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.

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

chararray.startswith(prefix, start=0, end=None) [source] Returns a boolean array which is True where the string element in self starts with prefix, otherwise False. See also char.startswith

numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None) [source] Estimate a covariance matrix, given data and weights. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, , then the covariance matrix element is the covariance of and . The element is the variance of . See the notes for an outline of the algorithm. Parameters: m : array_like A 1-D or 2-D array containing multiple variables and observatio

MaskedArray.__int__() [source] Convert to int.