2.7. Nuclear Empirical Parameters (NEP)#
In this tutorial, you will learn how to employ nucleardatapy to extract predictions for NEP.
Import the libraries that will be employed in this tutorial.
# Import numpy
import numpy as np
# Import matplotlib
import matplotlib.pyplot as plt
# Import nucleardatapy package
import nucleardatapy as nuda
%matplotlib inline
You can simply print out the properties of the nuda’s function that we will use:
# Explore the nucleardatapy module to find the correct attribute
print(dir(nuda.matter.setupNEPStat_models()))
param: BSK14
param: BSK16
param: BSK17
param: BSK27
param: BSkG1
param: BSkG2
param: F-
param: F+
param: F0
param: FPL
param: LNS
param: LNS1
param: LNS5
param: NRAPR
param: RATP
param: SAMI
param: SGII
param: SIII
param: SKGSIGMA
param: SKI2
param: SKI4
param: SKMP
param: SKMS
param: SKO
param: SKOP
param: SKP
param: SKRSIGMA
param: SKX
param: Skz2
param: SLY4
param: SLY5
param: SLY230A
param: SLY230B
param: SV
param: T6
param: T44
param: UNEDF0
param: UNEDF1
['Dmsat', 'Dmsat_mean', 'Dmsat_std', 'Esat', 'Esat_mean', 'Esat_std', 'Esym', 'Esym_mean', 'Esym_std', 'Ksat', 'Ksat_mean', 'Ksat_std', 'Ksym', 'Ksym_mean', 'Ksym_std', 'Lsym', 'Lsym_mean', 'Lsym_std', 'Qsat', 'Qsat_mean', 'Qsat_std', 'Qsym', 'Qsym_mean', 'Qsym_std', 'Zsat', 'Zsat_mean', 'Zsat_std', 'Zsym', 'Zsym_mean', 'Zsym_std', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'init_self', 'kappas', 'kappas_mean', 'kappas_std', 'kappasat', 'kappasat_mean', 'kappasat_std', 'kappasym', 'kappasym_mean', 'kappasym_std', 'kappav', 'kappav_mean', 'kappav_std', 'models', 'msat', 'msat_mean', 'msat_std', 'nsat', 'nsat_mean', 'nsat_std', 'params', 'print_latex', 'print_outputs']
Get the full list of models:
models, models_lower = nuda.matter.nep_models()
print('models:',models)
models: ['Skyrme', 'ESkyrme', 'GSkyrme', 'Skyrme2', 'Gogny', 'Fayans', 'NLRH', 'DDRH', 'DDRHF', 'xEFT']
Fix one model for which we want to extract the data:
model = 'Skyrme'
Instantiate pheno with the data from phenomonological model defined by model and param:
dist = nuda.matter.setupNEPStat_model( model = model )
dist.print_latex()
param: BSK14
param: BSK16
param: BSK17
param: BSK27
param: BSkG1
param: BSkG2
param: F-
param: F+
param: F0
param: FPL
param: LNS
param: LNS1
param: LNS5
param: NRAPR
param: RATP
param: SAMI
param: SGII
param: SIII
param: SKGSIGMA
param: SKI2
param: SKI4
param: SKMP
param: SKMS
param: SKO
param: SKOP
param: SKP
param: SKRSIGMA
param: SKX
param: Skz2
param: SLY4
param: SLY5
param: SLY230A
param: SLY230B
param: SV
param: T6
param: T44
param: UNEDF0
param: UNEDF1
- Print latex:
model: Skyrme 38 ['BSK14', 'BSK16', 'BSK17', 'BSK27', 'BSkG1', 'BSkG2', 'F-', 'F+', 'F0', 'FPL', 'LNS', 'LNS1', 'LNS5', 'NRAPR', 'RATP', 'SAMI', 'SGII', 'SIII', 'SKGSIGMA', 'SKI2', 'SKI4', 'SKMP', 'SKMS', 'SKO', 'SKOP', 'SKP', 'SKRSIGMA', 'SKX', 'Skz2', 'SLY4', 'SLY5', 'SLY230A', 'SLY230B', 'SV', 'T6', 'T44', 'UNEDF0', 'UNEDF1']
table:
NEP & $E_{\sat}$ & $n_{\sat}$ & $K_{\sat}$ & $Q_{\sat}$ & $Z_{\sat}$ & $E_{sym}$ & $L_{sym}$ & $K_{sym}$ & $Q_{sym}$ & $Z_{sym}$ & $m^*_{sat}/m$ & $\Delta m^*_{sat}/m$ \\\\
& MeV & fm$^{-3}n$ & MeV & MeV & MeV & MeV & MeV & MeV & MeV & MeV & & \\\\
centroid & -15.89 & 0.159 & 238.0 & -350 & 1445 & 30.88 & 49.3 & -135 & 372 & -2180 & 0.77 & 0.126 \\\\
std.dev. & 0.18 & 0.004 & 25.7 & 86 & 494 & 1.52 & 21.2 & 88 & 184 & 1044 & 0.14 & 0.300 \\\\
dist.print_outputs( )
- Print output:
model: Skyrme
NEP:
Esat: [-15.85 -16.05 -16.05 -16.05 -16.09 -16.07 -16.02 -16.04 -16.03 -15.92
-15.31 -15.9 -15.56 -15.85 -16.05 -15.93 -15.59 -15.85 -15.59 -15.76
-15.93 -15.56 -15.83 -15.75 -15.95 -15.59 -16.05 -16. -15.97 -15.98
-15.99 -15.97 -16.05 -15.96 -16.02 -16.06 -15.8 ]
nsat: [0.159 0.159 0.159 0.159 0.158 0.158 0.162 0.162 0.162 0.162 0.175 0.162
0.16 0.161 0.16 0.159 0.158 0.145 0.158 0.158 0.16 0.157 0.16 0.16
0.162 0.158 0.155 0.16 0.16 0.16 0.16 0.16 0.155 0.161 0.161 0.16
0.159]
Ksat: [239.34 241.67 241.69 241.66 237.8 237.45 230.02 230.03 230.01 217.06
210.79 244.22 240.24 225.66 239.52 245. 214.65 355.38 237.25 240.72
247.74 230.88 223.36 222.32 201.03 237.39 271.08 230.11 229.91 229.96
229.91 229.92 305.68 235.97 230.03 230.02 220.02]
Qsat: [-358.66 -363.56 -363.6 -363.51 -376.7 -376.26 -404.91 -406.16 -405.42
-399.45 -382.52 -324.63 -316.09 -362.53 -349.81 -338.68 -380.89 101.39
-348.71 -339.28 -330.8 -338.03 -392.86 -390.73 -435.56 -348.39 -297.42
-365.23 -363.08 -363.97 -364.18 -363.08 -175.76 -383.15 -365.9 -403.67
-404.18]
Zsat: [1434.7 1459.72 1459.89 1459.44 1548.3 1546.92 1702.4 1709.6 1705.43
1832.78 1748.62 1299.23 1255.13 1611.1 1451.85 1330.51 1741.62 -903.05
1379.43 1349.04 1277.72 1423.54 1720.29 1709.57 2127.56 1377.1 903.91
1597.65 1586.7 1591.72 1593.51 1586.7 183.37 1560.77 1603.34 1706.58
1781.27]
Esym: [30. 30. 30. 30. 32. 32. 32. 32. 32. 30.93 33.43 29.91
29.15 32.78 29.26 28.16 26.83 28.16 31.37 33.37 29.5 29.89 31.97 31.95
30. 30.58 31.1 32.01 32.01 32.03 31.99 32.01 32.82 29.97 32. 30.54
28.99]
Lsym: [ 43.92 34.89 36.3 28.51 51.7 53.03 43.79 41.53 42.42 42.77
61.47 30.92 50.93 59.65 32.39 43.68 37.62 9.91 94.02 104.32
60.41 70.29 79.12 68.94 19.66 85.68 33.18 16.81 45.99 48.28
44.32 45.96 96.1 30.86 50.04 45.07 40. ]
Ksym: [-152.09 -187.46 -181.92 -221.53 -156.4 -150.6 -105.1 -117.92 -113.48
-135.62 -127.41 -211.39 -119.04 -123.38 -191.13 -119.93 -145.83 -393.9
13.98 70.63 -40.62 -49.8 -43.14 -78.84 -266.55 -9.14 -252.
-259.6 -119.56 -112.41 -98.24 -119.66 24.18 -211.64 -106.71 -189.61
-179.45]
Qsym: [388.57 462.24 450.81 487.92 341.52 331.9 655.14 660.58 657.96 485.9
302.66 444.4 285.85 311.85 440.36 371.86 330.16 130.53 -26.74 51.77
351.43 159.33 131.01 223.47 508.14 22.24 379.4 682.41 521.03 500.98
603.11 521.11 48.01 472.72 481.27 287.74 323.83]
Zsym: [-2190.6 -2565.8 -2507.73 -2650.89 -1893.74 -1846.97 -3869.47 -3875.17
-3870.47 -2912.97 -1766.34 -2470.88 -1670.6 -1837.54 -2476.83 -2179.12
-1890.8 -798.85 -4.89 -610.47 -2234.92 -1019.99 -850.6 -1348.65
-2747.74 -255.06 -1888.91 -3893.87 -3197.27 -3087.04 -3785.62 -3197.55
-480.83 -2561.61 -2971.81 -1495.46 -1744.06]
kappas: [ 0.25 0.25 0.25 0.25 0.25 0.25 0.43 0.43 0.43 0.19 0.21 0.66
0.66 0.44 0.5 0.48 0.27 0.31 0.28 0.46 0.54 0.53 0.12 0.12
-0. 0.28 0.01 0.43 0.44 0.44 0.44 0.44 1.61 0. 0.43 -0.1
-0.01]
kappav: [0.28 0.28 0.28 0.39 0.39 0.39 0.15 0.6 0.43 0.03 0.38 1.09 0.97 0.66
0.78 0.51 0.49 0.53 0.48 0.24 0.25 0.71 0.17 0.15 0.35 0.48 0.33 0.57
0.25 0.25 0. 0.25 2.02 0. 0.25 0.25 0.25]
kappasat: [ 0.25 0.25 0.25 0.25 0.25 0.25 0.43 0.43 0.43 0.19 0.21 0.66
0.66 0.44 0.5 0.48 0.27 0.31 0.28 0.46 0.54 0.53 0.12 0.12
-0. 0.28 0.01 0.43 0.44 0.44 0.44 0.44 1.61 0. 0.43 -0.1
-0.01]
kappasym: [-0.03 -0.03 -0.03 -0.14 -0.14 -0.14 0.28 -0.17 -0. 0.16 -0.16 -0.44
-0.31 -0.22 -0.28 -0.03 -0.22 -0.21 -0.2 0.22 0.29 -0.18 -0.06 -0.03
-0.35 -0.2 -0.33 -0.15 0.19 0.19 0.44 0.19 -0.4 0. 0.18 -0.35
-0.26]
msat: [0.8 0.8 0.8 0.8 0.86 0.86 0.7 0.7 0.7 0.84 0.83 0.6 0.6 0.69
0.67 0.68 0.79 0.76 0.78 0.69 0.65 0.65 0.9 0.9 1. 0.78 0.99 0.7
0.69 0.7 0.7 0.69 0.38 1. 0.7 1.11 1.01]
Dmsat: [ 0.03 0.04 0.04 0.18 0.18 0.18 -0.28 0.17 0. -0.23 0.23 0.34
0.23 0.21 0.26 0.02 0.28 0.26 0.25 -0.21 -0.25 0.16 0.09 0.05
0.8 0.25 0.72 0.14 -0.19 -0.18 -0.47 -0.19 0.12 -0. -0.18 1.02
0.56]
plot:
# reference band:
models, models_lower = nuda.matter.nep_models()
nuda.fig.matter_setupNEPStats_fig( None, models )
Plot name: None
param:
BSK14
param: BSK16
param: BSK17
param: BSK27
param: BSkG1
param: BSkG2
param: F-
param: F+
param: F0
param: FPL
param: LNS
param: LNS1
param: LNS5
param: NRAPR
param: RATP
param: SAMI
param: SGII
param: SIII
param: SKGSIGMA
param: SKI2
param: SKI4
param: SKMP
param: SKMS
param: SKO
param: SKOP
param: SKP
param: SKRSIGMA
param: SKX
param: Skz2
param: SLY4
param: SLY5
param: SLY230A
param: SLY230B
param: SV
param: T6
param: T44
param: UNEDF0
param: UNEDF1
param: BSk22
param: BSk24
param: BSk25
param: BSk26
param: BSk31
param: BSk32
param: BSkG3
param: BSkG4
param: SkK180
param: SkK200
param: SkK220
param: SkK240
param: SkKM
param: SLy4
param: SkM*
param: SV-min
param: SV-bas
param: SV-K218
param: SV-K226
param: SV-K241
param: SV-mas07
param: SV-mas08
param: SV-mas10
param: SV-sym28
param: SV-sym32
param: SV-sym34
param: SV-kap00
param: SV-kap20
param: SV-kap60
param: D1S
param: D1
param: D250
param: D260
param: D280
param: D300
param: Fy(IVP)
param: Fy(Dr,HFB)
param: Fy(std)
param: NL-SH
param: NL3
param: NL3II
param: PK1
param: PK1R
param: TM1
param: DDME1
param: DDME2
param: DDMEd
param: PKDD
param: TW99
param: PKA1
param: PKO1
param: PKO2
param: PKO3
param: H1MM
param: H2MM
param: H3MM
param: H4MM
param: H5MM
param: H6MM
param: H7MM
