Tibeuleu/FOC_Reduction
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modify interaction, fix use of global variables

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This commit is contained in:
Thibault Barnouin
2024-03-22 15:28:14 +01:00
parent 762b857720
commit 483fd93f42
5 changed files with 151 additions and 181 deletions
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60
src/FOC_reduction.py
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@@ -11,10 +11,11 @@ import lib.fits as proj_fits # Functions to handle fits files
import lib.reduction as proj_red # Functions used in reduction pipeline
import lib.plots as proj_plots # Functions for plotting data
from lib.query import retrieve_products, path_exists, system
from lib.utils import sci_not, princ_angle
from matplotlib.colors import LogNorm
def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=0, interactive=0):
def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=False, interactive=False):
# Reduction parameters
# Deconvolution
deconvolve = False
@@ -34,7 +35,7 @@ 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 = 0.50
display_bkg = False
display_bkg = True
# Data binning
rebin = True
@@ -56,13 +57,9 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
rotate_data = False # rotation to North convention can give erroneous results
rotate_stokes = True
# Final crop
crop = True # Crop to desired ROI
interactive = True # Whether to output to intercative analysis tool
# Polarization map output
SNRp_cut = 3. # P measurments with SNR>3
SNRi_cut = 30. # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
SNRi_cut = 3. # 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
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
@@ -93,6 +90,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
figname = "_".join([target, "FOC"])
figtype = ""
if rebin:
if px_scale not in ['full']:
figtype = "".join(["b", "{0:.2f}".format(pxsize), px_scale]) # additionnal informations
@@ -124,8 +122,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)
if display_align:
proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min(
)*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, str(align_center)]), plots_folder=plots_folder)
proj_plots.plot_obs(data_array, headers, savename="_".join([figname, str(align_center)]), plots_folder=plots_folder, norm=LogNorm(
vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam']))
# Rebin data to desired pixel size.
if rebin:
@@ -140,8 +138,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Plot array for checking output
if display_data and px_scale.lower() not in ['full', 'integrate']:
proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min(
)*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "rebin"]), plots_folder=plots_folder)
proj_plots.plot_obs(data_array, headers, savename="_".join([figname, "rebin"]), plots_folder=plots_folder, norm=LogNorm(
vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam']))
background = np.array([np.array(bkg).reshape(1, 1) for bkg in background])
background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1'] == head['filtnam1'] for h in headers], dtype=bool)].mean())
@@ -171,51 +169,52 @@ 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(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
headers, data_mask, "_".join([figname, figtype]), data_folder=data_folder, return_hdul=True)
headers, data_mask, figname, data_folder=data_folder, return_hdul=True)
data_mask = Stokes_test[-1].data.astype(bool)
# Step 5:
# crop to desired region of interest (roi)
if crop:
figtype += "_crop"
figname += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, "_".join([figname, figtype+".fits"])]))
stokescrop.write_to("/".join([data_folder, figname+".fits"]))
Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop]
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(
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'], 2, out=int)))
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100., np.ceil(headers[0]['p_int_err']*1000.)/10.))
print("PA_int = {0:.1f} ± {1:.1f} °".format(headers[0]['pa_int'], np.ceil(headers[0]['pa_int_err']*10.)/10.))
print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(headers[0]['pa_int']), princ_angle(np.ceil(headers[0]['pa_int_err']*10.)/10.)))
# Background values
print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(
print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *sci_not(
I_bkg[0, 0]*headers[0]['photflam'], np.sqrt(S_cov_bkg[0, 0][0, 0])*headers[0]['photflam'], 2, out=int)))
print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0]*100., np.ceil(s_P_bkg[0, 0]*1000.)/10.))
print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0, 0], np.ceil(s_PA_bkg[0, 0]*10.)/10.))
print("PA_bkg = {0:.1f} ± {1:.1f} °".format(princ_angle(PA_bkg[0, 0]), princ_angle(np.ceil(s_PA_bkg[0, 0]*10.)/10.)))
# Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error).
if px_scale.lower() not in ['full', 'integrate'] and not interactive:
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype]), plots_folder=plots_folder)
step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname]), plots_folder=plots_folder)
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "I"]), plots_folder=plots_folder, display='Intensity')
vec_scale=vec_scale, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
vec_scale=vec_scale, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "P"]), plots_folder=plots_folder, display='Pol_deg')
vec_scale=vec_scale, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "PA"]), plots_folder=plots_folder, display='Pol_ang')
vec_scale=vec_scale, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "I_err"]), plots_folder=plots_folder, display='I_err')
vec_scale=vec_scale, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
vec_scale=vec_scale, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRi"]), plots_folder=plots_folder, display='SNRi')
vec_scale=vec_scale, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi')
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRp"]), plots_folder=plots_folder, display='SNRp')
vec_scale=vec_scale, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp')
elif not interactive:
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
savename="_".join([figname, figtype]), plots_folder=plots_folder, display='integrate')
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)
@@ -231,9 +230,8 @@ if __name__ == "__main__":
parser.add_argument('-f', '--files', metavar='path', required=False, nargs='*', help='the full or relative path to the data products', default=None)
parser.add_argument('-o', '--output_dir', metavar='directory_path', required=False,
help='output directory path for the data products', type=str, default="./data")
parser.add_argument('-c', '--crop', metavar='crop_boolean', required=False, help='whether to crop the analysis region', type=int, default=0)
parser.add_argument('-i', '--interactive', metavar='interactive_boolean', required=False,
help='whether to output to the interactive analysis tool', type=int, default=0)
parser.add_argument('-c', '--crop', action='store_true', required=False, help='whether to crop the analysis region')
parser.add_argument('-i', '--interactive', action='store_true', required=False, help='whether to output to the interactive analysis tool')
args = parser.parse_args()
exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files,
output_dir=args.output_dir, crop=args.crop, interactive=args.interactive)