mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-19 00:30:24 +01:00
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
from __future__ import annotations
|
|
import numpy as np
|
|
from .obs import derived_observable, Obs
|
|
from autograd import jacobian
|
|
from scipy.integrate import quad as squad
|
|
from numpy import ndarray
|
|
from typing import Callable, Union
|
|
|
|
|
|
def quad(func: Callable, p: Union[list[Union[float, Obs]], list[float], ndarray], a: Union[Obs, float, int], b: Union[Obs, float, int], **kwargs) -> Union[tuple[Obs, float], tuple[float, float], tuple[Obs, float, dict[str, Union[int, ndarray]]]]:
|
|
'''Performs a (one-dimensional) numeric integration of f(p, x) from a to b.
|
|
|
|
The integration is performed using scipy.integrate.quad().
|
|
All parameters that can be passed to scipy.integrate.quad may also be passed to this function.
|
|
The output is the same as for scipy.integrate.quad, the first element being an Obs.
|
|
|
|
Parameters
|
|
----------
|
|
func : object
|
|
function to integrate, has to be of the form
|
|
|
|
```python
|
|
import autograd.numpy as anp
|
|
|
|
def func(p, x):
|
|
return p[0] + p[1] * x + p[2] * anp.sinh(x)
|
|
```
|
|
where x is the integration variable.
|
|
p : list of floats or Obs
|
|
parameters of the function func.
|
|
a: float or Obs
|
|
Lower limit of integration (use -numpy.inf for -infinity).
|
|
b: float or Obs
|
|
Upper limit of integration (use -numpy.inf for -infinity).
|
|
All parameters of scipy.integrate.quad
|
|
|
|
Returns
|
|
-------
|
|
y : Obs
|
|
The integral of func from `a` to `b`.
|
|
abserr : float
|
|
An estimate of the absolute error in the result.
|
|
infodict : dict
|
|
A dictionary containing additional information.
|
|
Run scipy.integrate.quad_explain() for more information.
|
|
message
|
|
A convergence message.
|
|
explain
|
|
Appended only with 'cos' or 'sin' weighting and infinite
|
|
integration limits, it contains an explanation of the codes in
|
|
infodict['ierlst']
|
|
'''
|
|
|
|
Np = len(p)
|
|
isobs = [True if isinstance(pi, Obs) else False for pi in p]
|
|
pval = np.array([p[i].value if isobs[i] else p[i] for i in range(Np)],)
|
|
pobs = [p[i] for i in range(Np) if isobs[i]]
|
|
|
|
bounds = [a, b]
|
|
isobs_b = [True if isinstance(bi, Obs) else False for bi in bounds]
|
|
bval = np.array([bounds[i].value if isobs_b[i] else bounds[i] for i in range(2)])
|
|
bobs = [bounds[i] for i in range(2) if isobs_b[i]]
|
|
bsign = [-1, 1]
|
|
|
|
ifunc = np.vectorize(lambda x: func(pval, x))
|
|
|
|
intpars = squad.__code__.co_varnames[3:3 + len(squad.__defaults__)]
|
|
ikwargs = {k: kwargs[k] for k in intpars if k in kwargs}
|
|
|
|
integration_result = squad(ifunc, bval[0], bval[1], **ikwargs)
|
|
val = integration_result[0]
|
|
|
|
jac = jacobian(func)
|
|
|
|
derivint = []
|
|
for i in range(Np):
|
|
if isobs[i]:
|
|
ifunc = np.vectorize(lambda x: jac(pval, x)[i])
|
|
derivint.append(squad(ifunc, bounds[0], bounds[1], **ikwargs)[0])
|
|
|
|
for i in range(2):
|
|
if isobs_b[i]:
|
|
derivint.append(bsign[i] * func(pval, bval[i]))
|
|
|
|
if len(derivint) == 0:
|
|
return integration_result
|
|
|
|
res = derived_observable(lambda x, **kwargs: 0 * (x[0] + np.finfo(np.float64).eps) * (pval[0] + np.finfo(np.float64).eps) + val, pobs + bobs, man_grad=derivint)
|
|
|
|
return (res, *integration_result[1:])
|