numpy.polynomial.hermite_e.hermeval(x, c, tensor=True) [source] Evaluate an HermiteE series at points x. If c is of length n + 1, this function returns the value: The parameter x is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either x or its elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array, then p(x) will have the same shape as x. If c is multidimensional,

numpy.ma.atleast_3d(*arys) = View inputs as arrays with at least three dimensions. Parameters: arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved. Returns: res1, res2, ... : ndarray An array, or tuple of arrays, each with a.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape (N,) be

numpy.r_ = Translates slice objects to concatenation along the first axis. This is a simple way to build up arrays quickly. There are two use cases. If the index expression contains comma separated arrays, then stack them along their first axis. If the index expression contains slice notation or scalars then create a 1-D array with a range indicated by the slice notation. If slice notation is used, the syntax start:stop:step is equivalent to np.arange(start, stop, step) inside of the brack

ndarray.__getitem__ x.__getitem__(y) <==> x[y]

ndarray.__pow__(y[, z]) <==> pow(x, y[, z])

numpy.convolve(a, v, mode='full') [source] Returns the discrete, linear convolution of two one-dimensional sequences. The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal [R17]. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions. If v is longer than a, the arrays are swapped before computation. Parameters: a : (N,) ar

matrix.compress(condition, axis=None, out=None) Return selected slices of this array along given axis. Refer to numpy.compress for full documentation. See also numpy.compress equivalent function

recarray.imag The imaginary part of the array. Examples >>> x = np.sqrt([1+0j, 0+1j]) >>> x.imag array([ 0. , 0.70710678]) >>> x.imag.dtype dtype('float64')

numpy.linalg.slogdet(a) [source] Compute the sign and (natural) logarithm of the determinant of an array. If an array has a very small or very large determinant, then a call to det may overflow or underflow. This routine is more robust against such issues, because it computes the logarithm of the determinant rather than the determinant itself. Parameters: a : (..., M, M) array_like Input array, has to be a square 2-D array. Returns: sign : (...) array_like A number representing the si

numpy.array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix='', style=, formatter=None) [source] Return a string representation of an array. Parameters: a : ndarray Input array. max_line_width : int, optional The maximum number of columns the string should span. Newline characters splits the string appropriately after array elements. precision : int, optional Floating point precision. Default is the current printing precision (usually 8), whic