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and b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_P_flux.png differ diff --git a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_SNRi.png b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_SNRi.png new file mode 100644 index 0000000..bc9a74d Binary files /dev/null and b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_SNRi.png differ diff --git a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_SNRp.png b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_SNRp.png new file mode 100644 index 0000000..3eeeaf7 Binary files /dev/null and b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_withoutaxiserror_SNRp.png differ diff --git a/src/FOC_reduction.py b/src/FOC_reduction.py index c7dcee0..0534b98 100755 --- a/src/FOC_reduction.py +++ b/src/FOC_reduction.py @@ -113,7 +113,7 @@ def main(): rotate_data = False #rotation to North convention can give erroneous results # Polarization map output figname = 'NGC1068_FOC' #target/intrument name - figtype = '_combine_FWHM020_rot' #additionnal informations + figtype = '_combine_FWHM020_rot_withaxiserror' #additionnal informations SNRp_cut = 15. #P measurments with SNR>3 SNRi_cut = 80. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%. step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted diff --git a/src/lib/reduction.py b/src/lib/reduction.py index a09d0c1..6601f8a 100755 --- a/src/lib/reduction.py +++ b/src/lib/reduction.py @@ -1183,7 +1183,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers, # s_U_U = np.sqrt(Stokes_cov[2,2])/U_stokes*100. # s_U_axis_U = np.sqrt(s_U2_axis)/U_stokes*100. # -# fig, ax = plt.subplots(3,3) +# fig, ax = plt.subplots(3,3,figsize=(15,15)) # im = ax[0,0].imshow(s_I_I, origin='lower') # ax[0,0].set_title(r"$\frac{\sigma_{I}}{I}$") # fig.colorbar(im, ax=ax[0,0]) @@ -1304,7 +1304,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers): #Compute the total exposure time so that #I_stokes*exp_tot = N_tot the total number of events exp_tot = np.array([header['exptime'] for header in headers]).sum() - print("Total exposure time : {} sec".format(exp_tot)) + #print("Total exposure time : {} sec".format(exp_tot)) N_obs = I_stokes*exp_tot #Errors on P, PA supposing Poisson noise @@ -1313,13 +1313,13 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers): s_PA_P = np.ones(I_stokes.shape)*fmax s_PA_P[mask2] = s_P_P[mask2]/(2.*P[mask2])*180./np.pi -# # Nan handling : -# P[np.isnan(P)] = 0. -# s_P[np.isnan(s_P)] = fmax -# s_PA[np.isnan(s_PA)] = fmax -# debiased_P[np.isnan(debiased_P)] = 0. -# s_P_P[np.isnan(s_P_P)] = fmax -# s_PA_P[np.isnan(s_PA_P)] = fmax + # Nan handling : + P[np.isnan(P)] = 0. + s_P[np.isnan(s_P)] = fmax + s_PA[np.isnan(s_PA)] = fmax + debiased_P[np.isnan(debiased_P)] = 0. + s_P_P[np.isnan(s_P_P)] = fmax + s_PA_P[np.isnan(s_PA_P)] = fmax return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P