Histogram Mode Detector#

TL;DR - Get normalized sample images averaged along the rotation angle.#

[1]:

from ess.imaging.data import get_ymir_images_path
from ess.imaging.io import FilePath
from ess.imaging.normalize import NormalizedSampleImages
from ess.imaging.workflow import (
    YmirImageNormalizationWorkflow,
    ImageDetectorName,
    RotationMotionSensorName,
)

wf = YmirImageNormalizationWorkflow()
wf[FilePath] = (
    get_ymir_images_path()
)  # Replace with the path to your images in nexus file.
wf[ImageDetectorName] = 'orca'
wf[RotationMotionSensorName] = 'motion_cabinet_2'
normalized = wf.compute(NormalizedSampleImages)
normalized

Downloading file 'README.md' from 'https://public.esss.dk/groups/scipp/ess/imaging/README.md' to '/home/runner/.cache/essimaging/0'.
Downloading file 'small_ymir_images.hdf' from 'https://public.esss.dk/groups/scipp/ess/imaging/small_ymir_images.hdf' to '/home/runner/.cache/essimaging/0'.
/home/runner/work/essimaging/essimaging/.tox/docs/lib/python3.10/site-packages/ess/imaging/io.py:105: UserWarning: The unit of the histogram detector data is [None]. It is expected to be [counts]. The loader manually assigned the unit to be [counts].
  warnings.warn(
/home/runner/work/essimaging/essimaging/.tox/docs/lib/python3.10/site-packages/ess/imaging/normalize.py:47: UserWarning: Computing average dark current image assuming constant exposure time.
  warnings.warn(warning_message, stacklevel=1)
/home/runner/work/essimaging/essimaging/.tox/docs/lib/python3.10/site-packages/ess/imaging/normalize.py:47: UserWarning: Computing average open beam image assuming constant exposure time.
  warnings.warn(warning_message, stacklevel=1)
/home/runner/work/essimaging/essimaging/.tox/docs/lib/python3.10/site-packages/ess/imaging/normalize.py:47: UserWarning: Computing average background pixel counts assuming constant exposure time.
  warnings.warn(warning_message, stacklevel=1)
/home/runner/work/essimaging/essimaging/.tox/docs/lib/python3.10/site-packages/ess/imaging/normalize.py:47: UserWarning: Computing average sample pixel counts assuming constant exposure time.
  warnings.warn(warning_message, stacklevel=1)
/home/runner/work/essimaging/essimaging/.tox/docs/lib/python3.10/site-packages/ess/imaging/normalize.py:47: UserWarning: Computing normalized sample image stack assuming constant exposure time.
  warnings.warn(warning_message, stacklevel=1)

[1]:
Show/Hide data repr Show/Hide attributes
scipp.DataArray (18.01 MB out of 18.01 MB)
    • time: 36
    • dim_1: 256
    • dim_2: 256
    • dim_1
      (dim_1)
      int64
      𝟙
      0, 1, ..., 254, 255
      Values:
      array([  0,   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])
    • dim_2
      (dim_2)
      int64
      𝟙
      0, 1, ..., 254, 255
      Values:
      array([  0,   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])
    • rotation_angle
      (time)
      float64
      deg
      1.007, 11.034, ..., 341.951, 351.979
      Values:
      array([  1.00731091,  11.03435639,  21.06173445,  31.08968206,
        41.11822052,  51.14620862,  61.17432276,  71.20250242,
        81.22770456,  91.25705069, 101.28393277, 111.31280002,
       121.33981109, 131.36662637, 141.39534187, 151.42282542,
       161.45027075, 171.47811035, 181.50597898, 191.53482643,
       201.56200906, 211.58970896, 221.61798686, 231.64625132,
       241.67332848, 251.70052385, 261.72550702, 271.75563946,
       281.78514837, 291.81166234, 301.83935011, 311.86674052,
       321.89460518, 331.92353937, 341.95077558, 351.97860336])
    • time
      (time)
      datetime64
      ns
      2024-08-14T14:16:57.997255602, 2024-08-14T14:17:46.682255224, ..., 2024-08-14T14:44:41.925222722, 2024-08-14T14:45:30.825154517
      Values:
      array(['2024-08-14T14:16:57.997255602', '2024-08-14T14:17:46.682255224',
       '2024-08-14T14:18:35.853205911', '2024-08-14T14:19:24.975128719',
       '2024-08-14T14:20:13.955183413', '2024-08-14T14:21:02.993115782',
       '2024-08-14T14:21:51.865167018', '2024-08-14T14:22:40.700159043',
       '2024-08-14T14:23:30.120165136', '2024-08-14T14:24:19.211202454',
       '2024-08-14T14:25:07.781151541', '2024-08-14T14:25:56.672195037',
       '2024-08-14T14:26:45.768113531', '2024-08-14T14:27:34.491207424',
       '2024-08-14T14:28:23.784141401', '2024-08-14T14:29:12.714194238',
       '2024-08-14T14:30:01.238209345', '2024-08-14T14:30:50.167144757',
       '2024-08-14T14:31:38.958162332', '2024-08-14T14:32:27.831177947',
       '2024-08-14T14:33:16.678124259', '2024-08-14T14:34:05.873147398',
       '2024-08-14T14:34:54.972148922', '2024-08-14T14:35:43.545102329',
       '2024-08-14T14:36:32.141243180', '2024-08-14T14:37:21.510179315',
       '2024-08-14T14:38:10.617163791', '2024-08-14T14:39:00.045194764',
       '2024-08-14T14:39:48.289190220', '2024-08-14T14:40:36.650186469',
       '2024-08-14T14:41:25.774189010', '2024-08-14T14:42:15.149107963',
       '2024-08-14T14:43:03.915278596', '2024-08-14T14:43:53.077218381',
       '2024-08-14T14:44:41.925222722', '2024-08-14T14:45:30.825154517'],
      dtype='datetime64[ns]')
    • (time, dim_1, dim_2)
      float64
      𝟙
      1.048, 1.062, ..., 0.992, 0.997
      Values:
      array([[[1.0476282 , 1.06165906, 1.04531464, ..., 1.05644014,
         1.0414165 , 1.05037228],
        [1.05502979, 1.03332636, 1.05998197, ..., 1.04956501,
         1.05474718, 1.04747274],
        [1.04426372, 1.06543977, 1.02916858, ..., 1.06201682,
         1.05465958, 1.0536832 ],
        ...,
        [0.82613202, 0.81359233, 0.80103089, ..., 1.05969303,
         1.04164473, 1.03079426],
        [0.83522123, 0.80806379, 0.77136373, ..., 1.08613709,
         1.10307506, 0.97883198],
        [0.82829581, 0.76003729, 0.82939737, ..., 1.0779487 ,
         1.06083292, 1.0388744 ]],

       [[1.07754557, 1.06728106, 1.04837291, ..., 1.04092882,
         1.0414165 , 1.04447528],
        [1.05981017, 1.04834564, 1.07451759, ..., 1.05978473,
         1.06356613, 1.04971428],
        [1.04545417, 1.06432785, 1.03143697, ..., 1.07656832,
         1.05465958, 1.04840317],
        ...,
        [0.77715187, 0.76736549, 0.77057344, ..., 0.90499331,
         1.01437654, 1.0036681 ],
        [0.83522123, 0.7607163 , 0.78654806, ..., 1.00762116,
         1.00959412, 0.96574599],
        [0.79692097, 0.76003729, 0.79674393, ..., 0.9941703 ,
         0.96489196, 0.9696161 ]],

       [[1.08665086, 1.07740067, 1.05550886, ..., 1.04092882,
         1.04598111, 1.05037228],
        [1.07056602, 1.04950097, 1.07339946, ..., 1.05595233,
         1.07112524, 1.05307659],
        [1.05735868, 1.06655169, 1.02916858, ..., 1.0511032 ,
         1.05922619, 1.06001924],
        ...,
        [0.80980531, 0.75195655, 0.77057344, ..., 0.98234317,
         0.98710835, 0.96297885],
        [0.78758504, 0.7449338 , 0.77136373, ..., 0.96836319,
         0.94282202, 0.90031605],
        [0.82829581, 0.76003729, 0.79674393, ..., 0.98020724,
         0.99230366, 0.99731942]],

       ...,

       [[1.16209465, 1.11225709, 1.08099439, ..., 1.01945161,
         1.01631112, 1.04329588],
        [1.09685811, 1.08878215, 1.11812444, ..., 1.04317768,
         1.05222747, 1.03850657],
        [1.06331094, 1.07211128, 1.0473157 , ..., 1.07656832,
         1.03525147, 1.01777898],
        ...,
        [0.8914389 , 0.90604601, 0.87717451, ..., 0.94366824,
         1.00074245, 0.9222896 ],
        [0.86697869, 0.83962878, 0.87765401, ..., 1.02070714,
         0.9962397 , 0.92648802],
        [0.89104549, 0.86601858, 0.89470425, ..., 0.9941703 ,
         0.97859781, 0.98346776]],

       [[1.1607939 , 1.10888388, 1.07385844, ..., 1.00632665,
         0.99805267, 1.0279637 ],
        [1.09805321, 1.0864715 , 1.11029757, ..., 1.04828754,
         1.04214867, 1.03850657],
        [1.05973958, 1.07211128, 1.0484499 , ..., 1.06807995,
         1.03981808, 1.01355496],
        ...,
        [0.8914389 , 0.87522811, 0.89240323, ..., 0.94366824,
         1.02801063, 0.96297885],
        [0.89873615, 0.85541128, 0.86246969, ..., 0.99453517,
         0.95617644, 0.96574599],
        [0.85967065, 0.8508784 , 0.86205081, ..., 0.98020724,
         1.04712707, 0.9696161 ]],

       [[1.17510221, 1.11000828, 1.07080018, ..., 1.0146789 ,
         1.00489959, 1.02678431],
        [1.11000416, 1.08069485, 1.11477007, ..., 1.02657064,
         1.04340852, 1.03066117],
        [1.0764059 , 1.07099936, 1.04277892, ..., 1.07414307,
         1.04324304, 1.01989099],
        ...,
        [0.92409234, 0.92145495, 0.83148833, ..., 1.04680138,
         1.01437654, 0.97654193],
        [0.83522123, 0.88697627, 0.92320699, ..., 1.04687912,
         0.9962397 , 0.97883198],
        [0.93810775, 0.8508784 , 0.92735769, ..., 1.0360595 ,
         0.99230366, 0.99731942]]])
[2]:

normalized['time', 0].plot(
    title=f'Normalized Sample Image at Rotation Angle of {normalized.coords["rotation_angle"].values[0]:.3f}'
)

[2]:
../_images/user-guide_histogram_mode_detector_2_0.svg

You can use a slicer to browse images along the rotation angle.

%matplotlib widget
from plopp import slicer

slicer(normalized)

If you want to bin the images by linear steps of angles, you can use sc.groupby like below.

%matplotlib widget
import scipp as sc
from plopp import slicer

slicer(normalized.groupby(
    'rotation_angle',
    bins=sc.linspace(dim='rotation_angle', start=-0.5, stop=4.5, num=5, unit='deg'),
).mean('rotation_angle'))

IO#

I/O providers can load white beam images and slice them according to the image key.

Note that not like the rotation_angle, image_key is not assigned as a coordinate.

Instead, we slice the data according to the image_key and separate image stacks earlier due to performance limitation of 3-dimensional data binning.

For example, for 400 images of 2_048 x 2_048 pixels couldn’t be done in the regular laptop.

Load the data as a stack of images#

[3]:

from ess.imaging.data import get_ymir_images_path
from ess.imaging.io import (
    FilePath,
    SampleImageStacksWithLogs,
    RawSampleImageStacks,
    OpenBeamImageStacks,
    DarkCurrentImageStacks,
    AllImageStacks,
)
from ess.imaging.workflow import (
    YmirImageNormalizationWorkflow,
    ImageDetectorName,
    RotationMotionSensorName,
)

wf = YmirImageNormalizationWorkflow()
wf[FilePath] = get_ymir_images_path()
wf[ImageDetectorName] = 'orca'
wf[RotationMotionSensorName] = 'motion_cabinet_2'

[4]:

image_stack_types = (
    AllImageStacks,
    RawSampleImageStacks,
    OpenBeamImageStacks,
    DarkCurrentImageStacks,
    SampleImageStacksWithLogs,
)
wf.visualize(image_stack_types)

[4]:
../_images/user-guide_histogram_mode_detector_6_0.svg 
[5]:

results = wf.compute(image_stack_types)
results[SampleImageStacksWithLogs]

[5]:
Show/Hide data repr Show/Hide attributes
scipp.DataArray (9.01 MB out of 11.51 MB)
    • time: 36
    • dim_1: 256
    • dim_2: 256
    • dim_1
      (dim_1)
      int64
      𝟙
      0, 1, ..., 254, 255
      Values:
      array([  0,   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])
    • dim_2
      (dim_2)
      int64
      𝟙
      0, 1, ..., 254, 255
      Values:
      array([  0,   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])
    • rotation_angle
      (time)
      float64
      deg
      1.007, 11.034, ..., 341.951, 351.979
      Values:
      array([  1.00731091,  11.03435639,  21.06173445,  31.08968206,
        41.11822052,  51.14620862,  61.17432276,  71.20250242,
        81.22770456,  91.25705069, 101.28393277, 111.31280002,
       121.33981109, 131.36662637, 141.39534187, 151.42282542,
       161.45027075, 171.47811035, 181.50597898, 191.53482643,
       201.56200906, 211.58970896, 221.61798686, 231.64625132,
       241.67332848, 251.70052385, 261.72550702, 271.75563946,
       281.78514837, 291.81166234, 301.83935011, 311.86674052,
       321.89460518, 331.92353937, 341.95077558, 351.97860336])
    • time
      (time)
      datetime64
      ns
      2024-08-14T14:16:57.997255602, 2024-08-14T14:17:46.682255224, ..., 2024-08-14T14:44:41.925222722, 2024-08-14T14:45:30.825154517
      Values:
      array(['2024-08-14T14:16:57.997255602', '2024-08-14T14:17:46.682255224',
       '2024-08-14T14:18:35.853205911', '2024-08-14T14:19:24.975128719',
       '2024-08-14T14:20:13.955183413', '2024-08-14T14:21:02.993115782',
       '2024-08-14T14:21:51.865167018', '2024-08-14T14:22:40.700159043',
       '2024-08-14T14:23:30.120165136', '2024-08-14T14:24:19.211202454',
       '2024-08-14T14:25:07.781151541', '2024-08-14T14:25:56.672195037',
       '2024-08-14T14:26:45.768113531', '2024-08-14T14:27:34.491207424',
       '2024-08-14T14:28:23.784141401', '2024-08-14T14:29:12.714194238',
       '2024-08-14T14:30:01.238209345', '2024-08-14T14:30:50.167144757',
       '2024-08-14T14:31:38.958162332', '2024-08-14T14:32:27.831177947',
       '2024-08-14T14:33:16.678124259', '2024-08-14T14:34:05.873147398',
       '2024-08-14T14:34:54.972148922', '2024-08-14T14:35:43.545102329',
       '2024-08-14T14:36:32.141243180', '2024-08-14T14:37:21.510179315',
       '2024-08-14T14:38:10.617163791', '2024-08-14T14:39:00.045194764',
       '2024-08-14T14:39:48.289190220', '2024-08-14T14:40:36.650186469',
       '2024-08-14T14:41:25.774189010', '2024-08-14T14:42:15.149107963',
       '2024-08-14T14:43:03.915278596', '2024-08-14T14:43:53.077218381',
       '2024-08-14T14:44:41.925222722', '2024-08-14T14:45:30.825154517'],
      dtype='datetime64[ns]')
    • (time, dim_1, dim_2)
      int32
      counts
      950, 1090, ..., 204, 203
      Values:
      array([[[ 950, 1090, 1171, ..., 1058, 1087, 1065],
        [1027, 1039, 1094, ...,  996, 1012, 1108],
        [1022, 1104, 1054, ..., 1050, 1100, 1174],
        ...,
        [ 169,  171,  171, ...,  215,  209,  208],
        [ 171,  170,  169, ...,  213,  213,  205],
        [ 171,  169,  170, ...,  211,  209,  206]],

       [[ 973, 1095, 1174, ..., 1045, 1087, 1060],
        [1031, 1052, 1107, ..., 1004, 1019, 1110],
        [1023, 1103, 1056, ..., 1062, 1100, 1169],
        ...,
        [ 166,  168,  169, ...,  203,  207,  206],
        [ 171,  167,  170, ...,  207,  206,  204],
        [ 169,  169,  168, ...,  205,  202,  201]],

       [[ 980, 1104, 1181, ..., 1045, 1091, 1065],
        [1040, 1053, 1106, ..., 1001, 1025, 1113],
        [1033, 1105, 1054, ..., 1041, 1104, 1180],
        ...,
        [ 168,  167,  169, ...,  209,  205,  203],
        [ 168,  166,  169, ...,  204,  201,  199],
        [ 171,  169,  168, ...,  204,  204,  203]],

       ...,

       [[1038, 1135, 1206, ..., 1027, 1065, 1059],
        [1062, 1087, 1146, ...,  991, 1010, 1100],
        [1038, 1110, 1070, ..., 1062, 1083, 1140],
        ...,
        [ 173,  177,  176, ...,  206,  206,  200],
        [ 173,  172,  176, ...,  208,  205,  201],
        [ 175,  176,  174, ...,  205,  203,  202]],

       [[1037, 1132, 1199, ..., 1016, 1049, 1046],
        [1063, 1085, 1139, ...,  995, 1002, 1100],
        [1035, 1110, 1071, ..., 1055, 1087, 1136],
        ...,
        [ 173,  175,  177, ...,  206,  208,  203],
        [ 175,  173,  175, ...,  206,  202,  204],
        [ 173,  175,  172, ...,  204,  208,  201]],

       [[1048, 1133, 1196, ..., 1023, 1055, 1045],
        [1073, 1080, 1143, ...,  978, 1003, 1093],
        [1049, 1109, 1066, ..., 1060, 1090, 1142],
        ...,
        [ 175,  178,  173, ...,  214,  207,  204],
        [ 171,  175,  179, ...,  210,  205,  205],
        [ 178,  175,  176, ...,  208,  204,  203]]], dtype=int32)

Save Individual Frame#

[6]:

from ess.imaging.io import export_image_stacks_as_tiff, ImageKey

from pathlib import Path
from tqdm import tqdm


output_dir = Path("images")
export_image_stacks_as_tiff(
    output_dir=output_dir,
    image_stacks=results[AllImageStacks],
    merge_image_by_key=False,
    overwrite=True,
    progress_wrapper=tqdm,
    image_prefix_map={
        ImageKey.SAMPLE: "sample",
        ImageKey.DARK_CURRENT: "dc",
        ImageKey.OPEN_BEAM: "ob",
    },
)

  0%|          | 0/3 [00:00<?, ?it/s]
100%|██████████| 36/36 [00:00<00:00, 1951.85it/s]

100%|██████████| 5/5 [00:00<00:00, 1476.97it/s]

100%|██████████| 5/5 [00:00<00:00, 1373.92it/s]
100%|██████████| 3/3 [00:00<00:00, 86.74it/s]

[7]:

sorted(output_dir.iterdir())

[7]:

[PosixPath('images/dc_0000.tiff'),
 PosixPath('images/dc_0001.tiff'),
 PosixPath('images/dc_0002.tiff'),
 PosixPath('images/dc_0003.tiff'),
 PosixPath('images/dc_0004.tiff'),
 PosixPath('images/ob_0000.tiff'),
 PosixPath('images/ob_0001.tiff'),
 PosixPath('images/ob_0002.tiff'),
 PosixPath('images/ob_0003.tiff'),
 PosixPath('images/ob_0004.tiff'),
 PosixPath('images/sample_0000.tiff'),
 PosixPath('images/sample_0001.tiff'),
 PosixPath('images/sample_0002.tiff'),
 PosixPath('images/sample_0003.tiff'),
 PosixPath('images/sample_0004.tiff'),
 PosixPath('images/sample_0005.tiff'),
 PosixPath('images/sample_0006.tiff'),
 PosixPath('images/sample_0007.tiff'),
 PosixPath('images/sample_0008.tiff'),
 PosixPath('images/sample_0009.tiff'),
 PosixPath('images/sample_0010.tiff'),
 PosixPath('images/sample_0011.tiff'),
 PosixPath('images/sample_0012.tiff'),
 PosixPath('images/sample_0013.tiff'),
 PosixPath('images/sample_0014.tiff'),
 PosixPath('images/sample_0015.tiff'),
 PosixPath('images/sample_0016.tiff'),
 PosixPath('images/sample_0017.tiff'),
 PosixPath('images/sample_0018.tiff'),
 PosixPath('images/sample_0019.tiff'),
 PosixPath('images/sample_0020.tiff'),
 PosixPath('images/sample_0021.tiff'),
 PosixPath('images/sample_0022.tiff'),
 PosixPath('images/sample_0023.tiff'),
 PosixPath('images/sample_0024.tiff'),
 PosixPath('images/sample_0025.tiff'),
 PosixPath('images/sample_0026.tiff'),
 PosixPath('images/sample_0027.tiff'),
 PosixPath('images/sample_0028.tiff'),
 PosixPath('images/sample_0029.tiff'),
 PosixPath('images/sample_0030.tiff'),
 PosixPath('images/sample_0031.tiff'),
 PosixPath('images/sample_0032.tiff'),
 PosixPath('images/sample_0033.tiff'),
 PosixPath('images/sample_0034.tiff'),
 PosixPath('images/sample_0035.tiff')]

Save All Frames in One File#

[8]:

from ess.imaging.io import export_image_stacks_as_tiff, ImageKey
from tqdm import tqdm
from pathlib import Path


output_dir = Path("images")
export_image_stacks_as_tiff(
    output_dir=output_dir,
    image_stacks=results[AllImageStacks],
    merge_image_by_key=True,
    overwrite=True,
    progress_wrapper=tqdm,
    image_prefix_map={
        ImageKey.SAMPLE: "sample",
        ImageKey.DARK_CURRENT: "dc",
        ImageKey.OPEN_BEAM: "ob",
    },
)

100%|██████████| 3/3 [00:00<00:00, 308.03it/s]

[9]:

tuple(output_dir.iterdir())

[9]:

(PosixPath('images/sample_0000_0036.tiff'),
 PosixPath('images/dc_0000_0005.tiff'),
 PosixPath('images/ob_0000_0005.tiff'))

[10]:

!rm -rf images

Normalization#

See :attr:ess.imaging.normalize.normalize_sample_images for more details.

[11]:

from ess.imaging.normalize import NormalizedSampleImages

wf[OpenBeamImageStacks] = results[OpenBeamImageStacks]
wf[DarkCurrentImageStacks] = results[DarkCurrentImageStacks]
wf[SampleImageStacksWithLogs] = results[SampleImageStacksWithLogs]
wf.visualize(NormalizedSampleImages)

[11]:
../_images/user-guide_histogram_mode_detector_16_0.svg 
[12]:

wf.compute(NormalizedSampleImages)

[12]:
Show/Hide data repr Show/Hide attributes
scipp.DataArray (18.01 MB out of 18.01 MB)
    • time: 36
    • dim_1: 256
    • dim_2: 256
    • dim_1
      (dim_1)
      int64
      𝟙
      0, 1, ..., 254, 255
      Values:
      array([  0,   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])
    • dim_2
      (dim_2)
      int64
      𝟙
      0, 1, ..., 254, 255
      Values:
      array([  0,   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])
    • rotation_angle
      (time)
      float64
      deg
      1.007, 11.034, ..., 341.951, 351.979
      Values:
      array([  1.00731091,  11.03435639,  21.06173445,  31.08968206,
        41.11822052,  51.14620862,  61.17432276,  71.20250242,
        81.22770456,  91.25705069, 101.28393277, 111.31280002,
       121.33981109, 131.36662637, 141.39534187, 151.42282542,
       161.45027075, 171.47811035, 181.50597898, 191.53482643,
       201.56200906, 211.58970896, 221.61798686, 231.64625132,
       241.67332848, 251.70052385, 261.72550702, 271.75563946,
       281.78514837, 291.81166234, 301.83935011, 311.86674052,
       321.89460518, 331.92353937, 341.95077558, 351.97860336])
    • time
      (time)
      datetime64
      ns
      2024-08-14T14:16:57.997255602, 2024-08-14T14:17:46.682255224, ..., 2024-08-14T14:44:41.925222722, 2024-08-14T14:45:30.825154517
      Values:
      array(['2024-08-14T14:16:57.997255602', '2024-08-14T14:17:46.682255224',
       '2024-08-14T14:18:35.853205911', '2024-08-14T14:19:24.975128719',
       '2024-08-14T14:20:13.955183413', '2024-08-14T14:21:02.993115782',
       '2024-08-14T14:21:51.865167018', '2024-08-14T14:22:40.700159043',
       '2024-08-14T14:23:30.120165136', '2024-08-14T14:24:19.211202454',
       '2024-08-14T14:25:07.781151541', '2024-08-14T14:25:56.672195037',
       '2024-08-14T14:26:45.768113531', '2024-08-14T14:27:34.491207424',
       '2024-08-14T14:28:23.784141401', '2024-08-14T14:29:12.714194238',
       '2024-08-14T14:30:01.238209345', '2024-08-14T14:30:50.167144757',
       '2024-08-14T14:31:38.958162332', '2024-08-14T14:32:27.831177947',
       '2024-08-14T14:33:16.678124259', '2024-08-14T14:34:05.873147398',
       '2024-08-14T14:34:54.972148922', '2024-08-14T14:35:43.545102329',
       '2024-08-14T14:36:32.141243180', '2024-08-14T14:37:21.510179315',
       '2024-08-14T14:38:10.617163791', '2024-08-14T14:39:00.045194764',
       '2024-08-14T14:39:48.289190220', '2024-08-14T14:40:36.650186469',
       '2024-08-14T14:41:25.774189010', '2024-08-14T14:42:15.149107963',
       '2024-08-14T14:43:03.915278596', '2024-08-14T14:43:53.077218381',
       '2024-08-14T14:44:41.925222722', '2024-08-14T14:45:30.825154517'],
      dtype='datetime64[ns]')
    • (time, dim_1, dim_2)
      float64
      𝟙
      1.048, 1.062, ..., 0.992, 0.997
      Values:
      array([[[1.0476282 , 1.06165906, 1.04531464, ..., 1.05644014,
         1.0414165 , 1.05037228],
        [1.05502979, 1.03332636, 1.05998197, ..., 1.04956501,
         1.05474718, 1.04747274],
        [1.04426372, 1.06543977, 1.02916858, ..., 1.06201682,
         1.05465958, 1.0536832 ],
        ...,
        [0.82613202, 0.81359233, 0.80103089, ..., 1.05969303,
         1.04164473, 1.03079426],
        [0.83522123, 0.80806379, 0.77136373, ..., 1.08613709,
         1.10307506, 0.97883198],
        [0.82829581, 0.76003729, 0.82939737, ..., 1.0779487 ,
         1.06083292, 1.0388744 ]],

       [[1.07754557, 1.06728106, 1.04837291, ..., 1.04092882,
         1.0414165 , 1.04447528],
        [1.05981017, 1.04834564, 1.07451759, ..., 1.05978473,
         1.06356613, 1.04971428],
        [1.04545417, 1.06432785, 1.03143697, ..., 1.07656832,
         1.05465958, 1.04840317],
        ...,
        [0.77715187, 0.76736549, 0.77057344, ..., 0.90499331,
         1.01437654, 1.0036681 ],
        [0.83522123, 0.7607163 , 0.78654806, ..., 1.00762116,
         1.00959412, 0.96574599],
        [0.79692097, 0.76003729, 0.79674393, ..., 0.9941703 ,
         0.96489196, 0.9696161 ]],

       [[1.08665086, 1.07740067, 1.05550886, ..., 1.04092882,
         1.04598111, 1.05037228],
        [1.07056602, 1.04950097, 1.07339946, ..., 1.05595233,
         1.07112524, 1.05307659],
        [1.05735868, 1.06655169, 1.02916858, ..., 1.0511032 ,
         1.05922619, 1.06001924],
        ...,
        [0.80980531, 0.75195655, 0.77057344, ..., 0.98234317,
         0.98710835, 0.96297885],
        [0.78758504, 0.7449338 , 0.77136373, ..., 0.96836319,
         0.94282202, 0.90031605],
        [0.82829581, 0.76003729, 0.79674393, ..., 0.98020724,
         0.99230366, 0.99731942]],

       ...,

       [[1.16209465, 1.11225709, 1.08099439, ..., 1.01945161,
         1.01631112, 1.04329588],
        [1.09685811, 1.08878215, 1.11812444, ..., 1.04317768,
         1.05222747, 1.03850657],
        [1.06331094, 1.07211128, 1.0473157 , ..., 1.07656832,
         1.03525147, 1.01777898],
        ...,
        [0.8914389 , 0.90604601, 0.87717451, ..., 0.94366824,
         1.00074245, 0.9222896 ],
        [0.86697869, 0.83962878, 0.87765401, ..., 1.02070714,
         0.9962397 , 0.92648802],
        [0.89104549, 0.86601858, 0.89470425, ..., 0.9941703 ,
         0.97859781, 0.98346776]],

       [[1.1607939 , 1.10888388, 1.07385844, ..., 1.00632665,
         0.99805267, 1.0279637 ],
        [1.09805321, 1.0864715 , 1.11029757, ..., 1.04828754,
         1.04214867, 1.03850657],
        [1.05973958, 1.07211128, 1.0484499 , ..., 1.06807995,
         1.03981808, 1.01355496],
        ...,
        [0.8914389 , 0.87522811, 0.89240323, ..., 0.94366824,
         1.02801063, 0.96297885],
        [0.89873615, 0.85541128, 0.86246969, ..., 0.99453517,
         0.95617644, 0.96574599],
        [0.85967065, 0.8508784 , 0.86205081, ..., 0.98020724,
         1.04712707, 0.9696161 ]],

       [[1.17510221, 1.11000828, 1.07080018, ..., 1.0146789 ,
         1.00489959, 1.02678431],
        [1.11000416, 1.08069485, 1.11477007, ..., 1.02657064,
         1.04340852, 1.03066117],
        [1.0764059 , 1.07099936, 1.04277892, ..., 1.07414307,
         1.04324304, 1.01989099],
        ...,
        [0.92409234, 0.92145495, 0.83148833, ..., 1.04680138,
         1.01437654, 0.97654193],
        [0.83522123, 0.88697627, 0.92320699, ..., 1.04687912,
         0.9962397 , 0.97883198],
        [0.93810775, 0.8508784 , 0.92735769, ..., 1.0360595 ,
         0.99230366, 0.99731942]]])