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correction for s_P_P and nicer plots

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Thibault Barnouin
2025-04-16 15:26:29 +02:00
parent 042be2bad4
commit f4fdce3f07
2 changed files with 50 additions and 50 deletions
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50
package/lib/plots.py
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@@ -360,7 +360,7 @@ def polarization_map(
if fig is None:
ratiox = max(int(stkI.shape[1] / (stkI.shape[0])), 1)
ratioy = max(int((stkI.shape[0]) / stkI.shape[1]), 1)
fig = plt.figure(figsize=(7 * ratiox, 7 * ratioy), layout="constrained")
fig = plt.figure(figsize=(8 * ratiox, 8 * ratioy), layout="constrained")
if ax is None:
ax = fig.add_subplot(111, projection=wcs)
ax.set(aspect="equal", fc="k") # , ylim=[-0.05 * stkI.shape[0], 1.05 * stkI.shape[0]])
@@ -435,33 +435,33 @@ def polarization_map(
else:
vmin, vmax = 1.0 / 2.0 * np.median(np.sqrt(stk_cov[0, 0][stkI > 0.0]) * convert_flux), np.max(stkI[stkI > 0.0] * convert_flux)
pfmax = (stkI[stkI > 0.0] * pol[stkI > 0.0] * convert_flux).max()
im = ax.imshow(stkI * convert_flux * pol, norm=LogNorm(vmin, vmax), aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(stkI * convert_flux * pol, norm=LogNorm(vmin, vmax), aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (pol < pol_err))
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
# levelsPf = np.linspace(0.0.60, 0.50, 5) * pfmax
levelsPf = np.array([1.73, 13.0, 33.0, 66.0]) / 100.0 * pfmax
levelsPf = np.array([13.0, 33.0, 66.0]) / 100.0 * pfmax
print("Polarized flux density contour levels : ", levelsPf)
ax.contour(stkI * convert_flux * pol, levels=levelsPf, colors="grey", linewidths=0.5)
elif display.lower() in ["p", "pol", "pol_deg"]:
# Display polarization degree map
display = "p"
vmin, vmax = 0.0, min(pol[np.isfinite(pol)].max(), 1.0) * 100.0
im = ax.imshow(pol * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
vmin, vmax = 0.0, min(pol[pol > pol_err].max(), 1.0) * 100.0
im = ax.imshow(pol * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (pol < pol_err))
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$P$ [%]")
elif display.lower() in ["pa", "pang", "pol_ang"]:
# Display polarization degree map
display = "pa"
vmin, vmax = 0.0, 180.0
im = ax.imshow(princ_angle(pang), vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(princ_angle(pang), vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (pol < pol_err))
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$\theta_P$ [°]")
elif display.lower() in ["s_p", "pol_err", "pol_deg_err"]:
# Display polarization degree error map
display = "s_p"
if (SNRp > P_cut).any():
vmin, vmax = 0.0, np.max([pol_err[SNRp > P_cut].max(), 1.0]) * 100.0
im = ax.imshow(pol_err * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno_r", alpha=1.0)
im = ax.imshow(pol_err * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno_r", alpha=1.0 - 0.75 * (pol < pol_err))
else:
vmin, vmax = 0.0, 100.0
im = ax.imshow(pol_err * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno_r", alpha=1.0)
im = ax.imshow(pol_err * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno_r", alpha=1.0 - 0.75 * (pol < pol_err))
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$\sigma_P$ [%]")
elif display.lower() in ["s_i", "i_err"]:
# Display intensity error map
@@ -471,39 +471,41 @@ def polarization_map(
1.0 / 2.0 * np.median(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.0]) * convert_flux),
np.max(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.0]) * convert_flux),
)
im = ax.imshow(np.sqrt(stk_cov[0, 0]) * convert_flux, norm=LogNorm(vmin, vmax), aspect="equal", cmap="inferno_r", alpha=1.0)
im = ax.imshow(
np.sqrt(stk_cov[0, 0]) * convert_flux, norm=LogNorm(vmin, vmax), aspect="equal", cmap="inferno_r", alpha=1.0 - 0.75 * (pol < pol_err)
)
else:
im = ax.imshow(np.sqrt(stk_cov[0, 0]) * convert_flux, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(np.sqrt(stk_cov[0, 0]) * convert_flux, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (pol < pol_err))
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$\sigma_I$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
elif display.lower() in ["snri"]:
# Display I_stokes signal-to-noise map
display = "snri"
vmin, vmax = 0.0, np.max(SNRi[np.isfinite(SNRi)])
if vmax * 0.99 > SNRi_cut:
im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"])
levelsSNRi = np.linspace(SNRi_cut, vmax * 0.99, 5).astype(int)
print("SNRi contour levels : ", levelsSNRi)
ax.contour(SNRi, levels=levelsSNRi, colors="grey", linewidths=0.5)
else:
im = ax.imshow(SNRi, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(SNRi, aspect="equal", cmap=kwargs["cmap"])
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$I_{Stokes}/\sigma_{I}$")
elif display.lower() in ["snr", "snrp"]:
# Display polarization degree signal-to-noise map
display = "snrp"
vmin, vmax = 0.0, np.max(SNRp[np.isfinite(SNRp)])
if vmax * 0.99 > SNRp_cut:
im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
levelsSNRp = np.linspace(P_cut, vmax * 0.99, 5).astype(int)
if vmax * 0.99 > SNRp_cut + 3:
im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"])
levelsSNRp = np.linspace(SNRp_cut, vmax * 0.99, 3).astype(int)
print("SNRp contour levels : ", levelsSNRp)
ax.contour(SNRp, levels=levelsSNRp, colors="grey", linewidths=0.5)
else:
im = ax.imshow(SNRp, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(SNRp, aspect="equal", cmap=kwargs["cmap"])
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$P/\sigma_{P}$")
elif display.lower() in ["conf", "confp"]:
# Display polarization degree signal-to-noise map
display = "confp"
vmin, vmax = 0.0, 1.0
im = ax.imshow(confP, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0)
im = ax.imshow(confP, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"])
levelsconfp = np.array([0.500, 0.900, 0.990, 0.999])
print("confp contour levels : ", levelsconfp)
ax.contour(confP, levels=levelsconfp, colors="grey", linewidths=0.5)
@@ -1895,7 +1897,7 @@ class crop_map(object):
else:
self.ax = ax
self.mask_alpha = 0.75
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1])
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1], spancoords="pixels", useblit=True)
self.embedded = True
self.ax.set(xlabel="Right Ascension (J2000)", ylabel="Declination (J2000)")
self.display(self.data, self.wcs, self.map_convert, **self.kwargs)
@@ -1956,7 +1958,7 @@ class crop_map(object):
self.display()
if self.fig.canvas.manager.toolbar.mode == "":
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1])
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1], spancoords="pixels", useblit=True)
self.RSextent = deepcopy(self.extent)
self.RScenter = deepcopy(self.center)
@@ -2016,7 +2018,7 @@ class crop_map(object):
self.ax.set_ylim(0, ylim)
if self.fig.canvas.manager.toolbar.mode == "":
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1])
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1], spancoords="pixels", useblit=True)
self.fig.canvas.draw_idle()
@@ -2028,7 +2030,7 @@ class crop_map(object):
def crop(self) -> None:
if self.fig.canvas.manager.toolbar.mode == "":
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1])
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1], spancoords="pixels", useblit=True)
self.bapply.on_clicked(self.apply_crop)
self.breset.on_clicked(self.reset_crop)
self.fig.canvas.mpl_connect("close_event", self.on_close)
@@ -2100,7 +2102,7 @@ class crop_Stokes(crop_map):
self.on_close(event)
if self.fig.canvas.manager.toolbar.mode == "":
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1])
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()
@@ -3079,7 +3081,7 @@ class pol_map(object):
label = r"$P \cdot F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
elif self.display_selection.lower() in ["pol_deg"]:
self.data = self.P * 100.0
kwargs["vmin"], kwargs["vmax"] = 0.0, np.max(self.data[self.P > self.P_ERR])
kwargs["vmin"], kwargs["vmax"] = 0.0, min(np.max(self.data[self.P > self.P_ERR]), 100.0)
kwargs["alpha"] = 1.0 - 0.75 * (self.P < self.P_ERR)
label = r"$P$ [%]"
elif self.display_selection.lower() in ["pol_ang"]:
@@ -3092,14 +3094,12 @@ class pol_map(object):
SNRi = np.zeros(self.I.shape)
SNRi[s_I > 0.0] = self.I[s_I > 0.0] / s_I[s_I > 0.0]
self.data = SNRi
kwargs["alpha"] = 1.0 - 0.75 * (self.I < s_I)
kwargs["vmin"], kwargs["vmax"] = 0.0, np.max(self.data[self.data > 0.0])
label = r"$I_{Stokes}/\sigma_{I}$"
elif self.display_selection.lower() in ["snrp"]:
SNRp = np.zeros(self.P.shape)
SNRp[self.P_ERR > 0.0] = self.P[self.P_ERR > 0.0] / self.P_ERR[self.P_ERR > 0.0]
self.data = SNRp
kwargs["alpha"] = 1.0 - 0.75 * (self.P < self.P_ERR)
kwargs["vmin"], kwargs["vmax"] = 0.0, np.max(self.data[self.data > 0.0])
label = r"$P/\sigma_{P}$"

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])