# That's necesarry for getting the explosion time from the template
sed_directory = rappid_original_data + '/SEDs'
# get the lightcurves either generated using MOSFiT type 'mosfit'
# or using specral templates type 'templates'
generated_with = 'mosfit'
# get the DataHandler object who takes care of all the book keeping
thisDH = rappidDH.get_dhandler(generated_with, sed_directory=sed_directory)
# get the explosion times for the simulations
thisDH.get_explosion_times_from_template(ncpu=45)
# fit the lightcurves with the desired method (only 'mosfit' is good!)
method = 'mosfit'
fitter = Fitter.get_fitter(method)
logger.debug(f'fitter method {fitter.method_name} \n'
f'job-id {fitter.job_id}')
missing_indice_file = f'{pickle_dir}/{thisDH.name}/{fitter.method_name}/missing_indices.txt'
fitter.fit_lcs(
thisDH,
tasks_in_group=100,
# missing_indice_file=missing_indice_file # to be used when repeating the fit
)
# make a selection of lightcurves based on the available photometry
thisDH.select_and_adjust_selection_string()
# get the results
from estimate_explosion_time.core.data_prep.data import DataHandler
from estimate_explosion_time.analyses.rappid_simulations import rappidDH
from estimate_explosion_time.core.fit_data.fitlauncher.fitlauncher import Fitter
from estimate_explosion_time.analyses.rappid_simulations.convert_to_pickle_files import \
rappid_pkl_name, write_model_to_pickle, rappid_original_data
import os
# take the original data and convert it into pickles in the right format
# if not os.path.exists(pickle_filename):
# convert_to_pickles()
convert_to_pickles()
# get the DataHandler object who takes care of all the book keeping
thisDH = ExampleDH.get_dhandler()
fitter = Fitter.get_fitter('mosfit')
thisDH.pickle_dir = fitter.get_output_directory(thisDH)
thisDH.use_method('mosfit', None)
thisDH.save_me()
# # fit the lightcurves with the desired method (only 'mosfit' is good!)
# method = 'mosfit'
# fitter = Fitter.get_fitter(method)
#
# logger.debug(
# f'fitter method {fitter.method_name} \n'
# f'job-id {fitter.job_id}'
# )
#
# missing_indice_file = f'{pickle_dir}/{thisDH.name}/{fitter.method_name}/missing_indices.txt'
# fitter.fit_lcs(rappidDH,
logger = get_custom_logger(main_logger_name)
logger.setLevel(logging.DEBUG)
logger.debug('logging level is DEBUG')
import argparse
from estimate_explosion_time.analyses.ZTF20abdnpdo import ZTF20abdnpdoDataHandler
from estimate_explosion_time.core.fit_data.fitlauncher.fitlauncher import Fitter
from estimate_explosion_time.core.analyse_fits_from_simulation.plots import Plotter
parser = argparse.ArgumentParser()
parser.add_argument('-fn', '--force_new', type=bool, default=False)
args = parser.parse_args()
method = 'mosfit'
dh = ZTF20abdnpdoDataHandler.get_dhandler(force_new=args.force_new)
fitter = Fitter(method)
missing_indice_file = f'{pickle_dir}/{dh.name}/{fitter.method_name}/missing_indices.txt'
fitter.fit_lcs(dh, reduce_mosfit_output=False, mock_fit=True)
dh.select_and_adjust_selection_string(select_all=True)
dh.results(force=True, make_plots=False)
plotter = Plotter(dh, method)
for i in range(dh.nlcs):
plotter.plot_lc(i,
plot_corner=True,
plot_orig_template=False,
reduce_data=True)
def collect_results(self, force=False):
if force:
logger.debug('forcing to collect data')
if isinstance(self.collected_data, type(None)) or force:
if self.job_id:
job_status = wait_for_cluster(self.job_id)
else:
inp = input(
'No job id specified. Go on trying to collect fit results? [y/n] '
)
if inp in ['y', 'yes']:
job_status = True
else:
raise ResultError('no job_id specified')
# if all jobs are done, unset job id and continue with collecting results
if job_status:
self.job_id = None
if len(os.listdir(self.pickle_dir)) != 0:
listed_pickle_dir = os.listdir(self.pickle_dir)
# indices are file name minus one as file names start at 1, indices at 0!
indices = [
int(file.split('.')[0]) for file in listed_pickle_dir
if not 'missing' in file and not file.startswith('.')
]
# get a list of indices, that are not a file in result directory
missing_indices = []
for i in range(self.dhandler.nlcs):
if i not in indices:
missing_indices.append(i)
# if this list is not empty, try and fit the missing lightcurves
if len(missing_indices) > 0:
missing_indices_fname = f'{pickle_dir}/{self.dhandler.name}/{self.method}/missing_indices.txt'
with open(missing_indices_fname, 'w') as f:
for ind in missing_indices:
f.write(f"{ind}\n")
logger.info(
f'No result files for {len(missing_indices)} fits! '
f'Wrote list to {missing_indices_fname}')
inpt = input('Submit remaining lcs to fit? [y/n] ')
# if desired fit the remaining lightcurves, then exit
if inpt == 'y':
fitter = Fitter.get_fitter(self.method)
fitter.fit_lcs(
self.dhandler,
ntasks=len(missing_indices),
tasks_in_group=1,
missing_indice_file=missing_indices_fname)
logger.info('submitted fits, exiting ...')
inpt = input(
'continue without the missing results? [y/n] ')
if inpt not in ['y', 'yes']:
self.collect_results(force=force)
# sys.exit(0)
self.sub_collect_results(indices, listed_pickle_dir)
collected_data_filename = f'{self.pickle_dir}.pkl'
self.dhandler.collected_data = collected_data_filename
self.save_data()
self.dhandler.save_me()
else:
raise ResultError(f'No result files in {self.pickle_dir}!')
else:
raise ResultError(f'Exiting before all tasks were done.')
else:
logger.debug('results were already collected')