Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

better handling of data rotation, add information about reduction in header

Browse Source
This commit is contained in:
Thibault Barnouin
2024-07-03 17:25:34 +02:00
parent 6879a8b551
commit fdcf1cb323
5 changed files with 349 additions and 256 deletions
Download Patch File Download Diff File Expand all files Collapse all files

156
package/FOC_reduction.py
View File

@@ -17,7 +17,7 @@ from lib.utils import princ_angle, sci_not
from matplotlib.colors import LogNorm
def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False):
def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False, **kwargs):
# Reduction parameters
# Deconvolution
deconvolve = False
@@ -36,13 +36,12 @@ 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 = 1.0
subtract_error = 0.7
display_bkg = False
# Data binning
rebin = True
pxsize = 2
px_scale = "px" # pixel, arcsec or full
pxsize = 0.1
pxscale = "arcsec" # pixel, arcsec or full
rebin_operation = "sum" # sum or average
# Alignement
@@ -55,23 +54,45 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Smoothing
smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 1.5 # If None, no smoothing is done
smoothing_scale = "px" # pixel or arcsec
smoothing_FWHM = 0.2 # If None, no smoothing is done
smoothing_scale = "arcsec" # pixel or arcsec
# Rotation
rotate_data = False # rotation to North convention can give erroneous results
rotate_stokes = True
rotate_North = True
# Polarization map output
SNRp_cut = 3.0 # P measurments with SNR>3
SNRi_cut = 3.0 # 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
vec_scale = 3
SNRi_cut = 1.0 # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
flux_lim = 1e-19, 3e-17 # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
scale_vec = 5
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
# Pipeline start
# Step 0:
# Get parameters from kwargs
for key, value in [
["error_sub_type", error_sub_type],
["subtract_error", subtract_error],
["pxsize", pxsize],
["pxscale", pxscale],
["smoothing_function", smoothing_function],
["smoothing_FWHM", smoothing_FWHM],
["smoothing_scale", smoothing_scale],
["SNRp_cut", SNRp_cut],
["SNRi_cut", SNRi_cut],
["flux_lim", flux_lim],
["scale_vec", scale_vec],
["step_vec", step_vec],
]:
try:
value = kwargs[key]
except KeyError:
pass
rebin = True if pxsize is not None else False
# Step 1:
# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
outfiles = []
if infiles is not None:
prod = np.array([["/".join(filepath.split("/")[:-1]), filepath.split("/")[-1]] for filepath in infiles], dtype=str)
obs_dir = "/".join(infiles[0].split("/")[:-1])
@@ -85,7 +106,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
target, products = retrieve_products(target, proposal_id, output_dir=output_dir)
prod = products.pop()
for prods in products:
main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir, crop=crop, interactive=interactive)
outfiles.append(main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir, crop=crop, interactive=interactive)[0])
data_folder = prod[0][0]
try:
plots_folder = data_folder.replace("data", "plots")
@@ -99,8 +120,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
figname = "_".join([target, "FOC"])
figtype = ""
if rebin:
if px_scale not in ["full"]:
figtype = "".join(["b", "{0:.2f}".format(pxsize), px_scale]) # additionnal informations
if pxscale not in ["full"]:
figtype = "".join(["b", "{0:.2f}".format(pxsize), pxscale]) # additionnal informations
else:
figtype = "full"
if smoothing_FWHM is not None:
@@ -137,9 +158,34 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
return_background=True,
)
# Rotate data to have same orientation
rotate_data = np.unique([float(head["ORIENTAT"]) for head in headers]).size != 1
if rotate_data:
ang = np.mean([head["ORIENTAT"] for head in headers])
for head in headers:
head["ORIENTAT"] -= ang
data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers)
if display_data:
proj_plots.plot_obs(
data_array,
headers,
savename="_".join([figname, "rotate_data"]),
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"]
),
)
# Align and rescale images with oversampling.
data_array, error_array, headers, data_mask, shifts, error_shifts = proj_red.align_data(
data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=True
data_array,
headers,
error_array=error_array,
data_mask=data_mask,
background=background,
upsample_factor=10,
ref_center=align_center,
return_shifts=True,
)
if display_align:
@@ -155,17 +201,11 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Rebin data to desired pixel size.
if rebin:
data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(
data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask
data_array, error_array, headers, pxsize=pxsize, scale=pxscale, operation=rebin_operation, data_mask=data_mask
)
# Rotate data to have North up
if rotate_data:
data_mask = np.ones(data_array.shape[1:]).astype(bool)
alpha = headers[0]["orientat"]
data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
# Plot array for checking output
if display_data and px_scale.lower() not in ["full", "integrate"]:
if display_data and pxscale.lower() not in ["full", "integrate"]:
proj_plots.plot_obs(
data_array,
headers,
@@ -202,7 +242,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Step 3:
# Rotate images to have North up
if rotate_stokes:
if rotate_North:
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None
)
@@ -215,7 +255,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Step 4:
# Save image to FITS.
figname = "_".join([figname, figtype]) if figtype != "" else figname
Stokes_test = proj_fits.save_Stokes(
Stokes_hdul = proj_fits.save_Stokes(
I_stokes,
Q_stokes,
U_stokes,
@@ -238,25 +278,25 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# crop to desired region of interest (roi)
if crop:
figname += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname + ".fits"]))
Stokes_test, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
Stokes_hdul, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
data_mask = Stokes_test["data_mask"].data.astype(bool)
data_mask = Stokes_hdul["data_mask"].data.astype(bool)
print(
"F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
headers[0]["photplam"],
*sci_not(
Stokes_test[0].data[data_mask].sum() * headers[0]["photflam"],
np.sqrt(Stokes_test[3].data[0, 0][data_mask].sum()) * headers[0]["photflam"],
Stokes_hdul[0].data[data_mask].sum() * headers[0]["photflam"],
np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum()) * headers[0]["photflam"],
2,
out=int,
),
)
)
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]["p_int"] * 100.0, np.ceil(headers[0]["p_int_err"] * 1000.0) / 10.0))
print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(headers[0]["pa_int"]), princ_angle(np.ceil(headers[0]["pa_int_err"] * 10.0) / 10.0)))
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]["p_int"] * 100.0, np.ceil(headers[0]["sP_int"] * 1000.0) / 10.0))
print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(headers[0]["pa_int"]), princ_angle(np.ceil(headers[0]["sPA_int"] * 10.0) / 10.0)))
# Background values
print(
"F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
@@ -266,122 +306,124 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0] * 100.0, np.ceil(s_P_bkg[0, 0] * 1000.0) / 10.0))
print("PA_bkg = {0:.1f} ± {1:.1f} °".format(princ_angle(PA_bkg[0, 0]), princ_angle(np.ceil(s_PA_bkg[0, 0] * 10.0) / 10.0)))
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
if px_scale.lower() not in ["full", "integrate"] and not interactive:
if pxscale.lower() not in ["full", "integrate"] and not interactive:
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname]),
plots_folder=plots_folder,
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "I"]),
plots_folder=plots_folder,
display="Intensity",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "P_flux"]),
plots_folder=plots_folder,
display="Pol_Flux",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "P"]),
plots_folder=plots_folder,
display="Pol_deg",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "PA"]),
plots_folder=plots_folder,
display="Pol_ang",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "I_err"]),
plots_folder=plots_folder,
display="I_err",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "P_err"]),
plots_folder=plots_folder,
display="Pol_deg_err",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "SNRi"]),
plots_folder=plots_folder,
display="SNRi",
)
proj_plots.polarization_map(
deepcopy(Stokes_test),
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=vec_scale,
scale_vec=scale_vec,
savename="_".join([figname, "SNRp"]),
plots_folder=plots_folder,
display="SNRp",
)
elif not interactive:
proj_plots.polarization_map(
deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"
deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=figname, plots_folder=plots_folder, display="integrate"
)
elif px_scale.lower() not in ["full", "integrate"]:
proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
elif pxscale.lower() not in ["full", "integrate"]:
proj_plots.pol_map(Stokes_hdul, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
return 0
outfiles.append(Stokes_hdul[0].header["FILENAME"])
return outfiles
if __name__ == "__main__":
@@ -400,4 +442,4 @@ if __name__ == "__main__":
exitcode = main(
target=args.target, proposal_id=args.proposal_id, infiles=args.files, output_dir=args.output_dir, crop=args.crop, interactive=args.interactive
)
print("Finished with ExitCode: ", exitcode)
print("Written to: ", exitcode)