Tibeuleu/FOC_Reduction
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merge CZ fork to testing, prepare pipeline for clenup and fix

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Thibault Barnouin
2024-07-17 16:44:06 +02:00
parent 2d0d8105be 1c60e875e8
commit d505ef6f8a
3 changed files with 502 additions and 175 deletions
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package/FOC_reduction.py
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@@ -5,26 +5,19 @@ Main script where are progressively added the steps for the FOC pipeline reducti
"""
# Project libraries
from copy import deepcopy
import os
from os import system
from os.path import exists as path_exists
from matplotlib.colors import LogNorm
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.background import subtract_bkg
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
from lib.utils import princ_angle, sci_not
from matplotlib.colors import LogNorm
def main(target=None, proposal_id=None, data_dir=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):
# Reduction parameters
# Deconvolution
deconvolve = False
@@ -42,10 +35,8 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
display_crop = False
# Background estimation
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
@@ -55,7 +46,6 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Alignement
align_center = "center" # If None will not align the images
display_align = False
display_data = False
@@ -64,7 +54,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Smoothing
smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.1 # If None, no smoothing is done
smoothing_FWHM = 0.10 # If None, no smoothing is done
smoothing_scale = "arcsec" # pixel or arcsec
# Rotation
@@ -84,47 +74,37 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# 3. Use the same alignment as the routine
# 4. Skip the rebinning step
# 5. Calulate the Stokes parameters without smoothing
optimal_binning = True
optimal_binning = False
optimize = False
# Pipeline start
# Step 1:
# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
outfiles = []
if data_dir is None:
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])
if not path_exists(obs_dir):
system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots")))
if target is None:
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:
outfiles.append(main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir, crop=crop, interactive=interactive))
data_folder = prod[0][0]
infiles = [p[1] for p in prod]
data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
else:
infiles = [f for f in os.listdir(data_dir) if f.endswith('.fits') and f.startswith('x')]
data_folder = data_dir
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])
if not path_exists(obs_dir):
system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots")))
if target is None:
target = input("Target name:\n>")
data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
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:
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")
except ValueError:
plots_folder = "."
if not path_exists(plots_folder):
system("mkdir -p {0:s} ".format(plots_folder))
infiles = [p[1] for p in prod]
data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
figname = "_".join([target, "FOC"])
figtype = ""
@@ -133,65 +113,129 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
figtype = "".join(["b", "{0:.2f}".format(pxsize), pxscale]) # additionnal informations
else:
figtype = "full"
if smoothing_FWHM is not None and smoothing_scale is not None:
smoothstr = "".join([*[s[0] for s in smoothing_function.split("_")], "{0:.2f}".format(smoothing_FWHM), smoothing_scale])
figtype = "_".join([figtype, smoothstr] if figtype != "" else [smoothstr])
if deconvolve:
figtype = "_".join([figtype, "deconv"] if figtype != "" else ["deconv"])
if align_center is None:
figtype = "_".join([figtype, "not_aligned"] if figtype != "" else ["not_aligned"])
if optimal_binning:
options = {'optimize': optimize, 'optimal_binning': True}
from lib.background import subtract_bkg
options = {"optimize": optimize, "optimal_binning": True}
# Step 1: Load the data again and preserve the full images
_data_array, _headers = deepcopy(data_array), deepcopy(headers) # Preserve full images
_data_array, _headers = deepcopy(data_array), deepcopy(headers) # Preserve full images
_data_mask = np.ones(_data_array[0].shape, dtype=bool)
# Step 2: Skip the cropping step but use the same error and background estimation (I don't understand why this is wrong)
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)
background = None
_, _, _, background, error_bkg = 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)
_, _, _, background, error_bkg = 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,
)
# _background is the same as background, but for the optimal binning
_background = None
_data_array, _error_array, _, = proj_red.get_error(_data_array, _headers, error_array=None, data_mask=_data_mask, sub_type=error_sub_type, subtract_error=False, display=display_bkg, savename="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=False)
_data_array, _error_array, _ = proj_red.get_error(
_data_array,
_headers,
error_array=None,
data_mask=_data_mask,
sub_type=error_sub_type,
subtract_error=False,
display=display_bkg,
savename="_".join([figname, "errors"]),
plots_folder=plots_folder,
return_background=False,
)
_error_bkg = np.ones_like(_data_array) * error_bkg[:, 0, 0, np.newaxis, np.newaxis]
_data_array, _error_array, _background, _ = subtract_bkg(_data_array, _error_array, _data_mask, background, _error_bkg)
# Step 3: Align and rescale images with oversampling. (has to disable croping in align_data function)
_data_array, _error_array, _headers, _, 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, optimal_binning=True)
_data_array, _error_array, _headers, _, 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,
optimal_binning=True,
)
print("Image shifts: {} \nShifts uncertainty: {}".format(shifts, error_shifts))
_data_mask = np.ones(_data_array[0].shape, dtype=bool)
# Step 4: Compute Stokes I, Q, U
_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)])
_I_stokes, _Q_stokes, _U_stokes, _Stokes_cov, _header_stokes = proj_red.compute_Stokes(_data_array, _error_array, _data_mask, _headers,
FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr)
_I_bkg, _Q_bkg, _U_bkg, _S_cov_bkg, _header_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)
_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)
]
)
_I_stokes, _Q_stokes, _U_stokes, _Stokes_cov, _header_stokes = proj_red.compute_Stokes(
_data_array, _error_array, _data_mask, _headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr
)
_I_bkg, _Q_bkg, _U_bkg, _S_cov_bkg, _header_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 5: Compute polarimetric parameters (polarization degree and angle).
_P, _debiased_P, _s_P, _s_P_P, _PA, _s_PA, _s_PA_P = proj_red.compute_pol(_I_stokes, _Q_stokes, _U_stokes, _Stokes_cov, _header_stokes)
_P_bkg, _debiased_P_bkg, _s_P_bkg, _s_P_P_bkg, _PA_bkg, _s_PA_bkg, _s_PA_P_bkg = proj_red.compute_pol(_I_bkg, _Q_bkg, _U_bkg, _S_cov_bkg, _header_bkg)
# Step 6: Save image to FITS.
figname = "_".join([figname, figtype]) if figtype != "" else figname
_Stokes_hdul = 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,
_header_stokes, _data_mask, figname, data_folder=data_folder, return_hdul=True)
_Stokes_hdul = 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,
_header_stokes,
_data_mask,
figname,
data_folder=data_folder,
return_hdul=True,
)
# Step 6:
_data_mask = _Stokes_hdul['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(
_header_stokes["PHOTPLAM"],
@@ -208,66 +252,196 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Background values
print(
"F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
_header_stokes["PHOTFLAM"], *sci_not(_I_bkg[0, 0] * _header_stokes["PHOTFLAM"], np.sqrt(_S_cov_bkg[0, 0][0, 0]) * _header_stokes["PHOTFLAM"], 2, out=int)
_header_stokes["PHOTFLAM"],
*sci_not(_I_bkg[0, 0] * _header_stokes["PHOTFLAM"], np.sqrt(_S_cov_bkg[0, 0][0, 0]) * _header_stokes["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 pxscale.lower() not in ['full', 'integrate'] and not interactive:
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options)
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=scale_vec, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options)
if pxscale.lower() not in ["full", "integrate"] and not interactive:
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname]),
plots_folder=plots_folder,
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "I"]),
plots_folder=plots_folder,
display="Intensity",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "P_flux"]),
plots_folder=plots_folder,
display="Pol_Flux",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "P"]),
plots_folder=plots_folder,
display="Pol_deg",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "PA"]),
plots_folder=plots_folder,
display="Pol_ang",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "I_err"]),
plots_folder=plots_folder,
display="I_err",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "P_err"]),
plots_folder=plots_folder,
display="Pol_deg_err",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "SNRi"]),
plots_folder=plots_folder,
display="SNRi",
**options,
)
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
vec_scale=scale_vec,
savename="_".join([figname, "SNRp"]),
plots_folder=plots_folder,
display="SNRp",
**options,
)
elif not interactive:
proj_plots.polarization_map(deepcopy(_Stokes_hdul), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
savename=figname, plots_folder=plots_folder, display='integrate', **options)
elif pxscale.lower() not in ['full', 'integrate']:
proj_plots.polarization_map(
deepcopy(_Stokes_hdul),
_data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
savename=figname,
plots_folder=plots_folder,
display="integrate",
**options,
)
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)
else:
options = {'optimize': optimize, 'optimal_binning': False}
options = {"optimize": optimize, "optimal_binning": False}
# 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.
if deconvolve:
data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations, algo=algo)
data_array = proj_red.deconvolve_array(
data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations, algo=algo
)
# Estimate error from data background, estimated from sub-image of desired sub_shape.
background = None
data_array, error_array, headers, background, error_bkg = 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, error_bkg = 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 (pxsize is not None) and not (pxsize == 1 and pxscale.lower() in ["px", "pixel", "pixels"]):
data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(
data_array, error_array, headers, pxsize=pxsize, scale=pxscale, 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 same orientation
rotate_data = np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1
@@ -288,13 +462,29 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
)
# Plot array for checking output
if display_data and pxscale.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 pxscale.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
@@ -303,16 +493,28 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# 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, header_stokes = 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, header_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, header_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_North:
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes = proj_red.rotate_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, SNRi_cut=None)
I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, SNRi_cut=None)
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, SNRi_cut=None
)
I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg = proj_red.rotate_Stokes(
I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, SNRi_cut=None
)
# Compute polarimetric parameters (polarization degree and angle).
P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes)
@@ -321,8 +523,24 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Step 4:
# Save image to FITS.
figname = "_".join([figname, figtype]) if figtype != "" else figname
Stokes_hdul = 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,
header_stokes, data_mask, figname, data_folder=data_folder, return_hdul=True)
Stokes_hdul = 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,
header_stokes,
data_mask,
figname,
data_folder=data_folder,
return_hdul=True,
)
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
# Step 5:
@@ -331,11 +549,11 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
figname += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname+".fits"]))
stokescrop.write_to("/".join([data_folder, figname + ".fits"]))
Stokes_hdul, header_stokes = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
data_mask = Stokes_hdul['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(
header_stokes["PHOTPLAM"],
@@ -352,55 +570,161 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Background values
print(
"F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(
header_stokes["PHOTPLAM"], *sci_not(I_bkg[0, 0] * header_stokes["PHOTPLAM"], np.sqrt(S_cov_bkg[0, 0][0, 0]) * header_stokes["PHOTPLAM"], 2, out=int)
header_stokes["PHOTPLAM"],
*sci_not(I_bkg[0, 0] * header_stokes["PHOTPLAM"], np.sqrt(S_cov_bkg[0, 0][0, 0]) * header_stokes["PHOTPLAM"], 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 pxscale.lower() not in ['full', 'integrate'] and not interactive:
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_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', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vece=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_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', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_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', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_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', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_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', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_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', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, 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', **options)
if pxscale.lower() not in ["full", "integrate"] and not interactive:
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vec=scale_vec,
savename="_".join([figname]),
plots_folder=plots_folder,
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_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",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
flux_lim=flux_lim,
step_vec=step_vec,
scale_vece=scale_vec,
savename="_".join([figname, "P_flux"]),
plots_folder=plots_folder,
display="Pol_Flux",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_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",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_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",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_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",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_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",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_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",
**options,
)
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
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",
**options,
)
elif not interactive:
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
savename=figname, plots_folder=plots_folder, display='integrate', **options)
elif pxscale.lower() not in ['full', 'integrate']:
proj_plots.polarization_map(
deepcopy(Stokes_hdul),
data_mask,
SNRp_cut=SNRp_cut,
SNRi_cut=SNRi_cut,
savename=figname,
plots_folder=plots_folder,
display="integrate",
**options,
)
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 outfiles
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('-d', '--data_dir', metavar='directory_path', required=False, help='directory path to the data products', type=str, 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, data_dir=args.data_dir, infiles=args.files,
output_dir=args.output_dir, crop=args.crop, interactive=args.interactive)
print("Finished with ExitCode: ", exitcode)
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("Written to: ", exitcode)

27
package/lib/plots.py
View File

@@ -406,7 +406,7 @@ def polarization_map(
plt.rcdefaults()
ratiox = max(int(stkI.shape[1]/(stkI.shape[0])),1)
ratioy = max(int((stkI.shape[0])/stkI.shape[1]),1)
fig, ax = plt.subplots(figsize=(6*ratiox, 6*ratioy), layout="compressed", subplot_kw=dict(projection=wcs))
fig, ax = plt.subplots(figsize=(7*ratiox, 7*ratioy), layout="compressed", subplot_kw=dict(projection=wcs))
ax.set(aspect="equal", fc="k", ylim=[-stkI.shape[0]*0.10,stkI.shape[0]*1.15])
# fig.subplots_adjust(hspace=0, wspace=0, left=0.102, right=1.02)
@@ -531,8 +531,8 @@ def polarization_map(
ax.transAxes,
"E",
"N",
length=-0.05,
fontsize=0.02,
length=-0.07,
fontsize=0.03,
loc=1,
aspect_ratio=-(stkI.shape[1]/(stkI.shape[0]*1.25)),
sep_y=0.01,
@@ -736,7 +736,7 @@ class align_maps(object):
length=-0.08,
fontsize=0.03,
loc=1,
aspect_ratio=-(self.map_data.shape[1]/self.map_data.shape[0]),
aspect_ratio=-(self.map_ax.get_xbound()[1]-self.map_ax.get_xbound()[0])/(self.map_ax.get_ybound()[1]-self.map_ax.get_ybound()[0]),
sep_y=0.01,
sep_x=0.01,
angle=-self.map_header["orientat"],
@@ -788,13 +788,13 @@ class align_maps(object):
)
if "ORIENTAT" in list(self.other_header.keys()):
north_dir2 = AnchoredDirectionArrows(
self.map_ax.transAxes,
self.other_ax.transAxes,
"E",
"N",
length=-0.08,
fontsize=0.03,
loc=1,
aspect_ratio=-(self.other_data.shape[1]/self.other_data.shape[0]),
aspect_ratio=-(self.other_ax.get_xbound()[1]-self.other_ax.get_xbound()[0])/(self.other_ax.get_ybound()[1]-self.other_ax.get_ybound()[0]),
sep_y=0.01,
sep_x=0.01,
angle=-self.other_header["orientat"],
@@ -1338,7 +1338,9 @@ class overplot_pol(align_maps):
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({"font.size": 16})
self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(11, 10), subplot_kw=dict(projection=self.other_wcs))
ratiox = max(int(stkI.shape[1]/stkI.shape[0]),1)
ratioy = max(int(stkI.shape[0]/stkI.shape[1]),1)
self.fig_overplot, self.ax_overplot = plt.subplots(figsize=(10*ratiox, 10*ratioy), subplot_kw=dict(projection=self.other_wcs))
self.fig_overplot.subplots_adjust(hspace=0, wspace=0, bottom=0.1, left=0.1, top=0.80, right=1.02)
self.ax_overplot.set_xlabel(label="Right Ascension (J2000)")
@@ -1393,11 +1395,12 @@ class overplot_pol(align_maps):
)
# Display full size polarization vectors
px_scale = self.wcs_UV.wcs.get_cdelt()[0]/self.other_wcs.wcs.get_cdelt()[0]
if scale_vec is None:
self.scale_vec = 2.0
self.scale_vec = 2.0*px_scale
pol[np.isfinite(pol)] = 1.0 / 2.0
else:
self.scale_vec = scale_vec
self.scale_vec = scale_vec*px_scale
step_vec = 1
self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
self.U, self.V = pol * np.cos(np.pi / 2.0 + pang * np.pi / 180.0), pol * np.sin(np.pi / 2.0 + pang * np.pi / 180.0)
@@ -1414,8 +1417,8 @@ class overplot_pol(align_maps):
headwidth=0.0,
headlength=0.0,
headaxislength=0.0,
width=0.5,
linewidth=0.75,
width=0.5*px_scale,
linewidth=0.3*px_scale,
color="white",
edgecolor="black",
transform=self.ax_overplot.get_transform(self.wcs_UV),
@@ -1454,7 +1457,7 @@ class overplot_pol(align_maps):
length=-0.08,
fontsize=0.03,
loc=1,
aspect_ratio=-(stkI.shape[1]/stkI.shape[0]),
aspect_ratio=-(self.ax_overplot.get_xbound()[1]-self.ax_overplot.get_xbound()[0])/(self.ax_overplot.get_ybound()[1]-self.ax_overplot.get_ybound()[0]),
sep_y=0.01,
sep_x=0.01,
angle=-self.Stokes_UV[0].header["orientat"],

6
package/lib/reduction.py
View File

@@ -217,9 +217,9 @@ def bin_ndarray(ndarray, new_shape, operation="sum"):
elif operation.lower() in ["mean", "average", "avg"]:
ndarray = ndarray.mean(-1 * (i + 1))
else:
row_comp = np.mat(get_row_compressor(ndarray.shape[0], new_shape[0], operation))
col_comp = np.mat(get_column_compressor(ndarray.shape[1], new_shape[1], operation))
ndarray = np.array(row_comp * np.mat(ndarray) * col_comp)
row_comp = np.asmatrix(get_row_compressor(ndarray.shape[0], new_shape[0], operation))
col_comp = np.asmatrix(get_column_compressor(ndarray.shape[1], new_shape[1], operation))
ndarray = np.array(row_comp * np.asmatrix(ndarray) * col_comp)
return ndarray