numpy.polynomial.hermite_e.hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Hermite_e series. Returns the Hermite_e series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more i

class numpy.flatiter [source] Flat iterator object to iterate over arrays. A flatiter iterator is returned by x.flat for any array x. It allows iterating over the array as if it were a 1-D array, either in a for-loop or by calling its next method. Iteration is done in row-major, C-style order (the last index varying the fastest). The iterator can also be indexed using basic slicing or advanced indexing. See also ndarray.flat Return a flat iterator over an array. ndarray.flatten Retur

numpy.ma.column_stack(tup) = Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first. Parameters: tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns: stacked : 2-D array The array formed by stacking the given arrays. Notes The function is applied to both

ndarray.nonzero() Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero equivalent function

numpy.isneginf(x, y=None) [source] Test element-wise for negative infinity, return result as bool array. Parameters: x : array_like The input array. y : array_like, optional A boolean array with the same shape and type as x to store the result. Returns: y : ndarray A boolean array with the same dimensions as the input. If second argument is not supplied then a numpy boolean array is returned with values True where the corresponding element of the input is negative infinity and value

MaskedArray.tobytes(fill_value=None, order='C') [source] Return the array data as a string containing the raw bytes in the array. The array is filled with a fill value before the string conversion. New in version 1.9.0. Parameters: fill_value : scalar, optional Value used to fill in the masked values. Deafult is None, in which case MaskedArray.fill_value is used. order : {?C?,?F?,?A?}, optional Order of the data item in the copy. Default is ?C?. ?C? ? C order (row major). ?F? ? Fortra

Laguerre.cutdeg(deg) [source] Truncate series to the given degree. Reduce the degree of the series to deg by discarding the high order terms. If deg is greater than the current degree a copy of the current series is returned. This can be useful in least squares where the coefficients of the high degree terms may be very small. New in version 1.5.0. Parameters: deg : non-negative int The series is reduced to degree deg by discarding the high order terms. The value of deg must be a non-ne

matrix.A Return self as an ndarray object. Equivalent to np.asarray(self). Parameters: None Returns: ret : ndarray self as an ndarray Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.getA() array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])

numpy.ma.harden_mask(self) = Force the mask to hard. Whether the mask of a masked array is hard or soft is determined by its hardmask property. harden_mask sets hardmask to True. See also hardmask

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