Hermite.has_samedomain(other) [source] Check if domains match. New in version 1.6.0. Parameters: other : class instance The other class must have the domain attribute. Returns: bool : boolean True if the domains are the same, False otherwise.

MaskedArray.ravel(order='C') [source] Returns a 1D version of self, as a view. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional The elements of a are read using this index order. ?C? means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ?F? means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ?C? and ?F? options ta

numpy.polynomial.chebyshev.chebsub(c1, c2) [source] Subtract one Chebyshev series from another. Returns the difference of two Chebyshev series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series T_0 + 2*T_1 + 3*T_2. Parameters: c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns: out : ndarray Of Chebyshev series coefficients representing their difference. See also chebadd, c

MaskedArray.argmax(axis=None, fill_value=None, out=None) [source] Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value. Parameters: axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : {var}, optional Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. out : {None, array}, optional Ar

numpy.polynomial.legendre.legmulx(c) [source] Multiply a Legendre series by x. Multiply the Legendre series c by x, where x is the independent variable. Parameters: c : array_like 1-D array of Legendre series coefficients ordered from low to high. Returns: out : ndarray Array representing the result of the multiplication. Notes The multiplication uses the recursion relationship for Legendre polynomials in the form

matrix.dtype Data-type of the array?s elements. Parameters: None Returns: d : numpy dtype object See also numpy.dtype Examples >>> x array([[0, 1], [2, 3]]) >>> x.dtype dtype('int32') >>> type(x.dtype) <type 'numpy.dtype'>

numpy.core.defchararray.swapcase(a) [source] Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa. Calls str.swapcase element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like, {str, unicode} Input array. Returns: out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also str.swapcase Examples >>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c a

numpy.logical_or(x1, x2[, out]) = Compute the truth value of x1 OR x2 element-wise. Parameters: x1, x2 : array_like Logical OR is applied to the elements of x1 and x2. They have to be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical OR operation on elements of x1 and x2. See also logical_and, logical_not, logical_xor, bitwise_or Examples >>> np.logical_or(True, False) True >>> np.logical_or([True, Fal

ndarray.flat A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python?s built-in iterator object. See also flatten Return a copy of the array collapsed into one dimension. flatiter Examples >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(x

numpy.ma.masked_object(x, value, copy=True, shrink=True) [source] Mask the array x where the data are exactly equal to value. This function is similar to masked_values, but only suitable for object arrays: for floating point, use masked_values instead. Parameters: x : array_like Array to mask value : object Comparison value copy : {True, False}, optional Whether to return a copy of x. shrink : {True, False}, optional Whether to collapse a mask full of False to nomask Returns: res