207 lines
11 KiB
Python
Executable File
207 lines
11 KiB
Python
Executable File
#!/usr/bin/python
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#-*- coding:utf-8 -*-
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"""
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Main script where are progressively added the steps for the FOC pipeline reduction.
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"""
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#Project libraries
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import sys
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import numpy as np
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import copy
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import lib.fits as proj_fits #Functions to handle fits files
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import lib.reduction as proj_red #Functions used in reduction pipeline
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import lib.plots as proj_plots #Functions for plotting data
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from lib.convex_hull import image_hull
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def main():
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##### User inputs
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## Input and output locations
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globals()['data_folder'] = "../data/NGC1068_x274020/"
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infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
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'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
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'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
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globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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# globals()['data_folder'] = "../data/NGC1068_x14w010/"
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# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
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# 'x14w0104t_c0f.fits','x14w0105p_c0f.fits','x14w0106t_c0f.fits']
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# globals()['plots_folder'] = "../plots/NGC1068_x14w010/"
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# globals()['data_folder'] = "../data/3C405_x136060/"
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# infiles = ['x1360601t_c0f.fits','x1360602t_c0f.fits','x1360603t_c0f.fits']
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# globals()['plots_folder'] = "../plots/3C405_x136060/"
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# globals()['data_folder'] = "../data/CygnusA_x43w0/"
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# infiles = ['x43w0101r_c0f.fits', 'x43w0102r_c0f.fits', 'x43w0103r_c0f.fits',
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# 'x43w0104r_c0f.fits', 'x43w0105r_c0f.fits', 'x43w0106r_c0f.fits',
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# 'x43w0107r_c0f.fits', 'x43w0108r_c0f.fits', 'x43w0109r_c0f.fits']
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# infiles = ['x43w0201r_c0f.fits', 'x43w0202r_c0f.fits', 'x43w0203r_c0f.fits',
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# 'x43w0204r_c0f.fits', 'x43w0205r_c0f.fits', 'x43w0206r_c0f.fits']
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# globals()['plots_folder'] = "../plots/CygnusA_x43w0/"
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# globals()['data_folder'] = "../data/3C109_x3mc010/"
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# infiles = ['x3mc0101m_c0f.fits','x3mc0102m_c0f.fits','x3mc0103m_c0f.fits']
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# globals()['plots_folder'] = "../plots/3C109_x3mc010/"
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# globals()['data_folder'] = "../data/MKN463_x2rp030/"
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# infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
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# 'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
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# 'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
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# 'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
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# 'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
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# globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
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# globals()['data_folder'] = "../data/PG1630+377_x39510/"
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# infiles = ['x3990201m_c0f.fits', 'x3990205m_c0f.fits', 'x3995101r_c0f.fits',
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# 'x3995105r_c0f.fits', 'x3995109r_c0f.fits', 'x3995201r_c0f.fits',
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# 'x3995205r_c0f.fits', 'x3990202m_c0f.fits', 'x3990206m_c0f.fits',
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# 'x3995102r_c0f.fits', 'x3995106r_c0f.fits', 'x399510ar_c0f.fits',
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# 'x3995202r_c0f.fits','x3995206r_c0f.fits']
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# globals()['plots_folder'] = "../plots/PG1630+377_x39510/"
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# globals()['data_folder'] = "../data/IC5063_x3nl030/"
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# infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
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# globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
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# globals()['data_folder'] = "../data/MKN3_x3nl010/"
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# infiles = ['x3nl0101r_c0f.fits','x3nl0102r_c0f.fits','x3nl0103r_c0f.fits']
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# globals()['plots_folder'] = "../plots/MKN3_x3nl010/"
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# globals()['data_folder'] = "../data/MKN3_x3md010/"
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# infiles = ['x3md0101r_c0f.fits', 'x3md0102r_c0f.fits', 'x3md0103r_c0f.fits']
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# infiles = ['x3md0104r_c0f.fits', 'x3md0105r_c0f.fits', 'x3md0106r_c0f.fits']
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# globals()['plots_folder'] = "../plots/MKN3_x3md010/"
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# globals()['data_folder'] = "../data/MKN78_x3nl020/"
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# infiles = ['x3nl0201r_c0f.fits','x3nl0202r_c0f.fits','x3nl0203r_c0f.fits']
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# globals()['plots_folder'] = "../plots/MKN78_x3nl020/"
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## Reduction parameters
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# Deconvolution
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deconvolve = False
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if deconvolve:
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psf = 'gaussian' #Can be user-defined as well
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psf_FWHM = 0.10
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psf_scale = 'arcsec'
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psf_shape=(9,9)
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iterations = 10
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# Cropping
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display_crop = False
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# Error estimation
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error_sub_shape = (75,75)
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display_error = False
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# Data binning
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rebin = True
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if rebin:
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pxsize = 0.10
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px_scale = 'arcsec' #pixel or arcsec
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rebin_operation = 'sum' #sum or average
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# Alignement
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align_center = 'image' #If None will align image to image center
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display_data = False
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# Smoothing
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smoothing_function = 'combine' #gaussian_after, gaussian or combine
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smoothing_FWHM = 0.20 #If None, no smoothing is done
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smoothing_scale = 'arcsec' #pixel or arcsec
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# Rotation
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rotate_stokes = True #rotation to North convention can give erroneous results
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rotate_data = False #rotation to North convention can give erroneous results
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# Polarization map output
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figname = 'NGC1068_FOC' #target/intrument name
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figtype = '_combine_FWHM020_rot' #additionnal informations
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SNRp_cut = 10 #P measurments with SNR>3
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SNRi_cut = 130 #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
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##### Pipeline start
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## Step 1:
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# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
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data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 1 : ", data)
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# Crop data to remove outside blank margins.
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data_array, error_array = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 2 : ", data)
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# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
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if deconvolve:
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data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations)
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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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)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 3 : ", data)
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# Rebin data to desired pixel size.
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if rebin:
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data_array, error_array, headers, Dxy = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 4 : ", data)
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#Align and rescale images with oversampling.
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data_array, error_array, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 5 : ", data)
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vertex = image_hull((1.-data_mask),step=5,null_val=0.,inside=True)
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shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
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rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'w']
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# Rotate data to have North up
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ref_header = copy.deepcopy(headers[0])
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if rotate_data:
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alpha = ref_header['orientat']
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mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
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[np.sin(-alpha), np.cos(-alpha)]])
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rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2
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rectangle[4] = alpha
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data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -ref_header['orientat'])
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 6 : ", data)
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#Plot array for checking output
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if display_data:
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proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), rectangle =[rectangle,]*data_array.shape[0], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
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## Step 2:
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# Compute Stokes I, Q, U with smoothed polarized images
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# SMOOTHING DISCUSSION :
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# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
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# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
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# Bibcode : 1995chst.conf...10J
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I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
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## Step 3:
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# Rotate images to have North up
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ref_header = copy.deepcopy(headers[0])
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if rotate_stokes:
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alpha = ref_header['orientat']
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mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
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[np.sin(-alpha), np.cos(-alpha)]])
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rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2
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rectangle[4] = alpha
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I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, pol_flux, data_mask, headers, -ref_header['orientat'], SNRi_cut=None)
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# Compute polarimetric parameters (polarization degree and angle).
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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, pol_flux, headers)
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## Step 4:
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# Save image to FITS.
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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)
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## Step 5:
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# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux')
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, 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')
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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