numpy.random.permutation(x) Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. Parameters: x : int or array_like If x is an integer, randomly permute np.arange(x). If x is an array, make a copy and shuffle the elements randomly. Returns: out : ndarray Permuted sequence or array range. Examples >>> np.random.permutation(10) array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) >>> np.random.permuta

recarray.strides Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides) A more detailed explanation of strides can be found in the ?ndarray.rst? file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], dtype=np.int32) This array

numpy.tensordot(a, b, axes=2) [source] Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one), a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a?s and b?s elements (components) over the axes specified by a_axes and b_axes. The third argument can be a single non-negative integer_like scalar, N; if it is such, then the last N dimensions of a and the fi

numpy.polynomial.polynomial.polyvander(x, deg) [source] Vandermonde matrix of given degree. Returns the Vandermonde matrix of degree deg and sample points x. The Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the power of x. If c is a 1-D array of coefficients of length n + 1 and V is the matrix V = polyvander(x, n), then np.dot(V, c) and polyval(x, c) are the same up to roundoff. This equivalence is usef

recarray.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) Returns the variance of the array elements, along given axis. Refer to numpy.var for full documentation. See also numpy.var equivalent function

numpy.nanargmin(a, axis=None) [source] Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs. Parameters: a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. Returns: index_array : ndarray An array of indices or a single index value. See also argmin, nanargmax Examples >>&

classmethod Legendre.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None) [source] Least squares fit to data. Return a series instance that is the least squares fit to the data y sampled at x. The domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates o

numpy.ma.getmask(a) [source] Return the mask of a masked array, or nomask. Return the mask of a as an ndarray if a is a MaskedArray and the mask is not nomask, else return nomask. To guarantee a full array of booleans of the same shape as a, use getmaskarray. Parameters: a : array_like Input MaskedArray for which the mask is required. See also getdata Return the data of a masked array as an ndarray. getmaskarray Return the mask of a masked array, or full array of False. Examples

Hermite.has_sametype(other) [source] Check if types match. New in version 1.7.0. Parameters: other : object Class instance. Returns: bool : boolean True if other is same class as self

numpy.random.choice(a, size=None, replace=True, p=None) Generates a random sample from a given 1-D array New in version 1.7.0. Parameters: a : 1-D array-like or int If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a was np.arange(n) size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. replac