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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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+# 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)
generated by cgit v1.2.3 (git 2.25.1) at 2024-04-23 07:32:11 +0000