Accessing LPD data

The Large Pixel Detector (LPD) is made of 16 modules which record data separately. extra_data includes convenient interfaces to access this data together.

This example stands by itself, but if you need more generic access to the data, please see other examples, including Reading data to analyse in memory and Reading data train by train.

The example uses some empty sample files which are generated by this cell:

[1]:
!python3 -m extra_data.tests.make_examples
Written examples.

First, let’s load a run containing LPD data:

[2]:
from extra_data import RunDirectory, by_index

run = RunDirectory('fxe_example_run/')
# Using only the first three trains to keep this example light:
run = run.select_trains(by_index[:3])

run.instrument_sources
[2]:
frozenset({'FXE_DET_LPD1M-1/DET/0CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/10CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/11CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/12CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/13CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/14CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/15CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/1CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/2CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/3CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/4CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/5CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/6CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/7CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/8CH0:xtdf',
           'FXE_DET_LPD1M-1/DET/9CH0:xtdf',
           'FXE_XAD_GEC/CAM/CAMERA:daqOutput',
           'FXE_XAD_GEC/CAM/CAMERA_NODATA:daqOutput',
           'SA1_XTD2_XGM/DOOCS/MAIN:output',
           'SPB_XTD9_XGM/DOOCS/MAIN:output'})

Normal access methods give us each module separately:

[3]:
data_module0 = run.get_array('FXE_DET_LPD1M-1/DET/0CH0:xtdf', 'image.data')
data_module0.shape
[3]:
(384, 1, 256, 256)

The class extra_data.components.LPD1M can piece these together:

[4]:
from extra_data.components import LPD1M
lpd = LPD1M(run)
lpd
[4]:
<LPD1M: Data interface for detector 'FXE_DET_LPD1M-1' with 16 modules>
[5]:
image_data = lpd.get_array('image.data')
print("Data shape:", image_data.shape)
print("Dimensions:", image_data.dims)
Data shape: (16, 3, 128, 256, 256)
Dimensions: ('module', 'train', 'pulse', 'slow_scan', 'fast_scan')

Note: This class pulls the data together, but it doesn’t know how the modules are physically arranged, so it can’t produce a detector image. Other examples show how to use detector geometry to produce images.

You can also select only certain modules of the detector. For example, modules 2 (Q1M3), 7 (Q2M4), 8 (Q3M1) and 13 (Q4M2) are the four modules around the center of the detector:

[6]:
lpd = LPD1M(run, modules=[2, 7, 8, 13])
image_data = lpd.get_array('image.data')
print("Data shape:", image_data.shape)
print("Dimensions:", image_data.dims)

print()
print("Data for one pulse:")
print(image_data.sel(train=10000, pulse=0))
Data shape: (4, 3, 128, 256, 256)
Dimensions: ('module', 'train', 'pulse', 'slow_scan', 'fast_scan')

Data for one pulse:
<xarray.DataArray (module: 4, slow_scan: 256, fast_scan: 256)>
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]]], dtype=uint16)
Coordinates:
    pulse    uint64 0
    train    uint64 10000
  * module   (module) int64 2 7 8 13
Dimensions without coordinates: slow_scan, fast_scan

The returned array is an xarray object with labelled axes. See Indexing and selecting data in the xarray docs for more on what you can do with it.

This interface also supports iterating train-by-train through detector data, giving labelled arrays again:

[7]:
for tid, train_data in lpd.trains(pulses=by_index[:16]):
    print("Train", tid)
    print("Keys in data:", sorted(train_data.keys()))
    print("Image data shape:", train_data['image.data'].shape)
    print()
Train 10000
Keys in data: ['detector.data', 'detector.trainId', 'header.dataId', 'header.linkId', 'header.magicNumberBegin', 'header.majorTrainFormatVersion', 'header.minorTrainFormatVersion', 'header.pulseCount', 'header.reserved', 'header.trainId', 'image.cellId', 'image.data', 'image.length', 'image.pulseId', 'image.status', 'image.trainId', 'trailer.checksum', 'trailer.magicNumberEnd', 'trailer.status', 'trailer.trainId']
Image data shape: (4, 1, 16, 256, 256)

Train 10001
Keys in data: ['detector.data', 'detector.trainId', 'header.dataId', 'header.linkId', 'header.magicNumberBegin', 'header.majorTrainFormatVersion', 'header.minorTrainFormatVersion', 'header.pulseCount', 'header.reserved', 'header.trainId', 'image.cellId', 'image.data', 'image.length', 'image.pulseId', 'image.status', 'image.trainId', 'trailer.checksum', 'trailer.magicNumberEnd', 'trailer.status', 'trailer.trainId']
Image data shape: (4, 1, 16, 256, 256)

Train 10002
Keys in data: ['detector.data', 'detector.trainId', 'header.dataId', 'header.linkId', 'header.magicNumberBegin', 'header.majorTrainFormatVersion', 'header.minorTrainFormatVersion', 'header.pulseCount', 'header.reserved', 'header.trainId', 'image.cellId', 'image.data', 'image.length', 'image.pulseId', 'image.status', 'image.trainId', 'trailer.checksum', 'trailer.magicNumberEnd', 'trailer.status', 'trailer.trainId']
Image data shape: (4, 1, 16, 256, 256)

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