examples/plotParallaxErrorsSkyAvg.py
def makePlot(args):
  """
  Make the plot with parallax performance predictions.

  :argument args: command line arguments
  """
  gmag=np.linspace(5.7,20.0,101)

  vminiB1V=vminiFromSpt('B1V')
  vminiG2V=vminiFromSpt('G2V')
  vminiM6V=vminiFromSpt('M6V')
  
  vmagB1V=gmag-gminvFromVmini(vminiB1V)
  vmagG2V=gmag-gminvFromVmini(vminiG2V)
  vmagM6V=gmag-gminvFromVmini(vminiM6V)
  
  sigparB1V=parallaxErrorSkyAvg(gmag,vminiB1V)
  sigparB1Vmin=parallaxMinError(gmag,vminiB1V)
  sigparB1Vmax=parallaxMaxError(gmag,vminiB1V)
  
  sigparG2V=parallaxErrorSkyAvg(gmag,vminiG2V)
  sigparG2Vmin=parallaxMinError(gmag,vminiG2V)
  sigparG2Vmax=parallaxMaxError(gmag,vminiG2V)
  
  sigparM6V=parallaxErrorSkyAvg(gmag,vminiM6V)
  sigparM6Vmin=parallaxMinError(gmag,vminiM6V)
  sigparM6Vmax=parallaxMaxError(gmag,vminiM6V)
  
  fig=plt.figure(figsize=(10,6.5))
  
  if (args['gmagAbscissa']):
    plt.semilogy(gmag, sigparB1V, 'b', label='B1V')
    plt.semilogy(gmag, sigparG2V, 'g', label='G2V')
    plt.semilogy(gmag, sigparM6V, 'r', label='M6V')
    plt.xlim((5,20))
    plt.ylim((4,1000))
    plt.legend(loc=4)
    plt.xlabel('$G$ [mag]')
  else:
    ax=fig.add_subplot(111)
    plt.semilogy(vmagB1V, sigparB1V, 'b', label='B1V')
    #plt.semilogy(vmagG2V, sigparG2V, 'g', label='G2V')
    plt.semilogy(vmagM6V, sigparM6V, 'r', label='M6V')
    plt.fill_between(vmagB1V, sigparB1Vmin, sigparB1Vmax, color='b', alpha=0.3)
    plt.fill_between(vmagM6V, sigparM6Vmin, sigparM6Vmax, color='r', alpha=0.3)
    plt.xlim((5,22.5))
    plt.ylim((4,1000))
    plt.text(17.2,190,'B1V',color='b')
    plt.text(18,20,'M6V',color='r')
    plt.xlabel('$V$ [mag]')
    plt.text(7,17,'calibration noise floor', size=12, bbox=dict(boxstyle="round,pad=0.3",
                       ec=(0.0, 0.0, 0.0),
                       fc=(1.0, 1.0, 1.0),
                       ))
    plt.text(14.75,80,'photon noise', rotation=45, size=12, bbox=dict(boxstyle="round,pad=0.3",
                       ec=(0.0, 0.0, 0.0),
                       fc=(1.0, 1.0, 1.0),
                       ))
    ax.annotate('non-uniformity\nover the sky', xy=(21.5, 320),  xycoords='data',
                  xytext=(21.5,80), textcoords='data', ha='center', size='12',
                  bbox=dict(boxstyle="round,pad=0.3",ec=(0,0,0),fc=(1,1,1)),
                  arrowprops=dict(facecolor='black', shrink=0.15, width=1,
                    headwidth=6),
                  horizontalalignment='right', verticalalignment='top',
                  )
    ax.annotate('', xy=(21.5, 500),  xycoords='data',
                  xytext=(21.5,950), textcoords='data', ha='center', size='12',
                  arrowprops=dict(facecolor='black', shrink=0.15, width=1,
                    headwidth=6),
                  horizontalalignment='right', verticalalignment='bottom',
                  )
  
  plt.xticks(np.arange(6,24,2))
  ax = plt.gca().yaxis 
  ax.set_major_formatter(matplotlib.ticker.ScalarFormatter())
  plt.ticklabel_format(axis='y',style='plain')
  plt.grid(which='both')
  plt.ylabel('End-of-mission parallax standard error [$\mu$as]')
  
  if (args['pdfOutput']):
    plt.savefig('ParallaxErrors.pdf')
  elif (args['pngOutput']):
    plt.savefig('ParallaxErrors.png')
  else:
    plt.show()
def makePlot(args):
  """
  Make the plot with parallax performance predictions.

  :argument args: command line arguments
  """
  gmag=np.linspace(5.7,20.0,101)

  vminiB1V=vminiFromSpt('B1V')
  vminiG2V=vminiFromSpt('G2V')
  vminiM6V=vminiFromSpt('M6V')
  
  vmagB1V=gmag-gminvFromVmini(vminiB1V)
  vmagG2V=gmag-gminvFromVmini(vminiG2V)
  vmagM6V=gmag-gminvFromVmini(vminiM6V)
  
  sigparB1V=parallaxErrorSkyAvg(gmag,vminiB1V)
  sigparB1Vmin=parallaxMinError(gmag,vminiB1V)
  sigparB1Vmax=parallaxMaxError(gmag,vminiB1V)
  
  sigparG2V=parallaxErrorSkyAvg(gmag,vminiG2V)
  sigparG2Vmin=parallaxMinError(gmag,vminiG2V)
  sigparG2Vmax=parallaxMaxError(gmag,vminiG2V)
  
  sigparM6V=parallaxErrorSkyAvg(gmag,vminiM6V)
  sigparM6Vmin=parallaxMinError(gmag,vminiM6V)
  sigparM6Vmax=parallaxMaxError(gmag,vminiM6V)
  
  fig=plt.figure(figsize=(10,6.5))
  
  if (args['gmagAbscissa']):
    plt.semilogy(gmag, sigparB1V, 'b', label='B1V')
    plt.semilogy(gmag, sigparG2V, 'g', label='G2V')
    plt.semilogy(gmag, sigparM6V, 'r', label='M6V')
    plt.xlim((5,20))
    plt.ylim((4,1000))
    plt.legend(loc=4)
    plt.xlabel('$G$ [mag]')
  else:
    ax=fig.add_subplot(111)
    plt.semilogy(vmagB1V, sigparB1V, 'b', label='B1V')
    #plt.semilogy(vmagG2V, sigparG2V, 'g', label='G2V')
    plt.semilogy(vmagM6V, sigparM6V, 'r', label='M6V')
    plt.fill_between(vmagB1V, sigparB1Vmin, sigparB1Vmax, color='b', alpha=0.3)
    plt.fill_between(vmagM6V, sigparM6Vmin, sigparM6Vmax, color='r', alpha=0.3)
    plt.xlim((5,22.5))
    plt.ylim((4,1000))
    plt.text(17.2,190,'B1V',color='b')
    plt.text(18,20,'M6V',color='r')
    plt.xlabel('$V$ [mag]')
    plt.text(7,17,'calibration noise floor', size=12, bbox=dict(boxstyle="round,pad=0.3",
                       ec=(0.0, 0.0, 0.0),
                       fc=(1.0, 1.0, 1.0),
                       ))
    plt.text(14.75,80,'photon noise', rotation=45, size=12, bbox=dict(boxstyle="round,pad=0.3",
                       ec=(0.0, 0.0, 0.0),
                       fc=(1.0, 1.0, 1.0),
                       ))
    ax.annotate('non-uniformity\nover the sky', xy=(21.5, 320),  xycoords='data',
                  xytext=(21.5,80), textcoords='data', ha='center', size='12',
                  bbox=dict(boxstyle="round,pad=0.3",ec=(0,0,0),fc=(1,1,1)),
                  arrowprops=dict(facecolor='black', shrink=0.15, width=1,
                    headwidth=6),
                  horizontalalignment='right', verticalalignment='top',
                  )
    ax.annotate('', xy=(21.5, 500),  xycoords='data',
                  xytext=(21.5,950), textcoords='data', ha='center', size='12',
                  arrowprops=dict(facecolor='black', shrink=0.15, width=1,
                    headwidth=6),
                  horizontalalignment='right', verticalalignment='bottom',
                  )
  
  plt.xticks(np.arange(6,24,2))
  ax = plt.gca().yaxis 
  ax.set_major_formatter(matplotlib.ticker.ScalarFormatter())
  plt.ticklabel_format(axis='y',style='plain')
  plt.grid(which='both')
  plt.ylabel('End-of-mission parallax standard error [$\mu$as]')
  
  if (args['pdfOutput']):
    plt.savefig('ParallaxErrors.pdf')
  elif (args['pngOutput']):
    plt.savefig('ParallaxErrors.png')
  else:
    plt.show()
def makePlot ( args ) : gmag = np . linspace ( 5.7 , 20.0 , 101 ) vminiB1V = vminiFromSpt ( 'B1V' ) vminiG2V = vminiFromSpt ( 'G2V' ) vminiM6V = vminiFromSpt ( 'M6V' ) vmagB1V = gmag - gminvFromVmini ( vminiB1V ) vmagG2V = gmag - gminvFromVmini ( vminiG2V ) vmagM6V = gmag - gminvFromVmini ( vminiM6V ) sigparB1V = parallaxErrorSkyAvg ( gmag , vminiB1V ) sigparB1Vmin = parallaxMinError ( gmag , vminiB1V ) sigparB1Vmax = parallaxMaxError ( gmag , vminiB1V ) sigparG2V = parallaxErrorSkyAvg ( gmag , vminiG2V ) sigparG2Vmin = parallaxMinError ( gmag , vminiG2V ) sigparG2Vmax = parallaxMaxError ( gmag , vminiG2V ) sigparM6V = parallaxErrorSkyAvg ( gmag , vminiM6V ) sigparM6Vmin = parallaxMinError ( gmag , vminiM6V ) sigparM6Vmax = parallaxMaxError ( gmag , vminiM6V ) fig = plt . figure ( figsize = ( 10 , 6.5 ) ) if ( args [ 'gmagAbscissa' ] ) : plt . semilogy ( gmag , sigparB1V , 'b' , label = 'B1V' ) plt . semilogy ( gmag , sigparG2V , 'g' , label = 'G2V' ) plt . semilogy ( gmag , sigparM6V , 'r' , label = 'M6V' ) plt . xlim ( ( 5 , 20 ) ) plt . ylim ( ( 4 , 1000 ) ) plt . legend ( loc = 4 ) plt . xlabel ( '$G$ [mag]' ) else : ax = fig . add_subplot ( 111 ) plt . semilogy ( vmagB1V , sigparB1V , 'b' , label = 'B1V' ) #plt.semilogy(vmagG2V, sigparG2V, 'g', label='G2V') plt . semilogy ( vmagM6V , sigparM6V , 'r' , label = 'M6V' ) plt . fill_between ( vmagB1V , sigparB1Vmin , sigparB1Vmax , color = 'b' , alpha = 0.3 ) plt . fill_between ( vmagM6V , sigparM6Vmin , sigparM6Vmax , color = 'r' , alpha = 0.3 ) plt . xlim ( ( 5 , 22.5 ) ) plt . ylim ( ( 4 , 1000 ) ) plt . text ( 17.2 , 190 , 'B1V' , color = 'b' ) plt . text ( 18 , 20 , 'M6V' , color = 'r' ) plt . xlabel ( '$V$ [mag]' ) plt . text ( 7 , 17 , 'calibration noise floor' , size = 12 , bbox = dict ( boxstyle = "round,pad=0.3" , ec = ( 0.0 , 0.0 , 0.0 ) , fc = ( 1.0 , 1.0 , 1.0 ) , ) ) plt . text ( 14.75 , 80 , 'photon noise' , rotation = 45 , size = 12 , bbox = dict ( boxstyle = "round,pad=0.3" , ec = ( 0.0 , 0.0 , 0.0 ) , fc = ( 1.0 , 1.0 , 1.0 ) , ) ) ax . annotate ( 'non-uniformity\nover the sky' , xy = ( 21.5 , 320 ) , xycoords = 'data' , xytext = ( 21.5 , 80 ) , textcoords = 'data' , ha = 'center' , size = '12' , bbox = dict ( boxstyle = "round,pad=0.3" , ec = ( 0 , 0 , 0 ) , fc = ( 1 , 1 , 1 ) ) , arrowprops = dict ( facecolor = 'black' , shrink = 0.15 , width = 1 , headwidth = 6 ) , horizontalalignment = 'right' , verticalalignment = 'top' , ) ax . annotate ( '' , xy = ( 21.5 , 500 ) , xycoords = 'data' , xytext = ( 21.5 , 950 ) , textcoords = 'data' , ha = 'center' , size = '12' , arrowprops = dict ( facecolor = 'black' , shrink = 0.15 , width = 1 , headwidth = 6 ) , horizontalalignment = 'right' , verticalalignment = 'bottom' , ) plt . xticks ( np . arange ( 6 , 24 , 2 ) ) ax = plt . gca ( ) . yaxis ax . set_major_formatter ( matplotlib . ticker . ScalarFormatter ( ) ) plt . ticklabel_format ( axis = 'y' , style = 'plain' ) plt . grid ( which = 'both' ) plt . ylabel ( 'End-of-mission parallax standard error [$\mu$as]' ) if ( args [ 'pdfOutput' ] ) : plt . savefig ( 'ParallaxErrors.pdf' ) elif ( args [ 'pngOutput' ] ) : plt . savefig ( 'ParallaxErrors.png' ) else : plt . show ( )
Make the plot with parallax performance predictions.

  :argument args: command line arguments
Make the plot with parallax performance predictions .