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compute debiased integrated polarization degree

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This commit is contained in:
Thibault Barnouin
2025-04-22 17:46:59 +02:00
parent f4fdce3f07
commit 0a8336aea8
5 changed files with 253 additions and 140 deletions
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158
package/lib/plots.py
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@@ -2079,7 +2079,7 @@ class crop_Stokes(crop_map):
# Crop dataset
for dataset in self.hdul_crop:
if dataset.header["datatype"] == "IQU_cov_matrix":
if dataset.header["datatype"][-10:] == "cov_matrix":
stokes_cov = np.zeros((3, 3, shape[1], shape[0]))
for i in range(3):
for j in range(3):
@@ -2104,16 +2104,22 @@ class crop_Stokes(crop_map):
if self.fig.canvas.manager.toolbar.mode == "":
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1], spancoords="pixels", useblit=True)
# Update integrated values
mask = np.logical_and(self.hdul_crop["data_mask"].data.astype(bool), self.hdul_crop[0].data > 0)
I_diluted = self.hdul_crop["i_stokes"].data[mask].sum()
Q_diluted = self.hdul_crop["q_stokes"].data[mask].sum()
U_diluted = self.hdul_crop["u_stokes"].data[mask].sum()
I_diluted_err = np.sqrt(np.sum(self.hdul_crop["iqu_cov_matrix"].data[0, 0][mask]))
Q_diluted_err = np.sqrt(np.sum(self.hdul_crop["iqu_cov_matrix"].data[1, 1][mask]))
U_diluted_err = np.sqrt(np.sum(self.hdul_crop["iqu_cov_matrix"].data[2, 2][mask]))
IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop["iqu_cov_matrix"].data[0, 1][mask] ** 2))
IU_diluted_err = np.sqrt(np.sum(self.hdul_crop["iqu_cov_matrix"].data[0, 2][mask] ** 2))
QU_diluted_err = np.sqrt(np.sum(self.hdul_crop["iqu_cov_matrix"].data[1, 2][mask] ** 2))
mask = np.logical_and(self.hdul_crop["DATA_MASK"].data.astype(bool), self.hdul_crop[0].data > 0)
I_diluted = self.hdul_crop["I_STOKES"].data[mask].sum()
Q_diluted = self.hdul_crop["Q_STOKES"].data[mask].sum()
U_diluted = self.hdul_crop["U_STOKES"].data[mask].sum()
I_diluted_err = np.sqrt(np.sum(self.hdul_crop["IQU_COV_MATRIX"].data[0, 0][mask]))
Q_diluted_err = np.sqrt(np.sum(self.hdul_crop["IQU_COV_MATRIX"].data[1, 1][mask]))
U_diluted_err = np.sqrt(np.sum(self.hdul_crop["IQU_COV_MATRIX"].data[2, 2][mask]))
IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop["IQU_COV_MATRIX"].data[0, 1][mask] ** 2))
IU_diluted_err = np.sqrt(np.sum(self.hdul_crop["IQU_COV_MATRIX"].data[0, 2][mask] ** 2))
QU_diluted_err = np.sqrt(np.sum(self.hdul_crop["IQU_COV_MATRIX"].data[1, 2][mask] ** 2))
I_diluted_stat_err = np.sqrt(np.sum(self.hdul_crop["IQU_STAT_COV_MATRIX"].data[0, 0][mask]))
Q_diluted_stat_err = np.sqrt(np.sum(self.hdul_crop["IQU_STAT_COV_MATRIX"].data[1, 1][mask]))
U_diluted_stat_err = np.sqrt(np.sum(self.hdul_crop["IQU_STAT_COV_MATRIX"].data[2, 2][mask]))
IQ_diluted_stat_err = np.sqrt(np.sum(self.hdul_crop["IQU_STAT_COV_MATRIX"].data[0, 1][mask] ** 2))
IU_diluted_stat_err = np.sqrt(np.sum(self.hdul_crop["IQU_STAT_COV_MATRIX"].data[0, 2][mask] ** 2))
QU_diluted_stat_err = np.sqrt(np.sum(self.hdul_crop["IQU_STAT_COV_MATRIX"].data[1, 2][mask] ** 2))
P_diluted = np.sqrt(Q_diluted**2 + U_diluted**2) / I_diluted
P_diluted_err = (1.0 / I_diluted) * np.sqrt(
@@ -2122,6 +2128,18 @@ class crop_Stokes(crop_map):
- 2.0 * (Q_diluted / I_diluted) * IQ_diluted_err
- 2.0 * (U_diluted / I_diluted) * IU_diluted_err
)
P_diluted_stat_err = (
P_diluted
/ I_diluted
* np.sqrt(
I_diluted_stat_err
- 2.0 / (I_diluted * P_diluted**2) * (Q_diluted * IQ_diluted_stat_err + U_diluted * IU_diluted_stat_err)
+ 1.0
/ (I_diluted**2 * P_diluted**4)
* (Q_diluted**2 * Q_diluted_stat_err + U_diluted**2 * U_diluted_stat_err + 2.0 * Q_diluted * U_diluted * QU_diluted_stat_err)
)
)
debiased_P_diluted = np.sqrt(P_diluted**2 - P_diluted_stat_err**2) if P_diluted**2 > P_diluted_stat_err**2 else 0.0
PA_diluted = princ_angle((90.0 / np.pi) * np.arctan2(U_diluted, Q_diluted))
PA_diluted_err = (90.0 / (np.pi * (Q_diluted**2 + U_diluted**2))) * np.sqrt(
@@ -2131,7 +2149,7 @@ class crop_Stokes(crop_map):
for dataset in self.hdul_crop:
if dataset.header["FILENAME"][-4:] != "crop":
dataset.header["FILENAME"] += "_crop"
dataset.header["P_int"] = (P_diluted, "Integrated polarization degree")
dataset.header["P_int"] = (debiased_P_diluted, "Integrated polarization degree")
dataset.header["sP_int"] = (np.ceil(P_diluted_err * 1000.0) / 1000.0, "Integrated polarization degree error")
dataset.header["PA_int"] = (PA_diluted, "Integrated polarization angle")
dataset.header["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error")
@@ -2961,6 +2979,10 @@ class pol_map(object):
def IQU_cov(self):
return self.Stokes["IQU_COV_MATRIX"].data
@property
def IQU_stat_cov(self):
return self.Stokes["IQU_STAT_COV_MATRIX"].data
@property
def P(self):
return self.Stokes["POL_DEG_DEBIASED"].data
@@ -3283,27 +3305,26 @@ class pol_map(object):
s_I = np.sqrt(self.IQU_cov[0, 0])
I_reg = self.I.sum()
I_reg_err = np.sqrt(np.sum(s_I**2))
P_reg = self.Stokes[0].header["P_int"]
debiased_P_reg = self.Stokes[0].header["P_int"]
P_reg_err = self.Stokes[0].header["sP_int"]
PA_reg = self.Stokes[0].header["PA_int"]
PA_reg_err = self.Stokes[0].header["sPA_int"]
s_I = np.sqrt(self.IQU_cov[0, 0])
s_Q = np.sqrt(self.IQU_cov[1, 1])
s_U = np.sqrt(self.IQU_cov[2, 2])
s_IQ = self.IQU_cov[0, 1]
s_IU = self.IQU_cov[0, 2]
s_QU = self.IQU_cov[1, 2]
I_cut = self.I[self.cut].sum()
Q_cut = self.Q[self.cut].sum()
U_cut = self.U[self.cut].sum()
I_cut_err = np.sqrt(np.sum(s_I[self.cut] ** 2))
Q_cut_err = np.sqrt(np.sum(s_Q[self.cut] ** 2))
U_cut_err = np.sqrt(np.sum(s_U[self.cut] ** 2))
IQ_cut_err = np.sqrt(np.sum(s_IQ[self.cut] ** 2))
IU_cut_err = np.sqrt(np.sum(s_IU[self.cut] ** 2))
QU_cut_err = np.sqrt(np.sum(s_QU[self.cut] ** 2))
I_cut_err = np.sqrt(np.sum(self.IQU_cov[0, 0][self.cut]))
Q_cut_err = np.sqrt(np.sum(self.IQU_cov[1, 1][self.cut]))
U_cut_err = np.sqrt(np.sum(self.IQU_cov[2, 2][self.cut]))
IQ_cut_err = np.sqrt(np.sum(self.IQU_cov[0, 1][self.cut] ** 2))
IU_cut_err = np.sqrt(np.sum(self.IQU_cov[0, 2][self.cut] ** 2))
QU_cut_err = np.sqrt(np.sum(self.IQU_cov[1, 2][self.cut] ** 2))
I_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 0][self.cut]))
Q_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[1, 1][self.cut]))
U_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[2, 2][self.cut]))
IQ_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 1][self.cut] ** 2))
IU_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 2][self.cut] ** 2))
QU_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[1, 2][self.cut] ** 2))
with np.errstate(divide="ignore", invalid="ignore"):
P_cut = np.sqrt(Q_cut**2 + U_cut**2) / I_cut
@@ -3316,6 +3337,18 @@ class pol_map(object):
)
/ I_cut
)
P_cut_stat_err = (
P_cut
/ I_cut
* np.sqrt(
I_cut_stat_err
- 2.0 / (I_cut * P_cut**2) * (Q_cut * IQ_cut_stat_err + U_cut * IU_cut_stat_err)
+ 1.0 / (I_cut**2 * P_cut**4) * (Q_cut**2 * Q_cut_stat_err + U_cut**2 * U_cut_stat_err + 2.0 * Q_cut * U_cut * QU_cut_stat_err)
)
)
mask = P_cut**2 > P_cut_stat_err
debiased_P_cut = np.zeros(P_cut.shape)
debiased_P_cut[mask] = np.sqrt(P_cut[mask] ** 2 - P_cut_stat_err[mask] ** 2)
PA_cut = princ_angle((90.0 / np.pi) * np.arctan2(U_cut, Q_cut))
PA_cut_err = (90.0 / (np.pi * (Q_cut**2 + U_cut**2))) * np.sqrt(
@@ -3323,22 +3356,21 @@ class pol_map(object):
)
else:
s_I = np.sqrt(self.IQU_cov[0, 0])
s_Q = np.sqrt(self.IQU_cov[1, 1])
s_U = np.sqrt(self.IQU_cov[2, 2])
s_IQ = self.IQU_cov[0, 1]
s_IU = self.IQU_cov[0, 2]
s_QU = self.IQU_cov[1, 2]
I_reg = self.I[self.region].sum()
Q_reg = self.Q[self.region].sum()
U_reg = self.U[self.region].sum()
I_reg_err = np.sqrt(np.sum(s_I[self.region] ** 2))
Q_reg_err = np.sqrt(np.sum(s_Q[self.region] ** 2))
U_reg_err = np.sqrt(np.sum(s_U[self.region] ** 2))
IQ_reg_err = np.sqrt(np.sum(s_IQ[self.region] ** 2))
IU_reg_err = np.sqrt(np.sum(s_IU[self.region] ** 2))
QU_reg_err = np.sqrt(np.sum(s_QU[self.region] ** 2))
I_reg_err = np.sqrt(np.sum(self.IQU_cov[0, 0][self.region]))
Q_reg_err = np.sqrt(np.sum(self.IQU_cov[1, 1][self.region]))
U_reg_err = np.sqrt(np.sum(self.IQU_cov[2, 2][self.region]))
IQ_reg_err = np.sqrt(np.sum(self.IQU_cov[0, 1][self.region] ** 2))
IU_reg_err = np.sqrt(np.sum(self.IQU_cov[0, 2][self.region] ** 2))
QU_reg_err = np.sqrt(np.sum(self.IQU_cov[1, 2][self.region] ** 2))
I_reg_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 0][self.region]))
Q_reg_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[1, 1][self.region]))
U_reg_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[2, 2][self.region]))
IQ_reg_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 1][self.region] ** 2))
IU_reg_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 2][self.region] ** 2))
QU_reg_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[1, 2][self.region] ** 2))
conf = PCconf(QN=Q_reg / I_reg, QN_ERR=Q_reg_err / I_reg, UN=U_reg / I_reg, UN_ERR=U_reg_err / I_reg)
if 1.0 - conf > 1e-3:
@@ -3355,6 +3387,16 @@ class pol_map(object):
)
/ I_reg
)
P_reg_stat_err = (
P_reg
/ I_reg
* np.sqrt(
I_reg_stat_err
- 2.0 / (I_reg * P_reg**2) * (Q_reg * IQ_reg_stat_err + U_reg * IU_reg_stat_err)
+ 1.0 / (I_reg**2 * P_reg**4) * (Q_reg**2 * Q_reg_stat_err + U_reg**2 * U_reg_stat_err + 2.0 * Q_reg * U_reg * QU_reg_stat_err)
)
)
debiased_P_reg = np.sqrt(P_reg**2 - P_reg_stat_err**2) if P_reg**2 > P_reg_stat_err**2 else 0.0
PA_reg = princ_angle((90.0 / np.pi) * np.arctan2(U_reg, Q_reg))
PA_reg_err = (90.0 / (np.pi * (Q_reg**2 + U_reg**2))) * np.sqrt(
@@ -3365,12 +3407,18 @@ class pol_map(object):
I_cut = self.I[new_cut].sum()
Q_cut = self.Q[new_cut].sum()
U_cut = self.U[new_cut].sum()
I_cut_err = np.sqrt(np.sum(s_I[new_cut] ** 2))
Q_cut_err = np.sqrt(np.sum(s_Q[new_cut] ** 2))
U_cut_err = np.sqrt(np.sum(s_U[new_cut] ** 2))
IQ_cut_err = np.sqrt(np.sum(s_IQ[new_cut] ** 2))
IU_cut_err = np.sqrt(np.sum(s_IU[new_cut] ** 2))
QU_cut_err = np.sqrt(np.sum(s_QU[new_cut] ** 2))
I_cut_err = np.sqrt(np.sum(self.IQU_cov[0, 0][new_cut]))
Q_cut_err = np.sqrt(np.sum(self.IQU_cov[1, 1][new_cut]))
U_cut_err = np.sqrt(np.sum(self.IQU_cov[2, 2][new_cut]))
IQ_cut_err = np.sqrt(np.sum(self.IQU_cov[0, 1][new_cut] ** 2))
IU_cut_err = np.sqrt(np.sum(self.IQU_cov[0, 2][new_cut] ** 2))
QU_cut_err = np.sqrt(np.sum(self.IQU_cov[1, 2][new_cut] ** 2))
I_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 0][new_cut]))
Q_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[1, 1][new_cut]))
U_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[2, 2][new_cut]))
IQ_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 1][new_cut] ** 2))
IU_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[0, 2][new_cut] ** 2))
QU_cut_stat_err = np.sqrt(np.sum(self.IQU_stat_cov[1, 2][new_cut] ** 2))
with np.errstate(divide="ignore", invalid="ignore"):
P_cut = np.sqrt(Q_cut**2 + U_cut**2) / I_cut
@@ -3383,6 +3431,18 @@ class pol_map(object):
)
/ I_cut
)
P_cut_stat_err = (
P_cut
/ I_cut
* np.sqrt(
I_cut_stat_err
- 2.0 / (I_cut * P_cut**2) * (Q_cut * IQ_cut_stat_err + U_cut * IU_cut_stat_err)
+ 1.0 / (I_cut**2 * P_cut**4) * (Q_cut**2 * Q_cut_stat_err + U_cut**2 * U_cut_stat_err + 2.0 * Q_cut * U_cut * QU_cut_stat_err)
)
)
mask = P_cut**2 > P_cut_stat_err
debiased_P_cut = np.zeros(P_cut.shape)
debiased_P_cut[mask] = np.sqrt(P_cut[mask] ** 2 - P_cut_stat_err[mask] ** 2)
PA_cut = princ_angle((90.0 / np.pi) * np.arctan2(U_cut, Q_cut))
PA_cut_err = (90.0 / (np.pi * (Q_cut**2 + U_cut**2))) * np.sqrt(
@@ -3403,7 +3463,7 @@ class pol_map(object):
self.pivot_wav, sci_not(I_reg * self.map_convert, I_reg_err * self.map_convert, 2)
)
+ "\n"
+ r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg * 100.0, np.ceil(P_reg_err * 1000.0) / 10.0)
+ r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(debiased_P_reg * 100.0, np.ceil(P_reg_err * 1000.0) / 10.0)
+ "\n"
+ r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err * 10.0) / 10.0)
+ str_conf
@@ -3415,7 +3475,7 @@ class pol_map(object):
# self.pivot_wav, sci_not(I_cut * self.map_convert, I_cut_err * self.map_convert, 2)
# )
# + "\n"
# + r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0)
# + r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(debiased_P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0)
# + "\n"
# + r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err * 10.0) / 10.0)
# )
@@ -3439,7 +3499,7 @@ class pol_map(object):
self.pivot_wav, sci_not(I_reg * self.map_convert, I_reg_err * self.map_convert, 2)
)
+ "\n"
+ r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg * 100.0, np.ceil(P_reg_err * 1000.0) / 10.0)
+ r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(debiased_P_reg * 100.0, np.ceil(P_reg_err * 1000.0) / 10.0)
+ "\n"
+ r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg, np.ceil(PA_reg_err * 10.0) / 10.0)
+ str_conf
@@ -3451,7 +3511,7 @@ class pol_map(object):
# self.pivot_wav, sci_not(I_cut * self.map_convert, I_cut_err * self.map_convert, 2)
# )
# + "\n"
# + r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0)
# + r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(debiased_P_cut * 100.0, np.ceil(P_cut_err * 1000.0) / 10.0)
# + "\n"
# + r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut, np.ceil(PA_cut_err * 10.0) / 10.0)
# )