numpy.polynomial.legendre.legweight(x) [source] Weight function of the Legendre polynomials. The weight function is and the interval of integration is . The Legendre polynomials are orthogonal, but not normalized, with respect to this weight function. Parameters: x : array_like Values at which the weight function will be computed. Returns: w : ndarray The weight function at x. Notes

RandomState.negative_binomial(n, p, size=None) Draw samples from a negative binomial distribution. Samples are drawn from a negative binomial distribution with specified parameters, n trials and p probability of success where n is an integer > 0 and p is in the interval [0, 1]. Parameters: n : int Parameter, > 0. p : float Parameter, >= 0 and <=1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. D

Finding help lookfor(what[, module, import_modules, ...]) Do a keyword search on docstrings. Reading help info([object, maxwidth, output, toplevel]) Get help information for a function, class, or module. source(object[, output]) Print or write to a file the source code for a Numpy object.

Laguerre.truncate(size) [source] Truncate series to length size. Reduce the series to length size by discarding the high degree terms. The value of size must be a positive integer. This can be useful in least squares where the coefficients of the high degree terms may be very small. Parameters: size : positive int The series is reduced to length size by discarding the high degree terms. The value of size must be a positive integer. Returns: new_series : series New instance of series w

numpy.resize(a, new_shape) [source] Return a new array with the specified shape. If the new array is larger than the original array, then the new array is filled with repeated copies of a. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of a. Parameters: a : array_like Array to be resized. new_shape : int or tuple of int Shape of resized array. Returns: reshaped_array : ndarray The new array is formed from the data in t

numpy.core.defchararray.isdecimal(a) [source] For each element, return True if there are only decimal characters in the element. Calls unicode.isdecimal element-wise. Decimal characters include digit characters, and all characters that that can be used to form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Parameters: a : array_like, unicode Input array. Returns: out : ndarray, bool Array of booleans identical in shape to a. See also unicode.isdecimal

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

Chebyshev.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.

numpy.ma.atleast_1d(*arys) = Convert inputs to arrays with at least one dimension. Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved. Parameters: arys1, arys2, ... : array_like One or more input arrays. Returns: ret : ndarray An array, or sequence of arrays, each with a.ndim >= 1. Copies are made only if necessary. Notes The function is applied to both the _data and the _mask, if any. Examples >>> np.atleast_1d(1.0) arr

classmethod Laguerre.cast(series, domain=None, window=None) [source] Convert series to series of this class. The series is expected to be an instance of some polynomial series of one of the types supported by by the numpy.polynomial module, but could be some other class that supports the convert method. New in version 1.7.0. Parameters: series : series The series instance to be converted. domain : {None, array_like}, optional If given, the array must be of the form [beg, end], where b