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():
# possible values
from get_ssa import get_ssa
from Model import IrradianceModel
zenith = 53.1836240528
AMass = 1.66450160404
rel_h = 0.665
pressure = 950
AM = 5
ssa = get_ssa(rel_h, AM)
iteration = 20
alphas = np.zeros(len(range(1, iteration)) + 1)
x = np.linspace(200, 800, 100)
irr = IrradianceModel_python(AMass, rel_h, ssa, zenith, pressure)
irr_symbol = IrradianceModel(x, zenith, AMass, pressure, ssa)
func = irr_symbol._irradiance_ratio()
y = irr.irradiance_ratio(x, 2.5, 0.06, 0.0, 1.0, 1.0)
for i in range(0, iteration):
ssa = get_ssa(rel_h, AM)
print(ssa)
irr = IrradianceModel_python(AMass, rel_h, ssa, zenith, pressure)
yerror = np.random.normal(0, 0.009, len(x))
y = irr.irradiance_ratio(x, 1.5, 0.06, 0.0, 0.6, 0.9) + yerror
weights = 1 / yerror
gmod = Model(irr.irradiance_ratio, independent_vars=["x"], param_names=["alpha", "beta", "g_dsa", "g_dsr"])
gmod.set_param_hint("alpha", value=1.0, min=-0.2, max=2.5)
gmod.set_param_hint("beta", value=0.01, min=0.0, max=2.0)
gmod.set_param_hint("g_dsa", value=0.6, min=0.0, max=1.0)
gmod.set_param_hint("g_dsr", value=0.9, min=0.0, max=1.0)
print(gmod.param_hints)
print(gmod.param_names)
print(gmod.independent_vars)
result = gmod.fit(y, x=x)
print(result.fit_report())
alphas[i] = result.params["alpha"].value
# plt.plot(x, y, label='%s' % AM)
# plt.plot(x, result.best_fit, 'r-', label='fit')
y = irr.irradiance_ratio(x, 1.5, 0.06, 0.0, 0.6, 0.9)
y2 = irr.irradiance_ratio(x, 1.5, 0.08, 0.0, 0.6, 0.9)
plt.legend()
plt.show()