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

generic.argmax() 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.logical_and(x1, x2[, out]) = Compute the truth value of x1 AND x2 element-wise. Parameters: x1, x2 : array_like Input arrays. x1 and x2 must be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical AND operation on corresponding elements of x1 and x2. See also logical_or, logical_not, logical_xor, bitwise_and Examples >>> np.logical_and(True, False) False >>> np.logical_and([True, False], [False, Fal

numpy.polynomial.hermite.hermval2d(x, y, c) [source] Evaluate a 2-D Hermite 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 app

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

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

Introduction The Simple Wrapper and Interface Generator (or SWIG) is a powerful tool for generating wrapper code for interfacing to a wide variety of scripting languages. SWIG can parse header files, and using only the code prototypes, create an interface to the target language. But SWIG is not omnipotent. For example, it cannot know from the prototype: double rms(double* seq, int n); what exactly seq is. Is it a single value to be altered in-place? Is it an array, and if so what is its leng

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

MaskedArray.__le__ x.__le__(y) <==> x<=y