record.itemsize length of one element in bytes

numpy.polynomial.laguerre.lagline(off, scl) [source] Laguerre series whose graph is a straight line. Parameters: off, scl : scalars The specified line is given by off + scl*x. Returns: y : ndarray This module?s representation of the Laguerre series for off + scl*x. See also polyline, chebline Examples >>> from numpy.polynomial.laguerre import lagline, lagval >>> lagval(0,lagline(3, 2)) 3.0 >>> lagval(1,lagline(3, 2)) 5.0

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

MaskedArray.__abs__() <==> abs(x)

numpy.divide(x1, x2[, out]) = Divide arguments element-wise. Parameters: x1 : array_like Dividend array. x2 : array_like Divisor array. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs. Returns: y : ndarray or scalar The quotient x1/x2, element-wise. Returns a scalar if both x1 and x2 are scalars. See also seterr Set whether to raise or warn on overflow, underflow and di

Hermite.mapparms() [source] Return the mapping parameters. The returned values define a linear map off + scl*x that is applied to the input arguments before the series is evaluated. The map depends on the domain and window; if the current domain is equal to the window the resulting map is the identity. If the coefficients of the series instance are to be used by themselves outside this class, then the linear function must be substituted for the x in the standard representation of the base p

numpy.testing.assert_array_less(x, y, err_msg='', verbose=True) [source] Raises an AssertionError if two array_like objects are not ordered by less than. Given two array_like objects, check that the shape is equal and all elements of the first object are strictly smaller than those of the second object. An exception is raised at shape mismatch or incorrectly ordered values. Shape mismatch does not raise if an object has zero dimension. In contrast to the standard usage in numpy, NaNs are co

numpy.ma.min(obj, axis=None, out=None, fill_value=None) [source] Return the minimum along a given axis. Parameters: axis : {None, int}, optional Axis along which to operate. By default, axis is None and the flattened input is used. out : array_like, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. fill_value : {var}, optional Value used to fill in the masked values. If None, use the output of minimum_fi

MaskedArray.__setslice__(i, j, value) [source] x.__setslice__(i, j, value) <==> x[i:j]=value Set the slice (i,j) of a to value. If value is masked, mask those locations.

numpy.ma.transpose(a, axes=None) [source] Permute the dimensions of an array. This function is exactly equivalent to numpy.transpose. See also numpy.transpose Equivalent function in top-level NumPy module. Examples >>> import numpy.ma as ma >>> x = ma.arange(4).reshape((2,2)) >>> x[1, 1] = ma.masked >>>> x masked_array(data = [[0 1] [2 --]], mask = [[False False] [False True]], fill_value = 999999) >>> ma.transpos