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')
