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-rw-r--r--silx/math/test/histo_benchmarks.py269
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diff --git a/silx/math/test/histo_benchmarks.py b/silx/math/test/histo_benchmarks.py
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--- a/silx/math/test/histo_benchmarks.py
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-# coding: utf-8
-# /*##########################################################################
-# Copyright (C) 2016 European Synchrotron Radiation Facility
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-# THE SOFTWARE.
-#
-# ############################################################################*/
-"""
-histogramnd benchmarks, vs numpy.histogramdd (bin counts and weights).
-"""
-
-import numpy as np
-
-import time
-
-from silx.math import histogramnd
-
-
-def print_times(t0s, t1s, t2s, t3s):
- c_times = t1s - t0s
- np_times = t2s - t1s
- np_w_times = t3s - t2s
-
- time_txt = 'min : {0: <7.3f}; max : {1: <7.3f}; avg : {2: <7.3f}'
-
- print('\tTimes :')
- print('\tC : ' + time_txt.format(c_times.min(),
- c_times.max(),
- c_times.mean()))
- print('\tNP : ' + time_txt.format(np_times.min(),
- np_times.max(),
- np_times.mean()))
- print('\tNP(W) : ' + time_txt.format(np_w_times.min(),
- np_w_times.max(),
- np_w_times.mean()))
-
-
-def commpare_results(txt,
- times,
- result_c,
- result_np,
- result_np_w,
- sample,
- weights,
- raise_ex=False):
-
- if result_np:
- hits_cmp = np.array_equal(result_c[0], result_np[0])
- else:
- hits_cmp = None
-
- if result_np_w and result_c[1] is not None:
- weights_cmp = np.array_equal(result_c[1], result_np_w[0])
- else:
- weights_cmp = None
-
- if((hits_cmp is not None and not hits_cmp) or
- (weights_cmp is not None and not weights_cmp)):
- err_txt = (txt + ' : results arent the same : '
- 'hits : {0}, '
- 'weights : {1}.'
- ''.format('OK' if hits_cmp else 'NOK',
- 'OK' if weights_cmp else 'NOK'))
- print('\t' + err_txt)
- if raise_ex:
- raise ValueError(err_txt)
- return False
-
- result_txt = ' : results OK. c : {0: <7.3f};'.format(times[0])
- if result_np or result_np_w:
- result_txt += (' np : {0: <7.3f}; '
- 'np (weights) {1: <7.3f}.'
- ''.format(times[1], times[2]))
- print('\t' + txt + result_txt)
- return True
-
-
-def benchmark(n_loops,
- sample_shape,
- sample_rng,
- weights_rng,
- histo_range,
- n_bins,
- weight_min,
- weight_max,
- last_bin_closed,
- dtype=np.double,
- do_weights=True,
- do_numpy=True):
-
- int_min = 0
- int_max = 100000
-
- sample = np.random.randint(int_min,
- high=int_max,
- size=sample_shape).astype(np.double)
- sample = (sample_rng[0] +
- (sample - int_min) *
- (sample_rng[1] - sample_rng[0]) /
- (int_max - int_min))
- sample = sample.astype(dtype)
-
- if do_weights:
- weights = np.random.randint(int_min,
- high=int_max,
- size=(ssetup.pyample_shape[0],))
- weights = weights.astype(np.double)
- weights = (weights_rng[0] +
- (weights - int_min) *
- (weights_rng[1] - weights_rng[0]) /
- (int_max - int_min))
- else:
- weights = None
-
- t0s = []
- t1s = []
- t2s = []
- t3s = []
-
- for i in range(n_loops):
- t0s.append(time.time())
- result_c = histogramnd(sample,
- histo_range,
- n_bins,
- weights=weights,
- weight_min=weight_min,
- weight_max=weight_max,
- last_bin_closed=last_bin_closed)
- t1s.append(time.time())
- if do_numpy:
- result_np = np.histogramdd(sample,
- bins=n_bins,
- range=histo_range)
- t2s.append(time.time())
- result_np_w = np.histogramdd(sample,
- bins=n_bins,
- range=histo_range,
- weights=weights)
- t3s.append(time.time())
- else:
- result_np = None
- result_np_w = None
- t2s.append(0)
- t3s.append(0)
-
- commpare_results('Run {0}'.format(i),
- [t1s[-1] - t0s[-1], t2s[-1] - t1s[-1], t3s[-1] - t2s[-1]],
- result_c,
- result_np,
- result_np_w,
- sample,
- weights)
-
- print_times(np.array(t0s), np.array(t1s), np.array(t2s), np.array(t3s))
-
-
-def run_benchmark(dtype=np.double,
- do_weights=True,
- do_numpy=True):
- n_loops = 5
-
- weights_rng = [0., 100.]
- sample_rng = [0., 100.]
-
- weight_min = None
- weight_max = None
- last_bin_closed = True
-
- # ====================================================
- # ====================================================
- # 1D
- # ====================================================
- # ====================================================
-
- print('==========================')
- print(' 1D [{0}]'.format(dtype))
- print('==========================')
- sample_shape = (10**7,)
- histo_range = [[0., 100.]]
- n_bins = 30
-
- benchmark(n_loops,
- sample_shape,
- sample_rng,
- weights_rng,
- histo_range,
- n_bins,
- weight_min,
- weight_max,
- last_bin_closed,
- dtype=dtype,
- do_weights=True,
- do_numpy=do_numpy)
-
- # ====================================================
- # ====================================================
- # 2D
- # ====================================================
- # ====================================================
-
- print('==========================')
- print(' 2D [{0}]'.format(dtype))
- print('==========================')
- sample_shape = (10**7, 2)
- histo_range = [[0., 100.], [0., 100.]]
- n_bins = 30
-
- benchmark(n_loops,
- sample_shape,
- sample_rng,
- weights_rng,
- histo_range,
- n_bins,
- weight_min,
- weight_max,
- last_bin_closed,
- dtype=dtype,
- do_weights=True,
- do_numpy=do_numpy)
-
- # ====================================================
- # ====================================================
- # 3D
- # ====================================================
- # ====================================================
-
- print('==========================')
- print(' 3D [{0}]'.format(dtype))
- print('==========================')
- sample_shape = (10**7, 3)
- histo_range = np.array([[0., 100.], [0., 100.], [0., 100.]])
- n_bins = 30
-
- benchmark(n_loops,
- sample_shape,
- sample_rng,
- weights_rng,
- histo_range,
- n_bins,
- weight_min,
- weight_max,
- last_bin_closed,
- dtype=dtype,
- do_weights=True,
- do_numpy=do_numpy)
-
-if __name__ == '__main__':
- types = (np.double, np.int32, np.float32,)
-
- for t in types:
- run_benchmark(t,
- do_weights=True,
- do_numpy=True)
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