381 lines
26 KiB
Python
Executable File
381 lines
26 KiB
Python
Executable File
#!/usr/bin/python
|
|
# -*- coding:utf-8 -*-
|
|
"""
|
|
Main script where are progressively added the steps for the FOC pipeline reduction.
|
|
"""
|
|
|
|
# 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 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
|
|
|
|
|
|
|
|
|
|
|
|
def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir="./data", crop=False, interactive=False):
|
|
# Reduction parameters
|
|
# Deconvolution
|
|
deconvolve = False
|
|
if deconvolve:
|
|
# from lib.deconvolve import from_file_psf
|
|
psf = "gaussian" # Can be user-defined as well
|
|
# psf = from_file_psf(data_folder+psf_file)
|
|
psf_FWHM = 3.1
|
|
psf_scale = "px"
|
|
psf_shape = None # (151, 151)
|
|
iterations = 1
|
|
algo = "conjgrad"
|
|
|
|
# Initial 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 = 1.0
|
|
|
|
display_bkg = False
|
|
|
|
# Data binning
|
|
pxsize = 2
|
|
pxscale = "px" # pixel, arcsec or full
|
|
rebin_operation = "sum" # sum or average
|
|
|
|
# Alignement
|
|
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 = 2.0 # If None, no smoothing is done
|
|
smoothing_scale = "px" # pixel or arcsec
|
|
|
|
# Rotation
|
|
rotate_North = True
|
|
|
|
# Polarization map output
|
|
SNRp_cut = 3.0 # P measurments with SNR>3
|
|
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
|
|
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
|
|
|
|
# Adaptive binning
|
|
# in order to perfrom optimal binning, there are several steps to follow:
|
|
# 1. Load the data again and preserve the full images
|
|
# 2. Skip the cropping step but use the same error and background estimation
|
|
# 3. Use the same alignment as the routine
|
|
# 4. Skip the rebinning step
|
|
# 5. Calulate the Stokes parameters without smoothing
|
|
optimal_binning = True
|
|
optimize = False
|
|
|
|
# Pipeline start
|
|
|
|
# Step 1:
|
|
# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
|
|
if data_dir is None:
|
|
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])
|
|
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 target is None:
|
|
target = input("Target name:\n>")
|
|
|
|
data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
|
|
|
|
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))
|
|
|
|
figname = "_".join([target, "FOC"])
|
|
figtype = ""
|
|
if (pxsize is not None) and not (pxsize == 1 and pxscale.lower() in ["px", "pixel", "pixels"]):
|
|
if pxscale not in ["full"]:
|
|
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}
|
|
|
|
# Step 1: Load the data again and preserve the 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_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 is the same as background, but for the optimal binning
|
|
_background = None
|
|
_, _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=True)
|
|
_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)
|
|
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 = 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 = 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, _headers)
|
|
_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, _headers)
|
|
|
|
# Step 6: 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)
|
|
|
|
# Step 6:
|
|
_data_mask = _Stokes_test['data_mask'].data.astype(bool)
|
|
print(_data_array.shape, _data_mask.shape)
|
|
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.)))
|
|
# 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.)))
|
|
# 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_test), _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_test), _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_test), _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_test), _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_test), _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_test), _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_test), _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_test), _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_test), _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_test), _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_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
|
|
|
|
else:
|
|
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_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)
|
|
|
|
# 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)
|
|
|
|
# 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)
|
|
|
|
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']))
|
|
|
|
# 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)
|
|
|
|
# Rotate data to have same orientation
|
|
rotate_data = np.unique([np.round(float(head["ORIENTAT"]), 3) 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"]
|
|
),
|
|
)
|
|
|
|
# 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']))
|
|
|
|
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)])
|
|
|
|
# Step 2:
|
|
# Compute Stokes I, Q, U with smoothed polarized images
|
|
# SMOOTHING DISCUSSION :
|
|
# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
|
|
# 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)
|
|
|
|
# Step 3:
|
|
# Rotate images to have North up
|
|
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)
|
|
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).
|
|
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, headers)
|
|
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, headers)
|
|
|
|
# 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,
|
|
headers, data_mask, figname, data_folder=data_folder, return_hdul=True)
|
|
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
|
|
|
|
# Step 5:
|
|
# crop to desired region of interest (roi)
|
|
if crop:
|
|
figname += "_crop"
|
|
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm())
|
|
stokescrop.crop()
|
|
stokescrop.write_to("/".join([data_folder, figname+".fits"]))
|
|
Stokes_hdul, headers = 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)
|
|
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *sci_not(
|
|
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., 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.)))
|
|
# 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.)))
|
|
# 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)
|
|
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.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')
|
|
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) |