numpy.ma.transpose(a, axes=None) [source] Permute the dimensions of an array. This function is exactly equivalent to numpy.transpose. See also numpy.transpose Equivalent function in top-level NumPy module. Examples >>> import numpy.ma as ma >>> x = ma.arange(4).reshape((2,2)) >>> x[1, 1] = ma.masked >>>> x masked_array(data = [[0 1] [2 --]], mask = [[False False] [False True]], fill_value = 999999) >>> ma.transpos

numpy.ma.asanyarray(a, dtype=None) [source] Convert the input to a masked array, conserving subclasses. If a is a subclass of MaskedArray, its class is conserved. No copy is performed if the input is already an ndarray. Parameters: a : array_like Input data, in any form that can be converted to an array. dtype : dtype, optional By default, the data-type is inferred from the input data. order : {?C?, ?F?}, optional Whether to use row-major (?C?) or column-major (?FORTRAN?) memory repre

numpy.ma.innerproduct(a, b) [source] Inner product of two arrays. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes. Parameters: a, b : array_like If a and b are nonscalar, their last dimensions must match. Returns: out : ndarray out.shape = a.shape[:-1] + b.shape[:-1] Raises: ValueError If the last dimension of a and b has different size. See also tensordot Sum products over arbitrary axes.

numpy.ma.concatenate(arrays, axis=0) [source] Concatenate a sequence of arrays along the given axis. Parameters: arrays : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. Returns: result : MaskedArray The concatenated array with any masked entries preserved. See also numpy.concatenate Equivalent function in the top

MaskedArray.shape Tuple of array dimensions. Notes May be used to ?reshape? the array, as long as this would not require a change in the total number of elements Examples >>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]

numpy.ma.anomalies(self, axis=None, dtype=None) = Compute the anomalies (deviations from the arithmetic mean) along the given axis. Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis. Parameters: axis : int, optional Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference. dtype : dtype, optional Type to use in computing the variance. For arrays of integer typ

MaskedArray.__reduce__() [source] Return a 3-tuple for pickling a MaskedArray.

numpy.ma.asarray(a, dtype=None, order=None) [source] Convert the input to a masked array of the given data-type. No copy is performed if the input is already an ndarray. If a is a subclass of MaskedArray, a base class MaskedArray is returned. Parameters: a : array_like Input data, in any form that can be converted to a masked array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists, ndarrays and masked arrays. dtype : dtype, optional By default, the data-ty

ndarray.reshape(shape, order='C') Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape equivalent function

ndarray.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