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TODO

Aggregation via pandas

Sample data to be included in the overall df

  • consider lorentzian electric dipole strengths only!
  • consider isovector data only!
  • consider only the tin (Sn) isotopes, proton_number = 50!
ztes_lorvec.out: T = 0.0
ftes_lorvec.out: T = 0.5, 1.0, 2.0

sample [zf]tes_lorvec.out file

#RPA-results:
#Nucleus: SN 132
#Excitation: 1 -
...
#Real part: -1.797451e-13

#Parameterset: DD-ME2
#natural parity: yes
#Isovector result:

#width: 1.000000e+00
#maximum value: 9.166250e+00
#at energy: 1.523000e+01
0.000000e+00	0.000000e+00
1.000000e-02	2.881808e-02

  • the first column (energies) goes from 0 up to 50 MeV in increments of 0.01 MeV: np.linspace(0, 50, 5001, retstep=True)

  • TALYS contains the tabulated microscopic gamma-ray strength functions computed according to HF-QRPA [see manual, page 143]:

sample structure/gamma/hfb/Sn.psf

 Z=  50 A=  90
  U[MeV]  fE1[mb/MeV]
    0.100   6.291E-03
    0.200   6.776E-03
    .....   .........
    
 Z=  50 A=  91
  U[MeV]  fE1[mb/MeV]
    0.100   6.350E-03
    0.200   6.840E-03
    .....   .........
 ...
  • the energy axis U goes from 0.1 up to 30 MeV in increments of 0.1 MeV: np.linspace(0.1, 30, 300, retstep=True)
  • the file contains tin (Z=50) isotopes with masses from A=90 to A=178, with a gap from A = 171 to A = 177; in total there are 82 isotopes in this file

The rpa project workflow for generating each job's photon strength function (.psf) file, which is then passed to TALYS, is as follows:

st=>start: structure/gamma/hfb/Sn.psf
e=>end: talys_df
op=>operation: talys.api.fn_to_dict()
op2=>operation: talys.api.dict_to_df()
st->op->op2->e

st=>start: [zf]tes_lorvec.out
e=>end: lorvec_df
op=>operation: talys.api.lorvec_to_df()
st->op->e

st=>start: talys.api.replace_table(talys_df, lorvec_df)
e=>end: ${job._id}/Sn.psf
op=>operation: talys.api.df_to_dict()
op2=>operation: talys.api.dict_to_fn()

st->op->op2->e

Of particular interest here is the function mypackage.talys.api.lorvec_to_df(), which has the following diagram:

st=>start: read columns E and R from [zf]tes_lorvec.out
op=>operation: R = R * 4.022 mb / (e^2 * fm^2)
op2=>operation: select E between 0.1 and 30 MeV
op3=>operation: keep only every 10th row
e=>end: return dataframe

st->op->op2->op3->e

sample astrorate.g

# Reaction rate for 139Sn(n,g)
#    T       Rate       MACS
  0.0001 6.09702E+05 6.09702E+05
  0.0005 4.01579E+05 4.01579E+05
  ...... ........... ...........
  9.0000 8.01139E+02 8.01139E+02
 10.0000 2.96834E+02 2.96834E+02 

sample rp050140.tot

# n + 139Sn: Production of 140Sn - Total
# Q-value    = 3.16732E+00 mass= 139.963146
# E-threshold= 0.00000E+00
# # energies =    88
#     E          xs
 1.00000E-11 1.87949E+03
 2.53000E-08 3.73663E+01
 ........... ...........
 2.90000E+01 1.05665E-01
 3.00000E+01 9.39864E-02
  • the energy grid E is given as an input file, n0-30.grid

df structure

proton_number neutron_number mass_number model temperature excitation_energy neutron_energy strength_function_fm strength_function_mb cross_section capture_rate
temperature nuclear state model
0 ground RHB
> 0 ground FTRMF
0 excited QRPA
> 0 excited FTRPA
  • TALYS: HF-QRPA?

Final plots

One isotope(reaction) per row, all tempeatures

One temperature per row, all isotopes

Workflow

Step 0 involves building the required dataframe which should hold everything needed below.

  1. Select niso isotopes, eg. isotopes = (76, 86, 96), niso = 3 and a set of ntemp temperatures, eg. temperatures = (0.0, 1.0, 2.0), ntemp = 3
  2. For each isotope in isotopes, plot one figure with ntemp curves, showing all the temperatures; each figure depicts the variation of the electric dipole transition strength R [e${}^{2}$ fm${}^{2}$/MeV] vs E [MeV]; niso figures total
  3. For each temperature in temperatures, plot one figure with niso curves, showing all the isotopes; each figure depicts the variation of the electric dipole transition strength R [e${}^{2}$ fm${}^{2}$/MeV] vs E [MeV]; ntemp figures total
  4. Repeat steps 2&3, after converting the transition strength R [e${}^{2}$ fm${}^{2}$/MeV] to fE1 [mb/MeV];
  5. Repeat steps 2-4, for the neutron production cross-section [mb] instead of fE1 [mb/MeV];
  6. Plot the neutron capture rate versus mass number A, resulting in one figure with ntemp curves;
  7. Plot the neutron capture rate versus temperature T, resulting in one figure with niso curves; the temperature axis only has ntemp points;

Notes

  • each plot should take a type argument, with possible values lin-lin, log-log, log-lin (log on Y axis), lin-log (log on X axis)
  • each plot should take the limits of the energy axis, defaulting to [0.1, 10] MeV
  • each plot should have an optional include_talys argument, defaulting to False
  • each plot should operate on (and return) Axes instances
  • add model to pcolormesh plot

Finalize plots

  1. Do the analysis of particle-hole (p-h) transition components for some low-lying states of N=76,86,96. How to select these low-lying states: below E=10 MeV, find one or two discrete states with highest transition strength.

  2. Create new branch for new neutron capture (NC) figure. Do the calculations of neutron capture rates at T=1&2MeV but with E1 strength function at T=0MeV (from RHB+QRPA). Plot on the existing NC rates figure.

  • Q about TALYS result: why neutron capture rate is decreasing with increasing temperature?