numpy.arctanh(x[, out]) = Inverse hyperbolic tangent element-wise. Parameters: x : array_like Input array. Returns: out : ndarray Array of the same shape as x. See also emath.arctanh Notes arctanh is a multivalued function: for each x there are infinitely many numbers z such that tanh(z) = x. The convention is to return the z whose imaginary part lies in [-pi/2, pi/2]. For real-valued input data types, arctanh always returns real output. For each value that cannot be expressed as

ndarray.partition(kth, axis=-1, kind='introselect', order=None) Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined. New in version 1.8.0. Parameters: kth : int or sequence of ints Element index to partition by. The kt

numpy.ma.size(obj, axis=None) [source] Return the number of elements along a given axis. Parameters: a : array_like Input data. axis : int, optional Axis along which the elements are counted. By default, give the total number of elements. Returns: element_count : int Number of elements along the specified axis. See also shape dimensions of array ndarray.shape dimensions of array ndarray.size number of elements in array Examples >>> a = np.array([[1,2,3],[4,5,6]])

numpy.dstack(tup) [source] Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. Parameters: tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns: stacked : ndarray The array formed by stacking the given arrays.

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.fliplr(m) [source] Flip array in the left/right direction. Flip the entries in each row in the left/right direction. Columns are preserved, but appear in a different order than before. Parameters: m : array_like Input array, must be at least 2-D. Returns: f : ndarray A view of m with the columns reversed. Since a view is returned, this operation is . See also flipud Flip array in the up/down direction. rot90 Rotate array counterclockwise. Notes Equivalent to A[:,::-1].

numpy.core.defchararray.isnumeric(a) [source] For each element, return True if there are only numeric characters in the element. Calls unicode.isnumeric element-wise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION ONE FIFTH. Parameters: a : array_like, unicode Input array. Returns: out : ndarray, bool Array of booleans of same shape as a. See also unicode.isnumeric

matrix.var(axis=None, dtype=None, out=None, ddof=0) [source] Returns the variance of the matrix elements, along the given axis. Refer to numpy.var for full documentation. See also numpy.var Notes This is the same as ndarray.var, except that where an ndarray would be returned, a matrix object is returned instead. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4))) >>> x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.var

numpy.i0(x) [source] Modified Bessel function of the first kind, order 0. Usually denoted . This function does broadcast, but will not ?up-cast? int dtype arguments unless accompanied by at least one float or complex dtype argument (see Raises below). Parameters: x : array_like, dtype float or complex Argument of the Bessel function. Returns: out : ndarray, shape = x.shape, dtype = x.dtype The modified Bessel function evaluated at each of the elements of x. Raises: TypeError: array

numpy.polymul(a1, a2) [source] Find the product of two polynomials. Finds the polynomial resulting from the multiplication of the two input polynomials. Each input must be either a poly1d object or a 1D sequence of polynomial coefficients, from highest to lowest degree. Parameters: a1, a2 : array_like or poly1d object Input polynomials. Returns: out : ndarray or poly1d object The polynomial resulting from the multiplication of the inputs. If either inputs is a poly1d object, then the