numpy.arctanh(x[, out]) = Inverse hyperbolic tangent element-wise. Parameters: x : array_like Input array. Returns: out : ndarray Array of the same shape as x. See also emath.arctanh Notes arctanh is a multivalued function: for each x there are infinitely many numbers z such that tanh(z) = x. The convention is to return the z whose imaginary part lies in [-pi/2, pi/2]. For real-valued input data types, arctanh always returns real output. For each value that cannot be expressed as

numpy.polynomial.hermite_e.hermeval2d(x, y, c) [source] Evaluate a 2-D HermiteE series at points (x, y). This function returns the values: The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly

numpy.polynomial.chebyshev.chebval3d(x, y, z, c) [source] Evaluate a 3-D Chebyshev series at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dim

matrix.nonzero() Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero equivalent function

numpy.ma.make_mask_none(newshape, dtype=None) [source] Return a boolean mask of the given shape, filled with False. This function returns a boolean ndarray with all entries False, that can be used in common mask manipulations. If a complex dtype is specified, the type of each field is converted to a boolean type. Parameters: newshape : tuple A tuple indicating the shape of the mask. dtype : {None, dtype}, optional If None, use a MaskType instance. Otherwise, use a new datatype with the

RandomState.randint(low, high=None, size=None, dtype='l') Return random integers from low (inclusive) to high (exclusive). Return random integers from the ?discrete uniform? distribution of the specified dtype in the ?half-open? interval [low, high). If high is None (the default), then results are from [0, low). Parameters: low : int Lowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is the highest such integer). high : int, optional

numpy.tril_indices_from(arr, k=0) [source] Return the indices for the lower-triangle of arr. See tril_indices for full details. Parameters: arr : array_like The indices will be valid for square arrays whose dimensions are the same as arr. k : int, optional Diagonal offset (see tril for details). See also tril_indices, tril Notes New in version 1.4.0.

numpy.ravel_multi_index(multi_index, dims, mode='raise', order='C') Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index. Parameters: multi_index : tuple of array_like A tuple of integer arrays, one array for each dimension. dims : tuple of ints The shape of array into which the indices from multi_index apply. mode : {?raise?, ?wrap?, ?clip?}, optional Specifies how out-of-bounds indices are handled. Can specify either one mode or

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