Tibeuleu/FOC_Reduction
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fix WCS computation, cdelt should not be sorted

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This commit is contained in:
Thibault Barnouin
2025-04-01 17:02:31 +02:00
parent fce787df39
commit b577fc5afe
3 changed files with 10 additions and 10 deletions
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12
package/FOC_reduction.py
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@@ -45,8 +45,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
display_bkg = True display_bkg = True
# Data binning # Data binning
pxsize = 0.05 pxsize = 4
pxscale = "arcsec" # pixel, arcsec or full pxscale = "px" # pixel, arcsec or full
rebin_operation = "sum" # sum or average rebin_operation = "sum" # sum or average
# Alignement # Alignement
@@ -59,15 +59,15 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Smoothing # Smoothing
smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.075 # If None, no smoothing is done smoothing_FWHM = 1.5 # If None, no smoothing is done
smoothing_scale = "arcsec" # pixel or arcsec smoothing_scale = "px" # pixel or arcsec
# Rotation # Rotation
rotate_North = True rotate_North = True
# Polarization map output # Polarization map output
P_cut = 5 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U P_cut = 3 # if >=1.0 cut on the signal-to-noise else cut on the confidence level in Q, U
SNRi_cut = 5.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%. 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 flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
scale_vec = 3 scale_vec = 3
step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length

6
package/lib/fits.py
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@@ -16,7 +16,7 @@ from astropy.io import fits
from astropy.wcs import WCS from astropy.wcs import WCS
from .convex_hull import clean_ROI from .convex_hull import clean_ROI
from .utils import wcs_PA from .utils import wcs_PA, princ_angle
def get_obs_data(infiles, data_folder="", compute_flux=False): def get_obs_data(infiles, data_folder="", compute_flux=False):
@@ -72,13 +72,13 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
for key in keys: for key in keys:
header.remove(key, ignore_missing=True) header.remove(key, ignore_missing=True)
new_cdelt = np.linalg.eigvals(wcs.wcs.cd) new_cdelt = np.linalg.eigvals(wcs.wcs.cd)
new_cdelt.sort() # new_cdelt.sort()
new_wcs.wcs.pc = wcs.wcs.cd.dot(np.diag(1.0 / new_cdelt)) new_wcs.wcs.pc = wcs.wcs.cd.dot(np.diag(1.0 / new_cdelt))
new_wcs.wcs.cdelt = new_cdelt new_wcs.wcs.cdelt = new_cdelt
for key, val in new_wcs.to_header().items(): for key, val in new_wcs.to_header().items():
header[key] = val header[key] = val
try: try:
_ = header["ORIENTAT"] header["ORIENTAT"] = princ_angle(float(header["ORIENTAT"]))
except KeyError: except KeyError:
header["ORIENTAT"] = wcs_PA(new_wcs.wcs.pc[1, 0], np.diag(new_wcs.wcs.pc).mean()) header["ORIENTAT"] = wcs_PA(new_wcs.wcs.pc[1, 0], np.diag(new_wcs.wcs.pc).mean())

2
package/lib/utils.py
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@@ -177,4 +177,4 @@ def wcs_PA(PC21, PC22):
orient = np.arccos(PC22) * 180.0 / np.pi orient = np.arccos(PC22) * 180.0 / np.pi
elif (abs(PC21) < abs(PC22)) and (PC22 < 0): elif (abs(PC21) < abs(PC22)) and (PC22 < 0):
orient = -np.arccos(PC22) * 180.0 / np.pi orient = -np.arccos(PC22) * 180.0 / np.pi
return orient return princ_angle(orient)