def data():
data, _, profiles = fileio.read_compressed_track(
base_path / 'test_data/M1.080_M0.502_P192.67_Z0.01129.h5',
return_profiles=True)
stable, ce_age = common_envelope.is_stable(data,
criterion='Mdot',
value=-2)
data = data[data['age'] <= ce_age]
return data
def test_apply_ce():
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M1.205_M0.413_P505.12_Z0.h5')
stable, ce_age = common_envelope.is_stable(data,
criterion='J_div_Jdot_div_P',
value=10)
data = data[data['age'] <= ce_age]
data = common_envelope.apply_ce(data, ce_formalism='iben_tutukov1984')
assert data['period_days'][-1] == pytest.approx(25.62, abs=0.01)
assert data['binary_separation'][-1] == pytest.approx(38.2451, abs=1e-4)
assert data['star_1_mass'][-1] == pytest.approx(0.4477, abs=1e-4)
assert data['envelope_mass'][-1] == 0
assert data['star_2_mass'][-1] == pytest.approx(0.4278, abs=1e-4)
assert data['mass_ratio'][-1] == pytest.approx(1.0465, abs=1e-4)
assert data['rl_1'][-1] == pytest.approx(14.6426, abs=1e-4)
assert data['rl_2'][-1] == pytest.approx(14.3415, abs=1e-4)
assert data['CE_phase'][-1] == 1
assert data['CE_phase'][-2] == 1
assert data['CE_phase'][-3] == 0
data, _, profiles = fileio.read_compressed_track(
base_path / 'test_data/M1.080_M0.502_P192.67_Z0.01129.h5',
return_profiles=True)
stable, ce_age = common_envelope.is_stable(data,
criterion='Mdot',
value=-2)
data = data[data['age'] <= ce_age]
data = common_envelope.apply_ce(data,
profiles=profiles,
ce_formalism='dewi_tauris2000',
a_th=0.5)
assert data['binary_separation'][-1] == pytest.approx(6.394841,
abs=0.000001)
assert data['star_1_mass'][-1] == pytest.approx(0.38572328, abs=0.000001)
assert data['envelope_mass'][-1] > 0
with pytest.raises(ValueError):
data = common_envelope.apply_ce(data, ce_formalism='uk_formalism')
def test_dewi_tauris2000(data):
data, _, profiles = fileio.read_compressed_track(
base_path / 'test_data/M1.080_M0.502_P192.67_Z0.01129.h5',
return_profiles=True)
stable, ce_age = common_envelope.is_stable(data,
criterion='Mdot',
value=-2)
data = data[data['age'] <= ce_age]
profile = profiles['profile_1_mdot-2.0']
af, M1_final = common_envelope.dewi_tauris2000(data, profile, a_th=0)
assert af == pytest.approx(4.8597844860, abs=0.00001)
assert M1_final == pytest.approx(0.38321120, abs=0.00001)
af, M1_final = common_envelope.dewi_tauris2000(data, profile, a_th=1.0)
assert af == pytest.approx(9.17843738836, abs=0.00001)
assert M1_final == pytest.approx(0.39115816046997015, abs=0.00001)
def test_is_stable():
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M1.205_M0.413_P505.12_Z0.h5')
stable, ce_age, ce_mn = common_envelope.is_stable(data,
criterion='Mdot',
value=-3,
return_model_number=True)
assert stable is False
assert ce_age == pytest.approx(5179376593.6, abs=0.1)
assert ce_mn == 11928
stable, ce_age, ce_mn = common_envelope.is_stable(data,
criterion='delta',
value=0.03,
return_model_number=True)
assert stable is False
assert ce_age == pytest.approx(5179376616.1, abs=0.1)
assert ce_mn == 12057
stable, ce_age, ce_mn = common_envelope.is_stable(
data, criterion='J_div_Jdot_div_P', value=10, return_model_number=True)
assert stable is False
assert ce_age == pytest.approx(5179376617.0, abs=0.1)
assert ce_mn == 12108
stable, ce_age, ce_mn = common_envelope.is_stable(
data,
criterion='M_div_Mdot_div_P',
value=100,
return_model_number=True)
assert stable is False
assert ce_age == pytest.approx(5179376614.6, abs=0.1)
assert ce_mn == 12021
stable, ce_age, ce_mn = common_envelope.is_stable(data,
criterion='R_div_SMA',
value=0.5,
return_model_number=True)
assert stable is False
assert ce_age == pytest.approx(5179376602.9, abs=0.1)
assert ce_mn == 11946
with pytest.raises(ValueError):
stable, ce_age = common_envelope.is_stable(data,
criterion='uk_criterion',
value=0.5)
def extract_mesa(file_list,
stability_criterion='J_div_Jdot_div_P',
stability_limit=10,
n_ml_phases=0,
ce_formalism='iben_tutukov1984',
ce_parameters={'al': 1},
ce_profile_name=None,
parameters=[],
phase_flags=[],
extra_info_parameters=[],
add_setup_pars_to_result=True,
verbose=False,
flatten_output=False,
**kwargs):
parameters, column_names = _process_parameters(parameters)
extra_info_parameters, extra_names = _process_parameters(
extra_info_parameters)
columns = ['path', 'stability', 'n_ML_phases'
] + extra_names + column_names + phase_flags
if add_setup_pars_to_result:
columns += [
'stability_criterion', 'stability_limit', 'ce_profile_name',
'ce_formalism', 'ce_parameters'
]
columns += ['error_flags']
results = []
# check if the same extraction parameters are used for all models, or if specific parameters are already
# provided in the files list
file_list = process_file_list(file_list,
stability_criterion=stability_criterion,
stability_limit=stability_limit,
ce_formalism=ce_formalism,
ce_parameters=ce_parameters,
ce_profile_name=ce_profile_name,
verbose=verbose)
for i, model in file_list.iterrows():
if verbose:
print(i, model['path'])
# 1: Get the data
try:
data, extra_info, profiles = fileio.read_compressed_track(
model['path'], return_profiles=True)
except Exception as e:
if verbose:
print(e)
continue
# 2: check for stability and cut data at start of CE
stable, ce_age = common_envelope.is_stable(
data,
criterion=model['stability_criterion'],
value=model['stability_limit'])
stability = 'stable'
if not stable:
# if the model is not stable, cut of the evolution at the start of the CE as anything after than
# is non physical anyway.
data = data[data['age'] <= ce_age]
if ce_profile_name is not None:
try:
profiles = profiles[model['ce_profile_name']]
except Exception:
# todo: deal correctly with the missing profile!
print('CE: profile missing')
data = common_envelope.apply_ce(data,
profiles=profiles,
ce_formalism=model['ce_formalism'],
**model['ce_parameters'])
# check if CE is ejected or if the system is a merger or a contact binary
s = np.where((data['star_2_radius'] >= 0.99 * data['rl_2'])
& (data['star_1_radius'] >= 0.99 * data['rl_1']))
if data['binary_separation'][-1] <= 0:
stability = 'merger'
print('CE: Merged')
elif len(data['model_number'][s]) > 0:
stability = 'contact'
print('CE: Contact')
else:
stability = 'CE'
# 3: extract some standard parameters: Path, stability and nr of ML phases.
pars = [model['path'].split('/')[-1]]
pars += [stability, count_ml_phases(data)]
# 4: add the extra info to the output
for p in extra_info_parameters:
pars.append(extra_info[p])
# 5: extract the requested parameters & 6: add the requested phase flags
extracted_pars = extract_parameters(data,
parameters,
phase_flags,
n_ml_phases=n_ml_phases)
pars += extracted_pars
# 7: Add the extraction setup parameters if requested
if add_setup_pars_to_result:
setup_pars = [
model['stability_criterion'], model['stability_limit'],
model['ce_profile_name'], model['ce_formalism'],
model['ce_parameters']
]
pars += setup_pars
# 8: todo: check for some possible errors and flag them
error_flags = evolution_errors.check_error_flags(
data, extra_info['termination_code'])
pars.append(error_flags)
results.append(pars)
results = pd.DataFrame(results, columns=columns)
if flatten_output:
# go over all columns. If a column contains a list instead of a value flatten that column
results = _flatten_dataframe(results, n_ml_phases)
return results