Tibeuleu/FOC_Reduction
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small fixes and improvments

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This commit is contained in:
Thibault Barnouin
2024-10-17 17:05:35 +02:00
parent bd7cad46a1
commit f6d62bff73
8 changed files with 119 additions and 174 deletions
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4
README.md
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@@ -1,2 +1,6 @@
# FOC_Reduction
FOC reduction pipeline
TODO:
- Add all polarimetry capables instruments from HST (starting with FOS and ACS)
- Build science case for future UV polarimeters (AGN outflow geometry, dust scattering, torus outline ?)

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doc/pipeline.pdf Normal file
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134
package/FOC_reduction.py
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@@ -41,8 +41,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Background estimation
error_sub_type = "freedman-diaconis" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 0.5
display_bkg = False
subtract_error = 1.0
display_bkg = True
# Data binning
pxsize = 0.05
@@ -51,7 +51,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Alignement
align_center = "center" # If None will not align the images
display_align = False
display_align = True
display_data = False
# Transmittance correction
@@ -176,6 +176,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
proj_plots.plot_obs(
data_array,
headers,
shifts=shifts,
savename="_".join([figname, str(align_center)]),
plots_folder=plots_folder,
norm=LogNorm(vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"]),
@@ -305,114 +306,25 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
savename="_".join([figname]),
plots_folder=plots_folder,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "I"]),
plots_folder=plots_folder,
display="Intensity",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "P_flux"]),
plots_folder=plots_folder,
display="Pol_Flux",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "P"]),
plots_folder=plots_folder,
display="Pol_deg",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "PA"]),
plots_folder=plots_folder,
display="Pol_ang",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "I_err"]),
plots_folder=plots_folder,
display="I_err",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "P_err"]),
plots_folder=plots_folder,
display="Pol_deg_err",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "SNRi"]),
plots_folder=plots_folder,
display="SNRi",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut if P_cut >= 1.0 else 3.0,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, "SNRp"]),
plots_folder=plots_folder,
display="SNRp",
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut if P_cut < 1.0 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",
)
for figtype, figsuffix in zip(
["Intensity", "Pol_flux", "Pol_deg", "Pol_ang", "I_err", "P_err", "SNRi", "SNRp", "confp"],
["I", "P_flux", "P", "PA", "I_err", "P_err", "SNRi", "SNRp", "confP"],
):
try:
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
P_cut=P_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname, figsuffix]),
plots_folder=plots_folder,
display=figtype,
)
except ValueError:
pass
elif not interactive:
proj_plots.polarization_map(
deepcopy(Stokes_hdul), data_mask, P_cut=P_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"

60
package/lib/plots.py
View File

@@ -66,7 +66,7 @@ except ImportError:
from utils import PCconf, princ_angle, rot2D, sci_not
def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder="", **kwargs):
def plot_obs(data_array, headers, rectangle=None, shifts=None, savename=None, plots_folder="", **kwargs):
"""
Plots raw observation imagery with some information on the instrument and
filters.
@@ -77,16 +77,14 @@ def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder=""
single observation with multiple polarizers of an instrument
headers : header list
List of headers corresponding to the images in data_array
vmin : float, optional
Min pixel value that should be displayed.
Defaults to 0.
vmax : float, optional
Max pixel value that should be displayed.
Defaults to 6.
rectangle : numpy.ndarray, optional
Array of parameters for matplotlib.patches.Rectangle objects that will
be displayed on each output image. If None, no rectangle displayed.
Defaults to None.
shifts : numpy.ndarray, optional
Array of vector coordinates corresponding to images shifts with respect
to the others. If None, no shifts displayed.
Defaults to None.
savename : str, optional
Name of the figure the map should be saved to. If None, the map won't
be saved (only displayed).
@@ -100,6 +98,7 @@ def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder=""
nb_obs = np.max([np.sum([head["filtnam1"] == curr_pol for head in headers]) for curr_pol in ["POL0", "POL60", "POL120"]])
shape = np.array((3, nb_obs))
fig, ax = plt.subplots(shape[0], shape[1], figsize=(3 * shape[1], 3 * shape[0]), layout="constrained", sharex=True, sharey=True)
used = np.zeros(shape, dtype=bool)
r_pol = dict(pol0=0, pol60=1, pol120=2)
c_pol = dict(pol0=0, pol60=0, pol120=0)
for i, (data, head) in enumerate(zip(data_array, headers)):
@@ -112,15 +111,17 @@ def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder=""
c_pol[filt.lower()] += 1
if shape[1] != 1:
ax_curr = ax[r_ax][c_ax]
used[r_ax][c_ax] = True
else:
ax_curr = ax[r_ax]
ax_curr[r_ax] = True
# plots
if "vmin" in kwargs.keys() or "vmax" in kwargs.keys():
vmin, vmax = kwargs["vmin"], kwargs["vmax"]
del kwargs["vmin"], kwargs["vmax"]
else:
vmin, vmax = convert * data[data > 0.0].min() / 10.0, convert * data[data > 0.0].max()
for key, value in [["cmap", [["cmap", "gray"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
for key, value in [["cmap", [["cmap", "inferno"]]], ["norm", [["norm", LogNorm(vmin, vmax)]]]]:
try:
_ = kwargs[key]
except KeyError:
@@ -129,17 +130,29 @@ def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder=""
# im = ax[r_ax][c_ax].imshow(convert*data, origin='lower', **kwargs)
data[data * convert < vmin * 10.0] = vmin * 10.0 / convert
im = ax_curr.imshow(convert * data, origin="lower", **kwargs)
if shifts is not None:
x, y = np.array(data.shape[::-1]) / 2.0 - shifts[i]
dx, dy = shifts[i]
ax_curr.arrow(x, y, dx, dy, length_includes_head=True, width=0.1, head_width=0.3, color="g")
ax_curr.plot([x, x], [0, data.shape[0] - 1], "--", lw=2, color="g", alpha=0.85)
ax_curr.plot([0, data.shape[1] - 1], [y, y], "--", lw=2, color="g", alpha=0.85)
if rectangle is not None:
x, y, width, height, angle, color = rectangle[i]
ax_curr.add_patch(Rectangle((x, y), width, height, angle=angle, edgecolor=color, fill=False))
# position of centroid
ax_curr.plot([data.shape[1] / 2, data.shape[1] / 2], [0, data.shape[0] - 1], "--", lw=1, color="grey", alpha=0.5)
ax_curr.plot([0, data.shape[1] - 1], [data.shape[1] / 2, data.shape[1] / 2], "--", lw=1, color="grey", alpha=0.5)
ax_curr.plot([data.shape[1] / 2, data.shape[1] / 2], [0, data.shape[0] - 1], "--", lw=2, color="b", alpha=0.85)
ax_curr.plot([0, data.shape[1] - 1], [data.shape[0] / 2, data.shape[0] / 2], "--", lw=2, color="b", alpha=0.85)
ax_curr.annotate(
instr + ":" + rootname, color="white", fontsize=5, xy=(0.01, 1.00), xycoords="axes fraction", verticalalignment="top", horizontalalignment="left"
instr + ":" + rootname, color="white", fontsize=10, xy=(0.01, 1.00), xycoords="axes fraction", verticalalignment="top", horizontalalignment="left"
)
ax_curr.annotate(filt, color="white", fontsize=10, xy=(0.01, 0.01), xycoords="axes fraction", verticalalignment="bottom", horizontalalignment="left")
ax_curr.annotate(exptime, color="white", fontsize=5, xy=(1.00, 0.01), xycoords="axes fraction", verticalalignment="bottom", horizontalalignment="right")
ax_curr.annotate(filt, color="white", fontsize=15, xy=(0.01, 0.01), xycoords="axes fraction", verticalalignment="bottom", horizontalalignment="left")
ax_curr.annotate(
exptime, color="white", fontsize=10, xy=(1.00, 0.01), xycoords="axes fraction", verticalalignment="bottom", horizontalalignment="right"
)
unused = np.logical_not(used)
ii, jj = np.indices(shape)
for i, j in zip(ii[unused], jj[unused]):
fig.delaxes(ax[i][j])
# fig.subplots_adjust(hspace=0.01, wspace=0.01, right=1.02)
fig.colorbar(im, ax=ax, location="right", shrink=0.75, aspect=50, pad=0.025, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
@@ -349,7 +362,7 @@ def polarization_map(
fig = plt.figure(figsize=(7 * ratiox, 7 * ratioy), layout="constrained")
if ax is None:
ax = fig.add_subplot(111, projection=wcs)
ax.set(aspect="equal", fc="k", ylim=[-stkI.shape[0] * 0.10, stkI.shape[0] * 1.15])
ax.set(aspect="equal", fc="k", ylim=[-stkI.shape[0] * 0.01, stkI.shape[0] * 1.01])
# fig.subplots_adjust(hspace=0, wspace=0, left=0.102, right=1.02)
# ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
@@ -677,7 +690,7 @@ class align_maps(object):
except KeyError:
for key_i, val_i in value:
kwargs[key_i] = val_i
self.map_ax.imshow(self.map_data * self.map_convert, aspect="equal", **kwargs)
self.im = self.map_ax.imshow(self.map_data * self.map_convert, aspect="equal", **kwargs)
if kwargs["cmap"] in [
"inferno",
@@ -766,7 +779,7 @@ class align_maps(object):
except KeyError:
for key_i, val_i in value:
other_kwargs[key_i] = val_i
self.other_ax.imshow(self.other_data * self.other_convert, aspect="equal", **other_kwargs)
self.other_im = self.other_ax.imshow(self.other_data * self.other_convert, aspect="equal", **other_kwargs)
px_size2 = self.other_wcs.wcs.get_cdelt()[0] * 3600.0
px_sc2 = AnchoredSizeBar(
@@ -1561,6 +1574,7 @@ class overplot_pol(align_maps):
while not self.aligned:
self.align()
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):
if isinstance(position, str) and position == "center":
@@ -2148,7 +2162,7 @@ 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:
for coll in self.cont.collections:
coll.remove()
self.cont = self.ax.contour(self.mask.astype(float), levels=[0.5], colors="white", linewidths=1)
if not self.embedded:
@@ -2261,7 +2275,7 @@ 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:
for coll in self.cont.collections:
try:
coll.remove()
except AttributeError:
@@ -2364,7 +2378,7 @@ 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:
for coll in self.cont.collections:
try:
coll.remove()
except AttributeError:
@@ -2499,7 +2513,7 @@ 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:
for coll in self.select_instance.cont.collections:
coll.remove()
self.select_instance.lasso.set_active(False)
self.set_data_mask(deepcopy(self.region))
@@ -2543,7 +2557,7 @@ 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:
for coll in self.select_instance.cont.collections:
coll.remove()
self.select_instance.circ.set_visible(False)
self.set_data_mask(deepcopy(self.region))
@@ -2601,7 +2615,7 @@ 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:
for coll in self.select_instance.cont.collections:
coll.remove()
self.select_instance.rect.set_visible(False)
self.set_data_mask(deepcopy(self.region))
@@ -3324,7 +3338,7 @@ class pol_map(object):
)
if hasattr(self, "cont"):
for coll in self.cont:
for coll in self.cont.collections:
try:
coll.remove()
except AttributeError:

6
package/src/Combine.py
View File

@@ -173,7 +173,7 @@ def main(infiles, target=None, output_dir="./data/"):
# Reduction parameters
kwargs = {}
# Polarization map output
kwargs["SNRp_cut"] = 3.0
kwargs["P_cut"] = 0.99
kwargs["SNRi_cut"] = 1.0
kwargs["flux_lim"] = 1e-19, 3e-17
kwargs["scale_vec"] = 5
@@ -186,9 +186,7 @@ def main(infiles, target=None, output_dir="./data/"):
new_infiles = []
for i, group in enumerate(grouped_infiles):
new_infiles.append(
FOC_reduction(target=target + "-" + str(i + 1), infiles=["/".join([data_folder, file]) for file in group], interactive=True)[0]
)
new_infiles.append(FOC_reduction(target=target + "-" + str(i + 1), infiles=["/".join([data_folder, file]) for file in group], interactive=True)[0])
infiles = new_infiles

48
package/src/overplot_IC5063.py
View File

@@ -11,46 +11,46 @@ from lib.plots import overplot_pol, overplot_radio
from matplotlib.colors import LogNorm
Stokes_UV = fits.open("./data/IC5063/5918/IC5063_FOC_b0.10arcsec_c0.20arcsec.fits")
Stokes_18GHz = fits.open("./data/IC5063/radio/IC5063_18GHz.fits")
Stokes_24GHz = fits.open("./data/IC5063/radio/IC5063_24GHz.fits")
Stokes_103GHz = fits.open("./data/IC5063/radio/IC5063_103GHz.fits")
Stokes_229GHz = fits.open("./data/IC5063/radio/IC5063_229GHz.fits")
Stokes_357GHz = fits.open("./data/IC5063/radio/IC5063_357GHz.fits")
# Stokes_18GHz = fits.open("./data/IC5063/radio/IC5063_18GHz.fits")
# Stokes_24GHz = fits.open("./data/IC5063/radio/IC5063_24GHz.fits")
# Stokes_103GHz = fits.open("./data/IC5063/radio/IC5063_103GHz.fits")
# Stokes_229GHz = fits.open("./data/IC5063/radio/IC5063_229GHz.fits")
# Stokes_357GHz = fits.open("./data/IC5063/radio/IC5063_357GHz.fits")
# Stokes_S2 = fits.open("./data/IC5063/POLARIZATION_COMPARISON/S2_rot_crop.fits")
Stokes_IR = fits.open("./data/IC5063/IR/u2e65g01t_c0f_rot.fits")
# levelsMorganti = np.array([1.,2.,3.,8.,16.,32.,64.,128.])
levelsMorganti = np.logspace(-0.1249, 1.97, 7) / 100.0
# 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, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/18GHz_overplot.pdf", scale_vec=None)
# levels18GHz = levelsMorganti * Stokes_18GHz[0].data.max()
# A = overplot_radio(Stokes_UV, Stokes_18GHz)
# 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, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/24GHz_overplot.pdf", scale_vec=None)
# levels24GHz = levelsMorganti * Stokes_24GHz[0].data.max()
# B = overplot_radio(Stokes_UV, Stokes_24GHz)
# 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, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/103GHz_overplot.pdf", scale_vec=None)
# levels103GHz = levelsMorganti * Stokes_103GHz[0].data.max()
# C = overplot_radio(Stokes_UV, Stokes_103GHz)
# 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, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/229GHz_overplot.pdf", scale_vec=None)
# levels229GHz = levelsMorganti * Stokes_229GHz[0].data.max()
# D = overplot_radio(Stokes_UV, Stokes_229GHz)
# 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, P_cut=2.0, SNRi_cut=10.0, savename="./plots/IC5063/357GHz_overplot.pdf", scale_vec=None)
# levels357GHz = levelsMorganti * Stokes_357GHz[0].data.max()
# E = overplot_radio(Stokes_UV, Stokes_357GHz)
# 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(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(
P_cut=2.0,
SNRi_cut=10.0,
P_cut=0.99,
SNRi_cut=1.0,
savename="./plots/IC5063/IR_overplot.pdf",
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",
cmap="inferno",
)

35
package/src/overplot_MRK463E.py
View File

@@ -11,16 +11,33 @@ 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_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits")
Stokes_Xr = fits.open("./data/MRK463E/Chandra/X_ray_crop.fits")
# Stokes_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits")
# Stokes_Xr = fits.open("./data/MRK463E/Chandra/X_ray_crop.fits")
Radio = fits.open("./data/MRK463E/EMERLIN/Voorwerpjes_1356+1822_1356+1822_uniform-image.fits")
levels = np.geomspace(1.0, 99.0, 7)
# levels = np.geomspace(1.0, 99.0, 7)
A = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
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")
# A = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
# 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, 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")
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, 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")
C = overplot_pol(Stokes_UV, Radio, norm=LogNorm())
C.other_im.set(norm=LogNorm(1e-4, 2e-2))
C.plot(
levels=levels,
P_cut=0.999,
SNRi_cut=5.0,
scale_vec=3,
norm=LogNorm(1e-4, 2e-2),
cmap="inferno_r",
width=0.5,
linewidth=0.5,
savename="./plots/MRK463E/EMERLIN_overplot.pdf",
)
C.write_to(path1="./data/MRK463E/FOC_data_EMERLIN.fits", path2="./data/MRK463E/EMERLIN_data.fits", suffix="aligned")

6
package/src/overplot_NGC1068.py
View File

@@ -30,10 +30,10 @@ A.add_vector(
A.other_wcs.celestial.wcs.crpix - (1.0, 1.0),
pol_deg=0.124,
pol_ang=100.7,
width=2,
linewidth=2,
width=2.0,
linewidth=1.0,
scale=1.0 / (A.px_scale * 6.0),
edgecolor="b",
edgecolor="w",
color="b",
label=r"IXPE torus: P = $12.4 (\pm 3.6)$%, PA = $100.7 (\pm 8.3)$°",
)