Tibeuleu/FOC_Reduction
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Add diluted integrated values on display

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This commit is contained in:
Thibault Barnouin
2021-06-04 15:13:11 +02:00
parent 42f33e99ad
commit 0c57e0ed1c
52 changed files with 21 additions and 4 deletions
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4
src/FOC_reduction.py
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@@ -88,10 +88,10 @@ def main():
smoothing_FWHM = 0.10 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
rotate = False #rotation to North convention can give erroneous results
rotate = True #rotation to North convention can give erroneous results
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_gaussian_after_FWHM010' #additionnal informations
figtype = '_gaussian_after_FWHM010_rot' #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%.
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted

21
src/lib/plots.py
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@@ -209,7 +209,7 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
ax.add_artist(pol_sc)
# Compute integrated parameters and associated errors
# Compute integrated parameters and associated errors for pixels in the cut
I_int = stkI.data[mask].sum()
Q_int = stkQ.data[mask].sum()
U_int = stkU.data[mask].sum()
@@ -226,7 +226,24 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
PA_int = (90./np.pi)*np.arctan2(U_int,Q_int)+90.
PA_int_err = (90./(np.pi*(Q_int**2 + U_int**2)))*np.sqrt(U_int**2*Q_int_err**2 + Q_int**2*U_int_err**2 - 2.*Q_int*U_int*QU_int_err)
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1:.1e} $\pm$ {2:.1e} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,I_int*convert_flux,I_int_err*convert_flux)+"\n"+r"$P^{{int}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_int,P_int_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_int,PA_int_err), color='white', fontsize=11, xy=(0.01, 0.94), xycoords='axes fraction')
# Compute integrated parameters and associated errors for all pixels
I_diluted = stkI.data.sum()
Q_diluted = stkQ.data.sum()
U_diluted = stkU.data.sum()
I_diluted_err = np.sqrt(np.sum(stk_cov.data[0,0]))
Q_diluted_err = np.sqrt(np.sum(stk_cov.data[1,1]))
U_diluted_err = np.sqrt(np.sum(stk_cov.data[2,2]))
IQ_diluted_err = np.sqrt(np.sum(stk_cov.data[0,1]**2))
IU_diluted_err = np.sqrt(np.sum(stk_cov.data[0,2]**2))
QU_diluted_err = np.sqrt(np.sum(stk_cov.data[1,2]**2))
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted*100.
P_diluted_err = (100./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
PA_diluted = (90./np.pi)*np.arctan2(U_diluted,Q_diluted)+90.
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1:.1e} $\pm$ {2:.1e} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,I_int*convert_flux,I_int_err*convert_flux)+"\n"+r"$P^{{int}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_int,P_int_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_int,PA_int_err)+"\n"+r"$P^{{diluted}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_diluted,P_diluted_err)+"\n"+r"$\theta_{{P}}^{{diluted}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=11, xy=(0.01, 0.90), xycoords='axes fraction')
ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
ax.coords[0].set_axislabel('Right Ascension (J2000)')