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https://github.com/yann64/haikuports.git
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131 lines
5.1 KiB
Plaintext
131 lines
5.1 KiB
Plaintext
From b6e0d70785f85e4a8b9ab61b37f684ad0eca4d28 Mon Sep 17 00:00:00 2001
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From: Aleksei Gerasimov <aleksei.gerasimov@vutbr.cz>
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Date: Thu, 15 Oct 2020 14:13:11 +0200
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Subject: disable ndimage
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diff --git a/scipy/setup.py b/scipy/setup.py
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index 3bcdd48..b3ede6f 100644
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--- a/scipy/setup.py
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+++ b/scipy/setup.py
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@@ -21,7 +21,7 @@ def configuration(parent_package='',top_path=None):
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config.add_subpackage('spatial')
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config.add_subpackage('special')
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config.add_subpackage('stats')
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- config.add_subpackage('ndimage')
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+ #config.add_subpackage('ndimage')
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config.add_subpackage('_build_utils')
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config.add_subpackage('_lib')
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config.make_config_py()
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--
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2.30.2
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From f9e25e090297e27de3395bab18f104c70358d9ec Mon Sep 17 00:00:00 2001
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From: begasus <begasus@gmail.com>
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Date: Mon, 24 May 2021 18:40:26 +0000
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Subject: Haiku doesn't use -pthread, use -lpthread instead
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diff --git a/scipy/fft/_pocketfft/setup.py b/scipy/fft/_pocketfft/setup.py
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index 7e44565..3b0ead2 100644
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--- a/scipy/fft/_pocketfft/setup.py
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+++ b/scipy/fft/_pocketfft/setup.py
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@@ -15,9 +15,9 @@ def pre_build_hook(build_ext, ext):
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'int main(int argc, char **argv) {}')
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if has_pthreads:
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ext.define_macros.append(('POCKETFFT_PTHREADS', None))
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- if has_flag(cc, '-pthread'):
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- args.append('-pthread')
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- ext.extra_link_args.append('-pthread')
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+ if has_flag(cc, '-lpthread'):
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+ args.append('-lpthread')
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+ ext.extra_link_args.append('-lpthread')
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else:
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raise RuntimeError("Build failed: System has pthreads header "
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"but could not compile with -pthread option")
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--
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2.30.2
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From 0450b42c56cb1ed5d80a81447a766a4cfa757e63 Mon Sep 17 00:00:00 2001
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From: Aleksei Gerasimov <aleksei.gerasimov@vutbr.cz>
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Date: Tue, 24 Aug 2021 15:19:52 +0200
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Subject: comment out ndimage import. Only one function (_threshold_mgc_map) is
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directly affected.
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diff --git a/scipy/stats/stats.py b/scipy/stats/stats.py
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index 2b7dac8..c460637 100644
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--- a/scipy/stats/stats.py
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+++ b/scipy/stats/stats.py
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@@ -172,7 +172,7 @@ import numpy as np
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from numpy import array, asarray, ma
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from scipy.spatial.distance import cdist
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-from scipy.ndimage import measurements
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+#from scipy.ndimage import measurements
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from scipy._lib._util import (_lazywhere, check_random_state, MapWrapper,
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rng_integers, float_factorial)
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import scipy.special as special
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@@ -5208,32 +5208,33 @@ def _threshold_mgc_map(stat_mgc_map, samp_size):
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sig_connect : ndarray
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A binary matrix with 1's indicating the significant region.
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"""
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- m, n = stat_mgc_map.shape
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-
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- # 0.02 is simply an empirical threshold, this can be set to 0.01 or 0.05
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- # with varying levels of performance. Threshold is based on a beta
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- # approximation.
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- per_sig = 1 - (0.02 / samp_size) # Percentile to consider as significant
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- threshold = samp_size * (samp_size - 3)/4 - 1/2 # Beta approximation
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- threshold = distributions.beta.ppf(per_sig, threshold, threshold) * 2 - 1
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-
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- # the global scale at is the statistic calculated at maximial nearest
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- # neighbors. Threshold is the maximium on the global and local scales
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- threshold = max(threshold, stat_mgc_map[m - 1][n - 1])
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-
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- # find the largest connected component of significant correlations
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- sig_connect = stat_mgc_map > threshold
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- if np.sum(sig_connect) > 0:
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- sig_connect, _ = measurements.label(sig_connect)
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- _, label_counts = np.unique(sig_connect, return_counts=True)
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-
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- # skip the first element in label_counts, as it is count(zeros)
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- max_label = np.argmax(label_counts[1:]) + 1
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- sig_connect = sig_connect == max_label
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- else:
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- sig_connect = np.array([[False]])
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-
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- return sig_connect
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+ raise ImportError("Haiku's package of scipy does not contain ndimage module")
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+# m, n = stat_mgc_map.shape
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+#
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+# # 0.02 is simply an empirical threshold, this can be set to 0.01 or 0.05
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+# # with varying levels of performance. Threshold is based on a beta
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+# # approximation.
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+# per_sig = 1 - (0.02 / samp_size) # Percentile to consider as significant
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+# threshold = samp_size * (samp_size - 3)/4 - 1/2 # Beta approximation
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+# threshold = distributions.beta.ppf(per_sig, threshold, threshold) * 2 - 1
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+#
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+# # the global scale at is the statistic calculated at maximial nearest
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+# # neighbors. Threshold is the maximium on the global and local scales
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+# threshold = max(threshold, stat_mgc_map[m - 1][n - 1])
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+#
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+# # find the largest connected component of significant correlations
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+# sig_connect = stat_mgc_map > threshold
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+# if np.sum(sig_connect) > 0:
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+# sig_connect, _ = measurements.label(sig_connect)
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+# _, label_counts = np.unique(sig_connect, return_counts=True)
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+#
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+# # skip the first element in label_counts, as it is count(zeros)
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+# max_label = np.argmax(label_counts[1:]) + 1
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+# sig_connect = sig_connect == max_label
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+# else:
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+# sig_connect = np.array([[False]])
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+#
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+# return sig_connect
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def _smooth_mgc_map(sig_connect, stat_mgc_map):
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--
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2.30.2 |