matrix.flat A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python?s built-in iterator object. See also flatten Return a copy of the array collapsed into one dimension. flatiter Examples >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(x.

numpy.polynomial.hermite_e.hermegrid3d(x, y, z, c) [source] Evaluate a 3-D HermiteE series on the Cartesian product of x, y, and z. This function returns the values: where the points (a, b, c) consist of all triples formed by taking a from x, b from y, and c from z. The resulting points form a grid with x in the first dimension, y in the second, and z in the third. The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scala

MaskedArray.anom(axis=None, dtype=None) [source] 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 type

ndarray.__invert__ x.__invert__() <==> ~x

matrix.getfield(dtype, offset=0) Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes. Parameters:

recarray.nonzero() Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero equivalent function

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

Polynomial.has_samecoef(other) [source] Check if coefficients match. New in version 1.6.0. Parameters: other : class instance The other class must have the coef attribute. Returns: bool : boolean True if the coefficients are the same, False otherwise.

numpy.floor_divide(x1, x2[, out]) = Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python // operator and pairs with the Python % (remainder), function so that b = a % b + b * (a // b) up to roundoff. Parameters: x1 : array_like Numerator. x2 : array_like Denominator. Returns: y : ndarray y = floor(x1/x2) See also remainder Remainder complementary to floor_divide. divide Standard division. floor Round a number to the n

numpy.ma.var(self, axis=None, dtype=None, out=None, ddof=0) = Compute the variance along the specified axis. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted. axis : None or int or tuple of ints, optional Axis or axes along which