matter.setupMicro

nucleardatapy.matter.setup_micro.micro_mbs()

Return a list of many-bodys (mbs) approaches available in this toolkit and print them all on the prompt.

Returns:

The list of models with can be ‘VAR’, ‘AFDMC’, ‘BHF’, ‘QMC’, ‘MBPT’, ‘NLEFT’.

Return type:

list[str].

nucleardatapy.matter.setup_micro.micro_models_mb(mb)

Return a list with the name of the models available in this toolkit for a given mb appoach and print them all on the prompt.

Parameters:

mb (str.) – The mb approach for which there are parametrizations. They should be chosen among the following options: ‘VAR’, ‘AFDMC’, ‘BHF’, ‘QMC’, ‘MBPT’, ‘NLEFT’.

Returns:

The list of parametrizations.

These models are the following ones: If mb == ‘VAR’: ‘1981-VAR-AM-FP’, ‘1998-VAR-AM-APR’, ‘1998-VAR-AM-APR-fit’, If mb == ‘AFDMC’: ‘2012-AFDMC-NM-RES-1’, ‘2012-AFDMC-NM-RES-2’, ‘2012-AFDMC-NM-RES-3’, ‘2012-AFDMC-NM-RES-4’, ‘2012-AFDMC-NM-RES-5’, ‘2012-AFDMC-NM-RES-6’, ‘2012-AFDMC-NM-RES-7’, ‘2012-AFDMC-NM-FIT-1’, ‘2012-AFDMC-NM-FIT-2’, ‘2012-AFDMC-NM-FIT-3’, ‘2012-AFDMC-NM-FIT-4’, ‘2012-AFDMC-NM-FIT-5’, ‘2012-AFDMC-NM-FIT-6’, ‘2012-AFDMC-NM-FIT-7’, ‘2022-AFDMC-NM’, If mb == ‘BHF2’: ‘2024-BHF-AM-2BF-Av8p’, ‘2024-BHF-AM-2BF-Av18’, ‘2024-BHF-AM-2BF-BONN’, ‘2024-BHF-AM-2BF-CDBONN’, ‘2024-BHF-AM-2BF-NSC97a’, ‘2024-BHF-AM-2BF-NSC97b’, ‘2024-BHF-AM-2BF-NSC97c’, ‘2024-BHF-AM-2BF-NSC97d’, ‘2024-BHF-AM-2BF-NSC97e’, ‘2024-BHF-AM-2BF-NSC97f’, ‘2024-BHF-AM-2BF-SSCV14’, If mb == ‘BHF23’: ‘2006-BHF-AM-Av18’, ‘2024-BHF-AM-23BF-Av8p’, ‘2024-BHF-AM-23BF-Av18’, ‘2024-BHF-AM-23BF-BONN’, ‘2024-BHF-AM-23BF-CDBONN’, ‘2024-BHF-AM-23BF-NSC97a’, ‘2024-BHF-AM-23BF-NSC97b’, ‘2024-BHF-AM-23BF-NSC97c’, ‘2024-BHF-AM-23BF-NSC97d’, ‘2024-BHF-AM-23BF-NSC97e’, ‘2024-BHF-AM-23BF-NSC97f’, ‘2024-BHF-AM-23BF-SSCV14’, ‘2024-BHF-AM-23BFmicro-Av18’, ‘2024-BHF-AM-23BFmicro-BONNB’, ‘2024-BHF-AM-23BFmicro-NSC93’, If mb == ‘QMC’: ‘2008-QMC-NM-swave’, ‘2010-QMC-NM-AV4’, ‘2009-DLQMC-NM’, ‘2013-QMC-NM’, ‘2014-AFQMC-NM’, ‘2016-QMC-NM’, ‘2018-QMC-NM’, ‘2024-QMC-NM’, If mb == ‘MBPT’: ‘2010-MBPT-NM’, ‘2020-MBPT-AM’, ‘2019-MBPT-AM-L59’, ‘2019-MBPT-AM-L69’ If mb == ‘NLEFT’: ‘2024-NLEFT-AM’,

nucleardatapy.matter.setup_micro.micro_models_mb_NM_old(mb)

nucleardatapy.matter.setup_micro.micro_models_mb_SM_old(mb)

nucleardatapy.matter.setup_micro.micro_models_mb_matter(mb, matter)

matter can be ‘sm’, ‘SM’ or ‘nm’, ‘NM’

nucleardatapy.matter.setup_micro.micro_models_old()

Return a list with the name of the models available in this toolkit and print them all on the prompt. These models are the following ones: ‘1981-VAR-AM-FP’, ‘1998-VAR-AM-APR’, ‘1998-VAR-AM-APR-fit’, ‘2006-BHF-AM*’, ‘2012-AFDMC-NM-RES-1’, ‘2012-AFDMC-NM-RES-2’, ‘2012-AFDMC-NM-RES-3’, ‘2012-AFDMC-NM-RES-4’, ‘2012-AFDMC-NM-RES-5’, ‘2012-AFDMC-NM-RES-6’, ‘2012-AFDMC-NM-RES-7’, ‘2012-AFDMC-NM-FIT-1’, ‘2012-AFDMC-NM-FIT-2’, ‘2012-AFDMC-NM-FIT-3’, ‘2012-AFDMC-NM-FIT-4’, ‘2012-AFDMC-NM-FIT-5’, ‘2012-AFDMC-NM-FIT-6’, ‘2012-AFDMC-NM-FIT-7’, ‘2008-QMC-NM-swave’, ‘2010-QMC-NM-AV4’, ‘2009-DLQMC-NM’, ‘2010-MBPT-NM’, ‘2013-QMC-NM’, ‘2014-AFQMC-NM’, ‘2016-QMC-NM’, ‘2016-MBPT-AM’, ‘2018-QMC-NM’, ‘2019-MBPT-AM-L59’, ‘2019-MBPT-AM-L69’, ‘2020-MBPT-AM’, ‘2022-AFDMC-NM’, ‘2024-NLEFT-AM’, ‘2006-BHF-AM’, ‘2024-BHF-AM-2BF-Av8p’, ‘2024-BHF-AM-2BF-Av18’, ‘2024-BHF-AM-2BF-BONN’, ‘2024-BHF-AM-2BF-CDBONN’, ‘2024-BHF-AM-2BF-NSC97a’, ‘2024-BHF-AM-2BF-NSC97b’, ‘2024-BHF-AM-2BF-NSC97c’, ‘2024-BHF-AM-2BF-NSC97d’, ‘2024-BHF-AM-2BF-NSC97e’, ‘2024-BHF-AM-2BF-NSC97f’, ‘2024-BHF-AM-2BF-SSCV14’, ‘2024-BHF-AM-23BF-Av8p’, ‘2024-BHF-AM-23BF-Av18’, ‘2024-BHF-AM-23BF-BONN’, ‘2024-BHF-AM-23BF-CDBONN’, ‘2024-BHF-AM-23BF-NSC97a’, ‘2024-BHF-AM-23BF-NSC97b’, ‘2024-BHF-AM-23BF-NSC97c’, ‘2024-BHF-AM-23BF-NSC97d’, ‘2024-BHF-AM-23BF-NSC97e’, ‘2024-BHF-AM-23BF-NSC97f’, ‘2024-BHF-AM-23BF-SSCV14’, ‘2024-BHF-AM-23BFmicro-Av18’, ‘2024-BHF-AM-23BFmicro-BONNB’, ‘2024-BHF-AM-23BFmicro-NSC93’, ‘2024-QMC-NM’

Returns:

The list of models.

Return type:

list[str].

class nucleardatapy.matter.setup_micro.setupMicro(model='1998-VAR-AM-APR', var1=array([0.01, 0.03052632, 0.05105263, 0.07157895, 0.09210526, 0.11263158, 0.13315789, 0.15368421, 0.17421053, 0.19473684, 0.21526316, 0.23578947, 0.25631579, 0.27684211, 0.29736842, 0.31789474, 0.33842105, 0.35894737, 0.37947368, 0.4]), var2=0.0)

Instantiate the object with microscopic results choosen by the toolkit practitioner.

This choice is defined in model, which can chosen among the following choices: ‘1981-VAR-AM-FP’, ‘1998-VAR-AM-APR’, ‘1998-VAR-AM-APR-fit’, ‘2006-BHF-AM*’, ‘2008-QMC-NM-swave’, ‘2010-QMC-NM-AV4’, ‘2009-DLQMC-NM’, ‘2010-MBPT-NM’, ‘2012-AFDMC-NM-RES-1’, ‘2012-AFDMC-NM-RES-2’, ‘2012-AFDMC-NM-RES-3’, ‘2012-AFDMC-NM-RES-4’, ‘2012-AFDMC-NM-RES-5’, ‘2012-AFDMC-NM-RES-6’, ‘2012-AFDMC-NM-RES-7’, ‘2012-AFDMC-NM-FIT-1’, ‘2012-AFDMC-NM-FIT-2’, ‘2012-AFDMC-NM-FIT-3’, ‘2012-AFDMC-NM-FIT-4’, ‘2012-AFDMC-NM-FIT-5’, ‘2012-AFDMC-NM-FIT-6’, ‘2012-AFDMC-NM-FIT-7’, ‘2013-QMC-NM’, ‘2014-AFQMC-NM’, ‘2016-QMC-NM’, ‘2016-MBPT-AM’, ‘2018-QMC-NM’, ‘2019-MBPT-AM-L59’, ‘2019-MBPT-AM-L69’, ‘2020-MBPT-AM’, ‘2022-AFDMC-NM’, ‘2024-NLEFT-AM’, ‘2024-BHF-AM-2BF-Av8p’, ‘2024-BHF-AM-2BF-Av18’, ‘2024-BHF-AM-2BF-BONN’, ‘2024-BHF-AM-2BF-CDBONN’, ‘2024-BHF-AM-2BF-NSC97a’, ‘2024-BHF-AM-2BF-NSC97b’, ‘2024-BHF-AM-2BF-NSC97c’, ‘2024-BHF-AM-2BF-NSC97d’, ‘2024-BHF-AM-2BF-NSC97e’, ‘2024-BHF-AM-2BF-NSC97f’, ‘2024-BHF-AM-2BF-SSCV14’, ‘2024-BHF-AM-23BF-Av8p’, ‘2024-BHF-AM-23BF-Av18’, ‘2024-BHF-AM-23BF-BONN’, ‘2024-BHF-AM-23BF-CDBONN’, ‘2024-BHF-AM-23BF-NSC97a’, ‘2024-BHF-AM-23BF-NSC97b’, ‘2024-BHF-AM-23BF-NSC97c’, ‘2024-BHF-AM-23BF-NSC97d’, ‘2024-BHF-AM-23BF-NSC97e’, ‘2024-BHF-AM-23BF-NSC97f’, ‘2024-BHF-AM-23BF-SSCV14’, ‘2024-QMC-NM’

Parameters:

model (str, optional.) – Fix the name of model. Default value: ‘1998-VAR-AM-APR’.

Attributes:

Parameters:

• model (str, optional)

• between (The model to consider. Choose)

• var2 (var1 and)

• np.array([0.1 (var1 =)

• 0.15

• 0.16

• 0.17

• 0.2

• 0.25])

init_self()

Initialize variables in self.

model

Attribute model.

print_outputs()

Method which print outputs on terminal’s screen.

Here are a set of figures which are produced with the Python sample: /nucleardatapy_sample/matter_setupMicro_plot.py

This figure shows the energy in neutron matter (NM) over the free Fermi gas energy (top) and the energy per particle (bottom) as function of the density (left) and the neutron Fermi momentum (right) for the variational models available in the nucleardatapy toolkit.

This figure shows the energy in neutron matter (NM) over the free Fermi gas energy (top) and the energy per particle (bottom) as function of the density (left) and the neutron Fermi momentum (right) for the AFDMC models available in the nucleardatapy toolkit.

This figure shows the energy in neutron matter (NM) over the free Fermi gas energy (top) and the energy per particle (bottom) as function of the density (left) and the neutron Fermi momentum (right) for the BHF models available in the nucleardatapy toolkit.

This figure shows the energy in neutron matter (NM) over the free Fermi gas energy (top) and the energy per particle (bottom) as function of the density (left) and the neutron Fermi momentum (right) for the QMC models available in the nucleardatapy toolkit.

This figure shows the energy in neutron matter (NM) over the free Fermi gas energy (top) and the energy per particle (bottom) as function of the density (left) and the neutron Fermi momentum (right) for the MBPT models available in the nucleardatapy toolkit.