numpy.fft.irfftn(a, s=None, axes=None, norm=None) [source] Compute the inverse of the N-dimensional FFT of real input. This function computes the inverse of the N-dimensional discrete Fourier Transform for real input over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, irfftn(rfftn(a), a.shape) == a to within numerical accuracy. (The a.shape is necessary like len(a) is for irfft, and for the same reason.) The input should be ordered

numpy.asfarray(a, dtype=) [source] Return an array converted to a float type. Parameters: a : array_like The input array. dtype : str or dtype object, optional Float type code to coerce input array a. If dtype is one of the ?int? dtypes, it is replaced with float64. Returns: out : ndarray The input a as a float ndarray. Examples >>> np.asfarray([2, 3]) array([ 2., 3.]) >>> np.asfarray([2, 3], dtype='float') array([ 2., 3.]) >>> np.asfarray([2, 3], dtyp

numpy.bartlett(M) [source] Return the Bartlett window. The Bartlett window is very similar to a triangular window, except that the end points are at zero. It is often used in signal processing for tapering a signal, without generating too much ripple in the frequency domain. Parameters: M : int Number of points in the output window. If zero or less, an empty array is returned. Returns: out : array The triangular window, with the maximum value normalized to one (the value one appears o

record.newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following: ?S? - swap dtype from current to opposite endian {?<?, ?L?} - little endian {?>?, ?B?} - big endian {?=?, ?N?} - native order {?|?, ?I?} - ignore (no change to byte order) Parameters: new_order : str, optional Byte order to force; a value from the byte order specifications above. The

numpy.linalg.lstsq(a, b, rcond=-1) [source] Return the least-squares solution to a linear matrix equation. Solves the equation a x = b by computing a vector x that minimizes the Euclidean 2-norm || b - a x ||^2. The equation may be under-, well-, or over- determined (i.e., the number of linearly independent rows of a can be less than, equal to, or greater than its number of linearly independent columns). If a is square and of full rank, then x (but for round-off error) is the ?exact? soluti

numpy.random.weibull(a, size=None) Draw samples from a Weibull distribution. Draw samples from a 1-parameter Weibull distribution with the given shape parameter a. Here, U is drawn from the uniform distribution over (0,1]. The more common 2-parameter Weibull, including a scale parameter is just . Parameters: a : float Shape of the distribution. 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. Default is Non

numpy.ma.dumps(a) [source] Return a string corresponding to the pickling of a masked array. This is a wrapper around cPickle.dumps. Parameters: a : MaskedArray The array for which the string representation of the pickle is returned.

record.tobytes()

busdaycalendar.holidays A copy of the holiday array indicating additional invalid days.

ndarray.__long__() <==> long(x)