Tibeuleu/FOC_Reduction
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compare maps with/without polarizer axis error

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Thibault Barnouin
2022-02-15 12:04:07 +01:00
parent 4b26d2c320
commit 867999e93c
20 changed files with 10 additions and 10 deletions
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18
src/lib/reduction.py
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@@ -1183,7 +1183,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
# s_U_U = np.sqrt(Stokes_cov[2,2])/U_stokes*100.
# s_U_axis_U = np.sqrt(s_U2_axis)/U_stokes*100.
#
# fig, ax = plt.subplots(3,3)
# fig, ax = plt.subplots(3,3,figsize=(15,15))
# im = ax[0,0].imshow(s_I_I, origin='lower')
# ax[0,0].set_title(r"$\frac{\sigma_{I}}{I}$")
# fig.colorbar(im, ax=ax[0,0])
@@ -1304,7 +1304,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
#Compute the total exposure time so that
#I_stokes*exp_tot = N_tot the total number of events
exp_tot = np.array([header['exptime'] for header in headers]).sum()
print("Total exposure time : {} sec".format(exp_tot))
#print("Total exposure time : {} sec".format(exp_tot))
N_obs = I_stokes*exp_tot
#Errors on P, PA supposing Poisson noise
@@ -1313,13 +1313,13 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
s_PA_P = np.ones(I_stokes.shape)*fmax
s_PA_P[mask2] = s_P_P[mask2]/(2.*P[mask2])*180./np.pi
# # Nan handling :
# 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
# Nan handling :
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 P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P