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How to use should_be_kept method in localstack

Best Python code snippet using localstack_python

plot_configurations.py

Source:plot_configurations.py

1import pickle2import numpy as np3LOGS_PATH = "./all_logs/toate_logs_2022_02_06-23_13_10.pkl"4with open(LOGS_PATH, mode="rb") as pickle_file:5    logs = pickle.load(pickle_file)6def get_configs_with_attr_value(logs, attr_vals):7    query_result = {8        "query": f"{attr_vals}",9        "configs": [],10    }11    for config_name in logs:12        config = logs[config_name]["configuration"]13        should_be_kept = True14        for attr, val in attr_vals:15            if not (attr in config and config[attr] == val):16                should_be_kept = False17                break18        if should_be_kept is True:19            query_result["configs"].append(config_name)20    return query_result21def get_lines(logs, line_names, line_style, xs_dict_keys, ys_dict_keys):22    lines = []23    for line_name in line_names:24        line = {25            "line_name": line_name,26            "line_style": line_style,27            "xs_dict_keys": [line_name] + xs_dict_keys,28            "ys_dict_keys": [line_name] + ys_dict_keys,29        }30        lines.append(line)31    return lines32def get_particles_space_plot_config(33        logs, search_space_max, num_particles, xs_dict_keys, ys_dict_keys, x_label, y_label34):35    target_configs = get_configs_with_attr_value(36        logs, [("search_space_max", search_space_max), ("num_particles", num_particles)]37    )["configs"]38    return {39        "type": "graph",40        "name": f"Models with particles {num_particles} and search_space_max {search_space_max} {x_label} vs {y_label}",41        "x_label": x_label,42        "y_label": y_label,43        "lines": (44            get_lines(45                logs, line_names=target_configs, line_style="solid",46                xs_dict_keys=xs_dict_keys, ys_dict_keys=ys_dict_keys47            )48        )49    }50def get_particles_space_plot_configs(51        logs, search_space_max_options, num_particles_options, xs_dict_keys, ys_dict_keys, x_label, y_label52):53    plot_configs = []54    for search_space_max in search_space_max_options:55        for num_particles in num_particles_options:56            plot_config = get_particles_space_plot_config(57                logs, search_space_max=search_space_max, num_particles=num_particles,58                xs_dict_keys=xs_dict_keys, ys_dict_keys=ys_dict_keys,59                x_label=x_label, y_label=y_label60            )61            plot_configs.append(plot_config)62    return plot_configs63def sort_models_by_metric(logs, metric_key):  # logs_ys_eval_accuracy_epochs64    candidates = []65    for model_name in logs:66        candidate_best_metric = np.max(logs[model_name][metric_key])67        candidate_best_metric_epoch = np.argmax(logs[model_name][metric_key]) + 168        candidates.append((candidate_best_metric, candidate_best_metric_epoch, model_name))69    candidates.sort(reverse=True)70    return candidates71print(f"=========================================")72models_sorted_by_acc_particles_10 = []73models_sorted_by_acc_particles_100 = []74models_sorted_by_acc_particles_1000 = []75print(f"Models sorted by best test accuracy:")76print(f"Optimizer\t& Num particles\t& $V_{{max}}$\t& $SearchSpace_{{min}}$\t& $SearchSpace_{{max}}$\t& $\\varphi_1$\t& $\\varphi_2$\t& Test acc\t\\\\")77print(f"\\midrule")78models_sorted_by_acc = sort_models_by_metric(logs, "logs_ys_eval_accuracy_epochs")79for i_model, (acc, best_epoch, model_name) in enumerate(models_sorted_by_acc):80    # print(f"[{i_model + 1}]\t{acc}@e{best_epoch}\t{model_name}")  # Non-Latex81    config = logs[model_name]["configuration"]82    optimizer = config["optimizer"]83    num_particles = config.get("num_particles", "-")84    v_max = config.get("v_max", "-")85    search_space_min = config.get("search_space_min", "-")86    search_space_max = config.get("search_space_max", "-")87    cognitive_coeff = config.get("cognitive_coeff", "-")88    social_coeff = config.get("social_coeff", "-")89    print(f"{optimizer}\t& {num_particles}\t& {v_max}\t& {search_space_min}\t& {search_space_max}\t& {cognitive_coeff}\t& {social_coeff}\t& {acc*100:.2f}\\%\t\\\\")90    if "num_particles" not in config:91        continue92    if config["num_particles"] == 10:93        models_sorted_by_acc_particles_10.append((acc, best_epoch, model_name))94    elif config["num_particles"] == 100:95        models_sorted_by_acc_particles_100.append((acc, best_epoch, model_name))96    elif config["num_particles"] == 1000:97        models_sorted_by_acc_particles_1000.append((acc, best_epoch, model_name))98print(f"\\bottomrule")99print(f"=========================================")100print()101print(f"=========================================")102print(f"10 Particles Models sorted by best test accuracy:")103print(f"Optimizer\t& Num particles\t& $V_{{max}}$\t& $SearchSpace_{{min}}$\t& $SearchSpace_{{max}}$\t& $\\varphi_1$\t& $\\varphi_2$\t& Test acc\t\\\\")104print(f"\\midrule")105for i_model, (acc, best_epoch, model_name) in enumerate(models_sorted_by_acc_particles_10):106    # print(f"[{i_model + 1}]\t{acc}@e{best_epoch}\t{model_name}")107    config = logs[model_name]["configuration"]108    optimizer = config["optimizer"]109    num_particles = config.get("num_particles", "-")110    v_max = config.get("v_max", "-")111    search_space_min = config.get("search_space_min", "-")112    search_space_max = config.get("search_space_max", "-")113    cognitive_coeff = config.get("cognitive_coeff", "-")114    social_coeff = config.get("social_coeff", "-")115    print(f"{optimizer}\t& {num_particles}\t& {v_max}\t& {search_space_min}\t& {search_space_max}\t& {cognitive_coeff}\t& {social_coeff}\t& {acc*100:.2f}\\%\t\\\\")116print(f"\\bottomrule")117print(f"=========================================")118print()119print(f"=========================================")120print(f"100 Particles Models sorted by best test accuracy:")121print(f"Optimizer\t& Num particles\t& $V_{{max}}$\t& $SearchSpace_{{min}}$\t& $SearchSpace_{{max}}$\t& $\\varphi_1$\t& $\\varphi_2$\t& Test acc\t\\\\")122print(f"\\midrule")123for i_model, (acc, best_epoch, model_name) in enumerate(models_sorted_by_acc_particles_100):124    # print(f"[{i_model + 1}]\t{acc}@e{best_epoch}\t{model_name}")125    config = logs[model_name]["configuration"]126    optimizer = config["optimizer"]127    num_particles = config.get("num_particles", "-")128    v_max = config.get("v_max", "-")129    search_space_min = config.get("search_space_min", "-")130    search_space_max = config.get("search_space_max", "-")131    cognitive_coeff = config.get("cognitive_coeff", "-")132    social_coeff = config.get("social_coeff", "-")133    print(f"{optimizer}\t& {num_particles}\t& {v_max}\t& {search_space_min}\t& {search_space_max}\t& {cognitive_coeff}\t& {social_coeff}\t& {acc*100:.2f}\\%\t\\\\")134print(f"\\bottomrule")135print(f"=========================================")136print()137print(f"=========================================")138print(f"1000 Particles Models sorted by best test accuracy:")139print(f"Optimizer\t& Num particles\t& $V_{{max}}$\t& $SearchSpace_{{min}}$\t& $SearchSpace_{{max}}$\t& $\\varphi_1$\t& $\\varphi_2$\t& Test acc\t\\\\")140print(f"\\midrule")141for i_model, (acc, best_epoch, model_name) in enumerate(models_sorted_by_acc_particles_1000):142    # print(f"[{i_model + 1}]\t{acc}@e{best_epoch}\t{model_name}")143    config = logs[model_name]["configuration"]144    optimizer = config["optimizer"]145    num_particles = config.get("num_particles", "-")146    v_max = config.get("v_max", "-")147    search_space_min = config.get("search_space_min", "-")148    search_space_max = config.get("search_space_max", "-")149    cognitive_coeff = config.get("cognitive_coeff", "-")150    social_coeff = config.get("social_coeff", "-")151    print(f"{optimizer}\t& {num_particles}\t& {v_max}\t& {search_space_min}\t& {search_space_max}\t& {cognitive_coeff}\t& {social_coeff}\t& {acc*100:.2f}\\%\t\\\\")152print(f"\\bottomrule")153print(f"=========================================")154print()155models_sorted_by_acc_top3 = (156    models_sorted_by_acc[:2] +157    models_sorted_by_acc_particles_1000[:3] +158    models_sorted_by_acc_particles_100[:3] +159    models_sorted_by_acc_particles_10[:3]160)161print(f"=========================================")162print(f"Top 3 Models for all number of particles sorted by best test accuracy:")163print(f"Optimizer\t& Num particles\t& $V_{{max}}$\t& $SearchSpace_{{min}}$\t& $SearchSpace_{{max}}$\t& $\\varphi_1$\t& $\\varphi_2$\t& Test acc\t\\\\")164print(f"\\midrule")165for i_model, (acc, best_epoch, model_name) in enumerate(models_sorted_by_acc_top3):166    # print(f"[{i_model + 1}]\t{acc}@e{best_epoch}\t{model_name}")167    config = logs[model_name]["configuration"]168    optimizer = config["optimizer"]169    num_particles = config.get("num_particles", "-")170    v_max = config.get("v_max", "-")171    search_space_min = config.get("search_space_min", "-")172    search_space_max = config.get("search_space_max", "-")173    cognitive_coeff = config.get("cognitive_coeff", "-")174    social_coeff = config.get("social_coeff", "-")175    print(f"{optimizer}\t& {num_particles}\t& {v_max}\t& {search_space_min}\t& {search_space_max}\t& {cognitive_coeff}\t& {social_coeff}\t& {acc*100:.2f}\\%\t\\\\")176print(f"\\bottomrule")177print(f"=========================================")178print()179SEARCH_SPACE_MAX_OPTIONS = [0.21, 0.1, 0.005]180NUM_PARTICLES_OPTIONS = [10, 100, 1000]181CONFIGS_GRADIENT = (182        get_configs_with_attr_value(logs, [("optimizer", "sgd")])["configs"]183        + get_configs_with_attr_value(logs, [("optimizer", "adam")])["configs"]184)185CONFIGS_PARTICLES10 = get_configs_with_attr_value(logs, [("num_particles", 10)])["configs"]186CONFIGS_PARTICLES100 = get_configs_with_attr_value(logs, [("num_particles", 100)])["configs"]187CONFIGS_PARTICLES1000 = get_configs_with_attr_value(logs, [("num_particles", 1000)])["configs"]188CONFIGS_PARTICLES10_TOP10 = [model_name for (acc, best_epoch, model_name) in models_sorted_by_acc_particles_10[:10]]189CONFIGS_PARTICLES100_TOP10 = [model_name for (acc, best_epoch, model_name) in models_sorted_by_acc_particles_100[:10]]190CONFIGS_PARTICLES1000_TOP10 = [model_name for (acc, best_epoch, model_name) in models_sorted_by_acc_particles_1000[:10]]191CONFIGS_PARTICLES10_TOP3 = [model_name for (acc, best_epoch, model_name) in models_sorted_by_acc_particles_10[:3]]192CONFIGS_PARTICLES100_TOP3 = [model_name for (acc, best_epoch, model_name) in models_sorted_by_acc_particles_100[:3]]193CONFIGS_PARTICLES1000_TOP3 = [model_name for (acc, best_epoch, model_name) in models_sorted_by_acc_particles_1000[:3]]194CONFIGS_ALL = CONFIGS_GRADIENT + CONFIGS_PARTICLES10 + CONFIGS_PARTICLES100 + CONFIGS_PARTICLES1000195PLOT_CONFIGS_ALL = [196    {197        "type": "graph",198        "name": "All models Epoch vs Test accuracy",199        "x_label": "Num train epochs",200        "y_label": "Test accuracy",201        "lines": (202            get_lines(logs, line_names=CONFIGS_GRADIENT, line_style="solid", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +203            get_lines(logs, line_names=CONFIGS_PARTICLES10, line_style="dotted", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +204            get_lines(logs, line_names=CONFIGS_PARTICLES100, line_style="dashed", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +205            get_lines(logs, line_names=CONFIGS_PARTICLES1000, line_style="dashdot", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])206        )207    }208]209PLOT_CONFIGS_PARTICLES10 = [210    {211        "type": "graph",212        "name": "All models Epoch vs Test accuracy",213        "x_label": "Num train epochs",214        "y_label": "Test accuracy",215        "lines": (216                get_lines(logs, line_names=CONFIGS_PARTICLES10, line_style="dotted", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])217        )218    }219]220PLOT_CONFIGS_PARTICLES_SEARCH_SPACE = get_particles_space_plot_configs(221    logs,222    SEARCH_SPACE_MAX_OPTIONS,223    NUM_PARTICLES_OPTIONS,224    xs_dict_keys=["logs_xs_epochs"],225    ys_dict_keys=["logs_ys_eval_accuracy_epochs"],226    x_label="Train epochs",227    y_label="Test accuracy"228)229PLOT_CONFIGS_PARTICLES_TOP10 = [230    {231        "type": "graph",232        "name": "Top 10 10Particles models Epoch vs Test accuracy",233        "x_label": "Num train epochs",234        "y_label": "Test accuracy",235        "lines": (236            get_lines(logs, line_names=CONFIGS_GRADIENT, line_style="solid", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +237            get_lines(logs, line_names=CONFIGS_PARTICLES10_TOP10, line_style="dotted", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])238        )239    },240    {241        "type": "graph",242        "name": "Top 10 100Particles models Epoch vs Test accuracy",243        "x_label": "Num train epochs",244        "y_label": "Test accuracy",245        "lines": (246            get_lines(logs, line_names=CONFIGS_GRADIENT, line_style="solid", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +247            get_lines(logs, line_names=CONFIGS_PARTICLES100_TOP10, line_style="dashed", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])248        )249    },250    {251        "type": "graph",252        "name": "Top 10 1000Particles models Epoch vs Test accuracy",253        "x_label": "Num train epochs",254        "y_label": "Test accuracy",255        "lines": (256            get_lines(logs, line_names=CONFIGS_GRADIENT, line_style="solid", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +257            get_lines(logs, line_names=CONFIGS_PARTICLES1000_TOP10, line_style="dashdot", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])258        )259    }260]261PLOT_CONFIGS_PARTICLES_TOP3 = [262    {263        "type": "graph",264        "name": "Top 3 from each num Particles models Epoch vs Test accuracy",265        "x_label": "Num train epochs",266        "y_label": "Test accuracy",267        "lines": (268            get_lines(logs, line_names=CONFIGS_GRADIENT, line_style="solid", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +269            get_lines(logs, line_names=CONFIGS_PARTICLES10_TOP10[:3], line_style="dotted", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +270            get_lines(logs, line_names=CONFIGS_PARTICLES100_TOP10[:3], line_style="dashed", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +271            get_lines(logs, line_names=CONFIGS_PARTICLES1000_TOP10[:3], line_style="dashdot", xs_dict_keys=["logs_xs_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])272        )273    },274    {275        "type": "graph",276        "name": "Top 3 from each num Particles models Training duration vs Test accuracy",277        "x_label": "Training duration (s)",278        "y_label": "Test accuracy",279        "lines": (280            get_lines(logs, line_names=CONFIGS_GRADIENT, line_style="solid", xs_dict_keys=["logs_ys_durations_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +281            get_lines(logs, line_names=CONFIGS_PARTICLES10_TOP10[:3], line_style="dotted", xs_dict_keys=["logs_xs_durations_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +282            get_lines(logs, line_names=CONFIGS_PARTICLES100_TOP10[:3], line_style="dashed", xs_dict_keys=["logs_xs_durations_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"]) +283            get_lines(logs, line_names=CONFIGS_PARTICLES1000_TOP10[:3], line_style="dashdot", xs_dict_keys=["logs_xs_durations_epochs"], ys_dict_keys=["logs_ys_eval_accuracy_epochs"])284        )285    }...

spot-the-difference.py

Source:spot-the-difference.py

1import json2import os3import argparse4import copy5NEWLINE="\n"6def BFS(top_dict, looking_for_key):7    # queue8    dict_q = [top_dict] # list of dictionaries9    # magic math10    for d in dict_q:11        dict_keys = d.keys()12        for k in dict_keys:13            if k == looking_for_key:14                return d[k] 15            if isinstance(d[k], dict):16                dict_q.append(d[k])17    return ""18def find_key_with_value (key, value, element_dictionary):19    return BFS(element_dictionary, key) == value20def get_ignore_list_file_path():21    return os.path.join(os.getcwd(), "config", "ignore.json")22def get_acm_leaks_list_file_path():23    return os.path.join(os.getcwd(), "config", "acm-leaks.json")24def read_json_file(file_path):25    if file_path:26        with open(file_path) as f:27            return json.load(f)28def read_scan_file_as_dict(file_path):29    _resource_info_dict = dict()30    _details_file=file_path31    _read_file = open(_details_file, 'r') 32    while True:33        # _resource_type=_read_file.readline().strip()34        # if not _resource_type:35        #     break36        _resource_details_json=_read_file.readline().strip()37        if not _resource_details_json:38            break39        _resource_details_dict=json.loads(_resource_details_json)40        if _resource_details_dict:41            _resource_info_dict.update(_resource_details_dict)42            # _resource_info_dict[_resource_type]=_resource_details_dict43    _read_file.close ()44    return _resource_info_dict45def simplify_dict_list(dict_of_lists):46    _attributes_we_care_about = ["kind", "name", "namespace", "pod-template-hash"]47    _dict_keys=dict_of_lists.keys()48    _simplified_dict_list = dict()49    for k in _dict_keys:50        _simplified_dict_list[k] = []51        _resource_list = dict_of_lists[k]52        for r in _resource_list: # r is a dict for a particular resource53            _simplified_resource = dict()54            for a in _attributes_we_care_about:55                _simplified_resource[a] = BFS(r, a)56            _simplified_dict_list[k].append(_simplified_resource)57    return _simplified_dict_list58def fix_name (dict_of_lists):59    _short_name_dict = dict()60    _dict_keys=dict_of_lists.keys()61    for k in _dict_keys:62        _short_name_dict[k] = []63        _resource_list = dict_of_lists[k]64        for r in _resource_list: # r is a dict for a particular resource65            _short_name_resource = dict()66            _short_name_resource["kind"] = r["kind"]67            if r["pod-template-hash"] != "":68                _short_name_resource["name"] = r["name"].split(r["pod-template-hash"])[0]69            else:70                _short_name_resource["name"] = r["name"]71            _short_name_resource["namespace"] = r["namespace"]72            _short_name_dict[k].append(_short_name_resource)73    return _short_name_dict74def diffTheLists(list_one, list_two):75    results = {76        "both": [],77        "added": [],78        "removed": []79    }80    # gotta check both directions81    for n in list_one:82        if n in list_two:83            results["both"].append(n)84        else:85            results["removed"].append(n)86    87    for n in list_two:88        if n not in list_one:89            results["added"].append(n)90    return results91def spotTheDifference(json_dict_one,  json_dict_two):92    results = {93        "both": dict(),94        "added": dict(),95        "removed": dict()96    }97    _resource_list_one=json_dict_one.keys()98    _resource_list_two=json_dict_two.keys()99    _resource_diffs  = diffTheLists(_resource_list_one, _resource_list_two)100    for a in _resource_diffs["added"]:101        if len(json_dict_two[a]) > 0: 102            results["added"][a] = json_dict_two[a]103    for r in _resource_diffs["removed"]:104        results["removed"][r] = json_dict_one[r]105    for b in _resource_diffs["both"]:106        _row_diffs = diffTheLists(json_dict_one[b], json_dict_two[b])107        for a in _row_diffs["added"]:108            if b not in results["added"]:109                results["added"][b] = []110            results["added"][b].append(a)111        for r in _row_diffs["removed"]:112            if b not in results["removed"]:113                results["removed"][b] = []114            results["removed"][b].append(r)115        for bo in _row_diffs["both"]:116            if b not in results["both"]:117                results["both"][b] = []118            results["both"][b].append(bo)119    return results120# this returns a (sub) set of the list_of_resources, having removed remove_resource121def usefulFunctNoGoodName(list_of_resources, remove_resource):122    # list_of_resources_copy = copy.deepcopy(list_of_resources)123    list_of_resources_subset = []124    for r in list_of_resources:125        remove_resource_keys = remove_resource.keys()126        should_be_kept = False127        for key in remove_resource_keys:128            if not find_key_with_value(key, remove_resource[key], r): 129                should_be_kept = True130                break131        if should_be_kept:132            list_of_resources_subset.append(r)133    return list_of_resources_subset134def removeIgnoredItems(res_dict, ignore_list):135    if len(ignore_list) == 0:136        return res_dict137    ignore_list_copy = copy.deepcopy(ignore_list)138    for remove_resource in ignore_list_copy:139        if "kind" in remove_resource.keys(): #if kind specified140            resource_kind = remove_resource["kind"]141            if resource_kind in res_dict.keys() and len(remove_resource.keys()) == 1: # if the entire kind should be removed (and that kind exists)142                res_dict.pop(resource_kind, None)143                continue144            # else, don't remove _entire_ kind145            remove_resource.pop("kind", None) # don't check kind anymore146            if resource_kind in res_dict.keys() and res_dict[resource_kind]: # if kind even exists in file147                res_dict[resource_kind] = usefulFunctNoGoodName(res_dict[resource_kind], remove_resource)148        else: # kind not specified, gotta loop through each kind149            for resource_kind in res_dict.keys():150                if res_dict[resource_kind]:151                    res_dict[resource_kind] = usefulFunctNoGoodName(res_dict[resource_kind], remove_resource)     152    return res_dict153def removeEmptyResults(res_dict):154    copy_dict=copy.deepcopy(res_dict)155    dict_keys = copy_dict.keys()156    for k in dict_keys:157        if type(copy_dict[k]) is list and not copy_dict[k]: #empty list158            del res_dict[k]159    return res_dict160    161def writeJSON(file_path, res_dict):162    _file = open(file_path, "w")163    _file.write(json.dumps(res_dict, indent=2))164    _file.close()165def countTotals(res_dict):166    typeCount = 0167    totalCount = 0168    for x in res_dict: 169        typeCount += 1170        totalCount += len(res_dict[x])171    return typeCount, totalCount172def main():173    parser = argparse.ArgumentParser()174    parser.add_argument("-f", "--first_file", required=True)175    parser.add_argument("-s", "--second_file", required=True)176    parser.add_argument("-o", "--output_tag", required=True)177    args = vars(parser.parse_args())178    _first_file_path = args['first_file']179    _second_file_path = args['second_file']180    _output_tag = args['output_tag']181    _first_resource_dict_list = read_scan_file_as_dict(_first_file_path)182    _second_resource_dict_list = read_scan_file_as_dict(_second_file_path)183    _first_resource_dict_list_simplified = simplify_dict_list(_first_resource_dict_list)184    _second_resource_dict_list_simplified = simplify_dict_list(_second_resource_dict_list)185    _first_resource_dict_list_namefixed = fix_name(_first_resource_dict_list_simplified)186    _second_resource_dict_list_namefixed = fix_name(_second_resource_dict_list_simplified)187    188    _results = spotTheDifference(_first_resource_dict_list_namefixed, _second_resource_dict_list_namefixed)189    _res_both = _results["both"]190    _res_added = _results["added"]191    _res_removed = _results["removed"]192    _ignore_list = read_json_file(get_ignore_list_file_path())193    _acm_leaks_list = read_json_file(get_acm_leaks_list_file_path())194    _all_ignores_list = _ignore_list + _acm_leaks_list195    _res_both = removeIgnoredItems(_res_both, _all_ignores_list)196    _res_added = removeIgnoredItems(_res_added, _all_ignores_list)197    _res_removed = removeIgnoredItems(_res_removed, _all_ignores_list)198    _res_both = removeEmptyResults(_results["both"])199    _res_added = removeEmptyResults(_results["added"])200    _res_removed = removeEmptyResults(_results["removed"])201    typeCount, totalCount = countTotals(_res_added)202    with open('./results/count.txt', 'w') as f:203        f.write('Total Leak Count: '+str(totalCount))204        f.write('\n')205        f.write('Leaked Resource Kinds: '+str(typeCount))206        f.write('\n')207    # writeJSON("./results/both-results-"+_output_tag+".json", _res_both)208    # writeJSON("./results/removed-results-"+_output_tag+".json", _res_removed)209    writeJSON("./results/leaks-"+_output_tag+".json", _res_added)...

trim.py

Source:trim.py

1from ...imports import *2def trim_times(self, just_edges=True, when_to_give_up=1, minimum_acceptable_ok=1):3    """4    Trim times that are all (or mostly) not numbers.5    Parameters6    ----------7    just_edges : bool8        Should we only trim the outermost bad time bins?9            `True` = Just trim off the bad edges and keep10            interior bad values. Keeping interior data, even if11            they're all bad, often helps to make for more12            intuititive imshow plots.13            `False` = Trim off every bad time, whether it's on14            the edge or somewhere in the middle of the dataset.15            The resulting Rainbow will be smaller, but it might16            be a little tricky to visualize with imshow.17    when_to_give_up : float18        The fraction of wavelengths that must be nan or not OK19        for the entire time to be considered bad (default = 1).20            `1.0` = trim only if all wavelengths are bad21            `0.5` = trim if more than 50% of wavelengths are bad22            `0.0` = trim if any wavelengths are bad23    minimum_acceptable_ok : float24        The numbers in the `.ok` attribute express "how OK?" each25        data point is, ranging from 0 (not OK) to 1 (super OK).26        In most cases, `.ok` will be binary, but there may be times27        where it's intermediate (for example, if a bin was created28        from some data that were not OK and some that were).29        The `minimum_acceptable_ok` parameter allows you to specify what30        level of OK-ness for a point to not get trimmed.31    """32    # create a history entry for this action (before other variables are defined)33    h = self._create_history_entry("trim_times", locals())34    # figure out which times should be considered bad35    is_nan = np.isnan(self.flux)36    isnt_ok = self.ok < minimum_acceptable_ok37    fraction_bad = np.sum(is_nan | isnt_ok, axis=self.waveaxis) / self.nwave38    should_be_kept = fraction_bad < when_to_give_up39    # only make cuts on the edges (if desired)40    if just_edges:41        isnt_before_first = np.cumsum(should_be_kept) > 042        isnt_after_last = (np.cumsum(should_be_kept[::-1]) > 0)[::-1]43        isnt_edge = isnt_before_first & isnt_after_last44        should_be_kept = should_be_kept | isnt_edge45    # actually try the Rainbow46    new = self[:, should_be_kept]47    new._remove_last_history_entry()48    # append the history entry to the new Rainbow49    new._record_history_entry(h)50    # return the new Rainbow51    return new52def trim_wavelengths(self, just_edges=True, when_to_give_up=1, minimum_acceptable_ok=1):53    """54    Trim wavelengths that are all (or mostly) not numbers.55    Parameters56    ----------57    just_edges : bool58        Should we only trim the outermost bad wavelength bins?59            `True` = Just trim off the bad edges and keep60            interior bad values. Keeping interior data, even if61            they're all bad, often helps to make for more62            intuititive imshow plots.63            `False` = Trim off every bad wavelength, whether it's on64            the edge or somewhere in the middle of the dataset.65            The resulting Rainbow will be smaller, but it might66            be a little tricky to visualize with imshow.67    when_to_give_up : float68        The fraction of times that must be nan or not OK69        for the entire wavelength to be considered bad (default = 1).70            `1.0` = trim only if all times are bad71            `0.5` = trim if more than 50% of times are bad72            `0.0` = trim if any times are bad73    minimum_acceptable_ok : float74        The numbers in the `.ok` attribute express "how OK?" each75        data point is, ranging from 0 (not OK) to 1 (super OK).76        In most cases, `.ok` will be binary, but there may be times77        where it's intermediate (for example, if a bin was created78        from some data that were not OK and some that were).79        The `minimum_acceptable_ok` parameter allows you to specify what80        level of OK-ness for a point to not get trimmed.81    """82    # create a history entry for this action (before other variables are defined)83    h = self._create_history_entry("trim_wavelengths", locals())84    # figure out which wavelengths should be considered bad85    is_nan = np.isnan(self.flux)86    isnt_ok = self.ok < minimum_acceptable_ok87    fraction_bad = np.sum(is_nan | isnt_ok, axis=self.timeaxis) / self.ntime88    should_be_kept = fraction_bad < when_to_give_up89    # only make cuts on the edges (if desired)90    if just_edges:91        isnt_before_first = np.cumsum(should_be_kept) > 092        isnt_after_last = (np.cumsum(should_be_kept[::-1]) > 0)[::-1]93        isnt_edge = isnt_before_first & isnt_after_last94        should_be_kept = should_be_kept | isnt_edge95    # actually try the Rainbow96    new = self[should_be_kept, :]97    new._remove_last_history_entry()98    # append the history entry to the new Rainbow99    new._record_history_entry(h)100    # return the new Rainbow101    return new102def trim(self, just_edges=True, when_to_give_up=1, minimum_acceptable_ok=1):103    """104    just_edges : bool105        Should we only trim the outermost bad wavelength bins?106            `True` = Just trim off the bad edges and keep107            interior bad values. Keeping interior data, even if108            they're all bad, often helps to make for more109            intuititive imshow plots.110            `False` = Trim off every bad wavelength, whether it's on111            the edge or somewhere in the middle of the dataset.112            The resulting Rainbow will be smaller, but it might113            be a little tricky to visualize with imshow.114    when_to_give_up : float115        The fraction of times that must be nan or not OK116        for the entire wavelength to be considered bad (default = 1).117            `1.0` = trim only if all times are bad118            `0.5` = trim if more than 50% of times are bad119            `0.0` = trim if any times are bad120    minimum_acceptable_ok : float121        The numbers in the `.ok` attribute express "how OK?" each122        data point is, ranging from 0 (not OK) to 1 (super OK).123        In most cases, `.ok` will be binary, but there may be times124        where it's intermediate (for example, if a bin was created125        from some data that were not OK and some that were).126        The `minimum_acceptable_ok` parameter allows you to specify what127        level of OK-ness for a point to not get trimmed.128    """129    trimmed = self.trim_times(130        when_to_give_up=when_to_give_up,131        just_edges=just_edges,132        minimum_acceptable_ok=minimum_acceptable_ok,133    )134    trimmed = trimmed.trim_wavelengths(135        when_to_give_up=when_to_give_up,136        just_edges=just_edges,137        minimum_acceptable_ok=minimum_acceptable_ok,138    )...

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.