Numerically computes the derivative of , . Optionally, computes the -th derivative, , for any order .
Derivatives of a simple function:
>>> from mpmath import * >>> mp.dps = 15 >>> print diff(lambda x: x**2 + x, 1.0) 3.0 >>> print diff(lambda x: x**2 + x, 1.0, 2) 2.0 >>> print diff(lambda x: x**2 + x, 1.0, 3) 0.0
The exponential function is invariant under differentiation:
>>> nprint([diff(exp, 3, n) for n in range(5)]) [20.0855, 20.0855, 20.0855, 20.0855, 20.0855]
One of two differentiation algorithms can be chosen with the method keyword argument. The two options are 'step', and 'quad'. The default method is 'step'.
The derivative is computed using a finite difference approximation, with a small step h. This requires n+1 function evaluations and must be performed at (n+1) times the target precison. Accordingly, f must support fast evaluation at high precision.
The derivative is computed using complex numerical integration. This requires a larger number of function evaluations, but the advantage is that not much extra precision is required. For high order derivatives, this method may thus be faster if f is very expensive to evaluate at high precision.
With 'quad' the result is likely to have a small imaginary component even if the derivative is actually real:
>>> print diff(sqrt, 1, method='quad') (0.5 - 9.44048454290863e-27j)
The scale option specifies the scale of variation of f. The step size in the finite difference is taken to be approximately eps*scale. Thus, for example if , the scale should be set to 1/1000 and if , the scale should be 1000. By default, scale = 1.
(In practice, the default scale will work even for or . Changing this parameter is a good idea if the scale is something preposterous.)
If numerical integration is used, the radius of integration is taken to be equal to scale/2. Note that f must not have any singularities within the circle of radius scale/2 centered around x. If possible, a larger scale value is preferable because it typically makes the integration faster and more accurate.
By default, diff() uses a central difference approximation. This corresponds to direction=0. Alternatively, it can compute a left difference (direction=-1) or right difference (direction=1). This is useful for computing left- or right-sided derivatives of nonsmooth functions:
>>> print diff(abs, 0, direction=0) 0.0 >>> print diff(abs, 0, direction=1) 1.0 >>> print diff(abs, 0, direction=-1) -1.0
More generally, if the direction is nonzero, a right difference is computed where the step size is multiplied by sign(direction). For example, with direction=+j, the derivative from the positive imaginary direction will be computed.
This option only makes sense with method=’step’. If integration is used, it is assumed that f is analytic, implying that the derivative is the same in all directions.
Returns a generator that yields the sequence of derivatives
With method='step', diffs() uses only function evaluations to generate the first derivatives, rather than the roughly evaluations required if one calls diff() separate times.
With , the generator stops as soon as the -th derivative has been generated. If the exact number of needed derivatives is known in advance, this is further slightly more efficient.
>>> nprint(list(diffs(cos, 1, 5))) [0.540302, -0.841471, -0.540302, 0.841471, 0.540302, -0.841471] >>> for i, d in zip(range(6), diffs(cos, 1)): print i, d ... 0 0.54030230586814 1 -0.841470984807897 2 -0.54030230586814 3 0.841470984807897 4 0.54030230586814 5 -0.841470984807897