Tibeuleu/FOC_Reduction
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Add propagation of stat uncertainty for PA

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Thibault Barnouin
2025-04-15 17:34:10 +02:00
parent e4acb9755c
commit 042be2bad4
1 changed files with 26 additions and 13 deletions
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13
package/lib/reduction.py
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@@ -1549,6 +1549,9 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_s
s_PA_P = np.ones(I_stokes.shape) * fmax s_PA_P = np.ones(I_stokes.shape) * fmax
maskP = np.logical_and(mask, P > 0.0) maskP = np.logical_and(mask, P > 0.0)
if s_IQU_stat is not None: 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] = ( s_P_P[maskP] = (
P[maskP] P[maskP]
/ I_stokes[maskP] / I_stokes[maskP]
@@ -1563,7 +1566,17 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_s
) )
) )
) )
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: else:
# Approximate Poisson error for P and PA
s_P_P[mask] = np.sqrt(2.0 / N_obs[mask]) s_P_P[mask] = np.sqrt(2.0 / N_obs[mask])
s_PA_P[maskP] = s_P_P[maskP] / P[maskP] * 90.0 / np.pi s_PA_P[maskP] = s_P_P[maskP] / P[maskP] * 90.0 / np.pi