numpy.median(a, axis=None, out=None, overwrite_input=False, keepdims=False) [source] Compute the median along the specified axis. Returns the median of the array elements. Parameters: a : array_like Input array or object that can be converted to an array. axis : {int, sequence of int, None}, optional Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : n

Chebyshev.degree() [source] The degree of the series. New in version 1.5.0. Returns: degree : int Degree of the series, one less than the number of coefficients.

class numpy.record [source] A data-type scalar that allows field access as attribute lookup. Attributes T transpose base base object data pointer to start of data dtype dtype object flags integer value of flags flat a 1-d view of scalar imag imaginary part of scalar itemsize length of one element in bytes nbytes length of item in bytes ndim number of array dimensions real real part of scalar shape tuple of array dimensions size number of elements in the gentype strides tuple of bytes steps

MaskedArray.__add__(other) [source] Add self to other, and return a new masked array.

record.copy() 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

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

generic.strides tuple of bytes steps in each dimension

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