1 | from asap.scantable import scantable |
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2 | from asap._asap import _edgemarker |
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3 | |
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4 | class edgemarker: |
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5 | """ |
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6 | The edgemarker is a helper tool to calibrate OTF observation |
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7 | without explicit OFF scans. According to a few user-specified |
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8 | options, the class automatically detects an edge region of the |
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9 | map and mark integrations within this region as OFF. |
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10 | |
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11 | The edgemarker supports raster pattern as well as other generic |
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12 | ones (e.g. lissajous, double circle). The constructor takes |
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13 | one boolean parameter to specify whether scan pattern is raster |
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14 | or not. This is because that edge detection algorithms for raster |
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15 | and others are different. |
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16 | |
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17 | Current limitation of this class is that it cannot handle some |
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18 | complicated observed area. Typical case is that the area has |
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19 | clear 'dent' (e.g. a composite area consisting of two diamond- |
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20 | shaped areas that slightly overlap). In such case, the class |
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21 | will fail to detect such feature. |
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22 | |
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23 | Note that the class takes a copy of input data so that input |
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24 | data will not be overwritten. Result will be provided as a |
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25 | separate data whose contents are essentially the same as input |
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26 | except for that some integrations are marked as OFF. |
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27 | |
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28 | Here is typical usage: |
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29 | |
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30 | s = scantable( 'otf.asap', average=False ) |
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31 | marker = edgemarker( israster=False ) |
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32 | marker.setdata( s ) |
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33 | marker.setoption( fraction='15%', width=0.5 ) |
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34 | marker.mark() |
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35 | |
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36 | # get result as scantable instance |
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37 | s2 = marker.getresult() |
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38 | |
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39 | # save result on disk |
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40 | marker.save( 'otfwithoff.asap', overwrite=True ) |
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41 | """ |
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42 | def __init__( self, israster=False ): |
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43 | """ |
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44 | Constructor. |
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45 | |
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46 | israster -- Whether scan pattern is raster or not. Set True |
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47 | if scan pattern is raster. Default is False. |
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48 | """ |
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49 | self.israster = israster |
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50 | self.marker = _edgemarker( self.israster ) |
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51 | self.st = None |
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52 | |
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53 | def setdata( self, st ): |
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54 | """ |
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55 | Set data to be processed. |
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56 | |
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57 | st -- Data as scantable instance. |
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58 | """ |
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59 | self.st = st |
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60 | self.marker._setdata( self.st, False ) |
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61 | self.marker._examine() |
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62 | |
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63 | def setoption( self, *args, **kwargs ): |
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64 | """ |
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65 | Set options for edge detection. Valid options depend on |
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66 | whether scan pattern is raster or not (i.e. constructor |
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67 | is called with israster=True or False). |
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68 | |
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69 | === for raster (israster=True) === |
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70 | fraction -- Fraction of OFF integration in each raster |
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71 | row. Either numerical value (<1.0) or string |
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72 | is accepted. For string, its value should be |
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73 | 'auto' or format 'xx%'. For example, '10%' |
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74 | is same as 0.1. The 'auto' option estimates |
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75 | number of OFFs based on t_OFF = sqrt(N) t_ON. |
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76 | Default is 0.1. |
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77 | npts -- Number of OFF integration in each raster row. |
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78 | Default is -1 (use fraction). |
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79 | |
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80 | Note that number of integrations specified by the above |
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81 | parameters will be marked as OFF from both ends. So, twice |
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82 | of specified number/fraction will be marked as OFF. For |
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83 | example, if you specify fraction='10%', resultant fraction |
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84 | of OFF integrations will be 20%. |
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85 | |
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86 | Note also that, if both fraction and npts are specified, |
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87 | specification by npts will come before. |
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88 | |
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89 | === for non-raster (israster=False) === |
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90 | fraction -- Fraction of edge area with respect to whole |
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91 | observed area. Either numerical value (<1.0) |
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92 | or string is accepted. For string, its value |
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93 | should be in 'xx%' format. For example, '10%' |
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94 | is same as 0.1. Default is 0.1. |
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95 | width -- Pixel width for edge detection. It should be given |
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96 | as a fraction of the median spatial separation |
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97 | between neighboring integrations in time. Default |
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98 | is 0.5. In the most case, default value will be fine. |
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99 | Larger value will cause worse result. Smaller value |
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100 | may improve result. However, if too small value is |
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101 | set (e.g. 1.0e-5), the algorithm may not work. |
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102 | elongated -- Set True only if observed area is extremely |
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103 | elongated in one direction. Default is False. |
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104 | In most cases, default value will be fine. |
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105 | """ |
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106 | option = {} |
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107 | if self.israster: |
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108 | keys = [ 'fraction', 'npts' ] |
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109 | else: |
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110 | keys = [ 'fraction', 'width', 'elongated' ] |
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111 | for key in keys: |
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112 | if kwargs.has_key( key ): |
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113 | option[key] = kwargs[key] |
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114 | |
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115 | if len(option) > 0: |
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116 | self.marker._setoption( option ) |
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117 | |
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118 | def mark( self ): |
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119 | """ |
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120 | Process data. Edge region is detected according to detection |
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121 | parameters given by setoption(). Then, integrations within |
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122 | edge region will be marked as OFF. |
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123 | """ |
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124 | self.marker._detect() |
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125 | self.marker._mark() |
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126 | |
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127 | def getresult( self ): |
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128 | """ |
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129 | Get result as scantable instance. Returned scantable is |
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130 | copy of input scantable except for that some data are |
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131 | marked as OFF as a result of edge detection and marking. |
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132 | """ |
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133 | return scantable( self.marker._get() ) |
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134 | |
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135 | def save( self, name, overwrite=False ): |
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136 | """ |
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137 | Save result as scantable. |
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138 | |
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139 | name -- Name of the scantable. |
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140 | overwrite -- Overwrite existing data if True. Default is |
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141 | False. |
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142 | """ |
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143 | s = self.getresult() |
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144 | s.save( name, overwrite=overwrite ) |
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