Python timeit.repeat函数代码示例

def performance(name, size, loops=100):

    libpath = os.path.join(os.getcwd(), name)
    sys.path.append(libpath)

    try:
        atm
    except NameError:
        import isa
    imp.reload(isa)

    times = []
    for element in size:
        element = int(element)
        if element == 1:
            time = repeat('atm(0.)', setup='from isa import atm',
                          number=loops, repeat=3)
        elif element > 1:
            time = repeat('atm(h)',
                          setup='from isa import atm\n'
                                'from numpy import linspace\n'
                                'h = linspace(0., 11000., {})'
                                .format(element),
                          number=loops, repeat=3)
        time = 1e3 * min(time) / loops
        times.append(time)

    sys.path.remove(libpath)

    return times

def time_update(function, truncate, imsize, picture, input_im, sigma_r, sigma_s, lw, num_thread=None):

    #cython parameters
    imsize0 = imsize[0]
    imsize1 = imsize[1]
    output = picture*0.
    output5 =  np.array(output, np.float32)
    input_im5 = np.array(input_im, np.float32)

    if num_thread is None:
        times = timeit.repeat(lambda: function(sigma_s,
                                                        sigma_r,
                                                        input_im5,
                                                        imsize0,
                                                        imsize1,
                                                        output5,
                                                        lw),
                            number=3, repeat=5)
    else:
        times = timeit.repeat(lambda: function(sigma_s,
                                                    sigma_r,
                                                    input_im5,
                                                    imsize0,
                                                    imsize1,
                                                    output5,
                                                    lw,
                                                    num_thread),
                            number=3, repeat=5)
    print("{}: {}s".format(str(function) , min(times)))

    return min(times)

    def time_big_inputs(self):
        n_elems_range = np.linspace(1, 2**self.max_exponent, num=self.num_test_points, dtype=int).tolist()
        times_merge = []
        times_counting = []
        sort_case= BigInputsSortCase('merge')
        for i,n_elems in enumerate(n_elems_range):
            print(i)
            sort_case.setup(n_elems=n_elems, max_elem=n_elems)
            print('max:' + str(max(sort_case.test_input)))
            elapsed_time = min(timeit.repeat(sort_case.sort, number=1, repeat=self.num_repeats))*1000
            times_merge.append(elapsed_time)
        sort_case = BigInputsSortCase('counting')
        for i, n_elems in enumerate(n_elems_range):
            print(i)
            sort_case.setup(n_elems=n_elems, max_elem=n_elems)
            elapsed_time = min(timeit.repeat(sort_case.sort, number=1, repeat=self.num_repeats))*1000
            times_counting.append(elapsed_time)

        # plot both
        plt.plot(n_elems_range, times_merge, color='red', label='Merge sort', linestyle='-', marker='o')
        plt.plot(n_elems_range, times_counting, color='blue', label='Counting sort', linestyle='-', marker='o')
        plt.title('Big Inputs case')
        plt.xlabel('size (length) of the input')
        plt.ylabel('ms.')
        plt.legend(loc='upper left', frameon=True)
        plt.show()

def find_breaking_point(f1, f2, input_generator, start=1, step=1,
                        limit=1000000, trial_count=1000, repeat_count=3):
    """
    Find size of input arguments (n0) for which f2(n0) is faster than f1(n0).
    -  f1, f2 - functions to test.
    -  input_generator - function that receives current size of input arguments and returns input data in form of tuple with first item - list of non-keyword arguments and second item - dict of keyword arguments.
    -  start - initial input data size.
    -  step - iteration step.
    -  limit - maximum size of input data.
    -  trial_count - count of executions of f1/f2 on each iteration.
    -  repeat_count - to repeat trials several times and use average performance value.

    returns n0 - size of input data for which f2(n0) is faster than f1(n0)
            or None if reaches limit.
    """
    for n in range(start, limit+1):
        curr_input = input_generator(n)
        # Test first function
        f1_results = timeit.repeat(lambda: f1(*curr_input[0], **curr_input[1]),
                                   repeat=repeat_count, number=trial_count)
        f1_avg = sum(f1_results) / len(f1_results)
        # Test second function
        f2_results = timeit.repeat(lambda: f2(*curr_input[0], **curr_input[1]), repeat=repeat_count, number=trial_count)
        f2_avg = sum(f2_results) / len(f2_results)
        # Compare performance
        if f2_avg < f1_avg:
            return n
    return None

def main():
    '''test functions'''
    init()
    if len(argv) > 1:
        n = int(argv[1])
    else:
        print('nchess.py, usage:\nn print repeat functions')
        return
    if len(argv) > 2:
        print_sol = bool(int(argv[2]))

    functions = [perm_all, perm_op1, perm_op2, perm_op3, perm_op4, perm_op5]
    if len(argv) > 3:
        repeats = int(argv[3])
    else:
        repeats = 100
    if len(argv) > 4:
        for func in argv[4:]:
            print()
            funcstr = str(functions[int(func)]).split(' ')[1]
            print(funcstr)
            if print_sol:
                print(min(timeit.repeat('print({}({}))'.format(funcstr, n),
                                        setup='from __main__ import '+funcstr,
                                        repeat=repeats, number=1)))
            else:
                print(min(timeit.repeat('{}({})'.format(funcstr, n),
                                        setup='from __main__ import '+funcstr,
                                        repeat=repeats, number=1)))
    else:
        print(function(n) for function in functions[1:])

    def time_already_sorted(self):
        max_value_range = np.linspace(1, 2**self.max_exponent, num=self.num_test_points, dtype=int).tolist()
        times_diff_merge = []
        times_diff_counting = []
        for n_elems in max_value_range:
            sort_case = AlreadySortedSortCase('merge')
            sort_case.setup(n_elems=n_elems)
            elapsed_time_sorted = min(timeit.repeat(sort_case.sort, number=1, repeat=self.num_repeats))*1000
            sort_case = ManualSortCases('merge')
            sort_case.setup(np.random.permutation(n_elems).tolist())
            elapsed_time_permuted = min(timeit.repeat(sort_case.sort, number=1, repeat=self.num_repeats))*1000
            diff_merge = elapsed_time_permuted - elapsed_time_sorted
            print(diff_merge)
            times_diff_merge.append(diff_merge)
        for n_elems in max_value_range:
            sort_case = AlreadySortedSortCase('counting')
            sort_case.setup(n_elems=n_elems)
            elapsed_time_sorted = min(timeit.repeat(sort_case.sort, number=1, repeat=self.num_repeats))*1000
            sort_case = ManualSortCases('counting')
            sort_case.setup(np.random.permutation(n_elems).tolist())
            elapsed_time_permuted = min(timeit.repeat(sort_case.sort, number=1, repeat=self.num_repeats))*1000
            diff_count = elapsed_time_permuted - elapsed_time_sorted
            print(diff_count)
            times_diff_counting.append(diff_count)

        # plot both
        plt.plot(max_value_range, times_diff_merge, color='red', label='Merge sort', linestyle='-', marker='o')
        plt.plot(max_value_range, times_diff_counting, color='blue', label='Counting sort', linestyle='-', marker='o')
        plt.title('Already Sorted case')
        plt.xlabel('size (length) of the input')
        plt.ylabel(r'$RT_{PERMUTED} - RT_{SORTED}$ [ms.]')
        plt.legend(loc='upper left', frameon=True)
        plt.show()

def main():
    print("Calling on 10: " + str(sum_of_primes(10)))
    print("Timing new Prime Method:")
    print("Old Method")
    print(timeit.repeat("sum_of_primes(2000000)", "from __main__ import sum_of_primes", number =1))
    print("New Method")
    print(timeit.repeat("better_sum_of_primes(2000000)", "from __main__ import better_sum_of_primes", number =1))

def run_case(docs, words, word_range):
    SETUP = (
        'import random;'
        'from {module} import {func} as func;'
        'from __main__ import generate_doclist;'
        'random.seed("tidovsoctavian");'
        'docs_list = generate_doclist({docs}, {words}, {word_range})'
    )

    octavian = timeit.repeat(
            "func(docs_list)",
            setup=SETUP.format(
                module='set_similarity_octavian',
                func='similarity',
                docs=docs, words=words, word_range=word_range),
            number=NUMBER, repeat=REPEAT)
    tido = timeit.repeat(
            "func(docs_list)",
            setup=SETUP.format(
                module='set_similarity_tido',
                func='print_similar_docs',
                docs=docs, words=words, word_range=word_range),
            number=NUMBER, repeat=REPEAT)

    return {'octavian': octavian, 'tido': tido}

def main(conf):
    """Run timed benchmark"""
    read_sequence = [1, 2, 16, 256, 512, 1024, 2048, 4096, 8192, 16384,
                     32768, 65536, 262144]
    write_sequence = [1, 2, 16, 256, 512, 1024, 2048, 4096, 8192, 16384,
                      32768, 65536, 262144]
    read_results = []
    write_results = []

    prepare_files(conf)

    for i in read_sequence:
        read_results.append((i, min(
            timeit.repeat("read_mark(%s, filehandle)" % i,
                          setup = conf['setup_read'], repeat=conf['repeat'],
                          number=conf['number']))))

    for i in write_sequence:
        write_results.append((i, min(
            timeit.repeat("write_mark(%s, filehandle, data)" % i,
                          setup = conf['setup_write'], repeat=conf['repeat'],
                          number=conf['number']))))
    out = pprint.PrettyPrinter()
    out.pprint(read_results)
    out.pprint(write_results)

def time_fit_predict(clf, dfx, dfy, var='TF', num=10, rp=3):
    '''time fit and predict with classifier clf on training set dfx, dfy
       using num loops and rp repeats'''

#    print("time_fit_predict: var", var)
    # dfy['TF'] has two states (0, 1)
    def fit_clf():
        do_fit(clf, dfx, dfy['TF'])
    
    # should run predict on test not train data
    def predict_clf():
        do_predict(clf, dfx, dfy['TF'])
    
    # dfy['Y'] has six states (1-6)
    def fit_clf_multi():
        do_fit(clf, dfx, dfy['Y'])
    
    def predict_clf_multi():
        do_predict(clf, dfx, dfy['Y'])
    
    if var=='Y':
        tfit  = min(timeit.repeat(fit_clf_multi, repeat=rp, number=num))
        tpred = min(timeit.repeat(predict_clf_multi, repeat=rp, number=num))
    else:
        tfit  = min(timeit.repeat(fit_clf, repeat=rp, number=num))
        tpred = min(timeit.repeat(predict_clf, repeat=rp, number=num))
    tfit = tfit * 1e3 / num
    tpred = tpred * 1e3 / num

    return tfit, tpred

def _measure_performance():
    import timeit

    _sqdiff_numba = _make_sqdiff_numba()

    print "All times in ms                         numpy\tnumba"
    print "type    \tnumpy\tnumba\tC\tspeedup\tspeedup\tsize\talignment"
    for _ in range(100):
        frame_cropped, template, template_transparent = _random_template()

        for l, t in [("template ", template),
                     ("with mask", template_transparent),
                     ("unmasked ", template_transparent[:, :, :3])]:
            # pylint: disable=cell-var-from-loop

            np_time = min(timeit.repeat(
                lambda: _sqdiff_numpy(t, frame_cropped),
                repeat=3, number=10)) / 10
            c_time = min(timeit.repeat(
                lambda: _sqdiff_c(t, frame_cropped),
                repeat=3, number=10)) / 10
            if _sqdiff_numba:
                numba_time = min(timeit.repeat(
                    lambda: _sqdiff_numba(t, frame_cropped),
                    repeat=3, number=10)) / 10
            else:
                numba_time = float('nan')
            print "%s\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%i x %i \t%s" % (
                l, np_time * 1000, numba_time * 1000, c_time * 1000,
                np_time / c_time, numba_time / c_time,
                frame_cropped.shape[1], frame_cropped.shape[0],
                frame_cropped.ctypes.data % 8)

def do_it(cmd, data_str, num_threads, globals, number, repeat, divisor):
    if num_threads == 1:
        times = timeit.repeat('%s(%s, core)' % (cmd, data_str), globals=globals, number=number, repeat=repeat)
    else:
        times = timeit.repeat('_run_x(%s, %s, %s, core=core)' % (cmd, data_str, num_threads), globals=globals,
                              number=number, repeat=repeat)
    print_time(cmd, times, divisor)

 def measuringExecutionTimes(self):
     print "---- measuringExecutionTimes() ----"
     def f(x):
         return x * x
     import timeit
     print timeit.repeat("for x in range(100): lambda x: x*10","",
               number=100000)

def main():
    for m in maps:
        for y in range(MAP_HEIGHT):
            for x in range(MAP_WIDTH):
                tcod.map_set_properties(m, x, y, True, True)

    for thread in threads:
        thread.start()

    print('Python %s\n%s\n%s' % (sys.version, platform.platform(),
                                   platform.processor()))

    print('\nComputing field-of-view for %i empty %ix%i maps.' %
          (len(maps), MAP_WIDTH, MAP_HEIGHT))
    single_time = min(timeit.repeat(test_fov_single, number=1))
    print('1 thread: %.2fms' % (single_time * 1000))

    multi_time = min(timeit.repeat(test_fov_threads, number=1))
    print('%i threads: %.2fms' % (THREADS, multi_time * 1000))
    print('%.2f%% efficiency' %
          (single_time / (multi_time * THREADS) * 100))

    print('\nComputing AStar from corner to corner %i times on seperate empty'
          ' %ix%i maps.' % (PATH_NUMBER, MAP_WIDTH, MAP_HEIGHT))
    single_time = min(timeit.repeat(test_astar_single, number=1))
    print('1 thread: %.2fms' % (single_time * 1000))

    multi_time = min(timeit.repeat(test_astar_threads, number=1))
    print('%i threads: %.2fms' % (THREADS, multi_time * 1000))
    print('%.2f%% efficiency' %
          (single_time / (multi_time * THREADS) * 100))

    def test_performance(self):
        call, args = self.get_callable(*self.django_filter_args())
        df_time = min(repeat(
            lambda: call(*args),
            number=self.iterations,
            repeat=self.repeat,
        ))

        call, args = self.get_callable(*self.rest_framework_filters_args())
        drf_time = min(repeat(
            lambda: call(*args),
            number=self.iterations,
            repeat=self.repeat,
        ))

        diff = (drf_time - df_time) / df_time * 100.0

        if verbosity >= 2:
            print('\n' + '-' * 32)
            print('%s performance' % self.label)
            print('django-filter time:\t%.4fs' % df_time)
            print('drf-filters time:\t%.4fs' % drf_time)
            print('performance diff:\t%+.2f%% ' % diff)
            print('-' * 32)

        self.assertTrue(drf_time < (df_time * self.threshold))