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feat: Added numerical integration of generic functions (#201)
* feat: Added numerical integration of generic functions * refactored integration routines * tests: two trivial tests for integration added. * docs: quad docstring corrected. * Small bugfix for integration without obs --------- Co-authored-by: Fabian Joswig <fabian.joswig@ed.ac.uk>
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4 changed files with 141 additions and 2 deletions
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@ -485,5 +485,6 @@ from . import input
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from . import linalg
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from . import linalg
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from . import mpm
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from . import mpm
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from . import roots
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from . import roots
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from . import integrate
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from .version import __version__
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from .version import __version__
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87
pyerrors/integrate.py
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87
pyerrors/integrate.py
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import numpy as np
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from .obs import derived_observable, Obs
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from autograd import jacobian
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from scipy.integrate import quad as squad
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def quad(func, p, a, b, **kwargs):
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'''Performs a (one-dimensional) numeric integration of f(p, x) from a to b.
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The integration is performed using scipy.integrate.quad().
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All parameters that can be passed to scipy.integrate.quad may also be passed to this function.
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The output is the same as for scipy.integrate.quad, the first element being an Obs.
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Parameters
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----------
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func : object
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function to integrate, has to be of the form
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```python
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import autograd.numpy as anp
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def func(p, x):
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return p[0] + p[1] * x + p[2] * anp.sinh(x)
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```
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where x is the integration variable.
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p : list of floats or Obs
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parameters of the function func.
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a: float or Obs
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Lower limit of integration (use -numpy.inf for -infinity).
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b: float or Obs
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Upper limit of integration (use -numpy.inf for -infinity).
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All parameters of scipy.integrate.quad
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Returns
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-------
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y : Obs
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The integral of func from `a` to `b`.
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abserr : float
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An estimate of the absolute error in the result.
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infodict : dict
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A dictionary containing additional information.
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Run scipy.integrate.quad_explain() for more information.
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message
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A convergence message.
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explain
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Appended only with 'cos' or 'sin' weighting and infinite
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integration limits, it contains an explanation of the codes in
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infodict['ierlst']
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'''
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Np = len(p)
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isobs = [True if isinstance(pi, Obs) else False for pi in p]
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pval = np.array([p[i].value if isobs[i] else p[i] for i in range(Np)],)
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pobs = [p[i] for i in range(Np) if isobs[i]]
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bounds = [a, b]
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isobs_b = [True if isinstance(bi, Obs) else False for bi in bounds]
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bval = np.array([bounds[i].value if isobs_b[i] else bounds[i] for i in range(2)])
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bobs = [bounds[i] for i in range(2) if isobs_b[i]]
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bsign = [-1, 1]
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ifunc = np.vectorize(lambda x: func(pval, x))
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intpars = squad.__code__.co_varnames[3:3 + len(squad.__defaults__)]
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ikwargs = {k: kwargs[k] for k in intpars if k in kwargs}
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integration_result = squad(ifunc, bval[0], bval[1], **ikwargs)
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val = integration_result[0]
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jac = jacobian(func)
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derivint = []
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for i in range(Np):
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if isobs[i]:
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ifunc = np.vectorize(lambda x: jac(pval, x)[i])
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derivint.append(squad(ifunc, bounds[0], bounds[1], **ikwargs)[0])
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for i in range(2):
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if isobs_b[i]:
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derivint.append(bsign[i] * func(pval, bval[i]))
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if len(derivint) == 0:
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return integration_result
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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)
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return (res, *integration_result[1:])
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51
tests/integrate_test.py
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51
tests/integrate_test.py
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import numpy as np
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import autograd.numpy as anp
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import pyerrors as pe
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def test_integration():
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def f(p, x):
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return p[0] * x + p[1] * x**2 - p[2] / x
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def F(p, x):
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return p[0] * x**2 / 2. + p[1] * x**3 / 3. - anp.log(x) * p[2]
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def check_ana_vs_int(p, l, u, **kwargs):
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numint_full = pe.integrate.quad(f, p, l, u, **kwargs)
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numint = numint_full[0]
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anaint = F(p, u) - F(p, l)
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diff = (numint - anaint)
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if isinstance(numint, pe.Obs):
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numint.gm()
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anaint.gm()
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assert(diff.is_zero())
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else:
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assert(np.isclose(0, diff))
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pobs = np.array([pe.cov_Obs(1., .1**2, '0'), pe.cov_Obs(2., .2**2, '1'), pe.cov_Obs(2.2, .17**2, '2')])
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lobs = pe.cov_Obs(.123, .012**2, 'l')
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uobs = pe.cov_Obs(1., .05**2, 'u')
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check_ana_vs_int(pobs, lobs, uobs)
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check_ana_vs_int(pobs, lobs.value, uobs)
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check_ana_vs_int(pobs, lobs, uobs.value)
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check_ana_vs_int(pobs, lobs.value, uobs.value)
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for i in range(len(pobs)):
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p = [pi for pi in pobs]
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p[i] = pobs[i].value
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check_ana_vs_int(p, lobs, uobs)
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check_ana_vs_int([pi.value for pi in pobs], lobs, uobs)
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check_ana_vs_int([pi.value for pi in pobs], lobs.value, uobs.value)
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check_ana_vs_int(pobs, lobs, uobs, epsabs=1.e-9, epsrel=1.236e-10, limit=100)
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assert(len(pe.integrate.quad(f, pobs, lobs, uobs, full_output=True)) > 2)
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r1, _ = pe.integrate.quad(F, pobs, 1, 0.1)
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r2, _ = pe.integrate.quad(F, pobs, 0.1, 1)
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assert r1 == -r2
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iamzero, _ = pe.integrate.quad(F, pobs, 1, 1)
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assert iamzero == 0
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@ -14,9 +14,9 @@ def get_real_matrix(dimension):
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exponent_imag = np.random.normal(0, 1)
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exponent_imag = np.random.normal(0, 1)
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base_matrix[n, m] = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
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base_matrix[n, m] = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
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return base_matrix
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return base_matrix
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def get_complex_matrix(dimension):
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def get_complex_matrix(dimension):
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base_matrix = np.empty((dimension, dimension), dtype=object)
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base_matrix = np.empty((dimension, dimension), dtype=object)
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for (n, m), entry in np.ndenumerate(base_matrix):
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for (n, m), entry in np.ndenumerate(base_matrix):
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@ -109,7 +109,6 @@ def test_einsum():
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assert np.all([o.imag.is_zero_within_error(0.001) for o in arr])
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assert np.all([o.imag.is_zero_within_error(0.001) for o in arr])
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assert np.all([o.imag.dvalue < 0.001 for o in arr])
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assert np.all([o.imag.dvalue < 0.001 for o in arr])
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tt = [get_real_matrix(4), get_real_matrix(3)]
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tt = [get_real_matrix(4), get_real_matrix(3)]
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q = np.tensordot(tt[0], tt[1], 0)
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q = np.tensordot(tt[0], tt[1], 0)
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c1 = tt[1] @ q
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c1 = tt[1] @ q
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@ -355,3 +354,4 @@ def test_complex_matrix_real_entries():
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my_mat[0, 1] = 4
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my_mat[0, 1] = 4
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my_mat[2, 0] = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
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my_mat[2, 0] = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
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assert np.all((my_mat @ pe.linalg.inv(my_mat) - np.identity(4)) == 0)
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assert np.all((my_mat @ pe.linalg.inv(my_mat) - np.identity(4)) == 0)
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