def archetype_classification(self):
io_functions.span_window()
print('... determining archetype classification')
[
interface_main.archetype_classify_MC(spec)
for spec in self.spectra_array
]
### prepare output table if it's reasonable
if len(self.spectra_array) < 30:
output_table = ['NAME', "GI", "GII", "GIII"]
for spec in self.spectra_array:
row = np.concatenate([[spec.get_name()],
[
spec.LL_DICT[key][0].round(0)
for key in ["GI", "GII", "GIII"]
]])
output_table = np.vstack([output_table, row])
table = Texttable()
table.add_rows(output_table)
print(" ------ ARCHETYPE LIKELIHOODS -------")
print(table.draw())
return
def generate_plots(self):
io_functions.span_window()
print("... generating plots")
plot_functions.plot_spectra(self)
print("... generating corner plots")
plot_functions.plot_corner_array(self)
return
def generate_output_files(self):
io_functions.span_window()
print("... generating outputs")
final = pd.concat(
[spec.get_output_row() for spec in self.spectra_array])
try:
final.to_csv("output/" + self.io_params['output_name'] +
"_out.csv",
index=False)
except:
final.to_csv("output/" + self.io_params['output_name'] +
"1_out.csv",
index=False)
def mcmc_determination(self, pool=20):
### Main iterative method for the mcmc_determination
io_functions.span_window()
print('... performing MCMC determinations')
[spec.prepare_regions() for spec in self.spectra_array]
[
interface_main.mcmc_determination(spec, mode='COARSE', pool=pool)
for spec in self.spectra_array
]
print("... performing kde determinations")
[
interface_main.generate_kde_params(spec, mode="COARSE")
for spec in self.spectra_array
]
io_functions.span_window()
print("... running refined mcmc")
[
interface_main.mcmc_determination(spec, mode='REFINE', pool=pool)
for spec in self.spectra_array
]
print("... finalizing kde determinations")
[
interface_main.generate_kde_params(spec, mode='REFINE')
for spec in self.spectra_array
]
io_functions.span_window()
print("... complete")
io_functions.span_window()
return
import time
print(" Started CASPER and logging!")
sys.stdout=open('casper-output-log.txt', 'wt')
start_time = time.time()
print("... initializing spectra batch")
spec_batch = Batch(spectra_path, param_path, io_param_path)
################################################################################
### load spectra + params
spec_batch.load_params()
spec_batch.load_spectra(is_fits=True)
spec_batch.set_params()
io_functions.span_window()
#spec_batch.radial_correct()
spec_batch.build_frames()
io_functions.span_window()
################################################################################
#### Continuum normalization with GISIC
spec_batch.normalize()
################################################################################
#### Preliminaries
spec_batch.set_KP_bounds()
spec_batch.set_carbon_mode()
def calibrate_temperatures(self, default=True, teff_sigma=250):
## Here is where we will use the (J-K)0 values from the param_file
## along with the surface gravity class, if known
## We'll eventually want to update to override sigma, I'l come back to that
print("... determining photometric temperature")
### I don't think the spectra_array and the param_file are sorted the same
### so I need to be careful
for i, row in self.param_file.iterrows():
io_functions.span_window()
spec = self.spectra_array[i]
assert spec.name == row[
'name'], 'Parameter error in calibrate_temperatures()'
print("\t setting photometric temperature sigma: ", spec.T_SIGMA)
CLASS = row['class'].strip()
#### remember that there is a class definition here too
if np.isfinite(spec.HARD_TEFF):
print("\t setting hard teff: ", spec.HARD_TEFF)
spec.set_temp_frame(
TC.calibrate_temp_frame(float(spec.PHOTO_0['J-K']),
float(spec.PHOTO_0['g-r']),
CLASS=CLASS))
spec.TEMP_FRAME.loc['ADOPTED', 'VALUE'] = spec.HARD_TEFF
spec.set_temperature(spec.HARD_TEFF, spec.T_SIGMA, hard=True)
else:
spec.set_temp_frame(
TC.calibrate_temp_frame(float(spec.PHOTO_0['J-K']),
float(spec.PHOTO_0['g-r']),
CLASS=CLASS))
spec.set_temperature(spec.TEMP_FRAME.loc['ADOPTED', 'VALUE'],
sigma=spec.T_SIGMA)
#### NOW ASSEMBLE THE OUTPUT TABLE
HEADER = [
'NAME', 'Bergeat', 'Hernandez', 'Casagrande', 'Fukugita', 'ADOPTED'
]
if len(self.spectra_array) < 30:
output_table = HEADER
for spec in self.spectra_array:
row = np.concatenate(
[[spec.get_name().split(".fits")[0]],
[
spec.TEMP_FRAME.loc[CURRENT].values[0]
for CURRENT in HEADER[1:]
]])
output_table = np.vstack([output_table, row])
table = Texttable()
table.add_rows(output_table)
print(" ------ PHOTOMETRIC TEMPERATURES -------")
print(table.draw())
return