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improve interactive display lisibility

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This commit is contained in:
Thibault Barnouin
2025-04-14 14:40:27 +02:00
parent 26a353f118
commit 00fa050a95
1 changed files with 35 additions and 35 deletions
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70
package/lib/plots.py
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@@ -2463,9 +2463,11 @@ class pol_map(object):
ax_snr_reset = self.fig.add_axes([0.060, 0.020, 0.05, 0.02]) ax_snr_reset = self.fig.add_axes([0.060, 0.020, 0.05, 0.02])
ax_snr_conf = self.fig.add_axes([0.115, 0.020, 0.05, 0.02]) ax_snr_conf = self.fig.add_axes([0.115, 0.020, 0.05, 0.02])
SNRi_max = np.max(self.I[self.IQU_cov[0, 0] > 0.0] / np.sqrt(self.IQU_cov[0, 0][self.IQU_cov[0, 0] > 0.0])) SNRi_max = np.max(self.I[self.IQU_cov[0, 0] > 0.0] / np.sqrt(self.IQU_cov[0, 0][self.IQU_cov[0, 0] > 0.0]))
SNRp_max = np.max(self.P[self.s_P > 0.0] / self.s_P[self.s_P > 0.0]) SNRp_max = np.max(self.P[self.P_ERR > 0.0] / self.P_ERR[self.P_ERR > 0.0])
s_I_cut = Slider(ax_I_cut, r"$SNR^{I}_{cut}$", 1.0, int(SNRi_max * 0.95), valstep=1, valinit=self.SNRi_cut) s_I_cut = Slider(ax_I_cut, r"$SNR^{I}_{cut}$", 1.0, int(SNRi_max * 0.95), valstep=1, valinit=self.SNRi_cut)
self.s_P_cut = Slider(self.ax_P_cut, r"$Conf^{P}_{cut}$", 0.50, 1.0, valstep=[0.500, 0.900, 0.990, 0.999], valinit=self.P_cut if P_cut <= 1.0 else 0.99) self.P_ERR_cut = Slider(
self.ax_P_cut, r"$Conf^{P}_{cut}$", 0.50, 1.0, valstep=[0.500, 0.900, 0.990, 0.999], valinit=self.P_cut if P_cut <= 1.0 else 0.99
)
s_vec_sc = Slider(ax_vec_sc, r"Vec scale", 0.0, 10.0, valstep=1, valinit=self.scale_vec) s_vec_sc = Slider(ax_vec_sc, r"Vec scale", 0.0, 10.0, valstep=1, valinit=self.scale_vec)
b_snr_reset = Button(ax_snr_reset, "Reset") b_snr_reset = Button(ax_snr_reset, "Reset")
b_snr_reset.label.set_fontsize(8) b_snr_reset.label.set_fontsize(8)
@@ -2493,7 +2495,7 @@ class pol_map(object):
def reset_snr(event): def reset_snr(event):
s_I_cut.reset() s_I_cut.reset()
self.s_P_cut.reset() self.P_ERR_cut.reset()
s_vec_sc.reset() s_vec_sc.reset()
def toggle_snr_conf(event=None): def toggle_snr_conf(event=None):
@@ -2502,21 +2504,21 @@ class pol_map(object):
if self.snr_conf: if self.snr_conf:
self.snr_conf = 0 self.snr_conf = 0
b_snr_conf.label.set_text("Conf") b_snr_conf.label.set_text("Conf")
self.s_P_cut = Slider( self.P_ERR_cut = Slider(
self.ax_P_cut, r"$SNR^{P}_{cut}$", 1.0, max(int(SNRp_max * 0.95), 3), valstep=1, valinit=self.P_cut if P_cut >= 1.0 else 3 self.ax_P_cut, r"$SNR^{P}_{cut}$", 1.0, max(int(SNRp_max * 0.95), 3), valstep=1, valinit=self.P_cut if P_cut >= 1.0 else 3
) )
else: else:
self.snr_conf = 1 self.snr_conf = 1
b_snr_conf.label.set_text("SNR") b_snr_conf.label.set_text("SNR")
self.s_P_cut = Slider( self.P_ERR_cut = Slider(
self.ax_P_cut, r"$Conf^{P}_{cut}$", 0.50, 1.0, valstep=[0.500, 0.900, 0.990, 0.999], valinit=self.P_cut if P_cut <= 1.0 else 0.99 self.ax_P_cut, r"$Conf^{P}_{cut}$", 0.50, 1.0, valstep=[0.500, 0.900, 0.990, 0.999], valinit=self.P_cut if P_cut <= 1.0 else 0.99
) )
self.s_P_cut.on_changed(update_snrp) self.P_ERR_cut.on_changed(update_snrp)
update_snrp(self.s_P_cut.val) update_snrp(self.P_ERR_cut.val)
self.fig.canvas.draw_idle() self.fig.canvas.draw_idle()
s_I_cut.on_changed(update_snri) s_I_cut.on_changed(update_snri)
self.s_P_cut.on_changed(update_snrp) self.P_ERR_cut.on_changed(update_snrp)
s_vec_sc.on_changed(update_vecsc) s_vec_sc.on_changed(update_vecsc)
b_snr_reset.on_clicked(reset_snr) b_snr_reset.on_clicked(reset_snr)
b_snr_conf.on_clicked(toggle_snr_conf) b_snr_conf.on_clicked(toggle_snr_conf)
@@ -2962,7 +2964,7 @@ class pol_map(object):
return self.Stokes["POL_DEG_DEBIASED"].data return self.Stokes["POL_DEG_DEBIASED"].data
@property @property
def s_P(self): def P_ERR(self):
return self.Stokes["POL_DEG_ERR"].data return self.Stokes["POL_DEG_ERR"].data
@property @property
@@ -2970,7 +2972,7 @@ class pol_map(object):
return self.Stokes["POL_ANG"].data return self.Stokes["POL_ANG"].data
@property @property
def s_PA(self): def PA_ERR(self):
return self.Stokes["POL_ANG_ERR"].data return self.Stokes["POL_ANG_ERR"].data
@property @property
@@ -2986,7 +2988,7 @@ class pol_map(object):
SNRp_mask, SNRi_mask = (np.zeros(self.P.shape).astype(bool), np.zeros(self.I.shape).astype(bool)) SNRp_mask, SNRi_mask = (np.zeros(self.P.shape).astype(bool), np.zeros(self.I.shape).astype(bool))
SNRi_mask[s_I > 0.0] = self.I[s_I > 0.0] / s_I[s_I > 0.0] > self.SNRi SNRi_mask[s_I > 0.0] = self.I[s_I > 0.0] / s_I[s_I > 0.0] > self.SNRi
if self.SNRp >= 1.0: if self.SNRp >= 1.0:
SNRp_mask[self.s_P > 0.0] = self.P[self.s_P > 0.0] / self.s_P[self.s_P > 0.0] > self.SNRp SNRp_mask[self.P_ERR > 0.0] = self.P[self.P_ERR > 0.0] / self.P_ERR[self.P_ERR > 0.0] > self.SNRp
else: else:
SNRp_mask = self.conf > self.SNRp SNRp_mask = self.conf > self.SNRp
return np.logical_and(SNRi_mask, SNRp_mask) return np.logical_and(SNRi_mask, SNRp_mask)
@@ -3054,7 +3056,7 @@ class pol_map(object):
ax.add_artist(self.north_dir) ax.add_artist(self.north_dir)
def display(self, fig=None, ax=None, flux_lim=None): def display(self, fig=None, ax=None, flux_lim=None):
norm = None kwargs = dict([])
if self.display_selection is None: if self.display_selection is None:
self.display_selection = "total_flux" self.display_selection = "total_flux"
if flux_lim is None: if flux_lim is None:
@@ -3065,7 +3067,7 @@ class pol_map(object):
vmin, vmax = (1.0 / 2.0 * np.median(self.data[self.data > 0.0]), np.max(self.data[self.data > 0.0])) vmin, vmax = (1.0 / 2.0 * np.median(self.data[self.data > 0.0]), np.max(self.data[self.data > 0.0]))
else: else:
vmin, vmax = flux_lim vmin, vmax = flux_lim
norm = LogNorm(vmin, vmax) kwargs["norm"] = LogNorm(vmin, vmax)
label = r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]" label = r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
elif self.display_selection.lower() in ["pol_flux"]: elif self.display_selection.lower() in ["pol_flux"]:
self.data = self.I * self.map_convert * self.P self.data = self.I * self.map_convert * self.P
@@ -3073,28 +3075,32 @@ class pol_map(object):
vmin, vmax = (1.0 / 2.0 * np.median(self.I[self.I > 0.0] * self.map_convert), np.max(self.I[self.I > 0.0] * self.map_convert)) vmin, vmax = (1.0 / 2.0 * np.median(self.I[self.I > 0.0] * self.map_convert), np.max(self.I[self.I > 0.0] * self.map_convert))
else: else:
vmin, vmax = flux_lim vmin, vmax = flux_lim
norm = LogNorm(vmin, vmax) kwargs["norm"] = LogNorm(vmin, vmax)
label = r"$P \cdot F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]" label = r"$P \cdot F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
elif self.display_selection.lower() in ["pol_deg"]: elif self.display_selection.lower() in ["pol_deg"]:
self.data = self.P * 100.0 self.data = self.P * 100.0
vmin, vmax = 0.0, np.max(self.data[self.P > self.s_P]) kwargs["vmin"], kwargs["vmax"] = 0.0, np.max(self.data[self.P > self.P_ERR])
kwargs["alpha"] = 1.0 - 0.75 * (self.P < self.P_ERR)
label = r"$P$ [%]" label = r"$P$ [%]"
elif self.display_selection.lower() in ["pol_ang"]: elif self.display_selection.lower() in ["pol_ang"]:
self.data = princ_angle(self.PA) self.data = princ_angle(self.PA)
vmin, vmax = 0, 180.0 kwargs["vmin"], kwargs["vmax"] = 0, 180.0
kwargs["alpha"] = 1.0 - 0.75 * (self.P < self.P_ERR)
label = r"$\theta_{P}$ [°]" label = r"$\theta_{P}$ [°]"
elif self.display_selection.lower() in ["snri"]: elif self.display_selection.lower() in ["snri"]:
s_I = np.sqrt(self.IQU_cov[0, 0]) s_I = np.sqrt(self.IQU_cov[0, 0])
SNRi = np.zeros(self.I.shape) SNRi = np.zeros(self.I.shape)
SNRi[s_I > 0.0] = self.I[s_I > 0.0] / s_I[s_I > 0.0] SNRi[s_I > 0.0] = self.I[s_I > 0.0] / s_I[s_I > 0.0]
self.data = SNRi self.data = SNRi
vmin, vmax = 0.0, np.max(self.data[self.data > 0.0]) 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"$I_{Stokes}/\sigma_{I}$" label = r"$I_{Stokes}/\sigma_{I}$"
elif self.display_selection.lower() in ["snrp"]: elif self.display_selection.lower() in ["snrp"]:
SNRp = np.zeros(self.P.shape) SNRp = np.zeros(self.P.shape)
SNRp[self.s_P > 0.0] = self.P[self.s_P > 0.0] / self.s_P[self.s_P > 0.0] SNRp[self.P_ERR > 0.0] = self.P[self.P_ERR > 0.0] / self.P_ERR[self.P_ERR > 0.0]
self.data = SNRp self.data = SNRp
vmin, vmax = 0.0, np.max(self.data[self.data > 0.0]) 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"$P/\sigma_{P}$" label = r"$P/\sigma_{P}$"
if fig is None: if fig is None:
@@ -3105,20 +3111,14 @@ class pol_map(object):
self.cbar.remove() self.cbar.remove()
if hasattr(self, "im"): if hasattr(self, "im"):
self.im.remove() self.im.remove()
if norm is not None: self.im = ax.imshow(self.data, aspect="equal", cmap="inferno", **kwargs)
self.im = ax.imshow(self.data, norm=norm, aspect="equal", cmap="inferno")
else:
self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno")
plt.rcParams.update({"font.size": 14}) plt.rcParams.update({"font.size": 14})
self.cbar = fig.colorbar(self.im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=label) self.cbar = fig.colorbar(self.im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=label)
plt.rcParams.update({"font.size": 10}) plt.rcParams.update({"font.size": 10})
fig.canvas.draw_idle() fig.canvas.draw_idle()
return self.im return self.im
else: else:
if norm is not None: im = ax.imshow(self.data, aspect="equal", cmap="inferno", **kwargs)
im = ax.imshow(self.data, norm=norm, aspect="equal", cmap="inferno")
else:
im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno")
ax.set_xlim(0, self.data.shape[1]) ax.set_xlim(0, self.data.shape[1])
ax.set_ylim(0, self.data.shape[0]) ax.set_ylim(0, self.data.shape[0])
plt.rcParams.update({"font.size": 14}) plt.rcParams.update({"font.size": 14})
@@ -3163,12 +3163,12 @@ class pol_map(object):
) )
if self.pa_err: if self.pa_err:
XY_U_err1, XY_V_err1 = ( XY_U_err1, XY_V_err1 = (
P_cut * np.cos(np.pi / 2.0 + (self.PA + 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.cos(np.pi / 2.0 + (self.PA + 3.0 * self.P_ERRA) * np.pi / 180.0),
P_cut * np.sin(np.pi / 2.0 + (self.PA + 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.sin(np.pi / 2.0 + (self.PA + 3.0 * self.P_ERRA) * np.pi / 180.0),
) )
XY_U_err2, XY_V_err2 = ( XY_U_err2, XY_V_err2 = (
P_cut * np.cos(np.pi / 2.0 + (self.PA - 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.cos(np.pi / 2.0 + (self.PA - 3.0 * self.P_ERRA) * np.pi / 180.0),
P_cut * np.sin(np.pi / 2.0 + (self.PA - 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.sin(np.pi / 2.0 + (self.PA - 3.0 * self.P_ERRA) * np.pi / 180.0),
) )
if hasattr(self, "quiver_err1"): if hasattr(self, "quiver_err1"):
self.quiver_err1.remove() self.quiver_err1.remove()
@@ -3232,12 +3232,12 @@ class pol_map(object):
) )
if self.pa_err: if self.pa_err:
XY_U_err1, XY_V_err1 = ( XY_U_err1, XY_V_err1 = (
P_cut * np.cos(np.pi / 2.0 + (self.PA + 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.cos(np.pi / 2.0 + (self.PA + 3.0 * self.P_ERRA) * np.pi / 180.0),
P_cut * np.sin(np.pi / 2.0 + (self.PA + 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.sin(np.pi / 2.0 + (self.PA + 3.0 * self.P_ERRA) * np.pi / 180.0),
) )
XY_U_err2, XY_V_err2 = ( XY_U_err2, XY_V_err2 = (
P_cut * np.cos(np.pi / 2.0 + (self.PA - 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.cos(np.pi / 2.0 + (self.PA - 3.0 * self.P_ERRA) * np.pi / 180.0),
P_cut * np.sin(np.pi / 2.0 + (self.PA - 3.0 * self.s_PA) * np.pi / 180.0), P_cut * np.sin(np.pi / 2.0 + (self.PA - 3.0 * self.P_ERRA) * np.pi / 180.0),
) )
ax.quiver( ax.quiver(
X[:: self.step_vec, :: self.step_vec], X[:: self.step_vec, :: self.step_vec],