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

numpy.fromfunction(function, shape, **kwargs) [source] Construct an array by executing a function over each coordinate. The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z). Parameters: function : callable The function is called with N parameters, where N is the rank of shape. Each parameter represents the coordinates of the array varying along a specific axis. For example, if shape were (2, 2), then the parameters in turn be (0, 0), (0, 1), (1, 0), (1, 1). shape

Making proper sets unique(ar[, return_index, return_inverse, ...]) Find the unique elements of an array. Boolean operations in1d(ar1, ar2[, assume_unique, invert]) Test whether each element of a 1-D array is also present in a second array. intersect1d(ar1, ar2[, assume_unique]) Find the intersection of two arrays. setdiff1d(ar1, ar2[, assume_unique]) Find the set difference of two arrays. setxor1d(ar1, ar2[, assume_unique]) Find the set exclusive-or of two arrays. union1d(ar1, ar2) Fi

numpy.polynomial.laguerre.lagvander2d(x, y, deg) [source] Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the Laguerre polynomials. If V = lagvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a

Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks. The numpy.ma moduleRationale What is a masked array? The numpy.ma module Using numpy.maConstructing masked arrays Accessing the data Accessing the mask Accessing only the valid entries Modifying the mask Indexing and slicing Operations on masked arrays ExamplesData with a given value representing missing data Filli

matrix.I Returns the (multiplicative) inverse of invertible self. Parameters: None Returns: ret : matrix object If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size) all return True. Raises: numpy.linalg.LinAlgError: Singular matrix If self is singular. See also linalg.inv Examples >>> m = np.matrix('[1, 2; 3, 4]'); m matrix([[1, 2], [3, 4]]) >>> m.getI() matrix([[-2. , 1. ], [ 1.5, -0.5]]) >&

chararray.strides Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides) A more detailed explanation of strides can be found in the ?ndarray.rst? file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], dtype=np.int32) This array

class numpy.DataSource(destpath='.') [source] A generic data source file (file, http, ftp, ...). DataSources can be local files or remote files/URLs. The files may also be compressed or uncompressed. DataSource hides some of the low-level details of downloading the file, allowing you to simply pass in a valid file path (or URL) and obtain a file object. Parameters: destpath : str or None, optional Path to the directory where the source file gets downloaded to for use. If destpath is None,

apply_along_axis(func1d, axis, arr, *args, ...) Apply a function to 1-D slices along the given axis. apply_over_axes(func, a, axes) Apply a function repeatedly over multiple axes. vectorize(pyfunc[, otypes, doc, excluded, cache]) Generalized function class. frompyfunc(func, nin, nout) Takes an arbitrary Python function and returns a Numpy ufunc. piecewise(x, condlist, funclist, *args, **kw) Evaluate a piecewise-defined function.

matrix.setflags(write=None, align=None, uic=None) Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable