| 1 | from __future__ import absolute_import
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| 2 |
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| 3 | import collections
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| 4 | import math
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| 5 | import numpy as np
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| 6 | import pp
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| 7 |
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| 8 | import common.commonobjects as co
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| 9 |
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| 10 |
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| 11 | class DirtyImage(object):
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| 12 | """Class to compute dirty image cube from uvspectra.
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| 13 | """
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| 14 |
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| 15 | def __init__(self, previous_results, job_server):
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| 16 | self.previous_results = previous_results
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| 17 | self.job_server = job_server
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| 18 |
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| 19 | self.result = collections.OrderedDict()
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| 20 |
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| 21 | def run(self):
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| 22 | print 'DirtyImage.run'
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| 23 |
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| 24 | # get relevant fts info
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| 25 | fts = self.previous_results['fts']
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| 26 | fts_wnmin = fts['wnmin']
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| 27 |
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| 28 | # get primary beam info
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| 29 | beamsgenerator = self.previous_results['beamsgenerator']
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| 30 | npix = beamsgenerator['npix']
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| 31 | spatial_axis = beamsgenerator['spatial axis [arcsec]']
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| 32 |
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| 33 | # get observation list
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| 34 | uvspectra = self.previous_results['uvspectra']['uvspectra']
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| 35 | wavenumber = uvspectra[0].wavenumber
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| 36 | wavenumber = wavenumber[wavenumber>fts_wnmin]
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| 37 |
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| 38 | # dirty image cube
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| 39 | dirtyimage = np.zeros([npix, npix, len(wavenumber)], np.float)
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| 40 | dirtybeam = np.zeros([npix, npix, len(wavenumber)], np.float)
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| 41 |
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| 42 | for uvspectrum in uvspectra:
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| 43 |
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| 44 | b_x = uvspectrum.baseline_x
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| 45 | b_y = uvspectrum.baseline_y
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| 46 | spectrum = uvspectrum.spectrum
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| 47 | wn_spectrum = uvspectrum.wavenumber
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| 48 |
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| 49 | for iwn,wn in enumerate(wavenumber):
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| 50 | # must be a better way of doing this
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| 51 | argx = b_x * (wn * 100.0) * spatial_axis / 206265.0
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| 52 | argy = b_y * (wn * 100.0) * spatial_axis / 206265.0
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| 53 | valx = np.exp(2j * math.pi * argx)
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| 54 | valy = np.exp(2j * math.pi * argy)
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| 55 |
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| 56 | vis = spectrum[wn_spectrum==wn]
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| 57 |
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| 58 | # numpy arrays are [row,col] or here [y,x]
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| 59 | for iy,y in enumerate(spatial_axis):
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| 60 | contribution = (vis * valx * valy[iy]) + \
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| 61 | np.conjugate(vis * valx * valy[iy])
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| 62 | dirtyimage[iy,:,iwn] += contribution.real
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| 63 | contribution = (valx * valy[iy]) + \
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| 64 | np.conjugate(valx * valy[iy])
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| 65 | dirtybeam[iy,:,iwn] += contribution.real
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| 66 |
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| 67 | # normalise
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| 68 | for iwn,wn in enumerate(wavenumber):
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| 69 | dirtyimage[:,:,iwn] /= np.sum(dirtybeam[:,:,iwn])
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| 70 | dirtybeam[:,:,iwn] /= np.sum(dirtybeam[:,:,iwn])
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| 71 | # dirtyimage /= 2.0 * float(len(uvspectra))
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| 72 | # dirtybeam /= 2.0 * float(len(uvspectra))
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| 73 | print 'sum image', np.sum(dirtyimage[:,:,0])
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| 74 | print 'sum beam', np.sum(dirtybeam[:,:,0])
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| 75 |
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| 76 | self.result['dirtyimage'] = dirtyimage
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| 77 | self.result['dirtybeam'] = dirtybeam
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| 78 | self.result['spatial axis [arcsec]'] = spatial_axis
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| 79 | self.result['spatial axis'] = spatial_axis
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| 80 | self.result['wavenumber [cm-1]'] = wavenumber
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| 81 |
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| 82 | return self.result
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| 83 |
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| 84 | def __repr__(self):
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| 85 | return '''
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| 86 | DirtyImage:
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| 87 | '''.format(
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| 88 | num_uvspectra=len(self.uvspectra))
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| 89 |
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