[med-svn] [python-multipletau] 05/14: Drop obsoleted numpy_1.11.0.patch

Alex Mestiashvili malex-guest at moszumanska.debian.org
Fri Oct 20 21:29:46 UTC 2017


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malex-guest pushed a commit to branch master
in repository python-multipletau.

commit ae8f4a77c4a1064c674a7812dfe259b594368859
Author: Alexandre Mestiashvili <alex at biotec.tu-dresden.de>
Date:   Fri Oct 20 14:15:03 2017 +0200

    Drop obsoleted numpy_1.11.0.patch
---
 debian/patches/numpy_1.11.0.patch | 142 --------------------------------------
 1 file changed, 142 deletions(-)

diff --git a/debian/patches/numpy_1.11.0.patch b/debian/patches/numpy_1.11.0.patch
deleted file mode 100644
index 04c7e81..0000000
--- a/debian/patches/numpy_1.11.0.patch
+++ /dev/null
@@ -1,142 +0,0 @@
-Description: fixes #813983, starting from Numpy 1.11.0 
- "Indexing with floats raises IndexError, e.g., a[0, 0.0]."
-Author: upstream
---- python-multipletau.orig/multipletau/_multipletau.py
-+++ python-multipletau/multipletau/_multipletau.py
-@@ -136,11 +136,11 @@
-     # Check parameters
-     if np.around(m / 2) != m / 2:
-         mold = 1 * m
--        m = int((np.around(m / 2) + 1) * 2)
-+        m = np.int((np.around(m / 2) + 1) * 2)
-         warnings.warn("Invalid value of m={}. Using m={} instead"
-                       .format(mold, m))
-     else:
--        m = int(m)
-+        m = np.int(m)
- 
-     N = N0 = len(trace)
- 
-@@ -148,12 +148,12 @@
-     # The integer k defines how many times we can average over
-     # two neighboring array elements in order to obtain an array of
-     # length just larger than m.
--    k = int(np.floor(np.log2(N / m)))
-+    k = np.int(np.floor(np.log2(N / m)))
- 
-     # In the base2 multiple-tau scheme, the length of the correlation
-     # array is (only taking into account values that are computed from
-     # traces that are just larger than m):
--    lenG = np.int(np.floor(m + k * m / 2))
-+    lenG = np.int(np.floor(m + k * m // 2))
- 
-     G = np.zeros((lenG, 2), dtype=dtype)
- 
-@@ -182,12 +182,13 @@
-     # Add up every second element
-     trace = (trace[:N:2] + trace[1:N + 1:2]) / 2
-     N /= 2
-+    N = np.int(N)
-     # Start iteration for each m/2 values
-     for step in range(1, k + 1):
-         # Get the next m/2 values via correlation of the trace
--        for n in range(1, int(m / 2) + 1):
--            idx = int(m + n - 1 + (step - 1) * m / 2)
--            if len(trace[:N - (n + m / 2)]) == 0:
-+        for n in range(1, np.int(m // 2) + 1):
-+            idx = np.int(m + n - 1 + (step - 1) * m // 2)
-+            if len(trace[:N - (n + m // 2)]) == 0:
-                 # This is a shortcut that stops the iteration once the
-                 # length of the trace is too small to compute a corre-
-                 # lation. The actual length of the correlation function
-@@ -211,11 +212,11 @@
-                 # k in advance.
-                 break
-             else:
--                G[idx, 0] = deltat * (n + m / 2) * 2**step
-+                G[idx, 0] = deltat * (n + m // 2) * 2**step
-                 # This is the computationally intensive step
--                G[idx, 1] = np.sum(trace[:N - (n + m / 2)] *
--                                   trace[(n + m / 2):], dtype=dtype)
--                normstat[idx] = N - (n + m / 2)
-+                G[idx, 1] = np.sum(trace[:N - (n + m // 2)] *
-+                                   trace[(n + m // 2):], dtype=dtype)
-+                normstat[idx] = N - (n + m // 2)
-                 normnump[idx] = N
-         # Check if len(trace) is even:
-         if N % 2 == 1:
-@@ -223,6 +224,7 @@
-         # Add up every second element
-         trace = (trace[:N:2] + trace[1:N + 1:2]) / 2
-         N /= 2
-+        N = np.int(N)
- 
-     if normalize:
-         G[:, 1] /= traceavg**2 * normstat
-@@ -334,11 +336,11 @@
-     # Check parameters
-     if np.around(m / 2) != m / 2:
-         mold = 1 * m
--        m = int((np.around(m / 2) + 1) * 2)
-+        m = np.int((np.around(m / 2) + 1) * 2)
-         warnings.warn("Invalid value of m={}. Using m={} instead"
-                       .format(mold, m))
-     else:
--        m = int(m)
-+        m = np.int(m)
- 
-     if len(a) != len(v):
-         raise ValueError("Input arrays must be of equal length.")
-@@ -348,12 +350,12 @@
-     # The integer k defines how many times we can average over
-     # two neighboring array elements in order to obtain an array of
-     # length just larger than m.
--    k = int(np.floor(np.log2(N / m)))
-+    k = np.int(np.floor(np.log2(N / m)))
- 
-     # In the base2 multiple-tau scheme, the length of the correlation
-     # array is (only taking into account values that are computed from
-     # traces that are just larger than m):
--    lenG = np.int(np.floor(m + k * m / 2))
-+    lenG = np.int(np.floor(m + k * m // 2))
- 
-     G = np.zeros((lenG, 2), dtype=dtype)
-     normstat = np.zeros(lenG, dtype=dtype)
-@@ -379,22 +381,23 @@
-     trace1 = (trace1[:N:2] + trace1[1:N + 1:2]) / 2
-     trace2 = (trace2[:N:2] + trace2[1:N + 1:2]) / 2
-     N /= 2
-+    N = np.int(N)
- 
-     for step in range(1, k + 1):
-         # Get the next m/2 values of the trace
--        for n in range(1, int(m / 2) + 1):
--            idx = int(m + n - 1 + (step - 1) * m / 2)
--            if len(trace1[:N - (n + m / 2)]) == 0:
-+        for n in range(1, np.int(m // 2) + 1):
-+            idx = np.int(m + n - 1 + (step - 1) * m // 2)
-+            if len(trace1[:N - (n + m // 2)]) == 0:
-                 # Abort
-                 G = G[:idx - 1]
-                 normstat = normstat[:idx - 1]
-                 normnump = normnump[:idx - 1]
-                 break
-             else:
--                G[idx, 0] = deltat * (n + m / 2) * 2**step
-+                G[idx, 0] = deltat * (n + m // 2) * 2**step
-                 G[idx, 1] = np.sum(
--                    trace1[:N - (n + m / 2)] * trace2[(n + m / 2):])
--                normstat[idx] = N - (n + m / 2)
-+                    trace1[:N - (n + m // 2)] * trace2[(n + m // 2):])
-+                normstat[idx] = N - (n + m // 2)
-                 normnump[idx] = N
- 
-         # Check if len(trace) is even:
-@@ -404,6 +407,7 @@
-         trace1 = (trace1[:N:2] + trace1[1:N + 1:2]) / 2
-         trace2 = (trace2[:N:2] + trace2[1:N + 1:2]) / 2
-         N /= 2
-+        N = np.int(N)
- 
-     if normalize:
-         G[:, 1] /= traceavg1 * traceavg2 * normstat

-- 
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