numpy.ma.any(self, axis=None, out=None) = Check if any of the elements of a are true. Performs a logical_or over the given axis and returns the result. Masked values are considered as False during computation. Parameters: axis : {None, integer} Axis to perform the operation over. If None, perform over flattened array and return a scalar. out : {None, array}, optional Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output.

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

numpy.ma.allequal(a, b, fill_value=True) [source] Return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked. Parameters: a, b : array_like Input arrays to compare. fill_value : bool, optional Whether masked values in a or b are considered equal (True) or not (False). Returns: y : bool Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned. S

numpy.ma.allclose(a, b, masked_equal=True, rtol=1e-05, atol=1e-08) [source] Returns True if two arrays are element-wise equal within a tolerance. This function is equivalent to allclose except that masked values are treated as equal (default) or unequal, depending on the masked_equal argument. Parameters: a, b : array_like Input arrays to compare. masked_equal : bool, optional Whether masked values in a and b are considered equal (True) or not (False). They are considered equal by defau

numpy.lookfor(what, module=None, import_modules=True, regenerate=False, output=None) [source] Do a keyword search on docstrings. A list of of objects that matched the search is displayed, sorted by relevance. All given keywords need to be found in the docstring for it to be returned as a result, but the order does not matter. Parameters: what : str String containing words to look for. module : str or list, optional Name of module(s) whose docstrings to go through. import_modules : bool

numpy.ma.all(self, axis=None, out=None) = Check if all of the elements of a are true. Performs a logical_and over the given axis and returns the result. Masked values are considered as True during computation. For convenience, the output array is masked where ALL the values along the current axis are masked: if the output would have been a scalar and that all the values are masked, then the output is masked. Parameters: axis : {None, integer} Axis to perform the operation over. If None,

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None) [source] Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below). Parameters: start : float base ** start is the starting value of the sequence. stop : float base ** stop is the final value of the sequence, unless endpoint is False. In that case, num + 1 values are spaced over the interval in log

numpy.logical_xor(x1, x2[, out]) = Compute the truth value of x1 XOR x2, element-wise. Parameters: x1, x2 : array_like Logical XOR is applied to the elements of x1 and x2. They must be broadcastable to the same shape. Returns: y : bool or ndarray of bool Boolean result of the logical XOR operation applied to the elements of x1 and x2; the shape is determined by whether or not broadcasting of one or both arrays was required. See also logical_and, logical_or, logical_not, bitwise_xo

numpy.logical_and(x1, x2[, out]) = Compute the truth value of x1 AND x2 element-wise. Parameters: x1, x2 : array_like Input arrays. x1 and x2 must be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical AND operation on corresponding elements of x1 and x2. See also logical_or, logical_not, logical_xor, bitwise_and Examples >>> np.logical_and(True, False) False >>> np.logical_and([True, False], [False, Fal

numpy.logical_or(x1, x2[, out]) = Compute the truth value of x1 OR x2 element-wise. Parameters: x1, x2 : array_like Logical OR is applied to the elements of x1 and x2. They have to be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical OR operation on elements of x1 and x2. See also logical_and, logical_not, logical_xor, bitwise_or Examples >>> np.logical_or(True, False) True >>> np.logical_or([True, Fal