def test_args_kwds_are_used(self): # check that user defined args and kwds make their way into the user # function a = [1., 2.] x = np.linspace(0, 10, 11) y = a[0] + 1 + 2 * a[1] * x par = Parameters() par.add('p0', 1.5) par.add('p1', 2.5) def fun(x, p, *args, **kwds): assert_equal(args, a) return args[0] + p['p0'] + p['p1'] * a[1] * x g = CurveFitter(fun, (x, y), par, fcn_args=a) res = g.fit() assert_almost_equal(values(res.params), [1., 2.]) d = {'a': 1, 'b': 2} def fun(x, p, *args, **kwds): return kwds['a'] + p['p0'] + p['p1'] * kwds['b'] * x g = CurveFitter(fun, (x, y), par, fcn_kws=d) res = g.fit() assert_almost_equal(values(res.params), [1., 2.])
def test_reflectivity_fit(self): # a smoke test to make sure the reflectivity fit proceeds fitfunc = reflect.ReflectivityFitFunction() transform = reflect.Transform('logY') yt, et = transform.transform(self.qvals361, self.rvals361, self.evals361) kws = {'transform': transform.transform} fitter2 = CurveFitter(fitfunc, (self.qvals361, yt, et), self.params361, fcn_kws=kws, kws={'seed': 2}) fitter2.fit('differential_evolution')
def test_costfun(self): # test user defined costfun res = self.f.fit('nelder') def costfun(params, generative, y, e): return np.sum((y - generative / e) ** 2) g = CurveFitter(gauss, (self.xdata, self.ydata), self.params, costfun=costfun) res2 = g.fit('nelder') assert_almost_equal(self.pvals(res.params), self.pvals(res2.params))
def test_best_weighted(self): f = CurveFitter(gauss, (self.xvals, self.yvals, self.evals), self.params) res = f.fit() output = list(res.params.valuesdict().values()) assert_almost_equal(output, self.best_weighted, 4) assert_almost_equal(res.chisqr, self.best_weighted_chisqr) uncertainties = [res.params['p%d' % i].stderr for i in range(4)] assert_almost_equal(uncertainties, self.best_weighted_errors, 3)
def test_reflectivity_fit(self): # a smoke test to make sure the reflectivity fit proceeds params = curvefitter.to_parameters(self.coefs) params['p1'].value = 1.1 fitfunc = reflect.ReflectivityFitFunction() fitter = CurveFitter(fitfunc, self.qvals, self.rvals, params) fitter.fit() transform = reflect.Transform('logY') yt, et = transform.transform(self.qvals361, self.rvals361, self.evals361) kws = {'transform':transform.transform} fitter2 = CurveFitter(fitfunc, self.qvals361, yt, self.params361, edata=et, fcn_kws=kws) fitter2.fit('differential_evolution')
def test_reflectivity_emcee(self): transform = reflect.Transform('logY') yt, et = transform.transform(self.qvals361, self.rvals361, self.evals361) kws = {'transform': transform.transform} fitfunc = RFF(transform=transform.transform, dq=5.) fitter = CurveFitter(fitfunc, (self.qvals361, yt, et), self.params361, fcn_kws=kws) res = fitter.fit() res_em = fitter.emcee(steps=10)
def test_reflectivity_emcee(self): transform = reflect.Transform('logY') yt, et = transform.transform(self.qvals361, self.rvals361, self.evals361) kws = {'transform': transform.transform} fitfunc = RFF(transform=transform.transform, dq=5.) fitter = CurveFitter(fitfunc, (self.qvals361, yt, et), self.params361, fcn_kws=kws) res = fitter.fit() res_em = fitter.emcee(steps=10, seed=1) assert_allclose(values(res.params), values(res_em.params), rtol=1e-2)
class TestFitter(unittest.TestCase): def setUp(self): self.xdata = np.linspace(-4, 4, 100) self.p0 = np.array([0., 1., 0.0, 1.]) self.bounds = [(-1, 1), (0, 2), (-1, 1.), (0.001, 2)] self.params = curvefitter.to_parameters(self.p0 + 0.2, bounds=self.bounds) self.final_params = curvefitter.to_parameters(self.p0, bounds=self.bounds) self.ydata = gauss(self.xdata, self.final_params) self.f = CurveFitter(gauss, (self.xdata, self.ydata), self.params) def test_fitting(self): # the simplest test - a really simple gauss curve with perfect data res = self.f.fit() assert_almost_equal(values(res.params), self.p0) assert_almost_equal(res.chisqr, 0) def test_NIST(self): # Run all the NIST standard tests with leastsq for model in Models: try: NIST_runner(model) except Exception: print(model) raise def test_model_returns_function(self): ydata = gauss(self.xdata, self.final_params) model = self.f.model(self.final_params) assert_almost_equal(ydata, model) def test_residuals(self): resid = self.f.residuals(self.final_params) assert_almost_equal(np.sum(resid**2), 0) def test_cost(self): resid = self.f.residuals(self.final_params) assert_almost_equal(0, np.sum(resid**2)) def test_leastsq(self): # test that a custom method can be used with scipy.optimize.minimize res = self.f.fit() assert_almost_equal(values(res.params), self.p0) def test_resid_length(self): # the residuals length should be equal to the data length resid = self.f.residuals(self.params) assert_equal(resid.size, self.f.dataset.y.size) def test_scalar_minimize(self): assert_equal(values(self.params), self.p0 + 0.2) res = self.f.fit(method='differential_evolution') assert_almost_equal(values(res.params), self.p0, 3) def test_holding_parameter(self): # holding parameters means that those parameters shouldn't change # during a fit self.params['p0'].vary = False res = self.f.fit() assert_almost_equal(self.p0[0] + 0.2, self.params['p0'].value) assert_almost_equal(res.params['p0'].value, self.params['p0'].value) def test_fit_returns_MinimizerResult(self): self.params['p0'].vary = False res = self.f.fit() assert_(isinstance(res, MinimizerResult)) def test_costfun(self): # test user defined costfun res = self.f.fit('nelder') def costfun(params, generative, y, e): return np.sum((y - generative / e) ** 2) g = CurveFitter(gauss, (self.xdata, self.ydata), self.params, costfun=costfun) res2 = g.fit('nelder') assert_almost_equal(values(res.params), values(res2.params)) def test_args_kwds_are_used(self): # check that user defined args and kwds make their way into the user # function a = [1., 2.] x = np.linspace(0, 10, 11) y = a[0] + 1 + 2 * a[1] * x par = Parameters() par.add('p0', 1.5) par.add('p1', 2.5) def fun(x, p, *args, **kwds): assert_equal(args, a) return args[0] + p['p0'] + p['p1'] * a[1] * x g = CurveFitter(fun, (x, y), par, fcn_args=a) res = g.fit() assert_almost_equal(values(res.params), [1., 2.]) d = {'a': 1, 'b': 2} def fun(x, p, *args, **kwds): return kwds['a'] + p['p0'] + p['p1'] * kwds['b'] * x g = CurveFitter(fun, (x, y), par, fcn_kws=d) res = g.fit() assert_almost_equal(values(res.params), [1., 2.])
class TestFitter(unittest.TestCase): def setUp(self): self.xdata = np.linspace(-4, 4, 100) self.p0 = np.array([0., 1., 0.0, 1.]) self.bounds = [(-1, 1), (0, 2), (-1, 1.), (0.001, 2)] self.params = curvefitter.to_parameters(self.p0 + 0.2, bounds=self.bounds) self.final_params = curvefitter.to_parameters(self.p0, bounds=self.bounds) self.ydata = gauss(self.xdata, self.final_params) self.f = CurveFitter(gauss, (self.xdata, self.ydata), self.params) def pvals(self, params): return np.asfarray(list(params.valuesdict().values())) def test_fitting(self): # the simplest test - a really simple gauss curve with perfect data res = self.f.fit() assert_almost_equal(self.pvals(res.params), self.p0) assert_almost_equal(res.chisqr, 0) def test_NIST(self): # Run all the NIST standard tests with leastsq for model in Models.keys(): try: NIST_runner(model) except Exception: print(model) raise def test_model_returns_function(self): ydata = gauss(self.xdata, self.final_params) model = self.f.model(self.final_params) assert_almost_equal(ydata, model) def test_residuals(self): resid = self.f.residuals(self.final_params) assert_almost_equal(np.sum(resid**2), 0) def test_cost(self): resid = self.f.residuals(self.final_params) assert_almost_equal(0, np.sum(resid**2)) def test_leastsq(self): # test that a custom method can be used with scipy.optimize.minimize res = self.f.fit() assert_almost_equal(self.pvals(res.params), self.p0) def test_resid_length(self): # the residuals length should be equal to the data length resid = self.f.residuals(self.params) assert_equal(resid.size, self.f.ydata.size) def test_scalar_minimize(self): assert_equal(self.pvals(self.params), self.p0 + 0.2) res = self.f.fit(method='differential_evolution') assert_almost_equal(self.pvals(res.params), self.p0, 3) def test_holding_parameter(self): # holding parameters means that those parameters shouldn't change # during a fit self.params['p0'].vary = False res = self.f.fit() assert_almost_equal(self.p0[0] + 0.2, self.params['p0'].value) def test_fit_returns_MinimizerResult(self): self.params['p0'].vary = False res = self.f.fit() assert_(isinstance(res, MinimizerResult)) def test_costfun(self): # test user defined costfun res = self.f.fit('nelder') def costfun(params, generative, y, e): return np.sum((y - generative / e) ** 2) g = CurveFitter(gauss, (self.xdata, self.ydata), self.params, costfun=costfun) res2 = g.fit('nelder') assert_almost_equal(self.pvals(res.params), self.pvals(res2.params))