updated codes 2

Author: anaismoller

SIMGEN_NEFF_YPIPELINE_SEO1.input

@@ -2,7 +2,7 @@
 SIMLIB_FILE: $SNDATA_ROOT/INTERNAL/DES/simlibs/DES_DIFFIMG.SIMLIB 
 #SOLID_ANGLE: 0.00913851     	# 6 sq deg (steridian) for deep field + 24 sq deg (steridian) for shallow
 SIMLIB_FIELDLIST:  X3+C3+E1+E2+S1+S2+C1+C2+X1+X2 #all DES fields, should include solid angle inside
-NGEN_SEASON: 3.0    # 3 seasons
+NGEN_SEASON: 1.0    # 1 to make it faster 3 seasons
 
 # ----------------- sim setup  ---------------
 GENVERSION:  AMDES_NEFF_YPIPELINE_SEO1    # SNDATA version to generate
@@ -78,8 +78,8 @@ CUTWIN_T0GAPMAX: 0 20.
 NEWMJD_DIF:  0.4  #days.  Ensures multiple epochs needed for trigger
 
 # cosmological params for lightcurve generation and redshift distribution
-OMEGA_MATTER:  0.27
-OMEGA_LAMBDA:  0.73
+OMEGA_MATTER:  0.295
+OMEGA_LAMBDA:  0.705
 W0_LAMBDA:    -1.00
 H0:           70.0   
 

do_emcee_fit_sigmoid.py

@@ -128,10 +128,10 @@ def log_posterior(theta, x, y):
     samples = sampler.chain[:, nburn:, :].reshape((-1, ndim))
     plot_MCMC_results(xdata, ydata, samples)
 
-    import corner
-    plt.clf()
-    fig = corner.corner(samples, labels=list_key,quantiles=[0.5],bins=50)
-    fig.savefig(path_to_save+'/triangle_' + filt + ".png")
+    # import corner
+    # plt.clf()
+    # fig = corner.corner(samples, labels=list_key,quantiles=[0.5],bins=50)
+    # fig.savefig(path_to_save+'/triangle_' + filt + ".png")
 
     A_mcmc, alpha_mcmc, beta_mcmc = map(lambda v: (v[1], v[2] - v[1], v[1] - v[0]),
                                         zip(*np.percentile(samples, [16, 50, 84],

do_selection_function.py

@@ -15,15 +15,19 @@
 debugging=False
 
 #______LOAD DATA AND SIM, AND APPLY CUTS
-data_ori = pd.read_csv('../../../data_and_sim/DESALL_fitted_myself/FITOPT000.FITRES',
+data_ori = pd.read_csv('../../../data_and_sim/SMP_SPEC_v1/FITOPT000.FITRES',
                        index_col=False, comment='#',delimiter=' ')
+# print data_ori
+
 tmp = data_ori[(data_ori['c'] > -0.3) & (data_ori['c'] < 0.3) & (data_ori['x1'] > -3) & (data_ori['x1']
                                                                                          < 3) & (data_ori['z'] > 0.05) & (data_ori['z'] < 0.9) & (data_ori['FITPROB'] > 1E-05)]
 tmp2 = tmp[tmp.columns.values[:-1]]
 #Selecting type Ias!
-data=tmp2[tmp2['TYPE']==1]
+# data=tmp2[tmp2['TYPE']==1]
+#with SMP we don't have this issue
+data = tmp2
 
-sim = pd.read_csv('../sim/NEFF/FITOPT000.FITRES',
+sim = pd.read_csv('FITOPT000.FITRES',
                   index_col=False, comment='#', delimiter=' ')
 tmp2 = sim[(sim['c'] > -0.3) & (sim['c'] < 0.3) & (sim['x1'] > -3) & (sim['x1'] < 3)
            & (sim['z'] > 0.05) & (sim['z'] < 0.9) & (sim['FITPROB'] > 1E-05)]
@@ -59,12 +63,12 @@ def initial_plots(norm_bin):
         err_sim = []
         for ibin in range(nbins - 1):
             # data
-            bin_elements_dat = np.take(data[var],np.where(
+            bin_elements_dat = np.take(data[var].values,np.where(
                 index_of_bin_belonging_to_dat == ibin)[0])
             error_dat = np.sqrt(len(bin_elements_dat))
             err_dat.append(error_dat)
             # sim
-            bin_elements_sim = np.take(sim[var],np.where(index_of_bin_belonging_to_sim == ibin)[0])
+            bin_elements_sim = np.take(sim[var].values,np.where(index_of_bin_belonging_to_sim == ibin)[0])
             error_sim = np.sqrt(len(bin_elements_sim))
             err_sim.append(error_sim)
             del bin_elements_sim, bin_elements_dat
@@ -149,15 +153,16 @@ def plots_vs_z():
     z_bins_plot=np.arange(min_z+half_z_bin_step,max_z-half_z_bin_step,z_bin_step)
     color_dic={'data':'red','sim':'blue'}
 
-    Mb_arr=np.ones(len(z_bins)-1)*(19.4)
+    Mb=19.05
+    Mb_arr=np.ones(len(z_bins)-1)*(Mb)
     alpha=0.144 #from sim
     beta=3.1
 
     #Bias correction
     mean_mu_arr=[]
     mean_z_arr=[]
     err_mu_arr=[]
-    sim['new_mu']=np.array(sim['mB'])+19.38+np.array(alpha*sim['x1'])-np.array(beta*sim['c'])-np.array(dist_mu(sim['z']))
+    sim['new_mu']=np.array(sim['mB'])+Mb+np.array(alpha*sim['x1'])-np.array(beta*sim['c'])-np.array(dist_mu(sim['z']))
     for i, z_bin in enumerate(z_bins[:-1]):
             binned_indices=sim[(sim['z'] >= z_bin) & (sim['z'] < z_bins[i + 1])].index.tolist()
             binned_mu=sim['new_mu'][binned_indices]
@@ -170,7 +175,7 @@ def plots_vs_z():
     plt.errorbar(mean_z_arr,mean_mu_arr,yerr=np.array(err_mu_arr),fmt='o')
     plt.xlabel('z')
     plt.ylabel('bias correction')
-    plt.title('mB+19.38+alpha*x1-beta*c-dist_mu(z)')
+    plt.title('mB+%s+alpha*x1-beta*c-dist_mu(z)'%Mb)
     plt.savefig('%s/bias.png'%path_to_save)
     del fig
 

mu_cosmo.py

@@ -5,7 +5,7 @@
 
 def dist_mu(redshift):
     mu = cosmo.distmod(redshift)
-    # mu = cosmo.luminosity_distance(redshift)
+
     return mu.value
 
 if __name__ == "__main__":
@@ -16,10 +16,3 @@ def dist_mu(redshift):
     redshift = float(args.z)
 
     dist_mu(redshift)
-
-# import cosmolopy.distance as cd
-# cosmo = {'omega_M_0':0.295, 'omega_lambda_0':0.705, 'omega_k_0':0.0, 'h':0.70}
-# cosmo = cd.set_omega_k_0(cosmo)
-# def dist_mu2(redshift):
-#     mu = cd.luminosity_distance(redshift)
-#     return mu.value