| 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 | def dirty(b_x_list, b_y_list, spectra, wn_spectra, iwn, wn, spatial_axis, npix):
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| 11 | image = numpy.zeros([npix, npix], numpy.float)
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| 12 | beam = numpy.zeros([npix, npix], numpy.float)
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| 13 |
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| 14 | for ibx,b_x in enumerate(b_x_list):
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| 15 | b_x = b_x_list[ibx]
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| 16 | b_y = b_y_list[ibx]
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| 17 | spectrum = spectra[ibx]
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| 18 | wn_spectrum = wn_spectra[ibx]
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| 19 |
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| 20 | # must be a better way of doing this
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| 21 | argx = b_x * (wn * 100.0) * spatial_axis / 206265.0
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| 22 | argy = b_y * (wn * 100.0) * spatial_axis / 206265.0
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| 23 | valx = numpy.exp(2j * math.pi * argx)
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| 24 | valy = numpy.exp(2j * math.pi * argy)
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| 25 |
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| 26 | vis = spectrum[wn_spectrum==wn]
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| 27 |
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| 28 | # numpy arrays are [row,col] or here [y,x]
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| 29 | for iy,y in enumerate(spatial_axis):
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| 30 | contribution = (vis * valx * valy[iy]) + \
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| 31 | numpy.conjugate(vis * valx * valy[iy])
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| 32 | image[iy,:] += contribution.real
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| 33 | contribution = (valx * valy[iy]) + \
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| 34 | numpy.conjugate(valx * valy[iy])
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| 35 | beam[iy,:] += contribution.real
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| 36 |
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| 37 | # normalise
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| 38 | image /= numpy.sum(beam)
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| 39 | beam /= numpy.sum(beam)
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| 40 |
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| 41 | return image, beam
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| 42 |
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| 43 |
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| 44 | class DirtyImage(object):
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| 45 | """Class to compute dirty image cube from uvspectra.
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| 46 | """
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| 47 |
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| 48 | def __init__(self, previous_results, job_server):
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| 49 | self.previous_results = previous_results
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| 50 | self.job_server = job_server
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| 51 |
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| 52 | self.result = collections.OrderedDict()
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| 53 |
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| 54 | def run(self):
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| 55 | print 'DirtyImage.run'
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| 56 |
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| 57 | # get relevant fts info
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| 58 | fts = self.previous_results['fts']
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| 59 | fts_wnmin = fts['wnmin']
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| 60 |
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| 61 | # get primary beam info
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| 62 | beamsgenerator = self.previous_results['beamsgenerator']
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| 63 | npix = beamsgenerator['npix']
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| 64 | spatial_axis = beamsgenerator['spatial axis [arcsec]']
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| 65 |
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| 66 | # get observation list
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| 67 | uvspectra = self.previous_results['uvspectra']['uvspectra']
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| 68 | wavenumber = uvspectra[0].wavenumber
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| 69 | wavenumber = wavenumber[wavenumber>fts_wnmin]
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| 70 |
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| 71 | # dirty image cube
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| 72 | dirtyimage = np.zeros([npix, npix, len(wavenumber)], np.float)
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| 73 | dirtybeam = np.zeros([npix, npix, len(wavenumber)], np.float)
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| 74 |
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| 75 | # calculate dirty image for each wn
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| 76 | # uvspectrum objects don't pickle so unpack them first
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| 77 | b_x_list = []
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| 78 | b_y_list = []
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| 79 | spectra = []
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| 80 | wn_spectra = []
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| 81 | for uvspectrum in uvspectra:
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| 82 | b_x_list.append(uvspectrum.baseline_x)
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| 83 | b_y_list.append(uvspectrum.baseline_y)
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| 84 | spectra.append(uvspectrum.spectrum)
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| 85 | wn_spectra.append(uvspectrum.wavenumber)
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| 86 |
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| 87 | jobs = {}
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| 88 | for iwn,wn in enumerate(wavenumber):
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| 89 | # submit jobs
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| 90 | print 'starting dirty for', wn
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| 91 | indata = (b_x_list, b_y_list, spectra, wn_spectra, iwn, wn,
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| 92 | spatial_axis, npix,)
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| 93 | jobs[wn] = self.job_server.submit(dirty, indata, (),
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| 94 | ('numpy','math',))
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| 95 |
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| 96 | for iwn,wn in enumerate(wavenumber):
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| 97 | # collect and store results
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| 98 | dirtyimage[:,:,iwn], dirtybeam[:,:,iwn] = jobs[wn]()
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| 99 |
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| 100 | self.result['dirtyimage'] = dirtyimage
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| 101 | self.result['dirtybeam'] = dirtybeam
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| 102 | self.result['spatial axis [arcsec]'] = spatial_axis
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| 103 | self.result['spatial axis'] = spatial_axis
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| 104 | self.result['wavenumber [cm-1]'] = wavenumber
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| 105 |
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| 106 | return self.result
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| 107 |
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| 108 | def __repr__(self):
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| 109 | return '''
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| 110 | DirtyImage:
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| 111 | '''.format(
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| 112 | num_uvspectra=len(self.uvspectra))
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| 113 |
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