tdcrpy package

This section provides documentation for the modules and classes in the project.

Submodules

tdcrpy.TDCRPy module

Created on Mon Jan 23 16:01:49 2023

A Monte-Carlo code to calculate detection efficiency in TDCR measurements

@author: Romain Coulon, Jialin Hu Bureau International des Poids et Mesures

tdcrpy.TDCRPy.TDCRPy(L, TD, TAB, TBC, TAC, Rad, pmf_1, N, kB, V, mode, mode2, Display=False, barp=True)

This is the main function of the TDCRPy package running the Monte-Carlo Triple-to-Double Coincidence Ratio model. The computation is made for a given solution containing a radionuclide (or a mixture of radionuclides), a given volume of scintillator V and a given Birks constant kB.

It can operates in two modes:

–> In mode=”eff”, it calculates the efficiency of the TDCR system as a function of a value (triplet) of free parameter(s) L, the measurement data is not used;

—> In mode=”res”, it calculates the residual of the TDCR model parametrized by a value (or triplet) of free parameter(s) L and the measurement data TD, TAB, TBC, TAC.

also, two configuration can be set:

–> mode2=”sym”, where symmetry is considered between the 3 photomultiplier tubes - here L is a scalar and only the global TDCR value TD is used as measurement data.

—> mode2=”asym”, where an asymmetry between the 3 photomultiplier tubes is possible - here L is a triplet and only the specific TDCR values TAB, TBC, TAC are used as measurement data.

The parmeter N sets the number of Monte-Carlo trails used for the estimation. Each MC trial corresponds to a simulated radiactive decay. TDCRPY() used a set of fonctions from the tdcrpy.TDCR_model_lib module.

Advanced settings can be configured in the config.toml file.

–> By default Y = True so that the analytical model is applied for solution containing only pure beta emitting radionuclides. If you would like to apply the MC calculation also for these nuclides, set Y = False.

—> If you would like to change the number of bins nE to discretize the linear energy space for quenching calculation, you can change nE_electron and nE_alpha parameters for respectively electrons and alpha particles.

–> By default the calculation is set for Ultima-Gold cocktail mixed with a small amount of aqueous solution. You can adapt for a specific scintillator by changing the density, the mean charge number Z and the mean mass number A of the scintillator.

Parameters

LFloat (if mode2=”sym”) or a tuple (if mode2=”asym”)

Free parameter in keV-1.

TDfloat

triple-to-double coincidence ratio. Not consider if mode2=”asym”. Not consider if mode2=”asym”.

TABfloat

triple-to-double coincidence ratio (coincidences between channel A and B). Not consider if mode2=”sym”.

TBCfloat

triple-to-double coincidence ratio (coincidences between channel B and C). Not consider if mode2=”sym”.

TACfloat

triple-to-double coincidence ratio (coincidences between channel A and C). Not consider if mode2=”sym”.

Radstring

List of radionuclides (eg. “H-3, Co-60”).

pmf_1string

list of probability of each radionuclide (eg. “0.8, 0.2”).

Ninteger

Number of Monte-Carlo trials. recommanded N>10000 (see JCGM 101). Not applied in the case of pure beta emitting radionuclides.

kBfloat

Birks constant in cm/keV.

Vfloat

volume of the scintillator in ml. run only for 10 ml

modestring

“res” to return the residual, “eff” to return efficiencies.

mode2string

“sym” for symetrical model, “asym” for symetrical model.

DisplayBoolean, optional

“True” to display details on the decay sampling. The default is False.

barpBoolean, optional

“True” to display the calculation progress. The default is True.

Returns

resfloat

Residuals of the model compared the measurement data for (a) given free parmeters L. (only in mode=”res”)

mean_efficiency_Sfloat

Estimation of the efficiency of single counting events. (only in mode=”eff”)

std_efficiency_Sfloat

Standard uncertainty from calculation associated with the estimation of the efficiency of single counting events. (only in mode=”eff”)

mean_efficiency_Dfloat

Estimation of the efficiency of logic sum of double coincidences. (only in mode=”eff”)

std_efficiency_Dfloat

Standard uncertainty from calculation associated with the estimation of the efficiency of logic sum of double coincidences. (only in mode=”eff”)

mean_efficiency_Tfloat

Estimation of the efficiency of triple coincidences. (only in mode=”eff”)

std_efficiency_Tfloat

Standard uncertainty from calculation associated with the estimation of the efficiency of triple coincidences. (only in mode=”eff”)

tdcrpy.TDCR_model_lib module

Created on Mon Jan 23 16:04:46 2023

Library of function of the TDCRpy code

@author: Romain Coulon, Jialin Hu Bureau International des Poids et Mesures

tdcrpy.TDCR_model_lib.E_quench_a(e, kB, nE)

This function calculate the quenched energy alpha particles according to the Birks model of scintillation quenching

Parameters

efloat

energy of the alpha particle in keV.

kBfloat

Birks constant in cm/keV.

Returns

float

Quenched energy in keV.

tdcrpy.TDCR_model_lib.E_quench_e(e, kB, nE)

This function calculate the quenched energy of electrons according to the Birks model of scintillation quenching

Parameters

efloat

energy of the electron in eV.

kBfloat

Birks constant in cm/MeV.

Returns

float

Quenched energy in eV.

tdcrpy.TDCR_model_lib.TicTocGenerator()

Generator that returns time differences

tdcrpy.TDCR_model_lib.clear_terminal()

Function to clear the terminal screen

tdcrpy.TDCR_model_lib.display_header()

Function to display the header.

tdcrpy.TDCR_model_lib.energie_dep_beta(e_inci, *, matrice10_1=array([[1.0, 2.0, 3.0, ..., 198.0, 199.0, 200.0], [0.0, 0.0, 0.0, ..., 5.1e-05, 4.7e-05, 5.7e-05], [0.0, 2e-06, 2e-06, ..., 7.7e-05, 7.4e-05, 7e-05], ..., [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0]]), matrice10_2=array([[200.0, 202.0, 204.0, ..., 1996.0, 1998.0, 2000.0], [5.7e-05, 4.9e-05, 5.6e-05, ..., 0.001795, 0.001826, 0.00181], [0.000494, 0.000489, 0.000492, ..., 0.000912, 0.000912, 0.000913], ..., [0.0, 0.0, 0.0, ..., 0.48222, 6.8e-05, 7e-05], [0.0, 0.0, 0.0, ..., 5e-06, 0.481935, 6.6e-05], [0.0, 0.0, 0.0, ..., 3e-06, 6e-06, 0.481512]]), matrice10_3=array([[2000.0, 2010.0, 2020.0, ..., 9980.0, 9990.0, 10000.0], [0.00181, 0.001776, 0.001775, ..., 0.012191, 0.012222, 0.012252], [0.004217, 0.004213, 0.004184, ..., 0.004783, 0.004785, 0.004751], ..., [0.0, 0.0, 0.0, ..., 0.000646, 1.4e-05, 1.5e-05], [0.0, 0.0, 0.0, ..., 1e-06, 0.000645, 1.5e-05], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.00065]]), ed=array([[0.0, 0.0, 0.0], [0.0002008, 0.001999, 0.009991], [0.0004016, 0.003998, 0.019982], ..., [0.2006, 1.997, 9.981], [0.2008, 1.999, 9.991], [0.201, 2.001, 10.001]]))

This function samples the energy deposited by an electron in the scintillator using response calculated by the Monte-Carlo code MCNP6.

Parameters

e_incifloat

energy of the electron in keV.

matrice10_1list[list], optional

response matrix for electrons in the range [1-200] keV and for a scintillator volume of 10 ml.

matrice10_2list[list], optional

response matrix for electrons in the range [200-2000] keV and for a scintillator volume of 10 ml.

matrice10_3list[list], optional

response matrix for electrons in the range [2000-10000] keV and for a scintillator volume of 10 ml.

edlist[list], optional

matrix of input energies. column 0: [1-200] keV; column 1: [200-2000] keV; column 2: [2000-10000] keV

Returns

resultfloat

deposited energy in keV.

tdcrpy.TDCR_model_lib.energie_dep_gamma(e_inci, v, matrice10_1=array([[1.0, 2.0, 3.0, ..., 198.0, 199.0, 200.0], [0.0, 0.0, 0.0, ..., 1e-06, 2e-06, 2e-06], [0.0, 0.001795, 0.005885, ..., 0.873706, 0.87388, 0.87406], ..., [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0]]), matrice10_2=array([[200.0, 202.0, 204.0, ..., 1996.0, 1998.0, 2000.0], [2e-06, 1e-06, 0.0, ..., 4.5e-05, 4.2e-05, 4.8e-05], [0.877508, 0.877865, 0.878157, ..., 0.951362, 0.951381, 0.951434], ..., [0.0, 0.0, 0.0, ..., 3e-06, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 4e-06, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 4e-06]]), matrice10_3=array([[2000.0, 2010.0, 2020.0, ..., 9980.0, 9990.0, 10000.0], [6e-05, 3e-05, 8e-05, ..., 0.00017, 0.00019, 0.00015], [0.95212, 0.95221, 0.95236, ..., 0.97913, 0.97918, 0.97918], ..., [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0]]), matrice16_1=array([[1.0, 2.0, 3.0, ..., 198.0, 199.0, 200.0], [0.0, 0.0, 0.0, ..., 2e-06, 1e-06, 3e-06], [0.0, 0.001533, 0.005069, ..., 0.855719, 0.855893, 0.856118], ..., [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0]]), matrice16_2=array([[200.0, 202.0, 204.0, ..., 1996.0, 1998.0, 2000.0], [3e-06, 1e-06, 0.0, ..., 3.8e-05, 4.8e-05, 4.1e-05], [0.859989, 0.860337, 0.860676, ..., 0.943906, 0.943951, 0.944006], ..., [0.0, 0.0, 0.0, ..., 3e-06, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 4e-06, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 4e-06]]), matrice16_3=array([[2000.0, 2010.0, 2020.0, ..., 9980.0, 9990.0, 10000.0], [4.1e-05, 3.1e-05, 4.4e-05, ..., 0.000194, 0.00019, 0.000205], [0.944572, 0.944712, 0.944892, ..., 0.975521, 0.975553, 0.975526], ..., [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0]]), ed=array([[0.0, 0.0, 0.0], [0.0002008, 0.001999, 0.009991], [0.0004016, 0.003998, 0.019982], ..., [0.2006, 1.997, 9.981], [0.2008, 1.999, 9.991], [0.201, 2.001, 10.001]]))

This function samples the energy deposited by a x or gamma rays in the scintillator using response calculated by the Monte-Carlo code MCNP6.

Parameters

e_incifloat

energy of the photon in keV.

vfloat

volume of the scintillator in ml.

matrice10_1list[list], optional

response matrix for photons in the range [1-200] keV and for a scintillator volume of 10 ml.

matrice10_2list[list], optional

response matrix for photons in the range [200-2000] keV and for a scintillator volume of 10 ml.

matrice10_3list[list], optional

response matrix for photons in the range [2000-10000] keV and for a scintillator volume of 10 ml.

edlist[list], optional

matrix of input energies. column 0: [1-200] keV; column 1: [200-2000] keV; column 2: [2000-10000] keV

Returns

resultfloat

deposited energy in keV.

tdcrpy.TDCR_model_lib.modelAnalytical(L, TD, TAB, TBC, TAC, rad, kB, V, mode, mode2, ne)

TDCR analytical model that is used for pure beta emitting radionuclides

Parameters

Lfloat or tuple

free parameter(s).

TDfloat

triple-to-double coincidence ratio that was measured (logic sum).

TABfloat

triple-to-double coincidence ratio that was measured (channels A and B).

TBCflat

triple-to-double coincidence ratio that was measured (channels B and C).

TACfloat

triple-to-double coincidence ratio that was measured (channels A and C).

radstring

radionuclide (eg. “Na-22”).

kBfloat

Birks constant in cm/keV.

Vfloat

volume of the scintillator in ml. run only for 10 ml

modestring

“res” to return the residual, “eff” to return efficiencies.

mode2string

“sym” for symetrical model, “asym” for symetrical model.

nEinteger

Number of bins for the quenching function.

Returns

resfloat

Residuals of the model compared the measurement data for (a) given free parmeters L. (only in mode=”res”)

mean_efficiency_Sfloat

Estimation of the efficiency of single counting events. (only in mode=”eff”)

mean_efficiency_Dfloat

Estimation of the efficiency of logic sum of double coincidences. (only in mode=”eff”)

mean_efficiency_Tfloat

Estimation of the efficiency of triple coincidences. (only in mode=”eff”)

tdcrpy.TDCR_model_lib.normalise(p_x)

This function is used to ensure that the sum of probability is equal to 1.

Parameters

p_xlist

vector of probabilities.

Returns

plist

normalized probability vector.

tdcrpy.TDCR_model_lib.readBetaShape(rad, mode, level, z=<zipfile.ZipFile filename='C:\\Users\\romain.coulon\\Anaconda3\\lib\\site-packages\\tdcrpy\\decayData\\All-nuclides_BetaShape.zip' mode='r'>)

This funcion reads the beta spectra calculated by the code BetaShape and published in the DDEP web page.

refs:

Parameters

radstring

identifier of the radionuclide. e.g. ‘Na-22’

modestring

identifier of the decay mode. ‘beta-’ or ‘beta+’

levelint or string

level of the daughter after decay. 0,1,2,3 …. or ‘tot’ in case of pure beta emitting radionuclides

Returns

elist

the energy vector in keV.

dNdxlist

the probability density in keV-1.

tdcrpy.TDCR_model_lib.readEShape(rad, *, z=<zipfile.ZipFile filename='C:\\Users\\romain.coulon\\Anaconda3\\lib\\site-packages\\tdcrpy\\decayData\\All-nuclides_Ensdf.zip' mode='r'>)

This function reads the ENSDF zip files and format the data to be processed by TDCRPy.

Parameters

radstring

name of the radionuclide such as ‘Ag-108’.

zZipFile object

zip ENSDF file.

Returns

daug_namelist

daughter nucleus of the decay

Energylist

comprise all transition energies of the daughter nucleus.

Problist

comprise all transtion probabilities of the daughter nucleus.

Typelist

comprise all type of transition of the daughter nucleus.

tdcrpy.TDCR_model_lib.readPenNuc2(rad, z1=<zipfile.ZipFile filename='C:\\Users\\romain.coulon\\Anaconda3\\lib\\site-packages\\tdcrpy\\decayData\\All-nuclides_PenNuc.zip' mode='r'>)

This function is used to read PenNuc files to format the decay data in lists readable by TDCRPy.

Parameters

radstring

name of the radionculide (for example: “Am-241”).

Returns

daughterlist

list of the daughter nucleus – indice 0.

prob_dauglist

list of probabilities to produce daugter nuclei – indice 1.

energy_Qlist

list of Q value for each transition to a given daughter nucleus – indice 2.

desin_type_totlist[list]

list of type of decay branch / emitted particules – indice 3. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

desin_energy_totlist[list]

list of the energies of decay transition or the emitted particles – indice 4. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

desin_prob_totlist[list]

list of the prabability of decay transition or the emitted particles – indice 5. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

desin_level_totlist[list]

list of energy level that the daughter nucleus can have just after the decay of the mother nucleus – indice 6. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

prob_branch_totlist

list of branch probabilities – indice 7. It contains a sub-list for all possible branches of a given daughter nucleus.

tran_type_totlist[list]

list of all possible transitions – indice 8. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

tran_energy_totlist[list]

list of energy associated with transitions – indice 9. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

tran_prob_totlist[list]

list of probability associated with transitions – indice 10. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

tran_level_totlist[list]

list of corresponding branch levels – indice 11. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to the level before the transition.

tran_level_end_totlist[list]

list of level following given transitions – indice 12. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to the level after the transition.

level_energy_totlist[list]

list of energy levels – indice 13. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

prob_tran_totlist[list]

list of sum of transition of each branches – indice 14. It contains a sub-list for all possible branches of a given daughter nucleus and a sub-sub list related to possible decay mode of each branch.

tdcrpy.TDCR_model_lib.read_matrice(path, niveau)

This function read the response matrix calculated by MCNP6 simulation.

Parameters

pathstring

path to the response matrix.

niveauinteger or string

energy range of the response matrix. 0: [1-200] keV; 1: [200-2000] keV; 2: [2000-10000] keV. “e” for the input energy matrix.

Returns

matricelist[list]

formatted response matrix.

tdcrpy.TDCR_model_lib.relaxation_atom(daugther, rad, lacune='defaut')

This function simulates the atomic rearangement following a missing electron an inner shell of the daughter atom.

Parameters

daugtherstring

The daughter nucleus (for example NB95,PD110 etc.)

radstring

The mother nucleus (for exemple Am-241, C-11 etc.)

lacunestring

The shell where the electron is missing (for example ‘Atom_K’,’Atom_L’ etc.)

Returns

Type : type of transition Auger L or K, or X Ray. Energy : corresponding energy in keV.

tdcrpy.TDCR_model_lib.sampling(p_x)

This function aims to sample in a pdf or a pmf

Parameters

p_xfloat vector

Probability Density (or mass) Function (PDF or PMF) of the random variable x.

Returns

iinteger

index in x pointing the sampled value of the random variable X.

tdcrpy.TDCR_model_lib.stoppingpower(e, rho=0.96, Z=5.2, A=11.04, emin=0, file=array([0.0, 0.7, 1.4, ..., 12.491336, 12.490668, 8.98619049e-43]))

The stopping power of electrons between 20 keV and 1000 keV is a mixture of a radiative loss model [1], and a collision model [2] that has been validated agaisnt the NIST model ESTAR [3] recommanded by the ICRU Report 37 [4]. At low energy - between 10 eV and 20 keV - the model from Tan and Xia [5] is implemented.

Refs:

Parameters

efloat

Energy of the electron in eV.

rhofloat, optional

density of the source in g.cm-3. The default is 0.96.

Zfloat, optional

mean charge number of the source. The default is 5.2.

Afloat, optional

mean mass number of the source. The default is 11.04.

eminfloat, optional

the minimal energy to consider. The default is 0.

filelist, optional

tabulated data form the Tan and Xia model. The default is data_TanXia_f.

Returns

dEdxfloat

Calculated stopping power in MeV.cm-1.

tdcrpy.TDCR_model_lib.stoppingpowerA(e, rho=0.96, energy_alpha=[1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.5, 15.0, 17.5, 20.0, 22.5, 25.0, 27.5, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 350.0, 400.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1250.0, 1500.0, 1750.0, 2000.0, 2250.0, 2500.0, 2750.0, 3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0, 6000.0, 6500.0, 7000.0, 7500.0, 8000.0], dEdx_alpha=[426600.0, 442500.0, 456000.0, 468700.0, 480800.0, 504300.0, 526800.0, 548500.0, 569400.0, 589600.0, 609100.0, 628100.0, 673000.0, 715000.0, 754500.0, 791900.0, 827400.0, 861200.0, 893600.0, 924700.0, 983500.0, 1038000.0, 1090000.0, 1139000.0, 1185000.0, 1229000.0, 1271000.0, 1311000.0, 1350000.0, 1387000.0, 1423000.0, 1457000.0, 1490000.0, 1522000.0, 1668000.0, 1792000.0, 1900000.0, 1993000.0, 2074000.0, 2145000.0, 2206000.0, 2258000.0, 2340000.0, 2398000.0, 2436000.0, 2457000.0, 2465000.0, 2463000.0, 2453000.0, 2436000.0, 2415000.0, 2389000.0, 2361000.0, 2331000.0, 2299000.0, 2267000.0, 2111000.0, 1962000.0, 1819000.0, 1684000.0, 1561000.0, 1457000.0, 1369000.0, 1292000.0, 1164000.0, 1061000.0, 977400.0, 907300.0, 847700.0, 796300.0, 751400.0, 711800.0, 676600.0, 645000.0])

Estimation of the stopping power of alpha particles using tabulated values form the ASTAR code

ref:

Parameters

efloat

energy of the alpha particle in keV.

rhofloat, optional

density of the source in g.cm-3. The default is 0.96.

energy_alphalist, optional

the list of energy (in keV) for which the stopping power was calculated with ASTAR. The default is energy_alph.

dEdx_alphalist, optional

the list of stopping powers (in keV.cm2/g) associated with the energy vector. The default is dEdx_alph.

Returns

float

Interpolated ASTAR estimation of the stopping power.

tdcrpy.TDCR_model_lib.tic()

Records a time in TicToc, marks the beginning of a time interval

tdcrpy.TDCR_model_lib.toc(tempBool=True)

Prints the time difference yielded by generator instance TicToc

tdcrpy.TDCR_model_lib.transf_name(rad)

This function format the name of the nuclide to match with the PenNuc format.

Parameters

radstring

name of the radionculdie such as ‘108AG’.

Returns

RADstring

name of the radionuclide such as ‘AG108’ that match with PenNuc format.

tdcrpy.TDCR_model_lib.writeEffcurves(x, y, uy, rad, p, kB, SDT)

This function writes efficiency curves

Parameters

xTYPE

DESCRIPTION.

yTYPE

DESCRIPTION.

uyTYPE

DESCRIPTION.

radTYPE

DESCRIPTION.

pTYPE

DESCRIPTION.

kBTYPE

DESCRIPTION.

SDTTYPE

DESCRIPTION.

Returns

None.

tdcrpy.TDCRoptimize module

Created on Wed Jul 5 10:04:53 2023

@author: romain.coulon, jialin.hu

tdcrpy.TDCRoptimize.eff(TD, TAB, TBC, TAC, Rad, pmf_1, kB, V, mode2, N=1000, L=1)

Caclulation of the efficiency of a TDCR system based on the model TDCRPy.

Parameters

TDfloat

triple-to-double coincidence ratio. Not consider if mode2=”asym”. Not consider if mode2=”asym”.

TABfloat

triple-to-double coincidence ratio (coincidences between channel A and B). Not consider if mode2=”sym”.

TBCfloat

triple-to-double coincidence ratio (coincidences between channel B and C). Not consider if mode2=”sym”.

TACfloat

triple-to-double coincidence ratio (coincidences between channel A and C). Not consider if mode2=”sym”.

Radstring

List of radionuclides.

pmf_1string

list of probability of each radionuclide..

kBfloat

Birks constant.

Vfloat

volume of the scintillator in ml. run only for 10 ml

mode2string

“sym” for symetrical model, “asym” for symetrical model.

Ninterger, optional

number of Monte-Carlo trials. The default is 1000.

Lfloat, optional

free parameter(s) as initial guess. The default is 1.

Returns

L0float

global free parameter.

Ltuple

free parameters (relevant for the asymetric model).

eff_Sfloat

counting efficiency of single events.

u_eff_Sfloat

standard uncertainty of eff_S.

eff_Dfloat

counting efficiency of double coincidences.

u_eff_Dfloat

standard uncertainty of eff_D.

eff_Tfloat

counting efficiency of triple coincidences.

u_eff_Tfloat

standard uncertainty of eff_T.

Module contents