def sens(model='apogee-dr14-giants.model', teff=4500, logg=3, mh=0., xh=False):
'''
Plot sensitivities of spectra to changes in Cannon labels
'''
model = tc.load_model(model)
# get label names
label_names = model.vectorizer.label_names
# set default parameters
if xh:
d0 = mh
else:
d0 = 0.
pars = [
teff, logg, mh, 0., mh, d0, d0, d0, d0, d0, d0, d0, d0, d0, d0, d0, d0,
d0, d0, d0, d0, d0, d0, d0
] #,d0]
synth0 = model.predict(pars)[0]
fig, ax = plots.multi(1, 1)
axalt = ax.twinx()
wave = 10.**(4.179 + 6.e-6 * np.arange(8575))
figs = []
for i, name in enumerate(label_names):
print(name)
delta = np.zeros(len(pars))
if i == 0:
delta[i] += 100.
else:
delta[i] += 0.1
synth = model.predict(pars + delta)[0]
ax.cla()
axalt.cla()
plots.plotl(ax, wave, synth - synth0, yr=[-0.10, 0.05])
ax.text(0.1, 0.9, name, transform=ax.transAxes)
if name not in ['TEFF', 'LOGG', 'M_H', 'ALPHA_M']:
elem = name.split('_')[0]
print(elem, ' ', name)
aspcap.elemsens([elem],
plot=axalt,
teff=teff,
logg=logg,
feh=mh,
smooth=2,
ylim=[0.05, -0.10])
file = name + '_sens.jpg'
fig.savefig('plots/' + file)
figs.append([file])
pdb.set_trace()
#pdb.set_trace()
html.htmltab(figs, file='plots/sens.html')
def compCframe(plate,
frame,
apred='test',
ratio=True,
rows=range(300),
yr=None,
hdu=1):
load = apload.ApLoad(apred=apred)
mjd = 55562 + int(frame // 10000)
new = load.apCframe('M67', plate, mjd, frame)
old = {}
fig, ax = plots.multi(1, 3, hspace=0.001)
x = np.arange(2048)
for ichip, chip in enumerate(chips):
old[chip] = fits.open(os.environ['APOGEE_REDUX'] +
'/r8/apo25m/{:d}/{:d}/apCframe-{:s}-{:d}.fits'.
format(plate, mjd, chip, frame))
for row in rows:
if ratio:
plots.plotl(ax[ichip],
x,
new[chip][hdu].data[row, :] /
old[chip][hdu].data[row, :],
yr=[0, 1.5])
else:
plots.plotl(ax[ichip], x, new[chip][hdu].data[row, :], yr=yr)
plots.plotl(ax[ichip], x, old[chip][hdu].data[row, :], yr=yr)
plots.plotl(ax[ichip],
x,
new[chip][hdu].data[row, :] -
old[chip][hdu].data[row, :],
yr=yr)
def comp1d(frame, apred='test', rows=range(300)):
load = apload.ApLoad(apred=apred)
new = load.ap1D(frame)
old = {}
mjd = 55562 + int(frame // 10000)
fig, ax = plots.multi(1, 3, hspace=0.001)
x = np.arange(2048)
for ichip, chip in enumerate(chips):
old[chip] = fits.open(
os.environ['APOGEE_REDUX'] +
'/r8/red/{:d}/ap1D-{:s}-{:d}.fits'.format(mjd, chip, frame))
for row in rows:
plots.plotl(ax[ichip],
x,
new[chip][1].data[row, :] / old[chip][1].data[row, :],
yr=[0, 1.5])
def plot(ax,
iso,
x,
y,
xr=None,
yr=None,
color=None,
dx=0.,
dy=0.,
isoadj=False,
alpha=None):
''' plotting routine that handles tip of RGB '''
mdiff = iso['mini'][0:-1] - iso['mini'][1:]
j = np.where(abs(mdiff) < 1.e-8)[0]
if len(j) > 0:
j = j[0]
if isoadj:
a = 116.
b = 155.
c = -22.
dx = a + b * iso['feh'][0:j] + c * iso['logg'][0:j]
line = plots.plotl(ax,
iso[x][0:j] + dx,
iso[y][0:j] + dy,
xr=xr,
yr=yr,
color=color,
alpha=alpha)
plots.plotl(ax,
iso[x][j + 1:],
iso[y][j + 1:],
color=line[0].get_color(),
alpha=alpha)
else:
line = plots.plotl(ax,
iso[x] + dx,
iso[y] + dy,
xr=xr,
yr=yr,
color=color,
alpha=alpha)
plt.draw()
def chip(a, row=150, ax=None, pixel=False, visit=False, color=None):
""" Routine to plot 3 chips in 3 panels
"""
if ax is None: fig, ax = plots.multi(1, 3, hspace=0.3)
if visit: rows = range(3)
else: rows = [row, row, row]
chips = ['a', 'b', 'c']
x = np.arange(a['a'][1].header['NAXIS1'])
for ichip, chip in enumerate(chips):
if pixel:
plots.plotl(ax[ichip],
x,
a[chip][1].data[rows[ichip], :],
color=color)
else:
plots.plotl(ax[ichip],
a[chip][4].data[rows[ichip], :],
a[chip][1].data[rows[ichip], :],
color=color)
try:
return fig, ax
except:
return
def correct(field,libfile,plot=True,write=None,width=151) :
""" Read raw FERRE files and create correction to normalization based on ratio
"""
lib=ferre.rdlibhead(libfile+'.hdr')
out=ferre.read(field,libfile+'.hdr')
nspec=out[1]['obs'].shape[0]
norm=copy.copy(out[1]['obs'])
if plot : fig,ax=plots.multi(1,3)
for i in range(nspec) :
p1=0
for ichip in range(3) :
npix=lib[1][ichip]['NPIX']
obs=out[1]['obs'][i,p1:p1+npix]
# replace bad pixels with median filtered value
med=median_filter(obs,[5*width],mode='nearest')
bd=np.where(obs < 0.01)[0]
obs[bd] = med[bd]
# get ratio of observed / model, make sure edge is reasonable number
mdl=out[1]['mdl'][i,p1:p1+npix]
ratio=obs/mdl
ratio[0]=np.median(ratio[0:9])
ratio[-1]=np.median(ratio[-9:0])
corr=median_filter(obs/mdl,[width],mode='nearest')
norm[i,p1:p1+npix] = corr
if plot :
ax[ichip].cla()
plots.plotl(ax[ichip],np.arange(npix),obs,yr=[0.95,1.05],color='r')
plots.plotl(ax[ichip],np.arange(npix),mdl,yr=[0.95,1.05],color='b')
plots.plotl(ax[ichip],np.arange(npix),obs/mdl,yr=[0.95,1.05],color='g')
plots.plotl(ax[ichip],np.arange(npix),corr,color='k',linewidth=3,yr=[0.95,1.05])
plt.draw()
plt.show()
p1+=npix
if plot : pdb.set_trace()
if write is not None:
# make sure we have no zeros
bd=np.where(norm.ravel() < 0.01)[0]
norm.ravel()[bd]=1.
ferre.writespec(write,norm)
return norm
def plot(self, ax, **kwargs):
for row in range(self.wave.shape[0]):
gd = np.where(self.mask[row, :] == False)[0]
plots.plotl(ax, self.wave[row, gd], self.data[row, gd], **kwargs)
def norm(w, coef, ichip):
x = w - 16000.
logflux = coef[0] * x**3 + coef[1] * x**2 + coef[2] * x
if ichip == 0: logflux += coef[3]
elif ichip == 2: logflux += coef[4]
return 10.**logflux
for row in rows[0:30:5]:
for ichip, chip in enumerate(chips):
x = b[chip][4].data[row, :]
plots.plotl(ax[0],
x,
b[chip][1].data[row, :],
color='k',
semilogy=True)
plots.plotl(ax[0],
x,
b[chip][1].data[row, :] / norm(x, w, ichip),
color=colors[ichip],
semilogy=True)
r10 = apload.ApLoad(apred='r10')
b = r10.ap1D(3190056)
for row in rows[0:30:5]:
plots.plotl(ax[1],
b['c'][4].data[row, :],
b['c'][1].data[row, :],
color='b',
def test(planfile,grid='GKg',npiece=12,npca=75,runraw=True,runpca=True,fit=True, fast=False, niso=None, sns=[1000], rot=False) :
""" Routine to set up and run a series of tests for a PCA library
Includes comparison of raw and PCA spectra for a sample suite,
and several FERRE runs to recover parameters for the sample suite
Requires existing test.ipf file with sample suite parameters
Args :
planfile (str) : name of input plan file that includes filename
npiece (int) : number of PCA pieces (for directory/file names)
npca (int) : number of PCA pieces (for directory/file names)
runraw (bool) : create the sample and input FERRE spectra (default=True)
runpca (bool) : create the PCA-derived spectra (default=True)
fit (bool) : do the raw-PCA FERRE runs and comparison (default=True)
fit (bool) : submit the FERRE test runs to queue (default=True)
fast (bool) : use the sdss-fast queue (default=False)
"""
# Read planfile and set output file name
if not os.path.isfile(planfile):
print('{:s} does not exist'.format(planfile))
return
p=yanny.yanny(planfile,np=True)
outfile=os.path.basename(p['name'])
# create test directory
try : os.mkdir('test')
except : pass
# create test sample for this grid
if runraw: sample.sample('test/test',gridclass=grid,niso=niso)
# raw and PCA library file root names
flib='f_aps'+outfile
plib='p_aps'+outfile+('_{:03d}_{:03d}').format(npiece,npca)
# make raw directory and setup to produce test spectra
prefix=liblink(flib,'test/raw/')
try: os.remove('test/raw/test.ipf')
except: pass
os.symlink(prefix+'test/test_'+grid+'.ipf','test/raw/test.ipf')
if runraw :
l=ferre.rdlibhead('f_aps'+outfile+'.hdr')[0]
ferre.writenml('test/raw/input.nml','test',l,nov=0,ncpus=1,f_access=1)
mkslurm.write('ferre.x',outdir='test/raw/',runplans=False,cwd=os.getcwd()+'/test/raw',fast=fast)
print('running ferre in raw to create spectra')
subprocess.call(['test/raw/ferre.x'],shell=False)
# create uncertainty spectra
true=np.loadtxt('test/raw/test.mdl')
ferre.writespec('test/raw/test.err',true*0.+0.001)
else :
true=np.loadtxt('test/raw/test.mdl')
# add noise
for sn in sns :
if sn < 1000 :
name = 'test/raw/testsn{:d}'.format(sn)
if not os.path.isfile(name+'.mdl') :
dim=true.shape
truen=true.flatten()+np.random.normal(0.,1./sn,true.flatten().shape)
truen=np.reshape(truen,(dim[0],dim[1]))
ferre.writespec(name+'.mdl',truen)
ferre.writespec(name+'.err',truen*0.+1./sn)
# list of all the different tests and their input.nml files; latter are set below
# first fdir is just for creating PCA
root='test/pca_{:d}_{:d}'.format(npiece,npca)
fdirs = [root+'/',root+'/algor3/',root+'/algor1/',root+'/algor1_12/',root+'/algor3_12/',
root+'/algor3_001/',root+'/algor3_01/',root+'/algor3_1',
root+'/algor3_12_01/',root+'/algor3_12_1',
root+'/algor3_indi1234567/',root+'/algor3_indi3142567/',
root+'/algor3_4d' ]
# general setup for all FERRE directories using PCA library
l=ferre.rdlibhead('p_aps'+outfile+('_{:03d}_{:03d}.hdr').format(npiece,npca))[0]
print("set up...")
for fdir in fdirs :
# create library links and return relative directory
print(fdir)
prefix=liblink(plib,fdir)
# create input ipf
cmds=[]
for sn in sns :
if sn < 1000 : name = fdir+'/testsn{:d}'.format(sn)
else : name = fdir+'/test'
# create input ipf
try: os.remove(name+'.ipf')
except: pass
os.symlink(prefix+'test/test_'+grid+'.ipf',name+'.ipf')
# create obs file with true spectra
try: os.remove(name+'.obs')
except: pass
os.symlink(prefix+'test/raw/'+os.path.basename(name)+'.mdl',name+'.obs')
# create uncertainty file
try: os.remove(name+'.err')
except: pass
os.symlink(prefix+'test/raw/'+os.path.basename(name)+'.err',name+'.err')
cmds.extend(['cp '+os.path.basename(name)+'.nml input.nml','ferre.x'])
# create the command file to run FERRE
mkslurm.write(cmds,name='ferre.x',outdir=fdir+'/',runplans=False,cwd=os.getcwd()+'/'+fdir,time='48:00:00',fast=fast)
# test-specific input.nml files
ferre.writenml(root+'/input.nml','test',l,nov=0,ncpus=1)
for sn in sns :
if sn < 1000 : name = 'testsn{:d}'.format(sn)
else : name = 'test'
ferre.writenml(root+'/algor3/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,init=1)
ferre.writenml(root+'/algor1_12/'+name+'.nml',name,l,algor=1,renorm=4,obscont=1,ncpus=32,indini=[1,1,1,1,2,2,3],init=1)
ferre.writenml(root+'/algor1/'+name+'.nml',name,l,algor=1,renorm=4,obscont=1,ncpus=32,init=1)
ferre.writenml(root+'/algor3_12/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,indini=[1,1,1,1,2,2,3],init=1)
ferre.writenml(root+'/algor3_001/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,stopcr=0.001,init=1)
ferre.writenml(root+'/algor3_01/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,stopcr=0.01,init=1)
ferre.writenml(root+'/algor3_12_01/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,stopcr=0.01,indini=[1,1,1,1,2,2,3],init=1)
ferre.writenml(root+'/algor3_1/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,stopcr=0.1,init=1)
ferre.writenml(root+'/algor3_12_1/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,stopcr=0.1,indini=[1,1,1,1,2,2,3],init=1)
ferre.writenml(root+'/algor3_indi1234567/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,indi=[1,2,3,4,5,6,7],init=1)
ferre.writenml(root+'/algor3_indi3142567/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,indi=[3,1,4,2,5,6,7],init=1)
ferre.writenml(root+'/algor3_4d/'+name+'.nml',name,l,algor=3,renorm=4,obscont=1,ncpus=32,indv=[4,5,6,7],init=1)
# produce PCA version of test spectra
if runpca :
print('running ferre in {:d}_{_d} to create spectra'.format(npiece,npca))
subprocess.call([root+'/ferre.x'],shell=False)
pca=np.loadtxt(root+'/test.mdl')
# histogram of ratio of pca to true
print("making pca/raw comparison histogram ...")
fig,ax=plots.multi(1,2)
hist,bins=np.histogram((pca/true).flatten(),bins=np.linspace(0.9,1.1,4001))
plots.plotl(ax[0],np.linspace(0.8005,1.2,4000),hist/hist.sum(),semilogy=True,xt='pca/true')
ax[1].hist(np.abs((pca-true).flatten()),bins=np.logspace(-7,3,50),histtype='step',normed=True,cumulative=True,color='k')
ax[1].set_xlim(0.,0.01)
ax[1].set_ylim(0.,1.0)
ax[1].set_xlabel('|pca-true|')
ax[1].set_ylabel('Cumulative fraction')
fig.tight_layout()
fig.savefig(root+'/'+outfile+'_pca.png')
plt.close()
# use the PCA libraries to fit raw spectra
print("running/making plots...")
tab=[]
yt=[]
# first fdir is just for creating PCA
for fdir in fdirs[1:] :
print(fdir)
if fit : subprocess.call(['sbatch','ferre.x'],shell=False,cwd=fdir)
xtab=[]
xt=[]
tmp=''
for sn in sns :
if sn < 1000 : name = fdir+'/testsn{:d}'.format(sn)
else : name = fdir+'/test'
try :
sample.comp(name,hard=True,true='test/raw/test.ipf',rot=rot)
dt=subprocess.check_output("grep ellapsed "+fdir+"/ferre.x.out | tail -1 | awk '{print $3}'",shell=True)
except :
print('failed comp: ',name)
dt='-1.'
xtab.extend(['../'+name+'.png','../'+name+'_2.png'])
xt.extend(['S/N = '+str(sn),'S/N = '+str(sn)])
tmp=tmp+dt+','
tab.append(xtab)
yt.append('<a href=../'+fdir+'>'+fdir+'</a><br>FERRE time: '+tmp+' s')
header = '<h3>'+outfile+'</h3>\n'
header = header + '<br> Test sample: <br><img src=test_'+grid+'.png width=40%> <img src=test_'+grid+'_2.png width=40%>'
header = header + '<br> Histogram of pixel ratios between raw and PCA for test sample:<br>'
header = header + '<img src=../'+root+'/'+outfile+'_pca.png width=40%>\n'
html.htmltab(tab,file='test/test.html',header=header,ytitle=yt,xtitle=xt)
for iplt, vp in enumerate(vplist):
a = fits.open('test' + t1 + suffix +
'_lsfcombo5/new_ap00cp00np00vp' + vp + '.fits')[0]
b = fits.open('test' + t2 + suffix +
'_lsfcombo5/new_ap00cp00np00vp' + vp + '.fits')[0]
# loop over 3 Teff, 3 logg, 3 [M/H]
for i in range(3):
for j in range(3):
for k in range(3):
if k == 0: color = 'r'
elif k == 1: color = 'g'
elif k == 2: color = 'b'
plots.plotl(ax[iplt],
wave_fits(a.header),
a.data[i, j, k, :] / b.data[i, j, k, :],
yr=[0.95, 1.05],
color=color)
ax[iplt].text(0.05,
0.9,
r'$v_{micro}= $' +
'{:3.1f}'.format(float(vp) / 10.),
transform=ax[iplt].transAxes,
va='top')
fig.savefig('dw/' + t1 + '_' + t2 + suffix + '.png')
plt.close(fig)
x.append('dw/' + t1 + '_' + t2 + suffix + '.png')
xlab.append('dw={:4.3f}'.format(float(t2) / 100.))
y.append(x)
ylab.append(t1)
def plot(teff,
logg,
mh,
meanfib,
v,
vr,
fit1d,
fit2d,
xr=[5500, 3500],
yr=[5, 0],
vt='vmicro'):
'''
auxiliary plotting routine for vmicro, vmacro plots
'''
fig = plt.figure(figsize=(14, 8))
y, x = np.mgrid[yr[1]:yr[0]:200j, xr[1]:xr[0]:200j]
ax = fig.add_subplot(3, 2, 1)
plots.plotc(ax,
teff,
logg,
10.**v,
xr=xr,
yr=yr,
zr=vr,
xt='Teff',
yt='log g',
zt=vt,
colorbar=True,
size=15)
ax.imshow(10.**fit2d(x, y),
extent=[xr[1], xr[0], yr[1], yr[0]],
aspect='auto',
vmin=0,
vmax=vr[1],
origin='lower')
ax = fig.add_subplot(3, 2, 2)
plots.plotc(ax,
teff,
logg,
10.**v,
xr=xr,
yr=yr,
zr=vr,
xt='Teff',
yt='log g',
zt=vt,
colorbar=True,
size=15)
ax.imshow(10.**fit1d(y),
extent=[xr[1], xr[0], yr[1], yr[0]],
aspect='auto',
vmin=0,
vmax=vr[1],
origin='lower')
ax = fig.add_subplot(3, 2, 3)
plots.plotc(ax,
logg,
10.**fit2d(teff, logg),
mh,
xr=[0, 5],
yr=vr,
zr=[-2.5, 0.5],
size=10,
colorbar=True,
xt='log g',
yt=vt,
zt='[M/H]')
ax = fig.add_subplot(3, 2, 4)
plots.plotc(ax,
logg,
10.**fit1d(logg),
mh,
xr=[0, 5],
yr=vr,
zr=[-2.5, 0.5],
size=10,
colorbar=True,
xt='log g',
yt=vt,
zt='[M/H]')
x = np.arange(0, 5, 0.01)
plots.plotl(ax, x,
10.**(0.226 - 0.0228 * x + 0.0297 * x**2 - 0.0113 * x**3))
ax = fig.add_subplot(3, 2, 5)
plots.plotc(ax,
logg,
10.**v,
mh,
xr=[0, 5],
yr=vr,
zr=[-2.5, 0.5],
size=10,
colorbar=True,
xt='log g',
yt=vt,
zt='[M/H]')
ax = fig.add_subplot(3, 2, 6)
plots.plotc(ax,
logg,
10.**v,
meanfib,
xr=[0, 5],
yr=vr,
zr=[0, 300],
size=10,
colorbar=True,
xt='log g',
yt=vt,
zt='<fiber>')
plt.show()
def all(ymlfile,
display=None,
plot=None,
verbose=True,
clobber=True,
wclobber=None,
groups='all'):
""" Reduce full night(s) of data given input configuration file
"""
# read input configuration file for reductions
f = open(ymlfile, 'r')
d = yaml.load(f, Loader=yaml.FullLoader)
f.close()
if type(groups) is not list: groups = [groups]
# loop over multiple groups in input file
for group in d['groups']:
if 'skip' in group:
if group['skip']: continue
if groups[0] != 'all' and group['name'] not in groups: continue
# clear displays if given
if display is not None: display.clear()
if plot is not None: plot.clf()
# set up Reducer, Combiner, and output directory
inst = group['inst']
print('Instrument: {:s}'.format(inst))
try:
red = imred.Reducer(inst=group['inst'],
dir=group['rawdir'],
verbose=verbose,
nfowler=group['nfowler'])
except KeyError:
red = imred.Reducer(inst=group['inst'],
dir=group['rawdir'],
verbose=verbose)
reddir = group['reddir'] + '/'
try:
os.makedirs(reddir)
except FileExistsError:
pass
#create superbiases if biases given
if 'biases' in group:
sbias = mkcal(group['biases'],
'bias',
red,
reddir,
clobber=clobber,
display=display)
else:
print('no bias frames given')
sbias = None
#create superdarks if darks given
if 'darks' in group:
sdark = mkcal(group['darks'],
'dark',
red,
reddir,
clobber=clobber,
display=display,
sbias=sbias)
else:
print('no dark frames given')
sdark = None
#create superflats if darks given
if 'flats' in group:
sflat = mkcal(group['flats'],
'flat',
red,
reddir,
clobber=clobber,
display=display,
sbias=sbias,
sdark=sdark)
else:
print('no flat frames given')
sflat = None
# create wavecals if arcs given
if 'arcs' in group:
# existing trace template
traces = pickle.load(
open(ROOT + '/data/' + inst + '/' + inst + '_traces.pkl',
'rb'))
if wclobber is None: wclobber = clobber
wavedict = {}
wavecals = group['arcs']
for wavecal in wavecals:
print('create wavecal : {:s}'.format(wavecal['id']))
# existing wavecal template
waves = pickle.load(
open(
ROOT + '/data/' + inst + '/' + wavecal['wref'] +
'.pkl', 'rb'))
if wclobber:
make = True
else:
make = False
try:
waves_all = pickle.load(
open(reddir + wavecal['id'] + '.pkl', 'rb'))
except FileNotFoundError:
make = True
if make:
# combine frames
try:
superbias = sbias[wavecal['bias']]
except KeyError:
superbias = None
arcs = red.sum(wavecal['frames'],
return_list=True,
superbias=superbias,
crbox=[5, 1],
display=display)
print(' extract wavecal')
# loop over channels
waves_all = []
for arc, wave, trace in zip(arcs, waves, traces):
# loop over windows
waves_channel = []
for iwind, (wcal,
wtrace) in enumerate(zip(wave, trace)):
try:
file = wavecal['file']
except KeyError:
file = None
# extract and ID lines
if wavecal['wavecal_type'] == 'echelle':
arcec = wtrace.extract(arc, plot=display)
wcal.identify(spectrum=arcec,
rad=3,
display=display,
plot=plot,
file=file)
elif wavecal['wavecal_type'] == 'longslit':
r0 = wtrace.rows[0]
r1 = wtrace.rows[1]
# 1d for inspection
wtrace.pix0 += 30
arcec = wtrace.extract(arc,
plot=display,
rad=20)
arcec.data = arcec.data - scipy.signal.medfilt(
arcec.data, kernel_size=[1, 101])
wcal.identify(spectrum=arcec,
rad=7,
plot=plot,
display=display,
lags=range(-500, 500),
file=file)
wcal.fit()
print("doing 2D wavecal...")
arcec = wtrace.extract2d(arc)
print(" remove continuum and smooth in rows")
arcec.data = arcec.data - scipy.signal.medfilt(
arcec.data, kernel_size=[1, 101])
# smooth vertically for better S/N, then sample accordingly
image.smooth(arcec, [5, 1])
wcal.identify(spectrum=arcec,
rad=3,
display=display,
plot=plot,
nskip=5,
lags=range(-50, 50))
wcal.fit()
delattr(wcal, 'ax')
delattr(wcal, 'fig')
waves_channel.append(wcal)
waves_all.append(waves_channel)
if display is not None: display.clear()
pickle.dump(waves_all,
open(reddir + wavecal['id'] + '.pkl', 'wb'))
if plot is not None: plot.clf()
else:
print(' already made!')
wavedict[wavecal['id']] = waves_all
else:
print('no wavecal frames given')
w = None
# reduce objects
if 'objects' in group:
objects = group['objects']
if 'image' in objects:
# images
for obj in objects['image']:
try:
superbias = sbias[obj['bias']]
except KeyError:
superbias = None
try:
superdark = sdark[obj['dark']]
except KeyError:
superdark = None
try:
superflat = sflat[obj['flat']]
except KeyError:
superflat = None
pdb.set_trace()
for iframe, id in enumerate(obj['frames']):
frames = red.reduce(id,
superbias=superbias,
superdark=superdark,
superflat=superflat,
scat=red.scat,
return_list=True,
crbox=red.crbox,
display=display)
elif 'extract1d' in objects:
# 1D spectra
traces = pickle.load(
open(ROOT + '/data/' + inst + '/' + inst + '_traces.pkl',
'rb'))
for obj in objects['extract1d']:
try:
superbias = sbias[obj['bias']]
except KeyError:
superbias = None
try:
superdark = sdark[obj['dark']]
except KeyError:
superdark = None
try:
superflat = sflat[obj['flat']]
except KeyError:
superflat = None
waves_all = wavedict[obj['wavecal']]
if obj['flat_type'] == '1d':
print('extracting 1d flat')
ecflat = []
for trace in traces:
tmp = []
for wtrace in trace:
shift = wtrace.find(superflat, plot=display)
tmp.append(
wtrace.extract(superflat,
plot=display,
medfilt=101))
ecflat.append(tmp)
superflat = None
# now frames
for iframe, id in enumerate(obj['frames']):
if display is not None: display.clear()
print("extracting object {}".format(id))
frames = red.reduce(id,
superbias=superbias,
superdark=superdark,
superflat=superflat,
scat=red.scat,
return_list=True,
crbox=red.crbox,
display=display)
if 'skyframes' in obj:
id = obj['skyframes'][iframe]
skyframes = red.reduce(id,
superbias=superbias,
superdark=superdark,
superflat=superflat,
scat=red.scat,
return_list=True,
crbox=red.crbox,
display=display)
for iframe, (frame, skyframe) in enumerate(
zip(frames, skyframes)):
header = frame.header
frames[iframe] = frame.subtract(skyframe)
frames[iframe].header = header
# extraction radius
try:
rad = obj['rad']
except KeyError:
rad = None
# retrace?
try:
retrace = obj['retrace']
except KeyError:
retrace = True
# initialize plots
if plot is not None:
plot.clf()
ax = []
ax.append(plot.add_subplot(2, 1, 1))
ax.append(plot.add_subplot(2, 1, 2, sharex=ax[0]))
plot.subplots_adjust(hspace=0.001)
# loop over channels
max = 0
for ichannel, (frame, wave, trace) in enumerate(
zip(frames, waves_all, traces)):
# loop over windows
for iwind, (wcal,
wtrace) in enumerate(zip(wave, trace)):
if retrace:
print(' retracing ....')
shift = wtrace.retrace(frame,
plot=display,
thresh=10)
else:
shift = wtrace.find(frame, plot=display)
ec = wtrace.extract(frame,
plot=display,
rad=rad)
w = wcal.wave(image=np.array(ec.data.shape))
if obj['flat_type'] == '1d':
header = ec.header
ec = ec.divide(ecflat[ichannel][iwind])
ec.header = header
if plot is not None:
gd = np.where(ec.mask == False)
med = np.median(ec.data[gd[0], gd[1]])
max = np.max([
max,
scipy.signal.medfilt(
ec.data, [1, 101]).max()
])
for row in range(ec.data.shape[0]):
gd = np.where(
ec.mask[row, :] == False)[0]
plots.plotl(ax[0],
w[row, gd],
ec.data[row, gd],
yr=[0, 1.2 * max],
xt='Wavelength',
yt='Flux')
plots.plotl(
ax[1],
w[row, gd],
ec.data[row, gd] /
ec.uncertainty.array[row, gd],
xt='Wavelength',
yt='S/N')
plot.suptitle(ec.header['OBJNAME'])
plt.draw()
plot.canvas.draw_idle()
plt.pause(0.1)
input(" hit a key to continue")
#ec.write(reddir+ec.header['FILE'].replace('.fits','.ec.fits'),overwrite=True)
comb = wcal.scomb(ec,
10.**np.arange(3.5, 4.0, 5.5e-6),
average=False,
usemask=False)
plots.plotl(ax[0],
10.**np.arange(3.5, 4.0, 5.5e-6),
comb.data,
color='k')
plots.plotl(ax[1],
10.**np.arange(3.5, 4.0, 5.5e-6),
comb.data / comb.uncertainty.array,
color='k')
out = spectra.SpecData(ec, wave=w)
out.write(
reddir +
ec.header['FILE'].replace('.fits', '.ec.fits'))
out = spectra.SpecData(comb, wave=w)
out.write(reddir + comb.header['FILE'])
pdb.set_trace()
elif 'extract2d' in objects:
# 2D spectra
print('extract2d')
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')
print('y6: ')
lsfsum(y6)
y7 = load.apLSF(25560065)[chip]
print('y7: ')
lsfsum(y7)
ii = 0
for ipar in range(26):
iy = ipar % 3
ix = ipar // 3
pax[iy, ix].plot(y1[0].data[ii, :])
ii += 1
for icol in range(4):
for fiber in [150]:
col = 500 + icol * 500
n = y1[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y1[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y2[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y2[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y3[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y3[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y4[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y4[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y5[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y5[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y6[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y6[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
def ghb(allstar,glatmin=30.,ebvmax=0.03,trange=[3750,5500],loggrange=[-1,6],mhrange=[-2.5,0.75],alpha=False,out='teffcomp',yr=[-500,500],
calib=False,dr13=False,grid=None,cmap='rainbow') :
"""
Compares allstar ASPCPAP Teff with photometric Teff from GHB for sample of stars with GLAT>glatmin and SFD_EBV<ebvmax,
does fits
Args:
allstar : allStar structure
Keyword args:
glatmin (float) : minimum GLAT for sample (default=30)
ebvmax (float) : maximum SFD_EBV for sample (default=0.03)
dwarf (bool) : use dwarfs and dwarf GHB (default = False)
"""
# select data to use
if 'TARGFLAGS' in allstar.columns.names : badtarg = ['EMBEDDED','EXTENDED']
else : badtarg = None
gd=apselect.select(allstar,badval=['STAR_BAD'],badtarg=badtarg,teff=trange,mh=mhrange,logg=loggrange,raw=True)
allstar=allstar[gd]
#if dr13 :
# j=np.where((abs(allstar['GLAT'])>glatmin)&(allstar['SFD_EBV']<ebvmax))[0]
#else :
j=np.where((abs(allstar['GLAT'])>glatmin)&(allstar['SFD_EBV']>-0.01)&(allstar['SFD_EBV']<ebvmax)&(abs(allstar['J'])<90)&(abs(allstar['K'])<90))[0]
# remove second gen GC stars
#if not dr13 :
gcstars = ascii.read(os.environ['APOGEE_DIR']+'/data/calib/gc_szabolcs.dat')
bd=np.where(gcstars['pop'] != 1)[0]
j = [x for x in j if allstar[x]['APOGEE_ID'] not in gcstars['id'][bd]]
allstar=allstar[j]
ghb,dtdjk=cte_ghb(allstar['J']-allstar['K'],allstar['FPARAM'][:,3],dwarf=False)
ghb_dwarf,dtdjk_dwarf=cte_ghb(allstar['J']-allstar['K'],allstar['FPARAM'][:,3],dwarf=True)
# use dwarf relation for dwarfs
dw=np.where(allstar['FPARAM'][:,1] > 3.8)[0]
ghb[dw]=ghb_dwarf[dw]
dtdjk[dw]=dtdjk_dwarf[dw]
gd=np.where(abs(allstar['FPARAM'][:,0]-ghb) < 500)[0]
ghb=ghb[gd]
dtdjk=dtdjk[gd]
allstar=allstar[gd]
print('Teff calibration, number of stars: ', len(allstar))
if calib :
param='PARAM'
teff=allstar[param][:,0]
logg=allstar[param][:,1]
mh=allstar[param][:,3]
am=allstar[param][:,6]
elif grid is None :
param='FPARAM'
teff=allstar[param][:,0]
logg=allstar[param][:,1]
mh=allstar[param][:,3]
am=allstar[param][:,6]
else :
param='FPARAM_CLASS'
teff=allstar[param][:,grid,0]
logg=allstar[param][:,grid,1]
mh=allstar[param][:,grid,3]
am=allstar[param][:,grid,6]
out=out+'_grid{:1d}'.format(grid)
# plot Teff difference against metallicity, color-code by temperature
fig,ax=plots.multi(1,1,hspace=0.001,wspace=0.001,figsize=(12,6))
xr=[-3.0,1.0]
zr=trange
if dr13: zr=[3500,5500]
binsize=0.25
bins=np.arange(-2.5,0.75,binsize)
# diff color-coded by gravity as f([M/H])
if alpha :
plots.plotc(ax,mh,teff-ghb,am,zr=[-0.1,0.4],xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt=r'[$\alpha$/M]',rasterized=True,cmap=cmap)
else :
plots.plotc(ax,mh,teff-ghb,teff,xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt='$T_{eff}$',rasterized=True,zr=trange,cmap=cmap)
mean=bindata(mh,teff-ghb,bins,median=False)
if not dr13: plots.plotp(ax,bins+binsize/2.,mean,marker='o',size=40)
mean=bindata(mh,teff-ghb,bins,median=True)
if not dr13: plots.plotp(ax,bins+binsize/2.,mean,marker='o',size=40,color='b')
ax.text(0.1,0.9,'E(B-V)<{:6.2f}'.format(ebvmax),transform=ax.transAxes)
gd=np.where(np.isfinite(mean))[0]
tefit = fit.fit1d(bins[gd]+binsize/2.,mean[gd],degree=2,reject=0)
# 1D quadratic fit as a function of metallicity
allfit = fit.fit1d(mh,teff-ghb,ydata=teff,degree=2,reject=0)
fig2,ax2=plots.multi(1,1)
tefit2 = fit.fit2d(mh,teff,teff-ghb,reject=0,plot=ax2,zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\Delta Teff$')
#pfit = fit.fit2d(allstar[param][:,3],allstar[param][:,0],allstar[param][:,0]-ghb,plot=ax[0,0],zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\Delta Teff$')
#ejk=np.clip(np.sqrt(allstar['J_ERR']**2+allstar['K_ERR']**2),0.,0.02)
#errpar = err.errfit(teff,allstar['SNR'],mh,teff-tefit(mh)-ghb,title='Teff',out=out+'_phot',zr=[0,250],meanerr=abs(dtdjk)*ejk)
errpar = err.errfit(teff,allstar['SNR'],mh,teff-tefit(mh)-ghb,title='Teff',out=out,zr=[0,150])
x=np.linspace(-3,1,200)
rms = (teff-tefit(mh)-ghb).std()
if dr13:
plots.plotl(ax,x,-36.17+95.97*x-15.09*x**2,color='k')
print(allfit)
else :
plots.plotl(ax,x,tefit(x),color='k')
ax.text(0.98,0.9,'rms: {:6.1f}'.format(rms),transform=ax.transAxes,ha='right')
cmap = matplotlib.cm.get_cmap(cmap)
for t in np.arange(trange[0],trange[1],500.) :
rgba=cmap((t-trange[0])/(trange[1]-trange[0]))
y=x*0.+t
plots.plotl(ax,x,tefit2(x,y),color=rgba)
plots._data_x = mh
plots._data_y = teff-ghb
plots._data = allstar
plots.event(fig)
# separate fits for low/hi alpha/M if requested
if alpha :
gdlo=apselect.select(allstar,badval=['STAR_BAD'],teff=trange,mh=mhrange,logg=[0,3.8],alpha=[-0.1,0.1],raw=True)
mean=bindata(mh[gdlo],teff[gdlo]-ghb[gdlo],bins)
plots.plotp(ax,bins,mean,marker='o',size=40,color='g')
tmpfit = fit.fit1d(mh[gdlo],teff[gdlo]-ghb[gdlo],ydata=teff[gdlo],degree=2)
plots.plotl(ax,x,tmpfit(x))
print('low alpha: ', len(gdlo))
gdhi=apselect.select(allstar,badval=['STAR_BAD'],teff=trange,mh=mhrange,logg=[0,3.8],alpha=[0.1,0.5],raw=True)
mean=bindata(mh[gdhi],teff[gdhi]-ghb[gdhi],bins)
plots.plotp(ax,bins,mean,marker='o',size=40,color='b')
tmpfit = fit.fit1d(mh[gdhi],teff[gdhi]-ghb[gdhi],ydata=teff[gdhi],degree=2)
plots.plotl(ax,x,tmpfit(x))
print('hi alpha: ', len(gdhi))
fig.tight_layout()
fig.savefig(out+'.png')
plt.close()
plt.rc('font',size=14)
plt.rc('axes',titlesize=14)
plt.rc('axes',labelsize=14)
fig.savefig(out+'.pdf')
plt.close()
# auxiliary plots with different color-codings
try:
meanfib=allstar['MEANFIB']
except:
meanfib=teff*0.
fig,ax=plots.multi(2,2,hspace=0.001,wspace=0.001)
plots.plotc(ax[0,0],mh,teff-ghb,logg,zr=[0,5],xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt='log g')
plots.plotc(ax[0,1],mh,teff-ghb,meanfib,zr=[0,300],xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt='mean fiber')
pfit = fit.fit1d(mh,teff-ghb,ydata=teff,plot=ax[1,0],zr=[-500,200],xt='[M/H]',yt='$\Delta Teff$',xr=[-2.7,0.9],yr=[3500,5000],colorbar=True,zt='Teff')
pfit = fit.fit1d(teff,teff-ghb,ydata=mh,plot=ax[1,1],zr=[-500,200],xt='Teff',xr=trange,yr=[-2.5,0.5],colorbar=True,zt='[M/H]')
fig.tight_layout()
fig.savefig(out+'_b.png')
plt.close()
# do some test 2D and 1D fits and plots
#fig,ax=plots.multi(2,2,hspace=0.5,wspace=0.001)
#ax[0,1].xaxis.set_visible(False)
#ax[0,1].yaxis.set_visible(False)
#pfit = fit.fit2d(allstar[param][:,3],allstar[param][:,0],allstar[param][:,0]-ghb,plot=ax[0,0],zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\Delta Teff$')
#pfit = fit.fit1d(allstar[param][:,3],allstar[param][:,0]-ghb,ydata=allstar[param][:,0],plot=ax[1,0],zr=[-500,200],xt='[M/H]',yt='$\Delta Teff$',xr=[-2.7,0.9],yr=[3500,5000])
#pfit = fit.fit1d(allstar[param][:,0],allstar[param][:,0]-ghb,ydata=allstar[param][:,3],plot=ax[1,1],zr=[-500,200],xt='Teff',xr=[3900,5100],yr=[-2.5,0.5])
plt.draw()
return {'caltemin': 3000., 'caltemax': 10000., 'temin' : trange[0], 'temax': trange[1],
'mhmin': mhrange[0], 'mhmax' : mhrange[1],
'par': tefit.parameters, 'rms' :rms, 'par2d': tefit2.parameters, 'errpar' : errpar}
def comp(plate=7267, mjd=56654, fiber=150, frame=10920059, field='M67'):
r11 = apload.ApLoad(apred='r11')
v = r11.apVisit(plate, mjd, fiber)
a = r11.ap1D(frame)
c = r11.apCframe(field, plate, mjd, frame)
v14 = fits.open(os.environ['APOGEE_REDUX'] +
'/r8/apo25m/{:d}/{:d}/apVisit-r8-{:d}-{:d}-{:03d}.fits'.
format(plate, mjd, plate, mjd, fiber))
a14 = {}
c14 = {}
for chip in chips:
a14[chip] = fits.open(
os.environ['APOGEE_REDUX'] +
'/r8/red/{:d}/ap1D-{:s}-{:d}.fits'.format(mjd, chip, frame))
c14[chip] = fits.open(os.environ['APOGEE_REDUX'] +
'/r8/apo25m/{:d}/{:d}/apCframe-{:s}-{:08d}.fits'.
format(plate, mjd, chip, frame))
fig, ax = plots.multi(1, 3, hspace=0.01)
x = np.arange(4096)
pixmask = bitmask.PixelBitMask()
for ichip, chip in enumerate(chips):
y = v[1].data[ichip, :]
plots.plotl(ax[ichip], x, v[1].data[ichip, :] / v14[1].data[ichip, :])
bd = np.where(((v[3].data[ichip, :] & pixmask.badval()) > 0) | (
(v[3].data[ichip, :] & pixmask.getval('SIG_SKYLINE')) > 0))[0]
y[bd] = np.nan
plots.plotl(ax[ichip], x, y / v14[1].data[ichip, :])
fig, ax = plots.multi(3, 3, hspace=0.01)
x = np.arange(2048)
for ichip, chip in enumerate(chips):
plots.plotl(ax[ichip, 0], x, c[chip][1].data[300 - fiber, :])
plots.plotl(ax[ichip, 0], x, c14[chip][1].data[300 - fiber, :])
plots.plotl(
ax[ichip, 1], x,
c[chip][1].data[300 - fiber, :] / c14[chip][1].data[300 - fiber])
plots.plotl(
ax[ichip, 2], x,
a[chip][1].data[300 - fiber, :] / a14[chip][1].data[300 - fiber])
