def test_get_all_phases_ML_phases(self, base_path):
phase_names = ['init', 'MLstart', 'ML', 'HeIgnition']
# -- 2 Mass loss phases both with wind and rlof mass loss --
data, _ = fileio.read_compressed_track(base_path / 'test_data/M2.341_M1.782_P8.01_Z0.01412.h5',
return_profiles=False)
# when asking for n_ml_phases = 0, only 1 ML phase should be returned, directly as a np.where structure.
phases = evolution_phases.get_all_phases(phase_names, data, 0)
assert len(phases) == 4
for name in phase_names:
assert name in phases
assert type(phases['ML']) == tuple, \
"When passing n_ml_phases=0, the result should be a np.where type structure"
assert type(phases['ML'][0]) == np.ndarray,\
"When passing n_ml_phases=0, the result should be a np.where type structure"
# when asking for n_ml_phases = 1, only 1 ML phase should be returned, BUT as a list
phases = evolution_phases.get_all_phases(phase_names, data, 1)
assert type(phases['ML']) == list
assert len(phases['ML']) == 1
assert type(phases['ML'][0]) == tuple
assert type(phases['ML'][0][0]) == np.ndarray
# when asking for n_ml_phases = 2, 2 ML phases should be returned as a list
phases = evolution_phases.get_all_phases(phase_names, data, 2)
assert type(phases['ML']) == list
assert len(phases['ML']) == 2
assert type(phases['ML'][0]) == tuple
assert type(phases['ML'][0][0]) == np.ndarray
assert type(phases['ML'][1]) == tuple
assert type(phases['ML'][1][0]) == np.ndarray
def test_count_ml_phases(self, base_path):
# 1 ML phase
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M0.789_M0.304_P20.58_Z0.h5')
n_ml_phases = extract_mesa.count_ml_phases(data)
assert n_ml_phases == 1
# 2 separate ML phases
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M2.341_M1.782_P8.01_Z0.01412.h5')
n_ml_phases = extract_mesa.count_ml_phases(data)
assert n_ml_phases == 2
# 1 ML phases and 3 other ML phases due to wind mass loss which should not get counted.
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M1.276_M1.140_P333.11_Z0.h5')
n_ml_phases = extract_mesa.count_ml_phases(data)
assert n_ml_phases == 1
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_check_error_flags(base_path):
# check no errors
data, _ = fileio.read_compressed_track(
base_path /
'test_data/error_models/M0.851_M0.305_P358.92_Z0.00129_no_problems.h5')
error_flags = evolution_errors.check_error_flags(data, 'log_g_upper_limit')
assert error_flags == []
# check max_model error
error_flags = evolution_errors.check_error_flags(data, 'max_model_number')
assert 1 in error_flags
# check accretor overflow error
error_flags = evolution_errors.check_error_flags(
data, 'accretor_overflow_terminate')
assert 2 in error_flags
# check ML and He ignition error
data, _ = fileio.read_compressed_track(
base_path /
'test_data/error_models/M2.407_M0.432_P1.72_Z0.00706_He_ignition_problem.h5'
)
error_flags = evolution_errors.check_error_flags(data, '')
assert 4 in error_flags
# Check HeIgnition but no HeCoreBurning error
data, _ = fileio.read_compressed_track(
base_path /
'test_data/error_models/M1.490_M1.380_P144.55_Z0.01427_Jz1.000_HeIgnition_but_no_Coreburning.h5'
)
error_flags = evolution_errors.check_error_flags(data, '')
assert 4 in error_flags
# check CO core formation error
data, _ = fileio.read_compressed_track(
base_path /
'test_data/error_models/M1.699_M1.401_P260.07_Z0.02489_Jz1.000_HeCoreBurning_doesnt_finish.h5'
)
error_flags = evolution_errors.check_error_flags(data, '')
assert 5 in error_flags
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 test_extract_parameters(self, base_path):
#TODO: improve this test case and add more checks
# HB star with core and shell He burning
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M1.276_M1.140_P333.11_Z0.h5')
parameters = [
'star_1_mass__init', 'period_days__final', 'rl_1__max',
'rl_1__HeIgnition', 'age__ML__diff', 'he_core_mass__ML__rate',
'star_1_mass__lg_mstar_dot_1_max'
]
res = extract_mesa.extract_parameters(data, parameters)
res = {k: v for k, v in zip(parameters, res)}
assert res['star_1_mass__init'] == data['star_1_mass'][0]
assert res['period_days__final'] == data['period_days'][-1]
assert res['rl_1__max'] == np.max(data['rl_1'])
#assert np.isnan(res['rl_1__HeIgnition'])
# a1 = data['age'][data['lg_mstar_dot_1'] > -10][0]
# a2 = data['age'][(data['age'] > a1) & (data['lg_mstar_dot_1'] <= -10)][0]
# s = np.where((data['age'] >= a1) & (data['age'] <= a2))
# assert res['age__ML__diff'] == data['age'][s][-1] - data['age'][s][0]
# assert res['he_core_mass__ML__rate'] == (data['he_core_mass'][s][-1] - data['he_core_mass'][s][0]) / \
# (data['age'][s][-1] - data['age'][s][0])
assert res['rl_1__HeIgnition'] == pytest.approx(152.8606, abs=0.0001)
assert res['star_1_mass__lg_mstar_dot_1_max'] == pytest.approx(
0.5205, abs=0.0001)
phase_flags = ['ML', 'HeCoreBurning', 'He-WD']
res = extract_mesa.extract_parameters(data,
parameters,
phase_flags=phase_flags)
res = {k: v for k, v in zip(parameters + phase_flags, res)}
assert res['ML'] is True
assert res['HeCoreBurning'] is True
assert res['He-WD'] is False
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_extract_parameters_ml(self, base_path):
"""
Test that the ML phases are correctly returned.
n_ml_phases = 0:
result = [<val>, <val>]
n_ml_phases = 1:
result = [<val>, [<val>]]
n_ml_phases = 2:
result = [<val>, [<val>, <val>]]
"""
parameters = ['period_days__init', 'period_days__ML']
data, _ = fileio.read_compressed_track(
base_path / 'test_data/M2.341_M1.782_P8.01_Z0.01412.h5')
result = extract_mesa.extract_parameters(data,
parameters=parameters,
phase_flags=[],
n_ml_phases=0)
assert len(result) == 2
assert type(result[1]) != list
result = extract_mesa.extract_parameters(data,
parameters=parameters,
phase_flags=[],
n_ml_phases=1)
assert len(result) == 2
assert type(result[1]) == list
assert len(result[1]) == 1
result = extract_mesa.extract_parameters(data,
parameters=parameters,
phase_flags=[],
n_ml_phases=2)
assert len(result) == 2
assert type(result[1]) == list
assert len(result[1]) == 2
def test_ml_phase(self, base_path):
# -- 1 Mass loss phase consisting of a long wind ml phase and a short RLOF phase --
data, _ = fileio.read_compressed_track(base_path / 'test_data/M0.814_M0.512_P260.18_Z0.h5',
return_profiles=False)
# test returned ages
a1, a2 = evolution_phases.ML(data, mltype='rlof', return_age=True)
assert a1 == pytest.approx(12316067393.354174, abs=0.001)
assert a2 == pytest.approx(12316911998.086807, abs=0.001)
a1, a2 = evolution_phases.ML(data, mltype='wind', return_age=True)
assert a1 == pytest.approx(12283352829.773027, abs=0.001)
assert a2 == pytest.approx(12316994026.181108, abs=0.001)
a1, a2 = evolution_phases.ML(data, mltype='total', return_age=True)
assert a1 == pytest.approx(12283352829.773027, abs=0.001)
assert a2 == pytest.approx(12316994026.181108, abs=0.001)
# check that ML returns same start and end as MLstart and MLend
a1_ml, a2_ml = evolution_phases.ML(data, mltype='rlof', return_age=True)
a1_start = evolution_phases.MLstart(data, mltype='rlof', return_age=True)
a2_end = evolution_phases.MLend(data, mltype='rlof', return_age=True)
assert a1_ml == a1_start
assert a2_ml == a2_end
s_ml = evolution_phases.ML(data, mltype='rlof', return_age=False)
s_start = evolution_phases.MLstart(data, mltype='rlof', return_age=False)
s_end = evolution_phases.MLend(data, mltype='rlof', return_age=False)
assert data['model_number'][s_ml][0] == data['model_number'][s_start][0]
assert data['model_number'][s_ml][-1] == data['model_number'][s_end][0]
# -- 4 Mass loss phases: first with both rlof and wind, the other 3 only wind --
data, _ = fileio.read_compressed_track(base_path / 'test_data/M1.276_M1.140_P333.11_Z0.h5',
return_profiles=False)
# test returned ages
ages = evolution_phases.ML(data, mltype='rlof', return_age=True, return_multiple=True)
assert len(ages) == 1
a1, a2 = ages[0]
assert a1 == pytest.approx(3461120558.9109983, abs=0.001)
assert a2 == pytest.approx(3462394022.0863924, abs=0.001)
ages = evolution_phases.ML(data, mltype='wind', return_age=True, return_multiple=True)
assert len(ages) == 4
a1, a2 = ages[0]
assert a1 == pytest.approx(3440285863.557928, abs=0.001)
assert a2 == pytest.approx(3462498981.825389, abs=0.001)
ages = evolution_phases.ML(data, mltype='total', return_age=True, return_multiple=True)
assert len(ages) == 4
a1, a2 = ages[1]
assert a1 == pytest.approx(3462515931.0189767, abs=0.001)
assert a2 == pytest.approx(3463059499.121973, abs=0.001)
a1, a2 = ages[2]
assert a1 == pytest.approx(3572455931.1686773, abs=0.001)
assert a2 == pytest.approx(3576048979.7216263, abs=0.001)
a1, a2 = ages[3]
assert a1 == pytest.approx(3576114881.085102, abs=0.001)
assert a2 == pytest.approx(3576115123.911062, abs=0.001)
# -- 2 Mass loss phases both with wind and rlof mass loss --
data, _ = fileio.read_compressed_track(base_path / 'test_data/M2.341_M1.782_P8.01_Z0.01412.h5',
return_profiles=False)
# RLOF
ages = evolution_phases.ML(data, mltype='rlof', return_age=True, return_multiple=True)
assert len(ages) == 2
a1, a2 = ages[0]
assert a1 == pytest.approx(607563161.616631, abs=0.001)
assert a2 == pytest.approx(617932607.0240884, abs=0.001)
a1, a2 = ages[1]
assert a1 == pytest.approx(970323465.489477, abs=0.001)
assert a2 == pytest.approx(975582471.8171717, abs=0.001)
# Wind
ages = evolution_phases.ML(data, mltype='wind', return_age=True, return_multiple=True)
assert len(ages) == 2
a1, a2 = ages[0]
assert a1 == pytest.approx(612221727.4877452, abs=0.001)
assert a2 == pytest.approx(619258765.4160738, abs=0.001)
a1, a2 = ages[1]
assert a1 == pytest.approx(971574716.1957985, abs=0.001)
assert a2 == pytest.approx(976109951.0074742, abs=0.001)
# Total
ages = evolution_phases.ML(data, mltype='total', return_age=True, return_multiple=True)
assert len(ages) == 2
a1, a2 = ages[0]
assert a1 == pytest.approx(607563161.616631, abs=0.001)
assert a2 == pytest.approx(619258765.4160738, abs=0.001)
a1, a2 = ages[1]
assert a1 == pytest.approx(970323465.489477, abs=0.001)
assert a2 == pytest.approx(976109951.0074742, abs=0.001)
def test_get_all_phases(self, base_path):
phase_names = ['init', 'final', 'MS', 'MSstart', 'MSend', 'RGB', 'RGBstart', 'RGBend', 'MLstart', 'MLend',
'ML', 'CE', 'CEstart', 'CEend', 'HeIgnition', 'HeCoreBurning', 'HeShellBurning']
# test checking if all parameters are available.
data, _ = fileio.read_compressed_track(base_path / 'test_data/M0.789_M0.304_P20.58_Z0.h5', return_profiles=False)
data = data[['age', 'period_days']]
with pytest.raises(ValueError):
phases = evolution_phases.get_all_phases(['MS'], data)
# stable model without He ignition and struggles at the end
# age of the last 1470 time steps doesn't change!
data, _ = fileio.read_compressed_track(base_path / 'test_data/M0.789_M0.304_P20.58_Z0.h5', return_profiles=False)
phases = evolution_phases.get_all_phases(phase_names, data)
assert data['model_number'][phases['init']][0] == 3
assert data['model_number'][phases['final']][0] == 30000
assert data['model_number'][phases['MS']][0] == 3
assert data['model_number'][phases['MS']][-1] == 114
assert data['model_number'][phases['MSstart']][0] == 3
assert data['model_number'][phases['MSend']][0] == 114
assert data['model_number'][phases['RGB']][0] == 114
assert data['model_number'][phases['RGB']][-1] == 948
assert data['model_number'][phases['RGBstart']][0] == 114
assert data['model_number'][phases['RGBend']][0] == 948
assert data['model_number'][phases['MLstart']][0] == 936
assert data['model_number'][phases['MLend']][0] == 30000
assert data['model_number'][phases['ML']][0] == 936
assert data['model_number'][phases['ML']][-1] == 30000
assert phases['HeIgnition'] is None
# stable model without He ignition
data, _ = fileio.read_compressed_track(base_path / 'test_data/M0.814_M0.512_P260.18_Z0.h5', return_profiles=False)
phases = evolution_phases.get_all_phases(phase_names, data)
assert data['model_number'][phases['RGB']][0] == 111
assert data['model_number'][phases['RGB']][-1] == 6570
assert data['model_number'][phases['RGBstart']][0] == 111
assert data['model_number'][phases['RGBend']][0] == 6570
assert data['model_number'][phases['ML']][0] == 6006
assert data['model_number'][phases['ML']][-1] == 7098
assert phases['HeIgnition'] is None
assert phases['HeCoreBurning'] is None
assert phases['HeShellBurning'] is None
# stable model with degenerate He ignition but issues in the He burning phase, and a double ML phase
data, _ = fileio.read_compressed_track(base_path / 'test_data/M1.125_M0.973_P428.86_Z0.h5', return_profiles=False)
phases = evolution_phases.get_all_phases(phase_names, data)
assert data['model_number'][phases['ML']][0] == 8970
assert data['model_number'][phases['ML']][-1] == 14406
assert data['model_number'][phases['HeIgnition']][0] == 19947
assert phases['HeCoreBurning'] is None
assert phases['HeShellBurning'] is None
assert phases['CE'] is None
assert phases['CEstart'] is None
assert phases['CEend'] is None
# CE model
data, _ = fileio.read_compressed_track(base_path / 'test_data/M1.205_M0.413_P505.12_Z0.h5', return_profiles=False)
data = data[data['model_number'] <= 12111]
data['CE_phase'][-1] = 1
data['CE_phase'][-2] = 1
phases = evolution_phases.get_all_phases(phase_names, data)
assert data['model_number'][phases['ML']][0] == 11295
assert data['model_number'][phases['ML']][-1] == 12111
assert data['model_number'][phases['CE']][0] == 12108
assert data['model_number'][phases['CE']][1] == 12111
assert data['model_number'][phases['CEstart']][0] == 12108
assert data['model_number'][phases['CEend']][0] == 12111
assert phases['HeIgnition'] is None
assert phases['HeCoreBurning'] is None
assert phases['HeShellBurning'] is None
# HB star with core and shell He burning
data, _ = fileio.read_compressed_track(base_path / 'test_data/M1.276_M1.140_P333.11_Z0.h5', return_profiles=False)
phases = evolution_phases.get_all_phases(phase_names, data)
assert data['model_number'][phases['ML']][0] == 8823
assert data['model_number'][phases['ML']][-1] == 11892
assert data['model_number'][phases['HeIgnition']][0] == 11709
assert data['model_number'][phases['HeCoreBurning']][0] == 12492
assert data['model_number'][phases['HeCoreBurning']][-1] == 12594
assert data['model_number'][phases['HeShellBurning']][0] == 12597
assert data['model_number'][phases['HeShellBurning']][-1] == 14268
# sdB star with core and shell He burning
data, _ = fileio.read_compressed_track(base_path / 'test_data/M1.269_M1.229_P133.46_Z0.00320.h5',
return_profiles=False)
phases = evolution_phases.get_all_phases(['sdA', 'sdB', 'sdO'], data)
assert phases['sdA'] is None
assert data['model_number'][phases['sdB']][0] == 22608
assert data['model_number'][phases['sdB']][-1] == 22689
assert phases['sdO'] is None
a1, a2 = evolution_phases.HeCoreBurning(data, return_age=True)
assert a1 == pytest.approx(3232213210.6798477, abs=0.001)
assert a2 == pytest.approx(3316814816.4952917, abs=0.001)
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
