Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

correction for s_P_P and nicer plots

Browse Source
This commit is contained in:
Thibault Barnouin
2025-04-16 15:26:29 +02:00
parent 042be2bad4
commit f4fdce3f07
2 changed files with 50 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files

50
package/lib/reduction.py
View File

@@ -1314,8 +1314,8 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
for i in range(Stokes_cov.shape[0]):
s_IQU_stat[i, i] = np.sum([coeff_stokes[i, k] ** 2 * sigma_flux[k] ** 2 for k in range(len(sigma_flux))], axis=0)
for j in [k for k in range(3) if k > i]:
s_IQU_stat[i, j] = np.sum([coeff_stokes[i, k] * coeff_stokes[j, k] * sigma_flux[k] ** 2 for k in range(len(sigma_flux))], axis=0)
s_IQU_stat[j, i] = np.sum([coeff_stokes[i, k] * coeff_stokes[j, k] * sigma_flux[k] ** 2 for k in range(len(sigma_flux))], axis=0)
s_IQU_stat[i, j] = np.sum([abs(coeff_stokes[i, k] * coeff_stokes[j, k]) * sigma_flux[k] ** 2 for k in range(len(sigma_flux))], axis=0)
s_IQU_stat[j, i] = np.sum([abs(coeff_stokes[i, k] * coeff_stokes[j, k]) * sigma_flux[k] ** 2 for k in range(len(sigma_flux))], axis=0)
# Compute the derivative of each Stokes parameter with respect to the polarizer orientation
dIQU_dtheta = np.zeros(Stokes_cov.shape)
@@ -1373,10 +1373,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
s_IQU_axis[i, i] = np.sum([dIQU_dtheta[i, k] ** 2 * globals()["sigma_theta"][k] ** 2 for k in range(len(globals()["sigma_theta"]))], axis=0)
for j in [k for k in range(3) if k > i]:
s_IQU_axis[i, j] = np.sum(
[dIQU_dtheta[i, k] * dIQU_dtheta[j, k] * globals()["sigma_theta"][k] ** 2 for k in range(len(globals()["sigma_theta"]))], axis=0
[abs(dIQU_dtheta[i, k] * dIQU_dtheta[j, k]) * globals()["sigma_theta"][k] ** 2 for k in range(len(globals()["sigma_theta"]))], axis=0
)
s_IQU_axis[j, i] = np.sum(
[dIQU_dtheta[i, k] * dIQU_dtheta[j, k] * globals()["sigma_theta"][k] ** 2 for k in range(len(globals()["sigma_theta"]))], axis=0
[abs(dIQU_dtheta[i, k] * dIQU_dtheta[j, k]) * globals()["sigma_theta"][k] ** 2 for k in range(len(globals()["sigma_theta"]))], axis=0
)
# Add quadratically the uncertainty to the Stokes covariance matrix
@@ -1551,30 +1551,30 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_s
if s_IQU_stat is not None:
# If IQU covariance matrix containing only statistical error is given propagate to P and PA
# Catch Invalid value in sqrt when diagonal terms are big
with warnings.catch_warnings(record=True) as _:
s_P_P[maskP] = (
P[maskP]
/ I_stokes[maskP]
* np.sqrt(
s_IQU_stat[0, 0][maskP]
- 2.0 / (I_stokes[maskP] * P[maskP] ** 2) * (Q_stokes[maskP] * s_IQU_stat[0, 1][maskP] + U_stokes[maskP] * s_IQU_stat[0, 2][maskP])
+ 1.0
/ (I_stokes[maskP] ** 2 * P[maskP] ** 4)
* (
Q_stokes[maskP] ** 2 * s_IQU_stat[1, 1][maskP]
+ U_stokes[maskP] ** 2 * s_IQU_stat[2, 2][maskP] * Q_stokes[maskP] * U_stokes[maskP] * s_IQU_stat[1, 2][maskP]
)
)
)
s_PA_P[maskP] = (
90.0
/ (np.pi * I_stokes[maskP] ** 2 * P[maskP] ** 2)
s_P_P[maskP] = (
P[maskP]
/ I_stokes[maskP]
* np.sqrt(
s_IQU_stat[0, 0][maskP]
- 2.0 / (I_stokes[maskP] * P[maskP] ** 2) * (Q_stokes[maskP] * s_IQU_stat[0, 1][maskP] + U_stokes[maskP] * s_IQU_stat[0, 2][maskP])
+ 1.0
/ (I_stokes[maskP] ** 2 * P[maskP] ** 4)
* (
Q_stokes[maskP] ** 2 * s_IQU_stat[2, 2][maskP]
+ U_stokes[maskP] * s_IQU_stat[1, 1][maskP]
- 2.0 * Q_stokes[maskP] * U_stokes[maskP] * s_IQU_stat[1, 2][maskP]
Q_stokes[maskP] ** 2 * s_IQU_stat[1, 1][maskP]
+ U_stokes[maskP] ** 2 * s_IQU_stat[2, 2][maskP]
+ 2.0 * Q_stokes[maskP] * U_stokes[maskP] * s_IQU_stat[1, 2][maskP]
)
)
)
s_PA_P[maskP] = (
90.0
/ (np.pi * I_stokes[maskP] ** 2 * P[maskP] ** 2)
* (
Q_stokes[maskP] ** 2 * s_IQU_stat[2, 2][maskP]
+ U_stokes[maskP] * s_IQU_stat[1, 1][maskP]
- 2.0 * Q_stokes[maskP] * U_stokes[maskP] * s_IQU_stat[1, 2][maskP]
)
)
else:
# Approximate Poisson error for P and PA
s_P_P[mask] = np.sqrt(2.0 / N_obs[mask])