Libraries

`distribution`

Pre-defined distribution derived from the parent class Distribution.

class nexus.lib.distribution.Array(values, weight)

Distributions from two arrays.

Parameters:

delta (list or ndarray) – Array of delta values.
weight (list or ndarray) – Array of weight values.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.AsymmetricGaussian(points, hwhm_low, hwhm_high)

Asymmetric Gaussian distribution with lower and higher HWHMs.

Parameters:

points (int) – Number of points in the distribution.
hwhm_low (float or Var) – HWHM of the Gaussian distribution for the lower values.
hwhm_high (float) – HWHM of the Gaussian distribution for the higher values.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.DoubleGaussian(points, fwhm1, fwhm2, offset2, weight2)

Double Gaussian distribution with the given two FWHMs offset to each other and with a relative weight.

Parameters:

points (int) – Number of points in the distribution.
fwhm1 (float or Var) – FWHM of the first Gaussian distribution around the zero position.
fwhm2 (float or Var) – FWHM of the second Gaussian distribution.
offset2 (float or Var) – offset of the second Gaussian distributions.
weight2 (float or Var) – relative weight of the second Gaussian distributions.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.File(filename)

Distributions from a file. delta values in column 1. Relative weight in column 2.

Parameters:: filename (string) – filename (with path) to be loaded by numpy.loadtxt(filename).

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.Gaussian(points, fwhm)

Gaussian distribution with the given FWHM.

see https://en.wikipedia.org/wiki/Gaussian_function

Parameters:

points (int) – Number of points in the distribution.
fwhm (float or Var) – FWHM of the Gaussian distribution.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.Log(points, sigma)

Log distributions with the given sigma value.

see https://en.wikipedia.org/wiki/Log-normal_distribution

Parameters:

points (int) – Number of points in the distribution.
sigma (float or Var) – Sigma of the Log distribution.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.Logistic(points, sigma)

Logistic distributions with the given sigma value.

see https://en.wikipedia.org/wiki/Logistic_distribution

Parameters:

points (int) – Number of points in the distribution.
sigma (float or Var) – Sigma value of the Logistic distribution.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.Lorentzian(points, fwhm)

Lorentzian distributions with the given FWHM.

see https://en.wikipedia.org/wiki/Cauchy_distribution

Parameters:

points (int) – Number of points in the distribution.
fwhm (float or Var) – FWHM of the Lorentzian distribution.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.NegGaussian(points, fwhm, target_var)

Gaussian distribution with the given FWHM and cut to negative values only.

Parameters:

points (int) – Number of points in the distribution.
fwhm (float or Var) – FWHM of the Gaussian distribution.
target_var (Var) – Target Var.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.PosGaussian(points, fwhm, target_var)

Gaussian distribution with the given FWHM and cut to positive values only.

Parameters:

points (int) – Number of points in the distribution.
fwhm (float or Var) – FWHM of the Gaussian distribution.
target_var (Var) – Target Var.

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.Random(points, width)

Random distribution in the range of width with weight of one.

Parameters:

points (int) – Number of points in the distribution
width (float or Var) – width of the distribution in absolute values

DistributionFunction(): Call of the distribution function implementation from python.

class nexus.lib.distribution.Rectangle(points, width)

Equally spaced distribution in the range of width with values of one.

Parameters:

points (int) – Number of points in the distribution
width (float or Var) – width of the distribution in absolute values

DistributionFunction(): Call of the distribution function implementation from python.

Example

import matplotlib.pyplot as plt

# Gaussian distribution object from nexus.lib.distribution.
gauss_dist = nx.lib.distribution.Gaussian(points = 101, fwhm = nx.Var(3, min = 0, max = 7, fit = True))

# Call of the distribution function. delta and weight are set by the call.
gauss_dist.DistributionFunction()

plt.plot(gauss_dist.delta, gauss_dist.weight)
plt.show()

`material`

The material module serves as a library for standard materials which are instances of the Material class. Almost all elements as well as typical alloys and compounds are covered in the library. Literature values are used for the material composition and density. For materials with a Moessbauer isotope, also the natural abundance and the Lamb Moessbauer factor (at room temperature, when available) are set properly.

A material can be accessed via nx.lib.materials.Name. The .Name for elements is the element symbol, e.g. for carbon its nx.lib.material.C and so on.

For isotope material often an enriched version is defined as Name_enriched, e.g. nexus.material_lib.Fe_enriched.

Alloys can be accessed by their name or a typical abbreviation. For example, permalloy can be called via nexus.lib.material.permalloy or nexus.lib.material.Py.

Compounds can be accessed via their material composition. For example, silicon nitride is referenced as nexus.Materials.Si3N4.

Note

Defining a material with a Material from material should always done by the method Material.Template().

my_material = nx.Material.Template(nx.lib.material.Name)

nexus.lib.material.Ag = Material .id: Ag .composition: Ag 1.0 .density (g/cm^3) Var.value = 10.49, .min = 0.0, .max = 10.49, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.856430029647292e+28 .average_mole_mass (g/mole) = 107.8682 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Silver

nexus.lib.material.Al = Material .id: Al .composition: Al 1.0 .density (g/cm^3) Var.value = 2.7, .min = 0.0, .max = 2.7, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.026261294278164e+28 .average_mole_mass (g/mole) = 26.9815384 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Aluminum

nexus.lib.material.Al2O3 = Material .id: Al2O3 sapphire .composition: Al 2.0 O 3.0 .density (g/cm^3) Var.value = 3.98, .min = 0.0, .max = 3.98, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.1753678977613322e+29 .average_mole_mass (g/mole) = 20.392015360000002 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Sapphire

nexus.lib.material.As = Material .id: As .composition: As 1.0 .density (g/cm^3) Var.value = 5.727, .min = 0.0, .max = 5.727, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.603318994012341e+28 .average_mole_mass (g/mole) = 74.921595 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Arsenic

nexus.lib.material.Au = Material .id: Au .composition: Au 1.0 .density (g/cm^3) Var.value = 19.3, .min = 0.0, .max = 19.3, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.900865140109817e+28 .average_mole_mass (g/mole) = 196.96657 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Gold

nexus.lib.material.B = Material .id: B .composition: B 1.0 .density (g/cm^3) Var.value = 2.08, .min = 0.0, .max = 2.08, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.1587467882331173e+29 .average_mole_mass (g/mole) = 10.81 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Boron

nexus.lib.material.B4C = Material .id: B4C boron carbide .composition: B 4.0 C 1.0 .density (g/cm^3) Var.value = 2.52, .min = 0.0, .max = 2.52, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.3733502303306725e+29 .average_mole_mass (g/mole) = 11.050200000000002 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Boron carbide

nexus.lib.material.BN = Material .id: BN boron nitride .composition: B 1.0 N 1.0 .density (g/cm^3) Var.value = 2.1, .min = 0.0, .max = 2.1, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.0191800456138937e+29 .average_mole_mass (g/mole) = 12.4085 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : boron nitride

nexus.lib.material.Be = Material .id: Be .composition: Be 1.0 .density (g/cm^3) Var.value = 1.85, .min = 0.0, .max = 1.85, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.2362110581175387e+29 .average_mole_mass (g/mole) = 9.0121831 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Beryllium

nexus.lib.material.C = Material .id: C .composition: C 1.0 .density (g/cm^3) Var.value = 2.25, .min = 0.0, .max = 2.25, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.1281172849887603e+29 .average_mole_mass (g/mole) = 12.011 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Carbon

nexus.lib.material.Ca = Material .id: Ca .composition: Ca 1.0 .density (g/cm^3) Var.value = 1.55, .min = 0.0, .max = 1.55, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 2.329037920554918e+28 .average_mole_mass (g/mole) = 40.078 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Calcium

nexus.lib.material.Cd = Material .id: Cd .composition: Cd 1.0 .density (g/cm^3) Var.value = 8.65, .min = 0.0, .max = 8.65, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.633899476399736e+28 .average_mole_mass (g/mole) = 112.414 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Cadmium

nexus.lib.material.Co = Material .id: Co .composition: Co 1.0 .density (g/cm^3) Var.value = 8.9, .min = 0.0, .max = 8.9, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.09454402963464e+28 .average_mole_mass (g/mole) = 58.933194 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Cobalt

nexus.lib.material.Cr = Material .id: Cr .composition: Cr 1.0 .density (g/cm^3) Var.value = 7.19, .min = 0.0, .max = 7.19, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.327392259111741e+28 .average_mole_mass (g/mole) = 51.9961 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Chromium

nexus.lib.material.Cu = Material .id: Cu .composition: Cu 1.0 .density (g/cm^3) Var.value = 8.96, .min = 0.0, .max = 8.96, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.491231739149593e+28 .average_mole_mass (g/mole) = 63.546 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Copper

nexus.lib.material.Dy = Material .id: Dy .composition: Dy 1.0 .density (g/cm^3) Var.value = 8.6, .min = 0.0, .max = 8.6, .fit: False, .id: .isotope: 161-Dy .abundance Var.value = 0.18889, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.2605, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.187102186830769e+28 .average_mole_mass (g/mole) = 162.5 .isotope_number_density (1/m^3) = 6.02011732070464e+27 number of hyperfine sites 0 : Dysprosium

nexus.lib.material.Eu = Material .id: Eu .composition: Eu 1.0 .density (g/cm^3) Var.value = 5.245, .min = 0.0, .max = 5.245, .fit: False, .id: .isotope: 151-Eu .abundance Var.value = 0.47816, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.087, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 2.0785286802730547e+28 .average_mole_mass (g/mole) = 151.9638799597923 .isotope_number_density (1/m^3) = 9.938692737593639e+27 number of hyperfine sites 0 : Europium

nexus.lib.material.Fe = Material .id: Fe .composition: Fe 1.0 .density (g/cm^3) Var.value = 7.874, .min = 0.0, .max = 7.874, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.02119, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.796, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.491062108378548e+28 .average_mole_mass (g/mole) = 55.845 .isotope_number_density (1/m^3) = 1.7992560607654142e+27 number of hyperfine sites 0 : Iron

nexus.lib.material.Fe2O3 = Material .id: Fe2O3 hematite .composition: Fe 2.0 O 3.0 .density (g/cm^3) Var.value = 5.25, .min = 0.0, .max = 5.25, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.02, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.793, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.89960483004405e+28 .average_mole_mass (g/mole) = 31.936869736505756 .isotope_number_density (1/m^3) = 7.919683864035241e+26 number of hyperfine sites 0 : hematite

nexus.lib.material.Fe2O3_enriched = Material .id: 57-Fe2O3 hematite .composition: Fe 2.0 O 3.0 .density (g/cm^3) Var.value = 5.25, .min = 0.0, .max = 5.25, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.95, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.793, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.772794643161411e+28 .average_mole_mass (g/mole) = 32.351277341250295 .isotope_number_density (1/m^3) = 3.7136619644013365e+28 number of hyperfine sites 0 : hematite enriched in iron to 95%

nexus.lib.material.Fe3O4 = Material .id: Fe3O4 magnetite .composition: Fe 3.0 O 4.0 .density (g/cm^3) Var.value = 5.0, .min = 0.0, .max = 5.0, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.02, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.796, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.103685774871276e+28 .average_mole_mass (g/mole) = 33.07528900339902 .isotope_number_density (1/m^3) = 7.803159235603951e+26 number of hyperfine sites 0 : magnetite

nexus.lib.material.Fe3O4_enriched = Material .id: Fe3O4 magnetite .composition: Fe 3.0 O 4.0 .density (g/cm^3) Var.value = 5.0, .min = 0.0, .max = 5.0, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.95, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.796, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.983095219464237e+28 .average_mole_mass (g/mole) = 33.5192971513396 .isotope_number_density (1/m^3) = 3.6574030536390104e+28 number of hyperfine sites 0 : magnetite enriched in iron to 95%

nexus.lib.material.Fe_enriched = Material .id: 57-Fe .composition: Fe 1.0 .density (g/cm^3) Var.value = 7.874, .min = 0.0, .max = 7.874, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.95, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.796, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.33660196616271e+28 .average_mole_mass (g/mole) = 56.87969335312573 .isotope_number_density (1/m^3) = 7.919771867854575e+28 number of hyperfine sites 0 : Iron enriched to 95%

nexus.lib.material.Ga = Material .id: Ga .composition: Ga 1.0 .density (g/cm^3) Var.value = 5.91, .min = 0.0, .max = 5.91, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.104607072501183e+28 .average_mole_mass (g/mole) = 69.723 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Gallium

nexus.lib.material.Ge = Material .id: Ge .composition: Ge 1.0 .density (g/cm^3) Var.value = 5.323, .min = 0.0, .max = 5.323, .fit: False, .id: .isotope: 73-Ge .abundance Var.value = 0.0776, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.8416, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.413583266622608e+28 .average_mole_mass (g/mole) = 72.63 .isotope_number_density (1/m^3) = 3.424940614899144e+27 number of hyperfine sites 0 : Germanium

nexus.lib.material.I = Material .id: I .composition: I 1.0 .density (g/cm^3) Var.value = 4.933, .min = 0.0, .max = 4.933, .fit: False, .id: .isotope: 127-I .abundance Var.value = 1.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = nan .average_mole_mass (g/mole) = nan .isotope_number_density (1/m^3) = nan number of hyperfine sites 0 : Iodine

nexus.lib.material.In = Material .id: In .composition: In 1.0 .density (g/cm^3) Var.value = 7.31, .min = 0.0, .max = 7.31, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.83405467397098e+28 .average_mole_mass (g/mole) = 114.818 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Indium

nexus.lib.material.Ir = Material .id: Ir .composition: Ir 1.0 .density (g/cm^3) Var.value = 22.56, .min = 0.0, .max = 22.56, .fit: False, .id: .isotope: 193-Ir .abundance Var.value = 0.627, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.038, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.0680270499279444e+28 .average_mole_mass (g/mole) = 192.217 .isotope_number_density (1/m^3) = 4.431652960304821e+28 number of hyperfine sites 0 : Iridium

nexus.lib.material.K = Material .id: K .composition: K 1.0 .density (g/cm^3) Var.value = 0.89, .min = 0.0, .max = 0.89, .fit: False, .id: .isotope: 40-K .abundance Var.value = 0.0001171, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.3708282110811396e+28 .average_mole_mass (g/mole) = 39.09830008657998 .isotope_number_density (1/m^3) = 1.6052398351760146e+24 number of hyperfine sites 0 : Potassium

nexus.lib.material.Li = Material .id: Li .composition: Li 1.0 .density (g/cm^3) Var.value = 0.534, .min = 0.0, .max = 0.534, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.633750959423631e+28 .average_mole_mass (g/mole) = 6.94 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Lithium

nexus.lib.material.Mg = Material .id: Mg .composition: Mg 1.0 .density (g/cm^3) Var.value = 1.738, .min = 0.0, .max = 1.738, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.306307607850237e+28 .average_mole_mass (g/mole) = 24.305 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Magnesium

nexus.lib.material.MgO = Material .id: MgO magnesium oxide .composition: Mg 1.0 O 1.0 .density (g/cm^3) Var.value = 3.6, .min = 0.0, .max = 3.6, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.0758091869789598e+29 .average_mole_mass (g/mole) = 20.152 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Magnesium oxide

nexus.lib.material.Mn = Material .id: Mn .composition: Mn 1.0 .density (g/cm^3) Var.value = 7.21, .min = 0.0, .max = 7.21, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.903382157169304e+28 .average_mole_mass (g/mole) = 54.938043 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Manganese

nexus.lib.material.Mo = Material .id: Mo .composition: Mo 1.0 .density (g/cm^3) Var.value = 10.28, .min = 0.0, .max = 10.28, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.452069516706618e+28 .average_mole_mass (g/mole) = 95.95 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Molybdenum

nexus.lib.material.Na = Material .id: Na .composition: Na 1.0 .density (g/cm^3) Var.value = 0.968, .min = 0.0, .max = 0.968, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 2.5356636618147042e+28 .average_mole_mass (g/mole) = 22.98976928 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Sodium

nexus.lib.material.Nb = Material .id: Nb .composition: Nb 1.0 .density (g/cm^3) Var.value = 8.57, .min = 0.0, .max = 8.57, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.555027746019999e+28 .average_mole_mass (g/mole) = 92.90637 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Niobium

nexus.lib.material.Ni = Material .id: Ni .composition: Ni 1.0 .density (g/cm^3) Var.value = 8.908, .min = 0.0, .max = 8.908, .fit: False, .id: .isotope: 61-Ni .abundance Var.value = 0.011399, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0007719, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.13990838664654e+28 .average_mole_mass (g/mole) = 58.6934 .isotope_number_density (1/m^3) = 1.041858156993839e+27 number of hyperfine sites 0 : Nickel

nexus.lib.material.Pd = Material .id: Pd .composition: Pd 1.0 .density (g/cm^3) Var.value = 12.023, .min = 0.0, .max = 12.023, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.8036269834128925e+28 .average_mole_mass (g/mole) = 106.42 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Palladium .. versionchanged:: 2.0.0

Was not working in versions 1.X.X

nexus.lib.material.Pt = Material .id: Pt .composition: Pt 1.0 .density (g/cm^3) Var.value = 21.45, .min = 0.0, .max = 21.45, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.621502496463062e+28 .average_mole_mass (g/mole) = 195.084 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Platinum

nexus.lib.material.Py = Material .id: permalloy Ni80Fe20 wt% .composition: Ni 79.2 Fe 20.8 .density (g/cm^3) Var.value = 8.7, .min = 0.0, .max = 8.7, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.02, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.796, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.017561463238652e+28 .average_mole_mass (g/mole) = 58.100657062983 .isotope_number_density (1/m^3) = 3.751305568707279e+26 number of hyperfine sites 0 : permalloy

nexus.lib.material.Py_enriched = Material .id: 57-permalloy Ni80Fe20 wt% .composition: Ni 79.2 Fe 20.8 .density (g/cm^3) Var.value = 8.7, .min = 0.0, .max = 8.7, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.95, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.76, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.984239442203628e+28 .average_mole_mass (g/mole) = 58.31614901745016 .isotope_number_density (1/m^3) = 1.775285713779437e+28 number of hyperfine sites 0 : permalloy enriched in iron to 95%

nexus.lib.material.Rb = Material .id: Rb .composition: Rb 1.0 .density (g/cm^3) Var.value = 1.532, .min = 0.0, .max = 1.532, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.0794614631849656e+28 .average_mole_mass (g/mole) = 85.4678 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Rubidium

nexus.lib.material.Rh = Material .id: Rh .composition: Rh 1.0 .density (g/cm^3) Var.value = 12.41, .min = 0.0, .max = 12.41, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.2624664467950155e+28 .average_mole_mass (g/mole) = 102.90549 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Rhodium

nexus.lib.material.Ru = Material .id: Ru .composition: Ru 1.0 .density (g/cm^3) Var.value = 12.45, .min = 0.0, .max = 12.45, .fit: False, .id: .isotope: 99-Ru .abundance Var.value = 0.1276, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.033, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.418190606708222e+28 .average_mole_mass (g/mole) = 101.07 .isotope_number_density (1/m^3) = 9.465611214159691e+27 number of hyperfine sites 0 : Ruthenium

nexus.lib.material.SS = Material .id: stainless steel Fe55Cr25Ni20 wt% .composition: Fe 54.52 Cr 26.62 Ni 18.86 .density (g/cm^3) Var.value = 7.8, .min = 0.0, .max = 7.8, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.485315760186361e+28 .average_mole_mass (g/mole) = 55.357631059999996 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Stainless steel

nexus.lib.material.SS_enriched = Material .id: 57-stainless steel Fe55Cr25Ni20 wt% .composition: Fe 54.52 Cr 26.62 Ni 18.86 .density (g/cm^3) Var.value = 7.8, .min = 0.0, .max = 7.8, .fit: False, .id: .isotope: 57-Fe .abundance Var.value = 0.95, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.76, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.39971949946846e+28 .average_mole_mass (g/mole) = 55.92174587612415 .isotope_number_density (1/m^3) = 4.350550717554694e+28 number of hyperfine sites 0 : Stainless steel enriched in iron to 95%

nexus.lib.material.Sb = Material .id: Sb .composition: Sb 1.0 .density (g/cm^3) Var.value = 6.697, .min = 0.0, .max = 6.697, .fit: False, .id: .isotope: 121-Sb .abundance Var.value = 0.5725, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.057, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.3122981087845464e+28 .average_mole_mass (g/mole) = 121.75919964075717 .isotope_number_density (1/m^3) = 1.8962906672791528e+28 number of hyperfine sites 0 : Antimony

nexus.lib.material.Sc = Material .id: Sc .composition: Sc 1.0 .density (g/cm^3) Var.value = 2.985, .min = 0.0, .max = 2.985, .fit: False, .id: .isotope: 45-Sc .abundance Var.value = 1.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.837, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = nan .average_mole_mass (g/mole) = nan .isotope_number_density (1/m^3) = nan number of hyperfine sites 0 : Scandium

nexus.lib.material.Si = Material .id: Si .composition: Si 1.0 .density (g/cm^3) Var.value = 2.336, .min = 0.0, .max = 2.336, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.008980172818231e+28 .average_mole_mass (g/mole) = 28.085 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Silicon

nexus.lib.material.Si3N4 = Material .id: Si3N4 silicon nitride .composition: Si 3.0 N 4.0 .density (g/cm^3) Var.value = 3.17, .min = 0.0, .max = 3.17, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.525837304905085e+28 .average_mole_mass (g/mole) = 20.04042857142857 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Silicon nitride

nexus.lib.material.SiC = Material .id: SiC silicon carbide .composition: Si 1.0 C 1.0 .density (g/cm^3) Var.value = 3.16, .min = 0.0, .max = 3.16, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.49220111811652e+28 .average_mole_mass (g/mole) = 20.048000000000002 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Silicon carbide

nexus.lib.material.SiO2 = Material .id: SiO2 silicon dioxide .composition: Si 1.0 O 2.0 .density (g/cm^3) Var.value = 2.648, .min = 0.0, .max = 2.648, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.9622998514455e+28 .average_mole_mass (g/mole) = 20.02766666666667 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Silicon dioxide

nexus.lib.material.Sm = Material .id: Sm .composition: Sm 1.0 .density (g/cm^3) Var.value = 7.518, .min = 0.0, .max = 7.536, .fit: False, .id: .isotope: 149-Sm .abundance Var.value = 0.1382, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.327, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.0110703799999994e+28 .average_mole_mass (g/mole) = 150.36 .isotope_number_density (1/m^3) = 4.161299265159999e+27 number of hyperfine sites 0 

Samarium

versionadded:

nexus.lib.material.Sn = Material .id: Sn .composition: Sn 1.0 .density (g/cm^3) Var.value = 6.99, .min = 0.0, .max = 6.99, .fit: False, .id: .isotope: 119-Sn .abundance Var.value = 0.0859, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.04, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.546016671923174e+28 .average_mole_mass (g/mole) = 118.71 .isotope_number_density (1/m^3) = 3.046028321182007e+27 number of hyperfine sites 0 : Tin

nexus.lib.material.SnO = Material .id: SnO stannous oxide .composition: Sn 1.0 O 1.0 .density (g/cm^3) Var.value = 6.45, .min = 0.0, .max = 6.45, .fit: False, .id: .isotope: 119-Sn .abundance Var.value = 0.086, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.27, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.766920072769215e+28 .average_mole_mass (g/mole) = 67.35451057387047 .isotope_number_density (1/m^3) = 2.4797756312907626e+27 number of hyperfine sites 0 : Tin oxide, stannous oxide

nexus.lib.material.SnO2 = Material .id: SnO2 stannic oxide .composition: Sn 1.0 O 2.0 .density (g/cm^3) Var.value = 6.95, .min = 0.0, .max = 6.95, .fit: False, .id: .isotope: 119-Sn .abundance Var.value = 0.086, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.6289, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.331450037613483e+28 .average_mole_mass (g/mole) = 50.23600704924698 .isotope_number_density (1/m^3) = 2.3883490107825313e+27 number of hyperfine sites 0 : Tin dioxide, stannic oxide,

nexus.lib.material.SnO2_enriched = Material .id: SnO2 stannic oxide enriched .composition: Sn 1.0 O 2.0 .density (g/cm^3) Var.value = 6.95, .min = 0.0, .max = 6.95, .fit: False, .id: .isotope: 119-Sn .abundance Var.value = 0.97, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.6289, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 8.32112807964764e+28 .average_mole_mass (g/mole) = 50.29832239257193 .isotope_number_density (1/m^3) = 2.6904980790860696e+28 number of hyperfine sites 0 : Tin dioxide, stannic oxide, enriched in tin to 97%

nexus.lib.material.SnO_enriched = Material .id: SnO stannous oxide enriched .composition: Sn 1.0 O 1.0 .density (g/cm^3) Var.value = 6.45, .min = 0.0, .max = 6.45, .fit: False, .id: .isotope: 119-Sn .abundance Var.value = 0.97, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.27, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.758927967183389e+28 .average_mole_mass (g/mole) = 67.4479835888579 .isotope_number_density (1/m^3) = 2.7930800640839436e+28 number of hyperfine sites 0 : Tin oxide, stannous oxide, enriched in tin to 97%

nexus.lib.material.Sn_enriched = Material .id: Sn resonant .composition: Sn 1.0 .density (g/cm^3) Var.value = 6.99, .min = 0.0, .max = 6.99, .fit: False, .id: .isotope: 119-Sn .abundance Var.value = 0.97, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.04, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.540440510099873e+28 .average_mole_mass (g/mole) = 118.8969671777158 .isotope_number_density (1/m^3) = 3.434227294796877e+28 number of hyperfine sites 0 : Tin enriched to 97%

nexus.lib.material.Sr = Material .id: Sr .composition: Sr 1.0 .density (g/cm^3) Var.value = 2.64, .min = 0.0, .max = 2.64, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 1.8144774716274822e+28 .average_mole_mass (g/mole) = 87.62 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Strontium

nexus.lib.material.Ta = Material .id: Ta .composition: Ta 1.0 .density (g/cm^3) Var.value = 16.69, .min = 0.0, .max = 16.69, .fit: False, .id: .isotope: 181-Ta .abundance Var.value = 0.99988, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.966, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.554612147461579e+28 .average_mole_mass (g/mole) = 180.94788009696086 .isotope_number_density (1/m^3) = 5.553945594003884e+28 number of hyperfine sites 0 : Tantalum

nexus.lib.material.Ta2O5 = Material .id: Ta2O5 tantalum pentoxide .composition: Ta 2.0 O 5.0 .density (g/cm^3) Var.value = 8.37, .min = 0.0, .max = 8.37, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.984716112380353e+28 .average_mole_mass (g/mole) = 63.12725142857143 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Tantalum pentoxide

nexus.lib.material.Tc = Material .id: Tc .composition: Tc 1.0 .density (g/cm^3) Var.value = 11.0, .min = 0.0, .max = 11.0, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.6976133306102144e+28 .average_mole_mass (g/mole) = 98.9062 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Technetium

nexus.lib.material.Te = Material .id: Te .composition: Te 1.0 .density (g/cm^3) Var.value = 6.24, .min = 0.0, .max = 6.24, .fit: False, .id: .isotope: 125-Te .abundance Var.value = 0.0699, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0081, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 2.9449431789693093e+28 .average_mole_mass (g/mole) = 127.6023205159152 .isotope_number_density (1/m^3) = 2.0585152820995473e+27 number of hyperfine sites 0 : Tellurium

nexus.lib.material.Ti = Material .id: Ti .composition: Ti 1.0 .density (g/cm^3) Var.value = 4.506, .min = 0.0, .max = 4.506, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 5.66899247175716e+28 .average_mole_mass (g/mole) = 47.867 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Titanium .. versionadded:: 2.0.0

nexus.lib.material.V = Material .id: V .composition: V 1.0 .density (g/cm^3) Var.value = 6.11, .min = 0.0, .max = 6.11, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 7.223046051568956e+28 .average_mole_mass (g/mole) = 50.9415 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Vanadium

nexus.lib.material.W = Material .id: W .composition: W 1.0 .density (g/cm^3) Var.value = 19.3, .min = 0.0, .max = 19.3, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.322199557658833e+28 .average_mole_mass (g/mole) = 183.84 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Tungsten

nexus.lib.material.WC = Material .id: WC tungsten carbide .composition: W 1.0 C 1.0 .density (g/cm^3) Var.value = 15.63, .min = 0.0, .max = 15.63, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 9.612007095067168e+28 .average_mole_mass (g/mole) = 97.9255 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Tungsten carbide

nexus.lib.material.WO = Material .id: WO tungsten oxide .composition: W 1.0 O 1.0 .density (g/cm^3) Var.value = 7.16, .min = 0.0, .max = 7.16, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.315326622090783e+28 .average_mole_mass (g/mole) = 99.9195 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Tungsten oxide

nexus.lib.material.Y = Material .id: Y .composition: Y 1.0 .density (g/cm^3) Var.value = 4.472, .min = 0.0, .max = 4.472, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 3.029161355285547e+28 .average_mole_mass (g/mole) = 88.90584 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Yttrium

nexus.lib.material.Zn = Material .id: Zn .composition: Zn 1.0 .density (g/cm^3) Var.value = 7.14, .min = 0.0, .max = 7.14, .fit: False, .id: .isotope: 67-Zn .abundance Var.value = 0.0404, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 5.15e-12, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 6.576641943468951e+28 .average_mole_mass (g/mole) = 65.38 .isotope_number_density (1/m^3) = 2.6569633451614563e+27 number of hyperfine sites 0 : Zinc

nexus.lib.material.Zr = Material .id: Zr .composition: Zr 1.0 .density (g/cm^3) Var.value = 6.52, .min = 0.0, .max = 6.52, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.304169709199333e+28 .average_mole_mass (g/mole) = 91.224 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Zirconium

nexus.lib.material.air = Material .id: Air .composition: N 1.562 O 0.42 C 0.0003 Ar 0.0094 .density (g/cm^3) Var.value = 0.0012041, .min = 0.0, .max = 0.0012041, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.983956693686564e+25 .average_mole_mass (g/mole) = 14.549202841793443 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Air

nexus.lib.material.vacuum = Material .id: vacuum .composition: N 1.0 .density (g/cm^3) Var.value = 1e-299, .min = 0.0, .max = 2e-299, .fit: False, .id: .isotope: none .abundance Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: .lamb_moessbauer Var.value = 0.0, .min = 0.0, .max = 1.0, .fit: False, .id: derived parameters: .total_number_density (1/m^3) = 4.299379424573428e-271 .average_mole_mass (g/mole) = 14.007 .isotope_number_density (1/m^3) = 0.0 number of hyperfine sites 0 : Vacuum

`moessbauer`

Predefined Moessbauer isotopes. Instances of nexus.MoessbauerIsotope class. Data have mainly been taken from https://www.nndc.bnl.gov/ensdf/ or R. Roehlsberger, Nuclear Condensed Matter Physics with Synchrotron Radiation, Springer, Berlin, 2005.

nexus.lib.moessbauer.Dy161 = MoessbauerIsotope: 161-Dy .element = Dy .mass (u) = 160.9269334 .spin_ground = 2.5 .spin_excited = 2.5 .energy (eV) = 25655.0 .wavelength (m) = 4.832750e-11 .kvector (1/m) = 1.300126e+11 .lifetime (s) = 4.068400e-08 .half_lifetime (s) = 2.820000e-08 .gamma (eV) = 1.617864e-08 .gamma_photon (eV) = 4.829446e-09 .gamma_electron (eV) = 1.134920e-08 .quality_factor = 1.585732e+12 .internal_conversion = 2.35 .interference_term (beta) = 0.0 .multipolarity = E1 (L = 1, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -0.192 .gfactor_excited = 0.237 .quadrupole_ground (barn) = 2.507 .quadrupole_excited (barn) = 2.506 .magnetic_moment_ground (eV/T) = -1.5131766020015998e-08 .magnetic_moment_excited (eV/T) = 1.867827368095725e-08 .nuclear_cross_section (converted to kbarn) = 1109.5936528640366 .natural_abundance = 0.18889 : Dy-161 25.6 keV

nexus.lib.moessbauer.Eu151 = MoessbauerIsotope: 151-Eu .element = Eu .mass (u) = 150.91985026 .spin_ground = 2.5 .spin_excited = 3.5 .energy (eV) = 21541.418 .wavelength (m) = 5.755619e-11 .kvector (1/m) = 1.091661e+11 .lifetime (s) = 1.385000e-08 .half_lifetime (s) = 9.600088e-09 .gamma (eV) = 4.752433e-08 .gamma_photon (eV) = 1.605552e-09 .gamma_electron (eV) = 4.591878e-08 .quality_factor = 4.532714e+11 .internal_conversion = 28.6 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 1.388 .gfactor_excited = 0.7402 .quadrupole_ground (barn) = 0.903 .quadrupole_excited (barn) = 1.28 .magnetic_moment_ground (eV/T) = 1.0939005851969898e-07 .magnetic_moment_excited (eV/T) = 8.167055464178219e-08 .nuclear_cross_section (converted to kbarn) = 237.49324319908274 .natural_abundance = 0.4781 : Eu-151

nexus.lib.moessbauer.Fe57 = MoessbauerIsotope: 57-Fe .element = Fe .mass (u) = 56.9353933 .spin_ground = 0.5 .spin_excited = 1.5 .energy (eV) = 14412.497 .wavelength (m) = 8.602548e-11 .kvector (1/m) = 7.303865e+10 .lifetime (s) = 1.411100e-07 .half_lifetime (s) = 9.781000e-08 .gamma (eV) = 4.664531e-09 .gamma_photon (eV) = 4.879216e-10 .gamma_electron (eV) = 4.176609e-09 .quality_factor = 3.089806e+12 .internal_conversion = 8.56 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.18121 .gfactor_excited = -0.10348 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = 0.187 .magnetic_moment_ground (eV/T) = 2.8562784588407284e-09 .magnetic_moment_excited (eV/T) = -4.893234836722674e-09 .nuclear_cross_section (converted to kbarn) = 2464.032617084363 .natural_abundance = 0.02119 : Fe-57

Changed in version 1.2.0: internal conversion changed from 8.21 to 8.56

nexus.lib.moessbauer.Gd157 = MoessbauerIsotope: 157-Gd .element = Gd .mass (u) = 156.92396011 .spin_ground = 1.5 .spin_excited = 2.5 .energy (eV) = 63917.0 .wavelength (m) = 1.939769e-11 .kvector (1/m) = 3.239141e+11 .lifetime (s) = 6.636400e-07 .half_lifetime (s) = 4.600002e-07 .gamma (eV) = 9.918208e-10 .gamma_photon (eV) = 5.032069e-10 .gamma_electron (eV) = 4.886139e-10 .quality_factor = 6.444410e+13 .internal_conversion = 0.971 .interference_term (beta) = 0.0 .multipolarity = E1 (L = 1, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -0.3398 .gfactor_excited = -0.464 .quadrupole_ground (barn) = 1.38 .quadrupole_excited (barn) = 2.46 .magnetic_moment_ground (eV/T) = -1.606804404250449e-08 .magnetic_moment_excited (eV/T) = -3.6568434548372e-08 .nuclear_cross_section (converted to kbarn) = 455.74790191733223 .natural_abundance = 0.1565 : Gd-157 63.9 keV

nexus.lib.moessbauer.Ge73 = MoessbauerIsotope: 73-Ge .element = Ge .mass (u) = 72.9234589 .spin_ground = 4.5 .spin_excited = 2.5 .energy (eV) = 13275.0 .wavelength (m) = 9.339676e-11 .kvector (1/m) = 6.727413e+10 .lifetime (s) = 4.260000e-06 .half_lifetime (s) = 2.952807e-06 .gamma (eV) = 1.545098e-10 .gamma_photon (eV) = 1.409761e-13 .gamma_electron (eV) = 1.543689e-10 .quality_factor = 8.591685e+13 .internal_conversion = 1095.0 .interference_term (beta) = 0.0 .multipolarity = E2 (L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -0.19544444444444445 .gfactor_excited = -0.03764 .quadrupole_ground (barn) = -0.173 .quadrupole_excited (barn) = -0.4 .magnetic_moment_ground (eV/T) = -2.7725808780425153e-08 .magnetic_moment_excited (eV/T) = -2.96645663017397e-09 .nuclear_cross_section (converted to kbarn) = 7.600189930819303 .natural_abundance = 0.0776 : Ge-73 - 13.2 keV.

nexus.lib.moessbauer.Ge73_68keV = MoessbauerIsotope: 73-Ge .element = Ge .mass (u) = 72.9234589 .spin_ground = 4.5 .spin_excited = 3.5 .energy (eV) = 68752.0 .wavelength (m) = 1.803354e-11 .kvector (1/m) = 3.484166e+11 .lifetime (s) = 2.510000e-09 .half_lifetime (s) = 1.739799e-09 .gamma (eV) = 2.622358e-07 .gamma_photon (eV) = 2.137211e-07 .gamma_electron (eV) = 4.851470e-08 .quality_factor = 2.621762e+11 .internal_conversion = 0.227 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.0 .mixing_coefficient_1 (M1) = 1.0 .mixing_coefficient_2 (E2) = 0.0 .gfactor_ground = -0.19544444444444445 .gfactor_excited = -0.24 .quadrupole_ground (barn) = -0.173 .quadrupole_excited (barn) = 0.0 .magnetic_moment_ground (eV/T) = -2.7725808780425153e-08 .magnetic_moment_excited (eV/T) = -2.6480590535027996e-08 .nuclear_cross_section (converted to kbarn) = 337.4640858314025 .natural_abundance = 0.0776 : Ge-73 - 68.7 keV. Quadrupole value of excited state unknow (set to zero here). M1/E2 mixing ratio unknown (set to zero). Interference term unknown (set to zero).

Added in version 2.0.0.

nexus.lib.moessbauer.I127 = MoessbauerIsotope: 127-I .element = I .mass (u) = 126.904472681 .spin_ground = 2.5 .spin_excited = 3.5 .energy (eV) = 57606.0 .wavelength (m) = 2.152279e-11 .kvector (1/m) = 2.919317e+11 .lifetime (s) = 2.760000e-09 .half_lifetime (s) = 1.913086e-09 .gamma (eV) = 2.384826e-07 .gamma_photon (eV) = 4.999635e-08 .gamma_electron (eV) = 1.884862e-07 .quality_factor = 2.415522e+11 .internal_conversion = 3.77 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = -0.083 .mixing_coefficient_1 (M1) = 0.996573195313575 .mixing_coefficient_2 (E2) = -0.08271557521102674 .gfactor_ground = 1.125 .gfactor_excited = 0.726 .quadrupole_ground (barn) = -0.79 .quadrupole_excited (barn) = -0.71 .magnetic_moment_ground (eV/T) = 8.866269152353123e-08 .magnetic_moment_excited (eV/T) = 8.01037863684597e-08 .nuclear_cross_section (converted to kbarn) = 206.0808923604208 .natural_abundance = 1.0 : I-127

Added in version 1.0.4.

nexus.lib.moessbauer.Ir193 = MoessbauerIsotope: 193-Ir .element = Ir .mass (u) = 192.9629273 .spin_ground = 1.5 .spin_excited = 0.5 .energy (eV) = 73041.0 .wavelength (m) = 1.697460e-11 .kvector (1/m) = 3.701521e+11 .lifetime (s) = 8.598000e-09 .half_lifetime (s) = 5.959679e-09 .gamma (eV) = 7.655408e-08 .gamma_photon (eV) = 1.057377e-08 .gamma_electron (eV) = 6.598031e-08 .quality_factor = 9.541099e+11 .internal_conversion = 6.24 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.557 .mixing_coefficient_1 (M1) = 0.8736210332635719 .mixing_coefficient_2 (E2) = 0.4866069155278096 .gfactor_ground = 0.10607 .gfactor_excited = 1.009 .quadrupole_ground (barn) = 0.78 .quadrupole_excited (barn) = 0.0 .magnetic_moment_ground (eV/T) = 5.015707567947178e-09 .magnetic_moment_excited (eV/T) = 1.5904116577287647e-08 .nuclear_cross_section (converted to kbarn) = 31.670201461645334 .natural_abundance = 0.627 : Ir-193

nexus.lib.moessbauer.K40 = MoessbauerIsotope: 40-K .element = K .mass (u) = 39.96399848 .spin_ground = 4.0 .spin_excited = 3.0 .energy (eV) = 29834.0 .wavelength (m) = 4.155802e-11 .kvector (1/m) = 1.511907e+11 .lifetime (s) = 5.960000e-09 .half_lifetime (s) = 4.131157e-09 .gamma (eV) = 1.104382e-07 .gamma_photon (eV) = 1.453135e-08 .gamma_electron (eV) = 9.590690e-08 .quality_factor = 2.701419e+11 .internal_conversion = 6.6 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -0.3245 .gfactor_excited = -0.4 .quadrupole_ground (barn) = -0.061 .quadrupole_excited (barn) = 1e-200 .magnetic_moment_ground (eV/T) = -4.09188172791266e-08 .magnetic_moment_excited (eV/T) = -3.7829415050040004e-08 .nuclear_cross_section (converted to kbarn) = 281.3013240469893 .natural_abundance = 0.000117 : K-40

Warning

Quadrupole of excited state unknown. Set to 1e-200. Do not calculate with quadrupole interaction.

nexus.lib.moessbauer.Kr83 = MoessbauerIsotope: 83-Kr .element = Kr .mass (u) = 82.914136 .spin_ground = 4.5 .spin_excited = 3.5 .energy (eV) = 9396.0 .wavelength (m) = 1.319542e-10 .kvector (1/m) = 4.761640e+10 .lifetime (s) = 2.120800e-07 .half_lifetime (s) = 1.470027e-07 .gamma (eV) = 3.103602e-09 .gamma_photon (eV) = 1.642118e-10 .gamma_electron (eV) = 2.939390e-09 .quality_factor = 3.027450e+12 .internal_conversion = 17.9 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -0.2157 .gfactor_excited = -0.2694 .quadrupole_ground (barn) = 0.253 .quadrupole_excited (barn) = 0.495 .magnetic_moment_ground (eV/T) = -3.05992680986011e-08 .magnetic_moment_excited (eV/T) = -2.972446287556893e-08 .nuclear_cross_section (converted to kbarn) = 1172.9917981553613 .natural_abundance = 0.115 : Kr-83

Added in version 1.0.4.

nexus.lib.moessbauer.Ni61 = MoessbauerIsotope: 61-Ni .element = Ni .mass (u) = 60.931056 .spin_ground = 1.5 .spin_excited = 2.5 .energy (eV) = 67408.0 .wavelength (m) = 1.839310e-11 .kvector (1/m) = 3.416056e+11 .lifetime (s) = 7.600000e-09 .half_lifetime (s) = 5.267919e-09 .gamma (eV) = 8.660684e-08 .gamma_photon (eV) = 7.603761e-08 .gamma_electron (eV) = 1.056923e-08 .quality_factor = 7.783219e+11 .internal_conversion = 0.139 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.5 .gfactor_excited = 0.192 .quadrupole_ground (barn) = 0.162 .quadrupole_excited (barn) = -0.2 .magnetic_moment_ground (eV/T) = 2.3643384406274998e-08 .magnetic_moment_excited (eV/T) = 1.5131766020015998e-08 .nuclear_cross_section (converted to kbarn) = 709.0836315139229 .natural_abundance = 0.011399 : Ni-61

Added in version 1.0.4.

nexus.lib.moessbauer.Os187 = MoessbauerIsotope: 187-Os .element = Os .mass (u) = 186.95575044 .spin_ground = 0.5 .spin_excited = 1.5 .energy (eV) = 9778.0 .wavelength (m) = 1.267991e-10 .kvector (1/m) = 4.955227e+10 .lifetime (s) = 3.430000e-09 .half_lifetime (s) = 2.377495e-09 .gamma (eV) = 1.918985e-07 .gamma_photon (eV) = 6.829129e-10 .gamma_electron (eV) = 1.912156e-07 .quality_factor = 5.095401e+10 .internal_conversion = 280.0 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.03 .mixing_coefficient_1 (M1) = 0.9995503035223668 .mixing_coefficient_2 (E2) = 0.029986509105671005 .gfactor_ground = 0.1278 .gfactor_excited = 0.0 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = 1.46 .magnetic_moment_ground (eV/T) = 2.01441635141463e-09 .magnetic_moment_excited (eV/T) = 0.0 .nuclear_cross_section (converted to kbarn) = 182.12787428394796 .natural_abundance = 0.0196 : Os-187. Check value and sign of mixing ratio (<0.04, set to 0.03). Magnetic moment of excited state unknown (set to zero).

nexus.lib.moessbauer.Ru99 = MoessbauerIsotope: 99-Ru .element = Ru .mass (u) = 98.9059393 .spin_ground = 2.5 .spin_excited = 1.5 .energy (eV) = 89680.0 .wavelength (m) = 1.382518e-11 .kvector (1/m) = 4.544741e+11 .lifetime (s) = 2.958000e-08 .half_lifetime (s) = 2.050329e-08 .gamma (eV) = 2.225193e-08 .gamma_photon (eV) = 8.907897e-09 .gamma_electron (eV) = 1.334403e-08 .quality_factor = 4.030213e+12 .internal_conversion = 1.498 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = -1.65 .mixing_coefficient_1 (M1) = 0.5183017160933442 .mixing_coefficient_2 (E2) = -0.8551978315540179 .gfactor_ground = -0.2564 .gfactor_excited = -0.18933333333333333 .quadrupole_ground (barn) = 0.079 .quadrupole_excited (barn) = 0.231 .magnetic_moment_ground (eV/T) = -2.0207212539229702e-08 .magnetic_moment_excited (eV/T) = -8.952961561842799e-09 .nuclear_cross_section (converted to kbarn) = 81.18539659203296 .natural_abundance = 0.1276 : Ru-99

Added in version 1.2.0.

nexus.lib.moessbauer.Sb121 = MoessbauerIsotope: 121-Sb .element = Sb .mass (u) = 120.9038157 .spin_ground = 2.5 .spin_excited = 3.5 .energy (eV) = 37133.0 .wavelength (m) = 3.338922e-11 .kvector (1/m) = 1.881800e+11 .lifetime (s) = 4.990000e-09 .half_lifetime (s) = 3.458804e-09 .gamma (eV) = 1.319062e-07 .gamma_photon (eV) = 1.089234e-08 .gamma_electron (eV) = 1.210139e-07 .quality_factor = 2.815106e+11 .internal_conversion = 11.11 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 1.34536 .gfactor_excited = 0.71942857142 .quadrupole_ground (barn) = -0.36 .quadrupole_excited (barn) = -0.48 .magnetic_moment_ground (eV/T) = 1.0602954548275377e-07 .magnetic_moment_excited (eV/T) = 7.937872257905487e-08 .nuclear_cross_section (converted to kbarn) = 195.3562576578406 .natural_abundance = 0.5721 : Sb-121

Added in version 1.0.3.

nexus.lib.moessbauer.Sc45 = MoessbauerIsotope: 45-Sc .element = Sc .mass (u) = 44.95591276 .spin_ground = 3.5 .spin_excited = 1.5 .energy (eV) = 12389.59 .wavelength (m) = 1.000713e-10 .kvector (1/m) = 6.278711e+10 .lifetime (s) = 4.600000e-01 .half_lifetime (s) = 3.188477e-01 .gamma (eV) = 1.430896e-15 .gamma_photon (eV) = 2.260499e-18 .gamma_electron (eV) = 1.428635e-15 .quality_factor = 8.658626e+18 .internal_conversion = 632.0 .interference_term (beta) = 0.0 .multipolarity = M2 (L = 2, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 1.359 .gfactor_excited = 0.245333 .quadrupole_ground (barn) = -0.22 .quadrupole_excited (barn) = 0.318 .magnetic_moment_ground (eV/T) = 1.4994634390459605e-07 .magnetic_moment_excited (eV/T) = 1.160100485308933e-08 .nuclear_cross_section (converted to kbarn) = 12.589405827271353 .natural_abundance = 1.0 : Sc-45

Added in version 1.0.3.

nexus.lib.moessbauer.Sm149 = MoessbauerIsotope: 149-Sm .element = Sm .mass (u) = 148.9171847 .spin_ground = 3.5 .spin_excited = 2.5 .energy (eV) = 22494.0 .wavelength (m) = 5.511879e-11 .kvector (1/m) = 1.139935e+11 .lifetime (s) = 1.027000e-08 .half_lifetime (s) = 7.118622e-09 .gamma (eV) = 6.409075e-08 .gamma_photon (eV) = 2.108248e-09 .gamma_electron (eV) = 6.198250e-08 .quality_factor = 3.509711e+11 .internal_conversion = 29.4 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.075 .mixing_coefficient_1 (M1) = 0.9971993098884564 .mixing_coefficient_2 (E2) = 0.07478994824163422 .gfactor_ground = -0.192 .gfactor_excited = -0.2496 .quadrupole_ground (barn) = 0.075 .quadrupole_excited (barn) = 1.01 .magnetic_moment_ground (eV/T) = -2.11844724280224e-08 .magnetic_moment_excited (eV/T) = -1.9671295826020802e-08 .nuclear_cross_section (converted to kbarn) = 119.29084282091806 .natural_abundance = 0.1382 : Sm-149 please check mixing_ratio_E2M1

nexus.lib.moessbauer.Sn119 = MoessbauerIsotope: 119-Sn .element = Sn .mass (u) = 118.9033115 .spin_ground = 0.5 .spin_excited = 1.5 .energy (eV) = 23879.5 .wavelength (m) = 5.192077e-11 .kvector (1/m) = 1.210149e+11 .lifetime (s) = 2.530000e-08 .half_lifetime (s) = 1.753662e-08 .gamma (eV) = 2.601628e-08 .gamma_photon (eV) = 4.182682e-09 .gamma_electron (eV) = 2.183360e-08 .quality_factor = 9.178675e+11 .internal_conversion = 5.22 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -2.09456 .gfactor_excited = 0.422 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = -0.094 .magnetic_moment_ground (eV/T) = -3.301499149467158e-08 .magnetic_moment_excited (eV/T) = 1.9955016438896098e-08 .nuclear_cross_section (converted to kbarn) = 1379.5643455126014 .natural_abundance = 0.0859 : Sn-119

nexus.lib.moessbauer.Ta181 = MoessbauerIsotope: 181-Ta .element = Ta .mass (u) = 180.94799645 .spin_ground = 3.5 .spin_excited = 4.5 .energy (eV) = 6215.7 .wavelength (m) = 1.994694e-10 .kvector (1/m) = 3.149949e+10 .lifetime (s) = 8.728300e-06 .half_lifetime (s) = 6.049997e-06 .gamma (eV) = 7.541124e-11 .gamma_photon (eV) = 1.604495e-12 .gamma_electron (eV) = 7.380675e-11 .quality_factor = 8.242405e+13 .internal_conversion = 46.0 .interference_term (beta) = -0.08 .multipolarity = E1 (L = 1, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.6772857 .gfactor_excited = 1.173 .quadrupole_ground (barn) = 3.17 .quadrupole_excited (barn) = 3.71 .magnetic_moment_ground (eV/T) = 7.472885540387421e-08 .magnetic_moment_excited (eV/T) = 1.6640213945136346e-07 .nuclear_cross_section (converted to kbarn) = 1684.165915018059 .natural_abundance = 0.9998799 : Ta-181

nexus.lib.moessbauer.Te125 = MoessbauerIsotope: 125-Te .element = Te .mass (u) = 124.9044307 .spin_ground = 0.5 .spin_excited = 1.5 .energy (eV) = 35491.9 .wavelength (m) = 3.493310e-11 .kvector (1/m) = 1.798634e+11 .lifetime (s) = 2.140000e-09 .half_lifetime (s) = 1.483335e-09 .gamma (eV) = 3.075757e-07 .gamma_photon (eV) = 2.050505e-08 .gamma_electron (eV) = 2.870706e-07 .quality_factor = 1.153924e+11 .internal_conversion = 14.0 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.06016 .mixing_coefficient_1 (M1) = 0.9981952844796517 .mixing_coefficient_2 (E2) = 0.06005142831429584 .gfactor_ground = -1.777 .gfactor_excited = 0.4033333333333 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = -0.31 .magnetic_moment_ground (eV/T) = -2.800952939330045e-08 .magnetic_moment_excited (eV/T) = 1.9072330087726924e-08 .nuclear_cross_section (converted to kbarn) = 258.96021503566857 .natural_abundance = 0.0707 

Te-125

Added in version 1.0.3.

Please check the sign of the mixing ratio

nexus.lib.moessbauer.Tm169 = MoessbauerIsotope: 169-Tm .element = Tm .mass (u) = 168.93421429 .spin_ground = 0.5 .spin_excited = 1.5 .energy (eV) = 8410.0 .wavelength (m) = 1.474247e-10 .kvector (1/m) = 4.261962e+10 .lifetime (s) = 5.785000e-09 .half_lifetime (s) = 4.009856e-09 .gamma (eV) = 1.137791e-07 .gamma_photon (eV) = 3.818090e-10 .gamma_electron (eV) = 1.133973e-07 .quality_factor = 7.391517e+10 .internal_conversion = 297.0 .interference_term (beta) = 0.0 .multipolarity = M1 (L = 1, lambda = 0) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = -0.464 .gfactor_excited = 0.347 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = -1.3 .magnetic_moment_ground (eV/T) = -7.3136869096744005e-09 .magnetic_moment_excited (eV/T) = 1.6408508777954846e-08 .nuclear_cross_section (converted to kbarn) = 232.1531328914595 .natural_abundance = 1.0 : Tm-169

nexus.lib.moessbauer.Xe129 = MoessbauerIsotope: 129-Xe .element = Xe .mass (u) = 128.9047795 .spin_ground = 0.5 .spin_excited = 1.5 .energy (eV) = 39577.4 .wavelength (m) = 3.132702e-11 .kvector (1/m) = 2.005676e+11 .lifetime (s) = 1.400000e-09 .half_lifetime (s) = 9.704061e-10 .gamma (eV) = 4.701514e-07 .gamma_photon (eV) = 3.608223e-08 .gamma_electron (eV) = 4.340692e-07 .quality_factor = 8.418012e+10 .internal_conversion = 12.03 .interference_term (beta) = 0.0 .multipolarity = M1E2 (L = 1, lambda = 0 / L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.027 .mixing_coefficient_1 (M1) = 0.9996356991693832 .mixing_coefficient_2 (E2) = 0.026990163877573345 .gfactor_ground = -1.556 .gfactor_excited = 0.387 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = -0.393 .magnetic_moment_ground (eV/T) = -2.45260707574426e-08 .magnetic_moment_excited (eV/T) = 1.829997953045685e-08 .nuclear_cross_section (converted to kbarn) = 239.74185883191214 .natural_abundance = 0.2644 : Xe-129. Check mixing ratio sign.

Added in version 2.0.0.

nexus.lib.moessbauer.Yb172 = MoessbauerIsotope: 172-Yb .element = Yb .mass (u) = 171.9363815 .spin_ground = 0.0 .spin_excited = 2.0 .energy (eV) = 78742.7 .wavelength (m) = 1.574548e-11 .kvector (1/m) = 3.990468e+11 .lifetime (s) = 2.380000e-09 .half_lifetime (s) = 1.649690e-09 .gamma (eV) = 2.765596e-07 .gamma_photon (eV) = 2.942124e-08 .gamma_electron (eV) = 2.471384e-07 .quality_factor = 2.847223e+11 .internal_conversion = 8.4 .interference_term (beta) = 0.0 .multipolarity = E2 (L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.0 .gfactor_excited = 0.3345 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = 2.16 .magnetic_moment_ground (eV/T) = 0.0 .magnetic_moment_excited (eV/T) = 2.10898988903973e-08 .nuclear_cross_section (converted to kbarn) = 209.88159462273467 .natural_abundance = 0.21686 : Yb-172

Added in version 1.2.0.

nexus.lib.moessbauer.Yb174 = MoessbauerIsotope: 174-Yb .element = Yb .mass (u) = 173.9388621 .spin_ground = 0.0 .spin_excited = 2.0 .energy (eV) = 76471.0 .wavelength (m) = 1.621323e-11 .kvector (1/m) = 3.875344e+11 .lifetime (s) = 2.580000e-09 .half_lifetime (s) = 1.788320e-09 .gamma (eV) = 2.551209e-07 .gamma_photon (eV) = 2.446030e-08 .gamma_electron (eV) = 2.306606e-07 .quality_factor = 2.997441e+11 .internal_conversion = 9.43 .interference_term (beta) = 0.0 .multipolarity = E2 (L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.0 .gfactor_excited = 0.338 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = 2.12 .magnetic_moment_ground (eV/T) = 0.0 .magnetic_moment_excited (eV/T) = 2.13105704781892e-08 .nuclear_cross_section (converted to kbarn) = 200.5602978298222 .natural_abundance = 0.32025 : Yb-174

Added in version 1.2.0.

nexus.lib.moessbauer.Zn67 = MoessbauerIsotope: 67-Zn .element = Zn .mass (u) = 66.927128 .spin_ground = 2.5 .spin_excited = 0.5 .energy (eV) = 93312.0 .wavelength (m) = 1.328706e-11 .kvector (1/m) = 4.728801e+11 .lifetime (s) = 1.309000e-05 .half_lifetime (s) = 9.073297e-06 .gamma (eV) = 5.028357e-11 .gamma_photon (eV) = 2.684654e-11 .gamma_electron (eV) = 2.343703e-11 .quality_factor = 1.855715e+15 .internal_conversion = 0.873 .interference_term (beta) = 0.0 .multipolarity = E2 (L = 2, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.35024 .gfactor_excited = 1.174 .quadrupole_ground (barn) = 0.15 .quadrupole_excited (barn) = 0.0 .magnetic_moment_ground (eV/T) = 2.7602863181512515e-08 .magnetic_moment_excited (eV/T) = 1.8504888861977897e-08 .nuclear_cross_section (converted to kbarn) = 50.00561591319957 .natural_abundance = 0.0404 : Zn-67

Added in version 1.2.0.

nexus.lib.moessbauer.none = MoessbauerIsotope: none .element = none .mass (u) = 0.0 .spin_ground = 0.0 .spin_excited = 0.0 .energy (eV) = 0.0 .wavelength (m) = inf .kvector (1/m) = 0.000000e+00 .lifetime (s) = 0.000000e+00 .half_lifetime (s) = 0.000000e+00 .gamma (eV) = inf .gamma_photon (eV) = inf .gamma_electron (eV) = nan .quality_factor = 0.000000e+00 .internal_conversion = 0.0 .interference_term (beta) = 0.0 .multipolarity = E1 (L = 1, lambda = 1) .mixing_ratio_E2M1 = 0.0 .gfactor_ground = 0.0 .gfactor_excited = 0.0 .quadrupole_ground (barn) = 0.0 .quadrupole_excited (barn) = 0.0 .magnetic_moment_ground (eV/T) = 0.0 .magnetic_moment_excited (eV/T) = 0.0 .nuclear_cross_section (converted to kbarn) = inf .natural_abundance = 0.0 : none

`energy`

Common X-ray energies of nuclear transitions and K-alpha transition lines.

nexus.lib.energy.CoKalpha = 6925.7: Co K alpha transition energy (eV)

nexus.lib.energy.CuKalpha = 8047.8: Cu K alpha transition energy (eV)

nexus.lib.energy.Dy161 = 25655.0: Dy-161 transition energy (eV)

nexus.lib.energy.Eu151 = 21541.418: Eu-151 transition energy (eV)

nexus.lib.energy.Fe57 = 14412.497: Fe-57 transition energy (eV)

nexus.lib.energy.FeKalpha = 6399.8: Fe K alpha transition energy (eV)

nexus.lib.energy.Gd157 = 63917.0: Gd-157 transition energy (eV)

nexus.lib.energy.Ge73 = 13275.0: Ge-73 13.2 keV transition energy (eV)

nexus.lib.energy.Ge73_68keV = 68752.0: Ge-73 68 keV transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.I127 = 57606.0: I-127 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Ir193 = 73041.0: Ir-193 transition energy (eV)

nexus.lib.energy.K40 = 29834.0: K-40 transition energy (eV)

nexus.lib.energy.Kr83 = 9396.0: Kr-83 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.MoKalpha = 17450.0: Mo K alpha transition energy (eV)

nexus.lib.energy.Ni61 = 67408.0: Ni-61 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Os187 = 9778.0: Os-187 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Ru99 = 89680.0: Ru-99 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Sb121 = 37133.0: Sb-121 transition energy (eV)

Added in version 1.0.3.

nexus.lib.energy.Sc45 = 12389.59: Sc-45 transition energy (eV)

Added in version 1.0.3.

nexus.lib.energy.Sm149 = 22494.0: Sm-149 transition energy (eV)

nexus.lib.energy.Sn119 = 23879.5: Sn-119 transition energy (eV)

nexus.lib.energy.Ta181 = 6215.7: Ta-181 transition energy (eV)

nexus.lib.energy.Te125 = 35491.9: Te-125 transition energy (eV)

Added in version 1.0.3.

nexus.lib.energy.Tm169 = 8410.0: Tm-169 transition energy (eV)

nexus.lib.energy.Xe129 = 39577.4: Xe-129 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Yb172 = 78742.7: Yb-172 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Yb174 = 76471.0: Yb-174 transition energy (eV)

Added in version 2.0.0.

nexus.lib.energy.Zn67 = 93312.0: Zn-67 transition energy (eV)

Added in version 2.0.0.

`residual`

Predefined residuals derived from the parent class Residual.

class nexus.lib.residual.Difference(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference residual

\[residual = data - theory\]

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.Log(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference of the natural logarithm values

\[residual = \log(data) - \log(theory)\]

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.Log10(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference of the logarithm of base 10 values

\[residual = \log_{10}(data) - \log_{10}(theory)\]

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.LogLikelihood(Residual self, std::string const id, double const exponent, std::string const plot_string="")

The residual is given by [Odstrcil]

\[residual = - \left( data * ln(theory) - theory \right)\]

and gives the Poisson log-likelihood

\[\mathcal{L}_p = -\sum_i data_i * log(theory_i) - theory_i\]

Note, that this residual is not working with gradient based methods of the ceres solver (LevMar, DogLeg, SubDogLeg and LineSearch). In addition, the fit options.error_method must be None or Bootstrap.

For global fit methods, set the local method to Subplex or Newuoa.

In combined fitting all measurements need to have this residual, when used.

Added in version 2.0.0.

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.Norm(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Normalized difference residual

\[residual = \frac{data - theory}{\sum data}\]

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.Power(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference of a power law

\[residual = data^\beta - theory^\beta\]

with \(\beta\) being the exponent parameter. This model smoothly varies between a logarithmic residual for \(\beta \rightarrow 0\) and a difference residual for \(\beta = 1\). Typically, values between \(\beta\) = 0.2 and 0.4 are used to find a good compromise between logarithmic and square root models.

Added in version 2.0.0.

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.Sqrt(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference of the square root values

\[residual = \sqrt{data} - \sqrt{theory}\]

This an approximation of the Poisson likelihood function [Thibault]

\[\mathcal{L}_p \approx 2 \sum_i \left( \sqrt{data_i} - \sqrt{theory_i}) \right)^2\]

and the \(cost\) is

\[cost = \frac{1}{2} \sum_i \left( \sqrt{data_i} - \sqrt{theory_i}) \right)^2\]

Both only differ by a scaling factor that does not affect fitting.

It is the standard residual for most FitMeasurement types (if the residual parameter is set to None).

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.SqrtStdDev(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference of the square root values weighted by the standard deviation

\[residual = \frac{\sqrt{data} - \sqrt{theory}}{\sqrt{data}}\]

This gives the amplitude log-likelihood [Odstrcil]

\[cost = \mathcal{L}_a = \frac{1}{2} \sum_i \left(\frac{\sqrt{data_i} - \sqrt{theory_i}) }{\sqrt{data_i}}\right)^2\]

Added in version 1.2.0.

ResidualFunction(input_data, input_theory)

Call of the residual function implementation from python.

Returns:: List of residual values of measured and theoretical intensities.
Return type:: list or ndarray

class nexus.lib.residual.StdDev(Residual self, std::string const id, double const exponent, std::string const plot_string="")

Difference residual weighted by the standard deviation

\[residual = \frac{data - theory}{\sqrt{data}}\]

For histograms the variance is the measured data value \(V = data\) and the standard deviation is given by \(\sigma = \sqrt(V) = \sqrt(data)\). The best linear unbiased estimator for Gaussian noise is obtained by using the variance as a weight in the squared residual. This will result in the cost function

\[cost = norm \sum_i \frac{r_i^2}{V_i} = norm \sum_i \left(\frac{data_i- theory_i}{\sqrt(data_i)}\right)^2\]

This is a very typical cost function for data fitting. However, for Poisson statistics and low count rates a Poisson likelihood function is the better choice.

`instrument`

Pre-defined instrument functions from the parent class Instrument.

class nexus.lib.instrument.AsymmetricGaussian(Instrument self, std::string const id)

Asymmetric Gaussian instrument function with lower and higher HWHMs.

Parameters:

hwhm_low (float or Var) – HWHM of the Gaussian distribution for the lower values.
hwhm_high (float) – HWHM of the Gaussian distribution for the higher values.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.DoubleGaussian(Instrument self, std::string const id)

Double Gaussian instrument function

\[G_2(x) = \exp\left(-\frac{1}{2} \left(\frac{x - x_1}{\sigma_1}\right)^2\right) + w_2 \exp\left(-\frac{1}{2} \left(\frac{x - x_2}{\sigma_2}\right)^2\right)\]

with the full width half maximum \(FWHM_i = 2 \sqrt{2 \ln(2)} \sigma_i\), the offset \(x_i\) and the relative weight \(w_2\) of the second Gaussian distribution. The relative weight \(w_2\) is defined as the ratio of the maximum values of the second and the first Gaussian distribution. For example, for \(w_2 = 0.5\) the maximum of the second Gaussian distribution is half of the maximum of the first Gaussian distribution. Normalization is performed after the sum of the two Gaussian distributions.

Parameters:

points (int) – Number of points in the distribution.
fwhm1 (float or Var) – FWHM of the first Gaussian distribution.
fwhm2 (float or Var) – FWHM of the second Gaussian distribution.
center1 (float or Var) – center of the first Gaussian distribution.
center2 (float or Var) – center of the second Gaussian distributions.
weight2 (float or Var) – relative weight of the second Gaussian distributions.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.File(Instrument self, std::string const id)

Instrument function from a file. Grid values in column 1. Instrument values in column 2.

Please note that the grid values in the file must match the grid values of the detuning/time axis for convolution. The kernel will not be normalized by default, since the values in the file are assumed to be normalized. If normalization is required, the values in the file should be normalized accordingly.

Parameters:: filename (string) – filename (with path) to be loaded by numpy.loadtxt(filename).

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.GaussLorentz2D(Instrument self, std::string const id)

2D instrument function:

Gaussian in detuning
Lorentzian in time

Parameters:

detuning_fwhm (float or Var) – FWHM of the Gaussian distribution in detuning.
time_fwhm (float or Var) – FWHM of the Lorentzian distribution in time (ns).
relative_truncation_threshold – Relative value for truncation of the kernel.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction2D(detuning_grid, time_grid)

Call of the instrument function 2D implementation from python.

Parameters:

grid_x (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. drive_detuning for EnergyTimeSpectrum.
grid_y (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. time for EnergyTimeSpectrum..

Returns:

Implemented instrumental function.

Return type:

2D ndarray

class nexus.lib.instrument.Gaussian(Instrument self, std::string const id)

Gaussian instrument function

\[G(x) = \exp\left(-\frac{1}{2} \left(\frac{x}{\sigma}\right)^2\right)\]

with the full width half maximum \(FWHM = 2 \sqrt{2 \ln(2)} \sigma\).

see https://en.wikipedia.org/wiki/Gaussian_function

Parameters:

fwhm (float or Var) – FWHM of the Gaussian distribution.
relative_truncation_threshold – Relative value for truncation of the kernel. Set to zero if no truncation shall be performed. Default is 1e-3.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.Gaussian2D(Instrument self, std::string const id)

2D Gaussian instrument function

see https://en.wikipedia.org/wiki/Gaussian_function

Parameters:

detuning_fwhm (float or Var) – FWHM of the Gaussian distribution in detuning (\(\Gamma\)).
time_fwhm (float or Var) – FWHM of the Gaussian distribution in time (ns).
relative_truncation_threshold – Relative value for truncation of the kernel. Set to zero if no truncation shall be performed. Default is 1e-3.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction2D(detuning_grid, time_grid)

Call of the instrument function 2D implementation from python.

Parameters:

grid_x (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. drive_detuning for EnergyTimeSpectrum.
grid_y (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. time for EnergyTimeSpectrum..

Returns:

Implemented instrumental function.

Return type:

2D ndarray

class nexus.lib.instrument.GaussianSquared(Instrument self, std::string const id)

Squared Gaussian instrument function

\[G^2(x) = \exp\left(-\left(\frac{x}{\sigma}\right)^2\right)\]

with the full width half maximum \(FWHM = 2 \sqrt{\ln(2)} \sigma\).

see https://en.wikipedia.org/wiki/Gaussian_function

Parameters:

fwhm (float or Var) – FWHM of the squared Gaussian distribution.
relative_truncation_threshold – Relative value for truncation of the kernel. Set to zero if no truncation shall be performed. Default is 1e-3.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.Lorentz(Instrument self, std::string const id)

Lorentzian instrument function

\[L(x) = \frac{1}{1 + \left(\frac{x}{\gamma}\right)^2}\]

with the full width half maximum \(FWHM = 2 \gamma\).

see https://en.wikipedia.org/wiki/Cauchy_distribution

Parameters:

fwhm (float or Var) – FWHM of the Lorentzian distribution.
relative_truncation_threshold – Relative value for truncation of the kernel. Set to zero if no truncation shall be performed. Default is 1e-3.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.LorentzGauss2D(Instrument self, std::string const id)

2D instrument function:

Lorentzian in detuning
Gaussian in time

Parameters:

detuning_fwhm (float or Var) – FWHM of the Lorentzian distribution in detuning.
time_fwhm (float or Var) – FWHM of the Gaussian distribution in time (ns).
relative_truncation_threshold – Relative value for truncation of the kernel.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction2D(detuning_grid, time_grid)

Call of the instrument function 2D implementation from python.

Parameters:

grid_x (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. drive_detuning for EnergyTimeSpectrum.
grid_y (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. time for EnergyTimeSpectrum..

Returns:

Implemented instrumental function.

Return type:

2D ndarray

class nexus.lib.instrument.LorentzSquared(Instrument self, std::string const id)

Squared Lorentzian instrument function

\[L^2(x) = \frac{1}{\left(1 + \left(\frac{x}{\gamma}\right)^2\right)^2}\]

with the full width half maximum :math:`FWHM = 2 sqrt{sqrt{2} - 1} gamma `.

see https://en.wikipedia.org/wiki/Cauchy_distribution

Parameters:

fwhm (float or Var) – FWHM of the squared Lorentzian distribution.
relative_truncation_threshold – Relative value for truncation of the kernel. Set to zero if no truncation shall be performed. Default is 1e-3.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

class nexus.lib.instrument.Lorentzian2D(Instrument self, std::string const id)

2D Loretnzian instrument function

see https://en.wikipedia.org/wiki/Gaussian_function

Parameters:

detuning_fwhm (float or Var) – FWHM of the Lorentzian distribution in detuning (\(\Gamma\)).
time_fwhm (float or Var) – FWHM of the Lorentzian distribution in time (ns).
relative_truncation_threshold – Relative value for truncation of the kernel. Set to zero if no truncation shall be performed. Default is 1e-3.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction2D(detuning_grid, time_grid)

Call of the instrument function 2D implementation from python.

Parameters:

grid_x (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. drive_detuning for EnergyTimeSpectrum.
grid_y (list of list or 2D ndarray) – The grid for the instrument calculation, e.g. time for EnergyTimeSpectrum..

Returns:

Implemented instrumental function.

Return type:

2D ndarray

class nexus.lib.instrument.TripleGaussian(Instrument self, std::string const id)

Triple Gaussian instrument function with the given two FWHMs offset to each other and with a relative weight.

\[G_3(x) = \exp\left(-\frac{1}{2} \left(\frac{x - x_1}{\sigma_1}\right)^2\right) + w_2 \exp\left(-\frac{1}{2} \left(\frac{x - x_2}{\sigma_2}\right)^2\right) + w_3 \exp\left(-\frac{1}{2} \left(\frac{x - x_3}{\sigma_2}\right)^2\right)\]

with the full width half maximum \(FWHM_i = 2 \sqrt{2 \ln(2)} \sigma_i\), the offset \(x_i\) and the relative weight \(w_i`^. The relative weights are defined with respect to the maximum values the first Gaussian distribution. For example, for :math:`w_2 = 0.5\) the maximum of the second Gaussian distribution is half of the maximum of the first Gaussian distribution. Normalization is performed after the sum of the three Gaussian distributions.

Parameters:

points (int) – Number of points in the distribution.
fwhm1 (float or Var) – FWHM of the first Gaussian distribution.
fwhm2 (float or Var) – FWHM of the second Gaussian distribution.
fwhm3 (float or Var) – FWHM of the third Gaussian distribution.
center1 (float or Var) – center of the first Gaussian distribution.
center2 (float or Var) – center of the second Gaussian distributions.
center3 (float or Var) – center of the third Gaussian distributions.
weight2 (float or Var) – relative weight of the second Gaussian distributions.
weight3 (float or Var) – relative weight of the third Gaussian distributions.
norm_factor – Normalization factor for the kernel. Default is 1.0.

InstrumentFunction(grid)

Call of the instrument function implementation from python.

The function must return a normalized kernel to the sum of one in order to preserve intensity of the theoretical curve during the convolution. A normalization factor can be applied via NormalizeKernel(), which simulates the Lamb-Moessbauer factor of a Moessbauer source.

Args:
grid (list or ndarray): The grid for the instrument function calculation.

Returns:
list or ndarray: Implemented instrumental function.

Libraries

distribution

material

moessbauer

energy

residual

instrument

`distribution`

`material`

`moessbauer`

`energy`

`residual`

`instrument`