"""
Created on 27. aug. 2015
@author: pab
Author: John D'Errico
e-mail: woodchips@rochester.rr.com
Release: 1.0
Release date: 5/23/2008
"""
from __future__ import absolute_import, division, print_function
from collections import namedtuple
from functools import partial
import warnings
import numpy as np
from numdifftools.step_generators import MinStepGenerator
from numdifftools.extrapolation import Richardson, dea3
def _assert(cond, msg):
if not cond:
raise ValueError(msg)
[docs]class CStepGenerator(MinStepGenerator):
"""
Generates a sequence of steps
where
steps = base_step * step_nom * (exp(1j*dtheta) * step_ratio) ** (i + offset)
for i = 0, 1, ..., num_steps-1
Parameters
----------
base_step : float, array-like, default None
Defines the minimum step, if None, the value is set to EPS**(1/scale)
step_ratio : real scalar, optional, default 4.0
Ratio between sequential steps generated.
num_steps : scalar integer, optional,
defines number of steps generated.
If None the value is 2 * int(round(16.0/log(abs(step_ratio)))) + 1
step_nom : default maximum(log(exp(1)+|x|), 1)
Nominal step where x is supplied at runtime through the __call__ method.
offset : real scalar, optional, default 0
offset to the base step
use_exact_steps : boolean, default True.
If true make sure exact steps are generated.
scale : real scalar, default 1.2
scale used in base step.
path : 'radial' or 'spiral'
Specifies the type of path to take the limit along. Default 'radial'.
dtheta: real scalar, default pi/8
If the path is 'spiral' it will follow an exponential spiral into the
limit, with angular steps at dtheta radians.
"""
[docs] def __init__(self, base_step=None, step_ratio=4.0, num_steps=None, step_nom=None,
offset=0, scale=1.2, **options):
self.path = options.pop('path', 'radial')
self.dtheta = options.pop('dtheta', np.pi / 8)
super(CStepGenerator,
self).__init__(base_step=base_step, step_ratio=step_ratio,
num_steps=num_steps, step_nom=step_nom, offset=offset, scale=scale,
**options)
self._check_path()
def _check_path(self):
_assert(self.path in ['spiral', 'radial'], 'Invalid Path: {}'.format(str(self.path)))
@property
def step_ratio(self):
"""Ratio between sequential steps generated."""
dtheta = self.dtheta
_step_ratio = float(self._step_ratio) # radial path
if dtheta != 0:
_step_ratio = np.exp(1j * dtheta) * _step_ratio # a spiral path
return _step_ratio
@step_ratio.setter
def step_ratio(self, step_ratio):
self._step_ratio = step_ratio
@property
def dtheta(self):
"""Angular steps in radians used for the exponential spiral path."""
radial_path = self.path[0].lower() == 'r'
return 0 if radial_path else self._dtheta
@dtheta.setter
def dtheta(self, dtheta):
self._dtheta = dtheta
@property
def num_steps(self):
"""The number of steps generated"""
if self._num_steps is None:
return 2 * int(np.round(16.0 / np.log(np.abs(self.step_ratio)))) + 1
return self._num_steps
@num_steps.setter
def num_steps(self, num_steps):
self._num_steps = num_steps
class _Limit(object):
"""Common methods and member variables"""
info = namedtuple('info', ['error_estimate', 'final_step', 'index'])
def __init__(self, step=None, **options):
self.step = step, options
self.richardson = Richardson(step_ratio=1.6, step=1, order=1, num_terms=2)
@staticmethod
def _parse_step_options(step):
options = {}
if isinstance(step, tuple) and isinstance(step[-1], dict):
step, options = step
return step, options
@staticmethod
def _step_generator(step, options):
if hasattr(step, '__call__'):
return step
step_nom = None if step is None else 1
return CStepGenerator(base_step=step, step_nom=step_nom, **options)
@property
def step(self):
"""The step spacing(s) used in the approximation"""
return self._step
@step.setter
def step(self, step_options):
step, options = self._parse_step_options(step_options)
self._step = self._step_generator(step, options)
@staticmethod
def _get_arg_min(errors):
shape = errors.shape
try:
arg_mins = np.nanargmin(errors, axis=0)
min_errors = np.nanmin(errors, axis=0)
except ValueError as msg:
warnings.warn(str(msg))
return np.arange(shape[1])
for i, min_error in enumerate(min_errors):
idx = np.flatnonzero(errors[:, i] == min_error)
arg_mins[i] = idx[idx.size // 2]
return np.ravel_multi_index((arg_mins, np.arange(shape[1])), shape)
@staticmethod
def _add_error_to_outliers(der, trim_fact=10):
"""
discard any estimate that differs wildly from the
median of all estimates. A factor of 10 to 1 in either
direction is probably wild enough here. The actual
trimming factor is defined as a parameter.
"""
try:
if np.any(np.isnan(der)):
p25, median, p75 = np.nanpercentile(der, [25,50, 75], axis=0)
else:
p25, median, p75 = np.percentile(der, [25,50, 75], axis=0)
iqr = np.abs(p75 - p25)
except ValueError as msg:
warnings.warn(str(msg))
return 0 * der
a_median = np.abs(median)
outliers = (((abs(der) < (a_median / trim_fact)) +
(abs(der) > (a_median * trim_fact))) * (a_median > 1e-8) +
((der < p25 - 1.5 * iqr) + (p75 + 1.5 * iqr < der)))
errors = outliers * np.abs(der - median)
return errors
@staticmethod
def _get_best_estimate(der, errors, steps, shape):
errors += _Limit._add_error_to_outliers(der)
idx = _Limit._get_arg_min(errors)
final_step = steps.flat[idx].reshape(shape)
err = errors.flat[idx].reshape(shape)
return der.flat[idx].reshape(shape), _Limit.info(err, final_step, idx)
@staticmethod
def _wynn_extrapolate(der, steps):
der, errors = dea3(der[0:-2], der[1:-1], der[2:], symmetric=False)
return der, errors, steps[2:]
def _extrapolate(self, results, steps, shape):
# if len(results) > 2:
# der0, errors0, steps0 = self._wynn_extrapolate(results, steps)
# if len(der0) > 0:
# der2, errors2, steps2 = self._wynn_extrapolate(der0, steps0)
# else:
der1, errors1, steps = self.richardson(results, steps)
if len(der1) > 2:
der1, errors1, steps = self._wynn_extrapolate(der1, steps)
der, info = self._get_best_estimate(der1, errors1, steps, shape)
return der, info
@staticmethod
def _vstack(sequence, steps):
original_shape = np.shape(sequence[0])
f_del = np.vstack([np.ravel(r) for r in sequence])
one = np.ones(original_shape)
h = np.vstack([np.ravel(one * step) for step in steps])
_assert(f_del.size == h.size, 'fun did not return data of correct '
'size (it must be vectorized)')
return f_del, h, original_shape
[docs]class Limit(_Limit):
"""
Compute limit of a function at a given point
Parameters
----------
fun : callable
function fun(z, `*args`, `**kwds`) to compute the limit for z->z0.
The function, fun, is assumed to return a result of the same shape and
size as its input, `z`.
step: float, complex, array-like or StepGenerator object, optional
Defines the spacing used in the approximation.
Default is CStepGenerator(base_step=step, **options)
method : {'above', 'below'}
defines if the limit is taken from `above` or `below`
order: positive scalar integer, optional.
defines the order of approximation used to find the specified limit.
The order must be member of [1 2 3 4 5 6 7 8]. 4 is a good compromise.
full_output: bool
If true return additional info.
options:
options to pass on to CStepGenerator
Returns
-------
limit_fz: array like
estimated limit of f(z) as z --> z0
info:
Only given if full_output is True and contains the following:
error estimate: ndarray
95 % uncertainty estimate around the limit, such that
abs(limit_fz - lim z->z0 f(z)) < error_estimate
final_step: ndarray
final step used in approximation
Notes
-----
`Limit` computes the limit of a given function at a specified
point, z0. When the function is evaluable at the point in question,
this is a simple task. But when the function cannot be evaluated
at that location due to a singularity, you may need a tool to
compute the limit. `Limit` does this, as well as produce an
uncertainty estimate in the final result.
The methods used by `Limit` are Richardson extrapolation in a combination
with Wynn's epsilon algorithm which also yield an error estimate.
The user can specify the method order, as well as the path into
z0. z0 may be real or complex. `Limit` uses a proportionally cascaded
series of function evaluations, moving away from your point of evaluation
along a path along the real line (or in the complex plane for complex z0 or
step.) The `step_ratio` is the ratio used between sequential steps. The
sign of step allows you to specify a limit from above or below. Negative
values of step will cause the limit to be taken approaching z0 from below.
A smaller `step_ratio` means that `Limit` will take more function
evaluations to evaluate the limit, but the result will potentially be less
accurate. The `step_ratio` MUST be a scalar larger than 1. A value in the
range [2,100] is recommended. 4 seems a good compromise.
Examples
--------
Compute the limit of sin(x)./x, at x == 0. The limit is 1.
>>> import numpy as np
>>> from numdifftools.limits import Limit
>>> def f(x): return np.sin(x)/x
>>> lim_f0, err = Limit(f, full_output=True)(0)
>>> np.allclose(lim_f0, 1)
True
>>> np.allclose(err.error_estimate, 1.77249444610966e-15)
True
Compute the derivative of cos(x) at x == pi/2. It should
be -1. The limit will be taken as a function of the
differential parameter, dx.
>>> x0 = np.pi/2;
>>> def g(x): return (np.cos(x0+x)-np.cos(x0))/x
>>> lim_g0, err = Limit(g, full_output=True)(0)
>>> np.allclose(lim_g0, -1)
True
>>> err.error_estimate < 1e-14
True
Compute the residue at a first order pole at z = 0
The function 1./(1-exp(2*z)) has a pole at z == 0.
The residue is given by the limit of z*fun(z) as z --> 0.
Here, that residue should be -0.5.
>>> def h(z): return -z/(np.expm1(2*z))
>>> lim_h0, err = Limit(h, full_output=True)(0)
>>> np.allclose(lim_h0, -0.5)
True
>>> err.error_estimate < 1e-14
True
Compute the residue of function 1./sin(z)**2 at z = 0.
This pole is of second order thus the residue is given by the limit of
z**2*fun(z) as z --> 0.
>>> def g(z): return z**2/(np.sin(z)**2)
>>> lim_gpi, err = Limit(g, full_output=True)(0)
>>> np.allclose(lim_gpi, 1)
True
>>> err.error_estimate < 1e-14
True
A more difficult limit is one where there is significant
subtractive cancellation at the limit point. In the following
example, the cancellation is second order. The true limit
should be 0.5.
>>> def k(x): return (x*np.exp(x)-np.expm1(x))/x**2
>>> lim_k0,err = Limit(k, full_output=True)(0)
>>> np.allclose(lim_k0, 0.5)
True
>>> err.error_estimate < 1.0e-8
True
>>> def h(x): return (x-np.sin(x))/x**3
>>> lim_h0, err = Limit(h, full_output=True)(0)
>>> np.allclose(lim_h0, 1./6)
True
>>> err.error_estimate < 1e-8
True
"""
[docs] def __init__(self, fun, step=None, method='above', order=4, full_output=False, **options):
super(Limit, self).__init__(step=step, **options)
self.fun = fun
self.method = method
self.order = order
self.full_output = full_output
def _fun(self, z, d_z, args, kwds):
return self.fun(z + d_z, *args, **kwds)
def _get_steps(self, x_i):
return list(self.step(x_i)) # pylint: disable=not-callable
def _set_richardson_rule(self, step_ratio, num_terms=2):
self.richardson = Richardson(step_ratio=step_ratio, step=1, order=1,
num_terms=num_terms)
def _lim(self, f, z):
sign = dict(forward=1, above=1, backward=-1, below=-1)[self.method]
steps = [sign * step for step in self.step(z)] # pylint: disable=not-callable
# pylint: disable=no-member
self._set_richardson_rule(self.step.step_ratio, self.order + 1)
sequence = [f(z, h) for h in steps]
results = self._vstack(sequence, steps)
lim_fz, info = self._extrapolate(*results)
return lim_fz, info
[docs] def limit(self, x, *args, **kwds):
"""Return lim f(z) as z-> x"""
z = np.asarray(x)
f = partial(self._fun, args=args, kwds=kwds)
f_z, info = self._lim(f, z)
if self.full_output:
return f_z, info
return f_z
def _call_lim(self, f_z, z, f):
err = np.zeros_like(f_z, dtype=float)
final_step = np.zeros_like(f_z)
index = np.zeros_like(f_z, dtype=int)
k = np.flatnonzero(np.isnan(f_z))
if k.size > 0:
lim_fz, info1 = self._lim(f, z.flat[k])
zero = np.zeros(1, dtype=np.result_type(lim_fz))
f_z = np.where(np.isnan(f_z), zero, f_z)
np.put(f_z, k, lim_fz)
if self.full_output:
final_step = np.where(np.isnan(f_z), zero, final_step)
np.put(final_step, k, info1.final_step)
np.put(index, k, info1.index)
np.put(err, k, info1.error_estimate)
return f_z, self.info(err, final_step, index)
def __call__(self, x, *args, **kwds):
z = np.asarray(x)
f = partial(self._fun, args=args, kwds=kwds)
with np.errstate(divide='ignore', invalid='ignore'):
f_z = f(z, 0)
f_z, info = self._call_lim(f_z, z, f)
if self.full_output:
return f_z, info
return f_z
[docs]class Residue(Limit):
"""
Compute residue of a function at a given point
Parameters
----------
fun : callable
function fun(z, `*args`, `**kwds`) to compute the Residue at z=z0.
The function, fun, is assumed to return a result of the same shape and
size as its input, `z`.
step: float, complex, array-like or StepGenerator object, optional
Defines the spacing used in the approximation.
Default is CStepGenerator(base_step=step, **options)
method : {'above', 'below'}
defines if the limit is taken from `above` or `below`
order: positive scalar integer, optional.
defines the order of approximation used to find the specified limit.
The order must be member of [1 2 3 4 5 6 7 8]. 4 is a good compromise.
pole_order : scalar integer
specifies the order of the pole at z0.
full_output: bool
If true return additional info.
options:
options to pass on to CStepGenerator
Returns
-------
res_fz: array like
estimated residue, i.e., limit of f(z)*(z-z0)**pole_order as z --> z0
When the residue is estimated as approximately zero,
the wrong order pole may have been specified.
info: namedtuple,
Only given if full_output is True and contains the following:
error estimate: ndarray
95 % uncertainty estimate around the residue, such that
abs(res_fz - lim z->z0 f(z)*(z-z0)**pole_order) < error_estimate
Large uncertainties here suggest that the wrong order
pole was specified for f(z0).
final_step: ndarray
final step used in approximation
Notes
-----
Residue computes the residue of a given function at a simple first order
pole, or at a second order pole.
The methods used by residue are polynomial extrapolants, which also yield
an error estimate. The user can specify the method order, as well as the
order of the pole.
z0 - scalar point at which to compute the residue. z0 may be
real or complex.
See the document DERIVEST.pdf for more explanation of the
algorithms behind the parameters of Residue. In most cases,
the user should never need to specify anything other than possibly
the PoleOrder.
Examples
--------
A first order pole at z = 0
>>> import numpy as np
>>> from numdifftools.limits import Residue
>>> def f(z): return -1./(np.expm1(2*z))
>>> res_f, info = Residue(f, full_output=True)(0)
>>> np.allclose(res_f, -0.5)
True
>>> info.error_estimate < 1e-14
True
A second order pole around z = 0 and z = pi
>>> def h(z): return 1.0/np.sin(z)**2
>>> res_h, info = Residue(h, full_output=True, pole_order=2)([0, np.pi])
>>> np.allclose(res_h, 1)
True
>>> (info.error_estimate < 1e-10).all()
True
"""
[docs] def __init__(self, f, step=None, method='above', order=None, pole_order=1,
full_output=False, **options):
if order is None:
# MethodOrder will always = pole_order + 2
order = pole_order + 2
_assert(pole_order < order, 'order must be at least pole_order+1.')
self.pole_order = pole_order
super(Residue, self).__init__(f, step=step, method=method, order=order,
full_output=full_output, **options)
def _fun(self, z, d_z, args, kwds):
return self.fun(z + d_z, *args, **kwds) * (d_z ** self.pole_order)
def __call__(self, x, *args, **kwds):
return self.limit(x, *args, **kwds)
if __name__ == '__main__':
from numdifftools.testing import test_docstrings
test_docstrings(__file__)