def VALD(elem=None, xmin=3200., xmax=4800., outputdir=None):
"""
Request linelists from VALD for each ion seperately within a specific wavelength range.
elem = an array of ions e.g. ['CI','OII'], xmin and xmax: wavelength range in which the spectral lines are searched, outputdir = output directory chosen by the user.
If no elements are given, this function returns all of them.
@param elem: list of ions
@type elem: list of str
"""
if elem is None:
files = sorted(config.glob('VALD_individual', 'VALD_*.lijnen'))
elem = [
os.path.splitext(os.path.basename(ff))[0].split('_')[1]
for ff in files
]
all_lines = []
for i in range(len(elem)):
print elem[i]
filename = config.get_datafile('VALD_individual',
'VALD_' + elem[i] + '.lijnen')
if not os.path.isfile(filename):
logger.info('No data for element ' + str(elem[i]))
return None
newwav, newexc, newep, newgf = np.loadtxt(filename).T
lines = np.rec.fromarrays([newwav, newexc, newep, newgf],
names=['wavelength', 'ion', 'ep', 'gf'])
keep = (xmin <= lines['wavelength']) & (lines['wavelength'] <= xmax)
if not hasattr(keep, '__iter__'):
continue
lines = lines[keep]
if len(lines) and outputdir is not None:
ascii.write_array(lines,
outputdir + 'VALD_' + str(elem[i]) + '_' +
str(xmin) + '_' + str(xmax) + '.dat',
auto_width=True,
header=True,
formats=['%.3f', '%.1f', '%.3f', '%.3f'])
elif len(lines):
all_lines.append(lines)
else:
logger.debug('No lines of ' + str(elem[i]) +
' in given wavelength range')
return np.hstack(all_lines)
def update_info(zp=None):
"""
Update information in zeropoint file, e.g. after calibration.
Call first L{ivs.sed.model.calibrate} without arguments, and pass the output
to this function.
@param zp: updated contents from C{zeropoints.dat}
@type zp: recarray
"""
zp_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), "zeropoints.dat")
zp_, comms = ascii.read2recarray(zp_file, return_comments=True)
existing = [str(i.strip()) for i in zp_["photband"]]
resp_files = sorted(glob.glob(os.path.join(os.path.dirname(os.path.abspath(__file__)), "filters/*")))
resp_files = [os.path.basename(ff) for ff in resp_files if not os.path.basename(ff) in existing]
resp_files.remove("HUMAN.EYE")
resp_files.remove("HUMAN.CONES")
resp_files.remove("CONES.EYE")
if zp is None:
zp = zp_
logger.info("No new calibrations; previous information on existing response curves is copied")
else:
logger.info("Received new calibrations contents of zeropoints.dat will be updated")
# -- update info on previously non existing response curves
new_zp = np.zeros(len(resp_files), dtype=zp.dtype)
logger.info("Found {} new response curves, adding them with default information".format(len(resp_files)))
for i, respfile in enumerate(resp_files):
new_zp[i]["photband"] = respfile
new_zp[i]["eff_wave"] = float(eff_wave(respfile))
new_zp[i]["type"] = "CCD"
new_zp[i]["vegamag"] = np.nan
new_zp[i]["ABmag"] = np.nan
new_zp[i]["STmag"] = np.nan
new_zp[i]["Flam0_units"] = "erg/s/cm2/AA"
new_zp[i]["Fnu0_units"] = "erg/s/cm2/AA"
new_zp[i]["source"] = "nan"
zp = np.hstack([zp, new_zp])
sa = np.argsort(zp["photband"])
ascii.write_array(
zp[sa],
"zeropoints.dat",
header=True,
auto_width=True,
comments=["#" + line for line in comms[:-2]],
use_float="%g",
)
def search(ID,radius=1.,filename=None):
"""
Retrieve datafiles from the Coralie catalogue.
We search on coordinates, pulled from SIMBAD. If the star ID is not
recognised, a string search is performed to match the 'targ name' field in the
FITS headers.
Only the s1d_A data are searched.
@param ID: ID of the star, understandable by SIMBAD
@type ID: str
@param radius: search radius around the coordinates
@type radius: 1
@param filename: write summary to outputfile if not None
@type filename: str
@return: record array with summary information on the observations, as well
as their location (column 'filename')
@rtype: numpy rec array
"""
data = ascii.read2recarray(config.get_datafile(os.path.join('catalogs','coralie'),'CoralieFullDataOverview.tsv'),splitchar='\t')
info = sesame.search(ID)
if info:
ra,dec = info['jradeg'],info['jdedeg']
keep = np.sqrt((data['ra']-ra)**2 + (data['dec']-dec)**2) < radius/60.
else:
keep = [((re.compile(ID).search(objectn) is not None) and True or False) for objectn in data['object']]
keep = np.array(keep)
data = data[keep]
logger.info('Found %d spectra'%(len(data)))
if filename is not None:
ascii.write_array(data,filename,auto_width=True,header=True)
else:
return data
def VALD(elem=None,xmin=3200.,xmax=4800.,outputdir=None):
"""
Request linelists from VALD for each ion seperately within a specific wavelength range.
elem = an array of ions e.g. ['CI','OII'], xmin and xmax: wavelength range in which the spectral lines are searched, outputdir = output directory chosen by the user.
If no elements are given, this function returns all of them.
@param elem: list of ions
@type elem: list of str
"""
if elem is None:
files = sorted(config.glob('VALD_individual','VALD_*.lijnen'))
elem = [os.path.splitext(os.path.basename(ff))[0].split('_')[1] for ff in files]
all_lines = []
for i in range(len(elem)):
print elem[i]
filename = config.get_datafile('VALD_individual','VALD_' + elem[i] + '.lijnen')
if not os.path.isfile(filename):
logger.info('No data for element ' + str(elem[i]))
return None
newwav,newexc,newep,newgf = np.loadtxt(filename).T
lines = np.rec.fromarrays([newwav,newexc,newep,newgf],names=['wavelength','ion','ep','gf'])
keep = (xmin<=lines['wavelength']) & (lines['wavelength']<=xmax)
if not hasattr(keep,'__iter__'):
continue
lines = lines[keep]
if len(lines) and outputdir is not None:
ascii.write_array(lines,outputdir + 'VALD_' + str(elem[i]) + '_' + str(xmin) + '_' + str(xmax) + '.dat',auto_width=True,header=True,formats=['%.3f','%.1f','%.3f','%.3f'])
elif len(lines):
all_lines.append(lines)
else:
logger.debug('No lines of ' + str(elem[i]) + ' in given wavelength range')
return np.hstack(all_lines)
syn = [calibrator[0]] + list(syn)
obs = [calibrator[0]] + list(obs) + list(err)
synthetic.append(tuple(syn))
observed.append(tuple(obs))
#-- store in easy to use recarrays
dtype = [('name', 'a20')] + [(pb.split('.')[-1], 'f8') for pb in photbands]
synthetic = np.array(synthetic, dtype=dtype)
dtype = [('name', 'a20')] + [(pb.split('.')[-1] , 'f8') for pb in photbands] +\
[('e_'+pb.split('.')[-1] , 'f8') for pb in photbands]
observed = np.array(observed, dtype=dtype)
#-- write results to file
ascii.write_array(synthetic, synfile, sep=',')
ascii.write_array(observed, obsfile, sep=',')
else:
#-- load results from file
dtype = [('name', 'a20')] + [(pb.split('.')[-1], 'f8') for pb in photbands]
synthetic = ascii.read2recarray(synfile, splitchar=',', dtype=dtype)
dtype = [('name', 'a20')] + [(pb.split('.')[-1] , 'f8') for pb in photbands] +\
[('e_'+pb.split('.')[-1] , 'f8') for pb in photbands]
observed = ascii.read2recarray(obsfile, splitchar=',', dtype=dtype)
#-- add some minimal error is non is given observationaly
for band in ['e_' + pb.split('.')[-1] for pb in photbands]:
observed[band] = np.where(observed[band] <= minerror, minerror,
observed[band])
burntype[(burn <= 1e0)] = 0
burntype[(1e0 < burn) & (burn <= 1e3)] = 1
burntype[(1e3 < burn) & (burn <= 1e7)] = 2
burntype[(1e7 < burn)] = 3
x = starl[x]
#-- search for the limits of convection types
limits = [[burntype[0], x[0]]]
for i in range(1, len(x)):
if burntype[i] != limits[-1][0]:
limits[-1].append(x[i])
limits.append([burntype[i], x[i]])
limits[-1].append(x[-1])
alphas = [1.0, 0.5, 0.5, 0.5, 0.5]
colors = ['k', (1.00, 1.00, 0.0), (1.00, 0.50, 0.0), (1.00, 0.00, 0.0)]
for limit in limits:
if limit[0] == 0: continue
index = int(limit[0])
logger.info(
'Burning between %g-%g (%s,color=%s)' %
(limit[1], limit[2], ['Low', 'Mid', 'High'][index], colors[index]))
out = pl.axvspan(limit[1],
limit[2],
color=colors[index],
alpha=alphas[index])
#}
if __name__ == "__main__":
data = read_agsm('tempmodel_frq.gsm')
ascii.write_array(data, 'test.dat', header=True, auto_width=True)
def update_grid(gridfile,responses,threads=10):
"""
Add passbands to an existing grid.
"""
shutil.copy(gridfile,gridfile+'.backup')
hdulist = pyfits.open(gridfile,mode='update')
existing_responses = set(list(hdulist[1].columns.names))
responses = sorted(list(set(responses) - existing_responses))
if not len(responses):
hdulist.close()
print "No new responses to do"
return None
law = hdulist[1].header['REDLAW']
units = hdulist[1].header['FLUXTYPE']
teffs = hdulist[1].data.field('teff')
loggs = hdulist[1].data.field('logg')
ebvs = hdulist[1].data.field('ebv')
zs = hdulist[1].data.field('z')
rvs = hdulist[1].data.field('rv')
vrads = hdulist[1].data.field('vrad')
names = hdulist[1].columns.names
N = len(teffs)
index = np.arange(N)
output = np.zeros((len(responses),len(teffs)))
print N
#--- PARALLEL PROCESS
def do_process(teffs,loggs,ebvs,zs,rvs,index,arr):
output = np.zeros((len(responses)+1,len(teffs)))
c0 = time.time()
N = len(teffs)
for i,(teff,logg,ebv,z,rv,ind) in enumerate(zip(teffs,loggs,ebvs,zs,rvs,index)):
if i%100==0:
dt = time.time()-c0
print "ETA",index[0],(N-i)/100.*dt/3600.,'hr'
c0 = time.time()
#-- get model SED and absolute luminosity
model.set_defaults(z=z)
wave,flux = model.get_table(teff,logg)
Labs = model.luminosity(wave,flux)
flux_ = reddening.redden(flux,wave=wave,ebv=ebv,rtype='flux',law=law,Rv=rv)
#-- calculate synthetic fluxes
output[0,i] = ind
output[1:,i] = model.synthetic_flux(wave,flux_,responses,units=units)
arr.append(output)
#--- PARALLEL PROCESS
c0 = time.time()
manager = Manager()
arr = manager.list([])
all_processes = []
for j in range(threads):
all_processes.append(Process(target=do_process,args=(teffs[j::threads],\
loggs[j::threads],\
ebvs[j::threads],\
zs[j::threads],\
rvs[j::threads],\
index[j::threads],arr)))
all_processes[-1].start()
for p in all_processes:
p.join()
output = np.hstack([res for res in arr])
del arr
sa = np.argsort(output[0])
output = output[:,sa][1:]
ascii.write_array(np.rec.fromarrays(output,names=responses),'test.temp',header=True)
#-- copy old columns and append new ones
cols = []
for i,photband in enumerate(responses):
cols.append(pyfits.Column(name=photband,format='E',array=output[i]))
#-- create new table
table = pyfits.new_table(pyfits.ColDefs(cols))
table = pyfits.new_table(hdulist[1].columns + table.columns,header=hdulist[1].header)
hdulist[1] = table
hdulist.close()
def plot_add_burn(starl,x='q'):
burn = starl['eps_nuc']
burntype = np.zeros(len(burn))
burntype[(burn<=1e0)] = 0
burntype[(1e0<burn)&(burn<=1e3)] = 1
burntype[(1e3<burn)&(burn<=1e7)] = 2
burntype[(1e7<burn)] = 3
x = starl[x]
#-- search for the limits of convection types
limits = [[burntype[0],x[0]]]
for i in range(1,len(x)):
if burntype[i]!=limits[-1][0]:
limits[-1].append(x[i])
limits.append([burntype[i],x[i]])
limits[-1].append(x[-1])
alphas = [1.0,0.5,0.5,0.5,0.5]
colors = ['k',(1.00,1.00,0.0),(1.00,0.50,0.0),(1.00,0.00,0.0)]
for limit in limits:
if limit[0]==0: continue
index = int(limit[0])
logger.info('Burning between %g-%g (%s,color=%s)'%(limit[1],limit[2],
['Low','Mid','High'][index],
colors[index]))
out = pl.axvspan(limit[1],limit[2],color=colors[index],alpha=alphas[index])
#}
if __name__=="__main__":
data = read_agsm('tempmodel_frq.gsm')
ascii.write_array(data,'test.dat',header=True,auto_width=True)
def update_grid(gridfile, responses, threads=10):
"""
Add passbands to an existing grid.
"""
shutil.copy(gridfile, gridfile + '.backup')
hdulist = pyfits.open(gridfile, mode='update')
existing_responses = set(list(hdulist[1].columns.names))
responses = sorted(list(set(responses) - existing_responses))
if not len(responses):
hdulist.close()
print "No new responses to do"
return None
law = hdulist[1].header['REDLAW']
units = hdulist[1].header['FLUXTYPE']
teffs = hdulist[1].data.field('teff')
loggs = hdulist[1].data.field('logg')
ebvs = hdulist[1].data.field('ebv')
zs = hdulist[1].data.field('z')
rvs = hdulist[1].data.field('rv')
vrads = hdulist[1].data.field('vrad')
names = hdulist[1].columns.names
N = len(teffs)
index = np.arange(N)
output = np.zeros((len(responses), len(teffs)))
print N
#--- PARALLEL PROCESS
def do_process(teffs, loggs, ebvs, zs, rvs, index, arr):
output = np.zeros((len(responses) + 1, len(teffs)))
c0 = time.time()
N = len(teffs)
for i, (teff, logg, ebv, z, rv,
ind) in enumerate(zip(teffs, loggs, ebvs, zs, rvs, index)):
if i % 100 == 0:
dt = time.time() - c0
print "ETA", index[0], (N - i) / 100. * dt / 3600., 'hr'
c0 = time.time()
#-- get model SED and absolute luminosity
model.set_defaults(z=z)
wave, flux = model.get_table(teff, logg)
Labs = model.luminosity(wave, flux)
flux_ = reddening.redden(flux,
wave=wave,
ebv=ebv,
rtype='flux',
law=law,
Rv=rv)
#-- calculate synthetic fluxes
output[0, i] = ind
output[1:, i] = model.synthetic_flux(wave,
flux_,
responses,
units=units)
arr.append(output)
#--- PARALLEL PROCESS
c0 = time.time()
manager = Manager()
arr = manager.list([])
all_processes = []
for j in range(threads):
all_processes.append(Process(target=do_process,args=(teffs[j::threads],\
loggs[j::threads],\
ebvs[j::threads],\
zs[j::threads],\
rvs[j::threads],\
index[j::threads],arr)))
all_processes[-1].start()
for p in all_processes:
p.join()
output = np.hstack([res for res in arr])
del arr
sa = np.argsort(output[0])
output = output[:, sa][1:]
ascii.write_array(np.rec.fromarrays(output, names=responses),
'test.temp',
header=True)
#-- copy old columns and append new ones
cols = []
for i, photband in enumerate(responses):
cols.append(pyfits.Column(name=photband, format='E', array=output[i]))
#-- create new table
table = pyfits.new_table(pyfits.ColDefs(cols))
table = pyfits.new_table(hdulist[1].columns + table.columns,
header=hdulist[1].header)
hdulist[1] = table
hdulist.close()