Tibeuleu/FOC_Reduction
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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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12
src/lib/reduction.py
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@@ -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])