Tibeuleu/FOC_Reduction
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fix package calling and clean scripts

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This commit is contained in:
Thibault Barnouin
2024-09-17 21:07:26 +02:00
parent 10577352d4
commit db3deac6c2
12 changed files with 345 additions and 496 deletions
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40
package/FOC_reduction.py
View File

@@ -3,6 +3,10 @@
"""
Main script where are progressively added the steps for the FOC pipeline reduction.
"""
from pathlib import Path
from sys import path as syspath
syspath.append(str(Path(__file__).parent.parent))
# Project libraries
from copy import deepcopy
@@ -61,7 +65,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
rotate_North = True
# Polarization map output
SNRp_cut = 3.0 # P measurments with SNR>3
P_cut = 0.99 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U
SNRi_cut = 1.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
scale_vec = 3
@@ -292,7 +296,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -303,7 +307,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -315,7 +319,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -327,7 +331,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -339,7 +343,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -351,7 +355,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -363,7 +367,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -375,7 +379,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -387,7 +391,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
P_cut=P_cut if P_cut >= 1. else 3.,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
@@ -396,12 +400,24 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
plots_folder=plots_folder,
display="SNRp",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut if P_cut < 1. else 0.99,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "confP"]),
plots_folder=plots_folder,
display="confp",
)
elif not interactive:
proj_plots.polarization_map(
deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"
deepcopy(Stokes_hdul), data_mask, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"
)
elif pxscale.lower() not in ["full", "integrate"]:
proj_plots.pol_map(Stokes_hdul, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, scale_vec=scale_vec, flux_lim=flux_lim)
proj_plots.pol_map(Stokes_hdul, P_cut=P_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, scale_vec=scale_vec, flux_lim=flux_lim)
return outfiles

3
package/__init__.py
View File

@@ -1,3 +1,2 @@
from . import lib
from . import src
from .lib import *
from . import FOC_reduction

314
package/lib/plots.py
View File

@@ -9,7 +9,7 @@ prototypes :
- plot_Stokes(Stokes, savename, plots_folder)
Plot the I/Q/U maps from the Stokes HDUList.
- polarization_map(Stokes, data_mask, rectangle, SNRp_cut, SNRi_cut, step_vec, savename, plots_folder, display) -> fig, ax
- polarization_map(Stokes, data_mask, rectangle, P_cut, SNRi_cut, step_vec, savename, plots_folder, display) -> fig, ax
Plots polarization map of polarimetric parameters saved in an HDUList.
class align_maps(map, other_map, **kwargs)
@@ -36,7 +36,7 @@ prototypes :
class aperture(img, cdelt, radius, fig, ax)
Class to interactively simulate aperture integration.
class pol_map(Stokes, SNRp_cut, SNRi_cut, selection)
class pol_map(Stokes, P_cut, SNRi_cut, selection)
Class to interactively study polarization maps making use of the cropping and selecting tools.
"""
@@ -60,10 +60,7 @@ from mpl_toolkits.axes_grid1.anchored_artists import (
)
from scipy.ndimage import zoom as sc_zoom
try:
from .utils import PCconf, princ_angle, rot2D, sci_not
except ImportError:
from utils import PCconf, princ_angle, rot2D, sci_not
from .utils import PCconf, princ_angle, rot2D, sci_not
def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder="", **kwargs):
@@ -216,11 +213,11 @@ def polarization_map(
Stokes,
data_mask=None,
rectangle=None,
SNRp_cut=3.0,
SNRi_cut=3.0,
P_cut=0.99,
SNRi_cut=1.0,
flux_lim=None,
step_vec=1,
scale_vec=2.0,
scale_vec=3.0,
savename=None,
plots_folder="",
display="default",
@@ -236,9 +233,9 @@ def polarization_map(
Array of parameters for matplotlib.patches.Rectangle objects that will
be displayed on each output image. If None, no rectangle displayed.
Defaults to None.
SNRp_cut : float, optional
P_cut : float, optional
Cut that should be applied to the signal-to-noise ratio on P.
Any SNR < SNRp_cut won't be displayed.
Any SNR < P_cut won't be displayed.
Defaults to 3.
SNRi_cut : float, optional
Cut that should be applied to the signal-to-noise ratio on I.
@@ -276,15 +273,24 @@ def polarization_map(
"""
# Get data
stkI = Stokes["I_stokes"].data.copy()
stkQ = Stokes["Q_stokes"].data.copy()
stkU = Stokes["U_stokes"].data.copy()
stk_cov = Stokes["IQU_cov_matrix"].data.copy()
pol = Stokes["Pol_deg_debiased"].data.copy()
pol_err = Stokes["Pol_deg_err"].data.copy()
pang = Stokes["Pol_ang"].data.copy()
try:
if data_mask is None:
if data_mask is None:
try:
data_mask = Stokes["Data_mask"].data.astype(bool).copy()
except KeyError:
data_mask = np.ones(stkI.shape).astype(bool)
except KeyError:
data_mask = np.zeros(stkI.shape).astype(bool)
data_mask[stkI > 0.0] = True
# Compute confidence level map
QN, UN, QN_ERR, UN_ERR = np.full((4, stkI.shape[0], stkI.shape[1]), np.nan)
for nflux, sflux in zip([QN, UN, QN_ERR, UN_ERR], [stkQ, stkU, np.sqrt(stk_cov[1, 1]), np.sqrt(stk_cov[2, 2])]):
nflux[stkI > 0.0] = sflux[stkI > 0.0] / stkI[stkI > 0.0]
confP = PCconf(QN, UN, QN_ERR, UN_ERR)
for dataset in [stkI, pol, pol_err, pang]:
dataset[np.logical_not(data_mask)] = np.nan
@@ -302,15 +308,23 @@ def polarization_map(
# Compute SNR and apply cuts
poldata, pangdata = pol.copy(), pang.copy()
maskP = pol_err > 0
SNRp = np.ones(pol.shape) * np.nan
SNRp[maskP] = pol[maskP] / pol_err[maskP]
SNRi = np.full(stkI.shape, np.nan)
SNRi[stk_cov[0, 0] > 0.0] = stkI[stk_cov[0, 0] > 0.0] / np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.0])
maskI = np.zeros(stkI.shape, dtype=bool)
maskI[stk_cov[0, 0] > 0.0] = SNRi[stk_cov[0, 0] > 0.0] > SNRi_cut
maskI = stk_cov[0, 0] > 0
SNRi = np.ones(stkI.shape) * np.nan
SNRi[maskI] = stkI[maskI] / np.sqrt(stk_cov[0, 0][maskI])
SNRp = np.full(pol.shape, np.nan)
SNRp[pol_err > 0.0] = pol[pol_err > 0.0] / pol_err[pol_err > 0.0]
maskP = np.zeros(pol.shape, dtype=bool)
mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut)
if P_cut >= 1.0:
# MaskP on the signal-to-noise value
maskP[pol_err > 0.0] = SNRp[pol_err > 0.0] > P_cut
else:
# MaskP on the confidence value
maskP = confP > P_cut
mask = np.logical_and(maskI, maskP)
poldata[np.logical_not(mask)] = np.nan
pangdata[np.logical_not(mask)] = np.nan
@@ -381,8 +395,8 @@ def polarization_map(
elif display.lower() in ["s_p", "pol_err", "pol_deg_err"]:
# Display polarization degree error map
display = "s_p"
if (SNRp > SNRp_cut).any():
vmin, vmax = 0.0, np.max([pol_err[SNRp > SNRp_cut].max(), 1.0]) * 100.0
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)
else:
vmin, vmax = 0.0, 100.0
@@ -400,30 +414,39 @@ def polarization_map(
else:
im = ax.imshow(np.sqrt(stk_cov[0, 0]) * convert_flux, aspect="equal", cmap="inferno", alpha=1.0)
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$\sigma_I$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
elif display.lower() in ["snr", "snri"]:
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="inferno", alpha=1.0)
levelsSNRi = np.linspace(SNRi_cut, vmax * 0.99, 5)
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="inferno", alpha=1.0)
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$I_{Stokes}/\sigma_{I}$")
elif display.lower() in ["snrp"]:
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:
if vmax * 0.99 > P_cut:
im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect="equal", cmap="inferno", alpha=1.0)
levelsSNRp = np.linspace(SNRp_cut, vmax * 0.99, 5)
levelsSNRp = np.linspace(P_cut, vmax * 0.99, 5).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="inferno", alpha=1.0)
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, 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="inferno", alpha=1.0)
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)
fig.colorbar(im, ax=ax, aspect=50, shrink=0.75, pad=0.025, label=r"$Conf_{P}$")
else:
# Defaults to intensity map
if mask.sum() > 0.0:
@@ -463,7 +486,7 @@ def polarization_map(
arrow_props={"ec": "k", "fc": "w", "alpha": 1, "lw": 1},
)
if display.lower() in ["i", "s_i", "snri", "pf", "p", "pa", "s_p", "snrp"]:
if display.lower() in ["i", "s_i", "snri", "pf", "p", "pa", "s_p", "snrp", "confp"]:
if step_vec == 0:
poldata[np.isfinite(poldata)] = 1.0 / 2.0
step_vec = 1
@@ -536,7 +559,6 @@ def polarization_map(
savename += ".pdf"
fig.savefig(path_join(plots_folder, savename), bbox_inches="tight", dpi=150, facecolor="None")
plt.show()
return fig, ax
@@ -765,14 +787,14 @@ class align_maps(object):
x = event.xdata
y = event.ydata
self.cr_map.set(data=[x, y])
self.cr_map.set(data=[[x], [y]])
self.fig_align.canvas.draw_idle()
if (event.inaxes is not None) and (event.inaxes == self.other_ax):
x = event.xdata
y = event.ydata
self.cr_other.set(data=[x, y])
self.cr_other.set(data=[[x], [y]])
self.fig_align.canvas.draw_idle()
def reset_align(self, event):
@@ -843,16 +865,23 @@ class overplot_radio(align_maps):
Inherit from class align_maps in order to get the same WCS on both maps.
"""
def overplot(self, levels=None, SNRp_cut=3.0, SNRi_cut=3.0, scale_vec=2, savename=None, **kwargs):
def overplot(self, levels=None, P_cut=0.99, SNRi_cut=1.0, scale_vec=2, savename=None, **kwargs):
self.Stokes_UV = self.map
self.wcs_UV = self.map_wcs
# Get Data
obj = self.Stokes_UV[0].header["targname"]
stkI = self.Stokes_UV["I_STOKES"].data
stkQ = self.Stokes_UV["Q_STOKES"].data
stkU = self.Stokes_UV["U_STOKES"].data
stk_cov = self.Stokes_UV["IQU_COV_MATRIX"].data
pol = deepcopy(self.Stokes_UV["POL_DEG_DEBIASED"].data)
pol_err = self.Stokes_UV["POL_DEG_ERR"].data
pang = self.Stokes_UV["POL_ANG"].data
# Compute confidence level map
QN, UN, QN_ERR, UN_ERR = np.full((4, stkI.shape[0], stkI.shape[1]), np.nan)
for nflux, sflux in zip([QN, UN, QN_ERR, UN_ERR], [stkQ, stkU, np.sqrt(stk_cov[1, 1]), np.sqrt(stk_cov[2, 2])]):
nflux[stkI > 0.0] = sflux[stkI > 0.0] / stkI[stkI > 0.0]
confP = PCconf(QN, UN, QN_ERR, UN_ERR)
other_data = self.other_data
self.other_convert = 1.0
@@ -864,12 +893,17 @@ class overplot_radio(align_maps):
self.map_convert = self.Stokes_UV[0].header["photflam"]
# Compute SNR and apply cuts
pol[pol == 0.0] = np.nan
SNRp = pol / pol_err
SNRp[np.isnan(SNRp)] = 0.0
pol[SNRp < SNRp_cut] = np.nan
SNRi = stkI / np.sqrt(stk_cov[0, 0])
SNRi[np.isnan(SNRi)] = 0.0
maskP = np.zeros(pol.shape, dtype=bool)
if P_cut >= 1.0:
SNRp = np.zeros(pol.shape)
SNRp[pol_err > 0.0] = pol[pol_err > 0.0] / pol_err[pol_err > 0.0]
maskP = SNRp > P_cut
else:
maskP = confP > P_cut
pol[np.logical_not(maskP)] = np.nan
SNRi = np.zeros(stkI.shape)
SNRi[stk_cov[0, 0] > 0.0] = stkI[stk_cov[0, 0] > 0.0] / np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.0])
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({"font.size": 16})
@@ -1025,7 +1059,7 @@ class overplot_radio(align_maps):
(0, 0), (0, 1), arrowstyle="-", fc="w", ec="k", lw=2
)
labels.append("{0:s} contour".format(self.other_observer))
handles.append(Rectangle((0, 0), 1, 1, fill=False, lw=2, ec=other_cont.collections[0].get_edgecolor()[0]))
handles.append(Rectangle((0, 0), 1, 1, fill=False, lw=2, ec=other_cont.get_edgecolor()[0]))
self.legend = self.ax_overplot.legend(
handles=handles, labels=labels, bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), loc="lower left", mode="expand", borderaxespad=0.0
)
@@ -1037,10 +1071,10 @@ class overplot_radio(align_maps):
self.fig_overplot.canvas.draw()
def plot(self, levels=None, SNRp_cut=3.0, SNRi_cut=3.0, savename=None, **kwargs) -> None:
def plot(self, levels=None, P_cut=0.99, SNRi_cut=1.0, savename=None, **kwargs) -> None:
while not self.aligned:
self.align()
self.overplot(levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
self.overplot(levels=levels, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
plt.show(block=True)
@@ -1050,16 +1084,23 @@ class overplot_chandra(align_maps):
Inherit from class align_maps in order to get the same WCS on both maps.
"""
def overplot(self, levels=None, SNRp_cut=3.0, SNRi_cut=3.0, scale_vec=2, zoom=1, savename=None, **kwargs):
def overplot(self, levels=None, P_cut=0.99, SNRi_cut=1.0, scale_vec=2, zoom=1, savename=None, **kwargs):
self.Stokes_UV = self.map
self.wcs_UV = self.map_wcs
# Get Data
obj = self.Stokes_UV[0].header["targname"]
stkI = self.Stokes_UV["I_STOKES"].data
stkQ = self.Stokes_UV["Q_STOKES"].data
stkU = self.Stokes_UV["U_STOKES"].data
stk_cov = self.Stokes_UV["IQU_COV_MATRIX"].data
pol = deepcopy(self.Stokes_UV["POL_DEG_DEBIASED"].data)
pol_err = self.Stokes_UV["POL_DEG_ERR"].data
pang = self.Stokes_UV["POL_ANG"].data
# Compute confidence level map
QN, UN, QN_ERR, UN_ERR = np.full((4, stkI.shape[0], stkI.shape[1]), np.nan)
for nflux, sflux in zip([QN, UN, QN_ERR, UN_ERR], [stkQ, stkU, np.sqrt(stk_cov[1, 1]), np.sqrt(stk_cov[2, 2])]):
nflux[stkI > 0.0] = sflux[stkI > 0.0] / stkI[stkI > 0.0]
confP = PCconf(QN, UN, QN_ERR, UN_ERR)
other_data = deepcopy(self.other_data)
other_wcs = self.other_wcs.deepcopy()
@@ -1070,12 +1111,17 @@ class overplot_chandra(align_maps):
self.other_unit = "counts"
# Compute SNR and apply cuts
pol[pol == 0.0] = np.nan
SNRp = pol / pol_err
SNRp[np.isnan(SNRp)] = 0.0
pol[SNRp < SNRp_cut] = np.nan
SNRi = stkI / np.sqrt(stk_cov[0, 0])
SNRi[np.isnan(SNRi)] = 0.0
maskP = np.zeros(pol.shape, dtype=bool)
if P_cut >= 1.0:
SNRp = np.zeros(pol.shape)
SNRp[pol_err > 0.0] = pol[pol_err > 0.0] / pol_err[pol_err > 0.0]
maskP = SNRp > P_cut
else:
maskP = confP > P_cut
pol[np.logical_not(maskP)] = np.nan
SNRi = np.zeros(stkI.shape)
SNRi[stk_cov[0, 0] > 0.0] = stkI[stk_cov[0, 0] > 0.0] / np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.0])
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({"font.size": 16})
@@ -1224,7 +1270,7 @@ class overplot_chandra(align_maps):
(0, 0), (0, 1), arrowstyle="-", fc="w", ec="k", lw=2
)
labels.append("{0:s} contour in counts".format(self.other_observer))
handles.append(Rectangle((0, 0), 1, 1, fill=False, lw=2, ec=other_cont.collections[0].get_edgecolor()[0]))
handles.append(Rectangle((0, 0), 1, 1, fill=False, lw=2, ec=other_cont.get_edgecolor()[0]))
self.legend = self.ax_overplot.legend(
handles=handles, labels=labels, bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), loc="lower left", mode="expand", borderaxespad=0.0
)
@@ -1236,10 +1282,10 @@ class overplot_chandra(align_maps):
self.fig_overplot.canvas.draw()
def plot(self, levels=None, SNRp_cut=3.0, SNRi_cut=3.0, zoom=1, savename=None, **kwargs) -> None:
def plot(self, levels=None, P_cut=0.99, SNRi_cut=1.0, zoom=1, savename=None, **kwargs) -> None:
while not self.aligned:
self.align()
self.overplot(levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, zoom=zoom, savename=savename, **kwargs)
self.overplot(levels=levels, P_cut=P_cut, SNRi_cut=SNRi_cut, zoom=zoom, savename=savename, **kwargs)
plt.show(block=True)
@@ -1249,26 +1295,38 @@ class overplot_pol(align_maps):
Inherit from class align_maps in order to get the same WCS on both maps.
"""
def overplot(self, levels=None, SNRp_cut=3.0, SNRi_cut=3.0, scale_vec=2.0, savename=None, **kwargs):
def overplot(self, levels=None, P_cut=0.99, SNRi_cut=1.0, scale_vec=2.0, savename=None, **kwargs):
self.Stokes_UV = self.map
self.wcs_UV = self.map_wcs
# Get Data
obj = self.Stokes_UV[0].header["targname"]
stkI = self.Stokes_UV["I_STOKES"].data
stkQ = self.Stokes_UV["Q_STOKES"].data
stkU = self.Stokes_UV["U_STOKES"].data
stk_cov = self.Stokes_UV["IQU_COV_MATRIX"].data
pol = deepcopy(self.Stokes_UV["POL_DEG_DEBIASED"].data)
pol_err = self.Stokes_UV["POL_DEG_ERR"].data
pang = self.Stokes_UV["POL_ANG"].data
# Compute confidence level map
QN, UN, QN_ERR, UN_ERR = np.full((4, stkI.shape[0], stkI.shape[1]), np.nan)
for nflux, sflux in zip([QN, UN, QN_ERR, UN_ERR], [stkQ, stkU, np.sqrt(stk_cov[1, 1]), np.sqrt(stk_cov[2, 2])]):
nflux[stkI > 0.0] = sflux[stkI > 0.0] / stkI[stkI > 0.0]
confP = PCconf(QN, UN, QN_ERR, UN_ERR)
other_data = self.other_data
# Compute SNR and apply cuts
pol[pol == 0.0] = np.nan
SNRp = pol / pol_err
SNRp[np.isnan(SNRp)] = 0.0
pol[SNRp < SNRp_cut] = np.nan
SNRi = stkI / np.sqrt(stk_cov[0, 0])
SNRi[np.isnan(SNRi)] = 0.0
maskP = np.zeros(pol.shape, dtype=bool)
if P_cut >= 1.0:
SNRp = np.zeros(pol.shape)
SNRp[pol_err > 0.0] = pol[pol_err > 0.0] / pol_err[pol_err > 0.0]
maskP = SNRp > P_cut
else:
maskP = confP > P_cut
pol[np.logical_not(maskP)] = np.nan
SNRi = np.zeros(stkI.shape)
SNRi[stk_cov[0, 0] > 0.0] = stkI[stk_cov[0, 0] > 0.0] / np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.0])
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({"font.size": 16})
@@ -1437,7 +1495,7 @@ class overplot_pol(align_maps):
(0, 0), (0, 1), arrowstyle="-", fc="w", ec="k", lw=2
)
labels.append("{0:s} Stokes I contour".format(self.map_observer))
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=cont_stkI.collections[0].get_edgecolor()[0]))
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=cont_stkI.get_edgecolor()[0]))
self.legend = self.ax_overplot.legend(
handles=handles, labels=labels, bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), loc="lower left", mode="expand", borderaxespad=0.0
)
@@ -1449,10 +1507,10 @@ class overplot_pol(align_maps):
self.fig_overplot.canvas.draw()
def plot(self, levels=None, SNRp_cut=3.0, SNRi_cut=3.0, scale_vec=2.0, savename=None, **kwargs) -> None:
def plot(self, levels=None, P_cut=0.99, SNRi_cut=1.0, scale_vec=2.0, savename=None, **kwargs) -> None:
while not self.aligned:
self.align()
self.overplot(levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, scale_vec=scale_vec, savename=savename, **kwargs)
self.overplot(levels=levels, P_cut=P_cut, SNRi_cut=SNRi_cut, scale_vec=scale_vec, savename=savename, **kwargs)
plt.show(block=True)
def add_vector(self, position="center", pol_deg=1.0, pol_ang=0.0, **kwargs):
@@ -1462,7 +1520,12 @@ class overplot_pol(align_maps):
position = self.other_wcs.world_to_pixel(position)
u, v = pol_deg * np.cos(np.radians(pol_ang) + np.pi / 2.0), pol_deg * np.sin(np.radians(pol_ang) + np.pi / 2.0)
for key, value in [["scale", [["scale", self.scale_vec]]], ["width", [["width", 0.1]]], ["color", [["color", "k"]]], ["edgecolor", [["edgecolor", "w"]]]]:
for key, value in [
["scale", [["scale", self.scale_vec]]],
["width", [["width", 0.1]]],
["color", [["color", "k"]]],
["edgecolor", [["edgecolor", "w"]]],
]:
try:
_ = kwargs[key]
except KeyError:
@@ -1495,7 +1558,7 @@ class align_pol(object):
self.kwargs = kwargs
def single_plot(self, curr_map, wcs, v_lim=None, ax_lim=None, SNRp_cut=3.0, SNRi_cut=3.0, savename=None, **kwargs):
def single_plot(self, curr_map, wcs, v_lim=None, ax_lim=None, P_cut=3.0, SNRi_cut=3.0, savename=None, **kwargs):
# Get data
stkI = curr_map["I_STOKES"].data
stk_cov = curr_map["IQU_COV_MATRIX"].data
@@ -1518,7 +1581,7 @@ class align_pol(object):
SNRi = np.zeros(stkI.shape)
SNRi[maskI] = stkI[maskI] / np.sqrt(stk_cov[0, 0][maskI])
mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut) * (pol >= 0.0)
mask = (SNRp > P_cut) * (SNRi > SNRi_cut) * (pol >= 0.0)
pol[mask] = np.nan
# Plot the map
@@ -1627,7 +1690,7 @@ class align_pol(object):
self.wcs, self.wcs_other[i] = curr_align.align()
self.aligned[i] = curr_align.aligned
def plot(self, SNRp_cut=3.0, SNRi_cut=3.0, savename=None, **kwargs):
def plot(self, P_cut=3.0, SNRi_cut=3.0, savename=None, **kwargs):
while not self.aligned.all():
self.align()
eps = 1e-35
@@ -1673,13 +1736,13 @@ class align_pol(object):
)
v_lim = np.array([vmin, vmax])
fig, ax = self.single_plot(self.ref_map, self.wcs, v_lim=v_lim, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename + "_0", **kwargs)
fig, ax = self.single_plot(self.ref_map, self.wcs, v_lim=v_lim, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=savename + "_0", **kwargs)
x_lim, y_lim = ax.get_xlim(), ax.get_ylim()
ax_lim = np.array([self.wcs.pixel_to_world(x_lim[i], y_lim[i]) for i in range(len(x_lim))])
for i, curr_map in enumerate(self.other_maps):
self.single_plot(
curr_map, self.wcs_other[i], v_lim=v_lim, ax_lim=ax_lim, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename + "_" + str(i + 1), **kwargs
curr_map, self.wcs_other[i], v_lim=v_lim, ax_lim=ax_lim, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=savename + "_" + str(i + 1), **kwargs
)
@@ -1726,6 +1789,7 @@ class crop_map(object):
self.mask_alpha = 0.75
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop, button=[1])
self.embedded = True
self.ax.set(xlabel="Right Ascension (J2000)", ylabel="Declination (J2000)")
self.display(self.data, self.wcs, self.map_convert, **self.kwargs)
self.extent = np.array([0.0, self.data.shape[0], 0.0, self.data.shape[1]])
@@ -2024,7 +2088,7 @@ class image_lasso_selector(object):
self.mask = np.zeros(self.img.shape[:2], dtype=bool)
self.mask[self.indices] = True
if hasattr(self, "cont"):
for coll in self.cont.collections:
for coll in self.cont:
coll.remove()
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1)
if not self.embedded:
@@ -2137,7 +2201,7 @@ class slit(object):
for p in self.pix:
self.mask[tuple(p)] = (np.abs(np.dot(rot2D(-self.angle), p - self.rect.get_center()[::-1])) < (self.height / 2.0, self.width / 2.0)).all()
if hasattr(self, "cont"):
for coll in self.cont.collections:
for coll in self.cont:
try:
coll.remove()
except AttributeError:
@@ -2240,7 +2304,7 @@ class aperture(object):
x0, y0 = self.circ.center
self.mask = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2) < self.radius
if hasattr(self, "cont"):
for coll in self.cont.collections:
for coll in self.cont:
try:
coll.remove()
except AttributeError:
@@ -2258,21 +2322,22 @@ class pol_map(object):
Class to interactively study polarization maps.
"""
def __init__(self, Stokes, SNRp_cut=3.0, SNRi_cut=3.0, step_vec=1, scale_vec=3.0, flux_lim=None, selection=None, pa_err=False):
def __init__(self, Stokes, P_cut=0.99, SNRi_cut=1.0, step_vec=1, scale_vec=3.0, flux_lim=None, selection=None, pa_err=False):
if isinstance(Stokes, str):
Stokes = fits.open(Stokes)
self.Stokes = deepcopy(Stokes)
self.SNRp_cut = SNRp_cut
self.P_cut = P_cut
self.SNRi_cut = SNRi_cut
self.flux_lim = flux_lim
self.SNRi = deepcopy(self.SNRi_cut)
self.SNRp = deepcopy(self.SNRp_cut)
self.SNRp = deepcopy(self.P_cut)
self.region = None
self.data = None
self.display_selection = selection
self.step_vec = step_vec
self.scale_vec = scale_vec
self.pa_err = pa_err
self.conf = PCconf(self.Q / self.I, self.U / self.I, np.sqrt(self.IQU_cov[1, 1]) / self.I, np.sqrt(self.IQU_cov[2, 2]) / self.I)
# Get data
self.targ = self.Stokes[0].header["targname"]
@@ -2292,18 +2357,22 @@ class pol_map(object):
# Display integrated values in ROI
self.pol_int()
# Set axes for sliders (SNRp_cut, SNRi_cut)
ax_I_cut = self.fig.add_axes([0.120, 0.080, 0.230, 0.01])
ax_P_cut = self.fig.add_axes([0.120, 0.055, 0.230, 0.01])
ax_vec_sc = self.fig.add_axes([0.240, 0.030, 0.110, 0.01])
ax_snr_reset = self.fig.add_axes([0.080, 0.020, 0.05, 0.02])
# Set axes for sliders (P_cut, SNRi_cut)
ax_I_cut = self.fig.add_axes([0.120, 0.080, 0.220, 0.01])
self.ax_P_cut = self.fig.add_axes([0.120, 0.055, 0.220, 0.01])
ax_vec_sc = self.fig.add_axes([0.260, 0.030, 0.090, 0.01])
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])
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])
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_P_cut = Slider(ax_P_cut, r"$SNR^{P}_{cut}$", 1.0, int(SNRp_max * 0.95), valstep=1, valinit=self.SNRp_cut)
s_vec_sc = Slider(ax_vec_sc, r"Vectors scale", 0.0, 10.0, valstep=1, valinit=self.scale_vec)
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)
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.label.set_fontsize(8)
self.snr_conf = 1
b_snr_conf = Button(ax_snr_conf, "Conf")
b_snr_conf.label.set_fontsize(8)
def update_snri(val):
self.SNRi = val
@@ -2325,13 +2394,34 @@ class pol_map(object):
def reset_snr(event):
s_I_cut.reset()
s_P_cut.reset()
self.s_P_cut.reset()
s_vec_sc.reset()
def toggle_snr_conf(event=None):
self.ax_P_cut.remove()
self.ax_P_cut = self.fig.add_axes([0.120, 0.055, 0.220, 0.01])
if self.snr_conf:
self.snr_conf = 0
b_snr_conf.label.set_text("Conf")
self.s_P_cut = Slider(self.ax_P_cut, r"$SNR^{P}_{cut}$", 1.0, int(SNRp_max * 0.95), valstep=1, valinit=self.P_cut if P_cut >= 1.0 else 3)
else:
self.snr_conf = 1
b_snr_conf.label.set_text("SNR")
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.s_P_cut.on_changed(update_snrp)
update_snrp(self.s_P_cut.val)
self.fig.canvas.draw_idle()
s_I_cut.on_changed(update_snri)
s_P_cut.on_changed(update_snrp)
self.s_P_cut.on_changed(update_snrp)
s_vec_sc.on_changed(update_vecsc)
b_snr_reset.on_clicked(reset_snr)
b_snr_conf.on_clicked(toggle_snr_conf)
if self.P_cut >= 1.0:
toggle_snr_conf()
# Set axe for ROI selection
ax_select = self.fig.add_axes([0.375, 0.070, 0.05, 0.02])
@@ -2349,7 +2439,7 @@ class pol_map(object):
self.selected = False
self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections:
for coll in self.select_instance.cont:
coll.remove()
self.select_instance.lasso.set_active(False)
self.set_data_mask(deepcopy(self.region))
@@ -2393,7 +2483,7 @@ class pol_map(object):
self.select_instance.update_mask()
self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections[:]:
for coll in self.select_instance.cont:
coll.remove()
self.select_instance.circ.set_visible(False)
self.set_data_mask(deepcopy(self.region))
@@ -2451,7 +2541,7 @@ class pol_map(object):
self.select_instance.update_mask()
self.region = deepcopy(self.select_instance.mask.astype(bool))
self.select_instance.displayed.remove()
for coll in self.select_instance.cont.collections[:]:
for coll in self.select_instance.cont:
coll.remove()
self.select_instance.rect.set_visible(False)
self.set_data_mask(deepcopy(self.region))
@@ -2768,8 +2858,11 @@ class pol_map(object):
def cut(self):
s_I = np.sqrt(self.IQU_cov[0, 0])
SNRp_mask, SNRi_mask = np.zeros(self.P.shape).astype(bool), np.zeros(self.I.shape).astype(bool)
SNRp_mask[self.s_P > 0.0] = self.P[self.s_P > 0.0] / self.s_P[self.s_P > 0.0] > self.SNRp
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:
SNRp_mask[self.s_P > 0.0] = self.P[self.s_P > 0.0] / self.s_P[self.s_P > 0.0] > self.SNRp
else:
SNRp_mask = self.conf > self.SNRp
return np.logical_and(SNRi_mask, SNRp_mask)
def ax_cosmetics(self, ax=None):
@@ -3171,7 +3264,7 @@ class pol_map(object):
)
if hasattr(self, "cont"):
for coll in self.cont.collections:
for coll in self.cont:
try:
coll.remove()
except AttributeError:
@@ -3242,15 +3335,13 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Interactively plot the pipeline products")
parser.add_argument("-f", "--file", metavar="path", required=False, help="The full or relative path to the data product", type=str, default=None)
parser.add_argument(
"-p", "--snrp", metavar="snrp_cut", required=False, help="The cut in signal-to-noise for the polarization degree", type=float, default=3.0
)
parser.add_argument("-p", "--pcut", metavar="p_cut", required=False, help="The cut in signal-to-noise for the polarization degree", type=float, default=3.0)
parser.add_argument("-i", "--snri", metavar="snri_cut", required=False, help="The cut in signal-to-noise for the intensity", type=float, default=3.0)
parser.add_argument(
"-st", "--step-vec", metavar="step_vec", required=False, help="Quantity of vectors to be shown, 1 is all, 2 is every other, etc.", type=int, default=1
)
parser.add_argument(
"-sc", "--scale-vec", metavar="scale_vec", required=False, help="Size of the 100% polarization vector in pixel units", type=float, default=3.0
"-sc", "--scale-vec", metavar="scale_vec", required=False, help="Size of the 100%% polarization vector in pixel units", type=float, default=3.0
)
parser.add_argument("-pa", "--pang-err", action="store_true", required=False, help="Whether the polarization angle uncertainties should be displayed")
parser.add_argument("-l", "--lim", metavar="flux_lim", nargs=2, required=False, help="Limits for the intensity map", type=float, default=None)
@@ -3265,7 +3356,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3276,7 +3367,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3288,7 +3379,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3300,7 +3391,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3312,7 +3403,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3324,7 +3415,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3336,7 +3427,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3348,7 +3439,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3360,7 +3451,7 @@ if __name__ == "__main__":
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
SNRp_cut=args.snrp,
P_cut=args.p_cut if args.p_cut >= 1.0 else 3.0,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
@@ -3369,12 +3460,21 @@ if __name__ == "__main__":
plots_folder=args.static_pdf,
display="SNRp",
)
else:
pol_map(
Stokes_UV, SNRp_cut=args.snrp, SNRi_cut=args.snri, step_vec=args.step_vec, scale_vec=args.scale_vec, flux_lim=args.lim, pa_err=args.pang_err
polarization_map(
Stokes_UV,
Stokes_UV["DATA_MASK"].data.astype(bool),
P_cut=args.p_cut if args.p_cut < 1.0 else 0.99,
SNRi_cut=args.snri,
flux_lim=args.lim,
step_vec=args.step_vec,
scale_vec=args.scale_vec,
savename="_".join([Stokes_UV[0].header["FILENAME"], "confP"]),
plots_folder=args.static_pdf,
display="confp",
)
else:
pol_map(Stokes_UV, P_cut=args.p_cut, SNRi_cut=args.snri, step_vec=args.step_vec, scale_vec=args.scale_vec, flux_lim=args.lim, pa_err=args.pang_err)
else:
print(
"python3 plots.py -f <path_to_reduced_fits> -p <SNRp_cut> -i <SNRi_cut> -st <step_vec> -sc <scale_vec> -l <flux_lim> -pa <pa_err> --pdf <static_pdf>"
"python3 plots.py -f <path_to_reduced_fits> -p <P_cut> -i <SNRi_cut> -st <step_vec> -sc <scale_vec> -l <flux_lim> -pa <pa_err> --pdf <static_pdf>"
)

5
package/Combine.py → package/src/Combine.py
View File

@@ -1,6 +1,9 @@
#!/usr/bin/python
# -*- coding:utf-8 -*-
# Project libraries
from pathlib import Path
from sys import path as syspath
syspath.append(str(Path(__file__).parent.parent))
import numpy as np

0
package/src/__init__.py
View File

40
package/src/analysis.py
View File

@@ -1,40 +0,0 @@
#!/usr/bin/python
from getopt import error as get_error
from getopt import getopt
from sys import argv
arglist = argv[1:]
options = "hf:p:i:l:"
long_options = ["help", "fits=", "snrp=", "snri=", "lim="]
fits_path = None
SNRp_cut, SNRi_cut = 3, 3
flux_lim = None
out_txt = None
try:
arg, val = getopt(arglist, options, long_options)
for curr_arg, curr_val in arg:
if curr_arg in ("-h", "--help"):
print("python3 analysis.py -f <path_to_reduced_fits> -p <SNRp_cut> -i <SNRi_cut> -l <flux_lim>")
elif curr_arg in ("-f", "--fits"):
fits_path = str(curr_val)
elif curr_arg in ("-p", "--snrp"):
SNRp_cut = int(curr_val)
elif curr_arg in ("-i", "--snri"):
SNRi_cut = int(curr_val)
elif curr_arg in ("-l", "--lim"):
flux_lim = list("".join(curr_val).split(","))
except get_error as err:
print(str(err))
if fits_path is not None:
from astropy.io import fits
from lib.plots import pol_map
Stokes_UV = fits.open(fits_path)
p = pol_map(Stokes_UV, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
else:
print("python3 analysis.py -f <path_to_reduced_fits> -p <SNRp_cut> -i <SNRi_cut> -l <flux_lim>")

214
package/src/comparison_Kishimoto.py
View File

@@ -1,214 +0,0 @@
#!/usr/bin/python
from src.lib.background import gauss, bin_centers
from src.lib.deconvolve import zeropad
from src.lib.reduction import align_data
from src.lib.plots import princ_angle
from matplotlib.colors import LogNorm
from os.path import join as path_join
from astropy.io import fits
from astropy.wcs import WCS
from scipy.ndimage import shift
from scipy.optimize import curve_fit
import numpy as np
import matplotlib.pyplot as plt
root_dir = path_join('/home/t.barnouin/Documents/Thesis/HST')
root_dir_K = path_join(root_dir, 'Kishimoto', 'output')
root_dir_S = path_join(root_dir, 'FOC_Reduction', 'output')
root_dir_data_S = path_join(root_dir, 'FOC_Reduction', 'data', 'NGC1068', '5144')
root_dir_plot_S = path_join(root_dir, 'FOC_Reduction', 'plots', 'NGC1068', '5144', 'notaligned')
filename_S = "NGC1068_FOC_b10.00pixel_not_aligned.fits"
plt.rcParams.update({'font.size': 15})
SNRi_cut = 30.
SNRp_cut = 3.
data_K = {}
data_S = {}
for d, i in zip(['I', 'Q', 'U', 'P', 'PA', 'sI', 'sQ', 'sU', 'sP', 'sPA'], [0, 1, 2, 5, 8, (3, 0, 0), (3, 1, 1), (3, 2, 2), 6, 9]):
data_K[d] = np.loadtxt(path_join(root_dir_K, d+'.txt'))
with fits.open(path_join(root_dir_data_S, filename_S)) as f:
if not type(i) is int:
data_S[d] = np.sqrt(f[i[0]].data[i[1], i[2]])
else:
data_S[d] = f[i].data
if i == 0:
header = f[i].header
wcs = WCS(header)
convert_flux = header['photflam']
bkg_S = np.median(data_S['I'])/3
bkg_K = np.median(data_K['I'])/3
# zeropad data to get same size of array
shape = data_S['I'].shape
for d in data_K:
data_K[d] = zeropad(data_K[d], shape)
# shift array to get same information in same pixel
data_arr, error_ar, heads, data_msk, shifts, shifts_err = align_data(np.array([data_S['I'], data_K['I']]), [header, header], error_array=np.array(
[data_S['sI'], data_K['sI']]), background=np.array([bkg_S, bkg_K]), upsample_factor=10., ref_center='center', return_shifts=True)
for d in data_K:
data_K[d] = shift(data_K[d], shifts[1], order=1, cval=0.)
# compute pol components from shifted array
for d in [data_S, data_K]:
for i in d:
d[i][np.isnan(d[i])] = 0.
d['P'] = np.where(np.logical_and(np.isfinite(d['I']), d['I'] > 0.), np.sqrt(d['Q']**2+d['U']**2)/d['I'], 0.)
d['sP'] = np.where(np.logical_and(np.isfinite(d['I']), d['I'] > 0.), np.sqrt((d['Q']**2*d['sQ']**2+d['U']**2*d['sU']**2) /
(d['Q']**2+d['U']**2)+((d['Q']/d['I'])**2+(d['U']/d['I'])**2)*d['sI']**2)/d['I'], 0.)
d['d_P'] = np.where(np.logical_and(np.isfinite(d['P']), np.isfinite(d['sP'])), np.sqrt(d['P']**2-d['sP']**2), 0.)
d['PA'] = 0.5*np.arctan2(d['U'], d['Q'])+np.pi
d['SNRp'] = np.zeros(d['d_P'].shape)
d['SNRp'][d['sP'] > 0.] = d['d_P'][d['sP'] > 0.]/d['sP'][d['sP'] > 0.]
d['SNRi'] = np.zeros(d['I'].shape)
d['SNRi'][d['sI'] > 0.] = d['I'][d['sI'] > 0.]/d['sI'][d['sI'] > 0.]
d['mask'] = np.logical_and(d['SNRi'] > SNRi_cut, d['SNRp'] > SNRp_cut)
data_S['mask'], data_K['mask'] = np.logical_and(data_S['mask'], data_K['mask']), np.logical_and(data_S['mask'], data_K['mask'])
#
# Compute histogram of measured polarization in cut
#
bins = int(data_S['mask'].sum()/5)
bin_size = 1./bins
mod_p = np.linspace(0., 1., 300)
for d in [data_S, data_K]:
d['hist'], d['bin_edges'] = np.histogram(d['d_P'][d['mask']], bins=bins, range=(0., 1.))
d['binning'] = bin_centers(d['bin_edges'])
peak, bins_fwhm = d['binning'][np.argmax(d['hist'])], d['binning'][d['hist'] > d['hist'].max()/2.]
fwhm = bins_fwhm[1]-bins_fwhm[0]
p0 = [d['hist'].max(), peak, fwhm]
try:
popt, pcov = curve_fit(gauss, d['binning'], d['hist'], p0=p0)
except RuntimeError:
popt = p0
d['hist_chi2'] = np.sum((d['hist']-gauss(d['binning'], *popt))**2)/d['hist'].size
d['hist_popt'] = popt
fig_p, ax_p = plt.subplots(num="Polarization degree histogram", figsize=(10, 6), constrained_layout=True)
ax_p.errorbar(data_S['binning'], data_S['hist'], xerr=bin_size/2., fmt='b.', ecolor='b', label='P through this pipeline')
ax_p.plot(mod_p, gauss(mod_p, *data_S['hist_popt']), 'b--', label='mean = {1:.2f}, stdev = {2:.2f}'.format(*data_S['hist_popt']))
ax_p.errorbar(data_K['binning'], data_K['hist'], xerr=bin_size/2., fmt='r.', ecolor='r', label="P through Kishimoto's pipeline")
ax_p.plot(mod_p, gauss(mod_p, *data_K['hist_popt']), 'r--', label='mean = {1:.2f}, stdev = {2:.2f}'.format(*data_K['hist_popt']))
ax_p.set(xlabel="Polarization degree", ylabel="Counts", title="Histogram of polarization degree computed in the cut for both pipelines.")
ax_p.legend()
fig_p.savefig(path_join(root_dir_plot_S, "NGC1068_K_pol_deg.png"), bbox_inches="tight", dpi=300)
#
# Compute angular difference between the maps in cut
#
dtheta = np.where(data_S['mask'], 0.5*np.arctan((np.sin(2*data_S['PA'])*np.cos(2*data_K['PA'])-np.cos(2*data_S['PA']) *
np.cos(2*data_K['PA']))/(np.cos(2*data_S['PA'])*np.cos(2*data_K['PA'])+np.cos(2*data_S['PA'])*np.sin(2*data_K['PA']))), np.nan)
fig_pa = plt.figure(num="Polarization degree alignement")
ax_pa = fig_pa.add_subplot(111, projection=wcs)
cbar_ax_pa = fig_pa.add_axes([0.88, 0.12, 0.01, 0.75])
ax_pa.set_title(r"Degree of alignement $\zeta$ of the polarization angles from the 2 pipelines in the cut")
im_pa = ax_pa.imshow(np.cos(2*dtheta), vmin=-1., vmax=1., origin='lower', cmap='bwr', label=r"$\zeta$ between this pipeline and Kishimoto's")
cbar_pa = plt.colorbar(im_pa, cax=cbar_ax_pa, label=r"$\zeta = \cos\left( 2 \cdot \delta\theta_P \right)$")
ax_pa.coords[0].set_axislabel('Right Ascension (J2000)')
ax_pa.coords[1].set_axislabel('Declination (J2000)')
fig_pa.savefig(path_join(root_dir_plot_S, "NGC1068_K_pol_ang.png"), bbox_inches="tight", dpi=300)
#
# Compute power uncertainty difference between the maps in cut
#
eta = np.where(data_S['mask'], np.abs(data_K['d_P']-data_S['d_P'])/np.sqrt(data_S['sP']**2+data_K['sP']**2)/2., np.nan)
fig_dif_p = plt.figure(num="Polarization power difference ratio")
ax_dif_p = fig_dif_p.add_subplot(111, projection=wcs)
cbar_ax_dif_p = fig_dif_p.add_axes([0.88, 0.12, 0.01, 0.75])
ax_dif_p.set_title(r"Degree of difference $\eta$ of the polarization from the 2 pipelines in the cut")
im_dif_p = ax_dif_p.imshow(eta, vmin=0., vmax=2., origin='lower', cmap='bwr_r', label=r"$\eta$ between this pipeline and Kishimoto's")
cbar_dif_p = plt.colorbar(im_dif_p, cax=cbar_ax_dif_p, label=r"$\eta = \frac{2 \left|P^K-P^S\right|}{\sqrt{{\sigma^K_P}^2+{\sigma^S_P}^2}}$")
ax_dif_p.coords[0].set_axislabel('Right Ascension (J2000)')
ax_dif_p.coords[1].set_axislabel('Declination (J2000)')
fig_dif_p.savefig(path_join(root_dir_plot_S, "NGC1068_K_pol_diff.png"), bbox_inches="tight", dpi=300)
#
# Compute angle uncertainty difference between the maps in cut
#
eta = np.where(data_S['mask'], np.abs(data_K['PA']-data_S['PA'])/np.sqrt(data_S['sPA']**2+data_K['sPA']**2)/2., np.nan)
fig_dif_pa = plt.figure(num="Polarization angle difference ratio")
ax_dif_pa = fig_dif_pa.add_subplot(111, projection=wcs)
cbar_ax_dif_pa = fig_dif_pa.add_axes([0.88, 0.12, 0.01, 0.75])
ax_dif_pa.set_title(r"Degree of difference $\eta$ of the polarization from the 2 pipelines in the cut")
im_dif_pa = ax_dif_pa.imshow(eta, vmin=0., vmax=2., origin='lower', cmap='bwr_r', label=r"$\eta$ between this pipeline and Kishimoto's")
cbar_dif_pa = plt.colorbar(im_dif_pa, cax=cbar_ax_dif_pa,
label=r"$\eta = \frac{2 \left|\theta_P^K-\theta_P^S\right|}{\sqrt{{\sigma^K_{\theta_P}}^2+{\sigma^S_{\theta_P}}^2}}$")
ax_dif_pa.coords[0].set_axislabel('Right Ascension (J2000)')
ax_dif_pa.coords[1].set_axislabel('Declination (J2000)')
fig_dif_pa.savefig(path_join(root_dir_plot_S, "NGC1068_K_polang_diff.png"), bbox_inches="tight", dpi=300)
# display both polarization maps to check consistency
# plt.rcParams.update({'font.size': 15})
fig = plt.figure(num="Polarization maps comparison", figsize=(10, 10))
ax = fig.add_subplot(111, projection=wcs)
fig.subplots_adjust(right=0.85)
cbar_ax = fig.add_axes([0.88, 0.12, 0.01, 0.75])
for d in [data_S, data_K]:
d['X'], d['Y'] = np.meshgrid(np.arange(d['I'].shape[1]), np.arange(d['I'].shape[0]))
d['xy_U'], d['xy_V'] = np.where(d['mask'], d['d_P']*np.cos(np.pi/2.+d['PA']), np.nan), np.where(d['mask'], d['d_P']*np.sin(np.pi/2.+d['PA']), np.nan)
im0 = ax.imshow(data_S['I']*convert_flux, norm=LogNorm(data_S['I'][data_S['I'] > 0].min()*convert_flux, data_S['I']
[data_S['I'] > 0].max()*convert_flux), origin='lower', cmap='gray', label=r"$I_{STOKES}$ through this pipeline")
quiv0 = ax.quiver(data_S['X'], data_S['Y'], data_S['xy_U'], data_S['xy_V'], units='xy', angles='uv', scale=0.5, scale_units='xy',
pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.2, color='b', alpha=0.75, label="PA through this pipeline")
quiv1 = ax.quiver(data_K['X'], data_K['Y'], data_K['xy_U'], data_K['xy_V'], units='xy', angles='uv', scale=0.5, scale_units='xy',
pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, color='r', alpha=0.75, label="PA through Kishimoto's pipeline")
ax.set_title(r"$SNR_P \geq$ "+str(SNRi_cut)+r"$\; & \; SNR_I \geq $"+str(SNRp_cut))
# ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
ax.coords[0].set_axislabel('Right Ascension (J2000)')
ax.coords[0].set_axislabel_position('b')
ax.coords[0].set_ticklabel_position('b')
ax.coords[1].set_axislabel('Declination (J2000)')
ax.coords[1].set_axislabel_position('l')
ax.coords[1].set_ticklabel_position('l')
# ax.axis('equal')
cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
ax.legend(loc='upper right')
fig.savefig(path_join(root_dir_plot_S, "NGC1068_K_comparison.png"), bbox_inches="tight", dpi=300)
# compute integrated polarization parameters on a specific cut
for d in [data_S, data_K]:
d['I_dil'] = np.sum(d['I'][d['mask']])
d['sI_dil'] = np.sqrt(np.sum(d['sI'][d['mask']]**2))
d['Q_dil'] = np.sum(d['Q'][d['mask']])
d['sQ_dil'] = np.sqrt(np.sum(d['sQ'][d['mask']]**2))
d['U_dil'] = np.sum(d['U'][d['mask']])
d['sU_dil'] = np.sqrt(np.sum(d['sU'][d['mask']]**2))
d['P_dil'] = np.sqrt(d['Q_dil']**2+d['U_dil']**2)/d['I_dil']
d['sP_dil'] = np.sqrt((d['Q_dil']**2*d['sQ_dil']**2+d['U_dil']**2*d['sU_dil']**2)/(d['Q_dil']**2+d['U_dil']**2) +
((d['Q_dil']/d['I_dil'])**2+(d['U_dil']/d['I_dil'])**2)*d['sI_dil']**2)/d['I_dil']
d['d_P_dil'] = np.sqrt(d['P_dil']**2-d['sP_dil']**2)
d['PA_dil'] = princ_angle((90./np.pi)*np.arctan2(d['U_dil'], d['Q_dil']))
d['sPA_dil'] = princ_angle((90./(np.pi*(d['Q_dil']**2+d['U_dil']**2)))*np.sqrt(d['Q_dil']**2*d['sU_dil']**2+d['U_dil']**2*d['sU_dil']**2))
print('From this pipeline :\n', "P = {0:.2f} ± {1:.2f} %\n".format(
data_S['d_P_dil']*100., data_S['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_S['PA_dil'], data_S['sPA_dil']))
print("From Kishimoto's pipeline :\n", "P = {0:.2f} ± {1:.2f} %\n".format(
data_K['d_P_dil']*100., data_K['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_K['PA_dil'], data_K['sPA_dil']))
# compare different types of error
print("This pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_S['sI'][data_S['mask']]/data_S['I'][data_S['mask']]), np.mean(
data_S['sQ'][data_S['mask']]/data_S['Q'][data_S['mask']]), np.mean(data_S['sU'][data_S['mask']]/data_S['U'][data_S['mask']]), np.mean(data_S['sP'][data_S['mask']]/data_S['P'][data_S['mask']])))
print("Kishimoto's pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_K['sI'][data_S['mask']]/data_K['I'][data_S['mask']]), np.mean(
data_K['sQ'][data_S['mask']]/data_K['Q'][data_S['mask']]), np.mean(data_K['sU'][data_S['mask']]/data_K['U'][data_S['mask']]), np.mean(data_K['sP'][data_S['mask']]/data_K['P'][data_S['mask']])))
for d, i in zip(['I', 'Q', 'U', 'P', 'PA', 'sI', 'sQ', 'sU', 'sP', 'sPA'], [0, 1, 2, 5, 8, (3, 0, 0), (3, 1, 1), (3, 2, 2), 6, 9]):
data_K[d] = np.loadtxt(path_join(root_dir_K, d+'.txt'))
with fits.open(path_join(root_dir_data_S, filename_S)) as f:
if not type(i) is int:
data_S[d] = np.sqrt(f[i[0]].data[i[1], i[2]])
else:
data_S[d] = f[i].data
if i == 0:
header = f[i].header
# from Kishimoto's pipeline : IQU_dir, IQU_shift, IQU_stat, IQU_trans
# from my pipeline : raw_bg, raw_flat, raw_psf, raw_shift, raw_wav, IQU_dir
# but errors from my pipeline are propagated all along, how to compare then ?
plt.show()

77
package/src/emission_center.py Executable file
View File

@@ -0,0 +1,77 @@
#!/usr/bin/python
# -*- coding:utf-8 -*-
from pathlib import Path
from sys import path as syspath
syspath.append(str(Path(__file__).parent.parent))
def main(infile, target=None, output_dir=None):
from os.path import join as pathjoin
import numpy as np
from astropy.io.fits import open as fits_open
from astropy.wcs import WCS
from lib.plots import polarization_map
from lib.utils import CenterConf, PCconf
from matplotlib.patches import Rectangle
from matplotlib.pyplot import show
output = []
levelssnr = np.array([3.0, 4.0])
levelsconf = np.array([0.99])
Stokes = fits_open(infile)
stkI = Stokes["I_STOKES"].data
QN, UN, QN_ERR, UN_ERR = np.full((4, stkI.shape[0], stkI.shape[1]), np.nan)
for sflux, nflux in zip(
[Stokes["Q_STOKES"].data, Stokes["U_STOKES"].data, np.sqrt(Stokes["IQU_COV_MATRIX"].data[1, 1]), np.sqrt(Stokes["IQU_COV_MATRIX"].data[2, 2])],
[QN, UN, QN_ERR, UN_ERR],
):
nflux[stkI > 0.0] = sflux[stkI > 0.0] / stkI[stkI > 0.0]
Stokesconf = PCconf(QN, UN, QN_ERR, UN_ERR)
Stokesmask = Stokes["DATA_MASK"].data.astype(bool)
Stokessnr = np.zeros(Stokesmask.shape)
Stokessnr[Stokes["POL_DEG_ERR"].data > 0.0] = (
Stokes["POL_DEG_DEBIASED"].data[Stokes["POL_DEG_ERR"].data > 0.0] / Stokes["POL_DEG_ERR"].data[Stokes["POL_DEG_ERR"].data > 0.0]
)
Stokescentconf, Stokescenter = CenterConf(Stokesconf > 0.99, Stokes["POL_ANG"].data, Stokes["POL_ANG_ERR"].data)
Stokespos = WCS(Stokes[0].header).pixel_to_world(*Stokescenter)
if target is None:
target = Stokes[0].header["TARGNAME"]
fig, ax = polarization_map(Stokes, P_cut=0.99, step_vec=2, scale_vec=5, display="i")
snrcont = ax.contour(Stokessnr, levelssnr, colors="b")
confcont = ax.contour(Stokesconf, levelsconf, colors="r")
confcenter = ax.plot(*Stokescenter, marker="+", color="gray", label="Best confidence for center: {0}".format(Stokespos.to_string("hmsdms")))
confcentcont = ax.contour(Stokescentconf, [0.01], colors="gray")
handles, labels = ax.get_legend_handles_labels()
labels.append(r"$SNR_P \geq$ 3 and 4 contours")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=snrcont.get_edgecolor()[0]))
labels.append(r"Polarization $Conf_{99\%}$ contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=confcont.get_edgecolor()[0]))
labels.append(r"Center $Conf_{99\%}$ contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=confcentcont.get_edgecolor()[0]))
ax.legend(handles=handles, labels=labels, bbox_to_anchor=(0.0, -0.12, 1.0, 0.102), loc="lower left", mode="expand", borderaxespad=0.0)
show()
if output_dir is not None:
filename = pathjoin(output_dir, "%s_center.pdf" % target)
fig.savefig(filename, dpi=150, facecolor="None")
output.append(filename)
return output
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Look for the center of emission for a given reduced observation")
parser.add_argument("-t", "--target", metavar="targetname", required=False, help="the name of the target", type=str, default=None)
parser.add_argument("-f", "--file", metavar="path", required=False, help="The full or relative path to the data product", type=str, default=None)
parser.add_argument("-o", "--output_dir", metavar="directory_path", required=False, help="output directory path for the plots", type=str, default="./data")
args = parser.parse_args()
exitcode = main(infile=args.file, target=args.target, output_dir=args.output_dir)
print("Written to: ", exitcode)

5
package/src/get_cdelt.py
View File

@@ -1,4 +1,9 @@
#!/usr/bin/python
# -*- coding:utf-8 -*-
from pathlib import Path
from sys import path as syspath
syspath.append(str(Path(__file__).parent.parent))
def main(infiles=None):

22
package/overplot_IC5063.py → package/src/overplot_IC5063.py
View File

@@ -1,4 +1,10 @@
#!/usr/bin/python3
# -*- coding:utf-8 -*-
from pathlib import Path
from sys import path as syspath
syspath.append(str(Path(__file__).parent.parent))
import numpy as np
from astropy.io import fits
from lib.plots import overplot_pol, overplot_radio
@@ -18,33 +24,33 @@ levelsMorganti = np.logspace(-0.1249, 1.97, 7) / 100.0
levels18GHz = levelsMorganti * Stokes_18GHz[0].data.max()
A = overplot_radio(Stokes_UV, Stokes_18GHz)
A.plot(levels=levels18GHz, SNRp_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/18GHz_overplot.pdf", vec_scale=None)
A.plot(levels=levels18GHz, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/18GHz_overplot.pdf", scale_vec=None)
levels24GHz = levelsMorganti * Stokes_24GHz[0].data.max()
B = overplot_radio(Stokes_UV, Stokes_24GHz)
B.plot(levels=levels24GHz, SNRp_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/24GHz_overplot.pdf", vec_scale=None)
B.plot(levels=levels24GHz, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/24GHz_overplot.pdf", scale_vec=None)
levels103GHz = levelsMorganti * Stokes_103GHz[0].data.max()
C = overplot_radio(Stokes_UV, Stokes_103GHz)
C.plot(levels=levels103GHz, SNRp_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/103GHz_overplot.pdf", vec_scale=None)
C.plot(levels=levels103GHz, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/103GHz_overplot.pdf", scale_vec=None)
levels229GHz = levelsMorganti * Stokes_229GHz[0].data.max()
D = overplot_radio(Stokes_UV, Stokes_229GHz)
D.plot(levels=levels229GHz, SNRp_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/229GHz_overplot.pdf", vec_scale=None)
D.plot(levels=levels229GHz, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/229GHz_overplot.pdf", scale_vec=None)
levels357GHz = levelsMorganti * Stokes_357GHz[0].data.max()
E = overplot_radio(Stokes_UV, Stokes_357GHz)
E.plot(levels=levels357GHz, SNRp_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/357GHz_overplot.pdf", vec_scale=None)
E.plot(levels=levels357GHz, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/357GHz_overplot.pdf", scale_vec=None)
# F = overplot_pol(Stokes_UV, Stokes_S2)
# F.plot(SNRp_cut=3.0, SNRi_cut=80.0, savename='./plots/IC5063/S2_overplot.pdf', norm=LogNorm(vmin=5e-20,vmax=5e-18))
# F.plot(P_cut=3.0, SNRi_cut=80.0, savename='./plots/IC5063/S2_overplot.pdf', norm=LogNorm(vmin=5e-20,vmax=5e-18))
G = overplot_pol(Stokes_UV, Stokes_IR, cmap="inferno")
G.plot(
SNRp_cut=2.0,
P_cut=2.0,
SNRi_cut=10.0,
savename="./plots/IC5063/IR_overplot.pdf",
vec_scale=None,
scale_vec=None,
norm=LogNorm(Stokes_IR[0].data.max() * Stokes_IR[0].header["photflam"] / 1e3, Stokes_IR[0].data.max() * Stokes_IR[0].header["photflam"]),
cmap="inferno_r",
)

12
package/overplot_MRK463E.py → package/src/overplot_MRK463E.py
View File

@@ -1,20 +1,26 @@
#!/usr/bin/python3
# -*- coding:utf-8 -*-
from pathlib import Path
from sys import path as syspath
syspath.append(str(Path(__file__).parent.parent))
import numpy as np
from astropy.io import fits
from lib.plots import overplot_chandra, overplot_pol
from matplotlib.colors import LogNorm
Stokes_UV = fits.open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.10arcsec.fits")
Stokes_UV = fits.open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.07arcsec.fits")
Stokes_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits")
Stokes_Xr = fits.open("./data/MRK463E/Chandra/X_ray_crop.fits")
levels = np.geomspace(1.0, 99.0, 7)
A = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=3.0, vec_scale=5, zoom=1, savename="./plots/MRK463E/Chandra_overplot.pdf")
A.plot(levels=levels, P_cut=0.99, SNRi_cut=1.0, scale_vec=5, zoom=1, savename="./plots/MRK463E/Chandra_overplot.pdf")
A.write_to(path1="./data/MRK463E/FOC_data_Chandra.fits", path2="./data/MRK463E/Chandra_data.fits", suffix="aligned")
levels = np.array([0.8, 2, 5, 10, 20, 50]) / 100.0 * Stokes_UV[0].header["photflam"]
B = overplot_pol(Stokes_UV, Stokes_IR, norm=LogNorm())
B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=3.0, vec_scale=5, norm=LogNorm(8.5e-18, 2.5e-15), savename="./plots/MRK463E/IR_overplot.pdf")
B.plot(levels=levels, P_cut=0.99, SNRi_cut=1.0, scale_vec=5, norm=LogNorm(8.5e-18, 2.5e-15), savename="./plots/MRK463E/IR_overplot.pdf")
B.write_to(path1="./data/MRK463E/FOC_data_WFPC.fits", path2="./data/MRK463E/WFPC_data.fits", suffix="aligned")

109
package/test_center.py
View File

@@ -1,109 +0,0 @@
import matplotlib.pyplot as plt
import numpy as np
from astropy.io.fits import open as fits_open
from astropy.wcs import WCS
from lib.utils import CenterConf, PCconf
from matplotlib.colors import LogNorm
from matplotlib.patches import Rectangle
levelssnr = np.array([3.0, 4.0])
levelsconf = np.array([0.99])
NGC1068 = fits_open("./data/NGC1068/5144/NGC1068_FOC_b0.05arcsec_c0.07arcsec.fits")
NGC1068conf = PCconf(
NGC1068["Q_STOKES"].data / NGC1068["I_STOKES"].data,
NGC1068["U_STOKES"].data / NGC1068["I_STOKES"].data,
np.sqrt(NGC1068["IQU_COV_MATRIX"].data[1, 1]) / NGC1068["I_STOKES"].data,
np.sqrt(NGC1068["IQU_COV_MATRIX"].data[2, 2]) / NGC1068["I_STOKES"].data,
)
NGC1068mask = NGC1068["DATA_MASK"].data.astype(bool)
NGC1068snr = np.full(NGC1068mask.shape, np.nan)
NGC1068snr[NGC1068["POL_DEG_ERR"].data > 0.0] = (
NGC1068["POL_DEG_DEBIASED"].data[NGC1068["POL_DEG_ERR"].data > 0.0] / NGC1068["POL_DEG_ERR"].data[NGC1068["POL_DEG_ERR"].data > 0.0]
)
NGC1068centconf, NGC1068center = CenterConf(NGC1068conf > 0.99, NGC1068["POL_ANG"].data, NGC1068["POL_ANG_ERR"].data)
NGC1068pos = WCS(NGC1068[0].header).pixel_to_world(*NGC1068center)
figngc, axngc = plt.subplots(1, 2, layout="tight", figsize=(18, 9), subplot_kw=dict(projection=WCS(NGC1068[0].header)), sharex=True, sharey=True)
axngc[0].set(xlabel="RA", ylabel="DEC", title="NGC1069 intensity map with SNR and confidence contours")
vmin, vmax = (
0.5 * np.median(NGC1068["I_STOKES"].data[NGC1068mask]) * NGC1068[0].header["PHOTFLAM"],
np.max(NGC1068["I_STOKES"].data[NGC1068mask]) * NGC1068[0].header["PHOTFLAM"],
)
imngc = axngc[0].imshow(NGC1068["I_STOKES"].data * NGC1068["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(vmin, vmax), cmap="inferno")
ngcsnrcont = axngc[0].contour(NGC1068snr, levelssnr, colors="b")
ngcconfcont = axngc[0].contour(NGC1068conf, levelsconf, colors="r")
ngcconfcenter = axngc[0].plot(*NGC1068center, marker="+",color="gray", label="Best confidence for center: {0}".format(NGC1068pos.to_string('hmsdms')))
ngcconfcentcont = axngc[0].contour(NGC1068centconf, [0.01], colors="gray")
handles, labels = axngc[0].get_legend_handles_labels()
labels.append("SNR contours")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcsnrcont.collections[0].get_edgecolor()[0]))
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcconfcont.collections[0].get_edgecolor()[0]))
labels.append("Center CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngcconfcentcont.collections[0].get_edgecolor()[0]))
axngc[0].legend(handles=handles, labels=labels)
axngc[1].set(xlabel="RA", ylabel="DEC", title="Location of the nucleus confidence map")
ngccent = axngc[1].imshow(NGC1068centconf, vmin=0.0, cmap="inferno")
ngccentcont = axngc[1].contour(NGC1068centconf, [0.01], colors="gray")
ngccentcenter = axngc[1].plot(*NGC1068center, marker="+",color="gray", label="Best confidence for center: {0}".format(NGC1068pos.to_string('hmsdms')))
handles, labels = axngc[1].get_legend_handles_labels()
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=ngccentcont.collections[0].get_edgecolor()[0]))
axngc[1].legend(handles=handles, labels=labels)
figngc.savefig("NGC1068_center.pdf", dpi=150, facecolor="None")
###################################################################################################
MRK463E = fits_open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.07arcsec.fits")
MRK463Econf = PCconf(
MRK463E["Q_STOKES"].data / MRK463E["I_STOKES"].data,
MRK463E["U_STOKES"].data / MRK463E["I_STOKES"].data,
np.sqrt(MRK463E["IQU_COV_MATRIX"].data[1, 1]) / MRK463E["I_STOKES"].data,
np.sqrt(MRK463E["IQU_COV_MATRIX"].data[2, 2]) / MRK463E["I_STOKES"].data,
)
MRK463Emask = MRK463E["DATA_MASK"].data.astype(bool)
MRK463Esnr = np.full(MRK463Emask.shape, np.nan)
MRK463Esnr[MRK463E["POL_DEG_ERR"].data > 0.0] = (
MRK463E["POL_DEG_DEBIASED"].data[MRK463E["POL_DEG_ERR"].data > 0.0] / MRK463E["POL_DEG_ERR"].data[MRK463E["POL_DEG_ERR"].data > 0.0]
)
MRK463Ecentconf, MRK463Ecenter = CenterConf(MRK463Econf > 0.99, MRK463E["POL_ANG"].data, MRK463E["POL_ANG_ERR"].data)
MRK463Epos = WCS(MRK463E[0].header).pixel_to_world(*MRK463Ecenter)
figmrk, axmrk = plt.subplots(1, 2, layout="tight", figsize=(18, 9), subplot_kw=dict(projection=WCS(MRK463E[0].header)), sharex=True, sharey=True)
axmrk[0].set(xlabel="RA", ylabel="DEC", title="NGC1069 intensity map with SNR and confidence contours")
vmin, vmax = (
0.5 * np.median(MRK463E["I_STOKES"].data[MRK463Emask]) * MRK463E[0].header["PHOTFLAM"],
np.max(MRK463E["I_STOKES"].data[MRK463Emask]) * MRK463E[0].header["PHOTFLAM"],
)
immrk = axmrk[0].imshow(MRK463E["I_STOKES"].data * MRK463E["I_STOKES"].header["PHOTFLAM"], norm=LogNorm(vmin, vmax), cmap="inferno")
mrksnrcont = axmrk[0].contour(MRK463Esnr, levelssnr, colors="b")
mrkconfcont = axmrk[0].contour(MRK463Econf, levelsconf, colors="r")
mrkconfcenter = axmrk[0].plot(*MRK463Ecenter, marker="+",color="gray", label="Best confidence for center: {0}".format(MRK463Epos.to_string('hmsdms')))
mrkconfcentcont = axmrk[0].contour(MRK463Ecentconf, [0.01], colors="gray")
handles, labels = axmrk[0].get_legend_handles_labels()
labels.append("SNR contours")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrksnrcont.collections[0].get_edgecolor()[0]))
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkconfcont.collections[0].get_edgecolor()[0]))
labels.append("Center CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkconfcentcont.collections[0].get_edgecolor()[0]))
axmrk[0].legend(handles=handles, labels=labels)
axmrk[1].set(xlabel="RA", ylabel="DEC", title="Location of the nucleus confidence map")
mrkcent = axmrk[1].imshow(MRK463Ecentconf, vmin=0.0, cmap="inferno")
mrkcentcont = axmrk[1].contour(MRK463Ecentconf, [0.01], colors="gray")
mrkcentcenter = axmrk[1].plot(*MRK463Ecenter, marker="+",color="gray", label="Best confidence for center: {0}".format(MRK463Epos.to_string('hmsdms')))
handles, labels = axmrk[1].get_legend_handles_labels()
labels.append("CONF99 contour")
handles.append(Rectangle((0, 0), 1, 1, fill=False, ec=mrkcentcont.collections[0].get_edgecolor()[0]))
axmrk[1].legend(handles=handles, labels=labels)
figmrk.savefig("MRK463E_center.pdf", dpi=150, facecolor="None")
plt.show()