numpy.ma.hsplit(ary, indices_or_sections) = Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension. See also split Split an array into multiple sub-arrays of equal size. Notes The function is applied to both the _data and the _mask, if any. Examples >>> x = np.arange(16.0).reshape(4, 4) >>>

numpy.linalg.lstsq(a, b, rcond=-1) [source] Return the least-squares solution to a linear matrix equation. Solves the equation a x = b by computing a vector x that minimizes the Euclidean 2-norm || b - a x ||^2. The equation may be under-, well-, or over- determined (i.e., the number of linearly independent rows of a can be less than, equal to, or greater than its number of linearly independent columns). If a is square and of full rank, then x (but for round-off error) is the ?exact? soluti

numpy.asfarray(a, dtype=) [source] Return an array converted to a float type. Parameters: a : array_like The input array. dtype : str or dtype object, optional Float type code to coerce input array a. If dtype is one of the ?int? dtypes, it is replaced with float64. Returns: out : ndarray The input a as a float ndarray. Examples >>> np.asfarray([2, 3]) array([ 2., 3.]) >>> np.asfarray([2, 3], dtype='float') array([ 2., 3.]) >>> np.asfarray([2, 3], dtyp

dtype.type The type object used to instantiate a scalar of this data-type.

numpy.ascontiguousarray(a, dtype=None) [source] Return a contiguous array in memory (C order). Parameters: a : array_like Input array. dtype : str or dtype object, optional Data-type of returned array. Returns: out : ndarray Contiguous array of same shape and content as a, with type dtype if specified. See also asfortranarray Convert input to an ndarray with column-major memory order. require Return an ndarray that satisfies requirements. ndarray.flags Information about

numpy.diag(v, k=0) [source] Extract a diagonal or construct a diagonal array. See the more detailed documentation for numpy.diagonal if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using. Parameters: v : array_like If v is a 2-D array, return a copy of its k-th diagonal. If v is a 1-D array, return a 2-D array with v on the k-th diagonal. k : int, optional Diagonal in qu

record.ptp() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

numpy.unravel_index(indices, dims, order='C') Converts a flat index or array of flat indices into a tuple of coordinate arrays. Parameters: indices : array_like An integer array whose elements are indices into the flattened version of an array of dimensions dims. Before version 1.6.0, this function accepted just one index value. dims : tuple of ints The shape of the array to use for unraveling indices. order : {?C?, ?F?}, optional Determines whether the indices should be viewed as ind

numpy.polynomial.laguerre.laggrid2d(x, y, c) [source] Evaluate a 2-D Laguerre series on the Cartesian product of x and y. This function returns the values: where the points (a, b) consist of all pairs formed by taking a from x and b from y. The resulting points form a grid with x in the first dimension and y in the second. The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x and y or their ele

numpy.may_share_memory(a, b, max_work=None) Determine if two arrays might share memory A return of True does not necessarily mean that the two arrays share any element. It just means that they might. Only the memory bounds of a and b are checked by default. Parameters: a, b : ndarray Input arrays max_work : int, optional Effort to spend on solving the overlap problem. See shares_memory for details. Default for may_share_memory is to do a bounds check. Returns: out : bool See also s