numpy.ma.fromfunction(function, shape, **kwargs) = Construct an array by executing a function over each coordinate. The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z). Parameters: function : callable The function is called with N parameters, where N is the rank of shape. Each parameter represents the coordinates of the array varying along a specific axis. For example, if shape were (2, 2), then the parameters in turn be (0, 0), (0, 1), (1, 0), (1, 1). shape :

numpy.ma.frombuffer(buffer, dtype=float, count=-1, offset=0) = Interpret a buffer as a 1-dimensional array. Parameters: buffer : buffer_like An object that exposes the buffer interface. dtype : data-type, optional Data-type of the returned array; default: float. count : int, optional Number of items to read. -1 means all data in the buffer. offset : int, optional Start reading the buffer from this offset; default: 0. Notes If the buffer has data that is not in machine byte-order,

numpy.ma.flatnotmasked_edges(a) [source] Find the indices of the first and last unmasked values. Expects a 1-D MaskedArray, returns None if all values are masked. Parameters: a : array_like Input 1-D MaskedArray Returns: edges : ndarray or None The indices of first and last non-masked value in the array. Returns None if all values are masked. See also flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked Notes Only accepts 1-D arrays. Exampl

numpy.ma.flatnotmasked_contiguous(a) [source] Find contiguous unmasked data in a masked array along the given axis. Parameters: a : narray The input array. Returns: slice_list : list A sorted sequence of slices (start index, end index). See also flatnotmasked_edges, notmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked Notes Only accepts 2-D arrays at most. Examples >>> a = np.ma.arange(10) >>> np.ma.flatnotmasked_contiguous(a) slice(0, 10, None) &

numpy.ma.fix_invalid(a, mask=False, copy=True, fill_value=None) [source] Return input with invalid data masked and replaced by a fill value. Invalid data means values of nan, inf, etc. Parameters: a : array_like Input array, a (subclass of) ndarray. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as data. True indicates a masked (i.e. invalid) data. copy : bool, optional Whether to use a copy of a (True) or to fix a in place (False). Def

numpy.ma.filled(a, fill_value=None) [source] Return input as an array with masked data replaced by a fill value. If a is not a MaskedArray, a itself is returned. If a is a MaskedArray and fill_value is None, fill_value is set to a.fill_value. Parameters: a : MaskedArray or array_like An input object. fill_value : scalar, optional Filling value. Default is None. Returns: a : ndarray The filled array. See also compressed Examples >>> x = np.ma.array(np.arange(9).reshape(3

numpy.ma.expand_dims(x, axis) [source] Expand the shape of an array. Expands the shape of the array by including a new axis before the one specified by the axis parameter. This function behaves the same as numpy.expand_dims but preserves masked elements. See also numpy.expand_dims Equivalent function in top-level NumPy module. Examples >>> import numpy.ma as ma >>> x = ma.array([1, 2, 4]) >>> x[1] = ma.masked >>> x masked_array(data = [1 -- 4],

numpy.ma.empty_like(a, dtype=None, order='K', subok=True) = Return a new array with the same shape and type as a given array. Parameters: a : array_like The shape and data-type of a define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. New in version 1.6.0. order : {?C?, ?F?, ?A?, or ?K?}, optional Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous

numpy.ma.empty(shape, dtype=float, order='C') = Return a new array of given shape and type, without initializing entries. Parameters: shape : int or tuple of int Shape of the empty array dtype : data-type, optional Desired output data-type. order : {?C?, ?F?}, optional Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Returns: out : ndarray Array of uninitialized (arbitrary) data of the given shape, dtype, and order. O

numpy.ma.ediff1d(arr, to_end=None, to_begin=None) [source] Compute the differences between consecutive elements of an array. This function is the equivalent of numpy.ediff1d that takes masked values into account, see numpy.ediff1d for details. See also numpy.ediff1d Equivalent function for ndarrays.