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158 lines
5 KiB
Python
158 lines
5 KiB
Python
import autograd.numpy as np
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import math
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import scipy.optimize
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from scipy.odr import ODR, Model, RealData
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from scipy.linalg import cholesky
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from scipy.stats import norm
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import pyerrors as pe
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import pytest
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np.random.seed(0)
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def test_least_squares():
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dim = 10 + int(30 * np.random.rand())
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x = np.arange(dim)
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y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
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yerr = 0.1 + 0.1 * np.random.rand(dim)
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oy = []
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for i, item in enumerate(x):
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oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
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def f(x, a, b):
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return a * np.exp(-b * x)
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popt, pcov = scipy.optimize.curve_fit(f, x, y, sigma=[o.dvalue for o in oy], absolute_sigma=True)
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def func(a, x):
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y = a[0] * np.exp(-a[1] * x)
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return y
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out = pe.least_squares(x, oy, func)
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beta = out.fit_parameters
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for i in range(2):
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beta[i].gamma_method(S=1.0)
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assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
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assert math.isclose(pcov[i, i], beta[i].dvalue ** 2, abs_tol=1e-3)
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assert math.isclose(pe.covariance(beta[0], beta[1]), pcov[0, 1], abs_tol=1e-3)
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pe.Obs.e_tag_global = 0
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chi2_pyerrors = np.sum(((f(x, *[o.value for o in beta]) - y) / yerr) ** 2) / (len(x) - 2)
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chi2_scipy = np.sum(((f(x, *popt) - y) / yerr) ** 2) / (len(x) - 2)
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assert math.isclose(chi2_pyerrors, chi2_scipy, abs_tol=1e-10)
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out = pe.least_squares(x, oy, func, const_par=[beta[1]])
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assert((out.fit_parameters[0] - beta[0]).is_zero)
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assert((out.fit_parameters[1] - beta[1]).is_zero)
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num_samples = 400
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N = 10
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x = norm.rvs(size=(N, num_samples))
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r = np.zeros((N, N))
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for i in range(N):
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for j in range(N):
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r[i, j] = np.exp(-0.1 * np.fabs(i - j))
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errl = np.sqrt([3.4, 2.5, 3.6, 2.8, 4.2, 4.7, 4.9, 5.1, 3.2, 4.2])
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errl *= 4
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for i in range(N):
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for j in range(N):
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r[i, j] *= errl[i] * errl[j]
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c = cholesky(r, lower=True)
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y = np.dot(c, x)
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x = np.arange(N)
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for linear in [True, False]:
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data = []
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for i in range(N):
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if linear:
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data.append(pe.Obs([[i + 1 + o for o in y[i]]], ['ens']))
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else:
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data.append(pe.Obs([[np.exp(-(i + 1)) + np.exp(-(i + 1)) * o for o in y[i]]], ['ens']))
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[o.gamma_method() for o in data]
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if linear:
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def fitf(p, x):
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return p[1] + p[0] * x
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else:
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def fitf(p, x):
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return p[1] * np.exp(-p[0] * x)
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fitp = pe.least_squares(x, data, fitf, expected_chisquare=True)
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fitpc = pe.least_squares(x, data, fitf, correlated_fit=True)
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for i in range(2):
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assert((fitp[i] - fitpc[i]).is_zero_within_error)
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def test_total_least_squares():
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dim = 10 + int(30 * np.random.rand())
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x = np.arange(dim) + np.random.normal(0.0, 0.15, dim)
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xerr = 0.1 + 0.1 * np.random.rand(dim)
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y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
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yerr = 0.1 + 0.1 * np.random.rand(dim)
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ox = []
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for i, item in enumerate(x):
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ox.append(pe.pseudo_Obs(x[i], xerr[i], str(i)))
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oy = []
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for i, item in enumerate(x):
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oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
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def f(x, a, b):
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return a * np.exp(-b * x)
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def func(a, x):
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y = a[0] * np.exp(-a[1] * x)
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return y
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data = RealData([o.value for o in ox], [o.value for o in oy], sx=[o.dvalue for o in ox], sy=[o.dvalue for o in oy])
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model = Model(func)
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odr = ODR(data, model, [0, 0], partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=0, deriv=1)
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output = odr.run()
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out = pe.total_least_squares(ox, oy, func)
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beta = out.fit_parameters
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pe.Obs.e_tag_global = 5
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for i in range(2):
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beta[i].gamma_method(e_tag=5, S=1.0)
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assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
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assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
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assert math.isclose(pe.covariance(beta[0], beta[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
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out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
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assert((out.fit_parameters[0] - beta[0]).is_zero)
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assert((out.fit_parameters[1] - beta[1]).is_zero)
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pe.Obs.e_tag_global = 0
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def test_odr_derivatives():
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x = []
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y = []
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x_err = 0.01
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y_err = 0.01
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for n in np.arange(1, 9, 2):
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loc_xvalue = n + np.random.normal(0.0, x_err)
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x.append(pe.pseudo_Obs(loc_xvalue, x_err, str(n)))
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y.append(pe.pseudo_Obs((lambda x: x ** 2 - 1)(loc_xvalue) +
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np.random.normal(0.0, y_err), y_err, str(n)))
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def func(a, x):
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return a[0] + a[1] * x ** 2
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out = pe.total_least_squares(x, y, func)
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fit1 = out.fit_parameters
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with pytest.warns(DeprecationWarning):
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tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
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assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
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- np.array(list(tfit[1].deltas.values())))) < 10e-8
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