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display_error = True + display_error = False # Data binning rebin = True pxsize = 0.10 @@ -154,8 +154,8 @@ def main(): # Polarization map output figname = 'IC5063_FOC' #target/intrument name figtype = '_c_020' #additionnal informations - SNRp_cut = 3. #P measurments with SNR>3 - SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%. + SNRp_cut = 1. #P measurments with SNR>3 + SNRi_cut = 10. #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 # if step_vec = 0 then all vectors are displayed at full length diff --git a/src/lib/plots.py b/src/lib/plots.py index 0833e61..9fcd2f2 100755 --- a/src/lib/plots.py +++ b/src/lib/plots.py @@ -316,7 +316,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c if display.lower() in ['intensity']: # If no display selected, show intensity map display='i' - vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) + vmin, vmax = 1./3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.) cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") levelsI = np.linspace(vmax*0.01, vmax*0.99, 10) @@ -327,7 +327,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c # Display polarisation flux display='pf' pf_mask = (stkI.data > 0.) * (pol.data > 0.) - vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) + vmin, vmax = 1./3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) im = ax.imshow(stkI.data*convert_flux*pol.data, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.) cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") levelsPf = np.linspace(vmax*0.01, vmax*0.99, 10)