tidy up main script making use of new query library
This commit is contained in:
Thibault Barnouin
@@ -5,117 +5,16 @@ Main script where are progressively added the steps for the FOC pipeline reducti
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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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from copy import deepcopy
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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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from lib.deconvolve import from_file_psf
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import matplotlib.pyplot as plt
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from astropy.wcs import WCS
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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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globals()['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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#psf_file = 'NGC1068_f253m00.fits'
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globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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#globals()['data_folder'] = "../data/IC5063_x3nl030/"
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#globals()['infiles'] = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
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##psf_file = 'IC5063_f502m00.fits'
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#globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
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#globals()['data_folder'] = "../data/NGC1068_x14w010/"
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#globals()['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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#globals()['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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#globals()['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'] #F342W
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##globals()['infiles'] = ['x43w0201r_c0f.fits', 'x43w0202r_c0f.fits', 'x43w0203r_c0f.fits',
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## 'x43w0204r_c0f.fits', 'x43w0205r_c0f.fits', 'x43w0206r_c0f.fits'] #F275W
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#globals()['plots_folder'] = "../plots/CygnusA_x43w0/"
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#globals()['data_folder'] = "../data/3C109_x3mc010/"
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#globals()['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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#globals()['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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#globals()['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/MKN3_x3nl010/"
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#globals()['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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#globals()['infiles'] = ['x3md0101r_c0f.fits', 'x3md0102r_c0f.fits', 'x3md0103r_c0f.fits'] #F275W
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##globals()['infiles'] = ['x3md0104r_c0f.fits', 'x3md0105r_c0f.fits', 'x3md0106r_c0f.fits'] #F342W
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#globals()['plots_folder'] = "../plots/MKN3_x3md010/"
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#globals()['data_folder'] = "../data/MKN78_x3nl020/"
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#globals()['infiles'] = ['x3nl0201r_c0f.fits','x3nl0202r_c0f.fits','x3nl0203r_c0f.fits']
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#globals()['plots_folder'] = "../plots/MKN78_x3nl020/"
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#globals()['data_folder'] = "../data/MRK231_x4qr010/"
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#globals()['infiles'] = ['x4qr010ar_c0f.fits', 'x4qr010br_c0f.fits', 'x4qr010dr_c0f.fits',
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# 'x4qr010er_c0f.fits', 'x4qr010gr_c0f.fits', 'x4qr010hr_c0f.fits',
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# 'x4qr010jr_c0f.fits', 'x4qr010kr_c0f.fits', 'x4qr0104r_c0f.fits',
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# 'x4qr0105r_c0f.fits', 'x4qr0107r_c0f.fits', 'x4qr0108r_c0f.fits']
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#globals()['plots_folder'] = "../plots/MRK231_x4qr010/"
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#globals()['data_folder'] = "../data/3C273_x0u20/"
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#globals()['infiles'] = ['x0u20101t_c0f.fits','x0u20102t_c0f.fits','x0u20103t_c0f.fits',
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# 'x0u20104t_c0f.fits','x0u20105t_c0f.fits','x0u20106t_c0f.fits',
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# 'x0u20201t_c0f.fits','x0u20202t_c0f.fits','x0u20203t_c0f.fits',
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# 'x0u20204t_c0f.fits','x0u20205t_c0f.fits','x0u20206t_c0f.fits',
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# 'x0u20301t_c0f.fits','x0u20302t_c0f.fits','x0u20303t_c0f.fits',
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# 'x0u20304t_c0f.fits','x0u20305t_c0f.fits','x0u20306t_c0f.fits']
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#globals()['plots_folder'] = "../plots/3C273_x0u20/"
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#BEWARE: 5 observations separated by 1 year each (1995, 1996, 1997, 1998, 1999)
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#globals()['data_folder'] = "../data/M87/POS1/"
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##globals()['infiles'] = ['x2py010ct_c0f.fits','x2py010dt_c0f.fits','x2py010et_c0f.fits','x2py010ft_c0f.fits'] #1995
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##globals()['infiles'] = ['x3be010ct_c0f.fits','x3be010dt_c0f.fits','x3be010et_c0f.fits','x3be010ft_c0f.fits'] #1996
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##globals()['infiles'] = ['x43r010km_c0f.fits','x43r010mm_c0f.fits','x43r010om_c0f.fits','x43r010rm_c0f.fits'] #1997
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##globals()['infiles'] = ['x43r110kr_c0f.fits','x43r110mr_c0f.fits','x43r110or_c0f.fits','x43r110rr_c0f.fits'] #1998
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#globals()['infiles'] = ['x43r210kr_c0f.fits','x43r210mr_c0f.fits','x43r210or_c0f.fits','x43r210rr_c0f.fits'] #1999
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#globals()['plots_folder'] = "../plots/M87/POS1/"
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#BEWARE: 5 observations separated by 1 year each (1995, 1996, 1997, 1998, 1999)
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#globals()['data_folder'] = "../data/M87/POS3/"
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##globals()['infiles'] = ['x2py030at_c0f.fits','x2py030bt_c0f.fits','x2py030ct_c0f.fits','x2py0309t_c0f.fits'] #1995
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##globals()['infiles'] = ['x3be030at_c0f.fits','x3be030bt_c0f.fits','x3be030ct_c0f.fits','x3be0309t_c0f.fits'] #1996
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##globals()['infiles'] = ['x43r030em_c0f.fits','x43r030gm_c0f.fits','x43r030im_c0f.fits','x43r030lm_c0f.fits'] #1997
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##globals()['infiles'] = ['x43r130er_c0f.fits','x43r130fr_c0f.fits','x43r130ir_c0f.fits','x43r130lr_c0f.fits'] #1998
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#globals()['infiles'] = ['x43r230er_c0f.fits','x43r230fr_c0f.fits','x43r230ir_c0f.fits','x43r230lr_c0f.fits'] #1999
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#globals()['plots_folder'] = "../plots/M87/POS3/"
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from lib.query import retrieve_products
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def main():
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def main(target=None, proposal_id=None, infiles=None):
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## Reduction parameters
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# Deconvolution
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deconvolve = False
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@@ -127,35 +26,41 @@ def main():
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psf_shape=(25,25)
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iterations = 5
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algo="richardson"
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# Initial crop
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display_crop = True
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# Error estimation
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error_sub_type = (51,51)#'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51,51))
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display_crop = False
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# Background estimation
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error_sub_type = 'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51,51))
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subtract_error = 1.25
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display_error = True
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display_error = False
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# Data binning
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rebin = True
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pxsize = 0.10
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px_scale = 'arcsec' #pixel, arcsec or full
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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 = True
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display_data = False
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# Smoothing
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smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_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
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rotate_data = False #rotation to North convention can give erroneous results
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# Final crop
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crop = False #Crop to desired ROI
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final_display = True
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final_display = False #Whether to display all polarization map outputs
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# Polarization map output
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figname = 'NGC1068_FOC' #target/intrument name
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figtype = '_c_020' #additionnal informations
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SNRp_cut = 5. #P measurments with SNR>3
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SNRi_cut = 50. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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SNRp_cut = 3. #P measurments with SNR>3
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SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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vec_scale = 2.0
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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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# if step_vec = 0 then all vectors are displayed at full length
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@@ -163,8 +68,21 @@ def main():
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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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if not infiles is None:
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products = [np.array([["/".join(filepath.split('/')[:-1]),filepath.split('/')[-1]] for filepath in infiles],dtype=str)]
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else:
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products = retrieve_products(target,proposal_id)
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data_folder = products[0][0,0]
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try:
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plots_folder = data_folder.replace("data","plots")
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except:
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plots_folder = "."
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infiles = products[0][:,1]
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data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
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figname = "_".join([target,"FOC"])
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figtype = "_".join(["".join([s[0] for s in smoothing_function.split("_")]),str(smoothing_FWHM).replace(".","")]) #additionnal informations
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# Crop data to remove outside blank margins.
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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)
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@@ -191,11 +109,7 @@ def main():
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#Plot array for checking output
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if display_data and px_scale.lower() not in ['full','integrate']:
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vertex = image_hull(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., 'g']
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], rectangle =[rectangle,]*data_array.shape[0], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
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background = np.array([np.array(bkg).reshape(1,1) for bkg in background])
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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)])
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@@ -258,5 +172,17 @@ def main():
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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import argparse
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parser = argparse.ArgumentParser(description='Query MAST for target products')
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parser.add_argument('-t','--target', metavar='targetname', required=False,
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help='the name of the target', type=str, default=None)
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parser.add_argument('-p','--proposal_id', metavar='proposal_id', required=False,
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help='the proposal id of the data products', type=int, default=None)
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parser.add_argument('-f','--files', metavar='path', required=False, nargs='*',
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help='the full or relative path to the data products', default=None)
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args = parser.parse_args()
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prodpaths = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files)
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print(prodpaths)
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