numpy.getbuffer(obj[, offset[, size]]) Create a buffer object from the given object referencing a slice of length size starting at offset. Default is the entire buffer. A read-write buffer is attempted followed by a read-only buffer. Parameters: obj : object offset : int, optional size : int, optional Returns: buffer_obj : buffer Examples >>> buf = np.getbuffer(np.ones(5), 1, 3) >>> len(buf) 3 >>> buf[0] '\x00' >>> buf <read-write buffer for 0x8af1

Truth value testing all(a[, axis, out, keepdims]) Test whether all array elements along a given axis evaluate to True. any(a[, axis, out, keepdims]) Test whether any array element along a given axis evaluates to True. Array contents isfinite(x[, out]) Test element-wise for finiteness (not infinity or not Not a Number). isinf(x[, out]) Test element-wise for positive or negative infinity. isnan(x[, out]) Test element-wise for NaN and return result as a boolean array. isneginf(x[, y]) Te

numpy.apply_along_axis(func1d, axis, arr, *args, **kwargs) [source] Apply a function to 1-D slices along the given axis. Execute func1d(a, *args) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis. Parameters: func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of arr along the specified axis. axis : integer Axis along which arr is sliced. arr : ndarray Input array. args : any Additional arguments to func1d. kwargs: any Add

numpy.polynomial.hermite_e.hermefromroots(roots) [source] Generate a HermiteE series with given roots. The function returns the coefficients of the polynomial in HermiteE form, where the r_n are the roots specified in roots. If a zero has multiplicity n, then it must appear in roots n times. For instance, if 2 is a root of multiplicity three and 3 is a root of multiplicity 2, then roots looks something like [2, 2, 2, 3, 3]. The roots can appear in any order. If the returned coefficients

matrix.getT() [source] 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]])

numpy.ma.is_mask(m) [source] Return True if m is a valid, standard mask. This function does not check the contents of the input, only that the type is MaskType. In particular, this function returns False if the mask has a flexible dtype. Parameters: m : array_like Array to test. Returns: result : bool True if m.dtype.type is MaskType, False otherwise. See also isMaskedArray Test whether input is an instance of MaskedArray. Examples >>> import numpy.ma as ma >>>

numpy.polynomial.hermite.hermfromroots(roots) [source] Generate a Hermite series with given roots. The function returns the coefficients of the polynomial in Hermite form, where the r_n are the roots specified in roots. If a zero has multiplicity n, then it must appear in roots n times. For instance, if 2 is a root of multiplicity three and 3 is a root of multiplicity 2, then roots looks something like [2, 2, 2, 3, 3]. The roots can appear in any order. If the returned coefficients are c

numpy.polynomial.hermite.hermweight(x) [source] Weight function of the Hermite polynomials. The weight function is and the interval of integration is . the Hermite polynomials are orthogonal, but not normalized, with respect to this weight function. Parameters: x : array_like Values at which the weight function will be computed. Returns: w : ndarray The weight function at x. Notes

numpy.polynomial.hermite.hermcompanion(c) [source] Return the scaled companion matrix of c. The basis polynomials are scaled so that the companion matrix is symmetric when c is an Hermite basis polynomial. This provides better eigenvalue estimates than the unscaled case and for basis polynomials the eigenvalues are guaranteed to be real if numpy.linalg.eigvalsh is used to obtain them. Parameters: c : array_like 1-D array of Hermite series coefficients ordered from low to high degree. Re

numpy.linalg.qr(a, mode='reduced') [source] Compute the qr factorization of a matrix. Factor the matrix a as qr, where q is orthonormal and r is upper-triangular. Parameters: a : array_like, shape (M, N) Matrix to be factored. mode : {?reduced?, ?complete?, ?r?, ?raw?, ?full?, ?economic?}, optional If K = min(M, N), then ?reduced? : returns q, r with dimensions (M, K), (K, N) (default) ?complete? : returns q, r with dimensions (M, M), (M, N) ?r? : returns r only with dimensions (K, N) ?