DREAM data reduction#
We begin with relevant imports. We will be using tutorial data downloaded with pooch. If you get an error about a missing module pooch, you can install it with !pip install pooch:
[1]:
import scipp as sc
from scippneutron.io import cif
from ess import dream, powder
import ess.dream.data # noqa: F401
from ess.powder.types import *
Create and configure the workflow#
We begin by creating the Dream (Geant4) workflow object which is a skeleton for reducing Dream data, with pre-configured steps.
[2]:
workflow = dream.DreamGeant4Workflow()
We then need to set the missing parameters which are specific to each experiment (the keys are types defined in essdiffraction.powder.types):
[3]:
workflow[Filename[SampleRun]] = dream.data.simulated_diamond_sample()
workflow[Filename[VanadiumRun]] = dream.data.simulated_vanadium_sample()
workflow[Filename[BackgroundRun]] = dream.data.simulated_empty_can()
workflow[CalibrationFilename] = None
workflow[NeXusDetectorName] = "mantle"
# The upper bounds mode is not yet implemented.
workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.drop
# Edges for binning in d-spacing
workflow[DspacingBins] = sc.linspace("dspacing", 0.0, 2.3434, 201, unit="angstrom")
# Mask in time-of-flight to crop to valid range
workflow[TofMask] = lambda x: (x < sc.scalar(0.0, unit="ns")) | (
x > sc.scalar(86e6, unit="ns")
)
workflow[TwoThetaMask] = None
workflow[WavelengthMask] = None
# No pixel masks
workflow = powder.with_pixel_mask_filenames(workflow, [])
Downloading file 'DREAM_simple_pwd_workflow/data_dream_diamond_vana_container_sample_union.csv.zip' from 'https://public.esss.dk/groups/scipp/ess/dream/1/DREAM_simple_pwd_workflow/data_dream_diamond_vana_container_sample_union.csv.zip' to '/home/runner/.cache/ess/dream/1'.
Downloading file 'DREAM_simple_pwd_workflow/data_dream_vanadium.csv.zip' from 'https://public.esss.dk/groups/scipp/ess/dream/1/DREAM_simple_pwd_workflow/data_dream_vanadium.csv.zip' to '/home/runner/.cache/ess/dream/1'.
Downloading file 'DREAM_simple_pwd_workflow/data_dream_vana_container_sample_union.csv.zip' from 'https://public.esss.dk/groups/scipp/ess/dream/1/DREAM_simple_pwd_workflow/data_dream_vana_container_sample_union.csv.zip' to '/home/runner/.cache/ess/dream/1'.
We also need some parameters to configure the output file:
[4]:
workflow[CIFAuthors] = CIFAuthors([
cif.Author(
name="Jane Doe",
email="jane.doe@ess.eu",
orcid="0000-0000-0000-0001",
role="measurement",
),
])
Use the workflow#
We can visualize the graph for computing the final normalized result for intensity as a function of d-spacing:
[5]:
workflow.visualize(IofDspacing, graph_attr={"rankdir": "LR"})
[5]:
We then call compute() to compute the result: (The cif object will later be used to write the result to disk.)
[6]:
results = workflow.compute([IofDspacing, ReducedDspacingCIF])
result = results[IofDspacing]
cif_data = results[ReducedDspacingCIF]
[7]:
dspacing_histogram = result.hist()
dspacing_histogram.plot()
[7]:
We can now save the result to disk: (The comment is optional but helps to identify the file later.)
[8]:
cif_data.comment = """This file was generated with the DREAM data reduction user guide
in the documentation of ESSdiffraction.
See https://scipp.github.io/essdiffraction/
"""
cif_data.save('dspacing.cif')
Compute intermediate results#
For inspection and debugging purposes, we can also compute intermediate results. To avoid repeated computation (including costly loading of files), we can request multiple results at once, including the final result, if desired. For example:
[9]:
intermediates = workflow.compute(
(
DataWithScatteringCoordinates[SampleRun],
MaskedData[SampleRun],
)
)
intermediates[DataWithScatteringCoordinates[SampleRun]]
[9]:
scipp.DataArray (111.27 MB)
- module: 14
- segment: 6
- counter: 2
- wire: 32
- strip: 256
- counter(counter)int641, 2
Values:
array([1, 2]) - gravity()vector3m/s^2[ 0. -9.80665 0. ]
Values:
array([ 0. , -9.80665, 0. ]) - module(module)int641, 2, ..., 13, 14
Values:
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) - position(module, segment, counter, wire, strip)vector3mm[ 943.7585 552.66233333 -1089.20833333], [ 943.48333333 555.36133333 -1079.13555556], ..., [nan nan nan], [nan nan nan]
Values:
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nan, nan, nan]], [[ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan], ..., [ nan, nan, nan], [ nan, nan, nan], [ 735.856 , -1247.43 , 1435.59 ]]]], [[[[ 542.029 , -951.3346 , -1094.772 ], [ 540.7905 , -950.202125 , -1075.12875 ], [ 543.51261538, -949.19030769, -1062.73846154], ..., [ 546.37 , -949.768 , 1064.46 ], [ nan, nan, nan], [ 542.576 , -945.908 , 1088.18 ]], [[ 547.3626 , -963.3666 , -1105.54 ], [ 547.25633333, -962.57783333, -1087.13333333], [ 547.55666667, -965.75566667, -1080.88 ], ..., [ 549.58633333, -963.858 , 1079.56 ], [ 545.889 , -964.64 , 1084.38 ], [ 548.4695 , -960.387 , 1102.78 ]], [[ 557.474 , -969.78366667, -1114.29833333], [ 555.466 , -965.6665 , -1099.785 ], [ 555.106 , -974.258 , -1091.45 ], ..., [ 559.95325 , -972.64025 , 1088.285 ], [ 559.835 , -966.364 , 1096.97 ], [ 562.511 , -970.89 , 1113.21 ]], ..., [[ 746.8545 , -1204.44 , -1412.57 ], [ nan, nan, nan], [ nan, nan, nan], ..., [ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan]], [[ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan], ..., [ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan]], [[ nan, nan, nan], [ nan, nan, nan], [ 760.937 , -1227.1 , -1396.23 ], ..., [ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan]]], [[[ 531.313 , -956.38975 , -1089.17 ], [ 533.4215 , -953.57925 , -1073.2925 ], [ 530.5145 , -958.12275 , -1061.7575 ], ..., [ 528.432 , -957.681 , 1058.52 ], [ nan, nan, nan], [ 531.055 , -963.04 , 1094.09333333]], [[ nan, nan, nan], [ 542.3512 , -964.207 , -1090.736 ], [ 541.7365 , -966.25 , -1078.21 ], ..., [ nan, nan, nan], [ 543.5225 , -963.978 , 1090.315 ], [ 538.6965 , -964.471 , 1094.68 ]], [[ 547.4695 , -977.21683333, -1114.465 ], [ 548.0585 , -979.87325 , -1104.4125 ], [ 551.46466667, -980.02366667, -1097.29666667], ..., [ nan, nan, nan], [ 551.346 , -973.411 , 1105.57 ], [ 551.53433333, -974.734 , 1114.30666667]], ..., [[ nan, nan, nan], [ nan, nan, nan], [ 749.519 , -1215.74 , -1381.89 ], ..., [ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan]], [[ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan], ..., [ nan, nan, nan], [ 754.327 , -1220.94 , 1409.76 ], [ nan, nan, nan]], [[ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan], ..., [ nan, nan, nan], [ nan, nan, nan], [ nan, nan, nan]]]]]]) - sample_position()vector3mm[0. 0. 0.]
Values:
array([0., 0., 0.]) - segment(segment)int641, 2, ..., 5, 6
Values:
array([1, 2, 3, 4, 5, 6]) - source_position()vector3mm[-3.478e+00 0.000e+00 -7.655e+04]
Values:
array([-3.478e+00, 0.000e+00, -7.655e+04]) - strip(strip)int641, 2, ..., 255, 256
Values:
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256]) - two_theta(module, segment, counter, wire, strip)float64rad2.354, 2.349, ..., nan, nan
Values:
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2.34828207, 2.34260813, ..., nan, 0.79184334, 0.78920814], ..., [ nan, nan, nan, ..., nan, 0.79224065, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35235091, 2.34667372, 2.33998121, ..., 0.79951776, 0.7940407 , 0.78904102], [2.35422072, 2.34705264, 2.33968589, ..., nan, 0.79343835, 0.78824448], [2.35435413, 2.34830445, 2.33767322, ..., 0.80007065, 0.79615596, 0.78786998], ..., [2.35658607, nan, 2.34316486, ..., nan, nan, nan], [ nan, nan, 2.34334845, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], [[[2.35475709, 2.34849498, 2.34170979, ..., 0.79815575, 0.79564217, nan], [2.35529288, 2.34662011, 2.34157254, ..., 0.79872101, 0.79512153, 0.78504439], [2.35503776, 2.3473043 , 2.34376638, ..., 0.80170221, 0.79410223, 0.78668313], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35483872, 2.34683383, 2.34106816, ..., 0.79812782, 0.79574601, 0.78637206], [2.35496645, 2.34432268, 2.34036242, ..., 0.80308814, nan, nan], [2.35432064, 2.34995541, 2.34091676, ..., 0.8036874 , 0.79269401, nan], ..., [ nan, 2.34974958, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., 0.79987406, nan, nan]]], [[[2.35448713, 2.34819154, 2.34267056, ..., 0.79869982, 0.7936924 , nan], [2.35615757, 2.344556 , 2.34215509, ..., 0.80094332, 0.7925997 , 0.78663846], [2.35571293, nan, 2.34351492, ..., 0.79711259, 0.79645482, 0.78667343], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, 0.79209608, nan], [ nan, nan, nan, ..., nan, 0.79225571, nan]], [[2.35415508, 2.34719302, 2.34147478, ..., 0.80109217, 0.79671539, 0.78709305], [2.35334454, 2.34696312, 2.33990937, ..., nan, nan, 0.78592807], [2.35179783, 2.34735059, 2.33960528, ..., 0.80237389, nan, 0.78730023], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [2.35377248, nan, nan, ..., nan, nan, nan]]], [[[2.35452082, 2.34788653, 2.34135869, ..., 0.79762928, 0.79577597, 0.78522232], [2.35524549, 2.34898427, 2.34001521, ..., 0.80230618, 0.79399863, nan], [2.35582719, 2.34681154, 2.34074028, ..., nan, nan, 0.78682128], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., 0.79683606, 0.79598766, nan]], [[2.35312628, 2.3476695 , 2.34052282, ..., nan, 0.7923047 , 0.78840886], [2.3530582 , 2.34757994, 2.33888946, ..., nan, 0.79176442, 0.7870269 ], [2.35359128, 2.34666765, 2.34046285, ..., 0.79979411, 0.79574892, 0.78665539], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., 0.7974848 , nan, nan]]], [[[2.35423314, 2.34878365, 2.34057621, ..., 0.79733719, 0.79647873, 0.78468584], [2.35310805, 2.34761336, 2.34241663, ..., nan, 0.79163379, nan], [2.35340256, 2.34738428, 2.34135509, ..., nan, 0.79244428, 0.78687793], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, 2.3404166 , ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35259119, 2.34839384, 2.34117645, ..., nan, 0.79543978, 0.78577802], [2.35400737, 2.34951774, 2.33964657, ..., nan, 0.7933908 , 0.78638109], [2.35528777, nan, nan, ..., 0.80169858, 0.79400702, 0.7879074 ], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, 2.34016904, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]]], [[[[2.3553061 , 2.34747025, 2.34378526, ..., 0.79728449, 0.79444047, 0.78613414], [2.35589714, 2.3469945 , 2.3436264 , ..., nan, nan, 0.78568436], [2.35632727, 2.34765515, 2.34224148, ..., nan, nan, 0.7852713 ], ..., [ nan, 2.34943101, nan, ..., nan, 0.79393995, nan], [ nan, 2.34919089, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35412428, 2.34780316, 2.33929936, ..., 0.80065741, 0.79364371, 0.78531 ], [2.35497983, 2.34698737, 2.34105718, ..., 0.79943316, 0.79297596, 0.79010277], [2.35483344, 2.34885768, 2.34007732, ..., 0.79756803, 0.79431894, 0.78953579], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], [[[2.35143173, 2.3474956 , 2.3440804 , ..., 0.79992868, 0.79169335, 0.78521786], [2.35519004, 2.34722791, 2.34044233, ..., 0.7971764 , 0.79687431, 0.78692887], [2.35478762, 2.34862847, 2.34062396, ..., nan, 0.7914867 , 0.78788205], ..., [ nan, nan, nan, ..., nan, nan, nan], [2.35718774, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35207284, 2.34679695, 2.34257954, ..., 0.79860525, 0.79571418, 0.78603484], [2.35437759, 2.34695296, 2.34069977, ..., nan, 0.79512931, 0.78803826], [2.35469822, 2.34712199, 2.34152509, ..., 0.79867341, 0.79444255, 0.78880081], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., 0.79863482, 0.79625344, nan]]], [[[2.35527108, 2.34713256, 2.34279327, ..., nan, 0.79171388, 0.79053857], [2.3544004 , 2.35009153, 2.33995122, ..., nan, 0.79311724, 0.78612878], [2.35384824, 2.34749617, 2.34072804, ..., nan, 0.79451351, 0.78546752], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35439106, 2.34787691, 2.3404937 , ..., 0.79822674, 0.79422579, 0.78898749], [2.35228642, 2.34724457, 2.34016295, ..., 0.80267013, 0.79478954, 0.78676613], [2.35596048, 2.34663847, 2.34034047, ..., 0.80113267, 0.79147869, 0.79045811], ..., [ nan, nan, nan, ..., nan, nan, nan], [2.35691664, nan, nan, ..., nan, nan, nan], [2.35637384, nan, nan, ..., nan, nan, nan]]], [[[2.35427999, 2.34872254, 2.34178349, ..., nan, 0.7938018 , 0.78617602], [2.35479447, 2.34862225, 2.34083469, ..., nan, 0.79290609, 0.78670691], [2.35424847, 2.34912973, 2.34135535, ..., nan, 0.79611187, 0.79028498], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, 0.78471505], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35253708, 2.34712823, 2.34246471, ..., nan, 0.79259216, 0.78687352], [2.35208916, 2.34716962, 2.34083973, ..., 0.8007169 , 0.79491447, nan], [2.35470734, 2.34829066, 2.34131286, ..., nan, nan, 0.78878447], ..., [ nan, nan, nan, ..., nan, nan, nan], [2.35678522, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], [[[2.35433696, 2.34789013, 2.34126693, ..., nan, 0.79139924, 0.78516737], [2.35439094, 2.34844275, 2.34186572, ..., nan, 0.79400746, nan], [2.35356851, 2.34910279, 2.3415245 , ..., nan, nan, 0.78479479], ..., [2.3571161 , nan, nan, ..., 0.80186291, nan, nan], [2.35568369, nan, nan, ..., nan, nan, nan], [ nan, 2.34715931, nan, ..., nan, nan, nan]], [[2.35318619, 2.34578929, 2.3430436 , ..., 0.80025979, 0.79222039, 0.78628945], [2.35350203, 2.3480823 , 2.3410972 , ..., 0.80344111, 0.79304316, nan], [2.35424122, 2.34615569, 2.33939818, ..., 0.799017 , nan, 0.78700556], ..., [ nan, nan, 2.34431358, ..., nan, nan, nan], [2.35161301, 2.3509502 , nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], [[[2.35345267, 2.34835171, 2.34364572, ..., nan, 0.79078442, 0.78626318], [2.35347241, 2.34960918, 2.34131135, ..., nan, 0.79659674, 0.78683598], [2.35558885, 2.34676428, 2.34161452, ..., 0.79704049, 0.79658297, nan], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, 2.34434109, ..., nan, nan, nan]], [[2.35406013, 2.34652474, 2.34158892, ..., 0.80228696, nan, 0.78576748], [2.35303869, 2.3496428 , 2.34054792, ..., 0.80125932, 0.79550154, 0.78808772], [2.35383932, 2.34616576, 2.34217028, ..., nan, nan, nan], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]]], [[[[2.35374611, 2.34969463, 2.34248562, ..., 0.8020957 , nan, 0.78775733], [2.3560148 , 2.3475521 , 2.34111202, ..., nan, 0.79384281, 0.78517139], [2.35309854, 2.34909513, 2.343023 , ..., 0.79741443, 0.79575588, 0.78635492], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35512651, 2.34791853, 2.34117157, ..., 0.79941037, 0.79297116, 0.7878066 ], [2.35303316, 2.34784235, 2.3405693 , ..., 0.79980211, 0.79350068, 0.78717625], [2.35421369, 2.34620998, 2.34120696, ..., 0.79915636, 0.79616331, 0.78656349], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, 0.78565612], [ nan, nan, nan, ..., 0.79928365, nan, nan]]], [[[2.35459373, 2.34880893, 2.34191135, ..., 0.79902904, 0.79458856, 0.78644079], [2.35395066, 2.34748834, 2.34218414, ..., nan, 0.79272576, 0.78503893], [2.35506318, 2.3476248 , 2.34330465, ..., 0.79792852, 0.79122886, nan], ..., [ nan, nan, nan, ..., 0.80117438, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35370508, 2.34704486, 2.34107647, ..., 0.79959835, 0.79554683, nan], [2.35373857, 2.34720578, nan, ..., nan, 0.79407281, nan], [2.35383585, 2.34566569, 2.34119756, ..., 0.79797409, 0.79725178, 0.78801874], ..., [ nan, nan, nan, ..., nan, 0.795918 , nan], [ nan, nan, nan, ..., nan, 0.79588174, nan], [ nan, nan, nan, ..., nan, nan, nan]]], [[[2.3541437 , 2.34982399, 2.34124006, ..., 0.80069969, nan, 0.78500579], [2.35478772, 2.34832718, 2.33926921, ..., 0.802078 , 0.79625985, 0.78532084], [ nan, 2.34666813, 2.34375659, ..., 0.80085371, nan, 0.78873496], ..., [ nan, 2.34923363, nan, ..., nan, nan, 0.78589944], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.3528945 , 2.34674296, 2.34113812, ..., 0.80101256, 0.79125157, 0.79099957], [2.35303176, 2.34612023, 2.34019494, ..., 0.79709146, 0.79499662, 0.78758539], [2.35319867, 2.34722278, 2.34188887, ..., nan, 0.7929278 , 0.78931736], ..., [ nan, nan, nan, ..., nan, 0.79316026, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]]], [[[2.35457117, 2.34761892, 2.34135701, ..., 0.79766693, 0.79472339, 0.78659783], [2.3558373 , 2.34818438, 2.34181158, ..., 0.80010779, 0.79029319, 0.78862914], [2.35402451, 2.34834439, 2.33911526, ..., 0.79974196, 0.79146956, 0.78501599], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan]], [[2.35430429, 2.34771182, 2.33848477, ..., nan, nan, 0.78630206], [2.35320529, 2.34810488, 2.34025312, ..., 0.80051919, nan, 0.78859113], [2.35500425, 2.34650251, 2.33965069, ..., 0.80169069, 0.79564914, 0.7881267 ], ..., [ nan, nan, nan, ..., nan, nan, nan], [2.35705734, nan, nan, ..., nan, nan, nan], [2.35703704, nan, nan, ..., nan, nan, nan]]], [[[2.35416911, 2.34785152, 2.3409837 , ..., nan, 0.79429753, 0.78545773], [2.35337787, 2.34889789, 2.34024394, ..., nan, 0.79491954, 0.78670047], [2.35455807, 2.34841565, 2.34002431, ..., 0.80224213, 0.79084635, 0.78730825], ..., [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, 0.79021801, nan]], [[2.35274002, 2.34672014, 2.34163639, ..., 0.80046183, 0.79582759, 0.78609965], [2.35514213, 2.34678917, 2.34155188, ..., 0.80058414, 0.79520091, 0.78575966], [2.35273582, 2.346624 , 2.34165292, ..., nan, 0.79304336, 0.79126268], ..., [2.35552545, nan, 2.33895936, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, 0.78978143]]], [[[2.35610792, 2.34778528, 2.3417758 , ..., 0.79984075, nan, 0.78642789], [2.35505724, 2.34699618, 2.34279981, ..., 0.79906757, 0.79632431, 0.78681832], [2.35424702, 2.34973906, 2.3426813 , ..., 0.80076546, 0.79433921, 0.78934006], ..., [2.35453294, nan, nan, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, nan, nan], [ nan, nan, 2.33939339, ..., nan, nan, nan]], [[2.35393105, 2.34724206, 2.34067304, ..., 0.80176528, nan, 0.78795783], [ nan, 2.34910044, 2.34265806, ..., nan, 0.79281017, 0.78993921], [2.35364027, 2.34794842, 2.34391723, ..., nan, 0.7912831 , 0.7879033 ], ..., [ nan, nan, 2.33968645, ..., nan, nan, nan], [ nan, nan, nan, ..., nan, 0.79430461, nan], [ nan, nan, nan, ..., nan, nan, nan]]]]]) - wire(wire)int641, 2, ..., 31, 32
Values:
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32])
- (module, segment, counter, wire, strip)DataArrayViewbinned data [len=12, len=9, ..., len=0, len=0]
dim='event', content=DataArray( dims=(event: 1533595), data=float64[counts/uAh], coords={'tof':int64[ns], 'wavelength':float64[Å]}, masks={'tof':bool})
[10]:
two_theta = sc.linspace("two_theta", 0.8, 2.4, 301, unit="rad")
intermediates[MaskedData[SampleRun]].hist(two_theta=two_theta, wavelength=300).plot(
norm="log"
)
[10]:
Grouping by scattering angle#
The above workflow 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 workflow parameters by adding some binning in \(2\theta\):
[11]:
workflow[TwoThetaBins] = sc.linspace(
dim="two_theta", unit="rad", start=0.8, stop=2.4, num=17
)
[12]:
grouped_dspacing = workflow.compute(IofDspacingTwoTheta)
grouped_dspacing
[12]:
scipp.DataArray (30.46 MB)
- two_theta: 16
- dspacing: 200
- dspacing(dspacing [bin-edge])float64Å0.0, 0.012, ..., 2.332, 2.343
Values:
array([0. , 0.011717, 0.023434, 0.035151, 0.046868, 0.058585, 0.070302, 0.082019, 0.093736, 0.105453, 0.11717 , 0.128887, 0.140604, 0.152321, 0.164038, 0.175755, 0.187472, 0.199189, 0.210906, 0.222623, 0.23434 , 0.246057, 0.257774, 0.269491, 0.281208, 0.292925, 0.304642, 0.316359, 0.328076, 0.339793, 0.35151 , 0.363227, 0.374944, 0.386661, 0.398378, 0.410095, 0.421812, 0.433529, 0.445246, 0.456963, 0.46868 , 0.480397, 0.492114, 0.503831, 0.515548, 0.527265, 0.538982, 0.550699, 0.562416, 0.574133, 0.58585 , 0.597567, 0.609284, 0.621001, 0.632718, 0.644435, 0.656152, 0.667869, 0.679586, 0.691303, 0.70302 , 0.714737, 0.726454, 0.738171, 0.749888, 0.761605, 0.773322, 0.785039, 0.796756, 0.808473, 0.82019 , 0.831907, 0.843624, 0.855341, 0.867058, 0.878775, 0.890492, 0.902209, 0.913926, 0.925643, 0.93736 , 0.949077, 0.960794, 0.972511, 0.984228, 0.995945, 1.007662, 1.019379, 1.031096, 1.042813, 1.05453 , 1.066247, 1.077964, 1.089681, 1.101398, 1.113115, 1.124832, 1.136549, 1.148266, 1.159983, 1.1717 , 1.183417, 1.195134, 1.206851, 1.218568, 1.230285, 1.242002, 1.253719, 1.265436, 1.277153, 1.28887 , 1.300587, 1.312304, 1.324021, 1.335738, 1.347455, 1.359172, 1.370889, 1.382606, 1.394323, 1.40604 , 1.417757, 1.429474, 1.441191, 1.452908, 1.464625, 1.476342, 1.488059, 1.499776, 1.511493, 1.52321 , 1.534927, 1.546644, 1.558361, 1.570078, 1.581795, 1.593512, 1.605229, 1.616946, 1.628663, 1.64038 , 1.652097, 1.663814, 1.675531, 1.687248, 1.698965, 1.710682, 1.722399, 1.734116, 1.745833, 1.75755 , 1.769267, 1.780984, 1.792701, 1.804418, 1.816135, 1.827852, 1.839569, 1.851286, 1.863003, 1.87472 , 1.886437, 1.898154, 1.909871, 1.921588, 1.933305, 1.945022, 1.956739, 1.968456, 1.980173, 1.99189 , 2.003607, 2.015324, 2.027041, 2.038758, 2.050475, 2.062192, 2.073909, 2.085626, 2.097343, 2.10906 , 2.120777, 2.132494, 2.144211, 2.155928, 2.167645, 2.179362, 2.191079, 2.202796, 2.214513, 2.22623 , 2.237947, 2.249664, 2.261381, 2.273098, 2.284815, 2.296532, 2.308249, 2.319966, 2.331683, 2.3434 ]) - gravity()vector3m/s^2[ 0. -9.80665 0. ]
Values:
array([ 0. , -9.80665, 0. ]) - sample_position()vector3mm[0. 0. 0.]
Values:
array([0., 0., 0.]) - source_position()vector3mm[-3.478e+00 0.000e+00 -7.655e+04]
Values:
array([-3.478e+00, 0.000e+00, -7.655e+04]) - two_theta(two_theta [bin-edge])float64rad0.8, 0.9, ..., 2.3, 2.4
Values:
array([0.8, 0.9, 1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4])
- (two_theta, dspacing)DataArrayViewbinned data [len=0, len=0, ..., len=6, len=1]
dim='event', content=DataArray( dims=(event: 1275487), data=float64[dimensionless], coords={'dspacing':float64[Å]}, masks={'tof':bool})
[13]:
angle = sc.midpoints(grouped_dspacing.coords["two_theta"])
sc.plot(
{
f"{angle[group].value:.3f} {angle[group].unit}": grouped_dspacing[
"two_theta", group
].hist()
for group in range(2, 6)
}
)
[13]:
[14]:
grouped_dspacing.hist().plot(norm="log")
[14]:
