Reading and processing NeXus choppers

This guide shows how to extract the relevant data from a NeXus representation of a chopper and create a scippneutron.chopper.DiskChopper object.

We also demonstrate some utilities that Scippneutron provides to process and visualize the data inside NeXus choppers.

import scipp as sc

NeXus chopper data

Here, we use fake data which roughly represents what a real chopper loaded from NeXus looks like. ScippNeutron has a function for generating this data:

from scippneutron.data import chopper_mockup

chopper = chopper_mockup()
chopper

scipp.DataGroup

(time: None, slit: 4)

• beam_position

  scipp

  Variable

  ()

  float64

  rad

  0.0
• delay

  scipp

  DataGroup

  (time: 1)

  • value

    scipp

    DataArray

    (time: 1)

    int64

    ns

    30500000
• position

  scipp

  Variable

  ()

  vector3

  m

  [ 0. 0. 60.]
• radius

  scipp

  Variable

  ()

  float64

  m

  0.35
• rotation_speed

  scipp

  DataGroup

  (time: 180)

  • value

    scipp

    DataArray

    (time: 180)

    float64

    Hz

    1.287e-05, 0.359, ..., 0.352, -4.904e-07
• rotation_speed_setpoint

  scipp

  DataGroup

  ()

  • value

    scipp

    Variable

    ()

    float64

    Hz

    14.0
• slit_height

  scipp

  Variable

  ()

  float64

  m

  0.1
• slit_edges

  scipp

  Variable

  (slit: 4)

  float64

  deg

  30.0, 160.0, 210.0, 280.0
• slits

  int

  ()

  2
• top_dead_center

  scipp

  DataGroup

  (time: 2354)

  • time

    scipp

    Variable

    (time: 2354)

    datetime64

    ns

    2023-01-19T08:11:06.217830400, 2023-01-19T08:11:08.799053824, ..., 2023-01-19T08:14:39.174524416, 2023-01-19T08:14:40.640919296

We can see that there is some information about the slits and geometry of the chopper as well as some timing-related data. Take a look at the NXdisk_chopper documentation for an overview of the fields.

In this case, there already is a position. This typically needs to be computed first, see scippnexus.compute_positions.

Some fields are nested data groups which happens when a NeXus file contains NXlogs. We can extract the relevant arrays from them using extract_chopper_from_nexus:

from scippneutron.chopper import extract_chopper_from_nexus

chopper = extract_chopper_from_nexus(chopper)
chopper

scipp.DataGroup

(time: None, slit: 4)

• type

  scippneutron

  DiskChopperType

  ()

  Chopper type single
• beam_position

  scipp

  Variable

  ()

  float64

  rad

  0.0
• delay

  scipp

  DataArray

  ()

  int64

  ns

  30500000
• position

  scipp

  Variable

  ()

  vector3

  m

  [ 0. 0. 60.]
• radius

  scipp

  Variable

  ()

  float64

  m

  0.35
• rotation_speed

  scipp

  DataArray

  (time: 180)

  float64

  Hz

  1.287e-05, 0.359, ..., 0.352, -4.904e-07
• rotation_speed_setpoint

  scipp

  Variable

  ()

  float64

  Hz

  14.0
• slit_height

  scipp

  Variable

  ()

  float64

  m

  0.1
• slit_edges

  scipp

  Variable

  (slit: 4)

  float64

  deg

  30.0, 160.0, 210.0, 280.0
• slits

  int

  ()

  2
• top_dead_center

  scipp

  Variable

  (time: 2354)

  datetime64

  ns

  2023-01-19T08:11:06.217830400, 2023-01-19T08:11:08.799053824, ..., 2023-01-19T08:14:39.174524416, 2023-01-19T08:14:40.640919296

Converting to a DiskChopper

We can assemble all data into a scippneutron.chopper.DiskChopper object, which enables better exploration/visualization of the chopper properties.

from scippneutron.chopper import DiskChopper

disk_chopper = DiskChopper.from_nexus(chopper)
disk_chopper

DiskChopper

(slit: 2)

• axle_position

  scipp

  Variable

  ()

  vector3

  m

  [ 0. 0. 60.]
• frequency

  scipp

  Variable

  ()

  float64

  Hz

  14.0
• beam_position

  scipp

  Variable

  ()

  float64

  rad

  0.0
• phase

  scipp

  Variable

  ()

  float64

  rad

  2.6829201261656834
• slit_begin

  scipp

  Variable

  (slit: 2)

  float64

  deg

  30.0, 210.0
• slit_end

  scipp

  Variable

  (slit: 2)

  float64

  deg

  160.0, 280.0
• slit_height

  scipp

  Variable

  (slit: 2)

  float64

  m

  0.1, 0.1
• radius

  scipp

  Variable

  ()

  float64

  m

  0.35

Note that the chopper phase \(\phi\) was derived from the rotation frequency \(f\) and the delay \(\delta_{t}\) using \(\phi = 2 \pi f \delta_{t}\).

With the DiskChopper, we can for example see at what times the chopper is open and closed:

pulse_frequency = sc.scalar(14, unit="Hz")

print("Times open:  ", disk_chopper.time_offset_open(pulse_frequency=pulse_frequency))
print("Times closed:", disk_chopper.time_offset_close(pulse_frequency=pulse_frequency))

Times open:   <scipp.Variable> (slit: 4)    float64              [s]  [-0.00124603, -0.0250556, 0.0701825, 0.046373]
Times closed: <scipp.Variable> (slit: 4)    float64              [s]  [0.0245476, -0.0111667, 0.0959762, 0.0602619]

Inspecting time-dependent logs

Some of the entries in the raw NeXus data group are time-dependent. In this section we take a closer look at these logs to gain more insights in how the chopper data is actually recorded.

Identifying in-phase regions

Frame unwrapping and time-of-flight computation are only feasible when the choppers are in-phase with the neutron source pulses because, otherwise, the wavelength frames vary pulse-by-pulse.

To identify regions where a chopper is in-phase, we can inspect the rotation_speed which is the rotation frequency of the chopper.

rotation_speed = chopper['rotation_speed']
rotation_speed.name = 'rotation_speed'
rotation_speed

scipp.DataArray (3.68 KB)

• Dimensions:
  • time: 180
• Coordinates: (1)
  • time
    (time)
    datetime64
    ns
    2023-01-19T08:11:06.205830400, 2023-01-19T08:11:07.412763648, ..., 2023-01-19T08:14:41.039957504, 2023-01-19T08:14:42.246890752

    Values:
    array(['2023-01-19T08:11:06.205830400', '2023-01-19T08:11:07.412763648',
           '2023-01-19T08:11:08.619696896', '2023-01-19T08:11:09.826630400',
           '2023-01-19T08:11:11.033563648', '2023-01-19T08:11:12.240496896',
           '2023-01-19T08:11:13.447430144', '2023-01-19T08:11:14.654363392',
           '2023-01-19T08:11:15.861296896', '2023-01-19T08:11:17.068230144',
           '2023-01-19T08:11:18.275163392', '2023-01-19T08:11:19.482096640',
           '2023-01-19T08:11:20.689029888', '2023-01-19T08:11:21.895963392',
           '2023-01-19T08:11:23.102896640', '2023-01-19T08:11:24.309829888',
           '2023-01-19T08:11:25.516763136', '2023-01-19T08:11:26.723696384',
           '2023-01-19T08:11:27.930629888', '2023-01-19T08:11:29.137563136',
           '2023-01-19T08:11:30.344496384', '2023-01-19T08:11:31.551429632',
           '2023-01-19T08:11:32.758362880', '2023-01-19T08:11:33.965296128',
           '2023-01-19T08:11:35.172229632', '2023-01-19T08:11:36.379162880',
           '2023-01-19T08:11:37.586096128', '2023-01-19T08:11:38.793029376',
           '2023-01-19T08:11:39.999962624', '2023-01-19T08:11:41.206896128',
           '2023-01-19T08:11:42.413829376', '2023-01-19T08:11:43.620762624',
           '2023-01-19T08:11:44.827695872', '2023-01-19T08:11:46.034629120',
           '2023-01-19T08:11:47.241562624', '2023-01-19T08:11:48.448495872',
           '2023-01-19T08:11:49.655429120', '2023-01-19T08:11:50.862362368',
           '2023-01-19T08:11:52.069295616', '2023-01-19T08:11:53.276229120',
           '2023-01-19T08:11:54.483162368', '2023-01-19T08:11:55.690095616',
           '2023-01-19T08:11:56.897028864', '2023-01-19T08:11:58.103962112',
           '2023-01-19T08:11:59.310895616', '2023-01-19T08:12:00.517828864',
           '2023-01-19T08:12:01.724762112', '2023-01-19T08:12:02.931695360',
           '2023-01-19T08:12:04.138628608', '2023-01-19T08:12:05.345562112',
           '2023-01-19T08:12:06.552495360', '2023-01-19T08:12:07.759428608',
           '2023-01-19T08:12:08.966361856', '2023-01-19T08:12:10.173295104',
           '2023-01-19T08:12:11.380228608', '2023-01-19T08:12:12.587161856',
           '2023-01-19T08:12:13.794095104', '2023-01-19T08:12:15.001028352',
           '2023-01-19T08:12:16.207961600', '2023-01-19T08:12:17.414895104',
           '2023-01-19T08:12:18.621828352', '2023-01-19T08:12:19.828761600',
           '2023-01-19T08:12:21.035694848', '2023-01-19T08:12:22.242628096',
           '2023-01-19T08:12:23.449561600', '2023-01-19T08:12:24.656494848',
           '2023-01-19T08:12:25.863428096', '2023-01-19T08:12:27.070361344',
           '2023-01-19T08:12:28.277294592', '2023-01-19T08:12:29.484227840',
           '2023-01-19T08:12:30.691161344', '2023-01-19T08:12:31.898094592',
           '2023-01-19T08:12:33.105027840', '2023-01-19T08:12:34.311961088',
           '2023-01-19T08:12:35.518894336', '2023-01-19T08:12:36.725827840',
           '2023-01-19T08:12:37.932761088', '2023-01-19T08:12:39.139694336',
           '2023-01-19T08:12:40.346627584', '2023-01-19T08:12:41.553560832',
           '2023-01-19T08:12:42.760494336', '2023-01-19T08:12:43.967427584',
           '2023-01-19T08:12:45.174360832', '2023-01-19T08:12:46.381294080',
           '2023-01-19T08:12:47.588227328', '2023-01-19T08:12:48.795160832',
           '2023-01-19T08:12:50.002094080', '2023-01-19T08:12:51.209027328',
           '2023-01-19T08:12:52.415960576', '2023-01-19T08:12:53.622893824',
           '2023-01-19T08:12:54.829827328', '2023-01-19T08:12:56.036760576',
           '2023-01-19T08:12:57.243693824', '2023-01-19T08:12:58.450627072',
           '2023-01-19T08:12:59.657560320', '2023-01-19T08:13:00.864493824',
           '2023-01-19T08:13:02.071427072', '2023-01-19T08:13:03.278360320',
           '2023-01-19T08:13:04.485293568', '2023-01-19T08:13:05.692226816',
           '2023-01-19T08:13:06.899160320', '2023-01-19T08:13:08.106093568',
           '2023-01-19T08:13:09.313026816', '2023-01-19T08:13:10.519960064',
           '2023-01-19T08:13:11.726893312', '2023-01-19T08:13:12.933826816',
           '2023-01-19T08:13:14.140760064', '2023-01-19T08:13:15.347693312',
           '2023-01-19T08:13:16.554626560', '2023-01-19T08:13:17.761559808',
           '2023-01-19T08:13:18.968493312', '2023-01-19T08:13:20.175426560',
           '2023-01-19T08:13:21.382359808', '2023-01-19T08:13:22.589293056',
           '2023-01-19T08:13:23.796226304', '2023-01-19T08:13:25.003159552',
           '2023-01-19T08:13:26.210093056', '2023-01-19T08:13:27.417026304',
           '2023-01-19T08:13:28.623959552', '2023-01-19T08:13:29.830892800',
           '2023-01-19T08:13:31.037826048', '2023-01-19T08:13:32.244759552',
           '2023-01-19T08:13:33.451692800', '2023-01-19T08:13:34.658626048',
           '2023-01-19T08:13:35.865559296', '2023-01-19T08:13:37.072492544',
           '2023-01-19T08:13:38.279426048', '2023-01-19T08:13:39.486359296',
           '2023-01-19T08:13:40.693292544', '2023-01-19T08:13:41.900225792',
           '2023-01-19T08:13:43.107159040', '2023-01-19T08:13:44.314092544',
           '2023-01-19T08:13:45.521025792', '2023-01-19T08:13:46.727959040',
           '2023-01-19T08:13:47.934892288', '2023-01-19T08:13:49.141825536',
           '2023-01-19T08:13:50.348759040', '2023-01-19T08:13:51.555692288',
           '2023-01-19T08:13:52.762625536', '2023-01-19T08:13:53.969558784',
           '2023-01-19T08:13:55.176492032', '2023-01-19T08:13:56.383425536',
           '2023-01-19T08:13:57.590358784', '2023-01-19T08:13:58.797292032',
           '2023-01-19T08:14:00.004225280', '2023-01-19T08:14:01.211158528',
           '2023-01-19T08:14:02.418092032', '2023-01-19T08:14:03.625025280',
           '2023-01-19T08:14:04.831958528', '2023-01-19T08:14:06.038891776',
           '2023-01-19T08:14:07.245825024', '2023-01-19T08:14:08.452758528',
           '2023-01-19T08:14:09.659691776', '2023-01-19T08:14:10.866625024',
           '2023-01-19T08:14:12.073558272', '2023-01-19T08:14:13.280491520',
           '2023-01-19T08:14:14.487425024', '2023-01-19T08:14:15.694358272',
           '2023-01-19T08:14:16.901291520', '2023-01-19T08:14:18.108224768',
           '2023-01-19T08:14:19.315158016', '2023-01-19T08:14:20.522091264',
           '2023-01-19T08:14:21.729024768', '2023-01-19T08:14:22.935958016',
           '2023-01-19T08:14:24.142891264', '2023-01-19T08:14:25.349824512',
           '2023-01-19T08:14:26.556757760', '2023-01-19T08:14:27.763691264',
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           '2023-01-19T08:14:41.039957504', '2023-01-19T08:14:42.246890752'],
          dtype='datetime64[ns]')

• Data: (1)
  • (time)
    float64
    Hz
    1.287e-05, 0.359, ..., 0.352, -4.904e-07

    Values:
    array([ 1.28712703e-05,  3.58958178e-01,  7.17866971e-01,  1.07691359e+00,
            1.43591746e+00,  1.79487320e+00,  2.15385369e+00,  2.51281987e+00,
            2.87187121e+00,  3.23081226e+00,  3.58981891e+00,  3.94870842e+00,
            4.30769912e+00,  4.66666365e+00,  5.02562219e+00,  5.38460756e+00,
            5.74358259e+00,  6.10256219e+00,  6.46145332e+00,  6.82052313e+00,
            7.17950960e+00,  7.53843141e+00,  7.89749324e+00,  8.25639496e+00,
            8.61538232e+00,  8.97432454e+00,  9.33327696e+00,  9.69229285e+00,
            1.00513025e+01,  1.04102649e+01,  1.07692595e+01,  1.11281847e+01,
            1.14871807e+01,  1.18461841e+01,  1.22050969e+01,  1.25640798e+01,
            1.29231327e+01,  1.32820439e+01,  1.36409727e+01,  1.40000353e+01,
            1.40783218e+01,  1.40743732e+01,  1.40368964e+01,  1.39993821e+01,
            1.39790818e+01,  1.39768099e+01,  1.39851619e+01,  1.39956710e+01,
            1.40030625e+01,  1.40059890e+01,  1.40054666e+01,  1.40033109e+01,
            1.40010291e+01,  1.39994742e+01,  1.39987127e+01,  1.39987323e+01,
            1.39990919e+01,  1.39995409e+01,  1.39998985e+01,  1.40000804e+01,
            1.40002122e+01,  1.40001767e+01,  1.40002364e+01,  1.40000597e+01,
            1.40001066e+01,  1.40000218e+01,  1.40000392e+01,  1.39999286e+01,
            1.40000235e+01,  1.40000074e+01,  1.39999709e+01,  1.39999340e+01,
            1.39999981e+01,  1.40000250e+01,  1.40000138e+01,  1.39999544e+01,
            1.40000262e+01,  1.39999248e+01,  1.39999959e+01,  1.40000356e+01,
            1.39999859e+01,  1.40000064e+01,  1.40000152e+01,  1.39999306e+01,
            1.40000103e+01,  1.40000394e+01,  1.40000453e+01,  1.39999621e+01,
            1.39999829e+01,  1.40000255e+01,  1.40000156e+01,  1.40001212e+01,
            1.40000104e+01,  1.40000177e+01,  1.40000035e+01,  1.40000708e+01,
            1.39999885e+01,  1.40000239e+01,  1.39999674e+01,  1.39999966e+01,
            1.40000941e+01,  1.39999697e+01,  1.40000485e+01,  1.39999898e+01,
            1.39999805e+01,  1.40000370e+01,  1.39999218e+01,  1.39999741e+01,
            1.40000058e+01,  1.40000634e+01,  1.39999932e+01,  1.40000127e+01,
            1.39999972e+01,  1.40000141e+01,  1.40000016e+01,  1.39999793e+01,
            1.39999465e+01,  1.39997833e+01,  1.39994304e+01,  1.39975883e+01,
            1.39909586e+01,  1.39679657e+01,  1.39066992e+01,  1.38161344e+01,
            1.37547382e+01,  1.37317718e+01,  1.37251129e+01,  1.37233104e+01,
            1.37229065e+01,  1.37227973e+01,  1.37227971e+01,  1.37227141e+01,
            1.37227334e+01,  1.37227890e+01,  1.37228152e+01,  1.37227866e+01,
            1.37227864e+01,  1.37227676e+01,  1.37227788e+01,  1.37227187e+01,
            1.37227271e+01,  1.33708923e+01,  1.30189700e+01,  1.26671450e+01,
            1.23152892e+01,  1.19635346e+01,  1.16115960e+01,  1.12596457e+01,
            1.09078860e+01,  1.05559769e+01,  1.02041706e+01,  9.85215034e+00,
            9.50041097e+00,  9.14844059e+00,  8.79657999e+00,  8.44471935e+00,
            8.09286946e+00,  7.74101219e+00,  7.38913836e+00,  7.03729080e+00,
            6.68543505e+00,  6.33355645e+00,  5.98173710e+00,  5.62985157e+00,
            5.27793131e+00,  4.92608422e+00,  4.57427248e+00,  4.22239068e+00,
            3.87061422e+00,  3.51868112e+00,  3.16675315e+00,  2.81483757e+00,
            2.46306945e+00,  2.11123897e+00,  1.75938373e+00,  1.40747555e+00,
            1.05549357e+00,  7.03716252e-01,  3.51902452e-01, -4.90383720e-07])

The chopper has a long region of near-constant rotation speed surrounded by spin-up and spin-down regions:

rotation_speed.plot(markersize=2)

The central plateau is the section of the log where the chopper is in-phase with the source (the ESS source has a frequency of 14 Hz).

We use find_plateaus and collapse_plateaus to find one or more plateaus in the log data.

Note the atol and min_n_points parameters, they need to be tuned for the specific input data.

Warning

find_plateaus can potentially falsely identify regions with a small but steady slope as a plateau. See the function’s documentation for details.

from scippneutron.chopper import collapse_plateaus, find_plateaus

plateaus = find_plateaus(
    rotation_speed, atol=sc.scalar(1e-3, unit='Hz / s'), min_n_points=10
)
plateaus = collapse_plateaus(plateaus)
plateaus

scipp.DataArray (1.18 KB)

• Dimensions:
  • plateau: 2
• Coordinates: (2)
  • plateau
    (plateau)
    int64
    0, 1

    Values:
    array([0, 1])

  • time
    (plateau, time [bin-edge])
    datetime64
    ns
    2023-01-19T08:12:10.173295104, 2023-01-19T08:13:28.623959553, 2023-01-19T08:13:39.486359296, 2023-01-19T08:13:55.176492033

    Values:
    array([['2023-01-19T08:12:10.173295104', '2023-01-19T08:13:28.623959553'],
           ['2023-01-19T08:13:39.486359296', '2023-01-19T08:13:55.176492033']],
          dtype='datetime64[ns]')

• Data: (1)
  • (plateau)
    float64
    Hz
    14.000, 13.723

    Values:
    array([13.99993461, 13.7228163 ])

find_plateaus found two plateaus that we can plot with the following helper function:

def plot_plateaus(raw_data: sc.DataArray, plateaus: sc.DataArray) -> None:
    to_plot = sc.DataGroup({'Rotation Speed': raw_data})
    for plateau in plateaus:
        i = plateau.coords['plateau'].value
        to_plot[f'Plateau {i}'] = sc.DataArray(
            plateau.data.broadcast(dims=['time'], shape=[2]),
            coords={'time': plateau.coords['time']},
        )
    return to_plot.plot(
        ls={f'Plateau {i}': '-' for i in range(len(plateaus))},
        marker={f'Plateau {i}': '|' for i in range(len(plateaus))},
        markersize={'Rotation Speed': 2},
    )

plot_plateaus(rotation_speed, plateaus)

In this case, the source has a frequency of 14Hz which means that plateau 0 is in phase. But plateau 1 is not, it is a short region where the chopper slowed down before fully stopping.

We can use filter_in_phase to remove all out-of-phase plateaus:

pulse_frequency = sc.scalar(14.0, unit='Hz')

from scippneutron.chopper import filter_in_phase

frequency_in_phase = filter_in_phase(
    plateaus, reference=pulse_frequency, rtol=sc.scalar(1e-3)
)
frequency_in_phase

scipp.DataArray (1.15 KB)

• Dimensions:
  • plateau: 1
• Coordinates: (2)
  • plateau
    (plateau)
    int64
    0

    Values:
    array([0])

  • time
    (plateau, time [bin-edge])
    datetime64
    ns
    2023-01-19T08:12:10.173295104, 2023-01-19T08:13:28.623959553

    Values:
    array([['2023-01-19T08:12:10.173295104', '2023-01-19T08:13:28.623959553']],
          dtype='datetime64[ns]')

• Data: (1)
  • (plateau)
    float64
    Hz
    14.000

    Values:
    array([13.99993461])

plot_plateaus(rotation_speed, frequency_in_phase)

Since there is only one plateau left, we can simply index into it to get the chopper frequency:

frequency = frequency_in_phase['plateau', 0]
frequency

scipp.DataArray (1.15 KB)

• Dimensions:
• Coordinates: (2)
  • plateau
    ()
    int64
    0

    Values:
    array(0)

  • time
    (time [bin-edge])
    datetime64
    ns
    2023-01-19T08:12:10.173295104, 2023-01-19T08:13:28.623959553

    Values:
    array(['2023-01-19T08:12:10.173295104', '2023-01-19T08:13:28.623959553'],
          dtype='datetime64[ns]')

• Data: (1)
  • ()
    float64
    Hz
    13.999934613725587

    Values:
    array(13.99993461)

Inspecting TDC timestamps

The top-dead-center (TDC) timestamps are created every time a marker placed on the chopper disk passes in front of a sensor mounted on the chopper module. It is essentially reporting at what times the chopper completed a full rotation.

tdc = chopper['top_dead_center']
tdc

scipp.Variable (18.64 KB)

• • (time: 2354)
    datetime64
    ns
    2023-01-19T08:11:06.217830400, 2023-01-19T08:11:08.799053824, ..., 2023-01-19T08:14:39.174524416, 2023-01-19T08:14:40.640919296

    Values:
    array(['2023-01-19T08:11:06.217830400', '2023-01-19T08:11:08.799053824',
           '2023-01-19T08:11:09.873177088', ...,
           '2023-01-19T08:14:38.209520640', '2023-01-19T08:14:39.174524416',
           '2023-01-19T08:14:40.640919296'], dtype='datetime64[ns]')

The initial speed-up and late slow-down are visible when simply plotting all the TDC timestamps:

tdc.plot()

We can filter out the TDCs for the time when the chopper was in-phase by using the time range of the plateau we extracted above:

low = frequency.coords['time'][0]
high = frequency.coords['time'][1]
tdc_in_phase = tdc[(tdc > low) & (tdc < high)]
tdc_in_phase

scipp.Variable (8.83 KB)

• • (time: 1098)
    datetime64
    ns
    2023-01-19T08:12:10.228627200, 2023-01-19T08:12:10.300058880, ..., 2023-01-19T08:13:28.514769152, 2023-01-19T08:13:28.586200832

    Values:
    array(['2023-01-19T08:12:10.228627200', '2023-01-19T08:12:10.300058880',
           '2023-01-19T08:12:10.371490560', ...,
           '2023-01-19T08:13:28.443337984', '2023-01-19T08:13:28.514769152',
           '2023-01-19T08:13:28.586200832'], dtype='datetime64[ns]')

We can check that the rate at which the TDC triggers is indeed close to 14Hz.

diff = tdc_in_phase[1:] - tdc_in_phase[:-1]
rate = 1 / diff.to(unit='s', dtype='float64')
rate.min(), rate.max()

(<scipp.Variable> ()    float64             [Hz]  13.9986,
 <scipp.Variable> ()    float64             [Hz]  14.0002)

Computing chopper phase

When constructing the DiskChopper at the start of this notebook, the phase was derived from a single delay value and the rotation speed setpoint.

It is however sometimes useful for debugging to compute the actual (time-dependent) phase of the chopper from the TDC information.

The phase can be defined as \(\phi = \omega (TDC - T_0)\), where \(\omega\) is the angular frequency of the chopper, \(TDC\) is a TDC timestamp, and \(T_0\) a pulse time.

We already determined the TDC timestamps above. In practice, we would get \(T_0\) from the input NeXus file, but here, we simply make one up:

pulse_time = sc.datetime('2023-01-19T08:12:03.484012915', unit='ns')

Note

The pulse time is typically an array of timestamps and it can be difficult to determine which pulse goes with which chopper period.

# We multiply by 1 rad to get the proper `rad*Hz` unit in `omega`
omega = 2 * sc.constants.pi * frequency.data * sc.scalar(1, unit='rad')
phase = omega * (tdc - pulse_time)
phase = phase.to(unit='deg') % sc.scalar(360.0, unit='deg')

phase.plot()

We can clearly see the a central region where the phase is almost constant, and close to the value of 153.7° listed by the DiskChopper table at the top of the notebook (= 2.683 rad).

To each side of the central region are areas where the chopper is completely out of phase, during the spin-up and spin-down periods.

We can take a closer look at the data in the ‘in-phase’ region where we see some jitter around the target value:

phase = omega * (tdc_in_phase - pulse_time)
phase = phase.to(unit='deg') % sc.scalar(360.0, unit='deg')
phase.plot()