From f4fdce3f07fc0c454f940468bc362dce8e1b3a10 Mon Sep 17 00:00:00 2001 From: Thibault Barnouin Date: Wed, 16 Apr 2025 15:26:29 +0200 Subject: [PATCH] correction for s_P_P and nicer plots --- package/lib/plots.py | 50 ++++++++++++++++++++-------------------- package/lib/reduction.py | 50 ++++++++++++++++++++-------------------- 2 files changed, 50 insertions(+), 50 deletions(-) diff --git a/package/lib/plots.py b/package/lib/plots.py index 9268f08..5d1565a 100755 --- a/package/lib/plots.py +++ b/package/lib/plots.py @@ -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"$P/\sigma_{P}$" diff --git a/package/lib/reduction.py b/package/lib/reduction.py index 6c6816f..24f788f 100755 --- a/package/lib/reduction.py +++ b/package/lib/reduction.py @@ -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])