Simplified common_shape

wafo/gaussian.py

@@ -7,7 +7,6 @@ from scipy.special import ndtr as cdfnorm, ndtri as invnorm
 from scipy.special import erfc
 import warnings
 import numpy as np
-from .misc import common_shape
 
 try:
     from . import mvn  # @UnresolvedImport
@@ -836,10 +835,9 @@ def cdfnorm2d(b1, b2, r):
     #     pullman, wa 99164-3113
     #     email : alangenz@wsu.edu
 
-    cshape = common_shape(b1, b2, r, shape=[1, ])
+    b1, b2, r = np.broadcast_arrays(b1, b2, r)
-    one = ones(cshape)
+    cshape = b1.shape
-
+    h, k, r = -b1.ravel(), -b2.ravel(), r.ravel()
-    h, k, r = (-b1 * one).ravel(), (-b2 * one).ravel(), (r * one).ravel()
 
     bvn = where(abs(r) > 1, nan, 0.0)
 

wafo/misc.py

@@ -1764,48 +1764,10 @@ def common_shape(*args, ** kwds):
     --------
     broadcast, broadcast_arrays
     '''
-    args = [asarray(x) for x in args]
-    shapes = [x.shape for x in args]
     shape = kwds.get('shape')
-    if shape is not None:
+    x0 = 1 if shape is None else np.ones(shape)
-        if not isinstance(shape, (list, tuple)):
+    x1 = np.broadcast(x0, *args)
-            shape = (shape,)
+    return tuple(x1.shape)
-        shapes.append(tuple(shape))
-    if len(set(shapes)) == 1:
-        # Common case where nothing needs to be broadcasted.
-        return tuple(shapes[0])
-    shapes = [list(s) for s in shapes]
-    nds = [len(s) for s in shapes]
-    biggest = max(nds)
-    # Go through each array and prepend dimensions of length 1 to each of the
-    # shapes in order to make the number of dimensions equal.
-    for i in range(len(shapes)):
-        diff = biggest - nds[i]
-        if diff > 0:
-            shapes[i] = [1] * diff + shapes[i]
-
-    # Check each dimension for compatibility. A dimension length of 1 is
-    # accepted as compatible with any other length.
-    c_shape = []
-    for axis in range(biggest):
-        lengths = [s[axis] for s in shapes]
-        unique = set(lengths + [1])
-        if len(unique) > 2:
-            # There must be at least two non-1 lengths for this axis.
-            raise ValueError("shape mismatch: two or more arrays have "
-                             "incompatible dimensions on axis %r." % (axis,))
-        elif len(unique) == 2:
-            # There is exactly one non-1 length.
-            # The common shape will take this value.
-            unique.remove(1)
-            new_length = unique.pop()
-            c_shape.append(new_length)
-        else:
-            # Every array has a length of 1 on this axis. Strides can be left
-            # alone as nothing is broadcasted.
-            c_shape.append(1)
-
-    return tuple(c_shape)
 
 
 def argsreduce(condition, * args):
@@ -2787,7 +2749,7 @@ def num2pistr(x, n=3, numerator_max=10, denominator_max=10):
     return fmt % x
 
 
-def fourier(data, t=None, T=None, m=None, n=None, method='trapz'):
+def fourier(data, t=None, period=None, m=None, n=None, method='trapz'):
     '''
     Returns Fourier coefficients.
 
@@ -2797,7 +2759,7 @@ def fourier(data, t=None, T=None, m=None, n=None, method='trapz'):
         vector or matrix of row vectors with data points shape p x n.
     t : array-like
         vector with n values indexed from 1 to N.
-    T : real scalar, (default T = t[-1]-t[0])
+    period : real scalar, (default T = t[-1]-t[0])
         primitive period of signal, i.e., smallest period.
     m : scalar integer
         defines no of harmonics desired (default M = N)
@@ -2845,19 +2807,12 @@ def fourier(data, t=None, T=None, m=None, n=None, method='trapz'):
     '''
     x = np.atleast_2d(data)
     p, n = x.shape
-    if t is None:
+    t = np.arange(n) if t is None else np.atleast_1d(t)
-        t = np.arange(n)
-    else:
-        t = np.atleast_1d(t)
 
     n = len(t) if n is None else n
-    m = n if n is None else m
+    m = n if m is None else m
-    T = t[-1] - t[0] if T is None else T
+    period = t[-1] - t[0] if period is None else period
-
+    intfun = trapz if method.startswith('trapz') else simps
-    if method.startswith('trapz'):
-        intfun = trapz
-    elif method.startswith('simp'):
-        intfun = simps
 
     # Define the vectors for computing the Fourier coefficients
     t.shape = (1, -1)
@@ -2866,8 +2821,7 @@ def fourier(data, t=None, T=None, m=None, n=None, method='trapz'):
     a[0] = intfun(x, t, axis=-1)
 
     # Compute M-1 more coefficients
-    tmp = 2 * pi * t / T
+    tmp = 2 * pi * t / period
-    # tmp =  2*pi*(0:N-1).'/(N-1);
     for i in range(1, m):
         a[i] = intfun(x * cos(i * tmp), t, axis=-1)
         b[i] = intfun(x * sin(i * tmp), t, axis=-1)