Merge pull request #10618 from eric-wieser/avoid-nontuple-indices

MAINT: Stop using non-tuple indices internally

8 files changed, 33 insertions, 29 deletions

diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py
index 26882c593..3a02c9fce 100644
--- a/numpy/core/tests/test_indexing.py
+++ b/numpy/core/tests/test_indexing.py
@@ -499,7 +499,7 @@ class TestIndexing(object):
         arro = np.zeros((4, 4))
         arr = arro[::-1, ::-1]
 
-        slices = [slice(None), [0, 1, 2, 3]]
+        slices = (slice(None), [0, 1, 2, 3])
         arr[slices] = 10
         assert_array_equal(arr, 10.)
 
diff --git a/numpy/core/tests/test_memmap.py b/numpy/core/tests/test_memmap.py
index 1cd09ab21..c00867ce1 100644
--- a/numpy/core/tests/test_memmap.py
+++ b/numpy/core/tests/test_memmap.py
@@ -126,7 +126,7 @@ class TestMemmap(object):
     def test_indexing_drops_references(self):
         fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
                     shape=self.shape)
-        tmp = fp[[(1, 2), (2, 3)]]
+        tmp = fp[(1, 2), (2, 3)]
         if isinstance(tmp, memmap):
             assert_(tmp._mmap is not fp._mmap)
 
diff --git a/numpy/fft/fftpack.py b/numpy/fft/fftpack.py
index bd116b9cb..e17e1cb34 100644
--- a/numpy/fft/fftpack.py
+++ b/numpy/fft/fftpack.py
@@ -69,13 +69,13 @@ def _raw_fft(a, n=None, axis=-1, init_function=fftpack.cffti,
         if s[axis] > n:
             index = [slice(None)]*len(s)
             index[axis] = slice(0, n)
-            a = a[index]
+            a = a[tuple(index)]
         else:
             index = [slice(None)]*len(s)
             index[axis] = slice(0, s[axis])
             s[axis] = n
             z = zeros(s, a.dtype.char)
-            z[index] = a
+            z[tuple(index)] = a
             a = z
 
     if axis != -1:
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py
index cdc354a02..daaa68d06 100644
--- a/numpy/lib/arraypad.py
+++ b/numpy/lib/arraypad.py
@@ -1346,9 +1346,9 @@ def pad(array, pad_width, mode, **kwargs):
         # Create a new padded array
         rank = list(range(narray.ndim))
         total_dim_increase = [np.sum(pad_width[i]) for i in rank]
-        offset_slices = [slice(pad_width[i][0],
-                               pad_width[i][0] + narray.shape[i])
-                         for i in rank]
+        offset_slices = tuple(
+            slice(pad_width[i][0], pad_width[i][0] + narray.shape[i])
+            for i in rank)
         new_shape = np.array(narray.shape) + total_dim_increase
         newmat = np.zeros(new_shape, narray.dtype)
 
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index bff3798ca..1eb197c12 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -975,7 +975,7 @@ def gradient(f, *varargs, **kwargs):
         slice4[axis] = slice(2, None)
 
         if uniform_spacing:
-            out[slice1] = (f[slice4] - f[slice2]) / (2. * ax_dx)
+            out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / (2. * ax_dx)
         else:
             dx1 = ax_dx[0:-1]
             dx2 = ax_dx[1:]
@@ -987,7 +987,7 @@ def gradient(f, *varargs, **kwargs):
             shape[axis] = -1
             a.shape = b.shape = c.shape = shape
             # 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:]
-            out[slice1] = a * f[slice2] + b * f[slice3] + c * f[slice4]
+            out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
 
         # Numerical differentiation: 1st order edges
         if edge_order == 1:
@@ -996,14 +996,14 @@ def gradient(f, *varargs, **kwargs):
             slice3[axis] = 0
             dx_0 = ax_dx if uniform_spacing else ax_dx[0]
             # 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0])
-            out[slice1] = (f[slice2] - f[slice3]) / dx_0
+            out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0
 
             slice1[axis] = -1
             slice2[axis] = -1
             slice3[axis] = -2
             dx_n = ax_dx if uniform_spacing else ax_dx[-1]
             # 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2])
-            out[slice1] = (f[slice2] - f[slice3]) / dx_n
+            out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n
 
         # Numerical differentiation: 2nd order edges
         else:
@@ -1022,7 +1022,7 @@ def gradient(f, *varargs, **kwargs):
                 b = (dx1 + dx2) / (dx1 * dx2)
                 c = - dx1 / (dx2 * (dx1 + dx2))
             # 1D equivalent -- out[0] = a * f[0] + b * f[1] + c * f[2]
-            out[slice1] = a * f[slice2] + b * f[slice3] + c * f[slice4]
+            out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
 
             slice1[axis] = -1
             slice2[axis] = -3
@@ -1039,7 +1039,7 @@ def gradient(f, *varargs, **kwargs):
                 b = - (dx2 + dx1) / (dx1 * dx2)
                 c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2))
             # 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1]
-            out[slice1] = a * f[slice2] + b * f[slice3] + c * f[slice4]
+            out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
 
         outvals.append(out)
 
@@ -1377,6 +1377,7 @@ def unwrap(p, discont=pi, axis=-1):
     dd = diff(p, axis=axis)
     slice1 = [slice(None, None)]*nd     # full slices
     slice1[axis] = slice(1, None)
+    slice1 = tuple(slice1)
     ddmod = mod(dd + pi, 2*pi) - pi
     _nx.copyto(ddmod, pi, where=(ddmod == -pi) & (dd > 0))
     ph_correct = ddmod - dd
@@ -3359,6 +3360,7 @@ def _median(a, axis=None, out=None, overwrite_input=False):
         indexer[axis] = slice(index, index+1)
     else:
         indexer[axis] = slice(index-1, index+1)
+    indexer = tuple(indexer)
 
     # Check if the array contains any nan's
     if np.issubdtype(a.dtype, np.inexact) and sz > 0:
@@ -3751,12 +3753,12 @@ def trapz(y, x=None, dx=1.0, axis=-1):
     slice1[axis] = slice(1, None)
     slice2[axis] = slice(None, -1)
     try:
-        ret = (d * (y[slice1] + y[slice2]) / 2.0).sum(axis)
+        ret = (d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0).sum(axis)
     except ValueError:
         # Operations didn't work, cast to ndarray
         d = np.asarray(d)
         y = np.asarray(y)
-        ret = add.reduce(d * (y[slice1]+y[slice2])/2.0, axis)
+        ret = add.reduce(d * (y[tuple(slice1)]+y[tuple(slice2)])/2.0, axis)
     return ret
 
 
@@ -4045,7 +4047,7 @@ def delete(arr, obj, axis=None):
             pass
         else:
             slobj[axis] = slice(None, start)
-            new[slobj] = arr[slobj]
+            new[tuple(slobj)] = arr[tuple(slobj)]
         # copy end chunck
         if stop == N:
             pass
@@ -4053,7 +4055,7 @@ def delete(arr, obj, axis=None):
             slobj[axis] = slice(stop-numtodel, None)
             slobj2 = [slice(None)]*ndim
             slobj2[axis] = slice(stop, None)
-            new[slobj] = arr[slobj2]
+            new[tuple(slobj)] = arr[tuple(slobj2)]
         # copy middle pieces
         if step == 1:
             pass
@@ -4063,9 +4065,9 @@ def delete(arr, obj, axis=None):
             slobj[axis] = slice(start, stop-numtodel)
             slobj2 = [slice(None)]*ndim
             slobj2[axis] = slice(start, stop)
-            arr = arr[slobj2]
+            arr = arr[tuple(slobj2)]
             slobj2[axis] = keep
-            new[slobj] = arr[slobj2]
+            new[tuple(slobj)] = arr[tuple(slobj2)]
         if wrap:
             return wrap(new)
         else:
@@ -4092,11 +4094,11 @@ def delete(arr, obj, axis=None):
         newshape[axis] -= 1
         new = empty(newshape, arr.dtype, arrorder)
         slobj[axis] = slice(None, obj)
-        new[slobj] = arr[slobj]
+        new[tuple(slobj)] = arr[tuple(slobj)]
         slobj[axis] = slice(obj, None)
         slobj2 = [slice(None)]*ndim
         slobj2[axis] = slice(obj+1, None)
-        new[slobj] = arr[slobj2]
+        new[tuple(slobj)] = arr[tuple(slobj2)]
     else:
         if obj.size == 0 and not isinstance(_obj, np.ndarray):
             obj = obj.astype(intp)
@@ -4128,7 +4130,7 @@ def delete(arr, obj, axis=None):
 
         keep[obj, ] = False
         slobj[axis] = keep
-        new = arr[slobj]
+        new = arr[tuple(slobj)]
 
     if wrap:
         return wrap(new)
@@ -4299,13 +4301,13 @@ def insert(arr, obj, values, axis=None):
         newshape[axis] += numnew
         new = empty(newshape, arr.dtype, arrorder)
         slobj[axis] = slice(None, index)
-        new[slobj] = arr[slobj]
+        new[tuple(slobj)] = arr[tuple(slobj)]
         slobj[axis] = slice(index, index+numnew)
-        new[slobj] = values
+        new[tuple(slobj)] = values
         slobj[axis] = slice(index+numnew, None)
         slobj2 = [slice(None)] * ndim
         slobj2[axis] = slice(index, None)
-        new[slobj] = arr[slobj2]
+        new[tuple(slobj)] = arr[tuple(slobj2)]
         if wrap:
             return wrap(new)
         return new
@@ -4334,8 +4336,8 @@ def insert(arr, obj, values, axis=None):
     slobj2 = [slice(None)]*ndim
     slobj[axis] = indices
     slobj2[axis] = old_mask
-    new[slobj] = values
-    new[slobj2] = arr
+    new[tuple(slobj)] = values
+    new[tuple(slobj2)] = arr
 
     if wrap:
         return wrap(new)
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index ccc6c0616..cf40fcfe0 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -860,7 +860,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
         ni[i], ni[j] = ni[j], ni[i]
 
     # Remove outliers (indices 0 and -1 for each dimension).
-    core = D*[slice(1, -1)]
+    core = D*(slice(1, -1),)
     hist = hist[core]
 
     # Normalize if normed is True
diff --git a/numpy/ma/core.py b/numpy/ma/core.py
index 698d39cda..6deff0ef4 100644
--- a/numpy/ma/core.py
+++ b/numpy/ma/core.py
@@ -5538,6 +5538,7 @@ class MaskedArray(ndarray):
         else:
             idx = list(np.ix_(*[np.arange(x) for x in self.shape]))
             idx[axis] = sidx
+            idx = tuple(idx)
 
         self[...] = self[idx]
 
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index 99f5234d1..e247fe170 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -724,6 +724,7 @@ def _median(a, axis=None, out=None, overwrite_input=False):
         # as median (which is mean of empty slice = nan)
         indexer = [slice(None)] * asorted.ndim
         indexer[axis] = slice(0, 0)
+        indexer = tuple(indexer)
         return np.ma.mean(asorted[indexer], axis=axis, out=out)
 
     if asorted.ndim == 1:
@@ -1716,7 +1717,7 @@ def notmasked_contiguous(a, axis=None):
     #
     for i in range(a.shape[other]):
         idx[other] = i
-        result.append(flatnotmasked_contiguous(a[idx]) or None)
+        result.append(flatnotmasked_contiguous(a[tuple(idx)]) or None)
     return result