Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

How to use filter_line method in localstack

Best Python code snippet using localstack_python

generate_ablation_series.py

Source:generate_ablation_series.py

1from pathlib import Path2from csv import reader as csv_reader3from csv import writer as csv_writer4import random5import numpy as np6source_file = "C:\\Users\\Andrei\\Dropbox\\workspaces\\JHU\\Ewald Lab\\" \7              "Kp_Km data\\humanized_weighted_abs_log-fold.txt"8background_file = "C:\\Users\\Andrei\\Dropbox\\workspaces\\JHU\\Ewald Lab\\" \9                  "Kp_Km data\\humanized_genes_background.txt"10source_path = Path(source_file)11fname = source_path.stem12fname = fname + '_ablations'13storage_folder = Path(source_path.parent.joinpath(fname))14storage_folder.mkdir(parents=True, exist_ok=True)15def dump_experiment(experiment_name, corrected_lines, write_outs=[], dump_out=False):16    if dump_out:17        print(write_outs)18        return19    write_outs.append(experiment_name)20    updated_experiment_name = experiment_name + '.tsv'21    print('debug', storage_folder)22    print('debug2', updated_experiment_name)23    with open(Path(storage_folder).joinpath(updated_experiment_name), 'wt',24              newline='', encoding='utf-8') as destination:25        writer = csv_writer(destination, delimiter='\t')26        writer.writerows(corrected_lines)27lines = []28with open(source_file, 'rt') as source:29    reader = csv_reader(source, delimiter='\t')30    for line in reader:31        lines.append(line)32background_lines = []33with open(background_file, 'rt') as source:34    reader = csv_reader(source, delimiter='\t')35    for line in reader:36        background_lines.append(line[0])37for removal_value in [0.05, 0.1, 0.2, 0.5]:38    # padding filter vector39    filter_line = [True]*len(lines)40    start_point = int(len(filter_line)*removal_value)41    filter_line = np.array(filter_line)42    filter_line[-start_point:] = False43    filter_line = filter_line.tolist()44    corrected_lines = [duplet for _filtered, duplet in zip(filter_line, lines) if _filtered]45    dump_experiment('lowest_%d_percent_removed' % (removal_value*100), corrected_lines)46    corrected_lines = [duplet47                       if _filtered48                       else [background_lines[random.randint(0, len(background_lines))], duplet[1]]49                       for _filtered, duplet50                       in zip(filter_line, lines)]51    dump_experiment('lowest_%d_percent_set_to_random' % (removal_value*100), corrected_lines)52    random.shuffle(filter_line)53    corrected_lines = [duplet for _filtered, duplet in zip(filter_line, lines) if _filtered]54    dump_experiment('random_%d_percent_removed' % (removal_value*100), corrected_lines)55    random.shuffle(filter_line)56    corrected_lines = [duplet57                       if _filtered58                       else [background_lines[random.randint(0, len(background_lines))], duplet[1]]59                       for _filtered, duplet60                       in zip(filter_line, lines)]61    dump_experiment('random_%d_percent_set_to_random' % (removal_value*100), corrected_lines)62flat_line = [(duplet[0],) for duplet in lines]63dump_experiment('no_weights', flat_line)64for removal_value in [0.05, 0.1, 0.2, 0.5]:65    # padding filter vector66    filter_line = [True]*len(lines)67    start_point = int(len(filter_line)*removal_value)68    filter_line = np.array(filter_line)69    filter_line[-start_point:] = False70    filter_line = filter_line.tolist()71    corrected_lines = [(duplet[0],) for _filtered, duplet in zip(filter_line, lines) if _filtered]72    dump_experiment('no_weights_lowest_%d_percent_removed' % (removal_value*100), corrected_lines)73    corrected_lines = [(duplet[0],)74                       if _filtered75                       else (background_lines[random.randint(0, len(background_lines))], )76                       for _filtered, duplet77                       in zip(filter_line, lines)]78    dump_experiment('no_weights_lowest_%d_percent_set_to_random' % (removal_value*100), corrected_lines)79    random.shuffle(filter_line)80    corrected_lines = [(duplet[0],) for _filtered, duplet in zip(filter_line, lines) if _filtered]81    dump_experiment('no_weights_random_%d_percent_removed' % (removal_value*100), corrected_lines)82    random.shuffle(filter_line)83    corrected_lines = [(duplet[0],)84                       if _filtered85                       else (background_lines[random.randint(0, len(background_lines))], )86                       for _filtered, duplet87                       in zip(filter_line, lines)]88    dump_experiment('no_weights_random_%d_percent_set_to_random' % (removal_value*100), corrected_lines)89dump_experiment('', [], dump_out=True)...

log.py

Source:log.py

1import re, json2import urllib, ssl3def format_line(line):4    json_line = {5        'deviceName': '',6        'processId': 0,7        'processName': '',8        'description': '',9        'timeWindow': '',10        'numberOfOccurrence': 011    }12    pattern = r'\w+ \d+ (\d+):\d+:\d+ (\w+) ([\.|\w+]+)\[(\d+)\]( \(([\.|\w]+)\[(\d+)\]\))?: (.*(\n?.*)*)'13    filter_line = re.match(pattern, line)14    if filter_line:15        json_line["deviceName"] = filter_line.group(2)16        json_line['timeWindow'] = filter_line.group(1)17        json_line['description'] = filter_line.group(8)18        if filter_line.group(6) != None:19            json_line['processName'] = filter_line.group(6)20        else:21            json_line['processName'] = filter_line.group(3)22        if filter_line.group(7) != None:23            json_line['processId'] = filter_line.group(7)24        else:25            json_line['processId'] = filter_line.group(4)26    return json_line27def post_json(list):28    ctx = ssl.create_default_context()29    ctx.check_hostname = False30    ctx.verify_mode = ssl.CERT_NONE31    for i in list:32        params = urllib.urlencode(i)33        # f = urllib.urlopen("https://foo.com/bar", params, context=ctx)34        # print(f.read())35        print(json.dumps(i))36def main(startword, file_path):37    file = open(file_path, 'r')38    new_line = ''39    line_list = []40    try:41        while True:42            text_line = file.readline()43            # check multiline44            if text_line.startswith(startword):45                new_line = format_line(new_line)46                line_list.append(new_line)47                new_line = text_line48            elif text_line and not text_line.startswith(startword):49                new_line = new_line + text_line50            else:51                new_line = format_line(new_line)52                line_list.append(new_line)53                break54    finally:55        file.close()56    # remove duplicate line57    tmp_list = line_list58    for i in tmp_list:59        for j in line_list:60            if i["timeWindow"] == j["timeWindow"] and i["processId"] == j["processId"] and i["description"] == j["description"]:61                i["numberOfOccurrence"] = i["numberOfOccurrence"] + 162    line_list = []63    for i in tmp_list:64        if i not in line_list:65            line_list.append(i)66    post_json(line_list)67if __name__ == "__main__":...

xls_models_tools.py

Source:xls_models_tools.py

1from collections import defaultdict2def filter_line(txt, escape='\t'):3    return txt.split(escape)4def remove_undesired(txt, escapes=('', '\t', '\n')):5    return [x for x in txt if x not in escapes]6def corruption_level(txt, escape='.'):7    entire = txt.split(escape)8    return (entire[-2], entire[-1]) if len(entire) > 1 else (entire[-1], None)9def extract_results(filepath, reveal_error=False):10    with open(filepath, 'r') as infile:11        data = infile.readlines()12        models = remove_undesired(filter_line(data[0]))13        results = {model: defaultdict(dict) for model in models}14        for i in data[1:]:15            values = filter_line(i)16            corruption, level = corruption_level(values[0])17            for model, val in zip(models, values[1:]):18                res = 1 - float(val) if reveal_error else float(val)19                if level is None:20                    results[model][corruption] = res21                else:22                    results[model][corruption][level] = res23    return results24def extract_results_by_corruption(filepath, reveal_error=False):25    results = defaultdict(dict)26    with open(filepath, 'r') as infile:27        data = infile.readlines()28        models = remove_undesired(filter_line(data[0]))29        for i in data[1:]:30            values = filter_line(i)31            corruption, level = corruption_level(values[0])32            for model, val in zip(models, values[1:]):33                res = 1 - float(val) if reveal_error else float(val)34                if level is None:35                    results[corruption][model] = res36                elif level not in results[corruption]:37                    results[corruption][level] = {model : res}38                else:39                    results[corruption][level][model] = res40    return results41def mean_dict(dict_values):...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.