How to use This library¶

>>> import neutronbraggedge

From python, first you need to import the package

>>> from neutronbraggedge.braggedge import BraggEdge

Metadata of Elements¶

For a particular element, or a list of elements, you can retrieve:

lattice parameter
h, k and l values
Crystal structure
bragg edges values

For this example, we are retrieving the data for Fe and we are only interested by the first 4 crystal orientation.

>>> _handler = BraggEdge(material = 'Fe', number_of_bragg_edges = 4)
>>> print("Crystal Structure is: %s" %_handler.metadata['cyrstal_structure]))
'BCC'
>>> print("Lattice is %.2f" %_handler.metadata['lattice'])
2.87
>>> print("hkl are: " , _handler.hkl)
hkl are: [][1,1,0],[2,0,0],[2,1,1],[2,2,0]]
>>> print("bragg edges are: ", _handler.bragg_edges)
bragg edges are: [2.0268, 1.4332, 1.1702, 1.0134]

It is also possible to display all metadata at once

>>> print(_handler)
===================================
Material: Fe
Lattice: 2.8664A
Crystal Structure: BCC
Using local metadata Table: True
===================================
 h | k | l |   d(A)  |    BraggEdge
===================================
 1 | 1 | 0 |  2.0269 |    4.0537
 2 | 0 | 0 |  1.4332 |    2.8664
 2 | 1 | 1 |  1.1702 |    2.3404
 2 | 2 | 0 |  1.0134 |    2.0269
===================================

In this example, we are retrieving the data for Fe and Al

>>> _handler = BraggEdge(material=['Al', 'Fe'], number_of_bragg_edges = 10)
>>> print("Crystal Structure of Al is: %s" %_handler.metadata['Crystal_structure']['Al'])
'FCC'
>>> print("Lattice of Al is: %.2f" %_handler.metadata['lattice']['Al'])
4.45

Here, we display the metadata for a list of material

>>> print(_handler)
=============================================
Material: Al
Lattice : 4.0460Å
Crystal Structure: FCC
Using local metadata Table: True
=============================================
 h | k | l |     d (Å)  |        BraggEdge
---------------------------------------------
 1 | 1 | 1 |     2.33596 |       4.67192
 2 | 0 | 0 |     2.02300 |       4.04600
 2 | 2 | 0 |     1.43048 |       2.86095
 2 | 2 | 2 |     1.16798 |       2.33596
 3 | 1 | 1 |     1.21991 |       2.43983
 3 | 3 | 1 |     0.92822 |       1.85643
 3 | 3 | 3 |     0.77865 |       1.55731
 4 | 0 | 0 |     1.01150 |       2.02300
 4 | 2 | 0 |     0.90471 |       1.80943
 4 | 2 | 2 |     0.82589 |       1.65177
=============================================
=============================================
Material: Fe
Lattice : 2.8664Å
Crystal Structure: BCC
Using local metadata Table: True
=============================================
 h | k | l |     d (Å)  |        BraggEdge
---------------------------------------------
 1 | 1 | 0 |     2.02685 |       4.05370
 2 | 0 | 0 |     1.43320 |       2.86640
 2 | 1 | 1 |     1.17020 |       2.34041
 2 | 2 | 0 |     1.01343 |       2.02685
 2 | 2 | 2 |     0.82746 |       1.65492
 3 | 1 | 0 |     0.90644 |       1.81287
 3 | 2 | 1 |     0.76608 |       1.53216
 3 | 3 | 0 |     0.67562 |       1.35123
 3 | 3 | 2 |     0.61112 |       1.22224
 4 | 0 | 0 |     0.71660 |       1.43320
=============================================

It is possible to display the full list of material available

>>> from neutronbraggedge.material_handler.retrieve_material_metadata import RetrieveMaterialMetadata
>>> retrieve_all_material = RetrieveMaterialMetadata(material = 'all')
>>> print(retrieve_all_material.full_list_material())
['C (diamond)' 'C (graphite)' 'Si' 'Ge' 'AlAs' 'AlP' 'AlSb' 'GaP' 'GaAs'
    'GaSb' 'inconel' 'InP' 'InAs' 'InSb' 'MgO' 'SiC' 'CdS' 'CdSe' 'CdTe' 'ZnO'
    'ZnS' 'PbS' 'PbTe' 'BN' 'BP' 'CdS' 'ZnS' 'AlN' 'GaN' 'InN' 'LiF' 'LiCl'
    'LiBr' 'LiI' 'NaF' 'NaCl' 'NaBr' 'NaI' 'KF' 'KCl' 'KBr' 'KI' 'RbF' 'RbCl'
    'RbBr' 'RbI' 'CsF' 'CsCl' 'CsI' 'Al' 'Fe' 'Ni' 'Cu' 'Mo' 'Pd' 'Ag' 'W'
    'Pt' 'Au' 'Pb' 'TiN' 'ZrN' 'HfN' 'VN' 'CrN' 'NbN' 'TiC' 'ZrC0.97'
    'HfC0.99' 'VC0.97' 'NC0.99' 'TaC0.99' 'Cr3C2' 'WC' 'ScN' 'LiNbO3' 'KTaO3'
    'BaTiO3' 'SrTiO3' 'CaTiO3' 'PbTiO3' 'EuTiO3' 'SrVO3' 'CaVO3' 'BaMnO3'
    'CaMnO3' 'SrRuO3' 'YAlO3']

In the case where the material is not in the list of material available, it is possible to define “on the fly” this material such as demonstrated here

>>> _handler = BraggEdge(new_material = {'name': "AlSiC", 'lattice': 2.8664, 'crystal_structure': 'BCC'})
>>> print(_handler)
===================================
Material: AlSiC
Lattice : 2.8664Å
Crystal Structure: BCC
Using local metadata Table: True
=============================================
 h | k | l |     d (Å)  |        BraggEdge
---------------------------------------------
 1 | 1 | 0 |     2.02685 |       4.05370
 2 | 0 | 0 |     1.43320 |       2.86640
 2 | 1 | 1 |     1.17020 |       2.34041
 2 | 2 | 0 |     1.01343 |       2.02685
 2 | 2 | 2 |     0.82746 |       1.65492
 3 | 1 | 0 |     0.90644 |       1.81287
 3 | 2 | 1 |     0.76608 |       1.53216
 3 | 3 | 0 |     0.67562 |       1.35123
 3 | 3 | 2 |     0.61112 |       1.22224
 4 | 0 | 0 |     0.71660 |       1.43320
=============================================

Lambda Calculation¶

>>> import neutronbraggedge.experiment_handler

In order to convert a TOF range into lambda, you will need to provide:

distance source-detector (in meters)
detector offset (micros)
tof array

First, you need to load your TOF range. You can either provide this array

>>> _tof_handler = TOF(tof = [9.6000E-07, 1.1200E-05, 2.1440E-05, 3.1680E-05], units = 'micros')

or by providing the name of an ascii file name where each tof is on its own row

>>> _tof_handler = TOF(filename = my_tof.txt, units = 'micros')

Then it is possible to calculate the Lambda array

>>> distance_source_detector_m = 16.09
>>> detector_offset_micros = 4500
>>> _exp = Experiment(tof = _tof_handler.tof_array, distance_source_detector_m = distance_source_detector_m, detector_offset_micros = detector_offset_micros)
>>> print(_exp.lambda_array)
[1.10664e-10, 1.109165e-10, 1.111682e-10, 1.114200e-10]

To export lambda into a csv file

>>> _exp.export_lambda(filename = 'my_lambda_file.txt')

Distance source-Detector Calculation¶

>>> import neutronbraggedge.experiment_handler

In order to calculate the source-detector length, you must provide:

detector offset (micros)
tof array
lambda array (Angstroms)

First, you need to load your TOF range. You can either provide this array

>>> _tof_handler = TOF(tof = [9.6000E-07, 1.1200E-05, 2.1440E-05, 3.1680E-05], units = 'micros')

or by providing the name of an ascii file name where each tof is on its own row

>>> _tof_handler = TOF(filename = my_tof.txt, units = 'micros')

Same thing with lambda array

>>> _lambda_handler = LambdaWavelength(data = [1.10664e-10, 1.109165e-10, 1.111682e-10, 1.114200e-10])

or by providing the name of an ascii file where each lambda is on its own row

>>> _lambda_handler = LambdaWavelength(filename = 'my_lambda.txt')

Then

>>> detector_offset_micros = 4500
>>> _exp = Experiment(tof = _tof_handler.tof_array, lambda_array = _lambda_handler.lambda, detector_offset_micros = detector_offset_micros)
>>> print(_exp.distance_source_detector_m)
16.09

Detector Offset Calculation¶

>>> import neutronbraggedge.experiment_handler

In order to calculate the detector offsetr, you must provide:

distance source-detector (m)
tof array
lambda array (Angstroms)

First, you need to load your TOF range. You can either provide this array

>>> _tof_handler = TOF(tof = [9.6000E-07, 1.1200E-05, 2.1440E-05, 3.1680E-05], units = 'micros')

or by providing the name of an ascii file name where each tof is on its own row

>>> _tof_handler = TOF(filename = my_tof.txt, units = 'micros')

Same thing with lambda array

>>> _lambda_handler = LambdaWavelength(data = [1.10664e-9, 1.109165e-9, 1.111682e-9, 1.114200e-9])

or by providing the name of an ascii file where each lambda is on its own row

>>> _lambda_handler = LambdaWavelength(filename = 'my_lambda.txt')

Then

>>> distance_source_detector_m = 14.09
>>> _exp = Experiment(tof = _tof_handler.tof_array, lambda_array = _lambda_handler.lambda, distance_source_detector_m = distance_source_detector_m)
>>> print(_exp.detector_offset_micros)
4500

Lattice Calculator¶

>>> from neutronbraggedge.lattice_handler.lattice import Lattice

In order to calculate the average lattice for a given material, the following information must be provided:

material name

crystal structure

bragg edge array

example:

>>> o_lattice = Lattice(material = "Si",
                        crystal_structure = "FCC",
                        bragg_edge_array = [1.1, None, 3.3, 4.4])

The algorithm automatically calculate the hkl bragg edge sequence and the lattice

Those calculation can be display using

>>> o_lattice.display_hkl_bragg_edge()
  hkl Bragg Edge Table
  ==================================================
  hkl            Bragg Edge      Lattice
  --------------------------------------------------
  [1, 1, 1]      1.1000          0.9526
  [2, 0, 0]      nan             nan
  [2, 2, 0]      3.3000          4.6669
  [2, 2, 2]      4.4000          7.6210
  --------------------------------------------------

>>> o_lattice.display_lattice_statistics()
 Lattice Statistics
 ==================================================
 min: 0.95263
 max: 7.62102
 median: 4.66690
 mean: 4.41352
 std: 2.72825
 --------------------------------------------------

>>> o_lattice.display_recap()
  Recap
  ==================================================
  Material: 'Si'
  Crystal Structure: 'FCC'
  --------------------------------------------------
  hkl Bragg Edge Table
  ==================================================
  hkl            Bragg Edge      Lattice
  --------------------------------------------------
  [1, 1, 1]      1.1000          0.9526
  [2, 0, 0]      nan             nan
  [2, 2, 0]      3.3000          4.6669
  [2, 2, 2]      4.4000          7.6210
  --------------------------------------------------
  Lattice Statistics
  ==================================================
  min: 0.95263
  max: 7.62102
  median: 4.66690
  mean: 4.41352 +/- 0.0000
  std: 2.72825
  --------------------------------------------------

To retrieve the various values:

>>> print(o_lattice.hkl)
 [[1, 1, 1], [2, 0, 0], [2, 2, 0], [2, 2, 2]]

>>> print(o_lattice.lattice_statistics)
{'mean': 4.4135187510990521, 'min': 0.95262794416288255, 'median': 4.6669047558312133, 'max': 7.6210235533030604, 'std': 2.7282507644454284}

>>> print(o_lattice.lattice_statistics['mean'])
4.4135187511