KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use num_complete method in autotest

Best Python code snippet using autotest_python

plot_fig5_binomial.py

Source:plot_fig5_binomial.py

1#!/usr/bin/env python2# -*- coding: utf-8 -*-3""" Plot probabilities of entity occurrence.4Usage: python plot_fig5_binomial.py5Input data files: ../data/[app_name]_out/complete_user_[app_name].txt, ../data/[app_name]_out/user_[app_name]_all.txt6Time: ~5M7"""8import sys, os, platform9from collections import defaultdict, Counter10import numpy as np11from scipy.special import comb12from scipy import stats13import matplotlib as mpl14if platform.system() == 'Linux':15    mpl.use('Agg')  # no UI backend16import matplotlib.pyplot as plt17from matplotlib.ticker import FuncFormatter18sys.path.append(os.path.join(os.path.dirname(__file__), '../'))19from utils.helper import Timer20from utils.plot_conf import ColorPalette, hide_spines21def binomial(n, k, rho):22    # n: number of trials, k: number of success (being sampled)23    return comb(n, k, exact=True) * (rho ** k) * ((1 - rho) ** (n - k))24def main():25    timer = Timer()26    timer.start()27    cc4 = ColorPalette.CC428    blue = cc4[0]29    app_name = 'cyberbullying'30    rho = 0.527231    entity = 'user'32    fig, axes = plt.subplots(1, 2, figsize=(10, 3.3))33    print('for entity: {0}'.format(entity))34    sample_entity_freq_dict = defaultdict(int)35    with open('../data/{1}_out/{0}_{1}_all.txt'.format(entity, app_name), 'r') as sample_datefile:36        for line in sample_datefile:37            sample_entity_freq_dict[line.rstrip().split(',')[1]] += 138    complete_entity_freq_dict = defaultdict(int)39    with open('../data/{1}_out/complete_{0}_{1}.txt'.format(entity, app_name), 'r') as complete_datefile:40        for line in complete_datefile:41            complete_entity_freq_dict[line.rstrip().split(',')[1]] += 142    complete_to_sample_freq_dict = defaultdict(list)43    sample_to_complete_freq_dict = defaultdict(list)44    for item, complete_vol in complete_entity_freq_dict.items():45        if item in sample_entity_freq_dict:46            complete_to_sample_freq_dict[complete_vol].append(sample_entity_freq_dict[item])47        else:48            complete_to_sample_freq_dict[complete_vol].append(0)49    for item, sample_vol in sample_entity_freq_dict.items():50        sample_to_complete_freq_dict[sample_vol].append(complete_entity_freq_dict[item])51    for item in set(complete_entity_freq_dict.keys()) - set(sample_entity_freq_dict.keys()):52        sample_to_complete_freq_dict[0].append(complete_entity_freq_dict[item])53    ax1_x_axis = range(1, 101)54    ax1_y_axis = []55    empirical_mean_list = []56    expected_mean_list = []57    for num_complete in ax1_x_axis:58        # compute complete to sample59        empirical_cnt_dist = complete_to_sample_freq_dict[num_complete]60        binomial_cnt_dist = []61        for x in range(num_complete + 1):62            binomial_cnt_dist.extend([x] * int(binomial(num_complete, x, rho) * len(empirical_cnt_dist)))63        ks_test = stats.ks_2samp(empirical_cnt_dist, binomial_cnt_dist)64        empirical_mean = sum(empirical_cnt_dist) / len(empirical_cnt_dist)65        empirical_mean_list.append(empirical_mean)66        expected_mean = sum(binomial_cnt_dist) / len(binomial_cnt_dist)67        expected_mean_list.append(expected_mean)68        print('num_complete: {0}, number of Bernoulli trials: {1}, d_statistic: {2:.4f}, p: {3:.4f}, expected mean: {4:.2f}, empirical mean: {5:.2f}'69              .format(num_complete, len(empirical_cnt_dist), ks_test[0], ks_test[1], expected_mean, empirical_mean))70        ax1_y_axis.append(ks_test[0])71    axes[0].plot(ax1_x_axis, ax1_y_axis, c='k', lw=1.5, ls='-')72    axes[0].set_xlabel(r'complete frequency $n_c$', fontsize=16)73    axes[0].set_ylabel('D-statistic', fontsize=16)74    axes[0].set_xlim([-2, 102])75    axes[0].set_xticks([0, 25, 50, 75, 100])76    axes[0].set_ylim([0, 0.1])77    axes[0].yaxis.set_major_formatter(FuncFormatter(lambda x, _: '{0:.2f}'.format(x)))78    axes[0].tick_params(axis='both', which='major', labelsize=16)79    axes[0].set_title('(a)', fontsize=18, pad=-3*72, y=1.0001)80    # show an example81    num_complete = 2082    axes[0].scatter(num_complete, ax1_y_axis[num_complete - 1], s=40, c=blue, zorder=30)83    axes[0].set_yticks([0, ax1_y_axis[num_complete - 1], 0.05, 0.1])84    axes[0].plot([axes[0].get_xlim()[0], num_complete], [ax1_y_axis[num_complete - 1], ax1_y_axis[num_complete - 1]], color=blue, ls='--', lw=1)85    axes[0].plot([num_complete, num_complete], [axes[0].get_ylim()[0], ax1_y_axis[num_complete - 1]], color=blue, ls='--', lw=1)86    # plot complete to sample87    ax2_x_axis = range(num_complete + 1)88    num_items = len(complete_to_sample_freq_dict[num_complete])89    complete_to_sample_cnt = Counter(complete_to_sample_freq_dict[num_complete])90    ax2_y_axis = [complete_to_sample_cnt[x] / num_items for x in ax2_x_axis]91    ax2_binomial_axis = [binomial(num_complete, x, rho) for x in ax2_x_axis]92    axes[1].plot(ax2_x_axis, ax2_y_axis, c=blue, lw=1.5, ls='-', marker='o', zorder=20, label='empirical')93    axes[1].plot(ax2_x_axis, ax2_binomial_axis, c='k', lw=1.5, ls='-', marker='x', zorder=10, label='binomial')94    axes[1].set_xlabel(r'sample frequency $n_s$', fontsize=16)95    axes[1].set_ylabel(r'Pr($n_s$|$n_c$={0})'.format(num_complete), fontsize=16)96    axes[1].set_xticks([0, 5, 10, 15, 20])97    axes[1].set_ylim([-0.005, 0.27])98    axes[1].set_yticks([0, 0.1, 0.2])99    axes[1].tick_params(axis='both', which='major', labelsize=16)100    axes[1].legend(frameon=False, fontsize=16, ncol=1, fancybox=False, shadow=True, loc='upper left')101    axes[1].set_title('(b)', fontsize=18, pad=-3*72, y=1.0001)102    axes[1].plot([empirical_mean_list[num_complete - 1], empirical_mean_list[num_complete - 1]], [axes[1].get_ylim()[0], 0.21], color=blue, ls='--', lw=1)103    axes[1].plot([expected_mean_list[num_complete - 1], expected_mean_list[num_complete - 1]], [axes[1].get_ylim()[0], 0.21], color='k', ls='--', lw=1)104    hide_spines(axes)105    timer.stop()106    plt.tight_layout(rect=[0, 0.05, 1, 1])107    plt.savefig('../images/entity_binomial.pdf', bbox_inches='tight')108    if not platform.system() == 'Linux':109        plt.show()110if __name__ == '__main__':...

bench_auth.py

Source:bench_auth.py

1#!/usr/bin/python32import argparse3import copy4import json5import rados6import time7import multiprocessing8caps_base = ["mon", "profile rbd", "osd", "profile rbd pool=rbd namespace=test"]9def create_users(conn, num_namespaces, num_users):10    cmd = {'prefix': 'auth get-or-create'}11    for i in range(num_namespaces):12        caps_base[-1] += ", profile rbd pool=rbd namespace=namespace{}".format(i)13    cmd['caps'] = caps_base14    for i in range(num_users):15        cmd['entity'] = "client.{}".format(i)16        conn.mon_command(json.dumps(cmd), b'')17class Worker(multiprocessing.Process):18    def __init__(self, conn, num, queue, duration):19        super().__init__()20        self.conn = conn21        self.num = num22        self.queue = queue23        self.duration = duration24    def run(self):25        client = "client.{}".format(self.num)26        cmd = {'prefix': 'auth caps', 'entity': client}27        start_time = time.time()28        num_complete = 029        with rados.Rados(conffile='') as conn:30            while True:31                now = time.time()32                diff = now - start_time33                if diff > self.duration:34                    self.queue.put((num_complete, diff))35                    return36                caps = copy.deepcopy(caps_base)37                caps[-1] += ", profile rbd pool=rbd namespace=namespace{}".format(self.num * 10000 + num_complete)38                cmd['caps'] = caps39                cmd_start = time.time()40                ret, buf, out = conn.mon_command(json.dumps(cmd), b'')41                cmd_end = time.time()42                if ret != 0:43                    self.queue.put((Exception("{0}: {1}".format(ret, out)), 0))44                    return45                num_complete += 146                print("Process {} finished op {} - latency: {}".format(self.num, num_complete, cmd_end - cmd_start))47def main():48    parser = argparse.ArgumentParser(description="""49Benchmark updates to ceph users' capabilities. Run one update at a time in each thread.50""")51    parser.add_argument(52        '-n', '--num-namespaces',53        type=int,54        default=300,55        help='number of namespaces per user',56    )57    parser.add_argument(58        '-t', '--threads',59        type=int,60        default=10,61        help='number of threads (and thus parallel operations) to use',62    )63    parser.add_argument(64        '-d', '--duration',65        type=int,66        default=30,67        help='how long to run, in seconds',68    )69    args = parser.parse_args()70    num_namespaces = args.num_namespaces71    num_threads = args.threads72    duration = args.duration73    workers = []74    results = []75    q = multiprocessing.Queue()76    with rados.Rados(conffile=rados.Rados.DEFAULT_CONF_FILES) as conn:77        create_users(conn, num_namespaces, num_threads)78    for i in range(num_threads):79        workers.append(Worker(conn, i, q, duration))80        workers[-1].start()81    for i in range(num_threads):82        num_complete, seconds = q.get()83        if isinstance(num_complete, Exception):84            raise num_complete85        results.append((num_complete, seconds))86    total = 087    total_rate = 088    for num, sec in results:89        print("Completed {} in {} ({} / s)".format(num, sec, num / sec))90        total += num91        total_rate += num / sec92    print("Total: ", total)93    print("Avg rate: ", total_rate / len(results))94if __name__ == '__main__':...

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.