Workflow#

In this example, we will use McStas 3 simulation file.

Build Pipeline (Collect Parameters and Providers)#

Import the providers from load_mcstas_nexus to use the McStas simulation data workflow. MaximumProbability can be manually provided to derive more realistic number of events. It is because weights are given as probability, not number of events in a McStas file.

[1]:

from ess.nmx.mcstas import McStasWorkflow
from ess.nmx.data import small_mcstas_3_sample

from ess.nmx.types import *
from ess.nmx.reduction import merge_panels
from ess.nmx.nexus import export_as_nexus

wf = McStasWorkflow()
# Replace with the path to your own file
wf[FilePath] = small_mcstas_3_sample()
wf[MaximumCounts] = 10000
wf[TimeBinSteps] = 50

Downloading file 'small_mcstas_3_sample.h5' from 'https://public.esss.dk/groups/scipp/ess/nmx/small_mcstas_3_sample.h5' to '/home/runner/.cache/essnmx/0'.

To see what the workflow can produce, display it:

[2]:

wf

[2]:
Name Value Source
CrystalRotation
load_crystal_rotation ess.nmx.mcstas.load.load_crystal_rotation
DetectorBankPrefix
load_event_data_bank_name ess.nmx.mcstas.load.load_event_data_bank_name
DetectorIndex
DetectorName
detector_name_from_index ess.nmx.mcstas.load.detector_name_from_index
FilePath
/home/runner/.cache/essnmx/0/s... /home/runner/.cache/essnmx/0/small_mcstas_3_sample.h5
MaximumCounts 10000
MaximumProbability
maximum_probability ess.nmx.mcstas.load.maximum_probability
MaximumTimeOfArrival
calculate_maximum_toa ess.nmx.reduction.calculate_maximum_toa
McStasInstrument
read_mcstas_geometry_xml ess.nmx.mcstas.xml.read_mcstas_geometry_xml
McStasWeight2CountScaleFactor
mcstas_weight_to_probability_scalefactor ess.nmx.mcstas.load.mcstas_weight_to_probability_scalefactor
MinimumTimeOfArrival
calculate_minimum_toa ess.nmx.reduction.calculate_minimum_toa
NMXDetectorMetadata
load_detector_metadata ess.nmx.mcstas.load.load_detector_metadata
NMXExperimentMetadata
load_experiment_metadata ess.nmx.mcstas.load.load_experiment_metadata
NMXRawDataMetadata
retrieve_raw_data_metadata ess.nmx.mcstas.load.retrieve_raw_data_metadata
NMXRawEventCountsDataGroup
load_mcstas ess.nmx.mcstas.load.load_mcstas
NMXReducedCounts
raw_event_probability_to_counts ess.nmx.reduction.raw_event_probability_to_counts
NMXReducedDataGroup
format_nmx_reduced_data ess.nmx.reduction.format_nmx_reduced_data
NMXReducedProbability
reduce_raw_event_probability ess.nmx.reduction.reduce_raw_event_probability
PixelIds
retrieve_pixel_ids ess.nmx.mcstas.load.retrieve_pixel_ids
ProtonCharge
proton_charge_from_event_counts ess.nmx.reduction.proton_charge_from_event_counts
RawEventProbability
load_raw_event_data ess.nmx.mcstas.load.load_raw_event_data
TimeBinSteps 50

We want to reduce all three panels, so we map the relevant part of the workflow over a list of the three panels:

[3]:

# DetectorIndex selects what detector panels to include in the run
# in this case we select all three panels.
wf[NMXReducedDataGroup] = (
    wf[NMXReducedDataGroup]
    .map({DetectorIndex: sc.arange('panel', 3, unit=None)})
    .reduce(index="panel", func=merge_panels)
)

Build Workflow#

[4]:

wf.visualize(NMXReducedDataGroup, graph_attr={"rankdir": "TD"}, compact=True)

[4]:
../_images/user-guide_workflow_7_0.svg

Compute Desired Types#

[5]:

from cyclebane.graph import NodeName, IndexValues

# Data from all selected detectors binned by panel, pixel and timeslice
targets = [NodeName(NMXReducedDataGroup, IndexValues(("panel",), (i,))) for i in range(3)]
dg = merge_panels(*wf.compute(targets).values())
dg

[5]:
  • counts
    scipp
    DataArray
    (panel: 3, id: 1638400, t: 50)
    float64
    counts
    0.0, 0.0, ..., 0.0, 0.0
  • proton_charge
    scipp
    Variable
    (panel: 3)
    float64
    counts
    10.506, 9.440, 9.867
  • crystal_rotation
    scipp
    Variable
    ()
    vector3
    deg
    [0. 0. 0.]
  • sample_position
    scipp
    Variable
    ()
    vector3
    m
    [0. 0. 0.]
  • source_position
    scipp
    Variable
    ()
    vector3
    m
    [ -0.53123 0. -157.405 ]
  • sample_name
    scipp
    Variable
    ()
    string
    sampleMantid
  • fast_axis
    scipp
    Variable
    (panel: 3)
    vector3
    𝟙
    [ 0.999986 0. -0.00529148], [-0.00531614 0. -0.99998587], [0.00531614 0. 0.99998587]
  • slow_axis
    scipp
    Variable
    ()
    vector3
    𝟙
    [0. 1. 0.]
  • origin_position
    scipp
    Variable
    (panel: 3)
    vector3
    m
    [-0.248454 -0.25 0.292 ], [-0.288666 -0.25 0.252 ], [ 0.288667 -0.25 -0.251 ]
  • position
    scipp
    Variable
    (panel: 3, id: 1638400)
    vector3
    m
    [-0.248454 -0.25 0.292 ], [-0.24805401 -0.25 0.29199788], ..., [0.29138461 0.2616 0.26019278], [0.29138674 0.2616 0.26059277]
  • detector_shape
    scipp
    Variable
    ()
    PyObject
    (1280, 1280)
  • x_pixel_size
    scipp
    Variable
    ()
    float64
    m
    0.0004
  • y_pixel_size
    scipp
    Variable
    ()
    float64
    m
    0.0004
  • detector_name
    scipp
    Variable
    (panel: 3)
    string
    nD_Mantid_0, nD_Mantid_1, nD_Mantid_2
[6]:

dg['counts']

[6]:
Show/Hide data repr Show/Hide attributes
scipp.DataArray (1.87 GB)
    • panel: 3
    • id: 1638400
    • t: 50
    • id
      (panel, id)
      int64
      1, 2, ..., 5638398, 5638399
      Values:
      array([[      1,       2,       3, ..., 1638398, 1638399, 1638400],
       [2000000, 2000001, 2000002, ..., 3638397, 3638398, 3638399],
       [4000000, 4000001, 4000002, ..., 5638397, 5638398, 5638399]],
      shape=(3, 1638400))
    • t
      (panel, t [bin-edge])
      float64
      s
      0.097, 0.098, ..., 0.144, 0.145
      Values:
      array([[0.09725517, 0.09816732, 0.09907947, 0.09999161, 0.10090376,
        0.10181591, 0.10272805, 0.1036402 , 0.10455235, 0.1054645 ,
        0.10637664, 0.10728879, 0.10820094, 0.10911308, 0.11002523,
        0.11093738, 0.11184953, 0.11276167, 0.11367382, 0.11458597,
        0.11549811, 0.11641026, 0.11732241, 0.11823456, 0.1191467 ,
        0.12005885, 0.120971  , 0.12188314, 0.12279529, 0.12370744,
        0.12461959, 0.12553173, 0.12644388, 0.12735603, 0.12826817,
        0.12918032, 0.13009247, 0.13100462, 0.13191676, 0.13282891,
        0.13374106, 0.13465321, 0.13556535, 0.1364775 , 0.13738965,
        0.13830179, 0.13921394, 0.14012609, 0.14103824, 0.14195038,
        0.14286253],
       [0.09934708, 0.10022566, 0.10110424, 0.10198283, 0.10286141,
        0.10373999, 0.10461857, 0.10549715, 0.10637573, 0.10725431,
        0.10813289, 0.10901147, 0.10989006, 0.11076864, 0.11164722,
        0.1125258 , 0.11340438, 0.11428296, 0.11516154, 0.11604012,
        0.11691871, 0.11779729, 0.11867587, 0.11955445, 0.12043303,
        0.12131161, 0.12219019, 0.12306877, 0.12394736, 0.12482594,
        0.12570452, 0.1265831 , 0.12746168, 0.12834026, 0.12921884,
        0.13009742, 0.13097601, 0.13185459, 0.13273317, 0.13361175,
        0.13449033, 0.13536891, 0.13624749, 0.13712607, 0.13800465,
        0.13888324, 0.13976182, 0.1406404 , 0.14151898, 0.14239756,
        0.14327614],
       [0.09933721, 0.10025598, 0.10117475, 0.10209353, 0.1030123 ,
        0.10393107, 0.10484984, 0.10576862, 0.10668739, 0.10760616,
        0.10852493, 0.10944371, 0.11036248, 0.11128125, 0.11220002,
        0.1131188 , 0.11403757, 0.11495634, 0.11587511, 0.11679389,
        0.11771266, 0.11863143, 0.1195502 , 0.12046898, 0.12138775,
        0.12230652, 0.12322529, 0.12414406, 0.12506284, 0.12598161,
        0.12690038, 0.12781915, 0.12873793, 0.1296567 , 0.13057547,
        0.13149424, 0.13241302, 0.13333179, 0.13425056, 0.13516933,
        0.13608811, 0.13700688, 0.13792565, 0.13884442, 0.1397632 ,
        0.14068197, 0.14160074, 0.14251951, 0.14343829, 0.14435706,
        0.14527583]])
    • (panel, id, t)
      float64
      counts
      0.0, 0.0, ..., 0.0, 0.0
      Values:
      array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], shape=(3, 1638400, 50))

Export Results#

NMXReducedData object has a method to export the data into nexus or h5 file.

You can save the result as test.nxs, for example:

[7]:

export_as_nexus(dg, "test.nxs")

/tmp/ipykernel_3157/608274272.py:1: DeprecationWarning: Exporting to custom NeXus format will be deprecated in the near future.Please use ``export_as_nxlauetof`` instead.
  export_as_nexus(dg, "test.nxs")

Instrument View#

Pixel positions are not used for later steps, but it is included in the coordinates for instrument view.

All pixel positions are relative to the sample position, therefore the sample is at (0, 0, 0).

It might be very slow or not work in the ``VS Code`` jupyter notebook editor.

[8]:

import scippneutron as scn

da = dg["counts"]
da.coords["position"] = dg["position"]
# Plot one out of 100 pixels to reduce size of docs output
view = scn.instrument_view(da["id", ::100].sum('t'), pixel_size=0.0075)
view

[8]: