Merge pull request #426 from matthew-brett/gmm-plot-2d-fix

MRG: fix more numpy 1.12 indexing errors

More indexing errors found by running the examples.

examples/algorithms/ward_clustering.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
-from __future__ import print_function # Python 2/3 compatibility
+from __future__ import print_function, division # Python 2/3 compatibility
 __doc__ = """
 Demo ward clustering on a graph: various ways of forming clusters and dendrogram
 
@@ -28,7 +28,7 @@
 verbose = False
 
 X = randn(n, 2)
-X[:np.ceil(n / 3)] += 3
+X[:int(np.ceil(n / 3))] += 3
 G = knn(X, 5)
 tree = ward(G, X, verbose)
 
@@ -57,7 +57,7 @@
 
 nl = np.sum(tree.isleaf())
 validleaves = np.zeros(n)
-validleaves[:np.ceil(n / 4)] = 1
+validleaves[:int(np.ceil(n / 4))] = 1
 valid = np.zeros(tree.V, 'bool')
 valid[tree.isleaf()] = validleaves.astype('bool')
 nv = np.sum(validleaves)

examples/labs/permutation_test_fakedata.py

@@ -35,11 +35,11 @@ def make_data(n=10, mask_shape=(10, 10, 10), axis=0, r=3, signal=5):
 P = PT.permutation_test_onesample(data, XYZ)
 
 # clusters definition (height threshold, max diameter)
-c = [(P.random_Tvalues[P.ndraws * (0.95)], None)]
+c = [(P.random_Tvalues[int(P.ndraws * (0.95))], None)]
 
 # regions definition (label vector)
 r = np.ones(data.shape[1], int)
-r[data.shape[1]/2.:] *= 10
+r[int(data.shape[1] / 2.):] *= 10
 voxel_results, cluster_results, region_results = \
                 P.calibrate(nperms=100, clusters=c, regions=[r])
 
@@ -57,7 +57,7 @@ def make_data(n=10, mask_shape=(10, 10, 10), axis=0, r=3, signal=5):
 
 # rfx calibration
 P = PT.permutation_test_twosample(data1, data2, XYZ)
-c = [(P.random_Tvalues[P.ndraws * (0.95)], None)]
+c = [(P.random_Tvalues[int(P.ndraws * (0.95))], None)]
 voxel_results, cluster_results, region_results = P.calibrate(nperms=100,
                                                              clusters=c)
 

nipy/algorithms/clustering/gmm.py

@@ -206,7 +206,7 @@ def plot2D(x, my_gmm, z=None, with_dots=True, log_scale=False, mpaxes=None,
         ax = mpaxes
 
     gdx = gd1.n_bins[0]
-    Pdens = np.reshape(L, (gdx, np.size(L) / gdx))
+    Pdens = np.reshape(L, (gdx, -1))
     extent = [xm, xs, ym, ys]
     if log_scale:
         plt.imshow(np.log(Pdens.T), alpha=2.0, origin='lower',

nipy/algorithms/clustering/hierarchical_clustering.py

@@ -146,7 +146,7 @@ def plot(self, ax=None):
         rank = np.arange(self.V)
         temp[:n] = np.argsort(aux[:n])
         for i in range(n):
-            rank[temp[i]] = i
+            rank[int(temp[i])] = i
 
         # 2. derive the abscissa in the dendrogram
         idx = np.zeros(self.V)

nipy/info.py

@@ -191,7 +191,7 @@
 %s
 
 Check the instructions in the ``doc/users/install_data.rst`` file in the nipy
-source tree, or online at http://nipy.org/nipy/stable/users/install_data.html
+source tree, or online at http://nipy.org/nipy/users/install_data.html
 
 If you have the package, have you set the path to the package correctly?"""
 

nipy/labs/group/permutation_test.py

@@ -546,11 +546,11 @@ def height_threshold(self, pval):
         """
         tvals = self.random_Tvalues
         ndraws = tvals.size
-        idx = np.ceil(ndraws*(1-pval))
+        idx = int(np.ceil(ndraws * (1 - pval)))
         if idx >= ndraws:
             return np.inf
         candidate = tvals[idx]
-        if tvals[max(0, idx-1)]<candidate:
+        if tvals[max(0, idx-1)] < candidate:
             return candidate
         idx = np.searchsorted(tvals, candidate, 'right')
         if idx >= ndraws:

nipy/labs/statistical_mapping.py

@@ -127,10 +127,10 @@ def cluster_stats(zimg, mask, height_th, height_control='fpr',
 
         # Voxel-level corrected p-values
         p = None
-        if nulls['zmax'] == 'bonferroni':
-            p = bonferroni(pval, nvoxels)
-        elif isinstance(nulls['zmax'], np.ndarray):
+        if isinstance(nulls['zmax'], np.ndarray):
             p = simulated_pvalue(zscore, nulls['zmax'])
+        elif nulls['zmax'] == 'bonferroni':
+            p = bonferroni(pval, nvoxels)
         c['fwer_pvalue'] = p
 
         # Cluster-level p-values (corrected)

setup.py

@@ -88,7 +88,7 @@ def data_install_msgs():
         from nibabel.data import datasource_or_bomber
     except ImportError:
         log.warn('Cannot check for optional data packages: see: '
-                 'http://nipy.org/nipy/stable/users/install_data.html')
+                 'http://nipy.org/nipy/users/install_data.html')
         return
     DATA_PKGS = INFO_VARS['DATA_PKGS']
     templates = datasource_or_bomber(DATA_PKGS['nipy-templates'])