numpy.polynomial.legendre.legdiv(c1, c2) [source] Divide one Legendre series by another. Returns the quotient-with-remainder of two Legendre series c1 / c2. The arguments are sequences of coefficients from lowest order ?term? to highest, e.g., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Legendre series coefficients ordered from low to high. Returns: quo, rem : ndarrays Of Legendre series coefficients representing the quotient and re

classmethod HermiteE.identity(domain=None, window=None) [source] Identity function. If p is the returned series, then p(x) == x for all values of x. Parameters: domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where beg and end are the endpoints of the domain. If None is given then the class domain is used. The default is None. window : {None, array_like}, optional If given, the resulting array must be if the form [beg, end], where beg and end ar

MaskedArray.put(indices, values, mode='raise') [source] Set storage-indexed locations to corresponding values. Sets self._data.flat[n] = values[n] for each n in indices. If values is shorter than indices then it will repeat. If values has some masked values, the initial mask is updated in consequence, else the corresponding values are unmasked. Parameters: indices : 1-D array_like Target indices, interpreted as integers. values : array_like Values to place in self._data copy at target i

MaskedArray.harden_mask() [source] 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

numpy.core.defchararray.rjust(a, width, fillchar=' ') [source] Return an array with the elements of a right-justified in a string of length width. Calls str.rjust element-wise. Parameters: a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The character to use for padding Returns: out : ndarray Output array of str or unicode, depending on input type See also str.rjust

numpy.lexsort(keys, axis=-1) Perform an indirect sort using a sequence of keys. Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used for the primary sort order, the second-to-last key for the secondary sort order, and so on. The keys argument must be a sequence of objects that can be converted to arrays of the same shape. If a 2D a

matrix.transpose(*axes) Returns a view of the array with axes transposed. For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Param

matrix.squeeze(axis=None) [source] Return a possibly reshaped matrix. Refer to numpy.squeeze for more documentation. Parameters: axis : None or int or tuple of ints, optional Selects a subset of the single-dimensional entries in the shape. If an axis is selected with shape entry greater than one, an error is raised. Returns: squeezed : matrix The matrix, but as a (1, N) matrix if it had shape (N, 1). See also numpy.squeeze related function Notes If m has a single column then th

matrix.partition(kth, axis=-1, kind='introselect', order=None) Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined. New in version 1.8.0. Parameters: kth : int or sequence of ints Element index to partition by. The kth

matrix.clip(min=None, max=None, out=None) Return an array whose values are limited to [min, max]. One of max or min must be given. Refer to numpy.clip for full documentation. See also numpy.clip equivalent function