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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):...

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