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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__':...

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