POWGEN data reduction#

Introduction#

This notebook gives a concise overview of how to use the ESSDiffraction package with Sciline. It uses a simple reduction workflow for the SNS POWGEN experiment.

We begin with relevant imports:

[1]:

import scipp as sc
import scippneutron as scn
import sciline

import ess.diffraction
from ess.diffraction import powder
from ess.diffraction.external import powgen
from ess.diffraction.types import *

Define reduction parameters#

We define a dictionary containing the reduction parameters. The keys are types defined in essdiffraction.types.

[2]:

params = {
    # Input data
    Filename[SampleRun]: 'PG3_4844_event.zip',
    Filename[VanadiumRun]: 'PG3_4866_event.zip',
    CalibrationFilename: 'PG3_FERNS_d4832_2011_08_24.zip',

    # Crop data to this range in time-of-flight
    ValidTofRange: sc.array(dims=['tof'], values=[0.0, 16666.67], unit='us'),

    # Edges for binning in d-spacing
    DspacingBins: sc.linspace('dspacing', 0.0, 2.3434, 200, unit='angstrom'),
}

Create pipeline using Sciline#

We use the basic providers available in essdiffraction as well as the specialised powder and powgen providers.

[3]:

providers = [
    *ess.diffraction.providers,
    *powder.providers,
    *powgen.providers,
]
pipeline = sciline.Pipeline(
    providers,
    params=params,
)

Group by scattering angle#

The above pipeline focuses the data by merging all instrument pixels to produce a 1d d-spacing curve. If instead we want to group into \(2\theta\) bins, we can alter the pipeline by replacing the focussing step:

[15]:

from ess.diffraction.grouping import group_by_two_theta, merge_all_pixels

grouping_providers = list(providers)
grouping_providers.remove(merge_all_pixels)
grouping_providers = (*grouping_providers, group_by_two_theta)

We also need to specify the grouping with a new parameter:

[16]:

params[TwoThetaBins] = sc.linspace(dim='two_theta', unit='deg', start=25.0, stop=90.0, num=16)

[17]:

grouping_pipeline = sciline.Pipeline(
    grouping_providers,
    params=params
)

Inspect the graph to check that the new provider has been inserted:

[18]:

grouped_dspacing = grouping_pipeline.get(DspacingHistogram)
grouped_dspacing.visualize(graph_attr={'rankdir': 'LR'})

[18]:

../_images/examples_POWGEN_data_reduction_30_0.svg

Compute and plot the result:

[19]:

grouped_result = grouped_dspacing.compute()
grouped_result

[20]:

angle = sc.midpoints(grouped_result.coords['two_theta'])
sc.plot({
    f'{angle[group].value:.3f} {angle[group].unit}': grouped_result['two_theta', group]
    for group in range(2, 6)
})

[20]:

../_images/examples_POWGEN_data_reduction_33_0.svg

POWGEN data reduction#

Introduction#

Define reduction parameters#

Create pipeline using Sciline#

Use the pipeline#

Compute final result#

Save reduced data to file#

Compute intermediate results#

Group by scattering angle#