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correction for Stokes_cov and P_diluted_err propagation

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Thibault Barnouin
2025-05-05 17:32:26 +02:00
parent 3ac9102445
commit e9fd50ad17
1 changed files with 7 additions and 3 deletions
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10
package/lib/reduction.py
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@@ -1380,7 +1380,11 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
) )
# Add quadratically the uncertainty to the Stokes covariance matrix # Add quadratically the uncertainty to the Stokes covariance matrix
Stokes_cov += Stokes_axis_cov + Stokes_stat_cov for i in range(Stokes_cov.shape[0]):
Stokes_cov[i, i] += Stokes_axis_cov[i, i] + Stokes_stat_cov[i, i]
for j in [k for k in range(Stokes_cov.shape[0]) if k > i]:
Stokes_cov[i, j] = np.sqrt(Stokes_cov[i, j] ** 2 + Stokes_axis_cov[i, j] ** 2 + Stokes_stat_cov[i, j] ** 2)
Stokes_cov[j, i] = np.sqrt(Stokes_cov[j, i] ** 2 + Stokes_axis_cov[j, i] ** 2 + Stokes_stat_cov[j, i] ** 2)
# Save values to single header # Save values to single header
header_stokes = pol_headers[0] header_stokes = pol_headers[0]
@@ -1452,7 +1456,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
QU_diluted_stat_err = np.sqrt(np.sum(Stokes_stat_cov[1, 2][mask] ** 2)) QU_diluted_stat_err = np.sqrt(np.sum(Stokes_stat_cov[1, 2][mask] ** 2))
P_diluted = np.sqrt(Q_diluted**2 + U_diluted**2) / I_diluted P_diluted = np.sqrt(Q_diluted**2 + U_diluted**2) / I_diluted
P_diluted_err = np.sqrt( P_diluted_err = (1.0 / I_diluted) * np.sqrt(
(Q_diluted**2 * Q_diluted_err**2 + U_diluted**2 * U_diluted_err**2 + 2.0 * Q_diluted * U_diluted * QU_diluted_err) / (Q_diluted**2 + U_diluted**2) (Q_diluted**2 * Q_diluted_err**2 + U_diluted**2 * U_diluted_err**2 + 2.0 * Q_diluted * U_diluted * QU_diluted_err) / (Q_diluted**2 + U_diluted**2)
+ ((Q_diluted / I_diluted) ** 2 + (U_diluted / I_diluted) ** 2) * I_diluted_err**2 + ((Q_diluted / I_diluted) ** 2 + (U_diluted / I_diluted) ** 2) * I_diluted_err**2
- 2.0 * (Q_diluted / I_diluted) * IQ_diluted_err - 2.0 * (Q_diluted / I_diluted) * IQ_diluted_err
@@ -1762,7 +1766,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, Stokes_stat_cov, dat
QU_diluted_stat_err = np.sqrt(np.sum(Stokes_stat_cov[1, 2][mask] ** 2)) QU_diluted_stat_err = np.sqrt(np.sum(Stokes_stat_cov[1, 2][mask] ** 2))
P_diluted = np.sqrt(Q_diluted**2 + U_diluted**2) / I_diluted P_diluted = np.sqrt(Q_diluted**2 + U_diluted**2) / I_diluted
P_diluted_err = np.sqrt( P_diluted_err = (1.0 / I_diluted) * np.sqrt(
(Q_diluted**2 * Q_diluted_err**2 + U_diluted**2 * U_diluted_err**2 + 2.0 * Q_diluted * U_diluted * QU_diluted_err) / (Q_diluted**2 + U_diluted**2) (Q_diluted**2 * Q_diluted_err**2 + U_diluted**2 * U_diluted_err**2 + 2.0 * Q_diluted * U_diluted * QU_diluted_err) / (Q_diluted**2 + U_diluted**2)
+ ((Q_diluted / I_diluted) ** 2 + (U_diluted / I_diluted) ** 2) * I_diluted_err**2 + ((Q_diluted / I_diluted) ** 2 + (U_diluted / I_diluted) ** 2) * I_diluted_err**2
- 2.0 * (Q_diluted / I_diluted) * IQ_diluted_err - 2.0 * (Q_diluted / I_diluted) * IQ_diluted_err