From d2ad81b08894d21b3158c3f436dfa4d9744c5560 Mon Sep 17 00:00:00 2001 From: Jaakko Luttinen Date: Thu, 25 May 2023 11:33:35 +0300 Subject: [PATCH] pythonPackages.bayespy: 0.5.22 -> 0.5.26 --- .../python-modules/bayespy/default.nix | 18 +-- .../pr127-Fix-deprecated-numpy-types.patch | 129 ------------------ 2 files changed, 2 insertions(+), 145 deletions(-) delete mode 100644 pkgs/development/python-modules/bayespy/pr127-Fix-deprecated-numpy-types.patch diff --git a/pkgs/development/python-modules/bayespy/default.nix b/pkgs/development/python-modules/bayespy/default.nix index 7ec5dc5ec7d..0d2d87acfad 100644 --- a/pkgs/development/python-modules/bayespy/default.nix +++ b/pkgs/development/python-modules/bayespy/default.nix @@ -4,7 +4,7 @@ buildPythonPackage rec { pname = "bayespy"; - version = "0.5.22"; + version = "0.5.26"; # Python 2 not supported and not some old Python 3 because MPL doesn't support # them properly. @@ -12,23 +12,9 @@ buildPythonPackage rec { src = fetchPypi { inherit pname version; - sha256 = "ed0057dc22bd392df4b3bba23536117e1b2866e3201b12c5a37428d23421a5ba"; + sha256 = "sha256-NOvuqPKioRIqScd2jC7nakonDEovTo9qKp/uTk9z1BE="; }; - patches = [ - # Change from scipy to locally defined epsilon - # https://github.com/bayespy/bayespy/pull/126 - (fetchpatch { - name = "locally-defined-epsilon.patch"; - url = "https://github.com/bayespy/bayespy/commit/9be53bada763e19c2b6086731a6aa542ad33aad0.patch"; - hash = "sha256-KYt/0GcaNWR9K9/uS2OXgK7g1Z+Bayx9+IQGU75Mpuo="; - }) - - # Fix deprecated numpy types - # https://sources.debian.org/src/python-bayespy/0.5.22-5/debian/patches/pr127-Fix-deprecated-numpy-types.patch/ - ./pr127-Fix-deprecated-numpy-types.patch - ]; - nativeCheckInputs = [ pytestCheckHook nose glibcLocales ]; propagatedBuildInputs = [ numpy scipy matplotlib h5py ]; diff --git a/pkgs/development/python-modules/bayespy/pr127-Fix-deprecated-numpy-types.patch b/pkgs/development/python-modules/bayespy/pr127-Fix-deprecated-numpy-types.patch deleted file mode 100644 index 160a15eddb0..00000000000 --- a/pkgs/development/python-modules/bayespy/pr127-Fix-deprecated-numpy-types.patch +++ /dev/null @@ -1,129 +0,0 @@ -Description: Fix deprecated numpy types -From: Antti Mäkinen -Bug: https://github.com/bayespy/bayespy/pull/127 -Bug-Debian: https://bugs.debian.org/1027220 - ---- a/bayespy/inference/vmp/nodes/categorical_markov_chain.py -+++ b/bayespy/inference/vmp/nodes/categorical_markov_chain.py -@@ -171,7 +171,7 @@ class CategoricalMarkovChainDistribution - # Explicit broadcasting - P = P * np.ones(plates)[...,None,None,None] - # Allocate memory -- Z = np.zeros(plates + (self.N,), dtype=np.int) -+ Z = np.zeros(plates + (self.N,), dtype=np.int64) - # Draw initial state - Z[...,0] = random.categorical(p0, size=plates) - # Create [0,1,2,...,len(plate_axis)] indices for each plate axis and ---- a/bayespy/inference/vmp/nodes/concatenate.py -+++ b/bayespy/inference/vmp/nodes/concatenate.py -@@ -70,7 +70,7 @@ class Concatenate(Deterministic): - ) - - # Compute start indices for each parent on the concatenated plate axis -- self._indices = np.zeros(len(nodes)+1, dtype=np.int) -+ self._indices = np.zeros(len(nodes)+1, dtype=np.int64) - self._indices[1:] = np.cumsum([int(parent.plates[axis]) - for parent in self.parents]) - self._lengths = [parent.plates[axis] for parent in self.parents] ---- a/bayespy/inference/vmp/nodes/tests/test_binomial.py -+++ b/bayespy/inference/vmp/nodes/tests/test_binomial.py -@@ -43,7 +43,7 @@ class TestBinomial(TestCase): - X = Binomial(10, 0.7*np.ones((4,3))) - self.assertEqual(X.plates, - (4,3)) -- n = np.ones((4,3), dtype=np.int) -+ n = np.ones((4,3), dtype=np.int64) - X = Binomial(n, 0.7) - self.assertEqual(X.plates, - (4,3)) ---- a/bayespy/inference/vmp/nodes/tests/test_multinomial.py -+++ b/bayespy/inference/vmp/nodes/tests/test_multinomial.py -@@ -43,7 +43,7 @@ class TestMultinomial(TestCase): - X = Multinomial(10, 0.25*np.ones((2,3,4))) - self.assertEqual(X.plates, - (2,3)) -- n = 10 * np.ones((3,4), dtype=np.int) -+ n = 10 * np.ones((3,4), dtype=np.int64) - X = Multinomial(n, [0.1, 0.3, 0.6]) - self.assertEqual(X.plates, - (3,4)) ---- a/bayespy/inference/vmp/nodes/tests/test_take.py -+++ b/bayespy/inference/vmp/nodes/tests/test_take.py -@@ -89,7 +89,7 @@ class TestTake(TestCase): - - # Test matrix indices, no shape - X = GaussianARD(1, 1, plates=(3,), shape=(2,)) -- Y = Take(X, np.ones((4, 5), dtype=np.int)) -+ Y = Take(X, np.ones((4, 5), dtype=np.int64)) - self.assertEqual( - Y.plates, - (4, 5), -@@ -113,7 +113,7 @@ class TestTake(TestCase): - - # Test vector indices with more plate axes - X = GaussianARD(1, 1, plates=(4, 2), shape=()) -- Y = Take(X, np.ones(3, dtype=np.int)) -+ Y = Take(X, np.ones(3, dtype=np.int64)) - self.assertEqual( - Y.plates, - (4, 3), -@@ -125,7 +125,7 @@ class TestTake(TestCase): - - # Test take on other plate axis - X = GaussianARD(1, 1, plates=(4, 2), shape=()) -- Y = Take(X, np.ones(3, dtype=np.int), plate_axis=-2) -+ Y = Take(X, np.ones(3, dtype=np.int64), plate_axis=-2) - self.assertEqual( - Y.plates, - (3, 2), -@@ -141,7 +141,7 @@ class TestTake(TestCase): - ValueError, - Take, - X, -- np.ones(3, dtype=np.int), -+ np.ones(3, dtype=np.int64), - plate_axis=0, - ) - ---- a/bayespy/utils/tests/test_linalg.py -+++ b/bayespy/utils/tests/test_linalg.py -@@ -126,7 +126,7 @@ class TestBandedSolve(misc.TestCase): - # Random sizes of the blocks - #D = np.random.randint(5, 10, size=N) - # Fixed sizes of the blocks -- D = 5*np.ones(N, dtype=np.int) -+ D = 5*np.ones(N, dtype=np.int64) - - # Some helpful variables to create the covariances - W = [np.random.randn(D[i], 2*D[i]) ---- a/bayespy/utils/misc.py -+++ b/bayespy/utils/misc.py -@@ -355,7 +355,7 @@ class TestCase(unittest.TestCase): - ] - ) - ] -- ).astype(np.int) -+ ).astype(int) - - def pack(x): - return [ ---- a/bayespy/utils/random.py -+++ b/bayespy/utils/random.py -@@ -284,7 +284,7 @@ def categorical(p, size=None): - for ind in inds: - z[ind] = np.searchsorted(P[ind], x[ind]) - -- return z.astype(np.int) -+ return z.astype(int) - - - def multinomial(n, p, size=None): -@@ -313,7 +313,7 @@ def multinomial(n, p, size=None): - for i in misc.nested_iterator(size): - x[i] = np.random.multinomial(n[i], p[i]) - -- return x.astype(np.int) -+ return x.astype(int) - - - def gamma(a, b, size=None):