Tibeuleu/FOC_Reduction
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Change display, margin handling and redo all reductions

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This commit is contained in:
Thibault Barnouin
2021-06-17 22:00:52 +02:00
parent 8c0ab1fad1
commit 44a060e2ae
402 changed files with 176 additions and 2601 deletions
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64
src/lib/plots.py
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@@ -12,10 +12,17 @@ prototypes :
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
import matplotlib.font_manager as fm
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
from astropy.wcs import WCS
def sci_not(v,err,rnd=1):
power = - int(('%E' % v)[-3:])+1
return r"({0} $\pm$ {1})e{2}".format(
round(v*10**power,rnd),round(err*10**power,rnd),-power)
def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
savename=None, plots_folder=""):
"""
@@ -66,8 +73,8 @@ def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
#plots
im = ax.imshow(data, vmin=vmin, vmax=vmax, origin='lower')
if not(rectangle is None):
x, y, width, height = rectangle[i]
ax.add_patch(Rectangle((x, y), width, height, edgecolor='r', fill=False))
x, y, width, height, color = rectangle[i]
ax.add_patch(Rectangle((x, y), width, height, edgecolor=color, fill=False))
#position of centroid
ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
color='black')
@@ -137,7 +144,7 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
return 0
def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
def polarization_map(Stokes, data_mask, SNRp_cut=3., SNRi_cut=30., step_vec=1,
savename=None, plots_folder="", display=None):
"""
Plots polarization map from Stokes HDUList.
@@ -177,7 +184,8 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
stkQ = Stokes[np.argmax([Stokes[i].header['datatype']=='Q_stokes' for i in range(len(Stokes))])]
stkU = Stokes[np.argmax([Stokes[i].header['datatype']=='U_stokes' for i in range(len(Stokes))])]
stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg' for i in range(len(Stokes))])]
#pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg' for i in range(len(Stokes))])]
pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
#pol_err_Poisson = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err_Poisson_noise' for i in range(len(Stokes))])]
pang = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang' for i in range(len(Stokes))])]
@@ -210,11 +218,12 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
print("No pixel with polarization information above requested SNR.")
#Plot the map
plt.rcParams.update({'font.size': 16})
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(111, projection=wcs)
ax.set_facecolor('k')
fig.subplots_adjust(hspace=0, wspace=0, right=0.95)
cbar_ax = fig.add_axes([0.98, 0.12, 0.01, 0.75])
fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
cbar_ax = fig.add_axes([0.95, 0.12, 0.01, 0.75])
if display is None:
# If no display selected, show intensity map
@@ -239,6 +248,7 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
im = ax.imshow(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$I_{Stokes}/\sigma_{I}$")
levelsI = np.linspace(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
#print(levelsI)
cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
elif display.lower() in ['snrp']:
# Display polarization degree signal-to-noise map
@@ -255,26 +265,31 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
levelsI = np.linspace(SNRi_cut, SNRi.max(), 10)
cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
fontprops = fm.FontProperties(size=16)
px_size = wcs.wcs.get_cdelt()[0]
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
ax.add_artist(px_sc)
X, Y = np.meshgrid(np.linspace(-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.,stkI.data.shape[0]), np.linspace(-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,stkI.data.shape[1]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
Q = ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=50.,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
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')
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', fontproperties=fontprops)
ax.add_artist(pol_sc)
north_dir = AnchoredDirectionArrows(ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-Stokes[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
ax.add_artist(north_dir)
# Compute integrated parameters and associated errors for pixels in the cut
n_pix = mask.size
I_int = stkI.data[mask].sum()
Q_int = stkQ.data[mask].sum()
U_int = stkU.data[mask].sum()
I_int_err = np.sqrt(np.sum(stk_cov.data[0,0][mask]))
Q_int_err = np.sqrt(np.sum(stk_cov.data[1,1][mask]))
U_int_err = np.sqrt(np.sum(stk_cov.data[2,2][mask]))
IQ_int_err = np.sqrt(np.sum(stk_cov.data[0,1][mask]**2))
IU_int_err = np.sqrt(np.sum(stk_cov.data[0,2][mask]**2))
QU_int_err = np.sqrt(np.sum(stk_cov.data[1,2][mask]**2))
I_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0][mask]))
Q_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1][mask]))
U_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2][mask]))
IQ_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1][mask]**2))
IU_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2][mask]**2))
QU_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2][mask]**2))
P_int = np.sqrt(Q_int**2+U_int**2)/I_int*100.
P_int_err = (100./I_int)*np.sqrt((Q_int**2*Q_int_err**2 + U_int**2*U_int_err**2 + 2.*Q_int*U_int*QU_int_err)/(Q_int**2 + U_int**2) + ((Q_int/I_int)**2 + (U_int/I_int)**2)*I_int_err**2 - 2.*(Q_int/I_int)*IQ_int_err - 2.*(U_int/I_int)*IU_int_err)
@@ -283,23 +298,26 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
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)
# Compute integrated parameters and associated errors for all pixels
n_pix = stkI.data.size
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))
I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0]))
Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1]))
U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2]))
IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1]**2))
IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2]**2))
QU_diluted_err = np.sqrt(n_pix)*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)
P_diluted_err = np.sqrt(2/n_pix)*100.
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)
PA_diluted_err = P_diluted_err/(2.*P_diluted)*180./np.pi
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.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted,P_diluted_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=16, xy=(0.01, 0.92), xycoords='axes fraction')
ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
ax.coords[0].set_axislabel('Right Ascension (J2000)')
@@ -311,7 +329,7 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1,
ax.axis('equal')
if not savename is None:
fig.suptitle(savename)
#fig.suptitle(savename)
fig.savefig(plots_folder+savename+".png",bbox_inches='tight',dpi=200)
plt.show()