numpy.linalg.cholesky(a) [source] Cholesky decomposition. Return the Cholesky decomposition, L * L.H, of the square matrix a, where L is lower-triangular and .H is the conjugate transpose operator (which is the ordinary transpose if a is real-valued). a must be Hermitian (symmetric if real-valued) and positive-definite. Only L is actually returned. Parameters: a : (..., M, M) array_like Hermitian (symmetric if all elements are real), positive-definite input matrix. Returns: L : (..., M

numpy.ceil(x[, out]) = Return the ceiling of the input, element-wise. The ceil of the scalar x is the smallest integer i, such that i >= x. It is often denoted as . Parameters: x : array_like Input data. Returns: y : ndarray or scalar The ceiling of each element in x, with float dtype. See also floor, trunc, rint Examples >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> np.ceil(a) array([-1., -1., -0., 1., 2., 2., 2.])

numpy.ma.argmax(a, axis=None, fill_value=None) [source] Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value. Parameters: axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : {var}, optional Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. out : {None, array}, optional Array into w

numpy.core.defchararray.translate(a, table, deletechars=None) [source] For each element in a, return a copy of the string where all characters occurring in the optional argument deletechars are removed, and the remaining characters have been mapped through the given translation table. Calls str.translate element-wise. Parameters: a : array-like of str or unicode table : str of length 256 deletechars : str Returns: out : ndarray Output array of str or unicode, depending on input type

dtype.fields Dictionary of named fields defined for this data type, or None. The dictionary is indexed by keys that are the names of the fields. Each entry in the dictionary is a tuple fully describing the field: (dtype, offset[, title]) If present, the optional title can be any object (if it is a string or unicode then it will also be a key in the fields dictionary, otherwise it?s meta-data). Notice also that the first two elements of the tuple can be passed directly as arguments to the n

numpy.ma.getmask(a) [source] Return the mask of a masked array, or nomask. Return the mask of a as an ndarray if a is a MaskedArray and the mask is not nomask, else return nomask. To guarantee a full array of booleans of the same shape as a, use getmaskarray. Parameters: a : array_like Input MaskedArray for which the mask is required. See also getdata Return the data of a masked array as an ndarray. getmaskarray Return the mask of a masked array, or full array of False. Examples

numpy.shares_memory(a, b, max_work=None) Determine if two arrays share memory Parameters: a, b : ndarray Input arrays max_work : int, optional Effort to spend on solving the overlap problem (maximum number of candidate solutions to consider). The following special values are recognized: max_work=MAY_SHARE_EXACT (default) The problem is solved exactly. In this case, the function returns True only if there is an element shared between the arrays. max_work=MAY_SHARE_BOUNDS Only the memo

numpy.tan(x[, out]) = Compute tangent element-wise. Equivalent to np.sin(x)/np.cos(x) element-wise. Parameters: x : array_like Input array. out : ndarray, optional Output array of same shape as x. Returns: y : ndarray The corresponding tangent values. Raises: ValueError: invalid return array shape if out is provided and out.shape != x.shape (See Examples) Notes If out is provided, the function writes the result into it, and returns a reference to out. (See Examples) Reference

numpy.may_share_memory(a, b, max_work=None) Determine if two arrays might share memory A return of True does not necessarily mean that the two arrays share any element. It just means that they might. Only the memory bounds of a and b are checked by default. Parameters: a, b : ndarray Input arrays max_work : int, optional Effort to spend on solving the overlap problem. See shares_memory for details. Default for may_share_memory is to do a bounds check. Returns: out : bool See also s

MaskedArray.argmax(axis=None, fill_value=None, out=None) [source] Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value. Parameters: axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : {var}, optional Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. out : {None, array}, optional Ar