<jupyter_start><jupyter_text>Test New Tomography Experiment ControlTest the OOP-based Tomography experiment control<jupyter_code>import numpy as np
import matplotlib
import bluesky
import ophyd
import apstools
import databroker

from datetime import datetime
from seisidd.experiment import Tomography
# instantiate the experiment handle

testexp = Tomography()
testexp.mode = 'dryrun'
# expose internal RunEngine
RE = testexp.RE

# setup the Metadata as before
RE.md['beamline_id'] = 'APS 6-BM-A'
RE.md['versions'] = {}
RE.md['versions']['apstools'] = apstools.__version__
RE.md['versions']['bluesky'] = bluesky.__version__
RE.md['versions']['databroker'] = databroker.__version__
RE.md['versions']['matplotlib'] = matplotlib.__version__
RE.md['versions']['numpy'] = np.__version__
RE.md['versions']['ophyd'] = ophyd.__version__
RE.md['SESSION_STARTED'] = datetime.isoformat(datetime.now(), " ")
scan_cfg = 'seisidd/config/tomo_scan_template.yml'
# summarize plan
testexp.dryrun(scan_cfg)
testexp.run(scan_cfg)<jupyter_output>Transient Scan ID: 2     Time: 2019-08-22 12:47:10
Persistent Unique Scan ID: 'e4c9c153-1090-47f1-bf23-fc246ac11792'
New stream: 'primary'
+-----------+------------+
|   seq_num |       time |
+-----------+------------+
|         1 | 12:47:16.2 |
|         2 | 12:47:22.3 |
|         3 | 12:47:25.4 |
|         4 | 12:47:28.4 |
|         5 | 12:47:31.5 |
|         6 | 12:47:34.6 |
|         7 | 12:47:37.6 |
|         8 | 12:47:40.7 |
|         9 | 12:47:43.7 |
|        10 | 12:47:46.8 |
|        11 | 12:47:49.9 |
|        12 | 12:47:52.9 |
|        13 | 12:47:57.5 |
|        14 | 12:48:02.1 |
+-----------+------------+
generator tomo_scan ['e4c9c153'] (scan num: 2)
{'fraction_non_alphanumeric': 0.18974661166764878, 'fraction_numerical': 0.09487330583382439, 'mean_word_length': 2.9488372093023254, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '362424', 'license': 'MIT', 'n_tokens_mistral': 721, 'n_tokens_neox': 617, 'n_words': 123, 'path': 'tomo_nuevo.ipynb', 'repo_name': 'KedoKudo/jupyter-ht-hedm', 'size': '1697'}