def fit_signal(signal,l):
x1=np.linspace(0,1,len(signal))
piecewiseModel=Model(piecewise_prob)
piecewiseModel.set_param_hint('l', value=1,vary=False)
piecewiseModel.set_param_hint('C', vary=True, value=.1,min=0,max=1)
piecewiseModel.make_params()
res=piecewiseModel.fit(signal,x=x1,weights=np.sin(1.5*x1)+1.5)
return res
def lmfit_gaussian(x_array, y_array, err_array, x_boundarys):
# Find indeces for six points in spectrum
idcsW = np.searchsorted(x_array, x_boundarys)
# Emission region
idcsEmis = (x_array[idcsW[2]] <= x_array) & (x_array <= x_array[idcsW[3]])
# Return left and right continua merged in one array
idcsCont = (((x_array[idcsW[0]] <= x_array) &
(x_array <= x_array[idcsW[1]])) |
((x_array[idcsW[4]] <= x_array) &
(x_array <= x_array[idcsW[5]]))).squeeze()
emisWave, emisFlux = x_array[idcsEmis], y_array[idcsEmis]
contWave, contFlux = x_array[idcsCont], y_array[idcsCont]
idx_peak = np.argmax(emisFlux)
fit_model = Model(linear_model, prefix=f'{lineLabel}_cont_')
fit_model.set_param_hint(f'{lineLabel}_cont_slope', **{
'value': 0,
'vary': False
})
fit_model.set_param_hint(f'{lineLabel}_cont_intercept', **{
'value': contFlux.mean(),
'vary': False
})
fit_model += Model(gaussian_model, prefix=f'{lineLabel}_')
fit_model.set_param_hint(f'{lineLabel}_amp',
value=emisFlux[idx_peak] - contFlux.mean())
fit_model.set_param_hint(f'{lineLabel}_center', value=emisWave[idx_peak])
fit_model.set_param_hint(f'{lineLabel}_sigma', value=1.0)
x_fit = x_array[idcsEmis + idcsCont]
y_fit = y_array[idcsEmis + idcsCont]
w_fit = 1.0 / err_array[idcsEmis + idcsCont]
fit_params = fit_model.make_params()
obs_fit_output = fit_model.fit(y_fit, fit_params, x=x_fit, weights=w_fit)
# amp = obs_fit_output.params[f"{lineLabel}_amp"].value
mu = obs_fit_output.params[f"{lineLabel}_center"].value
sigma = obs_fit_output.params[f"{lineLabel}_sigma"].value
mu_err = obs_fit_output.params[f"{lineLabel}_center"].stderr
sigma_err = obs_fit_output.params[f"{lineLabel}_sigma"].stderr
x_obs, y_obs = obs_fit_output.userkws['x'], obs_fit_output.data
wave_obs = np.linspace(x_obs[0], x_obs[-1], 500)
flux_comps_obs = obs_fit_output.eval_components(x=wave_obs)
flux_obs = flux_comps_obs.get(f'{lineLabel}_cont_',
0.0) + flux_comps_obs[f'{lineLabel}_']
return x_fit, y_fit, wave_obs, flux_obs, mu, sigma, mu_err, sigma_err
def make_model(num):
pref = "f{0}_".format(num)
model = Model(Lorexponential, prefix=pref)
model.set_param_hint(pref + "simetricL", value=0.01)
model.set_param_hint(pref + "asymetricL", value=0.01)
model.set_param_hint(pref + "center", value=paramos[num]["center"])
model.set_param_hint(pref + "sigma", value=paramos[num]["sigma"])
model.set_param_hint(pref + "C", value=0.001)
return model
y1=smooth_signal(y1,10**3)
y1=np.real(sharpen_filter(y1))
plt.clf()
plt.plot(y1,'b')
plt.savefig("".join([sys.argv[1],"blak"])+".png")
print(sys.argv[1]+": Coverage OK ("+str(coveragePercentage)+" x).")
x1=np.linspace(0,genomeLen-1,len(y1))
### Korem fit
# fit piecewise using least squares (LM)
piecewiseModel=Model(piecewiseLinear)
piecewiseModel.set_param_hint('Ol', value=0.5*genomeLen,vary=True,min=0,max=genomeLen)
#piecewiseModel.set_param_hint('Tl', value=0.6*genomeLen,vary=True,min=0,max=genomeLen)
piecewiseModel.set_param_hint('Tc', value = np.median(np.log2(y1)), vary=True)
piecewiseModel.set_param_hint('Oc', value = np.median(np.log2(y1)), vary=True)
piecewiseModel.set_param_hint('delta', value = 0.545*genomeLen, min=0.45*genomeLen, max=0.55*genomeLen, vary=True)#,expr='Tl-Ol if Tl-Ol > 0 else Ol-Tl')
#piecewiseModel.set_param_hint('Tl', value=0.6*genomeLen,vary=True,min=0,max=genomeLen,expr='mod(Ol+delta,genLen)')
piecewiseModel.set_param_hint('genLen', vary=False, value=genomeLen)
piecewiseModel.make_params()
result=piecewiseModel.fit(np.log2(y1),x=x1)
resR2=R2(result.best_fit,y1)
# var=np.var(abs(np.log2(y1)-result.best_fit), dtype=np.float64)
#med=np.median(np.log2(y1))
# y1=[value for value in y1 if (np.log2(value) < med+1.45*np.sqrt(var)) & (np.log2(value)>med-1.45*np.sqrt(var))]
err_voxel_norm = flux_err / norm_flux
obsLineWaves = wave_regions * (1 + z_objs[i])
idcsEmis, idcsCont = obsLm.define_masks(wave, flux_voxel_norm,
obsLineWaves)
emisWave, emisFlux = wave[idcsEmis], flux_voxel_norm[idcsEmis]
contWave, contFlux = wave[idcsCont], flux_voxel_norm[idcsCont]
obsLm.line_properties(emisWave,
emisFlux,
contWave,
contFlux,
bootstrap_size=5000)
fit_model = Model(linear_model, prefix=f'{lineLabel}_cont_')
fit_model.set_param_hint(f'{lineLabel}_cont_slope', **{
'value': obsLm.m_cont,
'vary': False
})
fit_model.set_param_hint(f'{lineLabel}_cont_intercept', **{
'value': obsLm.n_cont,
'vary': False
})
fit_model += Model(gaussian_model, prefix=f'{lineLabel}_')
fit_model.set_param_hint(f'{lineLabel}_amplitude',
value=obsLm.peak_flux - obsLm.cont)
fit_model.set_param_hint(f'{lineLabel}_center', value=obsLm.peak_wave)
fit_model.set_param_hint(f'{lineLabel}_sigma', value=1.0)
x_array = wave[idcsEmis + idcsCont]
y_array = flux_voxel_norm[idcsEmis + idcsCont]
w_array = 1.0 / np.sqrt(err_voxel_norm[idcsEmis + idcsCont])
