| 1 | from asap.scantable import scantable
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| 2 | from asap._asap import _edgemarker
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| 3 | import numpy
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| 4 | import math
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| 5 |
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| 6 | class edgemarker:
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| 7 | """
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| 8 | The edgemarker is a helper tool to calibrate OTF observation
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| 9 | without explicit OFF scans. According to a few user-specified
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| 10 | options, the class automatically detects an edge region of the
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| 11 | map and mark integrations within this region as OFF.
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| 12 |
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| 13 | The edgemarker supports raster pattern as well as other generic
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| 14 | ones (e.g. lissajous, double circle). The constructor takes
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| 15 | one boolean parameter to specify whether scan pattern is raster
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| 16 | or not. This is because that edge detection algorithms for raster
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| 17 | and others are different.
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| 18 |
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| 19 | Current limitation of this class is that it cannot handle some
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| 20 | complicated observed area. Typical case is that the area has
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| 21 | clear 'dent' (e.g. a composite area consisting of two diamond-
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| 22 | shaped areas that slightly overlap). In such case, the class
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| 23 | will fail to detect such feature.
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| 24 |
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| 25 | Note that the class takes a copy of input data so that input
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| 26 | data will not be overwritten. Result will be provided as a
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| 27 | separate data whose contents are essentially the same as input
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| 28 | except for that some integrations are marked as OFF.
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| 29 |
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| 30 | Here is typical usage:
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| 31 |
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| 32 | s = scantable( 'otf.asap', average=False )
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| 33 | marker = edgemarker( israster=False )
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| 34 | marker.setdata( s )
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| 35 | marker.setoption( fraction='15%', width=0.5 )
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| 36 | marker.mark()
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| 37 |
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| 38 | # get result as scantable instance
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| 39 | s2 = marker.getresult()
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| 40 |
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| 41 | # save result on disk
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| 42 | marker.save( 'otfwithoff.asap', overwrite=True )
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| 43 | """
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| 44 | def __init__( self, israster=False ):
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| 45 | """
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| 46 | Constructor.
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| 47 |
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| 48 | israster -- Whether scan pattern is raster or not. Set True
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| 49 | if scan pattern is raster. Default is False.
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| 50 | """
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| 51 | self.israster = israster
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| 52 | self.marker = _edgemarker( self.israster )
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| 53 | self.st = None
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| 54 |
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| 55 | def setdata( self, st ):
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| 56 | """
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| 57 | Set data to be processed.
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| 58 |
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| 59 | st -- Data as scantable instance.
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| 60 | """
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| 61 | self.st = st
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| 62 | self.marker._setdata( self.st, False )
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| 63 | self.marker._examine()
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| 64 |
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| 65 | def setoption( self, *args, **kwargs ):
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| 66 | """
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| 67 | Set options for edge detection. Valid options depend on
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| 68 | whether scan pattern is raster or not (i.e. constructor
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| 69 | is called with israster=True or False).
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| 70 |
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| 71 | === for raster (israster=True) ===
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| 72 | fraction -- Fraction of OFF integration in each raster
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| 73 | row. Either numerical value (<1.0) or string
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| 74 | is accepted. For string, its value should be
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| 75 | 'auto' or format 'xx%'. For example, '10%'
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| 76 | is same as 0.1. The 'auto' option estimates
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| 77 | number of OFFs based on t_OFF = sqrt(N) t_ON.
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| 78 | Default is 0.1.
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| 79 | npts -- Number of OFF integration in each raster row.
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| 80 | Default is -1 (use fraction).
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| 81 |
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| 82 | Note that number of integrations specified by the above
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| 83 | parameters will be marked as OFF from both ends. So, twice
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| 84 | of specified number/fraction will be marked as OFF. For
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| 85 | example, if you specify fraction='10%', resultant fraction
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| 86 | of OFF integrations will be 20%.
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| 87 |
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| 88 | Note also that, if both fraction and npts are specified,
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| 89 | specification by npts will come before.
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| 90 |
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| 91 | === for non-raster (israster=False) ===
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| 92 | fraction -- Fraction of edge area with respect to whole
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| 93 | observed area. Either numerical value (<1.0)
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| 94 | or string is accepted. For string, its value
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| 95 | should be in 'xx%' format. For example, '10%'
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| 96 | is same as 0.1. Default is 0.1.
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| 97 | width -- Pixel width for edge detection. It should be given
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| 98 | as a fraction of the median spatial separation
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| 99 | between neighboring integrations in time. Default
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| 100 | is 0.5. In the most case, default value will be fine.
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| 101 | Larger value will cause worse result. Smaller value
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| 102 | may improve result. However, if too small value is
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| 103 | set (e.g. 1.0e-5), the algorithm may not work.
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| 104 | elongated -- Set True only if observed area is extremely
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| 105 | elongated in one direction. Default is False.
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| 106 | In most cases, default value will be fine.
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| 107 | """
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| 108 | option = {}
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| 109 | if self.israster:
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| 110 | keys = [ 'fraction', 'npts' ]
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| 111 | else:
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| 112 | keys = [ 'fraction', 'width', 'elongated' ]
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| 113 | for key in keys:
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| 114 | if kwargs.has_key( key ):
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| 115 | option[key] = kwargs[key]
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| 116 |
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| 117 | if len(option) > 0:
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| 118 | self.marker._setoption( option )
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| 119 |
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| 120 | def mark( self ):
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| 121 | """
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| 122 | Process data. Edge region is detected according to detection
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| 123 | parameters given by setoption(). Then, integrations within
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| 124 | edge region will be marked as OFF.
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| 125 | """
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| 126 | self.marker._detect()
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| 127 | self.marker._mark()
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| 128 |
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| 129 | def getresult( self ):
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| 130 | """
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| 131 | Get result as scantable instance. Returned scantable is
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| 132 | copy of input scantable except for that some data are
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| 133 | marked as OFF as a result of edge detection and marking.
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| 134 | """
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| 135 | return scantable( self.marker._get() )
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| 136 |
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| 137 | def save( self, name, overwrite=False ):
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| 138 | """
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| 139 | Save result as scantable.
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| 140 |
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| 141 | name -- Name of the scantable.
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| 142 | overwrite -- Overwrite existing data if True. Default is
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| 143 | False.
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| 144 | """
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| 145 | s = self.getresult()
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| 146 | s.save( name, overwrite=overwrite )
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| 147 |
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| 148 | def plot( self ):
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| 149 | """
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| 150 | """
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| 151 | from matplotlib import pylab as pl
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| 152 | from asap import selector
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| 153 | from asap._asap import srctype as st
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| 154 | pl.clf()
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| 155 |
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| 156 | # result as a scantable
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| 157 | s = self.getresult()
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| 158 |
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| 159 | # ON scan
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| 160 | sel = selector()
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| 161 | sel.set_types( int(st.pson) )
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| 162 | s.set_selection( sel )
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| 163 | diron = numpy.array( s.get_directionval() ).transpose()
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| 164 | diron[0] = rotate( diron[0] )
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| 165 | s.set_selection()
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| 166 | sel.reset()
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| 167 |
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| 168 | # OFF scan
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| 169 | sel.set_types( int(st.psoff) )
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| 170 | s.set_selection( sel )
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| 171 | diroff = numpy.array( s.get_directionval() ).transpose()
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| 172 | diroff[0] = rotate( diroff[0] )
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| 173 | s.set_selection()
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| 174 | sel.reset()
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| 175 | del s
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| 176 | del sel
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| 177 |
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| 178 | # plot
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| 179 | pl.ioff()
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| 180 | ax=pl.axes()
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| 181 | ax.set_aspect(1.0)
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| 182 | pl.plot( diron[0], diron[1], '.', color='blue', label='ON' )
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| 183 | pl.plot( diroff[0], diroff[1], '.', color='green', label='OFF' )
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| 184 | [xmin,xmax,ymin,ymax] = pl.axis()
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| 185 | pl.axis([xmax,xmin,ymin,ymax])
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| 186 | pl.legend(loc='best',prop={'size':'small'},numpoints=1)
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| 187 | pl.xlabel( 'R.A. [rad]' )
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| 188 | pl.ylabel( 'Declination [rad]' )
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| 189 | pl.title( 'edgemarker result' )
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| 190 | pl.ion()
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| 191 | pl.draw()
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| 192 |
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| 193 | def _0to2pi( v ):
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| 194 | return v % (2.0*math.pi)
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| 195 |
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| 196 | def quadrant( v ):
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| 197 | vl = _0to2pi( v )
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| 198 | base = 0.5 * math.pi
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| 199 | return int( vl / base )
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| 200 |
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| 201 | def quadrantList( a ):
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| 202 | n = len(a)
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| 203 | nquad = numpy.zeros( 4, dtype=int )
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| 204 | for i in xrange(n):
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| 205 | v = quadrant( a[i] )
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| 206 | nquad[v] += 1
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| 207 | #print nquad
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| 208 | return nquad
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| 209 |
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| 210 | def rotate( v ):
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| 211 | a = numpy.zeros( len(v), dtype=float )
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| 212 | for i in xrange(len(v)):
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| 213 | a[i] = _0to2pi( v[i] )
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| 214 | nquad = quadrantList( a )
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| 215 | quadList = [[],[],[],[]]
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| 216 | rot = numpy.zeros( 4, dtype=bool )
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| 217 | if all( nquad==0 ):
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| 218 | print 'no data'
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| 219 | elif all( nquad>0 ):
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| 220 | #print 'extends in all quadrants'
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| 221 | pass
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| 222 | elif nquad[0]>0 and nquad[3]>0:
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| 223 | #print 'need rotation'
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| 224 | rot[3] = True
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| 225 | rot[2] = nquad[1]==0 and nquad[2]>0
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| 226 | #print rot
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| 227 |
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| 228 | for i in xrange(len(a)):
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| 229 | if rot[quadrant(a[i])]:
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| 230 | a[i] -= 2*math.pi
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| 231 | return a
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