Tibeuleu/FOC_Reduction
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slightly improve plots

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This commit is contained in:
Thibault Barnouin
2025-07-02 18:36:13 +02:00
parent f7c50bf136
commit 49846d9497
2 changed files with 23 additions and 9 deletions
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8
package/lib/background.py
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@@ -365,10 +365,10 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
# Fit a gaussian to the log-intensity histogram # Fit a gaussian to the log-intensity histogram
bins_stdev = binning[-1][hist > hist.max() / 2.0] bins_stdev = binning[-1][hist > hist.max() / 2.0]
stdev = bins_stdev[-1] - bins_stdev[0] stdev = bins_stdev[-1] - bins_stdev[0]
p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev, 1e-3, 1e-3, 1e-3, 1e-3] # p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev, 1e-3, 1e-3, 1e-3, 1e-3]
popt, pcov = curve_fit(gausspol, binning[-1], hist, p0=p0) # popt, pcov = curve_fit(gausspol, binning[-1], hist, p0=p0)
# p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev] p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev]
# popt, pcov = curve_fit(gauss, binning[-1], hist, p0=p0) popt, pcov = curve_fit(gauss, binning[-1], hist, p0=p0)
coeff.append(popt) coeff.append(popt)
bkg = popt[1] + np.abs(popt[2]) * subtract_error bkg = popt[1] + np.abs(popt[2]) * subtract_error

24
package/lib/plots.py
View File

@@ -435,7 +435,13 @@ def polarization_map(
else: 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) 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() 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 - 0.75 * (SNRp <= 1.0)) im = ax.imshow(
stkI * convert_flux * pol,
norm=LogNorm(vmin, vmax),
aspect="equal",
cmap=kwargs["cmap"],
alpha=1.0 - 0.75 * (np.logical_or(SNRp <= 1.0, SNRi <= 3.0)),
)
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") 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(0.60, 0.50, 5) * pfmax # levelsPf = np.linspace(0.60, 0.50, 5) * pfmax
levelsPf = np.array([8.25, 16.5, 33.0, 66.0]) / 100.0 * pfmax levelsPf = np.array([8.25, 16.5, 33.0, 66.0]) / 100.0 * pfmax
@@ -444,14 +450,22 @@ def polarization_map(
elif display.lower() in ["p", "pol", "pol_deg"]: elif display.lower() in ["p", "pol", "pol_deg"]:
# Display polarization degree map # Display polarization degree map
display = "p" display = "p"
vmin, vmax = max(np.floor(100.0 * pol[SNRp > 1.0].min()), 0.0), min(np.ceil(100.0 * pol[SNRp > 1.0].max()), 100.0) vmin, vmax = (
im = ax.imshow(pol * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (SNRp <= 1.0)) max(np.floor(100.0 * pol[np.logical_and(SNRp > 1.0, SNRi > 3.0)].min()), 0.0),
min(np.ceil(100.0 * pol[np.logical_and(SNRp > 1.0, SNRi > 3.0)].max()), 100.0),
)
im = ax.imshow(pol * 100.0, vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (np.logical_or(SNRp <= 1.0, SNRi <= 3.0)))
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$P$ [%]") fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$P$ [%]")
elif display.lower() in ["pa", "pang", "pol_ang"]: elif display.lower() in ["pa", "pang", "pol_ang"]:
# Display polarization degree map # Display polarization degree map
display = "pa" display = "pa"
vmin, vmax = 0.0, 180.0 vmin, vmax = (
im = ax.imshow(princ_angle(pang), vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (SNRp <= 1.0)) max(np.floor(princ_angle(pang[np.logical_and(SNRp > 1.0, SNRi > 3.0)]).min()), 0.0),
min(np.ceil(princ_angle(pang[np.logical_and(SNRp > 1.0, SNRi > 3.0)]).max()), 180.0),
)
im = ax.imshow(
princ_angle(pang), vmin=vmin, vmax=vmax, aspect="equal", cmap=kwargs["cmap"], alpha=1.0 - 0.75 * (np.logical_or(SNRp <= 1.0, SNRi <= 3.0))
)
fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$\Psi$ [°]") fig.colorbar(im, ax=ax, aspect=50, shrink=0.60, pad=0.025, label=r"$\Psi$ [°]")
elif display.lower() in ["s_p", "pol_err", "pol_deg_err"]: elif display.lower() in ["s_p", "pol_err", "pol_deg_err"]:
# Display polarization degree error map # Display polarization degree error map