ORSO Integration

Introduction

The Open Reflectometry Standards Organization (ORSO) has defined file types for reflectometry that allow standardized header information to be reat programmatically. GenX integrates these capabilities when exporting the simulated data (File‣Export‣Export ORSO…). The text format *.ort allows to export all datasets into a single file and includes the GenX model, parameters and script. This makes it the perfect format to submit as supplement for publications, as the metadata makes it easier to replicate and check the results.

Note

The ORSO export only works, if the header information is confirm with the standard.

Metadata Dialog

GenX keeps track of metadata from imported measurements. Depending on the data loader, different amount of information is available. You can browse through the metadata using the Data panel, selecting one dataset and clicking the information button:

This will open the Dataset information dialog with a hirachical structure on the left and text display on the right hand side. When opening the SiO layer neutron example, you can see such structure:

This can be quite useful in reviewing some instrument parameters or finding the sample a certain measurement corresponds to.

It is possible to edit the leaf notes directly by double-click. A right click on a parent node allows to insert new items accoring to ORSO specification.

New Model from File

For a proper ORSO datafile it is possible to create a new reflectivity model, making use of existing information from the header. For this use the menu File‣New from file….

By default this defines the instrument parameters (neutron/x-ray etc.). If a model according to the ORSO model language has been provided by the user, GenX will already use the information to buid the sample.

If this options is used to with a *.ort file exported by GenX, the model is directly loaded and parameters arc configured accoring to the specification.

Exported Fit

When exporting data for publication using the ORSO format, a typical header would look like this. In addition to all header data from the imported measurement, GenX adds the analysis section. It includes not only the fit script but all fit parameters, uncertainties as well as a model accoring to the ORSO model language.

(In this example, a full statistical analysis accoring to Error Statistics from bumps library was performed.)

# ORSO reflectivity data file | 1.1 standard | YAML encoding | https://www.reflectometry.org/
data_source:
  owner:
    name: GenX
    affiliation: Paul Scherrer Institut
  experiment:
    title: Example
    instrument: SuperAdam
    start_date: null
    probe: neutron
  sample:
    name: SiO on Si
  measurement:
    instrument_settings:
      incident_angle: {min: 0.01, max: 4.2, unit: deg}
      wavelength: {magnitude: 4.4, unit: angstrom}
      polarization: unpolarized
    data_files: []
  file_name: for_import.ort
reduction:
  software: {name: null}
data_set: SiO ref
analysis:
  software:
    name: GenX
    version: 3.6.26
  model:
    stack: ambient | surf | substrate
    origin: GenX model
    sub_stacks:
      surf:
        repetitions: 1
        stack: SiO
    layers:
      SiO:
        thickness: 1212.22800714822
        roughness: 4.306906302304505
        material:
          sld: {real: 4.2969154511952156e-07, imag: 0.0}
    materials:
      ambient:
        sld: {real: 0.0, imag: 0.0}
      substrate:
        sld: {real: 2.0583957958990535e-07, imag: 0.0}
    globals:
      roughness: {magnitude: 0.3, unit: nm}
      length_unit: angstrom
      mass_density_unit: g/cm^3
      number_density_unit: 1/nm^3
      sld_unit: 1/angstrom^2
      magnetic_moment_unit: muB
  script: "import models.spec_nx as model\nfrom models.utils import UserVars, fp,\
    \ fw, bc, bw\nfrom numpy import *\n\n# BEGIN Instrument DO NOT CHANGE\nfrom models.utils\
    \ import create_fp, create_fw\ninst = model.Instrument(probe='neutron', wavelength=4.4,\
    \ coords='q', I0=14.998640637081689, res=0.001, restype='full conv and varying\
    \ res.', respoints=9, resintrange=3, beamw=1.361494002150504, footype='gauss beam',\
    \ samplelen=50.0, incangle=0.0, pol='uu', Ibkg=3.9351064727954855e-06, tthoff=0.0,)\n\
    inst_fp = create_fp(inst.wavelength); inst_fw = create_fw(inst.wavelength)\n\n\
    fp.set_wavelength(inst.wavelength); fw.set_wavelength(inst.wavelength)\n# END\
    \ Instrument\n\n# BEGIN Sample DO NOT CHANGE\nAmb = model.Layer(sigma=0.0, dens=1.0,\
    \ d=0.0, f=(1e-20+1e-20j), b=0, xs_ai=0.0, magn=0.0, magn_ang=0.0)\nSiO = model.Layer(sigma=2,\
    \ dens=0.026, d=1205, f=(1e-20+1e-20j), b=bc.Si + bc.O*2, xs_ai=0.0, magn=0.0,\
    \ magn_ang=0.0)\nSub = model.Layer(sigma=2, dens=8/5.443**3, d=0.0, f=(1e-20+1e-20j),\
    \ b=bc.Si, xs_ai=0.0, magn=0.0, magn_ang=0.0)\n\nsurf = model.Stack(Layers=[SiO],\
    \ Repetitions = 1)\n\nsample = model.Sample(Stacks = [surf], Ambient = Amb, Substrate\
    \ = Sub)\n# END Sample\n\n# BEGIN Parameters DO NOT CHANGE\ncp = UserVars()\n\
    cp.new_var('dtheta', 0.04)\ncp.new_var('dlol', 0.007)\ncp.new_sys_err('tth0',\
    \ 0.0, 0.0035)\n# END Parameters\n\nSLD = []\ndef Sim(data):\n    I = []\n   \
    \ SLD[:] = []\n    # BEGIN Dataset 0 DO NOT CHANGE\n    inst.setTthoff(cp.getTth0())\n\
    \    inst.setRes(sqrt((cp.dlol*data[0].x)**2 + (4*3.1415/4.4*cp.dtheta*pi/360)**2))\n\
    \    d = data[0]\n    I.append(sample.SimSpecular(d.x, inst))\n    if _sim: SLD.append(sample.SimSLD(None,
    \ None, inst))\n    # END Dataset 0\n    return I"
  parameters:
  - Parameter: SiO.setD
    Value: 1212.22800714822
    Fit: true
    Min: 903.75
    Max: 1506.25
    Error: (-4.222e-01, 4.558e-01)
  - Parameter: SiO.setB
    Value: 16.52659788921237
    Fit: true
    Min: 11.816324999999999
    Max: 19.693875
    Error: (-5.753e-02, 5.761e-02)
  - Parameter: SiO.setSigma
    Value: 4.306906302304505
    Fit: true
    Min: 1.5
    Max: 15.0
    Error: (-1.975e-01, 3.629e-01)
  - Parameter: Sub.setSigma
    Value: 4.563155140343594
    Fit: true
    Min: 1.5
    Max: 15.0
    Error: (-1.311e+00, 7.008e-01)
  - Parameter: inst.setI0
    Value: 5.8056171439193776
    Fit: false
    Min: 1.5
    Max: 15.0
    Error: '-'
  - Parameter: inst.setIbkg
    Value: 2.1398201626854554e-06
    Fit: false
    Min: 0.0
    Max: 1.0e-05
    Error: '-'
  - Parameter: cp.setTth0
    Value: -0.00014253852631751873
    Fit: false
    Min: -0.05
    Max: 0.05
    Error: '-'
  - Parameter: inst.setBeamw
    Value: 0.6
    Fit: false
    Min: 0.15000000000000002
    Max: 1.5
    Error: '-'
  - Parameter: cp.setDlol
    Value: 0.005
    Fit: false
    Min: 0.004
    Max: 0.008
    Error: '-'
  - Parameter: cp.setDtheta
    Value: 0.033
    Fit: false
    Min: 0.03
    Max: 0.05
    Error: '-'
  statistics_mcmc:
    library: bumps
    version: 0.9.3
    settings:
      pop: 8
      burn: 200
      samples: 100000
    parameters:
    - name: SiO_B
      value: 16.52659788921237
      error: 0.05757321442536423
      cross_correlations:
        SiO_B: 0.003169740268471557
        SiO_D: 0.0039037691554475317
        SiO_Sigma: 0.0010288173799339142
        Sub_Sigma: 0.013075369118058998
    - name: SiO_D
      value: 1212.22800714822
      error: 0.43902018031155876
      cross_correlations:
        SiO_B: 0.0039037691554475317
        SiO_D: 0.18902683733024617
        SiO_Sigma: 0.006523187144176223
        Sub_Sigma: 0.001940201735574769
    - name: SiO_Sigma
      value: 4.306906302304505
      error: 0.28023314693246926
      cross_correlations:
        SiO_B: 0.001028817379933914
        SiO_D: 0.006523187144176223
        SiO_Sigma: 0.06652948247714746
        Sub_Sigma: -0.19488424544167524
    - name: Sub_Sigma
      value: 4.563155140343594
      error: 1.0059575457738956
      cross_correlations:
        SiO_B: 0.013075369118058998
        SiO_D: 0.001940201735574769
        SiO_Sigma: -0.19488424544167524
        Sub_Sigma: 0.7994400246403878
  operator:
    name: glavic_a
  timestamp: '2024-07-23T14:56:01'
columns:
- {name: Qz, unit: 1/angstrom}
- {name: R}
- {error_of: R}
- {name: Rsim}
- {name: FOM}