0:\\n cnts = contours.sort_contours(cnts)[0]\\n\\n self.numbers = []\\n roi_h_size = []\\n\\n for c in cnts:\\n (x, y, w, h) = cv2.boundingRect(c)\\n if h > 40 and w > 5 and w < 60:\\n roi = thresh[y-np.min([y, 5]): y+h+np.min([thresh.shape[0]-y-h, 5]),\\n x-np.min([x, 5]): x+w+np.min([thresh.shape[1]-x-w, 5])]\\n if roi.shape[0] > 0 and roi.shape[1] > 0:\\n roi = cv2.morphologyEx(roi, cv2.MORPH_OPEN, np.ones(\\n (3, 3), np.uint8), iterations=1)\\n if len(self.numbers) < 2:\\n num = self.recognizeSign(\\n roi, self.plate_first_part_roi, self.plate_first_part_signs)\\n else:\\n num = self.recognizeSign(\\n roi, self.plate_second_part_roi, self.plate_second_part_signs)\\n\\n if w < 20 and (num != 'I' and num != 'J' and num != '1') or num == '':\\n continue\\n self.numbers.append(num)\\n roi_h_size.append(roi.shape[0])\\n\\n while len(roi_h_size) > 7:\\n self.numbers.pop(roi_h_size.index(min(roi_h_size)))\\n roi_h_size.remove(min(roi_h_size))\\n\\n if len(self.numbers) < 7 and threshold <= 150:\\n self.getCarPlateSigns(car_plate_img, th2+25, th2+25)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77181d99745c131b62c11fcc54ccd014\",\n \"score\": \"0.47613615\",\n \"text\": \"def filter_matching_inner_outer_finder_patterns(finder_patterns):\\n # Loop to compare relative difference between centroid points\\n # of each upper_left_ratio upper_right_sprite lower_left_sprite\\n # of each pattern.\\n # The centroid point positions of outer sprite and inner sprite nearly match\\n\\n for i, pattern1 in enumerate(finder_patterns):\\n for j, pattern2 in enumerate(finder_patterns):\\n if i >= j:\\n continue\\n # Calculate relative difference between centroid points\\n # to define outer and inner sprite\\n upper_left_ratio = _calculate_relative_difference(\\n pattern1[POSITION[\\\"upper_left_sprite\\\"]].centroid[0],\\n pattern2[POSITION[\\\"upper_left_sprite\\\"]].centroid[0])\\n if upper_left_ratio > SIMILAR_DISTANCE:\\n continue\\n\\n # Skip if upper_right_sprite of pattern1 match\\n # upper_right_sprite of pattern2\\n if pattern1[POSITION[\\\"upper_right_sprite\\\"]] == \\\\\\n pattern2[POSITION[\\\"upper_right_sprite\\\"]]:\\n continue\\n upper_right_ratio = _calculate_relative_difference(\\n pattern1[POSITION[\\\"upper_right_sprite\\\"]].centroid[0],\\n pattern2[POSITION[\\\"upper_right_sprite\\\"]].centroid[0])\\n if upper_right_ratio > SIMILAR_DISTANCE:\\n continue\\n\\n # Skip if lower_left_sprite of pattern1 match\\n # lower_left_sprite of pattern2\\n if pattern1[POSITION[\\\"lower_left_sprite\\\"]] == \\\\\\n pattern2[POSITION[\\\"lower_left_sprite\\\"]]:\\n continue\\n lower_left_ratio = _calculate_relative_difference(\\n pattern1[POSITION[\\\"lower_left_sprite\\\"]].centroid[0],\\n pattern2[POSITION[\\\"lower_left_sprite\\\"]].centroid[0])\\n if lower_left_ratio > SIMILAR_DISTANCE:\\n continue\\n\\n # Define pattern position as outer sprites\\n if (pattern1[POSITION[\\\"lower_left_sprite\\\"]].top_left >\\n pattern2[POSITION[\\\"lower_left_sprite\\\"]].top_left)\\\\\\n and pattern1[POSITION[\\\"lower_left_sprite\\\"]].bottom_right\\\\\\n < pattern2[POSITION[\\\"lower_left_sprite\\\"]].bottom_right:\\n\\n return pattern2\\n\\n return pattern1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe1351ef040eccde5a2e045ff0f9cb0\",\n \"score\": \"0.47596735\",\n \"text\": \"def estimate_text_mask(cropped_imgs, painting_bboxes, method, qs_images):\\n text_segm = get_method(method)\\n cropped_text_mask = []\\n bboxes_mask = []\\n bbox_show = []\\n\\n print(\\\"Extracting text boxes...\\\")\\n for paintings, pantings_bboxes in tqdm(zip(cropped_imgs,painting_bboxes)):\\n bboxes_paintings = []\\n cropped_text_mask_paintings = []\\n bbox_show_paintings = []\\n\\n for painting, painting_bbox in zip(paintings, pantings_bboxes):\\n result = text_segm(painting)\\n cropped_text_mask_paintings.append(result[0]) #.astype(bool))\\n bbox_show_paintings.append(result[1])\\n\\n bbox_relative = result[1]\\n bbox_relative = [bbox_relative[0]+painting_bbox[1], bbox_relative[1]+painting_bbox[0], \\\\\\n bbox_relative[2]+painting_bbox[1], bbox_relative[3]+painting_bbox[0]]\\n bboxes_paintings.append(bbox_relative)\\n\\n cropped_text_mask.append(cropped_text_mask_paintings)\\n bboxes_mask.append(bboxes_paintings)\\n bbox_show.append(bbox_show_paintings)\\n\\n if isDebug():\\n # Show images\\n i = 0\\n for paintings in cropped_imgs:\\n j = 0\\n for painting in paintings:\\n bbox = bbox_show[i][j]\\n painting_copy = painting.copy()\\n painting_copy = cv2.rectangle(painting_copy, (int(bbox[0]),int(bbox[1])), (int(bbox[2]),int(bbox[3])), (255,0 ,0), 10)\\n cv2.imshow(\\\"result text segm\\\", cv2.resize(painting_copy,(512,512)))\\n cv2.waitKey(0)\\n \\n j += 1\\n i += 1\\n\\n return [cropped_text_mask, bboxes_mask]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d05fb801fd0d64d02d736f6a9f63bb\",\n \"score\": \"0.47588116\",\n \"text\": \"def detect_with_image(self,interpreter, image, threshold):\\n self.set_input_tensor(interpreter, image)\\n interpreter.invoke()\\n\\n # Get all output details\\n boxes = self.get_output_tensor(interpreter, 0)\\n classes = self.get_output_tensor(interpreter, 1)\\n scores = self.get_output_tensor(interpreter, 2)\\n count = int(self.get_output_tensor(interpreter, 3))\\n results = []\\n for i in range(count):\\n if scores[i] >= threshold:\\n result = {\\n 'bounding_box': boxes[i],\\n 'class_id': classes[i],\\n 'score': scores[i]\\n }\\n results.append(result)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e109db9b3a014302ef81e70d3bede57\",\n \"score\": \"0.47573677\",\n \"text\": \"def get_contours(frame, crop, adjustments, is_ball=False):\\n try:\\n if is_ball:\\n frame = frame[crop[2]:crop[3], crop[0]:crop[1]]\\n if frame is None:\\n return [None, None, None]\\n\\n # hp = adjustments['highpass']\\n # if hp is None: hp = 0\\n\\n if adjustments['contrast'] >= 1.0:\\n frame = cv2.add(frame,\\n np.array([float(adjustments['contrast'])]))\\n\\n # Convert frame to HSV\\n frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\\n\\n # Create a mask\\n frame_mask = cv2.inRange(frame_hsv,\\n np.array(adjustments['min']),\\n np.array(adjustments['max']))\\n\\n # Does nothing since highpass is 0.0 everywhere in calibrations.json\\n # frame_mask = CalibrationGUI.highpass(frame_mask, frame, hp)\\n\\n # Find contours\\n if adjustments['open'] >= 1:\\n kernel = np.ones((2,2),np.uint8)\\n frame_mask = cv2.morphologyEx(frame_mask,\\n cv2.MORPH_OPEN,\\n kernel,\\n iterations=adjustments['open'])\\n if adjustments['close'] >= 1:\\n kernel = np.ones((2,2),np.uint8)\\n frame_mask = cv2.dilate(frame_mask,\\n kernel,\\n iterations=adjustments['close'])\\n if adjustments['erode'] >= 1:\\n kernel = np.ones((2,2),np.uint8)\\n frame_mask = cv2.erode(frame_mask,\\n kernel,\\n iterations=adjustments['erode'])\\n\\n tmp_placeholder, contours, hierarchy = cv2.findContours(\\n frame_mask,\\n cv2.RETR_TREE,\\n cv2.CHAIN_APPROX_SIMPLE\\n )\\n\\n return ([c for c in contours if cv2.contourArea(c) <= MAX_CONTOUR_AREA],\\n hierarchy, frame_mask)\\n except:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd8f1b895970a9ba48f1c1ebeed3e13\",\n \"score\": \"0.47546664\",\n \"text\": \"def detect(self, img, show=False):\\n img_y, img_x, _ = img.shape\\n if img_y > img_x:\\n img = imutils.rotate_bound(img,90)\\n\\n self.original_image = img\\n self.resized_image = imutils.resize(img, width=600) #bassler images\\n #self.resized_image = imutils.resize(img, width=1200) #sony images\\n\\n self.y_orig, self.x_orig, _ = img.shape\\n self.y_resize, self.x_resize, _ = self.resized_image.shape\\n\\n self.img_crop.clear()\\n\\n self.preprocess()\\n self.detect_ROIs(edge_limit=500)\\n\\n if len(self.keypoints) > 5:\\n self.keypoints_image = self.resized_image\\n return []\\n\\n self.crop_ROIs()\\n\\n # refined_crops = self.img_crop.copy()\\n # self.img_crop.clear()\\n # for i in range(len(refined_crops)):\\n # if refined_crops[i] is not None:\\n # self.img_crop.append(refined_crops[i])\\n\\n\\n self.keypoints_image = cv.drawKeypoints(self.resized_image, self.keypoints,\\n np.array([]), (0,0,255), cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\\n\\n if show:\\n # cv.imshow('Resized', self.resized_image)\\n # cv.imshow('Preprocess', self.preprocessed)\\n # cv.imshow('Canny', self.canny)\\n # cv.imshow('Flood', self.flood)\\n cv.imshow('Detection', self.keypoints_image)\\n key = cv.waitKey(1) & 0xFF\\n # for i in range(len(self.img_crop)):\\n # cv.imshow('Cropped Image %i' % (i), self.img_crop[i].crop)\\n # cv.waitKey(0)\\n # cv.destroyAllWindows()\\n\\n return self.img_crop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f1698775c2a0ad934adc9d091d3f95\",\n \"score\": \"0.4752597\",\n \"text\": \"def face_matches_to_dict(face1:Face, face2:Face,block1:Block,block2:Block):\\n match = {\\n 'block1':{\\n 'block_index':face1.BlockIndex,\\n 'IMIN':-1,'JMIN':-1,'KMIN':-1, # Lower Corner\\n 'IMAX':-1,'JMAX':-1,'KMAX':-1 # Upper Corner\\n },\\n 'block2':{\\n 'block_index':face2.BlockIndex,\\n 'IMIN':-1,'JMIN':-1,'KMIN':-1, # Lower Corner\\n 'IMAX':-1,'JMAX':-1,'KMAX':-1 # Upper Corner\\n }\\n }\\n \\n I1 = [face1.IMIN,face1.IMAX]\\n J1 = [face1.JMIN,face1.JMAX]\\n K1 = [face1.KMIN,face1.KMAX]\\n\\n I2 = [face2.IMIN,face2.IMAX]\\n J2 = [face2.JMIN,face2.JMAX]\\n K2 = [face2.KMIN,face2.KMAX]\\n\\n # Search for corners \\n x1_l = block1.X[I1[0],J1[0],K1[0]] # lower corner of block 1 \\n y1_l = block1.Y[I1[0],J1[0],K1[0]]\\n z1_l = block1.Z[I1[0],J1[0],K1[0]]\\n # Matches which corner in block 2 \\n search_results = list()\\n for p in I2: \\n for q in J2:\\n for r in K2:\\n x2 = block2.X[p,q,r] \\n y2 = block2.Y[p,q,r]\\n z2 = block2.Z[p,q,r]\\n dx = x2-x1_l; dy = y2-y1_l; dz = z2 -z1_l \\n search_results.append({'I':p,'J':q,'K':r,'d':math.sqrt(dx*dx + dy*dy + dz*dz)}) \\n df = pd.DataFrame(search_results)\\n df = df.sort_values(by=['d'])\\n match['block1']['IMIN'] = face1.IMIN\\n match['block1']['JMIN'] = face1.JMIN\\n match['block1']['KMIN'] = face1.KMIN\\n match['block2']['IMIN'] = int(df.iloc[0]['I'])\\n match['block2']['JMIN'] = int(df.iloc[0]['J'])\\n match['block2']['KMIN'] = int(df.iloc[0]['K'])\\n \\n # Search for corners \\n x1_u = block1.X[I1[1],J1[1],K1[1]] # lower corner of block 1 \\n y1_u = block1.Y[I1[1],J1[1],K1[1]]\\n z1_u = block1.Z[I1[1],J1[1],K1[1]]\\n # Matches which corner in block 2 \\n search_results = list()\\n for p in I2: \\n for q in J2:\\n for r in K2:\\n x2 = block2.X[p,q,r] \\n y2 = block2.Y[p,q,r]\\n z2 = block2.Z[p,q,r]\\n dx = x2-x1_u; dy = y2-y1_u; dz = z2 -z1_u\\n search_results.append({'I':p,'J':q,'K':r,'d':math.sqrt(dx*dx + dy*dy + dz*dz)})\\n df = pd.DataFrame(search_results)\\n df = df.sort_values(by=['d'])\\n match['block1']['IMAX'] = face1.IMAX\\n match['block1']['JMAX'] = face1.JMAX\\n match['block1']['KMAX'] = face1.KMAX\\n match['block2']['IMAX'] = int(df.iloc[0]['I'])\\n match['block2']['JMAX'] = int(df.iloc[0]['J'])\\n match['block2']['KMAX'] = int(df.iloc[0]['K'])\\n return match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bc34ef4a5251a255465f889a193ac3f\",\n \"score\": \"0.47515804\",\n \"text\": \"def qr(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e205d8403006a411578626ad409fbea\",\n \"score\": \"0.47507954\",\n \"text\": \"def get_red_box(resized_frame,threshold_area = 400):\\n hsv_colour_img = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2HSV)\\n #lower red\\n lower_red = np.array([0,110,110])\\n upper_red = np.array([10,255,255])\\n\\n #upper red\\n lower_red2 = np.array([170,50,50])\\n upper_red2 = np.array([180,255,255])\\n\\n mask = cv2.inRange(hsv_colour_img, lower_red, upper_red)\\n mask2 = cv2.inRange(hsv_colour_img, lower_red2, upper_red2)\\n\\n combined_mask = cv2.bitwise_or(mask,mask2)\\n \\n # Generate intermediate image; use morphological closing to keep parts of the brain together\\n morphed_mask = cv2.morphologyEx(combined_mask, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))\\n\\n final_mask = mask = np.zeros(mask.shape, dtype=np.uint8)\\n\\n contours, _ = cv2.findContours(morphed_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\\n blobs_greater_than_threshold = [blob for blob in contours if cv2.contourArea(blob) > threshold_area]\\n for blob in blobs_greater_than_threshold:\\n cv2.drawContours(final_mask, [blob], -1, (255), -1)\\n\\n final_result = cv2.bitwise_and(resized_frame,resized_frame, mask= final_mask)\\n\\n object_identified = final_mask.max() > 0\\n\\n return final_mask, final_result, object_identified\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5d9e6c7d1dc9be275b1a12a16f91bf\",\n \"score\": \"0.47504622\",\n \"text\": \"def detection_gui(frame,sample_res, obj_num, obj_size, roi_list, time_pass, frame_size, bg_detect_sensitivity = 2):\\r\\n frame = draw_rect_roi(frame, roi_list, obj_size, sample_res)\\r\\n rows, cols = frame_size\\r\\n\\r\\n if obj_num > bg_detect_sensitivity * sample_res: # return background estimation if too many possible blocks are counted\\r\\n cv2.putText(frame, \\\"[BACKGROUND MOVEMENT]\\\", (int(rows * 0.025), int(cols / 2)), font, 1.5, Red, 2,\\r\\n cv2.LINE_AA)\\r\\n cv2.putText(frame, \\\"Background movement detected\\\", (int(rows * 0.025), int(cols * 0.08)), font, 0.5, Red,\\r\\n 1, cv2.LINE_AA)\\r\\n\\r\\n # print all this very interesting shit\\r\\n cv2.putText(frame, \\\"RunTime: {}s\\\".format(time_pass), (int(rows * 0.025), int(cols * 0.025)), font, 0.5, Green,\\r\\n 1, cv2.LINE_AA)\\r\\n cv2.putText(frame, \\\"Objects Detected: {}\\\".format(obj_num), (int(rows * 0.025), int(cols * 0.05)),\\r\\n font, 0.5, Green, 1, cv2.LINE_AA)\\r\\n cv2.putText(frame, \\\"Resolution: {} Blocks\\\".format(sample_res), (int(rows * 0.6), int(cols * 0.05)),\\r\\n font, 0.5, Green, 1, cv2.LINE_AA)\\r\\n cv2.putText(frame, \\\"Image Resolution: {} x {}\\\".format(rows, cols), (int(rows * 0.6), int(cols * 0.025)),\\r\\n font, 0.5, Green, 1, cv2.LINE_AA)\\r\\n return frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bf20a516eda6da6c7ad453acc00839a\",\n \"score\": \"0.47482425\",\n \"text\": \"def bandCorner_Finder(orientation, tmpList, outList):\\n M0,M1,M2,M3 = [],[],[],[]\\n if orientation==QR_ORI_NORTH:\\n M0 = tmpList[0]\\n M1 = tmpList[1]\\n M2 = tmpList[2]\\n M3 = tmpList[3]\\n elif orientation==QR_ORI_EAST:\\n M0 = tmpList[1]\\n M1 = tmpList[2]\\n M2 = tmpList[3]\\n M3 = tmpList[0]\\n elif orientation==QR_ORI_SOUTH:\\n M0 = tmpList[2]\\n M1 = tmpList[3]\\n M2 = tmpList[0]\\n M3 = tmpList[1]\\n elif orientation==QR_ORI_WEST:\\n M0 = tmpList[3]\\n M1 = tmpList[0]\\n M2 = tmpList[1]\\n M3 = tmpList[2]\\n outList.append(M0)\\n outList.append(M1)\\n outList.append(M2)\\n outList.append(M3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aca4ad772721a2dc9e7b617276e8b4e\",\n \"score\": \"0.47411212\",\n \"text\": \"def getAll(self):\\n\\n\\t\\tvalueList = sorted(barcode.PROVIDED_BARCODES + [\\\"qr\\\"])\\n\\n\\t\\treturn valueList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f88afe56a36d1cb0c350ff96104aaab\",\n \"score\": \"0.47340658\",\n \"text\": \"def findReferenceMasks(self):\\n # mask upper left\\n mask_ul = Image.open(\\\"masks/ul.png\\\")\\n pos_ul = self.findPosition(mask_ul, x1=30, x2=110, y1=650,\\n y2=730, xoff=52, yoff=52)\\n\\n # mask upper middle\\n mask_um = Image.open(\\\"masks/ul.png\\\")\\n pos_um = self.findPosition(mask_um, x1=1070, x2=1150, y1=340,\\n y2=430, xoff=52, yoff=52)\\n\\n # mask upper right\\n mask_ur = Image.open(\\\"masks/ur.png\\\")\\n pos_ur = self.findPosition(mask_ur, x1=2300, x2=2400, y1=350,\\n y2=430, xoff=54, yoff=59)\\n\\n # mask lower left\\n mask_ll = Image.open(\\\"masks/ll.png\\\")\\n pos_ll = self.findPosition(mask_ll, x1=30, x2=110, y1=2720,\\n y2=2791, xoff=52, yoff=36)\\n\\n # mask lower right\\n mask_lr = Image.open(\\\"masks/lr.png\\\")\\n pos_lr = self.findPosition(mask_lr, x1=2300, x2=2400, y1=2710,\\n y2=2789, xoff=51, yoff=54)\\n\\n return pos_ul, pos_um, pos_ur, pos_ll, pos_lr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"987830023ae9fc08ed69720d9960bf54\",\n \"score\": \"0.47324678\",\n \"text\": \"def create_qr(data):\\n qr = qrcode.QRCode()\\n qr.add_data(data)\\n img = qr.make_image()\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6998762e95470844523d102b3c1cb8\",\n \"score\": \"0.47307363\",\n \"text\": \"def detect_shapes(self):\\n # if there is no existing image in the Image class then raise an exception and exit\\n if self.original_image is None:\\n try:\\n raise ImageNotExistError\\n except ImageNotExistError as e:\\n print(e.message)\\n exit(1)\\n # find the contours in the 'after thresh'\\n cnts = cv2.findContours(self.after_thresh.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\n cnts = imutils.grab_contours(cnts)\\n if self.debug is True:\\n cv2.drawContours(self.resized, cnts, -1, (255, 0, 0), thickness=8)\\n cv2.imshow('draw_contour', self.resized)\\n cv2.waitKey(0)\\n # if no existing contour then return False\\n if len(cnts) == 0:\\n self.reset()\\n print('There is no contours in the current image. Try another image!!!!')\\n return False\\n # if exists then>>>>>\\n for cnt in cnts:\\n m = cv2.moments(cnt)\\n cx = int((m[\\\"m10\\\"] / m[\\\"m00\\\"]) * self.ratio)\\n cy = int((m[\\\"m01\\\"] / m[\\\"m00\\\"]) * self.ratio)\\n peri = cv2.arcLength(cnt, True)\\n # approx is the collection of corner points\\n approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)\\n # convert the corner points into the list format\\n corner_points = [[point[0][0], point[0][1]] for point in approx]\\n # initialize thew shape class\\n shape_nm, angle_deviation, orientation, catch_point = self.detect_from_corner_points(corner_points,\\n color=None)\\n self.draw_cnt_with_name_and_rotation(cnt, shape_nm, cx, cy, angle_deviation, orientation, catch_point)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcf5af84e2032867fc0e6f5c766dee9\",\n \"score\": \"0.47292545\",\n \"text\": \"def align_image():\\n corners = []\\n try:\\n corners = json.loads(request.headers.get('X-CORNERS'))\\n except:\\n pass\\n use_exif = os.environ.get(\\\"CVMONITOR_ORIENT_BY_EXIF\\\", \\\"TRUE\\\") == \\\"TRUE\\\"\\n use_qr = os.environ.get(\\\"CVMONITOR_ORIENT_BY_QR\\\", \\\"FALSE\\\") == \\\"TRUE\\\"\\n qrprefix = str(os.environ.get(\\\"CVMONITOR_QR_PREFIX\\\", \\\"cvmonitor\\\"))\\n qrsize = int(os.environ.get(\\\"CVMONITOR_QR_TARGET_SIZE\\\", 100))\\n boundery = float(os.environ.get(\\\"CVMONITOR_QR_BOUNDERY_SIZE\\\", 50))\\n align_image_by_qr = (\\n os.environ.get(\\\"CVMONITOR_SKIP_ALIGN\\\", \\\"TRUE\\\") == \\\"FALSE\\\"\\n )\\n save_before_align = os.environ.get(\\\"CVMONITOR_SAVE_BEFORE_ALIGN\\\") == \\\"TRUE\\\"\\n save_after_align = os.environ.get(\\\"CVMONITOR_SAVE_AFTER_ALIGN\\\") == \\\"TRUE\\\"\\n\\n imdata = io.BytesIO(request.data)\\n image, detected_qrcode, _ = get_oriented_image(\\n imdata, use_exif=use_exif, use_qr=use_qr\\n )\\n\\n headers = {\\\"content-type\\\": \\\"image/jpeg\\\"}\\n if save_before_align:\\n imdata.seek(0)\\n with open(\\\"original_image.jpg\\\", \\\"wb\\\") as f:\\n f.write(imdata)\\n\\n if detected_qrcode is None:\\n if align_image_by_qr:\\n abort(\\n 400,\\n \\\"Could not align the image by qr code, no such code detected\\\",\\n )\\n else:\\n headers[\\\"X-MONITOR-ID\\\"] = detected_qrcode.data.decode()\\n\\n if align_image_by_qr:\\n logging.debug(\\\"Trying to align image by qr code\\\")\\n image, _ = align_by_qrcode(\\n image, detected_qrcode, qrsize, boundery, qrprefix\\n )\\n\\n if save_after_align:\\n imageio.imwrite(\\\"aligned_image.jpg\\\", image)\\n\\n b = io.BytesIO()\\n imageio.imwrite(b, image, format=\\\"jpeg\\\")\\n b.seek(0)\\n return b.read(), 200, headers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc885017e9fa5521429afdd4d7bbc08\",\n \"score\": \"0.47273517\",\n \"text\": \"def _gen_matches_codes(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8298824a16b2b2ca572574c1a32dea63\",\n \"score\": \"0.4720895\",\n \"text\": \"def scan_qr(\\n self,\\n content: Union[str, int] = None\\n ):\\n _res = self._r.post(\\n self._tele_url.format(\\n 'service-transfe/scanReceiveC2CQRCode'\\n ),\\n headers=self._header,\\n data=json.dumps(\\n {\\n \\\"content\\\": str(content)\\n }\\n )\\n )\\n if _res.json().get(\\\"code\\\") in [401]:\\n raise TokenExpired(\\n \\\"[ Error ] : Token Expired\\\"\\n )\\n if _res.json()['data']:\\n return _res.json()['data']\\n else:\\n raise QRExpiredError(\\n \\\"[ ERROR ] QR expired\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595561362d075e5495e2cd48dff98379\",\n \"score\": \"0.47202697\",\n \"text\": \"def partial_split_qr_decode(qr_code):\\n\\n # calculate padding that would have been stripped\\n padding = (len(qr_code) * 5 - 9 * 8) % 32\\n binary = base64.b32decode(qr_code + ('=' * padding))\\n\\n # check the header\\n if not binary.startswith('\\\\x84\\\\x7c\\\\x20'):\\n raise ValueError('invalid header')\\n\\n # the missing index for this set\\n missing = ord(binary[3])\\n\\n required = ord(binary[4])\\n checksum = binary[5:9]\\n\\n # extract each binary key and recompose the key\\n keys = set()\\n start = 9\\n index = 0\\n while start + 32 <= len(binary):\\n if index == missing: index += 1\\n key = '\\\\x10\\\\x01' + chr(index) + chr(required) + checksum + binary[start:start + 32]\\n keys.add(encode_check(key))\\n start += 32\\n index += 1\\n\\n return keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cd625bd93229a4d2fd23380557e49e1\",\n \"score\": \"0.47180784\",\n \"text\": \"def check_roi(display, mean_r, points):\\n bw = BoundingBoxWidget(display, mean_r, points)\\n checkCircle = True\\n while checkCircle:\\n\\n ##TEXT INFO\\n\\n # font\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n\\n # org\\n org = (20, 30)\\n org2 = (20, 50)\\n org3 = (20, 70)\\n # fontScale\\n fontScale = 0.7\\n\\n # Blue color in BGR\\n color = (255, 0, 0)\\n\\n # Line thickness of 2 px\\n thickness = 1\\n\\n # Using cv2.putText() method\\n image = cv2.putText(bw.show_image(), 'Left click - add ROI', org, font,\\n fontScale, color, thickness, cv2.LINE_AA)\\n\\n # Using cv2.putText() method\\n image = cv2.putText(bw.show_image(), 'Right click - restart', org2, font,\\n fontScale, color, thickness, cv2.LINE_AA)\\n\\n # Using cv2.putText() method\\n image = cv2.putText(bw.show_image(), 'Press q - Accept', org3, font,\\n fontScale, color, thickness, cv2.LINE_AA)\\n\\n ### END TEXT INFO\\n cv2.imshow('image', bw.show_image())\\n key = cv2.waitKey(1)\\n roi = bw.get_roi()\\n # Close program with keyboard 'q'\\n if key == ord('q'):\\n cv2.destroyAllWindows()\\n checkCircle = False\\n\\n return roi, bw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b8d9f6c348630d8160fd717457bf216\",\n \"score\": \"0.47168452\",\n \"text\": \"def _detect(self, signals, position, laps):\\n # get observed tuning\\n self.tuning_curves = self.tuning_strategy.calculate(\\n signals, position, laps)\\n if self.min_trials_active is not None:\\n self.rasters = self.tuning_strategy.calculate_rasters(\\n signals, position, laps)\\n\\n # generate null distribution and calculate thresholds\\n self._get_nulls(signals, position, laps)\\n self._get_thresholds()\\n\\n # detect place fields\\n return self._find_fields()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a0ba8a6c23a328fd371fdb30afc5ff\",\n \"score\": \"0.4715605\",\n \"text\": \"def _extract_bb_in_region(self, top_left_corner, bottom_right_corner):\\n patch_list = []\\n\\n x_ini, y_ini = top_left_corner\\n x_end, y_end = bottom_right_corner\\n step_y = (y_end - y_ini) / self.grid_shape[0]\\n step_x = (x_end - x_ini) / self.grid_shape[1]\\n h = step_y\\n for i in range(self.grid_shape[0]):\\n w = step_x\\n if i == self.grid_shape[0] - 1:\\n h += (y_end - y_ini) % self.grid_shape[0]\\n for j in range(self.grid_shape[1]):\\n if j == self.grid_shape[1] - 1:\\n w += (x_end - x_ini) % self.grid_shape[1]\\n y = i * step_y\\n x = j * step_x\\n patch_list.append([h, w, y, x])\\n return patch_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de4870f78214a3032226b8ec1905776\",\n \"score\": \"0.47099626\",\n \"text\": \"def check_roi_good(master_img, search_img, boxes, method, ratio=.75, modus=\\\"FLANN\\\", crosscheck=False):\\n # TODO: add jobs/multithreading\\n # just use surf as method for now\\n\\n dec = det.detector(method)\\n kp_master, des_master = det.detect(dec, master_img)\\n rois = []\\n for box in boxes: \\n mask = _make_mask(search_img.shape, box)\\n kp_child, des_child = det.detect(dec, search_img, mask)\\n good = None\\n if crosscheck:\\n good = det.match_and_crosscheck(des_master, des_child, modus, method)\\n else:\\n # use ratio test\\n matches = det.match(des_master, des_child, modus, method) \\n good = det.ratio(matches, ratio)\\n if good: # list is not empty, we dont want empty matchboxes\\n rois.append([box, kp_child, good, None, None, None])\\n return kp_master, rois\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b59ea9555a1377b0dfdf6f5a1229ea30\",\n \"score\": \"0.47070795\",\n \"text\": \"def find_bounding_box_coordinates(self,plate_dict):\\r\\n boxes = np.squeeze(self.boxes)\\r\\n scores = np.squeeze(self.scores)\\r\\n plate_dict = plate_dict.copy()\\r\\n\\r\\n finding_boxes = []\\r\\n\\r\\n for i,value in enumerate(scores):\\r\\n if value in plate_dict[i].values():\\r\\n index = np.where(scores == value)\\r\\n finding_boxes.append(boxes[index])\\r\\n \\r\\n return np.asarray(finding_boxes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5540fe458d5b27956481e176ce81159e\",\n \"score\": \"0.47050735\",\n \"text\": \"def vis_detections(im, class_name, dets, thresh=0.5):\\n inds = np.where(dets[:, -1] >= thresh)[0]\\n if len(inds) == 0:\\n return\\n mask = im\\n im = im[:, :, (2, 1, 0)]\\n #print(im.shape())\\n fig, ax = plt.subplots(figsize=(12, 12))\\n ax.imshow(im, aspect='equal')\\n #mask = im#.transpose((1, 2, 0)).astype(np.uint8).copy() #np.zeros_like(im, dtype=np.uint8).copy()\\n\\n for i in inds:\\n bbox = dets[i, :4]\\n score = dets[i, -1]\\n #'''\\n cv2.rectangle(mask, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 4)\\n font = cv2.FONT_HERSHEY_COMPLEX_SMALL\\n text = '{:s}\\\\{:.3f}'.format(class_name, score)\\n cv2.putText(mask, text, (int(bbox[0]), int(bbox[1] - 2)), font, 2, (0, 0, 255), 1)\\n #'''\\n ax.add_patch(\\n plt.Rectangle((bbox[0], bbox[1]),\\n bbox[2] - bbox[0],\\n bbox[3] - bbox[1], fill=False,\\n edgecolor='red', linewidth=3.5)\\n )\\n ax.text(bbox[0], bbox[1] - 2,\\n '{:s} {:.3f}'.format(class_name, score),\\n bbox=dict(facecolor='blue', alpha=0.5),\\n fontsize=14, color='white')\\n\\n ax.set_title(('{} detections with '\\n 'p({} | box) >= {:.1f}').format(class_name, class_name,\\n thresh),\\n fontsize=14)\\n plt.axis('off')\\n plt.tight_layout()\\n plt.draw()\\n return mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549e91ffbca8c9624279083dfaba8edb\",\n \"score\": \"0.4703638\",\n \"text\": \"def perform_q6(ref_image, target_image, mask, ref_points=None, search_win=5, ssd_win=40, nms_window=20):\\n\\n # Extract feature points for both images:\\n if ref_points is None:\\n ref_feature_points, ref_features_img = q2.harris_and_nms(ref_image, nms_window)\\n else:\\n print(\\\"predefined\\\")\\n ref_feature_points = ref_points\\n\\n target_feature_points, target_features_img = q2.harris_and_nms(target_image, nms_window)\\n\\n # filter out points that do not correspond to points in the mask\\n f_ref_points, f_seq_points = filter_points_not_in_mask(ref_feature_points, mask, seq_points=target_feature_points)\\n # marked_ref = q3.mark_points(ref_image, f_ref_points)\\n # marked_seq = q3.mark_points(target_image, f_seq_points)\\n # utils.compare_two_images(marked_ref, marked_seq, \\\"comp\\\")\\n if len(f_ref_points) == 0:\\n print(\\\"all points filtered\\\")\\n f_ref_points = ref_feature_points\\n f_seq_points = target_feature_points\\n # utils.compare_two_images(ref_features_img, target_features_img, \\\"Harris and nms - ref vs frame\\\")\\n\\n return match_images(ref_image, f_ref_points, target_image, f_seq_points, search_win, ssd_win)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4492a2c7a0f257947164867ae04ea4\",\n \"score\": \"0.47008467\",\n \"text\": \"def detect_objects(self, interpreter, image, threshold):\\n self.set_input_tensor(interpreter, image)\\n interpreter.invoke()\\n\\n # Get all output details\\n boxes = self.get_output_tensor(interpreter, 0)\\n classes = self.get_output_tensor(interpreter, 1)\\n scores = self.get_output_tensor(interpreter, 2)\\n count = int(self.get_output_tensor(interpreter, 3))\\n\\n results = []\\n for i in range(count):\\n if scores[i] >= threshold:\\n result = {\\n 'bounding_box': boxes[i],\\n 'class_id': classes[i],\\n 'score': scores[i]\\n }\\n results.append(result)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0953be1b200a2e37e1726000f27ce3\",\n \"score\": \"0.46943772\",\n \"text\": \"def get_results(cf, img_shape, detections, box_results_list=None, return_masks=False):\\r\\n detections = detections.cpu().data.numpy()\\r\\n #print(\\\"detections.shape\\\",detections.shape)\\r\\n # if cf.dim == 2:\\r\\n # detection_masks = detection_masks.permute(0, 2, 3, 1).cpu().data.numpy()\\r\\n # else:\\r\\n # detection_masks = detection_masks.permute(0, 2, 3, 4, 1).cpu().data.numpy()\\r\\n\\r\\n # restore batch dimension of merged detections using the batch_ix info.\\r\\n batch_ixs = detections[:, cf.dim*2]\\r\\n detections = [detections[batch_ixs == ix] for ix in range(img_shape[0])]\\r\\n #print(\\\"after detections.shape\\\",detections.shape)\\r\\n #mrcnn_mask = [detection_masks[batch_ixs == ix] for ix in range(img_shape[0])]\\r\\n\\r\\n # for test_forward, where no previous list exists.\\r\\n if box_results_list is None:\\r\\n box_results_list = [[] for _ in range(img_shape[0])]\\r\\n\\r\\n seg_preds = []\\r\\n # loop over batch and unmold detections.\\r\\n for ix in range(img_shape[0]):\\r\\n\\r\\n if 0 not in detections[ix].shape:\\r\\n boxes = detections[ix][:, :2 * cf.dim].astype(np.int32)\\r\\n class_ids = detections[ix][:, 2 * cf.dim + 1].astype(np.int32)\\r\\n scores = detections[ix][:, 2 * cf.dim + 2]\\r\\n #masks = mrcnn_mask[ix][np.arange(boxes.shape[0]), ..., class_ids]\\r\\n\\r\\n # Filter out detections with zero area. Often only happens in early\\r\\n # stages of training when the network weights are still a bit random.\\r\\n if cf.dim == 2:\\r\\n exclude_ix = np.where((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0)[0]\\r\\n else:\\r\\n exclude_ix = np.where(\\r\\n (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 5] - boxes[:, 4]) <= 0)[0]\\r\\n\\r\\n if exclude_ix.shape[0] > 0:\\r\\n boxes = np.delete(boxes, exclude_ix, axis=0)\\r\\n class_ids = np.delete(class_ids, exclude_ix, axis=0)\\r\\n scores = np.delete(scores, exclude_ix, axis=0)\\r\\n #masks = np.delete(masks, exclude_ix, axis=0)\\r\\n\\r\\n # Resize masks to original image size and set boundary threshold.\\r\\n full_masks = []\\r\\n permuted_image_shape = list(img_shape[2:]) + [img_shape[1]]\\r\\n if return_masks:\\r\\n for i in range(masks.shape[0]):\\r\\n # Convert neural network mask to full size mask.\\r\\n full_masks.append(mutils.unmold_mask_2D(masks[i], boxes[i], permuted_image_shape)\\r\\n if cf.dim == 2 else mutils.unmold_mask_3D(masks[i], boxes[i], permuted_image_shape))\\r\\n # if masks are returned, take max over binary full masks of all predictions in this image.\\r\\n # right now only binary masks for plotting/monitoring. for instance segmentation return all proposal maks.\\r\\n final_masks = np.max(np.array(full_masks), 0) if len(full_masks) > 0 else np.zeros(\\r\\n (*permuted_image_shape[:-1],))\\r\\n\\r\\n # add final perdictions to results.\\r\\n if 0 not in boxes.shape:\\r\\n for ix2, score in enumerate(scores):\\r\\n box_results_list[ix].append({'box_coords': boxes[ix2], 'box_score': score,\\r\\n 'box_type': 'det', 'box_pred_class_id': class_ids[ix2]})\\r\\n else:\\r\\n # pad with zero dummy masks.\\r\\n final_masks = np.zeros(img_shape[2:])\\r\\n\\r\\n seg_preds.append(final_masks)\\r\\n\\r\\n # create and fill results dictionary.\\r\\n results_dict = {'boxes': box_results_list,\\r\\n 'seg_preds': np.round(np.array(seg_preds))[:, np.newaxis].astype('uint8')}\\r\\n\\r\\n return results_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb860c3411781a88d93fc89d7026eea\",\n \"score\": \"0.46919227\",\n \"text\": \"def xqrs_detect(\\n sig, fs, sampfrom=0, sampto=\\\"end\\\", conf=None, learn=True, verbose=True\\n):\\n xqrs = XQRS(sig=sig, fs=fs, conf=conf)\\n xqrs.detect(sampfrom=sampfrom, sampto=sampto, verbose=verbose)\\n return xqrs.qrs_inds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e228c0286fa73e5b353181b2f0e2bc\",\n \"score\": \"0.4688179\",\n \"text\": \"def find_kp_red_block (kp, xyz_tfs, rgb_imgs):\\n \\n ### clicking set-up \\n class GetClick:\\n xy = None\\n done = False\\n def callback(self, event, x, y, flags, param):\\n if self.done:\\n return\\n elif event == cv2.EVENT_LBUTTONDOWN:\\n self.xy = (x,y)\\n self.done = True\\n\\n\\n rect_corners = []\\n nc = len(xyz_tfs)\\n rgb_plot = rgb_imgs[0].copy()\\n print colorize(\\\"Click at the corners of a rectangle which encompasses the red block.\\\", 'red', bold=True)\\n\\n cv2.imshow(WIN_NAME, rgb_plot)\\n for i in xrange(2):\\n gc = GetClick()\\n cv2.setMouseCallback(WIN_NAME, gc.callback)\\n while not gc.done:\\n cv2.waitKey(100)\\n rect_corners.append(gc.xy)\\n\\n xy_tl = np.array(rect_corners).min(axis=0)\\n xy_br = np.array(rect_corners).max(axis=0)\\n\\n cv2.rectangle(rgb_plot, tuple(xy_tl), tuple(xy_br), (0, 255, 0))\\n cv2.imshow(WIN_NAME, rgb_plot)\\n cv2.waitKey(100)\\n \\n colmin, rowmin = xy_tl\\n colmax, rowmax = xy_br\\n \\n high_red_xyz = []\\n\\n valid_pts = 0\\n # extract depths from drawn rectangle\\n\\n FOAM_HEIGHT = .85\\n \\n for i in range(nc): \\n z_rectangle = xyz_tfs[i][rowmin:rowmax, colmin:colmax, 2] # z values in extracted rectangle\\n \\n hsv = cv2.cvtColor(rgb_imgs[i][rowmin:rowmax, colmin:colmax].copy(),cv2.COLOR_BGR2HSV)\\n h=hsv[:,:,0]\\n s=hsv[:,:,1]\\n v=hsv[:,:,2]\\n \\n color_mask = ((h<20) | (h>150)) & (s > 100) & (v > 90)\\n print \\\"max z \\\", np.max(z_rectangle)\\n height_mask = z_rectangle > FOAM_HEIGHT + .025\\n \\n total_mask = color_mask & height_mask\\n print \\\"%ith image has %i valid points (above foam and red)\\\"%(i, total_mask.sum())\\n valid_pts += total_mask.sum()\\n \\n high_red_xyz.extend(xyz_tfs[i][rowmin:rowmax, colmin:colmax, :][total_mask])\\n \\n if valid_pts == 0:\\n return None, 0\\n \\n xyz_avg = np.median(high_red_xyz,axis=0)\\n \\n from rapprentice.pcl_utils import xyz2uv\\n row, col = xyz2uv(xyz_avg).astype('int')[0]\\n\\n cv2.circle(rgb_plot, (row, col), 3, (255, 0, 0), 2)\\n cv2.imshow(WIN_NAME, rgb_plot)\\n cv2.waitKey(100) \\n\\n print 'Key point location', xyz_avg\\n \\n return xyz_avg, valid_pts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd02443f8b993d2f951f305fd6e9d49\",\n \"score\": \"0.46856526\",\n \"text\": \"def matchDetections(self, detections, truthDetections, frameSize):\\n\\n self.log.info(\\\"Matching detection with ground truth...\\\")\\n detectionCanvas = np.zeros((frameSize[0], frameSize[1], 1), np.uint8)\\n for detection in detections:\\n detectionCanvas[detection['ymin']:detection['ymax'], detection['xmin']:detection['xmax']] = 255\\n\\n truthCanvas = np.zeros((frameSize[0], frameSize[1], 1), np.uint8)\\n for truthDetection in truthDetections:\\n truthCanvas[truthDetection['ymin']:truthDetection['ymax'], truthDetection['xmin']:truthDetection['xmax']] = 255\\n\\n\\n canvas = cv2.bitwise_and(detectionCanvas, truthCanvas)\\n\\n detectionCount = np.count_nonzero(detectionCanvas)\\n truthCount = np.count_nonzero(truthCanvas)\\n overlapCount = np.count_nonzero(canvas)\\n\\n self.log.debug(\\\"dc: %d, tc: %d, oc: %d\\\" % (detectionCount, truthCount, overlapCount))\\n\\n if detectionCount + truthCount == 0: # nothing detected and nothing to detect, so perfect\\n score = 1\\n elif detectionCount * truthCount == 0: # detect nothing but should, or detect but should not\\n score = 0\\n else:\\n score = overlapCount / math.sqrt(detectionCount*truthCount)\\n self.log.info(\\\"Score: %f\\\" % score)\\n\\n # calculate precision.\\n # If there should be detected something, but nothing is detected, then, precision is always 0%\\n # If nothing should be detected and there is nothing detected, then precision is always 100%\\n if detectionCount == 0 and truthCount != 0:\\n precision = 0\\n elif detectionCount == 0:\\n precision = 1\\n else:\\n precision = float(overlapCount) / detectionCount\\n\\n # calculate recall. If there is nothing to detect, then recall is always 100%\\n if truthCount == 0:\\n recall = 1\\n else:\\n recall = float(overlapCount) / truthCount\\n\\n return {\\n 'score': score,\\n 'precision': precision,\\n 'recall': recall,\\n 'detectionsOverlapRatios': self.matchDetectionsOverlap(detections, truthDetections, frameSize)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c7131328fb7c10623617831f421798\",\n \"score\": \"0.46847925\",\n \"text\": \"def find_blobs(tiff_array, results_path=None, result_id=None, with_blob_display=False):\\n # img = ((tiff_array - tiff_array.min()) * (1 / (tiff_array.max() - tiff_array.min()) * 255)).astype('uint8')\\n img = binarized_frame(movie_frame=tiff_array, filled_value=255, percentile_threshold=98)\\n # img = tiff_array.copy()\\n # # plt.imshow(img)\\n # # plt.show()\\n # threshold = np.percentile(img, 98)\\n #\\n # img[img < threshold] = 0\\n # img[img >= threshold] = 255\\n # # print(tiff_array.shape)\\n #\\n # # print(f\\\"im min {np.min(tiff_array[0])} max {np.max(tiff_array[0])}\\\")\\n # img = img.astype(\\\"uint8\\\")\\n\\n # img = np.reshape(img, (img.shape[0], img.shape[1], 1))\\n # print(img.shape)\\n # Detect blobs.\\n\\n # invert image (blob detection only works with white background)\\n img = cv2.bitwise_not(img)\\n\\n # Setup SimpleBlobDetector parameters.\\n params = cv2.SimpleBlobDetector_Params()\\n\\n # Change thresholds\\n # params.minThreshold = 10\\n # params.maxThreshold = 200\\n\\n # Filter by Area.\\n params.filterByArea = True\\n params.minArea = 25\\n\\n # Filter by Circularity\\n params.filterByCircularity = True\\n params.minCircularity = 0.1\\n\\n # Filter by Convexity\\n params.filterByConvexity = True\\n params.minConvexity = 0.3\\n\\n # Filter by Inertia\\n params.filterByInertia = False\\n params.minInertiaRatio = 0.01\\n\\n # plt.imshow(tiff_array[0], cmap=cm.Reds)\\n # plt.show()\\n # Set up the detector with default parameters.\\n ## check opencv version and construct the detector\\n is_v2 = cv2.__version__.startswith(\\\"2.\\\")\\n if is_v2:\\n detector = cv2.SimpleBlobDetector(params)\\n else:\\n detector = cv2.SimpleBlobDetector_create(params)\\n\\n keypoints = detector.detect(img)\\n # print(f\\\"len keypoints {len(keypoints)}\\\")\\n\\n # Draw detected blobs as red circles.\\n if with_blob_display:\\n # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob\\n im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0, 0, 255),\\n cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\\n\\n # Show keypoints\\n # cv2.imshow(\\\"Keypoints\\\", im_with_keypoints)\\n cv2.imwrite(os.path.join(results_path, f\\\"{result_id}_blobs.png\\\"), im_with_keypoints)\\n cv2.waitKey(0)\\n\\n from shapely.geometry import Polygon\\n\\n centers_of_gravity = [(keypoint.pt[0], keypoint.pt[1]) for keypoint in keypoints]\\n # for keypoint in keypoints:\\n # x = keypoint.pt[0]\\n # y = keypoint.pt[1]\\n # s = keypoint.size\\n #\\n # # polygon = Polygon([[0, 0], [1, 0], [1, 1], [0, 1]])\\n # # polygon.centroid\\n # print(f\\\"x {x}, y {y}\\\")\\n return centers_of_gravity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd8083ef9bf6d9580909e90f8a409da5\",\n \"score\": \"0.46784255\",\n \"text\": \"def findInVideo(self, video, scale_factor=1.1, min_neighbors=3, flags=0, min_size=(10, 10), progDialog=None):\\n raw_bounds = []\\n for frame in video.frames():\\n raw = self.findInImage(frame, scale_factor, min_neighbors, flags, min_size)\\n raw_bounds.append(raw)\\n if progDialog:\\n cont, _ = progDialog.Update(int(video.getRatio()*1000), \\\"Extracting Face Boundaries...\\\")\\n if not cont: return raw_bounds\\n return raw_bounds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb4d356a198a48d4f659d2ecde27f590\",\n \"score\": \"0.46681085\",\n \"text\": \"def find_cards(thresh_image):\\r\\n\\r\\n # Find contours and sort their indices by contour size\\r\\n cnts, hier = cv2.findContours(thresh_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\\r\\n index_sort = sorted(range(len(cnts)), key=lambda i: cv2.contourArea(cnts[i]), reverse=True)\\r\\n\\r\\n # If there are no contours, do nothing\\r\\n if len(cnts) == 0:\\r\\n return [], [], []\\r\\n\\r\\n # Otherwise, initialize empty sorted contour and hierarchy lists\\r\\n cnts_sort = []\\r\\n hier_sort = []\\r\\n cnt_is_card = np.zeros(len(cnts), dtype=int)\\r\\n\\r\\n # Fill empty lists with sorted contour and sorted hierarchy. Now,\\r\\n # the indices of the contour list still correspond with those of\\r\\n # the hierarchy list. The hierarchy array can be used to check if\\r\\n # the contours have parents or not.\\r\\n for i in index_sort:\\r\\n cnts_sort.append(cnts[i])\\r\\n hier_sort.append(hier[0][i])\\r\\n\\r\\n # Determine which of the contours are cards by applying the\\r\\n # following criteria: 1) Smaller area than the maximum card size,\\r\\n # 2), bigger area than the minimum card size, 3) have no parents,\\r\\n # and 4) have four corners\\r\\n crns = []\\r\\n box = []\\r\\n for i in range(len(cnts_sort)):\\r\\n size = cv2.contourArea(cnts_sort[i])\\r\\n peri = cv2.arcLength(cnts_sort[i], True)\\r\\n # approx = cv2.approxPolyDP(cnts_sort[i], 0.01 * peri, True)\\r\\n rect = cv2.minAreaRect(cnts_sort[i])\\r\\n box = cv2.boxPoints(rect)\\r\\n\\r\\n if ((size < CARD_MAX_AREA) and (size > CARD_MIN_AREA)\\r\\n and (hier_sort[i][3] == -1) and (len(box) == 4)):\\r\\n cnt_is_card[i] = 1\\r\\n # print(\\\"Hello\\\")\\r\\n crns = box\\r\\n\\r\\n return cnts_sort, cnt_is_card, crns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf5a82532ece0ef043642f05eba7a21\",\n \"score\": \"0.46625507\",\n \"text\": \"def blurred_detection(img_url, aperture):\\n img = cv2.imread(img_url)\\n\\n orig_img = cv2.imread(img_url)\\n img = cv2.medianBlur(orig_img, aperture)\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n _, img = cv2.threshold(img, 127, 255, 1)\\n\\n plt.gray()\\n\\n contours, hier = cv2.findContours(np.array(img), \\n cv2.RETR_EXTERNAL, \\n cv2.CHAIN_APPROX_SIMPLE)\\n\\n if len(contours) == 1: \\n #print cv2.boundingRect(contours[0])\\n cv2.drawContours(img, [contours[0]], 0, 0, 100) \\n\\n contours, hier = cv2.findContours(np.array(img), \\n cv2.RETR_EXTERNAL, \\n cv2.CHAIN_APPROX_SIMPLE)\\n total_height, total_width = img.shape\\n\\n censors = []\\n colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (0, 255, 255), (255, 0, 255), (255, 255, 0)]\\n\\n contours = sorted(contours, key=lambda cnt:cv2.contourArea(cnt), reverse=True)\\n\\n #simply get average minimum area of censors after spotting and make that the cutoff\\n for cnt in contours: \\n x, y, w, h = cv2.boundingRect(cnt) \\n area = cv2.contourArea(cnt)\\n if (100 < x < 750) and (100 < y < 870) \\\\\\n and area > 300:\\n cv2.drawContours(orig_img, [cnt], 0, colors[len(censors) % len(colors)], -1) \\n censors.append((x, y)) \\n\\n #plt.imshow(orig_img)\\n #plt.show()\\n\\n return censors, orig_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"514fe29fbe246398f03f20f23fa099f8\",\n \"score\": \"0.46624887\",\n \"text\": \"def qr_code(string, **kwargs):\\n width = kwargs.get('width', None)\\n if width is None:\\n return('[QR ' + string + ']')\\n else:\\n return('[QR ' + string + ', ' + width + ']')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40ab01811feebc1f0329fa55eded72f\",\n \"score\": \"0.4659152\",\n \"text\": \"def template_matching_with_metrics(mask, bb_list):\\n bb_classified = list()\\n for x,y,w,h in bb_list:\\n nameType = \\\"none\\\"\\n window = mask[y:y+h,x:x+w]\\n n_white = np.count_nonzero(window)\\n fratio = n_white/float(w*h)\\n if(fratio<0.60 and fratio>0.40):\\n n_white_top = np.count_nonzero(window[:round(h/2),:])\\n n_white_bottom = np.count_nonzero(window[round(h/2)+1:,:])\\n if (n_white_top > n_white_bottom):\\n #If the top is bigger than the bottom is a yield-like signal \\n nameType = \\\"B\\\"\\n else:\\n nameType = \\\"A\\\"\\n elif(fratio<0.85 and fratio>0.7):\\n nameType = \\\"CDE\\\"\\n elif(fratio > 0.85):\\n nameType = \\\"F\\\"\\n bb_classified.append((x,y,w,h,nameType))\\n return bb_classified\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbbb79c83e6cdb423d07f3878408a424\",\n \"score\": \"0.4653415\",\n \"text\": \"def detect_regions(self, **kwargs):\\n \\n # Get the normalized PDF\\n pdf = self.get_pdf(**kwargs)\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ecb8664d6bfce19752c054fe078ec0\",\n \"score\": \"0.46509865\",\n \"text\": \"def _get_raw_candidates(self):\\n out = self.get_fcnn_output()\\n r, out_bin = cv2.threshold(out, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\\n candidates = list()\\n # creating points\\n # x shape\\n xshape = self._image.shape[1]\\n xlist = []\\n x = 0\\n while x < xshape:\\n xlist.append(x)\\n x += self._pointcloud_stepsize\\n # y shape\\n yshape = self._image.shape[0]\\n ylist = []\\n y = 0\\n while y < yshape:\\n ylist.append(y)\\n y += self._pointcloud_stepsize\\n # generate carthesian product of list\\n points = list(itertools.product(xlist, ylist))\\n if self._shuffle_candidate_list:\\n random.shuffle(points)\\n # expand points\\n while points:\\n point = points[-1]\\n lx, uy = point\\n rx, ly = point\\n # expand to the left\\n if not out_bin[point[1]][point[0]]:\\n points.remove(point)\\n continue\\n next_lx = max(lx - self._expand_stepsize, 0)\\n while next_lx > 0 and out_bin[point[1]][next_lx]:\\n lx = next_lx\\n next_lx = max(lx - self._expand_stepsize, 0)\\n # expand to the right\\n next_rx = min(rx + self._expand_stepsize, out_bin.shape[1] - 1)\\n while next_rx < out_bin.shape[1] - 1 and out_bin[point[1]][next_rx]:\\n rx = next_rx\\n next_rx = min(rx + self._expand_stepsize, out_bin.shape[1] - 1)\\n # expand upwards\\n next_uy = max(uy - self._expand_stepsize, 0)\\n while next_uy > 0 and out_bin[next_uy][point[0]]:\\n uy = next_uy\\n next_uy = max(uy - self._expand_stepsize, 0)\\n # expand downwards (the lowest y is the highest number for y)\\n next_ly = min(ly + self._expand_stepsize, out_bin.shape[0] - 1)\\n while next_ly < out_bin.shape[0] - 1 and out_bin[next_ly][point[0]]:\\n ly = next_ly\\n next_ly = min(ly + self._expand_stepsize, out_bin.shape[0] - 1)\\n for i in range(self._candidate_refinement_iteration_count):\\n # expand from the middle of the borders of the found candidate\\n width, height = rx - lx, ly - uy\\n\\n buffer_x = lx + width // 2\\n buffer_y = uy + height // 2\\n\\n # expand to the left\\n next_lx = max(lx - self._expand_stepsize, 0)\\n while next_lx > 0 and out_bin[buffer_y][next_lx]:\\n lx = next_lx\\n next_lx = max(lx - self._expand_stepsize, 0)\\n\\n # expand to the right\\n next_rx = min(rx + self._expand_stepsize, out_bin.shape[1] - 1)\\n while next_rx < out_bin.shape[1] - 1 and out_bin[buffer_y][next_rx]:\\n rx = next_rx\\n next_rx = min(rx + self._expand_stepsize, out_bin.shape[1] - 1)\\n\\n # expand upwards\\n next_uy = max(uy - self._expand_stepsize, 0)\\n while next_uy > 0 and out_bin[next_uy][buffer_x]:\\n uy = next_uy\\n next_uy = max(uy - self._expand_stepsize, 0)\\n\\n # expand downwards\\n next_ly = min(ly + self._expand_stepsize, out_bin.shape[0] - 1)\\n while next_ly < out_bin.shape[0] - 1 and out_bin[next_ly][buffer_x]:\\n ly = next_ly\\n next_ly = min(ly + self._expand_stepsize, out_bin.shape[0] - 1)\\n\\n # calculate final width and height\\n width, height = rx - lx, ly - uy\\n candidates.append(Candidate(lx, uy, width, height))\\n points.remove(point)\\n points = [other_point for other_point in points if not (lx <= other_point[0] <= rx and uy <= other_point[1] <= ly)]\\n return candidates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2698e24d7c0a333b4ec1b1f40bfe100c\",\n \"score\": \"0.4643801\",\n \"text\": \"def corners(images):\\n\\n\\t# Define the flags and parameters\\n\\tcorners = []\\n\\tcb_flags = cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE\\n\\ttc_flags = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.6)\\n\\n\\t# Search the pattern in every image of the dataset\\t\\n\\tfor image in images:\\n\\t\\tcorners_image = cv2.findChessboardCorners(image, pattern_size,cb_flags)\\n\\t\\t# If the pattern has beem found, refine the result\\n\\t\\tif corners_image[0] == True:\\n\\t\\t\\timage_grey = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\\n\\t\\t\\tcv2.cornerSubPix(image_grey,corners_image[1],(10,10),(-1,-1),tc_flags)\\n\\t\\t\\t# Add the corners to the vector\\n\\t\\t\\tcorners.append(corners_image[1])\\n\\t\\t\\n\\t\\t# Draw the results\\n\\t\\tcv2.drawChessboardCorners(image,pattern_size,corners_image[1], corners_image[0])\\n\\t\\tcv2.imshow('img',image)\\n\\t\\tcv2.waitKey()\\n\\n\\treturn corners\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a79be3921dd483717bef144579687c\",\n \"score\": \"0.46427315\",\n \"text\": \"def find_markers_single_image(self, image):\\n corners, ids, rejects = cv2.aruco.detectMarkers(image, self._marker_dict)\\n\\n markedup = cv2.aruco.drawDetectedMarkers(image, corners, ids)\\n\\n print(\\\"corners: \\\" + repr(corners))\\n markers = {}\\n for i in range(0, len(ids)):\\n # print(str(i) + \\\": corners[i]: \\\" + repr(corners[i]))\\n # print(str(i) + \\\": vector_ul_lr: \\\" + repr(vector_ul_lr))\\n # print(str(i) + \\\": 0.25 * vector_ul_lr: \\\" + repr(0.25 * vector_ul_lr))\\n # print(str(i) + \\\": corners[i][0][2] + 0.25 * vector_ul_lr: \\\" + repr(corners[i][0][2] + 0.25 * vector_ul_lr))\\n # Compute a box 25% wider in every dimension.\\n # I sorted this out in longform and then condensed it. Sorry.\\n grown_box = np.array([corners[i][0][(c + 2) % 4] + 0.25 * (corners[i][0][(c + 2) % 4] - corners[i][0][c]) for c in range(0,4)], dtype=np.int32)\\n # print(str(i) + \\\": grown_box: \\\" + repr(grown_box))\\n poly_pts = grown_box.reshape((-1,1,2))\\n \\n # Create a mask that will select just the dots\\n mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)\\n cv2.fillConvexPoly(mask, poly_pts, 1, 0)\\n cv2.fillConvexPoly(mask, corners[i].astype(np.int32), 0, 0)\\n masked = cv2.bitwise_and(image, image, mask=mask)\\n cv2.polylines(markedup, [poly_pts], True, (0,255,0))\\n \\n dots = self._get_dots(masked)\\n # print(\\\"Dots at: \\\" + repr(dots))\\n # For debugging, we're just gonna return a marked up image for now \\n # return masked\\n return dots\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d164feb8310dee59ae278ab0697d83\",\n \"score\": \"0.46405926\",\n \"text\": \"def test_finder(self):\\n f = Finder(1320, RadialSize.Small,\\n [1170, 580, 580, 210, 700], [0.0, 3.3, 5.0, 0.0, 0.0], 5*[0.0], 5*[True],\\n False, False, False, True, True)\\n designs = f.find()\\n self.assertEqual(len(designs), 7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33dcfd66ae43f4a0c78c2c50b96a30e0\",\n \"score\": \"0.46385962\",\n \"text\": \"def extractSignatures(adjustedImg, y = 1985, s1 = (0,520), s2 = (495, 1060), s3 = (1035, -1), centerArea = 40, threshold = 250):\\n \\n # Crop\\n sig1 = adjustedImg[y:, s1[0]:s1[1]]\\n sig2 = adjustedImg[y:, s2[0]:s2[1]]\\n sig3 = adjustedImg[y:, s3[0]:s3[1]]\\n\\n # Calculate the mean of the pixel values of the center to see if it's white (empty box)\\n signatures = []\\n center = [sig1.shape[0]//2, sig1.shape[1]//2]\\n roi = sig1[center[0]-centerArea:center[0]+centerArea, center[1]-centerArea:center[1]+centerArea]\\n mean = cv2.mean(cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY))\\n if mean[0] < threshold:\\n signatures.append(sig1)\\n \\n center = [sig2.shape[0]//2, sig2.shape[1]//2]\\n roi = sig2[center[0]-centerArea:center[0]+centerArea, center[1]-centerArea:center[1]+centerArea]\\n mean = cv2.mean(cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY))\\n if mean[0] < threshold:\\n signatures.append(sig2)\\n\\n center = [sig3.shape[0]//2, sig3.shape[1]//2]\\n roi = sig3[center[0]-centerArea:center[0]+centerArea, center[1]-centerArea:center[1]+centerArea]\\n mean = cv2.mean(cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY))\\n if mean[0] < threshold:\\n signatures.append(sig3)\\n\\n return signatures\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994373,"cells":{"query_id":{"kind":"string","value":"b6aa43fb28b96ebce5ab8a1a7121c6b9"},"query":{"kind":"string","value":"The contacts are sorted here and displayed in the gui window for the user to view"},"positive_passages":{"kind":"list like","value":[{"docid":"dfbf9fc8ecc04218c96886edcbafd704","score":"0.6409214","text":"def selF():\n\tcontacts.sort()\n\tselect.delete(0,END)\n\tfor fname,lname,phone,address,email in contacts :\n\t\tselect.insert(END,fname+\",\"+lname)","title":""}],"string":"[\n {\n \"docid\": \"dfbf9fc8ecc04218c96886edcbafd704\",\n \"score\": \"0.6409214\",\n \"text\": \"def selF():\\n\\tcontacts.sort()\\n\\tselect.delete(0,END)\\n\\tfor fname,lname,phone,address,email in contacts :\\n\\t\\tselect.insert(END,fname+\\\",\\\"+lname)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"49341738e82647fa1972faf8cdbee629","score":"0.70360625","text":"def contact_list_update(self):\n cntcts = self.client_db.get_contacts()\n self.contacts_model = QStandardItemModel()\n for cnt in sorted(cntcts):\n itm = QStandardItem(cnt)\n itm.setEditable(False)\n self.contacts_model.appendRow(itm)\n self.gui.contactsList.setModel(self.contacts_model)","title":""},{"docid":"3cba96afad629e45018709dc67e0713c","score":"0.6894648","text":"def my_contacts(self):\n x = self.data.get_all_contacts(self.user_name)\n j = 0\n for i in x:\n j += 1\n print(f'{j}.{i[1].capitalize()} {i[2].capitalize()} know as {i[0].capitalize()}')","title":""},{"docid":"70dc0ce84e7854a8efeed16afe7caaf0","score":"0.6818373","text":"def displayContacts(self):\n print(\"\")\n strRepresentation = [str(i + 1) + \". \" + c.__str__() for i,c in enumerate(self.__contactList) if not c .getHide()]\n\n for s in strRepresentation:\n print (s)\n print(\"\")","title":""},{"docid":"c4a6c153d648abf383fa9077ad141f0a","score":"0.6751819","text":"def _load_contacts_in_combobox(self):\n self._addr_book = self._video_engine.get_adress_book()\n self.contacts_combobox.clear()\n self.contacts_combobox.insertItem(0, _(\"VISIO_Contacts\"))\n \n # For contacts\n if not self._addr_book:\n return\n\n self._index_to_address_dict = dict()\n for cnt, contact in enumerate(self._addr_book.contacts):\n self._index_to_address_dict[cnt+1] = contact.address\n text = str(\"%s\" % contact.name)\n self.contacts_combobox.insertItem(cnt+1, text)\n\n self.contacts_combobox.update()\n self.contacts_combobox.updateGeometry()","title":""},{"docid":"d82a9069425d8e26d38ec695c7c2a49b","score":"0.6693157","text":"def list_top_contact(self):\n print('-------------------------------------------------------------------------')\n print_contact({'name': 'NAME', 'phone': 'PHONE', 'outgoing': 'OUTGOING CALL'}, top=True)\n print('-------------------------------------------------------------------------')\n for item in sort_data(self.all_data, 2)[:5]:\n print_contact({'name': item[0], 'phone': item[1], 'outgoing': item[2]}, top=True)\n print('-------------------------------------------------------------------------')","title":""},{"docid":"a7d9982ffd056fcbde80c90c6a343ed9","score":"0.659237","text":"def sort_entries(self):\n \n global choice\n self.parent.sort_criterion = choice.get()\n self.parent.current_phonebook_list = \\\n self.parent.current_phonebook.sort(self.parent.sort_criterion,\\\n self.parent.current_phonebook.entries)\n self.parent.update()","title":""},{"docid":"c893c7729b82785491647011207919de","score":"0.63674235","text":"def _fill_contacts(self, contacts):\n treeview = self.view['contacts_treeview']\n model = treeview.get_model()\n model.add_contacts(contacts)","title":""},{"docid":"29b2837454e96e7fb8d5d4e563876f44","score":"0.631471","text":"def Display_Contacts_UI():\n for i in range(contact_Manager.Get_ContactList_Length()):\n contactDetails = contact_Manager.Get_Contact_Details(i)\n print(\"ID: \" + str(contactDetails[0]) + \", Name: \" + contactDetails[1] + \", Address: \" + contactDetails[2] + \", Phone Number: \" + contactDetails[3] + \", Birthday: \" + contactDetails[4])\n input(\"Press enter to continue...\")","title":""},{"docid":"fac3c11fa56e4ec303cd7000728aff03","score":"0.6263225","text":"def printing_contacts(self):\n print('\\n' + self.phonebook_name + '\\n')\n for any_name in self.contacts:\n any_name.__str__()","title":""},{"docid":"53935fe8afd34043d6b6f14e7e4b6d8c","score":"0.62523556","text":"def call_sort_dialog(self):\n \n if self.current_phonebook is not None:\n sort_dialog = SortChoice(self)\n else: \n info = \"Sortieren nicht möglich.\\n\\nAktuell kein Telefonbuch offen.\"\n messagebox.showinfo(title = \"Info\", message = info)\n self.update()","title":""},{"docid":"012384369b61ad6b14be50a16e7d2782","score":"0.6226227","text":"def display_contacts(cls):\n return cls.contact_list","title":""},{"docid":"1f930b96096020d44ef64950218d288b","score":"0.62225837","text":"def contacts():\n contacts = api.Api()\n print('Contacts:')\n print('---------')\n print(contacts.get_contacts())","title":""},{"docid":"ea1bffa5393b2e3c42afebaaa1d2e1c9","score":"0.6204785","text":"def show_all(self):\n for contact in self._contacts:\n self._print_contact(contact)","title":""},{"docid":"da079ce14a389616bfa3f90358e8ea26","score":"0.6147651","text":"def display_contact(cls):\n return cls.contact_list","title":""},{"docid":"54627ead7473a3f6edd5c83240f43ddf","score":"0.61353034","text":"def list_contacts():\n contact_list = ContactsController().list_contacts()\n\n click.echo(\"ID | FULL NAME | EMAIL | PHONE\")\n for contact in contact_list:\n contact[\"id\"] = \"{:02d}\".format(contact[\"id\"])\n click.echo(\"{id} | {name} {lastname} | {email} | {phone}\".format(**contact))","title":""},{"docid":"02688387be5e1bb4d3b3cf846c8c695d","score":"0.6121694","text":"def listcontacts(self):\r\n return self.contacts","title":""},{"docid":"0f6083a0b1c9802c79ba992dc83f2ed0","score":"0.61123896","text":"def list_all_contact(self):\n print('-------------------------------------------------------------')\n print_contact({'name': 'NAME', 'phone': 'PHONE'})\n print('-------------------------------------------------------------')\n for item in self.all_data:\n print_contact({'name': item[0], 'phone': item[1]})\n print('-------------------------------------------------------------')","title":""},{"docid":"e0bd5b30b8a09913a2e67992f9045651","score":"0.609338","text":"def get_lists_for_contact(self, contact):","title":""},{"docid":"16e9997fec53ac42906bad6d94e84df3","score":"0.6086291","text":"def contacts(self):\n if self._contacts is None:\n self._contacts = OrderedDict(\n sorted(((i, j), c) for (i, j), c in self.pairs.iteritems()\n if c.N > 0 and i < j))\n return self._contacts","title":""},{"docid":"e0b08b8849f94c93d12bf065d292f21e","score":"0.60662204","text":"def _fill_contact_list(self):\r\n self._add_contact('dx@emesene.org', 'XD', e3.status.ONLINE, '', False)\r\n self._add_contact('roger@emesene.org', 'r0x0r', e3.status.ONLINE,\r\n '', False)\r\n self._add_contact('boyska@emesene.org', 'boyska', e3.status.ONLINE,\r\n '', True)\r\n self._add_contact('pochu@emesene.org', '<3 debian', e3.status.BUSY,\r\n '', False)\r\n self._add_contact('cloud@emesene.org', 'nube', e3.status.BUSY,\r\n '', False)\r\n self._add_contact('otacon@emesene.org', 'Otacon', e3.status.BUSY,\r\n '', True)\r\n self._add_contact('federico@emesene.org', 'federico..', e3.status.AWAY,\r\n 'he loves guiness', False)\r\n self._add_contact('respawner@emesene.org', 'Respawner', e3.status.AWAY,\r\n '', False)\r\n self._add_contact('mohrtutchy@emesene.org', 'moh..', e3.status.AWAY,\r\n 'one love', True)\r\n self._add_contact('nassty@emesene.org', 'nassto', e3.status.IDLE,\r\n '', False)\r\n self._add_contact('j0hn@emesene.org', 'juán', e3.status.IDLE, '', False)\r\n self._add_contact('c0n0@emesene.org', 'conoconocono', e3.status.IDLE,\r\n '', True)\r\n self._add_contact('warlo@emesene.org', 'warlo', e3.status.OFFLINE,\r\n '', False)\r\n self._add_contact('wariano@emesene.org', 'wariano', e3.status.OFFLINE,\r\n '', False)\r\n self._add_contact('Faith_Nahn@emesene.org', 'Gtk styler',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('you@emesene.org', 'I\\'m on emesene code!',\r\n e3.status.OFFLINE, '', True)\r\n\r\n self._add_contact('one@hotmail.com', '- [b][c=48]Pαrκ¡[/c=30][/b]',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('two@hotmail.com',\r\n '[c=46]-๑๑test_test๑๑-[/c=2]', e3.status.BUSY, '', False)\r\n self._add_contact('three@hotmail.com',\r\n '[c=29]•°o.Orandom εïз stuff O.o°•[/c=36]·$28',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('four@hotmail.com',\r\n '[c=48][b]hy[/b][/c=11] ·#·$3,3\\'_·$#fcfcfc,#fcfcfc\\'_·$4,4\\'_·0·$28',\r\n e3.status.ONLINE, '', False)\r\n self._add_contact('five@hotmail.com',\r\n '·&·#·$9X.|̲̅·$10X·$9̲̅·$10x·$9̲̅·$10x·$9̲̅·$10x·$9̲̅·$10x·$9̲̅|·$10·#',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('six@hotmail.com', '[c=46][u][b]xafd! [/b][/u][/c]',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('seven@hotmail.com',\r\n '[c=5]((_...sdsdf..._))..)_<(_))(°.°)(...][/c=48][u][/u]',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('eight@hotmail.com',\r\n '[i][B][C=12]☆[/C=0][C=0]☆[/C=12][c=12]☆[/c=0] (W) [C=12]Bellamezz[/c=49] (F) [c=0]☆[/c=0][c=12]☆[/c=12][c=0]☆[/c=0][/B][/i]',\r\n e3.status.AWAY, '', False)\r\n self._add_contact('nine@hotmail.com',\r\n '[b](*) ... [c=12]Ricky[/c=33] ...(*)[/b]',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('ten@hotmail.com',\r\n '<:o)[c=yellow]Yellow:DYellow[/c][c=red]Red[c=blue]Blue:)Blue[c=green]Green[/c][/c][/c]',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('eleven@hotmail.com',\r\n '·$,32·$59«·$,41·$50«·$,50·$41«·$,59·$32«·$,66·$23«·$32,1« :: nube ::»·$,66·$23»·$,59·$32»·$,50·$41»·$,41·$50»·$,32·$59»·0 ·$0',\r\n e3.status.AWAY, '', False)\r\n self._add_contact('twelve@hotmail.com',\r\n '·$4Red·$11Aqua·$6,8PurpleOnYellow·$14GrayOnYellow·0Black·$9Lime',\r\n e3.status.BUSY, '', False)\r\n self._add_contact('thirteen@hotmail.com',\r\n '[c=7][b][I am][/c=yellow][/b][c=70][/c=89][c=89] so [A]wesome that [I] only use invalid tags [/c]',\r\n e3.status.ONLINE, '', False,\r\n '[ I am ] so [ A ]wesome that [ I ] only use invalid tags ')\r\n self._add_contact('fourteen@hotmail.com',\r\n '\")':)(H);)"\\')',\r\n e3.status.BUSY, '', False,\r\n '\\\")'(emo)(emo)(emo)"\\')')\r\n self._add_contact('fifteen@hotmail.com',\r\n '1:)2;)3\\')4(><)5;)6:)7',\r\n e3.status.BUSY, '', False,\r\n '1(emo)2(emo)3\\')4(><)5(emo)6(emo)7')\r\n self._add_contact('sixteen@hotmail.com',\r\n '[C=80][i][b][I][/b]Single i[/i][/c=30][b][/C]Single /C[/B]',\r\n e3.status.OFFLINE, '', False,\r\n '[ i ]Single i[ /C ]Single /C')\r\n self._add_contact('seventeen@hotmail.com',\r\n '[/c][c=48][c=12][b]Colorful nickname[/b][/c=50]',\r\n e3.status.OFFLINE, '', False,\r\n '[ /c ][ c=48 ]Colorful nickname')\r\n\r\n self._add_group('ninjas')\r\n self._add_group('pirätes')\r\n self._add_group('lulz')\r\n self._add_group('code quiz ninjas')\r\n self._add_group('empty')\r\n self._add_group('strange nicks')\r\n\r\n self._add_contact_to_group('you@emesene.org', 'pirätes')\r\n self._add_contact_to_group('boyska@emesene.org', 'pirätes')\r\n self._add_contact_to_group('j0hn@emesene.org', 'pirätes')\r\n self._add_contact_to_group('c0n0@emesene.org', 'pirätes')\r\n self._add_contact_to_group('nassty@emesene.org', 'lulz')\r\n self._add_contact_to_group('warlo@emesene.org', 'lulz')\r\n self._add_contact_to_group('you@emesene.org', 'lulz')\r\n self._add_contact_to_group('cloud@emesene.org', 'lulz')\r\n self._add_contact_to_group('dx@emesene.org', 'ninjas')\r\n self._add_contact_to_group('roger@emesene.org', 'ninjas')\r\n self._add_contact_to_group('c0n0@emesene.org', 'ninjas')\r\n self._add_contact_to_group('boyska@emesene.org', 'ninjas')\r\n self._add_contact_to_group('Faith_Nahn@emesene.org', 'code quiz ninjas')\r\n\r\n self._add_contact_to_group('one@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('two@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('three@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('four@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('five@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('six@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('seven@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('eight@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('nine@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('ten@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('eleven@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('twelve@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('thirteen@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('fourteen@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('fifteen@hotmail.com', 'strange nicks')\r\n self._add_contact_to_group('sixteen@hotmail.com', 'strange nicks')\r\n\r\n #test pending contact dialog\r\n #self._add_pending_contacts()\r","title":""},{"docid":"52850584d3179ad57f68444e3ce03cfe","score":"0.6038005","text":"def show_contacts():\n\n return render_template(\"contacts.html\", contacts=contacts)","title":""},{"docid":"069378b05f593c83951440b0f7f64cc8","score":"0.6034698","text":"def BCListViewSort(lv):","title":""},{"docid":"ac8abdfc21cad233c5f93db0c57d767f","score":"0.5995351","text":"def render_contacts(self, windowInfo):\n\n # draw contact point\n contact_point_color = (255, 0, 0)\n point_radius = 2\n for index in range(0, len(self.contacts)):\n tmpManifold = self.contacts[index]\n for index2 in range(0, tmpManifold.contact_count):\n c = tmpManifold.contacts[index2]\n pygame.draw.circle(\n windowInfo,\n contact_point_color,\n (int(c.x), int(c.y)),\n int(point_radius))\n\n\n # draw contact normal\n contact_nomral_color = (0, 255, 0)\n for index in range(0, len(self.contacts)):\n tmpManifold = self.contacts[index]\n n = tmpManifold.normal\n for index2 in range(0, tmpManifold.contact_count):\n\n c = tmpManifold.contacts[index2]\n c_l = Vector2(c.x, c.y)\n\n n *= 8\n c_l += n\n\n line_vertices = []\n line_vertices.append((c.x, c.y))\n line_vertices.append((c_l.x, c_l.y))\n\n pygame.draw.lines(\n windowInfo,\n contact_nomral_color,\n False,\n line_vertices)","title":""},{"docid":"813656f8e74d34ed330874a6c7514278","score":"0.59764063","text":"def __paintContactList(self):\n self.contactsArea = guiobjects.OcempImageContactList(130, 270, self.player.getAgenda())\n self.contactsArea.topleft = 20, 40\n self.contactsArea.connect_signal (ocempgui.widgets.Constants.SIG_SELECTCHANGED, self.__selectionChange)\n self.container.add_child(self.contactsArea)","title":""},{"docid":"a8d3866fefce99afd7a732bc5dbf177f","score":"0.59717494","text":"def contact_details():\n\n #Global variables that are use in other functions\n global scrollable_frame, num\n\n ##############################################################################\n # Scripts that creates a canvas and scrollbar from the tkinter module in #\n # order for multiple contacts to be shown by scrolling up and down # \n ##############################################################################\n canvas = Canvas(HomeFrame)\n scrollbar = Scrollbar(HomeFrame, orient=\"vertical\", command=canvas.yview)\n scrollable_frame = Frame(canvas)\n\n scrollable_frame.bind(\n \"<Configure>\",\n lambda e: canvas.configure(\n scrollregion=canvas.bbox(\"all\")\n )\n )\n canvas.create_window((0, 0), window=scrollable_frame, anchor=\"nw\")\n\n canvas.configure(yscrollcommand=scrollbar.set)\n HomeFrame.pack()\n canvas.pack(side=\"left\", fill=\"both\", expand=True)\n scrollbar.pack(side=\"right\", fill=\"y\")\n\n #Creates an empty list to store the number each user has\n contact_num = []\n\n #Calls the database function\n database()\n\n #Fectches all the contacts associated to the user\n cursor.execute(\"SELECT * FROM contacts WHERE `user_id` = ?\", (id_number,))\n contacts = cursor.fetchall()\n\n # Outputs the contacts if any\n if contacts:\n for contact in contacts:\n\n #Outputs each contact row by row\n number = Button(scrollable_frame, text = str(contacts.index(contact)+1) + \"\\t\\t\" + str(contact[1]) + \" \"*(18- len(str(contact[1]))) +\"\\t\\t\" + str(contact[2]) + \" \"*(26 - len(str(contact[2]))) + \"\\t\\t\" + str(contact[3]) + \" \"*(11 - len(str(contact[3]))) + \"\\t\\t\\t\" + str(contact[4]) + \" \"*(25 - len(str(contact[4]))), font = ('arial', 10, 'bold'), bg = \"AliceBlue\")\n number.pack(pady = 20, fill = BOTH, expand = True)\n\n\n else:\n #Output \"No Contacts Added yet\" if not contact was found in the database\n no_contact = Label(scrollable_frame, text =\"\\n\" + \"\\t\" * 7+ \"No Contacts Added yet\", font = ('arial', 12, 'bold'))\n no_contact.pack()","title":""},{"docid":"1037c52dd95c870870a44b94d6fe00d8","score":"0.5905942","text":"def updateContactList(self): \n self.contactsArea.setContacts(self.player.getAgendaData())","title":""},{"docid":"bc9a4cd2b0eb90570947728f673823b4","score":"0.59010136","text":"def get_contacts(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"6cd073edd12846000027725085de5c05","score":"0.5876278","text":"def plot_contacts(total_contacts):\n\n fig1, ax1 = plt.subplots()\n ax1.hist(total_contacts, \n bins = range(1, int(total_contacts.max() + 1)), \n edgecolor = 'k')","title":""},{"docid":"098fec5b99bc0044315cdca39b29a16d","score":"0.58402216","text":"def contact_list(request):\n c = {}\n c['messages'] = []\n c['errors'] = []\n l = pynag.Parsers.mk_livestatus(nagios_cfg_file=adagios.settings.nagios_config)\n c['contacts'] = l.query('GET contacts')\n c['contacts'] = pynag.Utils.grep(c['contacts'], **request.GET)\n c['contactgroups'] = l.query('GET contactgroups')\n return render_to_response('status_contacts.html', c, context_instance=RequestContext(request))","title":""},{"docid":"85701b9420253b4be2e01af3857849b6","score":"0.58238846","text":"def display():\n \n key_sort = input(\"User information is displayed in ascending order by name/age/contact? >>>\")\n if key_sort == 'age':\n users_sorted = sorted(usersData, key=lambda x:usersData[x][0])\n elif key_sort == 'contact':\n users_sorted = sorted(usersData, key=lambda x:usersData[x][1])\n else:\n users_sorted = sorted(usersData)\n \n print(\"{:<15}{:<10}{:<20}{:<15}\".format('Username', 'Age', 'Contact', 'Password'))\n print(\"-\"*60)\n for i in range(len(users_sorted)):\n print(\"{:<15}{:<10}{:<20}{:<15}\".format(users_sorted[i], usersData[users_sorted[i]][0],\\\n usersData[users_sorted[i]][1], '*'*len(usersData[users_sorted[i]][2])))","title":""},{"docid":"2ac0d77926ce7edcccd20ffcafb3fd66","score":"0.5801961","text":"def list_contacts_command():\n # Get command arguments from user\n filters = demisto.args()\n # Make request and get raw response\n contacts = list_contacts(filters)\n # Parse response into context\n contexts = []\n for contact in contacts:\n # Parse individual contact response in context\n context = format_contact_context(contact)\n contexts.append(context)\n filters_as_strings = ', '.join([f'{key}: {val}' for key, val in filters.items()])\n title = f'Contacts Filtered by {filters_as_strings}' if filters else 'All Contacts'\n human_readable = tableToMarkdown(title, contexts, removeNull=False)\n demisto.results({\n 'Type': entryTypes['note'],\n 'ContentsFormat': formats['json'],\n 'Contents': contacts,\n 'ReadableContentsFormat': formats['markdown'],\n 'HumanReadable': human_readable,\n 'EntryContext': {\n 'Freshdesk.Contact(val.ID && val.ID === obj.ID)': contexts\n }\n })","title":""},{"docid":"3e1ce7941c00194b69e0108f5e2d27c5","score":"0.5766897","text":"def start_sorting(self):\n self.sorting = True\n for i, line in enumerate(self.sort_list):\n if not self.sorting:\n break\n if i==0:\n continue\n del self.sort_list[i]\n self.window.itemconfig(line[1], fill='red')\n self.window.update()\n self.insert(i, line, 0)\n\n if i == len(self.sort_list)-1:\n self.sorting = False\n for complete_line in self.sort_list:\n self.window.itemconfig(complete_line[1], fill='green')\n self.window.update()","title":""},{"docid":"e85b47ce8292e8f8701b064c66a939d3","score":"0.5721245","text":"def contact_to_display(self, type='All contacts'):\n send_key(KEY_MENU)\n sleep(2)\n click_textview_by_text('Contacts to display')\n if search_text(type):\n log_test_framework(self.TAG, str(type))\n click_textview_by_text(type)\n else:\n log_test_framework(self.TAG, \"can not find \" + str(type) + \" in this options choise\")\n sleep(1)","title":""},{"docid":"3e86df4d7c828f5409becf716c4ced9a","score":"0.5715643","text":"def print_contact(self):","title":""},{"docid":"111b49a3fc8c2fd5c4a691740afc7456","score":"0.5698682","text":"def output_bbdb_file(contacts):\n print_bbdb_header()\n printed = set()\n for contact in contacts:\n # don't print the same contact twice\n if contact.id in printed:\n continue\n # don't print entries that have no email addresses\n if not contact.email:\n continue\n print(format_contact_bbdb(contact).encode(\"utf-8\"))\n printed.add(contact.id)","title":""},{"docid":"ca07c6b8db4c2e94749621afcb8d6e6a","score":"0.5686167","text":"def test_display_all_contact(self):\n\n return cls.contact_list","title":""},{"docid":"379c25f17a3294d105b7ed733cbac092","score":"0.5674813","text":"def get_contacts():\n \n #Firstname, Lastname, Zipcode, Phonenumber\n \n contacts = [\n [\"B@dN@m3\", \"Johnson\", \"23435\", \"808-123-1234\"],\n [\"Ada\", \"O'Mally\", \"12345\", \"000-000-0000\"],\n [\"Grace\", \"Hopper\", \"35254\", \"983-245-3535\"],\n [\"Mary\", \"Jackson-Smith\", \"23435\", \"757-389-8372\"],\n [\"Dorthy\", \"Vaughan\", \"56\",\"567-353-35654323456\"],\n [\"Mary\", \"Keller\", \"8\", \"875-98766-9\"],\n [\"Edith\", \"Clarke\",\"34533\", \"5678909876545678\"],\n [\"John\", \"Doe\", \"87624\", \"878-345-2463\"],\n [\"Jim\", \"Bob\", \"98769\", \"800-777-5555\"],\n [\"John\", \"Smith\", \"34535\", \"345-355-3452\"],\n [\"Anna\", \"Queen\", \"83752\", \"453-254-8472\"],\n [\"Moana\", \"Princess\", \"96707\", \"808-551-5555\"],\n [\"Sam\", \"Smith\", \"24553\", \"864-284-9328\"],\n [\"John\", \"Doe\", \"87624\", \"878-345-2463\"],\n [\"Jim\", \"Bob\", \"98769\", \"800-777-5555\"],\n [\"Katherine\", \"Johnson\", \"23435\", \"808-123-1234\"],\n [\"Ada\", \"Lovelace\", \"12345\", \"000-000-0000\"],\n [\"Grace\", \"Hopper\", \"35254\", \"983-245-3535\"],\n [\"Mary\", \"Jackson\", \"23435\", \"757-389-8372\"],\n [\"Dorthy\", \"Vaughan\", \"56\",\"567-353-35654323456\"],\n [\"Mary\", \"Keller\", \"8\", \"875-98766-9\"],\n [\"Edith\", \"Clarke\",\"34533\", \"5678909876545678\"],\n [\"John\", \"Doe\", \"87624\", \"878-345-2463\"],\n [\"Jim\", \"Bob\", \"98769\", \"800-777-5555\"],\n [\"John\", \"Smith\", \"34535\", \"345-355-3452\"]\n ]\n \n return contacts","title":""},{"docid":"a019bb4d094da9cafa8f71cf9b8735f4","score":"0.5652653","text":"def start_sorting(self):\n if self.sorting:\n return None\n self.sorting = True\n self.quick_sort(0, len(self.sort_list)-1)\n if self.sorting:\n for line in self.sort_list:\n self.window.itemconfig(line[1], fill='green')\n self.window.update()\n self.sorting = False","title":""},{"docid":"c2f4a37af27468b5364a2e330c526e40","score":"0.56387544","text":"def on_contact_display_name_changed(self, contact):\r\n pass","title":""},{"docid":"0d1381f5ec1de6e138776d9a79456167","score":"0.56109416","text":"def test_display_all_contacts(self):\n self.assertEqual(Contact.display_contacts(), Contact.contact_list)","title":""},{"docid":"679500a5a12676f5ead29798a71a2ace","score":"0.55970997","text":"def get_contact_list(self):\n # TODO: get all contacts\n self.logger.info(\"function: InstantMessengerVmmSide::get_contact_list\")\n if self.is_connected:\n self.guest_obj.send(\"application \" + \"instantMessenger \" + str(self.window_id) + \" get_contact_list \")\n else:\n self.logger.error(\"function: InstantMessengerVmmSide::get_contact_list: not connected!\")","title":""},{"docid":"befd77431a8c3d390148045ab1c79f39","score":"0.558324","text":"def get_contact_list(self, args):\n self.instantMessengerApp.get_contact_list(args)","title":""},{"docid":"8e0892439a91e2161dac309e58fc5420","score":"0.5580354","text":"def list_contacts(self):\n catalog = getToolByName(getSite(), 'portal_catalog')\n query = {'portal_type': 'opengever.contact.contact'}\n # make catalog query without checking security (allowedRolesAndUsers)\n # since the contacts are not visible for foreign users but should be\n # in the vocabulary anyway...\n brains = ZCatalog.searchResults(catalog, **query)\n return brains","title":""},{"docid":"61a2b171ab9a7c0d93718c8fee6b6c9c","score":"0.5568778","text":"def Search_Contacts_UI():\n print(\"\")\n print(\"+---------Search----------+\")\n print(\"| 1) Search Names |\")\n print(\"| 2) Search Address's |\")\n print(\"| 3) Search Phone Numbers |\")\n print(\"| 4) Search Birthdays |\")\n print(\"| Enter 'Exit' to return |\")\n print(\"| to menu. |\")\n print(\"+-------------------------+\")\n print(\"(1-4 / Exit)\")\n valid_input = False\n while valid_input == False: ## For sanitising files\n user_input = input(\"Enter your selection here: \")\n\n if user_input == \"1\": ########## Name search\n valid_input = True\n search_input = input(\"What name would you like to search for?: \")\n\n results = contact_Manager.Search_Contact_Name(search_input)\n if len(results) == 0:\n print(\"There are no contacts with that name...\")\n else:\n print(\"These are the contacts with that name:\")\n for i in range(len(results)):\n print(\"ID: \" + str(results[i][0]) + \", Name: \" + results[i][1] + \", Address: \" + results[i][2] + \", Phone Number: \" + results[i][3] + \", Birthday: \" + results[i][4])\n\n elif user_input == \"2\": ########## Address search\n valid_input = True\n search_input = input(\"What address would you like to search for?: \")\n\n results = contact_Manager.Search_Contact_Address(search_input)\n if len(results) == 0:\n print(\"There are no contacts with that address...\")\n else:\n print(\"These are the contacts with that address:\")\n for i in range(len(results)):\n print(\"ID: \" + str(results[i][0]) + \", Name: \" + results[i][1] + \", Address: \" + results[i][2] + \", Phone Number: \" + results[i][3] + \", Birthday: \" + results[i][4])\n\n elif user_input == \"3\": ########## Phone number search\n valid_input = True\n print(\"Phone numbers are always 10 numerical characters, e.g. 7945625056\")\n search_input = input(\"What phone number would you like to search for?: \")\n results = contact_Manager.Search_Contact_PhoneNumber(search_input)\n\n if len(results) == 0:\n print(\"There are no contacts with that phone number...\")\n else:\n print(\"These contacts have that phone number:\")\n for i in range(len(results)):\n print(\"ID: \" + str(results[i][0]) + \", Name: \" + results[i][1] + \", Address: \" + results[i][2] + \", Phone Number: \" + results[i][3] + \", Birthday: \" + results[i][4])\n\n elif user_input == \"4\": ########## Birthday Search\n valid_input = True\n print(\"Birthdays are always in the form of dd/mm/yyyy, e.g. 10/07/2002\")\n search_input = input(\"What birthday would you like to search for?: \") ##Sanitise\n\n results = contact_Manager.Search_Contact_Birthday(search_input)\n if len(results) == 0:\n print(\"There are no contacts with that birthday...\")\n else:\n print(\"These contacts have that birthday:\")\n for i in range(len(results)):\n print(\"ID: \" + str(results[i][0]) + \", Name: \" + results[i][1] + \", Address: \" + results[i][2] + \", Phone Number: \" + results[i][3] + \", Birthday: \" + results[i][4])\n\n elif user_input == \"Exit\":\n valid_input = True\n print(\"Exit has been selected...\")\n else:\n print(\"That is not a valid input, please try again...\")\n input(\"Press enter to continue...\")","title":""},{"docid":"85b7533e490ca712ecb27c03693df1ca","score":"0.55497","text":"def get_project_client_contacts(self):\n client = self.aq_parent.aq_parent\n pc = getToolByName(self, 'portal_catalog')\n brains = pc(portal_type='Contact', path={'query': \"/\".join(client.getPhysicalPath()), 'level': 0})\n contacts = []\n for brain in brains:\n obj = brain.getObject()\n contacts.append((obj.UID(), obj.Title(), obj.getEmailAddress()))\n contacts.sort(lambda x, y: cmp(x[1], y[1]))\n\n return contacts","title":""},{"docid":"012ce9300b8202f2fea6b42da5563bec","score":"0.55245584","text":"def list_contacts(self):\n for ls in self._contacts.keys():\n return ls","title":""},{"docid":"f071e88225aa2e2fabfec9b5b8c4b973","score":"0.55097026","text":"def contact_list():\n\n return render_template(\"contact-list.html\")","title":""},{"docid":"ab9fa9e78e893df6e7b83c32bbba95e3","score":"0.5508335","text":"def BCListViewSortOrder(lv):","title":""},{"docid":"dab2a876e9e85c9f6efa0ebd79e35844","score":"0.54984254","text":"def get_contact_list(self, args):\n self.logger.info(\"function: InstantMessengerGuestSide::send_msg_to\")\n self.agent_object.send(\"application \" + self.module_name + \" \" + str(self.imParent.window_id) + \" busy\")\n\n #\n # TODO: get contact list via plugin\n #\n self.send_to_plugin(\"get_contact_list\", [])\n\n self.agent_object.send(\"application \" + self.module_name + \" \" + str(self.imParent.window_id) + \" ready\")","title":""},{"docid":"5607cabd7916b28ff015b44347787545","score":"0.548218","text":"def update(self, contacts):\n rlist = []\n for contact in contacts:\n try:\n rlist.append(self._contacts_by_hash[contact.hash.value])\n # If the hash has not been seen yet\n # TODO: this could error out with an AttributeError (no hash)\n except KeyError:\n try:\n rcontact = self._contacts_by_addr[str(contact.address)]\n rlist.append(rcontact)\n if rcontact.needs_hash and not contact.needs_hash:\n rcontact.hash = contact.hash\n # Happens if the contact is not made\n except KeyError:\n contact.channels['bytelynx'].crypto.p = self._dh_p\n contact.on_hash += self.on_contact_hash\n contact.on_death += self.clean_contact\n self._contacts_by_addr[str(contact.address)] = contact\n if not contact.needs_hash:\n self._contacts_by_hash[contact.hash.value] = contact\n self._on_changed()\n rlist.append(contact)\n return rlist","title":""},{"docid":"74386c580cbfda0050be966172a51ad5","score":"0.5475833","text":"def sort(self):\n pass","title":""},{"docid":"13b35a13ccfbeb57cdbf506da72f500a","score":"0.54480034","text":"def sort_list():\n temp_list = list(listbox2.get(0, tk.END))\n temp_list.sort(key=str.lower)\n # delete contents of present listbox\n listbox2.delete(0, tk.END)\n # load listbox with sorted data\n for item in temp_list:\n listbox2.insert(ttk.END, item)","title":""},{"docid":"8505f49547c02c5a51bb24e5358c520e","score":"0.5447792","text":"def add_contact_window(self):\n global select_dialog\n global contact\n contact = self.ui.list_users.currentIndex().data()\n select_dialog = AddContactDialog(self.sock, self.database, contact)\n select_dialog.btn_ok.clicked.connect(lambda: self.add_contact_action(select_dialog))\n select_dialog.show()","title":""},{"docid":"e0054bfe34c078072ea8350f13395360","score":"0.54398054","text":"def get_contacts(self):\n try:\n self.get_roster()\n except IqError as err:\n print('IqError')\n except IqTimeout:\n print('IqTimeout')\n\n print('Buscando contactos...\\n')\n groups = self.client_roster.groups()\n\n for group in groups:\n for jid in groups[group]:\n connections = self.client_roster.presence(jid)\n for res, pres in connections.items():\n if pres['status']:\n status = pres['status']\n\n print(jid + ' - ' + status)","title":""},{"docid":"8d40efda7247cd6bd5561dc9db89d216","score":"0.54388374","text":"def display_roster(self):\r\n self.__roster.sort()\r\n print(self.__roster)\r\n print(\"Number of students: \", len(self.__roster))","title":""},{"docid":"fc77612e759ca637d40aad798e373df3","score":"0.5425731","text":"def show_contact(self):\n contact = input(\"Nick name of the contact :\")\n x = self.data.get_contact(self.user_name, contact)\n if x:\n print(f'{x[1].capitalize()} {x[2].capitalize()} know as {x[0].capitalize()}')\n else:\n print(\"The contact does not exist \")","title":""},{"docid":"2a26d0e81cbbf3e785663fd8cc9b4977","score":"0.54231584","text":"def get_contact_list(self):\n\t\tparameter = {\"start\": 1, \"limit\": 4, \"group_id\": 1}\n\t\tcontact_list = self._request_to_freebox(\"get_contact_list\", parameters=parameter)\n\t\treturn contact_list","title":""},{"docid":"1806b15f31d62de4040c81d9ef8cf19d","score":"0.5411482","text":"def get_contact_list():\n\n\tcond = ['`%s` like \"%s%%\"' % (f, \n\t\twebnotes.form.getvalue('txt')) for f in webnotes.form.getvalue('where').split(',')]\n\tcl = webnotes.conn.sql(\"select `%s` from `tab%s` where %s\" % (\n \t\t\t webnotes.form.getvalue('select')\n\t\t\t,webnotes.form.getvalue('from')\n\t\t\t,' OR '.join(cond)\n\t\t)\n\t)\n\twebnotes.response['cl'] = filter(None, [c[0] for c in cl])","title":""},{"docid":"380515bd255aa88a3389af546c14b659","score":"0.54079384","text":"def addContact(self, name, lastName, age, phoneNumber, email):\n newContact = Contact(name, lastName, age, phoneNumber, email)\n self.__contactListDict[newContact.getFullName()] = newContact\n self.__contactList.append(newContact)\n \n #Contacts inside the contact list are always sorted according to thei creation date\n #from the most receantly added to the latest.\n self.__contactList = sorted(self.__contactList, key = lambda x : x.getDate(), reverse = True )","title":""},{"docid":"007209a1cf1bbdcadda0e1616ac4a58f","score":"0.5396982","text":"def updateDict(self):\n newContactDict = {}\n for c in self.__contactList:\n newKey = c.getName() + \" \" + c.getLastName()\n newContactDict[newKey] = c\n self.__contactListDict = newContactDict","title":""},{"docid":"147adaba4f8ce0e160871ecb5dcb51ad","score":"0.53938055","text":"def request_contacts(self):\n SOCKET_LOGGER.debug(\n 'Запрос списка контактов пользователя: %s'\n % self.client_nickname\n )\n self.database.cleat_contact_list()\n request = {\n ACTION: GET_CONTACTS,\n TIME: time(),\n USER: self.client_nickname\n }\n SOCKET_LOGGER.debug(\n 'Сформирован запрос к серверу: %s' % request\n )\n with socket_lock:\n send_message(self.client_socket, request)\n response = receive_message(self.client_socket)\n SOCKET_LOGGER.debug(\n 'Получен ответ от сервера: %s' % response\n )\n if RESPONSE in response and response[RESPONSE] == 202:\n for contact in response[LIST_INFO]:\n self.database.add_user_to_contacts(contact)\n else:\n SOCKET_LOGGER.error(\n 'Не удалось обновить список контактов.'\n )","title":""},{"docid":"6b27a020478aa410b348c6d8ea1ce0f7","score":"0.53919035","text":"def sorted_contacts(vertices, normals, T_ar_object):\n N = 1000\n # prune vertices that are too close to the table so you dont smack into the table\n # you may want to change this line, to be how you see fit\n possible_indices = np.r_[:len(vertices)][vertices[:,2] + T_ar_object[2,3] >= 0.03]\n np.random.shuffle(possible_indices)\n\n # Finding grasp via vertex sampling. make sure to not consider grasps where the \n # vertices are too big for the gripper\n\n # possible metrics: compute_force_closure, compute_gravity_resistance, compute_custom_metric\n metric = compute_custom_metric\n grasp_indices = list()\n metric_scores = list()\n for i in range(N):\n candidate_indices = np.random.choice(possible_indices, 2, replace=False)\n point_dist = utils.length(vertices[candidate_indices[0]] - vertices[candidate_indices[1]])\n grasp_indices.append(candidate_indices)\n # assign lowest possible scores to impossible grasps\n if point_dist < MIN_HAND_DISTANCE or point_dist > MAX_HAND_DISTANCE:\n metric_scores.append(-sys.maxint - 1)\n else:\n contacts = vertices[candidate_indices]\n contact_normals = normals[candidate_indices]\n score = metric(contacts, contact_normals, CONTACT_MU, CONTACT_GAMMA, OBJECT_MASS)\n metric_scores.append(score)\n\n # sort metrics and return the sorted order\n best_metric_indices = sorted(list(range(N)), key=lambda i: metric_scores[i], reverse=True)\n return grasp_indices, best_metric_indices","title":""},{"docid":"79d917c37725028b6540a886ee83456a","score":"0.5380656","text":"def get_contact_list():\n\tcond = ['`%s` like \"%s%%\"' % (f, \n\t\twebnotes.form_dict.get('txt')) for f in webnotes.form_dict.get('where').split(',')]\n\tcl = webnotes.conn.sql(\"select `%s` from `tab%s` where %s\" % (\n \t\t\t webnotes.form_dict.get('select')\n\t\t\t,webnotes.form_dict.get('from')\n\t\t\t,' OR '.join(cond)\n\t\t)\n\t)\n\twebnotes.response['cl'] = filter(None, [c[0] for c in cl])","title":""},{"docid":"702c23f4ce8aca11f9023fcb7b09f9e3","score":"0.5341022","text":"def user_map(self):\n user_map_dialog = QDialog(self)\n user_map_dialog.setModal(True)\n bgg_to_ludo = self.get_bgg_to_ludo_users()\n user_list = [f'{key} -> {value}' for key, value in bgg_to_ludo.items()]\n list_widget = QListWidget(user_map_dialog)\n list_widget.addItems(user_list)\n list_widget.setResizeMode(QListView.Adjust)\n list_widget.sortItems()\n grid_layout = QGridLayout(user_map_dialog)\n grid_layout.addWidget(list_widget, 1, 1)\n user_map_dialog.resize(400, 400)\n user_map_dialog.show()","title":""},{"docid":"0190378d6b01f42d2a7dd156acee288b","score":"0.53393394","text":"def addF():\n\tcontacts.append([fname1.get(),lname1.get(),phone1.get(),address1.get(),email1.get()])\n\tselF()\n\tmsgBox.showinfo(\"Message\",\"Contact has been added.\")","title":""},{"docid":"d84223c56927be0b900c84622eabac0c","score":"0.53368086","text":"def BCListViewCreate(p, numCols, colNames, sorting):","title":""},{"docid":"6bbcffbd6a57d0ab988b02603ba070af","score":"0.53294826","text":"def sorter():\n pass","title":""},{"docid":"3e344cdd0a07f5547660e8ea7b9619c7","score":"0.53031","text":"def index(request):\n contacts = Contact.objects.filter(user=request.user.id)\n form = ContactForm()\n context = {'contacts': contacts,\n 'title': 'contacts',\n 'form': form,\n }\n return render(request, 'contacts/index.html', context)","title":""},{"docid":"decb8d0bc339469b9e675778d51b0cc3","score":"0.52942497","text":"def display_groups(acct):\n gdc = gdata.contacts.client.ContactsClient(source='charrington')\n gdc.ClientLogin(acct[\"login\"], acct[\"password\"], gdc.source)\n feed = gdc.GetGroups()\n for entry in feed.entry:\n print(\"Group Name: \"+entry.title.text)\n print(\"Atom Id: \"+entry.id.text+\"\\n\")","title":""},{"docid":"45be348ac5d942f4878f40d9c194115e","score":"0.52916986","text":"def __append_contact(self, contact):\r\n item = gtk.ImageMenuItem()\r\n item.set_label(gui.Plus.msnplus_strip(contact.nick))\r\n pict = self.__get_contact_pixbuf_or_default(contact.picture)\r\n item.set_image(pict)\r\n item.connect('activate', self._on_contact_clicked)\r\n self.item_to_contacts[item] = contact\r\n self.contacts_to_item[contact.account] = item\r\n\r\n item.show()\r\n self.add(item)","title":""},{"docid":"de2cedb782e20a8b3484179aaa1ec24f","score":"0.5277852","text":"def get_contact_list_from_server(self):\n self.database.clear_contacts()\n LOGGER.debug(f'Запрос списка контактов пользователя {self.username}.')\n get_contacts_dict = GET_CONTACTS_DICT\n get_contacts_dict[USER] = self.username\n LOGGER.debug(f'Сформирован запрос {get_contacts_dict}.')\n\n # Send a message to the server.\n with SOCK_LOCK:\n send_message(self.client_sock, get_contacts_dict)\n # Get a response from the server.\n server_answer = get_message(self.client_sock)\n LOGGER.debug(f'Получен ответ {server_answer}.')\n\n if RESPONSE in server_answer and server_answer[RESPONSE] == 202 \\\n and DATA in server_answer:\n for contact in server_answer[DATA]:\n self.database.add_contact(contact)\n else:\n LOGGER.error('Не удалось обновить список контактов.')","title":""},{"docid":"d5e79b38c9c731856d3ff9eccfe4f366","score":"0.52643657","text":"def sorted_contacts(vertices, normals, T_ar_object):\n \n # prune vertices that are too close to the table so you dont smack into the table\n # you may want to change this line, to be how you see fit\n possible_indices = np.r_[:len(vertices)][vertices[:,2] + T_ar_object[2,3] >= 0.03]\n\n # Finding grasp via vertex sampling. make sure to not consider grasps where the \n # vertices are too big for the gripper\n all_metrics = list()\n metric = compute_custom_metric\n grasp_indices = list()\n # for i in range(?????):\n # candidate_indices = np.random.choice(possible_indices, 2, replace=False)\n # grasp_indices.append(candidate_indices)\n # contacts = vertices[candidate_indices]\n # contact_normals = normals[candidate_indices]\n\n # # YOUR CODE HERE\n # all_metrics.append(????)\n\n # YOUR CODE HERE. sort metrics and return the sorted order\n\n return grasp_indices, best_metric_indices","title":""},{"docid":"2a4a0723c40fb09f7cb293cbbff76515","score":"0.525799","text":"def __showCompletionsList(self, completions):\n if Preferences.getEditor(\"AutoCompletionReversedList\"):\n acCompletions = sorted(\n list(completions),\n key=self.__replaceLeadingUnderscores)\n else:\n acCompletions = sorted(list(completions))\n self.showUserList(EditorAutoCompletionListID, acCompletions)","title":""},{"docid":"937b60721992ed7dc333d116a1d3d940","score":"0.5256559","text":"def delete_contact(self):\n search = input(\"Please enter name or number: \")\n new_data = []\n for item in self.all_data:\n if not fnmatch.fnmatch(str.lower(item[0]), '*{}*'.format(str.lower(search))) or fnmatch.fnmatch(\n item[1], '*{}*'.format(search)):\n new_data.append([item[0], item[1], item[2]])\n\n self.all_data = sort_data(new_data)\n\n return print(\"The contact was successfully deleted\\n\")","title":""},{"docid":"7f9d6b0f409896c240ca16146708ca1d","score":"0.52547616","text":"def _display_member_rows(self):\n\n for i, member in enumerate(self.master.members_list):\n self.master.members_widget_list[i].entry.delete(0, \"end\")\n self.master.members_widget_list[i].entry.insert(0, int(100/len(self.master.members_list)))\n\n self.master.members_widget_list[i].pack()","title":""},{"docid":"50de5960f7838fbad57e678dc577beb7","score":"0.52532786","text":"def run(self):\n while True:\n # load data to memory\n if not self.all_data:\n tmp_data = []\n with open(self.filename, 'r') as f:\n for line in f:\n data = line.strip().split(',')\n if validate(data[1]):\n tmp_data.append([data[0], validate(data[1]), 0])\n\n self.all_data = sort_data(tmp_data)\n\n print('''Greetings user! Let's make an addressbook!\n \nWould you like to:\n1.) Add a New Contact\n2.) List All Contacts\n3.) Search Contacts\n4.) Delete A Contact\n5.) Make Call\n6.) List top 5 Call\n7.) Quit Program\\n''')\n\n choice = input(\"Select an option: \")\n\n if choice == \"1\":\n try:\n self.create_contact()\n except NotAPhoneNumberException:\n print(\"The phone number entered is invalid, creation aborted!\")\n\n elif choice == \"2\":\n self.list_all_contact()\n\n elif choice == \"3\":\n self.search_contact()\n\n elif choice == \"4\":\n self.delete_contact()\n\n elif choice == \"5\":\n self.make_call()\n\n elif choice == \"6\":\n self.list_top_contact()\n\n elif choice == \"7\":\n print(\"Ending Contact Book.\\nHave a nice day!\")\n break\n\n else:\n print(\"Invalid Input! Try again.\")","title":""},{"docid":"e42abc1a043de2c7f9ef44a436299b6a","score":"0.52458614","text":"def BCListViewSortColumn(lv):","title":""},{"docid":"18b5177116994ed3f85f1c24b84b944a","score":"0.5220726","text":"def search_contact(self):\n search = input(\"Please enter name or phone: \")\n print('-------------------------------------------------------------')\n print_contact({'name': 'NAME', 'phone': 'PHONE'})\n print('-------------------------------------------------------------')\n for item in self.all_data:\n if fnmatch.fnmatch(str.lower(item[0]), '*{}*'.format(str.lower(search))) or fnmatch.fnmatch(\n item[1], '*{}*'.format(search)):\n print_contact({'name': item[0], 'phone': item[1]})\n print('-------------------------------------------------------------')","title":""},{"docid":"d95174731acd527373048f166ee49401","score":"0.52194005","text":"def Sort(self) -> None:\n ...","title":""},{"docid":"77b59b35c3992aef28f8966571a6932b","score":"0.52060205","text":"def __init__(self):\n # todo : encrypt ?\n self._contacts = []","title":""},{"docid":"929a4fda57f841906e9253cec2887805","score":"0.520311","text":"def get_all_contacts(self):\r\n self.contacts = []\r\n try:\r\n result = self.contact_db.find({}).sort(\"contact_id\")\r\n for res in result:\r\n self.contacts.append(ContactMongo(res))\r\n return self.contacts\r\n except Exception as error:\r\n return str(error)","title":""},{"docid":"6a37abdff99d8f954e5ad25f9eae1fb1","score":"0.520254","text":"def sort_search_results(self):\n\n sort_by = self.ui.comboBox_sort.currentText()\n if sort_by == \"A - z\":\n self.results = sorted(self.results, key=lambda i_: i_['codename'])\n return\n if sort_by == \"Z - a\":\n self.results = sorted(self.results, key=lambda i_: i_['codename'], reverse=True)\n return\n\n # Order code names by frequency\n # Get unique code names\n tmp_names = [r['codename'] for r in self.results]\n '''for r in self.results:\n tmp_names.append(r['codename'])'''\n codenames = list(set(tmp_names))\n # Create list dictionary of code name and code count\n name_and_count = []\n for codename in codenames:\n count = 0\n for r in self.results:\n if r['codename'] == codename:\n count += 1\n name_and_count.append({'codename': codename, 'count': count})\n tmp_results = []\n if sort_by == \"1 - 10\":\n small_to_large = sorted(name_and_count, key=lambda d: d['count'])\n for s in small_to_large:\n for r in self.results:\n if s['codename'] == r['codename']:\n tmp_results.append(r)\n self.results = tmp_results\n return\n if sort_by == \"10 - 1\":\n large_to_small = sorted(name_and_count, key=lambda d: d['count'], reverse=True)\n for s in large_to_small:\n for r in self.results:\n if s['codename'] == r['codename']:\n tmp_results.append(r)\n self.results = tmp_results\n return","title":""},{"docid":"b60676e3cd8ad5fb73ae16dfa08463a5","score":"0.51925224","text":"def sort(self):\r\n self.cards.sort()","title":""},{"docid":"b60676e3cd8ad5fb73ae16dfa08463a5","score":"0.51925224","text":"def sort(self):\r\n self.cards.sort()","title":""},{"docid":"fdd9a379a5a8171652085069e95188c6","score":"0.5188233","text":"def update_list(self):\n self.channel_listview.clear()\n names = self.cs.channel_metadata(tuple(self.order),'name')\n full_names = [str(self.order[i])+' : '+names[i] for i in range(len(names))]\n self.channel_listview.addItems(full_names)","title":""},{"docid":"03186a57153d85460063cd5db19fcbe9","score":"0.5187119","text":"def editF():\n\tcontacts[cselect()]=[fname1.get(),lname1.get(),phone1.get(),address1.get(),email1.get()]\n\tselF()\n\tmsgBox.showinfo(\"Message\",\"Contact has been updated.\")","title":""},{"docid":"ab63ce6d464310c681e468903200cf29","score":"0.51823086","text":"def contact_list(self):\n return self._contact_list","title":""},{"docid":"a6aac21db36e876ea0c143a00789f2c7","score":"0.51795834","text":"def contacts(self, *args, **kwargs) -> Any:\n pass","title":""},{"docid":"a6aac21db36e876ea0c143a00789f2c7","score":"0.51795834","text":"def contacts(self, *args, **kwargs) -> Any:\n pass","title":""},{"docid":"81d997c720e5f709e759f3a815c3ea00","score":"0.517044","text":"def contacts(self):\n return contacts.Contacts(self)","title":""},{"docid":"95827a642ac979dc082b838f5de91802","score":"0.5165047","text":"def list_contacts():\n get_contacts = Contact.query.all()\n contacts = serialize_contact(get_contacts)\n return make_response(jsonify({\"contacts\": contacts}))","title":""},{"docid":"daeac1c5a149878c842e0322f0db2f02","score":"0.5159857","text":"def add_contact_dialog(self):\n global contact_select\n contact_select = AddContactDialog(\n self.client_socket,\n self.client_db\n )\n contact_select.ok_btn.clicked.connect(\n lambda: self.add_contact_handler(contact_select))\n contact_select.show()","title":""},{"docid":"b8080c67bca8e8f263bfc4cd68678bc3","score":"0.5157543","text":"def test_contacts(self):\n contacts = map(_model_to_tuple, Contact.objects.all())\n self.assertIn(('Email', 'email'), contacts)\n self.assertIn(('Jabber', 'JID'), contacts)\n self.assertIn(('Skype', 'id'), contacts)\n self.assertIn(('', 'Mul'), contacts)\n self.assertIn(('', 'ti'), contacts)\n self.assertIn(('', 'line'), contacts)","title":""},{"docid":"f5bd75bd19e75d45f9b317852ddb0828","score":"0.5147904","text":"def BCListViewSetItemsSortedFunction(lv, funct, data):","title":""},{"docid":"4620b111d0870ae24547d0badf1b8e93","score":"0.5139449","text":"def init_display_order(apps, schema_editor):\n # We get the model from the versioned app registry;\n # if we directly import it, it'll be the wrong version.\n db_alias = schema_editor.connection.alias\n Note = apps.get_model(\"notepad\", \"Note\")\n notes = Note.objects.using(db_alias).all()\n notes.update(display_order=F('id')*1000)","title":""},{"docid":"978c8118a259f50d2f724ac7353c5c95","score":"0.5134643","text":"def sortLines(self):\n if not self.selectionIsRectangle():\n return\n \n from .SortOptionsDialog import SortOptionsDialog\n dlg = SortOptionsDialog()\n if dlg.exec_() == QDialog.Accepted:\n ascending, alnum, caseSensitive = dlg.getData()\n origStartLine, origStartIndex, origEndLine, origEndIndex = (\n self.getRectangularSelection()\n )\n # convert to upper-left to lower-right\n startLine = min(origStartLine, origEndLine)\n startIndex = min(origStartIndex, origEndIndex)\n endLine = max(origStartLine, origEndLine)\n endIndex = max(origStartIndex, origEndIndex)\n \n # step 1: extract the text of the rectangular selection and\n # the lines\n selText = {}\n txtLines = {}\n for line in range(startLine, endLine + 1):\n txtLines[line] = self.text(line)\n txt = txtLines[line][startIndex:endIndex].strip()\n if not alnum:\n try:\n txt = float(txt)\n except ValueError:\n E5MessageBox.critical(\n self,\n self.tr(\"Sort Lines\"),\n self.tr(\n \"\"\"The selection contains illegal data for a\"\"\"\n \"\"\" numerical sort.\"\"\"))\n return\n \n if txt in selText:\n selText[txt].append(line)\n else:\n selText[txt] = [line]\n \n # step 2: calculate the sort parameters\n reverse = not ascending\n if alnum and not caseSensitive:\n keyFun = str.lower\n else:\n keyFun = None\n \n # step 3: sort the lines\n eol = self.getLineSeparator()\n lastWithEol = True\n newLines = []\n for txt in sorted(selText.keys(), key=keyFun, reverse=reverse):\n for line in selText[txt]:\n txt = txtLines[line]\n if not txt.endswith(eol):\n lastWithEol = False\n txt += eol\n newLines.append(txt)\n if not lastWithEol:\n newLines[-1] = newLines[-1][:-len(eol)]\n \n # step 4: replace the lines by the sorted ones\n self.setSelection(startLine, 0, endLine + 1, 0)\n self.beginUndoAction()\n self.replaceSelectedText(\"\".join(newLines))\n self.endUndoAction()\n \n # step 5: reset the rectangular selection\n self.setRectangularSelection(origStartLine, origStartIndex,\n origEndLine, origEndIndex)\n self.selectionChanged.emit()","title":""},{"docid":"3e94c51cb24ec374f0fe328718241b3f","score":"0.5134035","text":"def ver():\n \n\tprint \"Lista de contactos\"\n\tprint \"------------------\"\n\tprint \"\"\n \n\tcon = sqlite3.connect(\"agenda.db\")\n\tcursor = con.cursor()\n \n\tcursor.execute(\"SELECT * FROM datos\")\n\tresultado = cursor.fetchall()\n \n\tfor i in resultado:\n\t\tprint \"%s %s %s %s\" % (i[0],i[1],i[2],i[3])\n \n\tcursor.close()\n \n\tprint \"\"\n\traw_input(\"Presione una tecla para continuar...\")\n \n\tmain()","title":""},{"docid":"9fa5113c8f857856ae6483533d2b605b","score":"0.5130038","text":"def get_contacts(config_dir=join(expanduser(\"~\"), \".baresip\")):\n\n with open(f\"{config_dir}/current_contact\") as cur:\n cur_con = cur.readline()\n with open(f\"{config_dir}/contacts\") as cons:\n raw = cons.readlines()\n contacts_list = []\n for line in raw:\n if not line.startswith(\"#\") and line != \"\\n\":\n contacts_list.append(line)\n contacts = {}\n for contact in contacts_list:\n name = contact.split('\"', 2)[1]\n addr = contact.split('<', 1)[1].split('>', 1)[0]\n contacts[name] = addr\n return contacts, cur_con","title":""},{"docid":"3085a23fa56a85ca64960105cce1791c","score":"0.5129176","text":"def test_search_contact_list(self):\n pass","title":""},{"docid":"7b0b9b1eef82da663eaedf5c77c0cbcf","score":"0.5123261","text":"def find_contacts(request, nick):\n redirect_to = request.REQUEST.get('redirect_to', '/')\n\n # these are for the find more contacts bits\n start_index = int(request.REQUEST.get('index', 1))\n max = 100\n token = request.REQUEST.get('token')\n contacts_more = int(request.REQUEST.get('contacts_more', 0))\n\n # this won't be seen unless contacts_more is positive,\n # so no worries about the possible negative value\n contacts_so_far = contacts_more - 1\n\n try:\n if not settings.GOOGLE_CONTACTS_IMPORT_ENABLED:\n raise exception.FeatureDisabledError('Google Contacts import is currently disabled')\n\n if 'lookup_remote_contacts' in request.POST:\n validate.nonce(request, 'lookup_remote_contacts')\n\n next_url = util.qsa(util.here(request),\n {'redirect_to': redirect_to,\n 'upgrade_auth_token': '',\n '_nonce': util.create_nonce(request.user,\n 'upgrade_auth_token'),\n }\n )\n auth_url = google_contacts.auth_sub_url(next_url)\n return http.HttpResponseRedirect(auth_url)\n elif 'actor_add_contacts' in request.POST:\n validate.nonce(request, 'actor_add_contacts')\n\n\n targets = request.POST.getlist('targets')\n owner = request.POST.get('owner', '')\n\n rv = api.actor_add_contacts(request.user, owner, targets)\n\n next_url = util.qsa(util.here(request),\n {'redirect_to': redirect_to,\n 'contacts_more': contacts_more,\n 'index': start_index,\n 'token': token,\n }\n )\n\n return util.RedirectFlash(next_url, 'Contacts added.')\n\n elif 'upgrade_auth_token' in request.GET:\n validate.nonce(request, 'upgrade_auth_token')\n\n auth_token = google_contacts.auth_sub_token_from_request(request)\n session_token = google_contacts.upgrade_to_session_token(auth_token)\n\n next_url = util.qsa(util.here(request),\n {'redirect_to': redirect_to,\n 'fetch_contacts': '',\n 'token': session_token.get_token_string(),\n '_nonce': util.create_nonce(request.user,\n 'fetch_contacts'),\n }\n )\n\n return http.HttpResponseRedirect(next_url)\n\n elif 'fetch_contacts' in request.REQUEST:\n validate.nonce(request, 'fetch_contacts')\n\n # start_index and max are gathered above\n session_token = google_contacts.auth_sub_token_from_request(request)\n\n # check for the \"My Contacts\" group, otherwise, fetch it\n my_contacts = memcache.client.get('%s/my_contacts' % token)\n if not my_contacts:\n my_contacts = google_contacts.get_system_group(session_token,\n 'Contacts')\n memcache.client.set('%s/my_contacts' % token, my_contacts)\n\n\n rv, more = google_contacts.get_contacts_emails(session_token,\n group=my_contacts,\n index=start_index,\n max=max)\n\n contacts = []\n\n for name, email in rv:\n logging.info('looking up \"%s\" %s', name, email)\n contacts.append(api.actor_lookup_email(request.user, email))\n\n contacts = [x for x in contacts if x]\n\n # for the template\n contacts_found = True\n contacts_more = more\n contacts_so_far = contacts_more - 1\n token = session_token.get_token_string()\n contacts_emails = rv\n\n # if no contacts were found and more are available, try some more\n if not contacts and contacts_more:\n next_url = util.qsa(util.here(request),\n {'fetch_contacts': '',\n 'contacts_more': contacts_more,\n 'index': contacts_more,\n 'token': token,\n '_nonce': util.create_nonce(request.user,\n 'fetch_contacts'),\n 'redirect_to': redirect_to,\n }\n )\n # TODO(termie): this can take a really long time, probably not really\n # viable until we can do it with javascript\n #return util.MetaRefresh(next_url, message='Still working...', second=1)\n #return http.HttpResponseRedirect(next_url)\n\n except:\n exception.handle_exception(request)\n\n\n # set the progress\n welcome_photo = True\n welcome_mobile = True\n\n view = request.user\n whose = 'You'\n area = 'people'\n\n c = template.RequestContext(request, locals())\n\n t = loader.get_template('actor/templates/findpeople_contacts.html')\n return http.HttpResponse(t.render(c))","title":""}],"string":"[\n {\n \"docid\": \"49341738e82647fa1972faf8cdbee629\",\n \"score\": \"0.70360625\",\n \"text\": \"def contact_list_update(self):\\n cntcts = self.client_db.get_contacts()\\n self.contacts_model = QStandardItemModel()\\n for cnt in sorted(cntcts):\\n itm = QStandardItem(cnt)\\n itm.setEditable(False)\\n self.contacts_model.appendRow(itm)\\n self.gui.contactsList.setModel(self.contacts_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cba96afad629e45018709dc67e0713c\",\n \"score\": \"0.6894648\",\n \"text\": \"def my_contacts(self):\\n x = self.data.get_all_contacts(self.user_name)\\n j = 0\\n for i in x:\\n j += 1\\n print(f'{j}.{i[1].capitalize()} {i[2].capitalize()} know as {i[0].capitalize()}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70dc0ce84e7854a8efeed16afe7caaf0\",\n \"score\": \"0.6818373\",\n \"text\": \"def displayContacts(self):\\n print(\\\"\\\")\\n strRepresentation = [str(i + 1) + \\\". \\\" + c.__str__() for i,c in enumerate(self.__contactList) if not c .getHide()]\\n\\n for s in strRepresentation:\\n print (s)\\n print(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4a6c153d648abf383fa9077ad141f0a\",\n \"score\": \"0.6751819\",\n \"text\": \"def _load_contacts_in_combobox(self):\\n self._addr_book = self._video_engine.get_adress_book()\\n self.contacts_combobox.clear()\\n self.contacts_combobox.insertItem(0, _(\\\"VISIO_Contacts\\\"))\\n \\n # For contacts\\n if not self._addr_book:\\n return\\n\\n self._index_to_address_dict = dict()\\n for cnt, contact in enumerate(self._addr_book.contacts):\\n self._index_to_address_dict[cnt+1] = contact.address\\n text = str(\\\"%s\\\" % contact.name)\\n self.contacts_combobox.insertItem(cnt+1, text)\\n\\n self.contacts_combobox.update()\\n self.contacts_combobox.updateGeometry()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82a9069425d8e26d38ec695c7c2a49b\",\n \"score\": \"0.6693157\",\n \"text\": \"def list_top_contact(self):\\n print('-------------------------------------------------------------------------')\\n print_contact({'name': 'NAME', 'phone': 'PHONE', 'outgoing': 'OUTGOING CALL'}, top=True)\\n print('-------------------------------------------------------------------------')\\n for item in sort_data(self.all_data, 2)[:5]:\\n print_contact({'name': item[0], 'phone': item[1], 'outgoing': item[2]}, top=True)\\n print('-------------------------------------------------------------------------')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d9982ffd056fcbde80c90c6a343ed9\",\n \"score\": \"0.659237\",\n \"text\": \"def sort_entries(self):\\n \\n global choice\\n self.parent.sort_criterion = choice.get()\\n self.parent.current_phonebook_list = \\\\\\n self.parent.current_phonebook.sort(self.parent.sort_criterion,\\\\\\n self.parent.current_phonebook.entries)\\n self.parent.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c893c7729b82785491647011207919de\",\n \"score\": \"0.63674235\",\n \"text\": \"def _fill_contacts(self, contacts):\\n treeview = self.view['contacts_treeview']\\n model = treeview.get_model()\\n model.add_contacts(contacts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29b2837454e96e7fb8d5d4e563876f44\",\n \"score\": \"0.631471\",\n \"text\": \"def Display_Contacts_UI():\\n for i in range(contact_Manager.Get_ContactList_Length()):\\n contactDetails = contact_Manager.Get_Contact_Details(i)\\n print(\\\"ID: \\\" + str(contactDetails[0]) + \\\", Name: \\\" + contactDetails[1] + \\\", Address: \\\" + contactDetails[2] + \\\", Phone Number: \\\" + contactDetails[3] + \\\", Birthday: \\\" + contactDetails[4])\\n input(\\\"Press enter to continue...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac3c11fa56e4ec303cd7000728aff03\",\n \"score\": \"0.6263225\",\n \"text\": \"def printing_contacts(self):\\n print('\\\\n' + self.phonebook_name + '\\\\n')\\n for any_name in self.contacts:\\n any_name.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53935fe8afd34043d6b6f14e7e4b6d8c\",\n \"score\": \"0.62523556\",\n \"text\": \"def call_sort_dialog(self):\\n \\n if self.current_phonebook is not None:\\n sort_dialog = SortChoice(self)\\n else: \\n info = \\\"Sortieren nicht möglich.\\\\n\\\\nAktuell kein Telefonbuch offen.\\\"\\n messagebox.showinfo(title = \\\"Info\\\", message = info)\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012384369b61ad6b14be50a16e7d2782\",\n \"score\": \"0.6226227\",\n \"text\": \"def display_contacts(cls):\\n return cls.contact_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f930b96096020d44ef64950218d288b\",\n \"score\": \"0.62225837\",\n \"text\": \"def contacts():\\n contacts = api.Api()\\n print('Contacts:')\\n print('---------')\\n print(contacts.get_contacts())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea1bffa5393b2e3c42afebaaa1d2e1c9\",\n \"score\": \"0.6204785\",\n \"text\": \"def show_all(self):\\n for contact in self._contacts:\\n self._print_contact(contact)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da079ce14a389616bfa3f90358e8ea26\",\n \"score\": \"0.6147651\",\n \"text\": \"def display_contact(cls):\\n return cls.contact_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54627ead7473a3f6edd5c83240f43ddf\",\n \"score\": \"0.61353034\",\n \"text\": \"def list_contacts():\\n contact_list = ContactsController().list_contacts()\\n\\n click.echo(\\\"ID | FULL NAME | EMAIL | PHONE\\\")\\n for contact in contact_list:\\n contact[\\\"id\\\"] = \\\"{:02d}\\\".format(contact[\\\"id\\\"])\\n click.echo(\\\"{id} | {name} {lastname} | {email} | {phone}\\\".format(**contact))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02688387be5e1bb4d3b3cf846c8c695d\",\n \"score\": \"0.6121694\",\n \"text\": \"def listcontacts(self):\\r\\n return self.contacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6083a0b1c9802c79ba992dc83f2ed0\",\n \"score\": \"0.61123896\",\n \"text\": \"def list_all_contact(self):\\n print('-------------------------------------------------------------')\\n print_contact({'name': 'NAME', 'phone': 'PHONE'})\\n print('-------------------------------------------------------------')\\n for item in self.all_data:\\n print_contact({'name': item[0], 'phone': item[1]})\\n print('-------------------------------------------------------------')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0bd5b30b8a09913a2e67992f9045651\",\n \"score\": \"0.609338\",\n \"text\": \"def get_lists_for_contact(self, contact):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e9997fec53ac42906bad6d94e84df3\",\n \"score\": \"0.6086291\",\n \"text\": \"def contacts(self):\\n if self._contacts is None:\\n self._contacts = OrderedDict(\\n sorted(((i, j), c) for (i, j), c in self.pairs.iteritems()\\n if c.N > 0 and i < j))\\n return self._contacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b08b8849f94c93d12bf065d292f21e\",\n \"score\": \"0.60662204\",\n \"text\": \"def _fill_contact_list(self):\\r\\n self._add_contact('dx@emesene.org', 'XD', e3.status.ONLINE, '', False)\\r\\n self._add_contact('roger@emesene.org', 'r0x0r', e3.status.ONLINE,\\r\\n '', False)\\r\\n self._add_contact('boyska@emesene.org', 'boyska', e3.status.ONLINE,\\r\\n '', True)\\r\\n self._add_contact('pochu@emesene.org', '<3 debian', e3.status.BUSY,\\r\\n '', False)\\r\\n self._add_contact('cloud@emesene.org', 'nube', e3.status.BUSY,\\r\\n '', False)\\r\\n self._add_contact('otacon@emesene.org', 'Otacon', e3.status.BUSY,\\r\\n '', True)\\r\\n self._add_contact('federico@emesene.org', 'federico..', e3.status.AWAY,\\r\\n 'he loves guiness', False)\\r\\n self._add_contact('respawner@emesene.org', 'Respawner', e3.status.AWAY,\\r\\n '', False)\\r\\n self._add_contact('mohrtutchy@emesene.org', 'moh..', e3.status.AWAY,\\r\\n 'one love', True)\\r\\n self._add_contact('nassty@emesene.org', 'nassto', e3.status.IDLE,\\r\\n '', False)\\r\\n self._add_contact('j0hn@emesene.org', 'juán', e3.status.IDLE, '', False)\\r\\n self._add_contact('c0n0@emesene.org', 'conoconocono', e3.status.IDLE,\\r\\n '', True)\\r\\n self._add_contact('warlo@emesene.org', 'warlo', e3.status.OFFLINE,\\r\\n '', False)\\r\\n self._add_contact('wariano@emesene.org', 'wariano', e3.status.OFFLINE,\\r\\n '', False)\\r\\n self._add_contact('Faith_Nahn@emesene.org', 'Gtk styler',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('you@emesene.org', 'I\\\\'m on emesene code!',\\r\\n e3.status.OFFLINE, '', True)\\r\\n\\r\\n self._add_contact('one@hotmail.com', '- [b][c=48]Pαrκ¡[/c=30][/b]',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('two@hotmail.com',\\r\\n '[c=46]-๑๑test_test๑๑-[/c=2]', e3.status.BUSY, '', False)\\r\\n self._add_contact('three@hotmail.com',\\r\\n '[c=29]•°o.Orandom εïз stuff O.o°•[/c=36]·$28',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('four@hotmail.com',\\r\\n '[c=48][b]hy[/b][/c=11] ·#·$3,3\\\\'_·$#fcfcfc,#fcfcfc\\\\'_·$4,4\\\\'_·0·$28',\\r\\n e3.status.ONLINE, '', False)\\r\\n self._add_contact('five@hotmail.com',\\r\\n '·&·#·$9X.|̲̅·$10X·$9̲̅·$10x·$9̲̅·$10x·$9̲̅·$10x·$9̲̅·$10x·$9̲̅|·$10·#',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('six@hotmail.com', '[c=46][u][b]xafd! [/b][/u][/c]',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('seven@hotmail.com',\\r\\n '[c=5]((_...sdsdf..._))..)_<(_))(°.°)(...][/c=48][u][/u]',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('eight@hotmail.com',\\r\\n '[i][B][C=12]☆[/C=0][C=0]☆[/C=12][c=12]☆[/c=0] (W) [C=12]Bellamezz[/c=49] (F) [c=0]☆[/c=0][c=12]☆[/c=12][c=0]☆[/c=0][/B][/i]',\\r\\n e3.status.AWAY, '', False)\\r\\n self._add_contact('nine@hotmail.com',\\r\\n '[b](*) ... [c=12]Ricky[/c=33] ...(*)[/b]',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('ten@hotmail.com',\\r\\n '<:o)[c=yellow]Yellow:DYellow[/c][c=red]Red[c=blue]Blue:)Blue[c=green]Green[/c][/c][/c]',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('eleven@hotmail.com',\\r\\n '·$,32·$59«·$,41·$50«·$,50·$41«·$,59·$32«·$,66·$23«·$32,1« :: nube ::»·$,66·$23»·$,59·$32»·$,50·$41»·$,41·$50»·$,32·$59»·0 ·$0',\\r\\n e3.status.AWAY, '', False)\\r\\n self._add_contact('twelve@hotmail.com',\\r\\n '·$4Red·$11Aqua·$6,8PurpleOnYellow·$14GrayOnYellow·0Black·$9Lime',\\r\\n e3.status.BUSY, '', False)\\r\\n self._add_contact('thirteen@hotmail.com',\\r\\n '[c=7][b][I am][/c=yellow][/b][c=70][/c=89][c=89] so [A]wesome that [I] only use invalid tags [/c]',\\r\\n e3.status.ONLINE, '', False,\\r\\n '[ I am ] so [ A ]wesome that [ I ] only use invalid tags ')\\r\\n self._add_contact('fourteen@hotmail.com',\\r\\n '\\\")':)(H);)"\\\\')',\\r\\n e3.status.BUSY, '', False,\\r\\n '\\\\\\\")'(emo)(emo)(emo)"\\\\')')\\r\\n self._add_contact('fifteen@hotmail.com',\\r\\n '1:)2;)3\\\\')4(><)5;)6:)7',\\r\\n e3.status.BUSY, '', False,\\r\\n '1(emo)2(emo)3\\\\')4(><)5(emo)6(emo)7')\\r\\n self._add_contact('sixteen@hotmail.com',\\r\\n '[C=80][i][b][I][/b]Single i[/i][/c=30][b][/C]Single /C[/B]',\\r\\n e3.status.OFFLINE, '', False,\\r\\n '[ i ]Single i[ /C ]Single /C')\\r\\n self._add_contact('seventeen@hotmail.com',\\r\\n '[/c][c=48][c=12][b]Colorful nickname[/b][/c=50]',\\r\\n e3.status.OFFLINE, '', False,\\r\\n '[ /c ][ c=48 ]Colorful nickname')\\r\\n\\r\\n self._add_group('ninjas')\\r\\n self._add_group('pirätes')\\r\\n self._add_group('lulz')\\r\\n self._add_group('code quiz ninjas')\\r\\n self._add_group('empty')\\r\\n self._add_group('strange nicks')\\r\\n\\r\\n self._add_contact_to_group('you@emesene.org', 'pirätes')\\r\\n self._add_contact_to_group('boyska@emesene.org', 'pirätes')\\r\\n self._add_contact_to_group('j0hn@emesene.org', 'pirätes')\\r\\n self._add_contact_to_group('c0n0@emesene.org', 'pirätes')\\r\\n self._add_contact_to_group('nassty@emesene.org', 'lulz')\\r\\n self._add_contact_to_group('warlo@emesene.org', 'lulz')\\r\\n self._add_contact_to_group('you@emesene.org', 'lulz')\\r\\n self._add_contact_to_group('cloud@emesene.org', 'lulz')\\r\\n self._add_contact_to_group('dx@emesene.org', 'ninjas')\\r\\n self._add_contact_to_group('roger@emesene.org', 'ninjas')\\r\\n self._add_contact_to_group('c0n0@emesene.org', 'ninjas')\\r\\n self._add_contact_to_group('boyska@emesene.org', 'ninjas')\\r\\n self._add_contact_to_group('Faith_Nahn@emesene.org', 'code quiz ninjas')\\r\\n\\r\\n self._add_contact_to_group('one@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('two@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('three@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('four@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('five@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('six@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('seven@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('eight@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('nine@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('ten@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('eleven@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('twelve@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('thirteen@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('fourteen@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('fifteen@hotmail.com', 'strange nicks')\\r\\n self._add_contact_to_group('sixteen@hotmail.com', 'strange nicks')\\r\\n\\r\\n #test pending contact dialog\\r\\n #self._add_pending_contacts()\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52850584d3179ad57f68444e3ce03cfe\",\n \"score\": \"0.6038005\",\n \"text\": \"def show_contacts():\\n\\n return render_template(\\\"contacts.html\\\", contacts=contacts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069378b05f593c83951440b0f7f64cc8\",\n \"score\": \"0.6034698\",\n \"text\": \"def BCListViewSort(lv):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8abdfc21cad233c5f93db0c57d767f\",\n \"score\": \"0.5995351\",\n \"text\": \"def render_contacts(self, windowInfo):\\n\\n # draw contact point\\n contact_point_color = (255, 0, 0)\\n point_radius = 2\\n for index in range(0, len(self.contacts)):\\n tmpManifold = self.contacts[index]\\n for index2 in range(0, tmpManifold.contact_count):\\n c = tmpManifold.contacts[index2]\\n pygame.draw.circle(\\n windowInfo,\\n contact_point_color,\\n (int(c.x), int(c.y)),\\n int(point_radius))\\n\\n\\n # draw contact normal\\n contact_nomral_color = (0, 255, 0)\\n for index in range(0, len(self.contacts)):\\n tmpManifold = self.contacts[index]\\n n = tmpManifold.normal\\n for index2 in range(0, tmpManifold.contact_count):\\n\\n c = tmpManifold.contacts[index2]\\n c_l = Vector2(c.x, c.y)\\n\\n n *= 8\\n c_l += n\\n\\n line_vertices = []\\n line_vertices.append((c.x, c.y))\\n line_vertices.append((c_l.x, c_l.y))\\n\\n pygame.draw.lines(\\n windowInfo,\\n contact_nomral_color,\\n False,\\n line_vertices)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"813656f8e74d34ed330874a6c7514278\",\n \"score\": \"0.59764063\",\n \"text\": \"def __paintContactList(self):\\n self.contactsArea = guiobjects.OcempImageContactList(130, 270, self.player.getAgenda())\\n self.contactsArea.topleft = 20, 40\\n self.contactsArea.connect_signal (ocempgui.widgets.Constants.SIG_SELECTCHANGED, self.__selectionChange)\\n self.container.add_child(self.contactsArea)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d3866fefce99afd7a732bc5dbf177f\",\n \"score\": \"0.59717494\",\n \"text\": \"def contact_details():\\n\\n #Global variables that are use in other functions\\n global scrollable_frame, num\\n\\n ##############################################################################\\n # Scripts that creates a canvas and scrollbar from the tkinter module in #\\n # order for multiple contacts to be shown by scrolling up and down # \\n ##############################################################################\\n canvas = Canvas(HomeFrame)\\n scrollbar = Scrollbar(HomeFrame, orient=\\\"vertical\\\", command=canvas.yview)\\n scrollable_frame = Frame(canvas)\\n\\n scrollable_frame.bind(\\n \\\"<Configure>\\\",\\n lambda e: canvas.configure(\\n scrollregion=canvas.bbox(\\\"all\\\")\\n )\\n )\\n canvas.create_window((0, 0), window=scrollable_frame, anchor=\\\"nw\\\")\\n\\n canvas.configure(yscrollcommand=scrollbar.set)\\n HomeFrame.pack()\\n canvas.pack(side=\\\"left\\\", fill=\\\"both\\\", expand=True)\\n scrollbar.pack(side=\\\"right\\\", fill=\\\"y\\\")\\n\\n #Creates an empty list to store the number each user has\\n contact_num = []\\n\\n #Calls the database function\\n database()\\n\\n #Fectches all the contacts associated to the user\\n cursor.execute(\\\"SELECT * FROM contacts WHERE `user_id` = ?\\\", (id_number,))\\n contacts = cursor.fetchall()\\n\\n # Outputs the contacts if any\\n if contacts:\\n for contact in contacts:\\n\\n #Outputs each contact row by row\\n number = Button(scrollable_frame, text = str(contacts.index(contact)+1) + \\\"\\\\t\\\\t\\\" + str(contact[1]) + \\\" \\\"*(18- len(str(contact[1]))) +\\\"\\\\t\\\\t\\\" + str(contact[2]) + \\\" \\\"*(26 - len(str(contact[2]))) + \\\"\\\\t\\\\t\\\" + str(contact[3]) + \\\" \\\"*(11 - len(str(contact[3]))) + \\\"\\\\t\\\\t\\\\t\\\" + str(contact[4]) + \\\" \\\"*(25 - len(str(contact[4]))), font = ('arial', 10, 'bold'), bg = \\\"AliceBlue\\\")\\n number.pack(pady = 20, fill = BOTH, expand = True)\\n\\n\\n else:\\n #Output \\\"No Contacts Added yet\\\" if not contact was found in the database\\n no_contact = Label(scrollable_frame, text =\\\"\\\\n\\\" + \\\"\\\\t\\\" * 7+ \\\"No Contacts Added yet\\\", font = ('arial', 12, 'bold'))\\n no_contact.pack()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1037c52dd95c870870a44b94d6fe00d8\",\n \"score\": \"0.5905942\",\n \"text\": \"def updateContactList(self): \\n self.contactsArea.setContacts(self.player.getAgendaData())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9a4cd2b0eb90570947728f673823b4\",\n \"score\": \"0.59010136\",\n \"text\": \"def get_contacts(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd073edd12846000027725085de5c05\",\n \"score\": \"0.5876278\",\n \"text\": \"def plot_contacts(total_contacts):\\n\\n fig1, ax1 = plt.subplots()\\n ax1.hist(total_contacts, \\n bins = range(1, int(total_contacts.max() + 1)), \\n edgecolor = 'k')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098fec5b99bc0044315cdca39b29a16d\",\n \"score\": \"0.58402216\",\n \"text\": \"def contact_list(request):\\n c = {}\\n c['messages'] = []\\n c['errors'] = []\\n l = pynag.Parsers.mk_livestatus(nagios_cfg_file=adagios.settings.nagios_config)\\n c['contacts'] = l.query('GET contacts')\\n c['contacts'] = pynag.Utils.grep(c['contacts'], **request.GET)\\n c['contactgroups'] = l.query('GET contactgroups')\\n return render_to_response('status_contacts.html', c, context_instance=RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85701b9420253b4be2e01af3857849b6\",\n \"score\": \"0.58238846\",\n \"text\": \"def display():\\n \\n key_sort = input(\\\"User information is displayed in ascending order by name/age/contact? >>>\\\")\\n if key_sort == 'age':\\n users_sorted = sorted(usersData, key=lambda x:usersData[x][0])\\n elif key_sort == 'contact':\\n users_sorted = sorted(usersData, key=lambda x:usersData[x][1])\\n else:\\n users_sorted = sorted(usersData)\\n \\n print(\\\"{:<15}{:<10}{:<20}{:<15}\\\".format('Username', 'Age', 'Contact', 'Password'))\\n print(\\\"-\\\"*60)\\n for i in range(len(users_sorted)):\\n print(\\\"{:<15}{:<10}{:<20}{:<15}\\\".format(users_sorted[i], usersData[users_sorted[i]][0],\\\\\\n usersData[users_sorted[i]][1], '*'*len(usersData[users_sorted[i]][2])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ac0d77926ce7edcccd20ffcafb3fd66\",\n \"score\": \"0.5801961\",\n \"text\": \"def list_contacts_command():\\n # Get command arguments from user\\n filters = demisto.args()\\n # Make request and get raw response\\n contacts = list_contacts(filters)\\n # Parse response into context\\n contexts = []\\n for contact in contacts:\\n # Parse individual contact response in context\\n context = format_contact_context(contact)\\n contexts.append(context)\\n filters_as_strings = ', '.join([f'{key}: {val}' for key, val in filters.items()])\\n title = f'Contacts Filtered by {filters_as_strings}' if filters else 'All Contacts'\\n human_readable = tableToMarkdown(title, contexts, removeNull=False)\\n demisto.results({\\n 'Type': entryTypes['note'],\\n 'ContentsFormat': formats['json'],\\n 'Contents': contacts,\\n 'ReadableContentsFormat': formats['markdown'],\\n 'HumanReadable': human_readable,\\n 'EntryContext': {\\n 'Freshdesk.Contact(val.ID && val.ID === obj.ID)': contexts\\n }\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1ce7941c00194b69e0108f5e2d27c5\",\n \"score\": \"0.5766897\",\n \"text\": \"def start_sorting(self):\\n self.sorting = True\\n for i, line in enumerate(self.sort_list):\\n if not self.sorting:\\n break\\n if i==0:\\n continue\\n del self.sort_list[i]\\n self.window.itemconfig(line[1], fill='red')\\n self.window.update()\\n self.insert(i, line, 0)\\n\\n if i == len(self.sort_list)-1:\\n self.sorting = False\\n for complete_line in self.sort_list:\\n self.window.itemconfig(complete_line[1], fill='green')\\n self.window.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85b47ce8292e8f8701b064c66a939d3\",\n \"score\": \"0.5721245\",\n \"text\": \"def contact_to_display(self, type='All contacts'):\\n send_key(KEY_MENU)\\n sleep(2)\\n click_textview_by_text('Contacts to display')\\n if search_text(type):\\n log_test_framework(self.TAG, str(type))\\n click_textview_by_text(type)\\n else:\\n log_test_framework(self.TAG, \\\"can not find \\\" + str(type) + \\\" in this options choise\\\")\\n sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e86df4d7c828f5409becf716c4ced9a\",\n \"score\": \"0.5715643\",\n \"text\": \"def print_contact(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"111b49a3fc8c2fd5c4a691740afc7456\",\n \"score\": \"0.5698682\",\n \"text\": \"def output_bbdb_file(contacts):\\n print_bbdb_header()\\n printed = set()\\n for contact in contacts:\\n # don't print the same contact twice\\n if contact.id in printed:\\n continue\\n # don't print entries that have no email addresses\\n if not contact.email:\\n continue\\n print(format_contact_bbdb(contact).encode(\\\"utf-8\\\"))\\n printed.add(contact.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca07c6b8db4c2e94749621afcb8d6e6a\",\n \"score\": \"0.5686167\",\n \"text\": \"def test_display_all_contact(self):\\n\\n return cls.contact_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379c25f17a3294d105b7ed733cbac092\",\n \"score\": \"0.5674813\",\n \"text\": \"def get_contacts():\\n \\n #Firstname, Lastname, Zipcode, Phonenumber\\n \\n contacts = [\\n [\\\"B@dN@m3\\\", \\\"Johnson\\\", \\\"23435\\\", \\\"808-123-1234\\\"],\\n [\\\"Ada\\\", \\\"O'Mally\\\", \\\"12345\\\", \\\"000-000-0000\\\"],\\n [\\\"Grace\\\", \\\"Hopper\\\", \\\"35254\\\", \\\"983-245-3535\\\"],\\n [\\\"Mary\\\", \\\"Jackson-Smith\\\", \\\"23435\\\", \\\"757-389-8372\\\"],\\n [\\\"Dorthy\\\", \\\"Vaughan\\\", \\\"56\\\",\\\"567-353-35654323456\\\"],\\n [\\\"Mary\\\", \\\"Keller\\\", \\\"8\\\", \\\"875-98766-9\\\"],\\n [\\\"Edith\\\", \\\"Clarke\\\",\\\"34533\\\", \\\"5678909876545678\\\"],\\n [\\\"John\\\", \\\"Doe\\\", \\\"87624\\\", \\\"878-345-2463\\\"],\\n [\\\"Jim\\\", \\\"Bob\\\", \\\"98769\\\", \\\"800-777-5555\\\"],\\n [\\\"John\\\", \\\"Smith\\\", \\\"34535\\\", \\\"345-355-3452\\\"],\\n [\\\"Anna\\\", \\\"Queen\\\", \\\"83752\\\", \\\"453-254-8472\\\"],\\n [\\\"Moana\\\", \\\"Princess\\\", \\\"96707\\\", \\\"808-551-5555\\\"],\\n [\\\"Sam\\\", \\\"Smith\\\", \\\"24553\\\", \\\"864-284-9328\\\"],\\n [\\\"John\\\", \\\"Doe\\\", \\\"87624\\\", \\\"878-345-2463\\\"],\\n [\\\"Jim\\\", \\\"Bob\\\", \\\"98769\\\", \\\"800-777-5555\\\"],\\n [\\\"Katherine\\\", \\\"Johnson\\\", \\\"23435\\\", \\\"808-123-1234\\\"],\\n [\\\"Ada\\\", \\\"Lovelace\\\", \\\"12345\\\", \\\"000-000-0000\\\"],\\n [\\\"Grace\\\", \\\"Hopper\\\", \\\"35254\\\", \\\"983-245-3535\\\"],\\n [\\\"Mary\\\", \\\"Jackson\\\", \\\"23435\\\", \\\"757-389-8372\\\"],\\n [\\\"Dorthy\\\", \\\"Vaughan\\\", \\\"56\\\",\\\"567-353-35654323456\\\"],\\n [\\\"Mary\\\", \\\"Keller\\\", \\\"8\\\", \\\"875-98766-9\\\"],\\n [\\\"Edith\\\", \\\"Clarke\\\",\\\"34533\\\", \\\"5678909876545678\\\"],\\n [\\\"John\\\", \\\"Doe\\\", \\\"87624\\\", \\\"878-345-2463\\\"],\\n [\\\"Jim\\\", \\\"Bob\\\", \\\"98769\\\", \\\"800-777-5555\\\"],\\n [\\\"John\\\", \\\"Smith\\\", \\\"34535\\\", \\\"345-355-3452\\\"]\\n ]\\n \\n return contacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a019bb4d094da9cafa8f71cf9b8735f4\",\n \"score\": \"0.5652653\",\n \"text\": \"def start_sorting(self):\\n if self.sorting:\\n return None\\n self.sorting = True\\n self.quick_sort(0, len(self.sort_list)-1)\\n if self.sorting:\\n for line in self.sort_list:\\n self.window.itemconfig(line[1], fill='green')\\n self.window.update()\\n self.sorting = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2f4a37af27468b5364a2e330c526e40\",\n \"score\": \"0.56387544\",\n \"text\": \"def on_contact_display_name_changed(self, contact):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d1381f5ec1de6e138776d9a79456167\",\n \"score\": \"0.56109416\",\n \"text\": \"def test_display_all_contacts(self):\\n self.assertEqual(Contact.display_contacts(), Contact.contact_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679500a5a12676f5ead29798a71a2ace\",\n \"score\": \"0.55970997\",\n \"text\": \"def get_contact_list(self):\\n # TODO: get all contacts\\n self.logger.info(\\\"function: InstantMessengerVmmSide::get_contact_list\\\")\\n if self.is_connected:\\n self.guest_obj.send(\\\"application \\\" + \\\"instantMessenger \\\" + str(self.window_id) + \\\" get_contact_list \\\")\\n else:\\n self.logger.error(\\\"function: InstantMessengerVmmSide::get_contact_list: not connected!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"befd77431a8c3d390148045ab1c79f39\",\n \"score\": \"0.558324\",\n \"text\": \"def get_contact_list(self, args):\\n self.instantMessengerApp.get_contact_list(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e0892439a91e2161dac309e58fc5420\",\n \"score\": \"0.5580354\",\n \"text\": \"def list_contacts(self):\\n catalog = getToolByName(getSite(), 'portal_catalog')\\n query = {'portal_type': 'opengever.contact.contact'}\\n # make catalog query without checking security (allowedRolesAndUsers)\\n # since the contacts are not visible for foreign users but should be\\n # in the vocabulary anyway...\\n brains = ZCatalog.searchResults(catalog, **query)\\n return brains\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a2b171ab9a7c0d93718c8fee6b6c9c\",\n \"score\": \"0.5568778\",\n \"text\": \"def Search_Contacts_UI():\\n print(\\\"\\\")\\n print(\\\"+---------Search----------+\\\")\\n print(\\\"| 1) Search Names |\\\")\\n print(\\\"| 2) Search Address's |\\\")\\n print(\\\"| 3) Search Phone Numbers |\\\")\\n print(\\\"| 4) Search Birthdays |\\\")\\n print(\\\"| Enter 'Exit' to return |\\\")\\n print(\\\"| to menu. |\\\")\\n print(\\\"+-------------------------+\\\")\\n print(\\\"(1-4 / Exit)\\\")\\n valid_input = False\\n while valid_input == False: ## For sanitising files\\n user_input = input(\\\"Enter your selection here: \\\")\\n\\n if user_input == \\\"1\\\": ########## Name search\\n valid_input = True\\n search_input = input(\\\"What name would you like to search for?: \\\")\\n\\n results = contact_Manager.Search_Contact_Name(search_input)\\n if len(results) == 0:\\n print(\\\"There are no contacts with that name...\\\")\\n else:\\n print(\\\"These are the contacts with that name:\\\")\\n for i in range(len(results)):\\n print(\\\"ID: \\\" + str(results[i][0]) + \\\", Name: \\\" + results[i][1] + \\\", Address: \\\" + results[i][2] + \\\", Phone Number: \\\" + results[i][3] + \\\", Birthday: \\\" + results[i][4])\\n\\n elif user_input == \\\"2\\\": ########## Address search\\n valid_input = True\\n search_input = input(\\\"What address would you like to search for?: \\\")\\n\\n results = contact_Manager.Search_Contact_Address(search_input)\\n if len(results) == 0:\\n print(\\\"There are no contacts with that address...\\\")\\n else:\\n print(\\\"These are the contacts with that address:\\\")\\n for i in range(len(results)):\\n print(\\\"ID: \\\" + str(results[i][0]) + \\\", Name: \\\" + results[i][1] + \\\", Address: \\\" + results[i][2] + \\\", Phone Number: \\\" + results[i][3] + \\\", Birthday: \\\" + results[i][4])\\n\\n elif user_input == \\\"3\\\": ########## Phone number search\\n valid_input = True\\n print(\\\"Phone numbers are always 10 numerical characters, e.g. 7945625056\\\")\\n search_input = input(\\\"What phone number would you like to search for?: \\\")\\n results = contact_Manager.Search_Contact_PhoneNumber(search_input)\\n\\n if len(results) == 0:\\n print(\\\"There are no contacts with that phone number...\\\")\\n else:\\n print(\\\"These contacts have that phone number:\\\")\\n for i in range(len(results)):\\n print(\\\"ID: \\\" + str(results[i][0]) + \\\", Name: \\\" + results[i][1] + \\\", Address: \\\" + results[i][2] + \\\", Phone Number: \\\" + results[i][3] + \\\", Birthday: \\\" + results[i][4])\\n\\n elif user_input == \\\"4\\\": ########## Birthday Search\\n valid_input = True\\n print(\\\"Birthdays are always in the form of dd/mm/yyyy, e.g. 10/07/2002\\\")\\n search_input = input(\\\"What birthday would you like to search for?: \\\") ##Sanitise\\n\\n results = contact_Manager.Search_Contact_Birthday(search_input)\\n if len(results) == 0:\\n print(\\\"There are no contacts with that birthday...\\\")\\n else:\\n print(\\\"These contacts have that birthday:\\\")\\n for i in range(len(results)):\\n print(\\\"ID: \\\" + str(results[i][0]) + \\\", Name: \\\" + results[i][1] + \\\", Address: \\\" + results[i][2] + \\\", Phone Number: \\\" + results[i][3] + \\\", Birthday: \\\" + results[i][4])\\n\\n elif user_input == \\\"Exit\\\":\\n valid_input = True\\n print(\\\"Exit has been selected...\\\")\\n else:\\n print(\\\"That is not a valid input, please try again...\\\")\\n input(\\\"Press enter to continue...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b7533e490ca712ecb27c03693df1ca\",\n \"score\": \"0.55497\",\n \"text\": \"def get_project_client_contacts(self):\\n client = self.aq_parent.aq_parent\\n pc = getToolByName(self, 'portal_catalog')\\n brains = pc(portal_type='Contact', path={'query': \\\"/\\\".join(client.getPhysicalPath()), 'level': 0})\\n contacts = []\\n for brain in brains:\\n obj = brain.getObject()\\n contacts.append((obj.UID(), obj.Title(), obj.getEmailAddress()))\\n contacts.sort(lambda x, y: cmp(x[1], y[1]))\\n\\n return contacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012ce9300b8202f2fea6b42da5563bec\",\n \"score\": \"0.55245584\",\n \"text\": \"def list_contacts(self):\\n for ls in self._contacts.keys():\\n return ls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f071e88225aa2e2fabfec9b5b8c4b973\",\n \"score\": \"0.55097026\",\n \"text\": \"def contact_list():\\n\\n return render_template(\\\"contact-list.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab9fa9e78e893df6e7b83c32bbba95e3\",\n \"score\": \"0.5508335\",\n \"text\": \"def BCListViewSortOrder(lv):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab2a876e9e85c9f6efa0ebd79e35844\",\n \"score\": \"0.54984254\",\n \"text\": \"def get_contact_list(self, args):\\n self.logger.info(\\\"function: InstantMessengerGuestSide::send_msg_to\\\")\\n self.agent_object.send(\\\"application \\\" + self.module_name + \\\" \\\" + str(self.imParent.window_id) + \\\" busy\\\")\\n\\n #\\n # TODO: get contact list via plugin\\n #\\n self.send_to_plugin(\\\"get_contact_list\\\", [])\\n\\n self.agent_object.send(\\\"application \\\" + self.module_name + \\\" \\\" + str(self.imParent.window_id) + \\\" ready\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5607cabd7916b28ff015b44347787545\",\n \"score\": \"0.548218\",\n \"text\": \"def update(self, contacts):\\n rlist = []\\n for contact in contacts:\\n try:\\n rlist.append(self._contacts_by_hash[contact.hash.value])\\n # If the hash has not been seen yet\\n # TODO: this could error out with an AttributeError (no hash)\\n except KeyError:\\n try:\\n rcontact = self._contacts_by_addr[str(contact.address)]\\n rlist.append(rcontact)\\n if rcontact.needs_hash and not contact.needs_hash:\\n rcontact.hash = contact.hash\\n # Happens if the contact is not made\\n except KeyError:\\n contact.channels['bytelynx'].crypto.p = self._dh_p\\n contact.on_hash += self.on_contact_hash\\n contact.on_death += self.clean_contact\\n self._contacts_by_addr[str(contact.address)] = contact\\n if not contact.needs_hash:\\n self._contacts_by_hash[contact.hash.value] = contact\\n self._on_changed()\\n rlist.append(contact)\\n return rlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74386c580cbfda0050be966172a51ad5\",\n \"score\": \"0.5475833\",\n \"text\": \"def sort(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b35a13ccfbeb57cdbf506da72f500a\",\n \"score\": \"0.54480034\",\n \"text\": \"def sort_list():\\n temp_list = list(listbox2.get(0, tk.END))\\n temp_list.sort(key=str.lower)\\n # delete contents of present listbox\\n listbox2.delete(0, tk.END)\\n # load listbox with sorted data\\n for item in temp_list:\\n listbox2.insert(ttk.END, item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8505f49547c02c5a51bb24e5358c520e\",\n \"score\": \"0.5447792\",\n \"text\": \"def add_contact_window(self):\\n global select_dialog\\n global contact\\n contact = self.ui.list_users.currentIndex().data()\\n select_dialog = AddContactDialog(self.sock, self.database, contact)\\n select_dialog.btn_ok.clicked.connect(lambda: self.add_contact_action(select_dialog))\\n select_dialog.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0054bfe34c078072ea8350f13395360\",\n \"score\": \"0.54398054\",\n \"text\": \"def get_contacts(self):\\n try:\\n self.get_roster()\\n except IqError as err:\\n print('IqError')\\n except IqTimeout:\\n print('IqTimeout')\\n\\n print('Buscando contactos...\\\\n')\\n groups = self.client_roster.groups()\\n\\n for group in groups:\\n for jid in groups[group]:\\n connections = self.client_roster.presence(jid)\\n for res, pres in connections.items():\\n if pres['status']:\\n status = pres['status']\\n\\n print(jid + ' - ' + status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d40efda7247cd6bd5561dc9db89d216\",\n \"score\": \"0.54388374\",\n \"text\": \"def display_roster(self):\\r\\n self.__roster.sort()\\r\\n print(self.__roster)\\r\\n print(\\\"Number of students: \\\", len(self.__roster))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc77612e759ca637d40aad798e373df3\",\n \"score\": \"0.5425731\",\n \"text\": \"def show_contact(self):\\n contact = input(\\\"Nick name of the contact :\\\")\\n x = self.data.get_contact(self.user_name, contact)\\n if x:\\n print(f'{x[1].capitalize()} {x[2].capitalize()} know as {x[0].capitalize()}')\\n else:\\n print(\\\"The contact does not exist \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a26d0e81cbbf3e785663fd8cc9b4977\",\n \"score\": \"0.54231584\",\n \"text\": \"def get_contact_list(self):\\n\\t\\tparameter = {\\\"start\\\": 1, \\\"limit\\\": 4, \\\"group_id\\\": 1}\\n\\t\\tcontact_list = self._request_to_freebox(\\\"get_contact_list\\\", parameters=parameter)\\n\\t\\treturn contact_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1806b15f31d62de4040c81d9ef8cf19d\",\n \"score\": \"0.5411482\",\n \"text\": \"def get_contact_list():\\n\\n\\tcond = ['`%s` like \\\"%s%%\\\"' % (f, \\n\\t\\twebnotes.form.getvalue('txt')) for f in webnotes.form.getvalue('where').split(',')]\\n\\tcl = webnotes.conn.sql(\\\"select `%s` from `tab%s` where %s\\\" % (\\n \\t\\t\\t webnotes.form.getvalue('select')\\n\\t\\t\\t,webnotes.form.getvalue('from')\\n\\t\\t\\t,' OR '.join(cond)\\n\\t\\t)\\n\\t)\\n\\twebnotes.response['cl'] = filter(None, [c[0] for c in cl])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380515bd255aa88a3389af546c14b659\",\n \"score\": \"0.54079384\",\n \"text\": \"def addContact(self, name, lastName, age, phoneNumber, email):\\n newContact = Contact(name, lastName, age, phoneNumber, email)\\n self.__contactListDict[newContact.getFullName()] = newContact\\n self.__contactList.append(newContact)\\n \\n #Contacts inside the contact list are always sorted according to thei creation date\\n #from the most receantly added to the latest.\\n self.__contactList = sorted(self.__contactList, key = lambda x : x.getDate(), reverse = True )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"007209a1cf1bbdcadda0e1616ac4a58f\",\n \"score\": \"0.5396982\",\n \"text\": \"def updateDict(self):\\n newContactDict = {}\\n for c in self.__contactList:\\n newKey = c.getName() + \\\" \\\" + c.getLastName()\\n newContactDict[newKey] = c\\n self.__contactListDict = newContactDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147adaba4f8ce0e160871ecb5dcb51ad\",\n \"score\": \"0.53938055\",\n \"text\": \"def request_contacts(self):\\n SOCKET_LOGGER.debug(\\n 'Запрос списка контактов пользователя: %s'\\n % self.client_nickname\\n )\\n self.database.cleat_contact_list()\\n request = {\\n ACTION: GET_CONTACTS,\\n TIME: time(),\\n USER: self.client_nickname\\n }\\n SOCKET_LOGGER.debug(\\n 'Сформирован запрос к серверу: %s' % request\\n )\\n with socket_lock:\\n send_message(self.client_socket, request)\\n response = receive_message(self.client_socket)\\n SOCKET_LOGGER.debug(\\n 'Получен ответ от сервера: %s' % response\\n )\\n if RESPONSE in response and response[RESPONSE] == 202:\\n for contact in response[LIST_INFO]:\\n self.database.add_user_to_contacts(contact)\\n else:\\n SOCKET_LOGGER.error(\\n 'Не удалось обновить список контактов.'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b27a020478aa410b348c6d8ea1ce0f7\",\n \"score\": \"0.53919035\",\n \"text\": \"def sorted_contacts(vertices, normals, T_ar_object):\\n N = 1000\\n # prune vertices that are too close to the table so you dont smack into the table\\n # you may want to change this line, to be how you see fit\\n possible_indices = np.r_[:len(vertices)][vertices[:,2] + T_ar_object[2,3] >= 0.03]\\n np.random.shuffle(possible_indices)\\n\\n # Finding grasp via vertex sampling. make sure to not consider grasps where the \\n # vertices are too big for the gripper\\n\\n # possible metrics: compute_force_closure, compute_gravity_resistance, compute_custom_metric\\n metric = compute_custom_metric\\n grasp_indices = list()\\n metric_scores = list()\\n for i in range(N):\\n candidate_indices = np.random.choice(possible_indices, 2, replace=False)\\n point_dist = utils.length(vertices[candidate_indices[0]] - vertices[candidate_indices[1]])\\n grasp_indices.append(candidate_indices)\\n # assign lowest possible scores to impossible grasps\\n if point_dist < MIN_HAND_DISTANCE or point_dist > MAX_HAND_DISTANCE:\\n metric_scores.append(-sys.maxint - 1)\\n else:\\n contacts = vertices[candidate_indices]\\n contact_normals = normals[candidate_indices]\\n score = metric(contacts, contact_normals, CONTACT_MU, CONTACT_GAMMA, OBJECT_MASS)\\n metric_scores.append(score)\\n\\n # sort metrics and return the sorted order\\n best_metric_indices = sorted(list(range(N)), key=lambda i: metric_scores[i], reverse=True)\\n return grasp_indices, best_metric_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79d917c37725028b6540a886ee83456a\",\n \"score\": \"0.5380656\",\n \"text\": \"def get_contact_list():\\n\\tcond = ['`%s` like \\\"%s%%\\\"' % (f, \\n\\t\\twebnotes.form_dict.get('txt')) for f in webnotes.form_dict.get('where').split(',')]\\n\\tcl = webnotes.conn.sql(\\\"select `%s` from `tab%s` where %s\\\" % (\\n \\t\\t\\t webnotes.form_dict.get('select')\\n\\t\\t\\t,webnotes.form_dict.get('from')\\n\\t\\t\\t,' OR '.join(cond)\\n\\t\\t)\\n\\t)\\n\\twebnotes.response['cl'] = filter(None, [c[0] for c in cl])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702c23f4ce8aca11f9023fcb7b09f9e3\",\n \"score\": \"0.5341022\",\n \"text\": \"def user_map(self):\\n user_map_dialog = QDialog(self)\\n user_map_dialog.setModal(True)\\n bgg_to_ludo = self.get_bgg_to_ludo_users()\\n user_list = [f'{key} -> {value}' for key, value in bgg_to_ludo.items()]\\n list_widget = QListWidget(user_map_dialog)\\n list_widget.addItems(user_list)\\n list_widget.setResizeMode(QListView.Adjust)\\n list_widget.sortItems()\\n grid_layout = QGridLayout(user_map_dialog)\\n grid_layout.addWidget(list_widget, 1, 1)\\n user_map_dialog.resize(400, 400)\\n user_map_dialog.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0190378d6b01f42d2a7dd156acee288b\",\n \"score\": \"0.53393394\",\n \"text\": \"def addF():\\n\\tcontacts.append([fname1.get(),lname1.get(),phone1.get(),address1.get(),email1.get()])\\n\\tselF()\\n\\tmsgBox.showinfo(\\\"Message\\\",\\\"Contact has been added.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d84223c56927be0b900c84622eabac0c\",\n \"score\": \"0.53368086\",\n \"text\": \"def BCListViewCreate(p, numCols, colNames, sorting):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bbcffbd6a57d0ab988b02603ba070af\",\n \"score\": \"0.53294826\",\n \"text\": \"def sorter():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e344cdd0a07f5547660e8ea7b9619c7\",\n \"score\": \"0.53031\",\n \"text\": \"def index(request):\\n contacts = Contact.objects.filter(user=request.user.id)\\n form = ContactForm()\\n context = {'contacts': contacts,\\n 'title': 'contacts',\\n 'form': form,\\n }\\n return render(request, 'contacts/index.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"decb8d0bc339469b9e675778d51b0cc3\",\n \"score\": \"0.52942497\",\n \"text\": \"def display_groups(acct):\\n gdc = gdata.contacts.client.ContactsClient(source='charrington')\\n gdc.ClientLogin(acct[\\\"login\\\"], acct[\\\"password\\\"], gdc.source)\\n feed = gdc.GetGroups()\\n for entry in feed.entry:\\n print(\\\"Group Name: \\\"+entry.title.text)\\n print(\\\"Atom Id: \\\"+entry.id.text+\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45be348ac5d942f4878f40d9c194115e\",\n \"score\": \"0.52916986\",\n \"text\": \"def __append_contact(self, contact):\\r\\n item = gtk.ImageMenuItem()\\r\\n item.set_label(gui.Plus.msnplus_strip(contact.nick))\\r\\n pict = self.__get_contact_pixbuf_or_default(contact.picture)\\r\\n item.set_image(pict)\\r\\n item.connect('activate', self._on_contact_clicked)\\r\\n self.item_to_contacts[item] = contact\\r\\n self.contacts_to_item[contact.account] = item\\r\\n\\r\\n item.show()\\r\\n self.add(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2cedb782e20a8b3484179aaa1ec24f\",\n \"score\": \"0.5277852\",\n \"text\": \"def get_contact_list_from_server(self):\\n self.database.clear_contacts()\\n LOGGER.debug(f'Запрос списка контактов пользователя {self.username}.')\\n get_contacts_dict = GET_CONTACTS_DICT\\n get_contacts_dict[USER] = self.username\\n LOGGER.debug(f'Сформирован запрос {get_contacts_dict}.')\\n\\n # Send a message to the server.\\n with SOCK_LOCK:\\n send_message(self.client_sock, get_contacts_dict)\\n # Get a response from the server.\\n server_answer = get_message(self.client_sock)\\n LOGGER.debug(f'Получен ответ {server_answer}.')\\n\\n if RESPONSE in server_answer and server_answer[RESPONSE] == 202 \\\\\\n and DATA in server_answer:\\n for contact in server_answer[DATA]:\\n self.database.add_contact(contact)\\n else:\\n LOGGER.error('Не удалось обновить список контактов.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e79b38c9c731856d3ff9eccfe4f366\",\n \"score\": \"0.52643657\",\n \"text\": \"def sorted_contacts(vertices, normals, T_ar_object):\\n \\n # prune vertices that are too close to the table so you dont smack into the table\\n # you may want to change this line, to be how you see fit\\n possible_indices = np.r_[:len(vertices)][vertices[:,2] + T_ar_object[2,3] >= 0.03]\\n\\n # Finding grasp via vertex sampling. make sure to not consider grasps where the \\n # vertices are too big for the gripper\\n all_metrics = list()\\n metric = compute_custom_metric\\n grasp_indices = list()\\n # for i in range(?????):\\n # candidate_indices = np.random.choice(possible_indices, 2, replace=False)\\n # grasp_indices.append(candidate_indices)\\n # contacts = vertices[candidate_indices]\\n # contact_normals = normals[candidate_indices]\\n\\n # # YOUR CODE HERE\\n # all_metrics.append(????)\\n\\n # YOUR CODE HERE. sort metrics and return the sorted order\\n\\n return grasp_indices, best_metric_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4a0723c40fb09f7cb293cbbff76515\",\n \"score\": \"0.525799\",\n \"text\": \"def __showCompletionsList(self, completions):\\n if Preferences.getEditor(\\\"AutoCompletionReversedList\\\"):\\n acCompletions = sorted(\\n list(completions),\\n key=self.__replaceLeadingUnderscores)\\n else:\\n acCompletions = sorted(list(completions))\\n self.showUserList(EditorAutoCompletionListID, acCompletions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937b60721992ed7dc333d116a1d3d940\",\n \"score\": \"0.5256559\",\n \"text\": \"def delete_contact(self):\\n search = input(\\\"Please enter name or number: \\\")\\n new_data = []\\n for item in self.all_data:\\n if not fnmatch.fnmatch(str.lower(item[0]), '*{}*'.format(str.lower(search))) or fnmatch.fnmatch(\\n item[1], '*{}*'.format(search)):\\n new_data.append([item[0], item[1], item[2]])\\n\\n self.all_data = sort_data(new_data)\\n\\n return print(\\\"The contact was successfully deleted\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f9d6b0f409896c240ca16146708ca1d\",\n \"score\": \"0.52547616\",\n \"text\": \"def _display_member_rows(self):\\n\\n for i, member in enumerate(self.master.members_list):\\n self.master.members_widget_list[i].entry.delete(0, \\\"end\\\")\\n self.master.members_widget_list[i].entry.insert(0, int(100/len(self.master.members_list)))\\n\\n self.master.members_widget_list[i].pack()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50de5960f7838fbad57e678dc577beb7\",\n \"score\": \"0.52532786\",\n \"text\": \"def run(self):\\n while True:\\n # load data to memory\\n if not self.all_data:\\n tmp_data = []\\n with open(self.filename, 'r') as f:\\n for line in f:\\n data = line.strip().split(',')\\n if validate(data[1]):\\n tmp_data.append([data[0], validate(data[1]), 0])\\n\\n self.all_data = sort_data(tmp_data)\\n\\n print('''Greetings user! Let's make an addressbook!\\n \\nWould you like to:\\n1.) Add a New Contact\\n2.) List All Contacts\\n3.) Search Contacts\\n4.) Delete A Contact\\n5.) Make Call\\n6.) List top 5 Call\\n7.) Quit Program\\\\n''')\\n\\n choice = input(\\\"Select an option: \\\")\\n\\n if choice == \\\"1\\\":\\n try:\\n self.create_contact()\\n except NotAPhoneNumberException:\\n print(\\\"The phone number entered is invalid, creation aborted!\\\")\\n\\n elif choice == \\\"2\\\":\\n self.list_all_contact()\\n\\n elif choice == \\\"3\\\":\\n self.search_contact()\\n\\n elif choice == \\\"4\\\":\\n self.delete_contact()\\n\\n elif choice == \\\"5\\\":\\n self.make_call()\\n\\n elif choice == \\\"6\\\":\\n self.list_top_contact()\\n\\n elif choice == \\\"7\\\":\\n print(\\\"Ending Contact Book.\\\\nHave a nice day!\\\")\\n break\\n\\n else:\\n print(\\\"Invalid Input! Try again.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e42abc1a043de2c7f9ef44a436299b6a\",\n \"score\": \"0.52458614\",\n \"text\": \"def BCListViewSortColumn(lv):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b5177116994ed3f85f1c24b84b944a\",\n \"score\": \"0.5220726\",\n \"text\": \"def search_contact(self):\\n search = input(\\\"Please enter name or phone: \\\")\\n print('-------------------------------------------------------------')\\n print_contact({'name': 'NAME', 'phone': 'PHONE'})\\n print('-------------------------------------------------------------')\\n for item in self.all_data:\\n if fnmatch.fnmatch(str.lower(item[0]), '*{}*'.format(str.lower(search))) or fnmatch.fnmatch(\\n item[1], '*{}*'.format(search)):\\n print_contact({'name': item[0], 'phone': item[1]})\\n print('-------------------------------------------------------------')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95174731acd527373048f166ee49401\",\n \"score\": \"0.52194005\",\n \"text\": \"def Sort(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b59b35c3992aef28f8966571a6932b\",\n \"score\": \"0.52060205\",\n \"text\": \"def __init__(self):\\n # todo : encrypt ?\\n self._contacts = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929a4fda57f841906e9253cec2887805\",\n \"score\": \"0.520311\",\n \"text\": \"def get_all_contacts(self):\\r\\n self.contacts = []\\r\\n try:\\r\\n result = self.contact_db.find({}).sort(\\\"contact_id\\\")\\r\\n for res in result:\\r\\n self.contacts.append(ContactMongo(res))\\r\\n return self.contacts\\r\\n except Exception as error:\\r\\n return str(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a37abdff99d8f954e5ad25f9eae1fb1\",\n \"score\": \"0.520254\",\n \"text\": \"def sort_search_results(self):\\n\\n sort_by = self.ui.comboBox_sort.currentText()\\n if sort_by == \\\"A - z\\\":\\n self.results = sorted(self.results, key=lambda i_: i_['codename'])\\n return\\n if sort_by == \\\"Z - a\\\":\\n self.results = sorted(self.results, key=lambda i_: i_['codename'], reverse=True)\\n return\\n\\n # Order code names by frequency\\n # Get unique code names\\n tmp_names = [r['codename'] for r in self.results]\\n '''for r in self.results:\\n tmp_names.append(r['codename'])'''\\n codenames = list(set(tmp_names))\\n # Create list dictionary of code name and code count\\n name_and_count = []\\n for codename in codenames:\\n count = 0\\n for r in self.results:\\n if r['codename'] == codename:\\n count += 1\\n name_and_count.append({'codename': codename, 'count': count})\\n tmp_results = []\\n if sort_by == \\\"1 - 10\\\":\\n small_to_large = sorted(name_and_count, key=lambda d: d['count'])\\n for s in small_to_large:\\n for r in self.results:\\n if s['codename'] == r['codename']:\\n tmp_results.append(r)\\n self.results = tmp_results\\n return\\n if sort_by == \\\"10 - 1\\\":\\n large_to_small = sorted(name_and_count, key=lambda d: d['count'], reverse=True)\\n for s in large_to_small:\\n for r in self.results:\\n if s['codename'] == r['codename']:\\n tmp_results.append(r)\\n self.results = tmp_results\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b60676e3cd8ad5fb73ae16dfa08463a5\",\n \"score\": \"0.51925224\",\n \"text\": \"def sort(self):\\r\\n self.cards.sort()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b60676e3cd8ad5fb73ae16dfa08463a5\",\n \"score\": \"0.51925224\",\n \"text\": \"def sort(self):\\r\\n self.cards.sort()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd9a379a5a8171652085069e95188c6\",\n \"score\": \"0.5188233\",\n \"text\": \"def update_list(self):\\n self.channel_listview.clear()\\n names = self.cs.channel_metadata(tuple(self.order),'name')\\n full_names = [str(self.order[i])+' : '+names[i] for i in range(len(names))]\\n self.channel_listview.addItems(full_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03186a57153d85460063cd5db19fcbe9\",\n \"score\": \"0.5187119\",\n \"text\": \"def editF():\\n\\tcontacts[cselect()]=[fname1.get(),lname1.get(),phone1.get(),address1.get(),email1.get()]\\n\\tselF()\\n\\tmsgBox.showinfo(\\\"Message\\\",\\\"Contact has been updated.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab63ce6d464310c681e468903200cf29\",\n \"score\": \"0.51823086\",\n \"text\": \"def contact_list(self):\\n return self._contact_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6aac21db36e876ea0c143a00789f2c7\",\n \"score\": \"0.51795834\",\n \"text\": \"def contacts(self, *args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6aac21db36e876ea0c143a00789f2c7\",\n \"score\": \"0.51795834\",\n \"text\": \"def contacts(self, *args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d997c720e5f709e759f3a815c3ea00\",\n \"score\": \"0.517044\",\n \"text\": \"def contacts(self):\\n return contacts.Contacts(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95827a642ac979dc082b838f5de91802\",\n \"score\": \"0.5165047\",\n \"text\": \"def list_contacts():\\n get_contacts = Contact.query.all()\\n contacts = serialize_contact(get_contacts)\\n return make_response(jsonify({\\\"contacts\\\": contacts}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daeac1c5a149878c842e0322f0db2f02\",\n \"score\": \"0.5159857\",\n \"text\": \"def add_contact_dialog(self):\\n global contact_select\\n contact_select = AddContactDialog(\\n self.client_socket,\\n self.client_db\\n )\\n contact_select.ok_btn.clicked.connect(\\n lambda: self.add_contact_handler(contact_select))\\n contact_select.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8080c67bca8e8f263bfc4cd68678bc3\",\n \"score\": \"0.5157543\",\n \"text\": \"def test_contacts(self):\\n contacts = map(_model_to_tuple, Contact.objects.all())\\n self.assertIn(('Email', 'email'), contacts)\\n self.assertIn(('Jabber', 'JID'), contacts)\\n self.assertIn(('Skype', 'id'), contacts)\\n self.assertIn(('', 'Mul'), contacts)\\n self.assertIn(('', 'ti'), contacts)\\n self.assertIn(('', 'line'), contacts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5bd75bd19e75d45f9b317852ddb0828\",\n \"score\": \"0.5147904\",\n \"text\": \"def BCListViewSetItemsSortedFunction(lv, funct, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4620b111d0870ae24547d0badf1b8e93\",\n \"score\": \"0.5139449\",\n \"text\": \"def init_display_order(apps, schema_editor):\\n # We get the model from the versioned app registry;\\n # if we directly import it, it'll be the wrong version.\\n db_alias = schema_editor.connection.alias\\n Note = apps.get_model(\\\"notepad\\\", \\\"Note\\\")\\n notes = Note.objects.using(db_alias).all()\\n notes.update(display_order=F('id')*1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"978c8118a259f50d2f724ac7353c5c95\",\n \"score\": \"0.5134643\",\n \"text\": \"def sortLines(self):\\n if not self.selectionIsRectangle():\\n return\\n \\n from .SortOptionsDialog import SortOptionsDialog\\n dlg = SortOptionsDialog()\\n if dlg.exec_() == QDialog.Accepted:\\n ascending, alnum, caseSensitive = dlg.getData()\\n origStartLine, origStartIndex, origEndLine, origEndIndex = (\\n self.getRectangularSelection()\\n )\\n # convert to upper-left to lower-right\\n startLine = min(origStartLine, origEndLine)\\n startIndex = min(origStartIndex, origEndIndex)\\n endLine = max(origStartLine, origEndLine)\\n endIndex = max(origStartIndex, origEndIndex)\\n \\n # step 1: extract the text of the rectangular selection and\\n # the lines\\n selText = {}\\n txtLines = {}\\n for line in range(startLine, endLine + 1):\\n txtLines[line] = self.text(line)\\n txt = txtLines[line][startIndex:endIndex].strip()\\n if not alnum:\\n try:\\n txt = float(txt)\\n except ValueError:\\n E5MessageBox.critical(\\n self,\\n self.tr(\\\"Sort Lines\\\"),\\n self.tr(\\n \\\"\\\"\\\"The selection contains illegal data for a\\\"\\\"\\\"\\n \\\"\\\"\\\" numerical sort.\\\"\\\"\\\"))\\n return\\n \\n if txt in selText:\\n selText[txt].append(line)\\n else:\\n selText[txt] = [line]\\n \\n # step 2: calculate the sort parameters\\n reverse = not ascending\\n if alnum and not caseSensitive:\\n keyFun = str.lower\\n else:\\n keyFun = None\\n \\n # step 3: sort the lines\\n eol = self.getLineSeparator()\\n lastWithEol = True\\n newLines = []\\n for txt in sorted(selText.keys(), key=keyFun, reverse=reverse):\\n for line in selText[txt]:\\n txt = txtLines[line]\\n if not txt.endswith(eol):\\n lastWithEol = False\\n txt += eol\\n newLines.append(txt)\\n if not lastWithEol:\\n newLines[-1] = newLines[-1][:-len(eol)]\\n \\n # step 4: replace the lines by the sorted ones\\n self.setSelection(startLine, 0, endLine + 1, 0)\\n self.beginUndoAction()\\n self.replaceSelectedText(\\\"\\\".join(newLines))\\n self.endUndoAction()\\n \\n # step 5: reset the rectangular selection\\n self.setRectangularSelection(origStartLine, origStartIndex,\\n origEndLine, origEndIndex)\\n self.selectionChanged.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e94c51cb24ec374f0fe328718241b3f\",\n \"score\": \"0.5134035\",\n \"text\": \"def ver():\\n \\n\\tprint \\\"Lista de contactos\\\"\\n\\tprint \\\"------------------\\\"\\n\\tprint \\\"\\\"\\n \\n\\tcon = sqlite3.connect(\\\"agenda.db\\\")\\n\\tcursor = con.cursor()\\n \\n\\tcursor.execute(\\\"SELECT * FROM datos\\\")\\n\\tresultado = cursor.fetchall()\\n \\n\\tfor i in resultado:\\n\\t\\tprint \\\"%s %s %s %s\\\" % (i[0],i[1],i[2],i[3])\\n \\n\\tcursor.close()\\n \\n\\tprint \\\"\\\"\\n\\traw_input(\\\"Presione una tecla para continuar...\\\")\\n \\n\\tmain()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa5113c8f857856ae6483533d2b605b\",\n \"score\": \"0.5130038\",\n \"text\": \"def get_contacts(config_dir=join(expanduser(\\\"~\\\"), \\\".baresip\\\")):\\n\\n with open(f\\\"{config_dir}/current_contact\\\") as cur:\\n cur_con = cur.readline()\\n with open(f\\\"{config_dir}/contacts\\\") as cons:\\n raw = cons.readlines()\\n contacts_list = []\\n for line in raw:\\n if not line.startswith(\\\"#\\\") and line != \\\"\\\\n\\\":\\n contacts_list.append(line)\\n contacts = {}\\n for contact in contacts_list:\\n name = contact.split('\\\"', 2)[1]\\n addr = contact.split('<', 1)[1].split('>', 1)[0]\\n contacts[name] = addr\\n return contacts, cur_con\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3085a23fa56a85ca64960105cce1791c\",\n \"score\": \"0.5129176\",\n \"text\": \"def test_search_contact_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b0b9b1eef82da663eaedf5c77c0cbcf\",\n \"score\": \"0.5123261\",\n \"text\": \"def find_contacts(request, nick):\\n redirect_to = request.REQUEST.get('redirect_to', '/')\\n\\n # these are for the find more contacts bits\\n start_index = int(request.REQUEST.get('index', 1))\\n max = 100\\n token = request.REQUEST.get('token')\\n contacts_more = int(request.REQUEST.get('contacts_more', 0))\\n\\n # this won't be seen unless contacts_more is positive,\\n # so no worries about the possible negative value\\n contacts_so_far = contacts_more - 1\\n\\n try:\\n if not settings.GOOGLE_CONTACTS_IMPORT_ENABLED:\\n raise exception.FeatureDisabledError('Google Contacts import is currently disabled')\\n\\n if 'lookup_remote_contacts' in request.POST:\\n validate.nonce(request, 'lookup_remote_contacts')\\n\\n next_url = util.qsa(util.here(request),\\n {'redirect_to': redirect_to,\\n 'upgrade_auth_token': '',\\n '_nonce': util.create_nonce(request.user,\\n 'upgrade_auth_token'),\\n }\\n )\\n auth_url = google_contacts.auth_sub_url(next_url)\\n return http.HttpResponseRedirect(auth_url)\\n elif 'actor_add_contacts' in request.POST:\\n validate.nonce(request, 'actor_add_contacts')\\n\\n\\n targets = request.POST.getlist('targets')\\n owner = request.POST.get('owner', '')\\n\\n rv = api.actor_add_contacts(request.user, owner, targets)\\n\\n next_url = util.qsa(util.here(request),\\n {'redirect_to': redirect_to,\\n 'contacts_more': contacts_more,\\n 'index': start_index,\\n 'token': token,\\n }\\n )\\n\\n return util.RedirectFlash(next_url, 'Contacts added.')\\n\\n elif 'upgrade_auth_token' in request.GET:\\n validate.nonce(request, 'upgrade_auth_token')\\n\\n auth_token = google_contacts.auth_sub_token_from_request(request)\\n session_token = google_contacts.upgrade_to_session_token(auth_token)\\n\\n next_url = util.qsa(util.here(request),\\n {'redirect_to': redirect_to,\\n 'fetch_contacts': '',\\n 'token': session_token.get_token_string(),\\n '_nonce': util.create_nonce(request.user,\\n 'fetch_contacts'),\\n }\\n )\\n\\n return http.HttpResponseRedirect(next_url)\\n\\n elif 'fetch_contacts' in request.REQUEST:\\n validate.nonce(request, 'fetch_contacts')\\n\\n # start_index and max are gathered above\\n session_token = google_contacts.auth_sub_token_from_request(request)\\n\\n # check for the \\\"My Contacts\\\" group, otherwise, fetch it\\n my_contacts = memcache.client.get('%s/my_contacts' % token)\\n if not my_contacts:\\n my_contacts = google_contacts.get_system_group(session_token,\\n 'Contacts')\\n memcache.client.set('%s/my_contacts' % token, my_contacts)\\n\\n\\n rv, more = google_contacts.get_contacts_emails(session_token,\\n group=my_contacts,\\n index=start_index,\\n max=max)\\n\\n contacts = []\\n\\n for name, email in rv:\\n logging.info('looking up \\\"%s\\\" %s', name, email)\\n contacts.append(api.actor_lookup_email(request.user, email))\\n\\n contacts = [x for x in contacts if x]\\n\\n # for the template\\n contacts_found = True\\n contacts_more = more\\n contacts_so_far = contacts_more - 1\\n token = session_token.get_token_string()\\n contacts_emails = rv\\n\\n # if no contacts were found and more are available, try some more\\n if not contacts and contacts_more:\\n next_url = util.qsa(util.here(request),\\n {'fetch_contacts': '',\\n 'contacts_more': contacts_more,\\n 'index': contacts_more,\\n 'token': token,\\n '_nonce': util.create_nonce(request.user,\\n 'fetch_contacts'),\\n 'redirect_to': redirect_to,\\n }\\n )\\n # TODO(termie): this can take a really long time, probably not really\\n # viable until we can do it with javascript\\n #return util.MetaRefresh(next_url, message='Still working...', second=1)\\n #return http.HttpResponseRedirect(next_url)\\n\\n except:\\n exception.handle_exception(request)\\n\\n\\n # set the progress\\n welcome_photo = True\\n welcome_mobile = True\\n\\n view = request.user\\n whose = 'You'\\n area = 'people'\\n\\n c = template.RequestContext(request, locals())\\n\\n t = loader.get_template('actor/templates/findpeople_contacts.html')\\n return http.HttpResponse(t.render(c))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994374,"cells":{"query_id":{"kind":"string","value":"c585ae41ab51e2548c2bb5e58a18990c"},"query":{"kind":"string","value":"Used as GIT_AUTHOR_NAME and GIT_COMMITTER_NAME environment variables"},"positive_passages":{"kind":"list like","value":[{"docid":"3237c40882c51a589d7559a266716f81","score":"0.0","text":"def generate_user_name(spawner):\n return spawner.user.name","title":""}],"string":"[\n {\n \"docid\": \"3237c40882c51a589d7559a266716f81\",\n \"score\": \"0.0\",\n \"text\": \"def generate_user_name(spawner):\\n return spawner.user.name\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"91535ca3321b8a6da14aa75ded759c7d","score":"0.7018517","text":"def add_author(context_dict: dict) -> dict:\n result = sp.run(\n [\"git\", \"config\", \"user.name\"],\n text=True,\n capture_output=True,\n check=False,\n )\n author = result.stdout.strip() if result.returncode == 0 else \"\"\n if author == \"\" and \"USER\" in os.environ:\n author = os.environ[\"USER\"]\n context_dict[\"author\"] = ask(\"author\", default=author)\n return context_dict","title":""},{"docid":"b2f4d46dd9aebff1ef88aad519f6fe60","score":"0.6914711","text":"def read_author(self):\n\n if \"CVS_USER\" in os.environ:\n full_name = os.environ[\"CVS_USER\"]\n else:\n full_name = os.environ[\"USER\"]\n\n self.meta[\"username\"] = full_name\n\n if full_name.find(\"@\") < 0: \n full_name = full_name.replace(\"#\", \"@\")\n\n if full_name.find(\"@\") < 0:\n full_name = full_name + \"@\" + self.meta[\"default_email_domain\"]\n\n self.meta[\"email\"] = full_name\n self.meta[\"real_name\"] = full_name[0:full_name.find(\"@\")]","title":""},{"docid":"72e13181d0349f85927739763aa9081a","score":"0.63373226","text":"def get_valid_git_username_email(self, name):\r\n return RE_GIT_SANITIZE_AUTHOR.sub('?', name.lstrip('< ').rstrip('> '))","title":""},{"docid":"65f23da1302c0d1d806878ec5460695d","score":"0.6268793","text":"def get_author(self, gitcommit):\n\t\treturn gitcommit.committer","title":""},{"docid":"66986565c3d6a9fe363f65aeb8dc9b52","score":"0.6113984","text":"def author_name(self):\n return self.author_email","title":""},{"docid":"e1e3655bf2b38db71cc83aafa3aaebbc","score":"0.60978794","text":"def author():\n return 'anonymous'","title":""},{"docid":"3a90d86dbe06d9dcb84726be0a94a1f9","score":"0.60789555","text":"def author(self) -> str | None:\n return APP_METADATA.get(self.__class__.__name__, {}).get(\n \"author\", \"Materials Project\"\n )","title":""},{"docid":"a1f862a5b5aab7549d677ecdebc806f6","score":"0.60657793","text":"def username():\n import os\n return os.getenv('USER', os.getenv('USERNAME', 'user'))","title":""},{"docid":"52dd216ac31b43480a8fba8ca9cfc8d0","score":"0.5956089","text":"def test_author(runcutty: RunCutty, template: Path) -> None:\n env = {\n \"GIT_AUTHOR_NAME\": \"The project author\",\n \"GIT_AUTHOR_EMAIL\": \"the.project.author@example.com\",\n }\n\n runcutty(\"create\", \"--non-interactive\", str(template), env=env)\n\n author = Repository.open(Path(\"example\")).head.commit.author\n\n assert env[\"GIT_AUTHOR_NAME\"] == author.name\n assert env[\"GIT_AUTHOR_EMAIL\"] == author.email","title":""},{"docid":"60747025bead1ecdd588ccf821ff9a23","score":"0.5931518","text":"def author_string(self) -> str:\n return f\"{self.author} <{self.email}>\"","title":""},{"docid":"fde477db57649337a5f1536ccb63bdb8","score":"0.5925964","text":"def getTitle(self):\n return \"GitHub account\"","title":""},{"docid":"99bea9be53b1d8d22f7087227ba525d6","score":"0.5904499","text":"def actor(self) -> str:\n return self._mandatory_str(\"GITHUB_ACTOR\")","title":""},{"docid":"b92fb2f573a9cf3b0e261b3895cf5f03","score":"0.58970803","text":"def _create_project_name(self, owner, repo):\n\n m = hashlib.md5()\n raw_project_name = f\"{owner}:{repo}\"\n m.update(raw_project_name.encode(encoding=\"utf-8\"))\n digest = m.hexdigest()\n logger.debug(f\"Hashed project name {raw_project_name} to {digest}.\")\n return digest","title":""},{"docid":"d07dc078a3f2b1b10715bbd4d2a36923","score":"0.58767134","text":"def _get_repo_name(self):\n return self.__repo_name","title":""},{"docid":"ef257c77c370b1e360002d1016056d81","score":"0.5803618","text":"def username():\n try:\n user = next(shell.git(\"config\", \"--global\", \"--get\", \"user.name\"))\n user = str(user).strip()\n except CalledProcessError:\n user = getpass.getuser()\n return user","title":""},{"docid":"26e9286e08c59d6d07d8a4e1266da972","score":"0.5798694","text":"def _get_author(self):\n\treturn self._name","title":""},{"docid":"3b08afef138b41555fece1b167337399","score":"0.575243","text":"def repo_name(self) -> str:\n return pulumi.get(self, \"repo_name\")","title":""},{"docid":"3961a53f05f7d6bc41f5c9fb7037445d","score":"0.5741477","text":"def get_author_name(self) -> str:\n return user_services.get_username(self.author_id)","title":""},{"docid":"12a96119e83ff68fe4b2e6100f5fbea5","score":"0.5731727","text":"def _defaults():\n name = os.getcwd().split('/')[-1]\n author = retrieve(['git', 'config', '--get', 'user.name'], default=getpass.getuser())\n email = retrieve(['git', 'config', '--get', 'user.email'], default=getpass.getuser() + '@gmail.com')\n return OrderedDict([\n ('name', name),\n ('version', '1.0.0'),\n ('author', author),\n ('email', email),\n ('repository', 'https://github.com/' + author + '/' + name),\n ('readme', 'README.md'),\n ('license', 'MIT'),\n ('package', name.replace('-','_')),\n ('entry', 'main.py'),\n ('description', ' '),\n ])","title":""},{"docid":"7d8542b8863ba17fda8ee4940970d656","score":"0.57004243","text":"def authors(ctx, draft=False, branch='master'):\n _, tmp = tempfile.mkstemp(suffix='.csv')\n log('Getting contributions')\n ctx.run(f'git fame --log ERROR --branch {branch} --format csv > {tmp}',\n encoding=\"utf-8\", hide=True)\n fame = open(tmp, 'r').readlines()\n os.remove(tmp)\n\n if not fame[0].startswith('Author'):\n log('Errors occurred while getting authors:', level='ERROR')\n errs = []\n for ln in fame:\n if ln.startswith('Author'):\n break\n errs.append(ln)\n print_block('\\n'.join(errs))\n sys.exit(1)\n\n log('Getting contributor information')\n r = ctx.run('git log --use-mailmap --format\"=%aN <%aE>\"',\n encoding=\"utf-8\", hide=True)\n author_emails = {}\n seen_authors = set()\n for author in r.stdout.splitlines():\n author = author.strip().split()\n email = None if '@' not in author[-1] else author[-1]\n author = ' '.join([x for x in author if '@' not in x])\n if email and author.lower() not in seen_authors:\n seen_authors.add(author.lower())\n author_emails[author] = email\n\n print(fame)\n\n log('Getting contributor names')\n auths = []\n for ln in fame[1:]:\n ln = ln.strip()\n if not ln:\n break\n t_auth = ln.split(',')[0]\n if t_auth.lower() not in ('douglas daly',):\n if t_auth in author_emails:\n t_auth += \" %s\" % author_emails[t_auth]\n auths.append(t_auth.strip())\n\n with open('AUTHORS', 'r') as fin:\n in_contents = fin.readlines()\n\n out_contents = []\n for ln in in_contents:\n ln = ln.strip('\\n')\n if ln == 'Contributors:':\n break\n out_contents.append(ln)\n\n if auths:\n out_contents.append('Contributors:\\n')\n for t_auth in auths:\n out_contents.append(' - %s' % t_auth)\n\n out_contents = '\\n'.join(out_contents)\n if draft:\n log('Would generate AUTHORS:')\n print_block(out_contents)\n else:\n with open('AUTHORS', 'w') as fout:\n fout.write(out_contents)\n log('Generated AUTHORS file')\n\n run(ctx, 'git add AUTHORS', draft)\n return","title":""},{"docid":"e63dea3aa3debb9b1fb9be4323302e0a","score":"0.5673067","text":"def git_str(self):\n dirty = '*' if self._git_is_dirty else ''\n if 'git' in self._pip_requirement:\n return self._pip_requirement + dirty\n if self._git_commit is None and self._git_remotes is None:\n return ''\n ref = self._git_tag if self._git_tag is not None else self._git_commit\n return '%s@%s%s' % (self.git_remote, ref, dirty)","title":""},{"docid":"daa95674b34c044389929692e01b1120","score":"0.564205","text":"def repo_name(self):\n return self.opts.get('repo')","title":""},{"docid":"d93a0a8cded13f3d8811facc0a0b3dd2","score":"0.5580957","text":"def get_author_name(self):\n\t\tname = self.author.get_full_name()\n\n\t\tif name:\n\t\t\treturn name\n\t\telse:\n\t\t\treturn self.author","title":""},{"docid":"6f2f77708f676577dd6b44259f556ebe","score":"0.5560378","text":"def get_user_name(self, author):\n\treturn author.name","title":""},{"docid":"fd43adc665d9652f0bd43582db0df7e9","score":"0.555978","text":"def get_name(self):\n return 'GitHub'","title":""},{"docid":"1ca99aafb44832f125e2f1f897cec502","score":"0.55355734","text":"def _constructor_dlrnauth_username(self):\n return os.environ.get('DLRNAPI_USERNAME', None)","title":""},{"docid":"3611b61cb58aecec895c77e58199cb7e","score":"0.55353624","text":"def author(self) -> str:\n return self.file.parts[-2].split(\"_\")[1]","title":""},{"docid":"ca19257e22307e9126491b4a11479a18","score":"0.5529529","text":"def get_author_name(self):\n if self.name != '':\n return self.name\n else:\n from django.contrib.auth.models import User\n u = User.objects.get(id=self.username)\n return u.username","title":""},{"docid":"fca1f23d7bc6048b5ff352616bd9472e","score":"0.551713","text":"def test_name_and_author(self):\n agent_config = AgentConfig(agent_name=\"my_agent\", author=\"fetchai\")\n agent_config.name = \"new_name\"\n agent_config.author = \"new_author\"","title":""},{"docid":"42b3e23119e32cd07ec5c052c4ce7732","score":"0.55151117","text":"def as_envvar(self) -> str:\n return f\"{PROJECT_NAME.upper()}_{self.name.upper()}\"","title":""},{"docid":"b964da88479e59a8fcf58c9029c7dc26","score":"0.5487111","text":"def name(self):\n if self.branch:\n return '{} [{}]'.format(self.repo_name, self.branch)\n return self.repo_name","title":""},{"docid":"07a0b3bd16691057ca12cad605fe4e0c","score":"0.54607177","text":"def get_author_name(self):\n if self.author_display:\n return self.author_display\n if self.author:\n return self.author.get_full_name()\n return ''","title":""},{"docid":"5715386b937af62d431088c0de8950ea","score":"0.5443686","text":"def get_username(self):\n return self.author.username","title":""},{"docid":"3a7317738a645d213727dbc0064a7b77","score":"0.5427597","text":"def get_repository_name(self):\n return None","title":""},{"docid":"411fa20ed1a084806774c58bc2b01782","score":"0.54052097","text":"def commit(author, email, committer, msg):\n author = '--author=\"{0} <{1}>\"'.format(author, email)\n msg = '-m {0}'.format(msg)\n\n # Only way to reset the committer without\n os.environ['GIT_COMMITTER_NAME'] = committer\n _git('commit',[author, msg])\n del os.environ[\"GIT_COMMITTER_NAME\"]","title":""},{"docid":"5e4b32d78102ac20c2ee74244fb520a8","score":"0.5401892","text":"def github_client_id():\n return os.environ.get(ENV_GITHUBCLIENTID,'')","title":""},{"docid":"f7d0e39c7cfe01df0ae39137533bf283","score":"0.53924876","text":"def setup_git(\n user_email: str = CLMUTISL_EMAIL,\n user_name: str = CLMUTISL_USER_NAME,\n priv_key: Union[str, Path] = CLMUTISL_PRIV_KEY,\n identity_file: Union[str, Path] = \"\", # default f\"~/.ssh/gh_{user_name}\"\n set_global: bool = True,\n host: str = \"github.com\",\n overwrite: bool = False, # do not overwrite identity_file\n) -> Optional[str]:\n # fmt: on\n if isinstance(identity_file, str) and not identity_file.strip(): # default to f\"gh_{user_name}\"\n identity_file = f\"~/.ssh/gh_{user_name}\"\n\n def check(arg: str) -> str:\n try:\n arg = arg.strip()\n except Exception as exc:\n logger.error(\"%s: %s\", arg, exc)\n raise SystemExit(exc)\n if not user_email:\n raise SystemExit(f\"Empty {arg}, cant continue\")\n return arg\n\n if isinstance(priv_key, Path):\n priv_key_path = priv_key.expanduser()\n if not priv_key_path.exists():\n logger.error(\" %s does not exist, cant continue\")\n raise SystemExit(1)\n\n try:\n _ = priv_key_path.read_bytes()\n encoding = chardet.detect(_).get(\"encoding\")\n priv_key = priv_key_path.read_text(encoding)\n except Exception as exc:\n logger.error(\" priv_key_path.read_text exc: %s\", exc)\n raise SystemExit(1)\n\n for arg in [user_email, user_name, priv_key]:\n arg = check(arg)\n\n # attach a needed \"\\n\"\n priv_key = priv_key.strip() + \"\\n\"\n\n if isinstance(identity_file, str):\n identity_file = identity_file.strip()\n\n # convert to str for IdentityFile entry in ~/.ssh/config\n try:\n identity_file_str = Path(identity_file).expanduser().as_posix()\n except Exception as exc:\n logger.error(\" identity_file %s cant be converted to str, exc: %s\", identity_file, exc)\n raise\n\n # create identity_file\n create_file(\n priv_key,\n identity_file,\n overwrite=overwrite,\n setmode=True # chmod 600\n )\n\n # set up an entry in ~/.ssh/config\n config_entry = f\"\"\"\nHost {host}\n HostName github.com\n User git\n IdentityFile {identity_file_str}\n \"\"\"\n\n # check for duplicate\n entry_exits = False\n if Path(\"~/.ssh/config\").expanduser().exists():\n fpath = Path(\"~/.ssh/config\").expanduser()\n try:\n _ = fpath.read_bytes()\n encoding = chardet.detect(_).get(\"encoding\")\n _ = fpath.read_text(encoding=encoding)\n except Exception as exc:\n logger.error(\"Reading %s exc: %s\", fpath, exc)\n raise\n if re.findall(rf\"{identity_file_str}\", _):\n entry_exits = True\n # write to ~/.ssh/config if the entry does not already exist\n if not entry_exits:\n append_content(config_entry, \"~/.ssh/config\")\n else:\n append_content(config_entry, \"~/.ssh/config\")\n\n # set up git config --global if set_global is True\n if set_global:\n run_cmd(f\"git config --global user.email {user_email}\")\n run_cmd(f\"git config --global user.name {user_name}\")\n\n # validate setup\n out_err = run_cmd1(\"ssh -T git@github.com -o StrictHostKeyChecking=no\")\n\n if \"successfully authenticated\" in str(out_err):\n logger.debug(str(out_err))\n logger.info(\"\\n\\t Congrats! You are good to go. \")\n else:\n # out_err = run_cmd1(\"ssh -T git@github.com -o StrictHostKeyChecking=no -v\")\n # out = out_err[0] if isinstance(out_err[0], str) else \"\"\n # err = out_err[1] if isinstance(out_err[1], str) else \"\"\n # logger.debug(out + \"\\n\" + err)\n logger.warning(\n \"\\n There appears to be some problem.\"\n \"\\nYou may wish to exam the debug messages above, \"\n \"\\nfix it and give it another try.\"\n )","title":""},{"docid":"e390bc49fb34bbe54dd02e8e203a4f90","score":"0.53803694","text":"def author_str(self):\n def reorder_name(name):\n if \",\" in name:\n name = \" \".join(name.split(\", \")[::-1]) \n return name \n else:\n return name \n \n authors = [reorder_name(name) for name in self.entry_info['author']]\n if (len(authors) == 1):\n return authors[0] \n elif (len(authors) == 2):\n return \"{} and {}\".format(authors[0], authors[1])\n elif (len(authors) > 2):\n auth_str = \", \".join(authors[:-1])\n auth_str += \", and {}\".format(authors[-1])\n return auth_str","title":""},{"docid":"811c9687808152f463cd519e962783fd","score":"0.5378399","text":"def get_git_sha():\n env = environ.Env()\n git_sha = env(\"GIT_SHA\", default=None)\n if not git_sha:\n git_sha = \"local development\"\n return git_sha","title":""},{"docid":"d30f5ce38fb86f00c47347014e3858b2","score":"0.5367428","text":"def prepare_author(self, obj):\n if hasattr(obj, 'metadata'):\n first_creator = obj.metadata.creators.filter(order=1).first()\n if first_creator.name is not None:\n return first_creator.name.lstrip()\n else:\n return 'none'\n else:\n return 'none'","title":""},{"docid":"f95bd8f9623f728373daabc3fc2d6418","score":"0.53582597","text":"def author_author(self):\n print self.author","title":""},{"docid":"71675ed44f357acf736ec430adbb866b","score":"0.5341204","text":"async def usernames(self) -> AbstractSet[str]:\n pull_request = self.request['pull_request']\n # Start with the author of the pull request.\n logins = {pull_request['user']['login']}\n # For each commit, get the author and committer.\n async for commit in self._gh.getiter(pull_request['commits_url']):\n author = commit['author']\n # When the author is missing there seems to typically be a\n # matching commit that **does** specify the author. (issue #56)\n if author is not None:\n author_login = author['login']\n if commit['commit']['author']['email'].lower() == GITHUB_EMAIL:\n self.server.log(\"Ignoring GitHub-managed username: \"\n + author_login)\n else:\n logins.add(author_login)\n\n committer = commit['committer']\n if committer is not None:\n committer_login = committer['login']\n if commit['commit']['committer']['email'].lower() == GITHUB_EMAIL:\n self.server.log(\"Ignoring GitHub-managed username: \"\n + committer_login)\n else:\n logins.add(committer_login)\n return frozenset(logins)","title":""},{"docid":"710245501271c17592caafcc5fd423f1","score":"0.5339868","text":"def name(self):\n return self.username","title":""},{"docid":"dde9034c15d9d30a9dfcb9fd420055b4","score":"0.5336341","text":"def author_email(self):\n pass","title":""},{"docid":"2ee52f48d3d4d84c813dd02a621e9ffb","score":"0.53342134","text":"def owner_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"owner_name\")","title":""},{"docid":"45e0ef2766077b22f5375c4538aed7bb","score":"0.53195107","text":"def author(self):\n return self._author.encode('utf-8')","title":""},{"docid":"1d9aefe118d7549341d8816887e30f1b","score":"0.5314272","text":"def name(self):\n return self.__username","title":""},{"docid":"9f51dc0eda9d5745e3a04aebfe764114","score":"0.5300927","text":"def get_git_shortlog_authors():\n authors = []\n command = (\"git\", \"shortlog\", \"--summary\", \"--numbered\")\n result = subprocess.check_output(command, stderr=subprocess.STDOUT).decode()\n data = result.split(\"\\n\")\n for row in data:\n parts = row.split(\"\\t\")\n\n if len(parts) == 2:\n n_commits, author = parts\n if author not in EXCLUDE_AUTHORS:\n authors.append(author)\n\n return authors","title":""},{"docid":"a3b5619a268cb4bdf54f329ad3df061e","score":"0.52974266","text":"def owner_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"owner_name\")","title":""},{"docid":"c053159298bfe72ff9f73a82f95407a2","score":"0.5289855","text":"def name(self):\n return self._username","title":""},{"docid":"915aec8e6cc44095f6abb70abb40eb83","score":"0.52877474","text":"def credential_username(self, entity_reference):\n pass","title":""},{"docid":"74ebdd1d0e0df9eff9ffeadc64911257","score":"0.52790654","text":"def set_name(self, arg):\n if arg.name == \"@\" and self.mod[\"provider\"] == \"aws\":\n self.mod[\"name\"] = \"\"\n elif arg.name == \"\" and self.mod[\"provider\"] == \"digitalocean\":\n self.mod[\"name\"] = \"@\"\n else:\n self.mod[\"name\"] = arg.name","title":""},{"docid":"2c717e31f9582f67b4dc06252f3d52ac","score":"0.52656794","text":"def repo_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"repo_name\")","title":""},{"docid":"99cdfd2ed09b12f7b8a5418475e3f861","score":"0.52531284","text":"def fill_user(self):\n self.user = '%s <%s>' % (self.author_name, self.author_email)","title":""},{"docid":"5f32fbb7753d9f766ed0f0c51c2a155f","score":"0.5250321","text":"def author():\n return \"Author of this program is Daniel Meeker\\n\"","title":""},{"docid":"d4cf615d19af41aa045cb53baa552773","score":"0.524859","text":"def get_username_from_env():\n username = None\n try:\n username = os.environ[\"JIRA_USERNAME\"]\n except KeyError:\n log.debug(\"JIRA_USERNAME environment variable not set\")\n\n return username","title":""},{"docid":"cd3a41e7bf65adeb81c62e9b83123faa","score":"0.5243285","text":"def user_env(self, env):\n import pwd\n # set HOME and SHELL for the Jupyter process\n env['HOME'] = '/home/' + self.user.name\n env['SHELL'] = '/bin/bash'\n return env","title":""},{"docid":"ee279b1c99b9b55572ed75e57176e7ea","score":"0.52410465","text":"def plugin_author(self) -> str:\n return pulumi.get(self, \"plugin_author\")","title":""},{"docid":"5179b3fdeefd2623d8213acd2e21d8a7","score":"0.52272636","text":"def repository_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"repository_name\")","title":""},{"docid":"5179b3fdeefd2623d8213acd2e21d8a7","score":"0.52272636","text":"def repository_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"repository_name\")","title":""},{"docid":"3657e98c4e5fd19f7fdd4de09863a120","score":"0.52051103","text":"def developer_name(self):\r\n if self.current_version:\r\n return self.current_version.developer_name","title":""},{"docid":"da7016d8477634a1fbc54d3d8a78d9fe","score":"0.5191454","text":"def _get_user_name(self):\n return self.__user_name","title":""},{"docid":"da7016d8477634a1fbc54d3d8a78d9fe","score":"0.5191454","text":"def _get_user_name(self):\n return self.__user_name","title":""},{"docid":"2f2e5fae526a7a103dff3da94a0baa89","score":"0.5186244","text":"def username(self):\n return self.cx_config.get(\"username\", \"Admin\")","title":""},{"docid":"3deaec88611063ee6b3edcaafd025e63","score":"0.51855624","text":"def _get_headers():\n headers = {}\n github_token = os.getenv(env.GH_TOKEN, None)\n if github_token is not None:\n headers['Authorization'] = 'token ' + github_token\n\n return headers","title":""},{"docid":"ed20b7ecea8989fea57ca11f68063c3c","score":"0.5185498","text":"def creator(self) -> str:\n return pulumi.get(self, \"creator\")","title":""},{"docid":"9b5c2b8c057e7e6374d408927e2218c1","score":"0.5171994","text":"def email():\n try:\n email = next(shell.git(\"config\", \"--global\", \"--get\", \"user.email\"))\n email = str(email).strip()\n except CalledProcessError:\n user = getpass.getuser()\n host = socket.gethostname()\n email = \"{user}@{host}\".format(user=user, host=host)\n return email","title":""},{"docid":"5eab62f4c9040f1344178af6209a1b53","score":"0.5168471","text":"def get_bot_username() -> str:\n me = github_whoami()\n return me[\"login\"]","title":""},{"docid":"da19e056766158019abe4e64d7252466","score":"0.5156977","text":"def get_contributor_sort_value(self, obj):\n user = obj.contributor\n\n if user.first_name or user.last_name:\n contributor = user.get_full_name()\n else:\n contributor = user.username\n\n return contributor.strip().lower()","title":""},{"docid":"5d18aea9ff90584b82a73e73e7f000cf","score":"0.5156564","text":"def set_itunes_author_name(self, tag):\n try:\n self.itunes_author_name = tag.string\n except AttributeError:\n self.itunes_author_name = None","title":""},{"docid":"7ddc20415871a5d8336565afb004b59c","score":"0.51304066","text":"def getauthor(name):\n\treturn basename(name).split(\".\")[0].split(\",\")[0].capitalize()","title":""},{"docid":"1b00a9dabc72e7e64a7857883e8f1cd3","score":"0.5125697","text":"def event_name(self) -> Optional[str]:\n return self._mandatory_str(\"GITHUB_EVENT_NAME\")","title":""},{"docid":"feee2aaf5ac28bbe2ba9db9be86d71e6","score":"0.5121286","text":"def get_status_author(self, statusObject):\n\treturn statusObject.author","title":""},{"docid":"ff81b958c94d2686b848a57e024d4095","score":"0.51176125","text":"def owner_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"owner_name\")","title":""},{"docid":"d6a7a4f46a8c8dd2a18bc84f9dbc815d","score":"0.51153344","text":"def author(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"author\")","title":""},{"docid":"d3dfb4227809b7d1f0291e99d661be18","score":"0.5114947","text":"def _apply_repo_env_var_overrides(self, info: Dict[str, Any]):\n self._override_repo_env_var(\"CUMULUSCI_REPO_BRANCH\", \"branch\", info)\n self._override_repo_env_var(\"CUMULUSCI_REPO_COMMIT\", \"commit\", info)\n self._override_repo_env_var(\"CUMULUSCI_REPO_ROOT\", \"root\", info)\n\n repo_url = os.environ.get(\"CUMULUSCI_REPO_URL\")\n if repo_url:\n if repo_url != info.get(\"url\"):\n self.logger.info(\n \"CUMULUSCI_REPO_URL found, using its value as the repo url, owner, and name\"\n )\n\n url_info = {}\n url_info[\"owner\"], url_info[\"name\"] = split_repo_url(repo_url)\n url_info[\"url\"] = repo_url\n info.update(url_info)","title":""},{"docid":"e114fde17b442ade2c1c80d5a1cdc216","score":"0.51127","text":"def env(self) -> str:\n _env = self.config[\"build\"].get(\"env\", \"\")\n return f\"ENV {_env}\" if _env else \"\"","title":""},{"docid":"0a6d4df556a3b2c9754b3d539d4bf8c9","score":"0.51094127","text":"def get_short_name(self):\n return f'@{self.username}'","title":""},{"docid":"564e0a32478a082bc481788d272fb494","score":"0.51089305","text":"def _getAuthor(self):\r\n if not self._author:\r\n self._author = self._parseAuthor()\r\n return self._author","title":""},{"docid":"2bf0a6a879a0ca729508fbb292d5f69f","score":"0.51079607","text":"def username(self):\n return self.name","title":""},{"docid":"4db10c3a6efa3753cee10e4b169fe4be","score":"0.5099829","text":"def owner_title(self):\n return self.properties.get('OwnerTitle', None)","title":""},{"docid":"d6db8ed65bad1b3e5db3811b22efe487","score":"0.5097656","text":"def developer_name(self):\n return self._developer_name","title":""},{"docid":"c20c2dcf432c06c1e355d0628c9dffe4","score":"0.50857025","text":"def _get_common_context(self, commits):\n submitter_name = self.review_request_details.submitter.get_full_name()\n include_author_name = not submitter_name\n to_expand = set()\n\n for commit in commits:\n if commit.author_name != submitter_name:\n include_author_name = True\n\n if commit.summary.strip() != commit.commit_message.strip():\n to_expand.add(commit.pk)\n\n return {\n 'include_author_name': include_author_name,\n 'to_expand': to_expand,\n }","title":""},{"docid":"d55222cee5d721371c8374971b396487","score":"0.50798035","text":"def git_askpass():\n if argv[1] == \"Username for 'https://github.com': \":\n print(environ[\"GIT_USERNAME\"])\n sys.exit()\n\n if argv[1] == \"Password for 'https://\" \"%(GIT_USERNAME)s@github.com': \" % environ:\n print(environ[\"GIT_PASSWORD\"])\n sys.exit()\n\n sys.exit(1)","title":""},{"docid":"273cfbdf07a922247351274ea2432e36","score":"0.50760096","text":"def publish_name(self):\n\n return \"{}_{}_repos_{}_{}\".format(self.basemirror_name, self.basemirror_version, self.project_name, self.project_version)","title":""},{"docid":"373af993f22a8f0fe065f137c3cf571b","score":"0.5066062","text":"def __set_name__(self, owner, name):\n\n self.private_name = '_' + name\n self.owner_name = name","title":""},{"docid":"3dc24872a395c8aab4d2c30aadfd1771","score":"0.5063197","text":"def choose_username(metadata, config):\n if config.username is not None:\n # we allow -- but do not encourage -- database username configuration\n return config.username\n\n if config.read_only:\n # by convention, we provision read-only username for every service\n return f\"{metadata.name}_ro\"\n\n return metadata.name","title":""},{"docid":"3dc24872a395c8aab4d2c30aadfd1771","score":"0.5063197","text":"def choose_username(metadata, config):\n if config.username is not None:\n # we allow -- but do not encourage -- database username configuration\n return config.username\n\n if config.read_only:\n # by convention, we provision read-only username for every service\n return f\"{metadata.name}_ro\"\n\n return metadata.name","title":""},{"docid":"86af86e78122b313148cf4584a7fe238","score":"0.5061744","text":"def context():\n return {\n 'cookiecutter': {\n 'email': 'raphael@hackebrot.de',\n 'full_name': 'Raphael Pierzina',\n 'github_username': 'hackebrot',\n 'version': '0.1.0',\n }\n }","title":""},{"docid":"3e54dfb581028c75313c4e960706c93f","score":"0.50565755","text":"def _get_username(self):\n return self.__username","title":""},{"docid":"aaac4531a7b9b754be6bb8bd09929eb8","score":"0.5051324","text":"def repository(self) -> str:\n return self._mandatory_str(\"GITHUB_REPOSITORY\")","title":""},{"docid":"acf105800924ba252eaa7994d9f63cd1","score":"0.5050525","text":"def sha(self) -> Optional[str]:\n return self.env.get(\"GITHUB_SHA\", None)","title":""},{"docid":"dea685eea8b237c535c4d6fb6c7a018c","score":"0.504619","text":"def creator(name):\n return \"dc.creator\" + quote(\"=\") + name","title":""},{"docid":"15c1dae90a592d2ea79b76d3fbce6bc3","score":"0.5031688","text":"def github_owner(account, cache, headers=None):\n url = f'https://api.github.com/users/{account}'\n profile = json.loads(get(url, cache, headers=headers))\n profile_name = profile['name'] if 'name' in profile else account\n return profile_name","title":""},{"docid":"1a711f457da32915ca668a3d6d243c52","score":"0.5031629","text":"def gitignore(cls):\n return \"%s\\n\" % (cls.name)","title":""},{"docid":"22d40f532f1566470bc7b8b9d575a19e","score":"0.50309235","text":"def admin_get_author(self):\n return self.Author.user.username","title":""},{"docid":"a3090e713f33c44ac4333f9d330027d2","score":"0.50107133","text":"def _get_author(self):\n\t\tauth = None\n\t\tif hasattr(self, 'author'): # most objects\n\t\t\tauth = self.author\n\t\telif hasattr(self, '_author'): # Reports\n\t\t\tauth = self._author\n\t\telif hasattr(self, 'juser'): # Jobs\n\t\t\tauth = self.juser\n\t\telif hasattr(self, 'added_by'): # OffsiteUser\n\t\t\tauth = self.added_by\n\t\telif hasattr(self, 'user'): # UserProfile\n\t\t\tauth = self.user\n\t\telif hasattr(self, 'script_buyer'): # CartInfo\n\t\t\tauth = self.script_buyer\n\t\treturn auth","title":""},{"docid":"e944171731bf47d565613845aba134b6","score":"0.5008719","text":"def zoe_user():\n return os.environ['ZOE_USER']","title":""},{"docid":"2eed35af81cd943dae63c5ad4b1b6f24","score":"0.5003071","text":"def user_name(self):\n return self._user_name","title":""},{"docid":"e3f85dfe7c65bb8eb355f8fb7b8a780e","score":"0.5001558","text":"def GetEmail(repo_root):\n # We could want to look at the svn cred when it has a svn remote but it\n # should be fine for now, users should simply configure their git settings.\n return GIT.Capture(['config', 'user.email'], repo_root).strip()","title":""},{"docid":"55cdc5379fca8b0dfbbd46b390d1d363","score":"0.5000409","text":"def check_for_environment_variables():\n _get_environment_variable('LUMOL_GH_USERNAME')\n _get_environment_variable('LUMOL_GH_TOKEN')","title":""}],"string":"[\n {\n \"docid\": \"91535ca3321b8a6da14aa75ded759c7d\",\n \"score\": \"0.7018517\",\n \"text\": \"def add_author(context_dict: dict) -> dict:\\n result = sp.run(\\n [\\\"git\\\", \\\"config\\\", \\\"user.name\\\"],\\n text=True,\\n capture_output=True,\\n check=False,\\n )\\n author = result.stdout.strip() if result.returncode == 0 else \\\"\\\"\\n if author == \\\"\\\" and \\\"USER\\\" in os.environ:\\n author = os.environ[\\\"USER\\\"]\\n context_dict[\\\"author\\\"] = ask(\\\"author\\\", default=author)\\n return context_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f4d46dd9aebff1ef88aad519f6fe60\",\n \"score\": \"0.6914711\",\n \"text\": \"def read_author(self):\\n\\n if \\\"CVS_USER\\\" in os.environ:\\n full_name = os.environ[\\\"CVS_USER\\\"]\\n else:\\n full_name = os.environ[\\\"USER\\\"]\\n\\n self.meta[\\\"username\\\"] = full_name\\n\\n if full_name.find(\\\"@\\\") < 0: \\n full_name = full_name.replace(\\\"#\\\", \\\"@\\\")\\n\\n if full_name.find(\\\"@\\\") < 0:\\n full_name = full_name + \\\"@\\\" + self.meta[\\\"default_email_domain\\\"]\\n\\n self.meta[\\\"email\\\"] = full_name\\n self.meta[\\\"real_name\\\"] = full_name[0:full_name.find(\\\"@\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e13181d0349f85927739763aa9081a\",\n \"score\": \"0.63373226\",\n \"text\": \"def get_valid_git_username_email(self, name):\\r\\n return RE_GIT_SANITIZE_AUTHOR.sub('?', name.lstrip('< ').rstrip('> '))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f23da1302c0d1d806878ec5460695d\",\n \"score\": \"0.6268793\",\n \"text\": \"def get_author(self, gitcommit):\\n\\t\\treturn gitcommit.committer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66986565c3d6a9fe363f65aeb8dc9b52\",\n \"score\": \"0.6113984\",\n \"text\": \"def author_name(self):\\n return self.author_email\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e3655bf2b38db71cc83aafa3aaebbc\",\n \"score\": \"0.60978794\",\n \"text\": \"def author():\\n return 'anonymous'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a90d86dbe06d9dcb84726be0a94a1f9\",\n \"score\": \"0.60789555\",\n \"text\": \"def author(self) -> str | None:\\n return APP_METADATA.get(self.__class__.__name__, {}).get(\\n \\\"author\\\", \\\"Materials Project\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f862a5b5aab7549d677ecdebc806f6\",\n \"score\": \"0.60657793\",\n \"text\": \"def username():\\n import os\\n return os.getenv('USER', os.getenv('USERNAME', 'user'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52dd216ac31b43480a8fba8ca9cfc8d0\",\n \"score\": \"0.5956089\",\n \"text\": \"def test_author(runcutty: RunCutty, template: Path) -> None:\\n env = {\\n \\\"GIT_AUTHOR_NAME\\\": \\\"The project author\\\",\\n \\\"GIT_AUTHOR_EMAIL\\\": \\\"the.project.author@example.com\\\",\\n }\\n\\n runcutty(\\\"create\\\", \\\"--non-interactive\\\", str(template), env=env)\\n\\n author = Repository.open(Path(\\\"example\\\")).head.commit.author\\n\\n assert env[\\\"GIT_AUTHOR_NAME\\\"] == author.name\\n assert env[\\\"GIT_AUTHOR_EMAIL\\\"] == author.email\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60747025bead1ecdd588ccf821ff9a23\",\n \"score\": \"0.5931518\",\n \"text\": \"def author_string(self) -> str:\\n return f\\\"{self.author} <{self.email}>\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde477db57649337a5f1536ccb63bdb8\",\n \"score\": \"0.5925964\",\n \"text\": \"def getTitle(self):\\n return \\\"GitHub account\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bea9be53b1d8d22f7087227ba525d6\",\n \"score\": \"0.5904499\",\n \"text\": \"def actor(self) -> str:\\n return self._mandatory_str(\\\"GITHUB_ACTOR\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92fb2f573a9cf3b0e261b3895cf5f03\",\n \"score\": \"0.58970803\",\n \"text\": \"def _create_project_name(self, owner, repo):\\n\\n m = hashlib.md5()\\n raw_project_name = f\\\"{owner}:{repo}\\\"\\n m.update(raw_project_name.encode(encoding=\\\"utf-8\\\"))\\n digest = m.hexdigest()\\n logger.debug(f\\\"Hashed project name {raw_project_name} to {digest}.\\\")\\n return digest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07dc078a3f2b1b10715bbd4d2a36923\",\n \"score\": \"0.58767134\",\n \"text\": \"def _get_repo_name(self):\\n return self.__repo_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef257c77c370b1e360002d1016056d81\",\n \"score\": \"0.5803618\",\n \"text\": \"def username():\\n try:\\n user = next(shell.git(\\\"config\\\", \\\"--global\\\", \\\"--get\\\", \\\"user.name\\\"))\\n user = str(user).strip()\\n except CalledProcessError:\\n user = getpass.getuser()\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e9286e08c59d6d07d8a4e1266da972\",\n \"score\": \"0.5798694\",\n \"text\": \"def _get_author(self):\\n\\treturn self._name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b08afef138b41555fece1b167337399\",\n \"score\": \"0.575243\",\n \"text\": \"def repo_name(self) -> str:\\n return pulumi.get(self, \\\"repo_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3961a53f05f7d6bc41f5c9fb7037445d\",\n \"score\": \"0.5741477\",\n \"text\": \"def get_author_name(self) -> str:\\n return user_services.get_username(self.author_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12a96119e83ff68fe4b2e6100f5fbea5\",\n \"score\": \"0.5731727\",\n \"text\": \"def _defaults():\\n name = os.getcwd().split('/')[-1]\\n author = retrieve(['git', 'config', '--get', 'user.name'], default=getpass.getuser())\\n email = retrieve(['git', 'config', '--get', 'user.email'], default=getpass.getuser() + '@gmail.com')\\n return OrderedDict([\\n ('name', name),\\n ('version', '1.0.0'),\\n ('author', author),\\n ('email', email),\\n ('repository', 'https://github.com/' + author + '/' + name),\\n ('readme', 'README.md'),\\n ('license', 'MIT'),\\n ('package', name.replace('-','_')),\\n ('entry', 'main.py'),\\n ('description', ' '),\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d8542b8863ba17fda8ee4940970d656\",\n \"score\": \"0.57004243\",\n \"text\": \"def authors(ctx, draft=False, branch='master'):\\n _, tmp = tempfile.mkstemp(suffix='.csv')\\n log('Getting contributions')\\n ctx.run(f'git fame --log ERROR --branch {branch} --format csv > {tmp}',\\n encoding=\\\"utf-8\\\", hide=True)\\n fame = open(tmp, 'r').readlines()\\n os.remove(tmp)\\n\\n if not fame[0].startswith('Author'):\\n log('Errors occurred while getting authors:', level='ERROR')\\n errs = []\\n for ln in fame:\\n if ln.startswith('Author'):\\n break\\n errs.append(ln)\\n print_block('\\\\n'.join(errs))\\n sys.exit(1)\\n\\n log('Getting contributor information')\\n r = ctx.run('git log --use-mailmap --format\\\"=%aN <%aE>\\\"',\\n encoding=\\\"utf-8\\\", hide=True)\\n author_emails = {}\\n seen_authors = set()\\n for author in r.stdout.splitlines():\\n author = author.strip().split()\\n email = None if '@' not in author[-1] else author[-1]\\n author = ' '.join([x for x in author if '@' not in x])\\n if email and author.lower() not in seen_authors:\\n seen_authors.add(author.lower())\\n author_emails[author] = email\\n\\n print(fame)\\n\\n log('Getting contributor names')\\n auths = []\\n for ln in fame[1:]:\\n ln = ln.strip()\\n if not ln:\\n break\\n t_auth = ln.split(',')[0]\\n if t_auth.lower() not in ('douglas daly',):\\n if t_auth in author_emails:\\n t_auth += \\\" %s\\\" % author_emails[t_auth]\\n auths.append(t_auth.strip())\\n\\n with open('AUTHORS', 'r') as fin:\\n in_contents = fin.readlines()\\n\\n out_contents = []\\n for ln in in_contents:\\n ln = ln.strip('\\\\n')\\n if ln == 'Contributors:':\\n break\\n out_contents.append(ln)\\n\\n if auths:\\n out_contents.append('Contributors:\\\\n')\\n for t_auth in auths:\\n out_contents.append(' - %s' % t_auth)\\n\\n out_contents = '\\\\n'.join(out_contents)\\n if draft:\\n log('Would generate AUTHORS:')\\n print_block(out_contents)\\n else:\\n with open('AUTHORS', 'w') as fout:\\n fout.write(out_contents)\\n log('Generated AUTHORS file')\\n\\n run(ctx, 'git add AUTHORS', draft)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63dea3aa3debb9b1fb9be4323302e0a\",\n \"score\": \"0.5673067\",\n \"text\": \"def git_str(self):\\n dirty = '*' if self._git_is_dirty else ''\\n if 'git' in self._pip_requirement:\\n return self._pip_requirement + dirty\\n if self._git_commit is None and self._git_remotes is None:\\n return ''\\n ref = self._git_tag if self._git_tag is not None else self._git_commit\\n return '%s@%s%s' % (self.git_remote, ref, dirty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa95674b34c044389929692e01b1120\",\n \"score\": \"0.564205\",\n \"text\": \"def repo_name(self):\\n return self.opts.get('repo')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d93a0a8cded13f3d8811facc0a0b3dd2\",\n \"score\": \"0.5580957\",\n \"text\": \"def get_author_name(self):\\n\\t\\tname = self.author.get_full_name()\\n\\n\\t\\tif name:\\n\\t\\t\\treturn name\\n\\t\\telse:\\n\\t\\t\\treturn self.author\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f2f77708f676577dd6b44259f556ebe\",\n \"score\": \"0.5560378\",\n \"text\": \"def get_user_name(self, author):\\n\\treturn author.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd43adc665d9652f0bd43582db0df7e9\",\n \"score\": \"0.555978\",\n \"text\": \"def get_name(self):\\n return 'GitHub'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca99aafb44832f125e2f1f897cec502\",\n \"score\": \"0.55355734\",\n \"text\": \"def _constructor_dlrnauth_username(self):\\n return os.environ.get('DLRNAPI_USERNAME', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3611b61cb58aecec895c77e58199cb7e\",\n \"score\": \"0.55353624\",\n \"text\": \"def author(self) -> str:\\n return self.file.parts[-2].split(\\\"_\\\")[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca19257e22307e9126491b4a11479a18\",\n \"score\": \"0.5529529\",\n \"text\": \"def get_author_name(self):\\n if self.name != '':\\n return self.name\\n else:\\n from django.contrib.auth.models import User\\n u = User.objects.get(id=self.username)\\n return u.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca1f23d7bc6048b5ff352616bd9472e\",\n \"score\": \"0.551713\",\n \"text\": \"def test_name_and_author(self):\\n agent_config = AgentConfig(agent_name=\\\"my_agent\\\", author=\\\"fetchai\\\")\\n agent_config.name = \\\"new_name\\\"\\n agent_config.author = \\\"new_author\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b3e23119e32cd07ec5c052c4ce7732\",\n \"score\": \"0.55151117\",\n \"text\": \"def as_envvar(self) -> str:\\n return f\\\"{PROJECT_NAME.upper()}_{self.name.upper()}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b964da88479e59a8fcf58c9029c7dc26\",\n \"score\": \"0.5487111\",\n \"text\": \"def name(self):\\n if self.branch:\\n return '{} [{}]'.format(self.repo_name, self.branch)\\n return self.repo_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07a0b3bd16691057ca12cad605fe4e0c\",\n \"score\": \"0.54607177\",\n \"text\": \"def get_author_name(self):\\n if self.author_display:\\n return self.author_display\\n if self.author:\\n return self.author.get_full_name()\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5715386b937af62d431088c0de8950ea\",\n \"score\": \"0.5443686\",\n \"text\": \"def get_username(self):\\n return self.author.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a7317738a645d213727dbc0064a7b77\",\n \"score\": \"0.5427597\",\n \"text\": \"def get_repository_name(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411fa20ed1a084806774c58bc2b01782\",\n \"score\": \"0.54052097\",\n \"text\": \"def commit(author, email, committer, msg):\\n author = '--author=\\\"{0} <{1}>\\\"'.format(author, email)\\n msg = '-m {0}'.format(msg)\\n\\n # Only way to reset the committer without\\n os.environ['GIT_COMMITTER_NAME'] = committer\\n _git('commit',[author, msg])\\n del os.environ[\\\"GIT_COMMITTER_NAME\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4b32d78102ac20c2ee74244fb520a8\",\n \"score\": \"0.5401892\",\n \"text\": \"def github_client_id():\\n return os.environ.get(ENV_GITHUBCLIENTID,'')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7d0e39c7cfe01df0ae39137533bf283\",\n \"score\": \"0.53924876\",\n \"text\": \"def setup_git(\\n user_email: str = CLMUTISL_EMAIL,\\n user_name: str = CLMUTISL_USER_NAME,\\n priv_key: Union[str, Path] = CLMUTISL_PRIV_KEY,\\n identity_file: Union[str, Path] = \\\"\\\", # default f\\\"~/.ssh/gh_{user_name}\\\"\\n set_global: bool = True,\\n host: str = \\\"github.com\\\",\\n overwrite: bool = False, # do not overwrite identity_file\\n) -> Optional[str]:\\n # fmt: on\\n if isinstance(identity_file, str) and not identity_file.strip(): # default to f\\\"gh_{user_name}\\\"\\n identity_file = f\\\"~/.ssh/gh_{user_name}\\\"\\n\\n def check(arg: str) -> str:\\n try:\\n arg = arg.strip()\\n except Exception as exc:\\n logger.error(\\\"%s: %s\\\", arg, exc)\\n raise SystemExit(exc)\\n if not user_email:\\n raise SystemExit(f\\\"Empty {arg}, cant continue\\\")\\n return arg\\n\\n if isinstance(priv_key, Path):\\n priv_key_path = priv_key.expanduser()\\n if not priv_key_path.exists():\\n logger.error(\\\" %s does not exist, cant continue\\\")\\n raise SystemExit(1)\\n\\n try:\\n _ = priv_key_path.read_bytes()\\n encoding = chardet.detect(_).get(\\\"encoding\\\")\\n priv_key = priv_key_path.read_text(encoding)\\n except Exception as exc:\\n logger.error(\\\" priv_key_path.read_text exc: %s\\\", exc)\\n raise SystemExit(1)\\n\\n for arg in [user_email, user_name, priv_key]:\\n arg = check(arg)\\n\\n # attach a needed \\\"\\\\n\\\"\\n priv_key = priv_key.strip() + \\\"\\\\n\\\"\\n\\n if isinstance(identity_file, str):\\n identity_file = identity_file.strip()\\n\\n # convert to str for IdentityFile entry in ~/.ssh/config\\n try:\\n identity_file_str = Path(identity_file).expanduser().as_posix()\\n except Exception as exc:\\n logger.error(\\\" identity_file %s cant be converted to str, exc: %s\\\", identity_file, exc)\\n raise\\n\\n # create identity_file\\n create_file(\\n priv_key,\\n identity_file,\\n overwrite=overwrite,\\n setmode=True # chmod 600\\n )\\n\\n # set up an entry in ~/.ssh/config\\n config_entry = f\\\"\\\"\\\"\\nHost {host}\\n HostName github.com\\n User git\\n IdentityFile {identity_file_str}\\n \\\"\\\"\\\"\\n\\n # check for duplicate\\n entry_exits = False\\n if Path(\\\"~/.ssh/config\\\").expanduser().exists():\\n fpath = Path(\\\"~/.ssh/config\\\").expanduser()\\n try:\\n _ = fpath.read_bytes()\\n encoding = chardet.detect(_).get(\\\"encoding\\\")\\n _ = fpath.read_text(encoding=encoding)\\n except Exception as exc:\\n logger.error(\\\"Reading %s exc: %s\\\", fpath, exc)\\n raise\\n if re.findall(rf\\\"{identity_file_str}\\\", _):\\n entry_exits = True\\n # write to ~/.ssh/config if the entry does not already exist\\n if not entry_exits:\\n append_content(config_entry, \\\"~/.ssh/config\\\")\\n else:\\n append_content(config_entry, \\\"~/.ssh/config\\\")\\n\\n # set up git config --global if set_global is True\\n if set_global:\\n run_cmd(f\\\"git config --global user.email {user_email}\\\")\\n run_cmd(f\\\"git config --global user.name {user_name}\\\")\\n\\n # validate setup\\n out_err = run_cmd1(\\\"ssh -T git@github.com -o StrictHostKeyChecking=no\\\")\\n\\n if \\\"successfully authenticated\\\" in str(out_err):\\n logger.debug(str(out_err))\\n logger.info(\\\"\\\\n\\\\t Congrats! You are good to go. \\\")\\n else:\\n # out_err = run_cmd1(\\\"ssh -T git@github.com -o StrictHostKeyChecking=no -v\\\")\\n # out = out_err[0] if isinstance(out_err[0], str) else \\\"\\\"\\n # err = out_err[1] if isinstance(out_err[1], str) else \\\"\\\"\\n # logger.debug(out + \\\"\\\\n\\\" + err)\\n logger.warning(\\n \\\"\\\\n There appears to be some problem.\\\"\\n \\\"\\\\nYou may wish to exam the debug messages above, \\\"\\n \\\"\\\\nfix it and give it another try.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e390bc49fb34bbe54dd02e8e203a4f90\",\n \"score\": \"0.53803694\",\n \"text\": \"def author_str(self):\\n def reorder_name(name):\\n if \\\",\\\" in name:\\n name = \\\" \\\".join(name.split(\\\", \\\")[::-1]) \\n return name \\n else:\\n return name \\n \\n authors = [reorder_name(name) for name in self.entry_info['author']]\\n if (len(authors) == 1):\\n return authors[0] \\n elif (len(authors) == 2):\\n return \\\"{} and {}\\\".format(authors[0], authors[1])\\n elif (len(authors) > 2):\\n auth_str = \\\", \\\".join(authors[:-1])\\n auth_str += \\\", and {}\\\".format(authors[-1])\\n return auth_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"811c9687808152f463cd519e962783fd\",\n \"score\": \"0.5378399\",\n \"text\": \"def get_git_sha():\\n env = environ.Env()\\n git_sha = env(\\\"GIT_SHA\\\", default=None)\\n if not git_sha:\\n git_sha = \\\"local development\\\"\\n return git_sha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d30f5ce38fb86f00c47347014e3858b2\",\n \"score\": \"0.5367428\",\n \"text\": \"def prepare_author(self, obj):\\n if hasattr(obj, 'metadata'):\\n first_creator = obj.metadata.creators.filter(order=1).first()\\n if first_creator.name is not None:\\n return first_creator.name.lstrip()\\n else:\\n return 'none'\\n else:\\n return 'none'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f95bd8f9623f728373daabc3fc2d6418\",\n \"score\": \"0.53582597\",\n \"text\": \"def author_author(self):\\n print self.author\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71675ed44f357acf736ec430adbb866b\",\n \"score\": \"0.5341204\",\n \"text\": \"async def usernames(self) -> AbstractSet[str]:\\n pull_request = self.request['pull_request']\\n # Start with the author of the pull request.\\n logins = {pull_request['user']['login']}\\n # For each commit, get the author and committer.\\n async for commit in self._gh.getiter(pull_request['commits_url']):\\n author = commit['author']\\n # When the author is missing there seems to typically be a\\n # matching commit that **does** specify the author. (issue #56)\\n if author is not None:\\n author_login = author['login']\\n if commit['commit']['author']['email'].lower() == GITHUB_EMAIL:\\n self.server.log(\\\"Ignoring GitHub-managed username: \\\"\\n + author_login)\\n else:\\n logins.add(author_login)\\n\\n committer = commit['committer']\\n if committer is not None:\\n committer_login = committer['login']\\n if commit['commit']['committer']['email'].lower() == GITHUB_EMAIL:\\n self.server.log(\\\"Ignoring GitHub-managed username: \\\"\\n + committer_login)\\n else:\\n logins.add(committer_login)\\n return frozenset(logins)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"710245501271c17592caafcc5fd423f1\",\n \"score\": \"0.5339868\",\n \"text\": \"def name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde9034c15d9d30a9dfcb9fd420055b4\",\n \"score\": \"0.5336341\",\n \"text\": \"def author_email(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee52f48d3d4d84c813dd02a621e9ffb\",\n \"score\": \"0.53342134\",\n \"text\": \"def owner_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"owner_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e0ef2766077b22f5375c4538aed7bb\",\n \"score\": \"0.53195107\",\n \"text\": \"def author(self):\\n return self._author.encode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d9aefe118d7549341d8816887e30f1b\",\n \"score\": \"0.5314272\",\n \"text\": \"def name(self):\\n return self.__username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f51dc0eda9d5745e3a04aebfe764114\",\n \"score\": \"0.5300927\",\n \"text\": \"def get_git_shortlog_authors():\\n authors = []\\n command = (\\\"git\\\", \\\"shortlog\\\", \\\"--summary\\\", \\\"--numbered\\\")\\n result = subprocess.check_output(command, stderr=subprocess.STDOUT).decode()\\n data = result.split(\\\"\\\\n\\\")\\n for row in data:\\n parts = row.split(\\\"\\\\t\\\")\\n\\n if len(parts) == 2:\\n n_commits, author = parts\\n if author not in EXCLUDE_AUTHORS:\\n authors.append(author)\\n\\n return authors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b5619a268cb4bdf54f329ad3df061e\",\n \"score\": \"0.52974266\",\n \"text\": \"def owner_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"owner_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c053159298bfe72ff9f73a82f95407a2\",\n \"score\": \"0.5289855\",\n \"text\": \"def name(self):\\n return self._username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"915aec8e6cc44095f6abb70abb40eb83\",\n \"score\": \"0.52877474\",\n \"text\": \"def credential_username(self, entity_reference):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74ebdd1d0e0df9eff9ffeadc64911257\",\n \"score\": \"0.52790654\",\n \"text\": \"def set_name(self, arg):\\n if arg.name == \\\"@\\\" and self.mod[\\\"provider\\\"] == \\\"aws\\\":\\n self.mod[\\\"name\\\"] = \\\"\\\"\\n elif arg.name == \\\"\\\" and self.mod[\\\"provider\\\"] == \\\"digitalocean\\\":\\n self.mod[\\\"name\\\"] = \\\"@\\\"\\n else:\\n self.mod[\\\"name\\\"] = arg.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c717e31f9582f67b4dc06252f3d52ac\",\n \"score\": \"0.52656794\",\n \"text\": \"def repo_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"repo_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99cdfd2ed09b12f7b8a5418475e3f861\",\n \"score\": \"0.52531284\",\n \"text\": \"def fill_user(self):\\n self.user = '%s <%s>' % (self.author_name, self.author_email)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f32fbb7753d9f766ed0f0c51c2a155f\",\n \"score\": \"0.5250321\",\n \"text\": \"def author():\\n return \\\"Author of this program is Daniel Meeker\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4cf615d19af41aa045cb53baa552773\",\n \"score\": \"0.524859\",\n \"text\": \"def get_username_from_env():\\n username = None\\n try:\\n username = os.environ[\\\"JIRA_USERNAME\\\"]\\n except KeyError:\\n log.debug(\\\"JIRA_USERNAME environment variable not set\\\")\\n\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3a41e7bf65adeb81c62e9b83123faa\",\n \"score\": \"0.5243285\",\n \"text\": \"def user_env(self, env):\\n import pwd\\n # set HOME and SHELL for the Jupyter process\\n env['HOME'] = '/home/' + self.user.name\\n env['SHELL'] = '/bin/bash'\\n return env\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee279b1c99b9b55572ed75e57176e7ea\",\n \"score\": \"0.52410465\",\n \"text\": \"def plugin_author(self) -> str:\\n return pulumi.get(self, \\\"plugin_author\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5179b3fdeefd2623d8213acd2e21d8a7\",\n \"score\": \"0.52272636\",\n \"text\": \"def repository_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"repository_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5179b3fdeefd2623d8213acd2e21d8a7\",\n \"score\": \"0.52272636\",\n \"text\": \"def repository_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"repository_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3657e98c4e5fd19f7fdd4de09863a120\",\n \"score\": \"0.52051103\",\n \"text\": \"def developer_name(self):\\r\\n if self.current_version:\\r\\n return self.current_version.developer_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7016d8477634a1fbc54d3d8a78d9fe\",\n \"score\": \"0.5191454\",\n \"text\": \"def _get_user_name(self):\\n return self.__user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7016d8477634a1fbc54d3d8a78d9fe\",\n \"score\": \"0.5191454\",\n \"text\": \"def _get_user_name(self):\\n return self.__user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f2e5fae526a7a103dff3da94a0baa89\",\n \"score\": \"0.5186244\",\n \"text\": \"def username(self):\\n return self.cx_config.get(\\\"username\\\", \\\"Admin\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3deaec88611063ee6b3edcaafd025e63\",\n \"score\": \"0.51855624\",\n \"text\": \"def _get_headers():\\n headers = {}\\n github_token = os.getenv(env.GH_TOKEN, None)\\n if github_token is not None:\\n headers['Authorization'] = 'token ' + github_token\\n\\n return headers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed20b7ecea8989fea57ca11f68063c3c\",\n \"score\": \"0.5185498\",\n \"text\": \"def creator(self) -> str:\\n return pulumi.get(self, \\\"creator\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b5c2b8c057e7e6374d408927e2218c1\",\n \"score\": \"0.5171994\",\n \"text\": \"def email():\\n try:\\n email = next(shell.git(\\\"config\\\", \\\"--global\\\", \\\"--get\\\", \\\"user.email\\\"))\\n email = str(email).strip()\\n except CalledProcessError:\\n user = getpass.getuser()\\n host = socket.gethostname()\\n email = \\\"{user}@{host}\\\".format(user=user, host=host)\\n return email\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eab62f4c9040f1344178af6209a1b53\",\n \"score\": \"0.5168471\",\n \"text\": \"def get_bot_username() -> str:\\n me = github_whoami()\\n return me[\\\"login\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da19e056766158019abe4e64d7252466\",\n \"score\": \"0.5156977\",\n \"text\": \"def get_contributor_sort_value(self, obj):\\n user = obj.contributor\\n\\n if user.first_name or user.last_name:\\n contributor = user.get_full_name()\\n else:\\n contributor = user.username\\n\\n return contributor.strip().lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d18aea9ff90584b82a73e73e7f000cf\",\n \"score\": \"0.5156564\",\n \"text\": \"def set_itunes_author_name(self, tag):\\n try:\\n self.itunes_author_name = tag.string\\n except AttributeError:\\n self.itunes_author_name = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ddc20415871a5d8336565afb004b59c\",\n \"score\": \"0.51304066\",\n \"text\": \"def getauthor(name):\\n\\treturn basename(name).split(\\\".\\\")[0].split(\\\",\\\")[0].capitalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b00a9dabc72e7e64a7857883e8f1cd3\",\n \"score\": \"0.5125697\",\n \"text\": \"def event_name(self) -> Optional[str]:\\n return self._mandatory_str(\\\"GITHUB_EVENT_NAME\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feee2aaf5ac28bbe2ba9db9be86d71e6\",\n \"score\": \"0.5121286\",\n \"text\": \"def get_status_author(self, statusObject):\\n\\treturn statusObject.author\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff81b958c94d2686b848a57e024d4095\",\n \"score\": \"0.51176125\",\n \"text\": \"def owner_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"owner_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a7a4f46a8c8dd2a18bc84f9dbc815d\",\n \"score\": \"0.51153344\",\n \"text\": \"def author(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"author\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3dfb4227809b7d1f0291e99d661be18\",\n \"score\": \"0.5114947\",\n \"text\": \"def _apply_repo_env_var_overrides(self, info: Dict[str, Any]):\\n self._override_repo_env_var(\\\"CUMULUSCI_REPO_BRANCH\\\", \\\"branch\\\", info)\\n self._override_repo_env_var(\\\"CUMULUSCI_REPO_COMMIT\\\", \\\"commit\\\", info)\\n self._override_repo_env_var(\\\"CUMULUSCI_REPO_ROOT\\\", \\\"root\\\", info)\\n\\n repo_url = os.environ.get(\\\"CUMULUSCI_REPO_URL\\\")\\n if repo_url:\\n if repo_url != info.get(\\\"url\\\"):\\n self.logger.info(\\n \\\"CUMULUSCI_REPO_URL found, using its value as the repo url, owner, and name\\\"\\n )\\n\\n url_info = {}\\n url_info[\\\"owner\\\"], url_info[\\\"name\\\"] = split_repo_url(repo_url)\\n url_info[\\\"url\\\"] = repo_url\\n info.update(url_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e114fde17b442ade2c1c80d5a1cdc216\",\n \"score\": \"0.51127\",\n \"text\": \"def env(self) -> str:\\n _env = self.config[\\\"build\\\"].get(\\\"env\\\", \\\"\\\")\\n return f\\\"ENV {_env}\\\" if _env else \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a6d4df556a3b2c9754b3d539d4bf8c9\",\n \"score\": \"0.51094127\",\n \"text\": \"def get_short_name(self):\\n return f'@{self.username}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564e0a32478a082bc481788d272fb494\",\n \"score\": \"0.51089305\",\n \"text\": \"def _getAuthor(self):\\r\\n if not self._author:\\r\\n self._author = self._parseAuthor()\\r\\n return self._author\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf0a6a879a0ca729508fbb292d5f69f\",\n \"score\": \"0.51079607\",\n \"text\": \"def username(self):\\n return self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db10c3a6efa3753cee10e4b169fe4be\",\n \"score\": \"0.5099829\",\n \"text\": \"def owner_title(self):\\n return self.properties.get('OwnerTitle', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6db8ed65bad1b3e5db3811b22efe487\",\n \"score\": \"0.5097656\",\n \"text\": \"def developer_name(self):\\n return self._developer_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c20c2dcf432c06c1e355d0628c9dffe4\",\n \"score\": \"0.50857025\",\n \"text\": \"def _get_common_context(self, commits):\\n submitter_name = self.review_request_details.submitter.get_full_name()\\n include_author_name = not submitter_name\\n to_expand = set()\\n\\n for commit in commits:\\n if commit.author_name != submitter_name:\\n include_author_name = True\\n\\n if commit.summary.strip() != commit.commit_message.strip():\\n to_expand.add(commit.pk)\\n\\n return {\\n 'include_author_name': include_author_name,\\n 'to_expand': to_expand,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d55222cee5d721371c8374971b396487\",\n \"score\": \"0.50798035\",\n \"text\": \"def git_askpass():\\n if argv[1] == \\\"Username for 'https://github.com': \\\":\\n print(environ[\\\"GIT_USERNAME\\\"])\\n sys.exit()\\n\\n if argv[1] == \\\"Password for 'https://\\\" \\\"%(GIT_USERNAME)s@github.com': \\\" % environ:\\n print(environ[\\\"GIT_PASSWORD\\\"])\\n sys.exit()\\n\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"273cfbdf07a922247351274ea2432e36\",\n \"score\": \"0.50760096\",\n \"text\": \"def publish_name(self):\\n\\n return \\\"{}_{}_repos_{}_{}\\\".format(self.basemirror_name, self.basemirror_version, self.project_name, self.project_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373af993f22a8f0fe065f137c3cf571b\",\n \"score\": \"0.5066062\",\n \"text\": \"def __set_name__(self, owner, name):\\n\\n self.private_name = '_' + name\\n self.owner_name = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc24872a395c8aab4d2c30aadfd1771\",\n \"score\": \"0.5063197\",\n \"text\": \"def choose_username(metadata, config):\\n if config.username is not None:\\n # we allow -- but do not encourage -- database username configuration\\n return config.username\\n\\n if config.read_only:\\n # by convention, we provision read-only username for every service\\n return f\\\"{metadata.name}_ro\\\"\\n\\n return metadata.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc24872a395c8aab4d2c30aadfd1771\",\n \"score\": \"0.5063197\",\n \"text\": \"def choose_username(metadata, config):\\n if config.username is not None:\\n # we allow -- but do not encourage -- database username configuration\\n return config.username\\n\\n if config.read_only:\\n # by convention, we provision read-only username for every service\\n return f\\\"{metadata.name}_ro\\\"\\n\\n return metadata.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86af86e78122b313148cf4584a7fe238\",\n \"score\": \"0.5061744\",\n \"text\": \"def context():\\n return {\\n 'cookiecutter': {\\n 'email': 'raphael@hackebrot.de',\\n 'full_name': 'Raphael Pierzina',\\n 'github_username': 'hackebrot',\\n 'version': '0.1.0',\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e54dfb581028c75313c4e960706c93f\",\n \"score\": \"0.50565755\",\n \"text\": \"def _get_username(self):\\n return self.__username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaac4531a7b9b754be6bb8bd09929eb8\",\n \"score\": \"0.5051324\",\n \"text\": \"def repository(self) -> str:\\n return self._mandatory_str(\\\"GITHUB_REPOSITORY\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf105800924ba252eaa7994d9f63cd1\",\n \"score\": \"0.5050525\",\n \"text\": \"def sha(self) -> Optional[str]:\\n return self.env.get(\\\"GITHUB_SHA\\\", None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea685eea8b237c535c4d6fb6c7a018c\",\n \"score\": \"0.504619\",\n \"text\": \"def creator(name):\\n return \\\"dc.creator\\\" + quote(\\\"=\\\") + name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c1dae90a592d2ea79b76d3fbce6bc3\",\n \"score\": \"0.5031688\",\n \"text\": \"def github_owner(account, cache, headers=None):\\n url = f'https://api.github.com/users/{account}'\\n profile = json.loads(get(url, cache, headers=headers))\\n profile_name = profile['name'] if 'name' in profile else account\\n return profile_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a711f457da32915ca668a3d6d243c52\",\n \"score\": \"0.5031629\",\n \"text\": \"def gitignore(cls):\\n return \\\"%s\\\\n\\\" % (cls.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22d40f532f1566470bc7b8b9d575a19e\",\n \"score\": \"0.50309235\",\n \"text\": \"def admin_get_author(self):\\n return self.Author.user.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3090e713f33c44ac4333f9d330027d2\",\n \"score\": \"0.50107133\",\n \"text\": \"def _get_author(self):\\n\\t\\tauth = None\\n\\t\\tif hasattr(self, 'author'): # most objects\\n\\t\\t\\tauth = self.author\\n\\t\\telif hasattr(self, '_author'): # Reports\\n\\t\\t\\tauth = self._author\\n\\t\\telif hasattr(self, 'juser'): # Jobs\\n\\t\\t\\tauth = self.juser\\n\\t\\telif hasattr(self, 'added_by'): # OffsiteUser\\n\\t\\t\\tauth = self.added_by\\n\\t\\telif hasattr(self, 'user'): # UserProfile\\n\\t\\t\\tauth = self.user\\n\\t\\telif hasattr(self, 'script_buyer'): # CartInfo\\n\\t\\t\\tauth = self.script_buyer\\n\\t\\treturn auth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e944171731bf47d565613845aba134b6\",\n \"score\": \"0.5008719\",\n \"text\": \"def zoe_user():\\n return os.environ['ZOE_USER']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eed35af81cd943dae63c5ad4b1b6f24\",\n \"score\": \"0.5003071\",\n \"text\": \"def user_name(self):\\n return self._user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f85dfe7c65bb8eb355f8fb7b8a780e\",\n \"score\": \"0.5001558\",\n \"text\": \"def GetEmail(repo_root):\\n # We could want to look at the svn cred when it has a svn remote but it\\n # should be fine for now, users should simply configure their git settings.\\n return GIT.Capture(['config', 'user.email'], repo_root).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55cdc5379fca8b0dfbbd46b390d1d363\",\n \"score\": \"0.5000409\",\n \"text\": \"def check_for_environment_variables():\\n _get_environment_variable('LUMOL_GH_USERNAME')\\n _get_environment_variable('LUMOL_GH_TOKEN')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994375,"cells":{"query_id":{"kind":"string","value":"8f3291a84e6c0e3fa8369c4ab540a6b4"},"query":{"kind":"string","value":"Configures the Naaya Site for the Naaya Survey Product"},"positive_passages":{"kind":"list like","value":[{"docid":"c6813efd2ff1d565592ea13407db75ca","score":"0.6621894","text":"def manage_configureSite(self, REQUEST=None):\n self.manage_configureCatalog()\n self.manage_configureEmailNotifications()\n self.manage_configureSecurity()\n #self.manage_updatePortlets() # disabled for the moment\n #self.getSite().searchable_content.append(SurveyQuestionnaire.meta_type) # disabled for the moment\n self.getSite().searchable_content.append(MegaSurvey.meta_type)","title":""}],"string":"[\n {\n \"docid\": \"c6813efd2ff1d565592ea13407db75ca\",\n \"score\": \"0.6621894\",\n \"text\": \"def manage_configureSite(self, REQUEST=None):\\n self.manage_configureCatalog()\\n self.manage_configureEmailNotifications()\\n self.manage_configureSecurity()\\n #self.manage_updatePortlets() # disabled for the moment\\n #self.getSite().searchable_content.append(SurveyQuestionnaire.meta_type) # disabled for the moment\\n self.getSite().searchable_content.append(MegaSurvey.meta_type)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3eba6b65f16ea0a4a4737e22011f3594","score":"0.58864015","text":"def test_com_adobe_cq_social_site_impl_site_configurator_impl(self):\n pass","title":""},{"docid":"d8a10b37dc069aa4d5a8f2345c084fdd","score":"0.5704494","text":"def setup_blog(self):\n blog_site, unused = Site.objects.get_or_create(domain='blog.tracon.fi', defaults=dict(\n name='Traconin blogi'\n ))\n blog_site_settings, unused = SiteSettings.objects.get_or_create(site=blog_site, defaults=dict(\n base_template='tracon11_base.jade',\n page_template='tracon11_page.jade',\n blog_index_template='tracon11_blog_index.jade',\n blog_post_template='tracon11_blog_post.jade',\n ))","title":""},{"docid":"8412e2daf929817dc66c1cc4730b6fcc","score":"0.5648642","text":"def setUpPloneSite(self, portal):\n applyProfile(portal, 'plone.mls.listing:default')\n quickInstallProduct(portal, 'raptus.article.core')","title":""},{"docid":"0ee1bc164fa3112adb3c8d94a19f6474","score":"0.54363644","text":"def step_impl_survey_set_to_alternate(context):\n context.bdd_helper.use_alternate_survey()","title":""},{"docid":"18995a0df4a61910a28ef9e70929b914","score":"0.5420643","text":"def step_impl_survey_set_to_default(context):\n context.bdd_helper.use_default_survey()","title":""},{"docid":"2b5e031a81eb3d73db8c8b362f8ec6bb","score":"0.5418436","text":"def setupVirtuoso(self):\n pass","title":""},{"docid":"aebf7ca98ee0b676569e0f1ea2d77002","score":"0.53359467","text":"def _get_sitebase(self, config):\n self.logo = config.get('sitebase', 'LOGO')\n self.title = config.get('sitebase', 'TITLE')","title":""},{"docid":"d49b1987a54ec1ee53b47ceb3825a4eb","score":"0.5324622","text":"def enable_site(site_name):\n if not is_site_enabled(site_name):\n run_as_root('a2ensite %s' % _site_config_filename(site_name))","title":""},{"docid":"36b6c7a692e63a03b95bd96903b2e0c0","score":"0.53240675","text":"def _set_site_configuration(self):\n _return = False\n\n _common_db = self.RAMSTK_SITE_DIR + '/ramstk_common.rtk'\n\n _config = ConfigParser.ConfigParser()\n\n _config.add_section('Backend')\n _config.set('Backend', 'host', 'localhost')\n _config.set('Backend', 'socket', 3306)\n _config.set('Backend', 'database', _common_db)\n _config.set('Backend', 'user', 'ramstk')\n _config.set('Backend', 'password', 'ramstk')\n _config.set('Backend', 'type', 'sqlite')\n _config.set('Backend', 'path', self.RAMSTK_SITE_DIR)\n\n try:\n _parser = open(self.RAMSTK_SITE_CONF, 'w')\n _config.write(_parser)\n _parser.close()\n except EnvironmentError:\n _return = True\n\n return _return","title":""},{"docid":"feeba8adf27c3125d5744e2e78e24d64","score":"0.5313803","text":"def setup_site(args, site_name, parameters):\n update_site_config(site_name, parameters)\n create_site_database(args, site_name)","title":""},{"docid":"56f2f6ebb214d146bc3ba75da67f0f13","score":"0.523121","text":"def setup_site(context):\n # Executado apenas se o estivermos no Profile correto\n if context.readDataFile('brasil.gov.agenda.txt') is None:\n return\n site = context.getSite()\n list_agendadiaria_calendar(site)","title":""},{"docid":"a657f5780fd4e905f87a5c9efdc588dd","score":"0.5218182","text":"def test_change_default(self):\n ss_conf = self.conf[\"Short Survëy\"]\n self.assertEqual(ss_conf.get(\"footer\"), \"Short survey footer.\")\n self.assertEqual(ss_conf.get(\"document_class\"), \"article\")\n # Test Management Survëy conf\n tm_survey = Survey.objects.get(name=\"Test Management Survëy\")\n tm_conf = self.conf.get(survey_name=tm_survey.name)\n self.assertEqual(tm_conf.get(\"footer\"), \"Test management footer.\")\n self.assertEqual(tm_conf.get(\"document_class\"), \"article\")\n self.assertEqual(self.conf.get(\"footer\", \"Test survëy\"), \"This is the footer.\")\n self.assertEqual(self.conf.get(key=\"document_class\", survey_name=\"Test survëy\"), \"report\")\n self.assertEqual(self.conf.get(\"footer\"), \"This is the footer.\")\n self.assertEqual(self.conf.get(\"document_class\"), \"article\")","title":""},{"docid":"6cd4201d74b3f9648472574f679972d3","score":"0.51966184","text":"def configurator(request):\n\n return render(request, CONF_PAGE)","title":""},{"docid":"4569b15cb508d96925cbd1b91da7137c","score":"0.5192934","text":"def setUp(self):\n self.new_site = Site(\"twitter\", \"ngugi\", \"asdf\")","title":""},{"docid":"07446c8a958c281ad3321195b9617d80","score":"0.5142763","text":"def test_use_default(self):\n short_survey_conf = self.conf.get(survey_name=\"Short Survëy\")\n self.assertEqual(short_survey_conf.get(\"document_class\"), \"article\")","title":""},{"docid":"d51440b29fafd9899f8f9af7c8b96950","score":"0.51254827","text":"def MockSiteConfig():\n result = config_lib.SiteConfig()\n\n # Add a single, simple build config.\n result.Add(\n 'x86-generic-paladin',\n active_waterfall='chromiumos',\n boards=['x86-generic'],\n build_type='paladin',\n chrome_sdk=True,\n chrome_sdk_build_chrome=False,\n description='Commit Queue',\n doc='http://mock_url/',\n image_test=True,\n images=['base', 'test'],\n important=True,\n manifest_version=True,\n prebuilts='public',\n trybot_list=True,\n upload_standalone_images=False,\n vm_tests=[config_lib.VMTestConfig('smoke_suite')],\n )\n\n return result","title":""},{"docid":"73f5c25623b144cec9fdbf137d7d46a3","score":"0.5024309","text":"def setUp(self):\n question = \"What language did you first learn to speak?\"\n self.my_survey = AnonymousSurvey(question) #cada vez que se ejecute el setUp(), habrá una nueva instancia.\n self.responses = ['English', 'Spanish', 'Mandarin']","title":""},{"docid":"522d109329e5ec8c5cc5be7e9bc3b542","score":"0.5021734","text":"def _config_sensor_domainconf(self, minimum_n):\n engine = {CONF_SENSOR_TYPE: 'mockcloudwatch',\n CONF_IAAS_SITE: 'fake',\n CONF_IAAS_ALLOCATION: 'also.fake',\n 'deployable_type': 'fake',\n 'minimum_vms': minimum_n,\n 'metric': 'load',\n 'monitor_sensors': ['load', ],\n 'monitor_domain_sensors': ['queuelen', ],\n 'sample_function': 'Average'}\n return {EPUM_CONF_ENGINE: engine}","title":""},{"docid":"fc78f3e1ae848a77fce9c5ba33dde53b","score":"0.5009647","text":"def setUp(self):\n import seo_link.settings as seo_link_settings\n seo_link_settings.BACKEND = \"seo_link.backends.advanced.LXMLCachedBackend\"\n seo_link.middleware.SEO_BACKEND = get_seo_link_backend_class(path=seo_link_settings.BACKEND)","title":""},{"docid":"201f372752aec6828e1066830f4d1203","score":"0.49818853","text":"def disable_site(site_name):\n if is_site_enabled(site_name):\n run_as_root('a2dissite %s' % _site_config_filename(site_name))","title":""},{"docid":"3295fa5dfa570c0066be3ece6ddedd68","score":"0.4954472","text":"def test_default_site(self):\n config = SiteConfigurationManager.get_blank_site()\n\n dnshost = config.dnshost\n self.assertEquals(dnshost.domain,\n SiteDefaultsManager.get_default_domain())\n self.assertEquals(dnshost.name, u'')\n self.assertEquals(dnshost.platform,\n SiteDefaultsManager.get_default_platform())\n self.assertTrue(dnshost.platform in \\\n SiteDefaultsManager.get_platforms())\n self.assertEquals(dnshost.description, u'')\n\n repository = config.repository\n self.assertEquals(repository.enabled, False)\n self.assertEquals(repository.done, False)\n self.assertEquals(repository.name, u'')\n self.assertEquals(repository.type,\n SiteDefaultsManager.get_default_repository_type())\n self.assertTrue(repository.type in \\\n SiteDefaultsManager.get_repository_types())\n\n site = config.website\n self.assertEquals(site.enabled, False)\n self.assertEquals(site.done, False)\n self.assertEquals(site.maintenance, False)\n self.assertEquals(site.template,\n SiteDefaultsManager.get_default_site_template())\n self.assertTrue(site.template in \\\n SiteDefaultsManager.get_site_templates())\n self.assertEquals(site.access,\n SiteDefaultsManager.get_default_site_access())\n self.assertTrue(site.access in \\\n SiteDefaultsManager.get_site_accesses())\n\n database = config.database\n self.assertEquals(database.enabled, False)\n self.assertEquals(database.done, False)\n self.assertEquals(database.name, u'')\n self.assertEquals(database.username, u'')\n self.assertEquals(database.password, u'')\n self.assertEquals(database.type,\n SiteDefaultsManager.get_default_database_type())\n self.assertTrue(database.type in \\\n SiteDefaultsManager.get_database_types())","title":""},{"docid":"6355e6c448db9911030f85f170d257ba","score":"0.4941305","text":"def add_domain(site,settings,pan_list):","title":""},{"docid":"0598387f97c7b13f2890958f9ec3936a","score":"0.49392506","text":"def configure_sites(self):\n\n self.sites = {}\n try:\n for site_name, site_conf in self.config['sites'].items():\n self.sites[site_name] = TaxiiSiteConnector(site_conf)\n logging.info(f\"loaded site {site_name}\")\n except handled_exceptions as e:\n\n logging.error(f\"Error in parsing config file: {e}\")","title":""},{"docid":"cadaa78bf77cca9afdd2503ea08ff88d","score":"0.4919648","text":"def setUp(self):\n question = \"What language did you first learn to speak?\"\n self.my_survey = AnonymousSurvey(question)\n self.responses = ['English', 'Spanish', 'Mandarin']","title":""},{"docid":"5b69af01e7695f404a8991ce7f0abef6","score":"0.49153143","text":"def executeSiteSetup():\r\n pass","title":""},{"docid":"b713dd410374d58088751eddaeec0795","score":"0.4908102","text":"def setup_plone(app, options=None):\n # Set up request for debug / bin/instance run mode.\n app = makerequest(app)\n\n # If no site specified in options, assume there's exactly one and use that.\n if not options or not options.site_root:\n plone = get_plone_site(app)\n else:\n plone = app.unrestrictedTraverse(options.site_root)\n\n # Set up Manager security context\n user = app.acl_users.getUser('zopemaster')\n user = user.__of__(app.acl_users)\n newSecurityManager(app, user)\n\n # Set up site to make component registry work\n setSite(plone)\n return plone","title":""},{"docid":"b2ac919f0c486d043a23c9a9cfc9e677","score":"0.49066913","text":"def setup_configs():\n COMMON_KEYWORDS = ['person', 'people', 'finder', 'person finder',\n 'people finder', 'crisis', 'survivor', 'family']\n\n # NOTE: the following two CAPTCHA keys are dummy keys for testing only.\n # (https://developers.google.com/recaptcha/docs/faq)\n # They should be replaced with real keys upon launch.\n config.set(captcha_site_key='6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI',\n captcha_secret_key='6LeIxAcTAAAAAGG-vFI1TnRWxMZNFuojJ4WifJWe',\n # A Google Translate API key with a very low quota, just for testing.\n translate_api_key='AIzaSyCXdz9x7LDL3BvieEP8Wcze64CC_iqslSE',\n repo_aliases={},\n sms_number_to_repo={},\n referrer_whitelist=[],\n initialized=True,\n notification_email=const.DEFAULT_NOTIFICATION_EMAIL,\n unreviewed_notes_threshold=(\n const.DEFAULT_UNREVIEWED_NOTES_THRESHOLD),\n )\n\n config.set_for_repo(\n 'haiti',\n # Appended to \"Google Person Finder\" in page titles.\n repo_titles={\n 'en': 'Haiti Earthquake',\n 'fr': u'S\\xe9isme en Ha\\xefti',\n 'ht': u'Tranbleman T\\xe8 an Ayiti',\n 'es': u'Terremoto en Hait\\xed'\n },\n # List of language codes that appear in the language menu.\n language_menu_options=['en', 'ht', 'fr', 'es'],\n # Content for the <meta name=\"keywords\"> tag.\n keywords=', '.join([\n 'haiti', 'earthquake', 'haiti earthquake', 'haitian',\n u'ha\\xefti', u's\\xe9isme', 'tremblement', 'tremblement de terre',\n 'famille', 'recherche de personnes', 'terremoto'\n ] + COMMON_KEYWORDS),\n # If false, hide the family_name field and use only given_name.\n use_family_name=True,\n # Presentation order for the given name and family name.\n family_name_first=False,\n # If true, show extra fields for alternate names.\n use_alternate_names=True,\n # If false, hide the home_zip field.\n use_postal_code=True,\n # Require at least this many letters in each word of a text query.\n min_query_word_length=2,\n # Show input fields for profile URLs in create page.\n show_profile_entry=True,\n # Default list of profile websites to show in create page.\n profile_websites=const.DEFAULT_PROFILE_WEBSITES,\n # Default map viewport for the location field in the note form.\n map_default_zoom=7,\n map_default_center=[18.968637, -72.284546],\n map_size_pixels=[400, 280],\n # If true, the feeds and read API require an authorization key.\n read_auth_key_required=False,\n # If true, the search API requires an authorization key.\n search_auth_key_required=False,\n # If true, show \"believed dead\" option in the note status dropdown\n allow_believed_dead_via_ui=True,\n # Custom html messages to show on main page, results page, view page,\n # and query form, keyed by language codes.\n start_page_custom_htmls={'en': '', 'fr': ''},\n results_page_custom_htmls={'en': '', 'fr': ''},\n view_page_custom_htmls={'en': '', 'fr': ''},\n seek_query_form_custom_htmls={'en': '', 'fr': ''},\n time_zone_offset=0,\n time_zone_abbreviation='UTC',\n published_date=get_timestamp(datetime(2010, 1, 12)),\n updated_date=get_timestamp(datetime(2010, 1, 12)),\n )\n\n config.set_for_repo(\n 'japan',\n language_menu_options=['ja', 'en', 'ko', 'zh-CN', 'zh-TW', 'pt-BR', 'es'],\n repo_titles={\n 'en': '2011 Japan Earthquake',\n 'zh-TW': u'2011 \\u65e5\\u672c\\u5730\\u9707',\n 'zh-CN': u'2011 \\u65e5\\u672c\\u5730\\u9707',\n 'pt-BR': u'2011 Terremoto no Jap\\xe3o',\n 'ja': u'2011 \\u65e5\\u672c\\u5730\\u9707',\n 'es': u'2011 Terremoto en Jap\\xf3n'\n },\n keywords=', '.join(COMMON_KEYWORDS),\n use_family_name=True,\n family_name_first=True,\n use_alternate_names=True,\n use_postal_code=True,\n min_query_word_length=1,\n show_profile_entry=True,\n profile_websites=const.DEFAULT_PROFILE_WEBSITES,\n map_default_zoom=7,\n map_default_center=[38, 140.7],\n map_size_pixels=[400, 400],\n search_auth_key_required=True,\n read_auth_key_required=True,\n allow_believed_dead_via_ui=True,\n start_page_custom_htmls={'en': 'Custom message', 'fr': 'French'},\n results_page_custom_htmls={'en': 'Custom message', 'fr': 'French'},\n view_page_custom_htmls={'en': 'Custom message', 'fr': 'French'},\n seek_query_form_custom_htmls={'en': '', 'fr': ''},\n # NOTE(kpy): These two configuration settings only work for locations\n # with a single, fixed time zone offset and no Daylight Saving Time.\n time_zone_offset=9, # UTC+9\n time_zone_abbreviation='JST',\n jp_mobile_carrier_redirect=True,\n published_date=get_timestamp(datetime(2011, 3, 11)),\n updated_date=get_timestamp(datetime(2011, 3, 11)),\n )\n\n config.set_for_repo(\n 'pakistan',\n repo_titles={\n 'en': 'Pakistan Floods',\n 'ur': u'\\u067e\\u0627\\u06a9\\u0633\\u062a\\u0627\\u0646\\u06cc \\u0633\\u06cc\\u0644\\u0627\\u0628'\n },\n language_menu_options=['en', 'ur'],\n keywords=', '.join([\n 'pakistan', 'flood', 'pakistan flood', 'pakistani'\n ] + COMMON_KEYWORDS),\n use_family_name=False,\n family_name_first=False,\n use_alternate_names=False,\n use_postal_code=False,\n min_query_word_length=1,\n map_default_zoom=6,\n map_default_center=[33.36, 73.26], # near Rawalpindi, Pakistan\n map_size_pixels=[400, 500],\n read_auth_key_required=False,\n search_auth_key_required=False,\n allow_believed_dead_via_ui=True,\n start_page_custom_htmls={'en': '', 'fr': ''},\n results_page_custom_htmls={'en': '', 'fr': ''},\n view_page_custom_htmls={'en': '', 'fr': ''},\n seek_query_form_custom_htmls={'en': '', 'fr': ''},\n time_zone_offset=0,\n time_zone_abbreviation='UTC',\n published_date=get_timestamp(datetime(2010, 8, 6)),\n updated_date=get_timestamp(datetime(2010, 8, 6)),\n )","title":""},{"docid":"305b7da778948be1eeead846382a26f8","score":"0.49029732","text":"def _config_simplest_domain_conf(self, n_preserving):\n engine_class = \"epu.decisionengine.impls.simplest.SimplestEngine\"\n general = {EPUM_CONF_ENGINE_CLASS: engine_class}\n health = {EPUM_CONF_HEALTH_MONITOR: False}\n engine = {CONF_PRESERVE_N: n_preserving}\n return {EPUM_CONF_GENERAL: general, EPUM_CONF_ENGINE: engine, EPUM_CONF_HEALTH: health}","title":""},{"docid":"d06290a512fff69082ba4de465963978","score":"0.4899163","text":"def project_settings(request):\n econnect_settings = ikwen_settings(request)\n econnect_settings['settings'].update({\n 'GOOGLE_MAPS_API_KEY': getattr(settings, 'GOOGLE_MAPS_API_KEY'),\n 'CREOLINK_MAPS_URL': getattr(settings, 'CREOLINK_MAPS_URL'),\n })\n try:\n econnect_settings['yup'] = PaymentMean.objects.get(slug='yup')\n except:\n pass\n return econnect_settings","title":""},{"docid":"455007a533ccb8b06633e309d2ab5750","score":"0.48983642","text":"def postInstall(context):\n # the right place for your custom code\n if isNotPloneConfContentTypesProfile(context): return\n site = context.getSite()","title":""},{"docid":"070495c197b0d0bec90bb2ebbe454721","score":"0.48926044","text":"def add_default_site(instance, **kwargs):\n if not settings.DBTEMPLATES_ADD_DEFAULT_SITE:\n return\n current_site = Site.objects.get_current()\n if current_site not in instance.sites.all():\n instance.sites.add(current_site)","title":""},{"docid":"810cedd5d589fefbea0367a2d4a09df3","score":"0.48878357","text":"def set_basic_config(request, info):# {{{\n username = request.user.username\n client_ip = request.META['REMOTE_ADDR']\n if username in settings.SUPER_USER_LIST:\n isSuperUser = True\n divided_logfile_query = \"%s/%s/%s\"%(SITE_ROOT,\n \"static/log\", \"submitted_seq.log\")\n divided_logfile_finished_jobid = \"%s/%s/%s\"%(SITE_ROOT,\n \"static/log\", \"failed_job.log\")\n else:\n isSuperUser = False\n divided_logfile_query = \"%s/%s/%s\"%(SITE_ROOT,\n \"static/log/divided\", \"%s_submitted_seq.log\"%(client_ip))\n divided_logfile_finished_jobid = \"%s/%s/%s\"%(SITE_ROOT,\n \"static/log/divided\", \"%s_failed_job.log\"%(client_ip))\n\n if isSuperUser:\n info['MAX_DAYS_TO_SHOW'] = g_params['BIG_NUMBER']\n else:\n info['MAX_DAYS_TO_SHOW'] = g_params['MAX_DAYS_TO_SHOW']\n\n\n info['username'] = username\n info['isSuperUser'] = isSuperUser\n info['divided_logfile_query'] = divided_logfile_query\n info['divided_logfile_finished_jobid'] = divided_logfile_finished_jobid\n info['client_ip'] = client_ip\n info['BASEURL'] = g_params['BASEURL']\n info['STATIC_URL'] = settings.STATIC_URL\n info['path_result'] = path_result","title":""},{"docid":"bc7dbcdfa2e99586375c21303868eeac","score":"0.4882148","text":"def configure_website(self, main_page_suffix=None, error_key=None,\n headers=None):\n if main_page_suffix:\n main_page_frag = self.WebsiteMainPageFragment % main_page_suffix\n else:\n main_page_frag = ''\n\n if error_key:\n error_frag = self.WebsiteErrorFragment % error_key\n else:\n error_frag = ''\n\n body = self.WebsiteBody % (main_page_frag, error_frag)\n response = self.connection.make_request('PUT', self.name, data=body,\n query_args='websiteConfig',\n headers=headers)\n body = response.read()\n if response.status == 200:\n return True\n else:\n raise self.connection.provider.storage_response_error(\n response.status, response.reason, body)","title":""},{"docid":"fdb715d062db1dfbf4a7abef96f1f383","score":"0.4859533","text":"def url(self):\n return dict(site=config.where.database.nma_ws_site.str, sites=config.where.database.nma_ws_sites.str)","title":""},{"docid":"a32963b726217336a7fc345b0e26287f","score":"0.48571873","text":"def create_products_ploneformgen_content(portal):","title":""},{"docid":"89195526e1fcf61376b0bc3923ca107f","score":"0.48505992","text":"def production():\n #TKTK\n env.settings = 'production'\n env.hosts = ['census.ire.org']\n env.user = 'ubuntu'\n env.s3_bucket = 'censusmedia.ire.org'\n env.site_domain = 'census.ire.org' \n env.cache_server = 'census.ire.org'","title":""},{"docid":"5333bd7ccba24d9469c2d2a5888b687b","score":"0.4847403","text":"def get_main_config():\n handler = AelVariableHandler()\n handler.add('counterparty',\n label='Client Party',\n cls='FParty',\n collection=acm.FParty.Select('').SortByProperty('Name'),\n hook=short_name_hook)\n handler.add('shortName',\n label='Client Short Name',\n alt=\"Client's short name to be used in portfolio name building.\",\n enabled=False)\n handler.add('startDate',\n label='Start Date',\n cls='date',\n default=acm.Time.DateToday(),\n alt='Start date of the PortfolioSwaps and Call Loans/Deposits.')\n handler.add('cfdAccount',\n label='CFD Hedge Equity Account',\n mandatory=False)\n handler.add('ceAccount',\n label='CE Stock Account',\n mandatory=False)\n handler.add_bool('fundAllocations',\n label='Fund Allocations',\n default=False)\n handler.add_bool('sblAgreement',\n label='SBL Agreement',\n default=False)\n handler.add_bool('dualListed',\n label='Dual Listed',\n default=False,\n alt='If checked, dual listed arb margin policy will be applied.')\n handler.add_bool('dryRun',\n label='Dry Run',\n default=True,\n alt=('If checked, do a dry run only - i.e. '\n 'do not save anything to the DB.'))\n return handler","title":""},{"docid":"e2cdc8739c879c2d6f0fe6a776ae419a","score":"0.48262924","text":"def setUp(self):\n self.driver = webdriver.Firefox()\n self.driver.get(\"https://svssso10ext.laadev.co.uk\")","title":""},{"docid":"f8d41f7d49f70599e919f69b8a98b6b3","score":"0.48208126","text":"def test_prepopulate_siteform(self):\n response = self.client.get(reverse(\n 'streamwebs:update_site',\n kwargs={'site_slug': self.site.site_slug}))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'streamwebs/update_site.html')\n self.assertContains(response, 'Creaky Creek')\n self.assertContains(response, self.site.site_slug)","title":""},{"docid":"4aace95249b08664caac8eacd3ea4e68","score":"0.4820754","text":"def default_site():\n if getattr(settings, 'SITE_ID', ''):\n return Site.objects.get(pk=settings.SITE_ID)","title":""},{"docid":"18c73ac6f87b35c11fccf829f481ced4","score":"0.48201337","text":"def enable_product_admin_panels():","title":""},{"docid":"ba2714892a662c69a12cd3d9a571ddd4","score":"0.4810867","text":"def setup_method(self):\n\n config.DOMAIN = \"https://google.com/\"\n self._set_up()","title":""},{"docid":"16f6192bb17c362dc6a3afa462fd3afa","score":"0.48070142","text":"def default_configs(cls):\n config = super().default_configs()\n config.update({\"model\": \"openie\"})\n return config","title":""},{"docid":"0fa19578ddc67d9438ccd5cf53da3dc4","score":"0.48062485","text":"def config(settings):\n\n T = current.T\n\n settings.base.system_name = T(\"Humanitarian Country Team (HCT) Relief and Rehabilitation System\")\n settings.base.system_name_short = T(\"SHARE\")\n\n # UI Settings\n settings.ui.menu_logo = URL(c = \"static\",\n f = \"themes\",\n args = [\"SHARE\", \"sharemenulogo.png\"],\n )\n\n # PrePopulate data\n settings.base.prepopulate += (\"SHARE\",)\n\n # Theme (folder to use for views/layout.html)\n #settings.base.theme = \"SHARE\"\n\n # Authentication settings\n # Should users be allowed to register themselves?\n #settings.security.self_registration = False\n # Do new users need to verify their email address?\n #settings.auth.registration_requires_verification = True\n # Do new users need to be approved by an administrator prior to being able to login?\n #settings.auth.registration_requires_approval = True\n settings.auth.registration_requests_organisation = True\n #settings.auth.registration_organisation_required = True\n #settings.auth.registration_requests_site = True\n\n settings.auth.registration_link_user_to = {\"staff\": T(\"Staff\"),\n \"volunteer\": T(\"Volunteer\"),\n #\"member\": T(\"Member\")\n }\n\n def registration_organisation_default(default):\n auth = current.auth\n has_role = auth.s3_has_role\n if has_role(\"ORG_ADMIN\") and not has_role(\"ADMIN\"):\n return auth.user.organisation_id\n else:\n return default\n\n settings.auth.registration_organisation_default = registration_organisation_default\n\n # Approval emails get sent to all admins\n settings.mail.approver = \"ADMIN\"\n\n # Restrict the Location Selector to just certain countries\n # NB This can also be over-ridden for specific contexts later\n # e.g. Activities filtered to those of parent Project\n #settings.gis.countries = (\"US\",)\n # Uncomment to display the Map Legend as a floating DIV\n settings.gis.legend = \"float\"\n # Uncomment to Disable the Postcode selector in the LocationSelector\n #settings.gis.postcode_selector = False # @ToDo: Vary by country (include in the gis_config!)\n # Uncomment to show the Print control:\n # http://eden.sahanafoundation.org/wiki/UserGuidelines/Admin/MapPrinting\n #settings.gis.print_button = True\n\n # L10n settings\n # Number formats (defaults to ISO 31-0)\n # Decimal separator for numbers (defaults to ,)\n settings.L10n.decimal_separator = \".\"\n # Thousands separator for numbers (defaults to space)\n settings.L10n.thousands_separator = \",\"\n\n # Security Policy\n # http://eden.sahanafoundation.org/wiki/S3AAA#System-widePolicy\n # 1: Simple (default): Global as Reader, Authenticated as Editor\n # 2: Editor role required for Update/Delete, unless record owned by session\n # 3: Apply Controller ACLs\n # 4: Apply both Controller & Function ACLs\n # 5: Apply Controller, Function & Table ACLs\n # 6: Apply Controller, Function, Table ACLs and Entity Realm\n # 7: Apply Controller, Function, Table ACLs and Entity Realm + Hierarchy\n # 8: Apply Controller, Function, Table ACLs, Entity Realm + Hierarchy and Delegations\n\n settings.security.policy = 6 # Controller, Function, Table ACLs and Entity Realm\n\n # -------------------------------------------------------------------------\n # Events\n settings.event.label = \"Disaster\"\n # Uncomment to not use Incidents under Events\n settings.event.incident = False\n\n # -------------------------------------------------------------------------\n # Messaging\n settings.msg.parser = \"SAMBRO\" # for parse_tweet\n\n # -------------------------------------------------------------------------\n # Organisations\n settings.org.sector = True\n # Show Organisation Types in the rheader\n settings.org.organisation_type_rheader = True\n\n # -------------------------------------------------------------------------\n # Projects\n # Don't use Beneficiaries\n settings.project.activity_beneficiaries = False\n # Don't use Item Catalog for Distributions\n settings.project.activity_items = False\n settings.project.activity_sectors = True\n # Links to Filtered Components for Donors & Partners\n settings.project.organisation_roles = {\n 1: T(\"Agency\"),\n 2: T(\"Implementing Partner\"),\n 3: T(\"Donor\"),\n }\n\n # -------------------------------------------------------------------------\n # Supply\n # Disable the use of Multiple Item Catalogs\n settings.supply.catalog_multi = False\n\n # -------------------------------------------------------------------------\n # Comment/uncomment modules here to disable/enable them\n # Modules menu is defined in modules/eden/menu.py\n settings.modules = OrderedDict([\n # Core modules which shouldn't be disabled\n (\"default\", Storage(\n name_nice = \"Home\",\n restricted = False, # Use ACLs to control access to this module\n access = None, # All Users (inc Anonymous) can see this module in the default menu & access the controller\n module_type = None # This item is not shown in the menu\n )),\n (\"admin\", Storage(\n name_nice = \"Administration\",\n #description = \"Site Administration\",\n restricted = True,\n access = \"|1|\", # Only Administrators can see this module in the default menu & access the controller\n module_type = None # This item is handled separately for the menu\n )),\n (\"appadmin\", Storage(\n name_nice = \"Administration\",\n #description = \"Site Administration\",\n restricted = True,\n module_type = None # No Menu\n )),\n (\"errors\", Storage(\n name_nice = \"Ticket Viewer\",\n #description = \"Needed for Breadcrumbs\",\n restricted = False,\n module_type = None # No Menu\n )),\n (\"setup\", Storage(\n name_nice = T(\"Setup\"),\n #description = \"WebSetup\",\n restricted = True,\n access = \"|1|\", # Only Administrators can see this module in the default menu & access the controller\n module_type = None # No Menu\n )),\n (\"sync\", Storage(\n name_nice = \"Synchronization\",\n #description = \"Synchronization\",\n restricted = True,\n access = \"|1|\", # Only Administrators can see this module in the default menu & access the controller\n module_type = None # This item is handled separately for the menu\n )),\n #(\"tour\", Storage(\n # name_nice = T(\"Guided Tour Functionality\"),\n # module_type = None,\n #)),\n (\"translate\", Storage(\n name_nice = T(\"Translation Functionality\"),\n #description = \"Selective translation of strings based on module.\",\n module_type = None,\n )),\n (\"gis\", Storage(\n name_nice = \"Map\",\n #description = \"Situation Awareness & Geospatial Analysis\",\n restricted = True,\n module_type = 6, # 6th item in the menu\n )),\n (\"pr\", Storage(\n name_nice = \"Person Registry\",\n #description = \"Central point to record details on People\",\n restricted = True,\n access = \"|1|\", # Only Administrators can see this module in the default menu (access to controller is possible to all still)\n module_type = 10\n )),\n (\"org\", Storage(\n name_nice = \"Organizations\",\n #description = 'Lists \"who is doing what & where\". Allows relief agencies to coordinate their activities',\n restricted = True,\n module_type = 1\n )),\n (\"hrm\", Storage(\n name_nice = \"Staff\",\n #description = \"Human Resources Management\",\n restricted = True,\n module_type = 2,\n )),\n (\"vol\", Storage(\n name_nice = T(\"Volunteers\"),\n #description = \"Human Resources Management\",\n restricted = True,\n module_type = 2,\n )),\n (\"cms\", Storage(\n name_nice = \"Content Management\",\n #description = \"Content Management System\",\n restricted = True,\n module_type = 10,\n )),\n (\"doc\", Storage(\n name_nice = \"Documents\",\n #description = \"A library of digital resources, such as photos, documents and reports\",\n restricted = True,\n module_type = 10,\n )),\n (\"msg\", Storage(\n name_nice = \"Messaging\",\n #description = \"Sends & Receives Alerts via Email & SMS\",\n restricted = True,\n # The user-visible functionality of this module isn't normally required. Rather it's main purpose is to be accessed from other modules.\n module_type = None,\n )),\n (\"supply\", Storage(\n name_nice = \"Supply Chain Management\",\n #description = \"Used within Inventory Management, Request Management and Asset Management\",\n restricted = True,\n module_type = None, # Not displayed\n )),\n (\"inv\", Storage(\n name_nice = T(\"Warehouses\"),\n #description = \"Receiving and Sending Items\",\n restricted = True,\n module_type = 4\n )),\n (\"asset\", Storage(\n name_nice = \"Assets\",\n #description = \"Recording and Assigning Assets\",\n restricted = True,\n module_type = 5,\n )),\n # Vehicle depends on Assets\n #(\"vehicle\", Storage(\n # name_nice = \"Vehicles\",\n # #description = \"Manage Vehicles\",\n # restricted = True,\n # module_type = 10,\n #)),\n (\"req\", Storage(\n name_nice = \"Requests\",\n #description = \"Manage requests for supplies, assets, staff or other resources. Matches against Inventories where supplies are requested.\",\n restricted = True,\n module_type = 10,\n )),\n (\"project\", Storage(\n name_nice = \"Tasks\",\n #description = \"Tracking of Projects, Activities and Tasks\",\n restricted = True,\n module_type = 2\n )),\n #(\"cr\", Storage(\n # name_nice = T(\"Shelters\"),\n # #description = \"Tracks the location, capacity and breakdown of victims in Shelters\",\n # restricted = True,\n # module_type = 10\n #)),\n #(\"hms\", Storage(\n # name_nice = T(\"Hospitals\"),\n # #description = \"Helps to monitor status of hospitals\",\n # restricted = True,\n # module_type = 10\n #)),\n #(\"dvr\", Storage(\n # name_nice = T(\"Disaster Victim Registry\"),\n # #description = \"Allow affected individuals & households to register to receive compensation and distributions\",\n # restricted = True,\n # module_type = 10,\n #)),\n (\"event\", Storage(\n name_nice = \"Events\",\n #description = \"Activate Events (e.g. from Scenario templates) for allocation of appropriate Resources (Human, Assets & Facilities).\",\n restricted = True,\n module_type = 10,\n )),\n #(\"transport\", Storage(\n # name_nice = T(\"Transport\"),\n # restricted = True,\n # module_type = 10,\n #)),\n (\"stats\", Storage(\n name_nice = T(\"Statistics\"),\n #description = \"Manages statistics\",\n restricted = True,\n module_type = None,\n )),\n ])\n\n # -------------------------------------------------------------------------\n def customise_event_sitrep_resource(r, tablename):\n\n current.response.s3.crud_strings[tablename] = Storage(\n label_create = T(\"Add Situational Update\"),\n title_display = T(\"HCT Activity and Response Report\"),\n title_list = T(\"Situational Updates\"),\n title_update = T(\"Edit Situational Update\"),\n title_upload = T(\"Import Situational Updates\"),\n label_list_button = T(\"List Situational Updates\"),\n label_delete_button = T(\"Delete Situational Update\"),\n msg_record_created = T(\"Situational Update added\"),\n msg_record_modified = T(\"Situational Update updated\"),\n msg_record_deleted = T(\"Situational Update deleted\"),\n msg_list_empty = T(\"No Situational Updates currently registered\"))\n\n settings.customise_event_sitrep_resource = customise_event_sitrep_resource\n\n # -------------------------------------------------------------------------\n def customise_msg_twitter_channel_resource(r, tablename):\n\n s3db = current.s3db\n def onaccept(form):\n # Normal onaccept\n s3db.msg_channel_onaccept(form)\n _id = form.vars.id\n db = current.db\n table = db.msg_twitter_channel\n channel_id = db(table.id == _id).select(table.channel_id,\n limitby=(0, 1)).first().channel_id\n # Link to Parser\n table = s3db.msg_parser\n _id = table.insert(channel_id=channel_id, function_name=\"parse_tweet\", enabled=True)\n s3db.msg_parser_enable(_id)\n\n run_async = current.s3task.async\n # Poll\n run_async(\"msg_poll\", args=[\"msg_twitter_channel\", channel_id])\n\n # Parse\n run_async(\"msg_parse\", args=[channel_id, \"parse_tweet\"])\n\n s3db.configure(tablename,\n create_onaccept = onaccept,\n )\n\n settings.customise_msg_twitter_channel_resource = customise_msg_twitter_channel_resource\n\n # -------------------------------------------------------------------------\n def customise_org_organisation_resource(r, tablename):\n\n s3db = current.s3db\n\n # Custom Components\n s3db.add_components(tablename,\n org_organisation_tag = (# Request Number\n {\"name\": \"req_number\",\n \"joinby\": \"organisation_id\",\n \"filterby\": {\"tag\": \"req_number\",\n },\n \"multiple\": False,\n },\n # Vision\n {\"name\": \"vision\",\n \"joinby\": \"organisation_id\",\n \"filterby\": {\"tag\": \"vision\",\n },\n \"multiple\": False,\n },\n ),\n )\n\n from s3 import S3SQLCustomForm, S3SQLInlineComponent, S3SQLInlineLink, s3_comments_widget\n\n # Individual settings for specific tag components\n components_get = s3db.resource(tablename).components.get\n\n vision = components_get(\"vision\")\n vision.table.value.widget = s3_comments_widget\n\n crud_form = S3SQLCustomForm(\"name\",\n \"acronym\",\n S3SQLInlineLink(\"organisation_type\",\n field = \"organisation_type_id\",\n # Default 10 options just triggers which adds unnecessary complexity to a commonly-used form & commonly an early one (create Org when registering)\n filter = False,\n label = T(\"Type\"),\n multiple = False,\n widget = \"multiselect\",\n ),\n S3SQLInlineLink(\"sector\",\n columns = 4,\n field = \"sector_id\",\n label = T(\"Sectors\"),\n ),\n #S3SQLInlineLink(\"service\",\n # columns = 4,\n # field = \"service_id\",\n # label = T(\"Services\"),\n # ),\n \"country\",\n \"phone\",\n \"website\",\n \"logo\",\n (T(\"About\"), \"comments\"),\n S3SQLInlineComponent(\"vision\",\n label = T(\"Vision\"),\n fields = [(\"\", \"value\")],\n multiple = False,\n ),\n S3SQLInlineComponent(\"req_number\",\n label = T(\"Request Number\"),\n fields = [(\"\", \"value\")],\n multiple = False,\n ),\n )\n\n s3db.configure(tablename,\n crud_form = crud_form,\n )\n\n settings.customise_org_organisation_resource = customise_org_organisation_resource\n\n # -------------------------------------------------------------------------\n def customise_org_sector_controller(**attr):\n\n s3db = current.s3db\n tablename = \"org_sector\"\n\n # Just 1 set of sectors / sector leads nationally\n # @ToDo: Deployment Setting\n #f = s3db.org_sector.location_id\n #f.readable = f.writable = False\n\n # Custom Component for Sector Leads\n s3db.add_components(tablename,\n org_sector_organisation = {\"name\": \"sector_lead\",\n \"joinby\": \"sector_id\",\n \"filterby\": {\"lead\": True,\n },\n },\n )\n\n from s3 import S3SQLCustomForm, S3SQLInlineComponent\n crud_form = S3SQLCustomForm(\"name\",\n \"abrv\",\n \"comments\",\n S3SQLInlineComponent(\"sector_lead\",\n label = T(\"Lead Organization(s)\"),\n fields = [(\"\", \"organisation_id\"),],\n ),\n )\n\n s3db.configure(tablename,\n crud_form = crud_form,\n list_fields = [\"name\",\n \"abrv\",\n (T(\"Lead Organization(s)\"), \"sector_lead.organisation_id\"),\n ],\n )\n\n return attr\n\n settings.customise_org_sector_controller = customise_org_sector_controller\n\n # -------------------------------------------------------------------------\n def req_need_status_update(need_id):\n \"\"\"\n Update the Need's fulfilment Status\n \"\"\"\n\n db = current.db\n s3db = current.s3db\n\n # Read the Need details\n nitable = s3db.req_need_item\n iptable = s3db.supply_item_pack\n query = (nitable.need_id == need_id) & \\\n (nitable.deleted == False) & \\\n (nitable.item_pack_id == iptable.id)\n need_items = db(query).select(nitable.id,\n nitable.item_id,\n nitable.quantity,\n iptable.quantity,\n )\n items = {}\n for item in need_items:\n pack_qty = item[\"supply_item_pack.quantity\"]\n item = item[\"req_need_item\"]\n quantity = item.quantity\n if quantity:\n quantity = quantity * pack_qty\n items[item.item_id] = {\"id\": item.id,\n \"quantity\": quantity or 0,\n \"quantity_committed\": 0,\n \"quantity_delivered\": 0,\n }\n\n ndtable = s3db.req_need_demographic\n query = (ndtable.need_id == need_id) & \\\n (ndtable.deleted == False)\n need_demographics = db(query).select(ndtable.parameter_id,\n ndtable.id,\n ndtable.value,\n )\n demographics = {}\n for demographic in need_demographics:\n demographics[demographic.parameter_id] = {\"id\": demographic.id,\n \"value\": demographic.value or 0,\n \"value_committed\": 0,\n \"value_reached\": 0,\n }\n\n # Lookup which Status means 'Cancelled'\n stable = s3db.project_status\n status = db(stable.name == \"Cancelled\").select(stable.id,\n limitby = (0, 1)\n ).first()\n try:\n CANCELLED = status.id\n except AttributeError:\n # Prepop not done? Name changed?\n current.log.debug(\"'Cancelled' Status not found\")\n CANCELLED = 999999\n\n # Read the details of all Activities linked to this Need\n atable = s3db.project_activity\n natable = s3db.req_need_activity\n query = (natable.need_id == need_id) & \\\n (natable.activity_id == atable.id) & \\\n (atable.deleted == False)\n activities = db(query).select(atable.id,\n atable.status_id,\n )\n aitable = s3db.project_activity_item\n adtable = s3db.project_activity_demographic\n for activity in activities:\n activity_id = activity.id\n status_id = activity.status_id\n query = (aitable.activity_id == activity_id) & \\\n (aitable.deleted == False) & \\\n (aitable.item_pack_id == iptable.id)\n rows = db(query).select(aitable.item_id,\n aitable.target_value,\n aitable.value,\n iptable.quantity,\n )\n for row in rows:\n pack_qty = row[\"supply_item_pack.quantity\"]\n row = row[\"project_activity_item\"]\n item_id = row.item_id\n if item_id in items:\n item = items[item_id]\n if status_id != CANCELLED:\n target_value = row.target_value\n if target_value:\n item[\"quantity_committed\"] += target_value * pack_qty\n value = row.value\n if value:\n item[\"quantity_delivered\"] += value * pack_qty\n else:\n # Ignore Items in Activity which don't match Need\n continue\n query = (adtable.activity_id == activity_id) & \\\n (adtable.deleted == False)\n rows = db(query).select(adtable.parameter_id,\n adtable.target_value,\n adtable.value,\n )\n for row in rows:\n parameter_id = row.parameter_id\n if parameter_id in demographics:\n demographic = demographics[parameter_id]\n if status_id != CANCELLED:\n target_value = row.target_value\n if target_value:\n demographic[\"value_committed\"] += target_value\n value = row.value\n if value:\n demographic[\"value_reached\"] += value\n else:\n # Ignore Demographics in Activity which don't match Need\n continue\n\n # Calculate Need values & Update\n statuses = []\n sappend = statuses.append\n for item_id in items:\n item = items[item_id]\n quantity_requested = item[\"quantity\"]\n quantity_committed = item[\"quantity_committed\"]\n quantity_uncommitted = max(quantity_requested - quantity_committed, 0)\n quantity_delivered = item[\"quantity_delivered\"]\n if quantity_delivered >= quantity_requested:\n status = 3\n elif quantity_uncommitted <= 0:\n status = 2\n elif quantity_committed > 0:\n status = 1\n else:\n status = 0\n sappend(status)\n db(nitable.id == item[\"id\"]).update(quantity_committed = quantity_committed,\n quantity_uncommitted = quantity_uncommitted,\n quantity_delivered = quantity_delivered,\n )\n\n for parameter_id in demographics:\n demographic = demographics[parameter_id]\n value_requested = demographic[\"value\"]\n value_committed = demographic[\"value_committed\"]\n value_uncommitted = max(value_requested - value_committed, 0)\n value_reached = demographic[\"value_reached\"]\n if value_reached >= value_requested:\n status = 3\n elif value_uncommitted <= 0:\n status = 2\n elif value_committed > 0:\n status = 1\n else:\n status = 0\n sappend(status)\n db(ndtable.id == demographic[\"id\"]).update(value_committed = value_committed,\n value_uncommitted = value_uncommitted,\n value_reached = value_reached,\n )\n\n if 1 in statuses:\n # 1 or more items/people are only partially committed\n status = 1\n elif 0 in statuses:\n if 2 in statuses or 3 in statuses:\n # Some items/people are not committed, but others are\n status = 1\n else:\n # No items/people have been committed\n status = 0\n elif 2 in statuses:\n # All Items/People are Committed, but at least some are not delivered/reached\n status = 2\n elif 3 in statuses:\n # All Items/People are Delivered/Reached\n status = 3\n else:\n # No Items/People: assume partial\n status = 1\n\n ntable = s3db.req_need\n need = db(ntable.id == need_id).select(ntable.id,\n ntable.status,\n limitby = (0, 1)\n ).first()\n if need.status != status:\n need.update_record(status = status)\n\n # -------------------------------------------------------------------------\n def project_activity_ondelete(row):\n \"\"\"\n Ensure that the Need (if-any) has the correct Status\n \"\"\"\n\n import json\n\n db = current.db\n s3db = current.s3db\n\n activity_id = row.get(\"id\")\n\n # Lookup the Need\n need_id = None\n natable = s3db.req_need_activity\n deleted_links = db(natable.deleted == True).select(natable.deleted_fk)\n for link in deleted_links:\n deleted_fk = json.loads(link.deleted_fk)\n if activity_id == deleted_fk[\"activity_id\"]:\n need_id = deleted_fk[\"need_id\"]\n break\n\n if not need_id:\n return\n\n # Check that the Need hasn't been deleted\n ntable = s3db.req_need\n need = db(ntable.id == need_id).select(ntable.deleted,\n limitby = (0, 1)\n ).first()\n\n if need and not need.deleted:\n req_need_status_update(need_id)\n\n # -------------------------------------------------------------------------\n def project_activity_postprocess(form):\n \"\"\"\n Ensure that the Need (if-any) has the correct Status\n \"\"\"\n\n s3db = current.s3db\n\n activity_id = form.vars.id\n\n # Lookup the Need\n ntable = s3db.req_need\n natable = s3db.req_need_activity\n query = (natable.activity_id == activity_id) & \\\n (natable.need_id == ntable.id) & \\\n (ntable.deleted == False)\n need = current.db(query).select(ntable.id,\n limitby = (0, 1)\n ).first()\n\n if need:\n req_need_status_update(need.id)\n\n # -------------------------------------------------------------------------\n def customise_project_activity_resource(r, tablename):\n\n db = current.db\n s3db = current.s3db\n\n # Custom Filtered Components\n s3db.add_components(tablename,\n project_activity_organisation = (# Agency\n {\"name\": \"agency\",\n \"joinby\": \"activity_id\",\n \"filterby\": {\"role\": 1,\n },\n #\"multiple\": False,\n },\n # Partners\n {\"name\": \"partner\",\n \"joinby\": \"activity_id\",\n \"filterby\": {\"role\": 2,\n },\n #\"multiple\": False,\n },\n # Donors\n {\"name\": \"donor\",\n \"joinby\": \"activity_id\",\n \"filterby\": {\"role\": 3,\n },\n #\"multiple\": False,\n },\n ),\n project_activity_tag = (# Modality\n {\"name\": \"modality\",\n \"joinby\": \"activity_id\",\n \"filterby\": {\"tag\": \"modality\",\n },\n \"multiple\": False,\n },\n # Number\n #{\"name\": \"number\",\n # \"joinby\": \"activity_id\",\n # \"filterby\": {\"tag\": \"number\",\n # },\n # \"multiple\": False,\n # },\n )\n )\n\n from s3 import S3LocationFilter, S3OptionsFilter, \\\n S3SQLCustomForm, S3SQLInlineComponent, S3SQLInlineLink\n\n # Individual settings for specific tag components\n from gluon import IS_EMPTY_OR, IS_IN_SET\n components_get = s3db.resource(tablename).components.get\n\n donor = components_get(\"donor\")\n donor.table.organisation_id.default = None\n\n partner = components_get(\"partner\")\n partner.table.organisation_id.default = None\n\n modality = components_get(\"modality\")\n modality.table.value.requires = IS_EMPTY_OR(IS_IN_SET((\"Cash\", \"In-kind\")))\n\n #number = components_get(\"number\")\n #number.table.value.requires = IS_EMPTY_OR(IS_INT_IN_RANGE())\n\n #sdtable = s3db.stats_demographic\n #demographics = db(sdtable.deleted == False).select(sdtable.name)\n #demographics = [d.name for d in demographics]\n #s3db.project_activity_data.unit.requires = IS_EMPTY_OR(IS_IN_SET(demographics))\n\n crud_fields = [S3SQLInlineLink(\"event\",\n field = \"event_id\",\n label = T(\"Disaster\"),\n multiple = False,\n #required = True,\n ),\n S3SQLInlineComponent(\"agency\",\n name = \"agency\",\n label = T(\"Agency\"),\n fields = [(\"\", \"organisation_id\"),],\n #multiple = False,\n required = True,\n ),\n # @ToDo: MultiSelectWidget is nicer UI but S3SQLInlineLink\n # requires the link*ed* table as component (not the\n # link table as applied here) and linked components\n # cannot currently be filtered by link table fields\n # (=> should solve the latter rather than the former)\n # @ToDo: Fix Create Popups\n S3SQLInlineComponent(\"partner\",\n name = \"partner\",\n label = T(\"Implementing Partner\"),\n fields = [(\"\", \"organisation_id\"),],\n ),\n S3SQLInlineComponent(\"donor\",\n name = \"donor\",\n label = T(\"Donor\"),\n fields = [(\"\", \"organisation_id\"),],\n ),\n \"location_id\",\n S3SQLInlineLink(\"sector\",\n field = \"sector_id\",\n filter = False,\n label = T(\"Sector\"),\n multiple = False,\n ),\n (T(\"Summary of Activity\"), \"name\"),\n S3SQLInlineComponent(\"modality\",\n name = \"modality\",\n label = T(\"Modality\"),\n fields = [(\"\", \"value\"),],\n multiple = False,\n ),\n #S3SQLInlineComponent(\"number\",\n # name = \"number\",\n # label = T(\"Number of Items/Kits/Activities\"),\n # fields = [(\"\", \"value\"),],\n # multiple = False,\n # ),\n (T(\"Activity Date (Planned/Start Date)\"), \"date\"),\n (T(\"Activity Date (Completion Date)\"), \"end_date\"),\n #S3SQLInlineComponent(\"activity_data\",\n # label = T(\"People / Households\"),\n # fields = [(T(\"Type\"), \"unit\"),\n # (T(\"Number Targeted\"), \"target_value\"),\n # (T(\"Number Reached\"), \"value\"),\n # ],\n # #multiple = False,\n # ),\n S3SQLInlineComponent(\"activity_demographic\",\n label = T(\"Beneficiaries\"),\n #link = False,\n fields = [(T(\"Type\"), \"parameter_id\"),\n (T(\"Estimated Delivery Time\"), \"timeframe\"),\n (T(\"Number Planned\"), \"target_value\"),\n (T(\"Number Reached\"), \"value\"),\n ],\n #multiple = False,\n ),\n S3SQLInlineComponent(\"activity_item\",\n label = T(\"Items\"),\n fields = [\"item_category_id\",\n \"item_id\",\n (T(\"Unit\"), \"item_pack_id\"),\n (T(\"Estimated Delivery Time\"), \"timeframe\"),\n (T(\"Number Planned\"), \"target_value\"),\n (T(\"Number Distributed\"), \"value\"),\n ],\n #multiple = False,\n ),\n (T(\"Activity Status\"), \"status_id\"),\n \"comments\",\n ]\n\n if r.id and r.resource.tablename == tablename:\n natable = s3db.req_need_activity\n need_link = db(natable.activity_id == r.id).select(natable.need_id,\n limitby = (0, 1)\n ).first()\n if need_link:\n from controllers import req_NeedRepresent\n f = natable.need_id\n f.represent = req_NeedRepresent()\n # This hides the widget from Update forms instead of just rendering read-only!\n #f.writable = False\n crud_fields.append(S3SQLInlineLink(\"need\",\n field = \"need_id\",\n label = T(\"Need\"),\n multiple = False,\n readonly = True,\n ))\n\n crud_form = S3SQLCustomForm(*crud_fields,\n postprocess = project_activity_postprocess)\n\n filter_widgets = [S3OptionsFilter(\"agency.organisation_id\",\n label = T(\"Agency\"),\n ),\n S3OptionsFilter(\"sector_activity.sector_id\"),\n S3LocationFilter(\"location_id\",\n # These levels are for SHARE/LK\n levels = (\"L2\", \"L3\", \"L4\"),\n ),\n S3OptionsFilter(\"event.event_type_id\",\n hidden = True,\n ),\n # @ToDo: Filter this list dynamically based on Event Type:\n S3OptionsFilter(\"event__link.event_id\",\n hidden = True,\n ),\n S3OptionsFilter(\"status_id\",\n cols = 4,\n label = T(\"Status\"),\n hidden = True,\n ),\n ]\n\n s3db.configure(tablename,\n crud_form = crud_form,\n filter_widgets = filter_widgets,\n list_fields = [(T(\"Agency\"), \"agency.organisation_id\"),\n (T(\"Implementing Partner\"), \"partner.organisation_id\"),\n (T(\"District\"), \"location_id$L1\"),\n (T(\"DS Division\"), \"location_id$L2\"),\n (T(\"GN Division\"), \"location_id$L3\"),\n (T(\"Sector\"), \"sector_activity.sector_id\"),\n (T(\"Summary of Activity\"), \"name\"),\n (T(\"Activity Status\"), \"status_id\"),\n (T(\"Modality\"), \"modality.value\"),\n (T(\"Activity Date (Planned/Start Date)\"), \"date\"),\n (T(\"Activity Date (Completion Date)\"), \"end_date\"),\n #(T(\"People / Households\"), \"activity_data.unit\"),\n #(T(\"Total Number of People/HH Targeted\"), \"activity_data.target_value\"),\n (T(\"People / Households\"), \"activity_demographic.parameter_id\"),\n (T(\"Total Number of People/HH Reached\"), \"activity_demographic.value\"),\n (T(\"Donor\"), \"donor.organisation_id\"),\n \"comments\",\n ],\n ondelete = project_activity_ondelete,\n )\n\n settings.customise_project_activity_resource = customise_project_activity_resource\n\n # -------------------------------------------------------------------------\n def customise_project_activity_controller(**attr):\n\n s3 = current.response.s3\n\n # Custom postp\n standard_postp = s3.postp\n def postp(r, output):\n # Call standard postp\n if callable(standard_postp):\n output = standard_postp(r, output)\n\n if r.interactive:\n # Inject the javascript to handle dropdown filtering\n # - normnally injected through AddResourceLink, but this isn't there in Inline widget\n # - we also need to turn the trigger & target into dicts\n s3.scripts.append(\"/%s/static/themes/SHARE/js/supply.js\" % r.application)\n\n return output\n s3.postp = postp\n\n return attr\n\n settings.customise_project_activity_controller = customise_project_activity_controller\n\n # -------------------------------------------------------------------------\n def req_need_postprocess(form):\n\n if form.record:\n # Update form\n return\n\n need_id = form.vars.id\n\n db = current.db\n s3db = current.s3db\n\n # Lookup Organisation\n notable = s3db.req_need_organisation\n org_link = db(notable.need_id == need_id).select(notable.organisation_id,\n limitby = (0, 1),\n ).first()\n if not org_link:\n return\n\n organisation_id = org_link.organisation_id\n\n # Lookup Request Number format\n ottable = s3db.org_organisation_tag\n query = (ottable.organisation_id == organisation_id) & \\\n (ottable.tag == \"req_number\")\n tag = db(query).select(ottable.value,\n limitby = (0, 1),\n ).first()\n if not tag:\n return\n\n # Lookup most recently-used value\n nttable = s3db.req_need_tag\n query = (nttable.tag == \"req_number\") & \\\n (nttable.need_id != need_id) & \\\n (nttable.need_id == notable.need_id) & \\\n (notable.organisation_id == organisation_id)\n\n need = db(query).select(nttable.value,\n limitby = (0, 1),\n orderby = ~nttable.created_on,\n ).first()\n if need:\n new_number = int(need.value.split(\"-\", 1)[1]) + 1\n req_number = \"%s-%s\" % (tag.value, str(new_number).zfill(6))\n else:\n req_number = \"%s-000001\" % tag.value\n\n nttable.insert(need_id = need_id,\n tag = \"req_number\",\n value = req_number,\n )\n\n # -------------------------------------------------------------------------\n def customise_req_need_resource(r, tablename):\n\n from gluon import IS_EMPTY_OR, IS_IN_SET, SPAN\n\n from s3 import S3LocationFilter, S3OptionsFilter, S3TextFilter, \\\n S3Represent, \\\n S3SQLCustomForm, S3SQLInlineComponent, S3SQLInlineLink\n\n s3db = current.s3db\n\n req_status_opts = {0: SPAN(T(\"None\"),\n _class = \"req_status_none\",\n ),\n 1: SPAN(T(\"Partially Committed\"),\n _class = \"req_status_partial\",\n ),\n 2: SPAN(T(\"Fully Committed\"),\n _class = \"req_status_complete\",\n ),\n 3: SPAN(T(\"Complete\"),\n _class = \"req_status_complete\",\n ),\n }\n\n f = s3db.req_need.status\n f.requires = IS_EMPTY_OR(IS_IN_SET(req_status_opts, zero = None))\n #f.represent = lambda opt: req_status_opts.get(opt, current.messages.UNKNOWN_OPT)\n f.represent = S3Represent(options = req_status_opts)\n\n # Custom Filtered Components\n s3db.add_components(tablename,\n req_need_tag = (# Req Number\n {\"name\": \"req_number\",\n \"joinby\": \"need_id\",\n \"filterby\": {\"tag\": \"req_number\",\n },\n \"multiple\": False,\n },\n # Verified\n {\"name\": \"verified\",\n \"joinby\": \"need_id\",\n \"filterby\": {\"tag\": \"verified\",\n },\n \"multiple\": False,\n },\n )\n )\n\n # Individual settings for specific tag components\n components_get = s3db.resource(tablename).components.get\n\n verified = components_get(\"verified\")\n f = verified.table.value\n f.requires = IS_EMPTY_OR(IS_IN_SET((True, False)))\n auth = current.auth\n if auth.s3_has_role(\"ADMIN\"):\n f.default = True\n else:\n user = auth.user\n if user and user.organisation_id:\n f.default = True\n else:\n f.default = False\n f.writable = False\n\n if r.id:\n # Read or Update\n table = s3db.req_need_item\n table.quantity.label = T(\"Quantity Requested\")\n table.quantity_committed.readable = True\n table.quantity_uncommitted.readable = True\n table.quantity_delivered.readable = True\n need_item = S3SQLInlineComponent(\"need_item\",\n label = T(\"Items Needed\"),\n fields = [\"item_category_id\",\n \"item_id\",\n (T(\"Unit\"), \"item_pack_id\"),\n (T(\"Needed within Timeframe\"), \"timeframe\"),\n \"quantity\",\n \"quantity_committed\",\n \"quantity_uncommitted\",\n \"quantity_delivered\",\n \"priority\",\n \"comments\",\n ],\n )\n table = s3db.req_need_demographic\n table.value.label = T(\"Number in Need\")\n table.value_committed.readable = True\n table.value_uncommitted.readable = True\n table.value_reached.readable = True\n demographic = S3SQLInlineComponent(\"need_demographic\",\n label = T(\"People Affected\"),\n fields = [(T(\"Type\"), \"parameter_id\"),\n (T(\"Needed within Timeframe\"), \"timeframe\"),\n \"value\",\n \"value_committed\",\n \"value_uncommitted\",\n \"value_reached\",\n \"comments\",\n ],\n )\n else:\n # Create\n need_item = S3SQLInlineComponent(\"need_item\",\n label = T(\"Items Needed\"),\n fields = [\"item_category_id\",\n \"item_id\",\n (T(\"Unit\"), \"item_pack_id\"),\n (T(\"Needed within Timeframe\"), \"timeframe\"),\n \"quantity\",\n \"priority\",\n \"comments\",\n ],\n )\n demographic = S3SQLInlineComponent(\"need_demographic\",\n label = T(\"People Affected\"),\n fields = [(T(\"Type\"), \"parameter_id\"),\n (T(\"Needed within Timeframe\"), \"timeframe\"),\n \"value\",\n \"comments\",\n ],\n )\n\n crud_fields = [S3SQLInlineLink(\"event\",\n field = \"event_id\",\n label = T(\"Disaster\"),\n multiple = False,\n #required = True,\n ),\n S3SQLInlineLink(\"organisation\",\n field = \"organisation_id\",\n filter = False,\n label = T(\"Organization\"),\n multiple = False,\n ),\n \"location_id\",\n \"date\",\n \"priority\",\n S3SQLInlineLink(\"sector\",\n field = \"sector_id\",\n filter = False,\n label = T(\"Sector\"),\n multiple = False,\n ),\n \"name\",\n demographic,\n need_item,\n S3SQLInlineComponent(\"document\",\n label = T(\"Attachment\"),\n fields = [(\"\", \"file\")],\n # multiple = True has reliability issues in at least Chrome\n multiple = False,\n ),\n (T(\"Verified\"), \"verified.value\"),\n \"comments\",\n ]\n\n if r.id and r.resource.tablename == tablename:\n # Read or Update\n req_number = components_get(\"verified\")\n req_number.table.value.writable = False\n crud_fields.insert(2, (T(\"Request Number\"), \"req_number.value\"))\n crud_fields.insert(-2, \"status\")\n natable = s3db.req_need_activity\n need_links = current.db(natable.need_id == r.id).select(natable.activity_id)\n if need_links:\n from controllers import project_ActivityRepresent\n f = natable.activity_id\n f.represent = project_ActivityRepresent()\n # This hides the widget from Update forms instead of just rendering read-only!\n #f.writable = False\n crud_fields.append(S3SQLInlineLink(\"activity\",\n field = \"activity_id\",\n label = T(\"Commits\"),\n readonly = True,\n ))\n\n crud_form = S3SQLCustomForm(*crud_fields,\n postprocess = req_need_postprocess)\n\n filter_widgets = [S3TextFilter([\"req_number.value\",\n \"need_item.item_id$name\",\n # These levels are for SHARE/LK\n #\"location_id$L1\",\n \"location_id$L2\",\n \"location_id$L3\",\n \"location_id$L4\",\n \"name\",\n \"comments\",\n ],\n label = T(\"Search\"),\n comment = T(\"Search for a Need by Request Number, Item, Location, Summary or Comments\"),\n ),\n S3LocationFilter(\"location_id\",\n # These levels are for SHARE/LK\n levels = (\"L2\", \"L3\", \"L4\"),\n ),\n S3OptionsFilter(\"need_item.item_id\"),\n S3OptionsFilter(\"status\",\n cols = 3,\n label = T(\"Status\"),\n ),\n S3OptionsFilter(\"event.event_type_id\",\n hidden = True,\n ),\n # @ToDo: Filter this list dynamically based on Event Type:\n S3OptionsFilter(\"event__link.event_id\"),\n S3OptionsFilter(\"sector__link.sector_id\",\n hidden = True,\n ),\n S3OptionsFilter(\"organisation__link.organisation_id\",\n hidden = True,\n ),\n S3OptionsFilter(\"verified.value\",\n cols = 2,\n label = T(\"Verified\"),\n hidden = True,\n ),\n ]\n\n s3db.configure(tablename,\n crud_form = crud_form,\n filter_widgets = filter_widgets,\n list_fields = [(T(\"Disaster\"), \"event__link.event_id\"),\n \"date\",\n \"organisation__link.organisation_id\",\n # These levels/Labels are for SHARE/LK\n (T(\"District\"), \"location_id$L2\"),\n (T(\"DS\"), \"location_id$L3\"),\n (T(\"Status\"), \"status\"),\n \"need_item.item_id\",\n \"sector__link.sector_id\",\n \"priority\",\n #\"name\",\n (T(\"Request Number\"), \"req_number.value\"),\n (T(\"Verified\"), \"verified.value\"),\n (T(\"GN\"), \"location_id$L4\"),\n ],\n )\n\n settings.customise_req_need_resource = customise_req_need_resource\n\n # -------------------------------------------------------------------------\n def req_need_commit(r, **attr):\n \"\"\"\n Custom method to Commit to a Need by creating an Activity\n \"\"\"\n\n # Create Activity with values from Need\n need_id = r.id\n\n db = current.db\n s3db = current.s3db\n ntable = s3db.req_need\n ntable_id = ntable.id\n netable = s3db.event_event_need\n nstable = s3db.req_need_sector\n left = [netable.on(netable.need_id == ntable_id),\n nstable.on(nstable.need_id == ntable_id),\n ]\n need = db(ntable_id == need_id).select(ntable.name,\n ntable.location_id,\n netable.event_id,\n nstable.sector_id,\n left = left,\n limitby = (0, 1)\n ).first()\n\n atable = s3db.project_activity\n activity_id = atable.insert(name = need[\"req_need.name\"],\n location_id = need[\"req_need.location_id\"],\n )\n organisation_id = current.auth.user.organisation_id\n if organisation_id:\n s3db.project_activity_organisation.insert(activity_id = activity_id,\n organisation_id = organisation_id,\n role = 1,\n )\n\n event_id = need[\"event_event_need.event_id\"]\n if event_id:\n aetable = s3db.event_activity\n aetable.insert(activity_id = activity_id,\n event_id = event_id,\n )\n\n sector_id = need[\"req_need_sector.sector_id\"]\n if sector_id:\n astable = s3db.project_sector_activity\n astable.insert(activity_id = activity_id,\n sector_id = sector_id,\n )\n\n nitable = s3db.req_need_item\n query = (nitable.need_id == need_id) & \\\n (nitable.deleted == False)\n items = db(query).select(nitable.item_category_id,\n nitable.item_id,\n nitable.item_pack_id,\n nitable.timeframe,\n nitable.quantity,\n )\n if items:\n iinsert = s3db.project_activity_item.insert\n for item in items:\n iinsert(activity_id = activity_id,\n item_category_id = item.item_category_id,\n item_id = item.item_id,\n item_pack_id = item.item_pack_id,\n timeframe = item.timeframe,\n target_value = item.quantity,\n )\n\n ndtable = s3db.req_need_demographic\n query = (ndtable.need_id == need_id) & \\\n (ndtable.deleted == False)\n demographics = db(query).select(ndtable.parameter_id,\n ndtable.timeframe,\n ndtable.value,\n )\n if demographics:\n dinsert = s3db.project_activity_demographic.insert\n for demographic in demographics:\n dinsert(activity_id = activity_id,\n parameter_id = demographic.parameter_id,\n timeframe = demographic.timeframe,\n target_value = demographic.value,\n )\n\n # Link to Need\n s3db.req_need_activity.insert(activity_id = activity_id,\n need_id = need_id,\n )\n\n # Update Need to show Fulfilled\n ntable = s3db.req_need\n need = current.db(ntable.id == need_id).select(ntable.id,\n ntable.status,\n limitby = (0, 1)\n ).first()\n if need.status in (0, 1):\n # Set to Fully Committed\n need.update_record(status = 2)\n\n # Redirect to Update\n from gluon import redirect\n redirect(URL(c= \"project\", f=\"activity\",\n args = [activity_id, \"update\"],\n ))\n\n # -------------------------------------------------------------------------\n def req_need_rheader(r):\n \"\"\"\n Resource Header for Needs\n \"\"\"\n\n if r.representation != \"html\":\n # RHeaders only used in interactive views\n return None\n\n record = r.record\n if not record:\n # RHeaders only used in single-record views\n return None\n\n if r.name == \"need\":\n # No Tabs (all done Inline)\n tabs = [(T(\"Basic Details\"), None),\n #(T(\"Demographics\"), \"demographic\"),\n #(T(\"Items\"), \"need_item\"),\n #(T(\"Skills\"), \"need_skill\"),\n #(T(\"Tags\"), \"tag\"),\n ]\n\n from s3 import s3_rheader_tabs\n rheader_tabs = s3_rheader_tabs(r, tabs)\n\n location_id = r.table.location_id\n from gluon import DIV, TABLE, TR, TH\n rheader = DIV(TABLE(TR(TH(\"%s: \" % location_id.label),\n location_id.represent(record.location_id),\n )),\n rheader_tabs)\n\n else:\n # Not defined, probably using wrong rheader\n rheader = None\n\n return rheader\n\n # -------------------------------------------------------------------------\n def customise_req_need_controller(**attr):\n\n # Custom commit method to create an Activity from a Need\n current.s3db.set_method(\"req\", \"need\",\n method = \"commit\",\n action = req_need_commit)\n\n s3 = current.response.s3\n\n # Custom postp\n standard_postp = s3.postp\n def postp(r, output):\n # Call standard postp\n if callable(standard_postp):\n output = standard_postp(r, output)\n\n if r.interactive:\n # Inject the javascript to handle dropdown filtering\n # - normnally injected through AddResourceLink, but this isn't there in Inline widget\n # - we also need to turn the trigger & target into dicts\n s3.scripts.append(\"/%s/static/themes/SHARE/js/supply.js\" % r.application)\n if current.auth.s3_has_permission(\"create\", \"project_activity\"):\n if r.id:\n # Custom RFooter\n from gluon import A\n s3.rfooter = A(T(\"Commit\"),\n _href = URL(args=[r.id, \"commit\"]),\n _class = \"action-btn\",\n #_id = \"commit-btn\",\n )\n #s3.jquery_ready.append(\n#'''S3.confirmClick('#commit-btn','%s')''' % T(\"Do you want to commit to this need?\"))\n else:\n from s3 import S3CRUD, s3_str\n # Normal Action Buttons\n S3CRUD.action_buttons(r)\n # Custom Action Buttons\n s3.actions += [{\"label\": s3_str(T(\"Commit\")),\n \"_class\": \"action-btn\",\n \"url\": URL(args=[\"[id]\", \"commit\"]),\n }\n ]\n\n return output\n s3.postp = postp\n\n attr[\"rheader\"] = req_need_rheader\n\n return attr\n\n settings.customise_req_need_controller = customise_req_need_controller","title":""},{"docid":"2068bc0d59325bc3a79f3c2dcc9621c2","score":"0.4791621","text":"def Site(self) -> str:","title":""},{"docid":"2068bc0d59325bc3a79f3c2dcc9621c2","score":"0.4791621","text":"def Site(self) -> str:","title":""},{"docid":"97ef6d064f02c1c18a42b42112fa4571","score":"0.4775658","text":"def iotlab_site(request):\n\n return getattr(request, \"param\", os.environ.get(\"IOTLAB_SITE\", DEFAULT_SITE))","title":""},{"docid":"aab9da175732c6876783151105233495","score":"0.4756351","text":"def go_to_custom_settings_setup(self):\n url = self.cumulusci.org.lightning_base_url\n url = \"{}/lightning/setup/CustomSettings/home\".format(url)\n self.selenium.go_to(url)\n self.salesforce.wait_until_loading_is_complete()","title":""},{"docid":"22f56c6549bca84248998275d112e228","score":"0.47474557","text":"def __init__(self, site_name, site_username, site_password):\n self.site_name = site_name\n self.site_username = site_username\n self.site_password = site_password","title":""},{"docid":"7b3ad68b43a9bf70da3a32a309797ce0","score":"0.47429588","text":"def test_set_site(self):\n config = SiteConfigurationManager.get_blank_site()\n\n # DNS Host attributes\n dnshost = config.dnshost\n dnshost.desc = 'desc'\n self.assertEquals(dnshost.desc, 'desc')\n\n dnshost.name = 'name'\n self.assertEquals(dnshost.name, 'name')\n self.assertRaises(ValidationError, lambda x: setattr(dnshost, 'name', x), \"f@ke\")\n\n dnshost.domain = SiteDefaultsManager.get_default_domain()\n self.assertEquals(dnshost.domain, SiteDefaultsManager.get_default_domain())\n self.assertRaises(ValidationError, lambda x: setattr(dnshost, 'domain', x), \"fake\")\n\n # Repository related attributes\n repository = config.repository\n\n repository.name = 'name'\n self.assertEquals(repository.name, 'name')\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'name', x), \"f@ke\")\n\n repository.type = SiteDefaultsManager.get_default_repository_type()\n self.assertEquals(repository.type,\n SiteDefaultsManager.get_default_repository_type())\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'type', x), \"fake\")\n\n repository.enabled = True\n self.assertEquals(repository.enabled, True)\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'enabled', x), 'fake')\n\n repository.done = True\n self.assertEquals(repository.done, True)\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), 'fake')\n\n # Web site related attributes\n site = config.website\n\n site.template = SiteDefaultsManager.get_default_site_template()\n self.assertEquals(site.template,\n SiteDefaultsManager.get_default_site_template())\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), \"fake\")\n\n site.access = SiteDefaultsManager.get_default_site_access()\n self.assertEquals(site.access,\n SiteDefaultsManager.get_default_site_access())\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), \"fake\")\n\n site.maintenance = True\n self.assertEquals(site.maintenance, True)\n self.assertRaises(ValidationError, lambda x: setattr(site, 'maintenance', x), 'fake')\n\n site.enabled = True\n self.assertEquals(site.enabled, True)\n self.assertRaises(ValidationError, lambda x: setattr(site, 'enabled', x), 'fake')\n\n site.done = True\n self.assertEquals(site.done, True)\n self.assertRaises(ValidationError, lambda x: setattr(site, 'done', x), 'fake')\n\n # Database related attribute\n database = config.database\n\n database.type = SiteDefaultsManager.get_default_database_type()\n self.assertEquals(database.type,\n SiteDefaultsManager.get_default_database_type())\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), \"fake\")\n\n database.name = 'name'\n self.assertEquals(database.name, 'name')\n self.assertRaises(ValidationError, lambda x: setattr(database, 'name', x), \"f@ke\")\n\n database.username = 'username'\n self.assertEquals(database.username, 'username')\n self.assertRaises(ValidationError, lambda x: setattr(database, 'username', x), \"f@ke\")\n\n database.password = 'password'\n self.assertEquals(database.password, 'password')\n\n database.enabled = True\n self.assertEquals(database.enabled, True)\n self.assertRaises(ValidationError, lambda x: setattr(database, 'enabled', x), 'fake')\n\n database.done = True\n self.assertEquals(database.done, True)\n self.assertRaises(ValidationError, lambda x: setattr(database, 'done', x), 'fake')","title":""},{"docid":"131f89358a5bd992160e6dd8e844ea17","score":"0.47285497","text":"def setUp(self):\n self.driver.get('http://taqc-opencart.epizy.com/')","title":""},{"docid":"3795b74bb262fa60abb6154af317cd50","score":"0.47279897","text":"def test_com_day_cq_analytics_sitecatalyst_impl_sitecatalyst_adapter_factory(self):\n pass","title":""},{"docid":"0a88627dc398912640e139643b0e288c","score":"0.47257218","text":"def make_shopping_site(self):\n context = aq_inner(self.context)\n alsoProvides(context, IShoppingSiteRoot)\n context.reindexObject(idxs=['object_provides'])\n\n notify(MakeShoppingSiteEvent(context))\n\n url = context.absolute_url()\n return self.request.response.redirect(url)","title":""},{"docid":"797420a73652add17f4fa00f9ce4866f","score":"0.47238547","text":"def config():\n OdooEnv_()._config()","title":""},{"docid":"cc7af834595020ca8a81949137240abe","score":"0.47172868","text":"def setUpBasicProducts(self):\n\n with zopeApp() as app:\n installProduct(app, 'Products.PluginIndexes')\n installProduct(app, 'Products.OFSP')","title":""},{"docid":"a85cd5fef82a47909740a6c35a27fe11","score":"0.47168154","text":"def _compute_survey_url(self):\n raise NotImplementedError","title":""},{"docid":"c5c6a7d9cef6aab5f90474ab15e3bc61","score":"0.47068766","text":"def activate(self):\n super(Zenoss, self).activate()\n\n if self.config is not None and set((\"ZENOSS_INST\", \"ZENOSS_USER\", \"ZENOSS_PASS\")) <= set(self.config):\n self.urlOpener = urllib2.build_opener(urllib2.HTTPCookieProcessor())\n self.reqCount = 1\n # Contruct POST params and submit login.\n loginParams = urllib.urlencode(dict(\n __ac_name = self.config['ZENOSS_USER'],\n __ac_password = self.config['ZENOSS_PASS'],\n submitted = 'true',\n came_from = self.config['ZENOSS_INST'] + '/zport/dmd'))\n self.urlOpener.open(self.config['ZENOSS_INST'] + '/zport/acl_users/cookieAuthHelper/login', loginParams)\n self.connected = True\n else:\n logger.warn(\"Unable to connect to Zenoss, plugin not configured.\")\n\n\tdef get_configuration_template(self):\n\t\t\"\"\"Defines the configuration structure this plugin supports\"\"\"\n\n\t\treturn {'ZENOSS_INST': 'http://server:8080',\n 'ZENOSS_USER': \"admin\",\n\t\t 'ZENOSS_PASS': \"admin\"\n\t\t }","title":""},{"docid":"0089f6e6d8104b1e9dd4a9981922f85b","score":"0.47057673","text":"def configure_panel(self):\n webroot = os.path.dirname(__file__)\n\n self.template_path = os.path.join(webroot, 'templates')\n aiohttp_jinja2.setup(\n self, loader=jinja2.FileSystemLoader(self.template_path),\n filters={'sorted': sorted, 'int': int}\n )\n\n self['static_root_url'] = '/static'\n self.router.add_view('/', Panel)\n self.router.add_static(\n '/static/', path=os.path.join(webroot, 'static'), name='static'\n )","title":""},{"docid":"aedb0246b3d6ccc9c346b655bc8702f2","score":"0.47027162","text":"def get_context_data(self, *args, **kwargs):\n context = super(ProductListAndCreateView, self).get_context_data(*args, **kwargs)\n context['view_url_name'] = resolve(self.request.path_info).url_name\n context['site_name'] = app_settings.PRICE_MONITOR_SITENAME\n return context","title":""},{"docid":"c61037639ac663ff9baa213a452f24f6","score":"0.4700974","text":"def configure_spider(self, spider_config: collections.OrderedDict):\n\n logging.debug(\"Trying to configure the AjaxSpider\")\n self.configure_scripts(config=spider_config)\n\n # Configure Spider (ajax or http)\n\n if self._is_not_empty_integer(\"maxDuration\", spider_config):\n self.check_zap_result(\n result=self.get_zap_spider.set_option_max_duration(\n integer=str(spider_config[\"maxDuration\"])\n ),\n method_name=\"set_option_max_duration\",\n )\n if self._is_not_empty_integer(\"maxDepth\", spider_config):\n self.check_zap_result(\n result=self.get_zap_spider.set_option_max_crawl_depth(\n integer=str(spider_config[\"maxDepth\"])\n ),\n method_name=\"set_option_max_crawl_depth\",\n )\n if self._is_not_empty_integer(\"maxStates\", spider_config):\n self.check_zap_result(\n result=self.get_zap_spider.set_option_max_crawl_states(\n integer=str(spider_config[\"maxStates\"])\n ),\n method_name=\"set_option_max_crawl_states\",\n )\n if self._is_not_empty_string(\"browserId\", spider_config):\n self.check_zap_result(\n result=self.get_zap_spider.set_option_browser_id(\n string=str(spider_config[\"browserId\"])\n ),\n method_name=\"set_option_browser_id\",\n )\n if self._is_not_empty_integer(\"browserCount\", spider_config):\n self.check_zap_result(\n result=self.get_zap_spider.set_option_number_of_browsers(\n integer=str(spider_config[\"browserCount\"])\n ),\n method_name=\"set_option_number_of_browsers\",\n )\n if self._is_not_empty_integer(\"randomInputs\", spider_config):\n self.check_zap_result(\n result=self.get_zap_spider.set_option_random_inputs(\n boolean=str(spider_config[\"randomInputs\"])\n ),\n method_name=\"set_option_random_inputs\",\n )\n\n if self._is_not_empty_integer(\"failIfFoundUrlsLessThan\", spider_config):\n self.failIfFoundUrlsLessThan = spider_config[\"failIfFoundUrlsLessThan\"]\n else:\n self.failIfFoundUrlsLessThan = 0 # Default value\n \n if self._is_not_empty_integer(\"warnIfFoundUrlsLessThan\", spider_config): \n self.warnIfFoundUrlsLessThan = spider_config[\"warnIfFoundUrlsLessThan\"]\n else:\n self.warnIfFoundUrlsLessThan = 0 # Default value ","title":""},{"docid":"1c2d4fa9b4264958c28405bf8e8e9b3b","score":"0.47008792","text":"def __init__(self, hard_negatives, include_title, **kwargs):\n super(RetrievalConfig, self).__init__(**kwargs)\n self.hard_negatives = hard_negatives\n self.include_title = include_title","title":""},{"docid":"04cf18af4f45d11352e7acb66c291192","score":"0.47001162","text":"def create_site(GlobalNetworkId=None, Description=None, Location=None, Tags=None):\n pass","title":""},{"docid":"a5b875634c0416f8a3895d20b5970b94","score":"0.46925145","text":"def create_survey(request):\n return HttpResponse(loader.get_template(\"paranoidApp/survey_creation_single_page.html\")\n .render({}, request))","title":""},{"docid":"93d12d2c60f3a3a34a18ba3f4c43374b","score":"0.46923384","text":"def build_site(simple_site):\n site = Site(simple_site['name'])\n site.nodes = SiteFactory._build_nodes(simple_site['nodes'])\n site.applications = SiteFactory._get_combined_applications(site.nodes)\n \n return site","title":""},{"docid":"498135a0de036a7014689e5f5e5bdcd9","score":"0.4688666","text":"def EpicsConfig(self):\n pass","title":""},{"docid":"464f22dc9fadc4e3295a8db3efa4ced4","score":"0.46879807","text":"def configure_designate_basic(*args):\n # If cluster relation is available it needs to passed in\n cluster = relations.endpoint_from_flag('cluster.available')\n if cluster is not None:\n args = args + (cluster, )\n dns_backend = relations.endpoint_from_flag('dns-backend.available')\n if dns_backend is not None:\n args = args + (dns_backend, )\n with charm.provide_charm_instance() as instance:\n instance.render_base_config(args)\n instance.disable_services()\n reactive.set_state('base-config.rendered')","title":""},{"docid":"a36adb9dce1958729ff8298cc8e397ab","score":"0.46870282","text":"def configure(self):\n super(Assembly, self).configure()\n self.add('lcoe_csm', lcoe_csm_assembly()) \n\n driver = self.add('driver', DakotaGlobalSAStudy())\n driver.workflow.add('lcoe_csm')\n driver.stdout = 'dakota.out'\n driver.stderr = 'dakota.err'\n driver.sample_type = 'random'\n driver.seed = 20\n driver.samples = 500 \n driver.vbd = True\n\n # Can't have parameter with no 'low' or 'high' defined.\n driver.add_parameter('lcoe_csm.rotor_diameter', low=126.0*0.5, high=126.0*1.5)\n driver.add_parameter('lcoe_csm.machine_rating', low=5000.0*0.5, high=5000.0*1.5)\n driver.add_parameter('lcoe_csm.hub_height', low=90.0*0.5, high=90.0*1.5)\n driver.add_parameter('lcoe_csm.max_tip_speed', low=80.0*0.5, high=80.0*1.5)\n driver.add_objective('lcoe_csm.coe')\n driver.add_objective('lcoe_csm.lcoe')\n driver.add_objective('lcoe_csm.net_aep')\n driver.add_objective('lcoe_csm.bos_costs')\n driver.add_objective('lcoe_csm.avg_annual_opex')\n driver.add_objective('lcoe_csm.turbine_cost')\n driver.add_objective('lcoe_csm.turbine_mass')\n driver.add_objective('lcoe_csm.rated_rotor_speed')","title":""},{"docid":"36720607587849677eff90722a279b80","score":"0.46729946","text":"def set_site(cls, archive, context, request):\n domain = request.host\n if \":\" in domain:\n domain = domain.split(\":\", 1)[0]\n\n site_instance = archive.sites.match(domain, context=context)\n\n if site_instance is None:\n log.error(\n 'no site matching domain \"{domain}\", consider adding [site:{domain}] to settings'.format(\n domain=domain\n )\n )\n return None\n\n context.root[\"sys\"][\"site\"] = site_instance\n try:\n context.root[\"sys\"][\"base\"] = archive.project_fs.getsyspath(\"/\")\n except NoSysPath:\n context.root[\"sys\"][\"base\"] = None\n context.root[\"site\"] = site_instance._data\n\n return site_instance","title":""},{"docid":"f2d4c423cdf84fa3875ce9c82dcd7372","score":"0.46708056","text":"def main(global_config, **settings):\n globalreg = getGlobalSiteManager()\n set_cache_regions_from_settings(settings)\n config = Configurator(registry=globalreg)\n\n config.include('pyramid_zcml')\n config.load_zcml('workflow.zcml')\n config.add_translation_dirs('por.dashboard:locale')\n\n from por.dashboard.views import PORRequest\n config.setup_registry(settings=settings,\n request_factory=PORRequest,\n root_factory='por.dashboard.views.DefaultContext')\n\n # por security\n from por.dashboard import security\n authentication_policy = RepozeWho1AuthenticationPolicy(identifier_name=\"auth_tkt\",callback=security.rolefinder)\n config._set_authentication_policy(authentication_policy)\n authorization_policy = ACLAuthorizationPolicy()\n config._set_authorization_policy(authorization_policy)\n config.scan('por.dashboard.security.views')\n config.include('por.dashboard.security.openid2')\n config.include('velruse.store.memstore')\n config.add_view('por.dashboard.security.views.forbidden', renderer='skin', context=\"pyramid.httpexceptions.HTTPForbidden\")\n\n #mailier\n config.include('pyramid_mailer')\n\n # por.models's configuration\n config.include('por.models')\n import por.dashboard.events; por.dashboard.events\n import por.dashboard.breadcrumbs; por.dashboard.breadcrumbs\n import por.dashboard.sidebar; por.dashboard.sidebar\n\n session_factory = session_factory_from_settings(settings)\n config.set_session_factory(session_factory)\n\n config.add_static_view('static', 'por.dashboard:static')\n config.scan('por.dashboard.views')\n\n config.add_route('tp', '/tp/*traverse', factory='por.dashboard.tp.TPContext')\n config.scan('por.dashboard.tp')\n config.scan('por.dashboard.smartadd')\n config.scan('por.dashboard.backlog')\n\n config.add_route('administrator', '/manage', factory='por.dashboard.manage.ManageContext')\n config.add_route('manage_svn_authz', '/manage/svn_authz', factory='por.dashboard.manage.ManageContext')\n config.scan('por.dashboard.manage')\n\n config.add_route('search', '/search')\n config.scan('por.dashboard.search')\n\n config.add_route('reports', '/reports/*traverse', factory='por.dashboard.reports.ReportContext')\n config.scan('por.dashboard.reports')\n config.add_renderer(name='xls_report', factory='por.dashboard.renderers.XLSReportRenderer')\n config.add_renderer(name='csv_report', factory='por.dashboard.renderers.CSVReportRenderer')\n\n config.include('pyramid_rpc.jsonrpc')\n config.add_jsonrpc_endpoint('DashboardAPI', '/apis/json/dashboard')\n config.scan('por.dashboard.api')\n\n config.add_renderer('skin', renderer_factory)\n config.include('pyramid_skins')\n config.register_path(\"por.dashboard:skins\", discovery=DISCOVERY)\n\n # por.gdata configuration\n config.include('por.gdata')\n\n # por.trac configuration\n # config.include('por.trac')\n\n # pyramid_formalchemy's configuration\n config.include('pyramid_formalchemy')\n config.include('fa.bootstrap')\n config.include('deform_bootstrap')\n\n config.formalchemy_admin('admin',\n package='por.dashboard',\n factory='por.dashboard.forms.CrudModels',\n models='por.models',\n view='por.dashboard.forms.ModelView',\n session_factory='por.models.DBSession')\n\n config.add_view(context='pyramid_formalchemy.resources.ModelListing',\n renderer='fa.bootstrap:templates/admin/new.pt',\n request_method='POST',\n route_name='admin',\n request_type='por.dashboard.interfaces.IPorRequest',\n view='por.dashboard.forms.security_create')\n\n config.add_static_view('deform_static', 'deform:static')\n config.add_route('navbar', '/navbar')\n config.add_route('favicon', '/favicon.ico')\n\n from por.dashboard.forms import include_forms\n include_forms(config)\n\n return config.make_wsgi_app()","title":""},{"docid":"8fd2f02deba83409a49d071e69ff58fd","score":"0.46655256","text":"def manage_addSurveyTool(context, REQUEST=None):\n portal_id = SurveyTool.portal_id\n ob = SurveyTool(portal_id, title=\"Portal Survey Manager\")\n context._setObject(portal_id, ob)\n ob = context._getOb(portal_id)\n ob.manage_configureSite()\n\n if REQUEST:\n return context.manage_main(context, REQUEST, update_menu=1)\n return portal_id","title":""},{"docid":"cc56262298e36fef4ee16f4a13d1018d","score":"0.46531948","text":"def configuredefaults(self):\r\n return self.configure(3800, 800, 100)","title":""},{"docid":"5d48d84e05ebfee5015ee4516b7e824b","score":"0.4642923","text":"def view_no_domain_config_error(request, is_test_page=False):\n\n # double checke to make sure it doesn't exist\n #\n app_config = AppConfiguration.get_config()\n if app_config and not is_test_page:\n return HttpResponseRedirect(reverse('home'))\n\n dinfo = dict(title='Two Ravens configuration error',\n IS_TEST_PAGE=is_test_page)\n\n return render(request,\n 'content_pages/no_domain_config_error.html',\n dinfo)","title":""},{"docid":"9a4cd8dbe5f9b662f4313e81345d3a72","score":"0.46390343","text":"def __init__(self, domain, site, ssl=True, debug=False, client=None):\n self.domain = domain\n self.site = site\n self.ssl = ssl\n self.debug = debug\n self.client = client","title":""},{"docid":"113681eb1aabae3a0e4b9164ebc52cfc","score":"0.46351382","text":"def website(self, website: str):\n\n self._website = website","title":""},{"docid":"d1a867230ba97574e830a2fca677a643","score":"0.46338075","text":"def __init__(self, config, survey_area=4 * np.pi):\n super().__init__(config)\n\n sacc_file = self.io.get_sacc_file()\n if \"clusters\" not in str(sacc_file.tracers.keys()):\n print(\n \"Clusters are not within the SACC file tracers.\"\n + \"Not performing cluster covariances.\"\n )\n return\n\n self.overdensity_delta = 200\n self.h0 = float(self.config[\"parameters\"].get(\"h\"))\n self.load_from_sacc(sacc_file)\n\n # NOTE Survey area should be pulled from SACC file but is currently not\n self.survey_area = survey_area\n\n cosmo = self.get_cosmology()\n self.load_from_cosmology(cosmo)\n self.fft_helper = FFTHelper(\n cosmo, self.z_lower_limit, self.z_upper_limit\n )\n\n # Quick key to skip P(Richness|M)\n self.has_mproxy = self.config.get(\"has_mproxy\", True)","title":""},{"docid":"2329b71ee245598a4629e12677ab2f53","score":"0.4630403","text":"def setup(request):\n global driver\n if(request.config.getoption(\"browser_name\") == \"Chrome\"):\n driver = webdriver.Chrome(ChromeDriverManager().install())\n log.getLogger().debug(\"Chrome is launched\")\n elif(request.config.getoption(\"browser_name\") == \"firefox\"):\n driver = webdriver.Firefox(GeckoDriverManager().install())\n log.getLogger().debug(\"Firefox is launched\")\n elif(request.config.getoption(\"browser_name\") == \"ie\"):\n driver = webdriver.Ie(IEDriverManager().install())\n log.getLogger().debug(\"IE is launched\")\n driver.get(data[\"url\"])\n log.getLogger().debug(\"Enter the url\" + data[\"url\"])\n driver.maximize_window()\n log.getLogger().debug(\"Maximize the window\")\n request.cls.driver = driver\n yield\n driver.quit()","title":""},{"docid":"20f411fe368519daeaaaa5f67266397d","score":"0.46236736","text":"def configure(self):\n \n # pylint: disable-msg=E1101\n\n # Create Optimizer instance\n self.add('driver', SLSQPdriver())\n \n # Create Paraboloid component instances\n self.add('paraboloid', Paraboloid())\n\n # Driver process definition\n self.driver.workflow.add('paraboloid')\n \n # SQLSQP Flags\n self.driver.iprint = 0\n \n # Objective \n self.driver.add_objective('paraboloid.f_xy')\n \n # Design Variables \n self.driver.add_parameter('paraboloid.x', low=-50., high=50.)\n self.driver.add_parameter('paraboloid.y', low=-50., high=50.)","title":""},{"docid":"09b64c68bb0dd33017712cc24cd8e55e","score":"0.46170565","text":"def load_siteconfig():\n from mezzanine.core.request import current_request\n try:\n from main.models import SiteConfiguration\n except ImportError:\n raise ImproperlyConfigured(\n \"You must create a SiteConfiguration model\"\n \"to use load_siteconfig tag.\")\n\n request = current_request()\n if hasattr(request, \"siteconfig\"):\n return request.siteconfig\n siteconfig = SiteConfiguration.objects.get_or_create()[0]\n request.siteconfig = siteconfig\n return siteconfig","title":""},{"docid":"d9e52b75a6784e37dfdf4d69b92e6e6b","score":"0.46158603","text":"def production():\n env.environment = 'production'\n env.project_path = \"/srv/pootle-production\"\n env.apache_server_name = \"i18n.haiku-os.org\"","title":""},{"docid":"8d984e5bd048b52ca1b0b7278f6bec22","score":"0.4613428","text":"def setup(self):\n self.aea_config_path = Path(\n CUR_PATH, \"data\", \"dummy_aea\", DEFAULT_AEA_CONFIG_FILE\n )\n self.loader = ConfigLoaders.from_package_type(PackageType.AGENT)\n self.aea_config: AgentConfig = self.loader.load(self.aea_config_path.open())\n self.dummy_skill_component_id = ComponentId(\n ComponentType.SKILL, DUMMY_SKILL_PUBLIC_ID\n )\n\n self.new_dummy_skill_config = {\n \"behaviours\": {\"dummy\": {\"args\": dict(behaviour_arg_1=42)}},\n \"handlers\": {\"dummy\": {\"args\": dict(handler_arg_1=42)}},\n \"models\": {\"dummy\": {\"args\": dict(model_arg_1=42)}},\n }","title":""},{"docid":"6450bac74eda4f900578772f4e351668","score":"0.46091795","text":"def __set_properties(self):\n self.SetTitle('ANW Admin - version %s' % anwp.func.globals.currentVersion)","title":""},{"docid":"19258f60aa64d245623abef191812ad7","score":"0.46067014","text":"def bootstrapSite(self):\n portal = self.getPortal()\n if 'MailHost' in portal.objectIds():\n portal.manage_delObjects(['MailHost'])\n portal._setObject('MailHost', DummyMailHost('MailHost'))\n\n portal.email_from_address = 'romain@nexedi.com'\n portal.email_to_address = 'romain@nexedi.com'\n\n self.clearCache()\n\n self.logMessage(\"Bootstrap Vifib...\")\n self.logMessage(\"Create Manager\")\n self.createManagerAndLogin()\n self.createVifibDocumentList()\n self.validateRule()\n self.logMessage(\"Create Administrator\")\n self.createAdministratorUser()\n self.logout()\n self.logMessage(\"Bootstrap Finished\")\n # XXX Hardcoded\n # XXX-Fx: No security is defined so administrator has no rights\n # so impossible to create vifib user with administrator\n #self.login(\"administrator\")\n self.login('manager')\n # XXX-Fx\n # XXX XXX\n self.createVifibUsers()\n self.logout()","title":""},{"docid":"24c95aa6a5a76828508d16524ad2ea77","score":"0.46028617","text":"def set_default_config(self):\n if self.m.properties.get('use_mirror', True): # We're on a bot\n self.set_config('production')\n else:\n self.set_config('testing')","title":""},{"docid":"9ac5a7e8a50b1538d9d7b2840ff5486c","score":"0.46006432","text":"def configure_request(app):\n global api_key,sources_api,sources_article_api,headlines_api\n api_key = app.config[\"API_KEY\"]\n sources_api = app.config[\"SOURCES_API\"]\n headlines_api = app.config[\"HEADLINES_API\"]\n sources_article_api = app.config[\"SOURCES_ARTICLE_API\"]\n search_api = app.config[\"SEARCH_API\"]","title":""},{"docid":"a3a0cf7e54f1b8269aa8ca5ddd698576","score":"0.4595866","text":"def setting(request):\n # Loading settings\n settings = load_settings.ArcherySettings(setting_file)\n\n # Loading OpenVAS Settings\n ov_user = settings.openvas_username()\n ov_pass = settings.openvas_pass()\n ov_ip = settings.openvas_host()\n lod_ov_user = signing.loads(ov_user)\n lod_ov_pass = signing.loads(ov_pass)\n lod_ov_ip = signing.loads(ov_ip)\n\n # Loading ZAP Settings\n lod_apikey = settings.zap_api_key()\n zap_host = settings.zap_host()\n zap_port = settings.zap_port()\n\n # Loading Burp Settings\n burp_host = settings.burp_host()\n burp_port = settings.burp_port()\n\n # Loading Email Settings\n email_subject = settings.email_subject()\n email_from = settings.email_from()\n to_email = settings.email_to()\n\n return render(request, 'setting.html',\n {'apikey': lod_apikey,\n 'zapath': zap_host,\n 'zap_port': zap_port,\n 'lod_ov_user': lod_ov_user,\n 'lod_ov_pass': lod_ov_pass,\n 'lod_ov_ip': lod_ov_ip,\n 'burp_path': burp_host,\n 'burp_port': burp_port,\n 'email_subject': email_subject,\n 'email_from': email_from,\n 'to_email': to_email})","title":""},{"docid":"c06659eca1f4783b0bb94e4fd8f2987b","score":"0.45922372","text":"def setup(self, conf):","title":""},{"docid":"40d4cfd78e5eda58d95f2d0b5988b0e1","score":"0.45918846","text":"def ProductArea1Admin(oRequest):\n dContext = {\n 'productarea1': REF_PRODUCT_AREA_1.objects.filter(is_inactive=0),\n 'errors': oRequest.session.pop('errors', None),\n 'message_type_is_error': oRequest.session.pop('message_is_error', False)\n }\n return Default(oRequest, 'BoMConfig/adminproductarea1.html', dContext)","title":""},{"docid":"6d463939ca25fe4f27a0d883817ace09","score":"0.4591799","text":"def setup(request):\n load_configuration()\n\n if config.default_browser == \"firefox\":\n allure.step(\"Starting Firefox.\")\n Selenium.driver = webdriver.Firefox()\n elif config.default_browser == \"chrome\":\n allure.step(\"Starting Chrome.\")\n Selenium.driver = webdriver.Chrome()\n elif config.default_browser == \"chrome-local-webdriver\":\n allure.step(\"Starting Chrome in webdriver local.\")\n options = webdriver.ChromeOptions()\n capabilities = options.to_capabilities()\n Selenium.driver = webdriver.Remote(\n command_executor=config.command_executor,\n desired_capabilities=capabilities)\n elif config.default_browser == \"firefox-local-webdriver\":\n allure.step(\"Starting Firefox in webdriver local.\")\n options = webdriver.FirefoxOptions()\n capabilities = options.to_capabilities()\n Selenium.driver = webdriver.Remote(\n command_executor=config.command_executor, # 'http://127.0.0.1:4444/wd/hub'\n desired_capabilities=capabilities)\n else:\n allure.step(\"Using standard Browser (chrome).\")\n Selenium.driver = webdriver.Chrome()\n Selenium.driver.implicitly_wait(config.implicit_wait) # 10 seconds\n Selenium.driver.maximize_window()","title":""},{"docid":"dc4619a407c3556a1283fdee7d491dc8","score":"0.45896873","text":"def selectSiteSite(self, site): \n self.siteSiteVar.set(site)","title":""},{"docid":"353b9ce434be79c193317e77270557d2","score":"0.458839","text":"def configure(self, config):","title":""},{"docid":"84c705b6bdc8db6f23dcf91809dd0684","score":"0.45879897","text":"def configure(self):\r\n pass","title":""},{"docid":"ffa2fc2b481059781b96e621deb06029","score":"0.458498","text":"def app_product(self):\n raise ConfigError('app_product')","title":""},{"docid":"e9f964d21a25c7535266a9c8e4ef9226","score":"0.4575019","text":"def setup_environment(self):\n self.environment = os.environ.get('AMPLIFY_ENVIRONMENT', 'production')\n self.imagename = os.environ.get('AMPLIFY_IMAGENAME')","title":""},{"docid":"8ae8548a32eb728992ed2b0b8a8031f2","score":"0.4570665","text":"def website(self, website):\n\n self._website = website","title":""},{"docid":"8ae8548a32eb728992ed2b0b8a8031f2","score":"0.4570665","text":"def website(self, website):\n\n self._website = website","title":""},{"docid":"8ae8548a32eb728992ed2b0b8a8031f2","score":"0.4570665","text":"def website(self, website):\n\n self._website = website","title":""},{"docid":"8ae8548a32eb728992ed2b0b8a8031f2","score":"0.4570665","text":"def website(self, website):\n\n self._website = website","title":""},{"docid":"318f4bb13a6912b06ce86ee5cf41bc33","score":"0.45651618","text":"def setup_add_ons(context):\n portal = _get_navigation_root(context)\n\n configure_collective_cover(context)\n\n create_products_carousel_content(portal)\n create_products_doormat_content(portal)\n create_products_ploneformgen_content(portal)\n create_collective_cover_content(portal)","title":""},{"docid":"509d1ebd270f28a11e153c4f03c39c84","score":"0.45585662","text":"def uni_form_setup(context):\n if 'STATIC_URL' not in context:\n context['STATIC_URL'] = settings.STATIC_URL\n return (context)","title":""},{"docid":"d10d9e4c57716a441c5d89fb39e408f5","score":"0.45529035","text":"def test_com_day_cq_dam_core_impl_assethome_asset_home_page_configuration(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"3eba6b65f16ea0a4a4737e22011f3594\",\n \"score\": \"0.58864015\",\n \"text\": \"def test_com_adobe_cq_social_site_impl_site_configurator_impl(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a10b37dc069aa4d5a8f2345c084fdd\",\n \"score\": \"0.5704494\",\n \"text\": \"def setup_blog(self):\\n blog_site, unused = Site.objects.get_or_create(domain='blog.tracon.fi', defaults=dict(\\n name='Traconin blogi'\\n ))\\n blog_site_settings, unused = SiteSettings.objects.get_or_create(site=blog_site, defaults=dict(\\n base_template='tracon11_base.jade',\\n page_template='tracon11_page.jade',\\n blog_index_template='tracon11_blog_index.jade',\\n blog_post_template='tracon11_blog_post.jade',\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8412e2daf929817dc66c1cc4730b6fcc\",\n \"score\": \"0.5648642\",\n \"text\": \"def setUpPloneSite(self, portal):\\n applyProfile(portal, 'plone.mls.listing:default')\\n quickInstallProduct(portal, 'raptus.article.core')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ee1bc164fa3112adb3c8d94a19f6474\",\n \"score\": \"0.54363644\",\n \"text\": \"def step_impl_survey_set_to_alternate(context):\\n context.bdd_helper.use_alternate_survey()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18995a0df4a61910a28ef9e70929b914\",\n \"score\": \"0.5420643\",\n \"text\": \"def step_impl_survey_set_to_default(context):\\n context.bdd_helper.use_default_survey()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b5e031a81eb3d73db8c8b362f8ec6bb\",\n \"score\": \"0.5418436\",\n \"text\": \"def setupVirtuoso(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aebf7ca98ee0b676569e0f1ea2d77002\",\n \"score\": \"0.53359467\",\n \"text\": \"def _get_sitebase(self, config):\\n self.logo = config.get('sitebase', 'LOGO')\\n self.title = config.get('sitebase', 'TITLE')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49b1987a54ec1ee53b47ceb3825a4eb\",\n \"score\": \"0.5324622\",\n \"text\": \"def enable_site(site_name):\\n if not is_site_enabled(site_name):\\n run_as_root('a2ensite %s' % _site_config_filename(site_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b6c7a692e63a03b95bd96903b2e0c0\",\n \"score\": \"0.53240675\",\n \"text\": \"def _set_site_configuration(self):\\n _return = False\\n\\n _common_db = self.RAMSTK_SITE_DIR + '/ramstk_common.rtk'\\n\\n _config = ConfigParser.ConfigParser()\\n\\n _config.add_section('Backend')\\n _config.set('Backend', 'host', 'localhost')\\n _config.set('Backend', 'socket', 3306)\\n _config.set('Backend', 'database', _common_db)\\n _config.set('Backend', 'user', 'ramstk')\\n _config.set('Backend', 'password', 'ramstk')\\n _config.set('Backend', 'type', 'sqlite')\\n _config.set('Backend', 'path', self.RAMSTK_SITE_DIR)\\n\\n try:\\n _parser = open(self.RAMSTK_SITE_CONF, 'w')\\n _config.write(_parser)\\n _parser.close()\\n except EnvironmentError:\\n _return = True\\n\\n return _return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feeba8adf27c3125d5744e2e78e24d64\",\n \"score\": \"0.5313803\",\n \"text\": \"def setup_site(args, site_name, parameters):\\n update_site_config(site_name, parameters)\\n create_site_database(args, site_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f2f6ebb214d146bc3ba75da67f0f13\",\n \"score\": \"0.523121\",\n \"text\": \"def setup_site(context):\\n # Executado apenas se o estivermos no Profile correto\\n if context.readDataFile('brasil.gov.agenda.txt') is None:\\n return\\n site = context.getSite()\\n list_agendadiaria_calendar(site)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a657f5780fd4e905f87a5c9efdc588dd\",\n \"score\": \"0.5218182\",\n \"text\": \"def test_change_default(self):\\n ss_conf = self.conf[\\\"Short Survëy\\\"]\\n self.assertEqual(ss_conf.get(\\\"footer\\\"), \\\"Short survey footer.\\\")\\n self.assertEqual(ss_conf.get(\\\"document_class\\\"), \\\"article\\\")\\n # Test Management Survëy conf\\n tm_survey = Survey.objects.get(name=\\\"Test Management Survëy\\\")\\n tm_conf = self.conf.get(survey_name=tm_survey.name)\\n self.assertEqual(tm_conf.get(\\\"footer\\\"), \\\"Test management footer.\\\")\\n self.assertEqual(tm_conf.get(\\\"document_class\\\"), \\\"article\\\")\\n self.assertEqual(self.conf.get(\\\"footer\\\", \\\"Test survëy\\\"), \\\"This is the footer.\\\")\\n self.assertEqual(self.conf.get(key=\\\"document_class\\\", survey_name=\\\"Test survëy\\\"), \\\"report\\\")\\n self.assertEqual(self.conf.get(\\\"footer\\\"), \\\"This is the footer.\\\")\\n self.assertEqual(self.conf.get(\\\"document_class\\\"), \\\"article\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd4201d74b3f9648472574f679972d3\",\n \"score\": \"0.51966184\",\n \"text\": \"def configurator(request):\\n\\n return render(request, CONF_PAGE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4569b15cb508d96925cbd1b91da7137c\",\n \"score\": \"0.5192934\",\n \"text\": \"def setUp(self):\\n self.new_site = Site(\\\"twitter\\\", \\\"ngugi\\\", \\\"asdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07446c8a958c281ad3321195b9617d80\",\n \"score\": \"0.5142763\",\n \"text\": \"def test_use_default(self):\\n short_survey_conf = self.conf.get(survey_name=\\\"Short Survëy\\\")\\n self.assertEqual(short_survey_conf.get(\\\"document_class\\\"), \\\"article\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d51440b29fafd9899f8f9af7c8b96950\",\n \"score\": \"0.51254827\",\n \"text\": \"def MockSiteConfig():\\n result = config_lib.SiteConfig()\\n\\n # Add a single, simple build config.\\n result.Add(\\n 'x86-generic-paladin',\\n active_waterfall='chromiumos',\\n boards=['x86-generic'],\\n build_type='paladin',\\n chrome_sdk=True,\\n chrome_sdk_build_chrome=False,\\n description='Commit Queue',\\n doc='http://mock_url/',\\n image_test=True,\\n images=['base', 'test'],\\n important=True,\\n manifest_version=True,\\n prebuilts='public',\\n trybot_list=True,\\n upload_standalone_images=False,\\n vm_tests=[config_lib.VMTestConfig('smoke_suite')],\\n )\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f5c25623b144cec9fdbf137d7d46a3\",\n \"score\": \"0.5024309\",\n \"text\": \"def setUp(self):\\n question = \\\"What language did you first learn to speak?\\\"\\n self.my_survey = AnonymousSurvey(question) #cada vez que se ejecute el setUp(), habrá una nueva instancia.\\n self.responses = ['English', 'Spanish', 'Mandarin']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"522d109329e5ec8c5cc5be7e9bc3b542\",\n \"score\": \"0.5021734\",\n \"text\": \"def _config_sensor_domainconf(self, minimum_n):\\n engine = {CONF_SENSOR_TYPE: 'mockcloudwatch',\\n CONF_IAAS_SITE: 'fake',\\n CONF_IAAS_ALLOCATION: 'also.fake',\\n 'deployable_type': 'fake',\\n 'minimum_vms': minimum_n,\\n 'metric': 'load',\\n 'monitor_sensors': ['load', ],\\n 'monitor_domain_sensors': ['queuelen', ],\\n 'sample_function': 'Average'}\\n return {EPUM_CONF_ENGINE: engine}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc78f3e1ae848a77fce9c5ba33dde53b\",\n \"score\": \"0.5009647\",\n \"text\": \"def setUp(self):\\n import seo_link.settings as seo_link_settings\\n seo_link_settings.BACKEND = \\\"seo_link.backends.advanced.LXMLCachedBackend\\\"\\n seo_link.middleware.SEO_BACKEND = get_seo_link_backend_class(path=seo_link_settings.BACKEND)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201f372752aec6828e1066830f4d1203\",\n \"score\": \"0.49818853\",\n \"text\": \"def disable_site(site_name):\\n if is_site_enabled(site_name):\\n run_as_root('a2dissite %s' % _site_config_filename(site_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3295fa5dfa570c0066be3ece6ddedd68\",\n \"score\": \"0.4954472\",\n \"text\": \"def test_default_site(self):\\n config = SiteConfigurationManager.get_blank_site()\\n\\n dnshost = config.dnshost\\n self.assertEquals(dnshost.domain,\\n SiteDefaultsManager.get_default_domain())\\n self.assertEquals(dnshost.name, u'')\\n self.assertEquals(dnshost.platform,\\n SiteDefaultsManager.get_default_platform())\\n self.assertTrue(dnshost.platform in \\\\\\n SiteDefaultsManager.get_platforms())\\n self.assertEquals(dnshost.description, u'')\\n\\n repository = config.repository\\n self.assertEquals(repository.enabled, False)\\n self.assertEquals(repository.done, False)\\n self.assertEquals(repository.name, u'')\\n self.assertEquals(repository.type,\\n SiteDefaultsManager.get_default_repository_type())\\n self.assertTrue(repository.type in \\\\\\n SiteDefaultsManager.get_repository_types())\\n\\n site = config.website\\n self.assertEquals(site.enabled, False)\\n self.assertEquals(site.done, False)\\n self.assertEquals(site.maintenance, False)\\n self.assertEquals(site.template,\\n SiteDefaultsManager.get_default_site_template())\\n self.assertTrue(site.template in \\\\\\n SiteDefaultsManager.get_site_templates())\\n self.assertEquals(site.access,\\n SiteDefaultsManager.get_default_site_access())\\n self.assertTrue(site.access in \\\\\\n SiteDefaultsManager.get_site_accesses())\\n\\n database = config.database\\n self.assertEquals(database.enabled, False)\\n self.assertEquals(database.done, False)\\n self.assertEquals(database.name, u'')\\n self.assertEquals(database.username, u'')\\n self.assertEquals(database.password, u'')\\n self.assertEquals(database.type,\\n SiteDefaultsManager.get_default_database_type())\\n self.assertTrue(database.type in \\\\\\n SiteDefaultsManager.get_database_types())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6355e6c448db9911030f85f170d257ba\",\n \"score\": \"0.4941305\",\n \"text\": \"def add_domain(site,settings,pan_list):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0598387f97c7b13f2890958f9ec3936a\",\n \"score\": \"0.49392506\",\n \"text\": \"def configure_sites(self):\\n\\n self.sites = {}\\n try:\\n for site_name, site_conf in self.config['sites'].items():\\n self.sites[site_name] = TaxiiSiteConnector(site_conf)\\n logging.info(f\\\"loaded site {site_name}\\\")\\n except handled_exceptions as e:\\n\\n logging.error(f\\\"Error in parsing config file: {e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cadaa78bf77cca9afdd2503ea08ff88d\",\n \"score\": \"0.4919648\",\n \"text\": \"def setUp(self):\\n question = \\\"What language did you first learn to speak?\\\"\\n self.my_survey = AnonymousSurvey(question)\\n self.responses = ['English', 'Spanish', 'Mandarin']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b69af01e7695f404a8991ce7f0abef6\",\n \"score\": \"0.49153143\",\n \"text\": \"def executeSiteSetup():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b713dd410374d58088751eddaeec0795\",\n \"score\": \"0.4908102\",\n \"text\": \"def setup_plone(app, options=None):\\n # Set up request for debug / bin/instance run mode.\\n app = makerequest(app)\\n\\n # If no site specified in options, assume there's exactly one and use that.\\n if not options or not options.site_root:\\n plone = get_plone_site(app)\\n else:\\n plone = app.unrestrictedTraverse(options.site_root)\\n\\n # Set up Manager security context\\n user = app.acl_users.getUser('zopemaster')\\n user = user.__of__(app.acl_users)\\n newSecurityManager(app, user)\\n\\n # Set up site to make component registry work\\n setSite(plone)\\n return plone\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ac919f0c486d043a23c9a9cfc9e677\",\n \"score\": \"0.49066913\",\n \"text\": \"def setup_configs():\\n COMMON_KEYWORDS = ['person', 'people', 'finder', 'person finder',\\n 'people finder', 'crisis', 'survivor', 'family']\\n\\n # NOTE: the following two CAPTCHA keys are dummy keys for testing only.\\n # (https://developers.google.com/recaptcha/docs/faq)\\n # They should be replaced with real keys upon launch.\\n config.set(captcha_site_key='6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI',\\n captcha_secret_key='6LeIxAcTAAAAAGG-vFI1TnRWxMZNFuojJ4WifJWe',\\n # A Google Translate API key with a very low quota, just for testing.\\n translate_api_key='AIzaSyCXdz9x7LDL3BvieEP8Wcze64CC_iqslSE',\\n repo_aliases={},\\n sms_number_to_repo={},\\n referrer_whitelist=[],\\n initialized=True,\\n notification_email=const.DEFAULT_NOTIFICATION_EMAIL,\\n unreviewed_notes_threshold=(\\n const.DEFAULT_UNREVIEWED_NOTES_THRESHOLD),\\n )\\n\\n config.set_for_repo(\\n 'haiti',\\n # Appended to \\\"Google Person Finder\\\" in page titles.\\n repo_titles={\\n 'en': 'Haiti Earthquake',\\n 'fr': u'S\\\\xe9isme en Ha\\\\xefti',\\n 'ht': u'Tranbleman T\\\\xe8 an Ayiti',\\n 'es': u'Terremoto en Hait\\\\xed'\\n },\\n # List of language codes that appear in the language menu.\\n language_menu_options=['en', 'ht', 'fr', 'es'],\\n # Content for the <meta name=\\\"keywords\\\"> tag.\\n keywords=', '.join([\\n 'haiti', 'earthquake', 'haiti earthquake', 'haitian',\\n u'ha\\\\xefti', u's\\\\xe9isme', 'tremblement', 'tremblement de terre',\\n 'famille', 'recherche de personnes', 'terremoto'\\n ] + COMMON_KEYWORDS),\\n # If false, hide the family_name field and use only given_name.\\n use_family_name=True,\\n # Presentation order for the given name and family name.\\n family_name_first=False,\\n # If true, show extra fields for alternate names.\\n use_alternate_names=True,\\n # If false, hide the home_zip field.\\n use_postal_code=True,\\n # Require at least this many letters in each word of a text query.\\n min_query_word_length=2,\\n # Show input fields for profile URLs in create page.\\n show_profile_entry=True,\\n # Default list of profile websites to show in create page.\\n profile_websites=const.DEFAULT_PROFILE_WEBSITES,\\n # Default map viewport for the location field in the note form.\\n map_default_zoom=7,\\n map_default_center=[18.968637, -72.284546],\\n map_size_pixels=[400, 280],\\n # If true, the feeds and read API require an authorization key.\\n read_auth_key_required=False,\\n # If true, the search API requires an authorization key.\\n search_auth_key_required=False,\\n # If true, show \\\"believed dead\\\" option in the note status dropdown\\n allow_believed_dead_via_ui=True,\\n # Custom html messages to show on main page, results page, view page,\\n # and query form, keyed by language codes.\\n start_page_custom_htmls={'en': '', 'fr': ''},\\n results_page_custom_htmls={'en': '', 'fr': ''},\\n view_page_custom_htmls={'en': '', 'fr': ''},\\n seek_query_form_custom_htmls={'en': '', 'fr': ''},\\n time_zone_offset=0,\\n time_zone_abbreviation='UTC',\\n published_date=get_timestamp(datetime(2010, 1, 12)),\\n updated_date=get_timestamp(datetime(2010, 1, 12)),\\n )\\n\\n config.set_for_repo(\\n 'japan',\\n language_menu_options=['ja', 'en', 'ko', 'zh-CN', 'zh-TW', 'pt-BR', 'es'],\\n repo_titles={\\n 'en': '2011 Japan Earthquake',\\n 'zh-TW': u'2011 \\\\u65e5\\\\u672c\\\\u5730\\\\u9707',\\n 'zh-CN': u'2011 \\\\u65e5\\\\u672c\\\\u5730\\\\u9707',\\n 'pt-BR': u'2011 Terremoto no Jap\\\\xe3o',\\n 'ja': u'2011 \\\\u65e5\\\\u672c\\\\u5730\\\\u9707',\\n 'es': u'2011 Terremoto en Jap\\\\xf3n'\\n },\\n keywords=', '.join(COMMON_KEYWORDS),\\n use_family_name=True,\\n family_name_first=True,\\n use_alternate_names=True,\\n use_postal_code=True,\\n min_query_word_length=1,\\n show_profile_entry=True,\\n profile_websites=const.DEFAULT_PROFILE_WEBSITES,\\n map_default_zoom=7,\\n map_default_center=[38, 140.7],\\n map_size_pixels=[400, 400],\\n search_auth_key_required=True,\\n read_auth_key_required=True,\\n allow_believed_dead_via_ui=True,\\n start_page_custom_htmls={'en': 'Custom message', 'fr': 'French'},\\n results_page_custom_htmls={'en': 'Custom message', 'fr': 'French'},\\n view_page_custom_htmls={'en': 'Custom message', 'fr': 'French'},\\n seek_query_form_custom_htmls={'en': '', 'fr': ''},\\n # NOTE(kpy): These two configuration settings only work for locations\\n # with a single, fixed time zone offset and no Daylight Saving Time.\\n time_zone_offset=9, # UTC+9\\n time_zone_abbreviation='JST',\\n jp_mobile_carrier_redirect=True,\\n published_date=get_timestamp(datetime(2011, 3, 11)),\\n updated_date=get_timestamp(datetime(2011, 3, 11)),\\n )\\n\\n config.set_for_repo(\\n 'pakistan',\\n repo_titles={\\n 'en': 'Pakistan Floods',\\n 'ur': u'\\\\u067e\\\\u0627\\\\u06a9\\\\u0633\\\\u062a\\\\u0627\\\\u0646\\\\u06cc \\\\u0633\\\\u06cc\\\\u0644\\\\u0627\\\\u0628'\\n },\\n language_menu_options=['en', 'ur'],\\n keywords=', '.join([\\n 'pakistan', 'flood', 'pakistan flood', 'pakistani'\\n ] + COMMON_KEYWORDS),\\n use_family_name=False,\\n family_name_first=False,\\n use_alternate_names=False,\\n use_postal_code=False,\\n min_query_word_length=1,\\n map_default_zoom=6,\\n map_default_center=[33.36, 73.26], # near Rawalpindi, Pakistan\\n map_size_pixels=[400, 500],\\n read_auth_key_required=False,\\n search_auth_key_required=False,\\n allow_believed_dead_via_ui=True,\\n start_page_custom_htmls={'en': '', 'fr': ''},\\n results_page_custom_htmls={'en': '', 'fr': ''},\\n view_page_custom_htmls={'en': '', 'fr': ''},\\n seek_query_form_custom_htmls={'en': '', 'fr': ''},\\n time_zone_offset=0,\\n time_zone_abbreviation='UTC',\\n published_date=get_timestamp(datetime(2010, 8, 6)),\\n updated_date=get_timestamp(datetime(2010, 8, 6)),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305b7da778948be1eeead846382a26f8\",\n \"score\": \"0.49029732\",\n \"text\": \"def _config_simplest_domain_conf(self, n_preserving):\\n engine_class = \\\"epu.decisionengine.impls.simplest.SimplestEngine\\\"\\n general = {EPUM_CONF_ENGINE_CLASS: engine_class}\\n health = {EPUM_CONF_HEALTH_MONITOR: False}\\n engine = {CONF_PRESERVE_N: n_preserving}\\n return {EPUM_CONF_GENERAL: general, EPUM_CONF_ENGINE: engine, EPUM_CONF_HEALTH: health}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d06290a512fff69082ba4de465963978\",\n \"score\": \"0.4899163\",\n \"text\": \"def project_settings(request):\\n econnect_settings = ikwen_settings(request)\\n econnect_settings['settings'].update({\\n 'GOOGLE_MAPS_API_KEY': getattr(settings, 'GOOGLE_MAPS_API_KEY'),\\n 'CREOLINK_MAPS_URL': getattr(settings, 'CREOLINK_MAPS_URL'),\\n })\\n try:\\n econnect_settings['yup'] = PaymentMean.objects.get(slug='yup')\\n except:\\n pass\\n return econnect_settings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"455007a533ccb8b06633e309d2ab5750\",\n \"score\": \"0.48983642\",\n \"text\": \"def postInstall(context):\\n # the right place for your custom code\\n if isNotPloneConfContentTypesProfile(context): return\\n site = context.getSite()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070495c197b0d0bec90bb2ebbe454721\",\n \"score\": \"0.48926044\",\n \"text\": \"def add_default_site(instance, **kwargs):\\n if not settings.DBTEMPLATES_ADD_DEFAULT_SITE:\\n return\\n current_site = Site.objects.get_current()\\n if current_site not in instance.sites.all():\\n instance.sites.add(current_site)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810cedd5d589fefbea0367a2d4a09df3\",\n \"score\": \"0.48878357\",\n \"text\": \"def set_basic_config(request, info):# {{{\\n username = request.user.username\\n client_ip = request.META['REMOTE_ADDR']\\n if username in settings.SUPER_USER_LIST:\\n isSuperUser = True\\n divided_logfile_query = \\\"%s/%s/%s\\\"%(SITE_ROOT,\\n \\\"static/log\\\", \\\"submitted_seq.log\\\")\\n divided_logfile_finished_jobid = \\\"%s/%s/%s\\\"%(SITE_ROOT,\\n \\\"static/log\\\", \\\"failed_job.log\\\")\\n else:\\n isSuperUser = False\\n divided_logfile_query = \\\"%s/%s/%s\\\"%(SITE_ROOT,\\n \\\"static/log/divided\\\", \\\"%s_submitted_seq.log\\\"%(client_ip))\\n divided_logfile_finished_jobid = \\\"%s/%s/%s\\\"%(SITE_ROOT,\\n \\\"static/log/divided\\\", \\\"%s_failed_job.log\\\"%(client_ip))\\n\\n if isSuperUser:\\n info['MAX_DAYS_TO_SHOW'] = g_params['BIG_NUMBER']\\n else:\\n info['MAX_DAYS_TO_SHOW'] = g_params['MAX_DAYS_TO_SHOW']\\n\\n\\n info['username'] = username\\n info['isSuperUser'] = isSuperUser\\n info['divided_logfile_query'] = divided_logfile_query\\n info['divided_logfile_finished_jobid'] = divided_logfile_finished_jobid\\n info['client_ip'] = client_ip\\n info['BASEURL'] = g_params['BASEURL']\\n info['STATIC_URL'] = settings.STATIC_URL\\n info['path_result'] = path_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7dbcdfa2e99586375c21303868eeac\",\n \"score\": \"0.4882148\",\n \"text\": \"def configure_website(self, main_page_suffix=None, error_key=None,\\n headers=None):\\n if main_page_suffix:\\n main_page_frag = self.WebsiteMainPageFragment % main_page_suffix\\n else:\\n main_page_frag = ''\\n\\n if error_key:\\n error_frag = self.WebsiteErrorFragment % error_key\\n else:\\n error_frag = ''\\n\\n body = self.WebsiteBody % (main_page_frag, error_frag)\\n response = self.connection.make_request('PUT', self.name, data=body,\\n query_args='websiteConfig',\\n headers=headers)\\n body = response.read()\\n if response.status == 200:\\n return True\\n else:\\n raise self.connection.provider.storage_response_error(\\n response.status, response.reason, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb715d062db1dfbf4a7abef96f1f383\",\n \"score\": \"0.4859533\",\n \"text\": \"def url(self):\\n return dict(site=config.where.database.nma_ws_site.str, sites=config.where.database.nma_ws_sites.str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a32963b726217336a7fc345b0e26287f\",\n \"score\": \"0.48571873\",\n \"text\": \"def create_products_ploneformgen_content(portal):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89195526e1fcf61376b0bc3923ca107f\",\n \"score\": \"0.48505992\",\n \"text\": \"def production():\\n #TKTK\\n env.settings = 'production'\\n env.hosts = ['census.ire.org']\\n env.user = 'ubuntu'\\n env.s3_bucket = 'censusmedia.ire.org'\\n env.site_domain = 'census.ire.org' \\n env.cache_server = 'census.ire.org'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5333bd7ccba24d9469c2d2a5888b687b\",\n \"score\": \"0.4847403\",\n \"text\": \"def get_main_config():\\n handler = AelVariableHandler()\\n handler.add('counterparty',\\n label='Client Party',\\n cls='FParty',\\n collection=acm.FParty.Select('').SortByProperty('Name'),\\n hook=short_name_hook)\\n handler.add('shortName',\\n label='Client Short Name',\\n alt=\\\"Client's short name to be used in portfolio name building.\\\",\\n enabled=False)\\n handler.add('startDate',\\n label='Start Date',\\n cls='date',\\n default=acm.Time.DateToday(),\\n alt='Start date of the PortfolioSwaps and Call Loans/Deposits.')\\n handler.add('cfdAccount',\\n label='CFD Hedge Equity Account',\\n mandatory=False)\\n handler.add('ceAccount',\\n label='CE Stock Account',\\n mandatory=False)\\n handler.add_bool('fundAllocations',\\n label='Fund Allocations',\\n default=False)\\n handler.add_bool('sblAgreement',\\n label='SBL Agreement',\\n default=False)\\n handler.add_bool('dualListed',\\n label='Dual Listed',\\n default=False,\\n alt='If checked, dual listed arb margin policy will be applied.')\\n handler.add_bool('dryRun',\\n label='Dry Run',\\n default=True,\\n alt=('If checked, do a dry run only - i.e. '\\n 'do not save anything to the DB.'))\\n return handler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2cdc8739c879c2d6f0fe6a776ae419a\",\n \"score\": \"0.48262924\",\n \"text\": \"def setUp(self):\\n self.driver = webdriver.Firefox()\\n self.driver.get(\\\"https://svssso10ext.laadev.co.uk\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8d41f7d49f70599e919f69b8a98b6b3\",\n \"score\": \"0.48208126\",\n \"text\": \"def test_prepopulate_siteform(self):\\n response = self.client.get(reverse(\\n 'streamwebs:update_site',\\n kwargs={'site_slug': self.site.site_slug}))\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(response, 'streamwebs/update_site.html')\\n self.assertContains(response, 'Creaky Creek')\\n self.assertContains(response, self.site.site_slug)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aace95249b08664caac8eacd3ea4e68\",\n \"score\": \"0.4820754\",\n \"text\": \"def default_site():\\n if getattr(settings, 'SITE_ID', ''):\\n return Site.objects.get(pk=settings.SITE_ID)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c73ac6f87b35c11fccf829f481ced4\",\n \"score\": \"0.48201337\",\n \"text\": \"def enable_product_admin_panels():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba2714892a662c69a12cd3d9a571ddd4\",\n \"score\": \"0.4810867\",\n \"text\": \"def setup_method(self):\\n\\n config.DOMAIN = \\\"https://google.com/\\\"\\n self._set_up()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f6192bb17c362dc6a3afa462fd3afa\",\n \"score\": \"0.48070142\",\n \"text\": \"def default_configs(cls):\\n config = super().default_configs()\\n config.update({\\\"model\\\": \\\"openie\\\"})\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa19578ddc67d9438ccd5cf53da3dc4\",\n \"score\": \"0.48062485\",\n \"text\": \"def config(settings):\\n\\n T = current.T\\n\\n settings.base.system_name = T(\\\"Humanitarian Country Team (HCT) Relief and Rehabilitation System\\\")\\n settings.base.system_name_short = T(\\\"SHARE\\\")\\n\\n # UI Settings\\n settings.ui.menu_logo = URL(c = \\\"static\\\",\\n f = \\\"themes\\\",\\n args = [\\\"SHARE\\\", \\\"sharemenulogo.png\\\"],\\n )\\n\\n # PrePopulate data\\n settings.base.prepopulate += (\\\"SHARE\\\",)\\n\\n # Theme (folder to use for views/layout.html)\\n #settings.base.theme = \\\"SHARE\\\"\\n\\n # Authentication settings\\n # Should users be allowed to register themselves?\\n #settings.security.self_registration = False\\n # Do new users need to verify their email address?\\n #settings.auth.registration_requires_verification = True\\n # Do new users need to be approved by an administrator prior to being able to login?\\n #settings.auth.registration_requires_approval = True\\n settings.auth.registration_requests_organisation = True\\n #settings.auth.registration_organisation_required = True\\n #settings.auth.registration_requests_site = True\\n\\n settings.auth.registration_link_user_to = {\\\"staff\\\": T(\\\"Staff\\\"),\\n \\\"volunteer\\\": T(\\\"Volunteer\\\"),\\n #\\\"member\\\": T(\\\"Member\\\")\\n }\\n\\n def registration_organisation_default(default):\\n auth = current.auth\\n has_role = auth.s3_has_role\\n if has_role(\\\"ORG_ADMIN\\\") and not has_role(\\\"ADMIN\\\"):\\n return auth.user.organisation_id\\n else:\\n return default\\n\\n settings.auth.registration_organisation_default = registration_organisation_default\\n\\n # Approval emails get sent to all admins\\n settings.mail.approver = \\\"ADMIN\\\"\\n\\n # Restrict the Location Selector to just certain countries\\n # NB This can also be over-ridden for specific contexts later\\n # e.g. Activities filtered to those of parent Project\\n #settings.gis.countries = (\\\"US\\\",)\\n # Uncomment to display the Map Legend as a floating DIV\\n settings.gis.legend = \\\"float\\\"\\n # Uncomment to Disable the Postcode selector in the LocationSelector\\n #settings.gis.postcode_selector = False # @ToDo: Vary by country (include in the gis_config!)\\n # Uncomment to show the Print control:\\n # http://eden.sahanafoundation.org/wiki/UserGuidelines/Admin/MapPrinting\\n #settings.gis.print_button = True\\n\\n # L10n settings\\n # Number formats (defaults to ISO 31-0)\\n # Decimal separator for numbers (defaults to ,)\\n settings.L10n.decimal_separator = \\\".\\\"\\n # Thousands separator for numbers (defaults to space)\\n settings.L10n.thousands_separator = \\\",\\\"\\n\\n # Security Policy\\n # http://eden.sahanafoundation.org/wiki/S3AAA#System-widePolicy\\n # 1: Simple (default): Global as Reader, Authenticated as Editor\\n # 2: Editor role required for Update/Delete, unless record owned by session\\n # 3: Apply Controller ACLs\\n # 4: Apply both Controller & Function ACLs\\n # 5: Apply Controller, Function & Table ACLs\\n # 6: Apply Controller, Function, Table ACLs and Entity Realm\\n # 7: Apply Controller, Function, Table ACLs and Entity Realm + Hierarchy\\n # 8: Apply Controller, Function, Table ACLs, Entity Realm + Hierarchy and Delegations\\n\\n settings.security.policy = 6 # Controller, Function, Table ACLs and Entity Realm\\n\\n # -------------------------------------------------------------------------\\n # Events\\n settings.event.label = \\\"Disaster\\\"\\n # Uncomment to not use Incidents under Events\\n settings.event.incident = False\\n\\n # -------------------------------------------------------------------------\\n # Messaging\\n settings.msg.parser = \\\"SAMBRO\\\" # for parse_tweet\\n\\n # -------------------------------------------------------------------------\\n # Organisations\\n settings.org.sector = True\\n # Show Organisation Types in the rheader\\n settings.org.organisation_type_rheader = True\\n\\n # -------------------------------------------------------------------------\\n # Projects\\n # Don't use Beneficiaries\\n settings.project.activity_beneficiaries = False\\n # Don't use Item Catalog for Distributions\\n settings.project.activity_items = False\\n settings.project.activity_sectors = True\\n # Links to Filtered Components for Donors & Partners\\n settings.project.organisation_roles = {\\n 1: T(\\\"Agency\\\"),\\n 2: T(\\\"Implementing Partner\\\"),\\n 3: T(\\\"Donor\\\"),\\n }\\n\\n # -------------------------------------------------------------------------\\n # Supply\\n # Disable the use of Multiple Item Catalogs\\n settings.supply.catalog_multi = False\\n\\n # -------------------------------------------------------------------------\\n # Comment/uncomment modules here to disable/enable them\\n # Modules menu is defined in modules/eden/menu.py\\n settings.modules = OrderedDict([\\n # Core modules which shouldn't be disabled\\n (\\\"default\\\", Storage(\\n name_nice = \\\"Home\\\",\\n restricted = False, # Use ACLs to control access to this module\\n access = None, # All Users (inc Anonymous) can see this module in the default menu & access the controller\\n module_type = None # This item is not shown in the menu\\n )),\\n (\\\"admin\\\", Storage(\\n name_nice = \\\"Administration\\\",\\n #description = \\\"Site Administration\\\",\\n restricted = True,\\n access = \\\"|1|\\\", # Only Administrators can see this module in the default menu & access the controller\\n module_type = None # This item is handled separately for the menu\\n )),\\n (\\\"appadmin\\\", Storage(\\n name_nice = \\\"Administration\\\",\\n #description = \\\"Site Administration\\\",\\n restricted = True,\\n module_type = None # No Menu\\n )),\\n (\\\"errors\\\", Storage(\\n name_nice = \\\"Ticket Viewer\\\",\\n #description = \\\"Needed for Breadcrumbs\\\",\\n restricted = False,\\n module_type = None # No Menu\\n )),\\n (\\\"setup\\\", Storage(\\n name_nice = T(\\\"Setup\\\"),\\n #description = \\\"WebSetup\\\",\\n restricted = True,\\n access = \\\"|1|\\\", # Only Administrators can see this module in the default menu & access the controller\\n module_type = None # No Menu\\n )),\\n (\\\"sync\\\", Storage(\\n name_nice = \\\"Synchronization\\\",\\n #description = \\\"Synchronization\\\",\\n restricted = True,\\n access = \\\"|1|\\\", # Only Administrators can see this module in the default menu & access the controller\\n module_type = None # This item is handled separately for the menu\\n )),\\n #(\\\"tour\\\", Storage(\\n # name_nice = T(\\\"Guided Tour Functionality\\\"),\\n # module_type = None,\\n #)),\\n (\\\"translate\\\", Storage(\\n name_nice = T(\\\"Translation Functionality\\\"),\\n #description = \\\"Selective translation of strings based on module.\\\",\\n module_type = None,\\n )),\\n (\\\"gis\\\", Storage(\\n name_nice = \\\"Map\\\",\\n #description = \\\"Situation Awareness & Geospatial Analysis\\\",\\n restricted = True,\\n module_type = 6, # 6th item in the menu\\n )),\\n (\\\"pr\\\", Storage(\\n name_nice = \\\"Person Registry\\\",\\n #description = \\\"Central point to record details on People\\\",\\n restricted = True,\\n access = \\\"|1|\\\", # Only Administrators can see this module in the default menu (access to controller is possible to all still)\\n module_type = 10\\n )),\\n (\\\"org\\\", Storage(\\n name_nice = \\\"Organizations\\\",\\n #description = 'Lists \\\"who is doing what & where\\\". Allows relief agencies to coordinate their activities',\\n restricted = True,\\n module_type = 1\\n )),\\n (\\\"hrm\\\", Storage(\\n name_nice = \\\"Staff\\\",\\n #description = \\\"Human Resources Management\\\",\\n restricted = True,\\n module_type = 2,\\n )),\\n (\\\"vol\\\", Storage(\\n name_nice = T(\\\"Volunteers\\\"),\\n #description = \\\"Human Resources Management\\\",\\n restricted = True,\\n module_type = 2,\\n )),\\n (\\\"cms\\\", Storage(\\n name_nice = \\\"Content Management\\\",\\n #description = \\\"Content Management System\\\",\\n restricted = True,\\n module_type = 10,\\n )),\\n (\\\"doc\\\", Storage(\\n name_nice = \\\"Documents\\\",\\n #description = \\\"A library of digital resources, such as photos, documents and reports\\\",\\n restricted = True,\\n module_type = 10,\\n )),\\n (\\\"msg\\\", Storage(\\n name_nice = \\\"Messaging\\\",\\n #description = \\\"Sends & Receives Alerts via Email & SMS\\\",\\n restricted = True,\\n # The user-visible functionality of this module isn't normally required. Rather it's main purpose is to be accessed from other modules.\\n module_type = None,\\n )),\\n (\\\"supply\\\", Storage(\\n name_nice = \\\"Supply Chain Management\\\",\\n #description = \\\"Used within Inventory Management, Request Management and Asset Management\\\",\\n restricted = True,\\n module_type = None, # Not displayed\\n )),\\n (\\\"inv\\\", Storage(\\n name_nice = T(\\\"Warehouses\\\"),\\n #description = \\\"Receiving and Sending Items\\\",\\n restricted = True,\\n module_type = 4\\n )),\\n (\\\"asset\\\", Storage(\\n name_nice = \\\"Assets\\\",\\n #description = \\\"Recording and Assigning Assets\\\",\\n restricted = True,\\n module_type = 5,\\n )),\\n # Vehicle depends on Assets\\n #(\\\"vehicle\\\", Storage(\\n # name_nice = \\\"Vehicles\\\",\\n # #description = \\\"Manage Vehicles\\\",\\n # restricted = True,\\n # module_type = 10,\\n #)),\\n (\\\"req\\\", Storage(\\n name_nice = \\\"Requests\\\",\\n #description = \\\"Manage requests for supplies, assets, staff or other resources. Matches against Inventories where supplies are requested.\\\",\\n restricted = True,\\n module_type = 10,\\n )),\\n (\\\"project\\\", Storage(\\n name_nice = \\\"Tasks\\\",\\n #description = \\\"Tracking of Projects, Activities and Tasks\\\",\\n restricted = True,\\n module_type = 2\\n )),\\n #(\\\"cr\\\", Storage(\\n # name_nice = T(\\\"Shelters\\\"),\\n # #description = \\\"Tracks the location, capacity and breakdown of victims in Shelters\\\",\\n # restricted = True,\\n # module_type = 10\\n #)),\\n #(\\\"hms\\\", Storage(\\n # name_nice = T(\\\"Hospitals\\\"),\\n # #description = \\\"Helps to monitor status of hospitals\\\",\\n # restricted = True,\\n # module_type = 10\\n #)),\\n #(\\\"dvr\\\", Storage(\\n # name_nice = T(\\\"Disaster Victim Registry\\\"),\\n # #description = \\\"Allow affected individuals & households to register to receive compensation and distributions\\\",\\n # restricted = True,\\n # module_type = 10,\\n #)),\\n (\\\"event\\\", Storage(\\n name_nice = \\\"Events\\\",\\n #description = \\\"Activate Events (e.g. from Scenario templates) for allocation of appropriate Resources (Human, Assets & Facilities).\\\",\\n restricted = True,\\n module_type = 10,\\n )),\\n #(\\\"transport\\\", Storage(\\n # name_nice = T(\\\"Transport\\\"),\\n # restricted = True,\\n # module_type = 10,\\n #)),\\n (\\\"stats\\\", Storage(\\n name_nice = T(\\\"Statistics\\\"),\\n #description = \\\"Manages statistics\\\",\\n restricted = True,\\n module_type = None,\\n )),\\n ])\\n\\n # -------------------------------------------------------------------------\\n def customise_event_sitrep_resource(r, tablename):\\n\\n current.response.s3.crud_strings[tablename] = Storage(\\n label_create = T(\\\"Add Situational Update\\\"),\\n title_display = T(\\\"HCT Activity and Response Report\\\"),\\n title_list = T(\\\"Situational Updates\\\"),\\n title_update = T(\\\"Edit Situational Update\\\"),\\n title_upload = T(\\\"Import Situational Updates\\\"),\\n label_list_button = T(\\\"List Situational Updates\\\"),\\n label_delete_button = T(\\\"Delete Situational Update\\\"),\\n msg_record_created = T(\\\"Situational Update added\\\"),\\n msg_record_modified = T(\\\"Situational Update updated\\\"),\\n msg_record_deleted = T(\\\"Situational Update deleted\\\"),\\n msg_list_empty = T(\\\"No Situational Updates currently registered\\\"))\\n\\n settings.customise_event_sitrep_resource = customise_event_sitrep_resource\\n\\n # -------------------------------------------------------------------------\\n def customise_msg_twitter_channel_resource(r, tablename):\\n\\n s3db = current.s3db\\n def onaccept(form):\\n # Normal onaccept\\n s3db.msg_channel_onaccept(form)\\n _id = form.vars.id\\n db = current.db\\n table = db.msg_twitter_channel\\n channel_id = db(table.id == _id).select(table.channel_id,\\n limitby=(0, 1)).first().channel_id\\n # Link to Parser\\n table = s3db.msg_parser\\n _id = table.insert(channel_id=channel_id, function_name=\\\"parse_tweet\\\", enabled=True)\\n s3db.msg_parser_enable(_id)\\n\\n run_async = current.s3task.async\\n # Poll\\n run_async(\\\"msg_poll\\\", args=[\\\"msg_twitter_channel\\\", channel_id])\\n\\n # Parse\\n run_async(\\\"msg_parse\\\", args=[channel_id, \\\"parse_tweet\\\"])\\n\\n s3db.configure(tablename,\\n create_onaccept = onaccept,\\n )\\n\\n settings.customise_msg_twitter_channel_resource = customise_msg_twitter_channel_resource\\n\\n # -------------------------------------------------------------------------\\n def customise_org_organisation_resource(r, tablename):\\n\\n s3db = current.s3db\\n\\n # Custom Components\\n s3db.add_components(tablename,\\n org_organisation_tag = (# Request Number\\n {\\\"name\\\": \\\"req_number\\\",\\n \\\"joinby\\\": \\\"organisation_id\\\",\\n \\\"filterby\\\": {\\\"tag\\\": \\\"req_number\\\",\\n },\\n \\\"multiple\\\": False,\\n },\\n # Vision\\n {\\\"name\\\": \\\"vision\\\",\\n \\\"joinby\\\": \\\"organisation_id\\\",\\n \\\"filterby\\\": {\\\"tag\\\": \\\"vision\\\",\\n },\\n \\\"multiple\\\": False,\\n },\\n ),\\n )\\n\\n from s3 import S3SQLCustomForm, S3SQLInlineComponent, S3SQLInlineLink, s3_comments_widget\\n\\n # Individual settings for specific tag components\\n components_get = s3db.resource(tablename).components.get\\n\\n vision = components_get(\\\"vision\\\")\\n vision.table.value.widget = s3_comments_widget\\n\\n crud_form = S3SQLCustomForm(\\\"name\\\",\\n \\\"acronym\\\",\\n S3SQLInlineLink(\\\"organisation_type\\\",\\n field = \\\"organisation_type_id\\\",\\n # Default 10 options just triggers which adds unnecessary complexity to a commonly-used form & commonly an early one (create Org when registering)\\n filter = False,\\n label = T(\\\"Type\\\"),\\n multiple = False,\\n widget = \\\"multiselect\\\",\\n ),\\n S3SQLInlineLink(\\\"sector\\\",\\n columns = 4,\\n field = \\\"sector_id\\\",\\n label = T(\\\"Sectors\\\"),\\n ),\\n #S3SQLInlineLink(\\\"service\\\",\\n # columns = 4,\\n # field = \\\"service_id\\\",\\n # label = T(\\\"Services\\\"),\\n # ),\\n \\\"country\\\",\\n \\\"phone\\\",\\n \\\"website\\\",\\n \\\"logo\\\",\\n (T(\\\"About\\\"), \\\"comments\\\"),\\n S3SQLInlineComponent(\\\"vision\\\",\\n label = T(\\\"Vision\\\"),\\n fields = [(\\\"\\\", \\\"value\\\")],\\n multiple = False,\\n ),\\n S3SQLInlineComponent(\\\"req_number\\\",\\n label = T(\\\"Request Number\\\"),\\n fields = [(\\\"\\\", \\\"value\\\")],\\n multiple = False,\\n ),\\n )\\n\\n s3db.configure(tablename,\\n crud_form = crud_form,\\n )\\n\\n settings.customise_org_organisation_resource = customise_org_organisation_resource\\n\\n # -------------------------------------------------------------------------\\n def customise_org_sector_controller(**attr):\\n\\n s3db = current.s3db\\n tablename = \\\"org_sector\\\"\\n\\n # Just 1 set of sectors / sector leads nationally\\n # @ToDo: Deployment Setting\\n #f = s3db.org_sector.location_id\\n #f.readable = f.writable = False\\n\\n # Custom Component for Sector Leads\\n s3db.add_components(tablename,\\n org_sector_organisation = {\\\"name\\\": \\\"sector_lead\\\",\\n \\\"joinby\\\": \\\"sector_id\\\",\\n \\\"filterby\\\": {\\\"lead\\\": True,\\n },\\n },\\n )\\n\\n from s3 import S3SQLCustomForm, S3SQLInlineComponent\\n crud_form = S3SQLCustomForm(\\\"name\\\",\\n \\\"abrv\\\",\\n \\\"comments\\\",\\n S3SQLInlineComponent(\\\"sector_lead\\\",\\n label = T(\\\"Lead Organization(s)\\\"),\\n fields = [(\\\"\\\", \\\"organisation_id\\\"),],\\n ),\\n )\\n\\n s3db.configure(tablename,\\n crud_form = crud_form,\\n list_fields = [\\\"name\\\",\\n \\\"abrv\\\",\\n (T(\\\"Lead Organization(s)\\\"), \\\"sector_lead.organisation_id\\\"),\\n ],\\n )\\n\\n return attr\\n\\n settings.customise_org_sector_controller = customise_org_sector_controller\\n\\n # -------------------------------------------------------------------------\\n def req_need_status_update(need_id):\\n \\\"\\\"\\\"\\n Update the Need's fulfilment Status\\n \\\"\\\"\\\"\\n\\n db = current.db\\n s3db = current.s3db\\n\\n # Read the Need details\\n nitable = s3db.req_need_item\\n iptable = s3db.supply_item_pack\\n query = (nitable.need_id == need_id) & \\\\\\n (nitable.deleted == False) & \\\\\\n (nitable.item_pack_id == iptable.id)\\n need_items = db(query).select(nitable.id,\\n nitable.item_id,\\n nitable.quantity,\\n iptable.quantity,\\n )\\n items = {}\\n for item in need_items:\\n pack_qty = item[\\\"supply_item_pack.quantity\\\"]\\n item = item[\\\"req_need_item\\\"]\\n quantity = item.quantity\\n if quantity:\\n quantity = quantity * pack_qty\\n items[item.item_id] = {\\\"id\\\": item.id,\\n \\\"quantity\\\": quantity or 0,\\n \\\"quantity_committed\\\": 0,\\n \\\"quantity_delivered\\\": 0,\\n }\\n\\n ndtable = s3db.req_need_demographic\\n query = (ndtable.need_id == need_id) & \\\\\\n (ndtable.deleted == False)\\n need_demographics = db(query).select(ndtable.parameter_id,\\n ndtable.id,\\n ndtable.value,\\n )\\n demographics = {}\\n for demographic in need_demographics:\\n demographics[demographic.parameter_id] = {\\\"id\\\": demographic.id,\\n \\\"value\\\": demographic.value or 0,\\n \\\"value_committed\\\": 0,\\n \\\"value_reached\\\": 0,\\n }\\n\\n # Lookup which Status means 'Cancelled'\\n stable = s3db.project_status\\n status = db(stable.name == \\\"Cancelled\\\").select(stable.id,\\n limitby = (0, 1)\\n ).first()\\n try:\\n CANCELLED = status.id\\n except AttributeError:\\n # Prepop not done? Name changed?\\n current.log.debug(\\\"'Cancelled' Status not found\\\")\\n CANCELLED = 999999\\n\\n # Read the details of all Activities linked to this Need\\n atable = s3db.project_activity\\n natable = s3db.req_need_activity\\n query = (natable.need_id == need_id) & \\\\\\n (natable.activity_id == atable.id) & \\\\\\n (atable.deleted == False)\\n activities = db(query).select(atable.id,\\n atable.status_id,\\n )\\n aitable = s3db.project_activity_item\\n adtable = s3db.project_activity_demographic\\n for activity in activities:\\n activity_id = activity.id\\n status_id = activity.status_id\\n query = (aitable.activity_id == activity_id) & \\\\\\n (aitable.deleted == False) & \\\\\\n (aitable.item_pack_id == iptable.id)\\n rows = db(query).select(aitable.item_id,\\n aitable.target_value,\\n aitable.value,\\n iptable.quantity,\\n )\\n for row in rows:\\n pack_qty = row[\\\"supply_item_pack.quantity\\\"]\\n row = row[\\\"project_activity_item\\\"]\\n item_id = row.item_id\\n if item_id in items:\\n item = items[item_id]\\n if status_id != CANCELLED:\\n target_value = row.target_value\\n if target_value:\\n item[\\\"quantity_committed\\\"] += target_value * pack_qty\\n value = row.value\\n if value:\\n item[\\\"quantity_delivered\\\"] += value * pack_qty\\n else:\\n # Ignore Items in Activity which don't match Need\\n continue\\n query = (adtable.activity_id == activity_id) & \\\\\\n (adtable.deleted == False)\\n rows = db(query).select(adtable.parameter_id,\\n adtable.target_value,\\n adtable.value,\\n )\\n for row in rows:\\n parameter_id = row.parameter_id\\n if parameter_id in demographics:\\n demographic = demographics[parameter_id]\\n if status_id != CANCELLED:\\n target_value = row.target_value\\n if target_value:\\n demographic[\\\"value_committed\\\"] += target_value\\n value = row.value\\n if value:\\n demographic[\\\"value_reached\\\"] += value\\n else:\\n # Ignore Demographics in Activity which don't match Need\\n continue\\n\\n # Calculate Need values & Update\\n statuses = []\\n sappend = statuses.append\\n for item_id in items:\\n item = items[item_id]\\n quantity_requested = item[\\\"quantity\\\"]\\n quantity_committed = item[\\\"quantity_committed\\\"]\\n quantity_uncommitted = max(quantity_requested - quantity_committed, 0)\\n quantity_delivered = item[\\\"quantity_delivered\\\"]\\n if quantity_delivered >= quantity_requested:\\n status = 3\\n elif quantity_uncommitted <= 0:\\n status = 2\\n elif quantity_committed > 0:\\n status = 1\\n else:\\n status = 0\\n sappend(status)\\n db(nitable.id == item[\\\"id\\\"]).update(quantity_committed = quantity_committed,\\n quantity_uncommitted = quantity_uncommitted,\\n quantity_delivered = quantity_delivered,\\n )\\n\\n for parameter_id in demographics:\\n demographic = demographics[parameter_id]\\n value_requested = demographic[\\\"value\\\"]\\n value_committed = demographic[\\\"value_committed\\\"]\\n value_uncommitted = max(value_requested - value_committed, 0)\\n value_reached = demographic[\\\"value_reached\\\"]\\n if value_reached >= value_requested:\\n status = 3\\n elif value_uncommitted <= 0:\\n status = 2\\n elif value_committed > 0:\\n status = 1\\n else:\\n status = 0\\n sappend(status)\\n db(ndtable.id == demographic[\\\"id\\\"]).update(value_committed = value_committed,\\n value_uncommitted = value_uncommitted,\\n value_reached = value_reached,\\n )\\n\\n if 1 in statuses:\\n # 1 or more items/people are only partially committed\\n status = 1\\n elif 0 in statuses:\\n if 2 in statuses or 3 in statuses:\\n # Some items/people are not committed, but others are\\n status = 1\\n else:\\n # No items/people have been committed\\n status = 0\\n elif 2 in statuses:\\n # All Items/People are Committed, but at least some are not delivered/reached\\n status = 2\\n elif 3 in statuses:\\n # All Items/People are Delivered/Reached\\n status = 3\\n else:\\n # No Items/People: assume partial\\n status = 1\\n\\n ntable = s3db.req_need\\n need = db(ntable.id == need_id).select(ntable.id,\\n ntable.status,\\n limitby = (0, 1)\\n ).first()\\n if need.status != status:\\n need.update_record(status = status)\\n\\n # -------------------------------------------------------------------------\\n def project_activity_ondelete(row):\\n \\\"\\\"\\\"\\n Ensure that the Need (if-any) has the correct Status\\n \\\"\\\"\\\"\\n\\n import json\\n\\n db = current.db\\n s3db = current.s3db\\n\\n activity_id = row.get(\\\"id\\\")\\n\\n # Lookup the Need\\n need_id = None\\n natable = s3db.req_need_activity\\n deleted_links = db(natable.deleted == True).select(natable.deleted_fk)\\n for link in deleted_links:\\n deleted_fk = json.loads(link.deleted_fk)\\n if activity_id == deleted_fk[\\\"activity_id\\\"]:\\n need_id = deleted_fk[\\\"need_id\\\"]\\n break\\n\\n if not need_id:\\n return\\n\\n # Check that the Need hasn't been deleted\\n ntable = s3db.req_need\\n need = db(ntable.id == need_id).select(ntable.deleted,\\n limitby = (0, 1)\\n ).first()\\n\\n if need and not need.deleted:\\n req_need_status_update(need_id)\\n\\n # -------------------------------------------------------------------------\\n def project_activity_postprocess(form):\\n \\\"\\\"\\\"\\n Ensure that the Need (if-any) has the correct Status\\n \\\"\\\"\\\"\\n\\n s3db = current.s3db\\n\\n activity_id = form.vars.id\\n\\n # Lookup the Need\\n ntable = s3db.req_need\\n natable = s3db.req_need_activity\\n query = (natable.activity_id == activity_id) & \\\\\\n (natable.need_id == ntable.id) & \\\\\\n (ntable.deleted == False)\\n need = current.db(query).select(ntable.id,\\n limitby = (0, 1)\\n ).first()\\n\\n if need:\\n req_need_status_update(need.id)\\n\\n # -------------------------------------------------------------------------\\n def customise_project_activity_resource(r, tablename):\\n\\n db = current.db\\n s3db = current.s3db\\n\\n # Custom Filtered Components\\n s3db.add_components(tablename,\\n project_activity_organisation = (# Agency\\n {\\\"name\\\": \\\"agency\\\",\\n \\\"joinby\\\": \\\"activity_id\\\",\\n \\\"filterby\\\": {\\\"role\\\": 1,\\n },\\n #\\\"multiple\\\": False,\\n },\\n # Partners\\n {\\\"name\\\": \\\"partner\\\",\\n \\\"joinby\\\": \\\"activity_id\\\",\\n \\\"filterby\\\": {\\\"role\\\": 2,\\n },\\n #\\\"multiple\\\": False,\\n },\\n # Donors\\n {\\\"name\\\": \\\"donor\\\",\\n \\\"joinby\\\": \\\"activity_id\\\",\\n \\\"filterby\\\": {\\\"role\\\": 3,\\n },\\n #\\\"multiple\\\": False,\\n },\\n ),\\n project_activity_tag = (# Modality\\n {\\\"name\\\": \\\"modality\\\",\\n \\\"joinby\\\": \\\"activity_id\\\",\\n \\\"filterby\\\": {\\\"tag\\\": \\\"modality\\\",\\n },\\n \\\"multiple\\\": False,\\n },\\n # Number\\n #{\\\"name\\\": \\\"number\\\",\\n # \\\"joinby\\\": \\\"activity_id\\\",\\n # \\\"filterby\\\": {\\\"tag\\\": \\\"number\\\",\\n # },\\n # \\\"multiple\\\": False,\\n # },\\n )\\n )\\n\\n from s3 import S3LocationFilter, S3OptionsFilter, \\\\\\n S3SQLCustomForm, S3SQLInlineComponent, S3SQLInlineLink\\n\\n # Individual settings for specific tag components\\n from gluon import IS_EMPTY_OR, IS_IN_SET\\n components_get = s3db.resource(tablename).components.get\\n\\n donor = components_get(\\\"donor\\\")\\n donor.table.organisation_id.default = None\\n\\n partner = components_get(\\\"partner\\\")\\n partner.table.organisation_id.default = None\\n\\n modality = components_get(\\\"modality\\\")\\n modality.table.value.requires = IS_EMPTY_OR(IS_IN_SET((\\\"Cash\\\", \\\"In-kind\\\")))\\n\\n #number = components_get(\\\"number\\\")\\n #number.table.value.requires = IS_EMPTY_OR(IS_INT_IN_RANGE())\\n\\n #sdtable = s3db.stats_demographic\\n #demographics = db(sdtable.deleted == False).select(sdtable.name)\\n #demographics = [d.name for d in demographics]\\n #s3db.project_activity_data.unit.requires = IS_EMPTY_OR(IS_IN_SET(demographics))\\n\\n crud_fields = [S3SQLInlineLink(\\\"event\\\",\\n field = \\\"event_id\\\",\\n label = T(\\\"Disaster\\\"),\\n multiple = False,\\n #required = True,\\n ),\\n S3SQLInlineComponent(\\\"agency\\\",\\n name = \\\"agency\\\",\\n label = T(\\\"Agency\\\"),\\n fields = [(\\\"\\\", \\\"organisation_id\\\"),],\\n #multiple = False,\\n required = True,\\n ),\\n # @ToDo: MultiSelectWidget is nicer UI but S3SQLInlineLink\\n # requires the link*ed* table as component (not the\\n # link table as applied here) and linked components\\n # cannot currently be filtered by link table fields\\n # (=> should solve the latter rather than the former)\\n # @ToDo: Fix Create Popups\\n S3SQLInlineComponent(\\\"partner\\\",\\n name = \\\"partner\\\",\\n label = T(\\\"Implementing Partner\\\"),\\n fields = [(\\\"\\\", \\\"organisation_id\\\"),],\\n ),\\n S3SQLInlineComponent(\\\"donor\\\",\\n name = \\\"donor\\\",\\n label = T(\\\"Donor\\\"),\\n fields = [(\\\"\\\", \\\"organisation_id\\\"),],\\n ),\\n \\\"location_id\\\",\\n S3SQLInlineLink(\\\"sector\\\",\\n field = \\\"sector_id\\\",\\n filter = False,\\n label = T(\\\"Sector\\\"),\\n multiple = False,\\n ),\\n (T(\\\"Summary of Activity\\\"), \\\"name\\\"),\\n S3SQLInlineComponent(\\\"modality\\\",\\n name = \\\"modality\\\",\\n label = T(\\\"Modality\\\"),\\n fields = [(\\\"\\\", \\\"value\\\"),],\\n multiple = False,\\n ),\\n #S3SQLInlineComponent(\\\"number\\\",\\n # name = \\\"number\\\",\\n # label = T(\\\"Number of Items/Kits/Activities\\\"),\\n # fields = [(\\\"\\\", \\\"value\\\"),],\\n # multiple = False,\\n # ),\\n (T(\\\"Activity Date (Planned/Start Date)\\\"), \\\"date\\\"),\\n (T(\\\"Activity Date (Completion Date)\\\"), \\\"end_date\\\"),\\n #S3SQLInlineComponent(\\\"activity_data\\\",\\n # label = T(\\\"People / Households\\\"),\\n # fields = [(T(\\\"Type\\\"), \\\"unit\\\"),\\n # (T(\\\"Number Targeted\\\"), \\\"target_value\\\"),\\n # (T(\\\"Number Reached\\\"), \\\"value\\\"),\\n # ],\\n # #multiple = False,\\n # ),\\n S3SQLInlineComponent(\\\"activity_demographic\\\",\\n label = T(\\\"Beneficiaries\\\"),\\n #link = False,\\n fields = [(T(\\\"Type\\\"), \\\"parameter_id\\\"),\\n (T(\\\"Estimated Delivery Time\\\"), \\\"timeframe\\\"),\\n (T(\\\"Number Planned\\\"), \\\"target_value\\\"),\\n (T(\\\"Number Reached\\\"), \\\"value\\\"),\\n ],\\n #multiple = False,\\n ),\\n S3SQLInlineComponent(\\\"activity_item\\\",\\n label = T(\\\"Items\\\"),\\n fields = [\\\"item_category_id\\\",\\n \\\"item_id\\\",\\n (T(\\\"Unit\\\"), \\\"item_pack_id\\\"),\\n (T(\\\"Estimated Delivery Time\\\"), \\\"timeframe\\\"),\\n (T(\\\"Number Planned\\\"), \\\"target_value\\\"),\\n (T(\\\"Number Distributed\\\"), \\\"value\\\"),\\n ],\\n #multiple = False,\\n ),\\n (T(\\\"Activity Status\\\"), \\\"status_id\\\"),\\n \\\"comments\\\",\\n ]\\n\\n if r.id and r.resource.tablename == tablename:\\n natable = s3db.req_need_activity\\n need_link = db(natable.activity_id == r.id).select(natable.need_id,\\n limitby = (0, 1)\\n ).first()\\n if need_link:\\n from controllers import req_NeedRepresent\\n f = natable.need_id\\n f.represent = req_NeedRepresent()\\n # This hides the widget from Update forms instead of just rendering read-only!\\n #f.writable = False\\n crud_fields.append(S3SQLInlineLink(\\\"need\\\",\\n field = \\\"need_id\\\",\\n label = T(\\\"Need\\\"),\\n multiple = False,\\n readonly = True,\\n ))\\n\\n crud_form = S3SQLCustomForm(*crud_fields,\\n postprocess = project_activity_postprocess)\\n\\n filter_widgets = [S3OptionsFilter(\\\"agency.organisation_id\\\",\\n label = T(\\\"Agency\\\"),\\n ),\\n S3OptionsFilter(\\\"sector_activity.sector_id\\\"),\\n S3LocationFilter(\\\"location_id\\\",\\n # These levels are for SHARE/LK\\n levels = (\\\"L2\\\", \\\"L3\\\", \\\"L4\\\"),\\n ),\\n S3OptionsFilter(\\\"event.event_type_id\\\",\\n hidden = True,\\n ),\\n # @ToDo: Filter this list dynamically based on Event Type:\\n S3OptionsFilter(\\\"event__link.event_id\\\",\\n hidden = True,\\n ),\\n S3OptionsFilter(\\\"status_id\\\",\\n cols = 4,\\n label = T(\\\"Status\\\"),\\n hidden = True,\\n ),\\n ]\\n\\n s3db.configure(tablename,\\n crud_form = crud_form,\\n filter_widgets = filter_widgets,\\n list_fields = [(T(\\\"Agency\\\"), \\\"agency.organisation_id\\\"),\\n (T(\\\"Implementing Partner\\\"), \\\"partner.organisation_id\\\"),\\n (T(\\\"District\\\"), \\\"location_id$L1\\\"),\\n (T(\\\"DS Division\\\"), \\\"location_id$L2\\\"),\\n (T(\\\"GN Division\\\"), \\\"location_id$L3\\\"),\\n (T(\\\"Sector\\\"), \\\"sector_activity.sector_id\\\"),\\n (T(\\\"Summary of Activity\\\"), \\\"name\\\"),\\n (T(\\\"Activity Status\\\"), \\\"status_id\\\"),\\n (T(\\\"Modality\\\"), \\\"modality.value\\\"),\\n (T(\\\"Activity Date (Planned/Start Date)\\\"), \\\"date\\\"),\\n (T(\\\"Activity Date (Completion Date)\\\"), \\\"end_date\\\"),\\n #(T(\\\"People / Households\\\"), \\\"activity_data.unit\\\"),\\n #(T(\\\"Total Number of People/HH Targeted\\\"), \\\"activity_data.target_value\\\"),\\n (T(\\\"People / Households\\\"), \\\"activity_demographic.parameter_id\\\"),\\n (T(\\\"Total Number of People/HH Reached\\\"), \\\"activity_demographic.value\\\"),\\n (T(\\\"Donor\\\"), \\\"donor.organisation_id\\\"),\\n \\\"comments\\\",\\n ],\\n ondelete = project_activity_ondelete,\\n )\\n\\n settings.customise_project_activity_resource = customise_project_activity_resource\\n\\n # -------------------------------------------------------------------------\\n def customise_project_activity_controller(**attr):\\n\\n s3 = current.response.s3\\n\\n # Custom postp\\n standard_postp = s3.postp\\n def postp(r, output):\\n # Call standard postp\\n if callable(standard_postp):\\n output = standard_postp(r, output)\\n\\n if r.interactive:\\n # Inject the javascript to handle dropdown filtering\\n # - normnally injected through AddResourceLink, but this isn't there in Inline widget\\n # - we also need to turn the trigger & target into dicts\\n s3.scripts.append(\\\"/%s/static/themes/SHARE/js/supply.js\\\" % r.application)\\n\\n return output\\n s3.postp = postp\\n\\n return attr\\n\\n settings.customise_project_activity_controller = customise_project_activity_controller\\n\\n # -------------------------------------------------------------------------\\n def req_need_postprocess(form):\\n\\n if form.record:\\n # Update form\\n return\\n\\n need_id = form.vars.id\\n\\n db = current.db\\n s3db = current.s3db\\n\\n # Lookup Organisation\\n notable = s3db.req_need_organisation\\n org_link = db(notable.need_id == need_id).select(notable.organisation_id,\\n limitby = (0, 1),\\n ).first()\\n if not org_link:\\n return\\n\\n organisation_id = org_link.organisation_id\\n\\n # Lookup Request Number format\\n ottable = s3db.org_organisation_tag\\n query = (ottable.organisation_id == organisation_id) & \\\\\\n (ottable.tag == \\\"req_number\\\")\\n tag = db(query).select(ottable.value,\\n limitby = (0, 1),\\n ).first()\\n if not tag:\\n return\\n\\n # Lookup most recently-used value\\n nttable = s3db.req_need_tag\\n query = (nttable.tag == \\\"req_number\\\") & \\\\\\n (nttable.need_id != need_id) & \\\\\\n (nttable.need_id == notable.need_id) & \\\\\\n (notable.organisation_id == organisation_id)\\n\\n need = db(query).select(nttable.value,\\n limitby = (0, 1),\\n orderby = ~nttable.created_on,\\n ).first()\\n if need:\\n new_number = int(need.value.split(\\\"-\\\", 1)[1]) + 1\\n req_number = \\\"%s-%s\\\" % (tag.value, str(new_number).zfill(6))\\n else:\\n req_number = \\\"%s-000001\\\" % tag.value\\n\\n nttable.insert(need_id = need_id,\\n tag = \\\"req_number\\\",\\n value = req_number,\\n )\\n\\n # -------------------------------------------------------------------------\\n def customise_req_need_resource(r, tablename):\\n\\n from gluon import IS_EMPTY_OR, IS_IN_SET, SPAN\\n\\n from s3 import S3LocationFilter, S3OptionsFilter, S3TextFilter, \\\\\\n S3Represent, \\\\\\n S3SQLCustomForm, S3SQLInlineComponent, S3SQLInlineLink\\n\\n s3db = current.s3db\\n\\n req_status_opts = {0: SPAN(T(\\\"None\\\"),\\n _class = \\\"req_status_none\\\",\\n ),\\n 1: SPAN(T(\\\"Partially Committed\\\"),\\n _class = \\\"req_status_partial\\\",\\n ),\\n 2: SPAN(T(\\\"Fully Committed\\\"),\\n _class = \\\"req_status_complete\\\",\\n ),\\n 3: SPAN(T(\\\"Complete\\\"),\\n _class = \\\"req_status_complete\\\",\\n ),\\n }\\n\\n f = s3db.req_need.status\\n f.requires = IS_EMPTY_OR(IS_IN_SET(req_status_opts, zero = None))\\n #f.represent = lambda opt: req_status_opts.get(opt, current.messages.UNKNOWN_OPT)\\n f.represent = S3Represent(options = req_status_opts)\\n\\n # Custom Filtered Components\\n s3db.add_components(tablename,\\n req_need_tag = (# Req Number\\n {\\\"name\\\": \\\"req_number\\\",\\n \\\"joinby\\\": \\\"need_id\\\",\\n \\\"filterby\\\": {\\\"tag\\\": \\\"req_number\\\",\\n },\\n \\\"multiple\\\": False,\\n },\\n # Verified\\n {\\\"name\\\": \\\"verified\\\",\\n \\\"joinby\\\": \\\"need_id\\\",\\n \\\"filterby\\\": {\\\"tag\\\": \\\"verified\\\",\\n },\\n \\\"multiple\\\": False,\\n },\\n )\\n )\\n\\n # Individual settings for specific tag components\\n components_get = s3db.resource(tablename).components.get\\n\\n verified = components_get(\\\"verified\\\")\\n f = verified.table.value\\n f.requires = IS_EMPTY_OR(IS_IN_SET((True, False)))\\n auth = current.auth\\n if auth.s3_has_role(\\\"ADMIN\\\"):\\n f.default = True\\n else:\\n user = auth.user\\n if user and user.organisation_id:\\n f.default = True\\n else:\\n f.default = False\\n f.writable = False\\n\\n if r.id:\\n # Read or Update\\n table = s3db.req_need_item\\n table.quantity.label = T(\\\"Quantity Requested\\\")\\n table.quantity_committed.readable = True\\n table.quantity_uncommitted.readable = True\\n table.quantity_delivered.readable = True\\n need_item = S3SQLInlineComponent(\\\"need_item\\\",\\n label = T(\\\"Items Needed\\\"),\\n fields = [\\\"item_category_id\\\",\\n \\\"item_id\\\",\\n (T(\\\"Unit\\\"), \\\"item_pack_id\\\"),\\n (T(\\\"Needed within Timeframe\\\"), \\\"timeframe\\\"),\\n \\\"quantity\\\",\\n \\\"quantity_committed\\\",\\n \\\"quantity_uncommitted\\\",\\n \\\"quantity_delivered\\\",\\n \\\"priority\\\",\\n \\\"comments\\\",\\n ],\\n )\\n table = s3db.req_need_demographic\\n table.value.label = T(\\\"Number in Need\\\")\\n table.value_committed.readable = True\\n table.value_uncommitted.readable = True\\n table.value_reached.readable = True\\n demographic = S3SQLInlineComponent(\\\"need_demographic\\\",\\n label = T(\\\"People Affected\\\"),\\n fields = [(T(\\\"Type\\\"), \\\"parameter_id\\\"),\\n (T(\\\"Needed within Timeframe\\\"), \\\"timeframe\\\"),\\n \\\"value\\\",\\n \\\"value_committed\\\",\\n \\\"value_uncommitted\\\",\\n \\\"value_reached\\\",\\n \\\"comments\\\",\\n ],\\n )\\n else:\\n # Create\\n need_item = S3SQLInlineComponent(\\\"need_item\\\",\\n label = T(\\\"Items Needed\\\"),\\n fields = [\\\"item_category_id\\\",\\n \\\"item_id\\\",\\n (T(\\\"Unit\\\"), \\\"item_pack_id\\\"),\\n (T(\\\"Needed within Timeframe\\\"), \\\"timeframe\\\"),\\n \\\"quantity\\\",\\n \\\"priority\\\",\\n \\\"comments\\\",\\n ],\\n )\\n demographic = S3SQLInlineComponent(\\\"need_demographic\\\",\\n label = T(\\\"People Affected\\\"),\\n fields = [(T(\\\"Type\\\"), \\\"parameter_id\\\"),\\n (T(\\\"Needed within Timeframe\\\"), \\\"timeframe\\\"),\\n \\\"value\\\",\\n \\\"comments\\\",\\n ],\\n )\\n\\n crud_fields = [S3SQLInlineLink(\\\"event\\\",\\n field = \\\"event_id\\\",\\n label = T(\\\"Disaster\\\"),\\n multiple = False,\\n #required = True,\\n ),\\n S3SQLInlineLink(\\\"organisation\\\",\\n field = \\\"organisation_id\\\",\\n filter = False,\\n label = T(\\\"Organization\\\"),\\n multiple = False,\\n ),\\n \\\"location_id\\\",\\n \\\"date\\\",\\n \\\"priority\\\",\\n S3SQLInlineLink(\\\"sector\\\",\\n field = \\\"sector_id\\\",\\n filter = False,\\n label = T(\\\"Sector\\\"),\\n multiple = False,\\n ),\\n \\\"name\\\",\\n demographic,\\n need_item,\\n S3SQLInlineComponent(\\\"document\\\",\\n label = T(\\\"Attachment\\\"),\\n fields = [(\\\"\\\", \\\"file\\\")],\\n # multiple = True has reliability issues in at least Chrome\\n multiple = False,\\n ),\\n (T(\\\"Verified\\\"), \\\"verified.value\\\"),\\n \\\"comments\\\",\\n ]\\n\\n if r.id and r.resource.tablename == tablename:\\n # Read or Update\\n req_number = components_get(\\\"verified\\\")\\n req_number.table.value.writable = False\\n crud_fields.insert(2, (T(\\\"Request Number\\\"), \\\"req_number.value\\\"))\\n crud_fields.insert(-2, \\\"status\\\")\\n natable = s3db.req_need_activity\\n need_links = current.db(natable.need_id == r.id).select(natable.activity_id)\\n if need_links:\\n from controllers import project_ActivityRepresent\\n f = natable.activity_id\\n f.represent = project_ActivityRepresent()\\n # This hides the widget from Update forms instead of just rendering read-only!\\n #f.writable = False\\n crud_fields.append(S3SQLInlineLink(\\\"activity\\\",\\n field = \\\"activity_id\\\",\\n label = T(\\\"Commits\\\"),\\n readonly = True,\\n ))\\n\\n crud_form = S3SQLCustomForm(*crud_fields,\\n postprocess = req_need_postprocess)\\n\\n filter_widgets = [S3TextFilter([\\\"req_number.value\\\",\\n \\\"need_item.item_id$name\\\",\\n # These levels are for SHARE/LK\\n #\\\"location_id$L1\\\",\\n \\\"location_id$L2\\\",\\n \\\"location_id$L3\\\",\\n \\\"location_id$L4\\\",\\n \\\"name\\\",\\n \\\"comments\\\",\\n ],\\n label = T(\\\"Search\\\"),\\n comment = T(\\\"Search for a Need by Request Number, Item, Location, Summary or Comments\\\"),\\n ),\\n S3LocationFilter(\\\"location_id\\\",\\n # These levels are for SHARE/LK\\n levels = (\\\"L2\\\", \\\"L3\\\", \\\"L4\\\"),\\n ),\\n S3OptionsFilter(\\\"need_item.item_id\\\"),\\n S3OptionsFilter(\\\"status\\\",\\n cols = 3,\\n label = T(\\\"Status\\\"),\\n ),\\n S3OptionsFilter(\\\"event.event_type_id\\\",\\n hidden = True,\\n ),\\n # @ToDo: Filter this list dynamically based on Event Type:\\n S3OptionsFilter(\\\"event__link.event_id\\\"),\\n S3OptionsFilter(\\\"sector__link.sector_id\\\",\\n hidden = True,\\n ),\\n S3OptionsFilter(\\\"organisation__link.organisation_id\\\",\\n hidden = True,\\n ),\\n S3OptionsFilter(\\\"verified.value\\\",\\n cols = 2,\\n label = T(\\\"Verified\\\"),\\n hidden = True,\\n ),\\n ]\\n\\n s3db.configure(tablename,\\n crud_form = crud_form,\\n filter_widgets = filter_widgets,\\n list_fields = [(T(\\\"Disaster\\\"), \\\"event__link.event_id\\\"),\\n \\\"date\\\",\\n \\\"organisation__link.organisation_id\\\",\\n # These levels/Labels are for SHARE/LK\\n (T(\\\"District\\\"), \\\"location_id$L2\\\"),\\n (T(\\\"DS\\\"), \\\"location_id$L3\\\"),\\n (T(\\\"Status\\\"), \\\"status\\\"),\\n \\\"need_item.item_id\\\",\\n \\\"sector__link.sector_id\\\",\\n \\\"priority\\\",\\n #\\\"name\\\",\\n (T(\\\"Request Number\\\"), \\\"req_number.value\\\"),\\n (T(\\\"Verified\\\"), \\\"verified.value\\\"),\\n (T(\\\"GN\\\"), \\\"location_id$L4\\\"),\\n ],\\n )\\n\\n settings.customise_req_need_resource = customise_req_need_resource\\n\\n # -------------------------------------------------------------------------\\n def req_need_commit(r, **attr):\\n \\\"\\\"\\\"\\n Custom method to Commit to a Need by creating an Activity\\n \\\"\\\"\\\"\\n\\n # Create Activity with values from Need\\n need_id = r.id\\n\\n db = current.db\\n s3db = current.s3db\\n ntable = s3db.req_need\\n ntable_id = ntable.id\\n netable = s3db.event_event_need\\n nstable = s3db.req_need_sector\\n left = [netable.on(netable.need_id == ntable_id),\\n nstable.on(nstable.need_id == ntable_id),\\n ]\\n need = db(ntable_id == need_id).select(ntable.name,\\n ntable.location_id,\\n netable.event_id,\\n nstable.sector_id,\\n left = left,\\n limitby = (0, 1)\\n ).first()\\n\\n atable = s3db.project_activity\\n activity_id = atable.insert(name = need[\\\"req_need.name\\\"],\\n location_id = need[\\\"req_need.location_id\\\"],\\n )\\n organisation_id = current.auth.user.organisation_id\\n if organisation_id:\\n s3db.project_activity_organisation.insert(activity_id = activity_id,\\n organisation_id = organisation_id,\\n role = 1,\\n )\\n\\n event_id = need[\\\"event_event_need.event_id\\\"]\\n if event_id:\\n aetable = s3db.event_activity\\n aetable.insert(activity_id = activity_id,\\n event_id = event_id,\\n )\\n\\n sector_id = need[\\\"req_need_sector.sector_id\\\"]\\n if sector_id:\\n astable = s3db.project_sector_activity\\n astable.insert(activity_id = activity_id,\\n sector_id = sector_id,\\n )\\n\\n nitable = s3db.req_need_item\\n query = (nitable.need_id == need_id) & \\\\\\n (nitable.deleted == False)\\n items = db(query).select(nitable.item_category_id,\\n nitable.item_id,\\n nitable.item_pack_id,\\n nitable.timeframe,\\n nitable.quantity,\\n )\\n if items:\\n iinsert = s3db.project_activity_item.insert\\n for item in items:\\n iinsert(activity_id = activity_id,\\n item_category_id = item.item_category_id,\\n item_id = item.item_id,\\n item_pack_id = item.item_pack_id,\\n timeframe = item.timeframe,\\n target_value = item.quantity,\\n )\\n\\n ndtable = s3db.req_need_demographic\\n query = (ndtable.need_id == need_id) & \\\\\\n (ndtable.deleted == False)\\n demographics = db(query).select(ndtable.parameter_id,\\n ndtable.timeframe,\\n ndtable.value,\\n )\\n if demographics:\\n dinsert = s3db.project_activity_demographic.insert\\n for demographic in demographics:\\n dinsert(activity_id = activity_id,\\n parameter_id = demographic.parameter_id,\\n timeframe = demographic.timeframe,\\n target_value = demographic.value,\\n )\\n\\n # Link to Need\\n s3db.req_need_activity.insert(activity_id = activity_id,\\n need_id = need_id,\\n )\\n\\n # Update Need to show Fulfilled\\n ntable = s3db.req_need\\n need = current.db(ntable.id == need_id).select(ntable.id,\\n ntable.status,\\n limitby = (0, 1)\\n ).first()\\n if need.status in (0, 1):\\n # Set to Fully Committed\\n need.update_record(status = 2)\\n\\n # Redirect to Update\\n from gluon import redirect\\n redirect(URL(c= \\\"project\\\", f=\\\"activity\\\",\\n args = [activity_id, \\\"update\\\"],\\n ))\\n\\n # -------------------------------------------------------------------------\\n def req_need_rheader(r):\\n \\\"\\\"\\\"\\n Resource Header for Needs\\n \\\"\\\"\\\"\\n\\n if r.representation != \\\"html\\\":\\n # RHeaders only used in interactive views\\n return None\\n\\n record = r.record\\n if not record:\\n # RHeaders only used in single-record views\\n return None\\n\\n if r.name == \\\"need\\\":\\n # No Tabs (all done Inline)\\n tabs = [(T(\\\"Basic Details\\\"), None),\\n #(T(\\\"Demographics\\\"), \\\"demographic\\\"),\\n #(T(\\\"Items\\\"), \\\"need_item\\\"),\\n #(T(\\\"Skills\\\"), \\\"need_skill\\\"),\\n #(T(\\\"Tags\\\"), \\\"tag\\\"),\\n ]\\n\\n from s3 import s3_rheader_tabs\\n rheader_tabs = s3_rheader_tabs(r, tabs)\\n\\n location_id = r.table.location_id\\n from gluon import DIV, TABLE, TR, TH\\n rheader = DIV(TABLE(TR(TH(\\\"%s: \\\" % location_id.label),\\n location_id.represent(record.location_id),\\n )),\\n rheader_tabs)\\n\\n else:\\n # Not defined, probably using wrong rheader\\n rheader = None\\n\\n return rheader\\n\\n # -------------------------------------------------------------------------\\n def customise_req_need_controller(**attr):\\n\\n # Custom commit method to create an Activity from a Need\\n current.s3db.set_method(\\\"req\\\", \\\"need\\\",\\n method = \\\"commit\\\",\\n action = req_need_commit)\\n\\n s3 = current.response.s3\\n\\n # Custom postp\\n standard_postp = s3.postp\\n def postp(r, output):\\n # Call standard postp\\n if callable(standard_postp):\\n output = standard_postp(r, output)\\n\\n if r.interactive:\\n # Inject the javascript to handle dropdown filtering\\n # - normnally injected through AddResourceLink, but this isn't there in Inline widget\\n # - we also need to turn the trigger & target into dicts\\n s3.scripts.append(\\\"/%s/static/themes/SHARE/js/supply.js\\\" % r.application)\\n if current.auth.s3_has_permission(\\\"create\\\", \\\"project_activity\\\"):\\n if r.id:\\n # Custom RFooter\\n from gluon import A\\n s3.rfooter = A(T(\\\"Commit\\\"),\\n _href = URL(args=[r.id, \\\"commit\\\"]),\\n _class = \\\"action-btn\\\",\\n #_id = \\\"commit-btn\\\",\\n )\\n #s3.jquery_ready.append(\\n#'''S3.confirmClick('#commit-btn','%s')''' % T(\\\"Do you want to commit to this need?\\\"))\\n else:\\n from s3 import S3CRUD, s3_str\\n # Normal Action Buttons\\n S3CRUD.action_buttons(r)\\n # Custom Action Buttons\\n s3.actions += [{\\\"label\\\": s3_str(T(\\\"Commit\\\")),\\n \\\"_class\\\": \\\"action-btn\\\",\\n \\\"url\\\": URL(args=[\\\"[id]\\\", \\\"commit\\\"]),\\n }\\n ]\\n\\n return output\\n s3.postp = postp\\n\\n attr[\\\"rheader\\\"] = req_need_rheader\\n\\n return attr\\n\\n settings.customise_req_need_controller = customise_req_need_controller\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2068bc0d59325bc3a79f3c2dcc9621c2\",\n \"score\": \"0.4791621\",\n \"text\": \"def Site(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2068bc0d59325bc3a79f3c2dcc9621c2\",\n \"score\": \"0.4791621\",\n \"text\": \"def Site(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ef6d064f02c1c18a42b42112fa4571\",\n \"score\": \"0.4775658\",\n \"text\": \"def iotlab_site(request):\\n\\n return getattr(request, \\\"param\\\", os.environ.get(\\\"IOTLAB_SITE\\\", DEFAULT_SITE))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab9da175732c6876783151105233495\",\n \"score\": \"0.4756351\",\n \"text\": \"def go_to_custom_settings_setup(self):\\n url = self.cumulusci.org.lightning_base_url\\n url = \\\"{}/lightning/setup/CustomSettings/home\\\".format(url)\\n self.selenium.go_to(url)\\n self.salesforce.wait_until_loading_is_complete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f56c6549bca84248998275d112e228\",\n \"score\": \"0.47474557\",\n \"text\": \"def __init__(self, site_name, site_username, site_password):\\n self.site_name = site_name\\n self.site_username = site_username\\n self.site_password = site_password\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3ad68b43a9bf70da3a32a309797ce0\",\n \"score\": \"0.47429588\",\n \"text\": \"def test_set_site(self):\\n config = SiteConfigurationManager.get_blank_site()\\n\\n # DNS Host attributes\\n dnshost = config.dnshost\\n dnshost.desc = 'desc'\\n self.assertEquals(dnshost.desc, 'desc')\\n\\n dnshost.name = 'name'\\n self.assertEquals(dnshost.name, 'name')\\n self.assertRaises(ValidationError, lambda x: setattr(dnshost, 'name', x), \\\"f@ke\\\")\\n\\n dnshost.domain = SiteDefaultsManager.get_default_domain()\\n self.assertEquals(dnshost.domain, SiteDefaultsManager.get_default_domain())\\n self.assertRaises(ValidationError, lambda x: setattr(dnshost, 'domain', x), \\\"fake\\\")\\n\\n # Repository related attributes\\n repository = config.repository\\n\\n repository.name = 'name'\\n self.assertEquals(repository.name, 'name')\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'name', x), \\\"f@ke\\\")\\n\\n repository.type = SiteDefaultsManager.get_default_repository_type()\\n self.assertEquals(repository.type,\\n SiteDefaultsManager.get_default_repository_type())\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'type', x), \\\"fake\\\")\\n\\n repository.enabled = True\\n self.assertEquals(repository.enabled, True)\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'enabled', x), 'fake')\\n\\n repository.done = True\\n self.assertEquals(repository.done, True)\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), 'fake')\\n\\n # Web site related attributes\\n site = config.website\\n\\n site.template = SiteDefaultsManager.get_default_site_template()\\n self.assertEquals(site.template,\\n SiteDefaultsManager.get_default_site_template())\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), \\\"fake\\\")\\n\\n site.access = SiteDefaultsManager.get_default_site_access()\\n self.assertEquals(site.access,\\n SiteDefaultsManager.get_default_site_access())\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), \\\"fake\\\")\\n\\n site.maintenance = True\\n self.assertEquals(site.maintenance, True)\\n self.assertRaises(ValidationError, lambda x: setattr(site, 'maintenance', x), 'fake')\\n\\n site.enabled = True\\n self.assertEquals(site.enabled, True)\\n self.assertRaises(ValidationError, lambda x: setattr(site, 'enabled', x), 'fake')\\n\\n site.done = True\\n self.assertEquals(site.done, True)\\n self.assertRaises(ValidationError, lambda x: setattr(site, 'done', x), 'fake')\\n\\n # Database related attribute\\n database = config.database\\n\\n database.type = SiteDefaultsManager.get_default_database_type()\\n self.assertEquals(database.type,\\n SiteDefaultsManager.get_default_database_type())\\n self.assertRaises(ValidationError, lambda x: setattr(repository, 'done', x), \\\"fake\\\")\\n\\n database.name = 'name'\\n self.assertEquals(database.name, 'name')\\n self.assertRaises(ValidationError, lambda x: setattr(database, 'name', x), \\\"f@ke\\\")\\n\\n database.username = 'username'\\n self.assertEquals(database.username, 'username')\\n self.assertRaises(ValidationError, lambda x: setattr(database, 'username', x), \\\"f@ke\\\")\\n\\n database.password = 'password'\\n self.assertEquals(database.password, 'password')\\n\\n database.enabled = True\\n self.assertEquals(database.enabled, True)\\n self.assertRaises(ValidationError, lambda x: setattr(database, 'enabled', x), 'fake')\\n\\n database.done = True\\n self.assertEquals(database.done, True)\\n self.assertRaises(ValidationError, lambda x: setattr(database, 'done', x), 'fake')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131f89358a5bd992160e6dd8e844ea17\",\n \"score\": \"0.47285497\",\n \"text\": \"def setUp(self):\\n self.driver.get('http://taqc-opencart.epizy.com/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3795b74bb262fa60abb6154af317cd50\",\n \"score\": \"0.47279897\",\n \"text\": \"def test_com_day_cq_analytics_sitecatalyst_impl_sitecatalyst_adapter_factory(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a88627dc398912640e139643b0e288c\",\n \"score\": \"0.47257218\",\n \"text\": \"def make_shopping_site(self):\\n context = aq_inner(self.context)\\n alsoProvides(context, IShoppingSiteRoot)\\n context.reindexObject(idxs=['object_provides'])\\n\\n notify(MakeShoppingSiteEvent(context))\\n\\n url = context.absolute_url()\\n return self.request.response.redirect(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797420a73652add17f4fa00f9ce4866f\",\n \"score\": \"0.47238547\",\n \"text\": \"def config():\\n OdooEnv_()._config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc7af834595020ca8a81949137240abe\",\n \"score\": \"0.47172868\",\n \"text\": \"def setUpBasicProducts(self):\\n\\n with zopeApp() as app:\\n installProduct(app, 'Products.PluginIndexes')\\n installProduct(app, 'Products.OFSP')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85cd5fef82a47909740a6c35a27fe11\",\n \"score\": \"0.47168154\",\n \"text\": \"def _compute_survey_url(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c6a7d9cef6aab5f90474ab15e3bc61\",\n \"score\": \"0.47068766\",\n \"text\": \"def activate(self):\\n super(Zenoss, self).activate()\\n\\n if self.config is not None and set((\\\"ZENOSS_INST\\\", \\\"ZENOSS_USER\\\", \\\"ZENOSS_PASS\\\")) <= set(self.config):\\n self.urlOpener = urllib2.build_opener(urllib2.HTTPCookieProcessor())\\n self.reqCount = 1\\n # Contruct POST params and submit login.\\n loginParams = urllib.urlencode(dict(\\n __ac_name = self.config['ZENOSS_USER'],\\n __ac_password = self.config['ZENOSS_PASS'],\\n submitted = 'true',\\n came_from = self.config['ZENOSS_INST'] + '/zport/dmd'))\\n self.urlOpener.open(self.config['ZENOSS_INST'] + '/zport/acl_users/cookieAuthHelper/login', loginParams)\\n self.connected = True\\n else:\\n logger.warn(\\\"Unable to connect to Zenoss, plugin not configured.\\\")\\n\\n\\tdef get_configuration_template(self):\\n\\t\\t\\\"\\\"\\\"Defines the configuration structure this plugin supports\\\"\\\"\\\"\\n\\n\\t\\treturn {'ZENOSS_INST': 'http://server:8080',\\n 'ZENOSS_USER': \\\"admin\\\",\\n\\t\\t 'ZENOSS_PASS': \\\"admin\\\"\\n\\t\\t }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0089f6e6d8104b1e9dd4a9981922f85b\",\n \"score\": \"0.47057673\",\n \"text\": \"def configure_panel(self):\\n webroot = os.path.dirname(__file__)\\n\\n self.template_path = os.path.join(webroot, 'templates')\\n aiohttp_jinja2.setup(\\n self, loader=jinja2.FileSystemLoader(self.template_path),\\n filters={'sorted': sorted, 'int': int}\\n )\\n\\n self['static_root_url'] = '/static'\\n self.router.add_view('/', Panel)\\n self.router.add_static(\\n '/static/', path=os.path.join(webroot, 'static'), name='static'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aedb0246b3d6ccc9c346b655bc8702f2\",\n \"score\": \"0.47027162\",\n \"text\": \"def get_context_data(self, *args, **kwargs):\\n context = super(ProductListAndCreateView, self).get_context_data(*args, **kwargs)\\n context['view_url_name'] = resolve(self.request.path_info).url_name\\n context['site_name'] = app_settings.PRICE_MONITOR_SITENAME\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61037639ac663ff9baa213a452f24f6\",\n \"score\": \"0.4700974\",\n \"text\": \"def configure_spider(self, spider_config: collections.OrderedDict):\\n\\n logging.debug(\\\"Trying to configure the AjaxSpider\\\")\\n self.configure_scripts(config=spider_config)\\n\\n # Configure Spider (ajax or http)\\n\\n if self._is_not_empty_integer(\\\"maxDuration\\\", spider_config):\\n self.check_zap_result(\\n result=self.get_zap_spider.set_option_max_duration(\\n integer=str(spider_config[\\\"maxDuration\\\"])\\n ),\\n method_name=\\\"set_option_max_duration\\\",\\n )\\n if self._is_not_empty_integer(\\\"maxDepth\\\", spider_config):\\n self.check_zap_result(\\n result=self.get_zap_spider.set_option_max_crawl_depth(\\n integer=str(spider_config[\\\"maxDepth\\\"])\\n ),\\n method_name=\\\"set_option_max_crawl_depth\\\",\\n )\\n if self._is_not_empty_integer(\\\"maxStates\\\", spider_config):\\n self.check_zap_result(\\n result=self.get_zap_spider.set_option_max_crawl_states(\\n integer=str(spider_config[\\\"maxStates\\\"])\\n ),\\n method_name=\\\"set_option_max_crawl_states\\\",\\n )\\n if self._is_not_empty_string(\\\"browserId\\\", spider_config):\\n self.check_zap_result(\\n result=self.get_zap_spider.set_option_browser_id(\\n string=str(spider_config[\\\"browserId\\\"])\\n ),\\n method_name=\\\"set_option_browser_id\\\",\\n )\\n if self._is_not_empty_integer(\\\"browserCount\\\", spider_config):\\n self.check_zap_result(\\n result=self.get_zap_spider.set_option_number_of_browsers(\\n integer=str(spider_config[\\\"browserCount\\\"])\\n ),\\n method_name=\\\"set_option_number_of_browsers\\\",\\n )\\n if self._is_not_empty_integer(\\\"randomInputs\\\", spider_config):\\n self.check_zap_result(\\n result=self.get_zap_spider.set_option_random_inputs(\\n boolean=str(spider_config[\\\"randomInputs\\\"])\\n ),\\n method_name=\\\"set_option_random_inputs\\\",\\n )\\n\\n if self._is_not_empty_integer(\\\"failIfFoundUrlsLessThan\\\", spider_config):\\n self.failIfFoundUrlsLessThan = spider_config[\\\"failIfFoundUrlsLessThan\\\"]\\n else:\\n self.failIfFoundUrlsLessThan = 0 # Default value\\n \\n if self._is_not_empty_integer(\\\"warnIfFoundUrlsLessThan\\\", spider_config): \\n self.warnIfFoundUrlsLessThan = spider_config[\\\"warnIfFoundUrlsLessThan\\\"]\\n else:\\n self.warnIfFoundUrlsLessThan = 0 # Default value \",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c2d4fa9b4264958c28405bf8e8e9b3b\",\n \"score\": \"0.47008792\",\n \"text\": \"def __init__(self, hard_negatives, include_title, **kwargs):\\n super(RetrievalConfig, self).__init__(**kwargs)\\n self.hard_negatives = hard_negatives\\n self.include_title = include_title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04cf18af4f45d11352e7acb66c291192\",\n \"score\": \"0.47001162\",\n \"text\": \"def create_site(GlobalNetworkId=None, Description=None, Location=None, Tags=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b875634c0416f8a3895d20b5970b94\",\n \"score\": \"0.46925145\",\n \"text\": \"def create_survey(request):\\n return HttpResponse(loader.get_template(\\\"paranoidApp/survey_creation_single_page.html\\\")\\n .render({}, request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d12d2c60f3a3a34a18ba3f4c43374b\",\n \"score\": \"0.46923384\",\n \"text\": \"def build_site(simple_site):\\n site = Site(simple_site['name'])\\n site.nodes = SiteFactory._build_nodes(simple_site['nodes'])\\n site.applications = SiteFactory._get_combined_applications(site.nodes)\\n \\n return site\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498135a0de036a7014689e5f5e5bdcd9\",\n \"score\": \"0.4688666\",\n \"text\": \"def EpicsConfig(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464f22dc9fadc4e3295a8db3efa4ced4\",\n \"score\": \"0.46879807\",\n \"text\": \"def configure_designate_basic(*args):\\n # If cluster relation is available it needs to passed in\\n cluster = relations.endpoint_from_flag('cluster.available')\\n if cluster is not None:\\n args = args + (cluster, )\\n dns_backend = relations.endpoint_from_flag('dns-backend.available')\\n if dns_backend is not None:\\n args = args + (dns_backend, )\\n with charm.provide_charm_instance() as instance:\\n instance.render_base_config(args)\\n instance.disable_services()\\n reactive.set_state('base-config.rendered')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a36adb9dce1958729ff8298cc8e397ab\",\n \"score\": \"0.46870282\",\n \"text\": \"def configure(self):\\n super(Assembly, self).configure()\\n self.add('lcoe_csm', lcoe_csm_assembly()) \\n\\n driver = self.add('driver', DakotaGlobalSAStudy())\\n driver.workflow.add('lcoe_csm')\\n driver.stdout = 'dakota.out'\\n driver.stderr = 'dakota.err'\\n driver.sample_type = 'random'\\n driver.seed = 20\\n driver.samples = 500 \\n driver.vbd = True\\n\\n # Can't have parameter with no 'low' or 'high' defined.\\n driver.add_parameter('lcoe_csm.rotor_diameter', low=126.0*0.5, high=126.0*1.5)\\n driver.add_parameter('lcoe_csm.machine_rating', low=5000.0*0.5, high=5000.0*1.5)\\n driver.add_parameter('lcoe_csm.hub_height', low=90.0*0.5, high=90.0*1.5)\\n driver.add_parameter('lcoe_csm.max_tip_speed', low=80.0*0.5, high=80.0*1.5)\\n driver.add_objective('lcoe_csm.coe')\\n driver.add_objective('lcoe_csm.lcoe')\\n driver.add_objective('lcoe_csm.net_aep')\\n driver.add_objective('lcoe_csm.bos_costs')\\n driver.add_objective('lcoe_csm.avg_annual_opex')\\n driver.add_objective('lcoe_csm.turbine_cost')\\n driver.add_objective('lcoe_csm.turbine_mass')\\n driver.add_objective('lcoe_csm.rated_rotor_speed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36720607587849677eff90722a279b80\",\n \"score\": \"0.46729946\",\n \"text\": \"def set_site(cls, archive, context, request):\\n domain = request.host\\n if \\\":\\\" in domain:\\n domain = domain.split(\\\":\\\", 1)[0]\\n\\n site_instance = archive.sites.match(domain, context=context)\\n\\n if site_instance is None:\\n log.error(\\n 'no site matching domain \\\"{domain}\\\", consider adding [site:{domain}] to settings'.format(\\n domain=domain\\n )\\n )\\n return None\\n\\n context.root[\\\"sys\\\"][\\\"site\\\"] = site_instance\\n try:\\n context.root[\\\"sys\\\"][\\\"base\\\"] = archive.project_fs.getsyspath(\\\"/\\\")\\n except NoSysPath:\\n context.root[\\\"sys\\\"][\\\"base\\\"] = None\\n context.root[\\\"site\\\"] = site_instance._data\\n\\n return site_instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d4c423cdf84fa3875ce9c82dcd7372\",\n \"score\": \"0.46708056\",\n \"text\": \"def main(global_config, **settings):\\n globalreg = getGlobalSiteManager()\\n set_cache_regions_from_settings(settings)\\n config = Configurator(registry=globalreg)\\n\\n config.include('pyramid_zcml')\\n config.load_zcml('workflow.zcml')\\n config.add_translation_dirs('por.dashboard:locale')\\n\\n from por.dashboard.views import PORRequest\\n config.setup_registry(settings=settings,\\n request_factory=PORRequest,\\n root_factory='por.dashboard.views.DefaultContext')\\n\\n # por security\\n from por.dashboard import security\\n authentication_policy = RepozeWho1AuthenticationPolicy(identifier_name=\\\"auth_tkt\\\",callback=security.rolefinder)\\n config._set_authentication_policy(authentication_policy)\\n authorization_policy = ACLAuthorizationPolicy()\\n config._set_authorization_policy(authorization_policy)\\n config.scan('por.dashboard.security.views')\\n config.include('por.dashboard.security.openid2')\\n config.include('velruse.store.memstore')\\n config.add_view('por.dashboard.security.views.forbidden', renderer='skin', context=\\\"pyramid.httpexceptions.HTTPForbidden\\\")\\n\\n #mailier\\n config.include('pyramid_mailer')\\n\\n # por.models's configuration\\n config.include('por.models')\\n import por.dashboard.events; por.dashboard.events\\n import por.dashboard.breadcrumbs; por.dashboard.breadcrumbs\\n import por.dashboard.sidebar; por.dashboard.sidebar\\n\\n session_factory = session_factory_from_settings(settings)\\n config.set_session_factory(session_factory)\\n\\n config.add_static_view('static', 'por.dashboard:static')\\n config.scan('por.dashboard.views')\\n\\n config.add_route('tp', '/tp/*traverse', factory='por.dashboard.tp.TPContext')\\n config.scan('por.dashboard.tp')\\n config.scan('por.dashboard.smartadd')\\n config.scan('por.dashboard.backlog')\\n\\n config.add_route('administrator', '/manage', factory='por.dashboard.manage.ManageContext')\\n config.add_route('manage_svn_authz', '/manage/svn_authz', factory='por.dashboard.manage.ManageContext')\\n config.scan('por.dashboard.manage')\\n\\n config.add_route('search', '/search')\\n config.scan('por.dashboard.search')\\n\\n config.add_route('reports', '/reports/*traverse', factory='por.dashboard.reports.ReportContext')\\n config.scan('por.dashboard.reports')\\n config.add_renderer(name='xls_report', factory='por.dashboard.renderers.XLSReportRenderer')\\n config.add_renderer(name='csv_report', factory='por.dashboard.renderers.CSVReportRenderer')\\n\\n config.include('pyramid_rpc.jsonrpc')\\n config.add_jsonrpc_endpoint('DashboardAPI', '/apis/json/dashboard')\\n config.scan('por.dashboard.api')\\n\\n config.add_renderer('skin', renderer_factory)\\n config.include('pyramid_skins')\\n config.register_path(\\\"por.dashboard:skins\\\", discovery=DISCOVERY)\\n\\n # por.gdata configuration\\n config.include('por.gdata')\\n\\n # por.trac configuration\\n # config.include('por.trac')\\n\\n # pyramid_formalchemy's configuration\\n config.include('pyramid_formalchemy')\\n config.include('fa.bootstrap')\\n config.include('deform_bootstrap')\\n\\n config.formalchemy_admin('admin',\\n package='por.dashboard',\\n factory='por.dashboard.forms.CrudModels',\\n models='por.models',\\n view='por.dashboard.forms.ModelView',\\n session_factory='por.models.DBSession')\\n\\n config.add_view(context='pyramid_formalchemy.resources.ModelListing',\\n renderer='fa.bootstrap:templates/admin/new.pt',\\n request_method='POST',\\n route_name='admin',\\n request_type='por.dashboard.interfaces.IPorRequest',\\n view='por.dashboard.forms.security_create')\\n\\n config.add_static_view('deform_static', 'deform:static')\\n config.add_route('navbar', '/navbar')\\n config.add_route('favicon', '/favicon.ico')\\n\\n from por.dashboard.forms import include_forms\\n include_forms(config)\\n\\n return config.make_wsgi_app()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd2f02deba83409a49d071e69ff58fd\",\n \"score\": \"0.46655256\",\n \"text\": \"def manage_addSurveyTool(context, REQUEST=None):\\n portal_id = SurveyTool.portal_id\\n ob = SurveyTool(portal_id, title=\\\"Portal Survey Manager\\\")\\n context._setObject(portal_id, ob)\\n ob = context._getOb(portal_id)\\n ob.manage_configureSite()\\n\\n if REQUEST:\\n return context.manage_main(context, REQUEST, update_menu=1)\\n return portal_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56262298e36fef4ee16f4a13d1018d\",\n \"score\": \"0.46531948\",\n \"text\": \"def configuredefaults(self):\\r\\n return self.configure(3800, 800, 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d48d84e05ebfee5015ee4516b7e824b\",\n \"score\": \"0.4642923\",\n \"text\": \"def view_no_domain_config_error(request, is_test_page=False):\\n\\n # double checke to make sure it doesn't exist\\n #\\n app_config = AppConfiguration.get_config()\\n if app_config and not is_test_page:\\n return HttpResponseRedirect(reverse('home'))\\n\\n dinfo = dict(title='Two Ravens configuration error',\\n IS_TEST_PAGE=is_test_page)\\n\\n return render(request,\\n 'content_pages/no_domain_config_error.html',\\n dinfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a4cd8dbe5f9b662f4313e81345d3a72\",\n \"score\": \"0.46390343\",\n \"text\": \"def __init__(self, domain, site, ssl=True, debug=False, client=None):\\n self.domain = domain\\n self.site = site\\n self.ssl = ssl\\n self.debug = debug\\n self.client = client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"113681eb1aabae3a0e4b9164ebc52cfc\",\n \"score\": \"0.46351382\",\n \"text\": \"def website(self, website: str):\\n\\n self._website = website\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a867230ba97574e830a2fca677a643\",\n \"score\": \"0.46338075\",\n \"text\": \"def __init__(self, config, survey_area=4 * np.pi):\\n super().__init__(config)\\n\\n sacc_file = self.io.get_sacc_file()\\n if \\\"clusters\\\" not in str(sacc_file.tracers.keys()):\\n print(\\n \\\"Clusters are not within the SACC file tracers.\\\"\\n + \\\"Not performing cluster covariances.\\\"\\n )\\n return\\n\\n self.overdensity_delta = 200\\n self.h0 = float(self.config[\\\"parameters\\\"].get(\\\"h\\\"))\\n self.load_from_sacc(sacc_file)\\n\\n # NOTE Survey area should be pulled from SACC file but is currently not\\n self.survey_area = survey_area\\n\\n cosmo = self.get_cosmology()\\n self.load_from_cosmology(cosmo)\\n self.fft_helper = FFTHelper(\\n cosmo, self.z_lower_limit, self.z_upper_limit\\n )\\n\\n # Quick key to skip P(Richness|M)\\n self.has_mproxy = self.config.get(\\\"has_mproxy\\\", True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2329b71ee245598a4629e12677ab2f53\",\n \"score\": \"0.4630403\",\n \"text\": \"def setup(request):\\n global driver\\n if(request.config.getoption(\\\"browser_name\\\") == \\\"Chrome\\\"):\\n driver = webdriver.Chrome(ChromeDriverManager().install())\\n log.getLogger().debug(\\\"Chrome is launched\\\")\\n elif(request.config.getoption(\\\"browser_name\\\") == \\\"firefox\\\"):\\n driver = webdriver.Firefox(GeckoDriverManager().install())\\n log.getLogger().debug(\\\"Firefox is launched\\\")\\n elif(request.config.getoption(\\\"browser_name\\\") == \\\"ie\\\"):\\n driver = webdriver.Ie(IEDriverManager().install())\\n log.getLogger().debug(\\\"IE is launched\\\")\\n driver.get(data[\\\"url\\\"])\\n log.getLogger().debug(\\\"Enter the url\\\" + data[\\\"url\\\"])\\n driver.maximize_window()\\n log.getLogger().debug(\\\"Maximize the window\\\")\\n request.cls.driver = driver\\n yield\\n driver.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f411fe368519daeaaaa5f67266397d\",\n \"score\": \"0.46236736\",\n \"text\": \"def configure(self):\\n \\n # pylint: disable-msg=E1101\\n\\n # Create Optimizer instance\\n self.add('driver', SLSQPdriver())\\n \\n # Create Paraboloid component instances\\n self.add('paraboloid', Paraboloid())\\n\\n # Driver process definition\\n self.driver.workflow.add('paraboloid')\\n \\n # SQLSQP Flags\\n self.driver.iprint = 0\\n \\n # Objective \\n self.driver.add_objective('paraboloid.f_xy')\\n \\n # Design Variables \\n self.driver.add_parameter('paraboloid.x', low=-50., high=50.)\\n self.driver.add_parameter('paraboloid.y', low=-50., high=50.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b64c68bb0dd33017712cc24cd8e55e\",\n \"score\": \"0.46170565\",\n \"text\": \"def load_siteconfig():\\n from mezzanine.core.request import current_request\\n try:\\n from main.models import SiteConfiguration\\n except ImportError:\\n raise ImproperlyConfigured(\\n \\\"You must create a SiteConfiguration model\\\"\\n \\\"to use load_siteconfig tag.\\\")\\n\\n request = current_request()\\n if hasattr(request, \\\"siteconfig\\\"):\\n return request.siteconfig\\n siteconfig = SiteConfiguration.objects.get_or_create()[0]\\n request.siteconfig = siteconfig\\n return siteconfig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9e52b75a6784e37dfdf4d69b92e6e6b\",\n \"score\": \"0.46158603\",\n \"text\": \"def production():\\n env.environment = 'production'\\n env.project_path = \\\"/srv/pootle-production\\\"\\n env.apache_server_name = \\\"i18n.haiku-os.org\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d984e5bd048b52ca1b0b7278f6bec22\",\n \"score\": \"0.4613428\",\n \"text\": \"def setup(self):\\n self.aea_config_path = Path(\\n CUR_PATH, \\\"data\\\", \\\"dummy_aea\\\", DEFAULT_AEA_CONFIG_FILE\\n )\\n self.loader = ConfigLoaders.from_package_type(PackageType.AGENT)\\n self.aea_config: AgentConfig = self.loader.load(self.aea_config_path.open())\\n self.dummy_skill_component_id = ComponentId(\\n ComponentType.SKILL, DUMMY_SKILL_PUBLIC_ID\\n )\\n\\n self.new_dummy_skill_config = {\\n \\\"behaviours\\\": {\\\"dummy\\\": {\\\"args\\\": dict(behaviour_arg_1=42)}},\\n \\\"handlers\\\": {\\\"dummy\\\": {\\\"args\\\": dict(handler_arg_1=42)}},\\n \\\"models\\\": {\\\"dummy\\\": {\\\"args\\\": dict(model_arg_1=42)}},\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6450bac74eda4f900578772f4e351668\",\n \"score\": \"0.46091795\",\n \"text\": \"def __set_properties(self):\\n self.SetTitle('ANW Admin - version %s' % anwp.func.globals.currentVersion)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19258f60aa64d245623abef191812ad7\",\n \"score\": \"0.46067014\",\n \"text\": \"def bootstrapSite(self):\\n portal = self.getPortal()\\n if 'MailHost' in portal.objectIds():\\n portal.manage_delObjects(['MailHost'])\\n portal._setObject('MailHost', DummyMailHost('MailHost'))\\n\\n portal.email_from_address = 'romain@nexedi.com'\\n portal.email_to_address = 'romain@nexedi.com'\\n\\n self.clearCache()\\n\\n self.logMessage(\\\"Bootstrap Vifib...\\\")\\n self.logMessage(\\\"Create Manager\\\")\\n self.createManagerAndLogin()\\n self.createVifibDocumentList()\\n self.validateRule()\\n self.logMessage(\\\"Create Administrator\\\")\\n self.createAdministratorUser()\\n self.logout()\\n self.logMessage(\\\"Bootstrap Finished\\\")\\n # XXX Hardcoded\\n # XXX-Fx: No security is defined so administrator has no rights\\n # so impossible to create vifib user with administrator\\n #self.login(\\\"administrator\\\")\\n self.login('manager')\\n # XXX-Fx\\n # XXX XXX\\n self.createVifibUsers()\\n self.logout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c95aa6a5a76828508d16524ad2ea77\",\n \"score\": \"0.46028617\",\n \"text\": \"def set_default_config(self):\\n if self.m.properties.get('use_mirror', True): # We're on a bot\\n self.set_config('production')\\n else:\\n self.set_config('testing')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac5a7e8a50b1538d9d7b2840ff5486c\",\n \"score\": \"0.46006432\",\n \"text\": \"def configure_request(app):\\n global api_key,sources_api,sources_article_api,headlines_api\\n api_key = app.config[\\\"API_KEY\\\"]\\n sources_api = app.config[\\\"SOURCES_API\\\"]\\n headlines_api = app.config[\\\"HEADLINES_API\\\"]\\n sources_article_api = app.config[\\\"SOURCES_ARTICLE_API\\\"]\\n search_api = app.config[\\\"SEARCH_API\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a0cf7e54f1b8269aa8ca5ddd698576\",\n \"score\": \"0.4595866\",\n \"text\": \"def setting(request):\\n # Loading settings\\n settings = load_settings.ArcherySettings(setting_file)\\n\\n # Loading OpenVAS Settings\\n ov_user = settings.openvas_username()\\n ov_pass = settings.openvas_pass()\\n ov_ip = settings.openvas_host()\\n lod_ov_user = signing.loads(ov_user)\\n lod_ov_pass = signing.loads(ov_pass)\\n lod_ov_ip = signing.loads(ov_ip)\\n\\n # Loading ZAP Settings\\n lod_apikey = settings.zap_api_key()\\n zap_host = settings.zap_host()\\n zap_port = settings.zap_port()\\n\\n # Loading Burp Settings\\n burp_host = settings.burp_host()\\n burp_port = settings.burp_port()\\n\\n # Loading Email Settings\\n email_subject = settings.email_subject()\\n email_from = settings.email_from()\\n to_email = settings.email_to()\\n\\n return render(request, 'setting.html',\\n {'apikey': lod_apikey,\\n 'zapath': zap_host,\\n 'zap_port': zap_port,\\n 'lod_ov_user': lod_ov_user,\\n 'lod_ov_pass': lod_ov_pass,\\n 'lod_ov_ip': lod_ov_ip,\\n 'burp_path': burp_host,\\n 'burp_port': burp_port,\\n 'email_subject': email_subject,\\n 'email_from': email_from,\\n 'to_email': to_email})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06659eca1f4783b0bb94e4fd8f2987b\",\n \"score\": \"0.45922372\",\n \"text\": \"def setup(self, conf):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40d4cfd78e5eda58d95f2d0b5988b0e1\",\n \"score\": \"0.45918846\",\n \"text\": \"def ProductArea1Admin(oRequest):\\n dContext = {\\n 'productarea1': REF_PRODUCT_AREA_1.objects.filter(is_inactive=0),\\n 'errors': oRequest.session.pop('errors', None),\\n 'message_type_is_error': oRequest.session.pop('message_is_error', False)\\n }\\n return Default(oRequest, 'BoMConfig/adminproductarea1.html', dContext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d463939ca25fe4f27a0d883817ace09\",\n \"score\": \"0.4591799\",\n \"text\": \"def setup(request):\\n load_configuration()\\n\\n if config.default_browser == \\\"firefox\\\":\\n allure.step(\\\"Starting Firefox.\\\")\\n Selenium.driver = webdriver.Firefox()\\n elif config.default_browser == \\\"chrome\\\":\\n allure.step(\\\"Starting Chrome.\\\")\\n Selenium.driver = webdriver.Chrome()\\n elif config.default_browser == \\\"chrome-local-webdriver\\\":\\n allure.step(\\\"Starting Chrome in webdriver local.\\\")\\n options = webdriver.ChromeOptions()\\n capabilities = options.to_capabilities()\\n Selenium.driver = webdriver.Remote(\\n command_executor=config.command_executor,\\n desired_capabilities=capabilities)\\n elif config.default_browser == \\\"firefox-local-webdriver\\\":\\n allure.step(\\\"Starting Firefox in webdriver local.\\\")\\n options = webdriver.FirefoxOptions()\\n capabilities = options.to_capabilities()\\n Selenium.driver = webdriver.Remote(\\n command_executor=config.command_executor, # 'http://127.0.0.1:4444/wd/hub'\\n desired_capabilities=capabilities)\\n else:\\n allure.step(\\\"Using standard Browser (chrome).\\\")\\n Selenium.driver = webdriver.Chrome()\\n Selenium.driver.implicitly_wait(config.implicit_wait) # 10 seconds\\n Selenium.driver.maximize_window()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4619a407c3556a1283fdee7d491dc8\",\n \"score\": \"0.45896873\",\n \"text\": \"def selectSiteSite(self, site): \\n self.siteSiteVar.set(site)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"353b9ce434be79c193317e77270557d2\",\n \"score\": \"0.458839\",\n \"text\": \"def configure(self, config):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84c705b6bdc8db6f23dcf91809dd0684\",\n \"score\": \"0.45879897\",\n \"text\": \"def configure(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa2fc2b481059781b96e621deb06029\",\n \"score\": \"0.458498\",\n \"text\": \"def app_product(self):\\n raise ConfigError('app_product')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f964d21a25c7535266a9c8e4ef9226\",\n \"score\": \"0.4575019\",\n \"text\": \"def setup_environment(self):\\n self.environment = os.environ.get('AMPLIFY_ENVIRONMENT', 'production')\\n self.imagename = os.environ.get('AMPLIFY_IMAGENAME')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae8548a32eb728992ed2b0b8a8031f2\",\n \"score\": \"0.4570665\",\n \"text\": \"def website(self, website):\\n\\n self._website = website\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae8548a32eb728992ed2b0b8a8031f2\",\n \"score\": \"0.4570665\",\n \"text\": \"def website(self, website):\\n\\n self._website = website\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae8548a32eb728992ed2b0b8a8031f2\",\n \"score\": \"0.4570665\",\n \"text\": \"def website(self, website):\\n\\n self._website = website\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae8548a32eb728992ed2b0b8a8031f2\",\n \"score\": \"0.4570665\",\n \"text\": \"def website(self, website):\\n\\n self._website = website\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318f4bb13a6912b06ce86ee5cf41bc33\",\n \"score\": \"0.45651618\",\n \"text\": \"def setup_add_ons(context):\\n portal = _get_navigation_root(context)\\n\\n configure_collective_cover(context)\\n\\n create_products_carousel_content(portal)\\n create_products_doormat_content(portal)\\n create_products_ploneformgen_content(portal)\\n create_collective_cover_content(portal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"509d1ebd270f28a11e153c4f03c39c84\",\n \"score\": \"0.45585662\",\n \"text\": \"def uni_form_setup(context):\\n if 'STATIC_URL' not in context:\\n context['STATIC_URL'] = settings.STATIC_URL\\n return (context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10d9e4c57716a441c5d89fb39e408f5\",\n \"score\": \"0.45529035\",\n \"text\": \"def test_com_day_cq_dam_core_impl_assethome_asset_home_page_configuration(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994376,"cells":{"query_id":{"kind":"string","value":"4a44748cebac2d3abfbd07691a73eca0"},"query":{"kind":"string","value":"r\"\"\" Finds a linear relation between the given list of OMSs. If they are LI, returns a list of all 0's."},"positive_passages":{"kind":"list like","value":[{"docid":"bd0dbbe42ede2a778913aa43010ca117","score":"0.6331311","text":"def linear_relation(self, List):\n for Phi in List:\n assert Phi.valuation() >= 0, \"Symbols must be integral\"\n \n R = self.base()\n ## NEED A COMMAND FOR RELATIVE PRECISION OF OMS\n M = List[0].precision_relative()\n p = self.prime()\n d = len(List)\n V = R**d\n \n if d == 1:\n if List[0].is_zero():\n return [R(1)]\n else:\n return [R(0)]\n # Would be better to use the library as follows. Unfortunately, matsolvemod\n # is not included in the gen class!!\n #from sage.libs.pari.gen import pari\n #cols = [List[c].list_of_total_measures() for c in range(len(List) - 1)]\n #A = pari(Matrix(ZZ, len(cols[0]), d - 1, lambda i, j : cols[j][i].lift()))\n #aug_col = pari([ZZ(a) for a in List[-1].list_of_total_measures()]).Col()\n #v = A.matsolvemod(p ** M, aug_col)\n \n s = '['\n for c in range(d-1):\n v = List[c].list_of_total_measures()\n for r in range(len(v)):\n s += str(ZZ(v[r]))\n if r < len(v) - 1:\n s += ','\n if c < d - 2:\n s += ';'\n s = s + ']'\n \n verbose(\"s = %s\"%(s))\n \n A = gp(s)\n verbose(\"A = %s\"%(A))\n if len(List) == 2:\n A = A.Mat()\n \n s = '['\n v = List[d-1].list_of_total_measures()\n for r in range(len(v)):\n s += str(ZZ(v[r]))\n if r < len(v) - 1:\n s += ','\n s += ']~'\n \n verbose(\"s = %s\"%(s))\n \n B = gp(s)\n \n verbose(\"B = %s\"%(B))\n \n v = A.mattranspose().matsolvemod(p**M,B)\n \n verbose(\"v = %s\"%(v))\n \n if v == 0:\n return [R(0) for a in range(len(v))]\n else:\n ## Move back to SAGE from Pari\n v = [R(v[a]) for a in range(1,len(v)+1)]\n return v + [R(-1)]","title":""}],"string":"[\n {\n \"docid\": \"bd0dbbe42ede2a778913aa43010ca117\",\n \"score\": \"0.6331311\",\n \"text\": \"def linear_relation(self, List):\\n for Phi in List:\\n assert Phi.valuation() >= 0, \\\"Symbols must be integral\\\"\\n \\n R = self.base()\\n ## NEED A COMMAND FOR RELATIVE PRECISION OF OMS\\n M = List[0].precision_relative()\\n p = self.prime()\\n d = len(List)\\n V = R**d\\n \\n if d == 1:\\n if List[0].is_zero():\\n return [R(1)]\\n else:\\n return [R(0)]\\n # Would be better to use the library as follows. Unfortunately, matsolvemod\\n # is not included in the gen class!!\\n #from sage.libs.pari.gen import pari\\n #cols = [List[c].list_of_total_measures() for c in range(len(List) - 1)]\\n #A = pari(Matrix(ZZ, len(cols[0]), d - 1, lambda i, j : cols[j][i].lift()))\\n #aug_col = pari([ZZ(a) for a in List[-1].list_of_total_measures()]).Col()\\n #v = A.matsolvemod(p ** M, aug_col)\\n \\n s = '['\\n for c in range(d-1):\\n v = List[c].list_of_total_measures()\\n for r in range(len(v)):\\n s += str(ZZ(v[r]))\\n if r < len(v) - 1:\\n s += ','\\n if c < d - 2:\\n s += ';'\\n s = s + ']'\\n \\n verbose(\\\"s = %s\\\"%(s))\\n \\n A = gp(s)\\n verbose(\\\"A = %s\\\"%(A))\\n if len(List) == 2:\\n A = A.Mat()\\n \\n s = '['\\n v = List[d-1].list_of_total_measures()\\n for r in range(len(v)):\\n s += str(ZZ(v[r]))\\n if r < len(v) - 1:\\n s += ','\\n s += ']~'\\n \\n verbose(\\\"s = %s\\\"%(s))\\n \\n B = gp(s)\\n \\n verbose(\\\"B = %s\\\"%(B))\\n \\n v = A.mattranspose().matsolvemod(p**M,B)\\n \\n verbose(\\\"v = %s\\\"%(v))\\n \\n if v == 0:\\n return [R(0) for a in range(len(v))]\\n else:\\n ## Move back to SAGE from Pari\\n v = [R(v[a]) for a in range(1,len(v)+1)]\\n return v + [R(-1)]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"703cf4c945aa3009e5d58dd9816096f2","score":"0.59615","text":"def eltL0(strEle,nodCoo):\n mesDat = []\n for elt in strEle:\n nodI = np.int(elt[0])\n nodJ = np.int(elt[1])\n dX = nodCoo[nodJ][0]-nodCoo[nodI][0]\n dY = nodCoo[nodJ][1]-nodCoo[nodI][1]\n eleLen = np.sqrt(dX**2+dY**2)\n if np.abs(eleLen) > 1e-16:\n cos = dX/eleLen\n sin = dY/eleLen\n else:\n cos = 1\n sin = 0\n mesDat.append([eleLen,cos,sin])\n \n return mesDat","title":""},{"docid":"007a1008b073b998104e9f227c1f3149","score":"0.58626366","text":"def function_2_3_1d_relu_list(l1: 'List of numbers') -> 'Relu output':\n return [a if a >= 0 else 0 for a in l1]","title":""},{"docid":"5673fe2fb5613229b60961e1938e5953","score":"0.551257","text":"def linear_structures(self):\n n = self.n\n m = self.m\n act = self.autocorrelation_matrix()\n ret = []\n\n for j in range(1, 1<<n):\n for i in range(1, 1<<m):\n if (abs(act[i,j]) == (1<<m)):\n c = ((1 - (act[i][j] >> self.m)) >> 1)\n ret.append((j, i, c))\n return ret","title":""},{"docid":"5290e0dfb9d5aa5761a3b245402dd96d","score":"0.5448406","text":"def listindoubles(linearlist):\r\n ddo = oddlist(linearlist)\r\n neve = evenlist(linearlist)\r\n return map((lambda ew,qa:[ew,qa]), ddo, neve)","title":""},{"docid":"53f1a65a9f6e6d664a8ca6108c113b12","score":"0.535384","text":"def find_rel_prop(s_coords, o_coords):\r\n if PRINT_MODEL:\r\n print(\"find rel prop with s_coords, o_coords: \", s_coords, o_coords)\r\n vector = list_substract(s_coords, o_coords)\r\n return normalize(vector)","title":""},{"docid":"860360ab33e375a487c5b235d6a8b85d","score":"0.5248786","text":"def dimacs_list2formula(lines):\n F_str = [item[0:-1] for item in lines if (('c' not in item) and ('p' not in item))]\n F = []\n clause = []\n for str_clause in F_str:\n for literal in str_clause:\n clause.append(int(literal))\n F.append(clause)\n clause = []\n return F","title":""},{"docid":"cadc8f7856f261c9a5495f3050e5bd65","score":"0.5165928","text":"def mols_are_linear(self):\n self.mol_a.geom.linear = True\n self.mol_b.geom.linear = True","title":""},{"docid":"d76efa5678225af1b1d673ce8f5d097f","score":"0.51525706","text":"def setup_linearSegment(joints = [],\n ):\n ml_joints = cgmMeta.validateObjListArg(joints,'cgmObject')\n ml_new = []\n mStartHandle = ml_joints[0]\n mEndHandle = ml_joints[-1]\n \n for mObj in ml_joints[1:-1]:\n mGroup = mObj.doGroup(True,True,asMeta=True,typeModifier = 'twist')\n mObj.parent = False\n \n _vList = DIST.get_normalizedWeightsByDistance(mGroup.mNode,[mStartHandle.mNode,mEndHandle.mNode])\n _point = mc.pointConstraint([mStartHandle.mNode,mEndHandle.mNode],mGroup.mNode,maintainOffset = False)#Point contraint loc to the object\n _orient = mc.orientConstraint([mStartHandle.mNode,mEndHandle.mNode],mGroup.mNode,\n skip = ['y','z'],\n maintainOffset = False)#Point contraint loc to the object\n \n for c in [_point,_orient]:\n CONSTRAINT.set_weightsByDistance(c[0],_vList)\n \n mObj.parent = mGroup","title":""},{"docid":"d2d9c7c2db20a787334e8683e1979e17","score":"0.51349175","text":"def my_relu(mylist: list)->list:\n result = [x if x>= 0 else 0 for x in mylist]\n return result","title":""},{"docid":"444a50256c04c44ce11e01fd9aadb137","score":"0.50859416","text":"def listinpairs(linearlist):\r\n ddo = oddlist(linearlist)\r\n neve = evenlist(linearlist)\r\n return map((lambda ew,qa:(ew,qa)), ddo, neve)","title":""},{"docid":"100103fe7cca660e520b298237e6cd56","score":"0.5054028","text":"def generate_aryne_resonance_structures(mol):\n cython.declare(rings=list, ring=list, new_mol_list=list, bond_list=list,\n i=cython.int, j=cython.int, bond_orders=str, new_orders=str,\n ind=cython.int, bond=Bond, new_mol=Molecule)\n\n rings = mol.get_relevant_cycles()\n rings = [ring for ring in rings if len(ring) == 6]\n\n new_mol_list = []\n for ring in rings:\n # Get bond orders\n bond_list = mol.get_edges_in_cycle(ring)\n bond_orders = ''.join([bond.get_order_str() for bond in bond_list])\n new_orders = None\n # Check for expected bond patterns\n if bond_orders.count('T') == 1:\n # Reorder the list so the triple bond is first\n ind = bond_orders.index('T')\n bond_orders = bond_orders[ind:] + bond_orders[:ind]\n bond_list = bond_list[ind:] + bond_list[:ind]\n # Check for patterns\n if bond_orders == 'TSDSDS':\n new_orders = 'DDSDSD'\n elif bond_orders.count('D') == 4:\n # Search for DDD and reorder the list so that it comes first\n if 'DDD' in bond_orders:\n ind = bond_orders.index('DDD')\n bond_orders = bond_orders[ind:] + bond_orders[:ind]\n bond_list = bond_list[ind:] + bond_list[:ind]\n elif bond_orders.startswith('DD') and bond_orders.endswith('D'):\n bond_orders = bond_orders[-1:] + bond_orders[:-1]\n bond_list = bond_list[-1:] + bond_list[:-1]\n elif bond_orders.startswith('D') and bond_orders.endswith('DD'):\n bond_orders = bond_orders[-2:] + bond_orders[:-2]\n bond_list = bond_list[-2:] + bond_list[:-2]\n # Check for patterns\n if bond_orders == 'DDDSDS':\n new_orders = 'STSDSD'\n\n if new_orders is not None:\n # We matched one of our patterns, so we can now change the bonds\n for i, bond in enumerate(bond_list):\n bond.set_order_str(new_orders[i])\n # Make a copy of the molecule\n new_mol = mol.copy(deep=True)\n # Undo the changes to the current molecule\n for i, bond in enumerate(bond_list):\n bond.set_order_str(bond_orders[i])\n # Try to update atom types\n try:\n new_mol.update_atomtypes(log_species=False)\n except AtomTypeError:\n pass # Don't append resonance structure if it creates an undefined atomtype\n else:\n new_mol_list.append(new_mol)\n\n return new_mol_list","title":""},{"docid":"a539c8e9db77bbe64b7ed1465d4566a6","score":"0.50122446","text":"def get_linear_formula(start_i, end_i):\n return ' + '.join('x' + str(i) for i in range(start_i, end_i + 1))","title":""},{"docid":"bfc14a33f8910577e3a2dd23e79cfc2a","score":"0.500917","text":"def normals(linelist):\n for i, (n, *points) in enumerate(linelist):\n x = points[0][0] > points[1][0]\n y = points[0][1] > points[1][1]\n\n # print(i, x, y)\n\n if not x and not y:\n n = [n[0], -n[1]]\n elif x and not y:\n n = [n[0], -n[1]]\n elif x and y:\n n = [n[0], -n[1]]\n elif not x and y:\n n = [n[0], -n[1]]\n else:\n #print(\"Zero\", i, x, y)\n n = [0, 0]\n\n #print(n)\n linelist[i][0] = tuple(n)\n\n return linelist","title":""},{"docid":"f5ed838259c610333509208ed86a7fac","score":"0.49844572","text":"def tris(x, cross_list):\n cross_list_name = [x.rel if isinstance(x, radb.ast.RelRef) else x.relname for x in cross_list]\n x1 = cross_list_name.index(x.inputs[0].rel)\n x2 = cross_list_name.index(x.inputs[1].rel)\n return x1 + x2","title":""},{"docid":"7dd57c95a53c7937850f3f48605e1d16","score":"0.49799547","text":"def obter_posicoes_livres(tab):\n if not(eh_tabuleiro(tab)):\n raise ValueError(\"obter_posicoes_livres: o argumento e invalido\")\n i = 1\n rslt = ()\n for linha in tab:\n for elem in linha:\n if elem == 0:\n rslt = rslt + (i,)\n i += 1\n return rslt","title":""},{"docid":"02a5dfda4fade99879e9ff4fb3946858","score":"0.49720404","text":"def translate_to_1_dimensional_list(l : List[Union[str,int]]) -> List[str]:\n if (len(l) == 1):\n return l[0]\n return l[0] + translate_to_1_dimensional_list(l[1:])","title":""},{"docid":"02e7a80a43d29434d87a74ec0d2ac17d","score":"0.49606594","text":"def 樹(p列表, l, a, f):\n if l > 3:\n 列表 = []\n for p in p列表:\n p.前進(l)\n q = p.複製()\n p.左轉(a)\n q.右轉(a)\n 列表.append(p)\n 列表.append(q)\n for x in 樹(列表, l*f, a, f):\n yield 無","title":""},{"docid":"72ef7ceea389f4b6d405606a481a1378","score":"0.49380693","text":"def link_celiac_finding(mention, findings):\n\t\tfinding_mention = mention.text\n\t\tif 'lymphocyte' in finding_mention or 'lymphocital' in finding_mention:\n\t\t\tif 'iel' in finding_mention:\n\t\t\t\ti_end = [tok.i for tok in mention.doc[mention.end:] if mention.doc[tok.i].text == ')']\n\t\t\t\tvalue = mention.doc[mention.end:i_end[0]]\n\t\t\t\treturn [[mention.text, 'lymphocyte'], [mention.text, 'intraepithelial lymphocyte count'], [mention.text, value.text]]\n\t\t\telse:\n\t\t\t\treturn [[mention.text, 'lymphocyte']]\n\t\telif 'lymphoplasmocitary' in finding_mention:\n\t\t\treturn[[mention.text, 'lymphocyte']]\n\t\telif 'granulocitary' in finding_mention:\n\t\t\treturn [[mention.text, 'granulocyte']]\n\t\telif any(k in finding_mention for k in findings):\n\t\t\tfind_mentions = [k for k in findings if k in finding_mention]\n\t\t\tmentions2concepts = []\n\t\t\tfor finding in find_mentions:\n\t\t\t\tmentions2concepts.append([mention.text, finding])\n\t\t\treturn mentions2concepts\n\t\telif finding_mention in findings:\n\t\t\treturn [[mention.text, [find for find in findings if finding_mention == find][0]]]\n\t\telse:\n\t\t\treturn [[mention.text, None]]","title":""},{"docid":"a6976afff0c615648b869fefd2831497","score":"0.49185857","text":"def correspondence_list(points: list, which_region_list: list, inside_points: list, lengths: tuple):\n\n correspondence_list = []\n for point, reg in zip(points, which_region_list):\n new_point = [point[0] - reg[0]*lengths[0],\n point[1] - reg[1]*lengths[1]]\n\n loop_worked = False\n for ind, point_compare in enumerate(inside_points):\n if point_close(new_point, point_compare):\n correspondence_list.append(ind)\n loop_worked = True\n break\n\n if loop_worked == False:\n # some points dont have an analog - they must be removed completely - we signify with None\n correspondence_list.append(None)\n\n return correspondence_list","title":""},{"docid":"867cdc7cd6db401d9e1faa8891362a13","score":"0.49073812","text":"def LP(S):\n T = [ [None]*len(S) for _ in range(len(S))]\n LP_aux(S, T, 0, len(S)-1)\n return T[0][len(S)-1]","title":""},{"docid":"22169fdaa3e6a252eb71718d14cbd126","score":"0.4904927","text":"def check_relations(self):\n if not check_zope_admin():\n return \"You must be a zope manager to run this script\"\n from zc.relation.interfaces import ICatalog\n from zope.app.intid.interfaces import IIntIds\n from zope.component import getUtility\n\n intids = getUtility(IIntIds)\n rels = getUtility(ICatalog)\n out = [\"check_intids\\n\"]\n relations = list(rels.findRelations())\n for rel in relations:\n if not rel.from_id or not intids.queryObject(rel.from_id, False):\n log_list(out, \"Missing from_id %s\" % relation_infos(rel))\n # rels.unindex(rel)\n elif not rel.to_id or not intids.queryObject(rel.to_id, False):\n log_list(out, \"Missing to_id %s\" % relation_infos(rel))\n elif not rel.from_object or not intids.queryId(rel.from_object, False):\n log_list(out, \"Missing from_object %s\" % relation_infos(rel))\n elif not rel.to_object or not intids.queryId(rel.to_object, False):\n log_list(out, \"Missing to_object %s\" % relation_infos(rel))\n if len(out) == 1:\n out.append(\"No problem found\")\n return \"\\n\".join(out)","title":""},{"docid":"60ea6ec08f7fd3f9e889cd15ca7a8c9e","score":"0.49016887","text":"def get_somme(l):\n somme = 0\n for nombre in l:\n somme += nombre","title":""},{"docid":"ba942eef7b97603edc588d1dabc755d3","score":"0.48698246","text":"def get_language_model_match(pairs_list, lm, text_min, text_max):\n\n all_text_parent = []\n all_text_child = []\n for pair in pairs_list:\n parent_ob = pair[0]\n child_ob = pair[1]\n\n all_text_parent.append(parent_ob[\"body\"])\n all_text_child.append(child_ob[\"body\"])\n\n res_parent, lengths_parent = language_model.text_scores(all_text_parent, lm, text_min, text_max)\n\n res_child, lengths_child = language_model.text_scores(all_text_child, lm, text_min, text_max)\n\n return zip(res_parent, res_child), zip(lengths_parent, lengths_child)","title":""},{"docid":"050b645a17a1d3d58d75f9d8934b0423","score":"0.48621327","text":"def internal_lemmas(self):\n node_stripped = copy.deepcopy(self.node)\n etree.strip_elements(node_stripped, 'lemVersions')\n return node_stripped.findall('.//lm')","title":""},{"docid":"da081aa08c4c4bdb8d50c828b9a9c405","score":"0.48545665","text":"def objectRel(rommat):\n\n assertMatrixSquare(rommat)\n oid=0\n rels=[]\n # Transposing because orig matrix stores link types \n # from colIdx to rowIdx\n for inRel in transposed(rommat):\n oid+=1\n relOids = map(\n lambda i: i+1, # oids start w/ 1, indexes w/ 0\n nonZeroIndexes(inRel))\n rels+=[ObjectRel(oid,relOids)]\n return rels;","title":""},{"docid":"f7b82046b6a00ccd84e405bffd303dec","score":"0.48538083","text":"def exact_sol(nodes = 100):\n exact = []\n axisx_exact = np.linspace(0, L, nodes)\n for x in axisx_exact:\n hasil = func_exact_sol(x)\n exact.append(hasil)\n return axisx_exact, exact, nodes","title":""},{"docid":"0baa27e902ba5ab4a8b2ae696f51d551","score":"0.48512816","text":"def contractMPOEntries(MPO,indexlist,direction=0):\n south,north = indexlist\n M = MPO[0][:,south[0],:,north[0]]\n for i in range(1,len(MPO)):\n M = np.dot(M,MPO[i][:,south[i],:,north[i]])\n return(M)","title":""},{"docid":"6f18af7786d52a54bc4667f7a9bb1ad6","score":"0.4846471","text":"def linear_landmarks(x, x0, x1):\n x = np.array(x)\n return pol1_transform(x, (x0,0.), (x1,1.))","title":""},{"docid":"dc8f8c92b74e20c6d3a8d70bd1426daf","score":"0.483458","text":"def generate_adj_lone_pair_multiple_bond_resonance_structures(mol):\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule, direction=cython.int)\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, bond12=Bond)\n cython.declare(v1=Vertex, v2=Vertex)\n\n structures = []\n for atom in mol.vertices:\n paths = pathfinder.find_adj_lone_pair_multiple_bond_delocalization_paths(atom)\n for atom1, atom2, bond12, direction in paths:\n if direction == 1: # The direction <increasing> the bond order\n atom1.decrement_lone_pairs()\n bond12.increment_order()\n elif direction == 2: # The direction <decreasing> the bond order\n atom1.increment_lone_pairs()\n bond12.decrement_order()\n atom1.update_charge()\n atom2.update_charge()\n # Make a copy of structure\n structure = mol.copy(deep=True)\n # Restore current structure\n if direction == 1: # The direction <increasing> the bond order\n atom1.increment_lone_pairs()\n bond12.decrement_order()\n elif direction == 2: # The direction <decreasing> the bond order\n atom1.decrement_lone_pairs()\n bond12.increment_order()\n atom1.update_charge()\n atom2.update_charge()\n try:\n structure.update_atomtypes(log_species=False)\n except AtomTypeError:\n pass # Don't append resonance structure if it creates an undefined atomtype\n else:\n if not (structure.get_net_charge() and structure.contains_surface_site()):\n structures.append(structure)\n return structures","title":""},{"docid":"f91b02612e742669fb6e83a1ce65a77c","score":"0.48295075","text":"def relu(input_list):\n output_list = [x if x > 0 else 0 for x in input_list]\n print(\"relu\", input_list, output_list)\n return output_list","title":""},{"docid":"17bf0f3e1b92540cf48d9066a7c395dc","score":"0.48288542","text":"def FindMainConcept(NP,LPObj):\n MainConcepts, SubConcepts =[], []\n SingularPhrase = ''\n if len(NP) >1:\n MainConcepts = [each for each in NP for e2 in each.split() if e2 in LPObj]\n SubConcepts = [each for each in NP if each not in MainConcepts ]\n return SingularPhrase, MainConcepts, SubConcepts\n else:\n SingularPhrase=NP\n MainConcepts,SubConcepts = [''], ['']\n return SingularPhrase, MainConcepts, SubConcepts","title":""},{"docid":"3ffdc45584fcce7c2375e1421b43edab","score":"0.48272952","text":"def solveMPO(X,MPO,MPO_,tr_sites=[]): \n pos = [i for i in range(len(MPO_)) if(len(MPO_[i]) == 0)]\n if(not(len(pos))) == 1:\n raise Exception(\"The second MPO_ must have EXACTLY ONE empty list occurence\")\n else:\n pos = pos[0]\n trs = list(set(tr_sites))\n \n L = contractMPO(prodMPO(MPO[:pos],MPO_[:pos],tr_sites = list(filter(lambda x:x<pos and x >= 0,trs))))\n R = contractMPO(prodMPO(MPO[pos+1:],MPO_[pos+1:],tr_sites = list(map(lambda x:x-pos-1,filter(lambda x:x>pos,trs))) + list(filter(lambda x:x<0,trs))))\n A = MPO[pos]\n #print([x.shape for x in L if len(x)>0])\n #print([x.shape for x in R if len(x)>0])\n \n d1,d2,d3,D1,D2 = L.shape[1],A.shape[1],R.shape[1],A.shape[0],A.shape[2]\n D1_,D2_ = MPO_[pos-1].shape[2],MPO_[pos+1].shape[0]\n \"\"\" Reshape X to vector \"\"\"\n X_ = np.reshape(X,[X.shape[0]**2])\n \"\"\" Index structure: s1,e1,e1',n1 x w2,s2,e2,n2 -tensordot-> s1,e1',n1,s2,e2,n2 \"\"\"\n M = np.tensordot(np.reshape(L,[L.shape[1],D1,L.shape[2]//D1,L.shape[3]]),A,[[1],[0]])\n \"\"\" Index structure: s1,e1',n1,s2,e2,n2 x w3,w3',s3,n3 -tensordot-> s1,e1',n1,s2,n2,w3',s3,n3 \"\"\"\n M = np.tensordot(M,np.reshape(R,[D2,R.shape[0]//D2,R.shape[1],R.shape[3]]),[[4],[0]])\n \"\"\" Index structure: s1,e1',n1,s2,n2,w3',s3,n3 x s4,n4 -tensordot-> s1,e1',n1,s2,n2,w3',s3,n3,s4,n4 \"\"\"\n M = np.tensordot(M,np.identity(d2),[[],[]])\n \"\"\" Transpose and reshape M to have index structure: (s1,s4,s3,n1,n2,n3);(e1',n4,w3',s2) \"\"\"\n M = np.reshape(np.transpose(M,[2,4,7,0,8,6,1,9,5,3]),[d1**2*d2**2*d3**2,D1_*D2_*d2**2])\n #print(M.shape,X_.shape)\n \n Y = la.lstsq(M,X_)[0]\n Ai = np.reshape(Y,[D1_,d2,D2_,d2])\n \n return(Ai)","title":""},{"docid":"9ede8e0205a16fc783ef0242c404b966","score":"0.48241144","text":"def get_ll(l, num):\n lbls = [(np.sum(np.where(l==i,1,0)),i) for i in range(1,num+1)]\n lbls.sort(reverse=True)\n return lbls[0][1]","title":""},{"docid":"84336c1c2ff8b7c5c27c246590edc086","score":"0.48222518","text":"def calculate_polynoms(x, coefficients):\n for i in range(len(x)):\n straight_polynom(x[i], coefficients)","title":""},{"docid":"69b15e796f71bcbfc39cc76017195229","score":"0.48183295","text":"def lagrange(x0, x):\n import numpy as np\n\n if len(x) == 1:\n return [1.0]\n\n n = len(x)\n L = np.zeros((n,))\n l = node_polynomial(x0, x)\n for j in range(n):\n L[j] = lagrange_basis(x0, j, x, l)\n \n return L","title":""},{"docid":"d4d8611c84d424b3eab54bab50d0ad2e","score":"0.48116142","text":"def calc_linhas_largura(dict_circ_desc, ponto):\n if dict_circ_desc[\"tipo_circulo\"] == \"meio\":\n linha_nao_intersecta_ponto = None\n point_circ = Point()\n point_circ.X = dict_circ_desc[\"pt_medios_circ\"][\"x_ptm\"]\n point_circ.Y = dict_circ_desc[\"pt_medios_circ\"][\"y_ptm\"]\n array = Array([point_circ, ponto.getPart(0)])\n linha_circulo = Polyline(array, projecao_geo)\n for parte_linha in dict_circ_desc[\"partes\"]:\n if not dict_circ_desc[\"partes\"][parte_linha][\"cruza_ponto\"]:\n linha_nao_intersecta_ponto = dict_circ_desc[\"partes\"][parte_linha][\"linha_geometria\"]\n if linha_circulo.disjoint(linha_nao_intersecta_ponto):\n array.removeAll()\n point_circ = Point()\n point_circ.X = dict_circ_desc[\"pt_medios_circ\"][\"x_ptm_inv\"]\n point_circ.Y = dict_circ_desc[\"pt_medios_circ\"][\"y_ptm_inv\"]\n array = Array([point_circ, ponto.getPart(0)])\n linha_circulo = Polyline(array, projecao_geo)\n linha_largura = linha_circulo.intersect(poligono_ma, 2)\n array.removeAll()\n else:\n linha_largura = linha_circulo.intersect(poligono_ma, 2)\n array.removeAll()\n return linha_largura, linha_circulo","title":""},{"docid":"3e09712ab3ed9e696134e1c052ced7bd","score":"0.48105964","text":"def COM(Lats,Lons):\r\n X = [Distance([0,0],[0,Lons[i][0]]) for i in range(0, len(Lons))]\r\n Y = [Distance([0,0],[Lats[i][0],0]) for i in range(0, len(Lons))]\r\n #Distance conversion is carried out to avoid distortion.\r\n Cx = sum(X) / len(X)\r\n Cy = sum(Y) / len(Y)\r\n return [DirectionDistance(Cy,0,0,0)[0],DirectionDistance(Cx,math.pi / 2,0,0)[1]]","title":""},{"docid":"6dae7bf1850ec74516916e94b8880275","score":"0.48101878","text":"def generate_lone_pair_multiple_bond_resonance_structures(mol):\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule)\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, atom3=Atom, bond12=Bond, bond23=Bond)\n cython.declare(v1=Vertex, v2=Vertex)\n\n structures = []\n for atom in mol.vertices:\n if atom.lone_pairs >= 1:\n paths = pathfinder.find_lone_pair_multiple_bond_paths(atom)\n for atom1, atom2, atom3, bond12, bond23 in paths:\n # Adjust to (potentially) new resonance structure\n atom1.decrement_lone_pairs()\n atom3.increment_lone_pairs()\n bond12.increment_order()\n bond23.decrement_order()\n atom1.update_charge()\n atom3.update_charge()\n # Make a copy of structure\n structure = mol.copy(deep=True)\n # Restore current structure\n atom1.increment_lone_pairs()\n atom3.decrement_lone_pairs()\n bond12.decrement_order()\n bond23.increment_order()\n atom1.update_charge()\n atom3.update_charge()\n try:\n structure.update_atomtypes(log_species=False)\n except AtomTypeError:\n pass # Don't append resonance structure if it creates an undefined atomtype\n else:\n structures.append(structure)\n return structures","title":""},{"docid":"4ef9198d0e04648fc2c232d99ba232da","score":"0.4800227","text":"def generate_adj_lone_pair_radical_multiple_bond_resonance_structures(mol):\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule, direction=cython.int)\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, bond12=Bond)\n cython.declare(v1=Vertex, v2=Vertex)\n\n structures = []\n if mol.is_radical(): # Iterate over radicals in structure\n for atom in mol.vertices:\n paths = pathfinder.find_adj_lone_pair_radical_multiple_bond_delocalization_paths(atom)\n for atom1, atom2, bond12, direction in paths:\n if direction == 1: # The direction <increasing> the bond order\n atom1.decrement_lone_pairs()\n bond12.increment_order()\n atom1.increment_radical()\n atom2.decrement_radical()\n elif direction == 2: # The direction <decreasing> the bond order\n atom1.increment_lone_pairs()\n bond12.decrement_order()\n atom1.decrement_radical()\n atom2.increment_radical()\n atom1.update_charge()\n atom2.update_charge()\n # Make a copy of structure\n structure = mol.copy(deep=True)\n # Restore current structure\n if direction == 1: # The direction <increasing> the bond order\n atom1.increment_lone_pairs()\n bond12.decrement_order()\n atom1.decrement_radical()\n atom2.increment_radical()\n elif direction == 2: # The direction <decreasing> the bond order\n atom1.decrement_lone_pairs()\n bond12.increment_order()\n atom1.increment_radical()\n atom2.decrement_radical()\n atom1.update_charge()\n atom2.update_charge()\n try:\n structure.update_atomtypes(log_species=False)\n except AtomTypeError:\n pass # Don't append resonance structure if it creates an undefined atomtype\n else:\n structures.append(structure)\n return structures","title":""},{"docid":"1759e71110b6798c57d2e1921b796ac2","score":"0.47827086","text":"def longest_oscillation(L):\r\n if L == None:\r\n return 0, []\r\n if len(L) == 0:\r\n return 0, []\r\n elif len(L) == 1:\r\n return 1, [0]\r\n else:\r\n res = [0]\r\n temp = L[0]\r\n count = 1 # count = 1, because 1st element will always be included.\r\n k = 1\r\n # skip duplicate elements at the start\r\n while L[k-1] == L[k] and k < len(L)-1:\r\n k += 1\r\n # direct True indicates upward, direct False indicate downwards.\r\n direct = L[0] < L[k]\r\n j = 1\r\n # start from k, (skipped all duplicate elements)\r\n for i in range(k, len(L)):\r\n # if current temp not equal to current element. (duplicate element)\r\n if L[i] != temp:\r\n # if current element and temp do not match \"direct\" upward expected but downward detected or vice versa. \r\n if (L[i] > temp and direct == False) or (L[i] < temp and direct == True):\r\n res[j-1] = i\r\n \r\n # if((L[i] > temp) and direct == True) or (L[i] < temp and direct == False)\r\n else:\r\n direct = not direct # reverse current direction \r\n count += 1 # add new item, hence increment count by 1\r\n res.append(i) # append current index\r\n j += 1\r\n # replace temp with current if L[i] != temp (not duplicated) \r\n temp = L[i]\r\n \r\n return count, res","title":""},{"docid":"07722ff3e680e8c18cdac7577b587a37","score":"0.47821546","text":"def getIntersectionNodes(self):\n ways=self.getWayList()\n nodeList=[]\n intersectionList=[]\n for wayElement in ways:\n nodes=wayElement.getElementsByTagName('nd')\n for nodeElement in nodes:\n nodeList.append(nodeElement.getAttribute('ref'))\n for nodeElement in nodeList:\n if nodeList.count(nodeElement)>1 and intersectionList.count(nodeElement)==0:\n intersectionList.append(nodeElement)\n return intersectionList","title":""},{"docid":"1f5bbf3e021d49e7e3cc00a5402d96d9","score":"0.47819144","text":"def general_poly (L):\n def fun(x):\n n = 0\n for i in L:\n n = x*n + i\n return n\n return fun","title":""},{"docid":"f5b5a858b1bfecbf292010a119cfa5aa","score":"0.4777859","text":"def get_linear_components(self, *args):\n def getlin(a):\n return SparsePair(*CPX_PROC.getqconstr_lin(\n self._env._e, self._cplex._lp, a))\n return apply_freeform_one_arg(\n getlin, self._conv, self.get_num(), args)","title":""},{"docid":"77e868492b42368f387c9b1319845596","score":"0.47776476","text":"def findAwsomeDiagonals(addList,R):\n\tpass\n\t\n\t#first, list the ","title":""},{"docid":"7f495e90081556064173effad4955707","score":"0.47745904","text":"def get_allen_relation(\n e1_start: int, e1_end: int, e2_start: int, e2_end: int\n) -> AllenRelation:\n if e1_start == e2_start:\n if e1_end == e2_end:\n return AllenRelation.E1_EQUALS_E2\n elif e1_end > e2_end:\n return AllenRelation.E2_STARTS_E1\n else:\n return AllenRelation.E1_STARTS_E2\n elif e1_start < e2_start:\n if e1_end == e2_start:\n return AllenRelation.E1_MEETS_E2\n elif e1_end < e2_start:\n return AllenRelation.E1_PRECEDES_E2\n elif e1_end == e2_end:\n return AllenRelation.E2_FINISHES_E1\n elif e1_end > e2_end:\n return AllenRelation.E2_DURING_E1\n else:\n return AllenRelation.E1_OVERLAPS_E2\n else: # e1_start > e2_start\n if e2_end == e2_start:\n return AllenRelation.E2_MEETS_E1\n elif e2_end < e2_start:\n return AllenRelation.E2_PRECEDES_E1\n elif e2_end == e1_end:\n return AllenRelation.E1_FINISHES_E2\n elif e2_end > e1_end:\n return AllenRelation.E1_DURING_E2\n else:\n return AllenRelation.E2_OVERLAPS_E1","title":""},{"docid":"46d0b5b6b9fc63228ea01461e28a7351","score":"0.47689426","text":"def _clar_transformation(mol, aromatic_ring):\n cython.declare(bondList=list, i=cython.int, atom1=Atom, atom2=Atom, bond=Bond)\n\n bond_list = []\n\n for i, atom1 in enumerate(aromatic_ring):\n for atom2 in aromatic_ring[i + 1:]:\n if mol.has_bond(atom1, atom2):\n bond_list.append(mol.get_bond(atom1, atom2))\n\n for bond in bond_list:\n bond.order = 1.5","title":""},{"docid":"2a49dc1515e42408d3ff59082ba28ea9","score":"0.47533303","text":"def function_2_4_1d_sigmoid_list(l1: 'List of numbers') -> 'Relu output':\n return [(1 / (1 + math.exp(-a)) )for a in l1]","title":""},{"docid":"15355e9cbc24ac9652d1edd7e3af9050","score":"0.4747987","text":"def linear_solver(coef, const):\n if len(coef) == 2:\n y = (const[0][0] * coef[1][0] - coef[0][0] * const[1][0]) / (-coef[0][0] * coef[1][1] + coef[1][0] * coef[0][1])\n x = (const[1][0] - coef[1][1] * y) / coef[1][0]\n\n return [x, y]\n\n return multiply(inverse(coef), const)","title":""},{"docid":"5e224e84adc9517ff4cf085a9f485537","score":"0.4738598","text":"def generate_allyl_delocalization_resonance_structures(mol):\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule)\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, atom3=Atom, bond12=Bond, bond23=Bond)\n cython.declare(v1=Vertex, v2=Vertex)\n\n structures = []\n if mol.is_radical(): # Iterate over radicals in structure\n for atom in mol.vertices:\n paths = pathfinder.find_allyl_delocalization_paths(atom)\n for atom1, atom2, atom3, bond12, bond23 in paths:\n # Adjust to (potentially) new resonance structure\n atom1.decrement_radical()\n atom3.increment_radical()\n bond12.increment_order()\n bond23.decrement_order()\n # Make a copy of structure\n structure = mol.copy(deep=True)\n # Restore current structure\n atom1.increment_radical()\n atom3.decrement_radical()\n bond12.decrement_order()\n bond23.increment_order()\n try:\n structure.update_atomtypes(log_species=False)\n except AtomTypeError:\n pass # Don't append resonance structure if it creates an undefined atomtype\n else:\n structures.append(structure)\n return structures","title":""},{"docid":"886169413d2cd4dfe71eea8a84d1a3de","score":"0.4735548","text":"def create_xref_statements(self):\n\n if not MondoNode.mrh:\n MondoNode.mrh = wdi_helpers.MappingRelationHelper(sparql_endpoint_url=self.graph.sparql_endpoint_url)\n\n # need a way to get from \"http://linkedlifedata.com/resource/umls/id/C0265218\" to UMLS:C0265218, for example\n # ignoring meddra and snomed\n uri_to_curie_map = {\n \"http://identifiers.org/omim/\": \"OMIM:\",\n \"http://identifiers.org/mesh/\": \"MESH:\",\n \"http://linkedlifedata.com/resource/umls/id/\": \"UMLS:\",\n \"http://www.orpha.net/ORDO/Orphanet_\": \"Orphanet:\",\n \"http://purl.obolibrary.org/obo/DOID_\": \"DOID:\",\n \"http://purl.obolibrary.org/obo/NCIT_\": \"NCIT:\"\n }\n\n def uri_to_curie(uri):\n for k, v in uri_to_curie_map.items():\n if k in uri:\n uri = uri.replace(k, v)\n return uri\n\n ss = []\n for mrt, xrefs in self.bpv.items():\n if mrt in self.mrh.ABV_MRT.values():\n for xref in xrefs:\n xref_curie = uri_to_curie(xref)\n if not xref_curie:\n continue\n self.xrefs.discard(xref_curie)\n s = self.create_xref_statement(xref_curie)\n if not s:\n continue\n self.mrh.set_mrt(s, mrt)\n ss.append(s)\n for xref in self.xrefs:\n s = self.create_xref_statement(xref)\n if s:\n ss.append(s)\n\n return ss","title":""},{"docid":"598435957f4522c6cbf0b22dbd5f8296","score":"0.47162804","text":"def generateLonePairRadicalResonanceIsomers(mol):\n cython.declare(isomers=list, paths=list, index=cython.int, isomer=Molecule)\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom)\n cython.declare(v1=Vertex, v2=Vertex)\n \n isomers = []\n\n # Radicals\n if mol.isRadical():\n # Iterate over radicals in structure\n for atom in mol.vertices:\n paths = pathfinder.findAllDelocalizationPathsLonePairRadical(atom)\n for atom1, atom2 in paths:\n # Adjust to (potentially) new resonance isomer\n atom1.decrementRadical()\n atom1.incrementLonePairs()\n atom1.updateCharge()\n atom2.incrementRadical()\n atom2.decrementLonePairs()\n atom2.updateCharge()\n # Make a copy of isomer\n isomer = mol.copy(deep=True)\n # Also copy the connectivity values, since they are the same\n # for all resonance forms\n for index in range(len(mol.vertices)):\n v1 = mol.vertices[index]\n v2 = isomer.vertices[index]\n v2.connectivity1 = v1.connectivity1\n v2.connectivity2 = v1.connectivity2\n v2.connectivity3 = v1.connectivity3\n v2.sortingLabel = v1.sortingLabel\n # Restore current isomer\n atom1.incrementRadical()\n atom1.decrementLonePairs()\n atom1.updateCharge()\n atom2.decrementRadical()\n atom2.incrementLonePairs()\n atom2.updateCharge()\n # Append to isomer list if unique\n isomer.updateAtomTypes()\n isomers.append(isomer)\n\n return isomers","title":""},{"docid":"7d167627a35dbb3d113ec836349c4559","score":"0.47136837","text":"def general_poly (L):\n #YOUR CODE HERE\n length = len(L)-1\n def degree(x):\n total = 0\n for index, coefficient in enumerate(L):\n total = total + coefficient*x**(length-index)\n return total\n return degree","title":""},{"docid":"53d767d496e06a1a912275530066b2f8","score":"0.4710089","text":"def get_relations(triples: torch.LongTensor) -> Set[int]:\n return set(triples[:, 1].tolist())","title":""},{"docid":"baebd5720a558316021d3b8397fffb15","score":"0.4705233","text":"def linear_tensors(self):\n return self._LT","title":""},{"docid":"f4fcd2fbd96c32461e07c3485b50533a","score":"0.47046304","text":"def ConvertInd_Res(mList, rStep):\n for mComp in mList:\n if mComp.Type == \"I\": # NOT G\n mList.append(Component(\"R\", mComp.Node1, mComp.Node2, str(float(mComp.Value) / float(rStep)), \"0\"))\n\n return mList","title":""},{"docid":"1c0ae85452cb54de0db09c99fe7ce270","score":"0.47020477","text":"def LM(m, v):\n\n lm = np.array([0.0, 0.0])\n natoms = int(len(v) / 2)\n for i in range(0, natoms):\n x = m[2 * i][2 * i] * vector(v, i)\n lm += x\n\n return (lm)","title":""},{"docid":"818d8a5c5eb0e73afcff9d07751e2519","score":"0.46927842","text":"def get_linear_components(self, *args):\n def getlin(begin, end=self.get_num() - 1):\n mat = _HBMatrix()\n mat.matbeg, mat.matind, mat.matval = CPX_PROC.getindconstr(\n self._env._e, self._cplex._lp, begin, end)\n return [m for m in mat]\n return apply_freeform_two_args(getlin, self._conv, args)","title":""},{"docid":"b0cdc54d75c682769ba456f1567ddbdc","score":"0.46793965","text":"def _lmindx(self,l,m): \n return l**2+l+m","title":""},{"docid":"44eeac8d2732e4d001f6392aef6d1f6f","score":"0.46769416","text":"def find_zeroes(self):\n\n # Интервалы, содержащие нули функции\n intervals = [[-5, -3], [-1, 0], [0.175, 1], [3, 5]]\n result = []\n\n i = 0\n while i < 4:\n x_0 = intervals[i][0]\n x_1 = intervals[i][1]\n\n f_0 = self.function(x_0)\n f_1 = self.function(x_1)\n while np.abs(f_1) > self._eps:\n x_1 = x_1 - f_1 / (f_1 - f_0) * (x_1 - x_0)\n f_1 = self.function(x_1)\n result.append(x_1)\n i += 1\n\n return result","title":""},{"docid":"7ad84dae30b2eb5476e047d1c8f2674d","score":"0.4676159","text":"def whenzero(list1, list2):\n\n if len(list1) == 1 and len(list2) == 0:\n return \"Line \" + \"0\" + \":\" + '\\n' + list1[0] + '\\n^\\n\\n'\n elif len(list1) == 0 and len(list2) == 1:\n return \"Line \" + \"0\" + \":\" + '\\n' + list2[0] + '\\n^\\n\\n'","title":""},{"docid":"dcc2861f8469512bdca3eb95801447a4","score":"0.46739873","text":"def latex_lincomb(coeff, vec):\n code=\"\"\n first = True\n for i in range(len(coeff)):\n if coeff[i] != 0:\n if not first and coeff[i] > 0:\n code += \"+ \"\n if coeff[i] == 1:\n code += vec[i]\n elif coeff[i] == -1:\n code+=\"-\"+vec[i]\n else:\n code+=latex(coeff[i])+\" \"+vec[i]\n first = False\n return code","title":""},{"docid":"f4cfdec01ac239d4131876c1491e59d6","score":"0.46733528","text":"def ll(freq_stdc,freq_refc,tk_stdc,tk_refc):\n O = float(freq_stdc+freq_refc)\n N1 = float(tk_stdc)\n N2 = float(tk_refc)\n E1 = N1*O/(N1+N2) \n E2 = N2*O/(N1+N2)\n if freq_stdc < 1 or E1 < 1:\n v1 = 0\n else:\n v1 = math.log(freq_stdc/E1)\n if freq_refc < 1 or E2 < 1:\n v2 = 0\n else:\n v2 = math.log(freq_refc/E2)\n LL = round(2 * ((freq_stdc * v1)+(freq_refc * v2)),2)\n Norm_stdc = freq_stdc/float(tk_stdc)\n Norm_refc = freq_refc/float(tk_refc)\n if Norm_stdc == 0:\n Norm_stdc = 0.5 / float(tk_stdc)\n if Norm_refc == 0:\n Norm_refc = 0.5 / float(tk_refc)\n if Norm_stdc < Norm_refc:\n LL = - LL\n return LL","title":""},{"docid":"05a1d7f4cc7b04bfff6d1779d5fbce54","score":"0.46666345","text":"def get_bondlist_coords(u):\n angles = u.angles\n bonds = angles.atom3.positions - angles.atom1.positions \n # coords = angles.atom2.positions\n norm = np.linalg.norm(bonds,axis=1)\n bonds /= norm[:, None] #the norm vector is a (nx1) and we have to create dummy directions -> (n,3)\n return bonds","title":""},{"docid":"b2b097896b830de272552c70259a3c4e","score":"0.46654844","text":"def invUnitRelation (self, M) :\n\t\trulenames = []\n\t\tfor i in range(len(M)) :\n\t\t\tfor key, units in self.unitrelation.items() :\n\t\t\t\tif M[i].nodetype in units :\n\t\t\t\t\tnode = UnitNode (M[i], key)\n\t\t\t\t\trulenames.append (node)\n\t\tM = rulenames\n\t\treturn rulenames","title":""},{"docid":"58d66011d05017858340a9249a822c07","score":"0.46602762","text":"def make_reference(mol):\n reference_graph = nx.Graph()\n residues = make_residue_graph(mol)\n\n for residx in residues:\n # TODO: make separate function for just one residue.\n # TODO: multiprocess this loop?\n # TODO: Merge degree 1 nodes (hydrogens!) with the parent node. And\n # check whether the node degrees match?\n\n resname = residues.node[residx]['resname']\n resid = residues.node[residx]['resid']\n chain = residues.node[residx]['chain']\n residue = residues.node[residx]['graph']\n reference = mol.force_field.reference_graphs[resname]\n add_element_attr(reference)\n add_element_attr(residue)\n # Assume reference >= residue\n matches = isomorphism(reference, residue)\n if not matches:\n # Maybe reference < residue? I.e. PTM or protonation\n matches = isomorphism(residue, reference)\n matches = [{v: k for k, v in match.items()} for match in matches]\n if not matches:\n LOGGER.debug('Doing MCS matching for residue {}{}', resname, resid,\n type='performance')\n # The problem is that some residues (termini in particular) will\n # contain more atoms than they should according to the reference.\n # Furthermore they will have too little atoms because X-Ray is\n # supposedly hard. This means we can't do the subgraph isomorphism\n # like we're used to. Instead, identify the atoms in the largest\n # common subgraph, and do the subgraph isomorphism/alignment on\n # those. MCS is ridiculously expensive, so we only do it when we\n # have to.\n try:\n mcs_match = max(maximum_common_subgraph(reference, residue, ['element']),\n key=lambda m: rate_match(reference, residue, m))\n except ValueError:\n raise ValueError('No common subgraph found between {} and '\n 'reference {}.'.format(resname, resname))\n # We could seed the isomorphism calculation with the knowledge from\n # the mcs_match, but thats to much effort for now.\n # TODO: see above\n res = residue.subgraph(mcs_match.values())\n matches = isomorphism(reference, res)\n # TODO: matches is sorted by isomorphism. So we should probably use\n # that with e.g. itertools.takewhile.\n if not matches:\n LOGGER.error(\"Can't find isomorphism between {}{} and its \"\n \"reference.\", resname, resid, type='inconsistent-data')\n continue\n\n matches = maxes(matches, key=lambda m: rate_match(reference, residue, m))\n if len(matches) > 1:\n LOGGER.warning(\"More than one way to fit {}{} on it's reference.\"\n \" I'm picking one arbitrarily. You might want to\"\n \" fix at least some atomnames.\", resname, resid,\n type='bad-atom-names')\n\n match = matches[0]\n reference_graph.add_node(residx, chain=chain, reference=reference,\n found=residue, resname=resname, resid=resid,\n match=match)\n reference_graph.add_edges_from(residues.edges())\n return reference_graph","title":""},{"docid":"b33284496ab44af77f060871ce32f555","score":"0.46537","text":"def find_ref_vec(curr_data):\n ref_LR = np.array([0,0,0])\n ref_SI = np.array([0,0,0])\n for organ in curr_data.keys():\n if(organ=='image' or organ=='zxy_dimention' or ('stdName' not in curr_data[organ].keys()) ):\n continue\n if(curr_data[organ]['stdName']!='Brainstem'):\n continue\n mask = curr_data[organ]['mask']\n ref_SI = find_axis(mask)\n \n for organ_R in curr_data.keys():\n if(organ_R=='image' or organ_R=='zxy_dimention' or ('stdName' not in curr_data[organ_R].keys()) ):\n continue\n \n if(curr_data[organ_R]['stdName']!='Parotid_R'):\n continue\n \n for organ_L in curr_data.keys():\n if(organ_L=='image' or organ_L=='zxy_dimention' or ('stdName' not in curr_data[organ_L].keys()) ):\n continue\n if(curr_data[organ_L]['stdName']!='Parotid_L'):\n continue\n result = geometry_relation(curr_data[organ_R]['mask'], curr_data[organ_L]['mask'],vec_ref=[[1,0,0],[0,1,0],[0,0,1]])\n ref_LR = result['direction']\n \n #print('ref_LR is', ref_LR)\n #print('ref_SI is', ref_SI)\n if (ref_LR==np.array([0,0,0])).all() or (ref_SI==np.array([0,0,0])).all():\n return [np.array([0,0,0]),np.array([0,0,0]) ,np.array([0,0,0])]\n ref_AP = np.cross(ref_SI, ref_LR) \n ref_vec = [ref_SI, ref_AP, ref_LR]\n return ref_vec","title":""},{"docid":"f9e223d47fd9de427f19cfb5a6c98c82","score":"0.46467122","text":"def get_list_number_for(pn, sl):","title":""},{"docid":"276c341e1bddff47d2257c2745e9eed0","score":"0.46465728","text":"def create_rules(points: list):\n rules = []\n length = len(points)\n for i in range(length):\n p1 = points[i]\n for j in range(i + 1, length):\n p2 = points[j]\n rules.append(Rule(p1=p1, p2=p2, label=1))\n rules.append(Rule(p1=p1, p2=p2, label=-1))\n # else:\n # if p1.x equals p2.x -> incline = coefficient = infinity & bias = 0\n # rules.append(Rule(point=p1, coefficient=np.inf))\n return rules","title":""},{"docid":"74eee13594ef6e14377cdc4c40b35807","score":"0.46454006","text":"def L(i, f, x0):\n L1 = \"\"\n num = 1 # the actual numeric result\n for j in range(len(f)):\n if j != i:\n num *= ((x0 - f[j][0]) / (f[i][0] - f[j][0]))\n L1 += \"((x - \" + str(f[j][0]) + \") / (\" + str(f[i][0]) + \" - \" + str(f[j][0]) + \"))\"\n if i == len(f) - 1:\n if j != len(f) - 2:\n L1 += \" + \"\n elif j != len(f) - 1:\n L1 += \" + \"\n return num, L1","title":""},{"docid":"9f5e54b4aa33fcdf8285d295cf5e4496","score":"0.46427548","text":"def l(self, s1:int, s2:int, obs:str) -> float:\n\t\tself._checkStateIndex(s1)\n\t\tself._checkStateIndex(s2)\n\t\tif self.labelling[s1] != obs:\n\t\t\treturn 0.0\n\t\treturn self.matrix[s1][s2]","title":""},{"docid":"e6dc10349a97ff8efd91d1a282370847","score":"0.464154","text":"def test_lt_corr_list():\n # Case where the L-operators of the low-temperature correction match the\n # L-operators of the hierarchy\n hops_ltc_modes_match_l_op = HOPS(\n sys_param_ltc_modes_match_l_op,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_match_l_op.initialize(psi_0)\n\n sys_ltc_modes_match = hops_ltc_modes_match_l_op.basis.mode\n assert list(sys_ltc_modes_match.lt_corr_param) == \\\n [250.0 / 1000.0, 250.0 / 2000.0]\n assert np.allclose(sys_ltc_modes_match.list_L2_coo[0].todense(),sparse_l0.todense())\n assert np.allclose(sys_ltc_modes_match.list_L2_coo[1].todense(),sparse_l1.todense())\n\n # Case where the low-temperature correction term list fed into HOPSSystem is empty.\n hops_ltc_modes_empty = HOPS(\n sys_param_ltc_modes_empty,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_empty.initialize(psi_0)\n\n sys_ltc_modes_empty = hops_ltc_modes_empty.basis.mode\n assert list(sys_ltc_modes_empty.lt_corr_param) == \\\n [0, 0]\n\n # Case where the L-operators of the low-temperature correction match the\n # L-operators of the hierarchy, but are swapped in order to test that the\n # coefficients are matched to the right hierarchy L-operators\n hops_ltc_modes_swap_l_op = HOPS(\n sys_param_ltc_modes_swap_l_op,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_swap_l_op.initialize(psi_0)\n\n sys_ltc_modes_swap = hops_ltc_modes_swap_l_op.basis.mode\n assert list(sys_ltc_modes_swap.lt_corr_param) == \\\n [250.0 / 2000.0, 250.0 / 1000.0]\n assert np.allclose(sys_ltc_modes_swap.list_L2_coo[0].todense(),\n sparse_l0.todense())\n assert np.allclose(sys_ltc_modes_swap.list_L2_coo[1].todense(),\n sparse_l1.todense())\n\n # Case where there is an extra L-operator not found in the hierarchy L-operator\n # list (currently, the correction associated with that L-operator is ignored)\n hops_ltc_modes_more_than_l_op = HOPS(\n sys_param_ltc_modes_more_than_l_op,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_more_than_l_op.initialize(psi_0)\n\n sys_ltc_modes_more_than = hops_ltc_modes_more_than_l_op.basis.mode\n assert list(sys_ltc_modes_more_than.lt_corr_param) == \\\n [250.0 / 1000.0, 250.0 / 2000.0]\n assert np.allclose(sys_ltc_modes_more_than.list_L2_coo[0].todense(),\n sparse_l0.todense())\n assert np.allclose(sys_ltc_modes_more_than.list_L2_coo[1].todense(),\n sparse_l1.todense())\n\n # Case where the low-temperature correction is only acted on one of the\n # L-operators found in the hierarchy\n hops_ltc_modes_less_than_l_op = HOPS(\n sys_param_ltc_modes_less_than_l_op,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_less_than_l_op.initialize(psi_0)\n\n hopsmodes_ltc_modes_less_than = hops_ltc_modes_less_than_l_op.basis.mode\n assert list(hopsmodes_ltc_modes_less_than.lt_corr_param) == \\\n [250.0 / 1000.0, 0]\n assert np.allclose(hopsmodes_ltc_modes_less_than.list_L2_coo[0].todense(),\n sparse_l0.todense())\n\n # Case where none of the L-operators of the low-temperature correction match the\n # L-operators of the hierarchy\n hops_ltc_modes_wrong_l_op = HOPS(\n sys_param_ltc_modes_wrong_l_op,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_wrong_l_op.initialize(psi_0)\n\n hopsmodes_ltc_modes_wrong = hops_ltc_modes_wrong_l_op.basis.mode\n assert list(hopsmodes_ltc_modes_wrong.lt_corr_param) == \\\n [0, 0]\n\n # Case where the L-operators of the low-temperature correction match the\n # L-operators of the hierarchy, run with aggressive adaptivity to reduce the basis\n # to a single state\n hops_ltc_modes_match_l_op_adaptive = HOPS(\n sys_param_ltc_modes_match_l_op,\n noise_param=noise_param,\n hierarchy_param=hier_param,\n eom_param=eom_param,\n integration_param=integrator_param,\n )\n hops_ltc_modes_match_l_op_adaptive.make_adaptive(1e-1, 1e-1)\n hops_ltc_modes_match_l_op_adaptive.initialize(psi_0)\n\n hopsmodes_ltc_modes_adap = hops_ltc_modes_match_l_op_adaptive.basis.mode\n hopsmodes_ltc_modes_adap.system.state_list = [0]\n assert list(hopsmodes_ltc_modes_adap.lt_corr_param) == \\\n [250.0 / 1000.0]\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[0].todense(),\n np.array([[1]]))\n\n hopsmodes_ltc_modes_adap.system.state_list = [1]\n assert list(hopsmodes_ltc_modes_adap.lt_corr_param) == \\\n [250.0 / 2000.0]\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[0].todense(),\n np.array([[1]]))\n\n hopsmodes_ltc_modes_adap.system.state_list = [0, 1]\n hopsmodes_ltc_modes_adap.list_absindex_mode = [0, 1, 2, 3]\n assert list(hopsmodes_ltc_modes_adap.lt_corr_param) == \\\n [250.0 / 1000.0, 250.0 / 2000.0]\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[0].todense(),\n sparse_l0.todense())\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[1].todense(),\n sparse_l1.todense())","title":""},{"docid":"49117041c6a698e1ae73d33f3e5256ab","score":"0.46407595","text":"def dnf(l: List[int]) -> int:\n low_idx = 0\n mid_idx = 0\n high_idx = len(l) - 1\n # stop when mid pointer is larger than high pointer\n while mid_idx <= high_idx:\n mid = l[mid_idx]\n # if mid pointer has low value switch it with low pointer and increase bothself.\n # this works because low pointer guaranteed to have mid value under it.\n # (except if it is first element)\n if mid == 0:\n l[mid_idx], l[low_idx] = l[low_idx], l[mid_idx]\n low_idx += 1\n mid_idx += 1\n # if mid pointer has mid value, location is correct just move the pointer.\n elif mid == 1:\n mid_idx += 1\n # if mid pointer has high value, switch them and decrease high pointer. we do not\n # increase mid pointer because we don't know what high pointer had under it.\n # we will check it on next iteration.\n elif mid == 2:\n l[mid_idx], l[high_idx] = l[high_idx], l[mid_idx]\n high_idx -= 1\n # return low pointer (first medium element)\n # and mid pointer (first high element)\n return (low_idx, mid_idx)","title":""},{"docid":"307a66d9c05d4f7b1a876ea64e6396b2","score":"0.4638022","text":"def solve(l, n):\n s = str(n)\n mid = len(s) / 2\n first_right = -1\n first_left = -1\n for i in range(mid, l)\n if s[sep] != '0'","title":""},{"docid":"5f8f986ceec5f1564fbe4525ac355b0f","score":"0.4637062","text":"def bin_to_lines(l: List[Tuple[str, str]]) -> List[List[str]]:\n return [[x[1] for x in l], [x[0] for x in l]]","title":""},{"docid":"0396ed2c73524939f200643ef291c34e","score":"0.46187776","text":"def __call__(self, features: List[List[float]]) -> List[List[float]]:\n nor = []\n for i in features:\n sum = 0\n inner = []\n for j in i:\n sum += j ** 2\n den = sum ** (1 / 2)\n for j in i:\n if (sum == 0):\n inner.append(0)\n else:\n inner.append(j / den)\n nor.append(inner)\n return nor","title":""},{"docid":"89bb75897d4949d78a7a60ebecd68d6e","score":"0.46182895","text":"def constant_propagation(list_of_lines,comp=[]):\n\tident_temp = dict() #stores identifiers/temporaries as key and constants as value\n\tfor line in list_of_lines:\n\t\tline = line.strip('\\n')\n\t\ttoks = line.split()\n\t\tlength = len(line.split())\n\t\tif(length == 3):\n\t\t\tif (toks[0] and toks[1] == '=' and isnum(toks[2])):\n\t\t\t\tif toks[0] not in ident_temp:\n\t\t\t\t\tident_temp[toks[0]] = toks[2]\n\tfinal_list = [] #stores the final list of lines in the ICG after constants are propagated\n\tfor line in list_of_lines:\n\t\tline = line.strip('\\n')\n\t\ttoks = line.split()\n\t\tlength = len(line.split())\n\t\tif(length == 5 and toks[3] in arith):\n\t\t\tfor i in range(1,length):\n\t\t\t\tif toks[i] in ident_temp:\n\t\t\t\t\ttoks[i] = ident_temp[toks[i]]\n\t\tif(length == 3 and toks[1] == '=' and toks[2] in ident_temp):\n\t\t\ttoks[2] = ident_temp[toks[2]]\n\t\tif not(length == 3 and istemp(toks[0])):\n\t\t\tfinal_list.append(' '.join(toks))\n\t#returns 0 if the new ICG generated is the same as the last one else 1\n\tif(final_list == comp):\n\t\treturn final_list,0\n\telse:\n\t\treturn final_list,1","title":""},{"docid":"44e1575aa88639c95eb32b72df6c098a","score":"0.46179226","text":"def linear(self, X):\r\n return X","title":""},{"docid":"9e3b655129c1caf19abf227bfa5c1abe","score":"0.46147618","text":"def test_relation():\n\n lexems = [\n \"=\",\n \"<>\",\n \"<\",\n \"<=\",\n \">=\",\n \">\",\n \"#\",\n \"a\",\n \"&%4$\",\n \"1\"\n ]\n\n validator = Validator()\n assert validator.validate_lexems(lexems) == [\n {\"=\": \"<RELATION>\"},\n {\"<>\": \"<RELATION>\"},\n {\"<\": \"<RELATION>\"},\n {\"<=\": \"<RELATION>\"},\n {\">=\": \"<RELATION>\"},\n {\">\": \"<RELATION>\"},\n {\"#\": None},\n {\"a\": \"<IDENTIFIER>\"},\n {\"&%4$\": None},\n {\"1\": \"<NUMBER>\"},\n ]","title":""},{"docid":"c4d6cf731942ab3d5e292111dbbe6277","score":"0.46122715","text":"def listRelations( self ):\n rels = []\n rels.append(self.relations[0][0])\n rels.append(self.relations[0][1])\n rels.append(self.relations[1][0])\n #for typ in 0,1:\n # for direction in 0,1:\n # rels.append(self.relations[typ][direction])\n return tuple(rels)","title":""},{"docid":"d057437362d7d921af94a2cfcc3dd829","score":"0.46098673","text":"def lfsr(M, gn, S0=[]):\n if type(gn)==int:\n L = gn\n if L==3:\n gn = [1,0,1,1]\n elif L==4:\n gn = [1,0,0,1,1]\n elif L==5:\n gn = [1,0,0,1,0,1]\n elif L==6:\n gn = [1,0,0,0,0,1,1]\n elif L==7:\n gn = [1,0,0,0,0,0,1,1]\n elif L==8:\n gn = [1,1,0,0,0,0,1,1,1]\n elif L==9:\n gn = [1,0,0,0,0,1,0,0,0,1]\n elif L==10:\n gn = [1,0,0,0,0,0,0,1,0,0,1]\n elif L==11:\n gn = [1,0,0,0,0,0,0,0,0,1,0,1]\n elif L==17:\n gn = [1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1]\n else:\n print('No default gn for L={}'.format(L))\n else:\n L = len(gn)-1\n gn = np.array(gn, int) # make array of integers\n if len(S0)==0:\n yn = iir2filt(gn, np.hstack((1, np.zeros(M-1))))\n else:\n yn = iir2filt(gn, np.zeros(M), S0)\n return yn","title":""},{"docid":"66334273c1c5cf410f1eed6d7ada98fe","score":"0.46077585","text":"def linear(x):\n\treturn x","title":""},{"docid":"caa7b293234c6238e7301919cdee837e","score":"0.4596243","text":"def repr_lincomb(symbols, coeffs):\n s = \"\"\n first = True\n i = 0\n\n all_atomic = True\n for c in coeffs:\n b = latex(symbols[i])\n if c != 0:\n if c == 1:\n s += b\n else:\n coeff = coeff_repr(c)\n if not first:\n coeff = \" + %s\"%coeff\n else:\n coeff = \"%s\"%coeff\n s += \"%s%s\"%(coeff, b)\n first = False\n i += 1\n if first:\n s = \"0\"\n s = s.replace(\"+ -\",\"- \")\n return s","title":""},{"docid":"827a5942651297e9c5f18b30706821d3","score":"0.45941898","text":"def linear_model_coefs(pipeline, all_docs):\n\n coef_df = get_coef_df(pipeline.named_steps['clf'],\n pipeline.named_steps['vectorizer'].vocabulary_)\n labels = coef_df['class'].unique()\n coef_lookup = coef_df.set_index(['class', 'feature'])['coef'].to_dict()\n\n for doc in all_docs:\n doc['linear_importances'] = [\n [coef_lookup.get(label, token.lower()) for label in labels]\n for token in doc]\n return all_docs","title":""},{"docid":"dc0bddd16cb65e1f0f429ef22416648a","score":"0.45913088","text":"def cover_relations(self):\n (_, rels) = self._lattice_initializer()\n return (_ for _ in rels)","title":""},{"docid":"9cb96589b70e72fb872b34f44d9bf457","score":"0.45834422","text":"def analysis_line_linearity(target_list, measure_list_mm, boundary_index_list, linearity_list):\n\n boundary_max_linearity = [0, [0, 0]]\n center_max_linearity = [0, [0, 0]]\n for i in range(len(linearity_list)):\n for j in range(len(linearity_list[i])):\n if j in boundary_index_list[i]:\n if linearity_list[i][j] > boundary_max_linearity[0]:\n boundary_max_linearity[0] = linearity_list[i][j]\n boundary_max_linearity[1] = [i, j]\n else:\n if linearity_list[i][j] > center_max_linearity[0]:\n center_max_linearity[0] = linearity_list[i][j]\n center_max_linearity[1] = [i, j]\n print(\"\\tLinearity:\")\n print_linearity_list = [[\"boundary\", boundary_max_linearity], [\"center \", center_max_linearity]]\n for print_linearity in print_linearity_list:\n name = print_linearity[0]\n max_L = print_linearity[1][0]\n max_i = print_linearity[1][1][0]\n max_j = print_linearity[1][1][1]\n print(\"\\t\\tMax %s linearity %f -------------- target: (%f %f)->(%f, %f), measure: (%f, %f), line %d\" \\\n % ( name, max_L, target_list[max_i][0], target_list[max_i][1], target_list[max_i][2], target_list[max_i][3], \\\n measure_list_mm[max_i][max_j][0], measure_list_mm[max_i][max_j][1], max_i + 1 ))\n return boundary_max_linearity, center_max_linearity","title":""},{"docid":"9de7808db8d8153d75b939198be4a959","score":"0.45819134","text":"def getOposta(matriz):\n matriz_oposta = []\n for linha in matriz:\n linhaOposta = []\n for item in linha:\n linhaOposta.append(-item)\n matriz_oposta.append(linhaOposta)\n \n return matriz_oposta","title":""},{"docid":"f41523fc8de9917b0226f9186b720b8e","score":"0.4581452","text":"def test_calc_LT_corr_linear():\n test_ltc_linear = np.array([[1, 0],[0, 1j]])\n assert np.allclose(\n calc_LT_corr_linear([-1, -1j], [loperator[0], loperator[1]]),\n test_ltc_linear\n )\n assert np.allclose(\n calc_LT_corr_linear([0, 0], [loperator[0], loperator[1]]),\n 0\n )\n assert np.allclose(\n calc_LT_corr_linear([-1, -1j], [np.zeros([2,2]), np.zeros([2,2])]),\n 0\n )\n assert np.allclose(\n calc_LT_corr_linear([], []),\n 0\n )","title":""},{"docid":"68234eff4a9e46698fc22553ecf616d2","score":"0.4580524","text":"def try_merge(line_wo_zeros):\n return [ get_new_value(line_wo_zeros, index, value) for index, value in enumerate(line_wo_zeros)]","title":""},{"docid":"93546f33bd9a73898adc67b01eb74f30","score":"0.45758918","text":"def LD(X_,Y_):\n\n # implement your codes here\n LD_array = [[0]*(len(Y_)+1) for x in range(len(X_)+1)]\n for x_idx in range(len(X_)+1):\n for y_idx in range(len(Y_)+1):\n if x_idx==0 or y_idx==0:\n LD_array[x_idx][y_idx] = max(x_idx,y_idx)\n else:\n LD_array[x_idx][y_idx] = min(LD_array[x_idx-1][y_idx]+1,LD_array[x_idx][y_idx-1]+1,LD_array[x_idx-1][y_idx-1]+(X_[x_idx-1]!=Y_[y_idx-1]))\n\n return LD_array[len(X_)][len(Y_)]","title":""},{"docid":"a2caab65c744fd0810e867164de33d75","score":"0.4575172","text":"def generate_adj_lone_pair_radical_resonance_structures(mol):\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule)\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom)\n cython.declare(v1=Vertex, v2=Vertex)\n\n structures = []\n if mol.is_radical(): # Iterate over radicals in structure\n for atom in mol.vertices:\n paths = pathfinder.find_adj_lone_pair_radical_delocalization_paths(atom)\n for atom1, atom2 in paths:\n # Adjust to (potentially) new resonance structure\n atom1.decrement_radical()\n atom1.increment_lone_pairs()\n atom1.update_charge()\n atom2.increment_radical()\n atom2.decrement_lone_pairs()\n atom2.update_charge()\n # Make a copy of structure\n structure = mol.copy(deep=True)\n # Restore current structure\n atom1.increment_radical()\n atom1.decrement_lone_pairs()\n atom1.update_charge()\n atom2.decrement_radical()\n atom2.increment_lone_pairs()\n atom2.update_charge()\n try:\n structure.update_atomtypes(log_species=False)\n except AtomTypeError:\n pass # Don't append resonance structure if it creates an undefined atomtype\n else:\n structures.append(structure)\n return structures","title":""},{"docid":"af843c9ed610d734ef862f365a45c320","score":"0.45724002","text":"def pair_distances(polyline, segments_to_zero=None):\n polyline = np.asanyarray(polyline)\n d_vec = polyline[1:, :] - polyline[:-1, :]\n result = np.linalg.norm(d_vec, axis=1)\n if segments_to_zero is not None:\n result[segments_to_zero] = 0\n return result","title":""},{"docid":"5bfe550fa28879c9fdf93e63f5b7f7a6","score":"0.4571191","text":"def search_lsi(self, doc):\n vec_bow = dictionary.doc2bow(doc)\n vec_lsi = lsi[vec_bow]\n\n return [(revind[x[0]], x[1]) for x in index_lsi[vec_lsi]]","title":""},{"docid":"45966961a56a2e4f17842e0bcdd75ff3","score":"0.45645532","text":"def get_hyponym(ss):\n out = []\n for ss_i in ss:\n xh = ss_i.hyponyms()\n out += xh\n if len(xh) != 0:\n lt = get_hyponym(xh)\n out += lt\n return out","title":""},{"docid":"d816b0689a8a740d9d76f4833a8cbec7","score":"0.45617288","text":"def linear_functions(self):\n return self._LF","title":""},{"docid":"54d4017eeea31d439374d5863ca22903","score":"0.4560552","text":"def convert_to_lm_dataset(S):\n in_word_index, out_word_index = [], []\n for sentence in S:\n for i in range(len(sentence)):\n if i < 2:\n continue\n in_word_index.append(sentence[i - 1])\n out_word_index.append(sentence[i])\n return in_word_index, out_word_index","title":""},{"docid":"a71246e074483a6489848f3ed88d9f31","score":"0.45603096","text":"def calc_si(l, qlms, len_neigh, qlms_neigh):\n qlm_sum = 0.\n for m in range(2*l+1):\n qlm_sum += abs(qlms[m])**2\n qlm_sum = sqrt(qlm_sum)\n\n si = 0.\n for i in range(len_neigh):\n qlm_sum_neigh = 0.\n for m in range(2*l+1):\n qlm_sum_neigh += abs(qlms_neigh[i, m])**2\n qlm_sum_neigh = sqrt(qlm_sum_neigh)\n si_inner = 0.\n for m in range(2*l+1):\n si_inner += (qlms[m]*qlms_neigh[i, m].conjugate()).real\n si += si_inner/(qlm_sum*qlm_sum_neigh)\n\n return si/len_neigh","title":""},{"docid":"77c1cd485044a6426e0ea1596f6c23cf","score":"0.4559043","text":"def solve_formulars(equations: List[List[Union[str, int]]], l_to_r: bool = True) -> List[int]:\n return [solve_l_to_r(equation, l_to_r) for equation in equations]","title":""},{"docid":"98daab9733fb8fe82dd1876ca3cf3a1d","score":"0.4551032","text":"def seq_real(l):\r\n i = 0\r\n start = 0\r\n max = 0\r\n m1 = 0\r\n while i < len(l) - 1:\r\n if (l[i][0]) < (l[i + 1][0]):\r\n m1 += 1\r\n else:\r\n if m1 > max:\r\n max = m1\r\n start = i - m1\r\n m1 = 0\r\n i += 1\r\n if m1 > max:\r\n max = m1\r\n start = i - m1\r\n if max == 0:\r\n return None\r\n return l[start:start + max + 1]","title":""},{"docid":"6544ae0ce90971b29166cc375d9faa50","score":"0.45488253","text":"def f_imo_dump1(moc_list, l_atoms, filter_tag, mo_id):\n\n #### change element which corresponds to l_atoms ordering, to check its existence with high MO coefficients\n atom_tag=np.zeros(len(l_atoms))\n if not moc_list:\n return 0\n else:\n f_imo_print(moc_list)\n return 1","title":""},{"docid":"f6671559be0a5a988908984cc824ad5c","score":"0.45390823","text":"def general_poly(L):\n def g(x) :\n total = 0 \n if L == [] :\n return 0\n else :\n for index in range(0,len(L)):\n total += L[index]*(x**(len(L)-1-index))\n return total\n \n return g","title":""}],"string":"[\n {\n \"docid\": \"703cf4c945aa3009e5d58dd9816096f2\",\n \"score\": \"0.59615\",\n \"text\": \"def eltL0(strEle,nodCoo):\\n mesDat = []\\n for elt in strEle:\\n nodI = np.int(elt[0])\\n nodJ = np.int(elt[1])\\n dX = nodCoo[nodJ][0]-nodCoo[nodI][0]\\n dY = nodCoo[nodJ][1]-nodCoo[nodI][1]\\n eleLen = np.sqrt(dX**2+dY**2)\\n if np.abs(eleLen) > 1e-16:\\n cos = dX/eleLen\\n sin = dY/eleLen\\n else:\\n cos = 1\\n sin = 0\\n mesDat.append([eleLen,cos,sin])\\n \\n return mesDat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"007a1008b073b998104e9f227c1f3149\",\n \"score\": \"0.58626366\",\n \"text\": \"def function_2_3_1d_relu_list(l1: 'List of numbers') -> 'Relu output':\\n return [a if a >= 0 else 0 for a in l1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5673fe2fb5613229b60961e1938e5953\",\n \"score\": \"0.551257\",\n \"text\": \"def linear_structures(self):\\n n = self.n\\n m = self.m\\n act = self.autocorrelation_matrix()\\n ret = []\\n\\n for j in range(1, 1<<n):\\n for i in range(1, 1<<m):\\n if (abs(act[i,j]) == (1<<m)):\\n c = ((1 - (act[i][j] >> self.m)) >> 1)\\n ret.append((j, i, c))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5290e0dfb9d5aa5761a3b245402dd96d\",\n \"score\": \"0.5448406\",\n \"text\": \"def listindoubles(linearlist):\\r\\n ddo = oddlist(linearlist)\\r\\n neve = evenlist(linearlist)\\r\\n return map((lambda ew,qa:[ew,qa]), ddo, neve)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f1a65a9f6e6d664a8ca6108c113b12\",\n \"score\": \"0.535384\",\n \"text\": \"def find_rel_prop(s_coords, o_coords):\\r\\n if PRINT_MODEL:\\r\\n print(\\\"find rel prop with s_coords, o_coords: \\\", s_coords, o_coords)\\r\\n vector = list_substract(s_coords, o_coords)\\r\\n return normalize(vector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"860360ab33e375a487c5b235d6a8b85d\",\n \"score\": \"0.5248786\",\n \"text\": \"def dimacs_list2formula(lines):\\n F_str = [item[0:-1] for item in lines if (('c' not in item) and ('p' not in item))]\\n F = []\\n clause = []\\n for str_clause in F_str:\\n for literal in str_clause:\\n clause.append(int(literal))\\n F.append(clause)\\n clause = []\\n return F\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cadc8f7856f261c9a5495f3050e5bd65\",\n \"score\": \"0.5165928\",\n \"text\": \"def mols_are_linear(self):\\n self.mol_a.geom.linear = True\\n self.mol_b.geom.linear = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76efa5678225af1b1d673ce8f5d097f\",\n \"score\": \"0.51525706\",\n \"text\": \"def setup_linearSegment(joints = [],\\n ):\\n ml_joints = cgmMeta.validateObjListArg(joints,'cgmObject')\\n ml_new = []\\n mStartHandle = ml_joints[0]\\n mEndHandle = ml_joints[-1]\\n \\n for mObj in ml_joints[1:-1]:\\n mGroup = mObj.doGroup(True,True,asMeta=True,typeModifier = 'twist')\\n mObj.parent = False\\n \\n _vList = DIST.get_normalizedWeightsByDistance(mGroup.mNode,[mStartHandle.mNode,mEndHandle.mNode])\\n _point = mc.pointConstraint([mStartHandle.mNode,mEndHandle.mNode],mGroup.mNode,maintainOffset = False)#Point contraint loc to the object\\n _orient = mc.orientConstraint([mStartHandle.mNode,mEndHandle.mNode],mGroup.mNode,\\n skip = ['y','z'],\\n maintainOffset = False)#Point contraint loc to the object\\n \\n for c in [_point,_orient]:\\n CONSTRAINT.set_weightsByDistance(c[0],_vList)\\n \\n mObj.parent = mGroup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d9c7c2db20a787334e8683e1979e17\",\n \"score\": \"0.51349175\",\n \"text\": \"def my_relu(mylist: list)->list:\\n result = [x if x>= 0 else 0 for x in mylist]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444a50256c04c44ce11e01fd9aadb137\",\n \"score\": \"0.50859416\",\n \"text\": \"def listinpairs(linearlist):\\r\\n ddo = oddlist(linearlist)\\r\\n neve = evenlist(linearlist)\\r\\n return map((lambda ew,qa:(ew,qa)), ddo, neve)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100103fe7cca660e520b298237e6cd56\",\n \"score\": \"0.5054028\",\n \"text\": \"def generate_aryne_resonance_structures(mol):\\n cython.declare(rings=list, ring=list, new_mol_list=list, bond_list=list,\\n i=cython.int, j=cython.int, bond_orders=str, new_orders=str,\\n ind=cython.int, bond=Bond, new_mol=Molecule)\\n\\n rings = mol.get_relevant_cycles()\\n rings = [ring for ring in rings if len(ring) == 6]\\n\\n new_mol_list = []\\n for ring in rings:\\n # Get bond orders\\n bond_list = mol.get_edges_in_cycle(ring)\\n bond_orders = ''.join([bond.get_order_str() for bond in bond_list])\\n new_orders = None\\n # Check for expected bond patterns\\n if bond_orders.count('T') == 1:\\n # Reorder the list so the triple bond is first\\n ind = bond_orders.index('T')\\n bond_orders = bond_orders[ind:] + bond_orders[:ind]\\n bond_list = bond_list[ind:] + bond_list[:ind]\\n # Check for patterns\\n if bond_orders == 'TSDSDS':\\n new_orders = 'DDSDSD'\\n elif bond_orders.count('D') == 4:\\n # Search for DDD and reorder the list so that it comes first\\n if 'DDD' in bond_orders:\\n ind = bond_orders.index('DDD')\\n bond_orders = bond_orders[ind:] + bond_orders[:ind]\\n bond_list = bond_list[ind:] + bond_list[:ind]\\n elif bond_orders.startswith('DD') and bond_orders.endswith('D'):\\n bond_orders = bond_orders[-1:] + bond_orders[:-1]\\n bond_list = bond_list[-1:] + bond_list[:-1]\\n elif bond_orders.startswith('D') and bond_orders.endswith('DD'):\\n bond_orders = bond_orders[-2:] + bond_orders[:-2]\\n bond_list = bond_list[-2:] + bond_list[:-2]\\n # Check for patterns\\n if bond_orders == 'DDDSDS':\\n new_orders = 'STSDSD'\\n\\n if new_orders is not None:\\n # We matched one of our patterns, so we can now change the bonds\\n for i, bond in enumerate(bond_list):\\n bond.set_order_str(new_orders[i])\\n # Make a copy of the molecule\\n new_mol = mol.copy(deep=True)\\n # Undo the changes to the current molecule\\n for i, bond in enumerate(bond_list):\\n bond.set_order_str(bond_orders[i])\\n # Try to update atom types\\n try:\\n new_mol.update_atomtypes(log_species=False)\\n except AtomTypeError:\\n pass # Don't append resonance structure if it creates an undefined atomtype\\n else:\\n new_mol_list.append(new_mol)\\n\\n return new_mol_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a539c8e9db77bbe64b7ed1465d4566a6\",\n \"score\": \"0.50122446\",\n \"text\": \"def get_linear_formula(start_i, end_i):\\n return ' + '.join('x' + str(i) for i in range(start_i, end_i + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc14a33f8910577e3a2dd23e79cfc2a\",\n \"score\": \"0.500917\",\n \"text\": \"def normals(linelist):\\n for i, (n, *points) in enumerate(linelist):\\n x = points[0][0] > points[1][0]\\n y = points[0][1] > points[1][1]\\n\\n # print(i, x, y)\\n\\n if not x and not y:\\n n = [n[0], -n[1]]\\n elif x and not y:\\n n = [n[0], -n[1]]\\n elif x and y:\\n n = [n[0], -n[1]]\\n elif not x and y:\\n n = [n[0], -n[1]]\\n else:\\n #print(\\\"Zero\\\", i, x, y)\\n n = [0, 0]\\n\\n #print(n)\\n linelist[i][0] = tuple(n)\\n\\n return linelist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5ed838259c610333509208ed86a7fac\",\n \"score\": \"0.49844572\",\n \"text\": \"def tris(x, cross_list):\\n cross_list_name = [x.rel if isinstance(x, radb.ast.RelRef) else x.relname for x in cross_list]\\n x1 = cross_list_name.index(x.inputs[0].rel)\\n x2 = cross_list_name.index(x.inputs[1].rel)\\n return x1 + x2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd57c95a53c7937850f3f48605e1d16\",\n \"score\": \"0.49799547\",\n \"text\": \"def obter_posicoes_livres(tab):\\n if not(eh_tabuleiro(tab)):\\n raise ValueError(\\\"obter_posicoes_livres: o argumento e invalido\\\")\\n i = 1\\n rslt = ()\\n for linha in tab:\\n for elem in linha:\\n if elem == 0:\\n rslt = rslt + (i,)\\n i += 1\\n return rslt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02a5dfda4fade99879e9ff4fb3946858\",\n \"score\": \"0.49720404\",\n \"text\": \"def translate_to_1_dimensional_list(l : List[Union[str,int]]) -> List[str]:\\n if (len(l) == 1):\\n return l[0]\\n return l[0] + translate_to_1_dimensional_list(l[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e7a80a43d29434d87a74ec0d2ac17d\",\n \"score\": \"0.49606594\",\n \"text\": \"def 樹(p列表, l, a, f):\\n if l > 3:\\n 列表 = []\\n for p in p列表:\\n p.前進(l)\\n q = p.複製()\\n p.左轉(a)\\n q.右轉(a)\\n 列表.append(p)\\n 列表.append(q)\\n for x in 樹(列表, l*f, a, f):\\n yield 無\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ef7ceea389f4b6d405606a481a1378\",\n \"score\": \"0.49380693\",\n \"text\": \"def link_celiac_finding(mention, findings):\\n\\t\\tfinding_mention = mention.text\\n\\t\\tif 'lymphocyte' in finding_mention or 'lymphocital' in finding_mention:\\n\\t\\t\\tif 'iel' in finding_mention:\\n\\t\\t\\t\\ti_end = [tok.i for tok in mention.doc[mention.end:] if mention.doc[tok.i].text == ')']\\n\\t\\t\\t\\tvalue = mention.doc[mention.end:i_end[0]]\\n\\t\\t\\t\\treturn [[mention.text, 'lymphocyte'], [mention.text, 'intraepithelial lymphocyte count'], [mention.text, value.text]]\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn [[mention.text, 'lymphocyte']]\\n\\t\\telif 'lymphoplasmocitary' in finding_mention:\\n\\t\\t\\treturn[[mention.text, 'lymphocyte']]\\n\\t\\telif 'granulocitary' in finding_mention:\\n\\t\\t\\treturn [[mention.text, 'granulocyte']]\\n\\t\\telif any(k in finding_mention for k in findings):\\n\\t\\t\\tfind_mentions = [k for k in findings if k in finding_mention]\\n\\t\\t\\tmentions2concepts = []\\n\\t\\t\\tfor finding in find_mentions:\\n\\t\\t\\t\\tmentions2concepts.append([mention.text, finding])\\n\\t\\t\\treturn mentions2concepts\\n\\t\\telif finding_mention in findings:\\n\\t\\t\\treturn [[mention.text, [find for find in findings if finding_mention == find][0]]]\\n\\t\\telse:\\n\\t\\t\\treturn [[mention.text, None]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6976afff0c615648b869fefd2831497\",\n \"score\": \"0.49185857\",\n \"text\": \"def correspondence_list(points: list, which_region_list: list, inside_points: list, lengths: tuple):\\n\\n correspondence_list = []\\n for point, reg in zip(points, which_region_list):\\n new_point = [point[0] - reg[0]*lengths[0],\\n point[1] - reg[1]*lengths[1]]\\n\\n loop_worked = False\\n for ind, point_compare in enumerate(inside_points):\\n if point_close(new_point, point_compare):\\n correspondence_list.append(ind)\\n loop_worked = True\\n break\\n\\n if loop_worked == False:\\n # some points dont have an analog - they must be removed completely - we signify with None\\n correspondence_list.append(None)\\n\\n return correspondence_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867cdc7cd6db401d9e1faa8891362a13\",\n \"score\": \"0.49073812\",\n \"text\": \"def LP(S):\\n T = [ [None]*len(S) for _ in range(len(S))]\\n LP_aux(S, T, 0, len(S)-1)\\n return T[0][len(S)-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22169fdaa3e6a252eb71718d14cbd126\",\n \"score\": \"0.4904927\",\n \"text\": \"def check_relations(self):\\n if not check_zope_admin():\\n return \\\"You must be a zope manager to run this script\\\"\\n from zc.relation.interfaces import ICatalog\\n from zope.app.intid.interfaces import IIntIds\\n from zope.component import getUtility\\n\\n intids = getUtility(IIntIds)\\n rels = getUtility(ICatalog)\\n out = [\\\"check_intids\\\\n\\\"]\\n relations = list(rels.findRelations())\\n for rel in relations:\\n if not rel.from_id or not intids.queryObject(rel.from_id, False):\\n log_list(out, \\\"Missing from_id %s\\\" % relation_infos(rel))\\n # rels.unindex(rel)\\n elif not rel.to_id or not intids.queryObject(rel.to_id, False):\\n log_list(out, \\\"Missing to_id %s\\\" % relation_infos(rel))\\n elif not rel.from_object or not intids.queryId(rel.from_object, False):\\n log_list(out, \\\"Missing from_object %s\\\" % relation_infos(rel))\\n elif not rel.to_object or not intids.queryId(rel.to_object, False):\\n log_list(out, \\\"Missing to_object %s\\\" % relation_infos(rel))\\n if len(out) == 1:\\n out.append(\\\"No problem found\\\")\\n return \\\"\\\\n\\\".join(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ea6ec08f7fd3f9e889cd15ca7a8c9e\",\n \"score\": \"0.49016887\",\n \"text\": \"def get_somme(l):\\n somme = 0\\n for nombre in l:\\n somme += nombre\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba942eef7b97603edc588d1dabc755d3\",\n \"score\": \"0.48698246\",\n \"text\": \"def get_language_model_match(pairs_list, lm, text_min, text_max):\\n\\n all_text_parent = []\\n all_text_child = []\\n for pair in pairs_list:\\n parent_ob = pair[0]\\n child_ob = pair[1]\\n\\n all_text_parent.append(parent_ob[\\\"body\\\"])\\n all_text_child.append(child_ob[\\\"body\\\"])\\n\\n res_parent, lengths_parent = language_model.text_scores(all_text_parent, lm, text_min, text_max)\\n\\n res_child, lengths_child = language_model.text_scores(all_text_child, lm, text_min, text_max)\\n\\n return zip(res_parent, res_child), zip(lengths_parent, lengths_child)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050b645a17a1d3d58d75f9d8934b0423\",\n \"score\": \"0.48621327\",\n \"text\": \"def internal_lemmas(self):\\n node_stripped = copy.deepcopy(self.node)\\n etree.strip_elements(node_stripped, 'lemVersions')\\n return node_stripped.findall('.//lm')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da081aa08c4c4bdb8d50c828b9a9c405\",\n \"score\": \"0.48545665\",\n \"text\": \"def objectRel(rommat):\\n\\n assertMatrixSquare(rommat)\\n oid=0\\n rels=[]\\n # Transposing because orig matrix stores link types \\n # from colIdx to rowIdx\\n for inRel in transposed(rommat):\\n oid+=1\\n relOids = map(\\n lambda i: i+1, # oids start w/ 1, indexes w/ 0\\n nonZeroIndexes(inRel))\\n rels+=[ObjectRel(oid,relOids)]\\n return rels;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7b82046b6a00ccd84e405bffd303dec\",\n \"score\": \"0.48538083\",\n \"text\": \"def exact_sol(nodes = 100):\\n exact = []\\n axisx_exact = np.linspace(0, L, nodes)\\n for x in axisx_exact:\\n hasil = func_exact_sol(x)\\n exact.append(hasil)\\n return axisx_exact, exact, nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0baa27e902ba5ab4a8b2ae696f51d551\",\n \"score\": \"0.48512816\",\n \"text\": \"def contractMPOEntries(MPO,indexlist,direction=0):\\n south,north = indexlist\\n M = MPO[0][:,south[0],:,north[0]]\\n for i in range(1,len(MPO)):\\n M = np.dot(M,MPO[i][:,south[i],:,north[i]])\\n return(M)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f18af7786d52a54bc4667f7a9bb1ad6\",\n \"score\": \"0.4846471\",\n \"text\": \"def linear_landmarks(x, x0, x1):\\n x = np.array(x)\\n return pol1_transform(x, (x0,0.), (x1,1.))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8f8c92b74e20c6d3a8d70bd1426daf\",\n \"score\": \"0.483458\",\n \"text\": \"def generate_adj_lone_pair_multiple_bond_resonance_structures(mol):\\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule, direction=cython.int)\\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, bond12=Bond)\\n cython.declare(v1=Vertex, v2=Vertex)\\n\\n structures = []\\n for atom in mol.vertices:\\n paths = pathfinder.find_adj_lone_pair_multiple_bond_delocalization_paths(atom)\\n for atom1, atom2, bond12, direction in paths:\\n if direction == 1: # The direction <increasing> the bond order\\n atom1.decrement_lone_pairs()\\n bond12.increment_order()\\n elif direction == 2: # The direction <decreasing> the bond order\\n atom1.increment_lone_pairs()\\n bond12.decrement_order()\\n atom1.update_charge()\\n atom2.update_charge()\\n # Make a copy of structure\\n structure = mol.copy(deep=True)\\n # Restore current structure\\n if direction == 1: # The direction <increasing> the bond order\\n atom1.increment_lone_pairs()\\n bond12.decrement_order()\\n elif direction == 2: # The direction <decreasing> the bond order\\n atom1.decrement_lone_pairs()\\n bond12.increment_order()\\n atom1.update_charge()\\n atom2.update_charge()\\n try:\\n structure.update_atomtypes(log_species=False)\\n except AtomTypeError:\\n pass # Don't append resonance structure if it creates an undefined atomtype\\n else:\\n if not (structure.get_net_charge() and structure.contains_surface_site()):\\n structures.append(structure)\\n return structures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91b02612e742669fb6e83a1ce65a77c\",\n \"score\": \"0.48295075\",\n \"text\": \"def relu(input_list):\\n output_list = [x if x > 0 else 0 for x in input_list]\\n print(\\\"relu\\\", input_list, output_list)\\n return output_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17bf0f3e1b92540cf48d9066a7c395dc\",\n \"score\": \"0.48288542\",\n \"text\": \"def FindMainConcept(NP,LPObj):\\n MainConcepts, SubConcepts =[], []\\n SingularPhrase = ''\\n if len(NP) >1:\\n MainConcepts = [each for each in NP for e2 in each.split() if e2 in LPObj]\\n SubConcepts = [each for each in NP if each not in MainConcepts ]\\n return SingularPhrase, MainConcepts, SubConcepts\\n else:\\n SingularPhrase=NP\\n MainConcepts,SubConcepts = [''], ['']\\n return SingularPhrase, MainConcepts, SubConcepts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffdc45584fcce7c2375e1421b43edab\",\n \"score\": \"0.48272952\",\n \"text\": \"def solveMPO(X,MPO,MPO_,tr_sites=[]): \\n pos = [i for i in range(len(MPO_)) if(len(MPO_[i]) == 0)]\\n if(not(len(pos))) == 1:\\n raise Exception(\\\"The second MPO_ must have EXACTLY ONE empty list occurence\\\")\\n else:\\n pos = pos[0]\\n trs = list(set(tr_sites))\\n \\n L = contractMPO(prodMPO(MPO[:pos],MPO_[:pos],tr_sites = list(filter(lambda x:x<pos and x >= 0,trs))))\\n R = contractMPO(prodMPO(MPO[pos+1:],MPO_[pos+1:],tr_sites = list(map(lambda x:x-pos-1,filter(lambda x:x>pos,trs))) + list(filter(lambda x:x<0,trs))))\\n A = MPO[pos]\\n #print([x.shape for x in L if len(x)>0])\\n #print([x.shape for x in R if len(x)>0])\\n \\n d1,d2,d3,D1,D2 = L.shape[1],A.shape[1],R.shape[1],A.shape[0],A.shape[2]\\n D1_,D2_ = MPO_[pos-1].shape[2],MPO_[pos+1].shape[0]\\n \\\"\\\"\\\" Reshape X to vector \\\"\\\"\\\"\\n X_ = np.reshape(X,[X.shape[0]**2])\\n \\\"\\\"\\\" Index structure: s1,e1,e1',n1 x w2,s2,e2,n2 -tensordot-> s1,e1',n1,s2,e2,n2 \\\"\\\"\\\"\\n M = np.tensordot(np.reshape(L,[L.shape[1],D1,L.shape[2]//D1,L.shape[3]]),A,[[1],[0]])\\n \\\"\\\"\\\" Index structure: s1,e1',n1,s2,e2,n2 x w3,w3',s3,n3 -tensordot-> s1,e1',n1,s2,n2,w3',s3,n3 \\\"\\\"\\\"\\n M = np.tensordot(M,np.reshape(R,[D2,R.shape[0]//D2,R.shape[1],R.shape[3]]),[[4],[0]])\\n \\\"\\\"\\\" Index structure: s1,e1',n1,s2,n2,w3',s3,n3 x s4,n4 -tensordot-> s1,e1',n1,s2,n2,w3',s3,n3,s4,n4 \\\"\\\"\\\"\\n M = np.tensordot(M,np.identity(d2),[[],[]])\\n \\\"\\\"\\\" Transpose and reshape M to have index structure: (s1,s4,s3,n1,n2,n3);(e1',n4,w3',s2) \\\"\\\"\\\"\\n M = np.reshape(np.transpose(M,[2,4,7,0,8,6,1,9,5,3]),[d1**2*d2**2*d3**2,D1_*D2_*d2**2])\\n #print(M.shape,X_.shape)\\n \\n Y = la.lstsq(M,X_)[0]\\n Ai = np.reshape(Y,[D1_,d2,D2_,d2])\\n \\n return(Ai)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ede8e0205a16fc783ef0242c404b966\",\n \"score\": \"0.48241144\",\n \"text\": \"def get_ll(l, num):\\n lbls = [(np.sum(np.where(l==i,1,0)),i) for i in range(1,num+1)]\\n lbls.sort(reverse=True)\\n return lbls[0][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84336c1c2ff8b7c5c27c246590edc086\",\n \"score\": \"0.48222518\",\n \"text\": \"def calculate_polynoms(x, coefficients):\\n for i in range(len(x)):\\n straight_polynom(x[i], coefficients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b15e796f71bcbfc39cc76017195229\",\n \"score\": \"0.48183295\",\n \"text\": \"def lagrange(x0, x):\\n import numpy as np\\n\\n if len(x) == 1:\\n return [1.0]\\n\\n n = len(x)\\n L = np.zeros((n,))\\n l = node_polynomial(x0, x)\\n for j in range(n):\\n L[j] = lagrange_basis(x0, j, x, l)\\n \\n return L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d8611c84d424b3eab54bab50d0ad2e\",\n \"score\": \"0.48116142\",\n \"text\": \"def calc_linhas_largura(dict_circ_desc, ponto):\\n if dict_circ_desc[\\\"tipo_circulo\\\"] == \\\"meio\\\":\\n linha_nao_intersecta_ponto = None\\n point_circ = Point()\\n point_circ.X = dict_circ_desc[\\\"pt_medios_circ\\\"][\\\"x_ptm\\\"]\\n point_circ.Y = dict_circ_desc[\\\"pt_medios_circ\\\"][\\\"y_ptm\\\"]\\n array = Array([point_circ, ponto.getPart(0)])\\n linha_circulo = Polyline(array, projecao_geo)\\n for parte_linha in dict_circ_desc[\\\"partes\\\"]:\\n if not dict_circ_desc[\\\"partes\\\"][parte_linha][\\\"cruza_ponto\\\"]:\\n linha_nao_intersecta_ponto = dict_circ_desc[\\\"partes\\\"][parte_linha][\\\"linha_geometria\\\"]\\n if linha_circulo.disjoint(linha_nao_intersecta_ponto):\\n array.removeAll()\\n point_circ = Point()\\n point_circ.X = dict_circ_desc[\\\"pt_medios_circ\\\"][\\\"x_ptm_inv\\\"]\\n point_circ.Y = dict_circ_desc[\\\"pt_medios_circ\\\"][\\\"y_ptm_inv\\\"]\\n array = Array([point_circ, ponto.getPart(0)])\\n linha_circulo = Polyline(array, projecao_geo)\\n linha_largura = linha_circulo.intersect(poligono_ma, 2)\\n array.removeAll()\\n else:\\n linha_largura = linha_circulo.intersect(poligono_ma, 2)\\n array.removeAll()\\n return linha_largura, linha_circulo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e09712ab3ed9e696134e1c052ced7bd\",\n \"score\": \"0.48105964\",\n \"text\": \"def COM(Lats,Lons):\\r\\n X = [Distance([0,0],[0,Lons[i][0]]) for i in range(0, len(Lons))]\\r\\n Y = [Distance([0,0],[Lats[i][0],0]) for i in range(0, len(Lons))]\\r\\n #Distance conversion is carried out to avoid distortion.\\r\\n Cx = sum(X) / len(X)\\r\\n Cy = sum(Y) / len(Y)\\r\\n return [DirectionDistance(Cy,0,0,0)[0],DirectionDistance(Cx,math.pi / 2,0,0)[1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dae7bf1850ec74516916e94b8880275\",\n \"score\": \"0.48101878\",\n \"text\": \"def generate_lone_pair_multiple_bond_resonance_structures(mol):\\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule)\\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, atom3=Atom, bond12=Bond, bond23=Bond)\\n cython.declare(v1=Vertex, v2=Vertex)\\n\\n structures = []\\n for atom in mol.vertices:\\n if atom.lone_pairs >= 1:\\n paths = pathfinder.find_lone_pair_multiple_bond_paths(atom)\\n for atom1, atom2, atom3, bond12, bond23 in paths:\\n # Adjust to (potentially) new resonance structure\\n atom1.decrement_lone_pairs()\\n atom3.increment_lone_pairs()\\n bond12.increment_order()\\n bond23.decrement_order()\\n atom1.update_charge()\\n atom3.update_charge()\\n # Make a copy of structure\\n structure = mol.copy(deep=True)\\n # Restore current structure\\n atom1.increment_lone_pairs()\\n atom3.decrement_lone_pairs()\\n bond12.decrement_order()\\n bond23.increment_order()\\n atom1.update_charge()\\n atom3.update_charge()\\n try:\\n structure.update_atomtypes(log_species=False)\\n except AtomTypeError:\\n pass # Don't append resonance structure if it creates an undefined atomtype\\n else:\\n structures.append(structure)\\n return structures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef9198d0e04648fc2c232d99ba232da\",\n \"score\": \"0.4800227\",\n \"text\": \"def generate_adj_lone_pair_radical_multiple_bond_resonance_structures(mol):\\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule, direction=cython.int)\\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, bond12=Bond)\\n cython.declare(v1=Vertex, v2=Vertex)\\n\\n structures = []\\n if mol.is_radical(): # Iterate over radicals in structure\\n for atom in mol.vertices:\\n paths = pathfinder.find_adj_lone_pair_radical_multiple_bond_delocalization_paths(atom)\\n for atom1, atom2, bond12, direction in paths:\\n if direction == 1: # The direction <increasing> the bond order\\n atom1.decrement_lone_pairs()\\n bond12.increment_order()\\n atom1.increment_radical()\\n atom2.decrement_radical()\\n elif direction == 2: # The direction <decreasing> the bond order\\n atom1.increment_lone_pairs()\\n bond12.decrement_order()\\n atom1.decrement_radical()\\n atom2.increment_radical()\\n atom1.update_charge()\\n atom2.update_charge()\\n # Make a copy of structure\\n structure = mol.copy(deep=True)\\n # Restore current structure\\n if direction == 1: # The direction <increasing> the bond order\\n atom1.increment_lone_pairs()\\n bond12.decrement_order()\\n atom1.decrement_radical()\\n atom2.increment_radical()\\n elif direction == 2: # The direction <decreasing> the bond order\\n atom1.decrement_lone_pairs()\\n bond12.increment_order()\\n atom1.increment_radical()\\n atom2.decrement_radical()\\n atom1.update_charge()\\n atom2.update_charge()\\n try:\\n structure.update_atomtypes(log_species=False)\\n except AtomTypeError:\\n pass # Don't append resonance structure if it creates an undefined atomtype\\n else:\\n structures.append(structure)\\n return structures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1759e71110b6798c57d2e1921b796ac2\",\n \"score\": \"0.47827086\",\n \"text\": \"def longest_oscillation(L):\\r\\n if L == None:\\r\\n return 0, []\\r\\n if len(L) == 0:\\r\\n return 0, []\\r\\n elif len(L) == 1:\\r\\n return 1, [0]\\r\\n else:\\r\\n res = [0]\\r\\n temp = L[0]\\r\\n count = 1 # count = 1, because 1st element will always be included.\\r\\n k = 1\\r\\n # skip duplicate elements at the start\\r\\n while L[k-1] == L[k] and k < len(L)-1:\\r\\n k += 1\\r\\n # direct True indicates upward, direct False indicate downwards.\\r\\n direct = L[0] < L[k]\\r\\n j = 1\\r\\n # start from k, (skipped all duplicate elements)\\r\\n for i in range(k, len(L)):\\r\\n # if current temp not equal to current element. (duplicate element)\\r\\n if L[i] != temp:\\r\\n # if current element and temp do not match \\\"direct\\\" upward expected but downward detected or vice versa. \\r\\n if (L[i] > temp and direct == False) or (L[i] < temp and direct == True):\\r\\n res[j-1] = i\\r\\n \\r\\n # if((L[i] > temp) and direct == True) or (L[i] < temp and direct == False)\\r\\n else:\\r\\n direct = not direct # reverse current direction \\r\\n count += 1 # add new item, hence increment count by 1\\r\\n res.append(i) # append current index\\r\\n j += 1\\r\\n # replace temp with current if L[i] != temp (not duplicated) \\r\\n temp = L[i]\\r\\n \\r\\n return count, res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07722ff3e680e8c18cdac7577b587a37\",\n \"score\": \"0.47821546\",\n \"text\": \"def getIntersectionNodes(self):\\n ways=self.getWayList()\\n nodeList=[]\\n intersectionList=[]\\n for wayElement in ways:\\n nodes=wayElement.getElementsByTagName('nd')\\n for nodeElement in nodes:\\n nodeList.append(nodeElement.getAttribute('ref'))\\n for nodeElement in nodeList:\\n if nodeList.count(nodeElement)>1 and intersectionList.count(nodeElement)==0:\\n intersectionList.append(nodeElement)\\n return intersectionList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5bbf3e021d49e7e3cc00a5402d96d9\",\n \"score\": \"0.47819144\",\n \"text\": \"def general_poly (L):\\n def fun(x):\\n n = 0\\n for i in L:\\n n = x*n + i\\n return n\\n return fun\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b5a858b1bfecbf292010a119cfa5aa\",\n \"score\": \"0.4777859\",\n \"text\": \"def get_linear_components(self, *args):\\n def getlin(a):\\n return SparsePair(*CPX_PROC.getqconstr_lin(\\n self._env._e, self._cplex._lp, a))\\n return apply_freeform_one_arg(\\n getlin, self._conv, self.get_num(), args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e868492b42368f387c9b1319845596\",\n \"score\": \"0.47776476\",\n \"text\": \"def findAwsomeDiagonals(addList,R):\\n\\tpass\\n\\t\\n\\t#first, list the \",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f495e90081556064173effad4955707\",\n \"score\": \"0.47745904\",\n \"text\": \"def get_allen_relation(\\n e1_start: int, e1_end: int, e2_start: int, e2_end: int\\n) -> AllenRelation:\\n if e1_start == e2_start:\\n if e1_end == e2_end:\\n return AllenRelation.E1_EQUALS_E2\\n elif e1_end > e2_end:\\n return AllenRelation.E2_STARTS_E1\\n else:\\n return AllenRelation.E1_STARTS_E2\\n elif e1_start < e2_start:\\n if e1_end == e2_start:\\n return AllenRelation.E1_MEETS_E2\\n elif e1_end < e2_start:\\n return AllenRelation.E1_PRECEDES_E2\\n elif e1_end == e2_end:\\n return AllenRelation.E2_FINISHES_E1\\n elif e1_end > e2_end:\\n return AllenRelation.E2_DURING_E1\\n else:\\n return AllenRelation.E1_OVERLAPS_E2\\n else: # e1_start > e2_start\\n if e2_end == e2_start:\\n return AllenRelation.E2_MEETS_E1\\n elif e2_end < e2_start:\\n return AllenRelation.E2_PRECEDES_E1\\n elif e2_end == e1_end:\\n return AllenRelation.E1_FINISHES_E2\\n elif e2_end > e1_end:\\n return AllenRelation.E1_DURING_E2\\n else:\\n return AllenRelation.E2_OVERLAPS_E1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d0b5b6b9fc63228ea01461e28a7351\",\n \"score\": \"0.47689426\",\n \"text\": \"def _clar_transformation(mol, aromatic_ring):\\n cython.declare(bondList=list, i=cython.int, atom1=Atom, atom2=Atom, bond=Bond)\\n\\n bond_list = []\\n\\n for i, atom1 in enumerate(aromatic_ring):\\n for atom2 in aromatic_ring[i + 1:]:\\n if mol.has_bond(atom1, atom2):\\n bond_list.append(mol.get_bond(atom1, atom2))\\n\\n for bond in bond_list:\\n bond.order = 1.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a49dc1515e42408d3ff59082ba28ea9\",\n \"score\": \"0.47533303\",\n \"text\": \"def function_2_4_1d_sigmoid_list(l1: 'List of numbers') -> 'Relu output':\\n return [(1 / (1 + math.exp(-a)) )for a in l1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15355e9cbc24ac9652d1edd7e3af9050\",\n \"score\": \"0.4747987\",\n \"text\": \"def linear_solver(coef, const):\\n if len(coef) == 2:\\n y = (const[0][0] * coef[1][0] - coef[0][0] * const[1][0]) / (-coef[0][0] * coef[1][1] + coef[1][0] * coef[0][1])\\n x = (const[1][0] - coef[1][1] * y) / coef[1][0]\\n\\n return [x, y]\\n\\n return multiply(inverse(coef), const)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e224e84adc9517ff4cf085a9f485537\",\n \"score\": \"0.4738598\",\n \"text\": \"def generate_allyl_delocalization_resonance_structures(mol):\\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule)\\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom, atom3=Atom, bond12=Bond, bond23=Bond)\\n cython.declare(v1=Vertex, v2=Vertex)\\n\\n structures = []\\n if mol.is_radical(): # Iterate over radicals in structure\\n for atom in mol.vertices:\\n paths = pathfinder.find_allyl_delocalization_paths(atom)\\n for atom1, atom2, atom3, bond12, bond23 in paths:\\n # Adjust to (potentially) new resonance structure\\n atom1.decrement_radical()\\n atom3.increment_radical()\\n bond12.increment_order()\\n bond23.decrement_order()\\n # Make a copy of structure\\n structure = mol.copy(deep=True)\\n # Restore current structure\\n atom1.increment_radical()\\n atom3.decrement_radical()\\n bond12.decrement_order()\\n bond23.increment_order()\\n try:\\n structure.update_atomtypes(log_species=False)\\n except AtomTypeError:\\n pass # Don't append resonance structure if it creates an undefined atomtype\\n else:\\n structures.append(structure)\\n return structures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886169413d2cd4dfe71eea8a84d1a3de\",\n \"score\": \"0.4735548\",\n \"text\": \"def create_xref_statements(self):\\n\\n if not MondoNode.mrh:\\n MondoNode.mrh = wdi_helpers.MappingRelationHelper(sparql_endpoint_url=self.graph.sparql_endpoint_url)\\n\\n # need a way to get from \\\"http://linkedlifedata.com/resource/umls/id/C0265218\\\" to UMLS:C0265218, for example\\n # ignoring meddra and snomed\\n uri_to_curie_map = {\\n \\\"http://identifiers.org/omim/\\\": \\\"OMIM:\\\",\\n \\\"http://identifiers.org/mesh/\\\": \\\"MESH:\\\",\\n \\\"http://linkedlifedata.com/resource/umls/id/\\\": \\\"UMLS:\\\",\\n \\\"http://www.orpha.net/ORDO/Orphanet_\\\": \\\"Orphanet:\\\",\\n \\\"http://purl.obolibrary.org/obo/DOID_\\\": \\\"DOID:\\\",\\n \\\"http://purl.obolibrary.org/obo/NCIT_\\\": \\\"NCIT:\\\"\\n }\\n\\n def uri_to_curie(uri):\\n for k, v in uri_to_curie_map.items():\\n if k in uri:\\n uri = uri.replace(k, v)\\n return uri\\n\\n ss = []\\n for mrt, xrefs in self.bpv.items():\\n if mrt in self.mrh.ABV_MRT.values():\\n for xref in xrefs:\\n xref_curie = uri_to_curie(xref)\\n if not xref_curie:\\n continue\\n self.xrefs.discard(xref_curie)\\n s = self.create_xref_statement(xref_curie)\\n if not s:\\n continue\\n self.mrh.set_mrt(s, mrt)\\n ss.append(s)\\n for xref in self.xrefs:\\n s = self.create_xref_statement(xref)\\n if s:\\n ss.append(s)\\n\\n return ss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598435957f4522c6cbf0b22dbd5f8296\",\n \"score\": \"0.47162804\",\n \"text\": \"def generateLonePairRadicalResonanceIsomers(mol):\\n cython.declare(isomers=list, paths=list, index=cython.int, isomer=Molecule)\\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom)\\n cython.declare(v1=Vertex, v2=Vertex)\\n \\n isomers = []\\n\\n # Radicals\\n if mol.isRadical():\\n # Iterate over radicals in structure\\n for atom in mol.vertices:\\n paths = pathfinder.findAllDelocalizationPathsLonePairRadical(atom)\\n for atom1, atom2 in paths:\\n # Adjust to (potentially) new resonance isomer\\n atom1.decrementRadical()\\n atom1.incrementLonePairs()\\n atom1.updateCharge()\\n atom2.incrementRadical()\\n atom2.decrementLonePairs()\\n atom2.updateCharge()\\n # Make a copy of isomer\\n isomer = mol.copy(deep=True)\\n # Also copy the connectivity values, since they are the same\\n # for all resonance forms\\n for index in range(len(mol.vertices)):\\n v1 = mol.vertices[index]\\n v2 = isomer.vertices[index]\\n v2.connectivity1 = v1.connectivity1\\n v2.connectivity2 = v1.connectivity2\\n v2.connectivity3 = v1.connectivity3\\n v2.sortingLabel = v1.sortingLabel\\n # Restore current isomer\\n atom1.incrementRadical()\\n atom1.decrementLonePairs()\\n atom1.updateCharge()\\n atom2.decrementRadical()\\n atom2.incrementLonePairs()\\n atom2.updateCharge()\\n # Append to isomer list if unique\\n isomer.updateAtomTypes()\\n isomers.append(isomer)\\n\\n return isomers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d167627a35dbb3d113ec836349c4559\",\n \"score\": \"0.47136837\",\n \"text\": \"def general_poly (L):\\n #YOUR CODE HERE\\n length = len(L)-1\\n def degree(x):\\n total = 0\\n for index, coefficient in enumerate(L):\\n total = total + coefficient*x**(length-index)\\n return total\\n return degree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53d767d496e06a1a912275530066b2f8\",\n \"score\": \"0.4710089\",\n \"text\": \"def get_relations(triples: torch.LongTensor) -> Set[int]:\\n return set(triples[:, 1].tolist())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baebd5720a558316021d3b8397fffb15\",\n \"score\": \"0.4705233\",\n \"text\": \"def linear_tensors(self):\\n return self._LT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4fcd2fbd96c32461e07c3485b50533a\",\n \"score\": \"0.47046304\",\n \"text\": \"def ConvertInd_Res(mList, rStep):\\n for mComp in mList:\\n if mComp.Type == \\\"I\\\": # NOT G\\n mList.append(Component(\\\"R\\\", mComp.Node1, mComp.Node2, str(float(mComp.Value) / float(rStep)), \\\"0\\\"))\\n\\n return mList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0ae85452cb54de0db09c99fe7ce270\",\n \"score\": \"0.47020477\",\n \"text\": \"def LM(m, v):\\n\\n lm = np.array([0.0, 0.0])\\n natoms = int(len(v) / 2)\\n for i in range(0, natoms):\\n x = m[2 * i][2 * i] * vector(v, i)\\n lm += x\\n\\n return (lm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"818d8a5c5eb0e73afcff9d07751e2519\",\n \"score\": \"0.46927842\",\n \"text\": \"def get_linear_components(self, *args):\\n def getlin(begin, end=self.get_num() - 1):\\n mat = _HBMatrix()\\n mat.matbeg, mat.matind, mat.matval = CPX_PROC.getindconstr(\\n self._env._e, self._cplex._lp, begin, end)\\n return [m for m in mat]\\n return apply_freeform_two_args(getlin, self._conv, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0cdc54d75c682769ba456f1567ddbdc\",\n \"score\": \"0.46793965\",\n \"text\": \"def _lmindx(self,l,m): \\n return l**2+l+m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44eeac8d2732e4d001f6392aef6d1f6f\",\n \"score\": \"0.46769416\",\n \"text\": \"def find_zeroes(self):\\n\\n # Интервалы, содержащие нули функции\\n intervals = [[-5, -3], [-1, 0], [0.175, 1], [3, 5]]\\n result = []\\n\\n i = 0\\n while i < 4:\\n x_0 = intervals[i][0]\\n x_1 = intervals[i][1]\\n\\n f_0 = self.function(x_0)\\n f_1 = self.function(x_1)\\n while np.abs(f_1) > self._eps:\\n x_1 = x_1 - f_1 / (f_1 - f_0) * (x_1 - x_0)\\n f_1 = self.function(x_1)\\n result.append(x_1)\\n i += 1\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad84dae30b2eb5476e047d1c8f2674d\",\n \"score\": \"0.4676159\",\n \"text\": \"def whenzero(list1, list2):\\n\\n if len(list1) == 1 and len(list2) == 0:\\n return \\\"Line \\\" + \\\"0\\\" + \\\":\\\" + '\\\\n' + list1[0] + '\\\\n^\\\\n\\\\n'\\n elif len(list1) == 0 and len(list2) == 1:\\n return \\\"Line \\\" + \\\"0\\\" + \\\":\\\" + '\\\\n' + list2[0] + '\\\\n^\\\\n\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc2861f8469512bdca3eb95801447a4\",\n \"score\": \"0.46739873\",\n \"text\": \"def latex_lincomb(coeff, vec):\\n code=\\\"\\\"\\n first = True\\n for i in range(len(coeff)):\\n if coeff[i] != 0:\\n if not first and coeff[i] > 0:\\n code += \\\"+ \\\"\\n if coeff[i] == 1:\\n code += vec[i]\\n elif coeff[i] == -1:\\n code+=\\\"-\\\"+vec[i]\\n else:\\n code+=latex(coeff[i])+\\\" \\\"+vec[i]\\n first = False\\n return code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4cfdec01ac239d4131876c1491e59d6\",\n \"score\": \"0.46733528\",\n \"text\": \"def ll(freq_stdc,freq_refc,tk_stdc,tk_refc):\\n O = float(freq_stdc+freq_refc)\\n N1 = float(tk_stdc)\\n N2 = float(tk_refc)\\n E1 = N1*O/(N1+N2) \\n E2 = N2*O/(N1+N2)\\n if freq_stdc < 1 or E1 < 1:\\n v1 = 0\\n else:\\n v1 = math.log(freq_stdc/E1)\\n if freq_refc < 1 or E2 < 1:\\n v2 = 0\\n else:\\n v2 = math.log(freq_refc/E2)\\n LL = round(2 * ((freq_stdc * v1)+(freq_refc * v2)),2)\\n Norm_stdc = freq_stdc/float(tk_stdc)\\n Norm_refc = freq_refc/float(tk_refc)\\n if Norm_stdc == 0:\\n Norm_stdc = 0.5 / float(tk_stdc)\\n if Norm_refc == 0:\\n Norm_refc = 0.5 / float(tk_refc)\\n if Norm_stdc < Norm_refc:\\n LL = - LL\\n return LL\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a1d7f4cc7b04bfff6d1779d5fbce54\",\n \"score\": \"0.46666345\",\n \"text\": \"def get_bondlist_coords(u):\\n angles = u.angles\\n bonds = angles.atom3.positions - angles.atom1.positions \\n # coords = angles.atom2.positions\\n norm = np.linalg.norm(bonds,axis=1)\\n bonds /= norm[:, None] #the norm vector is a (nx1) and we have to create dummy directions -> (n,3)\\n return bonds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b097896b830de272552c70259a3c4e\",\n \"score\": \"0.46654844\",\n \"text\": \"def invUnitRelation (self, M) :\\n\\t\\trulenames = []\\n\\t\\tfor i in range(len(M)) :\\n\\t\\t\\tfor key, units in self.unitrelation.items() :\\n\\t\\t\\t\\tif M[i].nodetype in units :\\n\\t\\t\\t\\t\\tnode = UnitNode (M[i], key)\\n\\t\\t\\t\\t\\trulenames.append (node)\\n\\t\\tM = rulenames\\n\\t\\treturn rulenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d66011d05017858340a9249a822c07\",\n \"score\": \"0.46602762\",\n \"text\": \"def make_reference(mol):\\n reference_graph = nx.Graph()\\n residues = make_residue_graph(mol)\\n\\n for residx in residues:\\n # TODO: make separate function for just one residue.\\n # TODO: multiprocess this loop?\\n # TODO: Merge degree 1 nodes (hydrogens!) with the parent node. And\\n # check whether the node degrees match?\\n\\n resname = residues.node[residx]['resname']\\n resid = residues.node[residx]['resid']\\n chain = residues.node[residx]['chain']\\n residue = residues.node[residx]['graph']\\n reference = mol.force_field.reference_graphs[resname]\\n add_element_attr(reference)\\n add_element_attr(residue)\\n # Assume reference >= residue\\n matches = isomorphism(reference, residue)\\n if not matches:\\n # Maybe reference < residue? I.e. PTM or protonation\\n matches = isomorphism(residue, reference)\\n matches = [{v: k for k, v in match.items()} for match in matches]\\n if not matches:\\n LOGGER.debug('Doing MCS matching for residue {}{}', resname, resid,\\n type='performance')\\n # The problem is that some residues (termini in particular) will\\n # contain more atoms than they should according to the reference.\\n # Furthermore they will have too little atoms because X-Ray is\\n # supposedly hard. This means we can't do the subgraph isomorphism\\n # like we're used to. Instead, identify the atoms in the largest\\n # common subgraph, and do the subgraph isomorphism/alignment on\\n # those. MCS is ridiculously expensive, so we only do it when we\\n # have to.\\n try:\\n mcs_match = max(maximum_common_subgraph(reference, residue, ['element']),\\n key=lambda m: rate_match(reference, residue, m))\\n except ValueError:\\n raise ValueError('No common subgraph found between {} and '\\n 'reference {}.'.format(resname, resname))\\n # We could seed the isomorphism calculation with the knowledge from\\n # the mcs_match, but thats to much effort for now.\\n # TODO: see above\\n res = residue.subgraph(mcs_match.values())\\n matches = isomorphism(reference, res)\\n # TODO: matches is sorted by isomorphism. So we should probably use\\n # that with e.g. itertools.takewhile.\\n if not matches:\\n LOGGER.error(\\\"Can't find isomorphism between {}{} and its \\\"\\n \\\"reference.\\\", resname, resid, type='inconsistent-data')\\n continue\\n\\n matches = maxes(matches, key=lambda m: rate_match(reference, residue, m))\\n if len(matches) > 1:\\n LOGGER.warning(\\\"More than one way to fit {}{} on it's reference.\\\"\\n \\\" I'm picking one arbitrarily. You might want to\\\"\\n \\\" fix at least some atomnames.\\\", resname, resid,\\n type='bad-atom-names')\\n\\n match = matches[0]\\n reference_graph.add_node(residx, chain=chain, reference=reference,\\n found=residue, resname=resname, resid=resid,\\n match=match)\\n reference_graph.add_edges_from(residues.edges())\\n return reference_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b33284496ab44af77f060871ce32f555\",\n \"score\": \"0.46537\",\n \"text\": \"def find_ref_vec(curr_data):\\n ref_LR = np.array([0,0,0])\\n ref_SI = np.array([0,0,0])\\n for organ in curr_data.keys():\\n if(organ=='image' or organ=='zxy_dimention' or ('stdName' not in curr_data[organ].keys()) ):\\n continue\\n if(curr_data[organ]['stdName']!='Brainstem'):\\n continue\\n mask = curr_data[organ]['mask']\\n ref_SI = find_axis(mask)\\n \\n for organ_R in curr_data.keys():\\n if(organ_R=='image' or organ_R=='zxy_dimention' or ('stdName' not in curr_data[organ_R].keys()) ):\\n continue\\n \\n if(curr_data[organ_R]['stdName']!='Parotid_R'):\\n continue\\n \\n for organ_L in curr_data.keys():\\n if(organ_L=='image' or organ_L=='zxy_dimention' or ('stdName' not in curr_data[organ_L].keys()) ):\\n continue\\n if(curr_data[organ_L]['stdName']!='Parotid_L'):\\n continue\\n result = geometry_relation(curr_data[organ_R]['mask'], curr_data[organ_L]['mask'],vec_ref=[[1,0,0],[0,1,0],[0,0,1]])\\n ref_LR = result['direction']\\n \\n #print('ref_LR is', ref_LR)\\n #print('ref_SI is', ref_SI)\\n if (ref_LR==np.array([0,0,0])).all() or (ref_SI==np.array([0,0,0])).all():\\n return [np.array([0,0,0]),np.array([0,0,0]) ,np.array([0,0,0])]\\n ref_AP = np.cross(ref_SI, ref_LR) \\n ref_vec = [ref_SI, ref_AP, ref_LR]\\n return ref_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9e223d47fd9de427f19cfb5a6c98c82\",\n \"score\": \"0.46467122\",\n \"text\": \"def get_list_number_for(pn, sl):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276c341e1bddff47d2257c2745e9eed0\",\n \"score\": \"0.46465728\",\n \"text\": \"def create_rules(points: list):\\n rules = []\\n length = len(points)\\n for i in range(length):\\n p1 = points[i]\\n for j in range(i + 1, length):\\n p2 = points[j]\\n rules.append(Rule(p1=p1, p2=p2, label=1))\\n rules.append(Rule(p1=p1, p2=p2, label=-1))\\n # else:\\n # if p1.x equals p2.x -> incline = coefficient = infinity & bias = 0\\n # rules.append(Rule(point=p1, coefficient=np.inf))\\n return rules\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74eee13594ef6e14377cdc4c40b35807\",\n \"score\": \"0.46454006\",\n \"text\": \"def L(i, f, x0):\\n L1 = \\\"\\\"\\n num = 1 # the actual numeric result\\n for j in range(len(f)):\\n if j != i:\\n num *= ((x0 - f[j][0]) / (f[i][0] - f[j][0]))\\n L1 += \\\"((x - \\\" + str(f[j][0]) + \\\") / (\\\" + str(f[i][0]) + \\\" - \\\" + str(f[j][0]) + \\\"))\\\"\\n if i == len(f) - 1:\\n if j != len(f) - 2:\\n L1 += \\\" + \\\"\\n elif j != len(f) - 1:\\n L1 += \\\" + \\\"\\n return num, L1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5e54b4aa33fcdf8285d295cf5e4496\",\n \"score\": \"0.46427548\",\n \"text\": \"def l(self, s1:int, s2:int, obs:str) -> float:\\n\\t\\tself._checkStateIndex(s1)\\n\\t\\tself._checkStateIndex(s2)\\n\\t\\tif self.labelling[s1] != obs:\\n\\t\\t\\treturn 0.0\\n\\t\\treturn self.matrix[s1][s2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dc10349a97ff8efd91d1a282370847\",\n \"score\": \"0.464154\",\n \"text\": \"def test_lt_corr_list():\\n # Case where the L-operators of the low-temperature correction match the\\n # L-operators of the hierarchy\\n hops_ltc_modes_match_l_op = HOPS(\\n sys_param_ltc_modes_match_l_op,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_match_l_op.initialize(psi_0)\\n\\n sys_ltc_modes_match = hops_ltc_modes_match_l_op.basis.mode\\n assert list(sys_ltc_modes_match.lt_corr_param) == \\\\\\n [250.0 / 1000.0, 250.0 / 2000.0]\\n assert np.allclose(sys_ltc_modes_match.list_L2_coo[0].todense(),sparse_l0.todense())\\n assert np.allclose(sys_ltc_modes_match.list_L2_coo[1].todense(),sparse_l1.todense())\\n\\n # Case where the low-temperature correction term list fed into HOPSSystem is empty.\\n hops_ltc_modes_empty = HOPS(\\n sys_param_ltc_modes_empty,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_empty.initialize(psi_0)\\n\\n sys_ltc_modes_empty = hops_ltc_modes_empty.basis.mode\\n assert list(sys_ltc_modes_empty.lt_corr_param) == \\\\\\n [0, 0]\\n\\n # Case where the L-operators of the low-temperature correction match the\\n # L-operators of the hierarchy, but are swapped in order to test that the\\n # coefficients are matched to the right hierarchy L-operators\\n hops_ltc_modes_swap_l_op = HOPS(\\n sys_param_ltc_modes_swap_l_op,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_swap_l_op.initialize(psi_0)\\n\\n sys_ltc_modes_swap = hops_ltc_modes_swap_l_op.basis.mode\\n assert list(sys_ltc_modes_swap.lt_corr_param) == \\\\\\n [250.0 / 2000.0, 250.0 / 1000.0]\\n assert np.allclose(sys_ltc_modes_swap.list_L2_coo[0].todense(),\\n sparse_l0.todense())\\n assert np.allclose(sys_ltc_modes_swap.list_L2_coo[1].todense(),\\n sparse_l1.todense())\\n\\n # Case where there is an extra L-operator not found in the hierarchy L-operator\\n # list (currently, the correction associated with that L-operator is ignored)\\n hops_ltc_modes_more_than_l_op = HOPS(\\n sys_param_ltc_modes_more_than_l_op,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_more_than_l_op.initialize(psi_0)\\n\\n sys_ltc_modes_more_than = hops_ltc_modes_more_than_l_op.basis.mode\\n assert list(sys_ltc_modes_more_than.lt_corr_param) == \\\\\\n [250.0 / 1000.0, 250.0 / 2000.0]\\n assert np.allclose(sys_ltc_modes_more_than.list_L2_coo[0].todense(),\\n sparse_l0.todense())\\n assert np.allclose(sys_ltc_modes_more_than.list_L2_coo[1].todense(),\\n sparse_l1.todense())\\n\\n # Case where the low-temperature correction is only acted on one of the\\n # L-operators found in the hierarchy\\n hops_ltc_modes_less_than_l_op = HOPS(\\n sys_param_ltc_modes_less_than_l_op,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_less_than_l_op.initialize(psi_0)\\n\\n hopsmodes_ltc_modes_less_than = hops_ltc_modes_less_than_l_op.basis.mode\\n assert list(hopsmodes_ltc_modes_less_than.lt_corr_param) == \\\\\\n [250.0 / 1000.0, 0]\\n assert np.allclose(hopsmodes_ltc_modes_less_than.list_L2_coo[0].todense(),\\n sparse_l0.todense())\\n\\n # Case where none of the L-operators of the low-temperature correction match the\\n # L-operators of the hierarchy\\n hops_ltc_modes_wrong_l_op = HOPS(\\n sys_param_ltc_modes_wrong_l_op,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_wrong_l_op.initialize(psi_0)\\n\\n hopsmodes_ltc_modes_wrong = hops_ltc_modes_wrong_l_op.basis.mode\\n assert list(hopsmodes_ltc_modes_wrong.lt_corr_param) == \\\\\\n [0, 0]\\n\\n # Case where the L-operators of the low-temperature correction match the\\n # L-operators of the hierarchy, run with aggressive adaptivity to reduce the basis\\n # to a single state\\n hops_ltc_modes_match_l_op_adaptive = HOPS(\\n sys_param_ltc_modes_match_l_op,\\n noise_param=noise_param,\\n hierarchy_param=hier_param,\\n eom_param=eom_param,\\n integration_param=integrator_param,\\n )\\n hops_ltc_modes_match_l_op_adaptive.make_adaptive(1e-1, 1e-1)\\n hops_ltc_modes_match_l_op_adaptive.initialize(psi_0)\\n\\n hopsmodes_ltc_modes_adap = hops_ltc_modes_match_l_op_adaptive.basis.mode\\n hopsmodes_ltc_modes_adap.system.state_list = [0]\\n assert list(hopsmodes_ltc_modes_adap.lt_corr_param) == \\\\\\n [250.0 / 1000.0]\\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[0].todense(),\\n np.array([[1]]))\\n\\n hopsmodes_ltc_modes_adap.system.state_list = [1]\\n assert list(hopsmodes_ltc_modes_adap.lt_corr_param) == \\\\\\n [250.0 / 2000.0]\\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[0].todense(),\\n np.array([[1]]))\\n\\n hopsmodes_ltc_modes_adap.system.state_list = [0, 1]\\n hopsmodes_ltc_modes_adap.list_absindex_mode = [0, 1, 2, 3]\\n assert list(hopsmodes_ltc_modes_adap.lt_corr_param) == \\\\\\n [250.0 / 1000.0, 250.0 / 2000.0]\\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[0].todense(),\\n sparse_l0.todense())\\n assert np.allclose(hopsmodes_ltc_modes_adap.list_L2_coo[1].todense(),\\n sparse_l1.todense())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49117041c6a698e1ae73d33f3e5256ab\",\n \"score\": \"0.46407595\",\n \"text\": \"def dnf(l: List[int]) -> int:\\n low_idx = 0\\n mid_idx = 0\\n high_idx = len(l) - 1\\n # stop when mid pointer is larger than high pointer\\n while mid_idx <= high_idx:\\n mid = l[mid_idx]\\n # if mid pointer has low value switch it with low pointer and increase bothself.\\n # this works because low pointer guaranteed to have mid value under it.\\n # (except if it is first element)\\n if mid == 0:\\n l[mid_idx], l[low_idx] = l[low_idx], l[mid_idx]\\n low_idx += 1\\n mid_idx += 1\\n # if mid pointer has mid value, location is correct just move the pointer.\\n elif mid == 1:\\n mid_idx += 1\\n # if mid pointer has high value, switch them and decrease high pointer. we do not\\n # increase mid pointer because we don't know what high pointer had under it.\\n # we will check it on next iteration.\\n elif mid == 2:\\n l[mid_idx], l[high_idx] = l[high_idx], l[mid_idx]\\n high_idx -= 1\\n # return low pointer (first medium element)\\n # and mid pointer (first high element)\\n return (low_idx, mid_idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307a66d9c05d4f7b1a876ea64e6396b2\",\n \"score\": \"0.4638022\",\n \"text\": \"def solve(l, n):\\n s = str(n)\\n mid = len(s) / 2\\n first_right = -1\\n first_left = -1\\n for i in range(mid, l)\\n if s[sep] != '0'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8f986ceec5f1564fbe4525ac355b0f\",\n \"score\": \"0.4637062\",\n \"text\": \"def bin_to_lines(l: List[Tuple[str, str]]) -> List[List[str]]:\\n return [[x[1] for x in l], [x[0] for x in l]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0396ed2c73524939f200643ef291c34e\",\n \"score\": \"0.46187776\",\n \"text\": \"def __call__(self, features: List[List[float]]) -> List[List[float]]:\\n nor = []\\n for i in features:\\n sum = 0\\n inner = []\\n for j in i:\\n sum += j ** 2\\n den = sum ** (1 / 2)\\n for j in i:\\n if (sum == 0):\\n inner.append(0)\\n else:\\n inner.append(j / den)\\n nor.append(inner)\\n return nor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bb75897d4949d78a7a60ebecd68d6e\",\n \"score\": \"0.46182895\",\n \"text\": \"def constant_propagation(list_of_lines,comp=[]):\\n\\tident_temp = dict() #stores identifiers/temporaries as key and constants as value\\n\\tfor line in list_of_lines:\\n\\t\\tline = line.strip('\\\\n')\\n\\t\\ttoks = line.split()\\n\\t\\tlength = len(line.split())\\n\\t\\tif(length == 3):\\n\\t\\t\\tif (toks[0] and toks[1] == '=' and isnum(toks[2])):\\n\\t\\t\\t\\tif toks[0] not in ident_temp:\\n\\t\\t\\t\\t\\tident_temp[toks[0]] = toks[2]\\n\\tfinal_list = [] #stores the final list of lines in the ICG after constants are propagated\\n\\tfor line in list_of_lines:\\n\\t\\tline = line.strip('\\\\n')\\n\\t\\ttoks = line.split()\\n\\t\\tlength = len(line.split())\\n\\t\\tif(length == 5 and toks[3] in arith):\\n\\t\\t\\tfor i in range(1,length):\\n\\t\\t\\t\\tif toks[i] in ident_temp:\\n\\t\\t\\t\\t\\ttoks[i] = ident_temp[toks[i]]\\n\\t\\tif(length == 3 and toks[1] == '=' and toks[2] in ident_temp):\\n\\t\\t\\ttoks[2] = ident_temp[toks[2]]\\n\\t\\tif not(length == 3 and istemp(toks[0])):\\n\\t\\t\\tfinal_list.append(' '.join(toks))\\n\\t#returns 0 if the new ICG generated is the same as the last one else 1\\n\\tif(final_list == comp):\\n\\t\\treturn final_list,0\\n\\telse:\\n\\t\\treturn final_list,1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44e1575aa88639c95eb32b72df6c098a\",\n \"score\": \"0.46179226\",\n \"text\": \"def linear(self, X):\\r\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3b655129c1caf19abf227bfa5c1abe\",\n \"score\": \"0.46147618\",\n \"text\": \"def test_relation():\\n\\n lexems = [\\n \\\"=\\\",\\n \\\"<>\\\",\\n \\\"<\\\",\\n \\\"<=\\\",\\n \\\">=\\\",\\n \\\">\\\",\\n \\\"#\\\",\\n \\\"a\\\",\\n \\\"&%4$\\\",\\n \\\"1\\\"\\n ]\\n\\n validator = Validator()\\n assert validator.validate_lexems(lexems) == [\\n {\\\"=\\\": \\\"<RELATION>\\\"},\\n {\\\"<>\\\": \\\"<RELATION>\\\"},\\n {\\\"<\\\": \\\"<RELATION>\\\"},\\n {\\\"<=\\\": \\\"<RELATION>\\\"},\\n {\\\">=\\\": \\\"<RELATION>\\\"},\\n {\\\">\\\": \\\"<RELATION>\\\"},\\n {\\\"#\\\": None},\\n {\\\"a\\\": \\\"<IDENTIFIER>\\\"},\\n {\\\"&%4$\\\": None},\\n {\\\"1\\\": \\\"<NUMBER>\\\"},\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d6cf731942ab3d5e292111dbbe6277\",\n \"score\": \"0.46122715\",\n \"text\": \"def listRelations( self ):\\n rels = []\\n rels.append(self.relations[0][0])\\n rels.append(self.relations[0][1])\\n rels.append(self.relations[1][0])\\n #for typ in 0,1:\\n # for direction in 0,1:\\n # rels.append(self.relations[typ][direction])\\n return tuple(rels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d057437362d7d921af94a2cfcc3dd829\",\n \"score\": \"0.46098673\",\n \"text\": \"def lfsr(M, gn, S0=[]):\\n if type(gn)==int:\\n L = gn\\n if L==3:\\n gn = [1,0,1,1]\\n elif L==4:\\n gn = [1,0,0,1,1]\\n elif L==5:\\n gn = [1,0,0,1,0,1]\\n elif L==6:\\n gn = [1,0,0,0,0,1,1]\\n elif L==7:\\n gn = [1,0,0,0,0,0,1,1]\\n elif L==8:\\n gn = [1,1,0,0,0,0,1,1,1]\\n elif L==9:\\n gn = [1,0,0,0,0,1,0,0,0,1]\\n elif L==10:\\n gn = [1,0,0,0,0,0,0,1,0,0,1]\\n elif L==11:\\n gn = [1,0,0,0,0,0,0,0,0,1,0,1]\\n elif L==17:\\n gn = [1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1]\\n else:\\n print('No default gn for L={}'.format(L))\\n else:\\n L = len(gn)-1\\n gn = np.array(gn, int) # make array of integers\\n if len(S0)==0:\\n yn = iir2filt(gn, np.hstack((1, np.zeros(M-1))))\\n else:\\n yn = iir2filt(gn, np.zeros(M), S0)\\n return yn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66334273c1c5cf410f1eed6d7ada98fe\",\n \"score\": \"0.46077585\",\n \"text\": \"def linear(x):\\n\\treturn x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa7b293234c6238e7301919cdee837e\",\n \"score\": \"0.4596243\",\n \"text\": \"def repr_lincomb(symbols, coeffs):\\n s = \\\"\\\"\\n first = True\\n i = 0\\n\\n all_atomic = True\\n for c in coeffs:\\n b = latex(symbols[i])\\n if c != 0:\\n if c == 1:\\n s += b\\n else:\\n coeff = coeff_repr(c)\\n if not first:\\n coeff = \\\" + %s\\\"%coeff\\n else:\\n coeff = \\\"%s\\\"%coeff\\n s += \\\"%s%s\\\"%(coeff, b)\\n first = False\\n i += 1\\n if first:\\n s = \\\"0\\\"\\n s = s.replace(\\\"+ -\\\",\\\"- \\\")\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827a5942651297e9c5f18b30706821d3\",\n \"score\": \"0.45941898\",\n \"text\": \"def linear_model_coefs(pipeline, all_docs):\\n\\n coef_df = get_coef_df(pipeline.named_steps['clf'],\\n pipeline.named_steps['vectorizer'].vocabulary_)\\n labels = coef_df['class'].unique()\\n coef_lookup = coef_df.set_index(['class', 'feature'])['coef'].to_dict()\\n\\n for doc in all_docs:\\n doc['linear_importances'] = [\\n [coef_lookup.get(label, token.lower()) for label in labels]\\n for token in doc]\\n return all_docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0bddd16cb65e1f0f429ef22416648a\",\n \"score\": \"0.45913088\",\n \"text\": \"def cover_relations(self):\\n (_, rels) = self._lattice_initializer()\\n return (_ for _ in rels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb96589b70e72fb872b34f44d9bf457\",\n \"score\": \"0.45834422\",\n \"text\": \"def analysis_line_linearity(target_list, measure_list_mm, boundary_index_list, linearity_list):\\n\\n boundary_max_linearity = [0, [0, 0]]\\n center_max_linearity = [0, [0, 0]]\\n for i in range(len(linearity_list)):\\n for j in range(len(linearity_list[i])):\\n if j in boundary_index_list[i]:\\n if linearity_list[i][j] > boundary_max_linearity[0]:\\n boundary_max_linearity[0] = linearity_list[i][j]\\n boundary_max_linearity[1] = [i, j]\\n else:\\n if linearity_list[i][j] > center_max_linearity[0]:\\n center_max_linearity[0] = linearity_list[i][j]\\n center_max_linearity[1] = [i, j]\\n print(\\\"\\\\tLinearity:\\\")\\n print_linearity_list = [[\\\"boundary\\\", boundary_max_linearity], [\\\"center \\\", center_max_linearity]]\\n for print_linearity in print_linearity_list:\\n name = print_linearity[0]\\n max_L = print_linearity[1][0]\\n max_i = print_linearity[1][1][0]\\n max_j = print_linearity[1][1][1]\\n print(\\\"\\\\t\\\\tMax %s linearity %f -------------- target: (%f %f)->(%f, %f), measure: (%f, %f), line %d\\\" \\\\\\n % ( name, max_L, target_list[max_i][0], target_list[max_i][1], target_list[max_i][2], target_list[max_i][3], \\\\\\n measure_list_mm[max_i][max_j][0], measure_list_mm[max_i][max_j][1], max_i + 1 ))\\n return boundary_max_linearity, center_max_linearity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de7808db8d8153d75b939198be4a959\",\n \"score\": \"0.45819134\",\n \"text\": \"def getOposta(matriz):\\n matriz_oposta = []\\n for linha in matriz:\\n linhaOposta = []\\n for item in linha:\\n linhaOposta.append(-item)\\n matriz_oposta.append(linhaOposta)\\n \\n return matriz_oposta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f41523fc8de9917b0226f9186b720b8e\",\n \"score\": \"0.4581452\",\n \"text\": \"def test_calc_LT_corr_linear():\\n test_ltc_linear = np.array([[1, 0],[0, 1j]])\\n assert np.allclose(\\n calc_LT_corr_linear([-1, -1j], [loperator[0], loperator[1]]),\\n test_ltc_linear\\n )\\n assert np.allclose(\\n calc_LT_corr_linear([0, 0], [loperator[0], loperator[1]]),\\n 0\\n )\\n assert np.allclose(\\n calc_LT_corr_linear([-1, -1j], [np.zeros([2,2]), np.zeros([2,2])]),\\n 0\\n )\\n assert np.allclose(\\n calc_LT_corr_linear([], []),\\n 0\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68234eff4a9e46698fc22553ecf616d2\",\n \"score\": \"0.4580524\",\n \"text\": \"def try_merge(line_wo_zeros):\\n return [ get_new_value(line_wo_zeros, index, value) for index, value in enumerate(line_wo_zeros)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93546f33bd9a73898adc67b01eb74f30\",\n \"score\": \"0.45758918\",\n \"text\": \"def LD(X_,Y_):\\n\\n # implement your codes here\\n LD_array = [[0]*(len(Y_)+1) for x in range(len(X_)+1)]\\n for x_idx in range(len(X_)+1):\\n for y_idx in range(len(Y_)+1):\\n if x_idx==0 or y_idx==0:\\n LD_array[x_idx][y_idx] = max(x_idx,y_idx)\\n else:\\n LD_array[x_idx][y_idx] = min(LD_array[x_idx-1][y_idx]+1,LD_array[x_idx][y_idx-1]+1,LD_array[x_idx-1][y_idx-1]+(X_[x_idx-1]!=Y_[y_idx-1]))\\n\\n return LD_array[len(X_)][len(Y_)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2caab65c744fd0810e867164de33d75\",\n \"score\": \"0.4575172\",\n \"text\": \"def generate_adj_lone_pair_radical_resonance_structures(mol):\\n cython.declare(structures=list, paths=list, index=cython.int, structure=Molecule)\\n cython.declare(atom=Atom, atom1=Atom, atom2=Atom)\\n cython.declare(v1=Vertex, v2=Vertex)\\n\\n structures = []\\n if mol.is_radical(): # Iterate over radicals in structure\\n for atom in mol.vertices:\\n paths = pathfinder.find_adj_lone_pair_radical_delocalization_paths(atom)\\n for atom1, atom2 in paths:\\n # Adjust to (potentially) new resonance structure\\n atom1.decrement_radical()\\n atom1.increment_lone_pairs()\\n atom1.update_charge()\\n atom2.increment_radical()\\n atom2.decrement_lone_pairs()\\n atom2.update_charge()\\n # Make a copy of structure\\n structure = mol.copy(deep=True)\\n # Restore current structure\\n atom1.increment_radical()\\n atom1.decrement_lone_pairs()\\n atom1.update_charge()\\n atom2.decrement_radical()\\n atom2.increment_lone_pairs()\\n atom2.update_charge()\\n try:\\n structure.update_atomtypes(log_species=False)\\n except AtomTypeError:\\n pass # Don't append resonance structure if it creates an undefined atomtype\\n else:\\n structures.append(structure)\\n return structures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af843c9ed610d734ef862f365a45c320\",\n \"score\": \"0.45724002\",\n \"text\": \"def pair_distances(polyline, segments_to_zero=None):\\n polyline = np.asanyarray(polyline)\\n d_vec = polyline[1:, :] - polyline[:-1, :]\\n result = np.linalg.norm(d_vec, axis=1)\\n if segments_to_zero is not None:\\n result[segments_to_zero] = 0\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bfe550fa28879c9fdf93e63f5b7f7a6\",\n \"score\": \"0.4571191\",\n \"text\": \"def search_lsi(self, doc):\\n vec_bow = dictionary.doc2bow(doc)\\n vec_lsi = lsi[vec_bow]\\n\\n return [(revind[x[0]], x[1]) for x in index_lsi[vec_lsi]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45966961a56a2e4f17842e0bcdd75ff3\",\n \"score\": \"0.45645532\",\n \"text\": \"def get_hyponym(ss):\\n out = []\\n for ss_i in ss:\\n xh = ss_i.hyponyms()\\n out += xh\\n if len(xh) != 0:\\n lt = get_hyponym(xh)\\n out += lt\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d816b0689a8a740d9d76f4833a8cbec7\",\n \"score\": \"0.45617288\",\n \"text\": \"def linear_functions(self):\\n return self._LF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54d4017eeea31d439374d5863ca22903\",\n \"score\": \"0.4560552\",\n \"text\": \"def convert_to_lm_dataset(S):\\n in_word_index, out_word_index = [], []\\n for sentence in S:\\n for i in range(len(sentence)):\\n if i < 2:\\n continue\\n in_word_index.append(sentence[i - 1])\\n out_word_index.append(sentence[i])\\n return in_word_index, out_word_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71246e074483a6489848f3ed88d9f31\",\n \"score\": \"0.45603096\",\n \"text\": \"def calc_si(l, qlms, len_neigh, qlms_neigh):\\n qlm_sum = 0.\\n for m in range(2*l+1):\\n qlm_sum += abs(qlms[m])**2\\n qlm_sum = sqrt(qlm_sum)\\n\\n si = 0.\\n for i in range(len_neigh):\\n qlm_sum_neigh = 0.\\n for m in range(2*l+1):\\n qlm_sum_neigh += abs(qlms_neigh[i, m])**2\\n qlm_sum_neigh = sqrt(qlm_sum_neigh)\\n si_inner = 0.\\n for m in range(2*l+1):\\n si_inner += (qlms[m]*qlms_neigh[i, m].conjugate()).real\\n si += si_inner/(qlm_sum*qlm_sum_neigh)\\n\\n return si/len_neigh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c1cd485044a6426e0ea1596f6c23cf\",\n \"score\": \"0.4559043\",\n \"text\": \"def solve_formulars(equations: List[List[Union[str, int]]], l_to_r: bool = True) -> List[int]:\\n return [solve_l_to_r(equation, l_to_r) for equation in equations]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98daab9733fb8fe82dd1876ca3cf3a1d\",\n \"score\": \"0.4551032\",\n \"text\": \"def seq_real(l):\\r\\n i = 0\\r\\n start = 0\\r\\n max = 0\\r\\n m1 = 0\\r\\n while i < len(l) - 1:\\r\\n if (l[i][0]) < (l[i + 1][0]):\\r\\n m1 += 1\\r\\n else:\\r\\n if m1 > max:\\r\\n max = m1\\r\\n start = i - m1\\r\\n m1 = 0\\r\\n i += 1\\r\\n if m1 > max:\\r\\n max = m1\\r\\n start = i - m1\\r\\n if max == 0:\\r\\n return None\\r\\n return l[start:start + max + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6544ae0ce90971b29166cc375d9faa50\",\n \"score\": \"0.45488253\",\n \"text\": \"def f_imo_dump1(moc_list, l_atoms, filter_tag, mo_id):\\n\\n #### change element which corresponds to l_atoms ordering, to check its existence with high MO coefficients\\n atom_tag=np.zeros(len(l_atoms))\\n if not moc_list:\\n return 0\\n else:\\n f_imo_print(moc_list)\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6671559be0a5a988908984cc824ad5c\",\n \"score\": \"0.45390823\",\n \"text\": \"def general_poly(L):\\n def g(x) :\\n total = 0 \\n if L == [] :\\n return 0\\n else :\\n for index in range(0,len(L)):\\n total += L[index]*(x**(len(L)-1-index))\\n return total\\n \\n return g\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994377,"cells":{"query_id":{"kind":"string","value":"36ca1ab1014ba94a94116668d7f0dc5e"},"query":{"kind":"string","value":"Checks if it's rush hour or not based on the time given"},"positive_passages":{"kind":"list like","value":[{"docid":"f5df4086e10a9c87b01c0088cf704504","score":"0.74231344","text":"def is_rush_hour(hour, minute):\n rush_hour = []\n\n if (5 <= hour <= 9) or (16 <= hour <= 18):\n if (hour == 5 and 45 <= minute < 60) or (5 < hour < 9):\n rush_hour = [1]\n elif (hour == 5 and minute < 45) or (hour == 9 and 0 < minute):\n rush_hour = [0]\n elif 16 <= hour or (hour <= 18 and minute == 0):\n rush_hour = [1]\n elif (9 < hour < 16) or (hour == 9 and 0 < minute < 60) or (\n 18 < hour < 24) or (0 < hour < 5):\n rush_hour = [0]\n\n return rush_hour","title":""}],"string":"[\n {\n \"docid\": \"f5df4086e10a9c87b01c0088cf704504\",\n \"score\": \"0.74231344\",\n \"text\": \"def is_rush_hour(hour, minute):\\n rush_hour = []\\n\\n if (5 <= hour <= 9) or (16 <= hour <= 18):\\n if (hour == 5 and 45 <= minute < 60) or (5 < hour < 9):\\n rush_hour = [1]\\n elif (hour == 5 and minute < 45) or (hour == 9 and 0 < minute):\\n rush_hour = [0]\\n elif 16 <= hour or (hour <= 18 and minute == 0):\\n rush_hour = [1]\\n elif (9 < hour < 16) or (hour == 9 and 0 < minute < 60) or (\\n 18 < hour < 24) or (0 < hour < 5):\\n rush_hour = [0]\\n\\n return rush_hour\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f33219fe39e54f981037f15652125072","score":"0.7023107","text":"def isPossibleHour(self, hour):\n if hour > 23 and self.isPossibleHour(0):\n hour = int(hour)\n if not self.overnight:\n return self.stTime.hour <= hour <= self.endTime.hour\n else:\n return self.stTime.hour <= hour <= 23 or \\\n 0 <= hour <= self.endTime.hour","title":""},{"docid":"b649b1c99b658d8b1329b2b2b50708f4","score":"0.6590877","text":"def is_shutdown_time_int(hour: Any, minute: Any) -> bool:\n return type(hour) is int and type(minute) is int","title":""},{"docid":"d11685a053e50f53da75eaf4e82e2b35","score":"0.65434754","text":"def hasHourInterval(self):\n \tif self.hourInterval[0] != -1: # Use -1 as invalid hour time\n \t\treturn True\n \telse:\n \t\treturn False","title":""},{"docid":"e8b8e71ca3bce6b989820ac2a2d3ceec","score":"0.6496155","text":"def _time_has_passed(self, time_container):\n now = self.now()\n return (time_container.hour < now.hour or\n (time_container.hour == now.hour and\n time_container.minute < now.minute))","title":""},{"docid":"6e7ca2492604534a45a16571efaf332e","score":"0.6490712","text":"def match_time(self, t):\n return False","title":""},{"docid":"cb96a0d752b8ea81cc847c8d40ca622b","score":"0.6484256","text":"def _is_before(check_time, start_time):\r\n if check_time.hour < start_time.hour:\r\n return True\r\n elif check_time.hour == start_time.hour:\r\n return check_time.minute < start_time.minute\r\n else:\r\n return False","title":""},{"docid":"e34c5ba3c2ddbcb6e8ae43ceb4fbaadc","score":"0.64039236","text":"def isTimeIncluded(self, time):\n # time filtering in Ladybug and honeybee is slightly different since\n # start hour and end hour will be applied for every day.\n # For instance 2/20 9am to 2/22 5pm means hour between 9-17\n # during 20, 21 and 22 of Feb.\n return time.moy in self._timestampsData","title":""},{"docid":"28ae55136e9d83d4fa383e29097347d7","score":"0.62854517","text":"def check_time(self, x, y):\n x_hours = float(x.split(\":\")[0])\n x_minutes = float(x.split(\":\")[1])\n x_seconds = float(x.split(\":\")[-1])\n y_hours = float(y.split(\":\")[0])\n y_minutes = float(y.split(\":\")[1])\n y_seconds = float(y.split(\":\")[-1])\n if x_hours == y_hours and x_minutes == y_minutes and abs(x_seconds - y_seconds) <= 1:\n return True\n return False","title":""},{"docid":"bfe8304e324699c154e45f9e21ef9b1f","score":"0.6256818","text":"def test_release_time_hour(self):\n result = orlo.stats.releases_by_time('hour', **self.ARGS)\n year = str(arrow.utcnow().year)\n month = str(arrow.utcnow().month)\n day = str(arrow.utcnow().day)\n hour = str(arrow.utcnow().hour)\n self.assertEqual(\n 7,\n result[year][month][day][hour]['normal']['successful'],\n )","title":""},{"docid":"72c96a4a17a87fdd12d2089f46416833","score":"0.6225677","text":"def is_time_for_canary_safety_scan():\n current_utc_time = time.gmtime()\n return current_utc_time.tm_hour == 16 and (0 < current_utc_time.tm_min < 20)","title":""},{"docid":"ea78f1835ef74b4f82122f39fab770d0","score":"0.6196998","text":"def check_if_time(self):\n if self._alarm_time[\"text\"] == self._current_time:\n self.reset()\n messagebox.showinfo(\"Time is up\", \"Your alarm went off\")","title":""},{"docid":"026f3f2b9c4bd572f69fe91aacabd605","score":"0.6142179","text":"def matchtime(self, t):\n return ((t.minute in self.mins) and\n (t.hour in self.hours) and\n (t.day in self.days) and\n (t.month in self.months) and\n (t.weekday() in self.dow))","title":""},{"docid":"3303b20657f6bdc13e823482f246d164","score":"0.61350214","text":"def test(hour, is_am):\r\n print hour,\r\n if is_am:\r\n print \"AM\",\r\n else:\r\n print \"PM\",\r\n if is_lunchtime(hour, is_am):\r\n print \"is lunchtime.\"\r\n else:\r\n print \"is not lunchtime.\"","title":""},{"docid":"de088e6f0e4a37ff0ce934d2ceaf3034","score":"0.6115739","text":"def main(hrs, mins):\n #Change [hours to mins]\n ##EX. hrs = 1, mins = 6 >> hand_h = 5+(0.5)\n hand_h = (hrs * 5) + (mins / 12)\n\n #find that both hand will equal or not\n if hand_h >= mins and (hand_h -mins) < 1:\n print(\"True\")\n else:\n print(\"False\")","title":""},{"docid":"9d69efebdbfbdf2364cab897f01d64f1","score":"0.607937","text":"def is_before_end_time(self):\n chile = pytz.timezone(\"America/Santiago\")\n timezone.activate(chile)\n current_date = chile.localize(datetime.utcnow())\n end_time = timezone.datetime(\n current_date.year,\n current_date.month,\n current_date.day,\n hour=self.time_over,\n minute=0,\n second=0,\n microsecond=0,\n tzinfo=chile,\n ).astimezone(pytz.utc)\n print(end_time)\n if current_date.hour < end_time.hour:\n return True\n return False","title":""},{"docid":"14f1dbfdf33593e2db0dc2b13e964496","score":"0.6071197","text":"def test_hour_range_pos(self):\n self.assertTrue( hour_man.checkHourRange(0) )","title":""},{"docid":"35a91f8ec5ec7d04017c5ab9857d9263","score":"0.60695815","text":"def valid_hour(hour):\n \n debug = False\n\n try:\n num_hour = int(hour)\n except:\n print 'Hour is not an integer:', hour\n return False\n if not(num_hour in range(0,24)):\n print 'There are only 24 hours in the day. Value too large!'\n return False\n elif len(hour) == 0:\n print 'No hour value provided!'\n return False\n else:\n if debug:\n print 'Valid hour detected:', hour \n return True","title":""},{"docid":"36d95340dd276b4dfe3be8d67b8dacab","score":"0.6062433","text":"def is_valid_time(hour, minute, second):\n return 0 <= int(hour) <= 23 and 0 <= int(minute) < 60 and 0 <= int(second) < 60","title":""},{"docid":"6d89fea85408dc30a816618e56210af0","score":"0.59975475","text":"def _is_time(self) -> bool:\n\n date_str = read_file(get_tmp_path('.stamp'))\n\n year, month, day = list(map(int, date_str.split('-')))\n\n last_run = datetime(year, month, day)\n\n diff = self.now - last_run\n\n logger.info(\"Time Interval: {}\".format(diff))\n\n if diff > timedelta(hours=self.INTERVAL):\n return True\n\n return False","title":""},{"docid":"f7a3e4e5bbf891e71d04a74a0685df7a","score":"0.59947306","text":"def test_hour_range_neg(self):\n self.assertFalse( hour_man.checkHourRange(24) )","title":""},{"docid":"eb9fc1f1c6013c564c914756b21a064f","score":"0.59694535","text":"def is_reasonable_hour_for_text_messages(self):\n tz = pytz.timezone('US/Eastern')\n now = (datetime.now(tz))\n eight_am = (datetime.now(tz).replace(hour=8, minute=0, second=0, microsecond=0))\n ten_pm = (datetime.now(tz).replace(hour=22, minute=0, second=0, microsecond=0))\n return eight_am <= now <= ten_pm","title":""},{"docid":"497a6c962ae321ffdf28c9ee8f6fae7d","score":"0.59676623","text":"def work_hour():\n #date planned\n date_planned = datetime.datetime.today()\n\n # 4:50 PM\n five_pm = datetime.datetime.today().replace(hour=16,minute=50,second=00)\n # current date\n now = datetime.datetime.today()\n # time left, in seconds, before 5:0 PM\n time_left = (five_pm-now).total_seconds()\n\n #if less than 10 min left before 5:00 PM\n #report to the day after\n if time_left < 0:\n date_planned = date_planned+datetime.timedelta(days=1)\n\n # if the planned/current day is weekend\n # report to monday\n weekday = date_planned.weekday()\n if not 0<=weekday<=4:\n date_planned = date_planned+datetime.timedelta(\n days=7-weekday)\n\n\n while True:\n hour,minute = _schedule()\n # schedule date\n sched = date_planned.replace(hour=hour,minute=minute)\n # timedelta\n delta = sched-now\n if (delta.days>0\n or (delta.days==0 and delta.seconds>0)):\n #date_planned = sched\n return sched #date_planned","title":""},{"docid":"8a786d95454c48007a50be8b1d6ab969","score":"0.59159666","text":"def is_valid(self, hour, minute, second):\n if hour < 0 or minute < 0 or second < 0:\n return False\n if minute >= 60 or second >= 60:\n return False\n return True","title":""},{"docid":"d5bbebd09183864c06d0de0bb9e0b6a2","score":"0.5866292","text":"def is_daytime(date_time):\r\n\r\n time = date_time.time()\r\n # When the start of daytime is earlier in the day than the start of nighttime\r\n if day_start < night_start:\r\n if time >= day_start and time < night_start:\r\n return True\r\n # When the start of nighttime is earlier in the day than the start of daytime\r\n elif night_start < day_start:\r\n if not (time >= night_start and time < day_start):\r\n return True\r\n\r\n return False","title":""},{"docid":"d8486b6750a1bea40c9a55e8c4634e03","score":"0.5849143","text":"def share_same_hour(d1: datetime, d2: datetime) -> bool:\n h1 = d1.replace(microsecond=0, second=0, minute=0)\n h2 = d2.replace(microsecond=0, second=0, minute=0)\n return h1 == h2","title":""},{"docid":"703194746a8edbaee4f8792130f12dc9","score":"0.5836504","text":"def test_package_time_hour(self):\n result = orlo.stats.packages_by_time('hour', **self.ARGS)\n year = str(arrow.utcnow().year)\n month = str(arrow.utcnow().month)\n day = str(arrow.utcnow().day)\n hour = str(arrow.utcnow().hour)\n self.assertEqual(\n 7,\n result[year][month][day][hour]['test-package']['normal']['successful'],\n )","title":""},{"docid":"ba1c7868ed23dd09fd34a2af1740ad66","score":"0.5835717","text":"def check_times(now,cron):\n # print now,cron\n\n if now['minute'] in cron['minute'] and now['hour'] in cron['hour'] and now['day'] in cron['day'] and now['month'] in cron['month'] and now['week'] in cron['week']:\n print 'Cron matched'\n return True\n else:\n return False","title":""},{"docid":"395eb2c036cd5e8705f4684dd97e3c9c","score":"0.58140504","text":"def is_unique_grid_time(self):\n dims = self.get_geolocation_field('time').dimensions.values()[0]\n return (dims == 1)","title":""},{"docid":"95e028a5d9b2b8b121778bd6d7ab603f","score":"0.5797887","text":"def match_time(self, t):\n if not self.enabled:\n return False\n\n if not self._next_run:\n self._next_run = t + timedelta(minutes=self._ev_minutes, hours=self._ev_hours, days=self._ev_days)\n\n if t >= self._next_run:\n self._next_run = t + timedelta(minutes=self._ev_minutes, hours=self._ev_hours, days=self._ev_days)\n return True\n\n return False","title":""},{"docid":"68438fa2a14f4e9b16921171c33b09bc","score":"0.5779742","text":"def hour_rounder(t):\n # Rounds to nearest hour by adding a timedelta hour if minute >= 30\n return (t.replace(second=0, microsecond=0, minute=0, hour=t.hour)\n +datetime.timedelta(hours=t.minute//30))","title":""},{"docid":"23d15fbb9acc50ab13740d111dd333bc","score":"0.5777177","text":"def same_time_with_margin(hora):\n\n if hora is None:\n return False\n\n\n tz = pytz.timezone('America/Sao_Paulo')\n today = datetime.now(tz)\n\n minutes_today = int(today.hour * 60 + today.minute)\n\n hours, minutes = map(int, hora.split(':'))\n minutes_notification = hours * 60 + minutes\n\n # print(hora, \"{}:{}\".format(today.hour, today.minute))\n return abs(minutes_today - minutes_notification) <= 5","title":""},{"docid":"164e402f8088014227da94b72aa87b3a","score":"0.5746601","text":"def checktime(id):\n # we distribute it randomly in beteen 5th-1435th minute of day\n return binascii.crc32(id)%1430+5 if not isinstance(id, int) else id%1430+5","title":""},{"docid":"da314095117238ab1ad935b4dbb1b5e8","score":"0.57456654","text":"def under_hours(self):\n if self.worked_hours == 0: # worked hours was just added, don't want false positives\n return False\n return self.worked_hours < self.allocated_hours","title":""},{"docid":"12485ee511e0f79fd2fa5db2acbce48d","score":"0.57390714","text":"def __contains__(self, time):\r\n t = parse_time(time)\r\n return t >= self.t1 and t <= self.t2","title":""},{"docid":"0d08f7fbe65632a8fe2c771a150ac31f","score":"0.5738579","text":"def wish_me():\r\n hour = int(datetime.now().hour)\r\n if hour <= 12:\r\n print(\"Leo:\\n\\tGood morning\")\r\n speak(\"Good morning\")\r\n elif 12 < hour <= 18:\r\n print(\"Leo:\\n\\tGood Afternoon\")\r\n speak(\"Good Afternoon\")\r\n elif 18 < hour <= 24:\r\n print(\"Leo:\\n\\tGood Evening\")\r\n speak(\"Good Evening\")\r\n else:\r\n speak(\"You are not earth \")","title":""},{"docid":"39f2b07c9098e921ae38fd4e484ba9cd","score":"0.5737583","text":"def is_booking_time():\r\n return datetime.time(2, 16) <= now_time() <= datetime.time(23, 59) or \\\r\n datetime.time(0, 0) <= now_time() <= datetime.time(1, 59)","title":""},{"docid":"fcc038385ad88cb440d361b787c75963","score":"0.5727017","text":"def match_time(self, dt):\n if self.onetime:\n return self.onetime <= dt\n return ((dt.minute in self.minutes) and (dt.hour in self.hours) and (dt.day in self.days) and\n (dt.month in self.months) and (dt.weekday() in self.dow))","title":""},{"docid":"e262e2b9294fdbe9484024aac10b7345","score":"0.5722993","text":"def matching_day_hour(day_num, hour, match_id):\n if get_day_num(match_id) == day_num and get_hour(match_id) == hour:\n return True\n else:\n return False","title":""},{"docid":"ae59d6690a09463730edcc40a701d7ee","score":"0.571834","text":"def is_valid_time(self, d):\r\n if not d:\r\n return str()\r\n elif len(d) != 4:\r\n return str()\r\n\r\n # Check hours\r\n h = d[0] * 10 + d[1]\r\n if h >= 24:\r\n return False\r\n\r\n # Check minutes\r\n m = d[2] * 10 + d[3]\r\n if m >= 60:\r\n return False\r\n\r\n # Found valid time\r\n return True","title":""},{"docid":"5c04a23f37fdc8d33d8b65089524e788","score":"0.5718189","text":"def __pass_time(self):\n seconds = calendar.timegm(time.gmtime())\n current_second = seconds % 60\n minutes = seconds // 60\n current_minute = minutes % 60\n hours = minutes // 60\n current_hour = hours % 24\n\n if 1 in current_minute:\n self.__hunger +=1\n self.__boredome += 1","title":""},{"docid":"633b2a69cd405ca77b244d839b9763c2","score":"0.5704044","text":"def test_datetime_hourly():\n clear_metrics()\n\n with freeze_time(TWELVE_FIFTY_NINE_AM - ONE_MINUTE):\n when = hourly()\n assert when is not None\n assert isinstance(when, datetime.datetime)\n assert when == TWELVE_FIFTY_NINE_AM - ONE_MINUTE\n\n with freeze_time(TWELVE_FIFTY_NINE_AM):\n when = hourly()\n assert when is None\n\n ####\n # NEXT HOUR\n ####\n\n with freeze_time(ONE_AM):\n when = hourly()\n assert when is not None\n assert isinstance(when, datetime.datetime)\n assert when == ONE_AM\n\n # Already triggered, shouldn't trigger again\n with freeze_time(ONE_AM + ONE_MINUTE):\n when = hourly()\n assert when is None","title":""},{"docid":"7392091c0c314ab491965c2fddceed49","score":"0.5683123","text":"async def test_time_window(hass):\n sixam = dt.parse_time(\"06:00:00\")\n sixpm = dt.parse_time(\"18:00:00\")\n\n with patch(\n \"homeassistant.helpers.condition.dt_util.now\",\n return_value=dt.now().replace(hour=3),\n ):\n assert not condition.time(after=sixam, before=sixpm)\n assert condition.time(after=sixpm, before=sixam)\n\n with patch(\n \"homeassistant.helpers.condition.dt_util.now\",\n return_value=dt.now().replace(hour=9),\n ):\n assert condition.time(after=sixam, before=sixpm)\n assert not condition.time(after=sixpm, before=sixam)\n\n with patch(\n \"homeassistant.helpers.condition.dt_util.now\",\n return_value=dt.now().replace(hour=15),\n ):\n assert condition.time(after=sixam, before=sixpm)\n assert not condition.time(after=sixpm, before=sixam)\n\n with patch(\n \"homeassistant.helpers.condition.dt_util.now\",\n return_value=dt.now().replace(hour=21),\n ):\n assert not condition.time(after=sixam, before=sixpm)\n assert condition.time(after=sixpm, before=sixam)","title":""},{"docid":"afc7d5b193be73950a4cdbf303505c65","score":"0.5667082","text":"def remaintime_hour(self):\n return self._get_time_info([\"Remain_Time_H\", \"remainTimeHour\"])","title":""},{"docid":"23cd3915a69168b35384be1f983ac420","score":"0.56623095","text":"def reservetime_hour(self):\n return self._get_time_info([\"Reserve_Time_H\", \"reserveTimeHour\"])","title":""},{"docid":"5af3ce01526a6cdc284c1bf82fa42a0b","score":"0.56597525","text":"def check_runtime():\n\n hours_to_run = CONFIG.get(\"hours_to_run\")\n if hours_to_run:\n return (datetime.now() - START_TIME).total_seconds() / 60 / 60 < hours_to_run\n else:\n return True","title":""},{"docid":"867a52b93f000d8c7aa84a61f85d6521","score":"0.56594294","text":"def check_time():\n times = get_times()\n time_difference = abs((times['local'] - times['target']).total_seconds())\n return time_difference < post_time_tol_seconds","title":""},{"docid":"726cc573d4fa4b4eb7f3b9ba4013f8bc","score":"0.5657636","text":"def _check_time(self, cr, uid, ids, context=None):\n for act in self.browse(cr, uid, ids, context):\n if act.cat_subtype == 'hall':\n if ( (act.start_time) >= (act.end_time) ):\n raise osv.except_osv(_(''), _(\"Contract Start Time Must Be Less Than End Time!\"))\n return True","title":""},{"docid":"97bfa5a05c3f03dabc007e8299ea3fc8","score":"0.565763","text":"def hours(datetime):\n raise RuntimeError('hours' + error_message)","title":""},{"docid":"f73fee70a013252acf5fabdd92c7be4e","score":"0.5648897","text":"def find_era_date_hour(radar_pass):\n t = radar_pass.get_representative_time()\n if t.minute >= 30:\n rounded_time = t.replace(\n second=0, microsecond=0, minute=0, hour=t.hour+1)\n else:\n rounded_time = t.replace(second=0, microsecond=0, minute=0)\n return datetime.datetime.strftime(rounded_time, '%Y%m%d'), rounded_time.hour","title":""},{"docid":"9cf9ad23f45e3183e682e15d2b060076","score":"0.5642401","text":"def is_time_question_expired(self, min = 20):\n return ((time.time() - self.time) / 3600) >= min","title":""},{"docid":"82327026814e1fbffafe9356c9e4a688","score":"0.562343","text":"def check_time(self, player1, player2):\n\n # if the current time is less than the end time\n if time.time() <= self.end_time:\n pass\n else:\n # if time expires and player1 has highest score\n if player1.score > player2.score:\n print('\\n\\tTime has expired!\\n\\t{} wins with a score of {}'.format(player1.name, player1.score))\n exit()\n # if time expires and player2 has highest score\n elif player2.score > player1.score:\n print('\\n\\tTime has expired!\\n\\t{} wins with a score of {}'.format(player2.name, player2.score))\n exit()\n # if time expires and players have equal scores\n elif player1.score == player2.score:\n print('\\n\\tTime has expired!\\n\\tHowever, a winner can\\'t be determined.')\n exit()","title":""},{"docid":"d63b3f4bc2c7a6d88480e9bb0c75a121","score":"0.5622015","text":"def get_target_hour(popular_hour_arg: int) -> int:\n utc_now = datetime.datetime.utcnow().hour\n target = popular_hour_arg - utc_now\n if target < -11:\n target += 22\n elif target > 12:\n target -= 22\n return target","title":""},{"docid":"48450ae52bc5fa0414b355271f82d65a","score":"0.5620814","text":"def hour(self):\n\t\treturn self.time.hour","title":""},{"docid":"f03614e188381db1f3e7f530d264fa06","score":"0.56165904","text":"def chek_last_time():\n time=db.fetch_time()\n if (datetime.timestamp(datetime.now())-time<600):\n return True\n else:\n return False","title":""},{"docid":"904af1108942a7c93eb43b39f3c03094","score":"0.5604639","text":"def over_hours(self):\n return self.worked_hours > self.allocated_hours","title":""},{"docid":"4f170083fabbda29139944e37f1bb4b6","score":"0.5598047","text":"def time_validator(time):\n try:\n datetime.datetime.strptime(time, TIME_MASK)\n except (ValueError, TypeError):\n return False\n\n return True","title":""},{"docid":"564ab12fcfd6094ed0bfd6857d81c6e8","score":"0.55786884","text":"def isNight(self):\n now = datetime.now().time()\n return (now > night_min) or ( now < night_max)","title":""},{"docid":"c538cede1cb23542b2cc1c1e861131ca","score":"0.5576612","text":"def same_time(self, time):\n return self.__time == time","title":""},{"docid":"4a2a6e9c0b802cd83cb01fca5a32d6c1","score":"0.5570722","text":"def time_to_call_it_a_day(settings, start_time):\n if settings.timeout <= 0:\n return False\n return time.time() >= start_time + settings.timeout","title":""},{"docid":"a8be4a6dba64e65bcad51cbfda3e1b04","score":"0.55681294","text":"def checkIsHours(value):\n if not isinstance(value, str):\n return False\n if '*' in value:\n return False\n if '+' in value:\n return False\n if '-' in value:\n return False\n\n return True","title":""},{"docid":"005251297c9dc51fab54419e04d7368b","score":"0.5548757","text":"def is_time(self) -> bool:\n eps = 0.0003\n cur_full_time = str(datetime.now())\n cur_time = cur_full_time.split(\" \")[-1]\n seconds = float(cur_time.split(\":\")[-1])\n submit_seconds = float(self.submit_time.split(\":\")[-1])\n if seconds >= submit_seconds or abs(submit_seconds-seconds) <= eps:\n self.submit()\n return True\n return False","title":""},{"docid":"de68ed8e60d4548e14e755637bfe0110","score":"0.5548292","text":"def isTimeReconnect(self):\r\n start = self.core.config['reconnect']['startTime'].split(\":\")\r\n end = self.core.config['reconnect']['endTime'].split(\":\")\r\n return compare_time(start, end) and self.core.config[\"reconnect\"][\"activated\"]","title":""},{"docid":"970a407a3836e4c46add7576a2b461d4","score":"0.55479705","text":"def within_schedule(utc, *timezones):\n utc = utc.replace(tzinfo=tz.UTC)\n\n for timezone in timezones:\n timezone = tz.gettz(timezone)\n if not timezone:\n raise ValueError\n date = utc.astimezone(timezone)\n\n if not date.hour in MEETING_HOURS:\n return False\n\n return True","title":""},{"docid":"29bd9f8197faa5304e03d53fd16b1df2","score":"0.55452836","text":"def diffTime(self, hrs, mins):\n return hrs != self._hours or mins != self._minutes","title":""},{"docid":"b527f16ce48c8bf4d361ede256c198ad","score":"0.55392283","text":"def isha_time(self):\n \n fmt = to_hrtime(self._isha)\n if \"00\" in fmt:\n fmt = fmt.replace('00','12')\n return fmt","title":""},{"docid":"7a5197d5fbcc0baf41eb744f5fe58294","score":"0.55368036","text":"def _should_refresh():\n (start_time, end_time) = app.config['REFRESH_INTERVAL']\n now = datetime.datetime.utcnow()\n\n start_offset = start_time.hour*60 + start_time.minute\n end_offset = end_time.hour*60 + end_time.minute\n cur_offset = now.hour*60 + now.minute\n\n # no refresh when outside from refresh interval boundaries\n return bool(cur_offset >= start_offset and cur_offset <= end_offset)","title":""},{"docid":"7aefa206062aa7f4bca384be656ee02b","score":"0.5530637","text":"def can_be_picked_same_time(order1, order2):\n\ttime = difference_in_placement_time(order1, order2)\n\tdist = difference_in_pickup_distance(order1, order2)\n\tif time < hour and dist < max_pickup_distance_apart:\n\t\treturn True\n\telse:\n\t\treturn False","title":""},{"docid":"cd50a589ff5b421b39f7b81f0eadd399","score":"0.55251795","text":"def _check_request_time(self, request_data):\r\n try:\r\n diff_time = int(int(time.time()) - int(request_data.get('Timestamp', 0)))\r\n if diff_time <= int(self.hmac_time):\r\n return True\r\n return False\r\n except:\r\n return False","title":""},{"docid":"8f3233c2fc40651d80eb337144fecd97","score":"0.5523324","text":"def test_invalid_time(request, historian, publish_agent, query_agent,\n clean_db_rows):\n\n global query_points, DEVICES_ALL_TOPIC\n # print('HOME', volttron_instance.volttron_home)\n print(\"\\n** test_invalid_time for {}**\".format(\n request.keywords.node.name))\n\n # Create timestamp\n now = '2015-12-17 60:00:00.000000'\n\n try:\n # query with invalid timestamp\n query_agent.vip.rpc.call(identity,\n 'query',\n topic=query_points['mixed_point'],\n start=now,\n count=20,\n order=\"LAST_TO_FIRST\").get(timeout=10)\n except RemoteError as error:\n print (\"exception: {}\".format(error))\n assert 'hour must be in 0..23' == error.message","title":""},{"docid":"f473c64231879be2dc9a5326e6c74633","score":"0.5514971","text":"def test_gst(self):\n gst_compare = np.array([6.5971168570494854, 18.629694220878296,\n 18.62997275921186, 18.630251297545389,\n 6.6631074284018244])\n gst = self.t1.sidereal_time('apparent', 'greenwich')\n assert allclose_hours(gst.value, gst_compare)","title":""},{"docid":"4ac456435d1056e6e8c40ce20874a54f","score":"0.5514044","text":"def show_is_live():\n if _debug_mode():\n return True\n\n now = datetime.datetime.now()\n day = now.strftime('%A')\n hour = now.strftime('%H')\n\n return day == 'Friday' and 8 <= int(hour) <= 11","title":""},{"docid":"f145e7e19d266906314d08f1569c9247","score":"0.5504251","text":"def check_interval_time(interval_time: Dict):\n\n check_status = False\n interval_types = interval_time.keys()\n for interval_type, interval_value in interval_time.items():\n if interval_type == 'minute' and interval_value in range(0, 60):\n check_status = True\n elif all([interval_type == 'hour',\n interval_value in range(0, 24),\n 'minute' in interval_types]):\n check_status = True\n elif all([interval_type == 'weekday',\n interval_value in range(0, 7),\n 'hour' in interval_types,\n 'minute' in interval_types]):\n check_status = True\n else:\n check_status = False\n if not check_status:\n break\n return check_status","title":""},{"docid":"f40be2429082117103b37b488f48accd","score":"0.5499477","text":"def determine_time(self, time_token):\n hour, minute = parse_ambiguous_time(time_token)\n if (hour or minute):\n # The ambiguous hours arranged in order, according to Google Calendar:\n # 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6\n if 1 <= hour and hour <= 6:\n hour += 12\n else:\n tmp, _ = self._extract_time(time_token, ACCEPTED_TIME_FORMATS)\n if not tmp:\n LOG.debug('%s did not match any expected time formats')\n return None\n hour = tmp.hour\n minute = tmp.minute\n return datetime.time(hour=hour, minute=minute)","title":""},{"docid":"21fdd35f259a2355d5c2d51ca59672eb","score":"0.5488933","text":"def timeout_time(self):\n if not self.timeout_hours == 0: # never timeout through time\n if self.timestamp_creation + datetime.timedelta(hours=self.timeout_hours) < timezone.now():\n return True","title":""},{"docid":"55f8f9b6db21bbce5c9526392c9a8698","score":"0.54867303","text":"def trading_day_started() -> bool:\n if SYSTEM_TIME[0] > 16:\n return True\n elif SYSTEM_TIME[0] >= 16 and SYSTEM_TIME[1] >= 30:\n return True\n else:\n return False","title":""},{"docid":"b22ea55918edd36fd859870572ab0502","score":"0.54853714","text":"def check_for_time_interval(self):\n return (time.time() - self.time_interval_start) >= self.time_interval","title":""},{"docid":"6530a55d326abf7b9c529bd6caa68cf5","score":"0.5482748","text":"def is_after(t1, t2):\n\tis_after_hour = t1.hour - t2.hour\n\tis_after_minute = t1.minute - t2.minute\n\tis_after_second = t1.second - t2.second\n\treturn (is_after_hour > 0) or (is_after_hour == 0 and is_after_minute > 0) or (is_after_hour == 0 and is_after_minute == 0 and is_after_second > 0)","title":""},{"docid":"b92b3ab93ec383e788d5b82cab22e9be","score":"0.5482427","text":"def areHoursVerified(self):\n # def\n result = True\n # store\n intervalSt = self._timekprAdminFormBuilder.get_object(\"TimekprHourIntervalsLS\")\n # loop through all\n for rInt in intervalSt:\n # check whether it has been verified (id = -1 means not verified)\n if rInt[0] < 0 and not (rInt[4] == 0 == rInt[5]):\n # not verified\n result = False\n break\n # result\n return result","title":""},{"docid":"e331f93b790b249e63c880801452b530","score":"0.5474753","text":"def test_round_to_hour():\n dt = datetime.datetime(2009,1,3,9,30,46)\n dt_rounded = datetime.datetime(2009,1,3,10,0,0)\n assert dateutil.round_to_hour(dt) == dt_rounded\n\n dt = datetime.datetime(2009,1,3,9,29,14)\n dt_rounded = datetime.datetime(2009,1,3,9,0,0)\n assert dateutil.round_to_hour(dt) == dt_rounded","title":""},{"docid":"d5bbc8db511adc95f34a66223b57aafa","score":"0.54691976","text":"def test_businss_hour_fails(self, json_loader, hour_processor):\n expected_response = {'success': False, 'message': 'Please provide the time data'}\n self.assertEqual(expected_response, get_business_hours())","title":""},{"docid":"64c8455977d57ad9ffcc2bb5e7ceaab3","score":"0.5459021","text":"def extract_hour(time):\n hour = int(time[-2] + time[-1])\n return(hour)","title":""},{"docid":"5a540ff1ca1a07ff27f1f940cf4e2b8a","score":"0.5450342","text":"def haspassed(value):\n now = timezone.now()\n return value < now","title":""},{"docid":"fe2b21f2abb9a7780cd9c19d2af114d1","score":"0.54282224","text":"def hour_match(inputs, pattern):\n\n try:\n shifter_data = inputs[\"qualified_shifter\"][\"confirmedshifthours\"]\n vacated_data = inputs[\"vacated_params\"]\n shifter_hour_list = []\n for data in shifter_data:\n status = DateTimeDuration.is_date_match(data, vacated_data, \"date\")\n if status is True:\n shifter_hour_list = DateTimeDuration.get_hour_list(data[\"starttime\"], data[\"endtime\"])\n vacated_hour_list = DateTimeDuration.get_hour_list(vacated_data[\"startTime\"], vacated_data[\"endTime\"])\n instance = {\n \"hour_match\": {\n \"input\": {\n \"shifter_available_hour\": shifter_hour_list,\n \"vacated_shifter_hour\": vacated_hour_list\n },\n \"pattern\": pattern\n }\n }\n return instance\n except BaseException:\n return None","title":""},{"docid":"5532fcd501c0e5cbd6afcb5c80869040","score":"0.5427947","text":"def is_time(self):\n now = datetime.now()\n start = datetime(now.year, now.month, now.day, self._start_time[0], self._start_time[1], 0)\n stop = datetime(now.year, now.month, now.day, self._stop_time[0], self._stop_time[1], 0)\n\n # check if today should be considered in the schedule\n if now.strftime(\"%A\") not in self._days:\n self._is_active = False\n return False\n\n if start <= now < stop:\n if not self._is_active:\n self._is_active = True\n return True\n else:\n return False\n else:\n self._is_active = False\n return False","title":""},{"docid":"7c45aa784dbacb87d021ab82061440ee","score":"0.541685","text":"def verify_x_hours(mail_date, hours):\n parsed_date = email.utils.parsedate_tz(mail_date)\n utc_timestamp = email.utils.mktime_tz(parsed_date)\n utc_datetime = datetime.datetime.utcfromtimestamp(utc_timestamp)\n utc_now = datetime.datetime.utcnow()\n return utc_datetime >= utc_now - datetime.timedelta(hours=hours)","title":""},{"docid":"ffaf23ce48839047c38b5099e282c770","score":"0.5405138","text":"def within_schedule(utc, *timezones):\n utc_tz = pytz.timezone('UTC')\n utc = utc_tz.localize(utc)\n hours = []\n for tz in timezones:\n if tz not in TIMEZONES:\n raise ValueError\n else:\n local_time = utc.astimezone(pytz.timezone(tz)).hour\n hours.append(local_time)\n if all(hour in MEETING_HOURS for hour in hours):\n return True\n return False","title":""},{"docid":"39101aadad59ad5e355673594e858993","score":"0.53969926","text":"def time_check(self, dt):\n alarm_time_dt = self.to_datetime(self.alarm_time)\n if (alarm_time_dt - timedelta(seconds=1) <= \n self.parent.parent.time <= alarm_time_dt + \n timedelta(seconds=1)) and not self.playing:\n self.playing = True\n self.sound.play()\n Clock.schedule_once(\n self.remove, 60\n )","title":""},{"docid":"369dbf599d3b23d1bf95637e40fea496","score":"0.5394961","text":"def test_hr(self):\n q = quantity.Time(1.0, \"hr\")\n self.assertAlmostEqual(q.value, 1.0, 6)\n self.assertAlmostEqual(q.value_si, 3600.0, delta=1e-6)\n self.assertEqual(q.units, \"hr\")","title":""},{"docid":"3183e2b0e776d4f7cff88b092538148a","score":"0.5394515","text":"def hour(self):\n return self.nanosecond_time // Time.HOUR","title":""},{"docid":"ac64713875816d4929d61cb59ace49fa","score":"0.53901285","text":"def is_time(self):\n curt = time.time()\n if curt - self.last_time > self.delay:\n self.last_time = curt\n return True\n return False","title":""},{"docid":"24844ef7a49f8e92062d3f4f5125fb18","score":"0.5370857","text":"def test_release_time_with_only_this_unit(self):\n result = orlo.stats.releases_by_time('hour', summarize_by_unit=True, **self.ARGS)\n hour = str(arrow.utcnow().hour)\n self.assertEqual(\n 7,\n result[hour]['normal']['successful'],\n )","title":""},{"docid":"0c84262c471022534c98cd2d32788c63","score":"0.53654754","text":"def has_irregular_time(self):\n\n irregular_start = self.course.default_start_time is not None \\\n and self._time(self.course.default_start_time) != self.start_time\n irregular_end = self.course.default_end_time is not None \\\n and self._time(self.course.default_end_time) != self.end_time\n return irregular_start or irregular_end","title":""},{"docid":"83a240d689aa5784dff2ca7717c56dd9","score":"0.53598255","text":"def valid_times(self):\n if not self.day[0].start_time == pam.utils.minutes_to_datetime(0):\n return False\n\n for i in range(self.length - 1):\n if not self.day[i].end_time == self.day[i+1].start_time:\n return False\n\n if not self.day[-1].end_time == pam.variables.END_OF_DAY:\n return False\n\n return True","title":""},{"docid":"f4e537b2f2ac5b02f16c75084e8a71f7","score":"0.534602","text":"def _in_range(filepath):\n # Get the date in form 2020-MM-DD-HH from file if present, None if not.\n file_date_hour = extract_date_hour(filepath)\n if not file_date_hour: # No date-hour in file\n return False\n if low_date_hour and file_date_hour < low_date_hour: # Too early.\n return False\n if high_date_hour and file_date_hour > high_date_hour: # Too late.\n return False\n return True","title":""},{"docid":"c2f5f12a05cd8d7e18ea7188ab3a3ad5","score":"0.5345923","text":"def _is_between(check_time, start_time, end_time):\r\n if start_time.hour < check_time.hour < end_time.hour:\r\n return True\r\n elif check_time.hour == start_time.hour and check_time.hour < \\\r\n end_time.hour:\r\n return check_time.minute >= start_time.minute\r\n elif check_time.hour > start_time.hour and check_time.hour == \\\r\n end_time.hour:\r\n return check_time.minute <= end_time.minute\r\n elif check_time.hour == start_time.hour and check_time.hour == \\\r\n end_time.hour:\r\n return start_time.minute <= check_time.minute <= end_time.minute\r\n else:\r\n return False","title":""},{"docid":"b5838f1c67f51f4cef2c5ba6e46f0c83","score":"0.53443825","text":"def is_night(self):\n day_length = self.configs[\"parameters\"][\"DAY_LENGTH\"]\n cycle_length = day_length + self.configs[\"parameters\"][\"NIGHT_LENGTH\"]\n return (self.state[\"turn\"] % cycle_length) >= day_length","title":""},{"docid":"dd9bf3d87077de747dd17ccf535c3658","score":"0.5333783","text":"def wish():\n hour = int(datetime.now().hour)\n if(hour>=0) and (hour<12):\n Ai_gttss.gttts('A Very Good Morning')\n elif(hour>=12) and (hour<16):\n Ai_gttss.gttts('Good Afternoon, How\\'s your day going?')\n elif(hour>=16) and (hour<20):\n Ai_gttss.gttts('Good Evening, How was your day ?')\n elif(hour>=20) and (hour<24):\n Ai_gttss.gttts('Welcome back!, hope you\\'re having a great day!')","title":""},{"docid":"6d7cb8275b1fbf8e0f906f421eaf58dc","score":"0.53114665","text":"def _test_time(control_time, test_time):\n return control_time > test_time","title":""},{"docid":"74c5cd1041b7af76501c11793a5cacdf","score":"0.53092146","text":"def _is_polling_time(self) -> bool:\n if self._poll_outside_execution_window:\n return True\n device_execution_windows = self._get_device_execution_windows()\n cur_time = datetime.utcnow()\n for window in device_execution_windows:\n day_no = cur_time.weekday()\n day_of_week = cur_time.strftime(\"%A\")\n if window.executionDay != ExecutionDay.EVERYDAY:\n if window.executionDay == ExecutionDay.WEEKDAYS and day_no >= 5:\n continue\n if window.executionDay == ExecutionDay.WEEKENDS and day_no < 5:\n continue\n if (\n window.executionDay not in [ExecutionDay.WEEKENDS, ExecutionDay.WEEKDAYS]\n and window.executionDay != day_of_week\n ):\n continue\n if (\n cur_time.time() >= window.windowStartHour\n and cur_time.time() <= window.windowEndHour\n ):\n return True\n return False","title":""},{"docid":"f032c4f4f01af41f8b2f1c575baf1416","score":"0.5306526","text":"def _getHourFromInput(rawHour: str, isPm: bool) -> int:\n rawHourInt = int(rawHour)\n if isPm:\n if rawHourInt == 12:\n return 12\n else:\n return (rawHourInt + 12)\n else:\n if rawHourInt == 12:\n return 0\n else:\n return rawHourInt","title":""}],"string":"[\n {\n \"docid\": \"f33219fe39e54f981037f15652125072\",\n \"score\": \"0.7023107\",\n \"text\": \"def isPossibleHour(self, hour):\\n if hour > 23 and self.isPossibleHour(0):\\n hour = int(hour)\\n if not self.overnight:\\n return self.stTime.hour <= hour <= self.endTime.hour\\n else:\\n return self.stTime.hour <= hour <= 23 or \\\\\\n 0 <= hour <= self.endTime.hour\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b649b1c99b658d8b1329b2b2b50708f4\",\n \"score\": \"0.6590877\",\n \"text\": \"def is_shutdown_time_int(hour: Any, minute: Any) -> bool:\\n return type(hour) is int and type(minute) is int\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11685a053e50f53da75eaf4e82e2b35\",\n \"score\": \"0.65434754\",\n \"text\": \"def hasHourInterval(self):\\n \\tif self.hourInterval[0] != -1: # Use -1 as invalid hour time\\n \\t\\treturn True\\n \\telse:\\n \\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b8e71ca3bce6b989820ac2a2d3ceec\",\n \"score\": \"0.6496155\",\n \"text\": \"def _time_has_passed(self, time_container):\\n now = self.now()\\n return (time_container.hour < now.hour or\\n (time_container.hour == now.hour and\\n time_container.minute < now.minute))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7ca2492604534a45a16571efaf332e\",\n \"score\": \"0.6490712\",\n \"text\": \"def match_time(self, t):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb96a0d752b8ea81cc847c8d40ca622b\",\n \"score\": \"0.6484256\",\n \"text\": \"def _is_before(check_time, start_time):\\r\\n if check_time.hour < start_time.hour:\\r\\n return True\\r\\n elif check_time.hour == start_time.hour:\\r\\n return check_time.minute < start_time.minute\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34c5ba3c2ddbcb6e8ae43ceb4fbaadc\",\n \"score\": \"0.64039236\",\n \"text\": \"def isTimeIncluded(self, time):\\n # time filtering in Ladybug and honeybee is slightly different since\\n # start hour and end hour will be applied for every day.\\n # For instance 2/20 9am to 2/22 5pm means hour between 9-17\\n # during 20, 21 and 22 of Feb.\\n return time.moy in self._timestampsData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28ae55136e9d83d4fa383e29097347d7\",\n \"score\": \"0.62854517\",\n \"text\": \"def check_time(self, x, y):\\n x_hours = float(x.split(\\\":\\\")[0])\\n x_minutes = float(x.split(\\\":\\\")[1])\\n x_seconds = float(x.split(\\\":\\\")[-1])\\n y_hours = float(y.split(\\\":\\\")[0])\\n y_minutes = float(y.split(\\\":\\\")[1])\\n y_seconds = float(y.split(\\\":\\\")[-1])\\n if x_hours == y_hours and x_minutes == y_minutes and abs(x_seconds - y_seconds) <= 1:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe8304e324699c154e45f9e21ef9b1f\",\n \"score\": \"0.6256818\",\n \"text\": \"def test_release_time_hour(self):\\n result = orlo.stats.releases_by_time('hour', **self.ARGS)\\n year = str(arrow.utcnow().year)\\n month = str(arrow.utcnow().month)\\n day = str(arrow.utcnow().day)\\n hour = str(arrow.utcnow().hour)\\n self.assertEqual(\\n 7,\\n result[year][month][day][hour]['normal']['successful'],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c96a4a17a87fdd12d2089f46416833\",\n \"score\": \"0.6225677\",\n \"text\": \"def is_time_for_canary_safety_scan():\\n current_utc_time = time.gmtime()\\n return current_utc_time.tm_hour == 16 and (0 < current_utc_time.tm_min < 20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea78f1835ef74b4f82122f39fab770d0\",\n \"score\": \"0.6196998\",\n \"text\": \"def check_if_time(self):\\n if self._alarm_time[\\\"text\\\"] == self._current_time:\\n self.reset()\\n messagebox.showinfo(\\\"Time is up\\\", \\\"Your alarm went off\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"026f3f2b9c4bd572f69fe91aacabd605\",\n \"score\": \"0.6142179\",\n \"text\": \"def matchtime(self, t):\\n return ((t.minute in self.mins) and\\n (t.hour in self.hours) and\\n (t.day in self.days) and\\n (t.month in self.months) and\\n (t.weekday() in self.dow))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3303b20657f6bdc13e823482f246d164\",\n \"score\": \"0.61350214\",\n \"text\": \"def test(hour, is_am):\\r\\n print hour,\\r\\n if is_am:\\r\\n print \\\"AM\\\",\\r\\n else:\\r\\n print \\\"PM\\\",\\r\\n if is_lunchtime(hour, is_am):\\r\\n print \\\"is lunchtime.\\\"\\r\\n else:\\r\\n print \\\"is not lunchtime.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de088e6f0e4a37ff0ce934d2ceaf3034\",\n \"score\": \"0.6115739\",\n \"text\": \"def main(hrs, mins):\\n #Change [hours to mins]\\n ##EX. hrs = 1, mins = 6 >> hand_h = 5+(0.5)\\n hand_h = (hrs * 5) + (mins / 12)\\n\\n #find that both hand will equal or not\\n if hand_h >= mins and (hand_h -mins) < 1:\\n print(\\\"True\\\")\\n else:\\n print(\\\"False\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d69efebdbfbdf2364cab897f01d64f1\",\n \"score\": \"0.607937\",\n \"text\": \"def is_before_end_time(self):\\n chile = pytz.timezone(\\\"America/Santiago\\\")\\n timezone.activate(chile)\\n current_date = chile.localize(datetime.utcnow())\\n end_time = timezone.datetime(\\n current_date.year,\\n current_date.month,\\n current_date.day,\\n hour=self.time_over,\\n minute=0,\\n second=0,\\n microsecond=0,\\n tzinfo=chile,\\n ).astimezone(pytz.utc)\\n print(end_time)\\n if current_date.hour < end_time.hour:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14f1dbfdf33593e2db0dc2b13e964496\",\n \"score\": \"0.6071197\",\n \"text\": \"def test_hour_range_pos(self):\\n self.assertTrue( hour_man.checkHourRange(0) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a91f8ec5ec7d04017c5ab9857d9263\",\n \"score\": \"0.60695815\",\n \"text\": \"def valid_hour(hour):\\n \\n debug = False\\n\\n try:\\n num_hour = int(hour)\\n except:\\n print 'Hour is not an integer:', hour\\n return False\\n if not(num_hour in range(0,24)):\\n print 'There are only 24 hours in the day. Value too large!'\\n return False\\n elif len(hour) == 0:\\n print 'No hour value provided!'\\n return False\\n else:\\n if debug:\\n print 'Valid hour detected:', hour \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d95340dd276b4dfe3be8d67b8dacab\",\n \"score\": \"0.6062433\",\n \"text\": \"def is_valid_time(hour, minute, second):\\n return 0 <= int(hour) <= 23 and 0 <= int(minute) < 60 and 0 <= int(second) < 60\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d89fea85408dc30a816618e56210af0\",\n \"score\": \"0.59975475\",\n \"text\": \"def _is_time(self) -> bool:\\n\\n date_str = read_file(get_tmp_path('.stamp'))\\n\\n year, month, day = list(map(int, date_str.split('-')))\\n\\n last_run = datetime(year, month, day)\\n\\n diff = self.now - last_run\\n\\n logger.info(\\\"Time Interval: {}\\\".format(diff))\\n\\n if diff > timedelta(hours=self.INTERVAL):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a3e4e5bbf891e71d04a74a0685df7a\",\n \"score\": \"0.59947306\",\n \"text\": \"def test_hour_range_neg(self):\\n self.assertFalse( hour_man.checkHourRange(24) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9fc1f1c6013c564c914756b21a064f\",\n \"score\": \"0.59694535\",\n \"text\": \"def is_reasonable_hour_for_text_messages(self):\\n tz = pytz.timezone('US/Eastern')\\n now = (datetime.now(tz))\\n eight_am = (datetime.now(tz).replace(hour=8, minute=0, second=0, microsecond=0))\\n ten_pm = (datetime.now(tz).replace(hour=22, minute=0, second=0, microsecond=0))\\n return eight_am <= now <= ten_pm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497a6c962ae321ffdf28c9ee8f6fae7d\",\n \"score\": \"0.59676623\",\n \"text\": \"def work_hour():\\n #date planned\\n date_planned = datetime.datetime.today()\\n\\n # 4:50 PM\\n five_pm = datetime.datetime.today().replace(hour=16,minute=50,second=00)\\n # current date\\n now = datetime.datetime.today()\\n # time left, in seconds, before 5:0 PM\\n time_left = (five_pm-now).total_seconds()\\n\\n #if less than 10 min left before 5:00 PM\\n #report to the day after\\n if time_left < 0:\\n date_planned = date_planned+datetime.timedelta(days=1)\\n\\n # if the planned/current day is weekend\\n # report to monday\\n weekday = date_planned.weekday()\\n if not 0<=weekday<=4:\\n date_planned = date_planned+datetime.timedelta(\\n days=7-weekday)\\n\\n\\n while True:\\n hour,minute = _schedule()\\n # schedule date\\n sched = date_planned.replace(hour=hour,minute=minute)\\n # timedelta\\n delta = sched-now\\n if (delta.days>0\\n or (delta.days==0 and delta.seconds>0)):\\n #date_planned = sched\\n return sched #date_planned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a786d95454c48007a50be8b1d6ab969\",\n \"score\": \"0.59159666\",\n \"text\": \"def is_valid(self, hour, minute, second):\\n if hour < 0 or minute < 0 or second < 0:\\n return False\\n if minute >= 60 or second >= 60:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5bbebd09183864c06d0de0bb9e0b6a2\",\n \"score\": \"0.5866292\",\n \"text\": \"def is_daytime(date_time):\\r\\n\\r\\n time = date_time.time()\\r\\n # When the start of daytime is earlier in the day than the start of nighttime\\r\\n if day_start < night_start:\\r\\n if time >= day_start and time < night_start:\\r\\n return True\\r\\n # When the start of nighttime is earlier in the day than the start of daytime\\r\\n elif night_start < day_start:\\r\\n if not (time >= night_start and time < day_start):\\r\\n return True\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8486b6750a1bea40c9a55e8c4634e03\",\n \"score\": \"0.5849143\",\n \"text\": \"def share_same_hour(d1: datetime, d2: datetime) -> bool:\\n h1 = d1.replace(microsecond=0, second=0, minute=0)\\n h2 = d2.replace(microsecond=0, second=0, minute=0)\\n return h1 == h2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703194746a8edbaee4f8792130f12dc9\",\n \"score\": \"0.5836504\",\n \"text\": \"def test_package_time_hour(self):\\n result = orlo.stats.packages_by_time('hour', **self.ARGS)\\n year = str(arrow.utcnow().year)\\n month = str(arrow.utcnow().month)\\n day = str(arrow.utcnow().day)\\n hour = str(arrow.utcnow().hour)\\n self.assertEqual(\\n 7,\\n result[year][month][day][hour]['test-package']['normal']['successful'],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1c7868ed23dd09fd34a2af1740ad66\",\n \"score\": \"0.5835717\",\n \"text\": \"def check_times(now,cron):\\n # print now,cron\\n\\n if now['minute'] in cron['minute'] and now['hour'] in cron['hour'] and now['day'] in cron['day'] and now['month'] in cron['month'] and now['week'] in cron['week']:\\n print 'Cron matched'\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"395eb2c036cd5e8705f4684dd97e3c9c\",\n \"score\": \"0.58140504\",\n \"text\": \"def is_unique_grid_time(self):\\n dims = self.get_geolocation_field('time').dimensions.values()[0]\\n return (dims == 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e028a5d9b2b8b121778bd6d7ab603f\",\n \"score\": \"0.5797887\",\n \"text\": \"def match_time(self, t):\\n if not self.enabled:\\n return False\\n\\n if not self._next_run:\\n self._next_run = t + timedelta(minutes=self._ev_minutes, hours=self._ev_hours, days=self._ev_days)\\n\\n if t >= self._next_run:\\n self._next_run = t + timedelta(minutes=self._ev_minutes, hours=self._ev_hours, days=self._ev_days)\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68438fa2a14f4e9b16921171c33b09bc\",\n \"score\": \"0.5779742\",\n \"text\": \"def hour_rounder(t):\\n # Rounds to nearest hour by adding a timedelta hour if minute >= 30\\n return (t.replace(second=0, microsecond=0, minute=0, hour=t.hour)\\n +datetime.timedelta(hours=t.minute//30))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d15fbb9acc50ab13740d111dd333bc\",\n \"score\": \"0.5777177\",\n \"text\": \"def same_time_with_margin(hora):\\n\\n if hora is None:\\n return False\\n\\n\\n tz = pytz.timezone('America/Sao_Paulo')\\n today = datetime.now(tz)\\n\\n minutes_today = int(today.hour * 60 + today.minute)\\n\\n hours, minutes = map(int, hora.split(':'))\\n minutes_notification = hours * 60 + minutes\\n\\n # print(hora, \\\"{}:{}\\\".format(today.hour, today.minute))\\n return abs(minutes_today - minutes_notification) <= 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"164e402f8088014227da94b72aa87b3a\",\n \"score\": \"0.5746601\",\n \"text\": \"def checktime(id):\\n # we distribute it randomly in beteen 5th-1435th minute of day\\n return binascii.crc32(id)%1430+5 if not isinstance(id, int) else id%1430+5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da314095117238ab1ad935b4dbb1b5e8\",\n \"score\": \"0.57456654\",\n \"text\": \"def under_hours(self):\\n if self.worked_hours == 0: # worked hours was just added, don't want false positives\\n return False\\n return self.worked_hours < self.allocated_hours\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12485ee511e0f79fd2fa5db2acbce48d\",\n \"score\": \"0.57390714\",\n \"text\": \"def __contains__(self, time):\\r\\n t = parse_time(time)\\r\\n return t >= self.t1 and t <= self.t2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d08f7fbe65632a8fe2c771a150ac31f\",\n \"score\": \"0.5738579\",\n \"text\": \"def wish_me():\\r\\n hour = int(datetime.now().hour)\\r\\n if hour <= 12:\\r\\n print(\\\"Leo:\\\\n\\\\tGood morning\\\")\\r\\n speak(\\\"Good morning\\\")\\r\\n elif 12 < hour <= 18:\\r\\n print(\\\"Leo:\\\\n\\\\tGood Afternoon\\\")\\r\\n speak(\\\"Good Afternoon\\\")\\r\\n elif 18 < hour <= 24:\\r\\n print(\\\"Leo:\\\\n\\\\tGood Evening\\\")\\r\\n speak(\\\"Good Evening\\\")\\r\\n else:\\r\\n speak(\\\"You are not earth \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f2b07c9098e921ae38fd4e484ba9cd\",\n \"score\": \"0.5737583\",\n \"text\": \"def is_booking_time():\\r\\n return datetime.time(2, 16) <= now_time() <= datetime.time(23, 59) or \\\\\\r\\n datetime.time(0, 0) <= now_time() <= datetime.time(1, 59)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc038385ad88cb440d361b787c75963\",\n \"score\": \"0.5727017\",\n \"text\": \"def match_time(self, dt):\\n if self.onetime:\\n return self.onetime <= dt\\n return ((dt.minute in self.minutes) and (dt.hour in self.hours) and (dt.day in self.days) and\\n (dt.month in self.months) and (dt.weekday() in self.dow))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e262e2b9294fdbe9484024aac10b7345\",\n \"score\": \"0.5722993\",\n \"text\": \"def matching_day_hour(day_num, hour, match_id):\\n if get_day_num(match_id) == day_num and get_hour(match_id) == hour:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae59d6690a09463730edcc40a701d7ee\",\n \"score\": \"0.571834\",\n \"text\": \"def is_valid_time(self, d):\\r\\n if not d:\\r\\n return str()\\r\\n elif len(d) != 4:\\r\\n return str()\\r\\n\\r\\n # Check hours\\r\\n h = d[0] * 10 + d[1]\\r\\n if h >= 24:\\r\\n return False\\r\\n\\r\\n # Check minutes\\r\\n m = d[2] * 10 + d[3]\\r\\n if m >= 60:\\r\\n return False\\r\\n\\r\\n # Found valid time\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c04a23f37fdc8d33d8b65089524e788\",\n \"score\": \"0.5718189\",\n \"text\": \"def __pass_time(self):\\n seconds = calendar.timegm(time.gmtime())\\n current_second = seconds % 60\\n minutes = seconds // 60\\n current_minute = minutes % 60\\n hours = minutes // 60\\n current_hour = hours % 24\\n\\n if 1 in current_minute:\\n self.__hunger +=1\\n self.__boredome += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"633b2a69cd405ca77b244d839b9763c2\",\n \"score\": \"0.5704044\",\n \"text\": \"def test_datetime_hourly():\\n clear_metrics()\\n\\n with freeze_time(TWELVE_FIFTY_NINE_AM - ONE_MINUTE):\\n when = hourly()\\n assert when is not None\\n assert isinstance(when, datetime.datetime)\\n assert when == TWELVE_FIFTY_NINE_AM - ONE_MINUTE\\n\\n with freeze_time(TWELVE_FIFTY_NINE_AM):\\n when = hourly()\\n assert when is None\\n\\n ####\\n # NEXT HOUR\\n ####\\n\\n with freeze_time(ONE_AM):\\n when = hourly()\\n assert when is not None\\n assert isinstance(when, datetime.datetime)\\n assert when == ONE_AM\\n\\n # Already triggered, shouldn't trigger again\\n with freeze_time(ONE_AM + ONE_MINUTE):\\n when = hourly()\\n assert when is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7392091c0c314ab491965c2fddceed49\",\n \"score\": \"0.5683123\",\n \"text\": \"async def test_time_window(hass):\\n sixam = dt.parse_time(\\\"06:00:00\\\")\\n sixpm = dt.parse_time(\\\"18:00:00\\\")\\n\\n with patch(\\n \\\"homeassistant.helpers.condition.dt_util.now\\\",\\n return_value=dt.now().replace(hour=3),\\n ):\\n assert not condition.time(after=sixam, before=sixpm)\\n assert condition.time(after=sixpm, before=sixam)\\n\\n with patch(\\n \\\"homeassistant.helpers.condition.dt_util.now\\\",\\n return_value=dt.now().replace(hour=9),\\n ):\\n assert condition.time(after=sixam, before=sixpm)\\n assert not condition.time(after=sixpm, before=sixam)\\n\\n with patch(\\n \\\"homeassistant.helpers.condition.dt_util.now\\\",\\n return_value=dt.now().replace(hour=15),\\n ):\\n assert condition.time(after=sixam, before=sixpm)\\n assert not condition.time(after=sixpm, before=sixam)\\n\\n with patch(\\n \\\"homeassistant.helpers.condition.dt_util.now\\\",\\n return_value=dt.now().replace(hour=21),\\n ):\\n assert not condition.time(after=sixam, before=sixpm)\\n assert condition.time(after=sixpm, before=sixam)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afc7d5b193be73950a4cdbf303505c65\",\n \"score\": \"0.5667082\",\n \"text\": \"def remaintime_hour(self):\\n return self._get_time_info([\\\"Remain_Time_H\\\", \\\"remainTimeHour\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23cd3915a69168b35384be1f983ac420\",\n \"score\": \"0.56623095\",\n \"text\": \"def reservetime_hour(self):\\n return self._get_time_info([\\\"Reserve_Time_H\\\", \\\"reserveTimeHour\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af3ce01526a6cdc284c1bf82fa42a0b\",\n \"score\": \"0.56597525\",\n \"text\": \"def check_runtime():\\n\\n hours_to_run = CONFIG.get(\\\"hours_to_run\\\")\\n if hours_to_run:\\n return (datetime.now() - START_TIME).total_seconds() / 60 / 60 < hours_to_run\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867a52b93f000d8c7aa84a61f85d6521\",\n \"score\": \"0.56594294\",\n \"text\": \"def check_time():\\n times = get_times()\\n time_difference = abs((times['local'] - times['target']).total_seconds())\\n return time_difference < post_time_tol_seconds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"726cc573d4fa4b4eb7f3b9ba4013f8bc\",\n \"score\": \"0.5657636\",\n \"text\": \"def _check_time(self, cr, uid, ids, context=None):\\n for act in self.browse(cr, uid, ids, context):\\n if act.cat_subtype == 'hall':\\n if ( (act.start_time) >= (act.end_time) ):\\n raise osv.except_osv(_(''), _(\\\"Contract Start Time Must Be Less Than End Time!\\\"))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97bfa5a05c3f03dabc007e8299ea3fc8\",\n \"score\": \"0.565763\",\n \"text\": \"def hours(datetime):\\n raise RuntimeError('hours' + error_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73fee70a013252acf5fabdd92c7be4e\",\n \"score\": \"0.5648897\",\n \"text\": \"def find_era_date_hour(radar_pass):\\n t = radar_pass.get_representative_time()\\n if t.minute >= 30:\\n rounded_time = t.replace(\\n second=0, microsecond=0, minute=0, hour=t.hour+1)\\n else:\\n rounded_time = t.replace(second=0, microsecond=0, minute=0)\\n return datetime.datetime.strftime(rounded_time, '%Y%m%d'), rounded_time.hour\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cf9ad23f45e3183e682e15d2b060076\",\n \"score\": \"0.5642401\",\n \"text\": \"def is_time_question_expired(self, min = 20):\\n return ((time.time() - self.time) / 3600) >= min\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82327026814e1fbffafe9356c9e4a688\",\n \"score\": \"0.562343\",\n \"text\": \"def check_time(self, player1, player2):\\n\\n # if the current time is less than the end time\\n if time.time() <= self.end_time:\\n pass\\n else:\\n # if time expires and player1 has highest score\\n if player1.score > player2.score:\\n print('\\\\n\\\\tTime has expired!\\\\n\\\\t{} wins with a score of {}'.format(player1.name, player1.score))\\n exit()\\n # if time expires and player2 has highest score\\n elif player2.score > player1.score:\\n print('\\\\n\\\\tTime has expired!\\\\n\\\\t{} wins with a score of {}'.format(player2.name, player2.score))\\n exit()\\n # if time expires and players have equal scores\\n elif player1.score == player2.score:\\n print('\\\\n\\\\tTime has expired!\\\\n\\\\tHowever, a winner can\\\\'t be determined.')\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d63b3f4bc2c7a6d88480e9bb0c75a121\",\n \"score\": \"0.5622015\",\n \"text\": \"def get_target_hour(popular_hour_arg: int) -> int:\\n utc_now = datetime.datetime.utcnow().hour\\n target = popular_hour_arg - utc_now\\n if target < -11:\\n target += 22\\n elif target > 12:\\n target -= 22\\n return target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48450ae52bc5fa0414b355271f82d65a\",\n \"score\": \"0.5620814\",\n \"text\": \"def hour(self):\\n\\t\\treturn self.time.hour\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03614e188381db1f3e7f530d264fa06\",\n \"score\": \"0.56165904\",\n \"text\": \"def chek_last_time():\\n time=db.fetch_time()\\n if (datetime.timestamp(datetime.now())-time<600):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"904af1108942a7c93eb43b39f3c03094\",\n \"score\": \"0.5604639\",\n \"text\": \"def over_hours(self):\\n return self.worked_hours > self.allocated_hours\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f170083fabbda29139944e37f1bb4b6\",\n \"score\": \"0.5598047\",\n \"text\": \"def time_validator(time):\\n try:\\n datetime.datetime.strptime(time, TIME_MASK)\\n except (ValueError, TypeError):\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564ab12fcfd6094ed0bfd6857d81c6e8\",\n \"score\": \"0.55786884\",\n \"text\": \"def isNight(self):\\n now = datetime.now().time()\\n return (now > night_min) or ( now < night_max)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c538cede1cb23542b2cc1c1e861131ca\",\n \"score\": \"0.5576612\",\n \"text\": \"def same_time(self, time):\\n return self.__time == time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2a6e9c0b802cd83cb01fca5a32d6c1\",\n \"score\": \"0.5570722\",\n \"text\": \"def time_to_call_it_a_day(settings, start_time):\\n if settings.timeout <= 0:\\n return False\\n return time.time() >= start_time + settings.timeout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8be4a6dba64e65bcad51cbfda3e1b04\",\n \"score\": \"0.55681294\",\n \"text\": \"def checkIsHours(value):\\n if not isinstance(value, str):\\n return False\\n if '*' in value:\\n return False\\n if '+' in value:\\n return False\\n if '-' in value:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005251297c9dc51fab54419e04d7368b\",\n \"score\": \"0.5548757\",\n \"text\": \"def is_time(self) -> bool:\\n eps = 0.0003\\n cur_full_time = str(datetime.now())\\n cur_time = cur_full_time.split(\\\" \\\")[-1]\\n seconds = float(cur_time.split(\\\":\\\")[-1])\\n submit_seconds = float(self.submit_time.split(\\\":\\\")[-1])\\n if seconds >= submit_seconds or abs(submit_seconds-seconds) <= eps:\\n self.submit()\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de68ed8e60d4548e14e755637bfe0110\",\n \"score\": \"0.5548292\",\n \"text\": \"def isTimeReconnect(self):\\r\\n start = self.core.config['reconnect']['startTime'].split(\\\":\\\")\\r\\n end = self.core.config['reconnect']['endTime'].split(\\\":\\\")\\r\\n return compare_time(start, end) and self.core.config[\\\"reconnect\\\"][\\\"activated\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970a407a3836e4c46add7576a2b461d4\",\n \"score\": \"0.55479705\",\n \"text\": \"def within_schedule(utc, *timezones):\\n utc = utc.replace(tzinfo=tz.UTC)\\n\\n for timezone in timezones:\\n timezone = tz.gettz(timezone)\\n if not timezone:\\n raise ValueError\\n date = utc.astimezone(timezone)\\n\\n if not date.hour in MEETING_HOURS:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29bd9f8197faa5304e03d53fd16b1df2\",\n \"score\": \"0.55452836\",\n \"text\": \"def diffTime(self, hrs, mins):\\n return hrs != self._hours or mins != self._minutes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b527f16ce48c8bf4d361ede256c198ad\",\n \"score\": \"0.55392283\",\n \"text\": \"def isha_time(self):\\n \\n fmt = to_hrtime(self._isha)\\n if \\\"00\\\" in fmt:\\n fmt = fmt.replace('00','12')\\n return fmt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5197d5fbcc0baf41eb744f5fe58294\",\n \"score\": \"0.55368036\",\n \"text\": \"def _should_refresh():\\n (start_time, end_time) = app.config['REFRESH_INTERVAL']\\n now = datetime.datetime.utcnow()\\n\\n start_offset = start_time.hour*60 + start_time.minute\\n end_offset = end_time.hour*60 + end_time.minute\\n cur_offset = now.hour*60 + now.minute\\n\\n # no refresh when outside from refresh interval boundaries\\n return bool(cur_offset >= start_offset and cur_offset <= end_offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aefa206062aa7f4bca384be656ee02b\",\n \"score\": \"0.5530637\",\n \"text\": \"def can_be_picked_same_time(order1, order2):\\n\\ttime = difference_in_placement_time(order1, order2)\\n\\tdist = difference_in_pickup_distance(order1, order2)\\n\\tif time < hour and dist < max_pickup_distance_apart:\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd50a589ff5b421b39f7b81f0eadd399\",\n \"score\": \"0.55251795\",\n \"text\": \"def _check_request_time(self, request_data):\\r\\n try:\\r\\n diff_time = int(int(time.time()) - int(request_data.get('Timestamp', 0)))\\r\\n if diff_time <= int(self.hmac_time):\\r\\n return True\\r\\n return False\\r\\n except:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3233c2fc40651d80eb337144fecd97\",\n \"score\": \"0.5523324\",\n \"text\": \"def test_invalid_time(request, historian, publish_agent, query_agent,\\n clean_db_rows):\\n\\n global query_points, DEVICES_ALL_TOPIC\\n # print('HOME', volttron_instance.volttron_home)\\n print(\\\"\\\\n** test_invalid_time for {}**\\\".format(\\n request.keywords.node.name))\\n\\n # Create timestamp\\n now = '2015-12-17 60:00:00.000000'\\n\\n try:\\n # query with invalid timestamp\\n query_agent.vip.rpc.call(identity,\\n 'query',\\n topic=query_points['mixed_point'],\\n start=now,\\n count=20,\\n order=\\\"LAST_TO_FIRST\\\").get(timeout=10)\\n except RemoteError as error:\\n print (\\\"exception: {}\\\".format(error))\\n assert 'hour must be in 0..23' == error.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f473c64231879be2dc9a5326e6c74633\",\n \"score\": \"0.5514971\",\n \"text\": \"def test_gst(self):\\n gst_compare = np.array([6.5971168570494854, 18.629694220878296,\\n 18.62997275921186, 18.630251297545389,\\n 6.6631074284018244])\\n gst = self.t1.sidereal_time('apparent', 'greenwich')\\n assert allclose_hours(gst.value, gst_compare)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ac456435d1056e6e8c40ce20874a54f\",\n \"score\": \"0.5514044\",\n \"text\": \"def show_is_live():\\n if _debug_mode():\\n return True\\n\\n now = datetime.datetime.now()\\n day = now.strftime('%A')\\n hour = now.strftime('%H')\\n\\n return day == 'Friday' and 8 <= int(hour) <= 11\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f145e7e19d266906314d08f1569c9247\",\n \"score\": \"0.5504251\",\n \"text\": \"def check_interval_time(interval_time: Dict):\\n\\n check_status = False\\n interval_types = interval_time.keys()\\n for interval_type, interval_value in interval_time.items():\\n if interval_type == 'minute' and interval_value in range(0, 60):\\n check_status = True\\n elif all([interval_type == 'hour',\\n interval_value in range(0, 24),\\n 'minute' in interval_types]):\\n check_status = True\\n elif all([interval_type == 'weekday',\\n interval_value in range(0, 7),\\n 'hour' in interval_types,\\n 'minute' in interval_types]):\\n check_status = True\\n else:\\n check_status = False\\n if not check_status:\\n break\\n return check_status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40be2429082117103b37b488f48accd\",\n \"score\": \"0.5499477\",\n \"text\": \"def determine_time(self, time_token):\\n hour, minute = parse_ambiguous_time(time_token)\\n if (hour or minute):\\n # The ambiguous hours arranged in order, according to Google Calendar:\\n # 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6\\n if 1 <= hour and hour <= 6:\\n hour += 12\\n else:\\n tmp, _ = self._extract_time(time_token, ACCEPTED_TIME_FORMATS)\\n if not tmp:\\n LOG.debug('%s did not match any expected time formats')\\n return None\\n hour = tmp.hour\\n minute = tmp.minute\\n return datetime.time(hour=hour, minute=minute)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21fdd35f259a2355d5c2d51ca59672eb\",\n \"score\": \"0.5488933\",\n \"text\": \"def timeout_time(self):\\n if not self.timeout_hours == 0: # never timeout through time\\n if self.timestamp_creation + datetime.timedelta(hours=self.timeout_hours) < timezone.now():\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f8f9b6db21bbce5c9526392c9a8698\",\n \"score\": \"0.54867303\",\n \"text\": \"def trading_day_started() -> bool:\\n if SYSTEM_TIME[0] > 16:\\n return True\\n elif SYSTEM_TIME[0] >= 16 and SYSTEM_TIME[1] >= 30:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b22ea55918edd36fd859870572ab0502\",\n \"score\": \"0.54853714\",\n \"text\": \"def check_for_time_interval(self):\\n return (time.time() - self.time_interval_start) >= self.time_interval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6530a55d326abf7b9c529bd6caa68cf5\",\n \"score\": \"0.5482748\",\n \"text\": \"def is_after(t1, t2):\\n\\tis_after_hour = t1.hour - t2.hour\\n\\tis_after_minute = t1.minute - t2.minute\\n\\tis_after_second = t1.second - t2.second\\n\\treturn (is_after_hour > 0) or (is_after_hour == 0 and is_after_minute > 0) or (is_after_hour == 0 and is_after_minute == 0 and is_after_second > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92b3ab93ec383e788d5b82cab22e9be\",\n \"score\": \"0.5482427\",\n \"text\": \"def areHoursVerified(self):\\n # def\\n result = True\\n # store\\n intervalSt = self._timekprAdminFormBuilder.get_object(\\\"TimekprHourIntervalsLS\\\")\\n # loop through all\\n for rInt in intervalSt:\\n # check whether it has been verified (id = -1 means not verified)\\n if rInt[0] < 0 and not (rInt[4] == 0 == rInt[5]):\\n # not verified\\n result = False\\n break\\n # result\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e331f93b790b249e63c880801452b530\",\n \"score\": \"0.5474753\",\n \"text\": \"def test_round_to_hour():\\n dt = datetime.datetime(2009,1,3,9,30,46)\\n dt_rounded = datetime.datetime(2009,1,3,10,0,0)\\n assert dateutil.round_to_hour(dt) == dt_rounded\\n\\n dt = datetime.datetime(2009,1,3,9,29,14)\\n dt_rounded = datetime.datetime(2009,1,3,9,0,0)\\n assert dateutil.round_to_hour(dt) == dt_rounded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5bbc8db511adc95f34a66223b57aafa\",\n \"score\": \"0.54691976\",\n \"text\": \"def test_businss_hour_fails(self, json_loader, hour_processor):\\n expected_response = {'success': False, 'message': 'Please provide the time data'}\\n self.assertEqual(expected_response, get_business_hours())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64c8455977d57ad9ffcc2bb5e7ceaab3\",\n \"score\": \"0.5459021\",\n \"text\": \"def extract_hour(time):\\n hour = int(time[-2] + time[-1])\\n return(hour)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a540ff1ca1a07ff27f1f940cf4e2b8a\",\n \"score\": \"0.5450342\",\n \"text\": \"def haspassed(value):\\n now = timezone.now()\\n return value < now\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2b21f2abb9a7780cd9c19d2af114d1\",\n \"score\": \"0.54282224\",\n \"text\": \"def hour_match(inputs, pattern):\\n\\n try:\\n shifter_data = inputs[\\\"qualified_shifter\\\"][\\\"confirmedshifthours\\\"]\\n vacated_data = inputs[\\\"vacated_params\\\"]\\n shifter_hour_list = []\\n for data in shifter_data:\\n status = DateTimeDuration.is_date_match(data, vacated_data, \\\"date\\\")\\n if status is True:\\n shifter_hour_list = DateTimeDuration.get_hour_list(data[\\\"starttime\\\"], data[\\\"endtime\\\"])\\n vacated_hour_list = DateTimeDuration.get_hour_list(vacated_data[\\\"startTime\\\"], vacated_data[\\\"endTime\\\"])\\n instance = {\\n \\\"hour_match\\\": {\\n \\\"input\\\": {\\n \\\"shifter_available_hour\\\": shifter_hour_list,\\n \\\"vacated_shifter_hour\\\": vacated_hour_list\\n },\\n \\\"pattern\\\": pattern\\n }\\n }\\n return instance\\n except BaseException:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5532fcd501c0e5cbd6afcb5c80869040\",\n \"score\": \"0.5427947\",\n \"text\": \"def is_time(self):\\n now = datetime.now()\\n start = datetime(now.year, now.month, now.day, self._start_time[0], self._start_time[1], 0)\\n stop = datetime(now.year, now.month, now.day, self._stop_time[0], self._stop_time[1], 0)\\n\\n # check if today should be considered in the schedule\\n if now.strftime(\\\"%A\\\") not in self._days:\\n self._is_active = False\\n return False\\n\\n if start <= now < stop:\\n if not self._is_active:\\n self._is_active = True\\n return True\\n else:\\n return False\\n else:\\n self._is_active = False\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c45aa784dbacb87d021ab82061440ee\",\n \"score\": \"0.541685\",\n \"text\": \"def verify_x_hours(mail_date, hours):\\n parsed_date = email.utils.parsedate_tz(mail_date)\\n utc_timestamp = email.utils.mktime_tz(parsed_date)\\n utc_datetime = datetime.datetime.utcfromtimestamp(utc_timestamp)\\n utc_now = datetime.datetime.utcnow()\\n return utc_datetime >= utc_now - datetime.timedelta(hours=hours)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffaf23ce48839047c38b5099e282c770\",\n \"score\": \"0.5405138\",\n \"text\": \"def within_schedule(utc, *timezones):\\n utc_tz = pytz.timezone('UTC')\\n utc = utc_tz.localize(utc)\\n hours = []\\n for tz in timezones:\\n if tz not in TIMEZONES:\\n raise ValueError\\n else:\\n local_time = utc.astimezone(pytz.timezone(tz)).hour\\n hours.append(local_time)\\n if all(hour in MEETING_HOURS for hour in hours):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39101aadad59ad5e355673594e858993\",\n \"score\": \"0.53969926\",\n \"text\": \"def time_check(self, dt):\\n alarm_time_dt = self.to_datetime(self.alarm_time)\\n if (alarm_time_dt - timedelta(seconds=1) <= \\n self.parent.parent.time <= alarm_time_dt + \\n timedelta(seconds=1)) and not self.playing:\\n self.playing = True\\n self.sound.play()\\n Clock.schedule_once(\\n self.remove, 60\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369dbf599d3b23d1bf95637e40fea496\",\n \"score\": \"0.5394961\",\n \"text\": \"def test_hr(self):\\n q = quantity.Time(1.0, \\\"hr\\\")\\n self.assertAlmostEqual(q.value, 1.0, 6)\\n self.assertAlmostEqual(q.value_si, 3600.0, delta=1e-6)\\n self.assertEqual(q.units, \\\"hr\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3183e2b0e776d4f7cff88b092538148a\",\n \"score\": \"0.5394515\",\n \"text\": \"def hour(self):\\n return self.nanosecond_time // Time.HOUR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac64713875816d4929d61cb59ace49fa\",\n \"score\": \"0.53901285\",\n \"text\": \"def is_time(self):\\n curt = time.time()\\n if curt - self.last_time > self.delay:\\n self.last_time = curt\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24844ef7a49f8e92062d3f4f5125fb18\",\n \"score\": \"0.5370857\",\n \"text\": \"def test_release_time_with_only_this_unit(self):\\n result = orlo.stats.releases_by_time('hour', summarize_by_unit=True, **self.ARGS)\\n hour = str(arrow.utcnow().hour)\\n self.assertEqual(\\n 7,\\n result[hour]['normal']['successful'],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c84262c471022534c98cd2d32788c63\",\n \"score\": \"0.53654754\",\n \"text\": \"def has_irregular_time(self):\\n\\n irregular_start = self.course.default_start_time is not None \\\\\\n and self._time(self.course.default_start_time) != self.start_time\\n irregular_end = self.course.default_end_time is not None \\\\\\n and self._time(self.course.default_end_time) != self.end_time\\n return irregular_start or irregular_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a240d689aa5784dff2ca7717c56dd9\",\n \"score\": \"0.53598255\",\n \"text\": \"def valid_times(self):\\n if not self.day[0].start_time == pam.utils.minutes_to_datetime(0):\\n return False\\n\\n for i in range(self.length - 1):\\n if not self.day[i].end_time == self.day[i+1].start_time:\\n return False\\n\\n if not self.day[-1].end_time == pam.variables.END_OF_DAY:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e537b2f2ac5b02f16c75084e8a71f7\",\n \"score\": \"0.534602\",\n \"text\": \"def _in_range(filepath):\\n # Get the date in form 2020-MM-DD-HH from file if present, None if not.\\n file_date_hour = extract_date_hour(filepath)\\n if not file_date_hour: # No date-hour in file\\n return False\\n if low_date_hour and file_date_hour < low_date_hour: # Too early.\\n return False\\n if high_date_hour and file_date_hour > high_date_hour: # Too late.\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2f5f12a05cd8d7e18ea7188ab3a3ad5\",\n \"score\": \"0.5345923\",\n \"text\": \"def _is_between(check_time, start_time, end_time):\\r\\n if start_time.hour < check_time.hour < end_time.hour:\\r\\n return True\\r\\n elif check_time.hour == start_time.hour and check_time.hour < \\\\\\r\\n end_time.hour:\\r\\n return check_time.minute >= start_time.minute\\r\\n elif check_time.hour > start_time.hour and check_time.hour == \\\\\\r\\n end_time.hour:\\r\\n return check_time.minute <= end_time.minute\\r\\n elif check_time.hour == start_time.hour and check_time.hour == \\\\\\r\\n end_time.hour:\\r\\n return start_time.minute <= check_time.minute <= end_time.minute\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5838f1c67f51f4cef2c5ba6e46f0c83\",\n \"score\": \"0.53443825\",\n \"text\": \"def is_night(self):\\n day_length = self.configs[\\\"parameters\\\"][\\\"DAY_LENGTH\\\"]\\n cycle_length = day_length + self.configs[\\\"parameters\\\"][\\\"NIGHT_LENGTH\\\"]\\n return (self.state[\\\"turn\\\"] % cycle_length) >= day_length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9bf3d87077de747dd17ccf535c3658\",\n \"score\": \"0.5333783\",\n \"text\": \"def wish():\\n hour = int(datetime.now().hour)\\n if(hour>=0) and (hour<12):\\n Ai_gttss.gttts('A Very Good Morning')\\n elif(hour>=12) and (hour<16):\\n Ai_gttss.gttts('Good Afternoon, How\\\\'s your day going?')\\n elif(hour>=16) and (hour<20):\\n Ai_gttss.gttts('Good Evening, How was your day ?')\\n elif(hour>=20) and (hour<24):\\n Ai_gttss.gttts('Welcome back!, hope you\\\\'re having a great day!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d7cb8275b1fbf8e0f906f421eaf58dc\",\n \"score\": \"0.53114665\",\n \"text\": \"def _test_time(control_time, test_time):\\n return control_time > test_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c5cd1041b7af76501c11793a5cacdf\",\n \"score\": \"0.53092146\",\n \"text\": \"def _is_polling_time(self) -> bool:\\n if self._poll_outside_execution_window:\\n return True\\n device_execution_windows = self._get_device_execution_windows()\\n cur_time = datetime.utcnow()\\n for window in device_execution_windows:\\n day_no = cur_time.weekday()\\n day_of_week = cur_time.strftime(\\\"%A\\\")\\n if window.executionDay != ExecutionDay.EVERYDAY:\\n if window.executionDay == ExecutionDay.WEEKDAYS and day_no >= 5:\\n continue\\n if window.executionDay == ExecutionDay.WEEKENDS and day_no < 5:\\n continue\\n if (\\n window.executionDay not in [ExecutionDay.WEEKENDS, ExecutionDay.WEEKDAYS]\\n and window.executionDay != day_of_week\\n ):\\n continue\\n if (\\n cur_time.time() >= window.windowStartHour\\n and cur_time.time() <= window.windowEndHour\\n ):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f032c4f4f01af41f8b2f1c575baf1416\",\n \"score\": \"0.5306526\",\n \"text\": \"def _getHourFromInput(rawHour: str, isPm: bool) -> int:\\n rawHourInt = int(rawHour)\\n if isPm:\\n if rawHourInt == 12:\\n return 12\\n else:\\n return (rawHourInt + 12)\\n else:\\n if rawHourInt == 12:\\n return 0\\n else:\\n return rawHourInt\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994378,"cells":{"query_id":{"kind":"string","value":"e2cf7445300fa4d7adccac4df0887b30"},"query":{"kind":"string","value":"Returns a string containing n random words from wordlist"},"positive_passages":{"kind":"list like","value":[{"docid":"22c166d3d90d1a348cc5f226f779dcc0","score":"0.8835607","text":"def random_string(wordlist, n):\n return \" \".join([random_word(wordlist) for _ in range(n)])","title":""}],"string":"[\n {\n \"docid\": \"22c166d3d90d1a348cc5f226f779dcc0\",\n \"score\": \"0.8835607\",\n \"text\": \"def random_string(wordlist, n):\\n return \\\" \\\".join([random_word(wordlist) for _ in range(n)])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"17466e947506faa4e0135d8c2c04f89a","score":"0.7959614","text":"def get_random_words(chars, start, stop, n):\n results = []\n for i in range(int(n)):\n len = random.randrange(int(start), int(stop))\n results.append(''.join(random.choice(chars) for _ in range(len)))\n return results","title":""},{"docid":"0d764323a84f04b00841b3f12b65ed3d","score":"0.79319835","text":"def randomWords(numberofwords = 5):\n\n return map(lambda x: randomWord(), [0] * numberofwords)","title":""},{"docid":"8684c27893399f714bf535d9b5257315","score":"0.78923345","text":"def rand_words():\n return [random.choice(words) for word in range(10000)]","title":""},{"docid":"ad6d7638a5a82bbf810db5c81f4e1272","score":"0.78462625","text":"def random_scrambled(wordlist, n):\n s = random_string(wordlist, n) + \" \"\n shifts = [(i, random.randint(0, 26)) for i in range(len(s)) if s[i-1] == ' ']\n return apply_shifts(s, shifts)[:-1]","title":""},{"docid":"e6a7de4d1f8640518fc43d817d3922cd","score":"0.7820909","text":"def make_random_word(N, x):\n random.shuffle(alphabets)\n return ''.join(random.choice(alphabets[:x]) for _ in range(N))","title":""},{"docid":"c95b8a067dfac2729d87b74eca5134bd","score":"0.78102386","text":"def generate_phrase(n=2):\n if n not in range(1, 4):\n raise Exception(\"Invalid number of words for phrase: {}.\".format(n))\n\n lists = [fruits, animals, colors]\n\n return ' '.join([random.choice(l) for l in random.sample(lists, n)])","title":""},{"docid":"4577b30f9ec544b863d12ea03cec44d2","score":"0.7747641","text":"def rand_word(word_list):\n\t\treturn random.choice(word_list)","title":""},{"docid":"42b1e1e197c719b575c79d9913e3ad91","score":"0.7726783","text":"def generate(n=4, sep=\"\"):\n random.seed(secrets.token_bytes(32))\n return sep.join(random.sample(words, n))","title":""},{"docid":"604f7b8cad2680970e901bf9f199886a","score":"0.7715267","text":"def _get_random_words(filename, n_words):\n words = open(filename, 'rb').read().decode('utf-8')\n # Remove blank lines and extraneous whitespace.\n words = [word.strip() for word in words.split('\\n') if word.strip()]\n\n # There's only 267 names in that file and the caller may have asked for more which will\n # cause an error when random.sample() is called. I make sure I have enough n_names before I\n # call sample().\n while len(words) < n_words:\n words = words + words\n\n return random.sample(words, n_words)","title":""},{"docid":"3ff60d544850f2d2c2e854deb0743863","score":"0.76914465","text":"def _fetch_random_words(n=1000):\n\n # Ensure the same words each run\n random.seed(42)\n\n # Download the corpus if not present\n nltk.download('words')\n\n word_list = nltk.corpus.words.words()\n\n random.shuffle(word_list)\n\n random_words = word_list[:n]\n\n return random_words","title":""},{"docid":"746ce32037ff35c1a8336411c7a49b52","score":"0.7677853","text":"def random_word(wordlist):\n return random.choice(wordlist)","title":""},{"docid":"2c87f0d099152c2bd5027ce1b0a53b8e","score":"0.7669144","text":"def getRandomWords(numWords):\n words = open(\"twelve_years.txt\", \"r\").read().split('\\n')\n newSentence = []\n for i in range(int(numWords)):\n newSentence.append(words[random.randint(0, len(words))] + \" \")\n return(''.join(newSentence))","title":""},{"docid":"e5b64dd27b1b34a16a07d79b0051837a","score":"0.7609371","text":"def choose_words (words, numberOfWords):\n return random.sample(words, numberOfWords)","title":""},{"docid":"8c1feac0dcbea127b2fd1a5fa9591d83","score":"0.75674075","text":"def get_random_word(word_list: List[str]) -> str:\n return random.choice(word_list)","title":""},{"docid":"ee758c84f05a9162793f383167ac3b68","score":"0.7410763","text":"def rnd_text() -> str:\n return ' '.join(rnd_word() for _ in range(8))","title":""},{"docid":"214bba440d12d8d747df9b36ae02e0c1","score":"0.7394398","text":"def rnd_word() -> str:\n return ''.join(random.choice(string.ascii_letters) for _ in range(4))","title":""},{"docid":"9781587722de84df2f3481f23c63c428","score":"0.73738056","text":"def get_words(n):\n # Let's make sure the words are unique\n words_used = set()\n with open(\"/usr/share/dict/words\", \"r\") as dictionary:\n word_string = dictionary.read()\n words = word_string.split(\"\\n\")\n\n acc = []\n while len(acc) < n:\n rando_word = random.choice(words)\n if rando_word.lower() not in words_used:\n # Weird issues with sorting and comparison of cases\n l_case_word = rando_word.lower()\n words_used.add(l_case_word)\n acc.append(l_case_word)\n\n # Sort the list\n acc.sort()\n\n return acc","title":""},{"docid":"dcb585c9f85db83d4e11d211c13eab6f","score":"0.73677856","text":"def random_words(count=100, supplemental_word_file_path='/usr/share/dict/words'):\n if count <= len(WORDS_1K):\n return random.sample(WORDS_1K, count)\n with codecs.open(supplemental_word_file_path, 'r', 'utf-8') as f:\n supplemental_word_list = f.read().splitlines()\n supplemental_word_list.extend(WORDS_1K)\n return random.sample(supplemental_word_list, count)","title":""},{"docid":"5483c87dd34a93af5fca364ea2e0ad53","score":"0.7346386","text":"def random_text_generation(markov_table, text_size, word_list, n): \r\n random.seed(SEED)\r\n tlist = [] # will contain randomized text as a list\r\n key = ' '.join(word_list[n: n+n])\r\n word_list = word_list[n+n:]\r\n tlist.extend(key.split())\r\n for i in range(text_size - 1):\r\n if markov_table.get(key) != None: \r\n suffix_list = markov_table.get(key)\r\n if len(suffix_list) > 1:\r\n rand = random.randint(0, len(suffix_list) - 1)\r\n word = suffix_list[rand]\r\n else:\r\n word = suffix_list[0]\r\n tlist.append(word)\r\n if n > 1:\r\n key = key.split()[1] + \" \" + word\r\n else:\r\n key = word\r\n return tlist","title":""},{"docid":"90de3810b84cba26e4498ae5fd717fe0","score":"0.72732323","text":"def get_word(words):\n\n return words[random.randrange(0, len(words) - 1)]","title":""},{"docid":"4791d4e07b0484f0ff7e61dac0df8ef9","score":"0.72707087","text":"def pickRandomWord(listOfWords):\n #i = random.randrange(0, len(listOfWords))\n #return listOfWords[i]\n return random.choice(listOfWords)","title":""},{"docid":"c6411d6a02f51c3551df751e48b0adff","score":"0.72475135","text":"def _generate_random_word(self, length):\n filtered_list = tuple(filter(lambda v : len(v) == length, WORD_LIST))\n return random.choice(filtered_list)","title":""},{"docid":"c76c96132275a1c665c59d260699d41e","score":"0.723356","text":"def __get_random_words__(self):\n with open(\"word_list.txt\", \"r\") as f :\n text = f.read().splitlines()\n for _ in range(self.max_rounds):\n words = []\n for _ in range(3):\n words.append(choice(text))\n yield words","title":""},{"docid":"72ff35b91fe9415cef7cdf5242673fd0","score":"0.7211744","text":"def generate_random_word():\n return random.choice(words)","title":""},{"docid":"1164ebd646b4affe79655df9d23bcdad","score":"0.7197997","text":"def create_word_list(n, filename='English.txt'):\n file = open(filename, 'r') # Open the file with read-only access\n file = file.read() # Read in the text from the file\n file = file.split('\\n') # Get each word, separated by '\\n'\n file = file[:-1] # Remove the last endline\n # Randomize, convert to a list, and return.\n return list(np.random.permutation(file))[:n]","title":""},{"docid":"eb332dcb2f210a7a43c6f34cf1a12c1a","score":"0.7195058","text":"def generate_words(wordcount):\n return [generate_word() for _ in xrange(wordcount)]","title":""},{"docid":"4b80a956404f553d417bde72f8267d9f","score":"0.7160264","text":"def random_filler_text(number_of_words=200):\n import random\n # dict_list = []\n # print(list(my_dict.values()))\n\n my_dict = make_filler_text_dictionary()\n # print(my_dict)\n words = []\n\n while True:\n randword = \"\"\n randkey = 0\n randkey = my_dict.get(random.randint(3,7))\n # print(f\"Printing randkey: {randkey}\")\n randword = randkey[random.randint(0,2)]\n # print(f\"Printing randword: {randword}\")\n words.append(str(randword))\n if len(words) == number_of_words:\n break\n # for key, value in my_dict.iteritems():\n # temp = [key, value]\n # dict_list.append(temp)\n\n return \" \".join(words)","title":""},{"docid":"5327d3c653c69254d30532ade80367f7","score":"0.7153511","text":"def random_word(word_list):\n correct_word = random.choice(word_list)\n return correct_word","title":""},{"docid":"d85ce6da59925599c9eae5ae4c1bd000","score":"0.71405166","text":"def get_words():\n list_of_rand = []\n rand_word = []\n\n with open(os.path.join(os.path.dirname(__file__), 'alice.txt'), \"r\") as f:\n for _ in range(40):\n list_of_rand.append(random.randrange(0, sum(1 for line in f)))\n\n for i, line in enumerate(f.readlines()):\n if i in list_of_rand:\n w_list = line.split(\" \")\n rand_word.append(\n w_list[random.randrange(0, len(w_list))].rstrip())\n\n return [w for w in rand_word if w] #rand_word contains \"\"","title":""},{"docid":"b39b7e801edab8b8a50bc94c8953a443","score":"0.7128379","text":"def random_text(n):\n # choose a random prefix (not weighted by frequency)\n start = random.choice(suffix_map.keys())\n \n for i in range(n):\n suffixes = suffix_map.get(start, None)\n if suffixes == None:\n # if the start isn't in map, we got to the end of the\n # original text, so we have to start again.\n random_text(n-i)\n return\n\n # choose a random suffix\n word = random.choice(suffixes)\n print word,\n start = shift(start, word)","title":""},{"docid":"94fef8dd40c2a1ded0f8b57e61b29229","score":"0.7125045","text":"def generate_input(input_size):\n words = ['balla', 'assdd']\n append_random(words, input_size)\n words.append('areae')\n append_random(words, input_size)\n words.append('lekbc')\n append_random(words, input_size)\n words.append('labyd')\n append_random(words, input_size)\n words.append('aecdz')\n return words","title":""},{"docid":"4890a9ecb6b64f7823704bfd4cf3e97e","score":"0.71038365","text":"def get_words(nwords, generator):\n result = \"\"\n for i in range(nwords):\n result += next(generator)\n return result","title":""},{"docid":"a1d66cea2e3c9479dd3abcea040b9969","score":"0.70701665","text":"def make_sparse_wordlist(n):\n words = get_words(n)\n random_word = random.choice(words)\n return sparsify(words), random_word","title":""},{"docid":"8aa5db6bd4ea2d32951f15accc6585aa","score":"0.7067373","text":"def random_word_generator(length: int = 6, chars: str = string.ascii_uppercase) -> str:\n return \"\".join(random.choice(chars) for _ in range(length))","title":""},{"docid":"eefef587d4edc161ca865a9850de72fe","score":"0.70577323","text":"def choose_words(database_words, word_count):\n\n if word_count is None:\n words = database_words \n random.shuffle(words)\n\n return words\n\n return random.sample(database_words, word_count)","title":""},{"docid":"e3637da7e529c84a3173dbe915fddb1a","score":"0.7046549","text":"def getword(wordlist, rng):\n return wordlist[rng.randrange(len(wordlist))]","title":""},{"docid":"7960f1dad9daed5227985d254f6e9e40","score":"0.70277274","text":"def randomword(length):\n return ''.join(random.choice(string.lowercase) for i in range(length))","title":""},{"docid":"61fa22e10a6de8fb367c1d718a0b2743","score":"0.69991434","text":"def sample_phrases(self, n=1):\n phrase = []\n final_word_count = 0\n for word in self:\n phrase.append(word)\n if is_final_word(word):\n final_word_count += 1\n if final_word_count >= n:\n break\n\n return \" \".join(phrase)","title":""},{"docid":"95ec490da261394f9fbbd37e2a8695b3","score":"0.69973415","text":"def ranstr(m,n=10):\n r,s = random.choice, string.ascii_lowercase\n l = [''.join(r(s) for _ in xrange(n)) for _ in xrange(m)]\n return l","title":""},{"docid":"97481eed5c93d035ae961563f46d5de0","score":"0.6992634","text":"def gen_word() -> str:\n word_len = randint(2, 9)\n return ''.join(pick_letter() for _ in range(word_len))","title":""},{"docid":"c10a35323415c502899aaa602b88bc46","score":"0.6991505","text":"def randomword(length):\n return ''.join(random.choice(string.lowercase) for i in range(length))","title":""},{"docid":"3b1da0b405d8dad98247df06fec68575","score":"0.69854677","text":"def random_text(self,n=100):\n # choose a random prefix (not weighted by frequency)\n start = random.choice(list(self.suffix_map.keys()))\n \n for i in range(n):\n suffixes = self.suffix_map.get(start, None)\n if suffixes == None:\n # if the start isn't in map, we got to the end of the\n # original text, so we have to start again.\n random_text(n-i)\n return\n\n # choose a random suffix\n word = random.choice(suffixes)\n print(word, end=' ')\n start = start[1:] + (word,)","title":""},{"docid":"ea3f4f636541a2e741c72263de04e11b","score":"0.6953873","text":"def random_string(n):\n return \"\".join([random.choice(string.ascii_lowercase) for number\n in range(n)])","title":""},{"docid":"b57a3143c92fddd5be4f33e6638fcc14","score":"0.695329","text":"def generate_word(chains):\n length = random.randint(3, 10)\n word = random.choice(string.ascii_lowercase)\n while len(word) < length:\n order = len(word) if len(word) < 4 else 4\n options = []\n while len(options) == 0:\n last = word[-order:]\n try:\n options = chains[order-1][last]\n except KeyError:\n order -= 1\n nxt = random.choice(options)\n word += nxt\n return word","title":""},{"docid":"c72f568704f0d20bfa8bd002a3eb777f","score":"0.6937143","text":"def randomNouns(index):\n\tstring = ''\n\tfor _ in range(index):\n\t\tif _ == index - 1:\n\t\t\tstring = string + randomNoun()\n\t\telse:\n\t\t\tstring = string + randomNoun() + ' '\n\treturn string","title":""},{"docid":"1c366cf6b8c741a601eb7dc279ab7fb0","score":"0.692876","text":"def gen(self, n=50):\n if not self.chain:\n # raise error?\n return\n \n wordlist = []\n for i in xrange(n):\n key = '#'\n word = '' # string to append to\n while key != '$':\n roll = random.random()\n for c, prob in self.chain[key].items():\n roll -= prob\n if roll <= 0:\n if key == '#':\n # first one, so add pair\n word += \"\".join(c)\n key = c\n elif c == '$':\n key = '$'\n else:\n word += c\n key = (key[1], c)\n break\n wordlist.append(word)\n word = ''\n \n return wordlist","title":""},{"docid":"bb2110382311a0cd58f25fdc46b75ba0","score":"0.69271725","text":"def random_word(corpus):\n syllables = 5\n while syllables > 4:\n word = random.choice(corpus)\n syllables = count_syllables(word)\n return (word, syllables)","title":""},{"docid":"8dfe33ae580f9a279e6d7bf08df154f4","score":"0.6925991","text":"def get_words(self, word_count): \n word_list = self.fake.words(nb=word_count, unique=True)\n words = ' '.join(word_list)\n return words","title":""},{"docid":"822de6dc73898cba6854d418b9013c9a","score":"0.69175094","text":"def gen_word_list(num_words=NUM_WORDS):\n return [str(x) for x in range(num_words)]","title":""},{"docid":"2492d3a7ef10f455814651c4b4c7a713","score":"0.69091445","text":"def get_random_word(self):\n return choice(self.word_list)","title":""},{"docid":"5a9491582f4efac8c4ea2fe48b75a1ea","score":"0.6904912","text":"def random_text(self, n=100):\n # choose a random prefix (not weighted by frequency)\n start = random.choice(list(self.suffix_map.keys()))\n\n for i in range(n):\n suffixes = self.suffix_map.get(start, None)\n if suffixes == None:\n # if the start isn't in map, we got to the end of the\n # original text, so we have to start again.\n self.random_text(n - i)\n return\n\n # choose a random suffix\n word = random.choice(suffixes)\n print(word, end=' ')\n start = shift(start, word)","title":""},{"docid":"bae0ab66ca41d3f1ddb0757f2adfd507","score":"0.6898953","text":"def randomword(length):\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(length))","title":""},{"docid":"f8425ff563d702f80f113606595fc2e4","score":"0.68897134","text":"def generate_text(word_dict,num_words):\r\n y = ''\r\n current_word = '$'\r\n next_word = ''\r\n for x in range(num_words):\r\n wordlist = word_dict[current_word]\r\n next_word = random.choice(wordlist)\r\n y += ' ' + next_word\r\n if next_word[-1] == '.' or next_word[-1] == '?' or next_word[-1] == '!':\r\n current_word = '$'\r\n else:\r\n current_word = next_word\r\n while len(y) > 140:\r\n f = y.rfind(' ')\r\n y = y[:f]\r\n return str(y)","title":""},{"docid":"df14ee3d6f8c168da3cecfdb2ca82802","score":"0.6882976","text":"def generateWord(self):\n length = random.randint(10,20)\n\n word = \"\"\n\n for i in range(length):\n c = random.choice(string.ascii_letters)\n word += c\n\n return word","title":""},{"docid":"adc5785f6cd128bccdc594d7c3e56301","score":"0.68705046","text":"def nrandom(lst, qnt=1):\n saida = \"\"\n qnt -= 1\n for i in range(qnt):\n saida += random.choice(lst) + \" \"\n\n # para nao ficar espaco na ultima palavra\n saida += random.choice(lst)\n return saida","title":""},{"docid":"55ed9d663a0dfbc7c9c515644fc86b73","score":"0.686637","text":"def randomWord():\n \n if wordlist == None:\n pickDictionary()\n word = ''\n fd = open(wordlist)\n size = os.path.getsize(wordlist)\n pos = random.randrange(size)\n fd.seek(pos)\n # toss partial line\n word = fd.readline()\n # keep next line\n word = fd.readline()\n\n # if at the end, loop to the beginning\n if word == '':\n fd.seek(0)\n # toss partial line\n word = fd.readline()\n fd.close()\n\n # beautify word\n word = word.strip().lower()\n # jwz has done also following translations which we will leave out for now\n # $word =~ s/ly$//;\n # $word =~ s/ys$/y/;\n # $word =~ s/ies$/y/;\n # $word =~ s/ally$//;\n # $word =~ s/ally$/al/;\n # $word =~ s/izes$/ize/;\n return word","title":""},{"docid":"52d3f2b2f00c62188b09a5b69b5f5ae3","score":"0.68633103","text":"def noun():\n return [random.randrange(5), random.randrange(2)]","title":""},{"docid":"6acde7051a6439220b4883c9eefae3e3","score":"0.68514085","text":"def pick_word(words):\n return random.choice(words)","title":""},{"docid":"a041430218764304a35fa5a318893363","score":"0.6847547","text":"def generate_words(min, max, chars='loweralpha'):\n import random\n length = random.randint(min, max)\n string = generate_password(length, chars)\n i = 0\n while i < len(string):\n i += random.randint(2, 10)\n string = string[:i] + ' ' + string[i:]\n return string","title":""},{"docid":"c29a6a685a93128d0d5146d0350f10c0","score":"0.6845153","text":"def pick_random_word():\r\n #open the words dictionary \r\n with open(\"words.txt\", 'r') as f:\r\n words = f.readlines()\r\n\r\n #generate a random index\r\n #-1 because len(words) is not valid index in the list 'words'\r\n index = random.randint (0,len (words) - 1)\r\n\r\n #print out the word at that index \\\r\n word = words[index].strip() \r\n return word","title":""},{"docid":"e56faa20f3e536cd44b26db34f7aaed8","score":"0.68353474","text":"def random_items(self, n):\n return [self.loremipsum[rr(0, len(self.loremipsum))] for _ in range(n)]","title":""},{"docid":"b16b58117364e88d41417a2e9b09d2c9","score":"0.6834861","text":"def generate_string(n):\n characters = \"abcdefghijklmnoprstuvwxyz\"\n result_str = \"\".join(random.choice(characters) for i in range(n))\n return result_str","title":""},{"docid":"e9b251a7bcc49a75bd1bf429a6c5e194","score":"0.6826035","text":"def n_long_words(words, n):\n\n return [word for word in words if len(word) > n] # Using comprehension to select the word that are longer than \"n\". Autogenerating a list.","title":""},{"docid":"46e66db12de522c365622ce30a5e2de2","score":"0.6818638","text":"def random_multiword(self,length):\n if length==0:\n return [self.word([])]\n numgens=len(self.gens)\n wordlist=[]\n totallength=0\n numberofwords=random.choice(range(1,max(2,1+int(sqrt(length)))))\n for i in range(1,numberofwords+1):\n if totallength<length:\n ilen=random.choice(range(1,1+length-totallength))\n w=self.randomword(ilen)\n wordlist.append(w)\n totallength+=len(w.letters)\n else:\n wordlist.append(self.word([]))\n return wordlist","title":""},{"docid":"ad8d3762ff155055b4d987facf640074","score":"0.68152857","text":"def time_rand_words(rand):\n start = time.time()\n [words.index(elem) for elem in rand]\n return time.time() - start","title":""},{"docid":"1974edf647e4f41cdbf1e770ba823a25","score":"0.6814763","text":"def append_random(words, input_size):\n letters = string.letters[:26]\n for _ in xrange(input_size):\n new_string = \"\"\n for _ in xrange(5):\n new_string += (random.choice(letters))\n words.append(new_string)","title":""},{"docid":"92605361fdddf51fab6dcad1d3949430","score":"0.6805226","text":"def random_word(h):\n t = []\n for word, freg in h.items():\n t.extend([word]*freg)\n\n return random.choice(t)","title":""},{"docid":"edd49dabf544f6015f9565b14498b844","score":"0.6797209","text":"def randomword(self,length):\n numgens=len(self.gens)\n letterlist = range(1,numgens+1)+range(-1,-(numgens+1),-1)\n letters = []\n for n in range(length):\n nextletter=random.choice(letterlist)\n if len(letters):\n while nextletter==-letters[-1]:\n nextletter=random.choice(letterlist)\n letters.append(nextletter)\n return self.word(letters)","title":""},{"docid":"32bf3e726e284f23cf03809aed7dcc9b","score":"0.67911","text":"def samples(self, nwords):\n n = self.n\n nminus1gram = ('',) * (n-1)\n output = []\n for i in range(nwords):\n if nminus1gram not in self.cond_prob:\n nminus1gram = ('',) * (n-1) # Cannot continue, so restart.\n wn = self.cond_prob[nminus1gram].sample()\n output.append(wn)\n nminus1gram = nminus1gram[1:] + (wn,)\n return ' '.join(output)","title":""},{"docid":"706d934708d25fe83864637242f62308","score":"0.67882264","text":"def sample_lines(lyrics, n):\n lyrics_lines = lyrics.split(\" \\n \")\n i = random.randint(0, max(len(lyrics_lines) - n, 0))\n sample = lyrics_lines[i:i + n]\n return \" \\n \".join(sample) + \" \\n \"","title":""},{"docid":"ca96093a0334b044bba3dbbb7644e44a","score":"0.6785711","text":"def words(self, count=1, common=False):\n if common:\n word_list = list(self._common_words)\n else:\n word_list = []\n clen = len(word_list)\n if count > clen:\n count -= clen\n while count > 0:\n clen = min(count, len(self._words))\n count -= clen\n word_list += random.sample(self._words, clen)\n else:\n word_list = word_list[:count]\n return u' '.join(word_list)","title":""},{"docid":"08052ae3133e02fb1fb80059eb5b226c","score":"0.67819315","text":"def get_top_n_words(word_list, n):\n\tget_top_n_words = word_list[0:n-1]\n\treturn get_top_n_words","title":""},{"docid":"ff2a0878585223dcd6ec462b5542b05a","score":"0.6775607","text":"def create_random_string(n=20):\n choices = list(string.ascii_letters + string.digits)\n return ''.join(np.random.choice(choices) for _ in range(n))","title":""},{"docid":"3fc032e2d9c7c671e8497e96467053a2","score":"0.67702234","text":"def noun3():\n return [random.randrange(3)] + noun()","title":""},{"docid":"413ba741bdd0b8f10fed7bc7bc9dd71d","score":"0.67700094","text":"def random_word():\n num = random.choice(range(9))\n if num == 0:\n return \"NOTORIOUS\"\n elif num == 1:\n return \"GLAMOROUS\"\n elif num == 2:\n return \"CAUTIOUS\"\n elif num == 3:\n return \"DEMOCRACY\"\n elif num == 4:\n return \"BOYCOTT\"\n elif num == 5:\n return \"ENTHUSIASTIC\"\n elif num == 6:\n return \"HOSPITALITY\"\n elif num == 7:\n return \"BUNDLE\"\n elif num == 8:\n return \"REFUND\"","title":""},{"docid":"548f8a79eabb1e40b6c0093a5b5e63e6","score":"0.6766102","text":"def get_word():\n open_file = open(\"Lexicon.txt\", \"r\")\n lines = open_file.readlines()\n words = []\n for line in lines:\n words.append(line.strip())\n index = random.randrange(len(words))\n return words[index]","title":""},{"docid":"3af2268d0a2e55c957785414c22af1f6","score":"0.67642826","text":"def n_long_words(word_list, n):\n\n new_list = []\n\n for word in word_list:\n if len(word) > n: # If the length of the word is longer than n characters\n new_list.append(word) # Add the word to the new list\n\n return new_list","title":""},{"docid":"393a41e8130b8c0ee9325144044c250c","score":"0.67637444","text":"def get_n_labels(n):\n try:\n # read all english labels in database\n labels = Labels.query.all()\n english_labels = [str(label.english) for label in labels]\n random_list = random.sample(english_labels, n)\n return random_list\n\n except Exception as e:\n raise Exception(\"Could not read Labels table: \" + str(e))","title":""},{"docid":"07728c8c6bf45f9d3a872872fd084c9e","score":"0.67553985","text":"def get_word():\n index = random.randrange(3)\n if index == 0:\n return 'HAPPY'\n elif index == 1:\n return 'PYTHON'\n else:\n return 'COMPUTER'","title":""},{"docid":"c1d1aa3c4ccd3e8adbcb7d99d9be4a1a","score":"0.6743171","text":"def get_word():\n f = open('Lexicon.txt')\n word_list = []\n for line in f:\n line = line.strip()\n word_list.append(line)\n\n index = random.randrange(len(word_list))\n return word_list[index]","title":""},{"docid":"676638de76fc7556499f324960a87a1f","score":"0.6742763","text":"def generate_word(wc=1):\n letter_list = [initial_consonants]\n for i in range(wc):\n letter_list.extend([vowels, final_consonants])\n return ''.join(random.choice(s) for s in letter_list)","title":""},{"docid":"5cb7d948b76dd7ab9075e3716456cd3b","score":"0.67331314","text":"def get_top_n_words(word_list, n):\n\t#creating a dictionary in which each unique word is a key and its value is the number of times it appears\n\tword_dict = {}\n\tfor word in word_list:\n\t\tif word not in word_dict:\n\t\t\tword_dict[word] = 1\n\t\telif word in word_dict:\n\t\t\tword_dict[word] += 1\n\tordered_by_frequency = sorted(word_dict, key=word_dict.get, reverse = True) #sorts by value, as given in the hints for this toolbox\n\treturn ordered_by_frequency[:n]","title":""},{"docid":"7bfc275fc7b751821b2fb59d5c50a649","score":"0.67253804","text":"def randstr(n):\n return ''.join(choice('abcdefghijklmnopqrstuvwxyz') for i in range(n))","title":""},{"docid":"f89221d204c2d251516dcabee3cb6a27","score":"0.67222947","text":"def get_top_n_words(word_list, n):\n dic = dict()\n dic = collections.Counter(word_list)\n topwords= []\n topwords = sorted(dic, key=dic.get, reverse= True)\n return topwords[:n]","title":""},{"docid":"6ba83b23c7a3a9a026c1899246be6cdf","score":"0.6722267","text":"def get_word():\r\n import random\r\n word=words_dict[random.randrange(0,len(words_dict))]\r\n return word","title":""},{"docid":"2e667febd6771fed7e9c69e49251b473","score":"0.67158604","text":"def phrase(no_words=2):\n\twords = \" \".join(random.sample(__all_words, no_words))\n\treturn re.sub(r'\\W+', '', words)","title":""},{"docid":"3936f0bc7af6db2219d9ea0699fa4d56","score":"0.6712832","text":"def get_word():\n word=words_dict[random.randrange(0,len(words_dict))]\n return word","title":""},{"docid":"a1333a07b6853bee403d986e9b2da2e8","score":"0.67101085","text":"def random_string(N):\n return ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(N))","title":""},{"docid":"19fc6868fcf220af75fc6dc4716d667f","score":"0.66978866","text":"def main(path, n):\n trigrams = generate_trigrams(book_into_list(path))\n\n output = random.choice(list(trigrams)).split()\n\n while len(output) < n:\n for word in range(n - 2):\n pair_to_check = output[-2] + ' ' + output[-1]\n word_to_add = trigrams.get(pair_to_check)[0]\n if word_to_add[0] == ' ':\n word_to_add = word_to_add[1:]\n output.append(word_to_add)\n\n print(' '.join(output))\n return ' '.join(output)","title":""},{"docid":"e29076a5729557163df5f4520194284e","score":"0.6697149","text":"def generate_word(self, n=10) -> list:\n assert hasattr(self, 'model'), 'Call the fit() method first!'\n words = []\n for i in range(n):\n words.append(self.proccess_output(self.model))\n # words.append(word + self.config.suffix)\n return words","title":""},{"docid":"a776fefa84d023e5e341ad1d9fa14ce4","score":"0.6696424","text":"def get_word():\n \"\"\"\n index = random.randrange(3)\n if index == 0:\n return 'HAPPY'\n elif index == 1:\n return 'PYTHON'\n else:\n return 'COMPUTER'\n\"\"\"\n word_list = []\n file = open(LEXICON_FILE)\n for line in file:\n word = line.strip()\n word_list.append(word)\n return random.choice(word_list)","title":""},{"docid":"ab419f54fbb2429a6c57fa6cb8752d39","score":"0.66961646","text":"def random_word(hist):\r\n t = []\r\n for word, freq in hist.items():\r\n t.extend([word] * freq)\r\n\r\n return random.choice(t)","title":""},{"docid":"df509e21e70ded86baad6743b21b5bbf","score":"0.6694445","text":"def generateText( D, n ):\n choice=random.choice(D['$'])\n string=choice\n for count in range(n):\n choice=random.choice(D[punk(choice)])\n string=string+' '+choice\n return string","title":""},{"docid":"0b6402b142c0da3203f011d8441456fd","score":"0.6659473","text":"def make_text(chains, n=2):\n start = random.choice(chains.keys())\n while start[0][0].isupper() == False:\n start = random.choice(chains.keys())\n first_word = list(start[-(n-1):]) \n next = random.choice(chains[start])\n result = list(start)\n result.append(next)\n\n\n while next[-1] != \".\" and next[-1]!= \"?\" and next[-1]!= \"!\" :\n new = tuple(first_word+[next]) #should be list converted to tuple--make sure it create n tuple\n next_word = random.choice(chains[new]) #check to see if changing variable \"next\" works\n result.append(next_word)\n first_word = result[-n:-1] #first_word returns n-1 words\n next = result[-1]\n\n\n return \" \".join(result)","title":""},{"docid":"00819b69c3f8dcc609b840aa83b5db52","score":"0.6654779","text":"def rdWord(wordLst,sep=\"|\"):\n\txu=wordLst.split(sep)\n\treturn(random.choice(xu))","title":""},{"docid":"2f02a8e06d65a82d4c758ac23d110ece","score":"0.66425335","text":"def get_inflation(word):\n word = list(word)\n for idx, l in enumerate(word):\n if random.random() * 100 > 60:\n word[idx] = word[idx] * int(random.random() * 10)\n # ['h','i', 'i', 'i'] becomes ['h', ['i', 'ii', 'iii']]\n return word","title":""},{"docid":"b3555f2eb4713dd85bdc6d298d9a8d96","score":"0.664112","text":"def generate_mnemonic_password(word_count: int = 4):\n return '-'.join([random.choice(WORDS) for _ in range(0, word_count)])","title":""},{"docid":"a216dcc12d1d4a2fba9910d47f5bb4fb","score":"0.663369","text":"def random_dna_strings(length, num_strings):\n return [random_dna_string(length) for i in range(num_strings)]","title":""},{"docid":"df1d47f6699df3bd4afea9c51681615c","score":"0.6627084","text":"def random_word(corpus):\n word = random.choice(corpus)\n num_syls = count_syllables(word)\n if num_syls > 4:\n random_word(corpus)\n else:\n logging.debug(\"random word & syllables = %s %s\\n\", word, num_syls)\n return (word, num_syls)","title":""},{"docid":"dd9f18c12b908384af189bb681dfe0eb","score":"0.66206944","text":"def pick_random_word(self) -> str:\n\n count_possible_words: int = len(self.possible_words)\n random_number: int = random.randint(0, count_possible_words - 1)\n random_word: str = self.possible_words[random_number]\n return random_word","title":""}],"string":"[\n {\n \"docid\": \"17466e947506faa4e0135d8c2c04f89a\",\n \"score\": \"0.7959614\",\n \"text\": \"def get_random_words(chars, start, stop, n):\\n results = []\\n for i in range(int(n)):\\n len = random.randrange(int(start), int(stop))\\n results.append(''.join(random.choice(chars) for _ in range(len)))\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d764323a84f04b00841b3f12b65ed3d\",\n \"score\": \"0.79319835\",\n \"text\": \"def randomWords(numberofwords = 5):\\n\\n return map(lambda x: randomWord(), [0] * numberofwords)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8684c27893399f714bf535d9b5257315\",\n \"score\": \"0.78923345\",\n \"text\": \"def rand_words():\\n return [random.choice(words) for word in range(10000)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad6d7638a5a82bbf810db5c81f4e1272\",\n \"score\": \"0.78462625\",\n \"text\": \"def random_scrambled(wordlist, n):\\n s = random_string(wordlist, n) + \\\" \\\"\\n shifts = [(i, random.randint(0, 26)) for i in range(len(s)) if s[i-1] == ' ']\\n return apply_shifts(s, shifts)[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6a7de4d1f8640518fc43d817d3922cd\",\n \"score\": \"0.7820909\",\n \"text\": \"def make_random_word(N, x):\\n random.shuffle(alphabets)\\n return ''.join(random.choice(alphabets[:x]) for _ in range(N))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c95b8a067dfac2729d87b74eca5134bd\",\n \"score\": \"0.78102386\",\n \"text\": \"def generate_phrase(n=2):\\n if n not in range(1, 4):\\n raise Exception(\\\"Invalid number of words for phrase: {}.\\\".format(n))\\n\\n lists = [fruits, animals, colors]\\n\\n return ' '.join([random.choice(l) for l in random.sample(lists, n)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4577b30f9ec544b863d12ea03cec44d2\",\n \"score\": \"0.7747641\",\n \"text\": \"def rand_word(word_list):\\n\\t\\treturn random.choice(word_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b1e1e197c719b575c79d9913e3ad91\",\n \"score\": \"0.7726783\",\n \"text\": \"def generate(n=4, sep=\\\"\\\"):\\n random.seed(secrets.token_bytes(32))\\n return sep.join(random.sample(words, n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604f7b8cad2680970e901bf9f199886a\",\n \"score\": \"0.7715267\",\n \"text\": \"def _get_random_words(filename, n_words):\\n words = open(filename, 'rb').read().decode('utf-8')\\n # Remove blank lines and extraneous whitespace.\\n words = [word.strip() for word in words.split('\\\\n') if word.strip()]\\n\\n # There's only 267 names in that file and the caller may have asked for more which will\\n # cause an error when random.sample() is called. I make sure I have enough n_names before I\\n # call sample().\\n while len(words) < n_words:\\n words = words + words\\n\\n return random.sample(words, n_words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ff60d544850f2d2c2e854deb0743863\",\n \"score\": \"0.76914465\",\n \"text\": \"def _fetch_random_words(n=1000):\\n\\n # Ensure the same words each run\\n random.seed(42)\\n\\n # Download the corpus if not present\\n nltk.download('words')\\n\\n word_list = nltk.corpus.words.words()\\n\\n random.shuffle(word_list)\\n\\n random_words = word_list[:n]\\n\\n return random_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746ce32037ff35c1a8336411c7a49b52\",\n \"score\": \"0.7677853\",\n \"text\": \"def random_word(wordlist):\\n return random.choice(wordlist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c87f0d099152c2bd5027ce1b0a53b8e\",\n \"score\": \"0.7669144\",\n \"text\": \"def getRandomWords(numWords):\\n words = open(\\\"twelve_years.txt\\\", \\\"r\\\").read().split('\\\\n')\\n newSentence = []\\n for i in range(int(numWords)):\\n newSentence.append(words[random.randint(0, len(words))] + \\\" \\\")\\n return(''.join(newSentence))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b64dd27b1b34a16a07d79b0051837a\",\n \"score\": \"0.7609371\",\n \"text\": \"def choose_words (words, numberOfWords):\\n return random.sample(words, numberOfWords)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c1feac0dcbea127b2fd1a5fa9591d83\",\n \"score\": \"0.75674075\",\n \"text\": \"def get_random_word(word_list: List[str]) -> str:\\n return random.choice(word_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee758c84f05a9162793f383167ac3b68\",\n \"score\": \"0.7410763\",\n \"text\": \"def rnd_text() -> str:\\n return ' '.join(rnd_word() for _ in range(8))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214bba440d12d8d747df9b36ae02e0c1\",\n \"score\": \"0.7394398\",\n \"text\": \"def rnd_word() -> str:\\n return ''.join(random.choice(string.ascii_letters) for _ in range(4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9781587722de84df2f3481f23c63c428\",\n \"score\": \"0.73738056\",\n \"text\": \"def get_words(n):\\n # Let's make sure the words are unique\\n words_used = set()\\n with open(\\\"/usr/share/dict/words\\\", \\\"r\\\") as dictionary:\\n word_string = dictionary.read()\\n words = word_string.split(\\\"\\\\n\\\")\\n\\n acc = []\\n while len(acc) < n:\\n rando_word = random.choice(words)\\n if rando_word.lower() not in words_used:\\n # Weird issues with sorting and comparison of cases\\n l_case_word = rando_word.lower()\\n words_used.add(l_case_word)\\n acc.append(l_case_word)\\n\\n # Sort the list\\n acc.sort()\\n\\n return acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb585c9f85db83d4e11d211c13eab6f\",\n \"score\": \"0.73677856\",\n \"text\": \"def random_words(count=100, supplemental_word_file_path='/usr/share/dict/words'):\\n if count <= len(WORDS_1K):\\n return random.sample(WORDS_1K, count)\\n with codecs.open(supplemental_word_file_path, 'r', 'utf-8') as f:\\n supplemental_word_list = f.read().splitlines()\\n supplemental_word_list.extend(WORDS_1K)\\n return random.sample(supplemental_word_list, count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5483c87dd34a93af5fca364ea2e0ad53\",\n \"score\": \"0.7346386\",\n \"text\": \"def random_text_generation(markov_table, text_size, word_list, n): \\r\\n random.seed(SEED)\\r\\n tlist = [] # will contain randomized text as a list\\r\\n key = ' '.join(word_list[n: n+n])\\r\\n word_list = word_list[n+n:]\\r\\n tlist.extend(key.split())\\r\\n for i in range(text_size - 1):\\r\\n if markov_table.get(key) != None: \\r\\n suffix_list = markov_table.get(key)\\r\\n if len(suffix_list) > 1:\\r\\n rand = random.randint(0, len(suffix_list) - 1)\\r\\n word = suffix_list[rand]\\r\\n else:\\r\\n word = suffix_list[0]\\r\\n tlist.append(word)\\r\\n if n > 1:\\r\\n key = key.split()[1] + \\\" \\\" + word\\r\\n else:\\r\\n key = word\\r\\n return tlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90de3810b84cba26e4498ae5fd717fe0\",\n \"score\": \"0.72732323\",\n \"text\": \"def get_word(words):\\n\\n return words[random.randrange(0, len(words) - 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4791d4e07b0484f0ff7e61dac0df8ef9\",\n \"score\": \"0.72707087\",\n \"text\": \"def pickRandomWord(listOfWords):\\n #i = random.randrange(0, len(listOfWords))\\n #return listOfWords[i]\\n return random.choice(listOfWords)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6411d6a02f51c3551df751e48b0adff\",\n \"score\": \"0.72475135\",\n \"text\": \"def _generate_random_word(self, length):\\n filtered_list = tuple(filter(lambda v : len(v) == length, WORD_LIST))\\n return random.choice(filtered_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c76c96132275a1c665c59d260699d41e\",\n \"score\": \"0.723356\",\n \"text\": \"def __get_random_words__(self):\\n with open(\\\"word_list.txt\\\", \\\"r\\\") as f :\\n text = f.read().splitlines()\\n for _ in range(self.max_rounds):\\n words = []\\n for _ in range(3):\\n words.append(choice(text))\\n yield words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ff35b91fe9415cef7cdf5242673fd0\",\n \"score\": \"0.7211744\",\n \"text\": \"def generate_random_word():\\n return random.choice(words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1164ebd646b4affe79655df9d23bcdad\",\n \"score\": \"0.7197997\",\n \"text\": \"def create_word_list(n, filename='English.txt'):\\n file = open(filename, 'r') # Open the file with read-only access\\n file = file.read() # Read in the text from the file\\n file = file.split('\\\\n') # Get each word, separated by '\\\\n'\\n file = file[:-1] # Remove the last endline\\n # Randomize, convert to a list, and return.\\n return list(np.random.permutation(file))[:n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb332dcb2f210a7a43c6f34cf1a12c1a\",\n \"score\": \"0.7195058\",\n \"text\": \"def generate_words(wordcount):\\n return [generate_word() for _ in xrange(wordcount)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b80a956404f553d417bde72f8267d9f\",\n \"score\": \"0.7160264\",\n \"text\": \"def random_filler_text(number_of_words=200):\\n import random\\n # dict_list = []\\n # print(list(my_dict.values()))\\n\\n my_dict = make_filler_text_dictionary()\\n # print(my_dict)\\n words = []\\n\\n while True:\\n randword = \\\"\\\"\\n randkey = 0\\n randkey = my_dict.get(random.randint(3,7))\\n # print(f\\\"Printing randkey: {randkey}\\\")\\n randword = randkey[random.randint(0,2)]\\n # print(f\\\"Printing randword: {randword}\\\")\\n words.append(str(randword))\\n if len(words) == number_of_words:\\n break\\n # for key, value in my_dict.iteritems():\\n # temp = [key, value]\\n # dict_list.append(temp)\\n\\n return \\\" \\\".join(words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5327d3c653c69254d30532ade80367f7\",\n \"score\": \"0.7153511\",\n \"text\": \"def random_word(word_list):\\n correct_word = random.choice(word_list)\\n return correct_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d85ce6da59925599c9eae5ae4c1bd000\",\n \"score\": \"0.71405166\",\n \"text\": \"def get_words():\\n list_of_rand = []\\n rand_word = []\\n\\n with open(os.path.join(os.path.dirname(__file__), 'alice.txt'), \\\"r\\\") as f:\\n for _ in range(40):\\n list_of_rand.append(random.randrange(0, sum(1 for line in f)))\\n\\n for i, line in enumerate(f.readlines()):\\n if i in list_of_rand:\\n w_list = line.split(\\\" \\\")\\n rand_word.append(\\n w_list[random.randrange(0, len(w_list))].rstrip())\\n\\n return [w for w in rand_word if w] #rand_word contains \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39b7e801edab8b8a50bc94c8953a443\",\n \"score\": \"0.7128379\",\n \"text\": \"def random_text(n):\\n # choose a random prefix (not weighted by frequency)\\n start = random.choice(suffix_map.keys())\\n \\n for i in range(n):\\n suffixes = suffix_map.get(start, None)\\n if suffixes == None:\\n # if the start isn't in map, we got to the end of the\\n # original text, so we have to start again.\\n random_text(n-i)\\n return\\n\\n # choose a random suffix\\n word = random.choice(suffixes)\\n print word,\\n start = shift(start, word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94fef8dd40c2a1ded0f8b57e61b29229\",\n \"score\": \"0.7125045\",\n \"text\": \"def generate_input(input_size):\\n words = ['balla', 'assdd']\\n append_random(words, input_size)\\n words.append('areae')\\n append_random(words, input_size)\\n words.append('lekbc')\\n append_random(words, input_size)\\n words.append('labyd')\\n append_random(words, input_size)\\n words.append('aecdz')\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4890a9ecb6b64f7823704bfd4cf3e97e\",\n \"score\": \"0.71038365\",\n \"text\": \"def get_words(nwords, generator):\\n result = \\\"\\\"\\n for i in range(nwords):\\n result += next(generator)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1d66cea2e3c9479dd3abcea040b9969\",\n \"score\": \"0.70701665\",\n \"text\": \"def make_sparse_wordlist(n):\\n words = get_words(n)\\n random_word = random.choice(words)\\n return sparsify(words), random_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa5db6bd4ea2d32951f15accc6585aa\",\n \"score\": \"0.7067373\",\n \"text\": \"def random_word_generator(length: int = 6, chars: str = string.ascii_uppercase) -> str:\\n return \\\"\\\".join(random.choice(chars) for _ in range(length))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefef587d4edc161ca865a9850de72fe\",\n \"score\": \"0.70577323\",\n \"text\": \"def choose_words(database_words, word_count):\\n\\n if word_count is None:\\n words = database_words \\n random.shuffle(words)\\n\\n return words\\n\\n return random.sample(database_words, word_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3637da7e529c84a3173dbe915fddb1a\",\n \"score\": \"0.7046549\",\n \"text\": \"def getword(wordlist, rng):\\n return wordlist[rng.randrange(len(wordlist))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7960f1dad9daed5227985d254f6e9e40\",\n \"score\": \"0.70277274\",\n \"text\": \"def randomword(length):\\n return ''.join(random.choice(string.lowercase) for i in range(length))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61fa22e10a6de8fb367c1d718a0b2743\",\n \"score\": \"0.69991434\",\n \"text\": \"def sample_phrases(self, n=1):\\n phrase = []\\n final_word_count = 0\\n for word in self:\\n phrase.append(word)\\n if is_final_word(word):\\n final_word_count += 1\\n if final_word_count >= n:\\n break\\n\\n return \\\" \\\".join(phrase)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ec490da261394f9fbbd37e2a8695b3\",\n \"score\": \"0.69973415\",\n \"text\": \"def ranstr(m,n=10):\\n r,s = random.choice, string.ascii_lowercase\\n l = [''.join(r(s) for _ in xrange(n)) for _ in xrange(m)]\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97481eed5c93d035ae961563f46d5de0\",\n \"score\": \"0.6992634\",\n \"text\": \"def gen_word() -> str:\\n word_len = randint(2, 9)\\n return ''.join(pick_letter() for _ in range(word_len))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10a35323415c502899aaa602b88bc46\",\n \"score\": \"0.6991505\",\n \"text\": \"def randomword(length):\\n return ''.join(random.choice(string.lowercase) for i in range(length))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b1da0b405d8dad98247df06fec68575\",\n \"score\": \"0.69854677\",\n \"text\": \"def random_text(self,n=100):\\n # choose a random prefix (not weighted by frequency)\\n start = random.choice(list(self.suffix_map.keys()))\\n \\n for i in range(n):\\n suffixes = self.suffix_map.get(start, None)\\n if suffixes == None:\\n # if the start isn't in map, we got to the end of the\\n # original text, so we have to start again.\\n random_text(n-i)\\n return\\n\\n # choose a random suffix\\n word = random.choice(suffixes)\\n print(word, end=' ')\\n start = start[1:] + (word,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3f4f636541a2e741c72263de04e11b\",\n \"score\": \"0.6953873\",\n \"text\": \"def random_string(n):\\n return \\\"\\\".join([random.choice(string.ascii_lowercase) for number\\n in range(n)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b57a3143c92fddd5be4f33e6638fcc14\",\n \"score\": \"0.695329\",\n \"text\": \"def generate_word(chains):\\n length = random.randint(3, 10)\\n word = random.choice(string.ascii_lowercase)\\n while len(word) < length:\\n order = len(word) if len(word) < 4 else 4\\n options = []\\n while len(options) == 0:\\n last = word[-order:]\\n try:\\n options = chains[order-1][last]\\n except KeyError:\\n order -= 1\\n nxt = random.choice(options)\\n word += nxt\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72f568704f0d20bfa8bd002a3eb777f\",\n \"score\": \"0.6937143\",\n \"text\": \"def randomNouns(index):\\n\\tstring = ''\\n\\tfor _ in range(index):\\n\\t\\tif _ == index - 1:\\n\\t\\t\\tstring = string + randomNoun()\\n\\t\\telse:\\n\\t\\t\\tstring = string + randomNoun() + ' '\\n\\treturn string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c366cf6b8c741a601eb7dc279ab7fb0\",\n \"score\": \"0.692876\",\n \"text\": \"def gen(self, n=50):\\n if not self.chain:\\n # raise error?\\n return\\n \\n wordlist = []\\n for i in xrange(n):\\n key = '#'\\n word = '' # string to append to\\n while key != '$':\\n roll = random.random()\\n for c, prob in self.chain[key].items():\\n roll -= prob\\n if roll <= 0:\\n if key == '#':\\n # first one, so add pair\\n word += \\\"\\\".join(c)\\n key = c\\n elif c == '$':\\n key = '$'\\n else:\\n word += c\\n key = (key[1], c)\\n break\\n wordlist.append(word)\\n word = ''\\n \\n return wordlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb2110382311a0cd58f25fdc46b75ba0\",\n \"score\": \"0.69271725\",\n \"text\": \"def random_word(corpus):\\n syllables = 5\\n while syllables > 4:\\n word = random.choice(corpus)\\n syllables = count_syllables(word)\\n return (word, syllables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dfe33ae580f9a279e6d7bf08df154f4\",\n \"score\": \"0.6925991\",\n \"text\": \"def get_words(self, word_count): \\n word_list = self.fake.words(nb=word_count, unique=True)\\n words = ' '.join(word_list)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"822de6dc73898cba6854d418b9013c9a\",\n \"score\": \"0.69175094\",\n \"text\": \"def gen_word_list(num_words=NUM_WORDS):\\n return [str(x) for x in range(num_words)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2492d3a7ef10f455814651c4b4c7a713\",\n \"score\": \"0.69091445\",\n \"text\": \"def get_random_word(self):\\n return choice(self.word_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9491582f4efac8c4ea2fe48b75a1ea\",\n \"score\": \"0.6904912\",\n \"text\": \"def random_text(self, n=100):\\n # choose a random prefix (not weighted by frequency)\\n start = random.choice(list(self.suffix_map.keys()))\\n\\n for i in range(n):\\n suffixes = self.suffix_map.get(start, None)\\n if suffixes == None:\\n # if the start isn't in map, we got to the end of the\\n # original text, so we have to start again.\\n self.random_text(n - i)\\n return\\n\\n # choose a random suffix\\n word = random.choice(suffixes)\\n print(word, end=' ')\\n start = shift(start, word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae0ab66ca41d3f1ddb0757f2adfd507\",\n \"score\": \"0.6898953\",\n \"text\": \"def randomword(length):\\n letters = string.ascii_lowercase\\n return ''.join(random.choice(letters) for i in range(length))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8425ff563d702f80f113606595fc2e4\",\n \"score\": \"0.68897134\",\n \"text\": \"def generate_text(word_dict,num_words):\\r\\n y = ''\\r\\n current_word = '$'\\r\\n next_word = ''\\r\\n for x in range(num_words):\\r\\n wordlist = word_dict[current_word]\\r\\n next_word = random.choice(wordlist)\\r\\n y += ' ' + next_word\\r\\n if next_word[-1] == '.' or next_word[-1] == '?' or next_word[-1] == '!':\\r\\n current_word = '$'\\r\\n else:\\r\\n current_word = next_word\\r\\n while len(y) > 140:\\r\\n f = y.rfind(' ')\\r\\n y = y[:f]\\r\\n return str(y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df14ee3d6f8c168da3cecfdb2ca82802\",\n \"score\": \"0.6882976\",\n \"text\": \"def generateWord(self):\\n length = random.randint(10,20)\\n\\n word = \\\"\\\"\\n\\n for i in range(length):\\n c = random.choice(string.ascii_letters)\\n word += c\\n\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adc5785f6cd128bccdc594d7c3e56301\",\n \"score\": \"0.68705046\",\n \"text\": \"def nrandom(lst, qnt=1):\\n saida = \\\"\\\"\\n qnt -= 1\\n for i in range(qnt):\\n saida += random.choice(lst) + \\\" \\\"\\n\\n # para nao ficar espaco na ultima palavra\\n saida += random.choice(lst)\\n return saida\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ed9d663a0dfbc7c9c515644fc86b73\",\n \"score\": \"0.686637\",\n \"text\": \"def randomWord():\\n \\n if wordlist == None:\\n pickDictionary()\\n word = ''\\n fd = open(wordlist)\\n size = os.path.getsize(wordlist)\\n pos = random.randrange(size)\\n fd.seek(pos)\\n # toss partial line\\n word = fd.readline()\\n # keep next line\\n word = fd.readline()\\n\\n # if at the end, loop to the beginning\\n if word == '':\\n fd.seek(0)\\n # toss partial line\\n word = fd.readline()\\n fd.close()\\n\\n # beautify word\\n word = word.strip().lower()\\n # jwz has done also following translations which we will leave out for now\\n # $word =~ s/ly$//;\\n # $word =~ s/ys$/y/;\\n # $word =~ s/ies$/y/;\\n # $word =~ s/ally$//;\\n # $word =~ s/ally$/al/;\\n # $word =~ s/izes$/ize/;\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d3f2b2f00c62188b09a5b69b5f5ae3\",\n \"score\": \"0.68633103\",\n \"text\": \"def noun():\\n return [random.randrange(5), random.randrange(2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6acde7051a6439220b4883c9eefae3e3\",\n \"score\": \"0.68514085\",\n \"text\": \"def pick_word(words):\\n return random.choice(words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a041430218764304a35fa5a318893363\",\n \"score\": \"0.6847547\",\n \"text\": \"def generate_words(min, max, chars='loweralpha'):\\n import random\\n length = random.randint(min, max)\\n string = generate_password(length, chars)\\n i = 0\\n while i < len(string):\\n i += random.randint(2, 10)\\n string = string[:i] + ' ' + string[i:]\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29a6a685a93128d0d5146d0350f10c0\",\n \"score\": \"0.6845153\",\n \"text\": \"def pick_random_word():\\r\\n #open the words dictionary \\r\\n with open(\\\"words.txt\\\", 'r') as f:\\r\\n words = f.readlines()\\r\\n\\r\\n #generate a random index\\r\\n #-1 because len(words) is not valid index in the list 'words'\\r\\n index = random.randint (0,len (words) - 1)\\r\\n\\r\\n #print out the word at that index \\\\\\r\\n word = words[index].strip() \\r\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e56faa20f3e536cd44b26db34f7aaed8\",\n \"score\": \"0.68353474\",\n \"text\": \"def random_items(self, n):\\n return [self.loremipsum[rr(0, len(self.loremipsum))] for _ in range(n)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b16b58117364e88d41417a2e9b09d2c9\",\n \"score\": \"0.6834861\",\n \"text\": \"def generate_string(n):\\n characters = \\\"abcdefghijklmnoprstuvwxyz\\\"\\n result_str = \\\"\\\".join(random.choice(characters) for i in range(n))\\n return result_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b251a7bcc49a75bd1bf429a6c5e194\",\n \"score\": \"0.6826035\",\n \"text\": \"def n_long_words(words, n):\\n\\n return [word for word in words if len(word) > n] # Using comprehension to select the word that are longer than \\\"n\\\". Autogenerating a list.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e66db12de522c365622ce30a5e2de2\",\n \"score\": \"0.6818638\",\n \"text\": \"def random_multiword(self,length):\\n if length==0:\\n return [self.word([])]\\n numgens=len(self.gens)\\n wordlist=[]\\n totallength=0\\n numberofwords=random.choice(range(1,max(2,1+int(sqrt(length)))))\\n for i in range(1,numberofwords+1):\\n if totallength<length:\\n ilen=random.choice(range(1,1+length-totallength))\\n w=self.randomword(ilen)\\n wordlist.append(w)\\n totallength+=len(w.letters)\\n else:\\n wordlist.append(self.word([]))\\n return wordlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8d3762ff155055b4d987facf640074\",\n \"score\": \"0.68152857\",\n \"text\": \"def time_rand_words(rand):\\n start = time.time()\\n [words.index(elem) for elem in rand]\\n return time.time() - start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1974edf647e4f41cdbf1e770ba823a25\",\n \"score\": \"0.6814763\",\n \"text\": \"def append_random(words, input_size):\\n letters = string.letters[:26]\\n for _ in xrange(input_size):\\n new_string = \\\"\\\"\\n for _ in xrange(5):\\n new_string += (random.choice(letters))\\n words.append(new_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92605361fdddf51fab6dcad1d3949430\",\n \"score\": \"0.6805226\",\n \"text\": \"def random_word(h):\\n t = []\\n for word, freg in h.items():\\n t.extend([word]*freg)\\n\\n return random.choice(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd49dabf544f6015f9565b14498b844\",\n \"score\": \"0.6797209\",\n \"text\": \"def randomword(self,length):\\n numgens=len(self.gens)\\n letterlist = range(1,numgens+1)+range(-1,-(numgens+1),-1)\\n letters = []\\n for n in range(length):\\n nextletter=random.choice(letterlist)\\n if len(letters):\\n while nextletter==-letters[-1]:\\n nextletter=random.choice(letterlist)\\n letters.append(nextletter)\\n return self.word(letters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32bf3e726e284f23cf03809aed7dcc9b\",\n \"score\": \"0.67911\",\n \"text\": \"def samples(self, nwords):\\n n = self.n\\n nminus1gram = ('',) * (n-1)\\n output = []\\n for i in range(nwords):\\n if nminus1gram not in self.cond_prob:\\n nminus1gram = ('',) * (n-1) # Cannot continue, so restart.\\n wn = self.cond_prob[nminus1gram].sample()\\n output.append(wn)\\n nminus1gram = nminus1gram[1:] + (wn,)\\n return ' '.join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706d934708d25fe83864637242f62308\",\n \"score\": \"0.67882264\",\n \"text\": \"def sample_lines(lyrics, n):\\n lyrics_lines = lyrics.split(\\\" \\\\n \\\")\\n i = random.randint(0, max(len(lyrics_lines) - n, 0))\\n sample = lyrics_lines[i:i + n]\\n return \\\" \\\\n \\\".join(sample) + \\\" \\\\n \\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca96093a0334b044bba3dbbb7644e44a\",\n \"score\": \"0.6785711\",\n \"text\": \"def words(self, count=1, common=False):\\n if common:\\n word_list = list(self._common_words)\\n else:\\n word_list = []\\n clen = len(word_list)\\n if count > clen:\\n count -= clen\\n while count > 0:\\n clen = min(count, len(self._words))\\n count -= clen\\n word_list += random.sample(self._words, clen)\\n else:\\n word_list = word_list[:count]\\n return u' '.join(word_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08052ae3133e02fb1fb80059eb5b226c\",\n \"score\": \"0.67819315\",\n \"text\": \"def get_top_n_words(word_list, n):\\n\\tget_top_n_words = word_list[0:n-1]\\n\\treturn get_top_n_words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff2a0878585223dcd6ec462b5542b05a\",\n \"score\": \"0.6775607\",\n \"text\": \"def create_random_string(n=20):\\n choices = list(string.ascii_letters + string.digits)\\n return ''.join(np.random.choice(choices) for _ in range(n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc032e2d9c7c671e8497e96467053a2\",\n \"score\": \"0.67702234\",\n \"text\": \"def noun3():\\n return [random.randrange(3)] + noun()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413ba741bdd0b8f10fed7bc7bc9dd71d\",\n \"score\": \"0.67700094\",\n \"text\": \"def random_word():\\n num = random.choice(range(9))\\n if num == 0:\\n return \\\"NOTORIOUS\\\"\\n elif num == 1:\\n return \\\"GLAMOROUS\\\"\\n elif num == 2:\\n return \\\"CAUTIOUS\\\"\\n elif num == 3:\\n return \\\"DEMOCRACY\\\"\\n elif num == 4:\\n return \\\"BOYCOTT\\\"\\n elif num == 5:\\n return \\\"ENTHUSIASTIC\\\"\\n elif num == 6:\\n return \\\"HOSPITALITY\\\"\\n elif num == 7:\\n return \\\"BUNDLE\\\"\\n elif num == 8:\\n return \\\"REFUND\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548f8a79eabb1e40b6c0093a5b5e63e6\",\n \"score\": \"0.6766102\",\n \"text\": \"def get_word():\\n open_file = open(\\\"Lexicon.txt\\\", \\\"r\\\")\\n lines = open_file.readlines()\\n words = []\\n for line in lines:\\n words.append(line.strip())\\n index = random.randrange(len(words))\\n return words[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af2268d0a2e55c957785414c22af1f6\",\n \"score\": \"0.67642826\",\n \"text\": \"def n_long_words(word_list, n):\\n\\n new_list = []\\n\\n for word in word_list:\\n if len(word) > n: # If the length of the word is longer than n characters\\n new_list.append(word) # Add the word to the new list\\n\\n return new_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"393a41e8130b8c0ee9325144044c250c\",\n \"score\": \"0.67637444\",\n \"text\": \"def get_n_labels(n):\\n try:\\n # read all english labels in database\\n labels = Labels.query.all()\\n english_labels = [str(label.english) for label in labels]\\n random_list = random.sample(english_labels, n)\\n return random_list\\n\\n except Exception as e:\\n raise Exception(\\\"Could not read Labels table: \\\" + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07728c8c6bf45f9d3a872872fd084c9e\",\n \"score\": \"0.67553985\",\n \"text\": \"def get_word():\\n index = random.randrange(3)\\n if index == 0:\\n return 'HAPPY'\\n elif index == 1:\\n return 'PYTHON'\\n else:\\n return 'COMPUTER'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d1aa3c4ccd3e8adbcb7d99d9be4a1a\",\n \"score\": \"0.6743171\",\n \"text\": \"def get_word():\\n f = open('Lexicon.txt')\\n word_list = []\\n for line in f:\\n line = line.strip()\\n word_list.append(line)\\n\\n index = random.randrange(len(word_list))\\n return word_list[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"676638de76fc7556499f324960a87a1f\",\n \"score\": \"0.6742763\",\n \"text\": \"def generate_word(wc=1):\\n letter_list = [initial_consonants]\\n for i in range(wc):\\n letter_list.extend([vowels, final_consonants])\\n return ''.join(random.choice(s) for s in letter_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb7d948b76dd7ab9075e3716456cd3b\",\n \"score\": \"0.67331314\",\n \"text\": \"def get_top_n_words(word_list, n):\\n\\t#creating a dictionary in which each unique word is a key and its value is the number of times it appears\\n\\tword_dict = {}\\n\\tfor word in word_list:\\n\\t\\tif word not in word_dict:\\n\\t\\t\\tword_dict[word] = 1\\n\\t\\telif word in word_dict:\\n\\t\\t\\tword_dict[word] += 1\\n\\tordered_by_frequency = sorted(word_dict, key=word_dict.get, reverse = True) #sorts by value, as given in the hints for this toolbox\\n\\treturn ordered_by_frequency[:n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bfc275fc7b751821b2fb59d5c50a649\",\n \"score\": \"0.67253804\",\n \"text\": \"def randstr(n):\\n return ''.join(choice('abcdefghijklmnopqrstuvwxyz') for i in range(n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f89221d204c2d251516dcabee3cb6a27\",\n \"score\": \"0.67222947\",\n \"text\": \"def get_top_n_words(word_list, n):\\n dic = dict()\\n dic = collections.Counter(word_list)\\n topwords= []\\n topwords = sorted(dic, key=dic.get, reverse= True)\\n return topwords[:n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba83b23c7a3a9a026c1899246be6cdf\",\n \"score\": \"0.6722267\",\n \"text\": \"def get_word():\\r\\n import random\\r\\n word=words_dict[random.randrange(0,len(words_dict))]\\r\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e667febd6771fed7e9c69e49251b473\",\n \"score\": \"0.67158604\",\n \"text\": \"def phrase(no_words=2):\\n\\twords = \\\" \\\".join(random.sample(__all_words, no_words))\\n\\treturn re.sub(r'\\\\W+', '', words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3936f0bc7af6db2219d9ea0699fa4d56\",\n \"score\": \"0.6712832\",\n \"text\": \"def get_word():\\n word=words_dict[random.randrange(0,len(words_dict))]\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1333a07b6853bee403d986e9b2da2e8\",\n \"score\": \"0.67101085\",\n \"text\": \"def random_string(N):\\n return ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(N))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19fc6868fcf220af75fc6dc4716d667f\",\n \"score\": \"0.66978866\",\n \"text\": \"def main(path, n):\\n trigrams = generate_trigrams(book_into_list(path))\\n\\n output = random.choice(list(trigrams)).split()\\n\\n while len(output) < n:\\n for word in range(n - 2):\\n pair_to_check = output[-2] + ' ' + output[-1]\\n word_to_add = trigrams.get(pair_to_check)[0]\\n if word_to_add[0] == ' ':\\n word_to_add = word_to_add[1:]\\n output.append(word_to_add)\\n\\n print(' '.join(output))\\n return ' '.join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e29076a5729557163df5f4520194284e\",\n \"score\": \"0.6697149\",\n \"text\": \"def generate_word(self, n=10) -> list:\\n assert hasattr(self, 'model'), 'Call the fit() method first!'\\n words = []\\n for i in range(n):\\n words.append(self.proccess_output(self.model))\\n # words.append(word + self.config.suffix)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a776fefa84d023e5e341ad1d9fa14ce4\",\n \"score\": \"0.6696424\",\n \"text\": \"def get_word():\\n \\\"\\\"\\\"\\n index = random.randrange(3)\\n if index == 0:\\n return 'HAPPY'\\n elif index == 1:\\n return 'PYTHON'\\n else:\\n return 'COMPUTER'\\n\\\"\\\"\\\"\\n word_list = []\\n file = open(LEXICON_FILE)\\n for line in file:\\n word = line.strip()\\n word_list.append(word)\\n return random.choice(word_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab419f54fbb2429a6c57fa6cb8752d39\",\n \"score\": \"0.66961646\",\n \"text\": \"def random_word(hist):\\r\\n t = []\\r\\n for word, freq in hist.items():\\r\\n t.extend([word] * freq)\\r\\n\\r\\n return random.choice(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df509e21e70ded86baad6743b21b5bbf\",\n \"score\": \"0.6694445\",\n \"text\": \"def generateText( D, n ):\\n choice=random.choice(D['$'])\\n string=choice\\n for count in range(n):\\n choice=random.choice(D[punk(choice)])\\n string=string+' '+choice\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b6402b142c0da3203f011d8441456fd\",\n \"score\": \"0.6659473\",\n \"text\": \"def make_text(chains, n=2):\\n start = random.choice(chains.keys())\\n while start[0][0].isupper() == False:\\n start = random.choice(chains.keys())\\n first_word = list(start[-(n-1):]) \\n next = random.choice(chains[start])\\n result = list(start)\\n result.append(next)\\n\\n\\n while next[-1] != \\\".\\\" and next[-1]!= \\\"?\\\" and next[-1]!= \\\"!\\\" :\\n new = tuple(first_word+[next]) #should be list converted to tuple--make sure it create n tuple\\n next_word = random.choice(chains[new]) #check to see if changing variable \\\"next\\\" works\\n result.append(next_word)\\n first_word = result[-n:-1] #first_word returns n-1 words\\n next = result[-1]\\n\\n\\n return \\\" \\\".join(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00819b69c3f8dcc609b840aa83b5db52\",\n \"score\": \"0.6654779\",\n \"text\": \"def rdWord(wordLst,sep=\\\"|\\\"):\\n\\txu=wordLst.split(sep)\\n\\treturn(random.choice(xu))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f02a8e06d65a82d4c758ac23d110ece\",\n \"score\": \"0.66425335\",\n \"text\": \"def get_inflation(word):\\n word = list(word)\\n for idx, l in enumerate(word):\\n if random.random() * 100 > 60:\\n word[idx] = word[idx] * int(random.random() * 10)\\n # ['h','i', 'i', 'i'] becomes ['h', ['i', 'ii', 'iii']]\\n return word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3555f2eb4713dd85bdc6d298d9a8d96\",\n \"score\": \"0.664112\",\n \"text\": \"def generate_mnemonic_password(word_count: int = 4):\\n return '-'.join([random.choice(WORDS) for _ in range(0, word_count)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a216dcc12d1d4a2fba9910d47f5bb4fb\",\n \"score\": \"0.663369\",\n \"text\": \"def random_dna_strings(length, num_strings):\\n return [random_dna_string(length) for i in range(num_strings)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1d47f6699df3bd4afea9c51681615c\",\n \"score\": \"0.6627084\",\n \"text\": \"def random_word(corpus):\\n word = random.choice(corpus)\\n num_syls = count_syllables(word)\\n if num_syls > 4:\\n random_word(corpus)\\n else:\\n logging.debug(\\\"random word & syllables = %s %s\\\\n\\\", word, num_syls)\\n return (word, num_syls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9f18c12b908384af189bb681dfe0eb\",\n \"score\": \"0.66206944\",\n \"text\": \"def pick_random_word(self) -> str:\\n\\n count_possible_words: int = len(self.possible_words)\\n random_number: int = random.randint(0, count_possible_words - 1)\\n random_word: str = self.possible_words[random_number]\\n return random_word\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994379,"cells":{"query_id":{"kind":"string","value":"954c4409a1ba7017f3b740af5b2d9f48"},"query":{"kind":"string","value":"Accepts a weekday and tests if today is that weekday."},"positive_passages":{"kind":"list like","value":[{"docid":"2e3dfcebad664d7447ebdf26e1ef2dce","score":"0.7773084","text":"def test_for_day(target_day):\n import time\n # Get the date object of today's date:\n todays_date = time.localtime().tm_wday\n # Form a dictionary of the days of the week, starting on Monday\n # since this is the time module's assumption:\n date_dict = dict(enumerate('Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.split()))\n # Find the weekday of today's date and compare to target:\n if date_dict[todays_date] == target_day:\n print(\"Today is the target (%s).\" % target_day)\n else:\n print(\"Today is %s, not %s.\" % (date_dict[todays_date], target_day))","title":""}],"string":"[\n {\n \"docid\": \"2e3dfcebad664d7447ebdf26e1ef2dce\",\n \"score\": \"0.7773084\",\n \"text\": \"def test_for_day(target_day):\\n import time\\n # Get the date object of today's date:\\n todays_date = time.localtime().tm_wday\\n # Form a dictionary of the days of the week, starting on Monday\\n # since this is the time module's assumption:\\n date_dict = dict(enumerate('Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.split()))\\n # Find the weekday of today's date and compare to target:\\n if date_dict[todays_date] == target_day:\\n print(\\\"Today is the target (%s).\\\" % target_day)\\n else:\\n print(\\\"Today is %s, not %s.\\\" % (date_dict[todays_date], target_day))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9f211aa22ce1299f4f4f6bb340a47ae8","score":"0.7757462","text":"def is_dayofweek(day, today):\n if isinstance(today, datetime):\n int_day = int(day)\n if today.weekday() == int_day - 1:\n return True\n return False\n else:\n raise Exception(\"{} is not a datetime instance\".format(today))","title":""},{"docid":"af5e48310abd063a9d4fd60cfc8d734c","score":"0.75927293","text":"def is_weekday(dtObj):\n return dtObj.weekday() < 5","title":""},{"docid":"b9a3647c56419fa75d6b60c5aebe7e7a","score":"0.74711937","text":"def is_today(self, dt: datetime.datetime) -> bool:\n\n if self is Day.DAILY:\n return True\n day = dt.weekday()\n if self is Day.WEEKDAY:\n return day < 5\n if self is Day.WEEKEND:\n return day >= 5\n return Day(day) == self","title":""},{"docid":"e8ba479a6b65c40e76ea465e926804f2","score":"0.74445426","text":"def check_weekday_of_date(self, date):\n return date.isoweekday() % 7","title":""},{"docid":"a83cea7bb6d896fa56f312f4160c702f","score":"0.70387244","text":"def is_weekday(day, halfDay):\n hours, days = halfDay.split('x')\n if day <= int(days)-1:\n return True\n else:\n return False","title":""},{"docid":"bd2ba322be25e79d0e306ebc56ae7247","score":"0.6779038","text":"def isoweekday(self, *args, **kwargs): # real signature unknown\r\n pass","title":""},{"docid":"68097d3fed7dfe3341368d626ff11037","score":"0.67623657","text":"def runs_today(self,s_id,day):\n if self.schedule_keys[s_id][day]==1:\n return True\n else:\n return False","title":""},{"docid":"dd9e2dca544118f38399d5d4b30b8d73","score":"0.6756849","text":"def sleep_in(weekday, vacation):\r\n if not weekday or vacation:\r\n return True\r\n else:\r\n return False","title":""},{"docid":"ee814151062f93977ffa90a35b5b211a","score":"0.6719428","text":"def sleep_in(weekday, vacation):\r\n if not weekday or vacation:\r\n return True\r\n return False","title":""},{"docid":"86cd85a03c21b874c5a37093552cd6c6","score":"0.6692384","text":"def is_weekend() -> bool:\n return datetime.today().weekday() > 3","title":""},{"docid":"d2bedf4b587f097ce050891d086e14c5","score":"0.66828525","text":"def check_weekday(self, date):\n week_next = self.next_seven_day()\n today = datetime.date.today().strftime('%Y-%m-%d')\n if not date or date > week_next or date < today: # check the date is within one week\n return False, \"Sorry you can only booking consultation up to next one week. Your booking date must before {}\".format(week_next)\n try:\n day_as_string = self.get_the_weekday(date)\n if day_as_string == \"Saturday\" or day_as_string == \"Sunday\":\n logger.info(\"Sorry, there is no consultation on weekends\")\n return False, \"Sorry, there is no consultation on weekends\"\n else:\n logger.info(\"It is on next {}\".format(day_as_string))\n return True, \"Your booking has been made on {} {}\".format(day_as_string, date)\n except ValueError as e:\n logger.error(str(e))\n return False, \"Please try again\"","title":""},{"docid":"2347a8ee2bacd5b32f134165be77f199","score":"0.66738766","text":"def isoweekday(self):\n return 0","title":""},{"docid":"2347a8ee2bacd5b32f134165be77f199","score":"0.66738766","text":"def isoweekday(self):\n return 0","title":""},{"docid":"85c4ddabefa8f752ed6a7d085f697eb3","score":"0.6667073","text":"def check_day(self, day_of_week):\n\n day_of_week -= 1\n if (day_of_week == -1):\n self.day_of_week = 6\n else:\n self.day_of_week = day_of_week","title":""},{"docid":"e6b5b213d95a01d7c9aa2c817f9e7a47","score":"0.6637579","text":"def get_weekday():\n result = datetime.today().weekday() + 1\n return result","title":""},{"docid":"bbf464cf9579063d7542c14cb5f32552","score":"0.66249675","text":"def week_init():\n week = input('Week to check: MM/DD/YYYY\\n')\n week = dtt.datetime.strptime(week,'%m/%d/%Y') #turns input to a datetime\n beforeday = input('Check days before date (Press enter to use today): MM/DD/YYYY\\n') or dtt.date.today()\n if (beforeday != dtt.date.today()):\n beforeday = dtt.datetime.strptime(beforeday,'%m/%d/%Y')\n return week, beforeday","title":""},{"docid":"c96e90d12e5a760993b2a8e440d57372","score":"0.66073847","text":"def is_dayofnewtrimester(day, today):\n if isinstance(today, datetime):\n if today.day == int(day) and today.month == 1:\n return True\n elif today.day == int(day) and today.month == 4:\n return True\n elif today.day == int(day) and today.month == 7:\n return True\n elif today.day == int(day) and today.month == 10:\n return True\n return False\n else:\n raise Exception(\"{} is not a datetime instance\".format(today))","title":""},{"docid":"9b12fa4237365fb033e96db4811d4928","score":"0.66064125","text":"def weekday(self, dt):\n days = {\n 0: self.MONDAY,\n 1: self.TUESDAY,\n 2: self.WEDNESDAY,\n 3: self.THURSDAY,\n 4: self.FRIDAY,\n 5: self.SATURDAY,\n 6: self.SUNDAY\n }\n return days.get(dt.weekday())","title":""},{"docid":"9767d7b222adc4cd9dc6e64871c2f845","score":"0.6583573","text":"def get_day_of_week_from_user():\n while True:\n day = input('Select the month to explore. Enter from monday, tuesday, wednesday, thursday, friday, '\n 'saturday, sunday or all: ').lower()\n\n if day in VALID_DAYS:\n confirm = input(\"You have selected {}. Press 'y' to confirm: \".format(day.title()))\n\n if confirm == 'y':\n break\n else:\n print(\"Try again.\\n\")\n else:\n print(\"Invalid input: {}. Try again.\\n\".format(day))\n return day","title":""},{"docid":"e63e7dcdce36e2da1a08bd8b6c1dd011","score":"0.65824914","text":"def day_of_the_week(arg):","title":""},{"docid":"c095a09efe27545fe04b9d0f02abfe7e","score":"0.6545656","text":"def _day_rule_matches(self, rule, dt):\n if dt.weekday() == 4:\n sat = dt + datetime.timedelta(days=1)\n if super(SiteHolidays, self)._day_rule_matches(rule, sat):\n return True\n elif dt.weekday() == 0:\n sun = dt - datetime.timedelta(days=1)\n if super(SiteHolidays, self)._day_rule_matches(rule, sun):\n return True\n return super(SiteHolidays, self)._day_rule_matches(rule, dt)","title":""},{"docid":"86f5de3c6b71d045d2906e4e7da80760","score":"0.649922","text":"def checkToday(ctx, cron):\n daze = ctx.obj['daze']\n if cron is not None:\n if date.today() in daze.dateDict.keys():\n sys.exit(1)\n else:\n sys.exit(0)\n click.echo(date.today() in daze.dateDict.keys())\n return date.today() in daze.dateDict.keys()","title":""},{"docid":"b34294557b1886e44436043c7b723a6b","score":"0.64887506","text":"def is_weekend(date):\n \n return date.weekday() == 5 or date.weekday() == 6","title":""},{"docid":"17883616cd8302376bc8cd318b44dc7b","score":"0.6487841","text":"def weekday(day):\n return (day % 7) - 1","title":""},{"docid":"f9b6ebc2e89595aa3fa45981b3ddc277","score":"0.64171106","text":"def isoweekday(self):\n # 1-Jan-0001 is a Monday\n return self.toordinal() % 7 or 7","title":""},{"docid":"464aa0080e91a3afbfa3ab2d5a7c721c","score":"0.63790303","text":"def business_day(self): \n\n if self.time_stamp.weekday() not in (5, 6) and not holiday(self.time_stamp):\n return True \n return False","title":""},{"docid":"730992da2b85bca8cc5c76e44de4ef86","score":"0.63676757","text":"def test_date_accept_this_week(self):\n spi_search = \"find date this week\"\n inv_search = \"year:\" + datetime.datetime.strftime(datetime.datetime.today()\\\n +dateutil.relativedelta.relativedelta(days=-(datetime.datetime.today().isoweekday()%7)), '%Y-%m-%d')\n self._compare_searches(inv_search, spi_search)","title":""},{"docid":"eaa18c9820e23f32279ed4ea1c60af2f","score":"0.63490075","text":"def has_tuesday(self):\n return self.products.filter(type=\"S\", weekday=2).exists()","title":""},{"docid":"67ab5aa69a9405972431975e303a472b","score":"0.63318884","text":"def next_weekday(weekday, d=datetime.datetime.now()):\n if weekday.lower() not in day_values:\n return None\n days_ahead = day_values[weekday.lower()] - d.weekday()\n if days_ahead <= 0: # Target day already happened this week\n days_ahead += 7\n return d + datetime.timedelta(days_ahead)","title":""},{"docid":"a38431ba44eea563bdc6fbc4e2ef8e9b","score":"0.6329821","text":"def weekday(self, *args, **kwargs): # real signature unknown\r\n pass","title":""},{"docid":"53cec41aeae0f546a1aeaef266d1bc2c","score":"0.6327355","text":"def has_wednesday(self):\n return self.products.filter(type=\"S\", weekday=3).exists()","title":""},{"docid":"76dce3b833747abfe5827e07b315e09f","score":"0.627691","text":"def day_of_week():\n return calendar.day_name[datetime.date.today().weekday()]","title":""},{"docid":"1763407be8da3c1d7028f662627495b5","score":"0.6231285","text":"def has_thursday(self):\n return self.products.filter(type=\"S\", weekday=4).exists()","title":""},{"docid":"7460dcd6af1c8ad51d54a6a83582f856","score":"0.6228567","text":"def weekday(self):\n return 0","title":""},{"docid":"7460dcd6af1c8ad51d54a6a83582f856","score":"0.6228567","text":"def weekday(self):\n return 0","title":""},{"docid":"60412cfd9aea5717c8e5b746ade96753","score":"0.6200328","text":"def day_of_week(dt):\n cday = dt\n mday = 2\n uday = cday.isocalendar()[2] + mday\n try:\n if uday > 7:\n CURRDAY = uday - 7\n log.debug(\"1;EME;RUNNING;000;Scheduler.py;Setting customized day of week>7 : \", CURRDAY)\n else:\n CURRDAY = uday\n log.debug(\"1;EME;RUNNING;000;Scheduler.py;Setting customized day of week : \", CURRDAY)\n return CURRDAY\n except Exception as e:\n log.exception(\"1;EME;FAILURE;700;SCHEDULE ERROR \" + str(e), exc_info=False)\n sys.exit(0)","title":""},{"docid":"135cfbc4d78dd877aacf48f2cbf007e0","score":"0.6147052","text":"def is_workfree(date):\n \n return date.weekday() == 6 or is_holiday(date)","title":""},{"docid":"ca7123058f54f1e4ad9c677e47c6b305","score":"0.61246353","text":"def has_monday(self):\n return self.products.filter(type=\"S\", weekday=1).exists()","title":""},{"docid":"9b7fbe3fe8183dbeb6ab9a1d2734f52e","score":"0.61017764","text":"def has_friday(self):\n return self.products.filter(type=\"S\", weekday=5).exists()","title":""},{"docid":"7d543a73c801d8da89c6b27f7d1b0439","score":"0.60952795","text":"def get_weekday():\n try:\n day = config.getint(\"threadbot\", \"debug_day\")\n except ConfigParser.NoOptionError:\n d = datetime.date.today()\n day = d.weekday()\n sort_by_new = False\n\n # 0 / Monday / Feedback thread\n # 1 / Tuesday / How do I make this sound thread\n # 2 / Wednesday / There are no stupid questions thread\n # 3 / Thursday / Marketplace thread\n dayname = \"waffles\"\n if day == 0:\n dayname = \"monday\"\n sort_by_new = True\n elif day == 1:\n dayname = \"tuesday\"\n sort_by_new = True\n elif day == 2:\n dayname = \"wednesday\"\n sort_by_new = True\n elif day == 3:\n dayname = \"thursday\"\n sort_by_new = False\n else:\n sys.exit(1) # woo inelegance\n\n return dayname, sort_by_new","title":""},{"docid":"e1685e27dfc413e876f046f23b4b1063","score":"0.6078282","text":"def day_of_week(self) -> pulumi.Input[Union[str, 'WeekDay']]:\n return pulumi.get(self, \"day_of_week\")","title":""},{"docid":"e1685e27dfc413e876f046f23b4b1063","score":"0.6078282","text":"def day_of_week(self) -> pulumi.Input[Union[str, 'WeekDay']]:\n return pulumi.get(self, \"day_of_week\")","title":""},{"docid":"7c0db65529beec4769c8ab227e02cde8","score":"0.6068204","text":"def select_day():\r\n day_of_week = input(\r\n '\\nWhich day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\n').title()\r\n\r\n if day_of_week == 'Monday':\r\n\r\n return \"Monday\"\r\n\r\n elif day_of_week == 'Tuesday':\r\n\r\n return \"Tuesday\"\r\n\r\n elif day_of_week == 'Wednesday':\r\n\r\n return \"Wednesday\"\r\n\r\n elif day_of_week == 'Thursday':\r\n\r\n return \"Thursday\"\r\n\r\n elif day_of_week == 'Friday':\r\n\r\n return \"Friday\"\r\n\r\n elif day_of_week == 'Saturday':\r\n\r\n return \"Saturday\"\r\n\r\n elif day_of_week == 'Sunday':\r\n\r\n return \"Sunday\"\r\n\r\n else:\r\n\r\n print(\"\\nI'm sorry, I'm not sure which day of the week you're trying to filter by. Let's try again.\")\r\n\r\n return select_day()","title":""},{"docid":"aa090c0a141d5673705c3be4f93e4c17","score":"0.604645","text":"def day(self) -> Optional[pulumi.Input[Union[str, 'WeekDay']]]:\n return pulumi.get(self, \"day\")","title":""},{"docid":"6d9ef608e73edc6c362043e67183def9","score":"0.60180813","text":"def get_day(today: Day, weekday_number: int) -> Day:\n assert type(today) is Day\n assert type(weekday_number) is int\n\n today = today.to_date_time()\n date_list = list(rrule(DAILY, count=1, wkst=MO, byweekday=weekday_number, dtstart=today))\n if date_list:\n return Day(date_list[0])","title":""},{"docid":"ec795b3476bbe4d8fb6ab3ddd4f1d86a","score":"0.59986645","text":"def dow(values, feature, parent): \r\n input_date = values[0]\r\n \r\n # Return dayOfWeek() % 7 so that values range from 0 (sun) to 6 (sat)\r\n # to match Postgresql behaviour\r\n if type(input_date) == QDateTime:\r\n return input_date.date().dayOfWeek() % 7\r\n elif type(input_date) == QDate:\r\n return input_date.dayOfWeek() % 7\r\n elif type(input_date) in (str, unicode): \r\n # Convert string to qdate\r\n input_qdate = QDate.fromString(input_date, 'yyyy-MM-dd')\r\n if input_qdate.isValid():\r\n return input_qdate.dayOfWeek() % 7 \r\n else:\r\n return None","title":""},{"docid":"ec795b3476bbe4d8fb6ab3ddd4f1d86a","score":"0.59986645","text":"def dow(values, feature, parent): \r\n input_date = values[0]\r\n \r\n # Return dayOfWeek() % 7 so that values range from 0 (sun) to 6 (sat)\r\n # to match Postgresql behaviour\r\n if type(input_date) == QDateTime:\r\n return input_date.date().dayOfWeek() % 7\r\n elif type(input_date) == QDate:\r\n return input_date.dayOfWeek() % 7\r\n elif type(input_date) in (str, unicode): \r\n # Convert string to qdate\r\n input_qdate = QDate.fromString(input_date, 'yyyy-MM-dd')\r\n if input_qdate.isValid():\r\n return input_qdate.dayOfWeek() % 7 \r\n else:\r\n return None","title":""},{"docid":"d0ab3b82924ea411c4407d3af17eb8f0","score":"0.5997146","text":"def is_current_time_in_schedule(frequency, hour_of_day, day_of_month=None, day_of_week=None):\n est_timezone = pytz.timezone('US/Eastern')\n current_est_time = datetime.datetime.now(est_timezone)\n current_hour_of_day = current_est_time.hour\n current_day_of_week = current_est_time.weekday()\n current_day_of_month = current_est_time.day\n\n # All configurations have an hour of the day, so the hour must always match in order to send a report.\n if hour_of_day == current_hour_of_day:\n # If reports should be sent monthly and today is the same as the day configured, return True\n if frequency == FREQUENCY_TYPE_MONTHLY and day_of_month == current_day_of_month:\n return True\n # If reports should be sent weekly and today is the same as the day configured, return True\n elif frequency == FREQUENCY_TYPE_WEEKLY and day_of_week == current_day_of_week:\n return True\n # If reports should be sent daily, return True\n elif frequency == FREQUENCY_TYPE_DAILY:\n return True\n\n return False","title":""},{"docid":"21e2bfb4676ffbea69b0eb8058bd5260","score":"0.5987851","text":"def get_day_today() -> str:\n day = datetime.now().strftime(\"%w\")\n if day == '0': # Sunday\n return '6'\n elif day == '6': # Saturday\n return '5'\n elif day == '1': # Monday\n return '0'\n elif day == '2': # Tuesday\n return '1'\n elif day == '3': # Wednesday\n return '2'\n elif day == '4': # Thursday\n return '3'\n elif day == '5': # Friday\n return '4'","title":""},{"docid":"fb2a834e1bf447c71768da53ab6b8125","score":"0.59823704","text":"def test_saturday(self):\n date = datetime.date(1985, 5, 4)\n self.assertEqual(date.isoweekday(), 6)\n start_date, end_date = get_weekspan(date)\n self.assertEqual(start_date.isoweekday(), 1)\n self.assertEqual(end_date.isoweekday(), 7)\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())","title":""},{"docid":"605aa0f54fb2bdbbc95c30f2a4eb1e49","score":"0.5965009","text":"def weekday(self) -> int:\n return WD_EN.index(self.time.day.lower())","title":""},{"docid":"7678c31ac4843a24a4c41f6de3d1f855","score":"0.59630364","text":"def properDayInput(day):\r\n possibleStrings = [\"m\",\"mon\",\"monday\",\"tu\",\"tue\",\"tues\",\"tuesday\",\"w\",\r\n \"we\",\"wed\",\"wednesday\",\"th\",\"tr\",\"r\", \"thu\",\"thur\",\"thurs\",\"thursday\",\"f\",\"fr\",\r\n \"fri\",\"friday\",\"sa\",\"sat\",\"saturday\",\"su\",\"sun\",\"sunday\"]\r\n \r\n validString = False\r\n for i in range(0, len(possibleStrings)):\r\n if possibleStrings[i] == day.lower():\r\n validString = True\r\n return validString","title":""},{"docid":"31755c33e9ad86340828464a1a6440f9","score":"0.59593356","text":"def _valid_day(self, date_find):\n try:\n datetime.strptime(date_find, settings.TIME_FORMAT)\n valid = True\n except ValueError:\n valid = False\n return valid","title":""},{"docid":"15192bdf10ce0e8b807b912b7a76fb46","score":"0.59176916","text":"def is_today_todo(todo_time):\n today_date = datetime.datetime.now().date()\n todo_date = todo_time.date()\n if today_date == todo_date:\n return True\n else:\n return False","title":""},{"docid":"a101aa69668c1c71c96be1327916d7b7","score":"0.5872289","text":"def test_weekdays():\n\n def make_date(year, month, day):\n \"\"\" Helper to create numpy datetimes \"\"\"\n return datetime64(datetime.datetime(year, month, day))\n\n assert make_date(2004, 5, 21) in TRADING_DATES.values\n assert make_date(2004, 5, 17) in TRADING_DATES.values\n assert make_date(2004, 5, 18) in TRADING_DATES.values\n assert make_date(2004, 5, 19) in TRADING_DATES.values\n assert make_date(2004, 5, 20) in TRADING_DATES.values","title":""},{"docid":"e64881496c8377ed66fe8207331772fe","score":"0.5864865","text":"def set_when(day, today):\n if day < today:\n return \"past\"\n if day == today:\n return \"present\"\n return \"future\"","title":""},{"docid":"02d966b4803a18f364bff63c4af148e2","score":"0.5852883","text":"def is_runnable(self, force):\n if force:\n return True\n else:\n weekday = datetime.datetime.now().weekday()\n if weekday == settings.LOAD_DAY:\n return True\n else:\n logger.info(\n 'Today is %s. This command only runs on %s. Exiting.',\n calendar.day_name[weekday],\n self.load_day,\n )\n return False","title":""},{"docid":"f6c200dfa254c6731e99e6905b61da4c","score":"0.5844109","text":"def is_dayofmonth(day, today):\n if isinstance(today, datetime):\n int_day = int(day)\n if today.day == int_day:\n return True\n return False\n else:\n raise Exception(\"{} is not a datetime instance\".format(today))","title":""},{"docid":"e5a0a38d14cf74c77d2391afd3f67f72","score":"0.5843719","text":"def is_first_day_of_week(timestamps):\n return extract_day_of_week(timestamps) == 1","title":""},{"docid":"fb874bcb2ae7d18736a5f2f0dc5e7302","score":"0.5843443","text":"def weekday(self):\n\n return func.extract('dow', self.start_date) + 1","title":""},{"docid":"98573b188d79c8a0a77f9f67f223cbde","score":"0.5842164","text":"def whatday(num):\n return days[num] if num in days.keys() else \"Wrong, please enter a number between 1 and 7\"","title":""},{"docid":"385034af9e181088b34c72d5e2c60ff6","score":"0.5841252","text":"def next_weekday(date, weekday):\n delta = weekday - date.weekday()\n if delta < 0:\n delta += 7\n return date + timedelta(days=int(delta))","title":""},{"docid":"b77f93da1c9a1dc3bf86678325bdbfb4","score":"0.5838028","text":"def test_sunday(self):\n date = datetime.date(1980, 5, 4)\n self.assertEqual(date.isoweekday(), 7)\n start_date, end_date = get_weekspan(date)\n self.assertEqual(start_date.isoweekday(), 1)\n self.assertEqual(end_date.isoweekday(), 7)\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())","title":""},{"docid":"7bbe54a849994f17f63659125ddf615c","score":"0.58141375","text":"def test_wednesday(self):\n date = datetime.date(1988, 5, 4)\n self.assertEqual(date.isoweekday(), 3)\n start_date, end_date = get_weekspan(date)\n self.assertEqual(start_date.isoweekday(), 1)\n self.assertEqual(end_date.isoweekday(), 7)\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())","title":""},{"docid":"a420ef44cd6f8de79e3dc4fffccf9dc2","score":"0.58036435","text":"def weekday(self):\n if self.month is not None and self.day is not None:\n return self.todate().weekday()\n else:\n return None","title":""},{"docid":"beeb4a9c9eb83d08a50f69b541ea87cd","score":"0.57834846","text":"def Day_of_week(day, month, year):\r\n if year % 4 == 0 and (year % 400 == 0 or year % 100 != 0):\r\n doomsday = [11, 29, 21, 4, 9, 6, 11, 8, 5, 10, 7, 12]\r\n else:\r\n doomsday = [10, 28, 21, 4, 9, 6, 11, 8, 5, 10, 7, 12]\r\n exact_day = ((day - doomsday[month-1]) + Dooms_day(year)) % 7\r\n character_day = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \r\n \"Friday\", \"Saturday\"]\r\n return character_day[exact_day]","title":""},{"docid":"6331b0bd07aeef7d1263ab9834fe627b","score":"0.5782986","text":"def weekday(self):\n return (self.toordinal() + 6) % 7","title":""},{"docid":"de8312fa3125565358fe3500fc9837b9","score":"0.5781655","text":"def test_tuesday(self):\n date = datetime.date(1982, 5, 4)\n self.assertEqual(date.isoweekday(), 2)\n start_date, end_date = get_weekspan(date)\n self.assertEqual(start_date.isoweekday(), 1)\n self.assertEqual(end_date.isoweekday(), 7)\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())","title":""},{"docid":"019237d7d4eee94d6118c913621702c4","score":"0.5762731","text":"def is_valid_day (val):\n if len(val) == 2 and count_digits(val) == 2:\n day = int(val)\n return day > 0 and day < 32\n return False","title":""},{"docid":"aa25ad15503297a3154019d542abe7ba","score":"0.57625574","text":"def set_week_day(self, wday):\r\n\t\twdays = ['Domingo', 'Lunes', 'Martes', 'Miercoles',\r\n\t\t\t\t 'Jueves', 'Viernes', 'Sabado']\r\n\t\tfor i in range(7):\r\n\t\t\tif wday == i: \r\n\t\t\t\treturn wdays[i]","title":""},{"docid":"d928a57f16351899f25c8a1c630ad118","score":"0.576091","text":"def findWetWeatherDays(self, dbsession, today):\n wetDays = dbsession.query(self.dt).filter(or_(self.weather_description == \"light rain\", self.weather_description == \"moderate rain\")).all()\n # if one of those days is today return it.\n # else just return a wet day.\n for i in range(len(wetDays)):\n if today == wetDays[i][0].weekday():\n return wetDays[i][0]\n else:\n return wetDays[0][0]","title":""},{"docid":"1ae5770c2071b9797db0799e3557613f","score":"0.5747703","text":"def make_tuesday(date):\n offset = (date.weekday() - 1) % 7\n tuesday = date - datetime.timedelta(days=offset)\n # Ensure that the database has this date\n with get_dbconn(\"postgis\") as conn:\n cursor = conn.cursor()\n cursor.execute(\"SELECT max(valid) from usdm\")\n maxdate = cursor.fetchone()[0]\n if maxdate is not None:\n tuesday = min([tuesday, maxdate])\n return tuesday","title":""},{"docid":"bdfb0f9bf49e8db10c20a00a206dd3b5","score":"0.57474643","text":"def is_election_day(self):\n return self.election_date == datetime.date.today()","title":""},{"docid":"957c1361e0f4cae1070cfbf18d1f6574","score":"0.5743691","text":"def dayofweek(day, month, year, formatresult=True):\n if formatresult is False:\n return calendar.weekday(year, month, day) + 1\n days = {\n 0: 'Monday',\n 1: \"Tuesday\",\n 2: \"Wednesday\",\n 3: \"Thursday\",\n 4: \"Friday\",\n 5: \"Saturday\",\n 6: \"Sunday\"\n }\n return days[calendar.weekday(year, month, day)]","title":""},{"docid":"4f5fc7fe3f4996fb1784ea3db2b1f43a","score":"0.5738809","text":"def is_working_day_appointment(self):\n # function helps hide appointments on weekend\n return 0 <= self.date.weekday() <= 4","title":""},{"docid":"2754969828d1940e7a73ae9097ef0242","score":"0.5725767","text":"def meetup_day(year, month, day_of_week, day_occurrence):\n \n cal = calendar.monthcalendar(year, month)\n day_of_week_index = days_of_week[day_of_week]\n \n not_teenth = day_occurrence != 'teenth'\n day_is_in_first_week = cal[0][day_of_week_index] != 0\n \n if not_teenth and day_is_in_first_week:\n week_index = week_indices[day_occurrence]\n \n elif not_teenth and not day_is_in_first_week:\n week_index = week_indices[day_occurrence] + 1\n \n else:\n for i in range(len(cal)):\n if cal[i][day_of_week_index] >= 10:\n week_index = i\n break\n\n date = cal[week_index][day_of_week_index]\n return datetime.date(year, month, date)","title":""},{"docid":"146c245cec2229c6f2ec4cfd3e855968","score":"0.5707259","text":"def fday(var, wrapper, message):\n if var.PHASE != \"night\":\n wrapper.pm(messages[\"not_nighttime\"])\n else:\n transition_day()","title":""},{"docid":"c9c01585ebf8536dca794b6e71de685f","score":"0.57061934","text":"def test_is_payday_positive6(self):\n # Overriding first_payday\n self.first_payday = date_class(2020,12,24)\n date_to_check = date_class(2021,1,8)\n is_payday = self.pay_cycle.is_payday(date_to_check)\n assert is_payday == True","title":""},{"docid":"11d2d1b40edca6d24eaabe473d1bcb4c","score":"0.57050455","text":"def test_check_args_weekend(self):\n test_date = dt.datetime(2021, 6, 20, 11, 0, 0)\n with self.assertRaises(ValueError) as context:\n self.duedate.check_args(test_date, self.test_turn_time)\n self.assertTrue(\n \"You can submit requests during weekdays only.\" in str(context.exception))","title":""},{"docid":"7edbf2162eb04aa5011cd330cf2d284e","score":"0.5697565","text":"def test_weekly_training_day(self):\n self.assertIsInstance(self.weekly_training.day, int)\n self.assertEqual(self.weekly_training.day, 7)","title":""},{"docid":"f040f97866e7030c52fcce06c0736c5c","score":"0.5694714","text":"def get_weekday(self):\n weekdays = dict(PRODUCT_WEEKDAYS)\n return weekdays.get(self.weekday, \"N/A\")","title":""},{"docid":"ddac16672af511b77b98c090089fe53a","score":"0.5678517","text":"def has_weekend(self):\n return self.products.filter(type=\"S\", weekday=10).exists()","title":""},{"docid":"c27120087b140f42317b793ed4a20439","score":"0.5672199","text":"def check_date(date):\n\timport datetime\n\tif date < datetime.date.today():\n\t\treturn date\n\telse:\n\t\treturn datetime.date.today()","title":""},{"docid":"b5ffbb47976896e10446029affe1caa0","score":"0.5662075","text":"def test_thursday(self):\n date = datetime.date(1989, 5, 4)\n self.assertEqual(date.isoweekday(), 4)\n start_date, end_date = get_weekspan(date)\n self.assertEqual(start_date.isoweekday(), 1)\n self.assertEqual(end_date.isoweekday(), 7)\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())","title":""},{"docid":"3821323efc7b8b11b4e3fc8da3331565","score":"0.5660576","text":"def test_date_accept_today(self):\n spi_search = \"find date today\"\n inv_search = \"year:\" + datetime.datetime.strftime(datetime.datetime.today(), '%Y-%m-%d')\n self._compare_searches(inv_search, spi_search)","title":""},{"docid":"07aaee0cddb62e02181960f70e63a8f8","score":"0.5615565","text":"def generate_day_type(date):\n cal = France()\n\n if cal.is_holiday(date):\n # If Mon-Friday\n if date.weekday() in range(5):\n return 0.\n else:\n return 1.\n else:\n if date.weekday() in range(5):\n return 1.\n else:\n return 0.","title":""},{"docid":"6807f6d5edf005b5b43b2e01cacac029","score":"0.56123865","text":"def doomsday(y):","title":""},{"docid":"9bc88d6eca022d5808a46b7ccd9b655c","score":"0.56072474","text":"def main():\n print(day_of_week(datetime.now()))\n print(day_of_week(datetime(2019, 7, 4)))\n print(day_of_week(datetime(2013, 12, 25)))\n print(day_of_week(datetime(2000, 1, 1)))","title":""},{"docid":"a24bf917a6f15246817a178e23781f4f","score":"0.5603632","text":"def dateweek(line, date):\r\n\tindex = datetime.weekday(date)\r\n\tdateweek = '%s%s%s' % (date.day, cn2en.DATE_WEEK, cn2en.WEEKDAYS[index])\r\n\t\r\n\treturn dateweek == line","title":""},{"docid":"db35dc8a3f0000cdc76c231cb16a7427","score":"0.5602552","text":"def day_of_week_for_start_day(self):\n import calendar\n\n day = self.idfobjects[\"RUNPERIOD\"][0][\"Day_of_Week_for_Start_Day\"]\n\n if day.lower() == \"sunday\":\n return calendar.SUNDAY\n elif day.lower() == \"monday\":\n return calendar.MONDAY\n elif day.lower() == \"tuesday\":\n return calendar.TUESDAY\n elif day.lower() == \"wednesday\":\n return calendar.WEDNESDAY\n elif day.lower() == \"thursday\":\n return calendar.THURSDAY\n elif day.lower() == \"friday\":\n return calendar.FRIDAY\n elif day.lower() == \"saturday\":\n return calendar.SATURDAY\n else:\n return 0","title":""},{"docid":"7f94d8432783bb7968937fe6cab41ac6","score":"0.5602278","text":"def checkio(from_date: date, to_date: date) -> int:\n result = 0\n while True:\n if from_date.weekday() == 5 or from_date.weekday() == 6:\n result += 1\n if from_date == to_date:\n break\n else:\n from_date += timedelta(days=1)\n return result","title":""},{"docid":"f743c2c559ece4732acaa148351317e5","score":"0.5595656","text":"def test_is_payday_positive1(self):\n date_to_check = date_class(2019,2,8)\n is_payday = self.pay_cycle.is_payday(date_to_check)\n assert is_payday == True","title":""},{"docid":"89dc7c9972f4094d4a7cfa7a2e1a3992","score":"0.559418","text":"def get_day():\n return handle_invalid_inputs(question_4, days)","title":""},{"docid":"1759749660aeaa76c549402e64e7100f","score":"0.5585214","text":"def test_friday(self):\n date = datetime.date(1984, 5, 4)\n self.assertEqual(date.isoweekday(), 5)\n start_date, end_date = get_weekspan(date)\n self.assertEqual(start_date.isoweekday(), 1)\n self.assertEqual(end_date.isoweekday(), 7)\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())","title":""},{"docid":"3965ebce5b734d6e52f9cc23892b4cba","score":"0.5580911","text":"def next_day_of_week(current, day_of_week):\n\n while current.weekday() != day_of_week:\n current += timedelta(1)\n return current","title":""},{"docid":"68326cd29cd4b7e7e3b0e36a736fb4e6","score":"0.5572329","text":"def _FirstSunday(self, dtz): # pylint: disable-msg=C0103,R0201\n return dtz + datetime.timedelta(days=(6-dtz.weekday()))","title":""},{"docid":"3dfe13878a87e89672182ae40b004e99","score":"0.5570097","text":"def get_next_closest_day(weekday):\n names = {\n 'monday': 0,\n 'tuesday': 1,\n 'wednesday': 2,\n 'thursday': 3,\n 'friday': 4,\n 'saturday': 5,\n 'sunday': 6\n }\n\n today = get_current_india_time().date()\n day_shift = (names[weekday] - today.weekday()) % 7\n next_day = datetime.datetime.combine(\n today + datetime.timedelta(days=day_shift), datetime.time.min)\n\n if next_day.weekday() == today.weekday():\n next_day = next_day + datetime.timedelta(days=7)\n return next_day","title":""},{"docid":"f3a504516ec0411ce5762a9654a492ae","score":"0.5560319","text":"def get_next_weekday(date, weekday):\n return date + dt.timedelta(days=(weekday - date.weekday() + 7) % 7)","title":""},{"docid":"87a0eb0d0bf5cc9e929ac3416d7e619f","score":"0.5551044","text":"def next_sunday(day):\n if day.weekday() == 6: # sunday\n return day + timedelta(days=7)\n else:\n return day + timedelta(days=(6 - day.weekday()))","title":""},{"docid":"d042f46ccecb52b8ad0267e5b60daa26","score":"0.55334145","text":"def get_weekday(self, as_str=False):\n\n # First we get the first 8 bits stored in the weekday register\n # and translate it to an integer\n wd_8bits = self.__read_register(_REGISTER_WEEKDAY)\n\n # Then we extract the weekday and return it\n wd = wd_8bits & 0x07 # 0x07 = 0b00000111\n\n if as_str is True: # if we want the weekday's name\n wd = WEEKDAY_STR[wd]\n\n return wd","title":""}],"string":"[\n {\n \"docid\": \"9f211aa22ce1299f4f4f6bb340a47ae8\",\n \"score\": \"0.7757462\",\n \"text\": \"def is_dayofweek(day, today):\\n if isinstance(today, datetime):\\n int_day = int(day)\\n if today.weekday() == int_day - 1:\\n return True\\n return False\\n else:\\n raise Exception(\\\"{} is not a datetime instance\\\".format(today))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5e48310abd063a9d4fd60cfc8d734c\",\n \"score\": \"0.75927293\",\n \"text\": \"def is_weekday(dtObj):\\n return dtObj.weekday() < 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9a3647c56419fa75d6b60c5aebe7e7a\",\n \"score\": \"0.74711937\",\n \"text\": \"def is_today(self, dt: datetime.datetime) -> bool:\\n\\n if self is Day.DAILY:\\n return True\\n day = dt.weekday()\\n if self is Day.WEEKDAY:\\n return day < 5\\n if self is Day.WEEKEND:\\n return day >= 5\\n return Day(day) == self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8ba479a6b65c40e76ea465e926804f2\",\n \"score\": \"0.74445426\",\n \"text\": \"def check_weekday_of_date(self, date):\\n return date.isoweekday() % 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a83cea7bb6d896fa56f312f4160c702f\",\n \"score\": \"0.70387244\",\n \"text\": \"def is_weekday(day, halfDay):\\n hours, days = halfDay.split('x')\\n if day <= int(days)-1:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd2ba322be25e79d0e306ebc56ae7247\",\n \"score\": \"0.6779038\",\n \"text\": \"def isoweekday(self, *args, **kwargs): # real signature unknown\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68097d3fed7dfe3341368d626ff11037\",\n \"score\": \"0.67623657\",\n \"text\": \"def runs_today(self,s_id,day):\\n if self.schedule_keys[s_id][day]==1:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9e2dca544118f38399d5d4b30b8d73\",\n \"score\": \"0.6756849\",\n \"text\": \"def sleep_in(weekday, vacation):\\r\\n if not weekday or vacation:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee814151062f93977ffa90a35b5b211a\",\n \"score\": \"0.6719428\",\n \"text\": \"def sleep_in(weekday, vacation):\\r\\n if not weekday or vacation:\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86cd85a03c21b874c5a37093552cd6c6\",\n \"score\": \"0.6692384\",\n \"text\": \"def is_weekend() -> bool:\\n return datetime.today().weekday() > 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2bedf4b587f097ce050891d086e14c5\",\n \"score\": \"0.66828525\",\n \"text\": \"def check_weekday(self, date):\\n week_next = self.next_seven_day()\\n today = datetime.date.today().strftime('%Y-%m-%d')\\n if not date or date > week_next or date < today: # check the date is within one week\\n return False, \\\"Sorry you can only booking consultation up to next one week. Your booking date must before {}\\\".format(week_next)\\n try:\\n day_as_string = self.get_the_weekday(date)\\n if day_as_string == \\\"Saturday\\\" or day_as_string == \\\"Sunday\\\":\\n logger.info(\\\"Sorry, there is no consultation on weekends\\\")\\n return False, \\\"Sorry, there is no consultation on weekends\\\"\\n else:\\n logger.info(\\\"It is on next {}\\\".format(day_as_string))\\n return True, \\\"Your booking has been made on {} {}\\\".format(day_as_string, date)\\n except ValueError as e:\\n logger.error(str(e))\\n return False, \\\"Please try again\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2347a8ee2bacd5b32f134165be77f199\",\n \"score\": \"0.66738766\",\n \"text\": \"def isoweekday(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2347a8ee2bacd5b32f134165be77f199\",\n \"score\": \"0.66738766\",\n \"text\": \"def isoweekday(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c4ddabefa8f752ed6a7d085f697eb3\",\n \"score\": \"0.6667073\",\n \"text\": \"def check_day(self, day_of_week):\\n\\n day_of_week -= 1\\n if (day_of_week == -1):\\n self.day_of_week = 6\\n else:\\n self.day_of_week = day_of_week\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b5b213d95a01d7c9aa2c817f9e7a47\",\n \"score\": \"0.6637579\",\n \"text\": \"def get_weekday():\\n result = datetime.today().weekday() + 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf464cf9579063d7542c14cb5f32552\",\n \"score\": \"0.66249675\",\n \"text\": \"def week_init():\\n week = input('Week to check: MM/DD/YYYY\\\\n')\\n week = dtt.datetime.strptime(week,'%m/%d/%Y') #turns input to a datetime\\n beforeday = input('Check days before date (Press enter to use today): MM/DD/YYYY\\\\n') or dtt.date.today()\\n if (beforeday != dtt.date.today()):\\n beforeday = dtt.datetime.strptime(beforeday,'%m/%d/%Y')\\n return week, beforeday\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96e90d12e5a760993b2a8e440d57372\",\n \"score\": \"0.66073847\",\n \"text\": \"def is_dayofnewtrimester(day, today):\\n if isinstance(today, datetime):\\n if today.day == int(day) and today.month == 1:\\n return True\\n elif today.day == int(day) and today.month == 4:\\n return True\\n elif today.day == int(day) and today.month == 7:\\n return True\\n elif today.day == int(day) and today.month == 10:\\n return True\\n return False\\n else:\\n raise Exception(\\\"{} is not a datetime instance\\\".format(today))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b12fa4237365fb033e96db4811d4928\",\n \"score\": \"0.66064125\",\n \"text\": \"def weekday(self, dt):\\n days = {\\n 0: self.MONDAY,\\n 1: self.TUESDAY,\\n 2: self.WEDNESDAY,\\n 3: self.THURSDAY,\\n 4: self.FRIDAY,\\n 5: self.SATURDAY,\\n 6: self.SUNDAY\\n }\\n return days.get(dt.weekday())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9767d7b222adc4cd9dc6e64871c2f845\",\n \"score\": \"0.6583573\",\n \"text\": \"def get_day_of_week_from_user():\\n while True:\\n day = input('Select the month to explore. Enter from monday, tuesday, wednesday, thursday, friday, '\\n 'saturday, sunday or all: ').lower()\\n\\n if day in VALID_DAYS:\\n confirm = input(\\\"You have selected {}. Press 'y' to confirm: \\\".format(day.title()))\\n\\n if confirm == 'y':\\n break\\n else:\\n print(\\\"Try again.\\\\n\\\")\\n else:\\n print(\\\"Invalid input: {}. Try again.\\\\n\\\".format(day))\\n return day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63e7dcdce36e2da1a08bd8b6c1dd011\",\n \"score\": \"0.65824914\",\n \"text\": \"def day_of_the_week(arg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c095a09efe27545fe04b9d0f02abfe7e\",\n \"score\": \"0.6545656\",\n \"text\": \"def _day_rule_matches(self, rule, dt):\\n if dt.weekday() == 4:\\n sat = dt + datetime.timedelta(days=1)\\n if super(SiteHolidays, self)._day_rule_matches(rule, sat):\\n return True\\n elif dt.weekday() == 0:\\n sun = dt - datetime.timedelta(days=1)\\n if super(SiteHolidays, self)._day_rule_matches(rule, sun):\\n return True\\n return super(SiteHolidays, self)._day_rule_matches(rule, dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f5de3c6b71d045d2906e4e7da80760\",\n \"score\": \"0.649922\",\n \"text\": \"def checkToday(ctx, cron):\\n daze = ctx.obj['daze']\\n if cron is not None:\\n if date.today() in daze.dateDict.keys():\\n sys.exit(1)\\n else:\\n sys.exit(0)\\n click.echo(date.today() in daze.dateDict.keys())\\n return date.today() in daze.dateDict.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b34294557b1886e44436043c7b723a6b\",\n \"score\": \"0.64887506\",\n \"text\": \"def is_weekend(date):\\n \\n return date.weekday() == 5 or date.weekday() == 6\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17883616cd8302376bc8cd318b44dc7b\",\n \"score\": \"0.6487841\",\n \"text\": \"def weekday(day):\\n return (day % 7) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b6ebc2e89595aa3fa45981b3ddc277\",\n \"score\": \"0.64171106\",\n \"text\": \"def isoweekday(self):\\n # 1-Jan-0001 is a Monday\\n return self.toordinal() % 7 or 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464aa0080e91a3afbfa3ab2d5a7c721c\",\n \"score\": \"0.63790303\",\n \"text\": \"def business_day(self): \\n\\n if self.time_stamp.weekday() not in (5, 6) and not holiday(self.time_stamp):\\n return True \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730992da2b85bca8cc5c76e44de4ef86\",\n \"score\": \"0.63676757\",\n \"text\": \"def test_date_accept_this_week(self):\\n spi_search = \\\"find date this week\\\"\\n inv_search = \\\"year:\\\" + datetime.datetime.strftime(datetime.datetime.today()\\\\\\n +dateutil.relativedelta.relativedelta(days=-(datetime.datetime.today().isoweekday()%7)), '%Y-%m-%d')\\n self._compare_searches(inv_search, spi_search)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa18c9820e23f32279ed4ea1c60af2f\",\n \"score\": \"0.63490075\",\n \"text\": \"def has_tuesday(self):\\n return self.products.filter(type=\\\"S\\\", weekday=2).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ab5aa69a9405972431975e303a472b\",\n \"score\": \"0.63318884\",\n \"text\": \"def next_weekday(weekday, d=datetime.datetime.now()):\\n if weekday.lower() not in day_values:\\n return None\\n days_ahead = day_values[weekday.lower()] - d.weekday()\\n if days_ahead <= 0: # Target day already happened this week\\n days_ahead += 7\\n return d + datetime.timedelta(days_ahead)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a38431ba44eea563bdc6fbc4e2ef8e9b\",\n \"score\": \"0.6329821\",\n \"text\": \"def weekday(self, *args, **kwargs): # real signature unknown\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53cec41aeae0f546a1aeaef266d1bc2c\",\n \"score\": \"0.6327355\",\n \"text\": \"def has_wednesday(self):\\n return self.products.filter(type=\\\"S\\\", weekday=3).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76dce3b833747abfe5827e07b315e09f\",\n \"score\": \"0.627691\",\n \"text\": \"def day_of_week():\\n return calendar.day_name[datetime.date.today().weekday()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1763407be8da3c1d7028f662627495b5\",\n \"score\": \"0.6231285\",\n \"text\": \"def has_thursday(self):\\n return self.products.filter(type=\\\"S\\\", weekday=4).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7460dcd6af1c8ad51d54a6a83582f856\",\n \"score\": \"0.6228567\",\n \"text\": \"def weekday(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7460dcd6af1c8ad51d54a6a83582f856\",\n \"score\": \"0.6228567\",\n \"text\": \"def weekday(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60412cfd9aea5717c8e5b746ade96753\",\n \"score\": \"0.6200328\",\n \"text\": \"def day_of_week(dt):\\n cday = dt\\n mday = 2\\n uday = cday.isocalendar()[2] + mday\\n try:\\n if uday > 7:\\n CURRDAY = uday - 7\\n log.debug(\\\"1;EME;RUNNING;000;Scheduler.py;Setting customized day of week>7 : \\\", CURRDAY)\\n else:\\n CURRDAY = uday\\n log.debug(\\\"1;EME;RUNNING;000;Scheduler.py;Setting customized day of week : \\\", CURRDAY)\\n return CURRDAY\\n except Exception as e:\\n log.exception(\\\"1;EME;FAILURE;700;SCHEDULE ERROR \\\" + str(e), exc_info=False)\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135cfbc4d78dd877aacf48f2cbf007e0\",\n \"score\": \"0.6147052\",\n \"text\": \"def is_workfree(date):\\n \\n return date.weekday() == 6 or is_holiday(date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7123058f54f1e4ad9c677e47c6b305\",\n \"score\": \"0.61246353\",\n \"text\": \"def has_monday(self):\\n return self.products.filter(type=\\\"S\\\", weekday=1).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7fbe3fe8183dbeb6ab9a1d2734f52e\",\n \"score\": \"0.61017764\",\n \"text\": \"def has_friday(self):\\n return self.products.filter(type=\\\"S\\\", weekday=5).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d543a73c801d8da89c6b27f7d1b0439\",\n \"score\": \"0.60952795\",\n \"text\": \"def get_weekday():\\n try:\\n day = config.getint(\\\"threadbot\\\", \\\"debug_day\\\")\\n except ConfigParser.NoOptionError:\\n d = datetime.date.today()\\n day = d.weekday()\\n sort_by_new = False\\n\\n # 0 / Monday / Feedback thread\\n # 1 / Tuesday / How do I make this sound thread\\n # 2 / Wednesday / There are no stupid questions thread\\n # 3 / Thursday / Marketplace thread\\n dayname = \\\"waffles\\\"\\n if day == 0:\\n dayname = \\\"monday\\\"\\n sort_by_new = True\\n elif day == 1:\\n dayname = \\\"tuesday\\\"\\n sort_by_new = True\\n elif day == 2:\\n dayname = \\\"wednesday\\\"\\n sort_by_new = True\\n elif day == 3:\\n dayname = \\\"thursday\\\"\\n sort_by_new = False\\n else:\\n sys.exit(1) # woo inelegance\\n\\n return dayname, sort_by_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1685e27dfc413e876f046f23b4b1063\",\n \"score\": \"0.6078282\",\n \"text\": \"def day_of_week(self) -> pulumi.Input[Union[str, 'WeekDay']]:\\n return pulumi.get(self, \\\"day_of_week\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1685e27dfc413e876f046f23b4b1063\",\n \"score\": \"0.6078282\",\n \"text\": \"def day_of_week(self) -> pulumi.Input[Union[str, 'WeekDay']]:\\n return pulumi.get(self, \\\"day_of_week\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c0db65529beec4769c8ab227e02cde8\",\n \"score\": \"0.6068204\",\n \"text\": \"def select_day():\\r\\n day_of_week = input(\\r\\n '\\\\nWhich day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\\\n').title()\\r\\n\\r\\n if day_of_week == 'Monday':\\r\\n\\r\\n return \\\"Monday\\\"\\r\\n\\r\\n elif day_of_week == 'Tuesday':\\r\\n\\r\\n return \\\"Tuesday\\\"\\r\\n\\r\\n elif day_of_week == 'Wednesday':\\r\\n\\r\\n return \\\"Wednesday\\\"\\r\\n\\r\\n elif day_of_week == 'Thursday':\\r\\n\\r\\n return \\\"Thursday\\\"\\r\\n\\r\\n elif day_of_week == 'Friday':\\r\\n\\r\\n return \\\"Friday\\\"\\r\\n\\r\\n elif day_of_week == 'Saturday':\\r\\n\\r\\n return \\\"Saturday\\\"\\r\\n\\r\\n elif day_of_week == 'Sunday':\\r\\n\\r\\n return \\\"Sunday\\\"\\r\\n\\r\\n else:\\r\\n\\r\\n print(\\\"\\\\nI'm sorry, I'm not sure which day of the week you're trying to filter by. Let's try again.\\\")\\r\\n\\r\\n return select_day()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa090c0a141d5673705c3be4f93e4c17\",\n \"score\": \"0.604645\",\n \"text\": \"def day(self) -> Optional[pulumi.Input[Union[str, 'WeekDay']]]:\\n return pulumi.get(self, \\\"day\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9ef608e73edc6c362043e67183def9\",\n \"score\": \"0.60180813\",\n \"text\": \"def get_day(today: Day, weekday_number: int) -> Day:\\n assert type(today) is Day\\n assert type(weekday_number) is int\\n\\n today = today.to_date_time()\\n date_list = list(rrule(DAILY, count=1, wkst=MO, byweekday=weekday_number, dtstart=today))\\n if date_list:\\n return Day(date_list[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec795b3476bbe4d8fb6ab3ddd4f1d86a\",\n \"score\": \"0.59986645\",\n \"text\": \"def dow(values, feature, parent): \\r\\n input_date = values[0]\\r\\n \\r\\n # Return dayOfWeek() % 7 so that values range from 0 (sun) to 6 (sat)\\r\\n # to match Postgresql behaviour\\r\\n if type(input_date) == QDateTime:\\r\\n return input_date.date().dayOfWeek() % 7\\r\\n elif type(input_date) == QDate:\\r\\n return input_date.dayOfWeek() % 7\\r\\n elif type(input_date) in (str, unicode): \\r\\n # Convert string to qdate\\r\\n input_qdate = QDate.fromString(input_date, 'yyyy-MM-dd')\\r\\n if input_qdate.isValid():\\r\\n return input_qdate.dayOfWeek() % 7 \\r\\n else:\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec795b3476bbe4d8fb6ab3ddd4f1d86a\",\n \"score\": \"0.59986645\",\n \"text\": \"def dow(values, feature, parent): \\r\\n input_date = values[0]\\r\\n \\r\\n # Return dayOfWeek() % 7 so that values range from 0 (sun) to 6 (sat)\\r\\n # to match Postgresql behaviour\\r\\n if type(input_date) == QDateTime:\\r\\n return input_date.date().dayOfWeek() % 7\\r\\n elif type(input_date) == QDate:\\r\\n return input_date.dayOfWeek() % 7\\r\\n elif type(input_date) in (str, unicode): \\r\\n # Convert string to qdate\\r\\n input_qdate = QDate.fromString(input_date, 'yyyy-MM-dd')\\r\\n if input_qdate.isValid():\\r\\n return input_qdate.dayOfWeek() % 7 \\r\\n else:\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0ab3b82924ea411c4407d3af17eb8f0\",\n \"score\": \"0.5997146\",\n \"text\": \"def is_current_time_in_schedule(frequency, hour_of_day, day_of_month=None, day_of_week=None):\\n est_timezone = pytz.timezone('US/Eastern')\\n current_est_time = datetime.datetime.now(est_timezone)\\n current_hour_of_day = current_est_time.hour\\n current_day_of_week = current_est_time.weekday()\\n current_day_of_month = current_est_time.day\\n\\n # All configurations have an hour of the day, so the hour must always match in order to send a report.\\n if hour_of_day == current_hour_of_day:\\n # If reports should be sent monthly and today is the same as the day configured, return True\\n if frequency == FREQUENCY_TYPE_MONTHLY and day_of_month == current_day_of_month:\\n return True\\n # If reports should be sent weekly and today is the same as the day configured, return True\\n elif frequency == FREQUENCY_TYPE_WEEKLY and day_of_week == current_day_of_week:\\n return True\\n # If reports should be sent daily, return True\\n elif frequency == FREQUENCY_TYPE_DAILY:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21e2bfb4676ffbea69b0eb8058bd5260\",\n \"score\": \"0.5987851\",\n \"text\": \"def get_day_today() -> str:\\n day = datetime.now().strftime(\\\"%w\\\")\\n if day == '0': # Sunday\\n return '6'\\n elif day == '6': # Saturday\\n return '5'\\n elif day == '1': # Monday\\n return '0'\\n elif day == '2': # Tuesday\\n return '1'\\n elif day == '3': # Wednesday\\n return '2'\\n elif day == '4': # Thursday\\n return '3'\\n elif day == '5': # Friday\\n return '4'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2a834e1bf447c71768da53ab6b8125\",\n \"score\": \"0.59823704\",\n \"text\": \"def test_saturday(self):\\n date = datetime.date(1985, 5, 4)\\n self.assertEqual(date.isoweekday(), 6)\\n start_date, end_date = get_weekspan(date)\\n self.assertEqual(start_date.isoweekday(), 1)\\n self.assertEqual(end_date.isoweekday(), 7)\\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"605aa0f54fb2bdbbc95c30f2a4eb1e49\",\n \"score\": \"0.5965009\",\n \"text\": \"def weekday(self) -> int:\\n return WD_EN.index(self.time.day.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7678c31ac4843a24a4c41f6de3d1f855\",\n \"score\": \"0.59630364\",\n \"text\": \"def properDayInput(day):\\r\\n possibleStrings = [\\\"m\\\",\\\"mon\\\",\\\"monday\\\",\\\"tu\\\",\\\"tue\\\",\\\"tues\\\",\\\"tuesday\\\",\\\"w\\\",\\r\\n \\\"we\\\",\\\"wed\\\",\\\"wednesday\\\",\\\"th\\\",\\\"tr\\\",\\\"r\\\", \\\"thu\\\",\\\"thur\\\",\\\"thurs\\\",\\\"thursday\\\",\\\"f\\\",\\\"fr\\\",\\r\\n \\\"fri\\\",\\\"friday\\\",\\\"sa\\\",\\\"sat\\\",\\\"saturday\\\",\\\"su\\\",\\\"sun\\\",\\\"sunday\\\"]\\r\\n \\r\\n validString = False\\r\\n for i in range(0, len(possibleStrings)):\\r\\n if possibleStrings[i] == day.lower():\\r\\n validString = True\\r\\n return validString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31755c33e9ad86340828464a1a6440f9\",\n \"score\": \"0.59593356\",\n \"text\": \"def _valid_day(self, date_find):\\n try:\\n datetime.strptime(date_find, settings.TIME_FORMAT)\\n valid = True\\n except ValueError:\\n valid = False\\n return valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15192bdf10ce0e8b807b912b7a76fb46\",\n \"score\": \"0.59176916\",\n \"text\": \"def is_today_todo(todo_time):\\n today_date = datetime.datetime.now().date()\\n todo_date = todo_time.date()\\n if today_date == todo_date:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a101aa69668c1c71c96be1327916d7b7\",\n \"score\": \"0.5872289\",\n \"text\": \"def test_weekdays():\\n\\n def make_date(year, month, day):\\n \\\"\\\"\\\" Helper to create numpy datetimes \\\"\\\"\\\"\\n return datetime64(datetime.datetime(year, month, day))\\n\\n assert make_date(2004, 5, 21) in TRADING_DATES.values\\n assert make_date(2004, 5, 17) in TRADING_DATES.values\\n assert make_date(2004, 5, 18) in TRADING_DATES.values\\n assert make_date(2004, 5, 19) in TRADING_DATES.values\\n assert make_date(2004, 5, 20) in TRADING_DATES.values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64881496c8377ed66fe8207331772fe\",\n \"score\": \"0.5864865\",\n \"text\": \"def set_when(day, today):\\n if day < today:\\n return \\\"past\\\"\\n if day == today:\\n return \\\"present\\\"\\n return \\\"future\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d966b4803a18f364bff63c4af148e2\",\n \"score\": \"0.5852883\",\n \"text\": \"def is_runnable(self, force):\\n if force:\\n return True\\n else:\\n weekday = datetime.datetime.now().weekday()\\n if weekday == settings.LOAD_DAY:\\n return True\\n else:\\n logger.info(\\n 'Today is %s. This command only runs on %s. Exiting.',\\n calendar.day_name[weekday],\\n self.load_day,\\n )\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c200dfa254c6731e99e6905b61da4c\",\n \"score\": \"0.5844109\",\n \"text\": \"def is_dayofmonth(day, today):\\n if isinstance(today, datetime):\\n int_day = int(day)\\n if today.day == int_day:\\n return True\\n return False\\n else:\\n raise Exception(\\\"{} is not a datetime instance\\\".format(today))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a0a38d14cf74c77d2391afd3f67f72\",\n \"score\": \"0.5843719\",\n \"text\": \"def is_first_day_of_week(timestamps):\\n return extract_day_of_week(timestamps) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb874bcb2ae7d18736a5f2f0dc5e7302\",\n \"score\": \"0.5843443\",\n \"text\": \"def weekday(self):\\n\\n return func.extract('dow', self.start_date) + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98573b188d79c8a0a77f9f67f223cbde\",\n \"score\": \"0.5842164\",\n \"text\": \"def whatday(num):\\n return days[num] if num in days.keys() else \\\"Wrong, please enter a number between 1 and 7\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"385034af9e181088b34c72d5e2c60ff6\",\n \"score\": \"0.5841252\",\n \"text\": \"def next_weekday(date, weekday):\\n delta = weekday - date.weekday()\\n if delta < 0:\\n delta += 7\\n return date + timedelta(days=int(delta))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77f93da1c9a1dc3bf86678325bdbfb4\",\n \"score\": \"0.5838028\",\n \"text\": \"def test_sunday(self):\\n date = datetime.date(1980, 5, 4)\\n self.assertEqual(date.isoweekday(), 7)\\n start_date, end_date = get_weekspan(date)\\n self.assertEqual(start_date.isoweekday(), 1)\\n self.assertEqual(end_date.isoweekday(), 7)\\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bbe54a849994f17f63659125ddf615c\",\n \"score\": \"0.58141375\",\n \"text\": \"def test_wednesday(self):\\n date = datetime.date(1988, 5, 4)\\n self.assertEqual(date.isoweekday(), 3)\\n start_date, end_date = get_weekspan(date)\\n self.assertEqual(start_date.isoweekday(), 1)\\n self.assertEqual(end_date.isoweekday(), 7)\\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a420ef44cd6f8de79e3dc4fffccf9dc2\",\n \"score\": \"0.58036435\",\n \"text\": \"def weekday(self):\\n if self.month is not None and self.day is not None:\\n return self.todate().weekday()\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beeb4a9c9eb83d08a50f69b541ea87cd\",\n \"score\": \"0.57834846\",\n \"text\": \"def Day_of_week(day, month, year):\\r\\n if year % 4 == 0 and (year % 400 == 0 or year % 100 != 0):\\r\\n doomsday = [11, 29, 21, 4, 9, 6, 11, 8, 5, 10, 7, 12]\\r\\n else:\\r\\n doomsday = [10, 28, 21, 4, 9, 6, 11, 8, 5, 10, 7, 12]\\r\\n exact_day = ((day - doomsday[month-1]) + Dooms_day(year)) % 7\\r\\n character_day = [\\\"Sunday\\\", \\\"Monday\\\", \\\"Tuesday\\\", \\\"Wednesday\\\", \\\"Thursday\\\", \\r\\n \\\"Friday\\\", \\\"Saturday\\\"]\\r\\n return character_day[exact_day]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6331b0bd07aeef7d1263ab9834fe627b\",\n \"score\": \"0.5782986\",\n \"text\": \"def weekday(self):\\n return (self.toordinal() + 6) % 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de8312fa3125565358fe3500fc9837b9\",\n \"score\": \"0.5781655\",\n \"text\": \"def test_tuesday(self):\\n date = datetime.date(1982, 5, 4)\\n self.assertEqual(date.isoweekday(), 2)\\n start_date, end_date = get_weekspan(date)\\n self.assertEqual(start_date.isoweekday(), 1)\\n self.assertEqual(end_date.isoweekday(), 7)\\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019237d7d4eee94d6118c913621702c4\",\n \"score\": \"0.5762731\",\n \"text\": \"def is_valid_day (val):\\n if len(val) == 2 and count_digits(val) == 2:\\n day = int(val)\\n return day > 0 and day < 32\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa25ad15503297a3154019d542abe7ba\",\n \"score\": \"0.57625574\",\n \"text\": \"def set_week_day(self, wday):\\r\\n\\t\\twdays = ['Domingo', 'Lunes', 'Martes', 'Miercoles',\\r\\n\\t\\t\\t\\t 'Jueves', 'Viernes', 'Sabado']\\r\\n\\t\\tfor i in range(7):\\r\\n\\t\\t\\tif wday == i: \\r\\n\\t\\t\\t\\treturn wdays[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d928a57f16351899f25c8a1c630ad118\",\n \"score\": \"0.576091\",\n \"text\": \"def findWetWeatherDays(self, dbsession, today):\\n wetDays = dbsession.query(self.dt).filter(or_(self.weather_description == \\\"light rain\\\", self.weather_description == \\\"moderate rain\\\")).all()\\n # if one of those days is today return it.\\n # else just return a wet day.\\n for i in range(len(wetDays)):\\n if today == wetDays[i][0].weekday():\\n return wetDays[i][0]\\n else:\\n return wetDays[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae5770c2071b9797db0799e3557613f\",\n \"score\": \"0.5747703\",\n \"text\": \"def make_tuesday(date):\\n offset = (date.weekday() - 1) % 7\\n tuesday = date - datetime.timedelta(days=offset)\\n # Ensure that the database has this date\\n with get_dbconn(\\\"postgis\\\") as conn:\\n cursor = conn.cursor()\\n cursor.execute(\\\"SELECT max(valid) from usdm\\\")\\n maxdate = cursor.fetchone()[0]\\n if maxdate is not None:\\n tuesday = min([tuesday, maxdate])\\n return tuesday\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdfb0f9bf49e8db10c20a00a206dd3b5\",\n \"score\": \"0.57474643\",\n \"text\": \"def is_election_day(self):\\n return self.election_date == datetime.date.today()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"957c1361e0f4cae1070cfbf18d1f6574\",\n \"score\": \"0.5743691\",\n \"text\": \"def dayofweek(day, month, year, formatresult=True):\\n if formatresult is False:\\n return calendar.weekday(year, month, day) + 1\\n days = {\\n 0: 'Monday',\\n 1: \\\"Tuesday\\\",\\n 2: \\\"Wednesday\\\",\\n 3: \\\"Thursday\\\",\\n 4: \\\"Friday\\\",\\n 5: \\\"Saturday\\\",\\n 6: \\\"Sunday\\\"\\n }\\n return days[calendar.weekday(year, month, day)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f5fc7fe3f4996fb1784ea3db2b1f43a\",\n \"score\": \"0.5738809\",\n \"text\": \"def is_working_day_appointment(self):\\n # function helps hide appointments on weekend\\n return 0 <= self.date.weekday() <= 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2754969828d1940e7a73ae9097ef0242\",\n \"score\": \"0.5725767\",\n \"text\": \"def meetup_day(year, month, day_of_week, day_occurrence):\\n \\n cal = calendar.monthcalendar(year, month)\\n day_of_week_index = days_of_week[day_of_week]\\n \\n not_teenth = day_occurrence != 'teenth'\\n day_is_in_first_week = cal[0][day_of_week_index] != 0\\n \\n if not_teenth and day_is_in_first_week:\\n week_index = week_indices[day_occurrence]\\n \\n elif not_teenth and not day_is_in_first_week:\\n week_index = week_indices[day_occurrence] + 1\\n \\n else:\\n for i in range(len(cal)):\\n if cal[i][day_of_week_index] >= 10:\\n week_index = i\\n break\\n\\n date = cal[week_index][day_of_week_index]\\n return datetime.date(year, month, date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"146c245cec2229c6f2ec4cfd3e855968\",\n \"score\": \"0.5707259\",\n \"text\": \"def fday(var, wrapper, message):\\n if var.PHASE != \\\"night\\\":\\n wrapper.pm(messages[\\\"not_nighttime\\\"])\\n else:\\n transition_day()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9c01585ebf8536dca794b6e71de685f\",\n \"score\": \"0.57061934\",\n \"text\": \"def test_is_payday_positive6(self):\\n # Overriding first_payday\\n self.first_payday = date_class(2020,12,24)\\n date_to_check = date_class(2021,1,8)\\n is_payday = self.pay_cycle.is_payday(date_to_check)\\n assert is_payday == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11d2d1b40edca6d24eaabe473d1bcb4c\",\n \"score\": \"0.57050455\",\n \"text\": \"def test_check_args_weekend(self):\\n test_date = dt.datetime(2021, 6, 20, 11, 0, 0)\\n with self.assertRaises(ValueError) as context:\\n self.duedate.check_args(test_date, self.test_turn_time)\\n self.assertTrue(\\n \\\"You can submit requests during weekdays only.\\\" in str(context.exception))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edbf2162eb04aa5011cd330cf2d284e\",\n \"score\": \"0.5697565\",\n \"text\": \"def test_weekly_training_day(self):\\n self.assertIsInstance(self.weekly_training.day, int)\\n self.assertEqual(self.weekly_training.day, 7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f040f97866e7030c52fcce06c0736c5c\",\n \"score\": \"0.5694714\",\n \"text\": \"def get_weekday(self):\\n weekdays = dict(PRODUCT_WEEKDAYS)\\n return weekdays.get(self.weekday, \\\"N/A\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddac16672af511b77b98c090089fe53a\",\n \"score\": \"0.5678517\",\n \"text\": \"def has_weekend(self):\\n return self.products.filter(type=\\\"S\\\", weekday=10).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27120087b140f42317b793ed4a20439\",\n \"score\": \"0.5672199\",\n \"text\": \"def check_date(date):\\n\\timport datetime\\n\\tif date < datetime.date.today():\\n\\t\\treturn date\\n\\telse:\\n\\t\\treturn datetime.date.today()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5ffbb47976896e10446029affe1caa0\",\n \"score\": \"0.5662075\",\n \"text\": \"def test_thursday(self):\\n date = datetime.date(1989, 5, 4)\\n self.assertEqual(date.isoweekday(), 4)\\n start_date, end_date = get_weekspan(date)\\n self.assertEqual(start_date.isoweekday(), 1)\\n self.assertEqual(end_date.isoweekday(), 7)\\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3821323efc7b8b11b4e3fc8da3331565\",\n \"score\": \"0.5660576\",\n \"text\": \"def test_date_accept_today(self):\\n spi_search = \\\"find date today\\\"\\n inv_search = \\\"year:\\\" + datetime.datetime.strftime(datetime.datetime.today(), '%Y-%m-%d')\\n self._compare_searches(inv_search, spi_search)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07aaee0cddb62e02181960f70e63a8f8\",\n \"score\": \"0.5615565\",\n \"text\": \"def generate_day_type(date):\\n cal = France()\\n\\n if cal.is_holiday(date):\\n # If Mon-Friday\\n if date.weekday() in range(5):\\n return 0.\\n else:\\n return 1.\\n else:\\n if date.weekday() in range(5):\\n return 1.\\n else:\\n return 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6807f6d5edf005b5b43b2e01cacac029\",\n \"score\": \"0.56123865\",\n \"text\": \"def doomsday(y):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc88d6eca022d5808a46b7ccd9b655c\",\n \"score\": \"0.56072474\",\n \"text\": \"def main():\\n print(day_of_week(datetime.now()))\\n print(day_of_week(datetime(2019, 7, 4)))\\n print(day_of_week(datetime(2013, 12, 25)))\\n print(day_of_week(datetime(2000, 1, 1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a24bf917a6f15246817a178e23781f4f\",\n \"score\": \"0.5603632\",\n \"text\": \"def dateweek(line, date):\\r\\n\\tindex = datetime.weekday(date)\\r\\n\\tdateweek = '%s%s%s' % (date.day, cn2en.DATE_WEEK, cn2en.WEEKDAYS[index])\\r\\n\\t\\r\\n\\treturn dateweek == line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db35dc8a3f0000cdc76c231cb16a7427\",\n \"score\": \"0.5602552\",\n \"text\": \"def day_of_week_for_start_day(self):\\n import calendar\\n\\n day = self.idfobjects[\\\"RUNPERIOD\\\"][0][\\\"Day_of_Week_for_Start_Day\\\"]\\n\\n if day.lower() == \\\"sunday\\\":\\n return calendar.SUNDAY\\n elif day.lower() == \\\"monday\\\":\\n return calendar.MONDAY\\n elif day.lower() == \\\"tuesday\\\":\\n return calendar.TUESDAY\\n elif day.lower() == \\\"wednesday\\\":\\n return calendar.WEDNESDAY\\n elif day.lower() == \\\"thursday\\\":\\n return calendar.THURSDAY\\n elif day.lower() == \\\"friday\\\":\\n return calendar.FRIDAY\\n elif day.lower() == \\\"saturday\\\":\\n return calendar.SATURDAY\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f94d8432783bb7968937fe6cab41ac6\",\n \"score\": \"0.5602278\",\n \"text\": \"def checkio(from_date: date, to_date: date) -> int:\\n result = 0\\n while True:\\n if from_date.weekday() == 5 or from_date.weekday() == 6:\\n result += 1\\n if from_date == to_date:\\n break\\n else:\\n from_date += timedelta(days=1)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f743c2c559ece4732acaa148351317e5\",\n \"score\": \"0.5595656\",\n \"text\": \"def test_is_payday_positive1(self):\\n date_to_check = date_class(2019,2,8)\\n is_payday = self.pay_cycle.is_payday(date_to_check)\\n assert is_payday == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89dc7c9972f4094d4a7cfa7a2e1a3992\",\n \"score\": \"0.559418\",\n \"text\": \"def get_day():\\n return handle_invalid_inputs(question_4, days)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1759749660aeaa76c549402e64e7100f\",\n \"score\": \"0.5585214\",\n \"text\": \"def test_friday(self):\\n date = datetime.date(1984, 5, 4)\\n self.assertEqual(date.isoweekday(), 5)\\n start_date, end_date = get_weekspan(date)\\n self.assertEqual(start_date.isoweekday(), 1)\\n self.assertEqual(end_date.isoweekday(), 7)\\n self.assertTrue(start_date.toordinal() <= date.toordinal() <= end_date.toordinal())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3965ebce5b734d6e52f9cc23892b4cba\",\n \"score\": \"0.5580911\",\n \"text\": \"def next_day_of_week(current, day_of_week):\\n\\n while current.weekday() != day_of_week:\\n current += timedelta(1)\\n return current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68326cd29cd4b7e7e3b0e36a736fb4e6\",\n \"score\": \"0.5572329\",\n \"text\": \"def _FirstSunday(self, dtz): # pylint: disable-msg=C0103,R0201\\n return dtz + datetime.timedelta(days=(6-dtz.weekday()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dfe13878a87e89672182ae40b004e99\",\n \"score\": \"0.5570097\",\n \"text\": \"def get_next_closest_day(weekday):\\n names = {\\n 'monday': 0,\\n 'tuesday': 1,\\n 'wednesday': 2,\\n 'thursday': 3,\\n 'friday': 4,\\n 'saturday': 5,\\n 'sunday': 6\\n }\\n\\n today = get_current_india_time().date()\\n day_shift = (names[weekday] - today.weekday()) % 7\\n next_day = datetime.datetime.combine(\\n today + datetime.timedelta(days=day_shift), datetime.time.min)\\n\\n if next_day.weekday() == today.weekday():\\n next_day = next_day + datetime.timedelta(days=7)\\n return next_day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a504516ec0411ce5762a9654a492ae\",\n \"score\": \"0.5560319\",\n \"text\": \"def get_next_weekday(date, weekday):\\n return date + dt.timedelta(days=(weekday - date.weekday() + 7) % 7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a0eb0d0bf5cc9e929ac3416d7e619f\",\n \"score\": \"0.5551044\",\n \"text\": \"def next_sunday(day):\\n if day.weekday() == 6: # sunday\\n return day + timedelta(days=7)\\n else:\\n return day + timedelta(days=(6 - day.weekday()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d042f46ccecb52b8ad0267e5b60daa26\",\n \"score\": \"0.55334145\",\n \"text\": \"def get_weekday(self, as_str=False):\\n\\n # First we get the first 8 bits stored in the weekday register\\n # and translate it to an integer\\n wd_8bits = self.__read_register(_REGISTER_WEEKDAY)\\n\\n # Then we extract the weekday and return it\\n wd = wd_8bits & 0x07 # 0x07 = 0b00000111\\n\\n if as_str is True: # if we want the weekday's name\\n wd = WEEKDAY_STR[wd]\\n\\n return wd\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994380,"cells":{"query_id":{"kind":"string","value":"3562d3dd13e277d752083a277071d3cb"},"query":{"kind":"string","value":"Starts reading the file in file_handle, populating the reactions and atomic_relaxation members."},"positive_passages":{"kind":"list like","value":[{"docid":"5bad6f63fa863c4b72bcec2806f2bf1a","score":"0.7447146","text":"def __init__(self, file_handle):\n\t\tself._file = file_handle\n\t\tself.reactions = {}\n\t\tself.atomic_relaxation = {}\n\n\t\t# Determine MAT number for this file,\n\t\t# while skipping ahead to start of first header\n\t\tMF = 0\n\t\twhile MF == 0:\n\t\t\tposition = self._file.tell()\n\t\t\tline = self._file.readline()\n\t\t\tMF = int(line[70:72])\n\t\tself.MAT = int(line[66:70])\n\t\tself._file.seek(position)\n\n\t\tself._read()","title":""}],"string":"[\n {\n \"docid\": \"5bad6f63fa863c4b72bcec2806f2bf1a\",\n \"score\": \"0.7447146\",\n \"text\": \"def __init__(self, file_handle):\\n\\t\\tself._file = file_handle\\n\\t\\tself.reactions = {}\\n\\t\\tself.atomic_relaxation = {}\\n\\n\\t\\t# Determine MAT number for this file,\\n\\t\\t# while skipping ahead to start of first header\\n\\t\\tMF = 0\\n\\t\\twhile MF == 0:\\n\\t\\t\\tposition = self._file.tell()\\n\\t\\t\\tline = self._file.readline()\\n\\t\\t\\tMF = int(line[70:72])\\n\\t\\tself.MAT = int(line[66:70])\\n\\t\\tself._file.seek(position)\\n\\n\\t\\tself._read()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"711d7934c80dc5febb6f5fa7ddea8f39","score":"0.5626557","text":"def _open_file(self):\n\n with open(self.filename) as file:\n self.file_data = file.readlines()\n\n self.file_length = len(self.file_data)\n self.line_index = 0","title":""},{"docid":"349691eaa708ab8ceeec0e7c0f4bf918","score":"0.5621808","text":"def __init__(self, filename, start_state=0):\n\n with open(filename, 'rt') as fin:\n self.start_state = start_state\n self.symbols = set()\n self.states_count = int(fin.readline())\n self.final_states = set(\n map(lambda e: int(e), fin.readline().split()))\n\n self.read_delta(fin)","title":""},{"docid":"dfd55b6f97ead99740bb23b4133e350f","score":"0.562007","text":"def load_reaction_data(self, file_path, num_processes):\n print('Stage 1/2: loading reaction data...')\n all_reactant_mols = []\n all_product_mols = []\n all_real_bond_changes = []\n ids_for_small_samples = []\n with open(file_path, 'r') as f:\n lines = f.readlines()\n\n def _update_from_line(id, loaded_result):\n reactants_mol, product_mol, reaction_real_bond_changes = loaded_result\n if reactants_mol is None:\n return\n all_product_mols.append(product_mol)\n all_reactant_mols.append(reactants_mol)\n all_real_bond_changes.append(reaction_real_bond_changes)\n if reactants_mol.GetNumAtoms() <= self.size_cutoff:\n ids_for_small_samples.append(id)\n\n if num_processes == 1:\n for id, li in enumerate(tqdm(lines)):\n loaded_line = load_one_reaction_rank(li)\n _update_from_line(id, loaded_line)\n else:\n with Pool(processes=num_processes) as pool:\n results = pool.map(\n load_one_reaction_rank,\n lines, chunksize=len(lines) // num_processes)\n for id in range(len(lines)):\n _update_from_line(id, results[id])\n\n return all_reactant_mols, all_product_mols, all_real_bond_changes, ids_for_small_samples","title":""},{"docid":"e3dec43569176f24309c6ae7056efa70","score":"0.5524501","text":"def read_init_file(self, filename=None):\n readline.read_init_file(filename)","title":""},{"docid":"ec0c316cc5ef6a3c120101661f9f34d0","score":"0.54611844","text":"def open_with_file(self, fname):\n if not L1Ana.l1init: L1Ana.init_l1_analysis()\n self.file_list = [fname]\n self.check_first_file()\n self.open_no_init()\n self.init_branches()\n L1Ana.log.info(\"Ready to analyse.\")\n self.init = True","title":""},{"docid":"1bdeda53916c5b7c29788c8163db9b16","score":"0.5424699","text":"def load_reaction_data(self, file_path, num_processes):\n all_mols = []\n all_reactions = []\n all_graph_edits = []\n with open(file_path, 'r') as f:\n lines = f.readlines()\n if num_processes == 1:\n results = []\n for li in lines:\n mol, reaction, graph_edits = load_one_reaction(li)\n results.append((mol, reaction, graph_edits))\n else:\n with Pool(processes=num_processes) as pool:\n results = pool.map(load_one_reaction, lines)\n\n for mol, reaction, graph_edits in results:\n if mol is None:\n continue\n all_mols.append(mol)\n all_reactions.append(reaction)\n all_graph_edits.append(graph_edits)\n\n return all_mols, all_reactions, all_graph_edits","title":""},{"docid":"1755fc26d83ec3ab8f3e4c9699d0cc05","score":"0.54223454","text":"def _loadFile(self):\n\n for line in open(self.filepath):\n # Only look at the \"ATOM\" lines in the file\n if not re.search('^ATOM',line):\n continue\n\n # The indices here are for wwPDB format, see\n #\n # http://www.wwpdb.org/documentation/format23/sect9.html\n #\n # for complete details. Here we use:\n #\n # 13-16 is atom name\n # 18-20 is residue name\n # 31-38 is x coord\n # 39-46 is y coord\n # 47-54 is z coord\n\n atom_name = line[12:16]\n point = Point(float(line[30:38]), float(line[38:46]), float(line[46:54]))\n\n # for this program we only conisder the alpha carbons\n # these are indicated with 'CA' in the PDB file. We ignore\n # any \"MODEL\" statements and just cover everything.\n\n if atom_name == \" CA \":\n\n # If we have an alpha carbon we create a new\n # alpha carbon object and add it to our list of alpha\n # alpha carbons\n\n residue_name = line[17:20]\n alpha_carbon = AlphaCarbon(residue_name, point)\n self.alpha_carbons.append(alpha_carbon)\n\n # As we read in the file we keep track of the minimum and\n # maximum position for each dimension.\n #\n # NOTE: putting these updates in this if statements means\n # that we only consider the CA atoms for the protein (and thus\n # grid used for part 2 (prediction). This was consistent with\n # the examples, but not with the instructions. There was a TA\n # recommendation to do it this way. To span the WHOLE protein\n # we would have to put these updates before this if statement.\n\n self._updateMin(point)\n self._updateMax(point)","title":""},{"docid":"9b3446d94727594c0b811876bef49910","score":"0.53114104","text":"def init_from_file(self, filepath, effects_log):\n # don't forget to update the module docstring with changes here\n input_template_name = 'emission_rates_egu'\n input_template_version = 0.3\n input_template_columns = [\n 'case',\n 'rate_name',\n 'independent_variable',\n 'last_year',\n 'kwh_demand_metric',\n 'kwh_consumption_metric',\n 'equation_rate_id',\n 'equation_kwh_demand_metric',\n 'equation_kwh_consumption_metric',\n ]\n\n df = read_input_file(filepath, effects_log)\n validate_template_version_info(df, input_template_name, input_template_version, effects_log)\n\n # read in the data portion of the input file\n df = read_input_file(filepath, effects_log, skiprows=1)\n validate_template_column_names(filepath, df, input_template_columns, effects_log)\n\n rate_keys = zip(\n df['case'],\n df['rate_name']\n )\n df.set_index(rate_keys, inplace=True)\n\n self._cases = df['case'].unique()\n self.kwh_demand_metric = df['kwh_demand_metric'][0]\n self.kwh_consumption_metric = df['kwh_consumption_metric'][0]\n self.calendar_year_min = df['initial_year'][0]\n self.calendar_year_max = df['last_year'][0]\n\n self._data = df.to_dict('index')","title":""},{"docid":"4f83651ce409d0380741cb3b0bc00f4e","score":"0.5262542","text":"def read_whole_file(self, fhandle):\n\n line = fhandle.readline().strip()\n if line[0] != \">\":\n raise ValueError(\"File is missing initial header!\")\n else:\n curr_entry = FastaEntry(header = line.rstrip().split()[0])\n line = fhandle.readline().strip()\n curr_seq = []\n while line != '':\n if line[0] == \">\":\n curr_entry.set_seq(''.join(curr_seq))\n self.data[curr_entry.chrm_name()] = curr_entry\n self.names.append(curr_entry.chrm_name())\n curr_seq = []\n curr_entry = FastaEntry(line)\n else:\n curr_seq.append(line)\n\n line = fhandle.readline().strip()\n\n curr_entry.set_seq(''.join(curr_seq))\n self.data[curr_entry.chrm_name()] = curr_entry","title":""},{"docid":"8d43e173e3c6c0449dd217bc17c7cfbe","score":"0.523327","text":"def read(self, filepath: str) -> None:\n self.filepath = filepath\n self.readFile()","title":""},{"docid":"17a8408d7fd00934fadbff69ae64df70","score":"0.5226424","text":"def init_from_file(self, filepath, effects_log):\n df = read_input_file(filepath, effects_log)\n\n input_template_name = 'context_implicit_price_deflators'\n input_template_version = 0.22\n input_template_columns = {\n 'calendar_year',\n 'price_deflator',\n }\n\n validate_template_version_info(df, input_template_name, input_template_version, effects_log)\n\n # read in the data portion of the input file\n df = read_input_file(filepath, effects_log, skiprows=1)\n\n validate_template_column_names(filepath, df, input_template_columns, effects_log)\n\n self._data = df.set_index('calendar_year').to_dict(orient='index')","title":""},{"docid":"92eb684588444f12cbaa12faa8d6cc96","score":"0.5223175","text":"def read(self):\n self.iteration, self.s, self.y, self.rho, \\\n self.r0, self.f0, self.e0, self.task = self.load()\n self.load_restart = True","title":""},{"docid":"5d441abb5b830f731662da093f4c4756","score":"0.52148175","text":"def run(self):\n with open(self._path) as f:\n rdef = RecordDef([{'name': 'line', 'type': 'STRING'}])\n\n if not self._read_existing_lines: # go to the end of file\n f.seek(0, 2)\n\n while True:\n if self._interrupted():\n self.add(rdef, None) # producer has end data-fetching\n return\n\n curpos = f.tell()\n line = f.readline()\n if line == '': # EOF\n f.seek(curpos)\n else:\n self.add(rdef, Record(line))\n time.sleep(self._sleep_sec)","title":""},{"docid":"8e3bcb13b4cf67f23bdbad54d7ce0a6b","score":"0.52064943","text":"def initiate_fast_reaction(data_dir):\n new_old_index_dict, new_ind_reaction_dict = psri.parse_reaction_and_its_index(\n data_dir)\n\n rxn_pair_dict = dict()\n\n # un-paired species\n unpaired = dict()\n for _, val1 in enumerate(new_old_index_dict):\n # print(idx, val1, new_old_index_dict[val1])\n this_value = int(new_old_index_dict[val1])\n neg_value = -1 * this_value\n if (neg_value in unpaired):\n # print(int(val1), unpaired[neg_value])\n rxn_pair_dict.update({int(val1): unpaired[neg_value]})\n rxn_pair_dict.update({unpaired[neg_value]: int(val1)})\n unpaired.pop(neg_value)\n else:\n unpaired.update({this_value: int(val1)})\n\n rxn_info = {}\n for _, val1 in enumerate(new_ind_reaction_dict):\n entry = {\n str(val1): {\n \"formula\": new_ind_reaction_dict[val1],\n # default value, 10^-100, assume the reaction is super slow\n \"time_scale\": -100,\n \"reverse_reaction\": \"None\"\n }\n }\n if int(val1) in rxn_pair_dict:\n entry[str(val1)][\"reverse_reaction\"] = \\\n str(rxn_pair_dict[int(val1)])\n rxn_info.update(entry)\n\n fn0 = os.path.join(data_dir, \"input\", \"reaction_info_base_backup.json\")\n fn1 = os.path.join(data_dir, \"input\", \"reaction_info_base.json\")\n\n if os.path.isfile(fn1):\n copy2(fn1, fn0)\n\n rwc.write_configuration(rxn_info, fn1)","title":""},{"docid":"902c940061a578a1cb0fb747120609da","score":"0.5204261","text":"def run_book(self):\n \n with open(self.stock_input_file, 'r') as handler:\n\n #Read from a file and update my_book:\n for stock_input in handler:\n self.my_book.process_all(stock_input)","title":""},{"docid":"b020686a545cdf003f324b5cfd6bed02","score":"0.51869375","text":"def run(self):\n try:\n self.__read_file()\n self.__process_lines()\n except ValueError as ve:\n print >> sys.stderr, ve","title":""},{"docid":"a49d66a2f022ef668901c2ca82952451","score":"0.51745516","text":"def populateFromFile(self,file_handle, ignore_errors=False):\n #read version info\n ver_line=file_handle.readline()\n if ver_line[:13] != '#=GENOME_DIFF':\n print(\"Invalid GenomeDiff file, header missing or malformed.\")\n else:\n self._parseLine(1,ver_line) #process the ver_line to store the version info\n for line_num,line in enumerate(file_handle.readlines()):\n try:\n self._parseLine(line_num,line)\n except GDFieldError as gdfe:\n print(\"Parse error in field {}:{}, could not parse {}:\".format(gdfe.field_num,gdfe.field_name,gdfe.field_value))\n print(\"Message: {}\".format(gdfe.msg))\n if gdfe.inner_exception_msg:\n print(\"Exception: {}\".format(gdfe.inner_exception_msg))\n if not ignore_errors:\n raise \n break\n except GDParseError as gdpe:\n print(\"There is an error in line {} of the GenomeDiff file.\".format(line_num+1))\n print(\"Error returned: {}\".format(gdpe.msg))\n if gdpe.inner_exception_msg:\n print(\"Exception: {}\".format(gdpe.inner_exception_msg))\n if not ignore_errors:\n raise \n break\n except Exception as ex:\n print(\"Unhandled exception on line {}:\".format(line_num))\n print(ex.message)\n raise\n break\n try:\n pass\n self._qcChecks()\n except GDFieldError as gdfe:\n print(\"QC Check failed:\")\n \n except GDParseError as gdpe:\n print(\"QC Check failed:\")","title":""},{"docid":"afbdb10bd4253e18b8d533ae1d7a8f82","score":"0.5172113","text":"def connect(self):\n print('Opening file %s' % (self.filename))\n self._fp = open(self.filename)","title":""},{"docid":"ecd575d277ba69cf147a9c1a0fa1c569","score":"0.5140673","text":"def __init__(self, file_name):\n self.file_name = file_name\n self.size = None\n self.number_of_food = None\n self.number_of_stick = None\n self.number_of_cbstacles = None\n self.__read__()","title":""},{"docid":"da7e9352a4e8d2932a555cc606c34ce0","score":"0.5139134","text":"def __init__(self,file_handle=None, ignore_errors=False):\n self.metadata={}\n self.data={'mutation':{},'evidence':{},'validation':{}}\n self.valid_types=self.mutation_types+self.evidence_types+self.validation_types\n if file_handle is not None:\n self.populateFromFile(file_handle, ignore_errors)","title":""},{"docid":"dc150594f58182e0e51210ea35b5a9a7","score":"0.5116372","text":"def read(self) -> None:\n self.setup_reader()\n self.read_data()","title":""},{"docid":"85d5ae6ee7277b3a600f0b94f6f0b890","score":"0.51154214","text":"def state_load(self, filename):\n with open(filename, \"rb\") as f:\n with self.lock:\n self.hc = pickle.load(f)\n self.htc = pickle.load(f)\n self.tc = pickle.load(f)\n self.thc = pickle.load(f)\n self.tweet_total = pickle.load(f)\n self.hashtag_total = pickle.load(f)\n print \"State sucessfully loaded from \" + filename","title":""},{"docid":"cb9823038a3911b4d9fbd1622e8cb34c","score":"0.5112699","text":"def __readFile(self, filePath):\n\n # Parameters used in reading the file\n iscomment = False\n\n # Read the file\n fid = open(filePath)\n for line in fid:\n\n # Strip the line\n line = line.strip()\n\n # Ignore the comment part\n if (line.startswith(\"###\")):\n iscomment = ~iscomment\n\n if (not(line.startswith(\"#\")) and (not iscomment) and len(line) > 0):\n\n # Used for the single degree of freedom (DOF) unit testing\n if (line.startswith(\"dof\")):\n\n # Get the values\n index, newValue, unit = line.split()[1:4]\n\n # There is the \"-1\" here for the index. The index in python array begins from 0.\n index -= 1\n\n # Assign the value\n self.stateV[index] = newValue\n\n # By default, use micron and arcsec as units\n if (unit == \"mm\"):\n self.stateV[index] *= 1e3\n\n elif (unit == \"deg\"):\n self.stateV[index] *= 3600\n\n # Camera mjd\n elif (line.startswith(\"mjd\")):\n self.time0 = Time(float(line.split()[1]), format=\"mjd\")\n\n # Budget of image quality\n elif (line.startswith(\"iqBudget\")):\n # Read in mas, convert to arcsec\n # mas: milli-arcsec\n self.iqBudget = np.sqrt(np.sum(np.array(line.split()[1:], dtype=float)**2))\n\n # Budget of ellipticity\n elif (line.startswith(\"eBudget\")):\n self.eBudget = float(line.split()[1])\n\n # Get the zenith angle or file name\n elif (line.startswith(\"zenithAngle\")):\n self.zAngle = line.split()[1]\n\n # Camera temperature in degree C\n # Ignore this if the instrument is comcam\n elif (line.startswith(\"camTB\") and self.inst == self.LSST):\n self.camTB = float(line.split()[1])\n\n # Update the budget of image quality\n self.iqBudget = np.sqrt(self.iqBudget**2 + float(line.split()[3])**2)\n\n # Camera rotation angle\n # Ignore this if the instrument is comcam\n elif (line.startswith(\"camRotation\") and self.inst == self.LSST):\n self.camRot = float(line.split()[1])\n\n self.iqBudget = np.sqrt(self.iqBudget**2 + float(line.split()[3])**2)\n\n elif (line.startswith(\"M1M3ForceError\")):\n self.M1M3ForceError = float(line.split()[1])\n\n elif (line.startswith(\"M1M3TxGrad\")):\n self.M1M3TxGrad = float(line.split()[1])\n\n elif (line.startswith(\"M1M3TyGrad\")):\n self.M1M3TyGrad = float(line.split()[1])\n\n elif (line.startswith(\"M1M3TzGrad\")):\n self.M1M3TzGrad = float(line.split()[1])\n\n elif (line.startswith(\"M1M3TrGrad\")):\n self.M1M3TrGrad = float(line.split()[1])\n\n elif (line.startswith(\"M1M3TBulk\")):\n self.M1M3TBulk = float(line.split()[1])\n\n elif (line.startswith(\"M2TzGrad\")):\n self.M2TzGrad = float(line.split()[1])\n\n elif (line.startswith(\"M2TrGrad\")):\n self.M2TrGrad = float(line.split()[1])\n\n # Maximum number of Zn to define the surface in perturbation file\n # Check I can use \"znPert = 20\" or not. This is for the annular Zk in my WEP code.\n # Check the reason to use the spherical Zk. If I use annular Zk, what will happen?\n # From the phosim command, does it mean only spherical Zk is allowed?\n # https://bitbucket.org/phosim/phosim_release/wiki/Physics%20Commands\n # But the dof and uk is in the basis of annular Zk, which is different from PhoSim.\n # Check with Bo for this\n elif (line.startswith(\"znPert\")):\n self.znPert = int(line.split()[1])\n\n elif (line.startswith(\"surfaceGridN\")):\n self.surfaceGridN = int(line.split()[1])\n\n # Size of OPD image (n x n matrix)\n elif (line.startswith(\"opd_size\")):\n self.opdSize = int(line.split()[1])\n\n # Make sure the value is odd\n if (self.opdSize % 2 == 0):\n self.opdSize -= 1\n\n # Use 0 for eimage only, without backgrounds\n elif (line.startswith(\"eimage\")):\n self.eimage = bool(int(line.split()[1]))\n\n # Magnitude of point spread function (PSF) star\n elif (line.startswith(\"psf_mag\")):\n self.psfMag = int(line.split()[1])\n\n # Magnitude of wavefront sensing star\n elif (line.startswith(\"cwfs_mag\")):\n self.cwfsMag = int(line.split()[1])\n\n # Wavefront sensing image stamp size\n elif (line.startswith(\"cwfs_stamp_size\")):\n self.cwfsStampSize = int(line.split()[1])\n\n fid.close()","title":""},{"docid":"5d9c0f83720a3218b6e126d03961067d","score":"0.51083213","text":"def __init__(self, file_path):\n\n with open(file_path, 'r') as f:\n lines = f.readlines()\n\n self.sequences = []\n\n for i in range(0, len(lines), 4):\n self.sequences.append(Read(sq=lines[i + 1].rstrip('\\n'),\n quality=lines[i + 3].rstrip('\\n'),\n name=lines[i].rstrip('\\n')))\n\n print('This library size is %d consensus reads' % len(self.sequences))","title":""},{"docid":"ea82b565aa2c83882a451ad3ae735a99","score":"0.50958514","text":"def operate_on_file(self, file_name, handle):\n file_ = open(file_name, 'r')\n for line in file_:\n handle(line)\n self.previous_line = line\n file_.close()","title":""},{"docid":"cc39a278c77b92927afdf2bb8d8dc5a9","score":"0.50855786","text":"def _read(self):\n with open(self.filename, 'r') as f:\n self.data = f.read()\n # HERE DO SOMETHING TO PREPARE THE DATA TO BE READ ############","title":""},{"docid":"720d3defa0c39f3d0a22fdca8b496bfc","score":"0.5071081","text":"def _read_file(self):\n\t\twith open(self._path, \"r\") as file:\n\t\t\tfor line in file:\n\t\t\t\tself.__oneLiner += line\n\t\tself._remove_documentation()\n\t\tself._clean_spaces()","title":""},{"docid":"5eb7bb00a758e866d4e96da69d517ecd","score":"0.5062394","text":"def __init__(self, filename):\n self.file = open(filename)\n self.queue = []","title":""},{"docid":"06b17446374e19e4b69a67d9050df52f","score":"0.503247","text":"def open(self):\n with _iotools.open_file(self.filepath, \"r\") as f:\n self.content = f.readlines()","title":""},{"docid":"5abb085160ec6ba2d6a113ce58dc0c56","score":"0.5028873","text":"def open_with_file_list(self, fname_list):\n if not L1Ana.l1init: L1Ana.init_l1_analysis()\n self.open_file_list(fname_list)\n self.check_first_file()\n self.open_no_init()\n self.init_branches()\n\n L1Ana.log.info(\"Ready to analyse.\")\n self.init = True","title":""},{"docid":"199bd1df566cf7a971f755929cad1dd3","score":"0.5027212","text":"def init_from_file(self, fname):\n\n self.status = 1 # init to failure\n\n # the looks_like options imply AfniData, else use Afni1D\n if self.looks_like:\n adata = LAD.AfniData(fname, verb=self.verb)\n self.dtype = 2\n else:\n adata = LAD.Afni1D(fname, verb=self.verb)\n self.dtype = 1\n\n if not adata.ready: return 1\n\n if self.verb > 2: print(\"++ read 1D data from file '%s'\" % fname)\n\n self.fname = fname\n self.adata = adata\n self.status = 0\n\n return 0","title":""},{"docid":"3789325b38ac37811e46c1c7d521c8ac","score":"0.50238985","text":"def initiate_from_file(self, fname, scoring_function, Prior):\n with open(fname, 'r') as f:\n smiles = []\n for line in f:\n smile = line.split()[0]\n if Chem.MolFromSmiles(smile):\n smiles.append(smile)\n scores = scoring_function(smiles)\n tokenized = [self.voc.tokenize(smile) for smile in smiles]\n encoded = [Variable(self.voc.encode(tokenized_i)) for tokenized_i in tokenized]\n encoded = MolData.collate_fn(encoded)\n prior_likelihood, _ = Prior.likelihood(encoded.long())\n prior_likelihood = prior_likelihood.data.cpu().numpy()\n new_experience = zip(smiles, scores, prior_likelihood)\n self.add_experience(new_experience)","title":""},{"docid":"578db05bdca74607a6394901300acfbd","score":"0.5019072","text":"def read_input_file(path):\n global observables, setups, initialSpecies, casetitle, tol\n\n full_path = os.path.abspath(os.path.expandvars(path))\n try:\n f = open(full_path)\n except IOError:\n logging.error('The input file \"{0}\" could not be opened.'.format(full_path))\n logging.info('Check that the file exists and that you have read access.')\n raise\n\n logging.info('Reading input file \"{0}\"...'.format(full_path))\n logging.info(f.read())\n f.seek(0) # return to beginning of file\n\n global_context = {'__builtins__': None}\n local_context = {\n '__builtins__': None,\n 'True': True,\n 'False': False,\n 'observable': observable,\n 'SMILES': SMILES,\n 'species': species,\n 'adjacencyList': adjacencyList,\n 'reactorSetups': reactorSetups,\n 'options': options,\n }\n\n try:\n exec(f.read(), global_context, local_context)\n except (NameError, TypeError, SyntaxError) as e:\n logging.error('The input file \"{0}\" was invalid:'.format(full_path))\n logging.exception(e)\n raise\n finally:\n f.close()\n\n # convert keys of the initial mole fraction dictionaries from labels into Species objects.\n imfs = setups[3]\n new_imfs = []\n for imf in imfs:\n new_dict = convert(imf, initialSpecies)\n new_imfs.append(new_dict)\n setups[3] = new_imfs\n\n return casetitle, observables, setups, tol","title":""},{"docid":"e849f4d8787b0b500be602e8740bc12b","score":"0.50122976","text":"def __read_file(self,input_file):\n \n logging.debug('Parsing model input file: %s' % input_file)\n\n assert type(input_file) is StringType, 'Invalid input file path \"%s\": inpu file path must be a string' % input_file\n\n #Check that the model file exists\n if not os.path.exists(input_file):\n raise BeakerException('Model file \"%s\" does not exist' % input_file)\n \n f = open(input_file,'r')\n self.definition = []\n for line in f:\n if not line.rstrip('\\n') == '':\n self.definition.append(line.rstrip('\\n'))\n f.close()\n \n logging.debug('Finished parsing model input file: %s' % input_file)","title":""},{"docid":"0066e11f0cc4ea4b2eaaba69904621ef","score":"0.5001973","text":"def read_one(self):\n self._clear()\n readline = self._readline\n endOfMol = self._endOfMol\n try:\n name = readline().strip()\n userLine = readline()\n comment = readline()\n line = readline()\n except MolReaderError, msg:\n if str(msg) == \"Unexpected end of file\":\n return None \n raise\n\n try:\n numAtoms, numBonds = map(int, (line[0:3], line[3:6]))\n except ValueError: # XXX FIX ME - trap exceptions and stuff\n print \"cannot parse atom, bond line\"\n self._read_to_next()\n return None\n atoms = self.atoms = []\n\n for index in range(numAtoms):\n line = readline()\n try:\n x,y,z,symbol,mass,charge,stereo,hcount,hcount_fixed = parse_atom(line)\n except: # XXX FIX ME - trap exceptions and stuff\n self._read_to_next()\n return None\n \n atom = Atom()\n atom._line = line\n atom.symbol = symbol\n atom.explicit_hcount = hcount\n atom.charge = charge\n\n #if hcount_fixed: \n #symbol, hcount, charge, weight=0, aromatic=0)\n \n # XXX FIX ME\n # a really bad hack here.\n # ignore please!\n atom._line = line\n atom.x = x\n atom.y = y\n atom.z = z\n if hcount_fixed: atom.fixed_hcount = 1\n if mass:\n atom.weight = atom.mass + mass\n atom.index = len(atoms)\n atoms.append(atom)\n if vfgraph:\n insert_node(atom)\n\n bonds = []\n mappings = []\n bondCount = [0] * len(atoms)\n closures = {}\n for index in range(numBonds):\n line = readline()\n try:\n a1, a2, bondtype, remainder = parse_bond(line)\n except:\n self._read_to_next()\n return None\n a1 -= 1\n a2 -= 1\n\n symbol, bondorder, bondtype, fixed = BOND_SYMBOL[bondtype]\n atom1 = atoms[a1]\n atom2 = atoms[a2]\n if stripHydrogen:\n if atom1.symbol == \"H\":\n atom2.hcount += 1\n atom2.hcount_fixed = 1\n if atom2.symbol == \"H\":\n atom1.hcount += 1\n atom1.hcount_fixed = 1\n else:\n bond = Bond(symbol, bondorder, bondtype, fixed)\n bond._line = line\n # XXX FIX ME\n # a really bad hack here\n # ignore please!\n bond._line = remainder\n bond.atoms = [a1, a2]\n a1.bonds.append(bond)\n a2.bonds.append(bond)\n a1.oatoms.append(a1)\n a2.oatoms.append(a2)\n bonds.append(bond)\n## mappings.append((bond, a1, a2))\n## bondCount[a1] += 1\n## bondCount[a2] += 1\n\n self.readMProps()\n \n fields, potentialName = self._readFields()\n\n if not name:\n name = potentialName\n elif name != potentialName:\n # XXX FIX ME, what do I do here?\n pass\n \n # we've tokenized the molecule, now we need to build one\n # XXX FIX ME - Split this up into a builder and a tokenizer ?\n mol = Molecule()\n mol.name = name\n mol.fields = fields\n# for atom in atoms:\n# mol.add_atom(atom)\n \n# for bond, a1, a2 in mappings:\n# atom1, atom2 = atoms[a1], atoms[a2]\n\n # XXX FIX ME\n # does this format mean the atom's hcount can't\n # change?\n# stripHydrogens = self.stripHydrogens\n# if not hasattr(atom1, \"number\"):\n# print atom\n# print atom.__dict__\n## if stripHydrogens and atom1.symbol == \"H\" and bondCount[a1] == 1:\n## atom2.hcount += 1\n## atom2.hcount_fixed = 1\n## bondCount[a2] -=1\n## mol.remove_atom(atom1)\n## elif stripHydrogens and atom2.symbol == \"H\" and bondCount[a2] == 1:\n## atom1.hcount += 1\n## atom1.hcount_fixed = 1\n## bondCount[a1] -= 1\n## mol.remove_atom(atom2)\n## else:\n## mol.add_bond(bond, atom1, atom2)\n## if bond.bondtype == 4:\n## atom1.aromatic = 1\n## atom2.aromatic = 1\n\n## # get rid of any non-bonded hydrogens\n## atomsToDelete = []\n## for atom in mol.atoms:\n## if atom.symbol == \"H\":\n## assert len(atom.bonds) == 0\n## atomsToDelete.append(atom)\n## for atom in atomsToDelete:\n## mol.remove_atom(atom)\n\n index = 0\n for atom in mol.atoms:\n assert atom.symbol != \"H\"\n if len(atom.bonds) > 1:\n atom._closure = 1\n atom.index = index\n index += 1\n\n return mol","title":""},{"docid":"5e1121e8749d641c9f994e5d431ccbc8","score":"0.4988269","text":"def loadInput(self, path=None):\n from input import readInputFile\n if path is None: path = self.inputFile\n readInputFile(path, self)\n self.reactionModel.kineticsEstimator = self.kineticsEstimator\n # If the output directory is not yet set, then set it to the same\n # directory as the input file by default\n if not self.outputDirectory:\n self.outputDirectory = os.path.dirname(path)\n if self.pressureDependence:\n self.pressureDependence.outputFile = self.outputDirectory\n self.reactionModel.pressureDependence = self.pressureDependence\n self.reactionModel.reactionGenerationOptions = self.reactionGenerationOptions\n self.reactionModel.verboseComments = self.verboseComments\n \n if self.quantumMechanics:\n self.quantumMechanics.setDefaultOutputDirectory(self.outputDirectory)\n self.reactionModel.quantumMechanics = self.quantumMechanics","title":""},{"docid":"8194df22570f99f001f094910f829af1","score":"0.49783206","text":"def readFile(self):\n states = [] #to be appended by self.states\n obs = []\n\n with open(self.filename, 'r') as f:\n for line in f:\n if line[0] == '#': #skip first line\n continue\n elif line[0] == '.': #separate sequences \n self.states.append(states) #append sequence\n self.observations.append(obs) #append sequence of obs\n states = [] #reset\n obs = [] #reset \n else:\n x, y, c = line.strip().split(',')\n states.append((int(x), int(y)))\n obs.append(c)\n if states != []:\n self.states.append(states)\n self.observations.append(obs)","title":""},{"docid":"48cd3cbad27f89d3c861b95661b1bd62","score":"0.49698448","text":"def load_from_file(self, filename):\n self.reset()\n transition_read_status = IOState.Ready\n current_transition_string = u\"\"\n transition_counter = 0\n with open(filename, 'r') as f:\n reading_transition_matrix = 0\n for linenum, line in enumerate(f):\n if linenum == 0:\n # read in n, m\n n, m = line.decode(\"utf-8-sig\").encode(\"utf-8\").split(' ')\n self.n = int(n)\n self.m = int(m)\n last_line_num = 2+self.m*(self.n+1)\n elif linenum > 1 and linenum < last_line_num:\n if transition_read_status == IOState.Ready:\n # read in first line of matrix\n current_transition_string += line\n transition_counter += 1\n transition_read_status = IOState.Reading\n elif transition_read_status == IOState.Reading and transition_counter < self.n:\n # keep constructing matrix string\n current_transition_string += line\n transition_counter += 1\n elif transition_read_status == IOState.Reading and transition_counter == self.n:\n # convert finished string into numpy array\n c = StringIO(current_transition_string)\n T = np.loadtxt(c)\n self.T.append(T)\n\n # reset state/counters\n transition_read_status = IOState.Ready\n current_transition_string = u\"\"\n transition_counter = 0\n elif linenum == last_line_num:\n # read in the rewards\n c = StringIO(u\"\"+line)\n self.R = np.loadtxt(c)","title":""},{"docid":"28a88ec2c4d5c0730afd9432479c76ba","score":"0.49671054","text":"def __init__(self, fname):\n self.read_file(fname)\n self.fname = fname","title":""},{"docid":"f0991c3c9bbfe4fde9369ea3fd71cd0a","score":"0.49552247","text":"def _open_file(self) -> None:\n if not self._entered:\n warnings.warn(\n \"StataReader is being used without using a context manager. \"\n \"Using StataReader as a context manager is the only supported method.\",\n ResourceWarning,\n stacklevel=find_stack_level(),\n )\n handles = get_handle(\n self._original_path_or_buf,\n \"rb\",\n storage_options=self._storage_options,\n is_text=False,\n compression=self._compression,\n )\n if hasattr(handles.handle, \"seekable\") and handles.handle.seekable():\n # If the handle is directly seekable, use it without an extra copy.\n self._path_or_buf = handles.handle\n self._close_file = handles.close\n else:\n # Copy to memory, and ensure no encoding.\n with handles:\n self._path_or_buf = BytesIO(handles.handle.read())\n self._close_file = self._path_or_buf.close\n\n self._read_header()\n self._setup_dtype()","title":""},{"docid":"d295569a2328f7e3964936be9857da2f","score":"0.49488863","text":"def init(args):\n file_handler = FileHandler(args.input, args.output, args.file_type)\n file_handler.readDataFromFile()\n\n file_handler.data = RankCalculator.calculate(\n file_handler.data, args.data_index, args.rank_index)\n\n file_handler.writeDatatoFile(args.skip_index)","title":""},{"docid":"05220fee7eb2757a4542fe067363e838","score":"0.49375388","text":"def _read_file(self):\n\n # Initialize readers of the different McStas instrument sections\n args = [self.Instr, self.write_file, self.product_filename,\n self._get_next_line, self._return_line]\n self.Definition_reader = DefinitionReader(*args)\n self.Declare_reader = DeclareReader(*args)\n self.Uservars_reader = UservarsReader(*args)\n self.Initialize_reader = InitializeReader(*args)\n self.Trace_reader = TraceReader(*args)\n self.Finally_reader = FinallyReader(*args)\n\n # A mode for each type that activates the correct reader function\n definition_mode = False\n declare_mode = False\n uservars_mode = False\n initialize_mode = False\n trace_mode = False\n finally_mode = False\n comment_mode = False\n any_mode = False\n\n # check if instrument name has be read from file yet\n instr_name_read = False\n\n self._open_file()\n\n # for line in self.file_data:\n while self.line_index < self.file_length:\n\n line = self._get_next_line()\n\n # Find appropriate mode\n if line.strip().startswith(\"DEFINE INSTRUMENT\") and not any_mode:\n definition_mode = True\n any_mode = True\n\n if line.strip().startswith(\"DECLARE\") and not any_mode:\n declare_mode = True\n any_mode = True\n\n if line.strip().startswith(\"USERVARS\") and not any_mode:\n uservars_mode = True\n any_mode = True\n\n if (line.strip().startswith(\"INITIALIZE\") or\n line.strip().startswith(\"INITIALISE\")) and not any_mode:\n initialize_mode = True\n any_mode = True\n\n if line.strip().startswith(\"TRACE\") and not any_mode:\n trace_mode = True\n any_mode = True\n\n if line.strip().startswith(\"FINALLY\") and not any_mode:\n finally_mode = True\n any_mode = True\n\n if line.strip().startswith(\"/*\"):\n comment_mode = True\n\n # Read with appropriate reader\n if definition_mode and not comment_mode:\n # Get instrument name\n if not instr_name_read:\n self.instr_name = line.split(\"(\")[0].strip().split(\" \")[-1]\n instr_name_read = True\n self.update_file_name()\n\n # Read line from definition\n definition_mode = self.Definition_reader.read_definition_line(line)\n # When read_definition finds the end, it will return False\n any_mode = definition_mode\n\n if declare_mode and not comment_mode:\n # Read line from declare\n declare_mode = self.Declare_reader.read_declare_line(line)\n # When read_declare finds the end, it will return False\n any_mode = declare_mode\n\n if uservars_mode and not comment_mode:\n # Read line from uservars\n uservars_mode = self.Uservars_reader.read_uservars_line(line)\n # When read_uservars finds the end, it will return False\n any_mode = uservars_mode\n\n if initialize_mode and not comment_mode:\n # Read line from initialize\n initialize_mode = self.Initialize_reader.read_initialize_line(line)\n # When read_initialize finds the end, it will return False\n any_mode = initialize_mode\n\n if trace_mode and not comment_mode:\n # Read line from initialize\n trace_mode = self.Trace_reader.read_trace_line(line)\n # When read_initialize finds the end, it will return False\n any_mode = trace_mode\n\n if finally_mode and not comment_mode:\n # Read line from finally\n finally_mode = self.Finally_reader.read_finally_line(line)\n # When read_finallyfinds the end, it will return False\n any_mode = finally_mode\n\n # Stop comment mode when end of comment block reached\n if \"*/\" in line.strip():\n comment_mode = False","title":""},{"docid":"6b56d44e11ea3923aa52e44e8e18dbbf","score":"0.49295652","text":"def main(compound_fname, reaction_fname):\n compound_data = read_compounds_file(compound_fname)\n reaction_data = read_reactions_file(reaction_fname)\n\n # only keep masses which have associated intensities\n intensities_level0 = match_masses(compound_data)\n compounds_level0 = {k: compound_data[k] for k in intensities_level0.keys()}\n\n for k in compounds_level0:\n compounds_level0[k]['intensities'] = intensities_level0[k]\n\n # find new compounds\n print('Starting with {} compounds'.format(len(compounds_level0)))\n\n # investigate results\n #list_structure_formulas(\n # compounds_level0, intensities_level0,\n # reaction_data)\n\n #investigate_reactions(\n # compounds_level0, intensities_level0,\n # reaction_data)\n\n find_small_motifs(compounds_level0)","title":""},{"docid":"bd787e6329d20bd2da5502888bd5e237","score":"0.49189228","text":"def __initialise_load_state(self, a_file_content):\n\n try:\n l_lines = a_file_content.splitlines()\n i = 0\n\n # Verify this is a formatted LegalDOc\n if l_lines[0] == \"FIELD DATA:\":\n i += 1\n\n # Read in field data\n while l_lines[i] != \"SECTIONS:\":\n l_line = l_lines[i].strip()\n\n # File name\n if l_line == \"FILE NAME:\":\n self.__f_file_name = l_lines[i+1].strip()\n i += 2\n continue\n\n # Case number\n if l_line == \"CASE NUMBER:\":\n self.__f_case_number = l_lines[i + 1].strip()\n i += 2\n continue\n\n # Judge name\n if l_line == \"JUDGE NAME:\":\n self.__f_judge_name = l_lines[i + 1].strip()\n i += 2\n continue\n\n # Defendant name\n if l_line == \"DEFENDANT NAME:\":\n self.__f_defendant_name = l_lines[i + 1].strip()\n i += 2\n continue\n\n # Sentencing document\n if l_line == \"PRISON DOCUMENT:\":\n self.__f_sentencing_document = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Punctuation removed\n if l_line == \"PUNCTUATION REMOVED:\":\n self.__f_punctuation_removed = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Stop words removed\n if l_line == \"STOP WORDS REMOVED:\":\n self.__f_stop_words_removed = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Lower case\n if l_line == \"LOWER CASE:\":\n self.__f_lower_case = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Stemmed\n if l_line == \"STEMMED:\":\n self.__f_stemmed = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Lemmatized\n if l_line == \"LEMMATIZED:\":\n self.__f_lemmatized = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Contains errors\n if l_line == \"CONTAINS ERRORS:\":\n self.__f_contains_errors = ast.literal_eval(l_lines[i + 1].strip())\n i += 2\n continue\n\n # Skip line\n i += 1\n\n # Read in section data\n l_section_index = -1\n i += 1\n while i < len(l_lines):\n l_line = l_lines[i].strip()\n\n # Reading a section heading\n if LegalDoc.__FILE_SECTION_PATTERN.match(l_line):\n l_section_index += 1\n self.__f_body.append([])\n\n # Reading a section's contents\n else:\n self.__f_body[l_section_index].append(l_line)\n i += 1\n\n # Create corpora\n for l_section in self.body:\n for l_sentence in l_section:\n self.__f_corpus += word_tokenize(l_sentence)\n\n return True\n except IndexError:\n LegalDoc.__note_exception(self.path,\n \"MAJOR ERROR: Failed to import formatted file, index out of bounds\", True)\n return False","title":""},{"docid":"a79b8fccf8371453e7ed0d95a1f626c7","score":"0.49075282","text":"def read(self):\n self._reader = OBJReader(self.filepath)\n self._parser = OBJParser(self._reader, precision=self.precision)\n self._reader.open()\n self._reader.pre()\n self._reader.read()\n self._reader.post()\n self._parser.parse()\n self._is_parsed = True","title":""},{"docid":"97cb40b829fdd03a72bf8318cc2305f9","score":"0.4907238","text":"def __init__(\n self,\n filename=\"\",\n atoms=None,\n chg=[],\n chgdif=None,\n aug=None,\n augdiff=None,\n nsets=1,\n ):\n self.filename = filename\n self.atoms = atoms\n self.chg = chg\n self.chgdif = chgdif\n self.aug = aug\n self.augdiff = augdiff\n self.nsets = nsets\n if self.atoms is None:\n self.read_file()","title":""},{"docid":"13e63f401c42c0acad9ba48040574de9","score":"0.48952138","text":"def read_file(file_name):\n with open(file_name) as myfile:\n lines = myfile.read()\n\n name = read_nome(lines)\n estados_nomes = read_estados(lines)\n alfabeto = read_alfabeto(lines)\n initial = read_initial(lines)\n finals = read_final(lines)\n transicoes = read_trans(lines.split()[2:])\n\n estados = []\n for estado in estados_nomes:\n estados.append(embed_transitions(estado, transicoes))\n\n mark_ini(estados, initial)\n mark_fin(estados, finals)\n\n myafn = Af(name, alfabeto, estados, initial)\n\n\n return myafn","title":""},{"docid":"b9b1da59cd84d270c893ff53c5484636","score":"0.4879051","text":"def process_file(fname, options):\n\n f = open(fname, 'rb+', 0)\n\n jobs = read_jobs(f)\n for job in jobs:\n # keep going until we find a pristine job\n if not ((job.state == '.') or (options.retry and (job.state == '!' or job.state == 'e'))):\n continue\n # try to claim the job\n if not set_job_state(f, job, '?'):\n continue\n # from here on out, the job is ours\n try:\n assert(set_job_state(f, job, 'r'))\n if run_job(job, options) == 0:\n assert(set_job_state(f, job, 'd'))\n if options.one_only:\n break\n else:\n assert(set_job_state(f, job, '!'))\n except:\n #print \"Error while executing:\", sys.exc_info()[0]\n assert(set_job_state(f, job, 'e'))\n raise\n\n f.close()","title":""},{"docid":"872450d7e4808b2597457b8d9bc03e5a","score":"0.4876162","text":"def from_file(self,datafile):\n data=pickle.load(open(datafile,\"rb\"))\n self.refant=copy.copy(data.refant)\n self.n_phase_iter=copy.copy(data.n_phase_iter)\n self.min_bl_per_ant=copy.copy(data.min_bl_per_ant)\n self.eps=copy.copy(data.eps)\n self.min_ant_times=copy.copy(data.min_ant_times)\n #self.trim_neff=copy.copy(data.trim_neff)\n self.id=copy.copy(data.id)\n self.flag_weights_file=copy.copy(data.flag_weights_file)\n self.model_file=copy.copy(data.model_file)\n self.data_file=copy.copy(data.data_file)\n self.model_file=copy.copy(data.model_file)\n self.spw=copy.copy(data.spw)\n self.t_avg=copy.copy(data.t_avg)\n self.n_cycles=copy.copy(data.n_cycles)\n self.Niterations=copy.copy(data.Niterations)\n self.Nfreqs=copy.copy(data.Nfreqs)\n self.Ntimes=copy.copy(data.Ntimes)\n self.Nfreqs=copy.copy(data.Nfreqs)\n self.Nants_data=copy.copy(data.Nants_data)\n self.Nants_telescope=copy.copy(data.Nants_telescope)\n self.chi_square_per_ant=copy.copy(data.chi_square_per_ant)\n self.dof_per_ant=copy.copy(data.dof_per_ant)\n self.noise_tblavg=copy.copy(data.noise_tblavg)\n self.stefcal_version_str=copy.copy(data.stefcal_version_str)\n self.Ntime_steps=copy.copy(data.Ntime_steps)\n self.chi_square=copy.copy(data.chi_square)\n self.dof=copy.copy(data.dof)\n del(data)","title":""},{"docid":"588a535d74c7be541c16b6d2ace63910","score":"0.48756564","text":"def open(self, path):\n self.path = path\n self.f = open(self.path, \"rb\")\n self.f.seek(0, 0)\n self.line_header = self.f.read(3200)\n b_h = self.f.read(400)\n self.order = self.__check_order(b_h)\n self.bin_head = BinHead(b_h, self.order)\n size_file = os.path.getsize(self.path)\n coef = self.__check_coef()\n self.count_trace = (size_file - 3600) / (self.bin_head.Samples * coef + 240)\n self.count_trace = int(self.count_trace)","title":""},{"docid":"46759425a6e692ca9b7e88e7e7529a52","score":"0.487373","text":"def read(self, filename):\n raise NotImplementedError()\n pass","title":""},{"docid":"cb5b21cc74fdf3d355133568c601c005","score":"0.48732078","text":"def __init__(self, filename=None):\n\n self.header = biceps_restraint_line_pf_header()\n self.comments = []\n self.lines = []\n\n if filename != None:\n self.read(filename)","title":""},{"docid":"2af9cbda5f01ff274bb6685d03fae0f6","score":"0.4872704","text":"def importf(self, filepath):\n with open(filepath, 'rb') as f:\n self.version, self.token, self.totalrent, self.balance, self.rent, self.venmo, self.members, self.transactions, self.operatorrent = pickle.load(f)","title":""},{"docid":"57a158b58b9a96f3aa84fb7c87ac1143","score":"0.48671028","text":"def process_file(self):\n\n # Skip Header\n for i in range(0,self.header):\n self.handler.readline()\n\n for line in self.handler:\n self.process_line(line.strip())","title":""},{"docid":"11208ccdbb5567c8ea5bc451027b6510","score":"0.48659375","text":"def open(self):\n self.file = open(self.filename, \"rb\", buffering=0)","title":""},{"docid":"c2be18285af8037ea5b8002256dba2b1","score":"0.48652273","text":"def read(self, filepath, lazy=False, assign_to=None):\n data = self._read_file(filepath)\n file_contains_dataset = True\n if assign_to is not None:\n data = {assign_to: data}\n file_contains_dataset = False\n if lazy:\n for attr in data:\n setattr(self, f\"{attr}\", None)\n self._attr_filemap[attr] = (filepath, file_contains_dataset)\n return\n for attr, value in data.items(): # pylint:disable=no-member\n if attr in condensed_hamiltonians:\n value = self.__dict_to_hamiltonian(value[\"terms\"], value[\"wire_map\"])\n setattr(self, f\"{attr}\", value)","title":""},{"docid":"97f794b304a186f207431f8e1b5d90f9","score":"0.48621124","text":"def read(self, file):\n\n self.wavelength = np.array([])\n self.transmission = np.array([])\n\n with open(file, 'r') as file:\n for line in file.readlines():\n if line[0] != '#':\n entries = line.split()\n self.wavelength = np.append(self.wavelength, float(entries[0]))\n self.transmission = np.append(self.transmission, float(entries[1]))\n\n # Update wavelength with the relevant factor to get the units into ang\n self.wavelength = self.wavelength * self.factor","title":""},{"docid":"4d30eab57ad5a013a4ce89d19e214189","score":"0.4860297","text":"def init_from_file(self, f):\n self.min_time = int(next(f).strip())\n self.buses = [\n None if bus == \"x\" else int(bus)\n for bus in next(f).strip().split(\",\")\n ]","title":""},{"docid":"460a6afaa06d91b3e22114785024bd0e","score":"0.48548278","text":"def __init__(self, file):\n self._priority_map = dict()\n self._updated = False\n self._file = file\n\n count = 0\n if os.path.exists(file):\n with open(file, \"r\") as f:\n while True:\n line = f.readline()\n if len(line) == 0:\n break\n setup_key = line.strip()\n if len(setup_key) > 0:\n self._priority_map[setup_key] = count\n count += 1","title":""},{"docid":"91ac6df0ee1d6edcbe373a8b2afbb923","score":"0.4854026","text":"def load_metabolic_model_reactions(filename, format = \"text\", output = \"normal\"):\n #\n # Distionary for reaction data\n #\n reactions = {}\n #\n # Conter to keep reaction order\n #\n counter = 0\n #\n # Ititialize mode\n #\n mode = \"start\"\n\n with open(filename, 'r') as f:\n if format == \"text\":\n reader = csv.reader(f, delimiter='\\t')\n elif format == \"csv\":\n reader = csv.reader(f, dialect='excel')\n else:\n print(\"Unknown format!\")\n f.close()\n return False\n\n for i, row in enumerate(reader):\n if len(row) == 0:\n continue\n if \"#\" == row[0][0]:\n continue\n row = [item for item in row if \"#\" not in item]\n\n if len(row) < 1:\n continue\n\n if \"//\" in row[0]:\n rid = row[0].replace(\" \", \"\")\n if rid == \"//Reactions\":\n mode = \"Reactions\"\n continue\n if rid == \"//End\":\n break\n mode = \"other\"\n # Remove '' from row\n row = list(filter(lambda s:s != '', row))\n\n\n if mode == \"Reactions\":\n if output == \"debug\":\n print(row)\n if len(row) < 6:\n continue\n rid = row[0].replace(\" \", \"\")\n stoichiometry = row[1].replace(\" \", \"\")\n reaction = row[2].replace(\" \", \"\")\n atommap = row[3].replace(\" \", \"\")\n exid = row[4]\n if rid == \"\":\n continue\n\n\n reactions[rid] = {\n 'stoichiometry': stoichiometry,\n 'reaction':reaction,\n 'atommap':atommap,\n 'externalids' :exid,\n 'order':int(counter)\n }\n if len(row) >= 6:\n reactions[rid]['lb'] = float(row[5].replace(\" \", \"\"))\n reactions[rid]['ub'] = float(row[6].replace(\" \", \"\"))\n counter = counter + 1\n\n return(reactions)","title":""},{"docid":"e77775b0dbf194a0215fae87f9c958ba","score":"0.48460564","text":"def from_file(self, filename: str):\n with open(filename, 'r') as fp:\n self.from_fp(fp)","title":""},{"docid":"ffcac89978cf248625bd4c712a49768f","score":"0.4843126","text":"def read_reactions(reaction_file, verbose=False):\n\n reactions2run = set()\n with open(reaction_file, 'r') as f:\n for li in f:\n if li.startswith('#'):\n continue\n if \"biomass\" in li.lower():\n if verbose:\n sys.stderr.write(\"Biomass reaction was skipped from the list as it is auto-imported\\n\")\n continue\n r = li.strip()\n if r in modeldata.reactions:\n reactions2run.add(r)\n return reactions2run","title":""},{"docid":"42ef75b43c3d65f0d36aefbdfc826a55","score":"0.48420078","text":"def loadFile(self, file_name, message_name_filter_list):\n self.file_name = file_name\n self.file_handle = open(file_name, \"rb\")\n\n # parse the whole file\n self.read_file_header()\n self.last_timestamp = self.start_timestamp\n self.read_file_definitions()\n self.read_file_data(message_name_filter_list)","title":""},{"docid":"b6f29d7d66ae58c89886b4417a3b14d2","score":"0.4832494","text":"def read_from_file(self, filename):\n raise NotImplemented","title":""},{"docid":"1810e4065effde14fa421f5565386f31","score":"0.4829278","text":"def read(self, filename):\n\n # read in the lines of from the input file\n fin = open(filename, 'r')\n lines = fin.readlines()\n fin.close()\n\n # parse the header\n if lines[0][0] == '#':\n self.header = lines.pop(0).strip()\n\n # store other '#' lines as comments\n while lines[0][0] == '#':\n self.comments.append( lines.pop(0).strip() )\n\n # read the other lines \n while len(lines) > 0:\n self.lines.append( lines.pop(0).strip() )","title":""},{"docid":"9dba0982b0c42556be882e2f92e98f80","score":"0.48265225","text":"def __init__(self, file, keep_linebreaks=False, sequence_class=Sequence):\n\t\tif isinstance(file, basestring):\n\t\t\tfile = xopen(file)\n\t\tself.fp = file\n\t\tself.sequence_class = sequence_class\n\t\tself.delivers_qualities = False\n\t\tself._delimiter = '\\n' if keep_linebreaks else ''","title":""},{"docid":"cb340f5e4712b89435199b8f3b809279","score":"0.4821202","text":"def __init__(self, file_name):\n # layer & output managers\n self.act_manager = act.ActivationTypeManager()\n self.output_manager = act.OutputTypeManager()\n\n # data files\n self.train_data_file = ''\n self.val_data_file = ''\n self.test_data_file = ''\n self.is_val = False\n self.is_test = False\n self.output_dir = ''\n self.output_filename_pattern = 'm%d'\n self.is_output = False\n self.loss_file = None\n self.is_regression = False\n\n # parameters for training\n self.learn_rate = 0.001\n self.init_scale = 0.001\n self.momentum = 0.9\n self.weight_decay = 0\n self.minibatch_size = 100\n self.epoch_to_display = 10\n self.display_winc = True \n self.epoch_to_save = 100\n self.num_epochs = 1000\n self.random_seed = None\n\n # information for each layer, read from the file\n self.num_layers = 0\n\n # default output layer\n self.output = OutputSpec(10, self.output_manager.linear)\n\n self._parse_cfg_file(file_name)","title":""},{"docid":"1b497a15db3d7a8cdf15cdb39be9b9b2","score":"0.48064095","text":"def read_file(self, file_name):\n f = open(file_name,'r')\n line = f.readline()\n line = line.split(' ')\n self.n = int(line[0])\n self.m = int(line[1])\n\n for i in range(self.n):\n self.D_out[i] = []\n self.D_in[i] = []\n\n while True:\n line = f.readline()\n if len(line) == 0:\n return\n line = line[:-1]\n line = line.split(' ')\n v1 = int(line[0])\n v2 = int(line[1])\n cost = int(line[2])\n self.D_out[v1].append(v2) \n self.D_in[v2].append(v1)\n self.D_cost[(v1,v2)] = cost","title":""},{"docid":"43d9b9d5ec021cf8f6e9fc07a9cc4496","score":"0.48020414","text":"def __read_input_file( self ):\n\n\t try:\n\t\tself.tpd = yaml.load( open( self.oprator_infile, 'r' ) )\n\t\tminion_hosts = self.tpd[\"hosts\"]\n\t\tfor i in minion_hosts.keys():\n\t\t if self.ping_host(minion_hosts[i]) == 0:\n \t self.restful_request(minion_hosts[i])\n\t\t else:\n\t\t\t self.common_logger.info(\"server \" + i + \" not reachable, check the connectivity\")\n\t\t\t self.common_logger.error(\"Orcestration failed, check logfile for more information\")\n\t\t\t sys.exit(1)\n\n\t except yaml.YAMLError as exc:\n\t\tself.common_logger.error(exc)","title":""},{"docid":"15dc471fef383147f1af3988f3909f3b","score":"0.4797811","text":"def readInternal(self) -> None:\n key = \"\"\n impedances: Dict = {}\n variances: Dict = {}\n freq: Union[List[float], np.ndarray] = []\n with open(self.filepath, \"r\") as inFile:\n lines = inFile.readlines()\n numLines = len(lines)\n # find the headers\n for i in range(0, numLines):\n line = lines[i].strip()\n if line == \"\":\n continue\n if \"Evaluation frequencies\" not in line:\n split = line.split(\"=\")\n self.headers[split[0].strip()] = split[1].strip()\n else:\n break\n # format headers\n self.formatHeaders()\n # read in the frequencies\n freq = np.loadtxt(lines[i + 1].strip().split(\",\"), dtype=\"float\")\n # read in the rest of the data\n for j in range(i + 2, numLines):\n line = lines[j].strip()\n if \"Z\" in line or \"Var\" in line:\n split = line.split(\"-\")\n key = split[1].strip()\n j += 1\n if \"Z\" in line:\n impedances[key] = np.loadtxt(\n lines[j].strip().split(\",\"), dtype=\"complex\"\n )\n if \"Var\" in line:\n variances[key] = np.loadtxt(\n lines[j].strip().split(\",\"), dtype=\"float\"\n )\n self.tfData = TransferFunctionData(freq, impedances, variances)","title":""},{"docid":"fcb69181cb8c204281bf362619864ef1","score":"0.47867498","text":"def read_date_file(self, date_file):\n try:\n with open(date_file, \"r\") as this_date_file:\n date_string, run_number = this_date_file.read().split()\n self.current_date = esm_calendar.Date(date_string)\n self.run_number = int(run_number)\n # NOTE: IOError might be called FileNotFoundError in python3+\n except IOError:\n logging.debug(\"The datefile %s was not found, assuming very first run\", date_file)\n self.update_dates()","title":""},{"docid":"6b3f68618fd84e10b10b716069f55e0f","score":"0.4784722","text":"def __init__(self, path_file):\n _generic_fasta_file.__init__(self, path_file)\n self.read_fasta_file()","title":""},{"docid":"0c4561ef0b3d7f59c067d1d63065afa5","score":"0.47800082","text":"def __init__(self,\n filename,\n killburn = False,\n silent = True):\n self.setup_logger(silent)\n filename = os.path.expanduser(filename)\n self.filename = os.path.expandvars(filename)\n self.file = FortranReader(self.filename)\n self._load(killburn = killburn)\n self.file.close()\n self.close_logger()","title":""},{"docid":"55a3771ce24dd8540797bbfba2f10b18","score":"0.47722954","text":"def __init__(self, file_path):\n self.file_path = file_path\n self.index_payload = []\n self.index_meta = {}\n self.shape_payload = []\n self.writer = None","title":""},{"docid":"fed387188f1f5c3e272c31c7bb2f4198","score":"0.4765135","text":"def __init__(self, path: str)->None:\n \"[Read all lines from the current file]\"\n self._configuration=[line.rstrip().split(\" \")\n for line in open(path, \"r\").readlines()]\n\n \"[Divide configuration into peaces]\"\n self._symbols, self._states=set(), set()\n self._start=self._configuration[2][0]\n self._terminal=self._configuration[3]\n self._rules={tuple(rule[:2]): rule[2]\n for rule in self._configuration[4:]}\n\n \"[Create states and alphabet]\"\n for rule in self._configuration[4:]:\n self._symbols.add(rule[1])\n self._states.add(rule[0])\n self._states.add(rule[2])\n\n \"[Delete unusable variables]\"\n del self._configuration","title":""},{"docid":"6e221bd2729f9ff87de6170f5b8d9713","score":"0.476245","text":"def _parsefile(self, filehandle):\n allentries = []\n current = None\n sumreport = 1\n suminterval = 10000\n repthresh = sumreport * suminterval\n try:\n while True:\n line = filehandle.readline()\n if line == '':\n break\n #for line in filehandle:\n if line.startswith(\"ID \"):\n # ID 001R_FRG3G Reviewed; 256 AA.\n # <prot_name>_<prot_spec>\n proteinid = line[5:16].strip()\n current = defaultdict(dict)\n current['proteinid'] = proteinid\n (protein, species) = proteinid.split('_')\n current['protein'] = protein\n current['species'] = species\n self.log.debug(\"Handling ID. New entry.\") \n \n elif line.startswith(\"AC \"):\n # AC Q6GZX4;\n # AC Q91896; O57469;\n self.log.debug(\"Handling AC.\")\n accession = line[5:11].strip()\n current['proteinacc'] = accession\n\n elif line.startswith(\"OX \"):\n #OX NCBI_TaxID=654924;\n self.log.debug(\"Handling OX.\")\n taxonid = \"\"\n val = line[5:]\n fields = val.split('=')\n if fields[0] == 'NCBI_TaxID':\n taxonid = fields[1].strip().replace(';','')\n current['taxonid'] = taxonid\n \n elif line.startswith(\"DR GO;\"):\n # DR GO; GO:0046782; P:regulation of viral transcription; IEA:InterPro.\n # P biological process, C cellular component, F molecular function. \n self.log.debug(\"Handling DR.\")\n fields = line.split(';')\n goterm = fields[1].strip()\n goinfo = fields[2]\n aspcode = goinfo.split(':')[0].strip()\n goaspect = UniProt.ASPECTMAP[aspcode]\n goevsrc = fields[3]\n (goevidence, evsrc) = goevsrc.split(':') \n goevidence = goevidence.strip()\n current['goterms'][goterm] = [ goaspect, goevidence]\n\n elif line.startswith(\"SQ SEQUENCE\"):\n self.log.debug(\"Handling SQ: XXX\")\n # line = filehandle.readline()\n\n elif line.startswith(\"GN \"):\n # Examples:\n # GN ABL1 {ECO:0000303|PubMed:21546455},\n # GN Name=BRCA1; Synonyms=RNF53;\n # GN ORFNames=T13E15.24/T13E15.23, T14P1.25/T14P1.24;\n # \n \n self.log.debug(\"Handling GN.\")\n val = line[5:]\n\n \n elif line.startswith(\"//\"):\n self.log.debug(\"End of entry.\")\n clist = self._handle_current(current)\n current = None\n allentries.extend(clist)\n self.log.debug(f\"All entries list now {len(allentries)} items... \")\n if len(allentries) >= repthresh:\n self.log.info(f\"Processed {len(allentries)} entries... \")\n sumreport +=1\n repthresh = sumreport * suminterval\n \n except Exception as e:\n traceback.print_exc(file=sys.stdout) \n \n self.log.info(f\"Parsed file with {len(allentries)} goterms\" )\n return allentries","title":""},{"docid":"de1ef3356a864140aeafb44d727bc45d","score":"0.4760311","text":"def __init__(self, filePath):\n\t\tself.gameState = readGame.readGameState(filePath)\n\t\tself.nodesExpanded = 0\n\t\tself.trace = []","title":""},{"docid":"0750043cafc4b164418544c340c6344b","score":"0.475434","text":"def read_and_test_file_content(self): # pragma: no cover\n\n # We print the CLI header.\n PyFunceble.CLICore.print_header()\n\n with open(self.file, \"r\", encoding=\"utf-8\") as file:\n # We open the file we have to test.\n\n with Manager() as manager:\n # We initiate a server process.\n\n # We process the test/save of the original list to test.\n self.__run_multiprocess_test(\n self._get_list_to_of_subjects_to_test_from_file(file), manager\n )\n\n # We get the list of mined data to test.\n to_test = chain(self.mining.list_of_mined())\n\n # We process the test/save of the mined data to test.\n self.__run_multiprocess_test(to_test, manager)\n\n # We get the list of complements to test.\n complements = self.get_complements()\n\n if complements:\n # We process the test/save of the original list to test.\n to_test = chain(complements)\n\n self.__run_multiprocess_test(to_test, manager)\n\n # We inform all subsystem that we are not testing for complements anymore.\n self.complements_test_started = False\n\n # We generate the JSON formatted files if needed.\n self.generate_json_format()\n # We clean the autocontinue subsystem, we finished\n # the test.\n self.autocontinue.clean()\n # We process the autosaving if necessary.\n self.autosave.process(test_completed=True)","title":""},{"docid":"c4e0e07cf75e321ad85eb27733adcc2a","score":"0.47543067","text":"def setup(self, path_to_conf_file):\n self.track = Track.SCENE_LAYOUT\n self.agent_engaged = False\n self.current_control = carla.VehicleControl()\n self.current_control.steer = 0.0\n self.current_control.throttle = 1.0\n self.current_control.brake = 0.0\n self.current_control.hand_brake = False\n self._hic = HumanTextInterface(self)\n self._thread = Thread(target=self._hic.run)\n self._thread.start()","title":""},{"docid":"f9d77c2d99c36eafb85956740611879d","score":"0.47484687","text":"def load_file(self,initial_condition=False,custom_name=None):\n\n\t\t# if no custom name, then simply load with current settings (TUI version)\n\t\t# if there is a custom name, update paths to the appropriate experiment name then attempt to load.\n\n\t\tself.update_paths()\n\t\tif custom_name is None:\n\t\t\tfilepath = self.raw_data_path\n\t\telse:\n\t\t\tfilepath = \"DATA/{}d/{}/{}.json\".format(self.dimension, custom_name,custom_name)\n\n\t\tprint(\"Loading file: {}\".format(filepath))\n\t\ttry:\n\t\t\twith open(filepath, 'r') as fp:\n\t\t\t\tif initial_condition:\n\t\t\t\t\tself.current_cone_chain = json.load(fp, cls=cone_chain.ConeChainInitialConditionExtractor)\n\t\t\t\t\tif not self.batch_mode or self.experiment_name is None:\n\t\t\t\t\t\tself.ask_experiment_name()\n\t\t\t\t\tself.update_paths()\n\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tself.current_cone_chain = json.load(fp, cls=cone_chain.ConeChainDecoder)\n\t\t\tprint('\\tloading successful...')\n\t\texcept:\n\t\t\tprint('\\tA file loading error has occured.')\n\t\tself.save_file()","title":""},{"docid":"78612c36f1f47a7a3e86a55866e9acef","score":"0.47437727","text":"def __init__(self, file, sequence_class=Sequence):\n\t\tif isinstance(file, basestring):\n\t\t\tfile = xopen(file)\n\t\tself.fp = file\n\t\tself.sequence_class = sequence_class\n\t\tself.delivers_qualities = True","title":""},{"docid":"362529be851a153e859b1e52dc569d90","score":"0.47407082","text":"def read_mol(self, filename):\n self.fname = filename\n lines = []\n with open(self.fname, \"r\") as xyzfile:\n for line in xyzfile:\n lines.append(line.rstrip('\\n'))\n\n self.atom_basis = lines[0].strip().lower() == \"atombasis\"\n if self.atom_basis:\n offset = 1\n else:\n offset = 2\n # read basis\n line = lines[1].split()\n self.basis = line[0]\n if len(line)>1:\n # read aux basis\n if line[1][0:3].lower() == \"aux\":\n self.aux_basis = line[1][4:]\n\n # read title lines:\n self.title1 = lines[offset]\n self.title2 = lines[offset+1]\n\n # read main line:\n line = lines[offset+2].split()\n for elmt in line:\n el = elmt.split(\"=\")\n test = el[0].lower()\n if test == \"atomtypes\":\n self.natom_types=int(float(el[1]))\n elif test == \"charge\":\n self.charge=int(float(el[1]))\n elif test == \"nosymmetry\":\n self.symmetry = False\n elif test == \"angstrom\":\n self.bohr = False \n\n # read all atom types\n iline = offset + 2\n self.atom_types = [] \n for natp in xrange(self.natom_types):\n iline += 1\n new_atype = atom_type()\n new_atype.read_from_mol(lines, iline, self.bohr)\n self.atom_types.append( new_atype )\n self.natoms += self.atom_types[-1].natoms\n iline += self.atom_types[-1].natoms\n\n self.natom_types = len(self.atom_types)\n return self.check() # check validity of data","title":""},{"docid":"5c29b039a4bb52ba5253ac12c2026dad","score":"0.4740215","text":"def main(file_to_process):\n logger.info('Processing {}'.format(file_to_process))\n # with open(os.path.join(PROCESSING_PATH, file_to_process), 'r') as f:\n # logger.info(f.readline())","title":""},{"docid":"42142dbce383293ca00e90b18b63e0b7","score":"0.47299087","text":"def importRedescriptionsFromFile(self, redescriptions_filename):\n self._startMessage('importing', redescriptions_filename)\n try:\n tmp_reds, tmp_rshowids = self._readRedescriptionsFromFile(redescriptions_filename)\n except IOError as arg:\n self.logger.printL(1,\"Cannot open: %s\" % arg, \"dw_error\", \"DW\")\n self._stopMessage()\n raise\n except Exception:\n self.logger.printL(1,\"Unexpected error while importing redescriptions from file %s!\\n%s\" % (redescriptions_filename, sys.exc_info()[1]), \"dw_error\", \"DW\")\n self._stopMessage()\n raise\n else:\n self.reds = tmp_reds\n self.rshowids = tmp_rshowids\n finally:\n self._stopMessage('importing')","title":""},{"docid":"cb885bfd12d9801426a4c73fc5aa1ae4","score":"0.47284946","text":"def __init__(self, filepath):\n self.filepath = filepath\n self.populate_words_and_print_num()","title":""},{"docid":"feff88cfdbd68633799f003d6c0cc9c5","score":"0.47260806","text":"def parse_ontology(self, file):\n self.ontology_name = file\n ont = ontospy.Ontospy(file)\n self.o = ont\n self.objectProperties = self.o.objectProperties # cache this\n self.obtain_class_hierarchy_and_signatures()","title":""},{"docid":"e84b8007af242ca4c7aaaa9667613852","score":"0.47260755","text":"def read(self,filename):\n if not self.empty:\n raise Error(\"GdxFile.read can only be used if the GdxFile is .empty\")\n\n # open the file\n rc = gdxcc.gdxOpenRead(self.H,str(filename))\n if not rc[0]:\n raise GdxError(self.H,f\"Could not open {filename!r}\")\n self._filename = filename\n\n # read in meta-data ...\n # ... for the file\n ret, self._version, self._producer = gdxcc.gdxFileVersion(self.H)\n if ret != 1: \n raise GdxError(self.H,\"Could not get file version\")\n ret, symbol_count, element_count = gdxcc.gdxSystemInfo(self.H)\n logger.debug(\"Opening '{}' with {} symbols and {} elements with lazy_load = {}.\".format(filename,symbol_count,element_count,self.lazy_load))\n # ... for the symbols\n ret, name, dims, data_type = gdxcc.gdxSymbolInfo(self.H,0)\n if ret != 1:\n raise GdxError(self.H,\"Could not get symbol info for the universal set\")\n self.universal_set = GdxSymbol(name,data_type,dims=dims,file=self,index=0)\n for i in range(symbol_count):\n index = i + 1\n ret, name, dims, data_type = gdxcc.gdxSymbolInfo(self.H,index)\n if ret != 1:\n raise GdxError(self.H,\"Could not get symbol info for symbol {}\".format(index))\n self.append(GdxSymbol(name,data_type,dims=dims,file=self,index=index))\n\n # read all symbols if not lazy_load\n if not self.lazy_load:\n for symbol in self:\n symbol.load()\n return","title":""},{"docid":"8a9b3b42ccddac769b7d41deda1d3bdc","score":"0.47232094","text":"def main():\n d, args = process_init()\n\n user_file_path = get_attr_by_flag(args, d, \"default_defaultfile_flags\")\n default_file_path = get_attr_by_flag(args, d, \"default_changefilename_flags\")\n open_default_file = get_attr_by_flag(args, d, \"default_open_flags\")\n initiate_loop = get_attr_by_flag(args, d, \"default_loop_flags\")\n topics = get_attr_by_flag(\n args, d, \"default_topic_flags\"\n ) # could be None or list of passed in topics\n # print(dir(args))\n this_version = getattr(args, \"version\")\n\n ensure_redundancy(REDUNDANCY_PATH, default_file_path)\n\n if open_default_file is True: ##launch default file\n if path.isfile(d.get(\"default_file\")):\n print(\"Opening {}\".format(d.get(\"default_file\")))\n startfile(\"{}\".format(d.get(\"default_file\")))\n else:\n print(f'{d.get(\"default_file\")} not found.')\n return\n\n if this_version is True: ##display version for user\n print(\"Notes > Memory, version:{}\".format(version))\n return\n\n if (user_file_path) != d[\n \"default_file\"\n ]: ##user changing defaultfile; update INIT_FILE\n with open(INIT_FILE, \"r\") as f:\n lines = f.readlines()\n new_INIT_FILE = [\n i\n if \"default_file\" not in i\n else \"default_file={}\\n\".format((user_file_path))\n for i in lines\n ]\n\n try:\n copy2(\n INIT_FILE,\n path.join(\n path.split(INIT_FILE)[0], f\"COPY - {path.split(INIT_FILE)[1]}\"\n ),\n )\n except FileNotFoundError:\n print(f\"Creating {INIT_FILE}\")\n\n with open(INIT_FILE, \"w\") as f:\n f.writelines(new_INIT_FILE)\n\n print(\"Default file changed to {}\".format(user_file_path))\n return\n\n if initiate_loop is True: ##user entering note-taking loop\n process_loop(args, init_dict=d)\n return\n\n if (\n getattr(args, d.get(\"default_note_flags\")[-1].strip())\n ) is None: ##no note means user wants note output\n try:\n with open((default_file_path), \"r\") as f:\n lines = f.readlines()\n except FileNotFoundError:\n print(\n f\"\\n\\tNo such file or directory: {(default_file_path)}\\n\\t>>Add notes to file before using topic tag.\"\n )\n return\n\n if topics is None:\n show_non_specific_lines(lines, d)\n return\n if \"ALL\" in topics:\n topics = show_all_topics(topics, lines, default_file_path)\n for i, line in enumerate(lines):\n if sum([1 for t in topics if t in line.split(\"::\")[0]]) > 0:\n process_line(line, d)\n return\n\n note_str = \" \".join(\n (getattr(args, d.get(\"default_note_flags\")[-1].strip()))\n ) ##(getattr(args, d.get(\"default_note_flags\")[-1].strip())) comes in as a list; convert to string\n\n try: ##read current notes file content\n with open((default_file_path), \"r\") as f:\n string = f.read()\n except FileNotFoundError:\n string = \"\"\n\n try: ##redundancy to preserve notes\n copy2((default_file_path), f\"COPY - {(default_file_path)}\")\n except FileNotFoundError:\n print(f\"Creating {(default_file_path)}\")\n for topic in topics:\n if topic in [\"ALL\", \"SHOW\", \"HELP\", \"TOPICS\"]:\n topics.remove(topic)\n ticker_set = set()\n try:\n with open((default_file_path), \"r\") as f:\n lines = f.readlines()\n except FileNotFoundError:\n print(\n f\"\\n\\tNo such file or directory: {(default_file_path)}\\n\\t>>Add notes to file before using topic tag.\"\n )\n for i, line in enumerate(lines):\n mat = match(r\"\\d{4}-\\d{2}-\\d{2}\\s\\d{2}:\\d{2}:\\d{2}--(.*?):\", line)\n group = mat.group(1)\n if len(group) > 0:\n ticker_set.add(group.upper())\n print(\n \"\\n Current Topics in {}:\".format(default_file_path)\n + colorama.Fore.MAGENTA\n + \"\\n\\t{}\".format(\"\\n\\t\".join(list(ticker_set)))\n + colorama.Fore.WHITE\n )\n with open(\n (default_file_path), \"w\"\n ) as f: # if we got this far, we want to write notes to file\n this_note = (\n str(datetime.today())[:19] + \"--misc::\" + note_str\n if topics is None\n else str(datetime.today())[:19] + \"--\" + \", \".join(topics) + \"::\" + note_str\n )\n if topics is None:\n f.write(this_note + \"\\n\" + string)\n else:\n f.write(this_note + \"\\n\" + string)\n process_line(this_note, d)","title":""},{"docid":"b82082cb223d2189fa431a8975098d03","score":"0.47228834","text":"def init_from_mol(self, mol_file):\n self.name = mol_file.fname.split(\"/\")[-1][:-4]\n self.note = mol_file.title1+\" \"+mol_file.title2\n self.charge = mol_file.charge\n self.natoms = mol_file.natoms\n self.natom_types = mol_file.natom_types\n self.atom_types = mol_file.to_angstrom().atom_types\n self.get_chem_name()\n return self.check() # check validity of mol_data\"","title":""},{"docid":"3c733683198f644b2ef09aac90f4b51b","score":"0.47202966","text":"def read_all(self):\n with open(self.filepath, 'r') as filepath:\n self.data = filepath.read()\n self.data = QtProParser.replace_multiline_data(self.data)\n self.data = QtProParser.replace_variables(self.data, self.variable)","title":""},{"docid":"7212b40358fdd23dc0736534f9547b06","score":"0.47162932","text":"def read(self, fileName):\n i = 0\n file = open(fileName)\n for line in file:\n if len(line) <= 1:\n pass\n elif i >= 1:\n self.append(data(line))\n else:\n self.header = data(line)\n i |= 1\n file.close()","title":""},{"docid":"ca472f65c59893a1ce9b826fa22ca3d0","score":"0.47142392","text":"def read_file(self, file_name):\n f = open(file_name,'r')\n line = f.readline()\n line = line.split(' ')\n self.n = int(line[0])\n self.m = int(line[1])\n\n for i in range(self.n):\n self.D_vertices[i] = []\n\n while True:\n line = f.readline()\n if len(line) == 0:\n return\n line = line[:-1]\n line = line.split(' ')\n v1 = int(line[0])\n v2 = int(line[1])\n self.D_vertices[v1].append(v2) \n self.D_vertices[v2].append(v1)\n self.D_edges.append( (v1, v2) )\n self.D_edges.append( (v2, v1) )","title":""},{"docid":"759aeadb48b6e9ec5d36c4aea9849475","score":"0.47117305","text":"def initialise(self, a_path, load_state):\n\n # Initialise path\n self.__f_path = a_path\n\n # Get file name\n head, tail = ntpath.split(self.path)\n self.__f_file_name = (tail or ntpath.basename(head))\n\n # Read in file\n l_file = None\n try:\n l_file = open(self.path)\n l_file_content = l_file.read()\n l_file.close()\n\n # Handle file error\n except IOError:\n LegalDoc.__note_exception(self.path, \"MAJOR ERROR: Unable to read file\", True)\n l_file.close()\n return False\n\n # Load state from a formatted file\n if load_state:\n\n # TODO - Timer start\n Timers.s_init_load_state_timer.start()\n\n l_succeeded = self.__initialise_load_state(l_file_content)\n\n # TODO - Timer stop\n Timers.s_init_load_state_timer.stop()\n\n if not l_succeeded:\n LegalDoc.s_file_error += 1\n return False\n\n # Generate state from an unformatted file\n else:\n\n # TODO - Timer start\n Timers.s_init_gen_state_timer.start()\n\n l_succeeded = self.__initialise_generate_state(l_file_content)\n\n # TODO - Timer stop\n Timers.s_init_gen_state_timer.stop()\n\n if not l_succeeded:\n return False\n self.__f_punctuation_removed = LegalDoc.REMOVE_PUNCTUATION\n self.__f_lower_case = LegalDoc.TO_LOWER_CASE\n self.__f_stop_words_removed = LegalDoc.REMOVE_STOP_WORDS\n self.__f_stemmed = LegalDoc.APPLY_STEMMING\n self.__f_lemmatized = LegalDoc.APPLY_LEMMATIZATION\n\n # Note successful initialisation\n LegalDoc.s_successful_init_count += 1\n\n # Add current LegalDoc to static dictionary of LegalDocs\n LegalDoc.s_legal_doc_dict[self.file_name] = self\n\n # Create judge and add it to static dictionary of judges\n Judge.add_legal_doc(self)\n\n return True","title":""},{"docid":"4fa83c2c3aa994bd2b374e69d8f790c5","score":"0.47115463","text":"def load(self):\n if self.dummy:\n raise Exception(\"Can't load() a dummy SynchronizedFile\")\n self._read(open(self.filename), self.filename)","title":""},{"docid":"e3d0bfdf99bed434493dddb480823c06","score":"0.4695643","text":"def read_file_and_perform_action(file_path, action):\n\n # Check if the file is exists to avoid errors.\n if not is_file_exists(file_path):\n raise FileNotFoundError('{} path given is not exists.'.format(file_path))\n\n # Perform action for each value found in the file.\n with open(file_path, 'r') as file_handle:\n for line in file_handle.readlines():\n values_in_line = line.split(',')\n for value in values_in_line:\n action(value.strip())","title":""},{"docid":"158a277ebe803311cf6962a859b500c9","score":"0.46930277","text":"def openfile_and_preload(logf):\r\n logf.write('opening file\\n')\r\n f = open('uploadData.csv', 'r')\r\n logf.write('file opened\\n')\r\n csvdata = csv.reader(f)\r\n for row in csvdata:\r\n MIG_IDS[row[1]] = True\r\n ROWS_TO_PROCESS.append(row)\r\n\r\n logf.write('File read and loaded\\n')","title":""},{"docid":"fe48a3b9488bce4bfb8fd67e8de1cfdb","score":"0.46920854","text":"def __init__(self, state_file, twitter_config, telegram_config, pretend=False):\n self.state_file = state_file\n self.pretend = pretend\n\n if not self.pretend:\n self.twitter = twitter.Api(**twitter_config)\n self.twitter.VerifyCredentials()\n \n self.telegram_config = telegram_config\n\n self.tz = pytz.timezone(self.TIMEZONE)\n\n self.log = logging.getLogger('mlbot')\n\n self.log.debug('Loading state from file')\n try:\n with open(self.state_file, 'rb') as f:\n self.status = pickle.load(f)\n except FileNotFoundError:\n self.log.warning('File not found, first run?')\n self.status = {}","title":""},{"docid":"7de274f61420e099eab24396d4661e87","score":"0.46868306","text":"def read(self, filepath):\n self.parser.read(filepath)","title":""},{"docid":"6a9e8a3833ce764d55c75789b6d38f6f","score":"0.46857572","text":"def read_recipe(self, filename):\n Global.LOGGER.debug(f\"reading recipe {filename}\")\n if not os.path.isfile(filename):\n Global.LOGGER.error(filename + \" recipe not found, skipping\")\n return\n\n config = configparser.ConfigParser(allow_no_value=True,\n delimiters=\"=\")\n\n config.read(filename)\n\n for section in config.sections():\n self.sections[section] = config[section]\n\n Global.LOGGER.debug(\"Read recipe \" + filename)","title":""},{"docid":"86a7a88ec27ade1e05ae583cf40588fc","score":"0.4665189","text":"def __init__(self, f):\n\t\topen_file = open(f, \"r\", encoding=\"utf-8\")\n\t\tself.file = open_file.readlines()\n\t\topen_file.close()\n\t\tself.length = len(self.file)\n\t\tself.line_counter = -1\n\t\tself.current_command = None","title":""}],"string":"[\n {\n \"docid\": \"711d7934c80dc5febb6f5fa7ddea8f39\",\n \"score\": \"0.5626557\",\n \"text\": \"def _open_file(self):\\n\\n with open(self.filename) as file:\\n self.file_data = file.readlines()\\n\\n self.file_length = len(self.file_data)\\n self.line_index = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"349691eaa708ab8ceeec0e7c0f4bf918\",\n \"score\": \"0.5621808\",\n \"text\": \"def __init__(self, filename, start_state=0):\\n\\n with open(filename, 'rt') as fin:\\n self.start_state = start_state\\n self.symbols = set()\\n self.states_count = int(fin.readline())\\n self.final_states = set(\\n map(lambda e: int(e), fin.readline().split()))\\n\\n self.read_delta(fin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd55b6f97ead99740bb23b4133e350f\",\n \"score\": \"0.562007\",\n \"text\": \"def load_reaction_data(self, file_path, num_processes):\\n print('Stage 1/2: loading reaction data...')\\n all_reactant_mols = []\\n all_product_mols = []\\n all_real_bond_changes = []\\n ids_for_small_samples = []\\n with open(file_path, 'r') as f:\\n lines = f.readlines()\\n\\n def _update_from_line(id, loaded_result):\\n reactants_mol, product_mol, reaction_real_bond_changes = loaded_result\\n if reactants_mol is None:\\n return\\n all_product_mols.append(product_mol)\\n all_reactant_mols.append(reactants_mol)\\n all_real_bond_changes.append(reaction_real_bond_changes)\\n if reactants_mol.GetNumAtoms() <= self.size_cutoff:\\n ids_for_small_samples.append(id)\\n\\n if num_processes == 1:\\n for id, li in enumerate(tqdm(lines)):\\n loaded_line = load_one_reaction_rank(li)\\n _update_from_line(id, loaded_line)\\n else:\\n with Pool(processes=num_processes) as pool:\\n results = pool.map(\\n load_one_reaction_rank,\\n lines, chunksize=len(lines) // num_processes)\\n for id in range(len(lines)):\\n _update_from_line(id, results[id])\\n\\n return all_reactant_mols, all_product_mols, all_real_bond_changes, ids_for_small_samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3dec43569176f24309c6ae7056efa70\",\n \"score\": \"0.5524501\",\n \"text\": \"def read_init_file(self, filename=None):\\n readline.read_init_file(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec0c316cc5ef6a3c120101661f9f34d0\",\n \"score\": \"0.54611844\",\n \"text\": \"def open_with_file(self, fname):\\n if not L1Ana.l1init: L1Ana.init_l1_analysis()\\n self.file_list = [fname]\\n self.check_first_file()\\n self.open_no_init()\\n self.init_branches()\\n L1Ana.log.info(\\\"Ready to analyse.\\\")\\n self.init = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bdeda53916c5b7c29788c8163db9b16\",\n \"score\": \"0.5424699\",\n \"text\": \"def load_reaction_data(self, file_path, num_processes):\\n all_mols = []\\n all_reactions = []\\n all_graph_edits = []\\n with open(file_path, 'r') as f:\\n lines = f.readlines()\\n if num_processes == 1:\\n results = []\\n for li in lines:\\n mol, reaction, graph_edits = load_one_reaction(li)\\n results.append((mol, reaction, graph_edits))\\n else:\\n with Pool(processes=num_processes) as pool:\\n results = pool.map(load_one_reaction, lines)\\n\\n for mol, reaction, graph_edits in results:\\n if mol is None:\\n continue\\n all_mols.append(mol)\\n all_reactions.append(reaction)\\n all_graph_edits.append(graph_edits)\\n\\n return all_mols, all_reactions, all_graph_edits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1755fc26d83ec3ab8f3e4c9699d0cc05\",\n \"score\": \"0.54223454\",\n \"text\": \"def _loadFile(self):\\n\\n for line in open(self.filepath):\\n # Only look at the \\\"ATOM\\\" lines in the file\\n if not re.search('^ATOM',line):\\n continue\\n\\n # The indices here are for wwPDB format, see\\n #\\n # http://www.wwpdb.org/documentation/format23/sect9.html\\n #\\n # for complete details. Here we use:\\n #\\n # 13-16 is atom name\\n # 18-20 is residue name\\n # 31-38 is x coord\\n # 39-46 is y coord\\n # 47-54 is z coord\\n\\n atom_name = line[12:16]\\n point = Point(float(line[30:38]), float(line[38:46]), float(line[46:54]))\\n\\n # for this program we only conisder the alpha carbons\\n # these are indicated with 'CA' in the PDB file. We ignore\\n # any \\\"MODEL\\\" statements and just cover everything.\\n\\n if atom_name == \\\" CA \\\":\\n\\n # If we have an alpha carbon we create a new\\n # alpha carbon object and add it to our list of alpha\\n # alpha carbons\\n\\n residue_name = line[17:20]\\n alpha_carbon = AlphaCarbon(residue_name, point)\\n self.alpha_carbons.append(alpha_carbon)\\n\\n # As we read in the file we keep track of the minimum and\\n # maximum position for each dimension.\\n #\\n # NOTE: putting these updates in this if statements means\\n # that we only consider the CA atoms for the protein (and thus\\n # grid used for part 2 (prediction). This was consistent with\\n # the examples, but not with the instructions. There was a TA\\n # recommendation to do it this way. To span the WHOLE protein\\n # we would have to put these updates before this if statement.\\n\\n self._updateMin(point)\\n self._updateMax(point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3446d94727594c0b811876bef49910\",\n \"score\": \"0.53114104\",\n \"text\": \"def init_from_file(self, filepath, effects_log):\\n # don't forget to update the module docstring with changes here\\n input_template_name = 'emission_rates_egu'\\n input_template_version = 0.3\\n input_template_columns = [\\n 'case',\\n 'rate_name',\\n 'independent_variable',\\n 'last_year',\\n 'kwh_demand_metric',\\n 'kwh_consumption_metric',\\n 'equation_rate_id',\\n 'equation_kwh_demand_metric',\\n 'equation_kwh_consumption_metric',\\n ]\\n\\n df = read_input_file(filepath, effects_log)\\n validate_template_version_info(df, input_template_name, input_template_version, effects_log)\\n\\n # read in the data portion of the input file\\n df = read_input_file(filepath, effects_log, skiprows=1)\\n validate_template_column_names(filepath, df, input_template_columns, effects_log)\\n\\n rate_keys = zip(\\n df['case'],\\n df['rate_name']\\n )\\n df.set_index(rate_keys, inplace=True)\\n\\n self._cases = df['case'].unique()\\n self.kwh_demand_metric = df['kwh_demand_metric'][0]\\n self.kwh_consumption_metric = df['kwh_consumption_metric'][0]\\n self.calendar_year_min = df['initial_year'][0]\\n self.calendar_year_max = df['last_year'][0]\\n\\n self._data = df.to_dict('index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f83651ce409d0380741cb3b0bc00f4e\",\n \"score\": \"0.5262542\",\n \"text\": \"def read_whole_file(self, fhandle):\\n\\n line = fhandle.readline().strip()\\n if line[0] != \\\">\\\":\\n raise ValueError(\\\"File is missing initial header!\\\")\\n else:\\n curr_entry = FastaEntry(header = line.rstrip().split()[0])\\n line = fhandle.readline().strip()\\n curr_seq = []\\n while line != '':\\n if line[0] == \\\">\\\":\\n curr_entry.set_seq(''.join(curr_seq))\\n self.data[curr_entry.chrm_name()] = curr_entry\\n self.names.append(curr_entry.chrm_name())\\n curr_seq = []\\n curr_entry = FastaEntry(line)\\n else:\\n curr_seq.append(line)\\n\\n line = fhandle.readline().strip()\\n\\n curr_entry.set_seq(''.join(curr_seq))\\n self.data[curr_entry.chrm_name()] = curr_entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d43e173e3c6c0449dd217bc17c7cfbe\",\n \"score\": \"0.523327\",\n \"text\": \"def read(self, filepath: str) -> None:\\n self.filepath = filepath\\n self.readFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a8408d7fd00934fadbff69ae64df70\",\n \"score\": \"0.5226424\",\n \"text\": \"def init_from_file(self, filepath, effects_log):\\n df = read_input_file(filepath, effects_log)\\n\\n input_template_name = 'context_implicit_price_deflators'\\n input_template_version = 0.22\\n input_template_columns = {\\n 'calendar_year',\\n 'price_deflator',\\n }\\n\\n validate_template_version_info(df, input_template_name, input_template_version, effects_log)\\n\\n # read in the data portion of the input file\\n df = read_input_file(filepath, effects_log, skiprows=1)\\n\\n validate_template_column_names(filepath, df, input_template_columns, effects_log)\\n\\n self._data = df.set_index('calendar_year').to_dict(orient='index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92eb684588444f12cbaa12faa8d6cc96\",\n \"score\": \"0.5223175\",\n \"text\": \"def read(self):\\n self.iteration, self.s, self.y, self.rho, \\\\\\n self.r0, self.f0, self.e0, self.task = self.load()\\n self.load_restart = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d441abb5b830f731662da093f4c4756\",\n \"score\": \"0.52148175\",\n \"text\": \"def run(self):\\n with open(self._path) as f:\\n rdef = RecordDef([{'name': 'line', 'type': 'STRING'}])\\n\\n if not self._read_existing_lines: # go to the end of file\\n f.seek(0, 2)\\n\\n while True:\\n if self._interrupted():\\n self.add(rdef, None) # producer has end data-fetching\\n return\\n\\n curpos = f.tell()\\n line = f.readline()\\n if line == '': # EOF\\n f.seek(curpos)\\n else:\\n self.add(rdef, Record(line))\\n time.sleep(self._sleep_sec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3bcb13b4cf67f23bdbad54d7ce0a6b\",\n \"score\": \"0.52064943\",\n \"text\": \"def initiate_fast_reaction(data_dir):\\n new_old_index_dict, new_ind_reaction_dict = psri.parse_reaction_and_its_index(\\n data_dir)\\n\\n rxn_pair_dict = dict()\\n\\n # un-paired species\\n unpaired = dict()\\n for _, val1 in enumerate(new_old_index_dict):\\n # print(idx, val1, new_old_index_dict[val1])\\n this_value = int(new_old_index_dict[val1])\\n neg_value = -1 * this_value\\n if (neg_value in unpaired):\\n # print(int(val1), unpaired[neg_value])\\n rxn_pair_dict.update({int(val1): unpaired[neg_value]})\\n rxn_pair_dict.update({unpaired[neg_value]: int(val1)})\\n unpaired.pop(neg_value)\\n else:\\n unpaired.update({this_value: int(val1)})\\n\\n rxn_info = {}\\n for _, val1 in enumerate(new_ind_reaction_dict):\\n entry = {\\n str(val1): {\\n \\\"formula\\\": new_ind_reaction_dict[val1],\\n # default value, 10^-100, assume the reaction is super slow\\n \\\"time_scale\\\": -100,\\n \\\"reverse_reaction\\\": \\\"None\\\"\\n }\\n }\\n if int(val1) in rxn_pair_dict:\\n entry[str(val1)][\\\"reverse_reaction\\\"] = \\\\\\n str(rxn_pair_dict[int(val1)])\\n rxn_info.update(entry)\\n\\n fn0 = os.path.join(data_dir, \\\"input\\\", \\\"reaction_info_base_backup.json\\\")\\n fn1 = os.path.join(data_dir, \\\"input\\\", \\\"reaction_info_base.json\\\")\\n\\n if os.path.isfile(fn1):\\n copy2(fn1, fn0)\\n\\n rwc.write_configuration(rxn_info, fn1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"902c940061a578a1cb0fb747120609da\",\n \"score\": \"0.5204261\",\n \"text\": \"def run_book(self):\\n \\n with open(self.stock_input_file, 'r') as handler:\\n\\n #Read from a file and update my_book:\\n for stock_input in handler:\\n self.my_book.process_all(stock_input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b020686a545cdf003f324b5cfd6bed02\",\n \"score\": \"0.51869375\",\n \"text\": \"def run(self):\\n try:\\n self.__read_file()\\n self.__process_lines()\\n except ValueError as ve:\\n print >> sys.stderr, ve\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a49d66a2f022ef668901c2ca82952451\",\n \"score\": \"0.51745516\",\n \"text\": \"def populateFromFile(self,file_handle, ignore_errors=False):\\n #read version info\\n ver_line=file_handle.readline()\\n if ver_line[:13] != '#=GENOME_DIFF':\\n print(\\\"Invalid GenomeDiff file, header missing or malformed.\\\")\\n else:\\n self._parseLine(1,ver_line) #process the ver_line to store the version info\\n for line_num,line in enumerate(file_handle.readlines()):\\n try:\\n self._parseLine(line_num,line)\\n except GDFieldError as gdfe:\\n print(\\\"Parse error in field {}:{}, could not parse {}:\\\".format(gdfe.field_num,gdfe.field_name,gdfe.field_value))\\n print(\\\"Message: {}\\\".format(gdfe.msg))\\n if gdfe.inner_exception_msg:\\n print(\\\"Exception: {}\\\".format(gdfe.inner_exception_msg))\\n if not ignore_errors:\\n raise \\n break\\n except GDParseError as gdpe:\\n print(\\\"There is an error in line {} of the GenomeDiff file.\\\".format(line_num+1))\\n print(\\\"Error returned: {}\\\".format(gdpe.msg))\\n if gdpe.inner_exception_msg:\\n print(\\\"Exception: {}\\\".format(gdpe.inner_exception_msg))\\n if not ignore_errors:\\n raise \\n break\\n except Exception as ex:\\n print(\\\"Unhandled exception on line {}:\\\".format(line_num))\\n print(ex.message)\\n raise\\n break\\n try:\\n pass\\n self._qcChecks()\\n except GDFieldError as gdfe:\\n print(\\\"QC Check failed:\\\")\\n \\n except GDParseError as gdpe:\\n print(\\\"QC Check failed:\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbdb10bd4253e18b8d533ae1d7a8f82\",\n \"score\": \"0.5172113\",\n \"text\": \"def connect(self):\\n print('Opening file %s' % (self.filename))\\n self._fp = open(self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd575d277ba69cf147a9c1a0fa1c569\",\n \"score\": \"0.5140673\",\n \"text\": \"def __init__(self, file_name):\\n self.file_name = file_name\\n self.size = None\\n self.number_of_food = None\\n self.number_of_stick = None\\n self.number_of_cbstacles = None\\n self.__read__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7e9352a4e8d2932a555cc606c34ce0\",\n \"score\": \"0.5139134\",\n \"text\": \"def __init__(self,file_handle=None, ignore_errors=False):\\n self.metadata={}\\n self.data={'mutation':{},'evidence':{},'validation':{}}\\n self.valid_types=self.mutation_types+self.evidence_types+self.validation_types\\n if file_handle is not None:\\n self.populateFromFile(file_handle, ignore_errors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc150594f58182e0e51210ea35b5a9a7\",\n \"score\": \"0.5116372\",\n \"text\": \"def read(self) -> None:\\n self.setup_reader()\\n self.read_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d5ae6ee7277b3a600f0b94f6f0b890\",\n \"score\": \"0.51154214\",\n \"text\": \"def state_load(self, filename):\\n with open(filename, \\\"rb\\\") as f:\\n with self.lock:\\n self.hc = pickle.load(f)\\n self.htc = pickle.load(f)\\n self.tc = pickle.load(f)\\n self.thc = pickle.load(f)\\n self.tweet_total = pickle.load(f)\\n self.hashtag_total = pickle.load(f)\\n print \\\"State sucessfully loaded from \\\" + filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9823038a3911b4d9fbd1622e8cb34c\",\n \"score\": \"0.5112699\",\n \"text\": \"def __readFile(self, filePath):\\n\\n # Parameters used in reading the file\\n iscomment = False\\n\\n # Read the file\\n fid = open(filePath)\\n for line in fid:\\n\\n # Strip the line\\n line = line.strip()\\n\\n # Ignore the comment part\\n if (line.startswith(\\\"###\\\")):\\n iscomment = ~iscomment\\n\\n if (not(line.startswith(\\\"#\\\")) and (not iscomment) and len(line) > 0):\\n\\n # Used for the single degree of freedom (DOF) unit testing\\n if (line.startswith(\\\"dof\\\")):\\n\\n # Get the values\\n index, newValue, unit = line.split()[1:4]\\n\\n # There is the \\\"-1\\\" here for the index. The index in python array begins from 0.\\n index -= 1\\n\\n # Assign the value\\n self.stateV[index] = newValue\\n\\n # By default, use micron and arcsec as units\\n if (unit == \\\"mm\\\"):\\n self.stateV[index] *= 1e3\\n\\n elif (unit == \\\"deg\\\"):\\n self.stateV[index] *= 3600\\n\\n # Camera mjd\\n elif (line.startswith(\\\"mjd\\\")):\\n self.time0 = Time(float(line.split()[1]), format=\\\"mjd\\\")\\n\\n # Budget of image quality\\n elif (line.startswith(\\\"iqBudget\\\")):\\n # Read in mas, convert to arcsec\\n # mas: milli-arcsec\\n self.iqBudget = np.sqrt(np.sum(np.array(line.split()[1:], dtype=float)**2))\\n\\n # Budget of ellipticity\\n elif (line.startswith(\\\"eBudget\\\")):\\n self.eBudget = float(line.split()[1])\\n\\n # Get the zenith angle or file name\\n elif (line.startswith(\\\"zenithAngle\\\")):\\n self.zAngle = line.split()[1]\\n\\n # Camera temperature in degree C\\n # Ignore this if the instrument is comcam\\n elif (line.startswith(\\\"camTB\\\") and self.inst == self.LSST):\\n self.camTB = float(line.split()[1])\\n\\n # Update the budget of image quality\\n self.iqBudget = np.sqrt(self.iqBudget**2 + float(line.split()[3])**2)\\n\\n # Camera rotation angle\\n # Ignore this if the instrument is comcam\\n elif (line.startswith(\\\"camRotation\\\") and self.inst == self.LSST):\\n self.camRot = float(line.split()[1])\\n\\n self.iqBudget = np.sqrt(self.iqBudget**2 + float(line.split()[3])**2)\\n\\n elif (line.startswith(\\\"M1M3ForceError\\\")):\\n self.M1M3ForceError = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M1M3TxGrad\\\")):\\n self.M1M3TxGrad = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M1M3TyGrad\\\")):\\n self.M1M3TyGrad = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M1M3TzGrad\\\")):\\n self.M1M3TzGrad = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M1M3TrGrad\\\")):\\n self.M1M3TrGrad = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M1M3TBulk\\\")):\\n self.M1M3TBulk = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M2TzGrad\\\")):\\n self.M2TzGrad = float(line.split()[1])\\n\\n elif (line.startswith(\\\"M2TrGrad\\\")):\\n self.M2TrGrad = float(line.split()[1])\\n\\n # Maximum number of Zn to define the surface in perturbation file\\n # Check I can use \\\"znPert = 20\\\" or not. This is for the annular Zk in my WEP code.\\n # Check the reason to use the spherical Zk. If I use annular Zk, what will happen?\\n # From the phosim command, does it mean only spherical Zk is allowed?\\n # https://bitbucket.org/phosim/phosim_release/wiki/Physics%20Commands\\n # But the dof and uk is in the basis of annular Zk, which is different from PhoSim.\\n # Check with Bo for this\\n elif (line.startswith(\\\"znPert\\\")):\\n self.znPert = int(line.split()[1])\\n\\n elif (line.startswith(\\\"surfaceGridN\\\")):\\n self.surfaceGridN = int(line.split()[1])\\n\\n # Size of OPD image (n x n matrix)\\n elif (line.startswith(\\\"opd_size\\\")):\\n self.opdSize = int(line.split()[1])\\n\\n # Make sure the value is odd\\n if (self.opdSize % 2 == 0):\\n self.opdSize -= 1\\n\\n # Use 0 for eimage only, without backgrounds\\n elif (line.startswith(\\\"eimage\\\")):\\n self.eimage = bool(int(line.split()[1]))\\n\\n # Magnitude of point spread function (PSF) star\\n elif (line.startswith(\\\"psf_mag\\\")):\\n self.psfMag = int(line.split()[1])\\n\\n # Magnitude of wavefront sensing star\\n elif (line.startswith(\\\"cwfs_mag\\\")):\\n self.cwfsMag = int(line.split()[1])\\n\\n # Wavefront sensing image stamp size\\n elif (line.startswith(\\\"cwfs_stamp_size\\\")):\\n self.cwfsStampSize = int(line.split()[1])\\n\\n fid.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d9c0f83720a3218b6e126d03961067d\",\n \"score\": \"0.51083213\",\n \"text\": \"def __init__(self, file_path):\\n\\n with open(file_path, 'r') as f:\\n lines = f.readlines()\\n\\n self.sequences = []\\n\\n for i in range(0, len(lines), 4):\\n self.sequences.append(Read(sq=lines[i + 1].rstrip('\\\\n'),\\n quality=lines[i + 3].rstrip('\\\\n'),\\n name=lines[i].rstrip('\\\\n')))\\n\\n print('This library size is %d consensus reads' % len(self.sequences))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea82b565aa2c83882a451ad3ae735a99\",\n \"score\": \"0.50958514\",\n \"text\": \"def operate_on_file(self, file_name, handle):\\n file_ = open(file_name, 'r')\\n for line in file_:\\n handle(line)\\n self.previous_line = line\\n file_.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc39a278c77b92927afdf2bb8d8dc5a9\",\n \"score\": \"0.50855786\",\n \"text\": \"def _read(self):\\n with open(self.filename, 'r') as f:\\n self.data = f.read()\\n # HERE DO SOMETHING TO PREPARE THE DATA TO BE READ ############\",\n \"title\": \"\"\n },\n {\n \"docid\": \"720d3defa0c39f3d0a22fdca8b496bfc\",\n \"score\": \"0.5071081\",\n \"text\": \"def _read_file(self):\\n\\t\\twith open(self._path, \\\"r\\\") as file:\\n\\t\\t\\tfor line in file:\\n\\t\\t\\t\\tself.__oneLiner += line\\n\\t\\tself._remove_documentation()\\n\\t\\tself._clean_spaces()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb7bb00a758e866d4e96da69d517ecd\",\n \"score\": \"0.5062394\",\n \"text\": \"def __init__(self, filename):\\n self.file = open(filename)\\n self.queue = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b17446374e19e4b69a67d9050df52f\",\n \"score\": \"0.503247\",\n \"text\": \"def open(self):\\n with _iotools.open_file(self.filepath, \\\"r\\\") as f:\\n self.content = f.readlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5abb085160ec6ba2d6a113ce58dc0c56\",\n \"score\": \"0.5028873\",\n \"text\": \"def open_with_file_list(self, fname_list):\\n if not L1Ana.l1init: L1Ana.init_l1_analysis()\\n self.open_file_list(fname_list)\\n self.check_first_file()\\n self.open_no_init()\\n self.init_branches()\\n\\n L1Ana.log.info(\\\"Ready to analyse.\\\")\\n self.init = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199bd1df566cf7a971f755929cad1dd3\",\n \"score\": \"0.5027212\",\n \"text\": \"def init_from_file(self, fname):\\n\\n self.status = 1 # init to failure\\n\\n # the looks_like options imply AfniData, else use Afni1D\\n if self.looks_like:\\n adata = LAD.AfniData(fname, verb=self.verb)\\n self.dtype = 2\\n else:\\n adata = LAD.Afni1D(fname, verb=self.verb)\\n self.dtype = 1\\n\\n if not adata.ready: return 1\\n\\n if self.verb > 2: print(\\\"++ read 1D data from file '%s'\\\" % fname)\\n\\n self.fname = fname\\n self.adata = adata\\n self.status = 0\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3789325b38ac37811e46c1c7d521c8ac\",\n \"score\": \"0.50238985\",\n \"text\": \"def initiate_from_file(self, fname, scoring_function, Prior):\\n with open(fname, 'r') as f:\\n smiles = []\\n for line in f:\\n smile = line.split()[0]\\n if Chem.MolFromSmiles(smile):\\n smiles.append(smile)\\n scores = scoring_function(smiles)\\n tokenized = [self.voc.tokenize(smile) for smile in smiles]\\n encoded = [Variable(self.voc.encode(tokenized_i)) for tokenized_i in tokenized]\\n encoded = MolData.collate_fn(encoded)\\n prior_likelihood, _ = Prior.likelihood(encoded.long())\\n prior_likelihood = prior_likelihood.data.cpu().numpy()\\n new_experience = zip(smiles, scores, prior_likelihood)\\n self.add_experience(new_experience)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"578db05bdca74607a6394901300acfbd\",\n \"score\": \"0.5019072\",\n \"text\": \"def read_input_file(path):\\n global observables, setups, initialSpecies, casetitle, tol\\n\\n full_path = os.path.abspath(os.path.expandvars(path))\\n try:\\n f = open(full_path)\\n except IOError:\\n logging.error('The input file \\\"{0}\\\" could not be opened.'.format(full_path))\\n logging.info('Check that the file exists and that you have read access.')\\n raise\\n\\n logging.info('Reading input file \\\"{0}\\\"...'.format(full_path))\\n logging.info(f.read())\\n f.seek(0) # return to beginning of file\\n\\n global_context = {'__builtins__': None}\\n local_context = {\\n '__builtins__': None,\\n 'True': True,\\n 'False': False,\\n 'observable': observable,\\n 'SMILES': SMILES,\\n 'species': species,\\n 'adjacencyList': adjacencyList,\\n 'reactorSetups': reactorSetups,\\n 'options': options,\\n }\\n\\n try:\\n exec(f.read(), global_context, local_context)\\n except (NameError, TypeError, SyntaxError) as e:\\n logging.error('The input file \\\"{0}\\\" was invalid:'.format(full_path))\\n logging.exception(e)\\n raise\\n finally:\\n f.close()\\n\\n # convert keys of the initial mole fraction dictionaries from labels into Species objects.\\n imfs = setups[3]\\n new_imfs = []\\n for imf in imfs:\\n new_dict = convert(imf, initialSpecies)\\n new_imfs.append(new_dict)\\n setups[3] = new_imfs\\n\\n return casetitle, observables, setups, tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e849f4d8787b0b500be602e8740bc12b\",\n \"score\": \"0.50122976\",\n \"text\": \"def __read_file(self,input_file):\\n \\n logging.debug('Parsing model input file: %s' % input_file)\\n\\n assert type(input_file) is StringType, 'Invalid input file path \\\"%s\\\": inpu file path must be a string' % input_file\\n\\n #Check that the model file exists\\n if not os.path.exists(input_file):\\n raise BeakerException('Model file \\\"%s\\\" does not exist' % input_file)\\n \\n f = open(input_file,'r')\\n self.definition = []\\n for line in f:\\n if not line.rstrip('\\\\n') == '':\\n self.definition.append(line.rstrip('\\\\n'))\\n f.close()\\n \\n logging.debug('Finished parsing model input file: %s' % input_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0066e11f0cc4ea4b2eaaba69904621ef\",\n \"score\": \"0.5001973\",\n \"text\": \"def read_one(self):\\n self._clear()\\n readline = self._readline\\n endOfMol = self._endOfMol\\n try:\\n name = readline().strip()\\n userLine = readline()\\n comment = readline()\\n line = readline()\\n except MolReaderError, msg:\\n if str(msg) == \\\"Unexpected end of file\\\":\\n return None \\n raise\\n\\n try:\\n numAtoms, numBonds = map(int, (line[0:3], line[3:6]))\\n except ValueError: # XXX FIX ME - trap exceptions and stuff\\n print \\\"cannot parse atom, bond line\\\"\\n self._read_to_next()\\n return None\\n atoms = self.atoms = []\\n\\n for index in range(numAtoms):\\n line = readline()\\n try:\\n x,y,z,symbol,mass,charge,stereo,hcount,hcount_fixed = parse_atom(line)\\n except: # XXX FIX ME - trap exceptions and stuff\\n self._read_to_next()\\n return None\\n \\n atom = Atom()\\n atom._line = line\\n atom.symbol = symbol\\n atom.explicit_hcount = hcount\\n atom.charge = charge\\n\\n #if hcount_fixed: \\n #symbol, hcount, charge, weight=0, aromatic=0)\\n \\n # XXX FIX ME\\n # a really bad hack here.\\n # ignore please!\\n atom._line = line\\n atom.x = x\\n atom.y = y\\n atom.z = z\\n if hcount_fixed: atom.fixed_hcount = 1\\n if mass:\\n atom.weight = atom.mass + mass\\n atom.index = len(atoms)\\n atoms.append(atom)\\n if vfgraph:\\n insert_node(atom)\\n\\n bonds = []\\n mappings = []\\n bondCount = [0] * len(atoms)\\n closures = {}\\n for index in range(numBonds):\\n line = readline()\\n try:\\n a1, a2, bondtype, remainder = parse_bond(line)\\n except:\\n self._read_to_next()\\n return None\\n a1 -= 1\\n a2 -= 1\\n\\n symbol, bondorder, bondtype, fixed = BOND_SYMBOL[bondtype]\\n atom1 = atoms[a1]\\n atom2 = atoms[a2]\\n if stripHydrogen:\\n if atom1.symbol == \\\"H\\\":\\n atom2.hcount += 1\\n atom2.hcount_fixed = 1\\n if atom2.symbol == \\\"H\\\":\\n atom1.hcount += 1\\n atom1.hcount_fixed = 1\\n else:\\n bond = Bond(symbol, bondorder, bondtype, fixed)\\n bond._line = line\\n # XXX FIX ME\\n # a really bad hack here\\n # ignore please!\\n bond._line = remainder\\n bond.atoms = [a1, a2]\\n a1.bonds.append(bond)\\n a2.bonds.append(bond)\\n a1.oatoms.append(a1)\\n a2.oatoms.append(a2)\\n bonds.append(bond)\\n## mappings.append((bond, a1, a2))\\n## bondCount[a1] += 1\\n## bondCount[a2] += 1\\n\\n self.readMProps()\\n \\n fields, potentialName = self._readFields()\\n\\n if not name:\\n name = potentialName\\n elif name != potentialName:\\n # XXX FIX ME, what do I do here?\\n pass\\n \\n # we've tokenized the molecule, now we need to build one\\n # XXX FIX ME - Split this up into a builder and a tokenizer ?\\n mol = Molecule()\\n mol.name = name\\n mol.fields = fields\\n# for atom in atoms:\\n# mol.add_atom(atom)\\n \\n# for bond, a1, a2 in mappings:\\n# atom1, atom2 = atoms[a1], atoms[a2]\\n\\n # XXX FIX ME\\n # does this format mean the atom's hcount can't\\n # change?\\n# stripHydrogens = self.stripHydrogens\\n# if not hasattr(atom1, \\\"number\\\"):\\n# print atom\\n# print atom.__dict__\\n## if stripHydrogens and atom1.symbol == \\\"H\\\" and bondCount[a1] == 1:\\n## atom2.hcount += 1\\n## atom2.hcount_fixed = 1\\n## bondCount[a2] -=1\\n## mol.remove_atom(atom1)\\n## elif stripHydrogens and atom2.symbol == \\\"H\\\" and bondCount[a2] == 1:\\n## atom1.hcount += 1\\n## atom1.hcount_fixed = 1\\n## bondCount[a1] -= 1\\n## mol.remove_atom(atom2)\\n## else:\\n## mol.add_bond(bond, atom1, atom2)\\n## if bond.bondtype == 4:\\n## atom1.aromatic = 1\\n## atom2.aromatic = 1\\n\\n## # get rid of any non-bonded hydrogens\\n## atomsToDelete = []\\n## for atom in mol.atoms:\\n## if atom.symbol == \\\"H\\\":\\n## assert len(atom.bonds) == 0\\n## atomsToDelete.append(atom)\\n## for atom in atomsToDelete:\\n## mol.remove_atom(atom)\\n\\n index = 0\\n for atom in mol.atoms:\\n assert atom.symbol != \\\"H\\\"\\n if len(atom.bonds) > 1:\\n atom._closure = 1\\n atom.index = index\\n index += 1\\n\\n return mol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e1121e8749d641c9f994e5d431ccbc8\",\n \"score\": \"0.4988269\",\n \"text\": \"def loadInput(self, path=None):\\n from input import readInputFile\\n if path is None: path = self.inputFile\\n readInputFile(path, self)\\n self.reactionModel.kineticsEstimator = self.kineticsEstimator\\n # If the output directory is not yet set, then set it to the same\\n # directory as the input file by default\\n if not self.outputDirectory:\\n self.outputDirectory = os.path.dirname(path)\\n if self.pressureDependence:\\n self.pressureDependence.outputFile = self.outputDirectory\\n self.reactionModel.pressureDependence = self.pressureDependence\\n self.reactionModel.reactionGenerationOptions = self.reactionGenerationOptions\\n self.reactionModel.verboseComments = self.verboseComments\\n \\n if self.quantumMechanics:\\n self.quantumMechanics.setDefaultOutputDirectory(self.outputDirectory)\\n self.reactionModel.quantumMechanics = self.quantumMechanics\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8194df22570f99f001f094910f829af1\",\n \"score\": \"0.49783206\",\n \"text\": \"def readFile(self):\\n states = [] #to be appended by self.states\\n obs = []\\n\\n with open(self.filename, 'r') as f:\\n for line in f:\\n if line[0] == '#': #skip first line\\n continue\\n elif line[0] == '.': #separate sequences \\n self.states.append(states) #append sequence\\n self.observations.append(obs) #append sequence of obs\\n states = [] #reset\\n obs = [] #reset \\n else:\\n x, y, c = line.strip().split(',')\\n states.append((int(x), int(y)))\\n obs.append(c)\\n if states != []:\\n self.states.append(states)\\n self.observations.append(obs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48cd3cbad27f89d3c861b95661b1bd62\",\n \"score\": \"0.49698448\",\n \"text\": \"def load_from_file(self, filename):\\n self.reset()\\n transition_read_status = IOState.Ready\\n current_transition_string = u\\\"\\\"\\n transition_counter = 0\\n with open(filename, 'r') as f:\\n reading_transition_matrix = 0\\n for linenum, line in enumerate(f):\\n if linenum == 0:\\n # read in n, m\\n n, m = line.decode(\\\"utf-8-sig\\\").encode(\\\"utf-8\\\").split(' ')\\n self.n = int(n)\\n self.m = int(m)\\n last_line_num = 2+self.m*(self.n+1)\\n elif linenum > 1 and linenum < last_line_num:\\n if transition_read_status == IOState.Ready:\\n # read in first line of matrix\\n current_transition_string += line\\n transition_counter += 1\\n transition_read_status = IOState.Reading\\n elif transition_read_status == IOState.Reading and transition_counter < self.n:\\n # keep constructing matrix string\\n current_transition_string += line\\n transition_counter += 1\\n elif transition_read_status == IOState.Reading and transition_counter == self.n:\\n # convert finished string into numpy array\\n c = StringIO(current_transition_string)\\n T = np.loadtxt(c)\\n self.T.append(T)\\n\\n # reset state/counters\\n transition_read_status = IOState.Ready\\n current_transition_string = u\\\"\\\"\\n transition_counter = 0\\n elif linenum == last_line_num:\\n # read in the rewards\\n c = StringIO(u\\\"\\\"+line)\\n self.R = np.loadtxt(c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a88ec2c4d5c0730afd9432479c76ba\",\n \"score\": \"0.49671054\",\n \"text\": \"def __init__(self, fname):\\n self.read_file(fname)\\n self.fname = fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0991c3c9bbfe4fde9369ea3fd71cd0a\",\n \"score\": \"0.49552247\",\n \"text\": \"def _open_file(self) -> None:\\n if not self._entered:\\n warnings.warn(\\n \\\"StataReader is being used without using a context manager. \\\"\\n \\\"Using StataReader as a context manager is the only supported method.\\\",\\n ResourceWarning,\\n stacklevel=find_stack_level(),\\n )\\n handles = get_handle(\\n self._original_path_or_buf,\\n \\\"rb\\\",\\n storage_options=self._storage_options,\\n is_text=False,\\n compression=self._compression,\\n )\\n if hasattr(handles.handle, \\\"seekable\\\") and handles.handle.seekable():\\n # If the handle is directly seekable, use it without an extra copy.\\n self._path_or_buf = handles.handle\\n self._close_file = handles.close\\n else:\\n # Copy to memory, and ensure no encoding.\\n with handles:\\n self._path_or_buf = BytesIO(handles.handle.read())\\n self._close_file = self._path_or_buf.close\\n\\n self._read_header()\\n self._setup_dtype()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d295569a2328f7e3964936be9857da2f\",\n \"score\": \"0.49488863\",\n \"text\": \"def init(args):\\n file_handler = FileHandler(args.input, args.output, args.file_type)\\n file_handler.readDataFromFile()\\n\\n file_handler.data = RankCalculator.calculate(\\n file_handler.data, args.data_index, args.rank_index)\\n\\n file_handler.writeDatatoFile(args.skip_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05220fee7eb2757a4542fe067363e838\",\n \"score\": \"0.49375388\",\n \"text\": \"def _read_file(self):\\n\\n # Initialize readers of the different McStas instrument sections\\n args = [self.Instr, self.write_file, self.product_filename,\\n self._get_next_line, self._return_line]\\n self.Definition_reader = DefinitionReader(*args)\\n self.Declare_reader = DeclareReader(*args)\\n self.Uservars_reader = UservarsReader(*args)\\n self.Initialize_reader = InitializeReader(*args)\\n self.Trace_reader = TraceReader(*args)\\n self.Finally_reader = FinallyReader(*args)\\n\\n # A mode for each type that activates the correct reader function\\n definition_mode = False\\n declare_mode = False\\n uservars_mode = False\\n initialize_mode = False\\n trace_mode = False\\n finally_mode = False\\n comment_mode = False\\n any_mode = False\\n\\n # check if instrument name has be read from file yet\\n instr_name_read = False\\n\\n self._open_file()\\n\\n # for line in self.file_data:\\n while self.line_index < self.file_length:\\n\\n line = self._get_next_line()\\n\\n # Find appropriate mode\\n if line.strip().startswith(\\\"DEFINE INSTRUMENT\\\") and not any_mode:\\n definition_mode = True\\n any_mode = True\\n\\n if line.strip().startswith(\\\"DECLARE\\\") and not any_mode:\\n declare_mode = True\\n any_mode = True\\n\\n if line.strip().startswith(\\\"USERVARS\\\") and not any_mode:\\n uservars_mode = True\\n any_mode = True\\n\\n if (line.strip().startswith(\\\"INITIALIZE\\\") or\\n line.strip().startswith(\\\"INITIALISE\\\")) and not any_mode:\\n initialize_mode = True\\n any_mode = True\\n\\n if line.strip().startswith(\\\"TRACE\\\") and not any_mode:\\n trace_mode = True\\n any_mode = True\\n\\n if line.strip().startswith(\\\"FINALLY\\\") and not any_mode:\\n finally_mode = True\\n any_mode = True\\n\\n if line.strip().startswith(\\\"/*\\\"):\\n comment_mode = True\\n\\n # Read with appropriate reader\\n if definition_mode and not comment_mode:\\n # Get instrument name\\n if not instr_name_read:\\n self.instr_name = line.split(\\\"(\\\")[0].strip().split(\\\" \\\")[-1]\\n instr_name_read = True\\n self.update_file_name()\\n\\n # Read line from definition\\n definition_mode = self.Definition_reader.read_definition_line(line)\\n # When read_definition finds the end, it will return False\\n any_mode = definition_mode\\n\\n if declare_mode and not comment_mode:\\n # Read line from declare\\n declare_mode = self.Declare_reader.read_declare_line(line)\\n # When read_declare finds the end, it will return False\\n any_mode = declare_mode\\n\\n if uservars_mode and not comment_mode:\\n # Read line from uservars\\n uservars_mode = self.Uservars_reader.read_uservars_line(line)\\n # When read_uservars finds the end, it will return False\\n any_mode = uservars_mode\\n\\n if initialize_mode and not comment_mode:\\n # Read line from initialize\\n initialize_mode = self.Initialize_reader.read_initialize_line(line)\\n # When read_initialize finds the end, it will return False\\n any_mode = initialize_mode\\n\\n if trace_mode and not comment_mode:\\n # Read line from initialize\\n trace_mode = self.Trace_reader.read_trace_line(line)\\n # When read_initialize finds the end, it will return False\\n any_mode = trace_mode\\n\\n if finally_mode and not comment_mode:\\n # Read line from finally\\n finally_mode = self.Finally_reader.read_finally_line(line)\\n # When read_finallyfinds the end, it will return False\\n any_mode = finally_mode\\n\\n # Stop comment mode when end of comment block reached\\n if \\\"*/\\\" in line.strip():\\n comment_mode = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b56d44e11ea3923aa52e44e8e18dbbf\",\n \"score\": \"0.49295652\",\n \"text\": \"def main(compound_fname, reaction_fname):\\n compound_data = read_compounds_file(compound_fname)\\n reaction_data = read_reactions_file(reaction_fname)\\n\\n # only keep masses which have associated intensities\\n intensities_level0 = match_masses(compound_data)\\n compounds_level0 = {k: compound_data[k] for k in intensities_level0.keys()}\\n\\n for k in compounds_level0:\\n compounds_level0[k]['intensities'] = intensities_level0[k]\\n\\n # find new compounds\\n print('Starting with {} compounds'.format(len(compounds_level0)))\\n\\n # investigate results\\n #list_structure_formulas(\\n # compounds_level0, intensities_level0,\\n # reaction_data)\\n\\n #investigate_reactions(\\n # compounds_level0, intensities_level0,\\n # reaction_data)\\n\\n find_small_motifs(compounds_level0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd787e6329d20bd2da5502888bd5e237\",\n \"score\": \"0.49189228\",\n \"text\": \"def __initialise_load_state(self, a_file_content):\\n\\n try:\\n l_lines = a_file_content.splitlines()\\n i = 0\\n\\n # Verify this is a formatted LegalDOc\\n if l_lines[0] == \\\"FIELD DATA:\\\":\\n i += 1\\n\\n # Read in field data\\n while l_lines[i] != \\\"SECTIONS:\\\":\\n l_line = l_lines[i].strip()\\n\\n # File name\\n if l_line == \\\"FILE NAME:\\\":\\n self.__f_file_name = l_lines[i+1].strip()\\n i += 2\\n continue\\n\\n # Case number\\n if l_line == \\\"CASE NUMBER:\\\":\\n self.__f_case_number = l_lines[i + 1].strip()\\n i += 2\\n continue\\n\\n # Judge name\\n if l_line == \\\"JUDGE NAME:\\\":\\n self.__f_judge_name = l_lines[i + 1].strip()\\n i += 2\\n continue\\n\\n # Defendant name\\n if l_line == \\\"DEFENDANT NAME:\\\":\\n self.__f_defendant_name = l_lines[i + 1].strip()\\n i += 2\\n continue\\n\\n # Sentencing document\\n if l_line == \\\"PRISON DOCUMENT:\\\":\\n self.__f_sentencing_document = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Punctuation removed\\n if l_line == \\\"PUNCTUATION REMOVED:\\\":\\n self.__f_punctuation_removed = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Stop words removed\\n if l_line == \\\"STOP WORDS REMOVED:\\\":\\n self.__f_stop_words_removed = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Lower case\\n if l_line == \\\"LOWER CASE:\\\":\\n self.__f_lower_case = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Stemmed\\n if l_line == \\\"STEMMED:\\\":\\n self.__f_stemmed = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Lemmatized\\n if l_line == \\\"LEMMATIZED:\\\":\\n self.__f_lemmatized = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Contains errors\\n if l_line == \\\"CONTAINS ERRORS:\\\":\\n self.__f_contains_errors = ast.literal_eval(l_lines[i + 1].strip())\\n i += 2\\n continue\\n\\n # Skip line\\n i += 1\\n\\n # Read in section data\\n l_section_index = -1\\n i += 1\\n while i < len(l_lines):\\n l_line = l_lines[i].strip()\\n\\n # Reading a section heading\\n if LegalDoc.__FILE_SECTION_PATTERN.match(l_line):\\n l_section_index += 1\\n self.__f_body.append([])\\n\\n # Reading a section's contents\\n else:\\n self.__f_body[l_section_index].append(l_line)\\n i += 1\\n\\n # Create corpora\\n for l_section in self.body:\\n for l_sentence in l_section:\\n self.__f_corpus += word_tokenize(l_sentence)\\n\\n return True\\n except IndexError:\\n LegalDoc.__note_exception(self.path,\\n \\\"MAJOR ERROR: Failed to import formatted file, index out of bounds\\\", True)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a79b8fccf8371453e7ed0d95a1f626c7\",\n \"score\": \"0.49075282\",\n \"text\": \"def read(self):\\n self._reader = OBJReader(self.filepath)\\n self._parser = OBJParser(self._reader, precision=self.precision)\\n self._reader.open()\\n self._reader.pre()\\n self._reader.read()\\n self._reader.post()\\n self._parser.parse()\\n self._is_parsed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97cb40b829fdd03a72bf8318cc2305f9\",\n \"score\": \"0.4907238\",\n \"text\": \"def __init__(\\n self,\\n filename=\\\"\\\",\\n atoms=None,\\n chg=[],\\n chgdif=None,\\n aug=None,\\n augdiff=None,\\n nsets=1,\\n ):\\n self.filename = filename\\n self.atoms = atoms\\n self.chg = chg\\n self.chgdif = chgdif\\n self.aug = aug\\n self.augdiff = augdiff\\n self.nsets = nsets\\n if self.atoms is None:\\n self.read_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13e63f401c42c0acad9ba48040574de9\",\n \"score\": \"0.48952138\",\n \"text\": \"def read_file(file_name):\\n with open(file_name) as myfile:\\n lines = myfile.read()\\n\\n name = read_nome(lines)\\n estados_nomes = read_estados(lines)\\n alfabeto = read_alfabeto(lines)\\n initial = read_initial(lines)\\n finals = read_final(lines)\\n transicoes = read_trans(lines.split()[2:])\\n\\n estados = []\\n for estado in estados_nomes:\\n estados.append(embed_transitions(estado, transicoes))\\n\\n mark_ini(estados, initial)\\n mark_fin(estados, finals)\\n\\n myafn = Af(name, alfabeto, estados, initial)\\n\\n\\n return myafn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b1da59cd84d270c893ff53c5484636\",\n \"score\": \"0.4879051\",\n \"text\": \"def process_file(fname, options):\\n\\n f = open(fname, 'rb+', 0)\\n\\n jobs = read_jobs(f)\\n for job in jobs:\\n # keep going until we find a pristine job\\n if not ((job.state == '.') or (options.retry and (job.state == '!' or job.state == 'e'))):\\n continue\\n # try to claim the job\\n if not set_job_state(f, job, '?'):\\n continue\\n # from here on out, the job is ours\\n try:\\n assert(set_job_state(f, job, 'r'))\\n if run_job(job, options) == 0:\\n assert(set_job_state(f, job, 'd'))\\n if options.one_only:\\n break\\n else:\\n assert(set_job_state(f, job, '!'))\\n except:\\n #print \\\"Error while executing:\\\", sys.exc_info()[0]\\n assert(set_job_state(f, job, 'e'))\\n raise\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"872450d7e4808b2597457b8d9bc03e5a\",\n \"score\": \"0.4876162\",\n \"text\": \"def from_file(self,datafile):\\n data=pickle.load(open(datafile,\\\"rb\\\"))\\n self.refant=copy.copy(data.refant)\\n self.n_phase_iter=copy.copy(data.n_phase_iter)\\n self.min_bl_per_ant=copy.copy(data.min_bl_per_ant)\\n self.eps=copy.copy(data.eps)\\n self.min_ant_times=copy.copy(data.min_ant_times)\\n #self.trim_neff=copy.copy(data.trim_neff)\\n self.id=copy.copy(data.id)\\n self.flag_weights_file=copy.copy(data.flag_weights_file)\\n self.model_file=copy.copy(data.model_file)\\n self.data_file=copy.copy(data.data_file)\\n self.model_file=copy.copy(data.model_file)\\n self.spw=copy.copy(data.spw)\\n self.t_avg=copy.copy(data.t_avg)\\n self.n_cycles=copy.copy(data.n_cycles)\\n self.Niterations=copy.copy(data.Niterations)\\n self.Nfreqs=copy.copy(data.Nfreqs)\\n self.Ntimes=copy.copy(data.Ntimes)\\n self.Nfreqs=copy.copy(data.Nfreqs)\\n self.Nants_data=copy.copy(data.Nants_data)\\n self.Nants_telescope=copy.copy(data.Nants_telescope)\\n self.chi_square_per_ant=copy.copy(data.chi_square_per_ant)\\n self.dof_per_ant=copy.copy(data.dof_per_ant)\\n self.noise_tblavg=copy.copy(data.noise_tblavg)\\n self.stefcal_version_str=copy.copy(data.stefcal_version_str)\\n self.Ntime_steps=copy.copy(data.Ntime_steps)\\n self.chi_square=copy.copy(data.chi_square)\\n self.dof=copy.copy(data.dof)\\n del(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"588a535d74c7be541c16b6d2ace63910\",\n \"score\": \"0.48756564\",\n \"text\": \"def open(self, path):\\n self.path = path\\n self.f = open(self.path, \\\"rb\\\")\\n self.f.seek(0, 0)\\n self.line_header = self.f.read(3200)\\n b_h = self.f.read(400)\\n self.order = self.__check_order(b_h)\\n self.bin_head = BinHead(b_h, self.order)\\n size_file = os.path.getsize(self.path)\\n coef = self.__check_coef()\\n self.count_trace = (size_file - 3600) / (self.bin_head.Samples * coef + 240)\\n self.count_trace = int(self.count_trace)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46759425a6e692ca9b7e88e7e7529a52\",\n \"score\": \"0.487373\",\n \"text\": \"def read(self, filename):\\n raise NotImplementedError()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5b21cc74fdf3d355133568c601c005\",\n \"score\": \"0.48732078\",\n \"text\": \"def __init__(self, filename=None):\\n\\n self.header = biceps_restraint_line_pf_header()\\n self.comments = []\\n self.lines = []\\n\\n if filename != None:\\n self.read(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2af9cbda5f01ff274bb6685d03fae0f6\",\n \"score\": \"0.4872704\",\n \"text\": \"def importf(self, filepath):\\n with open(filepath, 'rb') as f:\\n self.version, self.token, self.totalrent, self.balance, self.rent, self.venmo, self.members, self.transactions, self.operatorrent = pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a158b58b9a96f3aa84fb7c87ac1143\",\n \"score\": \"0.48671028\",\n \"text\": \"def process_file(self):\\n\\n # Skip Header\\n for i in range(0,self.header):\\n self.handler.readline()\\n\\n for line in self.handler:\\n self.process_line(line.strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11208ccdbb5567c8ea5bc451027b6510\",\n \"score\": \"0.48659375\",\n \"text\": \"def open(self):\\n self.file = open(self.filename, \\\"rb\\\", buffering=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2be18285af8037ea5b8002256dba2b1\",\n \"score\": \"0.48652273\",\n \"text\": \"def read(self, filepath, lazy=False, assign_to=None):\\n data = self._read_file(filepath)\\n file_contains_dataset = True\\n if assign_to is not None:\\n data = {assign_to: data}\\n file_contains_dataset = False\\n if lazy:\\n for attr in data:\\n setattr(self, f\\\"{attr}\\\", None)\\n self._attr_filemap[attr] = (filepath, file_contains_dataset)\\n return\\n for attr, value in data.items(): # pylint:disable=no-member\\n if attr in condensed_hamiltonians:\\n value = self.__dict_to_hamiltonian(value[\\\"terms\\\"], value[\\\"wire_map\\\"])\\n setattr(self, f\\\"{attr}\\\", value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97f794b304a186f207431f8e1b5d90f9\",\n \"score\": \"0.48621124\",\n \"text\": \"def read(self, file):\\n\\n self.wavelength = np.array([])\\n self.transmission = np.array([])\\n\\n with open(file, 'r') as file:\\n for line in file.readlines():\\n if line[0] != '#':\\n entries = line.split()\\n self.wavelength = np.append(self.wavelength, float(entries[0]))\\n self.transmission = np.append(self.transmission, float(entries[1]))\\n\\n # Update wavelength with the relevant factor to get the units into ang\\n self.wavelength = self.wavelength * self.factor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d30eab57ad5a013a4ce89d19e214189\",\n \"score\": \"0.4860297\",\n \"text\": \"def init_from_file(self, f):\\n self.min_time = int(next(f).strip())\\n self.buses = [\\n None if bus == \\\"x\\\" else int(bus)\\n for bus in next(f).strip().split(\\\",\\\")\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460a6afaa06d91b3e22114785024bd0e\",\n \"score\": \"0.48548278\",\n \"text\": \"def __init__(self, file):\\n self._priority_map = dict()\\n self._updated = False\\n self._file = file\\n\\n count = 0\\n if os.path.exists(file):\\n with open(file, \\\"r\\\") as f:\\n while True:\\n line = f.readline()\\n if len(line) == 0:\\n break\\n setup_key = line.strip()\\n if len(setup_key) > 0:\\n self._priority_map[setup_key] = count\\n count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ac6df0ee1d6edcbe373a8b2afbb923\",\n \"score\": \"0.4854026\",\n \"text\": \"def load_metabolic_model_reactions(filename, format = \\\"text\\\", output = \\\"normal\\\"):\\n #\\n # Distionary for reaction data\\n #\\n reactions = {}\\n #\\n # Conter to keep reaction order\\n #\\n counter = 0\\n #\\n # Ititialize mode\\n #\\n mode = \\\"start\\\"\\n\\n with open(filename, 'r') as f:\\n if format == \\\"text\\\":\\n reader = csv.reader(f, delimiter='\\\\t')\\n elif format == \\\"csv\\\":\\n reader = csv.reader(f, dialect='excel')\\n else:\\n print(\\\"Unknown format!\\\")\\n f.close()\\n return False\\n\\n for i, row in enumerate(reader):\\n if len(row) == 0:\\n continue\\n if \\\"#\\\" == row[0][0]:\\n continue\\n row = [item for item in row if \\\"#\\\" not in item]\\n\\n if len(row) < 1:\\n continue\\n\\n if \\\"//\\\" in row[0]:\\n rid = row[0].replace(\\\" \\\", \\\"\\\")\\n if rid == \\\"//Reactions\\\":\\n mode = \\\"Reactions\\\"\\n continue\\n if rid == \\\"//End\\\":\\n break\\n mode = \\\"other\\\"\\n # Remove '' from row\\n row = list(filter(lambda s:s != '', row))\\n\\n\\n if mode == \\\"Reactions\\\":\\n if output == \\\"debug\\\":\\n print(row)\\n if len(row) < 6:\\n continue\\n rid = row[0].replace(\\\" \\\", \\\"\\\")\\n stoichiometry = row[1].replace(\\\" \\\", \\\"\\\")\\n reaction = row[2].replace(\\\" \\\", \\\"\\\")\\n atommap = row[3].replace(\\\" \\\", \\\"\\\")\\n exid = row[4]\\n if rid == \\\"\\\":\\n continue\\n\\n\\n reactions[rid] = {\\n 'stoichiometry': stoichiometry,\\n 'reaction':reaction,\\n 'atommap':atommap,\\n 'externalids' :exid,\\n 'order':int(counter)\\n }\\n if len(row) >= 6:\\n reactions[rid]['lb'] = float(row[5].replace(\\\" \\\", \\\"\\\"))\\n reactions[rid]['ub'] = float(row[6].replace(\\\" \\\", \\\"\\\"))\\n counter = counter + 1\\n\\n return(reactions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77775b0dbf194a0215fae87f9c958ba\",\n \"score\": \"0.48460564\",\n \"text\": \"def from_file(self, filename: str):\\n with open(filename, 'r') as fp:\\n self.from_fp(fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcac89978cf248625bd4c712a49768f\",\n \"score\": \"0.4843126\",\n \"text\": \"def read_reactions(reaction_file, verbose=False):\\n\\n reactions2run = set()\\n with open(reaction_file, 'r') as f:\\n for li in f:\\n if li.startswith('#'):\\n continue\\n if \\\"biomass\\\" in li.lower():\\n if verbose:\\n sys.stderr.write(\\\"Biomass reaction was skipped from the list as it is auto-imported\\\\n\\\")\\n continue\\n r = li.strip()\\n if r in modeldata.reactions:\\n reactions2run.add(r)\\n return reactions2run\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ef75b43c3d65f0d36aefbdfc826a55\",\n \"score\": \"0.48420078\",\n \"text\": \"def loadFile(self, file_name, message_name_filter_list):\\n self.file_name = file_name\\n self.file_handle = open(file_name, \\\"rb\\\")\\n\\n # parse the whole file\\n self.read_file_header()\\n self.last_timestamp = self.start_timestamp\\n self.read_file_definitions()\\n self.read_file_data(message_name_filter_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f29d7d66ae58c89886b4417a3b14d2\",\n \"score\": \"0.4832494\",\n \"text\": \"def read_from_file(self, filename):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1810e4065effde14fa421f5565386f31\",\n \"score\": \"0.4829278\",\n \"text\": \"def read(self, filename):\\n\\n # read in the lines of from the input file\\n fin = open(filename, 'r')\\n lines = fin.readlines()\\n fin.close()\\n\\n # parse the header\\n if lines[0][0] == '#':\\n self.header = lines.pop(0).strip()\\n\\n # store other '#' lines as comments\\n while lines[0][0] == '#':\\n self.comments.append( lines.pop(0).strip() )\\n\\n # read the other lines \\n while len(lines) > 0:\\n self.lines.append( lines.pop(0).strip() )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dba0982b0c42556be882e2f92e98f80\",\n \"score\": \"0.48265225\",\n \"text\": \"def __init__(self, file, keep_linebreaks=False, sequence_class=Sequence):\\n\\t\\tif isinstance(file, basestring):\\n\\t\\t\\tfile = xopen(file)\\n\\t\\tself.fp = file\\n\\t\\tself.sequence_class = sequence_class\\n\\t\\tself.delivers_qualities = False\\n\\t\\tself._delimiter = '\\\\n' if keep_linebreaks else ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb340f5e4712b89435199b8f3b809279\",\n \"score\": \"0.4821202\",\n \"text\": \"def __init__(self, file_name):\\n # layer & output managers\\n self.act_manager = act.ActivationTypeManager()\\n self.output_manager = act.OutputTypeManager()\\n\\n # data files\\n self.train_data_file = ''\\n self.val_data_file = ''\\n self.test_data_file = ''\\n self.is_val = False\\n self.is_test = False\\n self.output_dir = ''\\n self.output_filename_pattern = 'm%d'\\n self.is_output = False\\n self.loss_file = None\\n self.is_regression = False\\n\\n # parameters for training\\n self.learn_rate = 0.001\\n self.init_scale = 0.001\\n self.momentum = 0.9\\n self.weight_decay = 0\\n self.minibatch_size = 100\\n self.epoch_to_display = 10\\n self.display_winc = True \\n self.epoch_to_save = 100\\n self.num_epochs = 1000\\n self.random_seed = None\\n\\n # information for each layer, read from the file\\n self.num_layers = 0\\n\\n # default output layer\\n self.output = OutputSpec(10, self.output_manager.linear)\\n\\n self._parse_cfg_file(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b497a15db3d7a8cdf15cdb39be9b9b2\",\n \"score\": \"0.48064095\",\n \"text\": \"def read_file(self, file_name):\\n f = open(file_name,'r')\\n line = f.readline()\\n line = line.split(' ')\\n self.n = int(line[0])\\n self.m = int(line[1])\\n\\n for i in range(self.n):\\n self.D_out[i] = []\\n self.D_in[i] = []\\n\\n while True:\\n line = f.readline()\\n if len(line) == 0:\\n return\\n line = line[:-1]\\n line = line.split(' ')\\n v1 = int(line[0])\\n v2 = int(line[1])\\n cost = int(line[2])\\n self.D_out[v1].append(v2) \\n self.D_in[v2].append(v1)\\n self.D_cost[(v1,v2)] = cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d9b9d5ec021cf8f6e9fc07a9cc4496\",\n \"score\": \"0.48020414\",\n \"text\": \"def __read_input_file( self ):\\n\\n\\t try:\\n\\t\\tself.tpd = yaml.load( open( self.oprator_infile, 'r' ) )\\n\\t\\tminion_hosts = self.tpd[\\\"hosts\\\"]\\n\\t\\tfor i in minion_hosts.keys():\\n\\t\\t if self.ping_host(minion_hosts[i]) == 0:\\n \\t self.restful_request(minion_hosts[i])\\n\\t\\t else:\\n\\t\\t\\t self.common_logger.info(\\\"server \\\" + i + \\\" not reachable, check the connectivity\\\")\\n\\t\\t\\t self.common_logger.error(\\\"Orcestration failed, check logfile for more information\\\")\\n\\t\\t\\t sys.exit(1)\\n\\n\\t except yaml.YAMLError as exc:\\n\\t\\tself.common_logger.error(exc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15dc471fef383147f1af3988f3909f3b\",\n \"score\": \"0.4797811\",\n \"text\": \"def readInternal(self) -> None:\\n key = \\\"\\\"\\n impedances: Dict = {}\\n variances: Dict = {}\\n freq: Union[List[float], np.ndarray] = []\\n with open(self.filepath, \\\"r\\\") as inFile:\\n lines = inFile.readlines()\\n numLines = len(lines)\\n # find the headers\\n for i in range(0, numLines):\\n line = lines[i].strip()\\n if line == \\\"\\\":\\n continue\\n if \\\"Evaluation frequencies\\\" not in line:\\n split = line.split(\\\"=\\\")\\n self.headers[split[0].strip()] = split[1].strip()\\n else:\\n break\\n # format headers\\n self.formatHeaders()\\n # read in the frequencies\\n freq = np.loadtxt(lines[i + 1].strip().split(\\\",\\\"), dtype=\\\"float\\\")\\n # read in the rest of the data\\n for j in range(i + 2, numLines):\\n line = lines[j].strip()\\n if \\\"Z\\\" in line or \\\"Var\\\" in line:\\n split = line.split(\\\"-\\\")\\n key = split[1].strip()\\n j += 1\\n if \\\"Z\\\" in line:\\n impedances[key] = np.loadtxt(\\n lines[j].strip().split(\\\",\\\"), dtype=\\\"complex\\\"\\n )\\n if \\\"Var\\\" in line:\\n variances[key] = np.loadtxt(\\n lines[j].strip().split(\\\",\\\"), dtype=\\\"float\\\"\\n )\\n self.tfData = TransferFunctionData(freq, impedances, variances)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb69181cb8c204281bf362619864ef1\",\n \"score\": \"0.47867498\",\n \"text\": \"def read_date_file(self, date_file):\\n try:\\n with open(date_file, \\\"r\\\") as this_date_file:\\n date_string, run_number = this_date_file.read().split()\\n self.current_date = esm_calendar.Date(date_string)\\n self.run_number = int(run_number)\\n # NOTE: IOError might be called FileNotFoundError in python3+\\n except IOError:\\n logging.debug(\\\"The datefile %s was not found, assuming very first run\\\", date_file)\\n self.update_dates()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b3f68618fd84e10b10b716069f55e0f\",\n \"score\": \"0.4784722\",\n \"text\": \"def __init__(self, path_file):\\n _generic_fasta_file.__init__(self, path_file)\\n self.read_fasta_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4561ef0b3d7f59c067d1d63065afa5\",\n \"score\": \"0.47800082\",\n \"text\": \"def __init__(self,\\n filename,\\n killburn = False,\\n silent = True):\\n self.setup_logger(silent)\\n filename = os.path.expanduser(filename)\\n self.filename = os.path.expandvars(filename)\\n self.file = FortranReader(self.filename)\\n self._load(killburn = killburn)\\n self.file.close()\\n self.close_logger()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a3771ce24dd8540797bbfba2f10b18\",\n \"score\": \"0.47722954\",\n \"text\": \"def __init__(self, file_path):\\n self.file_path = file_path\\n self.index_payload = []\\n self.index_meta = {}\\n self.shape_payload = []\\n self.writer = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fed387188f1f5c3e272c31c7bb2f4198\",\n \"score\": \"0.4765135\",\n \"text\": \"def __init__(self, path: str)->None:\\n \\\"[Read all lines from the current file]\\\"\\n self._configuration=[line.rstrip().split(\\\" \\\")\\n for line in open(path, \\\"r\\\").readlines()]\\n\\n \\\"[Divide configuration into peaces]\\\"\\n self._symbols, self._states=set(), set()\\n self._start=self._configuration[2][0]\\n self._terminal=self._configuration[3]\\n self._rules={tuple(rule[:2]): rule[2]\\n for rule in self._configuration[4:]}\\n\\n \\\"[Create states and alphabet]\\\"\\n for rule in self._configuration[4:]:\\n self._symbols.add(rule[1])\\n self._states.add(rule[0])\\n self._states.add(rule[2])\\n\\n \\\"[Delete unusable variables]\\\"\\n del self._configuration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e221bd2729f9ff87de6170f5b8d9713\",\n \"score\": \"0.476245\",\n \"text\": \"def _parsefile(self, filehandle):\\n allentries = []\\n current = None\\n sumreport = 1\\n suminterval = 10000\\n repthresh = sumreport * suminterval\\n try:\\n while True:\\n line = filehandle.readline()\\n if line == '':\\n break\\n #for line in filehandle:\\n if line.startswith(\\\"ID \\\"):\\n # ID 001R_FRG3G Reviewed; 256 AA.\\n # <prot_name>_<prot_spec>\\n proteinid = line[5:16].strip()\\n current = defaultdict(dict)\\n current['proteinid'] = proteinid\\n (protein, species) = proteinid.split('_')\\n current['protein'] = protein\\n current['species'] = species\\n self.log.debug(\\\"Handling ID. New entry.\\\") \\n \\n elif line.startswith(\\\"AC \\\"):\\n # AC Q6GZX4;\\n # AC Q91896; O57469;\\n self.log.debug(\\\"Handling AC.\\\")\\n accession = line[5:11].strip()\\n current['proteinacc'] = accession\\n\\n elif line.startswith(\\\"OX \\\"):\\n #OX NCBI_TaxID=654924;\\n self.log.debug(\\\"Handling OX.\\\")\\n taxonid = \\\"\\\"\\n val = line[5:]\\n fields = val.split('=')\\n if fields[0] == 'NCBI_TaxID':\\n taxonid = fields[1].strip().replace(';','')\\n current['taxonid'] = taxonid\\n \\n elif line.startswith(\\\"DR GO;\\\"):\\n # DR GO; GO:0046782; P:regulation of viral transcription; IEA:InterPro.\\n # P biological process, C cellular component, F molecular function. \\n self.log.debug(\\\"Handling DR.\\\")\\n fields = line.split(';')\\n goterm = fields[1].strip()\\n goinfo = fields[2]\\n aspcode = goinfo.split(':')[0].strip()\\n goaspect = UniProt.ASPECTMAP[aspcode]\\n goevsrc = fields[3]\\n (goevidence, evsrc) = goevsrc.split(':') \\n goevidence = goevidence.strip()\\n current['goterms'][goterm] = [ goaspect, goevidence]\\n\\n elif line.startswith(\\\"SQ SEQUENCE\\\"):\\n self.log.debug(\\\"Handling SQ: XXX\\\")\\n # line = filehandle.readline()\\n\\n elif line.startswith(\\\"GN \\\"):\\n # Examples:\\n # GN ABL1 {ECO:0000303|PubMed:21546455},\\n # GN Name=BRCA1; Synonyms=RNF53;\\n # GN ORFNames=T13E15.24/T13E15.23, T14P1.25/T14P1.24;\\n # \\n \\n self.log.debug(\\\"Handling GN.\\\")\\n val = line[5:]\\n\\n \\n elif line.startswith(\\\"//\\\"):\\n self.log.debug(\\\"End of entry.\\\")\\n clist = self._handle_current(current)\\n current = None\\n allentries.extend(clist)\\n self.log.debug(f\\\"All entries list now {len(allentries)} items... \\\")\\n if len(allentries) >= repthresh:\\n self.log.info(f\\\"Processed {len(allentries)} entries... \\\")\\n sumreport +=1\\n repthresh = sumreport * suminterval\\n \\n except Exception as e:\\n traceback.print_exc(file=sys.stdout) \\n \\n self.log.info(f\\\"Parsed file with {len(allentries)} goterms\\\" )\\n return allentries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de1ef3356a864140aeafb44d727bc45d\",\n \"score\": \"0.4760311\",\n \"text\": \"def __init__(self, filePath):\\n\\t\\tself.gameState = readGame.readGameState(filePath)\\n\\t\\tself.nodesExpanded = 0\\n\\t\\tself.trace = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0750043cafc4b164418544c340c6344b\",\n \"score\": \"0.475434\",\n \"text\": \"def read_and_test_file_content(self): # pragma: no cover\\n\\n # We print the CLI header.\\n PyFunceble.CLICore.print_header()\\n\\n with open(self.file, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n # We open the file we have to test.\\n\\n with Manager() as manager:\\n # We initiate a server process.\\n\\n # We process the test/save of the original list to test.\\n self.__run_multiprocess_test(\\n self._get_list_to_of_subjects_to_test_from_file(file), manager\\n )\\n\\n # We get the list of mined data to test.\\n to_test = chain(self.mining.list_of_mined())\\n\\n # We process the test/save of the mined data to test.\\n self.__run_multiprocess_test(to_test, manager)\\n\\n # We get the list of complements to test.\\n complements = self.get_complements()\\n\\n if complements:\\n # We process the test/save of the original list to test.\\n to_test = chain(complements)\\n\\n self.__run_multiprocess_test(to_test, manager)\\n\\n # We inform all subsystem that we are not testing for complements anymore.\\n self.complements_test_started = False\\n\\n # We generate the JSON formatted files if needed.\\n self.generate_json_format()\\n # We clean the autocontinue subsystem, we finished\\n # the test.\\n self.autocontinue.clean()\\n # We process the autosaving if necessary.\\n self.autosave.process(test_completed=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e0e07cf75e321ad85eb27733adcc2a\",\n \"score\": \"0.47543067\",\n \"text\": \"def setup(self, path_to_conf_file):\\n self.track = Track.SCENE_LAYOUT\\n self.agent_engaged = False\\n self.current_control = carla.VehicleControl()\\n self.current_control.steer = 0.0\\n self.current_control.throttle = 1.0\\n self.current_control.brake = 0.0\\n self.current_control.hand_brake = False\\n self._hic = HumanTextInterface(self)\\n self._thread = Thread(target=self._hic.run)\\n self._thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d77c2d99c36eafb85956740611879d\",\n \"score\": \"0.47484687\",\n \"text\": \"def load_file(self,initial_condition=False,custom_name=None):\\n\\n\\t\\t# if no custom name, then simply load with current settings (TUI version)\\n\\t\\t# if there is a custom name, update paths to the appropriate experiment name then attempt to load.\\n\\n\\t\\tself.update_paths()\\n\\t\\tif custom_name is None:\\n\\t\\t\\tfilepath = self.raw_data_path\\n\\t\\telse:\\n\\t\\t\\tfilepath = \\\"DATA/{}d/{}/{}.json\\\".format(self.dimension, custom_name,custom_name)\\n\\n\\t\\tprint(\\\"Loading file: {}\\\".format(filepath))\\n\\t\\ttry:\\n\\t\\t\\twith open(filepath, 'r') as fp:\\n\\t\\t\\t\\tif initial_condition:\\n\\t\\t\\t\\t\\tself.current_cone_chain = json.load(fp, cls=cone_chain.ConeChainInitialConditionExtractor)\\n\\t\\t\\t\\t\\tif not self.batch_mode or self.experiment_name is None:\\n\\t\\t\\t\\t\\t\\tself.ask_experiment_name()\\n\\t\\t\\t\\t\\tself.update_paths()\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.current_cone_chain = json.load(fp, cls=cone_chain.ConeChainDecoder)\\n\\t\\t\\tprint('\\\\tloading successful...')\\n\\t\\texcept:\\n\\t\\t\\tprint('\\\\tA file loading error has occured.')\\n\\t\\tself.save_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78612c36f1f47a7a3e86a55866e9acef\",\n \"score\": \"0.47437727\",\n \"text\": \"def __init__(self, file, sequence_class=Sequence):\\n\\t\\tif isinstance(file, basestring):\\n\\t\\t\\tfile = xopen(file)\\n\\t\\tself.fp = file\\n\\t\\tself.sequence_class = sequence_class\\n\\t\\tself.delivers_qualities = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"362529be851a153e859b1e52dc569d90\",\n \"score\": \"0.47407082\",\n \"text\": \"def read_mol(self, filename):\\n self.fname = filename\\n lines = []\\n with open(self.fname, \\\"r\\\") as xyzfile:\\n for line in xyzfile:\\n lines.append(line.rstrip('\\\\n'))\\n\\n self.atom_basis = lines[0].strip().lower() == \\\"atombasis\\\"\\n if self.atom_basis:\\n offset = 1\\n else:\\n offset = 2\\n # read basis\\n line = lines[1].split()\\n self.basis = line[0]\\n if len(line)>1:\\n # read aux basis\\n if line[1][0:3].lower() == \\\"aux\\\":\\n self.aux_basis = line[1][4:]\\n\\n # read title lines:\\n self.title1 = lines[offset]\\n self.title2 = lines[offset+1]\\n\\n # read main line:\\n line = lines[offset+2].split()\\n for elmt in line:\\n el = elmt.split(\\\"=\\\")\\n test = el[0].lower()\\n if test == \\\"atomtypes\\\":\\n self.natom_types=int(float(el[1]))\\n elif test == \\\"charge\\\":\\n self.charge=int(float(el[1]))\\n elif test == \\\"nosymmetry\\\":\\n self.symmetry = False\\n elif test == \\\"angstrom\\\":\\n self.bohr = False \\n\\n # read all atom types\\n iline = offset + 2\\n self.atom_types = [] \\n for natp in xrange(self.natom_types):\\n iline += 1\\n new_atype = atom_type()\\n new_atype.read_from_mol(lines, iline, self.bohr)\\n self.atom_types.append( new_atype )\\n self.natoms += self.atom_types[-1].natoms\\n iline += self.atom_types[-1].natoms\\n\\n self.natom_types = len(self.atom_types)\\n return self.check() # check validity of data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c29b039a4bb52ba5253ac12c2026dad\",\n \"score\": \"0.4740215\",\n \"text\": \"def main(file_to_process):\\n logger.info('Processing {}'.format(file_to_process))\\n # with open(os.path.join(PROCESSING_PATH, file_to_process), 'r') as f:\\n # logger.info(f.readline())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42142dbce383293ca00e90b18b63e0b7\",\n \"score\": \"0.47299087\",\n \"text\": \"def importRedescriptionsFromFile(self, redescriptions_filename):\\n self._startMessage('importing', redescriptions_filename)\\n try:\\n tmp_reds, tmp_rshowids = self._readRedescriptionsFromFile(redescriptions_filename)\\n except IOError as arg:\\n self.logger.printL(1,\\\"Cannot open: %s\\\" % arg, \\\"dw_error\\\", \\\"DW\\\")\\n self._stopMessage()\\n raise\\n except Exception:\\n self.logger.printL(1,\\\"Unexpected error while importing redescriptions from file %s!\\\\n%s\\\" % (redescriptions_filename, sys.exc_info()[1]), \\\"dw_error\\\", \\\"DW\\\")\\n self._stopMessage()\\n raise\\n else:\\n self.reds = tmp_reds\\n self.rshowids = tmp_rshowids\\n finally:\\n self._stopMessage('importing')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb885bfd12d9801426a4c73fc5aa1ae4\",\n \"score\": \"0.47284946\",\n \"text\": \"def __init__(self, filepath):\\n self.filepath = filepath\\n self.populate_words_and_print_num()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feff88cfdbd68633799f003d6c0cc9c5\",\n \"score\": \"0.47260806\",\n \"text\": \"def parse_ontology(self, file):\\n self.ontology_name = file\\n ont = ontospy.Ontospy(file)\\n self.o = ont\\n self.objectProperties = self.o.objectProperties # cache this\\n self.obtain_class_hierarchy_and_signatures()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e84b8007af242ca4c7aaaa9667613852\",\n \"score\": \"0.47260755\",\n \"text\": \"def read(self,filename):\\n if not self.empty:\\n raise Error(\\\"GdxFile.read can only be used if the GdxFile is .empty\\\")\\n\\n # open the file\\n rc = gdxcc.gdxOpenRead(self.H,str(filename))\\n if not rc[0]:\\n raise GdxError(self.H,f\\\"Could not open {filename!r}\\\")\\n self._filename = filename\\n\\n # read in meta-data ...\\n # ... for the file\\n ret, self._version, self._producer = gdxcc.gdxFileVersion(self.H)\\n if ret != 1: \\n raise GdxError(self.H,\\\"Could not get file version\\\")\\n ret, symbol_count, element_count = gdxcc.gdxSystemInfo(self.H)\\n logger.debug(\\\"Opening '{}' with {} symbols and {} elements with lazy_load = {}.\\\".format(filename,symbol_count,element_count,self.lazy_load))\\n # ... for the symbols\\n ret, name, dims, data_type = gdxcc.gdxSymbolInfo(self.H,0)\\n if ret != 1:\\n raise GdxError(self.H,\\\"Could not get symbol info for the universal set\\\")\\n self.universal_set = GdxSymbol(name,data_type,dims=dims,file=self,index=0)\\n for i in range(symbol_count):\\n index = i + 1\\n ret, name, dims, data_type = gdxcc.gdxSymbolInfo(self.H,index)\\n if ret != 1:\\n raise GdxError(self.H,\\\"Could not get symbol info for symbol {}\\\".format(index))\\n self.append(GdxSymbol(name,data_type,dims=dims,file=self,index=index))\\n\\n # read all symbols if not lazy_load\\n if not self.lazy_load:\\n for symbol in self:\\n symbol.load()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a9b3b42ccddac769b7d41deda1d3bdc\",\n \"score\": \"0.47232094\",\n \"text\": \"def main():\\n d, args = process_init()\\n\\n user_file_path = get_attr_by_flag(args, d, \\\"default_defaultfile_flags\\\")\\n default_file_path = get_attr_by_flag(args, d, \\\"default_changefilename_flags\\\")\\n open_default_file = get_attr_by_flag(args, d, \\\"default_open_flags\\\")\\n initiate_loop = get_attr_by_flag(args, d, \\\"default_loop_flags\\\")\\n topics = get_attr_by_flag(\\n args, d, \\\"default_topic_flags\\\"\\n ) # could be None or list of passed in topics\\n # print(dir(args))\\n this_version = getattr(args, \\\"version\\\")\\n\\n ensure_redundancy(REDUNDANCY_PATH, default_file_path)\\n\\n if open_default_file is True: ##launch default file\\n if path.isfile(d.get(\\\"default_file\\\")):\\n print(\\\"Opening {}\\\".format(d.get(\\\"default_file\\\")))\\n startfile(\\\"{}\\\".format(d.get(\\\"default_file\\\")))\\n else:\\n print(f'{d.get(\\\"default_file\\\")} not found.')\\n return\\n\\n if this_version is True: ##display version for user\\n print(\\\"Notes > Memory, version:{}\\\".format(version))\\n return\\n\\n if (user_file_path) != d[\\n \\\"default_file\\\"\\n ]: ##user changing defaultfile; update INIT_FILE\\n with open(INIT_FILE, \\\"r\\\") as f:\\n lines = f.readlines()\\n new_INIT_FILE = [\\n i\\n if \\\"default_file\\\" not in i\\n else \\\"default_file={}\\\\n\\\".format((user_file_path))\\n for i in lines\\n ]\\n\\n try:\\n copy2(\\n INIT_FILE,\\n path.join(\\n path.split(INIT_FILE)[0], f\\\"COPY - {path.split(INIT_FILE)[1]}\\\"\\n ),\\n )\\n except FileNotFoundError:\\n print(f\\\"Creating {INIT_FILE}\\\")\\n\\n with open(INIT_FILE, \\\"w\\\") as f:\\n f.writelines(new_INIT_FILE)\\n\\n print(\\\"Default file changed to {}\\\".format(user_file_path))\\n return\\n\\n if initiate_loop is True: ##user entering note-taking loop\\n process_loop(args, init_dict=d)\\n return\\n\\n if (\\n getattr(args, d.get(\\\"default_note_flags\\\")[-1].strip())\\n ) is None: ##no note means user wants note output\\n try:\\n with open((default_file_path), \\\"r\\\") as f:\\n lines = f.readlines()\\n except FileNotFoundError:\\n print(\\n f\\\"\\\\n\\\\tNo such file or directory: {(default_file_path)}\\\\n\\\\t>>Add notes to file before using topic tag.\\\"\\n )\\n return\\n\\n if topics is None:\\n show_non_specific_lines(lines, d)\\n return\\n if \\\"ALL\\\" in topics:\\n topics = show_all_topics(topics, lines, default_file_path)\\n for i, line in enumerate(lines):\\n if sum([1 for t in topics if t in line.split(\\\"::\\\")[0]]) > 0:\\n process_line(line, d)\\n return\\n\\n note_str = \\\" \\\".join(\\n (getattr(args, d.get(\\\"default_note_flags\\\")[-1].strip()))\\n ) ##(getattr(args, d.get(\\\"default_note_flags\\\")[-1].strip())) comes in as a list; convert to string\\n\\n try: ##read current notes file content\\n with open((default_file_path), \\\"r\\\") as f:\\n string = f.read()\\n except FileNotFoundError:\\n string = \\\"\\\"\\n\\n try: ##redundancy to preserve notes\\n copy2((default_file_path), f\\\"COPY - {(default_file_path)}\\\")\\n except FileNotFoundError:\\n print(f\\\"Creating {(default_file_path)}\\\")\\n for topic in topics:\\n if topic in [\\\"ALL\\\", \\\"SHOW\\\", \\\"HELP\\\", \\\"TOPICS\\\"]:\\n topics.remove(topic)\\n ticker_set = set()\\n try:\\n with open((default_file_path), \\\"r\\\") as f:\\n lines = f.readlines()\\n except FileNotFoundError:\\n print(\\n f\\\"\\\\n\\\\tNo such file or directory: {(default_file_path)}\\\\n\\\\t>>Add notes to file before using topic tag.\\\"\\n )\\n for i, line in enumerate(lines):\\n mat = match(r\\\"\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\s\\\\d{2}:\\\\d{2}:\\\\d{2}--(.*?):\\\", line)\\n group = mat.group(1)\\n if len(group) > 0:\\n ticker_set.add(group.upper())\\n print(\\n \\\"\\\\n Current Topics in {}:\\\".format(default_file_path)\\n + colorama.Fore.MAGENTA\\n + \\\"\\\\n\\\\t{}\\\".format(\\\"\\\\n\\\\t\\\".join(list(ticker_set)))\\n + colorama.Fore.WHITE\\n )\\n with open(\\n (default_file_path), \\\"w\\\"\\n ) as f: # if we got this far, we want to write notes to file\\n this_note = (\\n str(datetime.today())[:19] + \\\"--misc::\\\" + note_str\\n if topics is None\\n else str(datetime.today())[:19] + \\\"--\\\" + \\\", \\\".join(topics) + \\\"::\\\" + note_str\\n )\\n if topics is None:\\n f.write(this_note + \\\"\\\\n\\\" + string)\\n else:\\n f.write(this_note + \\\"\\\\n\\\" + string)\\n process_line(this_note, d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b82082cb223d2189fa431a8975098d03\",\n \"score\": \"0.47228834\",\n \"text\": \"def init_from_mol(self, mol_file):\\n self.name = mol_file.fname.split(\\\"/\\\")[-1][:-4]\\n self.note = mol_file.title1+\\\" \\\"+mol_file.title2\\n self.charge = mol_file.charge\\n self.natoms = mol_file.natoms\\n self.natom_types = mol_file.natom_types\\n self.atom_types = mol_file.to_angstrom().atom_types\\n self.get_chem_name()\\n return self.check() # check validity of mol_data\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c733683198f644b2ef09aac90f4b51b\",\n \"score\": \"0.47202966\",\n \"text\": \"def read_all(self):\\n with open(self.filepath, 'r') as filepath:\\n self.data = filepath.read()\\n self.data = QtProParser.replace_multiline_data(self.data)\\n self.data = QtProParser.replace_variables(self.data, self.variable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7212b40358fdd23dc0736534f9547b06\",\n \"score\": \"0.47162932\",\n \"text\": \"def read(self, fileName):\\n i = 0\\n file = open(fileName)\\n for line in file:\\n if len(line) <= 1:\\n pass\\n elif i >= 1:\\n self.append(data(line))\\n else:\\n self.header = data(line)\\n i |= 1\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca472f65c59893a1ce9b826fa22ca3d0\",\n \"score\": \"0.47142392\",\n \"text\": \"def read_file(self, file_name):\\n f = open(file_name,'r')\\n line = f.readline()\\n line = line.split(' ')\\n self.n = int(line[0])\\n self.m = int(line[1])\\n\\n for i in range(self.n):\\n self.D_vertices[i] = []\\n\\n while True:\\n line = f.readline()\\n if len(line) == 0:\\n return\\n line = line[:-1]\\n line = line.split(' ')\\n v1 = int(line[0])\\n v2 = int(line[1])\\n self.D_vertices[v1].append(v2) \\n self.D_vertices[v2].append(v1)\\n self.D_edges.append( (v1, v2) )\\n self.D_edges.append( (v2, v1) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"759aeadb48b6e9ec5d36c4aea9849475\",\n \"score\": \"0.47117305\",\n \"text\": \"def initialise(self, a_path, load_state):\\n\\n # Initialise path\\n self.__f_path = a_path\\n\\n # Get file name\\n head, tail = ntpath.split(self.path)\\n self.__f_file_name = (tail or ntpath.basename(head))\\n\\n # Read in file\\n l_file = None\\n try:\\n l_file = open(self.path)\\n l_file_content = l_file.read()\\n l_file.close()\\n\\n # Handle file error\\n except IOError:\\n LegalDoc.__note_exception(self.path, \\\"MAJOR ERROR: Unable to read file\\\", True)\\n l_file.close()\\n return False\\n\\n # Load state from a formatted file\\n if load_state:\\n\\n # TODO - Timer start\\n Timers.s_init_load_state_timer.start()\\n\\n l_succeeded = self.__initialise_load_state(l_file_content)\\n\\n # TODO - Timer stop\\n Timers.s_init_load_state_timer.stop()\\n\\n if not l_succeeded:\\n LegalDoc.s_file_error += 1\\n return False\\n\\n # Generate state from an unformatted file\\n else:\\n\\n # TODO - Timer start\\n Timers.s_init_gen_state_timer.start()\\n\\n l_succeeded = self.__initialise_generate_state(l_file_content)\\n\\n # TODO - Timer stop\\n Timers.s_init_gen_state_timer.stop()\\n\\n if not l_succeeded:\\n return False\\n self.__f_punctuation_removed = LegalDoc.REMOVE_PUNCTUATION\\n self.__f_lower_case = LegalDoc.TO_LOWER_CASE\\n self.__f_stop_words_removed = LegalDoc.REMOVE_STOP_WORDS\\n self.__f_stemmed = LegalDoc.APPLY_STEMMING\\n self.__f_lemmatized = LegalDoc.APPLY_LEMMATIZATION\\n\\n # Note successful initialisation\\n LegalDoc.s_successful_init_count += 1\\n\\n # Add current LegalDoc to static dictionary of LegalDocs\\n LegalDoc.s_legal_doc_dict[self.file_name] = self\\n\\n # Create judge and add it to static dictionary of judges\\n Judge.add_legal_doc(self)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa83c2c3aa994bd2b374e69d8f790c5\",\n \"score\": \"0.47115463\",\n \"text\": \"def load(self):\\n if self.dummy:\\n raise Exception(\\\"Can't load() a dummy SynchronizedFile\\\")\\n self._read(open(self.filename), self.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d0bfdf99bed434493dddb480823c06\",\n \"score\": \"0.4695643\",\n \"text\": \"def read_file_and_perform_action(file_path, action):\\n\\n # Check if the file is exists to avoid errors.\\n if not is_file_exists(file_path):\\n raise FileNotFoundError('{} path given is not exists.'.format(file_path))\\n\\n # Perform action for each value found in the file.\\n with open(file_path, 'r') as file_handle:\\n for line in file_handle.readlines():\\n values_in_line = line.split(',')\\n for value in values_in_line:\\n action(value.strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158a277ebe803311cf6962a859b500c9\",\n \"score\": \"0.46930277\",\n \"text\": \"def openfile_and_preload(logf):\\r\\n logf.write('opening file\\\\n')\\r\\n f = open('uploadData.csv', 'r')\\r\\n logf.write('file opened\\\\n')\\r\\n csvdata = csv.reader(f)\\r\\n for row in csvdata:\\r\\n MIG_IDS[row[1]] = True\\r\\n ROWS_TO_PROCESS.append(row)\\r\\n\\r\\n logf.write('File read and loaded\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe48a3b9488bce4bfb8fd67e8de1cfdb\",\n \"score\": \"0.46920854\",\n \"text\": \"def __init__(self, state_file, twitter_config, telegram_config, pretend=False):\\n self.state_file = state_file\\n self.pretend = pretend\\n\\n if not self.pretend:\\n self.twitter = twitter.Api(**twitter_config)\\n self.twitter.VerifyCredentials()\\n \\n self.telegram_config = telegram_config\\n\\n self.tz = pytz.timezone(self.TIMEZONE)\\n\\n self.log = logging.getLogger('mlbot')\\n\\n self.log.debug('Loading state from file')\\n try:\\n with open(self.state_file, 'rb') as f:\\n self.status = pickle.load(f)\\n except FileNotFoundError:\\n self.log.warning('File not found, first run?')\\n self.status = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de274f61420e099eab24396d4661e87\",\n \"score\": \"0.46868306\",\n \"text\": \"def read(self, filepath):\\n self.parser.read(filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9e8a3833ce764d55c75789b6d38f6f\",\n \"score\": \"0.46857572\",\n \"text\": \"def read_recipe(self, filename):\\n Global.LOGGER.debug(f\\\"reading recipe {filename}\\\")\\n if not os.path.isfile(filename):\\n Global.LOGGER.error(filename + \\\" recipe not found, skipping\\\")\\n return\\n\\n config = configparser.ConfigParser(allow_no_value=True,\\n delimiters=\\\"=\\\")\\n\\n config.read(filename)\\n\\n for section in config.sections():\\n self.sections[section] = config[section]\\n\\n Global.LOGGER.debug(\\\"Read recipe \\\" + filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a7a88ec27ade1e05ae583cf40588fc\",\n \"score\": \"0.4665189\",\n \"text\": \"def __init__(self, f):\\n\\t\\topen_file = open(f, \\\"r\\\", encoding=\\\"utf-8\\\")\\n\\t\\tself.file = open_file.readlines()\\n\\t\\topen_file.close()\\n\\t\\tself.length = len(self.file)\\n\\t\\tself.line_counter = -1\\n\\t\\tself.current_command = None\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994381,"cells":{"query_id":{"kind":"string","value":"9b3360467e6d9f991c4590a7f3bc1070"},"query":{"kind":"string","value":".timeof() calculate the whole time of the file"},"positive_passages":{"kind":"list like","value":[{"docid":"a77fd1bc51a17819331082ad160b622b","score":"0.7086473","text":"def timeof(self):\r\n time = self.mtr[-1,4] - self.mtr[0,4]\r\n if time < 0:\r\n time += 65536\r\n time += ( self.mtr[-1,3] - self.mtr[0,3] )*65536\r\n time_hours = (time / 1000000)/3600\r\n time_min = ((time / 1000000)%3600)/60\r\n time_sec = (time / 1000000)%60\r\n print 'hours = %d; minutes = %d; seconds = %d' % (time_hours,\r\n time_min, time_sec)","title":""}],"string":"[\n {\n \"docid\": \"a77fd1bc51a17819331082ad160b622b\",\n \"score\": \"0.7086473\",\n \"text\": \"def timeof(self):\\r\\n time = self.mtr[-1,4] - self.mtr[0,4]\\r\\n if time < 0:\\r\\n time += 65536\\r\\n time += ( self.mtr[-1,3] - self.mtr[0,3] )*65536\\r\\n time_hours = (time / 1000000)/3600\\r\\n time_min = ((time / 1000000)%3600)/60\\r\\n time_sec = (time / 1000000)%60\\r\\n print 'hours = %d; minutes = %d; seconds = %d' % (time_hours,\\r\\n time_min, time_sec)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7ae5ad7bc2d33e48e90728fc6be4c0bf","score":"0.69575024","text":"def time():","title":""},{"docid":"7a0b826d66927ef4fcb590f59bc4d1ac","score":"0.6760677","text":"def _measure_timing(self, header):\n return (rospy.get_rostime() - header.stamp).to_sec()","title":""},{"docid":"8c7c045326c1695556812d47aa77ad9b","score":"0.6693681","text":"def time(self):\r\n return conf.lib.clang_getFileTime(self)","title":""},{"docid":"cf212e10dc68b22e3db3e25f152fc4ee","score":"0.66304564","text":"def reading_time(self):","title":""},{"docid":"5b1d771180a932fa7412ac457d6c607e","score":"0.6587622","text":"def timing():\n return time.time()","title":""},{"docid":"163c57b4544a6345a405dc81a08cb755","score":"0.65783024","text":"def time(self):","title":""},{"docid":"43f7bcf26ee2380a7d8b78a5eac0669d","score":"0.6550655","text":"def time():\n return time.time()","title":""},{"docid":"dfcce7596f36c14e2b825620b59a8113","score":"0.65317684","text":"def _extract_execution_time(self, record):\n path_dir = self._get_path_reg_dir(record)\n path_time = os.path.join(path_dir, NAME_FILE_TIME)\n with open(path_time, 'r') as fp:\n t_exec = float(fp.read()) / 60.\n return t_exec","title":""},{"docid":"d86ab964f6ef324970ca3dee32d112aa","score":"0.64987504","text":"def time():\n return 0.0","title":""},{"docid":"8998a3d8959ddbfe8bbf409757eed639","score":"0.6471957","text":"def process_time():\n return time.process_time()","title":""},{"docid":"882a2636072b66ca02a9b220ce69c032","score":"0.6436795","text":"def time(self):\n pass","title":""},{"docid":"46e0d28d99a1711045bbcfc0234840e4","score":"0.63417697","text":"def getctime(pth):\n\n buf = struct_stat64()\n rv = stat64(pth.encode(\"utf-8\"), pointer(buf))\n if rv != 0:\n raise OSError(\"Couldn't stat file %r\" % pth)\n return buf.st_birthtimespec.tv_sec","title":""},{"docid":"efdeb73f9f007fd50e02656ca3d9cc21","score":"0.6317825","text":"def time(self):\n raise NotImplementedError","title":""},{"docid":"da5cf19d2ed5dfe546ac3ce126446391","score":"0.629848","text":"def gettime():\n return time.process_time()","title":""},{"docid":"575f014fe27b7f8040684f381b5b2929","score":"0.62965524","text":"def getctime(path):\n return 0.0","title":""},{"docid":"ddb3aafed086fc8a9ab1ba44324e10df","score":"0.6296194","text":"def _time(self):\n return time.time() - self._start_time","title":""},{"docid":"5c353018993bbcff7b2717534715bf47","score":"0.6285912","text":"def time(self):\n return timestamp() - self._base","title":""},{"docid":"0076601296f8c0008bca72e3a796aed4","score":"0.62828994","text":"def time():\n ...","title":""},{"docid":"0819a2b0adb82e10259af61b512396e5","score":"0.62504447","text":"def time(self):\n return","title":""},{"docid":"88dd7d9e7b4592ddafa0a57ca18134b0","score":"0.6250373","text":"def Time():\n return get_time() - init_time","title":""},{"docid":"ee6a6413281d1b45ad6422e3080bf95d","score":"0.62480253","text":"def time(self):\n return time.monotonic()","title":""},{"docid":"b72cc8644793e0ea61a0d40a783b8a66","score":"0.6228801","text":"def run_time(self):\n time_now = time.time() - self.time\n return time_now","title":""},{"docid":"636ac2b936555dde2a530da01334c52e","score":"0.6224945","text":"def getatime(filename: AnyStr_) -> float:\r\n return os.stat(filename).st_atime","title":""},{"docid":"5f47f3c589ed800214ad41e91d39e3ce","score":"0.6208159","text":"def getatime(path):\n return 0.0","title":""},{"docid":"6befb4f9c65439446c7e5aedd57a15ae","score":"0.61784506","text":"def getctime(filename: AnyStr_) -> float:\r\n return os.stat(filename).st_ctime","title":""},{"docid":"0a963ac4a1fe7cd39187bec7a4dabe5e","score":"0.6151988","text":"def Time(self):","title":""},{"docid":"fc591254b0ccd5530174f2be5e99cbac","score":"0.6149372","text":"def elapsed():\n return time.clock()","title":""},{"docid":"d8368ca3d9ae6e3880cebd923f6cf50e","score":"0.61424583","text":"def total_time() -> float:\n return 0","title":""},{"docid":"7a061bc8148354bbb845b3b669039d70","score":"0.61363965","text":"def currentTime():\n\n return time.time()","title":""},{"docid":"a0358779d55338e815e87d662dc0cac9","score":"0.6128019","text":"def time(self):\n return self._time() - self.start","title":""},{"docid":"fec2167f7a3e71b6a0c483804d1858bf","score":"0.61194557","text":"def filetime(file_path):\n epoch_timestamp = __get_epochtime(file_path)\n time_string = __build_timestring(epoch_timestamp)\n return time_string","title":""},{"docid":"cc32ab3762a618705bbb0c9d109023e1","score":"0.61027503","text":"def time( self ):\n return time.time()","title":""},{"docid":"dba3727dc4cc904e44f9c1ff8af5534f","score":"0.6033098","text":"def time():\n return (origtime.time() - epoch) * resolution","title":""},{"docid":"0effb5e4a41fd971f3b1f8e45822bceb","score":"0.6019206","text":"def get_time():\n return frame_counter * SKIP_TICKS","title":""},{"docid":"7e29a0288f1397ffad6dbfb3fd762d49","score":"0.6001766","text":"def baseTimeStamp(logFile):\r\n return os.lstat(logFile)[ST_CTIME]","title":""},{"docid":"8b44525763e7373f119807fdd5989112","score":"0.59988827","text":"def duration(self):\n return len(self.data) / self.fs","title":""},{"docid":"2893db60f75e80de00bbe3443f4abc51","score":"0.5978789","text":"def time(self):\n return self.get_time(self.frame, self.audio.samplerate)","title":""},{"docid":"f8016343de0c1a4a14cdd2b1e3e6045c","score":"0.5976678","text":"def clock(self, /) -> float:\n return time.time() - self.start","title":""},{"docid":"41809cd1f0e7a970c644d3e0bbf671d4","score":"0.59597707","text":"def getatime(self, filename):\n return os.path.getatime(self.abspath(filename))","title":""},{"docid":"d22d8be7e6a9eb56cc0fff3d6a335a91","score":"0.5954437","text":"def read_file_timed(path):\n s_time = time.time()\n try:\n f = open(path, mode=\"rb\")\n data = f.read()\n return data\n except IOError as err:\n logger.error(err)\n raise\n else:\n f.close()\n finally:\n stop_time = time.time()\n dt = stop_time - s_time\n logger.info(\"Time required for {file} = {time}\".format(file=path,\n time=dt))","title":""},{"docid":"2503ae58a8dcb42aa89cac2f22b163e8","score":"0.5948028","text":"def time(self):\n\n return self.starts[-1] - time.clock()","title":""},{"docid":"7be20bb9fbb78a75a6321363e5adafd9","score":"0.5943941","text":"def getTime():\n return float(time.perf_counter()*1000)","title":""},{"docid":"7be20bb9fbb78a75a6321363e5adafd9","score":"0.5943941","text":"def getTime():\n return float(time.perf_counter()*1000)","title":""},{"docid":"7be20bb9fbb78a75a6321363e5adafd9","score":"0.5943941","text":"def getTime():\n return float(time.perf_counter()*1000)","title":""},{"docid":"7be20bb9fbb78a75a6321363e5adafd9","score":"0.5943941","text":"def getTime():\n return float(time.perf_counter()*1000)","title":""},{"docid":"8bc9ac3acb1d738faa39995596386ef6","score":"0.5934402","text":"def _time():\n\t#return int(time.time())\n\treturn time.time()","title":""},{"docid":"89c36c31f85286bf957d5ca09fccdc71","score":"0.5932821","text":"def test_calculate_time_difference():\r\n r = Race(filename)\r\n assert r.calculate_time_difference(4201, 57411) == '+0:53.210'","title":""},{"docid":"af07a75954db7f39d6b032d613eb73d7","score":"0.59280205","text":"def get_time(self) -> int:\n return self.read32(2 * 4)","title":""},{"docid":"770fee269b3916e8abc1227f4210a388","score":"0.5926292","text":"def _get_time():\n return time.time()","title":""},{"docid":"e23cf1b2434d5f63287841aeb6c7a01c","score":"0.59233075","text":"def getatime():\n if isinstance(p, (fpath,dpath)):\n return p.getatime()\n return os.path.getatime(p)","title":""},{"docid":"4da95b33bbb59adf5a4c59ac03301c43","score":"0.5913929","text":"def get_time():\n return time.time()","title":""},{"docid":"07a4520ccd727b67df47a7c9035ce6ad","score":"0.5913543","text":"def readTimeStamp(fname,path):\n fname = os.path.join(os.path.abspath(path),fname)\n num_lines = sum(1 for line in open(fname))\n f = open(fname)\n for i in range(3):\n f.readline()\n strt = [f.readline().split()[1] for i in range(num_lines-4)]\n t = [float(i)/1000000 for i in strt]\n return t","title":""},{"docid":"50bb8f09abe097441e948593c3caf8a9","score":"0.5905055","text":"def stat(filename):\n import os\n rstat = os.stat(filename)\n return rstat[:7] + tuple(tim + TIME_OFFSET for tim in rstat[7:])","title":""},{"docid":"58e211faf3d3988ea00619e03f087bcb","score":"0.5901196","text":"def tic():\n\n return time.time()","title":""},{"docid":"7fe12454abcbbe385a8b67b64b52dac1","score":"0.58587563","text":"def _calculate_runtime(self):\n lines = self.file_dic['output'].splitlines()\n start_time = datetime.strptime(lines[0].strip(), self.timestring_format)\n fin_time = datetime.strptime(lines[-1].strip(), self.timestring_format)\n dif = fin_time - start_time\n self.date = fin_time.strftime('%d %b %Y')\n self.runtime = dif.total_seconds()","title":""},{"docid":"61a9dba7d04622c65564480631784f13","score":"0.5851342","text":"def GetTimeNow():","title":""},{"docid":"0b80808126fe994f2ed79ca43dc872bf","score":"0.58404726","text":"def time_stats(self):\n func_names = ['_read_graphs', 'run']\n time_deltas = collections.defaultdict(float)\n for fn in func_names:\n time_deltas[fn] = round(\n self.timestamps[fn + '_out'] - self.timestamps[fn + '_in'],\n 2\n )\n\n print('Read:\\t{} s'.format(time_deltas['_read_graphs']))\n print('Mine:\\t{} s'.format(\n time_deltas['run'] - time_deltas['_read_graphs']))\n print('Total:\\t{} s'.format(time_deltas['run']))\n return self","title":""},{"docid":"3b1d8ecebffcc8319029a5108fd58e5b","score":"0.5837847","text":"def get_time(self):\n return self.measure_state.time","title":""},{"docid":"bb18accc1e0e0b1b742714844f7c59d5","score":"0.58373195","text":"def time(self):\n\n return 649692000.0 + self.monotonic_time","title":""},{"docid":"d93137c7dbb7240cf6bb3074a975485d","score":"0.58195084","text":"def timestamp():\r\n return _clock_func()","title":""},{"docid":"4b2c945bb4ffdad2ff7201bc6ededc54","score":"0.580161","text":"def elapsed():\n global start_time\n return time.time() - start_time","title":""},{"docid":"a4097eb63a93fcd2bea9537ae3910c2a","score":"0.57996166","text":"def test_time_1M_file_size(self):\n t = timeit.Timer(stmt=\"wordcount('1MFile.txt')\", setup=\"from wordgroup import wordcount\")\n self.assertLess(t.timeit(number=1), 120)","title":""},{"docid":"560e4d94753f99b1b342dd33827a1733","score":"0.57904214","text":"def get_time():\n g.start = time.time()","title":""},{"docid":"636cb6be6689f6dfb5e1e9efb61ab94e","score":"0.5788313","text":"def time(self):\n return self._time_code*units.time","title":""},{"docid":"ef3d092ce8e826f89460c877db0745a5","score":"0.5782163","text":"def get_timestamp():\n return time.time() - start_time","title":""},{"docid":"f47050df4029f69991fdc81993e7d14b","score":"0.57779217","text":"def time(self):\n return self.t","title":""},{"docid":"2e87d6e9c4bee26315fca8db58d8ab75","score":"0.5772191","text":"def total_time(self):\n return self.time_records.total_time().total_seconds()","title":""},{"docid":"4daa80b55fbd716e83274d65e7c9b50c","score":"0.5770971","text":"def ctestGetElapsedTime():\n global visit_cur_time\n new_time = time.time()\n elapsed_time = new_time - visit_cur_time\n visit_cur_time = new_time\n return elapsed_time","title":""},{"docid":"f2013375cc7926278637f4fa20dbcba7","score":"0.5762016","text":"def t(self):\n return self._attr('t_secs') + self._attr('t_nsecs') * 1E-9","title":""},{"docid":"f482e16189823bc719e9be8bde7b06b5","score":"0.57619727","text":"def get_estimation_time(file):\n try:\n f = open(file, \"r\", errors=\"ignore\")\n li = f.readlines()\n f.close()\n except Exception as e: # pragma: no cover\n warnings.warn(f\"Issue with '{file}'\\n{type(e)}\\n{e}\", UserWarning)\n return 10\n try:\n s = ''.join(li)\n except Exception as e: # pragma: no cover\n warnings.warn(\n \"Probably an enconding issue for file '{0}'\\n{1}\\n{2}\".format(\n file, type(e), e), UserWarning)\n return 10\n c = re.compile(\"[(]time=([0-9]+)s[)]\").search(s)\n if c is None:\n return 0\n return int(c.groups()[0])","title":""},{"docid":"8f2a0737124d28de2f4f2613ebd28ea7","score":"0.5760847","text":"def elapsed_time(word_time):\n return word_time[1]","title":""},{"docid":"8f2a0737124d28de2f4f2613ebd28ea7","score":"0.5760847","text":"def elapsed_time(word_time):\n return word_time[1]","title":""},{"docid":"b197386e322faa36ca38321e91f0f41f","score":"0.5746732","text":"def time_measurement(cls):\n return cls._namespace_SIO('SIO_000391')","title":""},{"docid":"90e1f10a200b441126160750380a5579","score":"0.5727093","text":"def time(self):\r\n return self._time","title":""},{"docid":"cf8a9ab61d85eb410e93e190ac992a8e","score":"0.57202274","text":"def get_last_time(filename: str) -> float:\n\n current_time = time.time()\n\n if os.path.isfile(filename):\n fp = open(filename, 'r')\n t = fp.read()\n fp.close()\n try:\n t = float(t)\n return t\n except ValueError as e:\n print(\"Invalid time in {}\".format(filename))\n return None\n else:\n fp = open(filename, 'w')\n fp.write(str(current_time))\n fp.close()\n return current_time","title":""},{"docid":"c260439f57f9c0c1a3ee786d4f67660a","score":"0.57147145","text":"def time_for_logfile():\n return datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")","title":""},{"docid":"42b8264c759ff3ec83fd0fba9f7d3087","score":"0.5712143","text":"def __total_time(self):\n return utils.get_total_time(self.get_seconds())","title":""},{"docid":"eeb4d2fe56a70efb095d655dc6302953","score":"0.5708492","text":"def now(self):\n return time.time() - self.start","title":""},{"docid":"9baeb4ab7ef17caad12aeb693eb292c5","score":"0.57045394","text":"def test_time_10M_file_size(self):\n t = timeit.Timer(stmt=\"wordcount('10MFile.txt')\", setup=\"from wordgroup import wordcount\")\n self.assertLess(t.timeit(number=1), 60)","title":""},{"docid":"d8e35711b0266c5f325983bb2f4530ec","score":"0.5690758","text":"def getmtime(path):\n return 0.0","title":""},{"docid":"c7ef95cd6270fd1f684fbd2b3951811d","score":"0.5688324","text":"def getComputeTime(self) -> int:\n return self.timeTaken","title":""},{"docid":"10670184cadf8c119b4823aef5d47fa2","score":"0.5686727","text":"def command_time(self,):\n return time.time()","title":""},{"docid":"ada93c8020bf58842ed0209f13a57b21","score":"0.5685386","text":"def getModifiedTime():","title":""},{"docid":"2d842ff8ae49c08febf001ac9b5b65f8","score":"0.56840163","text":"def compute_total_diff(line, base_time):\n date = line.strip().split(':')[2].split('/')\n timestamp = datetime(2014, int(date[0][0:2]), int(date[0][2:4]),\n int(date[1][0:2]), int(date[1][2:4]), int(date[1][4:6]))\n return (timestamp - base_time).total_seconds()","title":""},{"docid":"3ae83a15c0675bdc6ee49f661cc73eff","score":"0.56764346","text":"def elapsed(self):\n return time.time() - self.seconds_since_epoch","title":""},{"docid":"5f5b2ae8e94662bd7a5294e025ddfb1e","score":"0.5672931","text":"def time(self):\n return self.__time","title":""},{"docid":"5f5b2ae8e94662bd7a5294e025ddfb1e","score":"0.5672931","text":"def time(self):\n return self.__time","title":""},{"docid":"bcb83b877260f0d1e2c1bd74f9174349","score":"0.56656355","text":"def real_time(time):\n return time * config.speed","title":""},{"docid":"9c4e28d990d8509ab2e6793291a47386","score":"0.56630296","text":"def remotetime(self, file):\n\t\tself.connection.request(\"HEAD\", self.url + file)\n\n\t\theaders = {}\n\t\theaders['last-modified'] = self.connection.getresponse().getheader('last-modified')\n\t\treturn totime(headers['last-modified'])","title":""},{"docid":"ea60ad9075376ee3fa5a4a4abc21ca3a","score":"0.5662272","text":"def mock_time():\n return real_time() - 61","title":""},{"docid":"adee4beb5fb184f2acfa5c955ab5e6ac","score":"0.56577694","text":"def duration(self):\n return perf_counter() - self.t1","title":""},{"docid":"cf46167d0b09772331a04bf9e578e082","score":"0.5651942","text":"def test_time_100M_file_size(self):\n t = timeit.Timer(stmt=\"wordcount('100MFile.txt')\", setup=\"from wordgroup import wordcount\")\n self.assertLess(t.timeit(number=1), 60)","title":""},{"docid":"9b7eec492362fe7ee38827f10c76be82","score":"0.56512743","text":"def _running_time(self):\n return perf_counter() - self._pc","title":""},{"docid":"28b866bfafe0dfa7a568a37678f8d6ed","score":"0.5650881","text":"def get_cache_time(self, path, modified, mime_type):\n ...","title":""},{"docid":"db52c9575b97fcd41ef04c71d1d82fdb","score":"0.564664","text":"def load_file(self):\n with open('save', 'rb') as fichier:\n identity = pickle.load(fichier)\n for key, value in identity.items():\n if(key in self.statKey):\n self.set_stat(key,value)\n #if(key in self.statKey):\n self.__class__ = identity[\"Type\"]\n self.change()\n old_time = identity[\"Time\"]\n now_time = time.time()\n return (now_time - old_time)/60","title":""},{"docid":"2546033c142d88b8c8f51bb2c22aa301","score":"0.5638738","text":"def time(self) :\n\t\ttry :\n\t\t\treturn self._time\n\t\texcept Exception as e:\n\t\t\traise e","title":""},{"docid":"cfd8e513afe8915e518259b553885a11","score":"0.5627741","text":"def clock():\n return time.clock(), 0.0","title":""},{"docid":"ce302804960ebb247901275e2212474a","score":"0.5627626","text":"def now():\n return time.time()","title":""},{"docid":"0222f1427cc28d169b1630ec8aa5b296","score":"0.56203526","text":"def time(self):\n return self._time","title":""},{"docid":"0222f1427cc28d169b1630ec8aa5b296","score":"0.56203526","text":"def time(self):\n return self._time","title":""}],"string":"[\n {\n \"docid\": \"7ae5ad7bc2d33e48e90728fc6be4c0bf\",\n \"score\": \"0.69575024\",\n \"text\": \"def time():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a0b826d66927ef4fcb590f59bc4d1ac\",\n \"score\": \"0.6760677\",\n \"text\": \"def _measure_timing(self, header):\\n return (rospy.get_rostime() - header.stamp).to_sec()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7c045326c1695556812d47aa77ad9b\",\n \"score\": \"0.6693681\",\n \"text\": \"def time(self):\\r\\n return conf.lib.clang_getFileTime(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf212e10dc68b22e3db3e25f152fc4ee\",\n \"score\": \"0.66304564\",\n \"text\": \"def reading_time(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b1d771180a932fa7412ac457d6c607e\",\n \"score\": \"0.6587622\",\n \"text\": \"def timing():\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"163c57b4544a6345a405dc81a08cb755\",\n \"score\": \"0.65783024\",\n \"text\": \"def time(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f7bcf26ee2380a7d8b78a5eac0669d\",\n \"score\": \"0.6550655\",\n \"text\": \"def time():\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfcce7596f36c14e2b825620b59a8113\",\n \"score\": \"0.65317684\",\n \"text\": \"def _extract_execution_time(self, record):\\n path_dir = self._get_path_reg_dir(record)\\n path_time = os.path.join(path_dir, NAME_FILE_TIME)\\n with open(path_time, 'r') as fp:\\n t_exec = float(fp.read()) / 60.\\n return t_exec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86ab964f6ef324970ca3dee32d112aa\",\n \"score\": \"0.64987504\",\n \"text\": \"def time():\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8998a3d8959ddbfe8bbf409757eed639\",\n \"score\": \"0.6471957\",\n \"text\": \"def process_time():\\n return time.process_time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"882a2636072b66ca02a9b220ce69c032\",\n \"score\": \"0.6436795\",\n \"text\": \"def time(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e0d28d99a1711045bbcfc0234840e4\",\n \"score\": \"0.63417697\",\n \"text\": \"def getctime(pth):\\n\\n buf = struct_stat64()\\n rv = stat64(pth.encode(\\\"utf-8\\\"), pointer(buf))\\n if rv != 0:\\n raise OSError(\\\"Couldn't stat file %r\\\" % pth)\\n return buf.st_birthtimespec.tv_sec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efdeb73f9f007fd50e02656ca3d9cc21\",\n \"score\": \"0.6317825\",\n \"text\": \"def time(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da5cf19d2ed5dfe546ac3ce126446391\",\n \"score\": \"0.629848\",\n \"text\": \"def gettime():\\n return time.process_time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575f014fe27b7f8040684f381b5b2929\",\n \"score\": \"0.62965524\",\n \"text\": \"def getctime(path):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb3aafed086fc8a9ab1ba44324e10df\",\n \"score\": \"0.6296194\",\n \"text\": \"def _time(self):\\n return time.time() - self._start_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c353018993bbcff7b2717534715bf47\",\n \"score\": \"0.6285912\",\n \"text\": \"def time(self):\\n return timestamp() - self._base\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0076601296f8c0008bca72e3a796aed4\",\n \"score\": \"0.62828994\",\n \"text\": \"def time():\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0819a2b0adb82e10259af61b512396e5\",\n \"score\": \"0.62504447\",\n \"text\": \"def time(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88dd7d9e7b4592ddafa0a57ca18134b0\",\n \"score\": \"0.6250373\",\n \"text\": \"def Time():\\n return get_time() - init_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee6a6413281d1b45ad6422e3080bf95d\",\n \"score\": \"0.62480253\",\n \"text\": \"def time(self):\\n return time.monotonic()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72cc8644793e0ea61a0d40a783b8a66\",\n \"score\": \"0.6228801\",\n \"text\": \"def run_time(self):\\n time_now = time.time() - self.time\\n return time_now\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636ac2b936555dde2a530da01334c52e\",\n \"score\": \"0.6224945\",\n \"text\": \"def getatime(filename: AnyStr_) -> float:\\r\\n return os.stat(filename).st_atime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f47f3c589ed800214ad41e91d39e3ce\",\n \"score\": \"0.6208159\",\n \"text\": \"def getatime(path):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6befb4f9c65439446c7e5aedd57a15ae\",\n \"score\": \"0.61784506\",\n \"text\": \"def getctime(filename: AnyStr_) -> float:\\r\\n return os.stat(filename).st_ctime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a963ac4a1fe7cd39187bec7a4dabe5e\",\n \"score\": \"0.6151988\",\n \"text\": \"def Time(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc591254b0ccd5530174f2be5e99cbac\",\n \"score\": \"0.6149372\",\n \"text\": \"def elapsed():\\n return time.clock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8368ca3d9ae6e3880cebd923f6cf50e\",\n \"score\": \"0.61424583\",\n \"text\": \"def total_time() -> float:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a061bc8148354bbb845b3b669039d70\",\n \"score\": \"0.61363965\",\n \"text\": \"def currentTime():\\n\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0358779d55338e815e87d662dc0cac9\",\n \"score\": \"0.6128019\",\n \"text\": \"def time(self):\\n return self._time() - self.start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec2167f7a3e71b6a0c483804d1858bf\",\n \"score\": \"0.61194557\",\n \"text\": \"def filetime(file_path):\\n epoch_timestamp = __get_epochtime(file_path)\\n time_string = __build_timestring(epoch_timestamp)\\n return time_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc32ab3762a618705bbb0c9d109023e1\",\n \"score\": \"0.61027503\",\n \"text\": \"def time( self ):\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba3727dc4cc904e44f9c1ff8af5534f\",\n \"score\": \"0.6033098\",\n \"text\": \"def time():\\n return (origtime.time() - epoch) * resolution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0effb5e4a41fd971f3b1f8e45822bceb\",\n \"score\": \"0.6019206\",\n \"text\": \"def get_time():\\n return frame_counter * SKIP_TICKS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e29a0288f1397ffad6dbfb3fd762d49\",\n \"score\": \"0.6001766\",\n \"text\": \"def baseTimeStamp(logFile):\\r\\n return os.lstat(logFile)[ST_CTIME]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b44525763e7373f119807fdd5989112\",\n \"score\": \"0.59988827\",\n \"text\": \"def duration(self):\\n return len(self.data) / self.fs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2893db60f75e80de00bbe3443f4abc51\",\n \"score\": \"0.5978789\",\n \"text\": \"def time(self):\\n return self.get_time(self.frame, self.audio.samplerate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8016343de0c1a4a14cdd2b1e3e6045c\",\n \"score\": \"0.5976678\",\n \"text\": \"def clock(self, /) -> float:\\n return time.time() - self.start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41809cd1f0e7a970c644d3e0bbf671d4\",\n \"score\": \"0.59597707\",\n \"text\": \"def getatime(self, filename):\\n return os.path.getatime(self.abspath(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22d8be7e6a9eb56cc0fff3d6a335a91\",\n \"score\": \"0.5954437\",\n \"text\": \"def read_file_timed(path):\\n s_time = time.time()\\n try:\\n f = open(path, mode=\\\"rb\\\")\\n data = f.read()\\n return data\\n except IOError as err:\\n logger.error(err)\\n raise\\n else:\\n f.close()\\n finally:\\n stop_time = time.time()\\n dt = stop_time - s_time\\n logger.info(\\\"Time required for {file} = {time}\\\".format(file=path,\\n time=dt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2503ae58a8dcb42aa89cac2f22b163e8\",\n \"score\": \"0.5948028\",\n \"text\": \"def time(self):\\n\\n return self.starts[-1] - time.clock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be20bb9fbb78a75a6321363e5adafd9\",\n \"score\": \"0.5943941\",\n \"text\": \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be20bb9fbb78a75a6321363e5adafd9\",\n \"score\": \"0.5943941\",\n \"text\": \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be20bb9fbb78a75a6321363e5adafd9\",\n \"score\": \"0.5943941\",\n \"text\": \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be20bb9fbb78a75a6321363e5adafd9\",\n \"score\": \"0.5943941\",\n \"text\": \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc9ac3acb1d738faa39995596386ef6\",\n \"score\": \"0.5934402\",\n \"text\": \"def _time():\\n\\t#return int(time.time())\\n\\treturn time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c36c31f85286bf957d5ca09fccdc71\",\n \"score\": \"0.5932821\",\n \"text\": \"def test_calculate_time_difference():\\r\\n r = Race(filename)\\r\\n assert r.calculate_time_difference(4201, 57411) == '+0:53.210'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af07a75954db7f39d6b032d613eb73d7\",\n \"score\": \"0.59280205\",\n \"text\": \"def get_time(self) -> int:\\n return self.read32(2 * 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"770fee269b3916e8abc1227f4210a388\",\n \"score\": \"0.5926292\",\n \"text\": \"def _get_time():\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e23cf1b2434d5f63287841aeb6c7a01c\",\n \"score\": \"0.59233075\",\n \"text\": \"def getatime():\\n if isinstance(p, (fpath,dpath)):\\n return p.getatime()\\n return os.path.getatime(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da95b33bbb59adf5a4c59ac03301c43\",\n \"score\": \"0.5913929\",\n \"text\": \"def get_time():\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07a4520ccd727b67df47a7c9035ce6ad\",\n \"score\": \"0.5913543\",\n \"text\": \"def readTimeStamp(fname,path):\\n fname = os.path.join(os.path.abspath(path),fname)\\n num_lines = sum(1 for line in open(fname))\\n f = open(fname)\\n for i in range(3):\\n f.readline()\\n strt = [f.readline().split()[1] for i in range(num_lines-4)]\\n t = [float(i)/1000000 for i in strt]\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50bb8f09abe097441e948593c3caf8a9\",\n \"score\": \"0.5905055\",\n \"text\": \"def stat(filename):\\n import os\\n rstat = os.stat(filename)\\n return rstat[:7] + tuple(tim + TIME_OFFSET for tim in rstat[7:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e211faf3d3988ea00619e03f087bcb\",\n \"score\": \"0.5901196\",\n \"text\": \"def tic():\\n\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe12454abcbbe385a8b67b64b52dac1\",\n \"score\": \"0.58587563\",\n \"text\": \"def _calculate_runtime(self):\\n lines = self.file_dic['output'].splitlines()\\n start_time = datetime.strptime(lines[0].strip(), self.timestring_format)\\n fin_time = datetime.strptime(lines[-1].strip(), self.timestring_format)\\n dif = fin_time - start_time\\n self.date = fin_time.strftime('%d %b %Y')\\n self.runtime = dif.total_seconds()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a9dba7d04622c65564480631784f13\",\n \"score\": \"0.5851342\",\n \"text\": \"def GetTimeNow():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b80808126fe994f2ed79ca43dc872bf\",\n \"score\": \"0.58404726\",\n \"text\": \"def time_stats(self):\\n func_names = ['_read_graphs', 'run']\\n time_deltas = collections.defaultdict(float)\\n for fn in func_names:\\n time_deltas[fn] = round(\\n self.timestamps[fn + '_out'] - self.timestamps[fn + '_in'],\\n 2\\n )\\n\\n print('Read:\\\\t{} s'.format(time_deltas['_read_graphs']))\\n print('Mine:\\\\t{} s'.format(\\n time_deltas['run'] - time_deltas['_read_graphs']))\\n print('Total:\\\\t{} s'.format(time_deltas['run']))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b1d8ecebffcc8319029a5108fd58e5b\",\n \"score\": \"0.5837847\",\n \"text\": \"def get_time(self):\\n return self.measure_state.time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb18accc1e0e0b1b742714844f7c59d5\",\n \"score\": \"0.58373195\",\n \"text\": \"def time(self):\\n\\n return 649692000.0 + self.monotonic_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d93137c7dbb7240cf6bb3074a975485d\",\n \"score\": \"0.58195084\",\n \"text\": \"def timestamp():\\r\\n return _clock_func()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b2c945bb4ffdad2ff7201bc6ededc54\",\n \"score\": \"0.580161\",\n \"text\": \"def elapsed():\\n global start_time\\n return time.time() - start_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4097eb63a93fcd2bea9537ae3910c2a\",\n \"score\": \"0.57996166\",\n \"text\": \"def test_time_1M_file_size(self):\\n t = timeit.Timer(stmt=\\\"wordcount('1MFile.txt')\\\", setup=\\\"from wordgroup import wordcount\\\")\\n self.assertLess(t.timeit(number=1), 120)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560e4d94753f99b1b342dd33827a1733\",\n \"score\": \"0.57904214\",\n \"text\": \"def get_time():\\n g.start = time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636cb6be6689f6dfb5e1e9efb61ab94e\",\n \"score\": \"0.5788313\",\n \"text\": \"def time(self):\\n return self._time_code*units.time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef3d092ce8e826f89460c877db0745a5\",\n \"score\": \"0.5782163\",\n \"text\": \"def get_timestamp():\\n return time.time() - start_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f47050df4029f69991fdc81993e7d14b\",\n \"score\": \"0.57779217\",\n \"text\": \"def time(self):\\n return self.t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e87d6e9c4bee26315fca8db58d8ab75\",\n \"score\": \"0.5772191\",\n \"text\": \"def total_time(self):\\n return self.time_records.total_time().total_seconds()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4daa80b55fbd716e83274d65e7c9b50c\",\n \"score\": \"0.5770971\",\n \"text\": \"def ctestGetElapsedTime():\\n global visit_cur_time\\n new_time = time.time()\\n elapsed_time = new_time - visit_cur_time\\n visit_cur_time = new_time\\n return elapsed_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2013375cc7926278637f4fa20dbcba7\",\n \"score\": \"0.5762016\",\n \"text\": \"def t(self):\\n return self._attr('t_secs') + self._attr('t_nsecs') * 1E-9\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f482e16189823bc719e9be8bde7b06b5\",\n \"score\": \"0.57619727\",\n \"text\": \"def get_estimation_time(file):\\n try:\\n f = open(file, \\\"r\\\", errors=\\\"ignore\\\")\\n li = f.readlines()\\n f.close()\\n except Exception as e: # pragma: no cover\\n warnings.warn(f\\\"Issue with '{file}'\\\\n{type(e)}\\\\n{e}\\\", UserWarning)\\n return 10\\n try:\\n s = ''.join(li)\\n except Exception as e: # pragma: no cover\\n warnings.warn(\\n \\\"Probably an enconding issue for file '{0}'\\\\n{1}\\\\n{2}\\\".format(\\n file, type(e), e), UserWarning)\\n return 10\\n c = re.compile(\\\"[(]time=([0-9]+)s[)]\\\").search(s)\\n if c is None:\\n return 0\\n return int(c.groups()[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2a0737124d28de2f4f2613ebd28ea7\",\n \"score\": \"0.5760847\",\n \"text\": \"def elapsed_time(word_time):\\n return word_time[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2a0737124d28de2f4f2613ebd28ea7\",\n \"score\": \"0.5760847\",\n \"text\": \"def elapsed_time(word_time):\\n return word_time[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b197386e322faa36ca38321e91f0f41f\",\n \"score\": \"0.5746732\",\n \"text\": \"def time_measurement(cls):\\n return cls._namespace_SIO('SIO_000391')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e1f10a200b441126160750380a5579\",\n \"score\": \"0.5727093\",\n \"text\": \"def time(self):\\r\\n return self._time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf8a9ab61d85eb410e93e190ac992a8e\",\n \"score\": \"0.57202274\",\n \"text\": \"def get_last_time(filename: str) -> float:\\n\\n current_time = time.time()\\n\\n if os.path.isfile(filename):\\n fp = open(filename, 'r')\\n t = fp.read()\\n fp.close()\\n try:\\n t = float(t)\\n return t\\n except ValueError as e:\\n print(\\\"Invalid time in {}\\\".format(filename))\\n return None\\n else:\\n fp = open(filename, 'w')\\n fp.write(str(current_time))\\n fp.close()\\n return current_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c260439f57f9c0c1a3ee786d4f67660a\",\n \"score\": \"0.57147145\",\n \"text\": \"def time_for_logfile():\\n return datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b8264c759ff3ec83fd0fba9f7d3087\",\n \"score\": \"0.5712143\",\n \"text\": \"def __total_time(self):\\n return utils.get_total_time(self.get_seconds())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb4d2fe56a70efb095d655dc6302953\",\n \"score\": \"0.5708492\",\n \"text\": \"def now(self):\\n return time.time() - self.start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9baeb4ab7ef17caad12aeb693eb292c5\",\n \"score\": \"0.57045394\",\n \"text\": \"def test_time_10M_file_size(self):\\n t = timeit.Timer(stmt=\\\"wordcount('10MFile.txt')\\\", setup=\\\"from wordgroup import wordcount\\\")\\n self.assertLess(t.timeit(number=1), 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e35711b0266c5f325983bb2f4530ec\",\n \"score\": \"0.5690758\",\n \"text\": \"def getmtime(path):\\n return 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ef95cd6270fd1f684fbd2b3951811d\",\n \"score\": \"0.5688324\",\n \"text\": \"def getComputeTime(self) -> int:\\n return self.timeTaken\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10670184cadf8c119b4823aef5d47fa2\",\n \"score\": \"0.5686727\",\n \"text\": \"def command_time(self,):\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada93c8020bf58842ed0209f13a57b21\",\n \"score\": \"0.5685386\",\n \"text\": \"def getModifiedTime():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d842ff8ae49c08febf001ac9b5b65f8\",\n \"score\": \"0.56840163\",\n \"text\": \"def compute_total_diff(line, base_time):\\n date = line.strip().split(':')[2].split('/')\\n timestamp = datetime(2014, int(date[0][0:2]), int(date[0][2:4]),\\n int(date[1][0:2]), int(date[1][2:4]), int(date[1][4:6]))\\n return (timestamp - base_time).total_seconds()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae83a15c0675bdc6ee49f661cc73eff\",\n \"score\": \"0.56764346\",\n \"text\": \"def elapsed(self):\\n return time.time() - self.seconds_since_epoch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f5b2ae8e94662bd7a5294e025ddfb1e\",\n \"score\": \"0.5672931\",\n \"text\": \"def time(self):\\n return self.__time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f5b2ae8e94662bd7a5294e025ddfb1e\",\n \"score\": \"0.5672931\",\n \"text\": \"def time(self):\\n return self.__time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb83b877260f0d1e2c1bd74f9174349\",\n \"score\": \"0.56656355\",\n \"text\": \"def real_time(time):\\n return time * config.speed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4e28d990d8509ab2e6793291a47386\",\n \"score\": \"0.56630296\",\n \"text\": \"def remotetime(self, file):\\n\\t\\tself.connection.request(\\\"HEAD\\\", self.url + file)\\n\\n\\t\\theaders = {}\\n\\t\\theaders['last-modified'] = self.connection.getresponse().getheader('last-modified')\\n\\t\\treturn totime(headers['last-modified'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea60ad9075376ee3fa5a4a4abc21ca3a\",\n \"score\": \"0.5662272\",\n \"text\": \"def mock_time():\\n return real_time() - 61\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adee4beb5fb184f2acfa5c955ab5e6ac\",\n \"score\": \"0.56577694\",\n \"text\": \"def duration(self):\\n return perf_counter() - self.t1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf46167d0b09772331a04bf9e578e082\",\n \"score\": \"0.5651942\",\n \"text\": \"def test_time_100M_file_size(self):\\n t = timeit.Timer(stmt=\\\"wordcount('100MFile.txt')\\\", setup=\\\"from wordgroup import wordcount\\\")\\n self.assertLess(t.timeit(number=1), 60)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7eec492362fe7ee38827f10c76be82\",\n \"score\": \"0.56512743\",\n \"text\": \"def _running_time(self):\\n return perf_counter() - self._pc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b866bfafe0dfa7a568a37678f8d6ed\",\n \"score\": \"0.5650881\",\n \"text\": \"def get_cache_time(self, path, modified, mime_type):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db52c9575b97fcd41ef04c71d1d82fdb\",\n \"score\": \"0.564664\",\n \"text\": \"def load_file(self):\\n with open('save', 'rb') as fichier:\\n identity = pickle.load(fichier)\\n for key, value in identity.items():\\n if(key in self.statKey):\\n self.set_stat(key,value)\\n #if(key in self.statKey):\\n self.__class__ = identity[\\\"Type\\\"]\\n self.change()\\n old_time = identity[\\\"Time\\\"]\\n now_time = time.time()\\n return (now_time - old_time)/60\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2546033c142d88b8c8f51bb2c22aa301\",\n \"score\": \"0.5638738\",\n \"text\": \"def time(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._time\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd8e513afe8915e518259b553885a11\",\n \"score\": \"0.5627741\",\n \"text\": \"def clock():\\n return time.clock(), 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce302804960ebb247901275e2212474a\",\n \"score\": \"0.5627626\",\n \"text\": \"def now():\\n return time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0222f1427cc28d169b1630ec8aa5b296\",\n \"score\": \"0.56203526\",\n \"text\": \"def time(self):\\n return self._time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0222f1427cc28d169b1630ec8aa5b296\",\n \"score\": \"0.56203526\",\n \"text\": \"def time(self):\\n return self._time\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994382,"cells":{"query_id":{"kind":"string","value":"d2aeec4836cdf76336be24b910aab608"},"query":{"kind":"string","value":"Create our initial stream and write to it over SMB1 protocol. Start with offset 0."},"positive_passages":{"kind":"list like","value":[{"docid":"72044df7bab733678cb137f8235a48e3","score":"0.68725437","text":"def test_065_create_and_write_stream_smb1(request):\n depends(request, [\"STREAM_TESTFILE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\n fd = c.create_file(\"streamstestfile:smb1_stream\", \"w\")\n c.write(fd, b'test1', 0)\n c.close(fd)\n\n fd2 = c.create_file(\"streamstestfile:smb1_stream\", \"w\")\n contents = c.read(fd2, 0, 5)\n c.close(fd2)\n c.disconnect()\n assert(contents.decode() == \"test1\")","title":""}],"string":"[\n {\n \"docid\": \"72044df7bab733678cb137f8235a48e3\",\n \"score\": \"0.68725437\",\n \"text\": \"def test_065_create_and_write_stream_smb1(request):\\n depends(request, [\\\"STREAM_TESTFILE_CREATED\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\\n fd = c.create_file(\\\"streamstestfile:smb1_stream\\\", \\\"w\\\")\\n c.write(fd, b'test1', 0)\\n c.close(fd)\\n\\n fd2 = c.create_file(\\\"streamstestfile:smb1_stream\\\", \\\"w\\\")\\n contents = c.read(fd2, 0, 5)\\n c.close(fd2)\\n c.disconnect()\\n assert(contents.decode() == \\\"test1\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bddce7b71ffc0e39fb548f3b8fd0246f","score":"0.65759665","text":"def test_061_create_and_write_stream_smb2(request):\n depends(request, [\"STREAM_TESTFILE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\n fd = c.create_file(\"streamstestfile:smb2_stream\", \"w\")\n c.write(fd, b'test1', 0)\n c.close(fd)\n\n fd2 = c.create_file(\"streamstestfile:smb2_stream\", \"w\")\n contents = c.read(fd2, 0, 5)\n c.close(fd2)\n c.disconnect()\n assert(contents.decode() == \"test1\")","title":""},{"docid":"126a19d4cc1fdba86ffdf318539b7405","score":"0.65454155","text":"def test_066_write_stream_large_offset_smb1(request):\n depends(request, [\"STREAM_WRITTEN_SMB1\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\n fd = c.create_file(\"streamstestfile:smb1_stream\", \"w\")\n c.write(fd, b'test2', 131072)\n c.close(fd)\n\n fd2 = c.create_file(\"streamstestfile:smb1_stream\", \"w\")\n contents = c.read(fd2, 131072, 5)\n c.close(fd2)\n c.disconnect()\n assert(contents.decode() == \"test2\")","title":""},{"docid":"d073a849be8994cbdb6180547473aa68","score":"0.62581897","text":"def test_062_write_stream_large_offset_smb2(request):\n depends(request, [\"STREAM_TESTFILE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\n fd = c.create_file(\"streamstestfile:smb2_stream\", \"w\")\n c.write(fd, b'test2', 131072)\n c.close(fd)\n\n fd2 = c.create_file(\"streamstestfile:smb2_stream\", \"w\")\n contents = c.read(fd2, 131072, 5)\n c.close(fd2)\n c.disconnect()\n assert(contents.decode() == \"test2\")","title":""},{"docid":"12c69e8bed0c0a18fd7e367600ec31ea","score":"0.61246866","text":"def init_stream(self):\r\n stream = Stream()\r\n while True:\r\n x = self.read_1b()\r\n if x != 0xff:\r\n stream.write_byte(x)\r\n else:\r\n y = self.read_1b()\r\n if y == 0x00: # remove byte padding 0x00\r\n stream.write_byte(x)\r\n else: # x is the first byte of the next marker\r\n self.pos -= 2\r\n break\r\n self.stream = stream","title":""},{"docid":"8db0219a7398d44bed5c3ecf48e4c8f7","score":"0.5964676","text":"def test_060_create_base_file_for_streams_tests(request):\n depends(request, [\"SMB_SHARE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\n fd = c.create_file(\"streamstestfile\", \"w\")\n c.close(fd)\n c.disconnect()","title":""},{"docid":"55ae7cc571877bb82b8d5ce901331d4d","score":"0.5924718","text":"def _initiate(self, stream, seek=None):\n if seek is not None:\n stream.seek(seek)\n\n record_type, = unpack('b', stream.read(BYTE_SIZE))\n root_id, header_id, major_version, minor_version = unpack(\n '4i', stream.read(INT_SIZE * 4)\n )\n self.record_type = record_type\n self.root_id = root_id\n self.header_id = header_id\n self.major_version = major_version\n self.minor_version = minor_version","title":""},{"docid":"fb455905f0e9949312dedd05d0e71f07","score":"0.5912581","text":"def _write_stream_content(self):\n self.add_debug('Write stream ...')\n\n self.__write_pool_creation_section()\n self.__get_source_plates()\n self.__write_prep_creation_section()\n self.__write_aliquot_part()","title":""},{"docid":"78b8a4240f0a457836000b8368035668","score":"0.576237","text":"def start_object(self):\r\n self.stream.write(u'{')","title":""},{"docid":"c0f748f960dfb28a8e4f7ea5796a0052","score":"0.57486194","text":"def SetStream():\n uart.write( b\"\\xFF\\x01\\x78\\x40\\x00\\x00\\x00\\x00\\x47\")\n return","title":""},{"docid":"61f9fe285d90f23bbbbe746d8414b50e","score":"0.56689066","text":"def _write_stream_content(self):\n self.add_debug('Write stream ...')\n\n self.__write_main_headline()\n self.__write_general_section()\n self.__write_destination_racks_section()\n self.__write_source_racks_section()","title":""},{"docid":"110f226f9c7494d5b0ecc845f9c903f5","score":"0.5573991","text":"def __init__(self, fid, offset, data):\n self.name = \"Twrite\"\n self.length = 23 + len(data)\n self.type = 118\n self.fid = fid\n self.offset = offset\n self.count = len(data)\n self.data = data\n self.messageBody = struct.pack('<IQI', self.fid, self.offset, self.count) + self.data\n self.body = \"%s, %s, %s, %s\" % (self.fid, self.offset, self.count, self.data[:20])","title":""},{"docid":"b4fc89b6367fcadf34e17c972add9e87","score":"0.5546117","text":"def write(self, stream):\n raise NotImplementedError","title":""},{"docid":"86ccea85f2ee13ea07511eb3ea33879b","score":"0.551612","text":"def write_stream(self, stream):\n pass","title":""},{"docid":"7ae6296518f1fc5e6150fcfc527155ed","score":"0.55077374","text":"def begin(self):\n self.wrote_header = False","title":""},{"docid":"dae2c57a667794211fbc433676cd815e","score":"0.5491221","text":"def write(self, buf):","title":""},{"docid":"f831f802ae0c126f58de40f039c121cd","score":"0.54895175","text":"def GetOutputStream(self):","title":""},{"docid":"1b42fe1153f2690893d7b8ba48e09f5e","score":"0.54695904","text":"def __init__(self):\n\t\tself.buffer = ''\n\t\tself.handshake()","title":""},{"docid":"0056172219873a1d5888fd40c8a42594","score":"0.54313385","text":"def __init__(self, stream):\n # Always add padding to make sure that last block has 16\n # bytes. Use the PKCS#5 format: '\\x01', '\\x02\\x02',\n # '\\x03\\x03\\x03', etc. See section 6.3 of\n # http://www.ietf.org/rfc/rfc3852.txt\n padding_bytes_needed = 16 - len(stream) % 16 or 16\n self.padding = chr(padding_bytes_needed) * padding_bytes_needed\n stream_length = len(stream) + len(self.padding)\n assert(stream_length % 16 == 0)\n RewindableSizedInputStream.__init__(self, stream_length)\n self.stream = stream\n self.stream_keep = stream\n self.rewind()","title":""},{"docid":"834c235785f14afd853d31114596257c","score":"0.5425964","text":"def InitKaldiOutputStream(stream, binary):\n if binary:\n stream.Write('\\0')\n stream.Write('B')\n return True","title":""},{"docid":"708d236036aa2ecd4bab4d789304e5a8","score":"0.5393589","text":"def write_chunk(stream, buf):\n nbytes = bytelen(buf)\n stream.write(magic_bytes)\n stream.write(struct.pack(\"@q\", nbytes))\n stream.write(bytedata(buf))\n padding = roundup(nbytes) - nbytes\n if padding > 0:\n stream.write(b\"\\0\" * padding)","title":""},{"docid":"06a61d908f3cd74a6194553e4d715d67","score":"0.53689986","text":"def BaseStream(self) -> _n_2_t_0:","title":""},{"docid":"e95ef031afb5a296fc229365e0641513","score":"0.53654003","text":"def test_067_stream_delete_on_close_smb1(request):\n depends(request, [\"STREAM_WRITTEN_SMB1\", \"LARGE_STREAM_WRITTEN_SMB1\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\n fd = c.create_file(\"streamstestfile:smb1_stream\", \"w\")\n c.close(fd, True)\n\n c.disconnect()","title":""},{"docid":"4110406ee3b4921b2dba2eed0ae2affd","score":"0.53506273","text":"def Open(self):\n ## Make Header\n hex_rep = self.NanonisTCP.make_header('BiasSpectr.Open', body_size=0)\n \n self.NanonisTCP.send_command(hex_rep)\n \n self.NanonisTCP.receive_response(0)","title":""},{"docid":"9e415eb1f273bbba39aae63c2d011fad","score":"0.5332037","text":"def __init__(self, bufsize=16384):\n self._raw = BytesIO()\n self.bufsize = bufsize\n self.write = self.write_stringio\n self.exceeded_bufsize = False","title":""},{"docid":"1c7f2fb8b89568c096d659445396e8ce","score":"0.53302926","text":"def initiate(self):\n self.write(\":INIT;*WAI;*OPC\")","title":""},{"docid":"875907b298de8838a08c157f31dbaf0b","score":"0.53085804","text":"def __writeFirstHeader(self):\n\n# CALCULAR PARAMETROS\n\n sizeLongHeader = self.systemHeaderObj.size + \\\n self.radarControllerHeaderObj.size + self.processingHeaderObj.size\n self.basicHeaderObj.size = self.basicHeaderSize + sizeLongHeader\n\n self.basicHeaderObj.write(self.fp)\n self.systemHeaderObj.write(self.fp)\n self.radarControllerHeaderObj.write(self.fp)\n self.processingHeaderObj.write(self.fp)","title":""},{"docid":"5b932e58cc7afe12f9d1a1f1390a85f0","score":"0.5300523","text":"def __init__(self, stream):\n SizedInputStream.__init__(self, len(stream))\n self.stream = stream\n self.stream_keep = stream\n self.buffer = ''","title":""},{"docid":"97530b2757eac9fb1d7c83f97d02e606","score":"0.52969205","text":"def stream(self):\n\t\t#self.SCI.send(self.config.data['opcodes']['start'],0)","title":""},{"docid":"68d0fa045b142455fe6f2eecd6910aa9","score":"0.52520484","text":"def start(self, stream_buffer):\n raise NotImplementedError()","title":""},{"docid":"2f94158969253d1382c6870a84f71b00","score":"0.5215718","text":"def reset(self):\n self._stream.reset()\n sleep(0.1)\n # Send some empty packets to flush any corrupt data\n self._stream.write(b\"\")\n self._stream.write(b\"\")\n self._stream.write(b\"\")\n self.get_version()","title":""},{"docid":"d26ee83b2d6478c6fa0ea2af883b812d","score":"0.52114683","text":"def _write_header(self, output_stream):\n output_stream.write_byte(self._version) # 1 byte\n output_stream.write_int64_unassigned(self.timestamp) # 8 byte\n output_stream.write_vector_3_int32(self.position) # 12 byte\n output_stream.write_bool(self.has_valid_data) # 1 byte","title":""},{"docid":"cd7c8ebf5232be0e7597ae0541e86682","score":"0.5204206","text":"def __init__(\n self,\n stream_opener: Callable[[], io.IOBase],\n offset: int = 0,\n stream_length: int | None = None,\n stream_sha1: Sha1HexDigest | None = None,\n ):\n super().__init__(\n stream_opener,\n stream_length=stream_length,\n stream_sha1=stream_sha1,\n )\n self._offset = offset","title":""},{"docid":"b05d3468cb4c57fb7b18dd13a0be1a1b","score":"0.52017593","text":"def _write_header(self):\n self._device_handle.seek(0)\n\n # Remember: we can only write a whole sector at a time\n first_sector = NOFS_HEADER\n first_sector += NOFS_TERMINAL\n first_sector += (NOFS_SECTOR_SIZE - len(NOFS_HEADER) -\n len(NOFS_TERMINAL)) * chr(0xFF)\n\n # Write it onto the device\n self._device_handle.write(first_sector)\n self._device_handle.flush()","title":""},{"docid":"2d252900015f4809c37fe765bc46af97","score":"0.5190342","text":"def _setup_streaming_request(self):\r\n self.raw = self._create_raw(chunked=True)","title":""},{"docid":"be5271c19f4015178d3ce07fc8a4539d","score":"0.5186833","text":"def write(self, output_stream):\n assert isinstance(output_stream, SMBinaryStream)\n self._version = max(self._valid_versions)\n self._write_header(output_stream)\n self._write_block_data(output_stream)","title":""},{"docid":"48a6b63fbd5b92ae471e4df3ccac92c5","score":"0.51842695","text":"def __init__(self, start, length):\n self._buffer = BinaryBuffer(start, length)\n self._offset = 0","title":""},{"docid":"7797fe428ab8a71f3f796daa7b3f13ed","score":"0.51745135","text":"def start(self):\r\n data_to_send = self.data_to_convert[:self.i]\r\n logger.info(\"Converting to hex data: '{}'\".format(data_to_send))\r\n # Write the data, and setup the _write_complete callback to be executed when the write finishes\r\n self.char.write(data_to_send).then(self._write_complete)","title":""},{"docid":"cedbd9d9d66e7cf58266bdc3a19b4cca","score":"0.5173928","text":"def OutputStream(self) -> _n_5_t_0:","title":""},{"docid":"3b96f173e150a3fbef89714f7fabbe95","score":"0.5166642","text":"def __init__(self, stream):\n self.__temp_filename = f'data/{str(uuid.uuid4())}'\n with open(self.__temp_filename, 'w') as out_fd:\n out_fd.write(stream.read())\n self.__reader = None","title":""},{"docid":"a27efaaab05ff896a035181b4080b8c0","score":"0.51446676","text":"def setup(self):\r\n self.connection = self.request # for doPOST\r\n self.rfile = socket._fileobject(self.request, \"rb\", self.rbufsize)\r\n self.wfile = socket._fileobject(self.request, \"wb\", self.wbufsize)","title":""},{"docid":"37cdf10b1c55b6dc90661a032c0c0659","score":"0.5133127","text":"def __init__(self, buf = None):\n\n super().__init__()\n\n self._rf = io.BytesIO(buf)\n self._wf = io.BytesIO()","title":""},{"docid":"98d280beffe79b410ae9b0ce2365b710","score":"0.51329637","text":"def __write_first_scenario(self, stream, output_path, bank):\n self.__log.info(\"Writing header from '%s': '%s'\", output_path, bank.header.rstrip(\"\\n\"))\n stream.write(bank.header)\n\n self.__log.info(\"Writing feature from '%s': '%s'\", output_path, bank.feature.rstrip(\"\\n\"))\n stream.write(bank.feature)\n\n self.__write_next_scenario(stream, output_path, bank)","title":""},{"docid":"2109fa5f14b17fa6b90b7b48133cfbbc","score":"0.5125137","text":"def __init__(self, tree, name):\n self.pipe_buffer = b\"\"\n super(OutputPipeBytes, self).__init__(tree, name)","title":""},{"docid":"25d7a7aa20bde6047ce9792846dd0ad9","score":"0.5123535","text":"def __init__(self):\n self.size = 0\n self.buf = []","title":""},{"docid":"3974e5d4f89e2a7c3242d3e45a89da94","score":"0.5111709","text":"def __init__(self, backingStream):\n \n self.backingStream = backingStream\n # We have no write listeners yet\n self.writeListeners = []","title":""},{"docid":"80733148946b065f712890dc61d0c047","score":"0.5096877","text":"def write_header(self, writeStream):\n\t\twriteStream.write(bytearray())","title":""},{"docid":"cd8fa78ce6b5c731d905b7351ec1aadc","score":"0.5095322","text":"def write(self, chunk):\n ...","title":""},{"docid":"a9b866bf8892425fe5deb562f86b3333","score":"0.5087629","text":"def sendType1(self):\n\n # Transforming all the header info into bytes\n msgType = self.msgType1.to_bytes(1, \"little\")\n serverAddress = self.serverAddress.to_bytes(1, \"little\")\n totalPackets = self.numberOfPackets.to_bytes(3, \"little\")\n fileExtension = bytes(\"\".join([\"@\" for i in range(5 - len(self.fileExtension))]), \"UTF-8\") + self.fileExtensionBA\n payloadSize = bytes([0])\n filler = bytes([0]) * 5\n\n # Build header\n # HEAD = msgType[1] + serverAddress[1] + totalPackets[3] + fileExtension[5] + payloadSize[1] + filler[5]\n head = msgType + serverAddress + totalPackets + payloadSize + fileExtension + filler\n\n # Sending the request\n self.com.sendData(head + self.eop)\n\n # Wait for the data to be sent.\n while (self.com.tx.getIsBussy()):\n pass\n\n self.log(\"Sent TYPE1.\", \"client\")","title":""},{"docid":"ea32b15e97ee6b492e444ec7c77f65d1","score":"0.508755","text":"def start(self):\r\n\r\n if not self.__started:\r\n if self.__status is None:\r\n self.setStatus(200)\r\n if self.__content_type is None:\r\n self.setContentType(\"text/plain\")\r\n\r\n headers = [(\"Content-Type\", self.__content_type)]\r\n headers.extend(self.__response_headers)\r\n\r\n self.__write = self.__start_response(self.__status, headers)\r\n self.__started = True","title":""},{"docid":"6e8e5ca3066f497fff95bedc7675a7d6","score":"0.5080573","text":"def __init__(self):\n self.buf = \"\"","title":""},{"docid":"0f10dbbd9703cc552d97a23cc6de96c4","score":"0.50719166","text":"def _write_block(self, start, data):\n with self._i2c as i2c:\n i2c.write(bytes((start,)) + data)","title":""},{"docid":"88cf91009838dcef439fbe4ddb4ca719","score":"0.5064834","text":"def write(self, stream, data):\n raise NotImplementedError","title":""},{"docid":"035c54bcc996231359534356e5027e88","score":"0.50421983","text":"def write(self, data):\n #\n # Size of the packet header\n #\n header_len = 10\n #\n # In data Recived from SDS, '\\x1B\\x1B' reserved for status change,\n # however transmitted data uses no such mechanism\n # Don't substitute data in sending packet.\n # data.replace('\\x1B\\x1B', '\\x1B\\x1B\\xFF')\n #\n # Check if we can wirte\n # \n if not self.is_open:\n raise portNotOpenError\n self._write_lock.acquire()\n try:\n try:\n #\n # Write to socket chunks of data, with-in limit:\n # ( 10 byte header + data ) <= MAX_PACKET_SIZE\n # after writing chunck, slice off\n #\n while len(data):\n #\n # Faster method to got largest chunck\n # \n if header_len + len(data) < MAX_PACKET_SIZE:\n position = len(data)\n else:\n position = MAX_PACKET_SIZE - header_len\n #\n # Line to be sent \n #\n line = data[:position]\n #\n # Cut line off processed data\n #\n data = data[position:]\n #\n # Increment sequence number 0 to 0xFFFF\n #\n self.packet_sequence_num += 1\n if self.packet_sequence_num >= 0xFFFF:\n self.packet_sequence_num = 0\n #\n # Make note in log\n #\n if self.logger:\n self.logger.info(\n (\n \"Writing to SDS: sequence:{} size:{} data:{}\"\n ).format(\n self.packet_sequence_num,\n position,\n line,\n )\n )\n #\n # Send message\n #\n self._socket.sendall(\n '\\x1B\\x1B\\x1B' # Header\n '\\x00' # Reseverd\n + struct.pack('!H', self.packet_sequence_num)\n + self.com.ctrl['TX_DATA']\n + self.com.ctrl['DEST_PORT']\n + struct.pack('!H', len(line)) # Data size\n + line\n )\n\n except socket.error as e:\n # Rais SerialException if conneciton fails,\n # so that caller can catch the exception\n raise SerialException(\n \"connection failed (socket error): {}\".format(e)\n )\n finally:\n self._write_lock.release()\n return len(data)","title":""},{"docid":"809edb61666b332d455cf736741c9ec3","score":"0.5025241","text":"def __init__(self, orientation=0):\n super().__init__(END_PIPE, orientation, False)","title":""},{"docid":"b147e0da2015475e1bb8260d8696c121","score":"0.50244826","text":"def Write(packer_type, buf, head, n):\n packer_type.pack_into(buf, head, n)","title":""},{"docid":"9f132b78320f3b47d563757867736d0f","score":"0.50182176","text":"def write(self, text, start=0):\r\n # simple scenario, let pickle create bytes\r\n if self.serialize:\r\n if CONQUE_PYTHON_VERSION == 3:\r\n tb = pickle.dumps(text, 0)\r\n else:\r\n tb = pickle.dumps(text, 0).encode(self.encoding)\r\n\r\n else:\r\n tb = text.encode(self.encoding, 'replace')\r\n\r\n # write to memory\r\n self.shm.seek(start * self.char_width)\r\n\r\n if self.fixed_length:\r\n self.shm.write(tb)\r\n else:\r\n self.shm.write(tb + self.TERMINATOR)","title":""},{"docid":"55be308a107a9d0e7790725be413224b","score":"0.5010571","text":"def stream_start(self):\n self.initiate()\n print(\" -- starting stream\")\n self.keepRecording=True # set this to False later to terminate stream\n self.data=None # will fill up with threaded recording data\n self.fft=None\n self.dataFiltered=None #same\n self.stream=self.p.open(format=pyaudio.paInt16,channels=1,\n rate=self.rate,input=True,frames_per_buffer=self.chunk)\n self.stream_thread_new()\n\n #main no UI","title":""},{"docid":"1dde02e8ad7285e38fb72d344d987bfe","score":"0.49900386","text":"def create_smbv1_conn(self):\n try:\n logging.debug('Attempting SMBv1 connection to {}'.format(self.host))\n logging.debug('using args.port: {}'.format(self.args.port))\n self.conn = impacket.smbconnection.SMBConnection(self.host, self.host, None, self.args.port) #, preferredDialect=impacket.smb.SMB_DIALECT)\n except socket.error as e:\n if str(e).find('Connection reset by peer') != -1:\n logging.debug('Connection was reset by target. SMBv1 might be disabled on {}'.format(self.host))\n elif str(e).find('No route to host') != -1:\n logging.debug('Could not connect to {}, no route to host. Can you ping it?'.format(self.host))\n else:\n logging.debug('Something went wrong, Could not connect to {}:{}, tried smbv1'.format(self.host, self.args.port))\n return False\n except Exception as e:\n logging.debug('Error creating SMBv1 connection to {}: {}'.format(self.host, e))\n return False\n logging.debug('Connected using SMBv1 to: {}:{}'.format(self.host, self.args.port))\n return True","title":""},{"docid":"665684b350a63ea1f6ce8311c1cac3e6","score":"0.4988296","text":"def handle_write(self):\n self.initiate_send()","title":""},{"docid":"c7ed9dd92deeec8683a6d01a91778260","score":"0.49792826","text":"def __init__(self):\n self._stream = os.dup(sys.stdout.fileno())\n self._touch()","title":""},{"docid":"e2ab1d3d42dfa8f168fed22b29a8d079","score":"0.4972222","text":"def WriteAll(self, buffer, size):","title":""},{"docid":"eb62299dd1d24effe7a1191227afa22c","score":"0.49719277","text":"def __init__(self, stream):\n self.stream = stream","title":""},{"docid":"eb62299dd1d24effe7a1191227afa22c","score":"0.49719277","text":"def __init__(self, stream):\n self.stream = stream","title":""},{"docid":"5cc512a50209a828421f4abc6fa55d5b","score":"0.4970963","text":"def test_writeSequence(self):\n data = b\"some bytes\"\n\n class SimpleSendingProtocol(Protocol):\n def connectionMade(self):\n self.transport.writeSequence(list(iterbytes(data)))\n\n return self.writeBeforeHandshakeTest(SimpleSendingProtocol, data)","title":""},{"docid":"bf9767c96a09bc4ab22acda95bfd7a43","score":"0.49663442","text":"def write(self, buf):\n if not self.connected:\n raise VppTransportSocketIOError(1, \"Not connected\")\n\n # Send header\n header = self.header.pack(0, len(buf), 0)\n try:\n self.socket.sendall(header)\n self.socket.sendall(buf)\n except socket.error as err:\n raise VppTransportSocketIOError(1, \"Sendall error: {err!r}\".format(err=err))","title":""},{"docid":"ba8f3c2e8d1db1223068f1c7ff492dac","score":"0.4963811","text":"def __init__(self, bufsize: int):\r\n self.buf = dict([(i, None) for i in range(Seq.MOD)]) # None mean empty packet\r\n self.base = Seq(0)\r\n self.bufsize = bufsize","title":""},{"docid":"7af645f421f15453353267e48a8f3c34","score":"0.49343464","text":"def _execute_task(self):\n self.add_info('Start rack transfer file generation ...')\n self.__stream = StringIO()\n self.__write_content()\n if not self.has_errors():\n self.return_value = self.__stream\n self.add_info('File creation completed.')","title":""},{"docid":"f42ba7bdaeae96c6beae48807f29d6f1","score":"0.49125788","text":"def _write_block_data(self, output_stream):\n if not self.has_valid_data:\n self.compressed_size = 0\n output_stream.write_int32_unassigned(self.compressed_size) # 4 byte\n else:\n byte_string = b\"\"\n set_of_valid_block_index = set(self.block_index_to_block.keys())\n for block_index in range(0, self._blocks_in_a_cube):\n if block_index in set_of_valid_block_index:\n block_int_24 = self.block_index_to_block[block_index].get_int_24()\n if self._version < 3:\n byte_string += SMBinaryStream.pack_int24(block_int_24)\n else:\n byte_string += SMBinaryStream.pack_int24b(block_int_24)\n continue\n byte_string += b\"\\0\" * 3\n compressed_data = zlib.compress(byte_string)\n self.compressed_size = len(compressed_data)\n output_stream.write_int32_unassigned(self.compressed_size) # 4 byte\n output_stream.write(compressed_data)\n\n output_stream.seek(49125-self.compressed_size, 1)\n output_stream.write(b\"\\0\") # this should fill the skipped positions with \\0","title":""},{"docid":"676c0437e95d0a096be94d5755118634","score":"0.49102533","text":"def test_063_stream_delete_on_close_smb2(request):\n depends(request, [\"STREAM_WRITTEN_SMB2\", \"LARGE_STREAM_WRITTEN_SMB2\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\n fd = c.create_file(\"streamstestfile:smb2_stream\", \"w\")\n c.close(fd, True)\n\n c.disconnect()","title":""},{"docid":"a94dc2d7a2828f9b2716ef7aa4e89981","score":"0.48913822","text":"def __call__(self, block0: bytes, block1: bytes) -> bytes:\n assert len(block0) == BLOCK_SIZE\n assert len(block1) == BLOCK_SIZE\n\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect(self.server)\n\n sock.sendall(self.build_message(block0, block1))\n\n r = sock.recv(18).decode('ascii')\n\n return int(r, 16).to_bytes(BLOCK_SIZE, 'big')","title":""},{"docid":"cdd20f54104d595f5888f072d346c905","score":"0.48900372","text":"def GetStream(self):","title":""},{"docid":"31cf4ef1dd9bcd0f4f42b8e61f71163e","score":"0.4863623","text":"def write(self, buf):\n raise NotImplementedError","title":""},{"docid":"94eb5a909e95d30d7d36b7320d3c839c","score":"0.48622474","text":"def init_bundle(file_name):\n fd = open(file_name, \"wb\")\n # Empty bundle file header, lots of magic numbers\n header = struct.pack(\"<4I3Q6I\",\n 3, # Version\n BSZ2, # numRecords\n 0, # maxRecord Size\n 5, # Offset Size\n 0, # Slack Space\n 64 + IDXSZ, # File Size\n 40, # User Header Offset\n 20 + IDXSZ, # User Header Size\n 3, # Legacy 1\n 16, # Legacy 2\n BSZ2, # Legacy 3\n 5, # Legacy 4\n IDXSZ # Index Size\n )\n fd.write(header)\n # Write empty index.\n fd.write(struct.pack(\"<{}Q\".format(BSZ2), *((0,) * BSZ2)))\n fd.close()","title":""},{"docid":"c3af2dae0f4f2b54673b7a7521e58a18","score":"0.48567945","text":"def _send(self, msg):\n if (msg != self.eof_msg) and (self.fd.tell() != 0):\n self.fd.seek(0)\n msg_ply, header = self.serializer.deserialize(msg)\n with open(self.current_address, 'rb') as fd:\n old_ply, header = self.serializer.deserialize(fd.read())\n old_ply.append(msg_ply)\n new_msg = self.serializer.serialize(old_ply)\n else:\n new_msg = msg\n return super(PlyFileComm, self)._send(new_msg)","title":""},{"docid":"ca967a075531f716fbf55d5c3b922be3","score":"0.4855195","text":"def write(self, output_stream, kmip_version=enums.KMIPVersion.KMIP_1_0):\n local_stream = utils.BytearrayStream()\n\n if self._major:\n self._major.write(local_stream, kmip_version=kmip_version)\n else:\n raise ValueError(\n \"Invalid struct missing the major protocol version number.\"\n )\n\n if self._minor:\n self._minor.write(local_stream, kmip_version=kmip_version)\n else:\n raise ValueError(\n \"Invalid struct missing the minor protocol version number.\"\n )\n\n self.length = local_stream.length()\n super(ProtocolVersion, self).write(\n output_stream,\n kmip_version=kmip_version\n )\n output_stream.write(local_stream.buffer)","title":""},{"docid":"e8ebb4854330f2683e363d6f6c5a8f66","score":"0.48438698","text":"def write_raw(self, data):\n # Initialize\n flags, chunk = 0, self.max_recv_size\n # Append terminator\n if self.term_char is not None:\n flags = OP_FLAG_TERMCHAR_SET\n term_char = str(self.term_char).encode('utf-8')[0]\n data += term_char\n # Loop over write requests\n offsets = range(0, len(data), chunk)\n for offset in offsets:\n # Get block\n if offset == offsets[-1]:\n flags |= OP_FLAG_END\n block = data[offset:offset+chunk]\n # Write request\n error, size = self.client.device_write(\n self.link, self.timeout_ms, self.lock_timeout_ms, flags, block)\n # Raise error\n if error:\n raise Vxi11Exception(error, 'write')\n elif size < len(block):\n raise Vxi11Exception(\"did not write complete block\", 'write')","title":""},{"docid":"92230d1b1e2d25ed7277556646f2b400","score":"0.48398197","text":"def initialize(self):\n self.bytes_read = 0\n self.content_length = 0\n self.content_type = ''\n self.data = bytes()\n self.error = None\n self.stream = None\n\n if self.request.headers and 'Content-Encoding' in self.request.headers:\n gen_log.warning(\"Unsupported Content-Encoding: %s\", self.request.headers['Content-Encoding'])\n return\n if self.request.headers and 'Content-Type' in self.request.headers:\n self.content_length = int(self.request.headers['Content-Length']) if 'Content-Length' in self.request.headers else 0\n self.content_type = self.request.headers['Content-Type']\n if self.content_type.startswith(\"application/x-www-form-urlencoded\"):\n return\n elif self.content_type.startswith(\"multipart/form-data\"):\n # If we have a POST that is multipart/form-data we will stream any file\n # content to disk. This will prevent excessive RAM usage. Clearly we\n # will need to keep tabs on the overall data size or someone could\n # still use too much RAM!\n self.stream = self.Stream()\n boundary = None\n fields = self.content_type.split(\";\")\n for field in fields:\n k, _, v = field.strip().partition(\"=\")\n if k == \"boundary\" and v:\n boundary = bytes(v, 'utf8')\n if not boundary:\n raise error.SnakeError('Content boundary not found')\n if boundary.startswith(b'\"') and boundary.endswith(b'\"'):\n boundary = boundary[1:-1]\n self.stream.boundary = boundary\n self.stream.working_dir = tempfile.TemporaryDirectory(dir=path.abspath(path.expanduser(snake_config['cache_dir'])))\n else:\n self.error = error.SnakeError('Unsupported Content-Type: %s' % self.content_type)","title":""},{"docid":"37164ceefb2a98e7cda8af5f9e409fd1","score":"0.48377302","text":"def __build(self):\n self.__body = self._buildBody()\n self.__convert = False\n self.__header = self.__header\n self.__length = len(self.__body)\n length = self.__length\n if self.isHalved(self.__header):\n if self.__archive:\n length = bits.bitSet(self.__length, 15)\n\n self.__rawData = pack(\"<BH\", self.__header, length)\n self.__rawData += self.__body\n self.__crc = crc16.Crc16.calcBinaryString(\n self.__rawData,\n crc16.INITIAL_MODBUS\n )\n self.__rawData += pack(\"<H\", self.__crc)","title":""},{"docid":"2814b68851547ff15b9d119479958da6","score":"0.4833357","text":"def __init__(self, stream, passphrase, testing_only_key=None):\n blocks_len = len(stream) - 16\n num_full_blocks = blocks_len / (MAC_BLOCK_SIZE + 32)\n num_partial_blocks = 0 if blocks_len % (MAC_BLOCK_SIZE + 32) == 0 else 1\n num_blocks = num_full_blocks + num_partial_blocks\n num_macs = max(1, num_blocks)\n stream_length = blocks_len - 32 * num_macs\n SizedInputStream.__init__(self, stream_length)\n self.stream = stream\n salt = stream.read(16)\n if len(salt) != 16:\n raise DataTruncatedException(\"File does not contain full MAC salt.\")\n key = testing_only_key or pbkdf2_256bit(passphrase, salt)\n self.mac = hmac.new(key, digestmod=hashlib.sha256)\n self.buffer = ''\n self.stream_at_start = True\n self.stream_at_eof = False","title":""},{"docid":"b48e74b225e968bde1197a11aa831bbf","score":"0.48329964","text":"def write(self, data):","title":""},{"docid":"89585053dae3fbbf7c5eb6cc6b12c6c3","score":"0.48303038","text":"def serialize(self, stream, encoding=\"utf-8\", **kwargs):\r\n pass # abstract\r","title":""},{"docid":"f8d5db4c020222ee2650a0527d4db3ec","score":"0.4819128","text":"def write(self, buff):\r\n pass","title":""},{"docid":"4895c91e88b09dd5c6b3e20b5d2bef28","score":"0.48156852","text":"def push(self, path, stream, is_new=True):\n if self.simulate: return\n\n segments = path.split('/')\n operation = 'A'\n if not is_new:\n operation = 'M'\n\n # Check if the file is located inside directory structure.\n # If yes, create the dirs if not exists.\n if len(segments) > 1:\n self.mkdir(segments[:-1])\n\n try:\n # Let's just always transfer the file as binary.\n self.session.storbinary('STOR %s' % path, stream)\n except Exception as e:\n # We don't want the whole operation stopped\n # instead, just log the status.\n self.failed_operation.append(OperationStatus(operation, path, e))\n else:\n self.success_operation.append(OperationStatus(operation, path))","title":""},{"docid":"f1f08ae6301432c49670a2dfd3c4105d","score":"0.48131764","text":"def _write_bytes(self, data):\n if self._request_started:\n print(\"Request has already written a response:\")\n traceback.print_stack()\n return\n\n self._outgoing_headers['content-length'] = len(data)\n\n response_lines = self.begin_response(len(data))\n response_lines.append(data)\n self.protocol.wfile.write(\"\\r\\n\".join(response_lines))\n if hasattr(self.protocol.wfile, 'flush'):\n self.protocol.wfile.flush()","title":""},{"docid":"5e7d3dab5b5614c6560772edb6feb53d","score":"0.48117247","text":"def new_audio_stream(self):\n self.close_stream()\n \n self.stream = sd.OutputStream(\n samplerate=self.audio.samplerate,\n channels=self.audio.channels,\n blocksize=self.blocksize,\n callback=self.callback,\n finished_callback=self.finished_callback,\n dtype='float32')\n\n self.raw_data = self.audio.read(dtype='float32')\n self.channel_mapping = np.arange(self.audio.channels)\n\n self.stream.start()\n self._stopped = False","title":""},{"docid":"f75bb7114ec2f6a66f201b713751b05b","score":"0.48059058","text":"def __init__(self, orientation=0):\n super().__init__(START_PIPE, orientation, False)","title":""},{"docid":"1cf0e31f3bb28682f2e1135b5f2870a4","score":"0.4802314","text":"def __init__(self, stream, location, mimetype, anchor, modif):","title":""},{"docid":"fc2db3af9a1b5e778583653c307753f5","score":"0.47909853","text":"def tx(data):\n\tbytes = (data).to_bytes(4, byteorder=BYTE_ORDER)\n\tser.write(bytes)\n\t# loopback for testing","title":""},{"docid":"f5cbc427c01088afef450afd2eb6f53d","score":"0.47834563","text":"def test_051_share_is_writable_smb1(request):\n depends(request, [\"SMB_SHARE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\n fd = c.create_file(\"testfile\", \"w\")\n c.close(fd, True)\n c.disconnect()","title":""},{"docid":"8f17e041716d9dac3c21aae683f97a1c","score":"0.47788328","text":"def write(self, data: bytes, offset: int = 0):\n self.__parent.basic_write(self.__base_address + offset, data)","title":""},{"docid":"f49b5e8145e78353f1155de23e4b50a4","score":"0.4777356","text":"def update(self):\n buffer = bytearray(17)\n buffer[1:] = self.buffer\n self.i2c.writeto(self.address, bytes(buffer))","title":""},{"docid":"dba2791253453c66d760559c433b391d","score":"0.47763595","text":"def start_slam_streaming(self):\n endpoint = 'slam/streaming/start'\n self.wrapper_post(endpoint)","title":""},{"docid":"28118e678054e7042b83c31062212c41","score":"0.47752145","text":"def test0_WriteReadLoopback(self):\n for i in range(self.N):\n q = bytes_0to255\n self.s.write(q)\n self.assertEqual(self.s.read(len(q)), q) # expected same which was written before\n self.assertEqual(self.s.inWaiting(), 0) # expected empty buffer after all sent chars are read","title":""},{"docid":"f9b96d17bedf9a8e0a0792839d28302b","score":"0.47739455","text":"def write(self, stream):\n\n stream.write(self.render())","title":""},{"docid":"9d9eefe4045ecf796dc6d1637b32e358","score":"0.47708577","text":"def xWrite(self, data, offset): # noqa: N802\n # Can't write to an HTTP server, ignore\n pass","title":""},{"docid":"16a0d26d4aef6ca6fae25c6116c3c835","score":"0.4761974","text":"def stream(self, f):\r\n self.stream_header(f)","title":""},{"docid":"3cc3a264d3ee28a776986fe7c6342dbd","score":"0.47603023","text":"def flush(self):\n self.__read_buffer = BufferIO(self._transform(self.__write_buffer.getvalue()))\n self.__write_buffer = BufferIO()","title":""},{"docid":"fded89f0d31cce7848b63f201b5f5bcf","score":"0.4759879","text":"def setup_io(self):\n sys.stdout = Stream(sys.stdout)\n sys.stderr = Stream(sys.stderr)","title":""},{"docid":"7ff3bc11b96b7fec4792691d9667e768","score":"0.47596627","text":"def __write_header(self):\n self.filed.write(struct.pack('I',0xa1b2c3d4))\n self.filed.write(struct.pack('H',2))\n self.filed.write(struct.pack('H',4))\n self.filed.write(struct.pack('i',0))\n self.filed.write(struct.pack('I',0))\n self.filed.write(struct.pack('I',65535))\n self.filed.write(struct.pack('I',1))","title":""},{"docid":"5cdcfd7ee5097a4b7df8c94f00436044","score":"0.47587278","text":"def record(cls):\n return cls._namespace_SIO('SIO_000088')","title":""}],"string":"[\n {\n \"docid\": \"bddce7b71ffc0e39fb548f3b8fd0246f\",\n \"score\": \"0.65759665\",\n \"text\": \"def test_061_create_and_write_stream_smb2(request):\\n depends(request, [\\\"STREAM_TESTFILE_CREATED\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\\n fd = c.create_file(\\\"streamstestfile:smb2_stream\\\", \\\"w\\\")\\n c.write(fd, b'test1', 0)\\n c.close(fd)\\n\\n fd2 = c.create_file(\\\"streamstestfile:smb2_stream\\\", \\\"w\\\")\\n contents = c.read(fd2, 0, 5)\\n c.close(fd2)\\n c.disconnect()\\n assert(contents.decode() == \\\"test1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"126a19d4cc1fdba86ffdf318539b7405\",\n \"score\": \"0.65454155\",\n \"text\": \"def test_066_write_stream_large_offset_smb1(request):\\n depends(request, [\\\"STREAM_WRITTEN_SMB1\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\\n fd = c.create_file(\\\"streamstestfile:smb1_stream\\\", \\\"w\\\")\\n c.write(fd, b'test2', 131072)\\n c.close(fd)\\n\\n fd2 = c.create_file(\\\"streamstestfile:smb1_stream\\\", \\\"w\\\")\\n contents = c.read(fd2, 131072, 5)\\n c.close(fd2)\\n c.disconnect()\\n assert(contents.decode() == \\\"test2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d073a849be8994cbdb6180547473aa68\",\n \"score\": \"0.62581897\",\n \"text\": \"def test_062_write_stream_large_offset_smb2(request):\\n depends(request, [\\\"STREAM_TESTFILE_CREATED\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\\n fd = c.create_file(\\\"streamstestfile:smb2_stream\\\", \\\"w\\\")\\n c.write(fd, b'test2', 131072)\\n c.close(fd)\\n\\n fd2 = c.create_file(\\\"streamstestfile:smb2_stream\\\", \\\"w\\\")\\n contents = c.read(fd2, 131072, 5)\\n c.close(fd2)\\n c.disconnect()\\n assert(contents.decode() == \\\"test2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12c69e8bed0c0a18fd7e367600ec31ea\",\n \"score\": \"0.61246866\",\n \"text\": \"def init_stream(self):\\r\\n stream = Stream()\\r\\n while True:\\r\\n x = self.read_1b()\\r\\n if x != 0xff:\\r\\n stream.write_byte(x)\\r\\n else:\\r\\n y = self.read_1b()\\r\\n if y == 0x00: # remove byte padding 0x00\\r\\n stream.write_byte(x)\\r\\n else: # x is the first byte of the next marker\\r\\n self.pos -= 2\\r\\n break\\r\\n self.stream = stream\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8db0219a7398d44bed5c3ecf48e4c8f7\",\n \"score\": \"0.5964676\",\n \"text\": \"def test_060_create_base_file_for_streams_tests(request):\\n depends(request, [\\\"SMB_SHARE_CREATED\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\\n fd = c.create_file(\\\"streamstestfile\\\", \\\"w\\\")\\n c.close(fd)\\n c.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ae7cc571877bb82b8d5ce901331d4d\",\n \"score\": \"0.5924718\",\n \"text\": \"def _initiate(self, stream, seek=None):\\n if seek is not None:\\n stream.seek(seek)\\n\\n record_type, = unpack('b', stream.read(BYTE_SIZE))\\n root_id, header_id, major_version, minor_version = unpack(\\n '4i', stream.read(INT_SIZE * 4)\\n )\\n self.record_type = record_type\\n self.root_id = root_id\\n self.header_id = header_id\\n self.major_version = major_version\\n self.minor_version = minor_version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb455905f0e9949312dedd05d0e71f07\",\n \"score\": \"0.5912581\",\n \"text\": \"def _write_stream_content(self):\\n self.add_debug('Write stream ...')\\n\\n self.__write_pool_creation_section()\\n self.__get_source_plates()\\n self.__write_prep_creation_section()\\n self.__write_aliquot_part()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78b8a4240f0a457836000b8368035668\",\n \"score\": \"0.576237\",\n \"text\": \"def start_object(self):\\r\\n self.stream.write(u'{')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0f748f960dfb28a8e4f7ea5796a0052\",\n \"score\": \"0.57486194\",\n \"text\": \"def SetStream():\\n uart.write( b\\\"\\\\xFF\\\\x01\\\\x78\\\\x40\\\\x00\\\\x00\\\\x00\\\\x00\\\\x47\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f9fe285d90f23bbbbe746d8414b50e\",\n \"score\": \"0.56689066\",\n \"text\": \"def _write_stream_content(self):\\n self.add_debug('Write stream ...')\\n\\n self.__write_main_headline()\\n self.__write_general_section()\\n self.__write_destination_racks_section()\\n self.__write_source_racks_section()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110f226f9c7494d5b0ecc845f9c903f5\",\n \"score\": \"0.5573991\",\n \"text\": \"def __init__(self, fid, offset, data):\\n self.name = \\\"Twrite\\\"\\n self.length = 23 + len(data)\\n self.type = 118\\n self.fid = fid\\n self.offset = offset\\n self.count = len(data)\\n self.data = data\\n self.messageBody = struct.pack('<IQI', self.fid, self.offset, self.count) + self.data\\n self.body = \\\"%s, %s, %s, %s\\\" % (self.fid, self.offset, self.count, self.data[:20])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4fc89b6367fcadf34e17c972add9e87\",\n \"score\": \"0.5546117\",\n \"text\": \"def write(self, stream):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86ccea85f2ee13ea07511eb3ea33879b\",\n \"score\": \"0.551612\",\n \"text\": \"def write_stream(self, stream):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ae6296518f1fc5e6150fcfc527155ed\",\n \"score\": \"0.55077374\",\n \"text\": \"def begin(self):\\n self.wrote_header = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae2c57a667794211fbc433676cd815e\",\n \"score\": \"0.5491221\",\n \"text\": \"def write(self, buf):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f831f802ae0c126f58de40f039c121cd\",\n \"score\": \"0.54895175\",\n \"text\": \"def GetOutputStream(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b42fe1153f2690893d7b8ba48e09f5e\",\n \"score\": \"0.54695904\",\n \"text\": \"def __init__(self):\\n\\t\\tself.buffer = ''\\n\\t\\tself.handshake()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0056172219873a1d5888fd40c8a42594\",\n \"score\": \"0.54313385\",\n \"text\": \"def __init__(self, stream):\\n # Always add padding to make sure that last block has 16\\n # bytes. Use the PKCS#5 format: '\\\\x01', '\\\\x02\\\\x02',\\n # '\\\\x03\\\\x03\\\\x03', etc. See section 6.3 of\\n # http://www.ietf.org/rfc/rfc3852.txt\\n padding_bytes_needed = 16 - len(stream) % 16 or 16\\n self.padding = chr(padding_bytes_needed) * padding_bytes_needed\\n stream_length = len(stream) + len(self.padding)\\n assert(stream_length % 16 == 0)\\n RewindableSizedInputStream.__init__(self, stream_length)\\n self.stream = stream\\n self.stream_keep = stream\\n self.rewind()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"834c235785f14afd853d31114596257c\",\n \"score\": \"0.5425964\",\n \"text\": \"def InitKaldiOutputStream(stream, binary):\\n if binary:\\n stream.Write('\\\\0')\\n stream.Write('B')\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708d236036aa2ecd4bab4d789304e5a8\",\n \"score\": \"0.5393589\",\n \"text\": \"def write_chunk(stream, buf):\\n nbytes = bytelen(buf)\\n stream.write(magic_bytes)\\n stream.write(struct.pack(\\\"@q\\\", nbytes))\\n stream.write(bytedata(buf))\\n padding = roundup(nbytes) - nbytes\\n if padding > 0:\\n stream.write(b\\\"\\\\0\\\" * padding)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a61d908f3cd74a6194553e4d715d67\",\n \"score\": \"0.53689986\",\n \"text\": \"def BaseStream(self) -> _n_2_t_0:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e95ef031afb5a296fc229365e0641513\",\n \"score\": \"0.53654003\",\n \"text\": \"def test_067_stream_delete_on_close_smb1(request):\\n depends(request, [\\\"STREAM_WRITTEN_SMB1\\\", \\\"LARGE_STREAM_WRITTEN_SMB1\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\\n fd = c.create_file(\\\"streamstestfile:smb1_stream\\\", \\\"w\\\")\\n c.close(fd, True)\\n\\n c.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4110406ee3b4921b2dba2eed0ae2affd\",\n \"score\": \"0.53506273\",\n \"text\": \"def Open(self):\\n ## Make Header\\n hex_rep = self.NanonisTCP.make_header('BiasSpectr.Open', body_size=0)\\n \\n self.NanonisTCP.send_command(hex_rep)\\n \\n self.NanonisTCP.receive_response(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e415eb1f273bbba39aae63c2d011fad\",\n \"score\": \"0.5332037\",\n \"text\": \"def __init__(self, bufsize=16384):\\n self._raw = BytesIO()\\n self.bufsize = bufsize\\n self.write = self.write_stringio\\n self.exceeded_bufsize = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7f2fb8b89568c096d659445396e8ce\",\n \"score\": \"0.53302926\",\n \"text\": \"def initiate(self):\\n self.write(\\\":INIT;*WAI;*OPC\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875907b298de8838a08c157f31dbaf0b\",\n \"score\": \"0.53085804\",\n \"text\": \"def __writeFirstHeader(self):\\n\\n# CALCULAR PARAMETROS\\n\\n sizeLongHeader = self.systemHeaderObj.size + \\\\\\n self.radarControllerHeaderObj.size + self.processingHeaderObj.size\\n self.basicHeaderObj.size = self.basicHeaderSize + sizeLongHeader\\n\\n self.basicHeaderObj.write(self.fp)\\n self.systemHeaderObj.write(self.fp)\\n self.radarControllerHeaderObj.write(self.fp)\\n self.processingHeaderObj.write(self.fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b932e58cc7afe12f9d1a1f1390a85f0\",\n \"score\": \"0.5300523\",\n \"text\": \"def __init__(self, stream):\\n SizedInputStream.__init__(self, len(stream))\\n self.stream = stream\\n self.stream_keep = stream\\n self.buffer = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97530b2757eac9fb1d7c83f97d02e606\",\n \"score\": \"0.52969205\",\n \"text\": \"def stream(self):\\n\\t\\t#self.SCI.send(self.config.data['opcodes']['start'],0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d0fa045b142455fe6f2eecd6910aa9\",\n \"score\": \"0.52520484\",\n \"text\": \"def start(self, stream_buffer):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f94158969253d1382c6870a84f71b00\",\n \"score\": \"0.5215718\",\n \"text\": \"def reset(self):\\n self._stream.reset()\\n sleep(0.1)\\n # Send some empty packets to flush any corrupt data\\n self._stream.write(b\\\"\\\")\\n self._stream.write(b\\\"\\\")\\n self._stream.write(b\\\"\\\")\\n self.get_version()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d26ee83b2d6478c6fa0ea2af883b812d\",\n \"score\": \"0.52114683\",\n \"text\": \"def _write_header(self, output_stream):\\n output_stream.write_byte(self._version) # 1 byte\\n output_stream.write_int64_unassigned(self.timestamp) # 8 byte\\n output_stream.write_vector_3_int32(self.position) # 12 byte\\n output_stream.write_bool(self.has_valid_data) # 1 byte\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7c8ebf5232be0e7597ae0541e86682\",\n \"score\": \"0.5204206\",\n \"text\": \"def __init__(\\n self,\\n stream_opener: Callable[[], io.IOBase],\\n offset: int = 0,\\n stream_length: int | None = None,\\n stream_sha1: Sha1HexDigest | None = None,\\n ):\\n super().__init__(\\n stream_opener,\\n stream_length=stream_length,\\n stream_sha1=stream_sha1,\\n )\\n self._offset = offset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05d3468cb4c57fb7b18dd13a0be1a1b\",\n \"score\": \"0.52017593\",\n \"text\": \"def _write_header(self):\\n self._device_handle.seek(0)\\n\\n # Remember: we can only write a whole sector at a time\\n first_sector = NOFS_HEADER\\n first_sector += NOFS_TERMINAL\\n first_sector += (NOFS_SECTOR_SIZE - len(NOFS_HEADER) -\\n len(NOFS_TERMINAL)) * chr(0xFF)\\n\\n # Write it onto the device\\n self._device_handle.write(first_sector)\\n self._device_handle.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d252900015f4809c37fe765bc46af97\",\n \"score\": \"0.5190342\",\n \"text\": \"def _setup_streaming_request(self):\\r\\n self.raw = self._create_raw(chunked=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be5271c19f4015178d3ce07fc8a4539d\",\n \"score\": \"0.5186833\",\n \"text\": \"def write(self, output_stream):\\n assert isinstance(output_stream, SMBinaryStream)\\n self._version = max(self._valid_versions)\\n self._write_header(output_stream)\\n self._write_block_data(output_stream)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48a6b63fbd5b92ae471e4df3ccac92c5\",\n \"score\": \"0.51842695\",\n \"text\": \"def __init__(self, start, length):\\n self._buffer = BinaryBuffer(start, length)\\n self._offset = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7797fe428ab8a71f3f796daa7b3f13ed\",\n \"score\": \"0.51745135\",\n \"text\": \"def start(self):\\r\\n data_to_send = self.data_to_convert[:self.i]\\r\\n logger.info(\\\"Converting to hex data: '{}'\\\".format(data_to_send))\\r\\n # Write the data, and setup the _write_complete callback to be executed when the write finishes\\r\\n self.char.write(data_to_send).then(self._write_complete)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cedbd9d9d66e7cf58266bdc3a19b4cca\",\n \"score\": \"0.5173928\",\n \"text\": \"def OutputStream(self) -> _n_5_t_0:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b96f173e150a3fbef89714f7fabbe95\",\n \"score\": \"0.5166642\",\n \"text\": \"def __init__(self, stream):\\n self.__temp_filename = f'data/{str(uuid.uuid4())}'\\n with open(self.__temp_filename, 'w') as out_fd:\\n out_fd.write(stream.read())\\n self.__reader = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27efaaab05ff896a035181b4080b8c0\",\n \"score\": \"0.51446676\",\n \"text\": \"def setup(self):\\r\\n self.connection = self.request # for doPOST\\r\\n self.rfile = socket._fileobject(self.request, \\\"rb\\\", self.rbufsize)\\r\\n self.wfile = socket._fileobject(self.request, \\\"wb\\\", self.wbufsize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37cdf10b1c55b6dc90661a032c0c0659\",\n \"score\": \"0.5133127\",\n \"text\": \"def __init__(self, buf = None):\\n\\n super().__init__()\\n\\n self._rf = io.BytesIO(buf)\\n self._wf = io.BytesIO()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d280beffe79b410ae9b0ce2365b710\",\n \"score\": \"0.51329637\",\n \"text\": \"def __write_first_scenario(self, stream, output_path, bank):\\n self.__log.info(\\\"Writing header from '%s': '%s'\\\", output_path, bank.header.rstrip(\\\"\\\\n\\\"))\\n stream.write(bank.header)\\n\\n self.__log.info(\\\"Writing feature from '%s': '%s'\\\", output_path, bank.feature.rstrip(\\\"\\\\n\\\"))\\n stream.write(bank.feature)\\n\\n self.__write_next_scenario(stream, output_path, bank)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2109fa5f14b17fa6b90b7b48133cfbbc\",\n \"score\": \"0.5125137\",\n \"text\": \"def __init__(self, tree, name):\\n self.pipe_buffer = b\\\"\\\"\\n super(OutputPipeBytes, self).__init__(tree, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d7a7aa20bde6047ce9792846dd0ad9\",\n \"score\": \"0.5123535\",\n \"text\": \"def __init__(self):\\n self.size = 0\\n self.buf = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3974e5d4f89e2a7c3242d3e45a89da94\",\n \"score\": \"0.5111709\",\n \"text\": \"def __init__(self, backingStream):\\n \\n self.backingStream = backingStream\\n # We have no write listeners yet\\n self.writeListeners = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80733148946b065f712890dc61d0c047\",\n \"score\": \"0.5096877\",\n \"text\": \"def write_header(self, writeStream):\\n\\t\\twriteStream.write(bytearray())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8fa78ce6b5c731d905b7351ec1aadc\",\n \"score\": \"0.5095322\",\n \"text\": \"def write(self, chunk):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9b866bf8892425fe5deb562f86b3333\",\n \"score\": \"0.5087629\",\n \"text\": \"def sendType1(self):\\n\\n # Transforming all the header info into bytes\\n msgType = self.msgType1.to_bytes(1, \\\"little\\\")\\n serverAddress = self.serverAddress.to_bytes(1, \\\"little\\\")\\n totalPackets = self.numberOfPackets.to_bytes(3, \\\"little\\\")\\n fileExtension = bytes(\\\"\\\".join([\\\"@\\\" for i in range(5 - len(self.fileExtension))]), \\\"UTF-8\\\") + self.fileExtensionBA\\n payloadSize = bytes([0])\\n filler = bytes([0]) * 5\\n\\n # Build header\\n # HEAD = msgType[1] + serverAddress[1] + totalPackets[3] + fileExtension[5] + payloadSize[1] + filler[5]\\n head = msgType + serverAddress + totalPackets + payloadSize + fileExtension + filler\\n\\n # Sending the request\\n self.com.sendData(head + self.eop)\\n\\n # Wait for the data to be sent.\\n while (self.com.tx.getIsBussy()):\\n pass\\n\\n self.log(\\\"Sent TYPE1.\\\", \\\"client\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea32b15e97ee6b492e444ec7c77f65d1\",\n \"score\": \"0.508755\",\n \"text\": \"def start(self):\\r\\n\\r\\n if not self.__started:\\r\\n if self.__status is None:\\r\\n self.setStatus(200)\\r\\n if self.__content_type is None:\\r\\n self.setContentType(\\\"text/plain\\\")\\r\\n\\r\\n headers = [(\\\"Content-Type\\\", self.__content_type)]\\r\\n headers.extend(self.__response_headers)\\r\\n\\r\\n self.__write = self.__start_response(self.__status, headers)\\r\\n self.__started = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8e5ca3066f497fff95bedc7675a7d6\",\n \"score\": \"0.5080573\",\n \"text\": \"def __init__(self):\\n self.buf = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f10dbbd9703cc552d97a23cc6de96c4\",\n \"score\": \"0.50719166\",\n \"text\": \"def _write_block(self, start, data):\\n with self._i2c as i2c:\\n i2c.write(bytes((start,)) + data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cf91009838dcef439fbe4ddb4ca719\",\n \"score\": \"0.5064834\",\n \"text\": \"def write(self, stream, data):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035c54bcc996231359534356e5027e88\",\n \"score\": \"0.50421983\",\n \"text\": \"def write(self, data):\\n #\\n # Size of the packet header\\n #\\n header_len = 10\\n #\\n # In data Recived from SDS, '\\\\x1B\\\\x1B' reserved for status change,\\n # however transmitted data uses no such mechanism\\n # Don't substitute data in sending packet.\\n # data.replace('\\\\x1B\\\\x1B', '\\\\x1B\\\\x1B\\\\xFF')\\n #\\n # Check if we can wirte\\n # \\n if not self.is_open:\\n raise portNotOpenError\\n self._write_lock.acquire()\\n try:\\n try:\\n #\\n # Write to socket chunks of data, with-in limit:\\n # ( 10 byte header + data ) <= MAX_PACKET_SIZE\\n # after writing chunck, slice off\\n #\\n while len(data):\\n #\\n # Faster method to got largest chunck\\n # \\n if header_len + len(data) < MAX_PACKET_SIZE:\\n position = len(data)\\n else:\\n position = MAX_PACKET_SIZE - header_len\\n #\\n # Line to be sent \\n #\\n line = data[:position]\\n #\\n # Cut line off processed data\\n #\\n data = data[position:]\\n #\\n # Increment sequence number 0 to 0xFFFF\\n #\\n self.packet_sequence_num += 1\\n if self.packet_sequence_num >= 0xFFFF:\\n self.packet_sequence_num = 0\\n #\\n # Make note in log\\n #\\n if self.logger:\\n self.logger.info(\\n (\\n \\\"Writing to SDS: sequence:{} size:{} data:{}\\\"\\n ).format(\\n self.packet_sequence_num,\\n position,\\n line,\\n )\\n )\\n #\\n # Send message\\n #\\n self._socket.sendall(\\n '\\\\x1B\\\\x1B\\\\x1B' # Header\\n '\\\\x00' # Reseverd\\n + struct.pack('!H', self.packet_sequence_num)\\n + self.com.ctrl['TX_DATA']\\n + self.com.ctrl['DEST_PORT']\\n + struct.pack('!H', len(line)) # Data size\\n + line\\n )\\n\\n except socket.error as e:\\n # Rais SerialException if conneciton fails,\\n # so that caller can catch the exception\\n raise SerialException(\\n \\\"connection failed (socket error): {}\\\".format(e)\\n )\\n finally:\\n self._write_lock.release()\\n return len(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"809edb61666b332d455cf736741c9ec3\",\n \"score\": \"0.5025241\",\n \"text\": \"def __init__(self, orientation=0):\\n super().__init__(END_PIPE, orientation, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b147e0da2015475e1bb8260d8696c121\",\n \"score\": \"0.50244826\",\n \"text\": \"def Write(packer_type, buf, head, n):\\n packer_type.pack_into(buf, head, n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f132b78320f3b47d563757867736d0f\",\n \"score\": \"0.50182176\",\n \"text\": \"def write(self, text, start=0):\\r\\n # simple scenario, let pickle create bytes\\r\\n if self.serialize:\\r\\n if CONQUE_PYTHON_VERSION == 3:\\r\\n tb = pickle.dumps(text, 0)\\r\\n else:\\r\\n tb = pickle.dumps(text, 0).encode(self.encoding)\\r\\n\\r\\n else:\\r\\n tb = text.encode(self.encoding, 'replace')\\r\\n\\r\\n # write to memory\\r\\n self.shm.seek(start * self.char_width)\\r\\n\\r\\n if self.fixed_length:\\r\\n self.shm.write(tb)\\r\\n else:\\r\\n self.shm.write(tb + self.TERMINATOR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55be308a107a9d0e7790725be413224b\",\n \"score\": \"0.5010571\",\n \"text\": \"def stream_start(self):\\n self.initiate()\\n print(\\\" -- starting stream\\\")\\n self.keepRecording=True # set this to False later to terminate stream\\n self.data=None # will fill up with threaded recording data\\n self.fft=None\\n self.dataFiltered=None #same\\n self.stream=self.p.open(format=pyaudio.paInt16,channels=1,\\n rate=self.rate,input=True,frames_per_buffer=self.chunk)\\n self.stream_thread_new()\\n\\n #main no UI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dde02e8ad7285e38fb72d344d987bfe\",\n \"score\": \"0.49900386\",\n \"text\": \"def create_smbv1_conn(self):\\n try:\\n logging.debug('Attempting SMBv1 connection to {}'.format(self.host))\\n logging.debug('using args.port: {}'.format(self.args.port))\\n self.conn = impacket.smbconnection.SMBConnection(self.host, self.host, None, self.args.port) #, preferredDialect=impacket.smb.SMB_DIALECT)\\n except socket.error as e:\\n if str(e).find('Connection reset by peer') != -1:\\n logging.debug('Connection was reset by target. SMBv1 might be disabled on {}'.format(self.host))\\n elif str(e).find('No route to host') != -1:\\n logging.debug('Could not connect to {}, no route to host. Can you ping it?'.format(self.host))\\n else:\\n logging.debug('Something went wrong, Could not connect to {}:{}, tried smbv1'.format(self.host, self.args.port))\\n return False\\n except Exception as e:\\n logging.debug('Error creating SMBv1 connection to {}: {}'.format(self.host, e))\\n return False\\n logging.debug('Connected using SMBv1 to: {}:{}'.format(self.host, self.args.port))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665684b350a63ea1f6ce8311c1cac3e6\",\n \"score\": \"0.4988296\",\n \"text\": \"def handle_write(self):\\n self.initiate_send()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ed9dd92deeec8683a6d01a91778260\",\n \"score\": \"0.49792826\",\n \"text\": \"def __init__(self):\\n self._stream = os.dup(sys.stdout.fileno())\\n self._touch()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ab1d3d42dfa8f168fed22b29a8d079\",\n \"score\": \"0.4972222\",\n \"text\": \"def WriteAll(self, buffer, size):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb62299dd1d24effe7a1191227afa22c\",\n \"score\": \"0.49719277\",\n \"text\": \"def __init__(self, stream):\\n self.stream = stream\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb62299dd1d24effe7a1191227afa22c\",\n \"score\": \"0.49719277\",\n \"text\": \"def __init__(self, stream):\\n self.stream = stream\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc512a50209a828421f4abc6fa55d5b\",\n \"score\": \"0.4970963\",\n \"text\": \"def test_writeSequence(self):\\n data = b\\\"some bytes\\\"\\n\\n class SimpleSendingProtocol(Protocol):\\n def connectionMade(self):\\n self.transport.writeSequence(list(iterbytes(data)))\\n\\n return self.writeBeforeHandshakeTest(SimpleSendingProtocol, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf9767c96a09bc4ab22acda95bfd7a43\",\n \"score\": \"0.49663442\",\n \"text\": \"def write(self, buf):\\n if not self.connected:\\n raise VppTransportSocketIOError(1, \\\"Not connected\\\")\\n\\n # Send header\\n header = self.header.pack(0, len(buf), 0)\\n try:\\n self.socket.sendall(header)\\n self.socket.sendall(buf)\\n except socket.error as err:\\n raise VppTransportSocketIOError(1, \\\"Sendall error: {err!r}\\\".format(err=err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8f3c2e8d1db1223068f1c7ff492dac\",\n \"score\": \"0.4963811\",\n \"text\": \"def __init__(self, bufsize: int):\\r\\n self.buf = dict([(i, None) for i in range(Seq.MOD)]) # None mean empty packet\\r\\n self.base = Seq(0)\\r\\n self.bufsize = bufsize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af645f421f15453353267e48a8f3c34\",\n \"score\": \"0.49343464\",\n \"text\": \"def _execute_task(self):\\n self.add_info('Start rack transfer file generation ...')\\n self.__stream = StringIO()\\n self.__write_content()\\n if not self.has_errors():\\n self.return_value = self.__stream\\n self.add_info('File creation completed.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f42ba7bdaeae96c6beae48807f29d6f1\",\n \"score\": \"0.49125788\",\n \"text\": \"def _write_block_data(self, output_stream):\\n if not self.has_valid_data:\\n self.compressed_size = 0\\n output_stream.write_int32_unassigned(self.compressed_size) # 4 byte\\n else:\\n byte_string = b\\\"\\\"\\n set_of_valid_block_index = set(self.block_index_to_block.keys())\\n for block_index in range(0, self._blocks_in_a_cube):\\n if block_index in set_of_valid_block_index:\\n block_int_24 = self.block_index_to_block[block_index].get_int_24()\\n if self._version < 3:\\n byte_string += SMBinaryStream.pack_int24(block_int_24)\\n else:\\n byte_string += SMBinaryStream.pack_int24b(block_int_24)\\n continue\\n byte_string += b\\\"\\\\0\\\" * 3\\n compressed_data = zlib.compress(byte_string)\\n self.compressed_size = len(compressed_data)\\n output_stream.write_int32_unassigned(self.compressed_size) # 4 byte\\n output_stream.write(compressed_data)\\n\\n output_stream.seek(49125-self.compressed_size, 1)\\n output_stream.write(b\\\"\\\\0\\\") # this should fill the skipped positions with \\\\0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"676c0437e95d0a096be94d5755118634\",\n \"score\": \"0.49102533\",\n \"text\": \"def test_063_stream_delete_on_close_smb2(request):\\n depends(request, [\\\"STREAM_WRITTEN_SMB2\\\", \\\"LARGE_STREAM_WRITTEN_SMB2\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\\n fd = c.create_file(\\\"streamstestfile:smb2_stream\\\", \\\"w\\\")\\n c.close(fd, True)\\n\\n c.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94dc2d7a2828f9b2716ef7aa4e89981\",\n \"score\": \"0.48913822\",\n \"text\": \"def __call__(self, block0: bytes, block1: bytes) -> bytes:\\n assert len(block0) == BLOCK_SIZE\\n assert len(block1) == BLOCK_SIZE\\n\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n sock.connect(self.server)\\n\\n sock.sendall(self.build_message(block0, block1))\\n\\n r = sock.recv(18).decode('ascii')\\n\\n return int(r, 16).to_bytes(BLOCK_SIZE, 'big')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd20f54104d595f5888f072d346c905\",\n \"score\": \"0.48900372\",\n \"text\": \"def GetStream(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31cf4ef1dd9bcd0f4f42b8e61f71163e\",\n \"score\": \"0.4863623\",\n \"text\": \"def write(self, buf):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94eb5a909e95d30d7d36b7320d3c839c\",\n \"score\": \"0.48622474\",\n \"text\": \"def init_bundle(file_name):\\n fd = open(file_name, \\\"wb\\\")\\n # Empty bundle file header, lots of magic numbers\\n header = struct.pack(\\\"<4I3Q6I\\\",\\n 3, # Version\\n BSZ2, # numRecords\\n 0, # maxRecord Size\\n 5, # Offset Size\\n 0, # Slack Space\\n 64 + IDXSZ, # File Size\\n 40, # User Header Offset\\n 20 + IDXSZ, # User Header Size\\n 3, # Legacy 1\\n 16, # Legacy 2\\n BSZ2, # Legacy 3\\n 5, # Legacy 4\\n IDXSZ # Index Size\\n )\\n fd.write(header)\\n # Write empty index.\\n fd.write(struct.pack(\\\"<{}Q\\\".format(BSZ2), *((0,) * BSZ2)))\\n fd.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3af2dae0f4f2b54673b7a7521e58a18\",\n \"score\": \"0.48567945\",\n \"text\": \"def _send(self, msg):\\n if (msg != self.eof_msg) and (self.fd.tell() != 0):\\n self.fd.seek(0)\\n msg_ply, header = self.serializer.deserialize(msg)\\n with open(self.current_address, 'rb') as fd:\\n old_ply, header = self.serializer.deserialize(fd.read())\\n old_ply.append(msg_ply)\\n new_msg = self.serializer.serialize(old_ply)\\n else:\\n new_msg = msg\\n return super(PlyFileComm, self)._send(new_msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca967a075531f716fbf55d5c3b922be3\",\n \"score\": \"0.4855195\",\n \"text\": \"def write(self, output_stream, kmip_version=enums.KMIPVersion.KMIP_1_0):\\n local_stream = utils.BytearrayStream()\\n\\n if self._major:\\n self._major.write(local_stream, kmip_version=kmip_version)\\n else:\\n raise ValueError(\\n \\\"Invalid struct missing the major protocol version number.\\\"\\n )\\n\\n if self._minor:\\n self._minor.write(local_stream, kmip_version=kmip_version)\\n else:\\n raise ValueError(\\n \\\"Invalid struct missing the minor protocol version number.\\\"\\n )\\n\\n self.length = local_stream.length()\\n super(ProtocolVersion, self).write(\\n output_stream,\\n kmip_version=kmip_version\\n )\\n output_stream.write(local_stream.buffer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8ebb4854330f2683e363d6f6c5a8f66\",\n \"score\": \"0.48438698\",\n \"text\": \"def write_raw(self, data):\\n # Initialize\\n flags, chunk = 0, self.max_recv_size\\n # Append terminator\\n if self.term_char is not None:\\n flags = OP_FLAG_TERMCHAR_SET\\n term_char = str(self.term_char).encode('utf-8')[0]\\n data += term_char\\n # Loop over write requests\\n offsets = range(0, len(data), chunk)\\n for offset in offsets:\\n # Get block\\n if offset == offsets[-1]:\\n flags |= OP_FLAG_END\\n block = data[offset:offset+chunk]\\n # Write request\\n error, size = self.client.device_write(\\n self.link, self.timeout_ms, self.lock_timeout_ms, flags, block)\\n # Raise error\\n if error:\\n raise Vxi11Exception(error, 'write')\\n elif size < len(block):\\n raise Vxi11Exception(\\\"did not write complete block\\\", 'write')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92230d1b1e2d25ed7277556646f2b400\",\n \"score\": \"0.48398197\",\n \"text\": \"def initialize(self):\\n self.bytes_read = 0\\n self.content_length = 0\\n self.content_type = ''\\n self.data = bytes()\\n self.error = None\\n self.stream = None\\n\\n if self.request.headers and 'Content-Encoding' in self.request.headers:\\n gen_log.warning(\\\"Unsupported Content-Encoding: %s\\\", self.request.headers['Content-Encoding'])\\n return\\n if self.request.headers and 'Content-Type' in self.request.headers:\\n self.content_length = int(self.request.headers['Content-Length']) if 'Content-Length' in self.request.headers else 0\\n self.content_type = self.request.headers['Content-Type']\\n if self.content_type.startswith(\\\"application/x-www-form-urlencoded\\\"):\\n return\\n elif self.content_type.startswith(\\\"multipart/form-data\\\"):\\n # If we have a POST that is multipart/form-data we will stream any file\\n # content to disk. This will prevent excessive RAM usage. Clearly we\\n # will need to keep tabs on the overall data size or someone could\\n # still use too much RAM!\\n self.stream = self.Stream()\\n boundary = None\\n fields = self.content_type.split(\\\";\\\")\\n for field in fields:\\n k, _, v = field.strip().partition(\\\"=\\\")\\n if k == \\\"boundary\\\" and v:\\n boundary = bytes(v, 'utf8')\\n if not boundary:\\n raise error.SnakeError('Content boundary not found')\\n if boundary.startswith(b'\\\"') and boundary.endswith(b'\\\"'):\\n boundary = boundary[1:-1]\\n self.stream.boundary = boundary\\n self.stream.working_dir = tempfile.TemporaryDirectory(dir=path.abspath(path.expanduser(snake_config['cache_dir'])))\\n else:\\n self.error = error.SnakeError('Unsupported Content-Type: %s' % self.content_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37164ceefb2a98e7cda8af5f9e409fd1\",\n \"score\": \"0.48377302\",\n \"text\": \"def __build(self):\\n self.__body = self._buildBody()\\n self.__convert = False\\n self.__header = self.__header\\n self.__length = len(self.__body)\\n length = self.__length\\n if self.isHalved(self.__header):\\n if self.__archive:\\n length = bits.bitSet(self.__length, 15)\\n\\n self.__rawData = pack(\\\"<BH\\\", self.__header, length)\\n self.__rawData += self.__body\\n self.__crc = crc16.Crc16.calcBinaryString(\\n self.__rawData,\\n crc16.INITIAL_MODBUS\\n )\\n self.__rawData += pack(\\\"<H\\\", self.__crc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2814b68851547ff15b9d119479958da6\",\n \"score\": \"0.4833357\",\n \"text\": \"def __init__(self, stream, passphrase, testing_only_key=None):\\n blocks_len = len(stream) - 16\\n num_full_blocks = blocks_len / (MAC_BLOCK_SIZE + 32)\\n num_partial_blocks = 0 if blocks_len % (MAC_BLOCK_SIZE + 32) == 0 else 1\\n num_blocks = num_full_blocks + num_partial_blocks\\n num_macs = max(1, num_blocks)\\n stream_length = blocks_len - 32 * num_macs\\n SizedInputStream.__init__(self, stream_length)\\n self.stream = stream\\n salt = stream.read(16)\\n if len(salt) != 16:\\n raise DataTruncatedException(\\\"File does not contain full MAC salt.\\\")\\n key = testing_only_key or pbkdf2_256bit(passphrase, salt)\\n self.mac = hmac.new(key, digestmod=hashlib.sha256)\\n self.buffer = ''\\n self.stream_at_start = True\\n self.stream_at_eof = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48e74b225e968bde1197a11aa831bbf\",\n \"score\": \"0.48329964\",\n \"text\": \"def write(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89585053dae3fbbf7c5eb6cc6b12c6c3\",\n \"score\": \"0.48303038\",\n \"text\": \"def serialize(self, stream, encoding=\\\"utf-8\\\", **kwargs):\\r\\n pass # abstract\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8d5db4c020222ee2650a0527d4db3ec\",\n \"score\": \"0.4819128\",\n \"text\": \"def write(self, buff):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4895c91e88b09dd5c6b3e20b5d2bef28\",\n \"score\": \"0.48156852\",\n \"text\": \"def push(self, path, stream, is_new=True):\\n if self.simulate: return\\n\\n segments = path.split('/')\\n operation = 'A'\\n if not is_new:\\n operation = 'M'\\n\\n # Check if the file is located inside directory structure.\\n # If yes, create the dirs if not exists.\\n if len(segments) > 1:\\n self.mkdir(segments[:-1])\\n\\n try:\\n # Let's just always transfer the file as binary.\\n self.session.storbinary('STOR %s' % path, stream)\\n except Exception as e:\\n # We don't want the whole operation stopped\\n # instead, just log the status.\\n self.failed_operation.append(OperationStatus(operation, path, e))\\n else:\\n self.success_operation.append(OperationStatus(operation, path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f08ae6301432c49670a2dfd3c4105d\",\n \"score\": \"0.48131764\",\n \"text\": \"def _write_bytes(self, data):\\n if self._request_started:\\n print(\\\"Request has already written a response:\\\")\\n traceback.print_stack()\\n return\\n\\n self._outgoing_headers['content-length'] = len(data)\\n\\n response_lines = self.begin_response(len(data))\\n response_lines.append(data)\\n self.protocol.wfile.write(\\\"\\\\r\\\\n\\\".join(response_lines))\\n if hasattr(self.protocol.wfile, 'flush'):\\n self.protocol.wfile.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7d3dab5b5614c6560772edb6feb53d\",\n \"score\": \"0.48117247\",\n \"text\": \"def new_audio_stream(self):\\n self.close_stream()\\n \\n self.stream = sd.OutputStream(\\n samplerate=self.audio.samplerate,\\n channels=self.audio.channels,\\n blocksize=self.blocksize,\\n callback=self.callback,\\n finished_callback=self.finished_callback,\\n dtype='float32')\\n\\n self.raw_data = self.audio.read(dtype='float32')\\n self.channel_mapping = np.arange(self.audio.channels)\\n\\n self.stream.start()\\n self._stopped = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75bb7114ec2f6a66f201b713751b05b\",\n \"score\": \"0.48059058\",\n \"text\": \"def __init__(self, orientation=0):\\n super().__init__(START_PIPE, orientation, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf0e31f3bb28682f2e1135b5f2870a4\",\n \"score\": \"0.4802314\",\n \"text\": \"def __init__(self, stream, location, mimetype, anchor, modif):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2db3af9a1b5e778583653c307753f5\",\n \"score\": \"0.47909853\",\n \"text\": \"def tx(data):\\n\\tbytes = (data).to_bytes(4, byteorder=BYTE_ORDER)\\n\\tser.write(bytes)\\n\\t# loopback for testing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5cbc427c01088afef450afd2eb6f53d\",\n \"score\": \"0.47834563\",\n \"text\": \"def test_051_share_is_writable_smb1(request):\\n depends(request, [\\\"SMB_SHARE_CREATED\\\"])\\n c = SMB()\\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\\n fd = c.create_file(\\\"testfile\\\", \\\"w\\\")\\n c.close(fd, True)\\n c.disconnect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f17e041716d9dac3c21aae683f97a1c\",\n \"score\": \"0.47788328\",\n \"text\": \"def write(self, data: bytes, offset: int = 0):\\n self.__parent.basic_write(self.__base_address + offset, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f49b5e8145e78353f1155de23e4b50a4\",\n \"score\": \"0.4777356\",\n \"text\": \"def update(self):\\n buffer = bytearray(17)\\n buffer[1:] = self.buffer\\n self.i2c.writeto(self.address, bytes(buffer))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba2791253453c66d760559c433b391d\",\n \"score\": \"0.47763595\",\n \"text\": \"def start_slam_streaming(self):\\n endpoint = 'slam/streaming/start'\\n self.wrapper_post(endpoint)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28118e678054e7042b83c31062212c41\",\n \"score\": \"0.47752145\",\n \"text\": \"def test0_WriteReadLoopback(self):\\n for i in range(self.N):\\n q = bytes_0to255\\n self.s.write(q)\\n self.assertEqual(self.s.read(len(q)), q) # expected same which was written before\\n self.assertEqual(self.s.inWaiting(), 0) # expected empty buffer after all sent chars are read\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b96d17bedf9a8e0a0792839d28302b\",\n \"score\": \"0.47739455\",\n \"text\": \"def write(self, stream):\\n\\n stream.write(self.render())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d9eefe4045ecf796dc6d1637b32e358\",\n \"score\": \"0.47708577\",\n \"text\": \"def xWrite(self, data, offset): # noqa: N802\\n # Can't write to an HTTP server, ignore\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a0d26d4aef6ca6fae25c6116c3c835\",\n \"score\": \"0.4761974\",\n \"text\": \"def stream(self, f):\\r\\n self.stream_header(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc3a264d3ee28a776986fe7c6342dbd\",\n \"score\": \"0.47603023\",\n \"text\": \"def flush(self):\\n self.__read_buffer = BufferIO(self._transform(self.__write_buffer.getvalue()))\\n self.__write_buffer = BufferIO()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fded89f0d31cce7848b63f201b5f5bcf\",\n \"score\": \"0.4759879\",\n \"text\": \"def setup_io(self):\\n sys.stdout = Stream(sys.stdout)\\n sys.stderr = Stream(sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ff3bc11b96b7fec4792691d9667e768\",\n \"score\": \"0.47596627\",\n \"text\": \"def __write_header(self):\\n self.filed.write(struct.pack('I',0xa1b2c3d4))\\n self.filed.write(struct.pack('H',2))\\n self.filed.write(struct.pack('H',4))\\n self.filed.write(struct.pack('i',0))\\n self.filed.write(struct.pack('I',0))\\n self.filed.write(struct.pack('I',65535))\\n self.filed.write(struct.pack('I',1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cdcfd7ee5097a4b7df8c94f00436044\",\n \"score\": \"0.47587278\",\n \"text\": \"def record(cls):\\n return cls._namespace_SIO('SIO_000088')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994383,"cells":{"query_id":{"kind":"string","value":"46adae9c98fd6026efdea1d766439e66"},"query":{"kind":"string","value":"Test select multiples choices are split within repeats in CSV exports."},"positive_passages":{"kind":"list like","value":[{"docid":"0d47b608d9ef2a061cc986110dfcf3d6","score":"0.72824013","text":"def test_split_select_multiples_within_repeats(self):\n self.maxDiff = None\n record = {\n \"name\": \"Tom\",\n \"age\": 23,\n \"browser_use\": [\n {\"browser_use/year\": \"2010\", \"browser_use/browsers\": \"firefox safari\"},\n {\"browser_use/year\": \"2011\", \"browser_use/browsers\": \"firefox chrome\"},\n ],\n } # yapf: disable\n expected_result = {\n \"name\": \"Tom\",\n \"age\": 23,\n \"browser_use\": [\n {\n \"browser_use/year\": \"2010\",\n \"browser_use/browsers/firefox\": True,\n \"browser_use/browsers/safari\": True,\n \"browser_use/browsers/ie\": False,\n \"browser_use/browsers/chrome\": False,\n },\n {\n \"browser_use/year\": \"2011\",\n \"browser_use/browsers/firefox\": True,\n \"browser_use/browsers/safari\": False,\n \"browser_use/browsers/ie\": False,\n \"browser_use/browsers/chrome\": True,\n },\n ],\n } # yapf: disable\n select_multiples = {\n \"browser_use/browsers\": [\n (\"browser_use/browsers/firefox\", \"firefox\", \"Firefox\"),\n (\"browser_use/browsers/safari\", \"safari\", \"Safari\"),\n (\"browser_use/browsers/ie\", \"ie\", \"Internet Explorer\"),\n (\"browser_use/browsers/chrome\", \"chrome\", \"Google Chrome\"),\n ]\n }\n # pylint: disable=protected-access\n result = CSVDataFrameBuilder._split_select_multiples(record, select_multiples)\n self.assertEqual(expected_result, result)","title":""}],"string":"[\n {\n \"docid\": \"0d47b608d9ef2a061cc986110dfcf3d6\",\n \"score\": \"0.72824013\",\n \"text\": \"def test_split_select_multiples_within_repeats(self):\\n self.maxDiff = None\\n record = {\\n \\\"name\\\": \\\"Tom\\\",\\n \\\"age\\\": 23,\\n \\\"browser_use\\\": [\\n {\\\"browser_use/year\\\": \\\"2010\\\", \\\"browser_use/browsers\\\": \\\"firefox safari\\\"},\\n {\\\"browser_use/year\\\": \\\"2011\\\", \\\"browser_use/browsers\\\": \\\"firefox chrome\\\"},\\n ],\\n } # yapf: disable\\n expected_result = {\\n \\\"name\\\": \\\"Tom\\\",\\n \\\"age\\\": 23,\\n \\\"browser_use\\\": [\\n {\\n \\\"browser_use/year\\\": \\\"2010\\\",\\n \\\"browser_use/browsers/firefox\\\": True,\\n \\\"browser_use/browsers/safari\\\": True,\\n \\\"browser_use/browsers/ie\\\": False,\\n \\\"browser_use/browsers/chrome\\\": False,\\n },\\n {\\n \\\"browser_use/year\\\": \\\"2011\\\",\\n \\\"browser_use/browsers/firefox\\\": True,\\n \\\"browser_use/browsers/safari\\\": False,\\n \\\"browser_use/browsers/ie\\\": False,\\n \\\"browser_use/browsers/chrome\\\": True,\\n },\\n ],\\n } # yapf: disable\\n select_multiples = {\\n \\\"browser_use/browsers\\\": [\\n (\\\"browser_use/browsers/firefox\\\", \\\"firefox\\\", \\\"Firefox\\\"),\\n (\\\"browser_use/browsers/safari\\\", \\\"safari\\\", \\\"Safari\\\"),\\n (\\\"browser_use/browsers/ie\\\", \\\"ie\\\", \\\"Internet Explorer\\\"),\\n (\\\"browser_use/browsers/chrome\\\", \\\"chrome\\\", \\\"Google Chrome\\\"),\\n ]\\n }\\n # pylint: disable=protected-access\\n result = CSVDataFrameBuilder._split_select_multiples(record, select_multiples)\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2d5cb44cdb85d97c0343e4af60eea444","score":"0.73833954","text":"def test_split_select_multiples_with_randomize(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label | parameters |\n | | text | name | Name | |\n | | integer | age | Age | |\n | | begin repeat | browser_use | Browser Use | |\n | | integer | year | Year | |\n | | select_multiple browsers | browsers | Browsers | randomize=true |\n | | end repeat | | | |\n\n | choices |\n | | list name | name | label |\n | | browsers | firefox | Firefox |\n | | browsers | chrome | Chrome |\n | | browsers | ie | Internet Explorer |\n | | browsers | safari | Safari |\n \"\"\" # noqa: E501\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [\n {\n \"name\": \"Tom\",\n \"age\": 23,\n \"browser_use\": [\n {\n \"browser_use/year\": \"2010\",\n \"browser_use/browsers\": \"firefox safari\",\n },\n {\n \"browser_use/year\": \"2011\",\n \"browser_use/browsers\": \"firefox chrome\",\n },\n ],\n }\n ] # yapf: disable\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=True,\n include_images=False,\n )\n record = cursor[0]\n select_multiples = CSVDataFrameBuilder._collect_select_multiples(xform)\n result = CSVDataFrameBuilder._split_select_multiples(record, select_multiples)\n # build a new dictionary only composed of the keys we want to use in\n # the comparison\n result = dict(\n [(key, result[key]) for key in list(result) if key in list(cursor[0])]\n )\n self.assertEqual(cursor[0], result)\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username, xform.id_string, binary_select_multiples=True\n )\n # pylint: disable=protected-access\n result = csv_df_builder._split_select_multiples(record, select_multiples)\n # build a new dictionary only composed of the keys we want to use in\n # the comparison\n result = dict(\n [(key, result[key]) for key in list(result) if key in list(cursor[0])]\n )\n self.assertEqual(cursor[0], result)","title":""},{"docid":"8f84d6ba2692a80b553fdf67185d3f10","score":"0.73285466","text":"def test_split_select_multiples(self):\n self._publish_nested_repeats_form()\n self._submit_fixture_instance(\"nested_repeats\", \"01\")\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username, self.xform.id_string, include_images=False\n )\n # pylint: disable=protected-access\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n record = cursor[0]\n select_multiples = CSVDataFrameBuilder._collect_select_multiples(self.xform)\n result = CSVDataFrameBuilder._split_select_multiples(record, select_multiples)\n expected_result = {\n \"web_browsers/ie\": True,\n \"web_browsers/safari\": True,\n \"web_browsers/firefox\": False,\n \"web_browsers/chrome\": False,\n }\n # build a new dictionary only composed of the keys we want to use in\n # the comparison\n result = dict(\n [(key, result[key]) for key in list(result) if key in list(expected_result)]\n )\n self.assertEqual(expected_result, result)\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username, self.xform.id_string, binary_select_multiples=True\n )\n # pylint: disable=protected-access\n result = csv_df_builder._split_select_multiples(record, select_multiples)\n expected_result = {\n \"web_browsers/ie\": 1,\n \"web_browsers/safari\": 1,\n \"web_browsers/firefox\": 0,\n \"web_browsers/chrome\": 0,\n }\n # build a new dictionary only composed of the keys we want to use in\n # the comparison\n result = dict(\n [(key, result[key]) for key in list(result) if key in list(expected_result)]\n )\n self.assertEqual(expected_result, result)","title":""},{"docid":"878fbee9e68d69a3bc62047e56187b42","score":"0.72794944","text":"def test_no_split_select_multiples(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label |\n | | text | name | Name |\n | | integer | age | Age |\n | | begin repeat | browser_use | Browser Use |\n | | integer | year | Year |\n | | select_multiple browsers | browsers | Browsers |\n | | end repeat | | |\n\n | choices |\n | | list name | name | label |\n | | browsers | firefox | Firefox |\n | | browsers | chrome | Chrome |\n | | browsers | ie | Internet Explorer |\n | | browsers | safari | Safari |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [\n {\n \"name\": \"Tom\",\n \"age\": 23,\n \"browser_use\": [\n {\n \"browser_use/year\": \"2010\",\n \"browser_use/browsers\": \"firefox safari\",\n },\n {\n \"browser_use/year\": \"2011\",\n \"browser_use/browsers\": \"firefox chrome\",\n },\n ],\n }\n ] # yapf: disable\n\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=False,\n include_images=False,\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [\n {\n \"name\": \"Tom\",\n \"age\": 23,\n \"browser_use[1]/year\": \"2010\",\n \"browser_use[1]/browsers\": \"firefox safari\",\n \"browser_use[2]/year\": \"2011\",\n \"browser_use[2]/browsers\": \"firefox chrome\",\n }\n ]\n self.maxDiff = None\n self.assertEqual(expected_result, result)","title":""},{"docid":"d4870f5ee6b1282eb432445e0d0f68a4","score":"0.7215538","text":"def test_split_select_multiples_values(self):\n self._publish_nested_repeats_form()\n self._submit_fixture_instance(\"nested_repeats\", \"01\")\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n record = cursor[0]\n select_multiples = CSVDataFrameBuilder._collect_select_multiples(self.xform)\n result = CSVDataFrameBuilder._split_select_multiples(\n record, select_multiples, value_select_multiples=True\n )\n expected_result = {\n \"web_browsers/ie\": \"ie\",\n \"web_browsers/safari\": \"safari\",\n \"web_browsers/firefox\": None,\n \"web_browsers/chrome\": None,\n }\n # build a new dictionary only composed of the keys we want to use in\n # the comparison\n result = dict(\n [(key, result[key]) for key in list(result) if key in list(expected_result)]\n )\n self.assertEqual(expected_result, result)","title":""},{"docid":"38563ed5d8daadc1cdcdcf5122ee1e04","score":"0.64926195","text":"def test_show_choice_labels_select_multiple(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label |\n | | text | name | Name |\n | | integer | age | Age |\n | | select_multiple fruits | fruit | Fruit |\n | | | | |\n | choices | list name | name | label |\n | | fruits | 1 | Mango |\n | | fruits | 2 | Orange |\n | | fruits | 3 | Apple |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1 2\"}] # yapf: disable\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=False,\n include_images=False,\n show_choice_labels=True,\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"Mango Orange\"}]\n self.maxDiff = None\n self.assertEqual(expected_result, result)","title":""},{"docid":"12718be74bd7221faa1a0ae46219dd93","score":"0.6464451","text":"def test_show_choice_labels_select_multiple_3(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label |\n | | text | name | Name |\n | | integer | age | Age |\n | | select_multiple fruits | fruit | Fruit |\n | | | | |\n | choices | list name | name | label |\n | | fruits | 1 | Mango |\n | | fruits | 2 | Orange |\n | | fruits | 3 | Apple |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1 2\"}] # yapf: disable\n # Split Select multiples, binary_select_multiples is True\n csv_df_builder_1 = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=True,\n binary_select_multiples=False,\n include_images=False,\n show_choice_labels=True,\n )\n result = [k for k in csv_df_builder_1._format_for_dataframe(cursor)]\n expected_result = [\n {\n \"name\": \"Maria\",\n \"age\": 25,\n \"fruit/Mango\": True,\n \"fruit/Orange\": True,\n \"fruit/Apple\": 0,\n }\n ]\n self.assertEqual(expected_result, result)","title":""},{"docid":"59dbe9e2e206d6c90943ec743caa2ab5","score":"0.64489794","text":"def test_show_choice_labels_select_multiple_1(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label |\n | | text | name | Name |\n | | integer | age | Age |\n | | select_multiple fruits | fruit | Fruit |\n | | | | |\n | choices | list name | name | label |\n | | fruits | 1 | Mango |\n | | fruits | 2 | Orange |\n | | fruits | 3 | Apple |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1 2\"}] # yapf: disable\n # Split Select multiples, value_select_multiples is True\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=True,\n value_select_multiples=True,\n include_images=False,\n show_choice_labels=True,\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [\n {\n \"name\": \"Maria\",\n \"age\": 25,\n \"fruit/Mango\": \"Mango\",\n \"fruit/Orange\": \"Orange\",\n \"fruit/Apple\": None,\n }\n ]\n self.assertEqual(expected_result, result)","title":""},{"docid":"26de22cbb9527e79737b7ed47c478551","score":"0.6430323","text":"def test_show_choice_labels_select_multiple_2(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label |\n | | text | name | Name |\n | | integer | age | Age |\n | | select_multiple fruits | fruit | Fruit |\n | | | | |\n | choices | list name | name | label |\n | | fruits | 1 | Mango |\n | | fruits | 2 | Orange |\n | | fruits | 3 | Apple |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1 2\"}] # yapf: disable\n # Split Select multiples, binary_select_multiples is True\n csv_df_builder_1 = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=True,\n binary_select_multiples=True,\n include_images=False,\n show_choice_labels=True,\n )\n result = [k for k in csv_df_builder_1._format_for_dataframe(cursor)]\n expected_result = [\n {\n \"name\": \"Maria\",\n \"age\": 25,\n \"fruit/Mango\": 1,\n \"fruit/Orange\": 1,\n \"fruit/Apple\": 0,\n }\n ]\n self.assertEqual(expected_result, result)","title":""},{"docid":"321909ff81dd2791a58a78b3cf9426e0","score":"0.61721087","text":"def test_to_csv(self):\n self.clean_option.get_merge_data()\n lines = self.clean_option.to_csv()\n\n self.assertGreater(len(lines), 1)\n for line in lines.split():\n print line\n data = line.split(',')\n self.assertEqual(len(data), 11)","title":""},{"docid":"281ec7075d7ee482a61036db7a5bfa35","score":"0.6168276","text":"def test_show_choice_labels_select_multiple_language(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label:Eng | label:Fr |\n | | text | name | Name | Prénom |\n | | integer | age | Age | Âge |\n | | select_multiple fruits | fruit | Fruit | Fruit |\n | | | | | |\n | choices | list name | name | label:Eng | label:Fr |\n | | fruits | 1 | Mango | Mangue |\n | | fruits | 2 | Orange | Orange |\n | | fruits | 3 | Apple | Pomme |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1 2\"}] # yapf: disable\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=False,\n include_images=False,\n show_choice_labels=True,\n language=\"Fr\",\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"Mangue Orange\"}]\n self.maxDiff = None\n self.assertEqual(expected_result, result)","title":""},{"docid":"40b0f50e5bd24cd7f793b4fb119df381","score":"0.6140882","text":"def test_show_choice_labels_select_multiple_1_language(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label:Eng | label:Fr |\n | | text | name | Name | Prénom |\n | | integer | age | Age | Âge |\n | | select_multiple fruits | fruit | Fruit | Fruit |\n | | | | | |\n | choices | list name | name | label:Eng | label:Fr |\n | | fruits | 1 | Mango | Mangue |\n | | fruits | 2 | Orange | Orange |\n | | fruits | 3 | Apple | Pomme |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1 2\"}] # yapf: disable\n # Split Select multiples, value_select_multiples is True\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=True,\n value_select_multiples=True,\n include_images=False,\n show_choice_labels=True,\n language=\"Fr\",\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [\n {\n \"name\": \"Maria\",\n \"age\": 25,\n \"fruit/Mangue\": \"Mangue\",\n \"fruit/Orange\": \"Orange\",\n \"fruit/Pomme\": None,\n }\n ]\n self.assertEqual(expected_result, result)","title":""},{"docid":"6052af7f0e70b94584269a96599356db","score":"0.56214225","text":"def test_to_csv(self):\n self.clean_option.get_merge_data()\n # self.clean_option.df_all = self.clean_option.df_all[\n # self.clean_option.df_all['option_code'] == 'TZA1120121C9'\n # ]\n self.clean_option.update_split_date()\n # ts(self.clean_option.df_all[['ask', 'bid', 'date', 'right', 'strike', 'close', 'close1']])\n lines = self.clean_option.to_csv()\n\n self.assertGreater(len(lines), 1)\n for line in lines.split():\n print line\n data = line.split(',')\n self.assertEqual(len(data), 12)","title":""},{"docid":"af669cef2b59404be2855936b11dd015","score":"0.55866694","text":"def pb4_clicked(self):\n list = self.view.selectedItems()\n if len(list) > 1:\n dlg = QtGui.QProgressDialog(\"Output CSV\", \"Cancel Output\", 0, len(list))\n dlg.setWindowModality(QtCore.Qt.WindowModal)\n dlg.setMinimumDuration(0)\n dlg.setWindowTitle(\"\")\n dlg.show()\n count = 0\n f = open('results.csv', 'wb')\n f.write('Variant, k1 from XXX, k1 from XX, k2 from XX, k1 from X, k2 from X\\n')\n for item in list: #item is the file_noext\n print(str(item.text()))\n kobs = self.fdict[str(item.text())].kobs\n print(kobs)\n print self.csv(kobs)\n alist = []\n f.write(str(item.text())+', ') #write the variant\n if len(kobs) > 1:\n for i in kobs:\n for j in i:\n alist.append(j)\n f.write(self.csv(alist))\n else:\n print kobs[0].shape\n print type(kobs[0])\n print self.csv(kobs[0].tolist())\n f.write(str(kobs[0].tolist()[0]))\n f.write('\\n')\n count += 1\n dlg.setValue(count)\n if dlg.wasCanceled():\n break\n f.close()\n return","title":""},{"docid":"f676686713a286b1ea0cc2afb8a08624","score":"0.55680466","text":"def test_multiple_repeats_column_order(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label | repeat_count | relevant |\n | | select_multiple food | food | Food: | | |\n | | integer | no_food | No. Food | | |\n | | begin repeat | food_repeat | Food Repeat | ${no_food} | |\n | | select_multiple food | food_group | Food: | | |\n | | end repeat | | | | |\n | | integer | no_food_2 | No. Food | | |\n | | begin repeat | food_repeat_2 | Food Repeat | ${no_food_2} | selected(${food}, 'Apple') |\n | | select_multiple food | food_group_2 | Food: | | |\n | | end repeat | | | | |\n | | geopoint | gps | GPS | | |\n | | | | | | |\n | choices | list name | name | label | | |\n | | food | Apple | Apple | | |\n | | food | Orange | Orange | | |\n | | food | Banana | Banana | | |\n | | food | Pizza | Pizza | | |\n | | food | Lasgna | Lasgna | | |\n | | food | Cake | Cake | | |\n | | food | Chocolate | Chocolate | | |\n | | food | Salad | Salad | | |\n | | food | Sandwich | Sandwich | | |\n \"\"\" # noqa: E501\n self.xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n\n cursor = [\n {\n \"food\": \"Orange\",\n \"no_food\": 2,\n \"food_repeat\": [\n {\"food_repeat/food_group\": \"Banana\"},\n {\"food_repeat/food_group\": \"Lasgna\"},\n ],\n },\n {\n \"food\": \"Apple\",\n \"no_food_2\": 2,\n \"food_repeat_2\": [\n {\"food_repeat_2/food_group_2\": \"Cake\"},\n {\"food_repeat_2/food_group_2\": \"Salad\"},\n ],\n },\n ]\n expected_header = [\n \"food/Apple\",\n \"food/Orange\",\n \"food/Banana\",\n \"food/Pizza\",\n \"food/Lasgna\",\n \"food/Cake\",\n \"food/Chocolate\",\n \"food/Salad\",\n \"food/Sandwich\",\n \"no_food\",\n \"food_repeat_count\",\n \"food_repeat[1]/food_group/Apple\",\n \"food_repeat[1]/food_group/Orange\",\n \"food_repeat[1]/food_group/Banana\",\n \"food_repeat[1]/food_group/Pizza\",\n \"food_repeat[1]/food_group/Lasgna\",\n \"food_repeat[1]/food_group/Cake\",\n \"food_repeat[1]/food_group/Chocolate\",\n \"food_repeat[1]/food_group/Salad\",\n \"food_repeat[1]/food_group/Sandwich\",\n \"food_repeat[2]/food_group/Apple\",\n \"food_repeat[2]/food_group/Orange\",\n \"food_repeat[2]/food_group/Banana\",\n \"food_repeat[2]/food_group/Pizza\",\n \"food_repeat[2]/food_group/Lasgna\",\n \"food_repeat[2]/food_group/Cake\",\n \"food_repeat[2]/food_group/Chocolate\",\n \"food_repeat[2]/food_group/Salad\",\n \"food_repeat[2]/food_group/Sandwich\",\n \"no_food_2\",\n \"food_repeat_2_count\",\n \"food_repeat_2[1]/food_group_2/Apple\",\n \"food_repeat_2[1]/food_group_2/Orange\",\n \"food_repeat_2[1]/food_group_2/Banana\",\n \"food_repeat_2[1]/food_group_2/Pizza\",\n \"food_repeat_2[1]/food_group_2/Lasgna\",\n \"food_repeat_2[1]/food_group_2/Cake\",\n \"food_repeat_2[1]/food_group_2/Chocolate\",\n \"food_repeat_2[1]/food_group_2/Salad\",\n \"food_repeat_2[1]/food_group_2/Sandwich\",\n \"food_repeat_2[2]/food_group_2/Apple\",\n \"food_repeat_2[2]/food_group_2/Orange\",\n \"food_repeat_2[2]/food_group_2/Banana\",\n \"food_repeat_2[2]/food_group_2/Pizza\",\n \"food_repeat_2[2]/food_group_2/Lasgna\",\n \"food_repeat_2[2]/food_group_2/Cake\",\n \"food_repeat_2[2]/food_group_2/Chocolate\",\n \"food_repeat_2[2]/food_group_2/Salad\",\n \"food_repeat_2[2]/food_group_2/Sandwich\",\n \"gps\",\n \"_gps_latitude\",\n \"_gps_longitude\",\n \"_gps_altitude\",\n \"_gps_precision\",\n \"meta/instanceID\",\n \"_id\",\n \"_uuid\",\n \"_submission_time\",\n \"_date_modified\",\n \"_tags\",\n \"_notes\",\n \"_version\",\n \"_duration\",\n \"_submitted_by\",\n \"_total_media\",\n \"_media_count\",\n \"_media_all_received\",\n ]\n\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username, self.xform.id_string, include_images=False\n )\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n header = next(csv_reader)\n\n self.assertEqual(header, expected_header)\n\n csv_file.close()","title":""},{"docid":"9faafc3451720364e01631237b2a37bd","score":"0.55194","text":"def is_multiple_selection_allowed(self):\n LOGGER.debug(\"Check if multiple selection is allowed\")\n self.web_element.is_multiple()","title":""},{"docid":"3ad261501cbbd72369a99eb0f91e2e4b","score":"0.55074614","text":"def test_csv_splits(self):\n from karld.loadump import split_csv_file\n\n split_csv_file(self.input_data_path, self.out_dir, max_lines=5)\n\n self.assertTrue(os.path.exists(self.expected_out_0))\n self.assertTrue(os.path.exists(self.expected_out_1))\n\n with open(self.expected_out_0, 'rb') as stream:\n data = stream.read()\n self.assertEqual(b'mushroom,fungus\\ntomato,fruit\\ntopaz,mineral\\n'\n b'iron,metal\\ndr\\xc3\\xb3\\xc5\\xbck\\xc4\\x85,'\n b'utf-8 sample\\n'.splitlines(), data.splitlines())","title":""},{"docid":"672758e8667c13365b511fe3e684209b","score":"0.55007154","text":"def test_exact_multiple(self):\n actual = parserutils.divide_into_groups(['a', 'b', 'c', 'd'], 2)\n expected = [['a', 'b'], ['c', 'd']]\n self.assertEqual(actual, expected)","title":""},{"docid":"fc4ae3b630577649d4b1c83b737bda15","score":"0.54716295","text":"def test_show_choice_labels_multi_language_1(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label:English | label:French |\n | | text | name | Name | Prénom |\n | | integer | age | Age | Âge |\n | | select one fruits | fruit | Fruit | Fruit |\n | | | | | |\n | choices | list name | name | label:English | label:French |\n | | fruits | 1 | Mango | Mangue |\n | | fruits | 2 | Orange | Orange |\n | | fruits | 3 | Apple | Pomme |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1\"}] # yapf: disable\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=False,\n include_images=False,\n show_choice_labels=True,\n language=\"English\",\n )\n # pylint: disable=protected-access\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"Mango\"}]\n self.maxDiff = None\n self.assertEqual(expected_result, result)","title":""},{"docid":"aea994eb5990c0aeada69938d9bedced","score":"0.5438774","text":"def test_show_choice_labels_multi_language(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label:English | label:French |\n | | text | name | Name | Prénom |\n | | integer | age | Age | Âge |\n | | select one fruits | fruit | Fruit | Fruit |\n | | | | | |\n | choices | list name | name | label:English | label:French |\n | | fruits | 1 | Mango | Mangue |\n | | fruits | 2 | Orange | Orange |\n | | fruits | 3 | Apple | Pomme |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1\"}] # yapf: disable\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=False,\n include_images=False,\n show_choice_labels=True,\n language=\"French\",\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"Mangue\"}]\n self.maxDiff = None\n self.assertEqual(expected_result, result)","title":""},{"docid":"3fdbfc6cdbe8743347650ab21fc5e1c3","score":"0.53887004","text":"def test_account_list_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\n account.entity.open()\n self.assert_util(\n account.entity.multiple_select.list_of_values(),\n [\"Option One\", \"Option Two\"]\n )","title":""},{"docid":"7d5657f9af8c256c673bea641e82fc14","score":"0.53864026","text":"def test_windows_excel_compatible_csv_export(self):\n self._publish_single_level_repeat_form()\n # submit 7 instances\n for _ in range(4):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n self._submit_fixture_instance(\"new_repeats\", \"02\")\n for _ in range(2):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n self.xform.id_string,\n remove_group_name=True,\n include_images=False,\n win_excel_utf8=True,\n )\n # pylint: disable=protected-access\n record_count = self.xform.instances.count()\n self.assertEqual(record_count, 7)\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n header = next(csv_reader)\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\n self.assertEqual(b\"\\xef\\xbb\\xbfname\", header[0].encode(\"utf-8\"))\n # close and delete file\n csv_file.close()\n os.unlink(temp_file.name)","title":""},{"docid":"76eef42f58070df11cd1f94c24d9d010","score":"0.5384241","text":"def _check_multiples(self, code):\n if len(self.data[code]) > 1 and not self.schema[code].multiple:\n strictness.problem(\n \"repeated option {} '{}' not permitted by pcapng spec\".format(\n code, self._get_name_alias(code)\n )\n )\n if strictness.should_fix():\n self.data[code] = self.data[code][:1]","title":""},{"docid":"d58f34ed5c250f70661ed78c2a0c3149","score":"0.53801537","text":"def test_not_exact_multiple(self):\n actual = parserutils.divide_into_groups(['a', 'b', 'c', 'd', 'e'], 2)\n expected = [['a', 'b'], ['c', 'd'], ['e']]\n self.assertEqual(actual, expected)","title":""},{"docid":"126f99224fad031cb41e6b10aea12325","score":"0.5376905","text":"def test_export_study_option_group(self, client):\n study_option_groups = client.export_option_groups()\n for option_group in study_option_groups:\n # Assert that the number of keys between the retrieved data and the model are the same\n assert len(option_group.keys()) == len(export_option_group_model.keys())\n for key in option_group:\n # Assert that each retrieved key should be in the model\n assert key in export_option_group_model.keys()\n # Assert that the value belonging to each key is according to the model\n assert type(option_group[key]) in export_option_group_model[key]","title":""},{"docid":"faec96ca872bfd8d9200f036e6ab3a4e","score":"0.53511304","text":"def write_csv_data_multi_question_hit(sample_name, sample_directory, questions_per_hit, sample_pair_list):\n\n # Erases and sets up a new CSV file, returning handles\n csvfile, writer = setup_csv_multi_question_hit(sample_name, sample_directory, questions_per_hit)\n\n counter = 1\n column_list = []\n for row in sample_pair_list:\n print(row)\n column_list = column_list + row\n if counter % questions_per_hit == 0:\n writer.writerow(column_list)\n column_list = []\n counter += 1","title":""},{"docid":"09a60402d8e561bbd30b3ac6bca6763c","score":"0.5277433","text":"def ask_for_multiple_list(input_ls: list, question: str, answers: list) -> list:\n output_list = []\n while True:\n line_no = ask_for_list(input_ls)\n output_list.append(line_no) if line_no not in output_list else print('Already selected.')\n more = ask_for(question, answers)\n if not more:\n break\n return output_list","title":""},{"docid":"e2c5b4fbcd550d5fb913756b92e89f80","score":"0.524852","text":"def test_export_two_scalar_csv_good_input(self):\n populate_database_with_data(self.factory.create_record_dao())\n export(\n factory=self.factory,\n id_list=['spam3', 'spam'],\n scalar_names=['spam_scal', 'spam_scal_2'],\n output_type='csv',\n output_file=self.test_file_path.name)\n\n with io.open(self.test_file_path.name, 'r', encoding='utf-8') as csvfile:\n reader = csv.reader(csvfile)\n rows = [row for row in reader]\n self.assertEqual(rows[0], ['id',\n 'spam_scal',\n 'spam_scal_2'])\n self.assertEqual(rows[1], ['spam3',\n '10.5',\n '10.5'])\n # AlmostEqual doesn't work with lists, but we expect floats from\n # SQL, hence this workaround\n self.assertEqual(rows[2], ['spam',\n '10.0',\n '200.0'])","title":""},{"docid":"9bbdda0cc9dd38c09dbf1ce900885fe9","score":"0.5224446","text":"def multiple_choice():\n # Here are 4 choices\n a = \"Training accuracy increases over time and this is a problem.\"\n b = \"Validation accuracy does not increase over time and this is a problem.\"\n c = \"Training loss decreases over time and this is a problem.\"\n d = \"Validation loss does not decrease over time and this is a problem.\"\n return a, b, c, d","title":""},{"docid":"9137ee475e04e1f3560688ff0ae2dd98","score":"0.52004576","text":"def test_show_choice_labels(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label |\n | | text | name | Name |\n | | integer | age | Age |\n | | select one fruits | fruit | Fruit |\n | | | | |\n | choices | list name | name | label |\n | | fruits | 1 | Mango |\n | | fruits | 2 | Orange |\n | | fruits | 3 | Apple |\n \"\"\"\n xform = self._publish_markdown(md_xform, self.user, id_string=\"b\")\n cursor = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"1\"}] # yapf: disable\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n xform.id_string,\n split_select_multiples=False,\n include_images=False,\n show_choice_labels=True,\n )\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\n expected_result = [{\"name\": \"Maria\", \"age\": 25, \"fruit\": \"Mango\"}]\n self.maxDiff = None\n self.assertEqual(expected_result, result)","title":""},{"docid":"2097a07d37602f50cee2a3ccab0d22c7","score":"0.5185491","text":"def test_account_select_value_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\n account.entity.open()\n account.entity.multiple_select.select(\"Option One\")\n account.entity.multiple_select.select(\"Option Two\")\n self.assert_util(\n account.entity.multiple_select.get_values,\n ['Option One', 'Option Two'])","title":""},{"docid":"c469afd657fbe397240ea16e6e27aab2","score":"0.5181445","text":"def test_csv_export_extra_columns(self):\n self._publish_single_level_repeat_form()\n # submit 7 instances\n for _ in range(4):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n self._submit_fixture_instance(\"new_repeats\", \"02\")\n for _ in range(2):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n self.xform.id_string,\n remove_group_name=True,\n include_labels=True,\n include_reviews=True,\n )\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n header = next(csv_reader)\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\n expected_header = [\n \"name\",\n \"age\",\n \"has_kids\",\n \"kids_name\",\n \"kids_age\",\n \"kids_name\",\n \"kids_age\",\n \"gps\",\n \"_gps_latitude\",\n \"_gps_longitude\",\n \"_gps_altitude\",\n \"_gps_precision\",\n \"web_browsers/firefox\",\n \"web_browsers/chrome\",\n \"web_browsers/ie\",\n \"web_browsers/safari\",\n \"instanceID\",\n \"_id\",\n \"_uuid\",\n \"_submission_time\",\n \"_date_modified\",\n \"_tags\",\n \"_notes\",\n \"_version\",\n \"_duration\",\n \"_submitted_by\",\n \"_total_media\",\n \"_media_count\",\n \"_media_all_received\",\n \"_xform_id\",\n \"_review_status\",\n \"_review_comment\",\n \"_review_date\",\n ]\n self.assertEqual(expected_header, header)\n # close and delete file\n csv_file.close()\n os.unlink(temp_file.name)\n\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n self.xform.id_string,\n remove_group_name=True,\n include_labels=True,\n include_reviews=True,\n )\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n header = next(csv_reader)\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\n self.assertEqual(expected_header, header)\n csv_file.close()\n os.unlink(temp_file.name)","title":""},{"docid":"1aa2c413100dc3517bdca9dc1c5420e6","score":"0.51507574","text":"def test_read_multiple_csv(tmp_path, copies):\n\n writer = Clumper.read_csv(\"tests/data/monopoly.csv\")\n\n for i in range(copies):\n writer.write_csv(tmp_path / f\"monopoly_copy_{i}.csv\")\n\n reader = Clumper.read_csv(str(tmp_path / \"*.csv\"))\n assert len(reader) == copies * len(writer)\n\n reader = Clumper.read_csv(list(Path(tmp_path).glob(\"*.csv\")))\n assert len(reader) == copies * len(writer)","title":""},{"docid":"a4d3b420551e208fbcddb8571e1c7fb8","score":"0.5133795","text":"def read_multiple():\r\n pass","title":""},{"docid":"86363eb83a831ac1c03ea38f4638f923","score":"0.5117611","text":"def OnExportCSVClick(self):\n if len(self.list)>0:\n f = tkFileDialog.asksaveasfile(mode='w', defaultextension=\".csv\", initialfile=\"common.csv\")\n if f is None:\n return\n writer = csv.writer(f)\n for keyword in self.list:\n writer.writerow([keyword])\n f.close \n tkMessageBox.showinfo(\"Done\", \"File exported as CSV!\")\n else:\n tkMessageBox.showwarning(\"Error\",\"No keywords to export.\")","title":""},{"docid":"5b84f18f25a95f65c7e6e01a55c5dfd1","score":"0.51058125","text":"def test_ls1_sample_csv(self):\n inputs = [(get_ls1_test_prob_point_data()), (get_ls1_test_prob_region_data())]\n\n for data, input_files, reference_files, output_file_index in inputs:\n with self.subTest(\n data=data,\n input_files=input_files,\n reference_files=reference_files,\n output_file_index=output_file_index,\n ):\n diff = run_write_test(\n data, input_files, reference_files, output_file_index\n )\n self.assertEqual(diff, \"\", diff)","title":""},{"docid":"a4d819606e84072e29b089c6d10a94da","score":"0.50775754","text":"def test_dataselect_bulk(self):\r\n clients = [self.client, self.client_auth]\r\n file = os.path.join(self.datapath, \"bulk.mseed\")\r\n expected = read(file)\r\n # test cases for providing lists of lists\r\n bulk = ((\"TA\", \"A25A\", \"\", \"BHZ\",\r\n UTCDateTime(\"2010-03-25T00:00:00\"),\r\n UTCDateTime(\"2010-03-25T00:00:04\")),\r\n (\"TA\", \"A25A\", \"\", \"BHE\",\r\n UTCDateTime(\"2010-03-25T00:00:00\"),\r\n UTCDateTime(\"2010-03-25T00:00:06\")),\r\n (\"IU\", \"ANMO\", \"*\", \"HHZ\",\r\n UTCDateTime(\"2010-03-25T00:00:00\"),\r\n UTCDateTime(\"2010-03-25T00:00:08\")))\r\n params = dict(quality=\"B\", longestonly=False, minimumlength=5)\r\n for client in clients:\r\n # test output to stream\r\n got = client.get_waveforms_bulk(bulk, **params)\r\n self.assertEqual(got, expected, failmsg(got, expected))\r\n # test output to file\r\n with NamedTemporaryFile() as tf:\r\n client.get_waveforms_bulk(bulk, filename=tf.name, **params)\r\n got = read(tf.name)\r\n self.assertEqual(got, expected, failmsg(got, expected))\r\n # test cases for providing a request string\r\n bulk = (\"quality=B\\n\"\r\n \"longestonly=false\\n\"\r\n \"minimumlength=5\\n\"\r\n \"TA A25A -- BHZ 2010-03-25T00:00:00 2010-03-25T00:00:04\\n\"\r\n \"TA A25A -- BHE 2010-03-25T00:00:00 2010-03-25T00:00:06\\n\"\r\n \"IU ANMO * HHZ 2010-03-25T00:00:00 2010-03-25T00:00:08\\n\")\r\n for client in clients:\r\n # test output to stream\r\n got = client.get_waveforms_bulk(bulk)\r\n self.assertEqual(got, expected, failmsg(got, expected))\r\n # test output to file\r\n with NamedTemporaryFile() as tf:\r\n client.get_waveforms_bulk(bulk, filename=tf.name)\r\n got = read(tf.name)\r\n self.assertEqual(got, expected, failmsg(got, expected))\r\n # test cases for providing a filename\r\n for client in clients:\r\n with NamedTemporaryFile() as tf:\r\n with open(tf.name, \"wt\") as fh:\r\n fh.write(bulk)\r\n got = client.get_waveforms_bulk(bulk)\r\n self.assertEqual(got, expected, failmsg(got, expected))\r\n # test cases for providing a file-like object\r\n for client in clients:\r\n got = client.get_waveforms_bulk(io.StringIO(bulk))\r\n self.assertEqual(got, expected, failmsg(got, expected))","title":""},{"docid":"dc50a830fc21c4848e9f23cd24baf88e","score":"0.50693756","text":"def multiple_choice_field_data(field, **kwargs):\n if field.choices:\n from django_any.functions import valid_choices \n l = list(valid_choices(field.choices))\n random.shuffle(l)\n choices = []\n count = xunit.any_int(min_value=1, max_value=len(field.choices))\n for i in six.moves.range(0, count):\n choices.append(l[i])\n return ' '.join(choices)\n return 'None'","title":""},{"docid":"54ffc4055b9b1ce93b879deb2884c488","score":"0.5062563","text":"def test_model_multiple_choice_number_of_queries(self):\r\n persons = [Person.objects.create(name=\"Person %s\" % i) for i in range(30)]\r\n\r\n f = forms.ModelMultipleChoiceField(queryset=Person.objects.all())\r\n self.assertNumQueries(1, f.clean, [p.pk for p in persons[1:11:2]])","title":""},{"docid":"51077527bcf99bf31d888e1a53124111","score":"0.5019203","text":"def test_csv_column_indices_in_groups_within_repeats(self):\n self._publish_xls_fixture_set_xform(\"groups_in_repeats\")\n self._submit_fixture_instance(\"groups_in_repeats\", \"01\")\n self.xform.get_keys()\n data_0 = self._csv_data_for_dataframe()[0]\n # remove dynamic fields\n ignore_list = [\n \"_uuid\",\n \"meta/instanceID\",\n \"formhub/uuid\",\n \"_submission_time\",\n \"_id\",\n \"_bamboo_dataset_id\",\n \"_date_modified\",\n ]\n for item in ignore_list:\n data_0.pop(item)\n expected_data_0 = {\n \"_xform_id_string\": \"groups_in_repeats\",\n \"_xform_id\": self.xform.pk,\n \"_status\": \"submitted_via_web\",\n \"_tags\": \"\",\n \"_notes\": \"\",\n \"_version\": self.xform.version,\n \"_submitted_by\": \"bob\",\n \"name\": \"Abe\",\n \"age\": 88,\n \"has_children\": \"1\",\n \"children[1]/childs_info/name\": \"Cain\",\n \"children[2]/childs_info/name\": \"Abel\",\n \"children[1]/childs_info/age\": 56,\n \"children[2]/childs_info/age\": 48,\n \"children[1]/immunization/immunization_received/polio_1\": True,\n \"children[1]/immunization/immunization_received/polio_2\": False,\n \"children[2]/immunization/immunization_received/polio_1\": True,\n \"children[2]/immunization/immunization_received/polio_2\": True,\n \"web_browsers/chrome\": True,\n \"web_browsers/firefox\": False,\n \"web_browsers/ie\": False,\n \"web_browsers/safari\": False,\n \"gps\": \"-1.2626156 36.7923571 0.0 30.0\",\n \"_geolocation\": [-1.2626156, 36.7923571],\n \"_duration\": \"\",\n \"_edited\": False,\n \"_gps_latitude\": \"-1.2626156\",\n \"_gps_longitude\": \"36.7923571\",\n \"_gps_altitude\": \"0.0\",\n \"_gps_precision\": \"30.0\",\n \"_attachments\": [],\n \"_total_media\": 0,\n \"_media_count\": 0,\n \"_media_all_received\": True,\n }\n self.maxDiff = None\n self.assertEqual(data_0, expected_data_0)","title":""},{"docid":"aa7fb32d53ba7e0c9085c9908634e74c","score":"0.5010031","text":"def test_split_list(self):\n\t\tself.spawn(\"./partnerup\").stdin(\"4\").stdin(\"McRae Gordon Harris McGill\").stdin(\"Harris McGill McRae Gordon\").stdout(\"good\").exit(0)","title":""},{"docid":"c7e97ffcf7bdb2db8c03260ff9e8ce48","score":"0.5009165","text":"def test_indexview_with_multiple_selected_and_unselected_products(self):\n\t\tproduct_test_one=CreateProduct(\"Sunflower seeds\",True)\n\t\tproduct_test_one.product_article_code=\"Sun01\"\n\t\tproduct_test_one.save()\n\n\t\tproduct_test_two=CreateProduct(\"Almond juce\",True)\n\t\tproduct_test_two.product_article_code=\"Alm01\"\n\t\tproduct_test_two.save()\n\n\t\tproduct_test_three=CreateProduct(\"Mango\",False)\n\t\tproduct_test_three.product_article_code=\"Man01\"\n\t\tproduct_test_three.save()\n\n\t\tproduct_test_four=CreateProduct(\"Cardamone seeds\",False)\n\t\tproduct_test_four.product_article_code=\"Car01\"\n\t\tproduct_test_four.save()\n\n\t\tresponse=self.client.get(reverse('products:index'))\n\t\tself.assertQuerysetEqual(response.context['product_list'],['<Product: Almond juce>','<Product: Sunflower seeds>'])","title":""},{"docid":"4ae72e9c0c790002292a6260093f8035","score":"0.50070167","text":"def test_account_search_value_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\n account.entity.open()\n self.assert_util(\n account.entity.multiple_select.search_get_list,\n [\"Option One\"],\n left_args={'value': \"Option One\"})","title":""},{"docid":"caee960e826653c00fcc131d60b159d9","score":"0.4999715","text":"def textoSelecionado(selections):\n\t\tfor selection in selections:\n\t\t\tif selection.a != selection.b:\n\t\t\t\treturn True\n\t\t\treturn False","title":""},{"docid":"4eef911b69d4832d3dee15da66f4dce7","score":"0.4973122","text":"def process_all(exclusion_sets, se_access_token, org_id):\n # Get data for all exclusion sets and save to file\n choice = json_or_csv()\n for exclusion_set in exclusion_sets:\n exclusion_set_data = get_exclusion_set_data(se_access_token, org_id, exclusion_set)\n if choice == '1':\n export_to_csv(exclusion_set_data, exclusion_set)\n elif choice == '2':\n export_to_json(exclusion_set_data, exclusion_set)\n exit(\"Processing complete\")","title":""},{"docid":"0440164a9890726c095ff7741c4cccfd","score":"0.49666315","text":"def test_csvdb_two_reports(self, csvdb):\n csvdb.add_files(BASIC_FILES)\n csvdb.connect()\n group_name = [path.split('/')[-1][:-4] for path in BASIC_FILES]\n\n csvdb_iter = csvdb.iter(HWPCModel(), False)\n for _ in range(2):\n report = next(csvdb_iter)\n for group in group_name:\n assert group in report.groups\n\n with pytest.raises(StopIteration) as pytest_wrapped:\n next(csvdb_iter)\n assert pytest_wrapped.type == StopIteration","title":""},{"docid":"b95f1b67ac01c0bae5c4255b9f7908ad","score":"0.49632078","text":"def test_csv_export_with_labels_only(self):\n self._publish_single_level_repeat_form()\n # submit 7 instances\n for _ in range(4):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n self._submit_fixture_instance(\"new_repeats\", \"02\")\n for _ in range(2):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n self.xform.id_string,\n remove_group_name=True,\n include_labels_only=True,\n include_reviews=True,\n )\n # pylint: disable=protected-access\n record_count = self.xform.instances.count()\n self.assertEqual(record_count, 7)\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n labels = next(csv_reader)\n self.assertEqual(len(labels), 17 + len(csv_df_builder.extra_columns))\n expected_labels = [\n \"Name\",\n \"age\",\n \"Do you have kids?\",\n \"Kids Name\",\n \"Kids Age\",\n \"Kids Name\",\n \"Kids Age\",\n \"5. Record your GPS coordinates.\",\n \"_gps_latitude\",\n \"_gps_longitude\",\n \"_gps_altitude\",\n \"_gps_precision\",\n \"web_browsers/Mozilla Firefox\",\n \"web_browsers/Google Chrome\",\n \"web_browsers/Internet Explorer\",\n \"web_browsers/Safari\",\n \"instanceID\",\n \"_id\",\n \"_uuid\",\n \"_submission_time\",\n \"_date_modified\",\n \"_tags\",\n \"_notes\",\n \"_version\",\n \"_duration\",\n \"_submitted_by\",\n \"_total_media\",\n \"_media_count\",\n \"_media_all_received\",\n \"_review_status\",\n \"_review_comment\",\n \"_review_date\",\n ]\n self.assertEqual(expected_labels, labels)\n rows = []\n for row in csv_reader:\n rows.append(row)\n self.assertEqual(len(rows), 7)\n self.assertEqual(rows[4][5], NA_REP)\n # close and delete file\n csv_file.close()\n os.unlink(temp_file.name)","title":""},{"docid":"ff325b6597d2544518f16a31eb9a6dfc","score":"0.49567202","text":"def select(self, competences):\n pass","title":""},{"docid":"d5d5ebf354925ef4cbeb74e46274b83b","score":"0.49405387","text":"def test_multiple(self):\n header = mupub.Header(mupub.LYLoader())\n prefix = os.path.join(os.path.dirname(__file__), TEST_DATA)\n header.load_table_list(prefix, ['hdr-split-a.ly', 'hdr-split-b.ly'])\n self.assertEqual(header.get_value('composer'), 'F. Sor')\n self.assertEqual(header.get_field('title'), '12 Etudes, No. 1')","title":""},{"docid":"52b20f5870d48c933757f1251b4e9777","score":"0.49400127","text":"def test_select_accepts(self):\n self.assertIsNone(self.client.select_header_accept([]))\n accepts = ['application/vnd.ms-excel', 'application/json']\n self.assertEqual('application/json', self.client.select_header_accept(accepts))\n accepts = ['application/vnd.ms-excel', 'text/csv']\n self.assertEqual(', '.join(accepts), self.client.select_header_accept(accepts))","title":""},{"docid":"db5cc2fe3aa36961d09516b913b5d901","score":"0.49351206","text":"def test_selection_some_results():\n result = [[\"Surname\", \"FirstName\", \"Age\", \"Salary\"],\n [\"Verdi\", \"Nico\", 36, 4500],\n [\"Smith\", \"Mark\", 40, 3900]]\n\n assert is_equal(result, selection(EMPLOYEES, filter_employees))","title":""},{"docid":"805629649568559888ef7762969645f8","score":"0.49208638","text":"def suite_test():\n\ttest(is_multiple(12,3))\n\ttest(is_multiple(12,4))\n\ttest(not is_multiple(12,5))\n\ttest(is_multiple(12,6))\n\ttest(not is_multiple(12,7))","title":""},{"docid":"8ba9464eddf8cc467afda766d500e50d","score":"0.4914326","text":"def parse_args_csv(self, args):\n\n self.report_args[\"csv_delimiter\"] = args.delimiter\n\n return True","title":""},{"docid":"df58f36d3ae16330d559cdfcc6fe2340","score":"0.48968482","text":"def test_flats_selector1(self):\n with open(flats_txt_path) as f:\n list_of_selected_flats = f.read().splitlines() \n try:\n actual = type(list_of_selected_flats)\n expected = list\n self.assertEqual(actual,expected)\n except:\n print(\"The list_of_selected_flats is not a list. It should be a list. Check the input text file.\")","title":""},{"docid":"44651f6b528aca6d7f3ceefaa7a0d4bd","score":"0.48933098","text":"def get_chosen_pairs():\n data = read_csv('training_results.csv')\n return [subject[3] for subject in data]","title":""},{"docid":"cd945f793b7a3b07dc0c50b6d201ca68","score":"0.48770088","text":"def test__export_csv(self):\n # Create temp data\n scalar_names = ['fake_scalar_1', 'fake_scalar_2']\n ids = ['a_fake_id_1', 'a_fake_id_2']\n data = OrderedDict()\n data[ids[0]] = {scalar_names[0]: {'value': 123},\n scalar_names[1]: {'value': 456}}\n data[ids[1]] = {scalar_names[0]: {'value': 0.1},\n scalar_names[1]: {'value': -12}}\n # Write to csv file\n _export_csv(data=data,\n scalar_names=scalar_names,\n output_file=self.test_file_path.name)\n\n # Validate csv file\n with io.open(self.test_file_path.name, 'r', encoding='utf-8') as csvfile:\n reader = csv.reader(csvfile)\n rows = [row for row in reader]\n self.assertEqual(len(rows), 3)\n self.assertEqual(rows[0], ['id'] + scalar_names)\n self.assertEqual(rows[1], ['a_fake_id_1', '123', '456'])\n self.assertEqual(rows[2], ['a_fake_id_2', '0.1', '-12'])","title":""},{"docid":"5663a18a042c3424aacf780585066a18","score":"0.48741752","text":"def test_splitter_shuffles(self, group_splitter, dummy_data, seed):\n group_splitter.random_state = seed\n train, test = group_splitter.split(dummy_data)\n concatenated = pd.concat([train, test], axis=0)\n # unique does not sort\n assert np.any(concatenated.group_col.unique() != dummy_data.group_col.unique())","title":""},{"docid":"0fceac7da37cfc5360412a3e9e1fb890","score":"0.48733902","text":"def split_and_check_count(data_entry_csv, expected_count):\n\n\tdata_entry_csv = data_entry_csv.rstrip()\n\tentries = [remove_quotes(entry) for entry in data_entry_csv.split(',')]\n\n\tactual_count = len(entries)\n\tif data_entry_csv.endswith(',') and entries[-1] == '':\n\t\t# Accomodate for the SOMETIMES trailing comma\n\t\tdata_entry_csv = data_entry_csv[:-1]\n\t\tentries = entries[:-1]\n\t\tactual_count = len(entries)\n\n\tif actual_count < expected_count:\n\t\t# Add empty strings to fill in the blanks.\n\t\tentries += ['' for i in range(expected_count - actual_count)]\n\telif actual_count > expected_count:\n\t\t# Crop off the excess entries.\n\t\tentries = entries[:expected_count]\n\n\treturn (entries, expected_count == actual_count, actual_count)","title":""},{"docid":"9ed09c1de21d45be9a64244f562420ed","score":"0.48667133","text":"def test_csv_export_remove_group_name(self):\n self._publish_single_level_repeat_form()\n # submit 7 instances\n for _ in range(4):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n self._submit_fixture_instance(\"new_repeats\", \"02\")\n for _ in range(2):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n self.xform.id_string,\n remove_group_name=True,\n include_images=False,\n include_reviews=True,\n )\n # pylint: disable=protected-access\n record_count = self.xform.instances.count()\n self.assertEqual(record_count, 7)\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n header = next(csv_reader)\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\n expected_header = [\n \"name\",\n \"age\",\n \"has_kids\",\n \"kids_name\",\n \"kids_age\",\n \"kids_name\",\n \"kids_age\",\n \"gps\",\n \"_gps_latitude\",\n \"_gps_longitude\",\n \"_gps_altitude\",\n \"_gps_precision\",\n \"web_browsers/firefox\",\n \"web_browsers/chrome\",\n \"web_browsers/ie\",\n \"web_browsers/safari\",\n \"instanceID\",\n \"_id\",\n \"_uuid\",\n \"_submission_time\",\n \"_date_modified\",\n \"_tags\",\n \"_notes\",\n \"_version\",\n \"_duration\",\n \"_submitted_by\",\n \"_total_media\",\n \"_media_count\",\n \"_media_all_received\",\n \"_review_status\",\n \"_review_comment\",\n \"_review_date\",\n ]\n self.assertEqual(expected_header, header)\n rows = []\n for row in csv_reader:\n rows.append(row)\n self.assertEqual(len(rows), 7)\n self.assertEqual(rows[4][5], NA_REP)\n # close and delete file\n csv_file.close()\n os.unlink(temp_file.name)","title":""},{"docid":"8163200aade0eb8325d47b63d3ad859a","score":"0.4865059","text":"def test_csv_export_with_labels(self):\n self._publish_single_level_repeat_form()\n # submit 7 instances\n for _ in range(4):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n self._submit_fixture_instance(\"new_repeats\", \"02\")\n for _ in range(2):\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n csv_df_builder = CSVDataFrameBuilder(\n self.user.username,\n self.xform.id_string,\n remove_group_name=True,\n include_labels=True,\n include_reviews=True,\n )\n # pylint: disable=protected-access\n record_count = self.xform.instances.count()\n self.assertEqual(record_count, 7)\n temp_file = NamedTemporaryFile(suffix=\".csv\", delete=False)\n cursor = (\n self.xform.instances.all().order_by(\"id\").values_list(\"json\", flat=True)\n )\n csv_df_builder.export_to(temp_file.name, cursor)\n csv_file = open(temp_file.name, \"r\")\n csv_reader = csv.reader(csv_file)\n header = next(csv_reader)\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\n expected_header = [\n \"name\",\n \"age\",\n \"has_kids\",\n \"kids_name\",\n \"kids_age\",\n \"kids_name\",\n \"kids_age\",\n \"gps\",\n \"_gps_latitude\",\n \"_gps_longitude\",\n \"_gps_altitude\",\n \"_gps_precision\",\n \"web_browsers/firefox\",\n \"web_browsers/chrome\",\n \"web_browsers/ie\",\n \"web_browsers/safari\",\n \"instanceID\",\n \"_id\",\n \"_uuid\",\n \"_submission_time\",\n \"_date_modified\",\n \"_tags\",\n \"_notes\",\n \"_version\",\n \"_duration\",\n \"_submitted_by\",\n \"_total_media\",\n \"_media_count\",\n \"_media_all_received\",\n \"_review_status\",\n \"_review_comment\",\n \"_review_date\",\n ]\n self.assertEqual(expected_header, header)\n labels = next(csv_reader)\n self.assertEqual(len(labels), 17 + len(csv_df_builder.extra_columns))\n expected_labels = [\n \"Name\",\n \"age\",\n \"Do you have kids?\",\n \"Kids Name\",\n \"Kids Age\",\n \"Kids Name\",\n \"Kids Age\",\n \"5. Record your GPS coordinates.\",\n \"_gps_latitude\",\n \"_gps_longitude\",\n \"_gps_altitude\",\n \"_gps_precision\",\n \"web_browsers/Mozilla Firefox\",\n \"web_browsers/Google Chrome\",\n \"web_browsers/Internet Explorer\",\n \"web_browsers/Safari\",\n \"instanceID\",\n \"_id\",\n \"_uuid\",\n \"_submission_time\",\n \"_date_modified\",\n \"_tags\",\n \"_notes\",\n \"_version\",\n \"_duration\",\n \"_submitted_by\",\n \"_total_media\",\n \"_media_count\",\n \"_media_all_received\",\n \"_review_status\",\n \"_review_comment\",\n \"_review_date\",\n ]\n self.assertEqual(expected_labels, labels)\n rows = []\n for row in csv_reader:\n rows.append(row)\n self.assertEqual(len(rows), 7)\n self.assertEqual(rows[4][5], NA_REP)\n # close and delete file\n csv_file.close()\n os.unlink(temp_file.name)","title":""},{"docid":"3e28b772bb2c23993990345dabe40b5c","score":"0.4848573","text":"def checkRepeatSelection(selList, checkSwitchJointList, checkMessage, isJoint=True):\n\tvalidSelection = True\n\tcheckList = checkSwitchJointList\n\tif isJoint: checkList = [sJ.getName() for sJ in checkSwitchJointList] #Collect Names since we have switch Joints, we need names to comapre to the selection\n\tintersectionSet = set(selList).intersection(set(checkList)) #Form a set from the intersection of these lists, if Items are in the set, then then they are repeated! \n\tif len(intersectionSet) > 0:\n\t\tvalidSelection = False\n\t\tcmds.warning('Incorrect Initial Selection - You have selected some items that match those in the ' + checkMessage + \" List\")#If we do not have 2 joints selected then exit\n\treturn validSelection","title":""},{"docid":"37c235cda361a0ba0f7596de7f29fd64","score":"0.48452136","text":"def test_format_mongo_data_for_csv(self):\n self.maxDiff = None\n self._publish_single_level_repeat_form()\n self._submit_fixture_instance(\"new_repeats\", \"01\")\n data_0 = self._csv_data_for_dataframe()[0]\n # remove AbstractDataFrameBuilder.INTERNAL_FIELDS\n for key in AbstractDataFrameBuilder.IGNORED_COLUMNS:\n if key in data_0:\n data_0.pop(key)\n for key in AbstractDataFrameBuilder.ADDITIONAL_COLUMNS:\n if key in data_0:\n data_0.pop(key)\n expected_data_0 = {\n \"gps\": \"-1.2627557 36.7926442 0.0 30.0\",\n \"_gps_latitude\": \"-1.2627557\",\n \"_gps_longitude\": \"36.7926442\",\n \"_gps_altitude\": \"0.0\",\n \"_gps_precision\": \"30.0\",\n \"kids/has_kids\": \"1\",\n \"info/age\": 80,\n \"kids/kids_details[1]/kids_name\": \"Abel\",\n \"kids/kids_details[1]/kids_age\": 50,\n \"kids/kids_details[2]/kids_name\": \"Cain\",\n \"kids/kids_details[2]/kids_age\": 76,\n \"web_browsers/chrome\": True,\n \"web_browsers/ie\": True,\n \"web_browsers/safari\": False,\n \"web_browsers/firefox\": False,\n \"info/name\": \"Adam\",\n \"_xform_id\": self.xform.pk,\n }\n self.assertEqual(expected_data_0, data_0)","title":""},{"docid":"a335e2451ab16b8c8fb8f4be6add8da6","score":"0.48365575","text":"def get_commaselectlist(self):\n return re.split(r'[,]+',self.comma_select.strip())","title":""},{"docid":"862b77ef3652813c3c0dab51f4a1e1e0","score":"0.48311725","text":"def log_selection_trace(self):\n self.logger.info(\"Writing selection logs (exclusions and special \"\n \"warnings).\")\n\n excluded_filename = self.parameters[\"OUTPUT_PREFIX\"] + \".excluded.log\"\n special_incl_filename = (\n self.parameters[\"OUTPUT_PREFIX\"] + \".special.log\"\n )\n\n variant_sets = [\n (excluded_filename, self.excluded),\n (special_incl_filename, self.special)\n ]\n\n for filename, variant_set in variant_sets:\n with open(filename, \"w\") as f:\n writer = csv.writer(f)\n writer.writerow([\"name\", \"chrom\", \"pos\", \"alleles\", \"reason\",\n \"details\"])\n\n for variant, reason, details in variant_set:\n alleles = \"/\".join(variant.alleles)\n writer.writerow([\n variant.name, variant.chrom, variant.pos,\n alleles, reason, details\n ])","title":""},{"docid":"62c9f4fd7661c9de4a8b10c399f7873a","score":"0.48307753","text":"def test_multiple_extract(self):\n to_extract = {\"rs785467\", \"rs140543381\"}\n reader = self.reader_f()\n extractor = Extractor(reader, names=to_extract)\n\n for i in range(2):\n seen = set()\n for genotype in extractor.iter_genotypes():\n name = genotype.variant.name\n truth = truth_genotypes[name]\n seen.add(name)\n\n self.assertEqual(genotype.variant, truth.variant)\n self.assertEqual(genotype.reference, truth.reference)\n self.assertEqual(genotype.coded, truth.coded)\n np.testing.assert_array_equal(genotype.genotypes,\n truth.genotypes)\n\n self.assertEqual(seen, to_extract)\n\n reader.close()","title":""},{"docid":"d009c0a67cc789b468b5080262daff81","score":"0.48097306","text":"def test_indewview_with_multiple_selected_product(self):\n\t\tproduct_test_one=CreateProduct(\"Sunflower seeds\",True)\n\t\tproduct_test_one.product_article_code=\"Sun01\"\n\t\tproduct_test_one.save()\n\n\t\tproduct_test_two=CreateProduct(\"Almond juce\",True)\n\t\tproduct_test_two.product_article_code=\"Alm01\"\n\t\tproduct_test_two.save()\n\n\t\tresponse=self.client.get(reverse('products:index'))\n\t\tself.assertQuerysetEqual(response.context['product_list'],['<Product: Almond juce>','<Product: Sunflower seeds>'])","title":""},{"docid":"031b105143b70afcc61fc1c3a092a190","score":"0.4809421","text":"def test_choice_duplicate_error(self):\n count = XForm.objects.count()\n xls_path = os.path.join(\n self.this_directory, 'fixtures', 'cascading_selects',\n 'duplicate_choice_form.xlsx')\n msg = (\n \"The name column for the 'counties' choice list\"\n \" contains these duplicates: 'king'. Duplicate \"\n \"names will be impossible to identify in analysis\"\n \" unless a previous value in a cascading select \"\n \"differentiates them. If this is intentional, \"\n \"you can set the allow_choice_duplicates \"\n \"setting to 'yes'. \"\n \"Learn more: https://xlsform.org/#choice-names.\")\n self.assertRaisesMessage(\n PyXFormError, msg, self._publish_xls_file, xls_path)\n self.assertEqual(XForm.objects.count(), count)","title":""},{"docid":"5e028a5c7836db94015acc4312b6fefb","score":"0.4808766","text":"def test_account_required_field_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\n account.entity.open()\n account.entity.name.set_value(ACCOUNT_CONFIG[\"name\"])\n account.entity.environment.select(\"Value2\")\n account.entity.username.set_value(\"TestEditUser\")\n account.entity.password.set_value(\"TestEditPassword\")\n account.entity.security_token.set_value(\"TestEditToken\")\n account.entity.account_radio.select(\"No\")\n self.assert_util(\n account.entity.save,\n 'Field Example Multiple Select is required',\n left_args={'expect_error': True}\n )","title":""},{"docid":"720d052d66be58de1f6df8ec55fe5729","score":"0.48055178","text":"def test_export_fac_csv(self):\n client = Client()\n response = client.get(\"/export/advanced-search/fac/csv?name__contains=Facility\")\n self.assertEqual(\n response.content.replace(\"\\r\\n\", \"\\n\").rstrip(),\n self.expected_data(\"fac\", \"csv\"),\n )","title":""},{"docid":"f60f8120dc988e2d918eb3582dc97081","score":"0.48021328","text":"def test_lang_choices_multiple_newsletters(self):\r\n form = NewsletterFooterForm('join-mozilla,firefox-tips', 'en-US')\r\n choices = [lang[0] for lang in form.fields['lang'].choices]\r\n self.assertEqual(choices, ['de', 'en', 'es', 'fr', 'pt', 'ru'])","title":""},{"docid":"6465fdd3edbe4f68464a316a944870be","score":"0.4801099","text":"def test_csv_row_to_enquiry_filter(self):\n csv_data = create_fake_enquiry_csv_row()\n expected_kwargs = {\n key.replace(\"enquirer_\", \"enquirer__\"): value for key, value in csv_data.items()\n }\n\n qs_kwargs = utils.csv_row_to_enquiry_filter_kwargs(csv_data)\n\n self.assertEqual(len(expected_kwargs.keys()), len(qs_kwargs.keys()))\n\n for key in expected_kwargs:\n self.assertTrue(key in qs_kwargs)\n self.assertEqual(qs_kwargs[key], expected_kwargs[key])","title":""},{"docid":"d4583e486d985260a584be595ee9a9c6","score":"0.47994888","text":"def test_iter_multiple(multiple_list):\n assert list(iter(multiple_list)) == [\"value 1\", \"value 2\"]","title":""},{"docid":"0daa03fee1c8b6d96a9fe2f0d9562779","score":"0.47833183","text":"def multiple():\n with timed('multiple (%d values)' % VALUES):\n\n script = [TPL_MANY]\n\n for i in range(VALUES):\n\n key = 'BENCH_{}'.format(i)\n value = 'VAL_SINGLE_{}'.format(i)\n\n script.append(TPL_LINE.format(key=key, value=value, bid=BUNDLE_ID))\n\n script = '\\n'.join(script)\n\n run_script(script)","title":""},{"docid":"360082979eb16f76782d9172bc7c98c2","score":"0.4744839","text":"def test_few_shot_subset(self):\n\n def helper(n_shot, seed):\n return self.dump_teacher_text(\n test_agents.SystemTeacher,\n test_agents.EPISODE_SETUP__MULTI_EPISODE,\n {\"episodes_randomization_seed\": seed, \"n_shot\": n_shot},\n )\n\n data_dumps_seed_zero = [helper(i, 0) for i in self.FEW_SHOT_SAMPLES]\n self._test_subsets(data_dumps_seed_zero)\n data_dumps_seed_three = [helper(i, 3) for i in self.FEW_SHOT_SAMPLES]\n self._test_subsets(data_dumps_seed_three)\n self.assertNotEqual(data_dumps_seed_zero[-1], data_dumps_seed_three[-1])","title":""},{"docid":"9bf53d133f53aa64a5a572eec53fc491","score":"0.47366884","text":"def test_write_csv(self):\n\t\tpath = profiles_and_competitions_synchronizer.ProfilesAndCompetitionsSynchronizer().write_csv('../data/test_allProfilesWithCompetitions.csv')\n\t\tcsvContent = csv_handler.CsvHandler().read_csv(path, 'r', 'latin-1')\n\t\tos.remove('../data/test_allProfilesWithCompetitions.csv')\n\t\tself.assertEqual(len(csvContent), 3782)","title":""},{"docid":"945bce42f679fc45b09c4a4398e35dcc","score":"0.4726768","text":"def determine_multiple(vals, mrn, data_type):\n if len(vals) > 1:\n raise ValueError('MRN: {0:d} has more than one {1:s}. Check data and try again.'.format(mrn, data_type))\n else:\n for vals in vals:\n break\n return vals","title":""},{"docid":"43bbcf0229b7daddfe645e2df666ce7f","score":"0.4717738","text":"def test_resource_output_module_resource_export_csvpost(self):\n pass","title":""},{"docid":"a9271ee47c7b5bb785026ca59574381d","score":"0.47153243","text":"def test_with_comma_delimited_multiple_words(self):\n\n self.assertEqual(parse_tag_input(',one'), ['one'])\n self.assertEqual(parse_tag_input(',one two'), ['one two'])\n self.assertEqual(parse_tag_input(',one two three'), ['one two three'])\n self.assertEqual(parse_tag_input('a-one, a-two and a-three'),\n ['a-one', 'a-two and a-three'])","title":""},{"docid":"f3c5ab222ef8c789cad1eacba957533a","score":"0.47057676","text":"def getSubsetFiles( self, data_path ):\n\n # load file as csv and shuffle\n df = pd.read_csv( os.path.join( data_path, 'train_v2.csv' ) )\n df = pd_shuffle( df )\n\n split_index = int( len( df ) * 0.8 )\n\n # assign 80% records as training\n train = df [ 0: split_index ]\n train.to_csv( os.path.join( data_path, 'train.csv' ), index=False )\n \n # assign remaining 20% records as validation / test\n test = df [ split_index : len( df ) ]\n test.to_csv( os.path.join( data_path, 'test.csv' ), index=False )\n \n return","title":""},{"docid":"f9c06f75636d605403060ed9c11b7d35","score":"0.47006464","text":"def select_features(csv_file):\n\n with open(csv_file) as f:\n available_features = \"\"\n\n reader = csv.reader(f)\n row1 = next(reader)\n\n for feature in row1:\n if feature == \"\":\n pass\n else:\n available_features += (feature + \", \")\n\n print(\"Available variables: \", available_features)\n input_features = input(\"Select feature(s) (separate with commas): \").replace(' ', '').strip(\"\\n\").split(\",\") \n\n if input_features == [\"all\"]:\n return row1\n elif all(elem in row1 for elem in input_features):\n if \"diabetes\" not in input_features:\n input_features.append(\"diabetes\") #Asserts diabetes column at the end\n return input_features\n else:\n print(\"One or more of the features are not available. Please try again.\")\n return select_features(csv_file)","title":""},{"docid":"fd5acfb121867fb7e6892196a097b864","score":"0.4687272","text":"def select_multiple_rows_from_compound_reference_price_list_grid(self):\n is_selected = None\n try:\n self.logger.info('Start: select multiple rows from compound reference price list grid')\n self._price_page.select_multiple_rows_from_compound_reference_price_list_grid()\n is_selected = True\n except WebDriverException as exp:\n is_selected = False\n self.logger.error(exp.msg)\n raise\n finally:\n self.logger.info('End: select multiple rows from compound reference price list grid')\n return is_selected","title":""},{"docid":"1fea15d0f59147daea7fb70d2b79a44b","score":"0.46828946","text":"def test_edx_multi_choice_with_named_choices(self):\n items_endpoint = '{0}/items'.format(self.url)\n\n # Use POST to create an item--right now user is Learner,\n # so this should throw unauthorized\n item_name = 'a really complicated item'\n item_desc = 'meant to differentiate students'\n question_body = 'can you manipulate this?'\n question_choices = [\n {'text' : 'I hope so.',\n 'name' : 'yes'},\n {'text' : 'I don\\'t think I can.',\n 'name' : 'no'},\n {'text' : 'Maybe tomorrow.',\n 'name' : 'maybe'}\n ]\n question_type = 'question-record-type%3Amulti-choice-edx%40ODL.MIT.EDU'\n answer = 2\n answer_type = 'answer-record-type%3Amulti-choice-edx%40ODL.MIT.EDU'\n payload = {\n \"name\" : item_name,\n \"description\" : item_desc,\n \"question\" : {\n \"type\" : question_type,\n \"questionString\" : question_body,\n \"choices\" : question_choices\n },\n \"answers\" : [{\n \"type\" : answer_type,\n \"choiceId\" : answer\n }],\n }\n req = self.app.post(items_endpoint,\n params=json.dumps(payload),\n headers={'content-type': 'application/json'})\n self.ok(req)","title":""},{"docid":"da12dc5f266e455c4774468e39a17a40","score":"0.4668556","text":"def test_api_create_csv_example(self):\n url = self.get_url(\"list\")\n data = b'\"Name\",\"Title\",\"level\"\\n\"Guido\",\"BDFL\",20\\n\\n\"Catherine\",,9,\"DBA\"\\n,\\n\"Tony\",\"Engineer\",10\\n'\n token = \"this1s@t0k3n\"\n self.client.credentials(HTTP_AUTHORIZATION=\"Token \" + token)\n response = self.client.post(url, data, content_type=\"text/csv\")\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n result = json.loads(response.content)[\"validation_results\"]\n self.assertFalse(result[\"valid\"])\n self.assertEqual(len(result[\"tables\"]), 1)\n self.assertEqual(result[\"tables\"][0][\"invalid_row_count\"], 3)\n self.assertEqual(result[\"tables\"][0][\"valid_row_count\"], 2)\n self.assertEqual(result[\"tables\"][0][\"whole_table_errors\"], [])","title":""},{"docid":"a380b41ff713a3888c071ed5dc7f739a","score":"0.46671355","text":"def __splitMulti(self):\n return self.string.split(\",\")","title":""},{"docid":"cd7fe258911e8a2014ffa6a0232678be","score":"0.46520945","text":"def test_selection_counter(self):\r\n response = self.client.get('/test_admin/admin/admin_views/subscriber/')\r\n self.assertContains(response, '0 of 2 selected')","title":""},{"docid":"191bffbd3ba9e7ceb9428505e22e59a2","score":"0.46509922","text":"def test_build_valid_extra_line_on_sample_list(self):\n sheet_file = path.join(path_to_module, \"fake_dir_data\",\n \"SampleList_with_space.csv\")\n\n sequencing_run = sample_parser.build_sequencing_run_from_samples(sheet_file)\n\n # Returns a SequencingRun\n self.assertEqual(type(sequencing_run), model.SequencingRun)\n # Includes 2 projects\n self.assertEqual(len(sequencing_run.project_list), 2)\n # is of type Project\n self.assertEqual(type(sequencing_run.project_list[0]), model.Project)\n # Project has 2 samples\n self.assertEqual(len(sequencing_run.project_list[0].sample_list), 2)\n # Other Project has 1 sample\n self.assertEqual(len(sequencing_run.project_list[1].sample_list), 1)\n # samples are of type Sample\n self.assertEqual(type(sequencing_run.project_list[0].sample_list[0]), model.Sample)\n # samples have SequenceFile\n self.assertEqual(type(sequencing_run.project_list[0].sample_list[0].sequence_file), model.SequenceFile)","title":""},{"docid":"49aaef794e37d79f1f77a471e7f7dd86","score":"0.46507782","text":"def test_different_feature_selections():\n\n f_selections = ['GST syn', 'LCS syn', 'TO syn', 'GST', 'TO', 'LCS',\n 'LCSubstring', 'Word Lengths', 'Word Level Levenshtein',\n 'String Matching', 'NE Coupling']\n\n feature_files = os.listdir(slash.join(processed_news_features_path))\n best_c_indeces = [75, 4, 61, 33, 104]\n best_corpora = [(feature_files[i], i) for i in best_c_indeces]\n\n\n with open(slash.join(saved_models_path + ['test_different_features_scores_file_new.csv']), 'w', newline='') as f:\n writer = csv.writer(f, delimiter=';')\n # add featureselection\n\n header = ['corpusnum', 'loss', 'accuracy', 'loss on meter', 'accuracy on meter',\n 'GST syn', 'LCS syn', 'TO syn', 'GST', 'TO', 'LCS',\n 'LCSubstring', 'Word Lengths', 'Word Level Levenshtein',\n 'String Matching', 'NE Coupling']\n\n writer.writerow(header)\n modelnum = 0\n for c_name in best_corpora:\n f_name = c_name[0]\n num = c_name[1]\n for i in range(5, 12):\n new_model_row = [0 for _ in list(range(len(header)))]\n f = f_selections[:i]\n start = time.time()\n model, score, rounded, y = classify(f, 0.1, f_name)\n loss, accuracy = score\n X_train, X_test, y_train, y_test = get_meter_features(model, 0.1, f, None)\n loss_on_meter, accuracy_on_meter = meter_classify(X_train, X_test, y_train, y_test)\n\n new_model_row[0] = num\n new_model_row[1] = loss\n new_model_row[2] = accuracy\n new_model_row[3] = loss_on_meter\n new_model_row[4] = accuracy_on_meter\n new_model_row[5] = 1 if 'GST syn' in f else 0\n new_model_row[6] = 1 if 'LCS syn' in f else 0\n new_model_row[7] = 1 if 'TO syn' in f else 0\n new_model_row[8] = 1 if 'GST' in f else 0\n new_model_row[9] = 1 if 'TO' in f else 0\n new_model_row[10] = 1 if 'LCS' in f else 0\n new_model_row[11] = 1 if 'LCSSubstring' in f else 0\n new_model_row[12] = 1 if 'Word Lenghts' in f else 0\n new_model_row[13] = 1 if 'Word Level Levenshtein' in f else 0\n new_model_row[14] = 1 if 'String Matching' in f else 0\n new_model_row[15] = 1 if 'NE Coupling' in f else 0\n\n writer.writerow(new_model_row)\n\n print(modelnum, 'done in', time.time() - start)\n K.clear_session()\n modelnum += 1\n if modelnum == 3:\n break","title":""},{"docid":"ef43719649cc745adf6c03c702281b75","score":"0.4644914","text":"def _check_parametrize_names(\n self, node: ast.Call, names: ast.AST\n ) -> Optional[bool]:\n\n multiple_names: Optional[bool] = None\n found_type: Optional[ParametrizeNamesType] = None\n if isinstance(names, ast.Str):\n if ',' in names.s:\n found_type = ParametrizeNamesType.CSV\n multiple_names = True\n else:\n multiple_names = False\n elif isinstance(names, (ast.List, ast.Tuple)):\n multiple_names = len(names.elts) > 1\n if not multiple_names:\n self.error_from_node(\n ParametrizeNamesWrongType, node, expected_type='string'\n )\n elif isinstance(names, ast.Tuple):\n found_type = ParametrizeNamesType.TUPLE\n else:\n found_type = ParametrizeNamesType.LIST\n if multiple_names and found_type != self.config.parametrize_names_type:\n self.error_from_node(\n ParametrizeNamesWrongType,\n node,\n expected_type=self.config.parametrize_names_type.value,\n )\n return multiple_names","title":""},{"docid":"7e95d93ef4108c34aaa031632794842c","score":"0.4644207","text":"def multiple_modal_select_browser(app, input_list, title):\r\n valid_selection = False\r\n is_folder = False\r\n\r\n while valid_selection == False:\r\n selection = app.ShowModalSelectBrowser(input_list, title)\r\n sel_len = len(selection)\r\n\r\n if sel_len == 0:\r\n return None\r\n elif sel_len >= 2:\r\n for sel in selection:\r\n if sel.GetClassName() == 'IntFolder':\r\n is_folder = True\r\n if is_folder:\r\n logger.error('Cannot select multiple objects if one is a folder. Please try again.')\r\n valid_selection = False\r\n else:\r\n valid_selection = True\r\n\r\n elif selection[0].GetClassName() == 'IntFolder':\r\n input_list = selection[0].GetContents()\r\n valid_selection = False\r\n else:\r\n valid_selection = True\r\n\r\n\r\n return selection","title":""},{"docid":"264369fde1a0cde36d3cfdfbef7bf7d7","score":"0.464374","text":"def test_multiple(self):\n f = list(MinimalFastaParser(self.threeseq))\n self.assertEqual(len(f), 3)\n a, b, c = f\n self.assertEqual(a, (\"123\", \"a\"))\n self.assertEqual(b, (\"abc\", \"caggac\"))\n self.assertEqual(c, (\"456\", \"cg\"))","title":""},{"docid":"505bcff9be2454d1d0391a020d006c13","score":"0.4640549","text":"def _separate_sets(filenames, validation_percent=0.2):\n filenames_rand = filenames\n np.random.shuffle(filenames_rand)\n num_total_files = len(filenames_rand)\n num_validation_files = int(num_total_files * validation_percent) or 1\n validation_set = filenames_rand[:num_validation_files]\n train_set = filenames_rand[num_validation_files:]\n\n return train_set, validation_set","title":""},{"docid":"00dc7dedb3cb640f92ddc91f5fc5f52d","score":"0.46349183","text":"def _validates_choices_should_be_unique_and_nonempty(\n self, choices: List[SubtitledHtml]\n ) -> None:\n seen_choices = []\n for choice in choices:\n if html_cleaner.is_html_empty(choice.html):\n raise utils.ValidationError(\n 'Choices should be non empty.'\n )\n\n if choice.html not in seen_choices:\n seen_choices.append(choice.html)\n else:\n raise utils.ValidationError(\n 'Choices should be unique.'\n )","title":""},{"docid":"96181655de7b8f3fad0dad94223e2711","score":"0.46337745","text":"def file_picker(self):\n d = self.data\n needed_files = []\n self.needed_columns = set()\n counties = \"data/us-counties.csv\"\n tests = \"data/tests-by-state.csv\"\n for category in self.categories:\n if category in d[\"aggregate\"].keys():\n for field in d[\"aggregate\"][category]:\n self.needed_columns.add(field)\n if (\n field in self.fields[\"counties\"]\n and counties not in needed_files\n ):\n needed_files.append(counties)\n if field in self.fields[\"tests\"] and tests not in needed_files:\n needed_files.append(tests)\n return needed_files","title":""},{"docid":"02be37b8cc56bd00df8bbdf485c3b993","score":"0.46270669","text":"def writebusinessdata():\r\n with open(\"business_data.csv\", \"w\") as f:\r\n f.write(main.csv_mutliline)\r\n print(\"Wrote multi-line string data to ./business_data.csv\")\r\n return True","title":""},{"docid":"b7f10705f7dbd0a10198943b380c4a70","score":"0.46228066","text":"def choices(self):","title":""},{"docid":"e81b3b049c66abd7d42562465b9f9838","score":"0.46153408","text":"def test_csv_quoting(self):\n resp = self.get_response({'formato': 'csv'},\n return_value='raw').decode()\n first_row = resp.splitlines()[1] # Saltear la cabecera CSV\n first_value = first_row.split(formatter.CSV_SEP)[0] # ID de la fila\n\n self.assertTrue(first_value.startswith('\"') and\n first_value.endswith('\"') and\n len(first_value) > 2)","title":""},{"docid":"1e934893eb261b6579570c1b6c54a34b","score":"0.4614544","text":"def test_chooseSettingValue(self):\n def csv(**kwargs):\n return _chooseSettingValue(db.LectureGlobalSetting(**kwargs))\n\n # Fixed values return None, not the value in question\n self.assertEqual(csv(value=4), None)\n\n # Random values are all within bounds\n for x in xrange(LOTS_OF_TESTS):\n out = float(csv(max=100))\n self.assertTrue(out >= 0)\n self.assertTrue(out < 100)\n for x in xrange(LOTS_OF_TESTS):\n out = float(csv(min=90, max=100))\n self.assertTrue(out >= 90)\n self.assertTrue(out < 100)\n\n # Can't specify shape without value\n with self.assertRaisesRegexp(ValueError, 'but no corresponding value'):\n csv(key='foo', shape=8)\n\n # Gamma values hit the mean\n out = 0\n for x in xrange(LOTS_OF_TESTS):\n out += float(csv(value=1000000, shape=2))\n out = out / LOTS_OF_TESTS\n self.assertTrue(abs(out - 2000000) < 5000)\n\n # String values come out unaltered, but can't be randomly chosen\n self.assertEqual(csv(key=\"iaa_type\", value=\"fun-size\"), None)\n with self.assertRaisesRegexp(ValueError, 'iaa_type'):\n csv(key=\"iaa_type\", value=\"fun-size\", shape=2)\n with self.assertRaisesRegexp(ValueError, 'iaa_type'):\n csv(key=\"iaa_type\", value=\"fun-size\", max=4)\n\n # Integer settings get rounded, don't have \"3.0\" at end\n for x in xrange(LOTS_OF_TESTS):\n self.assertIn(csv(key=\"grade_nmin\", max=9), '0 1 2 3 4 5 6 7 8 9'.split())","title":""},{"docid":"8e3ea6c30ebf4f896213aa0a0a86071b","score":"0.46059546","text":"def test_clean_dois(self):\n\n inputs = [\n {\"dois\": \"10.1093/nar/gks419\\n10.1093/nar/gkp323\"},\n {\"dois\": \"10.1093/nar/gkp323\\n10.1093/nar/gks419\"},\n {\"dois\": \"10.1093/nar/gkp323,\\n10.1093/nar/gks419\"},\n {\"dois\": \"10.1093/nar/gkp323,10.1093/nar/gks419\"},\n {\"dois\": \"10.1093/nar/gkp323,,10.1093/nar/gks419\"},\n {\"dois\": \"10.1093/nar/gkp323\\n\\n10.1093/nar/gks419\"},\n ]\n expected = [\"10.1093/nar/gkp323\", \"10.1093/nar/gks419\"]\n\n for input in inputs:\n\n form = EntryBatchImportForm(input)\n assert form.is_valid()\n\n dois = form.cleaned_data[\"dois\"]\n dois.sort()\n assert dois == expected","title":""},{"docid":"0b56533253c1d64e515cfdfeba3e980f","score":"0.46044","text":"def select_multiple_rows_from_customer_price_exception_request_grid(self):\n is_selected = None\n try:\n self.logger.info('Start: select multiple rows from customer price exception request grid')\n self._price_page.select_multiple_rows_from_customer_price_exception_request_grid()\n is_selected = True\n except WebDriverException as exp:\n is_selected = False\n self.logger.error(exp.msg)\n raise\n finally:\n self.logger.info('End: select multiple rows from customer price exception request grid')\n return is_selected","title":""},{"docid":"83edf8be6ca57fc27ed5c76218f6eb41","score":"0.46023008","text":"def get_choices(self):\n choice = \"\"\n (quote, unquote) = (\"`\", \"`\")\n quoted = False\n for char in self.choices:\n if not quoted and char == quote:\n quoted = True\n elif quoted and char == unquote:\n quoted = False\n elif char == \",\" and not quoted:\n choice = choice.strip()\n if choice:\n yield choice, choice\n choice = \"\"\n else:\n choice += char\n choice = choice.strip()\n if choice:\n yield choice, choice","title":""},{"docid":"96c5bb9a4affff4917d595675a984107","score":"0.46016142","text":"def test_model_multiple_choice_run_validators(self):\r\n for i in range(30):\r\n Person.objects.create(name=\"Person %s\" % i)\r\n\r\n self._validator_run = False\r\n def my_validator(value):\r\n self._validator_run = True\r\n\r\n f = forms.ModelMultipleChoiceField(queryset=Person.objects.all(),\r\n validators=[my_validator])\r\n\r\n f.clean([p.pk for p in Person.objects.all()[8:9]])\r\n self.assertTrue(self._validator_run)","title":""}],"string":"[\n {\n \"docid\": \"2d5cb44cdb85d97c0343e4af60eea444\",\n \"score\": \"0.73833954\",\n \"text\": \"def test_split_select_multiples_with_randomize(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label | parameters |\\n | | text | name | Name | |\\n | | integer | age | Age | |\\n | | begin repeat | browser_use | Browser Use | |\\n | | integer | year | Year | |\\n | | select_multiple browsers | browsers | Browsers | randomize=true |\\n | | end repeat | | | |\\n\\n | choices |\\n | | list name | name | label |\\n | | browsers | firefox | Firefox |\\n | | browsers | chrome | Chrome |\\n | | browsers | ie | Internet Explorer |\\n | | browsers | safari | Safari |\\n \\\"\\\"\\\" # noqa: E501\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [\\n {\\n \\\"name\\\": \\\"Tom\\\",\\n \\\"age\\\": 23,\\n \\\"browser_use\\\": [\\n {\\n \\\"browser_use/year\\\": \\\"2010\\\",\\n \\\"browser_use/browsers\\\": \\\"firefox safari\\\",\\n },\\n {\\n \\\"browser_use/year\\\": \\\"2011\\\",\\n \\\"browser_use/browsers\\\": \\\"firefox chrome\\\",\\n },\\n ],\\n }\\n ] # yapf: disable\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=True,\\n include_images=False,\\n )\\n record = cursor[0]\\n select_multiples = CSVDataFrameBuilder._collect_select_multiples(xform)\\n result = CSVDataFrameBuilder._split_select_multiples(record, select_multiples)\\n # build a new dictionary only composed of the keys we want to use in\\n # the comparison\\n result = dict(\\n [(key, result[key]) for key in list(result) if key in list(cursor[0])]\\n )\\n self.assertEqual(cursor[0], result)\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username, xform.id_string, binary_select_multiples=True\\n )\\n # pylint: disable=protected-access\\n result = csv_df_builder._split_select_multiples(record, select_multiples)\\n # build a new dictionary only composed of the keys we want to use in\\n # the comparison\\n result = dict(\\n [(key, result[key]) for key in list(result) if key in list(cursor[0])]\\n )\\n self.assertEqual(cursor[0], result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f84d6ba2692a80b553fdf67185d3f10\",\n \"score\": \"0.73285466\",\n \"text\": \"def test_split_select_multiples(self):\\n self._publish_nested_repeats_form()\\n self._submit_fixture_instance(\\\"nested_repeats\\\", \\\"01\\\")\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username, self.xform.id_string, include_images=False\\n )\\n # pylint: disable=protected-access\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n record = cursor[0]\\n select_multiples = CSVDataFrameBuilder._collect_select_multiples(self.xform)\\n result = CSVDataFrameBuilder._split_select_multiples(record, select_multiples)\\n expected_result = {\\n \\\"web_browsers/ie\\\": True,\\n \\\"web_browsers/safari\\\": True,\\n \\\"web_browsers/firefox\\\": False,\\n \\\"web_browsers/chrome\\\": False,\\n }\\n # build a new dictionary only composed of the keys we want to use in\\n # the comparison\\n result = dict(\\n [(key, result[key]) for key in list(result) if key in list(expected_result)]\\n )\\n self.assertEqual(expected_result, result)\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username, self.xform.id_string, binary_select_multiples=True\\n )\\n # pylint: disable=protected-access\\n result = csv_df_builder._split_select_multiples(record, select_multiples)\\n expected_result = {\\n \\\"web_browsers/ie\\\": 1,\\n \\\"web_browsers/safari\\\": 1,\\n \\\"web_browsers/firefox\\\": 0,\\n \\\"web_browsers/chrome\\\": 0,\\n }\\n # build a new dictionary only composed of the keys we want to use in\\n # the comparison\\n result = dict(\\n [(key, result[key]) for key in list(result) if key in list(expected_result)]\\n )\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878fbee9e68d69a3bc62047e56187b42\",\n \"score\": \"0.72794944\",\n \"text\": \"def test_no_split_select_multiples(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label |\\n | | text | name | Name |\\n | | integer | age | Age |\\n | | begin repeat | browser_use | Browser Use |\\n | | integer | year | Year |\\n | | select_multiple browsers | browsers | Browsers |\\n | | end repeat | | |\\n\\n | choices |\\n | | list name | name | label |\\n | | browsers | firefox | Firefox |\\n | | browsers | chrome | Chrome |\\n | | browsers | ie | Internet Explorer |\\n | | browsers | safari | Safari |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [\\n {\\n \\\"name\\\": \\\"Tom\\\",\\n \\\"age\\\": 23,\\n \\\"browser_use\\\": [\\n {\\n \\\"browser_use/year\\\": \\\"2010\\\",\\n \\\"browser_use/browsers\\\": \\\"firefox safari\\\",\\n },\\n {\\n \\\"browser_use/year\\\": \\\"2011\\\",\\n \\\"browser_use/browsers\\\": \\\"firefox chrome\\\",\\n },\\n ],\\n }\\n ] # yapf: disable\\n\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=False,\\n include_images=False,\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [\\n {\\n \\\"name\\\": \\\"Tom\\\",\\n \\\"age\\\": 23,\\n \\\"browser_use[1]/year\\\": \\\"2010\\\",\\n \\\"browser_use[1]/browsers\\\": \\\"firefox safari\\\",\\n \\\"browser_use[2]/year\\\": \\\"2011\\\",\\n \\\"browser_use[2]/browsers\\\": \\\"firefox chrome\\\",\\n }\\n ]\\n self.maxDiff = None\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4870f5ee6b1282eb432445e0d0f68a4\",\n \"score\": \"0.7215538\",\n \"text\": \"def test_split_select_multiples_values(self):\\n self._publish_nested_repeats_form()\\n self._submit_fixture_instance(\\\"nested_repeats\\\", \\\"01\\\")\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n record = cursor[0]\\n select_multiples = CSVDataFrameBuilder._collect_select_multiples(self.xform)\\n result = CSVDataFrameBuilder._split_select_multiples(\\n record, select_multiples, value_select_multiples=True\\n )\\n expected_result = {\\n \\\"web_browsers/ie\\\": \\\"ie\\\",\\n \\\"web_browsers/safari\\\": \\\"safari\\\",\\n \\\"web_browsers/firefox\\\": None,\\n \\\"web_browsers/chrome\\\": None,\\n }\\n # build a new dictionary only composed of the keys we want to use in\\n # the comparison\\n result = dict(\\n [(key, result[key]) for key in list(result) if key in list(expected_result)]\\n )\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38563ed5d8daadc1cdcdcf5122ee1e04\",\n \"score\": \"0.64926195\",\n \"text\": \"def test_show_choice_labels_select_multiple(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label |\\n | | text | name | Name |\\n | | integer | age | Age |\\n | | select_multiple fruits | fruit | Fruit |\\n | | | | |\\n | choices | list name | name | label |\\n | | fruits | 1 | Mango |\\n | | fruits | 2 | Orange |\\n | | fruits | 3 | Apple |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1 2\\\"}] # yapf: disable\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=False,\\n include_images=False,\\n show_choice_labels=True,\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"Mango Orange\\\"}]\\n self.maxDiff = None\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12718be74bd7221faa1a0ae46219dd93\",\n \"score\": \"0.6464451\",\n \"text\": \"def test_show_choice_labels_select_multiple_3(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label |\\n | | text | name | Name |\\n | | integer | age | Age |\\n | | select_multiple fruits | fruit | Fruit |\\n | | | | |\\n | choices | list name | name | label |\\n | | fruits | 1 | Mango |\\n | | fruits | 2 | Orange |\\n | | fruits | 3 | Apple |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1 2\\\"}] # yapf: disable\\n # Split Select multiples, binary_select_multiples is True\\n csv_df_builder_1 = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=True,\\n binary_select_multiples=False,\\n include_images=False,\\n show_choice_labels=True,\\n )\\n result = [k for k in csv_df_builder_1._format_for_dataframe(cursor)]\\n expected_result = [\\n {\\n \\\"name\\\": \\\"Maria\\\",\\n \\\"age\\\": 25,\\n \\\"fruit/Mango\\\": True,\\n \\\"fruit/Orange\\\": True,\\n \\\"fruit/Apple\\\": 0,\\n }\\n ]\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59dbe9e2e206d6c90943ec743caa2ab5\",\n \"score\": \"0.64489794\",\n \"text\": \"def test_show_choice_labels_select_multiple_1(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label |\\n | | text | name | Name |\\n | | integer | age | Age |\\n | | select_multiple fruits | fruit | Fruit |\\n | | | | |\\n | choices | list name | name | label |\\n | | fruits | 1 | Mango |\\n | | fruits | 2 | Orange |\\n | | fruits | 3 | Apple |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1 2\\\"}] # yapf: disable\\n # Split Select multiples, value_select_multiples is True\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=True,\\n value_select_multiples=True,\\n include_images=False,\\n show_choice_labels=True,\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [\\n {\\n \\\"name\\\": \\\"Maria\\\",\\n \\\"age\\\": 25,\\n \\\"fruit/Mango\\\": \\\"Mango\\\",\\n \\\"fruit/Orange\\\": \\\"Orange\\\",\\n \\\"fruit/Apple\\\": None,\\n }\\n ]\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26de22cbb9527e79737b7ed47c478551\",\n \"score\": \"0.6430323\",\n \"text\": \"def test_show_choice_labels_select_multiple_2(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label |\\n | | text | name | Name |\\n | | integer | age | Age |\\n | | select_multiple fruits | fruit | Fruit |\\n | | | | |\\n | choices | list name | name | label |\\n | | fruits | 1 | Mango |\\n | | fruits | 2 | Orange |\\n | | fruits | 3 | Apple |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1 2\\\"}] # yapf: disable\\n # Split Select multiples, binary_select_multiples is True\\n csv_df_builder_1 = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=True,\\n binary_select_multiples=True,\\n include_images=False,\\n show_choice_labels=True,\\n )\\n result = [k for k in csv_df_builder_1._format_for_dataframe(cursor)]\\n expected_result = [\\n {\\n \\\"name\\\": \\\"Maria\\\",\\n \\\"age\\\": 25,\\n \\\"fruit/Mango\\\": 1,\\n \\\"fruit/Orange\\\": 1,\\n \\\"fruit/Apple\\\": 0,\\n }\\n ]\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321909ff81dd2791a58a78b3cf9426e0\",\n \"score\": \"0.61721087\",\n \"text\": \"def test_to_csv(self):\\n self.clean_option.get_merge_data()\\n lines = self.clean_option.to_csv()\\n\\n self.assertGreater(len(lines), 1)\\n for line in lines.split():\\n print line\\n data = line.split(',')\\n self.assertEqual(len(data), 11)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"281ec7075d7ee482a61036db7a5bfa35\",\n \"score\": \"0.6168276\",\n \"text\": \"def test_show_choice_labels_select_multiple_language(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label:Eng | label:Fr |\\n | | text | name | Name | Prénom |\\n | | integer | age | Age | Âge |\\n | | select_multiple fruits | fruit | Fruit | Fruit |\\n | | | | | |\\n | choices | list name | name | label:Eng | label:Fr |\\n | | fruits | 1 | Mango | Mangue |\\n | | fruits | 2 | Orange | Orange |\\n | | fruits | 3 | Apple | Pomme |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1 2\\\"}] # yapf: disable\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=False,\\n include_images=False,\\n show_choice_labels=True,\\n language=\\\"Fr\\\",\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"Mangue Orange\\\"}]\\n self.maxDiff = None\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b0f50e5bd24cd7f793b4fb119df381\",\n \"score\": \"0.6140882\",\n \"text\": \"def test_show_choice_labels_select_multiple_1_language(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label:Eng | label:Fr |\\n | | text | name | Name | Prénom |\\n | | integer | age | Age | Âge |\\n | | select_multiple fruits | fruit | Fruit | Fruit |\\n | | | | | |\\n | choices | list name | name | label:Eng | label:Fr |\\n | | fruits | 1 | Mango | Mangue |\\n | | fruits | 2 | Orange | Orange |\\n | | fruits | 3 | Apple | Pomme |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1 2\\\"}] # yapf: disable\\n # Split Select multiples, value_select_multiples is True\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=True,\\n value_select_multiples=True,\\n include_images=False,\\n show_choice_labels=True,\\n language=\\\"Fr\\\",\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [\\n {\\n \\\"name\\\": \\\"Maria\\\",\\n \\\"age\\\": 25,\\n \\\"fruit/Mangue\\\": \\\"Mangue\\\",\\n \\\"fruit/Orange\\\": \\\"Orange\\\",\\n \\\"fruit/Pomme\\\": None,\\n }\\n ]\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6052af7f0e70b94584269a96599356db\",\n \"score\": \"0.56214225\",\n \"text\": \"def test_to_csv(self):\\n self.clean_option.get_merge_data()\\n # self.clean_option.df_all = self.clean_option.df_all[\\n # self.clean_option.df_all['option_code'] == 'TZA1120121C9'\\n # ]\\n self.clean_option.update_split_date()\\n # ts(self.clean_option.df_all[['ask', 'bid', 'date', 'right', 'strike', 'close', 'close1']])\\n lines = self.clean_option.to_csv()\\n\\n self.assertGreater(len(lines), 1)\\n for line in lines.split():\\n print line\\n data = line.split(',')\\n self.assertEqual(len(data), 12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af669cef2b59404be2855936b11dd015\",\n \"score\": \"0.55866694\",\n \"text\": \"def pb4_clicked(self):\\n list = self.view.selectedItems()\\n if len(list) > 1:\\n dlg = QtGui.QProgressDialog(\\\"Output CSV\\\", \\\"Cancel Output\\\", 0, len(list))\\n dlg.setWindowModality(QtCore.Qt.WindowModal)\\n dlg.setMinimumDuration(0)\\n dlg.setWindowTitle(\\\"\\\")\\n dlg.show()\\n count = 0\\n f = open('results.csv', 'wb')\\n f.write('Variant, k1 from XXX, k1 from XX, k2 from XX, k1 from X, k2 from X\\\\n')\\n for item in list: #item is the file_noext\\n print(str(item.text()))\\n kobs = self.fdict[str(item.text())].kobs\\n print(kobs)\\n print self.csv(kobs)\\n alist = []\\n f.write(str(item.text())+', ') #write the variant\\n if len(kobs) > 1:\\n for i in kobs:\\n for j in i:\\n alist.append(j)\\n f.write(self.csv(alist))\\n else:\\n print kobs[0].shape\\n print type(kobs[0])\\n print self.csv(kobs[0].tolist())\\n f.write(str(kobs[0].tolist()[0]))\\n f.write('\\\\n')\\n count += 1\\n dlg.setValue(count)\\n if dlg.wasCanceled():\\n break\\n f.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f676686713a286b1ea0cc2afb8a08624\",\n \"score\": \"0.55680466\",\n \"text\": \"def test_multiple_repeats_column_order(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label | repeat_count | relevant |\\n | | select_multiple food | food | Food: | | |\\n | | integer | no_food | No. Food | | |\\n | | begin repeat | food_repeat | Food Repeat | ${no_food} | |\\n | | select_multiple food | food_group | Food: | | |\\n | | end repeat | | | | |\\n | | integer | no_food_2 | No. Food | | |\\n | | begin repeat | food_repeat_2 | Food Repeat | ${no_food_2} | selected(${food}, 'Apple') |\\n | | select_multiple food | food_group_2 | Food: | | |\\n | | end repeat | | | | |\\n | | geopoint | gps | GPS | | |\\n | | | | | | |\\n | choices | list name | name | label | | |\\n | | food | Apple | Apple | | |\\n | | food | Orange | Orange | | |\\n | | food | Banana | Banana | | |\\n | | food | Pizza | Pizza | | |\\n | | food | Lasgna | Lasgna | | |\\n | | food | Cake | Cake | | |\\n | | food | Chocolate | Chocolate | | |\\n | | food | Salad | Salad | | |\\n | | food | Sandwich | Sandwich | | |\\n \\\"\\\"\\\" # noqa: E501\\n self.xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n\\n cursor = [\\n {\\n \\\"food\\\": \\\"Orange\\\",\\n \\\"no_food\\\": 2,\\n \\\"food_repeat\\\": [\\n {\\\"food_repeat/food_group\\\": \\\"Banana\\\"},\\n {\\\"food_repeat/food_group\\\": \\\"Lasgna\\\"},\\n ],\\n },\\n {\\n \\\"food\\\": \\\"Apple\\\",\\n \\\"no_food_2\\\": 2,\\n \\\"food_repeat_2\\\": [\\n {\\\"food_repeat_2/food_group_2\\\": \\\"Cake\\\"},\\n {\\\"food_repeat_2/food_group_2\\\": \\\"Salad\\\"},\\n ],\\n },\\n ]\\n expected_header = [\\n \\\"food/Apple\\\",\\n \\\"food/Orange\\\",\\n \\\"food/Banana\\\",\\n \\\"food/Pizza\\\",\\n \\\"food/Lasgna\\\",\\n \\\"food/Cake\\\",\\n \\\"food/Chocolate\\\",\\n \\\"food/Salad\\\",\\n \\\"food/Sandwich\\\",\\n \\\"no_food\\\",\\n \\\"food_repeat_count\\\",\\n \\\"food_repeat[1]/food_group/Apple\\\",\\n \\\"food_repeat[1]/food_group/Orange\\\",\\n \\\"food_repeat[1]/food_group/Banana\\\",\\n \\\"food_repeat[1]/food_group/Pizza\\\",\\n \\\"food_repeat[1]/food_group/Lasgna\\\",\\n \\\"food_repeat[1]/food_group/Cake\\\",\\n \\\"food_repeat[1]/food_group/Chocolate\\\",\\n \\\"food_repeat[1]/food_group/Salad\\\",\\n \\\"food_repeat[1]/food_group/Sandwich\\\",\\n \\\"food_repeat[2]/food_group/Apple\\\",\\n \\\"food_repeat[2]/food_group/Orange\\\",\\n \\\"food_repeat[2]/food_group/Banana\\\",\\n \\\"food_repeat[2]/food_group/Pizza\\\",\\n \\\"food_repeat[2]/food_group/Lasgna\\\",\\n \\\"food_repeat[2]/food_group/Cake\\\",\\n \\\"food_repeat[2]/food_group/Chocolate\\\",\\n \\\"food_repeat[2]/food_group/Salad\\\",\\n \\\"food_repeat[2]/food_group/Sandwich\\\",\\n \\\"no_food_2\\\",\\n \\\"food_repeat_2_count\\\",\\n \\\"food_repeat_2[1]/food_group_2/Apple\\\",\\n \\\"food_repeat_2[1]/food_group_2/Orange\\\",\\n \\\"food_repeat_2[1]/food_group_2/Banana\\\",\\n \\\"food_repeat_2[1]/food_group_2/Pizza\\\",\\n \\\"food_repeat_2[1]/food_group_2/Lasgna\\\",\\n \\\"food_repeat_2[1]/food_group_2/Cake\\\",\\n \\\"food_repeat_2[1]/food_group_2/Chocolate\\\",\\n \\\"food_repeat_2[1]/food_group_2/Salad\\\",\\n \\\"food_repeat_2[1]/food_group_2/Sandwich\\\",\\n \\\"food_repeat_2[2]/food_group_2/Apple\\\",\\n \\\"food_repeat_2[2]/food_group_2/Orange\\\",\\n \\\"food_repeat_2[2]/food_group_2/Banana\\\",\\n \\\"food_repeat_2[2]/food_group_2/Pizza\\\",\\n \\\"food_repeat_2[2]/food_group_2/Lasgna\\\",\\n \\\"food_repeat_2[2]/food_group_2/Cake\\\",\\n \\\"food_repeat_2[2]/food_group_2/Chocolate\\\",\\n \\\"food_repeat_2[2]/food_group_2/Salad\\\",\\n \\\"food_repeat_2[2]/food_group_2/Sandwich\\\",\\n \\\"gps\\\",\\n \\\"_gps_latitude\\\",\\n \\\"_gps_longitude\\\",\\n \\\"_gps_altitude\\\",\\n \\\"_gps_precision\\\",\\n \\\"meta/instanceID\\\",\\n \\\"_id\\\",\\n \\\"_uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_date_modified\\\",\\n \\\"_tags\\\",\\n \\\"_notes\\\",\\n \\\"_version\\\",\\n \\\"_duration\\\",\\n \\\"_submitted_by\\\",\\n \\\"_total_media\\\",\\n \\\"_media_count\\\",\\n \\\"_media_all_received\\\",\\n ]\\n\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username, self.xform.id_string, include_images=False\\n )\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n header = next(csv_reader)\\n\\n self.assertEqual(header, expected_header)\\n\\n csv_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9faafc3451720364e01631237b2a37bd\",\n \"score\": \"0.55194\",\n \"text\": \"def is_multiple_selection_allowed(self):\\n LOGGER.debug(\\\"Check if multiple selection is allowed\\\")\\n self.web_element.is_multiple()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad261501cbbd72369a99eb0f91e2e4b\",\n \"score\": \"0.55074614\",\n \"text\": \"def test_csv_splits(self):\\n from karld.loadump import split_csv_file\\n\\n split_csv_file(self.input_data_path, self.out_dir, max_lines=5)\\n\\n self.assertTrue(os.path.exists(self.expected_out_0))\\n self.assertTrue(os.path.exists(self.expected_out_1))\\n\\n with open(self.expected_out_0, 'rb') as stream:\\n data = stream.read()\\n self.assertEqual(b'mushroom,fungus\\\\ntomato,fruit\\\\ntopaz,mineral\\\\n'\\n b'iron,metal\\\\ndr\\\\xc3\\\\xb3\\\\xc5\\\\xbck\\\\xc4\\\\x85,'\\n b'utf-8 sample\\\\n'.splitlines(), data.splitlines())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672758e8667c13365b511fe3e684209b\",\n \"score\": \"0.55007154\",\n \"text\": \"def test_exact_multiple(self):\\n actual = parserutils.divide_into_groups(['a', 'b', 'c', 'd'], 2)\\n expected = [['a', 'b'], ['c', 'd']]\\n self.assertEqual(actual, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4ae3b630577649d4b1c83b737bda15\",\n \"score\": \"0.54716295\",\n \"text\": \"def test_show_choice_labels_multi_language_1(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label:English | label:French |\\n | | text | name | Name | Prénom |\\n | | integer | age | Age | Âge |\\n | | select one fruits | fruit | Fruit | Fruit |\\n | | | | | |\\n | choices | list name | name | label:English | label:French |\\n | | fruits | 1 | Mango | Mangue |\\n | | fruits | 2 | Orange | Orange |\\n | | fruits | 3 | Apple | Pomme |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1\\\"}] # yapf: disable\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=False,\\n include_images=False,\\n show_choice_labels=True,\\n language=\\\"English\\\",\\n )\\n # pylint: disable=protected-access\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"Mango\\\"}]\\n self.maxDiff = None\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea994eb5990c0aeada69938d9bedced\",\n \"score\": \"0.5438774\",\n \"text\": \"def test_show_choice_labels_multi_language(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label:English | label:French |\\n | | text | name | Name | Prénom |\\n | | integer | age | Age | Âge |\\n | | select one fruits | fruit | Fruit | Fruit |\\n | | | | | |\\n | choices | list name | name | label:English | label:French |\\n | | fruits | 1 | Mango | Mangue |\\n | | fruits | 2 | Orange | Orange |\\n | | fruits | 3 | Apple | Pomme |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1\\\"}] # yapf: disable\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=False,\\n include_images=False,\\n show_choice_labels=True,\\n language=\\\"French\\\",\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"Mangue\\\"}]\\n self.maxDiff = None\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fdbfc6cdbe8743347650ab21fc5e1c3\",\n \"score\": \"0.53887004\",\n \"text\": \"def test_account_list_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\\n account.entity.open()\\n self.assert_util(\\n account.entity.multiple_select.list_of_values(),\\n [\\\"Option One\\\", \\\"Option Two\\\"]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5657f9af8c256c673bea641e82fc14\",\n \"score\": \"0.53864026\",\n \"text\": \"def test_windows_excel_compatible_csv_export(self):\\n self._publish_single_level_repeat_form()\\n # submit 7 instances\\n for _ in range(4):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"02\\\")\\n for _ in range(2):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n self.xform.id_string,\\n remove_group_name=True,\\n include_images=False,\\n win_excel_utf8=True,\\n )\\n # pylint: disable=protected-access\\n record_count = self.xform.instances.count()\\n self.assertEqual(record_count, 7)\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n header = next(csv_reader)\\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\\n self.assertEqual(b\\\"\\\\xef\\\\xbb\\\\xbfname\\\", header[0].encode(\\\"utf-8\\\"))\\n # close and delete file\\n csv_file.close()\\n os.unlink(temp_file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76eef42f58070df11cd1f94c24d9d010\",\n \"score\": \"0.5384241\",\n \"text\": \"def _check_multiples(self, code):\\n if len(self.data[code]) > 1 and not self.schema[code].multiple:\\n strictness.problem(\\n \\\"repeated option {} '{}' not permitted by pcapng spec\\\".format(\\n code, self._get_name_alias(code)\\n )\\n )\\n if strictness.should_fix():\\n self.data[code] = self.data[code][:1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d58f34ed5c250f70661ed78c2a0c3149\",\n \"score\": \"0.53801537\",\n \"text\": \"def test_not_exact_multiple(self):\\n actual = parserutils.divide_into_groups(['a', 'b', 'c', 'd', 'e'], 2)\\n expected = [['a', 'b'], ['c', 'd'], ['e']]\\n self.assertEqual(actual, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"126f99224fad031cb41e6b10aea12325\",\n \"score\": \"0.5376905\",\n \"text\": \"def test_export_study_option_group(self, client):\\n study_option_groups = client.export_option_groups()\\n for option_group in study_option_groups:\\n # Assert that the number of keys between the retrieved data and the model are the same\\n assert len(option_group.keys()) == len(export_option_group_model.keys())\\n for key in option_group:\\n # Assert that each retrieved key should be in the model\\n assert key in export_option_group_model.keys()\\n # Assert that the value belonging to each key is according to the model\\n assert type(option_group[key]) in export_option_group_model[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faec96ca872bfd8d9200f036e6ab3a4e\",\n \"score\": \"0.53511304\",\n \"text\": \"def write_csv_data_multi_question_hit(sample_name, sample_directory, questions_per_hit, sample_pair_list):\\n\\n # Erases and sets up a new CSV file, returning handles\\n csvfile, writer = setup_csv_multi_question_hit(sample_name, sample_directory, questions_per_hit)\\n\\n counter = 1\\n column_list = []\\n for row in sample_pair_list:\\n print(row)\\n column_list = column_list + row\\n if counter % questions_per_hit == 0:\\n writer.writerow(column_list)\\n column_list = []\\n counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a60402d8e561bbd30b3ac6bca6763c\",\n \"score\": \"0.5277433\",\n \"text\": \"def ask_for_multiple_list(input_ls: list, question: str, answers: list) -> list:\\n output_list = []\\n while True:\\n line_no = ask_for_list(input_ls)\\n output_list.append(line_no) if line_no not in output_list else print('Already selected.')\\n more = ask_for(question, answers)\\n if not more:\\n break\\n return output_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c5b4fbcd550d5fb913756b92e89f80\",\n \"score\": \"0.524852\",\n \"text\": \"def test_export_two_scalar_csv_good_input(self):\\n populate_database_with_data(self.factory.create_record_dao())\\n export(\\n factory=self.factory,\\n id_list=['spam3', 'spam'],\\n scalar_names=['spam_scal', 'spam_scal_2'],\\n output_type='csv',\\n output_file=self.test_file_path.name)\\n\\n with io.open(self.test_file_path.name, 'r', encoding='utf-8') as csvfile:\\n reader = csv.reader(csvfile)\\n rows = [row for row in reader]\\n self.assertEqual(rows[0], ['id',\\n 'spam_scal',\\n 'spam_scal_2'])\\n self.assertEqual(rows[1], ['spam3',\\n '10.5',\\n '10.5'])\\n # AlmostEqual doesn't work with lists, but we expect floats from\\n # SQL, hence this workaround\\n self.assertEqual(rows[2], ['spam',\\n '10.0',\\n '200.0'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbdda0cc9dd38c09dbf1ce900885fe9\",\n \"score\": \"0.5224446\",\n \"text\": \"def multiple_choice():\\n # Here are 4 choices\\n a = \\\"Training accuracy increases over time and this is a problem.\\\"\\n b = \\\"Validation accuracy does not increase over time and this is a problem.\\\"\\n c = \\\"Training loss decreases over time and this is a problem.\\\"\\n d = \\\"Validation loss does not decrease over time and this is a problem.\\\"\\n return a, b, c, d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9137ee475e04e1f3560688ff0ae2dd98\",\n \"score\": \"0.52004576\",\n \"text\": \"def test_show_choice_labels(self):\\n md_xform = \\\"\\\"\\\"\\n | survey |\\n | | type | name | label |\\n | | text | name | Name |\\n | | integer | age | Age |\\n | | select one fruits | fruit | Fruit |\\n | | | | |\\n | choices | list name | name | label |\\n | | fruits | 1 | Mango |\\n | | fruits | 2 | Orange |\\n | | fruits | 3 | Apple |\\n \\\"\\\"\\\"\\n xform = self._publish_markdown(md_xform, self.user, id_string=\\\"b\\\")\\n cursor = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"1\\\"}] # yapf: disable\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n xform.id_string,\\n split_select_multiples=False,\\n include_images=False,\\n show_choice_labels=True,\\n )\\n result = [k for k in csv_df_builder._format_for_dataframe(cursor)]\\n expected_result = [{\\\"name\\\": \\\"Maria\\\", \\\"age\\\": 25, \\\"fruit\\\": \\\"Mango\\\"}]\\n self.maxDiff = None\\n self.assertEqual(expected_result, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2097a07d37602f50cee2a3ccab0d22c7\",\n \"score\": \"0.5185491\",\n \"text\": \"def test_account_select_value_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\\n account.entity.open()\\n account.entity.multiple_select.select(\\\"Option One\\\")\\n account.entity.multiple_select.select(\\\"Option Two\\\")\\n self.assert_util(\\n account.entity.multiple_select.get_values,\\n ['Option One', 'Option Two'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c469afd657fbe397240ea16e6e27aab2\",\n \"score\": \"0.5181445\",\n \"text\": \"def test_csv_export_extra_columns(self):\\n self._publish_single_level_repeat_form()\\n # submit 7 instances\\n for _ in range(4):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"02\\\")\\n for _ in range(2):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n self.xform.id_string,\\n remove_group_name=True,\\n include_labels=True,\\n include_reviews=True,\\n )\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n header = next(csv_reader)\\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\\n expected_header = [\\n \\\"name\\\",\\n \\\"age\\\",\\n \\\"has_kids\\\",\\n \\\"kids_name\\\",\\n \\\"kids_age\\\",\\n \\\"kids_name\\\",\\n \\\"kids_age\\\",\\n \\\"gps\\\",\\n \\\"_gps_latitude\\\",\\n \\\"_gps_longitude\\\",\\n \\\"_gps_altitude\\\",\\n \\\"_gps_precision\\\",\\n \\\"web_browsers/firefox\\\",\\n \\\"web_browsers/chrome\\\",\\n \\\"web_browsers/ie\\\",\\n \\\"web_browsers/safari\\\",\\n \\\"instanceID\\\",\\n \\\"_id\\\",\\n \\\"_uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_date_modified\\\",\\n \\\"_tags\\\",\\n \\\"_notes\\\",\\n \\\"_version\\\",\\n \\\"_duration\\\",\\n \\\"_submitted_by\\\",\\n \\\"_total_media\\\",\\n \\\"_media_count\\\",\\n \\\"_media_all_received\\\",\\n \\\"_xform_id\\\",\\n \\\"_review_status\\\",\\n \\\"_review_comment\\\",\\n \\\"_review_date\\\",\\n ]\\n self.assertEqual(expected_header, header)\\n # close and delete file\\n csv_file.close()\\n os.unlink(temp_file.name)\\n\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n self.xform.id_string,\\n remove_group_name=True,\\n include_labels=True,\\n include_reviews=True,\\n )\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n header = next(csv_reader)\\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\\n self.assertEqual(expected_header, header)\\n csv_file.close()\\n os.unlink(temp_file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa2c413100dc3517bdca9dc1c5420e6\",\n \"score\": \"0.51507574\",\n \"text\": \"def test_read_multiple_csv(tmp_path, copies):\\n\\n writer = Clumper.read_csv(\\\"tests/data/monopoly.csv\\\")\\n\\n for i in range(copies):\\n writer.write_csv(tmp_path / f\\\"monopoly_copy_{i}.csv\\\")\\n\\n reader = Clumper.read_csv(str(tmp_path / \\\"*.csv\\\"))\\n assert len(reader) == copies * len(writer)\\n\\n reader = Clumper.read_csv(list(Path(tmp_path).glob(\\\"*.csv\\\")))\\n assert len(reader) == copies * len(writer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d3b420551e208fbcddb8571e1c7fb8\",\n \"score\": \"0.5133795\",\n \"text\": \"def read_multiple():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86363eb83a831ac1c03ea38f4638f923\",\n \"score\": \"0.5117611\",\n \"text\": \"def OnExportCSVClick(self):\\n if len(self.list)>0:\\n f = tkFileDialog.asksaveasfile(mode='w', defaultextension=\\\".csv\\\", initialfile=\\\"common.csv\\\")\\n if f is None:\\n return\\n writer = csv.writer(f)\\n for keyword in self.list:\\n writer.writerow([keyword])\\n f.close \\n tkMessageBox.showinfo(\\\"Done\\\", \\\"File exported as CSV!\\\")\\n else:\\n tkMessageBox.showwarning(\\\"Error\\\",\\\"No keywords to export.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b84f18f25a95f65c7e6e01a55c5dfd1\",\n \"score\": \"0.51058125\",\n \"text\": \"def test_ls1_sample_csv(self):\\n inputs = [(get_ls1_test_prob_point_data()), (get_ls1_test_prob_region_data())]\\n\\n for data, input_files, reference_files, output_file_index in inputs:\\n with self.subTest(\\n data=data,\\n input_files=input_files,\\n reference_files=reference_files,\\n output_file_index=output_file_index,\\n ):\\n diff = run_write_test(\\n data, input_files, reference_files, output_file_index\\n )\\n self.assertEqual(diff, \\\"\\\", diff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d819606e84072e29b089c6d10a94da\",\n \"score\": \"0.50775754\",\n \"text\": \"def test_dataselect_bulk(self):\\r\\n clients = [self.client, self.client_auth]\\r\\n file = os.path.join(self.datapath, \\\"bulk.mseed\\\")\\r\\n expected = read(file)\\r\\n # test cases for providing lists of lists\\r\\n bulk = ((\\\"TA\\\", \\\"A25A\\\", \\\"\\\", \\\"BHZ\\\",\\r\\n UTCDateTime(\\\"2010-03-25T00:00:00\\\"),\\r\\n UTCDateTime(\\\"2010-03-25T00:00:04\\\")),\\r\\n (\\\"TA\\\", \\\"A25A\\\", \\\"\\\", \\\"BHE\\\",\\r\\n UTCDateTime(\\\"2010-03-25T00:00:00\\\"),\\r\\n UTCDateTime(\\\"2010-03-25T00:00:06\\\")),\\r\\n (\\\"IU\\\", \\\"ANMO\\\", \\\"*\\\", \\\"HHZ\\\",\\r\\n UTCDateTime(\\\"2010-03-25T00:00:00\\\"),\\r\\n UTCDateTime(\\\"2010-03-25T00:00:08\\\")))\\r\\n params = dict(quality=\\\"B\\\", longestonly=False, minimumlength=5)\\r\\n for client in clients:\\r\\n # test output to stream\\r\\n got = client.get_waveforms_bulk(bulk, **params)\\r\\n self.assertEqual(got, expected, failmsg(got, expected))\\r\\n # test output to file\\r\\n with NamedTemporaryFile() as tf:\\r\\n client.get_waveforms_bulk(bulk, filename=tf.name, **params)\\r\\n got = read(tf.name)\\r\\n self.assertEqual(got, expected, failmsg(got, expected))\\r\\n # test cases for providing a request string\\r\\n bulk = (\\\"quality=B\\\\n\\\"\\r\\n \\\"longestonly=false\\\\n\\\"\\r\\n \\\"minimumlength=5\\\\n\\\"\\r\\n \\\"TA A25A -- BHZ 2010-03-25T00:00:00 2010-03-25T00:00:04\\\\n\\\"\\r\\n \\\"TA A25A -- BHE 2010-03-25T00:00:00 2010-03-25T00:00:06\\\\n\\\"\\r\\n \\\"IU ANMO * HHZ 2010-03-25T00:00:00 2010-03-25T00:00:08\\\\n\\\")\\r\\n for client in clients:\\r\\n # test output to stream\\r\\n got = client.get_waveforms_bulk(bulk)\\r\\n self.assertEqual(got, expected, failmsg(got, expected))\\r\\n # test output to file\\r\\n with NamedTemporaryFile() as tf:\\r\\n client.get_waveforms_bulk(bulk, filename=tf.name)\\r\\n got = read(tf.name)\\r\\n self.assertEqual(got, expected, failmsg(got, expected))\\r\\n # test cases for providing a filename\\r\\n for client in clients:\\r\\n with NamedTemporaryFile() as tf:\\r\\n with open(tf.name, \\\"wt\\\") as fh:\\r\\n fh.write(bulk)\\r\\n got = client.get_waveforms_bulk(bulk)\\r\\n self.assertEqual(got, expected, failmsg(got, expected))\\r\\n # test cases for providing a file-like object\\r\\n for client in clients:\\r\\n got = client.get_waveforms_bulk(io.StringIO(bulk))\\r\\n self.assertEqual(got, expected, failmsg(got, expected))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc50a830fc21c4848e9f23cd24baf88e\",\n \"score\": \"0.50693756\",\n \"text\": \"def multiple_choice_field_data(field, **kwargs):\\n if field.choices:\\n from django_any.functions import valid_choices \\n l = list(valid_choices(field.choices))\\n random.shuffle(l)\\n choices = []\\n count = xunit.any_int(min_value=1, max_value=len(field.choices))\\n for i in six.moves.range(0, count):\\n choices.append(l[i])\\n return ' '.join(choices)\\n return 'None'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ffc4055b9b1ce93b879deb2884c488\",\n \"score\": \"0.5062563\",\n \"text\": \"def test_model_multiple_choice_number_of_queries(self):\\r\\n persons = [Person.objects.create(name=\\\"Person %s\\\" % i) for i in range(30)]\\r\\n\\r\\n f = forms.ModelMultipleChoiceField(queryset=Person.objects.all())\\r\\n self.assertNumQueries(1, f.clean, [p.pk for p in persons[1:11:2]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51077527bcf99bf31d888e1a53124111\",\n \"score\": \"0.5019203\",\n \"text\": \"def test_csv_column_indices_in_groups_within_repeats(self):\\n self._publish_xls_fixture_set_xform(\\\"groups_in_repeats\\\")\\n self._submit_fixture_instance(\\\"groups_in_repeats\\\", \\\"01\\\")\\n self.xform.get_keys()\\n data_0 = self._csv_data_for_dataframe()[0]\\n # remove dynamic fields\\n ignore_list = [\\n \\\"_uuid\\\",\\n \\\"meta/instanceID\\\",\\n \\\"formhub/uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_id\\\",\\n \\\"_bamboo_dataset_id\\\",\\n \\\"_date_modified\\\",\\n ]\\n for item in ignore_list:\\n data_0.pop(item)\\n expected_data_0 = {\\n \\\"_xform_id_string\\\": \\\"groups_in_repeats\\\",\\n \\\"_xform_id\\\": self.xform.pk,\\n \\\"_status\\\": \\\"submitted_via_web\\\",\\n \\\"_tags\\\": \\\"\\\",\\n \\\"_notes\\\": \\\"\\\",\\n \\\"_version\\\": self.xform.version,\\n \\\"_submitted_by\\\": \\\"bob\\\",\\n \\\"name\\\": \\\"Abe\\\",\\n \\\"age\\\": 88,\\n \\\"has_children\\\": \\\"1\\\",\\n \\\"children[1]/childs_info/name\\\": \\\"Cain\\\",\\n \\\"children[2]/childs_info/name\\\": \\\"Abel\\\",\\n \\\"children[1]/childs_info/age\\\": 56,\\n \\\"children[2]/childs_info/age\\\": 48,\\n \\\"children[1]/immunization/immunization_received/polio_1\\\": True,\\n \\\"children[1]/immunization/immunization_received/polio_2\\\": False,\\n \\\"children[2]/immunization/immunization_received/polio_1\\\": True,\\n \\\"children[2]/immunization/immunization_received/polio_2\\\": True,\\n \\\"web_browsers/chrome\\\": True,\\n \\\"web_browsers/firefox\\\": False,\\n \\\"web_browsers/ie\\\": False,\\n \\\"web_browsers/safari\\\": False,\\n \\\"gps\\\": \\\"-1.2626156 36.7923571 0.0 30.0\\\",\\n \\\"_geolocation\\\": [-1.2626156, 36.7923571],\\n \\\"_duration\\\": \\\"\\\",\\n \\\"_edited\\\": False,\\n \\\"_gps_latitude\\\": \\\"-1.2626156\\\",\\n \\\"_gps_longitude\\\": \\\"36.7923571\\\",\\n \\\"_gps_altitude\\\": \\\"0.0\\\",\\n \\\"_gps_precision\\\": \\\"30.0\\\",\\n \\\"_attachments\\\": [],\\n \\\"_total_media\\\": 0,\\n \\\"_media_count\\\": 0,\\n \\\"_media_all_received\\\": True,\\n }\\n self.maxDiff = None\\n self.assertEqual(data_0, expected_data_0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7fb32d53ba7e0c9085c9908634e74c\",\n \"score\": \"0.5010031\",\n \"text\": \"def test_split_list(self):\\n\\t\\tself.spawn(\\\"./partnerup\\\").stdin(\\\"4\\\").stdin(\\\"McRae Gordon Harris McGill\\\").stdin(\\\"Harris McGill McRae Gordon\\\").stdout(\\\"good\\\").exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e97ffcf7bdb2db8c03260ff9e8ce48\",\n \"score\": \"0.5009165\",\n \"text\": \"def test_indexview_with_multiple_selected_and_unselected_products(self):\\n\\t\\tproduct_test_one=CreateProduct(\\\"Sunflower seeds\\\",True)\\n\\t\\tproduct_test_one.product_article_code=\\\"Sun01\\\"\\n\\t\\tproduct_test_one.save()\\n\\n\\t\\tproduct_test_two=CreateProduct(\\\"Almond juce\\\",True)\\n\\t\\tproduct_test_two.product_article_code=\\\"Alm01\\\"\\n\\t\\tproduct_test_two.save()\\n\\n\\t\\tproduct_test_three=CreateProduct(\\\"Mango\\\",False)\\n\\t\\tproduct_test_three.product_article_code=\\\"Man01\\\"\\n\\t\\tproduct_test_three.save()\\n\\n\\t\\tproduct_test_four=CreateProduct(\\\"Cardamone seeds\\\",False)\\n\\t\\tproduct_test_four.product_article_code=\\\"Car01\\\"\\n\\t\\tproduct_test_four.save()\\n\\n\\t\\tresponse=self.client.get(reverse('products:index'))\\n\\t\\tself.assertQuerysetEqual(response.context['product_list'],['<Product: Almond juce>','<Product: Sunflower seeds>'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae72e9c0c790002292a6260093f8035\",\n \"score\": \"0.50070167\",\n \"text\": \"def test_account_search_value_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\\n account.entity.open()\\n self.assert_util(\\n account.entity.multiple_select.search_get_list,\\n [\\\"Option One\\\"],\\n left_args={'value': \\\"Option One\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caee960e826653c00fcc131d60b159d9\",\n \"score\": \"0.4999715\",\n \"text\": \"def textoSelecionado(selections):\\n\\t\\tfor selection in selections:\\n\\t\\t\\tif selection.a != selection.b:\\n\\t\\t\\t\\treturn True\\n\\t\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eef911b69d4832d3dee15da66f4dce7\",\n \"score\": \"0.4973122\",\n \"text\": \"def process_all(exclusion_sets, se_access_token, org_id):\\n # Get data for all exclusion sets and save to file\\n choice = json_or_csv()\\n for exclusion_set in exclusion_sets:\\n exclusion_set_data = get_exclusion_set_data(se_access_token, org_id, exclusion_set)\\n if choice == '1':\\n export_to_csv(exclusion_set_data, exclusion_set)\\n elif choice == '2':\\n export_to_json(exclusion_set_data, exclusion_set)\\n exit(\\\"Processing complete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0440164a9890726c095ff7741c4cccfd\",\n \"score\": \"0.49666315\",\n \"text\": \"def test_csvdb_two_reports(self, csvdb):\\n csvdb.add_files(BASIC_FILES)\\n csvdb.connect()\\n group_name = [path.split('/')[-1][:-4] for path in BASIC_FILES]\\n\\n csvdb_iter = csvdb.iter(HWPCModel(), False)\\n for _ in range(2):\\n report = next(csvdb_iter)\\n for group in group_name:\\n assert group in report.groups\\n\\n with pytest.raises(StopIteration) as pytest_wrapped:\\n next(csvdb_iter)\\n assert pytest_wrapped.type == StopIteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95f1b67ac01c0bae5c4255b9f7908ad\",\n \"score\": \"0.49632078\",\n \"text\": \"def test_csv_export_with_labels_only(self):\\n self._publish_single_level_repeat_form()\\n # submit 7 instances\\n for _ in range(4):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"02\\\")\\n for _ in range(2):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n self.xform.id_string,\\n remove_group_name=True,\\n include_labels_only=True,\\n include_reviews=True,\\n )\\n # pylint: disable=protected-access\\n record_count = self.xform.instances.count()\\n self.assertEqual(record_count, 7)\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n labels = next(csv_reader)\\n self.assertEqual(len(labels), 17 + len(csv_df_builder.extra_columns))\\n expected_labels = [\\n \\\"Name\\\",\\n \\\"age\\\",\\n \\\"Do you have kids?\\\",\\n \\\"Kids Name\\\",\\n \\\"Kids Age\\\",\\n \\\"Kids Name\\\",\\n \\\"Kids Age\\\",\\n \\\"5. Record your GPS coordinates.\\\",\\n \\\"_gps_latitude\\\",\\n \\\"_gps_longitude\\\",\\n \\\"_gps_altitude\\\",\\n \\\"_gps_precision\\\",\\n \\\"web_browsers/Mozilla Firefox\\\",\\n \\\"web_browsers/Google Chrome\\\",\\n \\\"web_browsers/Internet Explorer\\\",\\n \\\"web_browsers/Safari\\\",\\n \\\"instanceID\\\",\\n \\\"_id\\\",\\n \\\"_uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_date_modified\\\",\\n \\\"_tags\\\",\\n \\\"_notes\\\",\\n \\\"_version\\\",\\n \\\"_duration\\\",\\n \\\"_submitted_by\\\",\\n \\\"_total_media\\\",\\n \\\"_media_count\\\",\\n \\\"_media_all_received\\\",\\n \\\"_review_status\\\",\\n \\\"_review_comment\\\",\\n \\\"_review_date\\\",\\n ]\\n self.assertEqual(expected_labels, labels)\\n rows = []\\n for row in csv_reader:\\n rows.append(row)\\n self.assertEqual(len(rows), 7)\\n self.assertEqual(rows[4][5], NA_REP)\\n # close and delete file\\n csv_file.close()\\n os.unlink(temp_file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff325b6597d2544518f16a31eb9a6dfc\",\n \"score\": \"0.49567202\",\n \"text\": \"def select(self, competences):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d5ebf354925ef4cbeb74e46274b83b\",\n \"score\": \"0.49405387\",\n \"text\": \"def test_multiple(self):\\n header = mupub.Header(mupub.LYLoader())\\n prefix = os.path.join(os.path.dirname(__file__), TEST_DATA)\\n header.load_table_list(prefix, ['hdr-split-a.ly', 'hdr-split-b.ly'])\\n self.assertEqual(header.get_value('composer'), 'F. Sor')\\n self.assertEqual(header.get_field('title'), '12 Etudes, No. 1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b20f5870d48c933757f1251b4e9777\",\n \"score\": \"0.49400127\",\n \"text\": \"def test_select_accepts(self):\\n self.assertIsNone(self.client.select_header_accept([]))\\n accepts = ['application/vnd.ms-excel', 'application/json']\\n self.assertEqual('application/json', self.client.select_header_accept(accepts))\\n accepts = ['application/vnd.ms-excel', 'text/csv']\\n self.assertEqual(', '.join(accepts), self.client.select_header_accept(accepts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db5cc2fe3aa36961d09516b913b5d901\",\n \"score\": \"0.49351206\",\n \"text\": \"def test_selection_some_results():\\n result = [[\\\"Surname\\\", \\\"FirstName\\\", \\\"Age\\\", \\\"Salary\\\"],\\n [\\\"Verdi\\\", \\\"Nico\\\", 36, 4500],\\n [\\\"Smith\\\", \\\"Mark\\\", 40, 3900]]\\n\\n assert is_equal(result, selection(EMPLOYEES, filter_employees))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805629649568559888ef7762969645f8\",\n \"score\": \"0.49208638\",\n \"text\": \"def suite_test():\\n\\ttest(is_multiple(12,3))\\n\\ttest(is_multiple(12,4))\\n\\ttest(not is_multiple(12,5))\\n\\ttest(is_multiple(12,6))\\n\\ttest(not is_multiple(12,7))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba9464eddf8cc467afda766d500e50d\",\n \"score\": \"0.4914326\",\n \"text\": \"def parse_args_csv(self, args):\\n\\n self.report_args[\\\"csv_delimiter\\\"] = args.delimiter\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df58f36d3ae16330d559cdfcc6fe2340\",\n \"score\": \"0.48968482\",\n \"text\": \"def test_flats_selector1(self):\\n with open(flats_txt_path) as f:\\n list_of_selected_flats = f.read().splitlines() \\n try:\\n actual = type(list_of_selected_flats)\\n expected = list\\n self.assertEqual(actual,expected)\\n except:\\n print(\\\"The list_of_selected_flats is not a list. It should be a list. Check the input text file.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44651f6b528aca6d7f3ceefaa7a0d4bd\",\n \"score\": \"0.48933098\",\n \"text\": \"def get_chosen_pairs():\\n data = read_csv('training_results.csv')\\n return [subject[3] for subject in data]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd945f793b7a3b07dc0c50b6d201ca68\",\n \"score\": \"0.48770088\",\n \"text\": \"def test__export_csv(self):\\n # Create temp data\\n scalar_names = ['fake_scalar_1', 'fake_scalar_2']\\n ids = ['a_fake_id_1', 'a_fake_id_2']\\n data = OrderedDict()\\n data[ids[0]] = {scalar_names[0]: {'value': 123},\\n scalar_names[1]: {'value': 456}}\\n data[ids[1]] = {scalar_names[0]: {'value': 0.1},\\n scalar_names[1]: {'value': -12}}\\n # Write to csv file\\n _export_csv(data=data,\\n scalar_names=scalar_names,\\n output_file=self.test_file_path.name)\\n\\n # Validate csv file\\n with io.open(self.test_file_path.name, 'r', encoding='utf-8') as csvfile:\\n reader = csv.reader(csvfile)\\n rows = [row for row in reader]\\n self.assertEqual(len(rows), 3)\\n self.assertEqual(rows[0], ['id'] + scalar_names)\\n self.assertEqual(rows[1], ['a_fake_id_1', '123', '456'])\\n self.assertEqual(rows[2], ['a_fake_id_2', '0.1', '-12'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5663a18a042c3424aacf780585066a18\",\n \"score\": \"0.48741752\",\n \"text\": \"def test_splitter_shuffles(self, group_splitter, dummy_data, seed):\\n group_splitter.random_state = seed\\n train, test = group_splitter.split(dummy_data)\\n concatenated = pd.concat([train, test], axis=0)\\n # unique does not sort\\n assert np.any(concatenated.group_col.unique() != dummy_data.group_col.unique())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fceac7da37cfc5360412a3e9e1fb890\",\n \"score\": \"0.48733902\",\n \"text\": \"def split_and_check_count(data_entry_csv, expected_count):\\n\\n\\tdata_entry_csv = data_entry_csv.rstrip()\\n\\tentries = [remove_quotes(entry) for entry in data_entry_csv.split(',')]\\n\\n\\tactual_count = len(entries)\\n\\tif data_entry_csv.endswith(',') and entries[-1] == '':\\n\\t\\t# Accomodate for the SOMETIMES trailing comma\\n\\t\\tdata_entry_csv = data_entry_csv[:-1]\\n\\t\\tentries = entries[:-1]\\n\\t\\tactual_count = len(entries)\\n\\n\\tif actual_count < expected_count:\\n\\t\\t# Add empty strings to fill in the blanks.\\n\\t\\tentries += ['' for i in range(expected_count - actual_count)]\\n\\telif actual_count > expected_count:\\n\\t\\t# Crop off the excess entries.\\n\\t\\tentries = entries[:expected_count]\\n\\n\\treturn (entries, expected_count == actual_count, actual_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed09c1de21d45be9a64244f562420ed\",\n \"score\": \"0.48667133\",\n \"text\": \"def test_csv_export_remove_group_name(self):\\n self._publish_single_level_repeat_form()\\n # submit 7 instances\\n for _ in range(4):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"02\\\")\\n for _ in range(2):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n self.xform.id_string,\\n remove_group_name=True,\\n include_images=False,\\n include_reviews=True,\\n )\\n # pylint: disable=protected-access\\n record_count = self.xform.instances.count()\\n self.assertEqual(record_count, 7)\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n header = next(csv_reader)\\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\\n expected_header = [\\n \\\"name\\\",\\n \\\"age\\\",\\n \\\"has_kids\\\",\\n \\\"kids_name\\\",\\n \\\"kids_age\\\",\\n \\\"kids_name\\\",\\n \\\"kids_age\\\",\\n \\\"gps\\\",\\n \\\"_gps_latitude\\\",\\n \\\"_gps_longitude\\\",\\n \\\"_gps_altitude\\\",\\n \\\"_gps_precision\\\",\\n \\\"web_browsers/firefox\\\",\\n \\\"web_browsers/chrome\\\",\\n \\\"web_browsers/ie\\\",\\n \\\"web_browsers/safari\\\",\\n \\\"instanceID\\\",\\n \\\"_id\\\",\\n \\\"_uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_date_modified\\\",\\n \\\"_tags\\\",\\n \\\"_notes\\\",\\n \\\"_version\\\",\\n \\\"_duration\\\",\\n \\\"_submitted_by\\\",\\n \\\"_total_media\\\",\\n \\\"_media_count\\\",\\n \\\"_media_all_received\\\",\\n \\\"_review_status\\\",\\n \\\"_review_comment\\\",\\n \\\"_review_date\\\",\\n ]\\n self.assertEqual(expected_header, header)\\n rows = []\\n for row in csv_reader:\\n rows.append(row)\\n self.assertEqual(len(rows), 7)\\n self.assertEqual(rows[4][5], NA_REP)\\n # close and delete file\\n csv_file.close()\\n os.unlink(temp_file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8163200aade0eb8325d47b63d3ad859a\",\n \"score\": \"0.4865059\",\n \"text\": \"def test_csv_export_with_labels(self):\\n self._publish_single_level_repeat_form()\\n # submit 7 instances\\n for _ in range(4):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"02\\\")\\n for _ in range(2):\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n csv_df_builder = CSVDataFrameBuilder(\\n self.user.username,\\n self.xform.id_string,\\n remove_group_name=True,\\n include_labels=True,\\n include_reviews=True,\\n )\\n # pylint: disable=protected-access\\n record_count = self.xform.instances.count()\\n self.assertEqual(record_count, 7)\\n temp_file = NamedTemporaryFile(suffix=\\\".csv\\\", delete=False)\\n cursor = (\\n self.xform.instances.all().order_by(\\\"id\\\").values_list(\\\"json\\\", flat=True)\\n )\\n csv_df_builder.export_to(temp_file.name, cursor)\\n csv_file = open(temp_file.name, \\\"r\\\")\\n csv_reader = csv.reader(csv_file)\\n header = next(csv_reader)\\n self.assertEqual(len(header), 17 + len(csv_df_builder.extra_columns))\\n expected_header = [\\n \\\"name\\\",\\n \\\"age\\\",\\n \\\"has_kids\\\",\\n \\\"kids_name\\\",\\n \\\"kids_age\\\",\\n \\\"kids_name\\\",\\n \\\"kids_age\\\",\\n \\\"gps\\\",\\n \\\"_gps_latitude\\\",\\n \\\"_gps_longitude\\\",\\n \\\"_gps_altitude\\\",\\n \\\"_gps_precision\\\",\\n \\\"web_browsers/firefox\\\",\\n \\\"web_browsers/chrome\\\",\\n \\\"web_browsers/ie\\\",\\n \\\"web_browsers/safari\\\",\\n \\\"instanceID\\\",\\n \\\"_id\\\",\\n \\\"_uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_date_modified\\\",\\n \\\"_tags\\\",\\n \\\"_notes\\\",\\n \\\"_version\\\",\\n \\\"_duration\\\",\\n \\\"_submitted_by\\\",\\n \\\"_total_media\\\",\\n \\\"_media_count\\\",\\n \\\"_media_all_received\\\",\\n \\\"_review_status\\\",\\n \\\"_review_comment\\\",\\n \\\"_review_date\\\",\\n ]\\n self.assertEqual(expected_header, header)\\n labels = next(csv_reader)\\n self.assertEqual(len(labels), 17 + len(csv_df_builder.extra_columns))\\n expected_labels = [\\n \\\"Name\\\",\\n \\\"age\\\",\\n \\\"Do you have kids?\\\",\\n \\\"Kids Name\\\",\\n \\\"Kids Age\\\",\\n \\\"Kids Name\\\",\\n \\\"Kids Age\\\",\\n \\\"5. Record your GPS coordinates.\\\",\\n \\\"_gps_latitude\\\",\\n \\\"_gps_longitude\\\",\\n \\\"_gps_altitude\\\",\\n \\\"_gps_precision\\\",\\n \\\"web_browsers/Mozilla Firefox\\\",\\n \\\"web_browsers/Google Chrome\\\",\\n \\\"web_browsers/Internet Explorer\\\",\\n \\\"web_browsers/Safari\\\",\\n \\\"instanceID\\\",\\n \\\"_id\\\",\\n \\\"_uuid\\\",\\n \\\"_submission_time\\\",\\n \\\"_date_modified\\\",\\n \\\"_tags\\\",\\n \\\"_notes\\\",\\n \\\"_version\\\",\\n \\\"_duration\\\",\\n \\\"_submitted_by\\\",\\n \\\"_total_media\\\",\\n \\\"_media_count\\\",\\n \\\"_media_all_received\\\",\\n \\\"_review_status\\\",\\n \\\"_review_comment\\\",\\n \\\"_review_date\\\",\\n ]\\n self.assertEqual(expected_labels, labels)\\n rows = []\\n for row in csv_reader:\\n rows.append(row)\\n self.assertEqual(len(rows), 7)\\n self.assertEqual(rows[4][5], NA_REP)\\n # close and delete file\\n csv_file.close()\\n os.unlink(temp_file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e28b772bb2c23993990345dabe40b5c\",\n \"score\": \"0.4848573\",\n \"text\": \"def checkRepeatSelection(selList, checkSwitchJointList, checkMessage, isJoint=True):\\n\\tvalidSelection = True\\n\\tcheckList = checkSwitchJointList\\n\\tif isJoint: checkList = [sJ.getName() for sJ in checkSwitchJointList] #Collect Names since we have switch Joints, we need names to comapre to the selection\\n\\tintersectionSet = set(selList).intersection(set(checkList)) #Form a set from the intersection of these lists, if Items are in the set, then then they are repeated! \\n\\tif len(intersectionSet) > 0:\\n\\t\\tvalidSelection = False\\n\\t\\tcmds.warning('Incorrect Initial Selection - You have selected some items that match those in the ' + checkMessage + \\\" List\\\")#If we do not have 2 joints selected then exit\\n\\treturn validSelection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c235cda361a0ba0f7596de7f29fd64\",\n \"score\": \"0.48452136\",\n \"text\": \"def test_format_mongo_data_for_csv(self):\\n self.maxDiff = None\\n self._publish_single_level_repeat_form()\\n self._submit_fixture_instance(\\\"new_repeats\\\", \\\"01\\\")\\n data_0 = self._csv_data_for_dataframe()[0]\\n # remove AbstractDataFrameBuilder.INTERNAL_FIELDS\\n for key in AbstractDataFrameBuilder.IGNORED_COLUMNS:\\n if key in data_0:\\n data_0.pop(key)\\n for key in AbstractDataFrameBuilder.ADDITIONAL_COLUMNS:\\n if key in data_0:\\n data_0.pop(key)\\n expected_data_0 = {\\n \\\"gps\\\": \\\"-1.2627557 36.7926442 0.0 30.0\\\",\\n \\\"_gps_latitude\\\": \\\"-1.2627557\\\",\\n \\\"_gps_longitude\\\": \\\"36.7926442\\\",\\n \\\"_gps_altitude\\\": \\\"0.0\\\",\\n \\\"_gps_precision\\\": \\\"30.0\\\",\\n \\\"kids/has_kids\\\": \\\"1\\\",\\n \\\"info/age\\\": 80,\\n \\\"kids/kids_details[1]/kids_name\\\": \\\"Abel\\\",\\n \\\"kids/kids_details[1]/kids_age\\\": 50,\\n \\\"kids/kids_details[2]/kids_name\\\": \\\"Cain\\\",\\n \\\"kids/kids_details[2]/kids_age\\\": 76,\\n \\\"web_browsers/chrome\\\": True,\\n \\\"web_browsers/ie\\\": True,\\n \\\"web_browsers/safari\\\": False,\\n \\\"web_browsers/firefox\\\": False,\\n \\\"info/name\\\": \\\"Adam\\\",\\n \\\"_xform_id\\\": self.xform.pk,\\n }\\n self.assertEqual(expected_data_0, data_0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a335e2451ab16b8c8fb8f4be6add8da6\",\n \"score\": \"0.48365575\",\n \"text\": \"def get_commaselectlist(self):\\n return re.split(r'[,]+',self.comma_select.strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862b77ef3652813c3c0dab51f4a1e1e0\",\n \"score\": \"0.48311725\",\n \"text\": \"def log_selection_trace(self):\\n self.logger.info(\\\"Writing selection logs (exclusions and special \\\"\\n \\\"warnings).\\\")\\n\\n excluded_filename = self.parameters[\\\"OUTPUT_PREFIX\\\"] + \\\".excluded.log\\\"\\n special_incl_filename = (\\n self.parameters[\\\"OUTPUT_PREFIX\\\"] + \\\".special.log\\\"\\n )\\n\\n variant_sets = [\\n (excluded_filename, self.excluded),\\n (special_incl_filename, self.special)\\n ]\\n\\n for filename, variant_set in variant_sets:\\n with open(filename, \\\"w\\\") as f:\\n writer = csv.writer(f)\\n writer.writerow([\\\"name\\\", \\\"chrom\\\", \\\"pos\\\", \\\"alleles\\\", \\\"reason\\\",\\n \\\"details\\\"])\\n\\n for variant, reason, details in variant_set:\\n alleles = \\\"/\\\".join(variant.alleles)\\n writer.writerow([\\n variant.name, variant.chrom, variant.pos,\\n alleles, reason, details\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62c9f4fd7661c9de4a8b10c399f7873a\",\n \"score\": \"0.48307753\",\n \"text\": \"def test_multiple_extract(self):\\n to_extract = {\\\"rs785467\\\", \\\"rs140543381\\\"}\\n reader = self.reader_f()\\n extractor = Extractor(reader, names=to_extract)\\n\\n for i in range(2):\\n seen = set()\\n for genotype in extractor.iter_genotypes():\\n name = genotype.variant.name\\n truth = truth_genotypes[name]\\n seen.add(name)\\n\\n self.assertEqual(genotype.variant, truth.variant)\\n self.assertEqual(genotype.reference, truth.reference)\\n self.assertEqual(genotype.coded, truth.coded)\\n np.testing.assert_array_equal(genotype.genotypes,\\n truth.genotypes)\\n\\n self.assertEqual(seen, to_extract)\\n\\n reader.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d009c0a67cc789b468b5080262daff81\",\n \"score\": \"0.48097306\",\n \"text\": \"def test_indewview_with_multiple_selected_product(self):\\n\\t\\tproduct_test_one=CreateProduct(\\\"Sunflower seeds\\\",True)\\n\\t\\tproduct_test_one.product_article_code=\\\"Sun01\\\"\\n\\t\\tproduct_test_one.save()\\n\\n\\t\\tproduct_test_two=CreateProduct(\\\"Almond juce\\\",True)\\n\\t\\tproduct_test_two.product_article_code=\\\"Alm01\\\"\\n\\t\\tproduct_test_two.save()\\n\\n\\t\\tresponse=self.client.get(reverse('products:index'))\\n\\t\\tself.assertQuerysetEqual(response.context['product_list'],['<Product: Almond juce>','<Product: Sunflower seeds>'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031b105143b70afcc61fc1c3a092a190\",\n \"score\": \"0.4809421\",\n \"text\": \"def test_choice_duplicate_error(self):\\n count = XForm.objects.count()\\n xls_path = os.path.join(\\n self.this_directory, 'fixtures', 'cascading_selects',\\n 'duplicate_choice_form.xlsx')\\n msg = (\\n \\\"The name column for the 'counties' choice list\\\"\\n \\\" contains these duplicates: 'king'. Duplicate \\\"\\n \\\"names will be impossible to identify in analysis\\\"\\n \\\" unless a previous value in a cascading select \\\"\\n \\\"differentiates them. If this is intentional, \\\"\\n \\\"you can set the allow_choice_duplicates \\\"\\n \\\"setting to 'yes'. \\\"\\n \\\"Learn more: https://xlsform.org/#choice-names.\\\")\\n self.assertRaisesMessage(\\n PyXFormError, msg, self._publish_xls_file, xls_path)\\n self.assertEqual(XForm.objects.count(), count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e028a5c7836db94015acc4312b6fefb\",\n \"score\": \"0.4808766\",\n \"text\": \"def test_account_required_field_example_multiple_select(self, ucc_smartx_selenium_helper, ucc_smartx_rest_helper):\\n account = AccountPage(ucc_smartx_selenium_helper, ucc_smartx_rest_helper)\\n account.entity.open()\\n account.entity.name.set_value(ACCOUNT_CONFIG[\\\"name\\\"])\\n account.entity.environment.select(\\\"Value2\\\")\\n account.entity.username.set_value(\\\"TestEditUser\\\")\\n account.entity.password.set_value(\\\"TestEditPassword\\\")\\n account.entity.security_token.set_value(\\\"TestEditToken\\\")\\n account.entity.account_radio.select(\\\"No\\\")\\n self.assert_util(\\n account.entity.save,\\n 'Field Example Multiple Select is required',\\n left_args={'expect_error': True}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"720d052d66be58de1f6df8ec55fe5729\",\n \"score\": \"0.48055178\",\n \"text\": \"def test_export_fac_csv(self):\\n client = Client()\\n response = client.get(\\\"/export/advanced-search/fac/csv?name__contains=Facility\\\")\\n self.assertEqual(\\n response.content.replace(\\\"\\\\r\\\\n\\\", \\\"\\\\n\\\").rstrip(),\\n self.expected_data(\\\"fac\\\", \\\"csv\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60f8120dc988e2d918eb3582dc97081\",\n \"score\": \"0.48021328\",\n \"text\": \"def test_lang_choices_multiple_newsletters(self):\\r\\n form = NewsletterFooterForm('join-mozilla,firefox-tips', 'en-US')\\r\\n choices = [lang[0] for lang in form.fields['lang'].choices]\\r\\n self.assertEqual(choices, ['de', 'en', 'es', 'fr', 'pt', 'ru'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6465fdd3edbe4f68464a316a944870be\",\n \"score\": \"0.4801099\",\n \"text\": \"def test_csv_row_to_enquiry_filter(self):\\n csv_data = create_fake_enquiry_csv_row()\\n expected_kwargs = {\\n key.replace(\\\"enquirer_\\\", \\\"enquirer__\\\"): value for key, value in csv_data.items()\\n }\\n\\n qs_kwargs = utils.csv_row_to_enquiry_filter_kwargs(csv_data)\\n\\n self.assertEqual(len(expected_kwargs.keys()), len(qs_kwargs.keys()))\\n\\n for key in expected_kwargs:\\n self.assertTrue(key in qs_kwargs)\\n self.assertEqual(qs_kwargs[key], expected_kwargs[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4583e486d985260a584be595ee9a9c6\",\n \"score\": \"0.47994888\",\n \"text\": \"def test_iter_multiple(multiple_list):\\n assert list(iter(multiple_list)) == [\\\"value 1\\\", \\\"value 2\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0daa03fee1c8b6d96a9fe2f0d9562779\",\n \"score\": \"0.47833183\",\n \"text\": \"def multiple():\\n with timed('multiple (%d values)' % VALUES):\\n\\n script = [TPL_MANY]\\n\\n for i in range(VALUES):\\n\\n key = 'BENCH_{}'.format(i)\\n value = 'VAL_SINGLE_{}'.format(i)\\n\\n script.append(TPL_LINE.format(key=key, value=value, bid=BUNDLE_ID))\\n\\n script = '\\\\n'.join(script)\\n\\n run_script(script)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"360082979eb16f76782d9172bc7c98c2\",\n \"score\": \"0.4744839\",\n \"text\": \"def test_few_shot_subset(self):\\n\\n def helper(n_shot, seed):\\n return self.dump_teacher_text(\\n test_agents.SystemTeacher,\\n test_agents.EPISODE_SETUP__MULTI_EPISODE,\\n {\\\"episodes_randomization_seed\\\": seed, \\\"n_shot\\\": n_shot},\\n )\\n\\n data_dumps_seed_zero = [helper(i, 0) for i in self.FEW_SHOT_SAMPLES]\\n self._test_subsets(data_dumps_seed_zero)\\n data_dumps_seed_three = [helper(i, 3) for i in self.FEW_SHOT_SAMPLES]\\n self._test_subsets(data_dumps_seed_three)\\n self.assertNotEqual(data_dumps_seed_zero[-1], data_dumps_seed_three[-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf53d133f53aa64a5a572eec53fc491\",\n \"score\": \"0.47366884\",\n \"text\": \"def test_write_csv(self):\\n\\t\\tpath = profiles_and_competitions_synchronizer.ProfilesAndCompetitionsSynchronizer().write_csv('../data/test_allProfilesWithCompetitions.csv')\\n\\t\\tcsvContent = csv_handler.CsvHandler().read_csv(path, 'r', 'latin-1')\\n\\t\\tos.remove('../data/test_allProfilesWithCompetitions.csv')\\n\\t\\tself.assertEqual(len(csvContent), 3782)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945bce42f679fc45b09c4a4398e35dcc\",\n \"score\": \"0.4726768\",\n \"text\": \"def determine_multiple(vals, mrn, data_type):\\n if len(vals) > 1:\\n raise ValueError('MRN: {0:d} has more than one {1:s}. Check data and try again.'.format(mrn, data_type))\\n else:\\n for vals in vals:\\n break\\n return vals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43bbcf0229b7daddfe645e2df666ce7f\",\n \"score\": \"0.4717738\",\n \"text\": \"def test_resource_output_module_resource_export_csvpost(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9271ee47c7b5bb785026ca59574381d\",\n \"score\": \"0.47153243\",\n \"text\": \"def test_with_comma_delimited_multiple_words(self):\\n\\n self.assertEqual(parse_tag_input(',one'), ['one'])\\n self.assertEqual(parse_tag_input(',one two'), ['one two'])\\n self.assertEqual(parse_tag_input(',one two three'), ['one two three'])\\n self.assertEqual(parse_tag_input('a-one, a-two and a-three'),\\n ['a-one', 'a-two and a-three'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c5ab222ef8c789cad1eacba957533a\",\n \"score\": \"0.47057676\",\n \"text\": \"def getSubsetFiles( self, data_path ):\\n\\n # load file as csv and shuffle\\n df = pd.read_csv( os.path.join( data_path, 'train_v2.csv' ) )\\n df = pd_shuffle( df )\\n\\n split_index = int( len( df ) * 0.8 )\\n\\n # assign 80% records as training\\n train = df [ 0: split_index ]\\n train.to_csv( os.path.join( data_path, 'train.csv' ), index=False )\\n \\n # assign remaining 20% records as validation / test\\n test = df [ split_index : len( df ) ]\\n test.to_csv( os.path.join( data_path, 'test.csv' ), index=False )\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c06f75636d605403060ed9c11b7d35\",\n \"score\": \"0.47006464\",\n \"text\": \"def select_features(csv_file):\\n\\n with open(csv_file) as f:\\n available_features = \\\"\\\"\\n\\n reader = csv.reader(f)\\n row1 = next(reader)\\n\\n for feature in row1:\\n if feature == \\\"\\\":\\n pass\\n else:\\n available_features += (feature + \\\", \\\")\\n\\n print(\\\"Available variables: \\\", available_features)\\n input_features = input(\\\"Select feature(s) (separate with commas): \\\").replace(' ', '').strip(\\\"\\\\n\\\").split(\\\",\\\") \\n\\n if input_features == [\\\"all\\\"]:\\n return row1\\n elif all(elem in row1 for elem in input_features):\\n if \\\"diabetes\\\" not in input_features:\\n input_features.append(\\\"diabetes\\\") #Asserts diabetes column at the end\\n return input_features\\n else:\\n print(\\\"One or more of the features are not available. Please try again.\\\")\\n return select_features(csv_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd5acfb121867fb7e6892196a097b864\",\n \"score\": \"0.4687272\",\n \"text\": \"def select_multiple_rows_from_compound_reference_price_list_grid(self):\\n is_selected = None\\n try:\\n self.logger.info('Start: select multiple rows from compound reference price list grid')\\n self._price_page.select_multiple_rows_from_compound_reference_price_list_grid()\\n is_selected = True\\n except WebDriverException as exp:\\n is_selected = False\\n self.logger.error(exp.msg)\\n raise\\n finally:\\n self.logger.info('End: select multiple rows from compound reference price list grid')\\n return is_selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fea15d0f59147daea7fb70d2b79a44b\",\n \"score\": \"0.46828946\",\n \"text\": \"def test_edx_multi_choice_with_named_choices(self):\\n items_endpoint = '{0}/items'.format(self.url)\\n\\n # Use POST to create an item--right now user is Learner,\\n # so this should throw unauthorized\\n item_name = 'a really complicated item'\\n item_desc = 'meant to differentiate students'\\n question_body = 'can you manipulate this?'\\n question_choices = [\\n {'text' : 'I hope so.',\\n 'name' : 'yes'},\\n {'text' : 'I don\\\\'t think I can.',\\n 'name' : 'no'},\\n {'text' : 'Maybe tomorrow.',\\n 'name' : 'maybe'}\\n ]\\n question_type = 'question-record-type%3Amulti-choice-edx%40ODL.MIT.EDU'\\n answer = 2\\n answer_type = 'answer-record-type%3Amulti-choice-edx%40ODL.MIT.EDU'\\n payload = {\\n \\\"name\\\" : item_name,\\n \\\"description\\\" : item_desc,\\n \\\"question\\\" : {\\n \\\"type\\\" : question_type,\\n \\\"questionString\\\" : question_body,\\n \\\"choices\\\" : question_choices\\n },\\n \\\"answers\\\" : [{\\n \\\"type\\\" : answer_type,\\n \\\"choiceId\\\" : answer\\n }],\\n }\\n req = self.app.post(items_endpoint,\\n params=json.dumps(payload),\\n headers={'content-type': 'application/json'})\\n self.ok(req)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da12dc5f266e455c4774468e39a17a40\",\n \"score\": \"0.4668556\",\n \"text\": \"def test_api_create_csv_example(self):\\n url = self.get_url(\\\"list\\\")\\n data = b'\\\"Name\\\",\\\"Title\\\",\\\"level\\\"\\\\n\\\"Guido\\\",\\\"BDFL\\\",20\\\\n\\\\n\\\"Catherine\\\",,9,\\\"DBA\\\"\\\\n,\\\\n\\\"Tony\\\",\\\"Engineer\\\",10\\\\n'\\n token = \\\"this1s@t0k3n\\\"\\n self.client.credentials(HTTP_AUTHORIZATION=\\\"Token \\\" + token)\\n response = self.client.post(url, data, content_type=\\\"text/csv\\\")\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n result = json.loads(response.content)[\\\"validation_results\\\"]\\n self.assertFalse(result[\\\"valid\\\"])\\n self.assertEqual(len(result[\\\"tables\\\"]), 1)\\n self.assertEqual(result[\\\"tables\\\"][0][\\\"invalid_row_count\\\"], 3)\\n self.assertEqual(result[\\\"tables\\\"][0][\\\"valid_row_count\\\"], 2)\\n self.assertEqual(result[\\\"tables\\\"][0][\\\"whole_table_errors\\\"], [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a380b41ff713a3888c071ed5dc7f739a\",\n \"score\": \"0.46671355\",\n \"text\": \"def __splitMulti(self):\\n return self.string.split(\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7fe258911e8a2014ffa6a0232678be\",\n \"score\": \"0.46520945\",\n \"text\": \"def test_selection_counter(self):\\r\\n response = self.client.get('/test_admin/admin/admin_views/subscriber/')\\r\\n self.assertContains(response, '0 of 2 selected')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"191bffbd3ba9e7ceb9428505e22e59a2\",\n \"score\": \"0.46509922\",\n \"text\": \"def test_build_valid_extra_line_on_sample_list(self):\\n sheet_file = path.join(path_to_module, \\\"fake_dir_data\\\",\\n \\\"SampleList_with_space.csv\\\")\\n\\n sequencing_run = sample_parser.build_sequencing_run_from_samples(sheet_file)\\n\\n # Returns a SequencingRun\\n self.assertEqual(type(sequencing_run), model.SequencingRun)\\n # Includes 2 projects\\n self.assertEqual(len(sequencing_run.project_list), 2)\\n # is of type Project\\n self.assertEqual(type(sequencing_run.project_list[0]), model.Project)\\n # Project has 2 samples\\n self.assertEqual(len(sequencing_run.project_list[0].sample_list), 2)\\n # Other Project has 1 sample\\n self.assertEqual(len(sequencing_run.project_list[1].sample_list), 1)\\n # samples are of type Sample\\n self.assertEqual(type(sequencing_run.project_list[0].sample_list[0]), model.Sample)\\n # samples have SequenceFile\\n self.assertEqual(type(sequencing_run.project_list[0].sample_list[0].sequence_file), model.SequenceFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49aaef794e37d79f1f77a471e7f7dd86\",\n \"score\": \"0.46507782\",\n \"text\": \"def test_different_feature_selections():\\n\\n f_selections = ['GST syn', 'LCS syn', 'TO syn', 'GST', 'TO', 'LCS',\\n 'LCSubstring', 'Word Lengths', 'Word Level Levenshtein',\\n 'String Matching', 'NE Coupling']\\n\\n feature_files = os.listdir(slash.join(processed_news_features_path))\\n best_c_indeces = [75, 4, 61, 33, 104]\\n best_corpora = [(feature_files[i], i) for i in best_c_indeces]\\n\\n\\n with open(slash.join(saved_models_path + ['test_different_features_scores_file_new.csv']), 'w', newline='') as f:\\n writer = csv.writer(f, delimiter=';')\\n # add featureselection\\n\\n header = ['corpusnum', 'loss', 'accuracy', 'loss on meter', 'accuracy on meter',\\n 'GST syn', 'LCS syn', 'TO syn', 'GST', 'TO', 'LCS',\\n 'LCSubstring', 'Word Lengths', 'Word Level Levenshtein',\\n 'String Matching', 'NE Coupling']\\n\\n writer.writerow(header)\\n modelnum = 0\\n for c_name in best_corpora:\\n f_name = c_name[0]\\n num = c_name[1]\\n for i in range(5, 12):\\n new_model_row = [0 for _ in list(range(len(header)))]\\n f = f_selections[:i]\\n start = time.time()\\n model, score, rounded, y = classify(f, 0.1, f_name)\\n loss, accuracy = score\\n X_train, X_test, y_train, y_test = get_meter_features(model, 0.1, f, None)\\n loss_on_meter, accuracy_on_meter = meter_classify(X_train, X_test, y_train, y_test)\\n\\n new_model_row[0] = num\\n new_model_row[1] = loss\\n new_model_row[2] = accuracy\\n new_model_row[3] = loss_on_meter\\n new_model_row[4] = accuracy_on_meter\\n new_model_row[5] = 1 if 'GST syn' in f else 0\\n new_model_row[6] = 1 if 'LCS syn' in f else 0\\n new_model_row[7] = 1 if 'TO syn' in f else 0\\n new_model_row[8] = 1 if 'GST' in f else 0\\n new_model_row[9] = 1 if 'TO' in f else 0\\n new_model_row[10] = 1 if 'LCS' in f else 0\\n new_model_row[11] = 1 if 'LCSSubstring' in f else 0\\n new_model_row[12] = 1 if 'Word Lenghts' in f else 0\\n new_model_row[13] = 1 if 'Word Level Levenshtein' in f else 0\\n new_model_row[14] = 1 if 'String Matching' in f else 0\\n new_model_row[15] = 1 if 'NE Coupling' in f else 0\\n\\n writer.writerow(new_model_row)\\n\\n print(modelnum, 'done in', time.time() - start)\\n K.clear_session()\\n modelnum += 1\\n if modelnum == 3:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef43719649cc745adf6c03c702281b75\",\n \"score\": \"0.4644914\",\n \"text\": \"def _check_parametrize_names(\\n self, node: ast.Call, names: ast.AST\\n ) -> Optional[bool]:\\n\\n multiple_names: Optional[bool] = None\\n found_type: Optional[ParametrizeNamesType] = None\\n if isinstance(names, ast.Str):\\n if ',' in names.s:\\n found_type = ParametrizeNamesType.CSV\\n multiple_names = True\\n else:\\n multiple_names = False\\n elif isinstance(names, (ast.List, ast.Tuple)):\\n multiple_names = len(names.elts) > 1\\n if not multiple_names:\\n self.error_from_node(\\n ParametrizeNamesWrongType, node, expected_type='string'\\n )\\n elif isinstance(names, ast.Tuple):\\n found_type = ParametrizeNamesType.TUPLE\\n else:\\n found_type = ParametrizeNamesType.LIST\\n if multiple_names and found_type != self.config.parametrize_names_type:\\n self.error_from_node(\\n ParametrizeNamesWrongType,\\n node,\\n expected_type=self.config.parametrize_names_type.value,\\n )\\n return multiple_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e95d93ef4108c34aaa031632794842c\",\n \"score\": \"0.4644207\",\n \"text\": \"def multiple_modal_select_browser(app, input_list, title):\\r\\n valid_selection = False\\r\\n is_folder = False\\r\\n\\r\\n while valid_selection == False:\\r\\n selection = app.ShowModalSelectBrowser(input_list, title)\\r\\n sel_len = len(selection)\\r\\n\\r\\n if sel_len == 0:\\r\\n return None\\r\\n elif sel_len >= 2:\\r\\n for sel in selection:\\r\\n if sel.GetClassName() == 'IntFolder':\\r\\n is_folder = True\\r\\n if is_folder:\\r\\n logger.error('Cannot select multiple objects if one is a folder. Please try again.')\\r\\n valid_selection = False\\r\\n else:\\r\\n valid_selection = True\\r\\n\\r\\n elif selection[0].GetClassName() == 'IntFolder':\\r\\n input_list = selection[0].GetContents()\\r\\n valid_selection = False\\r\\n else:\\r\\n valid_selection = True\\r\\n\\r\\n\\r\\n return selection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"264369fde1a0cde36d3cfdfbef7bf7d7\",\n \"score\": \"0.464374\",\n \"text\": \"def test_multiple(self):\\n f = list(MinimalFastaParser(self.threeseq))\\n self.assertEqual(len(f), 3)\\n a, b, c = f\\n self.assertEqual(a, (\\\"123\\\", \\\"a\\\"))\\n self.assertEqual(b, (\\\"abc\\\", \\\"caggac\\\"))\\n self.assertEqual(c, (\\\"456\\\", \\\"cg\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505bcff9be2454d1d0391a020d006c13\",\n \"score\": \"0.4640549\",\n \"text\": \"def _separate_sets(filenames, validation_percent=0.2):\\n filenames_rand = filenames\\n np.random.shuffle(filenames_rand)\\n num_total_files = len(filenames_rand)\\n num_validation_files = int(num_total_files * validation_percent) or 1\\n validation_set = filenames_rand[:num_validation_files]\\n train_set = filenames_rand[num_validation_files:]\\n\\n return train_set, validation_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00dc7dedb3cb640f92ddc91f5fc5f52d\",\n \"score\": \"0.46349183\",\n \"text\": \"def _validates_choices_should_be_unique_and_nonempty(\\n self, choices: List[SubtitledHtml]\\n ) -> None:\\n seen_choices = []\\n for choice in choices:\\n if html_cleaner.is_html_empty(choice.html):\\n raise utils.ValidationError(\\n 'Choices should be non empty.'\\n )\\n\\n if choice.html not in seen_choices:\\n seen_choices.append(choice.html)\\n else:\\n raise utils.ValidationError(\\n 'Choices should be unique.'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96181655de7b8f3fad0dad94223e2711\",\n \"score\": \"0.46337745\",\n \"text\": \"def file_picker(self):\\n d = self.data\\n needed_files = []\\n self.needed_columns = set()\\n counties = \\\"data/us-counties.csv\\\"\\n tests = \\\"data/tests-by-state.csv\\\"\\n for category in self.categories:\\n if category in d[\\\"aggregate\\\"].keys():\\n for field in d[\\\"aggregate\\\"][category]:\\n self.needed_columns.add(field)\\n if (\\n field in self.fields[\\\"counties\\\"]\\n and counties not in needed_files\\n ):\\n needed_files.append(counties)\\n if field in self.fields[\\\"tests\\\"] and tests not in needed_files:\\n needed_files.append(tests)\\n return needed_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02be37b8cc56bd00df8bbdf485c3b993\",\n \"score\": \"0.46270669\",\n \"text\": \"def writebusinessdata():\\r\\n with open(\\\"business_data.csv\\\", \\\"w\\\") as f:\\r\\n f.write(main.csv_mutliline)\\r\\n print(\\\"Wrote multi-line string data to ./business_data.csv\\\")\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f10705f7dbd0a10198943b380c4a70\",\n \"score\": \"0.46228066\",\n \"text\": \"def choices(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81b3b049c66abd7d42562465b9f9838\",\n \"score\": \"0.46153408\",\n \"text\": \"def test_csv_quoting(self):\\n resp = self.get_response({'formato': 'csv'},\\n return_value='raw').decode()\\n first_row = resp.splitlines()[1] # Saltear la cabecera CSV\\n first_value = first_row.split(formatter.CSV_SEP)[0] # ID de la fila\\n\\n self.assertTrue(first_value.startswith('\\\"') and\\n first_value.endswith('\\\"') and\\n len(first_value) > 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e934893eb261b6579570c1b6c54a34b\",\n \"score\": \"0.4614544\",\n \"text\": \"def test_chooseSettingValue(self):\\n def csv(**kwargs):\\n return _chooseSettingValue(db.LectureGlobalSetting(**kwargs))\\n\\n # Fixed values return None, not the value in question\\n self.assertEqual(csv(value=4), None)\\n\\n # Random values are all within bounds\\n for x in xrange(LOTS_OF_TESTS):\\n out = float(csv(max=100))\\n self.assertTrue(out >= 0)\\n self.assertTrue(out < 100)\\n for x in xrange(LOTS_OF_TESTS):\\n out = float(csv(min=90, max=100))\\n self.assertTrue(out >= 90)\\n self.assertTrue(out < 100)\\n\\n # Can't specify shape without value\\n with self.assertRaisesRegexp(ValueError, 'but no corresponding value'):\\n csv(key='foo', shape=8)\\n\\n # Gamma values hit the mean\\n out = 0\\n for x in xrange(LOTS_OF_TESTS):\\n out += float(csv(value=1000000, shape=2))\\n out = out / LOTS_OF_TESTS\\n self.assertTrue(abs(out - 2000000) < 5000)\\n\\n # String values come out unaltered, but can't be randomly chosen\\n self.assertEqual(csv(key=\\\"iaa_type\\\", value=\\\"fun-size\\\"), None)\\n with self.assertRaisesRegexp(ValueError, 'iaa_type'):\\n csv(key=\\\"iaa_type\\\", value=\\\"fun-size\\\", shape=2)\\n with self.assertRaisesRegexp(ValueError, 'iaa_type'):\\n csv(key=\\\"iaa_type\\\", value=\\\"fun-size\\\", max=4)\\n\\n # Integer settings get rounded, don't have \\\"3.0\\\" at end\\n for x in xrange(LOTS_OF_TESTS):\\n self.assertIn(csv(key=\\\"grade_nmin\\\", max=9), '0 1 2 3 4 5 6 7 8 9'.split())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3ea6c30ebf4f896213aa0a0a86071b\",\n \"score\": \"0.46059546\",\n \"text\": \"def test_clean_dois(self):\\n\\n inputs = [\\n {\\\"dois\\\": \\\"10.1093/nar/gks419\\\\n10.1093/nar/gkp323\\\"},\\n {\\\"dois\\\": \\\"10.1093/nar/gkp323\\\\n10.1093/nar/gks419\\\"},\\n {\\\"dois\\\": \\\"10.1093/nar/gkp323,\\\\n10.1093/nar/gks419\\\"},\\n {\\\"dois\\\": \\\"10.1093/nar/gkp323,10.1093/nar/gks419\\\"},\\n {\\\"dois\\\": \\\"10.1093/nar/gkp323,,10.1093/nar/gks419\\\"},\\n {\\\"dois\\\": \\\"10.1093/nar/gkp323\\\\n\\\\n10.1093/nar/gks419\\\"},\\n ]\\n expected = [\\\"10.1093/nar/gkp323\\\", \\\"10.1093/nar/gks419\\\"]\\n\\n for input in inputs:\\n\\n form = EntryBatchImportForm(input)\\n assert form.is_valid()\\n\\n dois = form.cleaned_data[\\\"dois\\\"]\\n dois.sort()\\n assert dois == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b56533253c1d64e515cfdfeba3e980f\",\n \"score\": \"0.46044\",\n \"text\": \"def select_multiple_rows_from_customer_price_exception_request_grid(self):\\n is_selected = None\\n try:\\n self.logger.info('Start: select multiple rows from customer price exception request grid')\\n self._price_page.select_multiple_rows_from_customer_price_exception_request_grid()\\n is_selected = True\\n except WebDriverException as exp:\\n is_selected = False\\n self.logger.error(exp.msg)\\n raise\\n finally:\\n self.logger.info('End: select multiple rows from customer price exception request grid')\\n return is_selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83edf8be6ca57fc27ed5c76218f6eb41\",\n \"score\": \"0.46023008\",\n \"text\": \"def get_choices(self):\\n choice = \\\"\\\"\\n (quote, unquote) = (\\\"`\\\", \\\"`\\\")\\n quoted = False\\n for char in self.choices:\\n if not quoted and char == quote:\\n quoted = True\\n elif quoted and char == unquote:\\n quoted = False\\n elif char == \\\",\\\" and not quoted:\\n choice = choice.strip()\\n if choice:\\n yield choice, choice\\n choice = \\\"\\\"\\n else:\\n choice += char\\n choice = choice.strip()\\n if choice:\\n yield choice, choice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c5bb9a4affff4917d595675a984107\",\n \"score\": \"0.46016142\",\n \"text\": \"def test_model_multiple_choice_run_validators(self):\\r\\n for i in range(30):\\r\\n Person.objects.create(name=\\\"Person %s\\\" % i)\\r\\n\\r\\n self._validator_run = False\\r\\n def my_validator(value):\\r\\n self._validator_run = True\\r\\n\\r\\n f = forms.ModelMultipleChoiceField(queryset=Person.objects.all(),\\r\\n validators=[my_validator])\\r\\n\\r\\n f.clean([p.pk for p in Person.objects.all()[8:9]])\\r\\n self.assertTrue(self._validator_run)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994384,"cells":{"query_id":{"kind":"string","value":"292a4ae4224521db4e5539e2fe40e59b"},"query":{"kind":"string","value":"Displays the icon according to the operating system"},"positive_passages":{"kind":"list like","value":[{"docid":"410c25c1b09950d9e108c44cfe05ced7","score":"0.6957155","text":"def icones(self):\r\n if \"nt\" == name:\r\n self.root.iconbitmap(\"icons/icon.ico\")\r\n else:\r\n img = Image(\"photo\", file=\"icons/icon.png\")\r\n self.root.tk.call(\"wm\", \"iconphoto\", self.root._w, img)","title":""}],"string":"[\n {\n \"docid\": \"410c25c1b09950d9e108c44cfe05ced7\",\n \"score\": \"0.6957155\",\n \"text\": \"def icones(self):\\r\\n if \\\"nt\\\" == name:\\r\\n self.root.iconbitmap(\\\"icons/icon.ico\\\")\\r\\n else:\\r\\n img = Image(\\\"photo\\\", file=\\\"icons/icon.png\\\")\\r\\n self.root.tk.call(\\\"wm\\\", \\\"iconphoto\\\", self.root._w, img)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9e388977a27612c28387556e97934387","score":"0.68452096","text":"def icon():\n return \"icon.png\"","title":""},{"docid":"5e69561bb995b86026c5057fc57ea017","score":"0.6736521","text":"def icon(self):\n return \"as_ico_command.png\"","title":""},{"docid":"76a6a6bb204fb3c418427f5515c142ab","score":"0.667186","text":"def _icon(self, name, *args):\n myname = icons(\"32x32\", name)\n if os.path.exists(myname):\n return QtGui.QIcon(myname)\n else:\n return super()._icon(name, *args)","title":""},{"docid":"733d3b7979672fcd653489ca29da28c8","score":"0.6571517","text":"def run_tray_icon():\n SysTrayIcon.SysTrayIcon(resource_path('res/icon_vector.ico'), \"lifx_control_panel\",\n tray_options,\n on_quit=lambda_factory(self))","title":""},{"docid":"648f5e4c9562f5407a0aca6e89933373","score":"0.64446527","text":"def icon(self):\n\t\treturn ICON","title":""},{"docid":"92d7d8d1761034c86c1faea21b39c7a0","score":"0.63868445","text":"def icon(self):\n return TEMP_C_ICON","title":""},{"docid":"9a1a7916d9e5df2d4b412ffd90918e46","score":"0.63360023","text":"def icon(self):\n return \"as_ico_macro.png\"","title":""},{"docid":"ae196c8d423ab95599b014f7faadfde7","score":"0.6330747","text":"def platform_config():\n if sys.platform == 'win32' and mainmodel.is_win7():\n # Set taskbar icon to match application's icon\n # Code courtesy DamonJW\n # http://stackoverflow.com/questions/1551605/how-to-set-applications-taskbar-icon\n import ctypes\n\n myappid = 'tri.nditoolbox.bane.1' # arbitrary string\n ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(myappid)","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"a55263b8f361d5b5f111741fd6d5acbe","score":"0.6228661","text":"def icon(self):\n return ICON","title":""},{"docid":"bada5bfda56c923c35020d0e0f5cd895","score":"0.62069595","text":"def icon(self) -> str:\n return \"mdi:script-text-outline\" # Matches isy program icon","title":""},{"docid":"bd7230e1de0ab5e60e12cce2d93facc2","score":"0.6194612","text":"def icon(self) -> str:\n return \"mdi:server\"","title":""},{"docid":"bd7230e1de0ab5e60e12cce2d93facc2","score":"0.6194612","text":"def icon(self) -> str:\n return \"mdi:server\"","title":""},{"docid":"bd7230e1de0ab5e60e12cce2d93facc2","score":"0.6194612","text":"def icon(self) -> str:\n return \"mdi:server\"","title":""},{"docid":"e43454d0a7fb54e52ae06454d5dc4f79","score":"0.6175948","text":"def GetIcon(self, *args, **kwargs):\n pass","title":""},{"docid":"2ba88097a7aa7496124f5bb78b72391b","score":"0.6172751","text":"def tray(self):\n self.tray_icon = QtWidgets.QSystemTrayIcon(self)\n self.tray_icon.setIcon(QtGui.QIcon(ICON_FILE))\n self.tray_icon.setToolTip('MyApp') # Pop up lettering icons\n\n show_action = QtWidgets.QAction(\"Show\", self)\n hide_action = QtWidgets.QAction(\"Hide\", self)\n quit_action = QtWidgets.QAction(\"Exit\", self)\n show_action.triggered.connect(self.show)\n hide_action.triggered.connect(self.hide)\n quit_action.triggered.connect(self.close)\n\n tray_menu = QtWidgets.QMenu()\n tray_menu.addAction(show_action)\n tray_menu.addAction(hide_action)\n tray_menu.addAction(quit_action)\n self.tray_icon.setContextMenu(tray_menu)\n self.tray_icon.show()","title":""},{"docid":"4b478663dc9aa71c0bf75c2eb09f187f","score":"0.61332005","text":"def icon(self) -> str:\n if self.hvac_mode == HVAC_MODE_HEAT:\n return \"mdi:radiator\"\n return \"mdi:radiator-off\"","title":""},{"docid":"28a907c81dd5f67089d854cfdf2cf80a","score":"0.6095139","text":"def icon(self) -> str | None:\n if not self.device_class:\n return \"mdi:calculator\"\n return None","title":""},{"docid":"6a738a6532399514933f94f4eecb6953","score":"0.6091774","text":"def get_icon():\n return assets.load.image(settings.WINDOW_ICON_IMAGE)","title":""},{"docid":"e4af1e22e9b9ab3cc243e0380a0b6175","score":"0.60501987","text":"def icon(self):\n return None","title":""},{"docid":"e4af1e22e9b9ab3cc243e0380a0b6175","score":"0.60501987","text":"def icon(self):\n return None","title":""},{"docid":"e4af1e22e9b9ab3cc243e0380a0b6175","score":"0.60501987","text":"def icon(self):\n return None","title":""},{"docid":"5ec21ca25c30e30154fcf4455f4cbc78","score":"0.6040571","text":"def icon(self):\n if self._setting == 'up':\n return 'mdi:access-point'\n elif self._setting == 'earLeft':\n return 'mdi:arrow-top-left-bottom-right-bold'\n elif self._setting == 'earRight':\n return 'mdi:arrow-top-right-bottom-left-bold'\n elif self._setting == 'ledNose':\n return 'mdi:bell-alert'\n elif self._setting == 'ledInfo':\n return 'mdi:decagram'\n else:\n return 'mdi:flash'","title":""},{"docid":"944b09a5405d66245b5774ac3484e7a6","score":"0.6011727","text":"def icon(self):\r\n return None","title":""},{"docid":"30314cd1b8809ed8e730667be44a4fc0","score":"0.60094696","text":"def _icon_paths(self):\n if os.name == 'nt':\n self.error_icon = \"..\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-illegal')\n self.warning_icon = \"..\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-warning')\n else:\n self.error_icon = \"Packages\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-illegal.png')\n self.warning_icon = \"Packages\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-warning.png')","title":""},{"docid":"72c04a83a8af749e84814d648d1c4a63","score":"0.5986122","text":"def icon(self):\n return CO2_ICON","title":""},{"docid":"83a8d18acca02d7cffa01af5a45c9256","score":"0.5984402","text":"def app_display(app):\n app_template = loader.get_template('app-icon.html')\n app_dict = {'bk_color':app['background_color'],\n 'icon_img':app['icon'],\n 'name':app['name']}\n return mark_safe(app_template.render(Context(app_dict)))","title":""},{"docid":"6919674feef9db58454b0287e05c868a","score":"0.5973916","text":"def get_distributor_icon_file(self, os_name):\n os_name = os_name.lower()\n\n # No numix icon for DSGos, use our own.\n if \"DSGos\" in os_name:\n icons_path = os.path.join(self.settings.get('data'), \"icons/48x48\")\n icon_file = os.path.join(icons_path, \"distributor-logo-DSGos.png\")\n return icon_file\n\n icon_names = [\n \"lfs\", \"magiea\", \"manjaro\", \"mint\", \"archlinux\", \"chakra\",\n \"debian\", \"deepin\", \"fedora\", \"gentoo\", \"opensuse\", \"siduction\",\n \"kubuntu\", \"lubuntu\", \"ubuntu\", \"windows\"]\n prefix = \"distributor-logo-\"\n sufix = \".svg\"\n\n icons_path = os.path.join(self.settings.get('data'), \"icons/scalable\")\n default = os.path.join(icons_path, \"distributor-logo.svg\")\n\n for name in icon_names:\n if name in os_name:\n return os.path.join(icons_path, prefix + name + sufix)\n\n return default","title":""},{"docid":"294f87c7eb0529ee731d2f99120cbb80","score":"0.59551096","text":"async def icon(self, ctx):\n sname = '**' + ctx.message.server.name + '**'\n iurl = ctx.message.server.icon_url\n if iurl:\n await self.bot.say('Here is the link to the icon for ' + sname + ': <' + iurl + '>')\n else:\n await self.bot.say('The current server, ' + sname + ', does not have an icon set! :slight_frown:')","title":""},{"docid":"143a7105e033eea067731afc7110ef72","score":"0.595375","text":"def icon(self):\n return \"as_ico_variable.png\"","title":""},{"docid":"0cf0a44ca021a71a25841965003b2d3c","score":"0.5950367","text":"def icon(self):\n return \"mdi:soccer\"","title":""},{"docid":"295c7a72235188d601ec432f5a674f22","score":"0.5946909","text":"def draw_icon(self):\n text = str(self.currently.icon)\n self.draw_bloc(85, 75, 180, 180, text, 10)\n # Todo : make icons\n #ico = pygame.image.load('img/forecast.png').convert_alpha()\n #ico = pygame.transform.scale(ico, (180, 180))\n #self.screen.blit(ico, (85, 75))","title":""},{"docid":"f9ad5faa97a097128f9e9ecdf30185a2","score":"0.5942232","text":"def icon(self):\n return self.ICON","title":""},{"docid":"38504ac37a670407fb1e73b126bddce2","score":"0.5940487","text":"def icon(self):\n return DEFAULT_ICON","title":""},{"docid":"8ba0ccfd177b9bfa3b875f4fb720ac57","score":"0.59310436","text":"def icon(self):\n return 'mdi:cloud'","title":""},{"docid":"8fe63e563fee1aa80a0c3ae94a64006c","score":"0.59193534","text":"def icon(self):\n if self.zone_variable == \"temperature\":\n return \"mdi:thermometer\"\n if self.zone_variable == \"humidity\":\n return \"mdi:water-percent\"","title":""},{"docid":"3c1efaf2c76c0de33cf0746c080dd9bf","score":"0.5878178","text":"def icon(self):\n return HUMIDITY_ICON","title":""},{"docid":"61d7b0b0e76f426d96174a960210e8a3","score":"0.58707434","text":"def device_class(self):\n \"\"\"Icon to use in the frontend, if any.\"\"\"\n return 'carbon_dioxide_intensity'","title":""},{"docid":"8374e94801eab923ce2d042a04fbbd26","score":"0.5868734","text":"def findSearchIcon():\n # construct all icon paths\n paths = []\n if os.environ.get(\"XBMLANGPATH\"):\n paths = os.environ.get(\"XBMLANGPATH\").split(os.pathsep)\n\n # find icon\n for path in paths:\n filepath = os.path.join(path, \"rjCMDSearch.png\")\n if os.path.exists(filepath):\n return QIcon(filepath)\n \n return QIcon(\":/cmdWndIcon.png\")","title":""},{"docid":"3d7c4457055ba59b6d37fb15fb6de7d8","score":"0.58568114","text":"def icon(self) -> str:\n return \"mdi:wifi\" if self.is_on else \"mdi:wifi-off\"","title":""},{"docid":"d904c95df81654e63bc5612f36ff7fbb","score":"0.58423233","text":"def icon(*args, **kwargs):\n return Resource().icon(*args, **kwargs)","title":""},{"docid":"c7d50a9302a0b80e11cabe354b489739","score":"0.58366483","text":"def setup_system_tray():\n\n # This text is seen when the mouse hovers over the tray icon.\n hover_text = \"Keypad\"\n # These meanu options are available for the system tray.\n menu_options = (('Documentation', 'documentation.ico', show_documentation),\n ('Settings', 'settings.ico', show_config_keypress_file),\n ('Apply Settings', 'applysettings.ico', apply_settings_fun),\n ('Log', 'log.ico', show_log),\n ('Debugging', \"debugging.ico\", show_config_debugging_file)\n )\n # Setup the system tray.\n systrayicon = SysTrayIcon(\"ggzvs.ico\", hover_text, menu_options, on_quit=close_program, default_menu_index=1)\n # Start the system tray icon tread.\n systrayicon.start()","title":""},{"docid":"f2d559f3811f734de10d3a49fa777818","score":"0.5821726","text":"def icon(self):\n return self._device.SENSOR_ICON","title":""},{"docid":"78c4046be19a08b30a6c15e627ed4cf2","score":"0.5799704","text":"def getDeviceIcon(boardName, iconFormat=True):\n if boardName in SupportedBoards:\n iconName = SupportedBoards[boardName][\"icon\"]\n else:\n # return a generic MicroPython icon\n iconName = \"micropython48\"\n \n if iconFormat:\n return UI.PixmapCache.getIcon(iconName)\n else:\n return UI.PixmapCache.getPixmap(iconName)","title":""},{"docid":"ce4e5c9a13630216bc18c1ec417d35d1","score":"0.5776418","text":"def net_error_icon(): # -> string\n for _path in [\n \"HighContrast/32x32/status/network-offline.png\",\n \"hicolor/32x32/apps/nm-no-connection.png\",\n \"HighContrast/32x32/apps/preferences-system-network.png\",\n \"Adwaita/32x32/legacy/network-error.png\",\n \"Humanity/actions/32/stock_not.svg\",\n ]:\n _test = \"/usr/share/icons/%(_path)s\" % locals()\n if os.path.isfile(_test):\n return _test\n return \"\"","title":""},{"docid":"25bdd16a522993dc713969fe57f98225","score":"0.57668406","text":"def GetIcon(name):\n return QtGui.QIcon('bobdata/icon_%s.png' % name)","title":""},{"docid":"fad517d291b767a3a3adc4a3ef358451","score":"0.57664526","text":"def icon(self) -> str:\n return \"mdi:microphone\" if self.is_on else \"mdi:microphone-off\"","title":""},{"docid":"8ad366af1b6b54e2bca89c53ff7049f2","score":"0.5764568","text":"def icon(self):\n icon = DEFAULT_ICON\n return icon","title":""},{"docid":"11e89693fcc7fc65f21629cbcfe19543","score":"0.5757936","text":"def icon(self):\n return \"mdi:car\"","title":""},{"docid":"4e40c800e888f99eb0778ea7dd8fc558","score":"0.5735878","text":"def localOS():\n if cmds.about(mac=True):\n return \"mac\"\n elif cmds.about(linux=True):\n return \"linux\"\n return \"win\"","title":""},{"docid":"f30bd7ebdcfe9f027242d321eb0cb380","score":"0.5733379","text":"def icon(self) -> str | None:\n return self._config.get(CONF_ICON)","title":""},{"docid":"b891874cb56c7925520d50f5119ceb79","score":"0.57323235","text":"def icon(self):\n return 'mdi:bike'","title":""},{"docid":"841fc5ea5a658bc4b76ec54323daf690","score":"0.5726107","text":"def icon(self):\n icon = zope.component.queryMultiAdapter((self.context, self.request), \n name=self.iconName)\n if icon is not None:\n resource = getResource(icon.context, icon.rname, self.request)\n src = resource()\n return ('<img src=\"%s\" alt=\"icon\" width=\"%s\" height=\"%s\" ' \n 'border=\"0\" />' % (src, icon.width, icon.height))\n return u''","title":""},{"docid":"c6ff7a117469a2e01e7e4a9942323bce","score":"0.57181","text":"def icon(self):\n\n return tpDcc.ResourcesMgr().icon(self._name.lower().replace(' ', ''), theme=None, key='project')","title":""},{"docid":"9b9699a40bdacd3c144e55b88bc57099","score":"0.571622","text":"def icon(self) -> str:\n return \"mdi:cloud-outline\" if self.is_on else \"mdi:cloud-off-outline\"","title":""},{"docid":"eaa63a4c87a9c4232d0b91dd03e9ba1a","score":"0.5711513","text":"def icon(self):\n return \"as_ico_value.png\"","title":""},{"docid":"eaa63a4c87a9c4232d0b91dd03e9ba1a","score":"0.5711513","text":"def icon(self):\n return \"as_ico_value.png\"","title":""},{"docid":"eaa63a4c87a9c4232d0b91dd03e9ba1a","score":"0.5711513","text":"def icon(self):\n return \"as_ico_value.png\"","title":""},{"docid":"a7e943f8f959d0020efb9ba08ff7b3f7","score":"0.56543136","text":"def install_icon(filetype, iconspec):\n filetype_normalized = filetype.translate({ord('/'): '-'})\n cmd = [\"xdg-icon-resource\", \"install\", \"--context\", \"mimetypes\", \"--size\",\n \"64\", iconspec, filetype_normalized]\n subprocess.run(cmd, check=True)","title":""},{"docid":"ede2475ff804f7c85f83ec56e4b65e21","score":"0.56287056","text":"def app_icon_image(image_selection=\"\", asset_dir=\"images\"): # -> str\n path_root = os.path.split(os.path.realpath(__file__))[0]\n os_sep = os.sep\n folders = path_root.split(os_sep)\n count = len(folders)\n if not bool(image_selection):\n image_selection = \"textToSpeechAbout_42.png\"\n for _x in folders:\n test_root = os_sep.join(folders[:count])\n for image in [\n os.path.join(test_root, asset_dir, image_selection),\n os.path.join(test_root, asset_dir, \"textToSpeech.svg\"),\n os.path.join(test_root, asset_dir, \"schooltools_42.svg\"),\n ]:\n if os.path.isfile(image):\n return image\n if test_root.endswith(\"uno_packages\"):\n return \"\"\n count = count - 1\n return \"\"","title":""},{"docid":"e92d41ce8ac98a8845fd3a392c417b5c","score":"0.56262344","text":"def icon(self):\n return \"mdi:flower-outline\"","title":""},{"docid":"e92d41ce8ac98a8845fd3a392c417b5c","score":"0.56262344","text":"def icon(self):\n return \"mdi:flower-outline\"","title":""},{"docid":"c515317a2cdf434be07cfa10b594d00d","score":"0.5624574","text":"def initGui(self):\n\n icon_path = ':/plugins/Opera/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())","title":""},{"docid":"cf8514f371e5360406bb2e7a7ba42a3f","score":"0.56225514","text":"def icon_for_window(window):\n name = get_wm_name(window.window).reply()\n classes = get_wm_class(window.window).reply()\n\n if name:\n name = name[0].split()[0]\n if name in WINDOW_ICONS:\n return WINDOW_ICONS[name]\n\n if classes:\n for cls in classes:\n cls = cls.lower() # case-insensitive matching\n if cls in WINDOW_ICONS:\n return WINDOW_ICONS[cls]\n logging.info(\n 'No icon available for window with classes: %s' % str(classes))\n return DEFAULT_ICON","title":""},{"docid":"9a6225e0675a06229dbbc54a7fb46660","score":"0.562222","text":"def icon(self) -> str:\n return \"mdi:ruler-square\"","title":""},{"docid":"b7f85bad4d71cac5d4e369e40e357b22","score":"0.56205964","text":"def icon(self):\n if self._var_id == CONTAINER_INFO_STATUS:\n if self._state_extra == \"running\":\n return \"mdi:checkbox-marked-circle-outline\"\n else:\n return \"mdi:checkbox-blank-circle-outline\"\n elif self._var_id in [CONTAINER_INFO_ALLINONE, CONTAINER_INFO_STATE]:\n if self._state == \"running\":\n return \"mdi:checkbox-marked-circle-outline\"\n else:\n return \"mdi:checkbox-blank-circle-outline\"\n\n return self._var_icon","title":""},{"docid":"6ebd082b6f59d2fbecaad586c287bcfe","score":"0.5614865","text":"def icon(self) -> str | None:\n if self._config_option.option_type == libConfigurationOptionType.ADJUSTABLE_CHIME:\n if (\n current_value := self._config_option.current_value\n ) == libCameraSkybellControllerExtension.ChimeAdjustableVolume.OFF:\n return \"mdi:volume-mute\"\n if current_value == libCameraSkybellControllerExtension.ChimeAdjustableVolume.LOW:\n return \"mdi:volume-low\"\n if current_value == libCameraSkybellControllerExtension.ChimeAdjustableVolume.MEDIUM:\n return \"mdi:volume-medium\"\n if current_value == libCameraSkybellControllerExtension.ChimeAdjustableVolume.HIGH:\n return \"mdi:volume-high\"\n elif self._config_option.option_type == libConfigurationOptionType.MOTION_SENSITIVITY:\n return \"mdi:tune\"\n\n return super().icon if isinstance(super().icon, str) else None","title":""},{"docid":"bb575adf8089a6890a213bfd11dab19e","score":"0.56111014","text":"def _generate_shortcut_linux(name : str, working_dir:str, path:str, executable:Union[str,bool], icon:str) -> str:\n\n if not executable:\n shortcut_type = \"Directory\"\n executable_string = f\"\"\n else: # If executable is specified\n shortcut_type = \"Application\"\n executable_string = f\"\\nExec='cd {working_dir} && {executable}'\"\n\n # Generate the .desktop file Content\n data = f\"\"\"[Desktop Entry]\nEncoding=UTF-8\nVersion=1.0\nType={shortcut_type}\nTerminal=false{executable_string}\nPath='{path}'\nName={name}\nIcon='{icon}'\n\"\"\"\n return data","title":""},{"docid":"15d1f27e56ac308be80b4dc84c8a2809","score":"0.56005293","text":"def icon(icon_name='fracture'):\n return os.path.join(\n os.path.dirname(__file__),\n '_res',\n '%s.png' % icon_name,\n )","title":""},{"docid":"edb221b0dbb4f4cbc7ee392f3012c818","score":"0.5595696","text":"def icon(name):\n if name.startswith( 'icon-'):\n return mark_safe(\"<span class='%s'></span>\" % ICON_MAP[name]);\n else:\n return mark_safe(\"<i class='%s'></i>\" % ICON_MAP[name])","title":""},{"docid":"0c4c9a3f5863ffb0ff2bf24a1fb25604","score":"0.55945265","text":"def icon(self):\n return \"mdi:solar-power\"","title":""},{"docid":"0ddaab57059f8bf7f39ea5701e86ff10","score":"0.5583805","text":"def icon(self) -> str:\n return \"mdi:signal\" if self.is_on else \"mdi:signal-off\"","title":""},{"docid":"d57a7ffa5f201c0e9215c753ff3b013f","score":"0.5576548","text":"def icon(self):\n return \"mdi:gas-station\"","title":""},{"docid":"d57a7ffa5f201c0e9215c753ff3b013f","score":"0.5576548","text":"def icon(self):\n return \"mdi:gas-station\"","title":""},{"docid":"ca8d2e6bfb871010f2a437f26e2331c1","score":"0.5569407","text":"def iniciaUI(self):\n\n self.setGeometry(100,100, 200, 200)\n self.setWindowTitle(\"Exemplo com Icones\")\n self.displayWidgets()\n\n self.show()","title":""},{"docid":"7bf1e650e8db720182f16ced86264589","score":"0.55511516","text":"def icon(self):\n if self.coordinator.data:\n return \"mdi:lock\"\n\n return \"mdi:lock-open\"","title":""},{"docid":"101e232f419959fdd632cd81e5bc730c","score":"0.55440414","text":"def icon(self):\n return 'mdi:battery'","title":""},{"docid":"aa739167b3656530dd38058b62480b3a","score":"0.5539859","text":"def show_help_icon(self):\n\n st.sidebar.markdown(\n '<a href = \"{}\" target = \"_blank\"><img border=\"0\" alt=\"Help\" src=\"{}\"></a>'.format(cn.USER_MANUAL_LINK,\n cn.HELP_ICON),\n unsafe_allow_html=True)","title":""},{"docid":"1be5a0a81d79d65b9483e4472b6b1b00","score":"0.5536195","text":"def icon(self):\n return self._config[ATTR_SENSOR_ICON]","title":""},{"docid":"fd79339c0570242aa6ebf63989d610cf","score":"0.5534737","text":"def _set_icon(self, image):\n self._icon = image\n if not self._opened: return\n pygame.display.set_icon(image)","title":""},{"docid":"3a485290debb5aa1e09d7835ec78f703","score":"0.55344415","text":"def icon(self):\n return \"mdi:speaker-wireless\"","title":""},{"docid":"057f43ce0e01d21712461ee10b8b9495","score":"0.55052865","text":"def icon(self):\n if self._is_blocked:\n return \"mdi:network-off\"\n return \"mdi:network\"","title":""},{"docid":"3dfafa35619cea506396e49e8070ca7e","score":"0.55050606","text":"def favicon():\n return send_file(f'icons/basketball.png', mimetype='image/gif')","title":""},{"docid":"67bbf39e0c342f649d89d7b3ed9a570d","score":"0.5497408","text":"def get_ui_info():\n \n version = \"4.0\"\n title = \"WeatherLog\"\n menu_file = open(\"weatherlog_resources/menu.xml\", \"r\")\n menu_data = menu_file.read()\n menu_file.close()\n icon_small = \"weatherlog_resources/images/icon_small.png\"\n icon_medium = \"weatherlog_resources/images/icon_med.png\"\n icon_medium_about = \"../images/icon_med.png\"\n return version, title, menu_data, icon_small, icon_medium","title":""},{"docid":"517b8372e96a0a1058ff5ef167ade8d5","score":"0.54967034","text":"def icon(self):\n return 'mdi:periodic-table-co2'","title":""},{"docid":"526589334fd93727ed017ab2123b14ba","score":"0.54958737","text":"def icon(self):\n return STATUSES.get(self._mower_status, {}).get('icon', DEFAULT_ICON)","title":""},{"docid":"fe75ee42efb0087c2347642458c45784","score":"0.54926264","text":"def icon(self) -> str:\n native_value = self.native_value\n if not self.available or native_value is None:\n return \"mdi:battery-unknown\"\n\n # This is similar to the logic in helpers.icon, but we have delegated the\n # decision about what mdi:battery-alert is to the device.\n icon = \"mdi:battery\"\n is_charging = self.is_charging\n if is_charging and native_value > 10:\n percentage = int(round(native_value / 20 - 0.01)) * 20\n icon += f\"-charging-{percentage}\"\n elif is_charging:\n icon += \"-outline\"\n elif self.is_low_battery:\n icon += \"-alert\"\n elif native_value < 95:\n percentage = max(int(round(native_value / 10 - 0.01)) * 10, 10)\n icon += f\"-{percentage}\"\n\n return icon","title":""},{"docid":"51f8f95c3388b41c1c0d5b7bed4e8d14","score":"0.54814637","text":"def icon(self) -> str:\n if hasattr(self._node, \"protocol\") and self._node.protocol == PROTO_GROUP:\n return \"mdi:google-circles-communities\" # Matches isy scene icon\n return super().icon","title":""},{"docid":"3b9aab41bb10921bf9fc7a2c3b27f5a7","score":"0.54769814","text":"def icon(self):\n return \"mdi:water\" if self.current_operation == STATE_SCHEDULE else \"mdi:water-off\"","title":""},{"docid":"1e3e37737e194e56492b396b47af5bd9","score":"0.5472423","text":"def set_icon(self, variant):\r\n logging.debug('Setting icon to '+args.iconpath+'/btsync'+variant)\r\n self.ind.set_icon('btsync'+variant)\r\n return False","title":""},{"docid":"d04987ceef53eb01590ce47ad094473f","score":"0.5472229","text":"def sysinfo(dev):\n click.echo(click.style(\"== System info ==\", bold=True))\n click.echo(pf(dev.sys_info))","title":""},{"docid":"520a01c77ba097f7b05649f9f888b892","score":"0.5462993","text":"def icon(self):\n return SENSOR_TYPES[self.sensor_type][ATTR_ICON]","title":""},{"docid":"139e22b05af4d2a414725e6399b7c1a9","score":"0.5451079","text":"def _change_seticon(self):\n\n fname_orig = QFileDialog.getOpenFileName(self, 'Open file', \n \t\t\t\t USER_IMAGES_FILE_PATH, \"Image files (*.jpg *.png *.jpeg)\")\n fname = kks.find_image_from_computer(fname_orig)\n if fname != \"\":\n kks.update_card(self._card, fname, \"seticon\")\n self._update_card_layout()","title":""}],"string":"[\n {\n \"docid\": \"9e388977a27612c28387556e97934387\",\n \"score\": \"0.68452096\",\n \"text\": \"def icon():\\n return \\\"icon.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e69561bb995b86026c5057fc57ea017\",\n \"score\": \"0.6736521\",\n \"text\": \"def icon(self):\\n return \\\"as_ico_command.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a6a6bb204fb3c418427f5515c142ab\",\n \"score\": \"0.667186\",\n \"text\": \"def _icon(self, name, *args):\\n myname = icons(\\\"32x32\\\", name)\\n if os.path.exists(myname):\\n return QtGui.QIcon(myname)\\n else:\\n return super()._icon(name, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"733d3b7979672fcd653489ca29da28c8\",\n \"score\": \"0.6571517\",\n \"text\": \"def run_tray_icon():\\n SysTrayIcon.SysTrayIcon(resource_path('res/icon_vector.ico'), \\\"lifx_control_panel\\\",\\n tray_options,\\n on_quit=lambda_factory(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648f5e4c9562f5407a0aca6e89933373\",\n \"score\": \"0.64446527\",\n \"text\": \"def icon(self):\\n\\t\\treturn ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92d7d8d1761034c86c1faea21b39c7a0\",\n \"score\": \"0.63868445\",\n \"text\": \"def icon(self):\\n return TEMP_C_ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1a7916d9e5df2d4b412ffd90918e46\",\n \"score\": \"0.63360023\",\n \"text\": \"def icon(self):\\n return \\\"as_ico_macro.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae196c8d423ab95599b014f7faadfde7\",\n \"score\": \"0.6330747\",\n \"text\": \"def platform_config():\\n if sys.platform == 'win32' and mainmodel.is_win7():\\n # Set taskbar icon to match application's icon\\n # Code courtesy DamonJW\\n # http://stackoverflow.com/questions/1551605/how-to-set-applications-taskbar-icon\\n import ctypes\\n\\n myappid = 'tri.nditoolbox.bane.1' # arbitrary string\\n ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(myappid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55263b8f361d5b5f111741fd6d5acbe\",\n \"score\": \"0.6228661\",\n \"text\": \"def icon(self):\\n return ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bada5bfda56c923c35020d0e0f5cd895\",\n \"score\": \"0.62069595\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:script-text-outline\\\" # Matches isy program icon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7230e1de0ab5e60e12cce2d93facc2\",\n \"score\": \"0.6194612\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:server\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7230e1de0ab5e60e12cce2d93facc2\",\n \"score\": \"0.6194612\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:server\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7230e1de0ab5e60e12cce2d93facc2\",\n \"score\": \"0.6194612\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:server\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43454d0a7fb54e52ae06454d5dc4f79\",\n \"score\": \"0.6175948\",\n \"text\": \"def GetIcon(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba88097a7aa7496124f5bb78b72391b\",\n \"score\": \"0.6172751\",\n \"text\": \"def tray(self):\\n self.tray_icon = QtWidgets.QSystemTrayIcon(self)\\n self.tray_icon.setIcon(QtGui.QIcon(ICON_FILE))\\n self.tray_icon.setToolTip('MyApp') # Pop up lettering icons\\n\\n show_action = QtWidgets.QAction(\\\"Show\\\", self)\\n hide_action = QtWidgets.QAction(\\\"Hide\\\", self)\\n quit_action = QtWidgets.QAction(\\\"Exit\\\", self)\\n show_action.triggered.connect(self.show)\\n hide_action.triggered.connect(self.hide)\\n quit_action.triggered.connect(self.close)\\n\\n tray_menu = QtWidgets.QMenu()\\n tray_menu.addAction(show_action)\\n tray_menu.addAction(hide_action)\\n tray_menu.addAction(quit_action)\\n self.tray_icon.setContextMenu(tray_menu)\\n self.tray_icon.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b478663dc9aa71c0bf75c2eb09f187f\",\n \"score\": \"0.61332005\",\n \"text\": \"def icon(self) -> str:\\n if self.hvac_mode == HVAC_MODE_HEAT:\\n return \\\"mdi:radiator\\\"\\n return \\\"mdi:radiator-off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a907c81dd5f67089d854cfdf2cf80a\",\n \"score\": \"0.6095139\",\n \"text\": \"def icon(self) -> str | None:\\n if not self.device_class:\\n return \\\"mdi:calculator\\\"\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a738a6532399514933f94f4eecb6953\",\n \"score\": \"0.6091774\",\n \"text\": \"def get_icon():\\n return assets.load.image(settings.WINDOW_ICON_IMAGE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4af1e22e9b9ab3cc243e0380a0b6175\",\n \"score\": \"0.60501987\",\n \"text\": \"def icon(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4af1e22e9b9ab3cc243e0380a0b6175\",\n \"score\": \"0.60501987\",\n \"text\": \"def icon(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4af1e22e9b9ab3cc243e0380a0b6175\",\n \"score\": \"0.60501987\",\n \"text\": \"def icon(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec21ca25c30e30154fcf4455f4cbc78\",\n \"score\": \"0.6040571\",\n \"text\": \"def icon(self):\\n if self._setting == 'up':\\n return 'mdi:access-point'\\n elif self._setting == 'earLeft':\\n return 'mdi:arrow-top-left-bottom-right-bold'\\n elif self._setting == 'earRight':\\n return 'mdi:arrow-top-right-bottom-left-bold'\\n elif self._setting == 'ledNose':\\n return 'mdi:bell-alert'\\n elif self._setting == 'ledInfo':\\n return 'mdi:decagram'\\n else:\\n return 'mdi:flash'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944b09a5405d66245b5774ac3484e7a6\",\n \"score\": \"0.6011727\",\n \"text\": \"def icon(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30314cd1b8809ed8e730667be44a4fc0\",\n \"score\": \"0.60094696\",\n \"text\": \"def _icon_paths(self):\\n if os.name == 'nt':\\n self.error_icon = \\\"..\\\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-illegal')\\n self.warning_icon = \\\"..\\\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-warning')\\n else:\\n self.error_icon = \\\"Packages\\\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-illegal.png')\\n self.warning_icon = \\\"Packages\\\"+os.path.join(package_path.split('Packages')[1], 'icons', 'bright-warning.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c04a83a8af749e84814d648d1c4a63\",\n \"score\": \"0.5986122\",\n \"text\": \"def icon(self):\\n return CO2_ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a8d18acca02d7cffa01af5a45c9256\",\n \"score\": \"0.5984402\",\n \"text\": \"def app_display(app):\\n app_template = loader.get_template('app-icon.html')\\n app_dict = {'bk_color':app['background_color'],\\n 'icon_img':app['icon'],\\n 'name':app['name']}\\n return mark_safe(app_template.render(Context(app_dict)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6919674feef9db58454b0287e05c868a\",\n \"score\": \"0.5973916\",\n \"text\": \"def get_distributor_icon_file(self, os_name):\\n os_name = os_name.lower()\\n\\n # No numix icon for DSGos, use our own.\\n if \\\"DSGos\\\" in os_name:\\n icons_path = os.path.join(self.settings.get('data'), \\\"icons/48x48\\\")\\n icon_file = os.path.join(icons_path, \\\"distributor-logo-DSGos.png\\\")\\n return icon_file\\n\\n icon_names = [\\n \\\"lfs\\\", \\\"magiea\\\", \\\"manjaro\\\", \\\"mint\\\", \\\"archlinux\\\", \\\"chakra\\\",\\n \\\"debian\\\", \\\"deepin\\\", \\\"fedora\\\", \\\"gentoo\\\", \\\"opensuse\\\", \\\"siduction\\\",\\n \\\"kubuntu\\\", \\\"lubuntu\\\", \\\"ubuntu\\\", \\\"windows\\\"]\\n prefix = \\\"distributor-logo-\\\"\\n sufix = \\\".svg\\\"\\n\\n icons_path = os.path.join(self.settings.get('data'), \\\"icons/scalable\\\")\\n default = os.path.join(icons_path, \\\"distributor-logo.svg\\\")\\n\\n for name in icon_names:\\n if name in os_name:\\n return os.path.join(icons_path, prefix + name + sufix)\\n\\n return default\",\n \"title\": \"\"\n },\n {\n \"docid\": \"294f87c7eb0529ee731d2f99120cbb80\",\n \"score\": \"0.59551096\",\n \"text\": \"async def icon(self, ctx):\\n sname = '**' + ctx.message.server.name + '**'\\n iurl = ctx.message.server.icon_url\\n if iurl:\\n await self.bot.say('Here is the link to the icon for ' + sname + ': <' + iurl + '>')\\n else:\\n await self.bot.say('The current server, ' + sname + ', does not have an icon set! :slight_frown:')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"143a7105e033eea067731afc7110ef72\",\n \"score\": \"0.595375\",\n \"text\": \"def icon(self):\\n return \\\"as_ico_variable.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf0a44ca021a71a25841965003b2d3c\",\n \"score\": \"0.5950367\",\n \"text\": \"def icon(self):\\n return \\\"mdi:soccer\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295c7a72235188d601ec432f5a674f22\",\n \"score\": \"0.5946909\",\n \"text\": \"def draw_icon(self):\\n text = str(self.currently.icon)\\n self.draw_bloc(85, 75, 180, 180, text, 10)\\n # Todo : make icons\\n #ico = pygame.image.load('img/forecast.png').convert_alpha()\\n #ico = pygame.transform.scale(ico, (180, 180))\\n #self.screen.blit(ico, (85, 75))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ad5faa97a097128f9e9ecdf30185a2\",\n \"score\": \"0.5942232\",\n \"text\": \"def icon(self):\\n return self.ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38504ac37a670407fb1e73b126bddce2\",\n \"score\": \"0.5940487\",\n \"text\": \"def icon(self):\\n return DEFAULT_ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba0ccfd177b9bfa3b875f4fb720ac57\",\n \"score\": \"0.59310436\",\n \"text\": \"def icon(self):\\n return 'mdi:cloud'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe63e563fee1aa80a0c3ae94a64006c\",\n \"score\": \"0.59193534\",\n \"text\": \"def icon(self):\\n if self.zone_variable == \\\"temperature\\\":\\n return \\\"mdi:thermometer\\\"\\n if self.zone_variable == \\\"humidity\\\":\\n return \\\"mdi:water-percent\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1efaf2c76c0de33cf0746c080dd9bf\",\n \"score\": \"0.5878178\",\n \"text\": \"def icon(self):\\n return HUMIDITY_ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61d7b0b0e76f426d96174a960210e8a3\",\n \"score\": \"0.58707434\",\n \"text\": \"def device_class(self):\\n \\\"\\\"\\\"Icon to use in the frontend, if any.\\\"\\\"\\\"\\n return 'carbon_dioxide_intensity'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8374e94801eab923ce2d042a04fbbd26\",\n \"score\": \"0.5868734\",\n \"text\": \"def findSearchIcon():\\n # construct all icon paths\\n paths = []\\n if os.environ.get(\\\"XBMLANGPATH\\\"):\\n paths = os.environ.get(\\\"XBMLANGPATH\\\").split(os.pathsep)\\n\\n # find icon\\n for path in paths:\\n filepath = os.path.join(path, \\\"rjCMDSearch.png\\\")\\n if os.path.exists(filepath):\\n return QIcon(filepath)\\n \\n return QIcon(\\\":/cmdWndIcon.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7c4457055ba59b6d37fb15fb6de7d8\",\n \"score\": \"0.58568114\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:wifi\\\" if self.is_on else \\\"mdi:wifi-off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d904c95df81654e63bc5612f36ff7fbb\",\n \"score\": \"0.58423233\",\n \"text\": \"def icon(*args, **kwargs):\\n return Resource().icon(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7d50a9302a0b80e11cabe354b489739\",\n \"score\": \"0.58366483\",\n \"text\": \"def setup_system_tray():\\n\\n # This text is seen when the mouse hovers over the tray icon.\\n hover_text = \\\"Keypad\\\"\\n # These meanu options are available for the system tray.\\n menu_options = (('Documentation', 'documentation.ico', show_documentation),\\n ('Settings', 'settings.ico', show_config_keypress_file),\\n ('Apply Settings', 'applysettings.ico', apply_settings_fun),\\n ('Log', 'log.ico', show_log),\\n ('Debugging', \\\"debugging.ico\\\", show_config_debugging_file)\\n )\\n # Setup the system tray.\\n systrayicon = SysTrayIcon(\\\"ggzvs.ico\\\", hover_text, menu_options, on_quit=close_program, default_menu_index=1)\\n # Start the system tray icon tread.\\n systrayicon.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d559f3811f734de10d3a49fa777818\",\n \"score\": \"0.5821726\",\n \"text\": \"def icon(self):\\n return self._device.SENSOR_ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c4046be19a08b30a6c15e627ed4cf2\",\n \"score\": \"0.5799704\",\n \"text\": \"def getDeviceIcon(boardName, iconFormat=True):\\n if boardName in SupportedBoards:\\n iconName = SupportedBoards[boardName][\\\"icon\\\"]\\n else:\\n # return a generic MicroPython icon\\n iconName = \\\"micropython48\\\"\\n \\n if iconFormat:\\n return UI.PixmapCache.getIcon(iconName)\\n else:\\n return UI.PixmapCache.getPixmap(iconName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce4e5c9a13630216bc18c1ec417d35d1\",\n \"score\": \"0.5776418\",\n \"text\": \"def net_error_icon(): # -> string\\n for _path in [\\n \\\"HighContrast/32x32/status/network-offline.png\\\",\\n \\\"hicolor/32x32/apps/nm-no-connection.png\\\",\\n \\\"HighContrast/32x32/apps/preferences-system-network.png\\\",\\n \\\"Adwaita/32x32/legacy/network-error.png\\\",\\n \\\"Humanity/actions/32/stock_not.svg\\\",\\n ]:\\n _test = \\\"/usr/share/icons/%(_path)s\\\" % locals()\\n if os.path.isfile(_test):\\n return _test\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bdd16a522993dc713969fe57f98225\",\n \"score\": \"0.57668406\",\n \"text\": \"def GetIcon(name):\\n return QtGui.QIcon('bobdata/icon_%s.png' % name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad517d291b767a3a3adc4a3ef358451\",\n \"score\": \"0.57664526\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:microphone\\\" if self.is_on else \\\"mdi:microphone-off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad366af1b6b54e2bca89c53ff7049f2\",\n \"score\": \"0.5764568\",\n \"text\": \"def icon(self):\\n icon = DEFAULT_ICON\\n return icon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11e89693fcc7fc65f21629cbcfe19543\",\n \"score\": \"0.5757936\",\n \"text\": \"def icon(self):\\n return \\\"mdi:car\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e40c800e888f99eb0778ea7dd8fc558\",\n \"score\": \"0.5735878\",\n \"text\": \"def localOS():\\n if cmds.about(mac=True):\\n return \\\"mac\\\"\\n elif cmds.about(linux=True):\\n return \\\"linux\\\"\\n return \\\"win\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f30bd7ebdcfe9f027242d321eb0cb380\",\n \"score\": \"0.5733379\",\n \"text\": \"def icon(self) -> str | None:\\n return self._config.get(CONF_ICON)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b891874cb56c7925520d50f5119ceb79\",\n \"score\": \"0.57323235\",\n \"text\": \"def icon(self):\\n return 'mdi:bike'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"841fc5ea5a658bc4b76ec54323daf690\",\n \"score\": \"0.5726107\",\n \"text\": \"def icon(self):\\n icon = zope.component.queryMultiAdapter((self.context, self.request), \\n name=self.iconName)\\n if icon is not None:\\n resource = getResource(icon.context, icon.rname, self.request)\\n src = resource()\\n return ('<img src=\\\"%s\\\" alt=\\\"icon\\\" width=\\\"%s\\\" height=\\\"%s\\\" ' \\n 'border=\\\"0\\\" />' % (src, icon.width, icon.height))\\n return u''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ff7a117469a2e01e7e4a9942323bce\",\n \"score\": \"0.57181\",\n \"text\": \"def icon(self):\\n\\n return tpDcc.ResourcesMgr().icon(self._name.lower().replace(' ', ''), theme=None, key='project')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9699a40bdacd3c144e55b88bc57099\",\n \"score\": \"0.571622\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:cloud-outline\\\" if self.is_on else \\\"mdi:cloud-off-outline\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa63a4c87a9c4232d0b91dd03e9ba1a\",\n \"score\": \"0.5711513\",\n \"text\": \"def icon(self):\\n return \\\"as_ico_value.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa63a4c87a9c4232d0b91dd03e9ba1a\",\n \"score\": \"0.5711513\",\n \"text\": \"def icon(self):\\n return \\\"as_ico_value.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa63a4c87a9c4232d0b91dd03e9ba1a\",\n \"score\": \"0.5711513\",\n \"text\": \"def icon(self):\\n return \\\"as_ico_value.png\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e943f8f959d0020efb9ba08ff7b3f7\",\n \"score\": \"0.56543136\",\n \"text\": \"def install_icon(filetype, iconspec):\\n filetype_normalized = filetype.translate({ord('/'): '-'})\\n cmd = [\\\"xdg-icon-resource\\\", \\\"install\\\", \\\"--context\\\", \\\"mimetypes\\\", \\\"--size\\\",\\n \\\"64\\\", iconspec, filetype_normalized]\\n subprocess.run(cmd, check=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede2475ff804f7c85f83ec56e4b65e21\",\n \"score\": \"0.56287056\",\n \"text\": \"def app_icon_image(image_selection=\\\"\\\", asset_dir=\\\"images\\\"): # -> str\\n path_root = os.path.split(os.path.realpath(__file__))[0]\\n os_sep = os.sep\\n folders = path_root.split(os_sep)\\n count = len(folders)\\n if not bool(image_selection):\\n image_selection = \\\"textToSpeechAbout_42.png\\\"\\n for _x in folders:\\n test_root = os_sep.join(folders[:count])\\n for image in [\\n os.path.join(test_root, asset_dir, image_selection),\\n os.path.join(test_root, asset_dir, \\\"textToSpeech.svg\\\"),\\n os.path.join(test_root, asset_dir, \\\"schooltools_42.svg\\\"),\\n ]:\\n if os.path.isfile(image):\\n return image\\n if test_root.endswith(\\\"uno_packages\\\"):\\n return \\\"\\\"\\n count = count - 1\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92d41ce8ac98a8845fd3a392c417b5c\",\n \"score\": \"0.56262344\",\n \"text\": \"def icon(self):\\n return \\\"mdi:flower-outline\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92d41ce8ac98a8845fd3a392c417b5c\",\n \"score\": \"0.56262344\",\n \"text\": \"def icon(self):\\n return \\\"mdi:flower-outline\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c515317a2cdf434be07cfa10b594d00d\",\n \"score\": \"0.5624574\",\n \"text\": \"def initGui(self):\\n\\n icon_path = ':/plugins/Opera/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf8514f371e5360406bb2e7a7ba42a3f\",\n \"score\": \"0.56225514\",\n \"text\": \"def icon_for_window(window):\\n name = get_wm_name(window.window).reply()\\n classes = get_wm_class(window.window).reply()\\n\\n if name:\\n name = name[0].split()[0]\\n if name in WINDOW_ICONS:\\n return WINDOW_ICONS[name]\\n\\n if classes:\\n for cls in classes:\\n cls = cls.lower() # case-insensitive matching\\n if cls in WINDOW_ICONS:\\n return WINDOW_ICONS[cls]\\n logging.info(\\n 'No icon available for window with classes: %s' % str(classes))\\n return DEFAULT_ICON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6225e0675a06229dbbc54a7fb46660\",\n \"score\": \"0.562222\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:ruler-square\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f85bad4d71cac5d4e369e40e357b22\",\n \"score\": \"0.56205964\",\n \"text\": \"def icon(self):\\n if self._var_id == CONTAINER_INFO_STATUS:\\n if self._state_extra == \\\"running\\\":\\n return \\\"mdi:checkbox-marked-circle-outline\\\"\\n else:\\n return \\\"mdi:checkbox-blank-circle-outline\\\"\\n elif self._var_id in [CONTAINER_INFO_ALLINONE, CONTAINER_INFO_STATE]:\\n if self._state == \\\"running\\\":\\n return \\\"mdi:checkbox-marked-circle-outline\\\"\\n else:\\n return \\\"mdi:checkbox-blank-circle-outline\\\"\\n\\n return self._var_icon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebd082b6f59d2fbecaad586c287bcfe\",\n \"score\": \"0.5614865\",\n \"text\": \"def icon(self) -> str | None:\\n if self._config_option.option_type == libConfigurationOptionType.ADJUSTABLE_CHIME:\\n if (\\n current_value := self._config_option.current_value\\n ) == libCameraSkybellControllerExtension.ChimeAdjustableVolume.OFF:\\n return \\\"mdi:volume-mute\\\"\\n if current_value == libCameraSkybellControllerExtension.ChimeAdjustableVolume.LOW:\\n return \\\"mdi:volume-low\\\"\\n if current_value == libCameraSkybellControllerExtension.ChimeAdjustableVolume.MEDIUM:\\n return \\\"mdi:volume-medium\\\"\\n if current_value == libCameraSkybellControllerExtension.ChimeAdjustableVolume.HIGH:\\n return \\\"mdi:volume-high\\\"\\n elif self._config_option.option_type == libConfigurationOptionType.MOTION_SENSITIVITY:\\n return \\\"mdi:tune\\\"\\n\\n return super().icon if isinstance(super().icon, str) else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb575adf8089a6890a213bfd11dab19e\",\n \"score\": \"0.56111014\",\n \"text\": \"def _generate_shortcut_linux(name : str, working_dir:str, path:str, executable:Union[str,bool], icon:str) -> str:\\n\\n if not executable:\\n shortcut_type = \\\"Directory\\\"\\n executable_string = f\\\"\\\"\\n else: # If executable is specified\\n shortcut_type = \\\"Application\\\"\\n executable_string = f\\\"\\\\nExec='cd {working_dir} && {executable}'\\\"\\n\\n # Generate the .desktop file Content\\n data = f\\\"\\\"\\\"[Desktop Entry]\\nEncoding=UTF-8\\nVersion=1.0\\nType={shortcut_type}\\nTerminal=false{executable_string}\\nPath='{path}'\\nName={name}\\nIcon='{icon}'\\n\\\"\\\"\\\"\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d1f27e56ac308be80b4dc84c8a2809\",\n \"score\": \"0.56005293\",\n \"text\": \"def icon(icon_name='fracture'):\\n return os.path.join(\\n os.path.dirname(__file__),\\n '_res',\\n '%s.png' % icon_name,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb221b0dbb4f4cbc7ee392f3012c818\",\n \"score\": \"0.5595696\",\n \"text\": \"def icon(name):\\n if name.startswith( 'icon-'):\\n return mark_safe(\\\"<span class='%s'></span>\\\" % ICON_MAP[name]);\\n else:\\n return mark_safe(\\\"<i class='%s'></i>\\\" % ICON_MAP[name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4c9a3f5863ffb0ff2bf24a1fb25604\",\n \"score\": \"0.55945265\",\n \"text\": \"def icon(self):\\n return \\\"mdi:solar-power\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ddaab57059f8bf7f39ea5701e86ff10\",\n \"score\": \"0.5583805\",\n \"text\": \"def icon(self) -> str:\\n return \\\"mdi:signal\\\" if self.is_on else \\\"mdi:signal-off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57a7ffa5f201c0e9215c753ff3b013f\",\n \"score\": \"0.5576548\",\n \"text\": \"def icon(self):\\n return \\\"mdi:gas-station\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57a7ffa5f201c0e9215c753ff3b013f\",\n \"score\": \"0.5576548\",\n \"text\": \"def icon(self):\\n return \\\"mdi:gas-station\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8d2e6bfb871010f2a437f26e2331c1\",\n \"score\": \"0.5569407\",\n \"text\": \"def iniciaUI(self):\\n\\n self.setGeometry(100,100, 200, 200)\\n self.setWindowTitle(\\\"Exemplo com Icones\\\")\\n self.displayWidgets()\\n\\n self.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf1e650e8db720182f16ced86264589\",\n \"score\": \"0.55511516\",\n \"text\": \"def icon(self):\\n if self.coordinator.data:\\n return \\\"mdi:lock\\\"\\n\\n return \\\"mdi:lock-open\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101e232f419959fdd632cd81e5bc730c\",\n \"score\": \"0.55440414\",\n \"text\": \"def icon(self):\\n return 'mdi:battery'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa739167b3656530dd38058b62480b3a\",\n \"score\": \"0.5539859\",\n \"text\": \"def show_help_icon(self):\\n\\n st.sidebar.markdown(\\n '<a href = \\\"{}\\\" target = \\\"_blank\\\"><img border=\\\"0\\\" alt=\\\"Help\\\" src=\\\"{}\\\"></a>'.format(cn.USER_MANUAL_LINK,\\n cn.HELP_ICON),\\n unsafe_allow_html=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be5a0a81d79d65b9483e4472b6b1b00\",\n \"score\": \"0.5536195\",\n \"text\": \"def icon(self):\\n return self._config[ATTR_SENSOR_ICON]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd79339c0570242aa6ebf63989d610cf\",\n \"score\": \"0.5534737\",\n \"text\": \"def _set_icon(self, image):\\n self._icon = image\\n if not self._opened: return\\n pygame.display.set_icon(image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a485290debb5aa1e09d7835ec78f703\",\n \"score\": \"0.55344415\",\n \"text\": \"def icon(self):\\n return \\\"mdi:speaker-wireless\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057f43ce0e01d21712461ee10b8b9495\",\n \"score\": \"0.55052865\",\n \"text\": \"def icon(self):\\n if self._is_blocked:\\n return \\\"mdi:network-off\\\"\\n return \\\"mdi:network\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dfafa35619cea506396e49e8070ca7e\",\n \"score\": \"0.55050606\",\n \"text\": \"def favicon():\\n return send_file(f'icons/basketball.png', mimetype='image/gif')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67bbf39e0c342f649d89d7b3ed9a570d\",\n \"score\": \"0.5497408\",\n \"text\": \"def get_ui_info():\\n \\n version = \\\"4.0\\\"\\n title = \\\"WeatherLog\\\"\\n menu_file = open(\\\"weatherlog_resources/menu.xml\\\", \\\"r\\\")\\n menu_data = menu_file.read()\\n menu_file.close()\\n icon_small = \\\"weatherlog_resources/images/icon_small.png\\\"\\n icon_medium = \\\"weatherlog_resources/images/icon_med.png\\\"\\n icon_medium_about = \\\"../images/icon_med.png\\\"\\n return version, title, menu_data, icon_small, icon_medium\",\n \"title\": \"\"\n },\n {\n \"docid\": \"517b8372e96a0a1058ff5ef167ade8d5\",\n \"score\": \"0.54967034\",\n \"text\": \"def icon(self):\\n return 'mdi:periodic-table-co2'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"526589334fd93727ed017ab2123b14ba\",\n \"score\": \"0.54958737\",\n \"text\": \"def icon(self):\\n return STATUSES.get(self._mower_status, {}).get('icon', DEFAULT_ICON)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe75ee42efb0087c2347642458c45784\",\n \"score\": \"0.54926264\",\n \"text\": \"def icon(self) -> str:\\n native_value = self.native_value\\n if not self.available or native_value is None:\\n return \\\"mdi:battery-unknown\\\"\\n\\n # This is similar to the logic in helpers.icon, but we have delegated the\\n # decision about what mdi:battery-alert is to the device.\\n icon = \\\"mdi:battery\\\"\\n is_charging = self.is_charging\\n if is_charging and native_value > 10:\\n percentage = int(round(native_value / 20 - 0.01)) * 20\\n icon += f\\\"-charging-{percentage}\\\"\\n elif is_charging:\\n icon += \\\"-outline\\\"\\n elif self.is_low_battery:\\n icon += \\\"-alert\\\"\\n elif native_value < 95:\\n percentage = max(int(round(native_value / 10 - 0.01)) * 10, 10)\\n icon += f\\\"-{percentage}\\\"\\n\\n return icon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f8f95c3388b41c1c0d5b7bed4e8d14\",\n \"score\": \"0.54814637\",\n \"text\": \"def icon(self) -> str:\\n if hasattr(self._node, \\\"protocol\\\") and self._node.protocol == PROTO_GROUP:\\n return \\\"mdi:google-circles-communities\\\" # Matches isy scene icon\\n return super().icon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9aab41bb10921bf9fc7a2c3b27f5a7\",\n \"score\": \"0.54769814\",\n \"text\": \"def icon(self):\\n return \\\"mdi:water\\\" if self.current_operation == STATE_SCHEDULE else \\\"mdi:water-off\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e3e37737e194e56492b396b47af5bd9\",\n \"score\": \"0.5472423\",\n \"text\": \"def set_icon(self, variant):\\r\\n logging.debug('Setting icon to '+args.iconpath+'/btsync'+variant)\\r\\n self.ind.set_icon('btsync'+variant)\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d04987ceef53eb01590ce47ad094473f\",\n \"score\": \"0.5472229\",\n \"text\": \"def sysinfo(dev):\\n click.echo(click.style(\\\"== System info ==\\\", bold=True))\\n click.echo(pf(dev.sys_info))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520a01c77ba097f7b05649f9f888b892\",\n \"score\": \"0.5462993\",\n \"text\": \"def icon(self):\\n return SENSOR_TYPES[self.sensor_type][ATTR_ICON]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139e22b05af4d2a414725e6399b7c1a9\",\n \"score\": \"0.5451079\",\n \"text\": \"def _change_seticon(self):\\n\\n fname_orig = QFileDialog.getOpenFileName(self, 'Open file', \\n \\t\\t\\t\\t USER_IMAGES_FILE_PATH, \\\"Image files (*.jpg *.png *.jpeg)\\\")\\n fname = kks.find_image_from_computer(fname_orig)\\n if fname != \\\"\\\":\\n kks.update_card(self._card, fname, \\\"seticon\\\")\\n self._update_card_layout()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994385,"cells":{"query_id":{"kind":"string","value":"ea709676884608c4eb7bd6ba186f8058"},"query":{"kind":"string","value":"Return last average price by date for address"},"positive_passages":{"kind":"list like","value":[{"docid":"d64de475c378b783adcea981e8df5951","score":"0.7730563","text":"def last_avg_price(address: str, end_date: dt.date) -> Union[AvgPriceModel, None]:\n\n try:\n return (\n AvgPriceModel.select()\n .where(\n AvgPriceModel.address == address,\n AvgPriceModel.date < end_date,\n )\n .order_by(AvgPriceModel.date)[-1]\n )\n except IndexError:\n return None\n except PeeweeException as e:\n raise StoreException(\n f'Fail get average prices for daress: {address}, date: {end_date}.'\n ) from e","title":""}],"string":"[\n {\n \"docid\": \"d64de475c378b783adcea981e8df5951\",\n \"score\": \"0.7730563\",\n \"text\": \"def last_avg_price(address: str, end_date: dt.date) -> Union[AvgPriceModel, None]:\\n\\n try:\\n return (\\n AvgPriceModel.select()\\n .where(\\n AvgPriceModel.address == address,\\n AvgPriceModel.date < end_date,\\n )\\n .order_by(AvgPriceModel.date)[-1]\\n )\\n except IndexError:\\n return None\\n except PeeweeException as e:\\n raise StoreException(\\n f'Fail get average prices for daress: {address}, date: {end_date}.'\\n ) from e\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6fe6595bf749d4be707df8a261ccf555","score":"0.64064676","text":"def get_avg_prices_rows(offers: List[Dict[str, Any]]) -> List[Any]:\n avg_prices = []\n grouper = lambda x: x['address']\n for address, items in groupby(sorted(offers, key=grouper), key=grouper):\n items = list(items)\n date = items[0]['date']\n\n # avg price sq.m\n avg_price = sum([item['price'] / item['area'] for item in items]) / len(items)\n avg_price = round(avg_price, 2)\n\n # avg change\n avg_price_change = None\n if last_avg_price := AvgPriceModel.last_avg_price(address, date):\n avg_price_change = avg_price - last_avg_price.avg_price\n \n avg_prices.append([address, date, avg_price, avg_price_change])\n\n return avg_prices","title":""},{"docid":"b851f5f097af0936526d1a7ebd147854","score":"0.62795967","text":"def avg_prices(prices):","title":""},{"docid":"90470ba1dda201f3455d80628225eb2a","score":"0.5961269","text":"def address_price_duplicates(df, stats):\n\n # Dataframe of duplicate addresses and sale dates filtered, sorted by address and price\n df_dups = df[df.duplicated(['address', 'sale_date'], keep=False) == True].sort_values(by=['address', 'price'])\n\n # Price differences calculated for all duplicate properties\n df_dups['price_delta'] = df_dups.groupby(['address'])['price'].diff()\n\n # Electoral division mean is added to each property\n for i, x in df_dups.iterrows():\n if x['ed'] in stats.index:\n mean = stats.ix[x['ed'], 'mean']\n df_dups.ix[i, 'ed_mean'] = int(mean)\n\n # Price delta calculated\n df_dups['price_delta'] = abs(df_dups['price'] - df_dups['ed_mean'])","title":""},{"docid":"1607a66b6c15609d901722cdf2ad8ceb","score":"0.5822261","text":"def avg_price(self):\n raise NotImplementedError","title":""},{"docid":"b88d80f1f3417f4e949e783ec5480b40","score":"0.55665684","text":"def balance_latest(self, address: str, **kwargs) -> Dict:\n return self._address_query(\n address=address,\n route=\"account-balances/latest\",\n headers=self.initial_headers,\n params=kwargs,\n )","title":""},{"docid":"0940e6ade9e5372b6adf908f97c4f978","score":"0.55549246","text":"def check_price(self, date):\n return self.strategy.daily_avg_price(date)","title":""},{"docid":"2755f5c3837a7fc55a68ba867f86be73","score":"0.5517039","text":"def average_price(self, average_price):\n\n self._average_price = average_price","title":""},{"docid":"2755f5c3837a7fc55a68ba867f86be73","score":"0.5517039","text":"def average_price(self, average_price):\n\n self._average_price = average_price","title":""},{"docid":"b62e6d11f6219a72707e4cb08a835618","score":"0.548369","text":"def get_average_cost(user, symbol, price):\n r = {}\n t = Thread(\n target=get_previous_day_close_price, args=(symbol+\".NS\", r, \"cp\")\n )\n t.start()\n\n positions = Position.objects.filter(userid=user).order_by(\"id\")\n total_portfolio_units = positions.aggregate(units=Sum(\"size\"))\n\n symbol_positions = positions.filter(stockname__symbol=symbol)\n\n total_amount, total_units, total_return = 0.0, 0.0, 0.0\n\n if total_portfolio_units:\n total_portfolio_units = total_portfolio_units.get(\"units\", 0.0)\n if not total_portfolio_units:\n total_portfolio_units = 0.0\n else:\n total_portfolio_units = 0.0\n\n for position in symbol_positions:\n total_amount += (int(position.size) * float(position.transaction_details.price))\n total_units += int(position.size)\n\n if position.gl_history_position_rel.all():\n total_return += float(position.gl_history_position_rel.all()[0].unrealised_gainloss)\n\n # total_portfolio_units += position.size\n\n avg_cost = float(total_amount / total_units) if total_units else 0.0\n total_return_percent = float(total_return / total_amount) * 100 if total_amount else 0.0\n portfolio_diversity = float(total_units / total_portfolio_units) * 100.0 if total_portfolio_units else 0.0\n\n # Approach before Feb 8, 2021\n # today_return = (price - avg_cost)\n # today_return_percent = ((price - avg_cost) / avg_cost) * 100 if avg_cost else 0.0\n\n t.join()\n close_price = r.get('cp')\n today_return = (price - close_price)\n today_return_percent = (today_return / close_price) * 100.0 if close_price else 0.0\n today_return = today_return * total_units\n\n print(avg_cost, total_units, total_return, total_return_percent, today_return, \\\n today_return_percent, portfolio_diversity)\n\n return avg_cost, total_units, total_return, total_return_percent, today_return, \\\n today_return_percent, portfolio_diversity","title":""},{"docid":"7d29ca9e978be07e18aab1f6f9698c58","score":"0.5451101","text":"def date_average(self):\n avg = self._get_date('date-avg')\n if avg is None:\n start, end = self.date_start, self.date_end\n if start is not None and end is not None:\n avg = start + (end - start) / 2\n\n return avg","title":""},{"docid":"b50f009f9c5f5ad1d86d243e35b5c4bc","score":"0.5399872","text":"def get_last_price(assets):\n\n assets = utils.to_list(assets)\n data_feed_source = None\n ds = get_data_sources_dict(session = None)\n \n df = pd.DataFrame(columns = [\"asset\", \"last_price\"]).set_index(\"asset\")\n for asset in assets:\n data_source_names = utils.get_data_source_names_from_asset(asset = asset)\n\n for ds_name, ds_inst in ds.items():\n if ds_name in data_source_names:\n data_feed_source = ds_inst\n break\n\n try:\n lp = data_feed_source.get_last_price(assets = asset)\n except AttributeError:#If data_feed_source remains None\n lp = pd.DataFrame()\n \n df = pd.concat([df, lp])\n\n return df","title":""},{"docid":"18d965d0bc02c26d8beeca7c6545b88b","score":"0.5398656","text":"def get_closing_value_average_price(self, x):\n instr = str(x[TradeDataColumns.instrument.name])\n date = str(x[TradeDataColumns.date.name])\n condition = (self.df[TradeDataColumns.instrument.name] == instr) & (self.df[TradeDataColumns.date.name] == date)\n # Instrument closing value is last traded price\n instr_closing_value = self.df[condition].nlargest(1, TradeDataColumns.time_in_milsec.name)[TradeDataColumns.price.name].values[0]\n # Instrument average price is all instrument trades market values divided by total instrument quantity traded\n avg_price = self.df[condition][TradeDataColumns.market_val.name].sum()/self.df[condition][TradeDataColumns.quantity.name].sum()\n return instr_closing_value, avg_price","title":""},{"docid":"0f90575bb7513b38486b50d12375a9fe","score":"0.53256714","text":"def rent_avg(df, withARA=True):\n cols = ['Unnamed', 'Keskivuokra ARA-vuokra',\n 'Keskivuokra Vapaarah. vanhat', 'Keskivuokra Vapaarah. uudet']\n df = clean_rent(df)\n df.replace({'-': 0.0}, inplace=True)\n price = []\n lkm = []\n if df.empty:\n return np.nan\n else:\n df = df[df.columns[:4]]\n for i, row in df.iterrows():\n if row['Unnamed'] == 'Kaikki':\n price.append(float(row[cols[1]]))\n price.append(float(row[cols[2]]))\n price.append(float(row[cols[3]]))\n if row['Unnamed'] == 'Lkm':\n lkm.append(int(row[cols[1]]))\n lkm.append(int(row[cols[2]]))\n lkm.append(int(row[cols[3]]))\n if len(price) > 0 and len(price) == len(lkm):\n if withARA:\n mysum = sum([price[i]*lkm[i] for i in range(len(price))])\n if mysum != 0.0:\n return mysum/sum(lkm)\n else:\n return np.nan\n else:\n mysum = sum([price[i] * lkm[i] for i in range(1, len(price))])\n if mysum != 0.0:\n return mysum / (sum(lkm)-lkm[0])\n else:\n return np.nan","title":""},{"docid":"d7f81f6a22700ea6a58282773be342b4","score":"0.5323809","text":"def get_average(log_dict, numeric_var, max_date):\n past_vals = [ceil(log_dict[date][numeric_var] / log_dict[date]['no_runs']) for date in log_dict.keys() if date != max_date]\n if len(past_vals) > 1:\n mean_past_vals = mean(past_vals)\n elif len(past_vals) == 1:\n mean_past_vals = past_vals[0]\n else:\n mean_past_vals = 0\n return ceil(mean_past_vals)","title":""},{"docid":"f1e152c47e1f4e40e0d3f98ff23c280e","score":"0.52538943","text":"def get_last_price(self):\n if not self.last_price:\n self.last_price = float(self.df[self.price_column].tail(1))\n return self.last_price","title":""},{"docid":"f1e152c47e1f4e40e0d3f98ff23c280e","score":"0.52538943","text":"def get_last_price(self):\n if not self.last_price:\n self.last_price = float(self.df[self.price_column].tail(1))\n return self.last_price","title":""},{"docid":"1332df4e7aa69338ccbca6dcb79bd2f4","score":"0.51800114","text":"def get_daily_avg():\n conn = get_conn()\n curs = conn.cursor()\n curs.execute(\"select avg(tbl.num) from (select count(*) as num from events where type = 'open' group by DATE(ts)) as tbl\")\n ret = curs.fetchone()[0]\n curs.close()\n ret = round(float(ret), 3)\n return ret","title":""},{"docid":"78bbad12e81f9515bb6bc49310815bf9","score":"0.51718265","text":"def averagePrice(L):\n return sumList(L) / len(L)","title":""},{"docid":"c2d8cbf584d8301d4757c187f5f8fd53","score":"0.5153308","text":"def get_average_price(data_set, key='price'):\n total, counter = 0, 0\n for row in data_set:\n total += int(row[key])\n counter += 1\n return total // counter","title":""},{"docid":"fa881da3ef440335a1c0271e1aa1df2e","score":"0.50950754","text":"def calc_average(last, current, share_of_use):\n return last + current * share_of_use","title":""},{"docid":"8edeb15ac700490664d3fdad9da1a71d","score":"0.5094853","text":"def get_trailing_stop_pct(df):\n latest_price = df.iloc[-1]","title":""},{"docid":"aaf18303592d5a54d845c5cc2475fda3","score":"0.5066243","text":"def printing_get_date_avg(file_name):\n result = reports.get_date_avg(file_name)\n print (\"The average selling date is:\", result)\n print()","title":""},{"docid":"b335279f77a58df016a3a69ad236610a","score":"0.5051108","text":"def get_average_temp_by_date(date_string, connection):\n # Target date\n target_date = datetime.strptime(date_string, \"%Y-%m-%d\").date()\n min_date = target_date + timedelta(days=-3)\n max_date = target_date + timedelta(days=3)\n\n cursor = connection.cursor()\n sql = \"\"\"\n SELECT\n avg(value) as average\n FROM temperature_data\n WHERE date between ? and ?\n \"\"\"\n result = cursor.execute(sql, (min_date, max_date)).fetchone()\n return result[0] if result else None","title":""},{"docid":"626d99a1f398b54fbc16f503627ac176","score":"0.5007213","text":"def avg_trend(date):\n today = datetime.now()\n if today < convert_to_datetime(date): # Check if a future date is passed.\n return False, \"Can't get stock data for the future dates!\"\n elif convert_to_datetime(date).weekday() > 4: # Check if given date is a weekend.\n return False, \"Its a weekend, market closed on this date.\"\n else:\n result = db.comm_trend(date)\n if result is None: # If database is empty.\n return False, \"No company yet added.\"\n elif result is False: # Missing record/weekday holiday.\n return False, \"No records available for particular date.\"\n else:\n logger.info(\"Average of the date calculated.\")\n return True, result","title":""},{"docid":"d0d3cc0d5d2b97141e5b173a0fc97691","score":"0.50068915","text":"def address_date_duplicates(df):\n\n # Date column converted to datetime type\n df['sale_date'] = pd.to_datetime(df['sale_date'])\n\n # Duplicate properties isolated, sorted by address and sale date\n df_dups = df[df.duplicated(['address', 'price'], keep=False) == True].sort_values(by=['address', 'sale_date'])\n\n # Earliest sale date in the data set isolated\n min_date = df_dups['sale_date'].min()\n\n # Each sale date converted to integer based on the number of days it is away from min_date\n df_dups['date_int'] = (df_dups['sale_date'] - min_date).dt.days\n\n # Date delta calculated\n df_dups['date_delta'] = df_dups.groupby(['address'])['date_int'].diff()\n\n # Lambda function to highlight the properties of interested, based on a maximum delta gap\n # Default: 7 days\n df_dups['duplicates_of_interest'] = df_dups['date_delta'].apply(lambda x: 1 if x < 7 else 0)","title":""},{"docid":"8741be2efc97dbfe4f3c6d6369a7340f","score":"0.50020635","text":"def get_last_price(self, currency):\r\n\r\n return None","title":""},{"docid":"f1a8cba98954f8d044b48511eebf8003","score":"0.5001588","text":"def getDiscountedAverageAtGivenTime(self, timeIndex):","title":""},{"docid":"0964ce78e8bf0db11ccc5f472807eecd","score":"0.49431843","text":"def z_average(symbol):\n # Can't take average if the symbol evaluates on edges\n if symbol.evaluates_on_edges(\"primary\"):\n raise ValueError(\"Can't take the z-average of a symbol that evaluates on edges\")\n # Symbol must have domain [] or [\"current collector\"]\n if symbol.domain not in [[], [\"current collector\"]]:\n raise pybamm.DomainError(\n \"\"\"z-average only implemented in the 'current collector' domain,\n but symbol has domains {}\"\"\".format(\n symbol.domain\n )\n )\n # If symbol doesn't have a domain, its average value is itself\n if symbol.domain == []:\n return symbol\n # If symbol is a Broadcast, its average value is its child\n elif isinstance(symbol, pybamm.Broadcast):\n return symbol.reduce_one_dimension()\n # Average of a sum is sum of averages\n elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)):\n return _sum_of_averages(symbol, z_average)\n # Otherwise, define a ZAverage\n else:\n return ZAverage(symbol)","title":""},{"docid":"9adb7d8daa9a5459da1afc61882e6f5b","score":"0.49081734","text":"def CalcAvg(db: List[CurrencyRow], start_idx: int, back_count: int) -> float:\n # 1. safe check\n ret = _validate(db, start_idx, back_count)\n if -1 == ret:\n return -1\n\n # 2 avoid back count exceed the begin of the list\n begin_idx = start_idx - back_count\n if begin_idx < 0:\n begin_idx = 0\n\n # 3. calc average\n sum_all = 0.0\n for i in range(begin_idx, start_idx):\n sum_all += db[i].close\n\n avg = sum_all / back_count\n\n #print(\"Calc avg: %.5f\" % avg)\n return avg","title":""},{"docid":"c5f7b82b833d631d3cfe414057849451","score":"0.49029326","text":"def calculate_shipment_value(address: dict) -> float:\n\n try:\n with open(\"Utils/shipment/shipping_prices.json\", \"r\") as read_file:\n data = json.loads(read_file.read())\n except Exception as error:\n return error.args[0]\n for state in data:\n if state == address['user']['address']['address_state']:\n shipping_value = data[state]\n return shipping_value","title":""},{"docid":"40c80270cf52e461010f92367b43d28f","score":"0.48676726","text":"def YearlyAverage(lon, lat, field, month_start = 1, month_end = 12):\n\n\t#Take twice the amount of years for the month day\n\tmonth_days\t= np.asarray([31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31., 31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31.])\n\tmonth_days\t= month_days[month_start - 1:month_end]\n\tmonth_days\t= month_days / np.sum(month_days)\n\n\t#Fill the array's with the same dimensions\n\tmonth_days_all\t= ma.masked_all((len(month_days), len(lat), len(lon)))\n\n\tfor month_i in range(len(month_days)):\n\t\tmonth_days_all[month_i]\t\t= month_days[month_i]\n\n\t#Determine the mean\n\tfield_mean\t= np.sum(field * month_days_all, axis = 0)\n\n\treturn field_mean","title":""},{"docid":"5550071743b44ee4affd52cde93bb586","score":"0.48472494","text":"def calculate_avg_difference(initial_price, market_price):\n assert len(initial_price) == len(market_price)\n total_difference = 0\n for item in range(len(initial_price)):\n total_difference += market_price[item] - initial_price[item]\n\n return total_difference / len(initial_price)","title":""},{"docid":"766d09ee3ce0db391acd0a96bbfd059d","score":"0.48337725","text":"def get_daily_average_roundup(self, user_id):\n Transaction = apps.get_model('finance', 'Transaction')\n sum = Transaction.objects\\\n .filter(account__item__user_id=user_id)\\\n .aggregate(Sum('roundup'))['roundup__sum']\n\n if not sum:\n return 0\n\n first_transaction = Transaction.objects.filter(\n account__item__user_id=user_id).earliest('date')\n\n dt = first_transaction.date\n delta = datetime.date.today() - dt\n return sum / delta.days","title":""},{"docid":"5aac450ead67f336d2bd04f759f680d4","score":"0.48013544","text":"def tail_avg(timeseries):\n timeseries = np.array(timeseries)\n timeseries = timeseries[:, 1:]\n try:\n t = (timeseries[-1] + timeseries[-2] + timeseries[-3]) / 3\n return t\n except IndexError:\n return timeseries[-1]","title":""},{"docid":"72c4e3c977996af96e66fdf12861682e","score":"0.4784675","text":"def moving_average(self, symbol, period):\n self.period = period\n self.symbol = symbol\n symbol = symbol.upper()\n period = period.lower()\n url = \"https://fcsapi.com/api-v2/forex/ma_avg?symbol=\"+symbol+\"&period=\"+period+\"&access_key=\"+self.access_key\n payload = {}\n headers = {}\n response = requests.request(\"POST\", url, headers=headers, data = payload)\n res = json.loads(response.text.encode('utf8'))\n #Load the dict manually\n MA5=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA5\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA5\"][\"s\"])\n MA10=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA10\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA10\"][\"s\"])\n MA20=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA20\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA20\"][\"s\"])\n MA50=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA50\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA50\"][\"s\"])\n MA100=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA100\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA100\"][\"s\"])\n MA200=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA200\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"EMA\"][\"MA200\"][\"s\"])\n\n EMA = rdt.r_moving_average.EMA(\n MA5= MA5,\n MA10= MA10,\n MA20= MA20,\n MA50= MA50,\n MA100= MA100,\n MA200= MA200,\n )\n\n MA5=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA5\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA5\"][\"s\"])\n MA10=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA10\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA10\"][\"s\"])\n MA20=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA20\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA20\"][\"s\"])\n MA50=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA50\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA50\"][\"s\"])\n MA100=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA100\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA100\"][\"s\"])\n MA200=rdt.r_moving_average.v_s(\n v=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA200\"][\"v\"],\n s=res[\"response\"][\"ma_avg\"][\"SMA\"][\"MA200\"][\"s\"])\n\n SMA = rdt.r_moving_average.SMA(\n MA5= MA5,\n MA10= MA10,\n MA20= MA20,\n MA50= MA50,\n MA100= MA100,\n MA200= MA200,\n )\n\n ma_avg = rdt.r_moving_average.ma_avg(\n SMA=SMA,\n EMA=EMA,\n summary=res[\"response\"][\"ma_avg\"][\"summary\"]\n )\n\n count = rdt.r_moving_average.count(\n Total_Buy = res[\"response\"][\"count\"][\"Total_Buy\"],\n Total_Sell = res[\"response\"][\"count\"][\"Total_Sell\"], \n Total_Neutral = res[\"response\"][\"count\"][\"Total_Neutral\"], \n maBuy = res[\"response\"][\"count\"][\"maBuy\"], \n maSell = res[\"response\"][\"count\"][\"maSell\"]\n )\n response = rdt.r_moving_average.response(\n oa_summary = res[\"response\"][\"oa_summary\"],\n count = count,\n ma_avg = ma_avg\n )\n #Load info object\n info = rdt.r_moving_average.info(\n id=res[\"info\"][\"id\"],\n decimal = res[\"info\"][\"decimal\"],\n symbol = res[\"info\"][\"symbol\"],\n period = res[\"info\"][\"period\"],\n disclaimer = res[\"info\"][\"disclaimer\"],\n update=res[\"info\"][\"update\"],\n update_time=res[\"info\"][\"update_time\"],\n server_time=res[\"info\"][\"server_time\"],\n credit_count=res[\"info\"][\"credit_count\"]\n )\n #Load the main object\n obj = rdt.r_moving_average(\n status=res[\"status\"],\n code=res[\"code\"],\n msg=res[\"msg\"],\n response=response,\n info=info\n )\n\n return obj","title":""},{"docid":"55b883d07c09b142f478ba25f7ab9dd8","score":"0.47808328","text":"def yz_average(symbol):\n # Symbol must have domain [] or [\"current collector\"]\n if symbol.domain not in [[], [\"current collector\"]]:\n raise pybamm.DomainError(\n \"\"\"y-z-average only implemented in the 'current collector' domain,\n but symbol has domains {}\"\"\".format(\n symbol.domain\n )\n )\n # If symbol doesn't have a domain, its average value is itself\n if symbol.domain == []:\n return symbol\n # If symbol is a Broadcast, its average value is its child\n elif isinstance(symbol, pybamm.Broadcast):\n return symbol.reduce_one_dimension()\n # Average of a sum is sum of averages\n elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)):\n return _sum_of_averages(symbol, yz_average)\n # Otherwise, define a YZAverage\n else:\n return YZAverage(symbol)","title":""},{"docid":"15d0af5cb067c92767c68ef459ba752d","score":"0.47733572","text":"def average_data(self):\n self.month_total, self.month_average = 0, 0\n self.entry_count = 0\n\n if self.day_count == 0:\n return\n\n for day_data in self.data:\n day_total, day_average = day_data.total_sum, day_data.average\n self.entry_count += day_total // day_average\n self.month_total += day_total\n self.month_average = self.month_total // self.day_count","title":""},{"docid":"e0da6579c580d57beadb2a97279bbd6b","score":"0.47654012","text":"def date_price_duplicates(df):\n # Dataframe of duplicate price, sale dates and electoral division filtered, sorted by sale date and price\n df_dups = df[df.duplicated(['price', 'sale_date', 'ed']) == True].sort_values(by=['sale_date', 'price'])\n\n # Strip the house/apartment number from the beginning of the address\n df_dups['address_strip'] = df_dups['address'].apply(lambda x: int_strip(x))","title":""},{"docid":"1c8708159f0fd93c71f0f02567e415c9","score":"0.4764286","text":"def prices_asof(prices, date):\n date = pd.to_datetime(date)\n return prices.iloc[prices.index.get_loc(date, 'ffill')]","title":""},{"docid":"d9935a33baf7b7d1e50aa3f73693e886","score":"0.4758576","text":"def tail_avg(current_skyline_app, X, Y, timeseries, second_order_resolution_seconds, series, sigma_value):\n try:\n # t = (timeseries[-1][1] + timeseries[-2][1] + timeseries[-3][1]) / 3\n t = sum(Y[-sigma_value:]) / sigma_value\n return t\n except IndexError:\n return timeseries[-1][1]","title":""},{"docid":"4b49d673882ad863fa6a965c1c33bdb3","score":"0.47511798","text":"def monthly_average(merged_data):\n # Calculating the daily average from the database\n a = merged_data.groupby(merged_data.index.strftime(\"%m\"))\n return a.mean()","title":""},{"docid":"f233a799bc32d2b3558935427f1e09f8","score":"0.47470573","text":"def get_latest_price_date(self):\n return self.latestrealpricedate, self.latestrealprice","title":""},{"docid":"bb4c46f9bf2d143b5656ade6a1ad4dc3","score":"0.4731979","text":"def _set_moving_average(self, prices, window_size=MOVING_AVERAGE_WINDOW):\n prices_stack = []\n for price in prices:\n prices_stack.append(price[\"close\"])\n if len(prices_stack) == window_size:\n price[\"average\"] = np.mean(prices_stack)\n prices_stack.pop(0)\n else:\n price[\"average\"] = price[\"close\"]","title":""},{"docid":"1eba257a876ef2fd36a3b1d385e8dac2","score":"0.4728367","text":"def avg_amount(table, year):\n profit = get_profit_for_each_year(table)\n average = None\n for year_data in profit:\n if int(year_data[0]) == year:\n average = year_data[1]/year_data[2]\n return average","title":""},{"docid":"a752d9cb096118991a1fa06b2072eb6e","score":"0.47211987","text":"def sold_avg(df):\n df = clean_sale(df)\n if df[df.columns[5]].empty:\n return np.nan\n else:\n return df[df.columns[5]].mean()","title":""},{"docid":"2984a0ce99485527b77d7794cd974b14","score":"0.47099993","text":"def findAvg(couples, offspring, proportion):\n return couples * offspring * proportion","title":""},{"docid":"66b9916e8edc5bf7f6229d4a87586325","score":"0.47098908","text":"def daily_average_of_trips(self, dateframe: DataFrame) -> DataFrame:\n self.__logger.info(\"Transforming dataframe to get daily average of trips\")\n return dateframe.groupBy(\"tpep_dropoff_date\").agg(\n avg(\"trip_distance\").alias(\"daily_avg_trip_distance\")\n )","title":""},{"docid":"4dbaf1dc3339a4720c9ded1e9766e014","score":"0.47082162","text":"def _rolling(self, period, average=True):\n\n days = parse_period(period)\n rolling = self.data.price.pct_change(days).dropna()\n if average:\n return rolling.mean()\n else:\n return rolling","title":""},{"docid":"81841ab08eb549a3942e43ef00df33ba","score":"0.47032025","text":"def calc_average_age_by_year(ppl):\n \n \n age_earlier =[]\n age2013 =[]\n age2014 = []\n age2015 =[]\n age2016=[]\n\n years = [\"before 2013\",\"2013\",\"2014\",\"2015\",\"2016\"]\n\n\n for person in ppl:\n\n for program in person['Programs']:\n ad_date = program['Admission date']\n \n if (get_date_from_string(ad_date)).year == 2013:\n age = calc_age(person['DOB'], program['Admission date'])\n age2013.append(age.days/365.0)\n\n elif (get_date_from_string(ad_date)).year == 2014:\n age = calc_age(person['DOB'], program['Admission date'])\n age2014.append(age.days/365.0)\n \n elif (get_date_from_string(ad_date)).year == 2015:\n age = calc_age(person['DOB'], program['Admission date'])\n age2015.append(age.days/365.0)\n \n elif (get_date_from_string(ad_date)).year == 2016:\n age = calc_age(person['DOB'], program['Admission date'])\n age2016.append(age.days/365.0)\n \n else:\n age = calc_age(person['DOB'], program['Admission date'])\n age_earlier.append(age.days/365.0)\n \n \n age_list = [age_earlier, age2013, age2014, age2015, age2016]\n\n for age,year in zip(age_list,years):\n\n if len(age)>0:\n ave_age = np.mean(age)\n print(\"Average age for %-12s: %4.2f \" % (year,ave_age))\n else:\n print(\"No indviduals for %-12s\" % (year))\n \n return age_list","title":""},{"docid":"f816e2d2e98c379a9026259da8a68e8a","score":"0.47023392","text":"def averageVolumeLastMonth(self) -> float:\n return self.__averageVolumeLastMonth","title":""},{"docid":"22b3a949030d96571bc55f87bb2b5bb4","score":"0.47021013","text":"def get_average(self):\n self.average = sum(self.time_array)/float(len(self.time_array))\n return self.average","title":""},{"docid":"76743354cbe1570764b54ee11e6783fd","score":"0.47015372","text":"def predict_price(zipcode, test_date, hometype, all_parameters, all_means):\n if (check_date(test_date)):\n base_result = timeseries_predict(all_parameters, zipcode, test_date)\n if (type(base_result)!=str):\n final_result = adjust_predict(all_means, base_result, hometype, zipcode)\n return(final_result)\n else:\n return(base_result)\n else:\n return(\"The input date is invalid.\")","title":""},{"docid":"c5b8defcd66bd2091cd465d37e6d089a","score":"0.46900716","text":"def get_average_per_state(meta_data, weather_data, state):\n one_state = meta_data[meta_data['state'] == state]\n\n state_set = set(one_state['station_id'])\n weather_set = set(weather_data['station_id'])\n intersection_list = list(weather_set & state_set)\n\n print(\n f'There are {len(intersection_list)} stations in the state of {state}')\n\n all_stations_in_state = weather_data[weather_data['station_id'].isin(\n intersection_list)]\n pivoted = pd.pivot_table(all_stations_in_state, index=[\n 'station_id', 'measurement_date'],\n columns='measurement_type', values='measurement_flag')\n grouped_by_day = pivoted.groupby('measurement_date').mean()\n\n return grouped_by_day","title":""},{"docid":"bf9260af9dcd7f980ded1d481c00acb4","score":"0.4689363","text":"def get_highest_values(ordered_list):\n highest = f\"{ordered_list[-1]['promo_price']}{ordered_list[-1]['currency']}\"\n highest_date = strptime_to_str(ordered_list[-1][\"timestamp\"])\n highest_price_discount = (1 - float(ordered_list[-1][\"promo_price\"]) / float(\n ordered_list[-1][\"regular_price\"])) * 100\n highest_bookstore = ordered_list[-1][\"bookstore\"]\n return highest, highest_date, highest_price_discount, highest_bookstore","title":""},{"docid":"b643d65d0587a1cc2cf45923593df4d3","score":"0.46729523","text":"def balance_historical(self, address: str, **kwargs) -> Dict:\n return self._address_query(\n address=address,\n route=\"account-balances/historical\",\n headers=self.initial_headers,\n params=kwargs,\n )","title":""},{"docid":"22a0abc4366522496b0383fdc068fb50","score":"0.46631682","text":"def population_calculate_average(population):\n return sum(individual[1] for individual in population) / len(population)","title":""},{"docid":"48734e0b9d38104ef7f3f9d72947384d","score":"0.466062","text":"def _get_last_avail_price_date(self):\n return self.client.send_sql_request('SELECT MAX(timestamp) FROM {}'.format(self.indices['price']))","title":""},{"docid":"ee8cd6e55b82645462150ee473d456a1","score":"0.46571276","text":"def get_monthly_field_averages(fieldname):\n dbconn = opendb()\n curs = dbconn.cursor()\n \n fieldid = get_fieldid_for_field(fieldname)\n\n curs.execute(\"SELECT lat,lng,myquery.locid,monthtime,avggeoval FROM \" +\\\n \"(SELECT locid,EXTRACT(month FROM geotimespace.date) \"+\\\n \"AS monthtime,AVG(geoval) AS avggeoval FROM geotimespace \"+\\\n \"JOIN geovalue ON geovalue.geotsid = geotimespace.geotsid \"+\\\n \"WHERE geofieldid=%s AND locid IN \"+\\\n \"(SELECT locid FROM location WHERE sourceid = 'ground') \"+\\\n \"GROUP BY EXTRACT(month FROM geotimespace.date),locid ORDER BY locid,monthtime) \"+\\\n \"AS myquery JOIN location ON location.locid=myquery.locid;\", (fieldid,))\n return curs.fetchall()","title":""},{"docid":"668e1834b481944fbdf1a670bbd2bd5e","score":"0.46509257","text":"def price(self, date, exact=False):\n if isinstance(date, str):\n date = pd.Timestamp(datetime.strptime(date, '%Y-%m-%d')).to_numpy()\n if date not in self.data.index:\n if not exact:\n date = self._nearest(date)\n else:\n raise ValueError(f'Requested date {date} not in data')\n return self.data.loc[date].price","title":""},{"docid":"d0ffa244330c2b605f71a1ff138145a8","score":"0.46472782","text":"def get_last_es_data(self, ip_address):\n es_query = {\n \"size\": 1,\n \"sort\": [{\"@timestamp\": {\"order\": \"desc\"}}],\n \"query\": {\n \"bool\": {\n \"filter\": [\n {\n \"range\": {\n \"source.greynoise.query_timestamp\": {\n \"gte\": f\"now-{self.cache}s\",\n \"lte\": \"now\",\n }\n }\n },\n {\"term\": {\"tags\": \"enrich_greynoise\"}},\n {\"term\": {\"host.ip\": ip_address}},\n ]\n }\n },\n }\n\n es_results = raw_search(es_query, index=\"redirtraffic-*\")\n\n self.logger.debug(es_results)\n\n # Return the latest hit or False if not found\n if es_results and len(es_results[\"hits\"][\"hits\"]) > 0:\n return es_results[\"hits\"][\"hits\"][0]\n return False","title":""},{"docid":"aae03bd0954c1c4a387d021235e542d5","score":"0.46382073","text":"def get_return(values,beg_date,end_date):\r\n beg = values.loc[beg_date:end_date].head(1).values[0][0]\r\n end = values.loc[beg_date:end_date].tail(1).values[0][0]\r\n ret = (end/beg-1)*100\r\n sigma = values.loc[beg_date:end_date].pct_change().std().values[0]*100 \r\n return ret, sigma","title":""},{"docid":"31414e7f7e41a1de146e40c6c5d56dd6","score":"0.4632582","text":"def get_benchmark_returns(symbol, first_date, last_date):\n # data = pd_reader.DataReader(\n # symbol,\n # 'yahoo',\n # first_date,\n # last_date\n # )\n data = pd.read_csv('/Users/jonatjao/pdev/trade/backtest/SPY.csv', index_col=0, parse_dates=['date'])\n data = data['close']\n\n data[pd.Timestamp('2008-12-15')] = np.nan\n data[pd.Timestamp('2009-08-11')] = np.nan\n data[pd.Timestamp('2012-02-02')] = np.nan\n\n data = data.fillna(method='ffill')\n\n return data.sort_index().tz_localize('UTC').pct_change(1).iloc[1:]","title":""},{"docid":"50c20b50f4b17b2a5b8e134c26a08e37","score":"0.4629102","text":"def get_last_price(symbol):\n if not symbol:\n return None\n\n last_price = cache.get(f\"{symbol}_prev_price\")\n last_price_exp = cache.get(f\"{symbol}_prev_price_expiry\")\n\n if last_price and datetime.datetime.now() < last_price_exp:\n return last_price\n\n try:\n market_opened = is_market_opened_date()\n if market_opened:\n start = get_previous_business_day()\n else:\n start = get_previous_business_day(get_previous_business_day())\n\n last = si.get_data(symbol+\".NS\", start_date=start, end_date=start)\n if last.empty:\n return None\n last = last.iloc[-1]['close']\n cache.set(f\"{symbol}_prev_price\", last)\n cache.set(\n f\"{symbol}_prev_price_expiry\", get_day_range(datetime.datetime.now() + datetime.timedelta(days=1))[0]\n )\n return last\n except:\n return None","title":""},{"docid":"07802ba07611678efc3d693cb857862e","score":"0.46281484","text":"def calculate_daily_average_power_out(df=None, var=None):\n df_grp = df.groupby(['date_sent'])[var].mean()\n df_grp = pd.DataFrame(df_grp).reset_index()\n df_grp.sort_values(by='date_sent', inplace=True)\n df_grp.insert(0, 'day', range(1, len(df_grp) + 1))\n df_grp.rename(columns={var: 'avg_' + var}, inplace=True)\n \n return df_grp","title":""},{"docid":"5b6a709aef51ca043fa46a12709b88fc","score":"0.46046036","text":"def daily_profile_process(df):\n df = df.copy()\n df['weekday'] = df['date'].apply(lambda x: x.weekday())\n return df.groupby('weekday')['value'].agg(['sum', 'mean'])","title":""},{"docid":"540e2756990b03685dd6cca53a184fd5","score":"0.46021625","text":"def compute_average(self):\n if self.seconds != 0:\n self.average = float(self.requests) / self.seconds","title":""},{"docid":"de25adc3a9c425f6f8bccf8a8daf9654","score":"0.4600605","text":"def private_electric_spots_by_avg_brutto_income(parking_df, brutto_income_df):\n private_by_district_df = parking_df[parking_df['vejstatus'] == 'Privat fællesvej'].groupby('bydel')['antal_pladser'].agg(np.sum)\n electric_by_district_df = parking_df[parking_df['p_ordning'] == 'El-Bil plads'].groupby('bydel')['antal_pladser'].agg(np.sum)\n districts_list = private_by_district_df.index.tolist()\n avg_income_per_district = calculate_income_per_district(brutto_income_df, True)\n # print(avg_income_per_district)\n plt.private_electric_avg_income_multi_plot(districts_list, private_by_district_df.tolist(), electric_by_district_df.tolist(), avg_income_per_district)","title":""},{"docid":"1cfb30e042174a2b0fcf99a72c5f9b81","score":"0.45991313","text":"def calculate_return(start_date, end_date, price_df):\r\n return [(x / y) - 1 for x, y in\r\n zip(price_df[price_df.index == end_date].values, price_df[price_df.index == start_date].values)]","title":""},{"docid":"e2a92c67c2e5f7a320c66b872a1ad284","score":"0.45988208","text":"def average(self):\n return (self.current + self.last) / 2.0","title":""},{"docid":"457438b3b3977828aed560692e2974b4","score":"0.45890778","text":"def get_todays_prices(self, symbol):\n # needs to be changed sometime in the future into an optimized solution!\n path = os.path.join(os.getcwd(), 'ml_predictor', 'data', 'ml_format')\n _, y = data_handler.load_from_npz(symbol, path)\n return y[-1]","title":""},{"docid":"6c3365573d1eb6e221db56a9b57f6128","score":"0.4584877","text":"def get_persons_closest_to_average(table):\n\n current_year = 2017\n ages = [current_year - int(record[2]) for record in table]\n sum_ages = calculate_add(ages)\n average_age = sum_ages / len(table)\n\n names = []\n smallest_difference_to_avg = abs(current_year - int(table[0][2]) - average_age)\n for record in table:\n difference_to_avg = abs(current_year - int(record[2]) - average_age)\n\n if difference_to_avg < smallest_difference_to_avg:\n smallest_difference_to_avg = difference_to_avg\n names = [record[1]]\n elif difference_to_avg == smallest_difference_to_avg:\n names.append(record[1])\n\n return names","title":""},{"docid":"8cca4a2d30d71b1f8f896d51a2f2a879","score":"0.45811087","text":"def get_past_price(cls, date):\n data = cls._get_historical_data(date)\n price = data[\"bpi\"][date]\n return apply_format(str(price))","title":""},{"docid":"b11824c2e5fec65dd17d7d0be36c2bde","score":"0.45701414","text":"def get_acquired_cost(self):\n held_tradeable = requests.get('https://api.lemon.markets/rest/v1/accounts/{0}/portfolio/{1}/aggregated/'.format(self.account.uuid, self.isin), headers={'Authorization': self.account.auth})\n held_tradeable.raise_for_status(); held_tradeable = held_tradeable.json()\n \n if len(held_tradeable) <= 0: return -1\n return held_tradeable['average_price']","title":""},{"docid":"a726e24a445280e37312d8fe43281440","score":"0.4560129","text":"def calculate_mean(weather_data):\n pass","title":""},{"docid":"caaaaab1d438f0e08d046c6361457de3","score":"0.45600042","text":"def take_average(func):\n\n def inner(*args, **kwargs):\n results = _results(\n args[0],\n func.__name__,\n *args,\n **kwargs\n )\n\n sum_of_results = sum(results)\n\n if sum_of_results == 0:\n return sum_of_results\n\n return sum_of_results / len(results)\n\n return inner","title":""},{"docid":"5de2a12f0d09441dbbe6da8042e90883","score":"0.45590493","text":"def averageFillPrice(self) -> float:\n return self.__averageFillPrice","title":""},{"docid":"07be0ce36fc6a95b19f1db36e6a82320","score":"0.45579508","text":"def last_price(self, last_price):\n\n self._last_price = last_price","title":""},{"docid":"1614864b0e662b3419a7110538ffc467","score":"0.4545841","text":"def extract_cotations(index_list, previous_date):\n\n #If no more recent Trading day then abort\n if previous_date >= da.get_last_date():\n log_print(\"Pas de nouvelle cotation, arrêt de la procédure\")\n return\n\n\n #Else compute\n df = da.get_data(index_list)\n\n #Integration of a synthetic value to compute trends for all indexes\n\n syn_df = df.groupby('Trading_day', as_index=False, sort=False)['Calendar+1'].mean()\n\n syn_df['Calendar+2'] = df.groupby('Trading_day',\\\n as_index=False, sort=False)['Calendar+2'].mean()['Calendar+2']\n\n syn_df['Calendar+3'] = df.groupby('Trading_day',\\\n as_index=False, sort=False)['Calendar+3'].mean()['Calendar+3']\n\n\n syn_df['Synthetic'] = (syn_df['Calendar+1'] + syn_df['Calendar+2'] +\\\n syn_df['Calendar+3'])/3\n\n #Classement par date descendante utilisée pour les opérations suivantes\n syn_df = syn_df.sort_values('Trading_day', ascending=False)\n\n log_print(\"Synthetic index ready\")\n\n #Prix lors de l'appel d'offre\n origin_synthetic = round(syn_df.loc[syn_df['Trading_day'] == '2018-06-06']\\\n ['Synthetic'].item(), 2)\n\n #Dernier prix disponible a priori le closing de la veille.\n last_synthetic = round(syn_df.iloc[0]['Synthetic'].item(), 2) #Since we sorted we can use it\n\n\n #Evaluation of price evolution trends.\n tendance = 'BAISSE'\n previous_synthetic = round(syn_df.iloc[1]['Synthetic'].item(), 2)\n\n if last_synthetic > previous_synthetic:\n tendance = 'HAUSSE'\n tendance_pct = round((last_synthetic - previous_synthetic)/previous_synthetic*100, 2)\n\n texte = \"\\nRappel: L'index de prix synthétique est une moyenne sur les hubs \" +\\\n \"PEG Nord et TTF à partir des produits Cal+1, Cal+2 et Cal+3.\\n \" +\\\n \"Les prix sont obtenus via Powernext avec un jour de décalage et l'index est un \" +\\\n \"proxy pour les variations du prix de la molécule des offres fournisseurs.\\n\" +\\\n f\"\\nIndex du 6 juin : <b>{origin_synthetic} €/MWh</b>\\n\"+\\\n f\"Index à date : <b>{last_synthetic} €/MWh</b>\\n\"+\\\n \"Ecart de l'index à date vs l'index de l'appel d'offre) du 6 juin : \"+\\\n f\"<b>{round((last_synthetic - origin_synthetic)/origin_synthetic*100,2)}%</b>\\n\" +\\\n f\"\\nTendance de l'index par rapport à la cotation précédente : {tendance} de \" +\\\n f\"<b>{abs(tendance_pct)}%</b>\"\n\n #Computation of graphics\n fig = plt.figure()\n plt.plot(syn_df['Trading_day'], syn_df['Synthetic'])\n plt.suptitle(\"Evolution du prix de l'index gazier\", fontsize=15)\n plt.xlabel('Date', fontsize=13)\n plt.ylabel('€/MWh', fontsize=13)\n #Size\n fig.set_size_inches(9, 6)\n #Annotate\n plt.annotate(\"Appel d'offre du 6 juin 2018\",\n xy=(date(2018, 6, 6), origin_synthetic), xycoords='data',\n xytext=(-90, -50), textcoords='offset points', fontsize=10,\n arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n\n picture_path = os.path.join(FILE_PATH, 'Data', 'img.png')\n fig.savefig(picture_path)\n #plt.show()\n\n #Emails sending\n for receiver in RECEIVERS_EMAIL_LIST:\n send_html_email(receiver,\n f\"Evolution prix du gaz: {last_synthetic}€/MWH en {tendance}\",\n texte, picture_path)\n log_print(\"Emails sent\")","title":""},{"docid":"ec93ccbb45973e1a9f969f5cb6a10684","score":"0.4541558","text":"def yearly_average(arr, dt):\n assert_matching_time_coord(arr, dt)\n yr_str = TIME_STR + '.year'\n # Retain original data's mask.\n dt = dt.where(np.isfinite(arr))\n return ((arr*dt).groupby(yr_str, restore_coord_dims=True).sum(TIME_STR) /\n dt.groupby(yr_str, restore_coord_dims=True).sum(TIME_STR))","title":""},{"docid":"f20105c6a4c5e79367fb8d71f2df22eb","score":"0.45408043","text":"def sevenDayAverage(data):\n smooth = data.copy()\n for idx in range(len(data)):\n try:\n smooth[idx,1] = np.mean(data[idx-3:idx+3,1])\n except ZeroDivisionError:\n smooth[idx,1] = np.nan\n \n return smooth","title":""},{"docid":"ba4d9521e4cc89aaf9a306cc2f131f36","score":"0.45395842","text":"def _original_price(self):\n original_price = (self.baskets\n .groupby('product', as_index=False)['price']\n .max()\n .rename(columns={'price': 'original_price'})\n )\n\n return original_price","title":""},{"docid":"b96e2c5cf6d0305097b6837e6a75feed","score":"0.45390084","text":"def _compute_date_last_payroll(self):\n\t\tpayslip_obj = self.env['hr.payslip']\n\n\t\tfor employee in self:\n\n\t\t\tpayslip = payslip_obj.search([('employee_id', '=', employee.id),\n\t\t\t\t('employee_id.active', '=', True)],\n\t\t\t\torder='date_to desc', limit = 1)\n\t\t\t\n\t\t\tif payslip:\n\n\t\t\t\temployee.date_last_payroll = payslip.date_to","title":""},{"docid":"7db331c7c375bbc545df303200174e13","score":"0.4536336","text":"def excess_deaths_district(place='Birmingham',save=False):\n\tdistrict=CovidWeek.objects.filter(areaname=place,week__range=RANGE_WEEK).order_by('week')\n\tareacode=district[0].areacode\n\n\tall_deaths_2020=0\n\tall_carehome_deaths_2020=0\n\t_count=0 #count the number of weeks with data\n\t\n\n\t\n\tfor i in district:\n\t\tif i.weeklyalldeaths is None: #check for when data runs out\n\t\t\tbreak\n\t\tall_deaths_2020+=i.weeklyalldeaths\n\t\tif i.weeklycarehomedeaths is not None:\n\t\t\tall_carehome_deaths_2020+=i.weeklycarehomedeaths\n\t\t_count+=1\n\t\t\n\tdatarange=[RANGE_WEEK[0],RANGE_WEEK[0]+_count-1]\n\tlog.debug(datarange)\n\ttry:\n\t\t#gather the dates for same weeks\n\t\taverages=filter_averages(datarange,areacode)\n\texcept Exception as e:\n\t\tlog.error(f'No average data for {place} - {e}')\n\t\taverages=None\n\t\t\n\tif averages:\n\t\t_data=True\n\t\taverage_deaths=sum([i.weeklyalldeaths for i in averages])\n\t\t\n\t\ttry:\n\t\t\taverage_carehome_deaths=sum([i.weeklycarehomedeaths for i in averages])\n\t\texcept:\n\t\t\taverage_carehome_deaths=None\n\t\texcess=int(all_deaths_2020-average_deaths)\n\t\t\n\t\ttry:\n\t\t\texcess_carehomes=int(all_carehome_deaths_2020-average_carehome_deaths)\n\t\texcept:\n\t\t\texcess_carehomes=None\n\t\tlog.debug(f'Excess deaths in {place}: {excess} (care homes: {excess_carehomes})')\n\telse:\n\t\t_data=False\n\t\tlog.error(f'No average data for {place}')\n\tif save:\n\t\tav, created = CovidScores.objects.get_or_create(\n\t\t\tareaname=place,\n\t\t\t)\n\t\tif _data:\n\t\t\tav.excess_deaths=excess\n\t\t\tav.excess_deaths_carehomes=excess_carehomes\n\t\t\tav.save()\n\t\telse:\n\t\t\tav.excess_deaths=None\n\t\t\tav.excess_deaths_carehomes=None\n\t\t\tav.save()","title":""},{"docid":"7a64cdb3def4d1b4bda9461ee6c54646","score":"0.45334834","text":"def get_sma(data: List[dict], prices: int, parameter: str) -> float:\n return sum([get_data_from_parameter(data=period, parameter=parameter) for period in data]) / prices","title":""},{"docid":"52bb1fbb8790e2beaf31d864f5e9b47d","score":"0.45333534","text":"def closeprice(self, **kwargs):\n\n if 'date' in kwargs:\n lineidx = self.linebydate(kwargs['date'])\n dayidx = 4\n\n return float(self.lineitems[lineidx][self.itemsperday * dayidx + 2])","title":""},{"docid":"5d2ea2436bdc44cd6f5ac99345156867","score":"0.45315894","text":"def get_mean_for_last(client,time_slot,num_of_days = 10):\n db = db_connection()\n days_before = date.today() - timedelta(days=num_of_days)\n packets = Packet.get_packets_after(client,days_before,db)\n db.close_connection()\n packet_count_sum = sum(p.packet_count for p in packets)\n result = packet_count_sum / len(packets)\n return result","title":""},{"docid":"6c82ce6d4c7843d54726cd198b09021b","score":"0.4531074","text":"def base_a(data, start_date, end_date):\n df_index = list(data.index)\n end_date = last_month(end_date)\n start_date = '-'.join((start_date[0:4], start_date[4:]))\n end_date = '-'.join((end_date[0:4], end_date[4:]))\n output_data = data[start_date:end_date]\n\n month_start = df_index[df_index.index(output_data.index[0]) - 1]\n month_end = output_data.index[-1]\n output_data = data.ix[month_start:month_end]\n\n day_stamps = len(output_data.index) - 1\n\n standard_returns = output_data.values / output_data.values[0]\n annualized_return = ((standard_returns[-1] ** (250 / day_stamps)) - 1) * 100\n\n return annualized_return[0], standard_returns, list(output_data.index)","title":""},{"docid":"2b37252b0abb2020afc209de91a36e39","score":"0.45302132","text":"def get_monthly_averages(stock_info):\n monthly_averages[:] = []\n year_list = []\n month_list = []\n\n for entry in stock_info:\n # Extracting date from stock_info\n date = datetime.datetime.strptime(entry[\"Date\"], \"%Y-%m-%d\")\n if date.year not in year_list:\n year_list.append(date.year)\n if date.month not in month_list:\n month_list.append(date.month)\n for year_value in year_list:\n for month_value in month_list:\n product = 0\n sum_of_volumes = 0\n for item in stock_data:\n item_date = datetime.datetime.strptime(item[\"Date\"], \"%Y-%m-%d\")\n item_year = item_date.year\n item_month = item_date.month\n if item_year == year_value:\n if item_month == month_value:\n # Calculating average price\n # Step1 = (V1 * C1 + V2 * C2 + ... + Vn * Cn) where V is volume and C is close price\n product += item[\"Volume\"] * item[\"Close\"]\n # Step2 = V1 + V2 + .... + Vn\n sum_of_volumes += item[\"Volume\"]\n if sum_of_volumes != 0:\n # Step3 = Step1 / Step2\n month_average = product/sum_of_volumes\n month_average = round(month_average, 2)\n # Convert month value to 2 digits; ex - from 2 to 02\n month_value = '{0:0=2d}'.format(month_value)\n year_month = str(year_value) + \"/\" + str(month_value)\n # Tuple for each month with month/year and average stock price for that month\n month_tuple = (year_month, month_average)\n # monthly_averages list with appended tuples\n monthly_averages.append(month_tuple)","title":""},{"docid":"f5045b4e304b41b35d61cd90aa5f2a9a","score":"0.45262918","text":"def _make_historical_prices_req(self, address, days_count):\n symbol = self._get_symbol_by_address(address)\n url = 'https://min-api.cryptocompare.com/data/histoday?fsym={}&tsym=BTC&limit={}'.format(symbol, days_count)\n try:\n res = requests.get(url).json()\n for point in res['Data']:\n point['address'] = address\n return res['Data']\n except:\n print(\"No exchange rate for {}\".format(symbol))\n return","title":""},{"docid":"f6caf2ff7ba91273308fe34888edebb3","score":"0.45186773","text":"def item_avg_(task):\n if task in df_item_solved_avg.index:\n return df_item_solved_avg.loc[task].values[0]\n else:\n return global_avg","title":""},{"docid":"80e9e5b7c2da8ef5ab2a41486708a666","score":"0.45155996","text":"def average_holding_period(target_positions: pd.Series) -> float:\n\n pass","title":""},{"docid":"7b0296a5813083636e475f273827d44c","score":"0.4513126","text":"def price(self):\n auctioned_supply = self.auctioned_supply\n total_supply = self.mint.token.supply + auctioned_supply\n added_reserve = self.mint.curve.reserve(total_supply) - self.mint.reserve\n seniorage = auctioned_supply * self.mint.beneficiary.fraction\n sold_supply = auctioned_supply - seniorage\n avg_price = added_reserve / sold_supply # need to pay for seniorage tokens\n # print auctioned_supply, avg_price\n return avg_price","title":""},{"docid":"e3a28eb4848f46f01b2506f5db7f3867","score":"0.45092613","text":"def get_average_age(ages):\n average_age = 0\n total_population = sum(ages.values())\n for a in ages:\n average_age += ages[a] * a\n average_age = average_age/total_population\n return average_age","title":""},{"docid":"c5f0c2cccf53335bb69f4844f39f8fef","score":"0.4488714","text":"def get_year_bp_mean(years, year):\n bps = years[years['Year'] == year].BasePay\n yearMean = {'Year': year, 'BasePay': bps.mean()}\n\n return yearMean","title":""},{"docid":"6cc94bd91d6cddbbb35460d55c9d0354","score":"0.44876975","text":"def _get_average(x):\n dm, year, var_name, months = x\n return dm.get_seasonal_mean_for_year_of_2d_var(year, months=months, var_name=var_name)","title":""},{"docid":"daa7a15d9140203f064c49867e7adb73","score":"0.44815361","text":"def getEvolutionDiscountedAverage(self):\n \n #note this shortcup that Python offers us to run a for loop that \n #generates a list\n return [self.getDiscountedAverageAtGivenTime(timeIndex) \n for timeIndex in range(self.numberOfTimes)]","title":""},{"docid":"b6f8af0742dc22ab36384828de3a9a99","score":"0.44764084","text":"def get_avg_score(self):\n return PrestatiemetingResult.objects.filter(question=self).aggregate(avg=Avg('answer__gradation__score'))","title":""},{"docid":"42fe1ac94cbfdae12aff0b5e51487311","score":"0.4475752","text":"def average_stock(self):\r\n df = pd.DataFrame(columns = ['symbol','shares', 'price','currency'])\r\n i= 0\r\n for s in self.portfolio:\r\n if type(s).__name__ == 'Stock':\r\n i+=1\r\n #df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))\r\n df.loc[i]=[ s.symbol,s.shares, s.price,s.currency]\r\n\r\n grouped=df.groupby(['symbol','currency']).sum().reset_index()\r\n grouped['value']=grouped['shares'] * grouped['price']\r\n average_stock_price = grouped['value'].sum()/grouped['shares'].sum()\r\n\r\n return average_stock_price, grouped['shares'].sum() ,list(grouped['currency'].unique())","title":""},{"docid":"cf88eebb68223a3ecd4ff996af78e446","score":"0.4475254","text":"def fetch_avg_leadtime(leadtime: np.ndarray, shares: [np.ndarray, xr.DataArray]) -> int:\n\n return (shares * leadtime).sum().astype(int).values.item(0)","title":""},{"docid":"83efab11dd92c378958511f84201035d","score":"0.44737312","text":"def calc_daily_returns(self, dataset):\n self.daily_returns.clear()\n self.daily_returns.append(0)\n for ptr in range(0, len(dataset)):\n if ptr < len(dataset)-1:\n self.daily_returns.append(\n (dataset['Adj Close'][ptr+1] -\n dataset['Adj Close'][ptr])/\n dataset['Adj Close'][ptr])\n return self.daily_returns","title":""}],"string":"[\n {\n \"docid\": \"6fe6595bf749d4be707df8a261ccf555\",\n \"score\": \"0.64064676\",\n \"text\": \"def get_avg_prices_rows(offers: List[Dict[str, Any]]) -> List[Any]:\\n avg_prices = []\\n grouper = lambda x: x['address']\\n for address, items in groupby(sorted(offers, key=grouper), key=grouper):\\n items = list(items)\\n date = items[0]['date']\\n\\n # avg price sq.m\\n avg_price = sum([item['price'] / item['area'] for item in items]) / len(items)\\n avg_price = round(avg_price, 2)\\n\\n # avg change\\n avg_price_change = None\\n if last_avg_price := AvgPriceModel.last_avg_price(address, date):\\n avg_price_change = avg_price - last_avg_price.avg_price\\n \\n avg_prices.append([address, date, avg_price, avg_price_change])\\n\\n return avg_prices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b851f5f097af0936526d1a7ebd147854\",\n \"score\": \"0.62795967\",\n \"text\": \"def avg_prices(prices):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90470ba1dda201f3455d80628225eb2a\",\n \"score\": \"0.5961269\",\n \"text\": \"def address_price_duplicates(df, stats):\\n\\n # Dataframe of duplicate addresses and sale dates filtered, sorted by address and price\\n df_dups = df[df.duplicated(['address', 'sale_date'], keep=False) == True].sort_values(by=['address', 'price'])\\n\\n # Price differences calculated for all duplicate properties\\n df_dups['price_delta'] = df_dups.groupby(['address'])['price'].diff()\\n\\n # Electoral division mean is added to each property\\n for i, x in df_dups.iterrows():\\n if x['ed'] in stats.index:\\n mean = stats.ix[x['ed'], 'mean']\\n df_dups.ix[i, 'ed_mean'] = int(mean)\\n\\n # Price delta calculated\\n df_dups['price_delta'] = abs(df_dups['price'] - df_dups['ed_mean'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1607a66b6c15609d901722cdf2ad8ceb\",\n \"score\": \"0.5822261\",\n \"text\": \"def avg_price(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b88d80f1f3417f4e949e783ec5480b40\",\n \"score\": \"0.55665684\",\n \"text\": \"def balance_latest(self, address: str, **kwargs) -> Dict:\\n return self._address_query(\\n address=address,\\n route=\\\"account-balances/latest\\\",\\n headers=self.initial_headers,\\n params=kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0940e6ade9e5372b6adf908f97c4f978\",\n \"score\": \"0.55549246\",\n \"text\": \"def check_price(self, date):\\n return self.strategy.daily_avg_price(date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2755f5c3837a7fc55a68ba867f86be73\",\n \"score\": \"0.5517039\",\n \"text\": \"def average_price(self, average_price):\\n\\n self._average_price = average_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2755f5c3837a7fc55a68ba867f86be73\",\n \"score\": \"0.5517039\",\n \"text\": \"def average_price(self, average_price):\\n\\n self._average_price = average_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62e6d11f6219a72707e4cb08a835618\",\n \"score\": \"0.548369\",\n \"text\": \"def get_average_cost(user, symbol, price):\\n r = {}\\n t = Thread(\\n target=get_previous_day_close_price, args=(symbol+\\\".NS\\\", r, \\\"cp\\\")\\n )\\n t.start()\\n\\n positions = Position.objects.filter(userid=user).order_by(\\\"id\\\")\\n total_portfolio_units = positions.aggregate(units=Sum(\\\"size\\\"))\\n\\n symbol_positions = positions.filter(stockname__symbol=symbol)\\n\\n total_amount, total_units, total_return = 0.0, 0.0, 0.0\\n\\n if total_portfolio_units:\\n total_portfolio_units = total_portfolio_units.get(\\\"units\\\", 0.0)\\n if not total_portfolio_units:\\n total_portfolio_units = 0.0\\n else:\\n total_portfolio_units = 0.0\\n\\n for position in symbol_positions:\\n total_amount += (int(position.size) * float(position.transaction_details.price))\\n total_units += int(position.size)\\n\\n if position.gl_history_position_rel.all():\\n total_return += float(position.gl_history_position_rel.all()[0].unrealised_gainloss)\\n\\n # total_portfolio_units += position.size\\n\\n avg_cost = float(total_amount / total_units) if total_units else 0.0\\n total_return_percent = float(total_return / total_amount) * 100 if total_amount else 0.0\\n portfolio_diversity = float(total_units / total_portfolio_units) * 100.0 if total_portfolio_units else 0.0\\n\\n # Approach before Feb 8, 2021\\n # today_return = (price - avg_cost)\\n # today_return_percent = ((price - avg_cost) / avg_cost) * 100 if avg_cost else 0.0\\n\\n t.join()\\n close_price = r.get('cp')\\n today_return = (price - close_price)\\n today_return_percent = (today_return / close_price) * 100.0 if close_price else 0.0\\n today_return = today_return * total_units\\n\\n print(avg_cost, total_units, total_return, total_return_percent, today_return, \\\\\\n today_return_percent, portfolio_diversity)\\n\\n return avg_cost, total_units, total_return, total_return_percent, today_return, \\\\\\n today_return_percent, portfolio_diversity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d29ca9e978be07e18aab1f6f9698c58\",\n \"score\": \"0.5451101\",\n \"text\": \"def date_average(self):\\n avg = self._get_date('date-avg')\\n if avg is None:\\n start, end = self.date_start, self.date_end\\n if start is not None and end is not None:\\n avg = start + (end - start) / 2\\n\\n return avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50f009f9c5f5ad1d86d243e35b5c4bc\",\n \"score\": \"0.5399872\",\n \"text\": \"def get_last_price(assets):\\n\\n assets = utils.to_list(assets)\\n data_feed_source = None\\n ds = get_data_sources_dict(session = None)\\n \\n df = pd.DataFrame(columns = [\\\"asset\\\", \\\"last_price\\\"]).set_index(\\\"asset\\\")\\n for asset in assets:\\n data_source_names = utils.get_data_source_names_from_asset(asset = asset)\\n\\n for ds_name, ds_inst in ds.items():\\n if ds_name in data_source_names:\\n data_feed_source = ds_inst\\n break\\n\\n try:\\n lp = data_feed_source.get_last_price(assets = asset)\\n except AttributeError:#If data_feed_source remains None\\n lp = pd.DataFrame()\\n \\n df = pd.concat([df, lp])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d965d0bc02c26d8beeca7c6545b88b\",\n \"score\": \"0.5398656\",\n \"text\": \"def get_closing_value_average_price(self, x):\\n instr = str(x[TradeDataColumns.instrument.name])\\n date = str(x[TradeDataColumns.date.name])\\n condition = (self.df[TradeDataColumns.instrument.name] == instr) & (self.df[TradeDataColumns.date.name] == date)\\n # Instrument closing value is last traded price\\n instr_closing_value = self.df[condition].nlargest(1, TradeDataColumns.time_in_milsec.name)[TradeDataColumns.price.name].values[0]\\n # Instrument average price is all instrument trades market values divided by total instrument quantity traded\\n avg_price = self.df[condition][TradeDataColumns.market_val.name].sum()/self.df[condition][TradeDataColumns.quantity.name].sum()\\n return instr_closing_value, avg_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f90575bb7513b38486b50d12375a9fe\",\n \"score\": \"0.53256714\",\n \"text\": \"def rent_avg(df, withARA=True):\\n cols = ['Unnamed', 'Keskivuokra ARA-vuokra',\\n 'Keskivuokra Vapaarah. vanhat', 'Keskivuokra Vapaarah. uudet']\\n df = clean_rent(df)\\n df.replace({'-': 0.0}, inplace=True)\\n price = []\\n lkm = []\\n if df.empty:\\n return np.nan\\n else:\\n df = df[df.columns[:4]]\\n for i, row in df.iterrows():\\n if row['Unnamed'] == 'Kaikki':\\n price.append(float(row[cols[1]]))\\n price.append(float(row[cols[2]]))\\n price.append(float(row[cols[3]]))\\n if row['Unnamed'] == 'Lkm':\\n lkm.append(int(row[cols[1]]))\\n lkm.append(int(row[cols[2]]))\\n lkm.append(int(row[cols[3]]))\\n if len(price) > 0 and len(price) == len(lkm):\\n if withARA:\\n mysum = sum([price[i]*lkm[i] for i in range(len(price))])\\n if mysum != 0.0:\\n return mysum/sum(lkm)\\n else:\\n return np.nan\\n else:\\n mysum = sum([price[i] * lkm[i] for i in range(1, len(price))])\\n if mysum != 0.0:\\n return mysum / (sum(lkm)-lkm[0])\\n else:\\n return np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f81f6a22700ea6a58282773be342b4\",\n \"score\": \"0.5323809\",\n \"text\": \"def get_average(log_dict, numeric_var, max_date):\\n past_vals = [ceil(log_dict[date][numeric_var] / log_dict[date]['no_runs']) for date in log_dict.keys() if date != max_date]\\n if len(past_vals) > 1:\\n mean_past_vals = mean(past_vals)\\n elif len(past_vals) == 1:\\n mean_past_vals = past_vals[0]\\n else:\\n mean_past_vals = 0\\n return ceil(mean_past_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e152c47e1f4e40e0d3f98ff23c280e\",\n \"score\": \"0.52538943\",\n \"text\": \"def get_last_price(self):\\n if not self.last_price:\\n self.last_price = float(self.df[self.price_column].tail(1))\\n return self.last_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1e152c47e1f4e40e0d3f98ff23c280e\",\n \"score\": \"0.52538943\",\n \"text\": \"def get_last_price(self):\\n if not self.last_price:\\n self.last_price = float(self.df[self.price_column].tail(1))\\n return self.last_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1332df4e7aa69338ccbca6dcb79bd2f4\",\n \"score\": \"0.51800114\",\n \"text\": \"def get_daily_avg():\\n conn = get_conn()\\n curs = conn.cursor()\\n curs.execute(\\\"select avg(tbl.num) from (select count(*) as num from events where type = 'open' group by DATE(ts)) as tbl\\\")\\n ret = curs.fetchone()[0]\\n curs.close()\\n ret = round(float(ret), 3)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78bbad12e81f9515bb6bc49310815bf9\",\n \"score\": \"0.51718265\",\n \"text\": \"def averagePrice(L):\\n return sumList(L) / len(L)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d8cbf584d8301d4757c187f5f8fd53\",\n \"score\": \"0.5153308\",\n \"text\": \"def get_average_price(data_set, key='price'):\\n total, counter = 0, 0\\n for row in data_set:\\n total += int(row[key])\\n counter += 1\\n return total // counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa881da3ef440335a1c0271e1aa1df2e\",\n \"score\": \"0.50950754\",\n \"text\": \"def calc_average(last, current, share_of_use):\\n return last + current * share_of_use\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edeb15ac700490664d3fdad9da1a71d\",\n \"score\": \"0.5094853\",\n \"text\": \"def get_trailing_stop_pct(df):\\n latest_price = df.iloc[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf18303592d5a54d845c5cc2475fda3\",\n \"score\": \"0.5066243\",\n \"text\": \"def printing_get_date_avg(file_name):\\n result = reports.get_date_avg(file_name)\\n print (\\\"The average selling date is:\\\", result)\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b335279f77a58df016a3a69ad236610a\",\n \"score\": \"0.5051108\",\n \"text\": \"def get_average_temp_by_date(date_string, connection):\\n # Target date\\n target_date = datetime.strptime(date_string, \\\"%Y-%m-%d\\\").date()\\n min_date = target_date + timedelta(days=-3)\\n max_date = target_date + timedelta(days=3)\\n\\n cursor = connection.cursor()\\n sql = \\\"\\\"\\\"\\n SELECT\\n avg(value) as average\\n FROM temperature_data\\n WHERE date between ? and ?\\n \\\"\\\"\\\"\\n result = cursor.execute(sql, (min_date, max_date)).fetchone()\\n return result[0] if result else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626d99a1f398b54fbc16f503627ac176\",\n \"score\": \"0.5007213\",\n \"text\": \"def avg_trend(date):\\n today = datetime.now()\\n if today < convert_to_datetime(date): # Check if a future date is passed.\\n return False, \\\"Can't get stock data for the future dates!\\\"\\n elif convert_to_datetime(date).weekday() > 4: # Check if given date is a weekend.\\n return False, \\\"Its a weekend, market closed on this date.\\\"\\n else:\\n result = db.comm_trend(date)\\n if result is None: # If database is empty.\\n return False, \\\"No company yet added.\\\"\\n elif result is False: # Missing record/weekday holiday.\\n return False, \\\"No records available for particular date.\\\"\\n else:\\n logger.info(\\\"Average of the date calculated.\\\")\\n return True, result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d3cc0d5d2b97141e5b173a0fc97691\",\n \"score\": \"0.50068915\",\n \"text\": \"def address_date_duplicates(df):\\n\\n # Date column converted to datetime type\\n df['sale_date'] = pd.to_datetime(df['sale_date'])\\n\\n # Duplicate properties isolated, sorted by address and sale date\\n df_dups = df[df.duplicated(['address', 'price'], keep=False) == True].sort_values(by=['address', 'sale_date'])\\n\\n # Earliest sale date in the data set isolated\\n min_date = df_dups['sale_date'].min()\\n\\n # Each sale date converted to integer based on the number of days it is away from min_date\\n df_dups['date_int'] = (df_dups['sale_date'] - min_date).dt.days\\n\\n # Date delta calculated\\n df_dups['date_delta'] = df_dups.groupby(['address'])['date_int'].diff()\\n\\n # Lambda function to highlight the properties of interested, based on a maximum delta gap\\n # Default: 7 days\\n df_dups['duplicates_of_interest'] = df_dups['date_delta'].apply(lambda x: 1 if x < 7 else 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8741be2efc97dbfe4f3c6d6369a7340f\",\n \"score\": \"0.50020635\",\n \"text\": \"def get_last_price(self, currency):\\r\\n\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a8cba98954f8d044b48511eebf8003\",\n \"score\": \"0.5001588\",\n \"text\": \"def getDiscountedAverageAtGivenTime(self, timeIndex):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0964ce78e8bf0db11ccc5f472807eecd\",\n \"score\": \"0.49431843\",\n \"text\": \"def z_average(symbol):\\n # Can't take average if the symbol evaluates on edges\\n if symbol.evaluates_on_edges(\\\"primary\\\"):\\n raise ValueError(\\\"Can't take the z-average of a symbol that evaluates on edges\\\")\\n # Symbol must have domain [] or [\\\"current collector\\\"]\\n if symbol.domain not in [[], [\\\"current collector\\\"]]:\\n raise pybamm.DomainError(\\n \\\"\\\"\\\"z-average only implemented in the 'current collector' domain,\\n but symbol has domains {}\\\"\\\"\\\".format(\\n symbol.domain\\n )\\n )\\n # If symbol doesn't have a domain, its average value is itself\\n if symbol.domain == []:\\n return symbol\\n # If symbol is a Broadcast, its average value is its child\\n elif isinstance(symbol, pybamm.Broadcast):\\n return symbol.reduce_one_dimension()\\n # Average of a sum is sum of averages\\n elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)):\\n return _sum_of_averages(symbol, z_average)\\n # Otherwise, define a ZAverage\\n else:\\n return ZAverage(symbol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9adb7d8daa9a5459da1afc61882e6f5b\",\n \"score\": \"0.49081734\",\n \"text\": \"def CalcAvg(db: List[CurrencyRow], start_idx: int, back_count: int) -> float:\\n # 1. safe check\\n ret = _validate(db, start_idx, back_count)\\n if -1 == ret:\\n return -1\\n\\n # 2 avoid back count exceed the begin of the list\\n begin_idx = start_idx - back_count\\n if begin_idx < 0:\\n begin_idx = 0\\n\\n # 3. calc average\\n sum_all = 0.0\\n for i in range(begin_idx, start_idx):\\n sum_all += db[i].close\\n\\n avg = sum_all / back_count\\n\\n #print(\\\"Calc avg: %.5f\\\" % avg)\\n return avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f7b82b833d631d3cfe414057849451\",\n \"score\": \"0.49029326\",\n \"text\": \"def calculate_shipment_value(address: dict) -> float:\\n\\n try:\\n with open(\\\"Utils/shipment/shipping_prices.json\\\", \\\"r\\\") as read_file:\\n data = json.loads(read_file.read())\\n except Exception as error:\\n return error.args[0]\\n for state in data:\\n if state == address['user']['address']['address_state']:\\n shipping_value = data[state]\\n return shipping_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c80270cf52e461010f92367b43d28f\",\n \"score\": \"0.48676726\",\n \"text\": \"def YearlyAverage(lon, lat, field, month_start = 1, month_end = 12):\\n\\n\\t#Take twice the amount of years for the month day\\n\\tmonth_days\\t= np.asarray([31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31., 31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31.])\\n\\tmonth_days\\t= month_days[month_start - 1:month_end]\\n\\tmonth_days\\t= month_days / np.sum(month_days)\\n\\n\\t#Fill the array's with the same dimensions\\n\\tmonth_days_all\\t= ma.masked_all((len(month_days), len(lat), len(lon)))\\n\\n\\tfor month_i in range(len(month_days)):\\n\\t\\tmonth_days_all[month_i]\\t\\t= month_days[month_i]\\n\\n\\t#Determine the mean\\n\\tfield_mean\\t= np.sum(field * month_days_all, axis = 0)\\n\\n\\treturn field_mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5550071743b44ee4affd52cde93bb586\",\n \"score\": \"0.48472494\",\n \"text\": \"def calculate_avg_difference(initial_price, market_price):\\n assert len(initial_price) == len(market_price)\\n total_difference = 0\\n for item in range(len(initial_price)):\\n total_difference += market_price[item] - initial_price[item]\\n\\n return total_difference / len(initial_price)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766d09ee3ce0db391acd0a96bbfd059d\",\n \"score\": \"0.48337725\",\n \"text\": \"def get_daily_average_roundup(self, user_id):\\n Transaction = apps.get_model('finance', 'Transaction')\\n sum = Transaction.objects\\\\\\n .filter(account__item__user_id=user_id)\\\\\\n .aggregate(Sum('roundup'))['roundup__sum']\\n\\n if not sum:\\n return 0\\n\\n first_transaction = Transaction.objects.filter(\\n account__item__user_id=user_id).earliest('date')\\n\\n dt = first_transaction.date\\n delta = datetime.date.today() - dt\\n return sum / delta.days\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aac450ead67f336d2bd04f759f680d4\",\n \"score\": \"0.48013544\",\n \"text\": \"def tail_avg(timeseries):\\n timeseries = np.array(timeseries)\\n timeseries = timeseries[:, 1:]\\n try:\\n t = (timeseries[-1] + timeseries[-2] + timeseries[-3]) / 3\\n return t\\n except IndexError:\\n return timeseries[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c4e3c977996af96e66fdf12861682e\",\n \"score\": \"0.4784675\",\n \"text\": \"def moving_average(self, symbol, period):\\n self.period = period\\n self.symbol = symbol\\n symbol = symbol.upper()\\n period = period.lower()\\n url = \\\"https://fcsapi.com/api-v2/forex/ma_avg?symbol=\\\"+symbol+\\\"&period=\\\"+period+\\\"&access_key=\\\"+self.access_key\\n payload = {}\\n headers = {}\\n response = requests.request(\\\"POST\\\", url, headers=headers, data = payload)\\n res = json.loads(response.text.encode('utf8'))\\n #Load the dict manually\\n MA5=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA5\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA5\\\"][\\\"s\\\"])\\n MA10=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA10\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA10\\\"][\\\"s\\\"])\\n MA20=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA20\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA20\\\"][\\\"s\\\"])\\n MA50=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA50\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA50\\\"][\\\"s\\\"])\\n MA100=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA100\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA100\\\"][\\\"s\\\"])\\n MA200=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA200\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"EMA\\\"][\\\"MA200\\\"][\\\"s\\\"])\\n\\n EMA = rdt.r_moving_average.EMA(\\n MA5= MA5,\\n MA10= MA10,\\n MA20= MA20,\\n MA50= MA50,\\n MA100= MA100,\\n MA200= MA200,\\n )\\n\\n MA5=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA5\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA5\\\"][\\\"s\\\"])\\n MA10=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA10\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA10\\\"][\\\"s\\\"])\\n MA20=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA20\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA20\\\"][\\\"s\\\"])\\n MA50=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA50\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA50\\\"][\\\"s\\\"])\\n MA100=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA100\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA100\\\"][\\\"s\\\"])\\n MA200=rdt.r_moving_average.v_s(\\n v=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA200\\\"][\\\"v\\\"],\\n s=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"SMA\\\"][\\\"MA200\\\"][\\\"s\\\"])\\n\\n SMA = rdt.r_moving_average.SMA(\\n MA5= MA5,\\n MA10= MA10,\\n MA20= MA20,\\n MA50= MA50,\\n MA100= MA100,\\n MA200= MA200,\\n )\\n\\n ma_avg = rdt.r_moving_average.ma_avg(\\n SMA=SMA,\\n EMA=EMA,\\n summary=res[\\\"response\\\"][\\\"ma_avg\\\"][\\\"summary\\\"]\\n )\\n\\n count = rdt.r_moving_average.count(\\n Total_Buy = res[\\\"response\\\"][\\\"count\\\"][\\\"Total_Buy\\\"],\\n Total_Sell = res[\\\"response\\\"][\\\"count\\\"][\\\"Total_Sell\\\"], \\n Total_Neutral = res[\\\"response\\\"][\\\"count\\\"][\\\"Total_Neutral\\\"], \\n maBuy = res[\\\"response\\\"][\\\"count\\\"][\\\"maBuy\\\"], \\n maSell = res[\\\"response\\\"][\\\"count\\\"][\\\"maSell\\\"]\\n )\\n response = rdt.r_moving_average.response(\\n oa_summary = res[\\\"response\\\"][\\\"oa_summary\\\"],\\n count = count,\\n ma_avg = ma_avg\\n )\\n #Load info object\\n info = rdt.r_moving_average.info(\\n id=res[\\\"info\\\"][\\\"id\\\"],\\n decimal = res[\\\"info\\\"][\\\"decimal\\\"],\\n symbol = res[\\\"info\\\"][\\\"symbol\\\"],\\n period = res[\\\"info\\\"][\\\"period\\\"],\\n disclaimer = res[\\\"info\\\"][\\\"disclaimer\\\"],\\n update=res[\\\"info\\\"][\\\"update\\\"],\\n update_time=res[\\\"info\\\"][\\\"update_time\\\"],\\n server_time=res[\\\"info\\\"][\\\"server_time\\\"],\\n credit_count=res[\\\"info\\\"][\\\"credit_count\\\"]\\n )\\n #Load the main object\\n obj = rdt.r_moving_average(\\n status=res[\\\"status\\\"],\\n code=res[\\\"code\\\"],\\n msg=res[\\\"msg\\\"],\\n response=response,\\n info=info\\n )\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b883d07c09b142f478ba25f7ab9dd8\",\n \"score\": \"0.47808328\",\n \"text\": \"def yz_average(symbol):\\n # Symbol must have domain [] or [\\\"current collector\\\"]\\n if symbol.domain not in [[], [\\\"current collector\\\"]]:\\n raise pybamm.DomainError(\\n \\\"\\\"\\\"y-z-average only implemented in the 'current collector' domain,\\n but symbol has domains {}\\\"\\\"\\\".format(\\n symbol.domain\\n )\\n )\\n # If symbol doesn't have a domain, its average value is itself\\n if symbol.domain == []:\\n return symbol\\n # If symbol is a Broadcast, its average value is its child\\n elif isinstance(symbol, pybamm.Broadcast):\\n return symbol.reduce_one_dimension()\\n # Average of a sum is sum of averages\\n elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)):\\n return _sum_of_averages(symbol, yz_average)\\n # Otherwise, define a YZAverage\\n else:\\n return YZAverage(symbol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d0af5cb067c92767c68ef459ba752d\",\n \"score\": \"0.47733572\",\n \"text\": \"def average_data(self):\\n self.month_total, self.month_average = 0, 0\\n self.entry_count = 0\\n\\n if self.day_count == 0:\\n return\\n\\n for day_data in self.data:\\n day_total, day_average = day_data.total_sum, day_data.average\\n self.entry_count += day_total // day_average\\n self.month_total += day_total\\n self.month_average = self.month_total // self.day_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0da6579c580d57beadb2a97279bbd6b\",\n \"score\": \"0.47654012\",\n \"text\": \"def date_price_duplicates(df):\\n # Dataframe of duplicate price, sale dates and electoral division filtered, sorted by sale date and price\\n df_dups = df[df.duplicated(['price', 'sale_date', 'ed']) == True].sort_values(by=['sale_date', 'price'])\\n\\n # Strip the house/apartment number from the beginning of the address\\n df_dups['address_strip'] = df_dups['address'].apply(lambda x: int_strip(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c8708159f0fd93c71f0f02567e415c9\",\n \"score\": \"0.4764286\",\n \"text\": \"def prices_asof(prices, date):\\n date = pd.to_datetime(date)\\n return prices.iloc[prices.index.get_loc(date, 'ffill')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9935a33baf7b7d1e50aa3f73693e886\",\n \"score\": \"0.4758576\",\n \"text\": \"def tail_avg(current_skyline_app, X, Y, timeseries, second_order_resolution_seconds, series, sigma_value):\\n try:\\n # t = (timeseries[-1][1] + timeseries[-2][1] + timeseries[-3][1]) / 3\\n t = sum(Y[-sigma_value:]) / sigma_value\\n return t\\n except IndexError:\\n return timeseries[-1][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b49d673882ad863fa6a965c1c33bdb3\",\n \"score\": \"0.47511798\",\n \"text\": \"def monthly_average(merged_data):\\n # Calculating the daily average from the database\\n a = merged_data.groupby(merged_data.index.strftime(\\\"%m\\\"))\\n return a.mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f233a799bc32d2b3558935427f1e09f8\",\n \"score\": \"0.47470573\",\n \"text\": \"def get_latest_price_date(self):\\n return self.latestrealpricedate, self.latestrealprice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4c46f9bf2d143b5656ade6a1ad4dc3\",\n \"score\": \"0.4731979\",\n \"text\": \"def _set_moving_average(self, prices, window_size=MOVING_AVERAGE_WINDOW):\\n prices_stack = []\\n for price in prices:\\n prices_stack.append(price[\\\"close\\\"])\\n if len(prices_stack) == window_size:\\n price[\\\"average\\\"] = np.mean(prices_stack)\\n prices_stack.pop(0)\\n else:\\n price[\\\"average\\\"] = price[\\\"close\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eba257a876ef2fd36a3b1d385e8dac2\",\n \"score\": \"0.4728367\",\n \"text\": \"def avg_amount(table, year):\\n profit = get_profit_for_each_year(table)\\n average = None\\n for year_data in profit:\\n if int(year_data[0]) == year:\\n average = year_data[1]/year_data[2]\\n return average\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a752d9cb096118991a1fa06b2072eb6e\",\n \"score\": \"0.47211987\",\n \"text\": \"def sold_avg(df):\\n df = clean_sale(df)\\n if df[df.columns[5]].empty:\\n return np.nan\\n else:\\n return df[df.columns[5]].mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2984a0ce99485527b77d7794cd974b14\",\n \"score\": \"0.47099993\",\n \"text\": \"def findAvg(couples, offspring, proportion):\\n return couples * offspring * proportion\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b9916e8edc5bf7f6229d4a87586325\",\n \"score\": \"0.47098908\",\n \"text\": \"def daily_average_of_trips(self, dateframe: DataFrame) -> DataFrame:\\n self.__logger.info(\\\"Transforming dataframe to get daily average of trips\\\")\\n return dateframe.groupBy(\\\"tpep_dropoff_date\\\").agg(\\n avg(\\\"trip_distance\\\").alias(\\\"daily_avg_trip_distance\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dbaf1dc3339a4720c9ded1e9766e014\",\n \"score\": \"0.47082162\",\n \"text\": \"def _rolling(self, period, average=True):\\n\\n days = parse_period(period)\\n rolling = self.data.price.pct_change(days).dropna()\\n if average:\\n return rolling.mean()\\n else:\\n return rolling\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81841ab08eb549a3942e43ef00df33ba\",\n \"score\": \"0.47032025\",\n \"text\": \"def calc_average_age_by_year(ppl):\\n \\n \\n age_earlier =[]\\n age2013 =[]\\n age2014 = []\\n age2015 =[]\\n age2016=[]\\n\\n years = [\\\"before 2013\\\",\\\"2013\\\",\\\"2014\\\",\\\"2015\\\",\\\"2016\\\"]\\n\\n\\n for person in ppl:\\n\\n for program in person['Programs']:\\n ad_date = program['Admission date']\\n \\n if (get_date_from_string(ad_date)).year == 2013:\\n age = calc_age(person['DOB'], program['Admission date'])\\n age2013.append(age.days/365.0)\\n\\n elif (get_date_from_string(ad_date)).year == 2014:\\n age = calc_age(person['DOB'], program['Admission date'])\\n age2014.append(age.days/365.0)\\n \\n elif (get_date_from_string(ad_date)).year == 2015:\\n age = calc_age(person['DOB'], program['Admission date'])\\n age2015.append(age.days/365.0)\\n \\n elif (get_date_from_string(ad_date)).year == 2016:\\n age = calc_age(person['DOB'], program['Admission date'])\\n age2016.append(age.days/365.0)\\n \\n else:\\n age = calc_age(person['DOB'], program['Admission date'])\\n age_earlier.append(age.days/365.0)\\n \\n \\n age_list = [age_earlier, age2013, age2014, age2015, age2016]\\n\\n for age,year in zip(age_list,years):\\n\\n if len(age)>0:\\n ave_age = np.mean(age)\\n print(\\\"Average age for %-12s: %4.2f \\\" % (year,ave_age))\\n else:\\n print(\\\"No indviduals for %-12s\\\" % (year))\\n \\n return age_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f816e2d2e98c379a9026259da8a68e8a\",\n \"score\": \"0.47023392\",\n \"text\": \"def averageVolumeLastMonth(self) -> float:\\n return self.__averageVolumeLastMonth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22b3a949030d96571bc55f87bb2b5bb4\",\n \"score\": \"0.47021013\",\n \"text\": \"def get_average(self):\\n self.average = sum(self.time_array)/float(len(self.time_array))\\n return self.average\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76743354cbe1570764b54ee11e6783fd\",\n \"score\": \"0.47015372\",\n \"text\": \"def predict_price(zipcode, test_date, hometype, all_parameters, all_means):\\n if (check_date(test_date)):\\n base_result = timeseries_predict(all_parameters, zipcode, test_date)\\n if (type(base_result)!=str):\\n final_result = adjust_predict(all_means, base_result, hometype, zipcode)\\n return(final_result)\\n else:\\n return(base_result)\\n else:\\n return(\\\"The input date is invalid.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b8defcd66bd2091cd465d37e6d089a\",\n \"score\": \"0.46900716\",\n \"text\": \"def get_average_per_state(meta_data, weather_data, state):\\n one_state = meta_data[meta_data['state'] == state]\\n\\n state_set = set(one_state['station_id'])\\n weather_set = set(weather_data['station_id'])\\n intersection_list = list(weather_set & state_set)\\n\\n print(\\n f'There are {len(intersection_list)} stations in the state of {state}')\\n\\n all_stations_in_state = weather_data[weather_data['station_id'].isin(\\n intersection_list)]\\n pivoted = pd.pivot_table(all_stations_in_state, index=[\\n 'station_id', 'measurement_date'],\\n columns='measurement_type', values='measurement_flag')\\n grouped_by_day = pivoted.groupby('measurement_date').mean()\\n\\n return grouped_by_day\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf9260af9dcd7f980ded1d481c00acb4\",\n \"score\": \"0.4689363\",\n \"text\": \"def get_highest_values(ordered_list):\\n highest = f\\\"{ordered_list[-1]['promo_price']}{ordered_list[-1]['currency']}\\\"\\n highest_date = strptime_to_str(ordered_list[-1][\\\"timestamp\\\"])\\n highest_price_discount = (1 - float(ordered_list[-1][\\\"promo_price\\\"]) / float(\\n ordered_list[-1][\\\"regular_price\\\"])) * 100\\n highest_bookstore = ordered_list[-1][\\\"bookstore\\\"]\\n return highest, highest_date, highest_price_discount, highest_bookstore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b643d65d0587a1cc2cf45923593df4d3\",\n \"score\": \"0.46729523\",\n \"text\": \"def balance_historical(self, address: str, **kwargs) -> Dict:\\n return self._address_query(\\n address=address,\\n route=\\\"account-balances/historical\\\",\\n headers=self.initial_headers,\\n params=kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a0abc4366522496b0383fdc068fb50\",\n \"score\": \"0.46631682\",\n \"text\": \"def population_calculate_average(population):\\n return sum(individual[1] for individual in population) / len(population)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48734e0b9d38104ef7f3f9d72947384d\",\n \"score\": \"0.466062\",\n \"text\": \"def _get_last_avail_price_date(self):\\n return self.client.send_sql_request('SELECT MAX(timestamp) FROM {}'.format(self.indices['price']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee8cd6e55b82645462150ee473d456a1\",\n \"score\": \"0.46571276\",\n \"text\": \"def get_monthly_field_averages(fieldname):\\n dbconn = opendb()\\n curs = dbconn.cursor()\\n \\n fieldid = get_fieldid_for_field(fieldname)\\n\\n curs.execute(\\\"SELECT lat,lng,myquery.locid,monthtime,avggeoval FROM \\\" +\\\\\\n \\\"(SELECT locid,EXTRACT(month FROM geotimespace.date) \\\"+\\\\\\n \\\"AS monthtime,AVG(geoval) AS avggeoval FROM geotimespace \\\"+\\\\\\n \\\"JOIN geovalue ON geovalue.geotsid = geotimespace.geotsid \\\"+\\\\\\n \\\"WHERE geofieldid=%s AND locid IN \\\"+\\\\\\n \\\"(SELECT locid FROM location WHERE sourceid = 'ground') \\\"+\\\\\\n \\\"GROUP BY EXTRACT(month FROM geotimespace.date),locid ORDER BY locid,monthtime) \\\"+\\\\\\n \\\"AS myquery JOIN location ON location.locid=myquery.locid;\\\", (fieldid,))\\n return curs.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"668e1834b481944fbdf1a670bbd2bd5e\",\n \"score\": \"0.46509257\",\n \"text\": \"def price(self, date, exact=False):\\n if isinstance(date, str):\\n date = pd.Timestamp(datetime.strptime(date, '%Y-%m-%d')).to_numpy()\\n if date not in self.data.index:\\n if not exact:\\n date = self._nearest(date)\\n else:\\n raise ValueError(f'Requested date {date} not in data')\\n return self.data.loc[date].price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0ffa244330c2b605f71a1ff138145a8\",\n \"score\": \"0.46472782\",\n \"text\": \"def get_last_es_data(self, ip_address):\\n es_query = {\\n \\\"size\\\": 1,\\n \\\"sort\\\": [{\\\"@timestamp\\\": {\\\"order\\\": \\\"desc\\\"}}],\\n \\\"query\\\": {\\n \\\"bool\\\": {\\n \\\"filter\\\": [\\n {\\n \\\"range\\\": {\\n \\\"source.greynoise.query_timestamp\\\": {\\n \\\"gte\\\": f\\\"now-{self.cache}s\\\",\\n \\\"lte\\\": \\\"now\\\",\\n }\\n }\\n },\\n {\\\"term\\\": {\\\"tags\\\": \\\"enrich_greynoise\\\"}},\\n {\\\"term\\\": {\\\"host.ip\\\": ip_address}},\\n ]\\n }\\n },\\n }\\n\\n es_results = raw_search(es_query, index=\\\"redirtraffic-*\\\")\\n\\n self.logger.debug(es_results)\\n\\n # Return the latest hit or False if not found\\n if es_results and len(es_results[\\\"hits\\\"][\\\"hits\\\"]) > 0:\\n return es_results[\\\"hits\\\"][\\\"hits\\\"][0]\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aae03bd0954c1c4a387d021235e542d5\",\n \"score\": \"0.46382073\",\n \"text\": \"def get_return(values,beg_date,end_date):\\r\\n beg = values.loc[beg_date:end_date].head(1).values[0][0]\\r\\n end = values.loc[beg_date:end_date].tail(1).values[0][0]\\r\\n ret = (end/beg-1)*100\\r\\n sigma = values.loc[beg_date:end_date].pct_change().std().values[0]*100 \\r\\n return ret, sigma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31414e7f7e41a1de146e40c6c5d56dd6\",\n \"score\": \"0.4632582\",\n \"text\": \"def get_benchmark_returns(symbol, first_date, last_date):\\n # data = pd_reader.DataReader(\\n # symbol,\\n # 'yahoo',\\n # first_date,\\n # last_date\\n # )\\n data = pd.read_csv('/Users/jonatjao/pdev/trade/backtest/SPY.csv', index_col=0, parse_dates=['date'])\\n data = data['close']\\n\\n data[pd.Timestamp('2008-12-15')] = np.nan\\n data[pd.Timestamp('2009-08-11')] = np.nan\\n data[pd.Timestamp('2012-02-02')] = np.nan\\n\\n data = data.fillna(method='ffill')\\n\\n return data.sort_index().tz_localize('UTC').pct_change(1).iloc[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c20b50f4b17b2a5b8e134c26a08e37\",\n \"score\": \"0.4629102\",\n \"text\": \"def get_last_price(symbol):\\n if not symbol:\\n return None\\n\\n last_price = cache.get(f\\\"{symbol}_prev_price\\\")\\n last_price_exp = cache.get(f\\\"{symbol}_prev_price_expiry\\\")\\n\\n if last_price and datetime.datetime.now() < last_price_exp:\\n return last_price\\n\\n try:\\n market_opened = is_market_opened_date()\\n if market_opened:\\n start = get_previous_business_day()\\n else:\\n start = get_previous_business_day(get_previous_business_day())\\n\\n last = si.get_data(symbol+\\\".NS\\\", start_date=start, end_date=start)\\n if last.empty:\\n return None\\n last = last.iloc[-1]['close']\\n cache.set(f\\\"{symbol}_prev_price\\\", last)\\n cache.set(\\n f\\\"{symbol}_prev_price_expiry\\\", get_day_range(datetime.datetime.now() + datetime.timedelta(days=1))[0]\\n )\\n return last\\n except:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07802ba07611678efc3d693cb857862e\",\n \"score\": \"0.46281484\",\n \"text\": \"def calculate_daily_average_power_out(df=None, var=None):\\n df_grp = df.groupby(['date_sent'])[var].mean()\\n df_grp = pd.DataFrame(df_grp).reset_index()\\n df_grp.sort_values(by='date_sent', inplace=True)\\n df_grp.insert(0, 'day', range(1, len(df_grp) + 1))\\n df_grp.rename(columns={var: 'avg_' + var}, inplace=True)\\n \\n return df_grp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6a709aef51ca043fa46a12709b88fc\",\n \"score\": \"0.46046036\",\n \"text\": \"def daily_profile_process(df):\\n df = df.copy()\\n df['weekday'] = df['date'].apply(lambda x: x.weekday())\\n return df.groupby('weekday')['value'].agg(['sum', 'mean'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540e2756990b03685dd6cca53a184fd5\",\n \"score\": \"0.46021625\",\n \"text\": \"def compute_average(self):\\n if self.seconds != 0:\\n self.average = float(self.requests) / self.seconds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de25adc3a9c425f6f8bccf8a8daf9654\",\n \"score\": \"0.4600605\",\n \"text\": \"def private_electric_spots_by_avg_brutto_income(parking_df, brutto_income_df):\\n private_by_district_df = parking_df[parking_df['vejstatus'] == 'Privat fællesvej'].groupby('bydel')['antal_pladser'].agg(np.sum)\\n electric_by_district_df = parking_df[parking_df['p_ordning'] == 'El-Bil plads'].groupby('bydel')['antal_pladser'].agg(np.sum)\\n districts_list = private_by_district_df.index.tolist()\\n avg_income_per_district = calculate_income_per_district(brutto_income_df, True)\\n # print(avg_income_per_district)\\n plt.private_electric_avg_income_multi_plot(districts_list, private_by_district_df.tolist(), electric_by_district_df.tolist(), avg_income_per_district)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cfb30e042174a2b0fcf99a72c5f9b81\",\n \"score\": \"0.45991313\",\n \"text\": \"def calculate_return(start_date, end_date, price_df):\\r\\n return [(x / y) - 1 for x, y in\\r\\n zip(price_df[price_df.index == end_date].values, price_df[price_df.index == start_date].values)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a92c67c2e5f7a320c66b872a1ad284\",\n \"score\": \"0.45988208\",\n \"text\": \"def average(self):\\n return (self.current + self.last) / 2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457438b3b3977828aed560692e2974b4\",\n \"score\": \"0.45890778\",\n \"text\": \"def get_todays_prices(self, symbol):\\n # needs to be changed sometime in the future into an optimized solution!\\n path = os.path.join(os.getcwd(), 'ml_predictor', 'data', 'ml_format')\\n _, y = data_handler.load_from_npz(symbol, path)\\n return y[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3365573d1eb6e221db56a9b57f6128\",\n \"score\": \"0.4584877\",\n \"text\": \"def get_persons_closest_to_average(table):\\n\\n current_year = 2017\\n ages = [current_year - int(record[2]) for record in table]\\n sum_ages = calculate_add(ages)\\n average_age = sum_ages / len(table)\\n\\n names = []\\n smallest_difference_to_avg = abs(current_year - int(table[0][2]) - average_age)\\n for record in table:\\n difference_to_avg = abs(current_year - int(record[2]) - average_age)\\n\\n if difference_to_avg < smallest_difference_to_avg:\\n smallest_difference_to_avg = difference_to_avg\\n names = [record[1]]\\n elif difference_to_avg == smallest_difference_to_avg:\\n names.append(record[1])\\n\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cca4a2d30d71b1f8f896d51a2f2a879\",\n \"score\": \"0.45811087\",\n \"text\": \"def get_past_price(cls, date):\\n data = cls._get_historical_data(date)\\n price = data[\\\"bpi\\\"][date]\\n return apply_format(str(price))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b11824c2e5fec65dd17d7d0be36c2bde\",\n \"score\": \"0.45701414\",\n \"text\": \"def get_acquired_cost(self):\\n held_tradeable = requests.get('https://api.lemon.markets/rest/v1/accounts/{0}/portfolio/{1}/aggregated/'.format(self.account.uuid, self.isin), headers={'Authorization': self.account.auth})\\n held_tradeable.raise_for_status(); held_tradeable = held_tradeable.json()\\n \\n if len(held_tradeable) <= 0: return -1\\n return held_tradeable['average_price']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a726e24a445280e37312d8fe43281440\",\n \"score\": \"0.4560129\",\n \"text\": \"def calculate_mean(weather_data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caaaaab1d438f0e08d046c6361457de3\",\n \"score\": \"0.45600042\",\n \"text\": \"def take_average(func):\\n\\n def inner(*args, **kwargs):\\n results = _results(\\n args[0],\\n func.__name__,\\n *args,\\n **kwargs\\n )\\n\\n sum_of_results = sum(results)\\n\\n if sum_of_results == 0:\\n return sum_of_results\\n\\n return sum_of_results / len(results)\\n\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de2a12f0d09441dbbe6da8042e90883\",\n \"score\": \"0.45590493\",\n \"text\": \"def averageFillPrice(self) -> float:\\n return self.__averageFillPrice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07be0ce36fc6a95b19f1db36e6a82320\",\n \"score\": \"0.45579508\",\n \"text\": \"def last_price(self, last_price):\\n\\n self._last_price = last_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1614864b0e662b3419a7110538ffc467\",\n \"score\": \"0.4545841\",\n \"text\": \"def extract_cotations(index_list, previous_date):\\n\\n #If no more recent Trading day then abort\\n if previous_date >= da.get_last_date():\\n log_print(\\\"Pas de nouvelle cotation, arrêt de la procédure\\\")\\n return\\n\\n\\n #Else compute\\n df = da.get_data(index_list)\\n\\n #Integration of a synthetic value to compute trends for all indexes\\n\\n syn_df = df.groupby('Trading_day', as_index=False, sort=False)['Calendar+1'].mean()\\n\\n syn_df['Calendar+2'] = df.groupby('Trading_day',\\\\\\n as_index=False, sort=False)['Calendar+2'].mean()['Calendar+2']\\n\\n syn_df['Calendar+3'] = df.groupby('Trading_day',\\\\\\n as_index=False, sort=False)['Calendar+3'].mean()['Calendar+3']\\n\\n\\n syn_df['Synthetic'] = (syn_df['Calendar+1'] + syn_df['Calendar+2'] +\\\\\\n syn_df['Calendar+3'])/3\\n\\n #Classement par date descendante utilisée pour les opérations suivantes\\n syn_df = syn_df.sort_values('Trading_day', ascending=False)\\n\\n log_print(\\\"Synthetic index ready\\\")\\n\\n #Prix lors de l'appel d'offre\\n origin_synthetic = round(syn_df.loc[syn_df['Trading_day'] == '2018-06-06']\\\\\\n ['Synthetic'].item(), 2)\\n\\n #Dernier prix disponible a priori le closing de la veille.\\n last_synthetic = round(syn_df.iloc[0]['Synthetic'].item(), 2) #Since we sorted we can use it\\n\\n\\n #Evaluation of price evolution trends.\\n tendance = 'BAISSE'\\n previous_synthetic = round(syn_df.iloc[1]['Synthetic'].item(), 2)\\n\\n if last_synthetic > previous_synthetic:\\n tendance = 'HAUSSE'\\n tendance_pct = round((last_synthetic - previous_synthetic)/previous_synthetic*100, 2)\\n\\n texte = \\\"\\\\nRappel: L'index de prix synthétique est une moyenne sur les hubs \\\" +\\\\\\n \\\"PEG Nord et TTF à partir des produits Cal+1, Cal+2 et Cal+3.\\\\n \\\" +\\\\\\n \\\"Les prix sont obtenus via Powernext avec un jour de décalage et l'index est un \\\" +\\\\\\n \\\"proxy pour les variations du prix de la molécule des offres fournisseurs.\\\\n\\\" +\\\\\\n f\\\"\\\\nIndex du 6 juin : <b>{origin_synthetic} €/MWh</b>\\\\n\\\"+\\\\\\n f\\\"Index à date : <b>{last_synthetic} €/MWh</b>\\\\n\\\"+\\\\\\n \\\"Ecart de l'index à date vs l'index de l'appel d'offre) du 6 juin : \\\"+\\\\\\n f\\\"<b>{round((last_synthetic - origin_synthetic)/origin_synthetic*100,2)}%</b>\\\\n\\\" +\\\\\\n f\\\"\\\\nTendance de l'index par rapport à la cotation précédente : {tendance} de \\\" +\\\\\\n f\\\"<b>{abs(tendance_pct)}%</b>\\\"\\n\\n #Computation of graphics\\n fig = plt.figure()\\n plt.plot(syn_df['Trading_day'], syn_df['Synthetic'])\\n plt.suptitle(\\\"Evolution du prix de l'index gazier\\\", fontsize=15)\\n plt.xlabel('Date', fontsize=13)\\n plt.ylabel('€/MWh', fontsize=13)\\n #Size\\n fig.set_size_inches(9, 6)\\n #Annotate\\n plt.annotate(\\\"Appel d'offre du 6 juin 2018\\\",\\n xy=(date(2018, 6, 6), origin_synthetic), xycoords='data',\\n xytext=(-90, -50), textcoords='offset points', fontsize=10,\\n arrowprops=dict(arrowstyle=\\\"->\\\", connectionstyle=\\\"arc3,rad=.2\\\"))\\n\\n picture_path = os.path.join(FILE_PATH, 'Data', 'img.png')\\n fig.savefig(picture_path)\\n #plt.show()\\n\\n #Emails sending\\n for receiver in RECEIVERS_EMAIL_LIST:\\n send_html_email(receiver,\\n f\\\"Evolution prix du gaz: {last_synthetic}€/MWH en {tendance}\\\",\\n texte, picture_path)\\n log_print(\\\"Emails sent\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec93ccbb45973e1a9f969f5cb6a10684\",\n \"score\": \"0.4541558\",\n \"text\": \"def yearly_average(arr, dt):\\n assert_matching_time_coord(arr, dt)\\n yr_str = TIME_STR + '.year'\\n # Retain original data's mask.\\n dt = dt.where(np.isfinite(arr))\\n return ((arr*dt).groupby(yr_str, restore_coord_dims=True).sum(TIME_STR) /\\n dt.groupby(yr_str, restore_coord_dims=True).sum(TIME_STR))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f20105c6a4c5e79367fb8d71f2df22eb\",\n \"score\": \"0.45408043\",\n \"text\": \"def sevenDayAverage(data):\\n smooth = data.copy()\\n for idx in range(len(data)):\\n try:\\n smooth[idx,1] = np.mean(data[idx-3:idx+3,1])\\n except ZeroDivisionError:\\n smooth[idx,1] = np.nan\\n \\n return smooth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba4d9521e4cc89aaf9a306cc2f131f36\",\n \"score\": \"0.45395842\",\n \"text\": \"def _original_price(self):\\n original_price = (self.baskets\\n .groupby('product', as_index=False)['price']\\n .max()\\n .rename(columns={'price': 'original_price'})\\n )\\n\\n return original_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96e2c5cf6d0305097b6837e6a75feed\",\n \"score\": \"0.45390084\",\n \"text\": \"def _compute_date_last_payroll(self):\\n\\t\\tpayslip_obj = self.env['hr.payslip']\\n\\n\\t\\tfor employee in self:\\n\\n\\t\\t\\tpayslip = payslip_obj.search([('employee_id', '=', employee.id),\\n\\t\\t\\t\\t('employee_id.active', '=', True)],\\n\\t\\t\\t\\torder='date_to desc', limit = 1)\\n\\t\\t\\t\\n\\t\\t\\tif payslip:\\n\\n\\t\\t\\t\\temployee.date_last_payroll = payslip.date_to\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db331c7c375bbc545df303200174e13\",\n \"score\": \"0.4536336\",\n \"text\": \"def excess_deaths_district(place='Birmingham',save=False):\\n\\tdistrict=CovidWeek.objects.filter(areaname=place,week__range=RANGE_WEEK).order_by('week')\\n\\tareacode=district[0].areacode\\n\\n\\tall_deaths_2020=0\\n\\tall_carehome_deaths_2020=0\\n\\t_count=0 #count the number of weeks with data\\n\\t\\n\\n\\t\\n\\tfor i in district:\\n\\t\\tif i.weeklyalldeaths is None: #check for when data runs out\\n\\t\\t\\tbreak\\n\\t\\tall_deaths_2020+=i.weeklyalldeaths\\n\\t\\tif i.weeklycarehomedeaths is not None:\\n\\t\\t\\tall_carehome_deaths_2020+=i.weeklycarehomedeaths\\n\\t\\t_count+=1\\n\\t\\t\\n\\tdatarange=[RANGE_WEEK[0],RANGE_WEEK[0]+_count-1]\\n\\tlog.debug(datarange)\\n\\ttry:\\n\\t\\t#gather the dates for same weeks\\n\\t\\taverages=filter_averages(datarange,areacode)\\n\\texcept Exception as e:\\n\\t\\tlog.error(f'No average data for {place} - {e}')\\n\\t\\taverages=None\\n\\t\\t\\n\\tif averages:\\n\\t\\t_data=True\\n\\t\\taverage_deaths=sum([i.weeklyalldeaths for i in averages])\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\taverage_carehome_deaths=sum([i.weeklycarehomedeaths for i in averages])\\n\\t\\texcept:\\n\\t\\t\\taverage_carehome_deaths=None\\n\\t\\texcess=int(all_deaths_2020-average_deaths)\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\texcess_carehomes=int(all_carehome_deaths_2020-average_carehome_deaths)\\n\\t\\texcept:\\n\\t\\t\\texcess_carehomes=None\\n\\t\\tlog.debug(f'Excess deaths in {place}: {excess} (care homes: {excess_carehomes})')\\n\\telse:\\n\\t\\t_data=False\\n\\t\\tlog.error(f'No average data for {place}')\\n\\tif save:\\n\\t\\tav, created = CovidScores.objects.get_or_create(\\n\\t\\t\\tareaname=place,\\n\\t\\t\\t)\\n\\t\\tif _data:\\n\\t\\t\\tav.excess_deaths=excess\\n\\t\\t\\tav.excess_deaths_carehomes=excess_carehomes\\n\\t\\t\\tav.save()\\n\\t\\telse:\\n\\t\\t\\tav.excess_deaths=None\\n\\t\\t\\tav.excess_deaths_carehomes=None\\n\\t\\t\\tav.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a64cdb3def4d1b4bda9461ee6c54646\",\n \"score\": \"0.45334834\",\n \"text\": \"def get_sma(data: List[dict], prices: int, parameter: str) -> float:\\n return sum([get_data_from_parameter(data=period, parameter=parameter) for period in data]) / prices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52bb1fbb8790e2beaf31d864f5e9b47d\",\n \"score\": \"0.45333534\",\n \"text\": \"def closeprice(self, **kwargs):\\n\\n if 'date' in kwargs:\\n lineidx = self.linebydate(kwargs['date'])\\n dayidx = 4\\n\\n return float(self.lineitems[lineidx][self.itemsperday * dayidx + 2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d2ea2436bdc44cd6f5ac99345156867\",\n \"score\": \"0.45315894\",\n \"text\": \"def get_mean_for_last(client,time_slot,num_of_days = 10):\\n db = db_connection()\\n days_before = date.today() - timedelta(days=num_of_days)\\n packets = Packet.get_packets_after(client,days_before,db)\\n db.close_connection()\\n packet_count_sum = sum(p.packet_count for p in packets)\\n result = packet_count_sum / len(packets)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c82ce6d4c7843d54726cd198b09021b\",\n \"score\": \"0.4531074\",\n \"text\": \"def base_a(data, start_date, end_date):\\n df_index = list(data.index)\\n end_date = last_month(end_date)\\n start_date = '-'.join((start_date[0:4], start_date[4:]))\\n end_date = '-'.join((end_date[0:4], end_date[4:]))\\n output_data = data[start_date:end_date]\\n\\n month_start = df_index[df_index.index(output_data.index[0]) - 1]\\n month_end = output_data.index[-1]\\n output_data = data.ix[month_start:month_end]\\n\\n day_stamps = len(output_data.index) - 1\\n\\n standard_returns = output_data.values / output_data.values[0]\\n annualized_return = ((standard_returns[-1] ** (250 / day_stamps)) - 1) * 100\\n\\n return annualized_return[0], standard_returns, list(output_data.index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b37252b0abb2020afc209de91a36e39\",\n \"score\": \"0.45302132\",\n \"text\": \"def get_monthly_averages(stock_info):\\n monthly_averages[:] = []\\n year_list = []\\n month_list = []\\n\\n for entry in stock_info:\\n # Extracting date from stock_info\\n date = datetime.datetime.strptime(entry[\\\"Date\\\"], \\\"%Y-%m-%d\\\")\\n if date.year not in year_list:\\n year_list.append(date.year)\\n if date.month not in month_list:\\n month_list.append(date.month)\\n for year_value in year_list:\\n for month_value in month_list:\\n product = 0\\n sum_of_volumes = 0\\n for item in stock_data:\\n item_date = datetime.datetime.strptime(item[\\\"Date\\\"], \\\"%Y-%m-%d\\\")\\n item_year = item_date.year\\n item_month = item_date.month\\n if item_year == year_value:\\n if item_month == month_value:\\n # Calculating average price\\n # Step1 = (V1 * C1 + V2 * C2 + ... + Vn * Cn) where V is volume and C is close price\\n product += item[\\\"Volume\\\"] * item[\\\"Close\\\"]\\n # Step2 = V1 + V2 + .... + Vn\\n sum_of_volumes += item[\\\"Volume\\\"]\\n if sum_of_volumes != 0:\\n # Step3 = Step1 / Step2\\n month_average = product/sum_of_volumes\\n month_average = round(month_average, 2)\\n # Convert month value to 2 digits; ex - from 2 to 02\\n month_value = '{0:0=2d}'.format(month_value)\\n year_month = str(year_value) + \\\"/\\\" + str(month_value)\\n # Tuple for each month with month/year and average stock price for that month\\n month_tuple = (year_month, month_average)\\n # monthly_averages list with appended tuples\\n monthly_averages.append(month_tuple)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5045b4e304b41b35d61cd90aa5f2a9a\",\n \"score\": \"0.45262918\",\n \"text\": \"def _make_historical_prices_req(self, address, days_count):\\n symbol = self._get_symbol_by_address(address)\\n url = 'https://min-api.cryptocompare.com/data/histoday?fsym={}&tsym=BTC&limit={}'.format(symbol, days_count)\\n try:\\n res = requests.get(url).json()\\n for point in res['Data']:\\n point['address'] = address\\n return res['Data']\\n except:\\n print(\\\"No exchange rate for {}\\\".format(symbol))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6caf2ff7ba91273308fe34888edebb3\",\n \"score\": \"0.45186773\",\n \"text\": \"def item_avg_(task):\\n if task in df_item_solved_avg.index:\\n return df_item_solved_avg.loc[task].values[0]\\n else:\\n return global_avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80e9e5b7c2da8ef5ab2a41486708a666\",\n \"score\": \"0.45155996\",\n \"text\": \"def average_holding_period(target_positions: pd.Series) -> float:\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b0296a5813083636e475f273827d44c\",\n \"score\": \"0.4513126\",\n \"text\": \"def price(self):\\n auctioned_supply = self.auctioned_supply\\n total_supply = self.mint.token.supply + auctioned_supply\\n added_reserve = self.mint.curve.reserve(total_supply) - self.mint.reserve\\n seniorage = auctioned_supply * self.mint.beneficiary.fraction\\n sold_supply = auctioned_supply - seniorage\\n avg_price = added_reserve / sold_supply # need to pay for seniorage tokens\\n # print auctioned_supply, avg_price\\n return avg_price\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a28eb4848f46f01b2506f5db7f3867\",\n \"score\": \"0.45092613\",\n \"text\": \"def get_average_age(ages):\\n average_age = 0\\n total_population = sum(ages.values())\\n for a in ages:\\n average_age += ages[a] * a\\n average_age = average_age/total_population\\n return average_age\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f0c2cccf53335bb69f4844f39f8fef\",\n \"score\": \"0.4488714\",\n \"text\": \"def get_year_bp_mean(years, year):\\n bps = years[years['Year'] == year].BasePay\\n yearMean = {'Year': year, 'BasePay': bps.mean()}\\n\\n return yearMean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cc94bd91d6cddbbb35460d55c9d0354\",\n \"score\": \"0.44876975\",\n \"text\": \"def _get_average(x):\\n dm, year, var_name, months = x\\n return dm.get_seasonal_mean_for_year_of_2d_var(year, months=months, var_name=var_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa7a15d9140203f064c49867e7adb73\",\n \"score\": \"0.44815361\",\n \"text\": \"def getEvolutionDiscountedAverage(self):\\n \\n #note this shortcup that Python offers us to run a for loop that \\n #generates a list\\n return [self.getDiscountedAverageAtGivenTime(timeIndex) \\n for timeIndex in range(self.numberOfTimes)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f8af0742dc22ab36384828de3a9a99\",\n \"score\": \"0.44764084\",\n \"text\": \"def get_avg_score(self):\\n return PrestatiemetingResult.objects.filter(question=self).aggregate(avg=Avg('answer__gradation__score'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42fe1ac94cbfdae12aff0b5e51487311\",\n \"score\": \"0.4475752\",\n \"text\": \"def average_stock(self):\\r\\n df = pd.DataFrame(columns = ['symbol','shares', 'price','currency'])\\r\\n i= 0\\r\\n for s in self.portfolio:\\r\\n if type(s).__name__ == 'Stock':\\r\\n i+=1\\r\\n #df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))\\r\\n df.loc[i]=[ s.symbol,s.shares, s.price,s.currency]\\r\\n\\r\\n grouped=df.groupby(['symbol','currency']).sum().reset_index()\\r\\n grouped['value']=grouped['shares'] * grouped['price']\\r\\n average_stock_price = grouped['value'].sum()/grouped['shares'].sum()\\r\\n\\r\\n return average_stock_price, grouped['shares'].sum() ,list(grouped['currency'].unique())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf88eebb68223a3ecd4ff996af78e446\",\n \"score\": \"0.4475254\",\n \"text\": \"def fetch_avg_leadtime(leadtime: np.ndarray, shares: [np.ndarray, xr.DataArray]) -> int:\\n\\n return (shares * leadtime).sum().astype(int).values.item(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83efab11dd92c378958511f84201035d\",\n \"score\": \"0.44737312\",\n \"text\": \"def calc_daily_returns(self, dataset):\\n self.daily_returns.clear()\\n self.daily_returns.append(0)\\n for ptr in range(0, len(dataset)):\\n if ptr < len(dataset)-1:\\n self.daily_returns.append(\\n (dataset['Adj Close'][ptr+1] -\\n dataset['Adj Close'][ptr])/\\n dataset['Adj Close'][ptr])\\n return self.daily_returns\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994386,"cells":{"query_id":{"kind":"string","value":"42b9d9569f4770cbb15dac1a978a2cb4"},"query":{"kind":"string","value":"Test the calls to assert_equal_msg for the positional arguments."},"positive_passages":{"kind":"list like","value":[{"docid":"914cce41d87c6d6d68557a26d81ed1ac","score":"0.7829213","text":"def test_assert_equal_msg_calls_for_positional_arguments():\n\n with mock.patch(target=\"tubular.testing.helpers.assert_equal_msg\") as mocked_method:\n\n expected_args = [\"obj\", \"expected_method\", \"msg\"]\n\n h.test_function_arguments(\n h.test_object_method, expected_arguments=expected_args\n )\n\n assert mocked_method.call_count == len(\n expected_args\n ), f\"Unexpected number of calls to tubular.testing.helpers.assert_equal_msg -\\n Expected: {len(expected_args)}\\n Actual: {mocked_method.call_count}\"\n\n for call_n in range(mocked_method.call_count):\n\n call_n_args = mocked_method.call_args_list[call_n]\n call_n_pos_args = call_n_args[0]\n call_n_kwargs = call_n_args[1]\n\n call_n_expected_pos_arg = (\n expected_args[call_n],\n expected_args[call_n],\n f\"Incorrect arg at index {call_n}\",\n )\n\n assert len(call_n_pos_args) == len(\n call_n_expected_pos_arg\n ), f\"Unexpected number of positional args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\n Expected: {len(call_n_expected_pos_arg)}\\n Actual: {len(call_n_pos_args)}\"\n\n for i, (e, a) in enumerate(zip(call_n_expected_pos_arg, call_n_pos_args)):\n\n assert (\n e == a\n ), f\"Unexpected positional arg at index {i} -\\n Expected: {e}\\n Actual: {a}\"\n\n assert (\n call_n_kwargs == {}\n ), f\"Unexpected keyword args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\n Expected: None\\n Actual: {call_n_kwargs}\"","title":""}],"string":"[\n {\n \"docid\": \"914cce41d87c6d6d68557a26d81ed1ac\",\n \"score\": \"0.7829213\",\n \"text\": \"def test_assert_equal_msg_calls_for_positional_arguments():\\n\\n with mock.patch(target=\\\"tubular.testing.helpers.assert_equal_msg\\\") as mocked_method:\\n\\n expected_args = [\\\"obj\\\", \\\"expected_method\\\", \\\"msg\\\"]\\n\\n h.test_function_arguments(\\n h.test_object_method, expected_arguments=expected_args\\n )\\n\\n assert mocked_method.call_count == len(\\n expected_args\\n ), f\\\"Unexpected number of calls to tubular.testing.helpers.assert_equal_msg -\\\\n Expected: {len(expected_args)}\\\\n Actual: {mocked_method.call_count}\\\"\\n\\n for call_n in range(mocked_method.call_count):\\n\\n call_n_args = mocked_method.call_args_list[call_n]\\n call_n_pos_args = call_n_args[0]\\n call_n_kwargs = call_n_args[1]\\n\\n call_n_expected_pos_arg = (\\n expected_args[call_n],\\n expected_args[call_n],\\n f\\\"Incorrect arg at index {call_n}\\\",\\n )\\n\\n assert len(call_n_pos_args) == len(\\n call_n_expected_pos_arg\\n ), f\\\"Unexpected number of positional args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\\\n Expected: {len(call_n_expected_pos_arg)}\\\\n Actual: {len(call_n_pos_args)}\\\"\\n\\n for i, (e, a) in enumerate(zip(call_n_expected_pos_arg, call_n_pos_args)):\\n\\n assert (\\n e == a\\n ), f\\\"Unexpected positional arg at index {i} -\\\\n Expected: {e}\\\\n Actual: {a}\\\"\\n\\n assert (\\n call_n_kwargs == {}\\n ), f\\\"Unexpected keyword args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\\\n Expected: None\\\\n Actual: {call_n_kwargs}\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6ae70b8bb010cd704e9fce7bf977453e","score":"0.7293037","text":"def test_call_eq(self):\n self.assertCallEqual(\n 1,\n 2,\n func=str,\n args=[1],\n otherargs=[2],\n kwargs={'testing': True},\n otherkwargs={'testing': True},\n msg='Nope, not equal.',\n )","title":""},{"docid":"66c7a8c6833e89bb4bc9ffa8bef52dbc","score":"0.7197761","text":"def test_different_number_arguments_error():\n\n incorrect_expected_args = [\"actual\", \"expected\", \"msg_tag\"]\n\n with pytest.raises(\n AssertionError,\n match=f\"Incorrect number of arguments -\\n Expected: {len(incorrect_expected_args)}\\n Actual: 4\",\n ):\n\n h.test_function_arguments(\n h.assert_frame_equal_msg,\n expected_arguments=incorrect_expected_args,\n expected_default_values=(False,),\n )","title":""},{"docid":"19a5a6f492cda9c9c7740a5688c17877","score":"0.713591","text":"def test_call_tuple_eq(self):\n self.assertCallTupleEqual(\n (1,),\n (2,),\n func=str,\n args=[1],\n kwargs={'testing': True},\n otherargs=[2],\n otherkwargs={'testing': True},\n msg='Nope, not equal.',\n )","title":""},{"docid":"9553b31bf2adce0e60d50bf46cd72172","score":"0.6856742","text":"def assert_equal(self, first, second, msg=None):\n self.assertEqual(first, second, msg=msg)","title":""},{"docid":"3dd350d15d9985a8434ea545978a087f","score":"0.6849586","text":"def test_assert_equal_msg_calls_for_default_values():\n\n with mock.patch(target=\"tubular.testing.helpers.assert_equal_msg\") as mocked_method:\n\n expected_args = [\"self\", \"data\", \"index\", \"columns\", \"dtype\", \"copy\"]\n expected_default_values = (None, None, None, None, False)\n\n h.test_function_arguments(\n pd.DataFrame,\n expected_arguments=expected_args,\n expected_default_values=expected_default_values,\n )\n\n assert mocked_method.call_count == (\n len(expected_args) + len(expected_default_values)\n ), f\"Unexpected number of calls to tubular.testing.helpers.assert_equal_msg -\\n Expected: {len(expected_args) + len(expected_default_values)}\\n Actual: {mocked_method.call_count}\"\n\n for call_n in range(len(expected_args), mocked_method.call_count):\n\n call_n_args = mocked_method.call_args_list[call_n]\n call_n_pos_args = call_n_args[0]\n call_n_kwargs = call_n_args[1]\n\n call_n_expected_pos_arg = (\n expected_default_values[call_n - len(expected_args)],\n expected_default_values[call_n - len(expected_args)],\n f\"Incorrect default value at index {call_n - len(expected_args)} of default values\",\n )\n\n assert len(call_n_pos_args) == len(\n call_n_expected_pos_arg\n ), f\"Unexpected number of positional args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\n Expected: {len(call_n_expected_pos_arg)}\\n Actual: {len(call_n_pos_args)}\"\n\n for i, (e, a) in enumerate(zip(call_n_expected_pos_arg, call_n_pos_args)):\n\n assert (\n e == a\n ), f\"Unexpected default value at index {i} -\\n Expected: {e}\\n Actual: {a}\"\n\n assert (\n call_n_kwargs == {}\n ), f\"Unexpected keyword args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\n Expected: None\\n Actual: {call_n_kwargs}\"","title":""},{"docid":"dec0d5276fe92fef65b047cfb0c04940","score":"0.67891556","text":"def test_args_kwargs(self):\n\n self.assertEqual(tuple(self.arguments), self.result[0])\n self.assertEqual(self.kwarguments, self.result[1])","title":""},{"docid":"107776447c58903a2bffb208dd648c9d","score":"0.67358834","text":"def assert_equal(args: List, node):\n if len(args) != 2:\n evaluator.error(\"(special_forms.assert_value.assert-equal) 2 parameter expected, {} given\".format(len(args)), node)\n\n values = evaluator.evaluate_parallel_args(args)\n\n if values[0] != values[1]:\n evaluator.error(\"assertion failed: {} != {}\".format(values[0], values[1]), node)\n\n return None","title":""},{"docid":"a39cc3764b8c212a7a5bb80ba3bdadb9","score":"0.6706973","text":"def test_passing():\n assert (1, 2, 3) == (1, 2, 3)","title":""},{"docid":"4f0dc2567c3f1516640a57fc5f5bceb2","score":"0.66917634","text":"def assertEqual(self, left, right, msg=None, **kwargs):\n return assert_equal(left, right, explanation=msg, **kwargs)","title":""},{"docid":"c2afbb20f9cdddc3f8aea304229c9666","score":"0.6611375","text":"def assertCallArgsEqual(self, call_args_singles, mock_method):\n # convert from ['a', 'b'] into [(('a',),), (('b',),)]\n expected = [((arg,),) for arg in call_args_singles]\n self.assertEqual(expected, mock_method.call_args_list)","title":""},{"docid":"cb813c0f65794b4e5f0ebe01dce7577a","score":"0.6602467","text":"def test_arguments():\n\n expected_arguments = [\"obj\", \"cls\"]\n\n arg_spec = inspect.getfullargspec(h.assert_inheritance)\n\n arguments = arg_spec.args\n\n assert len(expected_arguments) == len(\n arguments\n ), f\"Incorrect number of arguments -\\n Expected: {len(expected_arguments)}\\n Actual: {len(arguments)}\"\n\n for i, (e, a) in enumerate(zip(expected_arguments, arguments)):\n\n assert e == a, f\"Incorrect arg at index {i} -\\n Expected: {e}\\n Actual: {a}\"","title":""},{"docid":"454ac85325c4baa9c6210ddeffabe7ec","score":"0.6544629","text":"def __assert_eq(self, *args, **kwargs):\n minified_exception = None\n try:\n self.assertEqual(*args, **kwargs)\n except Exception as e:\n str_e = str(e)\n minified_exception = \"\\nAssertionError:\\n\"\n lines = str_e.split(\"\\n\")\n countdown = 3\n countdown_on = False\n first_differing = False\n skip_lines = False\n for line in lines:\n if countdown_on:\n if not skip_lines:\n minified_exception += line + \"\\n\"\n countdown = countdown - 1\n if countdown == 0:\n countdown_on = False\n skip_lines = False\n elif line.startswith(\"First differing\"):\n first_differing = True\n countdown_on = True\n countdown = 3\n minified_exception += line + \"\\n\"\n elif line.startswith(\"First list\"):\n countdown_on = True\n countdown = 3\n if not first_differing:\n minified_exception += line + \"\\n\"\n else:\n skip_lines = True\n elif line.startswith(\"F\"):\n countdown_on = True\n countdown = 3\n minified_exception += line + \"\\n\"\n elif line.startswith(\"+\") or line.startswith(\"-\"):\n minified_exception += line + \"\\n\"\n elif line.startswith(\"?\"):\n minified_exception += line + \"\\n\"\n elif line.strip().startswith(\"*\"):\n minified_exception += line + \"\\n\"\n if minified_exception:\n raise VisualException(minified_exception)","title":""},{"docid":"6b8a66272c2964f3fb5fa4b0009489fa","score":"0.650816","text":"def test_assert_count_equal(testcase):\n testcase.assertCountEqual((1, 1, 2), (1, 2, 1))\n\n with pytest.raises(AssertionError):\n testcase.assertCountEqual((1, 1, 2), (2, 1, 2))","title":""},{"docid":"a3383affaf59d53d41daed21d68c2dd3","score":"0.6480526","text":"def assertTestLogEqual(self, messages):\n case = self._result_calls[0][1]\n self.assertEqual(messages, case._calls)","title":""},{"docid":"b0a17f25fc707b00ef95ce51252b70af","score":"0.64456534","text":"def test_call_neq(self):\n self.assertCallNotEqual(\n 1,\n 1,\n func=str,\n args=[1],\n kwargs={'testing': True},\n otherargs=[1],\n otherkwargs={'testing': True},\n msg='Oops, they are equal.',\n )","title":""},{"docid":"f605212c625de142f5dc6b0fbe22d3f8","score":"0.63874143","text":"def should_be_equal(got, expected, func, message=\"Incorrect result returned\"):\n\n assert expected == got, \\\n f\"[{func}] MSG: {message} [Expected: {expected}, got: {got}]\"","title":""},{"docid":"bba8449e6461585006b5599653dc4883","score":"0.63811386","text":"def _assert_msg(msg, test_case):\n assert msg.__class__.__name__ == test_case['name'], (\n \"test case name {} loaded class name {}\".format(test_case['name'], msg.__class__.__name__))\n if test_case['fields']:\n for field_name, field_value in test_case['fields'].items():\n assert field_eq(getattr(msg, field_name), _encoded_string(field_value)), \\\n \"Unequal field values (name: %s): got %r, but expected %r!\" \\\n % (field_name, getattr(msg, field_name), field_value)","title":""},{"docid":"d59cdbff67c9d6fc8512a372eae9b4af","score":"0.63465685","text":"def test_eq(self):\n self.assertEqual(1, 2, msg='Nope, not equal.')","title":""},{"docid":"647d7d8874f62dd2d6a7b6999ccf5674","score":"0.63397604","text":"def assert_equal(a, b, message='No message'):\n if a != b:\n raise ValueError('%s != %s:\\n %s' % (a, b, message))","title":""},{"docid":"7f63011b3120542c42d4ee6d117b72b5","score":"0.6299821","text":"def check_args(cc, args, expected):\n with set_env(SPACK_TEST_COMMAND=\"dump-args\"):\n cc_modified_args = cc(*args, output=str).strip().split(\"\\n\")\n assert expected == cc_modified_args","title":""},{"docid":"5969fe96c20076cb66da9042e32459a3","score":"0.6299272","text":"def _expected_cmd_args(self):","title":""},{"docid":"19cce1e948c800cab903b736e03ec609","score":"0.62879306","text":"def assertCountEqual(self, actual, expected, msg=None):\n try:\n super(BackpyTest, self).assertCountEqual(actual, expected, msg)\n except AttributeError:\n self.assertItemsEqual(actual, expected, msg)","title":""},{"docid":"167309107e4aff88d4e632ac11256715","score":"0.62486446","text":"def test_correct_num_args(self):\n arg_lengths = list(set([len(x) for x in self.m.tests]))\n self.assertEqual(len(arg_lengths), 1, 'some test is the wrong length')\n self.assertEqual(arg_lengths[0], len(signature(self.m.__dict__[self.gate]).parameters))","title":""},{"docid":"c34ce50c2f1eb5c9f577e31fdb611dbc","score":"0.6239252","text":"def assertItemsEqual(self, expected_seq, actual_seq, msg=None):\n\n if MAJOR_PY_VERSION == 2:\n return super(UnittestPythonCompatibility, self).assertItemsEqual(expected_seq, actual_seq, msg=msg)\n return super(UnittestPythonCompatibility, self).assertCountEqual(expected_seq, actual_seq, msg=msg)","title":""},{"docid":"6eaa0c04bb7597c64e6f870bdd4ffbf9","score":"0.6194282","text":"def test_case_function_call_with_args(self):\n value = AssertionChain().do(lambda x, y: x + y, 'no message', x=1, y=2).perform()\n assert value == 3","title":""},{"docid":"3fd7ca5bcaf022a8bbfc48ad1d6dcbae","score":"0.6174232","text":"def test_add_2_and_2_should_equal_5_fails_with_supplied_message():\n our_result = 2 + 2\n\n assert our_result == 5, \"2 and 2 should equal 5\"","title":""},{"docid":"f12d381abf76918ee8e58bcdfc62739e","score":"0.6162053","text":"def assertListEqual(self, expected, actual, msg=None):\n if msg is None:\n msg = ''\n assert_msg = f'{msg}: expected length {len(expected)} got {len(actual)}'\n self.assertEqual(len(expected), len(actual), assert_msg)\n idx = 0\n for exp, act in zip(expected, actual):\n if isinstance(exp, list):\n self.assertListEqual(exp, act, f'{msg}[{idx}]')\n elif isinstance(exp, dict):\n self.assertObjectEqual(exp, act, f'{msg}[{idx}]')\n else:\n assert_msg = f'{msg}[{idx}] expected \"{exp}\" got \"{act}\"'\n self.assertEqual(exp, act, assert_msg)\n idx += 1","title":""},{"docid":"24eb2364e819a888111265ea049e3329","score":"0.6120579","text":"def test_different_number_default_values_error():\n\n incorrect_defualt_value = (\"a\", \"b\", \"c\")\n\n with pytest.raises(\n AssertionError,\n match=f\"Incorrect number of default values -\\n Expected: {len(incorrect_defualt_value)}\\n Actual: 1\",\n ):\n\n h.test_function_arguments(\n h.assert_frame_equal_msg,\n expected_arguments=[\n \"actual\",\n \"expected\",\n \"msg_tag\",\n \"print_actual_and_expected\",\n ],\n expected_default_values=incorrect_defualt_value,\n )","title":""},{"docid":"82cfe72470dc47bb4bb113bc9a577ebd","score":"0.61125416","text":"def assertEqual(self, first, second):\n assert first == second","title":""},{"docid":"84095a092f97ae6c8e5097a279a9c628","score":"0.60950464","text":"def test_message(args, kwargs):\n fmt = ['{}']*len(args) + ['{' + name + '}' for name in kwargs]\n fmt = ' '.join(fmt)\n note('fmt={}'.format(fmt))\n counter = FormatCounter(fmt)\n msg = Message(counter, args, kwargs)\n assert counter.get_count() == 0\n assert str(msg) == fmt.format(*args, **kwargs)\n assert counter.get_count() == 1\n\n assert repr(msg) == '\"{}\".format(*{}, **{})'.format(fmt, args, kwargs)","title":""},{"docid":"4cadaf8c5949fc9897bbc931f5b972ac","score":"0.60931647","text":"def __my_assert_args(function, args, expected_output, check_type=True):\n argstr = str(args).replace(',)', ')')\n output = function(*args)\n\n # Controleer eerst het return-type (optioneel)\n if check_type:\n msg = f\"Fout: {function.__name__}{argstr} geeft geen {type(expected_output)} terug als return-type\"\n assert type(output) is type(expected_output), msg\n\n # Controleer of de functie-uitvoer overeenkomt met de gewenste uitvoer\n msg = f\"Fout: {function.__name__}{argstr} geeft {output} in plaats van {expected_output}\"\n if type(expected_output) is float:\n # Vergelijk bij float als return-type op 7 decimalen om afrondingsfouten te omzeilen\n assert round(output - expected_output, 7) == 0, msg\n else:\n assert output == expected_output, msg","title":""},{"docid":"f19b8a5ad3a8132f827ef04af4839f04","score":"0.6088724","text":"def assertEqual(self, a, b, msg=None):\n self._assert_true(a == b, a, b, msg, r'{} != {}')","title":""},{"docid":"d9a90ce06bbf71924fda65bf20be2ac6","score":"0.60836226","text":"def assertMultiLineEqual(self, expected, actual, msg=None):\n self.assertTrue(isinstance(expected, str),\n 'Expected argument is not a string')\n self.assertTrue(isinstance(actual, str),\n 'Actual argument is not a string')\n expected = map(self.normalize,open(expected, 'r').readlines())\n\n actual = map(self.normalize,open(actual,'r').readlines())\n diff = map(self.ignorable,difflib.ndiff(expected,actual))\n message = ''.join(diff)\n if len(message) > 0:\n if msg:\n message += \" : \" + msg\n self.fail(\"Multi-line strings are unequal:\\n\" + message)","title":""},{"docid":"69f635a46edc5c978203d2aa86fa57e7","score":"0.60514003","text":"def assertMultiLineEqual(self, first, second, msg=None):\n import difflib\n self.assertTrue(isinstance(first, str),\n 'First argument is not a string')\n self.assertTrue(isinstance(second, str),\n 'Second argument is not a string')\n if first != second:\n message = ''.join(difflib.ndiff(first.splitlines(True),\n second.splitlines(True)))\n if msg:\n message += \" : \" + msg\n self.fail(\"Multi-line strings are unequal:\\n\" + message)","title":""},{"docid":"82831caa03d8a45d98580fe889ca55dd","score":"0.60406125","text":"def verify_help(msg, expected, unexpected=[]):\n\n for str in expected:\n if str not in msg:\n self.assert_(False, \"{0} not in {1}\".format(\n str, msg))\n for str in unexpected:\n if str in msg:\n self.assert_(False, \"{0} in {1}\".format(\n str, msg))","title":""},{"docid":"ae8600cd52e03de7e6f50667bd0c1d62","score":"0.6032655","text":"def assertEqual_(self, output, expected):\n self.assertEqual(output, expected, 'Expected \"%s\". Got \"%s\".' % (expected, output))","title":""},{"docid":"f83ec7956d6f26582edaefcc1e8bdf03","score":"0.60318327","text":"def test04_args(self):\n s = Square(10, 2, 3, 12)\n self.assertEqual(s.size, 10)\n self.assertEqual(s.x, 2)\n self.assertEqual(s.y, 3)\n self.assertEqual(s.id, 12)","title":""},{"docid":"26baa93fcb8de452f0d2763157b7db40","score":"0.60161006","text":"def _verify(self, *args) -> bool:","title":""},{"docid":"0f622eb10183e55105e6aef9864e42a0","score":"0.6008199","text":"def _checkargs(self, args):\n if len(args) != self.Nargs:\n raise ValueError(\"Wrong number of arguments: %d given; %d \"\n \"expected.\" % (len(args), self.Nargs))","title":""},{"docid":"df92bcfd166e36ec42840c83780ce3cf","score":"0.6006135","text":"def test_VoteFewArgs(self):\n\n msg = \"Usage: ?vote \\\"Vote title\\\" \\\"Option 1\\\" \\\"Option 2\\\" \\\"...\\\"\"\n message, error = votes.vote(\"one\")\n self.assertEqual(error,\"NotEnoughArgs\")\n self.assertEqual(msg, message)","title":""},{"docid":"b6d759f2e7dcee8691900865c7ac5544","score":"0.60024685","text":"def assert_in(self, first, second, msg=None):\n self.assertIn(first, second, msg=msg)","title":""},{"docid":"e0859a19f6ce6535e3fd2cb1a7a7af42","score":"0.5998726","text":"def test_call_true(self):\n self.assertCallTrue(\n False,\n func=str,\n args=[1],\n kwargs={'testing': True},\n msg='Nope not true.',\n )","title":""},{"docid":"ea48ed7824b42d7a351315d9c0534a54","score":"0.59879446","text":"def test_meth(self, a, b, *args, **kwargs):\n print 'Inside test method'\n return '{} != {}'.format(a, b)","title":""},{"docid":"b6df6b9ed51fa025d0a4e5ee98fc0a83","score":"0.5972029","text":"def test_two_args(a,b):\r\n return a,b","title":""},{"docid":"bff4a757965dc8317ef5c3023e002515","score":"0.59715575","text":"def test_map_args_invalid():\n assert False","title":""},{"docid":"484c83b66035c269c5248743e13e9d8b","score":"0.594613","text":"def test_map_args_level():\n assert False","title":""},{"docid":"7db39fb86e41a1a107a5eb17df382e27","score":"0.594606","text":"def test_message(self, variables):\n assert Variable(variables[0]).message == variables[1]","title":""},{"docid":"20ca2de9e2ef62b950e7fca1d0429336","score":"0.5937386","text":"def eq_(*args, msg=None, precision=None):\n new_args = list(args)\n if msg is None:\n msg = \"Different values: %s\" % \", \".join(\n [str(arg) for arg in new_args])\n if precision is not None:\n for index, arg in enumerate(new_args):\n new_args[index] = float_round(arg, precision)\n assert all(new_args[0] == b for b in new_args[1:]), msg\n else:\n assert new_args[0] == new_args[1], msg","title":""},{"docid":"6a823ca25d41095589ef991386aa5dcb","score":"0.5921473","text":"def assertObjectEqual(self, expected, actual, msg=None, strict=False):\n for key, value in expected.items():\n if msg is None:\n key_name = key\n else:\n key_name = f'{msg}.{key}'\n if expected is None:\n self.assertIsNone(\n actual, f'{key_name}: {actual} should be None')\n else:\n self.assertIsNotNone(\n actual, f'{key_name}: {actual} should not be None')\n if isinstance(value, dict):\n self.assertObjectEqual(value, actual[key], key_name, strict=strict)\n elif isinstance(value, list):\n self.assertListEqual(value, actual[key], key_name)\n else:\n self.assertIn(key, actual,\n f'{key_name}: missing key {key}')\n assert_msg = f'{key_name}: expected \"{value}\" got \"{actual[key]}\"'\n self.assertEqual(value, actual[key], msg=assert_msg)\n if strict:\n for key in actual.keys():\n self.assertIn(key, expected)","title":""},{"docid":"c47f6f481f06d55795ca56e2eb9a7ea4","score":"0.5915513","text":"def test_parametrized_collect_with_wrong_args(pytester: Pytester) -> None:\n py_file = pytester.makepyfile(\n \"\"\"\n import pytest\n\n @pytest.mark.parametrize('foo, bar', [(1, 2, 3)])\n def test_func(foo, bar):\n pass\n \"\"\"\n )\n\n result = pytester.runpytest(py_file)\n result.stdout.fnmatch_lines(\n [\n 'test_parametrized_collect_with_wrong_args.py::test_func: in \"parametrize\" the number of names (2):',\n \" ['foo', 'bar']\",\n \"must be equal to the number of values (3):\",\n \" (1, 2, 3)\",\n ]\n )","title":""},{"docid":"3a75a02a3d33c090a6c2b961b271f198","score":"0.5914568","text":"def test_default_values_non_mismatch_2_error():\n\n incorrect_defualt_value = None\n\n with pytest.raises(\n AssertionError,\n match=\"Incorrect default values -\\n Expected: No default values\\n Actual: .*\",\n ):\n\n h.test_function_arguments(\n h.assert_frame_equal_msg,\n expected_arguments=[\n \"actual\",\n \"expected\",\n \"msg_tag\",\n \"print_actual_and_expected\",\n ],\n expected_default_values=incorrect_defualt_value,\n )","title":""},{"docid":"682f108133706c3b406dd916135dcfe2","score":"0.59049815","text":"def test_n_arg(self):\n Square_test = Square(8)\n message = '[Square] (1) 0/0 - 8'\n self.assertEqual(str(Square_test), message)\n Square_test = Square(8, 12)\n message = '[Square] (2) 12/0 - 8'\n self.assertEqual(str(Square_test), message)\n Square_test = Square(8, 12, 3)\n message = '[Square] (3) 12/3 - 8'\n self.assertEqual(str(Square_test), message)\n Square_test = Square(8, 12, 3, 4)\n message = '[Square] (4) 12/3 - 8'\n self.assertEqual(str(Square_test), message)","title":""},{"docid":"e51eafeb46f846d1031fc3465bd7a566","score":"0.5896019","text":"def test_comparison(self):\n self.assertIsInstance(hash(FREQ), int)\n self.assertNotEqual(FREQ, ENERGY)\n with self.assertRaises(TypeError):\n _ = ENERGY > FREQ\n\n # Test args in different order\n lot1 = LevelOfTheory('method', args=('arg1', 'arg2'))\n lot2 = LevelOfTheory('method', args=('arg2', 'arg1'))\n self.assertEqual(lot1, lot2)","title":""},{"docid":"dec55a48a2f8bc3a76b7f0cef9141959","score":"0.5894814","text":"def _check_arguments(assignment, issue_tracker):\n metadata = assignment.metadata\n mandatory = set(metadata['args']['mandatory'])\n optional = set(metadata['args']['optional'].keys())\n\n given_args = assignment.children\n given_idents = {a.ident.ident: a.ident for a in given_args}\n given_keys = given_idents.keys()\n given = set(given_keys)\n\n # Case 0: Duplicated arguments\n duplicates = set([x for x in given_keys if given_keys.count(x) > 1])\n for m in duplicates:\n reason = \"Duplicated argument: '{}'\".format(m)\n issue_tracker.add_error(reason, given_idents[m])\n\n # Case 1: Missing arguments\n missing = mandatory - given\n for m in missing:\n reason = \"Missing argument: '{}'\".format(m)\n issue_tracker.add_error(reason, assignment)\n\n # Case 2: Extra (unused) arguments\n # FIXME: Rename error to Excess argument\n unused = given - (mandatory | optional)\n for m in unused:\n reason = \"Excess argument: '{}'\".format(m)\n issue_tracker.add_error(reason, given_idents[m])\n\n # Case 3: Deprecation warning if optional args not explicitly given\n deprecated = optional - given\n for m in deprecated:\n reason = \"Using default value for implicit parameter '{}'\".format(m)\n issue_tracker.add_warning(reason, assignment)","title":""},{"docid":"70294f7077db7b7ee06c3489f997ca53","score":"0.5890335","text":"def assert_ex(\n msg: str,\n received: typing.Any,\n expected: typing.Any,\n hint: typing.Any = None):\n hint_str = '' if hint is None else '({!r})'.format(hint)\n\n return '{} {}\\nreceived > {}\\nexpected > {}'.format(\n msg, hint_str, received, expected)","title":""},{"docid":"b5d274a7e7e66eeb96e63839cff8593f","score":"0.5878507","text":"def check_example(msg):","title":""},{"docid":"944a7c63de25396f9a8735664c874c00","score":"0.5875338","text":"def test_wrong_args(self):\n #{{{\n self.assertEqual(\n find(self._pathto('baaaba'), '*', 41, 35), set())","title":""},{"docid":"2b40666de4b921b390f191238373910f","score":"0.5869969","text":"def test_arguments():\n\n expected_arguments = [\"func\", \"expected_arguments\", \"expected_default_values\"]\n\n expected_default_values = (None,)\n\n arg_spec = inspect.getfullargspec(h.test_function_arguments)\n\n arguments = arg_spec.args\n\n assert len(expected_arguments) == len(\n arguments\n ), f\"Incorrect number of arguments -\\n Expected: {len(expected_arguments)}\\n Actual: {len(arguments)}\"\n\n for i, (e, a) in enumerate(zip(expected_arguments, arguments)):\n\n assert e == a, f\"Incorrect arg at index {i} -\\n Expected: {e}\\n Actual: {a}\"\n\n default_values = arg_spec.defaults\n\n if default_values is None:\n\n if expected_default_values is not None:\n\n raise AssertionError(\n f\"Incorrect default values -\\n Expected: {expected_default_values}\\n Actual: No default values\"\n )\n\n else:\n\n if expected_default_values is None:\n\n raise AssertionError(\n f\"Incorrect default values -\\n Expected: No default values\\n Actual: {default_values}\"\n )\n\n if (default_values is not None) and (expected_default_values is not None):\n\n assert len(expected_default_values) == len(\n default_values\n ), f\"Incorrect number of default values -\\n Expected: {len(expected_default_values)}\\n Actual: {len(default_values)}\"\n\n for i, (e, a) in enumerate(zip(expected_default_values, default_values)):\n\n assert (\n e == a\n ), f\"Incorrect default value at index {i} of default values -\\n Expected: {e}\\n Actual: {a}\"","title":""},{"docid":"0b18c5bdad44362580396f0246f80f98","score":"0.5866624","text":"def test_all(self):\n message = build_message(\n b'COMMAND',\n b'param1',\n b'param2',\n prefix=b'something',\n suffix=b'suffix ftw!',\n )\n expected = b':something COMMAND param1 param2 :suffix ftw!\\r\\n'\n assert message == expected","title":""},{"docid":"1000b450c9464e81dc02f3dd17d413b4","score":"0.5863046","text":"def test_multiple_arguments(self):\n loc = translate_locator(\"test\", \"foo.message:hello,world\")\n assert loc == \"hello world\"","title":""},{"docid":"98986dc975ae0c4648ec29327ed64150","score":"0.5853667","text":"def CheckArguments(self):\n pass","title":""},{"docid":"315d72f9be87dd0d59de06a10c108088","score":"0.58467853","text":"def test_extra_args(self):\n loc = translate_locator(\"test\", \"foo.message:testing,one,two,three\")\n assert loc == \"testing one\"","title":""},{"docid":"08f830b4c6d83e4e924c04399dbe921e","score":"0.5831577","text":"def test_matches_arguments(self, mirror):\n signature = inspect.signature(mirror)\n assert 'a' in signature.parameters\n assert 'b' in signature.parameters\n assert 'args' not in signature.parameters","title":""},{"docid":"da40b11af68fdfac8e7ddc93c914b6a5","score":"0.58242786","text":"def _run_assert_output(args, message, **kwargs):\n output = run(args, **kwargs)\n if output.returncode != 0:\n raise RuntimeError(f\"{message}: (\" + ' '.join(args) + ')')","title":""},{"docid":"501d2012894bb7c66be1a25481f3da49","score":"0.5814888","text":"def testMatchAtAssertEqualUserDefinedWithMessage(self):\n parser = MockParser([' \\n'])\n atAssertEqualUserDefined = AtAssertEqualUserDefined(parser)\n\n parser.fileName = \"foo.pfunit\"\n parser.currentLineNumber = 8\n text = ' @assertEqualUserDefined(a,b,message=\"c\")\\n'\n atAssertEqualUserDefined.apply(text)\n self.assertEqual('#line 8 \"foo.pfunit\"\\n', parser.outLines[0])\n self.assertEqual(' call assertTrue(a==b, message=\"c\", &\\n',\n parser.outLines[1])\n self.assertEqual(\" & location=SourceLocation( &\\n\", parser.outLines[2])\n self.assertEqual(\" & 'foo.pfunit', &\\n\", parser.outLines[3])\n self.assertEqual(\" & 8)\", parser.outLines[4])\n self.assertEqual(\" )\\n\", parser.outLines[5])\n self.assertEqual(\" if (anyExceptions()) return\\n\", parser.outLines[6])\n self.assertEqual('#line 9 \"foo.pfunit\"\\n', parser.outLines[7])","title":""},{"docid":"102030103dae30078909285c29a6a63e","score":"0.58048826","text":"def test_update_args(self):\n\n # TODO: this\n pass","title":""},{"docid":"a4dc0689c568eebc120aa50de2b75c82","score":"0.5779741","text":"def test_neq(self):\n self.assertNotEqual(1, 1, msg='Oops, they are equal')","title":""},{"docid":"4b7b38e4b0b52f6556c763108a851ce2","score":"0.5766806","text":"def test_map_args_space():\n assert False","title":""},{"docid":"d09a70ac50a6d2e3c123256816c10f78","score":"0.5763555","text":"def __assertStringMatch(self, actual, expected, format_msg):\n def make_description(info):\n return \"Strings not equal: %s\" % info\n\n description = make_description(\"different characters\")\n msg = _make_message(actual, expected, format_msg, description=description)\n self.assertEqual(actual, expected, msg=msg)\n\n info = \"types different: %s != %s (actual)\" % (repr(type(expected)), repr(type(actual)))\n description = make_description(info)\n msg = _make_message(actual, expected, format_msg, description=description)\n self.assertEqual(type(expected), type(actual), msg=msg)","title":""},{"docid":"902096525d09116f7b23a96f9fd63ab5","score":"0.5763291","text":"def check_num_arguments(expected_number):\n if len(arg_values) != expected_number:\n if expected_number == 1:\n message = \"'\" + name + \"' must have exactly one argument.\"\n else:\n message = (\"'\" + name + \"' must have exactly \" +\n str(expected_number) + \" arguments.\")\n raise ValueError(message)","title":""},{"docid":"8f16ba5da1b977983e5fc5c1801d0f5e","score":"0.57550454","text":"def assert_iterables_similar(self, *args):\n\t\targs = list(map(list, args))\n\t\t[i.sort() for i in args]\n\t\tone, rest = args[0], args[1:]\n\t\tassert all([one == i for i in rest])","title":""},{"docid":"7c0ccfd8080ca35a924ef06475ecae7f","score":"0.5747624","text":"def assertion_error_with_test_case(err: AssertionError, test_case: str) -> AssertionError:\n msg = f'\\ntest case: {test_case}\\n{err.args[0]}'\n err.args = (msg,)\n return err","title":""},{"docid":"9c8840e19f0c358aa9cc5194ed25b8b4","score":"0.5741583","text":"def assert_not_equal(self, first, second, msg=None):\n self.assertNotEqual(first, second, msg=msg)","title":""},{"docid":"a725d5d031d23229c3ebff3a52378147","score":"0.5741329","text":"def assertCompositeEqual(self, ctx1, ctx2, msg=None):\n\n self.assertEqual(ctx1.GetNumberOfTransforms(), ctx2.GetNumberOfTransforms())\n self.assertEqual(ctx1.GetNumberOfFixedParameters(), ctx2.GetNumberOfFixedParameters())\n self.assertEqual(ctx1.GetFixedParameters(), ctx2.GetFixedParameters())\n self.assertEqual(ctx1.GetNumberOfParameters(), ctx2.GetNumberOfParameters())\n self.assertEqual(ctx1.GetParameters(), ctx2.GetParameters())\n self.assertTransformEqual(ctx1, ctx2, msg);\n\n for idx in range(ctx1.GetNumberOfTransforms()):\n tx1 = ctx1.GetNthTransform(idx)\n tx2 = ctx2.GetNthTransform(idx)\n\n self.assertTransformEqual(tx1, tx2, msg=\"Comparing transform {0}: {1}\".format(idx, msg))","title":""},{"docid":"261bb8ecf57bd0a28238fcb8ad1118a1","score":"0.5729832","text":"def test(record):\n assert record.getMessage() == expected\n if extra is not None:\n for key, val in extra.items():\n assert getattr(record, key, None) == val","title":""},{"docid":"7339801178967d30ea91bc654154456c","score":"0.572322","text":"def test_update_args(self):\n print(\"test_update_args\")\n self.assertEqual(self.s1.__str__(), \"[Square] ({}) 0/0 - 5\".format(self.s1.id))\n self.s1.update(69)\n self.assertEqual(self.s1.__str__(), \"[Square] (69) 0/0 - 5\")\n self.s1.update(2, 4)\n self.assertEqual(self.s1.__str__(), \"[Square] (2) 0/0 - 4\")\n self.s1.update(2, 4, 6)\n self.assertEqual(self.s1.__str__(), \"[Square] (2) 6/0 - 4\")\n self.s1.update(2, 4, 6, 8)\n self.assertEqual(self.s1.__str__(), \"[Square] (2) 6/8 - 4\")","title":""},{"docid":"ac8a874768619bb92e7fa4960e3e4457","score":"0.57147956","text":"def _repr_test_args(self):\n raise NotImplementedError()","title":""},{"docid":"33b050ebbaf2f8410c3cd655eceaad69","score":"0.5712951","text":"def test_multiplous_named_params():\n assert True","title":""},{"docid":"b3d39b61a42aaf058d5dcfca6b7e93cd","score":"0.5698148","text":"def test(*args, **kw):","title":""},{"docid":"7fe36d753391a2d8fbf886b2889b8663","score":"0.56976515","text":"def test_map_args_level_and_space():\n assert False","title":""},{"docid":"753454c0a0456ff9aa9cc0dadac02824","score":"0.568529","text":"def test_has_correct_number_of_keys_and_values(self):\n self.has_correct_number_of_keys_and_values(2, 1, args=self.default_args)","title":""},{"docid":"d2b646775659545ec8d098fa6460856c","score":"0.56828254","text":"def test_diagnosis_parametrize(a, b, expected):\n assert diagnosis(a, b) == expected","title":""},{"docid":"2693fbb826995ca7b180c9ec1868cc5b","score":"0.56717914","text":"def test_single_arg(a):\r\n return a","title":""},{"docid":"8bbe3655756ce1c9d48dd0370b033ad7","score":"0.5669148","text":"def test_text_second_argument():\n msg = Message('test', 'spam spam spam', 'test')\n text = Text()\n assert text.parse('spam', ['spam'], msg, 2) == 'spam spam'","title":""},{"docid":"43d1355577c68dadc40afb73678cccf7","score":"0.56649554","text":"def emulate_assertListEqual(self, seq1, seq2, msg=None, seq_type=None):\n if seq_type is not None:\n seq_type_name = seq_type.__name__\n if not isinstance(seq1, seq_type):\n raise self.failureException('First sequence is not a %s: %s'\n % (seq_type_name, safe_repr(seq1)))\n if not isinstance(seq2, seq_type):\n raise self.failureException('Second sequence is not a %s: %s'\n % (seq_type_name, safe_repr(seq2)))\n else:\n seq_type_name = \"sequence\"\n\n differing = None\n try:\n len1 = len(seq1)\n except (TypeError, NotImplementedError):\n differing = 'First %s has no length. Non-sequence?' % (\n seq_type_name)\n\n if differing is None:\n try:\n len2 = len(seq2)\n except (TypeError, NotImplementedError):\n differing = 'Second %s has no length. Non-sequence?' % (\n seq_type_name)\n\n if differing is None:\n if seq1 == seq2:\n return\n\n seq1_repr = safe_repr(seq1)\n seq2_repr = safe_repr(seq2)\n if len(seq1_repr) > 30:\n seq1_repr = seq1_repr[:30] + '...'\n if len(seq2_repr) > 30:\n seq2_repr = seq2_repr[:30] + '...'\n elements = (seq_type_name.capitalize(), seq1_repr, seq2_repr)\n differing = '%ss differ: %s != %s\\n' % elements\n\n for i in range(min(len1, len2)):\n try:\n item1 = seq1[i]\n except (TypeError, IndexError, NotImplementedError):\n differing += ('\\nUnable to index element %d of first %s\\n' %\n (i, seq_type_name))\n break\n\n try:\n item2 = seq2[i]\n except (TypeError, IndexError, NotImplementedError):\n differing += ('\\nUnable to index element %d of second %s\\n' %\n (i, seq_type_name))\n break\n\n if item1 != item2:\n differing += ('\\nFirst differing element %d:\\n%s\\n%s\\n' %\n (i, safe_repr(item1), safe_repr(item2)))\n break\n else:\n if (len1 == len2 and seq_type is None and\n type(seq1) != type(seq2)):\n # The sequences are the same, but have differing types.\n return\n\n if len1 > len2:\n differing += ('\\nFirst %s contains %d additional '\n 'elements.\\n' % (seq_type_name, len1 - len2))\n try:\n differing += ('First extra element %d:\\n%s\\n' %\n (len2, safe_repr(seq1[len2])))\n except (TypeError, IndexError, NotImplementedError):\n differing += ('Unable to index element %d '\n 'of first %s\\n' % (len2, seq_type_name))\n elif len1 < len2:\n differing += ('\\nSecond %s contains %d additional '\n 'elements.\\n' % (seq_type_name, len2 - len1))\n try:\n differing += ('First extra element %d:\\n%s\\n' %\n (len1, safe_repr(seq2[len1])))\n except (TypeError, IndexError, NotImplementedError):\n differing += ('Unable to index element %d '\n 'of second %s\\n' % (len1, seq_type_name))\n standardMsg = differing\n diffMsg = '\\n' + '\\n'.join(\n difflib.ndiff(pprint.pformat(seq1).splitlines(),\n pprint.pformat(seq2).splitlines()))\n standardMsg = self._truncateMessage(standardMsg, diffMsg)\n msg = self._formatMessage(msg, standardMsg)\n self.fail(msg)","title":""},{"docid":"207ea3ba2d8eb9388d97c2479f558c25","score":"0.56522644","text":"def assertTransformEqual(self, tx1, tx2, msg=None):\n\n self.assertEqual(tx1.__class__, tx2.__class__)\n self.assertEqual(tx1.GetDimension(), tx2.GetDimension())\n self.assertEqual(tx1.GetFixedParameters(), tx2.GetFixedParameters())\n self.assertEqual(tx1.GetParameters(), tx2.GetParameters())","title":""},{"docid":"506cc7c89ba30ba476234921f71637c5","score":"0.5648275","text":"def assertTraceCmpWas(self, call_args, left, right, op, left_is_const):\n #call_args: tuple(left, right, opid, idx, left_is_const)\n self.assertEqual(call_args[0], left)\n self.assertEqual(call_args[1], right)\n self.assertEqual(dis.cmp_op[call_args[2]], op)\n self.assertEqual(call_args[4], left_is_const)","title":""},{"docid":"825fa545cf412b04cf07015a542d3349","score":"0.56448895","text":"def test_dummy_message(kwargs, result):\n assert dummy_autotranslate(**kwargs) == result","title":""},{"docid":"b9dfc1819b4f42d828b651a766020bf0","score":"0.5639885","text":"def test_multiple_args(self):\n\n @self.check_arg_types(id=int, create_date=datetime, title=str, val=(int, float))\n def output_item(id, create_date, title, val):\n return (id, create_date, title, val)\n\n dt = datetime.now()\n\n self.assertEqual(output_item(id=1, create_date=dt, title='test', val=1), (1, dt, 'test', 1))\n\n self.assertEqual(output_item(id=1, create_date=dt, title='test', val=1.5), (1, dt, 'test', 1.5))\n\n with self.assertRaises(TypeError):\n output_item(id='1', create_date=dt, title='test', val=1)\n\n with self.assertRaises(TypeError):\n output_item(id=1, create_date=None, title='test', val=1)\n\n with self.assertRaises(TypeError):\n output_item(id=1, create_date=dt, title={'a': '1'}, val=1)\n\n with self.assertRaises(TypeError):\n output_item(id=1, create_date=dt, title='test', val=Decimal('-1.3'))\n\n with self.assertRaises(TypeError):\n output_item(id=None, create_date=None, title=None, val=None)","title":""},{"docid":"cd3a715d980511a66909d002b0b04cbc","score":"0.56375337","text":"def assert_equals(expected, actual):\n assert expected == actual","title":""},{"docid":"9869a9c38d07fb3751c2ec38beeb5d85","score":"0.56372213","text":"def test_arguments(self):\n xt = np.array([[1,2,3],[4,5,6],[7,8,9]])\n yt = np.array([0,1,0])\n self.check_arguments(TestModel, TestEstimator, xt, yt, skip_bm_args=False)","title":""},{"docid":"8a57fbc1c060b3223d5a54b854d6c2bb","score":"0.5616994","text":"def eq_(a, b, msg=None):\n if not a == b:\n raise AssertionError(msg or \"%r != %r\" % (a, b))","title":""},{"docid":"e14aba5ef7385efeb3d7880f90cb7b10","score":"0.5616082","text":"def check_args(self, args):\n logger.debug(\"check_args(args) was called.\")","title":""},{"docid":"a6ab896592641c37dac089a892d61d1a","score":"0.56114995","text":"def test_assertion(name, result, expected):\n assert result == expected, \"Expected %s, got %s for %s\" \\\n % (expected, result, name)","title":""},{"docid":"6f486cd486bb0a89afe9a436d7544113","score":"0.561105","text":"def verify(self, args):\n return","title":""},{"docid":"b1e8b88c6e68c1433a12e7b2442b788a","score":"0.5610687","text":"def test_multiple_args(self):\n\n @self.check_arg_types(value_on_error=None, id=int, create_date=datetime, title=str)\n def output_item(id, create_date, title):\n return (id, create_date, title)\n\n dt = datetime.now()\n\n self.assertEqual(output_item(id=1, create_date=dt, title='test'), (1, dt, 'test'))\n self.assertEqual(output_item(id='1', create_date=dt, title='test'), (1, dt, 'test'))\n self.assertEqual(output_item(id=1.4, create_date=dt, title='test'), (1, dt, 'test'))\n self.assertEqual(output_item(id=[1, 2], create_date=dt, title='test'), (None, dt, 'test'))\n\n self.assertEqual(output_item(id=1, create_date=dt, title='test'), (1, dt, 'test'))\n self.assertEqual(output_item(id=1, create_date='2019/01/25', title='test'), (1, None, 'test'))\n self.assertEqual(output_item(id=1, create_date=date.today(), title='test'), (1, None, 'test'))\n\n self.assertEqual(output_item(id=1, create_date=dt, title=1), (1, dt, '1'))\n self.assertEqual(output_item(id=1, create_date=dt, title=dt), (1, dt, str(dt)))\n self.assertEqual(output_item(id=1, create_date=dt, title=None), (1, dt, None))\n\n self.assertEqual(output_item(id=1, create_date=dt, title=[1, 2]), (1, dt, str([1, 2])))","title":""},{"docid":"c434ed3e45483868f39d93b6ed8b0783","score":"0.56064326","text":"def _test_allclose(a, b, msg=\"a, b\", atol=1e-6):\n L_inf = abs(a - b).max()\n L_1 = abs(a - b).sum()\n L_0 = 1 - np.isclose(a, b).sum() / a.size\n\n try:\n assert np.allclose(a, b, atol=atol), (\n f\"NotEqual: {msg}:\\nL_0: {L_0}\\nL_1: {L_1}\\nL_inf: {L_inf}\")\n except AssertionError:\n if DISPLAY:\n import matplotlib.pyplot as plt\n plt.imshow((1 - np.isclose(a, b)).sum(axis=(0, 1)))\n plt.show()\n raise","title":""},{"docid":"1464391e591c5c517a01aabdd41e5b64","score":"0.5603927","text":"def assertEqual(self, a, b):\n if a != b:\n raise EqualAssertionError(self, a, b)","title":""},{"docid":"55e8fd8b376d6d2317470c9fdb03483e","score":"0.56036377","text":"def my_test_equal(a, b):\n\n if a == b:\n print(\"Pass\")\n else:\n print(\"Fail\")","title":""},{"docid":"a061755ba902558b384b7bb0bf5a5a9e","score":"0.5601202","text":"def check_arg_count(self):\n if len(self.arguments) == self.arg_count(self.name):\n return\n raise XMLFormatException()","title":""}],"string":"[\n {\n \"docid\": \"6ae70b8bb010cd704e9fce7bf977453e\",\n \"score\": \"0.7293037\",\n \"text\": \"def test_call_eq(self):\\n self.assertCallEqual(\\n 1,\\n 2,\\n func=str,\\n args=[1],\\n otherargs=[2],\\n kwargs={'testing': True},\\n otherkwargs={'testing': True},\\n msg='Nope, not equal.',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c7a8c6833e89bb4bc9ffa8bef52dbc\",\n \"score\": \"0.7197761\",\n \"text\": \"def test_different_number_arguments_error():\\n\\n incorrect_expected_args = [\\\"actual\\\", \\\"expected\\\", \\\"msg_tag\\\"]\\n\\n with pytest.raises(\\n AssertionError,\\n match=f\\\"Incorrect number of arguments -\\\\n Expected: {len(incorrect_expected_args)}\\\\n Actual: 4\\\",\\n ):\\n\\n h.test_function_arguments(\\n h.assert_frame_equal_msg,\\n expected_arguments=incorrect_expected_args,\\n expected_default_values=(False,),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a5a6f492cda9c9c7740a5688c17877\",\n \"score\": \"0.713591\",\n \"text\": \"def test_call_tuple_eq(self):\\n self.assertCallTupleEqual(\\n (1,),\\n (2,),\\n func=str,\\n args=[1],\\n kwargs={'testing': True},\\n otherargs=[2],\\n otherkwargs={'testing': True},\\n msg='Nope, not equal.',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9553b31bf2adce0e60d50bf46cd72172\",\n \"score\": \"0.6856742\",\n \"text\": \"def assert_equal(self, first, second, msg=None):\\n self.assertEqual(first, second, msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd350d15d9985a8434ea545978a087f\",\n \"score\": \"0.6849586\",\n \"text\": \"def test_assert_equal_msg_calls_for_default_values():\\n\\n with mock.patch(target=\\\"tubular.testing.helpers.assert_equal_msg\\\") as mocked_method:\\n\\n expected_args = [\\\"self\\\", \\\"data\\\", \\\"index\\\", \\\"columns\\\", \\\"dtype\\\", \\\"copy\\\"]\\n expected_default_values = (None, None, None, None, False)\\n\\n h.test_function_arguments(\\n pd.DataFrame,\\n expected_arguments=expected_args,\\n expected_default_values=expected_default_values,\\n )\\n\\n assert mocked_method.call_count == (\\n len(expected_args) + len(expected_default_values)\\n ), f\\\"Unexpected number of calls to tubular.testing.helpers.assert_equal_msg -\\\\n Expected: {len(expected_args) + len(expected_default_values)}\\\\n Actual: {mocked_method.call_count}\\\"\\n\\n for call_n in range(len(expected_args), mocked_method.call_count):\\n\\n call_n_args = mocked_method.call_args_list[call_n]\\n call_n_pos_args = call_n_args[0]\\n call_n_kwargs = call_n_args[1]\\n\\n call_n_expected_pos_arg = (\\n expected_default_values[call_n - len(expected_args)],\\n expected_default_values[call_n - len(expected_args)],\\n f\\\"Incorrect default value at index {call_n - len(expected_args)} of default values\\\",\\n )\\n\\n assert len(call_n_pos_args) == len(\\n call_n_expected_pos_arg\\n ), f\\\"Unexpected number of positional args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\\\n Expected: {len(call_n_expected_pos_arg)}\\\\n Actual: {len(call_n_pos_args)}\\\"\\n\\n for i, (e, a) in enumerate(zip(call_n_expected_pos_arg, call_n_pos_args)):\\n\\n assert (\\n e == a\\n ), f\\\"Unexpected default value at index {i} -\\\\n Expected: {e}\\\\n Actual: {a}\\\"\\n\\n assert (\\n call_n_kwargs == {}\\n ), f\\\"Unexpected keyword args in call {call_n} to tubular.testing.helpers.assert_equal_msg -\\\\n Expected: None\\\\n Actual: {call_n_kwargs}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec0d5276fe92fef65b047cfb0c04940\",\n \"score\": \"0.67891556\",\n \"text\": \"def test_args_kwargs(self):\\n\\n self.assertEqual(tuple(self.arguments), self.result[0])\\n self.assertEqual(self.kwarguments, self.result[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"107776447c58903a2bffb208dd648c9d\",\n \"score\": \"0.67358834\",\n \"text\": \"def assert_equal(args: List, node):\\n if len(args) != 2:\\n evaluator.error(\\\"(special_forms.assert_value.assert-equal) 2 parameter expected, {} given\\\".format(len(args)), node)\\n\\n values = evaluator.evaluate_parallel_args(args)\\n\\n if values[0] != values[1]:\\n evaluator.error(\\\"assertion failed: {} != {}\\\".format(values[0], values[1]), node)\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a39cc3764b8c212a7a5bb80ba3bdadb9\",\n \"score\": \"0.6706973\",\n \"text\": \"def test_passing():\\n assert (1, 2, 3) == (1, 2, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f0dc2567c3f1516640a57fc5f5bceb2\",\n \"score\": \"0.66917634\",\n \"text\": \"def assertEqual(self, left, right, msg=None, **kwargs):\\n return assert_equal(left, right, explanation=msg, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2afbb20f9cdddc3f8aea304229c9666\",\n \"score\": \"0.6611375\",\n \"text\": \"def assertCallArgsEqual(self, call_args_singles, mock_method):\\n # convert from ['a', 'b'] into [(('a',),), (('b',),)]\\n expected = [((arg,),) for arg in call_args_singles]\\n self.assertEqual(expected, mock_method.call_args_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb813c0f65794b4e5f0ebe01dce7577a\",\n \"score\": \"0.6602467\",\n \"text\": \"def test_arguments():\\n\\n expected_arguments = [\\\"obj\\\", \\\"cls\\\"]\\n\\n arg_spec = inspect.getfullargspec(h.assert_inheritance)\\n\\n arguments = arg_spec.args\\n\\n assert len(expected_arguments) == len(\\n arguments\\n ), f\\\"Incorrect number of arguments -\\\\n Expected: {len(expected_arguments)}\\\\n Actual: {len(arguments)}\\\"\\n\\n for i, (e, a) in enumerate(zip(expected_arguments, arguments)):\\n\\n assert e == a, f\\\"Incorrect arg at index {i} -\\\\n Expected: {e}\\\\n Actual: {a}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454ac85325c4baa9c6210ddeffabe7ec\",\n \"score\": \"0.6544629\",\n \"text\": \"def __assert_eq(self, *args, **kwargs):\\n minified_exception = None\\n try:\\n self.assertEqual(*args, **kwargs)\\n except Exception as e:\\n str_e = str(e)\\n minified_exception = \\\"\\\\nAssertionError:\\\\n\\\"\\n lines = str_e.split(\\\"\\\\n\\\")\\n countdown = 3\\n countdown_on = False\\n first_differing = False\\n skip_lines = False\\n for line in lines:\\n if countdown_on:\\n if not skip_lines:\\n minified_exception += line + \\\"\\\\n\\\"\\n countdown = countdown - 1\\n if countdown == 0:\\n countdown_on = False\\n skip_lines = False\\n elif line.startswith(\\\"First differing\\\"):\\n first_differing = True\\n countdown_on = True\\n countdown = 3\\n minified_exception += line + \\\"\\\\n\\\"\\n elif line.startswith(\\\"First list\\\"):\\n countdown_on = True\\n countdown = 3\\n if not first_differing:\\n minified_exception += line + \\\"\\\\n\\\"\\n else:\\n skip_lines = True\\n elif line.startswith(\\\"F\\\"):\\n countdown_on = True\\n countdown = 3\\n minified_exception += line + \\\"\\\\n\\\"\\n elif line.startswith(\\\"+\\\") or line.startswith(\\\"-\\\"):\\n minified_exception += line + \\\"\\\\n\\\"\\n elif line.startswith(\\\"?\\\"):\\n minified_exception += line + \\\"\\\\n\\\"\\n elif line.strip().startswith(\\\"*\\\"):\\n minified_exception += line + \\\"\\\\n\\\"\\n if minified_exception:\\n raise VisualException(minified_exception)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8a66272c2964f3fb5fa4b0009489fa\",\n \"score\": \"0.650816\",\n \"text\": \"def test_assert_count_equal(testcase):\\n testcase.assertCountEqual((1, 1, 2), (1, 2, 1))\\n\\n with pytest.raises(AssertionError):\\n testcase.assertCountEqual((1, 1, 2), (2, 1, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3383affaf59d53d41daed21d68c2dd3\",\n \"score\": \"0.6480526\",\n \"text\": \"def assertTestLogEqual(self, messages):\\n case = self._result_calls[0][1]\\n self.assertEqual(messages, case._calls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a17f25fc707b00ef95ce51252b70af\",\n \"score\": \"0.64456534\",\n \"text\": \"def test_call_neq(self):\\n self.assertCallNotEqual(\\n 1,\\n 1,\\n func=str,\\n args=[1],\\n kwargs={'testing': True},\\n otherargs=[1],\\n otherkwargs={'testing': True},\\n msg='Oops, they are equal.',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f605212c625de142f5dc6b0fbe22d3f8\",\n \"score\": \"0.63874143\",\n \"text\": \"def should_be_equal(got, expected, func, message=\\\"Incorrect result returned\\\"):\\n\\n assert expected == got, \\\\\\n f\\\"[{func}] MSG: {message} [Expected: {expected}, got: {got}]\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba8449e6461585006b5599653dc4883\",\n \"score\": \"0.63811386\",\n \"text\": \"def _assert_msg(msg, test_case):\\n assert msg.__class__.__name__ == test_case['name'], (\\n \\\"test case name {} loaded class name {}\\\".format(test_case['name'], msg.__class__.__name__))\\n if test_case['fields']:\\n for field_name, field_value in test_case['fields'].items():\\n assert field_eq(getattr(msg, field_name), _encoded_string(field_value)), \\\\\\n \\\"Unequal field values (name: %s): got %r, but expected %r!\\\" \\\\\\n % (field_name, getattr(msg, field_name), field_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59cdbff67c9d6fc8512a372eae9b4af\",\n \"score\": \"0.63465685\",\n \"text\": \"def test_eq(self):\\n self.assertEqual(1, 2, msg='Nope, not equal.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647d7d8874f62dd2d6a7b6999ccf5674\",\n \"score\": \"0.63397604\",\n \"text\": \"def assert_equal(a, b, message='No message'):\\n if a != b:\\n raise ValueError('%s != %s:\\\\n %s' % (a, b, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f63011b3120542c42d4ee6d117b72b5\",\n \"score\": \"0.6299821\",\n \"text\": \"def check_args(cc, args, expected):\\n with set_env(SPACK_TEST_COMMAND=\\\"dump-args\\\"):\\n cc_modified_args = cc(*args, output=str).strip().split(\\\"\\\\n\\\")\\n assert expected == cc_modified_args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5969fe96c20076cb66da9042e32459a3\",\n \"score\": \"0.6299272\",\n \"text\": \"def _expected_cmd_args(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19cce1e948c800cab903b736e03ec609\",\n \"score\": \"0.62879306\",\n \"text\": \"def assertCountEqual(self, actual, expected, msg=None):\\n try:\\n super(BackpyTest, self).assertCountEqual(actual, expected, msg)\\n except AttributeError:\\n self.assertItemsEqual(actual, expected, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"167309107e4aff88d4e632ac11256715\",\n \"score\": \"0.62486446\",\n \"text\": \"def test_correct_num_args(self):\\n arg_lengths = list(set([len(x) for x in self.m.tests]))\\n self.assertEqual(len(arg_lengths), 1, 'some test is the wrong length')\\n self.assertEqual(arg_lengths[0], len(signature(self.m.__dict__[self.gate]).parameters))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34ce50c2f1eb5c9f577e31fdb611dbc\",\n \"score\": \"0.6239252\",\n \"text\": \"def assertItemsEqual(self, expected_seq, actual_seq, msg=None):\\n\\n if MAJOR_PY_VERSION == 2:\\n return super(UnittestPythonCompatibility, self).assertItemsEqual(expected_seq, actual_seq, msg=msg)\\n return super(UnittestPythonCompatibility, self).assertCountEqual(expected_seq, actual_seq, msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eaa0c04bb7597c64e6f870bdd4ffbf9\",\n \"score\": \"0.6194282\",\n \"text\": \"def test_case_function_call_with_args(self):\\n value = AssertionChain().do(lambda x, y: x + y, 'no message', x=1, y=2).perform()\\n assert value == 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd7ca5bcaf022a8bbfc48ad1d6dcbae\",\n \"score\": \"0.6174232\",\n \"text\": \"def test_add_2_and_2_should_equal_5_fails_with_supplied_message():\\n our_result = 2 + 2\\n\\n assert our_result == 5, \\\"2 and 2 should equal 5\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f12d381abf76918ee8e58bcdfc62739e\",\n \"score\": \"0.6162053\",\n \"text\": \"def assertListEqual(self, expected, actual, msg=None):\\n if msg is None:\\n msg = ''\\n assert_msg = f'{msg}: expected length {len(expected)} got {len(actual)}'\\n self.assertEqual(len(expected), len(actual), assert_msg)\\n idx = 0\\n for exp, act in zip(expected, actual):\\n if isinstance(exp, list):\\n self.assertListEqual(exp, act, f'{msg}[{idx}]')\\n elif isinstance(exp, dict):\\n self.assertObjectEqual(exp, act, f'{msg}[{idx}]')\\n else:\\n assert_msg = f'{msg}[{idx}] expected \\\"{exp}\\\" got \\\"{act}\\\"'\\n self.assertEqual(exp, act, assert_msg)\\n idx += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24eb2364e819a888111265ea049e3329\",\n \"score\": \"0.6120579\",\n \"text\": \"def test_different_number_default_values_error():\\n\\n incorrect_defualt_value = (\\\"a\\\", \\\"b\\\", \\\"c\\\")\\n\\n with pytest.raises(\\n AssertionError,\\n match=f\\\"Incorrect number of default values -\\\\n Expected: {len(incorrect_defualt_value)}\\\\n Actual: 1\\\",\\n ):\\n\\n h.test_function_arguments(\\n h.assert_frame_equal_msg,\\n expected_arguments=[\\n \\\"actual\\\",\\n \\\"expected\\\",\\n \\\"msg_tag\\\",\\n \\\"print_actual_and_expected\\\",\\n ],\\n expected_default_values=incorrect_defualt_value,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82cfe72470dc47bb4bb113bc9a577ebd\",\n \"score\": \"0.61125416\",\n \"text\": \"def assertEqual(self, first, second):\\n assert first == second\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84095a092f97ae6c8e5097a279a9c628\",\n \"score\": \"0.60950464\",\n \"text\": \"def test_message(args, kwargs):\\n fmt = ['{}']*len(args) + ['{' + name + '}' for name in kwargs]\\n fmt = ' '.join(fmt)\\n note('fmt={}'.format(fmt))\\n counter = FormatCounter(fmt)\\n msg = Message(counter, args, kwargs)\\n assert counter.get_count() == 0\\n assert str(msg) == fmt.format(*args, **kwargs)\\n assert counter.get_count() == 1\\n\\n assert repr(msg) == '\\\"{}\\\".format(*{}, **{})'.format(fmt, args, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cadaf8c5949fc9897bbc931f5b972ac\",\n \"score\": \"0.60931647\",\n \"text\": \"def __my_assert_args(function, args, expected_output, check_type=True):\\n argstr = str(args).replace(',)', ')')\\n output = function(*args)\\n\\n # Controleer eerst het return-type (optioneel)\\n if check_type:\\n msg = f\\\"Fout: {function.__name__}{argstr} geeft geen {type(expected_output)} terug als return-type\\\"\\n assert type(output) is type(expected_output), msg\\n\\n # Controleer of de functie-uitvoer overeenkomt met de gewenste uitvoer\\n msg = f\\\"Fout: {function.__name__}{argstr} geeft {output} in plaats van {expected_output}\\\"\\n if type(expected_output) is float:\\n # Vergelijk bij float als return-type op 7 decimalen om afrondingsfouten te omzeilen\\n assert round(output - expected_output, 7) == 0, msg\\n else:\\n assert output == expected_output, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19b8a5ad3a8132f827ef04af4839f04\",\n \"score\": \"0.6088724\",\n \"text\": \"def assertEqual(self, a, b, msg=None):\\n self._assert_true(a == b, a, b, msg, r'{} != {}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a90ce06bbf71924fda65bf20be2ac6\",\n \"score\": \"0.60836226\",\n \"text\": \"def assertMultiLineEqual(self, expected, actual, msg=None):\\n self.assertTrue(isinstance(expected, str),\\n 'Expected argument is not a string')\\n self.assertTrue(isinstance(actual, str),\\n 'Actual argument is not a string')\\n expected = map(self.normalize,open(expected, 'r').readlines())\\n\\n actual = map(self.normalize,open(actual,'r').readlines())\\n diff = map(self.ignorable,difflib.ndiff(expected,actual))\\n message = ''.join(diff)\\n if len(message) > 0:\\n if msg:\\n message += \\\" : \\\" + msg\\n self.fail(\\\"Multi-line strings are unequal:\\\\n\\\" + message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f635a46edc5c978203d2aa86fa57e7\",\n \"score\": \"0.60514003\",\n \"text\": \"def assertMultiLineEqual(self, first, second, msg=None):\\n import difflib\\n self.assertTrue(isinstance(first, str),\\n 'First argument is not a string')\\n self.assertTrue(isinstance(second, str),\\n 'Second argument is not a string')\\n if first != second:\\n message = ''.join(difflib.ndiff(first.splitlines(True),\\n second.splitlines(True)))\\n if msg:\\n message += \\\" : \\\" + msg\\n self.fail(\\\"Multi-line strings are unequal:\\\\n\\\" + message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82831caa03d8a45d98580fe889ca55dd\",\n \"score\": \"0.60406125\",\n \"text\": \"def verify_help(msg, expected, unexpected=[]):\\n\\n for str in expected:\\n if str not in msg:\\n self.assert_(False, \\\"{0} not in {1}\\\".format(\\n str, msg))\\n for str in unexpected:\\n if str in msg:\\n self.assert_(False, \\\"{0} in {1}\\\".format(\\n str, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8600cd52e03de7e6f50667bd0c1d62\",\n \"score\": \"0.6032655\",\n \"text\": \"def assertEqual_(self, output, expected):\\n self.assertEqual(output, expected, 'Expected \\\"%s\\\". Got \\\"%s\\\".' % (expected, output))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83ec7956d6f26582edaefcc1e8bdf03\",\n \"score\": \"0.60318327\",\n \"text\": \"def test04_args(self):\\n s = Square(10, 2, 3, 12)\\n self.assertEqual(s.size, 10)\\n self.assertEqual(s.x, 2)\\n self.assertEqual(s.y, 3)\\n self.assertEqual(s.id, 12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26baa93fcb8de452f0d2763157b7db40\",\n \"score\": \"0.60161006\",\n \"text\": \"def _verify(self, *args) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f622eb10183e55105e6aef9864e42a0\",\n \"score\": \"0.6008199\",\n \"text\": \"def _checkargs(self, args):\\n if len(args) != self.Nargs:\\n raise ValueError(\\\"Wrong number of arguments: %d given; %d \\\"\\n \\\"expected.\\\" % (len(args), self.Nargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df92bcfd166e36ec42840c83780ce3cf\",\n \"score\": \"0.6006135\",\n \"text\": \"def test_VoteFewArgs(self):\\n\\n msg = \\\"Usage: ?vote \\\\\\\"Vote title\\\\\\\" \\\\\\\"Option 1\\\\\\\" \\\\\\\"Option 2\\\\\\\" \\\\\\\"...\\\\\\\"\\\"\\n message, error = votes.vote(\\\"one\\\")\\n self.assertEqual(error,\\\"NotEnoughArgs\\\")\\n self.assertEqual(msg, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d759f2e7dcee8691900865c7ac5544\",\n \"score\": \"0.60024685\",\n \"text\": \"def assert_in(self, first, second, msg=None):\\n self.assertIn(first, second, msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0859a19f6ce6535e3fd2cb1a7a7af42\",\n \"score\": \"0.5998726\",\n \"text\": \"def test_call_true(self):\\n self.assertCallTrue(\\n False,\\n func=str,\\n args=[1],\\n kwargs={'testing': True},\\n msg='Nope not true.',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea48ed7824b42d7a351315d9c0534a54\",\n \"score\": \"0.59879446\",\n \"text\": \"def test_meth(self, a, b, *args, **kwargs):\\n print 'Inside test method'\\n return '{} != {}'.format(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6df6b9ed51fa025d0a4e5ee98fc0a83\",\n \"score\": \"0.5972029\",\n \"text\": \"def test_two_args(a,b):\\r\\n return a,b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff4a757965dc8317ef5c3023e002515\",\n \"score\": \"0.59715575\",\n \"text\": \"def test_map_args_invalid():\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"484c83b66035c269c5248743e13e9d8b\",\n \"score\": \"0.594613\",\n \"text\": \"def test_map_args_level():\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db39fb86e41a1a107a5eb17df382e27\",\n \"score\": \"0.594606\",\n \"text\": \"def test_message(self, variables):\\n assert Variable(variables[0]).message == variables[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ca2de9e2ef62b950e7fca1d0429336\",\n \"score\": \"0.5937386\",\n \"text\": \"def eq_(*args, msg=None, precision=None):\\n new_args = list(args)\\n if msg is None:\\n msg = \\\"Different values: %s\\\" % \\\", \\\".join(\\n [str(arg) for arg in new_args])\\n if precision is not None:\\n for index, arg in enumerate(new_args):\\n new_args[index] = float_round(arg, precision)\\n assert all(new_args[0] == b for b in new_args[1:]), msg\\n else:\\n assert new_args[0] == new_args[1], msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a823ca25d41095589ef991386aa5dcb\",\n \"score\": \"0.5921473\",\n \"text\": \"def assertObjectEqual(self, expected, actual, msg=None, strict=False):\\n for key, value in expected.items():\\n if msg is None:\\n key_name = key\\n else:\\n key_name = f'{msg}.{key}'\\n if expected is None:\\n self.assertIsNone(\\n actual, f'{key_name}: {actual} should be None')\\n else:\\n self.assertIsNotNone(\\n actual, f'{key_name}: {actual} should not be None')\\n if isinstance(value, dict):\\n self.assertObjectEqual(value, actual[key], key_name, strict=strict)\\n elif isinstance(value, list):\\n self.assertListEqual(value, actual[key], key_name)\\n else:\\n self.assertIn(key, actual,\\n f'{key_name}: missing key {key}')\\n assert_msg = f'{key_name}: expected \\\"{value}\\\" got \\\"{actual[key]}\\\"'\\n self.assertEqual(value, actual[key], msg=assert_msg)\\n if strict:\\n for key in actual.keys():\\n self.assertIn(key, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47f6f481f06d55795ca56e2eb9a7ea4\",\n \"score\": \"0.5915513\",\n \"text\": \"def test_parametrized_collect_with_wrong_args(pytester: Pytester) -> None:\\n py_file = pytester.makepyfile(\\n \\\"\\\"\\\"\\n import pytest\\n\\n @pytest.mark.parametrize('foo, bar', [(1, 2, 3)])\\n def test_func(foo, bar):\\n pass\\n \\\"\\\"\\\"\\n )\\n\\n result = pytester.runpytest(py_file)\\n result.stdout.fnmatch_lines(\\n [\\n 'test_parametrized_collect_with_wrong_args.py::test_func: in \\\"parametrize\\\" the number of names (2):',\\n \\\" ['foo', 'bar']\\\",\\n \\\"must be equal to the number of values (3):\\\",\\n \\\" (1, 2, 3)\\\",\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a75a02a3d33c090a6c2b961b271f198\",\n \"score\": \"0.5914568\",\n \"text\": \"def test_default_values_non_mismatch_2_error():\\n\\n incorrect_defualt_value = None\\n\\n with pytest.raises(\\n AssertionError,\\n match=\\\"Incorrect default values -\\\\n Expected: No default values\\\\n Actual: .*\\\",\\n ):\\n\\n h.test_function_arguments(\\n h.assert_frame_equal_msg,\\n expected_arguments=[\\n \\\"actual\\\",\\n \\\"expected\\\",\\n \\\"msg_tag\\\",\\n \\\"print_actual_and_expected\\\",\\n ],\\n expected_default_values=incorrect_defualt_value,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682f108133706c3b406dd916135dcfe2\",\n \"score\": \"0.59049815\",\n \"text\": \"def test_n_arg(self):\\n Square_test = Square(8)\\n message = '[Square] (1) 0/0 - 8'\\n self.assertEqual(str(Square_test), message)\\n Square_test = Square(8, 12)\\n message = '[Square] (2) 12/0 - 8'\\n self.assertEqual(str(Square_test), message)\\n Square_test = Square(8, 12, 3)\\n message = '[Square] (3) 12/3 - 8'\\n self.assertEqual(str(Square_test), message)\\n Square_test = Square(8, 12, 3, 4)\\n message = '[Square] (4) 12/3 - 8'\\n self.assertEqual(str(Square_test), message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e51eafeb46f846d1031fc3465bd7a566\",\n \"score\": \"0.5896019\",\n \"text\": \"def test_comparison(self):\\n self.assertIsInstance(hash(FREQ), int)\\n self.assertNotEqual(FREQ, ENERGY)\\n with self.assertRaises(TypeError):\\n _ = ENERGY > FREQ\\n\\n # Test args in different order\\n lot1 = LevelOfTheory('method', args=('arg1', 'arg2'))\\n lot2 = LevelOfTheory('method', args=('arg2', 'arg1'))\\n self.assertEqual(lot1, lot2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec55a48a2f8bc3a76b7f0cef9141959\",\n \"score\": \"0.5894814\",\n \"text\": \"def _check_arguments(assignment, issue_tracker):\\n metadata = assignment.metadata\\n mandatory = set(metadata['args']['mandatory'])\\n optional = set(metadata['args']['optional'].keys())\\n\\n given_args = assignment.children\\n given_idents = {a.ident.ident: a.ident for a in given_args}\\n given_keys = given_idents.keys()\\n given = set(given_keys)\\n\\n # Case 0: Duplicated arguments\\n duplicates = set([x for x in given_keys if given_keys.count(x) > 1])\\n for m in duplicates:\\n reason = \\\"Duplicated argument: '{}'\\\".format(m)\\n issue_tracker.add_error(reason, given_idents[m])\\n\\n # Case 1: Missing arguments\\n missing = mandatory - given\\n for m in missing:\\n reason = \\\"Missing argument: '{}'\\\".format(m)\\n issue_tracker.add_error(reason, assignment)\\n\\n # Case 2: Extra (unused) arguments\\n # FIXME: Rename error to Excess argument\\n unused = given - (mandatory | optional)\\n for m in unused:\\n reason = \\\"Excess argument: '{}'\\\".format(m)\\n issue_tracker.add_error(reason, given_idents[m])\\n\\n # Case 3: Deprecation warning if optional args not explicitly given\\n deprecated = optional - given\\n for m in deprecated:\\n reason = \\\"Using default value for implicit parameter '{}'\\\".format(m)\\n issue_tracker.add_warning(reason, assignment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70294f7077db7b7ee06c3489f997ca53\",\n \"score\": \"0.5890335\",\n \"text\": \"def assert_ex(\\n msg: str,\\n received: typing.Any,\\n expected: typing.Any,\\n hint: typing.Any = None):\\n hint_str = '' if hint is None else '({!r})'.format(hint)\\n\\n return '{} {}\\\\nreceived > {}\\\\nexpected > {}'.format(\\n msg, hint_str, received, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d274a7e7e66eeb96e63839cff8593f\",\n \"score\": \"0.5878507\",\n \"text\": \"def check_example(msg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944a7c63de25396f9a8735664c874c00\",\n \"score\": \"0.5875338\",\n \"text\": \"def test_wrong_args(self):\\n #{{{\\n self.assertEqual(\\n find(self._pathto('baaaba'), '*', 41, 35), set())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b40666de4b921b390f191238373910f\",\n \"score\": \"0.5869969\",\n \"text\": \"def test_arguments():\\n\\n expected_arguments = [\\\"func\\\", \\\"expected_arguments\\\", \\\"expected_default_values\\\"]\\n\\n expected_default_values = (None,)\\n\\n arg_spec = inspect.getfullargspec(h.test_function_arguments)\\n\\n arguments = arg_spec.args\\n\\n assert len(expected_arguments) == len(\\n arguments\\n ), f\\\"Incorrect number of arguments -\\\\n Expected: {len(expected_arguments)}\\\\n Actual: {len(arguments)}\\\"\\n\\n for i, (e, a) in enumerate(zip(expected_arguments, arguments)):\\n\\n assert e == a, f\\\"Incorrect arg at index {i} -\\\\n Expected: {e}\\\\n Actual: {a}\\\"\\n\\n default_values = arg_spec.defaults\\n\\n if default_values is None:\\n\\n if expected_default_values is not None:\\n\\n raise AssertionError(\\n f\\\"Incorrect default values -\\\\n Expected: {expected_default_values}\\\\n Actual: No default values\\\"\\n )\\n\\n else:\\n\\n if expected_default_values is None:\\n\\n raise AssertionError(\\n f\\\"Incorrect default values -\\\\n Expected: No default values\\\\n Actual: {default_values}\\\"\\n )\\n\\n if (default_values is not None) and (expected_default_values is not None):\\n\\n assert len(expected_default_values) == len(\\n default_values\\n ), f\\\"Incorrect number of default values -\\\\n Expected: {len(expected_default_values)}\\\\n Actual: {len(default_values)}\\\"\\n\\n for i, (e, a) in enumerate(zip(expected_default_values, default_values)):\\n\\n assert (\\n e == a\\n ), f\\\"Incorrect default value at index {i} of default values -\\\\n Expected: {e}\\\\n Actual: {a}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b18c5bdad44362580396f0246f80f98\",\n \"score\": \"0.5866624\",\n \"text\": \"def test_all(self):\\n message = build_message(\\n b'COMMAND',\\n b'param1',\\n b'param2',\\n prefix=b'something',\\n suffix=b'suffix ftw!',\\n )\\n expected = b':something COMMAND param1 param2 :suffix ftw!\\\\r\\\\n'\\n assert message == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1000b450c9464e81dc02f3dd17d413b4\",\n \"score\": \"0.5863046\",\n \"text\": \"def test_multiple_arguments(self):\\n loc = translate_locator(\\\"test\\\", \\\"foo.message:hello,world\\\")\\n assert loc == \\\"hello world\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98986dc975ae0c4648ec29327ed64150\",\n \"score\": \"0.5853667\",\n \"text\": \"def CheckArguments(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"315d72f9be87dd0d59de06a10c108088\",\n \"score\": \"0.58467853\",\n \"text\": \"def test_extra_args(self):\\n loc = translate_locator(\\\"test\\\", \\\"foo.message:testing,one,two,three\\\")\\n assert loc == \\\"testing one\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f830b4c6d83e4e924c04399dbe921e\",\n \"score\": \"0.5831577\",\n \"text\": \"def test_matches_arguments(self, mirror):\\n signature = inspect.signature(mirror)\\n assert 'a' in signature.parameters\\n assert 'b' in signature.parameters\\n assert 'args' not in signature.parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da40b11af68fdfac8e7ddc93c914b6a5\",\n \"score\": \"0.58242786\",\n \"text\": \"def _run_assert_output(args, message, **kwargs):\\n output = run(args, **kwargs)\\n if output.returncode != 0:\\n raise RuntimeError(f\\\"{message}: (\\\" + ' '.join(args) + ')')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501d2012894bb7c66be1a25481f3da49\",\n \"score\": \"0.5814888\",\n \"text\": \"def testMatchAtAssertEqualUserDefinedWithMessage(self):\\n parser = MockParser([' \\\\n'])\\n atAssertEqualUserDefined = AtAssertEqualUserDefined(parser)\\n\\n parser.fileName = \\\"foo.pfunit\\\"\\n parser.currentLineNumber = 8\\n text = ' @assertEqualUserDefined(a,b,message=\\\"c\\\")\\\\n'\\n atAssertEqualUserDefined.apply(text)\\n self.assertEqual('#line 8 \\\"foo.pfunit\\\"\\\\n', parser.outLines[0])\\n self.assertEqual(' call assertTrue(a==b, message=\\\"c\\\", &\\\\n',\\n parser.outLines[1])\\n self.assertEqual(\\\" & location=SourceLocation( &\\\\n\\\", parser.outLines[2])\\n self.assertEqual(\\\" & 'foo.pfunit', &\\\\n\\\", parser.outLines[3])\\n self.assertEqual(\\\" & 8)\\\", parser.outLines[4])\\n self.assertEqual(\\\" )\\\\n\\\", parser.outLines[5])\\n self.assertEqual(\\\" if (anyExceptions()) return\\\\n\\\", parser.outLines[6])\\n self.assertEqual('#line 9 \\\"foo.pfunit\\\"\\\\n', parser.outLines[7])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102030103dae30078909285c29a6a63e\",\n \"score\": \"0.58048826\",\n \"text\": \"def test_update_args(self):\\n\\n # TODO: this\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4dc0689c568eebc120aa50de2b75c82\",\n \"score\": \"0.5779741\",\n \"text\": \"def test_neq(self):\\n self.assertNotEqual(1, 1, msg='Oops, they are equal')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7b38e4b0b52f6556c763108a851ce2\",\n \"score\": \"0.5766806\",\n \"text\": \"def test_map_args_space():\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09a70ac50a6d2e3c123256816c10f78\",\n \"score\": \"0.5763555\",\n \"text\": \"def __assertStringMatch(self, actual, expected, format_msg):\\n def make_description(info):\\n return \\\"Strings not equal: %s\\\" % info\\n\\n description = make_description(\\\"different characters\\\")\\n msg = _make_message(actual, expected, format_msg, description=description)\\n self.assertEqual(actual, expected, msg=msg)\\n\\n info = \\\"types different: %s != %s (actual)\\\" % (repr(type(expected)), repr(type(actual)))\\n description = make_description(info)\\n msg = _make_message(actual, expected, format_msg, description=description)\\n self.assertEqual(type(expected), type(actual), msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"902096525d09116f7b23a96f9fd63ab5\",\n \"score\": \"0.5763291\",\n \"text\": \"def check_num_arguments(expected_number):\\n if len(arg_values) != expected_number:\\n if expected_number == 1:\\n message = \\\"'\\\" + name + \\\"' must have exactly one argument.\\\"\\n else:\\n message = (\\\"'\\\" + name + \\\"' must have exactly \\\" +\\n str(expected_number) + \\\" arguments.\\\")\\n raise ValueError(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f16ba5da1b977983e5fc5c1801d0f5e\",\n \"score\": \"0.57550454\",\n \"text\": \"def assert_iterables_similar(self, *args):\\n\\t\\targs = list(map(list, args))\\n\\t\\t[i.sort() for i in args]\\n\\t\\tone, rest = args[0], args[1:]\\n\\t\\tassert all([one == i for i in rest])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c0ccfd8080ca35a924ef06475ecae7f\",\n \"score\": \"0.5747624\",\n \"text\": \"def assertion_error_with_test_case(err: AssertionError, test_case: str) -> AssertionError:\\n msg = f'\\\\ntest case: {test_case}\\\\n{err.args[0]}'\\n err.args = (msg,)\\n return err\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c8840e19f0c358aa9cc5194ed25b8b4\",\n \"score\": \"0.5741583\",\n \"text\": \"def assert_not_equal(self, first, second, msg=None):\\n self.assertNotEqual(first, second, msg=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a725d5d031d23229c3ebff3a52378147\",\n \"score\": \"0.5741329\",\n \"text\": \"def assertCompositeEqual(self, ctx1, ctx2, msg=None):\\n\\n self.assertEqual(ctx1.GetNumberOfTransforms(), ctx2.GetNumberOfTransforms())\\n self.assertEqual(ctx1.GetNumberOfFixedParameters(), ctx2.GetNumberOfFixedParameters())\\n self.assertEqual(ctx1.GetFixedParameters(), ctx2.GetFixedParameters())\\n self.assertEqual(ctx1.GetNumberOfParameters(), ctx2.GetNumberOfParameters())\\n self.assertEqual(ctx1.GetParameters(), ctx2.GetParameters())\\n self.assertTransformEqual(ctx1, ctx2, msg);\\n\\n for idx in range(ctx1.GetNumberOfTransforms()):\\n tx1 = ctx1.GetNthTransform(idx)\\n tx2 = ctx2.GetNthTransform(idx)\\n\\n self.assertTransformEqual(tx1, tx2, msg=\\\"Comparing transform {0}: {1}\\\".format(idx, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb8ecf57bd0a28238fcb8ad1118a1\",\n \"score\": \"0.5729832\",\n \"text\": \"def test(record):\\n assert record.getMessage() == expected\\n if extra is not None:\\n for key, val in extra.items():\\n assert getattr(record, key, None) == val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7339801178967d30ea91bc654154456c\",\n \"score\": \"0.572322\",\n \"text\": \"def test_update_args(self):\\n print(\\\"test_update_args\\\")\\n self.assertEqual(self.s1.__str__(), \\\"[Square] ({}) 0/0 - 5\\\".format(self.s1.id))\\n self.s1.update(69)\\n self.assertEqual(self.s1.__str__(), \\\"[Square] (69) 0/0 - 5\\\")\\n self.s1.update(2, 4)\\n self.assertEqual(self.s1.__str__(), \\\"[Square] (2) 0/0 - 4\\\")\\n self.s1.update(2, 4, 6)\\n self.assertEqual(self.s1.__str__(), \\\"[Square] (2) 6/0 - 4\\\")\\n self.s1.update(2, 4, 6, 8)\\n self.assertEqual(self.s1.__str__(), \\\"[Square] (2) 6/8 - 4\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8a874768619bb92e7fa4960e3e4457\",\n \"score\": \"0.57147956\",\n \"text\": \"def _repr_test_args(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b050ebbaf2f8410c3cd655eceaad69\",\n \"score\": \"0.5712951\",\n \"text\": \"def test_multiplous_named_params():\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3d39b61a42aaf058d5dcfca6b7e93cd\",\n \"score\": \"0.5698148\",\n \"text\": \"def test(*args, **kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe36d753391a2d8fbf886b2889b8663\",\n \"score\": \"0.56976515\",\n \"text\": \"def test_map_args_level_and_space():\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753454c0a0456ff9aa9cc0dadac02824\",\n \"score\": \"0.568529\",\n \"text\": \"def test_has_correct_number_of_keys_and_values(self):\\n self.has_correct_number_of_keys_and_values(2, 1, args=self.default_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b646775659545ec8d098fa6460856c\",\n \"score\": \"0.56828254\",\n \"text\": \"def test_diagnosis_parametrize(a, b, expected):\\n assert diagnosis(a, b) == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2693fbb826995ca7b180c9ec1868cc5b\",\n \"score\": \"0.56717914\",\n \"text\": \"def test_single_arg(a):\\r\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bbe3655756ce1c9d48dd0370b033ad7\",\n \"score\": \"0.5669148\",\n \"text\": \"def test_text_second_argument():\\n msg = Message('test', 'spam spam spam', 'test')\\n text = Text()\\n assert text.parse('spam', ['spam'], msg, 2) == 'spam spam'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d1355577c68dadc40afb73678cccf7\",\n \"score\": \"0.56649554\",\n \"text\": \"def emulate_assertListEqual(self, seq1, seq2, msg=None, seq_type=None):\\n if seq_type is not None:\\n seq_type_name = seq_type.__name__\\n if not isinstance(seq1, seq_type):\\n raise self.failureException('First sequence is not a %s: %s'\\n % (seq_type_name, safe_repr(seq1)))\\n if not isinstance(seq2, seq_type):\\n raise self.failureException('Second sequence is not a %s: %s'\\n % (seq_type_name, safe_repr(seq2)))\\n else:\\n seq_type_name = \\\"sequence\\\"\\n\\n differing = None\\n try:\\n len1 = len(seq1)\\n except (TypeError, NotImplementedError):\\n differing = 'First %s has no length. Non-sequence?' % (\\n seq_type_name)\\n\\n if differing is None:\\n try:\\n len2 = len(seq2)\\n except (TypeError, NotImplementedError):\\n differing = 'Second %s has no length. Non-sequence?' % (\\n seq_type_name)\\n\\n if differing is None:\\n if seq1 == seq2:\\n return\\n\\n seq1_repr = safe_repr(seq1)\\n seq2_repr = safe_repr(seq2)\\n if len(seq1_repr) > 30:\\n seq1_repr = seq1_repr[:30] + '...'\\n if len(seq2_repr) > 30:\\n seq2_repr = seq2_repr[:30] + '...'\\n elements = (seq_type_name.capitalize(), seq1_repr, seq2_repr)\\n differing = '%ss differ: %s != %s\\\\n' % elements\\n\\n for i in range(min(len1, len2)):\\n try:\\n item1 = seq1[i]\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('\\\\nUnable to index element %d of first %s\\\\n' %\\n (i, seq_type_name))\\n break\\n\\n try:\\n item2 = seq2[i]\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('\\\\nUnable to index element %d of second %s\\\\n' %\\n (i, seq_type_name))\\n break\\n\\n if item1 != item2:\\n differing += ('\\\\nFirst differing element %d:\\\\n%s\\\\n%s\\\\n' %\\n (i, safe_repr(item1), safe_repr(item2)))\\n break\\n else:\\n if (len1 == len2 and seq_type is None and\\n type(seq1) != type(seq2)):\\n # The sequences are the same, but have differing types.\\n return\\n\\n if len1 > len2:\\n differing += ('\\\\nFirst %s contains %d additional '\\n 'elements.\\\\n' % (seq_type_name, len1 - len2))\\n try:\\n differing += ('First extra element %d:\\\\n%s\\\\n' %\\n (len2, safe_repr(seq1[len2])))\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('Unable to index element %d '\\n 'of first %s\\\\n' % (len2, seq_type_name))\\n elif len1 < len2:\\n differing += ('\\\\nSecond %s contains %d additional '\\n 'elements.\\\\n' % (seq_type_name, len2 - len1))\\n try:\\n differing += ('First extra element %d:\\\\n%s\\\\n' %\\n (len1, safe_repr(seq2[len1])))\\n except (TypeError, IndexError, NotImplementedError):\\n differing += ('Unable to index element %d '\\n 'of second %s\\\\n' % (len1, seq_type_name))\\n standardMsg = differing\\n diffMsg = '\\\\n' + '\\\\n'.join(\\n difflib.ndiff(pprint.pformat(seq1).splitlines(),\\n pprint.pformat(seq2).splitlines()))\\n standardMsg = self._truncateMessage(standardMsg, diffMsg)\\n msg = self._formatMessage(msg, standardMsg)\\n self.fail(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"207ea3ba2d8eb9388d97c2479f558c25\",\n \"score\": \"0.56522644\",\n \"text\": \"def assertTransformEqual(self, tx1, tx2, msg=None):\\n\\n self.assertEqual(tx1.__class__, tx2.__class__)\\n self.assertEqual(tx1.GetDimension(), tx2.GetDimension())\\n self.assertEqual(tx1.GetFixedParameters(), tx2.GetFixedParameters())\\n self.assertEqual(tx1.GetParameters(), tx2.GetParameters())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506cc7c89ba30ba476234921f71637c5\",\n \"score\": \"0.5648275\",\n \"text\": \"def assertTraceCmpWas(self, call_args, left, right, op, left_is_const):\\n #call_args: tuple(left, right, opid, idx, left_is_const)\\n self.assertEqual(call_args[0], left)\\n self.assertEqual(call_args[1], right)\\n self.assertEqual(dis.cmp_op[call_args[2]], op)\\n self.assertEqual(call_args[4], left_is_const)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825fa545cf412b04cf07015a542d3349\",\n \"score\": \"0.56448895\",\n \"text\": \"def test_dummy_message(kwargs, result):\\n assert dummy_autotranslate(**kwargs) == result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9dfc1819b4f42d828b651a766020bf0\",\n \"score\": \"0.5639885\",\n \"text\": \"def test_multiple_args(self):\\n\\n @self.check_arg_types(id=int, create_date=datetime, title=str, val=(int, float))\\n def output_item(id, create_date, title, val):\\n return (id, create_date, title, val)\\n\\n dt = datetime.now()\\n\\n self.assertEqual(output_item(id=1, create_date=dt, title='test', val=1), (1, dt, 'test', 1))\\n\\n self.assertEqual(output_item(id=1, create_date=dt, title='test', val=1.5), (1, dt, 'test', 1.5))\\n\\n with self.assertRaises(TypeError):\\n output_item(id='1', create_date=dt, title='test', val=1)\\n\\n with self.assertRaises(TypeError):\\n output_item(id=1, create_date=None, title='test', val=1)\\n\\n with self.assertRaises(TypeError):\\n output_item(id=1, create_date=dt, title={'a': '1'}, val=1)\\n\\n with self.assertRaises(TypeError):\\n output_item(id=1, create_date=dt, title='test', val=Decimal('-1.3'))\\n\\n with self.assertRaises(TypeError):\\n output_item(id=None, create_date=None, title=None, val=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd3a715d980511a66909d002b0b04cbc\",\n \"score\": \"0.56375337\",\n \"text\": \"def assert_equals(expected, actual):\\n assert expected == actual\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9869a9c38d07fb3751c2ec38beeb5d85\",\n \"score\": \"0.56372213\",\n \"text\": \"def test_arguments(self):\\n xt = np.array([[1,2,3],[4,5,6],[7,8,9]])\\n yt = np.array([0,1,0])\\n self.check_arguments(TestModel, TestEstimator, xt, yt, skip_bm_args=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a57fbc1c060b3223d5a54b854d6c2bb\",\n \"score\": \"0.5616994\",\n \"text\": \"def eq_(a, b, msg=None):\\n if not a == b:\\n raise AssertionError(msg or \\\"%r != %r\\\" % (a, b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e14aba5ef7385efeb3d7880f90cb7b10\",\n \"score\": \"0.5616082\",\n \"text\": \"def check_args(self, args):\\n logger.debug(\\\"check_args(args) was called.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6ab896592641c37dac089a892d61d1a\",\n \"score\": \"0.56114995\",\n \"text\": \"def test_assertion(name, result, expected):\\n assert result == expected, \\\"Expected %s, got %s for %s\\\" \\\\\\n % (expected, result, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f486cd486bb0a89afe9a436d7544113\",\n \"score\": \"0.561105\",\n \"text\": \"def verify(self, args):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e8b88c6e68c1433a12e7b2442b788a\",\n \"score\": \"0.5610687\",\n \"text\": \"def test_multiple_args(self):\\n\\n @self.check_arg_types(value_on_error=None, id=int, create_date=datetime, title=str)\\n def output_item(id, create_date, title):\\n return (id, create_date, title)\\n\\n dt = datetime.now()\\n\\n self.assertEqual(output_item(id=1, create_date=dt, title='test'), (1, dt, 'test'))\\n self.assertEqual(output_item(id='1', create_date=dt, title='test'), (1, dt, 'test'))\\n self.assertEqual(output_item(id=1.4, create_date=dt, title='test'), (1, dt, 'test'))\\n self.assertEqual(output_item(id=[1, 2], create_date=dt, title='test'), (None, dt, 'test'))\\n\\n self.assertEqual(output_item(id=1, create_date=dt, title='test'), (1, dt, 'test'))\\n self.assertEqual(output_item(id=1, create_date='2019/01/25', title='test'), (1, None, 'test'))\\n self.assertEqual(output_item(id=1, create_date=date.today(), title='test'), (1, None, 'test'))\\n\\n self.assertEqual(output_item(id=1, create_date=dt, title=1), (1, dt, '1'))\\n self.assertEqual(output_item(id=1, create_date=dt, title=dt), (1, dt, str(dt)))\\n self.assertEqual(output_item(id=1, create_date=dt, title=None), (1, dt, None))\\n\\n self.assertEqual(output_item(id=1, create_date=dt, title=[1, 2]), (1, dt, str([1, 2])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c434ed3e45483868f39d93b6ed8b0783\",\n \"score\": \"0.56064326\",\n \"text\": \"def _test_allclose(a, b, msg=\\\"a, b\\\", atol=1e-6):\\n L_inf = abs(a - b).max()\\n L_1 = abs(a - b).sum()\\n L_0 = 1 - np.isclose(a, b).sum() / a.size\\n\\n try:\\n assert np.allclose(a, b, atol=atol), (\\n f\\\"NotEqual: {msg}:\\\\nL_0: {L_0}\\\\nL_1: {L_1}\\\\nL_inf: {L_inf}\\\")\\n except AssertionError:\\n if DISPLAY:\\n import matplotlib.pyplot as plt\\n plt.imshow((1 - np.isclose(a, b)).sum(axis=(0, 1)))\\n plt.show()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1464391e591c5c517a01aabdd41e5b64\",\n \"score\": \"0.5603927\",\n \"text\": \"def assertEqual(self, a, b):\\n if a != b:\\n raise EqualAssertionError(self, a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e8fd8b376d6d2317470c9fdb03483e\",\n \"score\": \"0.56036377\",\n \"text\": \"def my_test_equal(a, b):\\n\\n if a == b:\\n print(\\\"Pass\\\")\\n else:\\n print(\\\"Fail\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a061755ba902558b384b7bb0bf5a5a9e\",\n \"score\": \"0.5601202\",\n \"text\": \"def check_arg_count(self):\\n if len(self.arguments) == self.arg_count(self.name):\\n return\\n raise XMLFormatException()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994387,"cells":{"query_id":{"kind":"string","value":"bc7378cf1929426a0488ca80370047de"},"query":{"kind":"string","value":"Provides the 3D genomic data corresponding to disease, chromosome"},"positive_passages":{"kind":"list like","value":[{"docid":"f01d911f63f4a85007244dd3506baff4","score":"0.5612504","text":"def real_genomic_data():\n def real_data_(disease, chrom):\n return scipy.io.loadmat(os.path.join(REAL_DATA_DIR, disease, 'chromo_{}_processed.mat'.format(chrom)))['X']\n\n return real_data_","title":""}],"string":"[\n {\n \"docid\": \"f01d911f63f4a85007244dd3506baff4\",\n \"score\": \"0.5612504\",\n \"text\": \"def real_genomic_data():\\n def real_data_(disease, chrom):\\n return scipy.io.loadmat(os.path.join(REAL_DATA_DIR, disease, 'chromo_{}_processed.mat'.format(chrom)))['X']\\n\\n return real_data_\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0b2b3e8f30a2655c858c3706f8711d0f","score":"0.5864309","text":"def read_3d_file(filename):\n return read_nd_file(filename, 3)","title":""},{"docid":"83ce175b10ea080bff84a3dd1ba9aafd","score":"0.58423555","text":"def geo_meta(dicomfile):\n\n # rows and columns\n rows = dicomfile[0x0028, 0x0010]\n rows = rows.value\n cols = dicomfile[0x0028, 0x0011]\n cols = cols.value\n matrix_size = [rows, cols]\n\n # per-frame functional group sequence\n elem = dicomfile[0x5200, 0x9230] # pydicom.dataelem.DataElement\n seq = elem.value # pydicom.sequence.Sequence\n elem3 = seq[0] # first frame\n elem4 = elem3.PixelMeasuresSequence # pydicom.sequence.Sequence\n\n for xx in elem4:\n st = xx.SliceThickness\n pixels_space = xx.PixelSpacing\n\n return matrix_size, st, pixels_space","title":""},{"docid":"20141910edd73dbce3ab00f7d81e96de","score":"0.57870525","text":"def process_xyz_gdb9(datafile):\n xyz_lines = [line.decode('UTF-8') for line in datafile.readlines()]\n\n num_atoms = int(xyz_lines[0])\n mol_props = xyz_lines[1].split()\n mol_xyz = xyz_lines[2:num_atoms+2]\n mol_freq = xyz_lines[num_atoms+2]\n\n atom_charges, atom_positions = [], []\n for line in mol_xyz:\n atom, posx, posy, posz, _ = line.replace('*^', 'e').split()\n atom_charges.append(charge_dict[atom])\n atom_positions.append([float(posx), float(posy), float(posz)])\n\n prop_strings = ['tag', 'index', 'A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'U0', 'U', 'H', 'G', 'Cv']\n prop_strings = prop_strings[1:]\n mol_props = [int(mol_props[1])] + [float(x) for x in mol_props[2:]]\n mol_props = dict(zip(prop_strings, mol_props))\n mol_props['omega1'] = max(float(omega) for omega in mol_freq.split())\n\n molecule = {'num_atoms': num_atoms, 'charges': atom_charges, 'positions': atom_positions}\n molecule.update(mol_props)\n molecule = {key: torch.tensor(val) for key, val in molecule.items()}\n\n return molecule","title":""},{"docid":"e8b1986cf0152e08ccefe434e16e6232","score":"0.5746226","text":"def _readCord3G(self, data, n):\r\n s = Struct(b'4i')\r\n nEntries = (len(data) - n) // 16\r\n for i in xrange(nEntries):\r\n eData = data[n:n + 16] # 4*4\r\n (cid, n1, n2, n3) = s.unpack(eData)\r\n dataIn = [cid, n1, n2, n3]\r\n coord = CORD3G(None, dataIn)\r\n self.add_coord(coord, allowOverwrites=True)\r\n n += 16\r\n self.card_count['CORD3G'] = nEntries\r\n return n","title":""},{"docid":"ce62f80437ee4d6696c5ef06338b11dd","score":"0.5725638","text":"def get_raw_output_3D(X,Y,Z):\n if isinstance( X, (float,int) ):\n X = np.array([X]); Y=np.array([Y]); Z=np.array([Z])\n elif isinstance( X, list ):\n X= np.array(X); Y=np.array(Y); Z=np.array(Z)\n\n classification_data = analytic_classification_3D(X,Y,Z)\n regression_data = analytic_regression_3D(X,Y,Z)\n return classification_data, regression_data","title":""},{"docid":"ad08e15ffd3d9fca4b6ee356566da784","score":"0.56601536","text":"def get_xyz_data(self):\n\t\treturn np.array([np.reshape(self.spliced_crds,\n\t\t\t\t\t\t\t\t\t(self.spliced_crds.shape[1] * self.spliced_crds.shape[2], 3))])","title":""},{"docid":"e715a547a3b4423645aed0c61cdd8474","score":"0.5564991","text":"def dataArr(filename):\r\n #Open the file\r\n f=h5py.File(filename,'r')\r\n \r\n #Initialize the data arrays\r\n cdata=[]\r\n idxset=[]\r\n vertices=[]\r\n \r\n #Open groups in the file\r\n for group in f.keys():\r\n# print('Group- '+group)\r\n \r\n #Get the group\r\n currGroup=f[group]\r\n \r\n #Open keys in the group\r\n for key in currGroup.keys():\r\n# print('Key- '+key)\r\n \r\n #Append the data to the respective arrays\r\n if key=='cdata(Complex)':\r\n cdataGroup=currGroup[key]\r\n \r\n imag=[]\r\n real=[]\r\n #Open the keys in cdata\r\n for subkey in cdataGroup.keys():\r\n# print('Subkey- '+subkey)\r\n \r\n #Get the real and imaginary parts of the array\r\n if subkey=='Imag':\r\n imag=cdataGroup[subkey][()]\r\n elif subkey=='Real':\r\n real=cdataGroup[subkey][()]\r\n \r\n #Convert lists to numpy arrays\r\n imag=np.array(imag)\r\n real=np.array(real)\r\n #Get the cdata value\r\n cdata=real+1j*imag\r\n \r\n elif key=='idxset':\r\n idxset=currGroup[key][()]\r\n elif key=='vertices':\r\n vertices=currGroup[key][()]\r\n \r\n #Remove the y component from the vertices\r\n xVals=[]\r\n yVals=[]\r\n newVertices=[]\r\n for vertex in vertices:\r\n xVals.append(vertex[0])\r\n yVals.append(vertex[2])\r\n newVertices.append([vertex[0],vertex[1]])\r\n vertices=newVertices\r\n \r\n #Convert to numpy arrays\r\n cdata=np.array(cdata)\r\n xVals=np.array(xVals)\r\n yVals=np.array(yVals)\r\n \r\n #Close the file\r\n f.close()\r\n \r\n return cdata, xVals, yVals","title":""},{"docid":"a40b4d44df69bbad2c79d6d4547d8b6d","score":"0.5561454","text":"def GSLIB2ndarray_3D(data_file, kcol,nreal, nx, ny, nz):\n if nz > 1 and ny > 1:\n array = np.ndarray(shape = (nreal, ny, nx, nz), dtype=float, order=\"F\")\n elif ny > 1:\n array = np.ndarray(shape=(nreal, ny, nx), dtype=float, order=\"F\")\n else:\n array = np.zeros(nreal, nx)\n\n with open(data_file) as f:\n head = [next(f) for _ in range(2)] # read first two lines\n line2 = head[1].split()\n ncol = int(line2[0]) # get the number of columns\n\n for icol in range(ncol): # read over the column names\n head = next(f)\n if icol == kcol:\n col_name = head.split()[0]\n for ineal in range(nreal):\t\t\n if nz > 1 and ny > 1:\n for iz in range(nz):\n for iy in range(ny):\n for ix in range(nx):\n head = next(f)\n array[ineal][ny - 1 - iy][ix][iz] = head.split()[kcol] \t\t\t\t\t\n elif ny > 1:\n for iy in range(ny):\n for ix in range(0, nx):\n head = next(f)\n array[ineal][ny - 1 - iy][ix] = head.split()[kcol]\n else:\n for ix in range(nx):\n head = next(f)\n array[ineal][ix] = head.split()[kcol]\n return array, col_name","title":""},{"docid":"894d2192eca7f9db94dfbde9925fc494","score":"0.55150336","text":"def gamv_3d(df, xcol, ycol, zcol, vcol, nlag, lagdist,lag_tol, azi, atol, bandh, dip, dtol, bandv, isill):\n lag = []\n gamma = []\n npair = []\n\n df_ext = pd.DataFrame({\"X\": df[xcol], \"Y\": df[ycol], \"Z\": df[zcol],\"Variable\": df[vcol]})\n Dataframe2GSLIB(\"gamv_out.dat\", df_ext)\n\n with open(\"gamv.par\", \"w\") as f:\n f.write(\" Parameters for GAMV \\n\")\n f.write(\" ******************* \\n\")\n f.write(\" \\n\")\n f.write(\"START OF PARAMETERS: \\n\")\n f.write(\"gamv_out.dat -file with data \\n\")\n f.write(\"1 2 3 - columns for X, Y, Z coordinates \\n\")\n f.write(\"1 4 0 - number of variables,col numbers \\n\")\n f.write(\"-1.0e21 1.0e21 - trimming limits \\n\")\n f.write(\"gamv.out -file for variogram output \\n\")\n f.write(str(nlag) + \" -number of lags \\n\")\n f.write(str(lagdist) + \" -lag separation distance \\n\")\n f.write(str(lag_tol) + \" -lag tolerance \\n\")\n f.write(\"1 -number of directions \\n\")\n f.write(str(azi) + \" \" + str(atol) + \" \" + str(bandh) + \" \" +str(dip) + \" \" + str(dtol) + \" \" + str(bandv)+ \" -azm,atol,bandh,dip,dtol,bandv \\n\")\n f.write(str(isill) + \" -standardize sills? (0=no, 1=yes) \\n\")\n f.write(\"1 -number of variograms \\n\")\n f.write(\"1 1 1 -tail var., head var., variogram type \\n\")\n\n os.system(\"gamv.exe gamv.par\")\n\n with open(\"gamv.out\") as f:\n next(f) # skip the first line\n\n for line in f:\n _, l, g, n, *_ = line.split()\n lag.append(float(l))\n gamma.append(float(g))\n npair.append(float(n))\n\n return lag, gamma, npair","title":""},{"docid":"4d70336328dc8e4f38aac9c8aedc8e0e","score":"0.549276","text":"def gen_3ps(img, cen, sz=16):\n tf_1 = get_tf_xynormal(cen)\n xy = gen_slice(img, tf_1, sz2ext(sz))\n\n tf_2 = get_tf_yznormal(cen)\n yz = gen_slice(img, tf_2, sz2ext(sz))\n\n tf_3 = get_tf_xznormal(cen)\n xz = gen_slice(img, tf_3, sz2ext(sz))\n\n return xy, yz, xz","title":""},{"docid":"3a716af21a6dde6d909ffc1ebe89c2d2","score":"0.54892606","text":"def data(self, pdb, **kwargs):\n\n # load the 3D structure file\n structure = self.structure(pdb)\n\n bond_orientations, error_message = annotate_bond_orientation(structure,True)\n\n if len(error_message) > 0:\n self.logger.info(error_message)\n\n for b in bond_orientations:\n yield mod.UnitAnnotations(unit_id=b[\"unit_id\"],\n pdb_id=pdb,\n category=\"orientation\",\n value=b[\"orientation\"])\n\n yield mod.UnitAnnotations(unit_id=b[\"unit_id\"],\n pdb_id=pdb,\n category=\"chi_degree\",\n value=b[\"chi_degree\"])","title":""},{"docid":"b96174733ed03fe32e98b168364f785b","score":"0.5475608","text":"def data():\n solutions = np.arange(5).reshape(5, 1)\n objectives = 2 * np.arange(5)\n behaviors = 3 * np.arange(5).reshape(5, 1)\n indices = [(idx,) for idx in 4 * np.arange(5)]\n metadata = [{\"a\": 1}, {\"b\": 2}, {\"c\": 3}, {\"d\": 4}, {\"e\": 5}]\n return solutions, objectives, behaviors, indices, metadata","title":""},{"docid":"31d134eb867c93a4e2c32a208cdf045e","score":"0.5474235","text":"def dataset_to_view_data(dataset):\n p3w = np.concatenate([s['p3w'] for s in dataset.values()], axis=0)\n p2 = np.concatenate([s['p2'] for s in dataset.values()], axis=0)\n r, t, f, c = [\n np.concatenate([[s.attrs[k]\n for i in range(s.attrs['n_frames'])]\n for s in dataset.values()])\n for k in ['r', 't', 'f', 'c']\n ]\n return p2, r, t, f, c, p3w","title":""},{"docid":"64136f2b8755fb4dea0815502db5434b","score":"0.5450458","text":"def getPts3D(self, idx = None):\n # YOU NEED TO IMPLEMENT THIS FUNCTION IN YOUR SUBCLASS\n pass","title":""},{"docid":"ed54ce620cc7f2c3162cdf3b442e6cb7","score":"0.5429246","text":"def DC3D0(self, ALPHA, X, Y, Z, DEPTH, DIP, POT1, POT2, POT3, POT4):\n\n# COMMON /C1/DUMMY(8),R,dumm(15)\n# DIMENSION U(12),DUA(12),DUB(12),DUC(12)\n\n F0 = 0.0\n U = num.zeros((12, 1), float)\n DUA = num.zeros((12, 1), float)\n DUB = num.zeros((12, 1), float)\n DUC = num.zeros((12, 1), float)\n\n if Z > 0:\n print('(''0** POSITIVE Z WAS GIVEN IN SUB-DC3D0'')')\n for I in range(0, 12):\n U[I] = F0\n DUA[I] = F0\n DUB[I] = F0\n DUC[I] = F0\n\n AALPHA = ALPHA\n DDIP = DIP\n self.DCC0N0(AALPHA, DDIP)\n# ======================================\n# ===== REAL-SOURCE CONTRIBUTION =====\n# ======================================\n XX = X\n YY = Y\n ZZ = Z\n DD = DEPTH+Z\n self.DCCON1(XX, YY, DD)\n R = self.R\n if R == F0:\n UX = F0\n UY = F0\n UZ = F0\n UXX = F0\n UYX = F0\n UZX = F0\n UXY = F0\n UYY = F0\n UZY = F0\n UXZ = F0\n UYZ = F0\n UZZ = F0\n IRET = 1\n return\n else:\n PP1 = POT1\n PP2 = POT2\n PP3 = POT3\n PP4 = POT4\n\n self.UA0(XX, YY, DD, PP1, PP2, PP3, PP4)\n DUA = self.DUA\n\n for I in range(0, 12):\n if I < 10:\n U[I] = U[I]-DUA[I]\n if I >= 10:\n U[I] = U[I]+DUA[I]\n# =======================================\n# ===== IMAGE-SOURCE CONTRIBUTION =====\n# =======================================\n DD = DEPTH-Z\n self.DCCON1(XX, YY, DD)\n self.UA0(XX, YY, DD, PP1, PP2, PP3, PP4)\n DUA = self.DUA\n self.UB0(XX, YY, DD, ZZ, PP1, PP2, PP3, PP4)\n DUB = self.DUB\n self.UC0(XX, YY, DD, ZZ, PP1, PP2, PP3, PP4)\n DUC = self.DUC\n\n# -----\n for I in range(0, 12):\n DU = DUA[I]+DUB[I]+ZZ*DUC[I]\n if I >= 9:\n DU = DU+DUC[I-9]\n U[I] = U[I]+DU\n# =====\n self.UX = U[0]\n self.UY = U[1]\n self.UZ = U[2]\n self.UXX = U[3]\n self.UYX = U[4]\n self.UZX = U[5]\n self.UXY = U[6]\n self.UYY = U[7]\n self.UZY = U[8]\n self.UXZ = U[9]\n self.UYZ = U[10]\n self.UZZ = U[11]\n self.IRET = 0","title":""},{"docid":"b9ccc97be30fe94f763a430500eab0ba","score":"0.5414495","text":"def parse_cluster3d(data):\n cluster_event = data[0]\n num_clusters = cluster_event.as_vector().size()\n clusters_voxels, clusters_data = [], []\n for i in range(num_clusters):\n cluster = cluster_event.as_vector()[i]\n num_points = cluster.as_vector().size()\n if num_points > 0:\n x = np.empty(shape=(num_points,), dtype=np.int32)\n y = np.empty(shape=(num_points,), dtype=np.int32)\n z = np.empty(shape=(num_points,), dtype=np.int32)\n value = np.empty(shape=(num_points,), dtype=np.int32)\n larcv.as_flat_arrays(cluster_event.as_vector()[i],\n cluster_event.meta(),\n x, y, z, value)\n value = np.full(shape=(cluster.as_vector().size(), 1),\n fill_value=i, dtype=np.int32)\n clusters_voxels.append(np.stack([x, y, z], axis=1))\n clusters_data.append(value)\n np_voxels = np.concatenate(clusters_voxels, axis=0)\n np_data = np.concatenate(clusters_data, axis=0)\n return np_voxels, np_data","title":""},{"docid":"f5dcca9b1ea9e36be91e8ad5b9c9c775","score":"0.5408329","text":"def actual():\n\n # Sets, in the order expected in the GDX file\n s = ['a', 'b', 'c', 'd', 'e', 'f', 'g']\n t = ['b', 'g', 'r', 'o', 'y', 'i', 'v']\n u = ['CA', 'US', 'CN', 'JP']\n star = s + t[2:] + u\n\n # Create the dataset\n ds = xr.Dataset({\n 'p1': ('s', np.full(len(s), np.nan)),\n 'p2': ('t', np.full(len(t), 0.1)),\n 'p3': (['s', 't'], np.full([len(s), len(t)], np.nan)),\n 'p5': ('*', np.full(len(star), np.nan)),\n },\n coords={\n 's': s,\n 't': t,\n 'u': u,\n '*': star,\n 's1': ['a', 'b', 'c', 'd'],\n 's2': ['e', 'f', 'g'],\n 's3': [],\n 's4': [],\n 's5': ['b', 'd', 'f'],\n 's6': ['b', 'd', 'f'],\n 's7': [],\n 's_': s,\n })\n\n # Contents of parameters\n ds['pi'] = 3.14\n ds['p1'].loc['a'] = 1\n ds['p3'].loc[:, 'y'] = 1\n ds['p4'] = (['s1'], np.ones(ds['s1'].size))\n ds['p6'] = (['s', 's1', 't'],\n np.full([len(s), ds['s1'].size, len(t)], np.nan))\n\n ds['p7'] = (['*', '*'],\n np.full([ds['*'].size] * 2, np.nan))\n ds['p7'].loc['a', 'o'] = 1\n ds['p7'].loc['r', 'US'] = 2\n ds['p7'].loc['CA', 'b'] = 3\n\n # Set the _gdx_index attribute on each variable\n order = ['*', 'pi', 's', 't', 'u', 's1', 's2', 's3', 's4', 's5', 's6',\n 's7', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'e1', 'v1', 'v2',\n 's_', ]\n for num, name in enumerate(order):\n try:\n ds[name].attrs['_gdx_index'] = num\n except KeyError:\n # These names do not appear in the loaded gdx.File object\n assert name in ['e1', 'v1', 'v2']\n\n return ds","title":""},{"docid":"9b15c678494255471865fb2a1f43f1a9","score":"0.5405561","text":"def det3x3(*args, **kwargs):\n \n pass","title":""},{"docid":"9b15c678494255471865fb2a1f43f1a9","score":"0.5403282","text":"def det3x3(*args, **kwargs):\n \n pass","title":""},{"docid":"79b60687c6bd73705729626549a99868","score":"0.5385744","text":"def get_data_dict(self):\r\n\r\n gene_dict = {'geneID': self.geneID, 'Name': self.name,\r\n 'seq_type': self.seq_type, 'seq_range': self.seq_range,\r\n 'orientation': self.orientation,\r\n 'chromosome': self.chromosome, 'transcripts': {}}\r\n\r\n transcripts = self.get_transcripts()\r\n\r\n # fetch each transcript data\r\n for ID in transcripts:\r\n\r\n transcript = transcripts[ID]\r\n\r\n transcript_dict = {}\r\n\r\n transcript_dict['seq_type'] = transcript.get_seq_type()\r\n transcript_dict['ID'] = transcript.get_ID()\r\n transcript_dict['seq_range'] = transcript.get_seq_range()\r\n\r\n # get exons\r\n transcript_dict['exons'] = []\r\n\r\n for exon in transcript.get_exons():\r\n\r\n transcript_dict['exons'].append(exon.get_seq_range())\r\n\r\n # get info for mRNA\r\n if transcript.get_seq_type() == 'mRNA':\r\n\r\n if transcript.get_CDSs() != []:\r\n\r\n # Calculate UTRs\r\n transcript.get_5pUTR()\r\n transcript.get_3pUTR()\r\n\r\n # get all sequences as list\r\n mrna_subseq = transcript.get_mRNA_subsequences()\r\n\r\n for seq in mrna_subseq:\r\n\r\n seq_type = seq.get_seq_type()\r\n\r\n seq_range = seq.get_seq_range()\r\n\r\n if seq_type not in transcript_dict:\r\n\r\n transcript_dict[seq_type] = [seq_range]\r\n\r\n else:\r\n transcript_dict[seq_type].append(seq_range)\r\n\r\n gene_dict['transcripts'][ID] = transcript_dict\r\n\r\n return gene_dict","title":""},{"docid":"07907335fcbf1b03901bab9c4ee0dcf6","score":"0.5359551","text":"def parse_cluster3d_groups(data):\n np_voxels, np_data = parse_cluster3d([data[0]])\n groups, edges = parse_particle_group([data[1]])\n for cluster_index, group_id in enumerate(groups):\n where = np.where(np_data == float(cluster_index))\n np_data[where] = group_id\n\n return np_voxels, np_data","title":""},{"docid":"e583547c92cdd47d7a6e0f2c963f4866","score":"0.5331808","text":"def kagome_vis(x1, x2, x3, z):\n\n pin = 1\n deltaz = 1\n # Bravais primitive unit vecs\n a = 2 * array([[cos(2 * pi * p / 3.), sin(2 * pi * p / 3.)] for p in range(3)])\n a1 = a[0];\n a2 = a[1];\n a3 = a[2];\n\n # make unit cell\n x = array([x1, x2, x3])\n y = [z / 3. + x[mod(p - 1, 3)] - x[mod(p + 1, 3)] for p in range(3)]\n s = array([x[p] * (a[mod(p - 1, 3)] - a[mod(p + 1, 3)]) + y[p] * a[p] for p in range(3)])\n s1 = s[0];\n s2 = s[1];\n d1 = a1 / 2. + s2\n d2 = a2 / 2. - s1\n d3 = a3 / 2.\n\n # nodes at R (from top to bottom) -- like fig 2a of KaneLubensky2014\n R = array([d1 + a2,\n d3 + a1 + a2,\n d2 + a1,\n d1,\n d3 + a1,\n d2 - a2,\n d1 + a3,\n d3,\n d2 + a3,\n d1 + a2 + a3,\n d3 + a2,\n d2])\n\n Ni = array([\n [11, 1, 0, 0], # 0 d1+a2,\n [3, 11, 0, 2], # 1 d3+a1+a2,\n [3, 1, 0, 0], # 2 d2+a1,\n [2, 5, 4, 1], # 3 d1,\n [3, 5, 0, 0], # 4 d3+a1,\n [3, 7, 4, 6], # 5 d2-a2,\n [7, 5, 0, 0], # 6 d1+a3,\n [6, 9, 5, 8], # 7 d3,\n [7, 9, 0, 0], # 8 d2+a3,\n [11, 7, 10, 8], # 9 d1+a2+a3,\n [11, 9, 0, 0], # 10 d3+a2,\n [0, 9, 10, 1], # 11 d2\n ])\n\n Nk = array([\n [1, 1, 0, 0], # 0 d1+a2,\n [1, 1, 1, 1], # 1 d3+a1+a2,\n [1, 1, 0, 0], # 2 d2+a1,\n [1, 1, 1, 1], # 3 d1,\n [1, 1, 0, 0], # 4 d3+a1,\n [1, 1, 1, 1], # 5 d2-a2,\n [1, 1, 0, 0], # 6 d1+a3,\n [1, 1, 1, 1], # 7 d3,\n [1, 1, 0, 0], # 8 d2+a3,\n [1, 1, 1, 1], # 9 d1+a2+a3,\n [1, 1, 0, 0], # 10 d3+a2,\n [1, 1, 1, 1], # 11 d2\n ])\n\n c1 = '#000000'\n c2 = '#B9B9B9'\n c3 = '#E51B1B'\n c4 = '#18CFCF'\n\n col = [c1, c2, c3, c4]\n\n A = col[0]\n B = col[1]\n C = col[2]\n\n cc1 = '#96adb4'\n cols = array([A, C, B, A, C, B, A, C, B, A, C, B])\n line_cols = array([cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1])\n\n return array(R), array(Ni), array(Nk), cols, line_cols","title":""},{"docid":"72e696be734f6dd38257eda5d315fe90","score":"0.5326278","text":"def make_sample_data_3D_3blobs(data_type=\"iris\", invert_xy=False):\n\n from iris.cube import Cube\n from iris.coords import DimCoord, AuxCoord\n\n t_0 = datetime.datetime(2000, 1, 1, 12, 0, 0)\n\n x = np.arange(0, 100e3, 1000)\n y = np.arange(0, 200e3, 1000)\n z = np.arange(0, 20e3, 1000)\n\n t = t_0 + np.arange(0, 50, 2) * datetime.timedelta(minutes=1)\n\n t_temp = np.arange(0, 60, 1)\n track1_t = t_0 + t_temp * datetime.timedelta(minutes=1)\n x_0_1 = 10e3\n y_0_1 = 10e3\n z_0_1 = 4e3\n track1_x = x_0_1 + 30 * t_temp * 60\n track1_y = y_0_1 + 14 * t_temp * 60\n track1_magnitude = 10 * np.ones(track1_x.shape)\n\n t_temp = np.arange(0, 30, 1)\n track2_t = t_0 + (t_temp + 40) * datetime.timedelta(minutes=1)\n x_0_2 = 20e3\n y_0_2 = 10e3\n z_0_2 = 6e3\n track2_x = x_0_2 + 24 * (t_temp * 60) ** 2 / 1000\n track2_y = y_0_2 + 12 * t_temp * 60\n track2_magnitude = 20 * np.ones(track2_x.shape)\n\n t_temp = np.arange(0, 20, 1)\n track3_t = t_0 + (t_temp + 50) * datetime.timedelta(minutes=1)\n x_0_3 = 70e3\n y_0_3 = 110e3\n z_0_3 = 8e3\n track3_x = x_0_3 + 20 * (t_temp * 60) ** 2 / 1000\n track3_y = y_0_3 + 20 * t_temp * 60\n track3_magnitude = 15 * np.ones(track3_x.shape)\n\n if invert_xy == False:\n zz, yy, xx = np.meshgrid(z, y, x, indexing=\"ij\")\n y_dim = 2\n x_dim = 3\n data = np.zeros((t.shape[0], z.shape[0], y.shape[0], x.shape[0]))\n\n else:\n zz, xx, yy = np.meshgrid(z, x, y, indexing=\"ij\")\n x_dim = 2\n y_dim = 3\n data = np.zeros((t.shape[0], z.shape[0], x.shape[0], y.shape[0]))\n\n for i_t, t_i in enumerate(t):\n if np.any(t_i in track1_t):\n x_i = track1_x[track1_t == t_i]\n y_i = track1_y[track1_t == t_i]\n z_i = z_0_1\n mag_i = track1_magnitude[track1_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0))) * np.exp(\n -np.power(zz - z_i, 2.0) / (2 * np.power(5e3, 2.0))\n )\n if np.any(t_i in track2_t):\n x_i = track2_x[track2_t == t_i]\n y_i = track2_y[track2_t == t_i]\n z_i = z_0_2\n mag_i = track2_magnitude[track2_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0))) * np.exp(\n -np.power(zz - z_i, 2.0) / (2 * np.power(5e3, 2.0))\n )\n\n if np.any(t_i in track3_t):\n x_i = track3_x[track3_t == t_i]\n y_i = track3_y[track3_t == t_i]\n z_i = z_0_3\n mag_i = track3_magnitude[track3_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0))) * np.exp(\n -np.power(zz - z_i, 2.0) / (2 * np.power(5e3, 2.0))\n )\n\n t_start = datetime.datetime(1970, 1, 1, 0, 0)\n t_points = (t - t_start).astype(\"timedelta64[ms]\").astype(int) / 1000\n t_coord = DimCoord(\n t_points,\n standard_name=\"time\",\n var_name=\"time\",\n units=\"seconds since 1970-01-01 00:00\",\n )\n z_coord = DimCoord(z, standard_name=\"geopotential_height\", var_name=\"z\", units=\"m\")\n y_coord = DimCoord(\n y, standard_name=\"projection_y_coordinate\", var_name=\"y\", units=\"m\"\n )\n x_coord = DimCoord(\n x, standard_name=\"projection_x_coordinate\", var_name=\"x\", units=\"m\"\n )\n lat_coord = AuxCoord(\n 24 + 1e-5 * xx[0], standard_name=\"latitude\", var_name=\"latitude\", units=\"degree\"\n )\n lon_coord = AuxCoord(\n 150 + 1e-5 * yy[0],\n standard_name=\"longitude\",\n var_name=\"longitude\",\n units=\"degree\",\n )\n sample_data = Cube(\n data,\n dim_coords_and_dims=[\n (t_coord, 0),\n (z_coord, 1),\n (y_coord, y_dim),\n (x_coord, x_dim),\n ],\n aux_coords_and_dims=[(lat_coord, (2, 3)), (lon_coord, (2, 3))],\n var_name=\"w\",\n units=\"m s-1\",\n )\n\n if data_type == \"xarray\":\n sample_data = DataArray.from_iris(sample_data)\n\n return sample_data","title":""},{"docid":"9012f0656580298817201d201ca626dd","score":"0.5276661","text":"def apr3read(filename):\n \n apr = {}\n flag = 0\n ##Radar varibles in hdf file found by hdf.datasets\n radar_freq = 'zhh14' #Ku\n radar_freq2 = 'zhh35' #Ka\n radar_freq3 = 'zvv95' #W\n radar_freq4 = 'ldr14' #LDR\n vel_str = 'vel14' #Doppler\n ##\n\n hdf = SD(filename, SDC.READ)\n \n listofkeys = hdf.datasets().keys()\n if 'zvv95' in listofkeys:\n radar3 = hdf.select(radar_freq3)\n radar_n3 = radar3.get()\n radar_n3 = radar_n3/100.\n else:\n radar_n3 = np.array([])\n flag = 1\n print('No W band')\n \n \n alt = hdf.select('alt3D')\n lat = hdf.select('lat')\n lon = hdf.select('lon')\n time = hdf.select('scantime').get()\n surf = hdf.select('surface_index').get()\n isurf = hdf.select('isurf').get()\n plane = hdf.select('alt_nav').get()\n radar = hdf.select(radar_freq)\n radar2 = hdf.select(radar_freq2)\n radar4 = hdf.select(radar_freq4)\n vel = hdf.select(vel_str)\n lon3d = hdf.select('lon3D')\n lat3d = hdf.select('lat3D')\n alt3d = hdf.select('alt3D')\n lat3d_scale = hdf.select('lat3D_scale').get()[0][0]\n lon3d_scale = hdf.select('lon3D_scale').get()[0][0]\n alt3d_scale = hdf.select('alt3D_scale').get()[0][0]\n lat3d_offset = hdf.select('lat3D_offset').get()[0][0]\n lon3d_offset = hdf.select('lon3D_offset').get()[0][0]\n alt3d_offset = hdf.select('alt3D_offset').get()[0][0]\n \n alt = alt.get()\n ngates = alt.shape[0]\n #alt = alt[:,scan,:]\n lat = lat.get()\n #lat = lat[scan,:]\n lon = lon.get()\n #lon = lon[scan,:]\n \n lat3d = lat3d.get()\n lat3d = (lat3d/lat3d_scale) + lat3d_offset\n lon3d = lon3d.get()\n lon3d = (lon3d/lon3d_scale) + lon3d_offset\n alt3d = alt3d.get()\n alt3d = (alt3d/alt3d_scale) + alt3d_offset\n \n #time = time[scan,:]\n #surf = surf[scan,:]\n #isurf = isurf[scan,:]\n #plane = plane[scan,:]\n radar_n = radar.get()\n radar_n = radar_n/100.\n radar_n2 = radar2.get()\n radar_n2 = radar_n2/100.\n\n radar_n4 = radar4.get()\n radar_n4 = radar_n4/100.\n vel_n = vel.get()\n vel_n = vel_n/100.\n \n #Quality control\n radar_n[radar_n <= -99] = np.nan\n radar_n2[radar_n2 <= -99] = np.nan\n radar_n4[radar_n4 <= -99] = np.nan\n \n ##convert time to datetimes\n time_dates = np.empty(time.shape,dtype=object)\n for i in np.arange(0,time.shape[0]):\n for j in np.arange(0,time.shape[1]):\n tmp = datetime.datetime.utcfromtimestamp(time[i,j])\n time_dates[i,j] = tmp\n \n #Create a time at each gate (assuming it is the same down each ray, there is a better way to do this) \n time_gate = np.empty(lat3d.shape,dtype=object)\n for k in np.arange(0,550):\n for i in np.arange(0,time_dates.shape[0]):\n for j in np.arange(0,time_dates.shape[1]):\n time_gate[k,i,j] = time_dates[i,j] \n\n apr['Ku'] = radar_n\n apr['Ka'] = radar_n2\n apr['W'] = radar_n3\n apr['DFR_1'] = radar_n - radar_n2 #Ku - Ka\n \n if flag == 0:\n apr['DFR_2'] = radar_n2 - radar_n3 #Ka - W\n apr['DFR_3'] = radar_n - radar_n3 #Ku - W\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward]'\n else:\n apr['DFR_2'] = np.array([]) #Ka - W\n apr['DFR_3'] = np.array([]) #Ku - W\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward], Note No W band avail'\n \n apr['ldr'] = radar_n4\n apr['vel'] = vel_n\n apr['lon'] = lon\n apr['lat'] = lat\n apr['alt_gate'] = alt3d\n apr['alt_plane'] = plane\n apr['surface'] = isurf \n apr['time']= time\n apr['timedates']= time_dates\n apr['time_gate'] = time_gate\n apr['lon_gate'] = lon3d\n apr['lat_gate'] = lat3d\n \n fileheader = hdf.select('fileheader')\n roll = hdf.select('roll').get()\n pitch = hdf.select('pitch').get()\n drift = hdf.select('drift').get()\n \n apr['fileheader'] = fileheader\n apr['ngates'] = ngates\n apr['roll'] = roll\n apr['pitch'] = pitch\n apr['drift'] = drift\n \n _range = np.arange(15,550*30,30)\n _range = np.asarray(_range,float)\n ind = np.where(_range >= plane.mean())\n _range[ind] = np.nan\n apr['range'] = _range\n \n return apr","title":""},{"docid":"cbb2c7f57335a4920d8bbf0ada3853fa","score":"0.5276661","text":"def apr3read(filename):\n \n apr = {}\n flag = 0\n ##Radar varibles in hdf file found by hdf.datasets\n radar_freq = 'zhh14' #Ku\n radar_freq2 = 'zhh35' #Ka\n radar_freq3 = 'zvv95' #W\n radar_freq4 = 'ldr14' #LDR\n vel_str = 'vel14' #Doppler\n ##\n\n hdf = SD(filename, SDC.READ)\n \n listofkeys = hdf.datasets().keys()\n if 'zvv95' in listofkeys:\n radar3 = hdf.select(radar_freq3)\n radar_n3 = radar3.get()\n radar_n3 = radar_n3/100.\n else:\n radar_n3 = np.array([])\n flag = 1\n print('No W band')\n \n \n alt = hdf.select('alt3D')\n lat = hdf.select('lat')\n lon = hdf.select('lon')\n time = hdf.select('scantime').get()\n surf = hdf.select('surface_index').get()\n isurf = hdf.select('isurf').get()\n plane = hdf.select('alt_nav').get()\n radar = hdf.select(radar_freq)\n radar2 = hdf.select(radar_freq2)\n radar4 = hdf.select(radar_freq4)\n vel = hdf.select(vel_str)\n lon3d = hdf.select('lon3D')\n lat3d = hdf.select('lat3D')\n alt3d = hdf.select('alt3D')\n lat3d_scale = hdf.select('lat3D_scale').get()[0][0]\n lon3d_scale = hdf.select('lon3D_scale').get()[0][0]\n alt3d_scale = hdf.select('alt3D_scale').get()[0][0]\n lat3d_offset = hdf.select('lat3D_offset').get()[0][0]\n lon3d_offset = hdf.select('lon3D_offset').get()[0][0]\n alt3d_offset = hdf.select('alt3D_offset').get()[0][0]\n \n alt = alt.get()\n ngates = alt.shape[0]\n #alt = alt[:,scan,:]\n lat = lat.get()\n #lat = lat[scan,:]\n lon = lon.get()\n #lon = lon[scan,:]\n \n lat3d = lat3d.get()\n lat3d = (lat3d/lat3d_scale) + lat3d_offset\n lon3d = lon3d.get()\n lon3d = (lon3d/lon3d_scale) + lon3d_offset\n alt3d = alt3d.get()\n alt3d = (alt3d/alt3d_scale) + alt3d_offset\n \n #time = time[scan,:]\n #surf = surf[scan,:]\n #isurf = isurf[scan,:]\n #plane = plane[scan,:]\n radar_n = radar.get()\n radar_n = radar_n/100.\n radar_n2 = radar2.get()\n radar_n2 = radar_n2/100.\n\n radar_n4 = radar4.get()\n radar_n4 = radar_n4/100.\n vel_n = vel.get()\n vel_n = vel_n/100.\n\n ##convert time to datetimes\n time_dates = np.empty(time.shape,dtype=object)\n for i in np.arange(0,time.shape[0]):\n for j in np.arange(0,time.shape[1]):\n tmp = datetime.datetime.utcfromtimestamp(time[i,j])\n time_dates[i,j] = tmp\n\n apr['Ku'] = radar_n\n apr['Ka'] = radar_n2\n apr['W'] = radar_n3\n apr['DFR_1'] = radar_n - radar_n2 #Ku - Ka\n \n if flag == 0:\n apr['DFR_2'] = radar_n2 - radar_n3 #Ka - W\n apr['DFR_3'] = radar_n - radar_n3 #Ku - W\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward]'\n else:\n apr['DFR_2'] = np.array([]) #Ka - W\n apr['DFR_3'] = np.array([]) #Ku - W\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward], Note No W band avail'\n \n apr['ldr'] = radar_n4\n apr['vel'] = vel_n\n apr['lon'] = lon\n apr['lat'] = lat\n apr['alt_gate'] = alt3d\n apr['alt_plane'] = plane\n apr['surface'] = isurf \n apr['time']= time\n apr['timedates']= time_dates\n apr['lon_gate'] = lon3d\n apr['lat_gate'] = lat3d\n \n fileheader = hdf.select('fileheader')\n roll = hdf.select('roll').get()\n pitch = hdf.select('pitch').get()\n drift = hdf.select('drift').get()\n \n apr['fileheader'] = fileheader\n apr['ngates'] = ngates\n apr['roll'] = roll\n apr['pitch'] = pitch\n apr['drift'] = drift\n \n _range = np.arange(15,550*30,30)\n _range = np.asarray(_range,float)\n ind = np.where(_range >= plane.mean())\n _range[ind] = np.nan\n apr['range'] = _range\n \n return apr","title":""},{"docid":"b870ffc194520cb215e546e7f57b4fcc","score":"0.5271329","text":"def process_xyz_md17(datafile):\n xyz_lines = [line.decode('UTF-8') for line in datafile.readlines()]\n\n line_counter = 0\n atom_positions = []\n atom_types = []\n for line in xyz_lines:\n if line[0] is '#':\n continue\n if line_counter is 0:\n num_atoms = int(line)\n elif line_counter is 1:\n split = line.split(';')\n assert (len(split) == 1 or len(split) == 2), 'Improperly formatted energy/force line.'\n if (len(split) == 1):\n e = split[0]\n f = None\n elif (len(split) == 2):\n e, f = split\n f = f.split('],[')\n atom_energy = float(e)\n atom_forces = [[float(x.strip('[]\\n')) for x in force.split(',')] for force in f]\n else:\n split = line.split()\n if len(split) is 4:\n type, x, y, z = split\n atom_types.append(split[0])\n atom_positions.append([float(x) for x in split[1:]])\n else:\n logging.debug(line)\n line_counter += 1\n\n atom_charges = [charge_dict[type] for type in atom_types]\n\n molecule = {'num_atoms': num_atoms, 'energy': atom_energy, 'charges': atom_charges,\n 'forces': atom_forces, 'positions': atom_positions}\n\n molecule = {key: torch.tensor(val) for key, val in molecule.items()}\n\n return molecule","title":""},{"docid":"a796156c9bae9b904e94b8584c613456","score":"0.5265736","text":"def syn_genomic_data():\n return h5py.File(features_path,'r')","title":""},{"docid":"14cf759d7a6c99df7d41b78b00653117","score":"0.52639365","text":"def sgsim_3D(nreal, df, xcol, ycol, zcol, vcol, nx, ny, nz, hsiz, vsiz, seed, var, output_file):\n x = df[xcol]\n y = df[ycol]\n z = df[zcol]\n v = df[vcol]\n var_min = v.values.min()\n var_max = v.values.max()\n df_temp = pd.DataFrame({\"X\": x, \"Y\": y, \"Z\": z, \"Var\": v})\n Dataframe2GSLIB(\"data_temp.dat\", df_temp)\n\n nug = var[\"nug\"]\n nst = var[\"nst\"]\n it1 = var[\"it1\"]\n cc1 = var[\"cc1\"]\n azi1 = var[\"azi1\"]\n dip1 = var[\"dip1\"] \n hmax1 = var[\"hmax1\"]\n hmed1 = var[\"hmed1\"]\n hmin1 = var[\"hmin1\"]\n it2 = var[\"it2\"]\n cc2 = var[\"cc2\"]\n azi2 = var[\"azi2\"]\n dip2 = var[\"dip2\"] \n hmax2 = var[\"hmax2\"]\n hmed2 = var[\"hmed2\"]\n hmin2 = var[\"hmin2\"]\n max_range = max(hmax1, hmax2)\n max_range_v = max(hmin1, hmin2) * 10\n hmn = hsiz * 0.5\n zmn = vsiz * 0.5\n hctab = int(max_range / hsiz) * 2 + 1\n\n with open(\"sgsim.par\", \"w\") as f:\n f.write(\" Parameters for SGSIM \\n\")\n f.write(\" ******************** \\n\")\n f.write(\" \\n\")\n f.write(\"START OF PARAMETER: \\n\")\n f.write(\"data_temp.dat -file with data \\n\")\n f.write(\"1 2 3 4 0 0 - columns for X,Y,Z,vr,wt,sec.var. \\n\")\n f.write(\"-1.0e21 1.0e21 - trimming limits \\n\")\n f.write(\"1 -transform the data (0=no, 1=yes) \\n\")\n f.write(\"none.trn - file for output trans table \\n\")\n f.write(\"0 - consider ref. dist (0=no, 1=yes) \\n\")\n f.write(\"none.dat - file with ref. dist distribution \\n\")\n f.write(\"1 0 - columns for vr and wt \\n\")\n f.write(str(var_min) + \" \" + str(var_max) + \" zmin,zmax(tail extrapolation) \\n\")\n f.write(\"1 \" + str(var_min) + \" - lower tail option, parameter \\n\")\n f.write(\"1 \" + str(var_max) + \" - upper tail option, parameter \\n\")\n f.write(\"0 -debugging level: 0,1,2,3 \\n\")\n f.write(\"nonw.dbg -file for debugging output \\n\")\n f.write(str(output_file) + \" -file for simulation output \\n\")\n f.write(str(nreal) + \" -number of realizations to generate \\n\")\n f.write(str(nx) + \" \" + str(hmn) + \" \" + str(hsiz) + \" \\n\")\n f.write(str(ny) + \" \" + str(hmn) + \" \" + str(hsiz) + \" \\n\")\n f.write(str(nz) + \" \" + str(zmn) + \" \" + str(vsiz) + \" \\n\")\n f.write(str(seed) + \" -random number seed \\n\")\n f.write(\"0 8 -min and max original data for sim \\n\")\n f.write(\"12 -number of simulated nodes to use \\n\")\n f.write(\"1 -assign data to nodes (0=no, 1=yes) \\n\")\n f.write(\"1 3 -multiple grid search (0=no, 1=yes),num \\n\")\n f.write(\"0 -maximum data per octant (0=not used) \\n\")\n f.write(str(max_range) + \" \" + str(max_range) +\" \"+ str(max_range_v) + \" -maximum search (hmax,hmin,vert) \\n\")\n f.write(str(azi1) + \" 0.0 0.0 -angles for search ellipsoid \\n\")\n f.write(str(hctab) + \" \" + str(hctab) + \" 1 -size of covariance lookup table \\n\")\n f.write(\"1 0.60 1.0 - ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC \\n\")\n f.write(\"none.dat - file with LVM, EXDR, or COLC variable \\n\")\n f.write(\"4 - column for secondary variable \\n\")\n f.write(str(nst) + \" \" + str(nug) + \" -nst, nugget effect \\n\")\n f.write(str(it1) + \" \" + str(cc1) + \" \" + str(azi1) + \" \" + str(dip1) +\" 0.0 -it,cc,ang1,ang2,ang3\\n\")\n f.write(\" \" + str(hmax1) + \" \t\" + str(hmed1) + \"\t\t\" + str(hmin1) + \" - a_hmax, a_hmin, a_vert \\n\")\n f.write(str(it2) + \" \" + str(cc2) + \" \t\" + str(azi2) + \"\t\t\" + str(dip2) +\" 0.0 -it,cc,ang1,ang2,ang3\\n\")\n f.write(\" \" + str(hmax2) + \" \" + str(hmed2) + \" \" +str(hmin2) + \" - a_hmax, a_hmin, a_vert \\n\")\n\n os.system(\"sgsim.exe sgsim.par\")\n sim_array = GSLIB2ndarray_3D(output_file, 0, nreal, nx, ny, nz)\n return sim_array[0]","title":""},{"docid":"73fcca3c13d863f202c0090e357678df","score":"0.52560854","text":"def geometric_data():\r\n\r\n Sw = 175 * 0.3048 * 0.3048 # m2\r\n c = 4.79 * 0.3048 # m\r\n span = 36.9 * 0.3048 # m\r\n\r\n return Sw, c, span","title":""},{"docid":"8aa1eb03f2548201c119cd3b802e6b4b","score":"0.52547175","text":"def read_3d_data( par ):\n \n # Load data\n train, _, rcams_train = load.load_3D( par['data_dir'], par, cam_id=par['cam_id'], subjects=par['train_subjects'], actions=par['actions'] )\n test, _, rcams_test = load.load_3D( par['data_dir'], par, cam_id=par['cam_id'], subjects=par['test_subjects'], actions=par['actions'] )\n \n #transform to camera coordinates\n train = transform_frame( train, rcams_train )\n test = transform_frame( test, rcams_test )\n \n # anchor points to body-coxa (to predict legjoints wrt body-coxas)\n train, _ = utils.anchor_to_root( train, par['roots'], par['target_sets'], par['out_dim'])\n test, offset = utils.anchor_to_root( test, par['roots'], par['target_sets'], par['out_dim'])\n\n # Standardize each dimension independently\n mean, std = stat.normalization_stats( train )\n train = stat.normalize( train, mean, std )\n test = stat.normalize( test, mean, std )\n \n #select coordinates to be predicted and return them as 'targets_3d'\n train, _ = utils.remove_roots(train, par['target_sets'], par['out_dim'])\n test, targets_3d = utils.remove_roots(test, par['target_sets'], par['out_dim'])\n\n return train, test, mean, std, targets_3d, rcams_test, offset","title":""},{"docid":"8b5240c59f9dfa89b16e1313c05a1cbc","score":"0.5251831","text":"def visualize3d_csv(algorithm, protein):\n # construct the path to the csv file\n filepath = pathlib.Path(\"Results/3d/{}/{}_{}.csv\".format(algorithm, algorithm, protein))\n\n # check if file exists\n if not filepath.exists():\n print(\"file cannot be found\")\n exit(1)\n\n # read in the file as a list of protein objects\n all_proteins = load_from_csv(filepath, protein)\n num_proteins = len(all_proteins)\n\n for protein_i, protein in enumerate(all_proteins):\n # get the attributes of the protein\n all_coordinates = protein.get_all_coordinates()\n amino_places = protein.get_amino_places()\n bonds = protein.get_bonds()\n\n # putting the coordinates in an x and an y list\n x_list, y_list, z_list, \\\n scat_hx_list, scat_hy_list, scat_hz_list, \\\n scat_px_list, scat_py_list, scat_pz_list, \\\n scat_cx_list, scat_cy_list, scat_cz_list = ([] for list in range(12))\n\n # print(all_coordinates)\n\n for coordinates in list(all_coordinates):\n # unpack the coordinate values\n x, y, z = coordinates\n x_list.append(x)\n y_list.append(y)\n z_list.append(z)\n\n # group the coordinates of the same amino kinds\n amino_kind = amino_places[\"{}\".format(coordinates)]\n if amino_kind == 'P':\n scat_px_list.append(x)\n scat_py_list.append(y)\n scat_pz_list.append(z)\n elif amino_kind == 'H':\n scat_hx_list.append(x)\n scat_hy_list.append(y)\n scat_hz_list.append(z)\n else:\n scat_cx_list.append(x)\n scat_cy_list.append(y)\n scat_cz_list.append(z)\n\n # Make plot axes\n ax = plt.axes(projection='3d')\n ax.set_xlim3d(min(x_list) - 1, max(x_list) + 1)\n ax.set_ylim3d(min(y_list) - 1, max(y_list) + 1)\n ax.set_zlim3d(min(z_list) - 1, max(z_list) + 1)\n\n # Plot cavalent line\n ax.plot3D(x_list, y_list, z_list, color='black', linestyle='solid', zorder=1)\n\n # use different colours to visualize the amino kind groups\n ax.scatter3D(scat_px_list, scat_py_list, scat_pz_list, color='red', zorder=2)\n ax.scatter3D(scat_hx_list, scat_hy_list, scat_hz_list, color='blue', zorder=2)\n ax.scatter3D(scat_cx_list, scat_cy_list, scat_cz_list, color='gold', zorder=2)\n\n # unpack the bonds neighboring_locations\n for bond in bonds:\n bonds_x, bonds_y, bonds_z = ([] for list in range(3))\n for coordinate in bond:\n x, y, z = coordinate\n bonds_x.append(x)\n bonds_y.append(y)\n bonds_z.append(z)\n\n # plot bond\n ax.plot3D(bonds_x, bonds_y, bonds_z, color='green', linestyle='--', zorder=1)\n\n # show figure\n plt.show()","title":""},{"docid":"a5b26ef912871a3382861bc3d20ca254","score":"0.5248432","text":"def region_2d_to_location_3d(*argv):","title":""},{"docid":"851c6f68450b9a84f5db79c639404b26","score":"0.52283657","text":"def val3p4c_depthfromIvarOLD(sample):\n nside = 1024 # Resolution of output maps\n nsideSTR='1024' # same as nside but in string format\n nsidesout = None # if you want full sky degraded maps to be written\n ratiores = 1 # Superresolution/oversampling ratio, simp mode doesn't allow anything other than 1\n mode = 1 # 1: fully sequential, 2: parallel then sequential, 3: fully parallel\n pixoffset = 0 # How many pixels are being removed on the edge of each CCD? 15 for DES.\n oversamp='1' # ratiores in string format\n \n band = sample.band\n catalogue_name = sample.catalog\n fname = sample.ccds \n localdir = sample.localdir\n extc = sample.extc\n\n #Read ccd file \n tbdata = pyfits.open(fname)[1].data\n \n # ------------------------------------------------------\n # Obtain indices\n auxstr='band_'+band\n sample_names = [auxstr]\n if(sample.DR == 'DR3'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \n elif(sample.DR == 'DR4'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['bitmask'] == 0)) \n elif(sample.DR == 'DR5'):\n if(sample.survey == 'DECaLS'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \n elif(sample.survey == 'DEShyb'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\n elif(sample.survey == 'NGCproxy'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \n elif(sample.DR == 'DR6'):\n inds = np.where((tbdata['filter'] == band)) \n elif(sample.DR == 'DR7'):\n if(sample.survey == 'DECaLS'):\n inds = np.where((tbdata['filter'] == band)) \n elif(sample.survey == 'DEShyb'):\n inds = np.where((tbdata['filter'] == band) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\n elif(sample.survey == 'NGCproxy'):\n inds = np.where((tbdata['filter'] == band) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \n\n #Read data \n #obtain invnoisesq here, including extinction \n nmag = Magtonanomaggies(tbdata['galdepth']-extc*tbdata['EBV'])/5.\n ivar= 1./nmag**2.\n\n hits=np.ones(np.shape(ivar)) \n\n # What properties do you want mapped?\n # Each each tuple has [(quantity to be projected, weighting scheme, operation),(etc..)] \n propertiesandoperations = [ ('ivar', '', 'total'), ('hits','','total') ]\n\n \n # What properties to keep when reading the images? \n # Should at least contain propertiesandoperations and the image corners.\n # MARCM - actually no need for ra dec image corners. \n # Only needs ra0 ra1 ra2 ra3 dec0 dec1 dec2 dec3 only if fast track appropriate quicksip subroutines were implemented \n #propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\n # \t+ ['RA', 'DEC', 'crval1', 'crval2', 'crpix1', 'crpix2', 'cd1_1', 'cd1_2', 'cd2_1', 'cd2_2','width','height']\n propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\n \t+ ['RA', 'DEC', 'ra0','ra1','ra2','ra3','dec0','dec1','dec2','dec3']\n \n # Create big table with all relevant properties. \n\n tbdata = np.core.records.fromarrays([tbdata[prop] for prop in propertiesToKeep] + [ivar] + [hits], names = propertiesToKeep + [ 'ivar', 'hits'])\n \n # Read the table, create Healtree, project it into healpix maps, and write these maps.\n # Done with Quicksip library, note it has quite a few hardcoded values (use new version by MARCM for BASS and MzLS) \n # project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, outroot, sample_names, inds, nside)\n #project_and_write_maps(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside, ratiores, pixoffset, nsidesout)\n project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside)\n \n # Read Haelpix maps from quicksip \n prop='ivar'\n op='total'\n vmin=21.0\n vmax=24.0\n\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\n f = fitsio.read(fname2)\n\n # HEALPIX DEPTH MAPS \n # convert ivar to depth \n import healpy as hp\n from healpix3 import pix2ang_ring,thphi2radec\n\n ral = []\n decl = []\n val = f['SIGNAL']\n pix = f['PIXEL']\n \n # Obtain values to plot \n if (prop == 'ivar'):\n myval = []\n mylabel='depth' \n \n below=0 \n for i in range(0,len(val)):\n depth=nanomaggiesToMag(sqrt(1./val[i]) * 5.)\n if(depth < vmin):\n below=below+1\n else:\n myval.append(depth)\n th,phi = hp.pix2ang(int(nside),pix[i])\n ra,dec = thphi2radec(th,phi)\n ral.append(ra)\n decl.append(dec)\n\n npix=len(pix)\n\n print('Area is ', npix/(float(nside)**2.*12)*360*360./pi, ' sq. deg.')\n print(below, 'of ', npix, ' pixels are not plotted as their ', mylabel,' < ', vmin)\n print('Within the plot, min ', mylabel, '= ', min(myval), ' and max ', mylabel, ' = ', max(myval))\n\n\n # Plot depth \n from matplotlib import pyplot as plt\n import matplotlib.cm as cm\n\n mapa = plt.scatter(ral,decl,c=myval, cmap=cm.rainbow,s=2., vmin=vmin, vmax=vmax, lw=0,edgecolors='none')\n cbar = plt.colorbar(mapa)\n plt.xlabel('r.a. (degrees)')\n plt.ylabel('declination (degrees)')\n plt.title('Map of '+ mylabel +' for '+catalogue_name+' '+band+'-band')\n plt.xlim(0,360)\n plt.ylim(-30,90)\n mapfile=localdir+mylabel+'_'+band+'_'+catalogue_name+str(nside)+'.png'\n print('saving plot to ', mapfile)\n plt.savefig(mapfile)\n plt.close()\n #plt.show()\n #cbar.set_label(r'5$\\sigma$ galaxy depth', rotation=270,labelpad=1)\n #plt.xscale('log')\n\n\n # Statistics depths\n\n deptharr=np.array(myval)\n p90=np.percentile(deptharr,10)\n p95=np.percentile(deptharr,5)\n p98=np.percentile(deptharr,2)\n med=np.percentile(deptharr,50)\n mean = sum(deptharr)/float(np.size(deptharr)) # 1M array, too long for precision\n std = sqrt(sum(deptharr**2.)/float(len(deptharr))-mean**2.)\n\n \n\n ndrawn=np.size(deptharr)\n print(\"Total pixels\", np.size(deptharr), \"probably too many for exact mean and std\")\n print(\"Mean = \", mean, \"; Median = \", med ,\"; Std = \", std)\n print(\"Results for 90% 95% and 98% are: \", p90, p95, p98)\n\n # Statistics pases\n prop = 'hits'\n op = 'total'\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\n f = fitsio.read(fname2)\n \n hitsb=f['SIGNAL']\n hist, bin_edges =np.histogram(hitsb,bins=[-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,100],density=True)\n #print hitsb[1000:10015]\n #hist, bin_edges =np.histogram(hitsb,density=True)\n print(\"Percentage of hits for 0,1,2., to >7 pases\\n\", end=' ') \n #print bin_edges \n print(hist)\n #print 100*hist\n\n return mapfile","title":""},{"docid":"8017e4b628252c7621511e47c1e71ecf","score":"0.52237123","text":"def get_data():\n size = 500\n weights = [0.2, 0.5, 0.3]\n cov = np.matrix([\n [1, 0],\n [0, 1]\n ])\n mean_1 = np.array([0, 0])\n mean_2 = np.array([3, 0])\n mean_3 = np.array([0, 3])\n data_1 = multivariate_normal(mean=mean_1, cov=cov, size=size)\n data_2 = multivariate_normal(mean=mean_2, cov=cov, size=size)\n data_3 = multivariate_normal(mean=mean_3, cov=cov, size=size)\n\n select = np.random.choice(range(3), size=500, p=weights)\n\n data = np.concatenate((data_1[select == 0], data_2[select == 1], data_3[select == 2]))\n\n return data","title":""},{"docid":"06a291c603fa0a327f7dfb97b7be9158","score":"0.5218129","text":"def DC3D(self, ALPHA, X, Y, Z, DEPTH, DIP, AL1, AL2, AW1, AW2, DISL1, DISL2, DISL3):\n\n # SUBROUTINE DC3D(ALPHA,X,Y,Z,DEPTH,DIP,\n # * AL1,AL2,AW1,AW2,DISL1,DISL2,DISL3,\n # * UX,UY,UZ,UXX,UYX,UZX,UXY,UYY,UZY,UXZ,UYZ,UZZ,IRET)\n # IMPLICIT REAL*8 (A-H,O-Z)\n # REAL*4 ALPHA,X,Y,Z,DEPTH,DIP,AL1,AL2,AW1,AW2,DISL1,DISL2,DISL3,\n # * UX,UY,UZ,UXX,UYX,UZX,UXY,UYY,UZY,UXZ,UYZ,UZZ,EPS\n#\n# COMMON /C0/DUMMY(5),SD,CD,dumm(5)\n # DIMENSION XI(2),ET(2),KXI(2),KET(2)\n # DIMENSION U(12),DU(12),DUA(12),DUB(12),DUC(12)\n from math import sqrt\n F0 = 0.0\n EPS = 0.000001\n\n XI = num.zeros(2, float)\n ET = num.zeros(2, float)\n KXI = num.zeros(2, float)\n KET = num.zeros(2, float)\n U = num.zeros(12, float)\n DU = num.zeros(12, float)\n DUA = num.zeros(12, float)\n DUB = num.zeros(12, float)\n DUC = num.zeros(12, float)\n\n# -----\n if Z > 0:\n log.critical('** POSITIVE Z WAS GIVEN IN SUB-DC3D')\n\n for I in range(0, 12):\n U[I] = F0\n DUA[I] = F0\n DUB[I] = F0\n DUC[I] = F0\n\n AALPHA = ALPHA\n DDIP = DIP\n self.DCC0N0(AALPHA, DDIP)\n CD = self.CD\n SD = self.SD\n\n# -----\n ZZ = Z\n DD1 = DISL1\n DD2 = DISL2\n DD3 = DISL3\n XI[0] = X-AL1\n XI[1] = X-AL2\n # if X==-6 and Y==-1:\n # print AL1\n # print AL2\n # print 'hello'\n\n if abs(XI[0]) < EPS:\n XI[0] = F0\n if abs(XI[1]) < EPS:\n XI[1] = F0\n# ======================================\n# ===== REAL-SOURCE CONTRIBUTION =====\n# ======================================\n D = DEPTH+Z\n P = Y*CD+D*SD\n Q = Y*SD-D*CD\n ET[0] = P-AW1\n ET[1] = P-AW2\n if abs(Q) < EPS:\n Q = F0\n if abs(ET[0]) < EPS:\n ET[0] = F0\n if abs(ET[1]) < EPS:\n ET[1] = F0\n\n# --------------------------------\n# ----- REJECT SINGULAR CASE -----\n# --------------------------------\n# ----- ON FAULT EDGE\n if (Q == F0 and ((XI[0]*XI[1] < F0 and ET[0]*ET[1] == F0)\n or (ET[0]*ET[1] < F0 and XI[0]*XI[1] == F0))):\n self.UX = F0\n self.UY = F0\n self.UZ = F0\n self.UXX = F0\n self.UYX = F0\n self.UZX = F0\n self.UXY = F0\n self.UYY = F0\n self.UZY = F0\n self.UXZ = F0\n self.UYZ = F0\n self.UZZ = F0\n self.IRET = 1\n return\n# ----- ON NEGATIVE EXTENSION OF FAULT EDGE\n KXI[0] = 0\n KXI[1] = 0\n KET[0] = 0\n KET[1] = 0\n R12 = sqrt(XI[0]*XI[0]+ET[1]*ET[1]+Q*Q)\n R21 = sqrt(XI[1]*XI[1]+ET[0]*ET[0]+Q*Q)\n R22 = sqrt(XI[1]*XI[1]+ET[1]*ET[1]+Q*Q)\n if XI[0] < F0 and R21+XI[1] < EPS:\n KXI[0] = 1\n if XI[0] < F0 and R22+XI[1] < EPS:\n KXI[1] = 1\n if ET[0] < F0 and R12+ET[1] < EPS:\n KET[0] = 1\n if ET[0] < F0 and R22+ET[1] < EPS:\n KET[1] = 1\n# =====\n for K in range(0, 2):\n for J in range(0, 2):\n self.DCCON2(XI[J], ET[K], Q, SD, CD, KXI[K], KET[J])\n self.UA(XI[J], ET[K], Q, DD1, DD2, DD3)\n DUA = self.DUA\n for I in range(0, 10, 3):\n DU[I] = -DUA[I]\n DU[I+1] = -DUA[I+1]*CD+DUA[I+2]*SD\n DU[I+2] = -DUA[I+1]*SD-DUA[I+2]*CD\n if I == 9:\n DU[I] = -DU[I]\n DU[I+1] = -DU[I+1]\n DU[I+2] = -DU[I+2]\n else:\n continue\n for I in range(0, 12):\n if (J+K) != 1:\n U[I] = U[I]+DU[I]\n if (J+K) == 1:\n U[I] = U[I]-DU[I]\n\n# =======================================\n# ===== IMAGE-SOURCE CONTRIBUTION =====\n# =======================================\n D = DEPTH-Z\n P = Y*CD+D*SD\n Q = Y*SD-D*CD\n ET[0] = P-AW1\n ET[1] = P-AW2\n if abs(Q) < EPS:\n Q = F0\n if abs(ET[0]) < EPS:\n ET[0] = F0\n if abs(ET[1]) < EPS:\n ET[1] = F0\n# --------------------------------\n# ----- REJECT SINGULAR CASE -----\n# --------------------------------\n# ----- ON FAULT EDGE\n if (Q == F0 and ((XI[0]*XI[1] < F0 and ET[0]*ET[1] == F0)\n or (ET[0]*ET[1] < F0 and XI[0]*XI[1] == F0))):\n self.UX = F0\n self.UY = F0\n self.UZ = F0\n self.UXX = F0\n self.UYX = F0\n self.UZX = F0\n self.UXY = F0\n self.UYY = F0\n self.UZY = F0\n self.UXZ = F0\n self.UYZ = F0\n self.UZZ = F0\n self.IRET = 1\n return\n# ----- ON NEGATIVE EXTENSION OF FAULT EDGE\n KXI[0] = 0\n KXI[1] = 0\n KET[0] = 0\n KET[1] = 0\n R12 = sqrt(XI[0]*XI[0]+ET[1]*ET[1]+Q*Q)\n R21 = sqrt(XI[1]*XI[1]+ET[0]*ET[0]+Q*Q)\n R22 = sqrt(XI[1]*XI[1]+ET[1]*ET[1]+Q*Q)\n if XI[0] < F0 and R21+XI[1] < EPS:\n KXI[0] = 1\n if XI[0] < F0 and R22+XI[1] < EPS:\n KXI[1] = 1\n if ET[0] < F0 and R12+ET[1] < EPS:\n KET[0] = 1\n if ET[0] < F0 and R22+ET[1] < EPS:\n KET[1] = 1\n# =====\n for K in range(0, 2):\n for J in range(0, 2):\n self.DCCON2(XI[J], ET[K], Q, SD, CD, KXI[K], KET[J])\n self.UA(XI[J], ET[K], Q, DD1, DD2, DD3)\n DUA = self.DUA\n self.UB(XI[J], ET[K], Q, DD1, DD2, DD3)\n DUB = self.DUB\n self.UC(XI[J], ET[K], Q, ZZ, DD1, DD2, DD3)\n DUC = self.DUC\n# -----\n for I in range(0, 10, 3):\n DU[I] = DUA[I]+DUB[I]+Z*DUC[I]\n DU[I+1] = ((DUA[I+1]+DUB[I+1]+Z*DUC[I+1])*CD\n - (DUA[I+2]+DUB[I+2]+Z*DUC[I+2])*SD)\n DU[I+2] = ((DUA[I+1]+DUB[I+1]-Z*DUC[I+1])*SD\n + (DUA[I+2]+DUB[I+2]-Z*DUC[I+2])*CD)\n if I == 9:\n DU[9] = DU[9]+DUC[0]\n DU[10] = DU[10]+DUC[1]*CD-DUC[2]*SD\n DU[11] = DU[11]-DUC[1]*SD-DUC[2]*CD\n for I in range(0, 12):\n if (J+K) != 1:\n U[I] = U[I]+DU[I]\n if (J+K) == 1:\n U[I] = U[I]-DU[I]\n# =====\n self.UX = U[0]\n self.UY = U[1]\n self.UZ = U[2]\n self.UXX = U[3]\n self.UYX = U[4]\n self.UZX = U[5]\n self.UXY = U[6]\n self.UYY = U[7]\n self.UZY = U[8]\n self.UXZ = U[9]\n self.UYZ = U[10]\n self.UZZ = U[11]\n self.IRET = 0","title":""},{"docid":"c429d441a31a3f6d37c031774b8ebf66","score":"0.52121","text":"def get_data(self, identifiers):\n out = dict()\n with h5py.File(self.path, 'r') as hf:\n for i, (person_identifier, index) in enumerate(identifiers):\n out[identifiers[i]] = {\n 'eye': hf[person_identifier]['image'][index][..., ::-1],\n 'gaze': hf[person_identifier]['gaze'][index]\n }\n return out","title":""},{"docid":"a059e7b9a6355b7cb3cf64d1256e1395","score":"0.5203842","text":"def load_all_functional_data(chromosomes, epi_sets):\n functional_data = {}\n\n for chrom in chromosomes:\n functional_data[chrom] = None\n for i, k in enumerate(epi_sets):\n s = np.loadtxt(f'/nfs/turbo/umms-drjieliu/proj/4dn/data/microC/mESC/processed/{chrom}/{chrom}_200_{k}.txt')\n s = zscore(s)\n print(chrom, k, len(s))\n if i == 0:\n functional_data[chrom] = s\n else:\n functional_data[chrom] = np.vstack((functional_data[chrom], s))\n functional_data[chrom] = functional_data[chrom].T\n print(functional_data[chrom].shape) # 大致形状： 1,000,000 * 11\n return functional_data","title":""},{"docid":"1805924055314130f267c18cb8ebac60","score":"0.5203016","text":"def Get_Var_Arrays(var, fignum):\n #create list of filenames\n ncfile_list = [\"/newtera/tera/phil/nchaparr/sam_grex_ensemble/sam_case\"+ str(i+2) + \"/OUT_STAT/NCHAPP1_testing_doscamiopdata.nc\" for i in range(1)]\n\n #create lists for variable arrays from each case\n vars_list = []\n height_list = []\n press_list = []\n time_list = []\n \n for i in range(len(ncfile_list)): #loop over list of nc files\n thefile = ncfile_list[i]\n print thefile \n ncdata = Dataset(thefile,'r')\n Vars = ncdata.variables[var][...]\n #print Vars.shape\n press = ncdata.variables['PRES'][...]\n height = ncdata.variables['z'][...]\n top = np.where(abs(height - 2000) < 50)[0][0]\n Vars = Vars[:, 0:top]\n height = height[0:top]\n time = ncdata.variables['time'][...]\n ncdata.close()\n \n vars_list.append(Vars) \n height_list.append(height)\n time_list.append(time) \n press_list.append(press)\n \n #get ensemble averages\n ens_vars = nc.Ensemble1_Average(vars_list)\n ens_press = nc.Ensemble1_Average(press_list)\n ens_press = np.transpose(ens_press)\n \n #TODO: verify this is in time order!\n \n #print 'ENSEMBLE AVERAGED', ens_vars.shape\n time = (time_list[0]-169)*24\n height = height_list[0] #TODO: time, height don't need to be averaged \n \n #set up plot\n theAx = nc.Do_Plot(fignum, 'Scaled ' + var + ' vs Scaled Height', 'Height/h', var+'/w*2', 111)\n #print ens_vars.shape, height.shape\n have_ens_vars = [] \n print len(time)\n for i in range(len(time)):\n if np.mod(i+1, 6)==0:\n #print i, time[i], 1.0*(i+1)/10, \"plotting\"\n #points = For_Plots(\"Mar52014\")\n #rinovals = points.rinovals()\n #print len(rinovals[:,2])\n #AvProfVars = points.AvProfVars()\n #invrinos: [rino, invrino, wstar, S, tau, mltheta, deltatheta, pi3, pi4]\n #wstar= rinovals[1.0*((i+1))*(6.0/6.0)-1, 2]\n #h= AvProfVars[1.0*((i+1))*(6.0/6.0)-1, 1]\n #h_index = np.where(height==h)[0]\n print time[i]\n have_ens_vars.append(1.0*np.sum(ens_vars[i]))\n #print have_ens_vars[i]\n theAx.plot(1.0*ens_vars[i], height, label=str(int(time[i])+1) + 'hrs')\n #height, time = np.meshgrid(height, time)\n #maxlev = np.max(ens_vars)\n #minlev = np.min(ens_vars)\n #step = (maxlev- minlev)/20\n #levels = [i for i in np.arange(minlev, maxlev, step)]\n #CS = plt.contourf(time, height, ens_vars, levels, cmap=plt.cm.bone)\n #cbar = plt.colorbar(CS)\n #cbar.ax.set_ylabel('colorbar')\n #print 'plotting'\n #theAx.plot(time, have_ens_vars, label=var)\n plt.ylim(0, 2000)\n plt.legend(loc = 'upper right', prop={'size':8})\n plt.show()","title":""},{"docid":"c06dc741256e73a12604cad5f6144de7","score":"0.5191207","text":"def spatial_example():\n\n imin, imax = 124, 126 # i.e. load DD0124 and DD0125\n ds_list = [None]*len(np.arange(imin,imax,1))\n j = 0\n for x in np.arange(imin,imax,1):\n name = 'DD%0004i/DD%0004i'%(x,x)\n if os.path.isfile(name):\n ds_list[j] = name\n j = j + 1\n\n ds_list = ds_list[:j]\n\n\n # use galaxy class to get consistent region parameters\n region_type = 'disk'\n gal = Galaxy( (ds_list[0]).split('/')[0] )\n region_kwargs = {}\n for k in ['center','normal','radius','height']:\n region_kwargs[k] = gal.disk_region[k]\n nbin = 100\n r = gal.disk_region['radius'].convert_to_units('pc').value\n z = gal.disk_region['height'].convert_to_units('pc').value * 0.5\n xbins = np.linspace(0.0, r, nbin)*yt.units.pc\n ybins = np.linspace(-z, z, nbin*(2.0*z)/r)*yt.units.pc\n\n del(gal) # don't need anymore\n\n pd = time_average_phase_diagram(160.0, 170.0, ds_list = ds_list,\n xfield = 'cylindrical_radius', yfield = 'cylindrical_z',\n x_bins = xbins, y_bins = ybins,\n zfield = \"G_o\", zunit = None, zlim = [0.01, 500.0], cmap = 'cubehelix',\n# zfield = 'Fe_over_H', zunit = None, zlim = [-7,1.5], cmap = 'algae',\n region_type = region_type, weight_field = None,\n region_kwargs = region_kwargs, xlog=False, ylog=False, zlog=True)\n\n pd = time_average_phase_diagram(160.0, 170.0, ds_list = ds_list,\n xfield = 'cylindrical_radius', yfield = 'cylindrical_z',\n x_bins = xbins, y_bins = ybins,\n zfield = 'Fe_over_H', zunit = None, zlim = [-7,1.5], cmap = 'algae',\n region_type = region_type, weight_field = 'cell_mass',\n region_kwargs = region_kwargs, xlog=False, ylog=False, zlog=True)\n\n# pd = time_average_phase_diagram(160.0, 170.0, ds_list = ds_list,\n# xfield = 'cylindrical_radius', yfield = 'cylindrical_z',\n# x_bins = xbins, y_bins = ybins,\n# zfield = 'Fe_over_H', zunit = None, zlim = [-7,1.5], cmap = 'algae',\n# region_type = region_type, weight_field = 'cell_mass',\n# region_kwargs = region_kwargs, xlog=False, ylog=False, zlog=True)\n\n\n\n return","title":""},{"docid":"f2bffa91ae27a2a723ba63d27349221f","score":"0.5189888","text":"def parse_gff_nc_genes():\n\t\n\tgff3 = sys.argv[1]\n\n\t#today = datetime.date.today()\n\n\t#ncRNA_gene_out = open('1_dmel_genes_ncRNA_%s_out.txt' %today, 'w')\n\n\tf= open(gff3, 'r')\n\tgff = [] # initiates gff list\n\tmel_ncRNA_coords = {}\n\twhile True:\n\t\tline = f.readline()\n\t\tif not line.startswith('#'):\n\t\t\tdata = line.strip().split(\"\\t\")\n\t\t\tif True == True:\n\t\n\t\t\t\ttry:\n\t\t\n\t\t\t\t\tif data[2] == 'gene':\n\t\t\t\t\t\t#print \"This is gene data\", data\n\t\t\t\t\t\tgff.append(data,)\n\t\t\t\t\t\t#print \"This is gff\", gff\n\t\t\t\t\t\t#ncRNA_gene_out.write('%s' %line)\n\t\t\t\n\t\t\t\t\telif data[2] == 'ncRNA':\n\t\t\t\t\t\t#ncRNA_gene_out.write('%s' %line)\n\t\t\t\t\t\t#print \"This is ncRNA data\", data\n\t\t\t\t\t\tgff.append(data,)\n\t\t\t\t\t\tpost_data = data[8].split(';')[0]\n\t\t\t\t\t\tmel_ncRNA_coords[post_data] = data[0], data[3], data[4], data[6]\n\t\t\t\t\t\t#print data\n\t\t\t\t\t\t#print mel_ncRNA_coords_dict\n\t\t\t\t\t\t#quit()\n\t\t\t\t\n\t\t\t\t\telse:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\n\t\t\t\texcept IndexError:\n\t\t\t\t\tif line.startswith('>'):\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif line.startswith('A'):\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif line.startswith('T'):\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif line.startswith('G'):\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif line.startswith('C'):\n\t\t\t\t\t\tbreak\n\t\t\t\t\n\t\t\tif not line:\n\t\t\t\tbreak\n\n\t#ncRNA_gene_out.close()\t\t\t\n\tf.close()\n\t#print \"This is gff:\", gff\n\treturn gff, mel_ncRNA_coords","title":""},{"docid":"5db3b04280faca78929728ebb428f300","score":"0.5184003","text":"def process_3D_data_csv(name, path=None):\n\n filename, name = _process_name(name, path, \".csv\")\n\n data = np.genfromtxt(filename, delimiter=',',skip_header=1)\n\n x1 = np.unique(data[:, 0])\n x2 = np.unique(data[:, 1])\n x3 = np.unique(data[:, 2])\n\n value = data[:, 3]\n\n x = (x1, x2, x3)\n\n value_data = value.reshape(len(x1), len(x2), len(x3), order=\"C\")\n\n formatted_data = (name, (x, value_data))\n\n return formatted_data","title":""},{"docid":"899ae48c28ba4eb9ab1fd0165eb6d482","score":"0.5174608","text":"def mogi(data, x0, y0, z0, dV):\n dx = data[0,:] - x0\n dy = data[1,:] - y0\n dz = 0 - z0\n c = dV * 3. / 4. * pi\n # or equivalently c= (3/4) a^3 dP / rigidity\n # where a = sphere radius, dP = delta Pressure\n r2 = dx*dx + dy*dy + dz*dz\n C = c / pow(r2, 1.5)\n return array((C*dx,C*dy,C*dz))","title":""},{"docid":"074005479ed7a27e190f85bddf4fe4b7","score":"0.5174326","text":"def get_output_3D(X,Y,Z):\n if isinstance( X, (float,int) ):\n X = np.array([X]); Y=np.array([Y]); Z=np.array([Z])\n elif isinstance( X, list ):\n X= np.array(X); Y=np.array(Y); Z=np.array(Z)\n\n classification_data, regression_data = get_raw_output_3D(X,Y,Z)\n if X.ndim > 1 or Y.ndim > 1 or Z.ndim > 1:\n classification_data = classification_data.flatten()\n regression_data = regression_data.flatten()\n X=X.flatten(); Y=Y.flatten(); Z=Z.flatten()\n data_frame = pd.DataFrame()\n data_frame[\"input_1\"] = X\n data_frame[\"input_2\"] = Y\n data_frame[\"input_3\"] = Z\n data_frame[\"class\"] = classification_data.flatten()\n data_frame[\"output_1\"] = regression_data.flatten()\n return data_frame","title":""},{"docid":"a7a0969a6a6e5f1c4ed2bcc237853cbb","score":"0.5160178","text":"def gen_3s(img, a, cen, sz=16):\n tf_xy = get_tf_xynormal(cen)\n tf_xy = tf_rotateXYZ(tf_xy, a)\n xy = gen_slice(img, tf_xy, sz2ext(sz))\n\n tf_yz = get_tf_yznormal(cen)\n tf_yz = tf_rotateXYZ(tf_yz, a)\n yz = gen_slice(img, tf_yz, sz2ext(sz))\n\n tf_xz = get_tf_xznormal(cen)\n tf_xz = tf_rotateXYZ(tf_xz, a)\n xz = gen_slice(img, tf_xz, sz2ext(sz))\n\n return xy, yz, xz","title":""},{"docid":"1e570eaa499ffc391804bd48b583b0dc","score":"0.5153892","text":"def extract_nucleus_features(data_path,area=500,m_m=2.8,solidity=0.4):\n seg = nuclei_segmenter(cropped_flag=1,area_threshold=area,major_minor=m_m,solidity_threshold=solidity)\n \n sipakmed_df_ext = pd.DataFrame()\n folders = listdir(data_path)[1:]\n for folder in folders:\n for blob in glob.glob(data_path+'{}/CROPPED/*.bmp'.format(folder)):\n seg.read_img(blob) # read the image\n df = seg.segment_nuclei() # segment the nuclei\n if not df.empty:\n df['cluster_id'] = int(blob.split('/')[-1][0:3]) # cluster id is the first three numbers of the file name\n df['Class'] = folder[3].lower() # class id is the first letter of the containing folder\n if df['Class'] in ['s','p']:\n df['Normal']=1\n else:\n df['Normal']=0\n sipakmed_df_ext = sipakmed_df_ext.append(df)\n sipakmed_df_ext.to_csv('../data/extracted/Sipakmed_nuclei_ext.csv')\n return sipakmed_df_ext","title":""},{"docid":"879b587e9d1636d2b035f7cfb69a050a","score":"0.515096","text":"def _get_calculation_data(self, grid_3d):\n dose_lut = [grid_3d[0], grid_3d[1]]\n dose_grid_points = get_dose_grid(dose_lut)\n x_delta = abs(grid_3d[0][0] - grid_3d[0][1])\n y_delta = abs(grid_3d[1][0] - grid_3d[1][1])\n # get structure slice Position\n # ordered_z = self.ordered_planes\n z_delta = self.structure['thickness']\n grid_delta = [x_delta, y_delta, z_delta]\n return self.planes, dose_lut, dose_grid_points, grid_delta","title":""},{"docid":"429752181166c2abc4bc32c5f3cdaef9","score":"0.51473665","text":"def zindex_to_grid3D(z):\n return numpy.array([unpart1by2(z ),\n unpart1by2(z >> 1),\n unpart1by2(z >> 2)])","title":""},{"docid":"447b84c00cd36cf5e6cc862531322cdd","score":"0.51440984","text":"def make_variogram_3D(\n nug,\n nst,\n it1,\n cc1,\n azi1,\n dip1,\n hmax1,\n hmed1,\n hmin1,\n it2=1,\n cc2=0,\n azi2=0,\n dip2=0,\n hmax2=0,\n hmed2=0,\n hmin2=0,\n):\n if cc2 == 0:\n nst = 1\n var = dict(\n [\n (\"nug\", nug),\n (\"nst\", nst),\n (\"it1\", it1),\n (\"cc1\", cc1),\n (\"azi1\", azi1),\n (\"dip1\", dip1),\n (\"hmax1\", hmax1),\n (\"hmed1\", hmed1),\n (\"hmin1\", hmin1),\n (\"it2\", it2),\n (\"cc2\", cc2),\n (\"azi2\", azi2),\n (\"dip2\", dip2),\n (\"hmax2\", hmax2),\n (\"hmed2\", hmed2),\n (\"hmin2\", hmin2),\n ]\n )\n if nug + cc1 + cc2 != 1:\n print(\n \"\\x1b[0;30;41m make_variogram Warning: \"\n \"sill does not sum to 1.0, do not use in simulation \\x1b[0m\"\n )\n if (\n cc1 < 0\n or cc2 < 0\n or nug < 0\n or hmax1 < 0\n or hmax2 < 0\n or hmin1 < 0\n or hmin2 < 0\n ):\n print(\n \"\\x1b[0;30;41m make_variogram Warning: \"\n \"contributions and ranges must be all positive \\x1b[0m\"\n )\n if hmax1 < hmed1 or hmax2 < hmed2:\n print(\n \"\\x1b[0;30;41m make_variogram Warning: \"\n \"major range should be greater than minor range \\x1b[0m\"\n )\n return var","title":""},{"docid":"597e376f7b065853f47b5c73d7b263b5","score":"0.51430905","text":"def template_ds(self):\n\n out_ds=self.g.write_to_xarray(mesh_name=\"Mesh2\",\n node_coordinates=\"Mesh2_node_x Mesh2_node_y\",\n face_node_connectivity='Mesh2_face_nodes',\n edge_node_connectivity='Mesh2_edge_nodes',\n face_dimension='nMesh2_face',\n edge_dimension='nMesh2_edge',\n node_dimension='nMesh2_node')\n out_ds=out_ds.rename({\n 'maxnode_per_face':'nMaxMesh2_face_nodes',\n 'node_per_edge':'Two'\n })\n\n # Additional grid information:\n # xarray wants the dimension made explicit here -- don't know why.\n out_ds['Mesh2_face_x']=('nMesh2_face',),self.mod_map_ds['Mesh2_face_x'].values\n out_ds['Mesh2_face_y']=('nMesh2_face',),self.mod_map_ds['Mesh2_face_y'].values\n\n out_ds['Mesh2_edge_x']=('nMesh2_edge',),self.mod_map_ds['Mesh2_edge_x'].values\n out_ds['Mesh2_edge_y']=('nMesh2_edge',),self.mod_map_ds['Mesh2_edge_y'].values\n\n e2c=self.g.edge_to_cells()\n out_ds['Mesh2_edge_faces']=('nMesh2_edge','Two'),e2c\n\n face_edges=np.array([self.g.cell_to_edges(c,pad=True)\n for c in range(self.g.Ncells())] )\n\n out_ds['Mesh2_face_edges']=('nMesh2_face','nMaxMesh2_face_nodes'),face_edges\n out_ds['Mesh2_face_depth']=('nMesh2_face',),-self.mod_map_ds['mesh2d_flowelem_bl'].values\n out_ds['Mesh2_face_depth'].attrs.update({'positive':'down',\n 'unit':'m',\n 'standard_name':'sea_floor_depth_below_geoid',\n 'mesh':'Mesh2',\n 'long_name':'Mean elevation of bed in face'})\n\n # recreate edge bed level based on a constant bedlevtype\n bedlevtype=int(self.model.mdu['geometry','BedLevType'])\n if bedlevtype==3:\n edge_z=self.map_ds.mesh2d_node_z.values[self.g.edges['nodes']].mean(axis=1)\n elif bedlevtype==4:\n edge_z=self.map_ds.mesh2d_node_z.values[self.g.edges['nodes']].min(axis=1)\n elif bedlevtype==6:\n c1 = e2c[:,0]\n c2 = e2c[:,1].copy()\n c2[c2<0] = c1[c2<0]\n edge_z=self.map_ds.mesh2d_flowelem_bl.values[np.c_[c1,c2]].max(axis=1)\n else:\n raise Exception(\"Only know how to deal with bed level type 3,4 not %d\"%bedlevtype)\n # mindful of positive-down sign convention needed by PTM\n out_ds['Mesh2_edge_depth']=('nMesh2_edge',),-edge_z\n out_ds['Mesh2_edge_depth'].attrs.update({'positive':'down',\n 'unit':'m',\n 'standard_name':'sea_floor_depth_below_geoid',\n 'mesh':'Mesh2',\n 'long_name':'Mean elevation of bed on edge'})\n\n out_ds['Mesh2_data_time']=self.mod_map_ds.nMesh2_data_time\n\n if 1:\n # This may not be necessary -- this keeps the freesurface\n # from appearing below the bed in cells, but these should\n # appear as dry based on ktop/kbot.\n # also, writing anything time-varying beyond the time stamps themselves\n # should probably be handled in the time loop [TODO]\n s1=np.maximum( self.mod_map_ds.mesh2d_s1, -out_ds['Mesh2_face_depth'])\n out_ds['Mesh2_sea_surface_elevation']=('nMesh2_face','nMesh2_data_time'),s1.T.values\n\n if 1: # edge and cell marks\n edge_marks=self.g.edges['mark']\n assert not np.any(edge_marks<0),\"Need to implement internal closed edges\"\n # this gets us to 0: internal, 1:boundary, 2: boundary_closed\n # this looks like 1 for stage or flow BC, 2 for land, 0 for internal.\n out_ds['Mesh2_edge_bc']=('nMesh2_edge',),edge_marks\n\n # 'facemark':'Mesh2_face_bc'\n out_ds['Mesh2_face_bc']=('nMesh2_face',),self.g.cells['mark']\n\n if 1: # layers\n ucx=self.mod_map_ds['mesh2d_ucx']\n if ucx.ndim==2:\n self.nkmax=1\n self.map_2d=True\n else:\n self.nkmax=ucx.shape[-1] # is it safe to assume nkmax is last in DFM?\n self.map_2d=False\n out_ds['nMesh2_layer_3d']=('nMesh2_layer_3d',),np.arange(self.nkmax)\n\n # based on sample output, the last of these is not used,\n # so this would be interfaces, starting with the top of the lowest layer.\n # fabricate something in sigma coordinates for now.\n #sigma_layers=np.linspace(-1,0,self.nkmax+1)[1:]\n #sigma_layers=self.mod_map_ds['mesh2d_interface_sigma'][1:]\n if 'mesh2d_interface_sigma' in self.mod_map_ds.keys():\n sigma_layers=self.mod_map_ds['mesh2d_interface_sigma'][1:]\n else:\n # 2022-06-10 RH: I think this change came in during the SFEI work\n # going to 3D. Not entirely clear what's going on here.\n sigma_layers=[1.0]\n out_ds['Mesh2_layer_3d']=('nMesh2_layer_3d',),sigma_layers\n attrs=dict(standard_name=\"ocean_sigma_coordinate\",\n dz_min=0.001, # not real, but ptm tries to read this.\n long_name=\"sigma layer coordinate at flow element top\",\n units=\"\",\n positive=\"up\", # kind of baked into the sigma definition\n formula_terms=\"sigma: Mesh2_layer_3d eta: Mesh2_sea_surface_elevation bedlevel: Mesh2_face_depth\"\n )\n out_ds['Mesh2_layer_3d'].attrs.update(attrs)\n\n # this would be for adding more scalars to be extracted at particle positions\n # Just guessing with 1 -- maybe 0 is more appropriate?\n out_ds['nsp']=('nsp',),np.arange(1)\n\n # from http://cfconventions.org/Data/cf-conventions/cf-conventions-1.0/build/apd.html\n # z(n,k,j,i) = eta(n,j,i) + sigma(k)*(depth(j,i)+eta(n,j,i))\n # sigma coordinate definition has z=positive:up baked in, likewise depth is\n # positive down, and eta positive up, with sigma ranging from -1 (bed) to 0 (surface)\n\n # for writing the output, use xarray to initialize the file, but\n # the big data part is best handled directly by netCDF4 so we can\n # control how much data is in RAM at a time.\n return out_ds","title":""},{"docid":"0427d731546ddc2bfab7a67b26cbefd0","score":"0.5141802","text":"def make_sample_data_2D_3blobs_inv(data_type=\"iris\"):\n\n from iris.cube import Cube\n from iris.coords import DimCoord, AuxCoord\n\n t_0 = datetime.datetime(2000, 1, 1, 12, 0, 0)\n x = np.arange(0, 100e3, 1000)\n y = np.arange(0, 200e3, 1000)\n t = t_0 + np.arange(0, 100, 1) * datetime.timedelta(minutes=1)\n yy, xx = np.meshgrid(y, x)\n\n t_temp = np.arange(0, 60, 1)\n track1_t = t_0 + t_temp * datetime.timedelta(minutes=1)\n x_0_1 = 10e3\n y_0_1 = 10e3\n track1_x = x_0_1 + 30 * t_temp * 60\n track1_y = y_0_1 + 14 * t_temp * 60\n track1_magnitude = 10 * np.ones(track1_x.shape)\n\n t_temp = np.arange(0, 30, 1)\n track2_t = t_0 + (t_temp + 40) * datetime.timedelta(minutes=1)\n x_0_2 = 20e3\n y_0_2 = 10e3\n track2_x = x_0_2 + 24 * (t_temp * 60) ** 2 / 1000\n track2_y = y_0_2 + 12 * t_temp * 60\n track2_magnitude = 20 * np.ones(track2_x.shape)\n\n t_temp = np.arange(0, 20, 1)\n track3_t = t_0 + (t_temp + 50) * datetime.timedelta(minutes=1)\n x_0_3 = 70e3\n y_0_3 = 110e3\n track3_x = x_0_3 + 20 * (t_temp * 60) ** 2 / 1000\n track3_y = y_0_3 + 20 * t_temp * 60\n track3_magnitude = 15 * np.ones(track3_x.shape)\n\n data = np.zeros((t.shape[0], x.shape[0], y.shape[0]))\n for i_t, t_i in enumerate(t):\n if np.any(t_i in track1_t):\n x_i = track1_x[track1_t == t_i]\n y_i = track1_y[track1_t == t_i]\n mag_i = track1_magnitude[track1_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\n if np.any(t_i in track2_t):\n x_i = track2_x[track2_t == t_i]\n y_i = track2_y[track2_t == t_i]\n mag_i = track2_magnitude[track2_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\n if np.any(t_i in track3_t):\n x_i = track3_x[track3_t == t_i]\n y_i = track3_y[track3_t == t_i]\n mag_i = track3_magnitude[track3_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\n\n t_start = datetime.datetime(1970, 1, 1, 0, 0)\n t_points = (t - t_start).astype(\"timedelta64[ms]\").astype(int) / 1000\n\n t_coord = DimCoord(\n t_points,\n standard_name=\"time\",\n var_name=\"time\",\n units=\"seconds since 1970-01-01 00:00\",\n )\n x_coord = DimCoord(\n x, standard_name=\"projection_x_coordinate\", var_name=\"x\", units=\"m\"\n )\n y_coord = DimCoord(\n y, standard_name=\"projection_y_coordinate\", var_name=\"y\", units=\"m\"\n )\n lat_coord = AuxCoord(\n 24 + 1e-5 * xx, standard_name=\"latitude\", var_name=\"latitude\", units=\"degree\"\n )\n lon_coord = AuxCoord(\n 150 + 1e-5 * yy, standard_name=\"longitude\", var_name=\"longitude\", units=\"degree\"\n )\n\n sample_data = Cube(\n data,\n dim_coords_and_dims=[(t_coord, 0), (y_coord, 2), (x_coord, 1)],\n aux_coords_and_dims=[(lat_coord, (1, 2)), (lon_coord, (1, 2))],\n var_name=\"w\",\n units=\"m s-1\",\n )\n\n if data_type == \"xarray\":\n sample_data = DataArray.from_iris(sample_data)\n\n return sample_data","title":""},{"docid":"1ab42fafe1207d9701a8e9b5a91b5755","score":"0.5138752","text":"def read_xspress3_data(file_path):\n data_output = {}\n\n # file_path = os.path.join(working_directory, file_name)\n with h5py.File(file_path, \"r\") as f:\n data = f[\"entry/instrument\"]\n\n # data from channel summed\n exp_data = np.asarray(data[\"detector/data\"])\n xval = np.asarray(data[\"NDAttributes/NpointX\"])\n yval = np.asarray(data[\"NDAttributes/NpointY\"])\n\n # data size is (ysize, xsize, num of frame, num of channel, energy channel)\n exp_data = np.sum(exp_data, axis=2)\n num_channel = exp_data.shape[2]\n # data from each channel\n for i in range(num_channel):\n channel_name = \"channel_\" + str(i + 1)\n data_output.update({channel_name: exp_data[:, :, i, :]})\n\n # change x,y to 2D array\n xval = xval.reshape(exp_data.shape[0:2])\n yval = yval.reshape(exp_data.shape[0:2])\n\n data_output.update({\"x_pos\": xval})\n data_output.update({\"y_pos\": yval})\n\n return data_output","title":""},{"docid":"e1894ca88e890aaec3a7619a40cbf654","score":"0.5134556","text":"def calc_zw3(ifile, var_id, ofile):\n\n # Read data\n dset_in = xarray.open_dataset(ifile)\n gio.check_xarrayDataset(dset_in, var_id)\n \n # Calculate the index\n groupby_op = get_groupby_op(dset_in['time'].values)\n index = {}\n for region in ['zw31', 'zw32', 'zw33']: \n south_lat, north_lat, west_lon, east_lon = gio.regions[region]\n darray = dset_in[var_id].sel(latitude=slice(south_lat, north_lat), longitude=slice(west_lon, east_lon)).mean(dim=['latitude', 'longitude'])\n\n clim = darray.groupby(groupby_op).mean(dim='time')\n anom = darray.groupby(groupby_op) - clim\n stdev = darray.groupby(groupby_op).std(dim='time')\n norm = anom.groupby(groupby_op) / stdev\n\n index[region] = norm.values\n\n zw3_timeseries = (index['zw31'] + index['zw32'] + index['zw33']) / 3.0\n \n # Write output file\n d = {}\n d['time'] = darray['time']\n d['zw3'] = (['time'], zw3_timeseries)\n dset_out = xarray.Dataset(d)\n \n dset_out['zw3'].attrs = {'id': 'zw3',\n 'long_name': 'zonal_wave_3_index',\n 'standard_name': 'zonal_wave_3_index',\n 'units': '',\n 'notes': 'Ref: ZW3 index of Raphael (2004)'}\n \n gio.set_global_atts(dset_out, dset_in.attrs, {ifile: dset_in.attrs['history']})\n dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')","title":""},{"docid":"268ee4ea37798536bbb16c589225a097","score":"0.5131933","text":"def ixy2virtual4C(\n d,\n fout,\n logger,\n chrom=\"\",\n start=-1,\n end=-1,\n viewStart=-1,\n viewEnd=-1,\n cut=0,\n mcut=-1,\n ):\n if start != -1 and end != -1 and end < start:\n logger.error(\"End %s is smaller than %s start.\" % (end, start))\n return\n if viewStart == -1 or viewEnd == -1:\n logger.error(\"viewStart or viewEnd not assigned.\")\n return\n if viewStart != -1 and viewEnd != -1 and viewEnd < viewStart:\n logger.error(\"viewEnd %s is smaller than %s viewStart.\" % (viewEnd, viewStart))\n return\n f = os.path.join(d,chrom+\".ixy\")\n if not os.path.isfile(f):\n logger.error(\"%s not exists, please check the input -virtual4C_chrom\"%f)\n return\n metaf = d + \"/petMeta.json\"\n meta = json.loads(open(metaf).read())\n tot = meta[\"Unique PETs\"] \n chrom, xy = parseIxy(f, cut=cut, mcut=mcut)\n if start == -1:\n start = np.min(xy)\n if end == -1:\n end = np.max(xy)\n ps = np.where((xy[:, 0] >= start) & (xy[:, 1] <= end))[0]\n xy = xy[ps, ]\n xy2 = XY(xy[:, 0], xy[:, 1]) #XY object\n virtual4Csig = getVirtual4CSig(xy2, start, end, viewStart, viewEnd)\n with open( fout + \"_4C.bdg\",\"w\") as fo:\n i = 0\n while i < len(virtual4Csig) -1 :\n if virtual4Csig[i] == 0 :\n i += 1\n continue\n for j in range(i+1, len(virtual4Csig)):\n if virtual4Csig[j] != virtual4Csig[i]:\n break\n #v = np.log2(virtual4Csig[i] / 1.0 / tot * 10**6)\n #v = virtual4Csig[i] / 1.0 / tot * 10**6\n v = np.log2(virtual4Csig[i])\n line = [chrom[0],start+i, start+j-1,v]\n fo.write(\"\\t\".join(list(map(str, line))) + \"\\n\")\n if j == len(virtual4Csig) - 1:\n break\n i = j","title":""},{"docid":"bed80c3c08699f844073c817180d1e37","score":"0.5128286","text":"def datacoords(self):\n return {\n k: v.values\n for k, v in self.cube.coords.items()\n if v.dims == (\"x\", \"y\", \"ch\")\n }","title":""},{"docid":"b2f202c165c613f3a0637f90ab76d268","score":"0.5127099","text":"def construct_3d_hdu(self):\n # Report\n if not self.quiet:\n log_output(self.loggers, 1, logging.INFO, 'Constructing stellar continuum datacube ...')\n\n bin_indx = self.hdu['BINID'].data.copy()\n model_flux = self.hdu['FLUX'].data.copy()\n model_mask = self.hdu['MASK'].data.copy()\n\n flux, mask = DAPFitsUtil.reconstruct_cube(self.shape, bin_indx.ravel(),\n [model_flux, model_mask])\n\n mask = self._finalize_cube_mask(mask)\n\n # Primary header is identical regardless of the shape of the\n # extensions\n hdr = self.hdu['PRIMARY'].header.copy()\n cube_hdr = DAPFitsUtil.build_cube_header(self.binned_spectra.cube,\n 'K Westfall <westfall@ucolick.org>')\n\n # Return the converted hdu without altering the internal hdu\n return fits.HDUList([ fits.PrimaryHDU(header=hdr),\n fits.ImageHDU(data=flux, header=cube_hdr, name='FLUX'),\n fits.ImageHDU(data=mask, header=cube_hdr, name='MASK'),\n self.hdu['WAVE'].copy(),\n self.hdu['BINID'].copy(),\n self.hdu['PAR'].copy()\n ])","title":""},{"docid":"75264387148e1d71a2e981c9847cabca","score":"0.512496","text":"def returnData(self):\n data = {var:self.majorData[:,i] for i, var in enumerate(self.headerName)}\n return data","title":""},{"docid":"381a86494efb468cd8ee768187105bf7","score":"0.51094353","text":"def get_dist_genomic(genomic_data,var_or_gene):\r\n gene_list=[]\r\n for patient in genomic_data:\r\n for i in range(len(genomic_data[patient])):\r\n if var_or_gene==\"Var\":\r\n if \"effect\" in list(genomic_data[patient][i].keys()) and \"gene\" in list(genomic_data[patient][i].keys()):\r\n gene_list.append([genomic_data[patient][i][\"gene\"],genomic_data[patient][i][\"effect\"]])\r\n else:\r\n gene_list.append([genomic_data[patient][i][\"gene\"],\"NA\"])\r\n elif var_or_gene==\"Gen\":\r\n if \"status\" in list(genomic_data[patient][i].keys()) and \"gene\" in list(genomic_data[patient][i].keys()):\r\n gene_list.append([genomic_data[patient][i][\"gene\"],genomic_data[patient][i][\"status\"]])\r\n else:\r\n gene_list.append([genomic_data[patient][i][\"gene\"],\"NA\"]) \r\n else:\r\n print(\"var_or_gene must be Var or Gen\")\r\n gene_effects=collec.Counter(np.array(gene_list)[:,1])\r\n return gene_effects","title":""},{"docid":"9d73373469b2ca0526c4b8ad3692343f","score":"0.5105838","text":"def sampledata_to_vcf(sample_data, filename):\n\n num_individuals = len(sample_data.individuals_metadata[:])\n ind_list = list()\n pos_geno_dict = {\"POS\": list()}\n\n for i in range(int(num_individuals/2)):\n pos_geno_dict[\"msp_\"+str(i)] = list()\n ind_list.append(\"msp_\"+str(i))\n\n # add all the sample positions and genotypes\n for i in sample_data.genotypes():\n pos = int(round(sample_data.sites_position[i[0]]))\n if pos not in pos_geno_dict[\"POS\"]:\n pos_geno_dict[\"POS\"].append(pos)\n for j in range(0, len(i[1]), 2):\n pos_geno_dict[\"msp_\" + str(int(j/2))].append(\n str(i[1][j]) + \"|\" + str(i[1][j+1]))\n\n df = pd.DataFrame(pos_geno_dict)\n\n df[\"#CHROM\"] = 1\n df[\"REF\"] = \"A\"\n df[\"ALT\"] = \"T\"\n df['ID'] = \".\"\n df['QUAL'] = \".\"\n df['FILTER'] = \"PASS\"\n df['INFO'] = \".\"\n df['FORMAT'] = \"GT\"\n\n cols = ['#CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER',\n 'INFO', 'FORMAT']+ind_list\n df = df[cols]\n\n header = \"\"\"##fileformat=VCFv4.2\n##source=msprime 0.6.0\n##FILTER=<ID=PASS, Description=\"All filters passed\">\n##contig=<ID=1, length=\"\"\" + str(int(sample_data.sequence_length)) + \"\"\">\n##FORMAT=<ID=GT, Number=1, Type=String, Description=\"Genotype\">\n\"\"\"\n output_VCF = \"tmp/\"+filename+\".vcf\"\n with open(output_VCF, 'w') as vcf:\n vcf.write(header)\n\n df.to_csv(output_VCF, sep=\"\\t\", mode='a', index=False)\n return df","title":""},{"docid":"f01a9c3ff6d8f737acc8b38063c4115a","score":"0.5103838","text":"def vex3D(S: np.array) -> np.array:\n return np.array([S[2, 1], S[0, 2], S[1, 0]])","title":""},{"docid":"ed18b3de9150778dea48ecebcc95ae28","score":"0.5102784","text":"def make_sample_data_2D_3blobs(data_type=\"iris\"):\n\n from iris.cube import Cube\n from iris.coords import DimCoord, AuxCoord\n\n t_0 = datetime.datetime(2000, 1, 1, 12, 0, 0)\n\n x = np.arange(0, 100e3, 1000)\n y = np.arange(0, 200e3, 1000)\n t = t_0 + np.arange(0, 100, 1) * datetime.timedelta(minutes=1)\n xx, yy = np.meshgrid(x, y)\n\n t_temp = np.arange(0, 60, 1)\n track1_t = t_0 + t_temp * datetime.timedelta(minutes=1)\n x_0_1 = 10e3\n y_0_1 = 10e3\n track1_x = x_0_1 + 30 * t_temp * 60\n track1_y = y_0_1 + 14 * t_temp * 60\n track1_magnitude = 10 * np.ones(track1_x.shape)\n\n t_temp = np.arange(0, 30, 1)\n track2_t = t_0 + (t_temp + 40) * datetime.timedelta(minutes=1)\n x_0_2 = 20e3\n y_0_2 = 10e3\n track2_x = x_0_2 + 24 * (t_temp * 60) ** 2 / 1000\n track2_y = y_0_2 + 12 * t_temp * 60\n track2_magnitude = 20 * np.ones(track2_x.shape)\n\n t_temp = np.arange(0, 20, 1)\n track3_t = t_0 + (t_temp + 50) * datetime.timedelta(minutes=1)\n x_0_3 = 70e3\n y_0_3 = 110e3\n track3_x = x_0_3 + 20 * (t_temp * 60) ** 2 / 1000\n track3_y = y_0_3 + 20 * t_temp * 60\n track3_magnitude = 15 * np.ones(track3_x.shape)\n\n data = np.zeros((t.shape[0], y.shape[0], x.shape[0]))\n for i_t, t_i in enumerate(t):\n if np.any(t_i in track1_t):\n x_i = track1_x[track1_t == t_i]\n y_i = track1_y[track1_t == t_i]\n mag_i = track1_magnitude[track1_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\n if np.any(t_i in track2_t):\n x_i = track2_x[track2_t == t_i]\n y_i = track2_y[track2_t == t_i]\n mag_i = track2_magnitude[track2_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\n if np.any(t_i in track3_t):\n x_i = track3_x[track3_t == t_i]\n y_i = track3_y[track3_t == t_i]\n mag_i = track3_magnitude[track3_t == t_i]\n data[i_t] = data[i_t] + mag_i * np.exp(\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\n t_start = datetime.datetime(1970, 1, 1, 0, 0)\n t_points = (t - t_start).astype(\"timedelta64[ms]\").astype(int) / 1000\n t_coord = DimCoord(\n t_points,\n standard_name=\"time\",\n var_name=\"time\",\n units=\"seconds since 1970-01-01 00:00\",\n )\n x_coord = DimCoord(\n x, standard_name=\"projection_x_coordinate\", var_name=\"x\", units=\"m\"\n )\n y_coord = DimCoord(\n y, standard_name=\"projection_y_coordinate\", var_name=\"y\", units=\"m\"\n )\n lat_coord = AuxCoord(\n 24 + 1e-5 * xx, standard_name=\"latitude\", var_name=\"latitude\", units=\"degree\"\n )\n lon_coord = AuxCoord(\n 150 + 1e-5 * yy, standard_name=\"longitude\", var_name=\"longitude\", units=\"degree\"\n )\n sample_data = Cube(\n data,\n dim_coords_and_dims=[(t_coord, 0), (y_coord, 1), (x_coord, 2)],\n aux_coords_and_dims=[(lat_coord, (1, 2)), (lon_coord, (1, 2))],\n var_name=\"w\",\n units=\"m s-1\",\n )\n\n if data_type == \"xarray\":\n sample_data = DataArray.from_iris(sample_data)\n\n return sample_data","title":""},{"docid":"118f0b7a9cb27f04dea5a50939e6f14d","score":"0.51012856","text":"def hyperconvert3d(data2d, rows, cols, channels):\n channels, pixnum = data2d.shape\n data3d = data2d.transpose().reshape(rows, cols, channels, order='F')\n return data3d","title":""},{"docid":"1114dfc0358b0c77cc3b55870665fad9","score":"0.5099725","text":"def detector_coverage_process_3D( args):\n #Error checking\n (inst, det, set_value) = args\n if det is None: return None\n if inst is None: return None\n message = \"Calculating coverage of detector '%s'...\" % det.name\n #Statusbar update\n #TODO: This notification does nothing in multiprocessing because this function runs in a separate process!\n messages.send_message(messages.MSG_UPDATE_MAIN_STATUSBAR, message)\n print message\n #This does the calculation\n coverage = inst.detector_coverage_3D_by_lines(det, set_value)\n #We have the coverage map, return it\n return coverage","title":""},{"docid":"5e00df047710fbe21f84cd0dda6c979a","score":"0.50968117","text":"def get_3d(self) -> \"VolumeGeometry\":\n if len(self._vol_shape_xyz) == 2:\n return dc_replace(self, _vol_shape_xyz=np.concatenate((self._vol_shape_xyz, [1])))\n else:\n return dc_replace(self)","title":""},{"docid":"ddaa129ccfec0cf285ec1adb215c36f4","score":"0.50952303","text":"def get_3d(self) -> \"ProjectionGeometry\":\n if self.ndim == 2:\n if self.det_shape_vu is not None:\n new_det_shape_vu = np.ones(2, dtype=int)\n new_det_shape_vu[-len(self.det_shape_vu) :] = self.det_shape_vu\n else:\n new_det_shape_vu = None\n return dc_replace(self, geom_type=self.geom_type.replace(\"2d\", \"3d\"), det_shape_vu=new_det_shape_vu)\n else:\n return dc_replace(self)","title":""},{"docid":"7efb5040bb41fce5d66bb766a48721d9","score":"0.50896275","text":"def region_2d_to_vector_3d(*argv):","title":""},{"docid":"2b3f42e8150cbf204c88a9a82fb4c863","score":"0.50859624","text":"def get_scene(self, index):\n in_xy, gt_xy, in_dxdy, gt_dxdy, pred_check, pad_mask = self.__getitem__(index)\n\n return {\"in_xy\": in_xy.permute(1, 0, 2),\n \"gt_xy\": gt_xy.permute(1, 0, 2),\n \"in_dxdy\": in_dxdy.permute(1, 0, 2),\n \"gt_dxdy\": gt_dxdy.permute(1, 0, 2),\n \"seq_start_end\": (0, in_xy.size(0)),\n \"pred_check\": pred_check,\n \"pad_mask\": pad_mask,\n }","title":""},{"docid":"4302c98b1fa75cfbcc90aa9fddb1b1ec","score":"0.5082406","text":"def geometryData(*args, **kwargs):\n \n pass","title":""},{"docid":"b906c11478cfbd03126c93b398fe08de","score":"0.5074803","text":"def array_data(self):\r\n self.INC_array=[]\r\n self.local_IEN_array=[]\r\n self.KV_xi_array=[]\r\n self.KV_eta_array=[]\r\n \r\n if self.dimension==1:\r\n \r\n self.INC_array.append(self.INC(0))\r\n self.local_IEN_array.append(self.local_IEN(0))\r\n self.KV_xi_array.append(self.knot_vector(self.number_elements,self.order,self.mp))\r\n \r\n elif self.dimension==2:\r\n \r\n for pnum in np.arange(len(self.num_bases)):\r\n \r\n self.INC_array.append(self.INC(pnum))\r\n self.local_IEN_array.append(self.local_IEN(pnum))\r\n self.KV_xi_array.append(self.knot_vector(self.number_elements[pnum,0],self.order[pnum,0],self.mp[pnum,0]))\r\n self.KV_eta_array.append(self.knot_vector(self.number_elements[pnum,1],self.order[pnum,1],self.mp[pnum,1]))","title":""},{"docid":"d27c9d4316a7c5527523180b67229873","score":"0.5065774","text":"def cartesianSlice(data=None,**keywords):\n \n # If the user did not pass in an array try to extract one from the\n # gui\n if data is None:\n data = guiDefinedData()\n \n attributes = keywords\n try:\n \n # Unless the user has explicitly specified different units we\n # treat the domain as a length box\n if \"units\" not in attributes:\n attributes[\"units\"] = VISUS_METERS\n\n # Try to infer as many data attributes as you can and return a\n # string describing/defining the data in the ViSUS internal format\n data_description = constructDataDescriptor(data,attributes)\n except:\n print \"Could not parse data. Unknown format\"\n raise\n\n # Create a font to be used in all the nodes that render text\n font = VisusFont()\n font.fontSize(6)\n \n # Construct a root node for the scene graph hierarchy. This node is\n # mainly used for the global transformation (e.g. rotating the\n # complete scene) and is a convinient way to collect independent\n # nodes (nodes that should have no ancestor-descendend\n # relationship. Note, the use of the construct function rather than\n # a standard \"constructor.\" ViSUS uses a smart pointer / reference\n # counting scheme which requires this.\n root = VisusGroup.construct()\n\n # To have a convinient reference point we want to display the\n # bounding box of the root node which will automatically adjust to\n # include the bounding box of all child nodes. \n root.drawBoundingBox(True)\n\n # Create two points that will form the left lower and right upper\n # corner of the data bounding box\n left = VectorDouble(3)\n right= VectorDouble(3)\n\n # Create a ViSUS data source based on the data description created earlier\n source = VisusDataSourceFactory.make(data_description)\n if not source.isValid():\n raise RuntimeError(\"Problem loading data set\")\n\n # Get the bounding box of the data whose value may or may not depend\n # on data attributes such as units. In particular, if this\n # information was available the bounding box will be given in\n # pyhsical units rather than in index space\n source.domainBoundingBox(left,right)\n bbox = VisusBoundingBox()\n bbox.set(left,right);\n \n # The ViSUS system is based on data \"requests\" that describe a\n # particular piece of data that should be displayed. The data\n # requests can be manipulated through the user interface or\n # directly. However, here we want to create a sensible default\n # request to see a portion of our data.\n \n # First, we create an empty request \n request = VisusDataRequest()\n\n # Second, we construct an array that describes the length in each\n # dimension of the box of data we want to extract in whatever units\n # the data source uses (typically physical space).\n extent = VectorDouble(3) \n extent[0] = (right[0] - left[0]) # We want to see all of the x-coordinate\n extent[1] = (right[1] - left[1]) # and all of the y-coordinate \n extent[2] = 0 # But we want to look at a plane so the z extend is 0\n\n # And we pass the extent information into the request\n request.extent(extent)\n\n # Second, we need to position our extent box in the data\n # set. Initially, the extent will be centered around the origin. To\n # see the middle of the data we want translate our request to the\n # center of the data's bounding box\n\n # So we create the corresponding transformation matrix\n matrix = translationMatrix((left[0]+right[0])/2,\n (left[1]+right[1])/2,\n (left[2]+right[2])/2,\n )\n # And pass this matrix to the request\n request.transformation(matrix)\n \n # Third, we want to indicate to the ViSUS system that we are\n # really interested in a 2D slice of the data (rather than a\n # degenerate volume) as slices are a special case and implement some\n # special user interactions\n request.requestType(VISUS_2D_REQUEST);\n\n # Finally, we describe the resolution at which we want to see our\n # data. The resolution is defined in terms ofo sub-sampling\n # \"strides\" where a stride of 1 defines the full resolution data a\n # stride of 2, the data sub-sampled by a factor of 2 etc.. Strides\n # must be powers of two but can be independently set for each\n # dimension. Furthermore, for large data it can be useful to\n # progressively refine the data starting at some higher strides /\n # coarser resolution, ending at some lower strides / finer\n # resolution. This preserves interactivity while still providing\n # high resolution data if the request remains unchanged long enough\n\n # Since, for default scenes we expect the data to be small we only\n # want to see the highest resolution\n start = [ 1, 1, 1 ]\n end = [ 1, 1, 1 ]\n request.setStrides(start,end)\n \n\n # Armed with the description of the data and the request we just\n # constructed we create a scene graph node that will extract the\n # data for us. This node, will continuously test its current request\n # and update the data accordingly. Therefore, one could adapt the\n # scene by simply passing different requests to this node (which is\n # what the graphical user interface does on mouse movements or\n # keyboard commands)\n \n # Create a producer node specialized to extract and axis aligned\n # bounding box from a data set. As before with the root node the\n # node must be explicitly constructed.\n extractor = VisusAxisAlignedExtractor.construct()\n\n # Set the data to extract our data from\n extractor.setValue(data_description)\n extractor.setValue(request) # Our default request\n extractor.setValue(bbox); # And the bounding box \n extractor.drawBoundingBox(False);\n\n\n # Now that we have a node that will extract the data we want to see\n # we construct another node to visualize the slice\n\n # We want to view an axis aligned slice of the data an the\n # appropriate visualization node is one for orthogonal slices\n slice = VisusOrthogonalSlice.construct()\n\n # Now we connect the output of the extractor to the input of the\n # visualization node\n if not slice.connectInput(extractor):\n raise RuntimeError(\"unable to connect extractor as slice input\")\n\n # For reasons internal to the underpinnings of the ViSUS system the\n # visualization node must know about the current data request. The\n # simplest way of achieving this is to attach the slice node as\n # child to the extractor \n extractor.attachSubTree(slice,True)\n\n # and to inherit/share the request \n slice.inherit(VisusSharedDataRequest.typeIndex(),True)\n\n # In this configuration the slice node will naturally draw the data\n # at the position of the data request which is the natural\n # place. However, by manipulating the transformation matrix of the\n # slice node we could draw the data anywhere in space / on the screen\n \n # Instead of the default greyscale color map we want to use a\n # typical red-to-blue map\n color_map = banded()\n slice.setValue(color_map)\n\n # In order to see the slice even if it contains no data (if the\n # request has been moved outside the data set, or the data is\n # extremly large) we draw the bounding box of the slice\n slice.drawBoundingBox(True);\n\n # Now, we want to create labeled tickmarks to give the user a notion\n # of the data range. \n\n # Create a tickmark node that by default will create tickmarks along\n # the x-axis of its bounding box\n xTickMarks = VisusTickMarks.construct()\n\n # ViSUS uses \"axis\" to control the labels of images, graphs, and\n # also tickmarks. Here we get the current axis \n axis = xTickMarks.axis()\n axis.legendText(\"longitude\") # Set the label \n axis.labelFont(font) # and font\n xTickMarks.axis(axis) # and update the axis\n\n # Finally, we attach the tickmarks node as a child to the\n # extractor. By default a tickmarks node inherits the bounding box\n # of its parent and thus this node will now annotate the x-axis of\n # our data set\n extractor.attachSubTree(xTickMarks,True)\n \n # Create tickmarks for the y-axis in an identical fashion\n yTickMarks = VisusTickMarks.construct()\n \n # Except that now we must name the axis explicitly \n yTickMarks.direction(TM_Y_AXIS)\n axis = yTickMarks.axis() \n axis.legendText(\"latitude\")\n axis.labelFont(font)\n yTickMarks.axis(axis)\n extractor.attachSubTree(yTickMarks,True)\n \n # Finally, we attach the whole subtree that renders the data plus\n # annotation to the root node\n root.attachSubTree(extractor,True)\n\n\n # To further enhance the scene we want to add the continental\n # outlines on top of the slice\n\n # First, we load the cdat internal world map \n world_map = loadWorldMap()\n\n # And parse it as a collection of polylines. A VisusIndexedData\n # object stores an array of n-dimensional points (n being 2 in this\n # case) and a set of elements defined as ordered lists of indices\n # into this array. Therefore, each contour of the world map is\n # parsed as a single element\n outlines = VisusIndexedData(2)\n for m in world_map:\n outlines.constructElement(m)\n\n # Once the data is constructed we create a display node designed to visualize it\n map = VisusIndexedDisplay.construct()\n\n # And load the data. \n map.loadData(outlines,0)\n\n # The cdat world map is given within (-180,-90)x(180,90). However,\n # in order to display the normal lat,long, elev data easily in\n # cartesian space we told ViSUS the data units are meters (meaning\n # general distance) which will draw (0,0)x(360,180). To adjust the\n # map to the slice we therefore must shift it\n #map.getValue(matrix);\n #matrix = matrix * translationMatrix(180,90,0.01)\n #map.setValue(matrix)\n\n # Attach the map as child of the extractor. Note, that from this moment\n # on the map will inherit all transformations from the extractor and\n # thus \"stick\" to the extractor if it is moved or rotated\n extractor.attachSubTree(map)\n \n # Lastly, we attach more annotations to the scene\n\n # A VisusLabelNode is a general one line text of arbitrary font and\n # size. First, we create a title string using the variable name if\n # we found one\n label_node = VisusLabelNode.construct()\n title = \"Variable: \" + attributes[\"name\"].replace(\"_\",\" \")\n label_node.text(title)\n label_node.setValue(font)\n\n # Positions of labels are given relative to the screen size\n label_node.position(-1,+0.8)\n root.attachSubTree(label_node,True)\n\n # If our data set is time-dependent we attach a label that displays\n # the current time step\n if \"time\" in attributes:\n time_node = VisusLabelNode.construct()\n t = attributes[\"time\"]\n title = \"Date: %02d/%02d/%04d\" % (t.month,t.day,t.year)\n time_node.text(title)\n time_node.setValue(font)\n time_node.position(0.6,-0.95)\n root.attachSubTree(time_node,True)\n\n # A VisusColorBar displays a bar with a color map on the inside and\n # allows us to attach tickmarks and labels\n color_bar = VisusColorBar.construct()\n\n # As before fopr the tickmark node we modify the labels via the Axis construct\n axis = color_bar.axis()\n\n # If we found a variable name we use it as label\n if \"var_name\" in attributes:\n axis.legendText(attributes[\"var_name\"])\n \n # If we found a range we use it as well. \n if \"range\" in attributes:\n axis.minValue(float(attributes[\"range\"][0]))\n axis.maxValue(float(attributes[\"range\"][1]))\n \n font.fontSize(4)\n axis.legendFont(font)\n axis.labelFont(font)\n color_bar.position(-1.2,-0.8)\n color_bar.axis(axis)\n\n # Finally, attach the color bar to the slice and make sure it\n # inherits its colormap from there\n slice.attachSubTree(color_bar,True)\n #color_bar.inherit(VisusSharedColorMap.typeIndex(),True)\n color_bar.inherit(8,True)\n\n\n # Lastly we must make sure that we actually see the data on the\n # screen which means we need to make sure that the data is scaled\n # and translated appropriately. (Alternatively, once can think of\n # this step as setting up the viewpoint of our virtual camera.\n\n # Using the current bounding box of the scene we adapt a default box\n # to the aspect ratios of of our scene and use it as our \"world\n # coordinate system\" \n world_box = constructWorldBox(bbox)\n root.setValue(bbox)\n\n # Now we make sure that the data is drawn in our standard coordinate\n # system\n root.mapToWorldBox(world_box)\n\n # Finally, most climate data contains significantly more samples in\n # x/y than in z. Since, for the cartesian slice we effecticely draw\n # the data in index space since is inconvinient as the volume wille\n # very \"flat.\" To compensate for this we simply scale the z\n # coordinate of our system\n root.getValue(matrix)\n matrix = matrix * scaleMatrix(1,1,10)\n root.setValue(matrix)\n\n # As the final step we create ourselfs a window to display the scene\n # graph hanging under the root node\n createWindow(root)\n \n return root,data_description","title":""},{"docid":"d90c29d17cda2196b97edb1489b615a6","score":"0.5062946","text":"def _load_3Dchairs(self, opts):\n self.data_dir = _data_dir(opts)\n filename = 'rendered_chairs.npz'\n filepath = os.path.join(self.data_dir,filename)\n assert os.path.isfile(filepath), '3Dchairs data file doesnt exist.'\n X = np.load(filepath)\n self.all_data = (255.*np.load(filepath)['data']).astype(np.uint8)\n num_data = opts['dataset_size']\n # datashape\n self.data_shape = datashapes[self.dataset]\n # plot set\n seed = 123\n np.random.seed(seed)\n idx = np.random.randint(self.all_data.shape[0],size=opts['plot_num_pics'])\n self.data_plot = self._sample_observations(idx)\n # shuffling data\n np.random.seed()\n idx_random = np.random.permutation(num_data)\n if opts['train_dataset_size']==-1 or opts['train_dataset_size']>num_data-10000:\n tr_stop = num_data - 10000\n else:\n tr_stop = opts['train_dataset_size']\n data_train = self.all_data[idx_random[:tr_stop]]\n data_test = self.all_data[idx_random[-10000:]]\n # dataset size\n self.train_size = data_train.shape[0]\n self.test_size = data_test.shape[0]\n # data for vizualisation\n seed = 123\n np.random.seed(seed)\n idx = np.random.randint(self.test_size, size=opts['plot_num_pics'])\n self.data_vizu = self._sample_observations(idx)\n # Create tf.dataset\n dataset_train = tf.data.Dataset.from_tensor_slices(data_train)\n dataset_test = tf.data.Dataset.from_tensor_slices(data_test)\n # map img to [0,1] and convert to float32\n def process_img(x):\n return tf.cast(x, dtype=tf.dtypes.float32) / 255.\n dataset_train = dataset_train.map(process_img,\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\n dataset_test = dataset_test.map(process_img,\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\n # normalize data if needed\n if opts['input_normalize_sym']:\n dataset_train = dataset_train.map(lambda x: (x - 0.5) * 2.,\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\n dataset_test = dataset_test.map(lambda x: (x - 0.5) * 2.,\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\n self.data_vizu = (self.data_vizu - 0.5) * 2.\n self.data_plot = (self.data_plot - 0.5) * 2.\n # Shuffle dataset\n dataset_train = dataset_train.shuffle(buffer_size=50*opts['batch_size'])\n dataset_test = dataset_test.shuffle(buffer_size=50*opts['batch_size'])\n # repeat for multiple epochs\n dataset_train = dataset_train.repeat()\n dataset_test = dataset_test.repeat()\n # Random batching\n dataset_train = dataset_train.batch(batch_size=opts['batch_size'])\n dataset_test = dataset_test.batch(batch_size=opts['batch_size'])\n # Prefetch\n self.dataset_train = dataset_train.prefetch(buffer_size=4*opts['batch_size'])\n self.dataset_test = dataset_test.prefetch(buffer_size=4*opts['batch_size'])\n # Iterator for each split\n self.iterator_train = dataset_train.make_initializable_iterator()\n self.iterator_test = dataset_test.make_initializable_iterator()\n\n # Global iterator\n self.handle = tf.placeholder(tf.string, shape=[])\n self.next_element = tf.data.Iterator.from_string_handle(\n self.handle, dataset_train.output_types, dataset_train.output_shapes).get_next()","title":""},{"docid":"b595c8271cbcbee186373beeeb837fbf","score":"0.5060969","text":"def fixture_family3_data(family_tag_names: List[str], third_family_vcf: Path) -> dict:\n return {\"tags\": family_tag_names, \"file\": third_family_vcf}","title":""},{"docid":"15f4499585a1ce2e605dc27eb5ad7677","score":"0.50575477","text":"def prepare_dataset_rapresentation(file) -> (dict, list):\n sequences_map = __load_df(file)\n\n gates = sorted(list(set(itertools.chain.from_iterable(sequences_map.values()))))\n gates_index = dict((gate, gates.index(gate)) for gate in gates)\n\n print(\"Start dictionary representation\")\n\n result = dict()\n\n for key, sequence in sequences_map.items():\n representation = np.zeros((len(sequence), len(gates)), dtype=np.int8)\n for i, gate in enumerate(sequence):\n representation[i, gates_index[gate]] = 1\n result[key] = representation\n\n return result, gates","title":""},{"docid":"b1e8991b4a941cbbe04f5533a0adc27d","score":"0.50547","text":"def _d3K_dr3(self, xa, xb):\n raise NotImplementedError","title":""},{"docid":"48e06f884283e023f523d7f9b2e933c3","score":"0.5048695","text":"def get_data(self):\n idxs = np.arange(self.size())\n self.rng.shuffle(idxs)\n for idx in idxs:\n # print(f\"{idx//2}, {idx}\")\n # obj = self.all_meta[idx // 2]\n obj = self.all_meta[idx // 2]\n yield [\n obj.img_path,\n obj.height,\n obj.width,\n obj.center,\n obj.bbox,\n obj.area,\n obj.num_keypoints,\n obj.masks_segments,\n obj.scale,\n obj.all_joints,\n obj.img,\n obj.mask,\n obj.aug_center,\n obj.aug_joints,\n idx\n ]","title":""},{"docid":"3560997dc20a835b827b1dfb23d6b040","score":"0.504588","text":"def get_gene_data(patient_phen, filename, var_or_gene):\r\n genomic_data={}\r\n with open(filename,\"r\") as pg:\r\n lines=pg.readlines()\r\n for line in lines:\r\n if line.split(\"<\")[0]==\"ID\":\r\n pid=line.split(\" \")[3].split(\"\\n\")[0]\r\n genomic_data[pid]=[]\r\n elif line.split(\"<\")[0]==var_or_gene:\r\n var=int(line.split(\" \")[1].split(\"\\n\")[0])\r\n genomic_data[pid].append({})\r\n else:\r\n if not(len(line.split(\" \"))==1):\r\n genomic_data[pid][var][line.split(\" \")[0].split(\"\\n\")[0]]=line.split(\" \")[1].split(\"\\n\")[0]\r\n for patient in genomic_data:\r\n if not(patient in list(patient_phen.keys())):\r\n genomic_data = removekey(genomic_data, patient)\r\n return genomic_data","title":""},{"docid":"9218fab908ce3f6fdcea42a75caeb55b","score":"0.50444394","text":"def get_data(self, identifiers):\n def get_file_path(path, person_identifier, index, postfix):\n return os.path.join(path, \"{}_{}.{}\".format(person_identifier, index, postfix))\n\n out = dict()\n for i, (person_identifier, index) in enumerate(identifiers):\n img = misc.imread(get_file_path(self.path, person_identifier, index, 'jpg'))\n with open(get_file_path(self.path, person_identifier, index, 'json'), 'r') as f:\n json_data = ujson.load(f)\n out[identifiers[i]] = {\n 'eye': img,\n 'gaze': json_data['gaze']\n }\n return out","title":""},{"docid":"f2e260ec343102f65481f72b3c57dd06","score":"0.50388235","text":"def extract3D(self, max_shifts=(8, 8, 2), strides=(48, 48, 4), overlaps=(24, 24, 2), \n gSig=(2, 2, 2.5), **kwargs):\n\n self.extract(max_shifts=max_shifts, strides=strides, overlaps=overlaps, \n gSig=gSig, **kwargs)","title":""},{"docid":"aecdbddc01478835f1e72b307ed82b40","score":"0.5037038","text":"def load_data():\n seq = open(os.path.join(mitty.test.example_data_dir, 'tiny.fasta')).readlines()[1]\n # df = vio.read_sample_from_vcf(os.path.join(mitty.test.example_data_dir, 'tiny.vcf'), 'g0_s0')\n df = vio.load_variant_file(\n os.path.join(mitty.test.example_data_dir, 'tiny.vcf.gz'),\n 'g0_s0',\n os.path.join(mitty.test.example_data_dir, 'tiny.whole.bed'))\n return seq, df","title":""},{"docid":"58598d48e32796a4896af062c96ef551","score":"0.503099","text":"def data():\n shape = (6,4,5)\n size = numpy.prod(shape)\n return numpy.arange(1, size + 1).reshape(shape) + 0.3","title":""},{"docid":"c6ad0ca681e29bd290298799325da572","score":"0.50309706","text":"def internalize(xyzfile):\n\n ccdata_xyz = parse.xyzfile(xyzfile, ccxyz=True)\n\n ccdata_xyz.build_zmatrix()\n\n ccdata_xyz.build_xyz()\n\n return ccdata_xyz","title":""},{"docid":"dcbc9719fb449c39e919d298a3a3d15e","score":"0.50289845","text":"def DC2Dsurvey(flag=\"PolePole\"):\n if flag == \"PoleDipole\":\n ntx, nmax = xr.size - 2, 8\n elif flag == \"DipolePole\":\n ntx, nmax = xr.size - 2, 8\n elif flag == \"DipoleDipole\":\n ntx, nmax = xr.size - 3, 8\n elif flag == \"PolePole\":\n ntx, nmax = xr.size - 2, 8\n else:\n raise Exception(\"Not Implemented\")\n xzlocs = getPseudoLocs(xr, ntx, nmax, flag)\n\n txList = []\n zloc = -2.5\n for i in range(ntx):\n if flag == \"PoleDipole\":\n A = np.r_[xr[i], zloc]\n B = np.r_[mesh.cell_centers_x.min(), zloc]\n if i < ntx - nmax + 1:\n M = np.c_[xr[i + 1 : i + 1 + nmax], np.ones(nmax) * zloc]\n N = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(nmax) * zloc]\n else:\n M = np.c_[xr[i + 1 : ntx + 1], np.ones(ntx - i) * zloc]\n N = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(ntx - i) * zloc]\n elif flag == \"DipolePole\":\n A = np.r_[xr[i], zloc]\n B = np.r_[xr[i + 1], zloc]\n if i < ntx - nmax + 1:\n M = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(nmax) * zloc]\n N = np.c_[np.ones(nmax) * mesh.cell_centers_x.max(), np.ones(nmax) * zloc]\n else:\n M = np.c_[xr[i + 2 : ntx + 2], np.ones(ntx - i) * zloc]\n N = np.c_[\n np.ones(ntx - i) * mesh.cell_centers_x.max(), np.ones(ntx - i) * zloc\n ]\n elif flag == \"DipoleDipole\":\n A = np.r_[xr[i], zloc]\n B = np.r_[xr[i + 1], zloc]\n if i < ntx - nmax:\n M = np.c_[\n xr[i + 2 : i + 2 + nmax],\n np.ones(len(xr[i + 2 : i + 2 + nmax])) * zloc,\n ]\n N = np.c_[\n xr[i + 3 : i + 3 + nmax],\n np.ones(len(xr[i + 3 : i + 3 + nmax])) * zloc,\n ]\n else:\n M = np.c_[\n xr[i + 2 : len(xr) - 1],\n np.ones(len(xr[i + 2 : len(xr) - 1])) * zloc,\n ]\n N = np.c_[xr[i + 3 : len(xr)], np.ones(len(xr[i + 3 : len(xr)])) * zloc]\n elif flag == \"PolePole\":\n A = np.r_[xr[i], zloc]\n B = np.r_[mesh.cell_centers_x.min(), zloc]\n\n if i < ntx - nmax + 1:\n M = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(nmax) * zloc]\n N = np.c_[np.ones(nmax) * mesh.cell_centers_x.max(), np.ones(nmax) * zloc]\n else:\n M = np.c_[xr[i + 2 : ntx + 2], np.ones(ntx - i) * zloc]\n N = np.c_[\n np.ones(ntx - i) * mesh.cell_centers_x.max(), np.ones(ntx - i) * zloc\n ]\n\n rx = DC.receivers.Dipole(M, N)\n src = DC.sources.Dipole([rx], A, B)\n txList.append(src)\n\n survey = DC.Survey(txList)\n simulation = DC.Simulation2DCellCentered(\n mesh, survey=survey, sigmaMap=mapping, solver=Pardiso\n )\n\n sigblk, sighalf, siglayer = 2e-2, 2e-3, 1e-3\n xc, yc, r, zh = -15, -8, 4, -5\n mtrue = np.r_[np.log(sighalf), np.log(siglayer), np.log(sigblk), xc, yc, r, zh]\n dtrue = simulation.dpred(mtrue)\n perc = 0.0001\n floor = np.linalg.norm(dtrue) * 1e-8\n np.random.seed([1])\n uncert = np.random.randn(survey.nD) * perc + floor\n dobs = dtrue + uncert\n\n return dobs, uncert, simulation, xzlocs","title":""},{"docid":"e065f51d00302f6d966fa897a2d04a65","score":"0.50286007","text":"def get_data(self, identifiers):\n from input.preprocessing import UnityPreprocessor\n\n def get_file_path(path, index, postfix):\n return os.path.join(path, \"{}.{}\".format(index, postfix))\n\n out = dict()\n for i, file_stem in enumerate(identifiers):\n img = misc.imread(get_file_path(self.path, file_stem, 'jpg'))\n with open(get_file_path(self.path, file_stem, 'json'), 'r') as f:\n json_data = ujson.load(f)\n out[identifiers[i]] = {\n 'eye': img,\n 'gaze': UnityPreprocessor.look_vec_to_gaze_vec(json_data)[0],\n 'original_gaze': UnityPreprocessor.look_vec_to_gaze_vec(json_data)[1]\n }\n return out","title":""},{"docid":"6fa16ae5a6838da69ef21c653932522d","score":"0.50258285","text":"def visualize_raw_data(filename):\n pointcloud = o3d.io.read_point_cloud(filename, format='xyz')\n o3d.visualization.draw_geometries([pointcloud])","title":""},{"docid":"acd477ef337f0729d16d5cc6e15c2df4","score":"0.5020945","text":"def get_3d_cube():\n V = np.zeros(8, [(IN_VERTEX_POS_NAME, np.float32, 3)])\n V[IN_VERTEX_POS_NAME] = np.array([(-1, 1, 1), (1, 1, 1), (1, -1, 1), (-1, -1, 1), (-1, 1, -1), (1, 1, -1), (1, -1, -1), (-1, -1, -1)])\n I = np.array([0, 1, 2, 0, 2, 3, 0, 4, 5, 0, 5, 1, 0, 4, 7, 0, 7, 3, 3, 7, 6, 3, 6, 2, 1, 5, 6, 1, 6, 2, 4, 5, 6, 4, 6, 7], dtype=np.uint32)\n return V, I","title":""},{"docid":"d76f6205786834dde7888b0796287329","score":"0.50156695","text":"def get_UBC_mesh(meshfile): \n\n fid = open(meshfile,'r')\n from numpy import zeros\n \n # Go through the log file and extract data and the last achieved misfit\n for ii in range (1, 6): \n \n line = fid.readline()\n line = line.split(' ') \n \n # First line: number of cells in i, j, k \n if ii == 1:\n \n numcell=[]\n \n for jj in range(len(line)):\n t = int(line[jj])\n numcell.append(t)\n \n nX = numcell[0]\n nY = numcell[1]\n nZ = numcell[2] \n # Second line: origin coordinate (X,Y,Z)\n elif ii==2:\n \n origin = []\n \n for jj in range(len(line)):\n t = float(line[jj])\n origin.append(t)\n \n \n # Other lines for the dX, dY ,dZ\n elif ii==3:\n \n dX=zeros((nX,1), dtype=float)\n count_entry = 0;\n count = 0;\n while (count<nX):\n\n if line[count_entry].find('*') != -1:\n \n ndx = line[count_entry].split('*')\n \n for kk in range(int(ndx[0])):\n dX[count] = (ndx[1])\n count = count+1\n count_entry=count_entry+1\n \n else:\n \n t = float(line[count_entry])\n dX[count]= t\n count = count+1 \n count_entry=count_entry+1\n \n elif ii==4:\n \n dY=zeros((nY,1), dtype=float)\n count_entry = 0;\n count = 0;\n while (count<nY):\n\n if line[count_entry].find('*') != -1:\n \n ndy = line[count_entry].split('*')\n \n for kk in range(int(ndy[0])):\n dY[count] = (ndy[1])\n count = count+1\n count_entry=count_entry+1\n \n else:\n \n t = float(line[count_entry])\n dY[count]= t\n count = count+1 \n count_entry=count_entry+1\n \n elif ii==5:\n \n dZ=zeros((nZ,1), dtype=float)\n count_entry = 0;\n count = 0;\n while (count<nZ):\n\n if line[count_entry].find('*') != -1:\n \n ndz = line[count_entry].split('*')\n \n for kk in range(int(ndz[0])):\n dZ[count] = (ndz[1])\n count = count+1\n count_entry=count_entry+1 \n else:\n \n t = float(line[count_entry])\n dZ[count]= t\n count = count+1 \n count_entry=count_entry+1\n fid.close(); \n return nX, nY, nZ, origin, dX, dY, dZ","title":""},{"docid":"132acde942eded30bccac9c593764b1f","score":"0.5001815","text":"def _prepare_data(self, grid_3d, upsample):\n if upsample:\n # upsample only small size structures\n # get structure slice Position\n # set up-sample only small size structures\n if self.volume_original < self.vol_lim:\n # self._set_upsample_delta()\n hi_resolution_structure, grid_ds, grid_delta, dose_lut = self.up_sampling(grid_3d, self.delta_mm)\n dose_grid_points = grid_ds[:, :2]\n return hi_resolution_structure, dose_lut, dose_grid_points, grid_delta\n\n else:\n return self._get_calculation_data(grid_3d)\n\n else:\n return self._get_calculation_data(grid_3d)","title":""},{"docid":"51beb5837dab4b7b75ec17c52ef18d27","score":"0.4996552","text":"def getRigBuildData (self):\n\n data = super (ArmComponentGuide, self).getRigBuildData ()\n\n # values\n bicepPosition = self.bicepCtrl.xfo.tr\n forearmPosition = self.forearmCtrl.xfo.tr\n wristPosition = self.wristCtrl.xfo.tr\n\n # Calculate Bicep Xfo\n rootToWrist = wristPosition.subtract (bicepPosition).unit ()\n rootToElbow = forearmPosition.subtract (bicepPosition).unit ()\n\n bone1Normal = rootToWrist.cross (rootToElbow).unit ()\n bone1ZAxis = rootToElbow.cross (bone1Normal).unit ()\n\n bicepXfo = Xfo ()\n bicepXfo.setFromVectors (rootToElbow, bone1Normal, bone1ZAxis, bicepPosition)\n\n # Calculate Forearm Xfo\n elbowToWrist = wristPosition.subtract (forearmPosition).unit ()\n elbowToRoot = bicepPosition.subtract (forearmPosition).unit ()\n bone2Normal = elbowToRoot.cross (elbowToWrist).unit ()\n bone2ZAxis = elbowToWrist.cross (bone2Normal).unit ()\n forearmXfo = Xfo ()\n forearmXfo.setFromVectors (elbowToWrist, bone2Normal, bone2ZAxis, forearmPosition)\n\n # Calculate Wrist Xfo\n wristXfo = Xfo ()\n wristXfo.tr = self.wristCtrl.xfo.tr\n wristXfo.ori = forearmXfo.ori\n\n upVXfo = xfoFromDirAndUpV (bicepPosition, wristPosition, forearmPosition)\n upVXfo.tr = forearmPosition\n upVXfo.tr = upVXfo.transformVector (Vec3 (0, 0, 5))\n\n # Lengths\n bicepLen = bicepPosition.subtract (forearmPosition).length ()\n forearmLen = forearmPosition.subtract (wristPosition).length ()\n\n data ['bicepXfo'] = bicepXfo\n data ['forearmXfo'] = forearmXfo\n data ['wristXfo'] = wristXfo\n data ['upVXfo'] = upVXfo\n data ['bicepLen'] = bicepLen\n data ['forearmLen'] = forearmLen\n\n return data","title":""},{"docid":"5b4cdfaea9d151085586f2c9f75715e7","score":"0.49907282","text":"def get_data(self):\n return {\n \"d_nums\": self.d_nums,\n \"d_dens\": self.d_dens,\n \"ds\": self.ds,\n \"lr_effs\": self.lr_effs\n }","title":""},{"docid":"f7bd13f1a06e2da21a1bf7c54dc7ca07","score":"0.49855143","text":"def read_xyz(self, fname):\n\n with open(fname, 'r') as f:\n natoms = int(f.readline().split()[0])\n f.readline() #comment\n xyz = numpy.zeros((natoms, 3))\n elements = []\n for i in range(natoms):\n line = f.readline().split()\n elements.append((line[0]).upper())\n xyz[i,:] = line[1:4]\n #if len(f.readlines()) > 0:\n # raise Exception('File '+fname+' longer than natoms+2: xyz movie?')\n #f = open(fname, 'r')V\n #natoms = string.atoi(string.split(f.readline())[0])\n #f.readline()\n #xyz = numpy.zeros((natoms, 3))\n #elements = []\n #for i in range(natoms):\n # line = string.split(f.readline())\n # elements.append(string.upper(line[0]))\n # xyz[i,:] = line[1:4]\n for i in range(len(elements)):\n if len(elements[i]) == 1:\n elements[i] += ' '\n elements = map(string.lower, elements)\n elements = map(string.capitalize, elements)\n self.set_elements(elements)\n self.set_natoms(natoms)\n self.set_masses()\n self.set_vdwr()\n self.set_xyz(xyz)\n return elements, xyz","title":""},{"docid":"962113ee0df684be065cfe5ab48cb753","score":"0.49794397","text":"def ecrire_v3d(acces_fichier, dict_numpy_arrays, dimensions, numbloc=0, fmt_fichier = \"bin\",\\\r\n precision=\"i4r8\", endian=\"big\", type_maillage=False):\r\n ## definition des dictionnaires:\r\n format_binaire = {\"i4\" : [4,\"l\"], \"i8\" : [8,\"q\"], \"r4\" : [4,\"f\"], \"r8\" : [8,\"d\"], \"big\":\">\", \r\n \"little\" : \"<\"}\r\n \r\n nb_vars = len(dict_numpy_arrays)\r\n if nb_vars == 0:\r\n print 'None to save'\r\n return 1\r\n \r\n ## ouverture du fichier:\r\n try: \r\n fichier_v3d = open(acces_fichier,\"w\" if fmt_fichier == 'fmt' else \"wb\")\r\n except:\r\n raise IOError, \"Le fichier ne peut pas etre ecrit\"\r\n else:\r\n print 'Ecriture %s'%(acces_fichier)\r\n \r\n ## ecriture du fichier:\r\n if fmt_fichier == \"fmt\":\r\n fichier_v3d.write((5*\" \" + str(nb_vars))[-5:] + \"\\n\")\r\n incr_var = 0\r\n for nom_var in dict_numpy_arrays if not type_maillage else ['x', 'y', 'z']:\r\n numpyArray = dict_numpy_arrays[nom_var]\r\n \r\n while len(dimensions) < 3:\r\n dimensions += (1,)\r\n numpyArray = numpyArray.ravel()\r\n \r\n if not type_maillage:\r\n var = \"va \" + nom_var\r\n else:\r\n var = nom_var\r\n \r\n if type_maillage:\r\n fichier_v3d.write((var + 20*\" \")[:20] + (20*\" \" + \"6e15.7\")[-20:] + \"\\n\" )\r\n fichier_v3d.write((6*\" \" + str(numbloc))[-6:] +\\\r\n (6*\" \" + str(dimensions[0]))[-6:] +\\\r\n (6*\" \" + str(dimensions[1]))[-6:] +\\\r\n (6*\" \" + str(dimensions[2]))[-6:] + \"\\n\")\r\n else:\r\n fichier_v3d.write((var + 20*\" \")[:20] + (20*\" \" + \"6e14.7\")[-20:] + \"\\n\")\r\n fichier_v3d.write((6*\" \" + str(incr_var + 1))[-6:] +\\\r\n (6*\" \" + str(numbloc))[-6:] +\\\r\n (6*\" \" + str(dimensions[0]))[-6:] +\\\r\n (6*\" \" + str(dimensions[1]))[-6:] +\\\r\n (6*\" \" + str(dimensions[2]))[-6:] + \"\\n\")\r\n incr_var += 1\r\n\r\n incr = 0\r\n if type_maillage:\r\n for value in numpyArray:\r\n incr += 1\r\n fichier_v3d.write(\r\n '{0: 15.7E}{1}'.format(value, \r\n \"\\n\" if incr%6 == 0 or incr == numpyArray.size\r\n else \"\"))\r\n else:\r\n for value in numpyArray:\r\n incr += 1\r\n fichier_v3d.write(\r\n '{0: 14.7E}{1}'.format(value, \r\n \"\\n\" if incr%6 == 0 or incr == numpyArray.size\r\n else \"\"))\r\n \r\n elif fmt_fichier == \"bin\":\r\n # options de lecture pour struct.unpack\r\n ordre_bin = '>' if endian == 'big' else '<'\r\n fmt_entier = \"l\" if precision[:2] == 'i4' else 'q'\r\n taille_entier = int(precision[1])\r\n fmt_reel = \"f\" if precision[-2:] == 'i4' else 'd'\r\n taille_reel = int(precision[3])\r\n \r\n fichier_v3d.write(struct.pack(ordre_bin + fmt_entier, taille_entier))\r\n fichier_v3d.write(struct.pack(ordre_bin + fmt_entier, nb_vars))\r\n fichier_v3d.write(struct.pack(ordre_bin + fmt_entier, taille_entier))\r\n \r\n incr_var = 0\r\n for nom_var in dict_numpy_arrays if not type_maillage else ['x', 'y', 'z']:\r\n numpyArray = dict_numpy_arrays[nom_var]\r\n \r\n while len(dimensions) < 3:\r\n dimensions += (1,)\r\n numpyArray = numpyArray.ravel()\r\n \r\n if not type_maillage:\r\n var = (\"va \" + nom_var + 20 * \" \")[:20]\r\n else:\r\n var = (nom_var + 20 * \" \")[:20]\r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", 20))\r\n fichier_v3d.write(struct.pack(\"20s\", var))\r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\" ,20))\r\n \r\n if type_maillage: \r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", 4 * taille_entier))\r\n fichier_v3d.write(struct.pack(ordre_bin + \"4\" + fmt_entier, \r\n numbloc, dimensions[0], dimensions[1], dimensions[2])\r\n )\r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", 4 * taille_entier))\r\n else:\r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", 5 * taille_entier))\r\n fichier_v3d.write(struct.pack(ordre_bin + \"5\" + fmt_entier,\r\n incr_var + 1 , numbloc, dimensions[0], dimensions[1], dimensions[2])\r\n )\r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", 5 * taille_entier))\r\n incr_var += 1\r\n \r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", numpy.prod(dimensions) * taille_reel))\r\n for value_point in numpyArray:\r\n fichier_v3d.write(struct.pack(ordre_bin + fmt_reel, value_point))\r\n fichier_v3d.write(struct.pack(ordre_bin + \"l\", numpy.prod(dimensions) * taille_reel))\r\n fichier_v3d.close()\r\n return 0","title":""},{"docid":"640484e65cde9a8344a1a94ad9b327b8","score":"0.49782774","text":"def _construct_contig_info(self, params):\n\n assembly_chromosome_dict = self.wsc.get_object_subset([{\n 'included': ['/contigs'],\n 'ref': self.vcf_info['assembly_ref']\n }])[0]['data']['contigs']\n\n\n contigs = []\n\n contig_infos = self.vcf_info['contigs']\n\n\n for contig_id in contig_infos:\n length_contig = assembly_chromosome_dict[contig_id].get(\"length\")\n contig_infos[contig_id][\"length\"] = length_contig\n contigs.append(contig_infos[contig_id])\n\n return contigs","title":""},{"docid":"b1df6994f10ebedbdb011994a8a2f78c","score":"0.49774212","text":"def to_3d(self, data):\n return data.reshape(self.original_dim)","title":""},{"docid":"26c7c36521caddaf785aebeacbaa4096","score":"0.49731585","text":"def get_genome_data(self, chroms_info, **kwargs):\n genome_data = []\n for chrom_info in chroms_info['chrom_info']:\n chrom = chrom_info['chrom']\n chrom_len = chrom_info['len']\n chrom_data = self.get_data(chrom, 0, chrom_len, **kwargs)\n # FIXME: data providers probably should never return None.\n # Some data providers return None when there's no data, so\n # create a dummy dict if necessary.\n if not chrom_data:\n chrom_data = {\n 'data': None\n }\n chrom_data['region'] = \"%s:%i-%i\" % (chrom, 0, chrom_len)\n genome_data.append(chrom_data)\n\n return {\n 'data': genome_data,\n 'dataset_type': self.dataset_type\n }","title":""},{"docid":"44cb3f7033b5a6119d6400b8476aed1d","score":"0.49645782","text":"def gen_slice_dicom_file(self,outdir, n, imageinfo,image_2d, \\\n sop_instance_uid,slice_dataset,meta_dataset):\n \n \n \n #get the image pixel array\n \n dim2 = image_2d.dimensions\n \n array_1d = image_2d.point_data.scalars.to_array() \n pixel_array = np.reshape(array_1d,(dim2[0],dim2[1])) \n \n # pixel_array = np.reshape(array_1d,(dim3[0],dim3[1])) \n slice_dataset.PixelData = pixel_array.tostring()\n \n \n \n slice_dataset.is_little_endian = True\n slice_dataset.is_implicit_VR = True \n \n #update the slice_dataset by imageinfo \n imageinfo.update_dataset(slice_dataset)\n \n \n \n meta_dataset.MediaStorageSOPInstanceUID = sop_instance_uid\n slice_dataset.SOPInstanceUID = sop_instance_uid\n \n #****************************************************\n #Write out dicom image file\n fn_prefix = self.export_property.file_name_prefix\n fn_sufix = self.export_property.file_name_suffix\n \n out_file_name =outdir + '/' + fn_prefix \\\n + str(n) + '.' + fn_sufix \n \n \n \n out_version = self.export_property.export_style \n \n \n \n \n \n \n if out_version == 'Dicom CTImage' or 'EcliseTPS-CTImage':\n \n# fds = FileDataset(out_file_name, slice_dataset) \n# fds.save_as(out_file_name)\n \n # Create the FileDataset instance (initially no data elements, but file_meta supplied)\n# \n fds = FileDataset(out_file_name, slice_dataset,file_meta=meta_dataset, preamble=\"\\0\"*128) \n fds.save_as(out_file_name)","title":""},{"docid":"7f105e6201ffa1729bcf7a44c82710ec","score":"0.49604526","text":"def readSampledGEOS(self):\n nc = Dataset(self.inFile)\n if self.aerosol:\n SDS = self.SDS_AER\n else:\n SDS = self.SDS_META\n for sds in SDS:\n self.__dict__[sds] = []\n if len(nc.variables[sds][:].shape) == 1:\n var = np.array([nc.variables[sds][pixel]])\n else:\n var = nc.variables[sds][pixel,:]\n\n var.shape = (1,) + var.shape\n self.__dict__[sds] = var","title":""},{"docid":"9931944dec879dcc24f127e2a8c7de50","score":"0.49536693","text":"def load_xyz(self, filename):\n xyz = torch.Tensor(np.load(filename)).float()[:, :, :3]\n xyz = xyz.permute(2, 0, 1)\n return xyz","title":""},{"docid":"36d40e919fe802f05251b103ee10daeb","score":"0.49517834","text":"def val3p4c_depthfromIvar(sample,Nexpmin=1,Nexpmax=500):\n nside = 1024 # Resolution of output maps\n nsideSTR='1024' # same as nside but in string format\n nsidesout = None # if you want full sky degraded maps to be written\n ratiores = 1 # Superresolution/oversampling ratio, simp mode doesn't allow anything other than 1\n mode = 1 # 1: fully sequential, 2: parallel then sequential, 3: fully parallel\n pixoffset = 0 # How many pixels are being removed on the edge of each CCD? 15 for DES.\n oversamp='1' # ratiores in string format\n \n band = sample.band\n catalogue_name = sample.catalog\n fname = sample.ccds \n localdir = sample.localdir\n extc = sample.extc\n\n #Read ccd file \n tbdata = pyfits.open(fname)[1].data\n \n # ------------------------------------------------------\n # Obtain indices\n auxstr='band_'+band\n sample_names = [auxstr]\n if(sample.DR == 'DR3'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \n elif(sample.DR == 'DR4'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['bitmask'] == 0)) \n elif(sample.DR == 'DR5'):\n if(sample.survey == 'DECaLS'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \n elif(sample.survey == 'DEShyb'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\n elif(sample.survey == 'NGCproxy'):\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \n elif(sample.DR == 'DR6'):\n inds = np.where((tbdata['filter'] == band)) \n elif(sample.DR == 'DR7'):\n if(sample.survey == 'DECaLS'):\n inds = np.where((tbdata['filter'] == band)) \n elif(sample.survey == 'DEShyb'):\n inds = np.where((tbdata['filter'] == band) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\n elif(sample.survey == 'NGCproxy'):\n inds = np.where((tbdata['filter'] == band) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \n\n\n #Read data \n #obtain invnoisesq here, including extinction \n nmag = Magtonanomaggies(tbdata['galdepth']-extc*tbdata['EBV'])/5.\n ivar= 1./nmag**2.\n\n hits=np.ones(np.shape(ivar)) \n\n # What properties do you want mapped?\n # Each each tuple has [(quantity to be projected, weighting scheme, operation),(etc..)] \n propertiesandoperations = [ ('ivar', '', 'total'), ('hits','','total') ]\n\n \n # What properties to keep when reading the images? \n # Should at least contain propertiesandoperations and the image corners.\n # MARCM - actually no need for ra dec image corners. \n # Only needs ra0 ra1 ra2 ra3 dec0 dec1 dec2 dec3 only if fast track appropriate quicksip subroutines were implemented \n #propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\n # \t+ ['RA', 'DEC', 'crval1', 'crval2', 'crpix1', 'crpix2', 'cd1_1', 'cd1_2', 'cd2_1', 'cd2_2','width','height']\n propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\n \t+ ['RA', 'DEC', 'ra0','ra1','ra2','ra3','dec0','dec1','dec2','dec3']\n \n # Create big table with all relevant properties. \n\n tbdata = np.core.records.fromarrays([tbdata[prop] for prop in propertiesToKeep] + [ivar] + [hits], names = propertiesToKeep + [ 'ivar', 'hits'])\n \n # Read the table, create Healtree, project it into healpix maps, and write these maps.\n # Done with Quicksip library, note it has quite a few hardcoded values (use new version by MARCM for BASS and MzLS) \n # project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, outroot, sample_names, inds, nside)\n #project_and_write_maps(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside, ratiores, pixoffset, nsidesout)\n project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside)\n \n # Read Haelpix maps from quicksip \n prop='ivar'\n op='total'\n vmin=21.0\n vmax=24.0\n\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\n f = fitsio.read(fname2)\n\n # HEALPIX DEPTH MAPS \n # convert ivar to depth \n import healpy as hp\n from healpix3 import pix2ang_ring,thphi2radec\n\n ral = []\n decl = []\n val = f['SIGNAL']\n pix = f['PIXEL']\n\n #get hits\n prop = 'hits'\n op = 'total'\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\n f = fitsio.read(fname2)\n hitsb=f['SIGNAL']\n \n # Obtain values to plot \n #if (prop == 'ivar'):\n myval = []\n mylabel='depth' \n \n below=0 \n for i in range(0,len(val)):\n depth=nanomaggiesToMag(sqrt(1./val[i]) * 5.)\n npases=hitsb[i]\n if(npases >= Nexpmin and npases <= Nexpmax ):\n myval.append(depth)\n th,phi = hp.pix2ang(int(nside),pix[i])\n ra,dec = thphi2radec(th,phi)\n ral.append(ra)\n decl.append(dec)\n if(depth < vmin):\n below=below+1\n\n npix=len(myval)\n\n print('Area is ', npix/(float(nside)**2.*12)*360*360./pi, ' sq. deg.')\n print(below, 'of ', npix, ' pixels are not plotted as their ', mylabel,' < ', vmin)\n print('Within the plot, min ', mylabel, '= ', min(myval), ' and max ', mylabel, ' = ', max(myval))\n\n\n # Plot depth \n #from matplotlib import pyplot as plt\n #import matplotlib.cm as cm\n\n #ralB = [ ra-360 if ra > 300 else ra for ra in ral ]\n #vmax = sample.recm\n #vmin = vmax - 2.0\n \n #mapa = plt.scatter(ralB,decl,c=myval, cmap=cm.rainbow,s=2., vmin=vmin, vmax=vmax, lw=0,edgecolors='none')\n #mapa = plt.scatter(ralB,decl,c=myval, cmap=cm.gnuplot,s=2., vmin=vmin, vmax=vmax, lw=0,edgecolors='none')\n #mapa.cmap.set_over('yellowgreen')\n #cbar = plt.colorbar(mapa,extend='both')\n #plt.xlabel('r.a. (degrees)')\n #plt.ylabel('declination (degrees)')\n #plt.title('Map of '+ mylabel +' for '+catalogue_name+' '+band+'-band \\n with 3 or more exposures')\n #plt.xlim(-60,300)\n #plt.ylim(-30,90)\n #mapfile=localdir+mylabel+'_'+band+'_'+catalogue_name+str(nside)+'.png'\n #print 'saving plot to ', mapfile\n #plt.savefig(mapfile)\n #plt.close()\n #plt.show()\n #cbar.set_label(r'5$\\sigma$ galaxy depth', rotation=270,labelpad=1)\n #plt.xscale('log')\n mapfile=localdir+mylabel+'_'+band+'_'+catalogue_name+str(nside)+'.png'\n mytitle='Map of '+ mylabel +' for '+catalogue_name+' '+band+'-band \\n with '+str(Nexpmin)+\\\n ' or more exposures'\n plot_magdepth2D(sample,ral,decl,myval,mapfile,mytitle)\n\n\n # Statistics depths\n\n deptharr=np.array(myval)\n p90=np.percentile(deptharr,10)\n p95=np.percentile(deptharr,5)\n p98=np.percentile(deptharr,2)\n med=np.percentile(deptharr,50)\n mean = sum(deptharr)/float(np.size(deptharr)) # 1M array, too long for precision\n std = sqrt(sum(deptharr**2.)/float(len(deptharr))-mean**2.)\n\n \n\n ndrawn=np.size(deptharr)\n print(\"Total pixels\", np.size(deptharr), \"probably too many for exact mean and std\")\n print(\"Mean = \", mean, \"; Median = \", med ,\"; Std = \", std)\n print(\"Results for 90% 95% and 98% are: \", p90, p95, p98)\n\n # Statistics pases\n #prop = 'hits'\n #op = 'total'\n #fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\n #catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\n #f = fitsio.read(fname2)\n \n #hitsb=f['SIGNAL']\n hist, bin_edges =np.histogram(hitsb,bins=[-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5, 11.5,12.5, 13.5, 14.5, 15.5, 16.15,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,100],density=True)\n #print hitsb[1000:10015]\n #hist, bin_edges =np.histogram(hitsb,density=True)\n print(\"Percentage of hits for 0,1,2., to >7 pases\\n\", end=' ') \n #print bin_edges \n print(hist)\n #print 100*hist\n\n return mapfile","title":""},{"docid":"b2d8ced3f07fdedc806a295c328af1a1","score":"0.4950398","text":"def get_data(self):\n # XXX is this actually necessary here?\n import numpy # import dependency\n\n # Get the image data\n return xds_cbf.reader.get_data(self)","title":""},{"docid":"d1b122146b08f796f17433e871f0efcc","score":"0.49495107","text":"def cell_to_morph3d(cell):\n g = nx.DiGraph()\n stack = [cell.soma]\n while len(stack) > 0:\n sec = stack.pop()\n # This is roundabout way is required because nrn7.4 does not\n # provide explicit equivalent of `access {section}`. In nrn7.5\n # the 3d functions are available as Section methods.\n h('access {}'.format(sec.name()))\n stype = nu.sectype(sec.name())\n pt3d = int(h.n3d())\n # pt3d = int(sec.n3d()): # only nrn >= 7.5\n for ii in range(pt3d): \n name = '{}_{}'.format(sec.name(), ii)\n x = h.x3d(ii)\n y = h.y3d(ii)\n z = h.z3d(ii)\n d = h.diam3d(ii)\n g.add_node(name, x=x, y=y, z=z, r=d/2.0, s=stype, orig=sec)\n for ii in range(1, pt3d):\n n1 = '{}_{}'.format(sec.name(), ii-1)\n n2 = '{}_{}'.format(sec.name(), ii)\n length = ng.eucd(g, n1, n2)\n g.add_edge(n1, n2, length=length)\n current = h.SectionRef(sec=sec)\n if current.has_parent():\n h('access {}'.format(current.parent.name())) \n n1 = '{}_{}'.format(current.parent.name(),\n int(h.n3d()-1))\n g.add_edge(n1, '{}_0'.format(sec.name()),\n length=0)\n for child in current.child:\n # print('Adding', child.name())\n stack.append(child)\n return g, '{}_0'.format(cell.soma.name())","title":""},{"docid":"76c494da22770be1f7eecf3fbc75b8de","score":"0.49494615","text":"def dbFiller3d(NRange = [2],nPhiRange = [256],\r\n VuRange = [-1.0],VpRange = [-1.0],VzRange = [-1.0],\r\n dVuRange = [1.0],dVpRange = [1.0],dVzRange = [1.0],\r\n VuuRange = [1.0],VppRange = [1.0],VzzRange = [1.0],\r\n dt = 0.1,drag = 0.1,v0 = 1,dims = 3):\r\n\r\n\r\n \r\n if dims == 2:\r\n\r\n VzRange= [0.0]\r\n dVzRange= [0]\r\n\r\n VzzRange = [0.0]\r\n conn = sqlite3.connect(parametersName)\r\n c = conn.cursor()\r\n for N in Nrange:\r\n for nPhi in nPhiRange:\r\n for Vu in VuRange:\r\n for Vp in VpRange:\r\n for Vz in VzRange:\r\n for dVu in dVuRange:\r\n for dVz in dVzRange: \r\n for Vuu in VuuRange:\r\n for Vpp in VppRange:\r\n for Vzz in VzzRange:\r\n expId = str(uuid.uuid4())\r\n values = [expId,N,nPhi,dt,v0,drag,Vuu,Vpp,Vzz,Vu,Vp,Vz,dVu,dVu,dVz]\r\n \r\n c.execute(\"INSERT INTO parameters VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\",values)\r\n conn.commit()\r\n conn.close()","title":""}],"string":"[\n {\n \"docid\": \"0b2b3e8f30a2655c858c3706f8711d0f\",\n \"score\": \"0.5864309\",\n \"text\": \"def read_3d_file(filename):\\n return read_nd_file(filename, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ce175b10ea080bff84a3dd1ba9aafd\",\n \"score\": \"0.58423555\",\n \"text\": \"def geo_meta(dicomfile):\\n\\n # rows and columns\\n rows = dicomfile[0x0028, 0x0010]\\n rows = rows.value\\n cols = dicomfile[0x0028, 0x0011]\\n cols = cols.value\\n matrix_size = [rows, cols]\\n\\n # per-frame functional group sequence\\n elem = dicomfile[0x5200, 0x9230] # pydicom.dataelem.DataElement\\n seq = elem.value # pydicom.sequence.Sequence\\n elem3 = seq[0] # first frame\\n elem4 = elem3.PixelMeasuresSequence # pydicom.sequence.Sequence\\n\\n for xx in elem4:\\n st = xx.SliceThickness\\n pixels_space = xx.PixelSpacing\\n\\n return matrix_size, st, pixels_space\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20141910edd73dbce3ab00f7d81e96de\",\n \"score\": \"0.57870525\",\n \"text\": \"def process_xyz_gdb9(datafile):\\n xyz_lines = [line.decode('UTF-8') for line in datafile.readlines()]\\n\\n num_atoms = int(xyz_lines[0])\\n mol_props = xyz_lines[1].split()\\n mol_xyz = xyz_lines[2:num_atoms+2]\\n mol_freq = xyz_lines[num_atoms+2]\\n\\n atom_charges, atom_positions = [], []\\n for line in mol_xyz:\\n atom, posx, posy, posz, _ = line.replace('*^', 'e').split()\\n atom_charges.append(charge_dict[atom])\\n atom_positions.append([float(posx), float(posy), float(posz)])\\n\\n prop_strings = ['tag', 'index', 'A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'U0', 'U', 'H', 'G', 'Cv']\\n prop_strings = prop_strings[1:]\\n mol_props = [int(mol_props[1])] + [float(x) for x in mol_props[2:]]\\n mol_props = dict(zip(prop_strings, mol_props))\\n mol_props['omega1'] = max(float(omega) for omega in mol_freq.split())\\n\\n molecule = {'num_atoms': num_atoms, 'charges': atom_charges, 'positions': atom_positions}\\n molecule.update(mol_props)\\n molecule = {key: torch.tensor(val) for key, val in molecule.items()}\\n\\n return molecule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b1986cf0152e08ccefe434e16e6232\",\n \"score\": \"0.5746226\",\n \"text\": \"def _readCord3G(self, data, n):\\r\\n s = Struct(b'4i')\\r\\n nEntries = (len(data) - n) // 16\\r\\n for i in xrange(nEntries):\\r\\n eData = data[n:n + 16] # 4*4\\r\\n (cid, n1, n2, n3) = s.unpack(eData)\\r\\n dataIn = [cid, n1, n2, n3]\\r\\n coord = CORD3G(None, dataIn)\\r\\n self.add_coord(coord, allowOverwrites=True)\\r\\n n += 16\\r\\n self.card_count['CORD3G'] = nEntries\\r\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce62f80437ee4d6696c5ef06338b11dd\",\n \"score\": \"0.5725638\",\n \"text\": \"def get_raw_output_3D(X,Y,Z):\\n if isinstance( X, (float,int) ):\\n X = np.array([X]); Y=np.array([Y]); Z=np.array([Z])\\n elif isinstance( X, list ):\\n X= np.array(X); Y=np.array(Y); Z=np.array(Z)\\n\\n classification_data = analytic_classification_3D(X,Y,Z)\\n regression_data = analytic_regression_3D(X,Y,Z)\\n return classification_data, regression_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad08e15ffd3d9fca4b6ee356566da784\",\n \"score\": \"0.56601536\",\n \"text\": \"def get_xyz_data(self):\\n\\t\\treturn np.array([np.reshape(self.spliced_crds,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t(self.spliced_crds.shape[1] * self.spliced_crds.shape[2], 3))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e715a547a3b4423645aed0c61cdd8474\",\n \"score\": \"0.5564991\",\n \"text\": \"def dataArr(filename):\\r\\n #Open the file\\r\\n f=h5py.File(filename,'r')\\r\\n \\r\\n #Initialize the data arrays\\r\\n cdata=[]\\r\\n idxset=[]\\r\\n vertices=[]\\r\\n \\r\\n #Open groups in the file\\r\\n for group in f.keys():\\r\\n# print('Group- '+group)\\r\\n \\r\\n #Get the group\\r\\n currGroup=f[group]\\r\\n \\r\\n #Open keys in the group\\r\\n for key in currGroup.keys():\\r\\n# print('Key- '+key)\\r\\n \\r\\n #Append the data to the respective arrays\\r\\n if key=='cdata(Complex)':\\r\\n cdataGroup=currGroup[key]\\r\\n \\r\\n imag=[]\\r\\n real=[]\\r\\n #Open the keys in cdata\\r\\n for subkey in cdataGroup.keys():\\r\\n# print('Subkey- '+subkey)\\r\\n \\r\\n #Get the real and imaginary parts of the array\\r\\n if subkey=='Imag':\\r\\n imag=cdataGroup[subkey][()]\\r\\n elif subkey=='Real':\\r\\n real=cdataGroup[subkey][()]\\r\\n \\r\\n #Convert lists to numpy arrays\\r\\n imag=np.array(imag)\\r\\n real=np.array(real)\\r\\n #Get the cdata value\\r\\n cdata=real+1j*imag\\r\\n \\r\\n elif key=='idxset':\\r\\n idxset=currGroup[key][()]\\r\\n elif key=='vertices':\\r\\n vertices=currGroup[key][()]\\r\\n \\r\\n #Remove the y component from the vertices\\r\\n xVals=[]\\r\\n yVals=[]\\r\\n newVertices=[]\\r\\n for vertex in vertices:\\r\\n xVals.append(vertex[0])\\r\\n yVals.append(vertex[2])\\r\\n newVertices.append([vertex[0],vertex[1]])\\r\\n vertices=newVertices\\r\\n \\r\\n #Convert to numpy arrays\\r\\n cdata=np.array(cdata)\\r\\n xVals=np.array(xVals)\\r\\n yVals=np.array(yVals)\\r\\n \\r\\n #Close the file\\r\\n f.close()\\r\\n \\r\\n return cdata, xVals, yVals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40b4d44df69bbad2c79d6d4547d8b6d\",\n \"score\": \"0.5561454\",\n \"text\": \"def GSLIB2ndarray_3D(data_file, kcol,nreal, nx, ny, nz):\\n if nz > 1 and ny > 1:\\n array = np.ndarray(shape = (nreal, ny, nx, nz), dtype=float, order=\\\"F\\\")\\n elif ny > 1:\\n array = np.ndarray(shape=(nreal, ny, nx), dtype=float, order=\\\"F\\\")\\n else:\\n array = np.zeros(nreal, nx)\\n\\n with open(data_file) as f:\\n head = [next(f) for _ in range(2)] # read first two lines\\n line2 = head[1].split()\\n ncol = int(line2[0]) # get the number of columns\\n\\n for icol in range(ncol): # read over the column names\\n head = next(f)\\n if icol == kcol:\\n col_name = head.split()[0]\\n for ineal in range(nreal):\\t\\t\\n if nz > 1 and ny > 1:\\n for iz in range(nz):\\n for iy in range(ny):\\n for ix in range(nx):\\n head = next(f)\\n array[ineal][ny - 1 - iy][ix][iz] = head.split()[kcol] \\t\\t\\t\\t\\t\\n elif ny > 1:\\n for iy in range(ny):\\n for ix in range(0, nx):\\n head = next(f)\\n array[ineal][ny - 1 - iy][ix] = head.split()[kcol]\\n else:\\n for ix in range(nx):\\n head = next(f)\\n array[ineal][ix] = head.split()[kcol]\\n return array, col_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894d2192eca7f9db94dfbde9925fc494\",\n \"score\": \"0.55150336\",\n \"text\": \"def gamv_3d(df, xcol, ycol, zcol, vcol, nlag, lagdist,lag_tol, azi, atol, bandh, dip, dtol, bandv, isill):\\n lag = []\\n gamma = []\\n npair = []\\n\\n df_ext = pd.DataFrame({\\\"X\\\": df[xcol], \\\"Y\\\": df[ycol], \\\"Z\\\": df[zcol],\\\"Variable\\\": df[vcol]})\\n Dataframe2GSLIB(\\\"gamv_out.dat\\\", df_ext)\\n\\n with open(\\\"gamv.par\\\", \\\"w\\\") as f:\\n f.write(\\\" Parameters for GAMV \\\\n\\\")\\n f.write(\\\" ******************* \\\\n\\\")\\n f.write(\\\" \\\\n\\\")\\n f.write(\\\"START OF PARAMETERS: \\\\n\\\")\\n f.write(\\\"gamv_out.dat -file with data \\\\n\\\")\\n f.write(\\\"1 2 3 - columns for X, Y, Z coordinates \\\\n\\\")\\n f.write(\\\"1 4 0 - number of variables,col numbers \\\\n\\\")\\n f.write(\\\"-1.0e21 1.0e21 - trimming limits \\\\n\\\")\\n f.write(\\\"gamv.out -file for variogram output \\\\n\\\")\\n f.write(str(nlag) + \\\" -number of lags \\\\n\\\")\\n f.write(str(lagdist) + \\\" -lag separation distance \\\\n\\\")\\n f.write(str(lag_tol) + \\\" -lag tolerance \\\\n\\\")\\n f.write(\\\"1 -number of directions \\\\n\\\")\\n f.write(str(azi) + \\\" \\\" + str(atol) + \\\" \\\" + str(bandh) + \\\" \\\" +str(dip) + \\\" \\\" + str(dtol) + \\\" \\\" + str(bandv)+ \\\" -azm,atol,bandh,dip,dtol,bandv \\\\n\\\")\\n f.write(str(isill) + \\\" -standardize sills? (0=no, 1=yes) \\\\n\\\")\\n f.write(\\\"1 -number of variograms \\\\n\\\")\\n f.write(\\\"1 1 1 -tail var., head var., variogram type \\\\n\\\")\\n\\n os.system(\\\"gamv.exe gamv.par\\\")\\n\\n with open(\\\"gamv.out\\\") as f:\\n next(f) # skip the first line\\n\\n for line in f:\\n _, l, g, n, *_ = line.split()\\n lag.append(float(l))\\n gamma.append(float(g))\\n npair.append(float(n))\\n\\n return lag, gamma, npair\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d70336328dc8e4f38aac9c8aedc8e0e\",\n \"score\": \"0.549276\",\n \"text\": \"def gen_3ps(img, cen, sz=16):\\n tf_1 = get_tf_xynormal(cen)\\n xy = gen_slice(img, tf_1, sz2ext(sz))\\n\\n tf_2 = get_tf_yznormal(cen)\\n yz = gen_slice(img, tf_2, sz2ext(sz))\\n\\n tf_3 = get_tf_xznormal(cen)\\n xz = gen_slice(img, tf_3, sz2ext(sz))\\n\\n return xy, yz, xz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a716af21a6dde6d909ffc1ebe89c2d2\",\n \"score\": \"0.54892606\",\n \"text\": \"def data(self, pdb, **kwargs):\\n\\n # load the 3D structure file\\n structure = self.structure(pdb)\\n\\n bond_orientations, error_message = annotate_bond_orientation(structure,True)\\n\\n if len(error_message) > 0:\\n self.logger.info(error_message)\\n\\n for b in bond_orientations:\\n yield mod.UnitAnnotations(unit_id=b[\\\"unit_id\\\"],\\n pdb_id=pdb,\\n category=\\\"orientation\\\",\\n value=b[\\\"orientation\\\"])\\n\\n yield mod.UnitAnnotations(unit_id=b[\\\"unit_id\\\"],\\n pdb_id=pdb,\\n category=\\\"chi_degree\\\",\\n value=b[\\\"chi_degree\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96174733ed03fe32e98b168364f785b\",\n \"score\": \"0.5475608\",\n \"text\": \"def data():\\n solutions = np.arange(5).reshape(5, 1)\\n objectives = 2 * np.arange(5)\\n behaviors = 3 * np.arange(5).reshape(5, 1)\\n indices = [(idx,) for idx in 4 * np.arange(5)]\\n metadata = [{\\\"a\\\": 1}, {\\\"b\\\": 2}, {\\\"c\\\": 3}, {\\\"d\\\": 4}, {\\\"e\\\": 5}]\\n return solutions, objectives, behaviors, indices, metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d134eb867c93a4e2c32a208cdf045e\",\n \"score\": \"0.5474235\",\n \"text\": \"def dataset_to_view_data(dataset):\\n p3w = np.concatenate([s['p3w'] for s in dataset.values()], axis=0)\\n p2 = np.concatenate([s['p2'] for s in dataset.values()], axis=0)\\n r, t, f, c = [\\n np.concatenate([[s.attrs[k]\\n for i in range(s.attrs['n_frames'])]\\n for s in dataset.values()])\\n for k in ['r', 't', 'f', 'c']\\n ]\\n return p2, r, t, f, c, p3w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64136f2b8755fb4dea0815502db5434b\",\n \"score\": \"0.5450458\",\n \"text\": \"def getPts3D(self, idx = None):\\n # YOU NEED TO IMPLEMENT THIS FUNCTION IN YOUR SUBCLASS\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed54ce620cc7f2c3162cdf3b442e6cb7\",\n \"score\": \"0.5429246\",\n \"text\": \"def DC3D0(self, ALPHA, X, Y, Z, DEPTH, DIP, POT1, POT2, POT3, POT4):\\n\\n# COMMON /C1/DUMMY(8),R,dumm(15)\\n# DIMENSION U(12),DUA(12),DUB(12),DUC(12)\\n\\n F0 = 0.0\\n U = num.zeros((12, 1), float)\\n DUA = num.zeros((12, 1), float)\\n DUB = num.zeros((12, 1), float)\\n DUC = num.zeros((12, 1), float)\\n\\n if Z > 0:\\n print('(''0** POSITIVE Z WAS GIVEN IN SUB-DC3D0'')')\\n for I in range(0, 12):\\n U[I] = F0\\n DUA[I] = F0\\n DUB[I] = F0\\n DUC[I] = F0\\n\\n AALPHA = ALPHA\\n DDIP = DIP\\n self.DCC0N0(AALPHA, DDIP)\\n# ======================================\\n# ===== REAL-SOURCE CONTRIBUTION =====\\n# ======================================\\n XX = X\\n YY = Y\\n ZZ = Z\\n DD = DEPTH+Z\\n self.DCCON1(XX, YY, DD)\\n R = self.R\\n if R == F0:\\n UX = F0\\n UY = F0\\n UZ = F0\\n UXX = F0\\n UYX = F0\\n UZX = F0\\n UXY = F0\\n UYY = F0\\n UZY = F0\\n UXZ = F0\\n UYZ = F0\\n UZZ = F0\\n IRET = 1\\n return\\n else:\\n PP1 = POT1\\n PP2 = POT2\\n PP3 = POT3\\n PP4 = POT4\\n\\n self.UA0(XX, YY, DD, PP1, PP2, PP3, PP4)\\n DUA = self.DUA\\n\\n for I in range(0, 12):\\n if I < 10:\\n U[I] = U[I]-DUA[I]\\n if I >= 10:\\n U[I] = U[I]+DUA[I]\\n# =======================================\\n# ===== IMAGE-SOURCE CONTRIBUTION =====\\n# =======================================\\n DD = DEPTH-Z\\n self.DCCON1(XX, YY, DD)\\n self.UA0(XX, YY, DD, PP1, PP2, PP3, PP4)\\n DUA = self.DUA\\n self.UB0(XX, YY, DD, ZZ, PP1, PP2, PP3, PP4)\\n DUB = self.DUB\\n self.UC0(XX, YY, DD, ZZ, PP1, PP2, PP3, PP4)\\n DUC = self.DUC\\n\\n# -----\\n for I in range(0, 12):\\n DU = DUA[I]+DUB[I]+ZZ*DUC[I]\\n if I >= 9:\\n DU = DU+DUC[I-9]\\n U[I] = U[I]+DU\\n# =====\\n self.UX = U[0]\\n self.UY = U[1]\\n self.UZ = U[2]\\n self.UXX = U[3]\\n self.UYX = U[4]\\n self.UZX = U[5]\\n self.UXY = U[6]\\n self.UYY = U[7]\\n self.UZY = U[8]\\n self.UXZ = U[9]\\n self.UYZ = U[10]\\n self.UZZ = U[11]\\n self.IRET = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ccc97be30fe94f763a430500eab0ba\",\n \"score\": \"0.5414495\",\n \"text\": \"def parse_cluster3d(data):\\n cluster_event = data[0]\\n num_clusters = cluster_event.as_vector().size()\\n clusters_voxels, clusters_data = [], []\\n for i in range(num_clusters):\\n cluster = cluster_event.as_vector()[i]\\n num_points = cluster.as_vector().size()\\n if num_points > 0:\\n x = np.empty(shape=(num_points,), dtype=np.int32)\\n y = np.empty(shape=(num_points,), dtype=np.int32)\\n z = np.empty(shape=(num_points,), dtype=np.int32)\\n value = np.empty(shape=(num_points,), dtype=np.int32)\\n larcv.as_flat_arrays(cluster_event.as_vector()[i],\\n cluster_event.meta(),\\n x, y, z, value)\\n value = np.full(shape=(cluster.as_vector().size(), 1),\\n fill_value=i, dtype=np.int32)\\n clusters_voxels.append(np.stack([x, y, z], axis=1))\\n clusters_data.append(value)\\n np_voxels = np.concatenate(clusters_voxels, axis=0)\\n np_data = np.concatenate(clusters_data, axis=0)\\n return np_voxels, np_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5dcca9b1ea9e36be91e8ad5b9c9c775\",\n \"score\": \"0.5408329\",\n \"text\": \"def actual():\\n\\n # Sets, in the order expected in the GDX file\\n s = ['a', 'b', 'c', 'd', 'e', 'f', 'g']\\n t = ['b', 'g', 'r', 'o', 'y', 'i', 'v']\\n u = ['CA', 'US', 'CN', 'JP']\\n star = s + t[2:] + u\\n\\n # Create the dataset\\n ds = xr.Dataset({\\n 'p1': ('s', np.full(len(s), np.nan)),\\n 'p2': ('t', np.full(len(t), 0.1)),\\n 'p3': (['s', 't'], np.full([len(s), len(t)], np.nan)),\\n 'p5': ('*', np.full(len(star), np.nan)),\\n },\\n coords={\\n 's': s,\\n 't': t,\\n 'u': u,\\n '*': star,\\n 's1': ['a', 'b', 'c', 'd'],\\n 's2': ['e', 'f', 'g'],\\n 's3': [],\\n 's4': [],\\n 's5': ['b', 'd', 'f'],\\n 's6': ['b', 'd', 'f'],\\n 's7': [],\\n 's_': s,\\n })\\n\\n # Contents of parameters\\n ds['pi'] = 3.14\\n ds['p1'].loc['a'] = 1\\n ds['p3'].loc[:, 'y'] = 1\\n ds['p4'] = (['s1'], np.ones(ds['s1'].size))\\n ds['p6'] = (['s', 's1', 't'],\\n np.full([len(s), ds['s1'].size, len(t)], np.nan))\\n\\n ds['p7'] = (['*', '*'],\\n np.full([ds['*'].size] * 2, np.nan))\\n ds['p7'].loc['a', 'o'] = 1\\n ds['p7'].loc['r', 'US'] = 2\\n ds['p7'].loc['CA', 'b'] = 3\\n\\n # Set the _gdx_index attribute on each variable\\n order = ['*', 'pi', 's', 't', 'u', 's1', 's2', 's3', 's4', 's5', 's6',\\n 's7', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'e1', 'v1', 'v2',\\n 's_', ]\\n for num, name in enumerate(order):\\n try:\\n ds[name].attrs['_gdx_index'] = num\\n except KeyError:\\n # These names do not appear in the loaded gdx.File object\\n assert name in ['e1', 'v1', 'v2']\\n\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b15c678494255471865fb2a1f43f1a9\",\n \"score\": \"0.5405561\",\n \"text\": \"def det3x3(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b15c678494255471865fb2a1f43f1a9\",\n \"score\": \"0.5403282\",\n \"text\": \"def det3x3(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79b60687c6bd73705729626549a99868\",\n \"score\": \"0.5385744\",\n \"text\": \"def get_data_dict(self):\\r\\n\\r\\n gene_dict = {'geneID': self.geneID, 'Name': self.name,\\r\\n 'seq_type': self.seq_type, 'seq_range': self.seq_range,\\r\\n 'orientation': self.orientation,\\r\\n 'chromosome': self.chromosome, 'transcripts': {}}\\r\\n\\r\\n transcripts = self.get_transcripts()\\r\\n\\r\\n # fetch each transcript data\\r\\n for ID in transcripts:\\r\\n\\r\\n transcript = transcripts[ID]\\r\\n\\r\\n transcript_dict = {}\\r\\n\\r\\n transcript_dict['seq_type'] = transcript.get_seq_type()\\r\\n transcript_dict['ID'] = transcript.get_ID()\\r\\n transcript_dict['seq_range'] = transcript.get_seq_range()\\r\\n\\r\\n # get exons\\r\\n transcript_dict['exons'] = []\\r\\n\\r\\n for exon in transcript.get_exons():\\r\\n\\r\\n transcript_dict['exons'].append(exon.get_seq_range())\\r\\n\\r\\n # get info for mRNA\\r\\n if transcript.get_seq_type() == 'mRNA':\\r\\n\\r\\n if transcript.get_CDSs() != []:\\r\\n\\r\\n # Calculate UTRs\\r\\n transcript.get_5pUTR()\\r\\n transcript.get_3pUTR()\\r\\n\\r\\n # get all sequences as list\\r\\n mrna_subseq = transcript.get_mRNA_subsequences()\\r\\n\\r\\n for seq in mrna_subseq:\\r\\n\\r\\n seq_type = seq.get_seq_type()\\r\\n\\r\\n seq_range = seq.get_seq_range()\\r\\n\\r\\n if seq_type not in transcript_dict:\\r\\n\\r\\n transcript_dict[seq_type] = [seq_range]\\r\\n\\r\\n else:\\r\\n transcript_dict[seq_type].append(seq_range)\\r\\n\\r\\n gene_dict['transcripts'][ID] = transcript_dict\\r\\n\\r\\n return gene_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07907335fcbf1b03901bab9c4ee0dcf6\",\n \"score\": \"0.5359551\",\n \"text\": \"def parse_cluster3d_groups(data):\\n np_voxels, np_data = parse_cluster3d([data[0]])\\n groups, edges = parse_particle_group([data[1]])\\n for cluster_index, group_id in enumerate(groups):\\n where = np.where(np_data == float(cluster_index))\\n np_data[where] = group_id\\n\\n return np_voxels, np_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e583547c92cdd47d7a6e0f2c963f4866\",\n \"score\": \"0.5331808\",\n \"text\": \"def kagome_vis(x1, x2, x3, z):\\n\\n pin = 1\\n deltaz = 1\\n # Bravais primitive unit vecs\\n a = 2 * array([[cos(2 * pi * p / 3.), sin(2 * pi * p / 3.)] for p in range(3)])\\n a1 = a[0];\\n a2 = a[1];\\n a3 = a[2];\\n\\n # make unit cell\\n x = array([x1, x2, x3])\\n y = [z / 3. + x[mod(p - 1, 3)] - x[mod(p + 1, 3)] for p in range(3)]\\n s = array([x[p] * (a[mod(p - 1, 3)] - a[mod(p + 1, 3)]) + y[p] * a[p] for p in range(3)])\\n s1 = s[0];\\n s2 = s[1];\\n d1 = a1 / 2. + s2\\n d2 = a2 / 2. - s1\\n d3 = a3 / 2.\\n\\n # nodes at R (from top to bottom) -- like fig 2a of KaneLubensky2014\\n R = array([d1 + a2,\\n d3 + a1 + a2,\\n d2 + a1,\\n d1,\\n d3 + a1,\\n d2 - a2,\\n d1 + a3,\\n d3,\\n d2 + a3,\\n d1 + a2 + a3,\\n d3 + a2,\\n d2])\\n\\n Ni = array([\\n [11, 1, 0, 0], # 0 d1+a2,\\n [3, 11, 0, 2], # 1 d3+a1+a2,\\n [3, 1, 0, 0], # 2 d2+a1,\\n [2, 5, 4, 1], # 3 d1,\\n [3, 5, 0, 0], # 4 d3+a1,\\n [3, 7, 4, 6], # 5 d2-a2,\\n [7, 5, 0, 0], # 6 d1+a3,\\n [6, 9, 5, 8], # 7 d3,\\n [7, 9, 0, 0], # 8 d2+a3,\\n [11, 7, 10, 8], # 9 d1+a2+a3,\\n [11, 9, 0, 0], # 10 d3+a2,\\n [0, 9, 10, 1], # 11 d2\\n ])\\n\\n Nk = array([\\n [1, 1, 0, 0], # 0 d1+a2,\\n [1, 1, 1, 1], # 1 d3+a1+a2,\\n [1, 1, 0, 0], # 2 d2+a1,\\n [1, 1, 1, 1], # 3 d1,\\n [1, 1, 0, 0], # 4 d3+a1,\\n [1, 1, 1, 1], # 5 d2-a2,\\n [1, 1, 0, 0], # 6 d1+a3,\\n [1, 1, 1, 1], # 7 d3,\\n [1, 1, 0, 0], # 8 d2+a3,\\n [1, 1, 1, 1], # 9 d1+a2+a3,\\n [1, 1, 0, 0], # 10 d3+a2,\\n [1, 1, 1, 1], # 11 d2\\n ])\\n\\n c1 = '#000000'\\n c2 = '#B9B9B9'\\n c3 = '#E51B1B'\\n c4 = '#18CFCF'\\n\\n col = [c1, c2, c3, c4]\\n\\n A = col[0]\\n B = col[1]\\n C = col[2]\\n\\n cc1 = '#96adb4'\\n cols = array([A, C, B, A, C, B, A, C, B, A, C, B])\\n line_cols = array([cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1, cc1])\\n\\n return array(R), array(Ni), array(Nk), cols, line_cols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e696be734f6dd38257eda5d315fe90\",\n \"score\": \"0.5326278\",\n \"text\": \"def make_sample_data_3D_3blobs(data_type=\\\"iris\\\", invert_xy=False):\\n\\n from iris.cube import Cube\\n from iris.coords import DimCoord, AuxCoord\\n\\n t_0 = datetime.datetime(2000, 1, 1, 12, 0, 0)\\n\\n x = np.arange(0, 100e3, 1000)\\n y = np.arange(0, 200e3, 1000)\\n z = np.arange(0, 20e3, 1000)\\n\\n t = t_0 + np.arange(0, 50, 2) * datetime.timedelta(minutes=1)\\n\\n t_temp = np.arange(0, 60, 1)\\n track1_t = t_0 + t_temp * datetime.timedelta(minutes=1)\\n x_0_1 = 10e3\\n y_0_1 = 10e3\\n z_0_1 = 4e3\\n track1_x = x_0_1 + 30 * t_temp * 60\\n track1_y = y_0_1 + 14 * t_temp * 60\\n track1_magnitude = 10 * np.ones(track1_x.shape)\\n\\n t_temp = np.arange(0, 30, 1)\\n track2_t = t_0 + (t_temp + 40) * datetime.timedelta(minutes=1)\\n x_0_2 = 20e3\\n y_0_2 = 10e3\\n z_0_2 = 6e3\\n track2_x = x_0_2 + 24 * (t_temp * 60) ** 2 / 1000\\n track2_y = y_0_2 + 12 * t_temp * 60\\n track2_magnitude = 20 * np.ones(track2_x.shape)\\n\\n t_temp = np.arange(0, 20, 1)\\n track3_t = t_0 + (t_temp + 50) * datetime.timedelta(minutes=1)\\n x_0_3 = 70e3\\n y_0_3 = 110e3\\n z_0_3 = 8e3\\n track3_x = x_0_3 + 20 * (t_temp * 60) ** 2 / 1000\\n track3_y = y_0_3 + 20 * t_temp * 60\\n track3_magnitude = 15 * np.ones(track3_x.shape)\\n\\n if invert_xy == False:\\n zz, yy, xx = np.meshgrid(z, y, x, indexing=\\\"ij\\\")\\n y_dim = 2\\n x_dim = 3\\n data = np.zeros((t.shape[0], z.shape[0], y.shape[0], x.shape[0]))\\n\\n else:\\n zz, xx, yy = np.meshgrid(z, x, y, indexing=\\\"ij\\\")\\n x_dim = 2\\n y_dim = 3\\n data = np.zeros((t.shape[0], z.shape[0], x.shape[0], y.shape[0]))\\n\\n for i_t, t_i in enumerate(t):\\n if np.any(t_i in track1_t):\\n x_i = track1_x[track1_t == t_i]\\n y_i = track1_y[track1_t == t_i]\\n z_i = z_0_1\\n mag_i = track1_magnitude[track1_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0))) * np.exp(\\n -np.power(zz - z_i, 2.0) / (2 * np.power(5e3, 2.0))\\n )\\n if np.any(t_i in track2_t):\\n x_i = track2_x[track2_t == t_i]\\n y_i = track2_y[track2_t == t_i]\\n z_i = z_0_2\\n mag_i = track2_magnitude[track2_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0))) * np.exp(\\n -np.power(zz - z_i, 2.0) / (2 * np.power(5e3, 2.0))\\n )\\n\\n if np.any(t_i in track3_t):\\n x_i = track3_x[track3_t == t_i]\\n y_i = track3_y[track3_t == t_i]\\n z_i = z_0_3\\n mag_i = track3_magnitude[track3_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0))) * np.exp(\\n -np.power(zz - z_i, 2.0) / (2 * np.power(5e3, 2.0))\\n )\\n\\n t_start = datetime.datetime(1970, 1, 1, 0, 0)\\n t_points = (t - t_start).astype(\\\"timedelta64[ms]\\\").astype(int) / 1000\\n t_coord = DimCoord(\\n t_points,\\n standard_name=\\\"time\\\",\\n var_name=\\\"time\\\",\\n units=\\\"seconds since 1970-01-01 00:00\\\",\\n )\\n z_coord = DimCoord(z, standard_name=\\\"geopotential_height\\\", var_name=\\\"z\\\", units=\\\"m\\\")\\n y_coord = DimCoord(\\n y, standard_name=\\\"projection_y_coordinate\\\", var_name=\\\"y\\\", units=\\\"m\\\"\\n )\\n x_coord = DimCoord(\\n x, standard_name=\\\"projection_x_coordinate\\\", var_name=\\\"x\\\", units=\\\"m\\\"\\n )\\n lat_coord = AuxCoord(\\n 24 + 1e-5 * xx[0], standard_name=\\\"latitude\\\", var_name=\\\"latitude\\\", units=\\\"degree\\\"\\n )\\n lon_coord = AuxCoord(\\n 150 + 1e-5 * yy[0],\\n standard_name=\\\"longitude\\\",\\n var_name=\\\"longitude\\\",\\n units=\\\"degree\\\",\\n )\\n sample_data = Cube(\\n data,\\n dim_coords_and_dims=[\\n (t_coord, 0),\\n (z_coord, 1),\\n (y_coord, y_dim),\\n (x_coord, x_dim),\\n ],\\n aux_coords_and_dims=[(lat_coord, (2, 3)), (lon_coord, (2, 3))],\\n var_name=\\\"w\\\",\\n units=\\\"m s-1\\\",\\n )\\n\\n if data_type == \\\"xarray\\\":\\n sample_data = DataArray.from_iris(sample_data)\\n\\n return sample_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9012f0656580298817201d201ca626dd\",\n \"score\": \"0.5276661\",\n \"text\": \"def apr3read(filename):\\n \\n apr = {}\\n flag = 0\\n ##Radar varibles in hdf file found by hdf.datasets\\n radar_freq = 'zhh14' #Ku\\n radar_freq2 = 'zhh35' #Ka\\n radar_freq3 = 'zvv95' #W\\n radar_freq4 = 'ldr14' #LDR\\n vel_str = 'vel14' #Doppler\\n ##\\n\\n hdf = SD(filename, SDC.READ)\\n \\n listofkeys = hdf.datasets().keys()\\n if 'zvv95' in listofkeys:\\n radar3 = hdf.select(radar_freq3)\\n radar_n3 = radar3.get()\\n radar_n3 = radar_n3/100.\\n else:\\n radar_n3 = np.array([])\\n flag = 1\\n print('No W band')\\n \\n \\n alt = hdf.select('alt3D')\\n lat = hdf.select('lat')\\n lon = hdf.select('lon')\\n time = hdf.select('scantime').get()\\n surf = hdf.select('surface_index').get()\\n isurf = hdf.select('isurf').get()\\n plane = hdf.select('alt_nav').get()\\n radar = hdf.select(radar_freq)\\n radar2 = hdf.select(radar_freq2)\\n radar4 = hdf.select(radar_freq4)\\n vel = hdf.select(vel_str)\\n lon3d = hdf.select('lon3D')\\n lat3d = hdf.select('lat3D')\\n alt3d = hdf.select('alt3D')\\n lat3d_scale = hdf.select('lat3D_scale').get()[0][0]\\n lon3d_scale = hdf.select('lon3D_scale').get()[0][0]\\n alt3d_scale = hdf.select('alt3D_scale').get()[0][0]\\n lat3d_offset = hdf.select('lat3D_offset').get()[0][0]\\n lon3d_offset = hdf.select('lon3D_offset').get()[0][0]\\n alt3d_offset = hdf.select('alt3D_offset').get()[0][0]\\n \\n alt = alt.get()\\n ngates = alt.shape[0]\\n #alt = alt[:,scan,:]\\n lat = lat.get()\\n #lat = lat[scan,:]\\n lon = lon.get()\\n #lon = lon[scan,:]\\n \\n lat3d = lat3d.get()\\n lat3d = (lat3d/lat3d_scale) + lat3d_offset\\n lon3d = lon3d.get()\\n lon3d = (lon3d/lon3d_scale) + lon3d_offset\\n alt3d = alt3d.get()\\n alt3d = (alt3d/alt3d_scale) + alt3d_offset\\n \\n #time = time[scan,:]\\n #surf = surf[scan,:]\\n #isurf = isurf[scan,:]\\n #plane = plane[scan,:]\\n radar_n = radar.get()\\n radar_n = radar_n/100.\\n radar_n2 = radar2.get()\\n radar_n2 = radar_n2/100.\\n\\n radar_n4 = radar4.get()\\n radar_n4 = radar_n4/100.\\n vel_n = vel.get()\\n vel_n = vel_n/100.\\n \\n #Quality control\\n radar_n[radar_n <= -99] = np.nan\\n radar_n2[radar_n2 <= -99] = np.nan\\n radar_n4[radar_n4 <= -99] = np.nan\\n \\n ##convert time to datetimes\\n time_dates = np.empty(time.shape,dtype=object)\\n for i in np.arange(0,time.shape[0]):\\n for j in np.arange(0,time.shape[1]):\\n tmp = datetime.datetime.utcfromtimestamp(time[i,j])\\n time_dates[i,j] = tmp\\n \\n #Create a time at each gate (assuming it is the same down each ray, there is a better way to do this) \\n time_gate = np.empty(lat3d.shape,dtype=object)\\n for k in np.arange(0,550):\\n for i in np.arange(0,time_dates.shape[0]):\\n for j in np.arange(0,time_dates.shape[1]):\\n time_gate[k,i,j] = time_dates[i,j] \\n\\n apr['Ku'] = radar_n\\n apr['Ka'] = radar_n2\\n apr['W'] = radar_n3\\n apr['DFR_1'] = radar_n - radar_n2 #Ku - Ka\\n \\n if flag == 0:\\n apr['DFR_2'] = radar_n2 - radar_n3 #Ka - W\\n apr['DFR_3'] = radar_n - radar_n3 #Ku - W\\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward]'\\n else:\\n apr['DFR_2'] = np.array([]) #Ka - W\\n apr['DFR_3'] = np.array([]) #Ku - W\\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward], Note No W band avail'\\n \\n apr['ldr'] = radar_n4\\n apr['vel'] = vel_n\\n apr['lon'] = lon\\n apr['lat'] = lat\\n apr['alt_gate'] = alt3d\\n apr['alt_plane'] = plane\\n apr['surface'] = isurf \\n apr['time']= time\\n apr['timedates']= time_dates\\n apr['time_gate'] = time_gate\\n apr['lon_gate'] = lon3d\\n apr['lat_gate'] = lat3d\\n \\n fileheader = hdf.select('fileheader')\\n roll = hdf.select('roll').get()\\n pitch = hdf.select('pitch').get()\\n drift = hdf.select('drift').get()\\n \\n apr['fileheader'] = fileheader\\n apr['ngates'] = ngates\\n apr['roll'] = roll\\n apr['pitch'] = pitch\\n apr['drift'] = drift\\n \\n _range = np.arange(15,550*30,30)\\n _range = np.asarray(_range,float)\\n ind = np.where(_range >= plane.mean())\\n _range[ind] = np.nan\\n apr['range'] = _range\\n \\n return apr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb2c7f57335a4920d8bbf0ada3853fa\",\n \"score\": \"0.5276661\",\n \"text\": \"def apr3read(filename):\\n \\n apr = {}\\n flag = 0\\n ##Radar varibles in hdf file found by hdf.datasets\\n radar_freq = 'zhh14' #Ku\\n radar_freq2 = 'zhh35' #Ka\\n radar_freq3 = 'zvv95' #W\\n radar_freq4 = 'ldr14' #LDR\\n vel_str = 'vel14' #Doppler\\n ##\\n\\n hdf = SD(filename, SDC.READ)\\n \\n listofkeys = hdf.datasets().keys()\\n if 'zvv95' in listofkeys:\\n radar3 = hdf.select(radar_freq3)\\n radar_n3 = radar3.get()\\n radar_n3 = radar_n3/100.\\n else:\\n radar_n3 = np.array([])\\n flag = 1\\n print('No W band')\\n \\n \\n alt = hdf.select('alt3D')\\n lat = hdf.select('lat')\\n lon = hdf.select('lon')\\n time = hdf.select('scantime').get()\\n surf = hdf.select('surface_index').get()\\n isurf = hdf.select('isurf').get()\\n plane = hdf.select('alt_nav').get()\\n radar = hdf.select(radar_freq)\\n radar2 = hdf.select(radar_freq2)\\n radar4 = hdf.select(radar_freq4)\\n vel = hdf.select(vel_str)\\n lon3d = hdf.select('lon3D')\\n lat3d = hdf.select('lat3D')\\n alt3d = hdf.select('alt3D')\\n lat3d_scale = hdf.select('lat3D_scale').get()[0][0]\\n lon3d_scale = hdf.select('lon3D_scale').get()[0][0]\\n alt3d_scale = hdf.select('alt3D_scale').get()[0][0]\\n lat3d_offset = hdf.select('lat3D_offset').get()[0][0]\\n lon3d_offset = hdf.select('lon3D_offset').get()[0][0]\\n alt3d_offset = hdf.select('alt3D_offset').get()[0][0]\\n \\n alt = alt.get()\\n ngates = alt.shape[0]\\n #alt = alt[:,scan,:]\\n lat = lat.get()\\n #lat = lat[scan,:]\\n lon = lon.get()\\n #lon = lon[scan,:]\\n \\n lat3d = lat3d.get()\\n lat3d = (lat3d/lat3d_scale) + lat3d_offset\\n lon3d = lon3d.get()\\n lon3d = (lon3d/lon3d_scale) + lon3d_offset\\n alt3d = alt3d.get()\\n alt3d = (alt3d/alt3d_scale) + alt3d_offset\\n \\n #time = time[scan,:]\\n #surf = surf[scan,:]\\n #isurf = isurf[scan,:]\\n #plane = plane[scan,:]\\n radar_n = radar.get()\\n radar_n = radar_n/100.\\n radar_n2 = radar2.get()\\n radar_n2 = radar_n2/100.\\n\\n radar_n4 = radar4.get()\\n radar_n4 = radar_n4/100.\\n vel_n = vel.get()\\n vel_n = vel_n/100.\\n\\n ##convert time to datetimes\\n time_dates = np.empty(time.shape,dtype=object)\\n for i in np.arange(0,time.shape[0]):\\n for j in np.arange(0,time.shape[1]):\\n tmp = datetime.datetime.utcfromtimestamp(time[i,j])\\n time_dates[i,j] = tmp\\n\\n apr['Ku'] = radar_n\\n apr['Ka'] = radar_n2\\n apr['W'] = radar_n3\\n apr['DFR_1'] = radar_n - radar_n2 #Ku - Ka\\n \\n if flag == 0:\\n apr['DFR_2'] = radar_n2 - radar_n3 #Ka - W\\n apr['DFR_3'] = radar_n - radar_n3 #Ku - W\\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward]'\\n else:\\n apr['DFR_2'] = np.array([]) #Ka - W\\n apr['DFR_3'] = np.array([]) #Ku - W\\n apr['info'] = 'The shape of these arrays are: Radar[Vertical gates,Time/DistanceForward], Note No W band avail'\\n \\n apr['ldr'] = radar_n4\\n apr['vel'] = vel_n\\n apr['lon'] = lon\\n apr['lat'] = lat\\n apr['alt_gate'] = alt3d\\n apr['alt_plane'] = plane\\n apr['surface'] = isurf \\n apr['time']= time\\n apr['timedates']= time_dates\\n apr['lon_gate'] = lon3d\\n apr['lat_gate'] = lat3d\\n \\n fileheader = hdf.select('fileheader')\\n roll = hdf.select('roll').get()\\n pitch = hdf.select('pitch').get()\\n drift = hdf.select('drift').get()\\n \\n apr['fileheader'] = fileheader\\n apr['ngates'] = ngates\\n apr['roll'] = roll\\n apr['pitch'] = pitch\\n apr['drift'] = drift\\n \\n _range = np.arange(15,550*30,30)\\n _range = np.asarray(_range,float)\\n ind = np.where(_range >= plane.mean())\\n _range[ind] = np.nan\\n apr['range'] = _range\\n \\n return apr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b870ffc194520cb215e546e7f57b4fcc\",\n \"score\": \"0.5271329\",\n \"text\": \"def process_xyz_md17(datafile):\\n xyz_lines = [line.decode('UTF-8') for line in datafile.readlines()]\\n\\n line_counter = 0\\n atom_positions = []\\n atom_types = []\\n for line in xyz_lines:\\n if line[0] is '#':\\n continue\\n if line_counter is 0:\\n num_atoms = int(line)\\n elif line_counter is 1:\\n split = line.split(';')\\n assert (len(split) == 1 or len(split) == 2), 'Improperly formatted energy/force line.'\\n if (len(split) == 1):\\n e = split[0]\\n f = None\\n elif (len(split) == 2):\\n e, f = split\\n f = f.split('],[')\\n atom_energy = float(e)\\n atom_forces = [[float(x.strip('[]\\\\n')) for x in force.split(',')] for force in f]\\n else:\\n split = line.split()\\n if len(split) is 4:\\n type, x, y, z = split\\n atom_types.append(split[0])\\n atom_positions.append([float(x) for x in split[1:]])\\n else:\\n logging.debug(line)\\n line_counter += 1\\n\\n atom_charges = [charge_dict[type] for type in atom_types]\\n\\n molecule = {'num_atoms': num_atoms, 'energy': atom_energy, 'charges': atom_charges,\\n 'forces': atom_forces, 'positions': atom_positions}\\n\\n molecule = {key: torch.tensor(val) for key, val in molecule.items()}\\n\\n return molecule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a796156c9bae9b904e94b8584c613456\",\n \"score\": \"0.5265736\",\n \"text\": \"def syn_genomic_data():\\n return h5py.File(features_path,'r')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14cf759d7a6c99df7d41b78b00653117\",\n \"score\": \"0.52639365\",\n \"text\": \"def sgsim_3D(nreal, df, xcol, ycol, zcol, vcol, nx, ny, nz, hsiz, vsiz, seed, var, output_file):\\n x = df[xcol]\\n y = df[ycol]\\n z = df[zcol]\\n v = df[vcol]\\n var_min = v.values.min()\\n var_max = v.values.max()\\n df_temp = pd.DataFrame({\\\"X\\\": x, \\\"Y\\\": y, \\\"Z\\\": z, \\\"Var\\\": v})\\n Dataframe2GSLIB(\\\"data_temp.dat\\\", df_temp)\\n\\n nug = var[\\\"nug\\\"]\\n nst = var[\\\"nst\\\"]\\n it1 = var[\\\"it1\\\"]\\n cc1 = var[\\\"cc1\\\"]\\n azi1 = var[\\\"azi1\\\"]\\n dip1 = var[\\\"dip1\\\"] \\n hmax1 = var[\\\"hmax1\\\"]\\n hmed1 = var[\\\"hmed1\\\"]\\n hmin1 = var[\\\"hmin1\\\"]\\n it2 = var[\\\"it2\\\"]\\n cc2 = var[\\\"cc2\\\"]\\n azi2 = var[\\\"azi2\\\"]\\n dip2 = var[\\\"dip2\\\"] \\n hmax2 = var[\\\"hmax2\\\"]\\n hmed2 = var[\\\"hmed2\\\"]\\n hmin2 = var[\\\"hmin2\\\"]\\n max_range = max(hmax1, hmax2)\\n max_range_v = max(hmin1, hmin2) * 10\\n hmn = hsiz * 0.5\\n zmn = vsiz * 0.5\\n hctab = int(max_range / hsiz) * 2 + 1\\n\\n with open(\\\"sgsim.par\\\", \\\"w\\\") as f:\\n f.write(\\\" Parameters for SGSIM \\\\n\\\")\\n f.write(\\\" ******************** \\\\n\\\")\\n f.write(\\\" \\\\n\\\")\\n f.write(\\\"START OF PARAMETER: \\\\n\\\")\\n f.write(\\\"data_temp.dat -file with data \\\\n\\\")\\n f.write(\\\"1 2 3 4 0 0 - columns for X,Y,Z,vr,wt,sec.var. \\\\n\\\")\\n f.write(\\\"-1.0e21 1.0e21 - trimming limits \\\\n\\\")\\n f.write(\\\"1 -transform the data (0=no, 1=yes) \\\\n\\\")\\n f.write(\\\"none.trn - file for output trans table \\\\n\\\")\\n f.write(\\\"0 - consider ref. dist (0=no, 1=yes) \\\\n\\\")\\n f.write(\\\"none.dat - file with ref. dist distribution \\\\n\\\")\\n f.write(\\\"1 0 - columns for vr and wt \\\\n\\\")\\n f.write(str(var_min) + \\\" \\\" + str(var_max) + \\\" zmin,zmax(tail extrapolation) \\\\n\\\")\\n f.write(\\\"1 \\\" + str(var_min) + \\\" - lower tail option, parameter \\\\n\\\")\\n f.write(\\\"1 \\\" + str(var_max) + \\\" - upper tail option, parameter \\\\n\\\")\\n f.write(\\\"0 -debugging level: 0,1,2,3 \\\\n\\\")\\n f.write(\\\"nonw.dbg -file for debugging output \\\\n\\\")\\n f.write(str(output_file) + \\\" -file for simulation output \\\\n\\\")\\n f.write(str(nreal) + \\\" -number of realizations to generate \\\\n\\\")\\n f.write(str(nx) + \\\" \\\" + str(hmn) + \\\" \\\" + str(hsiz) + \\\" \\\\n\\\")\\n f.write(str(ny) + \\\" \\\" + str(hmn) + \\\" \\\" + str(hsiz) + \\\" \\\\n\\\")\\n f.write(str(nz) + \\\" \\\" + str(zmn) + \\\" \\\" + str(vsiz) + \\\" \\\\n\\\")\\n f.write(str(seed) + \\\" -random number seed \\\\n\\\")\\n f.write(\\\"0 8 -min and max original data for sim \\\\n\\\")\\n f.write(\\\"12 -number of simulated nodes to use \\\\n\\\")\\n f.write(\\\"1 -assign data to nodes (0=no, 1=yes) \\\\n\\\")\\n f.write(\\\"1 3 -multiple grid search (0=no, 1=yes),num \\\\n\\\")\\n f.write(\\\"0 -maximum data per octant (0=not used) \\\\n\\\")\\n f.write(str(max_range) + \\\" \\\" + str(max_range) +\\\" \\\"+ str(max_range_v) + \\\" -maximum search (hmax,hmin,vert) \\\\n\\\")\\n f.write(str(azi1) + \\\" 0.0 0.0 -angles for search ellipsoid \\\\n\\\")\\n f.write(str(hctab) + \\\" \\\" + str(hctab) + \\\" 1 -size of covariance lookup table \\\\n\\\")\\n f.write(\\\"1 0.60 1.0 - ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC \\\\n\\\")\\n f.write(\\\"none.dat - file with LVM, EXDR, or COLC variable \\\\n\\\")\\n f.write(\\\"4 - column for secondary variable \\\\n\\\")\\n f.write(str(nst) + \\\" \\\" + str(nug) + \\\" -nst, nugget effect \\\\n\\\")\\n f.write(str(it1) + \\\" \\\" + str(cc1) + \\\" \\\" + str(azi1) + \\\" \\\" + str(dip1) +\\\" 0.0 -it,cc,ang1,ang2,ang3\\\\n\\\")\\n f.write(\\\" \\\" + str(hmax1) + \\\" \\t\\\" + str(hmed1) + \\\"\\t\\t\\\" + str(hmin1) + \\\" - a_hmax, a_hmin, a_vert \\\\n\\\")\\n f.write(str(it2) + \\\" \\\" + str(cc2) + \\\" \\t\\\" + str(azi2) + \\\"\\t\\t\\\" + str(dip2) +\\\" 0.0 -it,cc,ang1,ang2,ang3\\\\n\\\")\\n f.write(\\\" \\\" + str(hmax2) + \\\" \\\" + str(hmed2) + \\\" \\\" +str(hmin2) + \\\" - a_hmax, a_hmin, a_vert \\\\n\\\")\\n\\n os.system(\\\"sgsim.exe sgsim.par\\\")\\n sim_array = GSLIB2ndarray_3D(output_file, 0, nreal, nx, ny, nz)\\n return sim_array[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73fcca3c13d863f202c0090e357678df\",\n \"score\": \"0.52560854\",\n \"text\": \"def geometric_data():\\r\\n\\r\\n Sw = 175 * 0.3048 * 0.3048 # m2\\r\\n c = 4.79 * 0.3048 # m\\r\\n span = 36.9 * 0.3048 # m\\r\\n\\r\\n return Sw, c, span\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa1eb03f2548201c119cd3b802e6b4b\",\n \"score\": \"0.52547175\",\n \"text\": \"def read_3d_data( par ):\\n \\n # Load data\\n train, _, rcams_train = load.load_3D( par['data_dir'], par, cam_id=par['cam_id'], subjects=par['train_subjects'], actions=par['actions'] )\\n test, _, rcams_test = load.load_3D( par['data_dir'], par, cam_id=par['cam_id'], subjects=par['test_subjects'], actions=par['actions'] )\\n \\n #transform to camera coordinates\\n train = transform_frame( train, rcams_train )\\n test = transform_frame( test, rcams_test )\\n \\n # anchor points to body-coxa (to predict legjoints wrt body-coxas)\\n train, _ = utils.anchor_to_root( train, par['roots'], par['target_sets'], par['out_dim'])\\n test, offset = utils.anchor_to_root( test, par['roots'], par['target_sets'], par['out_dim'])\\n\\n # Standardize each dimension independently\\n mean, std = stat.normalization_stats( train )\\n train = stat.normalize( train, mean, std )\\n test = stat.normalize( test, mean, std )\\n \\n #select coordinates to be predicted and return them as 'targets_3d'\\n train, _ = utils.remove_roots(train, par['target_sets'], par['out_dim'])\\n test, targets_3d = utils.remove_roots(test, par['target_sets'], par['out_dim'])\\n\\n return train, test, mean, std, targets_3d, rcams_test, offset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b5240c59f9dfa89b16e1313c05a1cbc\",\n \"score\": \"0.5251831\",\n \"text\": \"def visualize3d_csv(algorithm, protein):\\n # construct the path to the csv file\\n filepath = pathlib.Path(\\\"Results/3d/{}/{}_{}.csv\\\".format(algorithm, algorithm, protein))\\n\\n # check if file exists\\n if not filepath.exists():\\n print(\\\"file cannot be found\\\")\\n exit(1)\\n\\n # read in the file as a list of protein objects\\n all_proteins = load_from_csv(filepath, protein)\\n num_proteins = len(all_proteins)\\n\\n for protein_i, protein in enumerate(all_proteins):\\n # get the attributes of the protein\\n all_coordinates = protein.get_all_coordinates()\\n amino_places = protein.get_amino_places()\\n bonds = protein.get_bonds()\\n\\n # putting the coordinates in an x and an y list\\n x_list, y_list, z_list, \\\\\\n scat_hx_list, scat_hy_list, scat_hz_list, \\\\\\n scat_px_list, scat_py_list, scat_pz_list, \\\\\\n scat_cx_list, scat_cy_list, scat_cz_list = ([] for list in range(12))\\n\\n # print(all_coordinates)\\n\\n for coordinates in list(all_coordinates):\\n # unpack the coordinate values\\n x, y, z = coordinates\\n x_list.append(x)\\n y_list.append(y)\\n z_list.append(z)\\n\\n # group the coordinates of the same amino kinds\\n amino_kind = amino_places[\\\"{}\\\".format(coordinates)]\\n if amino_kind == 'P':\\n scat_px_list.append(x)\\n scat_py_list.append(y)\\n scat_pz_list.append(z)\\n elif amino_kind == 'H':\\n scat_hx_list.append(x)\\n scat_hy_list.append(y)\\n scat_hz_list.append(z)\\n else:\\n scat_cx_list.append(x)\\n scat_cy_list.append(y)\\n scat_cz_list.append(z)\\n\\n # Make plot axes\\n ax = plt.axes(projection='3d')\\n ax.set_xlim3d(min(x_list) - 1, max(x_list) + 1)\\n ax.set_ylim3d(min(y_list) - 1, max(y_list) + 1)\\n ax.set_zlim3d(min(z_list) - 1, max(z_list) + 1)\\n\\n # Plot cavalent line\\n ax.plot3D(x_list, y_list, z_list, color='black', linestyle='solid', zorder=1)\\n\\n # use different colours to visualize the amino kind groups\\n ax.scatter3D(scat_px_list, scat_py_list, scat_pz_list, color='red', zorder=2)\\n ax.scatter3D(scat_hx_list, scat_hy_list, scat_hz_list, color='blue', zorder=2)\\n ax.scatter3D(scat_cx_list, scat_cy_list, scat_cz_list, color='gold', zorder=2)\\n\\n # unpack the bonds neighboring_locations\\n for bond in bonds:\\n bonds_x, bonds_y, bonds_z = ([] for list in range(3))\\n for coordinate in bond:\\n x, y, z = coordinate\\n bonds_x.append(x)\\n bonds_y.append(y)\\n bonds_z.append(z)\\n\\n # plot bond\\n ax.plot3D(bonds_x, bonds_y, bonds_z, color='green', linestyle='--', zorder=1)\\n\\n # show figure\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b26ef912871a3382861bc3d20ca254\",\n \"score\": \"0.5248432\",\n \"text\": \"def region_2d_to_location_3d(*argv):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"851c6f68450b9a84f5db79c639404b26\",\n \"score\": \"0.52283657\",\n \"text\": \"def val3p4c_depthfromIvarOLD(sample):\\n nside = 1024 # Resolution of output maps\\n nsideSTR='1024' # same as nside but in string format\\n nsidesout = None # if you want full sky degraded maps to be written\\n ratiores = 1 # Superresolution/oversampling ratio, simp mode doesn't allow anything other than 1\\n mode = 1 # 1: fully sequential, 2: parallel then sequential, 3: fully parallel\\n pixoffset = 0 # How many pixels are being removed on the edge of each CCD? 15 for DES.\\n oversamp='1' # ratiores in string format\\n \\n band = sample.band\\n catalogue_name = sample.catalog\\n fname = sample.ccds \\n localdir = sample.localdir\\n extc = sample.extc\\n\\n #Read ccd file \\n tbdata = pyfits.open(fname)[1].data\\n \\n # ------------------------------------------------------\\n # Obtain indices\\n auxstr='band_'+band\\n sample_names = [auxstr]\\n if(sample.DR == 'DR3'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \\n elif(sample.DR == 'DR4'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['bitmask'] == 0)) \\n elif(sample.DR == 'DR5'):\\n if(sample.survey == 'DECaLS'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \\n elif(sample.survey == 'DEShyb'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\\n elif(sample.survey == 'NGCproxy'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \\n elif(sample.DR == 'DR6'):\\n inds = np.where((tbdata['filter'] == band)) \\n elif(sample.DR == 'DR7'):\\n if(sample.survey == 'DECaLS'):\\n inds = np.where((tbdata['filter'] == band)) \\n elif(sample.survey == 'DEShyb'):\\n inds = np.where((tbdata['filter'] == band) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\\n elif(sample.survey == 'NGCproxy'):\\n inds = np.where((tbdata['filter'] == band) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \\n\\n #Read data \\n #obtain invnoisesq here, including extinction \\n nmag = Magtonanomaggies(tbdata['galdepth']-extc*tbdata['EBV'])/5.\\n ivar= 1./nmag**2.\\n\\n hits=np.ones(np.shape(ivar)) \\n\\n # What properties do you want mapped?\\n # Each each tuple has [(quantity to be projected, weighting scheme, operation),(etc..)] \\n propertiesandoperations = [ ('ivar', '', 'total'), ('hits','','total') ]\\n\\n \\n # What properties to keep when reading the images? \\n # Should at least contain propertiesandoperations and the image corners.\\n # MARCM - actually no need for ra dec image corners. \\n # Only needs ra0 ra1 ra2 ra3 dec0 dec1 dec2 dec3 only if fast track appropriate quicksip subroutines were implemented \\n #propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\\\\n # \\t+ ['RA', 'DEC', 'crval1', 'crval2', 'crpix1', 'crpix2', 'cd1_1', 'cd1_2', 'cd2_1', 'cd2_2','width','height']\\n propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\\\\n \\t+ ['RA', 'DEC', 'ra0','ra1','ra2','ra3','dec0','dec1','dec2','dec3']\\n \\n # Create big table with all relevant properties. \\n\\n tbdata = np.core.records.fromarrays([tbdata[prop] for prop in propertiesToKeep] + [ivar] + [hits], names = propertiesToKeep + [ 'ivar', 'hits'])\\n \\n # Read the table, create Healtree, project it into healpix maps, and write these maps.\\n # Done with Quicksip library, note it has quite a few hardcoded values (use new version by MARCM for BASS and MzLS) \\n # project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, outroot, sample_names, inds, nside)\\n #project_and_write_maps(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside, ratiores, pixoffset, nsidesout)\\n project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside)\\n \\n # Read Haelpix maps from quicksip \\n prop='ivar'\\n op='total'\\n vmin=21.0\\n vmax=24.0\\n\\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\\n f = fitsio.read(fname2)\\n\\n # HEALPIX DEPTH MAPS \\n # convert ivar to depth \\n import healpy as hp\\n from healpix3 import pix2ang_ring,thphi2radec\\n\\n ral = []\\n decl = []\\n val = f['SIGNAL']\\n pix = f['PIXEL']\\n \\n # Obtain values to plot \\n if (prop == 'ivar'):\\n myval = []\\n mylabel='depth' \\n \\n below=0 \\n for i in range(0,len(val)):\\n depth=nanomaggiesToMag(sqrt(1./val[i]) * 5.)\\n if(depth < vmin):\\n below=below+1\\n else:\\n myval.append(depth)\\n th,phi = hp.pix2ang(int(nside),pix[i])\\n ra,dec = thphi2radec(th,phi)\\n ral.append(ra)\\n decl.append(dec)\\n\\n npix=len(pix)\\n\\n print('Area is ', npix/(float(nside)**2.*12)*360*360./pi, ' sq. deg.')\\n print(below, 'of ', npix, ' pixels are not plotted as their ', mylabel,' < ', vmin)\\n print('Within the plot, min ', mylabel, '= ', min(myval), ' and max ', mylabel, ' = ', max(myval))\\n\\n\\n # Plot depth \\n from matplotlib import pyplot as plt\\n import matplotlib.cm as cm\\n\\n mapa = plt.scatter(ral,decl,c=myval, cmap=cm.rainbow,s=2., vmin=vmin, vmax=vmax, lw=0,edgecolors='none')\\n cbar = plt.colorbar(mapa)\\n plt.xlabel('r.a. (degrees)')\\n plt.ylabel('declination (degrees)')\\n plt.title('Map of '+ mylabel +' for '+catalogue_name+' '+band+'-band')\\n plt.xlim(0,360)\\n plt.ylim(-30,90)\\n mapfile=localdir+mylabel+'_'+band+'_'+catalogue_name+str(nside)+'.png'\\n print('saving plot to ', mapfile)\\n plt.savefig(mapfile)\\n plt.close()\\n #plt.show()\\n #cbar.set_label(r'5$\\\\sigma$ galaxy depth', rotation=270,labelpad=1)\\n #plt.xscale('log')\\n\\n\\n # Statistics depths\\n\\n deptharr=np.array(myval)\\n p90=np.percentile(deptharr,10)\\n p95=np.percentile(deptharr,5)\\n p98=np.percentile(deptharr,2)\\n med=np.percentile(deptharr,50)\\n mean = sum(deptharr)/float(np.size(deptharr)) # 1M array, too long for precision\\n std = sqrt(sum(deptharr**2.)/float(len(deptharr))-mean**2.)\\n\\n \\n\\n ndrawn=np.size(deptharr)\\n print(\\\"Total pixels\\\", np.size(deptharr), \\\"probably too many for exact mean and std\\\")\\n print(\\\"Mean = \\\", mean, \\\"; Median = \\\", med ,\\\"; Std = \\\", std)\\n print(\\\"Results for 90% 95% and 98% are: \\\", p90, p95, p98)\\n\\n # Statistics pases\\n prop = 'hits'\\n op = 'total'\\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\\n f = fitsio.read(fname2)\\n \\n hitsb=f['SIGNAL']\\n hist, bin_edges =np.histogram(hitsb,bins=[-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,100],density=True)\\n #print hitsb[1000:10015]\\n #hist, bin_edges =np.histogram(hitsb,density=True)\\n print(\\\"Percentage of hits for 0,1,2., to >7 pases\\\\n\\\", end=' ') \\n #print bin_edges \\n print(hist)\\n #print 100*hist\\n\\n return mapfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8017e4b628252c7621511e47c1e71ecf\",\n \"score\": \"0.52237123\",\n \"text\": \"def get_data():\\n size = 500\\n weights = [0.2, 0.5, 0.3]\\n cov = np.matrix([\\n [1, 0],\\n [0, 1]\\n ])\\n mean_1 = np.array([0, 0])\\n mean_2 = np.array([3, 0])\\n mean_3 = np.array([0, 3])\\n data_1 = multivariate_normal(mean=mean_1, cov=cov, size=size)\\n data_2 = multivariate_normal(mean=mean_2, cov=cov, size=size)\\n data_3 = multivariate_normal(mean=mean_3, cov=cov, size=size)\\n\\n select = np.random.choice(range(3), size=500, p=weights)\\n\\n data = np.concatenate((data_1[select == 0], data_2[select == 1], data_3[select == 2]))\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a291c603fa0a327f7dfb97b7be9158\",\n \"score\": \"0.5218129\",\n \"text\": \"def DC3D(self, ALPHA, X, Y, Z, DEPTH, DIP, AL1, AL2, AW1, AW2, DISL1, DISL2, DISL3):\\n\\n # SUBROUTINE DC3D(ALPHA,X,Y,Z,DEPTH,DIP,\\n # * AL1,AL2,AW1,AW2,DISL1,DISL2,DISL3,\\n # * UX,UY,UZ,UXX,UYX,UZX,UXY,UYY,UZY,UXZ,UYZ,UZZ,IRET)\\n # IMPLICIT REAL*8 (A-H,O-Z)\\n # REAL*4 ALPHA,X,Y,Z,DEPTH,DIP,AL1,AL2,AW1,AW2,DISL1,DISL2,DISL3,\\n # * UX,UY,UZ,UXX,UYX,UZX,UXY,UYY,UZY,UXZ,UYZ,UZZ,EPS\\n#\\n# COMMON /C0/DUMMY(5),SD,CD,dumm(5)\\n # DIMENSION XI(2),ET(2),KXI(2),KET(2)\\n # DIMENSION U(12),DU(12),DUA(12),DUB(12),DUC(12)\\n from math import sqrt\\n F0 = 0.0\\n EPS = 0.000001\\n\\n XI = num.zeros(2, float)\\n ET = num.zeros(2, float)\\n KXI = num.zeros(2, float)\\n KET = num.zeros(2, float)\\n U = num.zeros(12, float)\\n DU = num.zeros(12, float)\\n DUA = num.zeros(12, float)\\n DUB = num.zeros(12, float)\\n DUC = num.zeros(12, float)\\n\\n# -----\\n if Z > 0:\\n log.critical('** POSITIVE Z WAS GIVEN IN SUB-DC3D')\\n\\n for I in range(0, 12):\\n U[I] = F0\\n DUA[I] = F0\\n DUB[I] = F0\\n DUC[I] = F0\\n\\n AALPHA = ALPHA\\n DDIP = DIP\\n self.DCC0N0(AALPHA, DDIP)\\n CD = self.CD\\n SD = self.SD\\n\\n# -----\\n ZZ = Z\\n DD1 = DISL1\\n DD2 = DISL2\\n DD3 = DISL3\\n XI[0] = X-AL1\\n XI[1] = X-AL2\\n # if X==-6 and Y==-1:\\n # print AL1\\n # print AL2\\n # print 'hello'\\n\\n if abs(XI[0]) < EPS:\\n XI[0] = F0\\n if abs(XI[1]) < EPS:\\n XI[1] = F0\\n# ======================================\\n# ===== REAL-SOURCE CONTRIBUTION =====\\n# ======================================\\n D = DEPTH+Z\\n P = Y*CD+D*SD\\n Q = Y*SD-D*CD\\n ET[0] = P-AW1\\n ET[1] = P-AW2\\n if abs(Q) < EPS:\\n Q = F0\\n if abs(ET[0]) < EPS:\\n ET[0] = F0\\n if abs(ET[1]) < EPS:\\n ET[1] = F0\\n\\n# --------------------------------\\n# ----- REJECT SINGULAR CASE -----\\n# --------------------------------\\n# ----- ON FAULT EDGE\\n if (Q == F0 and ((XI[0]*XI[1] < F0 and ET[0]*ET[1] == F0)\\n or (ET[0]*ET[1] < F0 and XI[0]*XI[1] == F0))):\\n self.UX = F0\\n self.UY = F0\\n self.UZ = F0\\n self.UXX = F0\\n self.UYX = F0\\n self.UZX = F0\\n self.UXY = F0\\n self.UYY = F0\\n self.UZY = F0\\n self.UXZ = F0\\n self.UYZ = F0\\n self.UZZ = F0\\n self.IRET = 1\\n return\\n# ----- ON NEGATIVE EXTENSION OF FAULT EDGE\\n KXI[0] = 0\\n KXI[1] = 0\\n KET[0] = 0\\n KET[1] = 0\\n R12 = sqrt(XI[0]*XI[0]+ET[1]*ET[1]+Q*Q)\\n R21 = sqrt(XI[1]*XI[1]+ET[0]*ET[0]+Q*Q)\\n R22 = sqrt(XI[1]*XI[1]+ET[1]*ET[1]+Q*Q)\\n if XI[0] < F0 and R21+XI[1] < EPS:\\n KXI[0] = 1\\n if XI[0] < F0 and R22+XI[1] < EPS:\\n KXI[1] = 1\\n if ET[0] < F0 and R12+ET[1] < EPS:\\n KET[0] = 1\\n if ET[0] < F0 and R22+ET[1] < EPS:\\n KET[1] = 1\\n# =====\\n for K in range(0, 2):\\n for J in range(0, 2):\\n self.DCCON2(XI[J], ET[K], Q, SD, CD, KXI[K], KET[J])\\n self.UA(XI[J], ET[K], Q, DD1, DD2, DD3)\\n DUA = self.DUA\\n for I in range(0, 10, 3):\\n DU[I] = -DUA[I]\\n DU[I+1] = -DUA[I+1]*CD+DUA[I+2]*SD\\n DU[I+2] = -DUA[I+1]*SD-DUA[I+2]*CD\\n if I == 9:\\n DU[I] = -DU[I]\\n DU[I+1] = -DU[I+1]\\n DU[I+2] = -DU[I+2]\\n else:\\n continue\\n for I in range(0, 12):\\n if (J+K) != 1:\\n U[I] = U[I]+DU[I]\\n if (J+K) == 1:\\n U[I] = U[I]-DU[I]\\n\\n# =======================================\\n# ===== IMAGE-SOURCE CONTRIBUTION =====\\n# =======================================\\n D = DEPTH-Z\\n P = Y*CD+D*SD\\n Q = Y*SD-D*CD\\n ET[0] = P-AW1\\n ET[1] = P-AW2\\n if abs(Q) < EPS:\\n Q = F0\\n if abs(ET[0]) < EPS:\\n ET[0] = F0\\n if abs(ET[1]) < EPS:\\n ET[1] = F0\\n# --------------------------------\\n# ----- REJECT SINGULAR CASE -----\\n# --------------------------------\\n# ----- ON FAULT EDGE\\n if (Q == F0 and ((XI[0]*XI[1] < F0 and ET[0]*ET[1] == F0)\\n or (ET[0]*ET[1] < F0 and XI[0]*XI[1] == F0))):\\n self.UX = F0\\n self.UY = F0\\n self.UZ = F0\\n self.UXX = F0\\n self.UYX = F0\\n self.UZX = F0\\n self.UXY = F0\\n self.UYY = F0\\n self.UZY = F0\\n self.UXZ = F0\\n self.UYZ = F0\\n self.UZZ = F0\\n self.IRET = 1\\n return\\n# ----- ON NEGATIVE EXTENSION OF FAULT EDGE\\n KXI[0] = 0\\n KXI[1] = 0\\n KET[0] = 0\\n KET[1] = 0\\n R12 = sqrt(XI[0]*XI[0]+ET[1]*ET[1]+Q*Q)\\n R21 = sqrt(XI[1]*XI[1]+ET[0]*ET[0]+Q*Q)\\n R22 = sqrt(XI[1]*XI[1]+ET[1]*ET[1]+Q*Q)\\n if XI[0] < F0 and R21+XI[1] < EPS:\\n KXI[0] = 1\\n if XI[0] < F0 and R22+XI[1] < EPS:\\n KXI[1] = 1\\n if ET[0] < F0 and R12+ET[1] < EPS:\\n KET[0] = 1\\n if ET[0] < F0 and R22+ET[1] < EPS:\\n KET[1] = 1\\n# =====\\n for K in range(0, 2):\\n for J in range(0, 2):\\n self.DCCON2(XI[J], ET[K], Q, SD, CD, KXI[K], KET[J])\\n self.UA(XI[J], ET[K], Q, DD1, DD2, DD3)\\n DUA = self.DUA\\n self.UB(XI[J], ET[K], Q, DD1, DD2, DD3)\\n DUB = self.DUB\\n self.UC(XI[J], ET[K], Q, ZZ, DD1, DD2, DD3)\\n DUC = self.DUC\\n# -----\\n for I in range(0, 10, 3):\\n DU[I] = DUA[I]+DUB[I]+Z*DUC[I]\\n DU[I+1] = ((DUA[I+1]+DUB[I+1]+Z*DUC[I+1])*CD\\n - (DUA[I+2]+DUB[I+2]+Z*DUC[I+2])*SD)\\n DU[I+2] = ((DUA[I+1]+DUB[I+1]-Z*DUC[I+1])*SD\\n + (DUA[I+2]+DUB[I+2]-Z*DUC[I+2])*CD)\\n if I == 9:\\n DU[9] = DU[9]+DUC[0]\\n DU[10] = DU[10]+DUC[1]*CD-DUC[2]*SD\\n DU[11] = DU[11]-DUC[1]*SD-DUC[2]*CD\\n for I in range(0, 12):\\n if (J+K) != 1:\\n U[I] = U[I]+DU[I]\\n if (J+K) == 1:\\n U[I] = U[I]-DU[I]\\n# =====\\n self.UX = U[0]\\n self.UY = U[1]\\n self.UZ = U[2]\\n self.UXX = U[3]\\n self.UYX = U[4]\\n self.UZX = U[5]\\n self.UXY = U[6]\\n self.UYY = U[7]\\n self.UZY = U[8]\\n self.UXZ = U[9]\\n self.UYZ = U[10]\\n self.UZZ = U[11]\\n self.IRET = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c429d441a31a3f6d37c031774b8ebf66\",\n \"score\": \"0.52121\",\n \"text\": \"def get_data(self, identifiers):\\n out = dict()\\n with h5py.File(self.path, 'r') as hf:\\n for i, (person_identifier, index) in enumerate(identifiers):\\n out[identifiers[i]] = {\\n 'eye': hf[person_identifier]['image'][index][..., ::-1],\\n 'gaze': hf[person_identifier]['gaze'][index]\\n }\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a059e7b9a6355b7cb3cf64d1256e1395\",\n \"score\": \"0.5203842\",\n \"text\": \"def load_all_functional_data(chromosomes, epi_sets):\\n functional_data = {}\\n\\n for chrom in chromosomes:\\n functional_data[chrom] = None\\n for i, k in enumerate(epi_sets):\\n s = np.loadtxt(f'/nfs/turbo/umms-drjieliu/proj/4dn/data/microC/mESC/processed/{chrom}/{chrom}_200_{k}.txt')\\n s = zscore(s)\\n print(chrom, k, len(s))\\n if i == 0:\\n functional_data[chrom] = s\\n else:\\n functional_data[chrom] = np.vstack((functional_data[chrom], s))\\n functional_data[chrom] = functional_data[chrom].T\\n print(functional_data[chrom].shape) # 大致形状： 1,000,000 * 11\\n return functional_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1805924055314130f267c18cb8ebac60\",\n \"score\": \"0.5203016\",\n \"text\": \"def Get_Var_Arrays(var, fignum):\\n #create list of filenames\\n ncfile_list = [\\\"/newtera/tera/phil/nchaparr/sam_grex_ensemble/sam_case\\\"+ str(i+2) + \\\"/OUT_STAT/NCHAPP1_testing_doscamiopdata.nc\\\" for i in range(1)]\\n\\n #create lists for variable arrays from each case\\n vars_list = []\\n height_list = []\\n press_list = []\\n time_list = []\\n \\n for i in range(len(ncfile_list)): #loop over list of nc files\\n thefile = ncfile_list[i]\\n print thefile \\n ncdata = Dataset(thefile,'r')\\n Vars = ncdata.variables[var][...]\\n #print Vars.shape\\n press = ncdata.variables['PRES'][...]\\n height = ncdata.variables['z'][...]\\n top = np.where(abs(height - 2000) < 50)[0][0]\\n Vars = Vars[:, 0:top]\\n height = height[0:top]\\n time = ncdata.variables['time'][...]\\n ncdata.close()\\n \\n vars_list.append(Vars) \\n height_list.append(height)\\n time_list.append(time) \\n press_list.append(press)\\n \\n #get ensemble averages\\n ens_vars = nc.Ensemble1_Average(vars_list)\\n ens_press = nc.Ensemble1_Average(press_list)\\n ens_press = np.transpose(ens_press)\\n \\n #TODO: verify this is in time order!\\n \\n #print 'ENSEMBLE AVERAGED', ens_vars.shape\\n time = (time_list[0]-169)*24\\n height = height_list[0] #TODO: time, height don't need to be averaged \\n \\n #set up plot\\n theAx = nc.Do_Plot(fignum, 'Scaled ' + var + ' vs Scaled Height', 'Height/h', var+'/w*2', 111)\\n #print ens_vars.shape, height.shape\\n have_ens_vars = [] \\n print len(time)\\n for i in range(len(time)):\\n if np.mod(i+1, 6)==0:\\n #print i, time[i], 1.0*(i+1)/10, \\\"plotting\\\"\\n #points = For_Plots(\\\"Mar52014\\\")\\n #rinovals = points.rinovals()\\n #print len(rinovals[:,2])\\n #AvProfVars = points.AvProfVars()\\n #invrinos: [rino, invrino, wstar, S, tau, mltheta, deltatheta, pi3, pi4]\\n #wstar= rinovals[1.0*((i+1))*(6.0/6.0)-1, 2]\\n #h= AvProfVars[1.0*((i+1))*(6.0/6.0)-1, 1]\\n #h_index = np.where(height==h)[0]\\n print time[i]\\n have_ens_vars.append(1.0*np.sum(ens_vars[i]))\\n #print have_ens_vars[i]\\n theAx.plot(1.0*ens_vars[i], height, label=str(int(time[i])+1) + 'hrs')\\n #height, time = np.meshgrid(height, time)\\n #maxlev = np.max(ens_vars)\\n #minlev = np.min(ens_vars)\\n #step = (maxlev- minlev)/20\\n #levels = [i for i in np.arange(minlev, maxlev, step)]\\n #CS = plt.contourf(time, height, ens_vars, levels, cmap=plt.cm.bone)\\n #cbar = plt.colorbar(CS)\\n #cbar.ax.set_ylabel('colorbar')\\n #print 'plotting'\\n #theAx.plot(time, have_ens_vars, label=var)\\n plt.ylim(0, 2000)\\n plt.legend(loc = 'upper right', prop={'size':8})\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06dc741256e73a12604cad5f6144de7\",\n \"score\": \"0.5191207\",\n \"text\": \"def spatial_example():\\n\\n imin, imax = 124, 126 # i.e. load DD0124 and DD0125\\n ds_list = [None]*len(np.arange(imin,imax,1))\\n j = 0\\n for x in np.arange(imin,imax,1):\\n name = 'DD%0004i/DD%0004i'%(x,x)\\n if os.path.isfile(name):\\n ds_list[j] = name\\n j = j + 1\\n\\n ds_list = ds_list[:j]\\n\\n\\n # use galaxy class to get consistent region parameters\\n region_type = 'disk'\\n gal = Galaxy( (ds_list[0]).split('/')[0] )\\n region_kwargs = {}\\n for k in ['center','normal','radius','height']:\\n region_kwargs[k] = gal.disk_region[k]\\n nbin = 100\\n r = gal.disk_region['radius'].convert_to_units('pc').value\\n z = gal.disk_region['height'].convert_to_units('pc').value * 0.5\\n xbins = np.linspace(0.0, r, nbin)*yt.units.pc\\n ybins = np.linspace(-z, z, nbin*(2.0*z)/r)*yt.units.pc\\n\\n del(gal) # don't need anymore\\n\\n pd = time_average_phase_diagram(160.0, 170.0, ds_list = ds_list,\\n xfield = 'cylindrical_radius', yfield = 'cylindrical_z',\\n x_bins = xbins, y_bins = ybins,\\n zfield = \\\"G_o\\\", zunit = None, zlim = [0.01, 500.0], cmap = 'cubehelix',\\n# zfield = 'Fe_over_H', zunit = None, zlim = [-7,1.5], cmap = 'algae',\\n region_type = region_type, weight_field = None,\\n region_kwargs = region_kwargs, xlog=False, ylog=False, zlog=True)\\n\\n pd = time_average_phase_diagram(160.0, 170.0, ds_list = ds_list,\\n xfield = 'cylindrical_radius', yfield = 'cylindrical_z',\\n x_bins = xbins, y_bins = ybins,\\n zfield = 'Fe_over_H', zunit = None, zlim = [-7,1.5], cmap = 'algae',\\n region_type = region_type, weight_field = 'cell_mass',\\n region_kwargs = region_kwargs, xlog=False, ylog=False, zlog=True)\\n\\n# pd = time_average_phase_diagram(160.0, 170.0, ds_list = ds_list,\\n# xfield = 'cylindrical_radius', yfield = 'cylindrical_z',\\n# x_bins = xbins, y_bins = ybins,\\n# zfield = 'Fe_over_H', zunit = None, zlim = [-7,1.5], cmap = 'algae',\\n# region_type = region_type, weight_field = 'cell_mass',\\n# region_kwargs = region_kwargs, xlog=False, ylog=False, zlog=True)\\n\\n\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2bffa91ae27a2a723ba63d27349221f\",\n \"score\": \"0.5189888\",\n \"text\": \"def parse_gff_nc_genes():\\n\\t\\n\\tgff3 = sys.argv[1]\\n\\n\\t#today = datetime.date.today()\\n\\n\\t#ncRNA_gene_out = open('1_dmel_genes_ncRNA_%s_out.txt' %today, 'w')\\n\\n\\tf= open(gff3, 'r')\\n\\tgff = [] # initiates gff list\\n\\tmel_ncRNA_coords = {}\\n\\twhile True:\\n\\t\\tline = f.readline()\\n\\t\\tif not line.startswith('#'):\\n\\t\\t\\tdata = line.strip().split(\\\"\\\\t\\\")\\n\\t\\t\\tif True == True:\\n\\t\\n\\t\\t\\t\\ttry:\\n\\t\\t\\n\\t\\t\\t\\t\\tif data[2] == 'gene':\\n\\t\\t\\t\\t\\t\\t#print \\\"This is gene data\\\", data\\n\\t\\t\\t\\t\\t\\tgff.append(data,)\\n\\t\\t\\t\\t\\t\\t#print \\\"This is gff\\\", gff\\n\\t\\t\\t\\t\\t\\t#ncRNA_gene_out.write('%s' %line)\\n\\t\\t\\t\\n\\t\\t\\t\\t\\telif data[2] == 'ncRNA':\\n\\t\\t\\t\\t\\t\\t#ncRNA_gene_out.write('%s' %line)\\n\\t\\t\\t\\t\\t\\t#print \\\"This is ncRNA data\\\", data\\n\\t\\t\\t\\t\\t\\tgff.append(data,)\\n\\t\\t\\t\\t\\t\\tpost_data = data[8].split(';')[0]\\n\\t\\t\\t\\t\\t\\tmel_ncRNA_coords[post_data] = data[0], data[3], data[4], data[6]\\n\\t\\t\\t\\t\\t\\t#print data\\n\\t\\t\\t\\t\\t\\t#print mel_ncRNA_coords_dict\\n\\t\\t\\t\\t\\t\\t#quit()\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\n\\t\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\t\\tif line.startswith('>'):\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telif line.startswith('A'):\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telif line.startswith('T'):\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telif line.startswith('G'):\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telif line.startswith('C'):\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\n\\t\\t\\tif not line:\\n\\t\\t\\t\\tbreak\\n\\n\\t#ncRNA_gene_out.close()\\t\\t\\t\\n\\tf.close()\\n\\t#print \\\"This is gff:\\\", gff\\n\\treturn gff, mel_ncRNA_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5db3b04280faca78929728ebb428f300\",\n \"score\": \"0.5184003\",\n \"text\": \"def process_3D_data_csv(name, path=None):\\n\\n filename, name = _process_name(name, path, \\\".csv\\\")\\n\\n data = np.genfromtxt(filename, delimiter=',',skip_header=1)\\n\\n x1 = np.unique(data[:, 0])\\n x2 = np.unique(data[:, 1])\\n x3 = np.unique(data[:, 2])\\n\\n value = data[:, 3]\\n\\n x = (x1, x2, x3)\\n\\n value_data = value.reshape(len(x1), len(x2), len(x3), order=\\\"C\\\")\\n\\n formatted_data = (name, (x, value_data))\\n\\n return formatted_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"899ae48c28ba4eb9ab1fd0165eb6d482\",\n \"score\": \"0.5174608\",\n \"text\": \"def mogi(data, x0, y0, z0, dV):\\n dx = data[0,:] - x0\\n dy = data[1,:] - y0\\n dz = 0 - z0\\n c = dV * 3. / 4. * pi\\n # or equivalently c= (3/4) a^3 dP / rigidity\\n # where a = sphere radius, dP = delta Pressure\\n r2 = dx*dx + dy*dy + dz*dz\\n C = c / pow(r2, 1.5)\\n return array((C*dx,C*dy,C*dz))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"074005479ed7a27e190f85bddf4fe4b7\",\n \"score\": \"0.5174326\",\n \"text\": \"def get_output_3D(X,Y,Z):\\n if isinstance( X, (float,int) ):\\n X = np.array([X]); Y=np.array([Y]); Z=np.array([Z])\\n elif isinstance( X, list ):\\n X= np.array(X); Y=np.array(Y); Z=np.array(Z)\\n\\n classification_data, regression_data = get_raw_output_3D(X,Y,Z)\\n if X.ndim > 1 or Y.ndim > 1 or Z.ndim > 1:\\n classification_data = classification_data.flatten()\\n regression_data = regression_data.flatten()\\n X=X.flatten(); Y=Y.flatten(); Z=Z.flatten()\\n data_frame = pd.DataFrame()\\n data_frame[\\\"input_1\\\"] = X\\n data_frame[\\\"input_2\\\"] = Y\\n data_frame[\\\"input_3\\\"] = Z\\n data_frame[\\\"class\\\"] = classification_data.flatten()\\n data_frame[\\\"output_1\\\"] = regression_data.flatten()\\n return data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a0969a6a6e5f1c4ed2bcc237853cbb\",\n \"score\": \"0.5160178\",\n \"text\": \"def gen_3s(img, a, cen, sz=16):\\n tf_xy = get_tf_xynormal(cen)\\n tf_xy = tf_rotateXYZ(tf_xy, a)\\n xy = gen_slice(img, tf_xy, sz2ext(sz))\\n\\n tf_yz = get_tf_yznormal(cen)\\n tf_yz = tf_rotateXYZ(tf_yz, a)\\n yz = gen_slice(img, tf_yz, sz2ext(sz))\\n\\n tf_xz = get_tf_xznormal(cen)\\n tf_xz = tf_rotateXYZ(tf_xz, a)\\n xz = gen_slice(img, tf_xz, sz2ext(sz))\\n\\n return xy, yz, xz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e570eaa499ffc391804bd48b583b0dc\",\n \"score\": \"0.5153892\",\n \"text\": \"def extract_nucleus_features(data_path,area=500,m_m=2.8,solidity=0.4):\\n seg = nuclei_segmenter(cropped_flag=1,area_threshold=area,major_minor=m_m,solidity_threshold=solidity)\\n \\n sipakmed_df_ext = pd.DataFrame()\\n folders = listdir(data_path)[1:]\\n for folder in folders:\\n for blob in glob.glob(data_path+'{}/CROPPED/*.bmp'.format(folder)):\\n seg.read_img(blob) # read the image\\n df = seg.segment_nuclei() # segment the nuclei\\n if not df.empty:\\n df['cluster_id'] = int(blob.split('/')[-1][0:3]) # cluster id is the first three numbers of the file name\\n df['Class'] = folder[3].lower() # class id is the first letter of the containing folder\\n if df['Class'] in ['s','p']:\\n df['Normal']=1\\n else:\\n df['Normal']=0\\n sipakmed_df_ext = sipakmed_df_ext.append(df)\\n sipakmed_df_ext.to_csv('../data/extracted/Sipakmed_nuclei_ext.csv')\\n return sipakmed_df_ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879b587e9d1636d2b035f7cfb69a050a\",\n \"score\": \"0.515096\",\n \"text\": \"def _get_calculation_data(self, grid_3d):\\n dose_lut = [grid_3d[0], grid_3d[1]]\\n dose_grid_points = get_dose_grid(dose_lut)\\n x_delta = abs(grid_3d[0][0] - grid_3d[0][1])\\n y_delta = abs(grid_3d[1][0] - grid_3d[1][1])\\n # get structure slice Position\\n # ordered_z = self.ordered_planes\\n z_delta = self.structure['thickness']\\n grid_delta = [x_delta, y_delta, z_delta]\\n return self.planes, dose_lut, dose_grid_points, grid_delta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429752181166c2abc4bc32c5f3cdaef9\",\n \"score\": \"0.51473665\",\n \"text\": \"def zindex_to_grid3D(z):\\n return numpy.array([unpart1by2(z ),\\n unpart1by2(z >> 1),\\n unpart1by2(z >> 2)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"447b84c00cd36cf5e6cc862531322cdd\",\n \"score\": \"0.51440984\",\n \"text\": \"def make_variogram_3D(\\n nug,\\n nst,\\n it1,\\n cc1,\\n azi1,\\n dip1,\\n hmax1,\\n hmed1,\\n hmin1,\\n it2=1,\\n cc2=0,\\n azi2=0,\\n dip2=0,\\n hmax2=0,\\n hmed2=0,\\n hmin2=0,\\n):\\n if cc2 == 0:\\n nst = 1\\n var = dict(\\n [\\n (\\\"nug\\\", nug),\\n (\\\"nst\\\", nst),\\n (\\\"it1\\\", it1),\\n (\\\"cc1\\\", cc1),\\n (\\\"azi1\\\", azi1),\\n (\\\"dip1\\\", dip1),\\n (\\\"hmax1\\\", hmax1),\\n (\\\"hmed1\\\", hmed1),\\n (\\\"hmin1\\\", hmin1),\\n (\\\"it2\\\", it2),\\n (\\\"cc2\\\", cc2),\\n (\\\"azi2\\\", azi2),\\n (\\\"dip2\\\", dip2),\\n (\\\"hmax2\\\", hmax2),\\n (\\\"hmed2\\\", hmed2),\\n (\\\"hmin2\\\", hmin2),\\n ]\\n )\\n if nug + cc1 + cc2 != 1:\\n print(\\n \\\"\\\\x1b[0;30;41m make_variogram Warning: \\\"\\n \\\"sill does not sum to 1.0, do not use in simulation \\\\x1b[0m\\\"\\n )\\n if (\\n cc1 < 0\\n or cc2 < 0\\n or nug < 0\\n or hmax1 < 0\\n or hmax2 < 0\\n or hmin1 < 0\\n or hmin2 < 0\\n ):\\n print(\\n \\\"\\\\x1b[0;30;41m make_variogram Warning: \\\"\\n \\\"contributions and ranges must be all positive \\\\x1b[0m\\\"\\n )\\n if hmax1 < hmed1 or hmax2 < hmed2:\\n print(\\n \\\"\\\\x1b[0;30;41m make_variogram Warning: \\\"\\n \\\"major range should be greater than minor range \\\\x1b[0m\\\"\\n )\\n return var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597e376f7b065853f47b5c73d7b263b5\",\n \"score\": \"0.51430905\",\n \"text\": \"def template_ds(self):\\n\\n out_ds=self.g.write_to_xarray(mesh_name=\\\"Mesh2\\\",\\n node_coordinates=\\\"Mesh2_node_x Mesh2_node_y\\\",\\n face_node_connectivity='Mesh2_face_nodes',\\n edge_node_connectivity='Mesh2_edge_nodes',\\n face_dimension='nMesh2_face',\\n edge_dimension='nMesh2_edge',\\n node_dimension='nMesh2_node')\\n out_ds=out_ds.rename({\\n 'maxnode_per_face':'nMaxMesh2_face_nodes',\\n 'node_per_edge':'Two'\\n })\\n\\n # Additional grid information:\\n # xarray wants the dimension made explicit here -- don't know why.\\n out_ds['Mesh2_face_x']=('nMesh2_face',),self.mod_map_ds['Mesh2_face_x'].values\\n out_ds['Mesh2_face_y']=('nMesh2_face',),self.mod_map_ds['Mesh2_face_y'].values\\n\\n out_ds['Mesh2_edge_x']=('nMesh2_edge',),self.mod_map_ds['Mesh2_edge_x'].values\\n out_ds['Mesh2_edge_y']=('nMesh2_edge',),self.mod_map_ds['Mesh2_edge_y'].values\\n\\n e2c=self.g.edge_to_cells()\\n out_ds['Mesh2_edge_faces']=('nMesh2_edge','Two'),e2c\\n\\n face_edges=np.array([self.g.cell_to_edges(c,pad=True)\\n for c in range(self.g.Ncells())] )\\n\\n out_ds['Mesh2_face_edges']=('nMesh2_face','nMaxMesh2_face_nodes'),face_edges\\n out_ds['Mesh2_face_depth']=('nMesh2_face',),-self.mod_map_ds['mesh2d_flowelem_bl'].values\\n out_ds['Mesh2_face_depth'].attrs.update({'positive':'down',\\n 'unit':'m',\\n 'standard_name':'sea_floor_depth_below_geoid',\\n 'mesh':'Mesh2',\\n 'long_name':'Mean elevation of bed in face'})\\n\\n # recreate edge bed level based on a constant bedlevtype\\n bedlevtype=int(self.model.mdu['geometry','BedLevType'])\\n if bedlevtype==3:\\n edge_z=self.map_ds.mesh2d_node_z.values[self.g.edges['nodes']].mean(axis=1)\\n elif bedlevtype==4:\\n edge_z=self.map_ds.mesh2d_node_z.values[self.g.edges['nodes']].min(axis=1)\\n elif bedlevtype==6:\\n c1 = e2c[:,0]\\n c2 = e2c[:,1].copy()\\n c2[c2<0] = c1[c2<0]\\n edge_z=self.map_ds.mesh2d_flowelem_bl.values[np.c_[c1,c2]].max(axis=1)\\n else:\\n raise Exception(\\\"Only know how to deal with bed level type 3,4 not %d\\\"%bedlevtype)\\n # mindful of positive-down sign convention needed by PTM\\n out_ds['Mesh2_edge_depth']=('nMesh2_edge',),-edge_z\\n out_ds['Mesh2_edge_depth'].attrs.update({'positive':'down',\\n 'unit':'m',\\n 'standard_name':'sea_floor_depth_below_geoid',\\n 'mesh':'Mesh2',\\n 'long_name':'Mean elevation of bed on edge'})\\n\\n out_ds['Mesh2_data_time']=self.mod_map_ds.nMesh2_data_time\\n\\n if 1:\\n # This may not be necessary -- this keeps the freesurface\\n # from appearing below the bed in cells, but these should\\n # appear as dry based on ktop/kbot.\\n # also, writing anything time-varying beyond the time stamps themselves\\n # should probably be handled in the time loop [TODO]\\n s1=np.maximum( self.mod_map_ds.mesh2d_s1, -out_ds['Mesh2_face_depth'])\\n out_ds['Mesh2_sea_surface_elevation']=('nMesh2_face','nMesh2_data_time'),s1.T.values\\n\\n if 1: # edge and cell marks\\n edge_marks=self.g.edges['mark']\\n assert not np.any(edge_marks<0),\\\"Need to implement internal closed edges\\\"\\n # this gets us to 0: internal, 1:boundary, 2: boundary_closed\\n # this looks like 1 for stage or flow BC, 2 for land, 0 for internal.\\n out_ds['Mesh2_edge_bc']=('nMesh2_edge',),edge_marks\\n\\n # 'facemark':'Mesh2_face_bc'\\n out_ds['Mesh2_face_bc']=('nMesh2_face',),self.g.cells['mark']\\n\\n if 1: # layers\\n ucx=self.mod_map_ds['mesh2d_ucx']\\n if ucx.ndim==2:\\n self.nkmax=1\\n self.map_2d=True\\n else:\\n self.nkmax=ucx.shape[-1] # is it safe to assume nkmax is last in DFM?\\n self.map_2d=False\\n out_ds['nMesh2_layer_3d']=('nMesh2_layer_3d',),np.arange(self.nkmax)\\n\\n # based on sample output, the last of these is not used,\\n # so this would be interfaces, starting with the top of the lowest layer.\\n # fabricate something in sigma coordinates for now.\\n #sigma_layers=np.linspace(-1,0,self.nkmax+1)[1:]\\n #sigma_layers=self.mod_map_ds['mesh2d_interface_sigma'][1:]\\n if 'mesh2d_interface_sigma' in self.mod_map_ds.keys():\\n sigma_layers=self.mod_map_ds['mesh2d_interface_sigma'][1:]\\n else:\\n # 2022-06-10 RH: I think this change came in during the SFEI work\\n # going to 3D. Not entirely clear what's going on here.\\n sigma_layers=[1.0]\\n out_ds['Mesh2_layer_3d']=('nMesh2_layer_3d',),sigma_layers\\n attrs=dict(standard_name=\\\"ocean_sigma_coordinate\\\",\\n dz_min=0.001, # not real, but ptm tries to read this.\\n long_name=\\\"sigma layer coordinate at flow element top\\\",\\n units=\\\"\\\",\\n positive=\\\"up\\\", # kind of baked into the sigma definition\\n formula_terms=\\\"sigma: Mesh2_layer_3d eta: Mesh2_sea_surface_elevation bedlevel: Mesh2_face_depth\\\"\\n )\\n out_ds['Mesh2_layer_3d'].attrs.update(attrs)\\n\\n # this would be for adding more scalars to be extracted at particle positions\\n # Just guessing with 1 -- maybe 0 is more appropriate?\\n out_ds['nsp']=('nsp',),np.arange(1)\\n\\n # from http://cfconventions.org/Data/cf-conventions/cf-conventions-1.0/build/apd.html\\n # z(n,k,j,i) = eta(n,j,i) + sigma(k)*(depth(j,i)+eta(n,j,i))\\n # sigma coordinate definition has z=positive:up baked in, likewise depth is\\n # positive down, and eta positive up, with sigma ranging from -1 (bed) to 0 (surface)\\n\\n # for writing the output, use xarray to initialize the file, but\\n # the big data part is best handled directly by netCDF4 so we can\\n # control how much data is in RAM at a time.\\n return out_ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0427d731546ddc2bfab7a67b26cbefd0\",\n \"score\": \"0.5141802\",\n \"text\": \"def make_sample_data_2D_3blobs_inv(data_type=\\\"iris\\\"):\\n\\n from iris.cube import Cube\\n from iris.coords import DimCoord, AuxCoord\\n\\n t_0 = datetime.datetime(2000, 1, 1, 12, 0, 0)\\n x = np.arange(0, 100e3, 1000)\\n y = np.arange(0, 200e3, 1000)\\n t = t_0 + np.arange(0, 100, 1) * datetime.timedelta(minutes=1)\\n yy, xx = np.meshgrid(y, x)\\n\\n t_temp = np.arange(0, 60, 1)\\n track1_t = t_0 + t_temp * datetime.timedelta(minutes=1)\\n x_0_1 = 10e3\\n y_0_1 = 10e3\\n track1_x = x_0_1 + 30 * t_temp * 60\\n track1_y = y_0_1 + 14 * t_temp * 60\\n track1_magnitude = 10 * np.ones(track1_x.shape)\\n\\n t_temp = np.arange(0, 30, 1)\\n track2_t = t_0 + (t_temp + 40) * datetime.timedelta(minutes=1)\\n x_0_2 = 20e3\\n y_0_2 = 10e3\\n track2_x = x_0_2 + 24 * (t_temp * 60) ** 2 / 1000\\n track2_y = y_0_2 + 12 * t_temp * 60\\n track2_magnitude = 20 * np.ones(track2_x.shape)\\n\\n t_temp = np.arange(0, 20, 1)\\n track3_t = t_0 + (t_temp + 50) * datetime.timedelta(minutes=1)\\n x_0_3 = 70e3\\n y_0_3 = 110e3\\n track3_x = x_0_3 + 20 * (t_temp * 60) ** 2 / 1000\\n track3_y = y_0_3 + 20 * t_temp * 60\\n track3_magnitude = 15 * np.ones(track3_x.shape)\\n\\n data = np.zeros((t.shape[0], x.shape[0], y.shape[0]))\\n for i_t, t_i in enumerate(t):\\n if np.any(t_i in track1_t):\\n x_i = track1_x[track1_t == t_i]\\n y_i = track1_y[track1_t == t_i]\\n mag_i = track1_magnitude[track1_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\\n if np.any(t_i in track2_t):\\n x_i = track2_x[track2_t == t_i]\\n y_i = track2_y[track2_t == t_i]\\n mag_i = track2_magnitude[track2_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\\n if np.any(t_i in track3_t):\\n x_i = track3_x[track3_t == t_i]\\n y_i = track3_y[track3_t == t_i]\\n mag_i = track3_magnitude[track3_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\\n\\n t_start = datetime.datetime(1970, 1, 1, 0, 0)\\n t_points = (t - t_start).astype(\\\"timedelta64[ms]\\\").astype(int) / 1000\\n\\n t_coord = DimCoord(\\n t_points,\\n standard_name=\\\"time\\\",\\n var_name=\\\"time\\\",\\n units=\\\"seconds since 1970-01-01 00:00\\\",\\n )\\n x_coord = DimCoord(\\n x, standard_name=\\\"projection_x_coordinate\\\", var_name=\\\"x\\\", units=\\\"m\\\"\\n )\\n y_coord = DimCoord(\\n y, standard_name=\\\"projection_y_coordinate\\\", var_name=\\\"y\\\", units=\\\"m\\\"\\n )\\n lat_coord = AuxCoord(\\n 24 + 1e-5 * xx, standard_name=\\\"latitude\\\", var_name=\\\"latitude\\\", units=\\\"degree\\\"\\n )\\n lon_coord = AuxCoord(\\n 150 + 1e-5 * yy, standard_name=\\\"longitude\\\", var_name=\\\"longitude\\\", units=\\\"degree\\\"\\n )\\n\\n sample_data = Cube(\\n data,\\n dim_coords_and_dims=[(t_coord, 0), (y_coord, 2), (x_coord, 1)],\\n aux_coords_and_dims=[(lat_coord, (1, 2)), (lon_coord, (1, 2))],\\n var_name=\\\"w\\\",\\n units=\\\"m s-1\\\",\\n )\\n\\n if data_type == \\\"xarray\\\":\\n sample_data = DataArray.from_iris(sample_data)\\n\\n return sample_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab42fafe1207d9701a8e9b5a91b5755\",\n \"score\": \"0.5138752\",\n \"text\": \"def read_xspress3_data(file_path):\\n data_output = {}\\n\\n # file_path = os.path.join(working_directory, file_name)\\n with h5py.File(file_path, \\\"r\\\") as f:\\n data = f[\\\"entry/instrument\\\"]\\n\\n # data from channel summed\\n exp_data = np.asarray(data[\\\"detector/data\\\"])\\n xval = np.asarray(data[\\\"NDAttributes/NpointX\\\"])\\n yval = np.asarray(data[\\\"NDAttributes/NpointY\\\"])\\n\\n # data size is (ysize, xsize, num of frame, num of channel, energy channel)\\n exp_data = np.sum(exp_data, axis=2)\\n num_channel = exp_data.shape[2]\\n # data from each channel\\n for i in range(num_channel):\\n channel_name = \\\"channel_\\\" + str(i + 1)\\n data_output.update({channel_name: exp_data[:, :, i, :]})\\n\\n # change x,y to 2D array\\n xval = xval.reshape(exp_data.shape[0:2])\\n yval = yval.reshape(exp_data.shape[0:2])\\n\\n data_output.update({\\\"x_pos\\\": xval})\\n data_output.update({\\\"y_pos\\\": yval})\\n\\n return data_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1894ca88e890aaec3a7619a40cbf654\",\n \"score\": \"0.5134556\",\n \"text\": \"def calc_zw3(ifile, var_id, ofile):\\n\\n # Read data\\n dset_in = xarray.open_dataset(ifile)\\n gio.check_xarrayDataset(dset_in, var_id)\\n \\n # Calculate the index\\n groupby_op = get_groupby_op(dset_in['time'].values)\\n index = {}\\n for region in ['zw31', 'zw32', 'zw33']: \\n south_lat, north_lat, west_lon, east_lon = gio.regions[region]\\n darray = dset_in[var_id].sel(latitude=slice(south_lat, north_lat), longitude=slice(west_lon, east_lon)).mean(dim=['latitude', 'longitude'])\\n\\n clim = darray.groupby(groupby_op).mean(dim='time')\\n anom = darray.groupby(groupby_op) - clim\\n stdev = darray.groupby(groupby_op).std(dim='time')\\n norm = anom.groupby(groupby_op) / stdev\\n\\n index[region] = norm.values\\n\\n zw3_timeseries = (index['zw31'] + index['zw32'] + index['zw33']) / 3.0\\n \\n # Write output file\\n d = {}\\n d['time'] = darray['time']\\n d['zw3'] = (['time'], zw3_timeseries)\\n dset_out = xarray.Dataset(d)\\n \\n dset_out['zw3'].attrs = {'id': 'zw3',\\n 'long_name': 'zonal_wave_3_index',\\n 'standard_name': 'zonal_wave_3_index',\\n 'units': '',\\n 'notes': 'Ref: ZW3 index of Raphael (2004)'}\\n \\n gio.set_global_atts(dset_out, dset_in.attrs, {ifile: dset_in.attrs['history']})\\n dset_out.to_netcdf(ofile, format='NETCDF3_CLASSIC')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268ee4ea37798536bbb16c589225a097\",\n \"score\": \"0.5131933\",\n \"text\": \"def ixy2virtual4C(\\n d,\\n fout,\\n logger,\\n chrom=\\\"\\\",\\n start=-1,\\n end=-1,\\n viewStart=-1,\\n viewEnd=-1,\\n cut=0,\\n mcut=-1,\\n ):\\n if start != -1 and end != -1 and end < start:\\n logger.error(\\\"End %s is smaller than %s start.\\\" % (end, start))\\n return\\n if viewStart == -1 or viewEnd == -1:\\n logger.error(\\\"viewStart or viewEnd not assigned.\\\")\\n return\\n if viewStart != -1 and viewEnd != -1 and viewEnd < viewStart:\\n logger.error(\\\"viewEnd %s is smaller than %s viewStart.\\\" % (viewEnd, viewStart))\\n return\\n f = os.path.join(d,chrom+\\\".ixy\\\")\\n if not os.path.isfile(f):\\n logger.error(\\\"%s not exists, please check the input -virtual4C_chrom\\\"%f)\\n return\\n metaf = d + \\\"/petMeta.json\\\"\\n meta = json.loads(open(metaf).read())\\n tot = meta[\\\"Unique PETs\\\"] \\n chrom, xy = parseIxy(f, cut=cut, mcut=mcut)\\n if start == -1:\\n start = np.min(xy)\\n if end == -1:\\n end = np.max(xy)\\n ps = np.where((xy[:, 0] >= start) & (xy[:, 1] <= end))[0]\\n xy = xy[ps, ]\\n xy2 = XY(xy[:, 0], xy[:, 1]) #XY object\\n virtual4Csig = getVirtual4CSig(xy2, start, end, viewStart, viewEnd)\\n with open( fout + \\\"_4C.bdg\\\",\\\"w\\\") as fo:\\n i = 0\\n while i < len(virtual4Csig) -1 :\\n if virtual4Csig[i] == 0 :\\n i += 1\\n continue\\n for j in range(i+1, len(virtual4Csig)):\\n if virtual4Csig[j] != virtual4Csig[i]:\\n break\\n #v = np.log2(virtual4Csig[i] / 1.0 / tot * 10**6)\\n #v = virtual4Csig[i] / 1.0 / tot * 10**6\\n v = np.log2(virtual4Csig[i])\\n line = [chrom[0],start+i, start+j-1,v]\\n fo.write(\\\"\\\\t\\\".join(list(map(str, line))) + \\\"\\\\n\\\")\\n if j == len(virtual4Csig) - 1:\\n break\\n i = j\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed80c3c08699f844073c817180d1e37\",\n \"score\": \"0.5128286\",\n \"text\": \"def datacoords(self):\\n return {\\n k: v.values\\n for k, v in self.cube.coords.items()\\n if v.dims == (\\\"x\\\", \\\"y\\\", \\\"ch\\\")\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f202c165c613f3a0637f90ab76d268\",\n \"score\": \"0.5127099\",\n \"text\": \"def construct_3d_hdu(self):\\n # Report\\n if not self.quiet:\\n log_output(self.loggers, 1, logging.INFO, 'Constructing stellar continuum datacube ...')\\n\\n bin_indx = self.hdu['BINID'].data.copy()\\n model_flux = self.hdu['FLUX'].data.copy()\\n model_mask = self.hdu['MASK'].data.copy()\\n\\n flux, mask = DAPFitsUtil.reconstruct_cube(self.shape, bin_indx.ravel(),\\n [model_flux, model_mask])\\n\\n mask = self._finalize_cube_mask(mask)\\n\\n # Primary header is identical regardless of the shape of the\\n # extensions\\n hdr = self.hdu['PRIMARY'].header.copy()\\n cube_hdr = DAPFitsUtil.build_cube_header(self.binned_spectra.cube,\\n 'K Westfall <westfall@ucolick.org>')\\n\\n # Return the converted hdu without altering the internal hdu\\n return fits.HDUList([ fits.PrimaryHDU(header=hdr),\\n fits.ImageHDU(data=flux, header=cube_hdr, name='FLUX'),\\n fits.ImageHDU(data=mask, header=cube_hdr, name='MASK'),\\n self.hdu['WAVE'].copy(),\\n self.hdu['BINID'].copy(),\\n self.hdu['PAR'].copy()\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75264387148e1d71a2e981c9847cabca\",\n \"score\": \"0.512496\",\n \"text\": \"def returnData(self):\\n data = {var:self.majorData[:,i] for i, var in enumerate(self.headerName)}\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381a86494efb468cd8ee768187105bf7\",\n \"score\": \"0.51094353\",\n \"text\": \"def get_dist_genomic(genomic_data,var_or_gene):\\r\\n gene_list=[]\\r\\n for patient in genomic_data:\\r\\n for i in range(len(genomic_data[patient])):\\r\\n if var_or_gene==\\\"Var\\\":\\r\\n if \\\"effect\\\" in list(genomic_data[patient][i].keys()) and \\\"gene\\\" in list(genomic_data[patient][i].keys()):\\r\\n gene_list.append([genomic_data[patient][i][\\\"gene\\\"],genomic_data[patient][i][\\\"effect\\\"]])\\r\\n else:\\r\\n gene_list.append([genomic_data[patient][i][\\\"gene\\\"],\\\"NA\\\"])\\r\\n elif var_or_gene==\\\"Gen\\\":\\r\\n if \\\"status\\\" in list(genomic_data[patient][i].keys()) and \\\"gene\\\" in list(genomic_data[patient][i].keys()):\\r\\n gene_list.append([genomic_data[patient][i][\\\"gene\\\"],genomic_data[patient][i][\\\"status\\\"]])\\r\\n else:\\r\\n gene_list.append([genomic_data[patient][i][\\\"gene\\\"],\\\"NA\\\"]) \\r\\n else:\\r\\n print(\\\"var_or_gene must be Var or Gen\\\")\\r\\n gene_effects=collec.Counter(np.array(gene_list)[:,1])\\r\\n return gene_effects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d73373469b2ca0526c4b8ad3692343f\",\n \"score\": \"0.5105838\",\n \"text\": \"def sampledata_to_vcf(sample_data, filename):\\n\\n num_individuals = len(sample_data.individuals_metadata[:])\\n ind_list = list()\\n pos_geno_dict = {\\\"POS\\\": list()}\\n\\n for i in range(int(num_individuals/2)):\\n pos_geno_dict[\\\"msp_\\\"+str(i)] = list()\\n ind_list.append(\\\"msp_\\\"+str(i))\\n\\n # add all the sample positions and genotypes\\n for i in sample_data.genotypes():\\n pos = int(round(sample_data.sites_position[i[0]]))\\n if pos not in pos_geno_dict[\\\"POS\\\"]:\\n pos_geno_dict[\\\"POS\\\"].append(pos)\\n for j in range(0, len(i[1]), 2):\\n pos_geno_dict[\\\"msp_\\\" + str(int(j/2))].append(\\n str(i[1][j]) + \\\"|\\\" + str(i[1][j+1]))\\n\\n df = pd.DataFrame(pos_geno_dict)\\n\\n df[\\\"#CHROM\\\"] = 1\\n df[\\\"REF\\\"] = \\\"A\\\"\\n df[\\\"ALT\\\"] = \\\"T\\\"\\n df['ID'] = \\\".\\\"\\n df['QUAL'] = \\\".\\\"\\n df['FILTER'] = \\\"PASS\\\"\\n df['INFO'] = \\\".\\\"\\n df['FORMAT'] = \\\"GT\\\"\\n\\n cols = ['#CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER',\\n 'INFO', 'FORMAT']+ind_list\\n df = df[cols]\\n\\n header = \\\"\\\"\\\"##fileformat=VCFv4.2\\n##source=msprime 0.6.0\\n##FILTER=<ID=PASS, Description=\\\"All filters passed\\\">\\n##contig=<ID=1, length=\\\"\\\"\\\" + str(int(sample_data.sequence_length)) + \\\"\\\"\\\">\\n##FORMAT=<ID=GT, Number=1, Type=String, Description=\\\"Genotype\\\">\\n\\\"\\\"\\\"\\n output_VCF = \\\"tmp/\\\"+filename+\\\".vcf\\\"\\n with open(output_VCF, 'w') as vcf:\\n vcf.write(header)\\n\\n df.to_csv(output_VCF, sep=\\\"\\\\t\\\", mode='a', index=False)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f01a9c3ff6d8f737acc8b38063c4115a\",\n \"score\": \"0.5103838\",\n \"text\": \"def vex3D(S: np.array) -> np.array:\\n return np.array([S[2, 1], S[0, 2], S[1, 0]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed18b3de9150778dea48ecebcc95ae28\",\n \"score\": \"0.5102784\",\n \"text\": \"def make_sample_data_2D_3blobs(data_type=\\\"iris\\\"):\\n\\n from iris.cube import Cube\\n from iris.coords import DimCoord, AuxCoord\\n\\n t_0 = datetime.datetime(2000, 1, 1, 12, 0, 0)\\n\\n x = np.arange(0, 100e3, 1000)\\n y = np.arange(0, 200e3, 1000)\\n t = t_0 + np.arange(0, 100, 1) * datetime.timedelta(minutes=1)\\n xx, yy = np.meshgrid(x, y)\\n\\n t_temp = np.arange(0, 60, 1)\\n track1_t = t_0 + t_temp * datetime.timedelta(minutes=1)\\n x_0_1 = 10e3\\n y_0_1 = 10e3\\n track1_x = x_0_1 + 30 * t_temp * 60\\n track1_y = y_0_1 + 14 * t_temp * 60\\n track1_magnitude = 10 * np.ones(track1_x.shape)\\n\\n t_temp = np.arange(0, 30, 1)\\n track2_t = t_0 + (t_temp + 40) * datetime.timedelta(minutes=1)\\n x_0_2 = 20e3\\n y_0_2 = 10e3\\n track2_x = x_0_2 + 24 * (t_temp * 60) ** 2 / 1000\\n track2_y = y_0_2 + 12 * t_temp * 60\\n track2_magnitude = 20 * np.ones(track2_x.shape)\\n\\n t_temp = np.arange(0, 20, 1)\\n track3_t = t_0 + (t_temp + 50) * datetime.timedelta(minutes=1)\\n x_0_3 = 70e3\\n y_0_3 = 110e3\\n track3_x = x_0_3 + 20 * (t_temp * 60) ** 2 / 1000\\n track3_y = y_0_3 + 20 * t_temp * 60\\n track3_magnitude = 15 * np.ones(track3_x.shape)\\n\\n data = np.zeros((t.shape[0], y.shape[0], x.shape[0]))\\n for i_t, t_i in enumerate(t):\\n if np.any(t_i in track1_t):\\n x_i = track1_x[track1_t == t_i]\\n y_i = track1_y[track1_t == t_i]\\n mag_i = track1_magnitude[track1_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\\n if np.any(t_i in track2_t):\\n x_i = track2_x[track2_t == t_i]\\n y_i = track2_y[track2_t == t_i]\\n mag_i = track2_magnitude[track2_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\\n if np.any(t_i in track3_t):\\n x_i = track3_x[track3_t == t_i]\\n y_i = track3_y[track3_t == t_i]\\n mag_i = track3_magnitude[track3_t == t_i]\\n data[i_t] = data[i_t] + mag_i * np.exp(\\n -np.power(xx - x_i, 2.0) / (2 * np.power(10e3, 2.0))\\n ) * np.exp(-np.power(yy - y_i, 2.0) / (2 * np.power(10e3, 2.0)))\\n t_start = datetime.datetime(1970, 1, 1, 0, 0)\\n t_points = (t - t_start).astype(\\\"timedelta64[ms]\\\").astype(int) / 1000\\n t_coord = DimCoord(\\n t_points,\\n standard_name=\\\"time\\\",\\n var_name=\\\"time\\\",\\n units=\\\"seconds since 1970-01-01 00:00\\\",\\n )\\n x_coord = DimCoord(\\n x, standard_name=\\\"projection_x_coordinate\\\", var_name=\\\"x\\\", units=\\\"m\\\"\\n )\\n y_coord = DimCoord(\\n y, standard_name=\\\"projection_y_coordinate\\\", var_name=\\\"y\\\", units=\\\"m\\\"\\n )\\n lat_coord = AuxCoord(\\n 24 + 1e-5 * xx, standard_name=\\\"latitude\\\", var_name=\\\"latitude\\\", units=\\\"degree\\\"\\n )\\n lon_coord = AuxCoord(\\n 150 + 1e-5 * yy, standard_name=\\\"longitude\\\", var_name=\\\"longitude\\\", units=\\\"degree\\\"\\n )\\n sample_data = Cube(\\n data,\\n dim_coords_and_dims=[(t_coord, 0), (y_coord, 1), (x_coord, 2)],\\n aux_coords_and_dims=[(lat_coord, (1, 2)), (lon_coord, (1, 2))],\\n var_name=\\\"w\\\",\\n units=\\\"m s-1\\\",\\n )\\n\\n if data_type == \\\"xarray\\\":\\n sample_data = DataArray.from_iris(sample_data)\\n\\n return sample_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"118f0b7a9cb27f04dea5a50939e6f14d\",\n \"score\": \"0.51012856\",\n \"text\": \"def hyperconvert3d(data2d, rows, cols, channels):\\n channels, pixnum = data2d.shape\\n data3d = data2d.transpose().reshape(rows, cols, channels, order='F')\\n return data3d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1114dfc0358b0c77cc3b55870665fad9\",\n \"score\": \"0.5099725\",\n \"text\": \"def detector_coverage_process_3D( args):\\n #Error checking\\n (inst, det, set_value) = args\\n if det is None: return None\\n if inst is None: return None\\n message = \\\"Calculating coverage of detector '%s'...\\\" % det.name\\n #Statusbar update\\n #TODO: This notification does nothing in multiprocessing because this function runs in a separate process!\\n messages.send_message(messages.MSG_UPDATE_MAIN_STATUSBAR, message)\\n print message\\n #This does the calculation\\n coverage = inst.detector_coverage_3D_by_lines(det, set_value)\\n #We have the coverage map, return it\\n return coverage\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e00df047710fbe21f84cd0dda6c979a\",\n \"score\": \"0.50968117\",\n \"text\": \"def get_3d(self) -> \\\"VolumeGeometry\\\":\\n if len(self._vol_shape_xyz) == 2:\\n return dc_replace(self, _vol_shape_xyz=np.concatenate((self._vol_shape_xyz, [1])))\\n else:\\n return dc_replace(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddaa129ccfec0cf285ec1adb215c36f4\",\n \"score\": \"0.50952303\",\n \"text\": \"def get_3d(self) -> \\\"ProjectionGeometry\\\":\\n if self.ndim == 2:\\n if self.det_shape_vu is not None:\\n new_det_shape_vu = np.ones(2, dtype=int)\\n new_det_shape_vu[-len(self.det_shape_vu) :] = self.det_shape_vu\\n else:\\n new_det_shape_vu = None\\n return dc_replace(self, geom_type=self.geom_type.replace(\\\"2d\\\", \\\"3d\\\"), det_shape_vu=new_det_shape_vu)\\n else:\\n return dc_replace(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efb5040bb41fce5d66bb766a48721d9\",\n \"score\": \"0.50896275\",\n \"text\": \"def region_2d_to_vector_3d(*argv):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b3f42e8150cbf204c88a9a82fb4c863\",\n \"score\": \"0.50859624\",\n \"text\": \"def get_scene(self, index):\\n in_xy, gt_xy, in_dxdy, gt_dxdy, pred_check, pad_mask = self.__getitem__(index)\\n\\n return {\\\"in_xy\\\": in_xy.permute(1, 0, 2),\\n \\\"gt_xy\\\": gt_xy.permute(1, 0, 2),\\n \\\"in_dxdy\\\": in_dxdy.permute(1, 0, 2),\\n \\\"gt_dxdy\\\": gt_dxdy.permute(1, 0, 2),\\n \\\"seq_start_end\\\": (0, in_xy.size(0)),\\n \\\"pred_check\\\": pred_check,\\n \\\"pad_mask\\\": pad_mask,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4302c98b1fa75cfbcc90aa9fddb1b1ec\",\n \"score\": \"0.5082406\",\n \"text\": \"def geometryData(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b906c11478cfbd03126c93b398fe08de\",\n \"score\": \"0.5074803\",\n \"text\": \"def array_data(self):\\r\\n self.INC_array=[]\\r\\n self.local_IEN_array=[]\\r\\n self.KV_xi_array=[]\\r\\n self.KV_eta_array=[]\\r\\n \\r\\n if self.dimension==1:\\r\\n \\r\\n self.INC_array.append(self.INC(0))\\r\\n self.local_IEN_array.append(self.local_IEN(0))\\r\\n self.KV_xi_array.append(self.knot_vector(self.number_elements,self.order,self.mp))\\r\\n \\r\\n elif self.dimension==2:\\r\\n \\r\\n for pnum in np.arange(len(self.num_bases)):\\r\\n \\r\\n self.INC_array.append(self.INC(pnum))\\r\\n self.local_IEN_array.append(self.local_IEN(pnum))\\r\\n self.KV_xi_array.append(self.knot_vector(self.number_elements[pnum,0],self.order[pnum,0],self.mp[pnum,0]))\\r\\n self.KV_eta_array.append(self.knot_vector(self.number_elements[pnum,1],self.order[pnum,1],self.mp[pnum,1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27c9d4316a7c5527523180b67229873\",\n \"score\": \"0.5065774\",\n \"text\": \"def cartesianSlice(data=None,**keywords):\\n \\n # If the user did not pass in an array try to extract one from the\\n # gui\\n if data is None:\\n data = guiDefinedData()\\n \\n attributes = keywords\\n try:\\n \\n # Unless the user has explicitly specified different units we\\n # treat the domain as a length box\\n if \\\"units\\\" not in attributes:\\n attributes[\\\"units\\\"] = VISUS_METERS\\n\\n # Try to infer as many data attributes as you can and return a\\n # string describing/defining the data in the ViSUS internal format\\n data_description = constructDataDescriptor(data,attributes)\\n except:\\n print \\\"Could not parse data. Unknown format\\\"\\n raise\\n\\n # Create a font to be used in all the nodes that render text\\n font = VisusFont()\\n font.fontSize(6)\\n \\n # Construct a root node for the scene graph hierarchy. This node is\\n # mainly used for the global transformation (e.g. rotating the\\n # complete scene) and is a convinient way to collect independent\\n # nodes (nodes that should have no ancestor-descendend\\n # relationship. Note, the use of the construct function rather than\\n # a standard \\\"constructor.\\\" ViSUS uses a smart pointer / reference\\n # counting scheme which requires this.\\n root = VisusGroup.construct()\\n\\n # To have a convinient reference point we want to display the\\n # bounding box of the root node which will automatically adjust to\\n # include the bounding box of all child nodes. \\n root.drawBoundingBox(True)\\n\\n # Create two points that will form the left lower and right upper\\n # corner of the data bounding box\\n left = VectorDouble(3)\\n right= VectorDouble(3)\\n\\n # Create a ViSUS data source based on the data description created earlier\\n source = VisusDataSourceFactory.make(data_description)\\n if not source.isValid():\\n raise RuntimeError(\\\"Problem loading data set\\\")\\n\\n # Get the bounding box of the data whose value may or may not depend\\n # on data attributes such as units. In particular, if this\\n # information was available the bounding box will be given in\\n # pyhsical units rather than in index space\\n source.domainBoundingBox(left,right)\\n bbox = VisusBoundingBox()\\n bbox.set(left,right);\\n \\n # The ViSUS system is based on data \\\"requests\\\" that describe a\\n # particular piece of data that should be displayed. The data\\n # requests can be manipulated through the user interface or\\n # directly. However, here we want to create a sensible default\\n # request to see a portion of our data.\\n \\n # First, we create an empty request \\n request = VisusDataRequest()\\n\\n # Second, we construct an array that describes the length in each\\n # dimension of the box of data we want to extract in whatever units\\n # the data source uses (typically physical space).\\n extent = VectorDouble(3) \\n extent[0] = (right[0] - left[0]) # We want to see all of the x-coordinate\\n extent[1] = (right[1] - left[1]) # and all of the y-coordinate \\n extent[2] = 0 # But we want to look at a plane so the z extend is 0\\n\\n # And we pass the extent information into the request\\n request.extent(extent)\\n\\n # Second, we need to position our extent box in the data\\n # set. Initially, the extent will be centered around the origin. To\\n # see the middle of the data we want translate our request to the\\n # center of the data's bounding box\\n\\n # So we create the corresponding transformation matrix\\n matrix = translationMatrix((left[0]+right[0])/2,\\n (left[1]+right[1])/2,\\n (left[2]+right[2])/2,\\n )\\n # And pass this matrix to the request\\n request.transformation(matrix)\\n \\n # Third, we want to indicate to the ViSUS system that we are\\n # really interested in a 2D slice of the data (rather than a\\n # degenerate volume) as slices are a special case and implement some\\n # special user interactions\\n request.requestType(VISUS_2D_REQUEST);\\n\\n # Finally, we describe the resolution at which we want to see our\\n # data. The resolution is defined in terms ofo sub-sampling\\n # \\\"strides\\\" where a stride of 1 defines the full resolution data a\\n # stride of 2, the data sub-sampled by a factor of 2 etc.. Strides\\n # must be powers of two but can be independently set for each\\n # dimension. Furthermore, for large data it can be useful to\\n # progressively refine the data starting at some higher strides /\\n # coarser resolution, ending at some lower strides / finer\\n # resolution. This preserves interactivity while still providing\\n # high resolution data if the request remains unchanged long enough\\n\\n # Since, for default scenes we expect the data to be small we only\\n # want to see the highest resolution\\n start = [ 1, 1, 1 ]\\n end = [ 1, 1, 1 ]\\n request.setStrides(start,end)\\n \\n\\n # Armed with the description of the data and the request we just\\n # constructed we create a scene graph node that will extract the\\n # data for us. This node, will continuously test its current request\\n # and update the data accordingly. Therefore, one could adapt the\\n # scene by simply passing different requests to this node (which is\\n # what the graphical user interface does on mouse movements or\\n # keyboard commands)\\n \\n # Create a producer node specialized to extract and axis aligned\\n # bounding box from a data set. As before with the root node the\\n # node must be explicitly constructed.\\n extractor = VisusAxisAlignedExtractor.construct()\\n\\n # Set the data to extract our data from\\n extractor.setValue(data_description)\\n extractor.setValue(request) # Our default request\\n extractor.setValue(bbox); # And the bounding box \\n extractor.drawBoundingBox(False);\\n\\n\\n # Now that we have a node that will extract the data we want to see\\n # we construct another node to visualize the slice\\n\\n # We want to view an axis aligned slice of the data an the\\n # appropriate visualization node is one for orthogonal slices\\n slice = VisusOrthogonalSlice.construct()\\n\\n # Now we connect the output of the extractor to the input of the\\n # visualization node\\n if not slice.connectInput(extractor):\\n raise RuntimeError(\\\"unable to connect extractor as slice input\\\")\\n\\n # For reasons internal to the underpinnings of the ViSUS system the\\n # visualization node must know about the current data request. The\\n # simplest way of achieving this is to attach the slice node as\\n # child to the extractor \\n extractor.attachSubTree(slice,True)\\n\\n # and to inherit/share the request \\n slice.inherit(VisusSharedDataRequest.typeIndex(),True)\\n\\n # In this configuration the slice node will naturally draw the data\\n # at the position of the data request which is the natural\\n # place. However, by manipulating the transformation matrix of the\\n # slice node we could draw the data anywhere in space / on the screen\\n \\n # Instead of the default greyscale color map we want to use a\\n # typical red-to-blue map\\n color_map = banded()\\n slice.setValue(color_map)\\n\\n # In order to see the slice even if it contains no data (if the\\n # request has been moved outside the data set, or the data is\\n # extremly large) we draw the bounding box of the slice\\n slice.drawBoundingBox(True);\\n\\n # Now, we want to create labeled tickmarks to give the user a notion\\n # of the data range. \\n\\n # Create a tickmark node that by default will create tickmarks along\\n # the x-axis of its bounding box\\n xTickMarks = VisusTickMarks.construct()\\n\\n # ViSUS uses \\\"axis\\\" to control the labels of images, graphs, and\\n # also tickmarks. Here we get the current axis \\n axis = xTickMarks.axis()\\n axis.legendText(\\\"longitude\\\") # Set the label \\n axis.labelFont(font) # and font\\n xTickMarks.axis(axis) # and update the axis\\n\\n # Finally, we attach the tickmarks node as a child to the\\n # extractor. By default a tickmarks node inherits the bounding box\\n # of its parent and thus this node will now annotate the x-axis of\\n # our data set\\n extractor.attachSubTree(xTickMarks,True)\\n \\n # Create tickmarks for the y-axis in an identical fashion\\n yTickMarks = VisusTickMarks.construct()\\n \\n # Except that now we must name the axis explicitly \\n yTickMarks.direction(TM_Y_AXIS)\\n axis = yTickMarks.axis() \\n axis.legendText(\\\"latitude\\\")\\n axis.labelFont(font)\\n yTickMarks.axis(axis)\\n extractor.attachSubTree(yTickMarks,True)\\n \\n # Finally, we attach the whole subtree that renders the data plus\\n # annotation to the root node\\n root.attachSubTree(extractor,True)\\n\\n\\n # To further enhance the scene we want to add the continental\\n # outlines on top of the slice\\n\\n # First, we load the cdat internal world map \\n world_map = loadWorldMap()\\n\\n # And parse it as a collection of polylines. A VisusIndexedData\\n # object stores an array of n-dimensional points (n being 2 in this\\n # case) and a set of elements defined as ordered lists of indices\\n # into this array. Therefore, each contour of the world map is\\n # parsed as a single element\\n outlines = VisusIndexedData(2)\\n for m in world_map:\\n outlines.constructElement(m)\\n\\n # Once the data is constructed we create a display node designed to visualize it\\n map = VisusIndexedDisplay.construct()\\n\\n # And load the data. \\n map.loadData(outlines,0)\\n\\n # The cdat world map is given within (-180,-90)x(180,90). However,\\n # in order to display the normal lat,long, elev data easily in\\n # cartesian space we told ViSUS the data units are meters (meaning\\n # general distance) which will draw (0,0)x(360,180). To adjust the\\n # map to the slice we therefore must shift it\\n #map.getValue(matrix);\\n #matrix = matrix * translationMatrix(180,90,0.01)\\n #map.setValue(matrix)\\n\\n # Attach the map as child of the extractor. Note, that from this moment\\n # on the map will inherit all transformations from the extractor and\\n # thus \\\"stick\\\" to the extractor if it is moved or rotated\\n extractor.attachSubTree(map)\\n \\n # Lastly, we attach more annotations to the scene\\n\\n # A VisusLabelNode is a general one line text of arbitrary font and\\n # size. First, we create a title string using the variable name if\\n # we found one\\n label_node = VisusLabelNode.construct()\\n title = \\\"Variable: \\\" + attributes[\\\"name\\\"].replace(\\\"_\\\",\\\" \\\")\\n label_node.text(title)\\n label_node.setValue(font)\\n\\n # Positions of labels are given relative to the screen size\\n label_node.position(-1,+0.8)\\n root.attachSubTree(label_node,True)\\n\\n # If our data set is time-dependent we attach a label that displays\\n # the current time step\\n if \\\"time\\\" in attributes:\\n time_node = VisusLabelNode.construct()\\n t = attributes[\\\"time\\\"]\\n title = \\\"Date: %02d/%02d/%04d\\\" % (t.month,t.day,t.year)\\n time_node.text(title)\\n time_node.setValue(font)\\n time_node.position(0.6,-0.95)\\n root.attachSubTree(time_node,True)\\n\\n # A VisusColorBar displays a bar with a color map on the inside and\\n # allows us to attach tickmarks and labels\\n color_bar = VisusColorBar.construct()\\n\\n # As before fopr the tickmark node we modify the labels via the Axis construct\\n axis = color_bar.axis()\\n\\n # If we found a variable name we use it as label\\n if \\\"var_name\\\" in attributes:\\n axis.legendText(attributes[\\\"var_name\\\"])\\n \\n # If we found a range we use it as well. \\n if \\\"range\\\" in attributes:\\n axis.minValue(float(attributes[\\\"range\\\"][0]))\\n axis.maxValue(float(attributes[\\\"range\\\"][1]))\\n \\n font.fontSize(4)\\n axis.legendFont(font)\\n axis.labelFont(font)\\n color_bar.position(-1.2,-0.8)\\n color_bar.axis(axis)\\n\\n # Finally, attach the color bar to the slice and make sure it\\n # inherits its colormap from there\\n slice.attachSubTree(color_bar,True)\\n #color_bar.inherit(VisusSharedColorMap.typeIndex(),True)\\n color_bar.inherit(8,True)\\n\\n\\n # Lastly we must make sure that we actually see the data on the\\n # screen which means we need to make sure that the data is scaled\\n # and translated appropriately. (Alternatively, once can think of\\n # this step as setting up the viewpoint of our virtual camera.\\n\\n # Using the current bounding box of the scene we adapt a default box\\n # to the aspect ratios of of our scene and use it as our \\\"world\\n # coordinate system\\\" \\n world_box = constructWorldBox(bbox)\\n root.setValue(bbox)\\n\\n # Now we make sure that the data is drawn in our standard coordinate\\n # system\\n root.mapToWorldBox(world_box)\\n\\n # Finally, most climate data contains significantly more samples in\\n # x/y than in z. Since, for the cartesian slice we effecticely draw\\n # the data in index space since is inconvinient as the volume wille\\n # very \\\"flat.\\\" To compensate for this we simply scale the z\\n # coordinate of our system\\n root.getValue(matrix)\\n matrix = matrix * scaleMatrix(1,1,10)\\n root.setValue(matrix)\\n\\n # As the final step we create ourselfs a window to display the scene\\n # graph hanging under the root node\\n createWindow(root)\\n \\n return root,data_description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90c29d17cda2196b97edb1489b615a6\",\n \"score\": \"0.5062946\",\n \"text\": \"def _load_3Dchairs(self, opts):\\n self.data_dir = _data_dir(opts)\\n filename = 'rendered_chairs.npz'\\n filepath = os.path.join(self.data_dir,filename)\\n assert os.path.isfile(filepath), '3Dchairs data file doesnt exist.'\\n X = np.load(filepath)\\n self.all_data = (255.*np.load(filepath)['data']).astype(np.uint8)\\n num_data = opts['dataset_size']\\n # datashape\\n self.data_shape = datashapes[self.dataset]\\n # plot set\\n seed = 123\\n np.random.seed(seed)\\n idx = np.random.randint(self.all_data.shape[0],size=opts['plot_num_pics'])\\n self.data_plot = self._sample_observations(idx)\\n # shuffling data\\n np.random.seed()\\n idx_random = np.random.permutation(num_data)\\n if opts['train_dataset_size']==-1 or opts['train_dataset_size']>num_data-10000:\\n tr_stop = num_data - 10000\\n else:\\n tr_stop = opts['train_dataset_size']\\n data_train = self.all_data[idx_random[:tr_stop]]\\n data_test = self.all_data[idx_random[-10000:]]\\n # dataset size\\n self.train_size = data_train.shape[0]\\n self.test_size = data_test.shape[0]\\n # data for vizualisation\\n seed = 123\\n np.random.seed(seed)\\n idx = np.random.randint(self.test_size, size=opts['plot_num_pics'])\\n self.data_vizu = self._sample_observations(idx)\\n # Create tf.dataset\\n dataset_train = tf.data.Dataset.from_tensor_slices(data_train)\\n dataset_test = tf.data.Dataset.from_tensor_slices(data_test)\\n # map img to [0,1] and convert to float32\\n def process_img(x):\\n return tf.cast(x, dtype=tf.dtypes.float32) / 255.\\n dataset_train = dataset_train.map(process_img,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n dataset_test = dataset_test.map(process_img,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n # normalize data if needed\\n if opts['input_normalize_sym']:\\n dataset_train = dataset_train.map(lambda x: (x - 0.5) * 2.,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n dataset_test = dataset_test.map(lambda x: (x - 0.5) * 2.,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n self.data_vizu = (self.data_vizu - 0.5) * 2.\\n self.data_plot = (self.data_plot - 0.5) * 2.\\n # Shuffle dataset\\n dataset_train = dataset_train.shuffle(buffer_size=50*opts['batch_size'])\\n dataset_test = dataset_test.shuffle(buffer_size=50*opts['batch_size'])\\n # repeat for multiple epochs\\n dataset_train = dataset_train.repeat()\\n dataset_test = dataset_test.repeat()\\n # Random batching\\n dataset_train = dataset_train.batch(batch_size=opts['batch_size'])\\n dataset_test = dataset_test.batch(batch_size=opts['batch_size'])\\n # Prefetch\\n self.dataset_train = dataset_train.prefetch(buffer_size=4*opts['batch_size'])\\n self.dataset_test = dataset_test.prefetch(buffer_size=4*opts['batch_size'])\\n # Iterator for each split\\n self.iterator_train = dataset_train.make_initializable_iterator()\\n self.iterator_test = dataset_test.make_initializable_iterator()\\n\\n # Global iterator\\n self.handle = tf.placeholder(tf.string, shape=[])\\n self.next_element = tf.data.Iterator.from_string_handle(\\n self.handle, dataset_train.output_types, dataset_train.output_shapes).get_next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b595c8271cbcbee186373beeeb837fbf\",\n \"score\": \"0.5060969\",\n \"text\": \"def fixture_family3_data(family_tag_names: List[str], third_family_vcf: Path) -> dict:\\n return {\\\"tags\\\": family_tag_names, \\\"file\\\": third_family_vcf}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f4499585a1ce2e605dc27eb5ad7677\",\n \"score\": \"0.50575477\",\n \"text\": \"def prepare_dataset_rapresentation(file) -> (dict, list):\\n sequences_map = __load_df(file)\\n\\n gates = sorted(list(set(itertools.chain.from_iterable(sequences_map.values()))))\\n gates_index = dict((gate, gates.index(gate)) for gate in gates)\\n\\n print(\\\"Start dictionary representation\\\")\\n\\n result = dict()\\n\\n for key, sequence in sequences_map.items():\\n representation = np.zeros((len(sequence), len(gates)), dtype=np.int8)\\n for i, gate in enumerate(sequence):\\n representation[i, gates_index[gate]] = 1\\n result[key] = representation\\n\\n return result, gates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e8991b4a941cbbe04f5533a0adc27d\",\n \"score\": \"0.50547\",\n \"text\": \"def _d3K_dr3(self, xa, xb):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48e06f884283e023f523d7f9b2e933c3\",\n \"score\": \"0.5048695\",\n \"text\": \"def get_data(self):\\n idxs = np.arange(self.size())\\n self.rng.shuffle(idxs)\\n for idx in idxs:\\n # print(f\\\"{idx//2}, {idx}\\\")\\n # obj = self.all_meta[idx // 2]\\n obj = self.all_meta[idx // 2]\\n yield [\\n obj.img_path,\\n obj.height,\\n obj.width,\\n obj.center,\\n obj.bbox,\\n obj.area,\\n obj.num_keypoints,\\n obj.masks_segments,\\n obj.scale,\\n obj.all_joints,\\n obj.img,\\n obj.mask,\\n obj.aug_center,\\n obj.aug_joints,\\n idx\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3560997dc20a835b827b1dfb23d6b040\",\n \"score\": \"0.504588\",\n \"text\": \"def get_gene_data(patient_phen, filename, var_or_gene):\\r\\n genomic_data={}\\r\\n with open(filename,\\\"r\\\") as pg:\\r\\n lines=pg.readlines()\\r\\n for line in lines:\\r\\n if line.split(\\\"<\\\")[0]==\\\"ID\\\":\\r\\n pid=line.split(\\\" \\\")[3].split(\\\"\\\\n\\\")[0]\\r\\n genomic_data[pid]=[]\\r\\n elif line.split(\\\"<\\\")[0]==var_or_gene:\\r\\n var=int(line.split(\\\" \\\")[1].split(\\\"\\\\n\\\")[0])\\r\\n genomic_data[pid].append({})\\r\\n else:\\r\\n if not(len(line.split(\\\" \\\"))==1):\\r\\n genomic_data[pid][var][line.split(\\\" \\\")[0].split(\\\"\\\\n\\\")[0]]=line.split(\\\" \\\")[1].split(\\\"\\\\n\\\")[0]\\r\\n for patient in genomic_data:\\r\\n if not(patient in list(patient_phen.keys())):\\r\\n genomic_data = removekey(genomic_data, patient)\\r\\n return genomic_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9218fab908ce3f6fdcea42a75caeb55b\",\n \"score\": \"0.50444394\",\n \"text\": \"def get_data(self, identifiers):\\n def get_file_path(path, person_identifier, index, postfix):\\n return os.path.join(path, \\\"{}_{}.{}\\\".format(person_identifier, index, postfix))\\n\\n out = dict()\\n for i, (person_identifier, index) in enumerate(identifiers):\\n img = misc.imread(get_file_path(self.path, person_identifier, index, 'jpg'))\\n with open(get_file_path(self.path, person_identifier, index, 'json'), 'r') as f:\\n json_data = ujson.load(f)\\n out[identifiers[i]] = {\\n 'eye': img,\\n 'gaze': json_data['gaze']\\n }\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e260ec343102f65481f72b3c57dd06\",\n \"score\": \"0.50388235\",\n \"text\": \"def extract3D(self, max_shifts=(8, 8, 2), strides=(48, 48, 4), overlaps=(24, 24, 2), \\n gSig=(2, 2, 2.5), **kwargs):\\n\\n self.extract(max_shifts=max_shifts, strides=strides, overlaps=overlaps, \\n gSig=gSig, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aecdbddc01478835f1e72b307ed82b40\",\n \"score\": \"0.5037038\",\n \"text\": \"def load_data():\\n seq = open(os.path.join(mitty.test.example_data_dir, 'tiny.fasta')).readlines()[1]\\n # df = vio.read_sample_from_vcf(os.path.join(mitty.test.example_data_dir, 'tiny.vcf'), 'g0_s0')\\n df = vio.load_variant_file(\\n os.path.join(mitty.test.example_data_dir, 'tiny.vcf.gz'),\\n 'g0_s0',\\n os.path.join(mitty.test.example_data_dir, 'tiny.whole.bed'))\\n return seq, df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58598d48e32796a4896af062c96ef551\",\n \"score\": \"0.503099\",\n \"text\": \"def data():\\n shape = (6,4,5)\\n size = numpy.prod(shape)\\n return numpy.arange(1, size + 1).reshape(shape) + 0.3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ad0ca681e29bd290298799325da572\",\n \"score\": \"0.50309706\",\n \"text\": \"def internalize(xyzfile):\\n\\n ccdata_xyz = parse.xyzfile(xyzfile, ccxyz=True)\\n\\n ccdata_xyz.build_zmatrix()\\n\\n ccdata_xyz.build_xyz()\\n\\n return ccdata_xyz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbc9719fb449c39e919d298a3a3d15e\",\n \"score\": \"0.50289845\",\n \"text\": \"def DC2Dsurvey(flag=\\\"PolePole\\\"):\\n if flag == \\\"PoleDipole\\\":\\n ntx, nmax = xr.size - 2, 8\\n elif flag == \\\"DipolePole\\\":\\n ntx, nmax = xr.size - 2, 8\\n elif flag == \\\"DipoleDipole\\\":\\n ntx, nmax = xr.size - 3, 8\\n elif flag == \\\"PolePole\\\":\\n ntx, nmax = xr.size - 2, 8\\n else:\\n raise Exception(\\\"Not Implemented\\\")\\n xzlocs = getPseudoLocs(xr, ntx, nmax, flag)\\n\\n txList = []\\n zloc = -2.5\\n for i in range(ntx):\\n if flag == \\\"PoleDipole\\\":\\n A = np.r_[xr[i], zloc]\\n B = np.r_[mesh.cell_centers_x.min(), zloc]\\n if i < ntx - nmax + 1:\\n M = np.c_[xr[i + 1 : i + 1 + nmax], np.ones(nmax) * zloc]\\n N = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(nmax) * zloc]\\n else:\\n M = np.c_[xr[i + 1 : ntx + 1], np.ones(ntx - i) * zloc]\\n N = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(ntx - i) * zloc]\\n elif flag == \\\"DipolePole\\\":\\n A = np.r_[xr[i], zloc]\\n B = np.r_[xr[i + 1], zloc]\\n if i < ntx - nmax + 1:\\n M = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(nmax) * zloc]\\n N = np.c_[np.ones(nmax) * mesh.cell_centers_x.max(), np.ones(nmax) * zloc]\\n else:\\n M = np.c_[xr[i + 2 : ntx + 2], np.ones(ntx - i) * zloc]\\n N = np.c_[\\n np.ones(ntx - i) * mesh.cell_centers_x.max(), np.ones(ntx - i) * zloc\\n ]\\n elif flag == \\\"DipoleDipole\\\":\\n A = np.r_[xr[i], zloc]\\n B = np.r_[xr[i + 1], zloc]\\n if i < ntx - nmax:\\n M = np.c_[\\n xr[i + 2 : i + 2 + nmax],\\n np.ones(len(xr[i + 2 : i + 2 + nmax])) * zloc,\\n ]\\n N = np.c_[\\n xr[i + 3 : i + 3 + nmax],\\n np.ones(len(xr[i + 3 : i + 3 + nmax])) * zloc,\\n ]\\n else:\\n M = np.c_[\\n xr[i + 2 : len(xr) - 1],\\n np.ones(len(xr[i + 2 : len(xr) - 1])) * zloc,\\n ]\\n N = np.c_[xr[i + 3 : len(xr)], np.ones(len(xr[i + 3 : len(xr)])) * zloc]\\n elif flag == \\\"PolePole\\\":\\n A = np.r_[xr[i], zloc]\\n B = np.r_[mesh.cell_centers_x.min(), zloc]\\n\\n if i < ntx - nmax + 1:\\n M = np.c_[xr[i + 2 : i + 2 + nmax], np.ones(nmax) * zloc]\\n N = np.c_[np.ones(nmax) * mesh.cell_centers_x.max(), np.ones(nmax) * zloc]\\n else:\\n M = np.c_[xr[i + 2 : ntx + 2], np.ones(ntx - i) * zloc]\\n N = np.c_[\\n np.ones(ntx - i) * mesh.cell_centers_x.max(), np.ones(ntx - i) * zloc\\n ]\\n\\n rx = DC.receivers.Dipole(M, N)\\n src = DC.sources.Dipole([rx], A, B)\\n txList.append(src)\\n\\n survey = DC.Survey(txList)\\n simulation = DC.Simulation2DCellCentered(\\n mesh, survey=survey, sigmaMap=mapping, solver=Pardiso\\n )\\n\\n sigblk, sighalf, siglayer = 2e-2, 2e-3, 1e-3\\n xc, yc, r, zh = -15, -8, 4, -5\\n mtrue = np.r_[np.log(sighalf), np.log(siglayer), np.log(sigblk), xc, yc, r, zh]\\n dtrue = simulation.dpred(mtrue)\\n perc = 0.0001\\n floor = np.linalg.norm(dtrue) * 1e-8\\n np.random.seed([1])\\n uncert = np.random.randn(survey.nD) * perc + floor\\n dobs = dtrue + uncert\\n\\n return dobs, uncert, simulation, xzlocs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e065f51d00302f6d966fa897a2d04a65\",\n \"score\": \"0.50286007\",\n \"text\": \"def get_data(self, identifiers):\\n from input.preprocessing import UnityPreprocessor\\n\\n def get_file_path(path, index, postfix):\\n return os.path.join(path, \\\"{}.{}\\\".format(index, postfix))\\n\\n out = dict()\\n for i, file_stem in enumerate(identifiers):\\n img = misc.imread(get_file_path(self.path, file_stem, 'jpg'))\\n with open(get_file_path(self.path, file_stem, 'json'), 'r') as f:\\n json_data = ujson.load(f)\\n out[identifiers[i]] = {\\n 'eye': img,\\n 'gaze': UnityPreprocessor.look_vec_to_gaze_vec(json_data)[0],\\n 'original_gaze': UnityPreprocessor.look_vec_to_gaze_vec(json_data)[1]\\n }\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa16ae5a6838da69ef21c653932522d\",\n \"score\": \"0.50258285\",\n \"text\": \"def visualize_raw_data(filename):\\n pointcloud = o3d.io.read_point_cloud(filename, format='xyz')\\n o3d.visualization.draw_geometries([pointcloud])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd477ef337f0729d16d5cc6e15c2df4\",\n \"score\": \"0.5020945\",\n \"text\": \"def get_3d_cube():\\n V = np.zeros(8, [(IN_VERTEX_POS_NAME, np.float32, 3)])\\n V[IN_VERTEX_POS_NAME] = np.array([(-1, 1, 1), (1, 1, 1), (1, -1, 1), (-1, -1, 1), (-1, 1, -1), (1, 1, -1), (1, -1, -1), (-1, -1, -1)])\\n I = np.array([0, 1, 2, 0, 2, 3, 0, 4, 5, 0, 5, 1, 0, 4, 7, 0, 7, 3, 3, 7, 6, 3, 6, 2, 1, 5, 6, 1, 6, 2, 4, 5, 6, 4, 6, 7], dtype=np.uint32)\\n return V, I\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f6205786834dde7888b0796287329\",\n \"score\": \"0.50156695\",\n \"text\": \"def get_UBC_mesh(meshfile): \\n\\n fid = open(meshfile,'r')\\n from numpy import zeros\\n \\n # Go through the log file and extract data and the last achieved misfit\\n for ii in range (1, 6): \\n \\n line = fid.readline()\\n line = line.split(' ') \\n \\n # First line: number of cells in i, j, k \\n if ii == 1:\\n \\n numcell=[]\\n \\n for jj in range(len(line)):\\n t = int(line[jj])\\n numcell.append(t)\\n \\n nX = numcell[0]\\n nY = numcell[1]\\n nZ = numcell[2] \\n # Second line: origin coordinate (X,Y,Z)\\n elif ii==2:\\n \\n origin = []\\n \\n for jj in range(len(line)):\\n t = float(line[jj])\\n origin.append(t)\\n \\n \\n # Other lines for the dX, dY ,dZ\\n elif ii==3:\\n \\n dX=zeros((nX,1), dtype=float)\\n count_entry = 0;\\n count = 0;\\n while (count<nX):\\n\\n if line[count_entry].find('*') != -1:\\n \\n ndx = line[count_entry].split('*')\\n \\n for kk in range(int(ndx[0])):\\n dX[count] = (ndx[1])\\n count = count+1\\n count_entry=count_entry+1\\n \\n else:\\n \\n t = float(line[count_entry])\\n dX[count]= t\\n count = count+1 \\n count_entry=count_entry+1\\n \\n elif ii==4:\\n \\n dY=zeros((nY,1), dtype=float)\\n count_entry = 0;\\n count = 0;\\n while (count<nY):\\n\\n if line[count_entry].find('*') != -1:\\n \\n ndy = line[count_entry].split('*')\\n \\n for kk in range(int(ndy[0])):\\n dY[count] = (ndy[1])\\n count = count+1\\n count_entry=count_entry+1\\n \\n else:\\n \\n t = float(line[count_entry])\\n dY[count]= t\\n count = count+1 \\n count_entry=count_entry+1\\n \\n elif ii==5:\\n \\n dZ=zeros((nZ,1), dtype=float)\\n count_entry = 0;\\n count = 0;\\n while (count<nZ):\\n\\n if line[count_entry].find('*') != -1:\\n \\n ndz = line[count_entry].split('*')\\n \\n for kk in range(int(ndz[0])):\\n dZ[count] = (ndz[1])\\n count = count+1\\n count_entry=count_entry+1 \\n else:\\n \\n t = float(line[count_entry])\\n dZ[count]= t\\n count = count+1 \\n count_entry=count_entry+1\\n fid.close(); \\n return nX, nY, nZ, origin, dX, dY, dZ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132acde942eded30bccac9c593764b1f\",\n \"score\": \"0.5001815\",\n \"text\": \"def _prepare_data(self, grid_3d, upsample):\\n if upsample:\\n # upsample only small size structures\\n # get structure slice Position\\n # set up-sample only small size structures\\n if self.volume_original < self.vol_lim:\\n # self._set_upsample_delta()\\n hi_resolution_structure, grid_ds, grid_delta, dose_lut = self.up_sampling(grid_3d, self.delta_mm)\\n dose_grid_points = grid_ds[:, :2]\\n return hi_resolution_structure, dose_lut, dose_grid_points, grid_delta\\n\\n else:\\n return self._get_calculation_data(grid_3d)\\n\\n else:\\n return self._get_calculation_data(grid_3d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51beb5837dab4b7b75ec17c52ef18d27\",\n \"score\": \"0.4996552\",\n \"text\": \"def getRigBuildData (self):\\n\\n data = super (ArmComponentGuide, self).getRigBuildData ()\\n\\n # values\\n bicepPosition = self.bicepCtrl.xfo.tr\\n forearmPosition = self.forearmCtrl.xfo.tr\\n wristPosition = self.wristCtrl.xfo.tr\\n\\n # Calculate Bicep Xfo\\n rootToWrist = wristPosition.subtract (bicepPosition).unit ()\\n rootToElbow = forearmPosition.subtract (bicepPosition).unit ()\\n\\n bone1Normal = rootToWrist.cross (rootToElbow).unit ()\\n bone1ZAxis = rootToElbow.cross (bone1Normal).unit ()\\n\\n bicepXfo = Xfo ()\\n bicepXfo.setFromVectors (rootToElbow, bone1Normal, bone1ZAxis, bicepPosition)\\n\\n # Calculate Forearm Xfo\\n elbowToWrist = wristPosition.subtract (forearmPosition).unit ()\\n elbowToRoot = bicepPosition.subtract (forearmPosition).unit ()\\n bone2Normal = elbowToRoot.cross (elbowToWrist).unit ()\\n bone2ZAxis = elbowToWrist.cross (bone2Normal).unit ()\\n forearmXfo = Xfo ()\\n forearmXfo.setFromVectors (elbowToWrist, bone2Normal, bone2ZAxis, forearmPosition)\\n\\n # Calculate Wrist Xfo\\n wristXfo = Xfo ()\\n wristXfo.tr = self.wristCtrl.xfo.tr\\n wristXfo.ori = forearmXfo.ori\\n\\n upVXfo = xfoFromDirAndUpV (bicepPosition, wristPosition, forearmPosition)\\n upVXfo.tr = forearmPosition\\n upVXfo.tr = upVXfo.transformVector (Vec3 (0, 0, 5))\\n\\n # Lengths\\n bicepLen = bicepPosition.subtract (forearmPosition).length ()\\n forearmLen = forearmPosition.subtract (wristPosition).length ()\\n\\n data ['bicepXfo'] = bicepXfo\\n data ['forearmXfo'] = forearmXfo\\n data ['wristXfo'] = wristXfo\\n data ['upVXfo'] = upVXfo\\n data ['bicepLen'] = bicepLen\\n data ['forearmLen'] = forearmLen\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4cdfaea9d151085586f2c9f75715e7\",\n \"score\": \"0.49907282\",\n \"text\": \"def get_data(self):\\n return {\\n \\\"d_nums\\\": self.d_nums,\\n \\\"d_dens\\\": self.d_dens,\\n \\\"ds\\\": self.ds,\\n \\\"lr_effs\\\": self.lr_effs\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7bd13f1a06e2da21a1bf7c54dc7ca07\",\n \"score\": \"0.49855143\",\n \"text\": \"def read_xyz(self, fname):\\n\\n with open(fname, 'r') as f:\\n natoms = int(f.readline().split()[0])\\n f.readline() #comment\\n xyz = numpy.zeros((natoms, 3))\\n elements = []\\n for i in range(natoms):\\n line = f.readline().split()\\n elements.append((line[0]).upper())\\n xyz[i,:] = line[1:4]\\n #if len(f.readlines()) > 0:\\n # raise Exception('File '+fname+' longer than natoms+2: xyz movie?')\\n #f = open(fname, 'r')V\\n #natoms = string.atoi(string.split(f.readline())[0])\\n #f.readline()\\n #xyz = numpy.zeros((natoms, 3))\\n #elements = []\\n #for i in range(natoms):\\n # line = string.split(f.readline())\\n # elements.append(string.upper(line[0]))\\n # xyz[i,:] = line[1:4]\\n for i in range(len(elements)):\\n if len(elements[i]) == 1:\\n elements[i] += ' '\\n elements = map(string.lower, elements)\\n elements = map(string.capitalize, elements)\\n self.set_elements(elements)\\n self.set_natoms(natoms)\\n self.set_masses()\\n self.set_vdwr()\\n self.set_xyz(xyz)\\n return elements, xyz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962113ee0df684be065cfe5ab48cb753\",\n \"score\": \"0.49794397\",\n \"text\": \"def ecrire_v3d(acces_fichier, dict_numpy_arrays, dimensions, numbloc=0, fmt_fichier = \\\"bin\\\",\\\\\\r\\n precision=\\\"i4r8\\\", endian=\\\"big\\\", type_maillage=False):\\r\\n ## definition des dictionnaires:\\r\\n format_binaire = {\\\"i4\\\" : [4,\\\"l\\\"], \\\"i8\\\" : [8,\\\"q\\\"], \\\"r4\\\" : [4,\\\"f\\\"], \\\"r8\\\" : [8,\\\"d\\\"], \\\"big\\\":\\\">\\\", \\r\\n \\\"little\\\" : \\\"<\\\"}\\r\\n \\r\\n nb_vars = len(dict_numpy_arrays)\\r\\n if nb_vars == 0:\\r\\n print 'None to save'\\r\\n return 1\\r\\n \\r\\n ## ouverture du fichier:\\r\\n try: \\r\\n fichier_v3d = open(acces_fichier,\\\"w\\\" if fmt_fichier == 'fmt' else \\\"wb\\\")\\r\\n except:\\r\\n raise IOError, \\\"Le fichier ne peut pas etre ecrit\\\"\\r\\n else:\\r\\n print 'Ecriture %s'%(acces_fichier)\\r\\n \\r\\n ## ecriture du fichier:\\r\\n if fmt_fichier == \\\"fmt\\\":\\r\\n fichier_v3d.write((5*\\\" \\\" + str(nb_vars))[-5:] + \\\"\\\\n\\\")\\r\\n incr_var = 0\\r\\n for nom_var in dict_numpy_arrays if not type_maillage else ['x', 'y', 'z']:\\r\\n numpyArray = dict_numpy_arrays[nom_var]\\r\\n \\r\\n while len(dimensions) < 3:\\r\\n dimensions += (1,)\\r\\n numpyArray = numpyArray.ravel()\\r\\n \\r\\n if not type_maillage:\\r\\n var = \\\"va \\\" + nom_var\\r\\n else:\\r\\n var = nom_var\\r\\n \\r\\n if type_maillage:\\r\\n fichier_v3d.write((var + 20*\\\" \\\")[:20] + (20*\\\" \\\" + \\\"6e15.7\\\")[-20:] + \\\"\\\\n\\\" )\\r\\n fichier_v3d.write((6*\\\" \\\" + str(numbloc))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(dimensions[0]))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(dimensions[1]))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(dimensions[2]))[-6:] + \\\"\\\\n\\\")\\r\\n else:\\r\\n fichier_v3d.write((var + 20*\\\" \\\")[:20] + (20*\\\" \\\" + \\\"6e14.7\\\")[-20:] + \\\"\\\\n\\\")\\r\\n fichier_v3d.write((6*\\\" \\\" + str(incr_var + 1))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(numbloc))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(dimensions[0]))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(dimensions[1]))[-6:] +\\\\\\r\\n (6*\\\" \\\" + str(dimensions[2]))[-6:] + \\\"\\\\n\\\")\\r\\n incr_var += 1\\r\\n\\r\\n incr = 0\\r\\n if type_maillage:\\r\\n for value in numpyArray:\\r\\n incr += 1\\r\\n fichier_v3d.write(\\r\\n '{0: 15.7E}{1}'.format(value, \\r\\n \\\"\\\\n\\\" if incr%6 == 0 or incr == numpyArray.size\\r\\n else \\\"\\\"))\\r\\n else:\\r\\n for value in numpyArray:\\r\\n incr += 1\\r\\n fichier_v3d.write(\\r\\n '{0: 14.7E}{1}'.format(value, \\r\\n \\\"\\\\n\\\" if incr%6 == 0 or incr == numpyArray.size\\r\\n else \\\"\\\"))\\r\\n \\r\\n elif fmt_fichier == \\\"bin\\\":\\r\\n # options de lecture pour struct.unpack\\r\\n ordre_bin = '>' if endian == 'big' else '<'\\r\\n fmt_entier = \\\"l\\\" if precision[:2] == 'i4' else 'q'\\r\\n taille_entier = int(precision[1])\\r\\n fmt_reel = \\\"f\\\" if precision[-2:] == 'i4' else 'd'\\r\\n taille_reel = int(precision[3])\\r\\n \\r\\n fichier_v3d.write(struct.pack(ordre_bin + fmt_entier, taille_entier))\\r\\n fichier_v3d.write(struct.pack(ordre_bin + fmt_entier, nb_vars))\\r\\n fichier_v3d.write(struct.pack(ordre_bin + fmt_entier, taille_entier))\\r\\n \\r\\n incr_var = 0\\r\\n for nom_var in dict_numpy_arrays if not type_maillage else ['x', 'y', 'z']:\\r\\n numpyArray = dict_numpy_arrays[nom_var]\\r\\n \\r\\n while len(dimensions) < 3:\\r\\n dimensions += (1,)\\r\\n numpyArray = numpyArray.ravel()\\r\\n \\r\\n if not type_maillage:\\r\\n var = (\\\"va \\\" + nom_var + 20 * \\\" \\\")[:20]\\r\\n else:\\r\\n var = (nom_var + 20 * \\\" \\\")[:20]\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", 20))\\r\\n fichier_v3d.write(struct.pack(\\\"20s\\\", var))\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\" ,20))\\r\\n \\r\\n if type_maillage: \\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", 4 * taille_entier))\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"4\\\" + fmt_entier, \\r\\n numbloc, dimensions[0], dimensions[1], dimensions[2])\\r\\n )\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", 4 * taille_entier))\\r\\n else:\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", 5 * taille_entier))\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"5\\\" + fmt_entier,\\r\\n incr_var + 1 , numbloc, dimensions[0], dimensions[1], dimensions[2])\\r\\n )\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", 5 * taille_entier))\\r\\n incr_var += 1\\r\\n \\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", numpy.prod(dimensions) * taille_reel))\\r\\n for value_point in numpyArray:\\r\\n fichier_v3d.write(struct.pack(ordre_bin + fmt_reel, value_point))\\r\\n fichier_v3d.write(struct.pack(ordre_bin + \\\"l\\\", numpy.prod(dimensions) * taille_reel))\\r\\n fichier_v3d.close()\\r\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"640484e65cde9a8344a1a94ad9b327b8\",\n \"score\": \"0.49782774\",\n \"text\": \"def _construct_contig_info(self, params):\\n\\n assembly_chromosome_dict = self.wsc.get_object_subset([{\\n 'included': ['/contigs'],\\n 'ref': self.vcf_info['assembly_ref']\\n }])[0]['data']['contigs']\\n\\n\\n contigs = []\\n\\n contig_infos = self.vcf_info['contigs']\\n\\n\\n for contig_id in contig_infos:\\n length_contig = assembly_chromosome_dict[contig_id].get(\\\"length\\\")\\n contig_infos[contig_id][\\\"length\\\"] = length_contig\\n contigs.append(contig_infos[contig_id])\\n\\n return contigs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1df6994f10ebedbdb011994a8a2f78c\",\n \"score\": \"0.49774212\",\n \"text\": \"def to_3d(self, data):\\n return data.reshape(self.original_dim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26c7c36521caddaf785aebeacbaa4096\",\n \"score\": \"0.49731585\",\n \"text\": \"def get_genome_data(self, chroms_info, **kwargs):\\n genome_data = []\\n for chrom_info in chroms_info['chrom_info']:\\n chrom = chrom_info['chrom']\\n chrom_len = chrom_info['len']\\n chrom_data = self.get_data(chrom, 0, chrom_len, **kwargs)\\n # FIXME: data providers probably should never return None.\\n # Some data providers return None when there's no data, so\\n # create a dummy dict if necessary.\\n if not chrom_data:\\n chrom_data = {\\n 'data': None\\n }\\n chrom_data['region'] = \\\"%s:%i-%i\\\" % (chrom, 0, chrom_len)\\n genome_data.append(chrom_data)\\n\\n return {\\n 'data': genome_data,\\n 'dataset_type': self.dataset_type\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44cb3f7033b5a6119d6400b8476aed1d\",\n \"score\": \"0.49645782\",\n \"text\": \"def gen_slice_dicom_file(self,outdir, n, imageinfo,image_2d, \\\\\\n sop_instance_uid,slice_dataset,meta_dataset):\\n \\n \\n \\n #get the image pixel array\\n \\n dim2 = image_2d.dimensions\\n \\n array_1d = image_2d.point_data.scalars.to_array() \\n pixel_array = np.reshape(array_1d,(dim2[0],dim2[1])) \\n \\n # pixel_array = np.reshape(array_1d,(dim3[0],dim3[1])) \\n slice_dataset.PixelData = pixel_array.tostring()\\n \\n \\n \\n slice_dataset.is_little_endian = True\\n slice_dataset.is_implicit_VR = True \\n \\n #update the slice_dataset by imageinfo \\n imageinfo.update_dataset(slice_dataset)\\n \\n \\n \\n meta_dataset.MediaStorageSOPInstanceUID = sop_instance_uid\\n slice_dataset.SOPInstanceUID = sop_instance_uid\\n \\n #****************************************************\\n #Write out dicom image file\\n fn_prefix = self.export_property.file_name_prefix\\n fn_sufix = self.export_property.file_name_suffix\\n \\n out_file_name =outdir + '/' + fn_prefix \\\\\\n + str(n) + '.' + fn_sufix \\n \\n \\n \\n out_version = self.export_property.export_style \\n \\n \\n \\n \\n \\n \\n if out_version == 'Dicom CTImage' or 'EcliseTPS-CTImage':\\n \\n# fds = FileDataset(out_file_name, slice_dataset) \\n# fds.save_as(out_file_name)\\n \\n # Create the FileDataset instance (initially no data elements, but file_meta supplied)\\n# \\n fds = FileDataset(out_file_name, slice_dataset,file_meta=meta_dataset, preamble=\\\"\\\\0\\\"*128) \\n fds.save_as(out_file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f105e6201ffa1729bcf7a44c82710ec\",\n \"score\": \"0.49604526\",\n \"text\": \"def readSampledGEOS(self):\\n nc = Dataset(self.inFile)\\n if self.aerosol:\\n SDS = self.SDS_AER\\n else:\\n SDS = self.SDS_META\\n for sds in SDS:\\n self.__dict__[sds] = []\\n if len(nc.variables[sds][:].shape) == 1:\\n var = np.array([nc.variables[sds][pixel]])\\n else:\\n var = nc.variables[sds][pixel,:]\\n\\n var.shape = (1,) + var.shape\\n self.__dict__[sds] = var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9931944dec879dcc24f127e2a8c7de50\",\n \"score\": \"0.49536693\",\n \"text\": \"def load_xyz(self, filename):\\n xyz = torch.Tensor(np.load(filename)).float()[:, :, :3]\\n xyz = xyz.permute(2, 0, 1)\\n return xyz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d40e919fe802f05251b103ee10daeb\",\n \"score\": \"0.49517834\",\n \"text\": \"def val3p4c_depthfromIvar(sample,Nexpmin=1,Nexpmax=500):\\n nside = 1024 # Resolution of output maps\\n nsideSTR='1024' # same as nside but in string format\\n nsidesout = None # if you want full sky degraded maps to be written\\n ratiores = 1 # Superresolution/oversampling ratio, simp mode doesn't allow anything other than 1\\n mode = 1 # 1: fully sequential, 2: parallel then sequential, 3: fully parallel\\n pixoffset = 0 # How many pixels are being removed on the edge of each CCD? 15 for DES.\\n oversamp='1' # ratiores in string format\\n \\n band = sample.band\\n catalogue_name = sample.catalog\\n fname = sample.ccds \\n localdir = sample.localdir\\n extc = sample.extc\\n\\n #Read ccd file \\n tbdata = pyfits.open(fname)[1].data\\n \\n # ------------------------------------------------------\\n # Obtain indices\\n auxstr='band_'+band\\n sample_names = [auxstr]\\n if(sample.DR == 'DR3'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \\n elif(sample.DR == 'DR4'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['bitmask'] == 0)) \\n elif(sample.DR == 'DR5'):\\n if(sample.survey == 'DECaLS'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True)) \\n elif(sample.survey == 'DEShyb'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\\n elif(sample.survey == 'NGCproxy'):\\n inds = np.where((tbdata['filter'] == band) & (tbdata['photometric'] == True) & (tbdata['blacklist_ok'] == True) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \\n elif(sample.DR == 'DR6'):\\n inds = np.where((tbdata['filter'] == band)) \\n elif(sample.DR == 'DR7'):\\n if(sample.survey == 'DECaLS'):\\n inds = np.where((tbdata['filter'] == band)) \\n elif(sample.survey == 'DEShyb'):\\n inds = np.where((tbdata['filter'] == band) & (list(map(InDEShybFootprint,tbdata['ra'],tbdata['dec']))))\\n elif(sample.survey == 'NGCproxy'):\\n inds = np.where((tbdata['filter'] == band) & (list(map(InNGCproxyFootprint,tbdata['ra'])))) \\n\\n\\n #Read data \\n #obtain invnoisesq here, including extinction \\n nmag = Magtonanomaggies(tbdata['galdepth']-extc*tbdata['EBV'])/5.\\n ivar= 1./nmag**2.\\n\\n hits=np.ones(np.shape(ivar)) \\n\\n # What properties do you want mapped?\\n # Each each tuple has [(quantity to be projected, weighting scheme, operation),(etc..)] \\n propertiesandoperations = [ ('ivar', '', 'total'), ('hits','','total') ]\\n\\n \\n # What properties to keep when reading the images? \\n # Should at least contain propertiesandoperations and the image corners.\\n # MARCM - actually no need for ra dec image corners. \\n # Only needs ra0 ra1 ra2 ra3 dec0 dec1 dec2 dec3 only if fast track appropriate quicksip subroutines were implemented \\n #propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\\\\n # \\t+ ['RA', 'DEC', 'crval1', 'crval2', 'crpix1', 'crpix2', 'cd1_1', 'cd1_2', 'cd2_1', 'cd2_2','width','height']\\n propertiesToKeep = [ 'filter', 'FWHM','mjd_obs'] \\\\\\n \\t+ ['RA', 'DEC', 'ra0','ra1','ra2','ra3','dec0','dec1','dec2','dec3']\\n \\n # Create big table with all relevant properties. \\n\\n tbdata = np.core.records.fromarrays([tbdata[prop] for prop in propertiesToKeep] + [ivar] + [hits], names = propertiesToKeep + [ 'ivar', 'hits'])\\n \\n # Read the table, create Healtree, project it into healpix maps, and write these maps.\\n # Done with Quicksip library, note it has quite a few hardcoded values (use new version by MARCM for BASS and MzLS) \\n # project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, outroot, sample_names, inds, nside)\\n #project_and_write_maps(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside, ratiores, pixoffset, nsidesout)\\n project_and_write_maps_simp(mode, propertiesandoperations, tbdata, catalogue_name, localdir, sample_names, inds, nside)\\n \\n # Read Haelpix maps from quicksip \\n prop='ivar'\\n op='total'\\n vmin=21.0\\n vmax=24.0\\n\\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\\n f = fitsio.read(fname2)\\n\\n # HEALPIX DEPTH MAPS \\n # convert ivar to depth \\n import healpy as hp\\n from healpix3 import pix2ang_ring,thphi2radec\\n\\n ral = []\\n decl = []\\n val = f['SIGNAL']\\n pix = f['PIXEL']\\n\\n #get hits\\n prop = 'hits'\\n op = 'total'\\n fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\\\\n catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\\n f = fitsio.read(fname2)\\n hitsb=f['SIGNAL']\\n \\n # Obtain values to plot \\n #if (prop == 'ivar'):\\n myval = []\\n mylabel='depth' \\n \\n below=0 \\n for i in range(0,len(val)):\\n depth=nanomaggiesToMag(sqrt(1./val[i]) * 5.)\\n npases=hitsb[i]\\n if(npases >= Nexpmin and npases <= Nexpmax ):\\n myval.append(depth)\\n th,phi = hp.pix2ang(int(nside),pix[i])\\n ra,dec = thphi2radec(th,phi)\\n ral.append(ra)\\n decl.append(dec)\\n if(depth < vmin):\\n below=below+1\\n\\n npix=len(myval)\\n\\n print('Area is ', npix/(float(nside)**2.*12)*360*360./pi, ' sq. deg.')\\n print(below, 'of ', npix, ' pixels are not plotted as their ', mylabel,' < ', vmin)\\n print('Within the plot, min ', mylabel, '= ', min(myval), ' and max ', mylabel, ' = ', max(myval))\\n\\n\\n # Plot depth \\n #from matplotlib import pyplot as plt\\n #import matplotlib.cm as cm\\n\\n #ralB = [ ra-360 if ra > 300 else ra for ra in ral ]\\n #vmax = sample.recm\\n #vmin = vmax - 2.0\\n \\n #mapa = plt.scatter(ralB,decl,c=myval, cmap=cm.rainbow,s=2., vmin=vmin, vmax=vmax, lw=0,edgecolors='none')\\n #mapa = plt.scatter(ralB,decl,c=myval, cmap=cm.gnuplot,s=2., vmin=vmin, vmax=vmax, lw=0,edgecolors='none')\\n #mapa.cmap.set_over('yellowgreen')\\n #cbar = plt.colorbar(mapa,extend='both')\\n #plt.xlabel('r.a. (degrees)')\\n #plt.ylabel('declination (degrees)')\\n #plt.title('Map of '+ mylabel +' for '+catalogue_name+' '+band+'-band \\\\n with 3 or more exposures')\\n #plt.xlim(-60,300)\\n #plt.ylim(-30,90)\\n #mapfile=localdir+mylabel+'_'+band+'_'+catalogue_name+str(nside)+'.png'\\n #print 'saving plot to ', mapfile\\n #plt.savefig(mapfile)\\n #plt.close()\\n #plt.show()\\n #cbar.set_label(r'5$\\\\sigma$ galaxy depth', rotation=270,labelpad=1)\\n #plt.xscale('log')\\n mapfile=localdir+mylabel+'_'+band+'_'+catalogue_name+str(nside)+'.png'\\n mytitle='Map of '+ mylabel +' for '+catalogue_name+' '+band+'-band \\\\n with '+str(Nexpmin)+\\\\\\n ' or more exposures'\\n plot_magdepth2D(sample,ral,decl,myval,mapfile,mytitle)\\n\\n\\n # Statistics depths\\n\\n deptharr=np.array(myval)\\n p90=np.percentile(deptharr,10)\\n p95=np.percentile(deptharr,5)\\n p98=np.percentile(deptharr,2)\\n med=np.percentile(deptharr,50)\\n mean = sum(deptharr)/float(np.size(deptharr)) # 1M array, too long for precision\\n std = sqrt(sum(deptharr**2.)/float(len(deptharr))-mean**2.)\\n\\n \\n\\n ndrawn=np.size(deptharr)\\n print(\\\"Total pixels\\\", np.size(deptharr), \\\"probably too many for exact mean and std\\\")\\n print(\\\"Mean = \\\", mean, \\\"; Median = \\\", med ,\\\"; Std = \\\", std)\\n print(\\\"Results for 90% 95% and 98% are: \\\", p90, p95, p98)\\n\\n # Statistics pases\\n #prop = 'hits'\\n #op = 'total'\\n #fname2=localdir+catalogue_name+'/nside'+nsideSTR+'_oversamp'+oversamp+'/'+\\\\\\n #catalogue_name+'_band_'+band+'_nside'+nsideSTR+'_oversamp'+oversamp+'_'+prop+'__'+op+'.fits.gz'\\n #f = fitsio.read(fname2)\\n \\n #hitsb=f['SIGNAL']\\n hist, bin_edges =np.histogram(hitsb,bins=[-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5, 11.5,12.5, 13.5, 14.5, 15.5, 16.15,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,100],density=True)\\n #print hitsb[1000:10015]\\n #hist, bin_edges =np.histogram(hitsb,density=True)\\n print(\\\"Percentage of hits for 0,1,2., to >7 pases\\\\n\\\", end=' ') \\n #print bin_edges \\n print(hist)\\n #print 100*hist\\n\\n return mapfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d8ced3f07fdedc806a295c328af1a1\",\n \"score\": \"0.4950398\",\n \"text\": \"def get_data(self):\\n # XXX is this actually necessary here?\\n import numpy # import dependency\\n\\n # Get the image data\\n return xds_cbf.reader.get_data(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b122146b08f796f17433e871f0efcc\",\n \"score\": \"0.49495107\",\n \"text\": \"def cell_to_morph3d(cell):\\n g = nx.DiGraph()\\n stack = [cell.soma]\\n while len(stack) > 0:\\n sec = stack.pop()\\n # This is roundabout way is required because nrn7.4 does not\\n # provide explicit equivalent of `access {section}`. In nrn7.5\\n # the 3d functions are available as Section methods.\\n h('access {}'.format(sec.name()))\\n stype = nu.sectype(sec.name())\\n pt3d = int(h.n3d())\\n # pt3d = int(sec.n3d()): # only nrn >= 7.5\\n for ii in range(pt3d): \\n name = '{}_{}'.format(sec.name(), ii)\\n x = h.x3d(ii)\\n y = h.y3d(ii)\\n z = h.z3d(ii)\\n d = h.diam3d(ii)\\n g.add_node(name, x=x, y=y, z=z, r=d/2.0, s=stype, orig=sec)\\n for ii in range(1, pt3d):\\n n1 = '{}_{}'.format(sec.name(), ii-1)\\n n2 = '{}_{}'.format(sec.name(), ii)\\n length = ng.eucd(g, n1, n2)\\n g.add_edge(n1, n2, length=length)\\n current = h.SectionRef(sec=sec)\\n if current.has_parent():\\n h('access {}'.format(current.parent.name())) \\n n1 = '{}_{}'.format(current.parent.name(),\\n int(h.n3d()-1))\\n g.add_edge(n1, '{}_0'.format(sec.name()),\\n length=0)\\n for child in current.child:\\n # print('Adding', child.name())\\n stack.append(child)\\n return g, '{}_0'.format(cell.soma.name())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c494da22770be1f7eecf3fbc75b8de\",\n \"score\": \"0.49494615\",\n \"text\": \"def dbFiller3d(NRange = [2],nPhiRange = [256],\\r\\n VuRange = [-1.0],VpRange = [-1.0],VzRange = [-1.0],\\r\\n dVuRange = [1.0],dVpRange = [1.0],dVzRange = [1.0],\\r\\n VuuRange = [1.0],VppRange = [1.0],VzzRange = [1.0],\\r\\n dt = 0.1,drag = 0.1,v0 = 1,dims = 3):\\r\\n\\r\\n\\r\\n \\r\\n if dims == 2:\\r\\n\\r\\n VzRange= [0.0]\\r\\n dVzRange= [0]\\r\\n\\r\\n VzzRange = [0.0]\\r\\n conn = sqlite3.connect(parametersName)\\r\\n c = conn.cursor()\\r\\n for N in Nrange:\\r\\n for nPhi in nPhiRange:\\r\\n for Vu in VuRange:\\r\\n for Vp in VpRange:\\r\\n for Vz in VzRange:\\r\\n for dVu in dVuRange:\\r\\n for dVz in dVzRange: \\r\\n for Vuu in VuuRange:\\r\\n for Vpp in VppRange:\\r\\n for Vzz in VzzRange:\\r\\n expId = str(uuid.uuid4())\\r\\n values = [expId,N,nPhi,dt,v0,drag,Vuu,Vpp,Vzz,Vu,Vp,Vz,dVu,dVu,dVz]\\r\\n \\r\\n c.execute(\\\"INSERT INTO parameters VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\\\",values)\\r\\n conn.commit()\\r\\n conn.close()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994388,"cells":{"query_id":{"kind":"string","value":"efe4945b4bc959e32bc42da664ac3db0"},"query":{"kind":"string","value":"Overridden parent class destruction method method that adds additional logic to destroy the underlying toolkit widget. The destruction happens in dual pass topdown, then bottomup. On the topdown pass, the traits listeners for the abstract obj are unhooked so that no events get fired during destruction. On the bottom up pass, the abstract obj is destroyed and its ref set to None."},"positive_passages":{"kind":"list like","value":[{"docid":"56720abac1924d3ce7798466f7a7bfb7","score":"0.84241104","text":"def destroy(self):\n # Remove the abstract object as a trait listener so that it\n # does not try to update after destroying its internal widget.\n self.remove_trait_listener(self.abstract_obj, 'shell')\n\n # Traverse down the tree and have the children destroy themselves.\n super(BaseWidgetComponent, self).destroy()\n \n # On pass back up the tree, destroy the abstract_obj.\n self.abstract_obj.destroy()\n self.abstract_obj = None","title":""}],"string":"[\n {\n \"docid\": \"56720abac1924d3ce7798466f7a7bfb7\",\n \"score\": \"0.84241104\",\n \"text\": \"def destroy(self):\\n # Remove the abstract object as a trait listener so that it\\n # does not try to update after destroying its internal widget.\\n self.remove_trait_listener(self.abstract_obj, 'shell')\\n\\n # Traverse down the tree and have the children destroy themselves.\\n super(BaseWidgetComponent, self).destroy()\\n \\n # On pass back up the tree, destroy the abstract_obj.\\n self.abstract_obj.destroy()\\n self.abstract_obj = None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a53dd6a3c3388b044499111a8d792796","score":"0.6808209","text":"def destroy(self):\n del self.widget","title":""},{"docid":"90cc71eb89daca368a6bd631d5cba017","score":"0.65991217","text":"def cleanup_callback(self, obj, event):\n self.parent.rend.RemoveActor(self.actor)\n #self.vtk_interactor.RemoveObservers('LeftButtonPressEvent')\n self.parent.vtk_interactor.RemoveObserver(self.cleanup_observer)\n cleanup_observer = None","title":""},{"docid":"7ed01aa05890090fc0960a094e7d77dc","score":"0.6430921","text":"def destroy(self):\n self.roll_verified.set(HogGUI.KILL)\n super().destroy()","title":""},{"docid":"ca89335b2ce31fe29a74fc1a602eae98","score":"0.6430241","text":"def destroy(self):\n if self.active:\n super().destroy()\n del self.widgets\n del self.maxes\n del self.bar_var\n del self.bar_val\n self.active = False\n _ALL_SCREENS.discard(self)","title":""},{"docid":"58ba95fc30397e8e531a548d7416ddab","score":"0.64055765","text":"def __del__(self):\n safe_call(backend.get().af_destroy_window(self._wnd))","title":""},{"docid":"ef34951aca5a1b498a8bb84fdb5c5ea9","score":"0.63900644","text":"def destroy(self):\n\n # Make sure that any control panels are correctly destroyed\n for panelType, panel in self.__panels.items():\n self.__auiMgr.DetachPane(panel)\n panel.destroy()\n\n # Remove listeners from the overlay\n # list and display context\n lName = 'ViewPanel_{}'.format(self.name)\n\n self .removeListener('profile', lName)\n self.overlayList.removeListener('overlays', lName)\n self.displayCtx .removeListener('selectedOverlay', lName)\n\n # Disable the ProfileManager\n self.__profileManager.destroy()\n\n # Un-initialise the AUI manager\n self.__auiMgr.Unbind(aui.EVT_AUI_PANE_CLOSE)\n self.__auiMgr.Update()\n self.__auiMgr.UnInit()\n\n # The AUI manager does not clear its\n # reference to this panel, so let's\n # do it here.\n self.__auiMgr._frame = None\n self.__profileManager = None\n self.__auiMgr = None\n self.__panels = None\n self.__centrePanel = None\n\n fslpanel.FSLeyesPanel.destroy(self)","title":""},{"docid":"6413561c6316bd3fd41543a60d19b992","score":"0.6336812","text":"def destroy(self) -> None:\r\n self._tk.destroy()","title":""},{"docid":"3a22550f3c21a1f4ce07e1b449ce21a8","score":"0.6286755","text":"def __del__(self):\n\n # C# --> class UIA2Automation : AutomationBase --> abstract class AutomationBase : IDisposable\n self._automation.Dispose()","title":""},{"docid":"71b1f926667900af2cefe6b0fd967dd1","score":"0.62793624","text":"def __del__(self):\n print(\"Destroying object : \" + self.__class__.__name__ )","title":""},{"docid":"ae731f0d5bfca194d53ca88995413651","score":"0.62690437","text":"def __del__( self ):\n\t\tself.unbind()","title":""},{"docid":"6aeb4a831b73938990bfbf66f7e2748f","score":"0.62541896","text":"def __del__(self):\n super(self.__class__, self).__del__()","title":""},{"docid":"ceea0002d1b0869c37ff1a02d05d3c95","score":"0.6176663","text":"def destroy(self):\n raise NotImplementedError","title":""},{"docid":"dad277f63cb66efce5aa8cca1eb621ff","score":"0.61561614","text":"def destroy(self):\n\n self.detachAllFromParent()\n\n overlayList = self.__overlayList\n displays = self.__displays\n\n self.__overlayList = None\n self.__displays = None\n\n overlayList.removeListener('overlays', self.__name)\n\n if self.__child:\n self.removeListener('syncOverlayDisplay', self.__name)\n self.removeListener('syncOverlayVolume', self.__name)\n self.removeListener('displaySpace', self.__name)\n self.removeListener('location', self.__name)\n self.removeListener('worldLocation', self.__name)\n self.removeListener('vertexIndex', self.__name)\n else:\n for g in list(self.overlayGroups):\n self.overlayGroups.remove(g)\n g.destroy()\n\n for overlay, display in displays.items():\n display.destroy()","title":""},{"docid":"9d8f8ccc7ced462aab69ec2cf0664c64","score":"0.6153269","text":"def destroy(self):\n # in the future it might be nice to also remove associated events etc.\n self.world.deregister_world_object(self)","title":""},{"docid":"29999f319ebd7d9535148ce488de3ea7","score":"0.6152434","text":"def __del__(self):\n self.CleanUp()","title":""},{"docid":"88785339002a9311fb519ceb17d284e0","score":"0.6137018","text":"def destroy(self):\n self.read_callback = self.write_callback = None\n\n if hasattr(self, 'impl'):\n self.impl.stop()\n def _dummy(*args): pass\n self.impl.close(_dummy)\n del self.impl","title":""},{"docid":"1be3ae5becbfe00f7aa40cc0979490bb","score":"0.6130392","text":"def __del__(self):\n if self._created_container:\n self._teardown_container()","title":""},{"docid":"9921f6ec780635cfcc05102aaf9d3641","score":"0.6107894","text":"def __del__(self):\n try:\n self.disconnectBelt()\n except:\n pass","title":""},{"docid":"9d55d48df6ecf030263f902c2315e9f7","score":"0.60882986","text":"def destroy(self):\n self.tray.set_visible(False)","title":""},{"docid":"5f37cf9e648e93cb73551ac3bbc0fc86","score":"0.60728323","text":"def _uncanvased(self):\n if self['name'] and self._canvas:\n self._canvas._unregister_object_name(self['name'])\n self._wrap(None)\n self._canvas = None","title":""},{"docid":"7a5063de9ff5d19c076794cc62dc0b15","score":"0.6069728","text":"def teardown_method(self):\n event = threading.Event()\n self.factory.destroy(event)\n event.wait()","title":""},{"docid":"c411ee821f342722bbfe564766a04565","score":"0.6068623","text":"def fermeture_interface(self, object_tk):\n\n\t\t#je supprime la fenetre\n\t\tobject_tk.destroy()","title":""},{"docid":"83fd78ab971e86041352e68e3bcc5807","score":"0.60393226","text":"def _destroy(self, *_):\n if self.parent is not None:\n self.parent.mod_list_frame = None\n self.parent = None","title":""},{"docid":"978c00e42f97654e2d74a0b63fca4a9b","score":"0.60377616","text":"def destroy(self):\n self._destroyed = True","title":""},{"docid":"dca4460b07468b6c210eac5d581bc23d","score":"0.60207283","text":"def deinit(self) -> None:\n ...","title":""},{"docid":"dca4460b07468b6c210eac5d581bc23d","score":"0.60207283","text":"def deinit(self) -> None:\n ...","title":""},{"docid":"dca4460b07468b6c210eac5d581bc23d","score":"0.60207283","text":"def deinit(self) -> None:\n ...","title":""},{"docid":"1aa7c0076b22eb04c2a2770f1224aa37","score":"0.59679675","text":"def destroy(self):\n self.carla_actor = None\n super(Actor, self).destroy()","title":""},{"docid":"354f9011e825d347a9ef7529b0ed4b36","score":"0.5966708","text":"def destroy(self):\n self.function_frame.pack_forget()\n self.interactive_frame.pack_forget()\n self.is_show = False","title":""},{"docid":"d59ba04e551bc532742edd38c6c2f9ee","score":"0.5965096","text":"def onDestroy(self):\n pass","title":""},{"docid":"8141e9a23e6e2c401bf614bbe4db35e1","score":"0.59600407","text":"def destroy(self):\n self._root.destroy()","title":""},{"docid":"55efa311a1e1054ff494f2453d502d37","score":"0.5959752","text":"def _destroy(self):\n print(str(self) + ' has been destroyed.')\n Item.instances_in_level.remove(self) # Remove in 'instances_in_level'\n del self # Remove instance itself","title":""},{"docid":"04c2318dff5351b9ea649fca460f9903","score":"0.59529525","text":"def destroy(self):\n pass","title":""},{"docid":"04c2318dff5351b9ea649fca460f9903","score":"0.59529525","text":"def destroy(self):\n pass","title":""},{"docid":"04c2318dff5351b9ea649fca460f9903","score":"0.59529525","text":"def destroy(self):\n pass","title":""},{"docid":"04c2318dff5351b9ea649fca460f9903","score":"0.59529525","text":"def destroy(self):\n pass","title":""},{"docid":"04c2318dff5351b9ea649fca460f9903","score":"0.59529525","text":"def destroy(self):\n pass","title":""},{"docid":"a020c3cf03ed51f2592215a5227151a5","score":"0.5945861","text":"def __del__(cls):\r\n cls._events = None","title":""},{"docid":"44fe094181b884c1b7b9081f48570aa3","score":"0.59411633","text":"def destroy(self, widget, data=None):\n\t\tgtk.main_quit()","title":""},{"docid":"4de7f9dd9bdb6dfbdebba0005cb7cbc0","score":"0.5921413","text":"def destroy(self):\n if self.active:\n super().destroy()\n del self.maxes\n del self.splash\n del self.bar_val\n self.active = False\n _ALL_SCREENS.discard(self)","title":""},{"docid":"145447ed50baae6e1a5fc91419b0d836","score":"0.5914714","text":"def destroy(self):\n self.image_subscriber.unregister()\n self.collision_subscriber.unregister()\n self.lane_invasion_subscriber.unregister()","title":""},{"docid":"376b611773914e7871b1d39d290b7803","score":"0.59119856","text":"def destroy(self) -> None:\n if not self.is_alive:\n return\n\n self.process_sub_entities(func=lambda e: e.destroy())\n del self._sub_entities\n del self.seqend\n super().destroy()","title":""},{"docid":"4d6ce1462efd67a187a78405a06faf0c","score":"0.59066725","text":"def destroy(self, widget, data=None):\n gtk.main_quit()","title":""},{"docid":"6b17d7b4fd20149170b1021f182e5339","score":"0.58975387","text":"def __del__(self):\n self.clear()\n self._screen._removeTurtle(self)\n del self._turtle","title":""},{"docid":"d12a5fc00ed33a97b3df04ffcb520271","score":"0.58862853","text":"def __del__(self):\n self.cleanup(True)\n if self.manager is not None:\n del self.manager\n if self.world is not None:\n del self.world","title":""},{"docid":"87585c8030cf89b742bd13b6f3524735","score":"0.5858656","text":"def __del__(self) -> None:\n ...","title":""},{"docid":"87585c8030cf89b742bd13b6f3524735","score":"0.5858656","text":"def __del__(self) -> None:\n ...","title":""},{"docid":"050751bd3c8c430be72698471910cd9f","score":"0.5839367","text":"def OnDestroy(self, event):\n print(\"Destroy Window\")","title":""},{"docid":"01b12ae96e7f960e93977416b81dedb5","score":"0.581417","text":"def teardown(self):\n raise NotImplementedError","title":""},{"docid":"01b12ae96e7f960e93977416b81dedb5","score":"0.581417","text":"def teardown(self):\n raise NotImplementedError","title":""},{"docid":"a063f63023e5694874d846a99e836a27","score":"0.58102006","text":"def destroy(self) -> None:","title":""},{"docid":"0f25e01f7baac89e4fd767b39fd9dd76","score":"0.5803387","text":"def destroy (self,event=None):\n\n # This is enough to disable fillbox.\n self.top.withdraw()","title":""},{"docid":"21b3381f6b2be167723756804f06ecd8","score":"0.5801165","text":"def _close_ui(self):\n self.stash.renderer._stop_rendering()\n self.tk.destroy()","title":""},{"docid":"3fbcd1059c0835c345cdb138d62100c4","score":"0.5800343","text":"def __del__(self):\n pass","title":""},{"docid":"3fbcd1059c0835c345cdb138d62100c4","score":"0.5800343","text":"def __del__(self):\n pass","title":""},{"docid":"3fbcd1059c0835c345cdb138d62100c4","score":"0.5800343","text":"def __del__(self):\n pass","title":""},{"docid":"3fbcd1059c0835c345cdb138d62100c4","score":"0.5800343","text":"def __del__(self):\n pass","title":""},{"docid":"3fbcd1059c0835c345cdb138d62100c4","score":"0.5800343","text":"def __del__(self):\n pass","title":""},{"docid":"b5cf7d05143d394b2a933671180c6108","score":"0.57913226","text":"def _before_destroy(self):","title":""},{"docid":"24e1100f5c6aebc9372e250a19b4f821","score":"0.5781939","text":"def __del__(self):\n del self","title":""},{"docid":"ddfed205b2ce605f687f8f21ed3855c0","score":"0.57798415","text":"def deleter(instance):\n instance.__dict__.pop(name, None)","title":""},{"docid":"345f2d5f6ea59d353e55b2e4fde653a3","score":"0.5767714","text":"def evt_close(self, *args):\n self.Destroy()","title":""},{"docid":"c32e80e7dab9686ab2136f4af2dba453","score":"0.57661545","text":"def destroy(self) :\n self.stop_thread()\n if self.command_leave is not None :\n f = self.command_leave\n self.command_leave = None\n f()\n else :\n try:\n tkinter.Frame.destroy(self)\n except Exception as e :\n if \"application has been destroyed\" in str(e) :\n os._exit(0)","title":""},{"docid":"3f3a5477e0382fb8aee0ee3200520610","score":"0.5753696","text":"def do_unrealize(self):\r\n self.window.destroy()","title":""},{"docid":"3f3a5477e0382fb8aee0ee3200520610","score":"0.5753696","text":"def do_unrealize(self):\r\n self.window.destroy()","title":""},{"docid":"6a4853133efaa7ff0a2291e93201f1c2","score":"0.57463986","text":"def __del__(self):\n self.cleanup()","title":""},{"docid":"3e8c2b88299d15a4f8c77004c090401f","score":"0.5745602","text":"def destroy(self):\n self.overlayList.removeListener('overlays', self.__name)\n self.displayCtx .removeListener('selectedOverlay', self.__name)\n profiles.Profile.destroy(self)","title":""},{"docid":"1e4f63c32f3aa1620812edf44b7eb8b0","score":"0.5742261","text":"def dispose(self):\n\n delattr(self.class_, self.MANAGER_ATTR)","title":""},{"docid":"403a13018e01c58572d4d61962668ae4","score":"0.5738827","text":"def cleanup(self):\n self.removeObservers()","title":""},{"docid":"403a13018e01c58572d4d61962668ae4","score":"0.5738827","text":"def cleanup(self):\n self.removeObservers()","title":""},{"docid":"403a13018e01c58572d4d61962668ae4","score":"0.5738827","text":"def cleanup(self):\n self.removeObservers()","title":""},{"docid":"bad9ee80d29707e3e4023d8b47b6b4ea","score":"0.57382894","text":"def __del__(self):\n\n # delete the Py_Image reference, this should be automatically but just\n # to be sure\n if hasattr(self, \"_py_image\"):\n log_debug(self._logger, (\"Deleting this instance but deleting \" + \n \"the _py_image attribute before\"))\n del self._py_image","title":""},{"docid":"d713db30f023493223fa4bf1b296f1d3","score":"0.5733382","text":"def destroy(self):\n self.log.debug('Destroyed')","title":""},{"docid":"e9080bf75ad579b0d7c32027ebc5f44a","score":"0.5723832","text":"def destroy(self) -> None:\n pass","title":""},{"docid":"2888a34e76a2e25836aae53e32042977","score":"0.5722436","text":"def plugin_destroy(self):\n pass","title":""},{"docid":"4827813cf4ce4b774e225294eff0bc17","score":"0.5720652","text":"def __del__(self):\n self.release()","title":""},{"docid":"4827813cf4ce4b774e225294eff0bc17","score":"0.5720652","text":"def __del__(self):\n self.release()","title":""},{"docid":"85e02c66bb9e959d39a87c70946dc0af","score":"0.5718935","text":"def OnClose(self,event):\n self.Destroy()","title":""},{"docid":"d61fd7cb6372981f8dca6e1de96ed7c6","score":"0.57080543","text":"def destroy(self):\n # if this is the main_app - set the _main_app class variable to `None`.\n if self == App._main_app:\n App._main_app = None\n self.tk.destroy()","title":""},{"docid":"cbc90aed69575ec9c19b0930b5a1e747","score":"0.5707538","text":"def TearDownInstance(self, name):","title":""},{"docid":"9032279396a07af027e33fea8050ebf6","score":"0.5697297","text":"def destroy(self):\n \n pass","title":""},{"docid":"6ebd80edddafba691c72618d7c0e21c3","score":"0.56951076","text":"def cleanup(self): \n LOG(\"Releasing TC status view\")\n if self.__tcStatusView: \n CONN.releaseObject(self.__tcStatusView)\n \n if self.tcInjector: \n try:\n LOG(\"Releasing TC injector\")\n self.tcInjector.deregister()\n except:pass\n CONN.releaseObject(self.tcInjector)\n\n if self.__tcInjectorManager:\n LOG(\"Releasing TC injection manager\")\n CONN.releaseObject(self.__tcInjectorManager) \n LOG(\"TC injection interface clean\")","title":""},{"docid":"d4087df625b2dbd43840fc71695a4113","score":"0.5689629","text":"def destroy(self):\n self.__close_image()\n self.__config.destroy()\n logging.info('Close GUI')\n self.quit()","title":""},{"docid":"d798b11030edd230b3e8ea504036aa69","score":"0.5688169","text":"def destroy(self):\n self._callback_active.acquire()\n if self.carla_actor.is_listening:\n self.carla_actor.stop()\n super(Sensor, self).destroy()","title":""},{"docid":"b5e8f7842141d94294b7d8cf6f6e93ee","score":"0.5675714","text":"def __del__(self):\n try:\n self.stop()\n restore_original_display()\n except:\n pass","title":""},{"docid":"f0dbe0cb2d21874c3e954d3e41777b1b","score":"0.56654286","text":"def destroy(self):\n try:\n self.cancel_scheduled()\n self._before_destroy()\n except Exception as e:\n print(self.name, e, 'in close')\n super().destroy()","title":""},{"docid":"89f7175ceff8fc4f8bc4e17036afda19","score":"0.5657575","text":"def __init__(self, *args, **kwargs):\n newobj = _core.new_WindowDestroyEvent(*args, **kwargs)\n self.this = newobj.this\n self.thisown = 1\n del newobj.thisown","title":""},{"docid":"955584ce6a0ca80d4a64054f59a68433","score":"0.56567013","text":"def deinit(self):\n # deinit() not implemented in micropython\n pass # avoid raising a NotImplemented Error","title":""},{"docid":"c0ad74f00a587393a26aa65e5539f5c6","score":"0.56554824","text":"def destroy(self):\n if self._tclCommands is not None:\n for name in self._tclCommands:\n #print '- Tkinter: deleted command', name\n self.tk.deletecommand(name)\n self._tclCommands = None","title":""},{"docid":"b06ed83284558aa21b9d2b00e1f83862","score":"0.5649462","text":"def Close( self ):\n def Destroy( np, cookie ):\n wrpr = base.game.nodeMgr.Wrap( np )\n if wrpr is not None:\n wrpr.Destroy()\n \n self.Walk( Destroy )\n self.app.game.pluginMgr.OnSceneClose()\n \n # Now remove the root node. If the root node was render, reset base\n # in order to remove and recreate the default node set.\n if self.rootNp is render:\n base.Reset()\n \n # Reset references to render\n self.app.selection.marquee.rootNp = render\n \n self.rootNp.removeNode()","title":""},{"docid":"c528c9551ec88fe547441cebd9610be3","score":"0.5645579","text":"def __close(self):\r\n self.destroy()","title":""},{"docid":"7eef5ee95b4552098e68f105bd74eb49","score":"0.5643967","text":"def __del__(self):\n self.release()\n return","title":""},{"docid":"36054cb9edc19415ec4159b1c798dd95","score":"0.56384534","text":"def destroy(self):\n del self.engine","title":""},{"docid":"36054cb9edc19415ec4159b1c798dd95","score":"0.56384534","text":"def destroy(self):\n del self.engine","title":""},{"docid":"288fcdfce10f354ddcbc73c52053f8f4","score":"0.5630647","text":"def cleanup(self):\n super(CaseMWGUI, self).cleanup()\n close_app()","title":""},{"docid":"0daec4a1a0f32a4d86abf4a81dfbfdd7","score":"0.56171525","text":"def teardown(self):\n raise NotImplementedError()","title":""},{"docid":"43dde48884b8e735cf7f319d3768c7c5","score":"0.5612211","text":"def destroy(self):\n # XXX TODO: this entire method is just horrid. Basically, the initial\n # implementation of the ChangeSet class and various Change* subclasses\n # (AbstractChange, FileChange, DirectoryChange, etc) was rife with\n # circular links, because all child objects linked backed to their\n # parents (i.e. via self.parent). (And by \"initial implementation\", I\n # mean \"initial and still current implementation\".)\n #\n # This prevented a ChangeSet and all its child objects from being\n # garbage collected. Upon realizing the error of my ways, much\n # hacking was done to break the circular references so that objects\n # could be collected properly.\n #\n # This particular method is the biggest, ugliest artefact from that\n # hacking work. It is almost certainly overkill (del'ing objects,\n # then setting them to None... say wha'?), but eh, in its current\n # form, warts and all, it works. Call changeset.destroy(), and gc\n # will be able to clean up the object and all the children correctly.\n #\n # But, yeah, this whole approach should definitely get cleaned up when\n # ChangeSet and friends go in for their next refactoring service.\n\n assert not self.__destroyed\n self.__destroyed = True\n\n del self._nada\n del self._copy\n del self._create\n del self._modify\n del self._remove\n del self._replace\n del self._copied_from\n del self._renamed_from\n\n del self._raw_deletes\n del self._indirect_copy_then_modify\n del self._removes_that_might_be_renames\n\n del self.__all_propchanges\n del self.__all_propreplacements\n del self.__mergeinfo_propchanges\n\n del self.__notes\n del self.__errors\n del self.__warnings\n\n del self.__roots\n del self.__revprops\n\n del self.__fs\n del self.__ptr\n del self.__repo\n del self.__root\n del self.__baton\n del self.__base_root\n\n self._nada = None\n self._copy = None\n self._create = None\n self._modify = None\n self._remove = None\n self._rename = None\n self._replace = None\n self._copied_from = None\n self._renamed_from = None\n\n self.__all_propchanges = None\n self.__all_propreplacements = None\n self.__mergeinfo_propchanges = None\n\n self._raw_deletes = None\n self._indirect_copy_then_modify = None\n self._removes_that_might_be_renames = None\n\n self.__notes = None\n self.__errors = None\n self.__warnings = None\n\n self.__roots = None\n\n self.__fs = None\n self.__repo = None\n self.__root = None\n self.__revprops = None\n self.__base_root = None\n\n del self._tracker\n self._tracker = None\n\n for (k, c) in self._all_changes.items():\n c.unparent()\n c.destroy()\n del c\n\n for k in self._all_changes.keys():\n del self._all_changes[k]\n del self._all_changes\n self._all_changes = None\n\n if self.__pool is not None:\n self.__pool.destroy()\n self.__pool = None\n\n AbstractChangeSet.destroy(self)\n\n gc.collect()","title":""},{"docid":"19048dd0e2a0146ffb1bbb970e7f9013","score":"0.56084996","text":"def teardown(self) -> BaseStep:\n super().teardown()\n self.wrapped.teardown()\n self.is_initialized = False\n return self","title":""},{"docid":"0f74e69b79d3b66ec3926dcaae7840b5","score":"0.5579296","text":"def __del__(self):\n self.auto_pilot_enable_publisher.unregister()\n self.vehicle_control_publisher.unregister()\n self.vehicle_control_manual_override_publisher.unregister()","title":""},{"docid":"069095bc2350f141dbfba7a56713fd32","score":"0.55780935","text":"def destroy(self, log_level=\"\"):\n self.flexy_instance.destroy()","title":""}],"string":"[\n {\n \"docid\": \"a53dd6a3c3388b044499111a8d792796\",\n \"score\": \"0.6808209\",\n \"text\": \"def destroy(self):\\n del self.widget\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90cc71eb89daca368a6bd631d5cba017\",\n \"score\": \"0.65991217\",\n \"text\": \"def cleanup_callback(self, obj, event):\\n self.parent.rend.RemoveActor(self.actor)\\n #self.vtk_interactor.RemoveObservers('LeftButtonPressEvent')\\n self.parent.vtk_interactor.RemoveObserver(self.cleanup_observer)\\n cleanup_observer = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed01aa05890090fc0960a094e7d77dc\",\n \"score\": \"0.6430921\",\n \"text\": \"def destroy(self):\\n self.roll_verified.set(HogGUI.KILL)\\n super().destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca89335b2ce31fe29a74fc1a602eae98\",\n \"score\": \"0.6430241\",\n \"text\": \"def destroy(self):\\n if self.active:\\n super().destroy()\\n del self.widgets\\n del self.maxes\\n del self.bar_var\\n del self.bar_val\\n self.active = False\\n _ALL_SCREENS.discard(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ba95fc30397e8e531a548d7416ddab\",\n \"score\": \"0.64055765\",\n \"text\": \"def __del__(self):\\n safe_call(backend.get().af_destroy_window(self._wnd))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef34951aca5a1b498a8bb84fdb5c5ea9\",\n \"score\": \"0.63900644\",\n \"text\": \"def destroy(self):\\n\\n # Make sure that any control panels are correctly destroyed\\n for panelType, panel in self.__panels.items():\\n self.__auiMgr.DetachPane(panel)\\n panel.destroy()\\n\\n # Remove listeners from the overlay\\n # list and display context\\n lName = 'ViewPanel_{}'.format(self.name)\\n\\n self .removeListener('profile', lName)\\n self.overlayList.removeListener('overlays', lName)\\n self.displayCtx .removeListener('selectedOverlay', lName)\\n\\n # Disable the ProfileManager\\n self.__profileManager.destroy()\\n\\n # Un-initialise the AUI manager\\n self.__auiMgr.Unbind(aui.EVT_AUI_PANE_CLOSE)\\n self.__auiMgr.Update()\\n self.__auiMgr.UnInit()\\n\\n # The AUI manager does not clear its\\n # reference to this panel, so let's\\n # do it here.\\n self.__auiMgr._frame = None\\n self.__profileManager = None\\n self.__auiMgr = None\\n self.__panels = None\\n self.__centrePanel = None\\n\\n fslpanel.FSLeyesPanel.destroy(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6413561c6316bd3fd41543a60d19b992\",\n \"score\": \"0.6336812\",\n \"text\": \"def destroy(self) -> None:\\r\\n self._tk.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a22550f3c21a1f4ce07e1b449ce21a8\",\n \"score\": \"0.6286755\",\n \"text\": \"def __del__(self):\\n\\n # C# --> class UIA2Automation : AutomationBase --> abstract class AutomationBase : IDisposable\\n self._automation.Dispose()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71b1f926667900af2cefe6b0fd967dd1\",\n \"score\": \"0.62793624\",\n \"text\": \"def __del__(self):\\n print(\\\"Destroying object : \\\" + self.__class__.__name__ )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae731f0d5bfca194d53ca88995413651\",\n \"score\": \"0.62690437\",\n \"text\": \"def __del__( self ):\\n\\t\\tself.unbind()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aeb4a831b73938990bfbf66f7e2748f\",\n \"score\": \"0.62541896\",\n \"text\": \"def __del__(self):\\n super(self.__class__, self).__del__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceea0002d1b0869c37ff1a02d05d3c95\",\n \"score\": \"0.6176663\",\n \"text\": \"def destroy(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dad277f63cb66efce5aa8cca1eb621ff\",\n \"score\": \"0.61561614\",\n \"text\": \"def destroy(self):\\n\\n self.detachAllFromParent()\\n\\n overlayList = self.__overlayList\\n displays = self.__displays\\n\\n self.__overlayList = None\\n self.__displays = None\\n\\n overlayList.removeListener('overlays', self.__name)\\n\\n if self.__child:\\n self.removeListener('syncOverlayDisplay', self.__name)\\n self.removeListener('syncOverlayVolume', self.__name)\\n self.removeListener('displaySpace', self.__name)\\n self.removeListener('location', self.__name)\\n self.removeListener('worldLocation', self.__name)\\n self.removeListener('vertexIndex', self.__name)\\n else:\\n for g in list(self.overlayGroups):\\n self.overlayGroups.remove(g)\\n g.destroy()\\n\\n for overlay, display in displays.items():\\n display.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d8f8ccc7ced462aab69ec2cf0664c64\",\n \"score\": \"0.6153269\",\n \"text\": \"def destroy(self):\\n # in the future it might be nice to also remove associated events etc.\\n self.world.deregister_world_object(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29999f319ebd7d9535148ce488de3ea7\",\n \"score\": \"0.6152434\",\n \"text\": \"def __del__(self):\\n self.CleanUp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88785339002a9311fb519ceb17d284e0\",\n \"score\": \"0.6137018\",\n \"text\": \"def destroy(self):\\n self.read_callback = self.write_callback = None\\n\\n if hasattr(self, 'impl'):\\n self.impl.stop()\\n def _dummy(*args): pass\\n self.impl.close(_dummy)\\n del self.impl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be3ae5becbfe00f7aa40cc0979490bb\",\n \"score\": \"0.6130392\",\n \"text\": \"def __del__(self):\\n if self._created_container:\\n self._teardown_container()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9921f6ec780635cfcc05102aaf9d3641\",\n \"score\": \"0.6107894\",\n \"text\": \"def __del__(self):\\n try:\\n self.disconnectBelt()\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d55d48df6ecf030263f902c2315e9f7\",\n \"score\": \"0.60882986\",\n \"text\": \"def destroy(self):\\n self.tray.set_visible(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f37cf9e648e93cb73551ac3bbc0fc86\",\n \"score\": \"0.60728323\",\n \"text\": \"def _uncanvased(self):\\n if self['name'] and self._canvas:\\n self._canvas._unregister_object_name(self['name'])\\n self._wrap(None)\\n self._canvas = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5063de9ff5d19c076794cc62dc0b15\",\n \"score\": \"0.6069728\",\n \"text\": \"def teardown_method(self):\\n event = threading.Event()\\n self.factory.destroy(event)\\n event.wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c411ee821f342722bbfe564766a04565\",\n \"score\": \"0.6068623\",\n \"text\": \"def fermeture_interface(self, object_tk):\\n\\n\\t\\t#je supprime la fenetre\\n\\t\\tobject_tk.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fd78ab971e86041352e68e3bcc5807\",\n \"score\": \"0.60393226\",\n \"text\": \"def _destroy(self, *_):\\n if self.parent is not None:\\n self.parent.mod_list_frame = None\\n self.parent = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"978c00e42f97654e2d74a0b63fca4a9b\",\n \"score\": \"0.60377616\",\n \"text\": \"def destroy(self):\\n self._destroyed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca4460b07468b6c210eac5d581bc23d\",\n \"score\": \"0.60207283\",\n \"text\": \"def deinit(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca4460b07468b6c210eac5d581bc23d\",\n \"score\": \"0.60207283\",\n \"text\": \"def deinit(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dca4460b07468b6c210eac5d581bc23d\",\n \"score\": \"0.60207283\",\n \"text\": \"def deinit(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aa7c0076b22eb04c2a2770f1224aa37\",\n \"score\": \"0.59679675\",\n \"text\": \"def destroy(self):\\n self.carla_actor = None\\n super(Actor, self).destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"354f9011e825d347a9ef7529b0ed4b36\",\n \"score\": \"0.5966708\",\n \"text\": \"def destroy(self):\\n self.function_frame.pack_forget()\\n self.interactive_frame.pack_forget()\\n self.is_show = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59ba04e551bc532742edd38c6c2f9ee\",\n \"score\": \"0.5965096\",\n \"text\": \"def onDestroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8141e9a23e6e2c401bf614bbe4db35e1\",\n \"score\": \"0.59600407\",\n \"text\": \"def destroy(self):\\n self._root.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55efa311a1e1054ff494f2453d502d37\",\n \"score\": \"0.5959752\",\n \"text\": \"def _destroy(self):\\n print(str(self) + ' has been destroyed.')\\n Item.instances_in_level.remove(self) # Remove in 'instances_in_level'\\n del self # Remove instance itself\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c2318dff5351b9ea649fca460f9903\",\n \"score\": \"0.59529525\",\n \"text\": \"def destroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c2318dff5351b9ea649fca460f9903\",\n \"score\": \"0.59529525\",\n \"text\": \"def destroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c2318dff5351b9ea649fca460f9903\",\n \"score\": \"0.59529525\",\n \"text\": \"def destroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c2318dff5351b9ea649fca460f9903\",\n \"score\": \"0.59529525\",\n \"text\": \"def destroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c2318dff5351b9ea649fca460f9903\",\n \"score\": \"0.59529525\",\n \"text\": \"def destroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a020c3cf03ed51f2592215a5227151a5\",\n \"score\": \"0.5945861\",\n \"text\": \"def __del__(cls):\\r\\n cls._events = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44fe094181b884c1b7b9081f48570aa3\",\n \"score\": \"0.59411633\",\n \"text\": \"def destroy(self, widget, data=None):\\n\\t\\tgtk.main_quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de7f9dd9bdb6dfbdebba0005cb7cbc0\",\n \"score\": \"0.5921413\",\n \"text\": \"def destroy(self):\\n if self.active:\\n super().destroy()\\n del self.maxes\\n del self.splash\\n del self.bar_val\\n self.active = False\\n _ALL_SCREENS.discard(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"145447ed50baae6e1a5fc91419b0d836\",\n \"score\": \"0.5914714\",\n \"text\": \"def destroy(self):\\n self.image_subscriber.unregister()\\n self.collision_subscriber.unregister()\\n self.lane_invasion_subscriber.unregister()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"376b611773914e7871b1d39d290b7803\",\n \"score\": \"0.59119856\",\n \"text\": \"def destroy(self) -> None:\\n if not self.is_alive:\\n return\\n\\n self.process_sub_entities(func=lambda e: e.destroy())\\n del self._sub_entities\\n del self.seqend\\n super().destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d6ce1462efd67a187a78405a06faf0c\",\n \"score\": \"0.59066725\",\n \"text\": \"def destroy(self, widget, data=None):\\n gtk.main_quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b17d7b4fd20149170b1021f182e5339\",\n \"score\": \"0.58975387\",\n \"text\": \"def __del__(self):\\n self.clear()\\n self._screen._removeTurtle(self)\\n del self._turtle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12a5fc00ed33a97b3df04ffcb520271\",\n \"score\": \"0.58862853\",\n \"text\": \"def __del__(self):\\n self.cleanup(True)\\n if self.manager is not None:\\n del self.manager\\n if self.world is not None:\\n del self.world\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87585c8030cf89b742bd13b6f3524735\",\n \"score\": \"0.5858656\",\n \"text\": \"def __del__(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87585c8030cf89b742bd13b6f3524735\",\n \"score\": \"0.5858656\",\n \"text\": \"def __del__(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050751bd3c8c430be72698471910cd9f\",\n \"score\": \"0.5839367\",\n \"text\": \"def OnDestroy(self, event):\\n print(\\\"Destroy Window\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b12ae96e7f960e93977416b81dedb5\",\n \"score\": \"0.581417\",\n \"text\": \"def teardown(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b12ae96e7f960e93977416b81dedb5\",\n \"score\": \"0.581417\",\n \"text\": \"def teardown(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a063f63023e5694874d846a99e836a27\",\n \"score\": \"0.58102006\",\n \"text\": \"def destroy(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f25e01f7baac89e4fd767b39fd9dd76\",\n \"score\": \"0.5803387\",\n \"text\": \"def destroy (self,event=None):\\n\\n # This is enough to disable fillbox.\\n self.top.withdraw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b3381f6b2be167723756804f06ecd8\",\n \"score\": \"0.5801165\",\n \"text\": \"def _close_ui(self):\\n self.stash.renderer._stop_rendering()\\n self.tk.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbcd1059c0835c345cdb138d62100c4\",\n \"score\": \"0.5800343\",\n \"text\": \"def __del__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbcd1059c0835c345cdb138d62100c4\",\n \"score\": \"0.5800343\",\n \"text\": \"def __del__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbcd1059c0835c345cdb138d62100c4\",\n \"score\": \"0.5800343\",\n \"text\": \"def __del__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbcd1059c0835c345cdb138d62100c4\",\n \"score\": \"0.5800343\",\n \"text\": \"def __del__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbcd1059c0835c345cdb138d62100c4\",\n \"score\": \"0.5800343\",\n \"text\": \"def __del__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cf7d05143d394b2a933671180c6108\",\n \"score\": \"0.57913226\",\n \"text\": \"def _before_destroy(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e1100f5c6aebc9372e250a19b4f821\",\n \"score\": \"0.5781939\",\n \"text\": \"def __del__(self):\\n del self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfed205b2ce605f687f8f21ed3855c0\",\n \"score\": \"0.57798415\",\n \"text\": \"def deleter(instance):\\n instance.__dict__.pop(name, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"345f2d5f6ea59d353e55b2e4fde653a3\",\n \"score\": \"0.5767714\",\n \"text\": \"def evt_close(self, *args):\\n self.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c32e80e7dab9686ab2136f4af2dba453\",\n \"score\": \"0.57661545\",\n \"text\": \"def destroy(self) :\\n self.stop_thread()\\n if self.command_leave is not None :\\n f = self.command_leave\\n self.command_leave = None\\n f()\\n else :\\n try:\\n tkinter.Frame.destroy(self)\\n except Exception as e :\\n if \\\"application has been destroyed\\\" in str(e) :\\n os._exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3a5477e0382fb8aee0ee3200520610\",\n \"score\": \"0.5753696\",\n \"text\": \"def do_unrealize(self):\\r\\n self.window.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3a5477e0382fb8aee0ee3200520610\",\n \"score\": \"0.5753696\",\n \"text\": \"def do_unrealize(self):\\r\\n self.window.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a4853133efaa7ff0a2291e93201f1c2\",\n \"score\": \"0.57463986\",\n \"text\": \"def __del__(self):\\n self.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8c2b88299d15a4f8c77004c090401f\",\n \"score\": \"0.5745602\",\n \"text\": \"def destroy(self):\\n self.overlayList.removeListener('overlays', self.__name)\\n self.displayCtx .removeListener('selectedOverlay', self.__name)\\n profiles.Profile.destroy(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e4f63c32f3aa1620812edf44b7eb8b0\",\n \"score\": \"0.5742261\",\n \"text\": \"def dispose(self):\\n\\n delattr(self.class_, self.MANAGER_ATTR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403a13018e01c58572d4d61962668ae4\",\n \"score\": \"0.5738827\",\n \"text\": \"def cleanup(self):\\n self.removeObservers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403a13018e01c58572d4d61962668ae4\",\n \"score\": \"0.5738827\",\n \"text\": \"def cleanup(self):\\n self.removeObservers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403a13018e01c58572d4d61962668ae4\",\n \"score\": \"0.5738827\",\n \"text\": \"def cleanup(self):\\n self.removeObservers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad9ee80d29707e3e4023d8b47b6b4ea\",\n \"score\": \"0.57382894\",\n \"text\": \"def __del__(self):\\n\\n # delete the Py_Image reference, this should be automatically but just\\n # to be sure\\n if hasattr(self, \\\"_py_image\\\"):\\n log_debug(self._logger, (\\\"Deleting this instance but deleting \\\" + \\n \\\"the _py_image attribute before\\\"))\\n del self._py_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d713db30f023493223fa4bf1b296f1d3\",\n \"score\": \"0.5733382\",\n \"text\": \"def destroy(self):\\n self.log.debug('Destroyed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9080bf75ad579b0d7c32027ebc5f44a\",\n \"score\": \"0.5723832\",\n \"text\": \"def destroy(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2888a34e76a2e25836aae53e32042977\",\n \"score\": \"0.5722436\",\n \"text\": \"def plugin_destroy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4827813cf4ce4b774e225294eff0bc17\",\n \"score\": \"0.5720652\",\n \"text\": \"def __del__(self):\\n self.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4827813cf4ce4b774e225294eff0bc17\",\n \"score\": \"0.5720652\",\n \"text\": \"def __del__(self):\\n self.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e02c66bb9e959d39a87c70946dc0af\",\n \"score\": \"0.5718935\",\n \"text\": \"def OnClose(self,event):\\n self.Destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d61fd7cb6372981f8dca6e1de96ed7c6\",\n \"score\": \"0.57080543\",\n \"text\": \"def destroy(self):\\n # if this is the main_app - set the _main_app class variable to `None`.\\n if self == App._main_app:\\n App._main_app = None\\n self.tk.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbc90aed69575ec9c19b0930b5a1e747\",\n \"score\": \"0.5707538\",\n \"text\": \"def TearDownInstance(self, name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9032279396a07af027e33fea8050ebf6\",\n \"score\": \"0.5697297\",\n \"text\": \"def destroy(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebd80edddafba691c72618d7c0e21c3\",\n \"score\": \"0.56951076\",\n \"text\": \"def cleanup(self): \\n LOG(\\\"Releasing TC status view\\\")\\n if self.__tcStatusView: \\n CONN.releaseObject(self.__tcStatusView)\\n \\n if self.tcInjector: \\n try:\\n LOG(\\\"Releasing TC injector\\\")\\n self.tcInjector.deregister()\\n except:pass\\n CONN.releaseObject(self.tcInjector)\\n\\n if self.__tcInjectorManager:\\n LOG(\\\"Releasing TC injection manager\\\")\\n CONN.releaseObject(self.__tcInjectorManager) \\n LOG(\\\"TC injection interface clean\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4087df625b2dbd43840fc71695a4113\",\n \"score\": \"0.5689629\",\n \"text\": \"def destroy(self):\\n self.__close_image()\\n self.__config.destroy()\\n logging.info('Close GUI')\\n self.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d798b11030edd230b3e8ea504036aa69\",\n \"score\": \"0.5688169\",\n \"text\": \"def destroy(self):\\n self._callback_active.acquire()\\n if self.carla_actor.is_listening:\\n self.carla_actor.stop()\\n super(Sensor, self).destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e8f7842141d94294b7d8cf6f6e93ee\",\n \"score\": \"0.5675714\",\n \"text\": \"def __del__(self):\\n try:\\n self.stop()\\n restore_original_display()\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0dbe0cb2d21874c3e954d3e41777b1b\",\n \"score\": \"0.56654286\",\n \"text\": \"def destroy(self):\\n try:\\n self.cancel_scheduled()\\n self._before_destroy()\\n except Exception as e:\\n print(self.name, e, 'in close')\\n super().destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89f7175ceff8fc4f8bc4e17036afda19\",\n \"score\": \"0.5657575\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n newobj = _core.new_WindowDestroyEvent(*args, **kwargs)\\n self.this = newobj.this\\n self.thisown = 1\\n del newobj.thisown\",\n \"title\": \"\"\n },\n {\n \"docid\": \"955584ce6a0ca80d4a64054f59a68433\",\n \"score\": \"0.56567013\",\n \"text\": \"def deinit(self):\\n # deinit() not implemented in micropython\\n pass # avoid raising a NotImplemented Error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ad74f00a587393a26aa65e5539f5c6\",\n \"score\": \"0.56554824\",\n \"text\": \"def destroy(self):\\n if self._tclCommands is not None:\\n for name in self._tclCommands:\\n #print '- Tkinter: deleted command', name\\n self.tk.deletecommand(name)\\n self._tclCommands = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06ed83284558aa21b9d2b00e1f83862\",\n \"score\": \"0.5649462\",\n \"text\": \"def Close( self ):\\n def Destroy( np, cookie ):\\n wrpr = base.game.nodeMgr.Wrap( np )\\n if wrpr is not None:\\n wrpr.Destroy()\\n \\n self.Walk( Destroy )\\n self.app.game.pluginMgr.OnSceneClose()\\n \\n # Now remove the root node. If the root node was render, reset base\\n # in order to remove and recreate the default node set.\\n if self.rootNp is render:\\n base.Reset()\\n \\n # Reset references to render\\n self.app.selection.marquee.rootNp = render\\n \\n self.rootNp.removeNode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c528c9551ec88fe547441cebd9610be3\",\n \"score\": \"0.5645579\",\n \"text\": \"def __close(self):\\r\\n self.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eef5ee95b4552098e68f105bd74eb49\",\n \"score\": \"0.5643967\",\n \"text\": \"def __del__(self):\\n self.release()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36054cb9edc19415ec4159b1c798dd95\",\n \"score\": \"0.56384534\",\n \"text\": \"def destroy(self):\\n del self.engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36054cb9edc19415ec4159b1c798dd95\",\n \"score\": \"0.56384534\",\n \"text\": \"def destroy(self):\\n del self.engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"288fcdfce10f354ddcbc73c52053f8f4\",\n \"score\": \"0.5630647\",\n \"text\": \"def cleanup(self):\\n super(CaseMWGUI, self).cleanup()\\n close_app()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0daec4a1a0f32a4d86abf4a81dfbfdd7\",\n \"score\": \"0.56171525\",\n \"text\": \"def teardown(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43dde48884b8e735cf7f319d3768c7c5\",\n \"score\": \"0.5612211\",\n \"text\": \"def destroy(self):\\n # XXX TODO: this entire method is just horrid. Basically, the initial\\n # implementation of the ChangeSet class and various Change* subclasses\\n # (AbstractChange, FileChange, DirectoryChange, etc) was rife with\\n # circular links, because all child objects linked backed to their\\n # parents (i.e. via self.parent). (And by \\\"initial implementation\\\", I\\n # mean \\\"initial and still current implementation\\\".)\\n #\\n # This prevented a ChangeSet and all its child objects from being\\n # garbage collected. Upon realizing the error of my ways, much\\n # hacking was done to break the circular references so that objects\\n # could be collected properly.\\n #\\n # This particular method is the biggest, ugliest artefact from that\\n # hacking work. It is almost certainly overkill (del'ing objects,\\n # then setting them to None... say wha'?), but eh, in its current\\n # form, warts and all, it works. Call changeset.destroy(), and gc\\n # will be able to clean up the object and all the children correctly.\\n #\\n # But, yeah, this whole approach should definitely get cleaned up when\\n # ChangeSet and friends go in for their next refactoring service.\\n\\n assert not self.__destroyed\\n self.__destroyed = True\\n\\n del self._nada\\n del self._copy\\n del self._create\\n del self._modify\\n del self._remove\\n del self._replace\\n del self._copied_from\\n del self._renamed_from\\n\\n del self._raw_deletes\\n del self._indirect_copy_then_modify\\n del self._removes_that_might_be_renames\\n\\n del self.__all_propchanges\\n del self.__all_propreplacements\\n del self.__mergeinfo_propchanges\\n\\n del self.__notes\\n del self.__errors\\n del self.__warnings\\n\\n del self.__roots\\n del self.__revprops\\n\\n del self.__fs\\n del self.__ptr\\n del self.__repo\\n del self.__root\\n del self.__baton\\n del self.__base_root\\n\\n self._nada = None\\n self._copy = None\\n self._create = None\\n self._modify = None\\n self._remove = None\\n self._rename = None\\n self._replace = None\\n self._copied_from = None\\n self._renamed_from = None\\n\\n self.__all_propchanges = None\\n self.__all_propreplacements = None\\n self.__mergeinfo_propchanges = None\\n\\n self._raw_deletes = None\\n self._indirect_copy_then_modify = None\\n self._removes_that_might_be_renames = None\\n\\n self.__notes = None\\n self.__errors = None\\n self.__warnings = None\\n\\n self.__roots = None\\n\\n self.__fs = None\\n self.__repo = None\\n self.__root = None\\n self.__revprops = None\\n self.__base_root = None\\n\\n del self._tracker\\n self._tracker = None\\n\\n for (k, c) in self._all_changes.items():\\n c.unparent()\\n c.destroy()\\n del c\\n\\n for k in self._all_changes.keys():\\n del self._all_changes[k]\\n del self._all_changes\\n self._all_changes = None\\n\\n if self.__pool is not None:\\n self.__pool.destroy()\\n self.__pool = None\\n\\n AbstractChangeSet.destroy(self)\\n\\n gc.collect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19048dd0e2a0146ffb1bbb970e7f9013\",\n \"score\": \"0.56084996\",\n \"text\": \"def teardown(self) -> BaseStep:\\n super().teardown()\\n self.wrapped.teardown()\\n self.is_initialized = False\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f74e69b79d3b66ec3926dcaae7840b5\",\n \"score\": \"0.5579296\",\n \"text\": \"def __del__(self):\\n self.auto_pilot_enable_publisher.unregister()\\n self.vehicle_control_publisher.unregister()\\n self.vehicle_control_manual_override_publisher.unregister()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069095bc2350f141dbfba7a56713fd32\",\n \"score\": \"0.55780935\",\n \"text\": \"def destroy(self, log_level=\\\"\\\"):\\n self.flexy_instance.destroy()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994389,"cells":{"query_id":{"kind":"string","value":"583f4036233277798d30891d0491a2b2"},"query":{"kind":"string","value":"Query or edit the camera in a gl render panel. Derived from mel command `maya.cmds.hardwareRenderPanel`"},"positive_passages":{"kind":"list like","value":[{"docid":"02eb3dea994300a093267912afb97e41","score":"0.6226707","text":"def getCamera(self, **kwargs):\n \n pass","title":""}],"string":"[\n {\n \"docid\": \"02eb3dea994300a093267912afb97e41\",\n \"score\": \"0.6226707\",\n \"text\": \"def getCamera(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e1769c5d20baf8236d54fe6e955c1ac8","score":"0.6627869","text":"def GetRenderingCamera(self):\n pass","title":""},{"docid":"9db2bdb3b4eddcbdf4a5fd461d9e143e","score":"0.63430506","text":"def camera(self, camera):\n self.renderer.camera = camera","title":""},{"docid":"5fea7d7204fe4d7f2a1c55bf09a53e0b","score":"0.62744004","text":"def camera(self):\n if not self.renderer.camera.is_set:\n self.camera_position = self.get_default_cam_pos()\n self.reset_camera()\n self.renderer.camera.is_set = True\n return self.renderer.camera","title":""},{"docid":"6332cc65b8b96e8f94c8e46f45d65713","score":"0.623032","text":"def render(self, camera=None):\n self.gl_list.render()\n data.Texture.bound = None","title":""},{"docid":"1b40c22d40ff5f4283f5ab3b08def8bd","score":"0.61798275","text":"def update_camera(func):\r\n @wraps(func)\r\n def with_wrapped_func(*args, **kwargs):\r\n func(*args, **kwargs)\r\n wid = args[0]\r\n wid.camera.apply()\r\n wid.updateGL()\r\n wid.signal_camera_updated.emit(wid.camera)\r\n wid.state.signal_state_change.emit()\r\n return with_wrapped_func","title":""},{"docid":"73f34baec9c5977ac19bc0d05ec4b6c6","score":"0.6156473","text":"def onShowCamera(self, obj, evt, cam):\n\t\tself.setCamera(cam)\n\t\tself.render()","title":""},{"docid":"3feb3041f07536ab627c20315701b265","score":"0.60941774","text":"def storeViewerCamera(self):\n self._renderer.storeCamera()","title":""},{"docid":"238aff81f270b95e30cd31dfe8b0ca7b","score":"0.6076526","text":"def AddCamera(self, show_window=False):\n # Add renderer to scene graph\n renderer_name = \"kinova_camera_renderer\"\n self.scene_graph.AddRenderer(renderer_name, \n MakeRenderEngineVtk(RenderEngineVtkParams()))\n \n # Set camera properites. These roughly correspond\n # to the camera on the hardware (Intel Realsense D410).\n intrinsics = CameraInfo(width=480, height=270, fov_y=np.radians(40))\n clipping = ClippingRange(0.01,3.0)\n X_lens = RigidTransform()\n camera_core = RenderCameraCore(renderer_name, intrinsics, clipping, X_lens)\n depth_range = DepthRange(0.1, 2.0)\n\n # Create the camera model\n self.color_camera = ColorRenderCamera(camera_core, show_window=show_window)\n self.depth_camera = DepthRenderCamera(camera_core, depth_range)\n \n # Set the frame and position of the camera\n self.camera_parent_frame = self.plant.GetFrameByName(\"end_effector_link\", self.arm)\n self.X_camera = RigidTransform() # position of camera in parent frame\n self.X_camera.set_translation([0,0.1,0.0])\n\n self.has_camera = True","title":""},{"docid":"5a248d736498765e2720ac4c4c6278e7","score":"0.5931266","text":"def getCurrentCamera(self, **kwargs):\n \n pass","title":""},{"docid":"648fc36eb6d05600b34cfa692279370e","score":"0.5919756","text":"def SwitchViewerCamera(self,pCamera):\n pass","title":""},{"docid":"38b694572cbc50737e092c02bf212d7a","score":"0.58980113","text":"def camera_entity(self):\n return self._camera","title":""},{"docid":"38b694572cbc50737e092c02bf212d7a","score":"0.58980113","text":"def camera_entity(self):\n return self._camera","title":""},{"docid":"38b694572cbc50737e092c02bf212d7a","score":"0.58980113","text":"def camera_entity(self):\n return self._camera","title":""},{"docid":"bae25219dbd1b9f4ef4c97dfa963e7ad","score":"0.5856452","text":"def getGlRenderEditor(self, **kwargs):\n \n pass","title":""},{"docid":"9f7cf2cbd432638e80b91fa4ebf001c1","score":"0.584912","text":"def AddCamera(self, projectionmethod, fov, clippingplanes, scale = 1, name = \"\", viewport=(0, 0, 100, 100), clearcolor=True, cleardepth=True, layer=0, backgroundcolor=(1, 1, 1, 1), active=True):\n comp, inx = self.AddComponent(name, active) \n cam = comp.FusCamera\n cam.ProjectionMethod = projectionmethod\n cam.Fov = fov\n cam.ClippingPlanes.x = clippingplanes[0]\n cam.ClippingPlanes.y = clippingplanes[1]\n cam.Viewport.x = viewport[0]\n cam.Viewport.y = viewport[1]\n cam.Viewport.z = viewport[2]\n cam.Viewport.w = viewport[3]\n cam.ClearColor = clearcolor\n cam.ClearDepth = cleardepth\n cam.Layer = layer\n cam.BackgroundColor.x = backgroundcolor[0]\n cam.BackgroundColor.y = backgroundcolor[1]\n cam.BackgroundColor.z = backgroundcolor[2]\n cam.BackgroundColor.w = backgroundcolor[3]\n cam.Scale = scale","title":""},{"docid":"06a1cf64f67ff188510e854c805479dd","score":"0.58304435","text":"def render(self):\n self.camera.load()\n self.renderScene(Drawable.RenderState(self))","title":""},{"docid":"cd37fdfe23737bcc27865b299709aaee","score":"0.5824603","text":"def update(self):\r\n update = self._input_ubo.update_input\r\n\r\n # Get the current transform matrix of the camera\r\n view_mat = Globals.render.get_transform(self._showbase.cam).get_mat()\r\n\r\n # Compute the view matrix, but with a z-up coordinate system\r\n update(\"view_mat_z_up\", view_mat * Mat4.convert_mat(CS_zup_right, CS_yup_right))\r\n\r\n # Compute the view matrix without the camera rotation\r\n view_mat_billboard = Mat4(view_mat)\r\n view_mat_billboard.set_row(0, Vec3(1, 0, 0))\r\n view_mat_billboard.set_row(1, Vec3(0, 1, 0))\r\n view_mat_billboard.set_row(2, Vec3(0, 0, 1))\r\n update(\"view_mat_billboard\", view_mat_billboard)\r\n\r\n update(\"camera_pos\", self._showbase.camera.get_pos(Globals.render))\r\n\r\n # Compute last view projection mat\r\n curr_vp = self._input_ubo.get_input(\"view_proj_mat_no_jitter\")\r\n update(\"last_view_proj_mat_no_jitter\", curr_vp)\r\n curr_vp = Mat4(curr_vp)\r\n curr_vp.invert_in_place()\r\n curr_inv_vp = curr_vp\r\n update(\"last_inv_view_proj_mat_no_jitter\", curr_inv_vp)\r\n\r\n proj_mat = Mat4(self._showbase.camLens.get_projection_mat())\r\n\r\n # Set the projection matrix as an input, but convert it to the correct\r\n # coordinate system before.\r\n proj_mat_zup = Mat4.convert_mat(CS_yup_right, CS_zup_right) * proj_mat\r\n update(\"proj_mat\", proj_mat_zup)\r\n\r\n # Set the inverse projection matrix\r\n update(\"inv_proj_mat\", invert(proj_mat_zup))\r\n\r\n # Remove jitter and set the new view projection mat\r\n proj_mat.set_cell(1, 0, 0.0)\r\n proj_mat.set_cell(1, 1, 0.0)\r\n update(\"view_proj_mat_no_jitter\", view_mat * proj_mat)\r\n\r\n # Store the frame delta\r\n update(\"frame_delta\", Globals.clock.get_dt())\r\n update(\"smooth_frame_delta\", 1.0 / max(1e-5, Globals.clock.get_average_frame_rate()))\r\n update(\"frame_time\", Globals.clock.get_frame_time())\r\n\r\n # Store the current film offset, we use this to compute the pixel-perfect\r\n # velocity, which is otherwise not possible. Usually this is always 0\r\n # except when SMAA and reprojection is enabled\r\n update(\"current_film_offset\", self._showbase.camLens.get_film_offset())\r\n update(\"frame_index\", Globals.clock.get_frame_count())\r\n\r\n # Compute frustum corners in the order BL, BR, TL, TR\r\n ws_frustum_directions = Mat4()\r\n vs_frustum_directions = Mat4()\r\n inv_proj_mat = Globals.base.camLens.get_projection_mat_inv()\r\n view_mat_inv = Mat4(view_mat)\r\n view_mat_inv.invert_in_place()\r\n\r\n for i, point in enumerate(((-1, -1), (1, -1), (-1, 1), (1, 1))):\r\n result = inv_proj_mat.xform(Vec4(point[0], point[1], 1.0, 1.0))\r\n vs_dir = result.xyz.normalized()\r\n vs_frustum_directions.set_row(i, Vec4(vs_dir, 1))\r\n ws_dir = view_mat_inv.xform(Vec4(result.xyz, 0))\r\n ws_frustum_directions.set_row(i, ws_dir)\r\n\r\n update(\"vs_frustum_directions\", vs_frustum_directions)\r\n update(\"ws_frustum_directions\", ws_frustum_directions)","title":""},{"docid":"b71506f055e546d6f1ad8f894dd348e5","score":"0.58211446","text":"def camera_set(self):\n return self.renderer.camera.is_set","title":""},{"docid":"5cff41ab1510782c791b6915adf1d1cb","score":"0.57846403","text":"def setup_default_camera(self):\r\n self.add_camera(GLCamera(self, name=\"interactive\"))\r\n self.set_camera(\"interactive\")","title":""},{"docid":"e6c95220dec5ab6c0a5be77a870298a0","score":"0.5777523","text":"def update_camera(self):\n if self.params.camera_update_mode == 'ResetCamera':\n ## resetCamera() method:\n self.ui.plt.resetCamera() # resetCamera() updates the camera's position given the ignored components\n # This limits the meshes to just the active window's meshes: [start_bound_plane, end_bound_plane, active_window_only_axes]\n elif self.params.camera_update_mode == 'CenterToActiveWindow':\n ## center_camera_on_active_timewindow(): tries to compute the explicit center of the time window\n self.center_camera_on_active_timewindow()\n elif self.params.camera_update_mode == 'None':\n pass # do no automatic adjustment of the camera\n else:\n raise NotImplementedError","title":""},{"docid":"6113172ba50594f7491e55380209ac34","score":"0.576773","text":"def SetViewCamera(self, *args):\n return _MaxPlus.Viewport_SetViewCamera(self, *args)","title":""},{"docid":"bfe37da770bb6af92b64903b030786fb","score":"0.57476056","text":"def setCamera(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"bfe37da770bb6af92b64903b030786fb","score":"0.57467717","text":"def setCamera(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"bfe37da770bb6af92b64903b030786fb","score":"0.57467717","text":"def setCamera(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"bfe37da770bb6af92b64903b030786fb","score":"0.57439363","text":"def setCamera(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"b61d2ff7e0c4aa33e648b2523695df49","score":"0.57315207","text":"def camera_entity(self):\n return self._camera_entity","title":""},{"docid":"cc30a12a34a9e50e229337ee09656231","score":"0.57275444","text":"def getCamera(self):\n\t\tcam = None\n\t\tif self.renderer:\n\t\t\tcam = self.renderer.GetActiveCamera()\n\t\treturn cam","title":""},{"docid":"5631f257e689de6c5e440bfc500972be","score":"0.5725253","text":"def camera_set(self, is_set):\n self.renderer.camera.is_set = is_set","title":""},{"docid":"5e3ee16d7a98ecb70abd3ed4fdf9984a","score":"0.57177436","text":"def getCamera(self, viewName):\n camerasLogic = slicer.modules.cameras.logic()\n camera = camerasLogic.GetViewActiveCameraNode(slicer.util.getNode(viewName))\n return camera","title":""},{"docid":"829e92e358f91c7eb054ac199ba0c79e","score":"0.5712814","text":"def add_static_camera(self):\n \n bpy.ops.object.camera_add()\n camera = bpy.context.object\n camera.name = \"C_camera\"\n camera.location = (0, -1.1*self.y_size, self.y_size)\n camera.rotation_euler = (math.pi/4, 0, 0)","title":""},{"docid":"f4d5dacffa826a22b45007432369d741","score":"0.57105225","text":"def setCamera(self, cam):\n\t\tif self.renderer:\n\t\t\tself.renderer.SetActiveCamera(cam)","title":""},{"docid":"81bd07909578e40f2aa43891c9425698","score":"0.5706572","text":"def camera_menu(m):\n if m.selected in solar_system:\n scene.camera.follow(solar_system[m.selected].obj)","title":""},{"docid":"668c9a421af21891b21f0fcd3f444c72","score":"0.5686792","text":"def test_camera(self):\n # N.B. We don't need perception geometry in the scene -- we'll rely on\n # the RgbdSensor unit tests to check that cameras work as advertised.\n # Our only goal here is to achieve statement-level code coverage of the\n # ModelVisualizer code when show_rgbd_sensor=True.\n model = \"\"\"<?xml version=\"1.0\"?>\n <sdf version=\"1.9\">\n <model name=\"sample\">\n <link name=\"base\"/>\n </model>\n </sdf>\n \"\"\"\n dut = mut.ModelVisualizer(show_rgbd_sensor=True)\n dut.parser().AddModelsFromString(model, \"sdf\")\n dut.Run(loop_once=True)","title":""},{"docid":"f3a92c55cc493d19ea84ce2993a84187","score":"0.5685627","text":"def render(self, screen, camera=None):\n offset_x, offset_y = (camera.x, camera.y) if camera else (0, 0)\n rendering_position = (self.shape.x - offset_x, self.shape.y - offset_y)\n screen.blit(self.raw_image, rendering_position)","title":""},{"docid":"0ea4647303521f596c56dacd23f186fa","score":"0.56434876","text":"def GetViewCamera(self):\n return _MaxPlus.Viewport_GetViewCamera(self)","title":""},{"docid":"df3e626c296b7383c3caec606474741b","score":"0.5636412","text":"def render(self, num_camera=1):\r\n view_matrix1 = p.computeViewMatrixFromYawPitchRoll(\r\n cameraTargetPosition=self.camera_target_pos,\r\n distance=2.,\r\n yaw=145, # 145 degree\r\n pitch=-36, # -36 degree\r\n roll=0,\r\n upAxisIndex=2\r\n )\r\n proj_matrix1 = p.computeProjectionMatrixFOV(\r\n fov=60, aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,\r\n nearVal=0.1, farVal=100.0)\r\n # depth: the depth camera, mask: mask on different body ID\r\n (_, _, px, depth, mask) = p.getCameraImage(\r\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix1,\r\n projectionMatrix=proj_matrix1, renderer=p.ER_TINY_RENDERER)\r\n px, depth, mask = np.array(px), np.array(depth), np.array(mask)\r\n\r\n if self._renders:\r\n plt.ion()\r\n if num_camera == 1:\r\n figsize = np.array([3, 1]) * 5\r\n else:\r\n figsize = np.array([3, 2]) * 5\r\n fig = plt.figure(\"Inmoov\", figsize=figsize)\r\n\r\n if num_camera == 2:\r\n view_matrix2 = p.computeViewMatrixFromYawPitchRoll(\r\n cameraTargetPosition=(0.316, 0.316, 1.0),\r\n distance=1.2,\r\n yaw=90, # 145 degree\r\n pitch=-13, # -36 degree\r\n roll=0,\r\n upAxisIndex=2\r\n )\r\n proj_matrix2 = p.computeProjectionMatrixFOV(\r\n fov=60, aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,\r\n nearVal=0.1, farVal=100.0)\r\n (_, _, px2, depth2, mask2) = p.getCameraImage(\r\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix2,\r\n projectionMatrix=proj_matrix2, renderer=p.ER_TINY_RENDERER)\r\n ax1 = fig.add_subplot(231)\r\n ax1.imshow(px)\r\n ax1.set_title(\"rgb_1\")\r\n ax2 = fig.add_subplot(232)\r\n ax2.imshow(depth)\r\n ax2.set_title(\"depth_1\")\r\n ax3 = fig.add_subplot(233)\r\n ax3.imshow(mask)\r\n ax3.set_title(\"mask_1\")\r\n ax1 = fig.add_subplot(234)\r\n ax1.imshow(px2)\r\n ax1.set_title(\"rgb_2\")\r\n ax2 = fig.add_subplot(235)\r\n ax2.imshow(depth2)\r\n ax2.set_title(\"depth_2\")\r\n ax3 = fig.add_subplot(236)\r\n ax3.imshow(mask2)\r\n ax3.set_title(\"mask_2\")\r\n elif num_camera == 1: # only one camera\r\n ax1 = fig.add_subplot(131)\r\n ax1.imshow(px)\r\n ax1.set_title(\"rgb_1\")\r\n ax2 = fig.add_subplot(132)\r\n ax2.imshow(depth)\r\n ax2.set_title(\"depth_1\")\r\n ax3 = fig.add_subplot(133)\r\n ax3.imshow(mask)\r\n ax3.set_title(\"mask_1\")\r\n # rgb_array = np.array(px)\r\n # self.image_plot = plt.imshow(rgb_array)\r\n # self.image_plot.axes.grid(False)\r\n # plt.title(\"Inmoov Robot Simulation\")\r\n fig.suptitle('Inmoov Simulation: Two Cameras View', fontsize=32)\r\n plt.draw()\r\n # To avoid too fast drawing conflict\r\n plt.pause(0.00001)","title":""},{"docid":"30fcc4d096c815acb4b9d1aa79002ca2","score":"0.5627395","text":"def display(self, camera_tr_vec, camera_rot_mat, camera_fov_y, tracked_cam_track_pos_float):\n\n GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)\n\n view_mat = np.linalg.inv(_get_pose_matrix(data3d.Pose(camera_rot_mat, camera_tr_vec)))\n\n aspect_ratio = float(GLUT.glutGet(GLUT.GLUT_WINDOW_WIDTH)) / float(GLUT.glutGet(GLUT.GLUT_WINDOW_HEIGHT))\n focus_y = 1.0 / np.tan(camera_fov_y / 2.0)\n focus_x = focus_y / aspect_ratio\n\n near = 0.1\n far = 1000.0\n\n projection_mat = np.matrix([\n [focus_x, 0, 0, 0],\n [0, focus_y, 0, 0],\n [0, 0, -(far + near) / (far - near), -2 * far * near / (far - near)],\n [0, 0, -1, 0],\n ])\n\n pv_matrix = projection_mat.dot(view_mat)\n\n self._point_cloud_renderer.render(pv_matrix)\n self._cam_track_line_renderer.render(pv_matrix)\n self._cam_renderer.render(pv_matrix, tracked_cam_track_pos_float)\n\n GLUT.glutSwapBuffers()","title":""},{"docid":"af6d2e8ea2de753583cc98049caef5f5","score":"0.56040853","text":"def recallViewerCamera(self):\n self._renderer.recallCamera()\n self._cameraLensCombo.setCurrentIndex(self._renderer.activeSceneCamera().lens)","title":""},{"docid":"bc63e700a7dec0a2663208fbb74a6f3c","score":"0.5571779","text":"def loadCam(self):\n\t\tself.refresh_frame_range()\n\t\tsel = mc.ls(sl=True)\n\t\tprint sel\n\t\tif sel:\n\t\t\tfor s in sel:\n\t\t\t\ttry:\n\t\t\t\t\tcamXform, camShape = getCameraXformAndShape(s)\n\t\t\t\t\t# setting the first camera we found\n\t\t\t\t\ttrackingCamera = mc.textFieldButtonGrp(\n\t\t\t\t\t\tself.targetCam, e=True, text=camXform)\n\t\t\t\t\treturn\n\t\t\t\texcept:\n\t\t\t\t\t# couldn't get camera, handle the exception by moving\n\t\t\t\t\t# to the next item in the selection\n\t\t\t\t\tcontinue","title":""},{"docid":"cca93198e4999578de644489a25dd60a","score":"0.5561535","text":"def display(self, camera_tr_vec, camera_rot_mat, camera_fov_y, tracked_cam_track_pos_float):\n\n # a frame in which a tracked camera model and frustrum should be drawn\n # without interpolation\n tracked_cam_track_pos = int(tracked_cam_track_pos_float)\n\n GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)\n\n self._render_example_point(np.eye(4))\n\n GLUT.glutSwapBuffers()","title":""},{"docid":"444f4bb535c9cd2ecd047aa50f8a3a6d","score":"0.5543331","text":"def get_camera(self, name):\r\n return self.__cameras.get(name)","title":""},{"docid":"652545cc091327cf97c2efec74626c9f","score":"0.55427897","text":"def right_camera(self):\n return self._right_camera","title":""},{"docid":"8d48c1ac6ef3be7973497c9b19212675","score":"0.55250996","text":"def init(map_width, map_height):\n global camera\n camera = Rect(0, 0, map_width, map_height)","title":""},{"docid":"1500f259c7c89d9db26c5d8eeca4916f","score":"0.5524116","text":"def camera_selection(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n cambot, update = args\n cam_id = re.split(r'^.*(?=cam_)', update.message.text)[-1]\n cam = cambot._pool.get_instance(cam_id)\n args = args + (cam, cam_id)\n return func(*args, **kwargs)\n return wrapper","title":""},{"docid":"02e83852b339592ccc52c25a12b58233","score":"0.5497755","text":"def setCurrentCamera(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"408400362b013b5becc37997bded3f4a","score":"0.5460251","text":"def set_camera(self, camera):\r\n log.debug(\"[%s.set_camera] %s\" % (self, camera))\r\n if type(camera) in [str, str]:\r\n if \"/\" in camera:\r\n camera = os.path.split(\"/\")[-1]\r\n elif camera not in [cam.name for cam in self.state.cameras]:\r\n log.warn(\"camera not found: %s\" % camera)\r\n return\r\n self.camera = self.state.get_camera(camera)\r\n else:\r\n self.camera = camera\r\n self.camera.add_view(self)\r\n self.resizeGL(self.width(), self.height())\r\n self.signal_set_camera.emit(self.camera)","title":""},{"docid":"d555116a32c4a90edfe945f113317386","score":"0.5457226","text":"def __init__(self, scene: Scene, camera: Camera):\n super().__init__(scene, camera)\n self.ctx = mgl.create_context()\n super()._setup()\n\n self.viewport = None\n \"\"\" tuple: Viewport is a tuple of size four (x, y, width, height). \"\"\"","title":""},{"docid":"bf870e664465572e8ce4e03f25c3163b","score":"0.54435754","text":"def makeCamera(self, win, sort = 0, scene = None,\n displayRegion = (0, 1, 0, 1), stereo = None,\n aspectRatio = None, clearDepth = 0, clearColor = None,\n lens = None, camName = 'cam', mask = None,\n useCamera = None):\n # self.camera is the parent node of all cameras: a node that\n # we can move around to move all cameras as a group.\n if self.camera is None:\n # We make it a ModelNode with the PTLocal flag, so that\n # a wayward flatten operations won't attempt to mangle the\n # camera.\n self.camera = self.render.attachNewNode(ModelNode('camera'))\n self.camera.node().setPreserveTransform(ModelNode.PTLocal)\n builtins.camera = self.camera\n\n self.mouse2cam.node().setNode(self.camera.node())\n\n if useCamera:\n # Use the existing camera node.\n cam = useCamera\n camNode = useCamera.node()\n assert isinstance(camNode, Camera)\n lens = camNode.getLens()\n cam.reparentTo(self.camera)\n\n else:\n # Make a new Camera node.\n camNode = Camera(camName)\n if lens is None:\n lens = PerspectiveLens()\n\n if aspectRatio is None:\n aspectRatio = self.getAspectRatio(win)\n lens.setAspectRatio(aspectRatio)\n\n cam = self.camera.attachNewNode(camNode)\n\n if lens is not None:\n camNode.setLens(lens)\n\n if scene is not None:\n camNode.setScene(scene)\n\n if mask is not None:\n if isinstance(mask, int):\n mask = BitMask32(mask)\n camNode.setCameraMask(mask)\n\n if self.cam is None:\n self.cam = cam\n self.camNode = camNode\n self.camLens = lens\n\n self.camList.append(cam)\n\n # Now, make a DisplayRegion for the camera.\n if stereo is not None:\n if stereo:\n dr = win.makeStereoDisplayRegion(*displayRegion)\n else:\n dr = win.makeMonoDisplayRegion(*displayRegion)\n else:\n dr = win.makeDisplayRegion(*displayRegion)\n\n dr.setSort(sort)\n\n # By default, we do not clear 3-d display regions (the entire\n # window will be cleared, which is normally sufficient). But\n # we will if clearDepth is specified.\n if clearDepth:\n dr.setClearDepthActive(1)\n\n if clearColor:\n dr.setClearColorActive(1)\n dr.setClearColor(clearColor)\n\n dr.setCamera(cam)\n\n return cam","title":""},{"docid":"0dcce4558826edd4ee701eef0d4e6903","score":"0.5426206","text":"def test_global_camera(self):\n layout = Layout([\n Map(Layer(Source(SOURCE))),\n Map(Layer(Source(SOURCE)))\n ], viewport={'zoom': 5})\n\n assert layout._layout[0].get('camera') == {\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 5}\n assert layout._layout[1].get('camera') == {\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 5}","title":""},{"docid":"a06488315d919c8240ad73ad24962245","score":"0.54261726","text":"def _set_camera(self, c):\n if c.new == -1: return\n try:\n self.send({'type': 'camera', 'content': self.cameras[c.new]})\n except IndexError:\n pass","title":""},{"docid":"bb23d6d627945d0232abb7cb2f1a85f3","score":"0.5415432","text":"def display(self):\n if not hasattr(self.s, 'actor'):\n self.s.addActor()\n if not hasattr(self.m, 'actor'):\n self.m.addActor()\n # Generate a renderer window\n win = vtkRenWin()\n # Set the number of viewports\n win.setnumViewports(1)\n # Set the background colour\n win.setBackground([1,1,1])\n # Set camera projection \n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\n renderWindowInteractor.SetRenderWindow(win)\n renderWindowInteractor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\n # Set camera projection \n win.setView()\n self.s.actor.setColor([1.0, 0.0, 0.0])\n self.s.actor.setOpacity(0.5)\n self.m.actor.setColor([0.0, 0.0, 1.0])\n self.m.actor.setOpacity(0.5)\n win.renderActors([self.s.actor, self.m.actor])\n win.Render()\n win.rens[0].GetActiveCamera().Azimuth(180)\n win.rens[0].GetActiveCamera().SetParallelProjection(True)\n win.Render()\n return win","title":""},{"docid":"23fe24aedb2050d4022c78f2fc9f7396","score":"0.5412809","text":"def robot_render(self):\r\n right_eye_state = p.getLinkState(self.inmoov_id, 17)\r\n\r\n left_eye_state = p.getLinkState(self.inmoov_id, 21)\r\n right_eye_pos = np.array(right_eye_state[0])\r\n right_eye_orn = right_eye_state[1]\r\n left_eye_pos, left_eye_orn = np.array(left_eye_state[0]), left_eye_state[1]\r\n\r\n focus = 0.5\r\n\r\n r_rotation_matrix = np.array(p.getMatrixFromQuaternion(right_eye_orn)).reshape([3,3]).T\r\n l_rotation_matrix = np.array(p.getMatrixFromQuaternion(left_eye_orn)).reshape([3,3]).T\r\n front = np.array([0, focus, 0])\r\n right_target_pos = right_eye_pos + front@r_rotation_matrix\r\n left_target_pos = left_eye_pos + front @ l_rotation_matrix\r\n right_eye_matrix = p.computeViewMatrix(\r\n cameraEyePosition = right_eye_pos,\r\n cameraTargetPosition = right_target_pos,\r\n cameraUpVector = [0, 0, 1]\r\n )\r\n left_eye_matrix = p.computeViewMatrix(\r\n cameraEyePosition=left_eye_pos,\r\n cameraTargetPosition=left_target_pos,\r\n cameraUpVector=[0, 0, 1]\r\n )\r\n proj_matrix = p.computeProjectionMatrixFOV(\r\n fov=100, aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,\r\n nearVal=0.1, farVal=100.0)\r\n\r\n # depth: the depth camera, mask: mask on different body ID\r\n (_, _, px, depth, mask) = p.getCameraImage(\r\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=right_eye_matrix,\r\n projectionMatrix=proj_matrix, renderer=p.ER_TINY_RENDERER)\r\n right_px, right_depth, right_mask = np.array(px), np.array(depth), np.array(mask)\r\n (_, _, px, depth, mask) = p.getCameraImage(\r\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=left_eye_matrix,\r\n projectionMatrix=proj_matrix, renderer=p.ER_TINY_RENDERER)\r\n left_px, left_depth, left_mask = np.array(px), np.array(depth), np.array(mask)\r\n if self._renders:\r\n plt.ion()\r\n figsize = np.array([3, 5]) * 3\r\n fig = plt.figure(\"Inmoov\", figsize=figsize)\r\n ax1 = fig.add_subplot(321)\r\n ax1.imshow(left_px)\r\n ax1.set_title(\"Left\")\r\n ax2 = fig.add_subplot(322)\r\n ax2.imshow(right_px)\r\n ax2.set_title(\"Right\")\r\n ax3 = fig.add_subplot(323)\r\n ax3.imshow(left_depth)\r\n ax4 = fig.add_subplot(324)\r\n ax4.imshow(right_depth)\r\n ax5 = fig.add_subplot(325)\r\n ax5.imshow(left_mask)\r\n ax6 = fig.add_subplot(326)\r\n ax6.imshow(right_mask)\r\n # rgb_array = np.array(px)\r\n # self.image_plot = plt.imshow(rgb_array)\r\n # self.image_plot.axes.grid(False)\r\n # plt.title(\"Inmoov Robot Simulation\")\r\n fig.suptitle('Inmoov Simulation: Two Cameras View', fontsize=32)\r\n plt.draw()\r\n # To avoid too fast drawing conflict\r\n plt.pause(0.00001)\r\n return left_px, right_px, left_depth, right_depth, left_mask, right_mask","title":""},{"docid":"0049b700eaf630b90d5b3a8fe5c304b0","score":"0.54054385","text":"def switch_cameras(self):\n scene = bge.logic.getCurrentScene()\n index = bge.logic.current_camera_index\n next_camera = scene.cameras[index]\n scene.active_camera = next_camera\n logger.info(\"Showing view from camera: '%s'\" % next_camera.name)\n # Disable mouse cursor for Human camera\n if next_camera.name == \"Human_Camera\":\n bge.logic.mouse.visible = False\n else:\n bge.logic.mouse.visible = True\n # Update the index for the next call\n index = (index + 1) % len(scene.cameras)\n bge.logic.current_camera_index = index","title":""},{"docid":"119a8508a34bf317e1697475528d259f","score":"0.5405085","text":"def NewCamera(self, *args):\n return _MaxPlus.GenCamera_NewCamera(self, *args)","title":""},{"docid":"4088caf95eb91d943172e5dde8d5b6be","score":"0.5386747","text":"def getCurrentCameraRig(self, **kwargs):\n \n pass","title":""},{"docid":"50366c92169e985f928ceb3afdef8f3c","score":"0.53796756","text":"def _setup_video_texture(self):\n for child in self.blender_obj.children:\n # The camera object that will produce the image in Blender\n if 'CameraRobot' in child.name:\n camera = child\n # The object that contains the material where the image is rendered\n if 'CameraMesh' in child.name:\n screen = child\n # Considering it consists of a single mesh\n mesh = child.meshes[0] \n # Get the material name\n for material in mesh.materials:\n material_index = material.getMaterialIndex()\n mesh_material_name = mesh.getMaterialName(material_index)\n if 'MAScreenMat' in mesh_material_name:\n material_name = mesh_material_name\n\n try:\n logger.debug(\"\\tCAMERA: %s\" % camera.name)\n logger.debug(\"\\tSCREEN: %s\" % screen.name)\n logger.debug(\"\\tMATERIAL: %s\" % material_name)\n except UnboundLocalError as detail:\n logger.error(\"\"\"\n !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n ERROR: The video camera could not be properly initialized.\n The children object could not be found.\n Best solution is to re-link the camera.\n !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n \"\"\")\n return (False)\n\n # Get the reference to the scene\n scene = bge.logic.getCurrentScene()\n\n # Link the objects using bge.texture\n if not hasattr(bge.logic, 'cameras'):\n bge.logic.cameras = {}\n\n mat_id = bge.texture.materialID(screen, material_name)\n vt_camera = bge.texture.Texture(screen, mat_id)\n vt_camera.source = bge.texture.ImageRender(scene, camera)\n\n # Set the focal length of the camera using the Game Logic Property\n camera.lens = self.image_focal\n logger.info(\"\\tFocal length of the camera is: %s\" % camera.lens)\n\n # Set the background to be used for the render\n vt_camera.source.background = self.bg_color\n # Define an image size. It must be powers of two. Default 512 * 512\n vt_camera.source.capsize = [self.image_width, self.image_height]\n logger.info(\"Camera '%s': Exporting an image of capsize: %s pixels\" % \\\n (self.name(), vt_camera.source.capsize))\n\n # Reverse the image (boolean game-property)\n # cf. bge.logic.video.source.flip (bge.texture.ImageRender)\n # http://wiki.blender.org/index.php/Dev:Source/GameEngine/2.49/VideoTexture#Setup_the_source\n if 'Vertical_Flip' in self.blender_obj: # backward compatibility\n vt_camera.source.flip = self.blender_obj['Vertical_Flip']\n\n bge.logic.cameras[self.name()] = vt_camera","title":""},{"docid":"ee267e05cff5bc9876b64ac600a433ec","score":"0.537032","text":"def draw(self, screen, camera):\n self.camrect = pygame.Rect(self.x - camera.x,\n self.y - camera.y,\n self.size, self.size)\n screen.blit(self.image, self.camrect)","title":""},{"docid":"3e80045b3c30814c6192309792241de5","score":"0.53507864","text":"def update_edit_panel(self):\n\n if self.viewer and isfile2(self.photo):\n self.viewer.stop()\n if self.edit_panel_object:\n self.edit_panel_object.save_last()\n self.viewer.edit_mode = self.edit_panel\n edit_panel_container = self.ids['panelEdit']\n if self.edit_panel == 'main':\n self.edit_panel_object = EditMain(owner=self)\n self.edit_panel_object.update_programs()\n self.viewer.bypass = False\n else:\n self.viewer.bypass = True\n self.viewer.stop()\n if self.edit_panel == 'color':\n self.edit_panel_object = EditColorImage(owner=self)\n self.viewer.edit_image.bind(histogram=self.edit_panel_object.draw_histogram)\n elif self.edit_panel == 'advanced':\n self.edit_panel_object = EditColorImageAdvanced(owner=self)\n self.viewer.edit_image.bind(histogram=self.edit_panel_object.draw_histogram)\n elif self.edit_panel == 'filter':\n self.edit_panel_object = EditFilterImage(owner=self)\n elif self.edit_panel == 'border':\n self.edit_panel_object = EditBorderImage(owner=self)\n elif self.edit_panel == 'denoise':\n if opencv:\n self.edit_panel_object = EditDenoiseImage(owner=self, imagefile=self.photo, image_x=self.viewer.edit_image.original_width, image_y=self.viewer.edit_image.original_height)\n else:\n self.edit_panel = 'main'\n app = App.get_running_app()\n app.message(\"Could Not Denoise, OpenCV Not Found\")\n elif self.edit_panel == 'crop':\n self.edit_panel_object = EditCropImage(owner=self, image_x=self.viewer.edit_image.original_width, image_y=self.viewer.edit_image.original_height)\n self.viewer.edit_image.crop_controls = self.edit_panel_object\n elif self.edit_panel == 'rotate':\n self.edit_panel_object = EditRotateImage(owner=self)\n elif self.edit_panel == 'convert':\n if self.view_image:\n self.edit_panel_object = EditConvertImage(owner=self)\n else:\n self.edit_panel_object = EditConvertVideo(owner=self)\n edit_panel_container.change_panel(self.edit_panel_object)\n else:\n if self.edit_panel_object:\n self.edit_panel_object.save_last()\n self.viewer.edit_mode = self.edit_panel\n edit_panel_container = self.ids['panelEdit']\n edit_panel_container.change_panel(None)\n self.edit_panel_object = EditNone(owner=self)","title":""},{"docid":"a94734507f6c6459167fbcbc4857544f","score":"0.53454703","text":"def GetCameraMatrix(self,pMatrix,pType,pEvalInfo):\n pass","title":""},{"docid":"82508bdd10def3c51ce2f7888c8b3081","score":"0.53391844","text":"def getCameraImage(self):\n # compute eye and target position for viewMatrix\n pos, orn, _, _, _, _ = p.getLinkState(self.robotId, self.CAMERA_JOINT_INDEX)\n cameraPos = np.array(pos)\n cameraMat = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3).T\n eyePos = cameraPos + self.CAMERA_EYE_SCALE * cameraMat[self.CAMERA_EYE_INDEX]\n targetPos = cameraPos + self.CAMERA_TARGET_SCALE * cameraMat[self.CAMERA_EYE_INDEX]\n up = self.CAMERA_UP_SCALE * cameraMat[self.CAMERA_UP_INDEX]\n # p.addUserDebugLine(eyePos, targetPos, lineColorRGB=[1, 0, 0], lifeTime=0.1) # red line for camera vector\n # p.addUserDebugLine(eyePos, eyePos + up * 0.5, lineColorRGB=[0, 0, 1], lifeTime=0.1) # blue line for up vector\n viewMatrix = p.computeViewMatrix(eyePos, targetPos, up)\n image = p.getCameraImage(self.CAMERA_PIXEL_WIDTH, self.CAMERA_PIXEL_HEIGHT, viewMatrix, self.projectionMatrix, shadow=1, lightDirection=[1, 1, 1], renderer=p.ER_BULLET_HARDWARE_OPENGL)\n return image","title":""},{"docid":"0d15b49270b5f47e719280823fead278","score":"0.5325108","text":"def set_camera(self, xyz, hpr, tracking = 'none', body_to_track = 0 ):\n\n assert len(xyz) == 3\n assert len(hpr) == 3\n x, y, z = xyz\n h, p, r = hpr\n\n if tracking == 'none':\n tracking = 0\n elif tracking == 'pan':\n tracking = 1\n elif tracking == 'follow':\n tracking = 2\n else:\n tracking = 0\n\n # C.C. parameters oare singular values so \n # must write out vectors. May change in the future\n\n self._send_parameter('CameraX', x)\n self._send_parameter('CameraY', y)\n self._send_parameter('CameraZ', z)\n\n self._send_parameter('CameraH', h)\n self._send_parameter('CameraP', p)\n self._send_parameter('CameraR', r)\n\n self._send_parameter( 'CameraTracking', tracking )\n self._send_parameter( 'CameraBody', body_to_track )","title":""},{"docid":"1d77cde5c919e359007f67bb74dcbfe6","score":"0.5309324","text":"def setup_camera(app):\n camera_data = app.getCameraData()\n camera_data.eye = agx.Vec3(0.5, 0, 0.25)\n camera_data.center = agx.Vec3(0, 0, 0)\n camera_data.up = agx.Vec3(0, 0, 1)\n camera_data.nearClippingPlane = 0.1\n camera_data.farClippingPlane = 5000\n app.applyCameraData(camera_data)","title":""},{"docid":"bb574e3f310f635bba5fccaf1ab6f305","score":"0.530484","text":"def edit_camera_xform(world_fn,xform=None,title=None):\n world = WorldModel()\n world.readFile(world_fn)\n world.readFile(\"camera.rob\")\n robot = world.robot(0)\n sensor = robot.sensor(0)\n if xform is not None:\n sensing.set_sensor_xform(sensor,xform)\n vis.createWindow()\n if title is not None:\n vis.setWindowTitle(title)\n vis.resizeWindow(1024,768)\n vis.add(\"world\",world)\n vis.add(\"sensor\",sensor)\n vis.add(\"sensor_xform\",sensing.get_sensor_xform(sensor,robot))\n vis.edit(\"sensor_xform\")\n def update_sensor_xform():\n sensor_xform = vis.getItemConfig(\"sensor_xform\")\n sensor_xform = sensor_xform[:9],sensor_xform[9:]\n sensing.set_sensor_xform(sensor,sensor_xform)\n vis.loop(callback=update_sensor_xform)\n sensor_xform = vis.getItemConfig(\"sensor_xform\")\n return sensor_xform[:9],sensor_xform[9:]","title":""},{"docid":"9647c535ed24dbd52849ac734bbb9e0b","score":"0.5297465","text":"def GetDisplayableGeometryInCameraFrustum(self,pModelList,pCamera):\n pass","title":""},{"docid":"3ec1785bd80767a4a0c3404d99f6789c","score":"0.5283774","text":"def camera(self):\n if self.position in self.painted:\n return self.painted[self.position]\n\n return BLACK","title":""},{"docid":"86c918af3f3e00efefda4d5e34986aa3","score":"0.5278841","text":"def toggleCamera(self):\n self.groundCamEnabled = not self.groundCamEnabled\n if (self.groundCamEnabled):\n self.groundCam()\n else:\n self.frontCam()","title":""},{"docid":"0618c5d249dda76088ea0c466cc5c768","score":"0.5277198","text":"def test_custom_camera(self):\n layout = Layout([\n Map(Layer(Source(SOURCE)), viewport={'zoom': 2}),\n Map(Layer(Source(SOURCE)))\n ], viewport={'zoom': 5})\n\n assert layout._layout[0].get('camera') == {\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 2}\n assert layout._layout[1].get('camera') == {\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 5}","title":""},{"docid":"4a46e5a31be9d53d4bfe6b1bdac67d80","score":"0.5274758","text":"def patch_camera(set_provider=CAM.EXPLICIT):\n CoreCamera = core_camera.Camera\n set_provider = set_provider or CAM.IMPLICIT.get(PLATFORM)\n\n if CAM.ENABLE and set_provider:\n for provider in core_camera.providers:\n if set_provider == provider[0]:\n core_camera.providers = (provider,)\n _CoreCamera = core_select_lib(\"camera\", core_camera.providers)\n CoreCamera = _CoreCamera or CoreCamera\n break\n\n class PatchedCoreCamera(CoreCamera):\n\n def start(self, *args, **kwargs):\n ret = super(PatchedCoreCamera, self).start(*args, **kwargs)\n\n attrs = map(lambda attr: getattr(self, attr, None),\n [\"_update_ev\", \"fps\"])\n if all(attrs) and self.fps > 1.0:\n self._update_ev.cancel()\n self._update_ev = Clock.schedule_interval(\n self._update, 1.0 / self.fps\n )\n\n return ret\n\n core_camera.Camera = PatchedCoreCamera\n uix_camera.CoreCamera = core_camera.Camera","title":""},{"docid":"0ab1b61a0d5c391798d02db4b165d446","score":"0.52688134","text":"def SetEnvDisplay(self, *args):\n return _MaxPlus.CameraObject_SetEnvDisplay(self, *args)","title":""},{"docid":"b332cbebfc4567a9f05cf381e197b646","score":"0.52596724","text":"def __init__(self, screen_size=(800, 600), clear_color=(0, 0, 0),\n camera_initial_position=(0, 0, 0),\n camera_initial_direction=(0, 0, 1)):\n super(Graphx, self).__init__()\n pygame.init()\n screen = pygame.display.set_mode(screen_size, HWSURFACE|OPENGL|DOUBLEBUF)\n self.camera = Camera(camera_initial_position)\n self.screen_width, self.screen_height = self.screen_size = screen_size\n\n glEnable(GL_DEPTH_TEST)\n\n glShadeModel(GL_FLAT)\n glClearColor(*clear_color)\n\n glEnable(GL_COLOR_MATERIAL)\n\n glEnable(GL_LIGHTING)\n glEnable(GL_LIGHT0)\n glLight(GL_LIGHT0, GL_POSITION, (0, 1, 1, 0))\n\n self.resize(*screen_size)\n\n glMaterial(GL_FRONT, GL_AMBIENT, (0.1, 0.1, 0.1, 1.0))\n glMaterial(GL_FRONT, GL_DIFFUSE, (1.0, 1.0, 1.0, 1.0))","title":""},{"docid":"fd8f71a909982d629e7c2a7ed7744015","score":"0.5256443","text":"def GetEnvDisplay(self):\n return _MaxPlus.CameraObject_GetEnvDisplay(self)","title":""},{"docid":"9c96a9cdfda1cb6db825c82a2e9a992a","score":"0.5232319","text":"def render(self, camera_pose, target_id=\"\", render_pc=True):\n # Keep local to free OpenGl resources after use\n renderer = pyrender.OffscreenRenderer(\n viewport_width=self._width, viewport_height=self._height\n )\n\n # add camera and light to scene\n scene = self._scene.as_pyrender_scene()\n scene.add(self._camera, pose=camera_pose, name=\"camera\")\n light = pyrender.SpotLight(\n color=np.ones(4),\n intensity=3.0,\n innerConeAngle=np.pi / 16,\n outerConeAngle=np.pi / 6.0,\n )\n scene.add(light, pose=camera_pose, name=\"light\")\n\n # render the full scene\n color, depth = renderer.render(scene)\n\n segmentation = np.zeros(depth.shape, dtype=np.uint8)\n\n # hide all objects\n for node in scene.mesh_nodes:\n node.mesh.is_visible = False\n\n # Render only target object and add to segmentation mask\n for node in scene.mesh_nodes:\n if node.name == target_id:\n node.mesh.is_visible = True\n _, object_depth = renderer.render(scene)\n mask = np.logical_and(\n (np.abs(object_depth - depth) < 1e-6), np.abs(depth) > 0\n )\n segmentation[mask] = 1\n\n for node in scene.mesh_nodes:\n node.mesh.is_visible = True\n\n if render_pc:\n pc = self._to_pointcloud(depth)\n else:\n pc = None\n\n return color, depth, pc, segmentation","title":""},{"docid":"8c33dd1093734703e927beeba899442b","score":"0.5226646","text":"def setupCamera(self):\n self.camera = piggyphoto.Camera()\n self.camera.leave_locked()\n\n self.camRefresh = QTimer()\n self.camRefresh.timeout.connect(self.displayCameraPreview)\n self.camRefresh.setInterval(100)\n self.camRefresh.start()\n self.camHibernate.start()","title":""},{"docid":"f07bdf228b54f0735b4d114788d1863e","score":"0.52230614","text":"def set_view(self, x=0, y=-5, z=0, roll=0, pitch=0, yaw=0):\n sy = math.sin(yaw)\n cy = math.cos(yaw)\n sp = math.sin(pitch)\n cp = math.cos(pitch)\n sr = math.sin(roll)\n cr = math.cos(roll)\n self.cameraMatrix = [\n cy * cp, cy * sp * sr - sy * cr, cy * sp * cr + sy * sr, 0,\n sy * cp, sy * sp * sr + cy * cr, sy * sp * cr - cy * sr, 0,\n -sp, cp * sr, cp * cr, 0,\n x, y, z, 1\n ]\n self.SetMatrix(udRenderTargetMatrix.Camera, self.cameraMatrix)","title":""},{"docid":"1a71f70a2bdb45b3630d58d80daaf16d","score":"0.52169925","text":"def FrameCurrentCameraWithModels(self,pAll):\n pass","title":""},{"docid":"98e1c5308686ee88de76ee4dfa5d7fa5","score":"0.5215392","text":"def set_camera(self, camera_x, camera_y, camera_z,\n mrot_x, mrot_y):\n for i in range(len(self.vertices)):\n vert = self.vertices[i]\n xpos = vert[0] - camera_x\n ypos = vert[1] - camera_y\n zpos = vert[2]\n\n # rotate around Y axis\n ppos = mrot_y[0] * xpos + mrot_y[1] * ypos \\\n + mrot_y[2] * zpos\n qpos = mrot_y[3] * xpos + mrot_y[4] * ypos \\\n + mrot_y[5] * zpos\n rpos = mrot_y[6] * xpos + mrot_y[7] * ypos \\\n + mrot_y[8] * zpos\n\n # rotate around X axis\n self.pos[i][0] = mrot_x[0] * ppos + mrot_x[1] * qpos\\\n + mrot_x[2] * rpos\n self.pos[i][1] = mrot_x[3] * ppos + mrot_x[4] * qpos\\\n + mrot_x[5] * rpos\n self.pos[i][2] = mrot_x[6] * ppos + mrot_x[7] * qpos\\\n + mrot_x[8] * rpos - camera_z\n\n for surface in self.surfaces:\n surface.update()","title":""},{"docid":"5665193b2216a7cf4e850517bc9c2e67","score":"0.52153534","text":"def __load_camera_capture_screen(self):\n self.fc.flow_load_home_screen()\n self.home.select_bottom_nav_btn(self.home.NAV_CAMERA_SCAN_BTN)\n if self.camera_scan.verify_capture_no_access_screen(raise_e=False):\n self.camera_scan.select_camera_access_allow()\n self.camera_scan.check_run_time_permission()\n self.camera_scan.verify_capture_screen()","title":""},{"docid":"18b53fc43a718455cafc8ca0eb39ab82","score":"0.5209333","text":"def world_to_camera(self, points):\n points = np.atleast_2d(points)\n return tt.transform_points(points, self._world_to_camera_4x4)","title":""},{"docid":"4a37c69fdb9901d20a3fc74da83e02e1","score":"0.52090925","text":"def load_camera(hutch, cam_name):\n # Get config for cam_name\n try:\n with open(f'/reg/g/pcds/pyps/config/{hutch}/camviewer.cfg', 'r') as f:\n lines = f.readlines()\n except Exception as e:\n exit('Could not open camera config file, '\n f'/reg/g/pcds/pyps/config/{hutch}/camviewer.cfg', e)\n cam_line = [line for line in lines if cam_name.upper() in line]\n if not cam_line:\n exit('Did not find camera in camera config file, '\n f'/reg/g/pcds/pyps/config/{hutch}/camviewer.cfg, '\n 'check spelling of camera argument.')\n cam_info = interpret_lines(cam_line)\n camera = build_cam(*cam_info[0])\n return camera","title":""},{"docid":"78e06c312afd34bba36a8461c5ce0fc4","score":"0.5192894","text":"def resetViewerCamera(self):\n self._renderer.resetCamera()\n self._cameraLensCombo.setCurrentIndex(self._renderer.activeSceneCamera().lens)","title":""},{"docid":"bb5872a68d63bc51495f281feff7d634","score":"0.51910275","text":"def _update_camera(self, camera_center):\n self._world_tl_to_world_camera_rel.offset = (\n -self._world_to_world_tl.fwd_pt(camera_center) *\n self._world_tl_to_world_camera_rel.scale)","title":""},{"docid":"065b33b4c319c4b8d1a8b40a31c7d8a2","score":"0.51874024","text":"def resolve_camera(self, info, camera_id):\n return camera_id","title":""},{"docid":"bdc21617b9d52eba819655f60432a360","score":"0.51761484","text":"def filterCamera():\n logger.debug(\"filterCamera\")\n\n PYFILTER_MANAGER.run_operations_for_stage(\"filterCamera\")","title":""},{"docid":"d332e5580379bacf1d6b9f8ab9c9ff0a","score":"0.5172844","text":"def set_custom_camera(self, camera_settings):\n camera = self.ren.GetActiveCamera()\n cs = camera_settings\n if cs.clippingRange and cs.focalPoint and cs.position and cs.viewUp:\n camera.SetClippingRange(cs.clippingRange)\n camera.SetFocalPoint(cs.focalPoint)\n camera.SetPosition(cs.position)\n camera.SetViewUp(cs.viewUp)","title":""},{"docid":"8ccf716f68b7c17c02dadcece842ebcd","score":"0.5148847","text":"def testcam(self):\n print(self.dictionnaire_camera_pipe)","title":""},{"docid":"1dec95da736960c4c603123eae2546aa","score":"0.51484096","text":"def setCamera(self, height = 50):\n pass","title":""},{"docid":"c7761b2705d04388adb897b1bf4e93e4","score":"0.51424915","text":"def cam_stream(self):\n if self.cam.query_image():\n self.sight = self.cam.get_image(self.sight)\n self.sight = pygame.transform.flip(self.sight, True, False)\n self.screen.blit(self.sight, (0,0))","title":""},{"docid":"0e8fe41e6341f9a21e57318cbb2eae81","score":"0.512114","text":"def Render( self, mode):\n shaders.glUseProgram(self.shader)\n glutReshapeWindow( 512 , 512 )\n testcontext.ViewPlatform.setPosition(self, [0,0,512])\n try:\n self.vbo.bind()\n try:\n quad = gluNewQuadric()\n glEnableClientState(GL_VERTEX_ARRAY);\n glVertexPointerf( self.vbo )\n glDrawArrays(glusphere(quad,1,5,5), 0, len(self.vbo))\n finally:\n self.vbo.unbind()\n glDisableClientState(GL_VERTEX_ARRAY);\n finally:\n shaders.glUseProgram( 0 )","title":""},{"docid":"2280d9f7b34996019a30b7f2d71b9f3d","score":"0.51190746","text":"def _init_camera(\n self, feature_dimensions, map_size, camera_width_world_units):\n if not map_size or not camera_width_world_units:\n raise ValueError(\n \"Either pass the game_info with raw enabled, or map_size and \"\n \"camera_width_world_units in order to use feature_units.\")\n map_size = point.Point.build(map_size)\n self._world_to_world_tl = transform.Linear(point.Point(1, -1),\n point.Point(0, map_size.y))\n self._world_tl_to_world_camera_rel = transform.Linear(offset=-map_size / 4)\n world_camera_rel_to_feature_screen = transform.Linear(\n feature_dimensions.screen / camera_width_world_units,\n feature_dimensions.screen / 2)\n self._world_to_feature_screen_px = transform.Chain(\n self._world_to_world_tl,\n self._world_tl_to_world_camera_rel,\n world_camera_rel_to_feature_screen,\n transform.PixelToCoord())","title":""},{"docid":"edeaa3c8cb50168047a4ca7d35b5f330","score":"0.51099044","text":"def _set_camera(self, request: client_pb2.SetCameraRequest):\n if not self._env:\n # The client will not wait for a response.\n return\n camera_info = self._env.set_camera(request.index)\n if not camera_info:\n return\n self._send_response(\n set_camera=client_pb2.SetCameraResponse(\n index=camera_info.index, name=camera_info.name))\n if self._paused or self._sync:\n # Update the image if the environment is paused or in sync mode. The\n # reward will be 0, but this is a cosmetic issue. In async mode, it will\n # be updated with the next step.\n _, self._image = self._get_image()\n self._send_response(\n step=client_pb2.StepResponse(\n image=self._image,\n episode_index=self._episode_index + 1,\n episode_steps=self._episode_steps))","title":""},{"docid":"b40dfc63ad2cbe459ee787d10e741e1b","score":"0.51069397","text":"def render(self):\n\t\tself.theater.draw()\n\t\tif self.recorder.recording == 'Video':\n\t\t\tchanges = self.rootCamera.draw(True)\n\t\t\tself.recorder.addFrame(changes)\n\t\telse:\n\t\t\tself.rootCamera.draw(False)\n\t\t\n\t\tdirtyGui= self.gui.update(pygame.display.get_surface());\n\t\tif dirtyGui:\n\t\t\tpygame.display.update(dirtyGui);","title":""},{"docid":"24ddf01de96fa88397c9c3a2046b5306","score":"0.5106569","text":"def EvalCameraState(self, *args):\n return _MaxPlus.CameraObject_EvalCameraState(self, *args)","title":""},{"docid":"f81f2e6331c196b8d5d4a212f4438728","score":"0.5087296","text":"def setup_camera(self):\n\n camera_transform = carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15))\n self.camera = self.world.spawn_actor(self.camera_blueprint(), camera_transform, attach_to=self.car)\n weak_self = weakref.ref(self)\n self.camera.listen(lambda image: weak_self().set_image(weak_self, image))\n\n calibration = np.identity(3)\n calibration[0, 2] = VIEW_WIDTH / 2.0\n calibration[1, 2] = VIEW_HEIGHT / 2.0\n calibration[0, 0] = calibration[1, 1] = VIEW_WIDTH / (2.0 * np.tan(VIEW_FOV * np.pi / 360.0))\n self.camera.calibration = calibration","title":""},{"docid":"6f97aa211e21c611ea4d2473a82a5ac0","score":"0.5086536","text":"def render(self, object_pose):\n oTc = np.linalg.inv(object_pose)\n oTopengl = oTc @ self.cTopengl\n self.scene.set_pose(self.camera_node, oTopengl)\n # TODO: figure out DEPTH_ONLY rendering mode with OSMesa backend\n # DEPTH_ONLY + OSMesa does not work currently\n # so we have to render color also :(\n _, depth = self.renderer.render(self.scene)\n return self.flip_fn(depth)","title":""},{"docid":"0e88619a98831669fe7aa362224c1f01","score":"0.5077818","text":"def left_camera(self):\n return self._left_camera","title":""},{"docid":"e6acefef3f2fb1c70938917b89cd2ff3","score":"0.5076684","text":"def getViewCameraName(self, **kwargs):\n \n pass","title":""},{"docid":"cec3f95737d429776fecfde7f1092e6c","score":"0.50761527","text":"def __init__(self, width, height, depth = 3, title = \"Camera View\", parent = None, visible = 0, async=1):\n PyrobotImage.__init__(self, width, height, depth, 0)\n self.app = 0\n self.title = title\n self.filterMode = 1\n self.callbackList = []\n self.filterResults = []\n self.callbackTextList = []\n Device.__init__(self, 'camera', async=async)\n # specific camera type will define self.rgb = (0, 1, 2) offsets\n # and self.format = \"RGB\", for example\n self.lastWindowUpdate = 0\n self.updateWindowInterval = 0.0 # update window once a second\n self.update() # call it once to initialize\n self.image = []\n self.data = self.data\n self.grayscale = []\n self.height = self.height\n self.width = self.width\n self.depth = self.depth\n self.filters = self.callbackTextList\n self.lastX, self.lastY = 0, 0\n # Required:\n self.startDevice()\n # make these visible by default\n if visible and self.vision:\n self.makeWindow()","title":""}],"string":"[\n {\n \"docid\": \"e1769c5d20baf8236d54fe6e955c1ac8\",\n \"score\": \"0.6627869\",\n \"text\": \"def GetRenderingCamera(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db2bdb3b4eddcbdf4a5fd461d9e143e\",\n \"score\": \"0.63430506\",\n \"text\": \"def camera(self, camera):\\n self.renderer.camera = camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fea7d7204fe4d7f2a1c55bf09a53e0b\",\n \"score\": \"0.62744004\",\n \"text\": \"def camera(self):\\n if not self.renderer.camera.is_set:\\n self.camera_position = self.get_default_cam_pos()\\n self.reset_camera()\\n self.renderer.camera.is_set = True\\n return self.renderer.camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6332cc65b8b96e8f94c8e46f45d65713\",\n \"score\": \"0.623032\",\n \"text\": \"def render(self, camera=None):\\n self.gl_list.render()\\n data.Texture.bound = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b40c22d40ff5f4283f5ab3b08def8bd\",\n \"score\": \"0.61798275\",\n \"text\": \"def update_camera(func):\\r\\n @wraps(func)\\r\\n def with_wrapped_func(*args, **kwargs):\\r\\n func(*args, **kwargs)\\r\\n wid = args[0]\\r\\n wid.camera.apply()\\r\\n wid.updateGL()\\r\\n wid.signal_camera_updated.emit(wid.camera)\\r\\n wid.state.signal_state_change.emit()\\r\\n return with_wrapped_func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f34baec9c5977ac19bc0d05ec4b6c6\",\n \"score\": \"0.6156473\",\n \"text\": \"def onShowCamera(self, obj, evt, cam):\\n\\t\\tself.setCamera(cam)\\n\\t\\tself.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3feb3041f07536ab627c20315701b265\",\n \"score\": \"0.60941774\",\n \"text\": \"def storeViewerCamera(self):\\n self._renderer.storeCamera()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"238aff81f270b95e30cd31dfe8b0ca7b\",\n \"score\": \"0.6076526\",\n \"text\": \"def AddCamera(self, show_window=False):\\n # Add renderer to scene graph\\n renderer_name = \\\"kinova_camera_renderer\\\"\\n self.scene_graph.AddRenderer(renderer_name, \\n MakeRenderEngineVtk(RenderEngineVtkParams()))\\n \\n # Set camera properites. These roughly correspond\\n # to the camera on the hardware (Intel Realsense D410).\\n intrinsics = CameraInfo(width=480, height=270, fov_y=np.radians(40))\\n clipping = ClippingRange(0.01,3.0)\\n X_lens = RigidTransform()\\n camera_core = RenderCameraCore(renderer_name, intrinsics, clipping, X_lens)\\n depth_range = DepthRange(0.1, 2.0)\\n\\n # Create the camera model\\n self.color_camera = ColorRenderCamera(camera_core, show_window=show_window)\\n self.depth_camera = DepthRenderCamera(camera_core, depth_range)\\n \\n # Set the frame and position of the camera\\n self.camera_parent_frame = self.plant.GetFrameByName(\\\"end_effector_link\\\", self.arm)\\n self.X_camera = RigidTransform() # position of camera in parent frame\\n self.X_camera.set_translation([0,0.1,0.0])\\n\\n self.has_camera = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a248d736498765e2720ac4c4c6278e7\",\n \"score\": \"0.5931266\",\n \"text\": \"def getCurrentCamera(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648fc36eb6d05600b34cfa692279370e\",\n \"score\": \"0.5919756\",\n \"text\": \"def SwitchViewerCamera(self,pCamera):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b694572cbc50737e092c02bf212d7a\",\n \"score\": \"0.58980113\",\n \"text\": \"def camera_entity(self):\\n return self._camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b694572cbc50737e092c02bf212d7a\",\n \"score\": \"0.58980113\",\n \"text\": \"def camera_entity(self):\\n return self._camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b694572cbc50737e092c02bf212d7a\",\n \"score\": \"0.58980113\",\n \"text\": \"def camera_entity(self):\\n return self._camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae25219dbd1b9f4ef4c97dfa963e7ad\",\n \"score\": \"0.5856452\",\n \"text\": \"def getGlRenderEditor(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f7cf2cbd432638e80b91fa4ebf001c1\",\n \"score\": \"0.584912\",\n \"text\": \"def AddCamera(self, projectionmethod, fov, clippingplanes, scale = 1, name = \\\"\\\", viewport=(0, 0, 100, 100), clearcolor=True, cleardepth=True, layer=0, backgroundcolor=(1, 1, 1, 1), active=True):\\n comp, inx = self.AddComponent(name, active) \\n cam = comp.FusCamera\\n cam.ProjectionMethod = projectionmethod\\n cam.Fov = fov\\n cam.ClippingPlanes.x = clippingplanes[0]\\n cam.ClippingPlanes.y = clippingplanes[1]\\n cam.Viewport.x = viewport[0]\\n cam.Viewport.y = viewport[1]\\n cam.Viewport.z = viewport[2]\\n cam.Viewport.w = viewport[3]\\n cam.ClearColor = clearcolor\\n cam.ClearDepth = cleardepth\\n cam.Layer = layer\\n cam.BackgroundColor.x = backgroundcolor[0]\\n cam.BackgroundColor.y = backgroundcolor[1]\\n cam.BackgroundColor.z = backgroundcolor[2]\\n cam.BackgroundColor.w = backgroundcolor[3]\\n cam.Scale = scale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a1cf64f67ff188510e854c805479dd\",\n \"score\": \"0.58304435\",\n \"text\": \"def render(self):\\n self.camera.load()\\n self.renderScene(Drawable.RenderState(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd37fdfe23737bcc27865b299709aaee\",\n \"score\": \"0.5824603\",\n \"text\": \"def update(self):\\r\\n update = self._input_ubo.update_input\\r\\n\\r\\n # Get the current transform matrix of the camera\\r\\n view_mat = Globals.render.get_transform(self._showbase.cam).get_mat()\\r\\n\\r\\n # Compute the view matrix, but with a z-up coordinate system\\r\\n update(\\\"view_mat_z_up\\\", view_mat * Mat4.convert_mat(CS_zup_right, CS_yup_right))\\r\\n\\r\\n # Compute the view matrix without the camera rotation\\r\\n view_mat_billboard = Mat4(view_mat)\\r\\n view_mat_billboard.set_row(0, Vec3(1, 0, 0))\\r\\n view_mat_billboard.set_row(1, Vec3(0, 1, 0))\\r\\n view_mat_billboard.set_row(2, Vec3(0, 0, 1))\\r\\n update(\\\"view_mat_billboard\\\", view_mat_billboard)\\r\\n\\r\\n update(\\\"camera_pos\\\", self._showbase.camera.get_pos(Globals.render))\\r\\n\\r\\n # Compute last view projection mat\\r\\n curr_vp = self._input_ubo.get_input(\\\"view_proj_mat_no_jitter\\\")\\r\\n update(\\\"last_view_proj_mat_no_jitter\\\", curr_vp)\\r\\n curr_vp = Mat4(curr_vp)\\r\\n curr_vp.invert_in_place()\\r\\n curr_inv_vp = curr_vp\\r\\n update(\\\"last_inv_view_proj_mat_no_jitter\\\", curr_inv_vp)\\r\\n\\r\\n proj_mat = Mat4(self._showbase.camLens.get_projection_mat())\\r\\n\\r\\n # Set the projection matrix as an input, but convert it to the correct\\r\\n # coordinate system before.\\r\\n proj_mat_zup = Mat4.convert_mat(CS_yup_right, CS_zup_right) * proj_mat\\r\\n update(\\\"proj_mat\\\", proj_mat_zup)\\r\\n\\r\\n # Set the inverse projection matrix\\r\\n update(\\\"inv_proj_mat\\\", invert(proj_mat_zup))\\r\\n\\r\\n # Remove jitter and set the new view projection mat\\r\\n proj_mat.set_cell(1, 0, 0.0)\\r\\n proj_mat.set_cell(1, 1, 0.0)\\r\\n update(\\\"view_proj_mat_no_jitter\\\", view_mat * proj_mat)\\r\\n\\r\\n # Store the frame delta\\r\\n update(\\\"frame_delta\\\", Globals.clock.get_dt())\\r\\n update(\\\"smooth_frame_delta\\\", 1.0 / max(1e-5, Globals.clock.get_average_frame_rate()))\\r\\n update(\\\"frame_time\\\", Globals.clock.get_frame_time())\\r\\n\\r\\n # Store the current film offset, we use this to compute the pixel-perfect\\r\\n # velocity, which is otherwise not possible. Usually this is always 0\\r\\n # except when SMAA and reprojection is enabled\\r\\n update(\\\"current_film_offset\\\", self._showbase.camLens.get_film_offset())\\r\\n update(\\\"frame_index\\\", Globals.clock.get_frame_count())\\r\\n\\r\\n # Compute frustum corners in the order BL, BR, TL, TR\\r\\n ws_frustum_directions = Mat4()\\r\\n vs_frustum_directions = Mat4()\\r\\n inv_proj_mat = Globals.base.camLens.get_projection_mat_inv()\\r\\n view_mat_inv = Mat4(view_mat)\\r\\n view_mat_inv.invert_in_place()\\r\\n\\r\\n for i, point in enumerate(((-1, -1), (1, -1), (-1, 1), (1, 1))):\\r\\n result = inv_proj_mat.xform(Vec4(point[0], point[1], 1.0, 1.0))\\r\\n vs_dir = result.xyz.normalized()\\r\\n vs_frustum_directions.set_row(i, Vec4(vs_dir, 1))\\r\\n ws_dir = view_mat_inv.xform(Vec4(result.xyz, 0))\\r\\n ws_frustum_directions.set_row(i, ws_dir)\\r\\n\\r\\n update(\\\"vs_frustum_directions\\\", vs_frustum_directions)\\r\\n update(\\\"ws_frustum_directions\\\", ws_frustum_directions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b71506f055e546d6f1ad8f894dd348e5\",\n \"score\": \"0.58211446\",\n \"text\": \"def camera_set(self):\\n return self.renderer.camera.is_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cff41ab1510782c791b6915adf1d1cb\",\n \"score\": \"0.57846403\",\n \"text\": \"def setup_default_camera(self):\\r\\n self.add_camera(GLCamera(self, name=\\\"interactive\\\"))\\r\\n self.set_camera(\\\"interactive\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c95220dec5ab6c0a5be77a870298a0\",\n \"score\": \"0.5777523\",\n \"text\": \"def update_camera(self):\\n if self.params.camera_update_mode == 'ResetCamera':\\n ## resetCamera() method:\\n self.ui.plt.resetCamera() # resetCamera() updates the camera's position given the ignored components\\n # This limits the meshes to just the active window's meshes: [start_bound_plane, end_bound_plane, active_window_only_axes]\\n elif self.params.camera_update_mode == 'CenterToActiveWindow':\\n ## center_camera_on_active_timewindow(): tries to compute the explicit center of the time window\\n self.center_camera_on_active_timewindow()\\n elif self.params.camera_update_mode == 'None':\\n pass # do no automatic adjustment of the camera\\n else:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6113172ba50594f7491e55380209ac34\",\n \"score\": \"0.576773\",\n \"text\": \"def SetViewCamera(self, *args):\\n return _MaxPlus.Viewport_SetViewCamera(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe37da770bb6af92b64903b030786fb\",\n \"score\": \"0.57476056\",\n \"text\": \"def setCamera(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe37da770bb6af92b64903b030786fb\",\n \"score\": \"0.57467717\",\n \"text\": \"def setCamera(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe37da770bb6af92b64903b030786fb\",\n \"score\": \"0.57467717\",\n \"text\": \"def setCamera(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfe37da770bb6af92b64903b030786fb\",\n \"score\": \"0.57439363\",\n \"text\": \"def setCamera(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b61d2ff7e0c4aa33e648b2523695df49\",\n \"score\": \"0.57315207\",\n \"text\": \"def camera_entity(self):\\n return self._camera_entity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc30a12a34a9e50e229337ee09656231\",\n \"score\": \"0.57275444\",\n \"text\": \"def getCamera(self):\\n\\t\\tcam = None\\n\\t\\tif self.renderer:\\n\\t\\t\\tcam = self.renderer.GetActiveCamera()\\n\\t\\treturn cam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5631f257e689de6c5e440bfc500972be\",\n \"score\": \"0.5725253\",\n \"text\": \"def camera_set(self, is_set):\\n self.renderer.camera.is_set = is_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3ee16d7a98ecb70abd3ed4fdf9984a\",\n \"score\": \"0.57177436\",\n \"text\": \"def getCamera(self, viewName):\\n camerasLogic = slicer.modules.cameras.logic()\\n camera = camerasLogic.GetViewActiveCameraNode(slicer.util.getNode(viewName))\\n return camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"829e92e358f91c7eb054ac199ba0c79e\",\n \"score\": \"0.5712814\",\n \"text\": \"def add_static_camera(self):\\n \\n bpy.ops.object.camera_add()\\n camera = bpy.context.object\\n camera.name = \\\"C_camera\\\"\\n camera.location = (0, -1.1*self.y_size, self.y_size)\\n camera.rotation_euler = (math.pi/4, 0, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d5dacffa826a22b45007432369d741\",\n \"score\": \"0.57105225\",\n \"text\": \"def setCamera(self, cam):\\n\\t\\tif self.renderer:\\n\\t\\t\\tself.renderer.SetActiveCamera(cam)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81bd07909578e40f2aa43891c9425698\",\n \"score\": \"0.5706572\",\n \"text\": \"def camera_menu(m):\\n if m.selected in solar_system:\\n scene.camera.follow(solar_system[m.selected].obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"668c9a421af21891b21f0fcd3f444c72\",\n \"score\": \"0.5686792\",\n \"text\": \"def test_camera(self):\\n # N.B. We don't need perception geometry in the scene -- we'll rely on\\n # the RgbdSensor unit tests to check that cameras work as advertised.\\n # Our only goal here is to achieve statement-level code coverage of the\\n # ModelVisualizer code when show_rgbd_sensor=True.\\n model = \\\"\\\"\\\"<?xml version=\\\"1.0\\\"?>\\n <sdf version=\\\"1.9\\\">\\n <model name=\\\"sample\\\">\\n <link name=\\\"base\\\"/>\\n </model>\\n </sdf>\\n \\\"\\\"\\\"\\n dut = mut.ModelVisualizer(show_rgbd_sensor=True)\\n dut.parser().AddModelsFromString(model, \\\"sdf\\\")\\n dut.Run(loop_once=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a92c55cc493d19ea84ce2993a84187\",\n \"score\": \"0.5685627\",\n \"text\": \"def render(self, screen, camera=None):\\n offset_x, offset_y = (camera.x, camera.y) if camera else (0, 0)\\n rendering_position = (self.shape.x - offset_x, self.shape.y - offset_y)\\n screen.blit(self.raw_image, rendering_position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea4647303521f596c56dacd23f186fa\",\n \"score\": \"0.56434876\",\n \"text\": \"def GetViewCamera(self):\\n return _MaxPlus.Viewport_GetViewCamera(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df3e626c296b7383c3caec606474741b\",\n \"score\": \"0.5636412\",\n \"text\": \"def render(self, num_camera=1):\\r\\n view_matrix1 = p.computeViewMatrixFromYawPitchRoll(\\r\\n cameraTargetPosition=self.camera_target_pos,\\r\\n distance=2.,\\r\\n yaw=145, # 145 degree\\r\\n pitch=-36, # -36 degree\\r\\n roll=0,\\r\\n upAxisIndex=2\\r\\n )\\r\\n proj_matrix1 = p.computeProjectionMatrixFOV(\\r\\n fov=60, aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,\\r\\n nearVal=0.1, farVal=100.0)\\r\\n # depth: the depth camera, mask: mask on different body ID\\r\\n (_, _, px, depth, mask) = p.getCameraImage(\\r\\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix1,\\r\\n projectionMatrix=proj_matrix1, renderer=p.ER_TINY_RENDERER)\\r\\n px, depth, mask = np.array(px), np.array(depth), np.array(mask)\\r\\n\\r\\n if self._renders:\\r\\n plt.ion()\\r\\n if num_camera == 1:\\r\\n figsize = np.array([3, 1]) * 5\\r\\n else:\\r\\n figsize = np.array([3, 2]) * 5\\r\\n fig = plt.figure(\\\"Inmoov\\\", figsize=figsize)\\r\\n\\r\\n if num_camera == 2:\\r\\n view_matrix2 = p.computeViewMatrixFromYawPitchRoll(\\r\\n cameraTargetPosition=(0.316, 0.316, 1.0),\\r\\n distance=1.2,\\r\\n yaw=90, # 145 degree\\r\\n pitch=-13, # -36 degree\\r\\n roll=0,\\r\\n upAxisIndex=2\\r\\n )\\r\\n proj_matrix2 = p.computeProjectionMatrixFOV(\\r\\n fov=60, aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,\\r\\n nearVal=0.1, farVal=100.0)\\r\\n (_, _, px2, depth2, mask2) = p.getCameraImage(\\r\\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix2,\\r\\n projectionMatrix=proj_matrix2, renderer=p.ER_TINY_RENDERER)\\r\\n ax1 = fig.add_subplot(231)\\r\\n ax1.imshow(px)\\r\\n ax1.set_title(\\\"rgb_1\\\")\\r\\n ax2 = fig.add_subplot(232)\\r\\n ax2.imshow(depth)\\r\\n ax2.set_title(\\\"depth_1\\\")\\r\\n ax3 = fig.add_subplot(233)\\r\\n ax3.imshow(mask)\\r\\n ax3.set_title(\\\"mask_1\\\")\\r\\n ax1 = fig.add_subplot(234)\\r\\n ax1.imshow(px2)\\r\\n ax1.set_title(\\\"rgb_2\\\")\\r\\n ax2 = fig.add_subplot(235)\\r\\n ax2.imshow(depth2)\\r\\n ax2.set_title(\\\"depth_2\\\")\\r\\n ax3 = fig.add_subplot(236)\\r\\n ax3.imshow(mask2)\\r\\n ax3.set_title(\\\"mask_2\\\")\\r\\n elif num_camera == 1: # only one camera\\r\\n ax1 = fig.add_subplot(131)\\r\\n ax1.imshow(px)\\r\\n ax1.set_title(\\\"rgb_1\\\")\\r\\n ax2 = fig.add_subplot(132)\\r\\n ax2.imshow(depth)\\r\\n ax2.set_title(\\\"depth_1\\\")\\r\\n ax3 = fig.add_subplot(133)\\r\\n ax3.imshow(mask)\\r\\n ax3.set_title(\\\"mask_1\\\")\\r\\n # rgb_array = np.array(px)\\r\\n # self.image_plot = plt.imshow(rgb_array)\\r\\n # self.image_plot.axes.grid(False)\\r\\n # plt.title(\\\"Inmoov Robot Simulation\\\")\\r\\n fig.suptitle('Inmoov Simulation: Two Cameras View', fontsize=32)\\r\\n plt.draw()\\r\\n # To avoid too fast drawing conflict\\r\\n plt.pause(0.00001)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30fcc4d096c815acb4b9d1aa79002ca2\",\n \"score\": \"0.5627395\",\n \"text\": \"def display(self, camera_tr_vec, camera_rot_mat, camera_fov_y, tracked_cam_track_pos_float):\\n\\n GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)\\n\\n view_mat = np.linalg.inv(_get_pose_matrix(data3d.Pose(camera_rot_mat, camera_tr_vec)))\\n\\n aspect_ratio = float(GLUT.glutGet(GLUT.GLUT_WINDOW_WIDTH)) / float(GLUT.glutGet(GLUT.GLUT_WINDOW_HEIGHT))\\n focus_y = 1.0 / np.tan(camera_fov_y / 2.0)\\n focus_x = focus_y / aspect_ratio\\n\\n near = 0.1\\n far = 1000.0\\n\\n projection_mat = np.matrix([\\n [focus_x, 0, 0, 0],\\n [0, focus_y, 0, 0],\\n [0, 0, -(far + near) / (far - near), -2 * far * near / (far - near)],\\n [0, 0, -1, 0],\\n ])\\n\\n pv_matrix = projection_mat.dot(view_mat)\\n\\n self._point_cloud_renderer.render(pv_matrix)\\n self._cam_track_line_renderer.render(pv_matrix)\\n self._cam_renderer.render(pv_matrix, tracked_cam_track_pos_float)\\n\\n GLUT.glutSwapBuffers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6d2e8ea2de753583cc98049caef5f5\",\n \"score\": \"0.56040853\",\n \"text\": \"def recallViewerCamera(self):\\n self._renderer.recallCamera()\\n self._cameraLensCombo.setCurrentIndex(self._renderer.activeSceneCamera().lens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc63e700a7dec0a2663208fbb74a6f3c\",\n \"score\": \"0.5571779\",\n \"text\": \"def loadCam(self):\\n\\t\\tself.refresh_frame_range()\\n\\t\\tsel = mc.ls(sl=True)\\n\\t\\tprint sel\\n\\t\\tif sel:\\n\\t\\t\\tfor s in sel:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tcamXform, camShape = getCameraXformAndShape(s)\\n\\t\\t\\t\\t\\t# setting the first camera we found\\n\\t\\t\\t\\t\\ttrackingCamera = mc.textFieldButtonGrp(\\n\\t\\t\\t\\t\\t\\tself.targetCam, e=True, text=camXform)\\n\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t# couldn't get camera, handle the exception by moving\\n\\t\\t\\t\\t\\t# to the next item in the selection\\n\\t\\t\\t\\t\\tcontinue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca93198e4999578de644489a25dd60a\",\n \"score\": \"0.5561535\",\n \"text\": \"def display(self, camera_tr_vec, camera_rot_mat, camera_fov_y, tracked_cam_track_pos_float):\\n\\n # a frame in which a tracked camera model and frustrum should be drawn\\n # without interpolation\\n tracked_cam_track_pos = int(tracked_cam_track_pos_float)\\n\\n GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)\\n\\n self._render_example_point(np.eye(4))\\n\\n GLUT.glutSwapBuffers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444f4bb535c9cd2ecd047aa50f8a3a6d\",\n \"score\": \"0.5543331\",\n \"text\": \"def get_camera(self, name):\\r\\n return self.__cameras.get(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652545cc091327cf97c2efec74626c9f\",\n \"score\": \"0.55427897\",\n \"text\": \"def right_camera(self):\\n return self._right_camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d48c1ac6ef3be7973497c9b19212675\",\n \"score\": \"0.55250996\",\n \"text\": \"def init(map_width, map_height):\\n global camera\\n camera = Rect(0, 0, map_width, map_height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1500f259c7c89d9db26c5d8eeca4916f\",\n \"score\": \"0.5524116\",\n \"text\": \"def camera_selection(func):\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n cambot, update = args\\n cam_id = re.split(r'^.*(?=cam_)', update.message.text)[-1]\\n cam = cambot._pool.get_instance(cam_id)\\n args = args + (cam, cam_id)\\n return func(*args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e83852b339592ccc52c25a12b58233\",\n \"score\": \"0.5497755\",\n \"text\": \"def setCurrentCamera(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"408400362b013b5becc37997bded3f4a\",\n \"score\": \"0.5460251\",\n \"text\": \"def set_camera(self, camera):\\r\\n log.debug(\\\"[%s.set_camera] %s\\\" % (self, camera))\\r\\n if type(camera) in [str, str]:\\r\\n if \\\"/\\\" in camera:\\r\\n camera = os.path.split(\\\"/\\\")[-1]\\r\\n elif camera not in [cam.name for cam in self.state.cameras]:\\r\\n log.warn(\\\"camera not found: %s\\\" % camera)\\r\\n return\\r\\n self.camera = self.state.get_camera(camera)\\r\\n else:\\r\\n self.camera = camera\\r\\n self.camera.add_view(self)\\r\\n self.resizeGL(self.width(), self.height())\\r\\n self.signal_set_camera.emit(self.camera)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d555116a32c4a90edfe945f113317386\",\n \"score\": \"0.5457226\",\n \"text\": \"def __init__(self, scene: Scene, camera: Camera):\\n super().__init__(scene, camera)\\n self.ctx = mgl.create_context()\\n super()._setup()\\n\\n self.viewport = None\\n \\\"\\\"\\\" tuple: Viewport is a tuple of size four (x, y, width, height). \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf870e664465572e8ce4e03f25c3163b\",\n \"score\": \"0.54435754\",\n \"text\": \"def makeCamera(self, win, sort = 0, scene = None,\\n displayRegion = (0, 1, 0, 1), stereo = None,\\n aspectRatio = None, clearDepth = 0, clearColor = None,\\n lens = None, camName = 'cam', mask = None,\\n useCamera = None):\\n # self.camera is the parent node of all cameras: a node that\\n # we can move around to move all cameras as a group.\\n if self.camera is None:\\n # We make it a ModelNode with the PTLocal flag, so that\\n # a wayward flatten operations won't attempt to mangle the\\n # camera.\\n self.camera = self.render.attachNewNode(ModelNode('camera'))\\n self.camera.node().setPreserveTransform(ModelNode.PTLocal)\\n builtins.camera = self.camera\\n\\n self.mouse2cam.node().setNode(self.camera.node())\\n\\n if useCamera:\\n # Use the existing camera node.\\n cam = useCamera\\n camNode = useCamera.node()\\n assert isinstance(camNode, Camera)\\n lens = camNode.getLens()\\n cam.reparentTo(self.camera)\\n\\n else:\\n # Make a new Camera node.\\n camNode = Camera(camName)\\n if lens is None:\\n lens = PerspectiveLens()\\n\\n if aspectRatio is None:\\n aspectRatio = self.getAspectRatio(win)\\n lens.setAspectRatio(aspectRatio)\\n\\n cam = self.camera.attachNewNode(camNode)\\n\\n if lens is not None:\\n camNode.setLens(lens)\\n\\n if scene is not None:\\n camNode.setScene(scene)\\n\\n if mask is not None:\\n if isinstance(mask, int):\\n mask = BitMask32(mask)\\n camNode.setCameraMask(mask)\\n\\n if self.cam is None:\\n self.cam = cam\\n self.camNode = camNode\\n self.camLens = lens\\n\\n self.camList.append(cam)\\n\\n # Now, make a DisplayRegion for the camera.\\n if stereo is not None:\\n if stereo:\\n dr = win.makeStereoDisplayRegion(*displayRegion)\\n else:\\n dr = win.makeMonoDisplayRegion(*displayRegion)\\n else:\\n dr = win.makeDisplayRegion(*displayRegion)\\n\\n dr.setSort(sort)\\n\\n # By default, we do not clear 3-d display regions (the entire\\n # window will be cleared, which is normally sufficient). But\\n # we will if clearDepth is specified.\\n if clearDepth:\\n dr.setClearDepthActive(1)\\n\\n if clearColor:\\n dr.setClearColorActive(1)\\n dr.setClearColor(clearColor)\\n\\n dr.setCamera(cam)\\n\\n return cam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dcce4558826edd4ee701eef0d4e6903\",\n \"score\": \"0.5426206\",\n \"text\": \"def test_global_camera(self):\\n layout = Layout([\\n Map(Layer(Source(SOURCE))),\\n Map(Layer(Source(SOURCE)))\\n ], viewport={'zoom': 5})\\n\\n assert layout._layout[0].get('camera') == {\\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 5}\\n assert layout._layout[1].get('camera') == {\\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 5}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a06488315d919c8240ad73ad24962245\",\n \"score\": \"0.54261726\",\n \"text\": \"def _set_camera(self, c):\\n if c.new == -1: return\\n try:\\n self.send({'type': 'camera', 'content': self.cameras[c.new]})\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb23d6d627945d0232abb7cb2f1a85f3\",\n \"score\": \"0.5415432\",\n \"text\": \"def display(self):\\n if not hasattr(self.s, 'actor'):\\n self.s.addActor()\\n if not hasattr(self.m, 'actor'):\\n self.m.addActor()\\n # Generate a renderer window\\n win = vtkRenWin()\\n # Set the number of viewports\\n win.setnumViewports(1)\\n # Set the background colour\\n win.setBackground([1,1,1])\\n # Set camera projection \\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\\n renderWindowInteractor.SetRenderWindow(win)\\n renderWindowInteractor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\\n # Set camera projection \\n win.setView()\\n self.s.actor.setColor([1.0, 0.0, 0.0])\\n self.s.actor.setOpacity(0.5)\\n self.m.actor.setColor([0.0, 0.0, 1.0])\\n self.m.actor.setOpacity(0.5)\\n win.renderActors([self.s.actor, self.m.actor])\\n win.Render()\\n win.rens[0].GetActiveCamera().Azimuth(180)\\n win.rens[0].GetActiveCamera().SetParallelProjection(True)\\n win.Render()\\n return win\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fe24aedb2050d4022c78f2fc9f7396\",\n \"score\": \"0.5412809\",\n \"text\": \"def robot_render(self):\\r\\n right_eye_state = p.getLinkState(self.inmoov_id, 17)\\r\\n\\r\\n left_eye_state = p.getLinkState(self.inmoov_id, 21)\\r\\n right_eye_pos = np.array(right_eye_state[0])\\r\\n right_eye_orn = right_eye_state[1]\\r\\n left_eye_pos, left_eye_orn = np.array(left_eye_state[0]), left_eye_state[1]\\r\\n\\r\\n focus = 0.5\\r\\n\\r\\n r_rotation_matrix = np.array(p.getMatrixFromQuaternion(right_eye_orn)).reshape([3,3]).T\\r\\n l_rotation_matrix = np.array(p.getMatrixFromQuaternion(left_eye_orn)).reshape([3,3]).T\\r\\n front = np.array([0, focus, 0])\\r\\n right_target_pos = right_eye_pos + front@r_rotation_matrix\\r\\n left_target_pos = left_eye_pos + front @ l_rotation_matrix\\r\\n right_eye_matrix = p.computeViewMatrix(\\r\\n cameraEyePosition = right_eye_pos,\\r\\n cameraTargetPosition = right_target_pos,\\r\\n cameraUpVector = [0, 0, 1]\\r\\n )\\r\\n left_eye_matrix = p.computeViewMatrix(\\r\\n cameraEyePosition=left_eye_pos,\\r\\n cameraTargetPosition=left_target_pos,\\r\\n cameraUpVector=[0, 0, 1]\\r\\n )\\r\\n proj_matrix = p.computeProjectionMatrixFOV(\\r\\n fov=100, aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,\\r\\n nearVal=0.1, farVal=100.0)\\r\\n\\r\\n # depth: the depth camera, mask: mask on different body ID\\r\\n (_, _, px, depth, mask) = p.getCameraImage(\\r\\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=right_eye_matrix,\\r\\n projectionMatrix=proj_matrix, renderer=p.ER_TINY_RENDERER)\\r\\n right_px, right_depth, right_mask = np.array(px), np.array(depth), np.array(mask)\\r\\n (_, _, px, depth, mask) = p.getCameraImage(\\r\\n width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=left_eye_matrix,\\r\\n projectionMatrix=proj_matrix, renderer=p.ER_TINY_RENDERER)\\r\\n left_px, left_depth, left_mask = np.array(px), np.array(depth), np.array(mask)\\r\\n if self._renders:\\r\\n plt.ion()\\r\\n figsize = np.array([3, 5]) * 3\\r\\n fig = plt.figure(\\\"Inmoov\\\", figsize=figsize)\\r\\n ax1 = fig.add_subplot(321)\\r\\n ax1.imshow(left_px)\\r\\n ax1.set_title(\\\"Left\\\")\\r\\n ax2 = fig.add_subplot(322)\\r\\n ax2.imshow(right_px)\\r\\n ax2.set_title(\\\"Right\\\")\\r\\n ax3 = fig.add_subplot(323)\\r\\n ax3.imshow(left_depth)\\r\\n ax4 = fig.add_subplot(324)\\r\\n ax4.imshow(right_depth)\\r\\n ax5 = fig.add_subplot(325)\\r\\n ax5.imshow(left_mask)\\r\\n ax6 = fig.add_subplot(326)\\r\\n ax6.imshow(right_mask)\\r\\n # rgb_array = np.array(px)\\r\\n # self.image_plot = plt.imshow(rgb_array)\\r\\n # self.image_plot.axes.grid(False)\\r\\n # plt.title(\\\"Inmoov Robot Simulation\\\")\\r\\n fig.suptitle('Inmoov Simulation: Two Cameras View', fontsize=32)\\r\\n plt.draw()\\r\\n # To avoid too fast drawing conflict\\r\\n plt.pause(0.00001)\\r\\n return left_px, right_px, left_depth, right_depth, left_mask, right_mask\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0049b700eaf630b90d5b3a8fe5c304b0\",\n \"score\": \"0.54054385\",\n \"text\": \"def switch_cameras(self):\\n scene = bge.logic.getCurrentScene()\\n index = bge.logic.current_camera_index\\n next_camera = scene.cameras[index]\\n scene.active_camera = next_camera\\n logger.info(\\\"Showing view from camera: '%s'\\\" % next_camera.name)\\n # Disable mouse cursor for Human camera\\n if next_camera.name == \\\"Human_Camera\\\":\\n bge.logic.mouse.visible = False\\n else:\\n bge.logic.mouse.visible = True\\n # Update the index for the next call\\n index = (index + 1) % len(scene.cameras)\\n bge.logic.current_camera_index = index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119a8508a34bf317e1697475528d259f\",\n \"score\": \"0.5405085\",\n \"text\": \"def NewCamera(self, *args):\\n return _MaxPlus.GenCamera_NewCamera(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4088caf95eb91d943172e5dde8d5b6be\",\n \"score\": \"0.5386747\",\n \"text\": \"def getCurrentCameraRig(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50366c92169e985f928ceb3afdef8f3c\",\n \"score\": \"0.53796756\",\n \"text\": \"def _setup_video_texture(self):\\n for child in self.blender_obj.children:\\n # The camera object that will produce the image in Blender\\n if 'CameraRobot' in child.name:\\n camera = child\\n # The object that contains the material where the image is rendered\\n if 'CameraMesh' in child.name:\\n screen = child\\n # Considering it consists of a single mesh\\n mesh = child.meshes[0] \\n # Get the material name\\n for material in mesh.materials:\\n material_index = material.getMaterialIndex()\\n mesh_material_name = mesh.getMaterialName(material_index)\\n if 'MAScreenMat' in mesh_material_name:\\n material_name = mesh_material_name\\n\\n try:\\n logger.debug(\\\"\\\\tCAMERA: %s\\\" % camera.name)\\n logger.debug(\\\"\\\\tSCREEN: %s\\\" % screen.name)\\n logger.debug(\\\"\\\\tMATERIAL: %s\\\" % material_name)\\n except UnboundLocalError as detail:\\n logger.error(\\\"\\\"\\\"\\n !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\\n ERROR: The video camera could not be properly initialized.\\n The children object could not be found.\\n Best solution is to re-link the camera.\\n !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\\n \\\"\\\"\\\")\\n return (False)\\n\\n # Get the reference to the scene\\n scene = bge.logic.getCurrentScene()\\n\\n # Link the objects using bge.texture\\n if not hasattr(bge.logic, 'cameras'):\\n bge.logic.cameras = {}\\n\\n mat_id = bge.texture.materialID(screen, material_name)\\n vt_camera = bge.texture.Texture(screen, mat_id)\\n vt_camera.source = bge.texture.ImageRender(scene, camera)\\n\\n # Set the focal length of the camera using the Game Logic Property\\n camera.lens = self.image_focal\\n logger.info(\\\"\\\\tFocal length of the camera is: %s\\\" % camera.lens)\\n\\n # Set the background to be used for the render\\n vt_camera.source.background = self.bg_color\\n # Define an image size. It must be powers of two. Default 512 * 512\\n vt_camera.source.capsize = [self.image_width, self.image_height]\\n logger.info(\\\"Camera '%s': Exporting an image of capsize: %s pixels\\\" % \\\\\\n (self.name(), vt_camera.source.capsize))\\n\\n # Reverse the image (boolean game-property)\\n # cf. bge.logic.video.source.flip (bge.texture.ImageRender)\\n # http://wiki.blender.org/index.php/Dev:Source/GameEngine/2.49/VideoTexture#Setup_the_source\\n if 'Vertical_Flip' in self.blender_obj: # backward compatibility\\n vt_camera.source.flip = self.blender_obj['Vertical_Flip']\\n\\n bge.logic.cameras[self.name()] = vt_camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee267e05cff5bc9876b64ac600a433ec\",\n \"score\": \"0.537032\",\n \"text\": \"def draw(self, screen, camera):\\n self.camrect = pygame.Rect(self.x - camera.x,\\n self.y - camera.y,\\n self.size, self.size)\\n screen.blit(self.image, self.camrect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e80045b3c30814c6192309792241de5\",\n \"score\": \"0.53507864\",\n \"text\": \"def update_edit_panel(self):\\n\\n if self.viewer and isfile2(self.photo):\\n self.viewer.stop()\\n if self.edit_panel_object:\\n self.edit_panel_object.save_last()\\n self.viewer.edit_mode = self.edit_panel\\n edit_panel_container = self.ids['panelEdit']\\n if self.edit_panel == 'main':\\n self.edit_panel_object = EditMain(owner=self)\\n self.edit_panel_object.update_programs()\\n self.viewer.bypass = False\\n else:\\n self.viewer.bypass = True\\n self.viewer.stop()\\n if self.edit_panel == 'color':\\n self.edit_panel_object = EditColorImage(owner=self)\\n self.viewer.edit_image.bind(histogram=self.edit_panel_object.draw_histogram)\\n elif self.edit_panel == 'advanced':\\n self.edit_panel_object = EditColorImageAdvanced(owner=self)\\n self.viewer.edit_image.bind(histogram=self.edit_panel_object.draw_histogram)\\n elif self.edit_panel == 'filter':\\n self.edit_panel_object = EditFilterImage(owner=self)\\n elif self.edit_panel == 'border':\\n self.edit_panel_object = EditBorderImage(owner=self)\\n elif self.edit_panel == 'denoise':\\n if opencv:\\n self.edit_panel_object = EditDenoiseImage(owner=self, imagefile=self.photo, image_x=self.viewer.edit_image.original_width, image_y=self.viewer.edit_image.original_height)\\n else:\\n self.edit_panel = 'main'\\n app = App.get_running_app()\\n app.message(\\\"Could Not Denoise, OpenCV Not Found\\\")\\n elif self.edit_panel == 'crop':\\n self.edit_panel_object = EditCropImage(owner=self, image_x=self.viewer.edit_image.original_width, image_y=self.viewer.edit_image.original_height)\\n self.viewer.edit_image.crop_controls = self.edit_panel_object\\n elif self.edit_panel == 'rotate':\\n self.edit_panel_object = EditRotateImage(owner=self)\\n elif self.edit_panel == 'convert':\\n if self.view_image:\\n self.edit_panel_object = EditConvertImage(owner=self)\\n else:\\n self.edit_panel_object = EditConvertVideo(owner=self)\\n edit_panel_container.change_panel(self.edit_panel_object)\\n else:\\n if self.edit_panel_object:\\n self.edit_panel_object.save_last()\\n self.viewer.edit_mode = self.edit_panel\\n edit_panel_container = self.ids['panelEdit']\\n edit_panel_container.change_panel(None)\\n self.edit_panel_object = EditNone(owner=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94734507f6c6459167fbcbc4857544f\",\n \"score\": \"0.53454703\",\n \"text\": \"def GetCameraMatrix(self,pMatrix,pType,pEvalInfo):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82508bdd10def3c51ce2f7888c8b3081\",\n \"score\": \"0.53391844\",\n \"text\": \"def getCameraImage(self):\\n # compute eye and target position for viewMatrix\\n pos, orn, _, _, _, _ = p.getLinkState(self.robotId, self.CAMERA_JOINT_INDEX)\\n cameraPos = np.array(pos)\\n cameraMat = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3).T\\n eyePos = cameraPos + self.CAMERA_EYE_SCALE * cameraMat[self.CAMERA_EYE_INDEX]\\n targetPos = cameraPos + self.CAMERA_TARGET_SCALE * cameraMat[self.CAMERA_EYE_INDEX]\\n up = self.CAMERA_UP_SCALE * cameraMat[self.CAMERA_UP_INDEX]\\n # p.addUserDebugLine(eyePos, targetPos, lineColorRGB=[1, 0, 0], lifeTime=0.1) # red line for camera vector\\n # p.addUserDebugLine(eyePos, eyePos + up * 0.5, lineColorRGB=[0, 0, 1], lifeTime=0.1) # blue line for up vector\\n viewMatrix = p.computeViewMatrix(eyePos, targetPos, up)\\n image = p.getCameraImage(self.CAMERA_PIXEL_WIDTH, self.CAMERA_PIXEL_HEIGHT, viewMatrix, self.projectionMatrix, shadow=1, lightDirection=[1, 1, 1], renderer=p.ER_BULLET_HARDWARE_OPENGL)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d15b49270b5f47e719280823fead278\",\n \"score\": \"0.5325108\",\n \"text\": \"def set_camera(self, xyz, hpr, tracking = 'none', body_to_track = 0 ):\\n\\n assert len(xyz) == 3\\n assert len(hpr) == 3\\n x, y, z = xyz\\n h, p, r = hpr\\n\\n if tracking == 'none':\\n tracking = 0\\n elif tracking == 'pan':\\n tracking = 1\\n elif tracking == 'follow':\\n tracking = 2\\n else:\\n tracking = 0\\n\\n # C.C. parameters oare singular values so \\n # must write out vectors. May change in the future\\n\\n self._send_parameter('CameraX', x)\\n self._send_parameter('CameraY', y)\\n self._send_parameter('CameraZ', z)\\n\\n self._send_parameter('CameraH', h)\\n self._send_parameter('CameraP', p)\\n self._send_parameter('CameraR', r)\\n\\n self._send_parameter( 'CameraTracking', tracking )\\n self._send_parameter( 'CameraBody', body_to_track )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d77cde5c919e359007f67bb74dcbfe6\",\n \"score\": \"0.5309324\",\n \"text\": \"def setup_camera(app):\\n camera_data = app.getCameraData()\\n camera_data.eye = agx.Vec3(0.5, 0, 0.25)\\n camera_data.center = agx.Vec3(0, 0, 0)\\n camera_data.up = agx.Vec3(0, 0, 1)\\n camera_data.nearClippingPlane = 0.1\\n camera_data.farClippingPlane = 5000\\n app.applyCameraData(camera_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb574e3f310f635bba5fccaf1ab6f305\",\n \"score\": \"0.530484\",\n \"text\": \"def edit_camera_xform(world_fn,xform=None,title=None):\\n world = WorldModel()\\n world.readFile(world_fn)\\n world.readFile(\\\"camera.rob\\\")\\n robot = world.robot(0)\\n sensor = robot.sensor(0)\\n if xform is not None:\\n sensing.set_sensor_xform(sensor,xform)\\n vis.createWindow()\\n if title is not None:\\n vis.setWindowTitle(title)\\n vis.resizeWindow(1024,768)\\n vis.add(\\\"world\\\",world)\\n vis.add(\\\"sensor\\\",sensor)\\n vis.add(\\\"sensor_xform\\\",sensing.get_sensor_xform(sensor,robot))\\n vis.edit(\\\"sensor_xform\\\")\\n def update_sensor_xform():\\n sensor_xform = vis.getItemConfig(\\\"sensor_xform\\\")\\n sensor_xform = sensor_xform[:9],sensor_xform[9:]\\n sensing.set_sensor_xform(sensor,sensor_xform)\\n vis.loop(callback=update_sensor_xform)\\n sensor_xform = vis.getItemConfig(\\\"sensor_xform\\\")\\n return sensor_xform[:9],sensor_xform[9:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9647c535ed24dbd52849ac734bbb9e0b\",\n \"score\": \"0.5297465\",\n \"text\": \"def GetDisplayableGeometryInCameraFrustum(self,pModelList,pCamera):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec1785bd80767a4a0c3404d99f6789c\",\n \"score\": \"0.5283774\",\n \"text\": \"def camera(self):\\n if self.position in self.painted:\\n return self.painted[self.position]\\n\\n return BLACK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c918af3f3e00efefda4d5e34986aa3\",\n \"score\": \"0.5278841\",\n \"text\": \"def toggleCamera(self):\\n self.groundCamEnabled = not self.groundCamEnabled\\n if (self.groundCamEnabled):\\n self.groundCam()\\n else:\\n self.frontCam()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0618c5d249dda76088ea0c466cc5c768\",\n \"score\": \"0.5277198\",\n \"text\": \"def test_custom_camera(self):\\n layout = Layout([\\n Map(Layer(Source(SOURCE)), viewport={'zoom': 2}),\\n Map(Layer(Source(SOURCE)))\\n ], viewport={'zoom': 5})\\n\\n assert layout._layout[0].get('camera') == {\\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 2}\\n assert layout._layout[1].get('camera') == {\\n 'bearing': None, 'center': None, 'pitch': None, 'zoom': 5}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a46e5a31be9d53d4bfe6b1bdac67d80\",\n \"score\": \"0.5274758\",\n \"text\": \"def patch_camera(set_provider=CAM.EXPLICIT):\\n CoreCamera = core_camera.Camera\\n set_provider = set_provider or CAM.IMPLICIT.get(PLATFORM)\\n\\n if CAM.ENABLE and set_provider:\\n for provider in core_camera.providers:\\n if set_provider == provider[0]:\\n core_camera.providers = (provider,)\\n _CoreCamera = core_select_lib(\\\"camera\\\", core_camera.providers)\\n CoreCamera = _CoreCamera or CoreCamera\\n break\\n\\n class PatchedCoreCamera(CoreCamera):\\n\\n def start(self, *args, **kwargs):\\n ret = super(PatchedCoreCamera, self).start(*args, **kwargs)\\n\\n attrs = map(lambda attr: getattr(self, attr, None),\\n [\\\"_update_ev\\\", \\\"fps\\\"])\\n if all(attrs) and self.fps > 1.0:\\n self._update_ev.cancel()\\n self._update_ev = Clock.schedule_interval(\\n self._update, 1.0 / self.fps\\n )\\n\\n return ret\\n\\n core_camera.Camera = PatchedCoreCamera\\n uix_camera.CoreCamera = core_camera.Camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab1b61a0d5c391798d02db4b165d446\",\n \"score\": \"0.52688134\",\n \"text\": \"def SetEnvDisplay(self, *args):\\n return _MaxPlus.CameraObject_SetEnvDisplay(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b332cbebfc4567a9f05cf381e197b646\",\n \"score\": \"0.52596724\",\n \"text\": \"def __init__(self, screen_size=(800, 600), clear_color=(0, 0, 0),\\n camera_initial_position=(0, 0, 0),\\n camera_initial_direction=(0, 0, 1)):\\n super(Graphx, self).__init__()\\n pygame.init()\\n screen = pygame.display.set_mode(screen_size, HWSURFACE|OPENGL|DOUBLEBUF)\\n self.camera = Camera(camera_initial_position)\\n self.screen_width, self.screen_height = self.screen_size = screen_size\\n\\n glEnable(GL_DEPTH_TEST)\\n\\n glShadeModel(GL_FLAT)\\n glClearColor(*clear_color)\\n\\n glEnable(GL_COLOR_MATERIAL)\\n\\n glEnable(GL_LIGHTING)\\n glEnable(GL_LIGHT0)\\n glLight(GL_LIGHT0, GL_POSITION, (0, 1, 1, 0))\\n\\n self.resize(*screen_size)\\n\\n glMaterial(GL_FRONT, GL_AMBIENT, (0.1, 0.1, 0.1, 1.0))\\n glMaterial(GL_FRONT, GL_DIFFUSE, (1.0, 1.0, 1.0, 1.0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd8f71a909982d629e7c2a7ed7744015\",\n \"score\": \"0.5256443\",\n \"text\": \"def GetEnvDisplay(self):\\n return _MaxPlus.CameraObject_GetEnvDisplay(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c96a9cdfda1cb6db825c82a2e9a992a\",\n \"score\": \"0.5232319\",\n \"text\": \"def render(self, camera_pose, target_id=\\\"\\\", render_pc=True):\\n # Keep local to free OpenGl resources after use\\n renderer = pyrender.OffscreenRenderer(\\n viewport_width=self._width, viewport_height=self._height\\n )\\n\\n # add camera and light to scene\\n scene = self._scene.as_pyrender_scene()\\n scene.add(self._camera, pose=camera_pose, name=\\\"camera\\\")\\n light = pyrender.SpotLight(\\n color=np.ones(4),\\n intensity=3.0,\\n innerConeAngle=np.pi / 16,\\n outerConeAngle=np.pi / 6.0,\\n )\\n scene.add(light, pose=camera_pose, name=\\\"light\\\")\\n\\n # render the full scene\\n color, depth = renderer.render(scene)\\n\\n segmentation = np.zeros(depth.shape, dtype=np.uint8)\\n\\n # hide all objects\\n for node in scene.mesh_nodes:\\n node.mesh.is_visible = False\\n\\n # Render only target object and add to segmentation mask\\n for node in scene.mesh_nodes:\\n if node.name == target_id:\\n node.mesh.is_visible = True\\n _, object_depth = renderer.render(scene)\\n mask = np.logical_and(\\n (np.abs(object_depth - depth) < 1e-6), np.abs(depth) > 0\\n )\\n segmentation[mask] = 1\\n\\n for node in scene.mesh_nodes:\\n node.mesh.is_visible = True\\n\\n if render_pc:\\n pc = self._to_pointcloud(depth)\\n else:\\n pc = None\\n\\n return color, depth, pc, segmentation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c33dd1093734703e927beeba899442b\",\n \"score\": \"0.5226646\",\n \"text\": \"def setupCamera(self):\\n self.camera = piggyphoto.Camera()\\n self.camera.leave_locked()\\n\\n self.camRefresh = QTimer()\\n self.camRefresh.timeout.connect(self.displayCameraPreview)\\n self.camRefresh.setInterval(100)\\n self.camRefresh.start()\\n self.camHibernate.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f07bdf228b54f0735b4d114788d1863e\",\n \"score\": \"0.52230614\",\n \"text\": \"def set_view(self, x=0, y=-5, z=0, roll=0, pitch=0, yaw=0):\\n sy = math.sin(yaw)\\n cy = math.cos(yaw)\\n sp = math.sin(pitch)\\n cp = math.cos(pitch)\\n sr = math.sin(roll)\\n cr = math.cos(roll)\\n self.cameraMatrix = [\\n cy * cp, cy * sp * sr - sy * cr, cy * sp * cr + sy * sr, 0,\\n sy * cp, sy * sp * sr + cy * cr, sy * sp * cr - cy * sr, 0,\\n -sp, cp * sr, cp * cr, 0,\\n x, y, z, 1\\n ]\\n self.SetMatrix(udRenderTargetMatrix.Camera, self.cameraMatrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a71f70a2bdb45b3630d58d80daaf16d\",\n \"score\": \"0.52169925\",\n \"text\": \"def FrameCurrentCameraWithModels(self,pAll):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e1c5308686ee88de76ee4dfa5d7fa5\",\n \"score\": \"0.5215392\",\n \"text\": \"def set_camera(self, camera_x, camera_y, camera_z,\\n mrot_x, mrot_y):\\n for i in range(len(self.vertices)):\\n vert = self.vertices[i]\\n xpos = vert[0] - camera_x\\n ypos = vert[1] - camera_y\\n zpos = vert[2]\\n\\n # rotate around Y axis\\n ppos = mrot_y[0] * xpos + mrot_y[1] * ypos \\\\\\n + mrot_y[2] * zpos\\n qpos = mrot_y[3] * xpos + mrot_y[4] * ypos \\\\\\n + mrot_y[5] * zpos\\n rpos = mrot_y[6] * xpos + mrot_y[7] * ypos \\\\\\n + mrot_y[8] * zpos\\n\\n # rotate around X axis\\n self.pos[i][0] = mrot_x[0] * ppos + mrot_x[1] * qpos\\\\\\n + mrot_x[2] * rpos\\n self.pos[i][1] = mrot_x[3] * ppos + mrot_x[4] * qpos\\\\\\n + mrot_x[5] * rpos\\n self.pos[i][2] = mrot_x[6] * ppos + mrot_x[7] * qpos\\\\\\n + mrot_x[8] * rpos - camera_z\\n\\n for surface in self.surfaces:\\n surface.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5665193b2216a7cf4e850517bc9c2e67\",\n \"score\": \"0.52153534\",\n \"text\": \"def __load_camera_capture_screen(self):\\n self.fc.flow_load_home_screen()\\n self.home.select_bottom_nav_btn(self.home.NAV_CAMERA_SCAN_BTN)\\n if self.camera_scan.verify_capture_no_access_screen(raise_e=False):\\n self.camera_scan.select_camera_access_allow()\\n self.camera_scan.check_run_time_permission()\\n self.camera_scan.verify_capture_screen()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b53fc43a718455cafc8ca0eb39ab82\",\n \"score\": \"0.5209333\",\n \"text\": \"def world_to_camera(self, points):\\n points = np.atleast_2d(points)\\n return tt.transform_points(points, self._world_to_camera_4x4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a37c69fdb9901d20a3fc74da83e02e1\",\n \"score\": \"0.52090925\",\n \"text\": \"def load_camera(hutch, cam_name):\\n # Get config for cam_name\\n try:\\n with open(f'/reg/g/pcds/pyps/config/{hutch}/camviewer.cfg', 'r') as f:\\n lines = f.readlines()\\n except Exception as e:\\n exit('Could not open camera config file, '\\n f'/reg/g/pcds/pyps/config/{hutch}/camviewer.cfg', e)\\n cam_line = [line for line in lines if cam_name.upper() in line]\\n if not cam_line:\\n exit('Did not find camera in camera config file, '\\n f'/reg/g/pcds/pyps/config/{hutch}/camviewer.cfg, '\\n 'check spelling of camera argument.')\\n cam_info = interpret_lines(cam_line)\\n camera = build_cam(*cam_info[0])\\n return camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e06c312afd34bba36a8461c5ce0fc4\",\n \"score\": \"0.5192894\",\n \"text\": \"def resetViewerCamera(self):\\n self._renderer.resetCamera()\\n self._cameraLensCombo.setCurrentIndex(self._renderer.activeSceneCamera().lens)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb5872a68d63bc51495f281feff7d634\",\n \"score\": \"0.51910275\",\n \"text\": \"def _update_camera(self, camera_center):\\n self._world_tl_to_world_camera_rel.offset = (\\n -self._world_to_world_tl.fwd_pt(camera_center) *\\n self._world_tl_to_world_camera_rel.scale)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065b33b4c319c4b8d1a8b40a31c7d8a2\",\n \"score\": \"0.51874024\",\n \"text\": \"def resolve_camera(self, info, camera_id):\\n return camera_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc21617b9d52eba819655f60432a360\",\n \"score\": \"0.51761484\",\n \"text\": \"def filterCamera():\\n logger.debug(\\\"filterCamera\\\")\\n\\n PYFILTER_MANAGER.run_operations_for_stage(\\\"filterCamera\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d332e5580379bacf1d6b9f8ab9c9ff0a\",\n \"score\": \"0.5172844\",\n \"text\": \"def set_custom_camera(self, camera_settings):\\n camera = self.ren.GetActiveCamera()\\n cs = camera_settings\\n if cs.clippingRange and cs.focalPoint and cs.position and cs.viewUp:\\n camera.SetClippingRange(cs.clippingRange)\\n camera.SetFocalPoint(cs.focalPoint)\\n camera.SetPosition(cs.position)\\n camera.SetViewUp(cs.viewUp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ccf716f68b7c17c02dadcece842ebcd\",\n \"score\": \"0.5148847\",\n \"text\": \"def testcam(self):\\n print(self.dictionnaire_camera_pipe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dec95da736960c4c603123eae2546aa\",\n \"score\": \"0.51484096\",\n \"text\": \"def setCamera(self, height = 50):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7761b2705d04388adb897b1bf4e93e4\",\n \"score\": \"0.51424915\",\n \"text\": \"def cam_stream(self):\\n if self.cam.query_image():\\n self.sight = self.cam.get_image(self.sight)\\n self.sight = pygame.transform.flip(self.sight, True, False)\\n self.screen.blit(self.sight, (0,0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e8fe41e6341f9a21e57318cbb2eae81\",\n \"score\": \"0.512114\",\n \"text\": \"def Render( self, mode):\\n shaders.glUseProgram(self.shader)\\n glutReshapeWindow( 512 , 512 )\\n testcontext.ViewPlatform.setPosition(self, [0,0,512])\\n try:\\n self.vbo.bind()\\n try:\\n quad = gluNewQuadric()\\n glEnableClientState(GL_VERTEX_ARRAY);\\n glVertexPointerf( self.vbo )\\n glDrawArrays(glusphere(quad,1,5,5), 0, len(self.vbo))\\n finally:\\n self.vbo.unbind()\\n glDisableClientState(GL_VERTEX_ARRAY);\\n finally:\\n shaders.glUseProgram( 0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2280d9f7b34996019a30b7f2d71b9f3d\",\n \"score\": \"0.51190746\",\n \"text\": \"def _init_camera(\\n self, feature_dimensions, map_size, camera_width_world_units):\\n if not map_size or not camera_width_world_units:\\n raise ValueError(\\n \\\"Either pass the game_info with raw enabled, or map_size and \\\"\\n \\\"camera_width_world_units in order to use feature_units.\\\")\\n map_size = point.Point.build(map_size)\\n self._world_to_world_tl = transform.Linear(point.Point(1, -1),\\n point.Point(0, map_size.y))\\n self._world_tl_to_world_camera_rel = transform.Linear(offset=-map_size / 4)\\n world_camera_rel_to_feature_screen = transform.Linear(\\n feature_dimensions.screen / camera_width_world_units,\\n feature_dimensions.screen / 2)\\n self._world_to_feature_screen_px = transform.Chain(\\n self._world_to_world_tl,\\n self._world_tl_to_world_camera_rel,\\n world_camera_rel_to_feature_screen,\\n transform.PixelToCoord())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edeaa3c8cb50168047a4ca7d35b5f330\",\n \"score\": \"0.51099044\",\n \"text\": \"def _set_camera(self, request: client_pb2.SetCameraRequest):\\n if not self._env:\\n # The client will not wait for a response.\\n return\\n camera_info = self._env.set_camera(request.index)\\n if not camera_info:\\n return\\n self._send_response(\\n set_camera=client_pb2.SetCameraResponse(\\n index=camera_info.index, name=camera_info.name))\\n if self._paused or self._sync:\\n # Update the image if the environment is paused or in sync mode. The\\n # reward will be 0, but this is a cosmetic issue. In async mode, it will\\n # be updated with the next step.\\n _, self._image = self._get_image()\\n self._send_response(\\n step=client_pb2.StepResponse(\\n image=self._image,\\n episode_index=self._episode_index + 1,\\n episode_steps=self._episode_steps))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40dfc63ad2cbe459ee787d10e741e1b\",\n \"score\": \"0.51069397\",\n \"text\": \"def render(self):\\n\\t\\tself.theater.draw()\\n\\t\\tif self.recorder.recording == 'Video':\\n\\t\\t\\tchanges = self.rootCamera.draw(True)\\n\\t\\t\\tself.recorder.addFrame(changes)\\n\\t\\telse:\\n\\t\\t\\tself.rootCamera.draw(False)\\n\\t\\t\\n\\t\\tdirtyGui= self.gui.update(pygame.display.get_surface());\\n\\t\\tif dirtyGui:\\n\\t\\t\\tpygame.display.update(dirtyGui);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ddf01de96fa88397c9c3a2046b5306\",\n \"score\": \"0.5106569\",\n \"text\": \"def EvalCameraState(self, *args):\\n return _MaxPlus.CameraObject_EvalCameraState(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81f2e6331c196b8d5d4a212f4438728\",\n \"score\": \"0.5087296\",\n \"text\": \"def setup_camera(self):\\n\\n camera_transform = carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15))\\n self.camera = self.world.spawn_actor(self.camera_blueprint(), camera_transform, attach_to=self.car)\\n weak_self = weakref.ref(self)\\n self.camera.listen(lambda image: weak_self().set_image(weak_self, image))\\n\\n calibration = np.identity(3)\\n calibration[0, 2] = VIEW_WIDTH / 2.0\\n calibration[1, 2] = VIEW_HEIGHT / 2.0\\n calibration[0, 0] = calibration[1, 1] = VIEW_WIDTH / (2.0 * np.tan(VIEW_FOV * np.pi / 360.0))\\n self.camera.calibration = calibration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f97aa211e21c611ea4d2473a82a5ac0\",\n \"score\": \"0.5086536\",\n \"text\": \"def render(self, object_pose):\\n oTc = np.linalg.inv(object_pose)\\n oTopengl = oTc @ self.cTopengl\\n self.scene.set_pose(self.camera_node, oTopengl)\\n # TODO: figure out DEPTH_ONLY rendering mode with OSMesa backend\\n # DEPTH_ONLY + OSMesa does not work currently\\n # so we have to render color also :(\\n _, depth = self.renderer.render(self.scene)\\n return self.flip_fn(depth)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e88619a98831669fe7aa362224c1f01\",\n \"score\": \"0.5077818\",\n \"text\": \"def left_camera(self):\\n return self._left_camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6acefef3f2fb1c70938917b89cd2ff3\",\n \"score\": \"0.5076684\",\n \"text\": \"def getViewCameraName(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec3f95737d429776fecfde7f1092e6c\",\n \"score\": \"0.50761527\",\n \"text\": \"def __init__(self, width, height, depth = 3, title = \\\"Camera View\\\", parent = None, visible = 0, async=1):\\n PyrobotImage.__init__(self, width, height, depth, 0)\\n self.app = 0\\n self.title = title\\n self.filterMode = 1\\n self.callbackList = []\\n self.filterResults = []\\n self.callbackTextList = []\\n Device.__init__(self, 'camera', async=async)\\n # specific camera type will define self.rgb = (0, 1, 2) offsets\\n # and self.format = \\\"RGB\\\", for example\\n self.lastWindowUpdate = 0\\n self.updateWindowInterval = 0.0 # update window once a second\\n self.update() # call it once to initialize\\n self.image = []\\n self.data = self.data\\n self.grayscale = []\\n self.height = self.height\\n self.width = self.width\\n self.depth = self.depth\\n self.filters = self.callbackTextList\\n self.lastX, self.lastY = 0, 0\\n # Required:\\n self.startDevice()\\n # make these visible by default\\n if visible and self.vision:\\n self.makeWindow()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994390,"cells":{"query_id":{"kind":"string","value":"283f38a36cb317c6e166ea440bad40d8"},"query":{"kind":"string","value":"Stop and then start the daemon."},"positive_passages":{"kind":"list like","value":[{"docid":"511a9b1031de48879200e6d9c4222065","score":"0.0","text":"def restart(self):\n self.stop()\n self.start()","title":""}],"string":"[\n {\n \"docid\": \"511a9b1031de48879200e6d9c4222065\",\n \"score\": \"0.0\",\n \"text\": \"def restart(self):\\n self.stop()\\n self.start()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"24fdea183ea66334ccc720419bfc7b08","score":"0.7850297","text":"def start(self):\r\n if self.go_daemon:\r\n if not self.pidfile:\r\n self.log.error(\"Daemon needs pidfile\")\r\n sys.exit(1)\r\n self.run_func_safely(self._boot_daemon)","title":""},{"docid":"084f2c49f80b1d662b33b6191e21d676","score":"0.771307","text":"def run_daemon(self):\n if self.pidpath is None:\n print(\"No valid PID path.\")\n return\n self.ctx = DaemonContext(\n stdin=self.stdin,\n stdout=self.stdout,\n stderr=self.stderr,\n pidfile=LockFile(self.pidpath),\n\n )\n if len(sys.argv) > 1:\n if sys.argv[1] == \"start\":\n with self.ctx:\n self.run()\n elif sys.argv[1] == \"stop\":\n self._closectx()\n elif sys.argv[1] == \"restart\":\n self._closectx()\n with self.ctx:\n self.run()\n else:\n print(\"Unrecognised argument.\")\n else:\n self.run() # Run without daemon","title":""},{"docid":"b9e90a4d2cfe8898921d55d26fbf287e","score":"0.7563267","text":"def start(self):\n\n self.startup_clean()\n if self.as_daemon:\n self.daemon = Daemonize(app=config['GENERAL']['app_name'], pid=config['GENERAL']['pid'], action=self.run)\n self.daemon.start()\n else:\n self.run()","title":""},{"docid":"2cea1fa4e14ec0c6f0319090c2c4f337","score":"0.7385666","text":"def start(self):\n self.check_pid()\n self.add_signal_handlers()\n self.start_logging()\n\n try:\n self.check_pid_writable()\n self.check_server_accessibility()\n self.daemonize()\n except:\n logging.exception(\"failed to start due to an exception\")\n raise\n\n self.write_pid()\n try:\n try:\n self.run()\n except (KeyboardInterrupt, SystemExit):\n pass\n except OSError as exc:\n logging.exception(str(exc))\n pass\n except:\n logging.exception(\"stopping with an exception\")\n raise\n finally:\n self.remove_pid()","title":""},{"docid":"c62370fad04cd6b6d13ca79fd4037a84","score":"0.7352018","text":"def start(self):\n # Check for a pidfile to see if the daemon already runs\n try:\n pf = open(self._pid_file, 'r')\n pid = int(pf.read().strip())\n pf.close()\n except IOError:\n pid = None\n\n if pid:\n message = 'pidfile {} already exist. '\\\n 'Daemon already running?\\n'\n sys.stderr.write(message.format(self._pid_file))\n sys.exit(1)\n\n # Start the daemon\n self._daemonize()\n self.run()","title":""},{"docid":"2fe4945d09ca18cb3debd374f3344bef","score":"0.7237522","text":"def run(self):\n self.daemonize(write_pid=False)","title":""},{"docid":"f91126c5e2343e81c038c1b0aff66b9f","score":"0.6984598","text":"def no_daemon(self, **kwargs):\r\n kwargs['daemon'] = False\r\n return self.start(**kwargs)","title":""},{"docid":"ef33fb2f9fd5152892a465292a85c299","score":"0.6978491","text":"def run(self):\n\n if len(sys.argv) != 2:\n print('Usage: %s [start|stop]' % sys.argv[0])\n exit(1)\n\n # Daemon was commanded to start\n if sys.argv[1] == 'start':\n try:\n self.daemonize( self.pf_name )\n except Exception as err:\n print \"Fatal Exception: \"\n print err.args\n exit(1)\n\n self.connect_rx()\n\n # Daemon was commanded to stop\n elif sys.argv[1] == 'stop':\n if os.path.exists( self.pf_name ):\n pidFile = open( self.pf_name , 'r')\n pid = pidFile.read()\n os.kill(int(pid), signal.SIGTERM)\n else:\n print('Daemon not running!')\n exit(1)\n else:\n print('Unknown Command!')\n exit(1)\n\n count = 0\n\n self.build_dev_list()\n\n # event loop\n while True:\n\n count += 1\n self.listen()\n self.process_cmds()\n time.sleep(1)\n\n # Don't flood the network with requests\n if count >= 120:\n self.build_dev_list()\n count = 0","title":""},{"docid":"67223a0297ea9964a702618761923570","score":"0.68387085","text":"def start(self):\n if not self._installed:\n self.install()\n if not self._enabled:\n self.enable()\n if not self.is_running():\n self._systemctl(\"start\")\n self._stopped = False","title":""},{"docid":"3049e62a4275e4064cf0401d1e84100d","score":"0.6807076","text":"def _goDaemon():\n\n _doFork()\n os.chdir(\"/\") \n os.setsid() \n os.umask(0) \n _doFork()","title":""},{"docid":"37615df76de8e9077666d0aa723c2484","score":"0.6791836","text":"def daemonize(self):\n\n signal.signal(signal.SIGTERM, self.term_handler)\n signal.signal(signal.SIGINT, self.term_handler)\n\n if not self.settings.daemonize:\n self.log.init_log()\n return\n\n self.log.write(log.DEBUG1, \"daemonizing\")\n\n try:\n pid = os.fork()\n except OSError:\n # self.log.write(log.EMERGENCY, \"Failed to fork daemon\")\n raise\n\n if pid == 0:\n os.setsid()\n try:\n pid = os.fork()\n except OSError:\n # self.log.write(log.EMERGENCY, \"Failed to fork daemon\")\n raise\n\n if pid == 0:\n os.chdir(\"/tmp\")\n os.umask(0)\n else:\n exit(0)\n else:\n exit(0)\n\n self.settings.is_daemonized = True\n self.log.init_log()\n self.log.write(log.MESSAGE, \"daemonized.\")","title":""},{"docid":"05cd20f4be14d2a6257c2b84b515b848","score":"0.67631406","text":"def daemonize(self):\n\n signal.signal(signal.SIGTERM, self.term_handler)\n signal.signal(signal.SIGINT, self.term_handler)\n try:\n pid = os.fork()\n except OSError:\n print(\"Failed to fork daemon\")\n raise\n\n if pid == 0:\n os.setsid()\n try:\n pid = os.fork()\n except OSError:\n print(\"Failed to fork daemon\")\n raise\n\n if pid == 0:\n os.chdir(\"/tmp\")\n os.umask(0)\n else:\n exit(0)\n else:\n exit(0)","title":""},{"docid":"c4c684bfd171f17004e9a3dcc9e3543a","score":"0.6710145","text":"def start_server(self):\r\n if not self._config[\"enable\"]:\r\n print(\"Daemon not enabled. Check configuration!\")\r\n return\r\n\r\n # self.start_kernel('/Users/goanpeca/miniconda3/envs/zeroconf')\r\n logger.info(\"Starting daemon!\")\r\n logging.info(\"Starting daemon!\")\r\n context = zmq.Context()\r\n socket = context.socket(zmq.REP)\r\n\r\n self._server_port = find_free_port()\r\n socket.bind(f\"tcp://*:{self._server_port}\")\r\n print(f\"Bound to port {self._server_port}\")\r\n self.register_service()\r\n\r\n while self._running:\r\n self._busy = True\r\n message = socket.recv()\r\n\r\n try:\r\n reply = self.process_request(message)\r\n except Exception as err:\r\n reply = json.dumps({\"error\": str(err)})\r\n print(err)\r\n\r\n socket.send_string(reply)\r\n self._busy = False","title":""},{"docid":"33f961708a0ee48f2255f11f69c0b211","score":"0.6618145","text":"def start(self):\n if self.thread is not None:\n return\n self._pleaseStop=False\n self.thread = threading.Thread(target=self.run)\n self.thread.daemon=True\n self.log.debug(\"Starting\")\n self.thread.start()","title":""},{"docid":"8f9ca35353d8a08ae229017dba009574","score":"0.65436524","text":"def start(self):\r\n self.thread = threading.Thread(target=self._run)\r\n self.thread.setDaemon(True)\r\n self.thread.start()","title":""},{"docid":"bb068aecdef791277e8785f2de9ec839","score":"0.6530049","text":"def startDaemon(self, progName, pidFile=None):\n daemon.run(progName, pidFile)","title":""},{"docid":"30a95e300f7253aec257c5ae5befded7","score":"0.64391774","text":"def start(self):\n if not self.is_started():\n self._restart()","title":""},{"docid":"9aaeb7356f1c0b89faa1cde55034d24c","score":"0.6403249","text":"def start(self):\n if not self.running:\n self.exit.clear()\n self._remove_stop_items()\n self.running = True\n self.thread = threading.Thread(target=self.run)\n self.thread.start()","title":""},{"docid":"0723c768928f4829e19ad1b6e134b0f7","score":"0.6402869","text":"def run_daemon():\n # <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>\n print(\"Running Slackify daemon.\")\n # <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>\n # Run slackify_reminder() as a daemon.\n with daemon.DaemonContext(stdout=sys.stdout,\n stderr=sys.stdout,\n pidfile=filelock.FileLock('/tmp/slackify.pid', timeout=0)):\n slackify_reminder()","title":""},{"docid":"27ff0ebdde00161135049c560324e7dc","score":"0.63773906","text":"def daemonize():\r\n\r\n # launch new process, kill parent\r\n pid = os.fork()\r\n if pid != 0:\r\n os._exit(0)\r\n\r\n # start new session\r\n os.setsid()\r\n\r\n # stop i/o\r\n fd = os.open(\"/dev/null\", os.O_RDWR)\r\n os.dup2(fd, 0)\r\n os.dup2(fd, 1)\r\n os.dup2(fd, 2)\r\n if fd > 2:\r\n os.close(fd)","title":""},{"docid":"26ebb2d96d79ae2f85bd5f2de7efddc6","score":"0.6365857","text":"def run_daemon(self):\n\n # daemonize if possible\n if platform.system() == \"Windows\":\n # Nothing we can do; subproc was launched with\n # creationflags=CREATE_NEW_PROCESS_GROUP, hopefully that's enough\n pass\n else:\n # https://stackoverflow.com/questions/6011235/run-a-program-from-python-and-have-it-continue-to-run-after-the-script-is-kille\n #\n # Note that subproc has been created with preexec_fn=os.setsid, so\n # first fork has already occurred\n #\n pid = os.fork()\n if pid > 0:\n sys.exit(0)\n\n logger = self._init_logging()\n\n # somewhere for the daemon to store stateful info\n state = {\n \"logger\": logger\n }\n\n # copy variants into cache\n try:\n while True:\n keep_running = self._run_daemon_step(state)\n if not keep_running:\n break\n except Exception:\n logger.exception(\"An error occurred while adding variants to the cache\")\n raise\n\n # do some cleanup\n if config.package_cache_clean_limit > 0:\n try:\n self.clean(time_limit=config.package_cache_clean_limit)\n except Exception:\n logger.exception(\"An error occurred while cleaning the cache\")","title":""},{"docid":"6e07013ccdb7882125528be10b4914a1","score":"0.6365708","text":"def start(self):\n if not self.is_running:\n self._impl.start()\n self._is_running = True","title":""},{"docid":"cb7dafd3d7ac9db34ac861fcd0a79111","score":"0.6324681","text":"def run(self):\n if not DEBUG and not self.is_vassal:\n # No fork in debug mode and not in uwsgi vassal mode.\n self.daemonize()\n else:\n print('PiBlasterWorker running in debug or as vassal')\n self.uploader.start()\n self.idler.start()\n self.daemon_loop()\n MPDService.stop_idler()","title":""},{"docid":"2021ac70c6c6bcf3b2bd2a5046c2c9d7","score":"0.63153154","text":"def deamonize():\n d = Daemonizer(cherrypy.engine)\n d.subscribe()\n PIDFile(cherrypy.engine, 'daemon.pid').subscribe()","title":""},{"docid":"a79a357be3cbdfc9ff8427e60267bcc1","score":"0.630865","text":"def run_daemon(self):\n proc = Process(target=self._run, name=\"alchemy-sender\",)\n proc.start()\n proc.join()","title":""},{"docid":"25f5a90e75d470eed46dd86997ba43a9","score":"0.62992454","text":"def daemonize(self):\n try:\n pid = os.fork()\n if pid > 0:\n # exit first parent\n sys.exit(0)\n except OSError, e:\n sys.stderr.write(\"fork #1 failed: %d (%s)\\n\" % (e.errno, e.strerror))\n sys.exit(1)\n\n # decouple from parent environment\n os.chdir(\"/\")\n os.setsid()\n os.umask(0)\n\n # do second fork\n try:\n pid = os.fork()\n if pid > 0:\n # exit from second parent\n sys.exit(0)\n except OSError, e:\n sys.stderr.write(\"fork #2 failed: %d (%s)\\n\" % (e.errno, e.strerror))\n sys.exit(1)\n\n # write pidfile\n atexit.register(self.delpid)\n pid = str(os.getpid())\n logger.info(\"Daemon running as PID %s\", pid)\n file(self.pidfile, 'w+').write(\"%s\\n\" % pid)","title":""},{"docid":"3b9fdd5e1ba19da7da09fba455d2e1af","score":"0.6270956","text":"def start(self):\n import threading\n threading.Thread(target=self.run_in_thread, daemon=True).start()","title":""},{"docid":"f2b2db8d029b32361ce454fb73bebbe0","score":"0.62333083","text":"def start(self):\n self.background = spawn(self._run)","title":""},{"docid":"2003056cdfb463bac7ca3eb7a80fa714","score":"0.6220807","text":"def start(self):\n if self.__started is True:\n # No need to stop twice.\n return\n self.__stop_sign.acquire()\n self.__background.start()\n self.__started = True\n self.__stop_sign.release()","title":""},{"docid":"ea6f2358ddd59a26b69ddcdbec14021c","score":"0.62140447","text":"def start(self):\n logging.info(\"starting background listener thread\")\n for daemon in self._daemons:\n daemon.start()","title":""},{"docid":"9ef19e50e85f4a1c28d402ffffc3a20f","score":"0.6212811","text":"def run(self):\n log.debug('Starter::run()')\n try:\n Server(conf.port).run()\n except Exception as E:\n log.critical(E)","title":""},{"docid":"c7a9a8ff9466ca0eca63c259f87bf18d","score":"0.6200567","text":"def start_server(self):\n lsock = self.socket(self.listen_host, self.listen_port, False,\n self.prefer_ipv6,\n tcp_keepalive=self.tcp_keepalive,\n tcp_keepcnt=self.tcp_keepcnt,\n tcp_keepidle=self.tcp_keepidle,\n tcp_keepintvl=self.tcp_keepintvl)\n\n if self.daemon:\n keepfd = self.get_log_fd()\n keepfd.append(lsock.fileno())\n self.daemonize(keepfd=keepfd, chdir=self.web)\n\n self.started() # Some things need to happen after daemonizing\n\n # Allow override of signals\n original_signals = {\n signal.SIGINT: signal.getsignal(signal.SIGINT),\n signal.SIGTERM: signal.getsignal(signal.SIGTERM),\n signal.SIGCHLD: signal.getsignal(signal.SIGCHLD),\n }\n signal.signal(signal.SIGINT, self.do_SIGINT)\n signal.signal(signal.SIGTERM, self.do_SIGTERM)\n if not multiprocessing:\n # os.fork() (python 2.4) child reaper\n signal.signal(signal.SIGCHLD, self.fallback_SIGCHLD)\n else:\n # make sure that _cleanup is called when children die\n # by calling active_children on SIGCHLD\n signal.signal(signal.SIGCHLD, self.multiprocessing_SIGCHLD)\n\n last_active_time = self.launch_time\n try:\n while True:\n try:\n try:\n startsock = None\n pid = err = 0\n child_count = 0\n\n if multiprocessing:\n # Collect zombie child processes\n child_count = len(multiprocessing.active_children())\n\n time_elapsed = time.time() - self.launch_time\n if self.timeout and time_elapsed > self.timeout:\n self.msg('listener exit due to --timeout %s'\n % self.timeout)\n break\n\n if self.idle_timeout:\n idle_time = 0\n if child_count == 0:\n idle_time = time.time() - last_active_time\n else:\n idle_time = 0\n last_active_time = time.time()\n\n if idle_time > self.idle_timeout and child_count == 0:\n self.msg('listener exit due to --idle-timeout %s'\n % self.idle_timeout)\n break\n\n try:\n self.poll()\n\n ready = select.select([lsock], [], [], 1)[0]\n if lsock in ready:\n startsock, address = lsock.accept()\n else:\n continue\n except self.Terminate:\n raise\n except Exception:\n _, exc, _ = sys.exc_info()\n if hasattr(exc, 'errno'):\n err = exc.errno\n elif hasattr(exc, 'args'):\n err = exc.args[0]\n else:\n err = exc[0]\n if err == errno.EINTR:\n self.vmsg(\"Ignoring interrupted syscall\")\n continue\n else:\n raise\n\n if self.run_once:\n # Run in same process if run_once\n self.top_new_client(startsock, address)\n if self.ws_connection :\n self.msg('%s: exiting due to --run-once'\n % address[0])\n break\n elif multiprocessing:\n self.vmsg('%s: new handler Process' % address[0])\n p = multiprocessing.Process(\n target=self.top_new_client,\n args=(startsock, address))\n p.start()\n # child will not return\n else:\n # python 2.4\n self.vmsg('%s: forking handler' % address[0])\n pid = os.fork()\n if pid == 0:\n # child handler process\n self.top_new_client(startsock, address)\n break # child process exits\n\n # parent process\n self.handler_id += 1\n\n except (self.Terminate, SystemExit, KeyboardInterrupt):\n self.msg(\"In exit\")\n # terminate all child processes\n if multiprocessing and not self.run_once:\n children = multiprocessing.active_children()\n\n for child in children:\n self.msg(\"Terminating child %s\" % child.pid)\n child.terminate()\n\n break\n except Exception:\n exc = sys.exc_info()[1]\n self.msg(\"handler exception: %s\", str(exc))\n self.vmsg(\"exception\", exc_info=True)\n\n finally:\n if startsock:\n startsock.close()\n finally:\n # Close listen port\n self.vmsg(\"Closing socket listening at %s:%s\",\n self.listen_host, self.listen_port)\n lsock.close()\n\n # Restore signals\n for sig, func in original_signals.items():\n signal.signal(sig, func)","title":""},{"docid":"c6022bacdc93d6d41b1fc4467d4c4c5e","score":"0.6199782","text":"def start(self):\n self.running = True\n self.stopped = False\n self.thread.start()","title":""},{"docid":"c6022bacdc93d6d41b1fc4467d4c4c5e","score":"0.6199782","text":"def start(self):\n self.running = True\n self.stopped = False\n self.thread.start()","title":""},{"docid":"cabdfc2527f699f579c7a56d4041452b","score":"0.619683","text":"def daemonize(self):\n # Do first fork\n self.fork()\n\n # Decouple from parent environment\n self.dettach_env()\n # Do second fork\n self.fork()\n\n # Flush standart file descriptors\n sys.stdout.flush()\n sys.stderr.flush()\n\n #\n self.attach_stream('stdin', mode='r')\n self.create_pidfile()","title":""},{"docid":"1ef473a4367f08eee9fceb2e8b722517","score":"0.6194529","text":"def run(self):\n while 'program running':\n self._start.wait()\n if self._verbose:\n print('serving at port', self.port)\n self.httpd.serve_forever()","title":""},{"docid":"ec1785d39e4a7be074106f6a289597cf","score":"0.6190639","text":"def stop(self):\n\n if not os.path.exists(self._pid_file):\n sys.stderr.write(\n 'Pidfile {} does not exist; '\n 'Considered daemon is not running;\\n'.format(self._pid_file))\n sys.stderr.write('Stopping the daemon is failed')\n exit(1)\n\n # Get the pid from the pidfile\n pid = None\n try:\n with open(self._pid_file, 'r') as f:\n pid = int(f.read().strip())\n except IOError as e:\n sys.stderr.write(\n 'Error while reading pid from pidfile \"{}\": {}'\n .format(self._pid_file, e))\n sys.stderr.write('Stopping the daemon is failed\\n;')\n exit(1)\n\n try:\n cnt = 0\n while Daemon._is_process_running(pid):\n os.kill(pid, signal.SIGTERM)\n cnt += 1\n time.sleep(0.1)\n os.kill(pid, signal.SIGTERM)\n except OSError as e:\n if 'No such process' in str(e):\n # We here if daemon has terminated just after last\n # \"_is process_running()\" call and before the next \"kill\".\n # We OK with it.\n pass\n else:\n sys.stderr.write('{}\\n'.format(e))\n sys.stderr.write('Stopping the daemon if failed\\n')\n #Note: pidfile is not removed in this case\n sys.exit(1)\n finally:\n print('Kill try count: {}'.format(cnt))\n\n if os.path.exists(self._pid_file):\n self._remove_pid_file()","title":""},{"docid":"d37e5fbfc126323c888d25b764ed6804","score":"0.6183899","text":"def start(app):\n _service(app, 'start')","title":""},{"docid":"e9ffa9eb51133ce9be761df60417a086","score":"0.61708117","text":"def main():\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--daemon', dest='daemon_mode', action='store_true')\n arguments = parser.parse_args()\n \n daemon_running = Daemon.daemon_running()\n reminders = Reminders()\n\n if arguments.daemon_mode:\n if daemon_running:\n print('\\nDaemon already running\\n')\n sys.exit()\n daemon = Daemon(reminders)\n daemon.start()\n\n else:\n if not daemon_running:\n print('\\nDaemon not running\\n')\n sys.exit()\n interface = Urwid_Interface(reminders)\n interface.run()","title":""},{"docid":"a3562708fd14fc6ed6099ed16dff158b","score":"0.6153397","text":"def daemonize(self):\n try:\n pid = os.fork()\n if pid > 0:\n # be orphaned by the parent\n sys.exit(0)\n except OSError as e:\n sys.stderr.write(\"fork #1 failed: %d (%s)\\n\"%(e.errno, e.strerror))\n sys.exit(1)\n\n # decouple from the parent\n os.chdir('/home/mmalinow/.daemons')\n os.setsid()\n os.umask(0)\n\n # do the second fork in order to lift up leadership\n # session leader can still acquire access to tty\n try:\n pid=os.fork()\n if pid > 0:\n sys.exit(0)\n except OSError as e:\n sys.stderr.write(\"fork #2 failed: %d (%s)\\n\"%(e.errno, e.strerror))\n sys.exit(1)\n\n # redirect standard file descriptors\n sys.stdout.flush()\n sys.stdin.flush()\n sys.stderr.flush()\n\n si=file(self.stdin,'r')\n so=file(self.stdout, 'a+')\n se=file(self.stderr, 'a+', 0)\n\n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())\n\n atexit.register(self.delpid)\n pid=str(os.getpid())\n file(self.pidfile,'w+').write(\"%s\\n\" % pid)","title":""},{"docid":"9505ecd63424f92aafe5e664ef9ff507","score":"0.6124912","text":"def start(self):\n self._started = True\n self._pyrustic_app.start()","title":""},{"docid":"df985a1119025a8a3a2b2db8f1f5e831","score":"0.61214644","text":"def startOrStop(self):\n if(self.runningState):\n self.stop()\n else:\n self.start()","title":""},{"docid":"6f90cc730edb908af05d9dec57db0dc4","score":"0.61172587","text":"def onDaemonStartRun(daemon, event):\n from .zeneventdWorkers import EventDEventletWorker\n # Free up unnecessary database resources in parent zeneventd process\n if daemon.options.daemon:\n daemon.closedb()\n daemon.closeAll()\n daemon._workers.startWorkers()\n reactor.run()\n else:\n worker = EventDEventletWorker()\n worker.run()","title":""},{"docid":"730f6559b1209ef2ad6ba24e7f49f1bb","score":"0.6107526","text":"def stop_and_restart():\n\tupdater.stop()\n\tos.execl(sys.executable, sys.executable, *sys.argv)","title":""},{"docid":"2d2a1dd2924a4f8c000ce3677102b0bb","score":"0.6105633","text":"def restart(self):\n if not self._installed:\n return\n if self.is_running():\n self._systemctl(\"restart\")\n else:\n self._systemctl(\"start\")\n self._stopped = False","title":""},{"docid":"751cffb772f9e7d33537f108087ae99f","score":"0.6103976","text":"def start(self):\n LOG.info(\"Start service.\\n\")\n\n try:\n LOG.debug(\"Wait for game start.\\n\")\n self.wait_start()\n LOG.debug(\"Game started.\\n\")\n self.run()\n finally:\n LOG.info(\"Stop service.\\n\")\n GPIO.cleanup()","title":""},{"docid":"b581b72e52e8564176e26a5555042087","score":"0.6099174","text":"def start(self):\n self._process = p = Process(target=self._monitor)\n p.daemon = True\n p.start()","title":""},{"docid":"d95493c5f096765f8c4efe8e71a204ce","score":"0.60959375","text":"def _makeDaemon(self):\n\n try:\n pid = os.fork()\n if pid > 0:\n # Exit first parent.\n sys.exit(0)\n except OSError as e:\n m = \"Fork #1 failed: {}\".format(e)\n print(m)\n sys.exit(1)\n\n # Decouple from the parent environment.\n os.chdir(\"/\")\n os.setsid()\n os.umask(0)\n\n # Do second fork.\n try:\n pid = os.fork()\n if pid > 0:\n # Exit from second parent.\n sys.exit(0)\n except OSError as e:\n m = \"Fork #2 failed: {}\".format(e)\n print(m)\n sys.exit(1)\n\n m = \"The daemon process is going to background.\"\n print(m)\n\n # Redirect standard file descriptors.\n sys.stdout.flush()\n sys.stderr.flush()\n si = open(self.stdin, 'r')\n so = open(self.stdout, 'a+')\n se = open(self.stderr, 'a+')\n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())","title":""},{"docid":"49ae4e68179ce7be44619c06109478a7","score":"0.60894555","text":"def start(self):\n subprocess.check_call(\"sudo service rsyslog restart\", shell=True)","title":""},{"docid":"f642d54e37e66fac21b58c44f4408704","score":"0.60719544","text":"def test_daemon_runandstop(self) -> None:\n cmdstr = \"/usr/bin/rfcomm connect /dev/rfcomm0 88:6B:0F:86:4D:F9\"\n cmdstr = \"sleep 1000\"\n dt = Taskmeister.DaemonTaskMeister(self.logger, cmdstr, 0)\n gevent.sleep(1)\n stat = dt.get_status()\n print(\"GOT STAT: {}\".format(stat))\n assert stat == STATUS_RUNNING, \"expected status running\"\n print(\"numchecks: {}\".format(dt.numchecks))\n dt.stop_cmd(do_wait=True)\n stat = dt.get_status()\n print(\"GOT STAT: {}\".format(stat))\n assert stat == STATUS_STOPPED, \"expected status stopped\"\n # assert False, \"force fail!\"","title":""},{"docid":"b9c9c1fa2816b3abefbdabdfdb2f56be","score":"0.60676366","text":"def start(self):\n reaper = threading.Thread(target=self.cleanup)\n reaper.daemon = True\n reaper.start()","title":""},{"docid":"3f426db7ff9d5a83e7fc7d038425849d","score":"0.60526675","text":"def start(self):\n self.setIsMainloopAlive(True)\n self.run()","title":""},{"docid":"066bd1e8afa2abd582b19218eeb58d6f","score":"0.6051409","text":"def stop(self):\n if not self._installed or self._stopped:\n return\n if self.is_running():\n self._systemctl(\"stop\")\n self._stopped = True","title":""},{"docid":"58c9859a66793f1a8331bf06df862762","score":"0.6048351","text":"def shutdown(self):\n self._logger.info(\"user asked for daemon termination.\")\n self._stop = True","title":""},{"docid":"b22613f01b3567da40aad90b8aa1c90c","score":"0.6045158","text":"def run(self):\n self.srv = make_server(self.host, int(self.port), self.app, handler_class=self.handler_cls)\n try:\n self.srv.serve_forever()\n except Exception:\n import traceback\n traceback.print_exc()\n # Failed to start\n self.srv = None","title":""},{"docid":"9fa738604b9d0887c11f53794e6895bc","score":"0.60283566","text":"def datalogger_start(self):\n\n if not self.datalogger_active:\n self.__stop_event.clear()\n self.__start_event.set()\n self.__dump_event.clear()\n else:\n self.__logger.error('Start failed: datalogger is already running')","title":""},{"docid":"afa2f19968de366a21b8e3aa76af1d98","score":"0.60218036","text":"def start(self):\n t0 = threading.Thread(target=self.service, args=())\n t0.deamon = True\n t0.start()\n\n t1 = threading.Thread(target=self.processor, args=())\n t1.daemon = True\n t1.start()\n\n t2 = threading.Thread(target=self.link_egress, args=())\n t2.daemon = True\n t2.start()\n\n for neighbour in self.neighbours(self.vrid):\n if self.vrid < neighbour:\n t = threading.Thread(target=self.setup_link, args=(neighbour,))\n t.daemon = True\n t.start()\n pass\n pass","title":""},{"docid":"b92c99f647d2467f0f6d1a38a060e53b","score":"0.6019435","text":"def main():\n start()","title":""},{"docid":"bb5a2961a211a6bd788f8ccced99b1e9","score":"0.6008547","text":"def main():\n if 'start' in sys.argv:\n print \"Trying to run the app...\"\n\n if CLI_ARGS.daemonize is True:\n if os.path.isfile(PID_FILE_PATH) is not True:\n print \"Trying to run in daemonized mode. Check log file for further insight: \" \\\n \"%s\", LOG_FILE_PATH\n daemon = setup_daemon()\n daemon.start()\n else:\n print \"Tried to run the app but an existing pid file found. \" \\\n \"Looks like it was not shutdown gracefully last time.\" \\\n \"file: \" + PID_FILE_PATH\n else:\n print \"Running in foreground, CTRL+C to exit the process.\"\n start_leexportpy_jobs()\n\n elif 'stop' in sys.argv:\n try:\n print \"Trying to terminate the daemon...\"\n terminate()\n except IOError as exception:\n if exception.errno == 2: # meaning pid file not found and so there is no running\n # instance\n print \"No leexportpy instance found so it could not be stopped.\"\n elif exception.errno == 3:\n print \"Pid file found but no such process was found. Please remove the pid file \" \\\n \"at: \" + PID_FILE_PATH\n else:\n print \"An unexpected error occurred while stopping leexportpy: \", exception\n logging.exception(\"An unexpected error occurred while stopping leexportpy\")\n else:\n print \"This should not happen.\"","title":""},{"docid":"99e1b93cb27a72f3b8e4fcc469fcd122","score":"0.59968793","text":"def start(self):\n self.server.start()","title":""},{"docid":"99e1b93cb27a72f3b8e4fcc469fcd122","score":"0.59968793","text":"def start(self):\n self.server.start()","title":""},{"docid":"c24a55cb8e1dc983908bee8040e3b315","score":"0.5989902","text":"def start(self):\n\tif self.m_handle is not None:\n\t raise Error, 'Service already started.'\n\tself.m_stop = False\n\tself.m_handle = _cpg.initialize(self)\n\t_cpg.join(self.m_handle, self.m_name)","title":""},{"docid":"8dd1102379e066d3dc84988f4eabdb2d","score":"0.59896356","text":"def start(self):\n if not self._started.is_set():\n # If daemone=False python forces join to be called which closes the thread properly.\n self.daemon = self.force_non_daemon or self.daemon\n if self.force_non_daemon:\n self.daemon = False\n\n super(Thread, self).start()","title":""},{"docid":"5752e322bbc9a8a5e361324a1081d478","score":"0.5988487","text":"def start(conf_file_path=None):\n args = [sys.argv[0], '-daemon']\n if conf_file_path is not None:\n args.append('-configuration-path:{0}'.format(conf_file_path))\n\n with open(os.devnull, 'w') as devnull:\n subprocess.Popen(args, stdout=devnull, stderr=devnull)","title":""},{"docid":"5cf9cee1b1701db1e4c053d29b85d93b","score":"0.5977895","text":"def _daemonize(func, args, cfg):\n class _Daemonizer(_daemons.RunDaemon):\n def run(self):\n func(args, cfg)\n daemon = _Daemonizer(pidfile=args.pid_file)\n action = args.action\n if action == 'start':\n daemon.start()\n elif action == 'stop':\n daemon.stop()\n elif action == 'restart' or action == 'reload':\n daemon.restart()\n else:\n LOG.error('Unsupported daemon action %s, aborting!', action)","title":""},{"docid":"17048cfc384642f45113c7caa139f95e","score":"0.5969656","text":"def _daemonize():\n global run_proc\n try:\n print(\"Persisting process...\")\n signal.signal(signal.SIGTERM, _exit)\n signal.signal(signal.SIGINT, _exit)\n\n if(G.VERBOSE):\n print(\"Building engine...\")\n en = engine.Engine()\n\n if(G.VERBOSE):\n print(\"Building wrapper...\")\n ps = pubsub.Wrapper(en)\n\n print(\"Ready. Waiting for requests...\")\n\n while run_proc:\n ps.heartbeat()\n time.sleep(10)\n except KeyboardInterrupt:\n _exit()","title":""},{"docid":"b09249ed9038dbc6ba46f71e57f7e63f","score":"0.5967777","text":"def start(self):\n while self.stay_alive:\n if self.redis and self.needs_running:\n self._run()\n time.sleep(1)","title":""},{"docid":"f426011c770ff7d5b74e51eb10a3a2a1","score":"0.5960717","text":"def run(self):\n for c in self.servers:\n c.startup()\n\n try:\n self._loop.run_forever()\n except KeyboardInterrupt:\n print(\"\\nKeyboard Interrupt: shutting down...\")\n\n for c in self.servers:\n c.shutdown()","title":""},{"docid":"11be6a6994111b6f24d310ac46089d20","score":"0.59557205","text":"def start(self):\n self.run()","title":""},{"docid":"11be6a6994111b6f24d310ac46089d20","score":"0.59557205","text":"def start(self):\n self.run()","title":""},{"docid":"d035464e9c96a9bbebb4ee956cfff88e","score":"0.5954812","text":"def stop_global_daemon():\n address, pid = _read_daemon()\n if address is None:\n return\n\n try:\n Client(address=address)\n except DaemonNotRunningError:\n pass\n else:\n os.kill(pid, signal.SIGTERM)\n\n try:\n os.remove(os.path.join(CONFIG_DIR, 'daemon'))\n except OSError:\n pass","title":""},{"docid":"2efe8d4219941177fec531272b1f39dd","score":"0.59488726","text":"def start(self):\n if not self.python:\n self.prog = Popen(self.path,\n shell = True,\n stdin = PIPE,\n stdout = PIPE)\n else:\n print self.path\n self.prog = Popen(\"python %s -u\" % self.path,\n shell = True,\n stdin = PIPE,\n stdout = PIPE)\n self.alive = True","title":""},{"docid":"c590a2f91dec5324106465c4635d9f40","score":"0.5936039","text":"def daemon(self):\n return False","title":""},{"docid":"ef3b03b9c9d9472bcb7302ce1ed4e4f7","score":"0.5934361","text":"def daemonize(self):\n\t\tcount = 0\n\n\t\ttry: \n\t\t\tpid = os.fork() \n\t\t\tif pid > 0:\n\t\t\t\t# exit first parent\n\t\t\t\tsys.exit(0) \n\t\texcept OSError, e: \n\t\t\tsys.stderr.write(\"fork #1 failed: %d (%s)\\n\" % (e.errno, e.strerror))\n\t\t\tsys.exit(1)\n\t\n\t\t# decouple from parent environment\n\t\tos.setsid() \n\t\tos.umask(0) \n\t\n\t\t# do second fork\n\t\ttry: \n\t\t\tpid = os.fork() \n\t\t\tif pid > 0:\n\t\t\t\t# exit from second parent\n\t\t\t\tsys.exit(0) \n\t\texcept OSError, e: \n\t\t\tsys.stderr.write(\"fork #2 failed: %d (%s)\\n\" % (e.errno, e.strerror))\n\t\t\tsys.exit(1) \n\t\n\t\t# redirect standard file descriptors\n\t\tsys.stdout.flush()\n\t\tsys.stderr.flush()\n\t\n\t\tsi = file(self.stdin, 'r')\n\t\tso = file(self.stdout, 'a+')\n\t\tse = file(self.stderr, 'a+', 0)\n\n\t\tatexit.register(self.onstop)\n\t\tsignal(SIGTERM, lambda signum, stack_frame: exit())\n \t\n\t\t# write pidfile \n\t\tpid = str(os.getpid())\n\t\tfile(self.pidfile,'w+').write(\"%s\\n\" % pid)\n\n\t\t\n\t\taccess_token = \"925901916512641026-IMcQ5hTUfyfZp0hAWxBjKDPQxhmfdHE\"","title":""},{"docid":"61c665dab10458d42e7841f732f935f5","score":"0.5933297","text":"def start():\n sudo('service uwsgi start %s' % PROJECT_NAME)","title":""},{"docid":"809cc7b9f08bc0793ccf4145cd82c8b3","score":"0.592385","text":"def daemonize(self):\r\n try:\r\n pid = os.fork()\r\n if pid > 0:\r\n # Exit the first parent.\r\n sys.exit(0)\r\n except OSError, e:\r\n sys.stderr.write(\"fork #1 failed: %d (%s)\\n\" % (e.errno, e.strerror))\r\n sys.exit(1)\r\n\r\n # Decouple from the parent environment.\r\n os.chdir(\"/\")\r\n os.setsid()\r\n os.umask(0)\r\n\r\n # Do the second fork.\r\n try:\r\n pid = os.fork()\r\n if pid > 0:\r\n # Exit from the second parent.\r\n sys.exit(0)\r\n except OSError, e:\r\n sys.stderr.write(\"fork #2 failed: %d (%s)\\n\" % (e.errno, e.strerror))\r\n sys.exit(1)\r\n\r\n # Redirect standard file descriptors. Because the daemon has no\r\n # controlling terminal, we want to avoid side effects from reading and\r\n # writing to/from the standard file descriptors.\r\n sys.stdout.flush()\r\n sys.stderr.flush()\r\n si = file(self.stdin, 'r')\r\n so = file(self.stdout, 'a+')\r\n se = file(self.stderr, 'a+', 0)\r\n # Use os.dup2() with the fileno() instead of assigning directly to\r\n # sys.stdout because the former will also affect any C-level sys.stdout\r\n # calls.\r\n os.dup2(si.fileno(), sys.stdin.fileno()) # Essentially: sys.stdin = si\r\n os.dup2(so.fileno(), sys.stdout.fileno()) # Essentially: sys.stdout = so\r\n os.dup2(se.fileno(), sys.stderr.fileno()) # Essentially: sys.stderr = se\r\n\r\n # Write the pidfile.\r\n atexit.register(self.delpid)\r\n pid = str(os.getpid())\r\n open(self.pidfile, 'w+').write(\"%s\\n\" % pid)","title":""},{"docid":"809cc7b9f08bc0793ccf4145cd82c8b3","score":"0.592385","text":"def daemonize(self):\r\n try:\r\n pid = os.fork()\r\n if pid > 0:\r\n # Exit the first parent.\r\n sys.exit(0)\r\n except OSError, e:\r\n sys.stderr.write(\"fork #1 failed: %d (%s)\\n\" % (e.errno, e.strerror))\r\n sys.exit(1)\r\n\r\n # Decouple from the parent environment.\r\n os.chdir(\"/\")\r\n os.setsid()\r\n os.umask(0)\r\n\r\n # Do the second fork.\r\n try:\r\n pid = os.fork()\r\n if pid > 0:\r\n # Exit from the second parent.\r\n sys.exit(0)\r\n except OSError, e:\r\n sys.stderr.write(\"fork #2 failed: %d (%s)\\n\" % (e.errno, e.strerror))\r\n sys.exit(1)\r\n\r\n # Redirect standard file descriptors. Because the daemon has no\r\n # controlling terminal, we want to avoid side effects from reading and\r\n # writing to/from the standard file descriptors.\r\n sys.stdout.flush()\r\n sys.stderr.flush()\r\n si = file(self.stdin, 'r')\r\n so = file(self.stdout, 'a+')\r\n se = file(self.stderr, 'a+', 0)\r\n # Use os.dup2() with the fileno() instead of assigning directly to\r\n # sys.stdout because the former will also affect any C-level sys.stdout\r\n # calls.\r\n os.dup2(si.fileno(), sys.stdin.fileno()) # Essentially: sys.stdin = si\r\n os.dup2(so.fileno(), sys.stdout.fileno()) # Essentially: sys.stdout = so\r\n os.dup2(se.fileno(), sys.stderr.fileno()) # Essentially: sys.stderr = se\r\n\r\n # Write the pidfile.\r\n atexit.register(self.delpid)\r\n pid = str(os.getpid())\r\n open(self.pidfile, 'w+').write(\"%s\\n\" % pid)","title":""},{"docid":"14a9a2ca5f02056ff687afb75fa9eb94","score":"0.5923208","text":"def start(self):\n if self._isRunning:\n return\n\n if self._cease.is_set():\n self._cease.clear() # restart\n\n class Runner(threading.Thread):\n @classmethod\n def run(cls):\n nextRunAt = cls.setNextRun()\n\n while not self._cease.is_set():\n if datetime.now() >= nextRunAt:\n self._run()\n nextRunAt = cls.setNextRun()\n\n @classmethod\n def setNextRun(cls):\n # return datetime.now() + timedelta(seconds=self._interval)\n return self._interval.nextRunAt()\n\n runner = Runner()\n runner.setDaemon(True)\n runner.start()\n self._isRunning = True","title":""},{"docid":"371e0b0cd90e97837f12851832cbc044","score":"0.5920431","text":"def start(self):\n if not self.local_mode:\n self.client.start()\n self.switch.start()\n self.config.start()","title":""},{"docid":"59b487336402e0706a6bfc1f49d9a738","score":"0.59114337","text":"def handle_start(args):\n # do start up here\n cmds = build_dockerrun(args[\"latest\"])\n for cmd in cmds:\n # TODO get better solution than sleep\n run_subprocess(cmd, shell=True)\n time.sleep(8) # brute force give docker time to complete its stuff.\n time.sleep(10) # As above, but let docker catch up before login\n handle_login(args)","title":""},{"docid":"bdda8a6e0e30366dc20d32a1836f61d1","score":"0.5907747","text":"def stopDaemon(self):\n return _eveusb.Controller_stopDaemon(self)","title":""},{"docid":"831326dbb51a7e62cb7beac03f0af2b6","score":"0.5906053","text":"def start(self):\n self.runListenLoop()","title":""},{"docid":"8cc8335477dcc4d589c0795678185441","score":"0.590142","text":"def start() -> None:\n \n active_server_name = srv_mgr.get_active_server_name()\n if not active_server_name:\n print(f'No active server found. Set an active server?\\n')\n sys.exit(1)\n \n if srv_mgr.is_server_running():\n print(f'Looks like this server {active_server_name} is already running. Use restart instead of start.')\n sys.exit(1)\n\n if srv_mgr.start_server():\n print(f\"Server {active_server_name} started succesfully!\")\n sys.exit(0)\n\n print(f\"Server {active_server_name} could not be started!\")\n sys.exit(1)","title":""},{"docid":"b65bb5ca38c06afc23fd294ea9beadc7","score":"0.58973867","text":"async def run(self) -> None:\n try:\n await self.start()\n except (KeyboardInterrupt, SystemExit) as e:\n logging.info(e)\n await self.close()\n finally:\n await self.cleanup()","title":""},{"docid":"0258c4e6d6cae892fb8a42679e0f1b5f","score":"0.5895067","text":"def startService(handler, *args, **kwargs):\n\n preProcessHandler(handler)\n enforceSolitude(handler)\n if handler._dbus_kill: sys.exit()\n if handler._dbus_go_daemon: _goDaemon()\n\n print(\"{:s} daemon: {:d}\".format(handler._dbus_service, os.getpid()))\n\n postProcessHandler(handler, *args, **kwargs)","title":""},{"docid":"bee4842342cadde5aaa2f15a458c1641","score":"0.5889479","text":"def start(self):\n if self.service.getProcessDicts() == []:\n return\n\n def start2(process, nbr=None):\n\n cwd = process[\"cwd\"]\n # args['process'] = process\n if nbr is None:\n self.stop()\n\n tcmd = process[\"cmd\"]\n if tcmd == \"jspython\":\n tcmd = \"source %s/env.sh;jspython\" % (j.dirs.base)\n\n targs = process[\"args\"]\n tuser = process[\"user\"]\n if tuser == \"\":\n tuser = \"root\"\n tlog = self.service.hrd.getBool(\"process.log\", default=True)\n env = process[\"env\"]\n\n startupmethod = process[\"startupmanager\"]\n domain, name = self._getDomainName(process)\n if nbr is not None:\n name = \"%s.%d\" % (name, i)\n log(\"Starting %s:%s\" % (domain, name))\n\n if startupmethod == 'upstart':\n # check if we are in our docker image which uses myinit instead\n # of upstart\n if j.sal.fs.exists(path=\"/etc/my_init.d/\"):\n cmd2 = \"%s %s\" % (tcmd, targs)\n extracmds = \"\"\n if cmd2.find(\";\") != -1:\n parts = cmd2.split(\";\")\n extracmds = \"\\n\".join(parts[:-1])\n cmd2 = parts[-1]\n\n C = \"#!/bin/sh\\nset -e\\ncd %s\\nrm -f /var/log/%s.log\\n%s\\nexec %s >>/var/log/%s.log 2>&1\\n\" % (\n cwd, name, extracmds, cmd2, name)\n j.sal.fs.remove(\"/var/log/%s.log\" % name)\n j.sal.fs.createDir(\"/etc/service/%s\" % name)\n path2 = \"/etc/service/%s/run\" % name\n j.sal.fs.writeFile(path2, C)\n j.sal.fs.chmod(path2, 0o770)\n j.sal.process.execute(\n \"sv start %s\" % name, die=False, outputToStdout=False, outputStderr=False, captureout=False)\n else:\n j.sal.ubuntu.service_install(name, tcmd, pwd=cwd, env=env)\n j.sal.ubuntu.service_start(name)\n\n elif startupmethod == \"tmux\":\n j.tools.cuisine.local.tmux.executeInScreen(\n domain, name, tcmd + \" \" + targs, cwd=cwd, env=env, user=tuser) # , newscr=True)\n\n else:\n raise j.exceptions.RuntimeError(\n \"startup method not known or disabled:'%s'\" % startupmethod)\n\n # if msg==\"\":\n # pids=self.getPids(ifNoPidFail=False,wait=False)\n # if len(pids) != self.numprocesses:\n # msg=\"Could not start, did not find enough running instances, needed %s, found %s\"%(self.numprocesses,len(pids))\n\n # if msg==\"\" and pids!=[]:\n # for pid in pids:\n # test=j.sal.process.isPidAlive(pid)\n # if test==False:\n # msg=\"Could not start, pid:%s was not alive.\"%pid\n\n # if log!=\"\":\n # msg=\"%s\\nlog:\\n%s\\n\"%(msg,log)\n\n # self.raiseError(msg)\n # return\n\n isrunning = self.check_up(wait=False)\n if isrunning:\n return\n\n processes = self.service.getProcessDicts()\n for i, process in enumerate(processes):\n\n if \"platform\" in process:\n if not j.core.platformtype.myplatform.checkMatch(process[\"platform\"]):\n continue\n if len(processes) > 1:\n start2(process, nbr=i)\n else:\n start2(process)\n\n isrunning = self.check_up()\n if isrunning is False:\n msg = \"\"\n\n if self.service.getTCPPorts() == [0]:\n print('Done ...')\n elif self.service.getTCPPorts() != []:\n ports = \",\".join([str(item)\n for item in self.service.getTCPPorts()])\n msg = \"Could not start:%s, could not connect to ports %s.\" % (\n self.service, ports)\n j.events.opserror_critical(msg, \"service.start.failed.ports\")\n else:\n j.events.opserror_critical(\n \"could not start:%s\" % self.service, \"service.start.failed.other\")","title":""},{"docid":"e0d496efa82f1a3513067c86a8e98f2a","score":"0.58878255","text":"def start(self):\n self.process = Process(target=self.listen)\n self.process.start()\n self.process.join()","title":""},{"docid":"24a3e8a0e540cc5cd263e42c79cc22b0","score":"0.58815247","text":"def start_subprocess(self):\n \n self.server = InetdService(self)\n yield from self.server.run()\n\n self.loginfo(\"inetd service {0} listening on port {1}\".format(self.name, self.port))","title":""},{"docid":"c0ce12a6cfad5057f4f9c5074e0c9ff9","score":"0.58737564","text":"def daemonize(self):\n if os.fork(): # launch child and...\n os._exit(0) # kill off parent\n os.setsid()\n if os.fork(): # launch child and...\n os._exit(0) # kill off parent again.\n os.umask(63) # 077 in octal\n null = os.open('/dev/null', os.O_RDWR)\n for i in range(3):\n try:\n os.dup2(null, i)\n except OSError as e:\n if e.errno != errno.EBADF:\n raise\n os.close(null)","title":""},{"docid":"299579829c53783643e1857352e0c987","score":"0.5852781","text":"def stop(self):\n if self.pidfile is None:\n raise DaemonError('Cannot stop daemon without PID file')\n\n pid = self._read_pidfile()\n if pid is None:\n # I don't think this should be a fatal error\n self._emit_warning('{prog} is not running'.format(prog=self.prog))\n return\n\n self._emit_message('Stopping {prog} ... '.format(prog=self.prog))\n\n try:\n # Try to terminate the process\n os.kill(pid, signal.SIGTERM)\n except OSError as ex:\n self._emit_failed()\n self._emit_error(str(ex))\n sys.exit(1)\n\n _, alive = psutil.wait_procs([psutil.Process(pid)], timeout=self.stop_timeout)\n if alive:\n # The process didn't terminate for some reason\n os.kill(pid, signal.SIGKILL)\n time.sleep(0.5)\n # Hahahaha. Do you feel alive now?\n\n self._emit_ok()","title":""},{"docid":"6a5b72311bfe6eaff22fd5116e7fd7fc","score":"0.5849422","text":"def start(self) -> None:\n if self._runner:\n raise RuntimeError(\"Runner is already started and cannot restart...\")\n self._runner = threading.Thread(target=self._start_pull)\n self._runner.start()","title":""},{"docid":"b2f093b737479874b6fca297b35eba15","score":"0.5849257","text":"def run():\n stop()\n fabric.api.run('puppet agent --no-daemonize --onetime --verbose', warn_only=True)\n fabric.api.run('service puppet start')","title":""},{"docid":"a8ff27408b49de465f1991432b3c3db5","score":"0.5845146","text":"def start_(self):\n\t\tif not self.running:\n\t\t\tself._start = time.time() - self._stop\n\t\t\tself.running = True\n\t\t\tself.autoStop = False\n\t\t\tself.autoStop2 = False\n\t\t\tself.refresher.resume()","title":""},{"docid":"0c8e77cea00c5e405dcfee2e255cdca1","score":"0.58441657","text":"def start(self):\n self.log.debug(\"(Re)starting\")","title":""},{"docid":"06347100c5d25a23c13c075938520e7a","score":"0.5831052","text":"def start_service(service):\n subprocess.call([\"service\", service, \"start\"])","title":""},{"docid":"90475e139f22b1c1743513d6ef7a44bc","score":"0.5827442","text":"def daemonlized():\n \n WORKDIR='/'\n UMASK=0\n\n if os.path.exists(PIDFILE):\n raise RuntimeError('Already running')\n \n if hasattr(os, \"devnull\"):\n REDIRECT_TO = os.devnull\n else:\n REDIRECT_TO = \"/dev/null\"\n \n try:\n pid = os.fork()\n except OSError as e:\n raise Exception(\"%s [%d]\" % (e.strerror, e.errno))\n if pid == 0:\n os.setsid()\n signal.signal(signal.SIGHUP, signal.SIG_IGN)\n try:\n pid = os.fork()\n except OSError as e:\n raise Exception(\"%s [%d]\" % (e.strerror, e.errno))\n\n if pid == 0:\n os.chdir(WORKDIR)\n os.umask(UMASK)\n else:\n os._exit(0)\n else:\n os._exit(0)\n\n sys.stdout.flush()\n sys.stderr.flush()\n si = open(os.devnull, 'r')\n so = open(os.devnull, 'a+')\n se = open(os.devnull, 'a+')\n \n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())\n atexit.register(lambda: os.remove(PIDFILE))\n with open(PIDFILE,'w') as f:\n print(os.getpid(),file=f)","title":""},{"docid":"6b983bc86e55a779720cb90c8a4fe08f","score":"0.5817107","text":"def execute(self):\n self.start()\n while self.running:\n self.run()\n self.terminate()","title":""},{"docid":"920e84385a9a2129c18f11c1cbba5438","score":"0.58117104","text":"def stop(cls):\n if cls._pid:\n os.kill(cls._pid, signal.SIGTERM)\n cls._pid = None\n cls.datadir = None\n wait_until(lambda: not is_running(cls._port))\n cls._port = None","title":""},{"docid":"f60e7f462e0c4d783a3e2011a46c2d64","score":"0.57922107","text":"def start(self):\n for servo in self.servos:\n if not servo.is_running():\n servo.start()\n return self","title":""},{"docid":"19b152e9a21a5f896785908fdf9e8b2a","score":"0.5782499","text":"def start(keep_work_dir=False):\n\tprint \"Starting asd\"\n\n\tif not keep_work_dir:\n\t\tinit_work_dir()\n\n\tinit_state_dirs()\n\tinterceptor = init_interceptor()\n\tsearch_path = [os.sep + os.path.join(\"usr\", \"bin\")]\n\n\tif \"ASREPO\" in os.environ:\n\t\trepo = absolute_path(os.environ[\"ASREPO\"])\n\t\tuname = os.uname()\n\t\tsearch_path = [os.path.join(repo, \"target\", uname[0] + \"-\" + uname[4], \"bin\")] + search_path\n\n\tprint \"asd search path is\", search_path\n\n\tfor path in search_path:\n\t\tasd_path = os.path.join(path, \"asd\")\n\n\t\tif os.path.exists(asd_path):\n\t\t\tbreak\n\telse:\n\t\traise Exception(\"No asd executable found\")\n\n\tif not SHOW_ASD_OUTPUT:\n\t\tdev_null = open(os.devnull, \"w\")\n\t\tredirect = {\"stdout\": dev_null, \"stderr\": subprocess.STDOUT}\n\telse:\n\t\tredirect = {}\n\n\ttemp_file = absolute_path(\"aerospike.conf\")\n\tconf_file = []\n\tconf_file.append(create_conf_file(temp_file, 3000, None, 1))\n\tconf_file.append(create_conf_file(temp_file, 4000, 3000, 2))\n\n\tos.environ[\"LD_PRELOAD\"] = interceptor\n\n\tfor index in xrange(1, 3):\n\t\tcommand = [asd_path, \"--config-file\", conf_file[index - 1]]\n\t\tprint \"Executing\", command\n\t\tGLOBALS[\"asd-\" + str(index)] = subprocess.Popen(command, cwd=absolute_path(\".\"), **redirect)\n\n\tdel os.environ[\"LD_PRELOAD\"]\n\n\tprint \"Connecting client\"\n\tconfig = {\"hosts\": [(\"127.0.0.1\", PORT)]}\n\n\tfor attempt in xrange(CLIENT_ATTEMPTS):\n\t\ttry:\n\t\t\tGLOBALS[\"client\"] = aerospike.client(config).connect()\n\t\t\tbreak\n\t\texcept Exception:\n\t\t\tif attempt < CLIENT_ATTEMPTS - 1:\n\t\t\t\ttime.sleep(1)\n\t\t\telse:\n\t\t\t\traise\n\n\tprint \"Client connected\"\n\ttime.sleep(5) # let the cluster fully form and the client learn about it","title":""},{"docid":"8e8ae3603639a59299fc7ef6dd37bd86","score":"0.5780658","text":"def start(self):\n dc = docker.from_env()\n container = dc.containers.get(self._name)\n container.start()\n # Give it some time to start, so subsequent calls work\n time.sleep(1)","title":""}],"string":"[\n {\n \"docid\": \"24fdea183ea66334ccc720419bfc7b08\",\n \"score\": \"0.7850297\",\n \"text\": \"def start(self):\\r\\n if self.go_daemon:\\r\\n if not self.pidfile:\\r\\n self.log.error(\\\"Daemon needs pidfile\\\")\\r\\n sys.exit(1)\\r\\n self.run_func_safely(self._boot_daemon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"084f2c49f80b1d662b33b6191e21d676\",\n \"score\": \"0.771307\",\n \"text\": \"def run_daemon(self):\\n if self.pidpath is None:\\n print(\\\"No valid PID path.\\\")\\n return\\n self.ctx = DaemonContext(\\n stdin=self.stdin,\\n stdout=self.stdout,\\n stderr=self.stderr,\\n pidfile=LockFile(self.pidpath),\\n\\n )\\n if len(sys.argv) > 1:\\n if sys.argv[1] == \\\"start\\\":\\n with self.ctx:\\n self.run()\\n elif sys.argv[1] == \\\"stop\\\":\\n self._closectx()\\n elif sys.argv[1] == \\\"restart\\\":\\n self._closectx()\\n with self.ctx:\\n self.run()\\n else:\\n print(\\\"Unrecognised argument.\\\")\\n else:\\n self.run() # Run without daemon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e90a4d2cfe8898921d55d26fbf287e\",\n \"score\": \"0.7563267\",\n \"text\": \"def start(self):\\n\\n self.startup_clean()\\n if self.as_daemon:\\n self.daemon = Daemonize(app=config['GENERAL']['app_name'], pid=config['GENERAL']['pid'], action=self.run)\\n self.daemon.start()\\n else:\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cea1fa4e14ec0c6f0319090c2c4f337\",\n \"score\": \"0.7385666\",\n \"text\": \"def start(self):\\n self.check_pid()\\n self.add_signal_handlers()\\n self.start_logging()\\n\\n try:\\n self.check_pid_writable()\\n self.check_server_accessibility()\\n self.daemonize()\\n except:\\n logging.exception(\\\"failed to start due to an exception\\\")\\n raise\\n\\n self.write_pid()\\n try:\\n try:\\n self.run()\\n except (KeyboardInterrupt, SystemExit):\\n pass\\n except OSError as exc:\\n logging.exception(str(exc))\\n pass\\n except:\\n logging.exception(\\\"stopping with an exception\\\")\\n raise\\n finally:\\n self.remove_pid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c62370fad04cd6b6d13ca79fd4037a84\",\n \"score\": \"0.7352018\",\n \"text\": \"def start(self):\\n # Check for a pidfile to see if the daemon already runs\\n try:\\n pf = open(self._pid_file, 'r')\\n pid = int(pf.read().strip())\\n pf.close()\\n except IOError:\\n pid = None\\n\\n if pid:\\n message = 'pidfile {} already exist. '\\\\\\n 'Daemon already running?\\\\n'\\n sys.stderr.write(message.format(self._pid_file))\\n sys.exit(1)\\n\\n # Start the daemon\\n self._daemonize()\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe4945d09ca18cb3debd374f3344bef\",\n \"score\": \"0.7237522\",\n \"text\": \"def run(self):\\n self.daemonize(write_pid=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91126c5e2343e81c038c1b0aff66b9f\",\n \"score\": \"0.6984598\",\n \"text\": \"def no_daemon(self, **kwargs):\\r\\n kwargs['daemon'] = False\\r\\n return self.start(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef33fb2f9fd5152892a465292a85c299\",\n \"score\": \"0.6978491\",\n \"text\": \"def run(self):\\n\\n if len(sys.argv) != 2:\\n print('Usage: %s [start|stop]' % sys.argv[0])\\n exit(1)\\n\\n # Daemon was commanded to start\\n if sys.argv[1] == 'start':\\n try:\\n self.daemonize( self.pf_name )\\n except Exception as err:\\n print \\\"Fatal Exception: \\\"\\n print err.args\\n exit(1)\\n\\n self.connect_rx()\\n\\n # Daemon was commanded to stop\\n elif sys.argv[1] == 'stop':\\n if os.path.exists( self.pf_name ):\\n pidFile = open( self.pf_name , 'r')\\n pid = pidFile.read()\\n os.kill(int(pid), signal.SIGTERM)\\n else:\\n print('Daemon not running!')\\n exit(1)\\n else:\\n print('Unknown Command!')\\n exit(1)\\n\\n count = 0\\n\\n self.build_dev_list()\\n\\n # event loop\\n while True:\\n\\n count += 1\\n self.listen()\\n self.process_cmds()\\n time.sleep(1)\\n\\n # Don't flood the network with requests\\n if count >= 120:\\n self.build_dev_list()\\n count = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67223a0297ea9964a702618761923570\",\n \"score\": \"0.68387085\",\n \"text\": \"def start(self):\\n if not self._installed:\\n self.install()\\n if not self._enabled:\\n self.enable()\\n if not self.is_running():\\n self._systemctl(\\\"start\\\")\\n self._stopped = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3049e62a4275e4064cf0401d1e84100d\",\n \"score\": \"0.6807076\",\n \"text\": \"def _goDaemon():\\n\\n _doFork()\\n os.chdir(\\\"/\\\") \\n os.setsid() \\n os.umask(0) \\n _doFork()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37615df76de8e9077666d0aa723c2484\",\n \"score\": \"0.6791836\",\n \"text\": \"def daemonize(self):\\n\\n signal.signal(signal.SIGTERM, self.term_handler)\\n signal.signal(signal.SIGINT, self.term_handler)\\n\\n if not self.settings.daemonize:\\n self.log.init_log()\\n return\\n\\n self.log.write(log.DEBUG1, \\\"daemonizing\\\")\\n\\n try:\\n pid = os.fork()\\n except OSError:\\n # self.log.write(log.EMERGENCY, \\\"Failed to fork daemon\\\")\\n raise\\n\\n if pid == 0:\\n os.setsid()\\n try:\\n pid = os.fork()\\n except OSError:\\n # self.log.write(log.EMERGENCY, \\\"Failed to fork daemon\\\")\\n raise\\n\\n if pid == 0:\\n os.chdir(\\\"/tmp\\\")\\n os.umask(0)\\n else:\\n exit(0)\\n else:\\n exit(0)\\n\\n self.settings.is_daemonized = True\\n self.log.init_log()\\n self.log.write(log.MESSAGE, \\\"daemonized.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05cd20f4be14d2a6257c2b84b515b848\",\n \"score\": \"0.67631406\",\n \"text\": \"def daemonize(self):\\n\\n signal.signal(signal.SIGTERM, self.term_handler)\\n signal.signal(signal.SIGINT, self.term_handler)\\n try:\\n pid = os.fork()\\n except OSError:\\n print(\\\"Failed to fork daemon\\\")\\n raise\\n\\n if pid == 0:\\n os.setsid()\\n try:\\n pid = os.fork()\\n except OSError:\\n print(\\\"Failed to fork daemon\\\")\\n raise\\n\\n if pid == 0:\\n os.chdir(\\\"/tmp\\\")\\n os.umask(0)\\n else:\\n exit(0)\\n else:\\n exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4c684bfd171f17004e9a3dcc9e3543a\",\n \"score\": \"0.6710145\",\n \"text\": \"def start_server(self):\\r\\n if not self._config[\\\"enable\\\"]:\\r\\n print(\\\"Daemon not enabled. Check configuration!\\\")\\r\\n return\\r\\n\\r\\n # self.start_kernel('/Users/goanpeca/miniconda3/envs/zeroconf')\\r\\n logger.info(\\\"Starting daemon!\\\")\\r\\n logging.info(\\\"Starting daemon!\\\")\\r\\n context = zmq.Context()\\r\\n socket = context.socket(zmq.REP)\\r\\n\\r\\n self._server_port = find_free_port()\\r\\n socket.bind(f\\\"tcp://*:{self._server_port}\\\")\\r\\n print(f\\\"Bound to port {self._server_port}\\\")\\r\\n self.register_service()\\r\\n\\r\\n while self._running:\\r\\n self._busy = True\\r\\n message = socket.recv()\\r\\n\\r\\n try:\\r\\n reply = self.process_request(message)\\r\\n except Exception as err:\\r\\n reply = json.dumps({\\\"error\\\": str(err)})\\r\\n print(err)\\r\\n\\r\\n socket.send_string(reply)\\r\\n self._busy = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f961708a0ee48f2255f11f69c0b211\",\n \"score\": \"0.6618145\",\n \"text\": \"def start(self):\\n if self.thread is not None:\\n return\\n self._pleaseStop=False\\n self.thread = threading.Thread(target=self.run)\\n self.thread.daemon=True\\n self.log.debug(\\\"Starting\\\")\\n self.thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9ca35353d8a08ae229017dba009574\",\n \"score\": \"0.65436524\",\n \"text\": \"def start(self):\\r\\n self.thread = threading.Thread(target=self._run)\\r\\n self.thread.setDaemon(True)\\r\\n self.thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb068aecdef791277e8785f2de9ec839\",\n \"score\": \"0.6530049\",\n \"text\": \"def startDaemon(self, progName, pidFile=None):\\n daemon.run(progName, pidFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a95e300f7253aec257c5ae5befded7\",\n \"score\": \"0.64391774\",\n \"text\": \"def start(self):\\n if not self.is_started():\\n self._restart()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aaeb7356f1c0b89faa1cde55034d24c\",\n \"score\": \"0.6403249\",\n \"text\": \"def start(self):\\n if not self.running:\\n self.exit.clear()\\n self._remove_stop_items()\\n self.running = True\\n self.thread = threading.Thread(target=self.run)\\n self.thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0723c768928f4829e19ad1b6e134b0f7\",\n \"score\": \"0.6402869\",\n \"text\": \"def run_daemon():\\n # <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>\\n print(\\\"Running Slackify daemon.\\\")\\n # <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>\\n # Run slackify_reminder() as a daemon.\\n with daemon.DaemonContext(stdout=sys.stdout,\\n stderr=sys.stdout,\\n pidfile=filelock.FileLock('/tmp/slackify.pid', timeout=0)):\\n slackify_reminder()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27ff0ebdde00161135049c560324e7dc\",\n \"score\": \"0.63773906\",\n \"text\": \"def daemonize():\\r\\n\\r\\n # launch new process, kill parent\\r\\n pid = os.fork()\\r\\n if pid != 0:\\r\\n os._exit(0)\\r\\n\\r\\n # start new session\\r\\n os.setsid()\\r\\n\\r\\n # stop i/o\\r\\n fd = os.open(\\\"/dev/null\\\", os.O_RDWR)\\r\\n os.dup2(fd, 0)\\r\\n os.dup2(fd, 1)\\r\\n os.dup2(fd, 2)\\r\\n if fd > 2:\\r\\n os.close(fd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ebb2d96d79ae2f85bd5f2de7efddc6\",\n \"score\": \"0.6365857\",\n \"text\": \"def run_daemon(self):\\n\\n # daemonize if possible\\n if platform.system() == \\\"Windows\\\":\\n # Nothing we can do; subproc was launched with\\n # creationflags=CREATE_NEW_PROCESS_GROUP, hopefully that's enough\\n pass\\n else:\\n # https://stackoverflow.com/questions/6011235/run-a-program-from-python-and-have-it-continue-to-run-after-the-script-is-kille\\n #\\n # Note that subproc has been created with preexec_fn=os.setsid, so\\n # first fork has already occurred\\n #\\n pid = os.fork()\\n if pid > 0:\\n sys.exit(0)\\n\\n logger = self._init_logging()\\n\\n # somewhere for the daemon to store stateful info\\n state = {\\n \\\"logger\\\": logger\\n }\\n\\n # copy variants into cache\\n try:\\n while True:\\n keep_running = self._run_daemon_step(state)\\n if not keep_running:\\n break\\n except Exception:\\n logger.exception(\\\"An error occurred while adding variants to the cache\\\")\\n raise\\n\\n # do some cleanup\\n if config.package_cache_clean_limit > 0:\\n try:\\n self.clean(time_limit=config.package_cache_clean_limit)\\n except Exception:\\n logger.exception(\\\"An error occurred while cleaning the cache\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e07013ccdb7882125528be10b4914a1\",\n \"score\": \"0.6365708\",\n \"text\": \"def start(self):\\n if not self.is_running:\\n self._impl.start()\\n self._is_running = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb7dafd3d7ac9db34ac861fcd0a79111\",\n \"score\": \"0.6324681\",\n \"text\": \"def run(self):\\n if not DEBUG and not self.is_vassal:\\n # No fork in debug mode and not in uwsgi vassal mode.\\n self.daemonize()\\n else:\\n print('PiBlasterWorker running in debug or as vassal')\\n self.uploader.start()\\n self.idler.start()\\n self.daemon_loop()\\n MPDService.stop_idler()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2021ac70c6c6bcf3b2bd2a5046c2c9d7\",\n \"score\": \"0.63153154\",\n \"text\": \"def deamonize():\\n d = Daemonizer(cherrypy.engine)\\n d.subscribe()\\n PIDFile(cherrypy.engine, 'daemon.pid').subscribe()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a79a357be3cbdfc9ff8427e60267bcc1\",\n \"score\": \"0.630865\",\n \"text\": \"def run_daemon(self):\\n proc = Process(target=self._run, name=\\\"alchemy-sender\\\",)\\n proc.start()\\n proc.join()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f5a90e75d470eed46dd86997ba43a9\",\n \"score\": \"0.62992454\",\n \"text\": \"def daemonize(self):\\n try:\\n pid = os.fork()\\n if pid > 0:\\n # exit first parent\\n sys.exit(0)\\n except OSError, e:\\n sys.stderr.write(\\\"fork #1 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\n sys.exit(1)\\n\\n # decouple from parent environment\\n os.chdir(\\\"/\\\")\\n os.setsid()\\n os.umask(0)\\n\\n # do second fork\\n try:\\n pid = os.fork()\\n if pid > 0:\\n # exit from second parent\\n sys.exit(0)\\n except OSError, e:\\n sys.stderr.write(\\\"fork #2 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\n sys.exit(1)\\n\\n # write pidfile\\n atexit.register(self.delpid)\\n pid = str(os.getpid())\\n logger.info(\\\"Daemon running as PID %s\\\", pid)\\n file(self.pidfile, 'w+').write(\\\"%s\\\\n\\\" % pid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b9fdd5e1ba19da7da09fba455d2e1af\",\n \"score\": \"0.6270956\",\n \"text\": \"def start(self):\\n import threading\\n threading.Thread(target=self.run_in_thread, daemon=True).start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b2db8d029b32361ce454fb73bebbe0\",\n \"score\": \"0.62333083\",\n \"text\": \"def start(self):\\n self.background = spawn(self._run)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2003056cdfb463bac7ca3eb7a80fa714\",\n \"score\": \"0.6220807\",\n \"text\": \"def start(self):\\n if self.__started is True:\\n # No need to stop twice.\\n return\\n self.__stop_sign.acquire()\\n self.__background.start()\\n self.__started = True\\n self.__stop_sign.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6f2358ddd59a26b69ddcdbec14021c\",\n \"score\": \"0.62140447\",\n \"text\": \"def start(self):\\n logging.info(\\\"starting background listener thread\\\")\\n for daemon in self._daemons:\\n daemon.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ef19e50e85f4a1c28d402ffffc3a20f\",\n \"score\": \"0.6212811\",\n \"text\": \"def run(self):\\n log.debug('Starter::run()')\\n try:\\n Server(conf.port).run()\\n except Exception as E:\\n log.critical(E)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7a9a8ff9466ca0eca63c259f87bf18d\",\n \"score\": \"0.6200567\",\n \"text\": \"def start_server(self):\\n lsock = self.socket(self.listen_host, self.listen_port, False,\\n self.prefer_ipv6,\\n tcp_keepalive=self.tcp_keepalive,\\n tcp_keepcnt=self.tcp_keepcnt,\\n tcp_keepidle=self.tcp_keepidle,\\n tcp_keepintvl=self.tcp_keepintvl)\\n\\n if self.daemon:\\n keepfd = self.get_log_fd()\\n keepfd.append(lsock.fileno())\\n self.daemonize(keepfd=keepfd, chdir=self.web)\\n\\n self.started() # Some things need to happen after daemonizing\\n\\n # Allow override of signals\\n original_signals = {\\n signal.SIGINT: signal.getsignal(signal.SIGINT),\\n signal.SIGTERM: signal.getsignal(signal.SIGTERM),\\n signal.SIGCHLD: signal.getsignal(signal.SIGCHLD),\\n }\\n signal.signal(signal.SIGINT, self.do_SIGINT)\\n signal.signal(signal.SIGTERM, self.do_SIGTERM)\\n if not multiprocessing:\\n # os.fork() (python 2.4) child reaper\\n signal.signal(signal.SIGCHLD, self.fallback_SIGCHLD)\\n else:\\n # make sure that _cleanup is called when children die\\n # by calling active_children on SIGCHLD\\n signal.signal(signal.SIGCHLD, self.multiprocessing_SIGCHLD)\\n\\n last_active_time = self.launch_time\\n try:\\n while True:\\n try:\\n try:\\n startsock = None\\n pid = err = 0\\n child_count = 0\\n\\n if multiprocessing:\\n # Collect zombie child processes\\n child_count = len(multiprocessing.active_children())\\n\\n time_elapsed = time.time() - self.launch_time\\n if self.timeout and time_elapsed > self.timeout:\\n self.msg('listener exit due to --timeout %s'\\n % self.timeout)\\n break\\n\\n if self.idle_timeout:\\n idle_time = 0\\n if child_count == 0:\\n idle_time = time.time() - last_active_time\\n else:\\n idle_time = 0\\n last_active_time = time.time()\\n\\n if idle_time > self.idle_timeout and child_count == 0:\\n self.msg('listener exit due to --idle-timeout %s'\\n % self.idle_timeout)\\n break\\n\\n try:\\n self.poll()\\n\\n ready = select.select([lsock], [], [], 1)[0]\\n if lsock in ready:\\n startsock, address = lsock.accept()\\n else:\\n continue\\n except self.Terminate:\\n raise\\n except Exception:\\n _, exc, _ = sys.exc_info()\\n if hasattr(exc, 'errno'):\\n err = exc.errno\\n elif hasattr(exc, 'args'):\\n err = exc.args[0]\\n else:\\n err = exc[0]\\n if err == errno.EINTR:\\n self.vmsg(\\\"Ignoring interrupted syscall\\\")\\n continue\\n else:\\n raise\\n\\n if self.run_once:\\n # Run in same process if run_once\\n self.top_new_client(startsock, address)\\n if self.ws_connection :\\n self.msg('%s: exiting due to --run-once'\\n % address[0])\\n break\\n elif multiprocessing:\\n self.vmsg('%s: new handler Process' % address[0])\\n p = multiprocessing.Process(\\n target=self.top_new_client,\\n args=(startsock, address))\\n p.start()\\n # child will not return\\n else:\\n # python 2.4\\n self.vmsg('%s: forking handler' % address[0])\\n pid = os.fork()\\n if pid == 0:\\n # child handler process\\n self.top_new_client(startsock, address)\\n break # child process exits\\n\\n # parent process\\n self.handler_id += 1\\n\\n except (self.Terminate, SystemExit, KeyboardInterrupt):\\n self.msg(\\\"In exit\\\")\\n # terminate all child processes\\n if multiprocessing and not self.run_once:\\n children = multiprocessing.active_children()\\n\\n for child in children:\\n self.msg(\\\"Terminating child %s\\\" % child.pid)\\n child.terminate()\\n\\n break\\n except Exception:\\n exc = sys.exc_info()[1]\\n self.msg(\\\"handler exception: %s\\\", str(exc))\\n self.vmsg(\\\"exception\\\", exc_info=True)\\n\\n finally:\\n if startsock:\\n startsock.close()\\n finally:\\n # Close listen port\\n self.vmsg(\\\"Closing socket listening at %s:%s\\\",\\n self.listen_host, self.listen_port)\\n lsock.close()\\n\\n # Restore signals\\n for sig, func in original_signals.items():\\n signal.signal(sig, func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6022bacdc93d6d41b1fc4467d4c4c5e\",\n \"score\": \"0.6199782\",\n \"text\": \"def start(self):\\n self.running = True\\n self.stopped = False\\n self.thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6022bacdc93d6d41b1fc4467d4c4c5e\",\n \"score\": \"0.6199782\",\n \"text\": \"def start(self):\\n self.running = True\\n self.stopped = False\\n self.thread.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabdfc2527f699f579c7a56d4041452b\",\n \"score\": \"0.619683\",\n \"text\": \"def daemonize(self):\\n # Do first fork\\n self.fork()\\n\\n # Decouple from parent environment\\n self.dettach_env()\\n # Do second fork\\n self.fork()\\n\\n # Flush standart file descriptors\\n sys.stdout.flush()\\n sys.stderr.flush()\\n\\n #\\n self.attach_stream('stdin', mode='r')\\n self.create_pidfile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef473a4367f08eee9fceb2e8b722517\",\n \"score\": \"0.6194529\",\n \"text\": \"def run(self):\\n while 'program running':\\n self._start.wait()\\n if self._verbose:\\n print('serving at port', self.port)\\n self.httpd.serve_forever()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec1785d39e4a7be074106f6a289597cf\",\n \"score\": \"0.6190639\",\n \"text\": \"def stop(self):\\n\\n if not os.path.exists(self._pid_file):\\n sys.stderr.write(\\n 'Pidfile {} does not exist; '\\n 'Considered daemon is not running;\\\\n'.format(self._pid_file))\\n sys.stderr.write('Stopping the daemon is failed')\\n exit(1)\\n\\n # Get the pid from the pidfile\\n pid = None\\n try:\\n with open(self._pid_file, 'r') as f:\\n pid = int(f.read().strip())\\n except IOError as e:\\n sys.stderr.write(\\n 'Error while reading pid from pidfile \\\"{}\\\": {}'\\n .format(self._pid_file, e))\\n sys.stderr.write('Stopping the daemon is failed\\\\n;')\\n exit(1)\\n\\n try:\\n cnt = 0\\n while Daemon._is_process_running(pid):\\n os.kill(pid, signal.SIGTERM)\\n cnt += 1\\n time.sleep(0.1)\\n os.kill(pid, signal.SIGTERM)\\n except OSError as e:\\n if 'No such process' in str(e):\\n # We here if daemon has terminated just after last\\n # \\\"_is process_running()\\\" call and before the next \\\"kill\\\".\\n # We OK with it.\\n pass\\n else:\\n sys.stderr.write('{}\\\\n'.format(e))\\n sys.stderr.write('Stopping the daemon if failed\\\\n')\\n #Note: pidfile is not removed in this case\\n sys.exit(1)\\n finally:\\n print('Kill try count: {}'.format(cnt))\\n\\n if os.path.exists(self._pid_file):\\n self._remove_pid_file()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37e5fbfc126323c888d25b764ed6804\",\n \"score\": \"0.6183899\",\n \"text\": \"def start(app):\\n _service(app, 'start')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ffa9eb51133ce9be761df60417a086\",\n \"score\": \"0.61708117\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--daemon', dest='daemon_mode', action='store_true')\\n arguments = parser.parse_args()\\n \\n daemon_running = Daemon.daemon_running()\\n reminders = Reminders()\\n\\n if arguments.daemon_mode:\\n if daemon_running:\\n print('\\\\nDaemon already running\\\\n')\\n sys.exit()\\n daemon = Daemon(reminders)\\n daemon.start()\\n\\n else:\\n if not daemon_running:\\n print('\\\\nDaemon not running\\\\n')\\n sys.exit()\\n interface = Urwid_Interface(reminders)\\n interface.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3562708fd14fc6ed6099ed16dff158b\",\n \"score\": \"0.6153397\",\n \"text\": \"def daemonize(self):\\n try:\\n pid = os.fork()\\n if pid > 0:\\n # be orphaned by the parent\\n sys.exit(0)\\n except OSError as e:\\n sys.stderr.write(\\\"fork #1 failed: %d (%s)\\\\n\\\"%(e.errno, e.strerror))\\n sys.exit(1)\\n\\n # decouple from the parent\\n os.chdir('/home/mmalinow/.daemons')\\n os.setsid()\\n os.umask(0)\\n\\n # do the second fork in order to lift up leadership\\n # session leader can still acquire access to tty\\n try:\\n pid=os.fork()\\n if pid > 0:\\n sys.exit(0)\\n except OSError as e:\\n sys.stderr.write(\\\"fork #2 failed: %d (%s)\\\\n\\\"%(e.errno, e.strerror))\\n sys.exit(1)\\n\\n # redirect standard file descriptors\\n sys.stdout.flush()\\n sys.stdin.flush()\\n sys.stderr.flush()\\n\\n si=file(self.stdin,'r')\\n so=file(self.stdout, 'a+')\\n se=file(self.stderr, 'a+', 0)\\n\\n os.dup2(si.fileno(), sys.stdin.fileno())\\n os.dup2(so.fileno(), sys.stdout.fileno())\\n os.dup2(se.fileno(), sys.stderr.fileno())\\n\\n atexit.register(self.delpid)\\n pid=str(os.getpid())\\n file(self.pidfile,'w+').write(\\\"%s\\\\n\\\" % pid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9505ecd63424f92aafe5e664ef9ff507\",\n \"score\": \"0.6124912\",\n \"text\": \"def start(self):\\n self._started = True\\n self._pyrustic_app.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df985a1119025a8a3a2b2db8f1f5e831\",\n \"score\": \"0.61214644\",\n \"text\": \"def startOrStop(self):\\n if(self.runningState):\\n self.stop()\\n else:\\n self.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f90cc730edb908af05d9dec57db0dc4\",\n \"score\": \"0.61172587\",\n \"text\": \"def onDaemonStartRun(daemon, event):\\n from .zeneventdWorkers import EventDEventletWorker\\n # Free up unnecessary database resources in parent zeneventd process\\n if daemon.options.daemon:\\n daemon.closedb()\\n daemon.closeAll()\\n daemon._workers.startWorkers()\\n reactor.run()\\n else:\\n worker = EventDEventletWorker()\\n worker.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730f6559b1209ef2ad6ba24e7f49f1bb\",\n \"score\": \"0.6107526\",\n \"text\": \"def stop_and_restart():\\n\\tupdater.stop()\\n\\tos.execl(sys.executable, sys.executable, *sys.argv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2a1dd2924a4f8c000ce3677102b0bb\",\n \"score\": \"0.6105633\",\n \"text\": \"def restart(self):\\n if not self._installed:\\n return\\n if self.is_running():\\n self._systemctl(\\\"restart\\\")\\n else:\\n self._systemctl(\\\"start\\\")\\n self._stopped = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751cffb772f9e7d33537f108087ae99f\",\n \"score\": \"0.6103976\",\n \"text\": \"def start(self):\\n LOG.info(\\\"Start service.\\\\n\\\")\\n\\n try:\\n LOG.debug(\\\"Wait for game start.\\\\n\\\")\\n self.wait_start()\\n LOG.debug(\\\"Game started.\\\\n\\\")\\n self.run()\\n finally:\\n LOG.info(\\\"Stop service.\\\\n\\\")\\n GPIO.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b581b72e52e8564176e26a5555042087\",\n \"score\": \"0.6099174\",\n \"text\": \"def start(self):\\n self._process = p = Process(target=self._monitor)\\n p.daemon = True\\n p.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95493c5f096765f8c4efe8e71a204ce\",\n \"score\": \"0.60959375\",\n \"text\": \"def _makeDaemon(self):\\n\\n try:\\n pid = os.fork()\\n if pid > 0:\\n # Exit first parent.\\n sys.exit(0)\\n except OSError as e:\\n m = \\\"Fork #1 failed: {}\\\".format(e)\\n print(m)\\n sys.exit(1)\\n\\n # Decouple from the parent environment.\\n os.chdir(\\\"/\\\")\\n os.setsid()\\n os.umask(0)\\n\\n # Do second fork.\\n try:\\n pid = os.fork()\\n if pid > 0:\\n # Exit from second parent.\\n sys.exit(0)\\n except OSError as e:\\n m = \\\"Fork #2 failed: {}\\\".format(e)\\n print(m)\\n sys.exit(1)\\n\\n m = \\\"The daemon process is going to background.\\\"\\n print(m)\\n\\n # Redirect standard file descriptors.\\n sys.stdout.flush()\\n sys.stderr.flush()\\n si = open(self.stdin, 'r')\\n so = open(self.stdout, 'a+')\\n se = open(self.stderr, 'a+')\\n os.dup2(si.fileno(), sys.stdin.fileno())\\n os.dup2(so.fileno(), sys.stdout.fileno())\\n os.dup2(se.fileno(), sys.stderr.fileno())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ae4e68179ce7be44619c06109478a7\",\n \"score\": \"0.60894555\",\n \"text\": \"def start(self):\\n subprocess.check_call(\\\"sudo service rsyslog restart\\\", shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f642d54e37e66fac21b58c44f4408704\",\n \"score\": \"0.60719544\",\n \"text\": \"def test_daemon_runandstop(self) -> None:\\n cmdstr = \\\"/usr/bin/rfcomm connect /dev/rfcomm0 88:6B:0F:86:4D:F9\\\"\\n cmdstr = \\\"sleep 1000\\\"\\n dt = Taskmeister.DaemonTaskMeister(self.logger, cmdstr, 0)\\n gevent.sleep(1)\\n stat = dt.get_status()\\n print(\\\"GOT STAT: {}\\\".format(stat))\\n assert stat == STATUS_RUNNING, \\\"expected status running\\\"\\n print(\\\"numchecks: {}\\\".format(dt.numchecks))\\n dt.stop_cmd(do_wait=True)\\n stat = dt.get_status()\\n print(\\\"GOT STAT: {}\\\".format(stat))\\n assert stat == STATUS_STOPPED, \\\"expected status stopped\\\"\\n # assert False, \\\"force fail!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c9c1fa2816b3abefbdabdfdb2f56be\",\n \"score\": \"0.60676366\",\n \"text\": \"def start(self):\\n reaper = threading.Thread(target=self.cleanup)\\n reaper.daemon = True\\n reaper.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f426db7ff9d5a83e7fc7d038425849d\",\n \"score\": \"0.60526675\",\n \"text\": \"def start(self):\\n self.setIsMainloopAlive(True)\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"066bd1e8afa2abd582b19218eeb58d6f\",\n \"score\": \"0.6051409\",\n \"text\": \"def stop(self):\\n if not self._installed or self._stopped:\\n return\\n if self.is_running():\\n self._systemctl(\\\"stop\\\")\\n self._stopped = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c9859a66793f1a8331bf06df862762\",\n \"score\": \"0.6048351\",\n \"text\": \"def shutdown(self):\\n self._logger.info(\\\"user asked for daemon termination.\\\")\\n self._stop = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b22613f01b3567da40aad90b8aa1c90c\",\n \"score\": \"0.6045158\",\n \"text\": \"def run(self):\\n self.srv = make_server(self.host, int(self.port), self.app, handler_class=self.handler_cls)\\n try:\\n self.srv.serve_forever()\\n except Exception:\\n import traceback\\n traceback.print_exc()\\n # Failed to start\\n self.srv = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa738604b9d0887c11f53794e6895bc\",\n \"score\": \"0.60283566\",\n \"text\": \"def datalogger_start(self):\\n\\n if not self.datalogger_active:\\n self.__stop_event.clear()\\n self.__start_event.set()\\n self.__dump_event.clear()\\n else:\\n self.__logger.error('Start failed: datalogger is already running')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa2f19968de366a21b8e3aa76af1d98\",\n \"score\": \"0.60218036\",\n \"text\": \"def start(self):\\n t0 = threading.Thread(target=self.service, args=())\\n t0.deamon = True\\n t0.start()\\n\\n t1 = threading.Thread(target=self.processor, args=())\\n t1.daemon = True\\n t1.start()\\n\\n t2 = threading.Thread(target=self.link_egress, args=())\\n t2.daemon = True\\n t2.start()\\n\\n for neighbour in self.neighbours(self.vrid):\\n if self.vrid < neighbour:\\n t = threading.Thread(target=self.setup_link, args=(neighbour,))\\n t.daemon = True\\n t.start()\\n pass\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b92c99f647d2467f0f6d1a38a060e53b\",\n \"score\": \"0.6019435\",\n \"text\": \"def main():\\n start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb5a2961a211a6bd788f8ccced99b1e9\",\n \"score\": \"0.6008547\",\n \"text\": \"def main():\\n if 'start' in sys.argv:\\n print \\\"Trying to run the app...\\\"\\n\\n if CLI_ARGS.daemonize is True:\\n if os.path.isfile(PID_FILE_PATH) is not True:\\n print \\\"Trying to run in daemonized mode. Check log file for further insight: \\\" \\\\\\n \\\"%s\\\", LOG_FILE_PATH\\n daemon = setup_daemon()\\n daemon.start()\\n else:\\n print \\\"Tried to run the app but an existing pid file found. \\\" \\\\\\n \\\"Looks like it was not shutdown gracefully last time.\\\" \\\\\\n \\\"file: \\\" + PID_FILE_PATH\\n else:\\n print \\\"Running in foreground, CTRL+C to exit the process.\\\"\\n start_leexportpy_jobs()\\n\\n elif 'stop' in sys.argv:\\n try:\\n print \\\"Trying to terminate the daemon...\\\"\\n terminate()\\n except IOError as exception:\\n if exception.errno == 2: # meaning pid file not found and so there is no running\\n # instance\\n print \\\"No leexportpy instance found so it could not be stopped.\\\"\\n elif exception.errno == 3:\\n print \\\"Pid file found but no such process was found. Please remove the pid file \\\" \\\\\\n \\\"at: \\\" + PID_FILE_PATH\\n else:\\n print \\\"An unexpected error occurred while stopping leexportpy: \\\", exception\\n logging.exception(\\\"An unexpected error occurred while stopping leexportpy\\\")\\n else:\\n print \\\"This should not happen.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e1b93cb27a72f3b8e4fcc469fcd122\",\n \"score\": \"0.59968793\",\n \"text\": \"def start(self):\\n self.server.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99e1b93cb27a72f3b8e4fcc469fcd122\",\n \"score\": \"0.59968793\",\n \"text\": \"def start(self):\\n self.server.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24a55cb8e1dc983908bee8040e3b315\",\n \"score\": \"0.5989902\",\n \"text\": \"def start(self):\\n\\tif self.m_handle is not None:\\n\\t raise Error, 'Service already started.'\\n\\tself.m_stop = False\\n\\tself.m_handle = _cpg.initialize(self)\\n\\t_cpg.join(self.m_handle, self.m_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dd1102379e066d3dc84988f4eabdb2d\",\n \"score\": \"0.59896356\",\n \"text\": \"def start(self):\\n if not self._started.is_set():\\n # If daemone=False python forces join to be called which closes the thread properly.\\n self.daemon = self.force_non_daemon or self.daemon\\n if self.force_non_daemon:\\n self.daemon = False\\n\\n super(Thread, self).start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5752e322bbc9a8a5e361324a1081d478\",\n \"score\": \"0.5988487\",\n \"text\": \"def start(conf_file_path=None):\\n args = [sys.argv[0], '-daemon']\\n if conf_file_path is not None:\\n args.append('-configuration-path:{0}'.format(conf_file_path))\\n\\n with open(os.devnull, 'w') as devnull:\\n subprocess.Popen(args, stdout=devnull, stderr=devnull)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cf9cee1b1701db1e4c053d29b85d93b\",\n \"score\": \"0.5977895\",\n \"text\": \"def _daemonize(func, args, cfg):\\n class _Daemonizer(_daemons.RunDaemon):\\n def run(self):\\n func(args, cfg)\\n daemon = _Daemonizer(pidfile=args.pid_file)\\n action = args.action\\n if action == 'start':\\n daemon.start()\\n elif action == 'stop':\\n daemon.stop()\\n elif action == 'restart' or action == 'reload':\\n daemon.restart()\\n else:\\n LOG.error('Unsupported daemon action %s, aborting!', action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17048cfc384642f45113c7caa139f95e\",\n \"score\": \"0.5969656\",\n \"text\": \"def _daemonize():\\n global run_proc\\n try:\\n print(\\\"Persisting process...\\\")\\n signal.signal(signal.SIGTERM, _exit)\\n signal.signal(signal.SIGINT, _exit)\\n\\n if(G.VERBOSE):\\n print(\\\"Building engine...\\\")\\n en = engine.Engine()\\n\\n if(G.VERBOSE):\\n print(\\\"Building wrapper...\\\")\\n ps = pubsub.Wrapper(en)\\n\\n print(\\\"Ready. Waiting for requests...\\\")\\n\\n while run_proc:\\n ps.heartbeat()\\n time.sleep(10)\\n except KeyboardInterrupt:\\n _exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09249ed9038dbc6ba46f71e57f7e63f\",\n \"score\": \"0.5967777\",\n \"text\": \"def start(self):\\n while self.stay_alive:\\n if self.redis and self.needs_running:\\n self._run()\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f426011c770ff7d5b74e51eb10a3a2a1\",\n \"score\": \"0.5960717\",\n \"text\": \"def run(self):\\n for c in self.servers:\\n c.startup()\\n\\n try:\\n self._loop.run_forever()\\n except KeyboardInterrupt:\\n print(\\\"\\\\nKeyboard Interrupt: shutting down...\\\")\\n\\n for c in self.servers:\\n c.shutdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11be6a6994111b6f24d310ac46089d20\",\n \"score\": \"0.59557205\",\n \"text\": \"def start(self):\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11be6a6994111b6f24d310ac46089d20\",\n \"score\": \"0.59557205\",\n \"text\": \"def start(self):\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d035464e9c96a9bbebb4ee956cfff88e\",\n \"score\": \"0.5954812\",\n \"text\": \"def stop_global_daemon():\\n address, pid = _read_daemon()\\n if address is None:\\n return\\n\\n try:\\n Client(address=address)\\n except DaemonNotRunningError:\\n pass\\n else:\\n os.kill(pid, signal.SIGTERM)\\n\\n try:\\n os.remove(os.path.join(CONFIG_DIR, 'daemon'))\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efe8d4219941177fec531272b1f39dd\",\n \"score\": \"0.59488726\",\n \"text\": \"def start(self):\\n if not self.python:\\n self.prog = Popen(self.path,\\n shell = True,\\n stdin = PIPE,\\n stdout = PIPE)\\n else:\\n print self.path\\n self.prog = Popen(\\\"python %s -u\\\" % self.path,\\n shell = True,\\n stdin = PIPE,\\n stdout = PIPE)\\n self.alive = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c590a2f91dec5324106465c4635d9f40\",\n \"score\": \"0.5936039\",\n \"text\": \"def daemon(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef3b03b9c9d9472bcb7302ce1ed4e4f7\",\n \"score\": \"0.5934361\",\n \"text\": \"def daemonize(self):\\n\\t\\tcount = 0\\n\\n\\t\\ttry: \\n\\t\\t\\tpid = os.fork() \\n\\t\\t\\tif pid > 0:\\n\\t\\t\\t\\t# exit first parent\\n\\t\\t\\t\\tsys.exit(0) \\n\\t\\texcept OSError, e: \\n\\t\\t\\tsys.stderr.write(\\\"fork #1 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\n\\t\\t\\tsys.exit(1)\\n\\t\\n\\t\\t# decouple from parent environment\\n\\t\\tos.setsid() \\n\\t\\tos.umask(0) \\n\\t\\n\\t\\t# do second fork\\n\\t\\ttry: \\n\\t\\t\\tpid = os.fork() \\n\\t\\t\\tif pid > 0:\\n\\t\\t\\t\\t# exit from second parent\\n\\t\\t\\t\\tsys.exit(0) \\n\\t\\texcept OSError, e: \\n\\t\\t\\tsys.stderr.write(\\\"fork #2 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\n\\t\\t\\tsys.exit(1) \\n\\t\\n\\t\\t# redirect standard file descriptors\\n\\t\\tsys.stdout.flush()\\n\\t\\tsys.stderr.flush()\\n\\t\\n\\t\\tsi = file(self.stdin, 'r')\\n\\t\\tso = file(self.stdout, 'a+')\\n\\t\\tse = file(self.stderr, 'a+', 0)\\n\\n\\t\\tatexit.register(self.onstop)\\n\\t\\tsignal(SIGTERM, lambda signum, stack_frame: exit())\\n \\t\\n\\t\\t# write pidfile \\n\\t\\tpid = str(os.getpid())\\n\\t\\tfile(self.pidfile,'w+').write(\\\"%s\\\\n\\\" % pid)\\n\\n\\t\\t\\n\\t\\taccess_token = \\\"925901916512641026-IMcQ5hTUfyfZp0hAWxBjKDPQxhmfdHE\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61c665dab10458d42e7841f732f935f5\",\n \"score\": \"0.5933297\",\n \"text\": \"def start():\\n sudo('service uwsgi start %s' % PROJECT_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"809cc7b9f08bc0793ccf4145cd82c8b3\",\n \"score\": \"0.592385\",\n \"text\": \"def daemonize(self):\\r\\n try:\\r\\n pid = os.fork()\\r\\n if pid > 0:\\r\\n # Exit the first parent.\\r\\n sys.exit(0)\\r\\n except OSError, e:\\r\\n sys.stderr.write(\\\"fork #1 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\r\\n sys.exit(1)\\r\\n\\r\\n # Decouple from the parent environment.\\r\\n os.chdir(\\\"/\\\")\\r\\n os.setsid()\\r\\n os.umask(0)\\r\\n\\r\\n # Do the second fork.\\r\\n try:\\r\\n pid = os.fork()\\r\\n if pid > 0:\\r\\n # Exit from the second parent.\\r\\n sys.exit(0)\\r\\n except OSError, e:\\r\\n sys.stderr.write(\\\"fork #2 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\r\\n sys.exit(1)\\r\\n\\r\\n # Redirect standard file descriptors. Because the daemon has no\\r\\n # controlling terminal, we want to avoid side effects from reading and\\r\\n # writing to/from the standard file descriptors.\\r\\n sys.stdout.flush()\\r\\n sys.stderr.flush()\\r\\n si = file(self.stdin, 'r')\\r\\n so = file(self.stdout, 'a+')\\r\\n se = file(self.stderr, 'a+', 0)\\r\\n # Use os.dup2() with the fileno() instead of assigning directly to\\r\\n # sys.stdout because the former will also affect any C-level sys.stdout\\r\\n # calls.\\r\\n os.dup2(si.fileno(), sys.stdin.fileno()) # Essentially: sys.stdin = si\\r\\n os.dup2(so.fileno(), sys.stdout.fileno()) # Essentially: sys.stdout = so\\r\\n os.dup2(se.fileno(), sys.stderr.fileno()) # Essentially: sys.stderr = se\\r\\n\\r\\n # Write the pidfile.\\r\\n atexit.register(self.delpid)\\r\\n pid = str(os.getpid())\\r\\n open(self.pidfile, 'w+').write(\\\"%s\\\\n\\\" % pid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"809cc7b9f08bc0793ccf4145cd82c8b3\",\n \"score\": \"0.592385\",\n \"text\": \"def daemonize(self):\\r\\n try:\\r\\n pid = os.fork()\\r\\n if pid > 0:\\r\\n # Exit the first parent.\\r\\n sys.exit(0)\\r\\n except OSError, e:\\r\\n sys.stderr.write(\\\"fork #1 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\r\\n sys.exit(1)\\r\\n\\r\\n # Decouple from the parent environment.\\r\\n os.chdir(\\\"/\\\")\\r\\n os.setsid()\\r\\n os.umask(0)\\r\\n\\r\\n # Do the second fork.\\r\\n try:\\r\\n pid = os.fork()\\r\\n if pid > 0:\\r\\n # Exit from the second parent.\\r\\n sys.exit(0)\\r\\n except OSError, e:\\r\\n sys.stderr.write(\\\"fork #2 failed: %d (%s)\\\\n\\\" % (e.errno, e.strerror))\\r\\n sys.exit(1)\\r\\n\\r\\n # Redirect standard file descriptors. Because the daemon has no\\r\\n # controlling terminal, we want to avoid side effects from reading and\\r\\n # writing to/from the standard file descriptors.\\r\\n sys.stdout.flush()\\r\\n sys.stderr.flush()\\r\\n si = file(self.stdin, 'r')\\r\\n so = file(self.stdout, 'a+')\\r\\n se = file(self.stderr, 'a+', 0)\\r\\n # Use os.dup2() with the fileno() instead of assigning directly to\\r\\n # sys.stdout because the former will also affect any C-level sys.stdout\\r\\n # calls.\\r\\n os.dup2(si.fileno(), sys.stdin.fileno()) # Essentially: sys.stdin = si\\r\\n os.dup2(so.fileno(), sys.stdout.fileno()) # Essentially: sys.stdout = so\\r\\n os.dup2(se.fileno(), sys.stderr.fileno()) # Essentially: sys.stderr = se\\r\\n\\r\\n # Write the pidfile.\\r\\n atexit.register(self.delpid)\\r\\n pid = str(os.getpid())\\r\\n open(self.pidfile, 'w+').write(\\\"%s\\\\n\\\" % pid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a9a2ca5f02056ff687afb75fa9eb94\",\n \"score\": \"0.5923208\",\n \"text\": \"def start(self):\\n if self._isRunning:\\n return\\n\\n if self._cease.is_set():\\n self._cease.clear() # restart\\n\\n class Runner(threading.Thread):\\n @classmethod\\n def run(cls):\\n nextRunAt = cls.setNextRun()\\n\\n while not self._cease.is_set():\\n if datetime.now() >= nextRunAt:\\n self._run()\\n nextRunAt = cls.setNextRun()\\n\\n @classmethod\\n def setNextRun(cls):\\n # return datetime.now() + timedelta(seconds=self._interval)\\n return self._interval.nextRunAt()\\n\\n runner = Runner()\\n runner.setDaemon(True)\\n runner.start()\\n self._isRunning = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"371e0b0cd90e97837f12851832cbc044\",\n \"score\": \"0.5920431\",\n \"text\": \"def start(self):\\n if not self.local_mode:\\n self.client.start()\\n self.switch.start()\\n self.config.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59b487336402e0706a6bfc1f49d9a738\",\n \"score\": \"0.59114337\",\n \"text\": \"def handle_start(args):\\n # do start up here\\n cmds = build_dockerrun(args[\\\"latest\\\"])\\n for cmd in cmds:\\n # TODO get better solution than sleep\\n run_subprocess(cmd, shell=True)\\n time.sleep(8) # brute force give docker time to complete its stuff.\\n time.sleep(10) # As above, but let docker catch up before login\\n handle_login(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdda8a6e0e30366dc20d32a1836f61d1\",\n \"score\": \"0.5907747\",\n \"text\": \"def stopDaemon(self):\\n return _eveusb.Controller_stopDaemon(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831326dbb51a7e62cb7beac03f0af2b6\",\n \"score\": \"0.5906053\",\n \"text\": \"def start(self):\\n self.runListenLoop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cc8335477dcc4d589c0795678185441\",\n \"score\": \"0.590142\",\n \"text\": \"def start() -> None:\\n \\n active_server_name = srv_mgr.get_active_server_name()\\n if not active_server_name:\\n print(f'No active server found. Set an active server?\\\\n')\\n sys.exit(1)\\n \\n if srv_mgr.is_server_running():\\n print(f'Looks like this server {active_server_name} is already running. Use restart instead of start.')\\n sys.exit(1)\\n\\n if srv_mgr.start_server():\\n print(f\\\"Server {active_server_name} started succesfully!\\\")\\n sys.exit(0)\\n\\n print(f\\\"Server {active_server_name} could not be started!\\\")\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65bb5ca38c06afc23fd294ea9beadc7\",\n \"score\": \"0.58973867\",\n \"text\": \"async def run(self) -> None:\\n try:\\n await self.start()\\n except (KeyboardInterrupt, SystemExit) as e:\\n logging.info(e)\\n await self.close()\\n finally:\\n await self.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0258c4e6d6cae892fb8a42679e0f1b5f\",\n \"score\": \"0.5895067\",\n \"text\": \"def startService(handler, *args, **kwargs):\\n\\n preProcessHandler(handler)\\n enforceSolitude(handler)\\n if handler._dbus_kill: sys.exit()\\n if handler._dbus_go_daemon: _goDaemon()\\n\\n print(\\\"{:s} daemon: {:d}\\\".format(handler._dbus_service, os.getpid()))\\n\\n postProcessHandler(handler, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee4842342cadde5aaa2f15a458c1641\",\n \"score\": \"0.5889479\",\n \"text\": \"def start(self):\\n if self.service.getProcessDicts() == []:\\n return\\n\\n def start2(process, nbr=None):\\n\\n cwd = process[\\\"cwd\\\"]\\n # args['process'] = process\\n if nbr is None:\\n self.stop()\\n\\n tcmd = process[\\\"cmd\\\"]\\n if tcmd == \\\"jspython\\\":\\n tcmd = \\\"source %s/env.sh;jspython\\\" % (j.dirs.base)\\n\\n targs = process[\\\"args\\\"]\\n tuser = process[\\\"user\\\"]\\n if tuser == \\\"\\\":\\n tuser = \\\"root\\\"\\n tlog = self.service.hrd.getBool(\\\"process.log\\\", default=True)\\n env = process[\\\"env\\\"]\\n\\n startupmethod = process[\\\"startupmanager\\\"]\\n domain, name = self._getDomainName(process)\\n if nbr is not None:\\n name = \\\"%s.%d\\\" % (name, i)\\n log(\\\"Starting %s:%s\\\" % (domain, name))\\n\\n if startupmethod == 'upstart':\\n # check if we are in our docker image which uses myinit instead\\n # of upstart\\n if j.sal.fs.exists(path=\\\"/etc/my_init.d/\\\"):\\n cmd2 = \\\"%s %s\\\" % (tcmd, targs)\\n extracmds = \\\"\\\"\\n if cmd2.find(\\\";\\\") != -1:\\n parts = cmd2.split(\\\";\\\")\\n extracmds = \\\"\\\\n\\\".join(parts[:-1])\\n cmd2 = parts[-1]\\n\\n C = \\\"#!/bin/sh\\\\nset -e\\\\ncd %s\\\\nrm -f /var/log/%s.log\\\\n%s\\\\nexec %s >>/var/log/%s.log 2>&1\\\\n\\\" % (\\n cwd, name, extracmds, cmd2, name)\\n j.sal.fs.remove(\\\"/var/log/%s.log\\\" % name)\\n j.sal.fs.createDir(\\\"/etc/service/%s\\\" % name)\\n path2 = \\\"/etc/service/%s/run\\\" % name\\n j.sal.fs.writeFile(path2, C)\\n j.sal.fs.chmod(path2, 0o770)\\n j.sal.process.execute(\\n \\\"sv start %s\\\" % name, die=False, outputToStdout=False, outputStderr=False, captureout=False)\\n else:\\n j.sal.ubuntu.service_install(name, tcmd, pwd=cwd, env=env)\\n j.sal.ubuntu.service_start(name)\\n\\n elif startupmethod == \\\"tmux\\\":\\n j.tools.cuisine.local.tmux.executeInScreen(\\n domain, name, tcmd + \\\" \\\" + targs, cwd=cwd, env=env, user=tuser) # , newscr=True)\\n\\n else:\\n raise j.exceptions.RuntimeError(\\n \\\"startup method not known or disabled:'%s'\\\" % startupmethod)\\n\\n # if msg==\\\"\\\":\\n # pids=self.getPids(ifNoPidFail=False,wait=False)\\n # if len(pids) != self.numprocesses:\\n # msg=\\\"Could not start, did not find enough running instances, needed %s, found %s\\\"%(self.numprocesses,len(pids))\\n\\n # if msg==\\\"\\\" and pids!=[]:\\n # for pid in pids:\\n # test=j.sal.process.isPidAlive(pid)\\n # if test==False:\\n # msg=\\\"Could not start, pid:%s was not alive.\\\"%pid\\n\\n # if log!=\\\"\\\":\\n # msg=\\\"%s\\\\nlog:\\\\n%s\\\\n\\\"%(msg,log)\\n\\n # self.raiseError(msg)\\n # return\\n\\n isrunning = self.check_up(wait=False)\\n if isrunning:\\n return\\n\\n processes = self.service.getProcessDicts()\\n for i, process in enumerate(processes):\\n\\n if \\\"platform\\\" in process:\\n if not j.core.platformtype.myplatform.checkMatch(process[\\\"platform\\\"]):\\n continue\\n if len(processes) > 1:\\n start2(process, nbr=i)\\n else:\\n start2(process)\\n\\n isrunning = self.check_up()\\n if isrunning is False:\\n msg = \\\"\\\"\\n\\n if self.service.getTCPPorts() == [0]:\\n print('Done ...')\\n elif self.service.getTCPPorts() != []:\\n ports = \\\",\\\".join([str(item)\\n for item in self.service.getTCPPorts()])\\n msg = \\\"Could not start:%s, could not connect to ports %s.\\\" % (\\n self.service, ports)\\n j.events.opserror_critical(msg, \\\"service.start.failed.ports\\\")\\n else:\\n j.events.opserror_critical(\\n \\\"could not start:%s\\\" % self.service, \\\"service.start.failed.other\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d496efa82f1a3513067c86a8e98f2a\",\n \"score\": \"0.58878255\",\n \"text\": \"def start(self):\\n self.process = Process(target=self.listen)\\n self.process.start()\\n self.process.join()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24a3e8a0e540cc5cd263e42c79cc22b0\",\n \"score\": \"0.58815247\",\n \"text\": \"def start_subprocess(self):\\n \\n self.server = InetdService(self)\\n yield from self.server.run()\\n\\n self.loginfo(\\\"inetd service {0} listening on port {1}\\\".format(self.name, self.port))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0ce12a6cfad5057f4f9c5074e0c9ff9\",\n \"score\": \"0.58737564\",\n \"text\": \"def daemonize(self):\\n if os.fork(): # launch child and...\\n os._exit(0) # kill off parent\\n os.setsid()\\n if os.fork(): # launch child and...\\n os._exit(0) # kill off parent again.\\n os.umask(63) # 077 in octal\\n null = os.open('/dev/null', os.O_RDWR)\\n for i in range(3):\\n try:\\n os.dup2(null, i)\\n except OSError as e:\\n if e.errno != errno.EBADF:\\n raise\\n os.close(null)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"299579829c53783643e1857352e0c987\",\n \"score\": \"0.5852781\",\n \"text\": \"def stop(self):\\n if self.pidfile is None:\\n raise DaemonError('Cannot stop daemon without PID file')\\n\\n pid = self._read_pidfile()\\n if pid is None:\\n # I don't think this should be a fatal error\\n self._emit_warning('{prog} is not running'.format(prog=self.prog))\\n return\\n\\n self._emit_message('Stopping {prog} ... '.format(prog=self.prog))\\n\\n try:\\n # Try to terminate the process\\n os.kill(pid, signal.SIGTERM)\\n except OSError as ex:\\n self._emit_failed()\\n self._emit_error(str(ex))\\n sys.exit(1)\\n\\n _, alive = psutil.wait_procs([psutil.Process(pid)], timeout=self.stop_timeout)\\n if alive:\\n # The process didn't terminate for some reason\\n os.kill(pid, signal.SIGKILL)\\n time.sleep(0.5)\\n # Hahahaha. Do you feel alive now?\\n\\n self._emit_ok()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5b72311bfe6eaff22fd5116e7fd7fc\",\n \"score\": \"0.5849422\",\n \"text\": \"def start(self) -> None:\\n if self._runner:\\n raise RuntimeError(\\\"Runner is already started and cannot restart...\\\")\\n self._runner = threading.Thread(target=self._start_pull)\\n self._runner.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2f093b737479874b6fca297b35eba15\",\n \"score\": \"0.5849257\",\n \"text\": \"def run():\\n stop()\\n fabric.api.run('puppet agent --no-daemonize --onetime --verbose', warn_only=True)\\n fabric.api.run('service puppet start')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ff27408b49de465f1991432b3c3db5\",\n \"score\": \"0.5845146\",\n \"text\": \"def start_(self):\\n\\t\\tif not self.running:\\n\\t\\t\\tself._start = time.time() - self._stop\\n\\t\\t\\tself.running = True\\n\\t\\t\\tself.autoStop = False\\n\\t\\t\\tself.autoStop2 = False\\n\\t\\t\\tself.refresher.resume()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8e77cea00c5e405dcfee2e255cdca1\",\n \"score\": \"0.58441657\",\n \"text\": \"def start(self):\\n self.log.debug(\\\"(Re)starting\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06347100c5d25a23c13c075938520e7a\",\n \"score\": \"0.5831052\",\n \"text\": \"def start_service(service):\\n subprocess.call([\\\"service\\\", service, \\\"start\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90475e139f22b1c1743513d6ef7a44bc\",\n \"score\": \"0.5827442\",\n \"text\": \"def daemonlized():\\n \\n WORKDIR='/'\\n UMASK=0\\n\\n if os.path.exists(PIDFILE):\\n raise RuntimeError('Already running')\\n \\n if hasattr(os, \\\"devnull\\\"):\\n REDIRECT_TO = os.devnull\\n else:\\n REDIRECT_TO = \\\"/dev/null\\\"\\n \\n try:\\n pid = os.fork()\\n except OSError as e:\\n raise Exception(\\\"%s [%d]\\\" % (e.strerror, e.errno))\\n if pid == 0:\\n os.setsid()\\n signal.signal(signal.SIGHUP, signal.SIG_IGN)\\n try:\\n pid = os.fork()\\n except OSError as e:\\n raise Exception(\\\"%s [%d]\\\" % (e.strerror, e.errno))\\n\\n if pid == 0:\\n os.chdir(WORKDIR)\\n os.umask(UMASK)\\n else:\\n os._exit(0)\\n else:\\n os._exit(0)\\n\\n sys.stdout.flush()\\n sys.stderr.flush()\\n si = open(os.devnull, 'r')\\n so = open(os.devnull, 'a+')\\n se = open(os.devnull, 'a+')\\n \\n os.dup2(si.fileno(), sys.stdin.fileno())\\n os.dup2(so.fileno(), sys.stdout.fileno())\\n os.dup2(se.fileno(), sys.stderr.fileno())\\n atexit.register(lambda: os.remove(PIDFILE))\\n with open(PIDFILE,'w') as f:\\n print(os.getpid(),file=f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b983bc86e55a779720cb90c8a4fe08f\",\n \"score\": \"0.5817107\",\n \"text\": \"def execute(self):\\n self.start()\\n while self.running:\\n self.run()\\n self.terminate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"920e84385a9a2129c18f11c1cbba5438\",\n \"score\": \"0.58117104\",\n \"text\": \"def stop(cls):\\n if cls._pid:\\n os.kill(cls._pid, signal.SIGTERM)\\n cls._pid = None\\n cls.datadir = None\\n wait_until(lambda: not is_running(cls._port))\\n cls._port = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60e7f462e0c4d783a3e2011a46c2d64\",\n \"score\": \"0.57922107\",\n \"text\": \"def start(self):\\n for servo in self.servos:\\n if not servo.is_running():\\n servo.start()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b152e9a21a5f896785908fdf9e8b2a\",\n \"score\": \"0.5782499\",\n \"text\": \"def start(keep_work_dir=False):\\n\\tprint \\\"Starting asd\\\"\\n\\n\\tif not keep_work_dir:\\n\\t\\tinit_work_dir()\\n\\n\\tinit_state_dirs()\\n\\tinterceptor = init_interceptor()\\n\\tsearch_path = [os.sep + os.path.join(\\\"usr\\\", \\\"bin\\\")]\\n\\n\\tif \\\"ASREPO\\\" in os.environ:\\n\\t\\trepo = absolute_path(os.environ[\\\"ASREPO\\\"])\\n\\t\\tuname = os.uname()\\n\\t\\tsearch_path = [os.path.join(repo, \\\"target\\\", uname[0] + \\\"-\\\" + uname[4], \\\"bin\\\")] + search_path\\n\\n\\tprint \\\"asd search path is\\\", search_path\\n\\n\\tfor path in search_path:\\n\\t\\tasd_path = os.path.join(path, \\\"asd\\\")\\n\\n\\t\\tif os.path.exists(asd_path):\\n\\t\\t\\tbreak\\n\\telse:\\n\\t\\traise Exception(\\\"No asd executable found\\\")\\n\\n\\tif not SHOW_ASD_OUTPUT:\\n\\t\\tdev_null = open(os.devnull, \\\"w\\\")\\n\\t\\tredirect = {\\\"stdout\\\": dev_null, \\\"stderr\\\": subprocess.STDOUT}\\n\\telse:\\n\\t\\tredirect = {}\\n\\n\\ttemp_file = absolute_path(\\\"aerospike.conf\\\")\\n\\tconf_file = []\\n\\tconf_file.append(create_conf_file(temp_file, 3000, None, 1))\\n\\tconf_file.append(create_conf_file(temp_file, 4000, 3000, 2))\\n\\n\\tos.environ[\\\"LD_PRELOAD\\\"] = interceptor\\n\\n\\tfor index in xrange(1, 3):\\n\\t\\tcommand = [asd_path, \\\"--config-file\\\", conf_file[index - 1]]\\n\\t\\tprint \\\"Executing\\\", command\\n\\t\\tGLOBALS[\\\"asd-\\\" + str(index)] = subprocess.Popen(command, cwd=absolute_path(\\\".\\\"), **redirect)\\n\\n\\tdel os.environ[\\\"LD_PRELOAD\\\"]\\n\\n\\tprint \\\"Connecting client\\\"\\n\\tconfig = {\\\"hosts\\\": [(\\\"127.0.0.1\\\", PORT)]}\\n\\n\\tfor attempt in xrange(CLIENT_ATTEMPTS):\\n\\t\\ttry:\\n\\t\\t\\tGLOBALS[\\\"client\\\"] = aerospike.client(config).connect()\\n\\t\\t\\tbreak\\n\\t\\texcept Exception:\\n\\t\\t\\tif attempt < CLIENT_ATTEMPTS - 1:\\n\\t\\t\\t\\ttime.sleep(1)\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise\\n\\n\\tprint \\\"Client connected\\\"\\n\\ttime.sleep(5) # let the cluster fully form and the client learn about it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8ae3603639a59299fc7ef6dd37bd86\",\n \"score\": \"0.5780658\",\n \"text\": \"def start(self):\\n dc = docker.from_env()\\n container = dc.containers.get(self._name)\\n container.start()\\n # Give it some time to start, so subsequent calls work\\n time.sleep(1)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994391,"cells":{"query_id":{"kind":"string","value":"09f40ecffd6b941aa945901d297b2fa9"},"query":{"kind":"string","value":"u\"\"\"Przetwarzanie slowa, sprawdzenie jego typu i dodanie go do odpowiedniej listy."},"positive_passages":{"kind":"list like","value":[{"docid":"da2691c3ddbacfd0456af75842c22981","score":"0.0","text":"def __processWord(word):\n if len(word) == 3 or word.isupper():\n if __isRome(word):\n utility.add_to_dict(__rome, word)\n else:\n utility.add_to_dict(__shortcuts, word)\n else:\n utility.add_to_dict(__results, word)","title":""}],"string":"[\n {\n \"docid\": \"da2691c3ddbacfd0456af75842c22981\",\n \"score\": \"0.0\",\n \"text\": \"def __processWord(word):\\n if len(word) == 3 or word.isupper():\\n if __isRome(word):\\n utility.add_to_dict(__rome, word)\\n else:\\n utility.add_to_dict(__shortcuts, word)\\n else:\\n utility.add_to_dict(__results, word)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4f87fe47e49d703ab17987bcba1aea1a","score":"0.6794209","text":"def slow() -> None:","title":""},{"docid":"4780e645539e45c292c4aeed12fc3a85","score":"0.595264","text":"def slower(self):\n pass","title":""},{"docid":"d3a3f11a4c3460c2ca69554bda5f8005","score":"0.59418374","text":"def speed_list(self) -> list:\n return SPEED_LIST_HA","title":""},{"docid":"1d028d89ba29631d2ab2787adc39a3d9","score":"0.5633691","text":"def dyson_speed_list(self) -> list:\n return SPEED_LIST_DYSON","title":""},{"docid":"50c6e51e0e0c9f3d65c75514fd98ef83","score":"0.5611326","text":"def speed_list(self):\n return SPEED_LIST","title":""},{"docid":"50c6e51e0e0c9f3d65c75514fd98ef83","score":"0.5611326","text":"def speed_list(self):\n return SPEED_LIST","title":""},{"docid":"3871a88541c3e393d7e4ede88209c64b","score":"0.55062443","text":"def slowdown(self):\n self.speed = 3","title":""},{"docid":"3871a88541c3e393d7e4ede88209c64b","score":"0.55062443","text":"def slowdown(self):\n self.speed = 3","title":""},{"docid":"fee99b0e929a3b1b6ca15a44b651638a","score":"0.54974556","text":"def _print_test_list_timing(self, title, test_list):\n if self._printer.disabled('slowest'):\n return\n\n self._printer.print_timing(title)\n for test_tuple in test_list:\n filename = test_tuple.filename[len(\n self._port.layout_tests_dir()) + 1:]\n filename = filename.replace('\\\\', '/')\n test_run_time = round(test_tuple.test_run_time, 1)\n self._printer.print_timing(\" %s took %s seconds\" %\n (filename, test_run_time))","title":""},{"docid":"94652be197e251be1e4cc65aa2a3ccf9","score":"0.546838","text":"def speed_list(self: ToggleEntity) -> list:\n return []","title":""},{"docid":"b4ed1a6815a45bb7fac8c6c7858e2340","score":"0.5462859","text":"def faster(self):\n pass","title":""},{"docid":"be834a5ec390910f5828b4cd38684937","score":"0.54195976","text":"def slow_down_wav(lst):\r\n slowed_list = []\r\n prev_sample = None\r\n for idx, sample in enumerate(lst):\r\n if prev_sample:\r\n avg_sample = calc_avg_samples(prev_sample, sample)\r\n slowed_list.append(avg_sample)\r\n slowed_list.append(sample)\r\n prev_sample = sample\r\n return slowed_list","title":""},{"docid":"b56c87558ca66d53e465cd15a8679000","score":"0.5265163","text":"def full_speed(cls):\n cls.add_int_times, cls.add_tail_times = False, False","title":""},{"docid":"de62e4d348b0b611b69872c197c5615f","score":"0.52400833","text":"def slowDown(self):\n self.maxspeed = 5","title":""},{"docid":"dfd93d8123b48078c2eac5049b633c73","score":"0.5224358","text":"def slowest(self):\n raise NotImplementedError()","title":""},{"docid":"ac54097f9c922db4cabbaf5d6beb9a05","score":"0.52093387","text":"def speed_list(self) -> list:\n return self._speed_list","title":""},{"docid":"8fd5b6c7aff260f79dcba80af7781153","score":"0.51585144","text":"def timestable():","title":""},{"docid":"fc3060598c33220cbd6cf801786631c4","score":"0.5151539","text":"def slow_wave(lst):\n result = []\n for item in range((len(lst) - 1)):\n result.append(lst[item])\n result.append(get_average_item(item, lst))\n result.append(lst[-1])\n return result","title":""},{"docid":"6015ffba60293cd6745dba1a06c23786","score":"0.50686","text":"def speed_list(self) -> list:\n return [SPEED_OFF, SPEED_LOW, SPEED_MEDIUM, SPEED_HIGH]","title":""},{"docid":"fbaaca67d1bdf9068aebef3326b2170f","score":"0.5065023","text":"def speed_wave(lst):\n return lst[0::2]","title":""},{"docid":"1366d62797d3744311896f4e7dcf2d8a","score":"0.50134784","text":"def __show_speed(self):\r\n\r\n # Only shw speeds in verbose mode.\r\n if self._DigSpeed:\r\n\r\n # Iterate threw track points and grap speed and length.\r\n speedClips = []\r\n for trkPt in self._Speeds:\r\n speed = \"%2.1f\" % trkPt['speed']\r\n length = trkPt['duration']\r\n\r\n # Create TextClip for each track point.\r\n speedClips.append(mpy.TextClip( txt = speed,\r\n color = \"white\",\r\n bg_color = \"transparent\",\r\n fontsize = 30,\r\n print_cmd = False,\r\n tempfilename = \"text\" + speed + \".png\",\r\n remove_temp = True\r\n ).set_duration(length)\r\n )\r\n\r\n # Merge track point text clips.\r\n self._SpeedClip = mpy.concatenate_videoclips(speedClips)","title":""},{"docid":"f2d355101d35026756f66144b3c4ab48","score":"0.49842596","text":"def _optimize_microstep(self):","title":""},{"docid":"913c10954fc9b8cb745b64a75627b33c","score":"0.49528563","text":"def test(func:type(lambda:1), unsortedlist:tuple) -> float:\r\n #Set copy equal to a new copy (different mem. location) of unsortedList to preserve current list\r\n #using the [:] split command which returns a copy of the whole list but as a new object\r\n from time import perf_counter as clock\r\n copy = unsortedlist[:]\r\n start = clock() #Set start time. TODO - May convert to using timit module.\r\n func(copy) #Run bubble or shuttle sort with copy of unsorted list as argument\r\n duration = clock() - start #Work out how long the execution of algorithm took and set to duration\r\n return duration","title":""},{"docid":"c31f251eb10a86d01327f7d2ee245a3e","score":"0.49451625","text":"def pause_list() -> List:\n pause_list = ['utter_ask_fractions_parts_mcq_1',\n 'utter_ask_fractions_parts_mcq_2',\n 'utter_ask_fractions_parts_mcq_3',\n 'utter_ask_fractions_wholes_nrq_3'\n ]\n\n return pause_list","title":""},{"docid":"f71dcbf80672973a7d21ebbe9a4f6bb2","score":"0.49155867","text":"def millis():","title":""},{"docid":"39db5a5541581a87945b3281e75449cd","score":"0.48584422","text":"def brake(self):\n print(self.make.title() + \" \" + self.model.title() + \" is slowing down.\")","title":""},{"docid":"15bad40fb57fbf520fc039ea92052e62","score":"0.48531953","text":"def increase_speed(self):\n self.speed(\"fastest\")","title":""},{"docid":"8e5873938cbeb6faad790d1be7d08cbd","score":"0.48439124","text":"def iter_toc(self):\n self.iter_timer.pause()\n self.net_timer.pause()","title":""},{"docid":"8e5873938cbeb6faad790d1be7d08cbd","score":"0.48439124","text":"def iter_toc(self):\n self.iter_timer.pause()\n self.net_timer.pause()","title":""},{"docid":"8e5873938cbeb6faad790d1be7d08cbd","score":"0.48439124","text":"def iter_toc(self):\n self.iter_timer.pause()\n self.net_timer.pause()","title":""},{"docid":"8e5873938cbeb6faad790d1be7d08cbd","score":"0.48439124","text":"def iter_toc(self):\n self.iter_timer.pause()\n self.net_timer.pause()","title":""},{"docid":"c458654c011761a6fb941bc474a449b6","score":"0.48362437","text":"def iter_toc(self):\n self.iter_timer.pause()","title":""},{"docid":"c458654c011761a6fb941bc474a449b6","score":"0.48362437","text":"def iter_toc(self):\n self.iter_timer.pause()","title":""},{"docid":"5663ce4527ed266eff6f633cde5f3836","score":"0.48010963","text":"def fastest_words(data, send):\n words = [\"START\"] + data.get(\"prompt\", [\"\"])[0].split()\n del data[\"prompt\"]\n progress = send(data)\n start_times = [p[0][1] for p in progress]\n word_times = [[typing.word_time(w, p[1] - s) for w, p in zip(words, ps)] \\\n for s, ps in zip(start_times, progress)]\n\n return typing.fastest_words_report(word_times)","title":""},{"docid":"dfde5f60d40929a0fcbd2674650e101d","score":"0.47991157","text":"def slow_down(traj):\n\n new_traj = RobotTrajectory()\n new_traj.joint_trajectory = traj.joint_trajectory\n n_joints = len(traj.joint_trajectory.joint_names)\n n_points = len(traj.joint_trajectory.points)\n\n spd = 0.2 # Lower this when using the Gazebo Robot\n\n for i in range(n_points):\n new_traj.joint_trajectory.points[i].time_from_start = traj.joint_trajectory.points[i].time_from_start / spd\n\n # rospy.loginfo(type(traj.joint_trajectory.points[i]))\n v = list(new_traj.joint_trajectory.points[i].velocities)\n a = list(new_traj.joint_trajectory.points[i].accelerations)\n p = list(new_traj.joint_trajectory.points[i].positions)\n\n for j in range(n_joints):\n # rospy.loginfo(type(new_traj.joint_trajectory.points[i].velocities))\n v[j] = traj.joint_trajectory.points[i].velocities[j] * spd\n a[j] = traj.joint_trajectory.points[i].accelerations[j] * spd**2\n p[j] = traj.joint_trajectory.points[i].positions[j]\n\n # new_traj.joint_trajectory.points[i].accelerations[j] = traj.joint_trajectory.points[i].accelerations[j] * spd\n # new_traj.joint_trajectory.points[i].positions[j] = traj.joint_trajectory.points[i].positions[j]\n\n v = tuple(v)\n a = tuple(a)\n p = tuple(p)\n\n new_traj.joint_trajectory.points[i].velocities = v\n new_traj.joint_trajectory.points[i].accelerations = a\n new_traj.joint_trajectory.points[i].positions = p\n\n # rospy.loginfo( new_traj.joint_trajectory.points[i].velocities[j])\n # rospy.loginfo( new_traj.joint_trajectory.points[i].accelerations[j])\n # rospy.loginfo( new_traj.joint_trajectory.points[i].positions[j])\n return new_traj","title":""},{"docid":"822c55b0728c8d161429b0487dbf96d8","score":"0.47783932","text":"def smooth_n_cut(time_list: typing.Deque[typing.List], time_now: float) -> typing.List:\n while abs(time_list[0][-1] - time_now) > SECOND_SMOOTH_TIME:\n time_list.popleft()\n return smooth_func(time_list, time_now, FIRST_SMOOTH_TIME, SECOND_SMOOTH_TIME)","title":""},{"docid":"f6f13872ce1cae76d2633db6b818df31","score":"0.47746158","text":"def slower(self):\n if self.delayMultiplier < 10e6:\n self.delayMultiplier *= 5.0\n self.start()","title":""},{"docid":"3938737aa516e927fbf93c42232a7fce","score":"0.47731462","text":"def setSlowWhenEmpty(self, slow):\n getHandle().slowWhenEmpty = slow","title":""},{"docid":"0a1a716e5938a0144083afb57f49c691","score":"0.47617668","text":"def reorderList(self, head: ListNode) -> None:\n if not head or not head.next:\n return head\n fast, slow = head, head\n # 找到中点\n while fast.next and fast.next.next:\n fast = fast.next.next\n slow = slow.next\n # 反转后半链表cur\n cur, pre = slow.next, None\n slow.next = None # 此时head从此处断开，变成一半（left）\n while cur:\n pre, pre.next, cur = cur, pre, cur.next\n # 重排练表\n left = head\n while left and pre:\n left.next, pre.next, left, pre = pre, left.next, left.next, pre.next","title":""},{"docid":"d6a821baf0e072860b9e72526bc8cbc8","score":"0.47581175","text":"def __init__(self, num_frames, page_size, trace):\n Pager.__init__(self, num_frames, page_size)\n # TODO\n self.trace_ptr = -1\n self.future_pos = [None for i in xrange(num_frames)]\n\n\n self.list_of_list = [[] for i in xrange(1024/self.page_size+1)] # 这是一个查找表，以空间换时间！\n\n for i in xrange(len(trace)):\n self.list_of_list[trace[i]/self.page_size].append(i)\n # for list in self.list_of_list:\n # print list","title":""},{"docid":"d5aab81b0353fdfdb3f6bacedfd13ca0","score":"0.47260237","text":"async def slowmode(self, ctx: customContext, interval=None):\r\n if interval:\r\n\r\n def convert(time):\r\n pos = [\"s\", \"m\", \"h\", \"d\"]\r\n\r\n time_dict = {\"s\": 1, \"m\": 60, \"h\": 3600, \"d\": 3600 * 24}\r\n\r\n unit = time[-1]\r\n\r\n if unit not in pos:\r\n raise commands.BadArgument(\r\n f\"{self.bot.icons['redTick']} Number must end on a `s, m, h`\"\r\n )\r\n try:\r\n val = int(time[:-1])\r\n except Exception:\r\n return -2\r\n\r\n return val * time_dict[unit]\r\n\r\n interval1 = convert(interval)\r\n if interval1 < 1:\r\n raise commands.BadArgument(\r\n f\"{self.bot.icons['redTick']} The interval should be a positive number.\"\r\n )\r\n if interval1 < 21601:\r\n await ctx.channel.edit(slowmode_delay=interval1)\r\n await ctx.send(f\"Set slowmode to `{interval}`\")\r\n else:\r\n await ctx.send(f\"Slowmode should be less than or equal to **6h**\")\r\n else:\r\n await ctx.channel.edit(slowmode_delay=0)\r\n await ctx.send(f\"Removed the slowmode\")","title":""},{"docid":"457174128ab5cc75f258e73744ab69c9","score":"0.47244266","text":"def speed_up_wav(lst):\r\n boosted_list = []\r\n for idx, sample in enumerate(lst):\r\n if idx % 2 == 0: # only even\r\n boosted_list.append(sample)\r\n return boosted_list","title":""},{"docid":"63719021b9572b3f6c5af137dda2455a","score":"0.47212994","text":"def speedtest():\n jret = _run_speedify_cmd([\"speedtest\"], cmdtimeout=600)\n return jret","title":""},{"docid":"30f2ce141e8ced364a84519b5a0557cb","score":"0.4713104","text":"def short_time(self):\r\n length = len(self._object_lst)\r\n # print(length)\r\n min_dt = float('inf')\r\n for i in range(length):\r\n if i<(length-1): #to not test for collision with itself\r\n for j in range(i,length-1):\r\n temp1 = self._object_lst[i]\r\n temp2 = self._object_lst[j+1]\r\n dt = temp1.time_to_collision(temp2)\r\n \r\n if dt<min_dt:\r\n min_dt = dt\r\n index1 = i\r\n index2 = j+1\r\n col_data = [index1, index2, min_dt] #indices tell me which objects to collide\r\n return col_data","title":""},{"docid":"e9b9149996590356b6637d8f5add04de","score":"0.47007045","text":"def fullSpeed(self):\n\t\tself.debugger.setSpeed(self.debugger.MAX_SPEED)","title":""},{"docid":"628a968c97531ce01aef91cf34dd7627","score":"0.46986312","text":"def slowResponse(response, slow):\n respTime = response.elapsed.total_seconds()\n if (slow/1000 < respTime):\n return True\n return False","title":""},{"docid":"dc939d41a73977a3a15e6982f621256b","score":"0.46736017","text":"def speedUp(self):\n self.maxspeed = 10","title":""},{"docid":"d37833a0508e7d05424b2c90daf482a6","score":"0.46727547","text":"def tradingPerformance(self):","title":""},{"docid":"7fe829e975e67efc0c13db4dd594b070","score":"0.4669201","text":"def stop_timing(self):\r\n end_time = time.time()\r\n logging.info(\"Loading took {} seconds.\".format(round(end_time-self.start_time,1)))","title":""},{"docid":"88b050ddf5612ae9445dcc3f54c2f2fd","score":"0.46688503","text":"async def slowmode(self, ctx, slowmode: int=0, channel: typing.Optional[discord.TextChannel]=None):\n\t\tchannel = channel or ctx.channel\n\t\tif slowmode > 21600:\n\t\t\tawait ctx.send(f\"`{slowmode}` is too long of a delay! The new slowmode must be 21600 seconds or less (6 hours).\")\n\t\telif slowmode == 0:\n\t\t\tawait channel.edit(slowmode_delay=slowmode, reason=f\"Slowmode disabled by {ctx.author} ({ctx.author.id}).\")\n\t\t\tawait ctx.send(f\"Disabled slowmode for {channel.mention}!\")\n\t\telse:\n\t\t\tawait channel.edit(slowmode_delay=slowmode, reason=f\"Slowmode changed to {slowmode} seconds by {ctx.author} ({ctx.author.id}).\")\n\t\t\tawait ctx.send(f\"Changed {channel.mention}'s slowmode to {slowmode} seconds.\")","title":""},{"docid":"01d3fd8455f5d65780e7fb51c45b1888","score":"0.46584755","text":"def punto_a_stop(texto,longitud, costopcorta, costoplarga):\n lista = texto.split(' ')\n lista[-1]=lista[-1].rstrip(\".\")\n costopcortacant = 0\n costoplargacant = 0\n total = \"\"\n for i in range(len(lista)):\n if len(lista[i]) > longitud:\n total += lista[i][0:longitud]+\"@\" + \" \"\n costoplargacant = costoplargacant + 1\n \n for letra in lista[i]:\n if letra == \".\":\n print(lista[i][-1])\n total = f\"{total.strip()}{lista[i].replace(lista[i], 'STOP ')}\"\n else:\n total += lista[i] + \" \"\n costopcortacant = costopcortacant + 1\n for letra in lista[i]:\n if letra == \".\":\n print(lista[i][-1])\n total = f\"{lista[i].replace(lista[i], 'STOP ')}\" \n total = total.strip() + \"STOPSTOP\"\n costototal = costopcortacant * costopcorta + costoplargacant * costoplarga\n print (\"El telegrama queda \", total,\" con un costo de: \", costototal,\n \" con \", costopcortacant,\" palabras cortas y con \", costoplargacant,\" palabras largas \")","title":""},{"docid":"dd02bdf199936f3ffde198419324f500","score":"0.46379673","text":"def is_too_slow(fSpeed):\n return fSpeed < 2.0","title":""},{"docid":"fea5c1415748db54bb6e1182c708a167","score":"0.46258518","text":"def timeSync(self):","title":""},{"docid":"d826075796bf30402a2e1326ab6f5393","score":"0.4621075","text":"def slow_down(self):\n self.__speedy = self.__origin_speedy","title":""},{"docid":"4a31126454486207788c7f573c2ba06d","score":"0.46119767","text":"def positionToSpeed(self):\n\t\tself.real_data = []\n\t\tself.speed = True\n\n\t\torigin = parseDateTime(self.data[0][-1])\n\n\t\tfor i in xrange(1, len(self.data)):\n\t\t\td = self.data[i-1]\n\t\t\tif d[1] < self.data[i][1] or d[2] < self.data[i][2]:\n\t\t\t\tcontinue\n\t\t\tdate1 = parseDateTime(str(d[3]))\n\t\t\tdate2 = parseDateTime(str(self.data[i][3]))\n\t\t\ttimes = (date1-date2).seconds\n\t\t\t\n\t\t\tself.real_data.append([\n\t\t\t\t(d[1] - self.data[i][1])/times,\n\t\t\t\t(d[2] - self.data[i][2])/times,\n\t\t\t\t(origin-date1).seconds/60\n\t\t\t])","title":""},{"docid":"62aa085be8b532a86f61098e1a871135","score":"0.4611727","text":"def times(n):\n res = []\n t0 = time.clock()\n doskonale_skladana(n)\n res.append('{:.6f}'.format(time.clock()-t0))\n\n t1 = time.clock()\n doskonale_funkcyjna(n)\n res.append('{:.6f}'.format(time.clock()-t1))\n\n t2 = time.clock()\n doskonale_iter(n)\n res.append('{:.6f}'.format(time.clock()-t2))\n return res","title":""},{"docid":"5d97b8de6baa13596ebe92813c96f220","score":"0.46068934","text":"def calc_puiss(char,T,h=20,opt='ripples'):\n \n #T=open_data('BP1-BP2_Temps.txt')[0:20*512]\n global time\n puiss=[]\n Tpuiss=[]\n #Ti=[] #contient les i pour correspondant au temps\n Y=filtre(char,T,opt)\n if opt=='ripples': \n delta=100\n if opt=='epileptic':\n delta=50\n elif opt =='delta':\n delta=500 #correspond à la variation de la fenetre\n Y=filtre(char,T,opt)\n else : \n Y=open_data(char)[:time*512] #cas ou on cherche la puissance sans filtre\n delta= 50 #a changer en fonction de ce qui est normalisé\n i=0\n while i<=512*time-delta:#On a pas la puissance des derniers points à voir \n #print(i+delta)\n puiss+=[np.linalg.norm(Y[i:i+delta])]#portion de environ 20ms\n Tpuiss+=[i/512]\n #Ti+=[i]\n i=i+h\n \n #print(\"taille t , p \",len(Tpuiss)/512,len(Tpuiss))\n \n #print(i)\n return puiss,Tpuiss","title":""},{"docid":"b8cd33f6c4f4dfe2ca9f2710c7a300c1","score":"0.46052372","text":"def get_speed(self, info):\n return","title":""},{"docid":"3ff4c9727bfdca10d1cb361f712a790d","score":"0.46034938","text":"def streamtest():\n return _run_speedify_cmd([\"speedtest\"], cmdtimeout=600)","title":""},{"docid":"190d2c989c2530b3e19d888ba8638d67","score":"0.45989284","text":"def python_sort(a_list):\n start_time = time.time()\n a_list.sort()\n end_time = time.time()\n return end_time - start_time","title":""},{"docid":"a08a8d69684d064c0730ba57c3daa66e","score":"0.45872664","text":"def help_TIME_TO_SEC(self):","title":""},{"docid":"c78b77098d63d2452476ae7bb8af873b","score":"0.4578397","text":"def show_speedtest():\n return _run_speedify_cmd([\"show\", \"speedtest\"])","title":""},{"docid":"82a8a1d962850091db0bad42357b4cd6","score":"0.45741197","text":"def get_speed(self, speed):\n pass","title":""},{"docid":"848d8d9773ca53c81922bb641cd23bc2","score":"0.4571137","text":"def stop(self):\n if self.progress <= self.limit:\n sys.stdout.write('\\x1b[2K')\n sys.stdout.write(\"\\b\" * (self.numBarsWritten + 2))\n self.progress = self.limit\n if self.beginning:\n timetaken = time.clock() - self.beginning + self.time\n else:\n timetaken = self.time\n print \"\\033[92mTime taken for (%s) = %f\\033[0m\" % (self.string, timetaken)\n self.string = None\n self.beginning = None\n self.time = None\n self.limit = 0\n self.progress = 0\n self.numBars = 0\n self.oneBar = 0\n return timetaken","title":""},{"docid":"f4b436342e8522d1f713d957e91361e8","score":"0.4555844","text":"def __call__(self):\n deltat = time.clock() - self.last\n deltas = max([0, target - deltat])\n time.sleep(deltas)\n self.last = time.clock()","title":""},{"docid":"5853b4c52b19b0c5340e4c896608dcf3","score":"0.45524767","text":"def time_per_word(times_per_player, words): # ok -----> game(words, times) 一把过\n # BEGIN PROBLEM 9\n \"*** YOUR CODE HERE ***\"\n num_play = len(times_per_player)\n times = []\n for i in times_per_player:\n times_n = []\n t = 1\n while t < len(i):\n times_n.append(i[t]-i[t-1])\n t = t + 1\n times.append(times_n)\n return game(words, times)\n # END PROBLEM 9","title":""},{"docid":"55d6f1c408af32c799ca994810251edf","score":"0.4545329","text":"def testIndex(self):\n tlist = ['00:01:02','99:98:97']\n for i in tlist:\n val = str(_TrackTime(i))\n self.assertEqual(val,i)","title":""},{"docid":"995af5f010a57c0696009b93b549b07c","score":"0.45442498","text":"def fan_speeds(self, speeds):\r\n speeds = ['low','medium','high']\r\n return speeds","title":""},{"docid":"3c6271e326e7f3d991d02e3739893985","score":"0.4525652","text":"def _get_speed(self):\n return self.__speed","title":""},{"docid":"9ae7477b0b00e18883a76fe5d72147a6","score":"0.45163447","text":"def getMilliseconds(self):\n\t\treturn self.ldt.microsecond / 1000","title":""},{"docid":"eabe62508bc105d4999fae98990f30a7","score":"0.45130676","text":"def _dsuper_list_td(self, t, y, L_list):\n L = L_list[0][0]\n for n in range(1, len(L_list)):\n L = L + L_list[n][0] * L_list[n][1](t)\n return L * y","title":""},{"docid":"3938f2e0f58963db1364cf2b599d77a4","score":"0.45090756","text":"def timeTime():","title":""},{"docid":"529adc74941625ef809ed6cec99ca8c6","score":"0.4503853","text":"def alegra01():\n tl = TestList.TestList()\n t0 = time.time()\n #tl.scanDirectory( '/home/rrdrake/Alegra/Benchmarks' )\n tl.scanDirectory( '/scratch/rrdrake/ta/Benchmarks' )\n print3( 'time=', time.time()-t0 )","title":""},{"docid":"b540f539ef90fca284acae817733e474","score":"0.4499511","text":"def _stop_timing(self):\n self._load_end = time.time()","title":""},{"docid":"73bdd870062246b4d9596ae8ef701709","score":"0.44946814","text":"def __init__(self):\n self.head = [None] * 1000","title":""},{"docid":"bf61dcd777f9ee4ec6c302ad828a3bc3","score":"0.44898465","text":"def test_lst_search(self):\n sample = self.get_random()\n lst = self.words_lst[:990]\n self.start_timer()\n for word in sample:\n lst.__contains__(word)\n\n self.stop_timer(\"List search result:\")","title":""},{"docid":"011e1be759356fde8cfaa6d4455477a7","score":"0.44879758","text":"def text_tick():\n\n global layout_cache_old, layout_cache_new\n layout_cache_old = layout_cache_new\n layout_cache_new = { }\n\n global virtual_layout_cache_old, virtual_layout_cache_new\n virtual_layout_cache_old = layout_cache_new\n virtual_layout_cache_new = { }\n\n global slow_text\n slow_text = [ ]","title":""},{"docid":"7560038fc0f4790e5999eebe2c84abb8","score":"0.44855356","text":"def clean_list(orig_list):\n t_last = None\n eps = 10\n new_list = []\n if len(orig_list) < 2:\n return orig_list\n\n for item in orig_list:\n if t_last is not None:\n if abs((item - t_last).total_seconds()) > eps:\n new_list.append(item)\n else:\n new_list.append(item)\n t_last = item\n return new_list","title":""},{"docid":"bfa4f745ddd4f72ba6adf80b1847c4bd","score":"0.44786808","text":"def time_it(input_list):\n for i in range(501):\n start = time.time()\n radix_sort(input_list)\n time_passed = time.time() - start\n avg_time = time_passed / 500\n return avg_time","title":""},{"docid":"b1adf38bde8685ba7e10d9e985cca0fc","score":"0.44752553","text":"def breaktime():\n\turls = list(break_music.values())\n\tindex = randint(0, len(urls))\n\twebbrowser.open(urls[index])","title":""},{"docid":"3695cc55fa6b1ca7eb8eed35947fac0b","score":"0.44703335","text":"def getNData(self, maxSecs=-1):\n# temPraLer = self.getBufferLen()\n# print('leu %s ' + str(temPraLer) )\n\n # if self.getBufferLen() < size:\n # print(\"ERROS!!! TERIA DE LER %s E LEU APENAS %s\", (size,temPraLer))\n start = time.time()\n now = time.time()\n while (not(self.checkEop) or self.getBufferLen() == 0) and ((now - start < maxSecs) or (maxSecs == -1)):\n now = time.time()\n print(self.getBufferLen())\n # print(self.checkEop())\n time.sleep(0.1)\n\n self.buffer = self.getBuffer(self.getBufferLen())\n print(self.buffer)\n\n self.head_current_pkg = int.from_bytes(self.buffer[0:2], byteorder='big')\n self.head_total_pkg = int.from_bytes(self.buffer[2:4], byteorder='big')\n print(\">>>>>>>>>>>>>> pacote \" + str(self.head_current_pkg) +\n \" de \" + str(self.head_total_pkg))\n\n self.head_type = int.from_bytes(self.buffer[4:5], byteorder='big')\n self.size_head = int.from_bytes(self.buffer[5:14], byteorder='big')\n\n if self.head_type != 4:\n return (self.buffer, self.head_type, self.head_total_pkg, self.head_current_pkg)\n\n self.size_real = len(self.buffer)\n self.buffer = self.buffer[14:]\n lista_eops = []\n c = -1\n overhead = self.size_real/self.size_head\n throughput = overhead*self.fisica.baudrate\n print(\"Overhead: \" + str(overhead))\n print(\"Throughput: \" + str(throughput))\n while True:\n #print(self.buffer)\n c = self.buffer.find(self.eop, c+1)\n #print(c)\n if c == -1:\n #print(\"Nao tme eop\")\n break\n else:\n #print(\"Posição do eop bait: \" + str(c))\n lista_eops.append(c)\n print(lista_eops)\n lista_eops = lista_eops[:-1]\n if len(lista_eops) > 0:\n for i in lista_eops:\n self.buffer = self.buffer[:i - len(self.byte_stuff)] + self.buffer[i:]\n print(self.buffer)\n\n #if self.buffer.find(self.eop) != -1:\n #print(\"Posição de início de EOP: \" + str(self.buffer.find(self.eop)))\n #elif self.buffer.find(self.eop) == -1:\n #print(\"ERRO NÃO HÁ EOP\")\n\n self.buffer = self.buffer[:-len(self.eop)]\n print(len(self.buffer))\n print(self.size_head)\n if len(self.buffer) != self.size_head:\n print(\"ERRO TAMANHO INCOMPATIVEL\")\n return (self.buffer, self.head_type, self.head_total_pkg, self.head_current_pkg)","title":""},{"docid":"1082cebda77e6cecd83ad347a9c25d20","score":"0.44700176","text":"def Time():","title":""},{"docid":"6fda5328b525d08e7a9d337be9a2da9e","score":"0.44662005","text":"def pause(self):\n self.pausa = time.time()","title":""},{"docid":"5b6a95c1c52382bff20bae5c5cc50dc6","score":"0.44640958","text":"def GetSpeeds(results, column=5):\n speeds = []\n for t in results:\n pace = t[column]\n speed = ConvertPaceToSpeed(pace)\n speeds.append(speed)\n return speeds","title":""},{"docid":"f5b30098729b31a8c272fe111b41016e","score":"0.4463591","text":"def fast_strategy(self):\n pass","title":""},{"docid":"33d9d2347d0007ca73abc8be76cfd2d8","score":"0.44627616","text":"def test_throttling_blacklist_get(self):\n pass","title":""},{"docid":"76313981fa7f13b6127a1725114e4f18","score":"0.44617888","text":"def getSpeed(self, thresh=None, vec=False, laps=False):\n\n if laps:\n diffsTmp = []\n laps, lapTimes = self.getLaps()\n for item in lapTimes:\n lapStart = self.getIndexFromTime(item[0])\n lapEnd = self.getIndexFromTime(item[1])\n diffsTmp.append(numpy.diff(self.places[lapStart:lapEnd], axis=0)[:, 0:2])\n\n diffs = []\n for item in diffsTmp:\n for subitem in item:\n diffs.append(subitem)\n diffs = numpy.array(diffs)\n\n else:\n diffs = numpy.diff(self.places, axis=0)[:, 0:2]\n\n\n if vec:\n speed = diffs / self.dt\n else:\n speed = numpy.sqrt(diffs[:, 1] ** 2 + diffs[:, 0] ** 2) / self.dt # [space units/s]\n\n speed_dummy = []\n reducedTimes = []\n if thresh and not vec:\n for i, s in enumerate(speed):\n if s >= thresh:\n speed_dummy.append(s)\n reducedTimes.append(self.times[i])\n else:\n speed_dummy = speed\n reducedTimes = self.times[:-1]\n return numpy.array(reducedTimes), numpy.array(speed_dummy)","title":""},{"docid":"b526a7e940af23390a4477946318acae","score":"0.44600266","text":"def display_playback_progress(v):\n\n time_start = time.time()\n\n curses.setupterm()\n term_width = curses.tigetnum('cols')\n\n v_idx = max(0, jukebox.play_vector.index(v) - 1)\n beat = jukebox.play_vector[v_idx]['beat']\n min_sequence = jukebox.play_vector[v_idx]['seq_len']\n current_sequence = jukebox.play_vector[v_idx]['seq_pos']\n\n pct = float(beat) / len(jukebox.beats)\n term_pos = int( pct * term_width )\n\n prefix = \"\".ljust( min(term_pos, term_width - 4), '.') + '[' + str(min_sequence - current_sequence).zfill(2) + ']'\n log_line = prefix + \"\".ljust(term_width - len(prefix), '.')\n\n sys.stdout.write( log_line.ljust(term_width) + '\\r' )\n sys.stdout.flush()\n\n time_finish = time.time()\n\n return time_finish - time_start","title":""},{"docid":"48035828c6f3a95e8fbc2984f3a0c339","score":"0.4455816","text":"async def bedtime(self, ctx, member: discord.Member = None):\n sleep_list = [\"http://i.imgur.com/OC7uI2m.png\", \"http://i.imgur.com/0xflepR.png\", \"http://i.imgur.com/KklC7Zb.png\", \"http://i.imgur.com/1XGIPZ9.jpg\", \"http://i.imgur.com/xrDXOst.png\", \"http://i.imgur.com/Un08Qd7.png\", \"http://i.imgur.com/psj3lkd.png\",\n \"http://i.imgur.com/omBWRDQ.png\", \"http://i.imgur.com/TQgVrk0.png\", \"http://i.imgur.com/SV6wNJw.png\", \"http://i.imgur.com/LZgLYc1.png\", \"http://i.imgur.com/MaTF2cy.png\", \"http://i.imgur.com/qj5PPHM.gif\", \"http://i.imgur.com/CcTBuVx.jpg\",\n \"http://i.imgur.com/a6FFUer.png\", \"http://i.imgur.com/r9vuptx.jpg\", \"http://i.imgur.com/6eqbXNZ.png\", \"http://i.imgur.com/340X9UY.png\", \"http://i.imgur.com/5yfZssd.png\", \"http://i.imgur.com/uUEEgH4.png\", \"http://i.imgur.com/aHcB6gB.png\",\n \"http://i.imgur.com/ALpZVs9.png\", \"http://i.imgur.com/etvcqTQ.png\", \"http://i.imgur.com/5Hp7FqP.jpg\", \"http://i.imgur.com/GNqhhTA.jpg\", \"http://i.imgur.com/Aqam15S.png\", \"http://i.imgur.com/9z7OQPi.png\", \"http://i.imgur.com/tgLP4VE.png\",\n \"http://i.imgur.com/zm6OAhl.png\", \"http://i.imgur.com/0VnDJ30.png\", \"http://i.imgur.com/7qiTA2y.png\", \"http://i.imgur.com/U5lWjIO.jpg\", \"http://i.imgur.com/ihdDaW2.png\", \"http://i.imgur.com/DcPImeK.png\", \"http://i.imgur.com/zUpPCg8.png\"]\n if member is None:\n member = ctx.message.author\n randpic = random.randint(0, len(sleep_list)-1)\n caption = \"{} Should you still be up, poi?\".format(member.mention)\n await self.bot.say(\"{}\\n{}\".format(sleep_list[randpic], caption))","title":""},{"docid":"8fae2e8e60befdeab80535e88b47577f","score":"0.445395","text":"async def is_not_sync(self, text):\r\n text = BeautifulSoup(text, 'lxml')\r\n text = text.find(id=\"time\")\r\n time = current_time() - (int(text.attrs['time'])/1000) # ms in html whereas time() is in s\r\n time -= 25 # don't know why but it always gives about 30 secs before the song started\r\n return time","title":""},{"docid":"cca1a5d2e0155ef972bbe87a05b24f56","score":"0.44509152","text":"def setspeed(self, thr):\r\n return","title":""},{"docid":"853316b9f560f4c229d7e96a31287e93","score":"0.44425917","text":"async def speedtest(self, ctx):\n await ctx.trigger_typing()\n\n data = subprocess.getoutput(\"speedtest --share --simple\")\n data = re.search(\"(?P<url>https?://[^\\s]+)\", str(data)).group(\"url\")\n embed = discord.Embed(colour=3553599)\n embed.set_image(url=data)\n\n await ctx.send(embed=embed)","title":""},{"docid":"92f05cd723a25fc9f4980842151f677e","score":"0.44362083","text":"def wpm(typed, elapsed): # ok\n assert elapsed > 0, 'Elapsed time must be positive'\n # BEGIN PROBLEM 4\n \"*** YOUR CODE HERE ***\"\n n_char = len(typed) / 5\n speed = n_char / elapsed * 60\n return speed\n # END PROBLEM 4","title":""},{"docid":"24be43e4432a5e92e9bd4179eb192c14","score":"0.44266355","text":"def speed(self):\n return self.alio.sendCommand(\"P251\")[0].rstrip('\\r\\x06')","title":""},{"docid":"289064d0bd0df782d13d4b8ed87b9d52","score":"0.4419939","text":"def sleep(self):\n\t\t# Set GSM slow clock to 1","title":""},{"docid":"c268c92d18f47c8a116657c93f6dde8a","score":"0.44185248","text":"def FunTimer():\n global MusciIndex\n global files\n Lcd.SetText(\"scale: \\r\\n{}\".format(files[MusciIndex]))\n global timer\n timer = threading.Timer(0.25, FunTimer)\n timer.start()","title":""},{"docid":"b3f52329fc36957aed7585f5bbf9a886","score":"0.44137004","text":"def __init__(self,\n speeds: List['OfferingSpeed']) -> None:\n self.speeds = speeds","title":""},{"docid":"c815fc54fe391a9936764da22f254684","score":"0.44120252","text":"async def _speedtest(self, ctx):\n\n init_msg = await ctx.send(\"Running speedtest ...\")\n loop = asyncio.get_event_loop()\n s_test = await loop.run_in_executor(ThreadPoolExecutor(), run_speedtest)\n await init_msg.edit(content=f\"```{s_test}```\")","title":""},{"docid":"af94d7007a6ca4531e4c2bb0494fd614","score":"0.44113535","text":"def reorderList(self, head: ListNode) -> None:\n # 思路\n # 找到中断的点，将链表分成两段（快慢指针）\n # 反转后面那段，然后合并两个链表\n if not head:\n return head\n\n #找到中断点\n slow, fast = head, head.next\n while fast and fast.next:\n fast = fast.next.next\n slow = slow.next\n tail = slow.next\n slow.next = None # 断开\n #反转后半段\n tail = self.reverse(tail)\n #合并两端\n head = self.mergeTwoList(head, tail)\n return head","title":""},{"docid":"90ac23ff1666bb574953616496fb0fee","score":"0.44044945","text":"def reorderList(self, head: ListNode) -> None:\n if not head:\n return head\n fastNode = slowNode = head\n while fastNode.next != None and fastNode.next.next != None:\n fastNode = fastNode.next.next\n slowNode = slowNode.next\n prevNode = None\n curNode = slowNode.next\n slowNode.next = None\n while curNode != None:\n temp = curNode.next\n curNode.next = prevNode\n prevNode = curNode\n curNode = temp\n curNode = head\n dummy = ListNode(0, head)\n dummyIter = dummy\n while prevNode != None:\n dummyIter.next = curNode\n dummyIter = dummyIter.next\n curNode = curNode.next\n dummyIter.next = prevNode\n dummyIter = dummyIter.next\n prevNode = prevNode.next\n if curNode:\n dummyIter.next = curNode\n dummyIter = dummyIter.next\n dummyIter.next = None\n return dummy.next","title":""},{"docid":"51a787a11a2cbe20c0c94d8165492d45","score":"0.43999085","text":"def timsort(lst: list) -> None:\n runs = find_runs(lst)\n while len(runs) > 1:\n run1 = runs.pop()\n run2 = runs.pop()\n _merge(lst, run2[0], run2[1], run1[1])\n new_runs = find_runs(lst)\n for t in new_runs:\n runs.append(t)","title":""}],"string":"[\n {\n \"docid\": \"4f87fe47e49d703ab17987bcba1aea1a\",\n \"score\": \"0.6794209\",\n \"text\": \"def slow() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4780e645539e45c292c4aeed12fc3a85\",\n \"score\": \"0.595264\",\n \"text\": \"def slower(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a3f11a4c3460c2ca69554bda5f8005\",\n \"score\": \"0.59418374\",\n \"text\": \"def speed_list(self) -> list:\\n return SPEED_LIST_HA\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d028d89ba29631d2ab2787adc39a3d9\",\n \"score\": \"0.5633691\",\n \"text\": \"def dyson_speed_list(self) -> list:\\n return SPEED_LIST_DYSON\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c6e51e0e0c9f3d65c75514fd98ef83\",\n \"score\": \"0.5611326\",\n \"text\": \"def speed_list(self):\\n return SPEED_LIST\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c6e51e0e0c9f3d65c75514fd98ef83\",\n \"score\": \"0.5611326\",\n \"text\": \"def speed_list(self):\\n return SPEED_LIST\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3871a88541c3e393d7e4ede88209c64b\",\n \"score\": \"0.55062443\",\n \"text\": \"def slowdown(self):\\n self.speed = 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3871a88541c3e393d7e4ede88209c64b\",\n \"score\": \"0.55062443\",\n \"text\": \"def slowdown(self):\\n self.speed = 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee99b0e929a3b1b6ca15a44b651638a\",\n \"score\": \"0.54974556\",\n \"text\": \"def _print_test_list_timing(self, title, test_list):\\n if self._printer.disabled('slowest'):\\n return\\n\\n self._printer.print_timing(title)\\n for test_tuple in test_list:\\n filename = test_tuple.filename[len(\\n self._port.layout_tests_dir()) + 1:]\\n filename = filename.replace('\\\\\\\\', '/')\\n test_run_time = round(test_tuple.test_run_time, 1)\\n self._printer.print_timing(\\\" %s took %s seconds\\\" %\\n (filename, test_run_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94652be197e251be1e4cc65aa2a3ccf9\",\n \"score\": \"0.546838\",\n \"text\": \"def speed_list(self: ToggleEntity) -> list:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ed1a6815a45bb7fac8c6c7858e2340\",\n \"score\": \"0.5462859\",\n \"text\": \"def faster(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be834a5ec390910f5828b4cd38684937\",\n \"score\": \"0.54195976\",\n \"text\": \"def slow_down_wav(lst):\\r\\n slowed_list = []\\r\\n prev_sample = None\\r\\n for idx, sample in enumerate(lst):\\r\\n if prev_sample:\\r\\n avg_sample = calc_avg_samples(prev_sample, sample)\\r\\n slowed_list.append(avg_sample)\\r\\n slowed_list.append(sample)\\r\\n prev_sample = sample\\r\\n return slowed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56c87558ca66d53e465cd15a8679000\",\n \"score\": \"0.5265163\",\n \"text\": \"def full_speed(cls):\\n cls.add_int_times, cls.add_tail_times = False, False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de62e4d348b0b611b69872c197c5615f\",\n \"score\": \"0.52400833\",\n \"text\": \"def slowDown(self):\\n self.maxspeed = 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfd93d8123b48078c2eac5049b633c73\",\n \"score\": \"0.5224358\",\n \"text\": \"def slowest(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac54097f9c922db4cabbaf5d6beb9a05\",\n \"score\": \"0.52093387\",\n \"text\": \"def speed_list(self) -> list:\\n return self._speed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd5b6c7aff260f79dcba80af7781153\",\n \"score\": \"0.51585144\",\n \"text\": \"def timestable():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc3060598c33220cbd6cf801786631c4\",\n \"score\": \"0.5151539\",\n \"text\": \"def slow_wave(lst):\\n result = []\\n for item in range((len(lst) - 1)):\\n result.append(lst[item])\\n result.append(get_average_item(item, lst))\\n result.append(lst[-1])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6015ffba60293cd6745dba1a06c23786\",\n \"score\": \"0.50686\",\n \"text\": \"def speed_list(self) -> list:\\n return [SPEED_OFF, SPEED_LOW, SPEED_MEDIUM, SPEED_HIGH]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbaaca67d1bdf9068aebef3326b2170f\",\n \"score\": \"0.5065023\",\n \"text\": \"def speed_wave(lst):\\n return lst[0::2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1366d62797d3744311896f4e7dcf2d8a\",\n \"score\": \"0.50134784\",\n \"text\": \"def __show_speed(self):\\r\\n\\r\\n # Only shw speeds in verbose mode.\\r\\n if self._DigSpeed:\\r\\n\\r\\n # Iterate threw track points and grap speed and length.\\r\\n speedClips = []\\r\\n for trkPt in self._Speeds:\\r\\n speed = \\\"%2.1f\\\" % trkPt['speed']\\r\\n length = trkPt['duration']\\r\\n\\r\\n # Create TextClip for each track point.\\r\\n speedClips.append(mpy.TextClip( txt = speed,\\r\\n color = \\\"white\\\",\\r\\n bg_color = \\\"transparent\\\",\\r\\n fontsize = 30,\\r\\n print_cmd = False,\\r\\n tempfilename = \\\"text\\\" + speed + \\\".png\\\",\\r\\n remove_temp = True\\r\\n ).set_duration(length)\\r\\n )\\r\\n\\r\\n # Merge track point text clips.\\r\\n self._SpeedClip = mpy.concatenate_videoclips(speedClips)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d355101d35026756f66144b3c4ab48\",\n \"score\": \"0.49842596\",\n \"text\": \"def _optimize_microstep(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913c10954fc9b8cb745b64a75627b33c\",\n \"score\": \"0.49528563\",\n \"text\": \"def test(func:type(lambda:1), unsortedlist:tuple) -> float:\\r\\n #Set copy equal to a new copy (different mem. location) of unsortedList to preserve current list\\r\\n #using the [:] split command which returns a copy of the whole list but as a new object\\r\\n from time import perf_counter as clock\\r\\n copy = unsortedlist[:]\\r\\n start = clock() #Set start time. TODO - May convert to using timit module.\\r\\n func(copy) #Run bubble or shuttle sort with copy of unsorted list as argument\\r\\n duration = clock() - start #Work out how long the execution of algorithm took and set to duration\\r\\n return duration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31f251eb10a86d01327f7d2ee245a3e\",\n \"score\": \"0.49451625\",\n \"text\": \"def pause_list() -> List:\\n pause_list = ['utter_ask_fractions_parts_mcq_1',\\n 'utter_ask_fractions_parts_mcq_2',\\n 'utter_ask_fractions_parts_mcq_3',\\n 'utter_ask_fractions_wholes_nrq_3'\\n ]\\n\\n return pause_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71dcbf80672973a7d21ebbe9a4f6bb2\",\n \"score\": \"0.49155867\",\n \"text\": \"def millis():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39db5a5541581a87945b3281e75449cd\",\n \"score\": \"0.48584422\",\n \"text\": \"def brake(self):\\n print(self.make.title() + \\\" \\\" + self.model.title() + \\\" is slowing down.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15bad40fb57fbf520fc039ea92052e62\",\n \"score\": \"0.48531953\",\n \"text\": \"def increase_speed(self):\\n self.speed(\\\"fastest\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5873938cbeb6faad790d1be7d08cbd\",\n \"score\": \"0.48439124\",\n \"text\": \"def iter_toc(self):\\n self.iter_timer.pause()\\n self.net_timer.pause()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5873938cbeb6faad790d1be7d08cbd\",\n \"score\": \"0.48439124\",\n \"text\": \"def iter_toc(self):\\n self.iter_timer.pause()\\n self.net_timer.pause()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5873938cbeb6faad790d1be7d08cbd\",\n \"score\": \"0.48439124\",\n \"text\": \"def iter_toc(self):\\n self.iter_timer.pause()\\n self.net_timer.pause()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5873938cbeb6faad790d1be7d08cbd\",\n \"score\": \"0.48439124\",\n \"text\": \"def iter_toc(self):\\n self.iter_timer.pause()\\n self.net_timer.pause()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c458654c011761a6fb941bc474a449b6\",\n \"score\": \"0.48362437\",\n \"text\": \"def iter_toc(self):\\n self.iter_timer.pause()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c458654c011761a6fb941bc474a449b6\",\n \"score\": \"0.48362437\",\n \"text\": \"def iter_toc(self):\\n self.iter_timer.pause()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5663ce4527ed266eff6f633cde5f3836\",\n \"score\": \"0.48010963\",\n \"text\": \"def fastest_words(data, send):\\n words = [\\\"START\\\"] + data.get(\\\"prompt\\\", [\\\"\\\"])[0].split()\\n del data[\\\"prompt\\\"]\\n progress = send(data)\\n start_times = [p[0][1] for p in progress]\\n word_times = [[typing.word_time(w, p[1] - s) for w, p in zip(words, ps)] \\\\\\n for s, ps in zip(start_times, progress)]\\n\\n return typing.fastest_words_report(word_times)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfde5f60d40929a0fcbd2674650e101d\",\n \"score\": \"0.47991157\",\n \"text\": \"def slow_down(traj):\\n\\n new_traj = RobotTrajectory()\\n new_traj.joint_trajectory = traj.joint_trajectory\\n n_joints = len(traj.joint_trajectory.joint_names)\\n n_points = len(traj.joint_trajectory.points)\\n\\n spd = 0.2 # Lower this when using the Gazebo Robot\\n\\n for i in range(n_points):\\n new_traj.joint_trajectory.points[i].time_from_start = traj.joint_trajectory.points[i].time_from_start / spd\\n\\n # rospy.loginfo(type(traj.joint_trajectory.points[i]))\\n v = list(new_traj.joint_trajectory.points[i].velocities)\\n a = list(new_traj.joint_trajectory.points[i].accelerations)\\n p = list(new_traj.joint_trajectory.points[i].positions)\\n\\n for j in range(n_joints):\\n # rospy.loginfo(type(new_traj.joint_trajectory.points[i].velocities))\\n v[j] = traj.joint_trajectory.points[i].velocities[j] * spd\\n a[j] = traj.joint_trajectory.points[i].accelerations[j] * spd**2\\n p[j] = traj.joint_trajectory.points[i].positions[j]\\n\\n # new_traj.joint_trajectory.points[i].accelerations[j] = traj.joint_trajectory.points[i].accelerations[j] * spd\\n # new_traj.joint_trajectory.points[i].positions[j] = traj.joint_trajectory.points[i].positions[j]\\n\\n v = tuple(v)\\n a = tuple(a)\\n p = tuple(p)\\n\\n new_traj.joint_trajectory.points[i].velocities = v\\n new_traj.joint_trajectory.points[i].accelerations = a\\n new_traj.joint_trajectory.points[i].positions = p\\n\\n # rospy.loginfo( new_traj.joint_trajectory.points[i].velocities[j])\\n # rospy.loginfo( new_traj.joint_trajectory.points[i].accelerations[j])\\n # rospy.loginfo( new_traj.joint_trajectory.points[i].positions[j])\\n return new_traj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"822c55b0728c8d161429b0487dbf96d8\",\n \"score\": \"0.47783932\",\n \"text\": \"def smooth_n_cut(time_list: typing.Deque[typing.List], time_now: float) -> typing.List:\\n while abs(time_list[0][-1] - time_now) > SECOND_SMOOTH_TIME:\\n time_list.popleft()\\n return smooth_func(time_list, time_now, FIRST_SMOOTH_TIME, SECOND_SMOOTH_TIME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f13872ce1cae76d2633db6b818df31\",\n \"score\": \"0.47746158\",\n \"text\": \"def slower(self):\\n if self.delayMultiplier < 10e6:\\n self.delayMultiplier *= 5.0\\n self.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3938737aa516e927fbf93c42232a7fce\",\n \"score\": \"0.47731462\",\n \"text\": \"def setSlowWhenEmpty(self, slow):\\n getHandle().slowWhenEmpty = slow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1a716e5938a0144083afb57f49c691\",\n \"score\": \"0.47617668\",\n \"text\": \"def reorderList(self, head: ListNode) -> None:\\n if not head or not head.next:\\n return head\\n fast, slow = head, head\\n # 找到中点\\n while fast.next and fast.next.next:\\n fast = fast.next.next\\n slow = slow.next\\n # 反转后半链表cur\\n cur, pre = slow.next, None\\n slow.next = None # 此时head从此处断开，变成一半（left）\\n while cur:\\n pre, pre.next, cur = cur, pre, cur.next\\n # 重排练表\\n left = head\\n while left and pre:\\n left.next, pre.next, left, pre = pre, left.next, left.next, pre.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a821baf0e072860b9e72526bc8cbc8\",\n \"score\": \"0.47581175\",\n \"text\": \"def __init__(self, num_frames, page_size, trace):\\n Pager.__init__(self, num_frames, page_size)\\n # TODO\\n self.trace_ptr = -1\\n self.future_pos = [None for i in xrange(num_frames)]\\n\\n\\n self.list_of_list = [[] for i in xrange(1024/self.page_size+1)] # 这是一个查找表，以空间换时间！\\n\\n for i in xrange(len(trace)):\\n self.list_of_list[trace[i]/self.page_size].append(i)\\n # for list in self.list_of_list:\\n # print list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5aab81b0353fdfdb3f6bacedfd13ca0\",\n \"score\": \"0.47260237\",\n \"text\": \"async def slowmode(self, ctx: customContext, interval=None):\\r\\n if interval:\\r\\n\\r\\n def convert(time):\\r\\n pos = [\\\"s\\\", \\\"m\\\", \\\"h\\\", \\\"d\\\"]\\r\\n\\r\\n time_dict = {\\\"s\\\": 1, \\\"m\\\": 60, \\\"h\\\": 3600, \\\"d\\\": 3600 * 24}\\r\\n\\r\\n unit = time[-1]\\r\\n\\r\\n if unit not in pos:\\r\\n raise commands.BadArgument(\\r\\n f\\\"{self.bot.icons['redTick']} Number must end on a `s, m, h`\\\"\\r\\n )\\r\\n try:\\r\\n val = int(time[:-1])\\r\\n except Exception:\\r\\n return -2\\r\\n\\r\\n return val * time_dict[unit]\\r\\n\\r\\n interval1 = convert(interval)\\r\\n if interval1 < 1:\\r\\n raise commands.BadArgument(\\r\\n f\\\"{self.bot.icons['redTick']} The interval should be a positive number.\\\"\\r\\n )\\r\\n if interval1 < 21601:\\r\\n await ctx.channel.edit(slowmode_delay=interval1)\\r\\n await ctx.send(f\\\"Set slowmode to `{interval}`\\\")\\r\\n else:\\r\\n await ctx.send(f\\\"Slowmode should be less than or equal to **6h**\\\")\\r\\n else:\\r\\n await ctx.channel.edit(slowmode_delay=0)\\r\\n await ctx.send(f\\\"Removed the slowmode\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457174128ab5cc75f258e73744ab69c9\",\n \"score\": \"0.47244266\",\n \"text\": \"def speed_up_wav(lst):\\r\\n boosted_list = []\\r\\n for idx, sample in enumerate(lst):\\r\\n if idx % 2 == 0: # only even\\r\\n boosted_list.append(sample)\\r\\n return boosted_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63719021b9572b3f6c5af137dda2455a\",\n \"score\": \"0.47212994\",\n \"text\": \"def speedtest():\\n jret = _run_speedify_cmd([\\\"speedtest\\\"], cmdtimeout=600)\\n return jret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f2ce141e8ced364a84519b5a0557cb\",\n \"score\": \"0.4713104\",\n \"text\": \"def short_time(self):\\r\\n length = len(self._object_lst)\\r\\n # print(length)\\r\\n min_dt = float('inf')\\r\\n for i in range(length):\\r\\n if i<(length-1): #to not test for collision with itself\\r\\n for j in range(i,length-1):\\r\\n temp1 = self._object_lst[i]\\r\\n temp2 = self._object_lst[j+1]\\r\\n dt = temp1.time_to_collision(temp2)\\r\\n \\r\\n if dt<min_dt:\\r\\n min_dt = dt\\r\\n index1 = i\\r\\n index2 = j+1\\r\\n col_data = [index1, index2, min_dt] #indices tell me which objects to collide\\r\\n return col_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b9149996590356b6637d8f5add04de\",\n \"score\": \"0.47007045\",\n \"text\": \"def fullSpeed(self):\\n\\t\\tself.debugger.setSpeed(self.debugger.MAX_SPEED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628a968c97531ce01aef91cf34dd7627\",\n \"score\": \"0.46986312\",\n \"text\": \"def slowResponse(response, slow):\\n respTime = response.elapsed.total_seconds()\\n if (slow/1000 < respTime):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc939d41a73977a3a15e6982f621256b\",\n \"score\": \"0.46736017\",\n \"text\": \"def speedUp(self):\\n self.maxspeed = 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37833a0508e7d05424b2c90daf482a6\",\n \"score\": \"0.46727547\",\n \"text\": \"def tradingPerformance(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe829e975e67efc0c13db4dd594b070\",\n \"score\": \"0.4669201\",\n \"text\": \"def stop_timing(self):\\r\\n end_time = time.time()\\r\\n logging.info(\\\"Loading took {} seconds.\\\".format(round(end_time-self.start_time,1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b050ddf5612ae9445dcc3f54c2f2fd\",\n \"score\": \"0.46688503\",\n \"text\": \"async def slowmode(self, ctx, slowmode: int=0, channel: typing.Optional[discord.TextChannel]=None):\\n\\t\\tchannel = channel or ctx.channel\\n\\t\\tif slowmode > 21600:\\n\\t\\t\\tawait ctx.send(f\\\"`{slowmode}` is too long of a delay! The new slowmode must be 21600 seconds or less (6 hours).\\\")\\n\\t\\telif slowmode == 0:\\n\\t\\t\\tawait channel.edit(slowmode_delay=slowmode, reason=f\\\"Slowmode disabled by {ctx.author} ({ctx.author.id}).\\\")\\n\\t\\t\\tawait ctx.send(f\\\"Disabled slowmode for {channel.mention}!\\\")\\n\\t\\telse:\\n\\t\\t\\tawait channel.edit(slowmode_delay=slowmode, reason=f\\\"Slowmode changed to {slowmode} seconds by {ctx.author} ({ctx.author.id}).\\\")\\n\\t\\t\\tawait ctx.send(f\\\"Changed {channel.mention}'s slowmode to {slowmode} seconds.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d3fd8455f5d65780e7fb51c45b1888\",\n \"score\": \"0.46584755\",\n \"text\": \"def punto_a_stop(texto,longitud, costopcorta, costoplarga):\\n lista = texto.split(' ')\\n lista[-1]=lista[-1].rstrip(\\\".\\\")\\n costopcortacant = 0\\n costoplargacant = 0\\n total = \\\"\\\"\\n for i in range(len(lista)):\\n if len(lista[i]) > longitud:\\n total += lista[i][0:longitud]+\\\"@\\\" + \\\" \\\"\\n costoplargacant = costoplargacant + 1\\n \\n for letra in lista[i]:\\n if letra == \\\".\\\":\\n print(lista[i][-1])\\n total = f\\\"{total.strip()}{lista[i].replace(lista[i], 'STOP ')}\\\"\\n else:\\n total += lista[i] + \\\" \\\"\\n costopcortacant = costopcortacant + 1\\n for letra in lista[i]:\\n if letra == \\\".\\\":\\n print(lista[i][-1])\\n total = f\\\"{lista[i].replace(lista[i], 'STOP ')}\\\" \\n total = total.strip() + \\\"STOPSTOP\\\"\\n costototal = costopcortacant * costopcorta + costoplargacant * costoplarga\\n print (\\\"El telegrama queda \\\", total,\\\" con un costo de: \\\", costototal,\\n \\\" con \\\", costopcortacant,\\\" palabras cortas y con \\\", costoplargacant,\\\" palabras largas \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd02bdf199936f3ffde198419324f500\",\n \"score\": \"0.46379673\",\n \"text\": \"def is_too_slow(fSpeed):\\n return fSpeed < 2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fea5c1415748db54bb6e1182c708a167\",\n \"score\": \"0.46258518\",\n \"text\": \"def timeSync(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d826075796bf30402a2e1326ab6f5393\",\n \"score\": \"0.4621075\",\n \"text\": \"def slow_down(self):\\n self.__speedy = self.__origin_speedy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a31126454486207788c7f573c2ba06d\",\n \"score\": \"0.46119767\",\n \"text\": \"def positionToSpeed(self):\\n\\t\\tself.real_data = []\\n\\t\\tself.speed = True\\n\\n\\t\\torigin = parseDateTime(self.data[0][-1])\\n\\n\\t\\tfor i in xrange(1, len(self.data)):\\n\\t\\t\\td = self.data[i-1]\\n\\t\\t\\tif d[1] < self.data[i][1] or d[2] < self.data[i][2]:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tdate1 = parseDateTime(str(d[3]))\\n\\t\\t\\tdate2 = parseDateTime(str(self.data[i][3]))\\n\\t\\t\\ttimes = (date1-date2).seconds\\n\\t\\t\\t\\n\\t\\t\\tself.real_data.append([\\n\\t\\t\\t\\t(d[1] - self.data[i][1])/times,\\n\\t\\t\\t\\t(d[2] - self.data[i][2])/times,\\n\\t\\t\\t\\t(origin-date1).seconds/60\\n\\t\\t\\t])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62aa085be8b532a86f61098e1a871135\",\n \"score\": \"0.4611727\",\n \"text\": \"def times(n):\\n res = []\\n t0 = time.clock()\\n doskonale_skladana(n)\\n res.append('{:.6f}'.format(time.clock()-t0))\\n\\n t1 = time.clock()\\n doskonale_funkcyjna(n)\\n res.append('{:.6f}'.format(time.clock()-t1))\\n\\n t2 = time.clock()\\n doskonale_iter(n)\\n res.append('{:.6f}'.format(time.clock()-t2))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d97b8de6baa13596ebe92813c96f220\",\n \"score\": \"0.46068934\",\n \"text\": \"def calc_puiss(char,T,h=20,opt='ripples'):\\n \\n #T=open_data('BP1-BP2_Temps.txt')[0:20*512]\\n global time\\n puiss=[]\\n Tpuiss=[]\\n #Ti=[] #contient les i pour correspondant au temps\\n Y=filtre(char,T,opt)\\n if opt=='ripples': \\n delta=100\\n if opt=='epileptic':\\n delta=50\\n elif opt =='delta':\\n delta=500 #correspond à la variation de la fenetre\\n Y=filtre(char,T,opt)\\n else : \\n Y=open_data(char)[:time*512] #cas ou on cherche la puissance sans filtre\\n delta= 50 #a changer en fonction de ce qui est normalisé\\n i=0\\n while i<=512*time-delta:#On a pas la puissance des derniers points à voir \\n #print(i+delta)\\n puiss+=[np.linalg.norm(Y[i:i+delta])]#portion de environ 20ms\\n Tpuiss+=[i/512]\\n #Ti+=[i]\\n i=i+h\\n \\n #print(\\\"taille t , p \\\",len(Tpuiss)/512,len(Tpuiss))\\n \\n #print(i)\\n return puiss,Tpuiss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8cd33f6c4f4dfe2ca9f2710c7a300c1\",\n \"score\": \"0.46052372\",\n \"text\": \"def get_speed(self, info):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ff4c9727bfdca10d1cb361f712a790d\",\n \"score\": \"0.46034938\",\n \"text\": \"def streamtest():\\n return _run_speedify_cmd([\\\"speedtest\\\"], cmdtimeout=600)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"190d2c989c2530b3e19d888ba8638d67\",\n \"score\": \"0.45989284\",\n \"text\": \"def python_sort(a_list):\\n start_time = time.time()\\n a_list.sort()\\n end_time = time.time()\\n return end_time - start_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a08a8d69684d064c0730ba57c3daa66e\",\n \"score\": \"0.45872664\",\n \"text\": \"def help_TIME_TO_SEC(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c78b77098d63d2452476ae7bb8af873b\",\n \"score\": \"0.4578397\",\n \"text\": \"def show_speedtest():\\n return _run_speedify_cmd([\\\"show\\\", \\\"speedtest\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a8a1d962850091db0bad42357b4cd6\",\n \"score\": \"0.45741197\",\n \"text\": \"def get_speed(self, speed):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848d8d9773ca53c81922bb641cd23bc2\",\n \"score\": \"0.4571137\",\n \"text\": \"def stop(self):\\n if self.progress <= self.limit:\\n sys.stdout.write('\\\\x1b[2K')\\n sys.stdout.write(\\\"\\\\b\\\" * (self.numBarsWritten + 2))\\n self.progress = self.limit\\n if self.beginning:\\n timetaken = time.clock() - self.beginning + self.time\\n else:\\n timetaken = self.time\\n print \\\"\\\\033[92mTime taken for (%s) = %f\\\\033[0m\\\" % (self.string, timetaken)\\n self.string = None\\n self.beginning = None\\n self.time = None\\n self.limit = 0\\n self.progress = 0\\n self.numBars = 0\\n self.oneBar = 0\\n return timetaken\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b436342e8522d1f713d957e91361e8\",\n \"score\": \"0.4555844\",\n \"text\": \"def __call__(self):\\n deltat = time.clock() - self.last\\n deltas = max([0, target - deltat])\\n time.sleep(deltas)\\n self.last = time.clock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5853b4c52b19b0c5340e4c896608dcf3\",\n \"score\": \"0.45524767\",\n \"text\": \"def time_per_word(times_per_player, words): # ok -----> game(words, times) 一把过\\n # BEGIN PROBLEM 9\\n \\\"*** YOUR CODE HERE ***\\\"\\n num_play = len(times_per_player)\\n times = []\\n for i in times_per_player:\\n times_n = []\\n t = 1\\n while t < len(i):\\n times_n.append(i[t]-i[t-1])\\n t = t + 1\\n times.append(times_n)\\n return game(words, times)\\n # END PROBLEM 9\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d6f1c408af32c799ca994810251edf\",\n \"score\": \"0.4545329\",\n \"text\": \"def testIndex(self):\\n tlist = ['00:01:02','99:98:97']\\n for i in tlist:\\n val = str(_TrackTime(i))\\n self.assertEqual(val,i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"995af5f010a57c0696009b93b549b07c\",\n \"score\": \"0.45442498\",\n \"text\": \"def fan_speeds(self, speeds):\\r\\n speeds = ['low','medium','high']\\r\\n return speeds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6271e326e7f3d991d02e3739893985\",\n \"score\": \"0.4525652\",\n \"text\": \"def _get_speed(self):\\n return self.__speed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae7477b0b00e18883a76fe5d72147a6\",\n \"score\": \"0.45163447\",\n \"text\": \"def getMilliseconds(self):\\n\\t\\treturn self.ldt.microsecond / 1000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabe62508bc105d4999fae98990f30a7\",\n \"score\": \"0.45130676\",\n \"text\": \"def _dsuper_list_td(self, t, y, L_list):\\n L = L_list[0][0]\\n for n in range(1, len(L_list)):\\n L = L + L_list[n][0] * L_list[n][1](t)\\n return L * y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3938f2e0f58963db1364cf2b599d77a4\",\n \"score\": \"0.45090756\",\n \"text\": \"def timeTime():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"529adc74941625ef809ed6cec99ca8c6\",\n \"score\": \"0.4503853\",\n \"text\": \"def alegra01():\\n tl = TestList.TestList()\\n t0 = time.time()\\n #tl.scanDirectory( '/home/rrdrake/Alegra/Benchmarks' )\\n tl.scanDirectory( '/scratch/rrdrake/ta/Benchmarks' )\\n print3( 'time=', time.time()-t0 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b540f539ef90fca284acae817733e474\",\n \"score\": \"0.4499511\",\n \"text\": \"def _stop_timing(self):\\n self._load_end = time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73bdd870062246b4d9596ae8ef701709\",\n \"score\": \"0.44946814\",\n \"text\": \"def __init__(self):\\n self.head = [None] * 1000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf61dcd777f9ee4ec6c302ad828a3bc3\",\n \"score\": \"0.44898465\",\n \"text\": \"def test_lst_search(self):\\n sample = self.get_random()\\n lst = self.words_lst[:990]\\n self.start_timer()\\n for word in sample:\\n lst.__contains__(word)\\n\\n self.stop_timer(\\\"List search result:\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011e1be759356fde8cfaa6d4455477a7\",\n \"score\": \"0.44879758\",\n \"text\": \"def text_tick():\\n\\n global layout_cache_old, layout_cache_new\\n layout_cache_old = layout_cache_new\\n layout_cache_new = { }\\n\\n global virtual_layout_cache_old, virtual_layout_cache_new\\n virtual_layout_cache_old = layout_cache_new\\n virtual_layout_cache_new = { }\\n\\n global slow_text\\n slow_text = [ ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7560038fc0f4790e5999eebe2c84abb8\",\n \"score\": \"0.44855356\",\n \"text\": \"def clean_list(orig_list):\\n t_last = None\\n eps = 10\\n new_list = []\\n if len(orig_list) < 2:\\n return orig_list\\n\\n for item in orig_list:\\n if t_last is not None:\\n if abs((item - t_last).total_seconds()) > eps:\\n new_list.append(item)\\n else:\\n new_list.append(item)\\n t_last = item\\n return new_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa4f745ddd4f72ba6adf80b1847c4bd\",\n \"score\": \"0.44786808\",\n \"text\": \"def time_it(input_list):\\n for i in range(501):\\n start = time.time()\\n radix_sort(input_list)\\n time_passed = time.time() - start\\n avg_time = time_passed / 500\\n return avg_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1adf38bde8685ba7e10d9e985cca0fc\",\n \"score\": \"0.44752553\",\n \"text\": \"def breaktime():\\n\\turls = list(break_music.values())\\n\\tindex = randint(0, len(urls))\\n\\twebbrowser.open(urls[index])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3695cc55fa6b1ca7eb8eed35947fac0b\",\n \"score\": \"0.44703335\",\n \"text\": \"def getNData(self, maxSecs=-1):\\n# temPraLer = self.getBufferLen()\\n# print('leu %s ' + str(temPraLer) )\\n\\n # if self.getBufferLen() < size:\\n # print(\\\"ERROS!!! TERIA DE LER %s E LEU APENAS %s\\\", (size,temPraLer))\\n start = time.time()\\n now = time.time()\\n while (not(self.checkEop) or self.getBufferLen() == 0) and ((now - start < maxSecs) or (maxSecs == -1)):\\n now = time.time()\\n print(self.getBufferLen())\\n # print(self.checkEop())\\n time.sleep(0.1)\\n\\n self.buffer = self.getBuffer(self.getBufferLen())\\n print(self.buffer)\\n\\n self.head_current_pkg = int.from_bytes(self.buffer[0:2], byteorder='big')\\n self.head_total_pkg = int.from_bytes(self.buffer[2:4], byteorder='big')\\n print(\\\">>>>>>>>>>>>>> pacote \\\" + str(self.head_current_pkg) +\\n \\\" de \\\" + str(self.head_total_pkg))\\n\\n self.head_type = int.from_bytes(self.buffer[4:5], byteorder='big')\\n self.size_head = int.from_bytes(self.buffer[5:14], byteorder='big')\\n\\n if self.head_type != 4:\\n return (self.buffer, self.head_type, self.head_total_pkg, self.head_current_pkg)\\n\\n self.size_real = len(self.buffer)\\n self.buffer = self.buffer[14:]\\n lista_eops = []\\n c = -1\\n overhead = self.size_real/self.size_head\\n throughput = overhead*self.fisica.baudrate\\n print(\\\"Overhead: \\\" + str(overhead))\\n print(\\\"Throughput: \\\" + str(throughput))\\n while True:\\n #print(self.buffer)\\n c = self.buffer.find(self.eop, c+1)\\n #print(c)\\n if c == -1:\\n #print(\\\"Nao tme eop\\\")\\n break\\n else:\\n #print(\\\"Posição do eop bait: \\\" + str(c))\\n lista_eops.append(c)\\n print(lista_eops)\\n lista_eops = lista_eops[:-1]\\n if len(lista_eops) > 0:\\n for i in lista_eops:\\n self.buffer = self.buffer[:i - len(self.byte_stuff)] + self.buffer[i:]\\n print(self.buffer)\\n\\n #if self.buffer.find(self.eop) != -1:\\n #print(\\\"Posição de início de EOP: \\\" + str(self.buffer.find(self.eop)))\\n #elif self.buffer.find(self.eop) == -1:\\n #print(\\\"ERRO NÃO HÁ EOP\\\")\\n\\n self.buffer = self.buffer[:-len(self.eop)]\\n print(len(self.buffer))\\n print(self.size_head)\\n if len(self.buffer) != self.size_head:\\n print(\\\"ERRO TAMANHO INCOMPATIVEL\\\")\\n return (self.buffer, self.head_type, self.head_total_pkg, self.head_current_pkg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1082cebda77e6cecd83ad347a9c25d20\",\n \"score\": \"0.44700176\",\n \"text\": \"def Time():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fda5328b525d08e7a9d337be9a2da9e\",\n \"score\": \"0.44662005\",\n \"text\": \"def pause(self):\\n self.pausa = time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6a95c1c52382bff20bae5c5cc50dc6\",\n \"score\": \"0.44640958\",\n \"text\": \"def GetSpeeds(results, column=5):\\n speeds = []\\n for t in results:\\n pace = t[column]\\n speed = ConvertPaceToSpeed(pace)\\n speeds.append(speed)\\n return speeds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b30098729b31a8c272fe111b41016e\",\n \"score\": \"0.4463591\",\n \"text\": \"def fast_strategy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d9d2347d0007ca73abc8be76cfd2d8\",\n \"score\": \"0.44627616\",\n \"text\": \"def test_throttling_blacklist_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76313981fa7f13b6127a1725114e4f18\",\n \"score\": \"0.44617888\",\n \"text\": \"def getSpeed(self, thresh=None, vec=False, laps=False):\\n\\n if laps:\\n diffsTmp = []\\n laps, lapTimes = self.getLaps()\\n for item in lapTimes:\\n lapStart = self.getIndexFromTime(item[0])\\n lapEnd = self.getIndexFromTime(item[1])\\n diffsTmp.append(numpy.diff(self.places[lapStart:lapEnd], axis=0)[:, 0:2])\\n\\n diffs = []\\n for item in diffsTmp:\\n for subitem in item:\\n diffs.append(subitem)\\n diffs = numpy.array(diffs)\\n\\n else:\\n diffs = numpy.diff(self.places, axis=0)[:, 0:2]\\n\\n\\n if vec:\\n speed = diffs / self.dt\\n else:\\n speed = numpy.sqrt(diffs[:, 1] ** 2 + diffs[:, 0] ** 2) / self.dt # [space units/s]\\n\\n speed_dummy = []\\n reducedTimes = []\\n if thresh and not vec:\\n for i, s in enumerate(speed):\\n if s >= thresh:\\n speed_dummy.append(s)\\n reducedTimes.append(self.times[i])\\n else:\\n speed_dummy = speed\\n reducedTimes = self.times[:-1]\\n return numpy.array(reducedTimes), numpy.array(speed_dummy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b526a7e940af23390a4477946318acae\",\n \"score\": \"0.44600266\",\n \"text\": \"def display_playback_progress(v):\\n\\n time_start = time.time()\\n\\n curses.setupterm()\\n term_width = curses.tigetnum('cols')\\n\\n v_idx = max(0, jukebox.play_vector.index(v) - 1)\\n beat = jukebox.play_vector[v_idx]['beat']\\n min_sequence = jukebox.play_vector[v_idx]['seq_len']\\n current_sequence = jukebox.play_vector[v_idx]['seq_pos']\\n\\n pct = float(beat) / len(jukebox.beats)\\n term_pos = int( pct * term_width )\\n\\n prefix = \\\"\\\".ljust( min(term_pos, term_width - 4), '.') + '[' + str(min_sequence - current_sequence).zfill(2) + ']'\\n log_line = prefix + \\\"\\\".ljust(term_width - len(prefix), '.')\\n\\n sys.stdout.write( log_line.ljust(term_width) + '\\\\r' )\\n sys.stdout.flush()\\n\\n time_finish = time.time()\\n\\n return time_finish - time_start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48035828c6f3a95e8fbc2984f3a0c339\",\n \"score\": \"0.4455816\",\n \"text\": \"async def bedtime(self, ctx, member: discord.Member = None):\\n sleep_list = [\\\"http://i.imgur.com/OC7uI2m.png\\\", \\\"http://i.imgur.com/0xflepR.png\\\", \\\"http://i.imgur.com/KklC7Zb.png\\\", \\\"http://i.imgur.com/1XGIPZ9.jpg\\\", \\\"http://i.imgur.com/xrDXOst.png\\\", \\\"http://i.imgur.com/Un08Qd7.png\\\", \\\"http://i.imgur.com/psj3lkd.png\\\",\\n \\\"http://i.imgur.com/omBWRDQ.png\\\", \\\"http://i.imgur.com/TQgVrk0.png\\\", \\\"http://i.imgur.com/SV6wNJw.png\\\", \\\"http://i.imgur.com/LZgLYc1.png\\\", \\\"http://i.imgur.com/MaTF2cy.png\\\", \\\"http://i.imgur.com/qj5PPHM.gif\\\", \\\"http://i.imgur.com/CcTBuVx.jpg\\\",\\n \\\"http://i.imgur.com/a6FFUer.png\\\", \\\"http://i.imgur.com/r9vuptx.jpg\\\", \\\"http://i.imgur.com/6eqbXNZ.png\\\", \\\"http://i.imgur.com/340X9UY.png\\\", \\\"http://i.imgur.com/5yfZssd.png\\\", \\\"http://i.imgur.com/uUEEgH4.png\\\", \\\"http://i.imgur.com/aHcB6gB.png\\\",\\n \\\"http://i.imgur.com/ALpZVs9.png\\\", \\\"http://i.imgur.com/etvcqTQ.png\\\", \\\"http://i.imgur.com/5Hp7FqP.jpg\\\", \\\"http://i.imgur.com/GNqhhTA.jpg\\\", \\\"http://i.imgur.com/Aqam15S.png\\\", \\\"http://i.imgur.com/9z7OQPi.png\\\", \\\"http://i.imgur.com/tgLP4VE.png\\\",\\n \\\"http://i.imgur.com/zm6OAhl.png\\\", \\\"http://i.imgur.com/0VnDJ30.png\\\", \\\"http://i.imgur.com/7qiTA2y.png\\\", \\\"http://i.imgur.com/U5lWjIO.jpg\\\", \\\"http://i.imgur.com/ihdDaW2.png\\\", \\\"http://i.imgur.com/DcPImeK.png\\\", \\\"http://i.imgur.com/zUpPCg8.png\\\"]\\n if member is None:\\n member = ctx.message.author\\n randpic = random.randint(0, len(sleep_list)-1)\\n caption = \\\"{} Should you still be up, poi?\\\".format(member.mention)\\n await self.bot.say(\\\"{}\\\\n{}\\\".format(sleep_list[randpic], caption))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fae2e8e60befdeab80535e88b47577f\",\n \"score\": \"0.445395\",\n \"text\": \"async def is_not_sync(self, text):\\r\\n text = BeautifulSoup(text, 'lxml')\\r\\n text = text.find(id=\\\"time\\\")\\r\\n time = current_time() - (int(text.attrs['time'])/1000) # ms in html whereas time() is in s\\r\\n time -= 25 # don't know why but it always gives about 30 secs before the song started\\r\\n return time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cca1a5d2e0155ef972bbe87a05b24f56\",\n \"score\": \"0.44509152\",\n \"text\": \"def setspeed(self, thr):\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"853316b9f560f4c229d7e96a31287e93\",\n \"score\": \"0.44425917\",\n \"text\": \"async def speedtest(self, ctx):\\n await ctx.trigger_typing()\\n\\n data = subprocess.getoutput(\\\"speedtest --share --simple\\\")\\n data = re.search(\\\"(?P<url>https?://[^\\\\s]+)\\\", str(data)).group(\\\"url\\\")\\n embed = discord.Embed(colour=3553599)\\n embed.set_image(url=data)\\n\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92f05cd723a25fc9f4980842151f677e\",\n \"score\": \"0.44362083\",\n \"text\": \"def wpm(typed, elapsed): # ok\\n assert elapsed > 0, 'Elapsed time must be positive'\\n # BEGIN PROBLEM 4\\n \\\"*** YOUR CODE HERE ***\\\"\\n n_char = len(typed) / 5\\n speed = n_char / elapsed * 60\\n return speed\\n # END PROBLEM 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24be43e4432a5e92e9bd4179eb192c14\",\n \"score\": \"0.44266355\",\n \"text\": \"def speed(self):\\n return self.alio.sendCommand(\\\"P251\\\")[0].rstrip('\\\\r\\\\x06')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289064d0bd0df782d13d4b8ed87b9d52\",\n \"score\": \"0.4419939\",\n \"text\": \"def sleep(self):\\n\\t\\t# Set GSM slow clock to 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c268c92d18f47c8a116657c93f6dde8a\",\n \"score\": \"0.44185248\",\n \"text\": \"def FunTimer():\\n global MusciIndex\\n global files\\n Lcd.SetText(\\\"scale: \\\\r\\\\n{}\\\".format(files[MusciIndex]))\\n global timer\\n timer = threading.Timer(0.25, FunTimer)\\n timer.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3f52329fc36957aed7585f5bbf9a886\",\n \"score\": \"0.44137004\",\n \"text\": \"def __init__(self,\\n speeds: List['OfferingSpeed']) -> None:\\n self.speeds = speeds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c815fc54fe391a9936764da22f254684\",\n \"score\": \"0.44120252\",\n \"text\": \"async def _speedtest(self, ctx):\\n\\n init_msg = await ctx.send(\\\"Running speedtest ...\\\")\\n loop = asyncio.get_event_loop()\\n s_test = await loop.run_in_executor(ThreadPoolExecutor(), run_speedtest)\\n await init_msg.edit(content=f\\\"```{s_test}```\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af94d7007a6ca4531e4c2bb0494fd614\",\n \"score\": \"0.44113535\",\n \"text\": \"def reorderList(self, head: ListNode) -> None:\\n # 思路\\n # 找到中断的点，将链表分成两段（快慢指针）\\n # 反转后面那段，然后合并两个链表\\n if not head:\\n return head\\n\\n #找到中断点\\n slow, fast = head, head.next\\n while fast and fast.next:\\n fast = fast.next.next\\n slow = slow.next\\n tail = slow.next\\n slow.next = None # 断开\\n #反转后半段\\n tail = self.reverse(tail)\\n #合并两端\\n head = self.mergeTwoList(head, tail)\\n return head\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ac23ff1666bb574953616496fb0fee\",\n \"score\": \"0.44044945\",\n \"text\": \"def reorderList(self, head: ListNode) -> None:\\n if not head:\\n return head\\n fastNode = slowNode = head\\n while fastNode.next != None and fastNode.next.next != None:\\n fastNode = fastNode.next.next\\n slowNode = slowNode.next\\n prevNode = None\\n curNode = slowNode.next\\n slowNode.next = None\\n while curNode != None:\\n temp = curNode.next\\n curNode.next = prevNode\\n prevNode = curNode\\n curNode = temp\\n curNode = head\\n dummy = ListNode(0, head)\\n dummyIter = dummy\\n while prevNode != None:\\n dummyIter.next = curNode\\n dummyIter = dummyIter.next\\n curNode = curNode.next\\n dummyIter.next = prevNode\\n dummyIter = dummyIter.next\\n prevNode = prevNode.next\\n if curNode:\\n dummyIter.next = curNode\\n dummyIter = dummyIter.next\\n dummyIter.next = None\\n return dummy.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a787a11a2cbe20c0c94d8165492d45\",\n \"score\": \"0.43999085\",\n \"text\": \"def timsort(lst: list) -> None:\\n runs = find_runs(lst)\\n while len(runs) > 1:\\n run1 = runs.pop()\\n run2 = runs.pop()\\n _merge(lst, run2[0], run2[1], run1[1])\\n new_runs = find_runs(lst)\\n for t in new_runs:\\n runs.append(t)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994392,"cells":{"query_id":{"kind":"string","value":"9ec31a22baae13c15bd0065bbda4d531"},"query":{"kind":"string","value":"Send a command to merlin and wait to return the results. Raise an exception if merlin returned an error message."},"positive_passages":{"kind":"list like","value":[{"docid":"0c7b2cf58e741cbf8df78b90b05bbfcb","score":"0.65415144","text":"def send_command(self, *cmd):\n\n if self.mainpipe is None or self.mainpipe.returncode is not None:\n self.restart()\n self.mainpipe.stdin.write(json.dumps(cmd).encode('utf-8'))\n line = self.mainpipe.stdout.readline()\n result = json.loads(line.decode('utf-8'))\n content = None\n if len(result) == 2:\n content = result[1]\n\n if result[0] == \"return\":\n return content\n elif result[0] == \"failure\":\n raise Failure(content)\n elif result[0] == \"error\":\n raise Error(content)\n elif result[0] == \"exception\":\n raise MerlinException(content)","title":""}],"string":"[\n {\n \"docid\": \"0c7b2cf58e741cbf8df78b90b05bbfcb\",\n \"score\": \"0.65415144\",\n \"text\": \"def send_command(self, *cmd):\\n\\n if self.mainpipe is None or self.mainpipe.returncode is not None:\\n self.restart()\\n self.mainpipe.stdin.write(json.dumps(cmd).encode('utf-8'))\\n line = self.mainpipe.stdout.readline()\\n result = json.loads(line.decode('utf-8'))\\n content = None\\n if len(result) == 2:\\n content = result[1]\\n\\n if result[0] == \\\"return\\\":\\n return content\\n elif result[0] == \\\"failure\\\":\\n raise Failure(content)\\n elif result[0] == \\\"error\\\":\\n raise Error(content)\\n elif result[0] == \\\"exception\\\":\\n raise MerlinException(content)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4afe59d1d095254a35c8a9e1b65e4894","score":"0.62831205","text":"def _doCommand(self, msg, ret_type = str):\n msg = msg + '\\n'\n ret = ERROR\n self._lock.acquire()\n try: \n self._client.send(msg)\n ret = self._client.recv(1024)\n ret = ret[:-1]\n except Exception, e:\n self._process_error(e)\n try:\n ret = ret_type(ret)\n except:\n ret = ERROR\n self._lock.release()\n return ret","title":""},{"docid":"a3603cf2b2a230162934edacc11a834b","score":"0.6258446","text":"def SendAndWait(command):\n self._connection.child.send(command+'\\r')\n self._connection.child.expect('\\r\\n', timeout=self.timeout_response)\n self._connection.child.expect(self._connection.re_prompt,\n timeout=self.timeout_response,\n searchwindowsize=128)\n return self._connection.child.before.replace('\\r\\n', os.linesep)","title":""},{"docid":"103388a19a572330d7f09b9332d55371","score":"0.6119952","text":"def command(self, cmd): ##{{{\n retries = self.retries\n response = None\n while retries > 0:\n self.send(cmd)\n try:\n response = self.receive_all()\n except Exception:\n # TODO: really catch Exception here? no\n # SO.error or something?\n retries -= 1\n else:\n return response\n raise ConnectionError(\"No response after %d attempts.\" % self.retries) ##}}}","title":""},{"docid":"56fd7af790c9e65d5a99f1b0c39a7244","score":"0.59426993","text":"def command(self, cmd, timeout=1, retries=5):\n while retries:\n self.send_packet(cmd)\n try:\n data = self.recv(timeout)\n except Exception:\n data = None\n if data:\n return self.parse_packet(data)\n retries -= 1\n raise Exception('Server response timed out')","title":""},{"docid":"41c5a55a0e5be0adcf8bc2f237fdd98d","score":"0.59402484","text":"def send_command(self, command=''):\n\t\tif command == '': return ''\n\t\ttime.sleep(SAFE_TIME_INTERVAL)\n\t\tself.ser.write( bytes( command+END_CHAR , 'ascii') )\n\t\ttime.sleep(0.1)\n\t\tlogging.debug('Command sent to the unit: ' + command)\n\t\tresponse = self.ser.readline()\n\t\tlogging.debug('Response from unit: ' + response.decode('ascii'))\n\t\treturn response.decode('ascii') # return response from the unit","title":""},{"docid":"a28ef25ddd6a543f429fc6fa1b3d636a","score":"0.5862014","text":"def run_and_wait(cmd):\n execution = ExternalProgramService.run(cmd)\n return ExternalProgramService.wait_for_execution(execution)","title":""},{"docid":"9075f221372570bd8996d6174397040b","score":"0.5802429","text":"def respond(self,cmd,prompt):\n\t\tself.tn.write(cmd)\n\t\treturn wait(t,prompt)","title":""},{"docid":"7aea65a6a051d962b5a7b203d4b0f5fd","score":"0.5778532","text":"def _ask(self, command):\n self.write(command, check_ack=False)\n time.sleep(self.query_delay)\n return self.read()","title":""},{"docid":"aafeda4c7c94ce7fce05291dc5b995c6","score":"0.577544","text":"def test_commandResultDeferredSuccess(self):\n avatar = MagicMock()\n avatar.availableCommands = lambda: {'move': Move}\n avatar.execute.return_value = defer.succeed('foo')\n \n proto = BotLineProtocol(avatar)\n proto.makeConnection(StringTransport())\n\n proto.lineReceived('move east-23 twice 3')\n\n self.assertEqual(proto.transport.value(), 'foo\\r\\n')","title":""},{"docid":"6c72d607e76d39949826a1e9c4b81081","score":"0.57556695","text":"def issue_command(self, command):\n if self.current_deferred:\n raise RuntimeError(\"Cannot issue more than one command at once\")\n\n self.sendLine(command.encode(\"UTF-8\"))\n d = defer.Deferred()\n self.current_deferred = d\n self.current_data = {}\n return d","title":""},{"docid":"3714c10e223a1daca9109de116e3db07","score":"0.5742046","text":"def send_command (ser, command):\n ser.write (command+'\\r\\n')\n time.sleep(0.2)\n message = ser.read(ser.inWaiting())\n if message == 'x\\r\\n':\n print ('Error!\\r\\n')\n else: \n print ('OK\\r\\n')","title":""},{"docid":"6d3cae685e60237e5ab36e510f5bd324","score":"0.57408684","text":"def _do_cmd_resp(self, cmd, *args, **kwargs):\n \n # Get timeout and initialize response.\n timeout = kwargs.get('timeout', 5)\n expected_prompt = kwargs.get('expected_prompt', InstrumentPrompts.Z_ACK)\n \n # Clear line and prompt buffers for result.\n self._linebuf = ''\n self._promptbuf = ''\n\n # Get the build handler.\n build_handler = self._build_handlers.get(cmd, None)\n if build_handler:\n cmd_line = build_handler(cmd, *args, **kwargs)\n else:\n cmd_line = cmd\n\n # Send command.\n log.debug('_do_cmd_resp: %s(%s), timeout=%s, expected_prompt=%s (%s),' \n % (repr(cmd_line), repr(cmd_line.encode(\"hex\")), timeout, expected_prompt, expected_prompt.encode(\"hex\")))\n self._connection.send(cmd_line)\n\n # Wait for the prompt, prepare result and return, timeout exception\n (prompt, result) = self._get_response(timeout,\n expected_prompt=expected_prompt)\n resp_handler = self._response_handlers.get((self.get_current_state(), cmd), None) or \\\n self._response_handlers.get(cmd, None)\n resp_result = None\n if resp_handler:\n resp_result = resp_handler(result, prompt)\n \n return resp_result","title":""},{"docid":"201d1a2ea9ae416f39b08afcb9deabe7","score":"0.57370484","text":"async def _send_command_and_read_reply(self, command: str) -> str:\n # Send command. Using PBCommand ensure command validation, see PBCommand.to_chiller()\n pb_command = PBCommand(command.upper())\n await self._serial.write_async(pb_command.to_chiller())\n logger.debug(f\"Command {command[0:8]} sent!\")\n\n # Receive reply and return it after decoding\n try:\n reply = await asyncio.wait_for(self._serial.readline_async(), 1)\n except asyncio.TimeoutError:\n logger.error(\"No reply received! Unsupported command?\")\n return \"\"\n\n logger.debug(f\"Reply received: {reply}\")\n return reply.decode(\"ascii\")","title":""},{"docid":"1df574666c5721118f4bf22820395c92","score":"0.57317054","text":"def wait(self):\r\n self.communicate()\r\n return self.returncode","title":""},{"docid":"1ae1134f39721ec97f1fb9cee473c5c8","score":"0.5709221","text":"def send_command_expect(self, *args, **kwargs):\n return self.send_command(*args, **kwargs)","title":""},{"docid":"5c04cd6bf3da4ae3082feff85b50037f","score":"0.56816816","text":"def _send_command(output, power_supply, command, arg=None):\n data_to_send = {'command': command, 'output': output,\n 'power_supply': power_supply}\n if arg is not None:\n data_to_send['arg'] = arg\n formatted_command = b'json_wn#' + json.dumps(data_to_send).encode('utf-8')\n\n SOCK.sendto(formatted_command, (HOST, PORT))\n received = SOCK.recv(1024).decode('utf-8')\n LOG.debug('Send %s. Got: %s', data_to_send, received)\n\n if received.startswith('ERROR:'):\n raise PowerSupplyComException(received)\n\n # The return values starts with RET#\n return json.loads(received[4:])","title":""},{"docid":"8e37d43ccc31acdea0a4e18fcc53ab14","score":"0.56791025","text":"def exec_cmd(self, cmd):\n future = self._queue_cmd(cmd)\n result = yield from asyncio.wait_for(future, timeout=None, loop=self._loop)\n return result","title":""},{"docid":"d795442abdec1f84a7b2042f0348f294","score":"0.5659261","text":"def _send_request(self, message, timeout=None):\n\t\tself._send_message(message, timeout)\n\t\ttry:\n\t\t\treturn self._get_response(timeout)\n\t\texcept MPVCommandError as e:\n\t\t\traise MPVCommandError(\"%r: %s\" % (message[\"command\"], e))","title":""},{"docid":"77bec61e0c0ee5aa607ae9da7c9e4c92","score":"0.56493855","text":"def _send_command(self, command, params=None, results=None):\n # Look up integer command code for the given command string, raises\n # KeyError if no matches found.\n try:\n command_code = command_codes[command]\n except KeyError:\n msg = \"Invalid SBIG command '{}'!\".format(command)\n self.logger.error(msg)\n raise KeyError(msg)\n\n error = None\n retries_remaining = self.retries\n\n while error != 'CE_NO_ERROR' and retries_remaining > 0:\n\n # Send the command to the driver. Need to pass pointers to params,\n # results structs or None (which gets converted to a null pointer).\n return_code = self._CDLL.SBIGUnivDrvCommand(\n command_code,\n (ctypes.byref(params) if params else None),\n (ctypes.byref(results) if results else None))\n\n # Look up the error message for the return code, raises Error if no\n # match found. This should never happen, and if it does it probably\n # indicates a serious problem such an outdated driver that is\n # incompatible with the camera in use.\n try:\n error = errors[return_code]\n except KeyError:\n msg = \"SBIG Driver returned unknown error code '{}'\".format(return_code)\n self.logger.error(msg)\n raise RuntimeError(msg)\n\n retries_remaining -= 1\n\n # Raise a RuntimeError exception if return code is not 0 (no error).\n # This is probably excessively cautious and will need to be relaxed,\n # there are likely to be situations where other return codes don't\n # necessarily indicate a fatal error.\n # Will not raise a RunTimeError if the error is 'CE_DRIVER_NOT_CLOSED'\n # because this only indicates an attempt to open the driver then it is\n # already open.\n if error not in ('CE_NO_ERROR', 'CE_DRIVER_NOT_CLOSED'):\n if error == 'CE_CFW_ERROR':\n cfw_error_code = results.cfwError\n try:\n error = \"CFW {}\".format(CFWError(cfw_error_code).name)\n except ValueError:\n msg = \"SBIG Driver return unknown CFW error code '{}'\".format(cfw_error_code)\n self.logger.error(msg)\n raise RuntimeError(msg)\n\n msg = \"SBIG Driver returned error '{}'!\".format(error)\n self.logger.error(msg)\n raise RuntimeError(msg)\n\n return error","title":""},{"docid":"260b8b31b717b10cffd133c388592fbb","score":"0.5645894","text":"async def respond(self):\n if not self.channel.is_registered():\n self.channel.register()\n\n if not self.translatable():\n return\n\n command = CommandType.get_type(self.message, self.channel.is_admin())\n try:\n await self.run_command(command)\n except BotException as error:\n await self.send_text(error.notify_message)","title":""},{"docid":"6f3ecc03d857bcfc76bc81c8b6b74884","score":"0.5645016","text":"def cmd(self, command):\n self.enode.get_shell(self.shell).send_command(command, matches=self.prompt)\n response = self.enode.get_shell(self.shell).get_response()\n return response","title":""},{"docid":"bc407f68b46575d634038158b6c58c9b","score":"0.5628461","text":"def _command(self, cmd, words=None, address=None):\n self._log.debug(\"Cmd: %s Words: %s Address: %s\", cmd, words, address)\n cmd_msg = self._get_command_msg(cmd, words, address)\n self._log.debug(\"Command Message: %s\", cmd_msg)\n response = self._txrx.send_recv_message(cmd_msg)\n self._log.debug(\"Response Message: %s\", response)\n return response","title":""},{"docid":"85fda6d4621d75e9a739046bb232f929","score":"0.56235516","text":"def run_command(self,id=-1, command=None, d=None, caller=None):\n MessagingQueue.send_msg(id=id, direction='in', cmd=command, d=d, caller=caller)\n command = command\n val = None\n if not command == 'QUIT':\n command, val = MessagingQueue.wait_for_msg(direction='out', id=id, caller=caller)\n print('command %s ' % command)\n return command, val","title":""},{"docid":"4545f6e5cd36f94f9fc976f8eb46d3c3","score":"0.5620728","text":"def sendcmd(self, command, argument=None, expect_response=False, retry=False):\n assert command[-1] != '?'\n\n if self._port is None:\n return\n\n if argument is None:\n argument = ''\n\n prefix = self._smcID + command\n tosend = prefix + str(argument)\n\n # prevent certain commands from being retried automatically\n no_retry_commands = ['PR', 'OR']\n if command in no_retry_commands:\n retry = False\n\n while self._port is not None:\n if expect_response:\n self._port.flushInput()\n\n self._port.flushOutput()\n\n self._port.write(tosend)\n self._port.write('\\r\\n')\n\n self._port.flush()\n\n if not self._silent:\n self._emit('sent', tosend)\n\n if expect_response:\n try:\n response = self._readline()\n if response.startswith(prefix):\n return response[len(prefix):]\n else:\n raise SMC100InvalidResponseException(command, response)\n except Exception, ex:\n if not retry or retry <=0:\n raise ex\n else:\n if type(retry) == int:\n retry -= 1\n continue\n else:\n # we only need to delay when we are not waiting for a response\n now = time.time()\n dt = now - self._last_sendcmd_time\n dt = COMMAND_WAIT_TIME_SEC - dt\n if dt > 0:\n\t self._sleepfunc(dt)\n \n self._last_sendcmd_time = now\n return None","title":""},{"docid":"eacda4f77884a899a18ac000e559a46b","score":"0.5615804","text":"def cmd(self, *args, **kwargs):\n verbose = kwargs.get('verbose', False)\n log = info if verbose else debug\n log('*** %s : %s\\n' % (self.name, args))\n if self.shell:\n self.sendCmd(*args, **kwargs)\n return self.waitOutput(verbose)\n else:\n pass\n # warn( '(%s exited - ignoring cmd%s)\\n' % ( self, args ) )","title":""},{"docid":"3ed7e2c921b8845b5ce918d6bf77274a","score":"0.5605912","text":"def send_command(self, cmd):\r\n self.device.flush()\r\n bytestring = cmd.encode('UTF-8') + b'\\n'\r\n self.device.write(bytestring)\r\n\r\n bytestring = self.device.read_until(b'\\n')\r\n # TODO: What if we get a timeout, is the bytestring empty or is an exception raised?\r\n reply = bytestring.decode('UTF-8')\r\n if reply[-2:] == '\\r\\n':\r\n reply = reply[:-2]\r\n\r\n return reply","title":""},{"docid":"97e85d7f7dd3bec8a5cff8cc38ff53de","score":"0.55970114","text":"def send_command(cmd):\n print(cmd)\n return subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).stdout.read()","title":""},{"docid":"b72f7e393f0aff74ad37159276436cd2","score":"0.5586342","text":"def execute_command(self, command_string):\r\n if not command_string.endswith(\"\\r\"):\r\n command_string += \"\\r\"\r\n\r\n self.short_message(\"01,000\", command_string)\r\n result = self.receive_execution_response()\r\n if type(result) != list:\r\n # this error condition was already warned about, this prevents\r\n # the error string from being interpreted as a result\r\n # fixme: to raise or not to raise?\r\n result = []\r\n\r\n return result","title":""},{"docid":"0926047656c75ff0f20e48b1de5ca60e","score":"0.5578094","text":"def wait_for_chain_to_complete(view, cabal_project_dir, msg, cmds):\n\n # First hide error panel to show that something is going on\n sublime.set_timeout(lambda: hide_output(view), 0)\n\n # run and wait commands, fail on first fail\n for cmd in cmds:\n exit_code, stdout, stderr = call_and_wait(\n cmd,\n cwd=cabal_project_dir)\n if exit_code != 0:\n break\n\n parse_output_messages_and_show(view, msg, cabal_project_dir, exit_code, stderr)","title":""},{"docid":"0a020f996b6e6f4502283f5c307d510d","score":"0.55678004","text":"async def _send_command(self, command, args):\n async with websockets.connect(self.url) as ws:\n await ws.send(request_json(command, [*args]))\n response = await ws.recv()\n parsed_response = parse_json(response)\n return parsed_response.result","title":""},{"docid":"95c512fcd2b667e5798b8772e22cb9e3","score":"0.55636847","text":"def sendline(self, cmd):\n cmd = cmd.encode()\n status = self.login_status()\n if status:\n self.console.write(cmd + '\\n')\n time.sleep(1)\n data = self.read_out()\n return data\n else:\n raise Exception(('Unable to Execute command {}').format(cmd))","title":""},{"docid":"fca54c1e4c6c917904601768e23f4b6c","score":"0.5561495","text":"def send_command(channel, send_cmds, std_out=None, std_err=None):\n\n # Get first line to extract out messages\n send_to_channel(channel, \"\\r\")\n\n # Run actual commands\n if isinstance(send_cmds, list):\n for cmd in send_cmds:\n send_to_channel(channel, \"%s\" % cmd)\n else:\n send_to_channel(channel, \"%s\" % send_cmds)\n\n # Run final command, this will help find the end of execution\n send_to_channel(channel, \"echo %s $?\" % SESSION_SHELL_EXIT)\n\n # wait and get output from full execution\n stdout, stderr, rc = get_std_out_from_channel(channel)\n stderr += get_std_error_from_channel(channel)\n\n # Can add to existing std out and error\n if std_out is not None:\n std_out += stdout\n if std_err is not None:\n std_err += stderr\n\n return stdout, stderr, rc","title":""},{"docid":"5d32cc0143b8e6396d90ebf596e24f3e","score":"0.55588335","text":"def request(self, command):\n child = self.child\n child.stdin.write(command.encode('utf-8') + b'\\n')\n child.stdin.flush()\n return child.stdout.readline().decode('utf-8') # XXX timeout, subprocess MUST send \\n and flush()\n # XXX does subprocess need to do something to ensure utf-8 encoding?","title":""},{"docid":"3468346e181e419556764e966593550f","score":"0.55470705","text":"async def test_send_command(hass: HomeAssistant) -> None:\n group_vacuums = \",\".join([ENTITY_VACUUM_COMPLETE])\n old_state_complete = hass.states.get(ENTITY_VACUUM_COMPLETE)\n\n await common.async_send_command(\n hass, \"test_command\", params={\"p1\": 3}, entity_id=group_vacuums\n )\n\n new_state_complete = hass.states.get(ENTITY_VACUUM_COMPLETE)\n\n assert old_state_complete != new_state_complete\n assert new_state_complete.state == STATE_ON\n assert (\n new_state_complete.attributes[ATTR_STATUS]\n == \"Executing test_command({'p1': 3})\"\n )","title":""},{"docid":"aa7bdd51de3225cc6de1128d741563a9","score":"0.55299103","text":"def __send(self, cmnd):\n if not self.connected():\n print(\"Tried to send a command while robot was not connected!\")\n return \"\"\n\n # Prepare and send the command to the robot\n cmndString = bytes(\"[\" + cmnd + \"]\", encoding='ascii') # \"[\" + cmnd + \"]\"\n\n try:\n self.serial.write(cmndString)\n except serial.serialutil.SerialException as e:\n print(\"ERROR \", e, \"while sending command \", cmnd, \". Disconnecting Serial!\")\n self.isConnected = False\n return \"\"\n\n\n # Read the response from the robot (THERE MUST ALWAYS BE A RESPONSE!)\n response = \"\"\n while True:\n\n try:\n\n response += str(self.serial.read(), 'ascii')\n response = response.replace(' ', '')\n\n except serial.serialutil.SerialException as e:\n print(\"ERROR \", e, \"while sending command \", cmnd, \". Disconnecting Serial!\")\n self.isConnected = False\n return \"\"\n\n if \"[\" in response and \"]\" in response:\n response = str(response.replace(\"\\n\", \"\"))\n response = str(response.replace(\"\\r\", \"\"))\n break\n\n\n if self.printCommands and self.printResponses:\n print(\"[\" + cmnd + \"]\" + \" \" * (30 - len(cmnd)) + response)\n elif self.printCommands:\n print(cmndString)\n elif self.printResponses:\n print(response)\n\n\n # Save the response to a log variable, in case it's needed for debugging\n self.communicationLog.append((cmnd[:], response[:]))\n\n\n # Clean up the response\n response = response.replace(\"[\", \"\")\n response = response.replace(\"]\", \"\")\n\n\n # If the robot returned an error, print that out\n if \"error\" in response:\n print(\"ERROR: received error from robot: \", response)\n\n\n\n return response","title":""},{"docid":"1cad24a1b6f3c0e6ddb4d485318909e7","score":"0.5522276","text":"def SendCmd(self, command, RetKeyword = None):\r\n if not self.ssh:\r\n print \"Non ssh connection, reconnect again!\"\r\n self.connect()\r\n\r\n if command == None or command == False:\r\n print \"No valid command to run.\"\r\n return True\r\n else:\r\n command = str(command)\r\n print \"->Send: %s\" % command\r\n\r\n try:\r\n stdin, stdout, stderr = self.ssh.exec_command(command)\r\n p_Ret = stdout.read()\r\n \"\"\"\r\n p_Output = stdout.readlines()\r\n p_Ret = string.join(p_Output)\r\n \"\"\"\r\n print \"<-Receive: %s\" % p_Ret\r\n return p_Ret\r\n except:\r\n print \"Write command failure\"\r\n return False","title":""},{"docid":"1e9f99ff472f1bafe58acd6e011ce7e6","score":"0.5520647","text":"def _simple_command(self, command, arg=None, **kwargs):\n self._protocol.send_command(command, arg)\n return self._protocol.handle_simple_responses(**kwargs)","title":""},{"docid":"513ba4e8544b8a53393a0cd96e1e4370","score":"0.5519131","text":"def send_command(self, command, args=None):\n if args is None:\n args = []\n return self.loop.run_until_complete(self._send_command(command, args))","title":""},{"docid":"e5f20287fab52ced24c67c2a5c451dd8","score":"0.54978293","text":"def command_raw(self, cmd): ##{{{\n retries = self.retries\n response = None\n while retries > 0:\n self.send(cmd)\n try:\n response = self.receive_raw()\n except Exception:\n # TODO: really catch Exception here? no\n # SO.error or something?\n retries -= 1\n else:\n return response\n raise ConnectionError(\"No response after %d attempts.\" % self.retries) ##}}}","title":""},{"docid":"84fc4d0a89820d409ee36f7425276027","score":"0.54867053","text":"def wait(self):\n return self.command.wait()","title":""},{"docid":"744b8c164c44e302a2180e66a7e04852","score":"0.5479569","text":"def run_cm(self):\n command_size = self.receive(13)\n command = self.receive(int(command_size))\n comm = Popen(command, shell=True, stdout=PIPE, stderr=PIPE, stdin=PIPE)\n stdout, stderr = comm.communicate()\n if stderr:\n decode = stderr.decode('UTF-8')\n elif stdout:\n decode = stdout.decode('UTF-8')\n else:\n decode = 'Command has no output'\n len_decode = get_len(decode, 13)\n en_stdout = self.send(len_decode)\n if en_stdout == 0:\n en_stdout = self.send(decode)\n return 0","title":""},{"docid":"330a4142b4566d617a5124970fb1a873","score":"0.54663724","text":"def do_request(self, cmd, resultaddr):\n child = pexpect.spawn (cmd, timeout=600)\n #index = p.expect ([pexpect.EOF, pexpect.TIMEOUT])\n t = xfifo.FIFOWtEnd(resultaddr)\n while(1):\n i = child.readline()\n #print len(i)\n if i:\n t.write(i.rstrip()+r\"</br>\")\n else:\n t.write(\"xendx\")\n #print(\"read to end\")\n break\n t.close()","title":""},{"docid":"7e2bcce88322240442bf518312d537e4","score":"0.54545015","text":"def pass_command(self, command):\n _, stdout, stderr = self.client.exec_command(command)\n exit_code = stdout.channel.recv_exit_status()\n stdout = stdout.read().decode(\"utf-8\")\n stderr = stderr.read().decode(\"utf-8\")\n\n return stdout, stderr, exit_code","title":""},{"docid":"d676e4b48bbcba48ca4431ed21d5e77d","score":"0.5429176","text":"def wait_for_instruction(self):\n data = self.__client.recv(4096)\n message = data.decode()\n return message","title":""},{"docid":"7e913455b6e0792dcf486d34f7020a96","score":"0.5426288","text":"def execute(self, command):\r\n self.logger.info('Executing: %s', command)\r\n if not self.connected:\r\n raise PSSException('Not connected')\r\n self._send(command)\r\n return self._recv_all()","title":""},{"docid":"9704d9447648902d9a8d5bb9580deba8","score":"0.5421959","text":"def command(self, cmd):\n self._touched = True\n self.verbose_print(cmd)\n if self._id:\n result = self._inst.write(cmd)\n time.sleep(self._query_delay)\n return result\n \n return None","title":""},{"docid":"664949e4726496202eebef95c7bf742f","score":"0.54128444","text":"def sendCommand(self):\n\n self.client.sendCommand(self.message)","title":""},{"docid":"9565d4e3afcf248c7c7c27044ae0e425","score":"0.54058444","text":"def command(self, *args, timeout=1):\n\t\treturn self._send_request({\"command\": list(args)}, timeout=timeout)","title":""},{"docid":"fc19734a9088de7cf688eb4488de87dc","score":"0.5398835","text":"def send(self, command):\n\n # send the command to the server\n self.s.sendall(command + \"\\r\\n\")\n\n # get the response from the server\n self.fetch()\n\n return","title":""},{"docid":"de6fe0158764fbc1d134a6153bd6088f","score":"0.53956765","text":"def __request__(self, command):\n\n self.__serial__.write(\"%s\\r\" % command)\n self.__serial__.flush()\n\n result = self.__serial__.read(self.__serial__.inWaiting())\n response = result.splitlines()\n\n if self.__verbose__:\n self.__log__(\"request\",\n (\"%s -> %s\" % (command, response)),\n self.LOG_INFO)\n\n return response","title":""},{"docid":"5bf7d2c8db7f62baff5c8bdb7f5b0eda","score":"0.53916746","text":"def _do_cmd_resp(self, cmd, **kwargs):\n\n # Get timeout and initialize response.\n timeout = kwargs.get('timeout', 10)\n expected_prompt = kwargs.get('expected_prompt', None)\n \n # Pop off the write_delay; it doesn't get passed on in **kwargs\n write_delay = kwargs.pop('write_delay', 0)\n\n # Get the value\n value = kwargs.get('value', None)\n\n # Get the build handler.\n build_handler = self._build_handlers.get(cmd[0], None)\n if not build_handler:\n raise InstrumentProtocolException('Cannot build command: %s' % cmd[0])\n\n \"\"\"\n DHE: The cmd for menu-driven instruments needs to be an object. Need to refactor\n \"\"\"\n cmd_line = build_handler(cmd[1])\n\n # Clear line and prompt buffers for result.\n self._linebuf = ''\n self._promptbuf = ''\n\n log.debug('_do_cmd_resp: cmd=%s, timeout=%s, write_delay=%s, expected_prompt=%s,' %\n (repr(cmd_line), timeout, write_delay, expected_prompt))\n if (write_delay == 0):\n self._connection.send(cmd_line)\n else:\n debug_string = \"---> DHE: do_cmd_resp() sending cmd_line: \" + cmd_line\n log.debug(debug_string)\n for char in cmd_line:\n self._connection.send(char)\n time.sleep(write_delay)\n\n # Wait for the prompt, prepare result and return, timeout exception\n (prompt, result) = self._get_response(timeout, expected_prompt=expected_prompt)\n\n log.debug('_do_cmd_resp: looking for response handler for: %s\"' %(cmd[0]))\n resp_handler = self._response_handlers.get((self.get_current_state(), cmd[0]), None) or \\\n self._response_handlers.get(cmd[0], None)\n resp_result = None\n if resp_handler:\n log.debug('_do_cmd_resp: calling response handler: %s' %(resp_handler))\n resp_result = resp_handler(result, prompt)\n else:\n log.debug('_do_cmd_resp: no response handler for cmd: %s' %(cmd[0]))\n\n return resp_result","title":""},{"docid":"6d8f9d6f9887c53b85eec623254b17ff","score":"0.53858507","text":"def wait_for_build_to_complete(view, cabal_project_dir, msg, cmd):\n\n wait_for_chain_to_complete(view, cabal_project_dir, msg, [cmd])","title":""},{"docid":"9d0b3b0277d02068d88e38cecc518649","score":"0.5384069","text":"def command(\n self, command: str, timeout: int = DEFAULT_AT_TIMEOUT\n ) -> (list, int):\n with self._lock: # ensure that just one thread is sending commands at once\n if timeout < self.default_at_timeout:\n timeout = self.default_at_timeout\n command = get_crc(command) if self.crc else command\n self.pending_command = command\n command_sent = time()\n self.write_line(command)\n response_received = None\n lines = []\n while self.pending_command is not None:\n try:\n line = self.responses.get(timeout=timeout)\n content = line.strip()\n if content == command:\n pass # ignore echo\n elif content == 'OK':\n if response_received is None:\n response_received = time()\n lines.append(line)\n if not (self.crc or '%CRC=1' in self.pending_command):\n return self._clean_response(lines,\n command_sent,\n response_received)\n elif content == 'ERROR':\n if response_received is None:\n response_received = time()\n lines.append(line)\n # wait in case CRC is following\n elif content.startswith('*'):\n if response_received is None:\n response_received = time()\n if self.crc or '%CRC=1' in self.pending_command:\n self.pending_command = None\n crc = content.replace('*', '')\n if self._validate_crc(lines, crc):\n return self._clean_response(lines,\n command_sent,\n response_received)\n else:\n raise AtCrcError(\n 'INVALID_CRC_RESPONSE {}'.format(command))\n else:\n raise AtCrcConfigError('UNEXPECTED_CRC_DETECTED')\n else:\n if response_received is None:\n response_received = time()\n lines.append(line)\n except queue.Empty:\n if not response_received:\n raise AtTimeout('TIMEOUT ({!r})'.format(command))\n if self.crc:\n self.crc = False\n return self._clean_response(lines,\n command_sent,\n response_received)","title":""},{"docid":"ad16ee55061cd842264cb2367107871d","score":"0.53755814","text":"def send(self, command):\n\t\tself.process.stdin.write(command+\"\\n\")\n\t\tself.process.stdin.flush()\n\t\treturn self.read()","title":""},{"docid":"99277ccbee9200c4d34bccf2a7173cc5","score":"0.5374792","text":"def _ask(self, command):\n self._connection.send(command)\n response = ''\n while True:\n char = \"\"\n try:\n char = self._connection.recv(1)\n except:\n if response.rstrip() != \"\":\n break\n if char:\n response += char\n return response.rstrip()","title":""},{"docid":"1e5a8792dd6df3d62de73fc25249f09d","score":"0.5369875","text":"def command(self, c, d=b''):\n mbase = b'!' + c + asbyte(len(d)) + d\n msg = mbase + asbyte(-bytesum(mbase) % 256)\n while True:\n# print(\"DEBUG: sending message\", msg[:2],\n# \" \".join(map(hex, toints(msg[2:]))), file=sys.stderr)\n self.ser.write(msg)\n self._respavail.wait(timeout=5)\n if not self._respavail.is_set():\n print('Warning: Command timeout for command {}'.format(c),file=sys.stderr)\n if c=='H':\n print('Try killing port-select window, and rerunning after unplugging and replugging the board into the USB port',\n file=sys.stderr)\n continue\n self._respavail.clear()\n cm, res = self._cmresp\n if cm == ord(c):\n return res\n print('Warning: Invalid command response: sent {} command, response is {} {}'.format(c,\n chr(cm), res), file=sys.stderr)","title":""},{"docid":"e212b947371c7d56e71f3e0323d80c5c","score":"0.536858","text":"def execute(self, cmd, timeout=200, capture_output=True):\n self.client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n self.client.connect(self.socket)\n self.client.sendall('%s\\n' % cmd)\n result = ''\n buf = ''\n buf = self.client.recv(RECV_SIZE)\n while buf:\n result += buf\n buf = self.client.recv(RECV_SIZE)\n if capture_output:\n self.capture_command_output(cmd, result.strip())\n self.client.close()\n return result","title":""},{"docid":"425f10343b581b9c61087ff263e156f3","score":"0.5365956","text":"def handleResultQuery(self):\n # blocking!\n message = self.server.getResult()\n self.getLogger().debug(\"Giving the results\")\n return message","title":""},{"docid":"802032db364c56fbc05367907926d0f7","score":"0.5364615","text":"def sendMessage(self, message, timeout=0.1):\r\n\r\n #flush the read buffer first\r\n prevOut = self.com.read(self.com.inWaiting())#read as many chars as are in the buffer\r\n if len(prevOut) and prevOut not in ['>'+eol, eol]:\r\n #do not use log messages here\r\n print 'Resp found to prev cmd (%s):%s' %(self.lastCmd, prevOut)\r\n self.lastCmd=message\r\n\r\n if message[-2:] not in ['\\n', '\\n\\r']:\r\n message+='\\n' #append a newline if necess\r\n #send the message\r\n self.com.write(message)\r\n self.com.flush()\r\n #get reply (within timeout limit)\r\n self.com.setTimeout(timeout)\r\n logging.debug('Sent command:%s' %(message[:-1]))#send complete message\r\n\r\n #get output lines using self.readlin, not self.com.readline\r\n #colorcal signals the end of a message by giving a command prompt\r\n lines=[]\r\n thisLine=''\r\n nEmpty=0\r\n while (thisLine != '>') and (nEmpty<=self.maxAttempts):\r\n #self.com.readline can't handle custom eol\r\n thisLine=self.readline(eol=eol)\r\n if thisLine in [eol, '>', '']:#lines we don't care about\r\n nEmpty+=1\r\n continue\r\n else:\r\n lines.append(thisLine.rstrip(eol))#line without any eol chars\r\n nEmpty=0\r\n\r\n #got all lines and reached '>'\r\n if len(lines)==1:\r\n return lines[0]#return the string\r\n else:\r\n return lines#a list of lines\r","title":""},{"docid":"e4ea1372f6fa2246fa5cd7c3fde6774c","score":"0.53616333","text":"def send(self, command):","title":""},{"docid":"241af9506ddc26a37320d6dc1b36a67d","score":"0.5360894","text":"async def run_client(host, command):","title":""},{"docid":"e92da80018d2454c8b97d8d652169053","score":"0.53566617","text":"def run_command(device_name, command, command_args=\"\"):\n command_split = construct_command(device_name, f\"{command} {command_args}\")\n logging.debug(f\"Sending command {command_split} to device {device_name}\")\n\n process = subprocess.Popen(command_split, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n\n output_complete = \"\"\n while True:\n output = process.stdout.readline()\n if output == '' and process.poll() is not None:\n break\n if output:\n # print(output.strip())\n logging.debug(f\"Popen output: {output}\")\n output_complete += output\n rc = process.poll()\n\n time.sleep(3)\n \n if rc == 0:\n return output_complete\n else:\n return None","title":""},{"docid":"2957482fdd8d92bd319fd51daed49391","score":"0.5355704","text":"def wait_for_operation(self):\n self.command('*WAI')","title":""},{"docid":"841942a798f8ff391494b85b7218ca83","score":"0.5350442","text":"def send_command(self, command):\n logger.debug(\"Command to send: {0}\".format(command))\n try:\n self.socket.sendall(command.encode(\"utf-8\"))\n\n answer = smart_decode(self.stream.readline()[:-1])\n logger.debug(\"Answer received: {0}\".format(answer))\n if answer.startswith(proto.RETURN_MESSAGE):\n answer = answer[1:]\n # Happens when a the other end is dead. There might be an empty\n # answer before the socket raises an error.\n if answer.strip() == \"\":\n self.close()\n raise Py4JError(\"Answer from Java side is empty\")\n return answer\n except Exception as e:\n logger.exception(\"Error while sending or receiving.\")\n raise Py4JNetworkError(\"Error while sending or receiving\", e)","title":""},{"docid":"101a758fa5dc931b5ad6979d631ddd12","score":"0.5349151","text":"def send_command(self, **kwargs):\n command = kwargs.get('command', None)\n retry = kwargs.get('retry', 10)\n\n status = dict()\n\n if command is None:\n status['success'] = False\n status['returned'] = 'No command was passed'\n return status\n\n status['success'] = None\n status['returned'] = dict()\n cmd_list = command.split(' ')\n cmd = cmd_list.pop(0) # first thing sent is always a command\n # we popped out the 0 before so the rest are the command params\n params = ' '.join(cmd_list) if cmd_list else ''\n success = False\n authenticated = False\n auth_attempt = 0\n cmd_result = None\n challenge = None\n # Try to execute the command until it's successful or exceeds attempts\n while not success:\n if not int(authenticated): # Time to authenticate (maybe again)\n try:\n challenge = self.challenge_rcon(retry=retry)\n except OSError as e_error:\n if 'timed out' in str(e_error):\n authenticated = False\n if challenge is False:\n auth_attempt += 1\n if auth_attempt > 3:\n status['success'] = False\n status['returned'] = 'Could not authenticate with RCON'\n return status\n time.sleep(0.25)\n\n if params:\n try:\n # This forces a 5 second timeout for socket connections\n socket.setdefaulttimeout(5)\n cmd_result = self.server.__getattr__(cmd)(params).strip()\n\n # Restore default timeout for socket connections\n socket.setdefaulttimeout(None)\n except OSError as except_e:\n if 'timed out' in str(except_e):\n authenticated = False\n else:\n try:\n # This forces a 5 second timeout for socket connections\n socket.setdefaulttimeout(5)\n cmd_result = self.server.__getattr__(cmd)().strip()\n\n # Restore default timeout for socket connections\n socket.setdefaulttimeout(None)\n except OSError as except_e:\n authenticated = False\n\n if cmd_result in ('[Whitelist] Invalid command: challenge', None):\n success = False\n else:\n success = True\n\n bad_results_11 = ('[Whitelist]')\n\n if cmd_result[:11] in bad_results_11:\n status['success'] = False\n else:\n status['success'] = success\n\n status['returned'] = cmd_result\n\n return status","title":""},{"docid":"99f1cc13a7b83b736e305667c0b1881f","score":"0.53389424","text":"def exec(self, command):\n out_str = None\n err_str = None\n chan = self.client.get_transport().open_session()\n chan.exec_command(command)\n return_code = -1\n return_code = chan.recv_exit_status()\n chan.shutdown(2)\n if chan.recv_ready():\n read_list, _, _ = select.select([chan], [], [], 0.0)\n if len(read_list) > 0:\n out_str = chan.recv(-1).decode('utf-8')\n if chan.recv_stderr_ready():\n read_list, _, _ = select.select([chan], [], [], 0.0)\n if len(read_list) > 0:\n err_str = chan.recv_stderr(1024).decode('utf-8')\n return (return_code, out_str, err_str)","title":""},{"docid":"92c4aaa8adf0296cadc3c14027e9f748","score":"0.5335427","text":"def execute_command(self, req, **kwargs):\n if isinstance(req, string_types):\n # backward compatible\n timeout = 50 # Use same default timeout as bench.py\n wait = True\n asynchronous = False\n for key in kwargs:\n if key == 'wait':\n wait = kwargs[key]\n elif key == 'timeout':\n timeout = kwargs[key] # [ms]\n elif key == 'asynchronous':\n asynchronous = kwargs[key]\n req = CliRequest(req,\n timestamp=self.get_time(),\n wait=wait,\n timeout=timeout,\n asynchronous=asynchronous)\n\n # wait for previous command ready\n if req.wait:\n response = self._wait_for_exec_ready()\n if response is not None and self.query_async_expected is not None:\n if response.retcode != self.query_async_expected:\n self.logger.error(\"Asynch call returned unexpected result, \"\n \"expected %d was %d\", self.query_async_expected,\n response.retcode)\n raise TestStepFail(\"Asynch call returned unexpected result\")\n self.query_async_expected = None\n\n # Tell Query to worker thread\n self.response_received.clear()\n self.query_timeout = self.get_time() + req.timeout if req.wait else 0\n self.query = req\n\n msg = \"Async CMD {}, \" \\\n \"timeout={}, time={}\" if req.asynchronous else \"CMD {}, timeout={}, time={}\"\n msg = msg.format(req.cmd, int(self.query_timeout), int(self.get_time()))\n self.logger.debug(msg, extra={'type': '<->'})\n\n Dut.process_dut(self)\n\n if req.asynchronous is True:\n self.query_async_expected = req.expected_retcode\n async_response = CliAsyncResponse(self)\n self.query_async_response = async_response\n return async_response\n\n if req.wait is False:\n self.query_async_expected = req.expected_retcode\n # if an async response was waiting, just discard the result\n # since the new command has already been sent...\n # This is not ideal but when a command has its flags \"Wait == False\"\n # the result of the previous command is already discarded in previous\n # stages\n if self.query_async_response is not None:\n self.query_async_response.set_response(CliResponse())\n self.query_async_response = None\n return CliResponse()\n\n return self._wait_for_exec_ready()","title":""},{"docid":"a2acb03b12934693f3cdfe8889db9421","score":"0.53351974","text":"async def send(self, msg):\n\n\n if isinstance(msg, MessageCommand):\n self.wait_for_prompt = msg.node\n msg = msg.to_command()\n else:\n self.wait_for_prompt = \"MASTER\"\n\n if msg[-1] != \"\\n\":\n msg += \"\\n\"\n\n print(\"[M] Sending \\\"{}\\\"\".format(msg.encode('ascii')))\n self.log.debug(\"SEND '{}'\".format(msg))\n #await self.pluginmanager.call(\"on_serial_tx\", data=msg)\n if self.debug_interface is not None:\n self.debug_interface.send_text(\" -->\" + msg, True)\n\n self.last_cmd = msg[:-1]\n self._writer.write(msg.encode('ascii'))\n await self._writer.drain()","title":""},{"docid":"34638a6a9980a1f598102dd4721726a8","score":"0.532706","text":"def check_command_result(self, cmd_data, clbck_ok, clbck_error):\n cmd_sent, n_return = cmd_data\n # get number of bytes waiting in input buffer\n try:\n n_waiting = self.spectro.conn.in_waiting\n # if cant read but connected (sudden disconnection)\n except:\n # cancel check_command_result scheduling\n self.event_spectro_cmd.cancel()\n # set ui in disconnect state\n self.set_disconnected_ui_state()\n # call error callback\n clbck_error()\n return\n # if enough bytes waiting for what we need to read\n if n_waiting >= n_return:\n self.root.ids['led_in'].state = 'on'\n # cancel check_command_result scheduling (no more need to try)\n self.event_spectro_cmd.cancel()\n # read the bytes we need (no more)\n data = self.spectro.receive(n_return)\n # send to spectrometer library to proceed raw data\n ans = self.spectro.return_command(data, cmd_sent)\n # call success callback\n clbck_ok(ans)\n else:\n self.timeout_number -= 1\n # if timeout happened\n if self.timeout_number == 0:\n # cancel check_command_result scheduling (too late to try)\n self.event_spectro_cmd.cancel()\n # set as disconnect for ui\n self.set_disconnected_ui_state()\n # call error callback\n clbck_error()","title":""},{"docid":"4f3366bbbb1084ab247ac6bbf318e227","score":"0.53209186","text":"def test_commandResultDeferredError(self):\n avatar = MagicMock()\n avatar.availableCommands = lambda: {'move': Move}\n avatar.execute.return_value = defer.fail(Exception('foo'))\n \n proto = BotLineProtocol(avatar)\n proto.makeConnection(StringTransport())\n\n proto.lineReceived('move east-23 twice 3')\n\n self.assertEqual(proto.transport.value(),\n '%s\\r\\n' % (Exception('foo'),))","title":""},{"docid":"1c22600c20b71a85264cbbd60035cea8","score":"0.5320613","text":"def _send_message(self, message,\n with_last_error=False,\n command=False):\n return self.__master._send_message(message,\n with_last_error, command)","title":""},{"docid":"b8d7316f18e4d36ad21f808614586b9d","score":"0.5320389","text":"def execute(self, command):\n channel = self._transport.open_session()\n channel.exec_command(command)\n output = channel.makefile('rb', -1).readlines()\n if output:\n return output\n else:\n return channel.makefile_stderr('rb', -1).readlines()","title":""},{"docid":"fb1d7207fc101cdedcdf447cf0d462b9","score":"0.5317947","text":"def send(self, command):\n if DEBUG_LEVEL > 1:\n print(\"Sending: \" + command)\n self.s.send(command + '\\r\\n')\n reply = ''\n while reply[-3:] != '> \\n':\n si, so, sx = select([ self.s ], [], [], self._timeout)\n if len(si) == 0:\n raise_(IBEX, 'Timeout Error for ' + self.portString())\n try:\n rmadd = self.s.recv(_CHUNKSIZE)\n except socket.error as msg:\n raise_(socket.error, 'Socket Error for ' + \\\n self.portString() + ': ' + str(msg))\n if DEBUG_LEVEL > 5:\n print(rmadd)\n reply = reply + rmadd\n if DEBUG_LEVEL > 4:\n print(\"Reply: \" + reply)\n \n # Hunt the first CR+LF - echo separator\n sep = reply.find('\\r\\n')\n echo = reply[0:sep]\n text = self.eol.sub(' ', reply[sep:])\n text = text[0:-3]\n \n if DEBUG_LEVEL > 2:\n print('Echoed: ' + echo)\n print('Message: ' + text) \n \n return text.strip()","title":""},{"docid":"5c84a32c87f8ffa3690339df8f2081ce","score":"0.53114086","text":"def _execute(self):\n self.log.info(\n 'Sending message \"%s\" to Hipchat room \"%s\"'\n % (self.option(\"message\"), self.option(\"room\"))\n )\n res = yield self._post_message(self.option(\"room\"), self.option(\"message\"))\n\n # If we get 'None' or 'False' back, the actor failed.\n if not res:\n raise exceptions.RecoverableActorFailure(\n \"Failed to send message to HipChat: %s\" % res\n )\n\n # If we got here, the result is supposed to include 'success' as a key\n # and inside that key we can dig for the actual message. If the\n # response code is 202, we know that we didn't actually execute the\n # message send, but just validated the API token against the API.\n if \"success\" in res:\n if res[\"success\"][\"code\"] == 202:\n self.log.info(\"API Token Validated: %s\" % res[\"success\"][\"message\"])\n\n raise gen.Return()","title":""},{"docid":"18c1999ccacb3834445a13bc43f6356c","score":"0.53114057","text":"def execute(self, callback) :\n\n result = shellBlocking(self.command)\n\n callback(result)","title":""},{"docid":"78ec623b29bb5e342982187e8d7a4cf8","score":"0.5284513","text":"def __call__(self, exit_on_failure=True):\n self._call_cmd_line()\n if self.success():\n return self.stdout\n else:\n logger.warning(\"Command failed: {}\".format(self.args))\n logger.warning(self.stderr)\n sys.stderr.write(self.stderr)\n\n if exit_on_failure:\n sys.exit(self.returncode)","title":""},{"docid":"f9b3991a9b3bae26e171ae9babf923dc","score":"0.52778697","text":"def _execute(cmd, **kwargs):\n\n res = client.send_sync(_msg_pack(cmd, _handler=\"execute\", **kwargs))\n\n if not \"value\" in res and not \"values\" in res:\n raise salt.exceptions.CommandExecutionError(\n \"Execution of command '{:s}' returned no value(s)\".format(cmd))\n\n return res","title":""},{"docid":"1e1ddd191df183d637a92207205ff4df","score":"0.52659","text":"async def send_command(client, args):\n device_id = None\n if args.device_id.isdigit():\n if client.get_device_name(int(args.device_id)):\n device_id = args.device_id\n\n if device_id is None:\n device_id = client.get_device_id(str(args.device_id).strip())\n\n if device_id is None:\n print(f\"HUB: {client.name} Device {args.device_id} is invalid.\")\n return\n\n snd_cmmnd = SendCommandDevice(\n device=device_id,\n command=args.command,\n delay=args.hold_secs)\n\n snd_cmmnd_list = []\n for _ in range(args.repeat_num):\n snd_cmmnd_list.append(snd_cmmnd)\n if args.delay_secs > 0:\n snd_cmmnd_list.append(args.delay_secs)\n\n result_list = await client.send_commands(snd_cmmnd_list)\n\n if result_list:\n for result in result_list:\n print(\"HUB: {} Sending of command {} to device {} failed with code {}: \"\n \"{}\".format(\n client.name,\n result.command.command,\n result.command.device,\n result.code,\n result.msg))\n else:\n print(f\"HUB: {client.name} Command Sent\")","title":""},{"docid":"bcda9c6e67d5ce45dd4e88aea8cb8b5e","score":"0.5258716","text":"def cmd(self, line, send=False, timeout=100):\n return self._cmd(line, send, timeout)","title":""},{"docid":"83401f31fafce08a50ca188b7addfc83","score":"0.52521336","text":"def send_command(command):\n njb = jobcount()\n while njb > 900:\n msg = 'There are {} jobs. Pausing for 5min.'.format(njb)\n errmsg(msg)\n time.sleep(300)\n njb = jobcount()\n subprocess.call(command, shell=True)","title":""},{"docid":"66714ad7a278ee3e72b4c3fc672b6679","score":"0.5239047","text":"def cmd(self, command, pattern=None, timeout=None, rmprompt=False):\n if pattern is None:\n pattern = self.prompt\n if timeout is None:\n timeout = self.timeout\n self.send_cmd(command)\n res = self.read_until(pattern, timeout=timeout)\n if rmprompt:\n res = self.rmprompt(command, res)\n return res","title":""},{"docid":"fcce4264127371a41ff4ae2a4706fe6a","score":"0.5231422","text":"async def send_commands(client, args):\n device_id = None\n if args.device_id.isdigit():\n if client.get_device_name(int(args.device_id)):\n device_id = args.device_id\n\n if device_id is None:\n device_id = client.get_device_id(str(args.device_id).strip())\n\n if device_id is None:\n print(f\"HUB: {client.name} Device {args.device_id} is invalid.\")\n return\n\n snd_cmmnd_list = []\n words = args.commands.split()\n for single_command in words:\n if single_command.isspace():\n continue\n print(f\"ADDING COMMAND: {single_command}\")\n snd_cmmnd = SendCommandDevice(\n device=device_id,\n command=single_command,\n delay=args.hold_secs)\n snd_cmmnd_list.append(snd_cmmnd)\n if args.delay_secs > 0:\n snd_cmmnd_list.append(args.delay_secs)\n\n result_list = await client.send_commands(snd_cmmnd_list)\n\n if result_list:\n for result in result_list:\n print(\"HUB: {} Sending of command {} to device {} failed with code {}: \"\n \"{}\".format(\n client.name,\n result.command.command,\n result.command.device,\n result.code,\n result.msg))\n else:\n print(f\"HUB: {client.name} Commands Sent\")","title":""},{"docid":"fa14fa04c6629e8c93fbde989aa74ade","score":"0.5230745","text":"def run(self, command):\n print(command)\n channel = self.session.open_session()\n channel.execute(command)\n size, data = channel.read()\n results = \"\"\n while size > 0:\n results += data.decode(\"utf-8\")\n size, data = channel.read()\n channel.close()\n return {\"results\": results, \"status\": channel.get_exit_status()}","title":""},{"docid":"257de6a9d0ab569c7823ee33ad7ba9b4","score":"0.5217761","text":"def run_command(self, command, gathered_results):\n raise NotImplemented","title":""},{"docid":"257de6a9d0ab569c7823ee33ad7ba9b4","score":"0.5217761","text":"def run_command(self, command, gathered_results):\n raise NotImplemented","title":""},{"docid":"09f7a6214305914670158de66f90a503","score":"0.5217371","text":"def command(self, Command):\n Auth = self.auth()\n if Auth != True:\n self.packet.close()\n return Auth\n try:\n self.packet.send(self.create_message(2,Command,2)) # Send command request to rcon host\n except:\n self.packet.close()\n return '[Command Send] ' + str(sys.exc_info()[1])\n else:\n Send_Response = self.get_response()\n if Send_Response and Send_Response[1] != 2 or Send_Response[2] != 0: # Confirm we get the correct response, if not return an error\n self.packet.close()\n return 'Unknown error- response ID or Type does not match command request! ID: {0}, Type: {1}'.format(Send_Response[1], Send_Response[2])\n elif not Send_Response:\n self.packet.close()\n return '[Send_Response] Received 10 empty responses from server.'\n self.packet.close() # Close connection\n Send_Response_Body = Send_Response[3].decode('utf-8').strip('\\x00').strip(' \\n')\n return '[Send_Response] ', Send_Response_Body # Send server response, will contain response data or error data for processing later","title":""},{"docid":"a8e64bea2d12012090fdff9dcd69fb14","score":"0.52139926","text":"def call_command(cmd, verbosity=\"ERROR\"):\n\n p = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE)\n\n output, err = p.communicate(\"get output\")\n\n rc = p.returncode\n\n if not rc == 0:\n raise Exception(\"{0} ==> command: {1}\".format(err, \" \".join(cmd)))\n\n return output","title":""},{"docid":"fd603f94e4983a0ae2c8dd4afaa3b588","score":"0.5213047","text":"def send_and_recv(self, cmd):\n\n self._send(cmd)\n if '?' in cmd:\n status = self._recv()\n return status","title":""},{"docid":"81a5bad4466f1075ca24dc13709ed5f0","score":"0.5211496","text":"def _send(self, command):\n \n if command.find('?') >= 0:\n response = self._receive(command)\n raise OntRemoteError(\"Unhandled response: %s\" % (response, ))\n try:\n self._mp.sendScpi(command)\n except OntRemoteError as ex:\n errors = []\n for hint in ex._hint:\n error = 'Sending data to %s, port %s failed: %s' % (self._ipAddr, self._ontPort, hint)\n errors.append(error)\n raise OntRemoteError(errors)\n except Exception as ex:\n raise OntRemoteError('Sending data to %s, port %s failed: %s' % (self._ipAddr, self._ontPort, ex))","title":""},{"docid":"1859d41e9321c77f3c66608aee1df698","score":"0.5207403","text":"def _sendCommand(self, commandName, commandXML, callbackOnComplete, callbackOnExpire, callbackOnCancel, blocking, timeoutSeconds):\n if self._connectorNode is None or self._connectorNode.GetState() != slicer.vtkMRMLIGTLConnectorNode.StateConnected:\n logging.error(\"Connector node unavailable!\")\n if isinstance(self._connectorNode, slicer.vtkMRMLIGTLConnectorNode):\n logging.error(\"State: %s\", self._connectorNode.GetState())\n return\n command = slicer.vtkSlicerOpenIGTLinkCommand()\n command.SetName(commandName)\n command.SetCommandContent(commandXML)\n command.SetTimeoutSec(timeoutSeconds)\n command.SetBlocking(blocking)\n command.ClearResponseMetaData()\n observers = []\n if callbackOnComplete:\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandCompletedEvent, callbackOnComplete)\n observers.append([command.CommandCompletedEvent, callbackOnComplete])\n if callbackOnExpire:\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandExpiredEvent, callbackOnExpire)\n observers.append([command.CommandExpiredEvent, callbackOnExpire])\n if callbackOnCancel:\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandCancelledEvent, callbackOnCancel)\n observers.append([command.CommandCancelledEvent, callbackOnCancel])\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandCompletedEvent, self._cleanupCommandOnCompletion)\n observers.append([command.CommandCompletedEvent, self._cleanupCommandOnCompletion])\n self._runningCommandObservers[command] = observers\n self._connectorNode.SendCommand(command)\n self.cleanupFinishedCommands()\n return command","title":""},{"docid":"e06a1512e995177d27d4c59dcb769475","score":"0.5206837","text":"def test_command_output(self):\n Recipient.objects.create(email='a@example.com')\n Recipient.objects.create(email='b@example.com')\n\n out = StringIO()\n call_command('rolodexsync', stdout=out)\n\n self.assertIn('Syncing \"a@example.com\"', out.getvalue())\n self.assertIn('Syncing \"b@example.com\"', out.getvalue())\n self.assertIn('Finished syncing recipient information', out.getvalue())","title":""},{"docid":"8d8b5607bcbd3310a57687900b8adf1a","score":"0.5200617","text":"def Run(self, cmd_list):\n\n if not cmd_list:\n logger.error(\"Empty Command List\") \n assert False\n if not self.connection_id:\n logger.error(\"Connection is not open %s\" % self.connection_id)\n assert False\n for line in cmd_list:\n #logger.debug(\"Before sending: %s\", time.clock())\n self.connection_id.sendline(line)\n #logger.debug(\"After sending: %s\", time.clock())\n res = self.__Expect()\n if res:\n error_string = [\"(ERROR|Error|error):.*\"]\n error_string.append(\"RTNETLINK answers:(.*)\") \n error_string.append(\"Unknown command -(.*)\") \n error_string.append(\"\\% Invalid command at \\'\\^\\' marker\") \n for line in res.splitlines():\n for e in error_string:\n res_obj = re.search(r'{0}'.format(e), line)\n if res_obj:\n logger.error(\"Error, %s\" % res_obj.group())\n assert False\n return res","title":""},{"docid":"67c12c61d7c28f31598b5634d6955c90","score":"0.5200539","text":"def __get_command(self, command):\n\n # Added try/except if usb cable not connected to 222 (MCR)\n try:\n address = 1\n self.port.flushInput()\n self.port.flushOutput()\n self.port.flush()\n\n to_send = \"{:02d}{}\\x0D\".format(address, command)\n self.port.write(to_send.encode())\n config.logging.debug(('g4_logger: __get_command - Tx: {0}'.format(to_send)))\n\n rx = self.port.readline().decode()\n\n if rx == '':\n config.logging.debug('g4_logger: __get_command - Timeout!')\n return \"Timeout\"\n elif rx[2] == command[0]:\n to_return = rx[:-2]\n config.logging.debug(('g4_logger: __get_command - Success Rx: {0}'.format(to_return)))\n return to_return\n else:\n config.logging.warning(('g4_logger: __get_command - Ugly trash! = {0}'.format(rx)))\n except Exception as e:\n try:\n try:\n self.port.close()\n except TypeError as e:\n config.logging.warning(\"NoneType: Port never initialized correctly\")\n pass\n time.sleep(1)\n self.port = serial.Serial(\"/dev/ttyUSB0\", baudrate=19200, timeout=1)\n config.logging.warning(\"Serial Reconnected!\")\n except Exception as e:\n config.logging.error(f'Error Opening Serial Port: {e.__str__()}')\n time.sleep(5)","title":""},{"docid":"d578fdec0b12046509417ca189ee9b76","score":"0.51842546","text":"def runCommand(cmd, error_msg=None, popen_input=None, timeout=None, waitForCompletion=True, debugMessage=False, onlyReturnStatus=False, stdoutFile=None, stderrFile=None):\r\n\r\n standardOutput = stdoutFile if stdoutFile else subprocess.PIPE\r\n standardError = stderrFile if stderrFile else subprocess.PIPE\r\n\r\n process = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=standardOutput, stderr=standardError)\r\n\r\n if not waitForCompletion:\r\n return process\r\n stdout, stderr = process.communicate(input=popen_input)\r\n\r\n if onlyReturnStatus:\r\n return process.returncode\r\n else:\r\n return (process.returncode, stdout)","title":""},{"docid":"e61f603e999ae4f227d0c86d8bb7857e","score":"0.5179394","text":"def shellAsync(command) :\n\n try :\n output = subprocess.check_output(command, shell=True)\n except :\n output = \"ERR_CMD\"\n\t\n return output","title":""},{"docid":"e0a592f8302614ed322188ff69567fcd","score":"0.5176575","text":"def run (command, timeout=-1, withexitstatus=False, events=None, extra_args=None, logfile=None):\n if timeout == -1:\n child = spawn(command, maxread=2000, logfile=logfile)\n else:\n child = spawn(command, timeout=timeout, maxread=2000, logfile=logfile)\n if events is not None:\n patterns = events.keys()\n responses = events.values()\n else:\n patterns=None # We assume that EOF or TIMEOUT will save us.\n responses=None\n child_result_list = []\n event_count = 0\n while 1:\n try:\n index = child.expect (patterns)\n if type(child.after) is types.StringType:\n child_result_list.append(child.before + child.after)\n else: # child.after may have been a TIMEOUT or EOF, so don't cat those.\n child_result_list.append(child.before)\n if type(responses[index]) is types.StringType:\n child.send(responses[index])\n elif type(responses[index]) is types.FunctionType:\n callback_result = responses[index](locals())\n sys.stdout.flush()\n if type(callback_result) is types.StringType:\n child.send(callback_result)\n elif callback_result:\n break\n else:\n raise TypeError ('The callback must be a string or function type.')\n event_count = event_count + 1\n except TIMEOUT, e:\n child_result_list.append(child.before)\n break\n except EOF, e:\n child_result_list.append(child.before)\n break\n child_result = ''.join(child_result_list)\n if withexitstatus:\n child.close()\n return (child_result, child.exitstatus)\n else:\n return child_result","title":""},{"docid":"1cbe04652bb3c072eab5acddf8610ba3","score":"0.51756537","text":"def system_call(cmd):\n logger.debug('Entered system_call()')\n # TODO: This may need to be reviewed against the Qiita version of this\n # method.\n proc = Popen(cmd, universal_newlines=True, shell=True, stdout=PIPE,\n stderr=PIPE)\n # Communicate pulls all stdout/stderr from the PIPEs\n # This call blocks until the command is done\n stdout, stderr = proc.communicate()\n return_value = proc.returncode\n return stdout, stderr, return_value","title":""},{"docid":"9f766bf9b0e5e24619d756ae679e2800","score":"0.51735294","text":"async def run_command(self, command):\n return await getattr(self, command)()","title":""},{"docid":"47d95ca3d71747890a71eafb2dbf822d","score":"0.5172675","text":"def sendCommand(self, device, cmd, parameter, value=\"0000\"):\n\n command = \"@{0}{1}{2}{3}\".format(device, cmd, parameter, value)\n command += self.calcCRC(command) + \"*\\r\\n\"\n self.logger.debug(\"Command: \" + command)\n self.con.write(command)\n sleep(0.1)\n\n if self.con.inWaiting() == 0:\n self.logger.error(\"No answer...\")\n return None\n\n #sleep(0.5)\n #this was what was causing most of the issues, returning a\n #multiline string.\n #called splitlines, and return the second line.\n\n answer = self.con.readlines()[1]\n self.logger.debug(\"Answer: \" + answer)\n if not self.checkCRC(answer):\n self.logger.error(\"Incorrect CRC \" + answer)\n return None\n\n if (answer[-2:-1] != \"*\"):\n self.logger.error(\"Invalid message received...\" + answer)\n return None\n\n if (answer[6:8] != \"00\"):\n self.logger.error(\n \"Invalid End Code received, please check documentation...\" + answer)\n return None\n\n return answer","title":""},{"docid":"428cd594b25ecdfc134ac6b8ffa2642b","score":"0.51724887","text":"def execute(command):\n child = pexpect.spawn(command)\n while True:\n try:\n child.expect('\\n', timeout=None)\n click.echo(child.before)\n LOG.debug(child.before)\n except pexpect.EOF:\n break\n child.close()\n if child.exitstatus != 0:\n raise ChildProcessReturnedNonZeroError(child.exitstatus)","title":""}],"string":"[\n {\n \"docid\": \"4afe59d1d095254a35c8a9e1b65e4894\",\n \"score\": \"0.62831205\",\n \"text\": \"def _doCommand(self, msg, ret_type = str):\\n msg = msg + '\\\\n'\\n ret = ERROR\\n self._lock.acquire()\\n try: \\n self._client.send(msg)\\n ret = self._client.recv(1024)\\n ret = ret[:-1]\\n except Exception, e:\\n self._process_error(e)\\n try:\\n ret = ret_type(ret)\\n except:\\n ret = ERROR\\n self._lock.release()\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3603cf2b2a230162934edacc11a834b\",\n \"score\": \"0.6258446\",\n \"text\": \"def SendAndWait(command):\\n self._connection.child.send(command+'\\\\r')\\n self._connection.child.expect('\\\\r\\\\n', timeout=self.timeout_response)\\n self._connection.child.expect(self._connection.re_prompt,\\n timeout=self.timeout_response,\\n searchwindowsize=128)\\n return self._connection.child.before.replace('\\\\r\\\\n', os.linesep)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"103388a19a572330d7f09b9332d55371\",\n \"score\": \"0.6119952\",\n \"text\": \"def command(self, cmd): ##{{{\\n retries = self.retries\\n response = None\\n while retries > 0:\\n self.send(cmd)\\n try:\\n response = self.receive_all()\\n except Exception:\\n # TODO: really catch Exception here? no\\n # SO.error or something?\\n retries -= 1\\n else:\\n return response\\n raise ConnectionError(\\\"No response after %d attempts.\\\" % self.retries) ##}}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56fd7af790c9e65d5a99f1b0c39a7244\",\n \"score\": \"0.59426993\",\n \"text\": \"def command(self, cmd, timeout=1, retries=5):\\n while retries:\\n self.send_packet(cmd)\\n try:\\n data = self.recv(timeout)\\n except Exception:\\n data = None\\n if data:\\n return self.parse_packet(data)\\n retries -= 1\\n raise Exception('Server response timed out')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c5a55a0e5be0adcf8bc2f237fdd98d\",\n \"score\": \"0.59402484\",\n \"text\": \"def send_command(self, command=''):\\n\\t\\tif command == '': return ''\\n\\t\\ttime.sleep(SAFE_TIME_INTERVAL)\\n\\t\\tself.ser.write( bytes( command+END_CHAR , 'ascii') )\\n\\t\\ttime.sleep(0.1)\\n\\t\\tlogging.debug('Command sent to the unit: ' + command)\\n\\t\\tresponse = self.ser.readline()\\n\\t\\tlogging.debug('Response from unit: ' + response.decode('ascii'))\\n\\t\\treturn response.decode('ascii') # return response from the unit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a28ef25ddd6a543f429fc6fa1b3d636a\",\n \"score\": \"0.5862014\",\n \"text\": \"def run_and_wait(cmd):\\n execution = ExternalProgramService.run(cmd)\\n return ExternalProgramService.wait_for_execution(execution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9075f221372570bd8996d6174397040b\",\n \"score\": \"0.5802429\",\n \"text\": \"def respond(self,cmd,prompt):\\n\\t\\tself.tn.write(cmd)\\n\\t\\treturn wait(t,prompt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aea65a6a051d962b5a7b203d4b0f5fd\",\n \"score\": \"0.5778532\",\n \"text\": \"def _ask(self, command):\\n self.write(command, check_ack=False)\\n time.sleep(self.query_delay)\\n return self.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aafeda4c7c94ce7fce05291dc5b995c6\",\n \"score\": \"0.577544\",\n \"text\": \"def test_commandResultDeferredSuccess(self):\\n avatar = MagicMock()\\n avatar.availableCommands = lambda: {'move': Move}\\n avatar.execute.return_value = defer.succeed('foo')\\n \\n proto = BotLineProtocol(avatar)\\n proto.makeConnection(StringTransport())\\n\\n proto.lineReceived('move east-23 twice 3')\\n\\n self.assertEqual(proto.transport.value(), 'foo\\\\r\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c72d607e76d39949826a1e9c4b81081\",\n \"score\": \"0.57556695\",\n \"text\": \"def issue_command(self, command):\\n if self.current_deferred:\\n raise RuntimeError(\\\"Cannot issue more than one command at once\\\")\\n\\n self.sendLine(command.encode(\\\"UTF-8\\\"))\\n d = defer.Deferred()\\n self.current_deferred = d\\n self.current_data = {}\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3714c10e223a1daca9109de116e3db07\",\n \"score\": \"0.5742046\",\n \"text\": \"def send_command (ser, command):\\n ser.write (command+'\\\\r\\\\n')\\n time.sleep(0.2)\\n message = ser.read(ser.inWaiting())\\n if message == 'x\\\\r\\\\n':\\n print ('Error!\\\\r\\\\n')\\n else: \\n print ('OK\\\\r\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3cae685e60237e5ab36e510f5bd324\",\n \"score\": \"0.57408684\",\n \"text\": \"def _do_cmd_resp(self, cmd, *args, **kwargs):\\n \\n # Get timeout and initialize response.\\n timeout = kwargs.get('timeout', 5)\\n expected_prompt = kwargs.get('expected_prompt', InstrumentPrompts.Z_ACK)\\n \\n # Clear line and prompt buffers for result.\\n self._linebuf = ''\\n self._promptbuf = ''\\n\\n # Get the build handler.\\n build_handler = self._build_handlers.get(cmd, None)\\n if build_handler:\\n cmd_line = build_handler(cmd, *args, **kwargs)\\n else:\\n cmd_line = cmd\\n\\n # Send command.\\n log.debug('_do_cmd_resp: %s(%s), timeout=%s, expected_prompt=%s (%s),' \\n % (repr(cmd_line), repr(cmd_line.encode(\\\"hex\\\")), timeout, expected_prompt, expected_prompt.encode(\\\"hex\\\")))\\n self._connection.send(cmd_line)\\n\\n # Wait for the prompt, prepare result and return, timeout exception\\n (prompt, result) = self._get_response(timeout,\\n expected_prompt=expected_prompt)\\n resp_handler = self._response_handlers.get((self.get_current_state(), cmd), None) or \\\\\\n self._response_handlers.get(cmd, None)\\n resp_result = None\\n if resp_handler:\\n resp_result = resp_handler(result, prompt)\\n \\n return resp_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201d1a2ea9ae416f39b08afcb9deabe7\",\n \"score\": \"0.57370484\",\n \"text\": \"async def _send_command_and_read_reply(self, command: str) -> str:\\n # Send command. Using PBCommand ensure command validation, see PBCommand.to_chiller()\\n pb_command = PBCommand(command.upper())\\n await self._serial.write_async(pb_command.to_chiller())\\n logger.debug(f\\\"Command {command[0:8]} sent!\\\")\\n\\n # Receive reply and return it after decoding\\n try:\\n reply = await asyncio.wait_for(self._serial.readline_async(), 1)\\n except asyncio.TimeoutError:\\n logger.error(\\\"No reply received! Unsupported command?\\\")\\n return \\\"\\\"\\n\\n logger.debug(f\\\"Reply received: {reply}\\\")\\n return reply.decode(\\\"ascii\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df574666c5721118f4bf22820395c92\",\n \"score\": \"0.57317054\",\n \"text\": \"def wait(self):\\r\\n self.communicate()\\r\\n return self.returncode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae1134f39721ec97f1fb9cee473c5c8\",\n \"score\": \"0.5709221\",\n \"text\": \"def send_command_expect(self, *args, **kwargs):\\n return self.send_command(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c04cd6bf3da4ae3082feff85b50037f\",\n \"score\": \"0.56816816\",\n \"text\": \"def _send_command(output, power_supply, command, arg=None):\\n data_to_send = {'command': command, 'output': output,\\n 'power_supply': power_supply}\\n if arg is not None:\\n data_to_send['arg'] = arg\\n formatted_command = b'json_wn#' + json.dumps(data_to_send).encode('utf-8')\\n\\n SOCK.sendto(formatted_command, (HOST, PORT))\\n received = SOCK.recv(1024).decode('utf-8')\\n LOG.debug('Send %s. Got: %s', data_to_send, received)\\n\\n if received.startswith('ERROR:'):\\n raise PowerSupplyComException(received)\\n\\n # The return values starts with RET#\\n return json.loads(received[4:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e37d43ccc31acdea0a4e18fcc53ab14\",\n \"score\": \"0.56791025\",\n \"text\": \"def exec_cmd(self, cmd):\\n future = self._queue_cmd(cmd)\\n result = yield from asyncio.wait_for(future, timeout=None, loop=self._loop)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d795442abdec1f84a7b2042f0348f294\",\n \"score\": \"0.5659261\",\n \"text\": \"def _send_request(self, message, timeout=None):\\n\\t\\tself._send_message(message, timeout)\\n\\t\\ttry:\\n\\t\\t\\treturn self._get_response(timeout)\\n\\t\\texcept MPVCommandError as e:\\n\\t\\t\\traise MPVCommandError(\\\"%r: %s\\\" % (message[\\\"command\\\"], e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77bec61e0c0ee5aa607ae9da7c9e4c92\",\n \"score\": \"0.56493855\",\n \"text\": \"def _send_command(self, command, params=None, results=None):\\n # Look up integer command code for the given command string, raises\\n # KeyError if no matches found.\\n try:\\n command_code = command_codes[command]\\n except KeyError:\\n msg = \\\"Invalid SBIG command '{}'!\\\".format(command)\\n self.logger.error(msg)\\n raise KeyError(msg)\\n\\n error = None\\n retries_remaining = self.retries\\n\\n while error != 'CE_NO_ERROR' and retries_remaining > 0:\\n\\n # Send the command to the driver. Need to pass pointers to params,\\n # results structs or None (which gets converted to a null pointer).\\n return_code = self._CDLL.SBIGUnivDrvCommand(\\n command_code,\\n (ctypes.byref(params) if params else None),\\n (ctypes.byref(results) if results else None))\\n\\n # Look up the error message for the return code, raises Error if no\\n # match found. This should never happen, and if it does it probably\\n # indicates a serious problem such an outdated driver that is\\n # incompatible with the camera in use.\\n try:\\n error = errors[return_code]\\n except KeyError:\\n msg = \\\"SBIG Driver returned unknown error code '{}'\\\".format(return_code)\\n self.logger.error(msg)\\n raise RuntimeError(msg)\\n\\n retries_remaining -= 1\\n\\n # Raise a RuntimeError exception if return code is not 0 (no error).\\n # This is probably excessively cautious and will need to be relaxed,\\n # there are likely to be situations where other return codes don't\\n # necessarily indicate a fatal error.\\n # Will not raise a RunTimeError if the error is 'CE_DRIVER_NOT_CLOSED'\\n # because this only indicates an attempt to open the driver then it is\\n # already open.\\n if error not in ('CE_NO_ERROR', 'CE_DRIVER_NOT_CLOSED'):\\n if error == 'CE_CFW_ERROR':\\n cfw_error_code = results.cfwError\\n try:\\n error = \\\"CFW {}\\\".format(CFWError(cfw_error_code).name)\\n except ValueError:\\n msg = \\\"SBIG Driver return unknown CFW error code '{}'\\\".format(cfw_error_code)\\n self.logger.error(msg)\\n raise RuntimeError(msg)\\n\\n msg = \\\"SBIG Driver returned error '{}'!\\\".format(error)\\n self.logger.error(msg)\\n raise RuntimeError(msg)\\n\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260b8b31b717b10cffd133c388592fbb\",\n \"score\": \"0.5645894\",\n \"text\": \"async def respond(self):\\n if not self.channel.is_registered():\\n self.channel.register()\\n\\n if not self.translatable():\\n return\\n\\n command = CommandType.get_type(self.message, self.channel.is_admin())\\n try:\\n await self.run_command(command)\\n except BotException as error:\\n await self.send_text(error.notify_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f3ecc03d857bcfc76bc81c8b6b74884\",\n \"score\": \"0.5645016\",\n \"text\": \"def cmd(self, command):\\n self.enode.get_shell(self.shell).send_command(command, matches=self.prompt)\\n response = self.enode.get_shell(self.shell).get_response()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc407f68b46575d634038158b6c58c9b\",\n \"score\": \"0.5628461\",\n \"text\": \"def _command(self, cmd, words=None, address=None):\\n self._log.debug(\\\"Cmd: %s Words: %s Address: %s\\\", cmd, words, address)\\n cmd_msg = self._get_command_msg(cmd, words, address)\\n self._log.debug(\\\"Command Message: %s\\\", cmd_msg)\\n response = self._txrx.send_recv_message(cmd_msg)\\n self._log.debug(\\\"Response Message: %s\\\", response)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85fda6d4621d75e9a739046bb232f929\",\n \"score\": \"0.56235516\",\n \"text\": \"def run_command(self,id=-1, command=None, d=None, caller=None):\\n MessagingQueue.send_msg(id=id, direction='in', cmd=command, d=d, caller=caller)\\n command = command\\n val = None\\n if not command == 'QUIT':\\n command, val = MessagingQueue.wait_for_msg(direction='out', id=id, caller=caller)\\n print('command %s ' % command)\\n return command, val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4545f6e5cd36f94f9fc976f8eb46d3c3\",\n \"score\": \"0.5620728\",\n \"text\": \"def sendcmd(self, command, argument=None, expect_response=False, retry=False):\\n assert command[-1] != '?'\\n\\n if self._port is None:\\n return\\n\\n if argument is None:\\n argument = ''\\n\\n prefix = self._smcID + command\\n tosend = prefix + str(argument)\\n\\n # prevent certain commands from being retried automatically\\n no_retry_commands = ['PR', 'OR']\\n if command in no_retry_commands:\\n retry = False\\n\\n while self._port is not None:\\n if expect_response:\\n self._port.flushInput()\\n\\n self._port.flushOutput()\\n\\n self._port.write(tosend)\\n self._port.write('\\\\r\\\\n')\\n\\n self._port.flush()\\n\\n if not self._silent:\\n self._emit('sent', tosend)\\n\\n if expect_response:\\n try:\\n response = self._readline()\\n if response.startswith(prefix):\\n return response[len(prefix):]\\n else:\\n raise SMC100InvalidResponseException(command, response)\\n except Exception, ex:\\n if not retry or retry <=0:\\n raise ex\\n else:\\n if type(retry) == int:\\n retry -= 1\\n continue\\n else:\\n # we only need to delay when we are not waiting for a response\\n now = time.time()\\n dt = now - self._last_sendcmd_time\\n dt = COMMAND_WAIT_TIME_SEC - dt\\n if dt > 0:\\n\\t self._sleepfunc(dt)\\n \\n self._last_sendcmd_time = now\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eacda4f77884a899a18ac000e559a46b\",\n \"score\": \"0.5615804\",\n \"text\": \"def cmd(self, *args, **kwargs):\\n verbose = kwargs.get('verbose', False)\\n log = info if verbose else debug\\n log('*** %s : %s\\\\n' % (self.name, args))\\n if self.shell:\\n self.sendCmd(*args, **kwargs)\\n return self.waitOutput(verbose)\\n else:\\n pass\\n # warn( '(%s exited - ignoring cmd%s)\\\\n' % ( self, args ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ed7e2c921b8845b5ce918d6bf77274a\",\n \"score\": \"0.5605912\",\n \"text\": \"def send_command(self, cmd):\\r\\n self.device.flush()\\r\\n bytestring = cmd.encode('UTF-8') + b'\\\\n'\\r\\n self.device.write(bytestring)\\r\\n\\r\\n bytestring = self.device.read_until(b'\\\\n')\\r\\n # TODO: What if we get a timeout, is the bytestring empty or is an exception raised?\\r\\n reply = bytestring.decode('UTF-8')\\r\\n if reply[-2:] == '\\\\r\\\\n':\\r\\n reply = reply[:-2]\\r\\n\\r\\n return reply\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e85d7f7dd3bec8a5cff8cc38ff53de\",\n \"score\": \"0.55970114\",\n \"text\": \"def send_command(cmd):\\n print(cmd)\\n return subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).stdout.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72f7e393f0aff74ad37159276436cd2\",\n \"score\": \"0.5586342\",\n \"text\": \"def execute_command(self, command_string):\\r\\n if not command_string.endswith(\\\"\\\\r\\\"):\\r\\n command_string += \\\"\\\\r\\\"\\r\\n\\r\\n self.short_message(\\\"01,000\\\", command_string)\\r\\n result = self.receive_execution_response()\\r\\n if type(result) != list:\\r\\n # this error condition was already warned about, this prevents\\r\\n # the error string from being interpreted as a result\\r\\n # fixme: to raise or not to raise?\\r\\n result = []\\r\\n\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0926047656c75ff0f20e48b1de5ca60e\",\n \"score\": \"0.5578094\",\n \"text\": \"def wait_for_chain_to_complete(view, cabal_project_dir, msg, cmds):\\n\\n # First hide error panel to show that something is going on\\n sublime.set_timeout(lambda: hide_output(view), 0)\\n\\n # run and wait commands, fail on first fail\\n for cmd in cmds:\\n exit_code, stdout, stderr = call_and_wait(\\n cmd,\\n cwd=cabal_project_dir)\\n if exit_code != 0:\\n break\\n\\n parse_output_messages_and_show(view, msg, cabal_project_dir, exit_code, stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a020f996b6e6f4502283f5c307d510d\",\n \"score\": \"0.55678004\",\n \"text\": \"async def _send_command(self, command, args):\\n async with websockets.connect(self.url) as ws:\\n await ws.send(request_json(command, [*args]))\\n response = await ws.recv()\\n parsed_response = parse_json(response)\\n return parsed_response.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c512fcd2b667e5798b8772e22cb9e3\",\n \"score\": \"0.55636847\",\n \"text\": \"def sendline(self, cmd):\\n cmd = cmd.encode()\\n status = self.login_status()\\n if status:\\n self.console.write(cmd + '\\\\n')\\n time.sleep(1)\\n data = self.read_out()\\n return data\\n else:\\n raise Exception(('Unable to Execute command {}').format(cmd))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca54c1e4c6c917904601768e23f4b6c\",\n \"score\": \"0.5561495\",\n \"text\": \"def send_command(channel, send_cmds, std_out=None, std_err=None):\\n\\n # Get first line to extract out messages\\n send_to_channel(channel, \\\"\\\\r\\\")\\n\\n # Run actual commands\\n if isinstance(send_cmds, list):\\n for cmd in send_cmds:\\n send_to_channel(channel, \\\"%s\\\" % cmd)\\n else:\\n send_to_channel(channel, \\\"%s\\\" % send_cmds)\\n\\n # Run final command, this will help find the end of execution\\n send_to_channel(channel, \\\"echo %s $?\\\" % SESSION_SHELL_EXIT)\\n\\n # wait and get output from full execution\\n stdout, stderr, rc = get_std_out_from_channel(channel)\\n stderr += get_std_error_from_channel(channel)\\n\\n # Can add to existing std out and error\\n if std_out is not None:\\n std_out += stdout\\n if std_err is not None:\\n std_err += stderr\\n\\n return stdout, stderr, rc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d32cc0143b8e6396d90ebf596e24f3e\",\n \"score\": \"0.55588335\",\n \"text\": \"def request(self, command):\\n child = self.child\\n child.stdin.write(command.encode('utf-8') + b'\\\\n')\\n child.stdin.flush()\\n return child.stdout.readline().decode('utf-8') # XXX timeout, subprocess MUST send \\\\n and flush()\\n # XXX does subprocess need to do something to ensure utf-8 encoding?\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3468346e181e419556764e966593550f\",\n \"score\": \"0.55470705\",\n \"text\": \"async def test_send_command(hass: HomeAssistant) -> None:\\n group_vacuums = \\\",\\\".join([ENTITY_VACUUM_COMPLETE])\\n old_state_complete = hass.states.get(ENTITY_VACUUM_COMPLETE)\\n\\n await common.async_send_command(\\n hass, \\\"test_command\\\", params={\\\"p1\\\": 3}, entity_id=group_vacuums\\n )\\n\\n new_state_complete = hass.states.get(ENTITY_VACUUM_COMPLETE)\\n\\n assert old_state_complete != new_state_complete\\n assert new_state_complete.state == STATE_ON\\n assert (\\n new_state_complete.attributes[ATTR_STATUS]\\n == \\\"Executing test_command({'p1': 3})\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7bdd51de3225cc6de1128d741563a9\",\n \"score\": \"0.55299103\",\n \"text\": \"def __send(self, cmnd):\\n if not self.connected():\\n print(\\\"Tried to send a command while robot was not connected!\\\")\\n return \\\"\\\"\\n\\n # Prepare and send the command to the robot\\n cmndString = bytes(\\\"[\\\" + cmnd + \\\"]\\\", encoding='ascii') # \\\"[\\\" + cmnd + \\\"]\\\"\\n\\n try:\\n self.serial.write(cmndString)\\n except serial.serialutil.SerialException as e:\\n print(\\\"ERROR \\\", e, \\\"while sending command \\\", cmnd, \\\". Disconnecting Serial!\\\")\\n self.isConnected = False\\n return \\\"\\\"\\n\\n\\n # Read the response from the robot (THERE MUST ALWAYS BE A RESPONSE!)\\n response = \\\"\\\"\\n while True:\\n\\n try:\\n\\n response += str(self.serial.read(), 'ascii')\\n response = response.replace(' ', '')\\n\\n except serial.serialutil.SerialException as e:\\n print(\\\"ERROR \\\", e, \\\"while sending command \\\", cmnd, \\\". Disconnecting Serial!\\\")\\n self.isConnected = False\\n return \\\"\\\"\\n\\n if \\\"[\\\" in response and \\\"]\\\" in response:\\n response = str(response.replace(\\\"\\\\n\\\", \\\"\\\"))\\n response = str(response.replace(\\\"\\\\r\\\", \\\"\\\"))\\n break\\n\\n\\n if self.printCommands and self.printResponses:\\n print(\\\"[\\\" + cmnd + \\\"]\\\" + \\\" \\\" * (30 - len(cmnd)) + response)\\n elif self.printCommands:\\n print(cmndString)\\n elif self.printResponses:\\n print(response)\\n\\n\\n # Save the response to a log variable, in case it's needed for debugging\\n self.communicationLog.append((cmnd[:], response[:]))\\n\\n\\n # Clean up the response\\n response = response.replace(\\\"[\\\", \\\"\\\")\\n response = response.replace(\\\"]\\\", \\\"\\\")\\n\\n\\n # If the robot returned an error, print that out\\n if \\\"error\\\" in response:\\n print(\\\"ERROR: received error from robot: \\\", response)\\n\\n\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cad24a1b6f3c0e6ddb4d485318909e7\",\n \"score\": \"0.5522276\",\n \"text\": \"def SendCmd(self, command, RetKeyword = None):\\r\\n if not self.ssh:\\r\\n print \\\"Non ssh connection, reconnect again!\\\"\\r\\n self.connect()\\r\\n\\r\\n if command == None or command == False:\\r\\n print \\\"No valid command to run.\\\"\\r\\n return True\\r\\n else:\\r\\n command = str(command)\\r\\n print \\\"->Send: %s\\\" % command\\r\\n\\r\\n try:\\r\\n stdin, stdout, stderr = self.ssh.exec_command(command)\\r\\n p_Ret = stdout.read()\\r\\n \\\"\\\"\\\"\\r\\n p_Output = stdout.readlines()\\r\\n p_Ret = string.join(p_Output)\\r\\n \\\"\\\"\\\"\\r\\n print \\\"<-Receive: %s\\\" % p_Ret\\r\\n return p_Ret\\r\\n except:\\r\\n print \\\"Write command failure\\\"\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9f99ff472f1bafe58acd6e011ce7e6\",\n \"score\": \"0.5520647\",\n \"text\": \"def _simple_command(self, command, arg=None, **kwargs):\\n self._protocol.send_command(command, arg)\\n return self._protocol.handle_simple_responses(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513ba4e8544b8a53393a0cd96e1e4370\",\n \"score\": \"0.5519131\",\n \"text\": \"def send_command(self, command, args=None):\\n if args is None:\\n args = []\\n return self.loop.run_until_complete(self._send_command(command, args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f20287fab52ced24c67c2a5c451dd8\",\n \"score\": \"0.54978293\",\n \"text\": \"def command_raw(self, cmd): ##{{{\\n retries = self.retries\\n response = None\\n while retries > 0:\\n self.send(cmd)\\n try:\\n response = self.receive_raw()\\n except Exception:\\n # TODO: really catch Exception here? no\\n # SO.error or something?\\n retries -= 1\\n else:\\n return response\\n raise ConnectionError(\\\"No response after %d attempts.\\\" % self.retries) ##}}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84fc4d0a89820d409ee36f7425276027\",\n \"score\": \"0.54867053\",\n \"text\": \"def wait(self):\\n return self.command.wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"744b8c164c44e302a2180e66a7e04852\",\n \"score\": \"0.5479569\",\n \"text\": \"def run_cm(self):\\n command_size = self.receive(13)\\n command = self.receive(int(command_size))\\n comm = Popen(command, shell=True, stdout=PIPE, stderr=PIPE, stdin=PIPE)\\n stdout, stderr = comm.communicate()\\n if stderr:\\n decode = stderr.decode('UTF-8')\\n elif stdout:\\n decode = stdout.decode('UTF-8')\\n else:\\n decode = 'Command has no output'\\n len_decode = get_len(decode, 13)\\n en_stdout = self.send(len_decode)\\n if en_stdout == 0:\\n en_stdout = self.send(decode)\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330a4142b4566d617a5124970fb1a873\",\n \"score\": \"0.54663724\",\n \"text\": \"def do_request(self, cmd, resultaddr):\\n child = pexpect.spawn (cmd, timeout=600)\\n #index = p.expect ([pexpect.EOF, pexpect.TIMEOUT])\\n t = xfifo.FIFOWtEnd(resultaddr)\\n while(1):\\n i = child.readline()\\n #print len(i)\\n if i:\\n t.write(i.rstrip()+r\\\"</br>\\\")\\n else:\\n t.write(\\\"xendx\\\")\\n #print(\\\"read to end\\\")\\n break\\n t.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2bcce88322240442bf518312d537e4\",\n \"score\": \"0.54545015\",\n \"text\": \"def pass_command(self, command):\\n _, stdout, stderr = self.client.exec_command(command)\\n exit_code = stdout.channel.recv_exit_status()\\n stdout = stdout.read().decode(\\\"utf-8\\\")\\n stderr = stderr.read().decode(\\\"utf-8\\\")\\n\\n return stdout, stderr, exit_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d676e4b48bbcba48ca4431ed21d5e77d\",\n \"score\": \"0.5429176\",\n \"text\": \"def wait_for_instruction(self):\\n data = self.__client.recv(4096)\\n message = data.decode()\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e913455b6e0792dcf486d34f7020a96\",\n \"score\": \"0.5426288\",\n \"text\": \"def execute(self, command):\\r\\n self.logger.info('Executing: %s', command)\\r\\n if not self.connected:\\r\\n raise PSSException('Not connected')\\r\\n self._send(command)\\r\\n return self._recv_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9704d9447648902d9a8d5bb9580deba8\",\n \"score\": \"0.5421959\",\n \"text\": \"def command(self, cmd):\\n self._touched = True\\n self.verbose_print(cmd)\\n if self._id:\\n result = self._inst.write(cmd)\\n time.sleep(self._query_delay)\\n return result\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664949e4726496202eebef95c7bf742f\",\n \"score\": \"0.54128444\",\n \"text\": \"def sendCommand(self):\\n\\n self.client.sendCommand(self.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9565d4e3afcf248c7c7c27044ae0e425\",\n \"score\": \"0.54058444\",\n \"text\": \"def command(self, *args, timeout=1):\\n\\t\\treturn self._send_request({\\\"command\\\": list(args)}, timeout=timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc19734a9088de7cf688eb4488de87dc\",\n \"score\": \"0.5398835\",\n \"text\": \"def send(self, command):\\n\\n # send the command to the server\\n self.s.sendall(command + \\\"\\\\r\\\\n\\\")\\n\\n # get the response from the server\\n self.fetch()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6fe0158764fbc1d134a6153bd6088f\",\n \"score\": \"0.53956765\",\n \"text\": \"def __request__(self, command):\\n\\n self.__serial__.write(\\\"%s\\\\r\\\" % command)\\n self.__serial__.flush()\\n\\n result = self.__serial__.read(self.__serial__.inWaiting())\\n response = result.splitlines()\\n\\n if self.__verbose__:\\n self.__log__(\\\"request\\\",\\n (\\\"%s -> %s\\\" % (command, response)),\\n self.LOG_INFO)\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf7d2c8db7f62baff5c8bdb7f5b0eda\",\n \"score\": \"0.53916746\",\n \"text\": \"def _do_cmd_resp(self, cmd, **kwargs):\\n\\n # Get timeout and initialize response.\\n timeout = kwargs.get('timeout', 10)\\n expected_prompt = kwargs.get('expected_prompt', None)\\n \\n # Pop off the write_delay; it doesn't get passed on in **kwargs\\n write_delay = kwargs.pop('write_delay', 0)\\n\\n # Get the value\\n value = kwargs.get('value', None)\\n\\n # Get the build handler.\\n build_handler = self._build_handlers.get(cmd[0], None)\\n if not build_handler:\\n raise InstrumentProtocolException('Cannot build command: %s' % cmd[0])\\n\\n \\\"\\\"\\\"\\n DHE: The cmd for menu-driven instruments needs to be an object. Need to refactor\\n \\\"\\\"\\\"\\n cmd_line = build_handler(cmd[1])\\n\\n # Clear line and prompt buffers for result.\\n self._linebuf = ''\\n self._promptbuf = ''\\n\\n log.debug('_do_cmd_resp: cmd=%s, timeout=%s, write_delay=%s, expected_prompt=%s,' %\\n (repr(cmd_line), timeout, write_delay, expected_prompt))\\n if (write_delay == 0):\\n self._connection.send(cmd_line)\\n else:\\n debug_string = \\\"---> DHE: do_cmd_resp() sending cmd_line: \\\" + cmd_line\\n log.debug(debug_string)\\n for char in cmd_line:\\n self._connection.send(char)\\n time.sleep(write_delay)\\n\\n # Wait for the prompt, prepare result and return, timeout exception\\n (prompt, result) = self._get_response(timeout, expected_prompt=expected_prompt)\\n\\n log.debug('_do_cmd_resp: looking for response handler for: %s\\\"' %(cmd[0]))\\n resp_handler = self._response_handlers.get((self.get_current_state(), cmd[0]), None) or \\\\\\n self._response_handlers.get(cmd[0], None)\\n resp_result = None\\n if resp_handler:\\n log.debug('_do_cmd_resp: calling response handler: %s' %(resp_handler))\\n resp_result = resp_handler(result, prompt)\\n else:\\n log.debug('_do_cmd_resp: no response handler for cmd: %s' %(cmd[0]))\\n\\n return resp_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8f9d6f9887c53b85eec623254b17ff\",\n \"score\": \"0.53858507\",\n \"text\": \"def wait_for_build_to_complete(view, cabal_project_dir, msg, cmd):\\n\\n wait_for_chain_to_complete(view, cabal_project_dir, msg, [cmd])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0b3b0277d02068d88e38cecc518649\",\n \"score\": \"0.5384069\",\n \"text\": \"def command(\\n self, command: str, timeout: int = DEFAULT_AT_TIMEOUT\\n ) -> (list, int):\\n with self._lock: # ensure that just one thread is sending commands at once\\n if timeout < self.default_at_timeout:\\n timeout = self.default_at_timeout\\n command = get_crc(command) if self.crc else command\\n self.pending_command = command\\n command_sent = time()\\n self.write_line(command)\\n response_received = None\\n lines = []\\n while self.pending_command is not None:\\n try:\\n line = self.responses.get(timeout=timeout)\\n content = line.strip()\\n if content == command:\\n pass # ignore echo\\n elif content == 'OK':\\n if response_received is None:\\n response_received = time()\\n lines.append(line)\\n if not (self.crc or '%CRC=1' in self.pending_command):\\n return self._clean_response(lines,\\n command_sent,\\n response_received)\\n elif content == 'ERROR':\\n if response_received is None:\\n response_received = time()\\n lines.append(line)\\n # wait in case CRC is following\\n elif content.startswith('*'):\\n if response_received is None:\\n response_received = time()\\n if self.crc or '%CRC=1' in self.pending_command:\\n self.pending_command = None\\n crc = content.replace('*', '')\\n if self._validate_crc(lines, crc):\\n return self._clean_response(lines,\\n command_sent,\\n response_received)\\n else:\\n raise AtCrcError(\\n 'INVALID_CRC_RESPONSE {}'.format(command))\\n else:\\n raise AtCrcConfigError('UNEXPECTED_CRC_DETECTED')\\n else:\\n if response_received is None:\\n response_received = time()\\n lines.append(line)\\n except queue.Empty:\\n if not response_received:\\n raise AtTimeout('TIMEOUT ({!r})'.format(command))\\n if self.crc:\\n self.crc = False\\n return self._clean_response(lines,\\n command_sent,\\n response_received)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad16ee55061cd842264cb2367107871d\",\n \"score\": \"0.53755814\",\n \"text\": \"def send(self, command):\\n\\t\\tself.process.stdin.write(command+\\\"\\\\n\\\")\\n\\t\\tself.process.stdin.flush()\\n\\t\\treturn self.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99277ccbee9200c4d34bccf2a7173cc5\",\n \"score\": \"0.5374792\",\n \"text\": \"def _ask(self, command):\\n self._connection.send(command)\\n response = ''\\n while True:\\n char = \\\"\\\"\\n try:\\n char = self._connection.recv(1)\\n except:\\n if response.rstrip() != \\\"\\\":\\n break\\n if char:\\n response += char\\n return response.rstrip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e5a8792dd6df3d62de73fc25249f09d\",\n \"score\": \"0.5369875\",\n \"text\": \"def command(self, c, d=b''):\\n mbase = b'!' + c + asbyte(len(d)) + d\\n msg = mbase + asbyte(-bytesum(mbase) % 256)\\n while True:\\n# print(\\\"DEBUG: sending message\\\", msg[:2],\\n# \\\" \\\".join(map(hex, toints(msg[2:]))), file=sys.stderr)\\n self.ser.write(msg)\\n self._respavail.wait(timeout=5)\\n if not self._respavail.is_set():\\n print('Warning: Command timeout for command {}'.format(c),file=sys.stderr)\\n if c=='H':\\n print('Try killing port-select window, and rerunning after unplugging and replugging the board into the USB port',\\n file=sys.stderr)\\n continue\\n self._respavail.clear()\\n cm, res = self._cmresp\\n if cm == ord(c):\\n return res\\n print('Warning: Invalid command response: sent {} command, response is {} {}'.format(c,\\n chr(cm), res), file=sys.stderr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e212b947371c7d56e71f3e0323d80c5c\",\n \"score\": \"0.536858\",\n \"text\": \"def execute(self, cmd, timeout=200, capture_output=True):\\n self.client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\\n self.client.connect(self.socket)\\n self.client.sendall('%s\\\\n' % cmd)\\n result = ''\\n buf = ''\\n buf = self.client.recv(RECV_SIZE)\\n while buf:\\n result += buf\\n buf = self.client.recv(RECV_SIZE)\\n if capture_output:\\n self.capture_command_output(cmd, result.strip())\\n self.client.close()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"425f10343b581b9c61087ff263e156f3\",\n \"score\": \"0.5365956\",\n \"text\": \"def handleResultQuery(self):\\n # blocking!\\n message = self.server.getResult()\\n self.getLogger().debug(\\\"Giving the results\\\")\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"802032db364c56fbc05367907926d0f7\",\n \"score\": \"0.5364615\",\n \"text\": \"def sendMessage(self, message, timeout=0.1):\\r\\n\\r\\n #flush the read buffer first\\r\\n prevOut = self.com.read(self.com.inWaiting())#read as many chars as are in the buffer\\r\\n if len(prevOut) and prevOut not in ['>'+eol, eol]:\\r\\n #do not use log messages here\\r\\n print 'Resp found to prev cmd (%s):%s' %(self.lastCmd, prevOut)\\r\\n self.lastCmd=message\\r\\n\\r\\n if message[-2:] not in ['\\\\n', '\\\\n\\\\r']:\\r\\n message+='\\\\n' #append a newline if necess\\r\\n #send the message\\r\\n self.com.write(message)\\r\\n self.com.flush()\\r\\n #get reply (within timeout limit)\\r\\n self.com.setTimeout(timeout)\\r\\n logging.debug('Sent command:%s' %(message[:-1]))#send complete message\\r\\n\\r\\n #get output lines using self.readlin, not self.com.readline\\r\\n #colorcal signals the end of a message by giving a command prompt\\r\\n lines=[]\\r\\n thisLine=''\\r\\n nEmpty=0\\r\\n while (thisLine != '>') and (nEmpty<=self.maxAttempts):\\r\\n #self.com.readline can't handle custom eol\\r\\n thisLine=self.readline(eol=eol)\\r\\n if thisLine in [eol, '>', '']:#lines we don't care about\\r\\n nEmpty+=1\\r\\n continue\\r\\n else:\\r\\n lines.append(thisLine.rstrip(eol))#line without any eol chars\\r\\n nEmpty=0\\r\\n\\r\\n #got all lines and reached '>'\\r\\n if len(lines)==1:\\r\\n return lines[0]#return the string\\r\\n else:\\r\\n return lines#a list of lines\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ea1372f6fa2246fa5cd7c3fde6774c\",\n \"score\": \"0.53616333\",\n \"text\": \"def send(self, command):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241af9506ddc26a37320d6dc1b36a67d\",\n \"score\": \"0.5360894\",\n \"text\": \"async def run_client(host, command):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92da80018d2454c8b97d8d652169053\",\n \"score\": \"0.53566617\",\n \"text\": \"def run_command(device_name, command, command_args=\\\"\\\"):\\n command_split = construct_command(device_name, f\\\"{command} {command_args}\\\")\\n logging.debug(f\\\"Sending command {command_split} to device {device_name}\\\")\\n\\n process = subprocess.Popen(command_split, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\\n\\n output_complete = \\\"\\\"\\n while True:\\n output = process.stdout.readline()\\n if output == '' and process.poll() is not None:\\n break\\n if output:\\n # print(output.strip())\\n logging.debug(f\\\"Popen output: {output}\\\")\\n output_complete += output\\n rc = process.poll()\\n\\n time.sleep(3)\\n \\n if rc == 0:\\n return output_complete\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2957482fdd8d92bd319fd51daed49391\",\n \"score\": \"0.5355704\",\n \"text\": \"def wait_for_operation(self):\\n self.command('*WAI')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"841942a798f8ff391494b85b7218ca83\",\n \"score\": \"0.5350442\",\n \"text\": \"def send_command(self, command):\\n logger.debug(\\\"Command to send: {0}\\\".format(command))\\n try:\\n self.socket.sendall(command.encode(\\\"utf-8\\\"))\\n\\n answer = smart_decode(self.stream.readline()[:-1])\\n logger.debug(\\\"Answer received: {0}\\\".format(answer))\\n if answer.startswith(proto.RETURN_MESSAGE):\\n answer = answer[1:]\\n # Happens when a the other end is dead. There might be an empty\\n # answer before the socket raises an error.\\n if answer.strip() == \\\"\\\":\\n self.close()\\n raise Py4JError(\\\"Answer from Java side is empty\\\")\\n return answer\\n except Exception as e:\\n logger.exception(\\\"Error while sending or receiving.\\\")\\n raise Py4JNetworkError(\\\"Error while sending or receiving\\\", e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"101a758fa5dc931b5ad6979d631ddd12\",\n \"score\": \"0.5349151\",\n \"text\": \"def send_command(self, **kwargs):\\n command = kwargs.get('command', None)\\n retry = kwargs.get('retry', 10)\\n\\n status = dict()\\n\\n if command is None:\\n status['success'] = False\\n status['returned'] = 'No command was passed'\\n return status\\n\\n status['success'] = None\\n status['returned'] = dict()\\n cmd_list = command.split(' ')\\n cmd = cmd_list.pop(0) # first thing sent is always a command\\n # we popped out the 0 before so the rest are the command params\\n params = ' '.join(cmd_list) if cmd_list else ''\\n success = False\\n authenticated = False\\n auth_attempt = 0\\n cmd_result = None\\n challenge = None\\n # Try to execute the command until it's successful or exceeds attempts\\n while not success:\\n if not int(authenticated): # Time to authenticate (maybe again)\\n try:\\n challenge = self.challenge_rcon(retry=retry)\\n except OSError as e_error:\\n if 'timed out' in str(e_error):\\n authenticated = False\\n if challenge is False:\\n auth_attempt += 1\\n if auth_attempt > 3:\\n status['success'] = False\\n status['returned'] = 'Could not authenticate with RCON'\\n return status\\n time.sleep(0.25)\\n\\n if params:\\n try:\\n # This forces a 5 second timeout for socket connections\\n socket.setdefaulttimeout(5)\\n cmd_result = self.server.__getattr__(cmd)(params).strip()\\n\\n # Restore default timeout for socket connections\\n socket.setdefaulttimeout(None)\\n except OSError as except_e:\\n if 'timed out' in str(except_e):\\n authenticated = False\\n else:\\n try:\\n # This forces a 5 second timeout for socket connections\\n socket.setdefaulttimeout(5)\\n cmd_result = self.server.__getattr__(cmd)().strip()\\n\\n # Restore default timeout for socket connections\\n socket.setdefaulttimeout(None)\\n except OSError as except_e:\\n authenticated = False\\n\\n if cmd_result in ('[Whitelist] Invalid command: challenge', None):\\n success = False\\n else:\\n success = True\\n\\n bad_results_11 = ('[Whitelist]')\\n\\n if cmd_result[:11] in bad_results_11:\\n status['success'] = False\\n else:\\n status['success'] = success\\n\\n status['returned'] = cmd_result\\n\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f1cc13a7b83b736e305667c0b1881f\",\n \"score\": \"0.53389424\",\n \"text\": \"def exec(self, command):\\n out_str = None\\n err_str = None\\n chan = self.client.get_transport().open_session()\\n chan.exec_command(command)\\n return_code = -1\\n return_code = chan.recv_exit_status()\\n chan.shutdown(2)\\n if chan.recv_ready():\\n read_list, _, _ = select.select([chan], [], [], 0.0)\\n if len(read_list) > 0:\\n out_str = chan.recv(-1).decode('utf-8')\\n if chan.recv_stderr_ready():\\n read_list, _, _ = select.select([chan], [], [], 0.0)\\n if len(read_list) > 0:\\n err_str = chan.recv_stderr(1024).decode('utf-8')\\n return (return_code, out_str, err_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c4aaa8adf0296cadc3c14027e9f748\",\n \"score\": \"0.5335427\",\n \"text\": \"def execute_command(self, req, **kwargs):\\n if isinstance(req, string_types):\\n # backward compatible\\n timeout = 50 # Use same default timeout as bench.py\\n wait = True\\n asynchronous = False\\n for key in kwargs:\\n if key == 'wait':\\n wait = kwargs[key]\\n elif key == 'timeout':\\n timeout = kwargs[key] # [ms]\\n elif key == 'asynchronous':\\n asynchronous = kwargs[key]\\n req = CliRequest(req,\\n timestamp=self.get_time(),\\n wait=wait,\\n timeout=timeout,\\n asynchronous=asynchronous)\\n\\n # wait for previous command ready\\n if req.wait:\\n response = self._wait_for_exec_ready()\\n if response is not None and self.query_async_expected is not None:\\n if response.retcode != self.query_async_expected:\\n self.logger.error(\\\"Asynch call returned unexpected result, \\\"\\n \\\"expected %d was %d\\\", self.query_async_expected,\\n response.retcode)\\n raise TestStepFail(\\\"Asynch call returned unexpected result\\\")\\n self.query_async_expected = None\\n\\n # Tell Query to worker thread\\n self.response_received.clear()\\n self.query_timeout = self.get_time() + req.timeout if req.wait else 0\\n self.query = req\\n\\n msg = \\\"Async CMD {}, \\\" \\\\\\n \\\"timeout={}, time={}\\\" if req.asynchronous else \\\"CMD {}, timeout={}, time={}\\\"\\n msg = msg.format(req.cmd, int(self.query_timeout), int(self.get_time()))\\n self.logger.debug(msg, extra={'type': '<->'})\\n\\n Dut.process_dut(self)\\n\\n if req.asynchronous is True:\\n self.query_async_expected = req.expected_retcode\\n async_response = CliAsyncResponse(self)\\n self.query_async_response = async_response\\n return async_response\\n\\n if req.wait is False:\\n self.query_async_expected = req.expected_retcode\\n # if an async response was waiting, just discard the result\\n # since the new command has already been sent...\\n # This is not ideal but when a command has its flags \\\"Wait == False\\\"\\n # the result of the previous command is already discarded in previous\\n # stages\\n if self.query_async_response is not None:\\n self.query_async_response.set_response(CliResponse())\\n self.query_async_response = None\\n return CliResponse()\\n\\n return self._wait_for_exec_ready()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2acb03b12934693f3cdfe8889db9421\",\n \"score\": \"0.53351974\",\n \"text\": \"async def send(self, msg):\\n\\n\\n if isinstance(msg, MessageCommand):\\n self.wait_for_prompt = msg.node\\n msg = msg.to_command()\\n else:\\n self.wait_for_prompt = \\\"MASTER\\\"\\n\\n if msg[-1] != \\\"\\\\n\\\":\\n msg += \\\"\\\\n\\\"\\n\\n print(\\\"[M] Sending \\\\\\\"{}\\\\\\\"\\\".format(msg.encode('ascii')))\\n self.log.debug(\\\"SEND '{}'\\\".format(msg))\\n #await self.pluginmanager.call(\\\"on_serial_tx\\\", data=msg)\\n if self.debug_interface is not None:\\n self.debug_interface.send_text(\\\" -->\\\" + msg, True)\\n\\n self.last_cmd = msg[:-1]\\n self._writer.write(msg.encode('ascii'))\\n await self._writer.drain()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34638a6a9980a1f598102dd4721726a8\",\n \"score\": \"0.532706\",\n \"text\": \"def check_command_result(self, cmd_data, clbck_ok, clbck_error):\\n cmd_sent, n_return = cmd_data\\n # get number of bytes waiting in input buffer\\n try:\\n n_waiting = self.spectro.conn.in_waiting\\n # if cant read but connected (sudden disconnection)\\n except:\\n # cancel check_command_result scheduling\\n self.event_spectro_cmd.cancel()\\n # set ui in disconnect state\\n self.set_disconnected_ui_state()\\n # call error callback\\n clbck_error()\\n return\\n # if enough bytes waiting for what we need to read\\n if n_waiting >= n_return:\\n self.root.ids['led_in'].state = 'on'\\n # cancel check_command_result scheduling (no more need to try)\\n self.event_spectro_cmd.cancel()\\n # read the bytes we need (no more)\\n data = self.spectro.receive(n_return)\\n # send to spectrometer library to proceed raw data\\n ans = self.spectro.return_command(data, cmd_sent)\\n # call success callback\\n clbck_ok(ans)\\n else:\\n self.timeout_number -= 1\\n # if timeout happened\\n if self.timeout_number == 0:\\n # cancel check_command_result scheduling (too late to try)\\n self.event_spectro_cmd.cancel()\\n # set as disconnect for ui\\n self.set_disconnected_ui_state()\\n # call error callback\\n clbck_error()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3366bbbb1084ab247ac6bbf318e227\",\n \"score\": \"0.53209186\",\n \"text\": \"def test_commandResultDeferredError(self):\\n avatar = MagicMock()\\n avatar.availableCommands = lambda: {'move': Move}\\n avatar.execute.return_value = defer.fail(Exception('foo'))\\n \\n proto = BotLineProtocol(avatar)\\n proto.makeConnection(StringTransport())\\n\\n proto.lineReceived('move east-23 twice 3')\\n\\n self.assertEqual(proto.transport.value(),\\n '%s\\\\r\\\\n' % (Exception('foo'),))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c22600c20b71a85264cbbd60035cea8\",\n \"score\": \"0.5320613\",\n \"text\": \"def _send_message(self, message,\\n with_last_error=False,\\n command=False):\\n return self.__master._send_message(message,\\n with_last_error, command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d7316f18e4d36ad21f808614586b9d\",\n \"score\": \"0.5320389\",\n \"text\": \"def execute(self, command):\\n channel = self._transport.open_session()\\n channel.exec_command(command)\\n output = channel.makefile('rb', -1).readlines()\\n if output:\\n return output\\n else:\\n return channel.makefile_stderr('rb', -1).readlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb1d7207fc101cdedcdf447cf0d462b9\",\n \"score\": \"0.5317947\",\n \"text\": \"def send(self, command):\\n if DEBUG_LEVEL > 1:\\n print(\\\"Sending: \\\" + command)\\n self.s.send(command + '\\\\r\\\\n')\\n reply = ''\\n while reply[-3:] != '> \\\\n':\\n si, so, sx = select([ self.s ], [], [], self._timeout)\\n if len(si) == 0:\\n raise_(IBEX, 'Timeout Error for ' + self.portString())\\n try:\\n rmadd = self.s.recv(_CHUNKSIZE)\\n except socket.error as msg:\\n raise_(socket.error, 'Socket Error for ' + \\\\\\n self.portString() + ': ' + str(msg))\\n if DEBUG_LEVEL > 5:\\n print(rmadd)\\n reply = reply + rmadd\\n if DEBUG_LEVEL > 4:\\n print(\\\"Reply: \\\" + reply)\\n \\n # Hunt the first CR+LF - echo separator\\n sep = reply.find('\\\\r\\\\n')\\n echo = reply[0:sep]\\n text = self.eol.sub(' ', reply[sep:])\\n text = text[0:-3]\\n \\n if DEBUG_LEVEL > 2:\\n print('Echoed: ' + echo)\\n print('Message: ' + text) \\n \\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c84a32c87f8ffa3690339df8f2081ce\",\n \"score\": \"0.53114086\",\n \"text\": \"def _execute(self):\\n self.log.info(\\n 'Sending message \\\"%s\\\" to Hipchat room \\\"%s\\\"'\\n % (self.option(\\\"message\\\"), self.option(\\\"room\\\"))\\n )\\n res = yield self._post_message(self.option(\\\"room\\\"), self.option(\\\"message\\\"))\\n\\n # If we get 'None' or 'False' back, the actor failed.\\n if not res:\\n raise exceptions.RecoverableActorFailure(\\n \\\"Failed to send message to HipChat: %s\\\" % res\\n )\\n\\n # If we got here, the result is supposed to include 'success' as a key\\n # and inside that key we can dig for the actual message. If the\\n # response code is 202, we know that we didn't actually execute the\\n # message send, but just validated the API token against the API.\\n if \\\"success\\\" in res:\\n if res[\\\"success\\\"][\\\"code\\\"] == 202:\\n self.log.info(\\\"API Token Validated: %s\\\" % res[\\\"success\\\"][\\\"message\\\"])\\n\\n raise gen.Return()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c1999ccacb3834445a13bc43f6356c\",\n \"score\": \"0.53114057\",\n \"text\": \"def execute(self, callback) :\\n\\n result = shellBlocking(self.command)\\n\\n callback(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ec623b29bb5e342982187e8d7a4cf8\",\n \"score\": \"0.5284513\",\n \"text\": \"def __call__(self, exit_on_failure=True):\\n self._call_cmd_line()\\n if self.success():\\n return self.stdout\\n else:\\n logger.warning(\\\"Command failed: {}\\\".format(self.args))\\n logger.warning(self.stderr)\\n sys.stderr.write(self.stderr)\\n\\n if exit_on_failure:\\n sys.exit(self.returncode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b3991a9b3bae26e171ae9babf923dc\",\n \"score\": \"0.52778697\",\n \"text\": \"def _execute(cmd, **kwargs):\\n\\n res = client.send_sync(_msg_pack(cmd, _handler=\\\"execute\\\", **kwargs))\\n\\n if not \\\"value\\\" in res and not \\\"values\\\" in res:\\n raise salt.exceptions.CommandExecutionError(\\n \\\"Execution of command '{:s}' returned no value(s)\\\".format(cmd))\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1ddd191df183d637a92207205ff4df\",\n \"score\": \"0.52659\",\n \"text\": \"async def send_command(client, args):\\n device_id = None\\n if args.device_id.isdigit():\\n if client.get_device_name(int(args.device_id)):\\n device_id = args.device_id\\n\\n if device_id is None:\\n device_id = client.get_device_id(str(args.device_id).strip())\\n\\n if device_id is None:\\n print(f\\\"HUB: {client.name} Device {args.device_id} is invalid.\\\")\\n return\\n\\n snd_cmmnd = SendCommandDevice(\\n device=device_id,\\n command=args.command,\\n delay=args.hold_secs)\\n\\n snd_cmmnd_list = []\\n for _ in range(args.repeat_num):\\n snd_cmmnd_list.append(snd_cmmnd)\\n if args.delay_secs > 0:\\n snd_cmmnd_list.append(args.delay_secs)\\n\\n result_list = await client.send_commands(snd_cmmnd_list)\\n\\n if result_list:\\n for result in result_list:\\n print(\\\"HUB: {} Sending of command {} to device {} failed with code {}: \\\"\\n \\\"{}\\\".format(\\n client.name,\\n result.command.command,\\n result.command.device,\\n result.code,\\n result.msg))\\n else:\\n print(f\\\"HUB: {client.name} Command Sent\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcda9c6e67d5ce45dd4e88aea8cb8b5e\",\n \"score\": \"0.5258716\",\n \"text\": \"def cmd(self, line, send=False, timeout=100):\\n return self._cmd(line, send, timeout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83401f31fafce08a50ca188b7addfc83\",\n \"score\": \"0.52521336\",\n \"text\": \"def send_command(command):\\n njb = jobcount()\\n while njb > 900:\\n msg = 'There are {} jobs. Pausing for 5min.'.format(njb)\\n errmsg(msg)\\n time.sleep(300)\\n njb = jobcount()\\n subprocess.call(command, shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66714ad7a278ee3e72b4c3fc672b6679\",\n \"score\": \"0.5239047\",\n \"text\": \"def cmd(self, command, pattern=None, timeout=None, rmprompt=False):\\n if pattern is None:\\n pattern = self.prompt\\n if timeout is None:\\n timeout = self.timeout\\n self.send_cmd(command)\\n res = self.read_until(pattern, timeout=timeout)\\n if rmprompt:\\n res = self.rmprompt(command, res)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcce4264127371a41ff4ae2a4706fe6a\",\n \"score\": \"0.5231422\",\n \"text\": \"async def send_commands(client, args):\\n device_id = None\\n if args.device_id.isdigit():\\n if client.get_device_name(int(args.device_id)):\\n device_id = args.device_id\\n\\n if device_id is None:\\n device_id = client.get_device_id(str(args.device_id).strip())\\n\\n if device_id is None:\\n print(f\\\"HUB: {client.name} Device {args.device_id} is invalid.\\\")\\n return\\n\\n snd_cmmnd_list = []\\n words = args.commands.split()\\n for single_command in words:\\n if single_command.isspace():\\n continue\\n print(f\\\"ADDING COMMAND: {single_command}\\\")\\n snd_cmmnd = SendCommandDevice(\\n device=device_id,\\n command=single_command,\\n delay=args.hold_secs)\\n snd_cmmnd_list.append(snd_cmmnd)\\n if args.delay_secs > 0:\\n snd_cmmnd_list.append(args.delay_secs)\\n\\n result_list = await client.send_commands(snd_cmmnd_list)\\n\\n if result_list:\\n for result in result_list:\\n print(\\\"HUB: {} Sending of command {} to device {} failed with code {}: \\\"\\n \\\"{}\\\".format(\\n client.name,\\n result.command.command,\\n result.command.device,\\n result.code,\\n result.msg))\\n else:\\n print(f\\\"HUB: {client.name} Commands Sent\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa14fa04c6629e8c93fbde989aa74ade\",\n \"score\": \"0.5230745\",\n \"text\": \"def run(self, command):\\n print(command)\\n channel = self.session.open_session()\\n channel.execute(command)\\n size, data = channel.read()\\n results = \\\"\\\"\\n while size > 0:\\n results += data.decode(\\\"utf-8\\\")\\n size, data = channel.read()\\n channel.close()\\n return {\\\"results\\\": results, \\\"status\\\": channel.get_exit_status()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"257de6a9d0ab569c7823ee33ad7ba9b4\",\n \"score\": \"0.5217761\",\n \"text\": \"def run_command(self, command, gathered_results):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"257de6a9d0ab569c7823ee33ad7ba9b4\",\n \"score\": \"0.5217761\",\n \"text\": \"def run_command(self, command, gathered_results):\\n raise NotImplemented\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f7a6214305914670158de66f90a503\",\n \"score\": \"0.5217371\",\n \"text\": \"def command(self, Command):\\n Auth = self.auth()\\n if Auth != True:\\n self.packet.close()\\n return Auth\\n try:\\n self.packet.send(self.create_message(2,Command,2)) # Send command request to rcon host\\n except:\\n self.packet.close()\\n return '[Command Send] ' + str(sys.exc_info()[1])\\n else:\\n Send_Response = self.get_response()\\n if Send_Response and Send_Response[1] != 2 or Send_Response[2] != 0: # Confirm we get the correct response, if not return an error\\n self.packet.close()\\n return 'Unknown error- response ID or Type does not match command request! ID: {0}, Type: {1}'.format(Send_Response[1], Send_Response[2])\\n elif not Send_Response:\\n self.packet.close()\\n return '[Send_Response] Received 10 empty responses from server.'\\n self.packet.close() # Close connection\\n Send_Response_Body = Send_Response[3].decode('utf-8').strip('\\\\x00').strip(' \\\\n')\\n return '[Send_Response] ', Send_Response_Body # Send server response, will contain response data or error data for processing later\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8e64bea2d12012090fdff9dcd69fb14\",\n \"score\": \"0.52139926\",\n \"text\": \"def call_command(cmd, verbosity=\\\"ERROR\\\"):\\n\\n p = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE)\\n\\n output, err = p.communicate(\\\"get output\\\")\\n\\n rc = p.returncode\\n\\n if not rc == 0:\\n raise Exception(\\\"{0} ==> command: {1}\\\".format(err, \\\" \\\".join(cmd)))\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd603f94e4983a0ae2c8dd4afaa3b588\",\n \"score\": \"0.5213047\",\n \"text\": \"def send_and_recv(self, cmd):\\n\\n self._send(cmd)\\n if '?' in cmd:\\n status = self._recv()\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a5bad4466f1075ca24dc13709ed5f0\",\n \"score\": \"0.5211496\",\n \"text\": \"def _send(self, command):\\n \\n if command.find('?') >= 0:\\n response = self._receive(command)\\n raise OntRemoteError(\\\"Unhandled response: %s\\\" % (response, ))\\n try:\\n self._mp.sendScpi(command)\\n except OntRemoteError as ex:\\n errors = []\\n for hint in ex._hint:\\n error = 'Sending data to %s, port %s failed: %s' % (self._ipAddr, self._ontPort, hint)\\n errors.append(error)\\n raise OntRemoteError(errors)\\n except Exception as ex:\\n raise OntRemoteError('Sending data to %s, port %s failed: %s' % (self._ipAddr, self._ontPort, ex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1859d41e9321c77f3c66608aee1df698\",\n \"score\": \"0.5207403\",\n \"text\": \"def _sendCommand(self, commandName, commandXML, callbackOnComplete, callbackOnExpire, callbackOnCancel, blocking, timeoutSeconds):\\n if self._connectorNode is None or self._connectorNode.GetState() != slicer.vtkMRMLIGTLConnectorNode.StateConnected:\\n logging.error(\\\"Connector node unavailable!\\\")\\n if isinstance(self._connectorNode, slicer.vtkMRMLIGTLConnectorNode):\\n logging.error(\\\"State: %s\\\", self._connectorNode.GetState())\\n return\\n command = slicer.vtkSlicerOpenIGTLinkCommand()\\n command.SetName(commandName)\\n command.SetCommandContent(commandXML)\\n command.SetTimeoutSec(timeoutSeconds)\\n command.SetBlocking(blocking)\\n command.ClearResponseMetaData()\\n observers = []\\n if callbackOnComplete:\\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandCompletedEvent, callbackOnComplete)\\n observers.append([command.CommandCompletedEvent, callbackOnComplete])\\n if callbackOnExpire:\\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandExpiredEvent, callbackOnExpire)\\n observers.append([command.CommandExpiredEvent, callbackOnExpire])\\n if callbackOnCancel:\\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandCancelledEvent, callbackOnCancel)\\n observers.append([command.CommandCancelledEvent, callbackOnCancel])\\n self.addObserver(command, slicer.vtkSlicerOpenIGTLinkCommand.CommandCompletedEvent, self._cleanupCommandOnCompletion)\\n observers.append([command.CommandCompletedEvent, self._cleanupCommandOnCompletion])\\n self._runningCommandObservers[command] = observers\\n self._connectorNode.SendCommand(command)\\n self.cleanupFinishedCommands()\\n return command\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06a1512e995177d27d4c59dcb769475\",\n \"score\": \"0.5206837\",\n \"text\": \"def test_command_output(self):\\n Recipient.objects.create(email='a@example.com')\\n Recipient.objects.create(email='b@example.com')\\n\\n out = StringIO()\\n call_command('rolodexsync', stdout=out)\\n\\n self.assertIn('Syncing \\\"a@example.com\\\"', out.getvalue())\\n self.assertIn('Syncing \\\"b@example.com\\\"', out.getvalue())\\n self.assertIn('Finished syncing recipient information', out.getvalue())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8b5607bcbd3310a57687900b8adf1a\",\n \"score\": \"0.5200617\",\n \"text\": \"def Run(self, cmd_list):\\n\\n if not cmd_list:\\n logger.error(\\\"Empty Command List\\\") \\n assert False\\n if not self.connection_id:\\n logger.error(\\\"Connection is not open %s\\\" % self.connection_id)\\n assert False\\n for line in cmd_list:\\n #logger.debug(\\\"Before sending: %s\\\", time.clock())\\n self.connection_id.sendline(line)\\n #logger.debug(\\\"After sending: %s\\\", time.clock())\\n res = self.__Expect()\\n if res:\\n error_string = [\\\"(ERROR|Error|error):.*\\\"]\\n error_string.append(\\\"RTNETLINK answers:(.*)\\\") \\n error_string.append(\\\"Unknown command -(.*)\\\") \\n error_string.append(\\\"\\\\% Invalid command at \\\\'\\\\^\\\\' marker\\\") \\n for line in res.splitlines():\\n for e in error_string:\\n res_obj = re.search(r'{0}'.format(e), line)\\n if res_obj:\\n logger.error(\\\"Error, %s\\\" % res_obj.group())\\n assert False\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67c12c61d7c28f31598b5634d6955c90\",\n \"score\": \"0.5200539\",\n \"text\": \"def __get_command(self, command):\\n\\n # Added try/except if usb cable not connected to 222 (MCR)\\n try:\\n address = 1\\n self.port.flushInput()\\n self.port.flushOutput()\\n self.port.flush()\\n\\n to_send = \\\"{:02d}{}\\\\x0D\\\".format(address, command)\\n self.port.write(to_send.encode())\\n config.logging.debug(('g4_logger: __get_command - Tx: {0}'.format(to_send)))\\n\\n rx = self.port.readline().decode()\\n\\n if rx == '':\\n config.logging.debug('g4_logger: __get_command - Timeout!')\\n return \\\"Timeout\\\"\\n elif rx[2] == command[0]:\\n to_return = rx[:-2]\\n config.logging.debug(('g4_logger: __get_command - Success Rx: {0}'.format(to_return)))\\n return to_return\\n else:\\n config.logging.warning(('g4_logger: __get_command - Ugly trash! = {0}'.format(rx)))\\n except Exception as e:\\n try:\\n try:\\n self.port.close()\\n except TypeError as e:\\n config.logging.warning(\\\"NoneType: Port never initialized correctly\\\")\\n pass\\n time.sleep(1)\\n self.port = serial.Serial(\\\"/dev/ttyUSB0\\\", baudrate=19200, timeout=1)\\n config.logging.warning(\\\"Serial Reconnected!\\\")\\n except Exception as e:\\n config.logging.error(f'Error Opening Serial Port: {e.__str__()}')\\n time.sleep(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d578fdec0b12046509417ca189ee9b76\",\n \"score\": \"0.51842546\",\n \"text\": \"def runCommand(cmd, error_msg=None, popen_input=None, timeout=None, waitForCompletion=True, debugMessage=False, onlyReturnStatus=False, stdoutFile=None, stderrFile=None):\\r\\n\\r\\n standardOutput = stdoutFile if stdoutFile else subprocess.PIPE\\r\\n standardError = stderrFile if stderrFile else subprocess.PIPE\\r\\n\\r\\n process = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=standardOutput, stderr=standardError)\\r\\n\\r\\n if not waitForCompletion:\\r\\n return process\\r\\n stdout, stderr = process.communicate(input=popen_input)\\r\\n\\r\\n if onlyReturnStatus:\\r\\n return process.returncode\\r\\n else:\\r\\n return (process.returncode, stdout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61f603e999ae4f227d0c86d8bb7857e\",\n \"score\": \"0.5179394\",\n \"text\": \"def shellAsync(command) :\\n\\n try :\\n output = subprocess.check_output(command, shell=True)\\n except :\\n output = \\\"ERR_CMD\\\"\\n\\t\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a592f8302614ed322188ff69567fcd\",\n \"score\": \"0.5176575\",\n \"text\": \"def run (command, timeout=-1, withexitstatus=False, events=None, extra_args=None, logfile=None):\\n if timeout == -1:\\n child = spawn(command, maxread=2000, logfile=logfile)\\n else:\\n child = spawn(command, timeout=timeout, maxread=2000, logfile=logfile)\\n if events is not None:\\n patterns = events.keys()\\n responses = events.values()\\n else:\\n patterns=None # We assume that EOF or TIMEOUT will save us.\\n responses=None\\n child_result_list = []\\n event_count = 0\\n while 1:\\n try:\\n index = child.expect (patterns)\\n if type(child.after) is types.StringType:\\n child_result_list.append(child.before + child.after)\\n else: # child.after may have been a TIMEOUT or EOF, so don't cat those.\\n child_result_list.append(child.before)\\n if type(responses[index]) is types.StringType:\\n child.send(responses[index])\\n elif type(responses[index]) is types.FunctionType:\\n callback_result = responses[index](locals())\\n sys.stdout.flush()\\n if type(callback_result) is types.StringType:\\n child.send(callback_result)\\n elif callback_result:\\n break\\n else:\\n raise TypeError ('The callback must be a string or function type.')\\n event_count = event_count + 1\\n except TIMEOUT, e:\\n child_result_list.append(child.before)\\n break\\n except EOF, e:\\n child_result_list.append(child.before)\\n break\\n child_result = ''.join(child_result_list)\\n if withexitstatus:\\n child.close()\\n return (child_result, child.exitstatus)\\n else:\\n return child_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cbe04652bb3c072eab5acddf8610ba3\",\n \"score\": \"0.51756537\",\n \"text\": \"def system_call(cmd):\\n logger.debug('Entered system_call()')\\n # TODO: This may need to be reviewed against the Qiita version of this\\n # method.\\n proc = Popen(cmd, universal_newlines=True, shell=True, stdout=PIPE,\\n stderr=PIPE)\\n # Communicate pulls all stdout/stderr from the PIPEs\\n # This call blocks until the command is done\\n stdout, stderr = proc.communicate()\\n return_value = proc.returncode\\n return stdout, stderr, return_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f766bf9b0e5e24619d756ae679e2800\",\n \"score\": \"0.51735294\",\n \"text\": \"async def run_command(self, command):\\n return await getattr(self, command)()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d95ca3d71747890a71eafb2dbf822d\",\n \"score\": \"0.5172675\",\n \"text\": \"def sendCommand(self, device, cmd, parameter, value=\\\"0000\\\"):\\n\\n command = \\\"@{0}{1}{2}{3}\\\".format(device, cmd, parameter, value)\\n command += self.calcCRC(command) + \\\"*\\\\r\\\\n\\\"\\n self.logger.debug(\\\"Command: \\\" + command)\\n self.con.write(command)\\n sleep(0.1)\\n\\n if self.con.inWaiting() == 0:\\n self.logger.error(\\\"No answer...\\\")\\n return None\\n\\n #sleep(0.5)\\n #this was what was causing most of the issues, returning a\\n #multiline string.\\n #called splitlines, and return the second line.\\n\\n answer = self.con.readlines()[1]\\n self.logger.debug(\\\"Answer: \\\" + answer)\\n if not self.checkCRC(answer):\\n self.logger.error(\\\"Incorrect CRC \\\" + answer)\\n return None\\n\\n if (answer[-2:-1] != \\\"*\\\"):\\n self.logger.error(\\\"Invalid message received...\\\" + answer)\\n return None\\n\\n if (answer[6:8] != \\\"00\\\"):\\n self.logger.error(\\n \\\"Invalid End Code received, please check documentation...\\\" + answer)\\n return None\\n\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428cd594b25ecdfc134ac6b8ffa2642b\",\n \"score\": \"0.51724887\",\n \"text\": \"def execute(command):\\n child = pexpect.spawn(command)\\n while True:\\n try:\\n child.expect('\\\\n', timeout=None)\\n click.echo(child.before)\\n LOG.debug(child.before)\\n except pexpect.EOF:\\n break\\n child.close()\\n if child.exitstatus != 0:\\n raise ChildProcessReturnedNonZeroError(child.exitstatus)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994393,"cells":{"query_id":{"kind":"string","value":"8799ea90a06e49ddb628f103a556e4da"},"query":{"kind":"string","value":"Request stored feedback for this result from RTX"},"positive_passages":{"kind":"list like","value":[{"docid":"c412767fbe77f9d8b29dc58a479c952a","score":"0.0","text":"def get_result_feedback(response_id, result_id): # noqa: E501\n rtxFeedback = RTXFeedback()\n return rtxFeedback.getResultFeedback(response_id, result_id)","title":""}],"string":"[\n {\n \"docid\": \"c412767fbe77f9d8b29dc58a479c952a\",\n \"score\": \"0.0\",\n \"text\": \"def get_result_feedback(response_id, result_id): # noqa: E501\\n rtxFeedback = RTXFeedback()\\n return rtxFeedback.getResultFeedback(response_id, result_id)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9909e25386eb757d88738d6797469601","score":"0.60022223","text":"def estimate(self):\r\n self.store()\r\n # Validate Data Request\r\n # Send to queue\r\n # Return scheudle\r","title":""},{"docid":"6d3e70c19d7a64997aacfe7ce35ea546","score":"0.58360523","text":"def feedback(self, feedback):\n\n self._feedback = feedback","title":""},{"docid":"f4cf656b06689dde31feaec638029b79","score":"0.56802255","text":"def response(self, action):\n if self.config.args.task_mode == 'train':\n dataset = self.train_ctrl_dataset\n elif self.config.args.task_mode == 'test':\n dataset = self.train_task_dataset\n elif self.config.args.task_mode == 'baseline':\n dataset = self.train_task_dataset\n else:\n logger.exception('Unexcepted mode: {}'.\\\n format(self.config.args.task_mode))\n data = dataset.next_batch(self.config.batch_size)\n\n sess = self.sess\n x = data['input']\n y = data['target']\n feed_dict = {self.x_plh: x, self.y_plh: y}\n\n a = np.argmax(np.array(action))\n sess.run(self.update[a], feed_dict=feed_dict)\n\n fetch = [self.loss_ce, self.loss_l1, self.accuracy, self.ce_grad, self.l1_grad]\n loss_ce, loss_l1, acc, ce_grad, l1_grad = sess.run(fetch, feed_dict=feed_dict)\n valid_loss, valid_acc, _, _ = self.valid()\n train_loss, train_acc = loss_ce, acc\n\n # ----Update state.----\n self.previous_ce_loss = self.previous_ce_loss[1:] + [loss_ce.tolist()]\n self.previous_l1_loss = self.previous_l1_loss[1:] + [loss_l1.tolist()]\n self.step_number[0] += 1\n self.previous_valid_loss = self.previous_valid_loss[1:]\\\n + [valid_loss.tolist()]\n self.previous_train_loss = self.previous_train_loss[1:]\\\n + [train_loss.tolist()]\n self.previous_valid_acc = self.previous_valid_acc[1:]\\\n + [valid_acc.tolist()]\n self.previous_train_acc = self.previous_train_acc[1:]\\\n + [train_acc.tolist()]\n self.mag_ce_grad = self.get_grads_magnitude(ce_grad)\n self.mag_l1_grad = self.get_grads_magnitude(l1_grad)\n\n # Public variable\n self.extra_info = {'valid_loss': valid_loss,\n 'train_loss': train_loss,\n 'valid_acc': valid_acc,\n 'train_acc': train_acc}\n\n reward = self.get_step_reward()\n # ----Early stop and record best result.----\n dead = self.check_terminate()\n state = self.get_state()\n return state, reward, dead","title":""},{"docid":"dcf0e2d09900e41ca698c44d287dfe61","score":"0.5676246","text":"def feedback(data: FeedbackData) -> str:\n return model.feedback(data)","title":""},{"docid":"4c8f06da51d075e2d4ea83f4bdf26bbd","score":"0.55030507","text":"def get_feedback(self):\n return self.feedback","title":""},{"docid":"98b9967682629c38d556e3c8448b9e29","score":"0.54418194","text":"def lookup_feedback(self, feedback):","title":""},{"docid":"891e8ec733bf9c260e28b6ea536a9050","score":"0.5400174","text":"def feedback_train(self, X, y, feedback, dropout=0.5):\n # Combine the input dictionaries for all the features models\n feed_dict = {}\n feed_dict['phase:0'] = 1\n feed_dict['X:0'] = X\n feed_dict['y:0'] = y\n feed_dict['dropout:0'] = dropout\n # Sample the attention vector from the Bernoulli distribution\n attention = self.sess.run(tf.reshape(self.distribution.sample(self.num_samples), [-1, self.num_features]), feed_dict=feed_dict)\n feed_dict['a:0'] = attention\n feed_dict['feedback:0'] = feedback\n # Run the training step and write the results to Tensorboard\n summary, _ = self.sess.run([self.merged, self.feedback_train_step],\n feed_dict=feed_dict)\n self.train_writer.add_summary(summary, global_step=self.sess.run(self.global_step))\n # Run image summary and write the results to Tensorboard\n summary = self.sess.run(self.merged_images,\n feed_dict={self.X: X[0:10], self.feedback: feedback[0:10], self.attention: attention[0:10], self.phase: 1})\n self.train_writer.add_summary(summary, global_step=self.sess.run(self.global_step))\n # Regularly save the models parameters\n if self.sess.run(self.global_step) % 1000 == 0:\n self.saver.save(self.sess, self.directory + '/model.ckpt')","title":""},{"docid":"46c67ff60391b0ab95333e1486eb508b","score":"0.53869754","text":"def getFeedbackNode(self, **kwargs):\n \n pass","title":""},{"docid":"a2c58e71d6a0f496d53dc4db9f43a218","score":"0.53808475","text":"def replay_buffer(self):\n ###########################\n # YOUR IMPLEMENTATION HERE #\n training_states = self.buffer.replay_buffer_states\n training_actions = self.buffer.replay_buffer_actions\n training_rewards = self.buffer.replay_buffer_rewards\n training_next_states = self.buffer.replay_buffer_next_states\n training_dones = self.buffer.replay_buffer_dones\n return training_states, training_actions, training_rewards, training_next_states, training_dones\n \n \n ###########################","title":""},{"docid":"d01952604d31003b793f86c4b205cf5e","score":"0.53526837","text":"def send_learn(self):\n return","title":""},{"docid":"6305ce089a4d38453e3ac13287e61e4d","score":"0.5303253","text":"def update(self, feedback_value):\r\n error = self.SetPoint - feedback_value\r\n\r\n self.current_time = time.time()\r\n delta_time = self.current_time - self.last_time\r\n delta_error = error - self.last_error\r\n\r\n if (delta_time >= self.sample_time):\r\n self.PTerm = self.Kp * error\r\n self.ITerm += error * delta_time\r\n\r\n if (self.ITerm < -self.windup_guard):\r\n self.ITerm = -self.windup_guard\r\n elif (self.ITerm > self.windup_guard):\r\n self.ITerm = self.windup_guard\r\n\r\n self.DTerm = 0.0\r\n if delta_time > 0:\r\n self.DTerm = delta_error / delta_time\r\n\r\n # Remember last time and last error for next calculation\r\n self.last_time = self.current_time\r\n self.last_error = error\r\n\r\n self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)\r\n \r\n return self.output;","title":""},{"docid":"9143ab85a6971cddb59f5e00444eda96","score":"0.5287941","text":"def train(self, results: Results):\n pass","title":""},{"docid":"98120d3ab6aa71e06b0497c082461584","score":"0.52597606","text":"def init_feedback_node(self):\n \n # Validation!\n if not self.request.params.get('pling_id'):\n raise HTTPBadRequest(detail='Please supply a pling_id')\n pling_id = self.request.params.get('pling_id')\n pling = URIRef('http://plings.net/a/%s' % (pling_id))\n \n # Validation!\n submitted = self.request.params.get('submission_date', None)\n if submitted:\n try:\n submitted = datetime.datetime.strptime(submitted, '%Y-%m-%dT%H:%M:%S')\n except ValueError:\n raise HTTPBadRequest(detail='\"submission_date\" should be supplied in \"%Y-%m-%dT%H:%M:%S\" (iso-8601) format')\n \n \n plingback_type = self.request.params.get('plingback_type',\n 'unknown' )\n plingback_type_uri = URIRef(\"http://plingback.plings.net/applications/\" \n + urllib.quote_plus(plingback_type.strip()))\n \n self.context.store.add((self.feedback_uri, ns['RDF']['type'], ns['REV']['Review']))\n self.context.store.add((pling, ns['REV']['hasReview'], self.feedback_uri))\n self.context.store.add((pling, ns['RDF']['type'], ns['PBO']['Activity']))\n self.context.store.add((self.feedback_uri, ns['PBO']['isAbout'], pling))\n \n if submitted:\n self.context.store.add((self.feedback_uri, ns['DC']['date'], Literal(submitted)))\n else:\n self.context.store.add((self.feedback_uri, ns['DC']['date'], Literal(datetime.datetime.now())))\n \n self.context.store.add((self.feedback_uri, ns['PBO']['plingBackType'], plingback_type_uri))\n if self.request.params.get('plingback_version'):\n self.context.store.add((self.feedback_uri, \n ns['PBO']['plingBackVersion'], \n Literal(self.request.params.get('plingback_version'))))\n \n return 'OK'","title":""},{"docid":"c317515a39eaa96c44213cd8a8b0db71","score":"0.5235318","text":"def recordResult(self, evt, op, res):\n outcome = smtk.model.OperatorOutcome(\n res.findInt('outcome').value(0))\n #print 'Record Result of {nm} as {stat}'.format(nm=op.name(), outcome)\n if len(self.active) and self.active[-1][0] == op.name():\n self.history.append(self.active[-1] + [outcome,])\n self.active = self.active[:-1]\n else:\n self.history.append(\n ['***ERROR*** {nm}'.format(op.name()), outcome])\n return 0","title":""},{"docid":"5c9a387ffe2a57b561a3e9b3f88c0e0e","score":"0.5205191","text":"def _store_results_of_this_batch(self):\n xp = self.xp\n # get losses and predicted labels\n l2sq = self.l2_loss().data # l2 squared loss\n probs = self.model.predict_proba(self.batch_adv).data\n labs = xp.argmax(probs, axis=1).astype(xp.int32)\n probs = xp.max(probs, axis=1) \n \n # check if succeeded ? \n is_success = (labs == self.batch_targ) # is the model fooled ?\n # gather succeeded images\n self.batch_success_adv[is_success] = self.batch_adv.data[is_success].copy()\n self.batch_success_labs[is_success] = labs[is_success].copy()\n self.batch_success_probs[is_success] = probs[is_success].copy()\n self.batch_success_l2sq[is_success] = l2sq[is_success].copy()\n # retain results \n self.adv_images = np.append(self.adv_images, cuda.to_cpu(self.batch_success_adv), axis=0)\n self.adv_labels = np.append(self.adv_labels, cuda.to_cpu(self.batch_success_labs), axis=0)\n self.adv_probs = np.append(self.adv_probs, cuda.to_cpu(self.batch_success_probs), axis=0)\n self.adv_l2_squared = np.append(self.adv_l2_squared, cuda.to_cpu(self.batch_success_l2sq), axis=0)","title":""},{"docid":"063fa5935e87f6fe6c8cc4cd6ffe830d","score":"0.519186","text":"def get_data(self, filtered_batch):\n batched_rate_output, _, states_t = vmap(self.rate_layer._evolve_functional, in_axes=(None, None, 0))(self.lr_params, self.lr_state, filtered_batch)\n batched_res_inputs = states_t[\"res_inputs\"]\n batched_res_acts = states_t[\"res_acts\"]\n return batched_rate_output, batched_res_inputs, batched_res_acts","title":""},{"docid":"c6da465cf5177ca68e3497bc7eaf61b8","score":"0.5173925","text":"def eval(self, sample):\n print 'here our rr cost'\n T = sample.T\n dU = sample.dU\n dX = sample.dX\n\n sample_u = sample.get_U()\n sample_x = sample.get_X()\n\n wl2 = 0.3 # weight of state l2 cost\n wl1 = 0.0 # weight of state l1 cost\n wlog = 0.0 # weight of state log cost\n wu = 1.0 # weight of action cost\n wt = (np.arange(T)/float(T)) ** 2 # time dependent weight\n # final_weight_multiplier = 9\n # wt[-1] *= final_weight_multiplier\n alpha = 1e-5 # offset for log cost\n\n tgtM = np.tile(self.tgt, (T, 1))\n d = sample_x - tgtM\n dnorm = np.sum(d ** 2, axis=1)\n denominator = alpha + wt ** 2 * dnorm\n\n # TODO: for log, wt --> wt^2\n ll2 = 0.5 * wl2 * wt * dnorm\n ll1 = wl1 * wt * np.sqrt(dnorm)\n llog = 0.5 * wlog * np.log(denominator)\n lu = 0.5 * wu * np.sum(sample_u ** 2, axis=1)\n l = ll2 + ll1 + llog + lu\n print l\n\n data = {}\n costLogger.info(\"l2 cost\")\n costLogger.info(ll2)\n data['l2'] = ll2\n costLogger.info(\"l1 cost\")\n costLogger.info(ll1)\n data['l1'] = ll1\n costLogger.info(\"log cost\")\n costLogger.info(llog)\n data['log'] = llog\n costLogger.info(\"lu cost\")\n costLogger.info(lu)\n data['lu'] = lu\n costLogger.info(\"total cost\")\n costLogger.info(l)\n data['total'] = l\n\n lx = wl2 * wt.reshape((T, 1)) * d + \\\n wl1 * (wt / (np.sqrt(dnorm))).reshape((T, 1)) * d + \\\n wlog * (wt ** 2 / denominator).reshape((T, 1)) * d\n\n costLogger.info(\"lx\")\n costLogger.info(lx)\n\n lu = wu * sample_u\n lxx = np.zeros((T, dX, dX))\n for t in range(T):\n lxx[t] = wl1 * np.diag(wt[t]*(1/np.sqrt(dnorm[t])-dnorm[t]**(-1.5)*d[t]**2)) + \\\n wlog * np.diag(wt[t]**2/denominator[t]-2*wt[t]**4*d[t]**2/denominator[t]**2)\n lxx += wl2 * np.tile(np.eye(dX), [T, 1, 1]) * wt.reshape((T, 1, 1))\n luu = wu * np.tile(np.eye(dU), [T, 1, 1])\n lux = np.zeros((T, dU, dX))\n\n return l, lx, lu, lxx, luu, lux, data","title":""},{"docid":"5b0ae2d52cf4e6940b4195e290865f2e","score":"0.5126339","text":"def update(self, feedback_value):\r\n error = self.SetPoint - feedback_value\r\n\r\n self.current_time = time.time()\r\n delta_time = self.current_time - self.last_time\r\n delta_error = error - self.last_error\r\n\r\n if (delta_time >= self.sample_time):\r\n self.PTerm = self.Kp * error\r\n self.ITerm += error * delta_time\r\n\r\n if (self.ITerm < -self.windup_guard):\r\n self.ITerm = -self.windup_guard\r\n elif (self.ITerm > self.windup_guard):\r\n self.ITerm = self.windup_guard\r\n\r\n self.DTerm = 0.0\r\n if delta_time > 0:\r\n self.DTerm = delta_error / delta_time\r\n\r\n # Remember last time and last error for next calculation\r\n self.last_time = self.current_time\r\n self.last_error = error\r\n\r\n self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)","title":""},{"docid":"7b4726b20d15674547af7d3459f64f3c","score":"0.51262957","text":"def forward(self, belief_state, request_state):\n\n if self.recommend_flag != -1:\n self.recommend_flag += 1\n\n self.kb_result = {}\n DA = {}\n user_action = self.check_diff(belief_state, request_state)\n\n self.last_request_state = deepcopy(request_state)\n self.last_belief_state = deepcopy(belief_state)\n\n for user_act in user_action:\n domain, _ = user_act.split('-')\n\n # Respond to general greetings\n if domain == 'general':\n self._update_greeting(user_act, DA)\n\n # Book taxi for user\n elif domain == 'Taxi':\n self._book_taxi(belief_state, DA)\n\n elif domain == 'Booking':\n pass\n\n # User's talking about other domain\n elif domain != \"Train\":\n self._update_DA(user_act, user_action, belief_state, DA)\n\n # Info about train\n else:\n self._update_train(user_act, user_action, belief_state, DA)\n\n # Judge if user want to book\n self._judge_booking(user_act, user_action, DA)\n\n if 'Booking-Book' in DA:\n if random.random() < 0.5:\n DA['general-reqmore'] = []\n user_acts = []\n for user_act_ in DA:\n if user_act_ != 'Booking-Book':\n user_acts.append(user_act_)\n for user_act_ in user_acts:\n del DA[user_act_]\n\n if DA == {}:\n DA = {'general-greet': [['none', 'none']]}\n system_acts = []\n for domain_intent, svs in DA.items():\n domain, intent = domain_intent.split('-')\n if not svs and domain == 'general':\n system_acts.append([intent, domain, 'none', 'none'])\n else:\n for slot, value in svs:\n system_acts.append([intent, domain, slot, value])\n\n logging.debug(\"DPM output: %s\", system_acts)\n logging.debug(\"Belief State after DPM: %s\", belief_state)\n logging.debug(\"Request State after DPM: %s\", request_state)\n return belief_state, system_acts","title":""},{"docid":"1e030def18bbe2ab46c1313011de8061","score":"0.51215184","text":"def receive_feedbacks(self, session, feedbacks):\n print \"propagation started...\"\n for doc_fb in feedbacks.get(\"docs\", []):\n doc_id, fb = doc_fb\n doc = Document.get(doc_id)\n \n self.ppgt.fb_from_doc(doc, fb, session)\n\n for kw_fb in feedbacks.get(\"kws\", []):\n kw_id, fb = kw_fb\n kw = Keyword.get(kw_id)\n \n self.ppgt.fb_from_kw(kw, fb, session)\n\n for dockw_fb in feedbacks.get(\"dockws\", []):\n kw_id, doc_id, fb = dockw_fb\n doc = Document.get(doc_id)\n kw = Keyword.get(kw_id)\n \n self.ppgt.fb_from_dockw(kw, doc, fb, session)\n\n # propagation is done\n # updates the feedback value \n self.upd.update(session)\n print \"propagation finished\"","title":""},{"docid":"dfa58f79c2db518a84084fd90e07ba9a","score":"0.51064306","text":"def train(self):\n for i in range(self.iterations):\n action = self.agent.chooseAction()\n best_action = self.bandit.getBestAction()\n \n reward = self.bandit.getReward(action)\n if self.sample_avg:\n self.agent.updateEstimates(action, reward, 1.0/(1+i))\n else:\n self.agent.updateEstimates(action, reward, self.alpha)\n\n \n self.best_action.append(best_action)\n self.chosen_action.append(action)\n self.rewards_obtained.append(reward)","title":""},{"docid":"083630da2917d8876064c37db9760f1e","score":"0.50832766","text":"def processAlgorithm(self, parameters, context, feedback):\n\n # Retrieve the feature source and sink. The 'dest_id' variable is used\n # to uniquely identify the feature sink, and must be included in the\n # dictionary returned by the processAlgorithm function.\n rep_source = self.parameterAsFile(parameters, self.REP_SOURCE, context)\n rep_resultat=self.parameterAsFileOutput(parameters, self.REP_RESULTAT, context)\n prefixe_reseau=self.parameterAsString(parameters,self.PREFIXE_RESEAU,context)\n uic=self.parameterAsBool(parameters,self.UIC,context)\n split_formula=self.parameterAsString(parameters,self.SPLIT_FORMULA,context)\n \n print(rep_resultat,self.REP_RESULTAT)\n if os.path.split(rep_resultat)[1]==self.REP_RESULTAT:\n rep_resultat=os.path.split(rep_resultat)[0]\n self.importGTFS(rep_source,rep_resultat,prefixe_reseau,uic,split_formula)\n\n\n return {self.REP_SOURCE: self.REP_RESULTAT}","title":""},{"docid":"956cc8e98cbd1314427de4ee58b3ee97","score":"0.5080188","text":"def savedata():\n [hitId,assignmentId,workerId,user,to_return] = check_identity(request);\n if to_return!=None:\n return to_return;\n \n try:\n part = Participant.query.\\\n filter(Participant.assignmentId == assignmentId).\\\n filter(Participant.workerId == workerId).\\\n one()\n except:\n raise ExperimentError('improper_inputs')\n total = 0;\n for choice in part.choices:\n for decision in choice.decisions:\n if(decision.status==Decision.USER_MADE):\n total = total + decision.reward;\n \n part.status = Participant.COMPLETED\n user.endhit = datetime.now()\n db_session.add(user)\n db_session.commit()\n rewardCode = 0\n for code in user.codes:\n if code.status==Code.FREE:\n rewardCode = code.value\n code.status=Code.USED\n db_session.commit()\n break\n query_rs_keyCode= \"update keycode_gameset_user set status='USED',totalreward=\"+str(total)+\\\n \" where userid=\"+workerId+\" and gamesetid=\"+assignmentId\n \n connection.execute(query_rs_keyCode)\n \n query_rs_keyCode= \"select keyCodeEnd from keycode_gameset_user where userid=\"+workerId+\" and gamesetid=\"+assignmentId\n rs_keyCode=connection.execute(query_rs_keyCode)\n row_rs_keyCode=rs_keyCode.fetchone()\n email=user.email\n if email==None:\n email=''\n return render_template('/eg/debriefing.html', hitId = hitId, workerId=workerId, assignmentId=assignmentId,sessionId=user.sessionId, totalreward=total, rewardcode=row_rs_keyCode['keyCodeEnd'],email=email)","title":""},{"docid":"55eaa4f054fbce97af99fcbbf6606ce3","score":"0.50688714","text":"def learn(self):\n if len(self.replay_buffer) - self.n + 1 < self.batch_size:\n return\n \n self.replay_buffer.sample(self.batch_size)\n \n states = self.replay_buffer.states\n actions = self.replay_buffer.actions\n rewards = self.replay_buffer.rewards\n next_states = self.replay_buffer.next_states\n dones = self.replay_buffer.dones\n \n state_q_values = self.network(states).gather(1, actions)\n target_state_q_values = self._target_state_q_values(rewards,\n next_states,\n dones)\n \n if self.per:\n errors = target_state_q_values - state_q_values\n for i, index in enumerate(self.replay_buffer.indices):\n self.replay_buffer.update(index, errors[i].item())\n \n loss = self.criterion(state_q_values, target_state_q_values)\n loss *= self.replay_buffer.is_weight\n loss = loss.mean()\n else:\n loss = self.criterion(state_q_values, target_state_q_values)\n \n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n self._update_target_network()","title":""},{"docid":"f80fe9d94ce0bebb08ac143967e30554","score":"0.50652546","text":"def results(self) -> None:\n self.reset_res_fr()\n self.draw_res_fr()\n self.original = self.sent_input.get()\n self.results_rows = get_results2(self.res_fr, self.original)\n self.draw_results_labels()\n self.draw_results_rows()\n return None","title":""},{"docid":"c877664fc73452cc1e43dd8d85c25274","score":"0.5064237","text":"def sample_update(self):\n self.data = self.daq.getData()","title":""},{"docid":"8c0b8c73cf7bed4b72674efe9e117bb0","score":"0.5055165","text":"def update(self, feedback_value):\n error = self.SetPoint - feedback_value\n\n self.current_time = time.time()\n delta_time = self.current_time - self.last_time\n delta_error = error - self.last_error\n\n if (delta_time >= self.sample_time):\n self.PTerm = self.Kp * error\n self.ITerm += error * delta_time\n\n if (self.ITerm < -self.windup_guard):\n self.ITerm = -self.windup_guard\n elif (self.ITerm > self.windup_guard):\n self.ITerm = self.windup_guard\n\n self.DTerm = 0.0\n if delta_time > 0:\n self.DTerm = delta_error / delta_time\n\n # Remember last time and last error for next calculation\n self.last_time = self.current_time\n self.last_error = error\n\n self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)","title":""},{"docid":"4b993c12f550d163eac6d97db5ce29b2","score":"0.5044623","text":"def attention_model(context_memory, query_memory, init_status, hidden_dim, att_dim, max_steps = 5, init = glorot_uniform()):\n gru = GRU((hidden_dim*2, ), name='control_status')\n status = init_status\n output = [None]*max_steps*2\n sum_prob = None\n context_attention_score = attention_score(att_dim, name='context_attention')\n query_attention_score = attention_score(att_dim, name='query_attention')\n answer_attention_score = attention_score(att_dim, name='candidate_attention')\n stop_gate = termination_gate(name='terminate_prob')\n prev_stop = 0\n for step in range(max_steps):\n context_attention_weight = context_attention_score(status, context_memory)\n query_attention_weight = query_attention_score(status, query_memory)\n context_attention = sequence.reduce_sum(times(context_attention_weight, context_memory), name='C-Att')\n query_attention = sequence.reduce_sum(times(query_attention_weight, query_memory), name='Q-Att')\n attention = ops.splice(query_attention, context_attention, name='att-sp')\n status = gru(status, attention).output\n termination_prob = stop_gate(status)\n ans_attention = answer_attention_score(status, context_memory)\n output[step*2] = ans_attention\n if step < max_steps -1:\n stop_prob = prev_stop + ops.log(termination_prob, name='log_stop')\n else:\n stop_prob = prev_stop\n output[step*2+1] = sequence.broadcast_as(ops.exp(stop_prob, name='exp_log_stop'), output[step*2], name='Stop_{0}'.format(step))\n prev_stop += ops.log(1-termination_prob, name='log_non_stop')\n\n final_ans = None\n for step in range(max_steps):\n if final_ans is None:\n final_ans = output[step*2] * output[step*2+1]\n else:\n final_ans += output[step*2] * output[step*2+1]\n results = combine(output + [ final_ans ], name='Attention_func')\n return results","title":""},{"docid":"c4599a53c71061aace5770a7e94de7cd","score":"0.5043428","text":"def receive_trial_result(self, parameter_id, parameters, value, **kwargs):\n trial_info_idx = self.running_trials.pop(parameter_id, None)\n assert trial_info_idx is not None\n\n value = extract_scalar_reward(value)\n if self.optimize_mode == OptimizeMode.Minimize:\n value = -value\n\n self.trials_result[trial_info_idx] = value\n self.finished_trials += 1\n\n logger.debug('receive_trial_result, parameter_id %d, trial_info_idx %d, finished_trials %d, inf_batch_size %d',\n parameter_id, trial_info_idx, self.finished_trials, self.inf_batch_size)\n if self.finished_trials == self.inf_batch_size:\n logger.debug('Start next round inference in receive_trial_result')\n self._next_round_inference()","title":""},{"docid":"be0c87ab659aa3c919a6be393b41bc69","score":"0.50422555","text":"def fetch_training_data(self):\n return","title":""},{"docid":"e3caa0e547dafaf79e926ede712b6217","score":"0.5037924","text":"def observe_result(self, session_state, job, result):","title":""},{"docid":"31000a54aed57aa42140c38c032ff726","score":"0.5029414","text":"def post_result_feedback(response_id, result_id, body): # noqa: E501\n if connexion.request.is_json:\n ratingData = connexion.request.get_json() # noqa: E501\n rtxFeedback = RTXFeedback()\n response = rtxFeedback.addNewResultRating(result_id, ratingData)\n return(response)\n return( { \"status\": 502, \"title\": \"body content not JSON\", \"detail\": \"Required body content is not JSON\", \"type\": \"about:blank\" }, 502 )","title":""},{"docid":"687b9fbf65020492df54c3684221ba31","score":"0.50268143","text":"def results():","title":""},{"docid":"5035f08e7cc6080dff44bf6243f37834","score":"0.50247085","text":"def train(self, result: Results):\n result.action_space = self.action_space_change(result.action_space)\n result.observation = self.observation_change(result.observation)\n self.agent.train(result)","title":""},{"docid":"5b29131549be3977fddcc1b1cf525387","score":"0.50232613","text":"def __call__(self, observation, prev_action, prev_reward):\n prev_action = self.distribution.to_onehot(prev_action)\n model_inputs = buffer_to((observation, prev_action, prev_reward),\n device=self.device)\n q, _conv = self.model(*model_inputs)\n return q.cpu()","title":""},{"docid":"a4756dac2229c053358d2fb41c5f79bd","score":"0.50133973","text":"def tell(self, reward_table_result):\n\n reward_table = -np.array(reward_table_result)\n\n ## Compute reward decay if needed\n if self.weight_decay > 0:\n l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\n reward_table += l2_decay\n\n self.es.tell(self.solutions, (reward_table).tolist())","title":""},{"docid":"af9251018607d66d4e33469db7cc7d4a","score":"0.5011749","text":"def request_data(self):\n return {\n 'tank_id': self.tank_id,\n 'count': len(self.registered),\n 'failed': self.failed,\n }","title":""},{"docid":"ba18d48bd3ffa9cc25e37831730f8ea7","score":"0.50112474","text":"def AquireParam(self):\n ### Update instance variables\n # self.Ig_value = self.Ig_obj.Measure()\n self.Ig_value = self.Ig_obj.Fetch()\n # self.Ic_value = self.Ic_obj.Measure()\n self.Ic_value = self.Ic_obj.Fetch()\n\n self.P_value = self.P_obj.RP()\n self.time_now = float( \"{0:.2f}\".format(time.time() - self.time_start) )\n self.Ve_value = self.volt_now\n self.Ve_status = str(self.Ve_value)\n self.Ic_status = str(self.Ic_value)\n self.Ig_status = str(self.Ig_value)\n self.P_status = \"{0:1.2e}\".format(self.P_value)","title":""},{"docid":"197d0ae8d7a2688e9091f6e4e5a40cae","score":"0.5009342","text":"def reward(self) -> tf.Tensor:","title":""},{"docid":"fef935e49fe9b16155fa71c425188997","score":"0.49899712","text":"async def batch_predict():\n return {\"response\": \"not implemented yet, sry\"}","title":""},{"docid":"367fc83156b11d7b1ea9e43a1215483e","score":"0.4987145","text":"def receiveResults(self,storeId,data):\n messenger.send('TDS-results-%d'%storeId,sentArgs=[data])","title":""},{"docid":"662a525b2637a4bf6a1d2811ba49b3b7","score":"0.4979685","text":"def send_buffer(self):\n # Do nothing if we have no entities\n if self.node_count == 0 and self.relation_count == 0:\n return\n\n args = (\n [self.node_count, self.relation_count, len(self.labels), len(self.reltypes)]\n + self.labels\n + self.reltypes\n )\n # Prepend a \"BEGIN\" token if this is the first query\n if self.initial_query:\n args.insert(0, \"BEGIN\")\n self.initial_query = False\n\n task = self.pool.apipe(run, self.client, self.graphname, args)\n self.add_task(task)\n\n self.clear_buffer()","title":""},{"docid":"e697bd472e41d5be4a642c67c9dc4f8e","score":"0.4977921","text":"def getUseFeedbackList(self, **kwargs):\n \n pass","title":""},{"docid":"62f5b5035eb733697bb4c39b2072bc5e","score":"0.49767745","text":"def do_opt_tx(self, taps):\n time.sleep(0.001) # Give the GUI a chance to acknowledge user clicking the Abort button.\n\n if self.stopped():\n raise RuntimeError(\"Optimization aborted.\")\n\n pybert = self.pybert\n tuners = pybert.tx_tap_tuners\n taps = list(taps)\n for tuner in tuners:\n if tuner.enabled:\n tuner.value = taps.pop(0)\n return pybert.cost","title":""},{"docid":"5a2edc00052b6bfee893b8d1f1601492","score":"0.4961446","text":"def __getitem__(self, index):\n context_arr = self.data_info['context_arr'][index]\n context_arr = self.preprocess(context_arr, self.src_word2id, trg=False)\n response = self.data_info['response'][index]\n response = self.preprocess(response, self.trg_word2id)\n ptr_index = torch.Tensor(self.data_info['ptr_index'][index])\n selector_index = torch.Tensor(self.data_info['selector_index'][index])\n conv_arr = self.data_info['conv_arr'][index]\n conv_arr = self.preprocess(conv_arr, self.src_word2id, trg=False)\n kb_arr = self.data_info['kb_arr'][index]\n kb_arr = self.preprocess(kb_arr, self.src_word2id, trg=False)\n sketch_response = self.data_info['sketch_response'][index]\n sketch_response = self.preprocess(sketch_response, self.trg_word2id)\n\n # processed information\n data_info = {}\n for k in self.data_info.keys():\n try:\n data_info[k] = locals()[k]\n except:\n data_info[k] = self.data_info[k][index]\n\n # additional plain information\n data_info['context_arr_plain'] = self.data_info['context_arr'][index]\n data_info['response_plain'] = self.data_info['response'][index]\n data_info['kb_arr_plain'] = self.data_info['kb_arr'][index]\n\n return data_info","title":""},{"docid":"f6635967c7b0d7e30367d63a61ef81c4","score":"0.49571595","text":"def updateModelResults(curMod, res):\n\tcurMod.acc = res[0]\n\tcurMod.spec = res[1]\n\tcurMod.sens = res[2]\n\tcurMod.ppv = res[3]\n\tcurMod.npv = res[4]\n\tcurMod.fscore = res[5]","title":""},{"docid":"1ea4c970c231f81d150c4fde02922b61","score":"0.49548402","text":"async def predict(success: Success):\n\n campaign_id = 23548\n result = 'pass'\n return {\n 'campaign id': campaign_id,\n 'prediction': result\n }","title":""},{"docid":"bc9aad94b0838d0898ea11fd34e80666","score":"0.4951337","text":"def __update_model(self):\n\n for tau in range(self.trials):\n for t in range(self.T):\n if t == 0:\n response = None\n else:\n response = self.actions[tau, t-1]\n \n observation = self.observations[tau,t]\n \n self.agent.update_beliefs(tau, t, observation, response)\n self.agent.plan_behavior(tau, t)\n self.agent.estimate_response_probability(tau, t)","title":""},{"docid":"bc9aad94b0838d0898ea11fd34e80666","score":"0.4951337","text":"def __update_model(self):\n\n for tau in range(self.trials):\n for t in range(self.T):\n if t == 0:\n response = None\n else:\n response = self.actions[tau, t-1]\n \n observation = self.observations[tau,t]\n \n self.agent.update_beliefs(tau, t, observation, response)\n self.agent.plan_behavior(tau, t)\n self.agent.estimate_response_probability(tau, t)","title":""},{"docid":"8306dae9d8431df121243b23398e11f9","score":"0.49411935","text":"def training(self):","title":""},{"docid":"2449d30a486f73707d94a750d7772ce2","score":"0.49362722","text":"def model_train_process():\n train_data = read.get_train_data(\"../data/myratings.txt\")\n user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)\n\n recom_result = give_recom_result(user_vec, item_vec, \"1\")\n print(recom_result)\n\n ana_recom_result(train_data, \"1\", recom_result)\n\n # print(len(user_vec[\"1\"]))\n # print(item_vec[\"2455\"])","title":""},{"docid":"78c895da24f9c8fb9539be8ec5ecd1b9","score":"0.49323434","text":"def train(self):\n last_eval = 0\n last_record = 0\n scores_eval = []\n\n self.init_averages()\n scores_eval = [] # list of scores computed at iteration time\n\n for t in range(self.config.num_batches):\n\n # collect a minibatch of samples\n paths, total_rewards = self.sample_path(self.env)\n scores_eval = scores_eval + total_rewards\n observations = np.concatenate([path[\"observation\"] for path in paths])\n actions = np.concatenate([path[\"action\"] for path in paths])\n rewards = np.concatenate([path[\"reward\"] for path in paths])\n # compute Q-val estimates (discounted future returns) for each time step\n returns = self.get_returns(paths)\n advantages = self.calculate_advantage(returns, observations)\n\n # run training operations\n if self.config.use_baseline:\n self.update_baseline(returns, observations)\n self.sess.run(self.train_op, feed_dict={\n self.observation_placeholder : observations,\n self.action_placeholder : actions,\n self.advantage_placeholder : advantages})\n\n # tf stuff\n if (t % self.config.summary_freq == 0):\n self.update_averages(total_rewards, scores_eval)\n self.record_summary(t)\n\n # compute reward statistics for this batch and log\n avg_reward = np.mean(total_rewards)\n sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))\n msg = \"Average reward: {:04.2f} +/- {:04.2f}\".format(avg_reward, sigma_reward)\n self.logger.info(msg)\n\n if self.config.record and (last_record > self.config.record_freq):\n self.logger.info(\"Recording...\")\n last_record =0\n self.record()\n\n self.logger.info(\"- Training done.\")\n export_plot(scores_eval, \"Score\", config.env_name, self.config.plot_output)","title":""},{"docid":"614a1d1c4bc8a12dfd661f70cccebce5","score":"0.49255964","text":"def process(self, data_batch, data_samples) -> None:\n for sample in data_samples:\n gt_answer = sample.get('gt_answer')\n result = {\n 'pred_answer': sample.get('pred_answer'),\n 'gt_answer': gt_answer\n }\n\n self.results.append(result)","title":""},{"docid":"7b200bb3c8f8006d68692ccc93362536","score":"0.49205723","text":"def receive_result(self, op):\n tag = self._tags[op]\n rank = self._op_location[tag]\n _, _, data = self.receive_data(rank)\n return data","title":""},{"docid":"f8bcbb25bf3283c6e16eed0d31c39fd6","score":"0.4916548","text":"def result(self, state, action):\n raise NotImplementedError","title":""},{"docid":"f8bcbb25bf3283c6e16eed0d31c39fd6","score":"0.4916548","text":"def result(self, state, action):\n raise NotImplementedError","title":""},{"docid":"f8bcbb25bf3283c6e16eed0d31c39fd6","score":"0.4916548","text":"def result(self, state, action):\n raise NotImplementedError","title":""},{"docid":"d5d483db76ced009eafdb2483b6d55d0","score":"0.49134415","text":"def train(self):\n pass","title":""},{"docid":"d5d483db76ced009eafdb2483b6d55d0","score":"0.49134415","text":"def train(self):\n pass","title":""},{"docid":"d5d483db76ced009eafdb2483b6d55d0","score":"0.49134415","text":"def train(self):\n pass","title":""},{"docid":"d5d483db76ced009eafdb2483b6d55d0","score":"0.49134415","text":"def train(self):\n pass","title":""},{"docid":"6b9465fee31e4712e80b95ef5b81f8c5","score":"0.49132198","text":"def _replay(self):\n batch = self._Memory.get_samples(self._Model.batch_size)\n\n if len(batch) > 0: # if the memory is full enough\n states = np.array([val[0] for val in batch]) # extract states from the batch\n next_states = np.array([val[3] for val in batch]) # extract next states from the batch\n\n # prediction\n q_s_a = self._Model.predict_batch(states) # predict Q(state), for every sample\n q_s_a_d = self._Model.predict_batch(next_states) # predict Q(next_state), for every sample\n\n # setup training arrays\n x = np.zeros((len(batch), self._num_states))\n y = np.zeros((len(batch), self._num_actions))\n\n for i, b in enumerate(batch):\n state, action, reward, _ = b[0], b[1], b[2], b[3] # extract data from one sample\n current_q = q_s_a[i] # get the Q(state) predicted before\n current_q[action] = reward + self._gamma * np.amax(q_s_a_d[i]) # update Q(state, action)\n x[i] = state\n y[i] = current_q # Q(state) that includes the updated action value\n\n self._Model.train_batch(x, y) # train the NN","title":""},{"docid":"616dcff6eec4e549342268ce1cfb980b","score":"0.49123177","text":"def train(self):","title":""},{"docid":"0c6d23be54de8713e87588f66f029acc","score":"0.4908751","text":"def step_impl(context):\n rtx_api_url = \"https://arax.rtx.ai/api/rtx/v1\"\n rtx_headers = {'accept': 'application/json'}\n\n # First translate the natural language question, if there is one\n if hasattr(context, 'human_question'):\n translate_response = requests.post(rtx_api_url + \"/translate\", json=context.human_question, headers=rtx_headers)\n print(translate_response.status_code, translate_response.text) # Only displays if test fails\n assert translate_response.status_code == 200\n context.machine_question = translate_response.json()\n\n # Then query RTX with the translated question\n context.machine_question[\"bypass_cache\"] = \"true\"\n query_response = requests.post(rtx_api_url + \"/query\", json=context.machine_question, headers=rtx_headers)\n print(query_response.status_code, query_response.text) # Only displays if test fails\n assert query_response.status_code == 200\n context.response_json = query_response.json()","title":""},{"docid":"1a1ae01f21ab0f5dbe3bf9a44d67e2af","score":"0.4902007","text":"def train(self, request):\n pass","title":""},{"docid":"b75144358f20640b05223b7f4651352e","score":"0.48998815","text":"def train_rr(self):\n self.env = GentlyTerminating(self.env)\n print(\"Training in real environment started...\")\n reward_record = 0\n total_reward = 0\n episode = 0\n rew = []\n step = 0\n while step < self.total_steps:\n state = self.transformObservation(self.env.reset())\n done = False\n\n self.info_print(step, total_reward, reward_record)\n\n total_reward = 0\n i = 0\n while not done:\n\n action = self.action_selection(\n torch.tensor(state, dtype=torch.float32, device=self.device).unsqueeze(0)).squeeze()\n\n action = self.noise_torch.sample((self.action_dim,)) * self.noise_decay ** episode + action\n\n action = torch.clamp(action, min=self.env_low[0], max=self.env_high[0])\n\n action = action.to(\"cpu\").detach().numpy()\n next_state, reward, done, _ = self.env.step(action)\n done = done or i >= self.trial_horizon\n next_state = self.transformObservation(next_state)\n\n total_reward += reward\n\n step += 1\n i = i + 1\n\n self.replayBuffer.add(state, action, reward, next_state, done)\n state = next_state\n # we do this at end of every episode because it takes to much time between episodes\n if self.replayBuffer.count >= self.n_batches:\n actor_loss, critic_loss = self.update()\n\n self.update_actor(actor_loss)\n self.update_critic(critic_loss)\n\n self.soft_update(self.actor_network, self.actor_target)\n self.soft_update(self.critic_network, self.critic_target)\n\n if self.replayBuffer.count >= self.n_batches:\n if self.critic_lr_scheduler.get_lr()[0] > self.lr_min:\n self.critic_lr_scheduler.step()\n if self.actor_lr_scheduler.get_lr()[0] > self.lr_min:\n self.actor_lr_scheduler.step()\n episode += 1\n # if out actor is really good, test target actor. If the target actor is good too, save it.\n if reward_record < total_reward and total_reward > 50:\n trial_average_reward = self.trial()\n if trial_average_reward > reward_record:\n print(\"New record\")\n reward_record = trial_average_reward\n self.save_model(trial_average_reward)\n rew.append(total_reward)\n # test & save final model\n trial_average_reward = self.trial()\n self.save_model(\"{:.2f}_final\".format(trial_average_reward))\n\n return rew","title":""},{"docid":"830def6931517f049a4afb7a7c2294b2","score":"0.48982772","text":"def processAlgorithm(self, parameters, context, feedback):\n\n # Retrieve the feature source and sink. The 'dest_id' variable is used\n # to uniquely identify the feature sink, and must be included in the\n # dictionary returned by the processAlgorithm function.\n source = self.parameterAsSource(parameters, self.INPUT, context)\n (sink, dest_id) = self.parameterAsSink(\n parameters, self.OUTPUT,\n context, source.fields(),\n source.wkbType(), source.sourceCrs()\n )\n\n # Compute the number of steps to display within the progress bar and\n # get features from source\n total = 100.0 / source.featureCount() if source.featureCount() else 0\n features = source.getFeatures()\n\n for current, feature in enumerate(features):\n # Stop the algorithm if cancel button has been clicked\n if feedback.isCanceled():\n break\n\n # Add a feature in the sink\n sink.addFeature(feature, QgsFeatureSink.FastInsert)\n\n # Update the progress bar\n feedback.setProgress(int(current * total))\n\n # Return the results of the algorithm. In this case our only result is\n # the feature sink which contains the processed features, but some\n # algorithms may return multiple feature sinks, calculated numeric\n # statistics, etc. These should all be included in the returned\n # dictionary, with keys matching the feature corresponding parameter\n # or output names.\n return {self.OUTPUT: dest_id}","title":""},{"docid":"1cde5c8c3ff535f97b8595b6dd05fd65","score":"0.48916265","text":"def respond_feedback(cls):\n # Start our TwiML response\n response = VoiceResponse()\n\n # Use <Say> to give the caller some instructions\n response.say('Hello, Niche always welcomes feedback so thank you for your time and effort. End the call after you are done. We will start recording after the beep.')\n\n # Use <Record> to record the caller's message\n response.record(action=\"/feedback/save\", finishOnKey='', timeout=0)\n\n # End the call with <Hangup>\n response.hangup()\n\n return str(response), 200","title":""},{"docid":"488c3cc50a6872c005aca6d85c0e1893","score":"0.48891422","text":"def gather_feedback():\n\n # a neat way for accessing data from both GET and POST requests!\n data = request.values\n\n # We can have a peek at what the data looks like in the console if we\n # print the result we got from the user. The keys we used\n # (the `['name']` bit after `data`) to see the data matches the \"name\"\n # we give on the input field in our HTML form.\n print(f\"Name submitted was: {data['name']}\")\n print(f\"Email submitted was: {data['email']}\")\n\n return render_template(\"feedback.html\", form_data=data, title=\"Feedback response\")","title":""},{"docid":"6353c9eeb30a87ef442260d764a89ae3","score":"0.48881766","text":"def processAlgorithm(self, parameters, context, feedback):\n\n # Load all rasters\n input1_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\n ds_input1 = gdal.Open(input1_raster.dataProvider().dataSourceUri())\n arr_input1 = ds_input1.GetRasterBand(1).ReadAsArray()\n\n input2_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\n ds_input2 = gdal.Open(input2_raster.dataProvider().dataSourceUri())\n arr_input2 = ds_input2.GetRasterBand(1).ReadAsArray()\n\n input3_raster = self.parameterAsRasterLayer(parameters, self.INPUT3, context)\n ds_input3 = gdal.Open(input3_raster.dataProvider().dataSourceUri())\n arr_input3 = ds_input3.GetRasterBand(1).ReadAsArray()\n\n input4_raster = self.parameterAsRasterLayer(parameters, self.INPUT4, context)\n ds_input4 = gdal.Open(input4_raster.dataProvider().dataSourceUri())\n arr_input4 = ds_input4.GetRasterBand(1).ReadAsArray()\n\n input5_raster = self.parameterAsRasterLayer(parameters, self.INPUT5, context)\n ds_input5 = gdal.Open(input5_raster.dataProvider().dataSourceUri())\n arr_input5 = ds_input5.GetRasterBand(1).ReadAsArray()\n\n input6_raster = self.parameterAsRasterLayer(parameters, self.INPUT6, context)\n ds_input6 = gdal.Open(input6_raster.dataProvider().dataSourceUri())\n arr_input6 = ds_input6.GetRasterBand(1).ReadAsArray()\n\n input7_raster = self.parameterAsRasterLayer(parameters, self.INPUT7, context)\n ds_input7 = gdal.Open(input7_raster.dataProvider().dataSourceUri())\n arr_input7 = ds_input7.GetRasterBand(1).ReadAsArray()\n\n input8_raster = self.parameterAsRasterLayer(parameters, self.INPUT8, context)\n ds_input8 = gdal.Open(input8_raster.dataProvider().dataSourceUri())\n arr_input8 = ds_input8.GetRasterBand(1).ReadAsArray()\n\n input9_raster = self.parameterAsRasterLayer(parameters, self.INPUT9, context)\n ds_input9 = gdal.Open(input9_raster.dataProvider().dataSourceUri())\n arr_input9 = ds_input9.GetRasterBand(1).ReadAsArray()\n\n arr_total = arr_input1 + arr_input2 + arr_input3 + arr_input4 + arr_input5 + arr_input6 + arr_input7 + \\\n arr_input8 + arr_input9\n\n # Initialize and write on output raster\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\n file_output = path_output + '/SE_10_SE_total.tiff'\n driver = gdal.GetDriverByName(\"GTiff\")\n [rows, cols] = arr_total.shape\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\n parameters, self.PIXEL_RES, context)\n total_area = np.sum(arr_total) / (cols * rows * area_pixel)\n outdata = driver.Create(file_output, cols, rows, 1, gdal.GDT_Float64)\n outdata.SetGeoTransform(ds_input1.GetGeoTransform())##sets same geotransform as input\n outdata.SetProjection(ds_input1.GetProjection())##sets same projection as input\n outdata.GetRasterBand(1).WriteArray(arr_total)\n outdata.FlushCache() ##saves to disk!!\n report_output = path_output + '/SE_totale.txt'\n f = open(report_output, \"w+\")\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n f.write(\"Sommario dell'analisi dei servizi ecosistemici\\n\")\n f.write(\"Data: \" + today +\"\\n\\n\\n\")\n f.write(\"Differenze tra stato di progetto e stato attuale\\n\\n\")\n f.write(\"Differenza di valore totale (€): %f \\n\" % (np.sum(arr_total)))\n f.write(\"Differenza per unità di superficie (€/ha): %f \\n\" % (\n total_area * 10000))\n return {self.OUTPUT: total_area}\n\n \n # ----------------------------------------------------------------------------------- \n # Copyright (c) 2021 Città di Torino.\n # \n # This material is free software: you can redistribute it and/or modify\n # it under the terms of the GNU General Public License as published by\n # the Free Software Foundation, either version 2 of the License, or\n # (at your option) any later version.\n # \n # This program is distributed in the hope that it will be useful,\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n # GNU General Public License for more details.\n # \n # You should have received a copy of the GNU General Public License\n # along with this program. If not, see http://www.gnu.org/licenses.\n # ----------------------------------------------------------------------------------- ","title":""},{"docid":"18bac172fb51841b9bc78d978d6e6d25","score":"0.48840973","text":"def feedback(self, user, data=None, is_response=False):\n poll_configs = [\n ('Teaching Evaluate', 'teach_feedback', None),\n ('Other Question', 'other_questions', None),\n ]\n\n if is_response == True:\n # get response from data\n selected_option = data['selected_option']\n # response\n for option, response_function, parameters in poll_configs:\n if selected_option == option:\n # clean talk record\n self.talks.pop(user['id'])\n\n # call the function\n call_the_function = {\n 'response_function': response_function,\n 'parameters': {\n 'user': user,\n 'data': data,\n 'is_response': False\n }\n }\n return call_the_function\n\n # error\n else:\n message = \"You can select teaching evaluate and scoring for the class and make some suggestions!\"\n message += \"\\nIf you have some questions, but just can't find answer by our bot, you can select other questions option and tell us.\"\n call_the_function = {\n 'response_function': 'send_poll',\n 'parameters': {\n 'user': user,\n 'message': message,\n 'poll_configs': poll_configs,\n 'response_function': 'feedback',\n 'keep_current_talk': False\n }\n }\n return call_the_function\n\n\n return None","title":""},{"docid":"bf2a06aba0a4395be711ed35cf5702b3","score":"0.48790106","text":"def PredictAction(self, PC, CSV=False, Feedback=False):\n Samples = PC.Samples\n Costs = [0] * Samples\n Rewards = [0] * Samples\n PredictedActions = [0] * len(self.Plr.MDP.A)\n # Find what samples we already have.\n RIndices = [PC.ObjectNames.index(\n i) if i in PC.ObjectNames else -1 for i in self.Plr.Map.ObjectNames]\n CIndices = [PC.CostNames.index(\n i) if i in PC.CostNames else -1 for i in self.Plr.Map.StateNames]\n if Feedback:\n sys.stdout.write(\"\\n\")\n for i in range(Samples):\n if Feedback:\n Percentage = round(i * 100.0 / Samples, 2)\n sys.stdout.write(\"\\rProgress |\")\n roundper = int(math.floor(Percentage / 5))\n sys.stdout.write(\n self.begincolor + self.block * roundper + self.endcolor)\n sys.stdout.write(\" \" * (20 - roundper))\n sys.stdout.write(\"| \" + str(Percentage) + \"%\")\n sys.stdout.flush()\n # Resample the agent\n self.Plr.Agent.ResampleAgent()\n # and overwrite sample sections that we already have\n self.Plr.Agent.costs = [PC.CostSamples[i, CIndices[\n j]] if CIndices[j] != -1 else self.Plr.Agent.costs[j] for j in range(len(self.Plr.Agent.costs))]\n self.Plr.Agent.rewards = [PC.RewardSamples[i, RIndices[\n j]] if RIndices[j] != -1 else self.Plr.Agent.rewards[j] for j in range(len(self.Plr.Agent.rewards))]\n # save samples\n Costs[i] = self.Plr.Agent.costs\n Rewards[i] = self.Plr.Agent.rewards\n # Replan\n self.Plr.Prepare(self.Validate)\n # Get predicted actions\n ActionDistribution = self.Plr.GetActionDistribution()\n # Get the probability\n probability = np.exp(PC.LogLikelihoods[i])\n # Add all up\n PredictedActions = [ActionDistribution[\n x] * probability + PredictedActions[x] for x in range(len(PredictedActions))]\n # Finish printing progress bar\n if Feedback:\n # Print complete progress bar\n sys.stdout.write(\"\\rProgress |\")\n sys.stdout.write(self.begincolor + self.block * 20 + self.endcolor)\n sys.stdout.write(\"| 100.0%\")\n sys.stdout.flush()\n # PredictedActions is a list of arrays. Make it a list of integers.\n PredictedActions = [PredictedActions[i][0]\n for i in range(len(PredictedActions))]\n if not CSV:\n return [self.Plr.Map.ActionNames, PredictedActions]\n else:\n print((\",\".join(self.Plr.Map.ActionNames)))\n probs = [str(i) for i in PredictedActions]\n print((\",\".join(probs)))","title":""},{"docid":"9ae667f820003f81886af5d1d3f41f83","score":"0.48749357","text":"def train(self, replay_buffer, writer, iterations):\n self.step += 1\n \n for it in range(iterations):\n \n # Step 1: Sample a batch of transitions (s, s’, a, r) from the memory\n batch_states, batch_next_states, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(self.batch_size)\n # convert the numpy arrays to tensors object \n # if cuda is available send data to gpu\n state = torch.Tensor(batch_states).to(self.device)\n next_state = torch.Tensor(batch_next_states).to(self.device)\n action = torch.Tensor(batch_actions).to(self.device)\n reward = torch.Tensor(batch_rewards).to(self.device)\n done = torch.Tensor(batch_dones).to(self.device)\n \n # Step 2: use the Target Actor to create the action of the next\n # state (part of the TD-Target \n next_action = self.actor_target(next_state)\n \n # Step 3: Add Gaussian noise to the action for exploration \n # clip the action value in case its outside the boundaries \n noise = torch.Tensor(batch_actions).data.normal_(0, self.policy_noise).to(self.device)\n noise = noise.clamp(-self.noise_clip, self.noise_clip)\n next_action = (next_action + noise).clamp(-self.max_action, self.max_action)\n \n # Step 4: Use the differenet Target Critis (delayed update)\n # and min of two Critic from TD3 to create the different Q Targets\n # compute the average of all Q Targets to get a single value\n target_Q1, target_Q2 = self.target_critic(next_state, next_action) \n target_Q_min = torch.min(target_Q1, target_Q2)\n \n \n # Step 5: Create the update based on the bellman equation \n target_Q = reward + ((1 - done) * self.discount * target_Q).detach()\n \n # Step 6: Use the critic compute the Q estimate for current state and action\n current_Q1, current_Q2 = self.critic(state, action) \n \n # Step 7: Compute the critc loss with the mean squard error\n # loss function \n critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)\n writer.add_scalar('critic_loss', critic_loss , self.step)\n \n\n # Step 8: Backpropagate this Critic loss and update the parameters\n # of the two Critic models use the adam optimizer\n \n self.critic_optimizer.zero_grad()\n critic_loss.backward()\n self.critic_optimizer.step()\n \n # Step 9: Delayed update og Actor model\n if it % self.policy_freq == 0:\n actor_loss = -self.critic.Q1(state, self.actor(state)).mean()\n writer.add_scalar('actor_loss', actor_loss , self.step)\n self.actor_optimizer.zero_grad()\n actor_loss.backward()\n self.actor_optimizer.step()\n \n # Step 10: we update the weights of the Critic target by polyak averaging\n # hyperparameter tau determines the combination of current and\n # target weights\n for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):\n target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)\n \n for param, target_param in zip(self.critic.parameters(), self.target_critic.parameters()):\n target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)","title":""},{"docid":"ab47916e6920f04c9ab9e89213275495","score":"0.4872434","text":"def _report_model(self):\n # We will do this separately since posterior sampling complicates how we keep\n # track of the GPs. See _set_next_gp()\n pass","title":""},{"docid":"6e8c64b229d4f66479e272ea6627aab3","score":"0.48719215","text":"def act(self, state_action):\n sa = torch.from_numpy(state_action).float().unsqueeze(0).to(device)\n # self.reward_net.eval()\n # with torch.no_grad():\n # action_values = self.reward_net(state)\n # self.reward_net.train()\n return(self.reward_net(sa))","title":""},{"docid":"1dade5e9e6f599a264793da3ec712966","score":"0.4870841","text":"def _result(self):","title":""},{"docid":"be866a42cf46996d2c27a57760bc8797","score":"0.48698625","text":"def update(self, action_i, action, pred_r, opt_r):\n\n self.t += 1\n self.h_data.add(action_i, action, pred_r, opt_r)\n\n if action_i == -1:\n # No updates on fake action\n return\n\n z_action = self.bnn.sess.run(self.bnn.nn, feed_dict={self.bnn.x: self.data.actions[action_i].reshape((1, -1))})\n self.h_latent.add(action_i, z_action, pred_r, opt_r)\n\n if self.hparams.remove_actions:\n self.data.remove(action_i)\n\n # Retrain the network on the original data (data_h)\n if self.t % self.update_freq_nn == 0:\n\n if self.hparams.reset_lr:\n self.bnn.assign_lr()\n self.bnn.train(self.h_data, self.num_epochs)\n\n # Update the latent representation of every datapoint collected so far\n self.h_latent.actions = self.bnn.sess.run(self.bnn.nn, feed_dict={self.bnn.x: self.h_data.actions})\n\n # Update the Bayesian Linear Regression\n if self.t % self.update_freq_lr == 0:\n\n # Update action posterior with formulas: \\beta | z,y ~ N(mu_q, cov_q)\n z = self.h_latent.actions\n y = self.h_latent.opt_rewards\n\n # The algorithm could be improved with sequential formulas (cheaper)\n s = np.dot(z.T, z)\n\n # Some terms are removed as we assume prior mu_0 = 0.\n self.precision = s + self.lambda_prior * np.eye(self.latent_dim)\n self.cov = np.linalg.inv(self.precision)\n diagonalize = getattr(self.hparams, 'diagonalize', False)\n if diagonalize:\n if diagonalize == 'precision':\n self.precision = np.diag(np.diag(self.precision))\n self.cov = np.linalg.inv(self.precision)\n elif diagonalize == 'covariance':\n self.cov = np.diag(np.diag(self.cov))\n self.precision = np.linalg.inv(self.cov)\n else:\n raise ValueError('Wrong diagonalization parameter. Only \\'precision\\' and \\'covariance\\' supported.')\n self.mu = np.dot(self.cov, np.dot(z.T, y))\n\n # Inverse Gamma posterior update\n self.a = self.a0 + z.shape[0] / 2.0\n b_upd = 0.5 * np.dot(y.T, y)\n b_upd -= 0.5 * np.dot(self.mu.T, np.dot(self.precision, self.mu))\n self.b = self.b0 + b_upd","title":""},{"docid":"dd17991f91552ea1fc4574b6e246f122","score":"0.4866218","text":"def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):\n\n ########################################################################\n # Modify the reward to include exploration bonus\n ex2_vars = None\n old_re_n = re_n\n if self.dm_type == 'none':\n pass\n else:\n # 1. Fit density model\n if self.dm_type == 'ex2':\n ### PROBLEM 3\n ### YOUR CODE HERE\n ex2_vars = self.exploration.fit_density_model(ob_no)\n elif self.dm_type == 'hist' or self.dm_type == 'rbf':\n ### PROBLEM 1\n ### YOUR CODE HERE\n self.exploration.fit_density_model(ob_no)\n else:\n assert False\n\n # 2. Modify the reward\n ### PROBLEM 1\n ### YOUR CODE HERE\n re_n = self.exploration.modify_reward(re_n, ob_no)\n\n print('average state', np.mean(ob_no, axis=0))\n print('average action', np.mean(ac_na, axis=0))\n\n loss = OrderedDict()\n\n for critic_update in range(self.num_critic_updates_per_agent_update):\n loss['Critic_Loss'] = self.critic.update(ob_no, next_ob_no, re_n, terminal_n)\n\n adv_n = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n) # put final critic loss here\n\n for actor_update in range(self.num_actor_updates_per_agent_update):\n loss['Actor_Loss'] = self.actor.update(ob_no, ac_na, adv_n) # put final actor loss here\n\n return loss, ex2_vars","title":""},{"docid":"e317d0d9bc66bd8d88763465668ba007","score":"0.48601902","text":"def predict_adv_action(self):\n temp_obs = self.data.obs\n with torch.no_grad():\n self.data.obs = np.expand_dims(self.data.obs, axis=0)\n result = self.adv_policy(self.data, last_state=None)\n self.data.obs = temp_obs\n self.data.adv_act = to_numpy(result.act)\n return result","title":""},{"docid":"9fc83de48b2474a692af0244bc6881b2","score":"0.4856799","text":"def learn(self):\n num = len(self.buffer.memory) # Check the length of replay buffer\n \n states, actions, rewards, next_states, dones = self.buffer.sample(BATCH_SIZE)\n \n # Get max predicted Q-values (for next_states) from target model\n Q_targets_next = self.qnetwork_target(next_states.to(device)).detach().max(1)[0].unsqueeze(1)\n \n # Compute Qtargets for current states\n Q_targets = rewards.to(device) + (GAMMA*Q_targets_next * (1-dones.to(device)))\n \n # Get expected Q-values from local model\n Q_expected = self.qnetwork_local(states.to(device)).gather(1, actions.to(device))\n \n # learn\n loss = F.smooth_l1_loss(Q_expected.squeeze(),Q_targets.squeeze())\n #loss = F.mse_loss(Q_expected, Q_targets)\n \n # optimizer\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n \n # ------------------- update target network ------------------- #\n #self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) \n \n self.step_counter += 1\n if (self.step_counter > self.update_target_step):\n self.hard_update(self.qnetwork_local, self.qnetwork_target)\n self.step_counter = 0","title":""},{"docid":"a1fa0dda5ca3f1b6e7e881350389c6a7","score":"0.48529676","text":"def __init__(self):\n self.feedback = \"\"","title":""},{"docid":"f061b7fa4583d05d2bc94a2a401d5a3b","score":"0.48527768","text":"def edit_results(self):\n session = b_scorer.today_session\n self.results = ResultsInput(self, session)","title":""},{"docid":"1b15122fb91a8af24ca49c3b56c95530","score":"0.48448327","text":"def result(self, state, action):\n raise NotImplementedError()","title":""},{"docid":"59aa21db954143a583a42b25458512a3","score":"0.48413637","text":"def learn(self,action,ob,ob_):\n self.check_ifstate_exist(ob_)\n q_predict= self.q_table.ix[ob,action]\n #no need to worry about terminal\n # print 'check point ++++++++++++++++++++++++++++++++++++++++++++'\n # print self.total_reward\n # print(self.cur_state)\n # print self.next_state\n # print self.q_table.ix[self.next_state,:].max()\n #self.q_table.info()\n q_target= self.reward+self.gamma*self.q_table.ix[ob_,:].max()\n self.q_table.ix[ob,action] +=self.lr*(q_target-q_predict)","title":""},{"docid":"993ca53b7bf6a7ed63e2b89420db5828","score":"0.48365423","text":"def callbacktf(self,TensorOutput_msg):\n\n\t\tself.label = []\n\t\tself.label = TensorOutput_msg.result\n\t\tself.Xtensor= TensorOutput_msg.x\n\t\tself.Ytensor = TensorOutput_msg.y","title":""},{"docid":"dac0ddef16b109c410cb6d1b361cdf0a","score":"0.4836206","text":"def _process_data(self):\n msg = self.recv_msg().decode()\n #print(\"----------------------------\")\n #print(msg)\n #print(\"=>\")\n msg = msg.replace('-inf', '0')\n msg = msg.replace('inf', '0')\n #print(msg)\n data = eval(msg)\n r = data['reward']\n done = data['done']\n del data['reward']\n del data['done']\n\n state = []\n\n if self._use_gsi:\n for feature in self._general_state_features:\n state.append(data[feature])\n for heuristic_id in range(self.__num_heuristics): # process heuristic data\n for feature in self._heuristic_state_features:\n state.append(data[\"%d\" % heuristic_id][feature])\n\n if self._prev_state is None:\n self.__norm_vals = deepcopy(state)\n self._prev_state = deepcopy(state)\n if self.__state_type != StateType.RAW: # Transform state to DIFF state or normalize\n tmp_state = state\n state = list(map(self._transformation_func, state, self._prev_state, self.__norm_vals,\n self.__skip_transform))\n self._prev_state = tmp_state\n return np.array(state), r, done","title":""},{"docid":"c4ed6dd582dd13618a0e5210fef8c59a","score":"0.48331305","text":"def evaluate_model(self, agent, index, test_mode, print_result = 0):\n self.dialogue_manager.set_agent(agent=agent)\n all_goal_set = self.goal_set\n save_performance = self.parameter.get(\"save_performance\")\n train_mode = self.parameter.get(\"train_mode\")\n success_count = 0\n absolute_success_count = 0\n total_reward = 0\n total_truns = 0\n recall_score = 0\n \n if test_mode == 2:\n goal_set = all_goal_set[\"dev\"]\n evaluate_epoch_number = len(goal_set)\n else:\n goal_set = self.goal_test_set['test']\n evaluate_epoch_number = len(goal_set)\n \n \n inform_wrong_disease_count = 0\n if print_result:\n goal_slots = self.json_bulid(goal_set)\n \n \n for epoch_index in range(0, evaluate_epoch_number, 1):\n self.dialogue_manager.initialize(train_mode=test_mode, epoch_index=epoch_index)\n episode_over = False\n ids = goal_set[epoch_index]['consult_id']\n \n if ids == '10123471':\n print(1)\n \n while episode_over == False:\n reward, episode_over, dialogue_status, agent_action = self.dialogue_manager.next(save_record=False,train_mode=train_mode,greedy_strategy=0)\n total_reward += reward\n \n if print_result and agent_action['inform_slots']: \n goal_slots[ids]['Disease'] = list(agent_action['inform_slots'].values())[0]\n \n total_truns += self.dialogue_manager.state_tracker.turn\n inform_wrong_disease_count += self.dialogue_manager.inform_wrong_disease_count\n \n\n ############### f1 score calculation ###########################\n final_state = self.dialogue_manager.state_tracker.state['current_slots']['inform_slots']\n all_state = set(list(self.dialogue_manager.state_tracker.user.goal['goal']['explicit_inform_slots'].keys()) + list(self.dialogue_manager.state_tracker.user.goal['goal']['implicit_inform_slots'].keys())) \n if print_result:\n goal_slots[ids]['Symptoms'] = list(final_state.keys())\n \n correct_inform = 0\n for key, value in final_state.items():\n if value != \"I don't know.\":\n correct_inform += 1\n recall_score += correct_inform / len(all_state)\n #print(2 * correct_inform / (len(all_state) + len(final_state)))\n if dialogue_status == dialogue_configuration.DIALOGUE_STATUS_SUCCESS:\n success_count += 1\n if self.dialogue_manager.inform_wrong_disease_count == 0:\n absolute_success_count += 1\n ########################### print .json file ########################\n if print_result:\n with open('./../../../Evaluation/result.json','w') as file_obj:\n json.dump(goal_slots, file_obj)\n \n average_recall = float(\"%.3f\" % (float(recall_score) / evaluate_epoch_number))\n success_rate = float(\"%.3f\" % (float(success_count) / evaluate_epoch_number))\n absolute_success_rate = float(\"%.3f\" % (float(absolute_success_count) / evaluate_epoch_number))\n average_reward = float(\"%.3f\" % (float(total_reward) / evaluate_epoch_number))\n average_turn = float(\"%.3f\" % (float(total_truns) / evaluate_epoch_number))\n average_wrong_disease = float(\"%.3f\" % (float(inform_wrong_disease_count) / evaluate_epoch_number))\n res = {\"success_rate\":success_rate, \"average_reward\": average_reward, \"average_turn\": average_turn, \"average_wrong_disease\":average_wrong_disease,\"ab_success_rate\":absolute_success_rate, 'average_recall': average_recall}\n self.learning_curve.setdefault(index, dict())\n self.learning_curve[index][\"success_rate\"]=success_rate\n self.learning_curve[index][\"average_reward\"]=average_reward\n self.learning_curve[index][\"average_turn\"] = average_turn\n self.learning_curve[index][\"average_wrong_disease\"]=average_wrong_disease\n print(\"%3d simulation SR %s, ABSR %s, ave reward %s, ave turns %s, ave wrong disease %s, ave recall %s\" % (index,res['success_rate'], res[\"ab_success_rate\"],res['average_reward'], res['average_turn'], res[\"average_wrong_disease\"], res[\"average_recall\"]))\n return res","title":""},{"docid":"cefa7079bf4f5a2759137864a1cea73d","score":"0.4832951","text":"def on_result_recieved(self, *args):\n print '{0} received streaming data.'.format(self.name)\n self.vis.put(dict(id=self.id, type='mes', rec=args[0]))\n self.analyze.put(dict(id=self.id, rec=args[0]))","title":""},{"docid":"f0e3e26161c5bad33ca844617cdbe677","score":"0.48291898","text":"def result(self, state, action):\n pass","title":""},{"docid":"f0e3e26161c5bad33ca844617cdbe677","score":"0.48291898","text":"def result(self, state, action):\n pass","title":""},{"docid":"c68e11cc8742234d4a27dd15575959d7","score":"0.48272514","text":"def step(self):\n # Time counting updates\n if 'ID' in self.unprocessedReqs.keys():\n if not self.unprocessedReqs['inElections']:\n self.unprocessedReqs['delayT'] += 1\n else:\n self.unprocessedReqs['msgWait'] += 1\n\n # Reading and interpreting messages in the message buffer\n currentMessage = None\n if len(self.messageBuffer) > 0:\n # In this case there are messages to be interpreted\n currentMessage = self.messageBuffer.pop()\n\n if currentMessage:\n if self.log:\n print(\"Received message: {}\".format(currentMessage.nType.name))\n # print \"\\t %s\" % str(currentMessage.msgDict)\n\n # Case 1: Service request from client\n if currentMessage['type'] == MsgTypes.req.value:\n\n if self.trainMode != TrainModes.outOfOrder: # Checks if train can accept\n if not ('ID' in self.unprocessedReqs.keys()): # Checks if there are current processes ongoing\n\n if self.log:\n print(\"Processing Client Request\")\n\n clientID = currentMessage['sender']\n route, d = None, None\n # Calculate route\n if self.trainMode == TrainModes.wait:\n route, d = self.calculate_route( self.pos, currentMessage['pickUp'] )\n elif (self.trainMode == TrainModes.accept) or (self.trainMode == TrainModes.busy):\n route, d = self.calculate_route( self.path[-1], currentMessage['pickUp'] )\n self.unprocessedReqs = dict(ID=clientID, pickup=currentMessage['pickUp'],\n dropoff=currentMessage['dropOff'], delayT=0,\n inElections=False, simpleD=d, route=route, msgWait=0)\n # Train Responde Request!\n\n # Case 2: Election started\n elif currentMessage['type'] == MsgTypes.elec.value:\n\n if not self.trainMode == TrainModes.outOfOrder: # Checks if train can accept\n # if not self.outOfElec == currentMessage['clientID']: # Check if has already 'lost' election\n\n if 'ID' in self.unprocessedReqs.keys():\n if self.unprocessedReqs['ID'] == currentMessage['clientID']:\n # NOTE: I assume any car receives first the notice from the client\n if self.log:\n print(\"Received Election Message\")\n\n dTot = self.unprocessedReqs['simpleD'] + self.full_distance()\n\n if dTot < currentMessage['distance']:\n # This train is the leader himself\n self.silence_train(currentMessage['sender'],self.unprocessedReqs['ID'])\n if not self.unprocessedReqs['inElections']:\n # If It hasn't yet send its distance, should do so now\n self.start_election(dTot,self.unprocessedReqs['ID'])\n self.unprocessedReqs['inElections'] = True\n self.unprocessedReqs['msgWait'] = 0\n if self.log:\n print(\"\\t Win this elections round\")\n else:\n # Finishes current election process\n self.outOfElec = self.unprocessedReqs['ID']\n self.unprocessedReqs = {}\n if self.log:\n print(\"\\t Lost these elections\")\n # Case 3: Election answer\n elif currentMessage['type'] == MsgTypes.elec_ack.value:\n if \"ID\" in self.unprocessedReqs.keys():\n if self.unprocessedReqs['ID'] == currentMessage['clientID']: # Checks if this message is from current election\n # No need to check if message is destined to itself, because the receiving mechanism already does so.\n # Train lost current election. Finishes election process\n self.outOfElec = self.unprocessedReqs['ID']\n self.unprocessedReqs = {}\n\n if self.log:\n print(\"Silenced in these elections. Lost election.\")\n \n # Case 4: Leader Message\n # Conferir ! - Fiz modificações\n elif currentMessage['type'] == MsgTypes.leader:\n if \"ID\" in self.unprocessedReqs.keys():\n if self.unprocessedReqs['ID'] == currentMessage['clientID']: # Checks if this message is from current election\n self.outOfElec = self.unprocessedReqs['ID']\n self.unprocessedReqs = {}\n if self.log:\n print(\"Got an election Lider in these elections. Lost election.\")\n else:\n pass\n # Nothing to Do\n # ------------------------------------------\n # Election start\n if 'ID' in self.unprocessedReqs.keys():\n if not self.unprocessedReqs['inElections']:\n if self.unprocessedReqs['delayT'] == self.delayWanted:\n # Will start election\n if self.log:\n print(\"Starting Election!\")\n\n self.unprocessedReqs['inElections'] = True\n d = self.unprocessedReqs['simpleD'] + self.full_distance() # Needs to add the distance until the\n # final position in path\n self.start_election(d,self.unprocessedReqs['ID'])\n self.unprocessedReqs['msgWait'] = 0\n # ------------------------------------------\n # Elections finish\n else:\n if self.unprocessedReqs['msgWait'] == self.maximumMsgWait:\n # If no answer is given, election isn't silenced and I am current leader\n # self.broadcast_leader(self.id) # Inform others who's answering the request\n\n if self.log:\n print(\"Finishing election! I've won! (ID {})\".format(self.id))\n\n self.path += self.unprocessedReqs['route'] # Adds route to desired path\n # TODO: Think on pickup and dropoff. Might be strings instead of actual coordinates...\n # In this case I'd need to convert into coordinates\n self.client += [(self.unprocessedReqs['ID'], self.unprocessedReqs['pickup'], self.unprocessedReqs['dropoff'])]\n self.client_accept(self.unprocessedReqs['ID'])\n self.unprocessedReqs = {} # Finishes current election process\n\n if self.trainMode == TrainModes.wait:\n self.trainMode = TrainModes.accept\n self.currentGoal = self.client[0][1] # pickup\n # ------------------------------------------\n\n # Moving train and handling new position\n self.move()\n\n if self.pos == self.currentGoal: # Reached current destination\n if self.trainMode == TrainModes.accept:\n # Client boarding train\n self.trainMode = TrainModes.busy\n self.currentGoal = self.client[0][2] # dropoff\n\n elif self.trainMode == TrainModes.busy:\n # Client leaving the train\n self.client.pop() # taking out client from list\n if len(self.client) > 0:\n self.trainMode = TrainModes.accept\n self.currentGoal = self.client[0][1] # pickUp\n else:\n self.currentGoal = None\n self.trainMode = TrainModes.wait\n\n elif self.trainMode == TrainModes.outOfOrder:\n self.kill()\n # ------------------------------------------\n\n # Print new train on simulation screen\n self.draw()","title":""},{"docid":"4148f9b4d95786ab5f54f9ef02a5c812","score":"0.48264095","text":"def post_regression():\n query = request_query()\n submission = request_submission()\n\n # Pass data into function after vetting\n if not isinstance(submission, tuple):\n new_regression = create_regression(\n query['user_id'], \n query['source'], \n submission\n )\n\n # Return 201 on success\n if not isinstance(new_regression, tuple):\n return new_regression, 201\n \n # Return error code if could not create\n else:\n return new_regression\n \n # Return error code if problem with submission\n else:\n return submission","title":""},{"docid":"8cbd0576f4a3bb51d565124d310243a7","score":"0.48231646","text":"def getResult(self, **kwargs):\n \n pass","title":""},{"docid":"c81ded0bc40fde466c897388c4b22af1","score":"0.4817456","text":"def data_available(self, timestamp, data):\n try:\n # clean\n data = data.replace('\\r\\n', ',').replace('True', '1').replace('False', '0')\n data = np.array(data.split(',')).astype(np.float)\n\n # split into multiple rows\n data = data.reshape(-1, self.num_columns)\n\n # append to samples buffer\n if self.samples.shape[0] == 0:\n self.samples = data\n else:\n self.samples = np.vstack((self.samples, data))\n\n # once the minimum batch size is reached, queue a task\n # to update the model using a snapshot of the collected samples\n if self.samples.shape[0] >= self.min_batch_size:\n print(f'Queuing task to update model using {self.samples.shape} samples')\n\n dask_queue.put(dask_client.submit(\n SerialIoUpdateModel.update_model,\n self.timesteps,\n self.preprocessors_and_data,\n np.copy(self.samples)))\n\n self.samples = np.array([])\n\n except Exception as e:\n print(e)","title":""},{"docid":"c19a911442b83c0c4e000c65c04cc75b","score":"0.481654","text":"def process(self, sess):\n\n sess.run(self.sync) # copy weights from shared to local\n rollout = self.pull_batch_from_queue()\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\n\n should_compute_summary = self.task == 0 and self.local_steps % 11 == 0\n\n if should_compute_summary:\n fetches = [self.summary_op, self.train_op, self.global_step]\n else:\n fetches = [self.train_op, self.global_step]\n\n feed_dict = {\n self.local_network.x: batch.si,\n self.ac: batch.a,\n self.adv: batch.adv,\n self.r: batch.r,\n self.local_network.state_in[0]: batch.features[0],\n self.local_network.state_in[1]: batch.features[1],\n }\n\n fetched = sess.run(fetches, feed_dict=feed_dict)\n\n if should_compute_summary:\n self.summary_writer.add_summary(tf.Summary.FromString(fetched[0]), fetched[-1])\n self.summary_writer.flush()\n self.local_steps += 1","title":""},{"docid":"4fc94d452f0229756a7898d34afc3932","score":"0.4811831","text":"def add_supervised_result(tx):\n with open(\"supervised_result.pkl\",\"rb\") as f:\n result_dict = pickle.load(f)\n for account,score in result_dict.items():\n tx.run(\"MATCH (acc:Account) where acc.ACCT_ID = '{}' set acc.supervised_socre = {} return acc;\".format(account,score))\n\n\n with open(\"predicted_label.pkl\",\"rb\") as f:\n label_dict = pickle.load(f)\n for account,label in label_dict.items():\n tx.run(\"MATCH (acc:Account) where acc.ACCT_ID = '{}' set acc.supervised_label = {} return acc;\".format(account,label))","title":""},{"docid":"3af0117f71482b6508c8f4fa816fbdfe","score":"0.48117074","text":"def _done_communicating(self, dmlresult_obj):\n\t\tif self.num_averages_per_round:\n\t\t\treturn ActionableEventTypes.NOTHING.name, None\n\t\t# else continue training, don't need to wait to hear new weights\n\t\ttrain_job = DMLTrainJob(\n\t\t\tdatapoint_count=self.job_data[\"datapoint_count\"],\n\t\t\thyperparams=self.job_data[\"hyperparams\"],\n\t\t\tlabel_column_name=self.job_data[\"label_column_name\"],\n\t\t\tframework_type=self.job_data[\"framework_type\"],\n\t\t\tserialized_model=self.job_data[\"serialized_model\"],\n\t\t\tweights=self.job_data[\"weights\"],\n\t\t\tsession_filepath=self.job_data[\"session_filepath\"]\n\t\t)\n\t\treturn ActionableEventTypes.SCHEDULE_JOBS.name, [train_job]","title":""},{"docid":"7a316413144b995bc5bbed395de72cbf","score":"0.48105597","text":"def GetBuffer(self):","title":""},{"docid":"7a316413144b995bc5bbed395de72cbf","score":"0.48105597","text":"def GetBuffer(self):","title":""},{"docid":"66300ee8864f06f4a76d0647951b2866","score":"0.4809305","text":"def computeResult(self):\n pass","title":""},{"docid":"43981a19e6ada136114ea1021b70b6fd","score":"0.48045123","text":"def robot_result_handler(self):\n # Wait a result to complete\n for sample in subscribe_to_study():\n self.logger.info(f'Received new sample: {sample.ID}')\n\n # Make a choice for each variable\n output = self.opt_spec.search_template.create_new_sample()\n for key, acceptable_values in self.opt_spec.search_template.list_acceptable_input_values().items():\n output.inputs[key] = random.choice(acceptable_values)\n\n # Send it to the robot\n send_new_sample(output)","title":""}],"string":"[\n {\n \"docid\": \"9909e25386eb757d88738d6797469601\",\n \"score\": \"0.60022223\",\n \"text\": \"def estimate(self):\\r\\n self.store()\\r\\n # Validate Data Request\\r\\n # Send to queue\\r\\n # Return scheudle\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3e70c19d7a64997aacfe7ce35ea546\",\n \"score\": \"0.58360523\",\n \"text\": \"def feedback(self, feedback):\\n\\n self._feedback = feedback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4cf656b06689dde31feaec638029b79\",\n \"score\": \"0.56802255\",\n \"text\": \"def response(self, action):\\n if self.config.args.task_mode == 'train':\\n dataset = self.train_ctrl_dataset\\n elif self.config.args.task_mode == 'test':\\n dataset = self.train_task_dataset\\n elif self.config.args.task_mode == 'baseline':\\n dataset = self.train_task_dataset\\n else:\\n logger.exception('Unexcepted mode: {}'.\\\\\\n format(self.config.args.task_mode))\\n data = dataset.next_batch(self.config.batch_size)\\n\\n sess = self.sess\\n x = data['input']\\n y = data['target']\\n feed_dict = {self.x_plh: x, self.y_plh: y}\\n\\n a = np.argmax(np.array(action))\\n sess.run(self.update[a], feed_dict=feed_dict)\\n\\n fetch = [self.loss_ce, self.loss_l1, self.accuracy, self.ce_grad, self.l1_grad]\\n loss_ce, loss_l1, acc, ce_grad, l1_grad = sess.run(fetch, feed_dict=feed_dict)\\n valid_loss, valid_acc, _, _ = self.valid()\\n train_loss, train_acc = loss_ce, acc\\n\\n # ----Update state.----\\n self.previous_ce_loss = self.previous_ce_loss[1:] + [loss_ce.tolist()]\\n self.previous_l1_loss = self.previous_l1_loss[1:] + [loss_l1.tolist()]\\n self.step_number[0] += 1\\n self.previous_valid_loss = self.previous_valid_loss[1:]\\\\\\n + [valid_loss.tolist()]\\n self.previous_train_loss = self.previous_train_loss[1:]\\\\\\n + [train_loss.tolist()]\\n self.previous_valid_acc = self.previous_valid_acc[1:]\\\\\\n + [valid_acc.tolist()]\\n self.previous_train_acc = self.previous_train_acc[1:]\\\\\\n + [train_acc.tolist()]\\n self.mag_ce_grad = self.get_grads_magnitude(ce_grad)\\n self.mag_l1_grad = self.get_grads_magnitude(l1_grad)\\n\\n # Public variable\\n self.extra_info = {'valid_loss': valid_loss,\\n 'train_loss': train_loss,\\n 'valid_acc': valid_acc,\\n 'train_acc': train_acc}\\n\\n reward = self.get_step_reward()\\n # ----Early stop and record best result.----\\n dead = self.check_terminate()\\n state = self.get_state()\\n return state, reward, dead\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf0e2d09900e41ca698c44d287dfe61\",\n \"score\": \"0.5676246\",\n \"text\": \"def feedback(data: FeedbackData) -> str:\\n return model.feedback(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c8f06da51d075e2d4ea83f4bdf26bbd\",\n \"score\": \"0.55030507\",\n \"text\": \"def get_feedback(self):\\n return self.feedback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b9967682629c38d556e3c8448b9e29\",\n \"score\": \"0.54418194\",\n \"text\": \"def lookup_feedback(self, feedback):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891e8ec733bf9c260e28b6ea536a9050\",\n \"score\": \"0.5400174\",\n \"text\": \"def feedback_train(self, X, y, feedback, dropout=0.5):\\n # Combine the input dictionaries for all the features models\\n feed_dict = {}\\n feed_dict['phase:0'] = 1\\n feed_dict['X:0'] = X\\n feed_dict['y:0'] = y\\n feed_dict['dropout:0'] = dropout\\n # Sample the attention vector from the Bernoulli distribution\\n attention = self.sess.run(tf.reshape(self.distribution.sample(self.num_samples), [-1, self.num_features]), feed_dict=feed_dict)\\n feed_dict['a:0'] = attention\\n feed_dict['feedback:0'] = feedback\\n # Run the training step and write the results to Tensorboard\\n summary, _ = self.sess.run([self.merged, self.feedback_train_step],\\n feed_dict=feed_dict)\\n self.train_writer.add_summary(summary, global_step=self.sess.run(self.global_step))\\n # Run image summary and write the results to Tensorboard\\n summary = self.sess.run(self.merged_images,\\n feed_dict={self.X: X[0:10], self.feedback: feedback[0:10], self.attention: attention[0:10], self.phase: 1})\\n self.train_writer.add_summary(summary, global_step=self.sess.run(self.global_step))\\n # Regularly save the models parameters\\n if self.sess.run(self.global_step) % 1000 == 0:\\n self.saver.save(self.sess, self.directory + '/model.ckpt')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c67ff60391b0ab95333e1486eb508b\",\n \"score\": \"0.53869754\",\n \"text\": \"def getFeedbackNode(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c58e71d6a0f496d53dc4db9f43a218\",\n \"score\": \"0.53808475\",\n \"text\": \"def replay_buffer(self):\\n ###########################\\n # YOUR IMPLEMENTATION HERE #\\n training_states = self.buffer.replay_buffer_states\\n training_actions = self.buffer.replay_buffer_actions\\n training_rewards = self.buffer.replay_buffer_rewards\\n training_next_states = self.buffer.replay_buffer_next_states\\n training_dones = self.buffer.replay_buffer_dones\\n return training_states, training_actions, training_rewards, training_next_states, training_dones\\n \\n \\n ###########################\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01952604d31003b793f86c4b205cf5e\",\n \"score\": \"0.53526837\",\n \"text\": \"def send_learn(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6305ce089a4d38453e3ac13287e61e4d\",\n \"score\": \"0.5303253\",\n \"text\": \"def update(self, feedback_value):\\r\\n error = self.SetPoint - feedback_value\\r\\n\\r\\n self.current_time = time.time()\\r\\n delta_time = self.current_time - self.last_time\\r\\n delta_error = error - self.last_error\\r\\n\\r\\n if (delta_time >= self.sample_time):\\r\\n self.PTerm = self.Kp * error\\r\\n self.ITerm += error * delta_time\\r\\n\\r\\n if (self.ITerm < -self.windup_guard):\\r\\n self.ITerm = -self.windup_guard\\r\\n elif (self.ITerm > self.windup_guard):\\r\\n self.ITerm = self.windup_guard\\r\\n\\r\\n self.DTerm = 0.0\\r\\n if delta_time > 0:\\r\\n self.DTerm = delta_error / delta_time\\r\\n\\r\\n # Remember last time and last error for next calculation\\r\\n self.last_time = self.current_time\\r\\n self.last_error = error\\r\\n\\r\\n self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)\\r\\n \\r\\n return self.output;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9143ab85a6971cddb59f5e00444eda96\",\n \"score\": \"0.5287941\",\n \"text\": \"def train(self, results: Results):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98120d3ab6aa71e06b0497c082461584\",\n \"score\": \"0.52597606\",\n \"text\": \"def init_feedback_node(self):\\n \\n # Validation!\\n if not self.request.params.get('pling_id'):\\n raise HTTPBadRequest(detail='Please supply a pling_id')\\n pling_id = self.request.params.get('pling_id')\\n pling = URIRef('http://plings.net/a/%s' % (pling_id))\\n \\n # Validation!\\n submitted = self.request.params.get('submission_date', None)\\n if submitted:\\n try:\\n submitted = datetime.datetime.strptime(submitted, '%Y-%m-%dT%H:%M:%S')\\n except ValueError:\\n raise HTTPBadRequest(detail='\\\"submission_date\\\" should be supplied in \\\"%Y-%m-%dT%H:%M:%S\\\" (iso-8601) format')\\n \\n \\n plingback_type = self.request.params.get('plingback_type',\\n 'unknown' )\\n plingback_type_uri = URIRef(\\\"http://plingback.plings.net/applications/\\\" \\n + urllib.quote_plus(plingback_type.strip()))\\n \\n self.context.store.add((self.feedback_uri, ns['RDF']['type'], ns['REV']['Review']))\\n self.context.store.add((pling, ns['REV']['hasReview'], self.feedback_uri))\\n self.context.store.add((pling, ns['RDF']['type'], ns['PBO']['Activity']))\\n self.context.store.add((self.feedback_uri, ns['PBO']['isAbout'], pling))\\n \\n if submitted:\\n self.context.store.add((self.feedback_uri, ns['DC']['date'], Literal(submitted)))\\n else:\\n self.context.store.add((self.feedback_uri, ns['DC']['date'], Literal(datetime.datetime.now())))\\n \\n self.context.store.add((self.feedback_uri, ns['PBO']['plingBackType'], plingback_type_uri))\\n if self.request.params.get('plingback_version'):\\n self.context.store.add((self.feedback_uri, \\n ns['PBO']['plingBackVersion'], \\n Literal(self.request.params.get('plingback_version'))))\\n \\n return 'OK'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c317515a39eaa96c44213cd8a8b0db71\",\n \"score\": \"0.5235318\",\n \"text\": \"def recordResult(self, evt, op, res):\\n outcome = smtk.model.OperatorOutcome(\\n res.findInt('outcome').value(0))\\n #print 'Record Result of {nm} as {stat}'.format(nm=op.name(), outcome)\\n if len(self.active) and self.active[-1][0] == op.name():\\n self.history.append(self.active[-1] + [outcome,])\\n self.active = self.active[:-1]\\n else:\\n self.history.append(\\n ['***ERROR*** {nm}'.format(op.name()), outcome])\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c9a387ffe2a57b561a3e9b3f88c0e0e\",\n \"score\": \"0.5205191\",\n \"text\": \"def _store_results_of_this_batch(self):\\n xp = self.xp\\n # get losses and predicted labels\\n l2sq = self.l2_loss().data # l2 squared loss\\n probs = self.model.predict_proba(self.batch_adv).data\\n labs = xp.argmax(probs, axis=1).astype(xp.int32)\\n probs = xp.max(probs, axis=1) \\n \\n # check if succeeded ? \\n is_success = (labs == self.batch_targ) # is the model fooled ?\\n # gather succeeded images\\n self.batch_success_adv[is_success] = self.batch_adv.data[is_success].copy()\\n self.batch_success_labs[is_success] = labs[is_success].copy()\\n self.batch_success_probs[is_success] = probs[is_success].copy()\\n self.batch_success_l2sq[is_success] = l2sq[is_success].copy()\\n # retain results \\n self.adv_images = np.append(self.adv_images, cuda.to_cpu(self.batch_success_adv), axis=0)\\n self.adv_labels = np.append(self.adv_labels, cuda.to_cpu(self.batch_success_labs), axis=0)\\n self.adv_probs = np.append(self.adv_probs, cuda.to_cpu(self.batch_success_probs), axis=0)\\n self.adv_l2_squared = np.append(self.adv_l2_squared, cuda.to_cpu(self.batch_success_l2sq), axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063fa5935e87f6fe6c8cc4cd6ffe830d\",\n \"score\": \"0.519186\",\n \"text\": \"def get_data(self, filtered_batch):\\n batched_rate_output, _, states_t = vmap(self.rate_layer._evolve_functional, in_axes=(None, None, 0))(self.lr_params, self.lr_state, filtered_batch)\\n batched_res_inputs = states_t[\\\"res_inputs\\\"]\\n batched_res_acts = states_t[\\\"res_acts\\\"]\\n return batched_rate_output, batched_res_inputs, batched_res_acts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6da465cf5177ca68e3497bc7eaf61b8\",\n \"score\": \"0.5173925\",\n \"text\": \"def eval(self, sample):\\n print 'here our rr cost'\\n T = sample.T\\n dU = sample.dU\\n dX = sample.dX\\n\\n sample_u = sample.get_U()\\n sample_x = sample.get_X()\\n\\n wl2 = 0.3 # weight of state l2 cost\\n wl1 = 0.0 # weight of state l1 cost\\n wlog = 0.0 # weight of state log cost\\n wu = 1.0 # weight of action cost\\n wt = (np.arange(T)/float(T)) ** 2 # time dependent weight\\n # final_weight_multiplier = 9\\n # wt[-1] *= final_weight_multiplier\\n alpha = 1e-5 # offset for log cost\\n\\n tgtM = np.tile(self.tgt, (T, 1))\\n d = sample_x - tgtM\\n dnorm = np.sum(d ** 2, axis=1)\\n denominator = alpha + wt ** 2 * dnorm\\n\\n # TODO: for log, wt --> wt^2\\n ll2 = 0.5 * wl2 * wt * dnorm\\n ll1 = wl1 * wt * np.sqrt(dnorm)\\n llog = 0.5 * wlog * np.log(denominator)\\n lu = 0.5 * wu * np.sum(sample_u ** 2, axis=1)\\n l = ll2 + ll1 + llog + lu\\n print l\\n\\n data = {}\\n costLogger.info(\\\"l2 cost\\\")\\n costLogger.info(ll2)\\n data['l2'] = ll2\\n costLogger.info(\\\"l1 cost\\\")\\n costLogger.info(ll1)\\n data['l1'] = ll1\\n costLogger.info(\\\"log cost\\\")\\n costLogger.info(llog)\\n data['log'] = llog\\n costLogger.info(\\\"lu cost\\\")\\n costLogger.info(lu)\\n data['lu'] = lu\\n costLogger.info(\\\"total cost\\\")\\n costLogger.info(l)\\n data['total'] = l\\n\\n lx = wl2 * wt.reshape((T, 1)) * d + \\\\\\n wl1 * (wt / (np.sqrt(dnorm))).reshape((T, 1)) * d + \\\\\\n wlog * (wt ** 2 / denominator).reshape((T, 1)) * d\\n\\n costLogger.info(\\\"lx\\\")\\n costLogger.info(lx)\\n\\n lu = wu * sample_u\\n lxx = np.zeros((T, dX, dX))\\n for t in range(T):\\n lxx[t] = wl1 * np.diag(wt[t]*(1/np.sqrt(dnorm[t])-dnorm[t]**(-1.5)*d[t]**2)) + \\\\\\n wlog * np.diag(wt[t]**2/denominator[t]-2*wt[t]**4*d[t]**2/denominator[t]**2)\\n lxx += wl2 * np.tile(np.eye(dX), [T, 1, 1]) * wt.reshape((T, 1, 1))\\n luu = wu * np.tile(np.eye(dU), [T, 1, 1])\\n lux = np.zeros((T, dU, dX))\\n\\n return l, lx, lu, lxx, luu, lux, data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b0ae2d52cf4e6940b4195e290865f2e\",\n \"score\": \"0.5126339\",\n \"text\": \"def update(self, feedback_value):\\r\\n error = self.SetPoint - feedback_value\\r\\n\\r\\n self.current_time = time.time()\\r\\n delta_time = self.current_time - self.last_time\\r\\n delta_error = error - self.last_error\\r\\n\\r\\n if (delta_time >= self.sample_time):\\r\\n self.PTerm = self.Kp * error\\r\\n self.ITerm += error * delta_time\\r\\n\\r\\n if (self.ITerm < -self.windup_guard):\\r\\n self.ITerm = -self.windup_guard\\r\\n elif (self.ITerm > self.windup_guard):\\r\\n self.ITerm = self.windup_guard\\r\\n\\r\\n self.DTerm = 0.0\\r\\n if delta_time > 0:\\r\\n self.DTerm = delta_error / delta_time\\r\\n\\r\\n # Remember last time and last error for next calculation\\r\\n self.last_time = self.current_time\\r\\n self.last_error = error\\r\\n\\r\\n self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4726b20d15674547af7d3459f64f3c\",\n \"score\": \"0.51262957\",\n \"text\": \"def forward(self, belief_state, request_state):\\n\\n if self.recommend_flag != -1:\\n self.recommend_flag += 1\\n\\n self.kb_result = {}\\n DA = {}\\n user_action = self.check_diff(belief_state, request_state)\\n\\n self.last_request_state = deepcopy(request_state)\\n self.last_belief_state = deepcopy(belief_state)\\n\\n for user_act in user_action:\\n domain, _ = user_act.split('-')\\n\\n # Respond to general greetings\\n if domain == 'general':\\n self._update_greeting(user_act, DA)\\n\\n # Book taxi for user\\n elif domain == 'Taxi':\\n self._book_taxi(belief_state, DA)\\n\\n elif domain == 'Booking':\\n pass\\n\\n # User's talking about other domain\\n elif domain != \\\"Train\\\":\\n self._update_DA(user_act, user_action, belief_state, DA)\\n\\n # Info about train\\n else:\\n self._update_train(user_act, user_action, belief_state, DA)\\n\\n # Judge if user want to book\\n self._judge_booking(user_act, user_action, DA)\\n\\n if 'Booking-Book' in DA:\\n if random.random() < 0.5:\\n DA['general-reqmore'] = []\\n user_acts = []\\n for user_act_ in DA:\\n if user_act_ != 'Booking-Book':\\n user_acts.append(user_act_)\\n for user_act_ in user_acts:\\n del DA[user_act_]\\n\\n if DA == {}:\\n DA = {'general-greet': [['none', 'none']]}\\n system_acts = []\\n for domain_intent, svs in DA.items():\\n domain, intent = domain_intent.split('-')\\n if not svs and domain == 'general':\\n system_acts.append([intent, domain, 'none', 'none'])\\n else:\\n for slot, value in svs:\\n system_acts.append([intent, domain, slot, value])\\n\\n logging.debug(\\\"DPM output: %s\\\", system_acts)\\n logging.debug(\\\"Belief State after DPM: %s\\\", belief_state)\\n logging.debug(\\\"Request State after DPM: %s\\\", request_state)\\n return belief_state, system_acts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e030def18bbe2ab46c1313011de8061\",\n \"score\": \"0.51215184\",\n \"text\": \"def receive_feedbacks(self, session, feedbacks):\\n print \\\"propagation started...\\\"\\n for doc_fb in feedbacks.get(\\\"docs\\\", []):\\n doc_id, fb = doc_fb\\n doc = Document.get(doc_id)\\n \\n self.ppgt.fb_from_doc(doc, fb, session)\\n\\n for kw_fb in feedbacks.get(\\\"kws\\\", []):\\n kw_id, fb = kw_fb\\n kw = Keyword.get(kw_id)\\n \\n self.ppgt.fb_from_kw(kw, fb, session)\\n\\n for dockw_fb in feedbacks.get(\\\"dockws\\\", []):\\n kw_id, doc_id, fb = dockw_fb\\n doc = Document.get(doc_id)\\n kw = Keyword.get(kw_id)\\n \\n self.ppgt.fb_from_dockw(kw, doc, fb, session)\\n\\n # propagation is done\\n # updates the feedback value \\n self.upd.update(session)\\n print \\\"propagation finished\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa58f79c2db518a84084fd90e07ba9a\",\n \"score\": \"0.51064306\",\n \"text\": \"def train(self):\\n for i in range(self.iterations):\\n action = self.agent.chooseAction()\\n best_action = self.bandit.getBestAction()\\n \\n reward = self.bandit.getReward(action)\\n if self.sample_avg:\\n self.agent.updateEstimates(action, reward, 1.0/(1+i))\\n else:\\n self.agent.updateEstimates(action, reward, self.alpha)\\n\\n \\n self.best_action.append(best_action)\\n self.chosen_action.append(action)\\n self.rewards_obtained.append(reward)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083630da2917d8876064c37db9760f1e\",\n \"score\": \"0.50832766\",\n \"text\": \"def processAlgorithm(self, parameters, context, feedback):\\n\\n # Retrieve the feature source and sink. The 'dest_id' variable is used\\n # to uniquely identify the feature sink, and must be included in the\\n # dictionary returned by the processAlgorithm function.\\n rep_source = self.parameterAsFile(parameters, self.REP_SOURCE, context)\\n rep_resultat=self.parameterAsFileOutput(parameters, self.REP_RESULTAT, context)\\n prefixe_reseau=self.parameterAsString(parameters,self.PREFIXE_RESEAU,context)\\n uic=self.parameterAsBool(parameters,self.UIC,context)\\n split_formula=self.parameterAsString(parameters,self.SPLIT_FORMULA,context)\\n \\n print(rep_resultat,self.REP_RESULTAT)\\n if os.path.split(rep_resultat)[1]==self.REP_RESULTAT:\\n rep_resultat=os.path.split(rep_resultat)[0]\\n self.importGTFS(rep_source,rep_resultat,prefixe_reseau,uic,split_formula)\\n\\n\\n return {self.REP_SOURCE: self.REP_RESULTAT}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956cc8e98cbd1314427de4ee58b3ee97\",\n \"score\": \"0.5080188\",\n \"text\": \"def savedata():\\n [hitId,assignmentId,workerId,user,to_return] = check_identity(request);\\n if to_return!=None:\\n return to_return;\\n \\n try:\\n part = Participant.query.\\\\\\n filter(Participant.assignmentId == assignmentId).\\\\\\n filter(Participant.workerId == workerId).\\\\\\n one()\\n except:\\n raise ExperimentError('improper_inputs')\\n total = 0;\\n for choice in part.choices:\\n for decision in choice.decisions:\\n if(decision.status==Decision.USER_MADE):\\n total = total + decision.reward;\\n \\n part.status = Participant.COMPLETED\\n user.endhit = datetime.now()\\n db_session.add(user)\\n db_session.commit()\\n rewardCode = 0\\n for code in user.codes:\\n if code.status==Code.FREE:\\n rewardCode = code.value\\n code.status=Code.USED\\n db_session.commit()\\n break\\n query_rs_keyCode= \\\"update keycode_gameset_user set status='USED',totalreward=\\\"+str(total)+\\\\\\n \\\" where userid=\\\"+workerId+\\\" and gamesetid=\\\"+assignmentId\\n \\n connection.execute(query_rs_keyCode)\\n \\n query_rs_keyCode= \\\"select keyCodeEnd from keycode_gameset_user where userid=\\\"+workerId+\\\" and gamesetid=\\\"+assignmentId\\n rs_keyCode=connection.execute(query_rs_keyCode)\\n row_rs_keyCode=rs_keyCode.fetchone()\\n email=user.email\\n if email==None:\\n email=''\\n return render_template('/eg/debriefing.html', hitId = hitId, workerId=workerId, assignmentId=assignmentId,sessionId=user.sessionId, totalreward=total, rewardcode=row_rs_keyCode['keyCodeEnd'],email=email)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55eaa4f054fbce97af99fcbbf6606ce3\",\n \"score\": \"0.50688714\",\n \"text\": \"def learn(self):\\n if len(self.replay_buffer) - self.n + 1 < self.batch_size:\\n return\\n \\n self.replay_buffer.sample(self.batch_size)\\n \\n states = self.replay_buffer.states\\n actions = self.replay_buffer.actions\\n rewards = self.replay_buffer.rewards\\n next_states = self.replay_buffer.next_states\\n dones = self.replay_buffer.dones\\n \\n state_q_values = self.network(states).gather(1, actions)\\n target_state_q_values = self._target_state_q_values(rewards,\\n next_states,\\n dones)\\n \\n if self.per:\\n errors = target_state_q_values - state_q_values\\n for i, index in enumerate(self.replay_buffer.indices):\\n self.replay_buffer.update(index, errors[i].item())\\n \\n loss = self.criterion(state_q_values, target_state_q_values)\\n loss *= self.replay_buffer.is_weight\\n loss = loss.mean()\\n else:\\n loss = self.criterion(state_q_values, target_state_q_values)\\n \\n self.optimizer.zero_grad()\\n loss.backward()\\n self.optimizer.step()\\n self._update_target_network()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80fe9d94ce0bebb08ac143967e30554\",\n \"score\": \"0.50652546\",\n \"text\": \"def results(self) -> None:\\n self.reset_res_fr()\\n self.draw_res_fr()\\n self.original = self.sent_input.get()\\n self.results_rows = get_results2(self.res_fr, self.original)\\n self.draw_results_labels()\\n self.draw_results_rows()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c877664fc73452cc1e43dd8d85c25274\",\n \"score\": \"0.5064237\",\n \"text\": \"def sample_update(self):\\n self.data = self.daq.getData()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c0b8c73cf7bed4b72674efe9e117bb0\",\n \"score\": \"0.5055165\",\n \"text\": \"def update(self, feedback_value):\\n error = self.SetPoint - feedback_value\\n\\n self.current_time = time.time()\\n delta_time = self.current_time - self.last_time\\n delta_error = error - self.last_error\\n\\n if (delta_time >= self.sample_time):\\n self.PTerm = self.Kp * error\\n self.ITerm += error * delta_time\\n\\n if (self.ITerm < -self.windup_guard):\\n self.ITerm = -self.windup_guard\\n elif (self.ITerm > self.windup_guard):\\n self.ITerm = self.windup_guard\\n\\n self.DTerm = 0.0\\n if delta_time > 0:\\n self.DTerm = delta_error / delta_time\\n\\n # Remember last time and last error for next calculation\\n self.last_time = self.current_time\\n self.last_error = error\\n\\n self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b993c12f550d163eac6d97db5ce29b2\",\n \"score\": \"0.5044623\",\n \"text\": \"def attention_model(context_memory, query_memory, init_status, hidden_dim, att_dim, max_steps = 5, init = glorot_uniform()):\\n gru = GRU((hidden_dim*2, ), name='control_status')\\n status = init_status\\n output = [None]*max_steps*2\\n sum_prob = None\\n context_attention_score = attention_score(att_dim, name='context_attention')\\n query_attention_score = attention_score(att_dim, name='query_attention')\\n answer_attention_score = attention_score(att_dim, name='candidate_attention')\\n stop_gate = termination_gate(name='terminate_prob')\\n prev_stop = 0\\n for step in range(max_steps):\\n context_attention_weight = context_attention_score(status, context_memory)\\n query_attention_weight = query_attention_score(status, query_memory)\\n context_attention = sequence.reduce_sum(times(context_attention_weight, context_memory), name='C-Att')\\n query_attention = sequence.reduce_sum(times(query_attention_weight, query_memory), name='Q-Att')\\n attention = ops.splice(query_attention, context_attention, name='att-sp')\\n status = gru(status, attention).output\\n termination_prob = stop_gate(status)\\n ans_attention = answer_attention_score(status, context_memory)\\n output[step*2] = ans_attention\\n if step < max_steps -1:\\n stop_prob = prev_stop + ops.log(termination_prob, name='log_stop')\\n else:\\n stop_prob = prev_stop\\n output[step*2+1] = sequence.broadcast_as(ops.exp(stop_prob, name='exp_log_stop'), output[step*2], name='Stop_{0}'.format(step))\\n prev_stop += ops.log(1-termination_prob, name='log_non_stop')\\n\\n final_ans = None\\n for step in range(max_steps):\\n if final_ans is None:\\n final_ans = output[step*2] * output[step*2+1]\\n else:\\n final_ans += output[step*2] * output[step*2+1]\\n results = combine(output + [ final_ans ], name='Attention_func')\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4599a53c71061aace5770a7e94de7cd\",\n \"score\": \"0.5043428\",\n \"text\": \"def receive_trial_result(self, parameter_id, parameters, value, **kwargs):\\n trial_info_idx = self.running_trials.pop(parameter_id, None)\\n assert trial_info_idx is not None\\n\\n value = extract_scalar_reward(value)\\n if self.optimize_mode == OptimizeMode.Minimize:\\n value = -value\\n\\n self.trials_result[trial_info_idx] = value\\n self.finished_trials += 1\\n\\n logger.debug('receive_trial_result, parameter_id %d, trial_info_idx %d, finished_trials %d, inf_batch_size %d',\\n parameter_id, trial_info_idx, self.finished_trials, self.inf_batch_size)\\n if self.finished_trials == self.inf_batch_size:\\n logger.debug('Start next round inference in receive_trial_result')\\n self._next_round_inference()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be0c87ab659aa3c919a6be393b41bc69\",\n \"score\": \"0.50422555\",\n \"text\": \"def fetch_training_data(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3caa0e547dafaf79e926ede712b6217\",\n \"score\": \"0.5037924\",\n \"text\": \"def observe_result(self, session_state, job, result):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31000a54aed57aa42140c38c032ff726\",\n \"score\": \"0.5029414\",\n \"text\": \"def post_result_feedback(response_id, result_id, body): # noqa: E501\\n if connexion.request.is_json:\\n ratingData = connexion.request.get_json() # noqa: E501\\n rtxFeedback = RTXFeedback()\\n response = rtxFeedback.addNewResultRating(result_id, ratingData)\\n return(response)\\n return( { \\\"status\\\": 502, \\\"title\\\": \\\"body content not JSON\\\", \\\"detail\\\": \\\"Required body content is not JSON\\\", \\\"type\\\": \\\"about:blank\\\" }, 502 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687b9fbf65020492df54c3684221ba31\",\n \"score\": \"0.50268143\",\n \"text\": \"def results():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5035f08e7cc6080dff44bf6243f37834\",\n \"score\": \"0.50247085\",\n \"text\": \"def train(self, result: Results):\\n result.action_space = self.action_space_change(result.action_space)\\n result.observation = self.observation_change(result.observation)\\n self.agent.train(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b29131549be3977fddcc1b1cf525387\",\n \"score\": \"0.50232613\",\n \"text\": \"def __call__(self, observation, prev_action, prev_reward):\\n prev_action = self.distribution.to_onehot(prev_action)\\n model_inputs = buffer_to((observation, prev_action, prev_reward),\\n device=self.device)\\n q, _conv = self.model(*model_inputs)\\n return q.cpu()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4756dac2229c053358d2fb41c5f79bd\",\n \"score\": \"0.50133973\",\n \"text\": \"def tell(self, reward_table_result):\\n\\n reward_table = -np.array(reward_table_result)\\n\\n ## Compute reward decay if needed\\n if self.weight_decay > 0:\\n l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\\n reward_table += l2_decay\\n\\n self.es.tell(self.solutions, (reward_table).tolist())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9251018607d66d4e33469db7cc7d4a\",\n \"score\": \"0.5011749\",\n \"text\": \"def request_data(self):\\n return {\\n 'tank_id': self.tank_id,\\n 'count': len(self.registered),\\n 'failed': self.failed,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba18d48bd3ffa9cc25e37831730f8ea7\",\n \"score\": \"0.50112474\",\n \"text\": \"def AquireParam(self):\\n ### Update instance variables\\n # self.Ig_value = self.Ig_obj.Measure()\\n self.Ig_value = self.Ig_obj.Fetch()\\n # self.Ic_value = self.Ic_obj.Measure()\\n self.Ic_value = self.Ic_obj.Fetch()\\n\\n self.P_value = self.P_obj.RP()\\n self.time_now = float( \\\"{0:.2f}\\\".format(time.time() - self.time_start) )\\n self.Ve_value = self.volt_now\\n self.Ve_status = str(self.Ve_value)\\n self.Ic_status = str(self.Ic_value)\\n self.Ig_status = str(self.Ig_value)\\n self.P_status = \\\"{0:1.2e}\\\".format(self.P_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"197d0ae8d7a2688e9091f6e4e5a40cae\",\n \"score\": \"0.5009342\",\n \"text\": \"def reward(self) -> tf.Tensor:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef935e49fe9b16155fa71c425188997\",\n \"score\": \"0.49899712\",\n \"text\": \"async def batch_predict():\\n return {\\\"response\\\": \\\"not implemented yet, sry\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"367fc83156b11d7b1ea9e43a1215483e\",\n \"score\": \"0.4987145\",\n \"text\": \"def receiveResults(self,storeId,data):\\n messenger.send('TDS-results-%d'%storeId,sentArgs=[data])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"662a525b2637a4bf6a1d2811ba49b3b7\",\n \"score\": \"0.4979685\",\n \"text\": \"def send_buffer(self):\\n # Do nothing if we have no entities\\n if self.node_count == 0 and self.relation_count == 0:\\n return\\n\\n args = (\\n [self.node_count, self.relation_count, len(self.labels), len(self.reltypes)]\\n + self.labels\\n + self.reltypes\\n )\\n # Prepend a \\\"BEGIN\\\" token if this is the first query\\n if self.initial_query:\\n args.insert(0, \\\"BEGIN\\\")\\n self.initial_query = False\\n\\n task = self.pool.apipe(run, self.client, self.graphname, args)\\n self.add_task(task)\\n\\n self.clear_buffer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e697bd472e41d5be4a642c67c9dc4f8e\",\n \"score\": \"0.4977921\",\n \"text\": \"def getUseFeedbackList(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f5b5035eb733697bb4c39b2072bc5e\",\n \"score\": \"0.49767745\",\n \"text\": \"def do_opt_tx(self, taps):\\n time.sleep(0.001) # Give the GUI a chance to acknowledge user clicking the Abort button.\\n\\n if self.stopped():\\n raise RuntimeError(\\\"Optimization aborted.\\\")\\n\\n pybert = self.pybert\\n tuners = pybert.tx_tap_tuners\\n taps = list(taps)\\n for tuner in tuners:\\n if tuner.enabled:\\n tuner.value = taps.pop(0)\\n return pybert.cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a2edc00052b6bfee893b8d1f1601492\",\n \"score\": \"0.4961446\",\n \"text\": \"def __getitem__(self, index):\\n context_arr = self.data_info['context_arr'][index]\\n context_arr = self.preprocess(context_arr, self.src_word2id, trg=False)\\n response = self.data_info['response'][index]\\n response = self.preprocess(response, self.trg_word2id)\\n ptr_index = torch.Tensor(self.data_info['ptr_index'][index])\\n selector_index = torch.Tensor(self.data_info['selector_index'][index])\\n conv_arr = self.data_info['conv_arr'][index]\\n conv_arr = self.preprocess(conv_arr, self.src_word2id, trg=False)\\n kb_arr = self.data_info['kb_arr'][index]\\n kb_arr = self.preprocess(kb_arr, self.src_word2id, trg=False)\\n sketch_response = self.data_info['sketch_response'][index]\\n sketch_response = self.preprocess(sketch_response, self.trg_word2id)\\n\\n # processed information\\n data_info = {}\\n for k in self.data_info.keys():\\n try:\\n data_info[k] = locals()[k]\\n except:\\n data_info[k] = self.data_info[k][index]\\n\\n # additional plain information\\n data_info['context_arr_plain'] = self.data_info['context_arr'][index]\\n data_info['response_plain'] = self.data_info['response'][index]\\n data_info['kb_arr_plain'] = self.data_info['kb_arr'][index]\\n\\n return data_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6635967c7b0d7e30367d63a61ef81c4\",\n \"score\": \"0.49571595\",\n \"text\": \"def updateModelResults(curMod, res):\\n\\tcurMod.acc = res[0]\\n\\tcurMod.spec = res[1]\\n\\tcurMod.sens = res[2]\\n\\tcurMod.ppv = res[3]\\n\\tcurMod.npv = res[4]\\n\\tcurMod.fscore = res[5]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea4c970c231f81d150c4fde02922b61\",\n \"score\": \"0.49548402\",\n \"text\": \"async def predict(success: Success):\\n\\n campaign_id = 23548\\n result = 'pass'\\n return {\\n 'campaign id': campaign_id,\\n 'prediction': result\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9aad94b0838d0898ea11fd34e80666\",\n \"score\": \"0.4951337\",\n \"text\": \"def __update_model(self):\\n\\n for tau in range(self.trials):\\n for t in range(self.T):\\n if t == 0:\\n response = None\\n else:\\n response = self.actions[tau, t-1]\\n \\n observation = self.observations[tau,t]\\n \\n self.agent.update_beliefs(tau, t, observation, response)\\n self.agent.plan_behavior(tau, t)\\n self.agent.estimate_response_probability(tau, t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9aad94b0838d0898ea11fd34e80666\",\n \"score\": \"0.4951337\",\n \"text\": \"def __update_model(self):\\n\\n for tau in range(self.trials):\\n for t in range(self.T):\\n if t == 0:\\n response = None\\n else:\\n response = self.actions[tau, t-1]\\n \\n observation = self.observations[tau,t]\\n \\n self.agent.update_beliefs(tau, t, observation, response)\\n self.agent.plan_behavior(tau, t)\\n self.agent.estimate_response_probability(tau, t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8306dae9d8431df121243b23398e11f9\",\n \"score\": \"0.49411935\",\n \"text\": \"def training(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2449d30a486f73707d94a750d7772ce2\",\n \"score\": \"0.49362722\",\n \"text\": \"def model_train_process():\\n train_data = read.get_train_data(\\\"../data/myratings.txt\\\")\\n user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)\\n\\n recom_result = give_recom_result(user_vec, item_vec, \\\"1\\\")\\n print(recom_result)\\n\\n ana_recom_result(train_data, \\\"1\\\", recom_result)\\n\\n # print(len(user_vec[\\\"1\\\"]))\\n # print(item_vec[\\\"2455\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c895da24f9c8fb9539be8ec5ecd1b9\",\n \"score\": \"0.49323434\",\n \"text\": \"def train(self):\\n last_eval = 0\\n last_record = 0\\n scores_eval = []\\n\\n self.init_averages()\\n scores_eval = [] # list of scores computed at iteration time\\n\\n for t in range(self.config.num_batches):\\n\\n # collect a minibatch of samples\\n paths, total_rewards = self.sample_path(self.env)\\n scores_eval = scores_eval + total_rewards\\n observations = np.concatenate([path[\\\"observation\\\"] for path in paths])\\n actions = np.concatenate([path[\\\"action\\\"] for path in paths])\\n rewards = np.concatenate([path[\\\"reward\\\"] for path in paths])\\n # compute Q-val estimates (discounted future returns) for each time step\\n returns = self.get_returns(paths)\\n advantages = self.calculate_advantage(returns, observations)\\n\\n # run training operations\\n if self.config.use_baseline:\\n self.update_baseline(returns, observations)\\n self.sess.run(self.train_op, feed_dict={\\n self.observation_placeholder : observations,\\n self.action_placeholder : actions,\\n self.advantage_placeholder : advantages})\\n\\n # tf stuff\\n if (t % self.config.summary_freq == 0):\\n self.update_averages(total_rewards, scores_eval)\\n self.record_summary(t)\\n\\n # compute reward statistics for this batch and log\\n avg_reward = np.mean(total_rewards)\\n sigma_reward = np.sqrt(np.var(total_rewards) / len(total_rewards))\\n msg = \\\"Average reward: {:04.2f} +/- {:04.2f}\\\".format(avg_reward, sigma_reward)\\n self.logger.info(msg)\\n\\n if self.config.record and (last_record > self.config.record_freq):\\n self.logger.info(\\\"Recording...\\\")\\n last_record =0\\n self.record()\\n\\n self.logger.info(\\\"- Training done.\\\")\\n export_plot(scores_eval, \\\"Score\\\", config.env_name, self.config.plot_output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614a1d1c4bc8a12dfd661f70cccebce5\",\n \"score\": \"0.49255964\",\n \"text\": \"def process(self, data_batch, data_samples) -> None:\\n for sample in data_samples:\\n gt_answer = sample.get('gt_answer')\\n result = {\\n 'pred_answer': sample.get('pred_answer'),\\n 'gt_answer': gt_answer\\n }\\n\\n self.results.append(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b200bb3c8f8006d68692ccc93362536\",\n \"score\": \"0.49205723\",\n \"text\": \"def receive_result(self, op):\\n tag = self._tags[op]\\n rank = self._op_location[tag]\\n _, _, data = self.receive_data(rank)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8bcbb25bf3283c6e16eed0d31c39fd6\",\n \"score\": \"0.4916548\",\n \"text\": \"def result(self, state, action):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8bcbb25bf3283c6e16eed0d31c39fd6\",\n \"score\": \"0.4916548\",\n \"text\": \"def result(self, state, action):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8bcbb25bf3283c6e16eed0d31c39fd6\",\n \"score\": \"0.4916548\",\n \"text\": \"def result(self, state, action):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d483db76ced009eafdb2483b6d55d0\",\n \"score\": \"0.49134415\",\n \"text\": \"def train(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d483db76ced009eafdb2483b6d55d0\",\n \"score\": \"0.49134415\",\n \"text\": \"def train(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d483db76ced009eafdb2483b6d55d0\",\n \"score\": \"0.49134415\",\n \"text\": \"def train(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d483db76ced009eafdb2483b6d55d0\",\n \"score\": \"0.49134415\",\n \"text\": \"def train(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b9465fee31e4712e80b95ef5b81f8c5\",\n \"score\": \"0.49132198\",\n \"text\": \"def _replay(self):\\n batch = self._Memory.get_samples(self._Model.batch_size)\\n\\n if len(batch) > 0: # if the memory is full enough\\n states = np.array([val[0] for val in batch]) # extract states from the batch\\n next_states = np.array([val[3] for val in batch]) # extract next states from the batch\\n\\n # prediction\\n q_s_a = self._Model.predict_batch(states) # predict Q(state), for every sample\\n q_s_a_d = self._Model.predict_batch(next_states) # predict Q(next_state), for every sample\\n\\n # setup training arrays\\n x = np.zeros((len(batch), self._num_states))\\n y = np.zeros((len(batch), self._num_actions))\\n\\n for i, b in enumerate(batch):\\n state, action, reward, _ = b[0], b[1], b[2], b[3] # extract data from one sample\\n current_q = q_s_a[i] # get the Q(state) predicted before\\n current_q[action] = reward + self._gamma * np.amax(q_s_a_d[i]) # update Q(state, action)\\n x[i] = state\\n y[i] = current_q # Q(state) that includes the updated action value\\n\\n self._Model.train_batch(x, y) # train the NN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616dcff6eec4e549342268ce1cfb980b\",\n \"score\": \"0.49123177\",\n \"text\": \"def train(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6d23be54de8713e87588f66f029acc\",\n \"score\": \"0.4908751\",\n \"text\": \"def step_impl(context):\\n rtx_api_url = \\\"https://arax.rtx.ai/api/rtx/v1\\\"\\n rtx_headers = {'accept': 'application/json'}\\n\\n # First translate the natural language question, if there is one\\n if hasattr(context, 'human_question'):\\n translate_response = requests.post(rtx_api_url + \\\"/translate\\\", json=context.human_question, headers=rtx_headers)\\n print(translate_response.status_code, translate_response.text) # Only displays if test fails\\n assert translate_response.status_code == 200\\n context.machine_question = translate_response.json()\\n\\n # Then query RTX with the translated question\\n context.machine_question[\\\"bypass_cache\\\"] = \\\"true\\\"\\n query_response = requests.post(rtx_api_url + \\\"/query\\\", json=context.machine_question, headers=rtx_headers)\\n print(query_response.status_code, query_response.text) # Only displays if test fails\\n assert query_response.status_code == 200\\n context.response_json = query_response.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a1ae01f21ab0f5dbe3bf9a44d67e2af\",\n \"score\": \"0.4902007\",\n \"text\": \"def train(self, request):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75144358f20640b05223b7f4651352e\",\n \"score\": \"0.48998815\",\n \"text\": \"def train_rr(self):\\n self.env = GentlyTerminating(self.env)\\n print(\\\"Training in real environment started...\\\")\\n reward_record = 0\\n total_reward = 0\\n episode = 0\\n rew = []\\n step = 0\\n while step < self.total_steps:\\n state = self.transformObservation(self.env.reset())\\n done = False\\n\\n self.info_print(step, total_reward, reward_record)\\n\\n total_reward = 0\\n i = 0\\n while not done:\\n\\n action = self.action_selection(\\n torch.tensor(state, dtype=torch.float32, device=self.device).unsqueeze(0)).squeeze()\\n\\n action = self.noise_torch.sample((self.action_dim,)) * self.noise_decay ** episode + action\\n\\n action = torch.clamp(action, min=self.env_low[0], max=self.env_high[0])\\n\\n action = action.to(\\\"cpu\\\").detach().numpy()\\n next_state, reward, done, _ = self.env.step(action)\\n done = done or i >= self.trial_horizon\\n next_state = self.transformObservation(next_state)\\n\\n total_reward += reward\\n\\n step += 1\\n i = i + 1\\n\\n self.replayBuffer.add(state, action, reward, next_state, done)\\n state = next_state\\n # we do this at end of every episode because it takes to much time between episodes\\n if self.replayBuffer.count >= self.n_batches:\\n actor_loss, critic_loss = self.update()\\n\\n self.update_actor(actor_loss)\\n self.update_critic(critic_loss)\\n\\n self.soft_update(self.actor_network, self.actor_target)\\n self.soft_update(self.critic_network, self.critic_target)\\n\\n if self.replayBuffer.count >= self.n_batches:\\n if self.critic_lr_scheduler.get_lr()[0] > self.lr_min:\\n self.critic_lr_scheduler.step()\\n if self.actor_lr_scheduler.get_lr()[0] > self.lr_min:\\n self.actor_lr_scheduler.step()\\n episode += 1\\n # if out actor is really good, test target actor. If the target actor is good too, save it.\\n if reward_record < total_reward and total_reward > 50:\\n trial_average_reward = self.trial()\\n if trial_average_reward > reward_record:\\n print(\\\"New record\\\")\\n reward_record = trial_average_reward\\n self.save_model(trial_average_reward)\\n rew.append(total_reward)\\n # test & save final model\\n trial_average_reward = self.trial()\\n self.save_model(\\\"{:.2f}_final\\\".format(trial_average_reward))\\n\\n return rew\",\n \"title\": \"\"\n },\n {\n \"docid\": \"830def6931517f049a4afb7a7c2294b2\",\n \"score\": \"0.48982772\",\n \"text\": \"def processAlgorithm(self, parameters, context, feedback):\\n\\n # Retrieve the feature source and sink. The 'dest_id' variable is used\\n # to uniquely identify the feature sink, and must be included in the\\n # dictionary returned by the processAlgorithm function.\\n source = self.parameterAsSource(parameters, self.INPUT, context)\\n (sink, dest_id) = self.parameterAsSink(\\n parameters, self.OUTPUT,\\n context, source.fields(),\\n source.wkbType(), source.sourceCrs()\\n )\\n\\n # Compute the number of steps to display within the progress bar and\\n # get features from source\\n total = 100.0 / source.featureCount() if source.featureCount() else 0\\n features = source.getFeatures()\\n\\n for current, feature in enumerate(features):\\n # Stop the algorithm if cancel button has been clicked\\n if feedback.isCanceled():\\n break\\n\\n # Add a feature in the sink\\n sink.addFeature(feature, QgsFeatureSink.FastInsert)\\n\\n # Update the progress bar\\n feedback.setProgress(int(current * total))\\n\\n # Return the results of the algorithm. In this case our only result is\\n # the feature sink which contains the processed features, but some\\n # algorithms may return multiple feature sinks, calculated numeric\\n # statistics, etc. These should all be included in the returned\\n # dictionary, with keys matching the feature corresponding parameter\\n # or output names.\\n return {self.OUTPUT: dest_id}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cde5c8c3ff535f97b8595b6dd05fd65\",\n \"score\": \"0.48916265\",\n \"text\": \"def respond_feedback(cls):\\n # Start our TwiML response\\n response = VoiceResponse()\\n\\n # Use <Say> to give the caller some instructions\\n response.say('Hello, Niche always welcomes feedback so thank you for your time and effort. End the call after you are done. We will start recording after the beep.')\\n\\n # Use <Record> to record the caller's message\\n response.record(action=\\\"/feedback/save\\\", finishOnKey='', timeout=0)\\n\\n # End the call with <Hangup>\\n response.hangup()\\n\\n return str(response), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488c3cc50a6872c005aca6d85c0e1893\",\n \"score\": \"0.48891422\",\n \"text\": \"def gather_feedback():\\n\\n # a neat way for accessing data from both GET and POST requests!\\n data = request.values\\n\\n # We can have a peek at what the data looks like in the console if we\\n # print the result we got from the user. The keys we used\\n # (the `['name']` bit after `data`) to see the data matches the \\\"name\\\"\\n # we give on the input field in our HTML form.\\n print(f\\\"Name submitted was: {data['name']}\\\")\\n print(f\\\"Email submitted was: {data['email']}\\\")\\n\\n return render_template(\\\"feedback.html\\\", form_data=data, title=\\\"Feedback response\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6353c9eeb30a87ef442260d764a89ae3\",\n \"score\": \"0.48881766\",\n \"text\": \"def processAlgorithm(self, parameters, context, feedback):\\n\\n # Load all rasters\\n input1_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\\n ds_input1 = gdal.Open(input1_raster.dataProvider().dataSourceUri())\\n arr_input1 = ds_input1.GetRasterBand(1).ReadAsArray()\\n\\n input2_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\\n ds_input2 = gdal.Open(input2_raster.dataProvider().dataSourceUri())\\n arr_input2 = ds_input2.GetRasterBand(1).ReadAsArray()\\n\\n input3_raster = self.parameterAsRasterLayer(parameters, self.INPUT3, context)\\n ds_input3 = gdal.Open(input3_raster.dataProvider().dataSourceUri())\\n arr_input3 = ds_input3.GetRasterBand(1).ReadAsArray()\\n\\n input4_raster = self.parameterAsRasterLayer(parameters, self.INPUT4, context)\\n ds_input4 = gdal.Open(input4_raster.dataProvider().dataSourceUri())\\n arr_input4 = ds_input4.GetRasterBand(1).ReadAsArray()\\n\\n input5_raster = self.parameterAsRasterLayer(parameters, self.INPUT5, context)\\n ds_input5 = gdal.Open(input5_raster.dataProvider().dataSourceUri())\\n arr_input5 = ds_input5.GetRasterBand(1).ReadAsArray()\\n\\n input6_raster = self.parameterAsRasterLayer(parameters, self.INPUT6, context)\\n ds_input6 = gdal.Open(input6_raster.dataProvider().dataSourceUri())\\n arr_input6 = ds_input6.GetRasterBand(1).ReadAsArray()\\n\\n input7_raster = self.parameterAsRasterLayer(parameters, self.INPUT7, context)\\n ds_input7 = gdal.Open(input7_raster.dataProvider().dataSourceUri())\\n arr_input7 = ds_input7.GetRasterBand(1).ReadAsArray()\\n\\n input8_raster = self.parameterAsRasterLayer(parameters, self.INPUT8, context)\\n ds_input8 = gdal.Open(input8_raster.dataProvider().dataSourceUri())\\n arr_input8 = ds_input8.GetRasterBand(1).ReadAsArray()\\n\\n input9_raster = self.parameterAsRasterLayer(parameters, self.INPUT9, context)\\n ds_input9 = gdal.Open(input9_raster.dataProvider().dataSourceUri())\\n arr_input9 = ds_input9.GetRasterBand(1).ReadAsArray()\\n\\n arr_total = arr_input1 + arr_input2 + arr_input3 + arr_input4 + arr_input5 + arr_input6 + arr_input7 + \\\\\\n arr_input8 + arr_input9\\n\\n # Initialize and write on output raster\\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\\n file_output = path_output + '/SE_10_SE_total.tiff'\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n [rows, cols] = arr_total.shape\\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\\n parameters, self.PIXEL_RES, context)\\n total_area = np.sum(arr_total) / (cols * rows * area_pixel)\\n outdata = driver.Create(file_output, cols, rows, 1, gdal.GDT_Float64)\\n outdata.SetGeoTransform(ds_input1.GetGeoTransform())##sets same geotransform as input\\n outdata.SetProjection(ds_input1.GetProjection())##sets same projection as input\\n outdata.GetRasterBand(1).WriteArray(arr_total)\\n outdata.FlushCache() ##saves to disk!!\\n report_output = path_output + '/SE_totale.txt'\\n f = open(report_output, \\\"w+\\\")\\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\\n f.write(\\\"Sommario dell'analisi dei servizi ecosistemici\\\\n\\\")\\n f.write(\\\"Data: \\\" + today +\\\"\\\\n\\\\n\\\\n\\\")\\n f.write(\\\"Differenze tra stato di progetto e stato attuale\\\\n\\\\n\\\")\\n f.write(\\\"Differenza di valore totale (€): %f \\\\n\\\" % (np.sum(arr_total)))\\n f.write(\\\"Differenza per unità di superficie (€/ha): %f \\\\n\\\" % (\\n total_area * 10000))\\n return {self.OUTPUT: total_area}\\n\\n \\n # ----------------------------------------------------------------------------------- \\n # Copyright (c) 2021 Città di Torino.\\n # \\n # This material is free software: you can redistribute it and/or modify\\n # it under the terms of the GNU General Public License as published by\\n # the Free Software Foundation, either version 2 of the License, or\\n # (at your option) any later version.\\n # \\n # This program is distributed in the hope that it will be useful,\\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\\n # GNU General Public License for more details.\\n # \\n # You should have received a copy of the GNU General Public License\\n # along with this program. If not, see http://www.gnu.org/licenses.\\n # ----------------------------------------------------------------------------------- \",\n \"title\": \"\"\n },\n {\n \"docid\": \"18bac172fb51841b9bc78d978d6e6d25\",\n \"score\": \"0.48840973\",\n \"text\": \"def feedback(self, user, data=None, is_response=False):\\n poll_configs = [\\n ('Teaching Evaluate', 'teach_feedback', None),\\n ('Other Question', 'other_questions', None),\\n ]\\n\\n if is_response == True:\\n # get response from data\\n selected_option = data['selected_option']\\n # response\\n for option, response_function, parameters in poll_configs:\\n if selected_option == option:\\n # clean talk record\\n self.talks.pop(user['id'])\\n\\n # call the function\\n call_the_function = {\\n 'response_function': response_function,\\n 'parameters': {\\n 'user': user,\\n 'data': data,\\n 'is_response': False\\n }\\n }\\n return call_the_function\\n\\n # error\\n else:\\n message = \\\"You can select teaching evaluate and scoring for the class and make some suggestions!\\\"\\n message += \\\"\\\\nIf you have some questions, but just can't find answer by our bot, you can select other questions option and tell us.\\\"\\n call_the_function = {\\n 'response_function': 'send_poll',\\n 'parameters': {\\n 'user': user,\\n 'message': message,\\n 'poll_configs': poll_configs,\\n 'response_function': 'feedback',\\n 'keep_current_talk': False\\n }\\n }\\n return call_the_function\\n\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf2a06aba0a4395be711ed35cf5702b3\",\n \"score\": \"0.48790106\",\n \"text\": \"def PredictAction(self, PC, CSV=False, Feedback=False):\\n Samples = PC.Samples\\n Costs = [0] * Samples\\n Rewards = [0] * Samples\\n PredictedActions = [0] * len(self.Plr.MDP.A)\\n # Find what samples we already have.\\n RIndices = [PC.ObjectNames.index(\\n i) if i in PC.ObjectNames else -1 for i in self.Plr.Map.ObjectNames]\\n CIndices = [PC.CostNames.index(\\n i) if i in PC.CostNames else -1 for i in self.Plr.Map.StateNames]\\n if Feedback:\\n sys.stdout.write(\\\"\\\\n\\\")\\n for i in range(Samples):\\n if Feedback:\\n Percentage = round(i * 100.0 / Samples, 2)\\n sys.stdout.write(\\\"\\\\rProgress |\\\")\\n roundper = int(math.floor(Percentage / 5))\\n sys.stdout.write(\\n self.begincolor + self.block * roundper + self.endcolor)\\n sys.stdout.write(\\\" \\\" * (20 - roundper))\\n sys.stdout.write(\\\"| \\\" + str(Percentage) + \\\"%\\\")\\n sys.stdout.flush()\\n # Resample the agent\\n self.Plr.Agent.ResampleAgent()\\n # and overwrite sample sections that we already have\\n self.Plr.Agent.costs = [PC.CostSamples[i, CIndices[\\n j]] if CIndices[j] != -1 else self.Plr.Agent.costs[j] for j in range(len(self.Plr.Agent.costs))]\\n self.Plr.Agent.rewards = [PC.RewardSamples[i, RIndices[\\n j]] if RIndices[j] != -1 else self.Plr.Agent.rewards[j] for j in range(len(self.Plr.Agent.rewards))]\\n # save samples\\n Costs[i] = self.Plr.Agent.costs\\n Rewards[i] = self.Plr.Agent.rewards\\n # Replan\\n self.Plr.Prepare(self.Validate)\\n # Get predicted actions\\n ActionDistribution = self.Plr.GetActionDistribution()\\n # Get the probability\\n probability = np.exp(PC.LogLikelihoods[i])\\n # Add all up\\n PredictedActions = [ActionDistribution[\\n x] * probability + PredictedActions[x] for x in range(len(PredictedActions))]\\n # Finish printing progress bar\\n if Feedback:\\n # Print complete progress bar\\n sys.stdout.write(\\\"\\\\rProgress |\\\")\\n sys.stdout.write(self.begincolor + self.block * 20 + self.endcolor)\\n sys.stdout.write(\\\"| 100.0%\\\")\\n sys.stdout.flush()\\n # PredictedActions is a list of arrays. Make it a list of integers.\\n PredictedActions = [PredictedActions[i][0]\\n for i in range(len(PredictedActions))]\\n if not CSV:\\n return [self.Plr.Map.ActionNames, PredictedActions]\\n else:\\n print((\\\",\\\".join(self.Plr.Map.ActionNames)))\\n probs = [str(i) for i in PredictedActions]\\n print((\\\",\\\".join(probs)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae667f820003f81886af5d1d3f41f83\",\n \"score\": \"0.48749357\",\n \"text\": \"def train(self, replay_buffer, writer, iterations):\\n self.step += 1\\n \\n for it in range(iterations):\\n \\n # Step 1: Sample a batch of transitions (s, s’, a, r) from the memory\\n batch_states, batch_next_states, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(self.batch_size)\\n # convert the numpy arrays to tensors object \\n # if cuda is available send data to gpu\\n state = torch.Tensor(batch_states).to(self.device)\\n next_state = torch.Tensor(batch_next_states).to(self.device)\\n action = torch.Tensor(batch_actions).to(self.device)\\n reward = torch.Tensor(batch_rewards).to(self.device)\\n done = torch.Tensor(batch_dones).to(self.device)\\n \\n # Step 2: use the Target Actor to create the action of the next\\n # state (part of the TD-Target \\n next_action = self.actor_target(next_state)\\n \\n # Step 3: Add Gaussian noise to the action for exploration \\n # clip the action value in case its outside the boundaries \\n noise = torch.Tensor(batch_actions).data.normal_(0, self.policy_noise).to(self.device)\\n noise = noise.clamp(-self.noise_clip, self.noise_clip)\\n next_action = (next_action + noise).clamp(-self.max_action, self.max_action)\\n \\n # Step 4: Use the differenet Target Critis (delayed update)\\n # and min of two Critic from TD3 to create the different Q Targets\\n # compute the average of all Q Targets to get a single value\\n target_Q1, target_Q2 = self.target_critic(next_state, next_action) \\n target_Q_min = torch.min(target_Q1, target_Q2)\\n \\n \\n # Step 5: Create the update based on the bellman equation \\n target_Q = reward + ((1 - done) * self.discount * target_Q).detach()\\n \\n # Step 6: Use the critic compute the Q estimate for current state and action\\n current_Q1, current_Q2 = self.critic(state, action) \\n \\n # Step 7: Compute the critc loss with the mean squard error\\n # loss function \\n critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)\\n writer.add_scalar('critic_loss', critic_loss , self.step)\\n \\n\\n # Step 8: Backpropagate this Critic loss and update the parameters\\n # of the two Critic models use the adam optimizer\\n \\n self.critic_optimizer.zero_grad()\\n critic_loss.backward()\\n self.critic_optimizer.step()\\n \\n # Step 9: Delayed update og Actor model\\n if it % self.policy_freq == 0:\\n actor_loss = -self.critic.Q1(state, self.actor(state)).mean()\\n writer.add_scalar('actor_loss', actor_loss , self.step)\\n self.actor_optimizer.zero_grad()\\n actor_loss.backward()\\n self.actor_optimizer.step()\\n \\n # Step 10: we update the weights of the Critic target by polyak averaging\\n # hyperparameter tau determines the combination of current and\\n # target weights\\n for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):\\n target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)\\n \\n for param, target_param in zip(self.critic.parameters(), self.target_critic.parameters()):\\n target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab47916e6920f04c9ab9e89213275495\",\n \"score\": \"0.4872434\",\n \"text\": \"def _report_model(self):\\n # We will do this separately since posterior sampling complicates how we keep\\n # track of the GPs. See _set_next_gp()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8c64b229d4f66479e272ea6627aab3\",\n \"score\": \"0.48719215\",\n \"text\": \"def act(self, state_action):\\n sa = torch.from_numpy(state_action).float().unsqueeze(0).to(device)\\n # self.reward_net.eval()\\n # with torch.no_grad():\\n # action_values = self.reward_net(state)\\n # self.reward_net.train()\\n return(self.reward_net(sa))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dade5e9e6f599a264793da3ec712966\",\n \"score\": \"0.4870841\",\n \"text\": \"def _result(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be866a42cf46996d2c27a57760bc8797\",\n \"score\": \"0.48698625\",\n \"text\": \"def update(self, action_i, action, pred_r, opt_r):\\n\\n self.t += 1\\n self.h_data.add(action_i, action, pred_r, opt_r)\\n\\n if action_i == -1:\\n # No updates on fake action\\n return\\n\\n z_action = self.bnn.sess.run(self.bnn.nn, feed_dict={self.bnn.x: self.data.actions[action_i].reshape((1, -1))})\\n self.h_latent.add(action_i, z_action, pred_r, opt_r)\\n\\n if self.hparams.remove_actions:\\n self.data.remove(action_i)\\n\\n # Retrain the network on the original data (data_h)\\n if self.t % self.update_freq_nn == 0:\\n\\n if self.hparams.reset_lr:\\n self.bnn.assign_lr()\\n self.bnn.train(self.h_data, self.num_epochs)\\n\\n # Update the latent representation of every datapoint collected so far\\n self.h_latent.actions = self.bnn.sess.run(self.bnn.nn, feed_dict={self.bnn.x: self.h_data.actions})\\n\\n # Update the Bayesian Linear Regression\\n if self.t % self.update_freq_lr == 0:\\n\\n # Update action posterior with formulas: \\\\beta | z,y ~ N(mu_q, cov_q)\\n z = self.h_latent.actions\\n y = self.h_latent.opt_rewards\\n\\n # The algorithm could be improved with sequential formulas (cheaper)\\n s = np.dot(z.T, z)\\n\\n # Some terms are removed as we assume prior mu_0 = 0.\\n self.precision = s + self.lambda_prior * np.eye(self.latent_dim)\\n self.cov = np.linalg.inv(self.precision)\\n diagonalize = getattr(self.hparams, 'diagonalize', False)\\n if diagonalize:\\n if diagonalize == 'precision':\\n self.precision = np.diag(np.diag(self.precision))\\n self.cov = np.linalg.inv(self.precision)\\n elif diagonalize == 'covariance':\\n self.cov = np.diag(np.diag(self.cov))\\n self.precision = np.linalg.inv(self.cov)\\n else:\\n raise ValueError('Wrong diagonalization parameter. Only \\\\'precision\\\\' and \\\\'covariance\\\\' supported.')\\n self.mu = np.dot(self.cov, np.dot(z.T, y))\\n\\n # Inverse Gamma posterior update\\n self.a = self.a0 + z.shape[0] / 2.0\\n b_upd = 0.5 * np.dot(y.T, y)\\n b_upd -= 0.5 * np.dot(self.mu.T, np.dot(self.precision, self.mu))\\n self.b = self.b0 + b_upd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd17991f91552ea1fc4574b6e246f122\",\n \"score\": \"0.4866218\",\n \"text\": \"def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n):\\n\\n ########################################################################\\n # Modify the reward to include exploration bonus\\n ex2_vars = None\\n old_re_n = re_n\\n if self.dm_type == 'none':\\n pass\\n else:\\n # 1. Fit density model\\n if self.dm_type == 'ex2':\\n ### PROBLEM 3\\n ### YOUR CODE HERE\\n ex2_vars = self.exploration.fit_density_model(ob_no)\\n elif self.dm_type == 'hist' or self.dm_type == 'rbf':\\n ### PROBLEM 1\\n ### YOUR CODE HERE\\n self.exploration.fit_density_model(ob_no)\\n else:\\n assert False\\n\\n # 2. Modify the reward\\n ### PROBLEM 1\\n ### YOUR CODE HERE\\n re_n = self.exploration.modify_reward(re_n, ob_no)\\n\\n print('average state', np.mean(ob_no, axis=0))\\n print('average action', np.mean(ac_na, axis=0))\\n\\n loss = OrderedDict()\\n\\n for critic_update in range(self.num_critic_updates_per_agent_update):\\n loss['Critic_Loss'] = self.critic.update(ob_no, next_ob_no, re_n, terminal_n)\\n\\n adv_n = self.estimate_advantage(ob_no, next_ob_no, re_n, terminal_n) # put final critic loss here\\n\\n for actor_update in range(self.num_actor_updates_per_agent_update):\\n loss['Actor_Loss'] = self.actor.update(ob_no, ac_na, adv_n) # put final actor loss here\\n\\n return loss, ex2_vars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e317d0d9bc66bd8d88763465668ba007\",\n \"score\": \"0.48601902\",\n \"text\": \"def predict_adv_action(self):\\n temp_obs = self.data.obs\\n with torch.no_grad():\\n self.data.obs = np.expand_dims(self.data.obs, axis=0)\\n result = self.adv_policy(self.data, last_state=None)\\n self.data.obs = temp_obs\\n self.data.adv_act = to_numpy(result.act)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc83de48b2474a692af0244bc6881b2\",\n \"score\": \"0.4856799\",\n \"text\": \"def learn(self):\\n num = len(self.buffer.memory) # Check the length of replay buffer\\n \\n states, actions, rewards, next_states, dones = self.buffer.sample(BATCH_SIZE)\\n \\n # Get max predicted Q-values (for next_states) from target model\\n Q_targets_next = self.qnetwork_target(next_states.to(device)).detach().max(1)[0].unsqueeze(1)\\n \\n # Compute Qtargets for current states\\n Q_targets = rewards.to(device) + (GAMMA*Q_targets_next * (1-dones.to(device)))\\n \\n # Get expected Q-values from local model\\n Q_expected = self.qnetwork_local(states.to(device)).gather(1, actions.to(device))\\n \\n # learn\\n loss = F.smooth_l1_loss(Q_expected.squeeze(),Q_targets.squeeze())\\n #loss = F.mse_loss(Q_expected, Q_targets)\\n \\n # optimizer\\n self.optimizer.zero_grad()\\n loss.backward()\\n self.optimizer.step()\\n \\n # ------------------- update target network ------------------- #\\n #self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) \\n \\n self.step_counter += 1\\n if (self.step_counter > self.update_target_step):\\n self.hard_update(self.qnetwork_local, self.qnetwork_target)\\n self.step_counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fa0dda5ca3f1b6e7e881350389c6a7\",\n \"score\": \"0.48529676\",\n \"text\": \"def __init__(self):\\n self.feedback = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f061b7fa4583d05d2bc94a2a401d5a3b\",\n \"score\": \"0.48527768\",\n \"text\": \"def edit_results(self):\\n session = b_scorer.today_session\\n self.results = ResultsInput(self, session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b15122fb91a8af24ca49c3b56c95530\",\n \"score\": \"0.48448327\",\n \"text\": \"def result(self, state, action):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59aa21db954143a583a42b25458512a3\",\n \"score\": \"0.48413637\",\n \"text\": \"def learn(self,action,ob,ob_):\\n self.check_ifstate_exist(ob_)\\n q_predict= self.q_table.ix[ob,action]\\n #no need to worry about terminal\\n # print 'check point ++++++++++++++++++++++++++++++++++++++++++++'\\n # print self.total_reward\\n # print(self.cur_state)\\n # print self.next_state\\n # print self.q_table.ix[self.next_state,:].max()\\n #self.q_table.info()\\n q_target= self.reward+self.gamma*self.q_table.ix[ob_,:].max()\\n self.q_table.ix[ob,action] +=self.lr*(q_target-q_predict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"993ca53b7bf6a7ed63e2b89420db5828\",\n \"score\": \"0.48365423\",\n \"text\": \"def callbacktf(self,TensorOutput_msg):\\n\\n\\t\\tself.label = []\\n\\t\\tself.label = TensorOutput_msg.result\\n\\t\\tself.Xtensor= TensorOutput_msg.x\\n\\t\\tself.Ytensor = TensorOutput_msg.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac0ddef16b109c410cb6d1b361cdf0a\",\n \"score\": \"0.4836206\",\n \"text\": \"def _process_data(self):\\n msg = self.recv_msg().decode()\\n #print(\\\"----------------------------\\\")\\n #print(msg)\\n #print(\\\"=>\\\")\\n msg = msg.replace('-inf', '0')\\n msg = msg.replace('inf', '0')\\n #print(msg)\\n data = eval(msg)\\n r = data['reward']\\n done = data['done']\\n del data['reward']\\n del data['done']\\n\\n state = []\\n\\n if self._use_gsi:\\n for feature in self._general_state_features:\\n state.append(data[feature])\\n for heuristic_id in range(self.__num_heuristics): # process heuristic data\\n for feature in self._heuristic_state_features:\\n state.append(data[\\\"%d\\\" % heuristic_id][feature])\\n\\n if self._prev_state is None:\\n self.__norm_vals = deepcopy(state)\\n self._prev_state = deepcopy(state)\\n if self.__state_type != StateType.RAW: # Transform state to DIFF state or normalize\\n tmp_state = state\\n state = list(map(self._transformation_func, state, self._prev_state, self.__norm_vals,\\n self.__skip_transform))\\n self._prev_state = tmp_state\\n return np.array(state), r, done\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4ed6dd582dd13618a0e5210fef8c59a\",\n \"score\": \"0.48331305\",\n \"text\": \"def evaluate_model(self, agent, index, test_mode, print_result = 0):\\n self.dialogue_manager.set_agent(agent=agent)\\n all_goal_set = self.goal_set\\n save_performance = self.parameter.get(\\\"save_performance\\\")\\n train_mode = self.parameter.get(\\\"train_mode\\\")\\n success_count = 0\\n absolute_success_count = 0\\n total_reward = 0\\n total_truns = 0\\n recall_score = 0\\n \\n if test_mode == 2:\\n goal_set = all_goal_set[\\\"dev\\\"]\\n evaluate_epoch_number = len(goal_set)\\n else:\\n goal_set = self.goal_test_set['test']\\n evaluate_epoch_number = len(goal_set)\\n \\n \\n inform_wrong_disease_count = 0\\n if print_result:\\n goal_slots = self.json_bulid(goal_set)\\n \\n \\n for epoch_index in range(0, evaluate_epoch_number, 1):\\n self.dialogue_manager.initialize(train_mode=test_mode, epoch_index=epoch_index)\\n episode_over = False\\n ids = goal_set[epoch_index]['consult_id']\\n \\n if ids == '10123471':\\n print(1)\\n \\n while episode_over == False:\\n reward, episode_over, dialogue_status, agent_action = self.dialogue_manager.next(save_record=False,train_mode=train_mode,greedy_strategy=0)\\n total_reward += reward\\n \\n if print_result and agent_action['inform_slots']: \\n goal_slots[ids]['Disease'] = list(agent_action['inform_slots'].values())[0]\\n \\n total_truns += self.dialogue_manager.state_tracker.turn\\n inform_wrong_disease_count += self.dialogue_manager.inform_wrong_disease_count\\n \\n\\n ############### f1 score calculation ###########################\\n final_state = self.dialogue_manager.state_tracker.state['current_slots']['inform_slots']\\n all_state = set(list(self.dialogue_manager.state_tracker.user.goal['goal']['explicit_inform_slots'].keys()) + list(self.dialogue_manager.state_tracker.user.goal['goal']['implicit_inform_slots'].keys())) \\n if print_result:\\n goal_slots[ids]['Symptoms'] = list(final_state.keys())\\n \\n correct_inform = 0\\n for key, value in final_state.items():\\n if value != \\\"I don't know.\\\":\\n correct_inform += 1\\n recall_score += correct_inform / len(all_state)\\n #print(2 * correct_inform / (len(all_state) + len(final_state)))\\n if dialogue_status == dialogue_configuration.DIALOGUE_STATUS_SUCCESS:\\n success_count += 1\\n if self.dialogue_manager.inform_wrong_disease_count == 0:\\n absolute_success_count += 1\\n ########################### print .json file ########################\\n if print_result:\\n with open('./../../../Evaluation/result.json','w') as file_obj:\\n json.dump(goal_slots, file_obj)\\n \\n average_recall = float(\\\"%.3f\\\" % (float(recall_score) / evaluate_epoch_number))\\n success_rate = float(\\\"%.3f\\\" % (float(success_count) / evaluate_epoch_number))\\n absolute_success_rate = float(\\\"%.3f\\\" % (float(absolute_success_count) / evaluate_epoch_number))\\n average_reward = float(\\\"%.3f\\\" % (float(total_reward) / evaluate_epoch_number))\\n average_turn = float(\\\"%.3f\\\" % (float(total_truns) / evaluate_epoch_number))\\n average_wrong_disease = float(\\\"%.3f\\\" % (float(inform_wrong_disease_count) / evaluate_epoch_number))\\n res = {\\\"success_rate\\\":success_rate, \\\"average_reward\\\": average_reward, \\\"average_turn\\\": average_turn, \\\"average_wrong_disease\\\":average_wrong_disease,\\\"ab_success_rate\\\":absolute_success_rate, 'average_recall': average_recall}\\n self.learning_curve.setdefault(index, dict())\\n self.learning_curve[index][\\\"success_rate\\\"]=success_rate\\n self.learning_curve[index][\\\"average_reward\\\"]=average_reward\\n self.learning_curve[index][\\\"average_turn\\\"] = average_turn\\n self.learning_curve[index][\\\"average_wrong_disease\\\"]=average_wrong_disease\\n print(\\\"%3d simulation SR %s, ABSR %s, ave reward %s, ave turns %s, ave wrong disease %s, ave recall %s\\\" % (index,res['success_rate'], res[\\\"ab_success_rate\\\"],res['average_reward'], res['average_turn'], res[\\\"average_wrong_disease\\\"], res[\\\"average_recall\\\"]))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefa7079bf4f5a2759137864a1cea73d\",\n \"score\": \"0.4832951\",\n \"text\": \"def on_result_recieved(self, *args):\\n print '{0} received streaming data.'.format(self.name)\\n self.vis.put(dict(id=self.id, type='mes', rec=args[0]))\\n self.analyze.put(dict(id=self.id, rec=args[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e3e26161c5bad33ca844617cdbe677\",\n \"score\": \"0.48291898\",\n \"text\": \"def result(self, state, action):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e3e26161c5bad33ca844617cdbe677\",\n \"score\": \"0.48291898\",\n \"text\": \"def result(self, state, action):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c68e11cc8742234d4a27dd15575959d7\",\n \"score\": \"0.48272514\",\n \"text\": \"def step(self):\\n # Time counting updates\\n if 'ID' in self.unprocessedReqs.keys():\\n if not self.unprocessedReqs['inElections']:\\n self.unprocessedReqs['delayT'] += 1\\n else:\\n self.unprocessedReqs['msgWait'] += 1\\n\\n # Reading and interpreting messages in the message buffer\\n currentMessage = None\\n if len(self.messageBuffer) > 0:\\n # In this case there are messages to be interpreted\\n currentMessage = self.messageBuffer.pop()\\n\\n if currentMessage:\\n if self.log:\\n print(\\\"Received message: {}\\\".format(currentMessage.nType.name))\\n # print \\\"\\\\t %s\\\" % str(currentMessage.msgDict)\\n\\n # Case 1: Service request from client\\n if currentMessage['type'] == MsgTypes.req.value:\\n\\n if self.trainMode != TrainModes.outOfOrder: # Checks if train can accept\\n if not ('ID' in self.unprocessedReqs.keys()): # Checks if there are current processes ongoing\\n\\n if self.log:\\n print(\\\"Processing Client Request\\\")\\n\\n clientID = currentMessage['sender']\\n route, d = None, None\\n # Calculate route\\n if self.trainMode == TrainModes.wait:\\n route, d = self.calculate_route( self.pos, currentMessage['pickUp'] )\\n elif (self.trainMode == TrainModes.accept) or (self.trainMode == TrainModes.busy):\\n route, d = self.calculate_route( self.path[-1], currentMessage['pickUp'] )\\n self.unprocessedReqs = dict(ID=clientID, pickup=currentMessage['pickUp'],\\n dropoff=currentMessage['dropOff'], delayT=0,\\n inElections=False, simpleD=d, route=route, msgWait=0)\\n # Train Responde Request!\\n\\n # Case 2: Election started\\n elif currentMessage['type'] == MsgTypes.elec.value:\\n\\n if not self.trainMode == TrainModes.outOfOrder: # Checks if train can accept\\n # if not self.outOfElec == currentMessage['clientID']: # Check if has already 'lost' election\\n\\n if 'ID' in self.unprocessedReqs.keys():\\n if self.unprocessedReqs['ID'] == currentMessage['clientID']:\\n # NOTE: I assume any car receives first the notice from the client\\n if self.log:\\n print(\\\"Received Election Message\\\")\\n\\n dTot = self.unprocessedReqs['simpleD'] + self.full_distance()\\n\\n if dTot < currentMessage['distance']:\\n # This train is the leader himself\\n self.silence_train(currentMessage['sender'],self.unprocessedReqs['ID'])\\n if not self.unprocessedReqs['inElections']:\\n # If It hasn't yet send its distance, should do so now\\n self.start_election(dTot,self.unprocessedReqs['ID'])\\n self.unprocessedReqs['inElections'] = True\\n self.unprocessedReqs['msgWait'] = 0\\n if self.log:\\n print(\\\"\\\\t Win this elections round\\\")\\n else:\\n # Finishes current election process\\n self.outOfElec = self.unprocessedReqs['ID']\\n self.unprocessedReqs = {}\\n if self.log:\\n print(\\\"\\\\t Lost these elections\\\")\\n # Case 3: Election answer\\n elif currentMessage['type'] == MsgTypes.elec_ack.value:\\n if \\\"ID\\\" in self.unprocessedReqs.keys():\\n if self.unprocessedReqs['ID'] == currentMessage['clientID']: # Checks if this message is from current election\\n # No need to check if message is destined to itself, because the receiving mechanism already does so.\\n # Train lost current election. Finishes election process\\n self.outOfElec = self.unprocessedReqs['ID']\\n self.unprocessedReqs = {}\\n\\n if self.log:\\n print(\\\"Silenced in these elections. Lost election.\\\")\\n \\n # Case 4: Leader Message\\n # Conferir ! - Fiz modificações\\n elif currentMessage['type'] == MsgTypes.leader:\\n if \\\"ID\\\" in self.unprocessedReqs.keys():\\n if self.unprocessedReqs['ID'] == currentMessage['clientID']: # Checks if this message is from current election\\n self.outOfElec = self.unprocessedReqs['ID']\\n self.unprocessedReqs = {}\\n if self.log:\\n print(\\\"Got an election Lider in these elections. Lost election.\\\")\\n else:\\n pass\\n # Nothing to Do\\n # ------------------------------------------\\n # Election start\\n if 'ID' in self.unprocessedReqs.keys():\\n if not self.unprocessedReqs['inElections']:\\n if self.unprocessedReqs['delayT'] == self.delayWanted:\\n # Will start election\\n if self.log:\\n print(\\\"Starting Election!\\\")\\n\\n self.unprocessedReqs['inElections'] = True\\n d = self.unprocessedReqs['simpleD'] + self.full_distance() # Needs to add the distance until the\\n # final position in path\\n self.start_election(d,self.unprocessedReqs['ID'])\\n self.unprocessedReqs['msgWait'] = 0\\n # ------------------------------------------\\n # Elections finish\\n else:\\n if self.unprocessedReqs['msgWait'] == self.maximumMsgWait:\\n # If no answer is given, election isn't silenced and I am current leader\\n # self.broadcast_leader(self.id) # Inform others who's answering the request\\n\\n if self.log:\\n print(\\\"Finishing election! I've won! (ID {})\\\".format(self.id))\\n\\n self.path += self.unprocessedReqs['route'] # Adds route to desired path\\n # TODO: Think on pickup and dropoff. Might be strings instead of actual coordinates...\\n # In this case I'd need to convert into coordinates\\n self.client += [(self.unprocessedReqs['ID'], self.unprocessedReqs['pickup'], self.unprocessedReqs['dropoff'])]\\n self.client_accept(self.unprocessedReqs['ID'])\\n self.unprocessedReqs = {} # Finishes current election process\\n\\n if self.trainMode == TrainModes.wait:\\n self.trainMode = TrainModes.accept\\n self.currentGoal = self.client[0][1] # pickup\\n # ------------------------------------------\\n\\n # Moving train and handling new position\\n self.move()\\n\\n if self.pos == self.currentGoal: # Reached current destination\\n if self.trainMode == TrainModes.accept:\\n # Client boarding train\\n self.trainMode = TrainModes.busy\\n self.currentGoal = self.client[0][2] # dropoff\\n\\n elif self.trainMode == TrainModes.busy:\\n # Client leaving the train\\n self.client.pop() # taking out client from list\\n if len(self.client) > 0:\\n self.trainMode = TrainModes.accept\\n self.currentGoal = self.client[0][1] # pickUp\\n else:\\n self.currentGoal = None\\n self.trainMode = TrainModes.wait\\n\\n elif self.trainMode == TrainModes.outOfOrder:\\n self.kill()\\n # ------------------------------------------\\n\\n # Print new train on simulation screen\\n self.draw()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4148f9b4d95786ab5f54f9ef02a5c812\",\n \"score\": \"0.48264095\",\n \"text\": \"def post_regression():\\n query = request_query()\\n submission = request_submission()\\n\\n # Pass data into function after vetting\\n if not isinstance(submission, tuple):\\n new_regression = create_regression(\\n query['user_id'], \\n query['source'], \\n submission\\n )\\n\\n # Return 201 on success\\n if not isinstance(new_regression, tuple):\\n return new_regression, 201\\n \\n # Return error code if could not create\\n else:\\n return new_regression\\n \\n # Return error code if problem with submission\\n else:\\n return submission\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbd0576f4a3bb51d565124d310243a7\",\n \"score\": \"0.48231646\",\n \"text\": \"def getResult(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81ded0bc40fde466c897388c4b22af1\",\n \"score\": \"0.4817456\",\n \"text\": \"def data_available(self, timestamp, data):\\n try:\\n # clean\\n data = data.replace('\\\\r\\\\n', ',').replace('True', '1').replace('False', '0')\\n data = np.array(data.split(',')).astype(np.float)\\n\\n # split into multiple rows\\n data = data.reshape(-1, self.num_columns)\\n\\n # append to samples buffer\\n if self.samples.shape[0] == 0:\\n self.samples = data\\n else:\\n self.samples = np.vstack((self.samples, data))\\n\\n # once the minimum batch size is reached, queue a task\\n # to update the model using a snapshot of the collected samples\\n if self.samples.shape[0] >= self.min_batch_size:\\n print(f'Queuing task to update model using {self.samples.shape} samples')\\n\\n dask_queue.put(dask_client.submit(\\n SerialIoUpdateModel.update_model,\\n self.timesteps,\\n self.preprocessors_and_data,\\n np.copy(self.samples)))\\n\\n self.samples = np.array([])\\n\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c19a911442b83c0c4e000c65c04cc75b\",\n \"score\": \"0.481654\",\n \"text\": \"def process(self, sess):\\n\\n sess.run(self.sync) # copy weights from shared to local\\n rollout = self.pull_batch_from_queue()\\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\\n\\n should_compute_summary = self.task == 0 and self.local_steps % 11 == 0\\n\\n if should_compute_summary:\\n fetches = [self.summary_op, self.train_op, self.global_step]\\n else:\\n fetches = [self.train_op, self.global_step]\\n\\n feed_dict = {\\n self.local_network.x: batch.si,\\n self.ac: batch.a,\\n self.adv: batch.adv,\\n self.r: batch.r,\\n self.local_network.state_in[0]: batch.features[0],\\n self.local_network.state_in[1]: batch.features[1],\\n }\\n\\n fetched = sess.run(fetches, feed_dict=feed_dict)\\n\\n if should_compute_summary:\\n self.summary_writer.add_summary(tf.Summary.FromString(fetched[0]), fetched[-1])\\n self.summary_writer.flush()\\n self.local_steps += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc94d452f0229756a7898d34afc3932\",\n \"score\": \"0.4811831\",\n \"text\": \"def add_supervised_result(tx):\\n with open(\\\"supervised_result.pkl\\\",\\\"rb\\\") as f:\\n result_dict = pickle.load(f)\\n for account,score in result_dict.items():\\n tx.run(\\\"MATCH (acc:Account) where acc.ACCT_ID = '{}' set acc.supervised_socre = {} return acc;\\\".format(account,score))\\n\\n\\n with open(\\\"predicted_label.pkl\\\",\\\"rb\\\") as f:\\n label_dict = pickle.load(f)\\n for account,label in label_dict.items():\\n tx.run(\\\"MATCH (acc:Account) where acc.ACCT_ID = '{}' set acc.supervised_label = {} return acc;\\\".format(account,label))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3af0117f71482b6508c8f4fa816fbdfe\",\n \"score\": \"0.48117074\",\n \"text\": \"def _done_communicating(self, dmlresult_obj):\\n\\t\\tif self.num_averages_per_round:\\n\\t\\t\\treturn ActionableEventTypes.NOTHING.name, None\\n\\t\\t# else continue training, don't need to wait to hear new weights\\n\\t\\ttrain_job = DMLTrainJob(\\n\\t\\t\\tdatapoint_count=self.job_data[\\\"datapoint_count\\\"],\\n\\t\\t\\thyperparams=self.job_data[\\\"hyperparams\\\"],\\n\\t\\t\\tlabel_column_name=self.job_data[\\\"label_column_name\\\"],\\n\\t\\t\\tframework_type=self.job_data[\\\"framework_type\\\"],\\n\\t\\t\\tserialized_model=self.job_data[\\\"serialized_model\\\"],\\n\\t\\t\\tweights=self.job_data[\\\"weights\\\"],\\n\\t\\t\\tsession_filepath=self.job_data[\\\"session_filepath\\\"]\\n\\t\\t)\\n\\t\\treturn ActionableEventTypes.SCHEDULE_JOBS.name, [train_job]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a316413144b995bc5bbed395de72cbf\",\n \"score\": \"0.48105597\",\n \"text\": \"def GetBuffer(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a316413144b995bc5bbed395de72cbf\",\n \"score\": \"0.48105597\",\n \"text\": \"def GetBuffer(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66300ee8864f06f4a76d0647951b2866\",\n \"score\": \"0.4809305\",\n \"text\": \"def computeResult(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43981a19e6ada136114ea1021b70b6fd\",\n \"score\": \"0.48045123\",\n \"text\": \"def robot_result_handler(self):\\n # Wait a result to complete\\n for sample in subscribe_to_study():\\n self.logger.info(f'Received new sample: {sample.ID}')\\n\\n # Make a choice for each variable\\n output = self.opt_spec.search_template.create_new_sample()\\n for key, acceptable_values in self.opt_spec.search_template.list_acceptable_input_values().items():\\n output.inputs[key] = random.choice(acceptable_values)\\n\\n # Send it to the robot\\n send_new_sample(output)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994394,"cells":{"query_id":{"kind":"string","value":"fd9095af87de0d8f52177c1b88881c4e"},"query":{"kind":"string","value":"Setup some initial variables"},"positive_passages":{"kind":"list like","value":[{"docid":"8c2cf3b7a4cb399e343ce6af003017d0","score":"0.0","text":"def initialize2D(self, todraw, xtitle, xbins, ytitle, ybins, cuts):\n print \"============ initialize2D ==================================\"\n print \"2D shape systematics \", todraw, \" x \",xtitle, \" y \",ytitle,\" cuts \",cuts\n self.todraw2D = todraw\n self.xtitle = xtitle\n self.xbins = xbins\n self.cuts = cuts\n self.ytitle = ytitle\n self.ybins = ybins\n #self.shortname = shortname \n\n self.sample_systematic_hist = {}\n self.systematic_hist = {}\n self.finalhist = {}\n self.channel_shortname_systematic_hist = {}\n for channel in self.channels:\n self.channel_shortname_systematic_hist[channel] = {}\n\n print \"============ end of initialize2D ==================================\"","title":""}],"string":"[\n {\n \"docid\": \"8c2cf3b7a4cb399e343ce6af003017d0\",\n \"score\": \"0.0\",\n \"text\": \"def initialize2D(self, todraw, xtitle, xbins, ytitle, ybins, cuts):\\n print \\\"============ initialize2D ==================================\\\"\\n print \\\"2D shape systematics \\\", todraw, \\\" x \\\",xtitle, \\\" y \\\",ytitle,\\\" cuts \\\",cuts\\n self.todraw2D = todraw\\n self.xtitle = xtitle\\n self.xbins = xbins\\n self.cuts = cuts\\n self.ytitle = ytitle\\n self.ybins = ybins\\n #self.shortname = shortname \\n\\n self.sample_systematic_hist = {}\\n self.systematic_hist = {}\\n self.finalhist = {}\\n self.channel_shortname_systematic_hist = {}\\n for channel in self.channels:\\n self.channel_shortname_systematic_hist[channel] = {}\\n\\n print \\\"============ end of initialize2D ==================================\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cbcd256d2ab8970061afb0326dd780c5","score":"0.72661275","text":"def pre_initialize(self):\n pass","title":""},{"docid":"c92637668a066945e6c1624fbc4d1976","score":"0.7254438","text":"def initialize():","title":""},{"docid":"9b30fc65b0b6dbc9a7bcaf7554fcad17","score":"0.7237147","text":"def init():","title":""},{"docid":"441bf5e66c30bdff718b4a4bc99b2510","score":"0.7236415","text":"def _init_env_variables(self):\n self.reward = 0\n self.n_step = 0\n self.consecutive_errors = 0\n self.min_distance = 10","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"261bb3e0a80ba11d75ebe45417c4a81e","score":"0.7212496","text":"def initialise(self):\n pass","title":""},{"docid":"291196e549bed4b80153ccbf8a51d0ed","score":"0.717406","text":"def pre_init(self) -> None:\n pass","title":""},{"docid":"5a50ffb3071e0f3e05bbb2f723b19810","score":"0.71012735","text":"def init_setup(self):\n pass","title":""},{"docid":"5c00fded5fd325fc1705acaa35401c7e","score":"0.7048341","text":"def initialise(self, initial_conditions):\n pass","title":""},{"docid":"25f6e02326d9bc60c61b53a2d0dfc4f1","score":"0.7019434","text":"def _additionalInit(self):","title":""},{"docid":"531dcf03197837e64438c128fb4c0bf3","score":"0.7016814","text":"def initialization(self):\r\n pass","title":""},{"docid":"5d092eb6063bd1fc9e53fe7514aec1d4","score":"0.6923094","text":"def initialize(self):\r\n\r\n # Remains unimplemented at the moment\r\n pass","title":""},{"docid":"53e3523a88a4dcf33733f4bd06331c3b","score":"0.6900155","text":"def _init_env_variables(self):\n # TODO","title":""},{"docid":"4d78e0d3e028da7d7029e335ba77185e","score":"0.6842065","text":"def initialize():\n\tpass","title":""},{"docid":"19b364bcf6dd084e71d0dc12e4f04ae8","score":"0.68337095","text":"def __init__(self, initialVariables, merit):\n\n self._initialVariables = initialVariables\n self._merit = merit","title":""},{"docid":"301e57a4e4eb11de1fb2529955ef6e94","score":"0.68240744","text":"def initialize_variables(self):\n # counts the number of evaluations modulo WEIGHT_ADAPTION_DURATION for weight adaption heuristic\n self.candidate_counter = 0","title":""},{"docid":"5b273c4fd1e3b528e6e4e57228b49e15","score":"0.6817473","text":"def init():\n pass","title":""},{"docid":"5b273c4fd1e3b528e6e4e57228b49e15","score":"0.6817473","text":"def init():\n pass","title":""},{"docid":"5b273c4fd1e3b528e6e4e57228b49e15","score":"0.6817473","text":"def init():\n pass","title":""},{"docid":"458f64f5e604fcae867d7191cb3312e6","score":"0.68149453","text":"def do_init(self, kwargs):\n\t\tself.init_done = True","title":""},{"docid":"4a945885d147b48f026b1c72b6fd3d7e","score":"0.68090457","text":"def _init_params(self) -> None:\n self._mode = 7\n self._shuntADC = 12\n self._busADC = 12\n self._gain = 8\n self._vrange = 32\n\n self._currentLSB = 0\n self._powerLSB = 0\n self._calibration = 0","title":""},{"docid":"d47050ff98b0773e25379659ea5a5c04","score":"0.68007267","text":"def _init_env_variables(self):\n # For Info Purposes\n self.cumulated_reward = 0.0\n self.previous_y = -1\n self.max_steps_reached = False\n self.current_nsteps = 0\n self.goal_found = False\n # This is necessary to give the laser sensors to refresh in the new reseted position.\n rospy.logwarn(\"Waiting...\")\n time.sleep(0.5)\n rospy.logwarn(\"END Waiting...\")","title":""},{"docid":"1c5d98d679015449aa9a48490dc083a6","score":"0.6790453","text":"def initialize(self):\n\n pass","title":""},{"docid":"b2fcf80024a1bc9e1b89dadb1f1ba542","score":"0.67379296","text":"def init_vars(self):\n cycle_count = 0\n mandatory_array = ('name',\n 'num',\n '#text')\n optional_array = (\n 'rep',\n 'clk',\n 'aresetn',\n 'vendor',\n 'lib',\n 'version',\n 'id_port',\n 'm_axi',\n 's_axi',\n 's_axis',\n 'm_axis',\n 'wire_master',\n 'wire_slave',\n 'ip',\n 'mac',\n 'const',\n 'properties',\n 'board'\n )\n\n\n return cycle_count, mandatory_array, optional_array","title":""},{"docid":"fd276d4c8276a6f8190c313009da1401","score":"0.6720557","text":"def init_params(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"039b9c26b4efb4c4900e869cf33eef5c","score":"0.67159015","text":"def initialize(self):\n pass","title":""},{"docid":"15c10c728c220b450fdd3e4a756486ce","score":"0.6714265","text":"def setUp(self):\n self.maxDiff = None\n self.user_info = set_user()\n self.markets_id = set_markets()\n self.products = set_products()\n self.markets_loc = set_markets_loc()\n self.quantity = set_quantity()\n self. markets_images = markets_images()\n self.products_images = products_images()\n self.markets_ids = markets_ids()\n self.products_ids = products_ids()","title":""},{"docid":"041df3e2a73405b451969a1968d1a339","score":"0.67122203","text":"def _setup(self, X):\n self._t = 1","title":""},{"docid":"504ec146b5e803124d12cd33a8aa4c37","score":"0.66969776","text":"def __init__(self, **initials):\r\n self.useDefaults()\r\n \r\n for key, value in initials.items():\r\n if value:\r\n self.__setattr__(key, value)","title":""},{"docid":"d79c3279140379d80c82cd4032799d14","score":"0.6689902","text":"def init ( self ):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.66703105","text":"def init(self):\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6667226","text":"def initialize(self):\r\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6667226","text":"def initialize(self):\r\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6667226","text":"def initialize(self):\r\n pass","title":""},{"docid":"0ef09567fcf62de9f77b46dbfd264654","score":"0.6663883","text":"def Initialize(self):","title":""},{"docid":"1e20b44815d634b9769e7a5e5cb20688","score":"0.6663097","text":"def setUp(self):\n io._thru = dict()\n io._psf = dict()\n io._params = None\n io._fiberpos = None\n io._tiles = None","title":""},{"docid":"304329564d5339885edb9bbf0bc3734e","score":"0.66608423","text":"def _set_initial_config(self):\t\t\n\t\tself._set_users_number()\n\t\tself._set_tasks_number()\n\t\tself._set_min_task_point()\n\t\tself._set_max_users_point()\n\n\t\tlogger.debug('Tasks number: %d', self.tasks_number)\n\t\tlogger.debug('Minimum points of tasks: %d', self.min_task_point)\n\t\t# Set max remain user points equal max users point\n\t\tself.max_remain_user_point = self.max_users_point","title":""},{"docid":"3448450accefbd3c2c79c1483b6a42f0","score":"0.6655434","text":"def _init_env_variables(self):\n rospy.logdebug(\"Init Env Variables...\")\n rospy.logdebug(\"Init Env Variables...END\")","title":""},{"docid":"affc4f297652c37556fd6a5447753513","score":"0.66522956","text":"def _initialize(self):","title":""},{"docid":"ad7185b7679f52ca738f87e9b78bc476","score":"0.6642054","text":"def initialize(self):\n self._init_context()\n self._init_inputs()","title":""},{"docid":"b6c3822ea2c6f1c4f45e623c52b5ba6c","score":"0.6639069","text":"def init_variables(self):\n self.seqwidth = 60\n self.translation_tables = {}\n for i, table in CodonTable.unambiguous_dna_by_id.items():\n self.translation_tables[table.names[0]] = i\n self.translator = xbb_translations()","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.6632832","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.6632832","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.6632832","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.6632832","text":"def initialize(self):","title":""},{"docid":"04aa578daa87849bc7f6405ad2f1e824","score":"0.6632832","text":"def initialize(self):","title":""},{"docid":"f1f02610f91f5e035cd3601bdde8616e","score":"0.66255546","text":"def test_initialize(self):\n # setup and tear down, run initialize","title":""},{"docid":"e8fe1e937c04fd4085fafa927ae97339","score":"0.6619739","text":"def initialise(cls):","title":""},{"docid":"5b028602b9a7637a71389088824c020b","score":"0.6618061","text":"def setup_data(self):\n pass","title":""},{"docid":"ac423eba6e88c1080e12f1aadac5dd7c","score":"0.6604181","text":"def _init(self):\n pass","title":""},{"docid":"92a6a65a78ede0583a5710ee5e9129bb","score":"0.65985304","text":"def setUp(self):\n self.parser = CParser()\n self.visitor = SharedVarsVisitor()","title":""},{"docid":"8435291537875be52dc5fe104c2b5597","score":"0.65890247","text":"def init(self): \r\n pass","title":""},{"docid":"68d107aa2a88f0c3c4b45845d4d8f6c0","score":"0.65884346","text":"def init (self):\n pass","title":""},{"docid":"188c44f48e0976fe8e88f87852d6a51c","score":"0.65881866","text":"def initialize():\n pass","title":""},{"docid":"9c117b45ebe3fc6ae17855ec0f4a423a","score":"0.6582987","text":"def init(self):\r\n pass","title":""},{"docid":"945cc72591445f736b58674267346ff2","score":"0.6574042","text":"def _initialize(self):\n pass","title":""},{"docid":"945cc72591445f736b58674267346ff2","score":"0.6574042","text":"def _initialize(self):\n pass","title":""},{"docid":"94a20e2d665de96cb2ab52d7a6acead8","score":"0.6570266","text":"def init(self, **kwargs):","title":""},{"docid":"94a20e2d665de96cb2ab52d7a6acead8","score":"0.6570266","text":"def init(self, **kwargs):","title":""},{"docid":"94a20e2d665de96cb2ab52d7a6acead8","score":"0.6570266","text":"def init(self, **kwargs):","title":""},{"docid":"e3b18d01113aa19349cad235b54ecf01","score":"0.6569843","text":"def SetUp(self):\n pass","title":""},{"docid":"bc56a1397740a06b8fe991920328120f","score":"0.6561064","text":"def _init_env_variables(self):\n raise NotImplementedError()","title":""},{"docid":"bc56a1397740a06b8fe991920328120f","score":"0.6561064","text":"def _init_env_variables(self):\n raise NotImplementedError()","title":""},{"docid":"83191f01888d03c9cb21cd7725c43ba3","score":"0.6553158","text":"def _real_initialize(self):\n pass","title":""},{"docid":"b9472da504987363fa4079f47ec7ce4a","score":"0.6534056","text":"def init_basics(self):\n\n if self.find_tcat_dset(): return 1\n if self.find_x_mat(): return 1\n\n if self.guess_subject_id(): return 1\n if self.guess_xmat_nocen(): return 1\n if self.guess_num_stim(): return 1\n if self.guess_rm_trs(): return 1\n if self.guess_stats_dset(): return 1\n if self.guess_sum_ideal(): return 1\n if self.guess_volreg_dset(): return 1\n if self.guess_final_view(): return 1\n\n if self.guess_enorm_dset(): return 1\n if self.guess_motion_dset(): return 1\n if self.guess_outlier_dset():return 1\n if self.guess_final_anat(): return 1\n if self.guess_align_anat(): return 1\n if self.guess_template(): return 1\n if self.guess_mask_dset(): return 1\n if self.guess_tsnr_dset(): return 1\n if self.guess_errts_dset(): return 1\n if self.guess_gcor_dset(): return 1\n if self.guess_mask_corr_dset(): return 1\n\n if self.find_censor_file(): return 1\n\n if self.cvars.verb > 2:\n print '-' * 75\n self.uvars.show('post-init uvars', name=0)\n self.cvars.show('post-init cvars', name=0)\n for datr in self.dsets.attributes(getall=1):\n obj = self.dsets.val(datr)\n if isinstance(obj, BASE.afni_name):\n print '-- dset %-16s : %s' % (datr, obj.pv())\n elif isinstance(obj, LAD.Afni1D):\n print '-- Afni1D %-16s : %s' % (datr, obj.fname)\n print '-' * 75\n\n return 0","title":""},{"docid":"ebc208cfb204c75653912a7902c5ef5d","score":"0.65323734","text":"def testInit(self):\n\n pass","title":""},{"docid":"604bba0093963e0c9dd34296fdc1faf4","score":"0.652392","text":"def init_data(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"cbcd256d2ab8970061afb0326dd780c5\",\n \"score\": \"0.72661275\",\n \"text\": \"def pre_initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92637668a066945e6c1624fbc4d1976\",\n \"score\": \"0.7254438\",\n \"text\": \"def initialize():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b30fc65b0b6dbc9a7bcaf7554fcad17\",\n \"score\": \"0.7237147\",\n \"text\": \"def init():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441bf5e66c30bdff718b4a4bc99b2510\",\n \"score\": \"0.7236415\",\n \"text\": \"def _init_env_variables(self):\\n self.reward = 0\\n self.n_step = 0\\n self.consecutive_errors = 0\\n self.min_distance = 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261bb3e0a80ba11d75ebe45417c4a81e\",\n \"score\": \"0.7212496\",\n \"text\": \"def initialise(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291196e549bed4b80153ccbf8a51d0ed\",\n \"score\": \"0.717406\",\n \"text\": \"def pre_init(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a50ffb3071e0f3e05bbb2f723b19810\",\n \"score\": \"0.71012735\",\n \"text\": \"def init_setup(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c00fded5fd325fc1705acaa35401c7e\",\n \"score\": \"0.7048341\",\n \"text\": \"def initialise(self, initial_conditions):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f6e02326d9bc60c61b53a2d0dfc4f1\",\n \"score\": \"0.7019434\",\n \"text\": \"def _additionalInit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"531dcf03197837e64438c128fb4c0bf3\",\n \"score\": \"0.7016814\",\n \"text\": \"def initialization(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d092eb6063bd1fc9e53fe7514aec1d4\",\n \"score\": \"0.6923094\",\n \"text\": \"def initialize(self):\\r\\n\\r\\n # Remains unimplemented at the moment\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e3523a88a4dcf33733f4bd06331c3b\",\n \"score\": \"0.6900155\",\n \"text\": \"def _init_env_variables(self):\\n # TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d78e0d3e028da7d7029e335ba77185e\",\n \"score\": \"0.6842065\",\n \"text\": \"def initialize():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b364bcf6dd084e71d0dc12e4f04ae8\",\n \"score\": \"0.68337095\",\n \"text\": \"def __init__(self, initialVariables, merit):\\n\\n self._initialVariables = initialVariables\\n self._merit = merit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301e57a4e4eb11de1fb2529955ef6e94\",\n \"score\": \"0.68240744\",\n \"text\": \"def initialize_variables(self):\\n # counts the number of evaluations modulo WEIGHT_ADAPTION_DURATION for weight adaption heuristic\\n self.candidate_counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b273c4fd1e3b528e6e4e57228b49e15\",\n \"score\": \"0.6817473\",\n \"text\": \"def init():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b273c4fd1e3b528e6e4e57228b49e15\",\n \"score\": \"0.6817473\",\n \"text\": \"def init():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b273c4fd1e3b528e6e4e57228b49e15\",\n \"score\": \"0.6817473\",\n \"text\": \"def init():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458f64f5e604fcae867d7191cb3312e6\",\n \"score\": \"0.68149453\",\n \"text\": \"def do_init(self, kwargs):\\n\\t\\tself.init_done = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a945885d147b48f026b1c72b6fd3d7e\",\n \"score\": \"0.68090457\",\n \"text\": \"def _init_params(self) -> None:\\n self._mode = 7\\n self._shuntADC = 12\\n self._busADC = 12\\n self._gain = 8\\n self._vrange = 32\\n\\n self._currentLSB = 0\\n self._powerLSB = 0\\n self._calibration = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47050ff98b0773e25379659ea5a5c04\",\n \"score\": \"0.68007267\",\n \"text\": \"def _init_env_variables(self):\\n # For Info Purposes\\n self.cumulated_reward = 0.0\\n self.previous_y = -1\\n self.max_steps_reached = False\\n self.current_nsteps = 0\\n self.goal_found = False\\n # This is necessary to give the laser sensors to refresh in the new reseted position.\\n rospy.logwarn(\\\"Waiting...\\\")\\n time.sleep(0.5)\\n rospy.logwarn(\\\"END Waiting...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c5d98d679015449aa9a48490dc083a6\",\n \"score\": \"0.6790453\",\n \"text\": \"def initialize(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fcf80024a1bc9e1b89dadb1f1ba542\",\n \"score\": \"0.67379296\",\n \"text\": \"def init_vars(self):\\n cycle_count = 0\\n mandatory_array = ('name',\\n 'num',\\n '#text')\\n optional_array = (\\n 'rep',\\n 'clk',\\n 'aresetn',\\n 'vendor',\\n 'lib',\\n 'version',\\n 'id_port',\\n 'm_axi',\\n 's_axi',\\n 's_axis',\\n 'm_axis',\\n 'wire_master',\\n 'wire_slave',\\n 'ip',\\n 'mac',\\n 'const',\\n 'properties',\\n 'board'\\n )\\n\\n\\n return cycle_count, mandatory_array, optional_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd276d4c8276a6f8190c313009da1401\",\n \"score\": \"0.6720557\",\n \"text\": \"def init_params(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039b9c26b4efb4c4900e869cf33eef5c\",\n \"score\": \"0.67159015\",\n \"text\": \"def initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c10c728c220b450fdd3e4a756486ce\",\n \"score\": \"0.6714265\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\\n self.user_info = set_user()\\n self.markets_id = set_markets()\\n self.products = set_products()\\n self.markets_loc = set_markets_loc()\\n self.quantity = set_quantity()\\n self. markets_images = markets_images()\\n self.products_images = products_images()\\n self.markets_ids = markets_ids()\\n self.products_ids = products_ids()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041df3e2a73405b451969a1968d1a339\",\n \"score\": \"0.67122203\",\n \"text\": \"def _setup(self, X):\\n self._t = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"504ec146b5e803124d12cd33a8aa4c37\",\n \"score\": \"0.66969776\",\n \"text\": \"def __init__(self, **initials):\\r\\n self.useDefaults()\\r\\n \\r\\n for key, value in initials.items():\\r\\n if value:\\r\\n self.__setattr__(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d79c3279140379d80c82cd4032799d14\",\n \"score\": \"0.6689902\",\n \"text\": \"def init ( self ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.66703105\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6667226\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6667226\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6667226\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef09567fcf62de9f77b46dbfd264654\",\n \"score\": \"0.6663883\",\n \"text\": \"def Initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e20b44815d634b9769e7a5e5cb20688\",\n \"score\": \"0.6663097\",\n \"text\": \"def setUp(self):\\n io._thru = dict()\\n io._psf = dict()\\n io._params = None\\n io._fiberpos = None\\n io._tiles = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304329564d5339885edb9bbf0bc3734e\",\n \"score\": \"0.66608423\",\n \"text\": \"def _set_initial_config(self):\\t\\t\\n\\t\\tself._set_users_number()\\n\\t\\tself._set_tasks_number()\\n\\t\\tself._set_min_task_point()\\n\\t\\tself._set_max_users_point()\\n\\n\\t\\tlogger.debug('Tasks number: %d', self.tasks_number)\\n\\t\\tlogger.debug('Minimum points of tasks: %d', self.min_task_point)\\n\\t\\t# Set max remain user points equal max users point\\n\\t\\tself.max_remain_user_point = self.max_users_point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3448450accefbd3c2c79c1483b6a42f0\",\n \"score\": \"0.6655434\",\n \"text\": \"def _init_env_variables(self):\\n rospy.logdebug(\\\"Init Env Variables...\\\")\\n rospy.logdebug(\\\"Init Env Variables...END\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"affc4f297652c37556fd6a5447753513\",\n \"score\": \"0.66522956\",\n \"text\": \"def _initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad7185b7679f52ca738f87e9b78bc476\",\n \"score\": \"0.6642054\",\n \"text\": \"def initialize(self):\\n self._init_context()\\n self._init_inputs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c3822ea2c6f1c4f45e623c52b5ba6c\",\n \"score\": \"0.6639069\",\n \"text\": \"def init_variables(self):\\n self.seqwidth = 60\\n self.translation_tables = {}\\n for i, table in CodonTable.unambiguous_dna_by_id.items():\\n self.translation_tables[table.names[0]] = i\\n self.translator = xbb_translations()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.6632832\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.6632832\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.6632832\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.6632832\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aa578daa87849bc7f6405ad2f1e824\",\n \"score\": \"0.6632832\",\n \"text\": \"def initialize(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f02610f91f5e035cd3601bdde8616e\",\n \"score\": \"0.66255546\",\n \"text\": \"def test_initialize(self):\\n # setup and tear down, run initialize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8fe1e937c04fd4085fafa927ae97339\",\n \"score\": \"0.6619739\",\n \"text\": \"def initialise(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b028602b9a7637a71389088824c020b\",\n \"score\": \"0.6618061\",\n \"text\": \"def setup_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac423eba6e88c1080e12f1aadac5dd7c\",\n \"score\": \"0.6604181\",\n \"text\": \"def _init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a6a65a78ede0583a5710ee5e9129bb\",\n \"score\": \"0.65985304\",\n \"text\": \"def setUp(self):\\n self.parser = CParser()\\n self.visitor = SharedVarsVisitor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8435291537875be52dc5fe104c2b5597\",\n \"score\": \"0.65890247\",\n \"text\": \"def init(self): \\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d107aa2a88f0c3c4b45845d4d8f6c0\",\n \"score\": \"0.65884346\",\n \"text\": \"def init (self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188c44f48e0976fe8e88f87852d6a51c\",\n \"score\": \"0.65881866\",\n \"text\": \"def initialize():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c117b45ebe3fc6ae17855ec0f4a423a\",\n \"score\": \"0.6582987\",\n \"text\": \"def init(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945cc72591445f736b58674267346ff2\",\n \"score\": \"0.6574042\",\n \"text\": \"def _initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945cc72591445f736b58674267346ff2\",\n \"score\": \"0.6574042\",\n \"text\": \"def _initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a20e2d665de96cb2ab52d7a6acead8\",\n \"score\": \"0.6570266\",\n \"text\": \"def init(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a20e2d665de96cb2ab52d7a6acead8\",\n \"score\": \"0.6570266\",\n \"text\": \"def init(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a20e2d665de96cb2ab52d7a6acead8\",\n \"score\": \"0.6570266\",\n \"text\": \"def init(self, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b18d01113aa19349cad235b54ecf01\",\n \"score\": \"0.6569843\",\n \"text\": \"def SetUp(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc56a1397740a06b8fe991920328120f\",\n \"score\": \"0.6561064\",\n \"text\": \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc56a1397740a06b8fe991920328120f\",\n \"score\": \"0.6561064\",\n \"text\": \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83191f01888d03c9cb21cd7725c43ba3\",\n \"score\": \"0.6553158\",\n \"text\": \"def _real_initialize(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9472da504987363fa4079f47ec7ce4a\",\n \"score\": \"0.6534056\",\n \"text\": \"def init_basics(self):\\n\\n if self.find_tcat_dset(): return 1\\n if self.find_x_mat(): return 1\\n\\n if self.guess_subject_id(): return 1\\n if self.guess_xmat_nocen(): return 1\\n if self.guess_num_stim(): return 1\\n if self.guess_rm_trs(): return 1\\n if self.guess_stats_dset(): return 1\\n if self.guess_sum_ideal(): return 1\\n if self.guess_volreg_dset(): return 1\\n if self.guess_final_view(): return 1\\n\\n if self.guess_enorm_dset(): return 1\\n if self.guess_motion_dset(): return 1\\n if self.guess_outlier_dset():return 1\\n if self.guess_final_anat(): return 1\\n if self.guess_align_anat(): return 1\\n if self.guess_template(): return 1\\n if self.guess_mask_dset(): return 1\\n if self.guess_tsnr_dset(): return 1\\n if self.guess_errts_dset(): return 1\\n if self.guess_gcor_dset(): return 1\\n if self.guess_mask_corr_dset(): return 1\\n\\n if self.find_censor_file(): return 1\\n\\n if self.cvars.verb > 2:\\n print '-' * 75\\n self.uvars.show('post-init uvars', name=0)\\n self.cvars.show('post-init cvars', name=0)\\n for datr in self.dsets.attributes(getall=1):\\n obj = self.dsets.val(datr)\\n if isinstance(obj, BASE.afni_name):\\n print '-- dset %-16s : %s' % (datr, obj.pv())\\n elif isinstance(obj, LAD.Afni1D):\\n print '-- Afni1D %-16s : %s' % (datr, obj.fname)\\n print '-' * 75\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc208cfb204c75653912a7902c5ef5d\",\n \"score\": \"0.65323734\",\n \"text\": \"def testInit(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604bba0093963e0c9dd34296fdc1faf4\",\n \"score\": \"0.652392\",\n \"text\": \"def init_data(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994395,"cells":{"query_id":{"kind":"string","value":"52b2df338463293f04d969d487031b7d"},"query":{"kind":"string","value":"Runs the CollectionCost module and populates the IO dictionaries with calculated values."},"positive_passages":{"kind":"list like","value":[{"docid":"9af86f1781a4bd9c28a491fe7216f674","score":"0.62023646","text":"def run_module(self):\n\n try:\n self.create_ArraySystem()\n self.calculate_trench_properties(self.input_dict, self.output_dict)\n operation_data = self.estimate_construction_time(self.input_dict, self.output_dict)\n\n # pull only global inputs for weather delay from input_dict\n weather_data_keys = ('wind_shear_exponent',\n 'weather_window')\n\n # specify collection-specific weather delay inputs\n self.weather_input_dict = dict(\n [(i, self.input_dict[i]) for i in self.input_dict if i in set(weather_data_keys)])\n self.weather_input_dict[\n 'start_delay_hours'] = 0 # assume zero start for when collection construction begins (start at beginning of construction time)\n self.weather_input_dict[\n 'critical_wind_speed_m_per_s'] = self.input_dict['critical_speed_non_erection_wind_delays_m_per_s']\n self.weather_input_dict[\n 'wind_height_of_interest_m'] = self.input_dict['critical_height_non_erection_wind_delays_m']\n\n # Compute the duration of the construction for electrical collection\n duration_construction = operation_data['Time construct days'].max(skipna=True)\n days_per_month = 30\n duration_construction_months = duration_construction / days_per_month\n self.output_dict['collection_construction_months'] = duration_construction_months\n\n # compute and specify weather delay mission time for roads\n operational_hrs_per_day = self.input_dict['hour_day'][self.input_dict['time_construct']]\n mission_time_hrs = duration_construction * operational_hrs_per_day\n self.weather_input_dict['mission_time_hours'] = int(mission_time_hrs)\n\n self.calculate_weather_delay(self.weather_input_dict, self.output_dict)\n self.calculate_costs(self.input_dict, self.output_dict)\n self.outputs_for_detailed_tab(self.input_dict, self.output_dict)\n self.output_dict['collection_cost_module_type_operation'] = self.outputs_for_costs_by_module_type_operation(\n input_df=self.output_dict['total_collection_cost'],\n project_id=self.project_name,\n total_or_turbine=True\n )\n return 0, 0 # module ran successfully\n except Exception as error:\n traceback.print_exc()\n print(f\"Fail {self.project_name} CollectionCost\")\n return 1, error # module did not run successfully","title":""}],"string":"[\n {\n \"docid\": \"9af86f1781a4bd9c28a491fe7216f674\",\n \"score\": \"0.62023646\",\n \"text\": \"def run_module(self):\\n\\n try:\\n self.create_ArraySystem()\\n self.calculate_trench_properties(self.input_dict, self.output_dict)\\n operation_data = self.estimate_construction_time(self.input_dict, self.output_dict)\\n\\n # pull only global inputs for weather delay from input_dict\\n weather_data_keys = ('wind_shear_exponent',\\n 'weather_window')\\n\\n # specify collection-specific weather delay inputs\\n self.weather_input_dict = dict(\\n [(i, self.input_dict[i]) for i in self.input_dict if i in set(weather_data_keys)])\\n self.weather_input_dict[\\n 'start_delay_hours'] = 0 # assume zero start for when collection construction begins (start at beginning of construction time)\\n self.weather_input_dict[\\n 'critical_wind_speed_m_per_s'] = self.input_dict['critical_speed_non_erection_wind_delays_m_per_s']\\n self.weather_input_dict[\\n 'wind_height_of_interest_m'] = self.input_dict['critical_height_non_erection_wind_delays_m']\\n\\n # Compute the duration of the construction for electrical collection\\n duration_construction = operation_data['Time construct days'].max(skipna=True)\\n days_per_month = 30\\n duration_construction_months = duration_construction / days_per_month\\n self.output_dict['collection_construction_months'] = duration_construction_months\\n\\n # compute and specify weather delay mission time for roads\\n operational_hrs_per_day = self.input_dict['hour_day'][self.input_dict['time_construct']]\\n mission_time_hrs = duration_construction * operational_hrs_per_day\\n self.weather_input_dict['mission_time_hours'] = int(mission_time_hrs)\\n\\n self.calculate_weather_delay(self.weather_input_dict, self.output_dict)\\n self.calculate_costs(self.input_dict, self.output_dict)\\n self.outputs_for_detailed_tab(self.input_dict, self.output_dict)\\n self.output_dict['collection_cost_module_type_operation'] = self.outputs_for_costs_by_module_type_operation(\\n input_df=self.output_dict['total_collection_cost'],\\n project_id=self.project_name,\\n total_or_turbine=True\\n )\\n return 0, 0 # module ran successfully\\n except Exception as error:\\n traceback.print_exc()\\n print(f\\\"Fail {self.project_name} CollectionCost\\\")\\n return 1, error # module did not run successfully\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f9dfb65556c23d60c4b78e4d649e6ebe","score":"0.64189136","text":"def main():\n cost = Costs()\n cost.update_total_costs(5000000, turn=1)\n cost.update_total_costs(4000000, turn=2)\n cost.update_total_costs(3000000, turn=3)\n cost.update_total_costs(2000000, turn=4)\n cost.update_total_costs(1000000, turn=5)\n cost.costs_graph()","title":""},{"docid":"605ce4aea1ed4c21ca4ce1cd787541c4","score":"0.61276674","text":"def build_process_costs(self):\n # TODO: Do we have any process level methods to add here?\n pass","title":""},{"docid":"113d165ad88ee2a1372fc823fd757a7d","score":"0.59934086","text":"def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):\n\n # Set characteristics based on regressions / empirical data\n self.set_properties(inputs, discrete_inputs)\n\n # Set geometry profile\n self.set_geometry(inputs, outputs)\n\n # Write MAP input file and analyze the system at every angle\n self.runMAP(inputs, discrete_inputs, outputs)\n\n # Compute costs for the system\n self.compute_cost(inputs, discrete_inputs, outputs)","title":""},{"docid":"1240be144d63269b0a9c2a8b2ddaef03","score":"0.5879974","text":"def main(path_to_model, scaling_factors, path_to_output):\n model = calliope.read_netcdf(path_to_model)\n eur_per_kw = scaling_factors[\"power\"] / scaling_factors[\"monetary\"] * EUR_PER_KW\n ct_per_kw = scaling_factors[\"power\"] / scaling_factors[\"monetary\"] * CT_PER_KW\n\n energy_cap = (model.get_formatted_array(\"cost_energy_cap\")\n .squeeze(\"costs\")\n .reindex(techs=list(TECHS.keys()))\n .groupby(\"techs\")\n .mean(\"locs\")\n .fillna(0)\n .drop(\"costs\")) * eur_per_kw\n energy_cap.loc[\"ac_transmission\"] = transmission_investment_cost(model, eur_per_kw)\n annual_cost = (model.get_formatted_array(\"cost_om_annual\")\n .squeeze(\"costs\")\n .reindex(techs=list(TECHS.keys()))\n .groupby(\"techs\")\n .mean(\"locs\")\n .fillna(0)\n .drop(\"costs\")) * eur_per_kw\n annual_cost.loc[\"ac_transmission\"] = transmission_annual_cost(model, eur_per_kw)\n storage_cap = (model.get_formatted_array(\"cost_storage_cap\")\n .squeeze(\"costs\")\n .reindex(techs=list(TECHS.keys()))\n .groupby(\"techs\")\n .mean(\"locs\")\n .fillna(0)\n .drop(\"costs\")) * eur_per_kw\n lifetime = (model.get_formatted_array(\"lifetime\")\n .reindex(techs=list(TECHS.keys()))\n .groupby(\"techs\")\n .mean(\"locs\")\n .fillna(0))\n lifetime.loc[\"ac_transmission\"] = transmission_lifetime(model)\n variable_costs_prod = (model.get_formatted_array(\"cost_om_prod\")\n .squeeze(\"costs\")\n .reindex(techs=list(TECHS.keys()))\n .groupby(\"techs\")\n .mean(\"locs\")\n .fillna(0)\n .drop(\"costs\")) * ct_per_kw\n variable_costs_con = (model.get_formatted_array(\"cost_om_con\")\n .squeeze(\"costs\")\n .reindex(techs=list(TECHS.keys()))\n .groupby(\"techs\")\n .mean(\"locs\")\n .fillna(0)\n .drop(\"costs\")) * ct_per_kw\n variable_costs = variable_costs_prod + variable_costs_con\n\n all_costs = xr.Dataset({\n \"Overnight cost (€/kW)\": energy_cap,\n \"Overnight cost (€/kWh)\": storage_cap,\n \"Annual cost (€/kW/yr)\": annual_cost,\n \"Variable cost (€ct/kWh)\": variable_costs,\n \"Lifetime (yr)\": lifetime,\n \"Source\": pd.Series(COST_SOURCES).to_xarray().rename(index=\"techs\")\n })\n all_costs.rename(techs=\"Technology\").to_dataframe().rename(index=TECHS).to_csv(\n path_to_output,\n index=True,\n header=True,\n float_format=\"%.0f\"\n )","title":""},{"docid":"a3319c01b05f549111d998e7ad154085","score":"0.5801046","text":"def cost(self):\n raise NotImplementedError","title":""},{"docid":"aab5d2f3292c4ebe847a687d4960f704","score":"0.5799554","text":"def run(self):\n\n cost = {}; cost[\"best\"] = []; cost[\"mean\"] = []\n \n for itr in range(self.max_itrs):\n\n # employees phase\n for index in range(self.size):\n self.send_employee(index)\n \n # onlookers phase\n self.send_onlookers()\n\n # scouts phase\n self.send_scout()\n\n # computes best path\n self.find_best()\n\n # stores convergence information\n cost[\"best\"].append( self.best )\n cost[\"mean\"].append( sum( [ bee.value for bee in self.population ] ) / self.size )\n\n # prints out information about computation\n if self.verbose:\n self._verbose(itr, cost)\n #print(\"\\n\")\n #print(\"****BEST SOLUTION****\")\n #self.bestSolution._printSolution()\n\n #sequenceFromSolution(self.dna_sequences,self.bestSolution.solution)\n \n #for best in cost[\"best\"]:\n #print(best)\n return cost","title":""},{"docid":"08ef7e0512d66fa83cd69d5aead55832","score":"0.5765362","text":"def cost(results):\n # This is all extremely unsophisticated munging, sorry future me.\n al_df = alignment_df(results)\n co_df = convergence_df(results)\n\n # Convergence targets\n co_nc_after_t = convergence_targets[0]\n co_nc_before_t = convergence_targets[1]\n co_c_after_t = convergence_targets[2]\n co_c_before_t = convergence_targets[3]\n\n # Targets as proportions of a standard\n # co_nc_after_tp = 1\n co_nc_before_tp = co_nc_before_t / co_nc_after_t\n co_c_after_tp = co_c_after_t / co_nc_after_t\n co_c_before_tp = co_c_before_t / co_nc_after_t\n\n # Actual values\n co_nc_after = co_df[\n (co_df[\"Condition\"] == \"Non-clustered\") & (co_df[\"Time\"] == \"After\")\n ][\"Convergence\"].mean()\n co_nc_before = co_df[\n (co_df[\"Condition\"] == \"Non-clustered\") & (co_df[\"Time\"] == \"Before\")\n ][\"Convergence\"].mean()\n co_c_after = co_df[\n (co_df[\"Condition\"] == \"Clustered\") & (co_df[\"Time\"] == \"After\")\n ][\"Convergence\"].mean()\n co_c_before = co_df[\n (co_df[\"Condition\"] == \"Clustered\") & (co_df[\"Time\"] == \"Before\")\n ][\"Convergence\"].mean()\n\n # Actual values as proportions of a standard\n # co_nc_after_p = 1\n co_nc_before_p = co_nc_before / co_nc_after\n co_c_after_p = co_c_after / co_nc_after\n co_c_before_p = co_c_before / co_nc_after\n\n # Alignment targets\n # As proportions\n alignment_targets_p = alignment_targets / alignment_targets[0]\n\n # Actual values\n al_c_1 = al_df[(al_df[\"Condition\"] == \"Clustered\") & (al_df[\"Hops\"] == 1)][\n \"Alignment\"\n ].mean()\n al_c_2 = al_df[(al_df[\"Condition\"] == \"Clustered\") & (al_df[\"Hops\"] == 2)][\n \"Alignment\"\n ].mean()\n al_c_3 = al_df[(al_df[\"Condition\"] == \"Clustered\") & (al_df[\"Hops\"] == 3)][\n \"Alignment\"\n ].mean()\n al_c_4 = al_df[(al_df[\"Condition\"] == \"Clustered\") & (al_df[\"Hops\"] == 4)][\n \"Alignment\"\n ].mean()\n al_c_5 = al_df[(al_df[\"Condition\"] == \"Clustered\") & (al_df[\"Hops\"] == 5)][\n \"Alignment\"\n ].mean()\n al_nc_1 = al_df[(al_df[\"Condition\"] == \"Non-clustered\") & (al_df[\"Hops\"] == 1)][\n \"Alignment\"\n ].mean()\n al_nc_2 = al_df[(al_df[\"Condition\"] == \"Non-clustered\") & (al_df[\"Hops\"] == 2)][\n \"Alignment\"\n ].mean()\n al_nc_3 = al_df[(al_df[\"Condition\"] == \"Non-clustered\") & (al_df[\"Hops\"] == 3)][\n \"Alignment\"\n ].mean()\n alignment_values = np.array(\n [al_c_1, al_c_2, al_c_3, al_c_4, al_c_5, al_nc_1, al_nc_2, al_nc_3]\n )\n # As proportions\n alignment_values_p = alignment_values / alignment_values[0]\n\n diffs_co = [\n co_nc_before_p - co_nc_before_tp,\n co_c_after_p - co_c_after_tp,\n co_c_before_p - co_c_before_tp,\n ]\n diffs_al = alignment_values_p - alignment_targets_p\n cost_co = np.mean(np.abs(diffs_co))\n cost_al = np.mean(np.abs(diffs_al))\n\n # Calculate the linear effect of Hops on Alignment in the Clustered\n # condition and compare to the actual result\n\n al_df_clustered = al_df[al_df[\"Condition\"] == \"Clustered\"]\n if np.isnan(al_df_clustered[\"Alignment\"].values).any():\n cost_reg = np.nan\n else:\n scale = (\n al_df_clustered[al_df_clustered[\"Hops\"] == 1][\"Alignment\"].mean()\n / alignment_targets[0]\n )\n # Rise over run\n target_coef = (alignment_targets[4] * scale - alignment_targets[0] * scale) / 4\n\n reg = linear_model.LinearRegression()\n reg.fit(\n al_df_clustered[\"Hops\"].values.reshape(-1, 1), al_df_clustered[\"Alignment\"]\n )\n cost_reg = np.abs(reg.coef_[0] - target_coef)\n\n # Finally, take into account the absolute amount of alignment in both\n # the clustered and non-clustered conditions\n\n cost_al_abs = 1 - al_df_clustered[al_df_clustered[\"Hops\"] == 1][\"Alignment\"].mean()\n\n c = np.average([cost_co, cost_al, cost_reg, cost_al_abs], weights=[1, 3, 2, 3])\n\n return c, cost_co, cost_al, cost_reg, cost_al_abs","title":""},{"docid":"bdf18f07cf6ded1ec49d2dae94ba384f","score":"0.5737898","text":"def main(\n factory_dict: dict,\n order_dict: dict,\n sku_dict: dict,\n recipe_dict: dict,\n time_limit=True,\n exact=True,\n scaling=1,\n):\n\n model = cp_model.CpModel()\n # initalise the model\n # [START VARIABLES]\n sum_actual = {}\n all_orders = {}\n error_abs = {}\n n = len(factory_dict)\n mape_limit = n * 100\n for count, item in enumerate(order_dict.values()):\n\n all_orders[count] = item\n item.order_list(recipe_dict)\n\n # creates a dict of int:object for use by or_tools\n all_factories = {}\n fulfilled = {} # dict of [factory_id,order_id] : BoolVar\n factory_quantity = {} # dict of [factory_id, type_id] : Int\n\n factory_demand = {} # dict of [factory_id, type_id] : IntVar\n fulfilled_by = {} # dict of [order_id,factory_id] : BoolVar\n items = {} # dict of [order_id, type_id] : Int\n temp = {}\n\n for count, item in enumerate(factory_dict.values()):\n\n all_factories[count] = item\n # creates a dict of int:object for use by or_tools\n error_abs[count] = model.NewIntVar(\n 0, 1000000000000000000, \"x_loss_abs_%i\" % count\n )\n all_skus = {}\n\n for count, item in enumerate(sku_dict.values()):\n\n all_skus[count] = item\n\n all_skus[count].unit_cost = int(all_skus[count].unit_cost * scaling)\n\n # creates a dict of int:object for use by or_tools\n\n for count_factory, factory in all_factories.items():\n\n for count_sku, sku in all_skus.items():\n prefix = \"%s_%s_\" % (factory.factory_id, sku.sku_id)\n factory_quantity[(count_factory, count_sku)] = 0\n if factory.factory_inventory.get(sku.name) is None:\n factory_quantity[(count_factory, count_sku)] = (\n factory.factory_inventory[sku.name] * sku.unit_cost\n )\n else:\n factory_quantity[(count_factory, count_sku)] += (\n factory.factory_inventory[sku.name] * sku.unit_cost\n )\n\n # creates a measure of the current quantity in the factories\n\n for count_order, order in all_orders.items():\n for count_factory, factory in all_factories.items():\n prefix = \"%s_\" % (order.order_id)\n suffix = \"_%s\" % (factory.factory_id)\n fulfilled_by[(count_order, count_factory)] = model.NewBoolVar(\n prefix + \"fulfilled_by\" + suffix\n )\n for count_sku, sku in all_skus.items():\n if order.combined.get(sku.sku_id) is None:\n items[(count_order, count_sku)] = 0\n # incase there is none of an ingredient in the order\n else:\n items[(count_order, count_sku)] = (\n order.combined[sku.sku_id] * sku.unit_cost\n )\n\n if factory_demand.get((count_factory, count_sku)) is None:\n factory_demand[(count_factory, count_sku)] = (\n round(items[(count_order, count_sku)])\n * fulfilled_by[(count_order, count_factory)]\n )\n # create factory demand\n else:\n factory_demand[(count_factory, count_sku)] += (\n round(items[(count_order, count_sku)])\n * fulfilled_by[(count_order, count_factory)]\n )\n # adds to factory demand\n for count_factory, factory in all_factories.items():\n error_abs[count_factory] = sum(\n [\n factory_quantity[(count_factory, count_sku)]\n - factory_demand[(count_factory, count_sku)]\n for count_sku, sku in all_skus.items()\n ]\n )\n # calcuate the numerator for weighted mape\n sum_actual[count_factory] = sum(\n [\n factory_quantity[(count_factory, count_sku)]\n for count_sku, sku in all_skus.items()\n ]\n )\n print(sum_actual)\n # calculates the errors for the quantity and the demand\n\n # [END VARIABLES]\n\n # [START CONSTRAINTS]\n for factory in list(all_factories.keys()):\n for order in list(all_orders.keys()):\n if exact == True:\n\n model.Add(\n sum(\n [\n fulfilled_by[(order, factory_c)]\n for factory_c in list(all_factories.keys())\n ]\n )\n == 1\n ) # ensure that all orders have been fulfilled\n else:\n model.Add(\n sum(\n [\n fulfilled_by[(order, factory)]\n for factory in list(all_factories.keys())\n ]\n )\n <= 1\n ) # to allow for some orders not being fulfilled\n\n for sku in list(all_skus.keys()):\n model.Add(factory_demand[(factory, sku)] <= factory_quantity[factory, sku])\n # ensure the demand does not outweigh the supply for a factory\n # [END CONSTRAINTS]\n # [START OBJECTIVE]\n obj_var = model.NewIntVar(0, mape_limit, \"Sum WMAPE\")\n numerator = {}\n denom = {}\n wmape = {}\n\n for count_factory, factory in all_factories.items():\n numerator[count_factory] = model.NewIntVar(0, 5000000000000000000, \"sum_all\")\n\n denom[count_factory] = model.NewIntVar(0, 5000000000000000000, \"sum_quantity\")\n\n wmape[count_factory] = model.NewIntVar(0, 100, \"WMAPE\")\n\n # adds new varibles to calcuate the weighted mape\n model.Add(numerator[count_factory] == error_abs[count_factory] * 100)\n\n model.Add(denom[count_factory] == sum_actual[count_factory])\n model.AddDivisionEquality(\n wmape[count_factory], numerator[count_factory], denom[count_factory]\n )\n\n sum_wmape = model.NewIntVar(0, 500, \"sum_wmape\")\n model.Add(sum_wmape == sum([wmape[f] for f in all_factories]))\n model.Add(obj_var == sum_wmape)\n model.Minimize(obj_var)\n # [END OBJECTIVE]\n\n solver = cp_model.CpSolver()\n if time_limit == True:\n solver.parameters.max_time_in_seconds = 20000\n # adds an optional time limit\n\n status = solver.Solve(model)\n # solves the model\n\n print(\"Solve status: %s\" % solver.StatusName(status))\n print(\"Mean_Wmape: %i\" % (solver.ObjectiveValue() / len(all_factories)))\n\n wmape_dict = {}\n for num, factory in enumerate(factory_dict):\n wmape_dict[factory] = solver.Value(wmape[num])\n\n return (solver.ObjectiveValue(), wmape_dict)","title":""},{"docid":"b0a5dc64078d27c7a355305bbd0a9af0","score":"0.57017267","text":"def rig_cost(self):\n if self.electricity_cost and self.power_consumption is not None:\n mining_time = 24 * self.period * self.algo_percentage()\n mining_cost = (self.power_consumption / 1000) * self.electricity_cost\n value = mining_time * mining_cost\n else:\n value = 0\n\n if self.pool_fee:\n value += self.pool_cost() * self.usd_price * self.currency.value\n\n self.cost = {'value': Decimal(value), 'btc_value': Decimal(self.currency_to_btc(value=value))}","title":""},{"docid":"8d23ef30e34e7557a05fdf15a4890f03","score":"0.5646338","text":"def main():\n mainpath = (os.path.dirname(os.path.abspath(__name__)))\n run_all(start=1, end=10, targdir=mainpath, cpucount=30, cores_per_job=2,\n cae_cpu_limit=6, filename=\"UnitCell_{0}\", runtype='separate')\n collector(\"csv\", mainpath)","title":""},{"docid":"2f7426e490436cdae9e9b291be353c37","score":"0.56133294","text":"def cost(file_path: str, commodity: str, price_per_ton: float, \n trade_volume: float) -> None:\n\n output = []\n\n #check to make sure the values of price_per_ton and trade_volume\n # are greater than or equal to 0\n if price_per_ton < 0 or trade_volume < 0:\n error_string = (\n \"Error: trade_volume and price_per_ton arguments must \"\n \"be greater than or equal to zero.\"\n )\n print(error_string)\n sys.exit(1)\n\n # Parse the JSON file and get back a list of objects\n commodity = commodity.lower()\n fruit_list = parse_file(file_path)\n\n # Iterate through the list of objects and create a list of dicts\n # with the object as a key and the total price as the value.\n for fruit in fruit_list:\n if commodity == fruit.commodity:\n cost_dict = {}\n cost_dict[\"OBJECT\"] = fruit\n total_cost = (trade_volume \n * (price_per_ton + fruit.variable_overhead)\n + fruit.fixed_overhead)\n # Round to the nearest cent\n cost_dict[\"TOTAL_COST\"] = round(total_cost, 2)\n output.append(cost_dict)\n\n # Check if the output list is empty. If so return that the fruit\n # does not exist.\n if not output:\n error_string = (\n f\"Commodity {commodity} was not found. \"\n \"Please run fruit_vendor_cli.py list commodity for valid values\"\n )\n print(error_string)\n sys.exit(1)\n # Sort the list by the cost. Greatest to least.\n output.sort(key = lambda x: x[\"TOTAL_COST\"], reverse=True)\n \n # Iterate through the output list and print the correctly formatted \n # output\n # COUNTRY | (PRICE_PER_TON + VARIABLE_OVERHEAD) \n # * TRADE_VOLUME + FIXED_OVERHEAD\n for object_cost_dict in output:\n fruit = object_cost_dict[\"OBJECT\"]\n total_cost = object_cost_dict[\"TOTAL_COST\"]\n \n\n out_string = (\n f\"{fruit.country:3} {total_cost:9.2f} | \"\n f\"(({price_per_ton:5.2f} + {fruit.variable_overhead:5.2f})\"\n f\" * {trade_volume:5.2f}) + {fruit.fixed_overhead:5.2f}\"\n )\n print(out_string) \n return","title":""},{"docid":"dbfe935c7dda52325ec0e2fb0d4f99ef","score":"0.55586934","text":"def calculate_economics_diagnostics(self):\n\n start_index_for_costs = tool_kit.find_first_list_element_at_least(\n self.times, self.inputs.model_constants['recent_time'])\n self.cost_times = self.times[start_index_for_costs:]\n self.costs = numpy.zeros((len(self.cost_times), len(self.interventions_to_cost)))\n\n for i, inter in enumerate(self.interventions_to_cost):\n for t, time in enumerate(self.cost_times):\n # costs from cost-coverage curves\n cost = get_cost_from_coverage(self.scaleup_fns['int_prop_' + inter](time),\n self.params['econ_inflectioncost_' + inter],\n self.params['econ_saturation_' + inter],\n self.params['econ_unitcost_' + inter],\n self.var_array[start_index_for_costs + t, self.var_labels.index('popsize_' + inter)])\n\n # start-up costs\n if 'econ_startupcost_' + inter in self.inputs.model_constants \\\n and 'econ_startupduration_' + inter in self.inputs.model_constants \\\n and self.inputs.model_constants['econ_startupduration_' + inter] > 0.:\n cost += self.calculate_startup_cost(time, inter)\n\n # yearly cost\n if 'econ_yearlycost_' + inter in self.params.keys() and time > self.params['scenario_start_time']:\n cost += self.params['econ_yearlycost_' + inter]\n\n self.costs[t, i] = cost","title":""},{"docid":"cb06c03f5d3f71480b743923b76611dc","score":"0.55478036","text":"def compute_cost(self, inputs, discrete_inputs, outputs):\n # Unpack variables\n rhoWater = inputs['water_density']\n L_mooring = inputs['mooring_line_length']\n anchorType = discrete_inputs['anchor_type']\n costFact = inputs['mooring_cost_factor']\n n_connect = int(inputs['number_of_mooring_connections'])\n n_lines = int(inputs['mooring_lines_per_connection'])\n ntotal = n_connect * n_lines\n \n # Cost of anchors\n if type(anchorType) == type(''): anchorType = Anchor[anchorType.upper()]\n if anchorType == Anchor['DRAGEMBEDMENT']:\n anchor_rate = 1e-3 * self.min_break_load / gravity / 20*2000\n elif anchorType == Anchor['SUCTIONPILE']:\n anchor_rate = 150000.* np.sqrt(1e-3*self.min_break_load/gravity/1250.)\n else:\n raise ValueError('Anchor Type must be DRAGEMBEDMENT or SUCTIONPILE')\n anchor_total = anchor_rate*ntotal\n\n # Cost of all of the mooring lines\n legs_total = ntotal * self.cost_per_length * L_mooring\n\n # Total summations\n outputs['anchor_cost'] = anchor_total\n outputs['mooring_cost'] = costFact*(legs_total + anchor_total)\n outputs['mooring_mass'] = self.wet_mass_per_length*L_mooring*ntotal\n outputs['number_of_mooring_lines'] = ntotal","title":""},{"docid":"d9633462cef468eb0b09559315d3cfbb","score":"0.5537384","text":"def compute(self):\n\n self.cover, self.cost = {}, 0\n\n if self.options.verb:\n print('c2 computing smallest rule cover')\n\n if self.options.cover == 'mxsat':\n self.compute_mxsat()\n elif self.options.cover == 'cplex':\n self.compute_cplex()\n elif self.options.cover in ('gurobi', 'grb'):\n self.compute_gurobi()\n\n # recording time\n self.stime = resource.getrusage(resource.RUSAGE_SELF).ru_utime - self.init_stime\n self.ctime = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime - self.init_ctime\n self.time = self.stime + self.ctime\n\n return self.cover","title":""},{"docid":"84318fe510f11133106368f669d9691f","score":"0.5512912","text":"def _compute_detailed_cost(self):\n if self.ocp.n_threads > 1:\n raise NotImplementedError(\"Computing detailed cost with n_thread > 1 is not implemented yet\")\n\n self._detailed_cost = []\n\n for nlp in self.ocp.nlp:\n for penalty in nlp.J_internal + nlp.J:\n if not penalty:\n continue\n val, val_weighted = self._get_penalty_cost(nlp, penalty)\n self._detailed_cost += [\n {\"name\": penalty.type.__str__(), \"cost_value_weighted\": val_weighted, \"cost_value\": val}\n ]\n for penalty in self.ocp.J:\n val, val_weighted = self._get_penalty_cost(self.ocp.nlp[0], penalty)\n self._detailed_cost += [\n {\"name\": penalty.type.__str__(), \"cost_value_weighted\": val_weighted, \"cost_value\": val}\n ]\n return","title":""},{"docid":"bc8f4926a9183cf5f7dd3d63ec86cb05","score":"0.5462631","text":"def setUp(self):\n self.cost_func = make_cost_function()\n problem = self.cost_func.problem\n self.options = problem.options\n self.options.software = [\"scipy\", \"dfo\", \"scipy_ls\"]\n self.grabbed_output = output_grabber.OutputGrabber(self.options)\n self.start_values_index = 0\n self.scipy_len = len(self.options.minimizers[\"scipy\"])\n self.dfo_len = len(self.options.minimizers[\"dfo\"])\n self.scipy_ls_len = len(self.options.minimizers[\"scipy_ls\"])\n controller = ScipyController(self.cost_func)\n controller.parameter_set = 0\n self.result_args = {'controller': controller,\n 'accuracy': 1,\n 'runtimes': [1]}\n self.cp = Checkpoint(self.options)","title":""},{"docid":"de87d11cde28e971bda21b69dd2a1f8b","score":"0.54610795","text":"def main():\n # fileDB query. this could be moved to cmd line arg in the future\n que = 'run==111' # 30 min bad bkg run\n # que = 'run==110' # 3hr alpha run\n \n # load fileDB. use DataGroup, with a hack to fix use of relative file paths\n pwd = os.getcwd()\n os.chdir('../processing/')\n dg = DataGroup('cage.json', load=True)\n os.chdir(pwd)\n # print(dg.fileDB)\n \n # run query\n dg.fileDB = dg.fileDB.query(que)\n \n # check query result\n view_cols = ['daq_file', 'cycle', 'run', 'runtype', 'startTime', 'runtime']\n print(dg.fileDB[view_cols], '\\n')\n \n # -- run routines -- \n plot_dsp(dg)\n # show_wfs(dg)","title":""},{"docid":"2acfd8ef363b22c591a6d1db5586c1df","score":"0.5446036","text":"def test_calculate_task_cost(self):\r\n\r\n self.DUT.minimum_cost = 252.00\r\n self.DUT.average_cost = 368.00\r\n self.DUT.maximum_cost = 441.00\r\n self.DUT.confidence = 0.95\r\n\r\n self.assertFalse(self.DUT.calculate())\r\n self.assertAlmostEqual(self.DUT.mean_cost, 360.83333333)\r\n self.assertAlmostEqual(self.DUT.cost_variance, 992.25)","title":""},{"docid":"52bfc8217ca0db2e1890d6d3bf0c6649","score":"0.5432577","text":"def project_cost():\n return {\n \"type\": \"class\",\n \"base\": None,\n \"is_abstract\": False,\n \"is_document\": True,\n \"pstr\": (\"Production: {}Y, {}\", (\"useful_years\", \"useful_data\")),\n \"properties\": [\n (\n \"activity\",\n \"linked_to(activity.activity)\",\n \"1.1\",\n \"Project or Experiment of interest\",\n ),\n (\n \"platform\",\n \"linked_to(platform.machine)\",\n \"1.1\",\n \"Machine used for project\",\n ),\n (\n \"useful_years\",\n \"int\",\n \"1.1\",\n \"Number of useful years simulated (or to be simulated) during \"\n \"this project\",\n ),\n (\n \"useful_data\",\n \"shared.numeric\",\n \"0.1\",\n \"Volume of useful data to be analysed\",\n ),\n (\n \"useful_core_hours\",\n \"int\",\n \"0.1\",\n \"Number of core-hours used for useful simulations within \"\n \"the project\",\n ),\n (\n \"actual_years\",\n \"int\",\n \"0.1\",\n \"Number of actual years simulated, including spin-up tuning \"\n \"etc.\",\n ),\n (\n \"peak_data\",\n \"shared.numeric\",\n \"0.1\",\n \"Maximum volume of data held during project\",\n ),\n (\n \"total_core_hours\",\n \"int\",\n \"0.1\",\n \"Total number of core hours needed for all aspects of the \"\n \"project\",\n ),\n (\n \"total_energy_cost\",\n \"float\",\n \"0.1\",\n \"Total cost of project in Joules, if known\",\n ),\n ],\n }","title":""},{"docid":"ea04000952039c17a26c72c2ab51d83f","score":"0.5416007","text":"def compute_cost_per_unit_capital(self):\n\t\ttotal_wage = self.wage * self.labor\n\t\tself.cost = copy.copy(total_wage / self.output)","title":""},{"docid":"96addf55f6117598f02019e88ac37fd0","score":"0.5381483","text":"def cost(self, data_center_cost=2000):\n self.total_cluster_cost = 0\n self.cluster_cost={}\n for point, k_num in self.cluster_assign.items():\n self.cluster_cost[point] = haversine(point,self.centers[k_num])\n self.total_cluster_cost += self.cluster_cost[point]\n self.total_center_cost = self.k_num * data_center_cost\n self.total_cost = self.total_center_cost + self.total_cluster_cost","title":""},{"docid":"9137fe698b763e92f5d01e20a6c85507","score":"0.53786206","text":"def Run(self):\n if self.checkComplete() is False:\n raise ValueError('ERROR')\n \n sourceNorm=0\n for i in range(self.assembly.N):\n for j in range(self.assembly.M):\n sourceIn=[s in self.pins[self.assembly.fuelmap[i][j]]._materials for s in self.assembly.source]\n if True in sourceIn:\n sourceNorm=sourceNorm+1\n dTmaps=[]\n contributionMaps=[]\n contributionMapAves=[]\n geomefficiencies=[]\n geomefficiencyAves=[]\n sourcePoints=[]\n for k in range(self.randomNum):\n print('#%d is being calculated'%(k))\n dTmap={}\n sourcePoint={}\n for name in self.detectors:\n print(\"Distance travelled to detector \"+name+\" is being calculated\")\n dTmap[name],sourcePoint[name]=self.distanceTravelled(self.detectors[name]) \n dTmaps.append(dTmap)\n sourcePoints.append(sourcePoint) \n if self._elines is not None:\n if k==0:\n self.get_MuTable()\n geomefficiency={}\n geomefficiencyAve=np.zeros(len(self._elines))\n contributionMapAve={e: np.zeros((self.assembly.N,self.assembly.M)) for e in self._elines}\n contributionMap={}\n for name in self.detectors:\n print('Contribution to detector %s is calculated...'%(name))\n contributionMap[name]={}\n geomefficiency[name]=np.zeros(len(self._elines))\n for e in self._elines:\n print('...for gamma energy %s MeV'%(e))\n mue=self._mu[e]\n muem={key: mue[key]*self.materials[key].density for key in mue.keys()}\n contributionMap[name][e]=self.attenuation(dTmap[name],muem,self.detectors[name],sourcePoint[name])\n contributionMapAve[e]=contributionMapAve[e]+contributionMap[name][e]/len(self.detectors)\n geomefficiency[name]=np.array([np.sum(contribution) for contribution in contributionMap[name].values()])/sourceNorm\n geomefficiencyAve=geomefficiencyAve+geomefficiency[name]/len(self.detectors)\n contributionMaps.append(contributionMap)\n contributionMapAves.append(contributionMapAve)\n geomefficiencies.append(geomefficiency)\n geomefficiencyAves.append(geomefficiencyAve) \n self._sourcePoints=sourcePoints\n #Various Numpy manipulations to restructure the \"plural\" lists containing data for\n #each random sample. Then the mean and the std of the \"plural\" lists is calculated.\n \n #restructuring dTmaps: from the list of dictionaries, make a dictionary of lists\n #then take the mean and std of the maps in the inner list\n dTmapsRe={det: {mat: [dmap[det][mat] for dmap in dTmaps] for mat in self.materials} for det in self.detectors}\n dTmap={} #will be the average\n dTmapErr={} #will be the std\n for det in dTmapsRe:\n dTmap[det]={}\n dTmapErr[det]={}\n for mat in dTmapsRe[det]:\n dTmapToAve=np.array([np.ravel(dT) for dT in dTmapsRe[det][mat]])\n dTmap[det][mat]=np.mean(dTmapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\n #dTmap elements may be np.Inf if the ray did not pass through the collimator\n #this is useful to get 0 in attenuation() for those locations\n #and it makes std calculation impossible, thus we set it to 0. see not in docstring!\n dTmapToAve=np.where(dTmapToAve==np.Inf,0.0,dTmapToAve) \n dTmapErr[det][mat]=np.std(dTmapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\n self._dTmap=dTmap\n self._dTmapErr=dTmapErr\n self._dTmaps=dTmaps\n \n if self._elines is not None: \n #restructuring contributionMaps\n contributionMapsRe={det: {e: [cmap[det][e] for cmap in contributionMaps] for e in self._elines} for det in self.detectors}\n contributionMap={} #will be the average\n contributionMapErr={} #will be the stdcontributionMapAves\n for det in contributionMapsRe:\n contributionMap[det]={}\n contributionMapErr[det]={}\n for e in contributionMapsRe[det]:\n cMapToAve=np.array([np.ravel(cm) for cm in contributionMapsRe[det][e]])\n contributionMap[det][e]=np.mean(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\n contributionMapErr[det][e]=np.std(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\n self._contributionMap=contributionMap\n self._contributionMapErr=contributionMapErr\n self._contributionMaps=contributionMaps\n \n #restructuring contributionMapAves\n contributionMapAvesRe={e: [cmap[e] for cmap in contributionMapAves] for e in self._elines}\n contributionMapAve={} #will be the average\n contributionMapAveErr={} #will be the std\n for e in contributionMapAvesRe:\n cMapToAve=np.array([np.ravel(cm) for cm in contributionMapAvesRe[e]])\n contributionMapAve[e]=np.mean(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\n contributionMapAveErr[e]=np.std(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\n self._contributionMapAve=contributionMapAve\n self._contributionMapAveErr=contributionMapAveErr\n self._contributionMapAves=contributionMapAves\n \n #restructuring geomefficiencies\n geomefficienciesRe={det: [geff[det] for geff in geomefficiencies] for det in self._detectors}\n geomefficiency={} #will be the average\n geomefficiencyErr={} #will be the std\n for det in geomefficienciesRe:\n geffToAve=np.array([geff for geff in geomefficienciesRe[det]])\n geomefficiency[det]=np.mean(geffToAve,axis=0)\n geomefficiencyErr[det]=np.std(geffToAve,axis=0)\n self._geomEff=geomefficiency\n self._geomEffErr=geomefficiencyErr\n self._geomEffs=geomefficiencies\n \n #restructuring geomefficiencyAves\n geomefficiencyAve=np.mean(np.array([geff for geff in geomefficiencyAves]),axis=0)\n geomefficiencyAveErr=np.std(np.array([geff for geff in geomefficiencyAves]),axis=0)\n self._geomEffAve=geomefficiencyAve\n self._geomEffAveErr=geomefficiencyAveErr\n self._geomEffAves=geomefficiencyAves\n \n if self.output is not None:\n output=open(self.output,'w')\n for e,c in zip(self._elines,self._geomEffAve):\n output.write(e+'\\t'+str(c)+'\\n')\n output.close()","title":""},{"docid":"00d139e4e3944ed0ea867b0b2bb8cef4","score":"0.53743863","text":"def run_calculations(self):\n logger.debug(\"Beginning calculation run - %s\", self.cavity_name)\n self.calculate_attenuation_factor()\n self.calculate_attenuation()\n self.calculate_P_fc()\n self.calculate_P_rc()\n self.calculate_Q_lf()\n self.calculate_Q_lr()\n self.data_sync_start = \"now\"\n self.data_sync_end = \"a little later\"\n\n # TODO: Figure out the current interface with the IOC to get timestamp info\n # self.data_sync_start = self.STARTLQ.value\n # self.data_sync_end = self.ENDLQ.value","title":""},{"docid":"e040e514ea40edfe41e3aede1d35ca00","score":"0.53642964","text":"def GC_NLFC_Out(network, sScenarios, pDemand, qDemand, q0, prices, sellFactor, scens, pool, V_weight):\n\n n, T = pDemand.shape\n nS = len(network.battnodes)\n nE = len(network.nodeElist0)\n realS_sum = np.matrix(np.zeros((nS, T)))\n rootV2_sum = np.matrix(np.zeros((1, T)))\n Wn_sum = np.matrix(np.zeros((n, T)))\n Wre_sum = np.matrix(np.zeros((nE, T)))\n Wie_sum = np.matrix(np.zeros((nE, T)))\n\n ## Compute in parallel\n # Gather all scenarios data into a list\n demandList = []\n for i in range(scens):\n combDemand = pDemand\n combDemand[network.battnodes, :] = sScenarios[i]\n demandList.append(combDemand)\n\n # Make parallel pool\n # if __name__== \"__main__\":\n # print('start parallel pool')\n # pool = Pool()\n print 'Computing in pool'\n sols = pool.map(GC_NLFC_star, itertools.izip(itertools.repeat(network), demandList, itertools.repeat(qDemand),\n itertools.repeat(q0), itertools.repeat(prices),\n itertools.repeat(sellFactor), itertools.repeat(V_weight)))\n\n # Unpack all information\n for i in range(scens):\n if sols[i][2] != \"optimal\":\n print 'OPF status is: ', sols[i][2]\n realS_sum += sols[i][0]\n rootV2_sum += sols[i][1]\n Wn_sum += sols[i][3]\n Wre_sum += sols[i][4]\n Wie_sum += sols[i][5]\n\n realS = realS_sum / scens\n rootV2 = rootV2_sum / scens\n Wn = Wn_sum / scens\n Wre = Wre_sum / scens\n Wie = Wie_sum / scens\n\n return realS, rootV2, Wn, Wre, Wie\n\n \"\"\"\n #Sequential code\n for i in range(scens):\n combDemand = pDemand\n combDemand[network.battnodes,:] = sScenarios[i]\n realS_new, imagS_new, rootV2_new, status, cost = GC_NLFC(network, combDemand, qDemand, q0, prices)\n realS_sum = realS_sum + realS_new\n rootV2_sum = rootV2_sum + rootV2_new\n if status != \"optimal\":\n print('status is: ', status)\n \"\"\"","title":""},{"docid":"e6ca9c0e93831660f15547490df6be8c","score":"0.5361871","text":"def main():\n\n\t### Initialize dictionary specifying which functions to execute\n\tdriver_args = {\n\t\t\n\t\t### Preprocessing functions\n\t\t'preprocessing': [\n\t\t\t'export_parquet2csvs',\n\t\t\t'join_firms2ztrax',\t\n\t\t\t'clean_data',\n\t\t\t'get_countylevel_summarystats',\n\t\t\t'create_countygroups',\n\t\t\t],\n\n\t\t### Statistical functions\n\t\t'stats': [\n\t\t\t'fit_100yr_model',\n\t\t\t],\n\n\t\t### Postprocessing functions\n\t\t'postprocessing': [\n\t\t\t### Collect outputs from statistical models\n\t\t\t'collect_statsoutputs',\n\t\t\t\n\t\t\t### Calculate and summarize overvaluation\n\t\t\t'calculate_overvaluation',\n\t\t\t'summarize_countyoutputs',\n\t\t\t'combine_atriskproperties',\n\n\t\t\t### Monte Carlo simulation\n\t\t\t'montecarlo_simulation',\n\t\t\t'collect_montecarlo_outputs',\n\n\t\t\t### Miscellaneous postprocessing functions\n\t\t\t'get_censustract_outputs',\n\t\t\t'fiscalvulnerability_analysis',\n\n\t\t\t### Postprocessing functions to generate data to share with media\n\t\t\t'collect_keyoutputs',\n\n\t\t\t### Assume non-SFHA discount is the same as SFHA discount\n\t\t\t'calculate_overvaluation_nonsfhadiscount',\n\t\t\t'summarize_countyoutputs_nonsfhadiscount',\n\t\t\t'combine_atriskproperties_nonsfhadiscount',\n\t\t\t'get_censustract_outputs_nonsfhadiscount',\n\t\t\t\n\t\t\t### Assume discounts based on cross-sectional model\n\t\t\t'calculate_overvaluation_xsdiscount',\n\t\t\t'summarize_countyoutputs_xsdiscount',\n\t\t\t'combine_atriskproperties_xsdiscount',\n\t\t\t'get_censustract_outputs_xsdiscount',\n\t\t\t],\n\n\t\t### Figure functions\n\t\t'figures': [\n\t\t\t### Main text\n\t\t\t'overvaluation_maps', # Fig. 1\n\t\t\t'histograms_byfloodzone',\t# Fig. 2\n\t\t\t'overvaluation_bypopulationgroups_v2', # Fig. 3\n\t\t\t'overvaluation_munifinance', # Fig. 4 (upper panel)\n\t\t\t'munifinance_demographics', #Fig. 4 (lower panel)\n\n\t\t\t### Supplemental Materials\n\t\t\t'ztrax_transactions_map', # Fig. S1\n\t\t\t'fmv_map', # Fig. S2\n\t\t\t'npv_map', # Fig. S3\n\t\t\t'firmupdates_map', # Fig. S4\n\t\t\t'yaleclimatesurvey_map', # Fig. S5\t\t\t\t\t\t\n\t\t\t'npv_histograms', # Fig. S6\n\t\t\t'empiricaldiscount_combinedgroups', # Fig. S7\n\t\t\t'empiricaldiscount_combinedgroups_xsmodel', # Fig. S8\n\t\t\t'overvaluation_maps_dr7_low', # Fig. S9\n\t\t\t'overvaluation_maps_dr1_high', # Fig. S10\n\t\t\t'histograms_byfloodzone_nonsfhadiscount', # Fig. S11\n\t\t\t'histograms_byfloodzone_xsdiscount', # Fig. S12\n\t\t\t'overvaluation_bypopulationgroups_scatter', # Fig. S13\n\t\t\t'overvaluation_bypopulationgroups_v2_nonsfhadiscount', # Fig. S14\n\t\t\t'overvaluation_bypopulationgroups_v2_xsdiscount', # Fig. S15\n\t\t\t'uncertainty_plots', # Fig. S16\n\t\t\t'overvaluation_rankedbystate', # Fig. S17\n\t\t\t]\n\t\t}\n\n\t### Call execute function within driver.py\n\tdriver.execute(driver_args)","title":""},{"docid":"5cbb556cf09bfc116fa2275e6cb48dae","score":"0.5349653","text":"def outputs_for_detailed_tab(self, input_dict, output_dict):\n result = []\n module = 'Collection Cost'\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Total Number of Turbines',\n 'value': float(self.output_dict['total_turb'])\n })\n\n result.append({\n 'unit': 'km',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Total trench length',\n 'value': float(self.output_dict['trench_length_km'])\n })\n\n result.append({\n 'unit': 'km',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Total cable length',\n 'value': float(self.output_dict['total_cable_len_km'])\n })\n\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Number of Turbines Per String in Full String',\n 'value': float(self.output_dict['total_turb_per_string'])\n })\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Number of Full Strings',\n 'value': float(self.output_dict['num_full_strings'])\n })\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Number of Turbines in Partial String',\n 'value': float(self.output_dict['num_leftover_turb'])\n })\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Number of Partial Strings',\n 'value': float(self.output_dict['num_partial_strings'])\n })\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Total number of strings full + partial',\n 'value': float(self.output_dict['num_full_strings'] + self.output_dict['num_partial_strings'])\n })\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Trench Length to Substation (km)',\n 'value': float(self.output_dict['distance_to_grid_connection_km'])\n })\n result.append({\n 'unit': '',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Cable Length to Substation (km)',\n 'value': float(self.output_dict['cable_len_to_grid_connection_km'])\n })\n\n cables = ''\n n = 1 # to keep tab of number of cables input by user.\n for cable, specs in self.output_dict['cables'].items():\n if n == len(self.output_dict['cables']):\n cables += str(cable)\n else:\n cables += str(cable) + ' , '\n\n for variable, value in specs.__dict__.items():\n if variable == 'array_cable_len':\n result.append({\n 'unit': 'km',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Array cable length for cable ' + cable,\n 'value': float(value)\n })\n elif variable == 'total_length':\n result.append({\n 'unit': 'km',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Total cable length for cable ' + cable,\n 'value': float(value)\n })\n\n elif variable == 'total_cost':\n result.append({\n 'unit': 'usd',\n 'type': 'variable',\n 'variable_df_key_col_name': 'Total cable cost for cable ' + cable,\n 'value': float(value)\n })\n n += 1\n\n result.append({\n 'unit': '',\n 'type': 'list',\n 'variable_df_key_col_name': 'Number of turbines per cable type in full strings [' + cables + ']',\n\n 'value': str(self.output_dict['num_turb_per_cable'])\n })\n\n if self.input_dict['turbine_rating_MW'] > 0.1:\n for row in self.output_dict['management_crew'].itertuples():\n dashed_row = ' <--> '.join(str(x) for x in list(row))\n result.append({\n 'unit': '',\n 'type': 'dataframe',\n 'variable_df_key_col_name': 'Labor type ID <--> Hourly rate USD per hour <--> Per diem USD per day <--> Operation <--> Crew type <--> Crew name <--> Number of workers <--> Per Diem Total <--> Hourly costs total <--> Crew total cost ',\n 'value': dashed_row\n })\n\n result.append({\n 'unit': '',\n 'type': 'list',\n 'variable_df_key_col_name': 'Percent length of cable in partial string [' + cables + ']',\n\n 'value': str(self.output_dict['perc_partial_string'])\n })\n\n\n\n for row in self.output_dict['total_collection_cost'].itertuples():\n dashed_row = '{} <--> {} <--> {}'.format(row[1], row[3], math.ceil(row[2]))\n result.append({\n 'unit': '',\n 'type': 'dataframe',\n 'variable_df_key_col_name': 'Type of Cost <--> Phase of Construction <--> Cost in USD ',\n 'value': dashed_row,\n 'last_number': row[2]\n })\n\n\n for _dict in result:\n _dict['project_id_with_serial'] = self.project_name\n _dict['module'] = module\n\n self.output_dict['collection_cost_csv'] = result\n return result","title":""},{"docid":"38f87ddf7930c8007421be685e0b3916","score":"0.5342951","text":"def __definecost(self):\n \n if self.mode == 'Unsup' or self.mode == \"Mixte\":\n if self.depth_min == 0:\n in_sided = self.one_sided_in\n in_bounded = 1.\n else:\n in_sided = self.layers[self.depth_min-1].one_sided\n in_bounded = self.layers[self.depth_min-1].bounded\n out_bounded = self.layersdec[self.depth_min].bounded\n out_sided = self.layersdec[self.depth_min].one_sided\n \n self.unsup_cost = self.reconstruction_cost_fn( self.layers[self.depth_min].inp,\\\n self.layersdec[self.depth_min].out, self.layersdec[self.depth_min].lin,\\\n in_sided,out_sided,in_bounded,out_bounded,self.layersdec[self.depth_min].act)\n self.unsup_cost[0][0] = self.unsup_scaling * self.unsup_cost[0][0]\n self.unsup_cost[1] = self.unsup_scaling * self.unsup_cost[1]\n else:\n self.unsup_cost = [[],[]]\n \n wdaux = []\n paramsaux = []\n if self.aux and self.aux_active:\n if (self.update_type != 'special' or self.depth+self.auxdepth == self.depth_max):\n paramsaux += self.auxlayer.params\n if self.auxregularization:\n wdaux += [self.auxregularization*self.auxlayer.wd]\n self.aux_cost = self.reconstruction_cost_fn( self.auxtarget, self.auxlayer.out,\\\n self.auxlayer.lin,self.aux_one_sided,self.auxlayer.one_sided,\\\n 1.,self.auxlayer.bounded,self.auxlayer.act)\n self.aux_cost[0][0] = self.aux_scaling * self.aux_cost[0][0]\n self.aux_cost[1] = self.aux_scaling * self.aux_cost[1]\n else:\n self.aux_cost = [[],[]]\n \n if self.mode == 'Sup':\n self.cost = sum([self.sup_scaling * self.outlayer.cost(self.out)] +\\\n self.aux_cost[0] + self.wd + wdaux + self.sp)\n if self.mode == 'Unsup':\n self.cost = sum(self.unsup_cost[0] + self.aux_cost[0] + self.wd + wdaux + self.sp)\n if self.mode == 'Mixte':\n self.cost = sum(self.unsup_cost[0] + [self.sup_scaling * self.outlayer.cost(self.out)] +\\\n self.aux_cost[0] + self.wd + wdaux + self.sp)\n if self.mode == 'Aux':\n self.cost = sum(self.aux_cost[0] + self.wd + wdaux + self.sp)\n self.grad = T.grad(self.cost,self.params+paramsaux)\n self.updates = dict((p, p - self.lr * g) for p, g in zip(self.params+paramsaux, self.grad))","title":""},{"docid":"bbc005fd0ea0b9e7fd13a347b2e7f004","score":"0.53383005","text":"def task5():\n\t# load three tables of AR-Accounts_Rec_Listing File\n\tprint('Start task 5...')\n\tbsid_bkpf = pd.read_csv(\"BSID_BKPF_AltColTitles.csv\",\n\t\tdtype={'Company_Code':np.str,'Customer_Number':np.str},encoding='latin-1')\n\tknkk = pd.read_csv(\"KNKK_AltColTitles.csv\",dtype={'Customer_Number':np.str})\n\tt001 = pd.read_csv(\"T001_AltColTitles.csv\",dtype={'Company_Code':np.str})\n\n\t# join bsid_bkpf,knkk,t001 using the ERD diagram of AR-Accounts_Rec_Listing File and get the AR-Accounts_Rec_Listing File data\n\tbsid_bkpf_knkk = pd.merge(bsid_bkpf, knkk, \n\t\tleft_on=['Customer_Number'], right_on=['Customer_Number'], how='left')\n\tAccounts_Rec_Listing = pd.merge(bsid_bkpf_knkk, t001, \n\t\tleft_on=['Company_Code'], right_on=['Company_Code'], how='left')\n\n\t# For each Reference_Document_Number of each Customer_Number, calculate the amount of Debit and amount of Credit\n\ta = Accounts_Rec_Listing[Accounts_Rec_Listing['Debit_Credit_Indicator'] == 'S']\n\tb = Accounts_Rec_Listing[Accounts_Rec_Listing['Debit_Credit_Indicator'] == 'H']\n\ta_group_amount = a.groupby(['Customer_Number','Reference_Document_Number'])\\\n\t\t\t\t\t['Amount_in_Local_Currency'].sum().reset_index()\n\tb_group_amount = b.groupby(['Customer_Number','Reference_Document_Number'])\\\n\t\t\t\t\t['Amount_in_Local_Currency'].sum().reset_index()\n\ttotal_group_amount = pd.merge(a_group_amount,b_group_amount,\n\t\tleft_on =['Customer_Number','Reference_Document_Number'], \n\t\tright_on = ['Customer_Number','Reference_Document_Number'], \n\t\thow='outer')\n\ttotal_group_amount=total_group_amount.rename(columns = \n\t\t{'Amount_in_Local_Currency_x':'Amount_S','Amount_in_Local_Currency_y':'Amount_H'})\n\n\t# Using amount of Debit minus amount of Credit to get the balance \n\ttotal_group_amount['Amount_H'].fillna(0, inplace=True)\n\ttotal_group_amount['Amount_S'].fillna(0, inplace=True)\n\ttotal_group_amount['Balance'] = total_group_amount['Amount_S'] - total_group_amount['Amount_H']\n\tbalance_amount = pd.merge(total_group_amount,Accounts_Rec_Listing, \n\t\ton = ['Customer_Number','Reference_Document_Number'], how = 'left')\n\tbalance_amount = balance_amount[['Customer_Number','Reference_Document_Number',\n\t\t\t\t\t\t\t'Amount_S','Amount_H','Balance','Document_Date_in_Document']]\n\tbalance_amount.drop_duplicates(inplace=True)\n\n\t# get the minimum Document_Date for each Reference_Document_Number of each Customer_Number \n\ty = balance_amount.groupby(['Customer_Number','Reference_Document_Number'])\\\n\t\t\t\t\t['Document_Date_in_Document'].min().reset_index()\n\tbalance_amount = pd.merge(balance_amount, y, \n\t\ton = ['Customer_Number','Reference_Document_Number','Document_Date_in_Document'],how='inner')\n\n\t# For Balance <= 0, outstanding day is zero; \n\t# For Balance > 0,using the system date minus minimum Document_Date to calculate the outstanding day \n\tfor i in range(len(balance_amount)):\n\t\tif balance_amount.loc[i,'Balance'] <= 0:\n\t\t\tbalance_amount.loc[i,'outstanding_days'] = int(0)\n\t\telif balance_amount.loc[i,'Balance'] > 0:\n\t\t\tbalance_amount.loc[i,'outstanding_days'] = \\\n \t\tint((pd.to_datetime(datetime.today().strftime('%Y-%m-%d')) - \n \t\t\tpd.to_datetime(balance_amount.loc[i,'Document_Date_in_Document'])).days)\n\n\tbalance_amount['outstanding_days'] = balance_amount['outstanding_days'].astype('int64')\n\n # using the outstanding_days to find the type of '60-90', '90-120', '> 120', '< 60' or 'not outstanding'\n\tfor i in range(len(balance_amount)):\n\t if balance_amount.loc[i,'outstanding_days'] >= 60 and balance_amount.loc[i,'outstanding_days'] <= 90:\n\t balance_amount.loc[i,'type'] = '60-90'\n\t elif balance_amount.loc[i,'outstanding_days'] > 90 and balance_amount.loc[i,'outstanding_days'] <= 120:\n\t balance_amount.loc[i,'type'] = '90-120'\n\t elif balance_amount.loc[i,'outstanding_days'] > 120:\n\t balance_amount.loc[i,'type'] = '> 120'\n\t elif balance_amount.loc[i,'outstanding_days'] < 60 and balance_amount.loc[i,'outstanding_days'] > 0:\n\t balance_amount.loc[i,'type'] = '< 60'\n\t elif balance_amount.loc[i,'outstanding_days'] == 0:\n\t balance_amount.loc[i,'type'] = 'not outstanding'\n\t\n\t# only keep Outstanding Debtors Greater Than 60 Days\n\tbalance_amount = balance_amount[(balance_amount['type'] == '60-90')\\\n\t |(balance_amount['type'] == '90-120')|(balance_amount['type'] == '> 120')]\n\t\n\t# For each customer, get the total outstanding amount,\n\t# local Amount 60 – 90 Days,local Amount 90 – 120 Days and local Amount >120 days\n\tbalance_amount = balance_amount.sort_values(\"Customer_Number\").reset_index(drop=True)\n\tCounts = []\n\tcid = 0\n\ts1, s2, s3, s = 0, 0, 0, 0\n\tfor i in range(len(balance_amount)):\n\t balance = balance_amount.loc[i, \"Balance\"]\n\t if balance_amount.loc[i, \"Customer_Number\"] != cid:\n\t Counts.append([cid, s1, s2, s3, s])\n\t cid = balance_amount.loc[i, \"Customer_Number\"]\n\t s1, s2, s3, s = 0, 0, 0, balance\n\t if balance_amount.loc[i, \"type\"] == \"60-90\":\n\t s1 = balance\n\t elif balance_amount.loc[i, \"type\"] == \"90-120\":\n\t s2 = balance\n\t elif balance_amount.loc[i, \"type\"] == \"> 120\":\n\t s3 = balance\n\t else:\n\t s += balance\n\t if balance_amount.loc[i, \"type\"] == \"60-90\":\n\t s1 += balance\n\t elif balance_amount.loc[i, \"type\"] == \"90-120\":\n\t s2 += balance\n\t elif balance_amount.loc[i, \"type\"] == \"> 120\":\n\t s3 += balance\n\tCounts.append([cid, s1, s2, s3, s])\n\tsum_by_type = pd.DataFrame(Counts[1:], columns = ['Customer_Number', \"60-90\", \"90-120\", \"> 120\", \"total\"])\n\t\n\t# get some key output results fields\n\tx = Accounts_Rec_Listing.loc[:,['Customer_Number','Company_Code','Name_of_Company_Code_or_Company',\n\t 'Currency_Key_y','Customer_s_credit_limit','Credit_Control_Area']]\n\tx.drop_duplicates(inplace=True)\n\n\t# merge togther to get the whole key output results table\n\tsum_by_type = pd.merge(sum_by_type, x, on = 'Customer_Number',how= 'left')\n\t# exclude Credit_Control_Area \"CAN\" and keep Credit_Control_Area \"US01\" or NaN\n\tsum_by_type = sum_by_type[sum_by_type['Credit_Control_Area'] != 'CAN']\n\n\t# get the correct order and names of key output results fields\n\tsum_by_type = sum_by_type[['Customer_Number','Company_Code','Name_of_Company_Code_or_Company',\n\t 'total','Currency_Key_y','Customer_s_credit_limit','60-90','90-120','> 120']]\n\tsum_by_type= sum_by_type.rename(columns = {'Currency_Key_y':'Local_Currency_Key',\n\t\t 'total':'Total_Amount_Local_Currency',\n\t\t '60-90':'Local_Amount_60-90_Days',\n\t\t '90-120':'Local_Amount_90-120_Days',\n\t\t '> 120':'Local_Amount_>120_Days'})\n\n\t# output the result table\n\tsum_by_type.to_csv(\"task5.csv\")\n\tprint('Task 5 is finished. Output is ready.')","title":""},{"docid":"8d7b6fb78f22741cea5538061c58aa73","score":"0.5305617","text":"def setUp(self):\n cost_func = make_cost_function()\n self.problem = cost_func.problem\n self.options = self.problem.options\n self.grabbed_output = output_grabber.OutputGrabber(self.options)\n self.count = 0\n self.scipy_len = len(self.options.minimizers[\"scipy\"])\n controller = ScipyController(cost_func)\n controller.parameter_set = 0\n self.result_args = {'controller': controller,\n 'accuracy': 1,\n 'runtimes': [1]}\n self.cp = Checkpoint(self.options)","title":""},{"docid":"e43e3d946024f61f6a82686d350ab941","score":"0.5304694","text":"def compute(self):\n pass","title":""},{"docid":"e43e3d946024f61f6a82686d350ab941","score":"0.5304694","text":"def compute(self):\n pass","title":""},{"docid":"e43e3d946024f61f6a82686d350ab941","score":"0.5304694","text":"def compute(self):\n pass","title":""},{"docid":"e43e3d946024f61f6a82686d350ab941","score":"0.5304694","text":"def compute(self):\n pass","title":""},{"docid":"2d27446ad19f1991bcc7b3ea34fd727b","score":"0.53033954","text":"def compute_stats(self) -> None:\n pass","title":""},{"docid":"924f4ff753ae68febc07b055e7ed4869","score":"0.5302831","text":"def compute(self):\n ## Get the list of files\n self.list=self.folder.GetAutoList()\n if self.list==None:\n return 0\n summary={}\n ## compute lines of code and density\n for filepath, size in self.list.items(): \n reader=FileReader(filepath)\n ## compute the number of new lines in the file\n currentLines=reader.GetLines()\n ## extrapolate the extension from the full file name path\n extension=os.path.splitext(filepath)[1][1:].strip() \n ## if the extension is the one we want to consider then sum the lines\n if extension in summary.keys():\n summary[extension]+=currentLines\n ## otherwise keep the same number of lines\n else:\n summary[extension]=currentLines\n ## Add the entry into the database containg the full path, the file extension, \n ## number of lines and size\n self.database.addEntry(filepath, extension,currentLines, size)\n ## if we want a summary POST the results into the server\n if summary: self.UpdateResults(summary)\n ## log the summary if necessary\n logging.info(''.join(['File with .%s contains %s lines' % (key, value) for (key, value) in summary.items()]))","title":""},{"docid":"f1403608b9e298fa64dcbcd9f47452e3","score":"0.529","text":"def main(cfg):\n # Get a description of the preprocessed data that we will use as input.\n input_data = cfg['input_data'].values()\n\n # Demonstrate use of metadata access convenience functions.\n selection = select_metadata(input_data, short_name='pr', project='CMIP5')\n logger.info(\"Example of how to select only CMIP5 precipitation data:\\n%s\",\n pformat(selection))\n\n selection = sorted_metadata(selection, sort='dataset')\n logger.info(\"Example of how to sort this selection by dataset:\\n%s\",\n pformat(selection))\n\n grouped_input_data = group_metadata(\n input_data, 'standard_name', sort='dataset')\n logger.info(\n \"Example of how to group and sort input data by standard_name:\"\n \"\\n%s\", pformat(grouped_input_data))\n\n # Example of how to loop over variables/datasets in alphabetical order\n for standard_name in grouped_input_data:\n logger.info(\"Processing variable %s\", standard_name)\n for attributes in grouped_input_data[standard_name]:\n logger.info(\"Processing dataset %s\", attributes['dataset'])\n input_file = attributes['filename']\n cube = compute_diagnostic(input_file)\n\n output_basename = os.path.splitext(\n os.path.basename(input_file))[0] + '_mean'\n provenance_record = get_provenance_record(\n attributes, ancestor_files=[input_file])\n plot_diagnostic(cube, output_basename, provenance_record, cfg)","title":""},{"docid":"c0863a2dcf579526682b4ccbeb2d223b","score":"0.52851063","text":"def evaluateCost(self, runData, isPositive):\n raise NotImplementedError(\"Please implement this method\")","title":""},{"docid":"3590db484e8cceaf8d163712c310e18c","score":"0.52822393","text":"def process_run(self):\n selection = self.run_options.get('selection',None)\n self.run_the_calculations(selection)\n return {}","title":""},{"docid":"0b5b55b182b100353e5f528a7f9cb671","score":"0.5278725","text":"def compute_statistics(allocations: Dict[Campaign, Dict[Good, int]], expenditure: Dict[Campaign, Dict[Good, float]]):\n total_allocations = 0\n total_expenditure = 0\n total_effective_allocation = {}\n sigmoidal_effective_reach_ratio = {}\n utilities = {}\n for c, _ in allocations.items():\n total_c_allocations = sum([a for g, a in allocations[c].items()])\n total_c_expenditure = sum([a for g, a in expenditure[c].items()])\n total_allocations += total_c_allocations\n total_expenditure += total_c_expenditure\n if c not in total_effective_allocation:\n # The total effective allocation is the sum of the allocation across all market segments that match the campaign's target.\n total_effective_allocation[c] = sum([a if g.__matches__(c.target) else 0 for g, a in allocations[c].items()])\n else:\n raise Exception(\"1) This should NOT happen!\")\n if c not in sigmoidal_effective_reach_ratio:\n # The sigmoidal effective reach ratio is compute with respect to the total effective allocation.\n sigmoidal_effective_reach_ratio[c] = compute_sigmoidal_effective_reach_ratio(total_effective_allocation[c], c.reach)\n else:\n raise Exception(\"2) This should NOT happen!\")\n if c not in utilities:\n # The actual final utility.\n # print(\"Computing utilities:\")\n # print(\"\\t \", sigmoidal_effective_reach_ratio[c])\n # print(\"\\t \", c.budget)\n # print(\"\\t \", total_c_expenditure)\n utilities[c] = sigmoidal_effective_reach_ratio[c] * c.budget - total_c_expenditure\n else:\n raise Exception(\"3) This should NOT happen!\")\n return utilities, total_expenditure","title":""},{"docid":"144872ec3fad64dd600b58dcb7559387","score":"0.52768654","text":"def compute(self):\n \n pass","title":""},{"docid":"94708b24440c862e9171cd28eeff93e5","score":"0.5273084","text":"def updates(self, cost):\n return []","title":""},{"docid":"df6ec47dd07756eb74c523241109e04b","score":"0.52502304","text":"def c_costs_rule(m, cost_type):\n if cost_type == 'Inv':\n return m.costs['Inv'] == \\\n sum((m.gamma_1[p] * m.conversion.loc[p]['IX1'] + m.part_size_1[p] * \n m.conversion.loc[p]['investment-slope-1']) +\n (m.gamma_2[p] * m.conversion.loc[p]['IX2'] + m.part_size_2[p] * \n m.conversion.loc[p]['investment-slope-2']) for p in m.con)\n \n elif cost_type == 'Mai':\n return m.costs['Mai'] == \\\n sum((m.conversion.loc[p]['maint-percentage']/100) * \n ((m.gamma_1[p] * m.conversion.loc[p]['IX1'] + m.part_size_1[p] * \n m.conversion.loc[p]['investment-slope-1']) +\n (m.gamma_2[p] * m.conversion.loc[p]['IX2'] + m.part_size_2[p] * \n m.conversion.loc[p]['investment-slope-2'])) for p in m.con)\n \n elif cost_type == 'Fuel':\n gas_user_index = m.connectivity['NG'][m.connectivity['NG'] == 1].index\n# el_user_index = m.connectivity['EG'][m.connectivity['EG'] == 1].index\n return (m.costs['Fuel'] == \n ((sum(m.u[t, p] for t in m.t for p in gas_user_index) * \n m.economic['gas-tariff'][0]) + \n (sum(m.el_buy[t] * m.economic['el-tariff'][0] for t in m.t)) - \n (sum(m.el_sell[t] * m.economic['feedin-tariff'][0] for t in m.t))) * \n 2190)\n \n elif cost_type == 'Inc':\n return Constraint.Skip\n \n else:\n raise NotImplementedError(\"Unknown cost type.\")","title":""},{"docid":"0b127ef43f67a32fedec1d185e740a8f","score":"0.5249243","text":"def _compute_cost_total(self):\n for service in self:\n service.cost_total = service.qty * service.cost_price or 0.0","title":""},{"docid":"4144d32cee3b4da6d2205bac8aa170f3","score":"0.5233409","text":"def main(standard, denovo, nongenes):\n\n CAI_instance = CAI()\n\n index_generation(standard, CAI_instance)\n\n CAI_calculation(standard, CAI_instance)\n CAI_calculation(denovo, CAI_instance)\n CAI_calculation(nongenes, CAI_instance)","title":""},{"docid":"9ad0aeda22ece026abefe91a66b44caa","score":"0.52322775","text":"def cost(self, target, value, subentry=''):\n result = self.db.cost\n return result","title":""},{"docid":"f6ff311f6f43f053146e77d54cbd1f80","score":"0.521826","text":"def cost_calc(self,cf,ob):\r\n inibal = ob\r\n #bal = ob\r\n if len(self.daily_cost)>0:\r\n del self.daily_cost[:]\r\n for element in cf:\r\n trans = self.transfer(inibal)\r\n bal = inibal+trans+element\r\n self.daily_cost.append(self.trans_cost(trans)+self.holding_cost(bal))\r\n inibal = bal\r\n return self.daily_cost","title":""},{"docid":"2ad0ffb81f2ce64b4722843f80119464","score":"0.5217138","text":"def main(items, rate, datafile='factorio_recipes', modules='prod 3, prod 3, prod 3, prod 3, speed 3, speed 3, speed 3, speed 3', fractional=False, log='WARNING',\n stop_at='', beacon_speed=0., oil=False, verbose=False):\n\tlogging.basicConfig(level=log.upper())\n\n\titems = [item.strip().lower() for item in items.split(',')]\n\tmodules = [name.strip().lower() for name in modules.split(',')] if modules else []\n\tstop_items = [item.strip().lower() for item in stop_at.split(',')] if stop_at else []\n\trecipes = get_recipes(datafile, modules, beacon_speed)\n\trate = Fraction(rate)\n\titems = OrderedDict((item, rate) for item in items)\n\tif oil:\n\t\tresults, oil_buildings = solve_with_oil(recipes, items, stop_items)\n\telse:\n\t\tresults = solve_all(recipes, items, stop_items)\n\t\toil_buildings = []\n\n\tdef maybe_round(amount):\n\t\treturn '{:.2f}'.format(float(amount)) if fractional else int(math.ceil(amount))\n\n\tdef mods_str(mods):\n\t\treturn ' with {}'.format(', '.join(\n\t\t\t'{}x {}'.format(count, name)\n\t\t\tfor name, count in sorted(\n\t\t\t\tCounter(mods).items(), key=lambda (name, count): (count, name)\n\t\t\t)\n\t\t)) if mods else ''\n\n\tdef input_str(throughput, inputs):\n\t\tif not verbose or not inputs:\n\t\t\treturn ''\n\t\treturn ' using {}'.format(\n\t\t\t', '.join(\n\t\t\t\t'{:.2f}/sec {}'.format(float(throughput * item_amount), item)\n\t\t\t\tfor item, item_amount in inputs.items()\n\t\t\t)\n\t\t)\n\n\tdef format_extra(building, amount, throughput, mods, item, inputs):\n\t\tMAX_PER_ROW = 40 # one blue belt\n\t\tEXCLUDE = ['water', 'crude oil', 'petroleum', 'light oil', 'heavy oil', 'sulfuric acid', 'lubricant']\n\t\tto_consider = [\n\t\t\tthroughput * item_amount\n\t\t\tfor _item, item_amount in inputs.items()\n\t\t\tif _item not in EXCLUDE\n\t\t]\n\t\tif item not in EXCLUDE:\n\t\t\tto_consider.append(throughput)\n\t\trows = int(math.ceil(max(to_consider) / MAX_PER_ROW)) if to_consider else 1\n\t\tif not verbose or rows == 1:\n\t\t\treturn ''\n\t\treturn '\\n\\tin {} rows of {} each producing {:.2f}/sec{}'.format(\n\t\t\trows, maybe_round(amount/rows), float(throughput / rows), input_str(throughput/rows, inputs),\n\t\t)\n\n\tdef format_item(building, amount, throughput, mods, item, inputs):\n\t\treturn '{} {}{} producing {:.2f}/sec of {}{}{}'.format(\n\t\t\tmaybe_round(amount),\n\t\t\tbuilding, mods_str(mods), float(throughput), item, input_str(throughput, inputs),\n\t\t\tformat_extra(building, amount, throughput, mods, item, inputs),\n\t\t)\n\n\tfor item, amount in results.items():\n\t\tif item in recipes and item not in stop_items:\n\t\t\tbuilding, per_building, inputs, mods = recipes[item]\n\t\t\tthroughput = amount * per_building\n\t\t\tprint format_item(building, amount, throughput, mods, item, inputs)\n\t\telse:\n\t\t\tprint '{:.2f}/sec of {}'.format(float(amount), item)\n\tfor recipe, name, amount in oil_buildings:\n\t\tbuilding, per_building, _, mods = recipes[recipe]\n\t\tthroughput = amount * per_building # this doesn't make much sense, it's completed processes per sec\n\t\tprint format_item(building, amount, throughput, mods, name, {})","title":""},{"docid":"22be8ba0a6f944e6efbbd0346d08de0d","score":"0.5215419","text":"def _update_collection_operations(cname, opnames):\n operations = config[\"campaigns\"][cname][\"operations\"]\n filters = []\n for opname in opnames:\n qualif = operations[opname][\"qualifiers\"]\n filters.append((_build_filter(qualif, \"asset\"), opname))\n\n prices = _get_prices()\n data = {opname: {} for opname in opnames}\n\n for char_id, token in g.auth.get_valid_characters():\n logger.debug(\"Fetching assets for char id=%d campaign=%s \"\n \"operations=%s\", char_id, cname, \",\".join(opnames))\n try:\n assets = g.eve.esi(token).v1.characters(char_id).assets.get()\n except EVEAPIForbiddenError:\n logger.debug(\"Asset access denied for char id=%d\", char_id)\n continue\n\n for opname in opnames:\n data[opname][char_id] = {}\n\n logger.debug(\"Evaluating %d assets for char id=%d\",\n len(assets), char_id)\n for asset in assets:\n for filt, opname in filters:\n if filt(asset):\n typeid = asset[\"type_id\"]\n count = 1 if asset[\"is_singleton\"] else asset[\"quantity\"]\n value = prices.get(typeid, 0.0)\n char = data[opname][char_id]\n if typeid in char:\n char[typeid][0] += count\n char[typeid][1] += count * value\n else:\n char[typeid] = [count, count * value]\n\n g.campaign_db.update_items(cname, data)\n _save_collection_overview(cname, opnames, data)","title":""},{"docid":"fcc297591f45eb2919691b6e5d884de4","score":"0.52153736","text":"def compute_results(collection_fname):\n f = open(\"label_results.csv\", \"w\")\n f.close()\n gold_dict = get_gold_standard_dict()\n print gold_dict\n content = \"\"\n with open(collection_fname) as f:\n content = f.read()\n\n for line in content.split(\"\\n\"):\n if ',' in line:\n fname, m, gname = line.split(',')\n m = int(m)\n fbase = fname[:-4]\n logger.info(\"%s | %s\" % (fbase, fname))\n gold_url = gold_dict[fbase]\n alpha = compute_results_of_file(gname=gname, m=m, gold_url=gold_url.strip())\n if alpha >= -2:\n append_results(fbase, alpha)\n logger.info(\"gethering: %s\" % fbase)\n else:\n logger.warning(\"no graph for %s\" % fname)","title":""},{"docid":"b4d5ad6085d8e66e8538ea4bf432c383","score":"0.5214863","text":"def execute(self):\n config = ConfigParser.RawConfigParser()\n config.read('/etc/autoreduce/icat4.cfg')\n\n directory = \"/\" + self._facilityName + \"/\" + self._instrumentName + \"/\" + self._investigationName + \"/shared/autoreduce\"\n logging.info(\"reduction output directory: %s\" % directory)\n\n #set dataset name\n dataset = self._factory.create(\"dataset\")\n\n dsType = self._factory.create(\"datasetType\")\n dsType.id = config.get('DatasetType', 'reduced')\n dataset.type = dsType\n dataset.name = self._runNumber\n dataset.location = directory\n datafiles = []\n\n pattern = '*' + self._runNumber + '*'\n for dirpath, dirnames, filenames in os.walk(directory):\n listing = glob.glob(os.path.join(dirpath, pattern))\n for filepath in listing:\n filename =os.path.basename(filepath)\n logging.info(\"Filename: %s\" % filename)\n datafile = self._factory.create(\"datafile\")\n datafile.location = filepath\n datafile.name = filename\n extension = os.path.splitext(filename)[1][1:]\n dfFormat = self._factory.create(\"datafileFormat\")\n dfFormat.id = config.get('DatafileFormat', extension)\n datafile.datafileFormat = dfFormat\n modTime = os.path.getmtime(filepath)\n datafile.datafileCreateTime = epochToISO8601(modTime)\n datafile.fileSize = os.path.getsize(filepath)\n\n datafiles.append(datafile)\n\n dataset.datafiles = datafiles\n dataset.type = dsType\n\n dbDatasets = self._service.search(self._sessionId, \"Dataset INCLUDE Datafile [name = '\" + str(dataset.name) + \"'] <-> Investigation <-> Instrument [name = '\" + str(self._instrumentName) + \"'] <-> DatasetType [name = 'reduced']\")\n\n if len(dbDatasets) == 0:\n\n dbInvestigations = self._service.search(self._sessionId, \"Investigation INCLUDE Sample [name = '\" + str(self._investigationName) + \"'] <-> Instrument [name = '\" + self._instrumentName + \"'] <-> Dataset [name = '\" + str(dataset.name) + \"']\")\n\n if len(dbInvestigations) == 0:\n logging.error(\"No investigation entry found: try cataloging the raw data first.\")\n return\n else:\n investigation = dbInvestigations[0]\n if len(dbInvestigations)>1:\n logging.error(\"Multiple investigation entries found: using the first.\")\n\n logging.debug(\"Creating dataset: %s\" % datetime.now())\n dataset.investigation = investigation\n dataset.sample = investigation.samples[0]\n self._service.create(self._sessionId, dataset)\n\n elif len(dbDatasets) == 1:\n logging.debug(\"reduced dataset %s is already cataloged, updating reduced dataset... \" % (dataset.name))\n\n dbDataset = dbDatasets[0]\n # update \"one to many\" relationships\n if hasattr(dbDataset, \"datafiles\"):\n dfs = getattr(dbDataset, \"datafiles\")\n self._service.deleteMany(self._sessionId, dfs)\n\n for df in datafiles:\n df.dataset = dbDataset\n self._service.createMany(self._sessionId, datafiles)\n\n else:\n logging.error(\"ERROR, there should be only one dataset per run number per type reduced\")","title":""},{"docid":"b4bd8f7ee7cfee4f3da7d8c036acf4c4","score":"0.52134824","text":"def calculate_total_cost(state):\n ### STUDENT CODE GOES HERE ###","title":""},{"docid":"a782802b3593bf07ed7bf7f5fc07a25e","score":"0.5199693","text":"def evaluate(self):\n if self.filename:\n coco_gt = COCO(self.filename)\n else:\n coco_gt = COCO()\n coco_gt.dataset = self.dataset\n coco_gt.createIndex()\n\n if self.testdev_dir:\n # Run on test-dev dataset.\n box_result_list = []\n for det in self.detections:\n box_result_list.append({\n 'image_id': int(det[0]),\n 'category_id': int(det[6]),\n 'bbox': np.around(\n det[1:5].astype(np.float64), decimals=2).tolist(),\n 'score': float(np.around(det[5], decimals=3)),\n })\n json.encoder.FLOAT_REPR = lambda o: format(o, '.3f')\n # Must be in the formst of 'detections_test-dev2017_xxx_results'.\n fname = 'detections_test-dev2017_test_results'\n output_path = os.path.join(self.testdev_dir, fname + '.json')\n logging.info('Writing output json file to: %s', output_path)\n with tf.io.gfile.GFile(output_path, 'w') as fid:\n json.dump(box_result_list, fid)\n return np.array([-1.], dtype=np.float32)\n else:\n # Run on validation dataset.\n detections = np.array(self.detections)\n image_ids = list(set(detections[:, 0]))\n coco_dt = coco_gt.loadRes(detections)\n coco_eval = COCOeval(coco_gt, coco_dt, iouType='bbox')\n coco_eval.params.imgIds = image_ids\n coco_eval.evaluate()\n coco_eval.accumulate()\n coco_eval.summarize()\n coco_metrics = coco_eval.stats\n\n if self.label_map:\n # Get per_class AP, see pycocotools/cocoeval.py:334\n # TxRxKxAxM: iouThrs x recThrs x catIds x areaRng x maxDets\n # Use areaRng_id=0 ('all') and maxDets_id=-1 (200) in default\n precision = coco_eval.eval['precision'][:, :, :, 0, -1]\n # Ideally, label_map should match the eval set, but it is possible that\n # some classes has no data in the eval set.\n ap_perclass = [0] * max(precision.shape[-1], len(self.label_map))\n for c in range(precision.shape[-1]): # iterate over all classes\n precision_c = precision[:, :, c]\n # Only consider values if > -1.\n precision_c = precision_c[precision_c > -1]\n ap_c = np.mean(precision_c) if precision_c.size else -1.\n ap_perclass[c] = ap_c\n coco_metrics = np.concatenate((coco_metrics, ap_perclass))\n\n # Return the concat normal and per-class AP.\n return np.array(coco_metrics, dtype=np.float32)","title":""},{"docid":"aa77183210b05afaeb69dc58b4f5088d","score":"0.51991105","text":"def operating_cost(oper_hrs, fuel_cost_per_hour, crew_cost_per_hour, average_speed=None, CPI=1,\n profit=True, profit_margin=0.1, table=False, latex_format=False):\n\n data_set = { # based on 2018 USD\n 'crew': 268, # crew cost per flight hour\n 'maintenance': 731, # maintenance cost per flight hour\n 'annual fixed cost without depreciation': 102687, # annual fixed cost without depreciation\n 'annual depreciation': 155238, # annual depreciation price\n 'average annual hours': 456 # average annual hours\n }\n\n if profit is True:\n profit_margin = profit_margin\n else:\n profit_margin = 0.0\n\n # all costs below are annual costs\n crew_cost = crew_cost_per_hour * oper_hrs * (1 + profit_margin)\n\n fuel_cost = fuel_cost_per_hour * oper_hrs * (1 + profit_margin)\n\n maintenance_cost = data_set['maintenance'] * data_set['average annual hours'] * (1 + profit_margin) * CPI\n\n fixed_cost = data_set['annual fixed cost without depreciation'] * CPI * (1 + profit_margin)\n\n depreciation_cost = data_set['annual depreciation'] * CPI * (1 + profit_margin)\n\n annual_oper_cost = crew_cost + fuel_cost + maintenance_cost + fixed_cost + depreciation_cost\n\n annual_oper_cost_no_crew = fuel_cost + maintenance_cost + fixed_cost + depreciation_cost\n\n annual_oper_cost_bundle = \\\n ((crew_cost, fuel_cost, maintenance_cost, fixed_cost, depreciation_cost, annual_oper_cost),\n ('Crew', 'Fuel', 'Maintenance', 'Fixed', 'Depreciation', 'Total'))\n\n # print tabulated data\n if table:\n cost_bundle, name_bundle = annual_oper_cost_bundle\n round_digit = 1\n data = []\n\n if profit:\n data.append(['Profit Margin', '{:.2f}%'.format(profit_margin * 100)])\n data.append([' ', ' '])\n\n data.append(['Annual Operating Cost:', ' '])\n\n annual_adjust_num = 1e3\n for ii in range(len(cost_bundle)):\n data.append([name_bundle[ii], '{:.1f} Thousand USD'.format(round(cost_bundle[ii] / annual_adjust_num, round_digit))])\n\n data.append([' ', ' '])\n\n # per flight hour\n data.append(['Operating cost per flight hour:', ' '])\n\n for ii in range(len(cost_bundle)):\n data.append([name_bundle[ii], round(cost_bundle[ii] / oper_hrs, round_digit)])\n\n data.append([' ', ' '])\n\n # per mile per seat (with pilot)\n available_seat = 2 # total available seats in flight\n data.append(['Operating cost per mile per seat (with pilot)', ' '])\n\n if average_speed is None:\n raise Exception(\"Please provide an average speed in mph.\")\n\n for ii in range(len(cost_bundle)):\n data.append([name_bundle[ii],\n round(cost_bundle[ii] / oper_hrs / average_speed / available_seat, round_digit)])\n\n # per mile per seat (autonomous flight, without pilot)\n available_seat = 3 # total available seats in flight\n data.append(['Operating cost per mile per seat (autonomous)', ' '])\n\n if average_speed is None:\n raise Exception(\"Please provide an average speed in mph.\")\n\n for ii in range(1, len(cost_bundle) - 1):\n data.append([name_bundle[ii],\n round(cost_bundle[ii] / oper_hrs / average_speed / available_seat, round_digit)])\n\n data.append([name_bundle[-1],\n round(annual_oper_cost_no_crew / oper_hrs / average_speed / available_seat, round_digit)])\n\n headers = ['Category', r'Cost [USD]']\n\n if latex_format:\n table_out = tabulate(data, headers=headers, tablefmt=\"latex\",\n numalign='right', floatfmt=(None, '.0f', '.0f'))\n print(table_out)\n else:\n table_out = tabulate(data, headers=headers, tablefmt=\"fancy_grid\",\n numalign='right', floatfmt=(None, '.0f', '.0f'))\n print(table_out)\n\n return annual_oper_cost, annual_oper_cost_bundle","title":""},{"docid":"4f8d1c09d5343b3b52032ae5211d6531","score":"0.5196402","text":"def cost(pvals, obdict, oberrdict, dC, start, fin):\n pvallist = m.mod_list(pvals, dC, start, fin)\n yoblist, yerrlist = obscost(obdict, oberrdict)\n rmatrix = rmat(yerrlist)\n hx = hxcost(pvallist, obdict, dC)\n obcost = np.dot(np.dot((yoblist-hx),np.linalg.inv(rmatrix)),(yoblist-hx).T)\n #modcost = np.dot(np.dot((pvals-dC.pvals),np.linalg.inv(dC.B)),\\\n # (pvals-dC.pvals).T)\n cost = 0.5*obcost #+ 0.5*modcost\n return cost","title":""},{"docid":"f91ee745be1e01a964c3dcba2fbe24ef","score":"0.51940095","text":"def run(self,options):\n print(Gstr_title)\n\n self.clist = self.generateCOElist(options.coenum)\n print(\"clist:\" + str(self.clist))######################\n if len(self.clist) != 0:\n for cnum in self.clist:\n print(\"cur cnum is \" + str(cnum)) ######################\n c = int(cnum)\n obj = MatCal.MatMulBench(c)\n ms, st, ft, et = obj.Run()\n print(\"cur list of pars:\" + str([ms,st,ft,et])) ######################\n parse = [{'Matrix_Size': ms, 'Start_Time': st, 'Finish_Time': ft, 'Elapse_Time': et}]\n self.createOrUpdate(parse, options.inputdir, options.outputdir)\n print(parse)","title":""},{"docid":"f5e13c616427485e223a0bd392a814c5","score":"0.5193116","text":"def performreduction(self,settings,directorylist):\n\n self.settings = settings\n self.directorylist = directorylist\n\n print('Create Directories')\n self.status.set('Create Directories')\n \n self.createWorkingDirectories()\n\n print('Copy Images')\n self.copyImageTypes()\n\n print('Create Collections')\n bias_ic = ImageFileCollection(self.paths['bias_dir'], self.keywords)\n bias_table = bias_ic.summary\n\n flat_ic = ImageFileCollection(self.paths['flat_dir'], self.keywords)\n flat_table = flat_ic.summary\n\n # check and handle empty table ie no FITS files so collection empty\n\n flat_filters = np.unique(flat_table[self.keywords[1]].data).tolist()\n flat_exposures = np.unique(flat_table[self.keywords[3]].data).tolist()\n\n print('Flat Exposures')\n\n dark_ic = ImageFileCollection(self.paths['dark_dir'], self.keywords)\n dark_table = dark_ic.summary\n\n dark_exposures = np.unique(dark_table[self.keywords[3]].data).tolist()\n\n print('Dark Exposures')\n \n science_ic = ImageFileCollection(self.paths['science_dir'], self.keywords)\n science_table = science_ic.summary\n\n # need to handle 'None Type' in table\n science_filters = np.unique(science_table[self.keywords[1]].data).tolist()\n science_objects = np.unique(science_table[self.keywords[4]].data).tolist()\n science_exposures = np.unique(science_table[self.keywords[3]].data).tolist()\n\n print('Science Exposures')\n\n print('Copy Exposures')\n self.copyExposures(dark_ic,self.paths['dark_dir'],dark_exposures)\n\n print('Copy Filters')\n if self.usefitsfilterlist[0] == 'True':\n self.copyFilters(flat_ic,self.paths['flat_dir'],flat_filters)\n else:\n self.copyFiltersFname(self.paths['flat_dir'],self.flatfilterlist,self.updatefitsfilterlist[0])\n\n if self.usefitsfilterlist[1] == 'True':\n self.copyFilters(flat_ic,self.paths['flat_dir'],science_filters)\n else:\n self.copyFiltersFname(self.paths['science_dir'],self.sciencefilterlist,self.updatefitsfilterlist[1])\n\n print('Create Master Bias')\n self.createMasters(bias_ic,self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\n self.filemods['master_bias_header_value'],self.filemods['median_combine_bias'])\n\n print('Create Master Darks')\n self.createMasters(dark_ic,self.paths['master_dir'],self.filemods['master_dark_name'] + '.fit',\n self.filemods['master_dark_header_value'],self.filemods['median_combine_dark'])\n\n print('Create Master Flatsx')\n if self.usefitsfilterlist[0] == 'True':\n filternames = flat_filters\n else:\n filternames = self.flatfilterlist\n\n for filterType in filternames:\n print('Create Master Flat ' + filterType)\n masterFile = self.filemods['master_flat_name'] + '_' + filterType + '.fit'\n filter_dir = os.path.join(self.paths['flat_dir'],filterType)\n ImageCollection = ImageFileCollection(filter_dir)\n self.createMasters(ImageCollection,self.paths['master_dir'],masterFile,\n self.filemods['master_flat_header_value'],self.filemods['median_combine_flat'])\n\n print('Bias Removal')\n print('Bias Removal from Dark')\n if self.filemods['filename_mod_prefix']:\n self.removeBias(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\n self.filemods['master_dark_name'] + '.fit',self.filemods['bias_removal_mod'] + self.filemods['master_dark_name'] + '.fit',self.filemods['master_dark_header_value'])\n else:\n self.removeBias(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\n self.filemods['master_dark_name'] + '.fit',self.filemods['master_dark_name'] + self.filemods['bias_removal_mod'] +'.fit',self.filemods['master_dark_header_value'])\n\n print('Bias Removal from Flats')\n for filterType in filternames:\n print('Bias Removal from Flat ' + filterType)\n masterFile = self.filemods['master_flat_name'] + '_' + filterType + '.fit'\n if self.filemods['filename_mod_prefix']:\n masterFilebr = self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] + '_' + filterType + '.fit'\n else:\n masterFilebr = self.filemods['master_flat_name'] + '_' + filterType + self.filemods['bias_removal_mod'] + '.fit'\n\n self.removeBias(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\n masterFile,masterFilebr,self.filemods['master_flat_header_value'])\n\n print('Dark Removal from Flats')\n for filterType in filternames:\n print('Dark Removal from Flat ' + filterType)\n if self.filemods['filename_mod_prefix']:\n masterFilebr = self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] +'_' + filterType + '.fit'\n masterFilebrds = self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] +'_' + filterType + '.fit'\n self.removeDark(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['bias_removal_mod'] + self.filemods['master_dark_name'] + '.fit',\n masterFilebr,masterFilebrds,self.filemods['master_flat_header_value'])\n else:\n masterFilebr = self.filemods['master_flat_name'] +'_' + filterType + self.filemods['bias_removal_mod'] +'.fit'\n masterFilebrds = self.filemods['master_flat_name'] +'_' + filterType + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit'\n self.removeDark(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_dark_name'] + self.filemods['bias_removal_mod'] +'.fit',\n masterFilebr,masterFilebrds,self.filemods['master_flat_header_value'])\n \n print('Reduce Science File')\n\n print('Remove Bias & Dark')\n for fname in science_ic.files:\n fname_noext = os.path.splitext(fname)[0]\n\n if self.filemods['filename_mod_prefix']:\n self.removeBias(self.paths['master_dir'],self.paths['science_dir'],self.paths['output_dir'],self.filemods['master_bias_name'] + '.fit',\n fname,self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.imagelist[3])\n self.removeDark(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['bias_removal_mod'] + self.filemods['master_dark_name'] + '.fit',\n self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.imagelist[3])\n else:\n self.removeBias(self.paths['master_dir'],self.paths['science_dir'],self.paths['output_dir'],self.filemods['master_bias_name'] + '.fit',\n fname,fname_noext + self.filemods['bias_removal_mod'] + '.fit',self.imagelist[3])\n self.removeDark(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['master_dark_name'] + self.filemods['bias_removal_mod'] + '.fit',\n fname_noext + self.filemods['bias_removal_mod'] +'.fit',fname_noext + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit',self.imagelist[3])\n\n print('Flat Correction')\n for filterType in filternames:\n\n #need to do filenames by other than filter when not using fits header\n\n filter_dir = os.path.join(self.paths['science_dir'],filterType)\n ImageCollection = ImageFileCollection(filter_dir)\n\n for fname in ImageCollection.summary['file']:\n fname_noext = os.path.splitext(fname)[0]\n\n if self.filemods['filename_mod_prefix']:\n self.reduceFlat(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] + '_' + filterType + '.fit',self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.filemods['reduced_removal_mod'] + fname_noext + '.fit')\n else:\n self.reduceFlat(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['master_flat_name'] + '_' + filterType + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit',fname_noext + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit',fname_noext + self.filemods['reduced_removal_mod'] + '.fit')\n\n print('Reduction Complete')","title":""},{"docid":"10805538b233798437311cf99c41e3bc","score":"0.5191627","text":"def runRConly(self):\r\n print(\"Initializing Rule Compaction...\")\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n # CHECK FOR POPULATION REBOOT - Required for running Rule Compaction only on an existing saved rule population.\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n if not cons.doPopulationReboot:\r\n print(\"Algorithm: Error - Existing population required to run rule compaction alone.\")\r\n return\r\n\r\n try:\r\n fileObject = open(cons.popRebootPath+\"_PopStats.txt\", 'rU') # opens each datafile to read.\r\n except:\r\n #break\r\n print(\"Data-set Not Found!\")\r\n\r\n #Retrieve last training and testing accuracies from saved file---------\r\n tempLine = None\r\n for i in range(3):\r\n tempLine = fileObject.readline()\r\n tempList = tempLine.strip().split('\\t')\r\n trainAcc = float(tempList[0])\r\n if cons.testFile != 'None': #If a testing file is available.\r\n testAcc = float(tempList[1])\r\n else:\r\n testAcc = None\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n # RULE COMPACTION\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n cons.timer.startTimeRuleCmp()\r\n RuleCompaction(self.population, trainAcc, testAcc, self.exploreIter)\r\n cons.timer.stopTimeRuleCmp()\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n # GLOBAL EVALUATION OF COMPACTED RULE POPULATION\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n cons.timer.startTimeEvaluation()\r\n self.population.recalculateNumerositySum()\r\n self.population.runPopAveEval(self.exploreIter)\r\n self.population.runAttGeneralitySum()\r\n #----------------------------------------------------------\r\n cons.env.startEvaluationMode()\r\n if cons.testFile != 'None': #If a testing file is available.\r\n if cons.env.formatData.discretePhenotype:\r\n trainEval = self.doPopEvaluation(True)\r\n testEval = self.doPopEvaluation(False)\r\n else: #ContinuousCode #########################\r\n trainEval = self.doContPopEvaluation(True)\r\n testEval = self.doContPopEvaluation(False)\r\n elif cons.trainFile != 'None':\r\n if cons.env.formatData.discretePhenotype:\r\n trainEval = self.doPopEvaluation(True)\r\n testEval = None\r\n else: #ContinuousCode #########################\r\n trainEval = self.doContPopEvaluation(True)\r\n testEval = None\r\n else: #Online Environment\r\n trainEval = None\r\n testEval = None\r\n\r\n cons.env.stopEvaluationMode()\r\n cons.timer.stopTimeEvaluation()\r\n #------------------------------------------------------------------------------\r\n cons.timer.returnGlobalTimer()\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n # WRITE OUTPUT FILES\r\n #-----------------------------------------------------------------------------------------------------------------------------------------\r\n OutputFileManager().writePopStats(cons.outFileName+\"_RC_\"+cons.ruleCompactionMethod, trainEval, testEval, self.exploreIter + 1, self.population, self.correct)\r\n OutputFileManager().writePop(cons.outFileName+\"_RC_\"+cons.ruleCompactionMethod, self.exploreIter + 1, self.population)\r\n OutputFileManager().attCo_Occurence(cons.outFileName+\"_RC_\"+cons.ruleCompactionMethod, self.exploreIter + 1, self.population)\r\n OutputFileManager().writePredictions(self.exploreIter, cons.outFileName+\"_RC_\"+cons.ruleCompactionMethod, self.predictionList, self.realList, self.predictionSets)\r\n #------------------------------------------------------------------------------------------------------------\r\n print(\"Rule Compaction Complete\")","title":""},{"docid":"3bde4ca60e5ca9934955c98cf6cbf66b","score":"0.51821685","text":"def main(self):\r\n\r\n self.get_relations()\r\n self.get_ways()\r\n self.get_nodes()\r\n self.merge_all_data()","title":""},{"docid":"64044668fdf9b3f998a51edc8bcf48d5","score":"0.51724035","text":"def test_replacement_costs_aggregate(self):\n replacement_costs = self.load_replacement_costs_mavrouli()\n self.assertReplacementCostsAggregate(replacement_costs)","title":""},{"docid":"30372dbaacb30f0386383ec7652347ac","score":"0.51662207","text":"def cost(self):\n\t\tamt = 0.0\n\t\tfor i in self.timesheets.itervalues():\n\t\t\tamt += (float(i[1]) * float(self.rate))\n\t\tfor i in self.POs.itervalues():\n\t\t\tamt += i.quote.price\n\t\treturn amt","title":""},{"docid":"3fa7ea24171c73e0ecccc810c9533e98","score":"0.5163673","text":"def print_cost(self, cost_type: CostType = CostType.ALL):\n\n def print_penalty_list(nlp, penalties, print_only_weighted):\n running_total = 0\n\n for penalty in penalties:\n if not penalty:\n continue\n\n val, val_weighted = self._get_penalty_cost(nlp, penalty)\n running_total += val_weighted\n\n if penalty.node in [Node.MULTINODES, Node.TRANSITION]:\n node_name = penalty.node.name\n else:\n node_name = f\"{penalty.node[0]}\" if isinstance(penalty.node[0], int) else penalty.node[0].name\n\n if self._detailed_cost is not None:\n self._detailed_cost += [\n {\n \"name\": penalty.type.__str__(),\n \"penalty\": penalty.type.__str__().split(\".\")[0],\n \"function\": penalty.name,\n \"cost_value_weighted\": val_weighted,\n \"cost_value\": val,\n \"params\": penalty.params,\n \"derivative\": penalty.derivative,\n \"explicit_derivative\": penalty.explicit_derivative,\n \"integration_rule\": penalty.integration_rule.name,\n \"weight\": penalty.weight,\n \"expand\": penalty.expand,\n \"node\": node_name,\n }\n ]\n if print_only_weighted:\n print(f\"{penalty.type}: {val_weighted}\")\n else:\n print(f\"{penalty.type}: {val_weighted} (non weighted {val: .2f})\")\n\n return running_total\n\n def print_objective_functions(ocp):\n \"\"\"\n Print the values of each objective function to the console\n \"\"\"\n print(f\"\\n---- COST FUNCTION VALUES ----\")\n running_total = print_penalty_list(None, ocp.J_internal, False)\n running_total += print_penalty_list(None, ocp.J, False)\n if running_total:\n print(\"\")\n\n for nlp in ocp.nlp:\n print(f\"PHASE {nlp.phase_idx}\")\n running_total += print_penalty_list(nlp, nlp.J_internal, False)\n running_total += print_penalty_list(nlp, nlp.J, False)\n print(\"\")\n\n print(f\"Sum cost functions: {running_total}\")\n print(f\"------------------------------\")\n\n def print_constraints(ocp, sol):\n \"\"\"\n Print the values of each constraint with its lagrange multiplier to the console\n \"\"\"\n\n if sol.constraints is None:\n return\n\n # Todo, min/mean/max\n print(f\"\\n--------- CONSTRAINTS ---------\")\n if (\n print_penalty_list(None, ocp.g_internal, True)\n + print_penalty_list(None, ocp.g_implicit, True)\n + print_penalty_list(None, ocp.g, True)\n ):\n print(\"\")\n\n for idx_phase, nlp in enumerate(ocp.nlp):\n print(f\"PHASE {idx_phase}\")\n print_penalty_list(nlp, nlp.g_internal, True)\n print_penalty_list(nlp, nlp.g_implicit, True)\n print_penalty_list(nlp, nlp.g, True)\n print(\"\")\n print(f\"------------------------------\")\n\n if cost_type == CostType.OBJECTIVES:\n print_objective_functions(self.ocp)\n elif cost_type == CostType.CONSTRAINTS:\n print_constraints(self.ocp, self)\n elif cost_type == CostType.ALL:\n print(\n f\"Solver reported time: {self.solver_time_to_optimize} sec\\n\"\n f\"Real time: {self.real_time_to_optimize} sec\"\n )\n self.print_cost(CostType.OBJECTIVES)\n self.print_cost(CostType.CONSTRAINTS)\n else:\n raise ValueError(\"print can only be called with CostType.OBJECTIVES or CostType.CONSTRAINTS\")","title":""},{"docid":"178fa74e94980d0cce85160d2d30d284","score":"0.5162399","text":"def cost(self):\n params = {\n \"f\" : \"json\",\n \"token\" : self._securityHandler.token\n }\n url = self._url + \"/cost\" #% (self.portalId, )\n return self._do_post(url=url,\n param_dict=params,\n proxy_url=self._proxy_url,\n proxy_port=self._proxy_port)","title":""},{"docid":"2824110f39967816bf377e218f7ce1e1","score":"0.51601386","text":"def get_cost(self, inputs, targets):\n self.outputs[:] = self.costfunc(inputs, targets)\n self.be.mean(self.outputs, axis=1, out=self.cost_buffer)\n self.cost = self.cost_buffer.get()\n return self.cost","title":""},{"docid":"6d9989e01d130cbb9703171c2dca9c93","score":"0.5155409","text":"def main():\n global SCORES\n\n size_categories = [\"small\", \"medium\", \"large\"]\n if not os.path.isdir(path_to_outputs):\n os.mkdir(path_to_outputs)\n\n # Load previous scores from file if such file exists.\n if os.path.isfile(score_path):\n with open(score_path, 'r+') as f:\n lines = f.read()\n if lines:\n print(\"!!~~ LOADED PREVIOUS SCORES ~~!!\\n\")\n SCORES = json.loads(lines)\n\n t_start = time.time()\n for size in size_categories:\n category_path = path_to_inputs + \"/\" + size\n output_category_path = path_to_outputs + \"/\" + size\n category_dir = os.fsencode(category_path)\n\n if not os.path.isdir(output_category_path):\n os.mkdir(output_category_path)\n\n for input_folder in os.listdir(category_dir):\n input_name = os.fsdecode(input_folder)\n graph, num_buses, bus_size, constraints = parse_input(category_path + \"/\" + input_name)\n solver_instance = solve(graph, num_buses, bus_size, constraints, verbose=True)\n solver_instance.write(input_name, output_category_path, verbose=True)\n\n time_elapsed = datetime.timedelta(seconds=(time.time() - t_start))\n print(f\"Time Elapsed: {time_elapsed} hrs\")\n all_scores = list(SCORES.values())\n print(f\"Average Score (on leaderboard): {sum(all_scores) / len(all_scores)}\")","title":""},{"docid":"df663e2a27c5cede45a84bc971e02851","score":"0.5149468","text":"def cost_saved_pa(generation_1kw, load_kwh, capacity_kWp, cost_per_kWp, \n import_cost, export_price, expected_life_yrs,\n verbose=False, return_raw_data=False):\n import pandas as pd\n load = load_kwh.rename(columns={load_kwh.columns[0]:'load_kWh'})\n if not isinstance(generation_1kw, pd.DataFrame):\n logging.debug(str(type(generation_1kw)))\n# input('Press any key')\n generation_1kw = generation_1kw.rename(columns={\n generation_1kw.columns[0]:'1kWp_generation_kWh'})\n generation = generation_1kw * capacity_kWp\n df = generation.merge(load, \n how='inner', \n left_index=True, \n right_index=True)\n #days = (df.index[-1] - df.index[0]).days\n #confidence = days/365\n df['import_kWh'] = \\\n (df['load_kWh'] - df['1kWp_generation_kWh']).clip(lower=0)\n df['export_kWh'] = \\\n (df['load_kWh'] - df['1kWp_generation_kWh']).clip(upper=0)\n df = df.rename(columns={\n '1kWp_generation_kWh':\n f'{round(capacity_kWp,2)}_1kWp_generation_kWh'})\n if return_raw_data:\n return df\n df['export_kWh'] = df['export_kWh'].abs()\n \n df = df.sum() # total kWh for the entire year\n\n old_import_cost = import_cost*(df['load_kWh'])\n new_import_cost = import_cost*(df['import_kWh'])\n \n import_cost_savings = old_import_cost - new_import_cost\n \n export_revenue = export_price * df['export_kWh']\n install_cost = cost_per_kWp * capacity_kWp\n amortized_install_cost = install_cost / expected_life_yrs\n revenue_pa = import_cost_savings + export_revenue\n total_revenue = revenue_pa * expected_life_yrs\n profit_pa = import_cost_savings \\\n + export_revenue \\\n - amortized_install_cost\n total_profit = ( profit_pa * expected_life_yrs )\n \n if verbose:\n ROI = 100 * (total_profit / install_cost)\n payback_yrs = install_cost / revenue_pa\n summary = {'old_import_cost':old_import_cost,\n 'new_import_cost': new_import_cost,\n 'import_cost_savings': import_cost_savings,\n 'export_revenue': export_revenue,\n 'install_cost': install_cost,\n 'amortized_install_cost': amortized_install_cost,\n 'revenue_pa': revenue_pa,\n 'profit_pa': profit_pa,\n 'total_revenue':total_revenue,\n 'total_profit': total_profit,\n 'ROI%': ROI,\n 'payback_yrs': payback_yrs}\n return summary\n return profit_pa","title":""},{"docid":"1d0ab969db604a3cc7407e65b5c34c47","score":"0.5145199","text":"def main(registry=REGISTRY, deployer=DEPLOYER):\n balance = deployer.balance()\n registry = Registry.at(registry)\n pool_data = get_pool_data()\n\n print(\"Adding pools to registry...\")\n\n for name, data in pool_data.items():\n pool = data[\"swap_address\"]\n if registry.get_n_coins(pool)[0] == 0:\n print(f\"\\nAdding {name}...\")\n add_pool(data, registry, deployer)\n else:\n print(f\"\\n{name} has already been added to registry\")\n\n gauges = data[\"gauge_addresses\"]\n gauges = gauges + [\"0x0000000000000000000000000000000000000000\"] * (10 - len(gauges))\n\n if registry.get_gauges(pool)[0] == gauges:\n print(f\"{name} gauges are up-to-date\")\n continue\n\n print(f\"Updating gauges for {name}...\")\n for gauge in data[\"gauge_addresses\"]:\n try:\n Contract(GAUGE_CONTROLLER).gauge_types(gauge)\n except (ValueError, VirtualMachineError):\n print(f\"Gauge {gauge} is not known to GaugeController, cannot add to registry\")\n gauges = False\n break\n\n if gauges:\n registry.set_liquidity_gauges(\n pool, gauges, {\"from\": deployer, \"gas_price\": gas_strategy}\n )\n\n print(f\"Total gas used: {(balance - deployer.balance()) / 1e18:.4f} eth\")","title":""},{"docid":"cb9a210ccbddce13c87bcab0e68915ae","score":"0.51375043","text":"def test_replacement_costs_aggregate(self):\n replacement_costs = self.load_replacement_costs_torres()\n self.assertReplacementCostsAggregate(replacement_costs)","title":""},{"docid":"72e267e88301700dfd7360f3f68cb17c","score":"0.5132486","text":"def main():\n \n\t# Check file directory paths exist \n\tDirExists(OUTDIR)\n\t#FileExists(STATDIR + FILEDATE + \"_stat.csv\")\n\t#FileExists(NETDIR + FILEDATE + \"_net.csv\")\n\n\t# Get Dictionaries\n\tinterrupts = GetInterrupts(FILEDATE + \"_stat.csv\")\n\t#print interrupts\n\tcontext = GetContext(FILEDATE + \"_stat.csv\")\n\tnet = GetBytes(FILEDATE + \"_net.csv\")\n\n\n\t# Sort Dictionaries\n\tintDict = collections.OrderedDict(sorted(interrupts.items()))\n\tcontDict = collections.OrderedDict(sorted(context.items()))\n\tnetDict = collections.OrderedDict(sorted(net.items()))\n\n\t\"\"\"\n\t Process dictionaries to get metrics:\n\t \t# Interrupts Clean\n\t\tIBR = Interrupts/Bytes Received\n\t\tIBT = Interrupts/Bytes Transferred\n\t\tIPR = Interrupts/Packets Received\n\t\tIPR = Interrupts/Packets Transferred\n\t\tBTI = Context Switch/Bytes Transferred\n\t\tBRI = Context Switch/Bytes Received\n\t\tPTI = Context Switch/Packets Transferred\n\t\tPRI = Context Switch/Packets Received\n\n\t\t# Context Clean\n\t\tCBT = Context Switch/Bytes Transferred\n\t\tCBR = Context Switch/Bytes Received\n\t\tCPT = Context Switch/Packets Transferred\n\t\tCPR = Context Switch/Packets Received\n\n\t\tBTC = Context Switch/Bytes Transferred\n\t\tBRC = Context Switch/Bytes Received\n\t\tPTC = Context Switch/Packets Transferred\n\t\tPRC = Context Switch/Packets Received\n\t\"\"\"\n\n\t# Prepare csv writers to output in NuPic input format \n\t# See: http://nupic.docs.numenta.org/1.0.0/quick-start/example-data.html)\n\tinterruptsFile = OUTDIR + FILEDATE + '_interrupts_clean.csv'\n\tinterruptOutput = open(interruptsFile, 'wb')\n\tinterruptWriter = csv.writer(interruptOutput, delimiter=',')\n\n\tcontextFile = OUTDIR + FILEDATE + '_context_clean.csv'\n\tcontextOutput = open(contextFile, 'wb')\n\tcontextWriter = csv.writer(contextOutput, delimiter=',')\n\tintHeader = [\n\t\t\t\t\t\"timestamp\",\n\t\t\t\t\t\"Total Interrupts since Boot\",\n\t\t\t\t\t\"Interrupts per Second\",\n\t\t\t\t\t\"IBR\",\n\t\t\t\t\t\"IBT\",\n\t\t\t\t\t\"IPR\",\n\t\t\t\t\t\"IPT\"\n\t\t\t\t]\n\tfieldTypes = [\n\t\t\t\t\"datetime\",\n\t\t\t\t\"int\",\n\t\t\t\t\"int\",\n\t\t\t\t\"float\",\n\t\t\t\t\"float\",\n\t\t\t\t\"float\",\n\t\t\t\t\"float\",\n\t\t\t\t]\n\tflags = [\"T\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\"]\n\tinterruptWriter.writerow(intHeader)\n\tinterruptWriter.writerow(fieldTypes)\n\tinterruptWriter.writerow(flags)\n\t\n\tcontheader = [\n\t\t\t\t\t\"timestamp\",\n\t\t\t\t\t\"Total Context Switches since Boot\", \n\t\t\t\t\t\"Contect Switches per Second\",\n\t\t\t\t\t\"CBR\",\n\t\t\t\t\t\"CBT\",\n\t\t\t\t\t\"CPR\",\n\t\t\t\t\t\"CPT\"\n\t\t\t\t]\n\tcontextWriter.writerow(contheader)\n\tcontextWriter.writerow(fieldTypes)\n\tcontextWriter.writerow(flags)\n\n\t# Get Metrics\n\tGetMetrics(intDict, netDict, interruptWriter)\n\tprint (\"Results written to \" + interruptsFile)\n\tGetMetrics(contDict, netDict, contextWriter)\n\tprint (\"Results written to \" + contextFile)","title":""},{"docid":"cf56331879c2dbca77057f750c40d22e","score":"0.5124699","text":"def compute_basic(self):\n self.counter = Counter(self.data[-1])\n self.data_entropy = self.entropy(self.counter.values(), self.nr)","title":""},{"docid":"1caef852996b2dc2d7677d4449532416","score":"0.512332","text":"def test_replacement_costs_aggregate(self):\n replacement_costs = self.load_replacement_costs_suppasri()\n self.assertReplacementCostsAggregate(replacement_costs)","title":""},{"docid":"2b251e8f0b93198b58af2b74e1fc8398","score":"0.5122973","text":"def get_cost(self, inputs, targets, cost_type=None):\n self.outputs[:] = self.costfunc(inputs, targets, cost_type)\n self.be.mean(self.outputs, axis=1, out=self.cost_buffer)\n self.cost = self.cost_buffer.get()\n return self.cost","title":""},{"docid":"073d4fc15145990b823b1b725cc5b660","score":"0.510906","text":"def get_elec_cost():\n if self.tech_type in [\"BOILER\", \"FURNACE\", \"DSGLF\", \"PTC\", \"PTCTES\"]:\n return 0\n \n def get_demand_cost(load):\n \"\"\"Finds the demand cost for each month\"\"\"\n length = np.array([31,28,31,30,31,30,31,31,30,31,30,31]) * 24\n start = np.array([0,31,59,90,120,151,181,212,243,273,304,334])*24\n peaks = []\n peakind = []\n for month in range(len(length)):\n ind = np.argmax(load[start[month] : start[month] + length[month]])\n peakind.append(ind)\n peaks.append(load[ind])\n # single value demand vs demand rate by month\n if type(self.edrate) == list:\n peakrates = np.array([self.edrate[i] for i in peakind])\n demand_cost = sum(np.array(peaks) * peakrates)\n else:\n demand_cost = sum(self.edrate*np.array(peaks)) \n \n return demand_cost\n # new facility electricity grid load after addition of electrified equipment and potential renewables\n new_elec_load = (self.fe_load_8760 + self.load_8760/self.model.get_efficiency() - self.model.gen).clip(min=0) \n\n # demand charge change due to renewables\n new_elec_cost_demand = get_demand_cost(new_elec_load)\n old_elec_cost_demand = get_demand_cost(self.fe_load_8760)\n demand_diff = new_elec_cost_demand - old_elec_cost_demand\n \n # reduced energy charges from renewables \n ediff = self.model.elec_gen - self.fe_load_8760\n mit_cost = sum(self.model.elec_gen[ediff <= 0] * self.pd_curve[0][ediff <=0]) \\\n + sum(self.fe_load_8760[ediff > 0] *self.pd_curve[0][ediff > 0]) \n \n # For PV assisted technologies, determine the energy charge from the grids\n if self.tech_type == \"PVEB\": \n \n elec_energy_grid_cost = sum((self.load_8760 - self.model.load_met)/self.model.get_efficiency() * self.pd_curve[0])\n \n return demand_diff - mit_cost + elec_energy_grid_cost\n\n if self.tech_type == \"PVRH\":\n \n elec_energy_grid_cost = sum((self.load_8760 - self.model.load_met)/self.model.get_efficiency()* self.pd_curve[0])\n \n return demand_diff - mit_cost + elec_energy_grid_cost","title":""},{"docid":"7b462462a0762589aaf51f77f61d8e33","score":"0.51047206","text":"def _refresh_internal_vars(self):\n self.min_cost_flow = min_cost_flow.SimpleMinCostFlow()\n self.start_inds = []\n self.end_inds = []\n self.caps = []\n self.costs = []","title":""},{"docid":"23fbb3c39d7de0703cf764bbbe0e593e","score":"0.51004565","text":"def CABE(self):\n\n Results, G_values, G1_values, G2_values = {}, [], [], []\n\n pool = mp.Pool(self.n_processors)\n G_values.append(pool.map(self.CalculateFreeEnergy,self.reports))\n pool.terminate()\n\n G_values = self.DecompressList(G_values)\n\n for G_value_list in G_values:\n G1_values += G_value_list[0]\n G2_values += G_value_list[1]\n\n mean_G1 = n.mean(G1_values,axis=0); std_G1 = n.std(G1_values,axis=0)\n mean_G2 = n.mean(G2_values,axis=0); std_G2 = n.std(G2_values,axis=0)\n state_1 = len(G1_values) ; state_2 = len(G2_values)\n rel1 = 100*state_1/(state_1+state_2); rel2 = 100*state_2/(state_1+state_2)\n \n print(mean_G1-mean_G2,rel1,rel2)\n\n Results[\"mean,std, and frequency of state 1\"] = (mean_G1, std_G1, state_1, rel1)\n Results[\"mean,std, and frequency of state 2\"] = (mean_G2, std_G2, state_2, rel2)\n\n if self.perform_plots:\n G_names = [\"State 1\" for i in G1_values]+[\"State 2\" for i in G2_values]\n G_val_grouped = G1_values + G2_values\n print(len(G_names),len(G_val_grouped))\n if self.violin_plots:\n sns.violinplot(G_names, G_val_grouped, flierprops={\"marker\":\"o\", \"markerfacecolor\":\"darkgrey\", \"markeredgecolor\":\"k\", \"markersize\":10})\n else:\n sns.boxplot(G_names, G_val_grouped, flierprops={\"marker\":\"o\", \"markerfacecolor\":\"darkgrey\", \"markeredgecolor\":\"k\", \"markersize\":10})\n plt.savefig(self.output_path+\"_CABE\");plt.close()\n inf_CABE = open(\"{}_CABE.pkl\".format(self.output_path), \"wb\"); pickle.dump(G_val_grouped, inf_CABE); pickle.dump(G_names, inf_CABE); inf_CABE.close()\n\n\n df = pd.DataFrame(Results, index = [\"Mean\", \"Standard deviation\", \"Frequency\",\"Relative_abundance\"])\n df.to_csv(self.output_path+\".csv\")","title":""},{"docid":"8e07408ce52b85bf73494d852c3fe39e","score":"0.5096082","text":"def _main():\n rtts = []\n cbts = []\n congs = []\n manager = Manager()\n wlock = manager.Lock()\n cqueue = manager.Queue()\n rqueue = manager.Queue()\n oqueue = manager.Queue()\n pool = Pool()\n parser = ArgumentParser(description=\"Add statistical information\" +\n \"to probes\")\n parser.add_argument(\"--input\", type=str, required=True, help=\"Input file.\")\n parser.add_argument(\"--output\", type=str, required=True,\n help=\"Output file.\")\n args = parser.parse_args()\n assert args.input and exists(args.input), 'Invalid input file.'\n assert args.output and not exists(args.output), 'Invalid output file.'\n\n with open(args.input, 'r') as f:\n try:\n while True:\n try:\n probe = load(f)\n except EOFError:\n break\n if probe.cbt:\n cbts.append(probe.cbt)\n if len(probe.rtts) > 0 and isinstance(probe.rtts[0], int):\n rtts.append(probe.rtts[0])\n if probe.cong:\n congs.append(probe.cong)\n pool.apply_async(_update_probe, (probe, cbts, rtts,\n congs, wlock, cqueue,\n rqueue, oqueue, args.output))\n if len(cbts) > 1000:\n del cbts[0]\n if len(rtts) > 1000:\n del rtts[0]\n if len(congs) > 1000:\n del congs[0]\n # Run garbage collector after cleaning R's memory space.\n collect()\n except KeyboardInterrupt:\n pass\n except:\n sys.stderr.write(str(traceback.format_exc()))\n pool.close()\n pool.join()\n assert cqueue.qsize() in range(0, 2), 'Wrong size of queue!'\n assert rqueue.qsize() in range(0, 2), 'Wrong size of queue!'","title":""},{"docid":"4eb00aa3d59f54a6bd87e8362d02cbab","score":"0.50921494","text":"def main():\n\n opts, args = getopt.gnu_getopt(sys.argv[1:], '',\n ['file_path='])\n # process all the options\n for opt, arg in opts :\n if opt == '--file_path':\n file_path = arg\n # Input the data.\n data = create_data_model(file_path)\n\n # Create the routing/scheduling index manager.\n\n manager = pywrapcp.RoutingIndexManager(\n len(data['change_over_matrix']), data['num_lines'], data['starts'],\n data['ends'])\n\n # Create Routing Model.\n scheduling = pywrapcp.RoutingModel(manager)\n\n # Create and register a transit/schedule callback\n def time_callback(from_index, to_index):\n \"\"\"Returns the distance between the two nodes.\"\"\"\n # Convert from scheduling variable Index to time matrix NodeIndex.\n from_node = manager.IndexToNode(from_index)\n to_node = manager.IndexToNode(to_index)\n return data['change_over_matrix'][from_node][to_node] + \\\n data['product_production_time'][from_node]\n\n schedule_callback_index = scheduling.RegisterTransitCallback(time_callback)\n\n # Define cost of each arc.\n scheduling.SetArcCostEvaluatorOfAllVehicles(schedule_callback_index)\n\n # Add Time Windows constraint.\n time = 'Time'\n scheduling.AddDimension(\n schedule_callback_index,\n 0, # allow waiting time\n 12000, # maximum time per vehicle\n True, # Don't force start cumul to zero.\n time)\n time_dimension = scheduling.GetDimensionOrDie(time)\n # The method SetGlobalSpanCostCoefficient sets a large coefficient (100)\n # for the global span of the routes, which in this example is the\n # maximum of the distances of the routes. This makes the global span\n # the predominant factor in the objective function, so the program\n # minimizes the length of the longest route.\n time_dimension.SetGlobalSpanCostCoefficient(100)\n\n # Add time window constraints for each location except depot.\n for product, time_window in enumerate(data['time_windows']):\n if product == 0 or 1 or 2 or len(data['time_windows']) - 1:\n continue\n print(time_dimension.CumulVar(product))\n time_dimension.CumulVar(product).SetRange(time_window[0],\n time_window[1])\n\n\n # define the lines that can produce certain product\n\n for product, line_list in data['line_constraint'].items():\n allowed_line_list = [-1]\n allowed_index = manager.NodeToIndex(product)\n allowed_line_list.extend(line_list)\n\n scheduling.VehicleVar(product).SetValues(allowed_line_list)\n\n\n # Instantiate route start and end times to produce feasible times.\n for i in range(data['num_lines']):\n scheduling.AddVariableMinimizedByFinalizer(\n time_dimension.CumulVar(scheduling.Start(i)))\n scheduling.AddVariableMinimizedByFinalizer(\n time_dimension.CumulVar(scheduling.End(i)))\n\n # Setting first solution heuristic.\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.local_search_metaheuristic = (\n routing_enums_pb2.LocalSearchMetaheuristic.TABU_SEARCH)\n\n # search_parameters.first_solution_strategy = (\n # routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n\n search_parameters.time_limit.seconds = 30\n search_parameters.log_search = True\n # get the attribute of the search_parameters\n #print(dir(search_parameters))\n\n # Solve the problem.\n assignment = scheduling.SolveWithParameters(search_parameters)\n\n # Print solution on console.\n if assignment:\n #file_path = 'Y:\\\\Pyomo_projects\\\\PnG_CP'\n figure_name = 'Demo_figure_heuristic'\n excel_name = '/' + 'Detailed_schedule_heuristic.xlsx'\n excel_path = file_path + excel_name\n input_df = get_schedule(data, manager, scheduling, assignment)\n writer_reference = pd.ExcelWriter(excel_path)\n input_df.to_excel(writer_reference, startcol = 4, startrow = 1)\n worksheet = writer_reference.sheets['Sheet1']\n worksheet.write_string(10, 0, 'Detailed Schedule')\n writer_reference.save()\n\n Vis_Gantt_Chart(input_df, file_path, figure_name)\n # print_solution(data, manager, scheduling, assignment)\n else:\n print('No solution found !')","title":""},{"docid":"e7644d0854c79d4d72f4220f0a4ba1ba","score":"0.5087497","text":"def _determine_costs_dict(net, opf_task_overview):\n\n cost_dfs = [df for df in [\"poly_cost\", \"pwl_cost\"] if net[df].shape[0]]\n if not len(cost_dfs):\n opf_task_overview[\"flexibilities_without_costs\"] = \"all\"\n return\n\n flex_elements = [\"ext_grid\", \"gen\", \"sgen\", \"load\", \"dcline\", \"storage\"]\n\n for flex_element in flex_elements:\n\n # determine keys of opf_task_overview[\"flexibilities\"] ending with flex_element\n keys = [power_type + flex_element for power_type in [\"P\", \"Q\"] if (\n power_type + flex_element) in opf_task_overview[\"flexibilities\"].keys()]\n\n # determine indices of all flexibles\n idx_without_cost = set()\n for key in keys:\n idx_without_cost |= set(chain(*opf_task_overview[\"flexibilities\"][key][\"index\"]))\n # simple alternative without itertools.chain():\n # idx_without_cost |= {idx for idxs in opf_task_overview[\"flexibilities\"][key][\n # \"index\"] for idx in idxs}\n\n for cost_df in cost_dfs:\n idx_with_cost = set(net[cost_df].element[net[cost_df].et == flex_element].astype(int))\n if len(idx_with_cost - idx_without_cost):\n logger.warning(\"These \" + flex_element + \"s have cost data but aren't flexible or\" +\n \" have both, poly_cost and pwl_cost: \" +\n str(sorted(idx_with_cost - idx_without_cost)))\n idx_without_cost -= idx_with_cost\n\n if len(idx_without_cost):\n opf_task_overview[\"flexibilities_without_costs\"][flex_element] = list(idx_without_cost)","title":""},{"docid":"3d7b87b1d5fec329e635d6f6462a578e","score":"0.50856185","text":"def main():\n utility_name = '05-2018_Utility_Summary.xlsx'\n dining_name = '05-2018_Dining_Utility_Summary.xlsx'\n housing_name = '05-2018_Housing_Utility_Summary.xlsx'\n input_column = get_columns()\n kwh_rate, water_rate, gas_rate = calc_rates()\n fill_utility(utility_name, input_column, kwh_rate, water_rate, gas_rate)\n fill_dining(dining_name, input_column, kwh_rate, water_rate)\n fill_housing(housing_name, input_column, kwh_rate, water_rate, gas_rate)","title":""},{"docid":"2de49faf15f385b4c699d8548e5b2394","score":"0.5078285","text":"def main():\n urls = get_urls(URL_NOLOOKUP)\n all_data = []\n print('Scraping PCGS Number Data...')\n for i, (category, subcategories) in enumerate(urls.items()):\n print(f\"\\tStarting Category {i+1}/{len(urls.items())}: {category}...\")\n for subcat_name, subcat_url in tqdm(subcategories):\n time.sleep(1.0)\n subcat_data = scrape_nums(subcat_url)\n all_data.extend(subcat_data)\n print('Done with PCGS Number Data! Saving...')\n\n pickle.dump(all_data, open('data/number_data.pkl', 'wb'))\n\n print('Saved to data/number_data.pkl')","title":""},{"docid":"e5dba0f08c9511aff8ab47638abd0675","score":"0.5063765","text":"def main(file_xyz: str, cell: str, restart: str, basis: str, basis_folder: str) -> None:\n system = Molecule(file_xyz)\n\n # Set path for basis set\n basisCP2K = join(basis_folder, \"BASIS_MOLOPT\")\n potCP2K = join(basis_folder, \"GTH_POTENTIALS\")\n\n # Settings specifics\n s = templates.geometry\n s.basis = basis\n s.potential = \"GTH-PBE\"\n s.cell_parameters = cell\n s.specific.cp2k.force_eval.dft.basis_set_file_name = basisCP2K\n s.specific.cp2k.force_eval.dft.potential_file_name = potCP2K\n s.specific.cp2k.force_eval.dft.uks = ''\n s.specific.cp2k.force_eval.dft.charge = '-1'\n s.specific.cp2k.force_eval.dft.multiplicity = '2'\n s.specific.cp2k.force_eval.dft.wfn_restart_file_name = f'{restart}'\n\n# =======================\n# Compute OPT files with CP2k\n# =======================\n\n result = run(cp2k(s, system))\n\n# ======================\n# Output the results\n# ======================\n\n logger.info(result.energy)","title":""},{"docid":"3bf077c96e0cf1e18495f9ad358da060","score":"0.5061305","text":"def compute(self):\n drafter = Drafter.get_instance(None)\n\n if len(self.tasksToCompute) != 0:\n for task in self.tasksToCompute:\n self.usedPower += task.compute(self.tasksToCompute[task])\n\n finishedTasks = {k: v for k, v in self.tasksToCompute.items() if k.isCompleted()}\n for task in finishedTasks:\n self.currentComputingPower += self.tasksToCompute[task]\n del self.tasksToCompute[task]\n\n if len(task.divisionHistory) == 0:\n print(str(task) + \": FINISHED\")\n else:\n task.sourceDevice.receiveResults(task)\n #gdy task wraca zmien kolor na niebieski\n change_edge_on_pull(self.deviceID, task.sourceDevice.deviceID, drafter.canvas, drafter.configuration)\n if task.sourceDevice.masterDevice.deviceID is self.deviceID:\n change_node_on_pull(task.sourceDevice.deviceID, drafter.canvas, drafter.configuration)\n\n #zmien kolor node'a na aktualnie zajmowana moc obliczeniowa\n change_node_color(calculateColor(self.currentComputingPower / self.maxComputingPower), self.deviceID, drafter.canvas, drafter.configuration)","title":""},{"docid":"ba5b5111185368e4bee7547817e848a9","score":"0.5061063","text":"def estimate_construction_time(self, construction_time_input_data, construction_time_output_data):\n\n collection_construction_time = construction_time_input_data['construct_duration'] * 1 / 3 # assumes collection construction occurs for one-third of project duration\n\n throughput_operations = construction_time_input_data['rsmeans']\n trench_length_km = construction_time_output_data['trench_length_km']\n if construction_time_input_data['turbine_rating_MW'] >= 0.1:\n operation_data = throughput_operations.where(throughput_operations['Module'] == 'Collection').dropna(\n thresh=4)\n # from rsmeans data, only read in Collection related data and filter out the rest:\n cable_trenching = throughput_operations[throughput_operations.Module == 'Collection']\n else: #switch for small DW\n operation_data = throughput_operations.where(\n throughput_operations['Module'] == 'Small DW Collection').dropna(thresh=4)\n # from rsmeans data, only read in Collection related data and filter out the rest:\n cable_trenching = throughput_operations[throughput_operations.Module == 'Small DW Collection']\n # operation_data = pd.merge()\n\n # from rsmeans data, only read in Collection related data and filter out the rest:\n cable_trenching = throughput_operations[throughput_operations.Module == 'Collection']\n\n # Storing data with labor related inputs:\n trenching_labor = cable_trenching[cable_trenching.values == 'Labor']\n trenching_labor_usd_per_hr = trenching_labor['Rate USD per unit'].sum()\n\n construction_time_output_data['trenching_labor_usd_per_hr']=trenching_labor_usd_per_hr\n trenching_labor_daily_output = trenching_labor['Daily output'].values[0] # Units: LF/day -> where LF = Linear Foot\n trenching_labor_num_workers = trenching_labor['Number of workers'].sum()\n\n # Storing data with equipment related inputs:\n trenching_equipment = cable_trenching[cable_trenching.values == 'Equipment']\n trenching_cable_equipment_usd_per_hr = trenching_equipment['Rate USD per unit'].sum()\n construction_time_output_data['trenching_cable_equipment_usd_per_hr']=trenching_cable_equipment_usd_per_hr\n trenching_equipment_daily_output = trenching_equipment['Daily output'].values[0] # Units: LF/day -> where LF = Linear Foot\n construction_time_output_data['trenching_labor_daily_output'] = trenching_labor_daily_output\n construction_time_output_data['trenching_equipment_daily_output'] = trenching_equipment_daily_output\n\n operation_data['Number of days taken by single crew'] = ((trench_length_km / self._km_to_LF) / trenching_labor_daily_output)\n operation_data['Number of crews'] = np.ceil((operation_data['Number of days taken by single crew'] / 30) / collection_construction_time)\n operation_data['Cost USD without weather delays'] = ((trench_length_km / self._km_to_LF) / trenching_labor_daily_output) * (operation_data['Rate USD per unit'] * construction_time_input_data['operational_hrs_per_day'])\n alpha = operation_data[operation_data['Type of cost'] == 'Collection']\n operation_data_id_days_crews_workers = alpha[['Operation ID', 'Number of days taken by single crew', 'Number of crews', 'Number of workers']]\n\n alpha = operation_data[operation_data['Type of cost'] == 'Labor']\n operation_data_id_days_crews_workers = alpha[['Operation ID', 'Number of days taken by single crew', 'Number of crews', 'Number of workers']]\n\n # if more than one crew needed to complete within construction duration then assume that all construction\n # happens within that window and use that timeframe for weather delays;\n # if not, use the number of days calculated\n operation_data['time_construct_bool'] = operation_data['Number of days taken by single crew'] > collection_construction_time * 30\n boolean_dictionary = {True: collection_construction_time * 30, False: np.NAN}\n operation_data['time_construct_bool'] = operation_data['time_construct_bool'].map(boolean_dictionary)\n operation_data['Time construct days'] = operation_data[['time_construct_bool', 'Number of days taken by single crew']].min(axis=1)\n num_days = operation_data['Time construct days'].max()\n\n\n # No 'management crew' in small DW\n if construction_time_input_data['turbine_rating_MW'] >= 0.1:\n # pull out management data\n crew_cost = self.input_dict['crew_cost']\n crew = self.input_dict['crew'][self.input_dict['crew']['Crew type ID'].str.contains('M0')]\n management_crew = pd.merge(crew_cost, crew, on=['Labor type ID'])\n management_crew = management_crew.assign(per_diem_total=management_crew['Per diem USD per day'] * management_crew['Number of workers'] * num_days)\n management_crew = management_crew.assign(hourly_costs_total=management_crew['Hourly rate USD per hour'] * self.input_dict['hour_day'][self.input_dict['time_construct']] * num_days)\n management_crew = management_crew.assign(total_crew_cost_before_wind_delay=management_crew['per_diem_total'] + management_crew['hourly_costs_total'])\n self.output_dict['management_crew'] = management_crew\n self.output_dict['managament_crew_cost_before_wind_delay'] = management_crew['total_crew_cost_before_wind_delay'].sum()\n else:\n self.output_dict['managament_crew_cost_before_wind_delay'] = 0.0\n\n construction_time_output_data['operation_data_id_days_crews_workers'] = operation_data_id_days_crews_workers\n construction_time_output_data['operation_data_entire_farm'] = operation_data\n\n return construction_time_output_data['operation_data_entire_farm']","title":""},{"docid":"b14f7f01f06047a9903b7e837b55a88a","score":"0.50591457","text":"def main():\n # pre_process = PreProcess()\n # pre_process.transform_queries_qrels_new_ids()\n # pagerank = PageRank(\"data/pagerank.scoreOrder\", \"data/qrels_webtrack2009/webtrec2009.txt\")\n # pagerank = PageRank(\"data/ClueWeb09-En-PRranked.txt\", \"data/qrels_webtrack2009/webtrec2009.txt\")\n # pagerank.add_pagerank_qrels()\n # spamscore = SpamScore(\"data/processed_data/qrels_webtrack2014/qrels_with_pr_normalized.txt\", \"data/waterloo-spam-cw12-decoded/\")\n # spamscore = SpamScore(\"data/processed_data/qrels_webtrack2009/qrels_with_pr_normalized.txt\", \"data/clueweb09spam.Fusion\", collection=True)\n # spamscore.add_spam_score_to_qrels()\n # runs_track = RunsTrack()\n # runs_track.generate_runs_new_ids()\n\n \"\"\"Discretizing the values of the qrels\"\"\" \n # qrels = Qrels()\n\n \"\"\"Computing the Partial order relation among documents and generating new qrels.\"\"\"\n # distance_order = DistanceOrder(is_config=True)\n # skyline_order = SkylineOrder(is_config=True)\n # exit()\n \"\"\"Computing Dataset Statistics\"\"\"\n # dataset_statistics = DatasetStatistics()\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2009/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2009/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2010/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2010/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2011/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2011/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2012/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2012/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2013/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2013/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2014/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2014/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_3aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/decision/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/processed_data/qrels/qrels_2015_4aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/task2015/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/processed_data/qrels/qrels_2016_4aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/task2016/qrels_urbp.txt')\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_misinfo/qrels/misinfo-qrels', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/misinfo2020/qrels_urbp.txt')\n \n\n exit()\n \"\"\"Generating queries and searching in Indri\"\"\"\n # search = Search()\n # search.prepare_indri_queries()\n # search.search_griparameters()\n\n \"\"\"Evaluating the runs\"\"\"\n # eval = Eval()\n # val = eval.run_trec_eval(qrels=\"data/processed_data/qrels/qrels_only_rel.txt\", run=\"data/runs/bm25/0.01_0.6.txt\", metric='ndcg', queries=True)\n # eval.run_trec_eval(qrels=\"data/processed_data/qrels/qrels_only_rel.txt\", run=\"data/runs/bm25/0.01_0.6.txt\", metric='ndcg')\n #getting the top runs\n # eval.get_top_runs()\n # eval.get_average_scores()\n\n \"\"\"Generating results file with evaluation scores\"\"\"\n # results = Results()\n # results.generate_results()\n\n \"\"\"Running Experiments\"\"\"\n #### Discriminative Power #####\n # discriminative_power = DiscriminativePower()\n\n ##### Informativeness #####\n # Informativeness()\n\n \"\"\"Correlation Analysis\"\"\"\n # corr_analysis = CorrAnalysis()\n\n \"\"\"Intuitiveness Experiment\"\"\"\n # intuitiveness = Intuitiveness()\n # intuitiveness.experiment_intuitiveness()\n\n \"\"\"Unanimity Experiment\"\"\"\n # intuitiveness = Unanimity()\n # intuitiveness.experiment_unanimity()\n\n \"\"\"Pool Downsampling\"\"\"\n # pooldownsampling = PoolDownsampling()\n #\n # #### Task Track 2015 #####\n # \"\"\"Multiple random samples\"\"\"\n # # for i in range(1000):\n # # # Task Track 2015 and Task Track 2016 ##\n # # # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2015_4aspects.txt',\n # # # folder_new='data/multiple_downsampling_qrels_2015/'+str(i)+'/', step=15)\n # # # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2015/',\n # # # qrels_path_downsamples='data/multiple_downsampling_qrels_2015/'+str(i)+'/',\n # # # results_folder='data/multiple_results_downsampling_topicwise_2015/'+str(i)+'/',\n # # # metric='ndcg')\n # #\n # # # Decision Track ##\n # # pooldownsampling.generate_downsampling(qrels_path='data/qrels_decision/qrels_decision_3aspects.txt', folder_new='data/multiple_downsampling_qrels_decision/'+str(i)+'/', step=15, num_aspects=3)\n # # pooldownsampling.evaluate_downsampling_topicwize_decision(runs_folder='data/decisionRuns/',\n # # qrels_path_downsamples='data/multiple_downsampling_qrels_decision/'+str(i)+'/',\n # # results_folder='data/multiple_results_downsampling_topicwise_decision/'+str(i)+'/', metric='ndcg')\n # # exit()\n # dict_results = {}\n # dict_to_plot = {}\n # corr = True\n # for i in range(1000):\n # if corr is not None:\n # res = pooldownsampling.calc_corr_by_topic(results_folder='data/multiple_results_downsampling_topicwise_2016/'+str(i)+'/')\n # else:\n # res = pooldownsampling.calc_rmse_by_topic(\n # results_folder='data/multiple_results_downsampling_topicwise_2015/' + str(i) + '/')\n #\n # for key, percentages in res.items():\n # if key not in dict_results:\n # dict_results[key] = {}\n # for percentage, value in percentages.items():\n # if percentage not in dict_results[key]:\n # dict_results[key][percentage] = []\n # dict_results[key][percentage] += [value]\n # # pprint.pprint(dict_results)\n # # exit()\n # for key, percentages in dict_results.items():\n # if key not in dict_to_plot:\n # dict_to_plot[key] = {'mean':[],\n # 'lb':[],\n # 'ub':[]}\n # # print(sorted(percentages))\n # for percentage, values in sorted(percentages.items()):\n # if percentage == '0' and corr is None:\n # dict_to_plot[key]['mean'] += [0]\n # dict_to_plot[key]['lb'] += [0]\n # dict_to_plot[key]['ub'] += [0]\n # continue\n # elif percentage == '0' and corr:\n # dict_to_plot[key]['mean'] += [1]\n # dict_to_plot[key]['lb'] += [1]\n # dict_to_plot[key]['ub'] += [1]\n # continue\n # stddev = std(values, ddof=1)\n # t_bounds = t.interval(0.95, len(values) - 1)\n # ci = [average(values) + critval * stddev / sqrt(len(values)) for critval in t_bounds]\n # dict_to_plot[key]['mean'] += [average(values)]\n # dict_to_plot[key]['lb'] += [ci[0]]\n # dict_to_plot[key]['ub'] += [ci[1]]\n # # pprint.pprint(dict_to_plot)\n # # exit()\n # # pooldownsampling.plot_mean_and_CI(dict_to_plot, plot_path='rmse_downsampling_2016.jpeg', x_labels=sorted(list(dict_results[list(dict_results.keys())[0]].keys())))\n # pooldownsampling.plot_mean_and_CI(dict_to_plot, plot_path='correlation_downsampling_2016.jpeg', x_labels=sorted(list(dict_results[list(dict_results.keys())[0]].keys())), corr=corr)\n\n # print(\"After ..\")\n # pprint.pprint(dict_results)\n\n # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2015_4aspects.txt', folder_new='data/downsample_qrels_2015/',step=15)\n # pooldownsampling.evaluate_downsampling(runs_folder='data/runs_track_newids/2015/',qrels_path_downsamples='data/downsample_qrels_2015/',results_folder='data/results_downsampling_2015/' ,metric='ndcg')\n # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2015/',qrels_path_downsamples='data/downsample_qrels_2015/',results_folder='data/results_downsampling_topicwise_2015/' ,metric='ndcg')\n # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2015/',qrels_path_downsamples='data/downsample_qrels_2015/',results_folder='data/results_downsampling_topicwise_2015/' ,metric='ndcg')\n # pooldownsampling.plot_results_summary(\"data/results_downsampling_2015/\")\n\n #### Task Track 2016 #####\n # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2016_4aspects.txt', folder_new='data/downsample_qrels_2016/',step=15)\n # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2016_4aspects.txt', folder_new='data/downsample_qrels_2016/',step=15)\n # pooldownsampling.evaluate_downsampling(runs_folder='data/runs_track_newids/2016/',\n # qrels_path_downsamples='data/downsample_qrels_2016/',\n # results_folder='data/results_downsampling_2016/', metric='ndcg')\n # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2016/',\n # qrels_path_downsamples='data/downsample_qrels_2016/',\n # results_folder='data/results_downsampling_topicwise_2016/', metric='ndcg')\n # pooldownsampling.plot_results_summary(\"data/results_downsampling_2016/\")\n # pooldownsampling.calc_corr_by_topic(results_folder='data/results_downsampling_topicwise_2016/')\n # pooldownsampling.calc_rmse_by_topic(results_folder='data/results_downsampling_topicwise_2016/')\n # pooldownsampling.calc_rmse_by_topic(results_folder='data/results_downsampling_topicwise_2015/')\n\n #### Decision TRACK #####\n # pooldownsampling.generate_downsampling(qrels_path='data/qrels_decision/qrels_decision_3aspects.txt', folder_new='data/downsample_qrels_decision/',step=15, num_aspects=3)\n # pooldownsampling.evaluate_downsampling_decision(runs_folder='data/decisionRuns/',\n # qrels_path_downsamples='data/downsample_qrels_decision/',\n # results_folder='data/results_downsampling_decision/', metric='ndcg')\n # pooldownsampling.evaluate_downsampling_topicwize_decision(runs_folder='data/decisionRuns/',\n # qrels_path_downsamples='data/downsample_qrels_decision/',\n # results_folder='data/results_downsampling_decision_topicwise/', metric='ndcg')\n # pooldownsampling.plot_results_summary(\"data/results_downsampling_decision/\")\n #\n # pooldownsampling.calc_corr_by_topic(results_folder='data/results_downsampling_decision_topicwise/')\n # pooldownsampling.calc_rmse_by_topic(results_folder='data/results_downsampling_decision_topicwise/')\n\n\n ### Ploting distributions ###\n # distributions = Distributions()\n # distributions.get_frequency_by_rank_pos()\n # distributions.plot_num_relevant_per_query()\n\n ## COMPUTING LIMITATIONS ##\n\n # for year in [2012]:\n # # dt.plot_histogram_qrels()\n # dt.generate_individualIdealRankings(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_discrete_aspects.txt',outpath='data/run_test_limitations_webtrack'+str(year)+'/', num_aspects=3)\n # dt.generate_idealRuns_Single(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_webtrack'+str(year)+'_manhattan.txt',\n # out_path='data/run_test_limitations_webtrack'+str(year)+'/manhattan.txt', aspects=[0,1,2])\n # dt.generate_idealRuns_Single(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_webtrack'+str(year)+'_euclidean.txt',\n # out_path='data/run_test_limitations_webtrack'+str(year)+'/euclidean.txt', aspects=[0, 1, 2])\n # dt.generate_idealRuns_Single(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_webtrack'+str(year)+'_manhattan.txt',\n # out_path='data/run_test_limitations_webtrack'+str(year)+'/chebyshev.txt', aspects=[0, 1, 2])\n # dt = DatasetStatistics()\n #\n # dt.generate_individualIdealRankings(qrels_path=\"data/qrels_decision/qrels_decision_3aspects.txt\",\n # outpath='data/runs_test_limitations_decision/', num_aspects=3)\n # dt.generate_idealRuns_Single(\n # qrels_path=\"data/qrels_misinfo/qrels/qrels_misinfo_chebyshev.txt\",\n # out_path='data/run_test_limitations_misinformation/individual/chebyshev.txt', aspects=[0, 1, 2])\n # dt.generate_idealRuns_Single(\n # qrels_path=\"data/qrels_misinfo/qrels/qrels_misinfo_manhattan.txt\",\n # out_path='data/run_test_limitations_misinformation/individual/manhattan.txt', aspects=[0, 1, 2])\n # dt.generate_idealRuns_Single(\n # qrels_path=\"data/qrels_misinfo/qrels/qrels_misinfo_euclidean.txt\",\n # out_path='data/run_test_limitations_misinformation/individual/euclidean.txt', aspects=[0, 1, 2])\n # for year in ['2009','2010','2011','2012','2013','2014']:\n # for metric in ['ndcg','map']:\n # dt = DatasetStatistics()\n # dict_parameters = {'qrels_arr': [\n # \"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack\"+str(year)+\"/qrels_only_relevance.txt\",\n # \"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack\"+str(year)+\"/qrels_only_pagerank.txt\",\n # \"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack\"+str(year)+\"/qrels_only_spam.txt\",\n # ],\n # 'runs_folder': '/science/image/cluster-homedirs/krn788/LLMS_multieval/data/run_test_limitations_webtrack'+year+'/individual/',\n # 'task':'Webtrack'+year,\n # 'metric': metric,\n # # 'topic_range': [36, 85],\n # # 'single_runs': {'chebyshev': (\"data/processed_data/qrels_chebyshev_iteration_qrels_only_query.txt\",\n # # \"data/runs_test_limitations_decision/chebyshev.txt\"),\n # # 'euclidean': (\"data/processed_data/qrels_euclidean_iteration_qrels_only_query.txt\",\n # # \"data/runs_test_limitations_decision/euclidean.txt\"),\n # # 'manhattan': (\"data/processed_data/qrels_manhattan_iteration_qrels_only_query.txt\",\n # # \"data/runs_test_limitations_decision/manhattan.txt\")}\n # }\n #\n # dt.plot_scores_by_topic(dict_parameters)\n # exit()\n for metric in ['ndcg', 'map']:\n dt = DatasetStatistics()\n dict_parameters = {'qrels_arr': [\n \"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_rel.txt\",\n \"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_cred.txt\",\n \"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_correctness.txt\",\n ],\n 'runs_folder': '/science/image/cluster-homedirs/krn788/LLMS_multieval/data/runs_test_limitations_decision/individual/',\n 'task': 'Decision',\n 'metric': metric,\n }\n dt.plot_scores_by_topic(dict_parameters)\n\n\n # dt.create_box_plot_format_csv(\"data/results/WebTrack2009/\", \"data/results_to_boxplot/WebTrack2009/\")","title":""},{"docid":"df7687437fdfc08add0b3114b3fafca9","score":"0.5057288","text":"def run(self, expRef, butler):\n \n pool = basePool.Pool(\"poolSatellite\")\n pool.cacheClear()\n pool.storeSet(butler=butler)\n\n self.log.info(\"Beginning processing of ExpRef: \" + str(expRef.dataId))\n\n dataIdList = dict([(ccdRef.get(\"ccdExposureId\"), ccdRef.dataId)\n for ccdRef in expRef.subItems(\"ccd\") if ccdRef.datasetExists(\"src\")])\n dataIdList = collections.OrderedDict(sorted(dataIdList.items()))\n\n self.log.info(\"DataIdList to be processed: \" + str( dataIdList.values()))\n\n # Scatter: run each CCD separately\n structList = pool.map(self.process, dataIdList.values())","title":""},{"docid":"4c6b5588ef07271fc2d27ccaa35da873","score":"0.5051165","text":"def compute_pool():\n return {\n \"type\": \"class\",\n \"base\": None,\n \"is_abstract\": False,\n \"pstr\": (\"{} [{}]\", (\"name\", \"number_of_nodes\")),\n \"properties\": [\n (\"accelerator_type\", \"str\", \"0.1\", \"Type of accelerator.\"),\n (\n \"accelerators_per_node\",\n \"int\",\n \"0.1\",\n \"Number of accelerator units on a node.\",\n ),\n (\n \"clock_cycle_concurrency\",\n \"int\",\n \"0.1\",\n \"The number of operations which can be carried out concurrently in a single clock cycle of a single core. E.g. 4.\",\n ),\n (\n \"clock_speed\",\n \"shared.numeric\",\n \"0.1\",\n \"The clock speed of a single core, in units of GHz. E.g. 3.6.\",\n ),\n (\n \"compute_cores_per_node\",\n \"int\",\n \"0.1\",\n \"Number of CPU cores per node.\",\n ),\n (\"cpu_type\", \"str\", \"0.1\", \"CPU type.\"),\n (\"description\", \"str\", \"0.1\", \"Textural description of pool.\"),\n (\"memory_per_node\", \"shared.numeric\", \"0.1\", \"Memory per node.\"),\n (\"model_number\", \"str\", \"0.1\", \"Model/Board number/type.\"),\n (\"name\", \"str\", \"0.1\", \"Name of compute pool within a machine.\"),\n (\"number_of_nodes\", \"int\", \"0.1\", \"Number of nodes.\"),\n (\n \"vendor\",\n \"linked_to(shared.party)\",\n \"0.1\",\n \"Supplier of compute hardware in this pool\",\n ),\n (\n \"network_cards_per_node\",\n \"platform.nic\",\n \"0.N\",\n \"Available network interfaces on node\",\n ),\n ],\n \"derived\": [\n (\"total_cores\", \"compute_cores_per_node * number_of_nodes\"),\n (\"total_memory\", \"memory_per_node * number_of_nodes\"),\n ],\n }","title":""},{"docid":"cae245603f377bca69ba3150d919998b","score":"0.5047229","text":"def test_benchmark_manycore_scf(self):\n from os import makedirs\n\n seed = \"_LiC.res\"\n shutil.copy(REAL_PATH + \"data/structures/LiC.res\", \"_LiC.res\")\n\n cell_dict, s = cell2dict(\n REAL_PATH + \"/data/benchmark/LiC_scf/LiC_scf.cell\",\n verbosity=VERBOSITY,\n db=False,\n )\n self.assertTrue(s)\n param_dict, s = param2dict(\n REAL_PATH + \"/data/benchmark/LiC_scf/LiC_scf.param\",\n verbosity=VERBOSITY,\n db=False,\n )\n self.assertTrue(s)\n\n shutil.copy(REAL_PATH + \"data/pspots/Li_00PBE.usp\", \".\")\n shutil.copy(REAL_PATH + \"data/pspots/C_00PBE.usp\", \".\")\n with self.assertRaises(CalculationError):\n ComputeTask(\n ncores=NCORES,\n nnodes=None,\n node=None,\n res=seed,\n param_dict=param_dict,\n cell_dict=cell_dict,\n verbosity=0,\n executable=EXECUTABLE,\n start=True,\n )\n\n outputs_exist = [\n isfile(\"bad_castep/_LiC.res\"),\n isfile(\"bad_castep/_LiC.castep\"),\n ]\n\n results, s = castep2dict(\"bad_castep/_LiC.castep\", db=False)\n makedirs(REAL_PATH + \"/data/benchmark/results\", exist_ok=True)\n shutil.copy(\n \"bad_castep/_LiC.castep\",\n REAL_PATH\n + \"/data/benchmark/results/_LiC_{}core_castep{}.castep\".format(\n results.get(\"num_mpi_processes\", 0),\n results.get(\"castep_version\", \"xxx\"),\n ),\n )\n\n self.assertTrue(all(outputs_exist), \"couldn't find output files!\")\n self.assertTrue(s, \"couldn't read output files!\")\n print(results[\"_time_estimated\"])\n benchmark_data = {2: 2 * 7.4, 4: 4 * 4.0, 12: 16.8, 14: 22.4, 18: 23.4}\n warnings.warn(\n RuntimeWarning(\n \"Run took {} s with {} MPI processes, with cumulative CPU time of {:.1f} s. Benchmark data\\n = {}\".format(\n results[\"_time_estimated\"],\n results[\"num_mpi_processes\"],\n results[\"_time_estimated\"] * results[\"num_mpi_processes\"],\n benchmark_data,\n )\n )\n )","title":""},{"docid":"fb4c353d275ec8cdc6617f7c6b9f6ea3","score":"0.50456494","text":"def CDLP(capacities, preference_no_purchase, offer_sets: np.ndarray, filename_result=\"\", silent=True):\n offer_sets = pd.DataFrame(offer_sets)\n offer_sets = offer_sets.append(pd.DataFrame([np.zeros_like(products)])) # append the empty set to ensure all times some offerset is offered\n offer_sets = offer_sets.reset_index(drop=True)\n T = len(times)\n\n S = {}\n R = {}\n Q = {}\n for index, offer_array in offer_sets.iterrows():\n S[index] = tuple(offer_array)\n R[index] = revenue(tuple(offer_array), preference_weights, preference_no_purchase, arrival_probabilities,\n revenues)\n temp = {}\n for i in resources:\n temp[i] = quantity_i(tuple(offer_array), preference_weights, preference_no_purchase,\n arrival_probabilities, i, A)\n Q[index] = temp\n\n try:\n m = Model()\n\n # Variables\n mt = m.addVars(offer_sets.index.values, name=\"t\", lb=0.0) # last constraint\n\n # Objective Function\n m.setObjective(quicksum(R[s] * mt[s] for s in offer_sets.index.values), GRB.MAXIMIZE)\n\n mc = {}\n # Constraints\n for i in resources:\n mc[i] = m.addConstr(quicksum(Q[s][i] * mt[s] for s in offer_sets.index.values), GRB.LESS_EQUAL,\n capacities[i],\n name=\"constraintOnResource\")\n msigma = m.addConstr(quicksum(mt[s] for s in offer_sets.index.values), GRB.EQUAL, T)\n\n if silent:\n m.setParam(\"OutputFlag\", 0)\n\n m.optimize()\n\n ret = {}\n # have to get to index to reproduce the original offerset\n pat = r\".*?\\[(.*)\\].*\"\n for v in m.getVars():\n if v.X > 0:\n match = re.search(pat, v.VarName)\n erg_index = match.group(1)\n\n ret[int(erg_index)] = (tuple(offer_sets.loc[int(erg_index)]), v.X)\n\n dualPi = np.zeros_like(resources, dtype=float)\n for i in resources:\n dualPi[i] = mc[i].pi\n dualSigma = msigma.pi\n\n valOpt = m.objVal\n\n return ret, valOpt, dualPi, dualSigma\n\n except GurobiError:\n print('Error reported')","title":""},{"docid":"909028efb1b594b2703d25688e94003a","score":"0.5040765","text":"def run(self):\n\n self.logger.info('Started!')\n\n # Get inputs\n input_uids = self._data_helper.input_uids()\n assert input_uids, 'Error! No input arguments!'\n\n # Get outputs\n output_uids = self._data_helper.output_uids()\n assert output_uids, 'Error! No output arguments!'\n\n # Calculate percentiles\n out_uid = 0\n for in_uid in input_uids:\n percentile = self._calc_percentile(in_uid)\n for threshold, data in percentile['data'].items():\n self._data_helper.put(output_uids[out_uid], values=data, level=str(threshold),\n segment=percentile['@base_period'],\n longitudes=percentile['@longitude_grid'], latitudes=percentile['@latitude_grid'],\n times=percentile['@day_grid'], fill_value=percentile['@fill_value'],\n meta=percentile['meta'])\n out_uid += 1\n\n self.logger.info('Finished!')","title":""},{"docid":"2a9acace05dae16c64f6b7673c56eb83","score":"0.50396425","text":"def run_central_solution(K, data, ene_cap, ene_init, power_ch, power_dis, eff_ch, eff_dis, self_disch, dt):\n print(\"Running Central Opt Solution\")\n\n prices_round = data.round(2)\n len_opt = K\n p_ch = cp.Variable(len_opt, name='p_ch', nonneg=True) # EV Charging power\n p_dis = cp.Variable(len_opt, name='p_dis', nonneg=True) # EV Discharging power\n ene = cp.Variable(len_opt + 1, name='ene', nonneg=True) # ESS Energy\n constraints = []\n constraints += [ene <= np.tile(ene_cap, len_opt + 1)] # energy upper limit\n constraints += [ene >= np.tile(0, len_opt + 1)] # energy lower limit\n constraints += [p_ch <= np.tile(power_ch, len_opt)] # power upper limit\n constraints += [p_ch >= np.tile(0, len_opt)] # power lower limit\n constraints += [p_dis <= np.tile(power_dis, len_opt)] # power upper limit\n constraints += [p_dis >= np.tile(0, len_opt)] # power lower limit\n constraints += [ene[len_opt] == ene_init] # ending\n constraints += [ene[0] == ene_init] # ending\n for k in range(len_opt):\n constraints += [ene[k + 1] == ene[k] + (p_ch[k] - p_dis[k]) * dt - self_disch/100*ene[k]]\n\n costVector = cp.multiply(p_ch, prices_round[0:len_opt]) * dt * 1 / eff_ch - cp.multiply(p_dis, prices_round[0:len_opt]) * dt * eff_dis\n obj = cp.Minimize(cp.sum(costVector))\n\n # Form and solve problem using Gurobi\n prob = cp.Problem(obj, constraints)\n # start = time.time()\n prob.solve(verbose=False, solver=cp.GUROBI)\n # end = time.time()\n # print(end - start)\n #print(\"status:\", prob.status)\n #print(\"optimal value\", prob.value)\n\n # ensure ESS is not charging and discharging at the same time\n assert all(p_ch.value * p_dis.value == 0)\n assert prob.status == cp.OPTIMAL\n\n log_central = pd.DataFrame({'LMP': data.values, 'Energy': ene.value[1:K+1], 'Power': p_ch.value - p_dis.value,\n 'cumul_prof': -np.cumsum(costVector.value)}, index=data.index)\n\n return log_central, prob.value","title":""},{"docid":"f7e3eb7c5285129f521e4bfc4be91bc0","score":"0.5039089","text":"def _save_collection_overview(cname, opnames, data):\n operations = config[\"campaigns\"][cname][\"operations\"]\n for opname in opnames:\n primary = sum(count for d in data[opname].values()\n for (count, _) in d.values())\n show_isk = operations[opname].get(\"isk\", True)\n if show_isk:\n secondary = sum(value for d in data[opname].values()\n for (_, value) in d.values())\n else:\n secondary = None\n _save_operation(cname, opname, primary, secondary)","title":""},{"docid":"7adce0e603cc12f9a74a69597a0f1c3f","score":"0.50368994","text":"def processor(self):\n\n self.load_preferences()\n\n self.load_data()\n\n self.platforms = pd.unique(self.df['Destination platform']).tolist()\n self.cryptos = pd.unique(self.df['Destination currency']).tolist()\n\n # Keep the execution order\n self.calculate_fee()\n self.calculate_gain()\n self.calculate_equivalents()\n self.perf_by_topic()\n\n renderData = {\n 'data': self.df.to_dict(),\n 'fee': self.fee,\n 'gain': self.gain,\n 'eq_perf': self.eq_perf,\n 'perf_topic': self.perf_topic,\n }\n\n pprint(renderData)\n\n jsonData = json.dumps(renderData, indent=4)\n with open(\"output/renderData.json\",\"w\") as f:\n f.write(jsonData)\n\n return renderData","title":""},{"docid":"2d1b87ed1594f768170d0177cc4e6951","score":"0.5032212","text":"def tasks_ro_ops(self):\n config = get_json_config(self.file_name)\n self.get_data = {}\n self.get_calls = []\n self.get_call_name = {}\n self.delete_dict = {}\n self.execute_locust(config)","title":""},{"docid":"9bf825f384ae9d5fb7b8ae1d332aef85","score":"0.50262773","text":"def main(self):\n for db in self.databases:\n database = Database(db)\n logger.debug(database)\n queue_length = database.queue_length.get(self.options.queue)\n if queue_length >= self.options.critical:\n self.add_critical_result(database)\n elif queue_length >= self.options.warning:\n self.add_warning_result(database)\n\n # Perfdata\n self.perfdata.append(\n '\\'{datasource}_{queue}\\'={value};{warn};{crit};0;'.format(\n datasource=database.name,\n queue=self.options.queue,\n value=queue_length,\n warn=self.options.warning,\n crit=self.options.critical))\n\n # Exit\n self._prepare_output(problem_pattern='{num} {queue} queues length '\n 'more than {thr} !',\n ok_pattern='All database {queue} queues are '\n 'normal.')\n if self.have_criticals:\n self.critical(self.output())\n elif self.have_warnings:\n self.warning(self.output())\n else:\n self.ok(self.output())","title":""},{"docid":"87f5476c67cc2828b5f6f6e7b0bb7a27","score":"0.5018137","text":"def __init__(self, cost_map):\n self.cost_map = cost_map\n self.node_grid = NodeGrid(cost_map)","title":""},{"docid":"33c9711ecda4d710090b17abc08507d8","score":"0.50105363","text":"def consumption_production():\n day_data_set = []\n # Wind\n data = read_data.read_csv((\"/home/dutchman/Daten/2013_energy_feed_in/\"\n \"Windenergie_Hochrechnung_2013.csv\"),\n skip=5)\n day_data = daily_production.get_one_day(data, sys.argv[1],\n date_pos=0, data_pos=3,\n mult=1000)\n day_data_set.append(day_data)\n\n # Solar\n data = read_data.read_csv((\"/home/dutchman/Daten/2013_energy_feed_in/\"\n \"Solarenergie_Hochrechnung_2013.csv\"),\n skip=5)\n day_data = daily_production.get_one_day(data, sys.argv[1], date_pos=0,\n data_pos=3)\n day_data_set.append(day_data)\n\n # Renewable\n day_data = day_data_set[0] + day_data_set[1]\n R = day_data\n day_data_set.append(day_data)\n\n # Household \n\n filename = (\"/home/dutchman/Daten/debs_challenge_2014/\"\n \"debs_0_0.csv\")\n nofhouseholds = 10000\n\n total = np.zeros(97)\n devices = accumulate.accumulate_device(filename)\n for device_id in devices.keys():\n devices[device_id] = devices[device_id] / (1000000) * nofhouseholds\n total += devices[device_id]\n\n diff = target_function(day_data_set[2], total)\n print(\"||R-C|| = \", diff)\n\n\n # optimization\n start = -50\n maxrange = 50\n stepwidth = 1\n minval = target_function(R, total)\n best_step1 = 0\n best_step2 = 0\n\n for step1 in xrange(start, maxrange, stepwidth):\n for step2 in xrange(start, maxrange, stepwidth):\n oc1 = super_simple_optimization( devices[7], step=step1)\n oc2 = super_simple_optimization( devices[11], step=step2)\n\n total = np.zeros(97)\n for device_id in devices.keys():\n if device_id == 7:\n total += oc1\n\n elif device_id == 11:\n total += oc2\n\n else:\n total += devices[device_id]\n\n val = target_function(R, total)\n\n print(\"%d %d %f\"%(step1, step2, val))\n\n if val < minval:\n minval = val\n best_step1 = step1\n best_step2 = step2\n\n print(best_step1, best_step2, minval)","title":""},{"docid":"44031ea1707bb0ed493a7708f21afb6c","score":"0.5006965","text":"def _compute_cost_total(self):\n for route in self:\n cost_total = 0.0\n if route.service_ids:\n cost_total = sum(route.service_ids.mapped('cost_total'))\n route.cost_total = cost_total","title":""},{"docid":"dc80edc914e09059b8785073e5ea37b6","score":"0.50055057","text":"def calc_objectives(self):\n if self.debug_wait:\n time.sleep(self.debug_wait_time)\n \n self._entry_name = self.get_design_name(self.current_design_variables)\n\n self.current_objectives, self.current_constraints = self.problem.evaluate(self.current_design_variables)\n \n self._entry = {'design variables': self.current_design_variables, \n 'objective functions': self.current_objectives,\n 'constraints': self.current_constraints}","title":""},{"docid":"c34295ef183d45cf3e8f76836fba260c","score":"0.49993053","text":"def main():\n\n args = parser.parse_args()\n rnaseq = args.rnaseq\n sra_file = args.sra_file\n dcc_manifest = args.dcc_manifest\n gdc_manifest = args.gdc_manifest\n metadata_dest = args.metadata_dest\n db_dest = args.db_dest\n\n Log.logger.info(\"Downloading Data\")\n if dcc_manifest is not None:\n Downloader.download_icgc_project_metadata(Helper.get_icgc_project_list(dcc_manifest), metadata_dest)\n Helper.gunzip_files(os.path.join(metadata_dest, \"icgc\"))\n else:\n Log.logger.info(\"No ICGC DCC manifest file provided or Metadata already downloaded. SKIPPING\")\n\n if sra_file is not None:\n Downloader.download_sra_metadata(Helper.get_sra_accession_list(sra_file), metadata_dest)\n else:\n Log.logger.info(\"No SRA accession list provided or Metadata already downloaded. SKIPPING\")\n\n if gdc_manifest is not None:\n Downloader.download_gdc_file_metadata(Helper.get_gdc_filename_list(gdc_manifest), metadata_dest)\n else:\n Log.logger.info(\"No GDC manifest provided or Metadata already downloaded. SKIPPING\")\n\n Downloader.download_ensembl_metadata(metadata_dest)\n Downloader.download_hgnc(metadata_dest)\n\n Log.logger.info(\"Creating ProjectLayer csv files\")\n start_project_layer = time.time()\n project_db = DataProcessor.create_project_layer_tables(\n DataProcessor.concat_sra_meta(os.path.join(metadata_dest, \"sra\"), Helper.get_sra_accession_list(sra_file)),\n DataProcessor.concat_icgc_meta(metadata_dest, dcc_manifest),\n DataProcessor.concat_gdc_meta(os.path.join(metadata_dest, 'gdc/')),\n db_dest)\n end_project_layer = time.time() - start_project_layer\n Log.logger.info(\"Creating ProjectLayer took {} seconds\".format(time.time() - start_project_layer))\n\n Log.logger.info(\"Creating ExpressionLayer csv files\")\n raw_count_df = DataProcessor.create_countinfo_raw_count_table(project_db[3], rnaseq, db_dest)\n\n Log.logger.info(\"Starting to create expression table. This might take a while.\")\n start = time.time()\n expression_df = dd.concat(DataProcessor.stringtie_results_out(os.path.join(rnaseq, 'results/stringtieFPKM/')))\n DataProcessor.create_expression_table(expression_df,\n raw_count_df,\n db_dest)\n Log.logger.info(\"Expression table created in {} seconds\".format(time.time() - start))\n DataProcessor.create_gene_table(DataProcessor.process_ensembl_metadata(metadata_dest),\n DataProcessor.process_hgnc_metadata(metadata_dest),\n expression_df,\n db_dest)\n\n DataProcessor.create_pipeline_table(rnaseq, db_dest)\n\n SQLscripts.prepare_populate_script(db_dest)\n\n Log.logger.info(\"project layer {} -- \".format(end_project_layer))\n Log.logger.info(\"Peak memory usage was: {} GB\".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024 ** 2))","title":""},{"docid":"182f6fd288d8cbec731908a6d31f34f9","score":"0.4996449","text":"def __init__(self):\n self.collection_name = defect_collection_name\n self.collection = Collection(db, self.collection_name)\n #self._gather_defect_names()\n self._gather_all_mpids()\n self.to = TasksOperater() \n self.hgo = HseGapOperater()","title":""},{"docid":"94133e50d5caed1ac876fd07c057b56d","score":"0.49922913","text":"def main():\n\n b_start_hp = 109\n b_dmg = 8\n b_arm = 2\n\n\n weapons = {\n 'dagger' : {\n 'cost' : 8,\n 'dmg' : 4\n },\n 'shortsword' : {\n 'cost' : 10,\n 'dmg' : 5\n },\n 'warhammer' : {\n 'cost' : 25,\n 'dmg' : 6\n },\n 'longsword' : {\n 'cost' : 40,\n 'dmg' : 7\n },\n 'greataxe' : {\n 'cost' : 74,\n 'dmg' : 8\n }\n }\n\n armors = {\n 'no_armor' : {\n 'cost' : 0,\n 'arm' : 0\n },\n 'leather' : {\n 'cost' : 13,\n 'arm' : 1\n },\n 'chainmail' : {\n 'cost' : 31,\n 'arm' : 2\n },\n 'splintmail' : {\n 'cost' : 53,\n 'arm' : 3\n },\n 'bandedmail' : {\n 'cost' : 75,\n 'arm' : 4\n },\n 'platemail' : {\n 'cost' : 102,\n 'arm' : 5\n }\n }\n\n rings = {\n 'no_ring' : {\n 'cost' : 0,\n 'points' : 0\n },\n 'dmg_1' : {\n 'cost' : 25,\n 'points' : 1\n },\n 'dmg_2' : {\n 'cost' : 50,\n 'points' : 2\n },\n 'dmg_3' : {\n 'cost' : 100,\n 'points' : 3\n },\n 'def_1' : {\n 'cost' : 20,\n 'points' : -1\n },\n 'def_2' : {\n 'cost' : 40,\n 'points' : -2\n },\n 'def_3' : {\n 'cost' : 80,\n 'points' : -3\n }\n }\n\n worst_loadout = []\n worst_price = 0\n for weapon in weapons:\n for armor in armors:\n for ring1 in rings:\n for ring2 in rings:\n if ring2 == ring1:\n continue\n price = weapons[weapon]['cost'] + \\\n armors[armor]['cost'] + \\\n rings[ring1]['cost'] + \\\n rings[ring2]['cost']\n\n player = {\n 'hp' : 100,\n 'dmg' : weapons[weapon]['dmg'],\n 'arm' : armors[armor]['arm']\n }\n\n if rings[ring1]['points'] > 0:\n player['dmg'] += rings[ring1]['points']\n else:\n player['arm'] -= rings[ring1]['points']\n\n if rings[ring2]['points'] > 0:\n player['dmg'] += rings[ring2]['points']\n else:\n player['arm'] -= rings[ring2]['points']\n\n b_hp = b_start_hp\n while player['hp'] > 0:\n b_hp -= max(1, (player['dmg']-b_arm))\n\n if b_hp <= 0:\n break\n\n player['hp'] -= max(1, (b_dmg-player['arm']))\n\n if player['hp'] <= 0 and price >= worst_price:\n print 'player', player['hp'], 'boss', b_hp\n worst_loadout = [weapon, armor, ring1, ring2]\n worst_price = price\n print worst_price, worst_loadout\n\n print '*' * 30\n print worst_price, worst_loadout","title":""}],"string":"[\n {\n \"docid\": \"f9dfb65556c23d60c4b78e4d649e6ebe\",\n \"score\": \"0.64189136\",\n \"text\": \"def main():\\n cost = Costs()\\n cost.update_total_costs(5000000, turn=1)\\n cost.update_total_costs(4000000, turn=2)\\n cost.update_total_costs(3000000, turn=3)\\n cost.update_total_costs(2000000, turn=4)\\n cost.update_total_costs(1000000, turn=5)\\n cost.costs_graph()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"605ce4aea1ed4c21ca4ce1cd787541c4\",\n \"score\": \"0.61276674\",\n \"text\": \"def build_process_costs(self):\\n # TODO: Do we have any process level methods to add here?\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"113d165ad88ee2a1372fc823fd757a7d\",\n \"score\": \"0.59934086\",\n \"text\": \"def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):\\n\\n # Set characteristics based on regressions / empirical data\\n self.set_properties(inputs, discrete_inputs)\\n\\n # Set geometry profile\\n self.set_geometry(inputs, outputs)\\n\\n # Write MAP input file and analyze the system at every angle\\n self.runMAP(inputs, discrete_inputs, outputs)\\n\\n # Compute costs for the system\\n self.compute_cost(inputs, discrete_inputs, outputs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1240be144d63269b0a9c2a8b2ddaef03\",\n \"score\": \"0.5879974\",\n \"text\": \"def main(path_to_model, scaling_factors, path_to_output):\\n model = calliope.read_netcdf(path_to_model)\\n eur_per_kw = scaling_factors[\\\"power\\\"] / scaling_factors[\\\"monetary\\\"] * EUR_PER_KW\\n ct_per_kw = scaling_factors[\\\"power\\\"] / scaling_factors[\\\"monetary\\\"] * CT_PER_KW\\n\\n energy_cap = (model.get_formatted_array(\\\"cost_energy_cap\\\")\\n .squeeze(\\\"costs\\\")\\n .reindex(techs=list(TECHS.keys()))\\n .groupby(\\\"techs\\\")\\n .mean(\\\"locs\\\")\\n .fillna(0)\\n .drop(\\\"costs\\\")) * eur_per_kw\\n energy_cap.loc[\\\"ac_transmission\\\"] = transmission_investment_cost(model, eur_per_kw)\\n annual_cost = (model.get_formatted_array(\\\"cost_om_annual\\\")\\n .squeeze(\\\"costs\\\")\\n .reindex(techs=list(TECHS.keys()))\\n .groupby(\\\"techs\\\")\\n .mean(\\\"locs\\\")\\n .fillna(0)\\n .drop(\\\"costs\\\")) * eur_per_kw\\n annual_cost.loc[\\\"ac_transmission\\\"] = transmission_annual_cost(model, eur_per_kw)\\n storage_cap = (model.get_formatted_array(\\\"cost_storage_cap\\\")\\n .squeeze(\\\"costs\\\")\\n .reindex(techs=list(TECHS.keys()))\\n .groupby(\\\"techs\\\")\\n .mean(\\\"locs\\\")\\n .fillna(0)\\n .drop(\\\"costs\\\")) * eur_per_kw\\n lifetime = (model.get_formatted_array(\\\"lifetime\\\")\\n .reindex(techs=list(TECHS.keys()))\\n .groupby(\\\"techs\\\")\\n .mean(\\\"locs\\\")\\n .fillna(0))\\n lifetime.loc[\\\"ac_transmission\\\"] = transmission_lifetime(model)\\n variable_costs_prod = (model.get_formatted_array(\\\"cost_om_prod\\\")\\n .squeeze(\\\"costs\\\")\\n .reindex(techs=list(TECHS.keys()))\\n .groupby(\\\"techs\\\")\\n .mean(\\\"locs\\\")\\n .fillna(0)\\n .drop(\\\"costs\\\")) * ct_per_kw\\n variable_costs_con = (model.get_formatted_array(\\\"cost_om_con\\\")\\n .squeeze(\\\"costs\\\")\\n .reindex(techs=list(TECHS.keys()))\\n .groupby(\\\"techs\\\")\\n .mean(\\\"locs\\\")\\n .fillna(0)\\n .drop(\\\"costs\\\")) * ct_per_kw\\n variable_costs = variable_costs_prod + variable_costs_con\\n\\n all_costs = xr.Dataset({\\n \\\"Overnight cost (€/kW)\\\": energy_cap,\\n \\\"Overnight cost (€/kWh)\\\": storage_cap,\\n \\\"Annual cost (€/kW/yr)\\\": annual_cost,\\n \\\"Variable cost (€ct/kWh)\\\": variable_costs,\\n \\\"Lifetime (yr)\\\": lifetime,\\n \\\"Source\\\": pd.Series(COST_SOURCES).to_xarray().rename(index=\\\"techs\\\")\\n })\\n all_costs.rename(techs=\\\"Technology\\\").to_dataframe().rename(index=TECHS).to_csv(\\n path_to_output,\\n index=True,\\n header=True,\\n float_format=\\\"%.0f\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3319c01b05f549111d998e7ad154085\",\n \"score\": \"0.5801046\",\n \"text\": \"def cost(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab5d2f3292c4ebe847a687d4960f704\",\n \"score\": \"0.5799554\",\n \"text\": \"def run(self):\\n\\n cost = {}; cost[\\\"best\\\"] = []; cost[\\\"mean\\\"] = []\\n \\n for itr in range(self.max_itrs):\\n\\n # employees phase\\n for index in range(self.size):\\n self.send_employee(index)\\n \\n # onlookers phase\\n self.send_onlookers()\\n\\n # scouts phase\\n self.send_scout()\\n\\n # computes best path\\n self.find_best()\\n\\n # stores convergence information\\n cost[\\\"best\\\"].append( self.best )\\n cost[\\\"mean\\\"].append( sum( [ bee.value for bee in self.population ] ) / self.size )\\n\\n # prints out information about computation\\n if self.verbose:\\n self._verbose(itr, cost)\\n #print(\\\"\\\\n\\\")\\n #print(\\\"****BEST SOLUTION****\\\")\\n #self.bestSolution._printSolution()\\n\\n #sequenceFromSolution(self.dna_sequences,self.bestSolution.solution)\\n \\n #for best in cost[\\\"best\\\"]:\\n #print(best)\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ef7e0512d66fa83cd69d5aead55832\",\n \"score\": \"0.5765362\",\n \"text\": \"def cost(results):\\n # This is all extremely unsophisticated munging, sorry future me.\\n al_df = alignment_df(results)\\n co_df = convergence_df(results)\\n\\n # Convergence targets\\n co_nc_after_t = convergence_targets[0]\\n co_nc_before_t = convergence_targets[1]\\n co_c_after_t = convergence_targets[2]\\n co_c_before_t = convergence_targets[3]\\n\\n # Targets as proportions of a standard\\n # co_nc_after_tp = 1\\n co_nc_before_tp = co_nc_before_t / co_nc_after_t\\n co_c_after_tp = co_c_after_t / co_nc_after_t\\n co_c_before_tp = co_c_before_t / co_nc_after_t\\n\\n # Actual values\\n co_nc_after = co_df[\\n (co_df[\\\"Condition\\\"] == \\\"Non-clustered\\\") & (co_df[\\\"Time\\\"] == \\\"After\\\")\\n ][\\\"Convergence\\\"].mean()\\n co_nc_before = co_df[\\n (co_df[\\\"Condition\\\"] == \\\"Non-clustered\\\") & (co_df[\\\"Time\\\"] == \\\"Before\\\")\\n ][\\\"Convergence\\\"].mean()\\n co_c_after = co_df[\\n (co_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (co_df[\\\"Time\\\"] == \\\"After\\\")\\n ][\\\"Convergence\\\"].mean()\\n co_c_before = co_df[\\n (co_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (co_df[\\\"Time\\\"] == \\\"Before\\\")\\n ][\\\"Convergence\\\"].mean()\\n\\n # Actual values as proportions of a standard\\n # co_nc_after_p = 1\\n co_nc_before_p = co_nc_before / co_nc_after\\n co_c_after_p = co_c_after / co_nc_after\\n co_c_before_p = co_c_before / co_nc_after\\n\\n # Alignment targets\\n # As proportions\\n alignment_targets_p = alignment_targets / alignment_targets[0]\\n\\n # Actual values\\n al_c_1 = al_df[(al_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (al_df[\\\"Hops\\\"] == 1)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_c_2 = al_df[(al_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (al_df[\\\"Hops\\\"] == 2)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_c_3 = al_df[(al_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (al_df[\\\"Hops\\\"] == 3)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_c_4 = al_df[(al_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (al_df[\\\"Hops\\\"] == 4)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_c_5 = al_df[(al_df[\\\"Condition\\\"] == \\\"Clustered\\\") & (al_df[\\\"Hops\\\"] == 5)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_nc_1 = al_df[(al_df[\\\"Condition\\\"] == \\\"Non-clustered\\\") & (al_df[\\\"Hops\\\"] == 1)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_nc_2 = al_df[(al_df[\\\"Condition\\\"] == \\\"Non-clustered\\\") & (al_df[\\\"Hops\\\"] == 2)][\\n \\\"Alignment\\\"\\n ].mean()\\n al_nc_3 = al_df[(al_df[\\\"Condition\\\"] == \\\"Non-clustered\\\") & (al_df[\\\"Hops\\\"] == 3)][\\n \\\"Alignment\\\"\\n ].mean()\\n alignment_values = np.array(\\n [al_c_1, al_c_2, al_c_3, al_c_4, al_c_5, al_nc_1, al_nc_2, al_nc_3]\\n )\\n # As proportions\\n alignment_values_p = alignment_values / alignment_values[0]\\n\\n diffs_co = [\\n co_nc_before_p - co_nc_before_tp,\\n co_c_after_p - co_c_after_tp,\\n co_c_before_p - co_c_before_tp,\\n ]\\n diffs_al = alignment_values_p - alignment_targets_p\\n cost_co = np.mean(np.abs(diffs_co))\\n cost_al = np.mean(np.abs(diffs_al))\\n\\n # Calculate the linear effect of Hops on Alignment in the Clustered\\n # condition and compare to the actual result\\n\\n al_df_clustered = al_df[al_df[\\\"Condition\\\"] == \\\"Clustered\\\"]\\n if np.isnan(al_df_clustered[\\\"Alignment\\\"].values).any():\\n cost_reg = np.nan\\n else:\\n scale = (\\n al_df_clustered[al_df_clustered[\\\"Hops\\\"] == 1][\\\"Alignment\\\"].mean()\\n / alignment_targets[0]\\n )\\n # Rise over run\\n target_coef = (alignment_targets[4] * scale - alignment_targets[0] * scale) / 4\\n\\n reg = linear_model.LinearRegression()\\n reg.fit(\\n al_df_clustered[\\\"Hops\\\"].values.reshape(-1, 1), al_df_clustered[\\\"Alignment\\\"]\\n )\\n cost_reg = np.abs(reg.coef_[0] - target_coef)\\n\\n # Finally, take into account the absolute amount of alignment in both\\n # the clustered and non-clustered conditions\\n\\n cost_al_abs = 1 - al_df_clustered[al_df_clustered[\\\"Hops\\\"] == 1][\\\"Alignment\\\"].mean()\\n\\n c = np.average([cost_co, cost_al, cost_reg, cost_al_abs], weights=[1, 3, 2, 3])\\n\\n return c, cost_co, cost_al, cost_reg, cost_al_abs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdf18f07cf6ded1ec49d2dae94ba384f\",\n \"score\": \"0.5737898\",\n \"text\": \"def main(\\n factory_dict: dict,\\n order_dict: dict,\\n sku_dict: dict,\\n recipe_dict: dict,\\n time_limit=True,\\n exact=True,\\n scaling=1,\\n):\\n\\n model = cp_model.CpModel()\\n # initalise the model\\n # [START VARIABLES]\\n sum_actual = {}\\n all_orders = {}\\n error_abs = {}\\n n = len(factory_dict)\\n mape_limit = n * 100\\n for count, item in enumerate(order_dict.values()):\\n\\n all_orders[count] = item\\n item.order_list(recipe_dict)\\n\\n # creates a dict of int:object for use by or_tools\\n all_factories = {}\\n fulfilled = {} # dict of [factory_id,order_id] : BoolVar\\n factory_quantity = {} # dict of [factory_id, type_id] : Int\\n\\n factory_demand = {} # dict of [factory_id, type_id] : IntVar\\n fulfilled_by = {} # dict of [order_id,factory_id] : BoolVar\\n items = {} # dict of [order_id, type_id] : Int\\n temp = {}\\n\\n for count, item in enumerate(factory_dict.values()):\\n\\n all_factories[count] = item\\n # creates a dict of int:object for use by or_tools\\n error_abs[count] = model.NewIntVar(\\n 0, 1000000000000000000, \\\"x_loss_abs_%i\\\" % count\\n )\\n all_skus = {}\\n\\n for count, item in enumerate(sku_dict.values()):\\n\\n all_skus[count] = item\\n\\n all_skus[count].unit_cost = int(all_skus[count].unit_cost * scaling)\\n\\n # creates a dict of int:object for use by or_tools\\n\\n for count_factory, factory in all_factories.items():\\n\\n for count_sku, sku in all_skus.items():\\n prefix = \\\"%s_%s_\\\" % (factory.factory_id, sku.sku_id)\\n factory_quantity[(count_factory, count_sku)] = 0\\n if factory.factory_inventory.get(sku.name) is None:\\n factory_quantity[(count_factory, count_sku)] = (\\n factory.factory_inventory[sku.name] * sku.unit_cost\\n )\\n else:\\n factory_quantity[(count_factory, count_sku)] += (\\n factory.factory_inventory[sku.name] * sku.unit_cost\\n )\\n\\n # creates a measure of the current quantity in the factories\\n\\n for count_order, order in all_orders.items():\\n for count_factory, factory in all_factories.items():\\n prefix = \\\"%s_\\\" % (order.order_id)\\n suffix = \\\"_%s\\\" % (factory.factory_id)\\n fulfilled_by[(count_order, count_factory)] = model.NewBoolVar(\\n prefix + \\\"fulfilled_by\\\" + suffix\\n )\\n for count_sku, sku in all_skus.items():\\n if order.combined.get(sku.sku_id) is None:\\n items[(count_order, count_sku)] = 0\\n # incase there is none of an ingredient in the order\\n else:\\n items[(count_order, count_sku)] = (\\n order.combined[sku.sku_id] * sku.unit_cost\\n )\\n\\n if factory_demand.get((count_factory, count_sku)) is None:\\n factory_demand[(count_factory, count_sku)] = (\\n round(items[(count_order, count_sku)])\\n * fulfilled_by[(count_order, count_factory)]\\n )\\n # create factory demand\\n else:\\n factory_demand[(count_factory, count_sku)] += (\\n round(items[(count_order, count_sku)])\\n * fulfilled_by[(count_order, count_factory)]\\n )\\n # adds to factory demand\\n for count_factory, factory in all_factories.items():\\n error_abs[count_factory] = sum(\\n [\\n factory_quantity[(count_factory, count_sku)]\\n - factory_demand[(count_factory, count_sku)]\\n for count_sku, sku in all_skus.items()\\n ]\\n )\\n # calcuate the numerator for weighted mape\\n sum_actual[count_factory] = sum(\\n [\\n factory_quantity[(count_factory, count_sku)]\\n for count_sku, sku in all_skus.items()\\n ]\\n )\\n print(sum_actual)\\n # calculates the errors for the quantity and the demand\\n\\n # [END VARIABLES]\\n\\n # [START CONSTRAINTS]\\n for factory in list(all_factories.keys()):\\n for order in list(all_orders.keys()):\\n if exact == True:\\n\\n model.Add(\\n sum(\\n [\\n fulfilled_by[(order, factory_c)]\\n for factory_c in list(all_factories.keys())\\n ]\\n )\\n == 1\\n ) # ensure that all orders have been fulfilled\\n else:\\n model.Add(\\n sum(\\n [\\n fulfilled_by[(order, factory)]\\n for factory in list(all_factories.keys())\\n ]\\n )\\n <= 1\\n ) # to allow for some orders not being fulfilled\\n\\n for sku in list(all_skus.keys()):\\n model.Add(factory_demand[(factory, sku)] <= factory_quantity[factory, sku])\\n # ensure the demand does not outweigh the supply for a factory\\n # [END CONSTRAINTS]\\n # [START OBJECTIVE]\\n obj_var = model.NewIntVar(0, mape_limit, \\\"Sum WMAPE\\\")\\n numerator = {}\\n denom = {}\\n wmape = {}\\n\\n for count_factory, factory in all_factories.items():\\n numerator[count_factory] = model.NewIntVar(0, 5000000000000000000, \\\"sum_all\\\")\\n\\n denom[count_factory] = model.NewIntVar(0, 5000000000000000000, \\\"sum_quantity\\\")\\n\\n wmape[count_factory] = model.NewIntVar(0, 100, \\\"WMAPE\\\")\\n\\n # adds new varibles to calcuate the weighted mape\\n model.Add(numerator[count_factory] == error_abs[count_factory] * 100)\\n\\n model.Add(denom[count_factory] == sum_actual[count_factory])\\n model.AddDivisionEquality(\\n wmape[count_factory], numerator[count_factory], denom[count_factory]\\n )\\n\\n sum_wmape = model.NewIntVar(0, 500, \\\"sum_wmape\\\")\\n model.Add(sum_wmape == sum([wmape[f] for f in all_factories]))\\n model.Add(obj_var == sum_wmape)\\n model.Minimize(obj_var)\\n # [END OBJECTIVE]\\n\\n solver = cp_model.CpSolver()\\n if time_limit == True:\\n solver.parameters.max_time_in_seconds = 20000\\n # adds an optional time limit\\n\\n status = solver.Solve(model)\\n # solves the model\\n\\n print(\\\"Solve status: %s\\\" % solver.StatusName(status))\\n print(\\\"Mean_Wmape: %i\\\" % (solver.ObjectiveValue() / len(all_factories)))\\n\\n wmape_dict = {}\\n for num, factory in enumerate(factory_dict):\\n wmape_dict[factory] = solver.Value(wmape[num])\\n\\n return (solver.ObjectiveValue(), wmape_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a5dc64078d27c7a355305bbd0a9af0\",\n \"score\": \"0.57017267\",\n \"text\": \"def rig_cost(self):\\n if self.electricity_cost and self.power_consumption is not None:\\n mining_time = 24 * self.period * self.algo_percentage()\\n mining_cost = (self.power_consumption / 1000) * self.electricity_cost\\n value = mining_time * mining_cost\\n else:\\n value = 0\\n\\n if self.pool_fee:\\n value += self.pool_cost() * self.usd_price * self.currency.value\\n\\n self.cost = {'value': Decimal(value), 'btc_value': Decimal(self.currency_to_btc(value=value))}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d23ef30e34e7557a05fdf15a4890f03\",\n \"score\": \"0.5646338\",\n \"text\": \"def main():\\n mainpath = (os.path.dirname(os.path.abspath(__name__)))\\n run_all(start=1, end=10, targdir=mainpath, cpucount=30, cores_per_job=2,\\n cae_cpu_limit=6, filename=\\\"UnitCell_{0}\\\", runtype='separate')\\n collector(\\\"csv\\\", mainpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7426e490436cdae9e9b291be353c37\",\n \"score\": \"0.56133294\",\n \"text\": \"def cost(file_path: str, commodity: str, price_per_ton: float, \\n trade_volume: float) -> None:\\n\\n output = []\\n\\n #check to make sure the values of price_per_ton and trade_volume\\n # are greater than or equal to 0\\n if price_per_ton < 0 or trade_volume < 0:\\n error_string = (\\n \\\"Error: trade_volume and price_per_ton arguments must \\\"\\n \\\"be greater than or equal to zero.\\\"\\n )\\n print(error_string)\\n sys.exit(1)\\n\\n # Parse the JSON file and get back a list of objects\\n commodity = commodity.lower()\\n fruit_list = parse_file(file_path)\\n\\n # Iterate through the list of objects and create a list of dicts\\n # with the object as a key and the total price as the value.\\n for fruit in fruit_list:\\n if commodity == fruit.commodity:\\n cost_dict = {}\\n cost_dict[\\\"OBJECT\\\"] = fruit\\n total_cost = (trade_volume \\n * (price_per_ton + fruit.variable_overhead)\\n + fruit.fixed_overhead)\\n # Round to the nearest cent\\n cost_dict[\\\"TOTAL_COST\\\"] = round(total_cost, 2)\\n output.append(cost_dict)\\n\\n # Check if the output list is empty. If so return that the fruit\\n # does not exist.\\n if not output:\\n error_string = (\\n f\\\"Commodity {commodity} was not found. \\\"\\n \\\"Please run fruit_vendor_cli.py list commodity for valid values\\\"\\n )\\n print(error_string)\\n sys.exit(1)\\n # Sort the list by the cost. Greatest to least.\\n output.sort(key = lambda x: x[\\\"TOTAL_COST\\\"], reverse=True)\\n \\n # Iterate through the output list and print the correctly formatted \\n # output\\n # COUNTRY | (PRICE_PER_TON + VARIABLE_OVERHEAD) \\n # * TRADE_VOLUME + FIXED_OVERHEAD\\n for object_cost_dict in output:\\n fruit = object_cost_dict[\\\"OBJECT\\\"]\\n total_cost = object_cost_dict[\\\"TOTAL_COST\\\"]\\n \\n\\n out_string = (\\n f\\\"{fruit.country:3} {total_cost:9.2f} | \\\"\\n f\\\"(({price_per_ton:5.2f} + {fruit.variable_overhead:5.2f})\\\"\\n f\\\" * {trade_volume:5.2f}) + {fruit.fixed_overhead:5.2f}\\\"\\n )\\n print(out_string) \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbfe935c7dda52325ec0e2fb0d4f99ef\",\n \"score\": \"0.55586934\",\n \"text\": \"def calculate_economics_diagnostics(self):\\n\\n start_index_for_costs = tool_kit.find_first_list_element_at_least(\\n self.times, self.inputs.model_constants['recent_time'])\\n self.cost_times = self.times[start_index_for_costs:]\\n self.costs = numpy.zeros((len(self.cost_times), len(self.interventions_to_cost)))\\n\\n for i, inter in enumerate(self.interventions_to_cost):\\n for t, time in enumerate(self.cost_times):\\n # costs from cost-coverage curves\\n cost = get_cost_from_coverage(self.scaleup_fns['int_prop_' + inter](time),\\n self.params['econ_inflectioncost_' + inter],\\n self.params['econ_saturation_' + inter],\\n self.params['econ_unitcost_' + inter],\\n self.var_array[start_index_for_costs + t, self.var_labels.index('popsize_' + inter)])\\n\\n # start-up costs\\n if 'econ_startupcost_' + inter in self.inputs.model_constants \\\\\\n and 'econ_startupduration_' + inter in self.inputs.model_constants \\\\\\n and self.inputs.model_constants['econ_startupduration_' + inter] > 0.:\\n cost += self.calculate_startup_cost(time, inter)\\n\\n # yearly cost\\n if 'econ_yearlycost_' + inter in self.params.keys() and time > self.params['scenario_start_time']:\\n cost += self.params['econ_yearlycost_' + inter]\\n\\n self.costs[t, i] = cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb06c03f5d3f71480b743923b76611dc\",\n \"score\": \"0.55478036\",\n \"text\": \"def compute_cost(self, inputs, discrete_inputs, outputs):\\n # Unpack variables\\n rhoWater = inputs['water_density']\\n L_mooring = inputs['mooring_line_length']\\n anchorType = discrete_inputs['anchor_type']\\n costFact = inputs['mooring_cost_factor']\\n n_connect = int(inputs['number_of_mooring_connections'])\\n n_lines = int(inputs['mooring_lines_per_connection'])\\n ntotal = n_connect * n_lines\\n \\n # Cost of anchors\\n if type(anchorType) == type(''): anchorType = Anchor[anchorType.upper()]\\n if anchorType == Anchor['DRAGEMBEDMENT']:\\n anchor_rate = 1e-3 * self.min_break_load / gravity / 20*2000\\n elif anchorType == Anchor['SUCTIONPILE']:\\n anchor_rate = 150000.* np.sqrt(1e-3*self.min_break_load/gravity/1250.)\\n else:\\n raise ValueError('Anchor Type must be DRAGEMBEDMENT or SUCTIONPILE')\\n anchor_total = anchor_rate*ntotal\\n\\n # Cost of all of the mooring lines\\n legs_total = ntotal * self.cost_per_length * L_mooring\\n\\n # Total summations\\n outputs['anchor_cost'] = anchor_total\\n outputs['mooring_cost'] = costFact*(legs_total + anchor_total)\\n outputs['mooring_mass'] = self.wet_mass_per_length*L_mooring*ntotal\\n outputs['number_of_mooring_lines'] = ntotal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9633462cef468eb0b09559315d3cfbb\",\n \"score\": \"0.5537384\",\n \"text\": \"def compute(self):\\n\\n self.cover, self.cost = {}, 0\\n\\n if self.options.verb:\\n print('c2 computing smallest rule cover')\\n\\n if self.options.cover == 'mxsat':\\n self.compute_mxsat()\\n elif self.options.cover == 'cplex':\\n self.compute_cplex()\\n elif self.options.cover in ('gurobi', 'grb'):\\n self.compute_gurobi()\\n\\n # recording time\\n self.stime = resource.getrusage(resource.RUSAGE_SELF).ru_utime - self.init_stime\\n self.ctime = resource.getrusage(resource.RUSAGE_CHILDREN).ru_utime - self.init_ctime\\n self.time = self.stime + self.ctime\\n\\n return self.cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84318fe510f11133106368f669d9691f\",\n \"score\": \"0.5512912\",\n \"text\": \"def _compute_detailed_cost(self):\\n if self.ocp.n_threads > 1:\\n raise NotImplementedError(\\\"Computing detailed cost with n_thread > 1 is not implemented yet\\\")\\n\\n self._detailed_cost = []\\n\\n for nlp in self.ocp.nlp:\\n for penalty in nlp.J_internal + nlp.J:\\n if not penalty:\\n continue\\n val, val_weighted = self._get_penalty_cost(nlp, penalty)\\n self._detailed_cost += [\\n {\\\"name\\\": penalty.type.__str__(), \\\"cost_value_weighted\\\": val_weighted, \\\"cost_value\\\": val}\\n ]\\n for penalty in self.ocp.J:\\n val, val_weighted = self._get_penalty_cost(self.ocp.nlp[0], penalty)\\n self._detailed_cost += [\\n {\\\"name\\\": penalty.type.__str__(), \\\"cost_value_weighted\\\": val_weighted, \\\"cost_value\\\": val}\\n ]\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc8f4926a9183cf5f7dd3d63ec86cb05\",\n \"score\": \"0.5462631\",\n \"text\": \"def setUp(self):\\n self.cost_func = make_cost_function()\\n problem = self.cost_func.problem\\n self.options = problem.options\\n self.options.software = [\\\"scipy\\\", \\\"dfo\\\", \\\"scipy_ls\\\"]\\n self.grabbed_output = output_grabber.OutputGrabber(self.options)\\n self.start_values_index = 0\\n self.scipy_len = len(self.options.minimizers[\\\"scipy\\\"])\\n self.dfo_len = len(self.options.minimizers[\\\"dfo\\\"])\\n self.scipy_ls_len = len(self.options.minimizers[\\\"scipy_ls\\\"])\\n controller = ScipyController(self.cost_func)\\n controller.parameter_set = 0\\n self.result_args = {'controller': controller,\\n 'accuracy': 1,\\n 'runtimes': [1]}\\n self.cp = Checkpoint(self.options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de87d11cde28e971bda21b69dd2a1f8b\",\n \"score\": \"0.54610795\",\n \"text\": \"def main():\\n # fileDB query. this could be moved to cmd line arg in the future\\n que = 'run==111' # 30 min bad bkg run\\n # que = 'run==110' # 3hr alpha run\\n \\n # load fileDB. use DataGroup, with a hack to fix use of relative file paths\\n pwd = os.getcwd()\\n os.chdir('../processing/')\\n dg = DataGroup('cage.json', load=True)\\n os.chdir(pwd)\\n # print(dg.fileDB)\\n \\n # run query\\n dg.fileDB = dg.fileDB.query(que)\\n \\n # check query result\\n view_cols = ['daq_file', 'cycle', 'run', 'runtype', 'startTime', 'runtime']\\n print(dg.fileDB[view_cols], '\\\\n')\\n \\n # -- run routines -- \\n plot_dsp(dg)\\n # show_wfs(dg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2acfd8ef363b22c591a6d1db5586c1df\",\n \"score\": \"0.5446036\",\n \"text\": \"def test_calculate_task_cost(self):\\r\\n\\r\\n self.DUT.minimum_cost = 252.00\\r\\n self.DUT.average_cost = 368.00\\r\\n self.DUT.maximum_cost = 441.00\\r\\n self.DUT.confidence = 0.95\\r\\n\\r\\n self.assertFalse(self.DUT.calculate())\\r\\n self.assertAlmostEqual(self.DUT.mean_cost, 360.83333333)\\r\\n self.assertAlmostEqual(self.DUT.cost_variance, 992.25)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52bfc8217ca0db2e1890d6d3bf0c6649\",\n \"score\": \"0.5432577\",\n \"text\": \"def project_cost():\\n return {\\n \\\"type\\\": \\\"class\\\",\\n \\\"base\\\": None,\\n \\\"is_abstract\\\": False,\\n \\\"is_document\\\": True,\\n \\\"pstr\\\": (\\\"Production: {}Y, {}\\\", (\\\"useful_years\\\", \\\"useful_data\\\")),\\n \\\"properties\\\": [\\n (\\n \\\"activity\\\",\\n \\\"linked_to(activity.activity)\\\",\\n \\\"1.1\\\",\\n \\\"Project or Experiment of interest\\\",\\n ),\\n (\\n \\\"platform\\\",\\n \\\"linked_to(platform.machine)\\\",\\n \\\"1.1\\\",\\n \\\"Machine used for project\\\",\\n ),\\n (\\n \\\"useful_years\\\",\\n \\\"int\\\",\\n \\\"1.1\\\",\\n \\\"Number of useful years simulated (or to be simulated) during \\\"\\n \\\"this project\\\",\\n ),\\n (\\n \\\"useful_data\\\",\\n \\\"shared.numeric\\\",\\n \\\"0.1\\\",\\n \\\"Volume of useful data to be analysed\\\",\\n ),\\n (\\n \\\"useful_core_hours\\\",\\n \\\"int\\\",\\n \\\"0.1\\\",\\n \\\"Number of core-hours used for useful simulations within \\\"\\n \\\"the project\\\",\\n ),\\n (\\n \\\"actual_years\\\",\\n \\\"int\\\",\\n \\\"0.1\\\",\\n \\\"Number of actual years simulated, including spin-up tuning \\\"\\n \\\"etc.\\\",\\n ),\\n (\\n \\\"peak_data\\\",\\n \\\"shared.numeric\\\",\\n \\\"0.1\\\",\\n \\\"Maximum volume of data held during project\\\",\\n ),\\n (\\n \\\"total_core_hours\\\",\\n \\\"int\\\",\\n \\\"0.1\\\",\\n \\\"Total number of core hours needed for all aspects of the \\\"\\n \\\"project\\\",\\n ),\\n (\\n \\\"total_energy_cost\\\",\\n \\\"float\\\",\\n \\\"0.1\\\",\\n \\\"Total cost of project in Joules, if known\\\",\\n ),\\n ],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea04000952039c17a26c72c2ab51d83f\",\n \"score\": \"0.5416007\",\n \"text\": \"def compute_cost_per_unit_capital(self):\\n\\t\\ttotal_wage = self.wage * self.labor\\n\\t\\tself.cost = copy.copy(total_wage / self.output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96addf55f6117598f02019e88ac37fd0\",\n \"score\": \"0.5381483\",\n \"text\": \"def cost(self, data_center_cost=2000):\\n self.total_cluster_cost = 0\\n self.cluster_cost={}\\n for point, k_num in self.cluster_assign.items():\\n self.cluster_cost[point] = haversine(point,self.centers[k_num])\\n self.total_cluster_cost += self.cluster_cost[point]\\n self.total_center_cost = self.k_num * data_center_cost\\n self.total_cost = self.total_center_cost + self.total_cluster_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9137fe698b763e92f5d01e20a6c85507\",\n \"score\": \"0.53786206\",\n \"text\": \"def Run(self):\\n if self.checkComplete() is False:\\n raise ValueError('ERROR')\\n \\n sourceNorm=0\\n for i in range(self.assembly.N):\\n for j in range(self.assembly.M):\\n sourceIn=[s in self.pins[self.assembly.fuelmap[i][j]]._materials for s in self.assembly.source]\\n if True in sourceIn:\\n sourceNorm=sourceNorm+1\\n dTmaps=[]\\n contributionMaps=[]\\n contributionMapAves=[]\\n geomefficiencies=[]\\n geomefficiencyAves=[]\\n sourcePoints=[]\\n for k in range(self.randomNum):\\n print('#%d is being calculated'%(k))\\n dTmap={}\\n sourcePoint={}\\n for name in self.detectors:\\n print(\\\"Distance travelled to detector \\\"+name+\\\" is being calculated\\\")\\n dTmap[name],sourcePoint[name]=self.distanceTravelled(self.detectors[name]) \\n dTmaps.append(dTmap)\\n sourcePoints.append(sourcePoint) \\n if self._elines is not None:\\n if k==0:\\n self.get_MuTable()\\n geomefficiency={}\\n geomefficiencyAve=np.zeros(len(self._elines))\\n contributionMapAve={e: np.zeros((self.assembly.N,self.assembly.M)) for e in self._elines}\\n contributionMap={}\\n for name in self.detectors:\\n print('Contribution to detector %s is calculated...'%(name))\\n contributionMap[name]={}\\n geomefficiency[name]=np.zeros(len(self._elines))\\n for e in self._elines:\\n print('...for gamma energy %s MeV'%(e))\\n mue=self._mu[e]\\n muem={key: mue[key]*self.materials[key].density for key in mue.keys()}\\n contributionMap[name][e]=self.attenuation(dTmap[name],muem,self.detectors[name],sourcePoint[name])\\n contributionMapAve[e]=contributionMapAve[e]+contributionMap[name][e]/len(self.detectors)\\n geomefficiency[name]=np.array([np.sum(contribution) for contribution in contributionMap[name].values()])/sourceNorm\\n geomefficiencyAve=geomefficiencyAve+geomefficiency[name]/len(self.detectors)\\n contributionMaps.append(contributionMap)\\n contributionMapAves.append(contributionMapAve)\\n geomefficiencies.append(geomefficiency)\\n geomefficiencyAves.append(geomefficiencyAve) \\n self._sourcePoints=sourcePoints\\n #Various Numpy manipulations to restructure the \\\"plural\\\" lists containing data for\\n #each random sample. Then the mean and the std of the \\\"plural\\\" lists is calculated.\\n \\n #restructuring dTmaps: from the list of dictionaries, make a dictionary of lists\\n #then take the mean and std of the maps in the inner list\\n dTmapsRe={det: {mat: [dmap[det][mat] for dmap in dTmaps] for mat in self.materials} for det in self.detectors}\\n dTmap={} #will be the average\\n dTmapErr={} #will be the std\\n for det in dTmapsRe:\\n dTmap[det]={}\\n dTmapErr[det]={}\\n for mat in dTmapsRe[det]:\\n dTmapToAve=np.array([np.ravel(dT) for dT in dTmapsRe[det][mat]])\\n dTmap[det][mat]=np.mean(dTmapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\\n #dTmap elements may be np.Inf if the ray did not pass through the collimator\\n #this is useful to get 0 in attenuation() for those locations\\n #and it makes std calculation impossible, thus we set it to 0. see not in docstring!\\n dTmapToAve=np.where(dTmapToAve==np.Inf,0.0,dTmapToAve) \\n dTmapErr[det][mat]=np.std(dTmapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\\n self._dTmap=dTmap\\n self._dTmapErr=dTmapErr\\n self._dTmaps=dTmaps\\n \\n if self._elines is not None: \\n #restructuring contributionMaps\\n contributionMapsRe={det: {e: [cmap[det][e] for cmap in contributionMaps] for e in self._elines} for det in self.detectors}\\n contributionMap={} #will be the average\\n contributionMapErr={} #will be the stdcontributionMapAves\\n for det in contributionMapsRe:\\n contributionMap[det]={}\\n contributionMapErr[det]={}\\n for e in contributionMapsRe[det]:\\n cMapToAve=np.array([np.ravel(cm) for cm in contributionMapsRe[det][e]])\\n contributionMap[det][e]=np.mean(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\\n contributionMapErr[det][e]=np.std(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\\n self._contributionMap=contributionMap\\n self._contributionMapErr=contributionMapErr\\n self._contributionMaps=contributionMaps\\n \\n #restructuring contributionMapAves\\n contributionMapAvesRe={e: [cmap[e] for cmap in contributionMapAves] for e in self._elines}\\n contributionMapAve={} #will be the average\\n contributionMapAveErr={} #will be the std\\n for e in contributionMapAvesRe:\\n cMapToAve=np.array([np.ravel(cm) for cm in contributionMapAvesRe[e]])\\n contributionMapAve[e]=np.mean(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\\n contributionMapAveErr[e]=np.std(cMapToAve,axis=0).reshape((self.assembly.N,self.assembly.M))\\n self._contributionMapAve=contributionMapAve\\n self._contributionMapAveErr=contributionMapAveErr\\n self._contributionMapAves=contributionMapAves\\n \\n #restructuring geomefficiencies\\n geomefficienciesRe={det: [geff[det] for geff in geomefficiencies] for det in self._detectors}\\n geomefficiency={} #will be the average\\n geomefficiencyErr={} #will be the std\\n for det in geomefficienciesRe:\\n geffToAve=np.array([geff for geff in geomefficienciesRe[det]])\\n geomefficiency[det]=np.mean(geffToAve,axis=0)\\n geomefficiencyErr[det]=np.std(geffToAve,axis=0)\\n self._geomEff=geomefficiency\\n self._geomEffErr=geomefficiencyErr\\n self._geomEffs=geomefficiencies\\n \\n #restructuring geomefficiencyAves\\n geomefficiencyAve=np.mean(np.array([geff for geff in geomefficiencyAves]),axis=0)\\n geomefficiencyAveErr=np.std(np.array([geff for geff in geomefficiencyAves]),axis=0)\\n self._geomEffAve=geomefficiencyAve\\n self._geomEffAveErr=geomefficiencyAveErr\\n self._geomEffAves=geomefficiencyAves\\n \\n if self.output is not None:\\n output=open(self.output,'w')\\n for e,c in zip(self._elines,self._geomEffAve):\\n output.write(e+'\\\\t'+str(c)+'\\\\n')\\n output.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00d139e4e3944ed0ea867b0b2bb8cef4\",\n \"score\": \"0.53743863\",\n \"text\": \"def run_calculations(self):\\n logger.debug(\\\"Beginning calculation run - %s\\\", self.cavity_name)\\n self.calculate_attenuation_factor()\\n self.calculate_attenuation()\\n self.calculate_P_fc()\\n self.calculate_P_rc()\\n self.calculate_Q_lf()\\n self.calculate_Q_lr()\\n self.data_sync_start = \\\"now\\\"\\n self.data_sync_end = \\\"a little later\\\"\\n\\n # TODO: Figure out the current interface with the IOC to get timestamp info\\n # self.data_sync_start = self.STARTLQ.value\\n # self.data_sync_end = self.ENDLQ.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e040e514ea40edfe41e3aede1d35ca00\",\n \"score\": \"0.53642964\",\n \"text\": \"def GC_NLFC_Out(network, sScenarios, pDemand, qDemand, q0, prices, sellFactor, scens, pool, V_weight):\\n\\n n, T = pDemand.shape\\n nS = len(network.battnodes)\\n nE = len(network.nodeElist0)\\n realS_sum = np.matrix(np.zeros((nS, T)))\\n rootV2_sum = np.matrix(np.zeros((1, T)))\\n Wn_sum = np.matrix(np.zeros((n, T)))\\n Wre_sum = np.matrix(np.zeros((nE, T)))\\n Wie_sum = np.matrix(np.zeros((nE, T)))\\n\\n ## Compute in parallel\\n # Gather all scenarios data into a list\\n demandList = []\\n for i in range(scens):\\n combDemand = pDemand\\n combDemand[network.battnodes, :] = sScenarios[i]\\n demandList.append(combDemand)\\n\\n # Make parallel pool\\n # if __name__== \\\"__main__\\\":\\n # print('start parallel pool')\\n # pool = Pool()\\n print 'Computing in pool'\\n sols = pool.map(GC_NLFC_star, itertools.izip(itertools.repeat(network), demandList, itertools.repeat(qDemand),\\n itertools.repeat(q0), itertools.repeat(prices),\\n itertools.repeat(sellFactor), itertools.repeat(V_weight)))\\n\\n # Unpack all information\\n for i in range(scens):\\n if sols[i][2] != \\\"optimal\\\":\\n print 'OPF status is: ', sols[i][2]\\n realS_sum += sols[i][0]\\n rootV2_sum += sols[i][1]\\n Wn_sum += sols[i][3]\\n Wre_sum += sols[i][4]\\n Wie_sum += sols[i][5]\\n\\n realS = realS_sum / scens\\n rootV2 = rootV2_sum / scens\\n Wn = Wn_sum / scens\\n Wre = Wre_sum / scens\\n Wie = Wie_sum / scens\\n\\n return realS, rootV2, Wn, Wre, Wie\\n\\n \\\"\\\"\\\"\\n #Sequential code\\n for i in range(scens):\\n combDemand = pDemand\\n combDemand[network.battnodes,:] = sScenarios[i]\\n realS_new, imagS_new, rootV2_new, status, cost = GC_NLFC(network, combDemand, qDemand, q0, prices)\\n realS_sum = realS_sum + realS_new\\n rootV2_sum = rootV2_sum + rootV2_new\\n if status != \\\"optimal\\\":\\n print('status is: ', status)\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ca9c0e93831660f15547490df6be8c\",\n \"score\": \"0.5361871\",\n \"text\": \"def main():\\n\\n\\t### Initialize dictionary specifying which functions to execute\\n\\tdriver_args = {\\n\\t\\t\\n\\t\\t### Preprocessing functions\\n\\t\\t'preprocessing': [\\n\\t\\t\\t'export_parquet2csvs',\\n\\t\\t\\t'join_firms2ztrax',\\t\\n\\t\\t\\t'clean_data',\\n\\t\\t\\t'get_countylevel_summarystats',\\n\\t\\t\\t'create_countygroups',\\n\\t\\t\\t],\\n\\n\\t\\t### Statistical functions\\n\\t\\t'stats': [\\n\\t\\t\\t'fit_100yr_model',\\n\\t\\t\\t],\\n\\n\\t\\t### Postprocessing functions\\n\\t\\t'postprocessing': [\\n\\t\\t\\t### Collect outputs from statistical models\\n\\t\\t\\t'collect_statsoutputs',\\n\\t\\t\\t\\n\\t\\t\\t### Calculate and summarize overvaluation\\n\\t\\t\\t'calculate_overvaluation',\\n\\t\\t\\t'summarize_countyoutputs',\\n\\t\\t\\t'combine_atriskproperties',\\n\\n\\t\\t\\t### Monte Carlo simulation\\n\\t\\t\\t'montecarlo_simulation',\\n\\t\\t\\t'collect_montecarlo_outputs',\\n\\n\\t\\t\\t### Miscellaneous postprocessing functions\\n\\t\\t\\t'get_censustract_outputs',\\n\\t\\t\\t'fiscalvulnerability_analysis',\\n\\n\\t\\t\\t### Postprocessing functions to generate data to share with media\\n\\t\\t\\t'collect_keyoutputs',\\n\\n\\t\\t\\t### Assume non-SFHA discount is the same as SFHA discount\\n\\t\\t\\t'calculate_overvaluation_nonsfhadiscount',\\n\\t\\t\\t'summarize_countyoutputs_nonsfhadiscount',\\n\\t\\t\\t'combine_atriskproperties_nonsfhadiscount',\\n\\t\\t\\t'get_censustract_outputs_nonsfhadiscount',\\n\\t\\t\\t\\n\\t\\t\\t### Assume discounts based on cross-sectional model\\n\\t\\t\\t'calculate_overvaluation_xsdiscount',\\n\\t\\t\\t'summarize_countyoutputs_xsdiscount',\\n\\t\\t\\t'combine_atriskproperties_xsdiscount',\\n\\t\\t\\t'get_censustract_outputs_xsdiscount',\\n\\t\\t\\t],\\n\\n\\t\\t### Figure functions\\n\\t\\t'figures': [\\n\\t\\t\\t### Main text\\n\\t\\t\\t'overvaluation_maps', # Fig. 1\\n\\t\\t\\t'histograms_byfloodzone',\\t# Fig. 2\\n\\t\\t\\t'overvaluation_bypopulationgroups_v2', # Fig. 3\\n\\t\\t\\t'overvaluation_munifinance', # Fig. 4 (upper panel)\\n\\t\\t\\t'munifinance_demographics', #Fig. 4 (lower panel)\\n\\n\\t\\t\\t### Supplemental Materials\\n\\t\\t\\t'ztrax_transactions_map', # Fig. S1\\n\\t\\t\\t'fmv_map', # Fig. S2\\n\\t\\t\\t'npv_map', # Fig. S3\\n\\t\\t\\t'firmupdates_map', # Fig. S4\\n\\t\\t\\t'yaleclimatesurvey_map', # Fig. S5\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t'npv_histograms', # Fig. S6\\n\\t\\t\\t'empiricaldiscount_combinedgroups', # Fig. S7\\n\\t\\t\\t'empiricaldiscount_combinedgroups_xsmodel', # Fig. S8\\n\\t\\t\\t'overvaluation_maps_dr7_low', # Fig. S9\\n\\t\\t\\t'overvaluation_maps_dr1_high', # Fig. S10\\n\\t\\t\\t'histograms_byfloodzone_nonsfhadiscount', # Fig. S11\\n\\t\\t\\t'histograms_byfloodzone_xsdiscount', # Fig. S12\\n\\t\\t\\t'overvaluation_bypopulationgroups_scatter', # Fig. S13\\n\\t\\t\\t'overvaluation_bypopulationgroups_v2_nonsfhadiscount', # Fig. S14\\n\\t\\t\\t'overvaluation_bypopulationgroups_v2_xsdiscount', # Fig. S15\\n\\t\\t\\t'uncertainty_plots', # Fig. S16\\n\\t\\t\\t'overvaluation_rankedbystate', # Fig. S17\\n\\t\\t\\t]\\n\\t\\t}\\n\\n\\t### Call execute function within driver.py\\n\\tdriver.execute(driver_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cbb556cf09bfc116fa2275e6cb48dae\",\n \"score\": \"0.5349653\",\n \"text\": \"def outputs_for_detailed_tab(self, input_dict, output_dict):\\n result = []\\n module = 'Collection Cost'\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Total Number of Turbines',\\n 'value': float(self.output_dict['total_turb'])\\n })\\n\\n result.append({\\n 'unit': 'km',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Total trench length',\\n 'value': float(self.output_dict['trench_length_km'])\\n })\\n\\n result.append({\\n 'unit': 'km',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Total cable length',\\n 'value': float(self.output_dict['total_cable_len_km'])\\n })\\n\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Number of Turbines Per String in Full String',\\n 'value': float(self.output_dict['total_turb_per_string'])\\n })\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Number of Full Strings',\\n 'value': float(self.output_dict['num_full_strings'])\\n })\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Number of Turbines in Partial String',\\n 'value': float(self.output_dict['num_leftover_turb'])\\n })\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Number of Partial Strings',\\n 'value': float(self.output_dict['num_partial_strings'])\\n })\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Total number of strings full + partial',\\n 'value': float(self.output_dict['num_full_strings'] + self.output_dict['num_partial_strings'])\\n })\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Trench Length to Substation (km)',\\n 'value': float(self.output_dict['distance_to_grid_connection_km'])\\n })\\n result.append({\\n 'unit': '',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Cable Length to Substation (km)',\\n 'value': float(self.output_dict['cable_len_to_grid_connection_km'])\\n })\\n\\n cables = ''\\n n = 1 # to keep tab of number of cables input by user.\\n for cable, specs in self.output_dict['cables'].items():\\n if n == len(self.output_dict['cables']):\\n cables += str(cable)\\n else:\\n cables += str(cable) + ' , '\\n\\n for variable, value in specs.__dict__.items():\\n if variable == 'array_cable_len':\\n result.append({\\n 'unit': 'km',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Array cable length for cable ' + cable,\\n 'value': float(value)\\n })\\n elif variable == 'total_length':\\n result.append({\\n 'unit': 'km',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Total cable length for cable ' + cable,\\n 'value': float(value)\\n })\\n\\n elif variable == 'total_cost':\\n result.append({\\n 'unit': 'usd',\\n 'type': 'variable',\\n 'variable_df_key_col_name': 'Total cable cost for cable ' + cable,\\n 'value': float(value)\\n })\\n n += 1\\n\\n result.append({\\n 'unit': '',\\n 'type': 'list',\\n 'variable_df_key_col_name': 'Number of turbines per cable type in full strings [' + cables + ']',\\n\\n 'value': str(self.output_dict['num_turb_per_cable'])\\n })\\n\\n if self.input_dict['turbine_rating_MW'] > 0.1:\\n for row in self.output_dict['management_crew'].itertuples():\\n dashed_row = ' <--> '.join(str(x) for x in list(row))\\n result.append({\\n 'unit': '',\\n 'type': 'dataframe',\\n 'variable_df_key_col_name': 'Labor type ID <--> Hourly rate USD per hour <--> Per diem USD per day <--> Operation <--> Crew type <--> Crew name <--> Number of workers <--> Per Diem Total <--> Hourly costs total <--> Crew total cost ',\\n 'value': dashed_row\\n })\\n\\n result.append({\\n 'unit': '',\\n 'type': 'list',\\n 'variable_df_key_col_name': 'Percent length of cable in partial string [' + cables + ']',\\n\\n 'value': str(self.output_dict['perc_partial_string'])\\n })\\n\\n\\n\\n for row in self.output_dict['total_collection_cost'].itertuples():\\n dashed_row = '{} <--> {} <--> {}'.format(row[1], row[3], math.ceil(row[2]))\\n result.append({\\n 'unit': '',\\n 'type': 'dataframe',\\n 'variable_df_key_col_name': 'Type of Cost <--> Phase of Construction <--> Cost in USD ',\\n 'value': dashed_row,\\n 'last_number': row[2]\\n })\\n\\n\\n for _dict in result:\\n _dict['project_id_with_serial'] = self.project_name\\n _dict['module'] = module\\n\\n self.output_dict['collection_cost_csv'] = result\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38f87ddf7930c8007421be685e0b3916\",\n \"score\": \"0.5342951\",\n \"text\": \"def __definecost(self):\\n \\n if self.mode == 'Unsup' or self.mode == \\\"Mixte\\\":\\n if self.depth_min == 0:\\n in_sided = self.one_sided_in\\n in_bounded = 1.\\n else:\\n in_sided = self.layers[self.depth_min-1].one_sided\\n in_bounded = self.layers[self.depth_min-1].bounded\\n out_bounded = self.layersdec[self.depth_min].bounded\\n out_sided = self.layersdec[self.depth_min].one_sided\\n \\n self.unsup_cost = self.reconstruction_cost_fn( self.layers[self.depth_min].inp,\\\\\\n self.layersdec[self.depth_min].out, self.layersdec[self.depth_min].lin,\\\\\\n in_sided,out_sided,in_bounded,out_bounded,self.layersdec[self.depth_min].act)\\n self.unsup_cost[0][0] = self.unsup_scaling * self.unsup_cost[0][0]\\n self.unsup_cost[1] = self.unsup_scaling * self.unsup_cost[1]\\n else:\\n self.unsup_cost = [[],[]]\\n \\n wdaux = []\\n paramsaux = []\\n if self.aux and self.aux_active:\\n if (self.update_type != 'special' or self.depth+self.auxdepth == self.depth_max):\\n paramsaux += self.auxlayer.params\\n if self.auxregularization:\\n wdaux += [self.auxregularization*self.auxlayer.wd]\\n self.aux_cost = self.reconstruction_cost_fn( self.auxtarget, self.auxlayer.out,\\\\\\n self.auxlayer.lin,self.aux_one_sided,self.auxlayer.one_sided,\\\\\\n 1.,self.auxlayer.bounded,self.auxlayer.act)\\n self.aux_cost[0][0] = self.aux_scaling * self.aux_cost[0][0]\\n self.aux_cost[1] = self.aux_scaling * self.aux_cost[1]\\n else:\\n self.aux_cost = [[],[]]\\n \\n if self.mode == 'Sup':\\n self.cost = sum([self.sup_scaling * self.outlayer.cost(self.out)] +\\\\\\n self.aux_cost[0] + self.wd + wdaux + self.sp)\\n if self.mode == 'Unsup':\\n self.cost = sum(self.unsup_cost[0] + self.aux_cost[0] + self.wd + wdaux + self.sp)\\n if self.mode == 'Mixte':\\n self.cost = sum(self.unsup_cost[0] + [self.sup_scaling * self.outlayer.cost(self.out)] +\\\\\\n self.aux_cost[0] + self.wd + wdaux + self.sp)\\n if self.mode == 'Aux':\\n self.cost = sum(self.aux_cost[0] + self.wd + wdaux + self.sp)\\n self.grad = T.grad(self.cost,self.params+paramsaux)\\n self.updates = dict((p, p - self.lr * g) for p, g in zip(self.params+paramsaux, self.grad))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc005fd0ea0b9e7fd13a347b2e7f004\",\n \"score\": \"0.53383005\",\n \"text\": \"def task5():\\n\\t# load three tables of AR-Accounts_Rec_Listing File\\n\\tprint('Start task 5...')\\n\\tbsid_bkpf = pd.read_csv(\\\"BSID_BKPF_AltColTitles.csv\\\",\\n\\t\\tdtype={'Company_Code':np.str,'Customer_Number':np.str},encoding='latin-1')\\n\\tknkk = pd.read_csv(\\\"KNKK_AltColTitles.csv\\\",dtype={'Customer_Number':np.str})\\n\\tt001 = pd.read_csv(\\\"T001_AltColTitles.csv\\\",dtype={'Company_Code':np.str})\\n\\n\\t# join bsid_bkpf,knkk,t001 using the ERD diagram of AR-Accounts_Rec_Listing File and get the AR-Accounts_Rec_Listing File data\\n\\tbsid_bkpf_knkk = pd.merge(bsid_bkpf, knkk, \\n\\t\\tleft_on=['Customer_Number'], right_on=['Customer_Number'], how='left')\\n\\tAccounts_Rec_Listing = pd.merge(bsid_bkpf_knkk, t001, \\n\\t\\tleft_on=['Company_Code'], right_on=['Company_Code'], how='left')\\n\\n\\t# For each Reference_Document_Number of each Customer_Number, calculate the amount of Debit and amount of Credit\\n\\ta = Accounts_Rec_Listing[Accounts_Rec_Listing['Debit_Credit_Indicator'] == 'S']\\n\\tb = Accounts_Rec_Listing[Accounts_Rec_Listing['Debit_Credit_Indicator'] == 'H']\\n\\ta_group_amount = a.groupby(['Customer_Number','Reference_Document_Number'])\\\\\\n\\t\\t\\t\\t\\t['Amount_in_Local_Currency'].sum().reset_index()\\n\\tb_group_amount = b.groupby(['Customer_Number','Reference_Document_Number'])\\\\\\n\\t\\t\\t\\t\\t['Amount_in_Local_Currency'].sum().reset_index()\\n\\ttotal_group_amount = pd.merge(a_group_amount,b_group_amount,\\n\\t\\tleft_on =['Customer_Number','Reference_Document_Number'], \\n\\t\\tright_on = ['Customer_Number','Reference_Document_Number'], \\n\\t\\thow='outer')\\n\\ttotal_group_amount=total_group_amount.rename(columns = \\n\\t\\t{'Amount_in_Local_Currency_x':'Amount_S','Amount_in_Local_Currency_y':'Amount_H'})\\n\\n\\t# Using amount of Debit minus amount of Credit to get the balance \\n\\ttotal_group_amount['Amount_H'].fillna(0, inplace=True)\\n\\ttotal_group_amount['Amount_S'].fillna(0, inplace=True)\\n\\ttotal_group_amount['Balance'] = total_group_amount['Amount_S'] - total_group_amount['Amount_H']\\n\\tbalance_amount = pd.merge(total_group_amount,Accounts_Rec_Listing, \\n\\t\\ton = ['Customer_Number','Reference_Document_Number'], how = 'left')\\n\\tbalance_amount = balance_amount[['Customer_Number','Reference_Document_Number',\\n\\t\\t\\t\\t\\t\\t\\t'Amount_S','Amount_H','Balance','Document_Date_in_Document']]\\n\\tbalance_amount.drop_duplicates(inplace=True)\\n\\n\\t# get the minimum Document_Date for each Reference_Document_Number of each Customer_Number \\n\\ty = balance_amount.groupby(['Customer_Number','Reference_Document_Number'])\\\\\\n\\t\\t\\t\\t\\t['Document_Date_in_Document'].min().reset_index()\\n\\tbalance_amount = pd.merge(balance_amount, y, \\n\\t\\ton = ['Customer_Number','Reference_Document_Number','Document_Date_in_Document'],how='inner')\\n\\n\\t# For Balance <= 0, outstanding day is zero; \\n\\t# For Balance > 0,using the system date minus minimum Document_Date to calculate the outstanding day \\n\\tfor i in range(len(balance_amount)):\\n\\t\\tif balance_amount.loc[i,'Balance'] <= 0:\\n\\t\\t\\tbalance_amount.loc[i,'outstanding_days'] = int(0)\\n\\t\\telif balance_amount.loc[i,'Balance'] > 0:\\n\\t\\t\\tbalance_amount.loc[i,'outstanding_days'] = \\\\\\n \\t\\tint((pd.to_datetime(datetime.today().strftime('%Y-%m-%d')) - \\n \\t\\t\\tpd.to_datetime(balance_amount.loc[i,'Document_Date_in_Document'])).days)\\n\\n\\tbalance_amount['outstanding_days'] = balance_amount['outstanding_days'].astype('int64')\\n\\n # using the outstanding_days to find the type of '60-90', '90-120', '> 120', '< 60' or 'not outstanding'\\n\\tfor i in range(len(balance_amount)):\\n\\t if balance_amount.loc[i,'outstanding_days'] >= 60 and balance_amount.loc[i,'outstanding_days'] <= 90:\\n\\t balance_amount.loc[i,'type'] = '60-90'\\n\\t elif balance_amount.loc[i,'outstanding_days'] > 90 and balance_amount.loc[i,'outstanding_days'] <= 120:\\n\\t balance_amount.loc[i,'type'] = '90-120'\\n\\t elif balance_amount.loc[i,'outstanding_days'] > 120:\\n\\t balance_amount.loc[i,'type'] = '> 120'\\n\\t elif balance_amount.loc[i,'outstanding_days'] < 60 and balance_amount.loc[i,'outstanding_days'] > 0:\\n\\t balance_amount.loc[i,'type'] = '< 60'\\n\\t elif balance_amount.loc[i,'outstanding_days'] == 0:\\n\\t balance_amount.loc[i,'type'] = 'not outstanding'\\n\\t\\n\\t# only keep Outstanding Debtors Greater Than 60 Days\\n\\tbalance_amount = balance_amount[(balance_amount['type'] == '60-90')\\\\\\n\\t |(balance_amount['type'] == '90-120')|(balance_amount['type'] == '> 120')]\\n\\t\\n\\t# For each customer, get the total outstanding amount,\\n\\t# local Amount 60 – 90 Days,local Amount 90 – 120 Days and local Amount >120 days\\n\\tbalance_amount = balance_amount.sort_values(\\\"Customer_Number\\\").reset_index(drop=True)\\n\\tCounts = []\\n\\tcid = 0\\n\\ts1, s2, s3, s = 0, 0, 0, 0\\n\\tfor i in range(len(balance_amount)):\\n\\t balance = balance_amount.loc[i, \\\"Balance\\\"]\\n\\t if balance_amount.loc[i, \\\"Customer_Number\\\"] != cid:\\n\\t Counts.append([cid, s1, s2, s3, s])\\n\\t cid = balance_amount.loc[i, \\\"Customer_Number\\\"]\\n\\t s1, s2, s3, s = 0, 0, 0, balance\\n\\t if balance_amount.loc[i, \\\"type\\\"] == \\\"60-90\\\":\\n\\t s1 = balance\\n\\t elif balance_amount.loc[i, \\\"type\\\"] == \\\"90-120\\\":\\n\\t s2 = balance\\n\\t elif balance_amount.loc[i, \\\"type\\\"] == \\\"> 120\\\":\\n\\t s3 = balance\\n\\t else:\\n\\t s += balance\\n\\t if balance_amount.loc[i, \\\"type\\\"] == \\\"60-90\\\":\\n\\t s1 += balance\\n\\t elif balance_amount.loc[i, \\\"type\\\"] == \\\"90-120\\\":\\n\\t s2 += balance\\n\\t elif balance_amount.loc[i, \\\"type\\\"] == \\\"> 120\\\":\\n\\t s3 += balance\\n\\tCounts.append([cid, s1, s2, s3, s])\\n\\tsum_by_type = pd.DataFrame(Counts[1:], columns = ['Customer_Number', \\\"60-90\\\", \\\"90-120\\\", \\\"> 120\\\", \\\"total\\\"])\\n\\t\\n\\t# get some key output results fields\\n\\tx = Accounts_Rec_Listing.loc[:,['Customer_Number','Company_Code','Name_of_Company_Code_or_Company',\\n\\t 'Currency_Key_y','Customer_s_credit_limit','Credit_Control_Area']]\\n\\tx.drop_duplicates(inplace=True)\\n\\n\\t# merge togther to get the whole key output results table\\n\\tsum_by_type = pd.merge(sum_by_type, x, on = 'Customer_Number',how= 'left')\\n\\t# exclude Credit_Control_Area \\\"CAN\\\" and keep Credit_Control_Area \\\"US01\\\" or NaN\\n\\tsum_by_type = sum_by_type[sum_by_type['Credit_Control_Area'] != 'CAN']\\n\\n\\t# get the correct order and names of key output results fields\\n\\tsum_by_type = sum_by_type[['Customer_Number','Company_Code','Name_of_Company_Code_or_Company',\\n\\t 'total','Currency_Key_y','Customer_s_credit_limit','60-90','90-120','> 120']]\\n\\tsum_by_type= sum_by_type.rename(columns = {'Currency_Key_y':'Local_Currency_Key',\\n\\t\\t 'total':'Total_Amount_Local_Currency',\\n\\t\\t '60-90':'Local_Amount_60-90_Days',\\n\\t\\t '90-120':'Local_Amount_90-120_Days',\\n\\t\\t '> 120':'Local_Amount_>120_Days'})\\n\\n\\t# output the result table\\n\\tsum_by_type.to_csv(\\\"task5.csv\\\")\\n\\tprint('Task 5 is finished. Output is ready.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7b6fb78f22741cea5538061c58aa73\",\n \"score\": \"0.5305617\",\n \"text\": \"def setUp(self):\\n cost_func = make_cost_function()\\n self.problem = cost_func.problem\\n self.options = self.problem.options\\n self.grabbed_output = output_grabber.OutputGrabber(self.options)\\n self.count = 0\\n self.scipy_len = len(self.options.minimizers[\\\"scipy\\\"])\\n controller = ScipyController(cost_func)\\n controller.parameter_set = 0\\n self.result_args = {'controller': controller,\\n 'accuracy': 1,\\n 'runtimes': [1]}\\n self.cp = Checkpoint(self.options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43e3d946024f61f6a82686d350ab941\",\n \"score\": \"0.5304694\",\n \"text\": \"def compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43e3d946024f61f6a82686d350ab941\",\n \"score\": \"0.5304694\",\n \"text\": \"def compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43e3d946024f61f6a82686d350ab941\",\n \"score\": \"0.5304694\",\n \"text\": \"def compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43e3d946024f61f6a82686d350ab941\",\n \"score\": \"0.5304694\",\n \"text\": \"def compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d27446ad19f1991bcc7b3ea34fd727b\",\n \"score\": \"0.53033954\",\n \"text\": \"def compute_stats(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"924f4ff753ae68febc07b055e7ed4869\",\n \"score\": \"0.5302831\",\n \"text\": \"def compute(self):\\n ## Get the list of files\\n self.list=self.folder.GetAutoList()\\n if self.list==None:\\n return 0\\n summary={}\\n ## compute lines of code and density\\n for filepath, size in self.list.items(): \\n reader=FileReader(filepath)\\n ## compute the number of new lines in the file\\n currentLines=reader.GetLines()\\n ## extrapolate the extension from the full file name path\\n extension=os.path.splitext(filepath)[1][1:].strip() \\n ## if the extension is the one we want to consider then sum the lines\\n if extension in summary.keys():\\n summary[extension]+=currentLines\\n ## otherwise keep the same number of lines\\n else:\\n summary[extension]=currentLines\\n ## Add the entry into the database containg the full path, the file extension, \\n ## number of lines and size\\n self.database.addEntry(filepath, extension,currentLines, size)\\n ## if we want a summary POST the results into the server\\n if summary: self.UpdateResults(summary)\\n ## log the summary if necessary\\n logging.info(''.join(['File with .%s contains %s lines' % (key, value) for (key, value) in summary.items()]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1403608b9e298fa64dcbcd9f47452e3\",\n \"score\": \"0.529\",\n \"text\": \"def main(cfg):\\n # Get a description of the preprocessed data that we will use as input.\\n input_data = cfg['input_data'].values()\\n\\n # Demonstrate use of metadata access convenience functions.\\n selection = select_metadata(input_data, short_name='pr', project='CMIP5')\\n logger.info(\\\"Example of how to select only CMIP5 precipitation data:\\\\n%s\\\",\\n pformat(selection))\\n\\n selection = sorted_metadata(selection, sort='dataset')\\n logger.info(\\\"Example of how to sort this selection by dataset:\\\\n%s\\\",\\n pformat(selection))\\n\\n grouped_input_data = group_metadata(\\n input_data, 'standard_name', sort='dataset')\\n logger.info(\\n \\\"Example of how to group and sort input data by standard_name:\\\"\\n \\\"\\\\n%s\\\", pformat(grouped_input_data))\\n\\n # Example of how to loop over variables/datasets in alphabetical order\\n for standard_name in grouped_input_data:\\n logger.info(\\\"Processing variable %s\\\", standard_name)\\n for attributes in grouped_input_data[standard_name]:\\n logger.info(\\\"Processing dataset %s\\\", attributes['dataset'])\\n input_file = attributes['filename']\\n cube = compute_diagnostic(input_file)\\n\\n output_basename = os.path.splitext(\\n os.path.basename(input_file))[0] + '_mean'\\n provenance_record = get_provenance_record(\\n attributes, ancestor_files=[input_file])\\n plot_diagnostic(cube, output_basename, provenance_record, cfg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0863a2dcf579526682b4ccbeb2d223b\",\n \"score\": \"0.52851063\",\n \"text\": \"def evaluateCost(self, runData, isPositive):\\n raise NotImplementedError(\\\"Please implement this method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3590db484e8cceaf8d163712c310e18c\",\n \"score\": \"0.52822393\",\n \"text\": \"def process_run(self):\\n selection = self.run_options.get('selection',None)\\n self.run_the_calculations(selection)\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b5b55b182b100353e5f528a7f9cb671\",\n \"score\": \"0.5278725\",\n \"text\": \"def compute_statistics(allocations: Dict[Campaign, Dict[Good, int]], expenditure: Dict[Campaign, Dict[Good, float]]):\\n total_allocations = 0\\n total_expenditure = 0\\n total_effective_allocation = {}\\n sigmoidal_effective_reach_ratio = {}\\n utilities = {}\\n for c, _ in allocations.items():\\n total_c_allocations = sum([a for g, a in allocations[c].items()])\\n total_c_expenditure = sum([a for g, a in expenditure[c].items()])\\n total_allocations += total_c_allocations\\n total_expenditure += total_c_expenditure\\n if c not in total_effective_allocation:\\n # The total effective allocation is the sum of the allocation across all market segments that match the campaign's target.\\n total_effective_allocation[c] = sum([a if g.__matches__(c.target) else 0 for g, a in allocations[c].items()])\\n else:\\n raise Exception(\\\"1) This should NOT happen!\\\")\\n if c not in sigmoidal_effective_reach_ratio:\\n # The sigmoidal effective reach ratio is compute with respect to the total effective allocation.\\n sigmoidal_effective_reach_ratio[c] = compute_sigmoidal_effective_reach_ratio(total_effective_allocation[c], c.reach)\\n else:\\n raise Exception(\\\"2) This should NOT happen!\\\")\\n if c not in utilities:\\n # The actual final utility.\\n # print(\\\"Computing utilities:\\\")\\n # print(\\\"\\\\t \\\", sigmoidal_effective_reach_ratio[c])\\n # print(\\\"\\\\t \\\", c.budget)\\n # print(\\\"\\\\t \\\", total_c_expenditure)\\n utilities[c] = sigmoidal_effective_reach_ratio[c] * c.budget - total_c_expenditure\\n else:\\n raise Exception(\\\"3) This should NOT happen!\\\")\\n return utilities, total_expenditure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144872ec3fad64dd600b58dcb7559387\",\n \"score\": \"0.52768654\",\n \"text\": \"def compute(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94708b24440c862e9171cd28eeff93e5\",\n \"score\": \"0.5273084\",\n \"text\": \"def updates(self, cost):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df6ec47dd07756eb74c523241109e04b\",\n \"score\": \"0.52502304\",\n \"text\": \"def c_costs_rule(m, cost_type):\\n if cost_type == 'Inv':\\n return m.costs['Inv'] == \\\\\\n sum((m.gamma_1[p] * m.conversion.loc[p]['IX1'] + m.part_size_1[p] * \\n m.conversion.loc[p]['investment-slope-1']) +\\n (m.gamma_2[p] * m.conversion.loc[p]['IX2'] + m.part_size_2[p] * \\n m.conversion.loc[p]['investment-slope-2']) for p in m.con)\\n \\n elif cost_type == 'Mai':\\n return m.costs['Mai'] == \\\\\\n sum((m.conversion.loc[p]['maint-percentage']/100) * \\n ((m.gamma_1[p] * m.conversion.loc[p]['IX1'] + m.part_size_1[p] * \\n m.conversion.loc[p]['investment-slope-1']) +\\n (m.gamma_2[p] * m.conversion.loc[p]['IX2'] + m.part_size_2[p] * \\n m.conversion.loc[p]['investment-slope-2'])) for p in m.con)\\n \\n elif cost_type == 'Fuel':\\n gas_user_index = m.connectivity['NG'][m.connectivity['NG'] == 1].index\\n# el_user_index = m.connectivity['EG'][m.connectivity['EG'] == 1].index\\n return (m.costs['Fuel'] == \\n ((sum(m.u[t, p] for t in m.t for p in gas_user_index) * \\n m.economic['gas-tariff'][0]) + \\n (sum(m.el_buy[t] * m.economic['el-tariff'][0] for t in m.t)) - \\n (sum(m.el_sell[t] * m.economic['feedin-tariff'][0] for t in m.t))) * \\n 2190)\\n \\n elif cost_type == 'Inc':\\n return Constraint.Skip\\n \\n else:\\n raise NotImplementedError(\\\"Unknown cost type.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b127ef43f67a32fedec1d185e740a8f\",\n \"score\": \"0.5249243\",\n \"text\": \"def _compute_cost_total(self):\\n for service in self:\\n service.cost_total = service.qty * service.cost_price or 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4144d32cee3b4da6d2205bac8aa170f3\",\n \"score\": \"0.5233409\",\n \"text\": \"def main(standard, denovo, nongenes):\\n\\n CAI_instance = CAI()\\n\\n index_generation(standard, CAI_instance)\\n\\n CAI_calculation(standard, CAI_instance)\\n CAI_calculation(denovo, CAI_instance)\\n CAI_calculation(nongenes, CAI_instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad0aeda22ece026abefe91a66b44caa\",\n \"score\": \"0.52322775\",\n \"text\": \"def cost(self, target, value, subentry=''):\\n result = self.db.cost\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ff311f6f43f053146e77d54cbd1f80\",\n \"score\": \"0.521826\",\n \"text\": \"def cost_calc(self,cf,ob):\\r\\n inibal = ob\\r\\n #bal = ob\\r\\n if len(self.daily_cost)>0:\\r\\n del self.daily_cost[:]\\r\\n for element in cf:\\r\\n trans = self.transfer(inibal)\\r\\n bal = inibal+trans+element\\r\\n self.daily_cost.append(self.trans_cost(trans)+self.holding_cost(bal))\\r\\n inibal = bal\\r\\n return self.daily_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad0ffb81f2ce64b4722843f80119464\",\n \"score\": \"0.5217138\",\n \"text\": \"def main(items, rate, datafile='factorio_recipes', modules='prod 3, prod 3, prod 3, prod 3, speed 3, speed 3, speed 3, speed 3', fractional=False, log='WARNING',\\n stop_at='', beacon_speed=0., oil=False, verbose=False):\\n\\tlogging.basicConfig(level=log.upper())\\n\\n\\titems = [item.strip().lower() for item in items.split(',')]\\n\\tmodules = [name.strip().lower() for name in modules.split(',')] if modules else []\\n\\tstop_items = [item.strip().lower() for item in stop_at.split(',')] if stop_at else []\\n\\trecipes = get_recipes(datafile, modules, beacon_speed)\\n\\trate = Fraction(rate)\\n\\titems = OrderedDict((item, rate) for item in items)\\n\\tif oil:\\n\\t\\tresults, oil_buildings = solve_with_oil(recipes, items, stop_items)\\n\\telse:\\n\\t\\tresults = solve_all(recipes, items, stop_items)\\n\\t\\toil_buildings = []\\n\\n\\tdef maybe_round(amount):\\n\\t\\treturn '{:.2f}'.format(float(amount)) if fractional else int(math.ceil(amount))\\n\\n\\tdef mods_str(mods):\\n\\t\\treturn ' with {}'.format(', '.join(\\n\\t\\t\\t'{}x {}'.format(count, name)\\n\\t\\t\\tfor name, count in sorted(\\n\\t\\t\\t\\tCounter(mods).items(), key=lambda (name, count): (count, name)\\n\\t\\t\\t)\\n\\t\\t)) if mods else ''\\n\\n\\tdef input_str(throughput, inputs):\\n\\t\\tif not verbose or not inputs:\\n\\t\\t\\treturn ''\\n\\t\\treturn ' using {}'.format(\\n\\t\\t\\t', '.join(\\n\\t\\t\\t\\t'{:.2f}/sec {}'.format(float(throughput * item_amount), item)\\n\\t\\t\\t\\tfor item, item_amount in inputs.items()\\n\\t\\t\\t)\\n\\t\\t)\\n\\n\\tdef format_extra(building, amount, throughput, mods, item, inputs):\\n\\t\\tMAX_PER_ROW = 40 # one blue belt\\n\\t\\tEXCLUDE = ['water', 'crude oil', 'petroleum', 'light oil', 'heavy oil', 'sulfuric acid', 'lubricant']\\n\\t\\tto_consider = [\\n\\t\\t\\tthroughput * item_amount\\n\\t\\t\\tfor _item, item_amount in inputs.items()\\n\\t\\t\\tif _item not in EXCLUDE\\n\\t\\t]\\n\\t\\tif item not in EXCLUDE:\\n\\t\\t\\tto_consider.append(throughput)\\n\\t\\trows = int(math.ceil(max(to_consider) / MAX_PER_ROW)) if to_consider else 1\\n\\t\\tif not verbose or rows == 1:\\n\\t\\t\\treturn ''\\n\\t\\treturn '\\\\n\\\\tin {} rows of {} each producing {:.2f}/sec{}'.format(\\n\\t\\t\\trows, maybe_round(amount/rows), float(throughput / rows), input_str(throughput/rows, inputs),\\n\\t\\t)\\n\\n\\tdef format_item(building, amount, throughput, mods, item, inputs):\\n\\t\\treturn '{} {}{} producing {:.2f}/sec of {}{}{}'.format(\\n\\t\\t\\tmaybe_round(amount),\\n\\t\\t\\tbuilding, mods_str(mods), float(throughput), item, input_str(throughput, inputs),\\n\\t\\t\\tformat_extra(building, amount, throughput, mods, item, inputs),\\n\\t\\t)\\n\\n\\tfor item, amount in results.items():\\n\\t\\tif item in recipes and item not in stop_items:\\n\\t\\t\\tbuilding, per_building, inputs, mods = recipes[item]\\n\\t\\t\\tthroughput = amount * per_building\\n\\t\\t\\tprint format_item(building, amount, throughput, mods, item, inputs)\\n\\t\\telse:\\n\\t\\t\\tprint '{:.2f}/sec of {}'.format(float(amount), item)\\n\\tfor recipe, name, amount in oil_buildings:\\n\\t\\tbuilding, per_building, _, mods = recipes[recipe]\\n\\t\\tthroughput = amount * per_building # this doesn't make much sense, it's completed processes per sec\\n\\t\\tprint format_item(building, amount, throughput, mods, name, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22be8ba0a6f944e6efbbd0346d08de0d\",\n \"score\": \"0.5215419\",\n \"text\": \"def _update_collection_operations(cname, opnames):\\n operations = config[\\\"campaigns\\\"][cname][\\\"operations\\\"]\\n filters = []\\n for opname in opnames:\\n qualif = operations[opname][\\\"qualifiers\\\"]\\n filters.append((_build_filter(qualif, \\\"asset\\\"), opname))\\n\\n prices = _get_prices()\\n data = {opname: {} for opname in opnames}\\n\\n for char_id, token in g.auth.get_valid_characters():\\n logger.debug(\\\"Fetching assets for char id=%d campaign=%s \\\"\\n \\\"operations=%s\\\", char_id, cname, \\\",\\\".join(opnames))\\n try:\\n assets = g.eve.esi(token).v1.characters(char_id).assets.get()\\n except EVEAPIForbiddenError:\\n logger.debug(\\\"Asset access denied for char id=%d\\\", char_id)\\n continue\\n\\n for opname in opnames:\\n data[opname][char_id] = {}\\n\\n logger.debug(\\\"Evaluating %d assets for char id=%d\\\",\\n len(assets), char_id)\\n for asset in assets:\\n for filt, opname in filters:\\n if filt(asset):\\n typeid = asset[\\\"type_id\\\"]\\n count = 1 if asset[\\\"is_singleton\\\"] else asset[\\\"quantity\\\"]\\n value = prices.get(typeid, 0.0)\\n char = data[opname][char_id]\\n if typeid in char:\\n char[typeid][0] += count\\n char[typeid][1] += count * value\\n else:\\n char[typeid] = [count, count * value]\\n\\n g.campaign_db.update_items(cname, data)\\n _save_collection_overview(cname, opnames, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc297591f45eb2919691b6e5d884de4\",\n \"score\": \"0.52153736\",\n \"text\": \"def compute_results(collection_fname):\\n f = open(\\\"label_results.csv\\\", \\\"w\\\")\\n f.close()\\n gold_dict = get_gold_standard_dict()\\n print gold_dict\\n content = \\\"\\\"\\n with open(collection_fname) as f:\\n content = f.read()\\n\\n for line in content.split(\\\"\\\\n\\\"):\\n if ',' in line:\\n fname, m, gname = line.split(',')\\n m = int(m)\\n fbase = fname[:-4]\\n logger.info(\\\"%s | %s\\\" % (fbase, fname))\\n gold_url = gold_dict[fbase]\\n alpha = compute_results_of_file(gname=gname, m=m, gold_url=gold_url.strip())\\n if alpha >= -2:\\n append_results(fbase, alpha)\\n logger.info(\\\"gethering: %s\\\" % fbase)\\n else:\\n logger.warning(\\\"no graph for %s\\\" % fname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d5ad6085d8e66e8538ea4bf432c383\",\n \"score\": \"0.5214863\",\n \"text\": \"def execute(self):\\n config = ConfigParser.RawConfigParser()\\n config.read('/etc/autoreduce/icat4.cfg')\\n\\n directory = \\\"/\\\" + self._facilityName + \\\"/\\\" + self._instrumentName + \\\"/\\\" + self._investigationName + \\\"/shared/autoreduce\\\"\\n logging.info(\\\"reduction output directory: %s\\\" % directory)\\n\\n #set dataset name\\n dataset = self._factory.create(\\\"dataset\\\")\\n\\n dsType = self._factory.create(\\\"datasetType\\\")\\n dsType.id = config.get('DatasetType', 'reduced')\\n dataset.type = dsType\\n dataset.name = self._runNumber\\n dataset.location = directory\\n datafiles = []\\n\\n pattern = '*' + self._runNumber + '*'\\n for dirpath, dirnames, filenames in os.walk(directory):\\n listing = glob.glob(os.path.join(dirpath, pattern))\\n for filepath in listing:\\n filename =os.path.basename(filepath)\\n logging.info(\\\"Filename: %s\\\" % filename)\\n datafile = self._factory.create(\\\"datafile\\\")\\n datafile.location = filepath\\n datafile.name = filename\\n extension = os.path.splitext(filename)[1][1:]\\n dfFormat = self._factory.create(\\\"datafileFormat\\\")\\n dfFormat.id = config.get('DatafileFormat', extension)\\n datafile.datafileFormat = dfFormat\\n modTime = os.path.getmtime(filepath)\\n datafile.datafileCreateTime = epochToISO8601(modTime)\\n datafile.fileSize = os.path.getsize(filepath)\\n\\n datafiles.append(datafile)\\n\\n dataset.datafiles = datafiles\\n dataset.type = dsType\\n\\n dbDatasets = self._service.search(self._sessionId, \\\"Dataset INCLUDE Datafile [name = '\\\" + str(dataset.name) + \\\"'] <-> Investigation <-> Instrument [name = '\\\" + str(self._instrumentName) + \\\"'] <-> DatasetType [name = 'reduced']\\\")\\n\\n if len(dbDatasets) == 0:\\n\\n dbInvestigations = self._service.search(self._sessionId, \\\"Investigation INCLUDE Sample [name = '\\\" + str(self._investigationName) + \\\"'] <-> Instrument [name = '\\\" + self._instrumentName + \\\"'] <-> Dataset [name = '\\\" + str(dataset.name) + \\\"']\\\")\\n\\n if len(dbInvestigations) == 0:\\n logging.error(\\\"No investigation entry found: try cataloging the raw data first.\\\")\\n return\\n else:\\n investigation = dbInvestigations[0]\\n if len(dbInvestigations)>1:\\n logging.error(\\\"Multiple investigation entries found: using the first.\\\")\\n\\n logging.debug(\\\"Creating dataset: %s\\\" % datetime.now())\\n dataset.investigation = investigation\\n dataset.sample = investigation.samples[0]\\n self._service.create(self._sessionId, dataset)\\n\\n elif len(dbDatasets) == 1:\\n logging.debug(\\\"reduced dataset %s is already cataloged, updating reduced dataset... \\\" % (dataset.name))\\n\\n dbDataset = dbDatasets[0]\\n # update \\\"one to many\\\" relationships\\n if hasattr(dbDataset, \\\"datafiles\\\"):\\n dfs = getattr(dbDataset, \\\"datafiles\\\")\\n self._service.deleteMany(self._sessionId, dfs)\\n\\n for df in datafiles:\\n df.dataset = dbDataset\\n self._service.createMany(self._sessionId, datafiles)\\n\\n else:\\n logging.error(\\\"ERROR, there should be only one dataset per run number per type reduced\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4bd8f7ee7cfee4f3da7d8c036acf4c4\",\n \"score\": \"0.52134824\",\n \"text\": \"def calculate_total_cost(state):\\n ### STUDENT CODE GOES HERE ###\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a782802b3593bf07ed7bf7f5fc07a25e\",\n \"score\": \"0.5199693\",\n \"text\": \"def evaluate(self):\\n if self.filename:\\n coco_gt = COCO(self.filename)\\n else:\\n coco_gt = COCO()\\n coco_gt.dataset = self.dataset\\n coco_gt.createIndex()\\n\\n if self.testdev_dir:\\n # Run on test-dev dataset.\\n box_result_list = []\\n for det in self.detections:\\n box_result_list.append({\\n 'image_id': int(det[0]),\\n 'category_id': int(det[6]),\\n 'bbox': np.around(\\n det[1:5].astype(np.float64), decimals=2).tolist(),\\n 'score': float(np.around(det[5], decimals=3)),\\n })\\n json.encoder.FLOAT_REPR = lambda o: format(o, '.3f')\\n # Must be in the formst of 'detections_test-dev2017_xxx_results'.\\n fname = 'detections_test-dev2017_test_results'\\n output_path = os.path.join(self.testdev_dir, fname + '.json')\\n logging.info('Writing output json file to: %s', output_path)\\n with tf.io.gfile.GFile(output_path, 'w') as fid:\\n json.dump(box_result_list, fid)\\n return np.array([-1.], dtype=np.float32)\\n else:\\n # Run on validation dataset.\\n detections = np.array(self.detections)\\n image_ids = list(set(detections[:, 0]))\\n coco_dt = coco_gt.loadRes(detections)\\n coco_eval = COCOeval(coco_gt, coco_dt, iouType='bbox')\\n coco_eval.params.imgIds = image_ids\\n coco_eval.evaluate()\\n coco_eval.accumulate()\\n coco_eval.summarize()\\n coco_metrics = coco_eval.stats\\n\\n if self.label_map:\\n # Get per_class AP, see pycocotools/cocoeval.py:334\\n # TxRxKxAxM: iouThrs x recThrs x catIds x areaRng x maxDets\\n # Use areaRng_id=0 ('all') and maxDets_id=-1 (200) in default\\n precision = coco_eval.eval['precision'][:, :, :, 0, -1]\\n # Ideally, label_map should match the eval set, but it is possible that\\n # some classes has no data in the eval set.\\n ap_perclass = [0] * max(precision.shape[-1], len(self.label_map))\\n for c in range(precision.shape[-1]): # iterate over all classes\\n precision_c = precision[:, :, c]\\n # Only consider values if > -1.\\n precision_c = precision_c[precision_c > -1]\\n ap_c = np.mean(precision_c) if precision_c.size else -1.\\n ap_perclass[c] = ap_c\\n coco_metrics = np.concatenate((coco_metrics, ap_perclass))\\n\\n # Return the concat normal and per-class AP.\\n return np.array(coco_metrics, dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa77183210b05afaeb69dc58b4f5088d\",\n \"score\": \"0.51991105\",\n \"text\": \"def operating_cost(oper_hrs, fuel_cost_per_hour, crew_cost_per_hour, average_speed=None, CPI=1,\\n profit=True, profit_margin=0.1, table=False, latex_format=False):\\n\\n data_set = { # based on 2018 USD\\n 'crew': 268, # crew cost per flight hour\\n 'maintenance': 731, # maintenance cost per flight hour\\n 'annual fixed cost without depreciation': 102687, # annual fixed cost without depreciation\\n 'annual depreciation': 155238, # annual depreciation price\\n 'average annual hours': 456 # average annual hours\\n }\\n\\n if profit is True:\\n profit_margin = profit_margin\\n else:\\n profit_margin = 0.0\\n\\n # all costs below are annual costs\\n crew_cost = crew_cost_per_hour * oper_hrs * (1 + profit_margin)\\n\\n fuel_cost = fuel_cost_per_hour * oper_hrs * (1 + profit_margin)\\n\\n maintenance_cost = data_set['maintenance'] * data_set['average annual hours'] * (1 + profit_margin) * CPI\\n\\n fixed_cost = data_set['annual fixed cost without depreciation'] * CPI * (1 + profit_margin)\\n\\n depreciation_cost = data_set['annual depreciation'] * CPI * (1 + profit_margin)\\n\\n annual_oper_cost = crew_cost + fuel_cost + maintenance_cost + fixed_cost + depreciation_cost\\n\\n annual_oper_cost_no_crew = fuel_cost + maintenance_cost + fixed_cost + depreciation_cost\\n\\n annual_oper_cost_bundle = \\\\\\n ((crew_cost, fuel_cost, maintenance_cost, fixed_cost, depreciation_cost, annual_oper_cost),\\n ('Crew', 'Fuel', 'Maintenance', 'Fixed', 'Depreciation', 'Total'))\\n\\n # print tabulated data\\n if table:\\n cost_bundle, name_bundle = annual_oper_cost_bundle\\n round_digit = 1\\n data = []\\n\\n if profit:\\n data.append(['Profit Margin', '{:.2f}%'.format(profit_margin * 100)])\\n data.append([' ', ' '])\\n\\n data.append(['Annual Operating Cost:', ' '])\\n\\n annual_adjust_num = 1e3\\n for ii in range(len(cost_bundle)):\\n data.append([name_bundle[ii], '{:.1f} Thousand USD'.format(round(cost_bundle[ii] / annual_adjust_num, round_digit))])\\n\\n data.append([' ', ' '])\\n\\n # per flight hour\\n data.append(['Operating cost per flight hour:', ' '])\\n\\n for ii in range(len(cost_bundle)):\\n data.append([name_bundle[ii], round(cost_bundle[ii] / oper_hrs, round_digit)])\\n\\n data.append([' ', ' '])\\n\\n # per mile per seat (with pilot)\\n available_seat = 2 # total available seats in flight\\n data.append(['Operating cost per mile per seat (with pilot)', ' '])\\n\\n if average_speed is None:\\n raise Exception(\\\"Please provide an average speed in mph.\\\")\\n\\n for ii in range(len(cost_bundle)):\\n data.append([name_bundle[ii],\\n round(cost_bundle[ii] / oper_hrs / average_speed / available_seat, round_digit)])\\n\\n # per mile per seat (autonomous flight, without pilot)\\n available_seat = 3 # total available seats in flight\\n data.append(['Operating cost per mile per seat (autonomous)', ' '])\\n\\n if average_speed is None:\\n raise Exception(\\\"Please provide an average speed in mph.\\\")\\n\\n for ii in range(1, len(cost_bundle) - 1):\\n data.append([name_bundle[ii],\\n round(cost_bundle[ii] / oper_hrs / average_speed / available_seat, round_digit)])\\n\\n data.append([name_bundle[-1],\\n round(annual_oper_cost_no_crew / oper_hrs / average_speed / available_seat, round_digit)])\\n\\n headers = ['Category', r'Cost [USD]']\\n\\n if latex_format:\\n table_out = tabulate(data, headers=headers, tablefmt=\\\"latex\\\",\\n numalign='right', floatfmt=(None, '.0f', '.0f'))\\n print(table_out)\\n else:\\n table_out = tabulate(data, headers=headers, tablefmt=\\\"fancy_grid\\\",\\n numalign='right', floatfmt=(None, '.0f', '.0f'))\\n print(table_out)\\n\\n return annual_oper_cost, annual_oper_cost_bundle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f8d1c09d5343b3b52032ae5211d6531\",\n \"score\": \"0.5196402\",\n \"text\": \"def cost(pvals, obdict, oberrdict, dC, start, fin):\\n pvallist = m.mod_list(pvals, dC, start, fin)\\n yoblist, yerrlist = obscost(obdict, oberrdict)\\n rmatrix = rmat(yerrlist)\\n hx = hxcost(pvallist, obdict, dC)\\n obcost = np.dot(np.dot((yoblist-hx),np.linalg.inv(rmatrix)),(yoblist-hx).T)\\n #modcost = np.dot(np.dot((pvals-dC.pvals),np.linalg.inv(dC.B)),\\\\\\n # (pvals-dC.pvals).T)\\n cost = 0.5*obcost #+ 0.5*modcost\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91ee745be1e01a964c3dcba2fbe24ef\",\n \"score\": \"0.51940095\",\n \"text\": \"def run(self,options):\\n print(Gstr_title)\\n\\n self.clist = self.generateCOElist(options.coenum)\\n print(\\\"clist:\\\" + str(self.clist))######################\\n if len(self.clist) != 0:\\n for cnum in self.clist:\\n print(\\\"cur cnum is \\\" + str(cnum)) ######################\\n c = int(cnum)\\n obj = MatCal.MatMulBench(c)\\n ms, st, ft, et = obj.Run()\\n print(\\\"cur list of pars:\\\" + str([ms,st,ft,et])) ######################\\n parse = [{'Matrix_Size': ms, 'Start_Time': st, 'Finish_Time': ft, 'Elapse_Time': et}]\\n self.createOrUpdate(parse, options.inputdir, options.outputdir)\\n print(parse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e13c616427485e223a0bd392a814c5\",\n \"score\": \"0.5193116\",\n \"text\": \"def performreduction(self,settings,directorylist):\\n\\n self.settings = settings\\n self.directorylist = directorylist\\n\\n print('Create Directories')\\n self.status.set('Create Directories')\\n \\n self.createWorkingDirectories()\\n\\n print('Copy Images')\\n self.copyImageTypes()\\n\\n print('Create Collections')\\n bias_ic = ImageFileCollection(self.paths['bias_dir'], self.keywords)\\n bias_table = bias_ic.summary\\n\\n flat_ic = ImageFileCollection(self.paths['flat_dir'], self.keywords)\\n flat_table = flat_ic.summary\\n\\n # check and handle empty table ie no FITS files so collection empty\\n\\n flat_filters = np.unique(flat_table[self.keywords[1]].data).tolist()\\n flat_exposures = np.unique(flat_table[self.keywords[3]].data).tolist()\\n\\n print('Flat Exposures')\\n\\n dark_ic = ImageFileCollection(self.paths['dark_dir'], self.keywords)\\n dark_table = dark_ic.summary\\n\\n dark_exposures = np.unique(dark_table[self.keywords[3]].data).tolist()\\n\\n print('Dark Exposures')\\n \\n science_ic = ImageFileCollection(self.paths['science_dir'], self.keywords)\\n science_table = science_ic.summary\\n\\n # need to handle 'None Type' in table\\n science_filters = np.unique(science_table[self.keywords[1]].data).tolist()\\n science_objects = np.unique(science_table[self.keywords[4]].data).tolist()\\n science_exposures = np.unique(science_table[self.keywords[3]].data).tolist()\\n\\n print('Science Exposures')\\n\\n print('Copy Exposures')\\n self.copyExposures(dark_ic,self.paths['dark_dir'],dark_exposures)\\n\\n print('Copy Filters')\\n if self.usefitsfilterlist[0] == 'True':\\n self.copyFilters(flat_ic,self.paths['flat_dir'],flat_filters)\\n else:\\n self.copyFiltersFname(self.paths['flat_dir'],self.flatfilterlist,self.updatefitsfilterlist[0])\\n\\n if self.usefitsfilterlist[1] == 'True':\\n self.copyFilters(flat_ic,self.paths['flat_dir'],science_filters)\\n else:\\n self.copyFiltersFname(self.paths['science_dir'],self.sciencefilterlist,self.updatefitsfilterlist[1])\\n\\n print('Create Master Bias')\\n self.createMasters(bias_ic,self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\\n self.filemods['master_bias_header_value'],self.filemods['median_combine_bias'])\\n\\n print('Create Master Darks')\\n self.createMasters(dark_ic,self.paths['master_dir'],self.filemods['master_dark_name'] + '.fit',\\n self.filemods['master_dark_header_value'],self.filemods['median_combine_dark'])\\n\\n print('Create Master Flatsx')\\n if self.usefitsfilterlist[0] == 'True':\\n filternames = flat_filters\\n else:\\n filternames = self.flatfilterlist\\n\\n for filterType in filternames:\\n print('Create Master Flat ' + filterType)\\n masterFile = self.filemods['master_flat_name'] + '_' + filterType + '.fit'\\n filter_dir = os.path.join(self.paths['flat_dir'],filterType)\\n ImageCollection = ImageFileCollection(filter_dir)\\n self.createMasters(ImageCollection,self.paths['master_dir'],masterFile,\\n self.filemods['master_flat_header_value'],self.filemods['median_combine_flat'])\\n\\n print('Bias Removal')\\n print('Bias Removal from Dark')\\n if self.filemods['filename_mod_prefix']:\\n self.removeBias(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\\n self.filemods['master_dark_name'] + '.fit',self.filemods['bias_removal_mod'] + self.filemods['master_dark_name'] + '.fit',self.filemods['master_dark_header_value'])\\n else:\\n self.removeBias(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\\n self.filemods['master_dark_name'] + '.fit',self.filemods['master_dark_name'] + self.filemods['bias_removal_mod'] +'.fit',self.filemods['master_dark_header_value'])\\n\\n print('Bias Removal from Flats')\\n for filterType in filternames:\\n print('Bias Removal from Flat ' + filterType)\\n masterFile = self.filemods['master_flat_name'] + '_' + filterType + '.fit'\\n if self.filemods['filename_mod_prefix']:\\n masterFilebr = self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] + '_' + filterType + '.fit'\\n else:\\n masterFilebr = self.filemods['master_flat_name'] + '_' + filterType + self.filemods['bias_removal_mod'] + '.fit'\\n\\n self.removeBias(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_bias_name'] + '.fit',\\n masterFile,masterFilebr,self.filemods['master_flat_header_value'])\\n\\n print('Dark Removal from Flats')\\n for filterType in filternames:\\n print('Dark Removal from Flat ' + filterType)\\n if self.filemods['filename_mod_prefix']:\\n masterFilebr = self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] +'_' + filterType + '.fit'\\n masterFilebrds = self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] +'_' + filterType + '.fit'\\n self.removeDark(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['bias_removal_mod'] + self.filemods['master_dark_name'] + '.fit',\\n masterFilebr,masterFilebrds,self.filemods['master_flat_header_value'])\\n else:\\n masterFilebr = self.filemods['master_flat_name'] +'_' + filterType + self.filemods['bias_removal_mod'] +'.fit'\\n masterFilebrds = self.filemods['master_flat_name'] +'_' + filterType + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit'\\n self.removeDark(self.paths['master_dir'],self.paths['master_dir'],self.paths['master_dir'],self.filemods['master_dark_name'] + self.filemods['bias_removal_mod'] +'.fit',\\n masterFilebr,masterFilebrds,self.filemods['master_flat_header_value'])\\n \\n print('Reduce Science File')\\n\\n print('Remove Bias & Dark')\\n for fname in science_ic.files:\\n fname_noext = os.path.splitext(fname)[0]\\n\\n if self.filemods['filename_mod_prefix']:\\n self.removeBias(self.paths['master_dir'],self.paths['science_dir'],self.paths['output_dir'],self.filemods['master_bias_name'] + '.fit',\\n fname,self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.imagelist[3])\\n self.removeDark(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['bias_removal_mod'] + self.filemods['master_dark_name'] + '.fit',\\n self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.imagelist[3])\\n else:\\n self.removeBias(self.paths['master_dir'],self.paths['science_dir'],self.paths['output_dir'],self.filemods['master_bias_name'] + '.fit',\\n fname,fname_noext + self.filemods['bias_removal_mod'] + '.fit',self.imagelist[3])\\n self.removeDark(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['master_dark_name'] + self.filemods['bias_removal_mod'] + '.fit',\\n fname_noext + self.filemods['bias_removal_mod'] +'.fit',fname_noext + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit',self.imagelist[3])\\n\\n print('Flat Correction')\\n for filterType in filternames:\\n\\n #need to do filenames by other than filter when not using fits header\\n\\n filter_dir = os.path.join(self.paths['science_dir'],filterType)\\n ImageCollection = ImageFileCollection(filter_dir)\\n\\n for fname in ImageCollection.summary['file']:\\n fname_noext = os.path.splitext(fname)[0]\\n\\n if self.filemods['filename_mod_prefix']:\\n self.reduceFlat(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + self.filemods['master_flat_name'] + '_' + filterType + '.fit',self.filemods['dark_removal_mod'] + self.filemods['bias_removal_mod'] + fname_noext + '.fit',self.filemods['reduced_removal_mod'] + fname_noext + '.fit')\\n else:\\n self.reduceFlat(self.paths['master_dir'],self.paths['output_dir'],self.paths['output_dir'],self.filemods['master_flat_name'] + '_' + filterType + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit',fname_noext + self.filemods['bias_removal_mod'] + self.filemods['dark_removal_mod'] + '.fit',fname_noext + self.filemods['reduced_removal_mod'] + '.fit')\\n\\n print('Reduction Complete')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10805538b233798437311cf99c41e3bc\",\n \"score\": \"0.5191627\",\n \"text\": \"def runRConly(self):\\r\\n print(\\\"Initializing Rule Compaction...\\\")\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n # CHECK FOR POPULATION REBOOT - Required for running Rule Compaction only on an existing saved rule population.\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n if not cons.doPopulationReboot:\\r\\n print(\\\"Algorithm: Error - Existing population required to run rule compaction alone.\\\")\\r\\n return\\r\\n\\r\\n try:\\r\\n fileObject = open(cons.popRebootPath+\\\"_PopStats.txt\\\", 'rU') # opens each datafile to read.\\r\\n except:\\r\\n #break\\r\\n print(\\\"Data-set Not Found!\\\")\\r\\n\\r\\n #Retrieve last training and testing accuracies from saved file---------\\r\\n tempLine = None\\r\\n for i in range(3):\\r\\n tempLine = fileObject.readline()\\r\\n tempList = tempLine.strip().split('\\\\t')\\r\\n trainAcc = float(tempList[0])\\r\\n if cons.testFile != 'None': #If a testing file is available.\\r\\n testAcc = float(tempList[1])\\r\\n else:\\r\\n testAcc = None\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n # RULE COMPACTION\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n cons.timer.startTimeRuleCmp()\\r\\n RuleCompaction(self.population, trainAcc, testAcc, self.exploreIter)\\r\\n cons.timer.stopTimeRuleCmp()\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n # GLOBAL EVALUATION OF COMPACTED RULE POPULATION\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n cons.timer.startTimeEvaluation()\\r\\n self.population.recalculateNumerositySum()\\r\\n self.population.runPopAveEval(self.exploreIter)\\r\\n self.population.runAttGeneralitySum()\\r\\n #----------------------------------------------------------\\r\\n cons.env.startEvaluationMode()\\r\\n if cons.testFile != 'None': #If a testing file is available.\\r\\n if cons.env.formatData.discretePhenotype:\\r\\n trainEval = self.doPopEvaluation(True)\\r\\n testEval = self.doPopEvaluation(False)\\r\\n else: #ContinuousCode #########################\\r\\n trainEval = self.doContPopEvaluation(True)\\r\\n testEval = self.doContPopEvaluation(False)\\r\\n elif cons.trainFile != 'None':\\r\\n if cons.env.formatData.discretePhenotype:\\r\\n trainEval = self.doPopEvaluation(True)\\r\\n testEval = None\\r\\n else: #ContinuousCode #########################\\r\\n trainEval = self.doContPopEvaluation(True)\\r\\n testEval = None\\r\\n else: #Online Environment\\r\\n trainEval = None\\r\\n testEval = None\\r\\n\\r\\n cons.env.stopEvaluationMode()\\r\\n cons.timer.stopTimeEvaluation()\\r\\n #------------------------------------------------------------------------------\\r\\n cons.timer.returnGlobalTimer()\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n # WRITE OUTPUT FILES\\r\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\r\\n OutputFileManager().writePopStats(cons.outFileName+\\\"_RC_\\\"+cons.ruleCompactionMethod, trainEval, testEval, self.exploreIter + 1, self.population, self.correct)\\r\\n OutputFileManager().writePop(cons.outFileName+\\\"_RC_\\\"+cons.ruleCompactionMethod, self.exploreIter + 1, self.population)\\r\\n OutputFileManager().attCo_Occurence(cons.outFileName+\\\"_RC_\\\"+cons.ruleCompactionMethod, self.exploreIter + 1, self.population)\\r\\n OutputFileManager().writePredictions(self.exploreIter, cons.outFileName+\\\"_RC_\\\"+cons.ruleCompactionMethod, self.predictionList, self.realList, self.predictionSets)\\r\\n #------------------------------------------------------------------------------------------------------------\\r\\n print(\\\"Rule Compaction Complete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bde4ca60e5ca9934955c98cf6cbf66b\",\n \"score\": \"0.51821685\",\n \"text\": \"def main(self):\\r\\n\\r\\n self.get_relations()\\r\\n self.get_ways()\\r\\n self.get_nodes()\\r\\n self.merge_all_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64044668fdf9b3f998a51edc8bcf48d5\",\n \"score\": \"0.51724035\",\n \"text\": \"def test_replacement_costs_aggregate(self):\\n replacement_costs = self.load_replacement_costs_mavrouli()\\n self.assertReplacementCostsAggregate(replacement_costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30372dbaacb30f0386383ec7652347ac\",\n \"score\": \"0.51662207\",\n \"text\": \"def cost(self):\\n\\t\\tamt = 0.0\\n\\t\\tfor i in self.timesheets.itervalues():\\n\\t\\t\\tamt += (float(i[1]) * float(self.rate))\\n\\t\\tfor i in self.POs.itervalues():\\n\\t\\t\\tamt += i.quote.price\\n\\t\\treturn amt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa7ea24171c73e0ecccc810c9533e98\",\n \"score\": \"0.5163673\",\n \"text\": \"def print_cost(self, cost_type: CostType = CostType.ALL):\\n\\n def print_penalty_list(nlp, penalties, print_only_weighted):\\n running_total = 0\\n\\n for penalty in penalties:\\n if not penalty:\\n continue\\n\\n val, val_weighted = self._get_penalty_cost(nlp, penalty)\\n running_total += val_weighted\\n\\n if penalty.node in [Node.MULTINODES, Node.TRANSITION]:\\n node_name = penalty.node.name\\n else:\\n node_name = f\\\"{penalty.node[0]}\\\" if isinstance(penalty.node[0], int) else penalty.node[0].name\\n\\n if self._detailed_cost is not None:\\n self._detailed_cost += [\\n {\\n \\\"name\\\": penalty.type.__str__(),\\n \\\"penalty\\\": penalty.type.__str__().split(\\\".\\\")[0],\\n \\\"function\\\": penalty.name,\\n \\\"cost_value_weighted\\\": val_weighted,\\n \\\"cost_value\\\": val,\\n \\\"params\\\": penalty.params,\\n \\\"derivative\\\": penalty.derivative,\\n \\\"explicit_derivative\\\": penalty.explicit_derivative,\\n \\\"integration_rule\\\": penalty.integration_rule.name,\\n \\\"weight\\\": penalty.weight,\\n \\\"expand\\\": penalty.expand,\\n \\\"node\\\": node_name,\\n }\\n ]\\n if print_only_weighted:\\n print(f\\\"{penalty.type}: {val_weighted}\\\")\\n else:\\n print(f\\\"{penalty.type}: {val_weighted} (non weighted {val: .2f})\\\")\\n\\n return running_total\\n\\n def print_objective_functions(ocp):\\n \\\"\\\"\\\"\\n Print the values of each objective function to the console\\n \\\"\\\"\\\"\\n print(f\\\"\\\\n---- COST FUNCTION VALUES ----\\\")\\n running_total = print_penalty_list(None, ocp.J_internal, False)\\n running_total += print_penalty_list(None, ocp.J, False)\\n if running_total:\\n print(\\\"\\\")\\n\\n for nlp in ocp.nlp:\\n print(f\\\"PHASE {nlp.phase_idx}\\\")\\n running_total += print_penalty_list(nlp, nlp.J_internal, False)\\n running_total += print_penalty_list(nlp, nlp.J, False)\\n print(\\\"\\\")\\n\\n print(f\\\"Sum cost functions: {running_total}\\\")\\n print(f\\\"------------------------------\\\")\\n\\n def print_constraints(ocp, sol):\\n \\\"\\\"\\\"\\n Print the values of each constraint with its lagrange multiplier to the console\\n \\\"\\\"\\\"\\n\\n if sol.constraints is None:\\n return\\n\\n # Todo, min/mean/max\\n print(f\\\"\\\\n--------- CONSTRAINTS ---------\\\")\\n if (\\n print_penalty_list(None, ocp.g_internal, True)\\n + print_penalty_list(None, ocp.g_implicit, True)\\n + print_penalty_list(None, ocp.g, True)\\n ):\\n print(\\\"\\\")\\n\\n for idx_phase, nlp in enumerate(ocp.nlp):\\n print(f\\\"PHASE {idx_phase}\\\")\\n print_penalty_list(nlp, nlp.g_internal, True)\\n print_penalty_list(nlp, nlp.g_implicit, True)\\n print_penalty_list(nlp, nlp.g, True)\\n print(\\\"\\\")\\n print(f\\\"------------------------------\\\")\\n\\n if cost_type == CostType.OBJECTIVES:\\n print_objective_functions(self.ocp)\\n elif cost_type == CostType.CONSTRAINTS:\\n print_constraints(self.ocp, self)\\n elif cost_type == CostType.ALL:\\n print(\\n f\\\"Solver reported time: {self.solver_time_to_optimize} sec\\\\n\\\"\\n f\\\"Real time: {self.real_time_to_optimize} sec\\\"\\n )\\n self.print_cost(CostType.OBJECTIVES)\\n self.print_cost(CostType.CONSTRAINTS)\\n else:\\n raise ValueError(\\\"print can only be called with CostType.OBJECTIVES or CostType.CONSTRAINTS\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178fa74e94980d0cce85160d2d30d284\",\n \"score\": \"0.5162399\",\n \"text\": \"def cost(self):\\n params = {\\n \\\"f\\\" : \\\"json\\\",\\n \\\"token\\\" : self._securityHandler.token\\n }\\n url = self._url + \\\"/cost\\\" #% (self.portalId, )\\n return self._do_post(url=url,\\n param_dict=params,\\n proxy_url=self._proxy_url,\\n proxy_port=self._proxy_port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2824110f39967816bf377e218f7ce1e1\",\n \"score\": \"0.51601386\",\n \"text\": \"def get_cost(self, inputs, targets):\\n self.outputs[:] = self.costfunc(inputs, targets)\\n self.be.mean(self.outputs, axis=1, out=self.cost_buffer)\\n self.cost = self.cost_buffer.get()\\n return self.cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9989e01d130cbb9703171c2dca9c93\",\n \"score\": \"0.5155409\",\n \"text\": \"def main():\\n global SCORES\\n\\n size_categories = [\\\"small\\\", \\\"medium\\\", \\\"large\\\"]\\n if not os.path.isdir(path_to_outputs):\\n os.mkdir(path_to_outputs)\\n\\n # Load previous scores from file if such file exists.\\n if os.path.isfile(score_path):\\n with open(score_path, 'r+') as f:\\n lines = f.read()\\n if lines:\\n print(\\\"!!~~ LOADED PREVIOUS SCORES ~~!!\\\\n\\\")\\n SCORES = json.loads(lines)\\n\\n t_start = time.time()\\n for size in size_categories:\\n category_path = path_to_inputs + \\\"/\\\" + size\\n output_category_path = path_to_outputs + \\\"/\\\" + size\\n category_dir = os.fsencode(category_path)\\n\\n if not os.path.isdir(output_category_path):\\n os.mkdir(output_category_path)\\n\\n for input_folder in os.listdir(category_dir):\\n input_name = os.fsdecode(input_folder)\\n graph, num_buses, bus_size, constraints = parse_input(category_path + \\\"/\\\" + input_name)\\n solver_instance = solve(graph, num_buses, bus_size, constraints, verbose=True)\\n solver_instance.write(input_name, output_category_path, verbose=True)\\n\\n time_elapsed = datetime.timedelta(seconds=(time.time() - t_start))\\n print(f\\\"Time Elapsed: {time_elapsed} hrs\\\")\\n all_scores = list(SCORES.values())\\n print(f\\\"Average Score (on leaderboard): {sum(all_scores) / len(all_scores)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df663e2a27c5cede45a84bc971e02851\",\n \"score\": \"0.5149468\",\n \"text\": \"def cost_saved_pa(generation_1kw, load_kwh, capacity_kWp, cost_per_kWp, \\n import_cost, export_price, expected_life_yrs,\\n verbose=False, return_raw_data=False):\\n import pandas as pd\\n load = load_kwh.rename(columns={load_kwh.columns[0]:'load_kWh'})\\n if not isinstance(generation_1kw, pd.DataFrame):\\n logging.debug(str(type(generation_1kw)))\\n# input('Press any key')\\n generation_1kw = generation_1kw.rename(columns={\\n generation_1kw.columns[0]:'1kWp_generation_kWh'})\\n generation = generation_1kw * capacity_kWp\\n df = generation.merge(load, \\n how='inner', \\n left_index=True, \\n right_index=True)\\n #days = (df.index[-1] - df.index[0]).days\\n #confidence = days/365\\n df['import_kWh'] = \\\\\\n (df['load_kWh'] - df['1kWp_generation_kWh']).clip(lower=0)\\n df['export_kWh'] = \\\\\\n (df['load_kWh'] - df['1kWp_generation_kWh']).clip(upper=0)\\n df = df.rename(columns={\\n '1kWp_generation_kWh':\\n f'{round(capacity_kWp,2)}_1kWp_generation_kWh'})\\n if return_raw_data:\\n return df\\n df['export_kWh'] = df['export_kWh'].abs()\\n \\n df = df.sum() # total kWh for the entire year\\n\\n old_import_cost = import_cost*(df['load_kWh'])\\n new_import_cost = import_cost*(df['import_kWh'])\\n \\n import_cost_savings = old_import_cost - new_import_cost\\n \\n export_revenue = export_price * df['export_kWh']\\n install_cost = cost_per_kWp * capacity_kWp\\n amortized_install_cost = install_cost / expected_life_yrs\\n revenue_pa = import_cost_savings + export_revenue\\n total_revenue = revenue_pa * expected_life_yrs\\n profit_pa = import_cost_savings \\\\\\n + export_revenue \\\\\\n - amortized_install_cost\\n total_profit = ( profit_pa * expected_life_yrs )\\n \\n if verbose:\\n ROI = 100 * (total_profit / install_cost)\\n payback_yrs = install_cost / revenue_pa\\n summary = {'old_import_cost':old_import_cost,\\n 'new_import_cost': new_import_cost,\\n 'import_cost_savings': import_cost_savings,\\n 'export_revenue': export_revenue,\\n 'install_cost': install_cost,\\n 'amortized_install_cost': amortized_install_cost,\\n 'revenue_pa': revenue_pa,\\n 'profit_pa': profit_pa,\\n 'total_revenue':total_revenue,\\n 'total_profit': total_profit,\\n 'ROI%': ROI,\\n 'payback_yrs': payback_yrs}\\n return summary\\n return profit_pa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d0ab969db604a3cc7407e65b5c34c47\",\n \"score\": \"0.5145199\",\n \"text\": \"def main(registry=REGISTRY, deployer=DEPLOYER):\\n balance = deployer.balance()\\n registry = Registry.at(registry)\\n pool_data = get_pool_data()\\n\\n print(\\\"Adding pools to registry...\\\")\\n\\n for name, data in pool_data.items():\\n pool = data[\\\"swap_address\\\"]\\n if registry.get_n_coins(pool)[0] == 0:\\n print(f\\\"\\\\nAdding {name}...\\\")\\n add_pool(data, registry, deployer)\\n else:\\n print(f\\\"\\\\n{name} has already been added to registry\\\")\\n\\n gauges = data[\\\"gauge_addresses\\\"]\\n gauges = gauges + [\\\"0x0000000000000000000000000000000000000000\\\"] * (10 - len(gauges))\\n\\n if registry.get_gauges(pool)[0] == gauges:\\n print(f\\\"{name} gauges are up-to-date\\\")\\n continue\\n\\n print(f\\\"Updating gauges for {name}...\\\")\\n for gauge in data[\\\"gauge_addresses\\\"]:\\n try:\\n Contract(GAUGE_CONTROLLER).gauge_types(gauge)\\n except (ValueError, VirtualMachineError):\\n print(f\\\"Gauge {gauge} is not known to GaugeController, cannot add to registry\\\")\\n gauges = False\\n break\\n\\n if gauges:\\n registry.set_liquidity_gauges(\\n pool, gauges, {\\\"from\\\": deployer, \\\"gas_price\\\": gas_strategy}\\n )\\n\\n print(f\\\"Total gas used: {(balance - deployer.balance()) / 1e18:.4f} eth\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9a210ccbddce13c87bcab0e68915ae\",\n \"score\": \"0.51375043\",\n \"text\": \"def test_replacement_costs_aggregate(self):\\n replacement_costs = self.load_replacement_costs_torres()\\n self.assertReplacementCostsAggregate(replacement_costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e267e88301700dfd7360f3f68cb17c\",\n \"score\": \"0.5132486\",\n \"text\": \"def main():\\n \\n\\t# Check file directory paths exist \\n\\tDirExists(OUTDIR)\\n\\t#FileExists(STATDIR + FILEDATE + \\\"_stat.csv\\\")\\n\\t#FileExists(NETDIR + FILEDATE + \\\"_net.csv\\\")\\n\\n\\t# Get Dictionaries\\n\\tinterrupts = GetInterrupts(FILEDATE + \\\"_stat.csv\\\")\\n\\t#print interrupts\\n\\tcontext = GetContext(FILEDATE + \\\"_stat.csv\\\")\\n\\tnet = GetBytes(FILEDATE + \\\"_net.csv\\\")\\n\\n\\n\\t# Sort Dictionaries\\n\\tintDict = collections.OrderedDict(sorted(interrupts.items()))\\n\\tcontDict = collections.OrderedDict(sorted(context.items()))\\n\\tnetDict = collections.OrderedDict(sorted(net.items()))\\n\\n\\t\\\"\\\"\\\"\\n\\t Process dictionaries to get metrics:\\n\\t \\t# Interrupts Clean\\n\\t\\tIBR = Interrupts/Bytes Received\\n\\t\\tIBT = Interrupts/Bytes Transferred\\n\\t\\tIPR = Interrupts/Packets Received\\n\\t\\tIPR = Interrupts/Packets Transferred\\n\\t\\tBTI = Context Switch/Bytes Transferred\\n\\t\\tBRI = Context Switch/Bytes Received\\n\\t\\tPTI = Context Switch/Packets Transferred\\n\\t\\tPRI = Context Switch/Packets Received\\n\\n\\t\\t# Context Clean\\n\\t\\tCBT = Context Switch/Bytes Transferred\\n\\t\\tCBR = Context Switch/Bytes Received\\n\\t\\tCPT = Context Switch/Packets Transferred\\n\\t\\tCPR = Context Switch/Packets Received\\n\\n\\t\\tBTC = Context Switch/Bytes Transferred\\n\\t\\tBRC = Context Switch/Bytes Received\\n\\t\\tPTC = Context Switch/Packets Transferred\\n\\t\\tPRC = Context Switch/Packets Received\\n\\t\\\"\\\"\\\"\\n\\n\\t# Prepare csv writers to output in NuPic input format \\n\\t# See: http://nupic.docs.numenta.org/1.0.0/quick-start/example-data.html)\\n\\tinterruptsFile = OUTDIR + FILEDATE + '_interrupts_clean.csv'\\n\\tinterruptOutput = open(interruptsFile, 'wb')\\n\\tinterruptWriter = csv.writer(interruptOutput, delimiter=',')\\n\\n\\tcontextFile = OUTDIR + FILEDATE + '_context_clean.csv'\\n\\tcontextOutput = open(contextFile, 'wb')\\n\\tcontextWriter = csv.writer(contextOutput, delimiter=',')\\n\\tintHeader = [\\n\\t\\t\\t\\t\\t\\\"timestamp\\\",\\n\\t\\t\\t\\t\\t\\\"Total Interrupts since Boot\\\",\\n\\t\\t\\t\\t\\t\\\"Interrupts per Second\\\",\\n\\t\\t\\t\\t\\t\\\"IBR\\\",\\n\\t\\t\\t\\t\\t\\\"IBT\\\",\\n\\t\\t\\t\\t\\t\\\"IPR\\\",\\n\\t\\t\\t\\t\\t\\\"IPT\\\"\\n\\t\\t\\t\\t]\\n\\tfieldTypes = [\\n\\t\\t\\t\\t\\\"datetime\\\",\\n\\t\\t\\t\\t\\\"int\\\",\\n\\t\\t\\t\\t\\\"int\\\",\\n\\t\\t\\t\\t\\\"float\\\",\\n\\t\\t\\t\\t\\\"float\\\",\\n\\t\\t\\t\\t\\\"float\\\",\\n\\t\\t\\t\\t\\\"float\\\",\\n\\t\\t\\t\\t]\\n\\tflags = [\\\"T\\\", \\\"C\\\", \\\"C\\\", \\\"C\\\", \\\"C\\\", \\\"C\\\", \\\"C\\\"]\\n\\tinterruptWriter.writerow(intHeader)\\n\\tinterruptWriter.writerow(fieldTypes)\\n\\tinterruptWriter.writerow(flags)\\n\\t\\n\\tcontheader = [\\n\\t\\t\\t\\t\\t\\\"timestamp\\\",\\n\\t\\t\\t\\t\\t\\\"Total Context Switches since Boot\\\", \\n\\t\\t\\t\\t\\t\\\"Contect Switches per Second\\\",\\n\\t\\t\\t\\t\\t\\\"CBR\\\",\\n\\t\\t\\t\\t\\t\\\"CBT\\\",\\n\\t\\t\\t\\t\\t\\\"CPR\\\",\\n\\t\\t\\t\\t\\t\\\"CPT\\\"\\n\\t\\t\\t\\t]\\n\\tcontextWriter.writerow(contheader)\\n\\tcontextWriter.writerow(fieldTypes)\\n\\tcontextWriter.writerow(flags)\\n\\n\\t# Get Metrics\\n\\tGetMetrics(intDict, netDict, interruptWriter)\\n\\tprint (\\\"Results written to \\\" + interruptsFile)\\n\\tGetMetrics(contDict, netDict, contextWriter)\\n\\tprint (\\\"Results written to \\\" + contextFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf56331879c2dbca77057f750c40d22e\",\n \"score\": \"0.5124699\",\n \"text\": \"def compute_basic(self):\\n self.counter = Counter(self.data[-1])\\n self.data_entropy = self.entropy(self.counter.values(), self.nr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1caef852996b2dc2d7677d4449532416\",\n \"score\": \"0.512332\",\n \"text\": \"def test_replacement_costs_aggregate(self):\\n replacement_costs = self.load_replacement_costs_suppasri()\\n self.assertReplacementCostsAggregate(replacement_costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b251e8f0b93198b58af2b74e1fc8398\",\n \"score\": \"0.5122973\",\n \"text\": \"def get_cost(self, inputs, targets, cost_type=None):\\n self.outputs[:] = self.costfunc(inputs, targets, cost_type)\\n self.be.mean(self.outputs, axis=1, out=self.cost_buffer)\\n self.cost = self.cost_buffer.get()\\n return self.cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"073d4fc15145990b823b1b725cc5b660\",\n \"score\": \"0.510906\",\n \"text\": \"def get_elec_cost():\\n if self.tech_type in [\\\"BOILER\\\", \\\"FURNACE\\\", \\\"DSGLF\\\", \\\"PTC\\\", \\\"PTCTES\\\"]:\\n return 0\\n \\n def get_demand_cost(load):\\n \\\"\\\"\\\"Finds the demand cost for each month\\\"\\\"\\\"\\n length = np.array([31,28,31,30,31,30,31,31,30,31,30,31]) * 24\\n start = np.array([0,31,59,90,120,151,181,212,243,273,304,334])*24\\n peaks = []\\n peakind = []\\n for month in range(len(length)):\\n ind = np.argmax(load[start[month] : start[month] + length[month]])\\n peakind.append(ind)\\n peaks.append(load[ind])\\n # single value demand vs demand rate by month\\n if type(self.edrate) == list:\\n peakrates = np.array([self.edrate[i] for i in peakind])\\n demand_cost = sum(np.array(peaks) * peakrates)\\n else:\\n demand_cost = sum(self.edrate*np.array(peaks)) \\n \\n return demand_cost\\n # new facility electricity grid load after addition of electrified equipment and potential renewables\\n new_elec_load = (self.fe_load_8760 + self.load_8760/self.model.get_efficiency() - self.model.gen).clip(min=0) \\n\\n # demand charge change due to renewables\\n new_elec_cost_demand = get_demand_cost(new_elec_load)\\n old_elec_cost_demand = get_demand_cost(self.fe_load_8760)\\n demand_diff = new_elec_cost_demand - old_elec_cost_demand\\n \\n # reduced energy charges from renewables \\n ediff = self.model.elec_gen - self.fe_load_8760\\n mit_cost = sum(self.model.elec_gen[ediff <= 0] * self.pd_curve[0][ediff <=0]) \\\\\\n + sum(self.fe_load_8760[ediff > 0] *self.pd_curve[0][ediff > 0]) \\n \\n # For PV assisted technologies, determine the energy charge from the grids\\n if self.tech_type == \\\"PVEB\\\": \\n \\n elec_energy_grid_cost = sum((self.load_8760 - self.model.load_met)/self.model.get_efficiency() * self.pd_curve[0])\\n \\n return demand_diff - mit_cost + elec_energy_grid_cost\\n\\n if self.tech_type == \\\"PVRH\\\":\\n \\n elec_energy_grid_cost = sum((self.load_8760 - self.model.load_met)/self.model.get_efficiency()* self.pd_curve[0])\\n \\n return demand_diff - mit_cost + elec_energy_grid_cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b462462a0762589aaf51f77f61d8e33\",\n \"score\": \"0.51047206\",\n \"text\": \"def _refresh_internal_vars(self):\\n self.min_cost_flow = min_cost_flow.SimpleMinCostFlow()\\n self.start_inds = []\\n self.end_inds = []\\n self.caps = []\\n self.costs = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23fbb3c39d7de0703cf764bbbe0e593e\",\n \"score\": \"0.51004565\",\n \"text\": \"def CABE(self):\\n\\n Results, G_values, G1_values, G2_values = {}, [], [], []\\n\\n pool = mp.Pool(self.n_processors)\\n G_values.append(pool.map(self.CalculateFreeEnergy,self.reports))\\n pool.terminate()\\n\\n G_values = self.DecompressList(G_values)\\n\\n for G_value_list in G_values:\\n G1_values += G_value_list[0]\\n G2_values += G_value_list[1]\\n\\n mean_G1 = n.mean(G1_values,axis=0); std_G1 = n.std(G1_values,axis=0)\\n mean_G2 = n.mean(G2_values,axis=0); std_G2 = n.std(G2_values,axis=0)\\n state_1 = len(G1_values) ; state_2 = len(G2_values)\\n rel1 = 100*state_1/(state_1+state_2); rel2 = 100*state_2/(state_1+state_2)\\n \\n print(mean_G1-mean_G2,rel1,rel2)\\n\\n Results[\\\"mean,std, and frequency of state 1\\\"] = (mean_G1, std_G1, state_1, rel1)\\n Results[\\\"mean,std, and frequency of state 2\\\"] = (mean_G2, std_G2, state_2, rel2)\\n\\n if self.perform_plots:\\n G_names = [\\\"State 1\\\" for i in G1_values]+[\\\"State 2\\\" for i in G2_values]\\n G_val_grouped = G1_values + G2_values\\n print(len(G_names),len(G_val_grouped))\\n if self.violin_plots:\\n sns.violinplot(G_names, G_val_grouped, flierprops={\\\"marker\\\":\\\"o\\\", \\\"markerfacecolor\\\":\\\"darkgrey\\\", \\\"markeredgecolor\\\":\\\"k\\\", \\\"markersize\\\":10})\\n else:\\n sns.boxplot(G_names, G_val_grouped, flierprops={\\\"marker\\\":\\\"o\\\", \\\"markerfacecolor\\\":\\\"darkgrey\\\", \\\"markeredgecolor\\\":\\\"k\\\", \\\"markersize\\\":10})\\n plt.savefig(self.output_path+\\\"_CABE\\\");plt.close()\\n inf_CABE = open(\\\"{}_CABE.pkl\\\".format(self.output_path), \\\"wb\\\"); pickle.dump(G_val_grouped, inf_CABE); pickle.dump(G_names, inf_CABE); inf_CABE.close()\\n\\n\\n df = pd.DataFrame(Results, index = [\\\"Mean\\\", \\\"Standard deviation\\\", \\\"Frequency\\\",\\\"Relative_abundance\\\"])\\n df.to_csv(self.output_path+\\\".csv\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e07408ce52b85bf73494d852c3fe39e\",\n \"score\": \"0.5096082\",\n \"text\": \"def _main():\\n rtts = []\\n cbts = []\\n congs = []\\n manager = Manager()\\n wlock = manager.Lock()\\n cqueue = manager.Queue()\\n rqueue = manager.Queue()\\n oqueue = manager.Queue()\\n pool = Pool()\\n parser = ArgumentParser(description=\\\"Add statistical information\\\" +\\n \\\"to probes\\\")\\n parser.add_argument(\\\"--input\\\", type=str, required=True, help=\\\"Input file.\\\")\\n parser.add_argument(\\\"--output\\\", type=str, required=True,\\n help=\\\"Output file.\\\")\\n args = parser.parse_args()\\n assert args.input and exists(args.input), 'Invalid input file.'\\n assert args.output and not exists(args.output), 'Invalid output file.'\\n\\n with open(args.input, 'r') as f:\\n try:\\n while True:\\n try:\\n probe = load(f)\\n except EOFError:\\n break\\n if probe.cbt:\\n cbts.append(probe.cbt)\\n if len(probe.rtts) > 0 and isinstance(probe.rtts[0], int):\\n rtts.append(probe.rtts[0])\\n if probe.cong:\\n congs.append(probe.cong)\\n pool.apply_async(_update_probe, (probe, cbts, rtts,\\n congs, wlock, cqueue,\\n rqueue, oqueue, args.output))\\n if len(cbts) > 1000:\\n del cbts[0]\\n if len(rtts) > 1000:\\n del rtts[0]\\n if len(congs) > 1000:\\n del congs[0]\\n # Run garbage collector after cleaning R's memory space.\\n collect()\\n except KeyboardInterrupt:\\n pass\\n except:\\n sys.stderr.write(str(traceback.format_exc()))\\n pool.close()\\n pool.join()\\n assert cqueue.qsize() in range(0, 2), 'Wrong size of queue!'\\n assert rqueue.qsize() in range(0, 2), 'Wrong size of queue!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb00aa3d59f54a6bd87e8362d02cbab\",\n \"score\": \"0.50921494\",\n \"text\": \"def main():\\n\\n opts, args = getopt.gnu_getopt(sys.argv[1:], '',\\n ['file_path='])\\n # process all the options\\n for opt, arg in opts :\\n if opt == '--file_path':\\n file_path = arg\\n # Input the data.\\n data = create_data_model(file_path)\\n\\n # Create the routing/scheduling index manager.\\n\\n manager = pywrapcp.RoutingIndexManager(\\n len(data['change_over_matrix']), data['num_lines'], data['starts'],\\n data['ends'])\\n\\n # Create Routing Model.\\n scheduling = pywrapcp.RoutingModel(manager)\\n\\n # Create and register a transit/schedule callback\\n def time_callback(from_index, to_index):\\n \\\"\\\"\\\"Returns the distance between the two nodes.\\\"\\\"\\\"\\n # Convert from scheduling variable Index to time matrix NodeIndex.\\n from_node = manager.IndexToNode(from_index)\\n to_node = manager.IndexToNode(to_index)\\n return data['change_over_matrix'][from_node][to_node] + \\\\\\n data['product_production_time'][from_node]\\n\\n schedule_callback_index = scheduling.RegisterTransitCallback(time_callback)\\n\\n # Define cost of each arc.\\n scheduling.SetArcCostEvaluatorOfAllVehicles(schedule_callback_index)\\n\\n # Add Time Windows constraint.\\n time = 'Time'\\n scheduling.AddDimension(\\n schedule_callback_index,\\n 0, # allow waiting time\\n 12000, # maximum time per vehicle\\n True, # Don't force start cumul to zero.\\n time)\\n time_dimension = scheduling.GetDimensionOrDie(time)\\n # The method SetGlobalSpanCostCoefficient sets a large coefficient (100)\\n # for the global span of the routes, which in this example is the\\n # maximum of the distances of the routes. This makes the global span\\n # the predominant factor in the objective function, so the program\\n # minimizes the length of the longest route.\\n time_dimension.SetGlobalSpanCostCoefficient(100)\\n\\n # Add time window constraints for each location except depot.\\n for product, time_window in enumerate(data['time_windows']):\\n if product == 0 or 1 or 2 or len(data['time_windows']) - 1:\\n continue\\n print(time_dimension.CumulVar(product))\\n time_dimension.CumulVar(product).SetRange(time_window[0],\\n time_window[1])\\n\\n\\n # define the lines that can produce certain product\\n\\n for product, line_list in data['line_constraint'].items():\\n allowed_line_list = [-1]\\n allowed_index = manager.NodeToIndex(product)\\n allowed_line_list.extend(line_list)\\n\\n scheduling.VehicleVar(product).SetValues(allowed_line_list)\\n\\n\\n # Instantiate route start and end times to produce feasible times.\\n for i in range(data['num_lines']):\\n scheduling.AddVariableMinimizedByFinalizer(\\n time_dimension.CumulVar(scheduling.Start(i)))\\n scheduling.AddVariableMinimizedByFinalizer(\\n time_dimension.CumulVar(scheduling.End(i)))\\n\\n # Setting first solution heuristic.\\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\\n search_parameters.local_search_metaheuristic = (\\n routing_enums_pb2.LocalSearchMetaheuristic.TABU_SEARCH)\\n\\n # search_parameters.first_solution_strategy = (\\n # routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\\n\\n search_parameters.time_limit.seconds = 30\\n search_parameters.log_search = True\\n # get the attribute of the search_parameters\\n #print(dir(search_parameters))\\n\\n # Solve the problem.\\n assignment = scheduling.SolveWithParameters(search_parameters)\\n\\n # Print solution on console.\\n if assignment:\\n #file_path = 'Y:\\\\\\\\Pyomo_projects\\\\\\\\PnG_CP'\\n figure_name = 'Demo_figure_heuristic'\\n excel_name = '/' + 'Detailed_schedule_heuristic.xlsx'\\n excel_path = file_path + excel_name\\n input_df = get_schedule(data, manager, scheduling, assignment)\\n writer_reference = pd.ExcelWriter(excel_path)\\n input_df.to_excel(writer_reference, startcol = 4, startrow = 1)\\n worksheet = writer_reference.sheets['Sheet1']\\n worksheet.write_string(10, 0, 'Detailed Schedule')\\n writer_reference.save()\\n\\n Vis_Gantt_Chart(input_df, file_path, figure_name)\\n # print_solution(data, manager, scheduling, assignment)\\n else:\\n print('No solution found !')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7644d0854c79d4d72f4220f0a4ba1ba\",\n \"score\": \"0.5087497\",\n \"text\": \"def _determine_costs_dict(net, opf_task_overview):\\n\\n cost_dfs = [df for df in [\\\"poly_cost\\\", \\\"pwl_cost\\\"] if net[df].shape[0]]\\n if not len(cost_dfs):\\n opf_task_overview[\\\"flexibilities_without_costs\\\"] = \\\"all\\\"\\n return\\n\\n flex_elements = [\\\"ext_grid\\\", \\\"gen\\\", \\\"sgen\\\", \\\"load\\\", \\\"dcline\\\", \\\"storage\\\"]\\n\\n for flex_element in flex_elements:\\n\\n # determine keys of opf_task_overview[\\\"flexibilities\\\"] ending with flex_element\\n keys = [power_type + flex_element for power_type in [\\\"P\\\", \\\"Q\\\"] if (\\n power_type + flex_element) in opf_task_overview[\\\"flexibilities\\\"].keys()]\\n\\n # determine indices of all flexibles\\n idx_without_cost = set()\\n for key in keys:\\n idx_without_cost |= set(chain(*opf_task_overview[\\\"flexibilities\\\"][key][\\\"index\\\"]))\\n # simple alternative without itertools.chain():\\n # idx_without_cost |= {idx for idxs in opf_task_overview[\\\"flexibilities\\\"][key][\\n # \\\"index\\\"] for idx in idxs}\\n\\n for cost_df in cost_dfs:\\n idx_with_cost = set(net[cost_df].element[net[cost_df].et == flex_element].astype(int))\\n if len(idx_with_cost - idx_without_cost):\\n logger.warning(\\\"These \\\" + flex_element + \\\"s have cost data but aren't flexible or\\\" +\\n \\\" have both, poly_cost and pwl_cost: \\\" +\\n str(sorted(idx_with_cost - idx_without_cost)))\\n idx_without_cost -= idx_with_cost\\n\\n if len(idx_without_cost):\\n opf_task_overview[\\\"flexibilities_without_costs\\\"][flex_element] = list(idx_without_cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7b87b1d5fec329e635d6f6462a578e\",\n \"score\": \"0.50856185\",\n \"text\": \"def main():\\n utility_name = '05-2018_Utility_Summary.xlsx'\\n dining_name = '05-2018_Dining_Utility_Summary.xlsx'\\n housing_name = '05-2018_Housing_Utility_Summary.xlsx'\\n input_column = get_columns()\\n kwh_rate, water_rate, gas_rate = calc_rates()\\n fill_utility(utility_name, input_column, kwh_rate, water_rate, gas_rate)\\n fill_dining(dining_name, input_column, kwh_rate, water_rate)\\n fill_housing(housing_name, input_column, kwh_rate, water_rate, gas_rate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de49faf15f385b4c699d8548e5b2394\",\n \"score\": \"0.5078285\",\n \"text\": \"def main():\\n urls = get_urls(URL_NOLOOKUP)\\n all_data = []\\n print('Scraping PCGS Number Data...')\\n for i, (category, subcategories) in enumerate(urls.items()):\\n print(f\\\"\\\\tStarting Category {i+1}/{len(urls.items())}: {category}...\\\")\\n for subcat_name, subcat_url in tqdm(subcategories):\\n time.sleep(1.0)\\n subcat_data = scrape_nums(subcat_url)\\n all_data.extend(subcat_data)\\n print('Done with PCGS Number Data! Saving...')\\n\\n pickle.dump(all_data, open('data/number_data.pkl', 'wb'))\\n\\n print('Saved to data/number_data.pkl')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5dba0f08c9511aff8ab47638abd0675\",\n \"score\": \"0.5063765\",\n \"text\": \"def main(file_xyz: str, cell: str, restart: str, basis: str, basis_folder: str) -> None:\\n system = Molecule(file_xyz)\\n\\n # Set path for basis set\\n basisCP2K = join(basis_folder, \\\"BASIS_MOLOPT\\\")\\n potCP2K = join(basis_folder, \\\"GTH_POTENTIALS\\\")\\n\\n # Settings specifics\\n s = templates.geometry\\n s.basis = basis\\n s.potential = \\\"GTH-PBE\\\"\\n s.cell_parameters = cell\\n s.specific.cp2k.force_eval.dft.basis_set_file_name = basisCP2K\\n s.specific.cp2k.force_eval.dft.potential_file_name = potCP2K\\n s.specific.cp2k.force_eval.dft.uks = ''\\n s.specific.cp2k.force_eval.dft.charge = '-1'\\n s.specific.cp2k.force_eval.dft.multiplicity = '2'\\n s.specific.cp2k.force_eval.dft.wfn_restart_file_name = f'{restart}'\\n\\n# =======================\\n# Compute OPT files with CP2k\\n# =======================\\n\\n result = run(cp2k(s, system))\\n\\n# ======================\\n# Output the results\\n# ======================\\n\\n logger.info(result.energy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf077c96e0cf1e18495f9ad358da060\",\n \"score\": \"0.5061305\",\n \"text\": \"def compute(self):\\n drafter = Drafter.get_instance(None)\\n\\n if len(self.tasksToCompute) != 0:\\n for task in self.tasksToCompute:\\n self.usedPower += task.compute(self.tasksToCompute[task])\\n\\n finishedTasks = {k: v for k, v in self.tasksToCompute.items() if k.isCompleted()}\\n for task in finishedTasks:\\n self.currentComputingPower += self.tasksToCompute[task]\\n del self.tasksToCompute[task]\\n\\n if len(task.divisionHistory) == 0:\\n print(str(task) + \\\": FINISHED\\\")\\n else:\\n task.sourceDevice.receiveResults(task)\\n #gdy task wraca zmien kolor na niebieski\\n change_edge_on_pull(self.deviceID, task.sourceDevice.deviceID, drafter.canvas, drafter.configuration)\\n if task.sourceDevice.masterDevice.deviceID is self.deviceID:\\n change_node_on_pull(task.sourceDevice.deviceID, drafter.canvas, drafter.configuration)\\n\\n #zmien kolor node'a na aktualnie zajmowana moc obliczeniowa\\n change_node_color(calculateColor(self.currentComputingPower / self.maxComputingPower), self.deviceID, drafter.canvas, drafter.configuration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5b5111185368e4bee7547817e848a9\",\n \"score\": \"0.5061063\",\n \"text\": \"def estimate_construction_time(self, construction_time_input_data, construction_time_output_data):\\n\\n collection_construction_time = construction_time_input_data['construct_duration'] * 1 / 3 # assumes collection construction occurs for one-third of project duration\\n\\n throughput_operations = construction_time_input_data['rsmeans']\\n trench_length_km = construction_time_output_data['trench_length_km']\\n if construction_time_input_data['turbine_rating_MW'] >= 0.1:\\n operation_data = throughput_operations.where(throughput_operations['Module'] == 'Collection').dropna(\\n thresh=4)\\n # from rsmeans data, only read in Collection related data and filter out the rest:\\n cable_trenching = throughput_operations[throughput_operations.Module == 'Collection']\\n else: #switch for small DW\\n operation_data = throughput_operations.where(\\n throughput_operations['Module'] == 'Small DW Collection').dropna(thresh=4)\\n # from rsmeans data, only read in Collection related data and filter out the rest:\\n cable_trenching = throughput_operations[throughput_operations.Module == 'Small DW Collection']\\n # operation_data = pd.merge()\\n\\n # from rsmeans data, only read in Collection related data and filter out the rest:\\n cable_trenching = throughput_operations[throughput_operations.Module == 'Collection']\\n\\n # Storing data with labor related inputs:\\n trenching_labor = cable_trenching[cable_trenching.values == 'Labor']\\n trenching_labor_usd_per_hr = trenching_labor['Rate USD per unit'].sum()\\n\\n construction_time_output_data['trenching_labor_usd_per_hr']=trenching_labor_usd_per_hr\\n trenching_labor_daily_output = trenching_labor['Daily output'].values[0] # Units: LF/day -> where LF = Linear Foot\\n trenching_labor_num_workers = trenching_labor['Number of workers'].sum()\\n\\n # Storing data with equipment related inputs:\\n trenching_equipment = cable_trenching[cable_trenching.values == 'Equipment']\\n trenching_cable_equipment_usd_per_hr = trenching_equipment['Rate USD per unit'].sum()\\n construction_time_output_data['trenching_cable_equipment_usd_per_hr']=trenching_cable_equipment_usd_per_hr\\n trenching_equipment_daily_output = trenching_equipment['Daily output'].values[0] # Units: LF/day -> where LF = Linear Foot\\n construction_time_output_data['trenching_labor_daily_output'] = trenching_labor_daily_output\\n construction_time_output_data['trenching_equipment_daily_output'] = trenching_equipment_daily_output\\n\\n operation_data['Number of days taken by single crew'] = ((trench_length_km / self._km_to_LF) / trenching_labor_daily_output)\\n operation_data['Number of crews'] = np.ceil((operation_data['Number of days taken by single crew'] / 30) / collection_construction_time)\\n operation_data['Cost USD without weather delays'] = ((trench_length_km / self._km_to_LF) / trenching_labor_daily_output) * (operation_data['Rate USD per unit'] * construction_time_input_data['operational_hrs_per_day'])\\n alpha = operation_data[operation_data['Type of cost'] == 'Collection']\\n operation_data_id_days_crews_workers = alpha[['Operation ID', 'Number of days taken by single crew', 'Number of crews', 'Number of workers']]\\n\\n alpha = operation_data[operation_data['Type of cost'] == 'Labor']\\n operation_data_id_days_crews_workers = alpha[['Operation ID', 'Number of days taken by single crew', 'Number of crews', 'Number of workers']]\\n\\n # if more than one crew needed to complete within construction duration then assume that all construction\\n # happens within that window and use that timeframe for weather delays;\\n # if not, use the number of days calculated\\n operation_data['time_construct_bool'] = operation_data['Number of days taken by single crew'] > collection_construction_time * 30\\n boolean_dictionary = {True: collection_construction_time * 30, False: np.NAN}\\n operation_data['time_construct_bool'] = operation_data['time_construct_bool'].map(boolean_dictionary)\\n operation_data['Time construct days'] = operation_data[['time_construct_bool', 'Number of days taken by single crew']].min(axis=1)\\n num_days = operation_data['Time construct days'].max()\\n\\n\\n # No 'management crew' in small DW\\n if construction_time_input_data['turbine_rating_MW'] >= 0.1:\\n # pull out management data\\n crew_cost = self.input_dict['crew_cost']\\n crew = self.input_dict['crew'][self.input_dict['crew']['Crew type ID'].str.contains('M0')]\\n management_crew = pd.merge(crew_cost, crew, on=['Labor type ID'])\\n management_crew = management_crew.assign(per_diem_total=management_crew['Per diem USD per day'] * management_crew['Number of workers'] * num_days)\\n management_crew = management_crew.assign(hourly_costs_total=management_crew['Hourly rate USD per hour'] * self.input_dict['hour_day'][self.input_dict['time_construct']] * num_days)\\n management_crew = management_crew.assign(total_crew_cost_before_wind_delay=management_crew['per_diem_total'] + management_crew['hourly_costs_total'])\\n self.output_dict['management_crew'] = management_crew\\n self.output_dict['managament_crew_cost_before_wind_delay'] = management_crew['total_crew_cost_before_wind_delay'].sum()\\n else:\\n self.output_dict['managament_crew_cost_before_wind_delay'] = 0.0\\n\\n construction_time_output_data['operation_data_id_days_crews_workers'] = operation_data_id_days_crews_workers\\n construction_time_output_data['operation_data_entire_farm'] = operation_data\\n\\n return construction_time_output_data['operation_data_entire_farm']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14f7f01f06047a9903b7e837b55a88a\",\n \"score\": \"0.50591457\",\n \"text\": \"def main():\\n # pre_process = PreProcess()\\n # pre_process.transform_queries_qrels_new_ids()\\n # pagerank = PageRank(\\\"data/pagerank.scoreOrder\\\", \\\"data/qrels_webtrack2009/webtrec2009.txt\\\")\\n # pagerank = PageRank(\\\"data/ClueWeb09-En-PRranked.txt\\\", \\\"data/qrels_webtrack2009/webtrec2009.txt\\\")\\n # pagerank.add_pagerank_qrels()\\n # spamscore = SpamScore(\\\"data/processed_data/qrels_webtrack2014/qrels_with_pr_normalized.txt\\\", \\\"data/waterloo-spam-cw12-decoded/\\\")\\n # spamscore = SpamScore(\\\"data/processed_data/qrels_webtrack2009/qrels_with_pr_normalized.txt\\\", \\\"data/clueweb09spam.Fusion\\\", collection=True)\\n # spamscore.add_spam_score_to_qrels()\\n # runs_track = RunsTrack()\\n # runs_track.generate_runs_new_ids()\\n\\n \\\"\\\"\\\"Discretizing the values of the qrels\\\"\\\"\\\" \\n # qrels = Qrels()\\n\\n \\\"\\\"\\\"Computing the Partial order relation among documents and generating new qrels.\\\"\\\"\\\"\\n # distance_order = DistanceOrder(is_config=True)\\n # skyline_order = SkylineOrder(is_config=True)\\n # exit()\\n \\\"\\\"\\\"Computing Dataset Statistics\\\"\\\"\\\"\\n # dataset_statistics = DatasetStatistics()\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2009/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2009/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2010/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2010/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2011/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2011/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2012/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2012/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2013/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2013/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack2014/qrels_discrete_aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/qrels_webtrack2014/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_3aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/decision/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/processed_data/qrels/qrels_2015_4aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/task2015/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/processed_data/qrels/qrels_2016_4aspects.txt', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/task2016/qrels_urbp.txt')\\n qrels = Qrels_urbp(qrels='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_misinfo/qrels/misinfo-qrels', qrels_out='/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_weighting_distanceorder/misinfo2020/qrels_urbp.txt')\\n \\n\\n exit()\\n \\\"\\\"\\\"Generating queries and searching in Indri\\\"\\\"\\\"\\n # search = Search()\\n # search.prepare_indri_queries()\\n # search.search_griparameters()\\n\\n \\\"\\\"\\\"Evaluating the runs\\\"\\\"\\\"\\n # eval = Eval()\\n # val = eval.run_trec_eval(qrels=\\\"data/processed_data/qrels/qrels_only_rel.txt\\\", run=\\\"data/runs/bm25/0.01_0.6.txt\\\", metric='ndcg', queries=True)\\n # eval.run_trec_eval(qrels=\\\"data/processed_data/qrels/qrels_only_rel.txt\\\", run=\\\"data/runs/bm25/0.01_0.6.txt\\\", metric='ndcg')\\n #getting the top runs\\n # eval.get_top_runs()\\n # eval.get_average_scores()\\n\\n \\\"\\\"\\\"Generating results file with evaluation scores\\\"\\\"\\\"\\n # results = Results()\\n # results.generate_results()\\n\\n \\\"\\\"\\\"Running Experiments\\\"\\\"\\\"\\n #### Discriminative Power #####\\n # discriminative_power = DiscriminativePower()\\n\\n ##### Informativeness #####\\n # Informativeness()\\n\\n \\\"\\\"\\\"Correlation Analysis\\\"\\\"\\\"\\n # corr_analysis = CorrAnalysis()\\n\\n \\\"\\\"\\\"Intuitiveness Experiment\\\"\\\"\\\"\\n # intuitiveness = Intuitiveness()\\n # intuitiveness.experiment_intuitiveness()\\n\\n \\\"\\\"\\\"Unanimity Experiment\\\"\\\"\\\"\\n # intuitiveness = Unanimity()\\n # intuitiveness.experiment_unanimity()\\n\\n \\\"\\\"\\\"Pool Downsampling\\\"\\\"\\\"\\n # pooldownsampling = PoolDownsampling()\\n #\\n # #### Task Track 2015 #####\\n # \\\"\\\"\\\"Multiple random samples\\\"\\\"\\\"\\n # # for i in range(1000):\\n # # # Task Track 2015 and Task Track 2016 ##\\n # # # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2015_4aspects.txt',\\n # # # folder_new='data/multiple_downsampling_qrels_2015/'+str(i)+'/', step=15)\\n # # # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2015/',\\n # # # qrels_path_downsamples='data/multiple_downsampling_qrels_2015/'+str(i)+'/',\\n # # # results_folder='data/multiple_results_downsampling_topicwise_2015/'+str(i)+'/',\\n # # # metric='ndcg')\\n # #\\n # # # Decision Track ##\\n # # pooldownsampling.generate_downsampling(qrels_path='data/qrels_decision/qrels_decision_3aspects.txt', folder_new='data/multiple_downsampling_qrels_decision/'+str(i)+'/', step=15, num_aspects=3)\\n # # pooldownsampling.evaluate_downsampling_topicwize_decision(runs_folder='data/decisionRuns/',\\n # # qrels_path_downsamples='data/multiple_downsampling_qrels_decision/'+str(i)+'/',\\n # # results_folder='data/multiple_results_downsampling_topicwise_decision/'+str(i)+'/', metric='ndcg')\\n # # exit()\\n # dict_results = {}\\n # dict_to_plot = {}\\n # corr = True\\n # for i in range(1000):\\n # if corr is not None:\\n # res = pooldownsampling.calc_corr_by_topic(results_folder='data/multiple_results_downsampling_topicwise_2016/'+str(i)+'/')\\n # else:\\n # res = pooldownsampling.calc_rmse_by_topic(\\n # results_folder='data/multiple_results_downsampling_topicwise_2015/' + str(i) + '/')\\n #\\n # for key, percentages in res.items():\\n # if key not in dict_results:\\n # dict_results[key] = {}\\n # for percentage, value in percentages.items():\\n # if percentage not in dict_results[key]:\\n # dict_results[key][percentage] = []\\n # dict_results[key][percentage] += [value]\\n # # pprint.pprint(dict_results)\\n # # exit()\\n # for key, percentages in dict_results.items():\\n # if key not in dict_to_plot:\\n # dict_to_plot[key] = {'mean':[],\\n # 'lb':[],\\n # 'ub':[]}\\n # # print(sorted(percentages))\\n # for percentage, values in sorted(percentages.items()):\\n # if percentage == '0' and corr is None:\\n # dict_to_plot[key]['mean'] += [0]\\n # dict_to_plot[key]['lb'] += [0]\\n # dict_to_plot[key]['ub'] += [0]\\n # continue\\n # elif percentage == '0' and corr:\\n # dict_to_plot[key]['mean'] += [1]\\n # dict_to_plot[key]['lb'] += [1]\\n # dict_to_plot[key]['ub'] += [1]\\n # continue\\n # stddev = std(values, ddof=1)\\n # t_bounds = t.interval(0.95, len(values) - 1)\\n # ci = [average(values) + critval * stddev / sqrt(len(values)) for critval in t_bounds]\\n # dict_to_plot[key]['mean'] += [average(values)]\\n # dict_to_plot[key]['lb'] += [ci[0]]\\n # dict_to_plot[key]['ub'] += [ci[1]]\\n # # pprint.pprint(dict_to_plot)\\n # # exit()\\n # # pooldownsampling.plot_mean_and_CI(dict_to_plot, plot_path='rmse_downsampling_2016.jpeg', x_labels=sorted(list(dict_results[list(dict_results.keys())[0]].keys())))\\n # pooldownsampling.plot_mean_and_CI(dict_to_plot, plot_path='correlation_downsampling_2016.jpeg', x_labels=sorted(list(dict_results[list(dict_results.keys())[0]].keys())), corr=corr)\\n\\n # print(\\\"After ..\\\")\\n # pprint.pprint(dict_results)\\n\\n # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2015_4aspects.txt', folder_new='data/downsample_qrels_2015/',step=15)\\n # pooldownsampling.evaluate_downsampling(runs_folder='data/runs_track_newids/2015/',qrels_path_downsamples='data/downsample_qrels_2015/',results_folder='data/results_downsampling_2015/' ,metric='ndcg')\\n # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2015/',qrels_path_downsamples='data/downsample_qrels_2015/',results_folder='data/results_downsampling_topicwise_2015/' ,metric='ndcg')\\n # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2015/',qrels_path_downsamples='data/downsample_qrels_2015/',results_folder='data/results_downsampling_topicwise_2015/' ,metric='ndcg')\\n # pooldownsampling.plot_results_summary(\\\"data/results_downsampling_2015/\\\")\\n\\n #### Task Track 2016 #####\\n # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2016_4aspects.txt', folder_new='data/downsample_qrels_2016/',step=15)\\n # pooldownsampling.generate_downsampling(qrels_path='data/processed_data/qrels/qrels_2016_4aspects.txt', folder_new='data/downsample_qrels_2016/',step=15)\\n # pooldownsampling.evaluate_downsampling(runs_folder='data/runs_track_newids/2016/',\\n # qrels_path_downsamples='data/downsample_qrels_2016/',\\n # results_folder='data/results_downsampling_2016/', metric='ndcg')\\n # pooldownsampling.evaluate_downsampling_topicwize(runs_folder='data/runs_track_newids/2016/',\\n # qrels_path_downsamples='data/downsample_qrels_2016/',\\n # results_folder='data/results_downsampling_topicwise_2016/', metric='ndcg')\\n # pooldownsampling.plot_results_summary(\\\"data/results_downsampling_2016/\\\")\\n # pooldownsampling.calc_corr_by_topic(results_folder='data/results_downsampling_topicwise_2016/')\\n # pooldownsampling.calc_rmse_by_topic(results_folder='data/results_downsampling_topicwise_2016/')\\n # pooldownsampling.calc_rmse_by_topic(results_folder='data/results_downsampling_topicwise_2015/')\\n\\n #### Decision TRACK #####\\n # pooldownsampling.generate_downsampling(qrels_path='data/qrels_decision/qrels_decision_3aspects.txt', folder_new='data/downsample_qrels_decision/',step=15, num_aspects=3)\\n # pooldownsampling.evaluate_downsampling_decision(runs_folder='data/decisionRuns/',\\n # qrels_path_downsamples='data/downsample_qrels_decision/',\\n # results_folder='data/results_downsampling_decision/', metric='ndcg')\\n # pooldownsampling.evaluate_downsampling_topicwize_decision(runs_folder='data/decisionRuns/',\\n # qrels_path_downsamples='data/downsample_qrels_decision/',\\n # results_folder='data/results_downsampling_decision_topicwise/', metric='ndcg')\\n # pooldownsampling.plot_results_summary(\\\"data/results_downsampling_decision/\\\")\\n #\\n # pooldownsampling.calc_corr_by_topic(results_folder='data/results_downsampling_decision_topicwise/')\\n # pooldownsampling.calc_rmse_by_topic(results_folder='data/results_downsampling_decision_topicwise/')\\n\\n\\n ### Ploting distributions ###\\n # distributions = Distributions()\\n # distributions.get_frequency_by_rank_pos()\\n # distributions.plot_num_relevant_per_query()\\n\\n ## COMPUTING LIMITATIONS ##\\n\\n # for year in [2012]:\\n # # dt.plot_histogram_qrels()\\n # dt.generate_individualIdealRankings(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_discrete_aspects.txt',outpath='data/run_test_limitations_webtrack'+str(year)+'/', num_aspects=3)\\n # dt.generate_idealRuns_Single(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_webtrack'+str(year)+'_manhattan.txt',\\n # out_path='data/run_test_limitations_webtrack'+str(year)+'/manhattan.txt', aspects=[0,1,2])\\n # dt.generate_idealRuns_Single(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_webtrack'+str(year)+'_euclidean.txt',\\n # out_path='data/run_test_limitations_webtrack'+str(year)+'/euclidean.txt', aspects=[0, 1, 2])\\n # dt.generate_idealRuns_Single(qrels_path='data/qrels_webtrack'+str(year)+'/qrels_webtrack'+str(year)+'_manhattan.txt',\\n # out_path='data/run_test_limitations_webtrack'+str(year)+'/chebyshev.txt', aspects=[0, 1, 2])\\n # dt = DatasetStatistics()\\n #\\n # dt.generate_individualIdealRankings(qrels_path=\\\"data/qrels_decision/qrels_decision_3aspects.txt\\\",\\n # outpath='data/runs_test_limitations_decision/', num_aspects=3)\\n # dt.generate_idealRuns_Single(\\n # qrels_path=\\\"data/qrels_misinfo/qrels/qrels_misinfo_chebyshev.txt\\\",\\n # out_path='data/run_test_limitations_misinformation/individual/chebyshev.txt', aspects=[0, 1, 2])\\n # dt.generate_idealRuns_Single(\\n # qrels_path=\\\"data/qrels_misinfo/qrels/qrels_misinfo_manhattan.txt\\\",\\n # out_path='data/run_test_limitations_misinformation/individual/manhattan.txt', aspects=[0, 1, 2])\\n # dt.generate_idealRuns_Single(\\n # qrels_path=\\\"data/qrels_misinfo/qrels/qrels_misinfo_euclidean.txt\\\",\\n # out_path='data/run_test_limitations_misinformation/individual/euclidean.txt', aspects=[0, 1, 2])\\n # for year in ['2009','2010','2011','2012','2013','2014']:\\n # for metric in ['ndcg','map']:\\n # dt = DatasetStatistics()\\n # dict_parameters = {'qrels_arr': [\\n # \\\"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack\\\"+str(year)+\\\"/qrels_only_relevance.txt\\\",\\n # \\\"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack\\\"+str(year)+\\\"/qrels_only_pagerank.txt\\\",\\n # \\\"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_webtrack\\\"+str(year)+\\\"/qrels_only_spam.txt\\\",\\n # ],\\n # 'runs_folder': '/science/image/cluster-homedirs/krn788/LLMS_multieval/data/run_test_limitations_webtrack'+year+'/individual/',\\n # 'task':'Webtrack'+year,\\n # 'metric': metric,\\n # # 'topic_range': [36, 85],\\n # # 'single_runs': {'chebyshev': (\\\"data/processed_data/qrels_chebyshev_iteration_qrels_only_query.txt\\\",\\n # # \\\"data/runs_test_limitations_decision/chebyshev.txt\\\"),\\n # # 'euclidean': (\\\"data/processed_data/qrels_euclidean_iteration_qrels_only_query.txt\\\",\\n # # \\\"data/runs_test_limitations_decision/euclidean.txt\\\"),\\n # # 'manhattan': (\\\"data/processed_data/qrels_manhattan_iteration_qrels_only_query.txt\\\",\\n # # \\\"data/runs_test_limitations_decision/manhattan.txt\\\")}\\n # }\\n #\\n # dt.plot_scores_by_topic(dict_parameters)\\n # exit()\\n for metric in ['ndcg', 'map']:\\n dt = DatasetStatistics()\\n dict_parameters = {'qrels_arr': [\\n \\\"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_rel.txt\\\",\\n \\\"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_cred.txt\\\",\\n \\\"/science/image/cluster-homedirs/krn788/LLMS_multieval/data/qrels_decision/qrels_decision_correctness.txt\\\",\\n ],\\n 'runs_folder': '/science/image/cluster-homedirs/krn788/LLMS_multieval/data/runs_test_limitations_decision/individual/',\\n 'task': 'Decision',\\n 'metric': metric,\\n }\\n dt.plot_scores_by_topic(dict_parameters)\\n\\n\\n # dt.create_box_plot_format_csv(\\\"data/results/WebTrack2009/\\\", \\\"data/results_to_boxplot/WebTrack2009/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7687437fdfc08add0b3114b3fafca9\",\n \"score\": \"0.5057288\",\n \"text\": \"def run(self, expRef, butler):\\n \\n pool = basePool.Pool(\\\"poolSatellite\\\")\\n pool.cacheClear()\\n pool.storeSet(butler=butler)\\n\\n self.log.info(\\\"Beginning processing of ExpRef: \\\" + str(expRef.dataId))\\n\\n dataIdList = dict([(ccdRef.get(\\\"ccdExposureId\\\"), ccdRef.dataId)\\n for ccdRef in expRef.subItems(\\\"ccd\\\") if ccdRef.datasetExists(\\\"src\\\")])\\n dataIdList = collections.OrderedDict(sorted(dataIdList.items()))\\n\\n self.log.info(\\\"DataIdList to be processed: \\\" + str( dataIdList.values()))\\n\\n # Scatter: run each CCD separately\\n structList = pool.map(self.process, dataIdList.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6b5588ef07271fc2d27ccaa35da873\",\n \"score\": \"0.5051165\",\n \"text\": \"def compute_pool():\\n return {\\n \\\"type\\\": \\\"class\\\",\\n \\\"base\\\": None,\\n \\\"is_abstract\\\": False,\\n \\\"pstr\\\": (\\\"{} [{}]\\\", (\\\"name\\\", \\\"number_of_nodes\\\")),\\n \\\"properties\\\": [\\n (\\\"accelerator_type\\\", \\\"str\\\", \\\"0.1\\\", \\\"Type of accelerator.\\\"),\\n (\\n \\\"accelerators_per_node\\\",\\n \\\"int\\\",\\n \\\"0.1\\\",\\n \\\"Number of accelerator units on a node.\\\",\\n ),\\n (\\n \\\"clock_cycle_concurrency\\\",\\n \\\"int\\\",\\n \\\"0.1\\\",\\n \\\"The number of operations which can be carried out concurrently in a single clock cycle of a single core. E.g. 4.\\\",\\n ),\\n (\\n \\\"clock_speed\\\",\\n \\\"shared.numeric\\\",\\n \\\"0.1\\\",\\n \\\"The clock speed of a single core, in units of GHz. E.g. 3.6.\\\",\\n ),\\n (\\n \\\"compute_cores_per_node\\\",\\n \\\"int\\\",\\n \\\"0.1\\\",\\n \\\"Number of CPU cores per node.\\\",\\n ),\\n (\\\"cpu_type\\\", \\\"str\\\", \\\"0.1\\\", \\\"CPU type.\\\"),\\n (\\\"description\\\", \\\"str\\\", \\\"0.1\\\", \\\"Textural description of pool.\\\"),\\n (\\\"memory_per_node\\\", \\\"shared.numeric\\\", \\\"0.1\\\", \\\"Memory per node.\\\"),\\n (\\\"model_number\\\", \\\"str\\\", \\\"0.1\\\", \\\"Model/Board number/type.\\\"),\\n (\\\"name\\\", \\\"str\\\", \\\"0.1\\\", \\\"Name of compute pool within a machine.\\\"),\\n (\\\"number_of_nodes\\\", \\\"int\\\", \\\"0.1\\\", \\\"Number of nodes.\\\"),\\n (\\n \\\"vendor\\\",\\n \\\"linked_to(shared.party)\\\",\\n \\\"0.1\\\",\\n \\\"Supplier of compute hardware in this pool\\\",\\n ),\\n (\\n \\\"network_cards_per_node\\\",\\n \\\"platform.nic\\\",\\n \\\"0.N\\\",\\n \\\"Available network interfaces on node\\\",\\n ),\\n ],\\n \\\"derived\\\": [\\n (\\\"total_cores\\\", \\\"compute_cores_per_node * number_of_nodes\\\"),\\n (\\\"total_memory\\\", \\\"memory_per_node * number_of_nodes\\\"),\\n ],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae245603f377bca69ba3150d919998b\",\n \"score\": \"0.5047229\",\n \"text\": \"def test_benchmark_manycore_scf(self):\\n from os import makedirs\\n\\n seed = \\\"_LiC.res\\\"\\n shutil.copy(REAL_PATH + \\\"data/structures/LiC.res\\\", \\\"_LiC.res\\\")\\n\\n cell_dict, s = cell2dict(\\n REAL_PATH + \\\"/data/benchmark/LiC_scf/LiC_scf.cell\\\",\\n verbosity=VERBOSITY,\\n db=False,\\n )\\n self.assertTrue(s)\\n param_dict, s = param2dict(\\n REAL_PATH + \\\"/data/benchmark/LiC_scf/LiC_scf.param\\\",\\n verbosity=VERBOSITY,\\n db=False,\\n )\\n self.assertTrue(s)\\n\\n shutil.copy(REAL_PATH + \\\"data/pspots/Li_00PBE.usp\\\", \\\".\\\")\\n shutil.copy(REAL_PATH + \\\"data/pspots/C_00PBE.usp\\\", \\\".\\\")\\n with self.assertRaises(CalculationError):\\n ComputeTask(\\n ncores=NCORES,\\n nnodes=None,\\n node=None,\\n res=seed,\\n param_dict=param_dict,\\n cell_dict=cell_dict,\\n verbosity=0,\\n executable=EXECUTABLE,\\n start=True,\\n )\\n\\n outputs_exist = [\\n isfile(\\\"bad_castep/_LiC.res\\\"),\\n isfile(\\\"bad_castep/_LiC.castep\\\"),\\n ]\\n\\n results, s = castep2dict(\\\"bad_castep/_LiC.castep\\\", db=False)\\n makedirs(REAL_PATH + \\\"/data/benchmark/results\\\", exist_ok=True)\\n shutil.copy(\\n \\\"bad_castep/_LiC.castep\\\",\\n REAL_PATH\\n + \\\"/data/benchmark/results/_LiC_{}core_castep{}.castep\\\".format(\\n results.get(\\\"num_mpi_processes\\\", 0),\\n results.get(\\\"castep_version\\\", \\\"xxx\\\"),\\n ),\\n )\\n\\n self.assertTrue(all(outputs_exist), \\\"couldn't find output files!\\\")\\n self.assertTrue(s, \\\"couldn't read output files!\\\")\\n print(results[\\\"_time_estimated\\\"])\\n benchmark_data = {2: 2 * 7.4, 4: 4 * 4.0, 12: 16.8, 14: 22.4, 18: 23.4}\\n warnings.warn(\\n RuntimeWarning(\\n \\\"Run took {} s with {} MPI processes, with cumulative CPU time of {:.1f} s. Benchmark data\\\\n = {}\\\".format(\\n results[\\\"_time_estimated\\\"],\\n results[\\\"num_mpi_processes\\\"],\\n results[\\\"_time_estimated\\\"] * results[\\\"num_mpi_processes\\\"],\\n benchmark_data,\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb4c353d275ec8cdc6617f7c6b9f6ea3\",\n \"score\": \"0.50456494\",\n \"text\": \"def CDLP(capacities, preference_no_purchase, offer_sets: np.ndarray, filename_result=\\\"\\\", silent=True):\\n offer_sets = pd.DataFrame(offer_sets)\\n offer_sets = offer_sets.append(pd.DataFrame([np.zeros_like(products)])) # append the empty set to ensure all times some offerset is offered\\n offer_sets = offer_sets.reset_index(drop=True)\\n T = len(times)\\n\\n S = {}\\n R = {}\\n Q = {}\\n for index, offer_array in offer_sets.iterrows():\\n S[index] = tuple(offer_array)\\n R[index] = revenue(tuple(offer_array), preference_weights, preference_no_purchase, arrival_probabilities,\\n revenues)\\n temp = {}\\n for i in resources:\\n temp[i] = quantity_i(tuple(offer_array), preference_weights, preference_no_purchase,\\n arrival_probabilities, i, A)\\n Q[index] = temp\\n\\n try:\\n m = Model()\\n\\n # Variables\\n mt = m.addVars(offer_sets.index.values, name=\\\"t\\\", lb=0.0) # last constraint\\n\\n # Objective Function\\n m.setObjective(quicksum(R[s] * mt[s] for s in offer_sets.index.values), GRB.MAXIMIZE)\\n\\n mc = {}\\n # Constraints\\n for i in resources:\\n mc[i] = m.addConstr(quicksum(Q[s][i] * mt[s] for s in offer_sets.index.values), GRB.LESS_EQUAL,\\n capacities[i],\\n name=\\\"constraintOnResource\\\")\\n msigma = m.addConstr(quicksum(mt[s] for s in offer_sets.index.values), GRB.EQUAL, T)\\n\\n if silent:\\n m.setParam(\\\"OutputFlag\\\", 0)\\n\\n m.optimize()\\n\\n ret = {}\\n # have to get to index to reproduce the original offerset\\n pat = r\\\".*?\\\\[(.*)\\\\].*\\\"\\n for v in m.getVars():\\n if v.X > 0:\\n match = re.search(pat, v.VarName)\\n erg_index = match.group(1)\\n\\n ret[int(erg_index)] = (tuple(offer_sets.loc[int(erg_index)]), v.X)\\n\\n dualPi = np.zeros_like(resources, dtype=float)\\n for i in resources:\\n dualPi[i] = mc[i].pi\\n dualSigma = msigma.pi\\n\\n valOpt = m.objVal\\n\\n return ret, valOpt, dualPi, dualSigma\\n\\n except GurobiError:\\n print('Error reported')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"909028efb1b594b2703d25688e94003a\",\n \"score\": \"0.5040765\",\n \"text\": \"def run(self):\\n\\n self.logger.info('Started!')\\n\\n # Get inputs\\n input_uids = self._data_helper.input_uids()\\n assert input_uids, 'Error! No input arguments!'\\n\\n # Get outputs\\n output_uids = self._data_helper.output_uids()\\n assert output_uids, 'Error! No output arguments!'\\n\\n # Calculate percentiles\\n out_uid = 0\\n for in_uid in input_uids:\\n percentile = self._calc_percentile(in_uid)\\n for threshold, data in percentile['data'].items():\\n self._data_helper.put(output_uids[out_uid], values=data, level=str(threshold),\\n segment=percentile['@base_period'],\\n longitudes=percentile['@longitude_grid'], latitudes=percentile['@latitude_grid'],\\n times=percentile['@day_grid'], fill_value=percentile['@fill_value'],\\n meta=percentile['meta'])\\n out_uid += 1\\n\\n self.logger.info('Finished!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9acace05dae16c64f6b7673c56eb83\",\n \"score\": \"0.50396425\",\n \"text\": \"def run_central_solution(K, data, ene_cap, ene_init, power_ch, power_dis, eff_ch, eff_dis, self_disch, dt):\\n print(\\\"Running Central Opt Solution\\\")\\n\\n prices_round = data.round(2)\\n len_opt = K\\n p_ch = cp.Variable(len_opt, name='p_ch', nonneg=True) # EV Charging power\\n p_dis = cp.Variable(len_opt, name='p_dis', nonneg=True) # EV Discharging power\\n ene = cp.Variable(len_opt + 1, name='ene', nonneg=True) # ESS Energy\\n constraints = []\\n constraints += [ene <= np.tile(ene_cap, len_opt + 1)] # energy upper limit\\n constraints += [ene >= np.tile(0, len_opt + 1)] # energy lower limit\\n constraints += [p_ch <= np.tile(power_ch, len_opt)] # power upper limit\\n constraints += [p_ch >= np.tile(0, len_opt)] # power lower limit\\n constraints += [p_dis <= np.tile(power_dis, len_opt)] # power upper limit\\n constraints += [p_dis >= np.tile(0, len_opt)] # power lower limit\\n constraints += [ene[len_opt] == ene_init] # ending\\n constraints += [ene[0] == ene_init] # ending\\n for k in range(len_opt):\\n constraints += [ene[k + 1] == ene[k] + (p_ch[k] - p_dis[k]) * dt - self_disch/100*ene[k]]\\n\\n costVector = cp.multiply(p_ch, prices_round[0:len_opt]) * dt * 1 / eff_ch - cp.multiply(p_dis, prices_round[0:len_opt]) * dt * eff_dis\\n obj = cp.Minimize(cp.sum(costVector))\\n\\n # Form and solve problem using Gurobi\\n prob = cp.Problem(obj, constraints)\\n # start = time.time()\\n prob.solve(verbose=False, solver=cp.GUROBI)\\n # end = time.time()\\n # print(end - start)\\n #print(\\\"status:\\\", prob.status)\\n #print(\\\"optimal value\\\", prob.value)\\n\\n # ensure ESS is not charging and discharging at the same time\\n assert all(p_ch.value * p_dis.value == 0)\\n assert prob.status == cp.OPTIMAL\\n\\n log_central = pd.DataFrame({'LMP': data.values, 'Energy': ene.value[1:K+1], 'Power': p_ch.value - p_dis.value,\\n 'cumul_prof': -np.cumsum(costVector.value)}, index=data.index)\\n\\n return log_central, prob.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7e3eb7c5285129f521e4bfc4be91bc0\",\n \"score\": \"0.5039089\",\n \"text\": \"def _save_collection_overview(cname, opnames, data):\\n operations = config[\\\"campaigns\\\"][cname][\\\"operations\\\"]\\n for opname in opnames:\\n primary = sum(count for d in data[opname].values()\\n for (count, _) in d.values())\\n show_isk = operations[opname].get(\\\"isk\\\", True)\\n if show_isk:\\n secondary = sum(value for d in data[opname].values()\\n for (_, value) in d.values())\\n else:\\n secondary = None\\n _save_operation(cname, opname, primary, secondary)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7adce0e603cc12f9a74a69597a0f1c3f\",\n \"score\": \"0.50368994\",\n \"text\": \"def processor(self):\\n\\n self.load_preferences()\\n\\n self.load_data()\\n\\n self.platforms = pd.unique(self.df['Destination platform']).tolist()\\n self.cryptos = pd.unique(self.df['Destination currency']).tolist()\\n\\n # Keep the execution order\\n self.calculate_fee()\\n self.calculate_gain()\\n self.calculate_equivalents()\\n self.perf_by_topic()\\n\\n renderData = {\\n 'data': self.df.to_dict(),\\n 'fee': self.fee,\\n 'gain': self.gain,\\n 'eq_perf': self.eq_perf,\\n 'perf_topic': self.perf_topic,\\n }\\n\\n pprint(renderData)\\n\\n jsonData = json.dumps(renderData, indent=4)\\n with open(\\\"output/renderData.json\\\",\\\"w\\\") as f:\\n f.write(jsonData)\\n\\n return renderData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1b87ed1594f768170d0177cc4e6951\",\n \"score\": \"0.5032212\",\n \"text\": \"def tasks_ro_ops(self):\\n config = get_json_config(self.file_name)\\n self.get_data = {}\\n self.get_calls = []\\n self.get_call_name = {}\\n self.delete_dict = {}\\n self.execute_locust(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf825f384ae9d5fb7b8ae1d332aef85\",\n \"score\": \"0.50262773\",\n \"text\": \"def main(self):\\n for db in self.databases:\\n database = Database(db)\\n logger.debug(database)\\n queue_length = database.queue_length.get(self.options.queue)\\n if queue_length >= self.options.critical:\\n self.add_critical_result(database)\\n elif queue_length >= self.options.warning:\\n self.add_warning_result(database)\\n\\n # Perfdata\\n self.perfdata.append(\\n '\\\\'{datasource}_{queue}\\\\'={value};{warn};{crit};0;'.format(\\n datasource=database.name,\\n queue=self.options.queue,\\n value=queue_length,\\n warn=self.options.warning,\\n crit=self.options.critical))\\n\\n # Exit\\n self._prepare_output(problem_pattern='{num} {queue} queues length '\\n 'more than {thr} !',\\n ok_pattern='All database {queue} queues are '\\n 'normal.')\\n if self.have_criticals:\\n self.critical(self.output())\\n elif self.have_warnings:\\n self.warning(self.output())\\n else:\\n self.ok(self.output())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f5476c67cc2828b5f6f6e7b0bb7a27\",\n \"score\": \"0.5018137\",\n \"text\": \"def __init__(self, cost_map):\\n self.cost_map = cost_map\\n self.node_grid = NodeGrid(cost_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c9711ecda4d710090b17abc08507d8\",\n \"score\": \"0.50105363\",\n \"text\": \"def consumption_production():\\n day_data_set = []\\n # Wind\\n data = read_data.read_csv((\\\"/home/dutchman/Daten/2013_energy_feed_in/\\\"\\n \\\"Windenergie_Hochrechnung_2013.csv\\\"),\\n skip=5)\\n day_data = daily_production.get_one_day(data, sys.argv[1],\\n date_pos=0, data_pos=3,\\n mult=1000)\\n day_data_set.append(day_data)\\n\\n # Solar\\n data = read_data.read_csv((\\\"/home/dutchman/Daten/2013_energy_feed_in/\\\"\\n \\\"Solarenergie_Hochrechnung_2013.csv\\\"),\\n skip=5)\\n day_data = daily_production.get_one_day(data, sys.argv[1], date_pos=0,\\n data_pos=3)\\n day_data_set.append(day_data)\\n\\n # Renewable\\n day_data = day_data_set[0] + day_data_set[1]\\n R = day_data\\n day_data_set.append(day_data)\\n\\n # Household \\n\\n filename = (\\\"/home/dutchman/Daten/debs_challenge_2014/\\\"\\n \\\"debs_0_0.csv\\\")\\n nofhouseholds = 10000\\n\\n total = np.zeros(97)\\n devices = accumulate.accumulate_device(filename)\\n for device_id in devices.keys():\\n devices[device_id] = devices[device_id] / (1000000) * nofhouseholds\\n total += devices[device_id]\\n\\n diff = target_function(day_data_set[2], total)\\n print(\\\"||R-C|| = \\\", diff)\\n\\n\\n # optimization\\n start = -50\\n maxrange = 50\\n stepwidth = 1\\n minval = target_function(R, total)\\n best_step1 = 0\\n best_step2 = 0\\n\\n for step1 in xrange(start, maxrange, stepwidth):\\n for step2 in xrange(start, maxrange, stepwidth):\\n oc1 = super_simple_optimization( devices[7], step=step1)\\n oc2 = super_simple_optimization( devices[11], step=step2)\\n\\n total = np.zeros(97)\\n for device_id in devices.keys():\\n if device_id == 7:\\n total += oc1\\n\\n elif device_id == 11:\\n total += oc2\\n\\n else:\\n total += devices[device_id]\\n\\n val = target_function(R, total)\\n\\n print(\\\"%d %d %f\\\"%(step1, step2, val))\\n\\n if val < minval:\\n minval = val\\n best_step1 = step1\\n best_step2 = step2\\n\\n print(best_step1, best_step2, minval)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44031ea1707bb0ed493a7708f21afb6c\",\n \"score\": \"0.5006965\",\n \"text\": \"def _compute_cost_total(self):\\n for route in self:\\n cost_total = 0.0\\n if route.service_ids:\\n cost_total = sum(route.service_ids.mapped('cost_total'))\\n route.cost_total = cost_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc80edc914e09059b8785073e5ea37b6\",\n \"score\": \"0.50055057\",\n \"text\": \"def calc_objectives(self):\\n if self.debug_wait:\\n time.sleep(self.debug_wait_time)\\n \\n self._entry_name = self.get_design_name(self.current_design_variables)\\n\\n self.current_objectives, self.current_constraints = self.problem.evaluate(self.current_design_variables)\\n \\n self._entry = {'design variables': self.current_design_variables, \\n 'objective functions': self.current_objectives,\\n 'constraints': self.current_constraints}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34295ef183d45cf3e8f76836fba260c\",\n \"score\": \"0.49993053\",\n \"text\": \"def main():\\n\\n args = parser.parse_args()\\n rnaseq = args.rnaseq\\n sra_file = args.sra_file\\n dcc_manifest = args.dcc_manifest\\n gdc_manifest = args.gdc_manifest\\n metadata_dest = args.metadata_dest\\n db_dest = args.db_dest\\n\\n Log.logger.info(\\\"Downloading Data\\\")\\n if dcc_manifest is not None:\\n Downloader.download_icgc_project_metadata(Helper.get_icgc_project_list(dcc_manifest), metadata_dest)\\n Helper.gunzip_files(os.path.join(metadata_dest, \\\"icgc\\\"))\\n else:\\n Log.logger.info(\\\"No ICGC DCC manifest file provided or Metadata already downloaded. SKIPPING\\\")\\n\\n if sra_file is not None:\\n Downloader.download_sra_metadata(Helper.get_sra_accession_list(sra_file), metadata_dest)\\n else:\\n Log.logger.info(\\\"No SRA accession list provided or Metadata already downloaded. SKIPPING\\\")\\n\\n if gdc_manifest is not None:\\n Downloader.download_gdc_file_metadata(Helper.get_gdc_filename_list(gdc_manifest), metadata_dest)\\n else:\\n Log.logger.info(\\\"No GDC manifest provided or Metadata already downloaded. SKIPPING\\\")\\n\\n Downloader.download_ensembl_metadata(metadata_dest)\\n Downloader.download_hgnc(metadata_dest)\\n\\n Log.logger.info(\\\"Creating ProjectLayer csv files\\\")\\n start_project_layer = time.time()\\n project_db = DataProcessor.create_project_layer_tables(\\n DataProcessor.concat_sra_meta(os.path.join(metadata_dest, \\\"sra\\\"), Helper.get_sra_accession_list(sra_file)),\\n DataProcessor.concat_icgc_meta(metadata_dest, dcc_manifest),\\n DataProcessor.concat_gdc_meta(os.path.join(metadata_dest, 'gdc/')),\\n db_dest)\\n end_project_layer = time.time() - start_project_layer\\n Log.logger.info(\\\"Creating ProjectLayer took {} seconds\\\".format(time.time() - start_project_layer))\\n\\n Log.logger.info(\\\"Creating ExpressionLayer csv files\\\")\\n raw_count_df = DataProcessor.create_countinfo_raw_count_table(project_db[3], rnaseq, db_dest)\\n\\n Log.logger.info(\\\"Starting to create expression table. This might take a while.\\\")\\n start = time.time()\\n expression_df = dd.concat(DataProcessor.stringtie_results_out(os.path.join(rnaseq, 'results/stringtieFPKM/')))\\n DataProcessor.create_expression_table(expression_df,\\n raw_count_df,\\n db_dest)\\n Log.logger.info(\\\"Expression table created in {} seconds\\\".format(time.time() - start))\\n DataProcessor.create_gene_table(DataProcessor.process_ensembl_metadata(metadata_dest),\\n DataProcessor.process_hgnc_metadata(metadata_dest),\\n expression_df,\\n db_dest)\\n\\n DataProcessor.create_pipeline_table(rnaseq, db_dest)\\n\\n SQLscripts.prepare_populate_script(db_dest)\\n\\n Log.logger.info(\\\"project layer {} -- \\\".format(end_project_layer))\\n Log.logger.info(\\\"Peak memory usage was: {} GB\\\".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024 ** 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182f6fd288d8cbec731908a6d31f34f9\",\n \"score\": \"0.4996449\",\n \"text\": \"def __init__(self):\\n self.collection_name = defect_collection_name\\n self.collection = Collection(db, self.collection_name)\\n #self._gather_defect_names()\\n self._gather_all_mpids()\\n self.to = TasksOperater() \\n self.hgo = HseGapOperater()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94133e50d5caed1ac876fd07c057b56d\",\n \"score\": \"0.49922913\",\n \"text\": \"def main():\\n\\n b_start_hp = 109\\n b_dmg = 8\\n b_arm = 2\\n\\n\\n weapons = {\\n 'dagger' : {\\n 'cost' : 8,\\n 'dmg' : 4\\n },\\n 'shortsword' : {\\n 'cost' : 10,\\n 'dmg' : 5\\n },\\n 'warhammer' : {\\n 'cost' : 25,\\n 'dmg' : 6\\n },\\n 'longsword' : {\\n 'cost' : 40,\\n 'dmg' : 7\\n },\\n 'greataxe' : {\\n 'cost' : 74,\\n 'dmg' : 8\\n }\\n }\\n\\n armors = {\\n 'no_armor' : {\\n 'cost' : 0,\\n 'arm' : 0\\n },\\n 'leather' : {\\n 'cost' : 13,\\n 'arm' : 1\\n },\\n 'chainmail' : {\\n 'cost' : 31,\\n 'arm' : 2\\n },\\n 'splintmail' : {\\n 'cost' : 53,\\n 'arm' : 3\\n },\\n 'bandedmail' : {\\n 'cost' : 75,\\n 'arm' : 4\\n },\\n 'platemail' : {\\n 'cost' : 102,\\n 'arm' : 5\\n }\\n }\\n\\n rings = {\\n 'no_ring' : {\\n 'cost' : 0,\\n 'points' : 0\\n },\\n 'dmg_1' : {\\n 'cost' : 25,\\n 'points' : 1\\n },\\n 'dmg_2' : {\\n 'cost' : 50,\\n 'points' : 2\\n },\\n 'dmg_3' : {\\n 'cost' : 100,\\n 'points' : 3\\n },\\n 'def_1' : {\\n 'cost' : 20,\\n 'points' : -1\\n },\\n 'def_2' : {\\n 'cost' : 40,\\n 'points' : -2\\n },\\n 'def_3' : {\\n 'cost' : 80,\\n 'points' : -3\\n }\\n }\\n\\n worst_loadout = []\\n worst_price = 0\\n for weapon in weapons:\\n for armor in armors:\\n for ring1 in rings:\\n for ring2 in rings:\\n if ring2 == ring1:\\n continue\\n price = weapons[weapon]['cost'] + \\\\\\n armors[armor]['cost'] + \\\\\\n rings[ring1]['cost'] + \\\\\\n rings[ring2]['cost']\\n\\n player = {\\n 'hp' : 100,\\n 'dmg' : weapons[weapon]['dmg'],\\n 'arm' : armors[armor]['arm']\\n }\\n\\n if rings[ring1]['points'] > 0:\\n player['dmg'] += rings[ring1]['points']\\n else:\\n player['arm'] -= rings[ring1]['points']\\n\\n if rings[ring2]['points'] > 0:\\n player['dmg'] += rings[ring2]['points']\\n else:\\n player['arm'] -= rings[ring2]['points']\\n\\n b_hp = b_start_hp\\n while player['hp'] > 0:\\n b_hp -= max(1, (player['dmg']-b_arm))\\n\\n if b_hp <= 0:\\n break\\n\\n player['hp'] -= max(1, (b_dmg-player['arm']))\\n\\n if player['hp'] <= 0 and price >= worst_price:\\n print 'player', player['hp'], 'boss', b_hp\\n worst_loadout = [weapon, armor, ring1, ring2]\\n worst_price = price\\n print worst_price, worst_loadout\\n\\n print '*' * 30\\n print worst_price, worst_loadout\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994396,"cells":{"query_id":{"kind":"string","value":"982bead9450d57c40ef7b3fd8fad98e4"},"query":{"kind":"string","value":"Returns the operation's result data set, which is always an iterable. The difference with L{working_set} is that it returns the data B{after} all filters and ordering (not slicing) are applied."},"positive_passages":{"kind":"list like","value":[{"docid":"0e86eb69c7757314b480957eb0cbb11f","score":"0.0","text":"def data_set(self, request, *args, **kwargs):\n data = self.working_set(request, *args, **kwargs)\n\n filters = self.filters or {}\n if filters:\n for name, definition in filters.iteritems():\n values = request.GET.getlist(name)\n if values:\n try:\n data = self.filter_data(request, data, definition, values)\n except ValueError:\n # Happens when giving invalid filter type data, like\n # for example providing a string instead of integer.\n raise UnprocessableEntity('Invalid filter data')\n\n order = request.GET.getlist(self.order)\n if order:\n data = self.order_data(data, *order)\n\n return data","title":""}],"string":"[\n {\n \"docid\": \"0e86eb69c7757314b480957eb0cbb11f\",\n \"score\": \"0.0\",\n \"text\": \"def data_set(self, request, *args, **kwargs):\\n data = self.working_set(request, *args, **kwargs)\\n\\n filters = self.filters or {}\\n if filters:\\n for name, definition in filters.iteritems():\\n values = request.GET.getlist(name)\\n if values:\\n try:\\n data = self.filter_data(request, data, definition, values)\\n except ValueError:\\n # Happens when giving invalid filter type data, like\\n # for example providing a string instead of integer.\\n raise UnprocessableEntity('Invalid filter data')\\n\\n order = request.GET.getlist(self.order)\\n if order:\\n data = self.order_data(data, *order)\\n\\n return data\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ef49803b3e27b1fec23e275587dea74f","score":"0.70770305","text":"def result_set(self):\n if self.status != ExecutionStatus.RUNNING and self._result_set is None:\n response = self._temboo_session.get_content(self.get_session_path(), {'view':'outputs'})\n self._result_set = self._make_result_set(response, self._temboo_path)\n\n return self._result_set","title":""},{"docid":"c474eef76859dd5cf6d193795423d4ad","score":"0.6688541","text":"def get_all(self):\r\n return self.data_set","title":""},{"docid":"650ed0d282a2f09395be505345c31ddb","score":"0.6386779","text":"def result(self) -> List[OperationResult]:\n return OperationResult.from_operation_group(self.preapply())","title":""},{"docid":"55863d89b1ec819548ae55c6e80eb6a4","score":"0.63244617","text":"def results(self, use_cache=True):\n if not use_cache or (self._results is None):\n self.execute(use_cache=use_cache)\n return self._results.results","title":""},{"docid":"acdb4025310f9781f8192486fa6f0d1e","score":"0.61623484","text":"def get_results(self):\n return self.__results","title":""},{"docid":"ed18f599e459c70693a94b648d6894ef","score":"0.612515","text":"def get_results(self, stored=False):\n\n ret = []\n if self.cursor is None:\n return None\n\n if not stored:\n row = self.cursor.fetchone()\n while row is not None:\n ret.append(row)\n row = self.cursor.fetchone()\n\n else:\n if self.results_tup:\n ret.append(self.results_tup)\n else:\n for result in self.cursor.stored_results():\n ret.extend(result.fetchall())\n return ret","title":""},{"docid":"ef7a9638880145e78eb012f62f8fba1c","score":"0.6114545","text":"def get_data(self) -> list:\n return self.result","title":""},{"docid":"5955442971e0078bd041c8e8afbf7aa5","score":"0.6096633","text":"def getResults(self):\n return []","title":""},{"docid":"aa821180d6edb2d40fbd117ee4e5c034","score":"0.60365593","text":"def get_data_set(self):\n result = None\n for data_set in self._data_sets:\n if result is None:\n result = data_set\n continue\n if len(data_set.column_names) != len(result.column_names):\n raise RuntimeError('Multi DataSets are different col size')\n result.rows.extend(data_set.rows)\n\n return result","title":""},{"docid":"db816eac5d301713fe12f025ebc312db","score":"0.5979883","text":"def getResults(self):\r\n return self.Results","title":""},{"docid":"2072535d94fc5b7400e97c428e852862","score":"0.59362626","text":"def getResults(self):\n return self.results","title":""},{"docid":"59bb9c99abbf769923cd4001e1a8b4f8","score":"0.59169096","text":"def result(self):\n reset_graph()\n return self.data","title":""},{"docid":"d0f4531231c24b9604747522e6265945","score":"0.5894283","text":"def getResult(self, query_id, wf_id = \"\"):\n result_set = self._getResult(query_id, wf_id=wf_id)\n return result_set","title":""},{"docid":"0a167634d549d8f10e59c92129e68092","score":"0.58920753","text":"def iterator(self):\n return iter(self.execute())","title":""},{"docid":"bd1454c0df5c8535511fedbe812b397b","score":"0.58747673","text":"def __iter__(self):\n if self._cursor:\n return self\n else:\n if not self._cached_result:\n raise QueryResultException('requested data was not cached')\n return iter(self._cached_result)","title":""},{"docid":"c5524b4f4f5463f0c9850a48978f7705","score":"0.5841234","text":"def __iter__(self):\n return self._data()","title":""},{"docid":"b20319756282f518cb9093113824eda5","score":"0.5831365","text":"def get_results(self):\n pass","title":""},{"docid":"3c611b6e35d196c322d8747512d6e6b8","score":"0.5796339","text":"def getResult(self) -> any:\n\n return self.result","title":""},{"docid":"2f0b16c71c0b144001d973042238a7ec","score":"0.57795286","text":"def nextset(self):\n if not self._resultsets:\n self._header = None\n self._records = None\n self.rowcount = -1\n return None\n stuff = {}\n return self._nextset(stuff)","title":""},{"docid":"1b3cc36e12a3843a1a0d0e4191f184fe","score":"0.5770195","text":"def results(self):\n return self._results","title":""},{"docid":"1b3cc36e12a3843a1a0d0e4191f184fe","score":"0.5770195","text":"def results(self):\n return self._results","title":""},{"docid":"1b3cc36e12a3843a1a0d0e4191f184fe","score":"0.5770195","text":"def results(self):\n return self._results","title":""},{"docid":"9c2aa323879af86a8ab9c4d69fea3e1a","score":"0.5765634","text":"def result(self) -> Union[List[Dict[str, Any]], Dict[str, Any]]:\n if self.status == \"FAILED\":\n raise ValueError(f\"Job failed. Errors : {self.errors}\")\n else:\n result = self._fetch_remote_json()\n if self.type == 'export-data-rows':\n return result\n\n return [{\n 'id': data_row['id'],\n 'external_id': data_row.get('externalId'),\n 'row_data': data_row['rowData'],\n 'global_key': data_row.get('globalKey'),\n } for data_row in result['createdDataRows']]","title":""},{"docid":"224b5e15f48af7f80fd9dfce04017647","score":"0.5739327","text":"def get_data(self):\n\t\treturn self.__result","title":""},{"docid":"153e4d26a0c93132ac85a5343ddea034","score":"0.5732269","text":"def _extract(self):\n return pd.DataFrame(list(fetch_all()))","title":""},{"docid":"102d52747597df1e9d34b7b15247e764","score":"0.5689135","text":"def get_query_dataset(self):\n return self.query_dataset","title":""},{"docid":"ffa0eb1aedb96cb01b7568ef7e94c8fe","score":"0.56709754","text":"def getQueryResults(self):\n self.__condition.acquire()\n try:\n return [] + self.__results\n finally:\n self.__condition.release()","title":""},{"docid":"5fd9d23d8a0a83ea7b880e2def2b8380","score":"0.5654766","text":"def results(self, **kwargs):\n # Extra filter\n contentFilter = dict(self.request.get('contentFilter', {}))\n contentFilter.update(kwargs.get('contentFilter', {}))\n kwargs.setdefault('custom_query', contentFilter)\n kwargs.setdefault('batch', True)\n kwargs.setdefault('b_size', self.b_size)\n kwargs.setdefault('b_start', self.b_start)\n\n results = self.collection_behavior.results(**kwargs)\n return results","title":""},{"docid":"7dd2352d42ef7fec3c12cd636f568a4d","score":"0.56425834","text":"def get_rows(self):\n result, self.parsed_rows = self.parsed_rows, []\n return result","title":""},{"docid":"e498599d124722bdf2c5cb6763d9305e","score":"0.5640295","text":"def get_data(self):\n if self._uses_data:\n try:\n return self._all_data\n except AttributeError:\n self.rebuild_data()\n return self._all_data\n else:\n raise RuntimeError(\n f\"ContextManager.get_data was called, but this context manager is \"\n f\"not in data mode.\"\n )","title":""},{"docid":"160ab3b36608986b75718c707005eba7","score":"0.5637499","text":"def produce(self, data_set: DataSet) -> DataSet:\n return data_set","title":""},{"docid":"2829736ed117e6ed5d9812b42ea34989","score":"0.5631113","text":"def _build_result_set(self):\n return [self.table(**dict(zip(self.table_map, item))) for item in self.raw_result]","title":""},{"docid":"9cb8ff5bdd1e1f05f1b311460b90bfd0","score":"0.56240195","text":"def get_rows(self):\n return self.rows","title":""},{"docid":"1568d04db0e60d246b4b1d4421aefa3f","score":"0.5614452","text":"def get_query_set(self):\n if self.queryset:\n return self.queryset._clone()\n elif self.model:\n return self.model._default_manager.all()","title":""},{"docid":"feaca71c2529f33286a48f80e57ca309","score":"0.56080633","text":"def operation_list(self):\n return self._operation_list","title":""},{"docid":"bda3cfb2f2bc702bb455aad78f2aa383","score":"0.55940574","text":"def list_all():\n return get_dataset().copy()","title":""},{"docid":"37c3254db7dfc91a82e6fe9467b0dcdb","score":"0.5582492","text":"def fetchall(self):\n raise NotImplementedError","title":""},{"docid":"fd76248257740b49040f5ab69e23dc04","score":"0.5573997","text":"def result(self, data):\n return data","title":""},{"docid":"a92e7f9b47630885059e4f7c21c8088e","score":"0.5572655","text":"def get_data():\n all_data = data_refined_query(request.args)\n data = [d.full_json() for d in all_data]\n return QuerySetApiResult(data, 'json')","title":""},{"docid":"16bcbad95820ad3af9622e6a2365795c","score":"0.5566674","text":"def get_results_df(self) -> Mapping[BaseEntity, Tuple]:\n return pickle.loads(self.result)","title":""},{"docid":"cae9ad74b1b3bdf6b767e8f9310e1e7f","score":"0.5566325","text":"def getResult(self):\n return self._out_pars","title":""},{"docid":"314577f87f8cffa64440b2cb63584442","score":"0.55632687","text":"def __iter__(self):\n query_results = self.sql_engine.execute(self.query)\n result_set = query_results.fetchmany(self.chunksize)\n while result_set:\n for row in result_set:\n yield row\n result_set = query_results.fetchmany(self.chunksize)","title":""},{"docid":"d704b393f0d422d38ad8fb9b67921303","score":"0.5558822","text":"def __iter__(self):\n for result in self.results:\n yield result","title":""},{"docid":"59376d5723c6c6ca3cc2d9793e710c3b","score":"0.55457383","text":"def __iter__(self):\n return iter(self.cursor)","title":""},{"docid":"8394445616975176dc857e0f04f5eb7f","score":"0.55307704","text":"def get_rows(self):\n return self._rows","title":""},{"docid":"4a2f56fdf6567930027ad8dffd98807b","score":"0.5522482","text":"def fetchall(self):\n return self.cursor.fetchall()","title":""},{"docid":"18f2ef22a492715047359793f6a02368","score":"0.5522242","text":"def _simulate_list_functions_single_data(self):\n results = []\n for item in self._process_units:\n result_set = ResultSet(item.__name__, self._pool.map_async(\n item, self._process_data).get())\n results.append(result_set)\n\n return results","title":""},{"docid":"ffc396c381080fa173edebbe8f9c2b4c","score":"0.55198115","text":"def get_partial_results(self) -> Optional[Dict[str, Any]]:\n return self.kusto_response","title":""},{"docid":"9eaf827ce11d24138bfb098edda6314b","score":"0.5519431","text":"def Results(self):\r\n if self._results is None or len(self._results) == 0:\r\n return None\r\n return self._results","title":""},{"docid":"1921008e62eca739b229869e69f5211a","score":"0.5511051","text":"def results(self):\n data_mgr = ITileDataManager(self)\n data = data_mgr.get()\n return data","title":""},{"docid":"070d53e00a7199f59c2d333afe244339","score":"0.5501539","text":"def rows(self):\n return self._engine.rows","title":""},{"docid":"e99ec688261a4aaf3248b2a3f4e70cdf","score":"0.5484039","text":"def __iter__(self):\n return self._out","title":""},{"docid":"42586e8ba165932b76dc6709d968b397","score":"0.5470763","text":"def get_data_set_wrapper(self):\n result = None\n for data_set in self._data_sets:\n if result is None:\n result = data_set\n continue\n if len(data_set.column_names) != len(result.column_names):\n raise RuntimeError('Multi DataSets are different col size')\n result.rows.extend(data_set.rows)\n\n if result is None:\n return None\n return DataSetWrapper(result, self._decode_type)","title":""},{"docid":"ef4b08ae726aa3df9bf07133994437d1","score":"0.5467488","text":"def __iter__(self):\n return QueryIterator(self)","title":""},{"docid":"c82d0c160351458b7eb0ebc3aa3d7a47","score":"0.5461698","text":"def create_resultSet(self, session, rset=None):\n raise NotImplementedError","title":""},{"docid":"5d5035a1d3b9cfebbed70f2454dea610","score":"0.5460505","text":"def _get_resultdata(self):\n resultdata = keys = self.data['query'][self.resultkey]\n if isinstance(resultdata, dict):\n keys = list(resultdata)\n if 'results' in resultdata:\n resultdata = resultdata['results']\n elif 'pageids' in self.data['query']:\n # this ensures that page data will be iterated\n # in the same order as received from server\n resultdata = [resultdata[k]\n for k in self.data['query']['pageids']]\n else:\n resultdata = [resultdata[k]\n for k in sorted(resultdata)]\n pywikibot.debug(\n f'{type(self).__name__} received {keys}; limit={self.limit}')\n return resultdata","title":""},{"docid":"49ac2aae5ff239fe7635e7334f8e7adc","score":"0.5458381","text":"def __iter__(self):\n query = self.query and {'$and': self.query} or {}\n if self.sorting:\n return self.collection.find(query, self.projection).sort(self.sorting)\n\n return self.collection.find(query, self.projection)","title":""},{"docid":"6a9a43b6a84075ae1274291c960b8647","score":"0.54510725","text":"def get_datarecords(self):\n try:\n return set_models.Dataset.objects.all()\n except:\n raise","title":""},{"docid":"6a9a43b6a84075ae1274291c960b8647","score":"0.54510725","text":"def get_datarecords(self):\n try:\n return set_models.Dataset.objects.all()\n except:\n raise","title":""},{"docid":"e9ddc2292991ebd1959f54feb491d1d9","score":"0.54485184","text":"def operation_list(self):\n return None","title":""},{"docid":"9f96d6b99976b140b7e405eba0c7c461","score":"0.54449105","text":"def get_dataframe(self):\n return self.dataframe_all","title":""},{"docid":"a686eb0b23f20c9ac333a33a96d050d1","score":"0.54440224","text":"def get_sets(self):\n return self.set.filter(workout=self)","title":""},{"docid":"8ca29d86f6a71e20b7307d49dbe1c110","score":"0.5443771","text":"def get(self):\n return {\"results\": self.listAll()}","title":""},{"docid":"364c70ea628e7c84592fbb71a0faef5c","score":"0.54419374","text":"def fetchall(self):\n result = []\n while True:\n one = yield self.fetchone()\n if one is None:\n break\n else:\n result.append(one)\n raise Return(result)","title":""},{"docid":"5deb1884cbb33e5e76617af63d11b785","score":"0.5433278","text":"def result(self):\r\n raise NotImplementedError","title":""},{"docid":"cda3c80c69072b68ac3189ed93f1d9b1","score":"0.5427908","text":"def __call__(self):\n dataset = self\n return dataset","title":""},{"docid":"0c6d90dabc0e7c29ff42e9f25f8db9f9","score":"0.54218066","text":"def __iter__(self):\n return itertools.chain(self.stores, self.loads, self.mutates)","title":""},{"docid":"c4d50b46fc973537ef7c4df983bc82ed","score":"0.54119897","text":"def fetch(self, hint=None):\n queries = add_hints(self.sql, hint)\n\n max_rows_returned, data = fetch_data(self.dataset.database, queries, self.dimensions)\n return self._transform_for_return(data, max_rows_returned=max_rows_returned)","title":""},{"docid":"3f52f0b24b76085dfe87f0c677a099a9","score":"0.5411625","text":"def __iter__(self):\n return self._iterate_columns('id', 'curr_exchange', 'data')","title":""},{"docid":"04eb4d00c002cea1166a8cba3ae71d7e","score":"0.5410862","text":"def get_result(self):\n raise NotImplementedError()","title":""},{"docid":"8c1c9257296f70b05b204e8fc7dcaac9","score":"0.5407166","text":"def __iter__(self):\n return iter(self._q[:])","title":""},{"docid":"65d34ea31097a2bfc2cf1d3378c56d8d","score":"0.5403488","text":"def raw_results(self):\n if self._raw_results is None:\n self._raw_results = pd.read_csv(self.results_path)\n \n return self._raw_results","title":""},{"docid":"d8a7d89ef243c54ae3aa80ef0bc07438","score":"0.54002124","text":"def __iter__(self):\n return iter(self._data)","title":""},{"docid":"b52f20f7fd9d4d23316229cac3785e6c","score":"0.53972673","text":"def results(self, process, temp_dir, filename):\n return ResultSet(temp_dir, filename)","title":""},{"docid":"9f9b1b38850b85cf8211ae304c359757","score":"0.53824264","text":"def __iter__(self):\n\n if not self.liveSet:\n self.refresh_liveSet()\n\n return self.liveSet.__iter__()","title":""},{"docid":"827db4319c58af2725644b91c5115bef","score":"0.5378629","text":"def fetchall(self):\n funcs = tuple(_fetch_funcs(self._rs))\n return tuple(\n tuple(_fetch_row(self._rs, funcs))\n for _ in self._rs\n )","title":""},{"docid":"7a4f88478263ce09219199a77ab600df","score":"0.5375595","text":"def get_results(self):\n return dict(self.return_values)","title":""},{"docid":"cd2e01f14c652fea6907a4515a6ccdb1","score":"0.53740263","text":"def fetchall(self):\n return self._cursor.fetchall()","title":""},{"docid":"05c9ea367b4b61cfe9775d79a0bb664f","score":"0.53710765","text":"def iterable(self):\n return []","title":""},{"docid":"6f4ec492e372a12406abb3b47eec9c64","score":"0.5361873","text":"def get_queries(self):\r\n return self.queries","title":""},{"docid":"fb94d8f6f3866891074c17104f0d47e9","score":"0.5360864","text":"def get_all(self, stop_condition=lambda x: False):\n while(self.has_next()):\n if stop_condition(self.next()): break\n self.all_data.append(self._current_datum)\n return self.all_data","title":""},{"docid":"58744f870370c569631c53926be81381","score":"0.5354491","text":"def fetch_data(self):\n self.rows = self.cursor.fetchall()","title":""},{"docid":"9bb48ee3e60f002d286e61348ea197d7","score":"0.5352697","text":"def get_all(self):\n raise NotImplemented # pragma: no cover","title":""},{"docid":"d7f16ac8310b89f14368380d49c64251","score":"0.5347104","text":"def get_operations(self):\n self._build_operations()\n return self.operations","title":""},{"docid":"124a30b10d75c0ae9f69c655b04687ca","score":"0.53435117","text":"def raw_data_operations(self): \n try: \n return self.__raw_data.operations\n except: \n return None #No Operations Applied","title":""},{"docid":"0dcf343a755fc0690a483919ddceb222","score":"0.5335114","text":"def get_results(self):\n self.get_content_from_fields()\n self.post_process()\n self.add_content_to_db()\n pprint(self.content)\n return self.content","title":""},{"docid":"00bbf4add57cefd6efd7ec55b18858fa","score":"0.53225285","text":"def get_results(self):","title":""},{"docid":"8c8320dbf19a4e6165928d6e642d0b9b","score":"0.53132147","text":"def get_data_list(self):\n return self.data_list","title":""},{"docid":"09730391ce021d41ba3214e91e13beb0","score":"0.5307436","text":"def getSubset(self):\n return self.subset","title":""},{"docid":"60a5e9ac96cd2ab5cbdce3c0e8d75815","score":"0.53030884","text":"def to_response(self):\n self.ctx[\"datasets\"] = self.execute_op()\n return result_response(DatasetsListCtrl.RESPONSE_SERIALIZER, self.ctx)","title":""},{"docid":"08d726350c9a128e6315eca884f25165","score":"0.5290681","text":"def get_data(self, query: str):\n self.cur.execute(query)\n data = self.cur.fetchall()\n return data","title":""},{"docid":"09856192970776fcbe5b676a77baa50b","score":"0.5290236","text":"def return_all(self):\n return Repository.return_all(self)","title":""},{"docid":"449c052ac67803e8cbb4b82b238b0c8f","score":"0.5288833","text":"def __iter__(self):\n return iter(self.data)","title":""},{"docid":"51bd27f5f5eeb428dac735ab92bdafeb","score":"0.5287143","text":"def get_results(self):\n return self.mct.selection_with_N()","title":""},{"docid":"2bfe8e635a295a9d6d788208b3b4dd22","score":"0.5285504","text":"def _simulate_list_functions_list_data(self):\n results = []\n for counter, item in enumerate(self._process_units):\n result_set = ResultSet(item.__name__, self._pool.map_async(\n item, self._process_data[counter]).get())\n results.append(result_set)\n\n return results","title":""},{"docid":"78d5785f192c2c83381ca320eeca5ce7","score":"0.5281581","text":"def result(self) -> Any:\n raise NotImplementedError()","title":""},{"docid":"78d5785f192c2c83381ca320eeca5ce7","score":"0.5281581","text":"def result(self) -> Any:\n raise NotImplementedError()","title":""},{"docid":"135e75728b57a6a6d56a965d96719862","score":"0.5275679","text":"def data(self):\n return self.df","title":""},{"docid":"17af7a49a21e4d5bc7371708aaf409b9","score":"0.5271991","text":"def query_data(self, query, *args, **kw):\n if kw:\n if args:\n raise TypeError(\"Only positional or keyword arguments\"\n \" may be provided, not both.\")\n args = kw\n cursor = self._connection._storage.ex_cursor()\n try:\n cursor.execute(query, args)\n result = list(cursor)\n finally:\n try:\n cursor.close()\n except Exception:\n pass # whatever :)\n\n return result","title":""},{"docid":"2ccb8cd81502ec4eba9ae125433fe6f8","score":"0.52669233","text":"def batch(self):\n return self.container.batch()","title":""},{"docid":"6c00fb9018167f78b2b791be868537e3","score":"0.5265072","text":"def operations(self):\n return self._operations","title":""}],"string":"[\n {\n \"docid\": \"ef49803b3e27b1fec23e275587dea74f\",\n \"score\": \"0.70770305\",\n \"text\": \"def result_set(self):\\n if self.status != ExecutionStatus.RUNNING and self._result_set is None:\\n response = self._temboo_session.get_content(self.get_session_path(), {'view':'outputs'})\\n self._result_set = self._make_result_set(response, self._temboo_path)\\n\\n return self._result_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c474eef76859dd5cf6d193795423d4ad\",\n \"score\": \"0.6688541\",\n \"text\": \"def get_all(self):\\r\\n return self.data_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650ed0d282a2f09395be505345c31ddb\",\n \"score\": \"0.6386779\",\n \"text\": \"def result(self) -> List[OperationResult]:\\n return OperationResult.from_operation_group(self.preapply())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55863d89b1ec819548ae55c6e80eb6a4\",\n \"score\": \"0.63244617\",\n \"text\": \"def results(self, use_cache=True):\\n if not use_cache or (self._results is None):\\n self.execute(use_cache=use_cache)\\n return self._results.results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acdb4025310f9781f8192486fa6f0d1e\",\n \"score\": \"0.61623484\",\n \"text\": \"def get_results(self):\\n return self.__results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed18f599e459c70693a94b648d6894ef\",\n \"score\": \"0.612515\",\n \"text\": \"def get_results(self, stored=False):\\n\\n ret = []\\n if self.cursor is None:\\n return None\\n\\n if not stored:\\n row = self.cursor.fetchone()\\n while row is not None:\\n ret.append(row)\\n row = self.cursor.fetchone()\\n\\n else:\\n if self.results_tup:\\n ret.append(self.results_tup)\\n else:\\n for result in self.cursor.stored_results():\\n ret.extend(result.fetchall())\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7a9638880145e78eb012f62f8fba1c\",\n \"score\": \"0.6114545\",\n \"text\": \"def get_data(self) -> list:\\n return self.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5955442971e0078bd041c8e8afbf7aa5\",\n \"score\": \"0.6096633\",\n \"text\": \"def getResults(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa821180d6edb2d40fbd117ee4e5c034\",\n \"score\": \"0.60365593\",\n \"text\": \"def get_data_set(self):\\n result = None\\n for data_set in self._data_sets:\\n if result is None:\\n result = data_set\\n continue\\n if len(data_set.column_names) != len(result.column_names):\\n raise RuntimeError('Multi DataSets are different col size')\\n result.rows.extend(data_set.rows)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db816eac5d301713fe12f025ebc312db\",\n \"score\": \"0.5979883\",\n \"text\": \"def getResults(self):\\r\\n return self.Results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2072535d94fc5b7400e97c428e852862\",\n \"score\": \"0.59362626\",\n \"text\": \"def getResults(self):\\n return self.results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59bb9c99abbf769923cd4001e1a8b4f8\",\n \"score\": \"0.59169096\",\n \"text\": \"def result(self):\\n reset_graph()\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f4531231c24b9604747522e6265945\",\n \"score\": \"0.5894283\",\n \"text\": \"def getResult(self, query_id, wf_id = \\\"\\\"):\\n result_set = self._getResult(query_id, wf_id=wf_id)\\n return result_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a167634d549d8f10e59c92129e68092\",\n \"score\": \"0.58920753\",\n \"text\": \"def iterator(self):\\n return iter(self.execute())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1454c0df5c8535511fedbe812b397b\",\n \"score\": \"0.58747673\",\n \"text\": \"def __iter__(self):\\n if self._cursor:\\n return self\\n else:\\n if not self._cached_result:\\n raise QueryResultException('requested data was not cached')\\n return iter(self._cached_result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5524b4f4f5463f0c9850a48978f7705\",\n \"score\": \"0.5841234\",\n \"text\": \"def __iter__(self):\\n return self._data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20319756282f518cb9093113824eda5\",\n \"score\": \"0.5831365\",\n \"text\": \"def get_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c611b6e35d196c322d8747512d6e6b8\",\n \"score\": \"0.5796339\",\n \"text\": \"def getResult(self) -> any:\\n\\n return self.result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f0b16c71c0b144001d973042238a7ec\",\n \"score\": \"0.57795286\",\n \"text\": \"def nextset(self):\\n if not self._resultsets:\\n self._header = None\\n self._records = None\\n self.rowcount = -1\\n return None\\n stuff = {}\\n return self._nextset(stuff)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3cc36e12a3843a1a0d0e4191f184fe\",\n \"score\": \"0.5770195\",\n \"text\": \"def results(self):\\n return self._results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3cc36e12a3843a1a0d0e4191f184fe\",\n \"score\": \"0.5770195\",\n \"text\": \"def results(self):\\n return self._results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3cc36e12a3843a1a0d0e4191f184fe\",\n \"score\": \"0.5770195\",\n \"text\": \"def results(self):\\n return self._results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2aa323879af86a8ab9c4d69fea3e1a\",\n \"score\": \"0.5765634\",\n \"text\": \"def result(self) -> Union[List[Dict[str, Any]], Dict[str, Any]]:\\n if self.status == \\\"FAILED\\\":\\n raise ValueError(f\\\"Job failed. Errors : {self.errors}\\\")\\n else:\\n result = self._fetch_remote_json()\\n if self.type == 'export-data-rows':\\n return result\\n\\n return [{\\n 'id': data_row['id'],\\n 'external_id': data_row.get('externalId'),\\n 'row_data': data_row['rowData'],\\n 'global_key': data_row.get('globalKey'),\\n } for data_row in result['createdDataRows']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"224b5e15f48af7f80fd9dfce04017647\",\n \"score\": \"0.5739327\",\n \"text\": \"def get_data(self):\\n\\t\\treturn self.__result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153e4d26a0c93132ac85a5343ddea034\",\n \"score\": \"0.5732269\",\n \"text\": \"def _extract(self):\\n return pd.DataFrame(list(fetch_all()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102d52747597df1e9d34b7b15247e764\",\n \"score\": \"0.5689135\",\n \"text\": \"def get_query_dataset(self):\\n return self.query_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa0eb1aedb96cb01b7568ef7e94c8fe\",\n \"score\": \"0.56709754\",\n \"text\": \"def getQueryResults(self):\\n self.__condition.acquire()\\n try:\\n return [] + self.__results\\n finally:\\n self.__condition.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd9d23d8a0a83ea7b880e2def2b8380\",\n \"score\": \"0.5654766\",\n \"text\": \"def results(self, **kwargs):\\n # Extra filter\\n contentFilter = dict(self.request.get('contentFilter', {}))\\n contentFilter.update(kwargs.get('contentFilter', {}))\\n kwargs.setdefault('custom_query', contentFilter)\\n kwargs.setdefault('batch', True)\\n kwargs.setdefault('b_size', self.b_size)\\n kwargs.setdefault('b_start', self.b_start)\\n\\n results = self.collection_behavior.results(**kwargs)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd2352d42ef7fec3c12cd636f568a4d\",\n \"score\": \"0.56425834\",\n \"text\": \"def get_rows(self):\\n result, self.parsed_rows = self.parsed_rows, []\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e498599d124722bdf2c5cb6763d9305e\",\n \"score\": \"0.5640295\",\n \"text\": \"def get_data(self):\\n if self._uses_data:\\n try:\\n return self._all_data\\n except AttributeError:\\n self.rebuild_data()\\n return self._all_data\\n else:\\n raise RuntimeError(\\n f\\\"ContextManager.get_data was called, but this context manager is \\\"\\n f\\\"not in data mode.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"160ab3b36608986b75718c707005eba7\",\n \"score\": \"0.5637499\",\n \"text\": \"def produce(self, data_set: DataSet) -> DataSet:\\n return data_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2829736ed117e6ed5d9812b42ea34989\",\n \"score\": \"0.5631113\",\n \"text\": \"def _build_result_set(self):\\n return [self.table(**dict(zip(self.table_map, item))) for item in self.raw_result]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb8ff5bdd1e1f05f1b311460b90bfd0\",\n \"score\": \"0.56240195\",\n \"text\": \"def get_rows(self):\\n return self.rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1568d04db0e60d246b4b1d4421aefa3f\",\n \"score\": \"0.5614452\",\n \"text\": \"def get_query_set(self):\\n if self.queryset:\\n return self.queryset._clone()\\n elif self.model:\\n return self.model._default_manager.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feaca71c2529f33286a48f80e57ca309\",\n \"score\": \"0.56080633\",\n \"text\": \"def operation_list(self):\\n return self._operation_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda3cfb2f2bc702bb455aad78f2aa383\",\n \"score\": \"0.55940574\",\n \"text\": \"def list_all():\\n return get_dataset().copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c3254db7dfc91a82e6fe9467b0dcdb\",\n \"score\": \"0.5582492\",\n \"text\": \"def fetchall(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd76248257740b49040f5ab69e23dc04\",\n \"score\": \"0.5573997\",\n \"text\": \"def result(self, data):\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a92e7f9b47630885059e4f7c21c8088e\",\n \"score\": \"0.5572655\",\n \"text\": \"def get_data():\\n all_data = data_refined_query(request.args)\\n data = [d.full_json() for d in all_data]\\n return QuerySetApiResult(data, 'json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16bcbad95820ad3af9622e6a2365795c\",\n \"score\": \"0.5566674\",\n \"text\": \"def get_results_df(self) -> Mapping[BaseEntity, Tuple]:\\n return pickle.loads(self.result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae9ad74b1b3bdf6b767e8f9310e1e7f\",\n \"score\": \"0.5566325\",\n \"text\": \"def getResult(self):\\n return self._out_pars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"314577f87f8cffa64440b2cb63584442\",\n \"score\": \"0.55632687\",\n \"text\": \"def __iter__(self):\\n query_results = self.sql_engine.execute(self.query)\\n result_set = query_results.fetchmany(self.chunksize)\\n while result_set:\\n for row in result_set:\\n yield row\\n result_set = query_results.fetchmany(self.chunksize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d704b393f0d422d38ad8fb9b67921303\",\n \"score\": \"0.5558822\",\n \"text\": \"def __iter__(self):\\n for result in self.results:\\n yield result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59376d5723c6c6ca3cc2d9793e710c3b\",\n \"score\": \"0.55457383\",\n \"text\": \"def __iter__(self):\\n return iter(self.cursor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8394445616975176dc857e0f04f5eb7f\",\n \"score\": \"0.55307704\",\n \"text\": \"def get_rows(self):\\n return self._rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a2f56fdf6567930027ad8dffd98807b\",\n \"score\": \"0.5522482\",\n \"text\": \"def fetchall(self):\\n return self.cursor.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f2ef22a492715047359793f6a02368\",\n \"score\": \"0.5522242\",\n \"text\": \"def _simulate_list_functions_single_data(self):\\n results = []\\n for item in self._process_units:\\n result_set = ResultSet(item.__name__, self._pool.map_async(\\n item, self._process_data).get())\\n results.append(result_set)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc396c381080fa173edebbe8f9c2b4c\",\n \"score\": \"0.55198115\",\n \"text\": \"def get_partial_results(self) -> Optional[Dict[str, Any]]:\\n return self.kusto_response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eaf827ce11d24138bfb098edda6314b\",\n \"score\": \"0.5519431\",\n \"text\": \"def Results(self):\\r\\n if self._results is None or len(self._results) == 0:\\r\\n return None\\r\\n return self._results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1921008e62eca739b229869e69f5211a\",\n \"score\": \"0.5511051\",\n \"text\": \"def results(self):\\n data_mgr = ITileDataManager(self)\\n data = data_mgr.get()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070d53e00a7199f59c2d333afe244339\",\n \"score\": \"0.5501539\",\n \"text\": \"def rows(self):\\n return self._engine.rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e99ec688261a4aaf3248b2a3f4e70cdf\",\n \"score\": \"0.5484039\",\n \"text\": \"def __iter__(self):\\n return self._out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42586e8ba165932b76dc6709d968b397\",\n \"score\": \"0.5470763\",\n \"text\": \"def get_data_set_wrapper(self):\\n result = None\\n for data_set in self._data_sets:\\n if result is None:\\n result = data_set\\n continue\\n if len(data_set.column_names) != len(result.column_names):\\n raise RuntimeError('Multi DataSets are different col size')\\n result.rows.extend(data_set.rows)\\n\\n if result is None:\\n return None\\n return DataSetWrapper(result, self._decode_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef4b08ae726aa3df9bf07133994437d1\",\n \"score\": \"0.5467488\",\n \"text\": \"def __iter__(self):\\n return QueryIterator(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c82d0c160351458b7eb0ebc3aa3d7a47\",\n \"score\": \"0.5461698\",\n \"text\": \"def create_resultSet(self, session, rset=None):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5035a1d3b9cfebbed70f2454dea610\",\n \"score\": \"0.5460505\",\n \"text\": \"def _get_resultdata(self):\\n resultdata = keys = self.data['query'][self.resultkey]\\n if isinstance(resultdata, dict):\\n keys = list(resultdata)\\n if 'results' in resultdata:\\n resultdata = resultdata['results']\\n elif 'pageids' in self.data['query']:\\n # this ensures that page data will be iterated\\n # in the same order as received from server\\n resultdata = [resultdata[k]\\n for k in self.data['query']['pageids']]\\n else:\\n resultdata = [resultdata[k]\\n for k in sorted(resultdata)]\\n pywikibot.debug(\\n f'{type(self).__name__} received {keys}; limit={self.limit}')\\n return resultdata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ac2aae5ff239fe7635e7334f8e7adc\",\n \"score\": \"0.5458381\",\n \"text\": \"def __iter__(self):\\n query = self.query and {'$and': self.query} or {}\\n if self.sorting:\\n return self.collection.find(query, self.projection).sort(self.sorting)\\n\\n return self.collection.find(query, self.projection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9a43b6a84075ae1274291c960b8647\",\n \"score\": \"0.54510725\",\n \"text\": \"def get_datarecords(self):\\n try:\\n return set_models.Dataset.objects.all()\\n except:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9a43b6a84075ae1274291c960b8647\",\n \"score\": \"0.54510725\",\n \"text\": \"def get_datarecords(self):\\n try:\\n return set_models.Dataset.objects.all()\\n except:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ddc2292991ebd1959f54feb491d1d9\",\n \"score\": \"0.54485184\",\n \"text\": \"def operation_list(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f96d6b99976b140b7e405eba0c7c461\",\n \"score\": \"0.54449105\",\n \"text\": \"def get_dataframe(self):\\n return self.dataframe_all\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a686eb0b23f20c9ac333a33a96d050d1\",\n \"score\": \"0.54440224\",\n \"text\": \"def get_sets(self):\\n return self.set.filter(workout=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca29d86f6a71e20b7307d49dbe1c110\",\n \"score\": \"0.5443771\",\n \"text\": \"def get(self):\\n return {\\\"results\\\": self.listAll()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364c70ea628e7c84592fbb71a0faef5c\",\n \"score\": \"0.54419374\",\n \"text\": \"def fetchall(self):\\n result = []\\n while True:\\n one = yield self.fetchone()\\n if one is None:\\n break\\n else:\\n result.append(one)\\n raise Return(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5deb1884cbb33e5e76617af63d11b785\",\n \"score\": \"0.5433278\",\n \"text\": \"def result(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda3c80c69072b68ac3189ed93f1d9b1\",\n \"score\": \"0.5427908\",\n \"text\": \"def __call__(self):\\n dataset = self\\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6d90dabc0e7c29ff42e9f25f8db9f9\",\n \"score\": \"0.54218066\",\n \"text\": \"def __iter__(self):\\n return itertools.chain(self.stores, self.loads, self.mutates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d50b46fc973537ef7c4df983bc82ed\",\n \"score\": \"0.54119897\",\n \"text\": \"def fetch(self, hint=None):\\n queries = add_hints(self.sql, hint)\\n\\n max_rows_returned, data = fetch_data(self.dataset.database, queries, self.dimensions)\\n return self._transform_for_return(data, max_rows_returned=max_rows_returned)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f52f0b24b76085dfe87f0c677a099a9\",\n \"score\": \"0.5411625\",\n \"text\": \"def __iter__(self):\\n return self._iterate_columns('id', 'curr_exchange', 'data')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04eb4d00c002cea1166a8cba3ae71d7e\",\n \"score\": \"0.5410862\",\n \"text\": \"def get_result(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c1c9257296f70b05b204e8fc7dcaac9\",\n \"score\": \"0.5407166\",\n \"text\": \"def __iter__(self):\\n return iter(self._q[:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d34ea31097a2bfc2cf1d3378c56d8d\",\n \"score\": \"0.5403488\",\n \"text\": \"def raw_results(self):\\n if self._raw_results is None:\\n self._raw_results = pd.read_csv(self.results_path)\\n \\n return self._raw_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a7d89ef243c54ae3aa80ef0bc07438\",\n \"score\": \"0.54002124\",\n \"text\": \"def __iter__(self):\\n return iter(self._data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b52f20f7fd9d4d23316229cac3785e6c\",\n \"score\": \"0.53972673\",\n \"text\": \"def results(self, process, temp_dir, filename):\\n return ResultSet(temp_dir, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f9b1b38850b85cf8211ae304c359757\",\n \"score\": \"0.53824264\",\n \"text\": \"def __iter__(self):\\n\\n if not self.liveSet:\\n self.refresh_liveSet()\\n\\n return self.liveSet.__iter__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827db4319c58af2725644b91c5115bef\",\n \"score\": \"0.5378629\",\n \"text\": \"def fetchall(self):\\n funcs = tuple(_fetch_funcs(self._rs))\\n return tuple(\\n tuple(_fetch_row(self._rs, funcs))\\n for _ in self._rs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a4f88478263ce09219199a77ab600df\",\n \"score\": \"0.5375595\",\n \"text\": \"def get_results(self):\\n return dict(self.return_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd2e01f14c652fea6907a4515a6ccdb1\",\n \"score\": \"0.53740263\",\n \"text\": \"def fetchall(self):\\n return self._cursor.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c9ea367b4b61cfe9775d79a0bb664f\",\n \"score\": \"0.53710765\",\n \"text\": \"def iterable(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4ec492e372a12406abb3b47eec9c64\",\n \"score\": \"0.5361873\",\n \"text\": \"def get_queries(self):\\r\\n return self.queries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb94d8f6f3866891074c17104f0d47e9\",\n \"score\": \"0.5360864\",\n \"text\": \"def get_all(self, stop_condition=lambda x: False):\\n while(self.has_next()):\\n if stop_condition(self.next()): break\\n self.all_data.append(self._current_datum)\\n return self.all_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58744f870370c569631c53926be81381\",\n \"score\": \"0.5354491\",\n \"text\": \"def fetch_data(self):\\n self.rows = self.cursor.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb48ee3e60f002d286e61348ea197d7\",\n \"score\": \"0.5352697\",\n \"text\": \"def get_all(self):\\n raise NotImplemented # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f16ac8310b89f14368380d49c64251\",\n \"score\": \"0.5347104\",\n \"text\": \"def get_operations(self):\\n self._build_operations()\\n return self.operations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124a30b10d75c0ae9f69c655b04687ca\",\n \"score\": \"0.53435117\",\n \"text\": \"def raw_data_operations(self): \\n try: \\n return self.__raw_data.operations\\n except: \\n return None #No Operations Applied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dcf343a755fc0690a483919ddceb222\",\n \"score\": \"0.5335114\",\n \"text\": \"def get_results(self):\\n self.get_content_from_fields()\\n self.post_process()\\n self.add_content_to_db()\\n pprint(self.content)\\n return self.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00bbf4add57cefd6efd7ec55b18858fa\",\n \"score\": \"0.53225285\",\n \"text\": \"def get_results(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c8320dbf19a4e6165928d6e642d0b9b\",\n \"score\": \"0.53132147\",\n \"text\": \"def get_data_list(self):\\n return self.data_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09730391ce021d41ba3214e91e13beb0\",\n \"score\": \"0.5307436\",\n \"text\": \"def getSubset(self):\\n return self.subset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a5e9ac96cd2ab5cbdce3c0e8d75815\",\n \"score\": \"0.53030884\",\n \"text\": \"def to_response(self):\\n self.ctx[\\\"datasets\\\"] = self.execute_op()\\n return result_response(DatasetsListCtrl.RESPONSE_SERIALIZER, self.ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d726350c9a128e6315eca884f25165\",\n \"score\": \"0.5290681\",\n \"text\": \"def get_data(self, query: str):\\n self.cur.execute(query)\\n data = self.cur.fetchall()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09856192970776fcbe5b676a77baa50b\",\n \"score\": \"0.5290236\",\n \"text\": \"def return_all(self):\\n return Repository.return_all(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"449c052ac67803e8cbb4b82b238b0c8f\",\n \"score\": \"0.5288833\",\n \"text\": \"def __iter__(self):\\n return iter(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51bd27f5f5eeb428dac735ab92bdafeb\",\n \"score\": \"0.5287143\",\n \"text\": \"def get_results(self):\\n return self.mct.selection_with_N()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bfe8e635a295a9d6d788208b3b4dd22\",\n \"score\": \"0.5285504\",\n \"text\": \"def _simulate_list_functions_list_data(self):\\n results = []\\n for counter, item in enumerate(self._process_units):\\n result_set = ResultSet(item.__name__, self._pool.map_async(\\n item, self._process_data[counter]).get())\\n results.append(result_set)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d5785f192c2c83381ca320eeca5ce7\",\n \"score\": \"0.5281581\",\n \"text\": \"def result(self) -> Any:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d5785f192c2c83381ca320eeca5ce7\",\n \"score\": \"0.5281581\",\n \"text\": \"def result(self) -> Any:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"135e75728b57a6a6d56a965d96719862\",\n \"score\": \"0.5275679\",\n \"text\": \"def data(self):\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17af7a49a21e4d5bc7371708aaf409b9\",\n \"score\": \"0.5271991\",\n \"text\": \"def query_data(self, query, *args, **kw):\\n if kw:\\n if args:\\n raise TypeError(\\\"Only positional or keyword arguments\\\"\\n \\\" may be provided, not both.\\\")\\n args = kw\\n cursor = self._connection._storage.ex_cursor()\\n try:\\n cursor.execute(query, args)\\n result = list(cursor)\\n finally:\\n try:\\n cursor.close()\\n except Exception:\\n pass # whatever :)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ccb8cd81502ec4eba9ae125433fe6f8\",\n \"score\": \"0.52669233\",\n \"text\": \"def batch(self):\\n return self.container.batch()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c00fb9018167f78b2b791be868537e3\",\n \"score\": \"0.5265072\",\n \"text\": \"def operations(self):\\n return self._operations\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994397,"cells":{"query_id":{"kind":"string","value":"dcb1b8bf0fcce2f33fbceabbafe3e645"},"query":{"kind":"string","value":"Return a new slide based on slide and having partname, created from scratch."},"positive_passages":{"kind":"list like","value":[{"docid":"2159840511c60dfa1b051c38d56ff5bc","score":"0.6649468","text":"def new(cls, slide, partname, package):\n element = CT_NotesSlide.new()\n notes_slide = cls(partname, CT.PML_NOTES_SLIDE, element, package)\n return notes_slide","title":""}],"string":"[\n {\n \"docid\": \"2159840511c60dfa1b051c38d56ff5bc\",\n \"score\": \"0.6649468\",\n \"text\": \"def new(cls, slide, partname, package):\\n element = CT_NotesSlide.new()\\n notes_slide = cls(partname, CT.PML_NOTES_SLIDE, element, package)\\n return notes_slide\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4a340950d41017958ca74388058fd5ee","score":"0.7599987","text":"def new(cls, slide_layout, partname, package):\n slide_elm = CT_Slide.new()\n slide = cls(partname, CT.PML_SLIDE, slide_elm, package)\n slide.shapes.clone_layout_placeholders(slide_layout)\n slide.relate_to(slide_layout, RT.SLIDE_LAYOUT)\n return slide","title":""},{"docid":"05abfc3b8c295d623ed8878221b5ac04","score":"0.6525773","text":"def make_blank_slide():\n\n prs = pptx.Presentation()\n layout = prs.slide_layouts[6]\n slide = prs.slides.add_slide(layout)\n return prs, slide","title":""},{"docid":"3c2bcc9bebc8777156140f7ca05e47c8","score":"0.63209826","text":"def save(self, ppt, parent_name=None):\n if parent_name:\n section_name = parent_name + (\n (\" - \" + self.pretty_title) if self.pretty_title else \"\"\n )\n else:\n section_name = self.pretty_title\n slide_obj = ppt.slides.add_slide(slide_layout=self.layout)\n if self.title and slide_obj.shapes.title:\n slide_obj.shapes.title.text = section_name\n for index, component in enumerate(self.contents):\n if index + 1 < len(slide_obj.placeholders):\n placeholder = slide_obj.placeholders[index + 1]\n component.save(placeholder, slide_obj)\n else:\n raise Exception(\"Not enough shapes in the slide for the contents.\")\n return slide_obj","title":""},{"docid":"71e0db32b76a8eed2d130b2fc02329dd","score":"0.6138355","text":"def build_slides_easy_way(self):\n slide_part1 = None\n for photo in self.list_photos:\n if photo.get_orientation() == \"H\":\n # case the slide is horizontal\n self.list_slides.append(Slide(photo))\n else:\n # case the slide is vertical\n if slide_part1 is None:\n # add the vertical photo on memory\n slide_part1 = photo\n else:\n # create a slide with the photo on memory and the current photo\n self.list_slides.append(Slide(slide_part1, photo))\n slide_part1 = None\n import random\n random.shuffle(self.list_slides)","title":""},{"docid":"1a5459e341419226743e4a5007c2fba9","score":"0.57955754","text":"def register_projector_slide(name: str, slide: ProjectorSlide) -> None:\n projector_slides[name] = slide","title":""},{"docid":"d62d52f985465c3b6f564e1b26762332","score":"0.5696239","text":"def __getitem__(self, idx):\n for e in self._element.iter_ph_elms():\n if e.ph_idx == idx:\n return SlideShapeFactory(e, self)\n raise KeyError('no placeholder on this slide with idx == %d' % idx)","title":""},{"docid":"c522a37db8ee0083f34cb15e919df21e","score":"0.5528841","text":"def create_blank_slide(self):\n self.add_slide(Slide(mpfdisplay=self, priority=0, persist=True,\n machine=self.machine, name='blank'))","title":""},{"docid":"810b3fe7f2b93037e564651b3ba6a802","score":"0.5332095","text":"def slide(self):\n return self.part_related_by(RT.SLIDE)","title":""},{"docid":"2b1550036bf5059c772bc6e4f5805b60","score":"0.5321755","text":"def addslides(inputfile, outputfile, feature_dicts_list):\n # still missing launch date, price, carriers. , , is not existed in every page\n keys = [\"Display size\",\"Battery\",\"Dimensions\",\"Screen-to-body ratio\",\"System chip\",\"Camera\",\n \"Memory\",\"Biometrics\",\"Bluetooth\",\"Wi-Fi\", \"Bands\",\"OS\",\"Materials\",\"Release_date\", 'Carriers', 'MSRP price']\n \n prs = Presentation(inputfile)\n # slide_layouts is a slide, Presentation.slides, slide.shapes, shapes.title, shapes.placeholders[]\n spec_slide_layout = prs.slide_layouts[2] # 2 is no.2 mother template\n \n# add slides according to feature_dicts_list numbers\n for spec_dict in feature_dicts_list:\n slide = prs.slides.add_slide(spec_slide_layout)\n \n # add product name \n shapes = slide.shapes\n title_shape = shapes.title\n title_shape.text = spec_dict['phone_name']\n \n # shapes.placeholder -- body\n# body_shape = shapes.placeholders[12]\n# tf = body_shape.text_frame\n# tf.text = 'Presentation.slides.shapes.placeholders[].text_frame'\n# \n# p = tf.add_paragraph()\n# p.text = 'Presentation.slides.shapes.placeholders[].text_frame.paragraph level 1'\n# p.level = 1\n# \n# p = tf.add_paragraph()\n# p.text = 'Presentation.slides.shapes.placeholders[].text_frame.paragraph level 2'\n# p.level = 2\n \n # add product ID\n img_path = spec_dict['phone_id'] \n left = Inches(0.01)\n top = Inches(0.6)\n height = Inches(5)\n slide.shapes.add_picture(img_path, left, top, height=height)\n \n # add spec table\n rows = len(keys)+1\n cols = 2\n left = Inches(4.0)\n top = Inches(0.2)\n width = Inches(2.0)\n height = Inches(0.1)\n \n table = shapes.add_table(rows, cols, left, top, width, height).table\n \n # set column widths\n table.columns[0].width = Inches(1.6)\n table.columns[1].width = Inches(4.2)\n \n # write column headings\n table.cell(0, 0).text = 'Item'\n table.cell(0, 1).text = 'Spec'\n \n i=1\n for key in keys:\n # check if this items exist\n if(key in spec_dict.keys()): \n key_frame = table.cell(i,0).text_frame\n key_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\n p = key_frame.paragraphs[0]\n run = p.add_run()\n run.text = key \n font = run.font\n font.name = 'Calibri'\n font.size = Pt(12)\n font.bold = False\n font.italic = None # cause value to be inherited from theme\n # \n value_frame = table.cell(i,1).text_frame\n value_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\n p = value_frame.paragraphs[0]\n run = p.add_run()\n run.text = spec_dict[key]\n font = run.font\n font.name = 'Calibri'\n font.size = Pt(12)\n font.bold = False\n font.italic = None # cause value to be inherited from theme\n \n i += 1 \n \n# Store all the items\n# i=1\n# for k, v in spec_dict.items():\n# key_frame = table.cell(i,0).text_frame\n# key_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\n# p = key_frame.paragraphs[0]\n# run = p.add_run()\n# run.text = k \n# font = run.font\n# font.name = 'Calibri'\n# font.size = Pt(12)\n# font.bold = False\n# font.italic = None # cause value to be inherited from theme\n# # \n# value_frame = table.cell(i,1).text_frame\n# value_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\n# p = value_frame.paragraphs[0]\n# run = p.add_run()\n# run.text = v\n# font = run.font\n# font.name = 'Calibri'\n# font.size = Pt(12)\n# font.bold = False\n# font.italic = None # cause value to be inherited from theme\n# \n# i += 1\n \n prs.save(outputfile)","title":""},{"docid":"55cc6ed34827649af74f7b2fe70d4a36","score":"0.5268827","text":"def add_partition(self, part_name):\n\n if part_name == PART1_NAME:\n faked_partition = self.add_partition1()\n elif part_name == PART2_NAME:\n faked_partition = self.add_partition2()\n elif part_name == PART3_NAME:\n faked_partition = self.add_partition3()\n return faked_partition","title":""},{"docid":"0c4efa253badd3432f76a1f99aea1e5a","score":"0.52511925","text":"def set_titleorbody_of_slide(self, _property_, slide_number, _new_):\n if self.playing():\n self._edit.append( (_property_, slide_number, _new_) )\n \n else:\n if _property_ not in [\"title\", \"body\"]:\n raise Exception(\"Passed {0} crap to set_titleorbody_of_slide\".format(str(_property_)))\n reference = getattr(self.get_slide_reference(slide_number), _property_)\n reference.set(_new_)","title":""},{"docid":"6b8bc19d1694e74d2f8f8b822a306dfb","score":"0.5245959","text":"def NewPart(self):\n\t\tret = self._oleobj_.InvokeTypes(8, LCID, 1, (9, 0), (),)\n\t\tif ret is not None:\n\t\t\tret = Dispatch(ret, u'NewPart', None)\n\t\treturn ret","title":""},{"docid":"8d6e26e3f90650e4895b979b7ea8bf86","score":"0.5153214","text":"def it_knows_the_name_of_the_slide(self, base_slide):\n # setup ------------------------\n base_slide._element = _sldLayout1()\n # exercise ---------------------\n name = base_slide.name\n # verify -----------------------\n expected = 'Title Slide'\n actual = name\n msg = \"expected '%s', got '%s'\" % (expected, actual)\n assert actual == expected, msg","title":""},{"docid":"4d592463a94b9e82724621a4eafe0093","score":"0.5118758","text":"def create():\n try:\n slides = Slides(\n author=get_current_user()._id,\n title=request.form['title'],\n content=request.form['content'],\n course_id=ObjectId(request.form['course_id'])\n )\n slides.save()\n return jsonify({\n 'success': 1,\n 'slides_id': str(slides._id)\n })\n except ValidationError as e:\n logger.warning(\n 'Failed to create new slides. Exception: {}'.format(e.message))\n return jsonify({\n 'success': 0,\n 'msg': '{}: {}'.format(error_msg.ILLEGAL_ARGUMENT, ','.join(list(e.message.keys())))\n }), 400","title":""},{"docid":"7b056ceb0d29c0d03096de6a366061b1","score":"0.51153123","text":"def get_slide(self, index):\n return self._generate_slide(index)","title":""},{"docid":"ed553c45d15ad287e86adea2406c0b47","score":"0.5060784","text":"def makeNew(app=None):\n return Thumbnail(app)","title":""},{"docid":"0557fdca3fa70c1da109d2e18e2eeaa9","score":"0.49716058","text":"def next_slide(self):\n if self.old_slide is None and self.album_length > 1:\n self.index = (self.index + 1) % self.album_length\n self.old_slide = self.slide\n self.slide = self.pending_slides[1]\n self.slide.set_x(unit.size_pix[0])\n if self.fullscreen_on:\n self.cover_frame.add_child(self.slide)\n self.slide.set_size(unit.size_pix[0], unit.size_pix[1])\n else:\n self.add_child(self.slide)\n self.pending_slides = self.data_source.get_pending_slides(\n self.index)\n self.new_slide_transition.set_from(unit.size_pix[0])\n self.new_slide_transition.set_to(0)\n self.old_slide_transition.set_to(-1*unit.size_pix[0])\n self.old_slide.add_transition(\"x\", self.old_slide_transition)\n self.slide.add_transition(\"x\", self.new_slide_transition)\n self.emit(\"progressed\",\n float(self.index+1) / self.album_length,\n self.index+1)","title":""},{"docid":"462dbc5c8d5c8dd5d19afa626ac02e8a","score":"0.4883622","text":"def instance(self, path, partName, jointName, lodName=\"lodRoot\"):\n partBundleDict = self.__partBundleDict.get(lodName)\n if partBundleDict:\n subpartDef = self.__subpartDict.get(partName, Actor.SubpartDef(partName))\n partDef = partBundleDict.get(subpartDef.truePartName)\n if partDef:\n joint = partDef.partBundleNP.find(\"**/\" + jointName)\n if joint.isEmpty():\n Actor.notify.warning(\"%s not found!\" % (jointName))\n else:\n return path.instanceTo(joint)\n else:\n Actor.notify.warning(\"no part named %s!\" % (partName))\n else:\n Actor.notify.warning(\"no lod named %s!\" % (lodName))","title":""},{"docid":"ae98685117c6b74231c5521c57057370","score":"0.48793986","text":"def newPartPathAtIndex(dest, index):\n return os.path.join(dest, 'part_%08d.new' % index)","title":""},{"docid":"87ec95c35d3332a939ba70fe4dc2f263","score":"0.4879093","text":"def Copy(self, name=None):\n if name is None:\n name = self.name\n return Pmf(dict(self.d), name)","title":""},{"docid":"aec60de0da697e521dea97aebf7255f4","score":"0.48536906","text":"def _loaded_slidelayout(self, prs_slidemaster=None):\n # partname for related slideMaster\n sldmaster_partname = '/ppt/slideMasters/slideMaster1.xml'\n # path to test slideLayout XML\n slidelayout_path = absjoin(test_file_dir, 'slideLayout1.xml')\n # model-side slideMaster part\n if prs_slidemaster is None:\n prs_slidemaster = Mock(spec=SlideMaster)\n # a part dict containing the already-loaded model-side slideMaster\n loaded_part_dict = {sldmaster_partname: prs_slidemaster}\n # a slideMaster package part for rel target\n pkg_slidemaster_part = Mock(spec=package.Part)\n pkg_slidemaster_part.partname = sldmaster_partname\n # a package-side relationship from slideLayout to its slideMaster\n rel = Mock(name='pptx.package.Relationship')\n rel.rId = 'rId1'\n rel.reltype = RT.SLIDE_MASTER\n rel.target = pkg_slidemaster_part\n # the slideLayout package part to send to _load()\n pkg_slidelayout_part = Mock(spec=package.Part)\n pkg_slidelayout_part.relationships = [rel]\n with open(slidelayout_path, 'rb') as f:\n pkg_slidelayout_part.blob = f.read()\n # _load and return\n slidelayout = SlideLayout()\n return slidelayout._load(pkg_slidelayout_part, loaded_part_dict)","title":""},{"docid":"f48bf505ecf21cc371712724c7fba96a","score":"0.48324564","text":"def _generate_powerpoint(self, *args, **kwargs):\n \n self.ppt.generate_presentation()","title":""},{"docid":"cb49fc4be7e881fa91fcf674564846c0","score":"0.48054546","text":"def _fetch_presentation_by_slug(self, presentation_name):\n full_presentation = json.loads(self.api_proxy.get(\"/director_api/presentation/%s/\" % presentation_name))\n return full_presentation","title":""},{"docid":"baadefdbc2eb26686215099fec75110c","score":"0.47963375","text":"def previous_slide(self):\n if self.old_slide is None and self.album_length > 1:\n self.index = self.index - 1 if self.index > 0 \\\n else self.album_length - 1\n self.old_slide = self.slide\n self.slide = self.pending_slides[0]\n self.slide.set_x(-1*unit.size_pix[0])\n if self.fullscreen_on:\n self.cover_frame.add_child(self.slide)\n self.slide.set_size(unit.size_pix[0], unit.size_pix[1])\n else:\n self.add_child(self.slide)\n self.pending_slides = self.data_source.get_pending_slides(\n self.index)\n self.new_slide_transition.set_from(-1*unit.size_pix[0])\n self.new_slide_transition.set_to(0)\n self.old_slide_transition.set_to(unit.size_pix[0])\n self.old_slide.add_transition(\"x\", self.old_slide_transition)\n self.slide.add_transition(\"x\", self.new_slide_transition)\n self.emit(\"progressed\",\n float(self.index+1) / self.album_length,\n self.index+1)","title":""},{"docid":"5303e6dedddda46f73b332476dd9d9e4","score":"0.47775975","text":"def doNewIndex(self):\n\t\tself.__slide = self.__slides[self.__pos]\n\t\tself_ink = self.__slide.getElementsByTagName(\"self\")\n\t\tself.__instructor_tag = None\n\t\tself.__self_ink_tag = None\n\t\tself.__instructor_ink = []\n\t\tself.__self_ink = []\n\t\tself.__self_text = \"\"\n\t\tself.__text_tag = None\n\t\tself.__active_sub = -1\n\t\tif len(self_ink) > 0:\n\t\t\tself.__self_ink_tag = self_ink[0]\n\t\t\ttexttags = self.__self_ink_tag.getElementsByTagName(\"text\")\n\t\t\tif len(texttags) > 0:\n\t\t\t\tself.__text_tag = texttags[0]\n\t\t\telse:\n\t\t\t\tself.__text_tag = self.__dom.createElement(\"text\")\n\t\t\t\tself.__text_tag.appendChild(self.__dom.createTextNode(\"\"))\n\t\t\t\tself.__self_ink_tag.appendChild(self.__text_tag)\n\t\t\tpathtags = self.__self_ink_tag.getElementsByTagName(\"path\")\n\t\t\tfor pathstr in pathtags:\n\t\t\t\tself.__self_ink.append(pathstr.firstChild.nodeValue)\n\t\telse:\n\t\t\tself.__self_ink_tag = self.__dom.createElement(\"self\")\n\t\t\tself.__slide.appendChild(self.__self_ink_tag)\n\t\t\tself.__text_tag = self.__dom.createElement(\"text\")\n\t\t\tself.__text_tag.appendChild(self.__dom.createTextNode(\"\"))\n\t\t\tself.__self_ink_tag.appendChild(self.__text_tag)\n\t\tif self.__text_tag.firstChild:\n\t\t\tself.__self_text = self.__text_tag.firstChild.nodeValue\n\t\t\t\n\t\tself.emit(\"slide-changed\")\n\t\tself.emit(\"update-submissions\", self.__active_sub)\n\t\tself.emit(\"slide-redraw\")","title":""},{"docid":"6ea721347efaa80b54dbcbb6dbf560a6","score":"0.46951017","text":"def Part(\n self, name: str, embeddedSpace: SymbolicConstant, type: SymbolicConstant\n ) -> OdbPart:\n self.parts[name] = odbPart = OdbPart(name, embeddedSpace, type)\n return odbPart","title":""},{"docid":"02a94b5bfeb33ba63f0a66dcf2e8b6c7","score":"0.46780938","text":"def _current_slide(self,\n zoom, \n page):\n\n current_slide = self._slides[page-1]\n current_slide.width = self._default_width*zoom\n current_slide.height = self._default_height*zoom\n return current_slide","title":""},{"docid":"cde7fed329c29eb6d4854a5cdd489450","score":"0.46732825","text":"def __init__(self, \n slides_path,\n default_width=720,\n height_to_width_ratio=810/1080):\n\n self._default_width = default_width\n self._default_height = default_width*height_to_width_ratio # default height\n self._slides = self._read_slides(slides_path) # read and keep the slides ","title":""},{"docid":"a8ce08bd7ed3f2a0fef2f28ce3aaad6f","score":"0.46728823","text":"def transform_to_image(slide):\n return \"\"","title":""},{"docid":"c6a6a5b2ed7ffdf1d90b23d119de933b","score":"0.4667195","text":"def _newStepItem(stepName):\n newItem = QtGui.QTreeWidgetItem()\n newItem.setText(0, stepName)\n newItem.stepName = stepName\n return newItem","title":""},{"docid":"4ce63372a0b3bd4230825905d380c4ee","score":"0.46107632","text":"def create_piece(piece):\n\n piece_map = {\n 'rook': Rook,\n 'knight': Knight,\n 'queen': Queen,\n }\n\n if piece not in piece_map:\n raise PieceNotFoundError()\n\n return piece_map[piece]()","title":""},{"docid":"295b550d40992a87297abdf08a5f8483","score":"0.4596975","text":"def _get_piece(self, name):\n return Piece(name, tuple([(x, y) for y in range(len(self._state))\n for x in range(len(self._state[y])) if\n self._state[y][x] == name]))","title":""},{"docid":"45ef0f5a4414ab6c98db1bbfdadbad2c","score":"0.45958647","text":"def make_new_puzzle(self):\n #Define solution to the puzzle \n solution = np.append(np.arange(1, self.p.N**2), 0)\n\n #Create new puzzle \n new_puzzle = np.random.choice(solution, self.p.N**2, replace = False)\n\n #Check if created puzzle is solvable and if not make new ones until solvable one is found\n new_puzzle, blank_index = self.check_if_solvable(new_puzzle)\n\n #Convert 1D arrays to NxN arrays\n return new_puzzle.reshape(self.p.N, self.p.N), solution.reshape(self.p.N, self.p.N), blank_index","title":""},{"docid":"fd267035451d7a057f07811bfa044dd4","score":"0.4554759","text":"def _make_pep_from_seq(self):\n self._generate_template()\n self._generate_sidechains_scwrl()\n return self.pep","title":""},{"docid":"199dcc44e1b7944389b2e141d9db0b76","score":"0.455377","text":"def create_samplepartition(context, data):\n print data['part_id']\n partition = _createObjectByType('SamplePartition', context, data['part_id'])\n partition.unmarkCreationFlag()\n partition.reindexObject()\n\n return partition","title":""},{"docid":"9faa7c25d4f978490eb09c919734f990","score":"0.45443135","text":"def create_slides(data):\n slides = []\n for item in data.index:\n slides.append({\n \"key\": item,\n \"src\": data['asset.image_url'][item],\n \"header\": data['asset.name'][item],\n })\n return slides","title":""},{"docid":"a320603f903204d15d97b19c3689bb48","score":"0.45366773","text":"def slides():\n fig = new_slide()\n\n slide_heading(fig, 'Future Plans')\n\n props = dict(fontproperties=FONT, fontsize=56, alpha=0.7,\n verticalalignment='top')\n\n fig.text(0.05, 0.8, 'Next feature release: 3.4', **props)\n fig.text(0.1, 0.7, f'{BULLET} September 2020', **props)\n fig.text(0.1, 0.6,\n f'{BULLET} Dropping support for Python 3.6 & NumPy 1.15',\n **props)\n\n fig.text(0.05, 0.4, 'Google Season of Docs 2020', **props)\n\n fig.text(0.05, 0.2, 'Check out our blog!', **props)\n t = fig.text(0.1, 0.2, '\\nhttps://matplotlib.org/matplotblog/',\n **props)\n t.set_url('https://matplotlib.org/matplotblog/')\n\n return fig","title":""},{"docid":"c89b3f43ee652541ee4c93281f7d43f0","score":"0.4523472","text":"def _create_section_page(title: str, parent: Page) -> SectionPage:\n p = SectionPage()\n p.title = title\n parent.add_child(instance=p)\n p.save_revision().publish()\n return p","title":""},{"docid":"98640bcbda449380c529db91497aae6b","score":"0.4520602","text":"def slice1(self, x, slide):\n x1 = x[:, :slide, :, :]\n return x1","title":""},{"docid":"db272a2550eb294b9b0d92c4b091bbc7","score":"0.45179543","text":"def parse_recipe(name):\n name = name if name.endswith('.xml') else name + '.xml'\n absname = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'recipes', name)\n return Workflow(absname)","title":""},{"docid":"a91ed648e3dc99390514aad3ba733559","score":"0.45158225","text":"def Instance(self, name: str, objectToCopy: PartInstance):\n return self","title":""},{"docid":"1d92d798300000167abeda9df9f340f7","score":"0.44765404","text":"def part(self, part):\n\t\tself.current_part = part","title":""},{"docid":"2208089025fef69c53a7fb7522b97e4a","score":"0.4473693","text":"def newphotooverlay(self, **kwargs):\n photoover = PhotoOverlay(**kwargs)\n photoover._parent = self._placemark\n self._addfeature(photoover)\n return photoover","title":""},{"docid":"cb5f0747464d378043a014aecd272b90","score":"0.44586238","text":"def __getitem__(self, item):\n steps = list(self.steps.items())\n return PipelineDefinition(\n resources=self.resources,\n steps=dict(steps[self.getslice(item)])\n )","title":""},{"docid":"053d9e98a9330800ea3498cf853d34d1","score":"0.44475362","text":"def make_plan(text):\r\n site = build.Website(\"\", \"\", \"\", \"\", \"\", [])\r\n f = open(text)\r\n title = f.readline()\r\n site.title = title\r\n new_p = None\r\n para_list = []\r\n\r\n for line in f:\r\n line = line.strip()\r\n if len(line) == 0:\r\n para_list.append(new_p)\r\n elif line == \"!new_paragraph\":\r\n new_p = build.Paragraphs(\"\", \"\", [])\r\n elif line[0:6] == \"!title\":\r\n new_p.title = line[7:]\r\n elif line[0:6] == \"!image\":\r\n split = line[7:].split(\" \")\r\n image_name = split[0]\r\n image_width = \"100%\"\r\n if len(split) == 2:\r\n image_width = split[1]\r\n\r\n img = build.Images(image_name, image_width)\r\n new_p.image.append(img)\r\n else:\r\n new_p.text += line + \"\\n\"\r\n site.body = para_list\r\n f.close()\r\n return site","title":""},{"docid":"201ef4f1b869f0cdda471a9bb82caebf","score":"0.4422248","text":"def getPart(self, partName, lodName=\"lodRoot\"):\n partBundleDict = self.__partBundleDict.get(lodName)\n if not partBundleDict:\n Actor.notify.warning(\"no lod named: %s\" % (lodName))\n return None\n subpartDef = self.__subpartDict.get(partName, Actor.SubpartDef(partName))\n partDef = partBundleDict.get(subpartDef.truePartName)\n if partDef is not None:\n return partDef.partBundleNP\n return None","title":""},{"docid":"26888056a28a2905e4f563d0f5b70f23","score":"0.44220287","text":"def create_model_parts(self):\n if not self.id:\n raise ValueError(\"Cannot create parts if Song has not been saved yet.\")\n xml_parts = list(self.xml_song.xml_parts)\n names = {part.name for part in xml_parts}\n Section = apps.get_model(\"singers\", \"Section\")\n sections = dict(Section.objects.filter(name__in=names).values_list(\"name\", \"id\"))\n model_parts = []\n for xml_part in xml_parts:\n model_part = xml_part.to_model_part()\n model_part.song_id = self.id\n model_part.id = sections.get(model_part.name)\n model_parts.append(model_part)\n return model_parts","title":""},{"docid":"439a1ff47749b5f5d22ce30fa4c060a1","score":"0.44192353","text":"def init_plot_custom_slices_wf(\n output_dir,\n desc,\n name=\"plot_custom_slices_wf\",\n):\n # NOTE: These slices are almost certainly specific to a given MNI template and resolution.\n SINGLE_SLICES = [\"x\", \"x\", \"x\", \"y\", \"y\", \"y\", \"z\", \"z\", \"z\"]\n SLICE_NUMBERS = [36, 45, 52, 43, 54, 65, 23, 33, 39]\n\n workflow = Workflow(name=name)\n\n inputnode = pe.Node(\n niu.IdentityInterface(\n fields=[\n \"underlay_file\",\n \"overlay_file\",\n \"name_source\",\n ],\n ),\n name=\"inputnode\",\n )\n\n # slices/slicer does not do well trying to make the red outline when it\n # cannot find the edges, so cannot use the ROI files with some low intensities.\n binarize_edges = pe.Node(\n BinaryMath(expression=\"img.astype(bool).astype(int)\"),\n name=\"binarize_edges\",\n )\n\n workflow.connect([(inputnode, binarize_edges, [(\"overlay_file\", \"in_file\")])])\n\n make_image = pe.MapNode(\n fsl.Slicer(show_orientation=True, label_slices=True),\n name=\"make_image\",\n iterfield=[\"single_slice\", \"slice_number\"],\n )\n make_image.inputs.single_slice = SINGLE_SLICES\n make_image.inputs.slice_number = SLICE_NUMBERS\n\n # fmt:off\n workflow.connect([\n (inputnode, make_image, [(\"underlay_file\", \"in_file\")]),\n (binarize_edges, make_image, [(\"out_file\", \"image_edges\")]),\n ])\n # fmt:on\n\n combine_images = pe.Node(\n PNGAppend(out_file=\"out.png\"),\n name=\"combine_images\",\n )\n\n workflow.connect([(make_image, combine_images, [(\"out_file\", \"in_files\")])])\n\n ds_overlay_figure = pe.Node(\n DerivativesDataSink(\n base_directory=output_dir,\n dismiss_entities=[\"den\"],\n datatype=\"figures\",\n desc=desc,\n extension=\".png\",\n ),\n name=\"ds_overlay_figure\",\n run_without_submitting=True,\n )\n\n # fmt:off\n workflow.connect([\n (inputnode, ds_overlay_figure, [(\"name_source\", \"source_file\")]),\n (combine_images, ds_overlay_figure, [(\"out_file\", \"in_file\")]),\n ])\n # fmt:on\n\n return workflow","title":""},{"docid":"5e193ea321e809f44bf45c9082f8986d","score":"0.44169056","text":"def it_knows_the_minimal_element_xml_for_a_slide(self, slide):\n # setup ------------------------\n path = absjoin(test_file_dir, 'minimal_slide.xml')\n # exercise ---------------------\n sld = slide._minimal_element()\n # verify -----------------------\n with open(path, 'r') as f:\n expected_xml = f.read()\n assert actual_xml(sld) == expected_xml","title":""},{"docid":"faf9f460a9462cb4edb9a0316ff35f29","score":"0.44147587","text":"def it_establishes_a_relationship_to_its_slide_layout_on_construction(\n self, relate_to_):\n # setup ------------------------\n slidelayout = SlideLayout(None, None, _sldLayout1(), None)\n partname = PackURI('/ppt/slides/slide1.xml')\n # exercise ---------------------\n slide = Slide.new(slidelayout, partname, None)\n # verify ----------------------\n slide.relate_to.assert_called_once_with(slidelayout, RT.SLIDE_LAYOUT)","title":""},{"docid":"ca029bf23b98892ca12ec046e81f020e","score":"0.44146568","text":"def _get_image(self, path_raw, info):\n param = info\n path_wsi = glob(os.path.join(self.path_raw, param['name'] + '.*'))\n assert path_wsi, \"no wsi with name {}\".format(param['name'])\n assert len(path_wsi)<2, \"several wsi with name {}\".format(param['name'])\n slide = open_slide(path_wsi[0])\n image = slide.read_region(location=(param['x'], param['y']),\n level=param['level'], \n size=(448, 448))#(param['xsize'], param['ysize']))\n return image","title":""},{"docid":"1777e711b6734dc63b86280f178a36a4","score":"0.44108897","text":"def newphotooverlay(self, **kwargs):\n photoover = PhotoOverlay(**kwargs)\n photoover._parent = self\n self._addfeature(photoover)\n return photoover","title":""},{"docid":"e15330fd395b4ce668aef29b49abe16d","score":"0.44106802","text":"def replace_note(slide, s_msg=None):\n import xml.etree.ElementTree as ET\n import copy\n NameSpace={\n 'a':\"http://schemas.openxmlformats.org/drawingml/2006/main\",\n 'r':\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\",\n 'p':\"http://schemas.openxmlformats.org/presentationml/2006/main\"\n }\n\n v=self.slide.part_related_by(NameSpace['r']+'/notesSlide')\n r=ET.fromstring(v._blob)\n notes=r.findall('.//p:txBody', NameSpace)\n for note in notes:\n if note.find('.//a:p/a:fld', NameSpace):\n # maybe a slide # or something else\n continue\n for p in note.findall('./a:p', NameSpace):\n note.remove(p)\n msg=ET.fromstring('<a:p xmlns:a=\"%s\"><a:r><a:rPr lang=\"en-US\" dirty=\"0\" smtClean=\"0\"/><a:t></a:t></a:r><a:endParaRPr lang=\"en-US\" dirty=\"0\"/></a:p>' % NameSpace['a'])\n msg.find('.//a:t', NameSpace).text=s_msg\n note.append(msg)\n break\n v.blob=ET.tostring(r)","title":""},{"docid":"80b5063098531e9fa8815f9bea6e1b57","score":"0.44026652","text":"def clone(self):\r\n new_puzzle = Puzzle(self._height, self._width, self._grid)\r\n return new_puzzle","title":""},{"docid":"620c77aabf71ea74e275d6cd38b38a60","score":"0.43986902","text":"def makeMapItem(pl_id):\n\n\tmapItem = []\n\tmapItem.append(makeMapItemStand(pl_id))\n\tpub_id = db.select(\"SELECT p.p_id FROM Pub p WHERE p.pl_id = \"+ str(pl_id))\n\n\tif len(pub_id) != 0:\n\t\tfor row in pub_id:\n\t\t\tmapItem.append(makeMapItemPub(row['pub_id']))\n\n\treturn (mapItem)","title":""},{"docid":"505c4bd6ca4d5fa6f87109e93851f234","score":"0.43942648","text":"def load(self, name=None):\n return Partition(name)","title":""},{"docid":"b8e5c4f51124ad2108130d78a3eb2cf6","score":"0.43765423","text":"def slides(self):\n if not self.__slides:\n self.__populate_slides()\n return self.__slides","title":""},{"docid":"d5fadda1f910df5d955a2b8df0df5e90","score":"0.43700403","text":"def INewPart(self):\n\t\tret = self._oleobj_.InvokeTypes(20, LCID, 1, (9, 0), (),)\n\t\tif ret is not None:\n\t\t\tret = Dispatch(ret, u'INewPart', '{83A33D32-27C5-11CE-BFD4-00400513BB57}')\n\t\treturn ret","title":""},{"docid":"e52c3bde1eb172bb67d4507fa4472f98","score":"0.43663114","text":"def create_pupil_choice(self, text):\n Pupil = dd.resolve_model(config.pupil_model)\n kw = parse_name(text)\n if len(kw) != 2:\n raise ValidationError(\n \"Cannot find first and last names in %r to \\\n auto-create pupil\", text)\n p = Pupil(**kw)\n p.full_clean()\n p.save()\n return p","title":""},{"docid":"a178b145a8fba8ca476104355a9cd373","score":"0.4365004","text":"def _create_article_page(title: str, parent: Page) -> SectionPage:\n p = ArticlePage()\n p.title = title\n parent.add_child(instance=p)\n p.save_revision().publish()\n return p","title":""},{"docid":"a53d00576c94d10cfbf1c74fe35c3767","score":"0.43601096","text":"def newPart(category_id):\n if 'username' not in login_session:\n return redirect('/login')\n category = session.query(Category).filter_by(id=category_id).one()\n if request.method == 'POST':\n newPart = Part(name=request.form['name'],\n description=request.form['description'],\n category_id=category_id, user_id=category.user_id)\n session.add(newPart)\n session.commit()\n flash('New Part %s Successfully Created' % (newPart.name))\n return redirect(url_for('showParts', category_id=category_id))\n else:\n return render_template('newpart.html')","title":""},{"docid":"188749b558eb3e780fd1a4b88e6f6683","score":"0.43569982","text":"def as_pose(self,**kwargs):\n\t\tkwargs['copy'] = True\n\t\treturn pose(self,**kwargs)","title":""},{"docid":"8357f6d821e9d039a084436ec67d9c34","score":"0.43486828","text":"def create(self, validated_data):\n\t\treturn Snippet.objects.create(**validated_data)","title":""},{"docid":"8357f6d821e9d039a084436ec67d9c34","score":"0.43486828","text":"def create(self, validated_data):\n\t\treturn Snippet.objects.create(**validated_data)","title":""},{"docid":"1afa89494aeb03aedc5020b30b1814d5","score":"0.43473628","text":"def new_figure(name):\n\n width, height = bf.get_screen_size()\n if name == 'New Figure':\n fig = plt.figure(figsize=(width, height-3.2))\n else:\n fig = plt.figure(name, figsize=(width, height-3.2))\n return fig","title":""},{"docid":"4ab44bc20129f40a54c8e55c050323fd","score":"0.43344074","text":"def add_ptx(self, ptx, name):","title":""},{"docid":"e07ffd3f9c89a11fa790afed8c0f8e2b","score":"0.43290237","text":"def PCreate (name, image, err, hwidth=1):\n ################################################################\n # Checks\n if not Image.PIsA(image):\n raise TypeError,\"image MUST be a Python Obit Image\"\n if not OErr.OErrIsA(err):\n raise TypeError,\"err MUST be an OErr\"\n #\n out = ImageInterp(\"None\",image=image,hwidth=hwidth,err=err);\n out.me = Obit.ImageInterpCreate(name, image.me, hwidth, err.me)\n if err.isErr:\n OErr.printErrMsg(err, \"Error creating beam object\")\n return out;\n # end PCreate","title":""},{"docid":"3134019227f31287e75eaa4922d058e7","score":"0.4327695","text":"def _read_slides(self, slides_path):\n\n # filter the slide name which has a number in it to indicate its order \n # in the folder \n slide_names = [sn for sn in os.listdir(slides_path) if any(map(str.isdigit, sn))]\n\n try:\n # sort out the slide names based on the number to make sure the images\n # are displayed in sequence\n slide_names_sorted = natsort.natsorted(slide_names, reverse=False)\n except ValueError as e:\n print(f'Error: {e}, please make sure the images have numbers to indicate the order')\n raise\n\n # construct a list of slide images \n slides = [Image(filename=f'{slides_path}/{slide_name}', \n width=self._default_width,\n height=self._default_height) \\\n for slide_name in slide_names_sorted]\n\n return slides","title":""},{"docid":"13fc61a517a2d3b463120f8280342451","score":"0.4323946","text":"def new_duplicate(self, path):\n self.gui.newDoc(path)\n self.model = self.gui.getActiveModel()\n self.__wrap_object(self.model)","title":""},{"docid":"223eb0ea0e4cc75575b8f1533f4d925b","score":"0.43236238","text":"def make_new_item(self, data, summary):\n identification = {} # @todo: what does this do?\n result = self.repo.editEntity(identification, data, summary=summary)\n pywikibot.output(summary) # afterwards in case an error is raised\n\n # return the new item\n return self.QtoItemPage(result.get(u'entity').get('id'))","title":""},{"docid":"fcc1d6bbdec8bfec5ab3f6e0489604ab","score":"0.43199682","text":"def get_slides(display_date=None):\n display_date = display_date or date.today()\n return ('carousel/{0}'.format(name) for name in\n _get_slide_names(display_date))","title":""},{"docid":"1917a3daacd82abdd55b698fa20a1109","score":"0.43122667","text":"def removePart(self, partName, lodName=\"lodRoot\"):\n # find the corresponding part bundle dict\n partBundleDict = self.__partBundleDict.get(lodName)\n if not partBundleDict:\n Actor.notify.warning(\"no lod named: %s\" % (lodName))\n return\n\n # remove the part\n if partName in partBundleDict:\n partBundleDict[partName].partBundleNP.removeNode()\n del partBundleDict[partName]\n\n # find the corresponding anim control dict\n if self.mergeLODBundles:\n lodName = 'common'\n partDict = self.__animControlDict.get(lodName)\n if not partDict:\n Actor.notify.warning(\"no lod named: %s\" % (lodName))\n return\n\n # remove the animations\n if partName in partDict:\n del partDict[partName]","title":""},{"docid":"06c488a3fb3204b16dad1c8e47f8938e","score":"0.43102464","text":"def get_pending_slides(self, index):\n return (self._generate_slide(index-1),\n self._generate_slide((index+1)%len(self.photos)),)","title":""},{"docid":"ef329b4c45f39e0da9d0cd1559683912","score":"0.43048415","text":"def _make_new_obs(obs, im):\n\n psf_obs=Observation(obs.psf.image,\n jacobian=obs.psf.jacobian,\n gmix=obs.psf.gmix.copy())\n\n newobs=Observation(im,\n jacobian=obs.jacobian,\n weight=obs.weight,\n psf=psf_obs)\n return newobs","title":""},{"docid":"6428be2ba2a94bbc9b23217670bb96cb","score":"0.4302464","text":"def clone(self, *parts):\r\n return type(self)(str(self), *parts)","title":""},{"docid":"a1f4d107c8913482e9d33519ef734faf","score":"0.43002823","text":"def make_copy(self):\n\t\tother = GobbletPiece(self.player, self.size)\n\t\treturn other","title":""},{"docid":"2365d7440f103c6ecc07eddff76a9082","score":"0.429854","text":"def save(self, placeholder, slide_obj):\n self.content.save(placeholder, slide_obj)","title":""},{"docid":"4601e5287a0f5fd371e05779d9dc5b1c","score":"0.42948383","text":"def exposeJoint(self, node, partName, jointName, lodName=\"lodRoot\",\n localTransform = 0):\n partBundleDict = self.__partBundleDict.get(lodName)\n if not partBundleDict:\n Actor.notify.warning(\"no lod named: %s\" % (lodName))\n return None\n\n subpartDef = self.__subpartDict.get(partName, Actor.SubpartDef(partName))\n\n partDef = partBundleDict.get(subpartDef.truePartName)\n if partDef:\n bundle = partDef.getBundle()\n else:\n Actor.notify.warning(\"no part named %s!\" % (partName))\n return None\n\n # Get a handle to the joint.\n joint = bundle.findChild(jointName)\n\n if node is None:\n node = partDef.partBundleNP.attachNewNode(jointName)\n\n if joint:\n if localTransform:\n joint.addLocalTransform(node.node())\n else:\n joint.addNetTransform(node.node())\n else:\n Actor.notify.warning(\"no joint named %s!\" % (jointName))\n\n return node","title":""},{"docid":"1b0d343107ab93140e60d1467eb0cc67","score":"0.42939147","text":"def makeNew(deviceId):\n return Puck(deviceId)","title":""},{"docid":"cfa331371f4dab5eeac0a2064f0d7d03","score":"0.4293383","text":"def move_figure(self, figure, coord):\n playfield = deepcopy(self)\n playfield.figures[playfield.figures.index(figure)] = coord\n return playfield","title":""},{"docid":"4f45029c8ba5deb2240f5942f31cbf64","score":"0.42911437","text":"def import_foreign_slide(self, foreign_slideshow, foreign_slide_num):\n ____verbose = False\n\n current_slideshow = self.name()\n current_where = self.current_slide_num()\n\n self.move_slide(foreign_slideshow, foreign_slide_num, current_slideshow, current_where)","title":""},{"docid":"f711a66bedca8269a8500218959ee08e","score":"0.42881453","text":"def create_from_ui(self, partner):\n # image is a dataurl, get the data after the comma\n if partner.get('image'):\n partner['image'] = partner['image'].split(',')[1]\n partner_id = partner.pop('id', False)\n\n if partner_id: # Modifying existing partner\n partner.pop('vat', None)\n partner.pop('name', None)\n self.browse(partner_id).with_context(from_pos=True).write(partner)\n else:\n partner_id = self.create(partner).id\n return partner_id","title":""},{"docid":"d97fea49f84b1a10cb597deca1a7201c","score":"0.4287546","text":"def newPressStep(self, **attrlinks):\n return PressStep(self, **attrlinks)","title":""},{"docid":"9e17677bbfc6f82a1a10229a0ec77487","score":"0.42865676","text":"def findNewspaperByName(self, name):\n return self.db.newspapers.find_one({'name':name})","title":""},{"docid":"88795498bf42668add0731c6ec91060c","score":"0.4285261","text":"def _copy_rep(self, name=None):\n if self.name is None:\n return None\n if name is None:\n name = self.name\n else:\n name = self.name % name\n return varMeta(name,\n (self._units and self._units.copy()),\n list(self.dim_names),\n self._units_style,\n self.scale)","title":""},{"docid":"2ab87822ec8eb20438fbeabfc2ac2068","score":"0.42818317","text":"def addPart(part):\n\n item = et.SubElement(partsList, part[\"NAME\"])\n for name in part:\n data = part[name]\n if name != \"NAME\":\n # Handle tuples by assigning x, y, z coords\n if isinstance(data, tuple):\n num = re.findall(\"[0-9]+$\", name)\n xyz = [\"x\",\"y\",\"z\"]\n vector = et.SubElement(item, name)\n for i in range(len(data)):\n coord = et.SubElement(vector, xyz[i]+num[-1])\n coord.text = str(data[i])\n else:\n subItem = et.SubElement(item, name)\n subItem.text = str(data)\n #item.set(name, str(part[name]))","title":""},{"docid":"a190dffc7ad6b408e0117294677beab6","score":"0.4279772","text":"def pownur():\r\n return factory.create('POWNUR')","title":""},{"docid":"8ac99d305bc48f0038f5a24ea7f0f00d","score":"0.42782572","text":"def clone(self):\n new_puzzle = Puzzle(self._height, self._width, self._grid)\n return new_puzzle","title":""},{"docid":"8ac99d305bc48f0038f5a24ea7f0f00d","score":"0.42782572","text":"def clone(self):\n new_puzzle = Puzzle(self._height, self._width, self._grid)\n return new_puzzle","title":""},{"docid":"8ac99d305bc48f0038f5a24ea7f0f00d","score":"0.42782572","text":"def clone(self):\n new_puzzle = Puzzle(self._height, self._width, self._grid)\n return new_puzzle","title":""},{"docid":"8ac99d305bc48f0038f5a24ea7f0f00d","score":"0.42782572","text":"def clone(self):\n new_puzzle = Puzzle(self._height, self._width, self._grid)\n return new_puzzle","title":""},{"docid":"8ac99d305bc48f0038f5a24ea7f0f00d","score":"0.42782572","text":"def clone(self):\n new_puzzle = Puzzle(self._height, self._width, self._grid)\n return new_puzzle","title":""},{"docid":"8ac99d305bc48f0038f5a24ea7f0f00d","score":"0.42782572","text":"def clone(self):\n new_puzzle = Puzzle(self._height, self._width, self._grid)\n return new_puzzle","title":""},{"docid":"98a7b65370eb8770af044dbac01d9cbb","score":"0.42746702","text":"def make_projector(project):\n if project is None or project==\"cocnat\":\n return Img2Seq()\n elif project == \"max\":\n return ImgMaxSeq()\n elif project == \"sum\":\n return ImgSumSeq()\n elif project.startswith(\"lstm:\"):\n _, n = project.split(\":\")\n return Lstm2Dto1D(int(n))\n else:\n raise Exception(\"unknown projection: \"+project)","title":""},{"docid":"7c43d60724fabd6f7b1dd231a1dd03fd","score":"0.42737997","text":"def newPxlSlot(self, checked=False):\n plugin = self.application().plugin(\"Pxl\")\n if plugin:\n plugin.newFile()","title":""},{"docid":"a89fff95dcdeb350d36fe799f8c8aa00","score":"0.4269441","text":"def create(self, validated_data):\r\n return Snippet.objects.create(**validated_data)","title":""},{"docid":"a89fff95dcdeb350d36fe799f8c8aa00","score":"0.4269441","text":"def create(self, validated_data):\r\n return Snippet.objects.create(**validated_data)","title":""},{"docid":"ecbe3b9b260a71535b4a975904e36b20","score":"0.4263557","text":"def create(self, validated_data):\n return Snippet.objects.create(**validated_data)","title":""},{"docid":"ecbe3b9b260a71535b4a975904e36b20","score":"0.4263557","text":"def create(self, validated_data):\n return Snippet.objects.create(**validated_data)","title":""},{"docid":"ecbe3b9b260a71535b4a975904e36b20","score":"0.4263557","text":"def create(self, validated_data):\n return Snippet.objects.create(**validated_data)","title":""}],"string":"[\n {\n \"docid\": \"4a340950d41017958ca74388058fd5ee\",\n \"score\": \"0.7599987\",\n \"text\": \"def new(cls, slide_layout, partname, package):\\n slide_elm = CT_Slide.new()\\n slide = cls(partname, CT.PML_SLIDE, slide_elm, package)\\n slide.shapes.clone_layout_placeholders(slide_layout)\\n slide.relate_to(slide_layout, RT.SLIDE_LAYOUT)\\n return slide\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05abfc3b8c295d623ed8878221b5ac04\",\n \"score\": \"0.6525773\",\n \"text\": \"def make_blank_slide():\\n\\n prs = pptx.Presentation()\\n layout = prs.slide_layouts[6]\\n slide = prs.slides.add_slide(layout)\\n return prs, slide\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2bcc9bebc8777156140f7ca05e47c8\",\n \"score\": \"0.63209826\",\n \"text\": \"def save(self, ppt, parent_name=None):\\n if parent_name:\\n section_name = parent_name + (\\n (\\\" - \\\" + self.pretty_title) if self.pretty_title else \\\"\\\"\\n )\\n else:\\n section_name = self.pretty_title\\n slide_obj = ppt.slides.add_slide(slide_layout=self.layout)\\n if self.title and slide_obj.shapes.title:\\n slide_obj.shapes.title.text = section_name\\n for index, component in enumerate(self.contents):\\n if index + 1 < len(slide_obj.placeholders):\\n placeholder = slide_obj.placeholders[index + 1]\\n component.save(placeholder, slide_obj)\\n else:\\n raise Exception(\\\"Not enough shapes in the slide for the contents.\\\")\\n return slide_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e0db32b76a8eed2d130b2fc02329dd\",\n \"score\": \"0.6138355\",\n \"text\": \"def build_slides_easy_way(self):\\n slide_part1 = None\\n for photo in self.list_photos:\\n if photo.get_orientation() == \\\"H\\\":\\n # case the slide is horizontal\\n self.list_slides.append(Slide(photo))\\n else:\\n # case the slide is vertical\\n if slide_part1 is None:\\n # add the vertical photo on memory\\n slide_part1 = photo\\n else:\\n # create a slide with the photo on memory and the current photo\\n self.list_slides.append(Slide(slide_part1, photo))\\n slide_part1 = None\\n import random\\n random.shuffle(self.list_slides)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a5459e341419226743e4a5007c2fba9\",\n \"score\": \"0.57955754\",\n \"text\": \"def register_projector_slide(name: str, slide: ProjectorSlide) -> None:\\n projector_slides[name] = slide\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d62d52f985465c3b6f564e1b26762332\",\n \"score\": \"0.5696239\",\n \"text\": \"def __getitem__(self, idx):\\n for e in self._element.iter_ph_elms():\\n if e.ph_idx == idx:\\n return SlideShapeFactory(e, self)\\n raise KeyError('no placeholder on this slide with idx == %d' % idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c522a37db8ee0083f34cb15e919df21e\",\n \"score\": \"0.5528841\",\n \"text\": \"def create_blank_slide(self):\\n self.add_slide(Slide(mpfdisplay=self, priority=0, persist=True,\\n machine=self.machine, name='blank'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"810b3fe7f2b93037e564651b3ba6a802\",\n \"score\": \"0.5332095\",\n \"text\": \"def slide(self):\\n return self.part_related_by(RT.SLIDE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1550036bf5059c772bc6e4f5805b60\",\n \"score\": \"0.5321755\",\n \"text\": \"def addslides(inputfile, outputfile, feature_dicts_list):\\n # still missing launch date, price, carriers. , , is not existed in every page\\n keys = [\\\"Display size\\\",\\\"Battery\\\",\\\"Dimensions\\\",\\\"Screen-to-body ratio\\\",\\\"System chip\\\",\\\"Camera\\\",\\n \\\"Memory\\\",\\\"Biometrics\\\",\\\"Bluetooth\\\",\\\"Wi-Fi\\\", \\\"Bands\\\",\\\"OS\\\",\\\"Materials\\\",\\\"Release_date\\\", 'Carriers', 'MSRP price']\\n \\n prs = Presentation(inputfile)\\n # slide_layouts is a slide, Presentation.slides, slide.shapes, shapes.title, shapes.placeholders[]\\n spec_slide_layout = prs.slide_layouts[2] # 2 is no.2 mother template\\n \\n# add slides according to feature_dicts_list numbers\\n for spec_dict in feature_dicts_list:\\n slide = prs.slides.add_slide(spec_slide_layout)\\n \\n # add product name \\n shapes = slide.shapes\\n title_shape = shapes.title\\n title_shape.text = spec_dict['phone_name']\\n \\n # shapes.placeholder -- body\\n# body_shape = shapes.placeholders[12]\\n# tf = body_shape.text_frame\\n# tf.text = 'Presentation.slides.shapes.placeholders[].text_frame'\\n# \\n# p = tf.add_paragraph()\\n# p.text = 'Presentation.slides.shapes.placeholders[].text_frame.paragraph level 1'\\n# p.level = 1\\n# \\n# p = tf.add_paragraph()\\n# p.text = 'Presentation.slides.shapes.placeholders[].text_frame.paragraph level 2'\\n# p.level = 2\\n \\n # add product ID\\n img_path = spec_dict['phone_id'] \\n left = Inches(0.01)\\n top = Inches(0.6)\\n height = Inches(5)\\n slide.shapes.add_picture(img_path, left, top, height=height)\\n \\n # add spec table\\n rows = len(keys)+1\\n cols = 2\\n left = Inches(4.0)\\n top = Inches(0.2)\\n width = Inches(2.0)\\n height = Inches(0.1)\\n \\n table = shapes.add_table(rows, cols, left, top, width, height).table\\n \\n # set column widths\\n table.columns[0].width = Inches(1.6)\\n table.columns[1].width = Inches(4.2)\\n \\n # write column headings\\n table.cell(0, 0).text = 'Item'\\n table.cell(0, 1).text = 'Spec'\\n \\n i=1\\n for key in keys:\\n # check if this items exist\\n if(key in spec_dict.keys()): \\n key_frame = table.cell(i,0).text_frame\\n key_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\\n p = key_frame.paragraphs[0]\\n run = p.add_run()\\n run.text = key \\n font = run.font\\n font.name = 'Calibri'\\n font.size = Pt(12)\\n font.bold = False\\n font.italic = None # cause value to be inherited from theme\\n # \\n value_frame = table.cell(i,1).text_frame\\n value_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\\n p = value_frame.paragraphs[0]\\n run = p.add_run()\\n run.text = spec_dict[key]\\n font = run.font\\n font.name = 'Calibri'\\n font.size = Pt(12)\\n font.bold = False\\n font.italic = None # cause value to be inherited from theme\\n \\n i += 1 \\n \\n# Store all the items\\n# i=1\\n# for k, v in spec_dict.items():\\n# key_frame = table.cell(i,0).text_frame\\n# key_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\\n# p = key_frame.paragraphs[0]\\n# run = p.add_run()\\n# run.text = k \\n# font = run.font\\n# font.name = 'Calibri'\\n# font.size = Pt(12)\\n# font.bold = False\\n# font.italic = None # cause value to be inherited from theme\\n# # \\n# value_frame = table.cell(i,1).text_frame\\n# value_frame.auto_size = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT\\n# p = value_frame.paragraphs[0]\\n# run = p.add_run()\\n# run.text = v\\n# font = run.font\\n# font.name = 'Calibri'\\n# font.size = Pt(12)\\n# font.bold = False\\n# font.italic = None # cause value to be inherited from theme\\n# \\n# i += 1\\n \\n prs.save(outputfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55cc6ed34827649af74f7b2fe70d4a36\",\n \"score\": \"0.5268827\",\n \"text\": \"def add_partition(self, part_name):\\n\\n if part_name == PART1_NAME:\\n faked_partition = self.add_partition1()\\n elif part_name == PART2_NAME:\\n faked_partition = self.add_partition2()\\n elif part_name == PART3_NAME:\\n faked_partition = self.add_partition3()\\n return faked_partition\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4efa253badd3432f76a1f99aea1e5a\",\n \"score\": \"0.52511925\",\n \"text\": \"def set_titleorbody_of_slide(self, _property_, slide_number, _new_):\\n if self.playing():\\n self._edit.append( (_property_, slide_number, _new_) )\\n \\n else:\\n if _property_ not in [\\\"title\\\", \\\"body\\\"]:\\n raise Exception(\\\"Passed {0} crap to set_titleorbody_of_slide\\\".format(str(_property_)))\\n reference = getattr(self.get_slide_reference(slide_number), _property_)\\n reference.set(_new_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8bc19d1694e74d2f8f8b822a306dfb\",\n \"score\": \"0.5245959\",\n \"text\": \"def NewPart(self):\\n\\t\\tret = self._oleobj_.InvokeTypes(8, LCID, 1, (9, 0), (),)\\n\\t\\tif ret is not None:\\n\\t\\t\\tret = Dispatch(ret, u'NewPart', None)\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6e26e3f90650e4895b979b7ea8bf86\",\n \"score\": \"0.5153214\",\n \"text\": \"def it_knows_the_name_of_the_slide(self, base_slide):\\n # setup ------------------------\\n base_slide._element = _sldLayout1()\\n # exercise ---------------------\\n name = base_slide.name\\n # verify -----------------------\\n expected = 'Title Slide'\\n actual = name\\n msg = \\\"expected '%s', got '%s'\\\" % (expected, actual)\\n assert actual == expected, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d592463a94b9e82724621a4eafe0093\",\n \"score\": \"0.5118758\",\n \"text\": \"def create():\\n try:\\n slides = Slides(\\n author=get_current_user()._id,\\n title=request.form['title'],\\n content=request.form['content'],\\n course_id=ObjectId(request.form['course_id'])\\n )\\n slides.save()\\n return jsonify({\\n 'success': 1,\\n 'slides_id': str(slides._id)\\n })\\n except ValidationError as e:\\n logger.warning(\\n 'Failed to create new slides. Exception: {}'.format(e.message))\\n return jsonify({\\n 'success': 0,\\n 'msg': '{}: {}'.format(error_msg.ILLEGAL_ARGUMENT, ','.join(list(e.message.keys())))\\n }), 400\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b056ceb0d29c0d03096de6a366061b1\",\n \"score\": \"0.51153123\",\n \"text\": \"def get_slide(self, index):\\n return self._generate_slide(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed553c45d15ad287e86adea2406c0b47\",\n \"score\": \"0.5060784\",\n \"text\": \"def makeNew(app=None):\\n return Thumbnail(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0557fdca3fa70c1da109d2e18e2eeaa9\",\n \"score\": \"0.49716058\",\n \"text\": \"def next_slide(self):\\n if self.old_slide is None and self.album_length > 1:\\n self.index = (self.index + 1) % self.album_length\\n self.old_slide = self.slide\\n self.slide = self.pending_slides[1]\\n self.slide.set_x(unit.size_pix[0])\\n if self.fullscreen_on:\\n self.cover_frame.add_child(self.slide)\\n self.slide.set_size(unit.size_pix[0], unit.size_pix[1])\\n else:\\n self.add_child(self.slide)\\n self.pending_slides = self.data_source.get_pending_slides(\\n self.index)\\n self.new_slide_transition.set_from(unit.size_pix[0])\\n self.new_slide_transition.set_to(0)\\n self.old_slide_transition.set_to(-1*unit.size_pix[0])\\n self.old_slide.add_transition(\\\"x\\\", self.old_slide_transition)\\n self.slide.add_transition(\\\"x\\\", self.new_slide_transition)\\n self.emit(\\\"progressed\\\",\\n float(self.index+1) / self.album_length,\\n self.index+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462dbc5c8d5c8dd5d19afa626ac02e8a\",\n \"score\": \"0.4883622\",\n \"text\": \"def instance(self, path, partName, jointName, lodName=\\\"lodRoot\\\"):\\n partBundleDict = self.__partBundleDict.get(lodName)\\n if partBundleDict:\\n subpartDef = self.__subpartDict.get(partName, Actor.SubpartDef(partName))\\n partDef = partBundleDict.get(subpartDef.truePartName)\\n if partDef:\\n joint = partDef.partBundleNP.find(\\\"**/\\\" + jointName)\\n if joint.isEmpty():\\n Actor.notify.warning(\\\"%s not found!\\\" % (jointName))\\n else:\\n return path.instanceTo(joint)\\n else:\\n Actor.notify.warning(\\\"no part named %s!\\\" % (partName))\\n else:\\n Actor.notify.warning(\\\"no lod named %s!\\\" % (lodName))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae98685117c6b74231c5521c57057370\",\n \"score\": \"0.48793986\",\n \"text\": \"def newPartPathAtIndex(dest, index):\\n return os.path.join(dest, 'part_%08d.new' % index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ec95c35d3332a939ba70fe4dc2f263\",\n \"score\": \"0.4879093\",\n \"text\": \"def Copy(self, name=None):\\n if name is None:\\n name = self.name\\n return Pmf(dict(self.d), name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aec60de0da697e521dea97aebf7255f4\",\n \"score\": \"0.48536906\",\n \"text\": \"def _loaded_slidelayout(self, prs_slidemaster=None):\\n # partname for related slideMaster\\n sldmaster_partname = '/ppt/slideMasters/slideMaster1.xml'\\n # path to test slideLayout XML\\n slidelayout_path = absjoin(test_file_dir, 'slideLayout1.xml')\\n # model-side slideMaster part\\n if prs_slidemaster is None:\\n prs_slidemaster = Mock(spec=SlideMaster)\\n # a part dict containing the already-loaded model-side slideMaster\\n loaded_part_dict = {sldmaster_partname: prs_slidemaster}\\n # a slideMaster package part for rel target\\n pkg_slidemaster_part = Mock(spec=package.Part)\\n pkg_slidemaster_part.partname = sldmaster_partname\\n # a package-side relationship from slideLayout to its slideMaster\\n rel = Mock(name='pptx.package.Relationship')\\n rel.rId = 'rId1'\\n rel.reltype = RT.SLIDE_MASTER\\n rel.target = pkg_slidemaster_part\\n # the slideLayout package part to send to _load()\\n pkg_slidelayout_part = Mock(spec=package.Part)\\n pkg_slidelayout_part.relationships = [rel]\\n with open(slidelayout_path, 'rb') as f:\\n pkg_slidelayout_part.blob = f.read()\\n # _load and return\\n slidelayout = SlideLayout()\\n return slidelayout._load(pkg_slidelayout_part, loaded_part_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48bf505ecf21cc371712724c7fba96a\",\n \"score\": \"0.48324564\",\n \"text\": \"def _generate_powerpoint(self, *args, **kwargs):\\n \\n self.ppt.generate_presentation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb49fc4be7e881fa91fcf674564846c0\",\n \"score\": \"0.48054546\",\n \"text\": \"def _fetch_presentation_by_slug(self, presentation_name):\\n full_presentation = json.loads(self.api_proxy.get(\\\"/director_api/presentation/%s/\\\" % presentation_name))\\n return full_presentation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baadefdbc2eb26686215099fec75110c\",\n \"score\": \"0.47963375\",\n \"text\": \"def previous_slide(self):\\n if self.old_slide is None and self.album_length > 1:\\n self.index = self.index - 1 if self.index > 0 \\\\\\n else self.album_length - 1\\n self.old_slide = self.slide\\n self.slide = self.pending_slides[0]\\n self.slide.set_x(-1*unit.size_pix[0])\\n if self.fullscreen_on:\\n self.cover_frame.add_child(self.slide)\\n self.slide.set_size(unit.size_pix[0], unit.size_pix[1])\\n else:\\n self.add_child(self.slide)\\n self.pending_slides = self.data_source.get_pending_slides(\\n self.index)\\n self.new_slide_transition.set_from(-1*unit.size_pix[0])\\n self.new_slide_transition.set_to(0)\\n self.old_slide_transition.set_to(unit.size_pix[0])\\n self.old_slide.add_transition(\\\"x\\\", self.old_slide_transition)\\n self.slide.add_transition(\\\"x\\\", self.new_slide_transition)\\n self.emit(\\\"progressed\\\",\\n float(self.index+1) / self.album_length,\\n self.index+1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5303e6dedddda46f73b332476dd9d9e4\",\n \"score\": \"0.47775975\",\n \"text\": \"def doNewIndex(self):\\n\\t\\tself.__slide = self.__slides[self.__pos]\\n\\t\\tself_ink = self.__slide.getElementsByTagName(\\\"self\\\")\\n\\t\\tself.__instructor_tag = None\\n\\t\\tself.__self_ink_tag = None\\n\\t\\tself.__instructor_ink = []\\n\\t\\tself.__self_ink = []\\n\\t\\tself.__self_text = \\\"\\\"\\n\\t\\tself.__text_tag = None\\n\\t\\tself.__active_sub = -1\\n\\t\\tif len(self_ink) > 0:\\n\\t\\t\\tself.__self_ink_tag = self_ink[0]\\n\\t\\t\\ttexttags = self.__self_ink_tag.getElementsByTagName(\\\"text\\\")\\n\\t\\t\\tif len(texttags) > 0:\\n\\t\\t\\t\\tself.__text_tag = texttags[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.__text_tag = self.__dom.createElement(\\\"text\\\")\\n\\t\\t\\t\\tself.__text_tag.appendChild(self.__dom.createTextNode(\\\"\\\"))\\n\\t\\t\\t\\tself.__self_ink_tag.appendChild(self.__text_tag)\\n\\t\\t\\tpathtags = self.__self_ink_tag.getElementsByTagName(\\\"path\\\")\\n\\t\\t\\tfor pathstr in pathtags:\\n\\t\\t\\t\\tself.__self_ink.append(pathstr.firstChild.nodeValue)\\n\\t\\telse:\\n\\t\\t\\tself.__self_ink_tag = self.__dom.createElement(\\\"self\\\")\\n\\t\\t\\tself.__slide.appendChild(self.__self_ink_tag)\\n\\t\\t\\tself.__text_tag = self.__dom.createElement(\\\"text\\\")\\n\\t\\t\\tself.__text_tag.appendChild(self.__dom.createTextNode(\\\"\\\"))\\n\\t\\t\\tself.__self_ink_tag.appendChild(self.__text_tag)\\n\\t\\tif self.__text_tag.firstChild:\\n\\t\\t\\tself.__self_text = self.__text_tag.firstChild.nodeValue\\n\\t\\t\\t\\n\\t\\tself.emit(\\\"slide-changed\\\")\\n\\t\\tself.emit(\\\"update-submissions\\\", self.__active_sub)\\n\\t\\tself.emit(\\\"slide-redraw\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea721347efaa80b54dbcbb6dbf560a6\",\n \"score\": \"0.46951017\",\n \"text\": \"def Part(\\n self, name: str, embeddedSpace: SymbolicConstant, type: SymbolicConstant\\n ) -> OdbPart:\\n self.parts[name] = odbPart = OdbPart(name, embeddedSpace, type)\\n return odbPart\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02a94b5bfeb33ba63f0a66dcf2e8b6c7\",\n \"score\": \"0.46780938\",\n \"text\": \"def _current_slide(self,\\n zoom, \\n page):\\n\\n current_slide = self._slides[page-1]\\n current_slide.width = self._default_width*zoom\\n current_slide.height = self._default_height*zoom\\n return current_slide\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde7fed329c29eb6d4854a5cdd489450\",\n \"score\": \"0.46732825\",\n \"text\": \"def __init__(self, \\n slides_path,\\n default_width=720,\\n height_to_width_ratio=810/1080):\\n\\n self._default_width = default_width\\n self._default_height = default_width*height_to_width_ratio # default height\\n self._slides = self._read_slides(slides_path) # read and keep the slides \",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8ce08bd7ed3f2a0fef2f28ce3aaad6f\",\n \"score\": \"0.46728823\",\n \"text\": \"def transform_to_image(slide):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a6a5b2ed7ffdf1d90b23d119de933b\",\n \"score\": \"0.4667195\",\n \"text\": \"def _newStepItem(stepName):\\n newItem = QtGui.QTreeWidgetItem()\\n newItem.setText(0, stepName)\\n newItem.stepName = stepName\\n return newItem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce63372a0b3bd4230825905d380c4ee\",\n \"score\": \"0.46107632\",\n \"text\": \"def create_piece(piece):\\n\\n piece_map = {\\n 'rook': Rook,\\n 'knight': Knight,\\n 'queen': Queen,\\n }\\n\\n if piece not in piece_map:\\n raise PieceNotFoundError()\\n\\n return piece_map[piece]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295b550d40992a87297abdf08a5f8483\",\n \"score\": \"0.4596975\",\n \"text\": \"def _get_piece(self, name):\\n return Piece(name, tuple([(x, y) for y in range(len(self._state))\\n for x in range(len(self._state[y])) if\\n self._state[y][x] == name]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ef0f5a4414ab6c98db1bbfdadbad2c\",\n \"score\": \"0.45958647\",\n \"text\": \"def make_new_puzzle(self):\\n #Define solution to the puzzle \\n solution = np.append(np.arange(1, self.p.N**2), 0)\\n\\n #Create new puzzle \\n new_puzzle = np.random.choice(solution, self.p.N**2, replace = False)\\n\\n #Check if created puzzle is solvable and if not make new ones until solvable one is found\\n new_puzzle, blank_index = self.check_if_solvable(new_puzzle)\\n\\n #Convert 1D arrays to NxN arrays\\n return new_puzzle.reshape(self.p.N, self.p.N), solution.reshape(self.p.N, self.p.N), blank_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd267035451d7a057f07811bfa044dd4\",\n \"score\": \"0.4554759\",\n \"text\": \"def _make_pep_from_seq(self):\\n self._generate_template()\\n self._generate_sidechains_scwrl()\\n return self.pep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199dcc44e1b7944389b2e141d9db0b76\",\n \"score\": \"0.455377\",\n \"text\": \"def create_samplepartition(context, data):\\n print data['part_id']\\n partition = _createObjectByType('SamplePartition', context, data['part_id'])\\n partition.unmarkCreationFlag()\\n partition.reindexObject()\\n\\n return partition\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9faa7c25d4f978490eb09c919734f990\",\n \"score\": \"0.45443135\",\n \"text\": \"def create_slides(data):\\n slides = []\\n for item in data.index:\\n slides.append({\\n \\\"key\\\": item,\\n \\\"src\\\": data['asset.image_url'][item],\\n \\\"header\\\": data['asset.name'][item],\\n })\\n return slides\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a320603f903204d15d97b19c3689bb48\",\n \"score\": \"0.45366773\",\n \"text\": \"def slides():\\n fig = new_slide()\\n\\n slide_heading(fig, 'Future Plans')\\n\\n props = dict(fontproperties=FONT, fontsize=56, alpha=0.7,\\n verticalalignment='top')\\n\\n fig.text(0.05, 0.8, 'Next feature release: 3.4', **props)\\n fig.text(0.1, 0.7, f'{BULLET} September 2020', **props)\\n fig.text(0.1, 0.6,\\n f'{BULLET} Dropping support for Python 3.6 & NumPy 1.15',\\n **props)\\n\\n fig.text(0.05, 0.4, 'Google Season of Docs 2020', **props)\\n\\n fig.text(0.05, 0.2, 'Check out our blog!', **props)\\n t = fig.text(0.1, 0.2, '\\\\nhttps://matplotlib.org/matplotblog/',\\n **props)\\n t.set_url('https://matplotlib.org/matplotblog/')\\n\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89b3f43ee652541ee4c93281f7d43f0\",\n \"score\": \"0.4523472\",\n \"text\": \"def _create_section_page(title: str, parent: Page) -> SectionPage:\\n p = SectionPage()\\n p.title = title\\n parent.add_child(instance=p)\\n p.save_revision().publish()\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98640bcbda449380c529db91497aae6b\",\n \"score\": \"0.4520602\",\n \"text\": \"def slice1(self, x, slide):\\n x1 = x[:, :slide, :, :]\\n return x1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db272a2550eb294b9b0d92c4b091bbc7\",\n \"score\": \"0.45179543\",\n \"text\": \"def parse_recipe(name):\\n name = name if name.endswith('.xml') else name + '.xml'\\n absname = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'recipes', name)\\n return Workflow(absname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a91ed648e3dc99390514aad3ba733559\",\n \"score\": \"0.45158225\",\n \"text\": \"def Instance(self, name: str, objectToCopy: PartInstance):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d92d798300000167abeda9df9f340f7\",\n \"score\": \"0.44765404\",\n \"text\": \"def part(self, part):\\n\\t\\tself.current_part = part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2208089025fef69c53a7fb7522b97e4a\",\n \"score\": \"0.4473693\",\n \"text\": \"def newphotooverlay(self, **kwargs):\\n photoover = PhotoOverlay(**kwargs)\\n photoover._parent = self._placemark\\n self._addfeature(photoover)\\n return photoover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5f0747464d378043a014aecd272b90\",\n \"score\": \"0.44586238\",\n \"text\": \"def __getitem__(self, item):\\n steps = list(self.steps.items())\\n return PipelineDefinition(\\n resources=self.resources,\\n steps=dict(steps[self.getslice(item)])\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"053d9e98a9330800ea3498cf853d34d1\",\n \"score\": \"0.44475362\",\n \"text\": \"def make_plan(text):\\r\\n site = build.Website(\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", [])\\r\\n f = open(text)\\r\\n title = f.readline()\\r\\n site.title = title\\r\\n new_p = None\\r\\n para_list = []\\r\\n\\r\\n for line in f:\\r\\n line = line.strip()\\r\\n if len(line) == 0:\\r\\n para_list.append(new_p)\\r\\n elif line == \\\"!new_paragraph\\\":\\r\\n new_p = build.Paragraphs(\\\"\\\", \\\"\\\", [])\\r\\n elif line[0:6] == \\\"!title\\\":\\r\\n new_p.title = line[7:]\\r\\n elif line[0:6] == \\\"!image\\\":\\r\\n split = line[7:].split(\\\" \\\")\\r\\n image_name = split[0]\\r\\n image_width = \\\"100%\\\"\\r\\n if len(split) == 2:\\r\\n image_width = split[1]\\r\\n\\r\\n img = build.Images(image_name, image_width)\\r\\n new_p.image.append(img)\\r\\n else:\\r\\n new_p.text += line + \\\"\\\\n\\\"\\r\\n site.body = para_list\\r\\n f.close()\\r\\n return site\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201ef4f1b869f0cdda471a9bb82caebf\",\n \"score\": \"0.4422248\",\n \"text\": \"def getPart(self, partName, lodName=\\\"lodRoot\\\"):\\n partBundleDict = self.__partBundleDict.get(lodName)\\n if not partBundleDict:\\n Actor.notify.warning(\\\"no lod named: %s\\\" % (lodName))\\n return None\\n subpartDef = self.__subpartDict.get(partName, Actor.SubpartDef(partName))\\n partDef = partBundleDict.get(subpartDef.truePartName)\\n if partDef is not None:\\n return partDef.partBundleNP\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26888056a28a2905e4f563d0f5b70f23\",\n \"score\": \"0.44220287\",\n \"text\": \"def create_model_parts(self):\\n if not self.id:\\n raise ValueError(\\\"Cannot create parts if Song has not been saved yet.\\\")\\n xml_parts = list(self.xml_song.xml_parts)\\n names = {part.name for part in xml_parts}\\n Section = apps.get_model(\\\"singers\\\", \\\"Section\\\")\\n sections = dict(Section.objects.filter(name__in=names).values_list(\\\"name\\\", \\\"id\\\"))\\n model_parts = []\\n for xml_part in xml_parts:\\n model_part = xml_part.to_model_part()\\n model_part.song_id = self.id\\n model_part.id = sections.get(model_part.name)\\n model_parts.append(model_part)\\n return model_parts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439a1ff47749b5f5d22ce30fa4c060a1\",\n \"score\": \"0.44192353\",\n \"text\": \"def init_plot_custom_slices_wf(\\n output_dir,\\n desc,\\n name=\\\"plot_custom_slices_wf\\\",\\n):\\n # NOTE: These slices are almost certainly specific to a given MNI template and resolution.\\n SINGLE_SLICES = [\\\"x\\\", \\\"x\\\", \\\"x\\\", \\\"y\\\", \\\"y\\\", \\\"y\\\", \\\"z\\\", \\\"z\\\", \\\"z\\\"]\\n SLICE_NUMBERS = [36, 45, 52, 43, 54, 65, 23, 33, 39]\\n\\n workflow = Workflow(name=name)\\n\\n inputnode = pe.Node(\\n niu.IdentityInterface(\\n fields=[\\n \\\"underlay_file\\\",\\n \\\"overlay_file\\\",\\n \\\"name_source\\\",\\n ],\\n ),\\n name=\\\"inputnode\\\",\\n )\\n\\n # slices/slicer does not do well trying to make the red outline when it\\n # cannot find the edges, so cannot use the ROI files with some low intensities.\\n binarize_edges = pe.Node(\\n BinaryMath(expression=\\\"img.astype(bool).astype(int)\\\"),\\n name=\\\"binarize_edges\\\",\\n )\\n\\n workflow.connect([(inputnode, binarize_edges, [(\\\"overlay_file\\\", \\\"in_file\\\")])])\\n\\n make_image = pe.MapNode(\\n fsl.Slicer(show_orientation=True, label_slices=True),\\n name=\\\"make_image\\\",\\n iterfield=[\\\"single_slice\\\", \\\"slice_number\\\"],\\n )\\n make_image.inputs.single_slice = SINGLE_SLICES\\n make_image.inputs.slice_number = SLICE_NUMBERS\\n\\n # fmt:off\\n workflow.connect([\\n (inputnode, make_image, [(\\\"underlay_file\\\", \\\"in_file\\\")]),\\n (binarize_edges, make_image, [(\\\"out_file\\\", \\\"image_edges\\\")]),\\n ])\\n # fmt:on\\n\\n combine_images = pe.Node(\\n PNGAppend(out_file=\\\"out.png\\\"),\\n name=\\\"combine_images\\\",\\n )\\n\\n workflow.connect([(make_image, combine_images, [(\\\"out_file\\\", \\\"in_files\\\")])])\\n\\n ds_overlay_figure = pe.Node(\\n DerivativesDataSink(\\n base_directory=output_dir,\\n dismiss_entities=[\\\"den\\\"],\\n datatype=\\\"figures\\\",\\n desc=desc,\\n extension=\\\".png\\\",\\n ),\\n name=\\\"ds_overlay_figure\\\",\\n run_without_submitting=True,\\n )\\n\\n # fmt:off\\n workflow.connect([\\n (inputnode, ds_overlay_figure, [(\\\"name_source\\\", \\\"source_file\\\")]),\\n (combine_images, ds_overlay_figure, [(\\\"out_file\\\", \\\"in_file\\\")]),\\n ])\\n # fmt:on\\n\\n return workflow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e193ea321e809f44bf45c9082f8986d\",\n \"score\": \"0.44169056\",\n \"text\": \"def it_knows_the_minimal_element_xml_for_a_slide(self, slide):\\n # setup ------------------------\\n path = absjoin(test_file_dir, 'minimal_slide.xml')\\n # exercise ---------------------\\n sld = slide._minimal_element()\\n # verify -----------------------\\n with open(path, 'r') as f:\\n expected_xml = f.read()\\n assert actual_xml(sld) == expected_xml\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf9f460a9462cb4edb9a0316ff35f29\",\n \"score\": \"0.44147587\",\n \"text\": \"def it_establishes_a_relationship_to_its_slide_layout_on_construction(\\n self, relate_to_):\\n # setup ------------------------\\n slidelayout = SlideLayout(None, None, _sldLayout1(), None)\\n partname = PackURI('/ppt/slides/slide1.xml')\\n # exercise ---------------------\\n slide = Slide.new(slidelayout, partname, None)\\n # verify ----------------------\\n slide.relate_to.assert_called_once_with(slidelayout, RT.SLIDE_LAYOUT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca029bf23b98892ca12ec046e81f020e\",\n \"score\": \"0.44146568\",\n \"text\": \"def _get_image(self, path_raw, info):\\n param = info\\n path_wsi = glob(os.path.join(self.path_raw, param['name'] + '.*'))\\n assert path_wsi, \\\"no wsi with name {}\\\".format(param['name'])\\n assert len(path_wsi)<2, \\\"several wsi with name {}\\\".format(param['name'])\\n slide = open_slide(path_wsi[0])\\n image = slide.read_region(location=(param['x'], param['y']),\\n level=param['level'], \\n size=(448, 448))#(param['xsize'], param['ysize']))\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1777e711b6734dc63b86280f178a36a4\",\n \"score\": \"0.44108897\",\n \"text\": \"def newphotooverlay(self, **kwargs):\\n photoover = PhotoOverlay(**kwargs)\\n photoover._parent = self\\n self._addfeature(photoover)\\n return photoover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e15330fd395b4ce668aef29b49abe16d\",\n \"score\": \"0.44106802\",\n \"text\": \"def replace_note(slide, s_msg=None):\\n import xml.etree.ElementTree as ET\\n import copy\\n NameSpace={\\n 'a':\\\"http://schemas.openxmlformats.org/drawingml/2006/main\\\",\\n 'r':\\\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\\\",\\n 'p':\\\"http://schemas.openxmlformats.org/presentationml/2006/main\\\"\\n }\\n\\n v=self.slide.part_related_by(NameSpace['r']+'/notesSlide')\\n r=ET.fromstring(v._blob)\\n notes=r.findall('.//p:txBody', NameSpace)\\n for note in notes:\\n if note.find('.//a:p/a:fld', NameSpace):\\n # maybe a slide # or something else\\n continue\\n for p in note.findall('./a:p', NameSpace):\\n note.remove(p)\\n msg=ET.fromstring('<a:p xmlns:a=\\\"%s\\\"><a:r><a:rPr lang=\\\"en-US\\\" dirty=\\\"0\\\" smtClean=\\\"0\\\"/><a:t></a:t></a:r><a:endParaRPr lang=\\\"en-US\\\" dirty=\\\"0\\\"/></a:p>' % NameSpace['a'])\\n msg.find('.//a:t', NameSpace).text=s_msg\\n note.append(msg)\\n break\\n v.blob=ET.tostring(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80b5063098531e9fa8815f9bea6e1b57\",\n \"score\": \"0.44026652\",\n \"text\": \"def clone(self):\\r\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\r\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620c77aabf71ea74e275d6cd38b38a60\",\n \"score\": \"0.43986902\",\n \"text\": \"def makeMapItem(pl_id):\\n\\n\\tmapItem = []\\n\\tmapItem.append(makeMapItemStand(pl_id))\\n\\tpub_id = db.select(\\\"SELECT p.p_id FROM Pub p WHERE p.pl_id = \\\"+ str(pl_id))\\n\\n\\tif len(pub_id) != 0:\\n\\t\\tfor row in pub_id:\\n\\t\\t\\tmapItem.append(makeMapItemPub(row['pub_id']))\\n\\n\\treturn (mapItem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505c4bd6ca4d5fa6f87109e93851f234\",\n \"score\": \"0.43942648\",\n \"text\": \"def load(self, name=None):\\n return Partition(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e5c4f51124ad2108130d78a3eb2cf6\",\n \"score\": \"0.43765423\",\n \"text\": \"def slides(self):\\n if not self.__slides:\\n self.__populate_slides()\\n return self.__slides\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5fadda1f910df5d955a2b8df0df5e90\",\n \"score\": \"0.43700403\",\n \"text\": \"def INewPart(self):\\n\\t\\tret = self._oleobj_.InvokeTypes(20, LCID, 1, (9, 0), (),)\\n\\t\\tif ret is not None:\\n\\t\\t\\tret = Dispatch(ret, u'INewPart', '{83A33D32-27C5-11CE-BFD4-00400513BB57}')\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e52c3bde1eb172bb67d4507fa4472f98\",\n \"score\": \"0.43663114\",\n \"text\": \"def create_pupil_choice(self, text):\\n Pupil = dd.resolve_model(config.pupil_model)\\n kw = parse_name(text)\\n if len(kw) != 2:\\n raise ValidationError(\\n \\\"Cannot find first and last names in %r to \\\\\\n auto-create pupil\\\", text)\\n p = Pupil(**kw)\\n p.full_clean()\\n p.save()\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a178b145a8fba8ca476104355a9cd373\",\n \"score\": \"0.4365004\",\n \"text\": \"def _create_article_page(title: str, parent: Page) -> SectionPage:\\n p = ArticlePage()\\n p.title = title\\n parent.add_child(instance=p)\\n p.save_revision().publish()\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a53d00576c94d10cfbf1c74fe35c3767\",\n \"score\": \"0.43601096\",\n \"text\": \"def newPart(category_id):\\n if 'username' not in login_session:\\n return redirect('/login')\\n category = session.query(Category).filter_by(id=category_id).one()\\n if request.method == 'POST':\\n newPart = Part(name=request.form['name'],\\n description=request.form['description'],\\n category_id=category_id, user_id=category.user_id)\\n session.add(newPart)\\n session.commit()\\n flash('New Part %s Successfully Created' % (newPart.name))\\n return redirect(url_for('showParts', category_id=category_id))\\n else:\\n return render_template('newpart.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188749b558eb3e780fd1a4b88e6f6683\",\n \"score\": \"0.43569982\",\n \"text\": \"def as_pose(self,**kwargs):\\n\\t\\tkwargs['copy'] = True\\n\\t\\treturn pose(self,**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8357f6d821e9d039a084436ec67d9c34\",\n \"score\": \"0.43486828\",\n \"text\": \"def create(self, validated_data):\\n\\t\\treturn Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8357f6d821e9d039a084436ec67d9c34\",\n \"score\": \"0.43486828\",\n \"text\": \"def create(self, validated_data):\\n\\t\\treturn Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afa89494aeb03aedc5020b30b1814d5\",\n \"score\": \"0.43473628\",\n \"text\": \"def new_figure(name):\\n\\n width, height = bf.get_screen_size()\\n if name == 'New Figure':\\n fig = plt.figure(figsize=(width, height-3.2))\\n else:\\n fig = plt.figure(name, figsize=(width, height-3.2))\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab44bc20129f40a54c8e55c050323fd\",\n \"score\": \"0.43344074\",\n \"text\": \"def add_ptx(self, ptx, name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07ffd3f9c89a11fa790afed8c0f8e2b\",\n \"score\": \"0.43290237\",\n \"text\": \"def PCreate (name, image, err, hwidth=1):\\n ################################################################\\n # Checks\\n if not Image.PIsA(image):\\n raise TypeError,\\\"image MUST be a Python Obit Image\\\"\\n if not OErr.OErrIsA(err):\\n raise TypeError,\\\"err MUST be an OErr\\\"\\n #\\n out = ImageInterp(\\\"None\\\",image=image,hwidth=hwidth,err=err);\\n out.me = Obit.ImageInterpCreate(name, image.me, hwidth, err.me)\\n if err.isErr:\\n OErr.printErrMsg(err, \\\"Error creating beam object\\\")\\n return out;\\n # end PCreate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3134019227f31287e75eaa4922d058e7\",\n \"score\": \"0.4327695\",\n \"text\": \"def _read_slides(self, slides_path):\\n\\n # filter the slide name which has a number in it to indicate its order \\n # in the folder \\n slide_names = [sn for sn in os.listdir(slides_path) if any(map(str.isdigit, sn))]\\n\\n try:\\n # sort out the slide names based on the number to make sure the images\\n # are displayed in sequence\\n slide_names_sorted = natsort.natsorted(slide_names, reverse=False)\\n except ValueError as e:\\n print(f'Error: {e}, please make sure the images have numbers to indicate the order')\\n raise\\n\\n # construct a list of slide images \\n slides = [Image(filename=f'{slides_path}/{slide_name}', \\n width=self._default_width,\\n height=self._default_height) \\\\\\n for slide_name in slide_names_sorted]\\n\\n return slides\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13fc61a517a2d3b463120f8280342451\",\n \"score\": \"0.4323946\",\n \"text\": \"def new_duplicate(self, path):\\n self.gui.newDoc(path)\\n self.model = self.gui.getActiveModel()\\n self.__wrap_object(self.model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223eb0ea0e4cc75575b8f1533f4d925b\",\n \"score\": \"0.43236238\",\n \"text\": \"def make_new_item(self, data, summary):\\n identification = {} # @todo: what does this do?\\n result = self.repo.editEntity(identification, data, summary=summary)\\n pywikibot.output(summary) # afterwards in case an error is raised\\n\\n # return the new item\\n return self.QtoItemPage(result.get(u'entity').get('id'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc1d6bbdec8bfec5ab3f6e0489604ab\",\n \"score\": \"0.43199682\",\n \"text\": \"def get_slides(display_date=None):\\n display_date = display_date or date.today()\\n return ('carousel/{0}'.format(name) for name in\\n _get_slide_names(display_date))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1917a3daacd82abdd55b698fa20a1109\",\n \"score\": \"0.43122667\",\n \"text\": \"def removePart(self, partName, lodName=\\\"lodRoot\\\"):\\n # find the corresponding part bundle dict\\n partBundleDict = self.__partBundleDict.get(lodName)\\n if not partBundleDict:\\n Actor.notify.warning(\\\"no lod named: %s\\\" % (lodName))\\n return\\n\\n # remove the part\\n if partName in partBundleDict:\\n partBundleDict[partName].partBundleNP.removeNode()\\n del partBundleDict[partName]\\n\\n # find the corresponding anim control dict\\n if self.mergeLODBundles:\\n lodName = 'common'\\n partDict = self.__animControlDict.get(lodName)\\n if not partDict:\\n Actor.notify.warning(\\\"no lod named: %s\\\" % (lodName))\\n return\\n\\n # remove the animations\\n if partName in partDict:\\n del partDict[partName]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c488a3fb3204b16dad1c8e47f8938e\",\n \"score\": \"0.43102464\",\n \"text\": \"def get_pending_slides(self, index):\\n return (self._generate_slide(index-1),\\n self._generate_slide((index+1)%len(self.photos)),)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef329b4c45f39e0da9d0cd1559683912\",\n \"score\": \"0.43048415\",\n \"text\": \"def _make_new_obs(obs, im):\\n\\n psf_obs=Observation(obs.psf.image,\\n jacobian=obs.psf.jacobian,\\n gmix=obs.psf.gmix.copy())\\n\\n newobs=Observation(im,\\n jacobian=obs.jacobian,\\n weight=obs.weight,\\n psf=psf_obs)\\n return newobs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6428be2ba2a94bbc9b23217670bb96cb\",\n \"score\": \"0.4302464\",\n \"text\": \"def clone(self, *parts):\\r\\n return type(self)(str(self), *parts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f4d107c8913482e9d33519ef734faf\",\n \"score\": \"0.43002823\",\n \"text\": \"def make_copy(self):\\n\\t\\tother = GobbletPiece(self.player, self.size)\\n\\t\\treturn other\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2365d7440f103c6ecc07eddff76a9082\",\n \"score\": \"0.429854\",\n \"text\": \"def save(self, placeholder, slide_obj):\\n self.content.save(placeholder, slide_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4601e5287a0f5fd371e05779d9dc5b1c\",\n \"score\": \"0.42948383\",\n \"text\": \"def exposeJoint(self, node, partName, jointName, lodName=\\\"lodRoot\\\",\\n localTransform = 0):\\n partBundleDict = self.__partBundleDict.get(lodName)\\n if not partBundleDict:\\n Actor.notify.warning(\\\"no lod named: %s\\\" % (lodName))\\n return None\\n\\n subpartDef = self.__subpartDict.get(partName, Actor.SubpartDef(partName))\\n\\n partDef = partBundleDict.get(subpartDef.truePartName)\\n if partDef:\\n bundle = partDef.getBundle()\\n else:\\n Actor.notify.warning(\\\"no part named %s!\\\" % (partName))\\n return None\\n\\n # Get a handle to the joint.\\n joint = bundle.findChild(jointName)\\n\\n if node is None:\\n node = partDef.partBundleNP.attachNewNode(jointName)\\n\\n if joint:\\n if localTransform:\\n joint.addLocalTransform(node.node())\\n else:\\n joint.addNetTransform(node.node())\\n else:\\n Actor.notify.warning(\\\"no joint named %s!\\\" % (jointName))\\n\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b0d343107ab93140e60d1467eb0cc67\",\n \"score\": \"0.42939147\",\n \"text\": \"def makeNew(deviceId):\\n return Puck(deviceId)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfa331371f4dab5eeac0a2064f0d7d03\",\n \"score\": \"0.4293383\",\n \"text\": \"def move_figure(self, figure, coord):\\n playfield = deepcopy(self)\\n playfield.figures[playfield.figures.index(figure)] = coord\\n return playfield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f45029c8ba5deb2240f5942f31cbf64\",\n \"score\": \"0.42911437\",\n \"text\": \"def import_foreign_slide(self, foreign_slideshow, foreign_slide_num):\\n ____verbose = False\\n\\n current_slideshow = self.name()\\n current_where = self.current_slide_num()\\n\\n self.move_slide(foreign_slideshow, foreign_slide_num, current_slideshow, current_where)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f711a66bedca8269a8500218959ee08e\",\n \"score\": \"0.42881453\",\n \"text\": \"def create_from_ui(self, partner):\\n # image is a dataurl, get the data after the comma\\n if partner.get('image'):\\n partner['image'] = partner['image'].split(',')[1]\\n partner_id = partner.pop('id', False)\\n\\n if partner_id: # Modifying existing partner\\n partner.pop('vat', None)\\n partner.pop('name', None)\\n self.browse(partner_id).with_context(from_pos=True).write(partner)\\n else:\\n partner_id = self.create(partner).id\\n return partner_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97fea49f84b1a10cb597deca1a7201c\",\n \"score\": \"0.4287546\",\n \"text\": \"def newPressStep(self, **attrlinks):\\n return PressStep(self, **attrlinks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e17677bbfc6f82a1a10229a0ec77487\",\n \"score\": \"0.42865676\",\n \"text\": \"def findNewspaperByName(self, name):\\n return self.db.newspapers.find_one({'name':name})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88795498bf42668add0731c6ec91060c\",\n \"score\": \"0.4285261\",\n \"text\": \"def _copy_rep(self, name=None):\\n if self.name is None:\\n return None\\n if name is None:\\n name = self.name\\n else:\\n name = self.name % name\\n return varMeta(name,\\n (self._units and self._units.copy()),\\n list(self.dim_names),\\n self._units_style,\\n self.scale)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab87822ec8eb20438fbeabfc2ac2068\",\n \"score\": \"0.42818317\",\n \"text\": \"def addPart(part):\\n\\n item = et.SubElement(partsList, part[\\\"NAME\\\"])\\n for name in part:\\n data = part[name]\\n if name != \\\"NAME\\\":\\n # Handle tuples by assigning x, y, z coords\\n if isinstance(data, tuple):\\n num = re.findall(\\\"[0-9]+$\\\", name)\\n xyz = [\\\"x\\\",\\\"y\\\",\\\"z\\\"]\\n vector = et.SubElement(item, name)\\n for i in range(len(data)):\\n coord = et.SubElement(vector, xyz[i]+num[-1])\\n coord.text = str(data[i])\\n else:\\n subItem = et.SubElement(item, name)\\n subItem.text = str(data)\\n #item.set(name, str(part[name]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a190dffc7ad6b408e0117294677beab6\",\n \"score\": \"0.4279772\",\n \"text\": \"def pownur():\\r\\n return factory.create('POWNUR')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac99d305bc48f0038f5a24ea7f0f00d\",\n \"score\": \"0.42782572\",\n \"text\": \"def clone(self):\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac99d305bc48f0038f5a24ea7f0f00d\",\n \"score\": \"0.42782572\",\n \"text\": \"def clone(self):\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac99d305bc48f0038f5a24ea7f0f00d\",\n \"score\": \"0.42782572\",\n \"text\": \"def clone(self):\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac99d305bc48f0038f5a24ea7f0f00d\",\n \"score\": \"0.42782572\",\n \"text\": \"def clone(self):\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac99d305bc48f0038f5a24ea7f0f00d\",\n \"score\": \"0.42782572\",\n \"text\": \"def clone(self):\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac99d305bc48f0038f5a24ea7f0f00d\",\n \"score\": \"0.42782572\",\n \"text\": \"def clone(self):\\n new_puzzle = Puzzle(self._height, self._width, self._grid)\\n return new_puzzle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a7b65370eb8770af044dbac01d9cbb\",\n \"score\": \"0.42746702\",\n \"text\": \"def make_projector(project):\\n if project is None or project==\\\"cocnat\\\":\\n return Img2Seq()\\n elif project == \\\"max\\\":\\n return ImgMaxSeq()\\n elif project == \\\"sum\\\":\\n return ImgSumSeq()\\n elif project.startswith(\\\"lstm:\\\"):\\n _, n = project.split(\\\":\\\")\\n return Lstm2Dto1D(int(n))\\n else:\\n raise Exception(\\\"unknown projection: \\\"+project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c43d60724fabd6f7b1dd231a1dd03fd\",\n \"score\": \"0.42737997\",\n \"text\": \"def newPxlSlot(self, checked=False):\\n plugin = self.application().plugin(\\\"Pxl\\\")\\n if plugin:\\n plugin.newFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a89fff95dcdeb350d36fe799f8c8aa00\",\n \"score\": \"0.4269441\",\n \"text\": \"def create(self, validated_data):\\r\\n return Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a89fff95dcdeb350d36fe799f8c8aa00\",\n \"score\": \"0.4269441\",\n \"text\": \"def create(self, validated_data):\\r\\n return Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecbe3b9b260a71535b4a975904e36b20\",\n \"score\": \"0.4263557\",\n \"text\": \"def create(self, validated_data):\\n return Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecbe3b9b260a71535b4a975904e36b20\",\n \"score\": \"0.4263557\",\n \"text\": \"def create(self, validated_data):\\n return Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecbe3b9b260a71535b4a975904e36b20\",\n \"score\": \"0.4263557\",\n \"text\": \"def create(self, validated_data):\\n return Snippet.objects.create(**validated_data)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994398,"cells":{"query_id":{"kind":"string","value":"588d72d8e087dcd6d745b56320578dba"},"query":{"kind":"string","value":"Renders a given dictionary to a string using the given template fname."},"positive_passages":{"kind":"list like","value":[{"docid":"56e9e14f3b7681543a306823965edeb6","score":"0.6991874","text":"def renderDict(d, tmpl):\n import web.template as template\n renfunc = template.frender(tmpl, globals=RENDER_GLOBALS)\n s = str(renfunc(d))\n return s","title":""}],"string":"[\n {\n \"docid\": \"56e9e14f3b7681543a306823965edeb6\",\n \"score\": \"0.6991874\",\n \"text\": \"def renderDict(d, tmpl):\\n import web.template as template\\n renfunc = template.frender(tmpl, globals=RENDER_GLOBALS)\\n s = str(renfunc(d))\\n return s\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d701eaab31a42e6f92eb89d722014168","score":"0.70578563","text":"def render(template_string, context, *filepath):\n for key, value in context.items():\n template_string = template_string.replace('{{ %s }}' % key, value)\n template_file = open(os.path.join(*filepath), 'w')\n template_file.write(template_string)\n template_file.close()","title":""},{"docid":"199c7fc72af14938010d7872ffc48df6","score":"0.6854282","text":"def template(file, vars={}, **args):\n contents = open(file, 'r').read()\n return str(contents % (dict(vars, **args)))","title":""},{"docid":"58a08b75663d03a798e9da25696a32b1","score":"0.6831962","text":"def render(variables, template):\r\n return load(template).render(variables)","title":""},{"docid":"f437c05f1527910b704ca3f30be15534","score":"0.6680663","text":"def renderDictToFile(d, tmpl, outname=None, reldir=__file__, **kw):\n s = renderDict(d, tmpl)\n dir, fname = os.path.split(tmpl)\n fname = fname.rsplit('.tmpl')[0]\n if not outname:\n outname = os.path.join(dir, fname + '.html')\n print 'Rendering from %s to %s' % (os.path.join(dir, fname+'.tmpl'), outname)\n # make parent dirs\n try:\n os.makedirs(os.path.dirname(outname))\n except OSError: pass\n # make sure any existing output is first renamed so render doesn't use it\n try:\n os.rename(outname, '.'+outname)\n except OSError: pass\n # compute the relative path to the static dir\n cur = os.path.abspath(os.path.dirname(reldir))\n outdir = os.path.abspath(os.path.dirname(outname))\n prefix = os.path.commonprefix([cur, outdir])\n levels = len(outdir.replace(prefix, '', 1).split('/')) - 1\n if levels == 0:\n rel = '.'\n else:\n rel = '/'.join('..' for i in range(levels))\n #print cur, outdir, prefix, levels, rel\n s = str(html(d, [s], rel=rel, **kw))\n f = open(outname, 'wb')\n f.write(s)\n f.close()\n # remove old output\n try:\n os.remove('.'+outname)\n except OSError: pass\n return outname","title":""},{"docid":"fdb7759c67b48f50f1c6b9953a3160f3","score":"0.662638","text":"def renderTemplate(fileName, templateDict, projectFileNameDict):\n \n templateFilePath = projectFiles[fileName] % (projectFileNameDict)\n templatePath = os.path.dirname(templateFilePath)\n \n template = None\n if fileName in templateFiles:\n template = templateEnvironment.get_template(templateFiles[fileName])\n \n if 'metadata' in templateDict:\n templateDict['metadata']['fileName'] = os.path.basename(templateFilePath)\n \n renderedTemplate = template.render(templateDict) if template else \"\"\n \n if not os.path.isdir(templatePath):\n os.makedirs(templatePath)\n \n templateFile = open(templateFilePath, 'w')\n templateFile.write(renderedTemplate)\n templateFile.close()","title":""},{"docid":"2313ea4932c1e935452cbd994c9e8b1c","score":"0.65588355","text":"def render_template(template_file: str, **kwargs: Any) -> str:\n template = jinja2.Template(open(template_file).read())\n return template.render(kwargs)","title":""},{"docid":"9c391aad01c5725d96e109f871b6e5f7","score":"0.6553463","text":"def render_template(file, template_values):\n path = os.path.join(os.path.dirname(__file__), file)\n return template.render(path, template_values)","title":""},{"docid":"3107f9747adf9309c30c4c2fd3e97e8b","score":"0.6470931","text":"def render(self, template_name, **kwargs):\n return self.write(self.render_str(template_name, **kwargs))","title":""},{"docid":"e445985d6d1281b8fbac803ac4b37381","score":"0.63626456","text":"def render_to_string(self, *args, **kwargs):\r\n try:\r\n template_name, args = args[0], args[1:]\r\n except IndexError:\r\n raise TypeError('name of template required')\r\n return self.get_template(template_name).render(*args, **kwargs)","title":""},{"docid":"e445985d6d1281b8fbac803ac4b37381","score":"0.63626456","text":"def render_to_string(self, *args, **kwargs):\r\n try:\r\n template_name, args = args[0], args[1:]\r\n except IndexError:\r\n raise TypeError('name of template required')\r\n return self.get_template(template_name).render(*args, **kwargs)","title":""},{"docid":"821853dfdbffb059429243a5c9da43be","score":"0.6352444","text":"def template(tpl, context):\n\n try:\n tpl = open(tpl)\n except TypeError:\n pass\n\n return tpl.read().format(**context)","title":""},{"docid":"6fd019eeeb017d7277e6b357a3cceb09","score":"0.63516396","text":"def render_template(self, view_filename, params=None):\n if not params:\n params = {}\n user = self.user_info\n params['user'] = user\n path = os.path.join(os.path.dirname(__file__), view_filename)\n self.response.out.write(template.render(path, params))","title":""},{"docid":"c0924b9cce07326df335dc6d88a6bb11","score":"0.63515085","text":"def render(self,template_file,values):\r\n html=self.get_render(template_file,values)\r\n self.response.out.write(html)","title":""},{"docid":"785f2c13c15287465f5702f618918722","score":"0.6341369","text":"def format (tpl, args):\n\timport re\n\tfrom sys import stderr\n\ts = tpl\n\tm = re.findall (\"{{.+?}}\", s)\n\t\n\tfor n in m:\n\t\tnneat = n.strip(\"{}\")\n\t\tparts = split(nneat, \"|\")\n\t\tkey = parts.pop(0).strip()\n\t\tif not args.has_key (key):\n\t\t\tstderr.write (\"[KEY] ->\" + str(key) + \"\\n\")\n\t\t\tstderr.write (\"[DATA] ->\" + str(args) + \"\\n\")\n\t\t\tstderr.write (\"[TEMPLATE] ->\\n\")\n\t\t\tstderr.write (tpl + \"\\n\")\n\t\t\tstderr.write (\"-\" * 80 + \"\\n\")\n\t\t\traise KeyError (\"No key found in the data!\")\n\t\t\n\t\tvalue = args[key]\n\t\twhile parts:\n\t\t\tfunc = parts.pop(0).strip()\n\t\t\tval2replace = (\"'%s'\" % value) if isinstance(value, basestring) else (\"%s\" % value)\n\t\t\tfunc = re.sub(r\"(?<=\\(|\\s|,)_(?=\\)|,|\\s)\", val2replace, func, 1)\n\t\t\t\n\t\t\tif func.startswith(\".\") or func.startswith(\"[\"):\n\t\t\t\tvalue = eval ('%s%s' % (val2replace, func))\n\t\t\telif func.startswith ('lambda'):\n\t\t\t\tvalue = eval ('(%s)(%s)' % (func, val2replace))\n\t\t\telif format.shorts.has_key (func):\n\t\t\t\tvalue = eval ('(%s)(%s)' % (format.shorts[func], val2replace))\n\t\t\telse:\n\t\t\t\tvalue = eval (func)\n\n\t\ts = s.replace (n, str(value))\n\treturn s","title":""},{"docid":"3f1c741bf456f44e2653f57e149ec012","score":"0.634026","text":"def _render_template(request, template, item):\n out = StringIO.StringIO()\n data = load_file(template.data)\n temp = Template(StringIO.StringIO(data), out)\n temp.render({\"item\": item, \"print_item\": PrintValueGetter(item, request)})\n return out","title":""},{"docid":"a37bf7e00a2f98f24e0f0b82363d00a8","score":"0.63221455","text":"def render(template_file):\n def entangle(f):\n def render(environ, start_response, *args, **kwds):\n output = _render(f, template_file, environ, \\\n start_response, args, kwds)\n return output\n return render\n return entangle","title":""},{"docid":"1ff45d81e39d8fff6c3f20e163952203","score":"0.62809026","text":"def render2(self,template_file,template_vals={}):\r\n self.template_vals.update(template_vals)\r\n path = os.path.join(settings.ROOT_PATH, template_file)\r\n self.response.out.write(micolog_template.render2(path, self.template_vals,debug=settings.DEBUG))","title":""},{"docid":"f3ee635b902fd721e850a4c7f20a463c","score":"0.62744224","text":"def render_file(self, template, context):\n if '/' not in template:\n # template is not a full path, generate it now.\n template = posixpath.join(self.template_base, template)\n\n return render_to_string(template, context)","title":""},{"docid":"49a52158667bc36840f7971a903a7cb1","score":"0.6270007","text":"def render_template(self, _filename, **context):\n return self.environment.get_template(_filename).render(**context)","title":""},{"docid":"bdc8fe54e87ea881e764b29bfe652f6c","score":"0.62484366","text":"def render_str(template, **params):\n\n template_jinja = jinja_env.get_template(template)\n return template_jinja.render(params)","title":""},{"docid":"ea27f0002c4e7255c99407ddab0f459a","score":"0.6247025","text":"def render(file_path, **kwargs):\n module = _utils.caller_frame()\n path = os.path.dirname(os.path.realpath(module.f_globals['__file__']))\n env = Environment()\n env.loader = FileSystemLoader(path)\n template = env.get_template(file_path)\n return template.render(kwargs)","title":""},{"docid":"67d0a94be8c34fa5273f7677bfa482ca","score":"0.6202991","text":"def render_template(self, name, **kwargs):","title":""},{"docid":"3d07566782a1bd5f972f5e358a1f2880","score":"0.6202805","text":"def generic_path_render(file):\n return render_template(file)","title":""},{"docid":"447f0d47d6dc9561e03f8698d2d75c9e","score":"0.6194532","text":"def render_template(template_file, **kwargs):\n import jinja2\n template_loader = jinja2.FileSystemLoader(searchpath=\"./templates\")\n template_env = jinja2.Environment(loader=template_loader)\n template = template_env.get_template(template_file)\n output_text = template.render(**kwargs)\n return output_text","title":""},{"docid":"1c39a9e0544f8b1d8367beef1008c214","score":"0.61917746","text":"def render_template(self) -> builtins.str:\n ...","title":""},{"docid":"865ffe3d3e186eb6fa378d51f946bd0b","score":"0.61849207","text":"def template(filetpl, fileout=None, env = None):\n env = env if env else {}\n workdir = justpath(filetpl)\n workdir = workdir if workdir else \".\"\n environ = Environment(loader=FileSystemLoader(workdir))\n t = environ.get_template(justfname(filetpl))\n text = t.render(env).encode(\"utf-8\")\n if fileout:\n strtofile(text, fileout)\n return text","title":""},{"docid":"996ce13f5ca1faa9fca0c1f2ad1d8cfa","score":"0.6180003","text":"def render_template(data):\n t = \\\n \"\"\"{{ title }} {\n {% for key in kvps.keys() %}{% if kvps[key] is not mapping%} {{key}} {{kvps[key]}}\n {%else%} {{key}} {\n {%for k in kvps[key].keys()%} {{k}} {{kvps[key][k]}}\n {%endfor%}\n }{%endif%}{% endfor %}\n}\n\"\"\"\n template = Template(t)\n keys = data.keys()\n for key in keys:\n print template.render(title = key, kvps = data[key])","title":""},{"docid":"b6f2f5c69d1746e6de0ff065289027fb","score":"0.6160362","text":"def render_string(self, template_name: str, **kwargs: Any) -> bytes:\n # If no template_path is specified, use the path of the calling file\n template_path = self.get_template_path()\n if not template_path:\n frame = sys._getframe(0)\n web_file = frame.f_code.co_filename\n while frame.f_code.co_filename == web_file and frame.f_back is not None:\n frame = frame.f_back\n assert frame.f_code.co_filename is not None\n template_path = os.path.dirname(frame.f_code.co_filename)\n with RequestHandler._template_loader_lock:\n if template_path not in RequestHandler._template_loaders:\n loader = self.create_template_loader(template_path)\n RequestHandler._template_loaders[template_path] = loader\n else:\n loader = RequestHandler._template_loaders[template_path]\n t = loader.load(template_name)\n namespace = self.get_template_namespace()\n namespace.update(kwargs)\n return t.generate(**namespace)","title":""},{"docid":"3981998b307813c7cb6651abeab8ae8b","score":"0.6147519","text":"def render_str(self, templete, **kwargs):\n t = env.get_template(templete)\n return t.render(**kwargs)","title":""},{"docid":"d71d2170a7c3b9c1175e56e39b10d80b","score":"0.61349595","text":"def _render_template(tpl: jinja2.Template, **kw) -> str:\n return tpl.render(**{k: v for k, v in kw.items() if v})","title":""},{"docid":"405e7e3c9976f38c3699018f2a71b823","score":"0.61346585","text":"def template(*args, **kwargs):\n tpl = args[0] if args else None\n adapter = kwargs.pop('template_adapter', SimpleTemplate)\n lookup = kwargs.pop('template_lookup', TEMPLATE_PATH)\n tplid = (id(lookup), tpl)\n if tplid not in TEMPLATES or DEBUG:\n settings = kwargs.pop('template_settings', {})\n if isinstance(tpl, adapter):\n TEMPLATES[tplid] = tpl\n if settings: TEMPLATES[tplid].prepare(**settings)\n elif \"\\n\" in tpl or \"{\" in tpl or \"%\" in tpl or '$' in tpl:\n TEMPLATES[tplid] = adapter(source=tpl, lookup=lookup, **settings)\n else:\n TEMPLATES[tplid] = adapter(name=tpl, lookup=lookup, **settings)\n if not TEMPLATES[tplid]:\n abort(500, 'Template (%s) not found' % tpl)\n for dictarg in args[1:]: kwargs.update(dictarg)\n return TEMPLATES[tplid].render(kwargs)","title":""},{"docid":"b625db898d36847045cf1b602b3ab8dc","score":"0.61221105","text":"def fill_template(self, filename, **kwargs):\n # call get_user so we can invalidate sessions from external users,\n # if external auth has been disabled.\n self.get_user()\n if filename.endswith( \".mako\" ):\n return self.fill_template_mako( filename, **kwargs )\n else:\n template = Template( file=os.path.join(self.app.config.template_path, filename), \n searchList=[kwargs, self.template_context, dict(caller=self, t=self, h=helpers, util=util, request=self.request, response=self.response, app=self.app)] )\n return str( template )","title":""},{"docid":"0dbd6873b969c7957058fb15bec7bb18","score":"0.60940486","text":"def template(self, name, data = {}):\n self.response.write(jinjaEnv.get_template(name).render(data))","title":""},{"docid":"5c22a8678187f216716008052749ed46","score":"0.6083946","text":"def substituteTemplate(self, template_location, mapping_dict):\n return open(template_location, 'r').read() % mapping_dict","title":""},{"docid":"add1cdc682c598a268175513a25560cf","score":"0.607747","text":"def render(tpl, context, target):\n\n try:\n target = open(target, 'w')\n except TypeError:\n pass\n\n res = template(tpl, context)\n target.write(res)\n\n target.close()","title":""},{"docid":"c5febba5dac472784c76b1ae087c1b0a","score":"0.60751885","text":"def render_to_string_tmpl(pretext,aftertext,template_name, dictionary=None, context_instance=None,\n dirs=None):\n if isinstance(template_name, (list, tuple)):\n t = select_template(template_name, dirs)\n else:\n t = get_template(template_name, dirs)\n e = [pretext]\n e.extend(t.nodelist)\n t.nodelist = e\n t.nodelist.append(aftertext)\n if not context_instance:\n return t.render(Context(dictionary))\n if not dictionary:\n return t.render(context_instance)\n # Add the dictionary to the context stack, ensuring it gets removed again\n # to keep the context_instance in the same state it started in.\n with context_instance.push(dictionary):\n return t.render(context_instance)","title":""},{"docid":"dfb85fb8b92a2a56993dbc5f487574cb","score":"0.6072568","text":"def render_template(template_filename, context):\n return TEMPLATE_ENVIRONMENT.get_template(template_filename).render(context)","title":""},{"docid":"3aa4710fa76f0142ced2f261f93d559a","score":"0.60720766","text":"def render(self, template, **kw):\n self.write(self.render_str(template, extension='.html', **kw))","title":""},{"docid":"9036b731588ee98aef6a9b150089f3d8","score":"0.60540533","text":"def render_to_string(filename, context):\n # type: (str, Dict) -> str\n template_directory = \"./swagger_aor_generator/templates/aor\"\n loaders = [jinja2.FileSystemLoader(template_directory)]\n try:\n import swagger_aor_generator\n loaders.append(\n jinja2.PackageLoader(\"swagger_aor_generator\", \"templates/aor\")\n )\n except ImportError:\n pass\n\n return jinja2.Environment(\n loader=jinja2.ChoiceLoader(loaders),\n trim_blocks=True,\n lstrip_blocks=True\n ).get_template(filename).render(context)","title":""},{"docid":"4267249a90d85a54e8ac5c32ccf499ca","score":"0.6051346","text":"def template(jsonpath, template_name, output):\n with open(jsonpath) as _file:\n ctxt = json.load(_file, object_pairs_hook=collections.OrderedDict)\n chutie.render_template(ctxt,\n template_name=template_name, write_to_path=output)\n click.echo(pprint.pformat(locals()))\n return 0","title":""},{"docid":"2693d243243cd19b79b96736217463d6","score":"0.60197026","text":"def display(headers, template_name):\n lang = get_language(headers)\n return render_template(lang + '/' + template_name)","title":""},{"docid":"ca63c67ecb40e1acd54c97ae961484c8","score":"0.60184246","text":"def _render_output_template(posts, input_template, output_file):\n context = {}\n context['posts'] = posts\n with open(input_template, 'r') as read_template:\n payload = Template(read_template.read())\n return payload.render(**context)","title":""},{"docid":"356f6e40695be4d976b483f8a8f9576e","score":"0.6016078","text":"def render(template, **params):\n return re.sub(\n r\"{{\\s*([^}\\s]+)\\s*}}\",\n lambda match: str(params.get(match.group(1), match.group(0))),\n template,\n )","title":""},{"docid":"cdc302545dd530d7c84efa1731a7ca0f","score":"0.5998786","text":"def get_text_file(template,variables):\n\n template_class = Template(template)\n return template_class.render(variables)","title":""},{"docid":"2766fb602c67ed802a1bca577626247e","score":"0.59938693","text":"def render_template(path, context):\n\n with open(path) as f:\n template = Template(f.read())\n\n return template.render(**context)","title":""},{"docid":"8ed7ec73fcdf599f28a2e8a7e5639bb9","score":"0.598588","text":"def render_template(\n template_file: pathlib.Path, output_file: pathlib.Path,\n context: Dict\n) -> None:\n\n logging.debug(f\"Generating {output_file}\")\n with template_file.open() as t:\n template = Template(t.read(), keep_trailing_newline=True)\n output: str = template.render(context)\n with output_file.open(\"w\") as o:\n o.write(output)","title":""},{"docid":"50f22c8784fb55c6c02ff6c433073c65","score":"0.597313","text":"def render_file(cls, src, dst, render_variables, postfix=None, silent=True):\n if not os.path.isfile(src):\n raise IOError(\"source file for rendering does not exist: {}\".format(src))\n\n with open(src, \"r\") as f:\n try:\n content = f.read()\n except UnicodeDecodeError:\n if silent:\n return\n raise\n\n def postfix_fn(m):\n return cls.postfix_input_file(m.group(1), postfix=postfix)\n\n for key, value in six.iteritems(render_variables):\n # value might contain paths to be postfixed, denoted by \"__law_job_postfix__:...\"\n if postfix:\n value = re.sub(r\"\\_\\_law\\_job\\_postfix\\_\\_:([^\\s]+)\", postfix_fn, value)\n content = cls.render_string(content, key, value)\n\n # finally, replace all non-rendered keys with empty strings\n content = cls.render_key_cre.sub(\"\", content)\n\n with open(dst, \"w\") as f:\n f.write(content)","title":""},{"docid":"de0fecae20cd4cf468b56f631af23b80","score":"0.59598273","text":"def render(self, input_tpl, **params):\n #Check if the input is a readable file, if not treat it as a string\n try:\n with open(input_tpl) as f:\n input_tpl = f.read()\n except FileNotFoundError:\n pass\n #Replace the parameter variables\n str_template = string.Template(input_tpl)\n input_tpl = str_template.safe_substitute(params)\n #Parse the file to extract template code\n input_tpl = self.parse(input_tpl)\n #Execute the template code\n input_tpl = self.compile_insert(input_tpl)\n #Remove template escapes\n input_tpl = re.sub(r\"([\\\\])(~=|~!|~%|~py|{[a-zA-Z_][a-zA-Z0-9_]*})\", \"\\g<2>\", input_tpl)\n return input_tpl","title":""},{"docid":"7c5c78c3547036cf13a60ffb074e1746","score":"0.59350336","text":"def render_block_to_string(template_name, dictionary=None, context_instance=None):\n dictionary = dictionary or {}\n template = get_template(template_name)\n if context_instance:\n context_instance.update(dictionary)\n else:\n context_instance = dictionary\n return template.render(context_instance)","title":""},{"docid":"d6a76b01c0d4f6f3089082b247023fe4","score":"0.59140444","text":"def render_to_file(self, filename, target_filename, **data):\n template = self.env.get_template(filename)\n self.render_template(template, target_filename, **data)","title":""},{"docid":"7d7aa6e0dc2a7457b0feb2af67980a51","score":"0.59115463","text":"def render_report(stat_list, template_filename):\n\n # re-format stat to list\n stat_rows = []\n for data in stat_list:\n stat_rows.append({\n 'url': data['url'],\n 'count': data['count'],\n 'time_avg': '{0:.10f}'.format(data['time_avg']),\n 'time_max': '{0:.10f}'.format(data['time_max']),\n 'time_sum': '{0:.10f}'.format(data['time_sum']),\n 'time_med': '{0:.10f}'.format(data['time_med']),\n 'time_perc': '{0:.10f}'.format(data['time_perc']),\n 'count_perc': '{0:.10f}'.format(data['count_perc']),\n })\n\n stat_json = json.dumps(stat_rows)\n\n try:\n with open(template_filename, \"r\") as ftemp:\n template = \"\".join(ftemp.readlines())\n except Exception as e:\n logging.error('Failed to read report template file \"%s\": %s',\n template_filename, e.message)\n return None\n\n report_str = template.replace('$table_json', stat_json)\n return report_str","title":""},{"docid":"ce96017025e90cf46b939820b4dcecbb","score":"0.5911458","text":"def template_subs_file(in_file, out_fobj, d):\n with open(in_file, 'r') as in_fobj:\n for line in in_fobj:\n out_fobj.write(line.format(**d))","title":""},{"docid":"ce96017025e90cf46b939820b4dcecbb","score":"0.5911458","text":"def template_subs_file(in_file, out_fobj, d):\n with open(in_file, 'r') as in_fobj:\n for line in in_fobj:\n out_fobj.write(line.format(**d))","title":""},{"docid":"7b174f86f6f86b4b6fe7eb41e634a041","score":"0.5901367","text":"def template_to_dict(template_path, dict_path, context):\n template_file = open(template_path, \"r\")\n template_text = template_file.read()\n template_file.close()\n\n template = jinja2.Template(template_text)\n mesh_file = open(dict_path, \"w\")\n mesh_file.write(template.render(context))\n mesh_file.close()","title":""},{"docid":"4eb9ff7e3d4ae19f3e0c02184018d942","score":"0.58916366","text":"def render_str(self, template_name, **kwargs):\n template = JINJA_ENV.get_template(template_name)\n kwargs['user'] = self.user\n return template.render(kwargs)","title":""},{"docid":"9153e4b18476a98d4be10502df1f8d81","score":"0.585749","text":"def render(tpl_path, context):\n path, filename = os.path.split(tpl_path)\n return jinja2.Environment(\n loader=jinja2.FileSystemLoader(path or './')\n ).get_template(filename).render(context)","title":""},{"docid":"114edd8afaa535a2157354cef2087064","score":"0.5856374","text":"def render_template(self, filename, parts):\n\n searchpath = os.path.join(self.basedir, \"templates\")\n\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(searchpath))\n\n tmpl = env.get_template(filename)\n\n\n # Update the namespace with all the global definitions from the\n # configuration file. This is typically done for site name, \n # copyright information, etc.\n for key in self.config:\n if key.startswith(\"namespace.\"):\n parts[key[4:]] = self.config[key]\n\n return tmpl.render(parts)","title":""},{"docid":"d284265d27d0ce9a3a30fff0c32329a0","score":"0.58503664","text":"def render_template(template_path, context={}):\n template_str = load_resource(template_path)\n template = Template(template_str)\n return template.render(Context(context))","title":""},{"docid":"35437795a6e589014b2ba13e1569ecbe","score":"0.5820803","text":"def render_put(template_name, dest, params, mode=None, tempfile='tempfile'):\n data = loader.render_to_string(template_name, dictionary=params)\n open(tempfile, 'w').write(data)\n put(tempfile, dest, mode)\n os.remove(tempfile)","title":""},{"docid":"f866e72d87f8e3afaf443a037be40d6d","score":"0.5816048","text":"def render_jinja(template_filename, **variables):\n jenv = jinja2.Environment(\n loader=jinja2.FileSystemLoader(\n [\n Path(\".\"),\n Path(__file__).parent / \"templates\",\n ]\n ),\n autoescape=True,\n )\n jenv.filters[\"datetime\"] = datetime_format\n jenv.filters[\"label_color_css\"] = label_color_css\n template = jenv.get_template(template_filename)\n html = template.render(**variables)\n return html","title":""},{"docid":"04d1eab599f9595ad4a7ba4065bab685","score":"0.5810972","text":"def render(name, data):\n viewunit.template_called(name, data)\n return flask.render_template(name, **data)","title":""},{"docid":"19602fd473886f6cc4a681b2258561e5","score":"0.579852","text":"def format(self, template_name=None, **data):\n if not template_name:\n template_name = self.config.default_template\n content = self.templator.render(template_name, **data)\n return content","title":""},{"docid":"896174b95841a7f47af651e17475d0ef","score":"0.57946557","text":"def render_template(template_file, **context):\r\n if hasattr(self, 'template_env'):\r\n t_env = self.template_env\r\n template = t_env.get_template(template_file)\r\n body = template.render(**context or {})\r\n return body","title":""},{"docid":"db5970f98aaa1c327b25ddbb5f1a17f6","score":"0.57934415","text":"def render(cls, c={}):\n cls.context.update(c)\n t = Template( open(cls.template).read() )\n c = Context( cls.context )\n return t.render( c )","title":""},{"docid":"495ec2273755288280ddd368911f9dab","score":"0.57880586","text":"def render(self, handler, params={}):\n\n template_info = get_view_file(handler, params)\n aio.debug(\"view.render: Using template at %s\", template_info['file'])\n output = self.render_or_get_cache(handler, template_info, params)\n handler.response.out.write(output)","title":""},{"docid":"d23857176d741fcbf5918e1b56758cdd","score":"0.576992","text":"def fillinFileTemplate(f_path_read, f_path_write=None, dict={}):\n if not f_path_write:\n f_path_write = f_path_read\n f_tmpl = open(f_path_read, 'r')\n tmpl = f_tmpl.read()\n f_tmpl.close()\n f_tmpl = open(f_path_write, 'w')\n try:\n f_tmpl.write(tmpl % dict)\n except:\n raise str(tmpl)\n f_tmpl.close()","title":""},{"docid":"73661695f0c7400a475abfa4cdede2ab","score":"0.5758503","text":"def render_latex(output_file, latex_template_fn, \n start_date, end_date, \n sections,\n top_entry_count, \n top_n_users, \n user_data,\n user_details):\n latex_tpl = Template(filename=latex_template_fn)\n latex = latex_tpl(start_date=start_date, end_date=end_date, \n sections=sections,\n n=top_entry_count, \n top_n_users=top_n_users, \n user_data=user_data,\n user_details=user_details)\n output_file.write(latex)","title":""},{"docid":"5a47d70b4e71d23d59369fcb12274207","score":"0.57499623","text":"def render1f(path: str, *, args: Dict = None, as_json: bool = False) -> str:\n return _render1(render_file(path, args=args, as_json=as_json), as_json=as_json)","title":""},{"docid":"d55678748a56b773f726dae021a78446","score":"0.57116705","text":"def render_template(self, template_file,\r\n _status_code=WebMessageHandler._SUCCESS_CODE,\r\n **context):\r\n mustache_env = self.application.template_env\r\n\r\n template = mustache_env.load_template(template_file)\r\n body = mustache_env.render(template, context or {})\r\n\r\n self.set_body(body, status_code=_status_code)\r\n return self.render()","title":""},{"docid":"fb76f0952c0050377c13cc0a49d04838","score":"0.5687893","text":"def render_template_string(source, **context):\r\n ctx = _request_ctx_stack.top\r\n ctx.app.update_template_context(context)\r\n return _render(ctx.app.jinja_env.from_string(source),\r\n context, ctx.app)","title":""},{"docid":"3fe76dd75bdae51f33b1cd7848d3832b","score":"0.5670168","text":"def render_template(self, template_name, **kwargs):\n return template_name","title":""},{"docid":"0fa244e3bc05b29cfde0f0922639174f","score":"0.5669284","text":"def render_template(self, template_file,\n _status_code=WebMessageHandler._SUCCESS_CODE,\n **context):\n mustache_env = self.application.template_env\n\n template = mustache_env.load_template(template_file)\n body = mustache_env.render(template, context or {})\n\n self.set_body(body, status_code=_status_code)\n return self.render()","title":""},{"docid":"f65a5fe6020fa3a9e238e248de8e4706","score":"0.56625575","text":"def render_template(plain_text, vars):\n t = Template(plain_text)\n content = t.render(vars)\n return content","title":""},{"docid":"0ec286bceb62b6f41b9ce2c524ee3fae","score":"0.5656246","text":"def render_template(template, **kwargs):\n response = make_response(flask.render_template(template, **kwargs))\n response.headers[\"Content-type\"] = \"text/html\"\n return response","title":""},{"docid":"0f6ab1564ee9891baff7dfec1c80e5d1","score":"0.565567","text":"def render(self, template_name, **contexts):\n self.write(self.render_template(template_name, **contexts))","title":""},{"docid":"9ab4d870ce35c0a83a9dfbba43dd004b","score":"0.5641959","text":"async def render_jinja_to_file(template_filename, output_file, **variables):\n text = render_jinja(template_filename, **variables)\n text = emoji.emojize(text, language=\"alias\")\n async with aiofiles.open(output_file, \"w\", encoding=\"utf-8\") as out:\n await out.write(text)","title":""},{"docid":"9e2ef9ab05e59fce511ab65556984a1f","score":"0.56381524","text":"def fillHTMLTemplate(templateString, paramsDict):\r\n templateObject = jinja2.Template(templateString)\r\n htmlContent = templateObject.render(paramsDict)\r\n return htmlContent","title":""},{"docid":"612b5c83fce9515c9646a6383d121e47","score":"0.56256217","text":"def render_string(cls, s, key, value):\n return s.replace(\"{{\" + key + \"}}\", value)","title":""},{"docid":"39486ec33fcd300c58f6f8d4f19be1fb","score":"0.56168604","text":"def render_summary(data, template_filename):\n loader = PackageLoader(\"gagepy\", \"templates\")\n env = Environment(loader=loader)\n template = env.get_template(template_filename)\n\n return template.render(data)","title":""},{"docid":"60028c49ec00592672b349a1a49288ae","score":"0.5616435","text":"def _render_templates(self, obj, ns=None):\n if ns is None:\n ns = self.template_namespace()\n\n _fmt = partial(self._render_templates, ns=ns)\n\n if isinstance(obj, str):\n try:\n return obj.format(**ns)\n except (ValueError, KeyError):\n # not a valid format string\n # to avoid crashing leave invalid templates unrendered\n # otherwise, this unconditional formatting would not allow\n # strings with `{` characters in them\n return obj\n elif isinstance(obj, dict):\n return {_fmt(key): _fmt(value) for key, value in obj.items()}\n elif isinstance(obj, (list, tuple)):\n return type(obj)([_fmt(item) for item in obj])\n else:\n return obj","title":""},{"docid":"4d10a656ea83e9a8ad5462b0b9d4cc77","score":"0.56161284","text":"def render_template_string(source, **context):\n ctx = _request_ctx_stack.top\n ctx.app.update_template_context(context)\n return _render(ctx.app.jinja_env.from_string(source),\n context, ctx.app)","title":""},{"docid":"d0f6058eca47ace5a5ad3e364b4c64af","score":"0.5613586","text":"def _render(self, template_name, context):\n\n template = self.jinja_env.get_template(template_name)\n return template.render(**context)","title":""},{"docid":"648917c52a1aee39c56da62d68f8a613","score":"0.56032234","text":"def render_template_string(source, **context):\n ctx = _request_ctx_stack.top\n ctx.app.update_template_context(context)\n return ctx.app.jinja_env.from_string(source).render(context)","title":""},{"docid":"e8b4cefe84704f86258ed17203194ddd","score":"0.55944186","text":"def test_from_file_with_filename():\n fd, filename = tempfile.mkstemp()\n try:\n os.write(fd, \"Hello ${you}!\")\n finally:\n os.close(fd)\n try:\n t = Template.from_file(filename)\n assert t.render(you=\"World\") == \"Hello World!\"\n finally:\n os.unlink(filename)","title":""},{"docid":"781d0955d92c927501a57b11705cc6b9","score":"0.55912226","text":"def _render_template(self, path, context):\n raise NotImplementedError","title":""},{"docid":"018cc0a6b30b9d1350ec163a5424ce51","score":"0.5585938","text":"def render(self,name,data):\n return self._getTemplate(name).stream(data)","title":""},{"docid":"63565fcfb295fd6f948aef108a0afa3d","score":"0.55788225","text":"def replace(text, dic) -> str:\n s = Template(text)\n return s.safe_substitute(**dic)","title":""},{"docid":"8b1644834d27ac9e3b9cb624e85be417","score":"0.5565817","text":"def test_from_file_with_fileobject():\n t = Template.from_file(sio.StringIO(\"Hello ${you}!\"))\n assert t.render(you=\"World\") == \"Hello World!\"","title":""},{"docid":"b05f5e2e616722b84e932aa63d23ea22","score":"0.55491006","text":"def render_template(context, template, output_path=None, templates_dir=None,\n executable=False):\n env = _init_template_env(templates_dir)\n\n rendered_data = env.get_template(template).render(context)\n\n # Remove trailing spaces\n cleaned_lines = []\n for line in rendered_data.splitlines():\n cleaned_lines.append(line.rstrip())\n rendered_data = '\\n'.join(cleaned_lines)\n\n if output_path:\n with open(output_path, 'w', encoding='utf-8') as f:\n f.write(rendered_data)\n\n if executable:\n st = os.stat(output_path)\n os.chmod(output_path, st.st_mode | stat.S_IEXEC)\n\n return rendered_data","title":""},{"docid":"5e94b4ca206cfecd0f000221a8696694","score":"0.5548685","text":"def render_template(template_name_or_list, **context):\r\n ctx = _request_ctx_stack.top\r\n ctx.app.update_template_context(context)\r\n return _render(ctx.app.jinja_env.get_or_select_template(template_name_or_list),\r\n context, ctx.app)","title":""},{"docid":"b988ade66a4e779e9a4d4bb31274e596","score":"0.5544229","text":"def render_template(template_path=None, template_text=None, dest=None, **kwargs):\n template = get_template(template_path=template_path, template_text=template_text)\n\n try:\n template_string = template.render(**kwargs)\n except Exception as e:\n raise j.exceptions.Base(e)\n\n if dest:\n j.sals.fs.mkdirs(j.sals.fs.dirname(dest))\n j.sals.fs.write_file(dest, template_string)\n return template_string","title":""},{"docid":"cd1e78e62bc5bbcfbf04614a60b69ee1","score":"0.5541193","text":"def render_template(source: str, *args, **kwargs) -> str:\n template = PARSER_HANDLER.from_string(source)\n return template.render(*args, **kwargs)","title":""},{"docid":"9eabcdb9ca1139b248dfa699197f6119","score":"0.5540827","text":"def render_to_string(template, *args, **kwargs):\n template_lib = _get_template_lib(template, kwargs)\n if template_lib == DJANGO:\n return django_render_to_string(template, *args, **kwargs)\n elif template_lib == MAKO:\n return django_mako.render_to_string(template, *args, **kwargs)\n else:\n raise Exception(\"Bad template lib: %s\" % template_lib)","title":""},{"docid":"6f508108890eb5806432e39d00c697af","score":"0.55246764","text":"def table_template(dictionary, name='Nome', value='Valor'):\n html_string = '<table>' \\\n '<thead>' \\\n '<tr>' \\\n '<th> {} </th>' \\\n '<th> {} </th>' \\\n '</tr>' \\\n '</thead>' \\\n '<tbody style=\"overflow-: auto;\">'.format(name, value)\n if isinstance(dictionary, dict):\n for key, value in dictionary.items():\n html_string += '</tr>' \\\n '<td>{}</td>' \\\n '<td>{}</td>' \\\n '</tr>'.format(key, value)\n html_string += '</tbody>' \\\n '</table>'\n return mark_safe(html_string)","title":""},{"docid":"970e23f365a71216c98f474d53de889d","score":"0.55115443","text":"def template(request, name, mimetype=None, **kwd):\n # parameters that will always be available for the template\n kwd['request'] = request\n resp = render_to_response(name, kwd, context_instance=RequestContext(request))\n return resp","title":""},{"docid":"8acd8ecff68953409762bc034347875c","score":"0.55071265","text":"def writeTemplate(self, fileName, context):\n template = self.jinja.get_template(fileName)\n self.response.write(template.render(context))\n self.response.content_type = 'text/html'","title":""},{"docid":"bb096a9330fd61708a7e456bf2aad487","score":"0.5500604","text":"def convert_json_to_tex(param_dict, only_contents):\n if only_contents:\n tex = \"\"\n else:\n tex = r\"\"\"\\begin{table}[]\n \\centering\n \\begin{tabular}{cc}\n\"\"\"\n tex += r\"\"\" \\hline\n Parameter & Range \\\\\n \\hline\n\"\"\"\n for key, prange in param_dict.iteritems():\n key_tex = texify_key(key)\n # key_tex = key\n tex += r\"\"\" $%s$ & %g - %g \\\\\"\"\" % (key_tex, prange['min'], prange['max'])\n tex += \"\\n\"\n tex += r\"\"\" \\hline\"\"\"\n\n if not only_contents:\n tex += \"\\n\"\n tex += r\"\"\" \\end{tabular}\n \\caption{}\n \\label{tab:}\n\\end{table}\"\"\"\n return tex","title":""},{"docid":"91546a13b5e8790bbd03212020131f93","score":"0.5498525","text":"def apply(self, mapping):\n return self.template.render(**mapping)","title":""},{"docid":"109ef3a5621279491f8c258c979ad3b2","score":"0.5493685","text":"def render_template(name, style=None, **kwargs):\n if style is None:\n style = current_app.config.get(\"STYLE\", None)\n if style is not None:\n name = f\"{style}/{name}\"\n return flask.render_template(name, **kwargs)","title":""},{"docid":"1a2817c2ce38b5f9c57d70c55fb05e32","score":"0.5490736","text":"def settings_template(path, **kwargs):\r\n from tornado.template import Template\r\n with io.open(path, mode='r', encoding='utf-8') as f:\r\n template_data = f.read()\r\n t = Template(template_data)\r\n # NOTE: Tornado returns templates as bytes, not unicode. That's why we need\r\n # the decode() below...\r\n rendered = t.generate(**kwargs).decode('utf-8')\r\n out = \"\"\r\n for line in rendered.splitlines():\r\n if line.strip():\r\n out += line + \"\\n\"\r\n return out","title":""}],"string":"[\n {\n \"docid\": \"d701eaab31a42e6f92eb89d722014168\",\n \"score\": \"0.70578563\",\n \"text\": \"def render(template_string, context, *filepath):\\n for key, value in context.items():\\n template_string = template_string.replace('{{ %s }}' % key, value)\\n template_file = open(os.path.join(*filepath), 'w')\\n template_file.write(template_string)\\n template_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199c7fc72af14938010d7872ffc48df6\",\n \"score\": \"0.6854282\",\n \"text\": \"def template(file, vars={}, **args):\\n contents = open(file, 'r').read()\\n return str(contents % (dict(vars, **args)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a08b75663d03a798e9da25696a32b1\",\n \"score\": \"0.6831962\",\n \"text\": \"def render(variables, template):\\r\\n return load(template).render(variables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f437c05f1527910b704ca3f30be15534\",\n \"score\": \"0.6680663\",\n \"text\": \"def renderDictToFile(d, tmpl, outname=None, reldir=__file__, **kw):\\n s = renderDict(d, tmpl)\\n dir, fname = os.path.split(tmpl)\\n fname = fname.rsplit('.tmpl')[0]\\n if not outname:\\n outname = os.path.join(dir, fname + '.html')\\n print 'Rendering from %s to %s' % (os.path.join(dir, fname+'.tmpl'), outname)\\n # make parent dirs\\n try:\\n os.makedirs(os.path.dirname(outname))\\n except OSError: pass\\n # make sure any existing output is first renamed so render doesn't use it\\n try:\\n os.rename(outname, '.'+outname)\\n except OSError: pass\\n # compute the relative path to the static dir\\n cur = os.path.abspath(os.path.dirname(reldir))\\n outdir = os.path.abspath(os.path.dirname(outname))\\n prefix = os.path.commonprefix([cur, outdir])\\n levels = len(outdir.replace(prefix, '', 1).split('/')) - 1\\n if levels == 0:\\n rel = '.'\\n else:\\n rel = '/'.join('..' for i in range(levels))\\n #print cur, outdir, prefix, levels, rel\\n s = str(html(d, [s], rel=rel, **kw))\\n f = open(outname, 'wb')\\n f.write(s)\\n f.close()\\n # remove old output\\n try:\\n os.remove('.'+outname)\\n except OSError: pass\\n return outname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb7759c67b48f50f1c6b9953a3160f3\",\n \"score\": \"0.662638\",\n \"text\": \"def renderTemplate(fileName, templateDict, projectFileNameDict):\\n \\n templateFilePath = projectFiles[fileName] % (projectFileNameDict)\\n templatePath = os.path.dirname(templateFilePath)\\n \\n template = None\\n if fileName in templateFiles:\\n template = templateEnvironment.get_template(templateFiles[fileName])\\n \\n if 'metadata' in templateDict:\\n templateDict['metadata']['fileName'] = os.path.basename(templateFilePath)\\n \\n renderedTemplate = template.render(templateDict) if template else \\\"\\\"\\n \\n if not os.path.isdir(templatePath):\\n os.makedirs(templatePath)\\n \\n templateFile = open(templateFilePath, 'w')\\n templateFile.write(renderedTemplate)\\n templateFile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2313ea4932c1e935452cbd994c9e8b1c\",\n \"score\": \"0.65588355\",\n \"text\": \"def render_template(template_file: str, **kwargs: Any) -> str:\\n template = jinja2.Template(open(template_file).read())\\n return template.render(kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c391aad01c5725d96e109f871b6e5f7\",\n \"score\": \"0.6553463\",\n \"text\": \"def render_template(file, template_values):\\n path = os.path.join(os.path.dirname(__file__), file)\\n return template.render(path, template_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3107f9747adf9309c30c4c2fd3e97e8b\",\n \"score\": \"0.6470931\",\n \"text\": \"def render(self, template_name, **kwargs):\\n return self.write(self.render_str(template_name, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e445985d6d1281b8fbac803ac4b37381\",\n \"score\": \"0.63626456\",\n \"text\": \"def render_to_string(self, *args, **kwargs):\\r\\n try:\\r\\n template_name, args = args[0], args[1:]\\r\\n except IndexError:\\r\\n raise TypeError('name of template required')\\r\\n return self.get_template(template_name).render(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e445985d6d1281b8fbac803ac4b37381\",\n \"score\": \"0.63626456\",\n \"text\": \"def render_to_string(self, *args, **kwargs):\\r\\n try:\\r\\n template_name, args = args[0], args[1:]\\r\\n except IndexError:\\r\\n raise TypeError('name of template required')\\r\\n return self.get_template(template_name).render(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"821853dfdbffb059429243a5c9da43be\",\n \"score\": \"0.6352444\",\n \"text\": \"def template(tpl, context):\\n\\n try:\\n tpl = open(tpl)\\n except TypeError:\\n pass\\n\\n return tpl.read().format(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd019eeeb017d7277e6b357a3cceb09\",\n \"score\": \"0.63516396\",\n \"text\": \"def render_template(self, view_filename, params=None):\\n if not params:\\n params = {}\\n user = self.user_info\\n params['user'] = user\\n path = os.path.join(os.path.dirname(__file__), view_filename)\\n self.response.out.write(template.render(path, params))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0924b9cce07326df335dc6d88a6bb11\",\n \"score\": \"0.63515085\",\n \"text\": \"def render(self,template_file,values):\\r\\n html=self.get_render(template_file,values)\\r\\n self.response.out.write(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785f2c13c15287465f5702f618918722\",\n \"score\": \"0.6341369\",\n \"text\": \"def format (tpl, args):\\n\\timport re\\n\\tfrom sys import stderr\\n\\ts = tpl\\n\\tm = re.findall (\\\"{{.+?}}\\\", s)\\n\\t\\n\\tfor n in m:\\n\\t\\tnneat = n.strip(\\\"{}\\\")\\n\\t\\tparts = split(nneat, \\\"|\\\")\\n\\t\\tkey = parts.pop(0).strip()\\n\\t\\tif not args.has_key (key):\\n\\t\\t\\tstderr.write (\\\"[KEY] ->\\\" + str(key) + \\\"\\\\n\\\")\\n\\t\\t\\tstderr.write (\\\"[DATA] ->\\\" + str(args) + \\\"\\\\n\\\")\\n\\t\\t\\tstderr.write (\\\"[TEMPLATE] ->\\\\n\\\")\\n\\t\\t\\tstderr.write (tpl + \\\"\\\\n\\\")\\n\\t\\t\\tstderr.write (\\\"-\\\" * 80 + \\\"\\\\n\\\")\\n\\t\\t\\traise KeyError (\\\"No key found in the data!\\\")\\n\\t\\t\\n\\t\\tvalue = args[key]\\n\\t\\twhile parts:\\n\\t\\t\\tfunc = parts.pop(0).strip()\\n\\t\\t\\tval2replace = (\\\"'%s'\\\" % value) if isinstance(value, basestring) else (\\\"%s\\\" % value)\\n\\t\\t\\tfunc = re.sub(r\\\"(?<=\\\\(|\\\\s|,)_(?=\\\\)|,|\\\\s)\\\", val2replace, func, 1)\\n\\t\\t\\t\\n\\t\\t\\tif func.startswith(\\\".\\\") or func.startswith(\\\"[\\\"):\\n\\t\\t\\t\\tvalue = eval ('%s%s' % (val2replace, func))\\n\\t\\t\\telif func.startswith ('lambda'):\\n\\t\\t\\t\\tvalue = eval ('(%s)(%s)' % (func, val2replace))\\n\\t\\t\\telif format.shorts.has_key (func):\\n\\t\\t\\t\\tvalue = eval ('(%s)(%s)' % (format.shorts[func], val2replace))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvalue = eval (func)\\n\\n\\t\\ts = s.replace (n, str(value))\\n\\treturn s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1c741bf456f44e2653f57e149ec012\",\n \"score\": \"0.634026\",\n \"text\": \"def _render_template(request, template, item):\\n out = StringIO.StringIO()\\n data = load_file(template.data)\\n temp = Template(StringIO.StringIO(data), out)\\n temp.render({\\\"item\\\": item, \\\"print_item\\\": PrintValueGetter(item, request)})\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a37bf7e00a2f98f24e0f0b82363d00a8\",\n \"score\": \"0.63221455\",\n \"text\": \"def render(template_file):\\n def entangle(f):\\n def render(environ, start_response, *args, **kwds):\\n output = _render(f, template_file, environ, \\\\\\n start_response, args, kwds)\\n return output\\n return render\\n return entangle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff45d81e39d8fff6c3f20e163952203\",\n \"score\": \"0.62809026\",\n \"text\": \"def render2(self,template_file,template_vals={}):\\r\\n self.template_vals.update(template_vals)\\r\\n path = os.path.join(settings.ROOT_PATH, template_file)\\r\\n self.response.out.write(micolog_template.render2(path, self.template_vals,debug=settings.DEBUG))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ee635b902fd721e850a4c7f20a463c\",\n \"score\": \"0.62744224\",\n \"text\": \"def render_file(self, template, context):\\n if '/' not in template:\\n # template is not a full path, generate it now.\\n template = posixpath.join(self.template_base, template)\\n\\n return render_to_string(template, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a52158667bc36840f7971a903a7cb1\",\n \"score\": \"0.6270007\",\n \"text\": \"def render_template(self, _filename, **context):\\n return self.environment.get_template(_filename).render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc8fe54e87ea881e764b29bfe652f6c\",\n \"score\": \"0.62484366\",\n \"text\": \"def render_str(template, **params):\\n\\n template_jinja = jinja_env.get_template(template)\\n return template_jinja.render(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea27f0002c4e7255c99407ddab0f459a\",\n \"score\": \"0.6247025\",\n \"text\": \"def render(file_path, **kwargs):\\n module = _utils.caller_frame()\\n path = os.path.dirname(os.path.realpath(module.f_globals['__file__']))\\n env = Environment()\\n env.loader = FileSystemLoader(path)\\n template = env.get_template(file_path)\\n return template.render(kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d0a94be8c34fa5273f7677bfa482ca\",\n \"score\": \"0.6202991\",\n \"text\": \"def render_template(self, name, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d07566782a1bd5f972f5e358a1f2880\",\n \"score\": \"0.6202805\",\n \"text\": \"def generic_path_render(file):\\n return render_template(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"447f0d47d6dc9561e03f8698d2d75c9e\",\n \"score\": \"0.6194532\",\n \"text\": \"def render_template(template_file, **kwargs):\\n import jinja2\\n template_loader = jinja2.FileSystemLoader(searchpath=\\\"./templates\\\")\\n template_env = jinja2.Environment(loader=template_loader)\\n template = template_env.get_template(template_file)\\n output_text = template.render(**kwargs)\\n return output_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c39a9e0544f8b1d8367beef1008c214\",\n \"score\": \"0.61917746\",\n \"text\": \"def render_template(self) -> builtins.str:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865ffe3d3e186eb6fa378d51f946bd0b\",\n \"score\": \"0.61849207\",\n \"text\": \"def template(filetpl, fileout=None, env = None):\\n env = env if env else {}\\n workdir = justpath(filetpl)\\n workdir = workdir if workdir else \\\".\\\"\\n environ = Environment(loader=FileSystemLoader(workdir))\\n t = environ.get_template(justfname(filetpl))\\n text = t.render(env).encode(\\\"utf-8\\\")\\n if fileout:\\n strtofile(text, fileout)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996ce13f5ca1faa9fca0c1f2ad1d8cfa\",\n \"score\": \"0.6180003\",\n \"text\": \"def render_template(data):\\n t = \\\\\\n \\\"\\\"\\\"{{ title }} {\\n {% for key in kvps.keys() %}{% if kvps[key] is not mapping%} {{key}} {{kvps[key]}}\\n {%else%} {{key}} {\\n {%for k in kvps[key].keys()%} {{k}} {{kvps[key][k]}}\\n {%endfor%}\\n }{%endif%}{% endfor %}\\n}\\n\\\"\\\"\\\"\\n template = Template(t)\\n keys = data.keys()\\n for key in keys:\\n print template.render(title = key, kvps = data[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f2f5c69d1746e6de0ff065289027fb\",\n \"score\": \"0.6160362\",\n \"text\": \"def render_string(self, template_name: str, **kwargs: Any) -> bytes:\\n # If no template_path is specified, use the path of the calling file\\n template_path = self.get_template_path()\\n if not template_path:\\n frame = sys._getframe(0)\\n web_file = frame.f_code.co_filename\\n while frame.f_code.co_filename == web_file and frame.f_back is not None:\\n frame = frame.f_back\\n assert frame.f_code.co_filename is not None\\n template_path = os.path.dirname(frame.f_code.co_filename)\\n with RequestHandler._template_loader_lock:\\n if template_path not in RequestHandler._template_loaders:\\n loader = self.create_template_loader(template_path)\\n RequestHandler._template_loaders[template_path] = loader\\n else:\\n loader = RequestHandler._template_loaders[template_path]\\n t = loader.load(template_name)\\n namespace = self.get_template_namespace()\\n namespace.update(kwargs)\\n return t.generate(**namespace)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3981998b307813c7cb6651abeab8ae8b\",\n \"score\": \"0.6147519\",\n \"text\": \"def render_str(self, templete, **kwargs):\\n t = env.get_template(templete)\\n return t.render(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d71d2170a7c3b9c1175e56e39b10d80b\",\n \"score\": \"0.61349595\",\n \"text\": \"def _render_template(tpl: jinja2.Template, **kw) -> str:\\n return tpl.render(**{k: v for k, v in kw.items() if v})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"405e7e3c9976f38c3699018f2a71b823\",\n \"score\": \"0.61346585\",\n \"text\": \"def template(*args, **kwargs):\\n tpl = args[0] if args else None\\n adapter = kwargs.pop('template_adapter', SimpleTemplate)\\n lookup = kwargs.pop('template_lookup', TEMPLATE_PATH)\\n tplid = (id(lookup), tpl)\\n if tplid not in TEMPLATES or DEBUG:\\n settings = kwargs.pop('template_settings', {})\\n if isinstance(tpl, adapter):\\n TEMPLATES[tplid] = tpl\\n if settings: TEMPLATES[tplid].prepare(**settings)\\n elif \\\"\\\\n\\\" in tpl or \\\"{\\\" in tpl or \\\"%\\\" in tpl or '$' in tpl:\\n TEMPLATES[tplid] = adapter(source=tpl, lookup=lookup, **settings)\\n else:\\n TEMPLATES[tplid] = adapter(name=tpl, lookup=lookup, **settings)\\n if not TEMPLATES[tplid]:\\n abort(500, 'Template (%s) not found' % tpl)\\n for dictarg in args[1:]: kwargs.update(dictarg)\\n return TEMPLATES[tplid].render(kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b625db898d36847045cf1b602b3ab8dc\",\n \"score\": \"0.61221105\",\n \"text\": \"def fill_template(self, filename, **kwargs):\\n # call get_user so we can invalidate sessions from external users,\\n # if external auth has been disabled.\\n self.get_user()\\n if filename.endswith( \\\".mako\\\" ):\\n return self.fill_template_mako( filename, **kwargs )\\n else:\\n template = Template( file=os.path.join(self.app.config.template_path, filename), \\n searchList=[kwargs, self.template_context, dict(caller=self, t=self, h=helpers, util=util, request=self.request, response=self.response, app=self.app)] )\\n return str( template )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dbd6873b969c7957058fb15bec7bb18\",\n \"score\": \"0.60940486\",\n \"text\": \"def template(self, name, data = {}):\\n self.response.write(jinjaEnv.get_template(name).render(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c22a8678187f216716008052749ed46\",\n \"score\": \"0.6083946\",\n \"text\": \"def substituteTemplate(self, template_location, mapping_dict):\\n return open(template_location, 'r').read() % mapping_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"add1cdc682c598a268175513a25560cf\",\n \"score\": \"0.607747\",\n \"text\": \"def render(tpl, context, target):\\n\\n try:\\n target = open(target, 'w')\\n except TypeError:\\n pass\\n\\n res = template(tpl, context)\\n target.write(res)\\n\\n target.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5febba5dac472784c76b1ae087c1b0a\",\n \"score\": \"0.60751885\",\n \"text\": \"def render_to_string_tmpl(pretext,aftertext,template_name, dictionary=None, context_instance=None,\\n dirs=None):\\n if isinstance(template_name, (list, tuple)):\\n t = select_template(template_name, dirs)\\n else:\\n t = get_template(template_name, dirs)\\n e = [pretext]\\n e.extend(t.nodelist)\\n t.nodelist = e\\n t.nodelist.append(aftertext)\\n if not context_instance:\\n return t.render(Context(dictionary))\\n if not dictionary:\\n return t.render(context_instance)\\n # Add the dictionary to the context stack, ensuring it gets removed again\\n # to keep the context_instance in the same state it started in.\\n with context_instance.push(dictionary):\\n return t.render(context_instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb85fb8b92a2a56993dbc5f487574cb\",\n \"score\": \"0.6072568\",\n \"text\": \"def render_template(template_filename, context):\\n return TEMPLATE_ENVIRONMENT.get_template(template_filename).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa4710fa76f0142ced2f261f93d559a\",\n \"score\": \"0.60720766\",\n \"text\": \"def render(self, template, **kw):\\n self.write(self.render_str(template, extension='.html', **kw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9036b731588ee98aef6a9b150089f3d8\",\n \"score\": \"0.60540533\",\n \"text\": \"def render_to_string(filename, context):\\n # type: (str, Dict) -> str\\n template_directory = \\\"./swagger_aor_generator/templates/aor\\\"\\n loaders = [jinja2.FileSystemLoader(template_directory)]\\n try:\\n import swagger_aor_generator\\n loaders.append(\\n jinja2.PackageLoader(\\\"swagger_aor_generator\\\", \\\"templates/aor\\\")\\n )\\n except ImportError:\\n pass\\n\\n return jinja2.Environment(\\n loader=jinja2.ChoiceLoader(loaders),\\n trim_blocks=True,\\n lstrip_blocks=True\\n ).get_template(filename).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4267249a90d85a54e8ac5c32ccf499ca\",\n \"score\": \"0.6051346\",\n \"text\": \"def template(jsonpath, template_name, output):\\n with open(jsonpath) as _file:\\n ctxt = json.load(_file, object_pairs_hook=collections.OrderedDict)\\n chutie.render_template(ctxt,\\n template_name=template_name, write_to_path=output)\\n click.echo(pprint.pformat(locals()))\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2693d243243cd19b79b96736217463d6\",\n \"score\": \"0.60197026\",\n \"text\": \"def display(headers, template_name):\\n lang = get_language(headers)\\n return render_template(lang + '/' + template_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca63c67ecb40e1acd54c97ae961484c8\",\n \"score\": \"0.60184246\",\n \"text\": \"def _render_output_template(posts, input_template, output_file):\\n context = {}\\n context['posts'] = posts\\n with open(input_template, 'r') as read_template:\\n payload = Template(read_template.read())\\n return payload.render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"356f6e40695be4d976b483f8a8f9576e\",\n \"score\": \"0.6016078\",\n \"text\": \"def render(template, **params):\\n return re.sub(\\n r\\\"{{\\\\s*([^}\\\\s]+)\\\\s*}}\\\",\\n lambda match: str(params.get(match.group(1), match.group(0))),\\n template,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdc302545dd530d7c84efa1731a7ca0f\",\n \"score\": \"0.5998786\",\n \"text\": \"def get_text_file(template,variables):\\n\\n template_class = Template(template)\\n return template_class.render(variables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2766fb602c67ed802a1bca577626247e\",\n \"score\": \"0.59938693\",\n \"text\": \"def render_template(path, context):\\n\\n with open(path) as f:\\n template = Template(f.read())\\n\\n return template.render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed7ec73fcdf599f28a2e8a7e5639bb9\",\n \"score\": \"0.598588\",\n \"text\": \"def render_template(\\n template_file: pathlib.Path, output_file: pathlib.Path,\\n context: Dict\\n) -> None:\\n\\n logging.debug(f\\\"Generating {output_file}\\\")\\n with template_file.open() as t:\\n template = Template(t.read(), keep_trailing_newline=True)\\n output: str = template.render(context)\\n with output_file.open(\\\"w\\\") as o:\\n o.write(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f22c8784fb55c6c02ff6c433073c65\",\n \"score\": \"0.597313\",\n \"text\": \"def render_file(cls, src, dst, render_variables, postfix=None, silent=True):\\n if not os.path.isfile(src):\\n raise IOError(\\\"source file for rendering does not exist: {}\\\".format(src))\\n\\n with open(src, \\\"r\\\") as f:\\n try:\\n content = f.read()\\n except UnicodeDecodeError:\\n if silent:\\n return\\n raise\\n\\n def postfix_fn(m):\\n return cls.postfix_input_file(m.group(1), postfix=postfix)\\n\\n for key, value in six.iteritems(render_variables):\\n # value might contain paths to be postfixed, denoted by \\\"__law_job_postfix__:...\\\"\\n if postfix:\\n value = re.sub(r\\\"\\\\_\\\\_law\\\\_job\\\\_postfix\\\\_\\\\_:([^\\\\s]+)\\\", postfix_fn, value)\\n content = cls.render_string(content, key, value)\\n\\n # finally, replace all non-rendered keys with empty strings\\n content = cls.render_key_cre.sub(\\\"\\\", content)\\n\\n with open(dst, \\\"w\\\") as f:\\n f.write(content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0fecae20cd4cf468b56f631af23b80\",\n \"score\": \"0.59598273\",\n \"text\": \"def render(self, input_tpl, **params):\\n #Check if the input is a readable file, if not treat it as a string\\n try:\\n with open(input_tpl) as f:\\n input_tpl = f.read()\\n except FileNotFoundError:\\n pass\\n #Replace the parameter variables\\n str_template = string.Template(input_tpl)\\n input_tpl = str_template.safe_substitute(params)\\n #Parse the file to extract template code\\n input_tpl = self.parse(input_tpl)\\n #Execute the template code\\n input_tpl = self.compile_insert(input_tpl)\\n #Remove template escapes\\n input_tpl = re.sub(r\\\"([\\\\\\\\])(~=|~!|~%|~py|{[a-zA-Z_][a-zA-Z0-9_]*})\\\", \\\"\\\\g<2>\\\", input_tpl)\\n return input_tpl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5c78c3547036cf13a60ffb074e1746\",\n \"score\": \"0.59350336\",\n \"text\": \"def render_block_to_string(template_name, dictionary=None, context_instance=None):\\n dictionary = dictionary or {}\\n template = get_template(template_name)\\n if context_instance:\\n context_instance.update(dictionary)\\n else:\\n context_instance = dictionary\\n return template.render(context_instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a76b01c0d4f6f3089082b247023fe4\",\n \"score\": \"0.59140444\",\n \"text\": \"def render_to_file(self, filename, target_filename, **data):\\n template = self.env.get_template(filename)\\n self.render_template(template, target_filename, **data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7aa6e0dc2a7457b0feb2af67980a51\",\n \"score\": \"0.59115463\",\n \"text\": \"def render_report(stat_list, template_filename):\\n\\n # re-format stat to list\\n stat_rows = []\\n for data in stat_list:\\n stat_rows.append({\\n 'url': data['url'],\\n 'count': data['count'],\\n 'time_avg': '{0:.10f}'.format(data['time_avg']),\\n 'time_max': '{0:.10f}'.format(data['time_max']),\\n 'time_sum': '{0:.10f}'.format(data['time_sum']),\\n 'time_med': '{0:.10f}'.format(data['time_med']),\\n 'time_perc': '{0:.10f}'.format(data['time_perc']),\\n 'count_perc': '{0:.10f}'.format(data['count_perc']),\\n })\\n\\n stat_json = json.dumps(stat_rows)\\n\\n try:\\n with open(template_filename, \\\"r\\\") as ftemp:\\n template = \\\"\\\".join(ftemp.readlines())\\n except Exception as e:\\n logging.error('Failed to read report template file \\\"%s\\\": %s',\\n template_filename, e.message)\\n return None\\n\\n report_str = template.replace('$table_json', stat_json)\\n return report_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce96017025e90cf46b939820b4dcecbb\",\n \"score\": \"0.5911458\",\n \"text\": \"def template_subs_file(in_file, out_fobj, d):\\n with open(in_file, 'r') as in_fobj:\\n for line in in_fobj:\\n out_fobj.write(line.format(**d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce96017025e90cf46b939820b4dcecbb\",\n \"score\": \"0.5911458\",\n \"text\": \"def template_subs_file(in_file, out_fobj, d):\\n with open(in_file, 'r') as in_fobj:\\n for line in in_fobj:\\n out_fobj.write(line.format(**d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b174f86f6f86b4b6fe7eb41e634a041\",\n \"score\": \"0.5901367\",\n \"text\": \"def template_to_dict(template_path, dict_path, context):\\n template_file = open(template_path, \\\"r\\\")\\n template_text = template_file.read()\\n template_file.close()\\n\\n template = jinja2.Template(template_text)\\n mesh_file = open(dict_path, \\\"w\\\")\\n mesh_file.write(template.render(context))\\n mesh_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb9ff7e3d4ae19f3e0c02184018d942\",\n \"score\": \"0.58916366\",\n \"text\": \"def render_str(self, template_name, **kwargs):\\n template = JINJA_ENV.get_template(template_name)\\n kwargs['user'] = self.user\\n return template.render(kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9153e4b18476a98d4be10502df1f8d81\",\n \"score\": \"0.585749\",\n \"text\": \"def render(tpl_path, context):\\n path, filename = os.path.split(tpl_path)\\n return jinja2.Environment(\\n loader=jinja2.FileSystemLoader(path or './')\\n ).get_template(filename).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"114edd8afaa535a2157354cef2087064\",\n \"score\": \"0.5856374\",\n \"text\": \"def render_template(self, filename, parts):\\n\\n searchpath = os.path.join(self.basedir, \\\"templates\\\")\\n\\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(searchpath))\\n\\n tmpl = env.get_template(filename)\\n\\n\\n # Update the namespace with all the global definitions from the\\n # configuration file. This is typically done for site name, \\n # copyright information, etc.\\n for key in self.config:\\n if key.startswith(\\\"namespace.\\\"):\\n parts[key[4:]] = self.config[key]\\n\\n return tmpl.render(parts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d284265d27d0ce9a3a30fff0c32329a0\",\n \"score\": \"0.58503664\",\n \"text\": \"def render_template(template_path, context={}):\\n template_str = load_resource(template_path)\\n template = Template(template_str)\\n return template.render(Context(context))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35437795a6e589014b2ba13e1569ecbe\",\n \"score\": \"0.5820803\",\n \"text\": \"def render_put(template_name, dest, params, mode=None, tempfile='tempfile'):\\n data = loader.render_to_string(template_name, dictionary=params)\\n open(tempfile, 'w').write(data)\\n put(tempfile, dest, mode)\\n os.remove(tempfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f866e72d87f8e3afaf443a037be40d6d\",\n \"score\": \"0.5816048\",\n \"text\": \"def render_jinja(template_filename, **variables):\\n jenv = jinja2.Environment(\\n loader=jinja2.FileSystemLoader(\\n [\\n Path(\\\".\\\"),\\n Path(__file__).parent / \\\"templates\\\",\\n ]\\n ),\\n autoescape=True,\\n )\\n jenv.filters[\\\"datetime\\\"] = datetime_format\\n jenv.filters[\\\"label_color_css\\\"] = label_color_css\\n template = jenv.get_template(template_filename)\\n html = template.render(**variables)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d1eab599f9595ad4a7ba4065bab685\",\n \"score\": \"0.5810972\",\n \"text\": \"def render(name, data):\\n viewunit.template_called(name, data)\\n return flask.render_template(name, **data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19602fd473886f6cc4a681b2258561e5\",\n \"score\": \"0.579852\",\n \"text\": \"def format(self, template_name=None, **data):\\n if not template_name:\\n template_name = self.config.default_template\\n content = self.templator.render(template_name, **data)\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"896174b95841a7f47af651e17475d0ef\",\n \"score\": \"0.57946557\",\n \"text\": \"def render_template(template_file, **context):\\r\\n if hasattr(self, 'template_env'):\\r\\n t_env = self.template_env\\r\\n template = t_env.get_template(template_file)\\r\\n body = template.render(**context or {})\\r\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db5970f98aaa1c327b25ddbb5f1a17f6\",\n \"score\": \"0.57934415\",\n \"text\": \"def render(cls, c={}):\\n cls.context.update(c)\\n t = Template( open(cls.template).read() )\\n c = Context( cls.context )\\n return t.render( c )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"495ec2273755288280ddd368911f9dab\",\n \"score\": \"0.57880586\",\n \"text\": \"def render(self, handler, params={}):\\n\\n template_info = get_view_file(handler, params)\\n aio.debug(\\\"view.render: Using template at %s\\\", template_info['file'])\\n output = self.render_or_get_cache(handler, template_info, params)\\n handler.response.out.write(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23857176d741fcbf5918e1b56758cdd\",\n \"score\": \"0.576992\",\n \"text\": \"def fillinFileTemplate(f_path_read, f_path_write=None, dict={}):\\n if not f_path_write:\\n f_path_write = f_path_read\\n f_tmpl = open(f_path_read, 'r')\\n tmpl = f_tmpl.read()\\n f_tmpl.close()\\n f_tmpl = open(f_path_write, 'w')\\n try:\\n f_tmpl.write(tmpl % dict)\\n except:\\n raise str(tmpl)\\n f_tmpl.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73661695f0c7400a475abfa4cdede2ab\",\n \"score\": \"0.5758503\",\n \"text\": \"def render_latex(output_file, latex_template_fn, \\n start_date, end_date, \\n sections,\\n top_entry_count, \\n top_n_users, \\n user_data,\\n user_details):\\n latex_tpl = Template(filename=latex_template_fn)\\n latex = latex_tpl(start_date=start_date, end_date=end_date, \\n sections=sections,\\n n=top_entry_count, \\n top_n_users=top_n_users, \\n user_data=user_data,\\n user_details=user_details)\\n output_file.write(latex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a47d70b4e71d23d59369fcb12274207\",\n \"score\": \"0.57499623\",\n \"text\": \"def render1f(path: str, *, args: Dict = None, as_json: bool = False) -> str:\\n return _render1(render_file(path, args=args, as_json=as_json), as_json=as_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d55678748a56b773f726dae021a78446\",\n \"score\": \"0.57116705\",\n \"text\": \"def render_template(self, template_file,\\r\\n _status_code=WebMessageHandler._SUCCESS_CODE,\\r\\n **context):\\r\\n mustache_env = self.application.template_env\\r\\n\\r\\n template = mustache_env.load_template(template_file)\\r\\n body = mustache_env.render(template, context or {})\\r\\n\\r\\n self.set_body(body, status_code=_status_code)\\r\\n return self.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb76f0952c0050377c13cc0a49d04838\",\n \"score\": \"0.5687893\",\n \"text\": \"def render_template_string(source, **context):\\r\\n ctx = _request_ctx_stack.top\\r\\n ctx.app.update_template_context(context)\\r\\n return _render(ctx.app.jinja_env.from_string(source),\\r\\n context, ctx.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe76dd75bdae51f33b1cd7848d3832b\",\n \"score\": \"0.5670168\",\n \"text\": \"def render_template(self, template_name, **kwargs):\\n return template_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa244e3bc05b29cfde0f0922639174f\",\n \"score\": \"0.5669284\",\n \"text\": \"def render_template(self, template_file,\\n _status_code=WebMessageHandler._SUCCESS_CODE,\\n **context):\\n mustache_env = self.application.template_env\\n\\n template = mustache_env.load_template(template_file)\\n body = mustache_env.render(template, context or {})\\n\\n self.set_body(body, status_code=_status_code)\\n return self.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65a5fe6020fa3a9e238e248de8e4706\",\n \"score\": \"0.56625575\",\n \"text\": \"def render_template(plain_text, vars):\\n t = Template(plain_text)\\n content = t.render(vars)\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ec286bceb62b6f41b9ce2c524ee3fae\",\n \"score\": \"0.5656246\",\n \"text\": \"def render_template(template, **kwargs):\\n response = make_response(flask.render_template(template, **kwargs))\\n response.headers[\\\"Content-type\\\"] = \\\"text/html\\\"\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6ab1564ee9891baff7dfec1c80e5d1\",\n \"score\": \"0.565567\",\n \"text\": \"def render(self, template_name, **contexts):\\n self.write(self.render_template(template_name, **contexts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab4d870ce35c0a83a9dfbba43dd004b\",\n \"score\": \"0.5641959\",\n \"text\": \"async def render_jinja_to_file(template_filename, output_file, **variables):\\n text = render_jinja(template_filename, **variables)\\n text = emoji.emojize(text, language=\\\"alias\\\")\\n async with aiofiles.open(output_file, \\\"w\\\", encoding=\\\"utf-8\\\") as out:\\n await out.write(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e2ef9ab05e59fce511ab65556984a1f\",\n \"score\": \"0.56381524\",\n \"text\": \"def fillHTMLTemplate(templateString, paramsDict):\\r\\n templateObject = jinja2.Template(templateString)\\r\\n htmlContent = templateObject.render(paramsDict)\\r\\n return htmlContent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612b5c83fce9515c9646a6383d121e47\",\n \"score\": \"0.56256217\",\n \"text\": \"def render_string(cls, s, key, value):\\n return s.replace(\\\"{{\\\" + key + \\\"}}\\\", value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39486ec33fcd300c58f6f8d4f19be1fb\",\n \"score\": \"0.56168604\",\n \"text\": \"def render_summary(data, template_filename):\\n loader = PackageLoader(\\\"gagepy\\\", \\\"templates\\\")\\n env = Environment(loader=loader)\\n template = env.get_template(template_filename)\\n\\n return template.render(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60028c49ec00592672b349a1a49288ae\",\n \"score\": \"0.5616435\",\n \"text\": \"def _render_templates(self, obj, ns=None):\\n if ns is None:\\n ns = self.template_namespace()\\n\\n _fmt = partial(self._render_templates, ns=ns)\\n\\n if isinstance(obj, str):\\n try:\\n return obj.format(**ns)\\n except (ValueError, KeyError):\\n # not a valid format string\\n # to avoid crashing leave invalid templates unrendered\\n # otherwise, this unconditional formatting would not allow\\n # strings with `{` characters in them\\n return obj\\n elif isinstance(obj, dict):\\n return {_fmt(key): _fmt(value) for key, value in obj.items()}\\n elif isinstance(obj, (list, tuple)):\\n return type(obj)([_fmt(item) for item in obj])\\n else:\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d10a656ea83e9a8ad5462b0b9d4cc77\",\n \"score\": \"0.56161284\",\n \"text\": \"def render_template_string(source, **context):\\n ctx = _request_ctx_stack.top\\n ctx.app.update_template_context(context)\\n return _render(ctx.app.jinja_env.from_string(source),\\n context, ctx.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f6058eca47ace5a5ad3e364b4c64af\",\n \"score\": \"0.5613586\",\n \"text\": \"def _render(self, template_name, context):\\n\\n template = self.jinja_env.get_template(template_name)\\n return template.render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648917c52a1aee39c56da62d68f8a613\",\n \"score\": \"0.56032234\",\n \"text\": \"def render_template_string(source, **context):\\n ctx = _request_ctx_stack.top\\n ctx.app.update_template_context(context)\\n return ctx.app.jinja_env.from_string(source).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b4cefe84704f86258ed17203194ddd\",\n \"score\": \"0.55944186\",\n \"text\": \"def test_from_file_with_filename():\\n fd, filename = tempfile.mkstemp()\\n try:\\n os.write(fd, \\\"Hello ${you}!\\\")\\n finally:\\n os.close(fd)\\n try:\\n t = Template.from_file(filename)\\n assert t.render(you=\\\"World\\\") == \\\"Hello World!\\\"\\n finally:\\n os.unlink(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781d0955d92c927501a57b11705cc6b9\",\n \"score\": \"0.55912226\",\n \"text\": \"def _render_template(self, path, context):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018cc0a6b30b9d1350ec163a5424ce51\",\n \"score\": \"0.5585938\",\n \"text\": \"def render(self,name,data):\\n return self._getTemplate(name).stream(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63565fcfb295fd6f948aef108a0afa3d\",\n \"score\": \"0.55788225\",\n \"text\": \"def replace(text, dic) -> str:\\n s = Template(text)\\n return s.safe_substitute(**dic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1644834d27ac9e3b9cb624e85be417\",\n \"score\": \"0.5565817\",\n \"text\": \"def test_from_file_with_fileobject():\\n t = Template.from_file(sio.StringIO(\\\"Hello ${you}!\\\"))\\n assert t.render(you=\\\"World\\\") == \\\"Hello World!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05f5e2e616722b84e932aa63d23ea22\",\n \"score\": \"0.55491006\",\n \"text\": \"def render_template(context, template, output_path=None, templates_dir=None,\\n executable=False):\\n env = _init_template_env(templates_dir)\\n\\n rendered_data = env.get_template(template).render(context)\\n\\n # Remove trailing spaces\\n cleaned_lines = []\\n for line in rendered_data.splitlines():\\n cleaned_lines.append(line.rstrip())\\n rendered_data = '\\\\n'.join(cleaned_lines)\\n\\n if output_path:\\n with open(output_path, 'w', encoding='utf-8') as f:\\n f.write(rendered_data)\\n\\n if executable:\\n st = os.stat(output_path)\\n os.chmod(output_path, st.st_mode | stat.S_IEXEC)\\n\\n return rendered_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94b4ca206cfecd0f000221a8696694\",\n \"score\": \"0.5548685\",\n \"text\": \"def render_template(template_name_or_list, **context):\\r\\n ctx = _request_ctx_stack.top\\r\\n ctx.app.update_template_context(context)\\r\\n return _render(ctx.app.jinja_env.get_or_select_template(template_name_or_list),\\r\\n context, ctx.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b988ade66a4e779e9a4d4bb31274e596\",\n \"score\": \"0.5544229\",\n \"text\": \"def render_template(template_path=None, template_text=None, dest=None, **kwargs):\\n template = get_template(template_path=template_path, template_text=template_text)\\n\\n try:\\n template_string = template.render(**kwargs)\\n except Exception as e:\\n raise j.exceptions.Base(e)\\n\\n if dest:\\n j.sals.fs.mkdirs(j.sals.fs.dirname(dest))\\n j.sals.fs.write_file(dest, template_string)\\n return template_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1e78e62bc5bbcfbf04614a60b69ee1\",\n \"score\": \"0.5541193\",\n \"text\": \"def render_template(source: str, *args, **kwargs) -> str:\\n template = PARSER_HANDLER.from_string(source)\\n return template.render(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eabcdb9ca1139b248dfa699197f6119\",\n \"score\": \"0.5540827\",\n \"text\": \"def render_to_string(template, *args, **kwargs):\\n template_lib = _get_template_lib(template, kwargs)\\n if template_lib == DJANGO:\\n return django_render_to_string(template, *args, **kwargs)\\n elif template_lib == MAKO:\\n return django_mako.render_to_string(template, *args, **kwargs)\\n else:\\n raise Exception(\\\"Bad template lib: %s\\\" % template_lib)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f508108890eb5806432e39d00c697af\",\n \"score\": \"0.55246764\",\n \"text\": \"def table_template(dictionary, name='Nome', value='Valor'):\\n html_string = '<table>' \\\\\\n '<thead>' \\\\\\n '<tr>' \\\\\\n '<th> {} </th>' \\\\\\n '<th> {} </th>' \\\\\\n '</tr>' \\\\\\n '</thead>' \\\\\\n '<tbody style=\\\"overflow-: auto;\\\">'.format(name, value)\\n if isinstance(dictionary, dict):\\n for key, value in dictionary.items():\\n html_string += '</tr>' \\\\\\n '<td>{}</td>' \\\\\\n '<td>{}</td>' \\\\\\n '</tr>'.format(key, value)\\n html_string += '</tbody>' \\\\\\n '</table>'\\n return mark_safe(html_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970e23f365a71216c98f474d53de889d\",\n \"score\": \"0.55115443\",\n \"text\": \"def template(request, name, mimetype=None, **kwd):\\n # parameters that will always be available for the template\\n kwd['request'] = request\\n resp = render_to_response(name, kwd, context_instance=RequestContext(request))\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8acd8ecff68953409762bc034347875c\",\n \"score\": \"0.55071265\",\n \"text\": \"def writeTemplate(self, fileName, context):\\n template = self.jinja.get_template(fileName)\\n self.response.write(template.render(context))\\n self.response.content_type = 'text/html'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb096a9330fd61708a7e456bf2aad487\",\n \"score\": \"0.5500604\",\n \"text\": \"def convert_json_to_tex(param_dict, only_contents):\\n if only_contents:\\n tex = \\\"\\\"\\n else:\\n tex = r\\\"\\\"\\\"\\\\begin{table}[]\\n \\\\centering\\n \\\\begin{tabular}{cc}\\n\\\"\\\"\\\"\\n tex += r\\\"\\\"\\\" \\\\hline\\n Parameter & Range \\\\\\\\\\n \\\\hline\\n\\\"\\\"\\\"\\n for key, prange in param_dict.iteritems():\\n key_tex = texify_key(key)\\n # key_tex = key\\n tex += r\\\"\\\"\\\" $%s$ & %g - %g \\\\\\\\\\\"\\\"\\\" % (key_tex, prange['min'], prange['max'])\\n tex += \\\"\\\\n\\\"\\n tex += r\\\"\\\"\\\" \\\\hline\\\"\\\"\\\"\\n\\n if not only_contents:\\n tex += \\\"\\\\n\\\"\\n tex += r\\\"\\\"\\\" \\\\end{tabular}\\n \\\\caption{}\\n \\\\label{tab:}\\n\\\\end{table}\\\"\\\"\\\"\\n return tex\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91546a13b5e8790bbd03212020131f93\",\n \"score\": \"0.5498525\",\n \"text\": \"def apply(self, mapping):\\n return self.template.render(**mapping)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"109ef3a5621279491f8c258c979ad3b2\",\n \"score\": \"0.5493685\",\n \"text\": \"def render_template(name, style=None, **kwargs):\\n if style is None:\\n style = current_app.config.get(\\\"STYLE\\\", None)\\n if style is not None:\\n name = f\\\"{style}/{name}\\\"\\n return flask.render_template(name, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a2817c2ce38b5f9c57d70c55fb05e32\",\n \"score\": \"0.5490736\",\n \"text\": \"def settings_template(path, **kwargs):\\r\\n from tornado.template import Template\\r\\n with io.open(path, mode='r', encoding='utf-8') as f:\\r\\n template_data = f.read()\\r\\n t = Template(template_data)\\r\\n # NOTE: Tornado returns templates as bytes, not unicode. That's why we need\\r\\n # the decode() below...\\r\\n rendered = t.generate(**kwargs).decode('utf-8')\\r\\n out = \\\"\\\"\\r\\n for line in rendered.splitlines():\\r\\n if line.strip():\\r\\n out += line + \\\"\\\\n\\\"\\r\\n return out\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":994399,"cells":{"query_id":{"kind":"string","value":"2e685c8cb86050cc606e80b6aaf1cfc4"},"query":{"kind":"string","value":"Create s3 volume using IAM Role auth."},"positive_passages":{"kind":"list like","value":[{"docid":"feb91429653a7b28081ec37c69b34a51","score":"0.7573797","text":"def create_s3_volume_role_auth(cls, name, bucket, role_arn, external_id,\n access_mode, description=None, prefix=None,\n properties=None, api=None):\n service = {'type': VolumeType.S3,\n 'bucket': bucket,\n 'credentials': {'role_arn': role_arn,\n 'external_id': external_id\n }\n }\n if prefix:\n service['prefix'] = prefix\n if properties:\n service['properties'] = properties\n\n data = {'name': name,\n 'service': service,\n 'access_mode': access_mode\n }\n if description:\n data['description'] = description\n api = api or cls._API\n extra = {\n 'resource': cls.__name__,\n 'query': data\n }\n logger.info('Creating s3 volume using role auth', extra=extra)\n response = api.post(url=cls._URL['query'], data=data).json()\n return Volume(api=api, **response)","title":""}],"string":"[\n {\n \"docid\": \"feb91429653a7b28081ec37c69b34a51\",\n \"score\": \"0.7573797\",\n \"text\": \"def create_s3_volume_role_auth(cls, name, bucket, role_arn, external_id,\\n access_mode, description=None, prefix=None,\\n properties=None, api=None):\\n service = {'type': VolumeType.S3,\\n 'bucket': bucket,\\n 'credentials': {'role_arn': role_arn,\\n 'external_id': external_id\\n }\\n }\\n if prefix:\\n service['prefix'] = prefix\\n if properties:\\n service['properties'] = properties\\n\\n data = {'name': name,\\n 'service': service,\\n 'access_mode': access_mode\\n }\\n if description:\\n data['description'] = description\\n api = api or cls._API\\n extra = {\\n 'resource': cls.__name__,\\n 'query': data\\n }\\n logger.info('Creating s3 volume using role auth', extra=extra)\\n response = api.post(url=cls._URL['query'], data=data).json()\\n return Volume(api=api, **response)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"819226986a465f5a16f920a1aad2294b","score":"0.64687634","text":"def create_iam():\r\n iam = boto3.client('iam', region_name='us-east-1', aws_access_key_id=KEY,\r\n aws_secret_access_key=SECRET)\r\n try:\r\n iam.create_role(Path='/',\r\n RoleName=DWH_IAM_ROLE_NAME,\r\n AssumeRolePolicyDocument=json.dumps(\r\n {'Statement': [{'Action': 'sts:AssumeRole',\r\n 'Effect': 'Allow',\r\n 'Principal': {'Service': 'redshift.amazonaws.com'}}],\r\n 'Version': '2012-10-17'}),\r\n Description='Allow Redshift to access S3 bucket',\r\n )\r\n except Exception as e:\r\n print(e)\r\n try:\r\n iam.attach_role_policy(RoleName=DWH_IAM_ROLE_NAME,\r\n PolicyArn=\"arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess\"\r\n )['ResponseMetadata']['HTTPStatusCode']\r\n except Exception as e:\r\n print(e)\r\n roleArn = iam.get_role(RoleName=DWH_IAM_ROLE_NAME)\r\n return roleArn['Role']['Arn']","title":""},{"docid":"a8344c77178a59480a7c6b2bb948b64b","score":"0.6233874","text":"def s3_create_upload(filename):\n s3 = boto3.resource('s3', region_name=aws_region)\n s3client = boto3.client('s3', region_name=aws_region)\n accountID = boto3.client('sts').get_caller_identity().get('Account')\n bucket_name = \"rds-reports-\"+aws_region+\"-\"+accountID\n bucket_exist = True\n try:\n s3.meta.client.head_bucket(Bucket=bucket_name)\n print(\"Bucket Exists\")\n except ClientError as e:\n if int(e.response['Error']['Code']) == 403:\n print(\"Private Bucket. Forbidden Access!\")\n pass\n else:\n print(\"Bucket Does Not Exist!\")\n print(str(e))\n bucket_exist = False\n pass\n if not bucket_exist:\n if aws_region == \"us-east-1\":\n s3client.create_bucket(Bucket=bucket_name, ACL='private')\n else:\n location = {\"LocationConstraint\": aws_region}\n s3client.create_bucket(\n Bucket=bucket_name,\n ACL='private',\n CreateBucketConfiguration=location\n )\n s3client.put_bucket_encryption(Bucket=bucket_name, ServerSideEncryptionConfiguration={'Rules': [{'ApplyServerSideEncryptionByDefault': {'SSEAlgorithm': 'AES256', }}]})\n\n s3.meta.client.upload_file(filename, bucket_name,\n filename.split(\"tmp/\")[1])","title":""},{"docid":"a435b957bc254898dea0c0eff2663df4","score":"0.6226705","text":"def neo_create_bucket(**kwargs):\n auth = kwargs.get(\"auth\")\n acl = kwargs.get(\"acl\", \"private\")\n policy_id = kwargs.get(\"policy_id\", \"\")\n bucket_name = kwargs.get(\"bucket_name\")\n\n if kwargs.get(\"random_name\"):\n bucket_name = gen_random_name(bucket_name)\n\n endpoint = auth_lib.get_endpoint(\"storage\", bucket_name)\n headers = {\"x-gmt-policyid\": policy_id, \"x-amz-acl\": acl}\n\n if \".\" in bucket_name:\n response = requests.put(endpoint, auth=auth, headers=headers, verify=False)\n else:\n response = requests.put(endpoint, auth=auth, headers=headers)\n return response","title":""},{"docid":"14379bf437975ff8a6c2ac022046550a","score":"0.61634696","text":"def create_bucket(client, name, region):\n if region == 'us-east-1':\n # S3 in us-east-1 does not accept a location constraint\n client.create_bucket(Bucket=name, ACL='public-read')\n else:\n client.create_bucket(Bucket=name, ACL='public-read', CreateBucketConfiguration={ 'LocationConstraint': region })","title":""},{"docid":"f798d740e5dc585bbf7eab53c8cfac09","score":"0.61412","text":"def role_creator ():\n \n DWH_IAM_ROLE_NAME = config.get(\"DWH\", \"DWH_IAM_ROLE_NAME\")\n \n try:\n dwhRole = iam.create_role(\n Path='/',\n RoleName=DWH_IAM_ROLE_NAME,\n Description = \"Allows Redshift clusters to call AWS services on your behalf.\",\n AssumeRolePolicyDocument=json.dumps(\n {'Statement': [{'Action': 'sts:AssumeRole',\n 'Effect': 'Allow',\n 'Principal': {'Service': 'redshift.amazonaws.com'}}],\n 'Version': '2012-10-17'})\n )\n\n except Exception as e:\n print(e)\n \n try:\n iam.attach_role_policy(RoleName=DWH_IAM_ROLE_NAME,\n PolicyArn=\"arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess\"\n )['ResponseMetadata']['HTTPStatusCode']\n \n except Exception as e:\n print(e)","title":""},{"docid":"fef2ef5d540592addba57ead8c61c1a7","score":"0.6132003","text":"def create_bucket(\n bucket_name: str,\n region: str,\n s3_client,\n s3_resource,\n acl_type: str = \"private\",\n versions: bool = True,\n) -> bool:\n try:\n location = {\"LocationConstraint\": region}\n s3_client.create_bucket(\n Bucket=bucket_name, CreateBucketConfiguration=location, ACL=acl_type\n )\n except ClientError as error:\n # S3.Client.exceptions.BucketAlreadyExists\n if error.response[\"Error\"][\"Code\"] == \"BucketAlreadyOwnedByYou\":\n logger.warning(f\"Bucket {bucket_name} already exists! Using it.\")\n else:\n logger.exception(f\"Unable to create bucket {bucket_name}.\")\n raise\n return False\n if versions:\n try:\n bucket = s3_resource.Bucket(bucket_name)\n bucket.Versioning().enable()\n logger.info(f\"Enabled versioning on bucket {bucket_name}.\")\n except ClientError:\n logger.exception(f\"Failed to enable versioning on bucket {bucket_name}.\")\n raise\n return True","title":""},{"docid":"f77408c1c4fa2473d42243d6bc18dc17","score":"0.6019878","text":"def create_bucket(self):\n\n response = self.__bucket.create(\n ACL='public-read'\n )\n\n LOGGER.debug(\n f\"Bucket Status: {response['ResponseMetadata']['HTTPStatusCode']} | Bucket Location: {response['Location']}\")","title":""},{"docid":"6209ca6b112e06d482b745d7d626e174","score":"0.59149665","text":"def create_bucket():\r\n # Instantiates a client\r\n storage_client = storage.Client()\r\n\r\n # Creates the new bucket\r\n bucket = storage_client.create_bucket(BUCKET_NAME)","title":""},{"docid":"1cdb7bbabf622cdae0cd2358ed9b54ac","score":"0.5887031","text":"def create_ebs_volume(tags, az, volume_type, volume_size, iops, encrypted, snap_id=None):\n try:\n if snap_id:\n if iops:\n volume_id = ec2.create_volume(Size=int(volume_size),\n AvailabilityZone=az,\n Encrypted=encrypted,\n VolumeType=volume_type,\n Iops=iops,\n SnapshotId=snap_id)['VolumeId']\n else:\n volume_id = ec2.create_volume(Size=int(volume_size),\n AvailabilityZone=az,\n Encrypted=encrypted,\n VolumeType=volume_type,\n SnapshotId=snap_id)['VolumeId']\n else:\n if iops:\n volume_id = ec2.create_volume(Size=int(volume_size),\n AvailabilityZone=az,\n Encrypted=encrypted,\n VolumeType=volume_type,\n Iops=iops)['VolumeId']\n else:\n volume_id = ec2.create_volume(Size=int(volume_size),\n AvailabilityZone=az,\n Encrypted=encrypted,\n VolumeType=volume_type)['VolumeId']\n if check_the_resource_state('volume_available',\n 'VolumeIds',\n volume_id):\n ec2.create_tags(Resources=[volume_id], Tags=tags)\n return volume_id\n else:\n logger.error(\"Timeout while waiting for the availability of the new volume\")\n except botocore.exceptions.ClientError as e:\n logger.error(\"Can\\'t create new volume\" .format(str(e)))\n except Exception as e:\n logger.error(\"Can\\'t execute volume creation. Exception: {0}\" .format(str(e)))\n return None","title":""},{"docid":"7565b41fe7a59fe021409acafe976a74","score":"0.58865273","text":"def create_volume(self, volume):\n volume_path = self._get_volume_path(volume)\n volume_size = '%sG' % volume['size']\n volume_blocksize = six.text_type(self.volume_blocksize)\n volume_sparsed = self.volume_sparse\n volume_options = {}\n if self.volume_compression:\n volume_options['compression'] = self.volume_compression\n self.nms.zvol.create_with_props(volume_path, volume_size,\n volume_blocksize,\n volume_sparsed,\n volume_options)","title":""},{"docid":"b091022a100ecfe745c4e09ad7617ddd","score":"0.58749175","text":"def upload_template_to_s3(template, bucket_name, session=None, file_name=None):\n \n file_key = dt.utcnow().strftime('%Y/%m/%d/%H-%M')\n if not file_name:\n file_name = 'iam_roles.json'\n s3_path = f\"{file_key}/{file_name}\"\n if not session:\n client = boto3.client('s3')\n else:\n client = session.client('s3')\n try:\n client.put_object(\n Bucket=bucket_name,\n Body=template.to_json(),\n Key=s3_path,\n ContentEncoding='utf-8',\n ContentType='application/json',\n ServerSideEncryption='AES256'\n )\n return f\"s3://{bucket_name}/{s3_path}\"\n except Exception as e:\n print(e)","title":""},{"docid":"e8d1ce14c2000dd904db22e717cba31b","score":"0.5842075","text":"def create(self, name, size, zone, snapshot=None, description=None):\n AWSVolume.assert_valid_resource_name(name)\n\n zone_id = zone.id if isinstance(zone, PlacementZone) else zone\n snapshot_id = snapshot.id if isinstance(\n snapshot, AWSSnapshot) and snapshot else snapshot\n\n ec2_vol = self.provider.ec2_conn.create_volume(\n size,\n zone_id,\n snapshot=snapshot_id)\n cb_vol = AWSVolume(self.provider, ec2_vol)\n cb_vol.name = name\n if description:\n cb_vol.description = description\n return cb_vol","title":""},{"docid":"124e27e08397d9030419513950ccc442","score":"0.5833289","text":"def __init__(self,\n volume_id,\n aws_account,\n region,\n availability_zone,\n encrypted,\n name=None,\n device_name=None):\n\n super(AWSVolume, self).__init__(aws_account,\n region,\n availability_zone,\n encrypted,\n name)\n self.volume_id = volume_id\n self.device_name = device_name","title":""},{"docid":"7d97d2fcb84220f9ed4496af3d1e3f76","score":"0.5829424","text":"def _create_volume(size='0', snapshot_id=None):\n vol = {}\n vol['size'] = size\n vol['snapshot_id'] = snapshot_id\n vol['user_id'] = 'fake'\n vol['project_id'] = 'fake'\n vol['availability_zone'] = FLAGS.storage_availability_zone\n vol['status'] = \"creating\"\n vol['attach_status'] = \"detached\"\n return db.volume_create(context.get_admin_context(), vol)['id']","title":""},{"docid":"782356049daedb1c1637935dca84374f","score":"0.5820942","text":"def ShareWithAWSAccount(self, aws_account_id):\n\n snapshot = self.aws_account.ResourceApi(EC2_SERVICE).Snapshot(\n self.snapshot_id)\n snapshot.modify_attribute(\n Attribute='createVolumePermission',\n CreateVolumePermission={\n 'Add': [{\n 'UserId': aws_account_id\n }]\n },\n OperationType='add'\n )","title":""},{"docid":"36b6a4855f318eb9ed71869fe50cb352","score":"0.5815212","text":"def ensure_bucket_exists(storage: MediaS3):\n storage.create_bucket()","title":""},{"docid":"f8389cd36e14e6acd3733a5a5dff6153","score":"0.57987326","text":"def init_s3_buckets():\n os.system(f\"aws s3 mb s3://{cfg.PROJECT_NAME}.smtj.data\")\n os.system(f\"aws s3 mb s3://{cfg.PROJECT_NAME}.smtj.output\")\n os.system(f\"aws s3 sync {cfg.DATA_LOCAL_DIR} s3://{cfg.PROJECT_NAME}\" +\\\n f\".smtj.data/{cfg.DATA_NAME}\")\n os.system(f\"aws iam create-role --role-name {cfg.PROJECT}-SagemakerRole \" +\\\n \"--assume-role-policy-document file://./role-policy.json\")\n os.system(f\"aws iam attach-role-policy --role-name {cfg.PROJECT}-SagemakerRole \" +\\\n \"--policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccess\")\n build_s3_policy_from_template()\n os.system(f\"aws iam put-role-policy --role-name {cfg.PROJECT}-SagemakerRole \" +\\\n \"--policy-document file://./s3-policy.json --policy-name s3-policy\")","title":""},{"docid":"64ea3d6e66916767f92e535c8a3cbcc1","score":"0.5728218","text":"def __moto_setup(self):\r\n s3 = get_client()\r\n s3.create_bucket(Bucket=self.bucket)\r\n s3.put_object(Bucket=self.bucket, Key=self.key, Body=self.value)","title":""},{"docid":"c1a2f7d691a36a97f3e7b1aaeccd354a","score":"0.57208574","text":"def s3(ctx, bucket_name, data_file, region):\n if not ctx.data_file:\n ctx.data_file = data_file\n\n if not ctx.bucket_name:\n ctx.bucket_name = bucket_name\n\n if not ctx.region:\n ctx.region = region\n\n ctx.type = 's3'","title":""},{"docid":"e9e9de5e44bd97f24f99b36297230fdc","score":"0.5707849","text":"def create_volume(self, dataset_id, size):\n requested_volume = self.connection.create_volume(\n size=int(Byte(size).to_GB().value), zone=self.zone)\n\n # Stamp created volume with Flocker-specific tags.\n metadata = {\n METADATA_VERSION_LABEL: '1',\n CLUSTER_ID_LABEL: unicode(self.cluster_id),\n DATASET_ID_LABEL: unicode(dataset_id),\n }\n self.connection.create_tags([requested_volume.id],\n metadata)\n\n # Wait for created volume to reach 'available' state.\n _wait_for_volume(requested_volume)\n\n # Return created volume in BlockDeviceVolume format.\n return _blockdevicevolume_from_ebs_volume(requested_volume)","title":""},{"docid":"e2351450863477383c3c696fb9e45974","score":"0.5703524","text":"def create_s3_user():\n\n # Set up logging\n run_date = f'{date.today().strftime(\"%Y%m%d\")}'\n log_filename = f'create_s3_user_{run_date}.log'\n logging.basicConfig(filename=log_filename, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')\n logger = logging.getLogger()\n\n # Define input arguments\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-b\", \"--bucket_name\", default=None, dest='bucket_name', required=True,\n help=\"The name of the s3 bucket.\")\n parser.add_argument(\"-u\", \"--user_name\", default=None, dest='user_name', required=True,\n help=\"The name for the iam-user.\")\n\n # Parse input args\n args = parser.parse_args()\n bucket_name = args.bucket_name\n user_name = args.user_name\n logger.info(f'Creating iam-user {user_name} for bucket {bucket_name}')\n\n try:\n # Validate the bucket name and user name\n if not s3_user_utils.bucket_exists(bucket_name):\n exit_program(f'Bucket does not exist: {bucket_name}')\n if not is_valid_user_name(user_name):\n exit_program(f'Invalid user name: {user_name}. Only letters, numbers, underscores, and dashes are allowed.')\n\n # Exit if the user name is already in use for this account\n for existing_user in s3_user_utils.list_users():\n if user_name == existing_user.name:\n exit_program(f'This user already exists in the account: {user_name}')\n\n # Create the new iam user\n user = s3_user_utils.create_user(user_name)\n\n # Create s3 list- and get-policies and attach to user\n create_and_add_policies(user, bucket_name)\n\n # Generate user keys and write to local csv file\n key_pair = s3_user_utils.create_key(user_name)\n write_keys_to_csv(user_name,key_pair)\n\n except Exception as e:\n error_message = \"Error of type {0} occurred:{1!r}\".format(type(e).__name__, str(e))\n logger.error(error_message)\n exit_program(error_message)\n\n else: \n print('Success!')","title":""},{"docid":"b05c309de6ab163a6799710c1c72c160","score":"0.5702093","text":"def create(profile, bucket, key, contents=None, filepath=None):\n if filepath:\n norm_path = path.normpath(filepath)\n norm_path = norm_path.rstrip(path.sep)\n with open(norm_path, \"rb\") as f:\n data = f.read()\n elif contents:\n data = contents\n client = boto3client.get(\"s3\", profile)\n params = {}\n params[\"Body\"] = data\n params[\"Bucket\"] = bucket\n params[\"Key\"] = key\n return client.put_object(**params)","title":""},{"docid":"6d0b1d18109ad5ddd793408802e3e419","score":"0.56963795","text":"def create_bucket(name, region):\n bucket = S3_RESOURCE.Bucket(name)\n bucket.create(\n ACL='private',\n CreateBucketConfiguration={\n 'LocationConstraint': region\n }\n )\n bucket.Versioning().enable()","title":""},{"docid":"6b6b09450f6f8cd9b3ba783fecadf101","score":"0.5694659","text":"def create_bucket(self, **kwargs):\n # TODO add validation\n kwargs['request_type'] = 'create_bucket'\n resp = self.post(\n self._url('/api/storage/authorize/create_bucket'), json=kwargs\n )\n return resp.json()","title":""},{"docid":"dccd5fb13eb402aefbbb1e39b378fb66","score":"0.56570494","text":"def create_role(target_account_id, account_alias, role_name, policy_arn=''):\n try:\n\n print('Creating role \"{}\" in account {} (ID: {})'.format(\n role_name,\n get_account_alias(target_account_id),\n target_account_id)\n )\n\n if ARGS.dry_run:\n print(u'\\u2023 Skipped during dry-run \\u2023\\u2023\\u2023')\n return\n\n assert 'AWS' in CI_ROLE_POLICY_DOC_TEMPLATE.get('Statement').get('Principal'), \\\n \"Management user's ARN is not \" \\\n \"injected! have you called create \" \\\n \"user first?\"\n\n role = client(\"iam\", target_account_id).create_role(\n AssumeRolePolicyDocument=json.dumps(CI_ROLE_POLICY_DOC_TEMPLATE),\n Path='/',\n RoleName=role_name,\n )\n\n print(u'\\u2A36 Wait for role to be created')\n waiter = client(\"iam\", target_account_id).get_waiter('role_exists')\n waiter.wait(\n RoleName=role_name,\n WaiterConfig={\n 'Delay': 5,\n 'MaxAttempts': 10\n }\n )\n\n if role_name == DEFAULT_CI_ROLE_NAME:\n print(\"- Attache {} policy\".format(policy_arn))\n client(\"iam\", target_account_id).attach_role_policy(\n RoleName=role_name,\n PolicyArn=policy_arn\n )\n else:\n print(\"- Put role's inline policy\")\n\n TERRAFORM_STATE_POLICY_DOC_TEMPLATE[\"Statement\"][0][\"Resource\"] = \"arn:aws:s3:::{}\".format(S3_BUCKET_NAME)\n TERRAFORM_STATE_POLICY_DOC_TEMPLATE[\"Statement\"][1][\"Resource\"] = \"arn:aws:s3:::{}/*\".format(S3_BUCKET_NAME)\n TERRAFORM_STATE_POLICY_DOC_TEMPLATE[\"Statement\"][2][\"Resource\"] = \"arn:aws:dynamodb:{}:{}:table/{}\".format(\n AWS_REGION,\n target_account_id,\n DEFAULT_TERRAFORM_BACKEND_DYNAMODB_TABLE_NAME\n )\n\n client(\"iam\", target_account_id).put_role_policy(\n RoleName=role_name,\n PolicyName=DEFAULT_TERRAFORM_STATE_ROLE_NAME,\n PolicyDocument=json.dumps(TERRAFORM_STATE_POLICY_DOC_TEMPLATE)\n )\n\n print_completion()\n\n return role.get(\"Role\").get(\"Arn\")\n\n except ClientError as err:\n if err.response['Error']['Code'] == 'EntityAlreadyExists':\n print(\"- Role already exists >>\")\n print_completion()\n elif err.response['Error']['Code'] == 'InvalidClientTokenId':\n print(\"- Failed to create user, using defined Key & Secret for account {}!\" \\\n \"\\n Make sure its user has sufficient permission\".format(account_alias))\n else:\n raise err\n print(\"Unexpected error: %s\" % err)","title":""},{"docid":"e42b6ef5095b7a6a3c3c3822428a609a","score":"0.56483907","text":"def create_blank_volume(name, zone, size, iops, voltype='io1'):\n \n tag_specs = [{\n 'ResourceType': 'volume',\n 'Tags': [{\n 'Key': 'Name',\n 'Value': name\n }]\n }]\n volume = ec2.create_volume(Size=size, VolumeType=voltype,\n TagSpecifications=tag_specs,\n AvailabilityZone=zone,\n Iops=iops)\n return volume","title":""},{"docid":"dbcf0552c100a93d590cd844b105d596","score":"0.5635047","text":"def create_bucket(bucket_name):\r\n storage_client = storage.Client()\r\n storage_client.location = 'europe-west1'\r\n bucket = storage_client.create_bucket(bucket_name)\r\n print('Bucket {} created'.format(bucket.name))","title":""},{"docid":"e2b35ac6de3f07e5c019502eacde26c0","score":"0.563487","text":"def create_s3_bucket(s3_resource, bucket_name, location_constraint):\n\tprint(\"\\nCreating a S3 bucket with the name '\" + bucket_name + \"'...\")\n\ttry:\n\t\ts3_resource.create_bucket(\n\t\t\tBucket=bucket_name,\n\t\t\tCreateBucketConfiguration={'LocationConstraint': location_constraint}\n\t\t)\n\t\tprint(\"The S3 bucket was created successfully.\")\n\texcept Exception as e:\n\t\tprint(e)\n\t\tuser_error_message = \"There was an error with creating an S3 bucket.\"\n\t\tprint(user_error_message)\n\t\tcreate_log(user_error_message, e)\n\t\texit_program()","title":""},{"docid":"6f5961a92d763c7bbfcda552dbccc68b","score":"0.5630302","text":"def create_policy(name, description, actions, bucket_name, user_name):\n\n # For the s3 getobject we add the user folder to the resource arn\n if actions == 's3:GetObject':\n resource_arn = f'arn:aws:s3:::{bucket_name}/{user_name}/*'\n else:\n resource_arn = f'arn:aws:s3:::{bucket_name}'\n\n # Create the policy json\n policy_doc = {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Action\": actions,\n \"Resource\": resource_arn,\n }\n ]\n }\n\n # For the s3 listbucket we add a condition to restrict to listing the user's folder\n if actions == 's3:ListBucket':\n user_folder = f'{user_name}/'\n condition = { \n \"Condition\":{\n \"StringEquals\": {\n \"s3:prefix\": [\n \"\",\n user_folder\n ],\n \"s3:delimiter\": [\n \"/\"\n ]\n }\n }\n }\n # Update the statement dictionary with the condition\n policy_doc['Statement'][0].update(condition)\n\n logger.info(f'Policy doc: {policy_doc}')\n try:\n policy = iam.create_policy(\n PolicyName=name, Description=description,\n PolicyDocument=json.dumps(policy_doc))\n logger.info(f'Created policy {policy.arn}')\n except ClientError:\n logger.exception(f\"Couldn't create policy {name}\")\n raise\n else:\n return policy","title":""},{"docid":"ff254c68be8ca15b36c9bd277f0e684c","score":"0.5609218","text":"def create_s3_bucket(bucket_name, region):\n s3_client = boto3.client(\"s3\", region_name=region)\n if region != \"us-east-1\":\n s3_client.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={\"LocationConstraint\": region})\n else:\n s3_client.create_bucket(Bucket=bucket_name)","title":""},{"docid":"98a79bb57bab1ce85ad789e997bfb123","score":"0.5559002","text":"def test_create_bucket_us(self):\n settings = EnvironSettings(\n {\"storage.bucket\": \"new_bucket\", \"storage.region_name\": \"us-west-1\"}, {}\n )\n S3Storage.configure(settings)\n\n bucket = self.s3.Bucket(\"new_bucket\")\n bucket.load()","title":""},{"docid":"9a9495842d2841fee5377921f553573d","score":"0.5558702","text":"def create_bucket_policy(self):\n data = {}\n data['Version'] = \"2012-10-17\"\n data['Statement'] = []\n data['Statement'].append({})\n data['Statement'][0][\"Sid\"] = \"AllowRoot\"\n data['Statement'][0][\"Effect\"] = \"Allow\"\n data['Statement'][0][\"Principal\"] = {}\n data['Statement'][0][\"Principal\"][\"AWS\"] = []\n data['Statement'][0][\"Action\"] = []\n data['Statement'][0][\"Action\"].append(\"s3:*\")\n data['Statement'][0][\"Resource\"] = []\n data['Statement'][0][\"Resource\"].append(\"arn:aws:s3:::\"+str(self.bucket_name)+\"/*\")\n data['Statement'][0][\"Resource\"].append(\"arn:aws:s3:::\"+str(self.bucket_name))\n\n\n for principal in self.bucket_policy_principals:\n data[\"Statement\"][0][\"Principal\"][\"AWS\"].append(principal)\n\n self.bucket_policy = data","title":""},{"docid":"383a11c62eb8ce2a60a4b5520650f18a","score":"0.55436677","text":"def make_s3file(mode):\n return S3File('test-bucket', 'path/to/gettysburg.txt', mode)","title":""},{"docid":"d46ad1d935f5b0d448d97af81d956db7","score":"0.55304474","text":"def _create_role(self, region):\n random_string = \"\".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(8))\n iam_role_name = \"Integ_test_InstanceRole_{0}_{1}\".format(self.region, random_string)\n\n iam_policy_name_batch = \"\".join(\"Integ_test_InstancePolicy_batch\" + random_string)\n logging.info(\"iam policy for awsbatch is {0}\".format(iam_policy_name_batch))\n iam_policy_name_traditional = \"\".join(\"Integ_test_InstancePolicy\" + random_string)\n logging.info(\"iam_policy for traditional scheduler is {0}\".format(iam_policy_name_traditional))\n\n self.iam_client = boto3.client(\"iam\", region_name=region)\n\n # Create the iam role\n logging.info(\"creating iam role {0} for creating KMS key...\".format(iam_role_name))\n\n self.partition = get_arn_partition(region)\n aws_domain = get_aws_domain(self.region)\n\n # Add EC2 as trust entity of the IAM role\n trust_relationship_policy_ec2 = {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Effect\": \"Allow\",\n \"Principal\": {\"Service\": \"ec2.{0}\".format(aws_domain)},\n \"Action\": \"sts:AssumeRole\",\n }\n ],\n }\n iam_role_arn = self.iam_client.create_role(\n RoleName=iam_role_name,\n AssumeRolePolicyDocument=json.dumps(trust_relationship_policy_ec2),\n Description=\"Role for create custom KMS key\",\n Path=\"/parallelcluster/\",\n )[\"Role\"][\"Arn\"]\n # Having time.sleep here because it takes a while for the IAM role to become valid for use in the\n # put_key_policy step for creating KMS key, read the following link for reference :\n # https://stackoverflow.com/questions/20156043/how-long-should-i-wait-after-applying-an-aws-iam-policy-before-it-is-valid\n time.sleep(15)\n\n # create instance policies for awsbatch and traditional schedulers\n self.iam_policy_arn_batch = self._create_iam_policies(iam_policy_name_batch, \"awsbatch\")\n self.iam_policy_arn_traditional = self._create_iam_policies(iam_policy_name_traditional, \"traditional\")\n\n # attach the Instance policies to the role\n logging.info(\"Attaching iam policy to the role {0}...\".format(iam_role_name))\n\n # attach the Instance policy for awsBatch\n self.iam_client.attach_role_policy(RoleName=iam_role_name, PolicyArn=self.iam_policy_arn_batch)\n\n # attach the Instance policy for traditional scheduler\n self.iam_client.attach_role_policy(RoleName=iam_role_name, PolicyArn=self.iam_policy_arn_traditional)\n\n logging.info(\"Iam role is ready: {0}\".format(iam_role_name))\n return iam_role_name, iam_role_arn","title":""},{"docid":"f156736d3a57a196245233afe80575c4","score":"0.55155516","text":"def create_volume(self, volume):\n LOG.debug('Create volume %(volume)s',\n {'volume': volume['name']})\n url = 'storage/volumes'\n path = '/'.join([self.storage_pool, self.volume_group, volume['name']])\n data = {\n 'path': path,\n 'volumeSize': volume['size'] * units.Gi,\n 'volumeBlockSize': (\n self.configuration.nexenta_ns5_blocksize * units.Ki),\n 'sparseVolume': self.configuration.nexenta_sparse\n }\n self.nef.post(url, data)","title":""},{"docid":"d07192fd219e683331b5085b29bad99c","score":"0.54953617","text":"def internal_create_uls(self, name, region=None):\n if region is None:\n self.client.create_bucket(name)\n else:\n self.client.create_bucket(name, location=region)","title":""},{"docid":"723bbf06e4fc8fceaa33c7cc61406b95","score":"0.54832065","text":"def create_volume(self, dataset_id, size):\n return self.create_volume_with_profile(dataset_id, size, None)","title":""},{"docid":"d196743708753168532e6274bd9ec3a4","score":"0.54679465","text":"def create_bucket_user(sender, instance, created, **kwargs):\n if created:\n if instance is not None:\n bucket_user = BucketUser(user=instance)\n bucket_user.save()","title":""},{"docid":"0d312ce9ca8c99f574f7da6889c53015","score":"0.54606885","text":"def test_create_bucket_eu(self):\n settings = EnvironSettings(\n {\n \"storage.bucket\": \"new_bucket\",\n \"storage.region_name\": \"eu-central-1\",\n \"signature_version\": \"s3v4\",\n },\n {},\n )\n S3Storage.configure(settings)\n\n bucket = self.s3.Bucket(\"new_bucket\")\n bucket.load()","title":""},{"docid":"89bcf457077ea8edb13bc22153f84352","score":"0.5454787","text":"def create_bucket(bucket_name: str) -> bool:\n try:\n region=\"us-east-1\"\n secret_key, access_key = _get_api_keys()\n s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key)\n s3_client.create_bucket(Bucket=bucket_name)\n except ClientError as e:\n logging.error(e)\n return False\n return True","title":""},{"docid":"a3998c4475c5e64a4c2e2c032b3aedb8","score":"0.5453417","text":"def create_volume(self, volume):\n url = 'storage/pools/%(pool)s/volumeGroups/%(group)s/volumes' % {\n 'pool': self.storage_pool,\n 'group': self.volume_group,\n }\n data = {\n 'name': volume['name'],\n 'volumeSize': volume['size'] * units.Gi,\n 'volumeBlockSize': (\n self.configuration.nexenta_ns5_blocksize * units.Ki),\n 'sparseVolume': self.configuration.nexenta_sparse\n }\n self.nef.post(url, data)","title":""},{"docid":"abc217c41841b71db37cf0850378413e","score":"0.5439353","text":"def uploadObjectToS3(vpnConfiguration, bucketName, assumeRoleArn=None):\n try:\n s3Connection = boto3.resource('s3')\n fileName = vpnConfiguration['VpnConnection']['VpnConnectionId'] + '.xml'\n vpnConfig = vpnConfiguration['VpnConnection']['CustomerGatewayConfiguration']\n # open(filePath).write(vpnConfiguration['VpnConnection']['CustomerGatewayConfiguration'])\n if assumeRoleArn:\n stsConnection = boto3.client('sts')\n assumedrole = stsConnection.assume_role(RoleArn=assumeRoleArn, RoleSessionName=\"Sample\")\n s3 = boto3.resource('s3', aws_access_key_id=assumedrole['Credentials']['AccessKeyId'],\n aws_secret_access_key=assumedrole['Credentials']['SecretAccessKey'],\n aws_session_token=assumedrole['Credentials']['SessionToken'])\n s3.Object(bucketName, fileName).put(Body=vpnConfig)\n return True\n s3Connection.Object(bucketName, fileName).put(Body=vpnConfig)\n return True\n except Exception as e:\n logger.error(\"Error uploading file to S3 Bucket, Error : %s\" % str(e))\n return False","title":""},{"docid":"8ddcf35700e81494f5cf36f95d9853dc","score":"0.5420014","text":"async def make_bucket(self):\n self.__connect().make_bucket(self.__bucket())","title":""},{"docid":"136cc33bf861e4ddd193811947540ac5","score":"0.5403652","text":"def create_s3_bucket(bucket_name, region):\n s3_client = boto3.client(\"s3\")\n \"\"\" :type : pyboto3.s3 \"\"\"\n if region != \"us-east-1\":\n s3_client.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={\"LocationConstraint\": region})\n else:\n s3_client.create_bucket(Bucket=bucket_name)\n\n try:\n _configure_s3_bucket(bucket_name)\n except Exception as e:\n LOGGER.error(\"Failed when configuring cluster S3 bucket with error %s\", e)\n delete_s3_bucket(bucket_name)\n raise","title":""},{"docid":"316971480c5b5142e5b1fc772a48b169","score":"0.54006684","text":"def create_volume(\n self,\n name,\n account_id,\n total_size,\n enable512e=OPTIONAL,\n qos=OPTIONAL,\n attributes=OPTIONAL,\n associate_with_qos_policy=OPTIONAL,\n access=OPTIONAL,\n enable_snap_mirror_replication=OPTIONAL,\n qos_policy_id=OPTIONAL,\n protection_scheme=OPTIONAL,\n fifo_size=OPTIONAL,\n min_fifo_size=OPTIONAL,):\n\n self._check_connection_type(\"create_volume\", \"Cluster\")\n\n params = { \n \"name\": name,\n \"accountID\": account_id,\n \"totalSize\": total_size,\n }\n if enable512e is not None:\n params[\"enable512e\"] = enable512e\n if qos is not None:\n params[\"qos\"] = qos\n if attributes is not None:\n params[\"attributes\"] = attributes\n if associate_with_qos_policy is not None:\n if self.api_version < 10.0:\n raise ApiParameterVersionError(\"create_volume\", 10.0,\n [(\"associate_with_qos_policy\", associate_with_qos_policy, 10.0, False)])\n else:\n params[\"associateWithQoSPolicy\"] = associate_with_qos_policy\n if access is not None:\n if self.api_version < 10.0:\n raise ApiParameterVersionError(\"create_volume\", 10.0,\n [(\"access\", access, 10.0, False)])\n else:\n params[\"access\"] = access\n if enable_snap_mirror_replication is not None:\n if self.api_version < 10.0:\n raise ApiParameterVersionError(\"create_volume\", 10.0,\n [(\"enable_snap_mirror_replication\", enable_snap_mirror_replication, 10.0, False)])\n else:\n params[\"enableSnapMirrorReplication\"] = enable_snap_mirror_replication\n if qos_policy_id is not None:\n if self.api_version < 10.0:\n raise ApiParameterVersionError(\"create_volume\", 10.0,\n [(\"qos_policy_id\", qos_policy_id, 10.0, False)])\n else:\n params[\"qosPolicyID\"] = qos_policy_id\n if protection_scheme is not None:\n params[\"protectionScheme\"] = protection_scheme\n if fifo_size is not None:\n if self.api_version < 12.3:\n raise ApiParameterVersionError(\"create_volume\", 12.3,\n [(\"fifo_size\", fifo_size, 12.3, False)])\n else:\n params[\"fifoSize\"] = fifo_size\n if min_fifo_size is not None:\n if self.api_version < 12.3:\n raise ApiParameterVersionError(\"create_volume\", 12.3,\n [(\"min_fifo_size\", min_fifo_size, 12.3, False)])\n else:\n params[\"minFifoSize\"] = min_fifo_size\n \n # There is no adaptor.\n return self.send_request(\n 'CreateVolume',\n CreateVolumeResult,\n params,\n since=1.0\n )","title":""},{"docid":"0d85fa3498a5c916a6a90ad872342c27","score":"0.53986055","text":"def create(self, name, location=None):\n AWSBucket.assert_valid_resource_name(name)\n\n bucket = self.provider.s3_conn.create_bucket(\n name,\n location=location if location else '')\n return AWSBucket(self.provider, bucket)","title":""},{"docid":"0d1d3d5e8cfb8916aa2670e9344f8481","score":"0.53954875","text":"def create_bucket(self, bucket_name: str):\n self.response = self.s3.create_bucket(Bucket=bucket_name)","title":""},{"docid":"ecbcdc2f988b7443674b044b4dcf3fda","score":"0.53952366","text":"def create(config, volname):\n exec_command(config, \"create\", volname)","title":""},{"docid":"daab2e11dc25f73157ad34b831fbbc1f","score":"0.5392365","text":"def create_bucket_policy(storage: MediaS3, policy_path: str):\n with open(policy_path) as fyl:\n policy = fyl.read()\n storage.bucket.Policy().put(Policy=policy)","title":""},{"docid":"9c4815ee57b5a7db743083d3464e2cb0","score":"0.5386606","text":"def test_create_storage_v1beta1_volume_attachment(self):\n pass","title":""},{"docid":"15f270162076ab35716d3d481e3106fa","score":"0.53834045","text":"def _create_data_bucket(self):\n bucket_name = f\"capstone-uda-data\"\n bucket_id = f\"{bucket_name}-bucket\"\n\n bucket = s3.Bucket(\n self,\n id=bucket_id,\n bucket_name=bucket_name,\n encryption=s3.BucketEncryption.S3_MANAGED,\n block_public_access=s3.BlockPublicAccess(\n block_public_acls=True,\n block_public_policy=True,\n ignore_public_acls=True,\n restrict_public_buckets=True,\n ),\n )\n\n return bucket","title":""},{"docid":"a09fa7b5338c07f781600cf6778aab40","score":"0.5370811","text":"def create_volume_with_profile(self, dataset_id, size, profile_name):\n\n with startTask(logger,\n \"SFAgent:create_volume_with_profile\",\n datasetID=unicode(dataset_id),\n volSize=unicode(size),\n profile=unicode(profile_name)):\n\n # NOTE(jdg): dataset_id is unique so we use it as the\n # volume name on the cluster. We then use the resultant\n # solidfire vol ID as the blockdevice_id\n vname = '%s%s' % (self.volume_prefix, dataset_id)\n profile = self._process_profile(profile_name)\n params = {'name': vname,\n 'accountID': self.account_id,\n 'sliceCount': 1,\n 'totalSize': int(size),\n 'enable512e': True,\n 'attributes': {}}\n if profile:\n # We set these keys explicity from the profile, rather\n # than slurping in a dict inentionally, this handles\n # cases where there may be extra/invalid keys in the\n # profile. More importantly alows us to extend the\n # usage of profiles beyond QoS later.\n params['qos'] = {'minIOPS': profile['minIOPS'],\n 'maxIOPS': profile['maxIOPS'],\n 'burstIOPS': profile['burstIOPS']}\n result = self.client.issue_request('CreateVolume',\n params)\n\n params = {}\n params['volumeAccessGroupID'] = self.vag_id\n params['volumes'] = [int(result['volumeID'])]\n self.client.issue_request('AddVolumesToVolumeAccessGroup',\n params,\n version='7.0')\n return BlockDeviceVolume(\n blockdevice_id=unicode(result['volumeID']),\n size=size,\n attached_to=None,\n dataset_id=uuid.UUID(dataset_id))","title":""},{"docid":"37a333a1b54590428d0c044c29c9d050","score":"0.53617054","text":"def create_volume(self, project_id, data):\n url = get_api_server_url('/api/projects/{}/volumes/'.format(project_id))\n\n headers = {\n 'X-CSRFToken': self.client.cookies['csrftoken'],\n }\n response = self.client.post(url, data=data, headers=headers)\n # print(response.status_code)\n # print(response.text)\n if response.status_code == 201:\n return True\n return False","title":""},{"docid":"d5cdac7e75288aaacd16072a456fbbec","score":"0.53593695","text":"def new_bucket_config(\n bucket_name,\n region: str,\n s3_client,\n s3_resource,\n block_public: bool = True,\n encrypt: bool = True,\n):\n create_bucket(bucket_name, region, s3_client, s3_resource)\n if encrypt:\n encrypt_bucket(bucket_name, s3_client)\n if block_public:\n block_bucket_public_access(bucket_name, s3_client)","title":""},{"docid":"4573fbc43726e052d294a8f390ec2243","score":"0.5351521","text":"def create_bucket(self, conn, bucket_name):\n try:\n bucket = conn.create_bucket(bucket_name)\n except boto.exception.S3CreateError as e:\n print \"Error creating S3 bucket.\"\n print e\n print \"New S3 bucket\", bucket_name, \"was created.\"\n return bucket","title":""},{"docid":"dc09a7d5ae868d0739123af44e9bc2db","score":"0.5349416","text":"def create(array, vol_name, size):\n return array.create_volume(vol_name, size=size)","title":""},{"docid":"ded740bddbc27b7bc6362b05e26d459c","score":"0.5346279","text":"def create_bucket(bucket_name, region=None):\n\n # Create bucket\n try:\n if region is None:\n s3_client = boto3.client('s3')\n s3_client.create_bucket(Bucket=bucket_name)\n else:\n s3_client = boto3.client('s3', region_name=region)\n location = {'LocationConstraint': region}\n s3_client.create_bucket(Bucket=bucket_name,\n CreateBucketConfiguration=location)\n except ClientError as e:\n logging.error(e)\n return False\n return True","title":""},{"docid":"10f6a1d735c6028820aad0183a8f877a","score":"0.5344345","text":"def uploadFileToS3(fileName, bucketName, updates, assumeRoleArn=False):\n try:\n s3Connection = boto3.resource('s3')\n if assumeRoleArn:\n stsConnection = boto3.client('sts')\n assumedrole = stsConnection.assume_role(RoleArn=assumeRoleArn, RoleSessionName=\"Sample\")\n s3 = boto3.resource('s3', aws_access_key_id=assumedrole['Credentials']['AccessKeyId'],\n aws_secret_access_key=assumedrole['Credentials']['SecretAccessKey'],\n aws_session_token=assumedrole['Credentials']['SessionToken'])\n s3.Object(bucketName, fileName).put(Body=updates)\n return True\n result = s3Connection.Object(bucketName, fileName).put(Body=json.dumps(updates))\n print (\"result is {}\".format(result))\n return True\n except Exception as e:\n print(\"Error uploading file to S3 Bucket, Error : %s\" % str(e))\n return False","title":""},{"docid":"15049d31d1618c6f38387ce044aae076","score":"0.5333661","text":"def create(self, context):\n if self.obj_attr_is_set('id'):\n raise exception.ObjectActionError(action='create',\n reason='already created')\n\n values = self.obj_get_changes()\n db_volume = dbapi.create_volume(context, values)\n self._from_db_object(self, db_volume)","title":""},{"docid":"ff81c99d4b294ba8f3975eee18efc1f8","score":"0.5333074","text":"def _create_cuboid_bucket(self, bucket_name):\n client = boto3.client('s3', region_name=get_region())\n _ = client.create_bucket(\n ACL='private',\n Bucket=bucket_name\n )\n return client.get_waiter('bucket_exists')","title":""},{"docid":"a33194caa263c39ef7aba20263406bc9","score":"0.5330251","text":"def s3_client():\n\n os.environ[\"AWS_ACCESS_KEY_ID\"] = \"foo\"\n os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"bar\"\n\n return s3fs.S3FileSystem()","title":""},{"docid":"d7e05b8ed91ff4c43b67e7f188c9609b","score":"0.5326904","text":"def configure_bucket(policy_path: str):\n if not isinstance(default_storage, MediaS3):\n return\n ensure_bucket_exists(default_storage)\n create_bucket_policy(default_storage, policy_path)","title":""},{"docid":"4dabc2bd4e301ec346dce358407ec7a9","score":"0.53154504","text":"def create(self, name, volume, description=None):\n pass","title":""},{"docid":"1f67d67d7854b3db31e201859a39af4c","score":"0.53132844","text":"def create(self, **data):\n raise ClientException('Storage create method not found')","title":""},{"docid":"d3ccb1ebb228b19878141ca484ce9171","score":"0.5297375","text":"def main(**kwargs):\n cos = ibm_boto3.client(service_name='s3',\n ibm_api_key_id='xx-xxx',\n ibm_service_instance_id='xx-xx-xx-xx-xxx',\n ibm_auth_endpoint=\"https://iam.cloud.ibm.com/identity/token\",\n config=Config(signature_version='oauth'),\n endpoint_url='https://s3.us-south.cloud-object-storage.appdomain.cloud')\n try:\n res=cos.upload_file(Filename='test.txt', Bucket='cloud-object-storage-8w-cos-standard-c5u',Key='test.txt')\n except Exception as e:\n print(Exception, e)\n else:\n print('File Uploaded')","title":""},{"docid":"b8d06e6a75407a5ba9a50f3e73d433e3","score":"0.5297212","text":"def create_volume(self, size, image=None, **kwargs):\n if image:\n kwargs[\"imageRef\"] = image\n\n self._create_volume(size, **kwargs)","title":""},{"docid":"7d7ef54889d6f3e60e11b33bc8d69d21","score":"0.52943295","text":"def test_create_volume():\n # Create a new volume\n volume_name = 'test_volume'\n DC.create_volume(volume_name, 'local')\n assert \"test_volume\" in DC.get_volume_details(volume_name)['Name']\n\n # Cleaning\n DC.delete_volume(volume_name)\n assert \"test_volume\" not in DC.get_volume_list()","title":""},{"docid":"4641750e4e1bce950a50c35c107f195d","score":"0.5292725","text":"def _add_project_s3_bucket(name: str) -> NoReturn:\n cf_data = _get_cloudformation_template_data(name)\n cf_data[\"Resources\"][f\"{_get_constant('S3_STORE_PREF')}{name}\"] = {\n \"Type\": \"AWS::S3::Bucket\",\n \"Properties\": {\n \"BucketName\": f\"{name}-store-{_get_field_if_exists(name, _get_constant('SALT_KEY'))}\"\n },\n }\n _update_cloudformation_template_data(name, cf_data)","title":""},{"docid":"cf1ff0cbd305c68ad594812e6a369975","score":"0.52901405","text":"def create_bucket(s3, bucket_name):\n # First let's see if we already have a bucket of this name.\n # The lookup method will return a Bucket object if the\n # bucket exists and we have access to it or None.\n print \"Looking up bucket \" + bucket_name\n bucket = s3.lookup(bucket_name)\n if bucket:\n print 'Bucket (%s) already exists' % bucket_name\n return bucket\n else:\n # Let's try to create the bucket. This will fail if\n # the bucket has already been created by someone else.\n try:\n print \"Creating bucket \" + bucket_name\n bucket = s3.create_bucket(bucket_name)\n print \"Bucket created \" + bucket_name\n except s3.provider.storage_create_error, e:\n print 'Bucket (%s) is owned by another user' % bucket_name\n return bucket","title":""},{"docid":"a2bb666942557002e13b9af1870afe6f","score":"0.52878004","text":"def create_volume(\n self,\n size,\n name,\n location=None,\n snapshot=None,\n ex_volume_type=\"standard\",\n ex_iops=None,\n ex_encrypted=False,\n ex_kms_key_id=None,\n ex_throughput=None,\n ):\n\n params = {\"Action\": \"CreateVolume\", \"Size\": str(size)}\n\n if ex_volume_type and ex_volume_type not in VALID_VOLUME_TYPES:\n raise ValueError(\"Invalid volume type specified: %s\" % (ex_volume_type))\n\n if snapshot:\n params[\"SnapshotId\"] = snapshot.id\n\n # AvailabilityZone argument is mandatory so if one is not provided,\n # we select one\n if not location:\n location = self.list_locations()[0]\n\n params[\"AvailabilityZone\"] = location.availability_zone.name\n\n if ex_volume_type:\n params[\"VolumeType\"] = ex_volume_type\n\n if ex_volume_type in [\"io1\", \"io2\", \"gp3\"] and ex_iops:\n params[\"Iops\"] = ex_iops\n\n if ex_encrypted:\n params[\"Encrypted\"] = 1\n\n if ex_kms_key_id is not None:\n params[\"KmsKeyId\"] = ex_kms_key_id\n\n if ex_volume_type == \"gp3\" and ex_throughput:\n params[\"Throughput\"] = ex_throughput\n\n volume = self._to_volume(\n self.connection.request(self.path, params=params).object, name=name\n )\n\n if self.ex_create_tags(volume, {\"Name\": name}):\n volume.extra[\"tags\"][\"Name\"] = name\n\n return volume","title":""},{"docid":"c84a971a6950394e578e851749d2b2f6","score":"0.5275209","text":"def create_iam_role(\n client,\n role_name=\"sparkifydbRole\",\n policy_arn=\"arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess\",\n):\n try:\n logger.info(\"Creating a new IAM Role...\")\n response = client.create_role(\n Path=\"/\",\n RoleName=role_name,\n Description=\"Allows Redshift clusters to call AWS services on your behalf.\",\n AssumeRolePolicyDocument=json.dumps(\n {\n \"Statement\": [\n {\n \"Action\": \"sts:AssumeRole\",\n \"Effect\": \"Allow\",\n \"Principal\": {\"Service\": \"redshift.amazonaws.com\"},\n }\n ],\n \"Version\": \"2012-10-17\",\n }\n ),\n )\n logger.debug(\"Response after creating sparkifyRole follows...\")\n logger.debug(response)\n logger.info(f\"Attaching Policy {policy_arn}\")\n response = client.attach_role_policy(RoleName=role_name, PolicyArn=policy_arn)\n role_arn = client.get_role(RoleName=role_name)[\"Role\"][\"Arn\"]\n logger.debug(f\"Response after attaching policy {policy_arn} follows...\")\n logger.debug(response)\n\n return role_arn\n\n except ClientError:\n logger.exception(\"Issue while creating the Role (see below)\")","title":""},{"docid":"6d9a1062aa27374f982642fd06557931","score":"0.52693427","text":"def create_bucket(bucket_name, region=None):\n\tprint(\"Creating bucket: {} in {}\".format(bucket_name,region))\n\n\t#https://github.com/elastic/elasticsearch/issues/16978\n\tif region == \"us-east-1\":\n\t\tregion = None\n\t# Create bucket\n\ttry:\n\t\tif region is None:\n\t\t\ts3_client = boto3.client('s3')\n\t\t\ts3_client.create_bucket(Bucket=bucket_name)\n\t\telse:\n\t\t\ts3_client = boto3.client('s3', region_name=region)\n\t\t\tlocation = {'LocationConstraint': region}\n\t\t\ts3_client.create_bucket(Bucket=bucket_name,\n\t\t\t\t\t\t\t\t\tCreateBucketConfiguration=location)\n\texcept ClientError as e:\n\t\tlogging.error(e)\n\t\treturn False\n\tprint(\"bucket created..\")\n\treturn True","title":""},{"docid":"06a6793b74e2e66a124c5ded231a691f","score":"0.52644473","text":"def create_folder(bucket, directory_name):\r\n\r\n try:\r\n s3 = boto3.client('s3')\r\n key_path = directory_name + '/' \"Indicamos crear un folder\"\r\n s3.put_object(Bucket=bucket, Key=key_path)\r\n except Exception as err:\r\n print(err)","title":""},{"docid":"1704c823884f04f95083c2e5ec67898b","score":"0.52616787","text":"def copy_to_s3(\n self, path, aws_id, aws_secret, aws_region, bucket_name, namespace=\"\"\n ):\n self.log.debug(\"Sending to S3 bucket...\")\n from boto3.session import Session\n\n session = Session(\n aws_access_key_id=aws_id,\n aws_secret_access_key=aws_secret,\n region_name=aws_region,\n )\n s3 = session.resource(\"s3\")\n bucket = s3.Bucket(bucket_name)\n if namespace and not namespace.endswith(\"/\"):\n namespace = namespace + \"/\"\n\n with open(path, \"br\") as content:\n result = bucket.put_object(Key=namespace + Path(path).name, Body=content)\n self.log.info(result)\n return result","title":""},{"docid":"cecd97ac1b20be875cb4cadf876fd2c2","score":"0.52612793","text":"def create_bucket(self, bucket_name, headers=None,\n location=\"us-west-2\", policy=None):\n if bucket_name and isinstance(bucket_name, basestring):\n try:\n return super(IcsS3, self).create_bucket(\n bucket_name, headers, location, policy)\n except S3CreateError, e:\n if \"bucketalreadyownedbyyou\" in str(e).lower():\n log.info(\n \"this bucket already exists, do the updating...\")\n return super(IcsS3, self).get_bucket(bucket_name)\n raise\n else:\n raise IcsS3Exception(\"no bucket name found...\")","title":""},{"docid":"5c147285f67cd6774889dca0625d77d6","score":"0.5260567","text":"def internal_create_uls(self, name, region=None):\n if region is None:\n self.client.create_bucket(Bucket=name)\n else:\n self.client.create_bucket(\n Bucket=name, CreateBucketConfiguration={\"LocationConstraint\": region}\n )","title":""},{"docid":"2e0e9419d66989e80396ecf7520af328","score":"0.52579206","text":"def test_create_volume_in_availability_zone(self):\n\n # Create volume\n availability_zone = 'nova'\n created_volume = self.api.post_volume(\n {'volume': {'size': 1,\n 'availability_zone': availability_zone}})\n LOG.debug(\"created_volume: %s\", created_volume)\n self.assertTrue(created_volume['id'])\n created_volume_id = created_volume['id']\n\n # Check it's there and availability zone present\n found_volume = self.api.get_volume(created_volume_id)\n self.assertEqual(created_volume_id, found_volume['id'])\n self.assertEqual(availability_zone, found_volume['availability_zone'])","title":""},{"docid":"5d18b2f85e4e0aac053945a16bd8067e","score":"0.5227771","text":"def create_bucket(self, name, region):\n create_bucket_kwargs = {}\n create_bucket_config = {}\n # us-east-1 does not work with location specified.\n if region != 'us-east-1':\n create_bucket_config['LocationConstraint'] = region\n if create_bucket_config:\n create_bucket_kwargs['CreateBucketConfiguration'] = (\n create_bucket_config\n )\n bucket = self.s3.Bucket(name)\n response = bucket.create(**create_bucket_kwargs)\n msg = 'Created bucket \"{bucket_name}\" at \"{bucket_location}\" in ' \\\n 'region \"{region}\".'\n print(msg.format(\n bucket_name=name,\n bucket_location=response['Location'],\n region=region,\n ))\n print()\n print('\\tAWS_STORAGE_BUCKET_NAME', name)\n print()\n bucket.wait_until_exists()\n return bucket","title":""},{"docid":"4497b6c71e1cc84603ef048c8fab9991","score":"0.52225846","text":"def create_object(self, bucket_name: str, filename: str, key: str, public: bool = False):\n if not Path(filename).is_file():\n print(\"File not found. Location: {}\".format(filename))\n return\n\n if public:\n extra_args = {'ACL': 'public-read'}\n else:\n extra_args = {}\n\n if filename.split('.')[-1] == 'html':\n extra_args.update({'ContentType': 'text/html'})\n\n self.response = self.s3.upload_file(Bucket=bucket_name,\n Filename=filename,\n Key=key,\n ExtraArgs=extra_args)","title":""},{"docid":"831a52a9b1a01ce76bfb9809f3161663","score":"0.5216734","text":"def __init__(self):\n self.client = boto3.client(\n S3,\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 )","title":""},{"docid":"0effcecab152c1e63099266142baee39","score":"0.52128994","text":"def _start(self):\n create_definition = pcf_util.param_filter(self.get_desired_state_definition(), S3Bucket.START_PARAMS_FILTER)\n response = self.client.create_bucket(**create_definition)\n\n if self.custom_config.get(\"Tags\"):\n tags = self.custom_config.get(\"Tags\")\n tag_set = []\n for k, v in tags.items():\n tag_set.append({\n \"Key\": k,\n \"Value\": v\n })\n\n self.client.put_bucket_tagging(\n Bucket=self.bucket_name,\n Tagging={\n \"TagSet\":tag_set\n }\n )\n\n return response","title":""},{"docid":"5057d4a303a14afe34487bed574ce6ce","score":"0.5206667","text":"def attach_volume(study):\n # Check that mounting volumes is enabled\n if not settings.FEAT_CAVATICA_MOUNT_VOLUMES:\n return\n\n api = sbg.Api(\n url=settings.CAVATICA_URL, token=settings.CAVATICA_HARMONIZATION_TOKEN\n )\n\n access_key = settings.CAVATICA_READWRITE_ACCESS_KEY\n secret_key = settings.CAVATICA_READWRITE_SECRET_KEY\n access_mode = \"RW\"\n\n # Make sure that the keys were set correctly\n if access_key is None or secret_key is None:\n raise \"Volume mounting is enabled but keys are not set correctly\"\n\n # Now we know that all settings are in place to attach a volume\n\n new_volume = api.volumes.create_s3_volume(\n name=study.kf_id,\n description=f\"Created by the Study Creator for '{study.name}'\",\n bucket=study.bucket,\n access_key_id=access_key,\n secret_access_key=secret_key,\n access_mode=access_mode,\n )\n\n logger.info(\n f\"Adding {settings.CAVATICA_DELIVERY_ACCOUNT} \"\n \"as a member to the volume\"\n )\n\n try:\n member = new_volume.add_member(\n settings.CAVATICA_DELIVERY_ACCOUNT,\n permissions={\n \"write\": False,\n \"read\": True,\n \"copy\": True,\n \"admin\": False,\n },\n )\n except sbg.errors.Conflict as err:\n logger.warning(\n f\"Unable to add {settings.CAVATICA_DELIVERY_ACCOUNT} to the \"\n f\"volume. Perhaps they are already a member? {err}\"\n )\n\n return new_volume","title":""},{"docid":"aaed747cc0ee3bb48a89a1ba28819999","score":"0.5199138","text":"def create(self, project, vault, attachable_sgid, **kwargs):\n kwargs[\"attachable_sgid\"] = attachable_sgid\n\n return self._post(\"/buckets/{project}/vaults/{vault}/uploads.json\",\n project=project, vault=vault, json_dict=kwargs)","title":""},{"docid":"c6ce6a731e7505101f71b3b09cbf325b","score":"0.51949924","text":"def create_s3_image(self, instance_id, key_location, aws_user_id,\r\n ec2_cert, ec2_private_key, bucket, image_name=\"image\",\r\n description=None, kernel_id=None, ramdisk_id=None,\r\n remove_image_files=False, **kwargs):\r\n icreator = image.S3ImageCreator(self, instance_id, key_location,\r\n aws_user_id, ec2_cert,\r\n ec2_private_key, bucket,\r\n image_name=image_name,\r\n description=description,\r\n kernel_id=kernel_id,\r\n ramdisk_id=ramdisk_id,\r\n remove_image_files=remove_image_files)\r\n return icreator.create_image()","title":""},{"docid":"15c77ae908b7986a109e1a7c6853b02f","score":"0.5194965","text":"def create_bucket(self):\n if self.debug:\n print('create bucket'+lineno())\n print('bucket name: '+str(self.bucket_name))\n\n if self.check_bucket():\n # Bucket already exists\n return\n else:\n # Create bucket\n\n try:\n if self.debug:\n print('Creating bucket')\n response = self.client.create_bucket(Bucket=str(self.bucket_name))\n\n if self.debug:\n print('response: '+str(response)+lineno())\n\n except ClientError as err:\n print('Error: '+str(err))\n sys.exit(1)","title":""},{"docid":"35d4903869af148af0219cc4253f0d79","score":"0.5191022","text":"def __init__(self, profile_name=\"default\", bucketName=None):\n if bucketName is None:\n self.bucketName = \"altotechpublic\"\n\n session = boto3.Session(profile_name=profile_name)\n # self.s3 = boto3.client('s3', aws_access_key_id=self.access_key,\n # aws_secret_access_key=self.secret_key)\n self.s3_resource = session.resource('s3')","title":""},{"docid":"db07eebc643ad1baf9143423efeb712a","score":"0.51860887","text":"def create_aws_conn(access_key, secret_key):\n resource = boto.resource('s3', region_name='us-east-1')\n client = boto.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key)\n return client","title":""},{"docid":"3f15f626da7a204ed23ccbadbe8d5df2","score":"0.5183712","text":"def init():\n if click.confirm('Do you want to enable EBS support?'):\n click.echo('A user with proper IAM permissions need to be specified.')\n key = click.prompt('The access key id of the authorised user')\n access = click.prompt('The secret access key of the authorised user')\n source_dir = \"{}/actions/aws-ebs-csi-driver\".format(os.getenv('SNAP'))\n destination_dir = \"{}/actions/aws-ebs-csi-driver\".format(os.getenv('SNAP_DATA'))\n shutil.rmtree(destination_dir, ignore_errors=True)\n shutil.copytree(source_dir, destination_dir)\n with open(\"{}/secret.yaml\".format(source_dir), \"rt\") as f:\n data = f.read()\n data = data.replace('{{key_id}}', key)\n data = data.replace('{{access_key}}', access)\n secrets_file = \"{}/secret.yaml\".format(destination_dir)\n os.chmod(secrets_file, 0o600)\n with open(secrets_file, \"w+\") as s:\n s.write(data)\n Path('{}/var/lock/ebs-ready'.format(os.getenv('SNAP_DATA'))).touch()\n click.echo('')\n if click.confirm('Do you want to enable EFS support?'):\n fsid = click.prompt('The EFS file system ID')\n size = click.prompt('The size in GB of the volume to be created')\n source_dir = \"{}/actions/aws-efs-csi-driver\".format(os.getenv('SNAP'))\n destination_dir = \"{}/actions/aws-efs-csi-driver\".format(os.getenv('SNAP_DATA'))\n shutil.rmtree(destination_dir, ignore_errors=True)\n shutil.copytree(source_dir, destination_dir)\n with open(\"{}/setup/pv.yaml\".format(source_dir), \"rt\") as f:\n data = f.read()\n data = data.replace('{{fsid}}', fsid)\n data = data.replace('{{size}}', size)\n with open(\"{}/setup/pv.yaml\".format(destination_dir), \"w+\") as s:\n s.write(data)\n Path('{}/var/lock/efs-ready'.format(os.getenv('SNAP_DATA'))).touch()\n click.echo('')","title":""},{"docid":"94e0b245a425cbd66d119780d6544eeb","score":"0.5182366","text":"def create(self, size, name):\n cinder = cinder_workflow.BaseCinderActions()\n volume = cinder.create_volume(size, name)._info\n del volume['links']\n utils.print_dict(volume)","title":""},{"docid":"63ebd743ad900622da81b9db5c2291ef","score":"0.51684356","text":"def create_backend_bucket(bucket_name):\n target_account_id = ACCOUNTS[\"management\"]\n print('Creating backend\\'s S3 bucket \"{}\" in management account (ID: {})'.format(\n bucket_name,\n target_account_id\n ))\n\n if ARGS.dry_run:\n print(u'\\u2023 Skipped during dry-run \\u2023\\u2023\\u2023')\n return\n\n try:\n client('s3', ACCOUNTS[\"management\"]).create_bucket(\n Bucket=bucket_name,\n CreateBucketConfiguration={'LocationConstraint': AWS_REGION}\n )\n print_completion()\n except ClientError as err:\n if err.response['Error']['Code'] == 'BucketAlreadyExists':\n print(\"- S3 bucket \\\"{}\\\" already exists! Choose another name by providing -s argument \" \\\n \" and try again\".format(bucket_name))\n abort_setup()\n elif err.response['Error']['Code'] == 'InvalidClientTokenId':\n print(\"- Failed to create S3 bucket, using defined Key & Secret for profile {}!\" \\\n \"\\n Make sure its user has sufficient permission\".format(target_account_id))\n abort_setup()\n elif err.response['Error']['Code'] == 'InvalidAccessKeyId':\n print(\"- Failed to authenticate credentials provided by {} profile.\" \\\n \"\\n Make sure the Access keys is activated and credentials are valid.\".format(target_account_id))\n abort_setup()\n elif err.response['Error']['Code'] == 'BucketAlreadyOwnedByYou':\n print(u'\\u2023 You already own this bucket')\n print_completion()\n else:\n print(\"Unexpected error: %s\" % err)\n abort_setup()","title":""},{"docid":"14b22f7380e314d52a195e25f7090357","score":"0.5164482","text":"def CreateVolumeFromSnapshot(\n self,\n snapshot: ebs.AWSSnapshot,\n volume_name: Optional[str] = None,\n volume_name_prefix: Optional[str] = None,\n volume_type: str = 'gp2',\n kms_key_id: Optional[str] = None,\n tags: Optional[Dict[str, str]] = None) -> ebs.AWSVolume:\n\n if volume_type not in ['standard', 'io1', 'gp2', 'sc1', 'st1']:\n raise ValueError('Volume type must be one of [standard, io1, gp2, sc1, '\n 'st1]. Got: {0:s}'.format(volume_type))\n\n if not volume_name:\n volume_name = self._GenerateVolumeName(\n snapshot, volume_name_prefix=volume_name_prefix)\n\n if len(volume_name) > 255:\n raise ValueError(\n 'Volume name {0:s} is too long (>255 chars)'.format(volume_name))\n\n if not tags:\n tags = {}\n tags['Name'] = volume_name\n\n client = self.ClientApi(common.EC2_SERVICE)\n create_volume_args = {\n 'AvailabilityZone': snapshot.availability_zone,\n 'SnapshotId': snapshot.snapshot_id,\n 'TagSpecifications': [common.CreateTags(common.VOLUME, tags)],\n 'VolumeType': volume_type\n }\n if kms_key_id:\n create_volume_args['Encrypted'] = True\n create_volume_args['KmsKeyId'] = kms_key_id\n if volume_type == 'io1':\n # If using the io1 volume type, we must specify Iops, see\n # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/\n # services/ec2.html#EC2.Client.create_volume. io1 volumes allow for a\n # ratio of 50 IOPS per 1 GiB.\n create_volume_args['Iops'] = self.ResourceApi(\n common.EC2_SERVICE).Snapshot(snapshot.snapshot_id).volume_size * 50\n try:\n volume = client.create_volume(**create_volume_args)\n volume_id = volume['VolumeId']\n # Wait for volume creation completion\n client.get_waiter('volume_available').wait(VolumeIds=[volume_id])\n except (client.exceptions.ClientError,\n botocore.exceptions.WaiterError) as exception:\n raise RuntimeError('Could not create volume {0:s} from snapshot '\n '{1:s}: {2:s}'.format(volume_name, snapshot.name,\n str(exception)))\n\n zone = volume['AvailabilityZone']\n encrypted = volume['Encrypted']\n\n return ebs.AWSVolume(volume_id,\n self,\n self.default_region,\n zone,\n encrypted,\n name=volume_name)","title":""},{"docid":"0b9691dfdc0c1a7ad2e380a5eec80cfc","score":"0.5159286","text":"def get_s3_resource():\n AWS_ACCESS_KEY = \"\"\n AWS_SECRET_KEY = \"\"\n return boto3.client(\"s3\", AWS_ACCESS_KEY, AWS_SECRET_KEY)","title":""},{"docid":"cebb0268e8884fde80efd548ac8a5f2b","score":"0.51531386","text":"def _create_role(self, role_name, role_arn, role_policy):\n\n role_result = None\n role_policy_result = None\n # Check if the role with the name exists\n if self._check_if_role_exists(role_name):\n LOG.debug('Role ' + role_name + ' exists.')\n else:\n LOG.debug('Role ' + role_name + ' does not exist.'\n ' Creating default role for EC2: ' + role_name)\n # Create a create using the IAM Client with a particular triplet\n # (role_name, role_arn, assume_role_policy)\n role_result = self._create_role_with_role_policy(role_name,\n role_policy,\n role_arn)\n role_policy_result = self._get_role_policy(role_arn)\n return role_result, role_policy_result","title":""},{"docid":"0380785e99174e7abf26efd9cf6991ae","score":"0.5145229","text":"def write_catalog_to_s3(bucket, prefix, catalog):\n\n if \"license\" not in catalog:\n s3_catalog_file = prefix + \"/catalog.json\"\n else:\n s3_catalog_file = prefix + \"/collection.json\"\n\n get_client(\"s3\").put_object(\n Body=json.dumps(catalog, indent=2), Bucket=bucket, Key=s3_catalog_file\n )","title":""},{"docid":"34aedd4ed9257f3b4f99725dcf0d22dd","score":"0.5138609","text":"def create(self, size, name=None, description=None,\n volume_type=None, imageRef=None):\n vol_name = \"'\" + name + \"' \" if name else ''\n try:\n size_in_GB = int(size)\n except ValueError:\n # size is in the format 'XGB' or 'XTB'\n size_in_GB, unit = int(size[:-2]), size[-2:]\n if unit == 'TB':\n size_in_GB *= 1000\n\n logging.info(\"Creating volume %s...\" % vol_name)\n tenant = self.__init_client()\n try:\n if not self.dryrun:\n volume = tenant.volumes.create(size=size_in_GB,\n name=name,\n description=description,\n volume_type=volume_type,\n imageRef=imageRef)\n logging.info(\"Request to create volume %ssent\" % vol_name)\n return volume\n else:\n logging.info(\"Volume %snot created because dryrun is enabled\" % vol_name)\n except requests.exceptions.Timeout, error:\n raise AiToolsCinderError(error)\n except cinderclient.exceptions.ClientException, error:\n raise AiToolsCinderError(error)\n except cinderclient.exceptions.ConnectionError, error:\n raise AiToolsCinderError(error)","title":""},{"docid":"70ab1eb90e23dc9faa52867f52888796","score":"0.51372135","text":"def __init__(__self__, *,\n bucket_name: Any,\n linked_service_name: pulumi.Input['LinkedServiceReferenceArgs'],\n type: pulumi.Input[str],\n annotations: Optional[pulumi.Input[Sequence[Any]]] = None,\n compression: Optional[pulumi.Input[Union['DatasetBZip2CompressionArgs', 'DatasetDeflateCompressionArgs', 'DatasetGZipCompressionArgs', 'DatasetZipDeflateCompressionArgs']]] = None,\n description: Optional[pulumi.Input[str]] = None,\n format: Optional[pulumi.Input[Union['AvroFormatArgs', 'JsonFormatArgs', 'OrcFormatArgs', 'ParquetFormatArgs', 'TextFormatArgs']]] = None,\n key: Optional[Any] = None,\n parameters: Optional[pulumi.Input[Mapping[str, pulumi.Input['ParameterSpecificationArgs']]]] = None,\n prefix: Optional[Any] = None,\n structure: Optional[Any] = None,\n version: Optional[Any] = None):\n pulumi.set(__self__, \"bucket_name\", bucket_name)\n pulumi.set(__self__, \"linked_service_name\", linked_service_name)\n pulumi.set(__self__, \"type\", 'AmazonS3Object')\n if annotations is not None:\n pulumi.set(__self__, \"annotations\", annotations)\n if compression is not None:\n pulumi.set(__self__, \"compression\", compression)\n if description is not None:\n pulumi.set(__self__, \"description\", description)\n if format is not None:\n pulumi.set(__self__, \"format\", format)\n if key is not None:\n pulumi.set(__self__, \"key\", key)\n if parameters is not None:\n pulumi.set(__self__, \"parameters\", parameters)\n if prefix is not None:\n pulumi.set(__self__, \"prefix\", prefix)\n if structure is not None:\n pulumi.set(__self__, \"structure\", structure)\n if version is not None:\n pulumi.set(__self__, \"version\", version)","title":""},{"docid":"306d6f38ac2d44f9bc9182e01f2e5a7c","score":"0.51238364","text":"def storageCreate():\n pathJson = request.get_json()\n path = pathJson[\"path\"]\n storagePath = sys.argv[4]\n\n if not isValidPathHelper(path):\n constant.exceptionReturn[\"exception_type\"] = \"IllegalArgumentException\"\n constant.exceptionReturn[\"exception_info\"] = \"[storage_create] given path is invalid\"\n content = json.dumps(constant.exceptionReturn)\n response = make_command_response(content, 404)\n return response\n\n filepath = storagePath + path\n if path == \"/\" or os.path.isfile(filepath) or os.path.isdir(filepath):\n constant.boolReturn[\"success\"] = False\n response = make_command_response(json.dumps(constant.boolReturn), 200)\n return response\n\n try:\n path_arr = filepath.rsplit('/', 1)\n dir_path = path_arr[0]\n os.makedirs(dir_path, exist_ok=True)\n f = open(filepath, 'w+')\n f.close()\n constant.boolReturn[\"success\"] = True\n except:\n constant.boolReturn[\"success\"] = False\n\n response = make_command_response(json.dumps(constant.boolReturn), 200)\n return response","title":""},{"docid":"e45bbaf2be54101343a38dc6ec90933e","score":"0.512271","text":"def create(self, permission):\n if self.is_exist():\n raise Exception(\"Error, Bucket with same name already exist.\")\n\n self.bucket.create_bucket(permission=permission).headers\n\n return self","title":""},{"docid":"3c410e3ee149c36f12fbf8a48a955d24","score":"0.51150316","text":"def s3auth(accesskey, secretkey):\n\n s3client = boto3.client('s3', aws_access_key_id=accesskey, aws_secret_access_key=secretkey)\n\n return s3client","title":""},{"docid":"40f6c2a992ed8c3f5a090518c36f2c69","score":"0.51135015","text":"def test_s3_bucket_policy_template():\n _test_template('macros/iam/s3_bucket_policy.template')","title":""}],"string":"[\n {\n \"docid\": \"819226986a465f5a16f920a1aad2294b\",\n \"score\": \"0.64687634\",\n \"text\": \"def create_iam():\\r\\n iam = boto3.client('iam', region_name='us-east-1', aws_access_key_id=KEY,\\r\\n aws_secret_access_key=SECRET)\\r\\n try:\\r\\n iam.create_role(Path='/',\\r\\n RoleName=DWH_IAM_ROLE_NAME,\\r\\n AssumeRolePolicyDocument=json.dumps(\\r\\n {'Statement': [{'Action': 'sts:AssumeRole',\\r\\n 'Effect': 'Allow',\\r\\n 'Principal': {'Service': 'redshift.amazonaws.com'}}],\\r\\n 'Version': '2012-10-17'}),\\r\\n Description='Allow Redshift to access S3 bucket',\\r\\n )\\r\\n except Exception as e:\\r\\n print(e)\\r\\n try:\\r\\n iam.attach_role_policy(RoleName=DWH_IAM_ROLE_NAME,\\r\\n PolicyArn=\\\"arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess\\\"\\r\\n )['ResponseMetadata']['HTTPStatusCode']\\r\\n except Exception as e:\\r\\n print(e)\\r\\n roleArn = iam.get_role(RoleName=DWH_IAM_ROLE_NAME)\\r\\n return roleArn['Role']['Arn']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8344c77178a59480a7c6b2bb948b64b\",\n \"score\": \"0.6233874\",\n \"text\": \"def s3_create_upload(filename):\\n s3 = boto3.resource('s3', region_name=aws_region)\\n s3client = boto3.client('s3', region_name=aws_region)\\n accountID = boto3.client('sts').get_caller_identity().get('Account')\\n bucket_name = \\\"rds-reports-\\\"+aws_region+\\\"-\\\"+accountID\\n bucket_exist = True\\n try:\\n s3.meta.client.head_bucket(Bucket=bucket_name)\\n print(\\\"Bucket Exists\\\")\\n except ClientError as e:\\n if int(e.response['Error']['Code']) == 403:\\n print(\\\"Private Bucket. Forbidden Access!\\\")\\n pass\\n else:\\n print(\\\"Bucket Does Not Exist!\\\")\\n print(str(e))\\n bucket_exist = False\\n pass\\n if not bucket_exist:\\n if aws_region == \\\"us-east-1\\\":\\n s3client.create_bucket(Bucket=bucket_name, ACL='private')\\n else:\\n location = {\\\"LocationConstraint\\\": aws_region}\\n s3client.create_bucket(\\n Bucket=bucket_name,\\n ACL='private',\\n CreateBucketConfiguration=location\\n )\\n s3client.put_bucket_encryption(Bucket=bucket_name, ServerSideEncryptionConfiguration={'Rules': [{'ApplyServerSideEncryptionByDefault': {'SSEAlgorithm': 'AES256', }}]})\\n\\n s3.meta.client.upload_file(filename, bucket_name,\\n filename.split(\\\"tmp/\\\")[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a435b957bc254898dea0c0eff2663df4\",\n \"score\": \"0.6226705\",\n \"text\": \"def neo_create_bucket(**kwargs):\\n auth = kwargs.get(\\\"auth\\\")\\n acl = kwargs.get(\\\"acl\\\", \\\"private\\\")\\n policy_id = kwargs.get(\\\"policy_id\\\", \\\"\\\")\\n bucket_name = kwargs.get(\\\"bucket_name\\\")\\n\\n if kwargs.get(\\\"random_name\\\"):\\n bucket_name = gen_random_name(bucket_name)\\n\\n endpoint = auth_lib.get_endpoint(\\\"storage\\\", bucket_name)\\n headers = {\\\"x-gmt-policyid\\\": policy_id, \\\"x-amz-acl\\\": acl}\\n\\n if \\\".\\\" in bucket_name:\\n response = requests.put(endpoint, auth=auth, headers=headers, verify=False)\\n else:\\n response = requests.put(endpoint, auth=auth, headers=headers)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14379bf437975ff8a6c2ac022046550a\",\n \"score\": \"0.61634696\",\n \"text\": \"def create_bucket(client, name, region):\\n if region == 'us-east-1':\\n # S3 in us-east-1 does not accept a location constraint\\n client.create_bucket(Bucket=name, ACL='public-read')\\n else:\\n client.create_bucket(Bucket=name, ACL='public-read', CreateBucketConfiguration={ 'LocationConstraint': region })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f798d740e5dc585bbf7eab53c8cfac09\",\n \"score\": \"0.61412\",\n \"text\": \"def role_creator ():\\n \\n DWH_IAM_ROLE_NAME = config.get(\\\"DWH\\\", \\\"DWH_IAM_ROLE_NAME\\\")\\n \\n try:\\n dwhRole = iam.create_role(\\n Path='/',\\n RoleName=DWH_IAM_ROLE_NAME,\\n Description = \\\"Allows Redshift clusters to call AWS services on your behalf.\\\",\\n AssumeRolePolicyDocument=json.dumps(\\n {'Statement': [{'Action': 'sts:AssumeRole',\\n 'Effect': 'Allow',\\n 'Principal': {'Service': 'redshift.amazonaws.com'}}],\\n 'Version': '2012-10-17'})\\n )\\n\\n except Exception as e:\\n print(e)\\n \\n try:\\n iam.attach_role_policy(RoleName=DWH_IAM_ROLE_NAME,\\n PolicyArn=\\\"arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess\\\"\\n )['ResponseMetadata']['HTTPStatusCode']\\n \\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef2ef5d540592addba57ead8c61c1a7\",\n \"score\": \"0.6132003\",\n \"text\": \"def create_bucket(\\n bucket_name: str,\\n region: str,\\n s3_client,\\n s3_resource,\\n acl_type: str = \\\"private\\\",\\n versions: bool = True,\\n) -> bool:\\n try:\\n location = {\\\"LocationConstraint\\\": region}\\n s3_client.create_bucket(\\n Bucket=bucket_name, CreateBucketConfiguration=location, ACL=acl_type\\n )\\n except ClientError as error:\\n # S3.Client.exceptions.BucketAlreadyExists\\n if error.response[\\\"Error\\\"][\\\"Code\\\"] == \\\"BucketAlreadyOwnedByYou\\\":\\n logger.warning(f\\\"Bucket {bucket_name} already exists! Using it.\\\")\\n else:\\n logger.exception(f\\\"Unable to create bucket {bucket_name}.\\\")\\n raise\\n return False\\n if versions:\\n try:\\n bucket = s3_resource.Bucket(bucket_name)\\n bucket.Versioning().enable()\\n logger.info(f\\\"Enabled versioning on bucket {bucket_name}.\\\")\\n except ClientError:\\n logger.exception(f\\\"Failed to enable versioning on bucket {bucket_name}.\\\")\\n raise\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77408c1c4fa2473d42243d6bc18dc17\",\n \"score\": \"0.6019878\",\n \"text\": \"def create_bucket(self):\\n\\n response = self.__bucket.create(\\n ACL='public-read'\\n )\\n\\n LOGGER.debug(\\n f\\\"Bucket Status: {response['ResponseMetadata']['HTTPStatusCode']} | Bucket Location: {response['Location']}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6209ca6b112e06d482b745d7d626e174\",\n \"score\": \"0.59149665\",\n \"text\": \"def create_bucket():\\r\\n # Instantiates a client\\r\\n storage_client = storage.Client()\\r\\n\\r\\n # Creates the new bucket\\r\\n bucket = storage_client.create_bucket(BUCKET_NAME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cdb7bbabf622cdae0cd2358ed9b54ac\",\n \"score\": \"0.5887031\",\n \"text\": \"def create_ebs_volume(tags, az, volume_type, volume_size, iops, encrypted, snap_id=None):\\n try:\\n if snap_id:\\n if iops:\\n volume_id = ec2.create_volume(Size=int(volume_size),\\n AvailabilityZone=az,\\n Encrypted=encrypted,\\n VolumeType=volume_type,\\n Iops=iops,\\n SnapshotId=snap_id)['VolumeId']\\n else:\\n volume_id = ec2.create_volume(Size=int(volume_size),\\n AvailabilityZone=az,\\n Encrypted=encrypted,\\n VolumeType=volume_type,\\n SnapshotId=snap_id)['VolumeId']\\n else:\\n if iops:\\n volume_id = ec2.create_volume(Size=int(volume_size),\\n AvailabilityZone=az,\\n Encrypted=encrypted,\\n VolumeType=volume_type,\\n Iops=iops)['VolumeId']\\n else:\\n volume_id = ec2.create_volume(Size=int(volume_size),\\n AvailabilityZone=az,\\n Encrypted=encrypted,\\n VolumeType=volume_type)['VolumeId']\\n if check_the_resource_state('volume_available',\\n 'VolumeIds',\\n volume_id):\\n ec2.create_tags(Resources=[volume_id], Tags=tags)\\n return volume_id\\n else:\\n logger.error(\\\"Timeout while waiting for the availability of the new volume\\\")\\n except botocore.exceptions.ClientError as e:\\n logger.error(\\\"Can\\\\'t create new volume\\\" .format(str(e)))\\n except Exception as e:\\n logger.error(\\\"Can\\\\'t execute volume creation. Exception: {0}\\\" .format(str(e)))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7565b41fe7a59fe021409acafe976a74\",\n \"score\": \"0.58865273\",\n \"text\": \"def create_volume(self, volume):\\n volume_path = self._get_volume_path(volume)\\n volume_size = '%sG' % volume['size']\\n volume_blocksize = six.text_type(self.volume_blocksize)\\n volume_sparsed = self.volume_sparse\\n volume_options = {}\\n if self.volume_compression:\\n volume_options['compression'] = self.volume_compression\\n self.nms.zvol.create_with_props(volume_path, volume_size,\\n volume_blocksize,\\n volume_sparsed,\\n volume_options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b091022a100ecfe745c4e09ad7617ddd\",\n \"score\": \"0.58749175\",\n \"text\": \"def upload_template_to_s3(template, bucket_name, session=None, file_name=None):\\n \\n file_key = dt.utcnow().strftime('%Y/%m/%d/%H-%M')\\n if not file_name:\\n file_name = 'iam_roles.json'\\n s3_path = f\\\"{file_key}/{file_name}\\\"\\n if not session:\\n client = boto3.client('s3')\\n else:\\n client = session.client('s3')\\n try:\\n client.put_object(\\n Bucket=bucket_name,\\n Body=template.to_json(),\\n Key=s3_path,\\n ContentEncoding='utf-8',\\n ContentType='application/json',\\n ServerSideEncryption='AES256'\\n )\\n return f\\\"s3://{bucket_name}/{s3_path}\\\"\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d1ce14c2000dd904db22e717cba31b\",\n \"score\": \"0.5842075\",\n \"text\": \"def create(self, name, size, zone, snapshot=None, description=None):\\n AWSVolume.assert_valid_resource_name(name)\\n\\n zone_id = zone.id if isinstance(zone, PlacementZone) else zone\\n snapshot_id = snapshot.id if isinstance(\\n snapshot, AWSSnapshot) and snapshot else snapshot\\n\\n ec2_vol = self.provider.ec2_conn.create_volume(\\n size,\\n zone_id,\\n snapshot=snapshot_id)\\n cb_vol = AWSVolume(self.provider, ec2_vol)\\n cb_vol.name = name\\n if description:\\n cb_vol.description = description\\n return cb_vol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124e27e08397d9030419513950ccc442\",\n \"score\": \"0.5833289\",\n \"text\": \"def __init__(self,\\n volume_id,\\n aws_account,\\n region,\\n availability_zone,\\n encrypted,\\n name=None,\\n device_name=None):\\n\\n super(AWSVolume, self).__init__(aws_account,\\n region,\\n availability_zone,\\n encrypted,\\n name)\\n self.volume_id = volume_id\\n self.device_name = device_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d97d2fcb84220f9ed4496af3d1e3f76\",\n \"score\": \"0.5829424\",\n \"text\": \"def _create_volume(size='0', snapshot_id=None):\\n vol = {}\\n vol['size'] = size\\n vol['snapshot_id'] = snapshot_id\\n vol['user_id'] = 'fake'\\n vol['project_id'] = 'fake'\\n vol['availability_zone'] = FLAGS.storage_availability_zone\\n vol['status'] = \\\"creating\\\"\\n vol['attach_status'] = \\\"detached\\\"\\n return db.volume_create(context.get_admin_context(), vol)['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"782356049daedb1c1637935dca84374f\",\n \"score\": \"0.5820942\",\n \"text\": \"def ShareWithAWSAccount(self, aws_account_id):\\n\\n snapshot = self.aws_account.ResourceApi(EC2_SERVICE).Snapshot(\\n self.snapshot_id)\\n snapshot.modify_attribute(\\n Attribute='createVolumePermission',\\n CreateVolumePermission={\\n 'Add': [{\\n 'UserId': aws_account_id\\n }]\\n },\\n OperationType='add'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b6a4855f318eb9ed71869fe50cb352\",\n \"score\": \"0.5815212\",\n \"text\": \"def ensure_bucket_exists(storage: MediaS3):\\n storage.create_bucket()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8389cd36e14e6acd3733a5a5dff6153\",\n \"score\": \"0.57987326\",\n \"text\": \"def init_s3_buckets():\\n os.system(f\\\"aws s3 mb s3://{cfg.PROJECT_NAME}.smtj.data\\\")\\n os.system(f\\\"aws s3 mb s3://{cfg.PROJECT_NAME}.smtj.output\\\")\\n os.system(f\\\"aws s3 sync {cfg.DATA_LOCAL_DIR} s3://{cfg.PROJECT_NAME}\\\" +\\\\\\n f\\\".smtj.data/{cfg.DATA_NAME}\\\")\\n os.system(f\\\"aws iam create-role --role-name {cfg.PROJECT}-SagemakerRole \\\" +\\\\\\n \\\"--assume-role-policy-document file://./role-policy.json\\\")\\n os.system(f\\\"aws iam attach-role-policy --role-name {cfg.PROJECT}-SagemakerRole \\\" +\\\\\\n \\\"--policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccess\\\")\\n build_s3_policy_from_template()\\n os.system(f\\\"aws iam put-role-policy --role-name {cfg.PROJECT}-SagemakerRole \\\" +\\\\\\n \\\"--policy-document file://./s3-policy.json --policy-name s3-policy\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ea3d6e66916767f92e535c8a3cbcc1\",\n \"score\": \"0.5728218\",\n \"text\": \"def __moto_setup(self):\\r\\n s3 = get_client()\\r\\n s3.create_bucket(Bucket=self.bucket)\\r\\n s3.put_object(Bucket=self.bucket, Key=self.key, Body=self.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1a2f7d691a36a97f3e7b1aaeccd354a\",\n \"score\": \"0.57208574\",\n \"text\": \"def s3(ctx, bucket_name, data_file, region):\\n if not ctx.data_file:\\n ctx.data_file = data_file\\n\\n if not ctx.bucket_name:\\n ctx.bucket_name = bucket_name\\n\\n if not ctx.region:\\n ctx.region = region\\n\\n ctx.type = 's3'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e9de5e44bd97f24f99b36297230fdc\",\n \"score\": \"0.5707849\",\n \"text\": \"def create_volume(self, dataset_id, size):\\n requested_volume = self.connection.create_volume(\\n size=int(Byte(size).to_GB().value), zone=self.zone)\\n\\n # Stamp created volume with Flocker-specific tags.\\n metadata = {\\n METADATA_VERSION_LABEL: '1',\\n CLUSTER_ID_LABEL: unicode(self.cluster_id),\\n DATASET_ID_LABEL: unicode(dataset_id),\\n }\\n self.connection.create_tags([requested_volume.id],\\n metadata)\\n\\n # Wait for created volume to reach 'available' state.\\n _wait_for_volume(requested_volume)\\n\\n # Return created volume in BlockDeviceVolume format.\\n return _blockdevicevolume_from_ebs_volume(requested_volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2351450863477383c3c696fb9e45974\",\n \"score\": \"0.5703524\",\n \"text\": \"def create_s3_user():\\n\\n # Set up logging\\n run_date = f'{date.today().strftime(\\\"%Y%m%d\\\")}'\\n log_filename = f'create_s3_user_{run_date}.log'\\n logging.basicConfig(filename=log_filename, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')\\n logger = logging.getLogger()\\n\\n # Define input arguments\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-b\\\", \\\"--bucket_name\\\", default=None, dest='bucket_name', required=True,\\n help=\\\"The name of the s3 bucket.\\\")\\n parser.add_argument(\\\"-u\\\", \\\"--user_name\\\", default=None, dest='user_name', required=True,\\n help=\\\"The name for the iam-user.\\\")\\n\\n # Parse input args\\n args = parser.parse_args()\\n bucket_name = args.bucket_name\\n user_name = args.user_name\\n logger.info(f'Creating iam-user {user_name} for bucket {bucket_name}')\\n\\n try:\\n # Validate the bucket name and user name\\n if not s3_user_utils.bucket_exists(bucket_name):\\n exit_program(f'Bucket does not exist: {bucket_name}')\\n if not is_valid_user_name(user_name):\\n exit_program(f'Invalid user name: {user_name}. Only letters, numbers, underscores, and dashes are allowed.')\\n\\n # Exit if the user name is already in use for this account\\n for existing_user in s3_user_utils.list_users():\\n if user_name == existing_user.name:\\n exit_program(f'This user already exists in the account: {user_name}')\\n\\n # Create the new iam user\\n user = s3_user_utils.create_user(user_name)\\n\\n # Create s3 list- and get-policies and attach to user\\n create_and_add_policies(user, bucket_name)\\n\\n # Generate user keys and write to local csv file\\n key_pair = s3_user_utils.create_key(user_name)\\n write_keys_to_csv(user_name,key_pair)\\n\\n except Exception as e:\\n error_message = \\\"Error of type {0} occurred:{1!r}\\\".format(type(e).__name__, str(e))\\n logger.error(error_message)\\n exit_program(error_message)\\n\\n else: \\n print('Success!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05c309de6ab163a6799710c1c72c160\",\n \"score\": \"0.5702093\",\n \"text\": \"def create(profile, bucket, key, contents=None, filepath=None):\\n if filepath:\\n norm_path = path.normpath(filepath)\\n norm_path = norm_path.rstrip(path.sep)\\n with open(norm_path, \\\"rb\\\") as f:\\n data = f.read()\\n elif contents:\\n data = contents\\n client = boto3client.get(\\\"s3\\\", profile)\\n params = {}\\n params[\\\"Body\\\"] = data\\n params[\\\"Bucket\\\"] = bucket\\n params[\\\"Key\\\"] = key\\n return client.put_object(**params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0b1d18109ad5ddd793408802e3e419\",\n \"score\": \"0.56963795\",\n \"text\": \"def create_bucket(name, region):\\n bucket = S3_RESOURCE.Bucket(name)\\n bucket.create(\\n ACL='private',\\n CreateBucketConfiguration={\\n 'LocationConstraint': region\\n }\\n )\\n bucket.Versioning().enable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b6b09450f6f8cd9b3ba783fecadf101\",\n \"score\": \"0.5694659\",\n \"text\": \"def create_bucket(self, **kwargs):\\n # TODO add validation\\n kwargs['request_type'] = 'create_bucket'\\n resp = self.post(\\n self._url('/api/storage/authorize/create_bucket'), json=kwargs\\n )\\n return resp.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dccd5fb13eb402aefbbb1e39b378fb66\",\n \"score\": \"0.56570494\",\n \"text\": \"def create_role(target_account_id, account_alias, role_name, policy_arn=''):\\n try:\\n\\n print('Creating role \\\"{}\\\" in account {} (ID: {})'.format(\\n role_name,\\n get_account_alias(target_account_id),\\n target_account_id)\\n )\\n\\n if ARGS.dry_run:\\n print(u'\\\\u2023 Skipped during dry-run \\\\u2023\\\\u2023\\\\u2023')\\n return\\n\\n assert 'AWS' in CI_ROLE_POLICY_DOC_TEMPLATE.get('Statement').get('Principal'), \\\\\\n \\\"Management user's ARN is not \\\" \\\\\\n \\\"injected! have you called create \\\" \\\\\\n \\\"user first?\\\"\\n\\n role = client(\\\"iam\\\", target_account_id).create_role(\\n AssumeRolePolicyDocument=json.dumps(CI_ROLE_POLICY_DOC_TEMPLATE),\\n Path='/',\\n RoleName=role_name,\\n )\\n\\n print(u'\\\\u2A36 Wait for role to be created')\\n waiter = client(\\\"iam\\\", target_account_id).get_waiter('role_exists')\\n waiter.wait(\\n RoleName=role_name,\\n WaiterConfig={\\n 'Delay': 5,\\n 'MaxAttempts': 10\\n }\\n )\\n\\n if role_name == DEFAULT_CI_ROLE_NAME:\\n print(\\\"- Attache {} policy\\\".format(policy_arn))\\n client(\\\"iam\\\", target_account_id).attach_role_policy(\\n RoleName=role_name,\\n PolicyArn=policy_arn\\n )\\n else:\\n print(\\\"- Put role's inline policy\\\")\\n\\n TERRAFORM_STATE_POLICY_DOC_TEMPLATE[\\\"Statement\\\"][0][\\\"Resource\\\"] = \\\"arn:aws:s3:::{}\\\".format(S3_BUCKET_NAME)\\n TERRAFORM_STATE_POLICY_DOC_TEMPLATE[\\\"Statement\\\"][1][\\\"Resource\\\"] = \\\"arn:aws:s3:::{}/*\\\".format(S3_BUCKET_NAME)\\n TERRAFORM_STATE_POLICY_DOC_TEMPLATE[\\\"Statement\\\"][2][\\\"Resource\\\"] = \\\"arn:aws:dynamodb:{}:{}:table/{}\\\".format(\\n AWS_REGION,\\n target_account_id,\\n DEFAULT_TERRAFORM_BACKEND_DYNAMODB_TABLE_NAME\\n )\\n\\n client(\\\"iam\\\", target_account_id).put_role_policy(\\n RoleName=role_name,\\n PolicyName=DEFAULT_TERRAFORM_STATE_ROLE_NAME,\\n PolicyDocument=json.dumps(TERRAFORM_STATE_POLICY_DOC_TEMPLATE)\\n )\\n\\n print_completion()\\n\\n return role.get(\\\"Role\\\").get(\\\"Arn\\\")\\n\\n except ClientError as err:\\n if err.response['Error']['Code'] == 'EntityAlreadyExists':\\n print(\\\"- Role already exists >>\\\")\\n print_completion()\\n elif err.response['Error']['Code'] == 'InvalidClientTokenId':\\n print(\\\"- Failed to create user, using defined Key & Secret for account {}!\\\" \\\\\\n \\\"\\\\n Make sure its user has sufficient permission\\\".format(account_alias))\\n else:\\n raise err\\n print(\\\"Unexpected error: %s\\\" % err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e42b6ef5095b7a6a3c3c3822428a609a\",\n \"score\": \"0.56483907\",\n \"text\": \"def create_blank_volume(name, zone, size, iops, voltype='io1'):\\n \\n tag_specs = [{\\n 'ResourceType': 'volume',\\n 'Tags': [{\\n 'Key': 'Name',\\n 'Value': name\\n }]\\n }]\\n volume = ec2.create_volume(Size=size, VolumeType=voltype,\\n TagSpecifications=tag_specs,\\n AvailabilityZone=zone,\\n Iops=iops)\\n return volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbcf0552c100a93d590cd844b105d596\",\n \"score\": \"0.5635047\",\n \"text\": \"def create_bucket(bucket_name):\\r\\n storage_client = storage.Client()\\r\\n storage_client.location = 'europe-west1'\\r\\n bucket = storage_client.create_bucket(bucket_name)\\r\\n print('Bucket {} created'.format(bucket.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2b35ac6de3f07e5c019502eacde26c0\",\n \"score\": \"0.563487\",\n \"text\": \"def create_s3_bucket(s3_resource, bucket_name, location_constraint):\\n\\tprint(\\\"\\\\nCreating a S3 bucket with the name '\\\" + bucket_name + \\\"'...\\\")\\n\\ttry:\\n\\t\\ts3_resource.create_bucket(\\n\\t\\t\\tBucket=bucket_name,\\n\\t\\t\\tCreateBucketConfiguration={'LocationConstraint': location_constraint}\\n\\t\\t)\\n\\t\\tprint(\\\"The S3 bucket was created successfully.\\\")\\n\\texcept Exception as e:\\n\\t\\tprint(e)\\n\\t\\tuser_error_message = \\\"There was an error with creating an S3 bucket.\\\"\\n\\t\\tprint(user_error_message)\\n\\t\\tcreate_log(user_error_message, e)\\n\\t\\texit_program()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5961a92d763c7bbfcda552dbccc68b\",\n \"score\": \"0.5630302\",\n \"text\": \"def create_policy(name, description, actions, bucket_name, user_name):\\n\\n # For the s3 getobject we add the user folder to the resource arn\\n if actions == 's3:GetObject':\\n resource_arn = f'arn:aws:s3:::{bucket_name}/{user_name}/*'\\n else:\\n resource_arn = f'arn:aws:s3:::{bucket_name}'\\n\\n # Create the policy json\\n policy_doc = {\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n \\\"Statement\\\": [\\n {\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Action\\\": actions,\\n \\\"Resource\\\": resource_arn,\\n }\\n ]\\n }\\n\\n # For the s3 listbucket we add a condition to restrict to listing the user's folder\\n if actions == 's3:ListBucket':\\n user_folder = f'{user_name}/'\\n condition = { \\n \\\"Condition\\\":{\\n \\\"StringEquals\\\": {\\n \\\"s3:prefix\\\": [\\n \\\"\\\",\\n user_folder\\n ],\\n \\\"s3:delimiter\\\": [\\n \\\"/\\\"\\n ]\\n }\\n }\\n }\\n # Update the statement dictionary with the condition\\n policy_doc['Statement'][0].update(condition)\\n\\n logger.info(f'Policy doc: {policy_doc}')\\n try:\\n policy = iam.create_policy(\\n PolicyName=name, Description=description,\\n PolicyDocument=json.dumps(policy_doc))\\n logger.info(f'Created policy {policy.arn}')\\n except ClientError:\\n logger.exception(f\\\"Couldn't create policy {name}\\\")\\n raise\\n else:\\n return policy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff254c68be8ca15b36c9bd277f0e684c\",\n \"score\": \"0.5609218\",\n \"text\": \"def create_s3_bucket(bucket_name, region):\\n s3_client = boto3.client(\\\"s3\\\", region_name=region)\\n if region != \\\"us-east-1\\\":\\n s3_client.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={\\\"LocationConstraint\\\": region})\\n else:\\n s3_client.create_bucket(Bucket=bucket_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a79bb57bab1ce85ad789e997bfb123\",\n \"score\": \"0.5559002\",\n \"text\": \"def test_create_bucket_us(self):\\n settings = EnvironSettings(\\n {\\\"storage.bucket\\\": \\\"new_bucket\\\", \\\"storage.region_name\\\": \\\"us-west-1\\\"}, {}\\n )\\n S3Storage.configure(settings)\\n\\n bucket = self.s3.Bucket(\\\"new_bucket\\\")\\n bucket.load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a9495842d2841fee5377921f553573d\",\n \"score\": \"0.5558702\",\n \"text\": \"def create_bucket_policy(self):\\n data = {}\\n data['Version'] = \\\"2012-10-17\\\"\\n data['Statement'] = []\\n data['Statement'].append({})\\n data['Statement'][0][\\\"Sid\\\"] = \\\"AllowRoot\\\"\\n data['Statement'][0][\\\"Effect\\\"] = \\\"Allow\\\"\\n data['Statement'][0][\\\"Principal\\\"] = {}\\n data['Statement'][0][\\\"Principal\\\"][\\\"AWS\\\"] = []\\n data['Statement'][0][\\\"Action\\\"] = []\\n data['Statement'][0][\\\"Action\\\"].append(\\\"s3:*\\\")\\n data['Statement'][0][\\\"Resource\\\"] = []\\n data['Statement'][0][\\\"Resource\\\"].append(\\\"arn:aws:s3:::\\\"+str(self.bucket_name)+\\\"/*\\\")\\n data['Statement'][0][\\\"Resource\\\"].append(\\\"arn:aws:s3:::\\\"+str(self.bucket_name))\\n\\n\\n for principal in self.bucket_policy_principals:\\n data[\\\"Statement\\\"][0][\\\"Principal\\\"][\\\"AWS\\\"].append(principal)\\n\\n self.bucket_policy = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383a11c62eb8ce2a60a4b5520650f18a\",\n \"score\": \"0.55436677\",\n \"text\": \"def make_s3file(mode):\\n return S3File('test-bucket', 'path/to/gettysburg.txt', mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46ad1d935f5b0d448d97af81d956db7\",\n \"score\": \"0.55304474\",\n \"text\": \"def _create_role(self, region):\\n random_string = \\\"\\\".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(8))\\n iam_role_name = \\\"Integ_test_InstanceRole_{0}_{1}\\\".format(self.region, random_string)\\n\\n iam_policy_name_batch = \\\"\\\".join(\\\"Integ_test_InstancePolicy_batch\\\" + random_string)\\n logging.info(\\\"iam policy for awsbatch is {0}\\\".format(iam_policy_name_batch))\\n iam_policy_name_traditional = \\\"\\\".join(\\\"Integ_test_InstancePolicy\\\" + random_string)\\n logging.info(\\\"iam_policy for traditional scheduler is {0}\\\".format(iam_policy_name_traditional))\\n\\n self.iam_client = boto3.client(\\\"iam\\\", region_name=region)\\n\\n # Create the iam role\\n logging.info(\\\"creating iam role {0} for creating KMS key...\\\".format(iam_role_name))\\n\\n self.partition = get_arn_partition(region)\\n aws_domain = get_aws_domain(self.region)\\n\\n # Add EC2 as trust entity of the IAM role\\n trust_relationship_policy_ec2 = {\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n \\\"Statement\\\": [\\n {\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Principal\\\": {\\\"Service\\\": \\\"ec2.{0}\\\".format(aws_domain)},\\n \\\"Action\\\": \\\"sts:AssumeRole\\\",\\n }\\n ],\\n }\\n iam_role_arn = self.iam_client.create_role(\\n RoleName=iam_role_name,\\n AssumeRolePolicyDocument=json.dumps(trust_relationship_policy_ec2),\\n Description=\\\"Role for create custom KMS key\\\",\\n Path=\\\"/parallelcluster/\\\",\\n )[\\\"Role\\\"][\\\"Arn\\\"]\\n # Having time.sleep here because it takes a while for the IAM role to become valid for use in the\\n # put_key_policy step for creating KMS key, read the following link for reference :\\n # https://stackoverflow.com/questions/20156043/how-long-should-i-wait-after-applying-an-aws-iam-policy-before-it-is-valid\\n time.sleep(15)\\n\\n # create instance policies for awsbatch and traditional schedulers\\n self.iam_policy_arn_batch = self._create_iam_policies(iam_policy_name_batch, \\\"awsbatch\\\")\\n self.iam_policy_arn_traditional = self._create_iam_policies(iam_policy_name_traditional, \\\"traditional\\\")\\n\\n # attach the Instance policies to the role\\n logging.info(\\\"Attaching iam policy to the role {0}...\\\".format(iam_role_name))\\n\\n # attach the Instance policy for awsBatch\\n self.iam_client.attach_role_policy(RoleName=iam_role_name, PolicyArn=self.iam_policy_arn_batch)\\n\\n # attach the Instance policy for traditional scheduler\\n self.iam_client.attach_role_policy(RoleName=iam_role_name, PolicyArn=self.iam_policy_arn_traditional)\\n\\n logging.info(\\\"Iam role is ready: {0}\\\".format(iam_role_name))\\n return iam_role_name, iam_role_arn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f156736d3a57a196245233afe80575c4\",\n \"score\": \"0.55155516\",\n \"text\": \"def create_volume(self, volume):\\n LOG.debug('Create volume %(volume)s',\\n {'volume': volume['name']})\\n url = 'storage/volumes'\\n path = '/'.join([self.storage_pool, self.volume_group, volume['name']])\\n data = {\\n 'path': path,\\n 'volumeSize': volume['size'] * units.Gi,\\n 'volumeBlockSize': (\\n self.configuration.nexenta_ns5_blocksize * units.Ki),\\n 'sparseVolume': self.configuration.nexenta_sparse\\n }\\n self.nef.post(url, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07192fd219e683331b5085b29bad99c\",\n \"score\": \"0.54953617\",\n \"text\": \"def internal_create_uls(self, name, region=None):\\n if region is None:\\n self.client.create_bucket(name)\\n else:\\n self.client.create_bucket(name, location=region)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"723bbf06e4fc8fceaa33c7cc61406b95\",\n \"score\": \"0.54832065\",\n \"text\": \"def create_volume(self, dataset_id, size):\\n return self.create_volume_with_profile(dataset_id, size, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d196743708753168532e6274bd9ec3a4\",\n \"score\": \"0.54679465\",\n \"text\": \"def create_bucket_user(sender, instance, created, **kwargs):\\n if created:\\n if instance is not None:\\n bucket_user = BucketUser(user=instance)\\n bucket_user.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d312ce9ca8c99f574f7da6889c53015\",\n \"score\": \"0.54606885\",\n \"text\": \"def test_create_bucket_eu(self):\\n settings = EnvironSettings(\\n {\\n \\\"storage.bucket\\\": \\\"new_bucket\\\",\\n \\\"storage.region_name\\\": \\\"eu-central-1\\\",\\n \\\"signature_version\\\": \\\"s3v4\\\",\\n },\\n {},\\n )\\n S3Storage.configure(settings)\\n\\n bucket = self.s3.Bucket(\\\"new_bucket\\\")\\n bucket.load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bcf457077ea8edb13bc22153f84352\",\n \"score\": \"0.5454787\",\n \"text\": \"def create_bucket(bucket_name: str) -> bool:\\n try:\\n region=\\\"us-east-1\\\"\\n secret_key, access_key = _get_api_keys()\\n s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key)\\n s3_client.create_bucket(Bucket=bucket_name)\\n except ClientError as e:\\n logging.error(e)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3998c4475c5e64a4c2e2c032b3aedb8\",\n \"score\": \"0.5453417\",\n \"text\": \"def create_volume(self, volume):\\n url = 'storage/pools/%(pool)s/volumeGroups/%(group)s/volumes' % {\\n 'pool': self.storage_pool,\\n 'group': self.volume_group,\\n }\\n data = {\\n 'name': volume['name'],\\n 'volumeSize': volume['size'] * units.Gi,\\n 'volumeBlockSize': (\\n self.configuration.nexenta_ns5_blocksize * units.Ki),\\n 'sparseVolume': self.configuration.nexenta_sparse\\n }\\n self.nef.post(url, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abc217c41841b71db37cf0850378413e\",\n \"score\": \"0.5439353\",\n \"text\": \"def uploadObjectToS3(vpnConfiguration, bucketName, assumeRoleArn=None):\\n try:\\n s3Connection = boto3.resource('s3')\\n fileName = vpnConfiguration['VpnConnection']['VpnConnectionId'] + '.xml'\\n vpnConfig = vpnConfiguration['VpnConnection']['CustomerGatewayConfiguration']\\n # open(filePath).write(vpnConfiguration['VpnConnection']['CustomerGatewayConfiguration'])\\n if assumeRoleArn:\\n stsConnection = boto3.client('sts')\\n assumedrole = stsConnection.assume_role(RoleArn=assumeRoleArn, RoleSessionName=\\\"Sample\\\")\\n s3 = boto3.resource('s3', aws_access_key_id=assumedrole['Credentials']['AccessKeyId'],\\n aws_secret_access_key=assumedrole['Credentials']['SecretAccessKey'],\\n aws_session_token=assumedrole['Credentials']['SessionToken'])\\n s3.Object(bucketName, fileName).put(Body=vpnConfig)\\n return True\\n s3Connection.Object(bucketName, fileName).put(Body=vpnConfig)\\n return True\\n except Exception as e:\\n logger.error(\\\"Error uploading file to S3 Bucket, Error : %s\\\" % str(e))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ddcf35700e81494f5cf36f95d9853dc\",\n \"score\": \"0.5420014\",\n \"text\": \"async def make_bucket(self):\\n self.__connect().make_bucket(self.__bucket())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"136cc33bf861e4ddd193811947540ac5\",\n \"score\": \"0.5403652\",\n \"text\": \"def create_s3_bucket(bucket_name, region):\\n s3_client = boto3.client(\\\"s3\\\")\\n \\\"\\\"\\\" :type : pyboto3.s3 \\\"\\\"\\\"\\n if region != \\\"us-east-1\\\":\\n s3_client.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={\\\"LocationConstraint\\\": region})\\n else:\\n s3_client.create_bucket(Bucket=bucket_name)\\n\\n try:\\n _configure_s3_bucket(bucket_name)\\n except Exception as e:\\n LOGGER.error(\\\"Failed when configuring cluster S3 bucket with error %s\\\", e)\\n delete_s3_bucket(bucket_name)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"316971480c5b5142e5b1fc772a48b169\",\n \"score\": \"0.54006684\",\n \"text\": \"def create_volume(\\n self,\\n name,\\n account_id,\\n total_size,\\n enable512e=OPTIONAL,\\n qos=OPTIONAL,\\n attributes=OPTIONAL,\\n associate_with_qos_policy=OPTIONAL,\\n access=OPTIONAL,\\n enable_snap_mirror_replication=OPTIONAL,\\n qos_policy_id=OPTIONAL,\\n protection_scheme=OPTIONAL,\\n fifo_size=OPTIONAL,\\n min_fifo_size=OPTIONAL,):\\n\\n self._check_connection_type(\\\"create_volume\\\", \\\"Cluster\\\")\\n\\n params = { \\n \\\"name\\\": name,\\n \\\"accountID\\\": account_id,\\n \\\"totalSize\\\": total_size,\\n }\\n if enable512e is not None:\\n params[\\\"enable512e\\\"] = enable512e\\n if qos is not None:\\n params[\\\"qos\\\"] = qos\\n if attributes is not None:\\n params[\\\"attributes\\\"] = attributes\\n if associate_with_qos_policy is not None:\\n if self.api_version < 10.0:\\n raise ApiParameterVersionError(\\\"create_volume\\\", 10.0,\\n [(\\\"associate_with_qos_policy\\\", associate_with_qos_policy, 10.0, False)])\\n else:\\n params[\\\"associateWithQoSPolicy\\\"] = associate_with_qos_policy\\n if access is not None:\\n if self.api_version < 10.0:\\n raise ApiParameterVersionError(\\\"create_volume\\\", 10.0,\\n [(\\\"access\\\", access, 10.0, False)])\\n else:\\n params[\\\"access\\\"] = access\\n if enable_snap_mirror_replication is not None:\\n if self.api_version < 10.0:\\n raise ApiParameterVersionError(\\\"create_volume\\\", 10.0,\\n [(\\\"enable_snap_mirror_replication\\\", enable_snap_mirror_replication, 10.0, False)])\\n else:\\n params[\\\"enableSnapMirrorReplication\\\"] = enable_snap_mirror_replication\\n if qos_policy_id is not None:\\n if self.api_version < 10.0:\\n raise ApiParameterVersionError(\\\"create_volume\\\", 10.0,\\n [(\\\"qos_policy_id\\\", qos_policy_id, 10.0, False)])\\n else:\\n params[\\\"qosPolicyID\\\"] = qos_policy_id\\n if protection_scheme is not None:\\n params[\\\"protectionScheme\\\"] = protection_scheme\\n if fifo_size is not None:\\n if self.api_version < 12.3:\\n raise ApiParameterVersionError(\\\"create_volume\\\", 12.3,\\n [(\\\"fifo_size\\\", fifo_size, 12.3, False)])\\n else:\\n params[\\\"fifoSize\\\"] = fifo_size\\n if min_fifo_size is not None:\\n if self.api_version < 12.3:\\n raise ApiParameterVersionError(\\\"create_volume\\\", 12.3,\\n [(\\\"min_fifo_size\\\", min_fifo_size, 12.3, False)])\\n else:\\n params[\\\"minFifoSize\\\"] = min_fifo_size\\n \\n # There is no adaptor.\\n return self.send_request(\\n 'CreateVolume',\\n CreateVolumeResult,\\n params,\\n since=1.0\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d85fa3498a5c916a6a90ad872342c27\",\n \"score\": \"0.53986055\",\n \"text\": \"def create(self, name, location=None):\\n AWSBucket.assert_valid_resource_name(name)\\n\\n bucket = self.provider.s3_conn.create_bucket(\\n name,\\n location=location if location else '')\\n return AWSBucket(self.provider, bucket)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d1d3d5e8cfb8916aa2670e9344f8481\",\n \"score\": \"0.53954875\",\n \"text\": \"def create_bucket(self, bucket_name: str):\\n self.response = self.s3.create_bucket(Bucket=bucket_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecbcdc2f988b7443674b044b4dcf3fda\",\n \"score\": \"0.53952366\",\n \"text\": \"def create(config, volname):\\n exec_command(config, \\\"create\\\", volname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daab2e11dc25f73157ad34b831fbbc1f\",\n \"score\": \"0.5392365\",\n \"text\": \"def create_bucket_policy(storage: MediaS3, policy_path: str):\\n with open(policy_path) as fyl:\\n policy = fyl.read()\\n storage.bucket.Policy().put(Policy=policy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4815ee57b5a7db743083d3464e2cb0\",\n \"score\": \"0.5386606\",\n \"text\": \"def test_create_storage_v1beta1_volume_attachment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f270162076ab35716d3d481e3106fa\",\n \"score\": \"0.53834045\",\n \"text\": \"def _create_data_bucket(self):\\n bucket_name = f\\\"capstone-uda-data\\\"\\n bucket_id = f\\\"{bucket_name}-bucket\\\"\\n\\n bucket = s3.Bucket(\\n self,\\n id=bucket_id,\\n bucket_name=bucket_name,\\n encryption=s3.BucketEncryption.S3_MANAGED,\\n block_public_access=s3.BlockPublicAccess(\\n block_public_acls=True,\\n block_public_policy=True,\\n ignore_public_acls=True,\\n restrict_public_buckets=True,\\n ),\\n )\\n\\n return bucket\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09fa7b5338c07f781600cf6778aab40\",\n \"score\": \"0.5370811\",\n \"text\": \"def create_volume_with_profile(self, dataset_id, size, profile_name):\\n\\n with startTask(logger,\\n \\\"SFAgent:create_volume_with_profile\\\",\\n datasetID=unicode(dataset_id),\\n volSize=unicode(size),\\n profile=unicode(profile_name)):\\n\\n # NOTE(jdg): dataset_id is unique so we use it as the\\n # volume name on the cluster. We then use the resultant\\n # solidfire vol ID as the blockdevice_id\\n vname = '%s%s' % (self.volume_prefix, dataset_id)\\n profile = self._process_profile(profile_name)\\n params = {'name': vname,\\n 'accountID': self.account_id,\\n 'sliceCount': 1,\\n 'totalSize': int(size),\\n 'enable512e': True,\\n 'attributes': {}}\\n if profile:\\n # We set these keys explicity from the profile, rather\\n # than slurping in a dict inentionally, this handles\\n # cases where there may be extra/invalid keys in the\\n # profile. More importantly alows us to extend the\\n # usage of profiles beyond QoS later.\\n params['qos'] = {'minIOPS': profile['minIOPS'],\\n 'maxIOPS': profile['maxIOPS'],\\n 'burstIOPS': profile['burstIOPS']}\\n result = self.client.issue_request('CreateVolume',\\n params)\\n\\n params = {}\\n params['volumeAccessGroupID'] = self.vag_id\\n params['volumes'] = [int(result['volumeID'])]\\n self.client.issue_request('AddVolumesToVolumeAccessGroup',\\n params,\\n version='7.0')\\n return BlockDeviceVolume(\\n blockdevice_id=unicode(result['volumeID']),\\n size=size,\\n attached_to=None,\\n dataset_id=uuid.UUID(dataset_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37a333a1b54590428d0c044c29c9d050\",\n \"score\": \"0.53617054\",\n \"text\": \"def create_volume(self, project_id, data):\\n url = get_api_server_url('/api/projects/{}/volumes/'.format(project_id))\\n\\n headers = {\\n 'X-CSRFToken': self.client.cookies['csrftoken'],\\n }\\n response = self.client.post(url, data=data, headers=headers)\\n # print(response.status_code)\\n # print(response.text)\\n if response.status_code == 201:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5cdac7e75288aaacd16072a456fbbec\",\n \"score\": \"0.53593695\",\n \"text\": \"def new_bucket_config(\\n bucket_name,\\n region: str,\\n s3_client,\\n s3_resource,\\n block_public: bool = True,\\n encrypt: bool = True,\\n):\\n create_bucket(bucket_name, region, s3_client, s3_resource)\\n if encrypt:\\n encrypt_bucket(bucket_name, s3_client)\\n if block_public:\\n block_bucket_public_access(bucket_name, s3_client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4573fbc43726e052d294a8f390ec2243\",\n \"score\": \"0.5351521\",\n \"text\": \"def create_bucket(self, conn, bucket_name):\\n try:\\n bucket = conn.create_bucket(bucket_name)\\n except boto.exception.S3CreateError as e:\\n print \\\"Error creating S3 bucket.\\\"\\n print e\\n print \\\"New S3 bucket\\\", bucket_name, \\\"was created.\\\"\\n return bucket\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc09a7d5ae868d0739123af44e9bc2db\",\n \"score\": \"0.5349416\",\n \"text\": \"def create(array, vol_name, size):\\n return array.create_volume(vol_name, size=size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded740bddbc27b7bc6362b05e26d459c\",\n \"score\": \"0.5346279\",\n \"text\": \"def create_bucket(bucket_name, region=None):\\n\\n # Create bucket\\n try:\\n if region is None:\\n s3_client = boto3.client('s3')\\n s3_client.create_bucket(Bucket=bucket_name)\\n else:\\n s3_client = boto3.client('s3', region_name=region)\\n location = {'LocationConstraint': region}\\n s3_client.create_bucket(Bucket=bucket_name,\\n CreateBucketConfiguration=location)\\n except ClientError as e:\\n logging.error(e)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10f6a1d735c6028820aad0183a8f877a\",\n \"score\": \"0.5344345\",\n \"text\": \"def uploadFileToS3(fileName, bucketName, updates, assumeRoleArn=False):\\n try:\\n s3Connection = boto3.resource('s3')\\n if assumeRoleArn:\\n stsConnection = boto3.client('sts')\\n assumedrole = stsConnection.assume_role(RoleArn=assumeRoleArn, RoleSessionName=\\\"Sample\\\")\\n s3 = boto3.resource('s3', aws_access_key_id=assumedrole['Credentials']['AccessKeyId'],\\n aws_secret_access_key=assumedrole['Credentials']['SecretAccessKey'],\\n aws_session_token=assumedrole['Credentials']['SessionToken'])\\n s3.Object(bucketName, fileName).put(Body=updates)\\n return True\\n result = s3Connection.Object(bucketName, fileName).put(Body=json.dumps(updates))\\n print (\\\"result is {}\\\".format(result))\\n return True\\n except Exception as e:\\n print(\\\"Error uploading file to S3 Bucket, Error : %s\\\" % str(e))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15049d31d1618c6f38387ce044aae076\",\n \"score\": \"0.5333661\",\n \"text\": \"def create(self, context):\\n if self.obj_attr_is_set('id'):\\n raise exception.ObjectActionError(action='create',\\n reason='already created')\\n\\n values = self.obj_get_changes()\\n db_volume = dbapi.create_volume(context, values)\\n self._from_db_object(self, db_volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff81c99d4b294ba8f3975eee18efc1f8\",\n \"score\": \"0.5333074\",\n \"text\": \"def _create_cuboid_bucket(self, bucket_name):\\n client = boto3.client('s3', region_name=get_region())\\n _ = client.create_bucket(\\n ACL='private',\\n Bucket=bucket_name\\n )\\n return client.get_waiter('bucket_exists')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33194caa263c39ef7aba20263406bc9\",\n \"score\": \"0.5330251\",\n \"text\": \"def s3_client():\\n\\n os.environ[\\\"AWS_ACCESS_KEY_ID\\\"] = \\\"foo\\\"\\n os.environ[\\\"AWS_SECRET_ACCESS_KEY\\\"] = \\\"bar\\\"\\n\\n return s3fs.S3FileSystem()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7e05b8ed91ff4c43b67e7f188c9609b\",\n \"score\": \"0.5326904\",\n \"text\": \"def configure_bucket(policy_path: str):\\n if not isinstance(default_storage, MediaS3):\\n return\\n ensure_bucket_exists(default_storage)\\n create_bucket_policy(default_storage, policy_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dabc2bd4e301ec346dce358407ec7a9\",\n \"score\": \"0.53154504\",\n \"text\": \"def create(self, name, volume, description=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f67d67d7854b3db31e201859a39af4c\",\n \"score\": \"0.53132844\",\n \"text\": \"def create(self, **data):\\n raise ClientException('Storage create method not found')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ccb1ebb228b19878141ca484ce9171\",\n \"score\": \"0.5297375\",\n \"text\": \"def main(**kwargs):\\n cos = ibm_boto3.client(service_name='s3',\\n ibm_api_key_id='xx-xxx',\\n ibm_service_instance_id='xx-xx-xx-xx-xxx',\\n ibm_auth_endpoint=\\\"https://iam.cloud.ibm.com/identity/token\\\",\\n config=Config(signature_version='oauth'),\\n endpoint_url='https://s3.us-south.cloud-object-storage.appdomain.cloud')\\n try:\\n res=cos.upload_file(Filename='test.txt', Bucket='cloud-object-storage-8w-cos-standard-c5u',Key='test.txt')\\n except Exception as e:\\n print(Exception, e)\\n else:\\n print('File Uploaded')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d06e6a75407a5ba9a50f3e73d433e3\",\n \"score\": \"0.5297212\",\n \"text\": \"def create_volume(self, size, image=None, **kwargs):\\n if image:\\n kwargs[\\\"imageRef\\\"] = image\\n\\n self._create_volume(size, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7ef54889d6f3e60e11b33bc8d69d21\",\n \"score\": \"0.52943295\",\n \"text\": \"def test_create_volume():\\n # Create a new volume\\n volume_name = 'test_volume'\\n DC.create_volume(volume_name, 'local')\\n assert \\\"test_volume\\\" in DC.get_volume_details(volume_name)['Name']\\n\\n # Cleaning\\n DC.delete_volume(volume_name)\\n assert \\\"test_volume\\\" not in DC.get_volume_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4641750e4e1bce950a50c35c107f195d\",\n \"score\": \"0.5292725\",\n \"text\": \"def _add_project_s3_bucket(name: str) -> NoReturn:\\n cf_data = _get_cloudformation_template_data(name)\\n cf_data[\\\"Resources\\\"][f\\\"{_get_constant('S3_STORE_PREF')}{name}\\\"] = {\\n \\\"Type\\\": \\\"AWS::S3::Bucket\\\",\\n \\\"Properties\\\": {\\n \\\"BucketName\\\": f\\\"{name}-store-{_get_field_if_exists(name, _get_constant('SALT_KEY'))}\\\"\\n },\\n }\\n _update_cloudformation_template_data(name, cf_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1ff0cbd305c68ad594812e6a369975\",\n \"score\": \"0.52901405\",\n \"text\": \"def create_bucket(s3, bucket_name):\\n # First let's see if we already have a bucket of this name.\\n # The lookup method will return a Bucket object if the\\n # bucket exists and we have access to it or None.\\n print \\\"Looking up bucket \\\" + bucket_name\\n bucket = s3.lookup(bucket_name)\\n if bucket:\\n print 'Bucket (%s) already exists' % bucket_name\\n return bucket\\n else:\\n # Let's try to create the bucket. This will fail if\\n # the bucket has already been created by someone else.\\n try:\\n print \\\"Creating bucket \\\" + bucket_name\\n bucket = s3.create_bucket(bucket_name)\\n print \\\"Bucket created \\\" + bucket_name\\n except s3.provider.storage_create_error, e:\\n print 'Bucket (%s) is owned by another user' % bucket_name\\n return bucket\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bb666942557002e13b9af1870afe6f\",\n \"score\": \"0.52878004\",\n \"text\": \"def create_volume(\\n self,\\n size,\\n name,\\n location=None,\\n snapshot=None,\\n ex_volume_type=\\\"standard\\\",\\n ex_iops=None,\\n ex_encrypted=False,\\n ex_kms_key_id=None,\\n ex_throughput=None,\\n ):\\n\\n params = {\\\"Action\\\": \\\"CreateVolume\\\", \\\"Size\\\": str(size)}\\n\\n if ex_volume_type and ex_volume_type not in VALID_VOLUME_TYPES:\\n raise ValueError(\\\"Invalid volume type specified: %s\\\" % (ex_volume_type))\\n\\n if snapshot:\\n params[\\\"SnapshotId\\\"] = snapshot.id\\n\\n # AvailabilityZone argument is mandatory so if one is not provided,\\n # we select one\\n if not location:\\n location = self.list_locations()[0]\\n\\n params[\\\"AvailabilityZone\\\"] = location.availability_zone.name\\n\\n if ex_volume_type:\\n params[\\\"VolumeType\\\"] = ex_volume_type\\n\\n if ex_volume_type in [\\\"io1\\\", \\\"io2\\\", \\\"gp3\\\"] and ex_iops:\\n params[\\\"Iops\\\"] = ex_iops\\n\\n if ex_encrypted:\\n params[\\\"Encrypted\\\"] = 1\\n\\n if ex_kms_key_id is not None:\\n params[\\\"KmsKeyId\\\"] = ex_kms_key_id\\n\\n if ex_volume_type == \\\"gp3\\\" and ex_throughput:\\n params[\\\"Throughput\\\"] = ex_throughput\\n\\n volume = self._to_volume(\\n self.connection.request(self.path, params=params).object, name=name\\n )\\n\\n if self.ex_create_tags(volume, {\\\"Name\\\": name}):\\n volume.extra[\\\"tags\\\"][\\\"Name\\\"] = name\\n\\n return volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c84a971a6950394e578e851749d2b2f6\",\n \"score\": \"0.5275209\",\n \"text\": \"def create_iam_role(\\n client,\\n role_name=\\\"sparkifydbRole\\\",\\n policy_arn=\\\"arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess\\\",\\n):\\n try:\\n logger.info(\\\"Creating a new IAM Role...\\\")\\n response = client.create_role(\\n Path=\\\"/\\\",\\n RoleName=role_name,\\n Description=\\\"Allows Redshift clusters to call AWS services on your behalf.\\\",\\n AssumeRolePolicyDocument=json.dumps(\\n {\\n \\\"Statement\\\": [\\n {\\n \\\"Action\\\": \\\"sts:AssumeRole\\\",\\n \\\"Effect\\\": \\\"Allow\\\",\\n \\\"Principal\\\": {\\\"Service\\\": \\\"redshift.amazonaws.com\\\"},\\n }\\n ],\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n }\\n ),\\n )\\n logger.debug(\\\"Response after creating sparkifyRole follows...\\\")\\n logger.debug(response)\\n logger.info(f\\\"Attaching Policy {policy_arn}\\\")\\n response = client.attach_role_policy(RoleName=role_name, PolicyArn=policy_arn)\\n role_arn = client.get_role(RoleName=role_name)[\\\"Role\\\"][\\\"Arn\\\"]\\n logger.debug(f\\\"Response after attaching policy {policy_arn} follows...\\\")\\n logger.debug(response)\\n\\n return role_arn\\n\\n except ClientError:\\n logger.exception(\\\"Issue while creating the Role (see below)\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9a1062aa27374f982642fd06557931\",\n \"score\": \"0.52693427\",\n \"text\": \"def create_bucket(bucket_name, region=None):\\n\\tprint(\\\"Creating bucket: {} in {}\\\".format(bucket_name,region))\\n\\n\\t#https://github.com/elastic/elasticsearch/issues/16978\\n\\tif region == \\\"us-east-1\\\":\\n\\t\\tregion = None\\n\\t# Create bucket\\n\\ttry:\\n\\t\\tif region is None:\\n\\t\\t\\ts3_client = boto3.client('s3')\\n\\t\\t\\ts3_client.create_bucket(Bucket=bucket_name)\\n\\t\\telse:\\n\\t\\t\\ts3_client = boto3.client('s3', region_name=region)\\n\\t\\t\\tlocation = {'LocationConstraint': region}\\n\\t\\t\\ts3_client.create_bucket(Bucket=bucket_name,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tCreateBucketConfiguration=location)\\n\\texcept ClientError as e:\\n\\t\\tlogging.error(e)\\n\\t\\treturn False\\n\\tprint(\\\"bucket created..\\\")\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a6793b74e2e66a124c5ded231a691f\",\n \"score\": \"0.52644473\",\n \"text\": \"def create_folder(bucket, directory_name):\\r\\n\\r\\n try:\\r\\n s3 = boto3.client('s3')\\r\\n key_path = directory_name + '/' \\\"Indicamos crear un folder\\\"\\r\\n s3.put_object(Bucket=bucket, Key=key_path)\\r\\n except Exception as err:\\r\\n print(err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1704c823884f04f95083c2e5ec67898b\",\n \"score\": \"0.52616787\",\n \"text\": \"def copy_to_s3(\\n self, path, aws_id, aws_secret, aws_region, bucket_name, namespace=\\\"\\\"\\n ):\\n self.log.debug(\\\"Sending to S3 bucket...\\\")\\n from boto3.session import Session\\n\\n session = Session(\\n aws_access_key_id=aws_id,\\n aws_secret_access_key=aws_secret,\\n region_name=aws_region,\\n )\\n s3 = session.resource(\\\"s3\\\")\\n bucket = s3.Bucket(bucket_name)\\n if namespace and not namespace.endswith(\\\"/\\\"):\\n namespace = namespace + \\\"/\\\"\\n\\n with open(path, \\\"br\\\") as content:\\n result = bucket.put_object(Key=namespace + Path(path).name, Body=content)\\n self.log.info(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cecd97ac1b20be875cb4cadf876fd2c2\",\n \"score\": \"0.52612793\",\n \"text\": \"def create_bucket(self, bucket_name, headers=None,\\n location=\\\"us-west-2\\\", policy=None):\\n if bucket_name and isinstance(bucket_name, basestring):\\n try:\\n return super(IcsS3, self).create_bucket(\\n bucket_name, headers, location, policy)\\n except S3CreateError, e:\\n if \\\"bucketalreadyownedbyyou\\\" in str(e).lower():\\n log.info(\\n \\\"this bucket already exists, do the updating...\\\")\\n return super(IcsS3, self).get_bucket(bucket_name)\\n raise\\n else:\\n raise IcsS3Exception(\\\"no bucket name found...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c147285f67cd6774889dca0625d77d6\",\n \"score\": \"0.5260567\",\n \"text\": \"def internal_create_uls(self, name, region=None):\\n if region is None:\\n self.client.create_bucket(Bucket=name)\\n else:\\n self.client.create_bucket(\\n Bucket=name, CreateBucketConfiguration={\\\"LocationConstraint\\\": region}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0e9419d66989e80396ecf7520af328\",\n \"score\": \"0.52579206\",\n \"text\": \"def test_create_volume_in_availability_zone(self):\\n\\n # Create volume\\n availability_zone = 'nova'\\n created_volume = self.api.post_volume(\\n {'volume': {'size': 1,\\n 'availability_zone': availability_zone}})\\n LOG.debug(\\\"created_volume: %s\\\", created_volume)\\n self.assertTrue(created_volume['id'])\\n created_volume_id = created_volume['id']\\n\\n # Check it's there and availability zone present\\n found_volume = self.api.get_volume(created_volume_id)\\n self.assertEqual(created_volume_id, found_volume['id'])\\n self.assertEqual(availability_zone, found_volume['availability_zone'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d18b2f85e4e0aac053945a16bd8067e\",\n \"score\": \"0.5227771\",\n \"text\": \"def create_bucket(self, name, region):\\n create_bucket_kwargs = {}\\n create_bucket_config = {}\\n # us-east-1 does not work with location specified.\\n if region != 'us-east-1':\\n create_bucket_config['LocationConstraint'] = region\\n if create_bucket_config:\\n create_bucket_kwargs['CreateBucketConfiguration'] = (\\n create_bucket_config\\n )\\n bucket = self.s3.Bucket(name)\\n response = bucket.create(**create_bucket_kwargs)\\n msg = 'Created bucket \\\"{bucket_name}\\\" at \\\"{bucket_location}\\\" in ' \\\\\\n 'region \\\"{region}\\\".'\\n print(msg.format(\\n bucket_name=name,\\n bucket_location=response['Location'],\\n region=region,\\n ))\\n print()\\n print('\\\\tAWS_STORAGE_BUCKET_NAME', name)\\n print()\\n bucket.wait_until_exists()\\n return bucket\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4497b6c71e1cc84603ef048c8fab9991\",\n \"score\": \"0.52225846\",\n \"text\": \"def create_object(self, bucket_name: str, filename: str, key: str, public: bool = False):\\n if not Path(filename).is_file():\\n print(\\\"File not found. Location: {}\\\".format(filename))\\n return\\n\\n if public:\\n extra_args = {'ACL': 'public-read'}\\n else:\\n extra_args = {}\\n\\n if filename.split('.')[-1] == 'html':\\n extra_args.update({'ContentType': 'text/html'})\\n\\n self.response = self.s3.upload_file(Bucket=bucket_name,\\n Filename=filename,\\n Key=key,\\n ExtraArgs=extra_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831a52a9b1a01ce76bfb9809f3161663\",\n \"score\": \"0.5216734\",\n \"text\": \"def __init__(self):\\n self.client = boto3.client(\\n S3,\\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 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0effcecab152c1e63099266142baee39\",\n \"score\": \"0.52128994\",\n \"text\": \"def _start(self):\\n create_definition = pcf_util.param_filter(self.get_desired_state_definition(), S3Bucket.START_PARAMS_FILTER)\\n response = self.client.create_bucket(**create_definition)\\n\\n if self.custom_config.get(\\\"Tags\\\"):\\n tags = self.custom_config.get(\\\"Tags\\\")\\n tag_set = []\\n for k, v in tags.items():\\n tag_set.append({\\n \\\"Key\\\": k,\\n \\\"Value\\\": v\\n })\\n\\n self.client.put_bucket_tagging(\\n Bucket=self.bucket_name,\\n Tagging={\\n \\\"TagSet\\\":tag_set\\n }\\n )\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5057d4a303a14afe34487bed574ce6ce\",\n \"score\": \"0.5206667\",\n \"text\": \"def attach_volume(study):\\n # Check that mounting volumes is enabled\\n if not settings.FEAT_CAVATICA_MOUNT_VOLUMES:\\n return\\n\\n api = sbg.Api(\\n url=settings.CAVATICA_URL, token=settings.CAVATICA_HARMONIZATION_TOKEN\\n )\\n\\n access_key = settings.CAVATICA_READWRITE_ACCESS_KEY\\n secret_key = settings.CAVATICA_READWRITE_SECRET_KEY\\n access_mode = \\\"RW\\\"\\n\\n # Make sure that the keys were set correctly\\n if access_key is None or secret_key is None:\\n raise \\\"Volume mounting is enabled but keys are not set correctly\\\"\\n\\n # Now we know that all settings are in place to attach a volume\\n\\n new_volume = api.volumes.create_s3_volume(\\n name=study.kf_id,\\n description=f\\\"Created by the Study Creator for '{study.name}'\\\",\\n bucket=study.bucket,\\n access_key_id=access_key,\\n secret_access_key=secret_key,\\n access_mode=access_mode,\\n )\\n\\n logger.info(\\n f\\\"Adding {settings.CAVATICA_DELIVERY_ACCOUNT} \\\"\\n \\\"as a member to the volume\\\"\\n )\\n\\n try:\\n member = new_volume.add_member(\\n settings.CAVATICA_DELIVERY_ACCOUNT,\\n permissions={\\n \\\"write\\\": False,\\n \\\"read\\\": True,\\n \\\"copy\\\": True,\\n \\\"admin\\\": False,\\n },\\n )\\n except sbg.errors.Conflict as err:\\n logger.warning(\\n f\\\"Unable to add {settings.CAVATICA_DELIVERY_ACCOUNT} to the \\\"\\n f\\\"volume. Perhaps they are already a member? {err}\\\"\\n )\\n\\n return new_volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaed747cc0ee3bb48a89a1ba28819999\",\n \"score\": \"0.5199138\",\n \"text\": \"def create(self, project, vault, attachable_sgid, **kwargs):\\n kwargs[\\\"attachable_sgid\\\"] = attachable_sgid\\n\\n return self._post(\\\"/buckets/{project}/vaults/{vault}/uploads.json\\\",\\n project=project, vault=vault, json_dict=kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ce6a731e7505101f71b3b09cbf325b\",\n \"score\": \"0.51949924\",\n \"text\": \"def create_s3_image(self, instance_id, key_location, aws_user_id,\\r\\n ec2_cert, ec2_private_key, bucket, image_name=\\\"image\\\",\\r\\n description=None, kernel_id=None, ramdisk_id=None,\\r\\n remove_image_files=False, **kwargs):\\r\\n icreator = image.S3ImageCreator(self, instance_id, key_location,\\r\\n aws_user_id, ec2_cert,\\r\\n ec2_private_key, bucket,\\r\\n image_name=image_name,\\r\\n description=description,\\r\\n kernel_id=kernel_id,\\r\\n ramdisk_id=ramdisk_id,\\r\\n remove_image_files=remove_image_files)\\r\\n return icreator.create_image()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c77ae908b7986a109e1a7c6853b02f\",\n \"score\": \"0.5194965\",\n \"text\": \"def create_bucket(self):\\n if self.debug:\\n print('create bucket'+lineno())\\n print('bucket name: '+str(self.bucket_name))\\n\\n if self.check_bucket():\\n # Bucket already exists\\n return\\n else:\\n # Create bucket\\n\\n try:\\n if self.debug:\\n print('Creating bucket')\\n response = self.client.create_bucket(Bucket=str(self.bucket_name))\\n\\n if self.debug:\\n print('response: '+str(response)+lineno())\\n\\n except ClientError as err:\\n print('Error: '+str(err))\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d4903869af148af0219cc4253f0d79\",\n \"score\": \"0.5191022\",\n \"text\": \"def __init__(self, profile_name=\\\"default\\\", bucketName=None):\\n if bucketName is None:\\n self.bucketName = \\\"altotechpublic\\\"\\n\\n session = boto3.Session(profile_name=profile_name)\\n # self.s3 = boto3.client('s3', aws_access_key_id=self.access_key,\\n # aws_secret_access_key=self.secret_key)\\n self.s3_resource = session.resource('s3')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db07eebc643ad1baf9143423efeb712a\",\n \"score\": \"0.51860887\",\n \"text\": \"def create_aws_conn(access_key, secret_key):\\n resource = boto.resource('s3', region_name='us-east-1')\\n client = boto.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key)\\n return client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f15f626da7a204ed23ccbadbe8d5df2\",\n \"score\": \"0.5183712\",\n \"text\": \"def init():\\n if click.confirm('Do you want to enable EBS support?'):\\n click.echo('A user with proper IAM permissions need to be specified.')\\n key = click.prompt('The access key id of the authorised user')\\n access = click.prompt('The secret access key of the authorised user')\\n source_dir = \\\"{}/actions/aws-ebs-csi-driver\\\".format(os.getenv('SNAP'))\\n destination_dir = \\\"{}/actions/aws-ebs-csi-driver\\\".format(os.getenv('SNAP_DATA'))\\n shutil.rmtree(destination_dir, ignore_errors=True)\\n shutil.copytree(source_dir, destination_dir)\\n with open(\\\"{}/secret.yaml\\\".format(source_dir), \\\"rt\\\") as f:\\n data = f.read()\\n data = data.replace('{{key_id}}', key)\\n data = data.replace('{{access_key}}', access)\\n secrets_file = \\\"{}/secret.yaml\\\".format(destination_dir)\\n os.chmod(secrets_file, 0o600)\\n with open(secrets_file, \\\"w+\\\") as s:\\n s.write(data)\\n Path('{}/var/lock/ebs-ready'.format(os.getenv('SNAP_DATA'))).touch()\\n click.echo('')\\n if click.confirm('Do you want to enable EFS support?'):\\n fsid = click.prompt('The EFS file system ID')\\n size = click.prompt('The size in GB of the volume to be created')\\n source_dir = \\\"{}/actions/aws-efs-csi-driver\\\".format(os.getenv('SNAP'))\\n destination_dir = \\\"{}/actions/aws-efs-csi-driver\\\".format(os.getenv('SNAP_DATA'))\\n shutil.rmtree(destination_dir, ignore_errors=True)\\n shutil.copytree(source_dir, destination_dir)\\n with open(\\\"{}/setup/pv.yaml\\\".format(source_dir), \\\"rt\\\") as f:\\n data = f.read()\\n data = data.replace('{{fsid}}', fsid)\\n data = data.replace('{{size}}', size)\\n with open(\\\"{}/setup/pv.yaml\\\".format(destination_dir), \\\"w+\\\") as s:\\n s.write(data)\\n Path('{}/var/lock/efs-ready'.format(os.getenv('SNAP_DATA'))).touch()\\n click.echo('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94e0b245a425cbd66d119780d6544eeb\",\n \"score\": \"0.5182366\",\n \"text\": \"def create(self, size, name):\\n cinder = cinder_workflow.BaseCinderActions()\\n volume = cinder.create_volume(size, name)._info\\n del volume['links']\\n utils.print_dict(volume)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ebd743ad900622da81b9db5c2291ef\",\n \"score\": \"0.51684356\",\n \"text\": \"def create_backend_bucket(bucket_name):\\n target_account_id = ACCOUNTS[\\\"management\\\"]\\n print('Creating backend\\\\'s S3 bucket \\\"{}\\\" in management account (ID: {})'.format(\\n bucket_name,\\n target_account_id\\n ))\\n\\n if ARGS.dry_run:\\n print(u'\\\\u2023 Skipped during dry-run \\\\u2023\\\\u2023\\\\u2023')\\n return\\n\\n try:\\n client('s3', ACCOUNTS[\\\"management\\\"]).create_bucket(\\n Bucket=bucket_name,\\n CreateBucketConfiguration={'LocationConstraint': AWS_REGION}\\n )\\n print_completion()\\n except ClientError as err:\\n if err.response['Error']['Code'] == 'BucketAlreadyExists':\\n print(\\\"- S3 bucket \\\\\\\"{}\\\\\\\" already exists! Choose another name by providing -s argument \\\" \\\\\\n \\\" and try again\\\".format(bucket_name))\\n abort_setup()\\n elif err.response['Error']['Code'] == 'InvalidClientTokenId':\\n print(\\\"- Failed to create S3 bucket, using defined Key & Secret for profile {}!\\\" \\\\\\n \\\"\\\\n Make sure its user has sufficient permission\\\".format(target_account_id))\\n abort_setup()\\n elif err.response['Error']['Code'] == 'InvalidAccessKeyId':\\n print(\\\"- Failed to authenticate credentials provided by {} profile.\\\" \\\\\\n \\\"\\\\n Make sure the Access keys is activated and credentials are valid.\\\".format(target_account_id))\\n abort_setup()\\n elif err.response['Error']['Code'] == 'BucketAlreadyOwnedByYou':\\n print(u'\\\\u2023 You already own this bucket')\\n print_completion()\\n else:\\n print(\\\"Unexpected error: %s\\\" % err)\\n abort_setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b22f7380e314d52a195e25f7090357\",\n \"score\": \"0.5164482\",\n \"text\": \"def CreateVolumeFromSnapshot(\\n self,\\n snapshot: ebs.AWSSnapshot,\\n volume_name: Optional[str] = None,\\n volume_name_prefix: Optional[str] = None,\\n volume_type: str = 'gp2',\\n kms_key_id: Optional[str] = None,\\n tags: Optional[Dict[str, str]] = None) -> ebs.AWSVolume:\\n\\n if volume_type not in ['standard', 'io1', 'gp2', 'sc1', 'st1']:\\n raise ValueError('Volume type must be one of [standard, io1, gp2, sc1, '\\n 'st1]. Got: {0:s}'.format(volume_type))\\n\\n if not volume_name:\\n volume_name = self._GenerateVolumeName(\\n snapshot, volume_name_prefix=volume_name_prefix)\\n\\n if len(volume_name) > 255:\\n raise ValueError(\\n 'Volume name {0:s} is too long (>255 chars)'.format(volume_name))\\n\\n if not tags:\\n tags = {}\\n tags['Name'] = volume_name\\n\\n client = self.ClientApi(common.EC2_SERVICE)\\n create_volume_args = {\\n 'AvailabilityZone': snapshot.availability_zone,\\n 'SnapshotId': snapshot.snapshot_id,\\n 'TagSpecifications': [common.CreateTags(common.VOLUME, tags)],\\n 'VolumeType': volume_type\\n }\\n if kms_key_id:\\n create_volume_args['Encrypted'] = True\\n create_volume_args['KmsKeyId'] = kms_key_id\\n if volume_type == 'io1':\\n # If using the io1 volume type, we must specify Iops, see\\n # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/\\n # services/ec2.html#EC2.Client.create_volume. io1 volumes allow for a\\n # ratio of 50 IOPS per 1 GiB.\\n create_volume_args['Iops'] = self.ResourceApi(\\n common.EC2_SERVICE).Snapshot(snapshot.snapshot_id).volume_size * 50\\n try:\\n volume = client.create_volume(**create_volume_args)\\n volume_id = volume['VolumeId']\\n # Wait for volume creation completion\\n client.get_waiter('volume_available').wait(VolumeIds=[volume_id])\\n except (client.exceptions.ClientError,\\n botocore.exceptions.WaiterError) as exception:\\n raise RuntimeError('Could not create volume {0:s} from snapshot '\\n '{1:s}: {2:s}'.format(volume_name, snapshot.name,\\n str(exception)))\\n\\n zone = volume['AvailabilityZone']\\n encrypted = volume['Encrypted']\\n\\n return ebs.AWSVolume(volume_id,\\n self,\\n self.default_region,\\n zone,\\n encrypted,\\n name=volume_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b9691dfdc0c1a7ad2e380a5eec80cfc\",\n \"score\": \"0.5159286\",\n \"text\": \"def get_s3_resource():\\n AWS_ACCESS_KEY = \\\"\\\"\\n AWS_SECRET_KEY = \\\"\\\"\\n return boto3.client(\\\"s3\\\", AWS_ACCESS_KEY, AWS_SECRET_KEY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cebb0268e8884fde80efd548ac8a5f2b\",\n \"score\": \"0.51531386\",\n \"text\": \"def _create_role(self, role_name, role_arn, role_policy):\\n\\n role_result = None\\n role_policy_result = None\\n # Check if the role with the name exists\\n if self._check_if_role_exists(role_name):\\n LOG.debug('Role ' + role_name + ' exists.')\\n else:\\n LOG.debug('Role ' + role_name + ' does not exist.'\\n ' Creating default role for EC2: ' + role_name)\\n # Create a create using the IAM Client with a particular triplet\\n # (role_name, role_arn, assume_role_policy)\\n role_result = self._create_role_with_role_policy(role_name,\\n role_policy,\\n role_arn)\\n role_policy_result = self._get_role_policy(role_arn)\\n return role_result, role_policy_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0380785e99174e7abf26efd9cf6991ae\",\n \"score\": \"0.5145229\",\n \"text\": \"def write_catalog_to_s3(bucket, prefix, catalog):\\n\\n if \\\"license\\\" not in catalog:\\n s3_catalog_file = prefix + \\\"/catalog.json\\\"\\n else:\\n s3_catalog_file = prefix + \\\"/collection.json\\\"\\n\\n get_client(\\\"s3\\\").put_object(\\n Body=json.dumps(catalog, indent=2), Bucket=bucket, Key=s3_catalog_file\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34aedd4ed9257f3b4f99725dcf0d22dd\",\n \"score\": \"0.5138609\",\n \"text\": \"def create(self, size, name=None, description=None,\\n volume_type=None, imageRef=None):\\n vol_name = \\\"'\\\" + name + \\\"' \\\" if name else ''\\n try:\\n size_in_GB = int(size)\\n except ValueError:\\n # size is in the format 'XGB' or 'XTB'\\n size_in_GB, unit = int(size[:-2]), size[-2:]\\n if unit == 'TB':\\n size_in_GB *= 1000\\n\\n logging.info(\\\"Creating volume %s...\\\" % vol_name)\\n tenant = self.__init_client()\\n try:\\n if not self.dryrun:\\n volume = tenant.volumes.create(size=size_in_GB,\\n name=name,\\n description=description,\\n volume_type=volume_type,\\n imageRef=imageRef)\\n logging.info(\\\"Request to create volume %ssent\\\" % vol_name)\\n return volume\\n else:\\n logging.info(\\\"Volume %snot created because dryrun is enabled\\\" % vol_name)\\n except requests.exceptions.Timeout, error:\\n raise AiToolsCinderError(error)\\n except cinderclient.exceptions.ClientException, error:\\n raise AiToolsCinderError(error)\\n except cinderclient.exceptions.ConnectionError, error:\\n raise AiToolsCinderError(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70ab1eb90e23dc9faa52867f52888796\",\n \"score\": \"0.51372135\",\n \"text\": \"def __init__(__self__, *,\\n bucket_name: Any,\\n linked_service_name: pulumi.Input['LinkedServiceReferenceArgs'],\\n type: pulumi.Input[str],\\n annotations: Optional[pulumi.Input[Sequence[Any]]] = None,\\n compression: Optional[pulumi.Input[Union['DatasetBZip2CompressionArgs', 'DatasetDeflateCompressionArgs', 'DatasetGZipCompressionArgs', 'DatasetZipDeflateCompressionArgs']]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n format: Optional[pulumi.Input[Union['AvroFormatArgs', 'JsonFormatArgs', 'OrcFormatArgs', 'ParquetFormatArgs', 'TextFormatArgs']]] = None,\\n key: Optional[Any] = None,\\n parameters: Optional[pulumi.Input[Mapping[str, pulumi.Input['ParameterSpecificationArgs']]]] = None,\\n prefix: Optional[Any] = None,\\n structure: Optional[Any] = None,\\n version: Optional[Any] = None):\\n pulumi.set(__self__, \\\"bucket_name\\\", bucket_name)\\n pulumi.set(__self__, \\\"linked_service_name\\\", linked_service_name)\\n pulumi.set(__self__, \\\"type\\\", 'AmazonS3Object')\\n if annotations is not None:\\n pulumi.set(__self__, \\\"annotations\\\", annotations)\\n if compression is not None:\\n pulumi.set(__self__, \\\"compression\\\", compression)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\\n if format is not None:\\n pulumi.set(__self__, \\\"format\\\", format)\\n if key is not None:\\n pulumi.set(__self__, \\\"key\\\", key)\\n if parameters is not None:\\n pulumi.set(__self__, \\\"parameters\\\", parameters)\\n if prefix is not None:\\n pulumi.set(__self__, \\\"prefix\\\", prefix)\\n if structure is not None:\\n pulumi.set(__self__, \\\"structure\\\", structure)\\n if version is not None:\\n pulumi.set(__self__, \\\"version\\\", version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"306d6f38ac2d44f9bc9182e01f2e5a7c\",\n \"score\": \"0.51238364\",\n \"text\": \"def storageCreate():\\n pathJson = request.get_json()\\n path = pathJson[\\\"path\\\"]\\n storagePath = sys.argv[4]\\n\\n if not isValidPathHelper(path):\\n constant.exceptionReturn[\\\"exception_type\\\"] = \\\"IllegalArgumentException\\\"\\n constant.exceptionReturn[\\\"exception_info\\\"] = \\\"[storage_create] given path is invalid\\\"\\n content = json.dumps(constant.exceptionReturn)\\n response = make_command_response(content, 404)\\n return response\\n\\n filepath = storagePath + path\\n if path == \\\"/\\\" or os.path.isfile(filepath) or os.path.isdir(filepath):\\n constant.boolReturn[\\\"success\\\"] = False\\n response = make_command_response(json.dumps(constant.boolReturn), 200)\\n return response\\n\\n try:\\n path_arr = filepath.rsplit('/', 1)\\n dir_path = path_arr[0]\\n os.makedirs(dir_path, exist_ok=True)\\n f = open(filepath, 'w+')\\n f.close()\\n constant.boolReturn[\\\"success\\\"] = True\\n except:\\n constant.boolReturn[\\\"success\\\"] = False\\n\\n response = make_command_response(json.dumps(constant.boolReturn), 200)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45bbaf2be54101343a38dc6ec90933e\",\n \"score\": \"0.512271\",\n \"text\": \"def create(self, permission):\\n if self.is_exist():\\n raise Exception(\\\"Error, Bucket with same name already exist.\\\")\\n\\n self.bucket.create_bucket(permission=permission).headers\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c410e3ee149c36f12fbf8a48a955d24\",\n \"score\": \"0.51150316\",\n \"text\": \"def s3auth(accesskey, secretkey):\\n\\n s3client = boto3.client('s3', aws_access_key_id=accesskey, aws_secret_access_key=secretkey)\\n\\n return s3client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40f6c2a992ed8c3f5a090518c36f2c69\",\n \"score\": \"0.51135015\",\n \"text\": \"def test_s3_bucket_policy_template():\\n _test_template('macros/iam/s3_bucket_policy.template')\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9943,"numItemsPerPage":100,"numTotalItems":994482,"offset":994300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3ODQzODE5Nywic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc3ODQ0MTc5NywiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.60nubOHeC19rlxCjuhUEkm0eNmWKo3E4drAAKdAV9o_QyY5pvLSJ7LYZhMyER076PGhtXTpQdlqW5LcRDTVtAg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
e27485c88cc537c34fa20450ccaaf1de | Return the last used timestamp for the given key. | [
{
"docid": "e96f39853e92cb20cb0ea2093da76300",
"score": "0.9002394",
"text": "def get_last_used_timestamp(self, key):\n return self._last_used_time.get(key)",
"title": ""
}
] | [
{
"docid": "38ae52b0ac3fb3bf6532694212d73bf8",
"score": "0.6671453",
"text": "def touch(self, key):\n self._last_used_time[key] = datetime.datetime.utcnow()",
"title": ""
},
{
"docid": "4312e80b02cced927f55b5da24a4d82b",
"score": "0.6450842",
"text": "def ts_last_seen(self) ->... |
ebc216b1b46e9d33e36eeb35234fac34 | Initialize local Electric Kiwi auth implementation. | [
{
"docid": "165ccd164be6941e10239ccdd1fc09dd",
"score": "0.0",
"text": "async def async_resolve_external_data(self, external_data: Any) -> dict:\n data = {\n \"grant_type\": \"authorization_code\",\n \"code\": external_data[\"code\"],\n \"redirect_uri\": external_... | [
{
"docid": "e99c2e8d550cd93f264017d018e76779",
"score": "0.6848689",
"text": "def __init__(self):\n default_credentials_file = (\n \"%s/.config/mincer/credentials.yaml\" % os.environ['HOME'])\n if CONF.credentials_file:\n raw = self._get_from_file(CONF.credentials_fil... |
cb0ddeca3dc7638e06e9b469917ee008 | Returns the string representation of the model | [
{
"docid": "0f283634439620e9ecd215d87804ec00",
"score": "0.0",
"text": "def to_str(self):\n return pprint.pformat(self.to_dict())",
"title": ""
}
] | [
{
"docid": "aa510b1d67cd504f00d31d7600881756",
"score": "0.860173",
"text": "def _repr_model_(self):\n return str(self)",
"title": ""
},
{
"docid": "ade3a9362342e8a6cfb9797910bc27b2",
"score": "0.7877549",
"text": "def __str__(self):\n return str(self.save())",
"tit... |
39dac090929c00b61fb38c244bd1cce5 | Closes the underlying HDF5 file, if one is associated with the `MODDataset` object. Note that associated HDF5 files are closed by default when the `MODDataset` object is deleted. | [
{
"docid": "3d07e1a4f56f4d62f4ffc630b5b2917f",
"score": "0.0",
"text": "def close(self):\n\n if hasattr(self, '_hf'):\n self._hf.close()",
"title": ""
}
] | [
{
"docid": "ed7438f77267cd1234f8b21d006a2481",
"score": "0.71574867",
"text": "def close_hdf(self):\n self.h5file.close()\n self._hdf_open_for_reading = False\n logging.info(f\"{self.hdf_filename} closed.\")",
"title": ""
},
{
"docid": "5a6f59baddc1da89797c906a6aeb1e9f",... |
40374eaf82fe9ca653a42807f62bdffb | Returns the object used to fold the feature vectors. | [
{
"docid": "75ce5d0b9b26c4a28a92a6641ed2a2f0",
"score": "0.0",
"text": "def fingerprint_folder(self):\n folder_file = op.join(self.data_dir, 'folder.pkl')\n return joblib.load(folder_file) if op.isfile(folder_file) else None",
"title": ""
}
] | [
{
"docid": "7f2870ae80df541714378701d7aa4a06",
"score": "0.6042251",
"text": "def create_fold_feature(fold_x_train, fold_y_train, feature):\n\n # create vocabulary (i.e., for baseline model from James)\n if feature == 'vocab':\n\n vocabularies = create_vocabularies(fold_x_train, fold_y_trai... |
d7e5c800043f4b416bd8e4fc3d0a049b | Longest ORF of a sequence. | [
{
"docid": "07780d6d159ad2c335bd947c8c9ff737",
"score": "0.7650298",
"text": "def longest_orf(seq, n = 1):\n orfs = all_orfs(seq)\n\n if len(orfs) == 0:\n return ''\n elif n == 1 or len(orfs) == 1:\n return seq[orfs[0][0]:orfs[0][1]]\n else:\n return_list = []\n f... | [
{
"docid": "ec3fc318c81325cc42696c342bbe4e88",
"score": "0.8518749",
"text": "def longest_orf(seq):\n orfs = all_orfs(seq)\n\n if len(orfs) == 0:\n return ''\n else:\n return seq[orfs[0][0]:orfs[0][1]]",
"title": ""
},
{
"docid": "c5426894bb3d1ca95d491cfdd99de953",
... |
09081b9e0ee32c2dc635db0b2c5832b8 | Add a new contact for the alter. A contact is composed of a timestamp of a direct message sent by ego to the alter and the text of the message. | [
{
"docid": "51a63ed33d384c6d3a9e6bb6b6bdcbab",
"score": "0.81476605",
"text": "def add_contact(self, alter_id: Hashable, timestamp: int, contact_type: Hashable = \"__default__\", text: str = None,\n num_contacted_alters: int = 1, additional_info: ContactAdditionalInfo = None) ->FullCo... | [
{
"docid": "fa3ffc7c0a4f0a7e2c17cf85c2f7a500",
"score": "0.7900511",
"text": "def add_contact(self, timestamp: int, alter_id: Hashable, contact_type: Hashable = \"__default__\", text: str = None,\n num_contacted_alters: int = 1, additional_info: ContactAdditionalInfo = None):\n ... |
53b17545f9caea1aedec25b14087fbbb | Copy the files from Ubuntu packages installed on bare metal to the conainer's filesystem. | [
{
"docid": "6e2d007510919a6d6aa4da5ccae984c0",
"score": "0.5821541",
"text": "def copy_packages(self, *pkgnames):\n if self.prefix == '':\n return\n for pkgname in pkgnames:\n for f in sprepo.list_package_files(pkgname):\n if os.path.isfile(f):\n ... | [
{
"docid": "fe91febe1e85989e919ef028b17b022b",
"score": "0.69753873",
"text": "def copy_packages(c: Connection):\n # Build a list of package metadata were each row looks like:\n #\n # pkg_name version deb_name\n c.run('rm -rf /tmp/packages')\n c.run('rm -rf /tmp/packages.txt')\n c.ru... |
336162e5368378370b33225e963fe4dc | Name of the broker | [
{
"docid": "5b19ae13149cfad7ca0abfd8b63c181f",
"score": "0.0",
"text": "def get_name(self):\n raise NotImplementedError",
"title": ""
}
] | [
{
"docid": "9d93b4cfbf550da6f80558ca1231e830",
"score": "0.85580945",
"text": "def brokerName(self):\r\n raise NotImplementedError(self.name)",
"title": ""
},
{
"docid": "5f1755300940335a946a8bec4029c73b",
"score": "0.7313525",
"text": "def broker(self):\n return self.c... |
311c9678abfc712b3775be9438a3b9f3 | Computes normal component of vorticity in spherical coordinates | [
{
"docid": "e1334f70d17bd0c82a0b5b2852c83194",
"score": "0.6482742",
"text": "def vorticity_spher(u,v,theta,dtheta,dlamb):\n term1 = 1./(Re*np.cos(theta)) * d_dlamb(v,dlamb)\n term2 = 1./(Re*np.cos(theta)) * d_dtheta(u*np.cos(theta),dtheta)\n return term1 - term2",
"title": ""
}
] | [
{
"docid": "acdcd0df6db91f52a4c4ef0d64b3fc79",
"score": "0.70356363",
"text": "def vnorm(x1, y1, z1, x2, y2, z2):\n vx = x1 - x2\n vy = y1 - y2\n vz = z1 - z2\n \n mag = np.sqrt(vx * vx + vy * vy + vz * vz)\n return vx / mag, vy / mag, vz / mag",
"title": ""
},
{
"docid": "... |
19deb3a65eb4033d58da5a49833076f0 | Get environment variable and convert it. | [
{
"docid": "e2dd523920c1c4776f00af8fa13cf1bd",
"score": "0.7324171",
"text": "def get_env(env_name, default_val=None, conv_func=str):\n val = getenv(env_name, default_val)\n if val is not None:\n val = conv_func(val)\n return val",
"title": ""
}
] | [
{
"docid": "cb8a24078dcc5d1af3cad7dbaaa1fa89",
"score": "0.7759673",
"text": "def get_from_env(self, key):\n return os.environ[key]",
"title": ""
},
{
"docid": "2ffcb107456a2b5bd9dc7c9ad0f1913b",
"score": "0.7716886",
"text": "def _get_env(self, env_var):\n value = os.e... |
687ab02a9afa2ea964cebcb680c6a912 | reads a message from remote device | [
{
"docid": "9fd3d45178b322056ef3701dbf633514",
"score": "0.0",
"text": "def read(self):\n pass",
"title": ""
}
] | [
{
"docid": "8ca10efdf833f9840f8b117fb3e1f4f0",
"score": "0.7706736",
"text": "def read(self, message):",
"title": ""
},
{
"docid": "65ce0135e106133b3c503055c9ddbfd6",
"score": "0.7271204",
"text": "def readMessage(self):\n pass",
"title": ""
},
{
"docid": "251ef113... |
42189bd7c6f15631ededdcb5ec6c854b | Site = (;{NP_PP_63}Ser{/NP_PP_63};UNK) ({NP_PP_63}6{/NP_PP_63};{NP_PP_63}Ser{/NP_PP_63};UNK) | [
{
"docid": "3daf68b5c6b63f09eb346c7da6b3c161",
"score": "0.0",
"text": "def fix_std_site(cls, annotation):\n # when using method line to extracted the position of a site,\n # we may match the wrong annotation with the method line.\n # e.g., 10026262. In reindex(), the second site an... | [
{
"docid": "a99db169d03f8a51694b56be4cdecad7",
"score": "0.5823867",
"text": "def site():\n\n arlen = Site(\"College Track Arlen\", \"Arlen\")\n return arlen",
"title": ""
},
{
"docid": "2d0775f099097872ca16f71a5ea696af",
"score": "0.5358703",
"text": "def identify_site(self, *... |
2692b9ad0d9ab0bfcd04f695f44aa2de | Sets the advanced of this PromotionExecutionInfo. | [
{
"docid": "4682959b0303083cfc4925648cfe2093",
"score": "0.65633017",
"text": "def advanced(self, advanced):\n\n self._advanced = advanced",
"title": ""
}
] | [
{
"docid": "f0951060f92b23af25a8222dd28fb199",
"score": "0.49700913",
"text": "def set_advanced_var(self, name, values):\n if not self._sim_package:\n if self._child_type == \"array\" and values.size == self.model.size:\n values = values[self.model.nodetouser]\n\n ... |
b3eb2757e64054d1c9a35a06432cd978 | IDs of east, horizontal link neighbor. | [
{
"docid": "27d03e31428d10469e5794036d4fcd80",
"score": "0.67702675",
"text": "def horizontal_east_link_neighbor(shape, horizontal_ids,\n bad_index_value=-1):\n # First, we find the shape of the horizontal link array given the shape\n # of the raster model grid. In... | [
{
"docid": "e2c0aefb669b83b802540a8317c85587",
"score": "0.6813953",
"text": "def list_of_neighbors(self):\n ci,ne,we = self.to_neighb()\n ln = [[ne[ci[i]:ci[i+1]]] for i in range(self.V)]\n return ln",
"title": ""
},
{
"docid": "9417536fec13ca098104860960f1d58a",
"s... |
d8a352b981de1e979d6476cb3f2a8b43 | Convenience method for adding just an errback. See L{addCallbacks}. | [
{
"docid": "659dff91505542412966fafa1b402230",
"score": "0.78562236",
"text": "def addErrback(self, errback, *args, **kw):\n return self.addCallbacks(passthru, errback,\n errbackArgs=args,\n errbackKeywords=kw)",
"title": ""
}
... | [
{
"docid": "d83f5dba16b50232a252f90ca584ec15",
"score": "0.6833719",
"text": "def on_failure(self, callback):\r\n self.failure_callbacks.append(callback)",
"title": ""
},
{
"docid": "3a6a6132e1ba2a0656a23fb1f481d25f",
"score": "0.6811721",
"text": "def addCallbacks(self, callb... |
98089d82955af4bb3c4335bd0d71a868 | Apply user related filters to the queryset. | [
{
"docid": "41ca5f5edd654f302322aab732b08e32",
"score": "0.7535608",
"text": "def apply_user_related_qsets_filters(user, queryset):\n if not check_user_has_groups(user, 'admin'):\n queryset = queryset.filter(top_qset__users=user.id)\n\n return queryset",
"title": ""
}
] | [
{
"docid": "ac97a15041e55d174afe302822472e12",
"score": "0.7289657",
"text": "def apply_user_related_questions_filters(user, queryset):\n if not check_user_has_groups(user, 'admin'):\n queryset = queryset.filter(qset__top_qset__users=user.id)\n\n return queryset",
"title": "... |
4c2488b99bba076985c22fb06b7aabea | In the master process, read from the pipe and store the data read in the ``messages`` attribute. | [
{
"docid": "62ce237e6cc9c22aec26e3b8bcd26302",
"score": "0.6897093",
"text": "def read_messages(self):\n # It's absolutely important to execute only one read() system\n # call, more might block. Assuming that we used pselect()\n # correctly, one read() will not block.\n if se... | [
{
"docid": "461593e8ab0312ce49e03310ef753790",
"score": "0.6321657",
"text": "async def reader(self):\n try: \n while True:\n line = await self.subproc.stdout.readline()\n await alog.log(INFO, f\"{self.pid}>{line.decode().strip()}\")\n if no... |
b25bdccf8435170ab0e16f4a0752214d | The ``check connection`` subcommand method. | [
{
"docid": "09e34dff59de4eff7f58b1c46b70b07a",
"score": "0.62133867",
"text": "def check_connection(\n self,\n stdout: typing.Optional[bytes] = None,\n ) -> bool:\n if stdout is None:\n # call certgrinderd ping command\n stdout = self.run_certgrinderd(stdin=... | [
{
"docid": "654d074f47233818c5a8a034f6e790a0",
"score": "0.7333854",
"text": "def check_connection(self):\n return True",
"title": ""
},
{
"docid": "b4bc45c2595acf6af04258951212040e",
"score": "0.7121899",
"text": "def checkConnection(self):\n return self.xmlrpc.checkCo... |
c551ee82cc7bdc2a4f5d85c53fbc8bbf | Start instances and stop them before they are running, increase time to terminate on each iteration | [
{
"docid": "bfbb940ad630ea5c1c816c4c3d374169",
"score": "0.0",
"text": "def ChurnBFEBS(self):\n if self.reservation:\n self.tester.terminate_instances(self.reservation)\n self.image = self.tester.get_emi(root_device_type=\"ebs\")\n self.Churn()",
"title": ""
}
] | [
{
"docid": "0462467bae3e474dcb3402c3f3abce24",
"score": "0.75088894",
"text": "def stop_instances():\n pass",
"title": ""
},
{
"docid": "1e813929bf3576fb2eef0814d4c9b73c",
"score": "0.64349276",
"text": "def terminate_workers(self):\n instances = self.conn.get_only_instance... |
4f19a9c726442567766cb8e8c410c078 | Perform a statistical test on a PointPattern, based on the distribution of a PointPattern attribute over the patterns in this collection. | [
{
"docid": "0866a8d18096f0e4626d6ab953f8b112",
"score": "0.70142114",
"text": "def _pp_attr_test(self, attr, pattern, **kwargs):\n tsdist = self._pp_attr_series(attr, **kwargs).dropna()\n teststat = getattr(pattern, attr)(**kwargs)\n return 1.0 - 0.01 * percentileofscore(tsdist, tes... | [
{
"docid": "78481b5e6317ccde76cbf9ee5a6ae18b",
"score": "0.57883215",
"text": "def computePatternScore(self,pattern):\n fScore = 0\n #terminal\n if not isinstance(pattern, list):\n fScore += pattern.getCanonical().getWeight()\n else:\n for child in patt... |
891ee1cce5e0a4581e8b8cd6afde7ad5 | Callback for apply VOI set button. | [
{
"docid": "f740c393d4f7e650ced656f224339661",
"score": "0.67368996",
"text": "def applyVOISetButtonOnButtonClick( self, event ):\r\n if self.currentNIfTIFile is None: # No loaded image file\r\n mes = \"Please load extracted VOI file before applying VOI set!\"\r\n dlg = wx... | [
{
"docid": "7bb713c1f287c01327126ad8fece1de5",
"score": "0.6189505",
"text": "def set_iso_button_handler(self, func: classmethod) -> None:\n self.iso_button.clicked.connect(func)",
"title": ""
},
{
"docid": "941eda4108a30a4d6cf72d28c949171e",
"score": "0.61075985",
"text": "de... |
05aa7b56c87f19f6a2aaa4d7dfdb3687 | Testing the plotting of wind barbs | [
{
"docid": "c09cbf308ea5c29c318d266621a80e9b",
"score": "0.7092604",
"text": "def test_barbs():\n mp = MapPlot(continentalcolor='white', nocaption=True)\n data = [\n dict(lat=41.5, lon=-96, tmpf=50, dwpf=30, sknt=10, drct=100),\n dict(lat=42.0, lon=-95.5, tmpf=50, dwpf=30, sknt=20, d... | [
{
"docid": "04afd452cfabcac8a97642377935604f",
"score": "0.6511004",
"text": "def detection_rate_bin_plot_by_wind_speed(ax1,ax2,ax3, data, n_bins = 5, threshold = 25,\n wind_to_use = \"WS_windGust_logged_mps\"):\n\n detection, detection_prob, detection_prob_highwind, detection_prob_lowwind... |
bb87171357e923a4b22eb58d40033bbe | Extract the qubit from the computing host given the qubit id. | [
{
"docid": "26727ed0681e0091e9d40ff9b39fca7d",
"score": "0.7352578",
"text": "def _get_stored_qubit(self, qubit_id: str) -> Qubit:\n if qubit_id in self._qubits:\n return self._qubits[qubit_id]\n if qubit_id in self._pre_allocated_qubits:\n return self._pre_allocated_... | [
{
"docid": "089914a42f44b9ef3b1370e5c771b091",
"score": "0.61521566",
"text": "def unpack_qubit(qubit):\n if isinstance(qubit, integer_types):\n return Qubit(qubit)\n elif isinstance(qubit, Qubit):\n return qubit\n elif isinstance(qubit, QubitPlaceholder):\n return qubit\n ... |
ae49247d0bbb87ac7fb54ea86127569d | Format structured fields for the host. | [
{
"docid": "532eae5e3c9c1c170c7318b3b925f7ca",
"score": "0.50925964",
"text": "def format_inbound_structured_fields(structured_fields):\n bytes_ = bytearray([AID.STRUCTURED_FIELD.value])\n\n for (id_, data) in structured_fields:\n length = len(data) + 3\n\n bytes_.extend([(length >> ... | [
{
"docid": "8e353a8947863dc4aeb6fdc5bc037a42",
"score": "0.61566275",
"text": "def format(self, record):\n data = record.__dict__.copy()\n\n if record.args:\n msg = record.msg % record.args\n else:\n msg = record.msg\n\n data.update(\n usernam... |
5a69c5401cae7817f088ad2a755dd806 | move_iter will move iter relative to sibling or parent. Direction (before or after) tells us whether to insert_before or insert_after (with a sib) or to prepend or append (with a parent). | [
{
"docid": "656dcbe668d85651d3ec183983a4bb06",
"score": "0.77750164",
"text": "def move_iter (mod, iter, sibling=None, parent=None, direction=\"before\"):\r\n if direction != \"after\":\r\n direction = \"before\"\r\n path = mod.get_path(iter)\r\n if sibling:\r\n dpath = mod.get_pa... | [
{
"docid": "64bba7517875dfc168251ef587680c31",
"score": "0.5765652",
"text": "def move(self, direction):\r\n iterations = 0\r\n row_list = []\r\n merge_values = []\r\n merged_list = []\r\n if (direction == UP) or (direction == DOWN):\r\n iterations = self.ge... |
feda7cb8aae4b9467dcce25145a7bd1c | Updates prediction with respect to guessing paramter. | [
{
"docid": "c734ed45de23b636a8808e41306963c7",
"score": "0.5966786",
"text": "def respect_guess(self, prediction, options):\n if options:\n val = 1 / len(options)\n return val + (1 - val) * prediction\n else:\n return prediction",
"title": ""
}
] | [
{
"docid": "e20cb2ca91f19b8542b030b5dbe6592a",
"score": "0.73624593",
"text": "def setPrediction(self,new_predition):\r\n \r\n \tself.prediction=new_predition",
"title": ""
},
{
"docid": "fd411cda9031c16e6e829da09d90ee35",
"score": "0.6656664",
"text": "def update(self, ans... |
94f9a2326290a7a8e1a2648816c87bba | This function will get all the details associated to a path. | [
{
"docid": "4222c0dd9dbb39285853b10c7585386b",
"score": "0.0",
"text": "def getFull(self, name):\n\n if Paths.__PATHS__.get(name):\n return Paths.__PATHS__[name]\n return None",
"title": ""
}
] | [
{
"docid": "5ef1d25c309f86f184e1ade6cfb82220",
"score": "0.7070504",
"text": "def getdetails(self, path):\n # type: (Text) -> Info\n return self.getinfo(path, namespaces=[\"details\"])",
"title": ""
},
{
"docid": "ecc93235db727f7a5936061eb256d31b",
"score": "0.6697386",
... |
88c444a94be533e2bcdb4ff2e3eaed74 | Hacky placeholder. Modify if need custom directory etc | [
{
"docid": "cc102b9973ac23478af02938d7573f6c",
"score": "0.0",
"text": "def make_save_path(load_path):\n return load_path.replace('.sents', '.copr') # conll input format",
"title": ""
}
] | [
{
"docid": "a24a252db511065866f14d27445f13a2",
"score": "0.6812693",
"text": "def dir_path(self):",
"title": ""
},
{
"docid": "46ecc2d77e045544c92ed52dcb223569",
"score": "0.66484755",
"text": "def home_directory(self):",
"title": ""
},
{
"docid": "1be25fd7e776c0470b26bf8... |
2bd9abb9e5e6981c5488650fd9fa7d6f | Returns the default site instance if Django settings defines SITE_ID, else None | [
{
"docid": "4aace95249b08664caac8eacd3ea4e68",
"score": "0.9090551",
"text": "def default_site():\n if getattr(settings, 'SITE_ID', ''):\n return Site.objects.get(pk=settings.SITE_ID)",
"title": ""
}
] | [
{
"docid": "ece70d1f60a8c59bd50cfea285f9bd14",
"score": "0.76525426",
"text": "def get_current_site():\n return Site.objects.get(id=settings.SITE_ID)",
"title": ""
},
{
"docid": "a510296d91861d1f247248bbf84ce2f2",
"score": "0.74339956",
"text": "def site(self):\r\n if self.... |
4627071a6042ef80198cae2bfa4fff3d | Init registration client by phone. | [
{
"docid": "c7b4e079977c8f938c61c1af91a90268",
"score": "0.5142294",
"text": "def SendRegistrationCodeToPhone(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')",
... | [
{
"docid": "b28cbbb74ab31b2c2f5f2652c31de3f4",
"score": "0.67799324",
"text": "def RegisterClientByPhone(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')",
"titl... |
75fd02b3896272b5b944a9f88846358c | This function runs the neural style transfer algorithm. This is not to be used eternally outside the ML library | [
{
"docid": "81ee35ace130370d9a8c7b8136b304d9",
"score": "0.6481593",
"text": "def run_style_transfer(\n cnn,\n normalization_mean,\n normalization_std,\n content_img,\n style_img,\n input_img,\n num_steps=300,\n style_weight=1000000,\n content_weight=1,\n):\n print(\"Buildi... | [
{
"docid": "df380ec8af79ccc63b97d940fb0ad375",
"score": "0.6913298",
"text": "def transfer_learning():\n\n # 1. Load data, Cifar10 and Preprocess for adapting Inception v3 network.\n dataset = load_cifar10()\n preprocessor = PreprocessAdaptingCifer10toInception3(dataset)\n X_train, Y_train, ... |
1b371472a7aff42beae171d2e3cbbb0c | This function echoes back the value of a specific request header while allowing caching for 10 hours. | [
{
"docid": "fe2cbdc397c1090a3d4aede89ec99842",
"score": "0.67426294",
"text": "def EchoHeaderCache(self):\n\n return self.EchoHeaderHelper(\"/echoheadercache\")",
"title": ""
}
] | [
{
"docid": "64206d09ecc5c7bc3a93a504f86ea09d",
"score": "0.65378183",
"text": "def do_header(self, args):\n mem_key = 'header_%s_%d' % (args['base_url'], self.get_year(args))\n header = memcache.get(mem_key)\n #header = None\t# Uncomment when flushing the cached header in production... |
3377567ab150a2ea6aeb02aa63b35598 | M and M transpose required for differential equations | [
{
"docid": "f4673750c81c3c05b1baa6b871c5f84c",
"score": "0.57042927",
"text": "def get_mmt():\r\n # M^T\r\n MT = np.array([[-1, 0, 0, 0, 0, 0, 0, 0, 0],\r\n [1, -1, 0, 0, 0, 0, 0, 0, 0],\r\n [0, 1, -1, 0, 0, 0, 0, 0, 0],\r\n [0, 0, 1, -1, 0, 0,... | [
{
"docid": "df1cadd996013506d20c59066f32db71",
"score": "0.6352941",
"text": "def T(self):\n # TODO - your code here\n trans=[]\n for i in range(len(self.h)):\n t_column=[]\n for j in range(len(self.w)):\n t_column.append(self.g[self.w][self.h]) ... |
d518090bca6278e72aa53cd63f19b766 | Create a ValidResponse from a Swagger response. | [
{
"docid": "345f4bc9c084135b0af20007a5e2cdf4",
"score": "0.6405929",
"text": "def _create_valid_response(self, response, endpoint, kwargs):\n body, status, headers = response\n continuation_token = getattr(body, \"continuation_token\", None)\n total_item_count = getattr(body, \"tota... | [
{
"docid": "63e49095eac1db7215e16ecc817e8111",
"score": "0.68187684",
"text": "def validate_response(cls, response):\n if not hasattr(response, \"status_code\"):\n raise Exception(\"invalid response object\")\n if response.status_code != 200:\n raise Exception(\"elast... |
1d3faf96d7d8f66f85179b2a997ee865 | Test function that tests the in_board() method contained in the Piece class. | [
{
"docid": "5bee908b7695ff282fb2787956299fd3",
"score": "0.8882027",
"text": "def test_in_board():\r\n\tfor row in range(8):\r\n\t\tfor col in range(8):\r\n\t\t\tassert(test_piece.in_board(row,col))\r\n\r\n\tassert(not test_piece.in_board(-1,0))\r\n\tassert(not test_piece.in_board(0,8))\r\n\tassert(not ... | [
{
"docid": "3553e6d93b4ab464d3951cf0d89e8aff",
"score": "0.71696293",
"text": "def test_drawTileOnBoard(self):\n # this is tested graphically by running the program\n pass",
"title": ""
},
{
"docid": "3730d6821a7d30d92da32d4d3e47ccd2",
"score": "0.71515757",
"text": "de... |
0cb9a360ce0763c9560aa38928ce57ff | Setter method for graceful_restart, mapped from YANG variable /network_instances/network_instance/protocols/protocol/bgp/peer_groups/peer_group/graceful_restart (container) | [
{
"docid": "d41fb83bbffb1d446242221228531463",
"score": "0.79115885",
"text": "def _set_graceful_restart(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(\n v,\n base=graceful_restart.graceful... | [
{
"docid": "0c0ccfe49dbdc100b6e3f346acf03321",
"score": "0.6150033",
"text": "def _get_graceful_restart(self):\n return self.__graceful_restart",
"title": ""
},
{
"docid": "0c0ccfe49dbdc100b6e3f346acf03321",
"score": "0.6150033",
"text": "def _get_graceful_restart(self):\n ... |
bcb38aa07bf9a44c3c55e2ac81c0bfd0 | Return the corresponding FileMap or None. >>> find_file('notestation.js') | [
{
"docid": "e6203a3be7f9492a7444e2b043ae72c6",
"score": "0.7818548",
"text": "def find_file(filename):\n return maps.find_file(filename)",
"title": ""
}
] | [
{
"docid": "ca17e789130dd28b17238d5d3c6d56a8",
"score": "0.61601883",
"text": "def get_file(self, file_name):\n\n exploded_name = file_name[len(self.name) + 1:].split('/')\n desired_name = exploded_name[-1]\n node = self.get_node('/'.join(exploded_name[:-1]))\n\n for file_inf... |
bb950707955b1aeaf7acce83a20dd34c | /sell[POST] The name of the ticket is no longer than 60 characters negative error case | [
{
"docid": "d46efe971ba70e0703a6d9728ee2fe11",
"score": "0.6784815",
"text": "def test_nameLength_Negative(self): # Test case R4.2.1\n self.refresh()\n\n self.click(\"#btn-add-ticket\")\n self.type(\"#sell-ticket-name\", \"validName2\")\n self.type(\"#sell-ticket-quantity\", ... | [
{
"docid": "8b5a75dcc36d60828d29d1a6bd276501",
"score": "0.6867265",
"text": "def test_name_length(self, *_):\n # open logout page to invalid any logged-in sessions that may exist, then open login page\n self.open(base_url + '/logout')\n self.open(base_url + '/')\n\n # test t... |
2d7ff2588e7ca463c1be4f6e1bd3b1fe | Finds the positionon the last header item line This is then used by numpy.loadtxt thats wrides items to np.array | [
{
"docid": "e2d32de4ca8dfb0bbcaa3229f4e3fb4e",
"score": "0.54721785",
"text": "def FindHeaderLength():\n\n lookup = '#------------------------------'\n \n with open(filename) as myFile:\n for FoundPosition, line in enumerate(myFile, 1):\n if lookup in line:\n #p... | [
{
"docid": "e87e35ab286ace8506354f7b1364236e",
"score": "0.59801245",
"text": "def _last_index(self):\n return len(self.items) - 1",
"title": ""
},
{
"docid": "c157ca3726dea1722007c42d103fa987",
"score": "0.5859204",
"text": "def get_last_position(self):\n if self.posit... |
0346c74110e9004de352aeb59e4097b5 | Expands a verse series (list) into its constituent verses as a string. | [
{
"docid": "1a5103cc8bfc764b101d1d4a4f27cf5a",
"score": "0.6429531",
"text": "def expandSeries(vSeries):\n\n vSeries = re.findall(r'\\d+', vSeries)\n osisID = list()\n for n in vSeries:\n osisID.append('$BOOK$.$CHAP$.'+str(n))\n return ' '.join(... | [
{
"docid": "c6d26767684e484357f2c2bc144f8929",
"score": "0.5957183",
"text": "def AlgoSeqListToString(AlgoSequense):\n assert isinstance(AlgoSequense, list)\n AlgoSequenseString = str(AlgoSequense)\n return AlgoSequenseString",
"title": ""
},
{
"docid": "2feec3ef47c2eaa91f8c8c34aa19... |
a708e551738fc9930e0279dd30a37901 | Create a color array to store colors assigned to all veritces. Vertex number is used as index in this array. The value '1' of colorArr[i] is used to indicate that no color is assigned to vertex 'i'. The value 1 is used to indicate first color is assigned and value 0 indicates second color is assigned. | [
{
"docid": "3ccbea5164b8550be63accdd1496e097",
"score": "0.0",
"text": "def isBipartite(self, src):\n colorArr = [-1] * self.V \n \n # Assign first color to source \n colorArr[src] = 1\n \n # Create a queue of vertex numbers and enqueue source vertex for BFS traversal \n ... | [
{
"docid": "5b17d83ac7fc29dbeede94ed3d76d508",
"score": "0.6323064",
"text": "def gen_color(self, num):\n PHI = 0.618033988749895\n s = 0.5\n v = 0.95\n h = random()\n colorList = []\n for i in range(num):\n h += PHI\n h %= 1\n r... |
6ec7aaef55fd531188e47fe3a4679528 | Deletes the current index freeing all the momory it uses. The memory used by the dataset that was indexed is not freed. | [
{
"docid": "594435b2221d086ea0a32f2d8aa25714",
"score": "0.70329934",
"text": "def delete_index(self, **kwargs):\n\n self.__flann_parameters.update(kwargs)\n\n if self.__curindex is not None:\n flann.free_index[self.__curindex_type](\n self.__curindex, pointer(sel... | [
{
"docid": "c217e92ae6b4a1aa241c525ae8f5f88e",
"score": "0.7036752",
"text": "def clear(self):\n try:\n # Remove the cached reference to the lexicon\n # So that it is refreshed\n del self._v_lexicon\n except (AttributeError, KeyError):\n pass\n ... |
76fd7374b4e7794e72d96aa4126367ed | Print some info about the context in which the simulatotion was performed. | [
{
"docid": "dba651296beb40e2f54f02e90779df6f",
"score": "0.7098086",
"text": "def _print_sim_context(dataset):\n print \"Command line was:\", \" \".join(sys.argv)\n print \"Process ID is: %d\" % os.getpid()\n sys.stdout.write(\"hg identify: \")\n sys.stdout.flush()\n os.system(\"hg identi... | [
{
"docid": "cfe10f2ec84b47131d1720997fda9fbc",
"score": "0.6998889",
"text": "def info(ctx):",
"title": ""
},
{
"docid": "c42fd18df64cd6260a7a745d4fcc7e92",
"score": "0.6983037",
"text": "def info(self):\n print(\"Grrrr..I am an Orc Wolf Rider. Don't mess with me.\")",
"ti... |
c8aa07b18df4527da3fcd2593342a0d8 | Frequency Distirbution of a Bscan | [
{
"docid": "7546a4cae37963e590f3a0c0366edefd",
"score": "0.6529375",
"text": "def frequency_bscan(outputdata):\n spects = []\n freqs = []\n times = []\n\n for i in range(len(outputdata[0, :])):\n f, t, zxx = stft(outputdata, i)\n freqs.append(f)\n times.append(t)\n ... | [
{
"docid": "2ae7a55bd649f9f37b62c65dcb651aaa",
"score": "0.62273216",
"text": "def likelihood_per_bin(self):",
"title": ""
},
{
"docid": "7e14e65b1e03d3dcc85f1201cb61c9aa",
"score": "0.61921173",
"text": "def GetFrequency():\n return getfreq(DZERO)",
"title": ""
},
{
"... |
69c370ade6a042c33028e6acb2242268 | Update display. Dispatches display update to correct driver. | [
{
"docid": "1585eef1b9485bd999283248aa2604dc",
"score": "0.0",
"text": "def _update(self, buf):\n self.setup()\n\n self._send_command(UC8159_DTM1, buf)\n\n self._send_command(UC8159_PON)\n self._busy_wait(0.2)\n\n self._send_command(UC8159_DRF)\n self._busy_wait... | [
{
"docid": "1c3be0adf4f55a8bc659ad03076a7f1c",
"score": "0.72705704",
"text": "def update_display(self):\r\n self.update_slaves()",
"title": ""
},
{
"docid": "8d898d2b2abd521e3bc423b022389bb0",
"score": "0.7244171",
"text": "def update(self) -> None:\n self.command(_IL9... |
0aeaef8fe75cbb358e4b32b2170b64a2 | Semantic segmentation PointNet, input is BxNx3, output Bxnum_class | [
{
"docid": "cbb63303791e7580a7f37dc2f2d71cd8",
"score": "0.53639525",
"text": "def get_model_decoder(point_cloud, is_training, num_class, bn_decay=None):\n if isinstance(num_class, int):\n num_class = [num_class]\n batch_size = point_cloud.get_shape()[0].value\n num_point = point_cloud.g... | [
{
"docid": "908836ac918506243446e197e69c195a",
"score": "0.63908523",
"text": "def class_shape():\n\n return 0",
"title": ""
},
{
"docid": "41ddf838380e3c5f2fd6629f62ebb942",
"score": "0.6256854",
"text": "def class_predictor(num_anchors, num_classes):\n return nn.Conv2D(nu... |
af72df6c64e194192b02c31f2ed2cfb7 | Gather beams from nested structure of tensors. Each tensor in nested represents a batch of beams, where beam refers to a single search state (beam search involves searching through multiple states in parallel). This function is used to gather the top beams, specified by beam_indices, from the nested tensors. | [
{
"docid": "316a8ad574fe33266533529dee462087",
"score": "0.7261795",
"text": "def _gather_beams(nested, beam_indices, batch_size, new_beam_size):\n # Computes the i'th coodinate that contains the batch index for gather_nd.\n # Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..].\n batch_pos = ... | [
{
"docid": "7390362172fcd093fff92c0241d3a6f4",
"score": "0.7263369",
"text": "def _gather_beams(nested, beam_indices, batch_size, new_beam_size):\n # Computes the i'th coodinate that contains the batch index for gather_nd.\n # Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..].\n batch_pos = ... |
87f79b856add3fa9ef4ec525bb94de39 | we define our inputs that can be accessed by all methods | [
{
"docid": "33945de30f948fa075e8311fb8c5b628",
"score": "0.0",
"text": "def setUp(self):\n super(ModelTest, self).setUp()\n self.vggspa = VGGSPA(CFG)",
"title": ""
}
] | [
{
"docid": "16337ee04ec23171481e5c0243e52047",
"score": "0.8017303",
"text": "def inputs(self, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "cf7f1df4a844595c91f0b7ad6517f4dd",
"score": "0.7612797",
"text": "def input(self):\n pass",
"title": ""
},
{
"... |
9aabf93e79bc7c779bb0dbd4f3a412cd | Filter trace markers to remove certain file names. | [
{
"docid": "1755aab8c6fccfed9412ab99ad77ec6d",
"score": "0.6129329",
"text": "def filterTrace(mlist):\n assert (type(mlist) == list)\n if len(mlist) == 0:\n return mlist\n mlist = mlist[-1] #The last stack trace will be a super set.\n mlist = e... | [
{
"docid": "3e8dfbd9274e58d202beea3fab40a79d",
"score": "0.6482564",
"text": "def remove_trace(lines):\n f = None\n for sloc in lines or []:\n if sloc.file() != f:\n f = sloc.file()\n buf = GPS.EditorBuffer.get(f, open=False)\n if buf:\n overl... |
c376375a2c21359275a19f13621f818d | Returns context_lines before and after lineno from file. Returns (pre_context_lineno, pre_context, context_line, post_context). | [
{
"docid": "2ca0c1ae1dc2e56b361992622cfa969b",
"score": "0.7646606",
"text": "def _get_lines_from_file(filename, lineno, context_lines):\r\n try:\r\n source = open(filename).readlines()\r\n lower_bound = max(0, lineno - context_lines)\r\n upper_bound = lineno + co... | [
{
"docid": "280ff2b5366a81c7ef05f25b26ee29e8",
"score": "0.6324767",
"text": "def context(self):\n return (self.filename, self.lineno)",
"title": ""
},
{
"docid": "26e67b102ef95eeac957245cc82510ad",
"score": "0.5848709",
"text": "def get_lineno_uncommented(self):\n\n li... |
17c2814189a22ebe77b6326c60d6127d | Process received entity lists | [
{
"docid": "7a0177407fac53ce1a76aa3fd88c6508",
"score": "0.70364696",
"text": "def _recv_entity_list(self, msg):\n OpEnum = pyimc.EntityList.OperationEnum\n if msg.op == OpEnum.REPORT:\n try:\n node = self.resolve_node_id(msg)\n node.update_entity_l... | [
{
"docid": "366fa6d33ca97c3c3551d353c85998dc",
"score": "0.64658636",
"text": "def process(self):\r\n for entity_list in self.partition_dict.values():\r\n self._process_batch(entity_list)",
"title": ""
},
{
"docid": "364ec06d9c1fec9a79652d74410ccf73",
"score": "0.617647... |
e6735eddfe47a863f66df13c59671fc4 | the data_move process when UB can put not in N1 N0 X0 data | [
{
"docid": "1bfeccd255add2006cf8e15f1f56f394",
"score": "0.5550848",
"text": "def data_move_case_two(self, tik_instance, ub_ori, ub_trans, is_last,\n num_x0, loop_time, loop_n, loop_len):\n ori_hwc = self.src_shape[0] * self.src_shape[1] * C0\n src_gm_index = num_... | [
{
"docid": "8943af20b4bd092cad18df7199e9c53e",
"score": "0.57849103",
"text": "def data_move_case_one(self, tik_instance, ub_ori, ub_trans, core_loop,\n sum_core, align_loop, remainder, num_data_one_loop):\n is_x_padding = tik_instance.Scalar(\"uint64\", init_value=0)\n ... |
24f8e87a6db55525649cd6fa190c01e5 | The motor's electrical pitch. float with units of mm It gives the spacing between two magnets (full magnetic cycle) on a linear motor. Velocities and accelerations are in units of pitches/s and pitches/s^2, so it is important. Setting it sends an immediate command to the drive to change the electrical pitch. Notes This... | [
{
"docid": "30a8e7ea7fcb89be45fb915922f8cfcf",
"score": "0.76393646",
"text": "def electrical_pitch(self):\n return self._get_parameter('DMEPIT', float)",
"title": ""
}
] | [
{
"docid": "16e5e5c63a60e47e772a3bcb4f782b13",
"score": "0.60981715",
"text": "def pitch(self, direction):\n if self.heli.mainRotor.start:\n self.heli.speed += 0.0001 * direction",
"title": ""
},
{
"docid": "113eb2870ef38b970a5d1a11133d07b1",
"score": "0.60102075",
... |
4b16dbd47cf65225b7e0e3738dbd884c | Dataframe from namedtuple list | [
{
"docid": "f6cbc04c3997192a6b52987dfcf74b00",
"score": "0.74547327",
"text": "def df_from_nt(nt_list):\n cols = [x for x in nt_list[0]._fields] # List of Name Tuple element names\n data = {column: [] for column in cols} # Dict with list per tuple element\n\n for entry in nt_list:\n fo... | [
{
"docid": "304c1ff54cf967a3422eb6f90553fdcf",
"score": "0.73435163",
"text": "def create_dataframe(tuple_data):\n return pd.DataFrame.from_records(tuple_data[1:], columns=tuple_data[0])",
"title": ""
},
{
"docid": "4e01975757c2cfb2ab859e311eb12ea1",
"score": "0.7110491",
"text": ... |
d0c37e6d335b41ee62f31ad93736719e | Function that performs the instantiation of the class "Provincia" to create the provinces objects. Invokes the function "listaProvincia" and scrolls through the list , so that each item in the list will be the name of the object, and declares a counter initialised at 1 and incremented by 1, to assign an identifier to e... | [
{
"docid": "95aa1ff8dbb90f0bff1028377241de02",
"score": "0.8064755",
"text": "def creaObjetoProvincia():\r\n lista = []\r\n Provincias = listaProvincia()\r\n contador=1\r\n for provincia in Provincias:\r\n p = Provincia()\r\n p.set_nombre(provincia)\r\n p.set_identificad... | [
{
"docid": "a46b8f5fb62470f5abfde9434f0b8198",
"score": "0.6254899",
"text": "def creaObjetoAccesorio():\r\n lista = []\r\n accesorios = listaAccesorios()\r\n contador=1\r\n for accesorio in accesorios:\r\n a = Accesorio()\r\n a.set_nombre(accesorio)\r\n a.set_identifica... |
e3b9dd5a51ecf4a59fdeeb1a4825ebc7 | Creates a new guild user chunk event from the given data. | [
{
"docid": "f6090d0b7e97fddf6be5d56bddf6ec60",
"score": "0.7571761",
"text": "def from_data(cls, data):\n self = object.__new__(GuildUserChunkEvent)\n self.chunk_index = parse_chunk_index(data)\n self.chunk_count = parse_chunk_count(data)\n self.guild_id = guild_id = parse_gu... | [
{
"docid": "71990f7b65b7d22b952221aa09c8d973",
"score": "0.61346686",
"text": "def create_object(self, data, **kwargs):\n return UserEvents(**data)",
"title": ""
},
{
"docid": "748a5642eed20d597bb6d9fc46141961",
"score": "0.56513685",
"text": "async def on_chunk(self, data):\n... |
a9350568ba18537fb51b7eff12dea6a2 | Check global import / binary availability | [
{
"docid": "58c494fdf4330e2a836e983f846d45f5",
"score": "0.6543548",
"text": "def checkup():\n\n if gtk is None:\n error('Unable to import python GTK module, aborting', True)\n\n if YubiController is None:\n error('Unable to import python yubioath module. Is it installed?',\n ... | [
{
"docid": "fc7816f6d62ff5408f223c4afbf68418",
"score": "0.70505756",
"text": "def check_module_available(env, module):\n return execute_python_expr(env, 'import %s' % module) == 0",
"title": ""
},
{
"docid": "536bf5a641fedb2a53821482d3d533fa",
"score": "0.6998137",
"text": "def b... |
5f3d5bdcaeb0aaac9d82264861ad8b5a | Test if event was created for string description, functionbasedview with args, without logged in user. | [
{
"docid": "86655b6f6b3ce675cbaf4392c111794e",
"score": "0.6201935",
"text": "def test_audit_trail_string_not_logged_function_view_with_argument(self):\n @audit_trail(description=\"Test\")\n def with_url_argument_view(request, pk):\n return HttpResponse()\n\n events_befor... | [
{
"docid": "fe192525a56aace703867ebd265008be",
"score": "0.6047266",
"text": "def create_event(request):\n pass",
"title": ""
},
{
"docid": "1753b57ca8e1f1fa575d3d3cc8b7f15f",
"score": "0.5944244",
"text": "def test_audit_trail_lambda_logged_user_function_view(self):\n user... |
6a670641b117aeb2164a77f6dcbf25fe | Make variable `x` arc consistent with variable `y`. To do so, remove values from `self.domains[x]` for which there is no possible corresponding value for `y` in `self.domains[y]`. Return True if a revision was made to the domain of `x`; return False if no revision was made. | [
{
"docid": "2e068df78422e1b02b5208470fad0021",
"score": "0.6188503",
"text": "def revise(self, x, y):\n revised=False\n overl=self.crossword.overlaps[x,y]\n \n #Based on the function shown in the lecture\n #First find the variables with overlap, and identify the overla... | [
{
"docid": "e473e7185b5271ac809fdadadc08f7b5",
"score": "0.6721664",
"text": "def revise(self, x, y):\n flag = False\n overlap = self.crossword.overlaps[x,y]\n # If there is no point of overlap then the variables are arc consistent and there is no point of conflict\n if overl... |
b59495343a0e1d5439d12b8608c8bed0 | Test multiple executions of the same task. | [
{
"docid": "261399b87f08db1e381ff5359778cf73",
"score": "0.5937325",
"text": "def test_double_execution(nworkers: int) -> None:\n # This test will fail if a job is re-executed multiple times.\n # external\n from rq.job import get_current_job\n\n def track_runs(inp: bytes) -> bytes:\n ... | [
{
"docid": "a11a668122111160bc332671c3468d4d",
"score": "0.70503175",
"text": "def testTask(self):\n pass",
"title": ""
},
{
"docid": "adf6ae40e4acbc55d4faf53185e9595a",
"score": "0.6859597",
"text": "def test_task_based(self):\n\n # Using the dummy scheduler to test th... |
cad3fd9bd74c6d2d35193eaf55471060 | Take a quantity string and return a decimal. Handles one hundred, two hundred, three hundred twenty five, 1, 1 1/4, 1 and 1/4, 1.25, .25. Negative values should have the negative sign first, such as 1/4 or 1 1/4 | [
{
"docid": "64125d04a6795dfd9024eac25f9f9b15",
"score": "0.765134",
"text": "def quantity_to_decimal(quantity_string: str) -> Decimal:\n\n # get actual fraction-like strings to be N/N with no spaces\n quantity_string = quantity_string.strip()\n quantity_string = re.sub(r\"\\b(\\d+)\\s+/\\s+(\\d... | [
{
"docid": "e51deab5842154b50e00615d91f9b2f8",
"score": "0.68759644",
"text": "def parse_moneyline(string):\n if string.lower() in [\"ev\", \"even\", \"100\", \"+100\"]:\n return 1.0\n elif re.match(\"[+-][0-9]+?\", string):\n line = float(string[1:])\n if string[0] == '+':\n ... |
8e4a08dc063469d103cfda26e7cbe95e | ``dockerpty.pty`` PseudoTerminal '_hijack_tty' can handle some SSL errors | [
{
"docid": "1e1aa5f27bcb3069248f14c6e9ce9d48",
"score": "0.6853129",
"text": "def test_hijack_tty_ok_ssl_error(self, fake_Terminal, fake_get_stdin_pump, fake_select):\n fake_select.return_value = ([], [])\n fake_client = MagicMock()\n fake_run_operation = MagicMock()\n fake_p... | [
{
"docid": "56c965de0a73264b9a4f9dfe41c417c6",
"score": "0.67390096",
"text": "def test_hijack_tty_bad_ssl_error(self, fake_Terminal, fake_get_stdin_pump, fake_select):\n fake_select.return_value = ([], [])\n fake_client = MagicMock()\n fake_run_operation = MagicMock()\n fake... |
b2f51586cf2125f2c5b5fa60597b8535 | The purpose is to generate a pair of XY Maps for the specified alpha, beta and zoom parameters, the result XY Maps can be used later to remap the original fisheye image to the target angle image. | [
{
"docid": "040f366585ccd84d974df3c080865f19",
"score": "0.6853466",
"text": "def create_anypoint_maps(self, alpha, beta, zoom, mode=1):\n if mode == 1:\n if beta < 0:\n beta = beta + 360\n if alpha < -90 or alpha > 90 or beta < 0 or beta > 360:\n ... | [
{
"docid": "9c560f76911f7c9c8175fb5ea2835633",
"score": "0.6331403",
"text": "def create_panorama_maps(self, alpha_min, alpha_max):\n self.__moildev.Panorama(self.__map_x, self.__map_y, alpha_min, alpha_max)\n return self.__map_x, self.__map_y",
"title": ""
},
{
"docid": "c1e14... |
c6672f6c6a0371d7e88c2ae6712f1f81 | Chop a string up into k mers of given length | [
{
"docid": "d1541a8888b8786b200a4ea22c2a29a8",
"score": "0.619577",
"text": "def chop(st, k):\n for i in xrange(0, len(st)-(k-1)):\n yield (st[i:i+k], st[i:i+k-1], st[i+1:i+k])",
"title": ""
}
] | [
{
"docid": "0db958df6e725ab6efe6f7f50077bcc6",
"score": "0.6994418",
"text": "def kmer_list(s, k):\n kmer = []\n for i in range(0,len(s)):\n string = s[i:i+k]\n if len(string) == k:\n kmer.append(string)\n return kmer",
"title": ""
},
{
"docid": "6337a974ebf... |
a5ecab4932699ebc6db733bd522b39b9 | Get a bit from a shuffled key. | [
{
"docid": "6cf8c0d418c0d56f18339943a3d695b8",
"score": "0.8303021",
"text": "def get_bit(self, key, shuffle_index):\n key_index = self._shuffle_index_to_key_index[shuffle_index]\n return key.get_bit(key_index)",
"title": ""
}
] | [
{
"docid": "9ef8323bfc76dbd41921419d3627f652",
"score": "0.73012364",
"text": "def getbit(self, key, offset):\n return self.execute_command(\"GETBIT\", key, offset)",
"title": ""
},
{
"docid": "24c9ca61d2375971833a57867f46c307",
"score": "0.70907825",
"text": "def getbit(self,... |
5fc98f12a4c8288c371b6daf030bc732 | The fasta file aligned with muscle. | [
{
"docid": "151b68736fbed15de97786492bab40f7",
"score": "0.6559242",
"text": "def alignment(self):\n alignment = AlignedFASTA(self.p.muscle)\n if not alignment:\n assert self.fasta\n print \"-> Making alignment.\"\n self.fasta.align(alignment)\n retu... | [
{
"docid": "3e3ea8cbd1c4ed441a1cf89fa9255cf2",
"score": "0.7138664",
"text": "def __muscle_align(self, in_file):\n\n #Align sequences with MUSCLE\n muscle_exe = f\"{self.files_path}/muscle.exe\"\n out_file = f'{in_file[:-4]}_aligned.fas'\n # Creating the command line\n ... |
872391643242cf132c4a2350a51432e1 | Construct a classifier VMR optinally supplying entries. | [
{
"docid": "4545568340713a83348a17e1eabfb3bc",
"score": "0.52344483",
"text": "def __init__(self, table, vmr=None):\n\n self._table = table\n self._vmr = []\n self._default_entry = None\n\n if vmr is None:\n return\n for entry in vmr:\n if self._t... | [
{
"docid": "be647706d72a44f8a4f74915f18282ea",
"score": "0.6275436",
"text": "def __init__(self, labels=3, tnorm=\"product\", rw=\"pcf\", frm=\"wr\"):\n print(\"Initializing classifier:\\n\")\n \"\"\"\n self.labels = labels\n self.tnorm = tnorm\n self.rw = rw\n ... |
a5fe3cfd2beb9722ed020bfce764e3b9 | Return the vertex with highest degree. | [
{
"docid": "33b57282217aaf672ceb206014eb9eb3",
"score": "0.8553951",
"text": "def highest_degreevertex(self):\r\n hd = -1\r\n hdv = None\r\n for v in self._vertices:\r\n if self.degree(v) > hd:\r\n hd = self.degree(v)\r\n hdv = v\r\n r... | [
{
"docid": "34efc8ddd04697dadcadf909cb657b27",
"score": "0.84826887",
"text": "def highest_inDegreevertex(self):\r\n hd = -1\r\n hdv = None\r\n for v in self._inedges: #_vertices would also work for the loop\r\n if self.inDegree(v) > hd:\r\n hd = self.inDe... |
af8c5d32807710392910806686573551 | The epoch timestamp for the overlapping checkin | [
{
"docid": "ae327feb09b28b35359cf8f77e16aad5",
"score": "0.0",
"text": "def getCreatedAt(self):\n return self.base.get(\"createdAt\", [])",
"title": ""
}
] | [
{
"docid": "c05d1339b961ac05e5fc29130bf1b798",
"score": "0.6936067",
"text": "def epoch_time(self):\n return time.time() - self._begin_time",
"title": ""
},
{
"docid": "98133f60f02de6e5ae9059f9cd822443",
"score": "0.6546636",
"text": "def _get_epoch(self):\n\n return se... |
0b02cf4b3d10cf04f8e63d0eb009280f | Check int limits information. Get information from ``sys`` library. | [
{
"docid": "f7114b15bf5c1e211774f1b174e16720",
"score": "0.52950776",
"text": "def check_int():\n info = {'INTEGER': {}}\n\n if sys.version_info >= (3, 1):\n info['INTEGER'].update(\n {'Bits per Digit': sys.int_info.bits_per_digit,\n 'Size of Digit': sys.int_info.size... | [
{
"docid": "79c9cdd92a1ce3082a80fe3986ddf841",
"score": "0.71252376",
"text": "def describe_limits():\n pass",
"title": ""
},
{
"docid": "3583ad4d767da787be8af56ee8c44547",
"score": "0.7047186",
"text": "def _CheckLimit(self, limit):\n return _CheckInteger(limit, 'limit', upper... |
ff69d8a2c046d534a92753ba6a9968c7 | Display a slice of a 3D pseudocolor plot of radial velocity generated by VisIt for this timestep. If the plot image isn't available, then generate it with VisIt on lens. | [
{
"docid": "717c9b612a29688bddce04ff40c87db3",
"score": "0.65592563",
"text": "def plotRVSlice(self, step, poll=False):\n self._out.plotRVSlice(step, poll, self._initmod._mycut.an_cut, self._initmod._mycut.sp_cen_den, self._initmod._mycut.sp_st_fac)",
"title": ""
}
] | [
{
"docid": "483ef13168e0ea0e989781594bddbb38",
"score": "0.6781416",
"text": "def plotRV3D(self, step, min_rv, poll, anelastic_cutoff, sp_cen_den, sp_st_fac):\n for s in self._slices:\n if s.timestamp[0] == step:\n s.plotRV3D(min_rv, poll, anelastic_cutoff, sp_cen_den, sp_st_fac)... |
a7a86ceb47f4ea496fef90aeea676d9f | QR code detection return a list of dictionaries, which contain 'hypothesis_bounding_rect' as bounding box of qrcode hypothesis, 'finder_patterns' as finder pattern contours, 'is_containing_qr_code' as the indicator of true, false, or probable qrcodes | [
{
"docid": "156f2f7c905eee0f6a02ebc2d6e73322",
"score": "0.7080438",
"text": "def detect_qr_code(img, is_pop_display):\r\n\r\n img_blur, img_blur_gray, img_blur_binarize = pre_process_img(img)\r\n img_integral = integrate_img(img_blur, img_blur_binarize)\r\n block_mask, block_mask_raw = get_blo... | [
{
"docid": "4f87db3d9222b554f769a239c45222ab",
"score": "0.76213753",
"text": "def get_finder_patterns_in_hypotheses(img, bounding_rects_hypotheses, is_pop_display):\r\n qr_code_hypothesis_detections = []\r\n\r\n \"\"\" Process the hypotheses ONE-BY-ONE \"\"\"\r\n for br in bounding_rects_hypot... |
b6aa43fb28b96ebce5ab8a1a7121c6b9 | The contacts are sorted here and displayed in the gui window for the user to view | [
{
"docid": "dfbf9fc8ecc04218c96886edcbafd704",
"score": "0.6409214",
"text": "def selF():\n\tcontacts.sort()\n\tselect.delete(0,END)\n\tfor fname,lname,phone,address,email in contacts :\n\t\tselect.insert(END,fname+\",\"+lname)",
"title": ""
}
] | [
{
"docid": "49341738e82647fa1972faf8cdbee629",
"score": "0.70360625",
"text": "def contact_list_update(self):\n cntcts = self.client_db.get_contacts()\n self.contacts_model = QStandardItemModel()\n for cnt in sorted(cntcts):\n itm = QStandardItem(cnt)\n itm.set... |
c585ae41ab51e2548c2bb5e58a18990c | Used as GIT_AUTHOR_NAME and GIT_COMMITTER_NAME environment variables | [
{
"docid": "3237c40882c51a589d7559a266716f81",
"score": "0.0",
"text": "def generate_user_name(spawner):\n return spawner.user.name",
"title": ""
}
] | [
{
"docid": "91535ca3321b8a6da14aa75ded759c7d",
"score": "0.7018517",
"text": "def add_author(context_dict: dict) -> dict:\n result = sp.run(\n [\"git\", \"config\", \"user.name\"],\n text=True,\n capture_output=True,\n check=False,\n )\n author = result.stdout.strip(... |
8f3291a84e6c0e3fa8369c4ab540a6b4 | Configures the Naaya Site for the Naaya Survey Product | [
{
"docid": "c6813efd2ff1d565592ea13407db75ca",
"score": "0.6621894",
"text": "def manage_configureSite(self, REQUEST=None):\n self.manage_configureCatalog()\n self.manage_configureEmailNotifications()\n self.manage_configureSecurity()\n #self.manage_updatePortlets() # disable... | [
{
"docid": "3eba6b65f16ea0a4a4737e22011f3594",
"score": "0.58864015",
"text": "def test_com_adobe_cq_social_site_impl_site_configurator_impl(self):\n pass",
"title": ""
},
{
"docid": "d8a10b37dc069aa4d5a8f2345c084fdd",
"score": "0.5704494",
"text": "def setup_blog(self):\n ... |
4a44748cebac2d3abfbd07691a73eca0 | r""" Finds a linear relation between the given list of OMSs. If they are LI, returns a list of all 0's. | [
{
"docid": "bd0dbbe42ede2a778913aa43010ca117",
"score": "0.6331311",
"text": "def linear_relation(self, List):\n for Phi in List:\n assert Phi.valuation() >= 0, \"Symbols must be integral\"\n \n R = self.base()\n ## NEED A COMMAND FOR RELATIVE PRECISION OF OMS\n ... | [
{
"docid": "703cf4c945aa3009e5d58dd9816096f2",
"score": "0.59615",
"text": "def eltL0(strEle,nodCoo):\n mesDat = []\n for elt in strEle:\n nodI = np.int(elt[0])\n nodJ = np.int(elt[1])\n dX = nodCoo[nodJ][0]-nodCoo[nodI][0]\n dY = nodCoo[nodJ][1]-nodCoo[nodI][1]\n ... |
36ca1ab1014ba94a94116668d7f0dc5e | Checks if it's rush hour or not based on the time given | [
{
"docid": "f5df4086e10a9c87b01c0088cf704504",
"score": "0.74231344",
"text": "def is_rush_hour(hour, minute):\n rush_hour = []\n\n if (5 <= hour <= 9) or (16 <= hour <= 18):\n if (hour == 5 and 45 <= minute < 60) or (5 < hour < 9):\n rush_hour = [1]\n ... | [
{
"docid": "f33219fe39e54f981037f15652125072",
"score": "0.7023107",
"text": "def isPossibleHour(self, hour):\n if hour > 23 and self.isPossibleHour(0):\n hour = int(hour)\n if not self.overnight:\n return self.stTime.hour <= hour <= self.endTime.hour\n else:\n... |
e2cf7445300fa4d7adccac4df0887b30 | Returns a string containing n random words from wordlist | [
{
"docid": "22c166d3d90d1a348cc5f226f779dcc0",
"score": "0.8835607",
"text": "def random_string(wordlist, n):\n return \" \".join([random_word(wordlist) for _ in range(n)])",
"title": ""
}
] | [
{
"docid": "17466e947506faa4e0135d8c2c04f89a",
"score": "0.7959614",
"text": "def get_random_words(chars, start, stop, n):\n results = []\n for i in range(int(n)):\n len = random.randrange(int(start), int(stop))\n results.append(''.join(random.choice(chars) for _ in range(len)))\n ... |
954c4409a1ba7017f3b740af5b2d9f48 | Accepts a weekday and tests if today is that weekday. | [
{
"docid": "2e3dfcebad664d7447ebdf26e1ef2dce",
"score": "0.7773084",
"text": "def test_for_day(target_day):\n import time\n # Get the date object of today's date:\n todays_date = time.localtime().tm_wday\n # Form a dictionary of the days of the week, starting on Monday\n # since this is t... | [
{
"docid": "9f211aa22ce1299f4f4f6bb340a47ae8",
"score": "0.7757462",
"text": "def is_dayofweek(day, today):\n if isinstance(today, datetime):\n int_day = int(day)\n if today.weekday() == int_day - 1:\n return True\n return False\n else:\n raise Exception(\"{}... |
3562d3dd13e277d752083a277071d3cb | Starts reading the file in file_handle, populating the reactions and atomic_relaxation members. | [
{
"docid": "5bad6f63fa863c4b72bcec2806f2bf1a",
"score": "0.7447146",
"text": "def __init__(self, file_handle):\n\t\tself._file = file_handle\n\t\tself.reactions = {}\n\t\tself.atomic_relaxation = {}\n\n\t\t# Determine MAT number for this file,\n\t\t# while skipping ahead to start of first header\n\t\tMF... | [
{
"docid": "711d7934c80dc5febb6f5fa7ddea8f39",
"score": "0.5626557",
"text": "def _open_file(self):\n\n with open(self.filename) as file:\n self.file_data = file.readlines()\n\n self.file_length = len(self.file_data)\n self.line_index = 0",
"title": ""
},
{
"d... |
9b3360467e6d9f991c4590a7f3bc1070 | .timeof() calculate the whole time of the file | [
{
"docid": "a77fd1bc51a17819331082ad160b622b",
"score": "0.7086473",
"text": "def timeof(self):\r\n time = self.mtr[-1,4] - self.mtr[0,4]\r\n if time < 0:\r\n time += 65536\r\n time += ( self.mtr[-1,3] - self.mtr[0,3] )*65536\r\n time_hours = (time / 1000000)/3600\... | [
{
"docid": "7ae5ad7bc2d33e48e90728fc6be4c0bf",
"score": "0.69575024",
"text": "def time():",
"title": ""
},
{
"docid": "7a0b826d66927ef4fcb590f59bc4d1ac",
"score": "0.6760677",
"text": "def _measure_timing(self, header):\n return (rospy.get_rostime() - header.stamp).to_sec()",... |
d2aeec4836cdf76336be24b910aab608 | Create our initial stream and write to it over SMB1 protocol. Start with offset 0. | [
{
"docid": "72044df7bab733678cb137f8235a48e3",
"score": "0.68725437",
"text": "def test_065_create_and_write_stream_smb1(request):\n depends(request, [\"STREAM_TESTFILE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=True)\n fd = c.creat... | [
{
"docid": "bddce7b71ffc0e39fb548f3b8fd0246f",
"score": "0.65759665",
"text": "def test_061_create_and_write_stream_smb2(request):\n depends(request, [\"STREAM_TESTFILE_CREATED\"])\n c = SMB()\n c.connect(host=ip, share=SMB_NAME, username=SMB_USER, password=SMB_PWD, smb1=False)\n fd = c.crea... |
46adae9c98fd6026efdea1d766439e66 | Test select multiples choices are split within repeats in CSV exports. | [
{
"docid": "0d47b608d9ef2a061cc986110dfcf3d6",
"score": "0.72824013",
"text": "def test_split_select_multiples_within_repeats(self):\n self.maxDiff = None\n record = {\n \"name\": \"Tom\",\n \"age\": 23,\n \"browser_use\": [\n {\"browser_use/... | [
{
"docid": "2d5cb44cdb85d97c0343e4af60eea444",
"score": "0.73833954",
"text": "def test_split_select_multiples_with_randomize(self):\n md_xform = \"\"\"\n | survey |\n | | type | name | label | parameters |\n | | text ... |
292a4ae4224521db4e5539e2fe40e59b | Displays the icon according to the operating system | [
{
"docid": "410c25c1b09950d9e108c44cfe05ced7",
"score": "0.6957155",
"text": "def icones(self):\r\n if \"nt\" == name:\r\n self.root.iconbitmap(\"icons/icon.ico\")\r\n else:\r\n img = Image(\"photo\", file=\"icons/icon.png\")\r\n self.root.tk.call(\"wm\", \... | [
{
"docid": "9e388977a27612c28387556e97934387",
"score": "0.68452096",
"text": "def icon():\n return \"icon.png\"",
"title": ""
},
{
"docid": "5e69561bb995b86026c5057fc57ea017",
"score": "0.6736521",
"text": "def icon(self):\n return \"as_ico_command.png\"",
"title": ""
... |
ea709676884608c4eb7bd6ba186f8058 | Return last average price by date for address | [
{
"docid": "d64de475c378b783adcea981e8df5951",
"score": "0.7730563",
"text": "def last_avg_price(address: str, end_date: dt.date) -> Union[AvgPriceModel, None]:\n\n try:\n return (\n AvgPriceModel.select()\n .where(\n AvgPriceModel.addre... | [
{
"docid": "6fe6595bf749d4be707df8a261ccf555",
"score": "0.64064676",
"text": "def get_avg_prices_rows(offers: List[Dict[str, Any]]) -> List[Any]:\n avg_prices = []\n grouper = lambda x: x['address']\n for address, items in groupby(sorted(offers, key=grouper), key=grouper):\n items = lis... |
42b9d9569f4770cbb15dac1a978a2cb4 | Test the calls to assert_equal_msg for the positional arguments. | [
{
"docid": "914cce41d87c6d6d68557a26d81ed1ac",
"score": "0.7829213",
"text": "def test_assert_equal_msg_calls_for_positional_arguments():\n\n with mock.patch(target=\"tubular.testing.helpers.assert_equal_msg\") as mocked_method:\n\n expected_args = [\"obj\", \"expected_method\", \"msg\"]\n\n ... | [
{
"docid": "6ae70b8bb010cd704e9fce7bf977453e",
"score": "0.7293037",
"text": "def test_call_eq(self):\n self.assertCallEqual(\n 1,\n 2,\n func=str,\n args=[1],\n otherargs=[2],\n kwargs={'testing': True},\n otherkwargs={... |
bc7378cf1929426a0488ca80370047de | Provides the 3D genomic data corresponding to disease, chromosome | [
{
"docid": "f01d911f63f4a85007244dd3506baff4",
"score": "0.5612504",
"text": "def real_genomic_data():\n def real_data_(disease, chrom):\n return scipy.io.loadmat(os.path.join(REAL_DATA_DIR, disease, 'chromo_{}_processed.mat'.format(chrom)))['X']\n\n return real_data_",
"title": ""
}
... | [
{
"docid": "0b2b3e8f30a2655c858c3706f8711d0f",
"score": "0.5864309",
"text": "def read_3d_file(filename):\n return read_nd_file(filename, 3)",
"title": ""
},
{
"docid": "83ce175b10ea080bff84a3dd1ba9aafd",
"score": "0.58423555",
"text": "def geo_meta(dicomfile):\n\n # rows a... |
efe4945b4bc959e32bc42da664ac3db0 | Overridden parent class destruction method method that adds additional logic to destroy the underlying toolkit widget. The destruction happens in dual pass topdown, then bottomup. On the topdown pass, the traits listeners for the abstract obj are unhooked so that no events get fired during destruction. On the bottom up... | [
{
"docid": "56720abac1924d3ce7798466f7a7bfb7",
"score": "0.84241104",
"text": "def destroy(self):\n # Remove the abstract object as a trait listener so that it\n # does not try to update after destroying its internal widget.\n self.remove_trait_listener(self.abstract_obj, 'shell')\n... | [
{
"docid": "a53dd6a3c3388b044499111a8d792796",
"score": "0.6808209",
"text": "def destroy(self):\n del self.widget",
"title": ""
},
{
"docid": "90cc71eb89daca368a6bd631d5cba017",
"score": "0.65991217",
"text": "def cleanup_callback(self, obj, event):\n self.parent.rend.... |
583f4036233277798d30891d0491a2b2 | Query or edit the camera in a gl render panel. Derived from mel command `maya.cmds.hardwareRenderPanel` | [
{
"docid": "02eb3dea994300a093267912afb97e41",
"score": "0.6226707",
"text": "def getCamera(self, **kwargs):\n \n pass",
"title": ""
}
] | [
{
"docid": "e1769c5d20baf8236d54fe6e955c1ac8",
"score": "0.6627869",
"text": "def GetRenderingCamera(self):\n pass",
"title": ""
},
{
"docid": "9db2bdb3b4eddcbdf4a5fd461d9e143e",
"score": "0.63430506",
"text": "def camera(self, camera):\n self.renderer.camera = camera",... |
283f38a36cb317c6e166ea440bad40d8 | Stop and then start the daemon. | [
{
"docid": "511a9b1031de48879200e6d9c4222065",
"score": "0.0",
"text": "def restart(self):\n self.stop()\n self.start()",
"title": ""
}
] | [
{
"docid": "24fdea183ea66334ccc720419bfc7b08",
"score": "0.7850297",
"text": "def start(self):\r\n if self.go_daemon:\r\n if not self.pidfile:\r\n self.log.error(\"Daemon needs pidfile\")\r\n sys.exit(1)\r\n self.run_func_safely(self._boot_daemon)",... |
09f40ecffd6b941aa945901d297b2fa9 | u"""Przetwarzanie slowa, sprawdzenie jego typu i dodanie go do odpowiedniej listy. | [
{
"docid": "da2691c3ddbacfd0456af75842c22981",
"score": "0.0",
"text": "def __processWord(word):\n if len(word) == 3 or word.isupper():\n if __isRome(word):\n utility.add_to_dict(__rome, word)\n else:\n utility.add_to_dict(__shortcuts, word)\n else:\n uti... | [
{
"docid": "4f87fe47e49d703ab17987bcba1aea1a",
"score": "0.6794209",
"text": "def slow() -> None:",
"title": ""
},
{
"docid": "4780e645539e45c292c4aeed12fc3a85",
"score": "0.595264",
"text": "def slower(self):\n pass",
"title": ""
},
{
"docid": "d3a3f11a4c3460c2ca6... |
9ec31a22baae13c15bd0065bbda4d531 | Send a command to merlin and wait to return the results. Raise an exception if merlin returned an error message. | [
{
"docid": "0c7b2cf58e741cbf8df78b90b05bbfcb",
"score": "0.65415144",
"text": "def send_command(self, *cmd):\n\n if self.mainpipe is None or self.mainpipe.returncode is not None:\n self.restart()\n self.mainpipe.stdin.write(json.dumps(cmd).encode('utf-8'))\n line = self.m... | [
{
"docid": "4afe59d1d095254a35c8a9e1b65e4894",
"score": "0.62831205",
"text": "def _doCommand(self, msg, ret_type = str):\n msg = msg + '\\n'\n ret = ERROR\n self._lock.acquire()\n try: \n self._client.send(msg)\n ret = self._client.recv(1024)\n ... |
8799ea90a06e49ddb628f103a556e4da | Request stored feedback for this result from RTX | [
{
"docid": "c412767fbe77f9d8b29dc58a479c952a",
"score": "0.0",
"text": "def get_result_feedback(response_id, result_id): # noqa: E501\n rtxFeedback = RTXFeedback()\n return rtxFeedback.getResultFeedback(response_id, result_id)",
"title": ""
}
] | [
{
"docid": "9909e25386eb757d88738d6797469601",
"score": "0.60022223",
"text": "def estimate(self):\r\n self.store()\r\n # Validate Data Request\r\n # Send to queue\r\n # Return scheudle\r",
"title": ""
},
{
"docid": "6d3e70c19d7a64997aacfe7ce35ea546",
"score":... |
fd9095af87de0d8f52177c1b88881c4e | Setup some initial variables | [
{
"docid": "8c2cf3b7a4cb399e343ce6af003017d0",
"score": "0.0",
"text": "def initialize2D(self, todraw, xtitle, xbins, ytitle, ybins, cuts):\n print \"============ initialize2D ==================================\"\n print \"2D shape systematics \", todraw, \" x \",xtitle, \" y \",ytitle... | [
{
"docid": "cbcd256d2ab8970061afb0326dd780c5",
"score": "0.72661275",
"text": "def pre_initialize(self):\n pass",
"title": ""
},
{
"docid": "c92637668a066945e6c1624fbc4d1976",
"score": "0.7254438",
"text": "def initialize():",
"title": ""
},
{
"docid": "9b30fc65b0b... |
52b2df338463293f04d969d487031b7d | Runs the CollectionCost module and populates the IO dictionaries with calculated values. | [
{
"docid": "9af86f1781a4bd9c28a491fe7216f674",
"score": "0.62023646",
"text": "def run_module(self):\n\n try:\n self.create_ArraySystem()\n self.calculate_trench_properties(self.input_dict, self.output_dict)\n operation_data = self.estimate_construction_time(self.... | [
{
"docid": "f9dfb65556c23d60c4b78e4d649e6ebe",
"score": "0.64189136",
"text": "def main():\n cost = Costs()\n cost.update_total_costs(5000000, turn=1)\n cost.update_total_costs(4000000, turn=2)\n cost.update_total_costs(3000000, turn=3)\n cost.update_total_costs(2000000, turn=4)\n cost... |
982bead9450d57c40ef7b3fd8fad98e4 | Returns the operation's result data set, which is always an iterable. The difference with L{working_set} is that it returns the data B{after} all filters and ordering (not slicing) are applied. | [
{
"docid": "0e86eb69c7757314b480957eb0cbb11f",
"score": "0.0",
"text": "def data_set(self, request, *args, **kwargs):\n data = self.working_set(request, *args, **kwargs)\n\n filters = self.filters or {}\n if filters:\n for name, definition in filters.iteritems():\n ... | [
{
"docid": "ef49803b3e27b1fec23e275587dea74f",
"score": "0.70770305",
"text": "def result_set(self):\n if self.status != ExecutionStatus.RUNNING and self._result_set is None:\n response = self._temboo_session.get_content(self.get_session_path(), {'view':'outputs'})\n self._r... |
dcb1b8bf0fcce2f33fbceabbafe3e645 | Return a new slide based on slide and having partname, created from scratch. | [
{
"docid": "2159840511c60dfa1b051c38d56ff5bc",
"score": "0.6649468",
"text": "def new(cls, slide, partname, package):\n element = CT_NotesSlide.new()\n notes_slide = cls(partname, CT.PML_NOTES_SLIDE, element, package)\n return notes_slide",
"title": ""
}
] | [
{
"docid": "4a340950d41017958ca74388058fd5ee",
"score": "0.7599987",
"text": "def new(cls, slide_layout, partname, package):\n slide_elm = CT_Slide.new()\n slide = cls(partname, CT.PML_SLIDE, slide_elm, package)\n slide.shapes.clone_layout_placeholders(slide_layout)\n slide.r... |
588d72d8e087dcd6d745b56320578dba | Renders a given dictionary to a string using the given template fname. | [
{
"docid": "56e9e14f3b7681543a306823965edeb6",
"score": "0.6991874",
"text": "def renderDict(d, tmpl):\n import web.template as template\n renfunc = template.frender(tmpl, globals=RENDER_GLOBALS)\n s = str(renfunc(d))\n return s",
"title": ""
}
] | [
{
"docid": "d701eaab31a42e6f92eb89d722014168",
"score": "0.70578563",
"text": "def render(template_string, context, *filepath):\n for key, value in context.items():\n template_string = template_string.replace('{{ %s }}' % key, value)\n template_file = open(os.path.join(*filepath), 'w')\n ... |
2e685c8cb86050cc606e80b6aaf1cfc4 | Create s3 volume using IAM Role auth. | [
{
"docid": "feb91429653a7b28081ec37c69b34a51",
"score": "0.7573797",
"text": "def create_s3_volume_role_auth(cls, name, bucket, role_arn, external_id,\n access_mode, description=None, prefix=None,\n properties=None, api=None):\n ... | [
{
"docid": "819226986a465f5a16f920a1aad2294b",
"score": "0.64687634",
"text": "def create_iam():\r\n iam = boto3.client('iam', region_name='us-east-1', aws_access_key_id=KEY,\r\n aws_secret_access_key=SECRET)\r\n try:\r\n iam.create_role(Path='/',\r\n ... |