def train_quadfit(\
trainingfilename=os.path.join(os.path.dirname(os.path.realpath(__file__)),
'cannon','training',
'training_apokasc_gc_ind_feh_fix.txt'),
outfilename=os.path.join(os.path.dirname(os.path.realpath(__file__)),
'cannon','trained',
'trained_apokasc_gc_ind_feh_fix.txt'),
baseline_labels=[4500.,2.,-0.3,0.05]):
"""
NAME:
train_quadfit
PURPOSE:
train a quadratic polynomial fit to training data
INPUT:
trainingfilename= name of the file that has the training data
outfilename= name of the file that will hold the output (scatter is in file with .txt replaced by _scatter.txt)
baseline_labels= baseline to subtract from the labels
OUTPUT:
(none; just writes the output to a file)
HISTORY:
2015-02-28 - Written - Bovy (IAS)
2018-02-05 - Updated to account for changing detector ranges - Price-Jones (UofT)
"""
# Read the training data
loc_ids, ap_ids, labels = _read_training(trainingfilename)
new_labels = (labels[0] - baseline_labels[0], )
for ii in range(1, len(labels)):
new_labels = new_labels + (labels[ii] - baseline_labels[ii], )
labels = new_labels
# Load the spectra for these data
aspcapBlu_start, aspcapGre_start, aspcapRed_start, aspcapTotal = _aspcapPixelLimits(
dr=None)
spec = numpy.empty((len(loc_ids), aspcapTotal))
specerr = numpy.empty((len(loc_ids), aspcapTotal))
for ii in range(len(loc_ids)):
spec[ii] = apread.aspcapStar(loc_ids[ii],
ap_ids[ii],
ext=1,
header=False,
aspcapWavegrid=True)
specerr[ii] = apread.aspcapStar(loc_ids[ii],
ap_ids[ii],
ext=2,
header=False,
aspcapWavegrid=True)
# Train
qout = cannon.quadfit(spec, specerr, *labels)
# Save to file
numpy.savetxt(outfilename, qout[0])
numpy.savetxt(outfilename.replace('.txt', '_scatter.txt'), qout[1])
numpy.savetxt(outfilename.replace('.txt', '_baseline_labels.txt'),
baseline_labels)
return None
def train_quadfit(\
trainingfilename=os.path.join(os.path.dirname(os.path.realpath(__file__)),
'cannon','training',
'training_apokasc_gc_ind_feh_fix.txt'),
outfilename=os.path.join(os.path.dirname(os.path.realpath(__file__)),
'cannon','trained',
'trained_apokasc_gc_ind_feh_fix.txt'),
baseline_labels=[4500.,2.,-0.3,0.05]):
"""
NAME:
train_quadfit
PURPOSE:
train a quadratic polynomial fit to training data
INPUT:
trainingfilename= name of the file that has the training data
outfilename= name of the file that will hold the output (scatter is in file with .txt replaced by _scatter.txt)
baseline_labels= baseline to subtract from the labels
OUTPUT:
(none; just writes the output to a file)
HISTORY:
2015-02-28 - Written - Bovy (IAS)
2018-02-05 - Updated to account for changing detector ranges - Price-Jones (UofT)
"""
# Read the training data
loc_ids, ap_ids, labels= _read_training(trainingfilename)
new_labels= (labels[0]-baseline_labels[0],)
for ii in range(1,len(labels)):
new_labels= new_labels+(labels[ii]-baseline_labels[ii],)
labels= new_labels
# Load the spectra for these data
aspcapBlu_start,aspcapGre_start,aspcapRed_start,aspcapTotal = _aspcapPixelLimits(dr=None)
spec= numpy.empty((len(loc_ids),aspcapTotal))
specerr= numpy.empty((len(loc_ids),aspcapTotal))
for ii in range(len(loc_ids)):
spec[ii]= apread.aspcapStar(loc_ids[ii],ap_ids[ii],ext=1,header=False,
aspcapWavegrid=True)
specerr[ii]= apread.aspcapStar(loc_ids[ii],ap_ids[ii],ext=2,
header=False,
aspcapWavegrid=True)
# Train
qout= cannon.quadfit(spec,specerr,*labels)
# Save to file
numpy.savetxt(outfilename,qout[0])
numpy.savetxt(outfilename.replace('.txt','_scatter.txt'),qout[1])
numpy.savetxt(outfilename.replace('.txt','_baseline_labels.txt'),
baseline_labels)
return None
def pixels_cannon(*args, **kwargs):
"""
NAME:
pixels_cannon
PURPOSE:
determine continuum pixels using a Cannon-like technique (Ness et al. 2015)
INPUT:
Either:
a) Input for running the apogee.spec.cannon:
spec - spectra to fit (nspec,nlambda)
specerrs - errors on the spectra (nspec,nlambda); assume no covariances
label1, label2, ... - labels (nspec); best to subtract reference values before running this
type= ('lin') type of Cannon to run:
'lin' - linear Cannon
'quad' - quadratic Cannon
b) Output from a previous Cannon run:
coefficients - coefficients from the fit (ncoeffs,nlambda)
scatter - scatter from the fit (nlambda)
KEYWORDS:
baseline_dev= (0.015) maximum deviation from baseline
label1_max= (10.**-5.) maximum deviation in first linear coefficient
label2_max= (0.006) similar for the second
label3_max= (0.012) similar for the third
labelN_max= same with default 0.03
...
scatter_max= (0.015) maximum scatter of residuals
dr= (module-wide default) data release
OUTPUT:
Boolean index into the wavelength range with True for continuum pixels
HISTORY:
2015-02-05 - Written - Bovy (IAS@KITP)
"""
# Grab kwargs
type = kwargs.pop('type', 'lin')
dr = kwargs.pop('dr', path._default_dr())
# Parse input
if len(args) == 0: # Use default fit
from apogee.spec._train_cannon import load_fit
coeffs, scatter, baseline_labels = load_fit()
type = 'quad'
else:
spec = args[0]
specerr = args[1]
# Determine the type of input
if len(specerr.shape) == 2:
# Run the Cannon
if type.lower() == 'lin':
coeffs, scatter = cannon.linfit(*args)
elif type.lower() == 'quad':
coeffs, scatter = cannon.quadfit(*args)
else:
coeffs = spec
scatter = specerr
ncoeffs = coeffs.shape[0]
if type.lower() == 'lin':
nlabels = ncoeffs - 1
elif type.lower() == 'quad':
nlabels = int((-3 + numpy.sqrt(9 + 8 * (ncoeffs - 1)))) // 2
# Determine continuum pixels
out = numpy.ones(len(scatter), dtype='bool')
# Deviation from baseline
out[numpy.fabs(coeffs[0] - 1.) > kwargs.get('baseline_dev', 0.015)] = False
# Large dependence on labels
maxs = numpy.zeros(nlabels)
maxs[0] = kwargs.get('label1_max', 10.**-5.)
maxs[1] = kwargs.get('label2_max', 0.006)
maxs[2] = kwargs.get('label3_max', 0.012)
for ii in range(nlabels - 3):
maxs[ii + 3] = kwargs.get('label%i_max' % (ii + 4), 0.03)
for ii in range(1, nlabels + 1):
out[numpy.fabs(coeffs[ii]) > maxs[ii - 1]] = False
# Large residuals
out[scatter > kwargs.get('scatter_max', 0.015)] = False
_, _, _, aspcapDR12length = _aspcapPixelLimits(dr='12')
if int(dr) > 12 and coeffs.shape[1] == aspcapDR12length:
# Want continuum pixels on >DR12 ASPCAP grid, but using coefficients
# from <= DR12 grid
dr_module = path._default_dr()
path.change_dr(12)
out = toApStarGrid(out)
path.change_dr(dr)
out = toAspcapGrid(out)
path.change_dr(dr_module)
return out
def pixels_cannon(*args,**kwargs):
"""
NAME:
pixels_cannon
PURPOSE:
determine continuum pixels using a Cannon-like technique (Ness et al. 2015)
INPUT:
Either:
a) Input for running the apogee.spec.cannon:
spec - spectra to fit (nspec,nlambda)
specerrs - errors on the spectra (nspec,nlambda); assume no covariances
label1, label2, ... - labels (nspec); best to subtract reference values before running this
type= ('lin') type of Cannon to run:
'lin' - linear Cannon
'quad' - quadratic Cannon
b) Output from a previous Cannon run:
coefficients - coefficients from the fit (ncoeffs,nlambda)
scatter - scatter from the fit (nlambda)
KEYWORDS:
baseline_dev= (0.015) maximum deviation from baseline
label1_max= (10.**-5.) maximum deviation in first linear coefficient
label2_max= (0.006) similar for the second
label3_max= (0.012) similar for the third
labelN_max= same with default 0.03
...
scatter_max= (0.015) maximum scatter of residuals
OUTPUT:
Boolean index into the wavelength range with True for continuum pixels
HISTORY:
2015-02-05 - Written - Bovy (IAS@KITP)
"""
# Grab kwargs
type= kwargs.pop('type','lin')
# Parse input
if len(args) == 0: # Use default fit
from apogee.spec._train_cannon import load_fit
coeffs, scatter, baseline_labels= load_fit()
type= 'quad'
else:
spec= args[0]
specerr= args[1]
# Determine the type of input
if len(specerr.shape) == 2:
# Run the Cannon
if type.lower() == 'lin':
coeffs, scatter= cannon.linfit(*args)
elif type.lower() == 'quad':
coeffs, scatter= cannon.quadfit(*args)
else:
coeffs= spec
scatter= specerr
ncoeffs= coeffs.shape[0]
if type.lower() == 'lin':
nlabels= ncoeffs-1
elif type.lower() == 'quad':
nlabels= int((-3+numpy.sqrt(9+8*(ncoeffs-1))))//2
# Determine continuum pixels
out= numpy.ones(len(scatter),dtype='bool')
# Deviation from baseline
out[numpy.fabs(coeffs[0]-1.) > kwargs.get('baseline_dev',0.015)]= False
# Large dependence on labels
maxs= numpy.zeros(nlabels)
maxs[0]= kwargs.get('label1_max',10.**-5.)
maxs[1]= kwargs.get('label2_max',0.006)
maxs[2]= kwargs.get('label3_max',0.012)
for ii in range(nlabels-3):
maxs[ii+3]= kwargs.get('label%i_max' % (ii+4),0.03)
for ii in range(1,nlabels+1):
out[numpy.fabs(coeffs[ii]) > maxs[ii-1]]= False
# Large residuals
out[scatter > kwargs.get('scatter_max',0.015)]= False
return out
def plot_afe_spectra(savename,plotname):
# Load the data
data= define_rcsample.get_rcsample()
data= data[data['SNR'] > 200.]
fehindx= (data['FE_H'] <= -0.35)*(data['FE_H'] > -0.45)
fehdata= data[fehindx]
# First compute the residuals and do the EM-PCA smoothing
if not os.path.exists(savename):
nspec= len(fehdata)
spec= numpy.zeros((nspec,7214))
specerr= numpy.zeros((nspec,7214))
for ii in range(nspec):
sys.stdout.write('\r'+"Loading spectrum %i / %i ...\r" % (ii+1,nspec))
sys.stdout.flush()
spec[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=1,header=False,aspcapWavegrid=True)
specerr[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=2,header=False,
aspcapWavegrid=True)
teffs= fehdata['FPARAM'][:,paramIndx('teff')]
loggs= fehdata['FPARAM'][:,paramIndx('logg')]
metals= fehdata[define_rcsample._FEHTAG]
cf, s, r= apcannon.quadfit(spec,specerr,
teffs-4800.,loggs-2.85,metals+0.3,
return_residuals=True)
pr= numpy.zeros_like(r)
# Deal w/ bad data
_MAXERR= 0.02
npca= 8
pca_input= r
pca_weights= (1./specerr**2.)
pca_weights[pca_weights < 1./_MAXERR**2.]= 0.
nanIndx= numpy.isnan(pca_input) + numpy.isnan(pca_weights)
pca_weights[nanIndx]= 0.
pca_input[nanIndx]= 0.
# Run EM-PCA
m= empca.empca(pca_input,pca_weights,nvec=npca,niter=25)#,silent=False)
for jj in range(nspec):
for kk in range(npca):
pr[jj]+= m.coeff[jj,kk]*m.eigvec[kk]
save_pickles(savename,pr,r,cf)
else:
with open(savename,'rb') as savefile:
pr= pickle.load(savefile)
# Now plot the various elements
colormap= cm.seismic
colorFunc= lambda afe: afe/0.25
widths= [3.5,2.]
yranges= [[-0.05,0.02],[-0.03,0.01]]
for ee, elem in enumerate(['S','Ca1']):
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),elem,)
kwargs= {'markLines':ii==4,
'yrange':yranges[ee],
'ylabel':'',
'cleanZero':False,
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':widths[ee]}
if ii>0: kwargs.pop('fig_width')
splot.windows(*args,**kwargs)
bovy_plot.bovy_end_print(plotname.replace('ELEM',
elem.lower().capitalize()))
# Also do Mg
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[3012,3120,3990],
'endindxs':[3083,3158,4012],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15745.017,15753.189,15770.055,15958.836],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':4.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Mg}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Mg'))
# Also do Si
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[4469, 4624,5171, 7205, 7843],
'endindxs':[4488, 4644,5182, 7243, 7871],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Si'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':6.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Si}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Si2'))
# Also do Oxygen
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startlams':[15558,16242,16536,16720],
'endlams':[15566,16250,16544,16728],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15562,16246,16539,16723.5],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':5.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{O}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','O'))
# Also do Ti
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[1116,2100,2899],
'endindxs':[1146,2124,2922],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Ti'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':3.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Ti}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Ti'))
return None
