def combine_run_tables(batch, source, table_name):
"""Combine summary files of runs into a batch table
"""
print(f'Combining model summary tables:')
n_runs = grid_tools.get_nruns(batch, source)
runs = np.arange(n_runs) + 1
table_list = []
for run in runs:
sys.stdout.write(f'\r{source}{batch} {run}/{runs[-1]}')
run_table = load_run_table(run, batch, source=source, table=table_name)
if table_name == 'bursts':
run_table['run'] = run
table_list += [run_table]
sys.stdout.write('\n')
combined_table = pd.concat(table_list, ignore_index=True, sort=False)
table_str = combined_table.to_string(index=False, justify='left')
filepath = grid_strings.batch_table_filepath(batch, source, table_name=table_name)
print(f'Saving: {filepath}')
with open(filepath, 'w') as f:
f.write(table_str)
return combined_table
def extract_batches(source,
batches=None,
save_plots=True,
multithread=True,
reload=False,
load_bursts=False,
load_summary=False,
basename='xrb',
param_table=None):
"""Do burst analysis on arbitrary number of batches"""
t0 = time.time()
if param_table is not None:
print('Using models from table provided')
batches = np.unique(param_table['batch'])
else:
batches = grid_tools.ensure_np_list(batches)
for batch in batches:
print_title(f'Batch {batch}')
analysis_path = grid_strings.batch_analysis_path(batch, source)
for folder in ['input', 'output']:
path = os.path.join(analysis_path, folder)
grid_tools.try_mkdir(path, skip=True)
if param_table is not None:
subset = grid_tools.reduce_table(param_table,
params={'batch': batch})
runs = np.array(subset['run'])
else:
n_runs = grid_tools.get_nruns(batch, source)
runs = np.arange(n_runs) + 1
if multithread:
args = []
for run in runs:
args.append((run, batch, source, save_plots, reload,
load_bursts, load_summary, basename))
with mp.Pool(processes=8) as pool:
pool.starmap(extract_runs, args)
else:
extract_runs(runs,
batch,
source,
reload=reload,
save_plots=save_plots,
load_bursts=load_bursts,
load_summary=load_summary,
basename=basename)
print_title('Combining run tables')
for table_name in ('summary', 'bursts'):
burst_tools.combine_run_tables(batch,
source,
table_name=table_name)
t1 = time.time()
dt = t1 - t0
print_title(f'Time taken: {dt:.1f} s ({dt/60:.2f} min)')
def batch_save(batch, source, runs=None, basename='xrb', reload=True, **kwargs):
"""Loads a collection of models and saves their lightcurves
"""
if runs is None:
runs = grid_tools.get_nruns(batch, source)
runs = grid_tools.expand_runs(runs)
for run in runs:
load_lum(run, batch, source, basename=basename, reload=reload, **kwargs)
def extract_batch_profiles(batch, source='frank', basename='xrb'):
"""Extracts and saves all profiles of all runs from a batch
"""
nruns = grid_tools.get_nruns(batch=batch, source=source, basename=basename)
for run in range(1, nruns + 1):
extract_run_profiles(run=run,
batch=batch,
source=source,
basename=basename)
def save_all_average_lightcurves(batches,
source,
align='t_peak',
reload=False):
"""Saves all lightcurve files for each model in given batches
"""
for batch in batches:
n_runs = grid_tools.get_nruns(batch, source=source)
for run in range(1, n_runs + 1):
model = burst_analyser.BurstRun(run,
batch=batch,
source=source,
reload=reload)
model.save_average_lightcurve(align=align)
def combine_run_summaries(batch, source):
"""Combines summary files of individual batch runs into a single table
"""
print(f'Combining model summary tables:')
n_runs = grid_tools.get_nruns(batch, source)
runs = np.arange(n_runs) + 1
run_tables = []
for run in runs:
sys.stdout.write(f'\r{source}{batch} {run}/{runs[-1]}')
run_tables += [load_run_table(run, batch, source=source, table='summary')]
sys.stdout.write('\n')
combined_table = pd.concat(run_tables, ignore_index=True)
table_str = combined_table.to_string(index=False, justify='left')
filepath = get_table_filepath(batch, source)
print(f'Saving: {filepath}')
with open(filepath, 'w') as f:
f.write(table_str)
return combined_table