ndarray.reshape(shape, order='C') Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape equivalent function

numpy.common_type(*arrays) [source] Return a scalar type which is common to the input arrays. The return type will always be an inexact (i.e. floating point) scalar type, even if all the arrays are integer arrays. If one of the inputs is an integer array, the minimum precision type that is returned is a 64-bit floating point dtype. All input arrays can be safely cast to the returned dtype without loss of information. Parameters: array1, array2, ... : ndarrays Input arrays. Returns: out

numpy.linalg.det(a) [source] Compute the determinant of an array. Parameters: a : (..., M, M) array_like Input array to compute determinants for. Returns: det : (...) array_like Determinant of a. See also slogdet Another way to representing the determinant, more suitable for large matrices where underflow/overflow may occur. Notes New in version 1.8.0. Broadcasting rules apply, see the numpy.linalg documentation for details. The determinant is computed via LU factorization us

numpy.divide(x1, x2[, out]) = Divide arguments element-wise. Parameters: x1 : array_like Dividend array. x2 : array_like Divisor array. 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: y : ndarray or scalar The quotient x1/x2, element-wise. Returns a scalar if both x1 and x2 are scalars. See also seterr Set whether to raise or warn on overflow, underflow and di

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 >>>