def test_bursts(n_bursts, run=1, batch=281, source='biggrid2', plot=True):
"""Test burst_analyser when model has zero bursts
"""
model_ref = burst_analyser.BurstRun(run,
batch=batch,
source=source,
analyse=False,
plot=False,
reload=False,
load_bursts=True,
load_summary=True,
load_lum=False)
model_test = burst_analyser.BurstRun(run,
batch=batch,
source=source,
analyse=False,
plot=False,
reload=False,
load_bursts=False,
load_summary=False,
load_lum=True)
pre_idx = model_ref.bursts['t_pre_i'][n_bursts]
model_test.lum = model_test.lum[:pre_idx]
model_test.analyse()
if plot:
model_test.plot()
model_test.plot_convergence()
def check_n_bursts(batches, source, kgrid):
"""Compares n_bursts detected with kepler_analyser against burstfit_1808
"""
mismatch = np.zeros(4)
filename = f'mismatch_{source}_{batches[0]}-{batches[-1]}.txt'
filepath = os.path.join(GRIDS_PATH, filename)
for batch in batches:
summ = kgrid.get_summ(batch)
n_runs = len(summ)
for i in range(n_runs):
run = i + 1
n_bursts1 = summ.iloc[i]['num']
sys.stdout.write(f'\r{source}_{batch} xrb{run:02}')
model = burst_analyser.BurstRun(run, batch, source, verbose=False)
model.analyse()
n_bursts2 = model.n_bursts
if n_bursts1 != n_bursts2:
m_new = np.array((batch, run, n_bursts1, n_bursts2))
mismatch = np.vstack((mismatch, m_new))
np.savetxt(filepath, mismatch)
return mismatch
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)