MaskedArray.__array__(|dtype) ? reference if type unchanged, copy otherwise. Returns either a new reference to self if dtype is not given or a new array of provided data type if dtype is different from the current dtype of the array.

numpy.polynomial.laguerre.lagpow(c, pow, maxpower=16) [source] Raise a Laguerre series to a power. Returns the Laguerre series c raised to the power pow. The argument c is a sequence of coefficients ordered from low to high. i.e., [1,2,3] is the series P_0 + 2*P_1 + 3*P_2. Parameters: c : array_like 1-D array of Laguerre series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is mainl

numpy.place(arr, mask, vals) [source] Change elements of an array based on conditional and input values. Similar to np.copyto(arr, vals, where=mask), the difference is that place uses the first N elements of vals, where N is the number of True values in mask, while copyto uses the elements where mask is True. Note that extract does the exact opposite of place. Parameters: arr : ndarray Array to put data into. mask : array_like Boolean mask array. Must have the same size as a. vals : 1-

numpy.polynomial.legendre.legline(off, scl) [source] Legendre series whose graph is a straight line. Parameters: off, scl : scalars The specified line is given by off + scl*x. Returns: y : ndarray This module?s representation of the Legendre series for off + scl*x. See also polyline, chebline Examples >>> import numpy.polynomial.legendre as L >>> L.legline(3,2) array([3, 2]) >>> L.legval(-3, L.legline(3,2)) # should be -3 -3.0

numpy.outer(a, b, out=None) [source] Compute the outer product of two vectors. Given two vectors, a = [a0, a1, ..., aM] and b = [b0, b1, ..., bN], the outer product [R55] is: [[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters: a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A loc

numpy.polynomial.chebyshev.chebgrid2d(x, y, c) [source] Evaluate a 2-D Chebyshev series on the Cartesian product of x and y. This function returns the values: where the points (a, b) consist of all pairs formed by taking a from x and b from y. The resulting points form a grid with x in the first dimension and y in the second. The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x and y or their

ndarray.all(axis=None, out=None, keepdims=False) Returns True if all elements evaluate to True. Refer to numpy.all for full documentation. See also numpy.all equivalent function

numpy.amin(a, axis=None, out=None, keepdims=False) [source] Return the minimum of an array or minimum along an axis. Parameters: a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of

ndarray.__float__() <==> float(x)

chararray.istitle() [source] Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise. See also char.istitle