generic.size number of elements in the gentype

numpy.sinc(x) [source] Return the sinc function. The sinc function is . Parameters: x : ndarray Array (possibly multi-dimensional) of values for which to to calculate sinc(x). Returns: out : ndarray sinc(x), which has the same shape as the input. Notes sinc(0) is the limit value 1. The name sinc is short for ?sine cardinal? or ?sinus cardinalis?. The sinc function is used in various signal processing applications, including in anti-aliasing, in the construction of a Lanczos resampli

chararray.dumps() Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array. Parameters: None

numpy.polynomial.legendre.legint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Legendre series. Returns the Legendre 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 inform

numpy.genfromtxt(fname, dtype=, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None) [source] Load data from a text file, with missing values handled as specified. Each line past the first skip_header lines is split a

recarray.T Same as self.transpose(), except that self is returned if self.ndim < 2. Examples >>> x = np.array([[1.,2.],[3.,4.]]) >>> x array([[ 1., 2.], [ 3., 4.]]) >>> x.T array([[ 1., 3.], [ 2., 4.]]) >>> x = np.array([1.,2.,3.,4.]) >>> x array([ 1., 2., 3., 4.]) >>> x.T array([ 1., 2., 3., 4.])

record.compress() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

Background The API exposed by NumPy for third-party extensions has grown over years of releases, and has allowed programmers to directly access NumPy functionality from C. This API can be best described as ?organic?. It has emerged from multiple competing desires and from multiple points of view over the years, strongly influenced by the desire to make it easy for users to move to NumPy from Numeric and Numarray. The core API originated with Numeric in 1995 and there are patterns such as the

numpy.asarray_chkfinite(a, dtype=None, order=None) [source] Convert the input to an array, checking for NaNs or Infs. Parameters: a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {?C?, ?F?}, optional Whether to use row-major (C-style) or col

generic.byteswap() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The