Merge pull request #233949 from jluttine/bayespy-0.5.26

pythonPackages.bayespy: 0.5.22 -> 0.5.26

pkgs/development/python-modules/bayespy/default.nix

···
 
 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.
···
 
   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 ];
 

pkgs/development/python-modules/bayespy/pr127-Fix-deprecated-numpy-types.patch

···
-Description: Fix deprecated numpy types
-From: Antti Mäkinen <[e-mail]>
-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):