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scipy.differentiate.

# derivative

scipy.differentiate.derivative(f, x, *, args=(), tolerances=None, maxiter=10, order=8, initial_step=0.5, step_factor=2.0, step_direction=0, preserve_shape=False, callback=None)

  Evaluate the derivative of a elementwise, real scalar function numerically.

  For each element of the output of f, `derivative` approximates the first derivative of f at the corresponding element of x using finite difference differentiation.

  This function works elementwise when x, step_direction, and args contain (broadcastable) arrays.

  Parameters:
    f callable

      The function whose derivative is desired. The signature must be:

          f(xi: ndarray, *argsi) -> ndarray

      where each element of `xi` is a finite real number and `argsi` is a tuple, which may contain an arbitrary number of arrays that are broadcastable with `xi`. f must be an elementwise function: each scalar element `f(xi)[j]` must equal `f(xi[j])` for valid indices `j`. It must not mutate the array `xi` or the arrays in `argsi`.

    x float array_like

      Abscissae at which to evaluate the derivative. Must be broadcastable with args and step_direction.

    args tuple of array_like, optional

      Additional positional array arguments to be passed to f. Arrays must be broadcastable with one another and the arrays of init. If the callable for which the root is desired requires arguments that are not broadcastable with x, wrap that callable with f such that f accepts only x and broadcastable `*args`.

    tolerances dictionary of floats, optional

      Absolute and relative tolerances. Valid keys of the dictionary are:

      - `atol` - absolute tolerance on the derivative

      - `rtol` - relative tolerance on the derivative

      Iteration will stop when `res.error < atol + rtol * abs(res.df)`. The default atol is the smallest normal number of the appropriate dtype, and the default rtol is the square root of the precision of the appropriate dtype.

    order int, default: 8

      The (positive integer) order of the finite difference formula to be used. Odd integers will be rounded up to the next even integer.

    initial_step float array_like, default: 0.5

      The (absolute) initial step size for the finite difference derivative approximation.

    step_factor float, default: 2.0

      The factor by which the step size is reduced in each iteration; i.e. the step size in iteration 1 is `initial_step/step_factor`. If `step_factor < 1`, subsequent steps will be greater than the initial step; this may be useful if steps smaller than some threshold are undesirable (e.g. due to subtractive cancellation error).

    maxiter int, default: 10

      The maximum number of iterations of the algorithm to perform. See Notes.

    step_direction integer array_like

      An array representing the direction of the finite difference steps (for use when x lies near to the boundary of the domain of the function.) Must be broadcastable with x and all args. Where 0 (default), central differences are used; where negative (e.g. -1), steps are non-positive; and where positive (e.g. 1), all steps are non-negative.

    preserve_shape bool, default: False

      In the following, “arguments of f” refers to the array `xi` and any arrays within `argsi`. Let `shape` be the broadcasted shape of x and all elements of args (which is conceptually distinct from `xi` and ``argsi` passed into f).

      - When `preserve_shape=False` (default), f must accept arguments of any broadcastable shapes.

      - When `preserve_shape=True`, f must accept arguments of shape `shape` or `shape + (n,)`, where `(n,)` is the number of abscissae at which the function is being evaluated.

      In either case, for each scalar element `xi[j]` within `xi`, the array returned by f must include the scalar `f(xi[j])` at the same index. Consequently, the shape of the output is always the shape of the input `xi`.

      See Examples.

    callback callable, optional

      An optional user-supplied function to be called before the first iteration and after each iteration. Called as `callback(res)`, where `res` is a `_RichResult` similar to that returned by `derivative` (but containing the current iterate’s values of all variables). If callback raises a `StopIteration`, the algorithm will terminate immediately and `derivative` will return a result. callback must not mutate res or its attributes.

  Returns:
    res_RichResult

      An object similar to an instance of `scipy.optimize.OptimizeResult` with the following attributes. The descriptions are written as though the values will be scalars; however, if f returns an array, the outputs will be arrays of the same shape.

      successbool array

        `True` where the algorithm terminated successfully (status `0`); `False` otherwise.

      statusint array

        An integer representing the exit status of the algorithm.

        - `0` : The algorithm converged to the specified tolerances.

        - `-1` : The error estimate increased, so iteration was terminated.

        - `-2` : The maximum number of iterations was reached.

        - `-3` : A non-finite value was encountered.

        - `-4` : Iteration was terminated by callback.

        - `1` : The algorithm is proceeding normally (in callback only).

      dffloat array

        The derivative of f at x, if the algorithm terminated successfully.

      errorfloat array

        An estimate of the error: the magnitude of the difference between the current estimate of the derivative and the estimate in the previous iteration.

      nitint array

        The number of iterations of the algorithm that were performed.

      nfevint array

        The number of points at which f was evaluated.

      xfloat array

        The value at which the derivative of f was evaluated (after broadcasting with args and step_direction).

  See also

  `jacobian`, `hessian`

  Notes

  The implementation was inspired by jacobi [1], numdifftools [2], and DERIVEST [3], but the implementation follows the theory of Taylor series more straightforwardly (and arguably naively so). In the first iteration, the derivative is estimated using a finite difference formula of order order with maximum step size initial_step. Each subsequent iteration, the maximum step size is reduced by step_factor, and the derivative is estimated again until a termination condition is reached. The error estimate is the magnitude of the difference between the current derivative approximation and that of the previous iteration.

  The stencils of the finite difference formulae are designed such that abscissae are “nested”: after f is evaluated at `order + 1` points in the first iteration, f is evaluated at only two new points in each subsequent iteration; `order - 1` previously evaluated function values required by the finite difference formula are reused, and two function values (evaluations at the points furthest from x) are unused.

  Step sizes are absolute. When the step size is small relative to the magnitude of x, precision is lost; for example, if x is `1e20`, the default initial step size of `0.5` cannot be resolved. Accordingly, consider using larger initial step sizes for large magnitudes of x.

  The default tolerances are challenging to satisfy at points where the true derivative is exactly zero. If the derivative may be exactly zero, consider specifying an absolute tolerance (e.g. `atol=1e-12`) to improve convergence.

  References

  [1]

  Hans Dembinski (@HDembinski). jacobi. HDembinski/jacobi

  [2]

  Per A. Brodtkorb and John D’Errico. numdifftools. https://numdifftools.readthedocs.io/en/latest/

  [3]

  John D’Errico. DERIVEST: Adaptive Robust Numerical Differentiation. https://www.mathworks.com/matlabcentral/fileexchange/13490-adaptive-robust-numerical-differentiation

  [4]

  Numerical Differentition. Wikipedia. https://en.wikipedia.org/wiki/Numerical_differentiation

  Examples

  Evaluate the derivative of `np.exp` at several points `x`.

      >>> import numpy as np
      >>> from scipy.differentiate import derivative
      >>> f = np.exp
      >>> df = np.exp  # true derivative
      >>> x = np.linspace(1, 2, 5)
      >>> res = derivative(f, x)
      >>> res.df  # approximation of the derivative
      array([2.71828183, 3.49034296, 4.48168907, 5.75460268, 7.3890561 ])
      >>> res.error  # estimate of the error
      array([7.13740178e-12, 9.16600129e-12, 1.17594823e-11, 1.51061386e-11,
             1.94262384e-11])
      >>> abs(res.df - df(x))  # true error
      array([2.53130850e-14, 3.55271368e-14, 5.77315973e-14, 5.59552404e-14,
             6.92779167e-14])

  Show the convergence of the approximation as the step size is reduced. Each iteration, the step size is reduced by step_factor, so for sufficiently small initial step, each iteration reduces the error by a factor of `1/step_factor**order` until finite precision arithmetic inhibits further improvement.

      >>> import matplotlib.pyplot as plt
      >>> iter = list(range(1, 12))  # maximum iterations
      >>> hfac = 2  # step size reduction per iteration
      >>> hdir = [-1, 0, 1]  # compare left-, central-, and right- steps
      >>> order = 4  # order of differentiation formula
      >>> x = 1
      >>> ref = df(x)
      >>> errors = []  # true error
      >>> for i in iter:
      ...     res = derivative(f, x, maxiter=i, step_factor=hfac,
      ...                      step_direction=hdir, order=order,
      ...                      # prevent early termination
      ...                      tolerances=dict(atol=0, rtol=0))
      ...     errors.append(abs(res.df - ref))
      >>> errors = np.array(errors)
      >>> plt.semilogy(iter, errors[:, 0], label='left differences')
      >>> plt.semilogy(iter, errors[:, 1], label='central differences')
      >>> plt.semilogy(iter, errors[:, 2], label='right differences')
      >>> plt.xlabel('iteration')
      >>> plt.ylabel('error')
      >>> plt.legend()
      >>> plt.show()

      >>> (errors[1, 1] / errors[0, 1], 1 / hfac**order)
      (0.06215223140159822, 0.0625)

  The implementation is vectorized over x, step_direction, and args. The function is evaluated once before the first iteration to perform input validation and standardization, and once per iteration thereafter.

      >>> def f(x, p):
      ...     f.nit += 1
      ...     return x**p
      >>> f.nit = 0
      >>> def df(x, p):
      ...     return p*x**(p-1)
      >>> x = np.arange(1, 5)
      >>> p = np.arange(1, 6).reshape((-1, 1))
      >>> hdir = np.arange(-1, 2).reshape((-1, 1, 1))
      >>> res = derivative(f, x, args=(p,), step_direction=hdir, maxiter=1)
      >>> np.allclose(res.df, df(x, p))
      True
      >>> res.df.shape
      (3, 5, 4)
      >>> f.nit
      2

  By default, preserve_shape is False, and therefore the callable f may be called with arrays of any broadcastable shapes. For example:

      >>> shapes = []
      >>> def f(x, c):
      ...    shape = np.broadcast_shapes(x.shape, c.shape)
      ...    shapes.append(shape)
      ...    return np.sin(c*x)
      >>>
      >>> c = [1, 5, 10, 20]
      >>> res = derivative(f, 0, args=(c,))
      >>> shapes
      [(4,), (4, 8), (4, 2), (3, 2), (2, 2), (1, 2)]

  To understand where these shapes are coming from - and to better understand how `derivative` computes accurate results - note that higher values of `c` correspond with higher frequency sinusoids. The higher frequency sinusoids make the function’s derivative change faster, so more function evaluations are required to achieve the target accuracy:

      >>> res.nfev
      array([11, 13, 15, 17], dtype=int32)

  The initial `shape`, `(4,)`, corresponds with evaluating the function at a single abscissa and all four frequencies; this is used for input validation and to determine the size and dtype of the arrays that store results. The next shape corresponds with evaluating the function at an initial grid of abscissae and all four frequencies. Successive calls to the function evaluate the function at two more abscissae, increasing the effective order of the approximation by two. However, in later function evaluations, the function is evaluated at fewer frequencies because the corresponding derivative has already converged to the required tolerance. This saves function evaluations to improve performance, but it requires the function to accept arguments of any shape.

  “Vector-valued” functions are unlikely to satisfy this requirement. For example, consider

      >>> def f(x):
      ...    return [x, np.sin(3*x), x+np.sin(10*x), np.sin(20*x)*(x-1)**2]

  This integrand is not compatible with `derivative` as written; for instance, the shape of the output will not be the same as the shape of `x`. Such a function could be converted to a compatible form with the introduction of additional parameters, but this would be inconvenient. In such cases, a simpler solution would be to use preserve_shape.

      >>> shapes = []
      >>> def f(x):
      ...     shapes.append(x.shape)
      ...     x0, x1, x2, x3 = x
      ...     return [x0, np.sin(3*x1), x2+np.sin(10*x2), np.sin(20*x3)*(x3-1)**2]
      >>>
      >>> x = np.zeros(4)
      >>> res = derivative(f, x, preserve_shape=True)
      >>> shapes
      [(4,), (4, 8), (4, 2), (4, 2), (4, 2), (4, 2)]

  Here, the shape of `x` is `(4,)`. With `preserve_shape=True`, the function may be called with argument `x` of shape `(4,)` or `(4, n)`, and this is what we observe.