numpy.random.bytes(length) Return random bytes. Parameters: length : int Number of random bytes. Returns: out : str String of length length. Examples >>> np.random.bytes(10) ' eh\x85\x022SZ\xbf\xa4' #random

ndarray.__ifloordiv__ x.__ifloordiv__(y) <==> x//y

recarray.put(indices, values, mode='raise') Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put equivalent function

numpy.polynomial.legendre.legval2d(x, y, c) [source] Evaluate a 2-D Legendre series at points (x, y). This function returns the values: The parameters x and y 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 and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly ap

numpy.polynomial.legendre.legder(c, m=1, scl=1, axis=0) [source] Differentiate a Legendre series. Returns the Legendre series coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series 1*L_0 + 2*L_1 + 3*L_2 while [[1,2],[1,2]] represents 1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y

record.searchsorted() 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.polynomial.chebyshev.chebfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Chebyshev series to data. Return the coefficients of a Legendre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) a

ndarray.__oct__() <==> oct(x)

classmethod Chebyshev.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

numpy.polynomial.laguerre.lagfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Laguerre series to data. Return the coefficients of a Laguerre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are