numpy.ma.dumps(a) [source] Return a string corresponding to the pickling of a masked array. This is a wrapper around cPickle.dumps. Parameters: a : MaskedArray The array for which the string representation of the pickle is returned.

numpy.ma.dstack(tup) = Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. Parameters: tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns: stacked : ndarray The array formed by stacking the given arrays.

numpy.ma.dump(a, F) [source] Pickle a masked array to a file. This is a wrapper around cPickle.dump. Parameters: a : MaskedArray The array to be pickled. F : str or file-like object The file to pickle a to. If a string, the full path to the file.

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,

numpy.ma.default_fill_value(obj) [source] Return the default fill value for the argument object. The default filling value depends on the datatype of the input array or the type of the input scalar: datatype default bool True int 999999 float 1.e20 complex 1.e20+0j object ??? string ?N/A? Parameters: obj : ndarray, dtype or scalar The array data-type or scalar for which the default fill value is returned. Returns: fill_value : scalar The default fill value. Examples >>> np

numpy.ma.diag(v, k=0) [source] Extract a diagonal or construct a diagonal array. This function is the equivalent of numpy.diag that takes masked values into account, see numpy.diag for details. See also numpy.diag Equivalent function for ndarrays.

numpy.ma.cumprod(self, axis=None, dtype=None, out=None) = Return the cumulative product of the elements along the given axis. The cumulative product is taken over the flattened array by default, otherwise over the specified axis. Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the product is computed. The default (axis = None)

numpy.ma.cumsum(self, axis=None, dtype=None, out=None) = Return the cumulative sum of the elements along the given axis. The cumulative sum is calculated over the flattened array by default, otherwise over the specified axis. Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to co

numpy.ma.count(a, axis=None) [source] Count the non-masked elements of the array along the given axis. Parameters: axis : int, optional Axis along which to count the non-masked elements. If axis is None, all non-masked elements are counted. Returns: result : int or ndarray If axis is None, an integer count is returned. When axis is not None, an array with shape determined by the lengths of the remaining axes, is returned. See also count_masked Count masked elements in array or a

numpy.ma.cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None) [source] Estimate the covariance matrix. Except for the handling of missing data this function does the same as numpy.cov. For more details and examples, see numpy.cov. By default, masked values are recognized as such. If x and y have the same shape, a common mask is allocated: if x[i,j] is masked, then y[i,j] will also be masked. Setting allow_masked to False will raise an exception if values are missing in eith