| 1 | import numpy |
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| 2 | from asap import rcParams |
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| 3 | from asap.scantable import scantable |
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| 4 | from asap.selector import selector |
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| 5 | from asap._asap import stgrid |
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| 6 | import pylab as pl |
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| 7 | from logging import asaplog |
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| 8 | |
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| 9 | class asapgrid: |
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| 10 | """ |
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| 11 | The asapgrid class is defined to convolve data onto regular |
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| 12 | spatial grid. Typical usage is as follows: |
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| 13 | |
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| 14 | # create asapgrid instance with two input data |
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| 15 | g = asapgrid( ['testimage1.asap','testimage2.asap'] ) |
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| 16 | # set IFNO if necessary |
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| 17 | g.setIF( 0 ) |
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| 18 | # set POLNOs if necessary |
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| 19 | g.setPolList( [0,1] ) |
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| 20 | # set SCANNOs if necessary |
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| 21 | g.setScanList( [22,23,24] ) |
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| 22 | # define image with full specification |
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| 23 | # you can skip some parameters (see help for defineImage) |
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| 24 | g.defineImage( nx=12, ny=12, cellx='10arcsec', celly='10arcsec', |
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| 25 | center='J2000 10h10m10s -5d05m05s' ) |
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| 26 | # set convolution function |
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| 27 | g.setFunc( func='sf', width=3 ) |
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| 28 | # enable min/max clipping |
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| 29 | g.enableClip() |
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| 30 | # or, disable min/max clipping |
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| 31 | #g.disableClip() |
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| 32 | # actual gridding |
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| 33 | g.grid() |
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| 34 | # save result |
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| 35 | g.save( outfile='grid.asap' ) |
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| 36 | # plot result |
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| 37 | g.plot( plotchan=1246, plotpol=-1, plotgrid=True, plotobs=True ) |
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| 38 | """ |
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| 39 | def __init__( self, infile ): |
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| 40 | """ |
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| 41 | Create asapgrid instance. |
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| 42 | |
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| 43 | infile -- input data as a string or string list if you want |
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| 44 | to grid more than one data at once. |
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| 45 | """ |
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| 46 | self.outfile = None |
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| 47 | self.ifno = None |
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| 48 | self.gridder = stgrid() |
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| 49 | self.setData( infile ) |
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| 50 | |
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| 51 | def setData( self, infile ): |
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| 52 | """ |
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| 53 | Set data to be processed. |
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| 54 | |
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| 55 | infile -- input data as a string or string list if you want |
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| 56 | to grid more than one data at once. |
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| 57 | """ |
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| 58 | if isinstance( infile, str ): |
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| 59 | self.gridder._setin( infile ) |
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| 60 | else: |
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| 61 | self.gridder._setfiles( infile ) |
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| 62 | self.infile = infile |
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| 63 | |
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| 64 | def setIF( self, ifno ): |
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| 65 | """ |
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| 66 | Set IFNO to be processed. Currently, asapgrid allows to process |
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| 67 | only one IFNO for one gridding run even if the data contains |
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| 68 | multiple IFs. If you didn't specify IFNO, default value, which |
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| 69 | is IFNO in the first spectrum, will be processed. |
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| 70 | |
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| 71 | ifno -- IFNO to be processed. |
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| 72 | """ |
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| 73 | self.ifno = ifno |
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| 74 | self.gridder._setif( self.ifno ) |
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| 75 | |
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| 76 | def setPolList( self, pollist ): |
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| 77 | """ |
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| 78 | Set list of polarization components you want to process. |
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| 79 | If not specified, all POLNOs will be processed. |
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| 80 | |
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| 81 | pollist -- list of POLNOs. |
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| 82 | """ |
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| 83 | self.gridder._setpollist( pollist ) |
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| 84 | |
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| 85 | def setScanList( self, scanlist ): |
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| 86 | """ |
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| 87 | Set list of scans you want to process. If not specified, all |
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| 88 | scans will be processed. |
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| 89 | |
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| 90 | scanlist -- list of SCANNOs. |
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| 91 | """ |
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| 92 | self.gridder._setscanlist( scanlist ) |
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| 93 | |
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| 94 | def defineImage( self, nx=-1, ny=-1, cellx='', celly='', center='' ): |
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| 95 | """ |
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| 96 | Define spatial grid. |
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| 97 | |
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| 98 | First two parameters, nx and ny, define number of pixels of |
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| 99 | the grid. If which of those is not specified, it will be set |
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| 100 | to the same value as the other. If none of them are specified, |
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| 101 | it will be determined from map extent and cell size. |
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| 102 | |
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| 103 | Next two parameters, cellx and celly, define size of pixel. |
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| 104 | You should set those parameters as string, which is constructed |
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| 105 | numerical value and unit, e.g. '0.5arcmin', or numerical value. |
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| 106 | If those values are specified as numerical value, their units |
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| 107 | will be assumed to 'arcmin'. If which of those is not specified, |
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| 108 | it will be set to the same value as the other. If none of them |
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| 109 | are specified, it will be determined from map extent and number |
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| 110 | of pixels, or set to '1arcmin' if neither nx nor ny is set. |
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| 111 | |
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| 112 | The last parameter, center, define the central coordinate of |
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| 113 | the grid. You should specify its value as a string, like, |
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| 114 | |
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| 115 | 'J2000 05h08m50s -16d23m30s' |
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| 116 | |
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| 117 | or |
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| 118 | |
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| 119 | 'J2000 05:08:50 -16.23.30' |
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| 120 | |
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| 121 | You can omit equinox when you specify center coordinate. In that |
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| 122 | case, J2000 is assumed. If center is not specified, it will be |
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| 123 | determined from the observed positions of input data. |
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| 124 | |
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| 125 | nx -- number of pixels along x (R.A.) direction. |
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| 126 | ny -- number of pixels along y (Dec.) direction. |
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| 127 | cellx -- size of pixel in x (R.A.) direction. |
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| 128 | celly -- size of pixel in y (Dec.) direction. |
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| 129 | center -- central position of the grid. |
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| 130 | """ |
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| 131 | if not isinstance( cellx, str ): |
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| 132 | cellx = '%sarcmin'%(cellx) |
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| 133 | if not isinstance( celly, str ): |
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| 134 | celly = '%sarcmin'%(celly) |
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| 135 | self.gridder._defineimage( nx, ny, cellx, celly, center ) |
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| 136 | |
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| 137 | def setFunc( self, func='box', width=-1 ): |
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| 138 | """ |
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| 139 | Set convolution function. Possible options are 'box' (Box-car, |
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| 140 | default), 'sf' (prolate spheroidal), and 'gauss' (Gaussian). |
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| 141 | Width of convolution function can be set using width parameter. |
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| 142 | By default (-1), width is automatically set depending on each |
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| 143 | convolution function. Default values for width are: |
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| 144 | |
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| 145 | 'box': 1 pixel |
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| 146 | 'sf': 3 pixels |
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| 147 | 'gauss': 3 pixels (width is used as HWHM) |
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| 148 | |
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| 149 | func -- Function type ('box', 'sf', 'gauss'). |
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| 150 | width -- Width of convolution function. Default (-1) is to |
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| 151 | choose pre-defined value for each convolution function. |
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| 152 | """ |
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| 153 | self.gridder._setfunc( func, width ) |
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| 154 | |
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| 155 | def setWeight( self, weightType='uniform' ): |
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| 156 | """ |
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| 157 | Set weight type. Possible options are 'uniform' (default), |
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| 158 | 'tint' (weight by integration time), 'tsys' (weight by |
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| 159 | Tsys: 1/Tsys**2), and 'tintsys' (weight by integration time |
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| 160 | as well as Tsys: tint/Tsys**2). |
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| 161 | |
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| 162 | weightType -- weight type ('uniform', 'tint', 'tsys', 'tintsys') |
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| 163 | """ |
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| 164 | self.gridder._setweight( weightType ) |
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| 165 | |
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| 166 | def enableClip( self ): |
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| 167 | """ |
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| 168 | Enable min/max clipping. |
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| 169 | |
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| 170 | By default, min/max clipping is disabled so that you should |
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| 171 | call this method before actual gridding if you want to do |
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| 172 | clipping. |
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| 173 | """ |
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| 174 | self.gridder._enableclip() |
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| 175 | |
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| 176 | def disableClip( self ): |
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| 177 | """ |
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| 178 | Disable min/max clipping. |
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| 179 | """ |
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| 180 | self.gridder._disableclip() |
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| 181 | |
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| 182 | def grid( self ): |
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| 183 | """ |
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| 184 | Actual gridding which will be done based on several user inputs. |
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| 185 | """ |
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| 186 | self.gridder._grid() |
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| 187 | |
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| 188 | def save( self, outfile='' ): |
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| 189 | """ |
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| 190 | Save result. By default, output data name will be constructed |
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| 191 | from first element of input data name list (e.g. 'input.asap.grid'). |
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| 192 | |
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| 193 | outfile -- output data name. |
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| 194 | """ |
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| 195 | self.outfile = self.gridder._save( outfile ) |
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| 196 | |
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| 197 | def plot( self, plotchan=-1, plotpol=-1, plotobs=False, plotgrid=False ): |
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| 198 | """ |
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| 199 | Plot gridded data. |
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| 200 | |
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| 201 | plotchan -- Which channel you want to plot. Default (-1) is |
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| 202 | to average all the channels. |
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| 203 | plotpol -- Which polarization component you want to plot. |
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| 204 | Default (-1) is to average all the polarization |
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| 205 | components. |
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| 206 | plotobs -- Also plot observed position if True. Default |
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| 207 | is False. Setting True for large amount of spectra |
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| 208 | might be time consuming. |
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| 209 | plotgrid -- Also plot grid center if True. Default is False. |
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| 210 | Setting True for large number of grids might be |
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| 211 | time consuming. |
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| 212 | """ |
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| 213 | import time |
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| 214 | t0=time.time() |
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| 215 | # to load scantable on disk |
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| 216 | storg = rcParams['scantable.storage'] |
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| 217 | rcParams['scantable.storage'] = 'disk' |
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| 218 | plotter = _SDGridPlotter( self.infile, self.outfile, self.ifno ) |
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| 219 | plotter.plot( chan=plotchan, pol=plotpol, plotobs=plotobs, plotgrid=plotgrid ) |
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| 220 | # back to original setup |
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| 221 | rcParams['scantable.storage'] = storg |
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| 222 | t1=time.time() |
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| 223 | asaplog.push('plot: elapsed time %s sec'%(t1-t0)) |
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| 224 | asaplog.post('DEBUG','asapgrid.plot') |
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| 225 | |
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| 226 | class _SDGridPlotter: |
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| 227 | def __init__( self, infile, outfile=None, ifno=-1 ): |
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| 228 | if isinstance( infile, str ): |
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| 229 | self.infile = [infile] |
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| 230 | else: |
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| 231 | self.infile = infile |
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| 232 | self.outfile = outfile |
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| 233 | if self.outfile is None: |
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| 234 | self.outfile = self.infile[0].rstrip('/')+'.grid' |
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| 235 | self.nx = -1 |
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| 236 | self.ny = -1 |
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| 237 | self.nchan = 0 |
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| 238 | self.npol = 0 |
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| 239 | self.pollist = [] |
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| 240 | self.cellx = 0.0 |
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| 241 | self.celly = 0.0 |
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| 242 | self.center = [0.0,0.0] |
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| 243 | self.nonzero = [[0.0],[0.0]] |
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| 244 | self.ifno = ifno |
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| 245 | self.tablein = None |
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| 246 | self.nrow = 0 |
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| 247 | self.blc = None |
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| 248 | self.trc = None |
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| 249 | self.get() |
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| 250 | |
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| 251 | def get( self ): |
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| 252 | s = scantable( self.outfile, average=False ) |
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| 253 | self.nchan = len(s._getspectrum(0)) |
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| 254 | nrow = s.nrow() |
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| 255 | pols = numpy.ones( nrow, dtype=int ) |
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| 256 | for i in xrange(nrow): |
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| 257 | pols[i] = s.getpol(i) |
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| 258 | self.pollist, indices = numpy.unique( pols, return_inverse=True ) |
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| 259 | self.npol = len(self.pollist) |
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| 260 | self.pollist = self.pollist[indices[:self.npol]] |
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| 261 | #print 'pollist=',self.pollist |
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| 262 | #print 'npol=',self.npol |
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| 263 | #print 'nrow=',nrow |
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| 264 | |
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| 265 | idx = 0 |
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| 266 | d0 = s.get_direction( 0 ).split()[-1] |
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| 267 | while ( s.get_direction(self.npol*idx).split()[-1] == d0 ): |
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| 268 | idx += 1 |
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| 269 | |
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| 270 | self.nx = idx |
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| 271 | self.ny = nrow / (self.npol * idx ) |
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| 272 | #print 'nx,ny=',self.nx,self.ny |
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| 273 | |
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| 274 | self.blc = s.get_directionval( 0 ) |
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| 275 | self.trc = s.get_directionval( nrow-self.npol ) |
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| 276 | #print self.blc |
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| 277 | #print self.trc |
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| 278 | incrx = s.get_directionval( self.npol ) |
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| 279 | incry = s.get_directionval( self.nx*self.npol ) |
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| 280 | self.cellx = abs( self.blc[0] - incrx[0] ) |
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| 281 | self.celly = abs( self.blc[1] - incry[1] ) |
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| 282 | #print 'cellx,celly=',self.cellx,self.celly |
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| 283 | |
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| 284 | def plot( self, chan=-1, pol=-1, plotobs=False, plotgrid=False ): |
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| 285 | if pol < 0: |
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| 286 | opt = 'averaged over pol' |
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| 287 | else: |
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| 288 | opt = 'pol %s'%(pol) |
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| 289 | if chan < 0: |
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| 290 | opt += ', averaged over channel' |
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| 291 | else: |
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| 292 | opt += ', channel %s'%(chan) |
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| 293 | data = self.getData( chan, pol ) |
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| 294 | title = 'Gridded Image (%s)'%(opt) |
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| 295 | pl.figure(10) |
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| 296 | pl.clf() |
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| 297 | # plot grid position |
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| 298 | if plotgrid: |
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| 299 | x = numpy.arange(self.blc[0],self.trc[0]+0.5*self.cellx,self.cellx,dtype=float) |
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| 300 | #print 'len(x)=',len(x) |
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| 301 | #print 'x=',x |
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| 302 | ybase = numpy.ones(self.nx,dtype=float)*self.blc[1] |
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| 303 | #print 'len(ybase)=',len(ybase) |
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| 304 | incr = self.celly |
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| 305 | for iy in xrange(self.ny): |
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| 306 | y = ybase + iy * incr |
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| 307 | #print y |
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| 308 | pl.plot(x,y,',',color='blue') |
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| 309 | # plot observed position |
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| 310 | if plotobs: |
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| 311 | for i in xrange(len(self.infile)): |
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| 312 | self.createTableIn( self.infile[i] ) |
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| 313 | irow = 0 |
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| 314 | while ( irow < self.nrow ): |
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| 315 | chunk = self.getPointingChunk( irow ) |
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| 316 | #print chunk |
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| 317 | pl.plot(chunk[0],chunk[1],',',color='green') |
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| 318 | irow += chunk.shape[1] |
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| 319 | #print irow |
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| 320 | # show image |
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| 321 | extent=[self.blc[0]-0.5*self.cellx, |
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| 322 | self.trc[0]+0.5*self.cellx, |
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| 323 | self.blc[1]-0.5*self.celly, |
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| 324 | self.trc[1]+0.5*self.celly] |
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| 325 | pl.imshow(data,extent=extent,origin='lower',interpolation='nearest') |
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| 326 | pl.colorbar() |
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| 327 | pl.xlabel('R.A. [rad]') |
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| 328 | pl.ylabel('Dec. [rad]') |
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| 329 | pl.title( title ) |
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| 330 | |
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| 331 | def createTableIn( self, tab ): |
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| 332 | del self.tablein |
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| 333 | self.tablein = scantable( tab, average=False ) |
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| 334 | if self.ifno < 0: |
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| 335 | ifno = self.tablein.getif(0) |
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| 336 | print 'ifno=',ifno |
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| 337 | else: |
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| 338 | ifno = self.ifno |
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| 339 | sel = selector() |
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| 340 | sel.set_ifs( ifno ) |
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| 341 | self.tablein.set_selection( sel ) |
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| 342 | self.nchan = len(self.tablein._getspectrum(0)) |
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| 343 | self.nrow = self.tablein.nrow() |
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| 344 | del sel |
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| 345 | |
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| 346 | |
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| 347 | def getPointingChunk( self, irow ): |
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| 348 | numchunk = 1000 |
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| 349 | nrow = min( self.nrow-irow, numchunk ) |
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| 350 | #print 'nrow=',nrow |
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| 351 | v = numpy.zeros( (2,nrow), dtype=float ) |
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| 352 | idx = 0 |
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| 353 | for i in xrange(irow,irow+nrow): |
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| 354 | d = self.tablein.get_directionval( i ) |
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| 355 | v[0,idx] = d[0] |
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| 356 | v[1,idx] = d[1] |
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| 357 | idx += 1 |
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| 358 | return v |
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| 359 | |
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| 360 | def getData( self, chan=-1, pol=-1 ): |
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| 361 | if chan == -1: |
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| 362 | spectra = self.__chanAverage() |
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| 363 | else: |
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| 364 | spectra = self.__chanIndex( chan ) |
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| 365 | data = spectra.reshape( (self.npol,self.ny,self.nx) ) |
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| 366 | if pol == -1: |
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| 367 | retval = data.mean(axis=0) |
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| 368 | else: |
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| 369 | retval = data[pol] |
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| 370 | return retval |
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| 371 | |
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| 372 | def __chanAverage( self ): |
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| 373 | s = scantable( self.outfile, average=False ) |
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| 374 | nrow = s.nrow() |
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| 375 | spectra = numpy.zeros( (self.npol,nrow/self.npol), dtype=float ) |
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| 376 | irow = 0 |
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| 377 | sp = [0 for i in xrange(self.nchan)] |
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| 378 | for i in xrange(nrow/self.npol): |
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| 379 | for ip in xrange(self.npol): |
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| 380 | sp = s._getspectrum( irow ) |
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| 381 | spectra[ip,i] = numpy.mean( sp ) |
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| 382 | irow += 1 |
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| 383 | return spectra |
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| 384 | |
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| 385 | def __chanIndex( self, idx ): |
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| 386 | s = scantable( self.outfile, average=False ) |
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| 387 | nrow = s.nrow() |
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| 388 | spectra = numpy.zeros( (self.npol,nrow/self.npol), dtype=float ) |
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| 389 | irow = 0 |
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| 390 | sp = [0 for i in xrange(self.nchan)] |
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| 391 | for i in xrange(nrow/self.npol): |
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| 392 | for ip in xrange(self.npol): |
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| 393 | sp = s._getspectrum( irow ) |
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| 394 | spectra[ip,i] = sp[idx] |
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| 395 | irow += 1 |
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| 396 | return spectra |
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| 397 | |
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| 398 | |
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