numpy.random.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.

matrix.imag The imaginary part of the array. Examples >>> x = np.sqrt([1+0j, 0+1j]) >>> x.imag array([ 0. , 0.70710678]) >>> x.imag.dtype dtype('float64')

class numpy.lib.user_array.container(data, dtype=None, copy=True) [source] Standard container-class for easy multiple-inheritance. Methods copy tostring byteswap astype

MaskedArray.diagonal(offset=0, axis1=0, axis2=1) [source] Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed. Refer to numpy.diagonal for full documentation. See also numpy.diagonal equivalent function

MaskedArray.choose(choices, out=None, mode='raise') Use an index array to construct a new array from a set of choices. Refer to numpy.choose for full documentation. See also numpy.choose equivalent function

numpy.restoredot() [source] Restore dot, vdot, and innerproduct to the default non-BLAS implementations. Typically, the user will only need to call this when troubleshooting and installation problem, reproducing the conditions of a build without an accelerated BLAS, or when being very careful about benchmarking linear algebra operations. Note Deprecated in Numpy 1.10 The cblas functions have been integrated into the multarray module and restoredot now longer does anything. It will be remov

numpy.ma.masked_where(condition, a, copy=True) [source] Mask an array where a condition is met. Return a as an array masked where condition is True. Any masked values of a or condition are also masked in the output. Parameters: condition : array_like Masking condition. When condition tests floating point values for equality, consider using masked_values instead. a : array_like Array to mask. copy : bool If True (default) make a copy of a in the result. If False modify a in place and r

numpy.polynomial.chebyshev.chebone = array([1])

ndarray.__neg__ x.__neg__() <==> -x

numpy.take(a, indices, axis=None, out=None, mode='raise') [source] Take elements from an array along an axis. This function does the same thing as ?fancy? indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. Parameters: a : array_like The source array. indices : array_like The indices of the values to extract. New in version 1.8.0. Also allow scalars for indices. axis : int, optional The axis over which to select values.