matrix.dtype Data-type of the array?s elements. Parameters: None Returns: d : numpy dtype object See also numpy.dtype Examples >>> x array([[0, 1], [2, 3]]) >>> x.dtype dtype('int32') >>> type(x.dtype) <type 'numpy.dtype'>

numpy.linalg.pinv(a, rcond=1e-15) [source] Compute the (Moore-Penrose) pseudo-inverse of a matrix. Calculate the generalized inverse of a matrix using its singular-value decomposition (SVD) and including all large singular values. Parameters: a : (M, N) array_like Matrix to be pseudo-inverted. rcond : float Cutoff for small singular values. Singular values smaller (in modulus) than rcond * largest_singular_value (again, in modulus) are set to zero. Returns: B : (N, M) ndarray The ps

numpy.asanyarray(a, dtype=None, order=None) [source] Convert the input to an ndarray, but pass ndarray subclasses through. Parameters: a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {?C?, ?F?}, optional Whether to use row-major (C-style) or column-major (Fortran-

numpy.ma.atleast_2d(*arys) = View inputs as arrays with at least two dimensions. Parameters: arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Returns: res, res2, ... : ndarray An array, or tuple of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned. Notes The function is applied to both the _dat

numpy.polynomial.hermite.hermval3d(x, y, z, c) [source] Evaluate a 3-D Hermite series at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dimensi

numpy.arccosh(x[, out]) = Inverse hyperbolic cosine, element-wise. Parameters: x : array_like Input array. out : ndarray, optional Array of the same shape as x, to store results in. See doc.ufuncs (Section ?Output arguments?) for details. Returns: arccosh : ndarray Array of the same shape as x. See also cosh, arcsinh, sinh, arctanh, tanh Notes arccosh is a multivalued function: for each x there are infinitely many numbers z such that cosh(z) = x. The convention is to return the

numpy.ma.loads(strg) [source] Load a pickle from the current string. The result of cPickle.loads(strg) is returned. Parameters: strg : str The string to load. See also dumps Return a string corresponding to the pickling of a masked array.

numpy.matlib.zeros(shape, dtype=None, order='C') [source] Return a matrix of given shape and type, filled with zeros. Parameters: shape : int or sequence of ints Shape of the matrix dtype : data-type, optional The desired data-type for the matrix, default is float. order : {?C?, ?F?}, optional Whether to store the result in C- or Fortran-contiguous order, default is ?C?. Returns: out : matrix Zero matrix of given shape, dtype, and order. See also numpy.zeros Equivalent array

numpy.bitwise_or(x1, x2[, out]) = Compute the bit-wise OR of two arrays element-wise. Computes the bit-wise OR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator |. Parameters: x1, x2 : array_like Only integer and boolean types are handled. 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: out : array_

numpy.random.randn(d0, d1, ..., dn) Return a sample (or samples) from the ?standard normal? distribution. If positive, int_like or int-convertible arguments are provided, randn generates an array of shape (d0, d1, ..., dn), filled with random floats sampled from a univariate ?normal? (Gaussian) distribution of mean 0 and variance 1 (if any of the are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument