def _iterate(simulation, variables, expected, guess, bounds, biased_parameter, model, input_values, logger, len_parameter=3): biased_parameters = np.zeros((len(input_values), len_parameter)) for i, input_ in enumerate(input_values): model.setVariable(biased_parameter, input_) logger.debug(">>>> Input for %s: %s" % (biased_parameter, input_)) logger.debug("Fit variables %s: " % model.get_Symbols()) res = Residuum(model, 'ratio') residuals = FitWrapper(res.getResiduals()) Fit = FitModel() resultls = Fit._least_squares(residuals, guess, simulation, bounds) biased_parameters[i] = np.array(resultls.x) - np.array(expected) return biased_parameters
def test_main(): from get_ssa import get_ssa zenith = 53.1836240528 AMass = 1.66450160404 rel_h = 0.665 pressure = 950 AM = 5 ssa = get_ssa(rel_h, AM) x = np.linspace(200, 800, 100) # config variables = ['alpha', 'beta', 'g_dsa', 'g_dsr'] # config expected_values = [2.5, 0.06, 0.6, 0.5] print('Expected: %s' % expected_values) guess = [1.0, 0.01, 0.5, 0.8] # config bounds = [(-0.2, 4), (0., 3), (0., 2.), (0., 2.)] # config # Theano irr_symbol = IrradianceModel_sym(x, zenith, AMass, pressure, ssa, variables) getIrrRatio = irr_symbol.getcompiledModel('ratio') y_theano = getIrrRatio(*expected_values) res = Residuum(irr_symbol, 'ratio') residuum = FitWrapper(res.getResiduum()) residuals = FitWrapper(res.getResiduals()) derivative = FitWrapper(res.getDerivative()) Fit = FitModel() result = Fit._minimize(residuum, guess, y_theano, bounds, jacobian=derivative) print("Got %s" % result.x) resultls = Fit._least_squares(residuals, guess, y_theano, bounds) print("Got %s" % resultls.x) # Python IrradianceObject = IrradianceModel_python(AMass, rel_h, ssa, zenith, pressure) y_python = IrradianceObject.irradiance_ratio(x, 2.5, 0.06,0.0, 0.6, 0.5) gmod = Model(IrradianceObject.irradiance_ratio, independent_vars=['x'], param_names=variables) gmod.set_param_hint('alpha', value=guess[0], min=bounds[0][0], max=bounds[0][1]) gmod.set_param_hint('beta', value=guess[1], min=bounds[1][0], max=bounds[1][1]) gmod.set_param_hint('g_dsa', value=guess[2], min=bounds[2][0], max=bounds[2][1]) gmod.set_param_hint('g_dsr', value=guess[3], min=bounds[3][0], max=bounds[3][1]) result_lmfit = gmod.fit(y_python, x=x) print(result_lmfit.fit_report()) plt.plot(x, y_theano) x_new = np.linspace(300, 900,150) irr_symbol.set_wavelengthAOI(x_new) getIrrRatio = irr_symbol.getcompiledModel('ratio') y_new = getIrrRatio(*expected_values) plt.plot(x_new, y_new, '+', label='different wavelengths') plt.legend() plt.show()
def example_gaussian(): #Config output x = np.linspace(-20, 20, 100) a = 3.4 b = 2.4 c = 5 bounds = [(3, 4), (2, 3), (4.2, 5.2)] guess = [a,b,c] variables = ['a', 'b', 'c'] y = gaussian(x, guess) + np.random.normal(0, 0.1, len(x)) #Auswertung model= GaussModel(x, variables) res = Residuum(model, 'gauss') function_to_fit = FitWrapper(res.getResiduum()) test = FitModel(function_to_fit) result = test.fit(guess, y, bounds) y_fitted = gaussian(x, result.x) plt.plot(x,y, 'b') plt.plot(x, y_fitted, 'r')