#!/usr/bin/python #-*- coding:utf-8 -*- """ Main script where are progressively added the steps for the FOC pipeline reduction. """ #Project libraries import sys import numpy as np import copy 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 def main(): ##### User inputs ## Input and output locations globals()['data_folder'] = "../data/NGC1068_x274020/" infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits', 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits', 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits'] globals()['plots_folder'] = "../plots/NGC1068_x274020/" # globals()['data_folder'] = "../data/NGC1068_x14w010/" # infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits', # 'x14w0104t_c0f.fits','x14w0105p_c0f.fits','x14w0106t_c0f.fits'] # globals()['plots_folder'] = "../plots/NGC1068_x14w010/" # globals()['data_folder'] = "../data/3C405_x136060/" # infiles = ['x1360601t_c0f.fits','x1360602t_c0f.fits','x1360603t_c0f.fits'] # globals()['plots_folder'] = "../plots/3C405_x136060/" # globals()['data_folder'] = "../data/CygnusA_x43w0/" # infiles = ['x43w0101r_c0f.fits', 'x43w0102r_c0f.fits', 'x43w0103r_c0f.fits', # 'x43w0104r_c0f.fits', 'x43w0105r_c0f.fits', 'x43w0106r_c0f.fits', # 'x43w0107r_c0f.fits', 'x43w0108r_c0f.fits', 'x43w0109r_c0f.fits'] # infiles = ['x43w0201r_c0f.fits', 'x43w0202r_c0f.fits', 'x43w0203r_c0f.fits', # 'x43w0204r_c0f.fits', 'x43w0205r_c0f.fits', 'x43w0206r_c0f.fits'] # globals()['plots_folder'] = "../plots/CygnusA_x43w0/" # globals()['data_folder'] = "../data/3C109_x3mc010/" # infiles = ['x3mc0101m_c0f.fits','x3mc0102m_c0f.fits','x3mc0103m_c0f.fits'] # globals()['plots_folder'] = "../plots/3C109_x3mc010/" # globals()['data_folder'] = "../data/MKN463_x2rp030/" # infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits', # 'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits', # 'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits', # 'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits', # 'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits'] # globals()['plots_folder'] = "../plots/MKN463_x2rp030/" # globals()['data_folder'] = "../data/PG1630+377_x39510/" # infiles = ['x3990201m_c0f.fits', 'x3990205m_c0f.fits', 'x3995101r_c0f.fits', # 'x3995105r_c0f.fits', 'x3995109r_c0f.fits', 'x3995201r_c0f.fits', # 'x3995205r_c0f.fits', 'x3990202m_c0f.fits', 'x3990206m_c0f.fits', # 'x3995102r_c0f.fits', 'x3995106r_c0f.fits', 'x399510ar_c0f.fits', # 'x3995202r_c0f.fits','x3995206r_c0f.fits'] # globals()['plots_folder'] = "../plots/PG1630+377_x39510/" # globals()['data_folder'] = "../data/IC5063_x3nl030/" # infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits'] # globals()['plots_folder'] = "../plots/IC5063_x3nl030/" # globals()['data_folder'] = "../data/MKN3_x3nl010/" # infiles = ['x3nl0101r_c0f.fits','x3nl0102r_c0f.fits','x3nl0103r_c0f.fits'] # globals()['plots_folder'] = "../plots/MKN3_x3nl010/" # globals()['data_folder'] = "../data/MKN3_x3md010/" # infiles = ['x3md0101r_c0f.fits', 'x3md0102r_c0f.fits', 'x3md0103r_c0f.fits'] # infiles = ['x3md0104r_c0f.fits', 'x3md0105r_c0f.fits', 'x3md0106r_c0f.fits'] # globals()['plots_folder'] = "../plots/MKN3_x3md010/" # globals()['data_folder'] = "../data/MKN78_x3nl020/" # infiles = ['x3nl0201r_c0f.fits','x3nl0202r_c0f.fits','x3nl0203r_c0f.fits'] # globals()['plots_folder'] = "../plots/MKN78_x3nl020/" ## Reduction parameters # Deconvolution deconvolve = False if deconvolve: psf = 'gaussian' #Can be user-defined as well psf_FWHM = 0.10 psf_scale = 'arcsec' psf_shape=(9,9) iterations = 10 # Error estimation error_sub_shape = (75,75) display_error = False # Data binning rebin = True if rebin: pxsize = 0.10 px_scale = 'arcsec' #pixel or arcsec rebin_operation = 'sum' #sum or average # Alignement align_center = 'image' #If None will align image to image center display_data = False # Smoothing smoothing_function = 'gaussian' #gaussian_after, gaussian or combine smoothing_FWHM = 0.10 #If None, no smoothing is done smoothing_scale = 'arcsec' #pixel or arcsec # Rotation rotate_stokes = True #rotation to North convention can give erroneous results rotate_data = False #rotation to North convention can give erroneous results # Polarization map output figname = 'NGC1068_FOC' #target/intrument name figtype = '_gaussian_FWHM010_rot' #additionnal informations 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%. step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted ##### Pipeline start ## Step 1: # Get data from fits files and translate to flux in erg/cm²/s/Angstrom. data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True) for data in data_array: if (data < 0.).any(): print("ETAPE 1 : ", data) # Crop data to remove outside blank margins. data_array, error_array = proj_red.crop_array(data_array, step=5, null_val=0., inside=True) for data in data_array: if (data < 0.).any(): print("ETAPE 2 : ", data) # 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) # Estimate error from data background, estimated from sub-image of desired sub_shape. data_array, error_array = proj_red.get_error(data_array, sub_shape=error_sub_shape, display=display_error, headers=headers, savename=figname+"_errors", plots_folder=plots_folder) for data in data_array: if (data < 0.).any(): print("ETAPE 3 : ", data) # Rebin data to desired pixel size. if rebin: data_array, error_array, headers, Dxy = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation) for data in data_array: if (data < 0.).any(): print("ETAPE 4 : ", data) #Align and rescale images with oversampling. data_array, error_array = proj_red.align_data(data_array, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False) for data in data_array: if (data < 0.).any(): print("ETAPE 5 : ", data) # Rotate data to have North up ref_header = copy.deepcopy(headers[0]) if rotate_data: data_array, error_array, headers = proj_red.rotate_data(data_array, error_array, headers, -ref_header['orientat']) for data in data_array: if (data < 0.).any(): print("ETAPE 6 : ", data) #Plot array for checking output if display_data: proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), savename=figname+"_center_"+align_center, plots_folder=plots_folder) ## 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, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function) ## Step 3: # Rotate images to have North up if rotate_stokes: ref_header = copy.deepcopy(headers[0]) I_stokes, Q_stokes, U_stokes, Stokes_cov, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, -ref_header['orientat'], 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) ## Step 4: # Save image to FITS. 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, figname+figtype, data_folder=data_folder, return_hdul=True) ## Step 5: # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None) proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg') proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err') proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi') proj_plots.polarization_map(copy.deepcopy(Stokes_test), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp') return 0 if __name__ == "__main__": sys.exit(main())