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()
class Minimize: def __init__(self, model, config, param_dict, logger): self.model = model self.param_dict = param_dict self.config = config del self.config['independent']['x'] for key, value in self.config['independent'].items(): logger.debug("Set %s for %s " % (value, key)) self.model.setVariable(key, value) self.res = Residuum(model) self.callable = FitWrapper(self.res.getResiduum()) self.symbols = model.get_Symbols() self.logger = logger if config['jac_flag']: logger.info("Using jacobian") self.jacobian = FitWrapper(self.res.getDerivative()) else: assert config['jac_flag'] == False self.jacobian = False def fit(self): self.logger.info("Symbols: %s" % self.symbols) self.logger.info('Method %s' % self.config['method']) self.result = minimize(self.callable, self.config['initial_values'], args=(self.param_dict['spectra_range']), jac=self.jacobian, method=self.config['method'], bounds=self.config['limits']) self.param_dict['variables'] = dict() for idx, symbol in enumerate(self.symbols): self.param_dict['variables'][symbol.name] = dict() self.param_dict['variables'][symbol.name]['stderr'] = None self.param_dict['variables'][symbol.name]['value'] = self.result.x[idx] self._calc_fitted_spectra() self._calc_residuals() return Result(self.result, self.fitted_spectra, self.residuals, self.param_dict['wave_range']), self.param_dict def _calc_fitted_spectra(self): f = self.model.get_compiled() self.fitted_spectra = f(*self.result.x) def _calc_residuals(self): #TODO self.residuals = self.param_dict['spectra_range'] - self.fitted_spectra
class Minimize: def __init__(self, model, config, param_dict, logger): self.model = model self.res = Residuum(model, 'ratio') self.callable = FitWrapper(self.res.getResiduum()) self.symbols = model.get_Symbols() self.param_dict = param_dict self.config = config self.logger = logger if config['jac_flag']: logger.info("Using jacobian") self.jacobian = FitWrapper(self.res.getDerivative()) else: assert config['jac_flag'] == False self.jacobian = False def fit(self): self.logger.info('Method %s' % self.config['method']) self.result = minimize(self.callable, self.config['initial_values'], args=(self.param_dict['spectra_range']), jac=self.jacobian, method=self.config['method'], bounds=self.config['limits']) for idx, symbol in enumerate(self.symbols): self.param_dict[symbol] = dict() self.param_dict[symbol]['stderr'] = None self.param_dict[symbol]['value'] = self.result.x[idx] self._calc_fitted_spectra() self._calc_residuals() return Result(self.result, self.fitted_spectra, self.residuals, self.param_dict['wave_range']), self.param_dict def _calc_fitted_spectra(self): f = self.model.getcompiledModel('ratio') self.fitted_spectra = f(*self.result.x) def _calc_residuals(self): #TODO self.residuals = self.param_dict['spectra_range'] - self.fitted_spectra