MaskedArray.__rsub__(other) [source] Subtract self from other, and return a new masked array.

numpy.core.records.array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, copy=True) [source] Construct a record array from a wide-variety of objects.

ndarray.__setslice__ x.__setslice__(i, j, y) <==> x[i:j]=y Use of negative indices is not supported.

record.squeeze() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

numpy.asmatrix(data, dtype=None) [source] Interpret the input as a matrix. Unlike matrix, asmatrix does not make a copy if the input is already a matrix or an ndarray. Equivalent to matrix(data, copy=False). Parameters: data : array_like Input data. dtype : data-type Data-type of the output matrix. Returns: mat : matrix data interpreted as a matrix. Examples >>> x = np.array([[1, 2], [3, 4]]) >>> m = np.asmatrix(x) >>> x[0,0] = 5 >>> m matrix

numpy.ma.dot(a, b, strict=False, out=None) [source] Return the dot product of two arrays. This function is the equivalent of numpy.dot that takes masked values into account. Note that strict and out are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory. Note Works only with 2-D arrays at the moment. Parameters: a,

RandomState.hypergeometric(ngood, nbad, nsample, size=None) Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified parameters, ngood (ways to make a good selection), nbad (ways to make a bad selection), and nsample = number of items sampled, which is less than or equal to the sum ngood + nbad. Parameters: ngood : int or array_like Number of ways to make a good selection. Must be nonnegative. nbad : int or array_like Number of

numpy.random.multivariate_normal(mean, cov[, size]) Draw random samples from a multivariate normal distribution. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. Such a distribution is specified by its mean and covariance matrix. These parameters are analogous to the mean (average or ?center?) and variance (standard deviation, or ?width,? squared) of the one-dimensional normal distribution. Par

Common test support for all numpy test scripts. This single module should provide all the common functionality for numpy tests in a single location, so that test scripts can just import it and work right away. Asserts assert_almost_equal(actual, desired[, ...]) Raises an AssertionError if two items are not equal up to desired precision. assert_approx_equal(actual, desired[, ...]) Raises an AssertionError if two items are not equal up to significant digits. assert_array_almost_equal(x, y[,

ndarray.__ipow__ x.__ipow__(y) <==> x**=y