Tibeuleu/FOC_Reduction
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default snrp to 3 and image output to pdf everywhere

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This commit is contained in:
Thibault Barnouin
2024-05-21 17:12:59 +02:00
parent d3915b3706
commit 1f43a3194d
2 changed files with 14 additions and 9 deletions
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16
src/lib/reduction.py
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@@ -308,8 +308,8 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, nu
fig.colorbar(im, ax=ax, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") fig.colorbar(im, ax=ax, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
if savename is not None: if savename is not None:
fig.savefig("/".join([plots_folder, savename+'_'+filt+'_crop_region.png']), fig.savefig("/".join([plots_folder, savename+'_'+filt+'_crop_region.pdf']),
bbox_inches='tight') bbox_inches='tight', dpi=200)
plot_obs(data_array, headers, vmin=convert_flux*data_array[data_array > 0.].mean()/5., plot_obs(data_array, headers, vmin=convert_flux*data_array[data_array > 0.].mean()/5.,
vmax=convert_flux*data_array[data_array > 0.].max(), rectangle=[rectangle,]*len(headers), vmax=convert_flux*data_array[data_array > 0.].max(), rectangle=[rectangle,]*len(headers),
savename=savename+'_crop_region', plots_folder=plots_folder) savename=savename+'_crop_region', plots_folder=plots_folder)
@@ -1228,10 +1228,12 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
QU_diluted_err = np.sqrt(np.sum(Stokes_cov[1, 2][mask]**2)) QU_diluted_err = np.sqrt(np.sum(Stokes_cov[1, 2][mask]**2))
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
P_diluted_err = (1./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) P_diluted_err = (1./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 = princ_angle((90./np.pi)*np.arctan2(U_diluted, Q_diluted)) PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted, Q_diluted))
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) 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)
for header in headers: for header in headers:
header['P_int'] = (P_diluted, 'Integrated polarisation degree') header['P_int'] = (P_diluted, 'Integrated polarisation degree')
@@ -1487,10 +1489,12 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
QU_diluted_err = np.sqrt(np.sum(new_Stokes_cov[1, 2][mask]**2)) QU_diluted_err = np.sqrt(np.sum(new_Stokes_cov[1, 2][mask]**2))
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
P_diluted_err = (1./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) P_diluted_err = (1./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 = princ_angle((90./np.pi)*np.arctan2(U_diluted, Q_diluted)) PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted, Q_diluted))
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) 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)
for header in new_headers: for header in new_headers:
header['P_int'] = (P_diluted, 'Integrated polarisation degree') header['P_int'] = (P_diluted, 'Integrated polarisation degree')

7
src/overplot_MRK463E.py
View File

@@ -6,7 +6,7 @@ from matplotlib.colors import LogNorm
Stokes_UV = fits.open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.10arcsec.fits") Stokes_UV = fits.open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.10arcsec.fits")
Stokes_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits") Stokes_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits")
Stokes_Xr = fits.open("./data/MRK463E/Chandra/4913/primary/acisf04913N004_cntr_img2.fits") Stokes_Xr = fits.open("./data/MRK463E/Chandra/X_ray_crop.fits")
levels = np.geomspace(1., 99., 7) levels = np.geomspace(1., 99., 7)
@@ -14,12 +14,13 @@ levels = np.geomspace(1., 99., 7)
# A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot.pdf') # A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot.pdf')
B = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm()) B = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=30.0, vec_scale=3, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf') B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=3.0, vec_scale=5, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf')
B.write_to(path1="./data/MRK463E/FOC_data_Chandra.fits", path2="./data/MRK463E/Chandra_data.fits", suffix="aligned") B.write_to(path1="./data/MRK463E/FOC_data_Chandra.fits", path2="./data/MRK463E/Chandra_data.fits", suffix="aligned")
# C = overplot_pol(Stokes_UV, Stokes_IR) # C = overplot_pol(Stokes_UV, Stokes_IR)
# C.plot(SNRp_cut=3.0, SNRi_cut=20.0, savename='./plots/MRK463E/IR_overplot.pdf') # C.plot(SNRp_cut=3.0, SNRi_cut=20.0, savename='./plots/MRK463E/IR_overplot.pdf')
levels = np.array([0.8, 2, 5, 10, 20, 50])/100.*Stokes_UV[0].header['photflam']
D = overplot_pol(Stokes_UV, Stokes_IR, norm=LogNorm()) D = overplot_pol(Stokes_UV, Stokes_IR, norm=LogNorm())
D.plot(SNRp_cut=3.0, SNRi_cut=30.0, vec_scale=3, norm=LogNorm(1e-18, 1e-15), savename='./plots/MRK463E/IR_overplot_forced.pdf') D.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=3.0, vec_scale=5, norm=LogNorm(8.5e-18, 2.5e-15), savename='./plots/MRK463E/IR_overplot_forced.pdf')
D.write_to(path1="./data/MRK463E/FOC_data_WFPC.fits", path2="./data/MRK463E/WFPC_data.fits", suffix="aligned") D.write_to(path1="./data/MRK463E/FOC_data_WFPC.fits", path2="./data/MRK463E/WFPC_data.fits", suffix="aligned")