Check the Test Rig is in Working Order#
Note
Assuming you’ve followed the quickstart guide or otherwise got an IPython terminal to control a test rig…
There are a series of plans for exercising the test rig, which assert that the hardware and Ophyd devices are behaving as expected.
In [1]: RE(exercise_beamline(det, sample_stage))
Excercising sample_stage_x
Excercising sample_stage_theta
Excercising AdAravisDetector(prefix='BL46P-EA-DET-01:', name='det', read_attrs=['cam', 'cam.acquire_period', 'cam.acquire_time', 'hdf'], configuration_attrs=['cam', 'cam.acquire_period', 'cam.acquire_time', 'cam.image_mode', 'cam.manufacturer', 'cam.model', 'cam.num_exposures', 'cam.num_images', 'cam.trigger_mode', 'hdf'])
Transient Scan ID: 3 Time: 2023-04-04 13:34:07
Persistent Unique Scan ID: '09f5ce67-adb6-46ab-a2ed-9779f01a80b9'
/venv/lib/python3.10/site-packages/dodal/devices/areadetector/adutils.py:35: UserWarning: .dispatch is deprecated, use .generate_datum instead
self.dispatch(self._image_name, ttime.time())
New stream: 'primary'
+-----------+------------+
| seq_num | time |
+-----------+------------+
| 1 | 13:34:07.7 |
+-----------+------------+
generator count ['09f5ce67'] (scan num: 3)
Excercising scan
Transient Scan ID: 4 Time: 2023-04-04 13:34:08
Persistent Unique Scan ID: '2bde468e-00ff-48e3-8c22-4c457e890509'
New stream: 'primary'
+-----------+------------+--------------------+
| seq_num | time | sample_stage_theta |
+-----------+------------+--------------------+
| 1 | 13:34:10.2 | -180.000 |
| 2 | 13:34:10.8 | -140.004 |
| 3 | 13:34:11.3 | -100.008 |
| 4 | 13:34:11.8 | -59.994 |
| 5 | 13:34:12.3 | -19.998 |
| 6 | 13:34:12.9 | 19.998 |
| 7 | 13:34:13.4 | 59.994 |
| 8 | 13:34:13.9 | 99.990 |
| 9 | 13:34:14.4 | 140.004 |
| 10 | 13:34:15.0 | 180.000 |
+-----------+------------+--------------------+
generator scan ['2bde468e'] (scan num: 4)
Out[3]:
('09f5ce67-adb6-46ab-a2ed-9779f01a80b9',
'2bde468e-00ff-48e3-8c22-4c457e890509')
Note
The test rig hardware is not production quality and may occasionally behave inconsistently. If the exercises fail, it may be worth trying them again a couple of times.