sandy.pfns module

This module contains all classes and functions dedicated to handling PFNS data and, more in general, any tabulated energy distribution provided in MF5 sections.

class sandy.pfns.Edistr(df, **kwargs)

Bases: object

Object to store tabulate energy distributions.

Attributes:

datapandas.DataFrame: Dataframe of energy distribution data with the following columns:

Methods

`add_energy_point`(mat, mt, k, enew)	Add outgoing energy distribution at one additional incident energy by interpolation.
`add_energy_points`(mat, mt, k, elist)	Add outgoing energy distributions at additional incident energies by interpolation.
`custom_perturbation`(pert, mat, mt, k, ...)	Given a peruration object (fractions), a MAT number, a MT number, a subsection number, a lower and an upper incoming energy bound, apply the perturbation to the outgoing energy distributions for all incident energies comprised within the given boundaries.
`filter_by`(key, value)	Apply condition to source data and return filtered results.
`from_endf6`(endf6)	Extract energy distributions from Endf6 instance.
`get_integrals`()	Calculate the integral of each energy distribution.
`get_table`(mat, mt, k)	Pivot dataframe of tabulated energy spectra.
`normalize`()	Renormalize each outgoing energy distribution to 1.

add_energy_point(mat, mt, k, enew)

Add outgoing energy distribution at one additional incident energy by interpolation.

Parameters:

matint: MAT number.
mtint: MT number.
kint: subsection.
enewfloat: energy point in eV

Returns:

sandy.Edistr: energy distribution with an additional additional incoming energy.

Examples

>>> orig = Edistr(minimal_edistrtest)
>>> new = orig.add_energy_point(9437, 18, 0, 1.5)
>>> new
    MAT  MT  K         EIN        EOUT       VALUE
9437  18  0 1.00000e+00 1.00000e-05 4.00000e-01
9437  18  0 1.00000e+00 2.00000e+07 6.00000e-01
9437  18  0 1.50000e+00 1.00000e-05 2.00000e-01
9437  18  0 1.50000e+00 1.00000e-04 3.00000e-01
9437  18  0 1.50000e+00 1.00000e+00 5.50000e-01
9437  18  0 1.50000e+00 1.00000e+07 3.00000e-01
9437  18  0 1.50000e+00 2.00000e+07 3.00000e-01
9437  18  0 2.00000e+00 1.00000e-04 2.00000e-01
9437  18  0 2.00000e+00 1.00000e+00 7.00000e-01
9437  18  0 2.00000e+00 1.00000e+07 1.00000e-01

>>> new.get_table(9437, 18, 0)
EOUT         1.00000e-05  1.00000e-04  1.00000e+00  1.00000e+07  2.00000e+07
EIN                                                                         
1.00000e+00  4.00000e-01  4.00000e-01  4.00000e-01  5.00000e-01  6.00000e-01
1.50000e+00  2.00000e-01  3.00000e-01  5.50000e-01  3.00000e-01  3.00000e-01
2.00000e+00  0.00000e+00  2.00000e-01  7.00000e-01  1.00000e-01  0.00000e+00

If energy point is already present >>> copy = orig.add_energy_point(9437, 18, 0, 1) >>> pd.testing.assert_frame_equal(orig.data, copy.data)

add_energy_points(mat, mt, k, elist)

Add outgoing energy distributions at additional incident energies by interpolation.

Parameters:

matint: MAT number.
mtint: MT number.
kint: subsection.
elistiterable of float: energy points in eV

Returns:

newsandy.Edistr: energy distribution with an additional additional incoming energies.

Examples

>>> orig = Edistr(minimal_edistrtest)
>>> new = orig.add_energy_points(9437, 18, 0, [1, 1.5, 1.7])
>>> new
     MAT  MT  K         EIN        EOUT       VALUE
 9437  18  0 1.00000e+00 1.00000e-05 4.00000e-01
 9437  18  0 1.00000e+00 2.00000e+07 6.00000e-01
 9437  18  0 1.50000e+00 1.00000e-05 2.00000e-01
 9437  18  0 1.50000e+00 1.00000e-04 3.00000e-01
 9437  18  0 1.50000e+00 1.00000e+00 5.50000e-01
 9437  18  0 1.50000e+00 1.00000e+07 3.00000e-01
 9437  18  0 1.50000e+00 2.00000e+07 3.00000e-01
 9437  18  0 1.70000e+00 1.00000e-05 1.20000e-01
 9437  18  0 1.70000e+00 1.00000e-04 2.60000e-01
 9437  18  0 1.70000e+00 1.00000e+00 6.10000e-01
9437  18  0 1.70000e+00 1.00000e+07 2.20000e-01
9437  18  0 1.70000e+00 2.00000e+07 1.80000e-01
9437  18  0 2.00000e+00 1.00000e-04 2.00000e-01
9437  18  0 2.00000e+00 1.00000e+00 7.00000e-01
9437  18  0 2.00000e+00 1.00000e+07 1.00000e-01

custom_perturbation(pert, mat, mt, k, ein_low, ein_high)

Given a peruration object (fractions), a MAT number, a MT number, a subsection number, a lower and an upper incoming energy bound, apply the perturbation to the outgoing energy distributions for all incident energies comprised within the given boundaries.

Parameters:

pertsandy.Pert: perturbation object.
matint: MAT number.
mtint: MT number.
kint: subsection.
ein_lowTYPE: lower energy boundary in eV.
ein_highTYPE: upper energy boundary in eV.

Returns:

sandy.Edistr: perturbed distributions.

Notes

Note

the output distributions are not renormalized.

Note

The energy grid of the perturbation object refers to the outgoing energy distribution.

Note

If the perturbation exceeds 100%, it is truncated.

Examples

>>> orig = Edistr(minimal_edistrtest)
>>> pert = sandy.Pert([1.3], index=[1e-3])
>>> orig.custom_perturbation(pert, 9437, 18, 0, 1.5, 2.5)
    MAT  MT  K         EIN        EOUT       VALUE
0  9437  18  0 1.00000e+00 1.00000e-05 4.00000e-01
1  9437  18  0 1.00000e+00 2.00000e+07 6.00000e-01
2  9437  18  0 2.00000e+00 1.00000e-04 2.60000e-01
3  9437  18  0 2.00000e+00 1.00000e+00 7.00000e-01
4  9437  18  0 2.00000e+00 1.00000e+07 1.00000e-01

property data

Dataframe of energy distribution data with the following columns:

MAT : MAT number

MT : MT number

K : subsection number

EIN : incoming energy

EOUT : outgoing energy

VALUE : tabulated value of the distribution

Returns:

pandas.DataFrame: tabulated energy distribution

Notes

Note

tabulated values are assumed to be interpolated linearly

Examples

>>> Edistr(minimal_edistrtest)
    MAT  MT  K         EIN        EOUT       VALUE
9437  18  0 1.00000e+00 1.00000e-05 4.00000e-01
9437  18  0 1.00000e+00 2.00000e+07 6.00000e-01
9437  18  0 2.00000e+00 1.00000e-04 2.00000e-01
9437  18  0 2.00000e+00 1.00000e+00 7.00000e-01
9437  18  0 2.00000e+00 1.00000e+07 1.00000e-01

filter_by(key, value)

Apply condition to source data and return filtered results.

Parameters:

`key`str: any label present in the columns of data
`value`int or float: value used as filtering condition

Returns:

sandy.Edistr: filtered dataframe of energy distributions

Raises:

sandy.Error: if applied filter returned empty dataframe

Notes

Note

The primary function of this method is to make sure that the filtered dataframe is still returned as a Edistr object.

Examples

>>> Edistr(minimal_edistrtest).filter_by("EIN", 2)
    MAT  MT  K         EIN        EOUT       VALUE
0  9437  18  0 2.00000e+00 1.00000e-04 2.00000e-01
1  9437  18  0 2.00000e+00 1.00000e+00 7.00000e-01
2  9437  18  0 2.00000e+00 1.00000e+07 1.00000e-01

classmethod from_endf6(endf6)

Extract energy distributions from Endf6 instance.

Parameters:

endf6sandy.Endf6: object containing the ENDF-6 text

Returns:

sandy.Edistr: object with tabulated energy distributions

get_integrals()

Calculate the integral of each energy distribution.

Returns:

pandas.DataFrame: dataframe of integrals

Examples

>>> Edistr(minimal_edistrtest).get_integrals()
    MAT  MT  K         EIN    INTEGRAL
0  9437  18  0 1.00000e+00 1.00000e+07
1  9437  18  0 2.00000e+00 4.00000e+06

get_table(mat, mt, k)

Pivot dataframe of tabulated energy spectra.

Parameters:

matint: MAT number to filter data
mtint: MT number to filter data
kint: subsection number to filter data

Returns:

pandas.DataFrame: tabulated energy spectra with incident energies as index and outgoing energies as columns.

Examples

>>> Edistr(minimal_edistrtest).get_table(9437, 18, 0)
EOUT         1.00000e-05  1.00000e-04  1.00000e+00  1.00000e+07  2.00000e+07
EIN                                                                         
1.00000e+00  4.00000e-01  4.00000e-01  4.00000e-01  5.00000e-01  6.00000e-01
2.00000e+00  0.00000e+00  2.00000e-01  7.00000e-01  1.00000e-01  0.00000e+00

normalize()

Renormalize each outgoing energy distribution to 1.

Returns:

sandy.Edistr: renormalized energy distributions

Examples

>>> new = Edistr(minimal_edistrtest).normalize()
>>> new
    MAT  MT  K         EIN        EOUT       VALUE
0  9437  18  0 1.00000e+00 1.00000e-05 4.00000e-08
1  9437  18  0 1.00000e+00 2.00000e+07 6.00000e-08
2  9437  18  0 2.00000e+00 1.00000e-04 5.00000e-08
3  9437  18  0 2.00000e+00 1.00000e+00 1.75000e-07
4  9437  18  0 2.00000e+00 1.00000e+07 2.50000e-08

>>> new.get_integrals()
    MAT  MT  K         EIN    INTEGRAL
0  9437  18  0 1.00000e+00 1.00000e+00
1  9437  18  0 2.00000e+00 1.00000e+00