1 | from asap.asaplot import ASAPlot |
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2 | from asap import rcParams |
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3 | |
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4 | class asapplotter: |
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5 | """ |
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6 | The ASAP plotter. |
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7 | By default the plotter is set up to plot polarisations |
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8 | 'colour stacked' and scantables across panels. |
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9 | Note: |
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10 | Currenly it only plots 'spectra' not Tsys or |
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11 | other variables. |
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12 | """ |
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13 | def __init__(self): |
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14 | self._plotter = ASAPlot() |
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15 | |
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16 | self._tdict = {'Time':'t','time':'t','t':'t','T':'t'} |
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17 | self._bdict = {'Beam':'b','beam':'b','b':'b','B':'b'} |
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18 | self._idict = {'IF':'i','if':'i','i':'i','I':'i'} |
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19 | self._pdict = {'Pol':'p','pol':'p','p':'p'} |
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20 | self._sdict = {'scan':'s','Scan':'s','s':'s','S':'s'} |
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21 | self._cdict = {'t':'scan.nrow()', |
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22 | 'b':'scan.nbeam()', |
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23 | 'i':'scan.nif()', |
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24 | 'p':'scan.npol()', |
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25 | 's':'len(scans)'} |
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26 | self._ldict = {'b':'Beam', |
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27 | 'i':'IF', |
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28 | 'p':'Pol', |
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29 | 's':'Scan'} |
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30 | self._dicts = [self._tdict,self._bdict, |
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31 | self._idict,self._pdict, |
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32 | self._sdict] |
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33 | self._panels = 's' |
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34 | self._stacking = rcParams['plotter.stacking'] |
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35 | self._rows = None |
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36 | self._cols = None |
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37 | self._autoplot = False |
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38 | self._minmax = None |
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39 | self._data = None |
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40 | self._lmap = [] |
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41 | self._title = None |
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42 | self._ordinate = None |
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43 | self._abcissa = None |
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44 | |
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45 | def _translate(self, name): |
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46 | for d in self._dicts: |
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47 | if d.has_key(name): |
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48 | return d[name] |
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49 | return None |
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50 | |
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51 | def plot(self,*args): |
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52 | """ |
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53 | Plot a (list of) scantables. |
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54 | Parameters: |
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55 | one or more comma separated scantables |
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56 | Note: |
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57 | If a (list) of scantables was specified in a previous call |
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58 | to plot, no argument has to be given to 'replot' |
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59 | NO checking is done that the abscissas of the scantables |
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60 | are consistent e.g. all 'channel' or all 'velocity' etc. |
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61 | """ |
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62 | if self._plotter.is_dead: |
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63 | self._plotter = ASAPlot() |
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64 | self._plotter.clear() |
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65 | self._plotter.hold() |
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66 | if len(args) > 0: |
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67 | self._data = tuple(args) |
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68 | if self._panels == 't': |
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69 | if self._data[0].nrow() > 25: |
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70 | print "Scan to be plotted contains more than 25 rows.\nCan't plot that many panels..." |
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71 | return |
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72 | self._plot_time(self._data[0], self._stacking) |
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73 | elif self._panels == 's': |
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74 | self._plot_scans(self._data, self._stacking) |
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75 | else: |
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76 | self._plot_other(self._data, self._stacking) |
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77 | if self._minmax is not None: |
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78 | self._plotter.set_limits(xlim=self._minmax) |
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79 | self._plotter.release() |
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80 | return |
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81 | |
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82 | def _plot_time(self, scan, colmode): |
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83 | if colmode == 't': |
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84 | return |
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85 | n = scan.nrow() |
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86 | cdict = {'b':'scan.setbeam(j)', |
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87 | 'i':'scan.setif(j)', |
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88 | 'p':'scan.setpol(j)'} |
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89 | if self._stacking is not None: |
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90 | ncol = eval(self._cdict.get(colmode)) |
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91 | self._plotter.set_panels() |
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92 | if n > 1: |
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93 | if self._rows and self._cols: |
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94 | n = min(n,self._rows*self._cols) |
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95 | self._plotter.set_panels(rows=self._rows,cols=self._cols, |
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96 | nplots=n) |
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97 | else: |
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98 | self._plotter.set_panels(rows=n,cols=0,nplots=n) |
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99 | for i in range(n): |
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100 | if n > 1: |
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101 | self._plotter.palette(1) |
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102 | self._plotter.subplot(i) |
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103 | for j in range(ncol): |
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104 | eval(cdict.get(colmode)) |
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105 | x = None |
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106 | y = None |
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107 | m = None |
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108 | if not self._title: |
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109 | tlab = scan._getsourcename(i) |
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110 | else: |
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111 | if len(self._title) == n: |
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112 | tlab = self._title[i] |
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113 | else: |
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114 | tlab = scan._getsourcename(i) |
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115 | x,xlab = scan.get_abcissa(i) |
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116 | if self._abcissa: xlab = self._abcissa |
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117 | y = scan._getspectrum(i) |
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118 | if self._ordinate: |
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119 | ylab = self._ordinate |
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120 | else: |
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121 | ylab = 'Flux ('+scan.get_fluxunit()+')' |
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122 | m = scan._getmask(i) |
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123 | if self._lmap and len(self._lmap) > 0: |
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124 | llab = self._lmap[j] |
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125 | else: |
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126 | llab = self._ldict.get(colmode)+' '+str(j) |
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127 | self._plotter.set_line(label=llab) |
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128 | self._plotter.plot(x,y,m) |
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129 | xlim=[min(x),max(x)] |
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130 | self._plotter.axes.set_xlim(xlim) |
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131 | self._plotter.set_axes('xlabel',xlab) |
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132 | self._plotter.set_axes('ylabel',ylab) |
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133 | self._plotter.set_axes('title',tlab) |
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134 | return |
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135 | |
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136 | def _plot_scans(self, scans, colmode): |
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137 | if colmode == 's': |
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138 | return |
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139 | cdict = {'b':'scan.setbeam(j)', |
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140 | 'i':'scan.setif(j)', |
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141 | 'p':'scan.setpol(j)'} |
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142 | n = len(scans) |
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143 | if self._stacking is not None: |
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144 | scan = scans[0] |
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145 | ncol = eval(self._cdict.get(colmode)) |
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146 | self._plotter.set_panels() |
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147 | if n > 1: |
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148 | if self._rows and self._cols: |
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149 | n = min(n,self._rows*self._cols) |
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150 | self._plotter.set_panels(rows=self._rows,cols=self._cols, |
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151 | nplots=n) |
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152 | else: |
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153 | self._plotter.set_panels(rows=n,cols=0,nplots=n) |
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154 | i = 0 |
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155 | for scan in scans: |
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156 | if n > 1: |
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157 | self._plotter.subplot(i) |
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158 | self._plotter.palette(1) |
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159 | for j in range(ncol): |
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160 | eval(cdict.get(colmode)) |
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161 | x = None |
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162 | y = None |
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163 | m = None |
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164 | tlab = self._title |
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165 | if not self._title: |
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166 | tlab = scan._getsourcename() |
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167 | x,xlab = scan.get_abcissa() |
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168 | if self._abcissa: xlab = self._abcissa |
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169 | y = scan._getspectrum() |
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170 | if self._ordinate: |
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171 | ylab = self._ordinate |
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172 | else: |
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173 | ylab = 'Flux ('+scan.get_fluxunit()+')' |
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174 | m = scan._getmask() |
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175 | if self._lmap and len(self._lmap) > 0: |
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176 | llab = self._lmap[j] |
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177 | else: |
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178 | llab = self._ldict.get(colmode)+' '+str(j) |
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179 | self._plotter.set_line(label=llab) |
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180 | self._plotter.plot(x,y,m) |
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181 | xlim=[min(x),max(x)] |
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182 | self._plotter.axes.set_xlim(xlim) |
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183 | |
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184 | self._plotter.set_axes('xlabel',xlab) |
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185 | self._plotter.set_axes('ylabel',ylab) |
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186 | self._plotter.set_axes('title',tlab) |
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187 | i += 1 |
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188 | return |
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189 | |
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190 | def _plot_other(self,scans,colmode): |
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191 | if colmode == self._panels: |
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192 | return |
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193 | cdict = {'b':'scan.setbeam(j)', |
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194 | 'i':'scan.setif(j)', |
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195 | 'p':'scan.setpol(j)', |
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196 | 's':'scans[j]'} |
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197 | scan = scans[0] |
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198 | n = eval(self._cdict.get(self._panels)) |
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199 | if self._stacking is not None: |
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200 | ncol = eval(self._cdict.get(colmode)) |
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201 | self._plotter.set_panels() |
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202 | if n > 1: |
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203 | if self._rows and self._cols: |
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204 | n = min(n,self._rows*self._cols) |
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205 | self._plotter.set_panels(rows=self._rows,cols=self._cols, |
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206 | nplots=n) |
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207 | else: |
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208 | print n |
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209 | self._plotter.set_panels(rows=n,cols=0,nplots=n) |
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210 | for i in range(n): |
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211 | if n>1: |
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212 | self._plotter.subplot(i) |
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213 | self._plotter.palette(1) |
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214 | k=0 |
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215 | j=i |
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216 | eval(cdict.get(self._panels)) |
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217 | for j in range(ncol): |
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218 | if colmode == 's': |
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219 | scan = eval(cdict.get(colmode)) |
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220 | elif colmode == 't': |
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221 | k = j |
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222 | else: |
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223 | eval(cdict.get(colmode)) |
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224 | x = None |
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225 | y = None |
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226 | m = None |
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227 | x,xlab = scan.get_abcissa(k) |
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228 | if self._abcissa: xlab = self._abcissa |
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229 | y = scan._getspectrum(k) |
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230 | if self._ordinate: |
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231 | ylab = self._ordinate |
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232 | else: |
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233 | ylab = 'Flux ('+scan.get_fluxunit()+')' |
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234 | m = scan._getmask(k) |
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235 | if colmode == 's' or colmode == 't': |
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236 | if not self._title: |
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237 | tlab = self._ldict.get(self._panels)+' '+str(i) |
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238 | else: |
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239 | if len(self.title) == n: |
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240 | tlab = self._title[i] |
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241 | else: |
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242 | tlab = self._ldict.get(self._panels)+' '+str(i) |
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243 | llab = scan._getsourcename(k) |
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244 | else: |
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245 | if self._title and len(self._title) > 0: |
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246 | tlab = self._title[i] |
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247 | else: |
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248 | tlab = self._ldict.get(self._panels)+' '+str(i) |
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249 | if self._lmap and len(self._lmap) > 0: |
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250 | llab = self._lmap[j] |
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251 | else: |
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252 | llab = self._ldict.get(colmode)+' '+str(j) |
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253 | self._plotter.set_line(label=llab) |
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254 | self._plotter.plot(x,y,m) |
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255 | xlim=[min(x),max(x)] |
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256 | self._plotter.axes.set_xlim(xlim) |
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257 | |
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258 | self._plotter.set_axes('xlabel',xlab) |
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259 | self._plotter.set_axes('ylabel',ylab) |
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260 | self._plotter.set_axes('title',tlab) |
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261 | |
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262 | return |
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263 | |
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264 | |
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265 | def set_mode(self, stacking=None, panelling=None): |
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266 | """ |
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267 | Set the plots look and feel, i.e. what you want to see on the plot. |
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268 | Parameters: |
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269 | stacking: tell the plotter which variable to plot |
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270 | as line colour overlays (default 'pol') |
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271 | panelling: tell the plotter which variable to plot |
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272 | across multiple panels (default 'scan' |
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273 | Note: |
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274 | Valid modes are: |
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275 | 'beam' 'Beam' 'b': Beams |
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276 | 'if' 'IF' 'i': IFs |
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277 | 'pol' 'Pol' 'p': Polarisations |
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278 | 'scan' 'Scan' 's': Scans |
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279 | 'time' 'Time' 't': Times |
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280 | """ |
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281 | if not self.set_panels(panelling): |
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282 | print "Invalid mode" |
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283 | return |
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284 | if not self.set_stacking(stacking): |
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285 | print "Invalid mode" |
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286 | return |
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287 | if self._data: self.plot() |
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288 | return |
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289 | |
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290 | def set_panels(self, what=None): |
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291 | """ |
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292 | """ |
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293 | if not what: |
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294 | what = rcParams['plotter.panelling'] |
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295 | md = self._translate(what) |
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296 | if md: |
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297 | self._panels = md |
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298 | self._title = None |
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299 | return True |
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300 | return False |
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301 | |
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302 | def set_layout(self,rows=None,cols=None): |
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303 | """ |
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304 | Set the multi-panel layout, i.e. how many rows and columns plots |
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305 | are visible. |
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306 | Parameters: |
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307 | rows: The number of rows of plots |
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308 | cols: The number of columns of plots |
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309 | Note: |
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310 | If no argument is given, the potter reverts to its auto-plot |
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311 | behaviour. |
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312 | """ |
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313 | self._rows = rows |
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314 | self._cols = cols |
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315 | if self._data: self.plot() |
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316 | return |
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317 | |
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318 | def set_stacking(self, what=None): |
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319 | if not what: |
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320 | what = rcParams['plotter.stacking'] |
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321 | md = self._translate(what) |
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322 | if md: |
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323 | self._stacking = md |
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324 | self._lmap = None |
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325 | return True |
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326 | return False |
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327 | |
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328 | def set_range(self,start=None,end=None): |
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329 | """ |
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330 | Set the range of interest on the abcissa of the plot |
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331 | Parameters: |
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332 | start,end: The start an end point of the 'zoom' window |
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333 | Note: |
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334 | These become non-sensical when the unit changes. |
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335 | use plotter.set_range() without parameters to reset |
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336 | |
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337 | """ |
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338 | if start is None and end is None: |
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339 | self._minmax = None |
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340 | if self._data: self.plot() |
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341 | else: |
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342 | self._minmax = [start,end] |
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343 | if self._data: self.plot() |
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344 | return |
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345 | |
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346 | def set_legend(self, mp=None): |
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347 | """ |
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348 | Specify a mapping for the legend instead of using the default |
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349 | indices: |
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350 | Parameters: |
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351 | mp: a list of 'strings'. This should have the same length |
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352 | as the number of elements on the legend and then maps |
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353 | to the indeces in order |
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354 | |
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355 | Example: |
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356 | If the data has two IFs/rest frequencies with index 0 and 1 |
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357 | for CO and SiO: |
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358 | plotter.set_stacking('i') |
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359 | plotter.set_legend_map(['CO','SiO']) |
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360 | plotter.plot() |
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361 | """ |
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362 | self._lmap = mp |
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363 | if self._data: self.plot() |
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364 | return |
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365 | |
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366 | def set_title(self, title=None): |
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367 | self._title = title |
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368 | if self._data: self.plot() |
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369 | return |
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370 | |
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371 | def set_ordinate(self, ordinate=None): |
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372 | self._ordinate = ordinate |
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373 | if self._data: self.plot() |
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374 | return |
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375 | |
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376 | def set_abcissa(self, abcissa=None): |
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377 | self._abcissa = abcissa |
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378 | if self._data: self.plot() |
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379 | return |
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380 | |
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381 | def save(self, filename=None): |
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382 | """ |
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383 | Save the plot to a file. The know formats are 'png', 'ps', 'eps'. |
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384 | Parameters: |
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385 | filename: The name of the output file. This is optional |
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386 | and autodetects the image format from the file |
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387 | suffix. If non filename is specified a file |
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388 | called 'yyyymmdd_hhmmss.png' is created in the |
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389 | current directory. |
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390 | """ |
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391 | self._plotter.save(filename) |
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392 | return |
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393 | |
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394 | if __name__ == '__main__': |
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395 | plotter = asapplotter() |
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