MAINT: replace len(x.shape) with x.ndim

19 files changed, 48 insertions, 48 deletions

diff --git a/numpy/core/arrayprint.py b/numpy/core/arrayprint.py
index 349f8ea39..318ad5495 100644
--- a/numpy/core/arrayprint.py
+++ b/numpy/core/arrayprint.py
@@ -331,7 +331,7 @@ def _array2string(a, max_line_width, precision, suppress_small, separator=' ',
     # skip over array(
     next_line_prefix += " "*len(prefix)
 
-    lst = _formatArray(a, format_function, len(a.shape), max_line_width,
+    lst = _formatArray(a, format_function, a.ndim, max_line_width,
                        next_line_prefix, separator,
                        _summaryEdgeItems, summary_insert)[:-1]
     return lst
diff --git a/numpy/core/einsumfunc.py b/numpy/core/einsumfunc.py
index 0b15c213b..c54a4a263 100644
--- a/numpy/core/einsumfunc.py
+++ b/numpy/core/einsumfunc.py
@@ -360,7 +360,7 @@ def _parse_einsum_input(operands):
                 if operands[num].shape == ():
                     ellipse_count = 0
                 else:
-                    ellipse_count = max(len(operands[num].shape), 1)
+                    ellipse_count = max(operands[num].ndim, 1)
                     ellipse_count -= (len(sub) - 3)
 
                 if ellipse_count > longest:
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index d4d4045a0..896ad7f6a 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -1354,9 +1354,9 @@ def tensordot(a, b, axes=2):
 
     a, b = asarray(a), asarray(b)
     as_ = a.shape
-    nda = len(a.shape)
+    nda = a.ndim
     bs = b.shape
-    ndb = len(b.shape)
+    ndb = b.ndim
     equal = True
     if na != nb:
         equal = False
@@ -1461,7 +1461,7 @@ def roll(a, shift, axis=None):
 
     else:
         broadcasted = broadcast(shift, axis)
-        if len(broadcasted.shape) > 1:
+        if broadcasted.ndim > 1:
             raise ValueError(
                 "'shift' and 'axis' should be scalars or 1D sequences")
         shifts = {ax: 0 for ax in range(a.ndim)}
diff --git a/numpy/core/records.py b/numpy/core/records.py
index 91b70614c..7ad0c111a 100644
--- a/numpy/core/records.py
+++ b/numpy/core/records.py
@@ -613,8 +613,8 @@ def fromarrays(arrayList, dtype=None, shape=None, formats=None,
         shape = shape[:-nn]
 
     for k, obj in enumerate(arrayList):
-        nn = len(descr[k].shape)
-        testshape = obj.shape[:len(obj.shape) - nn]
+        nn = descr[k].ndim
+        testshape = obj.shape[:obj.ndim - nn]
         if testshape != shape:
             raise ValueError("array-shape mismatch in array %d" % k)
 
diff --git a/numpy/core/shape_base.py b/numpy/core/shape_base.py
index 58b0dcaac..22ed17836 100644
--- a/numpy/core/shape_base.py
+++ b/numpy/core/shape_base.py
@@ -49,7 +49,7 @@ def atleast_1d(*arys):
     res = []
     for ary in arys:
         ary = asanyarray(ary)
-        if len(ary.shape) == 0:
+        if ary.ndim == 0:
             result = ary.reshape(1)
         else:
             result = ary
@@ -99,9 +99,9 @@ def atleast_2d(*arys):
     res = []
     for ary in arys:
         ary = asanyarray(ary)
-        if len(ary.shape) == 0:
+        if ary.ndim == 0:
             result = ary.reshape(1, 1)
-        elif len(ary.shape) == 1:
+        elif ary.ndim == 1:
             result = ary[newaxis,:]
         else:
             result = ary
@@ -163,11 +163,11 @@ def atleast_3d(*arys):
     res = []
     for ary in arys:
         ary = asanyarray(ary)
-        if len(ary.shape) == 0:
+        if ary.ndim == 0:
             result = ary.reshape(1, 1, 1)
-        elif len(ary.shape) == 1:
+        elif ary.ndim == 1:
             result = ary[newaxis,:, newaxis]
-        elif len(ary.shape) == 2:
+        elif ary.ndim == 2:
             result = ary[:,:, newaxis]
         else:
             result = ary
diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py
index f7b66f90c..8a5e7f603 100644
--- a/numpy/core/tests/test_ufunc.py
+++ b/numpy/core/tests/test_ufunc.py
@@ -1013,7 +1013,7 @@ class TestUfunc(TestCase):
                 MyThing.getitem_count += 1
                 if not isinstance(i, tuple):
                     i = (i,)
-                if len(i) > len(self.shape):
+                if len(i) > self.ndim:
                     raise IndexError("boo")
 
                 return MyThing(self.shape[len(i):])
diff --git a/numpy/fft/helper.py b/numpy/fft/helper.py
index 0832bc5a4..0856d6759 100644
--- a/numpy/fft/helper.py
+++ b/numpy/fft/helper.py
@@ -64,7 +64,7 @@ def fftshift(x, axes=None):
 
     """
     tmp = asarray(x)
-    ndim = len(tmp.shape)
+    ndim = tmp.ndim
     if axes is None:
         axes = list(range(ndim))
     elif isinstance(axes, integer_types):
@@ -113,7 +113,7 @@ def ifftshift(x, axes=None):
 
     """
     tmp = asarray(x)
-    ndim = len(tmp.shape)
+    ndim = tmp.ndim
     if axes is None:
         axes = list(range(ndim))
     elif isinstance(axes, integer_types):
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py
index 15e3ed957..2dad99c34 100644
--- a/numpy/lib/arraypad.py
+++ b/numpy/lib/arraypad.py
@@ -1338,7 +1338,7 @@ def pad(array, pad_width, mode, **kwargs):
         function = mode
 
         # Create a new padded array
-        rank = list(range(len(narray.shape)))
+        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])
diff --git a/numpy/lib/arrayterator.py b/numpy/lib/arrayterator.py
index fb52ada86..f2d4fe9fd 100644
--- a/numpy/lib/arrayterator.py
+++ b/numpy/lib/arrayterator.py
@@ -106,7 +106,7 @@ class Arrayterator(object):
         if not isinstance(index, tuple):
             index = (index,)
         fixed = []
-        length, dims = len(index), len(self.shape)
+        length, dims = len(index), self.ndim
         for slice_ in index:
             if slice_ is Ellipsis:
                 fixed.extend([slice(None)] * (dims-length+1))
@@ -186,7 +186,7 @@ class Arrayterator(object):
         start = self.start[:]
         stop = self.stop[:]
         step = self.step[:]
-        ndims = len(self.var.shape)
+        ndims = self.var.ndim
 
         while True:
             count = self.buf_size or reduce(mul, self.shape)
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index fc49a6fd7..1cf57d617 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -1674,7 +1674,7 @@ def gradient(f, *varargs, **kwargs):
             S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_.
     """
     f = np.asanyarray(f)
-    N = len(f.shape)  # number of dimensions
+    N = f.ndim  # number of dimensions
 
     axes = kwargs.pop('axis', None)
     if axes is None:
@@ -1900,7 +1900,7 @@ def diff(a, n=1, axis=-1):
         raise ValueError(
             "order must be non-negative but got " + repr(n))
     a = asanyarray(a)
-    nd = len(a.shape)
+    nd = a.ndim
     slice1 = [slice(None)]*nd
     slice2 = [slice(None)]*nd
     slice1[axis] = slice(1, None)
@@ -2144,7 +2144,7 @@ def unwrap(p, discont=pi, axis=-1):
 
     """
     p = asarray(p)
-    nd = len(p.shape)
+    nd = p.ndim
     dd = diff(p, axis=axis)
     slice1 = [slice(None, None)]*nd     # full slices
     slice1[axis] = slice(1, None)
@@ -4488,7 +4488,7 @@ def trapz(y, x=None, dx=1.0, axis=-1):
             d = d.reshape(shape)
         else:
             d = diff(x, axis=axis)
-    nd = len(y.shape)
+    nd = y.ndim
     slice1 = [slice(None)]*nd
     slice2 = [slice(None)]*nd
     slice1[axis] = slice(1, None)
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index c575cc030..0dee6b333 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -925,7 +925,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
                 flat_dt, flat_packing = flatten_dtype(tp)
                 types.extend(flat_dt)
                 # Avoid extra nesting for subarrays
-                if len(tp.shape) > 0:
+                if tp.ndim > 0:
                     packing.extend(flat_packing)
                 else:
                     packing.append((len(flat_dt), flat_packing))
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 281d79ec5..f00d95b6c 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -201,7 +201,7 @@ def roots(p):
     """
     # If input is scalar, this makes it an array
     p = atleast_1d(p)
-    if len(p.shape) != 1:
+    if p.ndim != 1:
         raise ValueError("Input must be a rank-1 array.")
 
     # find non-zero array entries
@@ -1051,7 +1051,7 @@ class poly1d(object):
         if r:
             c_or_r = poly(c_or_r)
         c_or_r = atleast_1d(c_or_r)
-        if len(c_or_r.shape) > 1:
+        if c_or_r.ndim > 1:
             raise ValueError("Polynomial must be 1d only.")
         c_or_r = trim_zeros(c_or_r, trim='f')
         if len(c_or_r) == 0:
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index 62798286f..8ebcf04b4 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -390,7 +390,7 @@ def dstack(tup):
 
 def _replace_zero_by_x_arrays(sub_arys):
     for i in range(len(sub_arys)):
-        if len(_nx.shape(sub_arys[i])) == 0:
+        if _nx.ndim(sub_arys[i]) == 0:
             sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
         elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
             sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
@@ -577,9 +577,9 @@ def hsplit(ary, indices_or_sections):
            [[ 6.,  7.]]])]
 
     """
-    if len(_nx.shape(ary)) == 0:
+    if _nx.ndim(ary) == 0:
         raise ValueError('hsplit only works on arrays of 1 or more dimensions')
-    if len(ary.shape) > 1:
+    if ary.ndim > 1:
         return split(ary, indices_or_sections, 1)
     else:
         return split(ary, indices_or_sections, 0)
@@ -631,7 +631,7 @@ def vsplit(ary, indices_or_sections):
             [ 6.,  7.]]])]
 
     """
-    if len(_nx.shape(ary)) < 2:
+    if _nx.ndim(ary) < 2:
         raise ValueError('vsplit only works on arrays of 2 or more dimensions')
     return split(ary, indices_or_sections, 0)
 
@@ -676,7 +676,7 @@ def dsplit(ary, indices_or_sections):
      array([], dtype=float64)]
 
     """
-    if len(_nx.shape(ary)) < 3:
+    if _nx.ndim(ary) < 3:
         raise ValueError('dsplit only works on arrays of 3 or more dimensions')
     return split(ary, indices_or_sections, 2)
 
diff --git a/numpy/lib/user_array.py b/numpy/lib/user_array.py
index 62398fc3c..f1510a7b1 100644
--- a/numpy/lib/user_array.py
+++ b/numpy/lib/user_array.py
@@ -34,7 +34,7 @@ class container(object):
         self.array = array(data, dtype, copy=copy)
 
     def __repr__(self):
-        if len(self.shape) > 0:
+        if self.ndim > 0:
             return self.__class__.__name__ + repr(self.array)[len("array"):]
         else:
             return self.__class__.__name__ + "(" + repr(self.array) + ")"
@@ -183,7 +183,7 @@ class container(object):
         return self._rc(invert(self.array))
 
     def _scalarfunc(self, func):
-        if len(self.shape) == 0:
+        if self.ndim == 0:
             return func(self[0])
         else:
             raise TypeError(
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index 6002c63b9..84e450b12 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -192,15 +192,15 @@ def _fastCopyAndTranspose(type, *arrays):
 
 def _assertRank2(*arrays):
     for a in arrays:
-        if len(a.shape) != 2:
+        if a.ndim != 2:
             raise LinAlgError('%d-dimensional array given. Array must be '
-                    'two-dimensional' % len(a.shape))
+                    'two-dimensional' % a.ndim)
 
 def _assertRankAtLeast2(*arrays):
     for a in arrays:
-        if len(a.shape) < 2:
+        if a.ndim < 2:
             raise LinAlgError('%d-dimensional array given. Array must be '
-                    'at least two-dimensional' % len(a.shape))
+                    'at least two-dimensional' % a.ndim)
 
 def _assertSquareness(*arrays):
     for a in arrays:
@@ -231,7 +231,7 @@ def tensorsolve(a, b, axes=None):
 
     It is assumed that all indices of `x` are summed over in the product,
     together with the rightmost indices of `a`, as is done in, for example,
-    ``tensordot(a, x, axes=len(b.shape))``.
+    ``tensordot(a, x, axes=b.ndim)``.
 
     Parameters
     ----------
@@ -1917,7 +1917,7 @@ def lstsq(a, b, rcond=-1):
     import math
     a, _ = _makearray(a)
     b, wrap = _makearray(b)
-    is_1d = len(b.shape) == 1
+    is_1d = b.ndim == 1
     if is_1d:
         b = b[:, newaxis]
     _assertRank2(a, b)
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py
index fc4f98ed7..31fde186f 100644
--- a/numpy/linalg/tests/test_linalg.py
+++ b/numpy/linalg/tests/test_linalg.py
@@ -743,7 +743,7 @@ class TestLstsq(LinalgSquareTestCase, LinalgNonsquareTestCase):
             expect_resids = (
                 np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
             expect_resids = np.asarray(expect_resids)
-            if len(np.asarray(b).shape) == 1:
+            if np.asarray(b).ndim == 1:
                 expect_resids.shape = (1,)
                 assert_equal(residuals.shape, expect_resids.shape)
         else:
diff --git a/numpy/ma/core.py b/numpy/ma/core.py
index 30ef5dbfc..3b2b39b18 100644
--- a/numpy/ma/core.py
+++ b/numpy/ma/core.py
@@ -3204,7 +3204,7 @@ class MaskedArray(ndarray):
                     # If we're indexing a multidimensional field in a
                     # structured array (such as dtype("(2,)i2,(2,)i1")),
                     # dimensionality goes up (M[field].ndim == M.ndim +
-                    # len(M.dtype[field].shape)).  That's fine for
+                    # M.dtype[field].ndim).  That's fine for
                     # M[field] but problematic for M[field].fill_value
                     # which should have shape () to avoid breaking several
                     # methods. There is no great way out, so set to
@@ -3846,7 +3846,7 @@ class MaskedArray(ndarray):
         Literal string representation.
 
         """
-        n = len(self.shape)
+        n = self.ndim
         if self._baseclass is np.ndarray:
             name = 'array'
         else:
@@ -7319,9 +7319,9 @@ def inner(a, b):
     """
     fa = filled(a, 0)
     fb = filled(b, 0)
-    if len(fa.shape) == 0:
+    if fa.ndim == 0:
         fa.shape = (1,)
-    if len(fb.shape) == 0:
+    if fb.ndim == 0:
         fb.shape = (1,)
     return np.inner(fa, fb).view(MaskedArray)
 inner.__doc__ = doc_note(np.inner.__doc__,
diff --git a/numpy/ma/mrecords.py b/numpy/ma/mrecords.py
index 45359cc81..ef5f5fd53 100644
--- a/numpy/ma/mrecords.py
+++ b/numpy/ma/mrecords.py
@@ -625,7 +625,7 @@ def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,
         maskrecordlength = len(mask.dtype)
         if maskrecordlength:
             mrec._mask.flat = mask
-        elif len(mask.shape) == 2:
+        elif mask.ndim == 2:
             mrec._mask.flat = [tuple(m) for m in mask]
         else:
             mrec.__setmask__(mask)
@@ -646,9 +646,9 @@ def _guessvartypes(arr):
     """
     vartypes = []
     arr = np.asarray(arr)
-    if len(arr.shape) == 2:
+    if arr.ndim == 2:
         arr = arr[0]
-    elif len(arr.shape) > 2:
+    elif arr.ndim > 2:
         raise ValueError("The array should be 2D at most!")
     # Start the conversion loop.
     for f in arr:
diff --git a/numpy/matrixlib/defmatrix.py b/numpy/matrixlib/defmatrix.py
index 6c7640cb8..bd14846c6 100644
--- a/numpy/matrixlib/defmatrix.py
+++ b/numpy/matrixlib/defmatrix.py
@@ -169,7 +169,7 @@ def matrix_power(M, n):
 
     """
     M = asanyarray(M)
-    if len(M.shape) != 2 or M.shape[0] != M.shape[1]:
+    if M.ndim != 2 or M.shape[0] != M.shape[1]:
         raise ValueError("input must be a square array")
     if not issubdtype(type(n), int):
         raise TypeError("exponent must be an integer")