def initializeStandards(spt,
sdss=True,
reset=False,
sd=False,
esd=False,
usd=False,
beta=False,
giant=False,
gamma=False,
verbose=False):
if isinstance(spt, list) == False: spt = [spt]
if isinstance(spt[0], float) == True or isinstance(spt[0], int) == True:
if len(spt) == 2: spt = numpy.arange(spt[0], spt[1] + 1)
spt = [kastredux.typeToNum(s) for s in spt]
if sd == True and 'sd' not in spt[0]: spt = ['sd' + s for s in spt]
if esd == True and 'esd' not in spt[0]: spt = ['esd' + s for s in spt]
if usd == True and 'usd' not in spt[0]: spt = ['usd' + s for s in spt]
if beta == True and 'b' not in spt[0]: spt = [s + 'b' for s in spt]
if gamma == True and 'g' not in spt[0]: spt = [s + 'g' for s in spt]
if giant == True and 'I' not in spt[0]: spt = [s + 'I' for s in spt]
for s in spt:
if s not in list(kastredux.SPTSTDS.keys()) or reset == True:
f = numpy.array(
glob.glob('{}/{}*.txt'.format(kastredux.SPTSTDFOLDER,
s.replace('.', ''))))
if len(f) > 1:
if sdss == True and len(f[['SDSS' in x for x in f]]) > 0:
f = f[['SDSS' in x for x in f]]
# for subdwarfs
elif (sd == True or esd == True
or usd == True) and len(f[['lepine2007' in x
for x in f]]) > 0:
# print(len(f))
f = f[['lepine2007' in x for x in f]]
else:
pass
if len(f) > 0:
kastredux.SPTSTDS[s] = kastredux.readSpectrum(
f[0], name='{} STD'.format(s))
else:
if verbose == True:
print(
'Warning: cannot find a spectral standard for type {}'.
format(s))
return
def classify_by_standard(spec, ref='lepine', plot=False, diag_path=None):
lepine_str = 'lepine'
kirkpatrick_str = 'kirkpatrick'
burgasser_str = 'burgasser'
sdss_str = 'SDSS'
all_str = 'all'
STANDARD_SPECTRA = []
LEPINE_STANDARDS = []
KIRKPATRICK_STANDARDS = []
BURGASSER_STANDARDS = []
SDSS_STANDARDS = []
STANDARD_PATHS = glob.glob(kastredux.SPTSTDFOLDER + '*.txt')
for path in STANDARD_PATHS:
name = os.path.basename(os.path.splitext(path)[0])
stand_spec = kastredux.readSpectrum(path)
stand_spec.name = name
STANDARD_SPECTRA.append(stand_spec)
if (lepine_str in name):
LEPINE_STANDARDS.append(stand_spec)
elif (kirkpatrick_str in name):
KIRKPATRICK_STANDARDS.append(stand_spec)
elif (burgasser_str in name):
BURGASSER_STANDARDS.append(stand_spec)
elif sdss_str in name:
SDSS_STANDARDS.append(stand_spec)
minimal = 1e90
minimal_scale = None
minimal_standard = None
minimal_standard_name = None
COMPARISON = []
if (ref in lepine_str):
COMPARISON = LEPINE_STANDARDS
elif (ref in kirkpatrick_str):
COMPARISON = KIRKPATRICK_STANDARDS
elif (ref in burgasser_str):
COMPARISON = BURGASSER_STANDARDS
elif ref in sdss_str:
COMPARISON = SDSS_STANDARDS
else:
COMPARISON = STANDARD_SPECTRA
for stand in COMPARISON:
chi2, scalefactor = kastredux.compareSpectra_simple(spec, stand)
if chi2 < minimal:
minimal = chi2
minimal_scale = scalefactor
minimal_standard = stand
minimal_standard_name = stand.name
if plot or (diag_path is not None):
placeholder = kastredux.compareSpectra_simple(spec,
minimal_standard,
plot=True)
if diag_path is not None:
plt.savefig(diag_path + spec.name + '_standardComparison.png')
if plot:
plt.show()
plt.close()
spt = spt_from_name(minimal_standard_name)
return spt, minimal_standard_name, minimal, minimal_scale
def calc_spec_info(filepath,
plot=False,
no_index_trials=100,
no_EW_trials=30,
diag_path=None):
t1 = datetime.datetime.now()
#collecting header info
print('Reading in {}'.format(filepath))
spec = kastredux.readSpectrum(filepath)
spec.name = fits.getheader(filepath)['OBJECT']
ra = fits.getheader(filepath)['RA']
dec = fits.getheader(filepath)['DEC']
date = fits.getheader(filepath)['DATE']
filename = os.path.basename(filepath)
if plot:
spec.plot()
plt.show()
if diag_path is not None:
spec.plot()
plt.savefig(diag_path + spec.name + '_spec.png')
print('Classifying by standard comparison...')
spt, stand_name, spt_chi2, spt_scale = classify_by_standard(
spec, ref='all', plot=plot, diag_path=diag_path)
print('Done!')
print('Measuring indices...')
indices = {}
for ref in list(kastclassify.defs.index_dict):
print('Mesuring index set {}...'.format(ref))
indices[ref] = measure_index_set(spec,
ref=ref,
no_trials=no_index_trials)
print('Done!')
print('Done!')
print('Measuring Equivalent Widths...')
EWs = {}
for ref in list(kastclassify_EW_dict):
print('Measuring EW for {}...'.format(ref))
EWs[ref] = measure_EW_element(spec,
ref=ref,
plot=plot,
no_trials=no_EW_trials,
diag_path=diag_path)
print('Done!')
print('Done!')
#calculating with the measurements
print('Calculating spectral types by index...')
index_spt, zeta_info, metallicity_class = classify_by_index(
spec, index_dict=indices)
print('Done!')
print('Calculating L_Halpha/L_bol...')
Lratio, Lratio_err = calculate_L_Lbol(EWs['Halpha']['EW'],
EWs['Halpha']['EW_err'], spt)
print('Done!')
plt.close('all')
#unpacking results
print('Packaging results...')
results = {}
results['name'] = spec.name
results['filename'] = filename
results['RA'] = ra
results['DEC'] = dec
results['OBSDATE'] = date
results['spt'] = spt
results['subtype'] = typeToNum(spt) - 10
results['spt_chi2'] = spt_chi2
results['spt_scale'] = spt_scale
for ref in list(indices):
for name in list(indices[ref]):
results[ref + '_' + name] = indices[ref][name][0]
results[ref + '_' + name + '_err'] = indices[ref][name][1]
for ref in list(EWs):
for name in list(EWs[ref]):
results[ref + '_' + name + '_(Angstrom)'] = EWs[ref][name]
for ref in list(index_spt):
results[ref] = index_spt[ref]
results['zeta'] = zeta_info[0]
results['zeta_err'] = zeta_info[1]
results['metallicity_class'] = metallicity_class
results['L_Halpha/L_bol'] = Lratio
results['L_Halpha/L_bol_err'] = Lratio_err
print('Done!')
t2 = datetime.datetime.now()
dt = t2 - t1
print('All done in {}! All done at {}'.format(dt, t2))
return results
def reduction_batch(dates):
for date in dates:
folder = '{}/{}/'.format(base_folder, date)
# red
instruct_file = '{}/{}'.format(folder, red_input)
if os.path.exists(instruct_file):
print('\n\nReducing RED data from {}\n\n'.format(date))
par = kastredux.readInstructions(instruct_file)
# first run a profileCheck
tmp = kastredux.profileCheck(instruct_file)
# now reduce
redux = kastredux.reduce(
instructions=instruct_file,
bias_file='bias_RED.fits',
flat_file='flat_RED.fits',
mask_file='mask_RED.fits',
cal_flux_file='cal_flux_RED.pkl',
reset=True) #,cal_wave_file='cal_wave_BLUE.pkl')
# classify results
print('\n\nClassifying RED data from {}\n\n'.format(date))
sources = list(par['SOURCE'].keys())
for src in sources:
spec = kastredux.readSpectrum('{}/kastRED_{}_20{}.fits'.format(
par['REDUCTION_FOLDER'], src, date))
spec.name = src
stats = []
stds = list(kastredux.SPTSTDS.keys())
for s in stds:
st, scl = kastredux.compareSpectra_simple(
spec,
kastredux.SPTSTDS[s],
fit_range=[6800, 8800],
plot=False)
stats.append(st)
spt = stds[numpy.argmin(stats)]
kastredux.compareSpectra_simple(
spec,
kastredux.SPTSTDS[spt],
fit_range=[6800, 8800],
plot=True,
plot_file='{}/classify{}_{}.pdf'.format(
par['REDUCTION_FOLDER'], par['MODE'], src))
# except:
# print('Problem reducing RED data from {}'.format(date))
# blue
instruct_file = '{}/{}'.format(folder, blue_input)
if os.path.exists(instruct_file):
print('Reducing BLUE data from {}'.format(date))
par = kastredux.readInstructions(instruct_file)
# first run a profileCheck
tmp = kastredux.profileCheck(instruct_file)
# now reduce
redux = kastredux.reduce(
instructions=instruct_file,
bias_file='bias_BLUE.fits',
flat_file='flat_BLUE.fits',
mask_file='mask_BLUE.fits',
cal_flux_file='cal_flux_BLUE.pkl',
reset=True) #,cal_wave_file='cal_wave_BLUE.pkl')
# classify results
# sources = list(par['SOURCE'].keys())
# for src in sources:
# spec = kastredux.readSpectrum('{}/kastBLUE_{}_20{}.fits'.format(par['REDUCTION_FOLDER'],src,date))
# spec.name = src
# stats = []
# stds = list(kastredux.SPTSTDS.keys())
# for s in stds:
# st,scl = kastredux.compareSpectra_simple(spec,kastredux.SPTSTDS[s],fit_range=[4500,5300],plot=False)
# stats.append(st)
# spt = stds[numpy.argmin(stats)]
# kastredux.compareSpectra_simple(spec,kastredux.SPTSTDS[spt],fit_range=[4500,5300],plot=True,plot_file='{}/classify{}_{}.pdf'.format(par['REDUCTION_FOLDER'],par['MODE'],src))
# except:
# print('Problem reducing BLUE data from {}'.format(date))
return