Tibeuleu/FOC_Reduction
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Roll back IQU=0 for pol > 1, test multiple smoothings with nan handling

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Thibault Barnouin
2021-06-15 11:27:59 +02:00
parent a259bbc0c4
commit b0ecc47361
32 changed files with 20 additions and 4 deletions
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20
src/lib/reduction.py
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@@ -1018,8 +1018,8 @@ def compute_Stokes(data_array, error_array, headers, FWHM=None,
plt.show()
#I_stokes[mask]=0.
Q_stokes[mask]=0.
U_stokes[mask]=0.
#Q_stokes[mask]=0.
#U_stokes[mask]=0.
#Stokes covariance matrix
Stokes_cov = np.zeros((3,3,I_stokes.shape[0],I_stokes.shape[1]))
@@ -1427,4 +1427,20 @@ def rotate2_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, ang):
new_headers.append(new_header)
# Nan handling :
fmax = np.finfo(np.float64).max
new_I_stokes[np.isnan(new_I_stokes)] = 0.
new_Q_stokes[new_I_stokes == 0.] = 0.
new_U_stokes[new_I_stokes == 0.] = 0.
new_Q_stokes[np.isnan(new_Q_stokes)] = 0.
new_U_stokes[np.isnan(new_U_stokes)] = 0.
new_Stokes_cov[np.isnan(new_Stokes_cov)] = fmax
P[np.isnan(P)] = 0.
s_P[np.isnan(s_P)] = fmax
s_PA[np.isnan(s_PA)] = fmax
debiased_P[np.isnan(debiased_P)] = 0.
s_P_P[np.isnan(s_P_P)] = fmax
s_PA_P[np.isnan(s_PA_P)] = fmax
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, new_headers