chararray.isdecimal() [source] For each element in self, return True if there are only decimal characters in the element. See also char.isdecimal

numpy.core.defchararray.rindex(a, sub, start=0, end=None) [source] Like rfind, but raises ValueError when the substring sub is not found. Calls str.rindex element-wise. Parameters: a : array-like of str or unicode sub : str or unicode start, end : int, optional Returns: out : ndarray Output array of ints. See also rfind, str.rindex

numpy.polynomial.hermite_e.hermevander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the HermiteE polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the array V = hermevander(x, n), then np.dot(V, c) and hermeval(x, c) are the

numpy.logaddexp(x1, x2[, out]) = Logarithm of the sum of exponentiations of the inputs. Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the logarithm of the calculated probability is stored. This function allows adding probabilities stored in such a fashion. Parameters: x1, x2 : array_like Input values. Returns: result : ndarray

numpy.polynomial.hermite.hermadd(c1, c2) [source] Add one Hermite series to another. Returns the sum of two Hermite series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Array representing the Hermite series of their sum. See also hermsub, hermmul, hermdiv,

numpy.polynomial.hermite.hermsub(c1, c2) [source] Subtract one Hermite series from another. Returns the difference of two Hermite series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Of Hermite series coefficients representing their difference. See also hermadd, hermmul, he

chararray.dump(file) Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load. Parameters: file : str A string naming the dump file.

numpy.ma.column_stack(tup) = Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first. Parameters: tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns: stacked : 2-D array The array formed by stacking the given arrays. Notes The function is applied to both

numpy.ma.maximum_fill_value(obj) [source] Return the minimum value that can be represented by the dtype of an object. This function is useful for calculating a fill value suitable for taking the maximum of an array with a given dtype. Parameters: obj : {ndarray, dtype} An object that can be queried for it?s numeric type. Returns: val : scalar The minimum representable value. Raises: TypeError If obj isn?t a suitable numeric type. See also minimum_fill_value The inverse functi

matrix.T Returns the transpose of the matrix. Does not conjugate! For the complex conjugate transpose, use .H. Parameters: None Returns: ret : matrix object The (non-conjugated) transpose of the matrix. See also transpose, getH Examples >>> m = np.matrix('[1, 2; 3, 4]') >>> m matrix([[1, 2], [3, 4]]) >>> m.getT() matrix([[1, 3], [2, 4]])