File: numpy-1.24.patch

package info (click to toggle)
python-pymbar 3.1.0-4
  • links: PTS, VCS
  • area: main
  • in suites: bookworm
  • size: 37,828 kB
  • sloc: python: 5,326; makefile: 154; ansic: 59; perl: 52; sh: 46
file content (38 lines) | stat: -rw-r--r-- 1,796 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Description: Fix test failure with Numpy 1.24.
Author: Bas Couwenberg <sebastic@debian.org>
Forwarded: not-needed
Bug-Debian: https://bugs.debian.org/1027217

--- a/pymbar/testsystems/exponential_distributions.py
+++ b/pymbar/testsystems/exponential_distributions.py
@@ -138,7 +138,7 @@ class ExponentialTestCase(object):
         x_kn = np.zeros([self.n_states, N_max], np.float64)
         u_kln = np.zeros([self.n_states, self.n_states, N_max], np.float64)
         x_n = np.zeros([N_tot], np.float64)
-        s_n = np.zeros([N_tot], np.int)
+        s_n = np.zeros([N_tot], int)
         u_kn = np.zeros([self.n_states, N_tot], np.float64)
         index = 0
         for k, N in enumerate(N_k):
--- a/pymbar/testsystems/harmonic_oscillators.py
+++ b/pymbar/testsystems/harmonic_oscillators.py
@@ -145,7 +145,7 @@ class HarmonicOscillatorsTestCase(object
         x_kn = np.zeros([self.n_states, N_max], np.float64)
         u_kln = np.zeros([self.n_states, self.n_states, N_max], np.float64)
         x_n = np.zeros([N_tot], np.float64)
-        s_n = np.zeros([N_tot], np.int)
+        s_n = np.zeros([N_tot], int)
         u_kn = np.zeros([self.n_states, N_tot], np.float64)
         index = 0
         for k, N in enumerate(N_k):
--- a/pymbar/tests/test_mbar.py
+++ b/pymbar/tests/test_mbar.py
@@ -374,7 +374,7 @@ def test_mbar_computePMF():
     xmin = test.O_k[refstate] - 1
     xmax = test.O_k[refstate] + 1
     within_bounds = (x_n >= xmin) & (x_n < xmax)
-    bin_centers = dx*np.arange(np.int(xmin/dx),np.int(xmax/dx)) + dx/2
+    bin_centers = dx*np.arange(int(xmin/dx),int(xmax/dx)) + dx/2
     bin_n = np.zeros(len(x_n),int)
     bin_n[within_bounds] = 1 + np.floor((x_n[within_bounds]-xmin)/dx)
     # 0 is reserved for samples outside the domain.  We will ignore this state