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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