def compstars(d1,d2,out=None) :
'''
Creates plots to compare 2 different version
'''
v1=fits.open(d1+'/'+d1+'/allCal-'+d1+'.fits')[1].data
v2=fits.open(d2+'/'+d2+'/allCal-'+d2+'.fits')[1].data
i1,i2=match.match(v1['APOGEE_ID'],v2['APOGEE_ID'])
fig,ax=plots.multi(1,7,hspace=0.001,figsize=(8,20))
plots.plotc(ax[0],v1['FPARAM'][i1,0],v2['FPARAM'][i2,0]-v1['FPARAM'][i1,0],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta Teff$',yr=[-1000,1000],xt='Teff')
plots.plotc(ax[1],v1['FPARAM'][i1,0],v2['FPARAM'][i2,1]-v1['FPARAM'][i1,1],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta log g$',yr=[-1,1],xt='Teff')
plots.plotc(ax[2],v1['FPARAM'][i1,0],10.**v2['FPARAM'][i2,2]-10.**v1['FPARAM'][i1,2],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta vmicro$',yr=[-1,1],xt='Teff')
plots.plotc(ax[3],v1['FPARAM'][i1,0],v2['FPARAM'][i2,3]-v1['FPARAM'][i1,3],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta [M/H]$',yr=[-0.75,0.75],xt='Teff')
plots.plotc(ax[4],v1['FPARAM'][i1,0],v2['FPARAM'][i2,4]-v1['FPARAM'][i1,4],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta [C/M]$',yr=[-0.75,0.75],xt='Teff')
plots.plotc(ax[5],v1['FPARAM'][i1,0],v2['FPARAM'][i2,5]-v1['FPARAM'][i1,5],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta [N/M]$',yr=[-0.75,0.75],xt='Teff')
plots.plotc(ax[6],v1['FPARAM'][i1,0],v2['FPARAM'][i2,6]-v1['FPARAM'][i1,6],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta \alpha/M]$',yr=[-0.75,0.75],xt='Teff')
if out is not None:
plt.savefig(out+'.png')
# plots as a function of delta logvmicro
fig,ax=plots.multi(1,7,hspace=0.001,figsize=(8,20))
plots.plotc(ax[0],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],v2['FPARAM'][i2,0]-v1['FPARAM'][i1,0],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta Teff$',yr=[-1000,1000],xt=r'$\Delta log vmicro$' )
plots.plotc(ax[1],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],v2['FPARAM'][i2,1]-v1['FPARAM'][i1,1],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta log g$',yr=[-1,1],xt=r'$\Delta log vmicro$' )
plots.plotc(ax[2],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],10.**v2['FPARAM'][i2,2]-10.**v1['FPARAM'][i1,2],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta vmicro$',yr=[-1,1],xt=r'$\Delta log vmicro$' )
plots.plotc(ax[3],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],v2['FPARAM'][i2,3]-v1['FPARAM'][i1,3],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta [M/H]$',yr=[-0.75,0.75],xt=r'$\Delta log vmicro$' )
plots.plotc(ax[4],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],v2['FPARAM'][i2,4]-v1['FPARAM'][i1,4],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta [C/M]$',yr=[-0.75,0.75],xt=r'$\Delta log vmicro$' )
plots.plotc(ax[5],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],v2['FPARAM'][i2,5]-v1['FPARAM'][i1,5],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta [N/M]$',yr=[-0.75,0.75],xt=r'$\Delta log vmicro$' )
plots.plotc(ax[6],v1['FPARAM'][i1,2]-v2['FPARAM'][i2,2],v2['FPARAM'][i2,6]-v1['FPARAM'][i1,6],v1['FPARAM'][i1,3],zr=[-2,0.5],colorbar=True,zt='[M/H]',yt=r'$\Delta \alpha/M]$',yr=[-0.75,0.75],xt=r'$\Delta log vmicro$' )
if out is not None:
plt.savefig(out+'_dvmicro.png')
def apolco(hdulist, out='./', snmin=150):
""" histograms of LCO-APO parameters and abundances
"""
gd = apselect.select(hdulist[1].data, badval='STAR_BAD', sn=[snmin, 10000])
a = hdulist[1].data[gd]
apo = np.where(a['TELESCOPE'] == 'apo25m')[0]
lco = np.where(a['TELESCOPE'] == 'lco25m')[0]
i1, i2 = match.match(a['APOGEE_ID'][apo], a['APOGEE_ID'][lco])
grid = []
yt = []
for iparam, param in enumerate(hdulist[3].data['PARAM_SYMBOL'][0]):
fig, ax = plots.multi(1, 1, figsize=(6, 4.5))
diff = a['FPARAM'][lco[i2], iparam] - a['FPARAM'][apo[i1], iparam]
if iparam == 0: ax.hist(diff, bins=np.arange(-100., 100., 1.))
else: ax.hist(diff, bins=np.arange(-0.5, 0.5, 0.01))
ax.set_xlabel('{:s} (LCO-APO)'.format(param))
ax.text(0.1, 0.9, 'S/N> {:d}'.format(snmin), transform=ax.transAxes)
ax.text(0.1,
0.8,
'mean: {:8.3f}'.format(diff.mean()),
transform=ax.transAxes)
ax.text(0.1,
0.7,
'std: {:8.3f}'.format(diff.std()),
transform=ax.transAxes)
outfile = out + 'apolco_param_{:d}.png'.format(iparam)
fig.savefig(outfile)
plt.close()
grid.append([os.path.basename(outfile)])
yt.append(param)
for ielem, el in enumerate(hdulist[3].data['ELEM_SYMBOL'][0]):
fig, ax = plots.multi(1, 1, figsize=(6, 4.5))
diff = a['FELEM'][lco[i2], ielem] - a['FELEM'][apo[i1], ielem]
ax.hist(diff, bins=np.arange(-0.5, 0.5, 0.01))
ax.set_xlabel('{:s} (LCO-APO)'.format(el))
ax.text(0.1, 0.9, 'S/N> {:d}'.format(snmin), transform=ax.transAxes)
ax.text(0.1,
0.8,
'mean: {:8.3f}'.format(diff.mean()),
transform=ax.transAxes)
ax.text(0.1,
0.7,
'std: {:8.3f}'.format(diff.std()),
transform=ax.transAxes)
outfile = out + 'apolco_{:s}.png'.format(el)
fig.savefig(outfile)
plt.close()
grid.append([os.path.basename(outfile)])
yt.append(el)
html.htmltab(grid, file=out + 'apolco.html', ytitle=yt)
def dr13dr12() :
'''
compare dr13 dr12 Teff
'''
dr12load=apload.ApLoad(dr='dr12')
dr12=dr12load.allStar()[1].data
dr13load=apload.ApLoad(dr='dr13')
dr13=dr13load.allStar()[1].data
i1,i2 = match.match(dr12['APOGEE_ID'],dr13['APOGEE_ID'])
dr12=dr12[i1]
dr13=dr13[i2]
fig,ax=plots.multi(1,2,hspace=0.001,wspace=0.001)
plots.plotc(ax[0],dr13['M_H'],dr13['TEFF']-dr12['TEFF'],dr13['TEFF'],xr=[-2.5,0.75],yr=[-300,300],zr=[3500,5000])
plots.plotc(ax[1],dr13['TEFF'],dr13['TEFF']-dr12['TEFF'],dr13['M_H'],xr=[6500,3000],yr=[-300,300],zr=[-2,0.5])
def hrsample(indata,hrdata,maxbin=50,raw=True) :
'''
selects stars covering HR diagram as best as possible from input sample
'''
i1,i2 = match.match(indata['APOGEE_ID'],hrdata['APOGEE_ID'])
gd=[]
for teff in np.arange(3000,6000,500) :
gdteff=apselect.select(hrdata[i2],badval=['STAR_BAD'],badtarg=['EMBEDDED','EXTENDED'],teff=[teff,teff+500],sn=[100,1000],raw=raw)
for logg in np.arange(-0.5,5.5,1) :
j=apselect.select(hrdata[i2[gdteff]],logg=[logg,logg+1],raw=True)
gdlogg=gdteff[j]
for mh in np.arange(-2.5,0.5,0.5) :
j=apselect.select(hrdata[i2[gdlogg]],mh=[mh,mh+0.5],raw=True)
j=gdlogg[j]
js=np.argsort(hrdata[i2[j]]['SNR'])[::-1]
x = j[js] if len(j) < maxbin else j[js[0:maxbin]]
gd.extend(x)
return i1[gd],i2[gd]
def allCal(files=['clust???/aspcapField-*.fits','cal???/aspcapField-*.fits'],nelem=15,out='allCal.fits',allfield=None) :
'''
Concatenate aspcapField files, adding ELEM tags if not there
'''
# concatenate the structures
all=struct.concat(files,verbose=True,fixfield=True)
# add elements tags if we don't have them
try :
test=all['FELEM'][0]
except :
n=len(all)
form='{:<d}f4'.format(nelem)
iform='{:<d}f4'.format(nelem)
all=struct.add_cols(all,np.zeros(n,dtype=[('FELEM',form),('FELEM_ERR',form),
('ELEM',form),('ELEM_ERR',form),('ELEMFLAG',iform)]))
# add in NINST information from allField file
if allfield is not None:
a=fits.open(allfield)[1].data
i1,i2=match.match(np.core.defchararray.add(all['APOGEE_ID'],all['LOCATION_ID'].astype(str)),
np.core.defchararray.add(a['APOGEE_ID'],a['LOCATION_ID'].astype(str)))
n=len(all)
all=struct.add_cols(all,np.zeros(n,dtype=[('NINST','3i4')]))
all['NINST'][i1]=a['NINST'][i2]
# write out the file
if out is not None:
print('writing',out)
#struct.wrfits(all,out)
hdulist=fits.HDUList()
hdu=fits.BinTableHDU.from_columns(all)
hdulist.append(hdu)
filelist=glob.glob(files[0])
hdu=fits.open(filelist[0])[3]
hdulist.append(hdu)
hdu=fits.open(filelist[0])[3]
hdulist.append(hdu)
hdulist.writeto(out,overwrite=True)
def calsample(indata=None,file='clust.html',plot=True,clusters=True,apokasc='APOKASC_cat_v4.4.2',
cal1m=True,coolstars=True,dir='cal',hrdata=None,optical='cal_stars_20190329.txt',ns=True,special=None,Ce=True,ebvmax=None,snmin=75,mkall=True) :
'''
selects a calibration subsample from an input apField structure, including several calibration sub-classes:
cluster, APOKASC stars, 1m calibration stars. Creates cluster web pages/plots if requested
'''
if indata is None :
indata=allField(files=['apo25m/*/a?Field-*.fits','lco25m/*/a?Field-*.fits','apo1m/calibration/a?Field-*.fits'],out=None,verbose=True)
j=np.where((indata['COMMISS'] == 0) & (indata['SNR'] > 75) )[0]
data=indata[j]
try: os.mkdir(dir)
except: pass
all=[]
if clusters :
jc=[]
clusts=apselect.clustdata()
fstars=open(dir+'/allclust.txt','w')
f=html.head(file=dir+'/'+file)
f.write('<A HREF=allclust.txt> cluster stars list </a>')
f.write('<TABLE BORDER=2>\n')
f.write('<TR><TD>NAME<TD>RA<TD>DEC<TD>Radius<TD>RV<TD>Delta RV<TD>Position criterion<TD>RV criterion<TD>PM criterion<TD>Parallax criterion<TD> CMD')
clust=apselect.clustdata()
for ic in range(len(clust.name)) :
print(clust[ic].name)
j=apselect.clustmember(data,clust[ic].name,plot=plot,hard=dir)
print(clust[ic].name,len(j))
# clusters to exclude here
if (clust[ic].name not in ['OmegaCen','Pal1','Pal6','Pal5','Terzan12']) and (len(j) >= 5): jc.extend(j)
f.write('<TR><TD><A HREF='+clust[ic].name+'.txt>'+clust[ic].name+'</A><TD>{:12.6f}<TD>{:12.6f}<TD>{:8.2f}<TD>{:8.2f}<TD>{:8.2f}\n'.format(
clust[ic].ra,clust[ic].dec,clust[ic].rad,clust[ic].rv,clust[ic].drv))
f.write('<TD><A HREF='+clust[ic].name+'_pos.png><IMG SRC='+clust[ic].name+'_pos.png width=300></A>\n')
f.write('<TD><A HREF='+clust[ic].name+'_rv.png><IMG SRC='+clust[ic].name+'_rv.png width=300></A>\n')
f.write('<TD><A HREF='+clust[ic].name+'_pm.png><IMG SRC='+clust[ic].name+'_pm.png width=300></A>\n')
f.write('<TD><A HREF='+clust[ic].name+'_parallax.png><IMG SRC='+clust[ic].name+'_parallax.png width=300></A>\n')
f.write('<TD><A HREF='+clust[ic].name+'_cmd.png><IMG SRC='+clust[ic].name+'_cmd.png width=300></A>\n')
np.savetxt(dir+'/'+clust[ic].name+'.txt',data[jc]['APOGEE_ID'],fmt='%s')
for star in data[jc]['APOGEE_ID'] : fstars.write('{:s} {:s}\n'.format(star,clust[ic].name))
html.tail(f)
fstars.close()
print('Number of cluster stars: ',len(jc))
mklinks(data,jc,dir+'_clust')
all.extend(jc)
if apokasc is not None :
jc=[]
apokasc = fits.open(os.environ['APOGEE_DIR']+'/data/apokasc/'+apokasc+'.fits')[1].data
i1,i2=match.match(data['APOGEE_ID'],apokasc['2MASS_ID'])
rgb=np.where(apokasc['CONS_EVSTATES'][i2] == 'RGB')[0]
print('Number of APOKASC RGB stars (every 4th): ',len(rgb[0:-1:3]))
jc.extend(i1[rgb][0:-1:4])
rc=np.where(apokasc['CONS_EVSTATES'][i2] == 'RC')[0]
print('Number of APOKASC RC stars (every 2nd): ',len(rc[0:-2:2]))
jc.extend(i1[rc][0:-1:2])
rc=np.where(apokasc['CONS_EVSTATES'][i2] == '2CL')[0]
print('Number of APOKASC 2CL stars: ',len(rc))
jc.extend(i1[rc])
rc=np.where(apokasc['CONS_EVSTATES'][i2] == 'RC/2CL')[0]
print('Number of APOKASC RC/2CL stars: ',len(rc))
jc.extend(i1[rc])
logg = 'APOKASC2_LOGG' # older version used LOGG_SYSD_SCALING
lowg=np.where((apokasc[logg][i2] < 2) & (apokasc[logg][i2] > 0.1))[0]
print('Number of APOKASC low log g stars: ',len(lowg))
jc.extend(i1[lowg])
highg=np.where((apokasc[logg][i2] > 3.8) & (apokasc[logg][i2] < 5.5))[0]
print('Number of APOKASC high log g stars: ',len(highg))
jc.extend(i1[highg])
highg=np.where((apokasc['LOGG_DW'][i2] > 3.8) & (apokasc['LOGG_DW'][i2] < 5.5))[0]
print('Number of APOKASC high LOGG_DW stars: ',len(highg))
jc.extend(i1[highg])
lowz=np.where((apokasc['FE_H_ADOP_COR'][i2] < -1.) & (apokasc['FE_H_ADOP_COR'][i2] > -90.))[0]
print('Number of APOKASC low [Fe/H] stars: ',len(lowz))
jc.extend(i1[lowz])
mklinks(data,jc,dir+'_apokasc')
all.extend(jc)
if coolstars :
jc=[]
j=np.where(data['FIELD'] == 'GALCEN')[0]
print('Number of GALCEN stars: ',len(j))
jc.extend(j)
stars = ascii.read(os.environ['APOGEE_DIR']+'/data/calib/coolstars.txt',names=['id'],format='fixed_width_no_header')
i1,i2=match.match(data['APOGEE_ID'],stars['id'])
print('Number of cool stars: ',len(i1))
jc.extend(i1)
mklinks(data,jc,dir+'_galcen')
all.extend(jc)
if cal1m :
j=np.where(data['FIELD'] == 'calibration')[0]
print('Number of 1m calibration stars: ',len(j))
all.extend(j)
j=np.where(data['FIELD'] == 'RCB')[0]
print('Number of 1m RCB stars: ',len(j))
all.extend(j)
mklinks(data,j,dir+'_cal1m')
if optical is not None:
#stars = ascii.read(os.environ['APOGEE_DIR']+'/data/calib/validation_stars_DR16.txt',names=['id'],format='fixed_width_no_header')
stars = ascii.read(os.environ['APOGEE_DIR']+'/data/calib/'+optical,names=['id'],format='fixed_width_no_header')
i1,i2=match.match(data['APOGEE_ID'],stars['id'])
print('Number of optical validation stars: ',len(i1))
mklinks(data,i1,dir+'_optical')
all.extend(i1)
if Ce :
stars = np.loadtxt(os.environ['APOGEE_DIR']+'/data/calib/Ce_test_stars.txt',dtype=str)[:,0]
i1,i2=match.match(data['APOGEE_ID'],stars)
print('Number of Ce test stars: ',len(i1))
mklinks(data,i1,dir+'_Ce')
all.extend(i1)
if ns :
#north-south overlap
jn=np.where((data['FIELD'] == 'N2243') | (data['FIELD'] == '000+08') |
(data['FIELD'] == '300+75') | (data['FIELD'] == 'M12-N') )[0]
js=np.where((data['FIELD'] == 'N2243-S') | (data['FIELD'] == '000+08-S') |
(data['FIELD'] == '300+75-S') | (data['FIELD'] == 'M12-S') )[0]
i1,i2=match.match(data['APOGEE_ID'][jn], data['APOGEE_ID'][js])
jc=list(jn[i1])
jc.extend(js[i2])
stars = ascii.read(os.environ['APOGEE_DIR']+'/data/calib/apogee_overlap.txt',names=['id'],format='fixed_width_no_header')
jn=np.where(data['TELESCOPE'] == 'apo25m')[0]
js=np.where(data['TELESCOPE'] == 'lco25m')[0]
i1,i2=match.match(data['APOGEE_ID'][jn],stars['id'])
jc.extend(jn[i1])
i1,i2=match.match(data['APOGEE_ID'][js],stars['id'])
jc.extend(js[i2])
mklinks(data,jc,dir+'_ns')
print('Number of N/S overlap stars: ',len(jc))
all.extend(jc)
if special is not None:
stars = ascii.read(os.environ['APOGEE_DIR']+'/data/calib/'+special,names=['id'],format='fixed_width_no_header')
jn=np.where(data['TELESCOPE'] == 'apo25m')[0]
js=np.where(data['TELESCOPE'] == 'lco25m')[0]
i1,i2=match.match(data['APOGEE_ID'][jn],stars['id'])
jc=list(jn[i1])
i1,i2=match.match(data['APOGEE_ID'][js],stars['id'])
jc.extend(js[i2])
print('Number of '+special+' stars: ',len(i1))
mklinks(data,jc,dir+'_special')
all.extend(jc)
if ebvmax is not None:
jc=np.where( (data['SFD_EBV']>0) & (data['SFD_EBV'] < ebvmax) )[0]
print('Number of low E(B-V) stars: ',len(jc))
mklinks(data,jc,dir+'_ebv')
all.extend(jc)
if hrdata is not None:
i1, i2 = hrsample(data,hrdata)
print('Number of HR sample stars: ',len(i1))
mklinks(data,i1,dir+'_hr')
all.extend(i1)
# create "all" directories with all stars, removing duplicates
print('Total number of stars: ',len(list(set(all))))
if mkall: mklinks(data,list(set(all)),dir+'_all')
return indata
def add_gaia(data,
gaia_1='gaia_2mass_xmatch.fits.gz',
gaia_2='gaia_posn_xmatch.fits.gz'):
""" Add GAIA data to allStar file, with coordinate match to (cross-matched) GAIA reference file
"""
tab = Table(data)
in_names = ('source_id', 'parallax', 'parallax_error', 'pmra',
'pmra_error', 'pmdec', 'pmdec_error', 'phot_g_mean_mag',
'phot_bp_mean_mag', 'phot_rp_mean_mag', 'a_g_val',
'e_bp_min_rp_val', 'radial_velocity', 'radial_velocity_error',
'r_est', 'r_lo', 'r_hi')
dtypes = ('i8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f4', 'f4', 'f4', 'f4',
'f4', 'f8', 'f8', 'f8', 'f8', 'f8')
out_names = []
for name in in_names:
out_names.append(('gaia_' + name).upper())
newcols = Table(np.zeros([len(tab), len(out_names)]) - 9999.,
names=out_names,
dtype=dtypes)
# for source_id, default to 0, not -9999.
newcols['GAIA_SOURCE_ID'] = 0
# get rid of targetting proper motions to avoid confusion!
#tab.remove_columns(['PMRA','PMDEC','PM_SRC'])
# change to rename
tab.rename_column('PMRA', 'TARG_PMRA')
tab.rename_column('PMDEC', 'TARG_PMDEC')
tab.rename_column('PM_SRC', 'TARG_PM_SRC')
# add unpopulated columns
tab.add_columns(newcols.columns.values())
# read gaia 2MASS matched file, match by 2MASS ID, and populate
gaia = fits.open(gaia_1)[1].data
print('number in GAIA-2MASS xmatch catalog: ', len(gaia),
len(set(gaia['original_ext_source_id'])))
while True:
# loop for matches since we have repeats and want them all matched
j = np.where(tab['GAIA_SOURCE_ID'] == 0)[0]
print('Number missing gaia_source_id: ', len(j))
m1, m2 = match.match(
np.core.defchararray.replace(tab['APOGEE_ID'][j], '2M', ''),
gaia['original_ext_source_id'])
print('Number matched by 2MASS: ', len(m1))
if len(m1) == 0: break
for inname, outname in zip(in_names, out_names):
tab[outname][j[m1]] = gaia[inname][m2]
#if len(m1) < 100 :
# for i in m1 : print(tab['APOGEE_ID'][j[m1]])
j = np.where(tab['GAIA_SOURCE_ID'] > 0)[0]
print('number of unique APOGEE_ID matches: ',
len(set(tab['APOGEE_ID'][j])))
j = np.where(tab['GAIA_SOURCE_ID'] == 0)[0]
print('missing sources after 2MASS matches: ', len(j))
gaia = fits.open(gaia_2)[1].data
h = htm.HTM()
# now do a positional match, take the brightest object within 3 arcsec (which is the max from the GAIA crossmatch)
maxrad = 3. / 3600.
m1, m2, rad = h.match(tab['RA'][j],
tab['DEC'][j],
gaia['RA'],
gaia['DEC'],
maxrad,
maxmatch=10)
for m in set(m1):
jj = np.where(m1 == m)[0]
ii = np.argsort(gaia['phot_rp_mean_mag'][m2[jj]])
#print(tab['RA'][j[m]],tab['DEC'][j[m]],tab['TARGET_ID'][j[m]])
#print(gaia['RA'][m2[jj]],gaia['DEC'][m2[jj]],gaia['PHOT_RP_MEAN_MAG'][m2[jj]])
#print(ii)
for inname, outname in zip(in_names, out_names):
tab[outname][j[m]] = gaia[inname][m2[jj[ii[0]]]]
j = np.where(tab['GAIA_SOURCE_ID'] == 0)[0]
print('missing sources after second match: ', len(j))
# replace NaNs
for name in out_names:
bd = np.where(np.isnan(tab[name]))[0]
tab[name][bd] = -9999.
return tab
def read(name, libfile):
""" Read all of the FERRE files associated with a FERRE run
"""
# get library headers and load wavelength array
libhead0, libhead = rdlibhead(libfile)
wave = []
for ichip in range(len(libhead)):
wave.extend(libhead[ichip]['WAVE'][0] +
np.arange(libhead[ichip]['NPIX']) *
libhead[ichip]['WAVE'][1])
wave = 10.**np.array(wave)
nwave = len(wave)
nparam = libhead0['N_OF_DIM']
# input ipf and output spm files, match object names
ipfobj, ipf = readferredata(name + '.ipf')
spmobj, spm = readferredata(name + '.spm')
i1, i2 = match.match(ipfobj, spmobj)
nobj = len(ipfobj)
param = spm[:, 0:nparam]
paramerr = spm[:, nparam:2 * nparam]
chi2 = 10.**spm[:, 2 * nparam + 2]
covar = spm[:, 2 * nparam + 3:2 * nparam + 3 + nparam**2]
covar = np.reshape(covar, (nobj, nparam, nparam))
# load param array
params, tagnames, flagnames = aspcap.params()
ntotparams = len(params)
index = np.zeros(ntotparams, dtype=int)
a = np.zeros(nobj,
dtype=[('APOGEE_ID', 'S100'), ('FPARAM', 'f4', (ntotparams)),
('FPARAM_COV', 'f4', (ntotparams, ntotparams)),
('PARAM_CHI2', 'f4'),
('PARAMFLAG', 'i4', (ntotparams)),
('ASPCAPFLAG', 'i4')])
a['APOGEE_ID'] = ipfobj
a['PARAM_CHI2'] = chi2
parammask = bitmask.ParamBitMask()
aspcapmask = bitmask.AspcapBitMask()
for i in range(nparam):
# load FPARAM and FPARAM_COV into the right order of parameters
pname = libhead0['LABEL'][i].decode()
index = np.where(params == pname)[0][0]
val = param[:, i]
a['FPARAM'][:, index] = val
for j in range(nparam):
jindex = np.where(params == libhead0['LABEL'][j].decode())[0][0]
a['FPARAM_COV'][:, index, jindex] = covar[:, i, j]
# check for grid edge and flag
warn = np.where(
(val < libhead0['LLIMITS'][i] + libhead0['STEPS'][i] / 2.)
| (val > libhead0['LLIMITS'][i] + libhead0['STEPS'][i] *
(libhead0['N_P'][i] - 1 - 1. / 2)))[0]
a['PARAMFLAG'][warn, index] |= parammask.getval('GRIDEDGE_WARN')
a['ASPCAPFLAG'][warn] |= aspcapmask.getval(flagnames[index] + '_WARN')
bad = np.where(
(val < libhead0['LLIMITS'][i] + libhead0['STEPS'][i] / 8.))[0]
if pname != 'N' and pname != 'LOG10VDOP' and pname != 'LGVSINI':
a['PARAMFLAG'][bad, index] |= parammask.getval('GRIDEDGE_BAD')
a['ASPCAPFLAG'][bad] |= aspcapmask.getval(flagnames[index] +
'_BAD')
bad = np.where((val > libhead0['LLIMITS'][i] + libhead0['STEPS'][i] *
(libhead0['N_P'][i] - 1 - 1 / 8.)))[0]
if pname != 'N':
a['PARAMFLAG'][bad, index] |= parammask.getval('GRIDEDGE_BAD')
a['ASPCAPFLAG'][bad] |= aspcapmask.getval(flagnames[index] +
'_BAD')
# put spectral data into a structured array
form = '{:d}f4'.format(nwave)
sform = '{:d}f4'.format(spm.shape[1])
out = np.empty(nobj,
dtype=[('obj', 'S24'), ('spm', sform), ('obs', form),
('frd', form), ('err', form), ('mdl', form),
('chi2', form)])
out['obj'] = ipfobj
out['spm'][i1, :] = spm[i2, :]
out['obs'] = readspec(name + '.obs')[i2, :]
out['frd'] = readspec(name + '.frd')[i2, :]
out['err'] = readspec(name + '.err')[i2, :]
out['mdl'] = readspec(name + '.mdl')[i2, :]
out['chi2'] = (out['obs'] - out['mdl'])**2 / out['err']**2
return a, out, wave
def fit_vmacro(data,
teffrange=[3550, 5500],
mhrange=[-2.5, 1.],
loggrange=[-1, 3.8],
vrange=[1, 15],
degree=1,
maxerr=0.1,
apokasc='APOKASC_cat_v3.6.0.fits',
nopersist=False,
reject=0,
out='vmacro'):
"""
Fit macroturbulence relation with 1D f(log g) and 2D f(Teff, logg) fits, plots
Args:
data : calib structure
Keyword args :
degree : degree of fit (default=1)
mhrange : range of [M/H] (default=[1.,1])
loggrange : range of log g (default=[1.,3.8])
vrange : scaling range for vmacro plot, vrange[1] sets maximum good vmacro
Returns:
fit1d, fit2d : 1D and 2D polynomial fits
"""
teff = data['FPARAM'][:, 0]
logg = data['FPARAM'][:, 1]
vmicro = data['FPARAM'][:, 2]
mh = data['FPARAM'][:, 3]
vmacro = data['FPARAM'][:, 7]
verr = np.sqrt(data['FPARAM_COV'][:, 7, 7])
try:
meanfib = data['MEANFIB']
except:
meanfib = 0. * vmicro
print('mhrange: ', mhrange)
print('loggrange: ', loggrange)
print('vrange: ', vrange)
print('maxerr: ', maxerr)
print('nopersist: ', nopersist)
print('reject: ', reject)
gd = np.where((mh > mhrange[0]) & (mh < mhrange[1]) & (logg > loggrange[0])
& (logg < loggrange[1]) & (10.**vmacro < vrange[1])
& (teff > teffrange[0]) & (teff < teffrange[1])
& (verr < maxerr))[0]
if nopersist:
j = np.where((data['STARFLAG'][gd] & bitmask.persist()) == 0)[0]
gd = gd[j]
print('len(gd)', len(gd))
# remove non-1st generation GC stars
gcstars = ascii.read(os.environ['IDLWRAP_DIR'] + '/data/gc_szabolcs.dat')
bd = np.where(gcstars['pop'] != 1)[0]
gd = [x for x in gd if data[x]['APOGEE_ID'] not in gcstars['id'][bd]]
apokasc = fits.open(os.environ['IDLWRAP_DIR'] + '/data/' + apokasc)[1].data
i1, i2 = match.match(data['APOGEE_ID'], apokasc['2MASS_ID'])
rgb = np.where(apokasc['CONS_EVSTATES'][i2] == 'RGB')[0]
rc = np.where(apokasc['CONS_EVSTATES'][i2] == 'RC')[0]
type = mh * 0.
type[i1[rgb]] = 1
type[i1[rc]] = -1
#fig,ax=plots.multi(2,2,figsize=(12,8))
#fit1d = fit.fit1d(logg[gd], vmacro[gd],degree=degree,plot=ax[0,0],xt='log g', yt='vmacro',ydata=mh[gd],colorbar=True,zt='[M/H]',reject=reject,log=True)
#fit1d = fit.fit1d(logg[gd], vmacro[gd],degree=degree,plot=ax[0,1],xt='log g', yt='vmacro',ydata=teff[gd],colorbar=True,zt='Teff',reject=reject,log=True)
#fit1d = fit.fit1d(logg[gd], vmacro[gd],degree=degree,plot=ax[1,0],xt='log g', yt='vmacro',ydata=meanfib[gd],colorbar=True,zt='mean fib',reject=reject,log=True)
#fit1d = fit.fit1d(logg[gd], vmacro[gd],degree=degree,plot=ax[1,1],xt='log g', yt='vmacro',ydata=type[gd],colorbar=True,zt='RGB/RC',reject=reject,log=True)
#fig.tight_layout()
#fig.savefig(out+'.png')
#fig,ax=plots.multi(1,2)
fig, ax = plots.multi(2, 3, figsize=(12, 8))
print('2D logg, mh')
fit2d = fit.fit2d(logg[gd],
mh[gd],
vmacro[gd],
degree=degree,
plot=ax[0, 0],
xt='log g',
yt='[M/H]',
zt='log(vmacro)',
reject=reject,
zr=[0, 15],
log=True)
plots.plotc(ax[0, 1],
teff[gd],
10.**vmacro[gd] - 10.**fit2d(logg[gd], mh[gd]),
mh[gd],
xt='Teff',
yt=r'$\Delta vmacro$',
zt='[M/H]',
xr=[5500, 3500],
yr=[-10, 20],
colorbar=True,
yerr=verr[gd] * vmacro[gd] * math.log(10))
parprint(
[fit2d.parameters[0], 0., fit2d.parameters[1], fit2d.parameters[2]])
#print('2D teff, logg')
fit2d_b = fit.fit2d(teff[gd],
logg[gd],
vmacro[gd],
degree=degree,
plot=ax[1, 0],
xt='Teff',
yt='log g',
zt='log(vmacro)',
reject=reject,
xr=[6000, 3000],
yr=[5, -1],
zr=[0, 15],
log=True)
plots.plotc(ax[1, 1],
teff[gd],
10.**vmacro[gd] - 10.**fit2d_b(teff[gd], logg[gd]),
mh[gd],
xt='Teff',
yt=r'$\Delta vmacro$',
zt='[M/H]',
xr=[5500, 3500],
yr=[-10, 20],
colorbar=True,
yerr=verr[gd] * vmacro[gd] * math.log(10))
parprint([
fit2d_b.parameters[0], fit2d_b.parameters[1], fit2d_b.parameters[2], 0.
])
#print('2D teff, [M/H]')
#fit2d_c = fit.fit2d(teff[gd], mh[gd], vmacro[gd],degree=degree,plot=ax[0,1],xt='Teff', yt='[M/H]',zt='log(vmacro)',reject=reject,zr=[0,15],log=True)
#parprint([fit2d_c.parameters[0],fit2d_c.parameters[1],0.,fit2d_c.parameters[2]])
fit1d = fit.fit1d(mh[gd],
vmacro[gd],
degree=degree,
plot=ax[2, 0],
xt='[M/H]',
yt='vmacro',
colorbar=True,
reject=reject,
log=True,
ydata=logg[gd],
yr=[0, 5],
zt='log g')
parprint([fit1d.parameters[0], 0., 0., fit1d.parameters[1]])
plots.plotc(ax[2, 1],
teff[gd],
10.**vmacro[gd] - 10.**fit1d(mh[gd]),
mh[gd],
xt='Teff',
yt=r'$\Delta vmacro$',
zt='[M/H]',
xr=[5500, 3500],
yr=[-10, 20],
colorbar=True,
yerr=verr[gd] * vmacro[gd] * math.log(10),
zr=[-2, 0.5])
#plots.plotc(ax[1,1],teff[gd],logg[gd],10.**vmacro[gd],xr=[6000,3000],yr=[5,-1],xt='Teff',yt='log g', zr=[0,15])
# DR13 fit
#dr13fit=models.Polynomial2D(degree=1)
#dr13fit.parameters=[0.741,-0.0998,-0.225]
#junk = fit.fit2d(logg[gd], mh[gd], vmacro[gd],degree=degree,plot=ax[1],xt='log g', yt='[M/H]',zt='log(vmacro)',pfit=dr13fit)
fig.tight_layout()
fig.savefig(out + '_2d.png')
# single plot with 1D fit
fig, ax = plots.multi(1, 1, figsize=(12, 4))
fit1d = fit.fit1d(mh[gd],
vmacro[gd],
degree=degree,
plot=ax,
xt='[M/H]',
yt='vmacro',
colorbar=True,
reject=reject,
log=True,
ydata=logg[gd],
yr=[0, 5],
zt='log g')
fig.tight_layout()
fig.savefig(out + '_1d.pdf')
#pdb.set_trace()
#plot(teff, logg, mh, meanfib, vmacro, vrange, fit1d, fit2d, vt='vmacro')
return
def fit_vmicro(data,
teffrange=[3550, 5500],
mhrange=[-2.5, 1],
loggrange=[-0.5, 4.75],
vrange=[0, 4],
vmrange=[0, 6],
maxerr=0.1,
degree=1,
reject=0,
apokasc='APOKASC_cat_v3.6.0.fits',
nopersist=False,
out='vmicro',
func=None):
"""
Fit microturbulence relation with 1D f(log g) and 2D f(Teff, logg) fits, plots
Args:
data : calib structure
Keyword args :
degree : degree of fit (default=1)
mhrange : range of [M/H] (default=[-1,1])
loggrange : range of log g (default=[-1,3.8])
maxerr : maximum uncertainty in vmicro
vrange : scaling range for vmacro plot, vrange[1] sets maximum good vmacro
Returns:
fit1d, fit2d : 1D and 2D polynomial fits
"""
vmicro = data['FPARAM'][:, 2]
vmacro = data['FPARAM'][:, 7]
# fix locked vmacro by hand (bad!)
#j=np.where(np.isclose(vmacro,1.))[0]
#vmacro[j] = 0.6
teff = data['FPARAM'][:, 0]
logg = data['FPARAM'][:, 1]
mh = data['FPARAM'][:, 3]
try:
meanfib = data['MEANFIB']
except:
meanfib = 0. * vmicro
try:
ninst = data['NINST'][:, 1] - data['NINST'][:, 2]
except:
ninst = data['NVISITS'] * 0
apokasc = fits.open(os.environ['APOGEE_DIR'] + '/data/apokasc/' +
apokasc)[1].data
i1, i2 = match.match(data['APOGEE_ID'], apokasc['2MASS_ID'])
rgb = np.where(apokasc['CONS_EVSTATES'][i2] == 'RGB')[0]
rc = np.where(apokasc['CONS_EVSTATES'][i2] == 'RC')[0]
type = mh * 0.
type[i1[rgb]] = 1
type[i1[rc]] = -1
print('mhrange: ', mhrange)
print('teffrange: ', teffrange)
print('loggrange: ', loggrange)
print('vmacrorange: ', vmrange)
print('vmicrorange: ', vrange)
print('maxerr: ', maxerr)
print('nopersist: ', nopersist)
print('reject: ', reject)
gd = np.where((mh > mhrange[0]) & (mh < mhrange[1]) & (logg > loggrange[0])
& (logg < loggrange[1]) & (teff > teffrange[0])
& (teff < teffrange[1]) & (10.**vmacro > vmrange[0])
& (10.**vmacro < vmrange[1])
& (np.sqrt(data['FPARAM_COV'][:, 2, 2]) < maxerr)
& (10.**vmicro > vrange[0]) & (10.**vmicro < vrange[1]))[0]
if nopersist:
j = np.where((data['STARFLAG'][gd] & bitmask.persist()) == 0)[0]
gd = gd[j]
# remove non-1st generation GC stars
gcstars = ascii.read(os.environ['APOGEE_DIR'] +
'/data/calib/gc_szabolcs.dat')
bd = np.where(gcstars['pop'] != 1)[0]
gd = [x for x in gd if data[x]['APOGEE_ID'] not in gcstars['id'][bd]]
# 1D plots a f(log g)
#fig,ax = plots.multi(2,3,figsize=(15,15))
#fit1d = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[0,0],ydata=mh[gd],log=True,xt='log g',yt='vmicro ([M/H])',yr=[-2.5,0.5],colorbar=True,zt='[M/H]',xr=[0,4.5])
## plot ALL points (even outside of fit range)
#plots.plotc(ax[0,0],logg,10.**vmicro,mh,zr=[-2.5,0.5],xr=[-1,5],size=1)
#
# junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[0,1],ydata=teff[gd],log=True,xt='log g',yt='vmicro',yr=[3500,5500],pfit=fit1d,colorbar=True,zt='Teff',xr=[0,4.5])
# plots.plotc(ax[0,1],logg,10.**vmicro,teff,zr=[3500,5500],xr=[-1,5],size=1)
# junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[1,0],ydata=meanfib[gd],log=True,xt='log g',yt='vmicro',yr=[0,300],pfit=fit1d,colorbar=True,zt='mean fiber',xr=[0,4.5])
# plots.plotc(ax[1,0],logg,10.**vmicro,meanfib,zr=[0,300],xr=[-1,5],size=1)
# junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[1,1],ydata=10.**vmacro[gd],log=True,xt='log g',yt='vmicro',yr=[0,15],pfit=fit1d,colorbar=True,zt='vmacro',xr=[0,4.5])
# plots.plotc(ax[1,1],logg,10.**vmicro,10.**vmacro,zr=[0,15],xr=[-1,5],size=1)
#
# junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[2,0],ydata=ninst[gd],log=True,xt='log g',yt='vmicro',yr=[-1,1],pfit=fit1d,colorbar=True,zt='ninst1-ninst2',xr=[0,4.5])
# plots.plotc(ax[2,0],logg,10.**vmicro,ninst,zr=[-1,1],xr=[-1,5],size=1)
# #plots.plotc(ax[3,1],logg[gd1],10.**vmicro[gd1],mh[gd1],zr=[-2.5,0.5],xr=[-1,5])
# # 2d plot
# #junk = fit.fit1d(logg, vmicro,degree=degree,reject=reject,plot=ax[2,0],plot2d=True,ydata=teff,log=True,yt='Teff',xt='log g',xr=[5,-0.5],yr=[6000,3000],pfit=fit1d,zr=[0,4])
# junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[2,1],ydata=type[gd],log=True,xt='log g',yt='vmicro',yr=[-1,1],pfit=fit1d,colorbar=True,zt='RGB/RC',xr=[0,4.5])
# plots.plotc(ax[2,0],logg,10.**vmicro,ninst,zr=[-1,1],xr=[-1,5],size=1)
#
#
# # plot with DR13 relation
# #dr13fit=models.Polynomial1D(degree=3)
# #dr13fit.parameters=[0.226,-0.0228,0.0297,-0.013]
# #junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,reject=reject,plot=ax[2,1],ydata=mh[gd],pfit=dr13fit,log=True,xt='log g',yt='vmicro',colorbar=True,zt='[M/H]',yr=[-2.5,0.5])
# #plots.plotc(ax[2,0],logg,10.**vmicro,mh,zr=[-2.5,0.5],xr=[-1,5],size=1)
##
# fig.tight_layout()
# fig.savefig(out+'.png')
#
# fig,ax=plots.multi(1,3,figsize=(6,10))
# plots.plotc(ax[0],teff[gd],10.**vmicro[gd]-10.**fit1d(logg[gd]),logg[gd],xt='Teff',yt=r'$\Delta vmicro$',xr=[3500,6000],yr=[-1,1],zr=[0,5],zt='log g',size=5)
# plots.plotc(ax[0],teff,10.**vmicro-10.**fit1d(logg),logg,xt='Teff',yt=r'$\Delta vmicro$',xr=[3500,6000],yr=[-1,1],zr=[0,5],zt='log g',colorbar=True,size=1)
# plots.plotc(ax[1],mh[gd],10.**vmicro[gd]-10.**fit1d(logg[gd]),teff[gd],xt='[M/H]',yt=r'$\Delta vmicro$',xr=[-2.5,0.5],yr=[-1,1],zr=[3500,5500],zt='Teff',size=5)
# plots.plotc(ax[1],mh,10.**vmicro-10.**fit1d(logg),teff,xt='[M/H]',yt=r'$\Delta vmicro$',xr=[-2.5,0.5],yr=[-1,1],zr=[3500,5500],zt='Teff',colorbar=True,size=1)
# plots.plotc(ax[2],meanfib[gd],10.**vmicro[gd]-10.**fit1d(logg[gd]),logg[gd],xt='mean fiber',yt=r'$\Delta vmicro$',xr=[0,300],yr=[-1,1],zr=[0,5],zt='log g',size=5)
# plots.plotc(ax[2],meanfib,10.**vmicro-10.**fit1d(logg),logg,xt='mean fiber',yt=r'$\Delta vmicro$',xr=[0,300],yr=[-1,1],zr=[0,5],zt='log g',colorbar=True,size=1)
# fig.tight_layout()
# fig.savefig(out+'_res.png')
#
#fig,ax=plots.multi(1,2)
#xr=[
#junk = fit.fit1d(logg[gd], vmicro[gd],degree=degree,plot=ax[0,0],plot2d=True,yr=[0,5],xr=[0,5],xt='Teff',yt='log g')
#y, x = np.mgrid[yr[1]:yr[0]:200j, xr[1]:xr[0]:200j]
# 2D plots a f(teff, logg)
fig, ax = plots.multi(1, 5, figsize=(6, 12))
#fit2d = fit.fit2d(logg[gd], mh[gd], vmicro[gd],degree=degree,plot=ax[0],yr=[-2.5,1],xr=[0,5],xt='logg',yt='[M/H]',log=True)
if func == vm1t:
print('gd:', len(gd))
x1 = logg
xr = [-0.5, 5.5]
xt = 'logg'
x2 = mh
yr = [-2, 0.5]
yt = '[M/H]'
#x3=logg
#x3_0=1.0
#yr=[-0.5,5.5]
#yt='log g'
des = func(x1[gd], x2[gd])
soln, inv = fit.linear(vmicro[gd], des)
y, x = np.mgrid[yr[0]:yr[1]:200j, xr[0]:xr[1]:200j]
ax[0].imshow(10.**func(x, y, soln=soln),
extent=[xr[0], xr[1], yr[0], yr[1]],
aspect='auto',
vmin=0.,
vmax=3.,
origin='lower')
plots.plotc(ax[0],
x1,
x2,
10.**vmicro,
xr=xr,
yr=yr,
zr=[0., 3.],
xt=xt,
yt=yt,
zt='vmicro',
size=5,
linewidth=0)
plots.plotc(ax[0],
x1[gd],
x2[gd],
10.**vmicro[gd],
xr=xr,
yr=yr,
zr=[0., 3.],
xt=xt,
yt=yt,
zt='vmicro',
colorbar=True,
size=15,
linewidth=0.2)
cs = ax[0].contour(x, y, 10**func(x, y, soln=soln), colors='k')
ax[0].clabel(cs)
else:
des = func(logg[gd], mh[gd])
soln, inv = fit.linear(vmicro[gd], des)
y, x = np.mgrid[-2.5:1.:200j, 0:5.:200j]
ax[0].imshow(10.**func(x, y, soln=soln),
extent=[0, 5, -2.5, 1.],
aspect='auto',
vmin=0.,
vmax=3.,
origin='lower')
plots.plotc(ax[0],
logg[gd],
mh[gd],
10.**vmicro[gd],
xr=[-0.5, 5],
yr=[-2.5, 1],
zr=[0., 3.],
xt='log g',
yt='[M/H]',
zt='vmicro',
colorbar=True,
size=15,
linewidth=1)
cs = ax[0].contour(x, y, 10**func(x, y, soln=soln), colors='k')
ax[0].clabel(cs, color='k')
# create independent variable grid for model and display
plots.plotc(ax[1],
logg[gd],
10.**vmicro[gd],
mh[gd],
xt='logg',
yt=r'$vmicro$',
xr=[-0.5, 5.],
yr=[0, 4],
zr=[-2., 0.5],
zt='[M/H]',
size=5)
plots.plotc(ax[1],
logg,
10.**vmicro,
mh,
xt='log g',
yt=r'$vmicro$',
xr=[-0.5, 5.],
yr=[0, 4],
zr=[-2., 0.5],
zt='[M/H]',
colorbar=True,
size=1)
plots.plotc(ax[2],
logg[gd],
10.**vmicro[gd],
teff[gd],
xt='logg',
yt=r'$vmicro$',
xr=[-0.5, 5.],
yr=[0, 4],
zr=[3000, 5500],
zt='Teff',
size=5)
plots.plotc(ax[2],
logg,
10.**vmicro,
teff,
xt='log g',
yt=r'$vmicro$',
xr=[-0.5, 5.],
yr=[0, 4],
zr=[3000, 5500],
zt='Teff',
colorbar=True,
size=1)
plots.plotc(ax[3],
logg[gd],
10.**vmicro[gd] - 10.**func(x1[gd], x2[gd], soln=soln),
teff[gd],
xt='logg',
yt=r'$\Delta vmicro$',
xr=[-0.5, 5.],
yr=[-1, 1],
zr=[3000, 5500],
zt='Teff',
size=5)
plots.plotc(ax[3],
logg,
10.**vmicro - 10.**func(x1, x2, soln=soln),
teff,
xt='log g',
yt=r'$\Delta vmicro$',
xr=[-0.5, 5.],
yr=[-1, 1],
zr=[3000, 5500],
zt='Teff',
colorbar=True,
size=1)
plots.plotc(ax[4],
logg[gd],
10.**vmicro[gd] - 10.**func(x1[gd], x2[gd], soln=soln),
mh[gd],
xt='logg',
yt=r'$\Delta vmicro$',
xr=[-0.5, 5.],
yr=[-1, 1],
zr=[-2.0, 0.5],
zt='[M/H]',
size=5)
plots.plotc(ax[4],
logg,
10.**vmicro - 10.**func(x1, x2, soln=soln),
mh,
xt='log g',
yt=r'$\Delta vmicro$',
xr=[-0.5, 5.],
yr=[-1, 1],
zr=[-2.0, 0.5],
zt='[M/H]',
colorbar=True,
size=1)
fig.tight_layout()
fig.savefig(out + '_res.png')
#summary plots
#plot(teff, logg, mh, meanfib, vmicro, vrange, fit1d, fit2d, vt='vmicro')
print('{', end="")
for i in range(len(soln)):
print('{:12.8f}'.format(soln[i]), end="")
print('}')
# return fit1d
return
def dr_compare():
# load the DRs, select stars with SN>150
dr12load = apload.ApLoad(dr='dr12')
dr12 = dr12load.allStar()[1].data
gd = np.where(dr12['SNR'] > 150)[0]
dr12 = dr12[gd]
dr13load = apload.ApLoad(dr='dr13')
dr13 = dr13load.allStar()[1].data
gd = np.where(dr13['SNR'] > 150)[0]
dr13 = dr13[gd]
dr14load = apload.ApLoad(dr='dr14')
dr14 = dr14load.allStar()[1].data
gd = np.where(dr14['SNR'] > 150)[0]
dr14 = dr14[gd]
c = apload.allStar()[3].data
# match them
m1a, m2a = match.match(dr12['APOGEE_ID'], dr13['APOGEE_ID'])
m1b, m2b = match.match(dr12['APOGEE_ID'], dr14['APOGEE_ID'])
m1c, m2c = match.match(dr13['APOGEE_ID'], dr14['APOGEE_ID'])
# parameter figures
figu, axu = plots.multi(3, 7, hspace=0.001, wspace=0.001)
figc, axc = plots.multi(3, 7, hspace=0.001, wspace=0.001)
tit = [
r'T$_{\rm eff}$', 'log g', r'V$_{\rm micro}$', '[M/H]', '[C/M]',
'[N/M]', r'[$\alpha$/M]'
]
for iparam in range(7):
print(iparam)
for iy, param in enumerate(['FPARAM', 'PARAM']):
if iy == 0:
ax = axu
else:
ax = axc
yt = r'$\Delta$' + tit[iparam]
if iparam == 6: xt = r'T$_{\rm eff}$'
else: xt = None
if iparam == 0:
ax[iparam, 0].text(0.5,
1.0,
'DR13-DR12',
transform=ax[iparam, 0].transAxes,
ha='center',
va='bottom')
ax[iparam, 1].text(0.5,
1.0,
'DR14-DR12',
transform=ax[iparam, 1].transAxes,
ha='center',
va='bottom')
ax[iparam, 2].text(0.5,
1.0,
'DR14-DR13',
transform=ax[iparam, 2].transAxes,
ha='center',
va='bottom')
if iparam == 0:
yr = [-300, 300]
elif iparam == 1:
yr = [-0.5, 0.5]
else:
yr = [-0.3, 0.3]
xr = [3500, 6000]
axim = plots.plotc(ax[iparam, 0],
dr12['TEFF'][m1a],
dr13[param][m2a, iparam] -
dr12[param][m1a, iparam],
dr12[param][m1a, 3],
size=1,
xr=xr,
yr=yr,
zr=[-1, 0.5],
yt=yt,
xt=xt,
rasterized=True)
plots.plotl(ax[iparam, 0], xr, [0., 0.], ls=':')
plots.plotc(ax[iparam, 1],
dr12['TEFF'][m1b],
dr14[param][m2b, iparam] - dr12[param][m1b, iparam],
dr12[param][m1b, 3],
size=1,
xr=xr,
yr=yr,
zr=[-1, 0.5],
xt=xt,
rasterized=True)
plots.plotl(ax[iparam, 1], xr, [0., 0.], ls=':')
plots.plotc(ax[iparam, 2],
dr13['TEFF'][m1c],
dr14[param][m2c, iparam] - dr13[param][m1c, iparam],
dr13[param][m1c, 3],
size=1,
xr=xr,
yr=yr,
zr=[-1, 0.5],
xt=xt,
rasterized=True)
plots.plotl(ax[iparam, 2], xr, [0., 0.], ls=':')
for iax in range(3):
ax[iparam, iax].tick_params(axis='both', labelsize=8)
# add colorbar
for fig in [figu, figc]:
cbaxes = fig.add_axes([0.91, 0.1, 0.01, 0.8])
cb = plt.colorbar(axim, cax=cbaxes)
cb.set_label('[M/H]')
cbaxes.tick_params(axis='both', labelsize=8)
figu.savefig('drcomp_uncal.pdf')
figc.savefig('drcomp_cal.pdf')
plots.close()
# abundance figure
fig, ax = plots.multi(3, 14, hspace=0.001, wspace=0.001, figsize=(8, 32))
for ielem, elem in enumerate([
'C', 'N', 'O', 'Na', 'Mg', 'Al', 'Si', 'S', 'K', 'Ca', 'Ti', 'V',
'Mn', 'Ni'
]):
print(elem)
yt = r'$\Delta$' + elem
if ielem == 13: xt = r'T$_{\rm eff}$'
else: xt = None
if ielem == 0:
ax[ielem, 0].text(0.5,
1.0,
'DR13-DR12',
transform=ax[ielem, 0].transAxes,
ha='center',
va='bottom')
ax[ielem, 1].text(0.5,
1.0,
'DR14-DR12',
transform=ax[ielem, 1].transAxes,
ha='center',
va='bottom')
ax[ielem, 2].text(0.5,
1.0,
'DR14-DR13',
transform=ax[ielem, 2].transAxes,
ha='center',
va='bottom')
yr = [-0.5, 0.5]
dr12elem = dr12[elem.upper() + '_H'][m1a] - dr12['FE_H'][m1a]
dr13elem = dr13[elem.upper() + '_FE'][m2a]
gd = np.where((dr12elem > -99) & (dr13elem > -99))[0]
plots.plotc(ax[ielem, 0],
dr12['TEFF'][m1a[gd]],
dr13elem[gd] - dr12elem[gd],
dr12['PARAM'][m1a[gd], 3],
size=1,
xr=[3500, 6000],
yr=yr,
zr=[-1, 0.5],
yt=yt,
xt=xt,
nytick=5,
rasterized=True)
plots.plotl(ax[ielem, 0], xr, [0., 0.], ls=':')
ax[ielem, 0].tick_params(axis='both', labelsize=8)
dr12elem = dr12[elem.upper() + '_H'][m1b] - dr12['FE_H'][m1b]
dr14elem = dr14[elem.upper() + '_FE'][m2b]
gd = np.where((dr12elem > -99) & (dr14elem > -99))[0]
plots.plotc(ax[ielem, 1],
dr12['TEFF'][m1b[gd]],
dr14elem[gd] - dr12elem[gd],
dr12['PARAM'][m1b[gd], 3],
size=1,
xr=[3500, 6000],
yr=yr,
zr=[-1, 0.5],
xt=xt,
nytick=5,
rasterized=True)
plots.plotl(ax[ielem, 1], xr, [0., 0.], ls=':')
ax[ielem, 1].tick_params(axis='both', labelsize=8)
dr13elem = dr13[elem.upper() + '_FE'][m1c]
dr14elem = dr14[elem.upper() + '_FE'][m2c]
gd = np.where((dr13elem > -99) & (dr14elem > -99))[0]
plots.plotc(ax[ielem, 2],
dr13['TEFF'][m1c[gd]],
dr14elem[gd] - dr13elem[gd],
dr13['PARAM'][m1c[gd], 3],
size=1,
xr=[3500, 6000],
yr=yr,
zr=[-1, 0.5],
xt=xt,
nytick=5,
rasterized=True)
plots.plotl(ax[ielem, 2], xr, [0., 0.], ls=':')
ax[ielem, 2].tick_params(axis='both', labelsize=8)
cbaxes = fig.add_axes([0.91, 0.1, 0.01, 0.8])
cb = plt.colorbar(axim, cax=cbaxes)
cb.set_label('[M/H]')
cbaxes.tick_params(axis='both', labelsize=8)
for item in (cbaxes.get_xticklabels() + cbaxes.get_yticklabels()):
item.set_fontsize(8)
fig.savefig('drcomp_elem.pdf')
def irfm(allstar,trange=[4000,5000],mhrange=[-2.5,0.75],out='dteff') :
'''
Compares allstar ASPCPAP Teff with various photometric Teff from JAJ compilation (SAGA, CL, TH, SFD)
Does fits
Args:
allstar : allStar structure
'''
# select stars
gd=apselect.select(allstar,badval=['STAR_BAD'],teff=trange,mh=mhrange,raw=True)
allstar=allstar[gd]
# get IRFM data
irfm=fits.open(os.environ['APOGEE_DIR']+'/data/calib/irfm_temp.fits')[1].data
# get the subsamples and match. Note that we have to do this separately for each subsample because some
# stars appear in more than one subsample
saga=np.where(irfm['SOURCE'] == 'SAGA')[0]
saga1,saga2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][saga]))
cl=np.where(irfm['SOURCE'] == 'CL')[0]
cl1,cl2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][cl]))
th=np.where(irfm['SOURCE'] == 'TH')[0]
th1,th2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][th]))
sfd=np.where(irfm['SOURCE'] == 'SFD')[0]
sfd1,sfd2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][sfd]))
# plot diff color-coded by gravity as f([M/H])
fig,ax=plots.multi(2,2,hspace=0.001,wspace=0.001)
xr=[-3.0,1.0]
yr=[-400,300]
zr=[3500,6000]
bins=np.arange(-2.5,0.75,0.25)
# SAGA
plots.plotc(ax[0,0],allstar['FPARAM'][saga1,3],allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]],allstar['FPARAM'][saga1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff')
mean=bindata(allstar['FPARAM'][saga1,3],allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]],bins)
plots.plotp(ax[0,0],bins,mean,marker='o',size=40)
ax[0,0].text(0.1,0.9,'SAGA',transform=ax[0,0].transAxes)
# CL
plots.plotc(ax[0,1],allstar['FPARAM'][cl1,3],allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]],allstar['FPARAM'][cl1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]')
mean=bindata(allstar['FPARAM'][cl1,3],(allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]]),bins)
plots.plotp(ax[0,1],bins,mean,marker='o',size=40)
ax[0,1].text(0.1,0.9,'CL',transform=ax[0,1].transAxes)
# TH
plots.plotc(ax[1,0],allstar['FPARAM'][th1,3],allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]],allstar['FPARAM'][th1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff')
mean=bindata(allstar['FPARAM'][th1,3],(allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]]),bins)
plots.plotp(ax[1,0],bins,mean,marker='o',size=40)
ax[1,0].text(0.1,0.9,'TH',transform=ax[1,0].transAxes)
# SFD
plots.plotc(ax[1,1],allstar['FPARAM'][sfd1,3],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],allstar['FPARAM'][sfd1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]')
mean=bindata(allstar['FPARAM'][sfd1,3],(allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]]),bins)
plots.plotp(ax[1,1],bins,mean,marker='o',size=40)
ax[1,1].text(0.1,0.9,'SFD',transform=ax[1,1].transAxes)
fig.savefig(out+'_mh.png')
# plot diff color-coded by gravity as f([M/H])
fig,ax=plots.multi(2,2,hspace=0.001,wspace=0.001)
zr=[-2.0,0.5]
yr=[-400,300]
xr=[6000,3500]
bins=np.arange(3500,5500,250)
# SAGA
plots.plotc(ax[0,0],allstar['FPARAM'][saga1,0],allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]],allstar['FPARAM'][saga1,3],zr=zr,xr=xr,yr=yr,xt='Teff',yt='ASPCAP-photometric Teff')
mean=bindata(allstar['FPARAM'][saga1,0],(allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]]),bins)
plots.plotp(ax[0,0],bins,mean,marker='o',size=40)
ax[0,0].text(0.1,0.9,'SAGA',transform=ax[0,0].transAxes)
# CL
plots.plotc(ax[0,1],allstar['FPARAM'][cl1,0],allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]],allstar['FPARAM'][cl1,3],zr=zr,xr=xr,yr=yr,xt='Teff')
mean=bindata(allstar['FPARAM'][cl1,0],(allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]]),bins)
plots.plotp(ax[0,1],bins,mean,marker='o',size=40)
ax[0,1].text(0.1,0.9,'CL',transform=ax[0,1].transAxes)
# TH
plots.plotc(ax[1,0],allstar['FPARAM'][th1,0],allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]],allstar['FPARAM'][th1,3],zr=zr,xr=xr,yr=yr,xt='Teff',yt='ASPCAP-photometric Teff')
mean=bindata(allstar['FPARAM'][th1,0],(allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]]),bins)
plots.plotp(ax[1,0],bins,mean,marker='o',size=40)
ax[1,0].text(0.1,0.9,'TH',transform=ax[1,0].transAxes)
# SFD
plots.plotc(ax[1,1],allstar['FPARAM'][sfd1,0],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],allstar['FPARAM'][sfd1,3],zr=zr,xr=xr,yr=yr,xt='Teff')
mean=bindata(allstar['FPARAM'][sfd1,0],(allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]]),bins)
plots.plotp(ax[1,1],bins,mean,marker='o',size=40)
ax[1,1].text(0.1,0.9,'SFD',transform=ax[1,1].transAxes)
fig.savefig(out+'_teff.png')
# do 2D fits with Teff and [M/H], and 1D fits with each
fig,ax=plots.multi(2,2,hspace=0.5,wspace=0.001)
ax[0,1].xaxis.set_visible(False)
ax[0,1].yaxis.set_visible(False)
pfit = fit.fit2d(ax[0,0],allstar['FPARAM'][sfd1,3],allstar['FPARAM'][sfd1,0],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],plot=True,zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\Delta Teff$')
pfit = fit.fit1d(ax[1,0],allstar['FPARAM'][sfd1,3],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],ydata=allstar['FPARAM'][sfd1,0],plot=True,zr=[-500,200],xt='[M/H]',yt='$\Delta Teff$',xr=[-2.7,0.9],yr=[3500,5000])
pfit = fit.fit1d(ax[1,1],allstar['FPARAM'][sfd1,0],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],ydata=allstar['FPARAM'][sfd1,3],plot=True,zr=[-500,200],xt='Teff',xr=[3900,5100],yr=[-2.5,0.5])
pdb.set_trace()
return pfit
def plotparamdiffs(data, bdata, title=None, cal=False, out=None, elem=True):
""" Plot parameter differences between two different runs
"""
a = data[1].data
b = bdata[1].data
paramnames, tagnames, flagnames = aspcap.params()
i1, i2 = match.match(a['APOGEE_ID'], b['APOGEE_ID'])
print('number of matching stars: ', len(i1))
# parameters first
if cal: param = 'PARAM'
else: param = 'FPARAM'
grid = []
yt = []
for i in range(8):
if i == 0:
yr = [-200, 200]
elif i == 1:
xr = [-0.5, 5]
yr = [-0.5, 0.5]
else:
xr = [-2.5, 1.0]
yr = [-0.5, 0.5]
row = []
yt.append(paramnames[i])
for j in range(3):
fig, ax = plots.multi(1, 2, hspace=0.001)
if j == 0:
plots.plotc(ax[0],
a['FPARAM'][i1, 0],
b['FPARAM'][i2, i] - a['FPARAM'][i1, i],
a['FPARAM'][i1, 3],
yt=r'$\Delta$' + tagnames[i],
xt='Teff',
yr=yr,
xr=[3000, 8000],
zr=[-2, 0.5])
plots.plotc(ax[1],
a['PARAM'][i1, 0],
b['PARAM'][i2, i] - a['PARAM'][i1, i],
a['PARAM'][i1, 3],
yt=r'$\Delta$' + tagnames[i],
xt='Teff',
yr=yr,
xr=[3000, 8000],
zr=[-2, 0.5])
elif j == 1:
plots.plotc(ax[0],
a['FPARAM'][i1, 1],
b['FPARAM'][i2, i] - a['FPARAM'][i1, i],
a['FPARAM'][i1, 3],
yt=r'$\Delta$' + tagnames[i],
xt='log g',
yr=yr,
xr=[-1, 6],
zr=[-2, 0.5])
plots.plotc(ax[1],
a['PARAM'][i1, 1],
b['PARAM'][i2, i] - a['PARAM'][i1, i],
a['PARAM'][i1, 3],
yt=r'$\Delta$' + tagnames[i],
xt='log g',
yr=yr,
xr=[-1, 6],
zr=[-2, 0.5])
elif j == 2:
plots.plotc(ax[0],
a['FPARAM'][i1, 3],
b['FPARAM'][i2, i] - a['FPARAM'][i1, i],
a['FPARAM'][i1, 3],
yt=r'$\Delta$' + tagnames[i],
xt='[M/H]',
yr=yr,
xr=[-2.5, 1.0],
zr=[-2, 0.5])
plots.plotc(ax[1],
a['PARAM'][i1, 3],
b['PARAM'][i2, i] - a['PARAM'][i1, i],
a['PARAM'][i1, 3],
yt=r'$\Delta$' + tagnames[i],
xt='[M/H]',
yr=yr,
xr=[-2.5, 1.0],
zr=[-2, 0.5])
ax[0].text(0.1, 0.9, 'Uncalibrated', transform=ax[0].transAxes)
ax[1].text(0.1, 0.9, 'Calibrated', transform=ax[1].transAxes)
if out is not None:
outfile = out + 'paramdiffs_{:1d}_{:1d}.png'.format(i, j)
fig.savefig(outfile)
row.append(os.path.basename(outfile))
else:
pdb.set_trace()
plt.close(fig)
grid.append(row)
ptab = html.table(grid, ytitle=yt)
html.htmltab(grid, file=out + 'paramdiffs.html', ytitle=yt)
# now elements
if elem:
grid = []
yt = []
elemnames = data[3].data['ELEM_SYMBOL'][0]
belemnames = bdata[3].data['ELEM_SYMBOL'][0]
if cal: elem = 'ELEM'
else: elem = 'FELEM'
for i, el in enumerate(elemnames):
ii = np.where(belemnames == el)[0]
if len(ii) > 0:
yr = [-0.5, 0.5]
row = []
yt.append(el)
if len(a[elem].shape) == 3: abun = a[elem][i1, 0, i]
else: abun = a[elem][i1, i]
if len(b[elem].shape) == 3: abun_b = b[elem][i2, 0, ii]
else: abun_b = b[elem][i2, i]
for j in range(3):
fig, ax = plots.multi(1, 2)
if j == 0:
plots.plotc(ax[0],
a['FPARAM'][i1, 0],
abun_b - abun,
a['FPARAM'][i1, 3],
yt=r'$\Delta$' + el,
xt='Teff',
yr=yr,
xr=[3000, 8000],
zr=[-2, 0.5])
plots.plotc(ax[1],
a['PARAM'][i1, 0],
abun_b - abun,
a['PARAM'][i1, 3],
yt=r'$\Delta$' + el,
xt='Teff',
yr=yr,
xr=[3000, 8000],
zr=[-2, 0.5])
elif j == 1:
plots.plotc(ax[0],
a['FPARAM'][i1, 1],
abun_b - abun,
a['FPARAM'][i1, 3],
yt=r'$\Delta$' + el,
xt='log g',
yr=yr,
xr=[-1, 6],
zr=[-2, 0.5])
plots.plotc(ax[1],
a['PARAM'][i1, 1],
abun_b - abun,
a['PARAM'][i1, 3],
yt=r'$\Delta$' + el,
xt='log g',
yr=yr,
xr=[-1, 6],
zr=[-2, 0.5])
elif j == 2:
plots.plotc(ax[0],
a['FPARAM'][i1, 3],
abun_b - abun,
a['FPARAM'][i1, 3],
yt=r'$\Delta$' + el,
xt='[M/H]',
yr=yr,
xr=[-2.5, 1.0],
zr=[-2, 0.5])
plots.plotc(ax[1],
a['PARAM'][i1, 3],
abun_b - abun,
a['PARAM'][i1, 3],
yt=r'$\Delta$' + el,
xt='[M/H]',
yr=yr,
xr=[-2.5, 1.0],
zr=[-2, 0.5])
ax[0].text(0.1,
0.9,
'Uncalibrated',
transform=ax[0].transAxes)
ax[1].text(0.1,
0.9,
'Calibrated',
transform=ax[1].transAxes)
if out is not None:
outfile = out + el + '_diff_{:1d}.png'.format(j)
fig.savefig(outfile)
row.append(os.path.basename(outfile))
else:
pdb.set_trace()
plt.close(fig)
grid.append(row)
etab = html.table(grid, ytitle=yt)
html.htmltab(grid, file=out + 'elemdiffs.html', ytitle=yt)
# HR diagrams
grid = []
row = []
aspcap.hr(a[i1], hard=out + 'hr_match1.png', grid=True, size=1)
row.append(os.path.basename(out + 'hr_match1.png'))
aspcap.hr(b[i2], hard=out + 'hr_match2.png', grid=True, size=1)
row.append(os.path.basename(out + 'hr_match2.png'))
grid.append(row)
row = []
aspcap.hr(a[i1],
hard=out + 'hrcal_match1.png',
param='PARAM',
grid=True,
size=1)
row.append(os.path.basename(out + 'hrcal_match1.png'))
aspcap.hr(b[i2],
hard=out + 'hrcal_match2.png',
param='PARAM',
grid=True,
size=1)
row.append(os.path.basename(out + 'hrcal_match2.png'))
grid.append(row)
hrtab = html.table(grid)
fp = html.head(out + 'diffs.html')
fp.write('<h2> HR diagrams, raw(top), calibrated(bottom)')
fp.write(hrtab)
fp.write('<h2> Parameter differences as f(Teff, logg, [M/H]')
fp.write(ptab)
fp.write('<h2> Abundance differences as f(Teff, logg, [M/H]')
if elem: fp.write(etab)
html.tail(fp)
return
def comp(file,
true=None,
truespec=None,
hard=False,
plot=False,
minchi2=0.,
testid=None,
rot=False):
""" Compare input parameters with output results
"""
if true is None: true = file + '.ipf'
if rot:
names = [
'id', 'vmicro', 'cm', 'nm', 'am', 'vrot', 'mh', 'logg', 'teff'
]
names_spm = [
'id', 'vmicro', 'cm', 'nm', 'am', 'vrot', 'mh', 'logg', 'teff',
'evmicro', 'ecm', 'enm', 'eam', 'evrot', 'emh', 'elogg', 'eteff',
'a', 'b', 'chi2'
]
else:
names = ['id', 'vmicro', 'cm', 'nm', 'am', 'mh', 'logg', 'teff']
names_spm = [
'id', 'vmicro', 'cm', 'nm', 'am', 'mh', 'logg', 'teff', 'evmicro',
'ecm', 'enm', 'eam', 'emh', 'elogg', 'eteff', 'a', 'b', 'chi2'
]
true = ascii.read(true, names=names)
##spec=np.loadtxt('test.dat')
obs = ascii.read(file + '.spm')
for i in range(len(names_spm)):
obs.rename_column('col{:d}'.format(i + 1), names_spm[i])
i1, i2 = match.match(true['id'], obs['id'])
# write out file with differences
f = open(file + '.out', 'w')
for i in range(len(i1)):
f.write(('{:<15s}' + '{:7.2f}' * 14 + '\n').format(
true[i1[i]]['id'], true[i1[i]]['teff'], true[i1[i]]['logg'],
true[i1[i]]['mh'], true[i1[i]]['am'], true[i1[i]]['cm'],
true[i1[i]]['nm'], true[i1[i]]['vmicro'],
obs[i2[i]]['teff'] - true[i1[i]]['teff'],
obs[i2[i]]['logg'] - true[i1[i]]['logg'],
obs[i2[i]]['mh'] - true[i1[i]]['mh'],
obs[i2[i]]['am'] - true[i1[i]]['am'],
obs[i2[i]]['cm'] - true[i1[i]]['cm'],
obs[i2[i]]['nm'] - true[i1[i]]['nm'],
obs[i2[i]]['vmicro'] - true[i1[i]]['vmicro']))
f.close()
# histogram of differences, in linear and log histograms
fig, ax = plots.multi(8,
2,
wspace=0.001,
hspace=0.001,
figsize=(12, 4),
xtickrot=60)
for iy, log in enumerate([False, True]):
ax[iy, 0].hist(dclip(obs[i2]['teff'] - true[i1]['teff'],
lim=[-200, 200]),
bins=np.arange(-200, 201, 10),
histtype='step',
log=log)
ax[iy, 0].set_xlabel('$\Delta$Teff')
ax[iy, 1].hist(dclip(obs[i2]['logg'] - true[i1]['logg']),
bins=np.arange(-0.5, 0.51, 0.01),
histtype='step',
log=log)
ax[iy, 1].set_xlabel('$\Delta$logg')
ax[iy, 2].hist(dclip(obs[i2]['mh'] - true[i1]['mh']),
bins=np.arange(-0.5, 0.51, 0.01),
histtype='step',
log=log)
ax[iy, 2].set_xlabel('$\Delta$[M/H]')
ax[iy, 3].hist(dclip(obs[i2]['am'] - true[i1]['am']),
bins=np.arange(-0.5, 0.51, 0.01),
histtype='step',
log=log)
ax[iy, 3].set_xlabel('$\Delta$[alpha/M]')
ax[iy, 4].hist(dclip(obs[i2]['cm'] - true[i1]['cm']),
bins=np.arange(-0.5, 0.51, 0.01),
histtype='step',
log=log)
ax[iy, 4].set_xlabel('$\Delta$[C/M]')
ax[iy, 5].hist(dclip(obs[i2]['nm'] - true[i1]['nm']),
bins=np.arange(-0.5, 0.51, 0.01),
histtype='step',
log=log)
ax[iy, 5].set_xlabel('$\Delta$[N/M]')
ax[iy, 6].hist(dclip(obs[i2]['vmicro'] - true[i1]['vmicro']),
bins=np.arange(-0.5, 0.51, 0.01),
histtype='step',
log=log)
ax[iy, 6].set_xlabel('$\Delta$vmicro')
ax[iy, 7].hist(dclip(10.**obs['chi2'], lim=[0, 50]),
bins=np.arange(0, 51, 0.1),
log=log)
ax[iy, 7].set_xlabel('chi2')
fig.suptitle(file)
if hard:
fig.savefig(file + '.png')
plt.close()
# plots of differences vs Teff, color-coded by various quantities
fig, ax = plots.multi(6,
7,
hspace=0.001,
wspace=0.001,
figsize=(16, 8),
xtickrot=60)
x = true['teff'][i1] + np.random.uniform(-45., 45., size=len(i1))
for ix in range(6):
yt = ''
if ix == 0:
z = true['logg'][i1]
tit = 'color: logg'
zr = [0, 5]
elif ix == 1:
z = true['mh'][i1]
tit = 'color: [M/H]'
zr = [-1.5, 0.5]
elif ix == 2:
z = true['mh'][i1] + true['am'][i1]
tit = 'color: [alpha/H]'
zr = [-1.5, 1.5]
elif ix == 3:
z = true['mh'][i1] + true['cm'][i1]
tit = 'color: [C/H]'
zr = [-1.5, 1.5]
elif ix == 4:
z = true['mh'][i1] + true['nm'][i1]
tit = 'color: [N/H]'
zr = [-1.5, 1.5]
elif ix == 5:
z = obs['chi2'][i2]
tit = 'color: chi2'
zr = [0, 10.]
ax[0, ix].set_title(tit)
if ix == 0:
plots.plotc(ax[0, ix],
x,
obs['teff'][i2] - true['teff'][i1],
z,
xt='Teff',
yt=r'$\Delta$Teff',
yr=[-1000, 1000],
zr=zr)
plots.plotc(ax[1, ix],
x,
obs['logg'][i2] - true['logg'][i1],
z,
xt='Teff',
yt=r'$\Delta$logg',
yr=[-2.0, 2.0],
zr=zr)
plots.plotc(ax[2, ix],
x,
obs['mh'][i2] - true['mh'][i1],
z,
xt='Teff',
yt=r'$\Delta$[M/H]',
yr=[-0.5, 0.5],
zr=zr)
plots.plotc(ax[3, ix],
x,
obs['am'][i2] - true['am'][i1],
z,
xt='Teff',
yt=r'$\Delta$[a/M]',
yr=[-0.5, 0.5],
zr=zr)
plots.plotc(ax[4, ix],
x,
obs['cm'][i2] - true['cm'][i1],
z,
xt='Teff',
yt=r'$\Delta$[C/M]',
yr=[-1.5, 1.5],
zr=zr)
plots.plotc(ax[5, ix],
x,
obs['nm'][i2] - true['nm'][i1],
z,
xt='Teff',
yt=r'$\Delta$[N/M]',
yr=[-1.5, 1.5],
zr=zr)
plots.plotc(ax[6, ix],
x,
10.**obs['vmicro'][i2] - 10.**true['vmicro'][i1],
z,
xt='Teff',
yt=r'$\Delta$vmicro',
yr=[-1.0, 1.0],
zr=zr)
else:
plots.plotc(ax[0, ix],
x,
obs['teff'][i2] - true['teff'][i1],
z,
xt='Teff',
yr=[-1000, 1000],
zr=zr)
plots.plotc(ax[1, ix],
x,
obs['logg'][i2] - true['logg'][i1],
z,
xt='Teff',
yr=[-2, 2],
zr=zr)
plots.plotc(ax[2, ix],
x,
obs['mh'][i2] - true['mh'][i1],
z,
xt='Teff',
yr=[-0.5, 0.5],
zr=zr)
plots.plotc(ax[3, ix],
x,
obs['am'][i2] - true['am'][i1],
z,
xt='Teff',
yr=[-0.5, 0.5],
zr=zr)
plots.plotc(ax[4, ix],
x,
obs['cm'][i2] - true['cm'][i1],
z,
xt='Teff',
yr=[-1.5, 1.5],
zr=zr)
plots.plotc(ax[5, ix],
x,
obs['nm'][i2] - true['nm'][i1],
z,
xt='Teff',
yr=[-1.5, 1.5],
zr=zr)
plots.plotc(ax[6, ix],
x,
10.**obs['vmicro'][i2] - 10.**true['vmicro'][i1],
z,
xt='Teff',
yr=[-1.0, 1.0],
zr=zr)
fig.suptitle(file)
plt.show()
if hard:
fig.savefig(file + '_2.png')
plt.close()
plt.show()
if plot:
pdb.set_trace()
obsspec = np.loadtxt(file + '.mdl')
if truespec is None: truespec = file + '.frd'
truespec = np.loadtxt(truespec)
if testid is None: testid = range(1, len(i1) + 1)
for tid in testid:
i = np.where(
np.core.defchararray.find(obs['id'][i2], 'test' +
str(tid)) >= 0)[0][0]
if 10.**obs['chi2'][i2[i]] > minchi2:
plt.clf()
plt.plot(truespec[i1[i], :], color='b')
plt.plot(obsspec[i2[i], :], color='r')
plt.plot(obsspec[i2[i], :] / truespec[i1[i], :] + 0.1,
color='g')
plt.draw()
print(true['id'][i1[i]])
print(
'{:8.1f}{:7.2f}{:7.2f}{:7.2f}{:7.2f}{:7.2f}{:7.2f}'.format(
true['teff'][i1[i]], true['logg'][i1[i]],
true['mh'][i1[i]], true['am'][i1[i]],
true['cm'][i1[i]], true['nm'][i1[i]],
true['vmicro'][i1[i]]))
print(
'{:8.1f}{:7.2f}{:7.2f}{:7.2f}{:7.2f}{:7.2f}{:7.2f}{:10.2f}'
.format(obs['teff'][i2[i]], obs['logg'][i2[i]],
obs['mh'][i2[i]], obs['am'][i2[i]],
obs['cm'][i2[i]], obs['nm'][i2[i]],
obs['vmicro'][i2[i]], obs['chi2'][i2[i]]))
pdb.set_trace()
def merge(planfile, fields=None, outfile=None, clobber=True):
'''
Match Cannon results to existing allStar file to make new table
'''
p = yanny.yanny(planfile, np=True)
apred = p['apred_vers'].strip("'")
apstar = p['apstar_vers'].strip("'")
aspcap_vers = p['aspcap_vers'].strip("'")
results = p['results_vers'].strip("'")
apl = apload.ApLoad(apred=apred,
apstar=apstar,
aspcap=aspcap_vers,
results=results)
a = apl.allStar()[1].data
t = Table(a)
out = Table()
out['APOGEE_ID'] = t['APOGEE_ID']
length = len(out)
if fields is None:
fields = glob('*/cannonField*.fits')
else:
fields = glob(fields)
c = fits.open(fields[0])[1].data
for i, name in enumerate(c.names):
print(name)
if name != 'APOGEE_ID' and name != 'model_flux' and name != 'fvec' and name != 'flux' and name != 'ivar':
out.add_column(Column(name=name, dtype=c.dtype[i], length=length))
print(name, type(out[name][0]))
if type(out[name][0]) is np.string_:
print('str!')
out[name] = ''
else:
out[name] = -9999.
# add X_M tag
#out.add_column(Column(name='X_M',dtype='{:d}f4'.format(len(a['X_M'])),length=length))
for field in fields:
print('field', field)
c = fits.open(field)[1].data
j1 = np.where(a['FIELD'] == c['FIELD'][0])[0]
i1, i2 = match.match(a['APOGEE_ID'][j1], c['APOGEE_ID'])
bd = np.where(c['chi_sq'][i2] <= 0.)[0]
print(len(bd))
for name in out.columns:
out[name][j1[i1]] = c[name][i2]
if type(c[name][0]) is np.string_:
bad = ''
else:
bad = -9999.
if name is not 'APOGEE_ID': out[name][j1[i1[bd]]] = bad
# for DR14, "fix" NA_H
bd = np.where(out['NA_H'] < -1)[0]
out['NA_H'][bd] = -9999.
out['CANNON_ID'] = t['ASPCAP_ID']
if outfile is None:
outfile = 'allStarCannon-' + results + '.fits'
prihdr = fits.Header()
prihdr['HISTORY'] = 'IDLWRAP_VERSION: ' + subprocess.check_output(
'idlwrap_version').strip('\n')
prihdu = fits.PrimaryHDU(header=prihdr)
hdu = fits.BinTableHDU.from_columns(np.array(out))
hdulist = fits.HDUList([prihdu, hdu])
hdulist.writeto(outfile, overwrite=clobber)
##out.write(outfile,overwrite=clobber)
return out
