Commit 8a5e014a by BURGARELLA Denis

### Now, correctly takes into account the age of the universe in the analysis

parent 5dcf0ec9
 ... @@ -209,15 +209,15 @@ def analysis(idx, obs): ... @@ -209,15 +209,15 @@ def analysis(idx, obs): global gbl_mod_fluxes, gbl_obs_fluxes, gbl_obs_errors global gbl_mod_fluxes, gbl_obs_fluxes, gbl_obs_errors # We pick up the closest redshift assuming we have limited the number of # We pick up the models with closest redshift assuming we have limited # decimals (usually set to 2 decimals). # the number of decimals (usually set to 2 decimals). w = np.where(gbl_w_redshifts[gbl_redshifts[np.abs(obs['redshift'] - w = np.where(gbl_w_redshifts[gbl_redshifts[np.abs(obs['redshift'] - gbl_redshifts).argmin()]]) gbl_redshifts).argmin()]]) # We only keep model with fluxes >= -90. If not => no data # We only keep model with fluxes >= -90. If not => no data # Probably because age > age of the universe (see function sed(idx) above). model_fluxes = np.ma.masked_less(gbl_model_fluxes[w[0], :], -90.) model_fluxes = np.ma.masked_less(gbl_model_fluxes[w[0], :], -90.) model_variables = np.ma.masked_where(np.ma.getmask(model_fluxes), model_variables = np.ma.masked_less(gbl_model_variables[w[0], :], -90.) gbl_model_variables[w[0], :]) obs_fluxes = np.array([obs[name] for name in gbl_filters]) obs_fluxes = np.array([obs[name] for name in gbl_filters]) obs_errors = np.array([obs[name + "_err"] for name in gbl_filters]) obs_errors = np.array([obs[name + "_err"] for name in gbl_filters]) ... @@ -342,17 +342,23 @@ def analysis(idx, obs): ... @@ -342,17 +342,23 @@ def analysis(idx, obs): for i, val in enumerate(analysed_averages): for i, val in enumerate(analysed_averages): pdf_binsize[i] = FDbinSize(model_variables[:, i]) pdf_binsize[i] = FDbinSize(model_variables[:, i]) if np.min(model_variables[:, i]) > 0.: if pdf_binsize[i]==0.: min_hist[i] = max(0., np.min(model_variables[:, i]) - # if only 1 bin, we cheat to have 1 point in the histogram pdf_binsize[i]) min_hist[i] = min(model_variables[:, i]) max_hist[i] = np.max(model_variables[:, i]) + pdf_binsize[i] max_hist[i] = min_hist[i] elif np.max(model_variables[:, i]) < 0.: pdf_binsize[i] = 1. min_hist[i] = np.min(model_variables[:, i]) - pdf_binsize[i] else: max_hist[i] = min(0., np.max(model_variables[:, i]) + if np.min(model_variables[:, i]) > 0.: pdf_binsize[i]) min_hist[i] = max(0., np.min(model_variables[:, i]) - else: pdf_binsize[i]) min_hist[i] = np.min(model_variables[:, i]) - pdf_binsize[i] max_hist[i] = np.max(model_variables[:, i]) + pdf_binsize[i] max_hist[i] = np.max(model_variables[:, i]) + pdf_binsize[i] elif np.max(model_variables[:, i]) < 0.: min_hist[i] = np.min(model_variables[:, i]) - pdf_binsize[i] max_hist[i] = min(0., np.max(model_variables[:, i]) + pdf_binsize[i]) else: min_hist[i] = np.min(model_variables[:, i]) - pdf_binsize[i] max_hist[i] = np.max(model_variables[:, i]) + pdf_binsize[i] pdf_Npoints = np.around((max_hist - min_hist) / pdf_binsize) + 1 pdf_Npoints = np.around((max_hist - min_hist) / pdf_binsize) + 1 ... ...
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