numpy.any(a, axis=None, out=None, keepdims=False) [source] Test whether any array element along a given axis evaluates to True. Returns single boolean unless axis is not None Parameters: a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical OR reduction is performed. The default (axis = None) is to perform a logical OR over all the dimensions of the input array. axis may be negative, in wh

ndarray.tofile(fid, sep="", format="%s") Write array to a file as text or binary (default). Data is always written in ?C? order, independent of the order of a. The data produced by this method can be recovered using the function fromfile(). Parameters: fid : file or str An open file object, or a string containing a filename. sep : str Separator between array items for text output. If ?? (empty), a binary file is written, equivalent to file.write(a.tobytes()). format : str Format strin

numpy.random.beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function where the normalisation, B, is the beta function, It is often seen in Bayesian inference and order statistics. Parameters: a : float Alpha, non-negative. b : float Beta, non-negative. size : int or tuple of ints, optional Output shape. If the given sh

RandomState.geometric(p, size=None) Draw samples from the geometric distribution. Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to achieve success. It is therefore supported on the positive integers, k = 1, 2, .... The probability mass function of the geometric distribution is where p is the probability of success of an in

ndarray.prod(axis=None, dtype=None, out=None, keepdims=False) Return the product of the array elements over the given axis Refer to numpy.prod for full documentation. See also numpy.prod equivalent function

numpy.polynomial.legendre.legtrim(c, tol=0) [source] Remove ?small? ?trailing? coefficients from a polynomial. ?Small? means ?small in absolute value? and is controlled by the parameter tol; ?trailing? means highest order coefficient(s), e.g., in [0, 1, 1, 0, 0] (which represents 0 + x + x**2 + 0*x**3 + 0*x**4) both the 3-rd and 4-th order coefficients would be ?trimmed.? Parameters: c : array_like 1-d array of coefficients, ordered from lowest order to highest. tol : number, optional T

numpy.core.defchararray.startswith(a, prefix, start=0, end=None) [source] Returns a boolean array which is True where the string element in a starts with prefix, otherwise False. Calls str.startswith element-wise. Parameters: a : array_like of str or unicode prefix : str start, end : int, optional With optional start, test beginning at that position. With optional end, stop comparing at that position. Returns: out : ndarray Array of booleans See also str.startswith

recarray.searchsorted(v, side='left', sorter=None) Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted equivalent function

HermiteE.roots() [source] Return the roots of the series polynomial. Compute the roots for the series. Note that the accuracy of the roots decrease the further outside the domain they lie. Returns: roots : ndarray Array containing the roots of the series.

numpy.multiply(x1, x2[, out]) = Multiply arguments element-wise. Parameters: x1, x2 : array_like Input arrays to be multiplied. Returns: y : ndarray The product of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars. Notes Equivalent to x1 * x2 in terms of array broadcasting. Examples >>> np.multiply(2.0, 4.0) 8.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.]