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]])

classmethod Legendre.fromroots(roots, domain=[], window=None) [source] Return series instance that has the specified roots. Returns a series representing the product (x - r[0])*(x - r[1])*...*(x - r[n-1]), where r is a list of roots. Parameters: roots : array_like List of roots. domain : {[], None, array_like}, optional Domain for the resulting series. If None the domain is the interval from the smallest root to the largest. If [] the domain is the class domain. The default is []. wind

recarray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

numpy.fmax(x1, x2[, out]) = Element-wise maximum of array elements. Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible. Parameters: x1, x2

numpy.fmin(x1, x2[, out]) = Element-wise minimum of array elements. Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are ignored when possible. Parameters: x1, x2

chararray.resize(new_shape, refcheck=True) Change shape and size of array in-place. Parameters: new_shape : tuple of ints, or n ints Shape of resized array. refcheck : bool, optional If False, reference count will not be checked. Default is True. Returns: None Raises: ValueError If a does not own its own data or references or views to it exist, and the data memory must be changed. SystemError If the order keyword argument is specified. This behaviour is a bug in NumPy. See al

numpy.putmask(a, mask, values) Changes elements of an array based on conditional and input values. Sets a.flat[n] = values[n] for each n where mask.flat[n]==True. If values is not the same size as a and mask then it will repeat. This gives behavior different from a[mask] = values. Parameters: a : array_like Target array. mask : array_like Boolean mask array. It has to be the same shape as a. values : array_like Values to put into a where mask is True. If values is smaller than a it wi

Legendre.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.

ndarray.squeeze(axis=None) Remove single-dimensional entries from the shape of a. Refer to numpy.squeeze for full documentation. See also numpy.squeeze equivalent function

classmethod Polynomial.fromroots(roots, domain=[], window=None) [source] Return series instance that has the specified roots. Returns a series representing the product (x - r[0])*(x - r[1])*...*(x - r[n-1]), where r is a list of roots. Parameters: roots : array_like List of roots. domain : {[], None, array_like}, optional Domain for the resulting series. If None the domain is the interval from the smallest root to the largest. If [] the domain is the class domain. The default is []. wi