def makePlot(args):
gmag=np.linspace(<NUM_LIT>,<NUM_LIT>,<NUM_LIT>)<EOL>vminiB1V=vminiFromSpt('<STR_LIT>')<EOL>vminiG2V=vminiFromSpt('<STR_LIT>')<EOL>vminiM6V=vminiFromSpt('<STR_LIT>')<EOL>vmagB1V=gmag-gminvFromVmini(vminiB1V)<EOL>vmagG2V=gmag-gminvFromVmini(vminiG2V)<EOL>vmagM6V=gmag-gminvFromVmini(vminiM6V)<EOL>sigmualphaB1V, sigmudeltaB1V = properMotionErrorSkyAvg(gmag,vminiB1V)<EOL>sigmuB1V = np.sqrt(<NUM_LIT:0.5>*sigmualphaB1V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaB1V**<NUM_LIT:2>)<EOL>sigmualphaB1V, sigmudeltaB1V = properMotionMinError(gmag,vminiB1V)<EOL>sigmuB1Vmin = np.sqrt(<NUM_LIT:0.5>*sigmualphaB1V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaB1V**<NUM_LIT:2>)<EOL>sigmualphaB1V, sigmudeltaB1V = properMotionMaxError(gmag,vminiB1V)<EOL>sigmuB1Vmax = np.sqrt(<NUM_LIT:0.5>*sigmualphaB1V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaB1V**<NUM_LIT:2>)<EOL>sigmualphaG2V, sigmudeltaG2V = properMotionErrorSkyAvg(gmag,vminiG2V)<EOL>sigmuG2V = np.sqrt(<NUM_LIT:0.5>*sigmualphaG2V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaG2V**<NUM_LIT:2>)<EOL>sigmualphaG2V, sigmudeltaG2V = properMotionMinError(gmag,vminiG2V)<EOL>sigmuG2Vmin = np.sqrt(<NUM_LIT:0.5>*sigmualphaG2V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaG2V**<NUM_LIT:2>)<EOL>sigmualphaG2V, sigmudeltaG2V = properMotionMaxError(gmag,vminiG2V)<EOL>sigmuG2Vmax = np.sqrt(<NUM_LIT:0.5>*sigmualphaG2V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaG2V**<NUM_LIT:2>)<EOL>sigmualphaM6V, sigmudeltaM6V = properMotionErrorSkyAvg(gmag,vminiM6V)<EOL>sigmuM6V = np.sqrt(<NUM_LIT:0.5>*sigmualphaM6V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaM6V**<NUM_LIT:2>)<EOL>sigmualphaM6V, sigmudeltaM6V = properMotionMinError(gmag,vminiM6V)<EOL>sigmuM6Vmin = np.sqrt(<NUM_LIT:0.5>*sigmualphaM6V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaM6V**<NUM_LIT:2>)<EOL>sigmualphaM6V, sigmudeltaM6V = properMotionMaxError(gmag,vminiM6V)<EOL>sigmuM6Vmax = np.sqrt(<NUM_LIT:0.5>*sigmualphaM6V**<NUM_LIT:2>+<NUM_LIT:0.5>*sigmudeltaM6V**<NUM_LIT:2>)<EOL>fig=plt.figure(figsize=(<NUM_LIT:10>,<NUM_LIT>))<EOL>if (args['<STR_LIT>']):<EOL><INDENT>plt.semilogy(gmag, sigmuB1V, '<STR_LIT:b>', label='<STR_LIT>')<EOL>plt.semilogy(gmag, sigmuG2V, '<STR_LIT:g>', label='<STR_LIT>')<EOL>plt.semilogy(gmag, sigmuM6V, '<STR_LIT:r>', label='<STR_LIT>')<EOL>plt.xlim((<NUM_LIT:5>,<NUM_LIT:20>))<EOL>plt.ylim((<NUM_LIT:1>,<NUM_LIT>))<EOL>plt.legend(loc=<NUM_LIT:4>)<EOL><DEDENT>else:<EOL><INDENT>ax=fig.add_subplot(<NUM_LIT>)<EOL>plt.semilogy(vmagB1V, sigmuB1V, '<STR_LIT:b>', label='<STR_LIT>')<EOL>plt.semilogy(vmagM6V, sigmuM6V, '<STR_LIT:r>', label='<STR_LIT>')<EOL>plt.fill_between(vmagB1V, sigmuB1Vmin, sigmuB1Vmax, color='<STR_LIT:b>', alpha=<NUM_LIT>)<EOL>plt.fill_between(vmagM6V, sigmuM6Vmin, sigmuM6Vmax, color='<STR_LIT:r>', alpha=<NUM_LIT>)<EOL>plt.xlim((<NUM_LIT:5>,<NUM_LIT>))<EOL>plt.ylim((<NUM_LIT:1>,<NUM_LIT>))<EOL>plt.text(<NUM_LIT>,<NUM_LIT:100>,'<STR_LIT>',color='<STR_LIT:b>')<EOL>plt.text(<NUM_LIT>,<NUM_LIT:10>,'<STR_LIT>',color='<STR_LIT:r>')<EOL>plt.text(<NUM_LIT:7>,<NUM_LIT:11>,'<STR_LIT>', size=<NUM_LIT:12>, bbox=dict(boxstyle="<STR_LIT>",<EOL>ec=(<NUM_LIT:0.0>, <NUM_LIT:0.0>, <NUM_LIT:0.0>),<EOL>fc=(<NUM_LIT:1.0>, <NUM_LIT:1.0>, <NUM_LIT:1.0>),<EOL>))<EOL>plt.text(<NUM_LIT>,<NUM_LIT:50>,'<STR_LIT>', rotation=<NUM_LIT>, size=<NUM_LIT:12>, bbox=dict(boxstyle="<STR_LIT>",<EOL>ec=(<NUM_LIT:0.0>, <NUM_LIT:0.0>, <NUM_LIT:0.0>),<EOL>fc=(<NUM_LIT:1.0>, <NUM_LIT:1.0>, <NUM_LIT:1.0>),<EOL>))<EOL>ax.annotate('<STR_LIT>', xy=(<NUM_LIT>, <NUM_LIT>),  xycoords='<STR_LIT:data>',<EOL>xytext=(<NUM_LIT>,<NUM_LIT:30>), textcoords='<STR_LIT:data>', ha='<STR_LIT>', size='<STR_LIT>',<EOL>bbox=dict(boxstyle="<STR_LIT>",ec=(<NUM_LIT:0>,<NUM_LIT:0>,<NUM_LIT:0>),fc=(<NUM_LIT:1>,<NUM_LIT:1>,<NUM_LIT:1>)),<EOL>arrowprops=dict(facecolor='<STR_LIT>', shrink=<NUM_LIT>, width=<NUM_LIT:1>,<EOL>headwidth=<NUM_LIT:6>),<EOL>horizontalalignment='<STR_LIT:right>', verticalalignment='<STR_LIT>',<EOL>)<EOL>ax.annotate('<STR_LIT>', xy=(<NUM_LIT>, <NUM_LIT>),  xycoords='<STR_LIT:data>',<EOL>xytext=(<NUM_LIT>,<NUM_LIT>), textcoords='<STR_LIT:data>', ha='<STR_LIT>', size='<STR_LIT>',<EOL>arrowprops=dict(facecolor='<STR_LIT>', shrink=<NUM_LIT>, width=<NUM_LIT:1>,<EOL>headwidth=<NUM_LIT:6>),<EOL>horizontalalignment='<STR_LIT:right>', verticalalignment='<STR_LIT>',<EOL>)<EOL><DEDENT>plt.xticks(np.arange(<NUM_LIT:6>,<NUM_LIT>,<NUM_LIT:2>))<EOL>ax = plt.gca().yaxis <EOL>ax.set_major_formatter(matplotlib.ticker.ScalarFormatter())<EOL>plt.ticklabel_format(axis='<STR_LIT:y>',style='<STR_LIT>')<EOL>plt.grid(which='<STR_LIT>')<EOL>plt.xlabel('<STR_LIT>')<EOL>plt.ylabel('<STR_LIT>')<EOL>basename = '<STR_LIT>'<EOL>if (args['<STR_LIT>']):<EOL><INDENT>plt.savefig(basename+'<STR_LIT>')<EOL><DEDENT>elif (args['<STR_LIT>']):<EOL><INDENT>plt.savefig(basename+'<STR_LIT>')<EOL><DEDENT>else:<EOL><INDENT>plt.show()<EOL><DEDENT>
Make the plot with proper motion performance predictions. The predictions are for the TOTAL proper
motion under the assumption of equal components mu_alpha* and mu_delta.

:argument args: command line arguments