numpy.obj2sctype(rep, default=None) [source] Return the scalar dtype or NumPy equivalent of Python type of an object. Parameters: rep : any The object of which the type is returned. default : any, optional If given, this is returned for objects whose types can not be determined. If not given, None is returned for those objects. Returns: dtype : dtype or Python type The data type of rep. See also sctype2char, issctype, issubsctype, issubdtype, maximum_sctype Examples >>>

numpy.ma.outer(a, b) [source] Compute the outer product of two vectors. Given two vectors, a = [a0, a1, ..., aM] and b = [b0, b1, ..., bN], the outer product [R50] is: [[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters: a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A location w

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

numpy.ma.dstack(tup) = Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. Parameters: tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns: stacked : ndarray The array formed by stacking the given arrays.

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.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.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

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.triu_indices_from(arr, k=0) [source] Return the indices for the upper-triangle of arr. See triu_indices for full details. Parameters: arr : ndarray, shape(N, N) The indices will be valid for square arrays. k : int, optional Diagonal offset (see triu for details). Returns: triu_indices_from : tuple, shape(2) of ndarray, shape(N) Indices for the upper-triangle of arr. See also triu_indices, triu Notes New in version 1.4.0.