matter.setupMicroEsym

nucleardatapy.matter.setup_micro_esym.micro_esym_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_esym.micro_esym_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 == ‘BHF’: ‘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’, If mb == ‘MBPT’: ‘2010-MBPT-NM’, ‘2020-MBPT-AM’, ‘2019-MBPT-AM-L59’, ‘2019-MBPT-AM-L69’ If mb == ‘SCGF’: “2020-SCGF-AM-N3LO-414”, “2020-SCGF-AM-N3LO-450”, “2020-SCGF-AM-N3LO-500”, “2024-SCGF-AM-DN2LO-450”, “2024-SCGF-AM-DN2LO-500”, “2024-SCGF-AM-DN2LOgo-394”, “2024-SCGF-AM-DN2LOgo-450”, “2024-SCGF-AM-N2LOsat”, If mb == ‘NLEFT’: ‘2024-NLEFT-AM’, If mb == ‘CC’: “2024-CC-AM-DN2LO-450”, “2024-CC-AM-DN2LO-500”, “2024-CC-AM-DN2LOgo-394”, “2024-CC-AM-DN2LOgo-450”, “2024-CC-AM-N2LOsat”,

class nucleardatapy.matter.setup_micro_esym.setupMicroEsym(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*’, ‘2016-MBPT-AM’, ‘2019-MBPT-AM-L59’, ‘2019-MBPT-AM-L69’, ‘2020-MBPT-AM’, ‘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’

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_setupMicroEsym_plot.py

This figure shows the symmetry energy as function of the density (left) and the neutron Fermi momentum (right) for the models available in the nucleardatapy toolkit.