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

reformat code using python-lsp-ruff

Browse Source
This commit is contained in:
Thibault Barnouin
2024-07-01 15:21:52 +02:00
parent 271ecbb631
commit 5a62fa4983
13 changed files with 1271 additions and 860 deletions
Download Patch File Download Diff File Expand all files Collapse all files

337
package/FOC_reduction.py
View File

@@ -5,14 +5,15 @@ Main script where are progressively added the steps for the FOC pipeline reducti
"""
# Project libraries
import numpy as np
from copy import deepcopy
from os import system
from os.path import exists as path_exists
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.utils import sci_not, princ_angle
import lib.fits as proj_fits # Functions to handle fits files
import lib.plots as proj_plots # Functions for plotting data
import lib.reduction as proj_red # Functions used in reduction pipeline
import numpy as np
from lib.utils import princ_angle, sci_not
from matplotlib.colors import LogNorm
@@ -22,10 +23,10 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
deconvolve = False
if deconvolve:
# from lib.deconvolve import from_file_psf
psf = 'gaussian' # Can be user-defined as well
psf = "gaussian" # Can be user-defined as well
# psf = from_file_psf(data_folder+psf_file)
psf_FWHM = 3.1
psf_scale = 'px'
psf_scale = "px"
psf_shape = None # (151, 151)
iterations = 1
algo = "conjgrad"
@@ -34,45 +35,45 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
display_crop = False
# Background estimation
error_sub_type = 'freedman-diaconis' # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 0.01
display_bkg = True
error_sub_type = "freedman-diaconis" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
subtract_error = 1.0
display_bkg = False
# Data binning
rebin = True
pxsize = 2
px_scale = 'px' # pixel, arcsec or full
rebin_operation = 'sum' # sum or average
px_scale = "px" # pixel, arcsec or full
rebin_operation = "sum" # sum or average
# Alignement
align_center = 'center' # If None will not align the images
display_align = True
align_center = "center" # If None will not align the images
display_align = False
display_data = False
# Transmittance correction
transmitcorr = True
# Smoothing
smoothing_function = 'combine' # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = None # If None, no smoothing is done
smoothing_scale = 'px' # pixel or arcsec
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
# Rotation
rotate_data = False # rotation to North convention can give erroneous results
rotate_data = False # rotation to North convention can give erroneous results
rotate_stokes = True
# Polarization map output
SNRp_cut = 3. # P measurments with SNR>3
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 = 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
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
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 1:
# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
if infiles is not None:
prod = np.array([["/".join(filepath.split('/')[:-1]), filepath.split('/')[-1]] for filepath in infiles], dtype=str)
prod = np.array([["/".join(filepath.split("/")[:-1]), filepath.split("/")[-1]] for filepath in infiles], dtype=str)
obs_dir = "/".join(infiles[0].split("/")[:-1])
if not path_exists(obs_dir):
system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots")))
@@ -80,6 +81,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
target = input("Target name:\n>")
else:
from lib.query import retrieve_products
target, products = retrieve_products(target, proposal_id, output_dir=output_dir)
prod = products.pop()
for prods in products:
@@ -97,21 +99,23 @@ 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 px_scale not in ["full"]:
figtype = "".join(["b", "{0:.2f}".format(pxsize), px_scale]) # additionnal informations
else:
figtype = "full"
if smoothing_FWHM is not None:
figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]),
"{0:.2f}".format(smoothing_FWHM), smoothing_scale]) # additionnal informations
figtype += "_" + "".join(
["".join([s[0] for s in smoothing_function.split("_")]), "{0:.2f}".format(smoothing_FWHM), smoothing_scale]
) # additionnal informations
if deconvolve:
figtype += "_deconv"
if align_center is None:
figtype += "_not_aligned"
# Crop data to remove outside blank margins.
data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0.,
inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
data_array, error_array, headers = proj_red.crop_array(
data_array, headers, step=5, null_val=0.0, inside=True, display=display_crop, savename=figname, plots_folder=plots_folder
)
data_mask = np.ones(data_array[0].shape, dtype=bool)
# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
@@ -120,36 +124,68 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Estimate error from data background, estimated from sub-image of desired sub_shape.
background = None
data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, data_mask=data_mask, sub_type=error_sub_type, subtract_error=subtract_error, display=display_bkg, savename="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=True)
data_array, error_array, headers, background = proj_red.get_error(
data_array,
headers,
error_array,
data_mask=data_mask,
sub_type=error_sub_type,
subtract_error=subtract_error,
display=display_bkg,
savename="_".join([figname, "errors"]),
plots_folder=plots_folder,
return_background=True,
)
# 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, background=background, upsample_factor=10, ref_center=align_center, return_shifts=True
)
if display_align:
print("Image shifts: {} \nShifts uncertainty: {}".format(shifts, error_shifts))
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']))
proj_plots.plot_obs(
data_array,
headers,
savename="_".join([figname, str(align_center)]),
plots_folder=plots_folder,
norm=LogNorm(vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"]),
)
# 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=px_scale, 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']
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']:
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']))
if display_data and px_scale.lower() not in ["full", "integrate"]:
proj_plots.plot_obs(
data_array,
headers,
savename="_".join([figname, "rebin"]),
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"]),
)
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())
** 2/np.sum([h['filtnam1'] == head['filtnam1'] for h in headers]))).reshape(1, 1) for bkg, head in zip(background, headers)])
background_error = np.array(
[
np.array(
np.sqrt(
(bkg - background[np.array([h["filtnam1"] == head["filtnam1"] for h in headers], dtype=bool)].mean()) ** 2
/ np.sum([h["filtnam1"] == head["filtnam1"] for h in headers])
)
).reshape(1, 1)
for bkg, head in zip(background, headers)
]
)
# Step 2:
# Compute Stokes I, Q, U with smoothed polarized images
@@ -158,15 +194,18 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
# Bibcode : 1995chst.conf...10J
I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(
data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr)
I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(
1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr
)
I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(
background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False
)
# Step 3:
# Rotate images to have North up
if rotate_stokes:
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)
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None
)
I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), headers, SNRi_cut=None)
# Compute polarimetric parameters (polarization degree and angle).
@@ -176,8 +215,24 @@ 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, figname, data_folder=data_folder, return_hdul=True)
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,
figname,
data_folder=data_folder,
return_hdul=True,
)
# Step 5:
# crop to desired region of interest (roi)
@@ -185,43 +240,145 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
figname += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname+".fits"]))
stokescrop.write_to("/".join([data_folder, figname + ".fits"]))
Stokes_test, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
data_mask = Stokes_test['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'], 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(princ_angle(headers[0]['pa_int']), princ_angle(np.ceil(headers[0]['pa_int_err']*10.)/10.)))
data_mask = Stokes_test["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"],
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)))
# Background values
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(princ_angle(PA_bkg[0, 0]), princ_angle(np.ceil(s_PA_bkg[0, 0]*10.)/10.)))
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] * 100.0, 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:
proj_plots.polarization_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]), plots_folder=plots_folder)
proj_plots.polarization_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, "I"]), plots_folder=plots_folder, display='Intensity')
proj_plots.polarization_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, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
proj_plots.polarization_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, "P"]), plots_folder=plots_folder, display='Pol_deg')
proj_plots.polarization_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, "PA"]), plots_folder=plots_folder, display='Pol_ang')
proj_plots.polarization_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, "I_err"]), plots_folder=plots_folder, display='I_err')
proj_plots.polarization_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, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
proj_plots.polarization_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, "SNRi"]), plots_folder=plots_folder, display='SNRi')
proj_plots.polarization_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, "SNRp"]), plots_folder=plots_folder, display='SNRp')
if px_scale.lower() not in ["full", "integrate"] and not interactive:
proj_plots.polarization_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]),
plots_folder=plots_folder,
)
proj_plots.polarization_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, "I"]),
plots_folder=plots_folder,
display="Intensity",
)
proj_plots.polarization_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, "P_flux"]),
plots_folder=plots_folder,
display="Pol_Flux",
)
proj_plots.polarization_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, "P"]),
plots_folder=plots_folder,
display="Pol_deg",
)
proj_plots.polarization_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, "PA"]),
plots_folder=plots_folder,
display="Pol_ang",
)
proj_plots.polarization_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, "I_err"]),
plots_folder=plots_folder,
display="I_err",
)
proj_plots.polarization_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, "P_err"]),
plots_folder=plots_folder,
display="Pol_deg_err",
)
proj_plots.polarization_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, "SNRi"]),
plots_folder=plots_folder,
display="SNRi",
)
proj_plots.polarization_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, "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')
elif px_scale.lower() not in ['full', 'integrate']:
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"
)
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)
return 0
@@ -230,15 +387,17 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Query MAST for target products')
parser.add_argument('-t', '--target', metavar='targetname', required=False, help='the name of the target', type=str, default=None)
parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False, help='the proposal id of the data products', type=int, default=None)
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', 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')
parser = argparse.ArgumentParser(description="Query MAST for target products")
parser.add_argument("-t", "--target", metavar="targetname", required=False, help="the name of the target", type=str, default=None)
parser.add_argument("-p", "--proposal_id", metavar="proposal_id", required=False, help="the proposal id of the data products", type=int, default=None)
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", 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)
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)