Commit 1731e663 by Médéric Boquien

### Now that we leave the models with NaN fluxes, the scaling factors and χ² may...

`Now that we leave the models with NaN fluxes, the scaling factors and χ² may be NaN too. Handle this case.`
parent 44e99b1d
 ... @@ -221,7 +221,7 @@ def analysis(idx, obs): ... @@ -221,7 +221,7 @@ def analysis(idx, obs): # We select only models that have at least 0.1% of the probability of # We select only models that have at least 0.1% of the probability of # the best model to reproduce the observations. It helps eliminating # the best model to reproduce the observations. It helps eliminating # very bad models. # very bad models. maxchi2 = st.chi2.isf(st.chi2.sf(np.min(chi2), nobs - 1) * 1e-3, nobs - 1) maxchi2 = st.chi2.isf(st.chi2.sf(np.nanmin(chi2), nobs-1) * 1e-3, nobs-1) wlikely = np.where(chi2 < maxchi2) wlikely = np.where(chi2 < maxchi2) if wlikely[0].size == 0: if wlikely[0].size == 0: ... @@ -241,7 +241,7 @@ def analysis(idx, obs): ... @@ -241,7 +241,7 @@ def analysis(idx, obs): # We define the best fitting model for each observation as the one # We define the best fitting model for each observation as the one # with the least χ². # with the least χ². best_index_z = chi2.argmin() # index for models at given z best_index_z = np.nanargmin(chi2) # index for models at given z best_index = wz.start + best_index_z * wz.step # index for all models best_index = wz.start + best_index_z * wz.step # index for all models # We compute once again the best sed to obtain its info # We compute once again the best sed to obtain its info ... ...
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