Use ndim, size whereever appropriate.

Author: anntzer

examples/statistics/errorbar_limits.py

@@ -52,7 +52,7 @@
 # Plot a series with lower and upper limits in both x & y
 # constant x-error with varying y-error
 xerr = 0.2
-yerr = np.zeros(x.shape) + 0.2
+yerr = np.zeros_like(x) + 0.2
 yerr[[3, 6]] = 0.3
 
 # mock up some limits by modifying previous data

lib/matplotlib/axes/_axes.py

@@ -5200,10 +5200,10 @@ def _pcolorargs(funcname, *args, **kw):
 
         Nx = X.shape[-1]
         Ny = Y.shape[0]
-        if len(X.shape) != 2 or X.shape[0] == 1:
+        if X.ndim != 2 or X.shape[0] == 1:
             x = X.reshape(1, Nx)
             X = x.repeat(Ny, axis=0)
-        if len(Y.shape) != 2 or Y.shape[1] == 1:
+        if Y.ndim != 2 or Y.shape[1] == 1:
             y = Y.reshape(Ny, 1)
             Y = y.repeat(Nx, axis=1)
         if X.shape != Y.shape:

lib/matplotlib/image.py

@@ -848,8 +848,7 @@ def set_data(self, x, y, A):
         x = np.array(x, np.float32)
         y = np.array(y, np.float32)
         A = cbook.safe_masked_invalid(A, copy=True)
-        if len(x.shape) != 1 or len(y.shape) != 1\
-           or A.shape[0:2] != (y.shape[0], x.shape[0]):
+        if not (x.ndim == y.ndim == 1 and A.shape[0:2] == y.shape + x.shape):
             raise TypeError("Axes don't match array shape")
         if A.ndim not in [2, 3]:
             raise TypeError("Can only plot 2D or 3D data")

lib/matplotlib/mlab.py

@@ -802,7 +802,7 @@ def _single_spectrum_helper(x, mode, Fs=None, window=None, pad_to=None,
     if mode != 'complex':
         spec = spec.real
 
-    if len(spec.shape) == 2 and spec.shape[1] == 1:
+    if spec.ndim == 2 and spec.shape[1] == 1:
         spec = spec[:, 0]
 
     return spec, freqs
@@ -1013,7 +1013,7 @@ def csd(x, y, NFFT=None, Fs=None, detrend=None, window=None,
                                      sides=sides, scale_by_freq=scale_by_freq,
                                      mode='psd')
 
-    if len(Pxy.shape) == 2:
+    if Pxy.ndim == 2:
         if Pxy.shape[1] > 1:
             Pxy = Pxy.mean(axis=1)
         else:
@@ -1671,16 +1671,12 @@ def project(self, x, minfrac=0.):
         of variance<minfrac
         '''
         x = np.asarray(x)
-
-        ndims = len(x.shape)
-
-        if (x.shape[-1] != self.numcols):
+        if x.shape[-1] != self.numcols:
             raise ValueError('Expected an array with dims[-1]==%d' %
                              self.numcols)
-
         Y = np.dot(self.Wt, self.center(x).T).T
         mask = self.fracs >= minfrac
-        if ndims == 2:
+        if x.ndim == 2:
             Yreduced = Y[:, mask]
         else:
             Yreduced = Y[mask]

lib/matplotlib/testing/compare.py

@@ -254,7 +254,7 @@ def calculate_rms(expectedImage, actualImage):
         raise ImageComparisonFailure(
             "image sizes do not match expected size: {0} "
             "actual size {1}".format(expectedImage.shape, actualImage.shape))
-    num_values = np.prod(expectedImage.shape)
+    num_values = expectedImage.size
     abs_diff_image = abs(expectedImage - actualImage)
     histogram = np.bincount(abs_diff_image.ravel(), minlength=256)
     sum_of_squares = np.sum(histogram * np.arange(len(histogram)) ** 2)

lib/matplotlib/tests/test_axes.py

@@ -2390,13 +2390,13 @@ def test_errorbar_limits():
     plt.errorbar(x, y, xerr=xerr, yerr=yerr, ls=ls, color='blue')
 
     # including upper limits
-    uplims = np.zeros(x.shape)
+    uplims = np.zeros_like(x)
     uplims[[1, 5, 9]] = True
     plt.errorbar(x, y+0.5, xerr=xerr, yerr=yerr, uplims=uplims, ls=ls,
                  color='green')
 
     # including lower limits
-    lolims = np.zeros(x.shape)
+    lolims = np.zeros_like(x)
     lolims[[2, 4, 8]] = True
     plt.errorbar(x, y+1.0, xerr=xerr, yerr=yerr, lolims=lolims, ls=ls,
                  color='red')
@@ -2407,12 +2407,12 @@ def test_errorbar_limits():
 
     # including xlower and xupper limits
     xerr = 0.2
-    yerr = np.zeros(x.shape) + 0.2
+    yerr = np.zeros_like(x) + 0.2
     yerr[[3, 6]] = 0.3
     xlolims = lolims
     xuplims = uplims
-    lolims = np.zeros(x.shape)
-    uplims = np.zeros(x.shape)
+    lolims = np.zeros_like(x)
+    uplims = np.zeros_like(x)
     lolims[[6]] = True
     uplims[[3]] = True
     plt.errorbar(x, y+2.1, marker='o', ms=8, xerr=xerr, yerr=yerr,

lib/matplotlib/tri/triangulation.py

@@ -41,7 +41,7 @@ class Triangulation(object):
     def __init__(self, x, y, triangles=None, mask=None):
         self.x = np.asarray(x, dtype=np.float64)
         self.y = np.asarray(y, dtype=np.float64)
-        if self.x.shape != self.y.shape or len(self.x.shape) != 1:
+        if self.x.shape != self.y.shape or self.x.ndim != 1:
             raise ValueError("x and y must be equal-length 1-D arrays")
 
         self.mask = None
@@ -67,8 +67,7 @@ def __init__(self, x, y, triangles=None, mask=None):
 
         if mask is not None:
             self.mask = np.asarray(mask, dtype=np.bool)
-            if (len(self.mask.shape) != 1 or
-                    self.mask.shape[0] != self.triangles.shape[0]):
+            if self.mask.shape != (self.triangles.shape[0],):
                 raise ValueError('mask array must have same length as '
                                  'triangles array')
 
@@ -202,8 +201,7 @@ def set_mask(self, mask):
             self.mask = None
         else:
             self.mask = np.asarray(mask, dtype=np.bool)
-            if (len(self.mask.shape) != 1 or
-                    self.mask.shape[0] != self.triangles.shape[0]):
+            if self.mask.shape != (self.triangles.shape[0],):
                 raise ValueError('mask array must have same length as '
                                  'triangles array')
 

lib/matplotlib/tri/triinterpolate.py

@@ -166,7 +166,7 @@ def _interpolate_multikeys(self, x, y, tri_index=None,
         x = np.asarray(x, dtype=np.float64)
         y = np.asarray(y, dtype=np.float64)
         sh_ret = x.shape
-        if (x.shape != y.shape):
+        if x.shape != y.shape:
             raise ValueError("x and y shall have same shapes."
                              " Given: {0} and {1}".format(x.shape, y.shape))
         x = np.ravel(x)

lib/mpl_toolkits/mplot3d/proj3d.py

@@ -152,14 +152,7 @@ def inv_transform(xs, ys, zs, M):
     return vecr[0], vecr[1], vecr[2]
 
 def vec_pad_ones(xs, ys, zs):
-    try:
-        try:
-            vec = np.array([xs,ys,zs,np.ones(xs.shape)])
-        except (AttributeError,TypeError):
-            vec = np.array([xs,ys,zs,np.ones((len(xs)))])
-    except TypeError:
-        vec = np.array([xs,ys,zs,1])
-    return vec
+    return np.array([xs, ys, zs, np.ones_like(xs)])
 
 def proj_transform(xs, ys, zs, M):
     """