def cofe(self,save=False,texcmd=False):
"""
Plots C/O as a function of Fe/H
save - save the file
"""
p = getelnum.Getelnum('O')
cmd0 = 'SELECT distinct(mystars.oid),'+\
' mystars.o_abund,mystars.c_abund,mystars.fe_abund '+\
' FROM mystars LEFT JOIN exo ON exo.oid = mystars.oid '+\
' WHERE '+postfit.globcut('C')+' AND '+postfit.globcut('O')
qtype= [('oid','|S10'),('o_abund',float),('c_abund',float),('feh',float)]
#Grab Planet Sample
cmd = cmd0 +' AND exo.name IS NOT NULL'
self.cur.execute(cmd)
#pull arrays and subtract solar offset
arrhost = np.array( self.cur.fetchall() ,dtype=qtype)
nhost = len(arrhost)
#Grab Comparison Sample
cmd = cmd0 +' AND exo.name IS NULL'
self.cur.execute(cmd)
#pull arrays and subtract solar offset
arrcomp = np.array( self.cur.fetchall() ,dtype=qtype)
ncomp = len(arrcomp)
#calculate C/O logeps(c) - logeps(o)
c2ohost = 10**(arrhost['c_abund']-arrhost['o_abund'])
c2ocomp = 10**(arrcomp['c_abund']-arrcomp['o_abund'])
f = plt.figure( figsize=(6,4) )
ax = plt.subplot(111)
ax.plot(arrcomp['feh'],c2ocomp,'bo')
ax.plot(arrhost['feh'],c2ohost,'go')
ax.legend(('Comparision','Hosts'),loc='best')
ax.set_xlabel('[Fe/H]')
ax.set_ylabel('C/O')
c2o = 10**(self.stats['C']-self.stats['O'])
yerr = np.array([s.std_dev() for s in c2o]).reshape((2,1))
ax = self.errpt(ax,(0.1,0.7),xerr=p.feherr,yerr=yerr)
ax.set_ybound(0,ax.get_ylim()[1])
ax.axhline(1.)
plt.show()
if save:
plt.savefig('Thesis/pyplots/cofe.ps')
return ax
def __init__(self):
###Pull up database.
self.conn = sqlite3.connect(os.environ['STARSDB'])
self.cur = self.conn.cursor()
elements = ['O','C']
self.stats = {}
for elstr in elements:
cmd = 'SELECT %s_abund,%s_staterrlo,%s_staterrhi FROM mystars '\
% (elstr,elstr,elstr)
wcmd = ' WHERE '+postfit.globcut(elstr)
self.cur.execute(cmd+wcmd)
temptype =[('abund',float),('staterrlo',float),('staterrhi',float)]
arr = np.array(self.cur.fetchall(),dtype=temptype)
m,slo,shi = np.mean(arr['abund']),np.abs(np.median(arr['staterrlo'])),np.median(arr['staterrhi'])
self.stats[elstr] = unumpy.uarray(([m,m],[slo,shi]))
def compmany(self,elstr='o'):
if elstr == 'o':
tables = ['mystars','luckstars','ramstars']
if elstr =='c':
tables = ['mystars','luckstars','red06']
ncomp = len(tables)
for i in range(ncomp):
for j in range(ncomp):
if i != j:
tabx = tables[i]
taby = tables[j]
colx = tabx+'.'+elstr+'_abund'
coly = taby+'.'+elstr+'_abund'
cut = ' WHERE '+tabx+'.oid = '+taby+'.oid '+'AND '+\
colx+' IS NOT NULL AND '+coly+' IS NOT NULL '
if tabx == 'mystars' or taby == 'mystars':
cut = cut+' AND '+postfit.globcut(elstr)
ax = plt.subplot(ncomp,ncomp,i*ncomp+j+1)
cmd = 'SELECT DISTINCT '+colx+','+coly+' FROM '+tabx+','+taby+cut
self.cur.execute(cmd)
arr = np.array(self.cur.fetchall())
if len(arr) > 0:
(x,y) = arr[:,0],arr[:,1]
ax.scatter(x-x.mean(),y-y.mean())
ax.set_xlabel(tabx)
ax.set_ylabel(taby)
xlim = ax.get_xlim()
x = np.linspace(xlim[0],xlim[1],10)
ax.plot(x,x,color='red')
plt.draw()
self.conn.close()
def feh(self,save=False,noratio=False):
"""
Show the trends of X/Fe as a function of Fe/H.
save - saves the file
noratio - plots X/H as opposed to X/Fe
"""
#pull in fitted abundances from tfit
lines = [6300,6587]
nel = len(lines)
flags = ['dn','dk']
binmin = -0.5
binwid = 0.1
nbins = 11
qtype= [('abund',float),('feh',float),('staterrlo',float),
('staterrhi',float),('pop_flag','|S10')]
bins = np.linspace(binmin,binmin+binwid*nbins,nbins)
subplot = ((1,2))
f = plt.figure( figsize=(6,6) )
f.subplots_adjust(hspace=0.0001)
ax = []
for i in range(nel):
if i is 0:
ax.append(plt.subplot(nel,1,i+1))
else:
ax.append(plt.subplot(nel,1,i+1,sharex=ax[0]))
p = getelnum.Getelnum(lines[i])
elstr = p.elstr
cmd = 'SELECT %s_abund,fe_abund,%s_staterrlo,%s_staterrhi,pop_flag FROM mystars ' % (elstr,elstr,elstr)
wcmd = ' WHERE '+postfit.globcut(elstr)+' AND pop_flag = "dn"'
self.cur.execute(cmd+wcmd)
arrthin = np.array(self.cur.fetchall(),dtype=qtype)
arrthin['abund'] -= p.abnd_sol
wcmd = ' WHERE '+postfit.globcut(elstr)+' AND pop_flag = "dk"'
self.cur.execute(cmd+wcmd)
arrthick = np.array(self.cur.fetchall(),
dtype=qtype)
arrthick['abund'] -= p.abnd_sol
if noratio:
ythin = arrthin['abund']
ythick = arrthick['abund']
ax[i].set_ylabel('[%s/H]' % (elstr) )
savename = 'xonh'
else:
ythin = arrthin['abund'] -arrthin['feh']
ythick = arrthick['abund'] -arrthick['feh']
ax[i].set_ylabel('[%s/Fe]' % (elstr) )
savename = 'xonfe'
### Compute Avg Thin disk in bins ###
binind = np.digitize(arrthin['feh'],bins)
binx,biny = [] , []
for j in np.arange(nbins-1)+1:
ind = (np.where(binind == j))[0]
midbin = binmin+binwid*(j-0.5)
binmean = np.mean(ythin[ind])
nbin = len(ythin[ind]) # numer of points in a bin
mederr = 0.5*(arrthin['staterrhi'][ind]-arrthin['staterrlo'][ind])
pull = (ythin[ind] - binmean)/mederr
binx.append(midbin)
biny.append(binmean)
print 'Bin %.2f: n = %i, stdpull = %.2f ' % (midbin,nbin,np.std(pull))
binx,biny = np.array(binx),np.array(biny)
ax[i].plot(arrthin['feh'],ythin,'bo')
ax[i].plot(arrthick['feh'],ythick,'go')
ax[i].plot(binx,biny,'rx-',lw=2,ms=5,mew=2)
#plot typical errorbars
yerr = np.array([s.std_dev() for s in self.stats[elstr]])
if not noratio:
yerr = np.sqrt(yerr**2 + p.feherr**2)
yerr = yerr.reshape((2,1))
ax[i] = self.errpt(ax[i],(0.9,0.9),xerr=p.feherr,yerr=yerr)
if i is nel-1:
ax[i].set_xlabel('[Fe/H]')
else:
ax[i].set_xticklabels('',visible=False)
#remove overlapping zeroes
yticks = ax[i].get_yticks()[2:] #hack
ax[i].set_yticks(yticks)
leg = ax[0].legend( ('Thin Disk','Thick Disk','Binned Thin Disk'), loc='best')
if save:
plt.savefig('Thesis/pyplots/%s.ps' % savename)
def exo(self,save=False,prob=True,texcmd=False):
"""
Show a histogram of Carbon and Oxygen for planet harboring stars, and
comparison stars.
"""
f = plt.figure( figsize=(6,8) )
f.subplots_adjust(hspace=0.0001)
ax1 = plt.subplot(211)
elements = ['O','C','Fe']
nel = len(elements)
sol_abnd = [8.7,8.5,0]
ax = [] #empty list to store axes
outex = []
statdict = { 'host':{},'comp':{} }
for i in range(nel): #loop over the different elements
if i is 0:
ax.append(plt.subplot(nel,1,i+1))
else:
ax.append(plt.subplot(nel,1,i+1,sharex=ax[0]))
elstr = elements[i]
if elstr is 'Fe':
cmd0 = 'SELECT distinct(mystars.oid),mystars.'+elstr+'_abund '+\
' FROM mystars LEFT JOIN exo ON exo.oid = mystars.oid WHERE '
else:
cmd0 = 'SELECT distinct(mystars.oid),mystars.'+elstr+'_abund '+\
' FROM mystars LEFT JOIN exo ON exo.oid = mystars.oid '+\
' WHERE '+postfit.globcut(elstr)+' AND '
#Grab Planet Sample
cmd = cmd0 +' exo.name IS NOT NULL'
self.cur.execute(cmd)
#pull arrays and subtract solar offset
arrhost = np.array( self.cur.fetchall() ,dtype=float)[:,1] - sol_abnd[i]
nhost = len(arrhost)
#Grab Comparison Sample
cmd = cmd0 +' exo.name IS NULL'
self.cur.execute(cmd)
#pull arrays and subtract solar offset
arrcomp = np.array( self.cur.fetchall() ,dtype=float)[:,1] - sol_abnd[i]
ncomp = len(arrcomp)
binwid = 0.1
rng = [-0.5,0.5]
nbins = (rng[1]-rng[0])/binwid
if prob:
exohist,bins = np.histogram(arrhost,bins=nbins,range=rng)
comphist,bins = np.histogram(arrcomp,bins=nbins,range=rng)
exohisterr = unumpy.uarray( (exohist,np.sqrt(exohist)))
comphisterr = unumpy.uarray( (comphist,np.sqrt(comphist)))
ratio = exohisterr/(comphisterr+exohisterr)*100.
y = unumpy.nominal_values(ratio)
yerr = unumpy.std_devs(ratio)
x = bins[:-1]+0.5*binwid
ax[i].hist(x,bins=bins,weights=y)
ax[i].errorbar(x,y,yerr=yerr,marker='o',elinewidth=2,ls='None')
else:
# Make histogram
ax[i].hist([arrcomp,arrhost], bins=nbins,range=rng, histtype='bar',
label=['Comparison','Planet Hosts'])
ax[i].legend(loc='upper left')
if i is nel-1:
ax[i].set_xlabel('[X/H]')
ax[i].set_ylabel('% Stars with Planets')
else:
ax[i].set_xlabel('['+elstr+'/H]')
ax[i].set_xticklabels('',visible=False)
#remove overlapping zeroes
yticks = ax[i].get_yticks()[1:]
ax[i].set_yticks(yticks)
#Annotate to show which lable we're on
inv = ax[i].transData.inverted()
txtpt = inv.transform( ax[i].transAxes.transform( (0.05,0.85) ) )
ax[i].annotate('[%s/H]' % elstr,txtpt)
######## Compute KS - statistic or probablity #########
mhost,shost,mcomp,scomp = np.mean(arrhost),np.std(arrhost),\
np.mean(arrcomp),np.std(arrcomp)
D,p = stats.ks_2samp(arrhost,arrcomp)
if save:
# element number mean std ncomp compmean compstd KS p
outex.append(r'$\text{[%s/H]}$ & %i & %.2f & %.2f & & %i & %.2f & %.2f & %s \\' % (elstr,nhost,mhost,shost,ncomp,mcomp,scomp,flt2tex.flt2tex(p,sigfig=1) ) )
else:
print elstr+' in stars w/ planets: N = %i Mean = %f Std %f ' \
% (nhost,mhost,shost)
print elstr+' in stars w/o planets: N = %i Mean = %f Std %f ' \
% (ncomp,mcomp,scomp)
print 'KS Test: D = %f p = %f ' % (D,p)
statdict['host'][elstr] = {'n':nhost,'m':mhost,'s':shost}
statdict['comp'][elstr] = {'n':ncomp,'m':mcomp,'s':scomp}
plt.draw()
if save:
plt.savefig('Thesis/pyplots/exo.ps')
f = open('Thesis/tables/exo.tex','w')
f.writelines(outex)
if texcmd:
return statdict
def comp(self,save=False,texcmd=False):
"""
Plots my results as a function of literature
save - saves the file
"""
tables = [['ben05'],['luckstars']]
offset = [[0],[8.5]]
literr = [[0.06],[0.1]]
lines = [6300,6587]
color = ['blue','red','green']
f = plt.figure( figsize=(6,8) )
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
ax1.set_xlabel('[O/H], Bensby 2005')
ax2.set_xlabel('[C/H], Luck 2006')
ax1.set_ylabel('[O/H], This Work')
ax2.set_ylabel('[C/H], This Work')
ax = [ax1,ax2]
ncomp = []
stdcomp = []
for i in [0,1]:
xtot =[] #total array of comparison studies
ytot =[] #total array of comparison studies
p = getelnum.Getelnum(lines[i])
elstr = p.elstr
abnd_sol = p.abnd_sol
print abnd_sol
for j in range(len(tables[i])):
table = tables[i][j]
#SELECT
cmd = """
SELECT DISTINCT
mystars.%s_abund,mystars.%s_staterrlo,
mystars.%s_staterrhi,%s.%s_abund """ % (elstr,elstr,elstr,table,elstr)
if table is 'luckstars':
cmd += ','+table+'.c_staterr '
#FROM WHERE
cmd +="""
FROM mystars,%s WHERE mystars.oid = %s.oid
AND %s.%s_abund IS NOT NULL AND %s""" % (table,table,table,elstr,postfit.globcut(elstr))
if table is 'luckstars':
cmd = cmd+' AND '+table+'.c_staterr < 0.3'
self.cur.execute(cmd)
arr = np.array(self.cur.fetchall())
x = arr[:,3] - offset[i][j]
y = arr[:,0] -abnd_sol
###pull literature errors###
if table is 'ben05':
xerr = np.zeros( (2,len(x)) ) + 0.06
if table is 'luckstars':
xerr = arr[:,4].tolist()
xerr = np.array([xerr,xerr])
yerr = np.abs(arr[:,1:3])
print cmd
n = len(x)
ncomp.append(n)
print str(n) + 'comparisons'
ax[i].errorbar(x,y,xerr=xerr,yerr=yerr.transpose(),color=color[j],
marker='o',ls='None',capsize=0,markersize=5)
xtot.append( x.tolist() )
ytot.append( y.tolist() )
line = np.linspace(-3,3,10)
xtot=np.array(xtot)
ytot=np.array(ytot)
symerr = (yerr[:,0]+yerr[:,1])/2.
ax[i].plot(line,line)
ax[i].set_xlim((-0.6,+0.6))
ax[i].set_ylim((-0.6,+0.6))
S = np.std(xtot[0]-ytot[0])
print S
stdcomp.append(S)
plt.draw()
if texcmd:
return ncomp,stdcomp
if save:
plt.savefig('Thesis/pyplots/comp.ps')
def abundhist(self,save=False,texcmd=False,uplim=False):
"""
Plot the distributions of abundances. Possibly not needed with the exo
plot.
save - save the plot
texcmd - returns what the tex command dumper wants
"""
#pull in fitted abundances from tfit
line = [6300,6587]
subplot = ((1,2))
f = plt.figure( figsize=(6,6) )
f.subplots_adjust(hspace=0.0001)
ax1 = plt.subplot(211)
ax1.set_xticklabels('',visible=False)
ax1.set_yticks(np.arange(0,200,50))
ax2 = plt.subplot(212,sharex=ax1)
ax2.set_yticks(np.arange(0,200,50))
ax2.set_xlabel('[X/H]')
ax = (ax1,ax2)
nstars = []
outex = []
for i in range(2):
p = getelnum.Getelnum(line[i])
elstr = p.elstr
cut = postfit.globcut(elstr,uplim=uplim)
cmd = 'SELECT %s_abund from MYSTARS WHERE %s'% (elstr,cut)
self.cur.execute(cmd)
out = self.cur.fetchall()
abund = np.array(out,dtype=float).flatten()-p.abnd_sol
ax[i].set_ylabel('Number of Stars')
ax[i].hist(abund,range=(-1,1),bins=20,fc='gray')
ax[i].set_ylim(0,200)
#Annotate to show which lable we're on
antxt = '[%s/H]' % elstr
inv = ax[i].transData.inverted()
txtpt = inv.transform( ax[i].transAxes.transform( (0.05,0.85) ) )
ax[i].annotate(antxt,txtpt)
N,m,s,min,max = abund.size,abund.mean(), \
abund.std(),abund.min(),abund.max()
nstars.append(N)
if save:
#output moments for tex write up
outex.append(r'$\text {%s}$& %i & %.2f & %.2f & %.2f & %.2f\\'
% (antxt,N,m,s,min,max))
else:
print 'N, mean, std, min, max' + antxt
print '(%i,%f,%f,%f,%f)' % (N,m,s,min,max)
if texcmd:
return nstars
if save:
plt.savefig('Thesis/pyplots/abundhist.ps')
f = open('Thesis/tables/abundhist.tex','w')
f.writelines(outex)
def dump(file='Thesis/texcmd.tex'):
"""
Dumps the short hands used in the latex paper.
"""
conn = sqlite3.connect(os.environ['STARSDB'])
cur = conn.cursor()
lines = [6300,6587]
el = ['O','C']
f = open(file,'w')
line = []
plotter = plotgen.Plotgen()
ncomp,stdcomp = plotter.comp(texcmd=True)
nstars= plotter.abundhist(texcmd=True)
#number of different disk populations
popnames = ['Thin','Thick','Halo','Bdr']
popsym = ['dn','dk','h','dn/dk']
npoptot = []
for i in range(len(popnames)):
cmd = """
SELECT count(pop_flag) FROM mystars WHERE pop_flag = "%s"
AND
((%s) OR (%s))
""" % (popsym[i],postfit.globcut('O'),postfit.globcut('C') )
print cmd
cur.execute(cmd)
npop = (cur.fetchall())[0][0]
npoptot.append(npop)
line.append(r'\nc{\n%s}{%i} %% # of stars in %s pop.' % (popnames[i],npop,popnames[i]))
npoptot = np.array(npoptot).sum()
line.append(r'\nc{\nPop}{%i} %% # of stars with pop prob' % npoptot)
#Cuts specific to each line.
for i in range(len(lines)):
p = getelnum.Getelnum(el[i])
line.append(r'\nc{\vsiniCut%s}{%i} %% vsinicut for %s' % (el[i],p.vsinicut,el[i]))
line.append(r'\nc{\teffCut%slo}{%i} %% teff for %s' % (el[i],p.teffrng[0],el[i]))
line.append(r'\nc{\teffCut%shi}{%i} %% teff for %s' % (el[i],p.teffrng[1],el[i]))
fitabund,x,x,abund = postfit.tfit(lines[i])
maxTcorr = max(np.abs(fitabund-abund))
line.append(r'\nc{\maxT%s}{%.2f} %% max temp correction %s' % (el[i],maxTcorr,el[i]))
line.append(r'\nc{\nComp%s}{%i} %% # of comparison stars %s' % (el[i],ncomp[i],el[i]))
line.append(r'\nc{\StdComp%s}{%.2f} %% std of comparison stars %s' % (el[i],stdcomp[i],el[i]))
line.append(r'\nc{\nStars%s}{%i} %% Number of stars with %s analysis' % (el[i],nstars[i],el[i]))
#Values sepecific to both lines
line.append(r'\nc{\coThresh}{%.2f} %% Theshhold for high co' % (p.coThresh))
line.append(r'\nc{\scatterCut}{%.2f} %% Cut on the scatter' % (p.scattercut))
line.append(r'\nc{\teffSol}{%i} %% Solar Effective Temp' % (p.teff_sol))
statdict = plotter.exo(texcmd=True)
for pop in statdict.keys():
for elstr in statdict[pop].keys():
for moment in statdict[pop][elstr].keys():
value = statdict[pop][elstr][moment]
if moment[0] is 'n':
line.append(r'\nc{\%s%s%s}{%i} %% %s - %s - %s'%
(pop,elstr,moment,value,pop,elstr,moment))
else:
line.append(r'\nc{\%s%s%s}{%.2f} %% %s - %s - %s'%
(pop,elstr,moment,value,pop,elstr,moment))
statdict = table.dump_stars(texcmd=True)
for key in statdict.keys():
value = statdict[key]
if key[0] is 'n': #treat integers a certain way
line.append(r'\nc{\%s}{%d} %% '% (key,value))
else:
line.append(r'\nc{\%s}{%.2f} %% '% (key,value))
for l in line:
l = l.replace('\nc','\newcommand')
f.write(l+'\n')
return line
def dump_stars(save=True,texcmd=False):
conn = sqlite3.connect(os.environ['STARSDB'])
cur = conn.cursor()
elements = ['O','C']
cuts={}
texdict = {}
cuts['C'] = postfit.globcut('C',table='t3')
cuts['O'] = postfit.globcut('O',table='t2')
cmd0 = """
SELECT
distinct(t1.name),t1.vmag,t1.d,t1.teff,t1.logg,t1.pop_flag,t1.monh,t1.ni_abund,
t2.o_nfits,t2.o_abund,t2.o_staterrlo,t2.o_staterrhi,
t3.c_nfits,t3.c_abund,t3.c_staterrlo,t3.c_staterrhi,
exo.name
FROM
mystars t1
LEFT JOIN
mystars t3
ON
t1.id = t3.id AND (%s)
LEFT JOIN
mystars t2
ON
t1.id = t2.id AND (%s)
LEFT JOIN
exo
ON
exo.oid = t1.oid
WHERE (%s) OR (%s)
ORDER BY
t1.name
""" % (cuts['C'],cuts['O'],cuts['C'],cuts['O'])
temptype = [('name','|S10'),('vmag',float),('d',int),('teff',int),
('logg',float),('pop_flag','|S10'),('monh',float),('ni_abund',float),
('o_nfits',float),('o_abund',float),('o_staterrlo',float),('o_staterrhi',float),
('c_nfits',float),('c_abund',float),('c_staterrlo',float),('c_staterrhi',float),
('exoname','|S10')]
cur.execute(cmd0)
out = cur.fetchall()#,dtype=temptype)
out = np.array(out,dtype=temptype)
outstr = []
out['o_nfits'][np.where(np.isnan(out['o_nfits']))[0]] = 0
out['c_nfits'][np.where(np.isnan(out['c_nfits']))[0]] = 0
#subtract of solar abundance
elements = ['O','C']
abund,errhi,errlo,abndone,logeps = {},{},{},{},{}
c2oarr = np.array([])
for el in elements:
p = getelnum.Getelnum(el)
elo = el.lower()
abund[el] = out['%s_abund' % elo] - p.abnd_sol
logeps[el] = out['%s_abund' % elo]
errhi[el] = np.abs(out['%s_staterrlo' % elo])
errlo[el] = out['%s_staterrhi' % elo]
for i in range(len(out)):
abundarr = np.array([abund['C'][i],abund['O'][i]])
if np.isnan(abundarr).any():
c2ostr = '$nan_{nan}^{+nan}$'
else:
for el in elements:
cent = logeps[el][i]
err = [errlo[el][i],errhi[el][i]]
abndone[el] = unumpy.uarray(([cent,cent],err))
c2o = 10**(abndone['C']-abndone['O'])
c2ostr = '$%.2f_{-%.2f}^{+%.2f}$' % (c2o[0].nominal_value,c2o[0].std_dev(),c2o[1].std_dev() )
c2oarr = np.append(c2oarr,c2o[0].nominal_value)
o = out[i]
planetstr = 'no'
if o['exoname'] != 'None':
planetstr = 'yes'
#Indentation character
a = '\\\[0ex] '
#Global parameters for stars
a += '%s & %.2f & %d & %d & '%(o['name'],o['vmag'],o['d'],o['teff'])
a += '%.2f & %s & %s &'%(o['logg'],o['pop_flag'],planetstr)
#Metalicity and Nickle abundances
a += '%.2f & %.2f & '%(o['monh'],o['ni_abund'])
#Oxygen
a += '%d & $%.2f_{%.2f}^{+%.2f}$ & '%(o['o_nfits'],abund['O'][i],o['o_staterrlo'],o['o_staterrhi'])
#Carbon
a += '%d & $%.2f_{%.2f}^{+%.2f}$ & '%(o['c_nfits'],abund['C'][i],o['c_staterrlo'],o['c_staterrhi'])
#The Ratio
a += '%s \\\ \n ' % c2ostr
a = a.replace('$nan_{nan}^{+nan}$',r'\nd')
a = a.replace('None',r'\nd')
a = a.replace('nan',r'\nd')
outstr.append(a)
c2ohist = np.histogram(c2oarr,bins = [0.,p.coThresh,1000.])[0]
# Total number of c2o measurments
texdict['nPlanetTot'] = len(np.where(out['exoname'] != 'None')[0])
texdict['nStarsTot'] = len(out)
texdict['ncoStarsTot'] = len(c2oarr)
texdict['ncoLtThresh'],texdict['ncoGtThresh'] = tuple(c2ohist)
texdict['coMin'],texdict['coMax'] = c2oarr.min(),c2oarr.max()
if texcmd:
return texdict
if save:
f = open('Thesis/tables/bigtable.tex','w')
f.writelines(outstr)
