1 | from asap import rcParams, print_log, selector |
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2 | from asap import NUM |
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3 | import matplotlib.axes |
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4 | import sre |
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5 | |
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6 | class asapplotter: |
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7 | """ |
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8 | The ASAP plotter. |
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9 | By default the plotter is set up to plot polarisations |
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10 | 'colour stacked' and scantables across panels. |
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11 | Note: |
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12 | Currenly it only plots 'spectra' not Tsys or |
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13 | other variables. |
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14 | """ |
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15 | def __init__(self, visible=None): |
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16 | self._visible = rcParams['plotter.gui'] |
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17 | if visible is not None: |
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18 | self._visible = visible |
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19 | self._plotter = self._newplotter() |
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20 | |
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21 | self._panelling = None |
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22 | self._stacking = None |
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23 | self.set_panelling() |
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24 | self.set_stacking() |
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25 | self._rows = None |
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26 | self._cols = None |
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27 | self._autoplot = False |
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28 | self._minmaxx = None |
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29 | self._minmaxy = None |
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30 | self._datamask = None |
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31 | self._data = None |
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32 | self._lmap = None |
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33 | self._title = None |
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34 | self._ordinate = None |
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35 | self._abcissa = None |
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36 | self._abcunit = None |
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37 | self._usermask = [] |
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38 | self._maskselection = None |
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39 | self._selection = selector() |
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40 | self._hist = rcParams['plotter.histogram'] |
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41 | |
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42 | def _translate(self, instr): |
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43 | keys = "s b i p t".split() |
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44 | if isinstance(instr, str): |
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45 | for key in keys: |
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46 | if instr.lower().startswith(key): |
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47 | return key |
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48 | return None |
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49 | |
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50 | def _newplotter(self): |
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51 | if self._visible: |
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52 | from asap.asaplotgui import asaplotgui as asaplot |
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53 | else: |
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54 | from asap.asaplot import asaplot |
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55 | return asaplot() |
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56 | |
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57 | |
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58 | def plot(self, scan=None): |
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59 | """ |
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60 | Plot a scantable. |
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61 | Parameters: |
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62 | scan: a scantable |
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63 | Note: |
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64 | If a scantable was specified in a previous call |
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65 | to plot, no argument has to be given to 'replot' |
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66 | NO checking is done that the abcissas of the scantable |
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67 | are consistent e.g. all 'channel' or all 'velocity' etc. |
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68 | """ |
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69 | if self._plotter.is_dead: |
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70 | self._plotter = self._newplotter() |
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71 | self._plotter.hold() |
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72 | self._plotter.clear() |
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73 | from asap import scantable |
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74 | if not self._data and not scan: |
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75 | msg = "Input is not a scantable" |
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76 | if rcParams['verbose']: |
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77 | print msg |
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78 | return |
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79 | raise TypeError(msg) |
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80 | if isinstance(scan, scantable): |
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81 | if self._data is not None: |
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82 | if scan != self._data: |
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83 | self._data = scan |
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84 | # reset |
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85 | self._reset() |
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86 | else: |
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87 | self._data = scan |
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88 | self._reset() |
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89 | # ranges become invalid when unit changes |
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90 | if self._abcunit and self._abcunit != self._data.get_unit(): |
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91 | self._minmaxx = None |
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92 | self._minmaxy = None |
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93 | self._abcunit = self._data.get_unit() |
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94 | self._datamask = None |
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95 | self._plot(self._data) |
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96 | if self._minmaxy is not None: |
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97 | self._plotter.set_limits(ylim=self._minmaxy) |
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98 | self._plotter.release() |
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99 | self._plotter.tidy() |
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100 | self._plotter.show(hardrefresh=False) |
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101 | print_log() |
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102 | return |
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103 | |
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104 | |
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105 | # forwards to matplotlib axes |
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106 | def text(self, *args, **kwargs): |
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107 | self._axes_callback("text", *args, **kwargs) |
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108 | text. __doc__ = matplotlib.axes.Axes.text.__doc__ |
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109 | def arrow(self, *args, **kwargs): |
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110 | self._axes_callback("arrow", *args, **kwargs) |
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111 | arrow. __doc__ = matplotlib.axes.Axes.arrow.__doc__ |
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112 | def axvline(self, *args, **kwargs): |
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113 | self._axes_callback("axvline", *args, **kwargs) |
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114 | axvline. __doc__ = matplotlib.axes.Axes.axvline.__doc__ |
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115 | def axhline(self, *args, **kwargs): |
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116 | self._axes_callback("axhline", *args, **kwargs) |
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117 | axhline. __doc__ = matplotlib.axes.Axes.axhline.__doc__ |
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118 | def axvspan(self, *args, **kwargs): |
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119 | self._axes_callback("axvspan", *args, **kwargs) |
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120 | # hack to preventy mpl from redrawing the patch |
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121 | # it seem to convert the patch into lines on every draw. |
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122 | # This doesn't happen in a test script??? |
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123 | del self._plotter.axes.patches[-1] |
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124 | axvspan. __doc__ = matplotlib.axes.Axes.axvspan.__doc__ |
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125 | |
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126 | def axhspan(self, *args, **kwargs): |
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127 | self._axes_callback("axhspan", *args, **kwargs) |
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128 | # hack to preventy mpl from redrawing the patch |
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129 | # it seem to convert the patch into lines on every draw. |
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130 | # This doesn't happen in a test script??? |
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131 | del self._plotter.axes.patches[-1] |
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132 | axhspan. __doc__ = matplotlib.axes.Axes.axhspan.__doc__ |
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133 | |
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134 | def _axes_callback(self, axesfunc, *args, **kwargs): |
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135 | panel = 0 |
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136 | if kwargs.has_key("panel"): |
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137 | panel = kwargs.pop("panel") |
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138 | coords = None |
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139 | if kwargs.has_key("coords"): |
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140 | coords = kwargs.pop("coords") |
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141 | if coords.lower() == 'world': |
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142 | kwargs["transform"] = self._plotter.axes.transData |
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143 | elif coords.lower() == 'relative': |
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144 | kwargs["transform"] = self._plotter.axes.transAxes |
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145 | self._plotter.subplot(panel) |
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146 | self._plotter.axes.set_autoscale_on(False) |
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147 | getattr(self._plotter.axes, axesfunc)(*args, **kwargs) |
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148 | self._plotter.show(False) |
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149 | self._plotter.axes.set_autoscale_on(True) |
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150 | # end matplotlib.axes fowarding functions |
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151 | |
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152 | def set_mode(self, stacking=None, panelling=None): |
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153 | """ |
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154 | Set the plots look and feel, i.e. what you want to see on the plot. |
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155 | Parameters: |
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156 | stacking: tell the plotter which variable to plot |
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157 | as line colour overlays (default 'pol') |
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158 | panelling: tell the plotter which variable to plot |
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159 | across multiple panels (default 'scan' |
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160 | Note: |
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161 | Valid modes are: |
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162 | 'beam' 'Beam' 'b': Beams |
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163 | 'if' 'IF' 'i': IFs |
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164 | 'pol' 'Pol' 'p': Polarisations |
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165 | 'scan' 'Scan' 's': Scans |
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166 | 'time' 'Time' 't': Times |
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167 | """ |
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168 | msg = "Invalid mode" |
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169 | if not self.set_panelling(panelling) or \ |
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170 | not self.set_stacking(stacking): |
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171 | if rcParams['verbose']: |
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172 | print msg |
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173 | return |
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174 | else: |
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175 | raise TypeError(msg) |
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176 | if self._data: self.plot(self._data) |
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177 | return |
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178 | |
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179 | def set_panelling(self, what=None): |
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180 | mode = what |
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181 | if mode is None: |
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182 | mode = rcParams['plotter.panelling'] |
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183 | md = self._translate(mode) |
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184 | if md: |
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185 | self._panelling = md |
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186 | self._title = None |
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187 | return True |
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188 | return False |
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189 | |
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190 | def set_layout(self,rows=None,cols=None): |
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191 | """ |
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192 | Set the multi-panel layout, i.e. how many rows and columns plots |
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193 | are visible. |
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194 | Parameters: |
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195 | rows: The number of rows of plots |
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196 | cols: The number of columns of plots |
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197 | Note: |
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198 | If no argument is given, the potter reverts to its auto-plot |
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199 | behaviour. |
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200 | """ |
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201 | self._rows = rows |
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202 | self._cols = cols |
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203 | if self._data: self.plot(self._data) |
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204 | return |
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205 | |
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206 | def set_stacking(self, what=None): |
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207 | mode = what |
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208 | if mode is None: |
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209 | mode = rcParams['plotter.stacking'] |
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210 | md = self._translate(mode) |
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211 | if md: |
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212 | self._stacking = md |
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213 | self._lmap = None |
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214 | return True |
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215 | return False |
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216 | |
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217 | def set_range(self,xstart=None,xend=None,ystart=None,yend=None): |
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218 | """ |
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219 | Set the range of interest on the abcissa of the plot |
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220 | Parameters: |
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221 | [x,y]start,[x,y]end: The start and end points of the 'zoom' window |
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222 | Note: |
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223 | These become non-sensical when the unit changes. |
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224 | use plotter.set_range() without parameters to reset |
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225 | |
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226 | """ |
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227 | if xstart is None and xend is None: |
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228 | self._minmaxx = None |
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229 | else: |
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230 | self._minmaxx = [xstart,xend] |
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231 | if ystart is None and yend is None: |
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232 | self._minmaxy = None |
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233 | else: |
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234 | self._minmaxy = [ystart,yend] |
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235 | if self._data: self.plot(self._data) |
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236 | return |
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237 | |
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238 | def set_legend(self, mp=None, fontsize = None, mode = 0): |
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239 | """ |
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240 | Specify a mapping for the legend instead of using the default |
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241 | indices: |
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242 | Parameters: |
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243 | mp: a list of 'strings'. This should have the same length |
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244 | as the number of elements on the legend and then maps |
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245 | to the indeces in order. It is possible to uses latex |
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246 | math expression. These have to be enclosed in r'', |
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247 | e.g. r'$x^{2}$' |
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248 | fontsize: The font size of the label (default None) |
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249 | mode: where to display the legend |
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250 | Any other value for loc else disables the legend: |
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251 | 0: auto |
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252 | 1: upper right |
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253 | 2: upper left |
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254 | 3: lower left |
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255 | 4: lower right |
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256 | 5: right |
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257 | 6: center left |
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258 | 7: center right |
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259 | 8: lower center |
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260 | 9: upper center |
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261 | 10: center |
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262 | |
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263 | Example: |
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264 | If the data has two IFs/rest frequencies with index 0 and 1 |
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265 | for CO and SiO: |
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266 | plotter.set_stacking('i') |
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267 | plotter.set_legend(['CO','SiO']) |
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268 | plotter.plot() |
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269 | plotter.set_legend([r'$^{12}CO$', r'SiO']) |
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270 | """ |
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271 | self._lmap = mp |
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272 | self._plotter.legend(mode) |
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273 | if isinstance(fontsize, int): |
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274 | from matplotlib import rc as rcp |
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275 | rcp('legend', fontsize=fontsize) |
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276 | if self._data: |
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277 | self.plot(self._data) |
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278 | return |
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279 | |
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280 | def set_title(self, title=None, fontsize=None): |
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281 | """ |
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282 | Set the title of the plot. If multiple panels are plotted, |
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283 | multiple titles have to be specified. |
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284 | Example: |
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285 | # two panels are visible on the plotter |
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286 | plotter.set_title(["First Panel","Second Panel"]) |
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287 | """ |
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288 | self._title = title |
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289 | if isinstance(fontsize, int): |
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290 | from matplotlib import rc as rcp |
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291 | rcp('axes', titlesize=fontsize) |
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292 | if self._data: self.plot(self._data) |
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293 | return |
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294 | |
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295 | def set_ordinate(self, ordinate=None, fontsize=None): |
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296 | """ |
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297 | Set the y-axis label of the plot. If multiple panels are plotted, |
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298 | multiple labels have to be specified. |
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299 | Parameters: |
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300 | ordinate: a list of ordinate labels. None (default) let |
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301 | data determine the labels |
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302 | Example: |
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303 | # two panels are visible on the plotter |
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304 | plotter.set_ordinate(["First Y-Axis","Second Y-Axis"]) |
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305 | """ |
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306 | self._ordinate = ordinate |
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307 | if isinstance(fontsize, int): |
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308 | from matplotlib import rc as rcp |
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309 | rcp('axes', labelsize=fontsize) |
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310 | rcp('ytick', labelsize=fontsize) |
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311 | if self._data: self.plot(self._data) |
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312 | return |
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313 | |
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314 | def set_abcissa(self, abcissa=None, fontsize=None): |
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315 | """ |
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316 | Set the x-axis label of the plot. If multiple panels are plotted, |
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317 | multiple labels have to be specified. |
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318 | Parameters: |
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319 | abcissa: a list of abcissa labels. None (default) let |
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320 | data determine the labels |
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321 | Example: |
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322 | # two panels are visible on the plotter |
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323 | plotter.set_ordinate(["First X-Axis","Second X-Axis"]) |
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324 | """ |
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325 | self._abcissa = abcissa |
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326 | if isinstance(fontsize, int): |
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327 | from matplotlib import rc as rcp |
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328 | rcp('axes', labelsize=fontsize) |
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329 | rcp('xtick', labelsize=fontsize) |
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330 | if self._data: self.plot(self._data) |
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331 | return |
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332 | |
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333 | def set_colors(self, colmap): |
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334 | """ |
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335 | Set the colours to be used. The plotter will cycle through |
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336 | these colours when lines are overlaid (stacking mode). |
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337 | Parameters: |
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338 | colmap: a list of colour names |
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339 | Example: |
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340 | plotter.set_colors("red green blue") |
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341 | # If for example four lines are overlaid e.g I Q U V |
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342 | # 'I' will be 'red', 'Q' will be 'green', U will be 'blue' |
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343 | # and 'V' will be 'red' again. |
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344 | """ |
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345 | if isinstance(colmap,str): |
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346 | colmap = colmap.split() |
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347 | self._plotter.palette(0, colormap=colmap) |
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348 | if self._data: self.plot(self._data) |
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349 | |
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350 | # alias for english speakers |
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351 | set_colours = set_colors |
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352 | |
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353 | def set_histogram(self, hist=True, linewidth=None): |
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354 | """ |
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355 | Enable/Disable histogram-like plotting. |
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356 | Parameters: |
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357 | hist: True (default) or False. The fisrt default |
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358 | is taken from the .asaprc setting |
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359 | plotter.histogram |
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360 | """ |
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361 | self._hist = hist |
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362 | if isinstance(linewidth, float) or isinstance(linewidth, int): |
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363 | from matplotlib import rc as rcp |
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364 | rcp('lines', linewidth=linewidth) |
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365 | if self._data: self.plot(self._data) |
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366 | |
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367 | def set_linestyles(self, linestyles=None, linewidth=None): |
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368 | """ |
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369 | Set the linestyles to be used. The plotter will cycle through |
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370 | these linestyles when lines are overlaid (stacking mode) AND |
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371 | only one color has been set. |
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372 | Parameters: |
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373 | linestyles: a list of linestyles to use. |
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374 | 'line', 'dashed', 'dotted', 'dashdot', |
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375 | 'dashdotdot' and 'dashdashdot' are |
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376 | possible |
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377 | |
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378 | Example: |
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379 | plotter.set_colors("black") |
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380 | plotter.set_linestyles("line dashed dotted dashdot") |
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381 | # If for example four lines are overlaid e.g I Q U V |
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382 | # 'I' will be 'solid', 'Q' will be 'dashed', |
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383 | # U will be 'dotted' and 'V' will be 'dashdot'. |
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384 | """ |
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385 | if isinstance(linestyles,str): |
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386 | linestyles = linestyles.split() |
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387 | self._plotter.palette(color=0,linestyle=0,linestyles=linestyles) |
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388 | if isinstance(linewidth, float) or isinstance(linewidth, int): |
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389 | from matplotlib import rc as rcp |
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390 | rcp('lines', linewidth=linewidth) |
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391 | if self._data: self.plot(self._data) |
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392 | |
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393 | def set_font(self, family=None, style=None, weight=None, size=None): |
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394 | """ |
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395 | Set font properties. |
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396 | Parameters: |
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397 | family: one of 'sans-serif', 'serif', 'cursive', 'fantasy', 'monospace' |
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398 | style: one of 'normal' (or 'roman'), 'italic' or 'oblique' |
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399 | weight: one of 'normal or 'bold' |
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400 | size: the 'general' font size, individual elements can be adjusted |
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401 | seperately |
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402 | """ |
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403 | from matplotlib import rc as rcp |
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404 | if isinstance(family, str): |
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405 | rcp('font', family=family) |
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406 | if isinstance(style, str): |
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407 | rcp('font', style=style) |
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408 | if isinstance(weight, str): |
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409 | rcp('font', weight=weight) |
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410 | if isinstance(size, float) or isinstance(size, int): |
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411 | rcp('font', size=size) |
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412 | if self._data: self.plot(self._data) |
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413 | |
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414 | def plot_lines(self, linecat=None, doppler=0.0, deltachan=10, rotate=90.0, |
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415 | location=None): |
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416 | """ |
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417 | Plot a line catalog. |
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418 | Parameters: |
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419 | linecat: the linecatalog to plot |
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420 | doppler: the velocity shift to apply to the frequencies |
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421 | deltachan: the number of channels to include each side of the |
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422 | line to determine a local maximum/minimum |
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423 | rotate: the rotation (in degrees) )for the text label (default 90.0) |
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424 | location: the location of the line annotation from the 'top', |
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425 | 'bottom' or alternate (None - the default) |
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426 | Notes: |
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427 | If the spectrum is flagged no line will be drawn in that location. |
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428 | """ |
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429 | if not self._data: |
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430 | raise RuntimeError("No scantable has been plotted yet.") |
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431 | from asap._asap import linecatalog |
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432 | if not isinstance(linecat, linecatalog): |
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433 | raise ValueError("'linecat' isn't of type linecatalog.") |
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434 | if not self._data.get_unit().endswith("Hz"): |
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435 | raise RuntimeError("Can only overlay linecatalogs when data is in frequency.") |
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436 | from matplotlib.numerix import ma |
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437 | for j in range(len(self._plotter.subplots)): |
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438 | self._plotter.subplot(j) |
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439 | lims = self._plotter.axes.get_xlim() |
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440 | for row in range(linecat.nrow()): |
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441 | # get_frequency returns MHz |
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442 | base = { "GHz": 1000.0, "MHz": 1.0, "Hz": 1.0e-6 } |
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443 | restf = linecat.get_frequency(row)/base[self._data.get_unit()] |
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444 | c = 299792.458 |
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445 | freq = restf*(1.0-doppler/c) |
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446 | if lims[0] < freq < lims[1]: |
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447 | if location is None: |
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448 | loc = 'bottom' |
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449 | if row%2: loc='top' |
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450 | else: loc = location |
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451 | maxys = [] |
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452 | for line in self._plotter.axes.lines: |
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453 | v = line._x |
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454 | asc = v[0] < v[-1] |
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455 | |
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456 | idx = None |
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457 | if not asc: |
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458 | if v[len(v)-1] <= freq <= v[0]: |
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459 | i = len(v)-1 |
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460 | while i>=0 and v[i] < freq: |
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461 | idx = i |
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462 | i-=1 |
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463 | else: |
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464 | if v[0] <= freq <= v[len(v)-1]: |
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465 | i = 0 |
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466 | while i<len(v) and v[i] < freq: |
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467 | idx = i |
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468 | i+=1 |
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469 | if idx is not None: |
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470 | lower = idx - deltachan |
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471 | upper = idx + deltachan |
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472 | if lower < 0: lower = 0 |
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473 | if upper > len(v): upper = len(v) |
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474 | s = slice(lower, upper) |
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475 | y = line._y[s] |
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476 | maxy = ma.maximum(y) |
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477 | if isinstance( maxy, float): |
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478 | maxys.append(maxy) |
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479 | if len(maxys): |
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480 | peak = max(maxys) |
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481 | if peak > self._plotter.axes.get_ylim()[1]: |
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482 | loc = 'bottom' |
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483 | else: |
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484 | continue |
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485 | self._plotter.vline_with_label(freq, peak, |
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486 | linecat.get_name(row), |
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487 | location=loc, rotate=rotate) |
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488 | self._plotter.show(hardrefresh=False) |
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489 | |
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490 | |
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491 | def save(self, filename=None, orientation=None, dpi=None): |
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492 | """ |
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493 | Save the plot to a file. The know formats are 'png', 'ps', 'eps'. |
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494 | Parameters: |
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495 | filename: The name of the output file. This is optional |
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496 | and autodetects the image format from the file |
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497 | suffix. If non filename is specified a file |
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498 | called 'yyyymmdd_hhmmss.png' is created in the |
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499 | current directory. |
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500 | orientation: optional parameter for postscript only (not eps). |
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501 | 'landscape', 'portrait' or None (default) are valid. |
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502 | If None is choosen for 'ps' output, the plot is |
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503 | automatically oriented to fill the page. |
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504 | dpi: The dpi of the output non-ps plot |
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505 | """ |
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506 | self._plotter.save(filename,orientation,dpi) |
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507 | return |
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508 | |
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509 | |
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510 | def set_mask(self, mask=None, selection=None): |
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511 | """ |
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512 | Set a plotting mask for a specific polarization. |
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513 | This is useful for masking out "noise" Pangle outside a source. |
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514 | Parameters: |
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515 | mask: a mask from scantable.create_mask |
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516 | selection: the spectra to apply the mask to. |
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517 | Example: |
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518 | select = selector() |
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519 | select.setpolstrings("Pangle") |
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520 | plotter.set_mask(mymask, select) |
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521 | """ |
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522 | if not self._data: |
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523 | msg = "Can only set mask after a first call to plot()" |
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524 | if rcParams['verbose']: |
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525 | print msg |
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526 | return |
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527 | else: |
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528 | raise RuntimeError(msg) |
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529 | if len(mask): |
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530 | if isinstance(mask, list) or isinstance(mask, tuple): |
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531 | self._usermask = array(mask) |
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532 | else: |
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533 | self._usermask = mask |
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534 | if mask is None and selection is None: |
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535 | self._usermask = [] |
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536 | self._maskselection = None |
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537 | if isinstance(selection, selector): |
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538 | self._maskselection = {'b': selection.get_beams(), |
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539 | 's': selection.get_scans(), |
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540 | 'i': selection.get_ifs(), |
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541 | 'p': selection.get_pols(), |
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542 | 't': [] } |
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543 | else: |
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544 | self._maskselection = None |
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545 | self.plot(self._data) |
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546 | |
---|
547 | def _slice_indeces(self, data): |
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548 | mn = self._minmaxx[0] |
---|
549 | mx = self._minmaxx[1] |
---|
550 | asc = data[0] < data[-1] |
---|
551 | start=0 |
---|
552 | end = len(data)-1 |
---|
553 | inc = 1 |
---|
554 | if not asc: |
---|
555 | start = len(data)-1 |
---|
556 | end = 0 |
---|
557 | inc = -1 |
---|
558 | # find min index |
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559 | while start > 0 and data[start] < mn: |
---|
560 | start+= inc |
---|
561 | # find max index |
---|
562 | while end > 0 and data[end] > mx: |
---|
563 | end-=inc |
---|
564 | if end > 0: end +=1 |
---|
565 | if start > end: |
---|
566 | return end,start |
---|
567 | return start,end |
---|
568 | |
---|
569 | def _reset(self): |
---|
570 | self._usermask = [] |
---|
571 | self._usermaskspectra = None |
---|
572 | self.set_selection(None, False) |
---|
573 | |
---|
574 | def _plot(self, scan): |
---|
575 | savesel = scan.get_selection() |
---|
576 | sel = savesel + self._selection |
---|
577 | d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO', |
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578 | 'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME' } |
---|
579 | order = [d0[self._panelling],d0[self._stacking]] |
---|
580 | sel.set_order(order) |
---|
581 | scan.set_selection(sel) |
---|
582 | d = {'b': scan.getbeam, 's': scan.getscan, |
---|
583 | 'i': scan.getif, 'p': scan.getpol, 't': scan._gettime } |
---|
584 | |
---|
585 | polmodes = dict(zip(self._selection.get_pols(), |
---|
586 | self._selection.get_poltypes())) |
---|
587 | # this returns either a tuple of numbers or a length (ncycles) |
---|
588 | # convert this into lengths |
---|
589 | n0,nstack0 = self._get_selected_n(scan) |
---|
590 | if isinstance(n0, int): n = n0 |
---|
591 | else: n = len(n0) |
---|
592 | if isinstance(nstack0, int): nstack = nstack0 |
---|
593 | else: nstack = len(nstack0) |
---|
594 | maxpanel, maxstack = 16,8 |
---|
595 | if n > maxpanel or nstack > maxstack: |
---|
596 | from asap import asaplog |
---|
597 | maxn = 0 |
---|
598 | if nstack > maxstack: maxn = maxstack |
---|
599 | if n > maxpanel: maxn = maxpanel |
---|
600 | msg ="Scan to be plotted contains more than %d selections.\n" \ |
---|
601 | "Selecting first %d selections..." % (maxn, maxn) |
---|
602 | asaplog.push(msg) |
---|
603 | print_log() |
---|
604 | n = min(n,maxpanel) |
---|
605 | nstack = min(nstack,maxstack) |
---|
606 | if n > 1: |
---|
607 | ganged = rcParams['plotter.ganged'] |
---|
608 | if self._rows and self._cols: |
---|
609 | n = min(n,self._rows*self._cols) |
---|
610 | self._plotter.set_panels(rows=self._rows,cols=self._cols, |
---|
611 | nplots=n,ganged=ganged) |
---|
612 | else: |
---|
613 | self._plotter.set_panels(rows=n,cols=0,nplots=n,ganged=ganged) |
---|
614 | else: |
---|
615 | self._plotter.set_panels() |
---|
616 | r=0 |
---|
617 | nr = scan.nrow() |
---|
618 | a0,b0 = -1,-1 |
---|
619 | allxlim = [] |
---|
620 | allylim = [] |
---|
621 | newpanel=True |
---|
622 | panelcount,stackcount = 0,0 |
---|
623 | while r < nr: |
---|
624 | a = d[self._panelling](r) |
---|
625 | b = d[self._stacking](r) |
---|
626 | if a > a0 and panelcount < n: |
---|
627 | if n > 1: |
---|
628 | self._plotter.subplot(panelcount) |
---|
629 | self._plotter.palette(0) |
---|
630 | #title |
---|
631 | xlab = self._abcissa and self._abcissa[panelcount] \ |
---|
632 | or scan._getabcissalabel() |
---|
633 | ylab = self._ordinate and self._ordinate[panelcount] \ |
---|
634 | or scan._get_ordinate_label() |
---|
635 | self._plotter.set_axes('xlabel',xlab) |
---|
636 | self._plotter.set_axes('ylabel',ylab) |
---|
637 | lbl = self._get_label(scan, r, self._panelling, self._title) |
---|
638 | if isinstance(lbl, list) or isinstance(lbl, tuple): |
---|
639 | if 0 <= panelcount < len(lbl): |
---|
640 | lbl = lbl[panelcount] |
---|
641 | else: |
---|
642 | # get default label |
---|
643 | lbl = self._get_label(scan, r, self._panelling, None) |
---|
644 | self._plotter.set_axes('title',lbl) |
---|
645 | newpanel = True |
---|
646 | stackcount =0 |
---|
647 | panelcount += 1 |
---|
648 | if (b > b0 or newpanel) and stackcount < nstack: |
---|
649 | y = [] |
---|
650 | if len(polmodes): |
---|
651 | y = scan._getspectrum(r, polmodes[scan.getpol(r)]) |
---|
652 | else: |
---|
653 | y = scan._getspectrum(r) |
---|
654 | m = scan._getmask(r) |
---|
655 | from matplotlib.numerix import logical_not, logical_and |
---|
656 | if self._maskselection and len(self._usermask) == len(m): |
---|
657 | if d[self._stacking](r) in self._maskselection[self._stacking]: |
---|
658 | m = logical_and(m, self._usermask) |
---|
659 | x = scan._getabcissa(r) |
---|
660 | from matplotlib.numerix import ma, array |
---|
661 | y = ma.masked_array(y,mask=logical_not(array(m,copy=False))) |
---|
662 | if self._minmaxx is not None: |
---|
663 | s,e = self._slice_indeces(x) |
---|
664 | x = x[s:e] |
---|
665 | y = y[s:e] |
---|
666 | if len(x) > 1024 and rcParams['plotter.decimate']: |
---|
667 | fac = len(x)/1024 |
---|
668 | x = x[::fac] |
---|
669 | y = y[::fac] |
---|
670 | llbl = self._get_label(scan, r, self._stacking, self._lmap) |
---|
671 | if isinstance(llbl, list) or isinstance(llbl, tuple): |
---|
672 | if 0 <= stackcount < len(llbl): |
---|
673 | # use user label |
---|
674 | llbl = llbl[stackcount] |
---|
675 | else: |
---|
676 | # get default label |
---|
677 | llbl = self._get_label(scan, r, self._stacking, None) |
---|
678 | self._plotter.set_line(label=llbl) |
---|
679 | plotit = self._plotter.plot |
---|
680 | if self._hist: plotit = self._plotter.hist |
---|
681 | if len(x) > 0: |
---|
682 | plotit(x,y) |
---|
683 | xlim= self._minmaxx or [min(x),max(x)] |
---|
684 | allxlim += xlim |
---|
685 | ylim= self._minmaxy or [ma.minimum(y),ma.maximum(y)] |
---|
686 | allylim += ylim |
---|
687 | stackcount += 1 |
---|
688 | # last in colour stack -> autoscale x |
---|
689 | if stackcount == nstack: |
---|
690 | allxlim.sort() |
---|
691 | self._plotter.axes.set_xlim([allxlim[0],allxlim[-1]]) |
---|
692 | # clear |
---|
693 | allxlim =[] |
---|
694 | |
---|
695 | newpanel = False |
---|
696 | a0=a |
---|
697 | b0=b |
---|
698 | # ignore following rows |
---|
699 | if (panelcount == n) and (stackcount == nstack): |
---|
700 | # last panel -> autoscale y if ganged |
---|
701 | if rcParams['plotter.ganged']: |
---|
702 | allylim.sort() |
---|
703 | self._plotter.set_limits(ylim=[allylim[0],allylim[-1]]) |
---|
704 | break |
---|
705 | r+=1 # next row |
---|
706 | #reset the selector to the scantable's original |
---|
707 | scan.set_selection(savesel) |
---|
708 | |
---|
709 | def set_selection(self, selection=None, refresh=True): |
---|
710 | self._selection = isinstance(selection,selector) and selection or selector() |
---|
711 | d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO', |
---|
712 | 'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME' } |
---|
713 | order = [d0[self._panelling],d0[self._stacking]] |
---|
714 | self._selection.set_order(order) |
---|
715 | if self._data and refresh: self.plot(self._data) |
---|
716 | |
---|
717 | def _get_selected_n(self, scan): |
---|
718 | d1 = {'b': scan.getbeamnos, 's': scan.getscannos, |
---|
719 | 'i': scan.getifnos, 'p': scan.getpolnos, 't': scan.ncycle } |
---|
720 | d2 = { 'b': self._selection.get_beams(), |
---|
721 | 's': self._selection.get_scans(), |
---|
722 | 'i': self._selection.get_ifs(), |
---|
723 | 'p': self._selection.get_pols(), |
---|
724 | 't': self._selection.get_cycles() } |
---|
725 | n = d2[self._panelling] or d1[self._panelling]() |
---|
726 | nstack = d2[self._stacking] or d1[self._stacking]() |
---|
727 | return n,nstack |
---|
728 | |
---|
729 | def _get_label(self, scan, row, mode, userlabel=None): |
---|
730 | if isinstance(userlabel, list) and len(userlabel) == 0: |
---|
731 | userlabel = " " |
---|
732 | pms = dict(zip(self._selection.get_pols(),self._selection.get_poltypes())) |
---|
733 | if len(pms): |
---|
734 | poleval = scan._getpollabel(scan.getpol(row),pms[scan.getpol(row)]) |
---|
735 | else: |
---|
736 | poleval = scan._getpollabel(scan.getpol(row),scan.poltype()) |
---|
737 | d = {'b': "Beam "+str(scan.getbeam(row)), |
---|
738 | 's': scan._getsourcename(row), |
---|
739 | 'i': "IF"+str(scan.getif(row)), |
---|
740 | 'p': poleval, |
---|
741 | 't': str(scan.get_time(row)) } |
---|
742 | return userlabel or d[mode] |
---|
743 | |
---|