Legendre.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

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.apply_along_axis(func1d, axis, arr, *args, **kwargs) [source] Apply a function to 1-D slices along the given axis. Execute func1d(a, *args) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis. Parameters: func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of arr along the specified axis. axis : integer Axis along which arr is sliced. arr : ndarray Input array. args : any Additional arguments to func1d. kwargs: any Add

class numpy.core.defchararray.chararray [source] Provides a convenient view on arrays of string and unicode values. Note The chararray class exists for backwards compatibility with Numarray, it is not recommended for new development. Starting from numpy 1.4, if one needs arrays of strings, it is recommended to use arrays of dtype object_, string_ or unicode_, and use the free functions in the numpy.char module for fast vectorized string operations. Versus a regular Numpy array of type str

numpy.fft.ifftshift(x, axes=None) [source] The inverse of fftshift. Although identical for even-length x, the functions differ by one sample for odd-length x. Parameters: x : array_like Input array. axes : int or shape tuple, optional Axes over which to calculate. Defaults to None, which shifts all axes. Returns: y : ndarray The shifted array. See also fftshift Shift zero-frequency component to the center of the spectrum. Examples >>> freqs = np.fft.fftfreq(9, d=1./9

numpy.fft.fftfreq(n, d=1.0) [source] Return the Discrete Fourier Transform sample frequencies. The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. Given a window length n and a sample spacing d: f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is o

numpy.ma.compress_rows(a) [source] Suppress whole rows of a 2-D array that contain masked values. This is equivalent to np.ma.compress_rowcols(a, 0), see extras.compress_rowcols for details. See also extras.compress_rowcols

numpy.fv(rate, nper, pmt, pv, when='end') [source] Compute the future value. Given: a present value, pv an interest rate compounded once per period, of which there are nper total a (fixed) payment, pmt, paid either at the beginning (when = {?begin?, 1}) or the end (when = {?end?, 0}) of each period Return: the value at the end of the nper periods Parameters: rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period nper : scalar or array_like of

poly1d.integ(m=1, k=0) [source] Return an antiderivative (indefinite integral) of this polynomial. Refer to polyint for full documentation. See also polyint equivalent function

RandomState.zipf(a, size=None) Draw samples from a Zipf distribution. Samples are drawn from a Zipf distribution with specified parameter a > 1. The Zipf distribution (also known as the zeta distribution) is a continuous probability distribution that satisfies Zipf?s law: the frequency of an item is inversely proportional to its rank in a frequency table. Parameters: a : float > 1 Distribution parameter. size : int or tuple of ints, optional Output shape. If the given shape is, e.