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correction on error propagation in compute_Stokes

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Thibault Barnouin
2022-03-17 11:40:00 +01:00
parent 4c8533ab94
commit 44c96dc8a2
75 changed files with 47 additions and 35 deletions
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33
src/lib/plots.py
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@@ -328,7 +328,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
data_mask = np.ones(stkI.shape).astype(bool)
#Plot Stokes parameters map
if display is None:
if display is None or display.lower() == 'default':
plot_Stokes(Stokes, savename=savename, plots_folder=plots_folder)
#Compute SNR and apply cuts
@@ -406,26 +406,27 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
cont = ax.contour(SNRp, levels=levelsP, colors='grey', linewidths=0.5)
else:
# Defaults to intensity map
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux*2.)
im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
levelsI = np.linspace(SNRi_cut, SNRi.max(), 10)
cont = ax.contour(SNRi, levels=levelsI, colors='grey', linewidths=0.5)
fontprops = fm.FontProperties(size=16)
px_size = wcs.wcs.get_cdelt()[0]*3600.
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)
if (display is None) or not(display.lower() in ['default']):
fontprops = fm.FontProperties(size=16)
px_size = wcs.wcs.get_cdelt()[0]*3600.
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)
#pol.data[np.isfinite(pol.data)] = 1./2.
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[0],stkI.data.shape[0]), np.linspace(0,stkI.data.shape[1],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=0.5,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', fontproperties=fontprops)
ax.add_artist(pol_sc)
if step_vec == 0:
pol.data[np.isfinite(pol.data)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[0],stkI.data.shape[0])-0.5, np.linspace(0,stkI.data.shape[1],stkI.data.shape[1])-0.5)
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=0.5,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', 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)
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)
# Display instrument FOV
if not(rectangle is None):

12
src/lib/reduction.py
View File

@@ -568,11 +568,12 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
Dxy = np.array([pxsize,]*2)
elif scale.lower() in ['arcsec','arcseconds']:
Dxy = np.floor(pxsize/w.wcs.cdelt/3600.).astype(int)
elif scale.lower() in ['full','integrate']:
Dxy = np.floor(image.shape).astype(int)
else:
raise ValueError("'{0:s}' invalid scale for binning.".format(scale))
if (Dxy <= 1.).any():
print(Dxy, pxsize, w.wcs.cdelt*3600.)
raise ValueError("Requested pixel size is below resolution.")
new_shape = (image.shape//Dxy).astype(int)
@@ -1049,7 +1050,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
# Routine for the FOC instrument
if instr == 'FOC':
# Get image from each polarizer and covariance matrix
pol_array, pol_cov = polarizer_avg(data_array, error_array, data_mask,
pol_array, pol_cov_m = polarizer_avg(data_array, error_array, data_mask,
headers, FWHM=FWHM, scale=scale, smoothing=smoothing)
pol0, pol60, pol120 = pol_array
@@ -1088,6 +1089,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
# Uncertainties on the orientation of the polarizers' axes taken to be 3deg (see Nota et. al 1996, p36; Robinson & Thomson 1995)
sigma_theta = np.array([3.*np.pi/180., 3.*np.pi/180., 3.*np.pi/180.])
pol_flux = 2.*np.array([pol0/transmit[0], pol60/transmit[1], pol120/transmit[2]])
pol_cov = np.array([[4.*pol_cov_m[i,j]/(transmit[i]*transmit[j]) for j in range(pol_cov_m.shape[1])] for i in range(pol_cov_m.shape[0])])
# Normalization parameter for Stokes parameters computation
A = pol_eff[1]*pol_eff[2]*np.sin(-2.*theta[1]+2.*theta[2]) \
@@ -1141,9 +1143,9 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
s_U2_axis = (dU_dtheta1**2*sigma_theta[0]**2 + dU_dtheta2**2*sigma_theta[1]**2 + dU_dtheta3**2*sigma_theta[2]**2)
# Add quadratically the uncertainty to the Stokes covariance matrix ## THIS IS WHERE THE PROBLEMATIC UNCERTAINTY IS ADDED TO THE PIPELINE
#Stokes_cov[0,0] += s_I2_axis
#Stokes_cov[1,1] += s_Q2_axis
#Stokes_cov[2,2] += s_U2_axis
Stokes_cov[0,0] += s_I2_axis
Stokes_cov[1,1] += s_Q2_axis
Stokes_cov[2,2] += s_U2_axis
if not(FWHM is None) and (smoothing.lower() in ['gaussian_after','gauss_after']):
Stokes_array = np.array([I_stokes, Q_stokes, U_stokes])