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jobovy/plot_distance_cohorts.py

Last active August 29, 2015 14:27
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Some simple scripts to look at the distance distribution of APOGEE-1 stars in different parts of the Galaxy with different color cuts, in preparation for APOGEE-2
Raw
plot_distance_cohorts.py
import sys
import numpy
from galpy.util import bovy_plot
import apogee.tools.read as apread
from matplotlib import pyplot
_DISTTAG= 'DISO_GAL'
def plot_distance_cohorts(plotfilename,region='inner'):
#Read the data
data= apread.allStar(adddist=True,distredux='v402',main=True)
#Cut the data to the relevant region
if region.lower() == 'inner':
data=\
data[((data['GLON'] <= 45.)+(data['GLON'] >= (360.-45.)))\
*(numpy.fabs(data['GLAT']) <= 15.)\
*(data[_DISTTAG] >= -1.)]
elif region.lower() == 'innernobulge':
data=\
data[(data['GLON'] <= 45.)*(data['GLON'] >= 20.)\
*(numpy.fabs(data['GLAT']) <= 15.)\
*(data[_DISTTAG] >= -1.)]
elif region.lower() == 'innernobulgeexpanded':
data=\
data[(data['GLON'] <= 65.)*(data['GLON'] >= 20.)\
*(numpy.fabs(data['GLAT']) <= 15.)\
*(data[_DISTTAG] >= -1.)]
elif region.lower() == 'outer':
data=\
data[(data['GLON'] > 45.)*(data['GLON'] < (360.-45.))\
*(numpy.fabs(data['GLAT']) <= 15.)\
*(data[_DISTTAG] >= -1.)]
elif region.lower() == 'halo':
data=\
data[(numpy.fabs(data['GLAT']) > 15.)\
*(data[_DISTTAG] >= -1.)]
elif region.lower() == 'allhalo':
data= apread.allStar(adddist=True,distredux='v402',main=False)
data=\
data[(numpy.fabs(data['GLAT']) > 15.)\
*(data[_DISTTAG] >= -1.)]
elif region.lower() == 'all':
data=\
data[(data[_DISTTAG] >= -1.)]
#Short cohorts, 0.5 <= J-Ks < 0.8
tindx= (data['H'] <= 12.2)\
*(data['J0']-data['K0'] >= 0.5)\
*(data['J0']-data['K0'] < 0.8)
nshortblue= numpy.sum(tindx)
chist= 1.-numpy.cumsum(numpy.ones(numpy.sum(tindx)))/float(numpy.sum(tindx))
bovy_plot.bovy_print()
lineshortblue= bovy_plot.bovy_plot(sorted(data[_DISTTAG][tindx]),chist,'k-',
xrange=[0.,15.],yrange=[0.,1.05],
xlabel=r'$\mathrm{distance\,(kpc)}$',
ylabel=r'$\mathrm{fraction\ at}\ >\ \mathrm{distance}$',
lw=2.)
bovy_plot.bovy_plot([sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))],
sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))]],
[0.,0.5],'k-',overplot=True)
#Short cohorts, J-Ks >= 0.8
tindx= (data['H'] <= 12.2)\
*(data['J0']-data['K0'] >= 0.8)
nshortred= numpy.sum(tindx)
chist= 1.-numpy.cumsum(numpy.ones(numpy.sum(tindx)))/float(numpy.sum(tindx))
lineshortred= bovy_plot.bovy_plot(sorted(data[_DISTTAG][tindx]),chist,'k--',
overplot=True,lw=2.)
bovy_plot.bovy_plot([sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))],
sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))]],
[0.,0.5],'k--',overplot=True)
#Medium cohorts, 0.5 <= J-Ks < 0.8
tindx= (data['H'] > 12.2)*(data['H'] <= 12.8)\
*(data['J0']-data['K0'] >= 0.5)\
*(data['J0']-data['K0'] < 0.8)
nmediumblue= numpy.sum(tindx)
chist= 1.-numpy.cumsum(numpy.ones(numpy.sum(tindx)))/float(numpy.sum(tindx))
bovy_plot.bovy_print()
linemediumblue= bovy_plot.bovy_plot(sorted(data[_DISTTAG][tindx]),chist,'r-',
overplot=True,lw=2.)
bovy_plot.bovy_plot([sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))],
sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))]],
[0.,0.5],'r-',overplot=True)
#Medium cohorts, J-Ks >= 0.8
tindx= (data['H'] > 12.2)*(data['H'] <= 12.8)\
*(data['J0']-data['K0'] >= 0.8)
nmediumred= numpy.sum(tindx)
chist= 1.-numpy.cumsum(numpy.ones(numpy.sum(tindx)))/float(numpy.sum(tindx))
linemediumlong= bovy_plot.bovy_plot(sorted(data[_DISTTAG][tindx]),chist,'r--',
overplot=True,lw=2.)
bovy_plot.bovy_plot([sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))],
sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))]],
[0.,0.5],'r--',overplot=True)
#Long cohorts, 0.5 <= J-Ks < 0.8
tindx= (data['H'] > 12.8)\
*(data['J0']-data['K0'] >= 0.5)\
*(data['J0']-data['K0'] < 0.8)
nlongblue= numpy.sum(tindx)
chist= 1.-numpy.cumsum(numpy.ones(numpy.sum(tindx)))/float(numpy.sum(tindx))
bovy_plot.bovy_print()
linelongblue= bovy_plot.bovy_plot(sorted(data[_DISTTAG][tindx]),chist,'-',color='lightgreen',
overplot=True,lw=2.)
bovy_plot.bovy_plot([sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))],
sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))]],
[0.,0.5],'-',color='lightgreen',overplot=True)
#Long cohorts, J-Ks >= 0.8
tindx= (data['H'] > 12.8)\
*(data['J0']-data['K0'] >= 0.8)
nlongred= numpy.sum(tindx)
chist= 1.-numpy.cumsum(numpy.ones(numpy.sum(tindx)))/float(numpy.sum(tindx))
if numpy.sum(tindx) > 0:
linelongred= bovy_plot.bovy_plot(sorted(data[_DISTTAG][tindx]),chist,'--',
color='lightgreen',
overplot=True,lw=2.)
bovy_plot.bovy_plot([sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))],
sorted(data[_DISTTAG][tindx])[numpy.argmin(numpy.fabs(chist-0.5))]],
[0.,0.5],'--',color='lightgreen',overplot=True)
#Add legend
pyplot.legend((lineshortblue[0],linemediumblue[0],linelongblue[0]),
(r'$\mathrm{short\ (%i,%i)}$' % (nshortblue,nshortred),
r'$\mathrm{medium\ (%i,%i)}$' % (nmediumblue,nmediumred),
r'$\mathrm{long\ (%i,%i)}$' % (nlongblue,nlongred)),
loc='upper right',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':14},
frameon=False)
if region.lower() == 'inner':
bovy_plot.bovy_text(r'$|l| \leq 45^\circ, |b| \leq 15^\circ$',
bottom_left=True,size=14.)
elif region.lower() == 'innernobulge':
bovy_plot.bovy_text(r'$20 \leq l \leq 45^\circ, |b| \leq 15^\circ$',
bottom_left=True,size=14.)
elif region.lower() == 'innernobulgeexpanded':
bovy_plot.bovy_text(r'$20 \leq l \leq 65^\circ, |b| \leq 15^\circ$',
bottom_left=True,size=14.)
elif region.lower() == 'outer':
bovy_plot.bovy_text(r'$|l| > 45^\circ, |b| \leq 15^\circ$',
bottom_left=True,size=14.)
elif region.lower() == 'halo':
bovy_plot.bovy_text(r'$|b| > 15^\circ$',
bottom_left=True,size=14.)
bovy_plot.bovy_end_print(plotfilename)
return None
if __name__ == '__main__':
if len(sys.argv) > 2:
plot_distance_cohorts(sys.argv[1],sys.argv[2])
else:
plot_distance_cohorts(sys.argv[1])
Raw
read_targeting.py
import os, os.path
import numpy
_TARGETFILE= os.path.join(os.getenv('DATADIR'),'bovy','apogee-srd',
'APOGEE2_Visit_Plan_Dec2013.csv')
def read_targeting(targetfile=None):
if targetfile is None:
targetfile= _TARGETFILE
ls= []
bs= []
nvis= []
type= []
targetfile= open(targetfile,'r')
for line in targetfile:
if line[0] == ';': continue
lines= line.split()
ls.append(float(lines[2]))
bs.append(float(lines[3]))
nvis.append(int(lines[4]))
type.append(lines[5])
return (numpy.array(ls),numpy.array(bs),numpy.array(nvis,dtype='int'),
numpy.array(type))
def calc_disk_targeting_numbers(targetfile=None,
fiber_alloc=[90,90,45],
fracred=0.5):
data= read_targeting(targetfile=targetfile)
#Number of visits in the various categories
nVis= numpy.sum((data[3] == 'disk')*(data[2] == 3))*3\
+numpy.sum((data[3] == 'disk')*(data[2] == 6))*6\
+numpy.sum((data[3] == 'disk')*(data[2] == 12))*12\
+numpy.sum((data[3] == 'disk')*(data[2] == 24))*24
nshortVis= (numpy.sum((data[3] == 'disk')*(data[2] == 3))+2*numpy.sum((data[3] == 'disk')*(data[2] == 6))+4*numpy.sum((data[3] == 'disk')*(data[2] == 12))+6*numpy.sum((data[3] == 'disk')*(data[2] == 24)))
nmediumVis= (numpy.sum((data[3] == 'disk')*(data[2] == 6))+2*numpy.sum((data[3] == 'disk')*(data[2] == 12))+2*numpy.sum((data[3] == 'disk')*(data[2] == 24)))
nlongVis= (numpy.sum((data[3] == 'disk')*(data[2] == 12))+numpy.sum((data[3] == 'disk')*(data[2] == 24)))
#Number of targets in the various categories
nshort= nshortVis*fiber_alloc[0]
nmedium= nmediumVis*fiber_alloc[1]
nlong= nlongVis*fiber_alloc[2]
print "Total number of stars:", nshort+nmedium+nlong
print "Total number of visits:", nVis
print "Number of short, medium, and long cohorts:", nshortVis, nmediumVis,\
nlongVis
print "Number of short, medium, and long stars:", nshort, nmedium,\
nlong
#Definitions
fracGtr5Blue= [0.15,0.4,0.5]
fracGtr5Red= [0.5,0.75,0.75]
fracGtr8Blue= [0.04,0.06,0.1]
fracGtr8Red= [0.15,0.3,0.3]
fracblue= 1.-fracred
#Calculate fraction of stars at > 5 kpc
print "Number of stars at distances > 5 kpc:",\
nshort*(fracblue*fracGtr5Blue[0]+fracred*fracGtr5Red[0])\
+nmedium*(fracblue*fracGtr5Blue[1]+fracred*fracGtr5Red[1])\
+nlong*(fracblue*fracGtr5Blue[2]+fracred*fracGtr5Red[2])
#Calculate fraction of stars at > 8 kpc
print "Number of stars at distances > 8 kpc:",\
nshort*(fracblue*fracGtr8Blue[0]+fracred*fracGtr8Red[0])\
+nmedium*(fracblue*fracGtr8Blue[1]+fracred*fracGtr8Red[1])\
+nlong*(fracblue*fracGtr8Blue[2]+fracred*fracGtr8Red[2])
return None
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