Shadow Simulations#
This section is based on the TES beamline example.
Hint: See the List of predefined simulations in Sirepo for examples of identifiers for different beamlines.
Run “Beamline” Shadow3 simulations#
This example repeats the first simulation in “Optical Elements as Ophyd Objects”, but instead uses Shadow3 to run the simulation.
Start ipython and run the following where sim_id is the UID for the simulation we are working with:
[1]:
from sirepo_bluesky import prepare_re_env
%run -i $prepare_re_env.__file__
from sirepo_bluesky.sirepo_bluesky import SirepoBluesky
from sirepo_bluesky.sirepo_ophyd import BeamStatisticsReport, create_classes
connection = SirepoBluesky("http://localhost:8000")
data, schema = connection.auth("shadow", sim_id="00000002")
classes, objects = create_classes(connection=connection)
globals().update(**objects)
print(f"Number of points before change: {data['models']['simulation']['npoint']}")
data["models"]["simulation"]["npoint"] = 1000000
print(f"Number of points after change: {data['models']['simulation']['npoint']}")
aperture.horizontalSize.kind = "hinted"
w9.duration.kind = "hinted"
(uid,) = RE(bp.scan([w9], aperture.horizontalSize, 0, 2, 6))
hdr = db[uid]
tbl = hdr.table(fill=True)
print(tbl)
w9_image = np.array(list(hdr.data("w9_image")))
Number of points before change: 100000
Number of points after change: 1000000
Transient Scan ID: 1 Time: 2023-08-19 16:54:12
Persistent Unique Scan ID: '37b7be0b-dd3a-45ce-b135-5ca6f4e4409c'
New stream: 'primary'
+-----------+------------+-------------------------+-------------+------------+
| seq_num | time | aperture_horizontalSize | w9_duration | w9_flux |
+-----------+------------+-------------------------+-------------+------------+
| 1 | 16:54:33.8 | 0.000 | 19.323 | 0.000 |
| 2 | 16:54:52.1 | 0.400 | 16.232 | 5138.520 |
| 3 | 16:55:10.5 | 0.800 | 16.225 | 10251.164 |
| 4 | 16:55:28.9 | 1.200 | 16.284 | 15406.536 |
| 5 | 16:55:47.4 | 1.600 | 16.243 | 20619.667 |
| 6 | 16:56:05.7 | 2.000 | 16.231 | 25819.524 |
+-----------+------------+-------------------------+-------------+------------+
generator scan ['37b7be0b'] (scan num: 1)
time \
seq_num
1 2023-08-19 16:54:33.845823765
2 2023-08-19 16:54:52.169906378
3 2023-08-19 16:55:10.515789747
4 2023-08-19 16:55:28.922255754
5 2023-08-19 16:55:47.416069984
6 2023-08-19 16:56:05.779393435
w9_sirepo_data_json \
seq_num
1 {"models": {"beamStatisticsReport": {"driftDiv...
2 {"models": {"beamStatisticsReport": {"driftDiv...
3 {"models": {"beamStatisticsReport": {"driftDiv...
4 {"models": {"beamStatisticsReport": {"driftDiv...
5 {"models": {"beamStatisticsReport": {"driftDiv...
6 {"models": {"beamStatisticsReport": {"driftDiv...
w9_sirepo_data_hash w9_duration \
seq_num
1 b21f59de0f2a128774aa69e087d8d88f0ab9550be9fd2c... 19.322850
2 864811b059113828eb518392d146dbb54038113349986b... 16.232152
3 1173d167fcee94f49ca78a7e5ed8fe7b18acd1c2acb1f4... 16.225222
4 41596e16c15c160b3c583046992f9862d540682e059e30... 16.284376
5 3f4a3bef8d820d8a683aec278b6ee918c3d56c6e5f5386... 16.243160
6 5cdff1353a7620b0feb848b2e0f296bfeb07d2e33feee3... 16.230777
w9_image w9_shape \
seq_num
1 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... [100, 100]
2 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... [100, 100]
3 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... [100, 100]
4 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... [100, 100]
5 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... [100, 100]
6 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... [100, 100]
w9_flux w9_mean w9_x w9_y w9_fwhm_x w9_fwhm_y \
seq_num
1 0.000000 0.000000 NaN NaN NaN NaN
2 5138.520058 0.513852 0.197259 -0.152572 1.108809 0.701000
3 10251.164164 1.025116 0.196433 -0.145201 1.141733 0.681383
4 15406.536271 1.540654 0.201374 -0.137640 1.186571 0.659964
5 20619.667190 2.061967 0.232234 -0.137607 1.242215 0.634798
6 25819.524119 2.581952 0.264168 -0.132240 1.296800 0.613439
w9_photon_energy w9_horizontal_extent \
seq_num
1 2500.00041 [-1000.0, 1000.0]
2 2500.00041 [-1.2336298496814044, 1.405929122101391]
3 2500.00041 [-1.3001551718158006, 1.4759384530988693]
4 2500.00041 [-1.3684114360191872, 1.5466178480457953]
5 2500.00041 [-1.4309472940233552, 1.6109566331651013]
6 2500.00041 [-1.5036858677506284, 1.695592271987839]
w9_vertical_extent w9_id w9_title w9_type \
seq_num
1 [-1000.0, 1000.0] 12.0 W9 watch
2 [-0.7103102913794072, 0.4591117629407924] 12.0 W9 watch
3 [-0.7162298148739806, 0.47877690974846177] 12.0 W9 watch
4 [-0.7162298148739806, 0.49331718361385357] 12.0 W9 watch
5 [-0.7162298148739806, 0.49331718361385357] 12.0 W9 watch
6 [-0.7162298148739806, 0.5058855096921268] 12.0 W9 watch
w9_element_position aperture_horizontalSize
seq_num
1 57.142 0.0
2 57.142 0.4
3 57.142 0.8
4 57.142 1.2
5 57.142 1.6
6 57.142 2.0
[2]:
from mpl_toolkits.axes_grid1 import ImageGrid
print(f"Data shape: {w9_image.shape}")
num_frames = w9_image.shape[0]
ncols = 2
nrows = int(np.ceil(num_frames / ncols))
fig = plt.figure()
grid = ImageGrid(
fig,
(1, 2, ncols, nrows),
nrows_ncols=(nrows, ncols),
axes_pad=0.5,
aspect=False,
)
for ax, im in zip(grid, w9_image[:, ...]):
ax.imshow(im, aspect="auto")
Data shape: (6, 100, 100)
Run “Beam Statistics” Sirepo/Shadow app simulations#
In this example, we scan the toroidal mirror’s radius and collect the results of the “Beam Statistics” report on the “Beamline” page of Sirepo/Shadow app. At the end of the execution, we provide the timing report for these simulations.
[3]:
from sirepo_bluesky import prepare_re_env
%run -i $prepare_re_env.__file__
from sirepo_bluesky.sirepo_bluesky import SirepoBluesky
from sirepo_bluesky.sirepo_ophyd import BeamStatisticsReport, create_classes
connection = SirepoBluesky("http://localhost:8000")
data, schema = connection.auth("shadow", sim_id="00000002")
classes, objects = create_classes(connection=connection)
globals().update(**objects)
bsr = BeamStatisticsReport(name="bsr", connection=connection)
toroid.r_maj.kind = "hinted"
bsr.duration.kind = "hinted"
scan_range = (10_000, 50_000, 21)
(uid,) = RE(bp.scan([bsr], toroid.r_maj, *scan_range))
hdr = db[uid]
tbl = hdr.table()
print(tbl)
Transient Scan ID: 1 Time: 2023-08-19 16:56:16
Persistent Unique Scan ID: '4d82ddc6-bbec-4dfe-ab5e-14dd090798b0'
New stream: 'primary'
+-----------+------------+--------------+--------------+
| seq_num | time | toroid_r_maj | bsr_duration |
+-----------+------------+--------------+--------------+
| 1 | 16:56:19.7 | 10000.000 | 3.066 |
| 2 | 16:56:22.8 | 12000.000 | 3.059 |
| 3 | 16:56:25.9 | 14000.000 | 3.060 |
| 4 | 16:56:29.1 | 16000.000 | 3.058 |
| 5 | 16:56:32.2 | 18000.000 | 3.058 |
| 6 | 16:56:35.3 | 20000.000 | 3.062 |
| 7 | 16:56:38.5 | 22000.000 | 3.060 |
| 8 | 16:56:41.6 | 24000.000 | 3.057 |
| 9 | 16:56:44.7 | 26000.000 | 3.060 |
| 10 | 16:56:47.9 | 28000.000 | 3.058 |
| 11 | 16:56:51.0 | 30000.000 | 3.058 |
| 12 | 16:56:54.1 | 32000.000 | 3.059 |
| 13 | 16:56:57.2 | 34000.000 | 3.058 |
| 14 | 16:57:00.4 | 36000.000 | 3.059 |
| 15 | 16:57:03.5 | 38000.000 | 3.059 |
| 16 | 16:57:06.6 | 40000.000 | 3.059 |
| 17 | 16:57:09.8 | 42000.000 | 3.058 |
| 18 | 16:57:12.9 | 44000.000 | 3.060 |
| 19 | 16:57:16.0 | 46000.000 | 3.056 |
| 20 | 16:57:19.2 | 48000.000 | 3.060 |
| 21 | 16:57:22.3 | 50000.000 | 3.058 |
+-----------+------------+--------------+--------------+
generator scan ['4d82ddc6'] (scan num: 1)
time toroid_r_maj \
seq_num
1 2023-08-19 16:56:19.729102850 10000.0
2 2023-08-19 16:56:22.864973783 12000.0
3 2023-08-19 16:56:25.998396873 14000.0
4 2023-08-19 16:56:29.127140045 16000.0
5 2023-08-19 16:56:32.259178162 18000.0
6 2023-08-19 16:56:35.388878584 20000.0
7 2023-08-19 16:56:38.519377708 22000.0
8 2023-08-19 16:56:41.646494389 24000.0
9 2023-08-19 16:56:44.779374123 26000.0
10 2023-08-19 16:56:47.910736322 28000.0
11 2023-08-19 16:56:51.040197372 30000.0
12 2023-08-19 16:56:54.167011499 32000.0
13 2023-08-19 16:56:57.293066263 34000.0
14 2023-08-19 16:57:00.420836687 36000.0
15 2023-08-19 16:57:03.549652338 38000.0
16 2023-08-19 16:57:06.679706335 40000.0
17 2023-08-19 16:57:09.808405876 42000.0
18 2023-08-19 16:57:12.942813396 44000.0
19 2023-08-19 16:57:16.070492268 46000.0
20 2023-08-19 16:57:19.203583717 48000.0
21 2023-08-19 16:57:22.331832647 50000.0
bsr_sirepo_data_json \
seq_num
1 {"models": {"beamStatisticsReport": {"driftDiv...
2 {"models": {"beamStatisticsReport": {"driftDiv...
3 {"models": {"beamStatisticsReport": {"driftDiv...
4 {"models": {"beamStatisticsReport": {"driftDiv...
5 {"models": {"beamStatisticsReport": {"driftDiv...
6 {"models": {"beamStatisticsReport": {"driftDiv...
7 {"models": {"beamStatisticsReport": {"driftDiv...
8 {"models": {"beamStatisticsReport": {"driftDiv...
9 {"models": {"beamStatisticsReport": {"driftDiv...
10 {"models": {"beamStatisticsReport": {"driftDiv...
11 {"models": {"beamStatisticsReport": {"driftDiv...
12 {"models": {"beamStatisticsReport": {"driftDiv...
13 {"models": {"beamStatisticsReport": {"driftDiv...
14 {"models": {"beamStatisticsReport": {"driftDiv...
15 {"models": {"beamStatisticsReport": {"driftDiv...
16 {"models": {"beamStatisticsReport": {"driftDiv...
17 {"models": {"beamStatisticsReport": {"driftDiv...
18 {"models": {"beamStatisticsReport": {"driftDiv...
19 {"models": {"beamStatisticsReport": {"driftDiv...
20 {"models": {"beamStatisticsReport": {"driftDiv...
21 {"models": {"beamStatisticsReport": {"driftDiv...
bsr_sirepo_data_hash bsr_duration \
seq_num
1 23384cb65d21308e202362ab7fcf312822bb1880b9c3f4... 3.065517
2 47908827c35d92e6cf7b4d5196ab8189fa8ead530449dc... 3.059052
3 778cb1b625d1399eeb84dca1ab0e849beb572b6ceb571e... 3.059620
4 c748573d5063a7cf69aeb17f787aa22d5d76b94a5bbbbe... 3.057929
5 3aced853bd345b13438f1f4842bc6c9bb20dadd19a2b37... 3.058033
6 70e525b680558671de280fc7123d090c7edf86654b7a05... 3.061732
7 7d360f61203fd15abeaf658c58cdc5a86a906d84b24df6... 3.060186
8 2644f0030b40981dd2ac70cb09069be1a103b30c57585a... 3.057071
9 c544e7fef721b07417014fcd6256d745abd819fb255fd2... 3.059635
10 8acf44c01070626f2dfe8f14a8f1c82c1dc54ce4439dd6... 3.058188
11 e3d9eef16674a150d3b09b8ac6044d6d8643816d79efc4... 3.058486
12 c39fc9a2e76af52670ec5ec7c22280553700367864f300... 3.059361
13 7cc5747f57774f0085e5db12f68edc716fb899ea6678c2... 3.058004
14 265dd233bb319ee2d21e764949c113c406edb1a83d0ed7... 3.058593
15 486c25ef692b50505eebf3dc6ee6d45d3838c32d58777f... 3.058763
16 b97c739828f7a04e65fd966eba32174a830d545e6802d5... 3.058876
17 9577121f52402a3482dfdd269a6586add965fb64240b66... 3.058004
18 f52211c94bd668ee0272940ac83bcf8ee1519ff6a7094b... 3.060068
19 45002de58bf16bd5f10fa211ccc6e77a44bb113a595d0e... 3.056486
20 07d13e3a7473027d0f8cb683ddc8f62ca53695ab9b4655... 3.059801
21 ef48ecfb7c098bb9530aea89096cf8b6f7aa588f06e12a... 3.057635
bsr_report
seq_num
1 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
2 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
3 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
4 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
5 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
6 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
7 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
8 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
9 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
10 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
11 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
12 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
13 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
14 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
15 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
16 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
17 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
18 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
19 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
20 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
21 {"angxpzp": [0.0, -1.7793448589854113e-20, -3....
[4]:
import json
import matplotlib.pyplot as plt
for idx in range(1, tbl["toroid_r_maj"].size + 1):
d = json.loads(tbl["bsr_report"][idx])
plt.figure(figsize=(16, 4))
plt.plot(d["s"], d["sigmax"], label="Horizonal RMS Beam Size [m]")
plt.plot(d["s"], d["sigmaz"], label="Vertical RMS Beam Size [m]")
plt.xlabel("Distance Along Beamline [m]")
plt.legend()
plt.title(f"toroid_r_maj = {tbl['toroid_r_maj'][idx]:.0f}")
/tmp/ipykernel_5424/2351232241.py:6: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
plt.figure(figsize=(16, 4))
[5]:
data = np.array(tbl["time"].diff(), dtype=float)[1:] / 1e9
fig = plt.figure()
ax = fig.add_subplot()
ax.plot(np.linspace(*scan_range)[1:], data, label="Duration of simulations")
ax.set_ylabel("Duration of simulations [s]")
ax.set_xlabel("Torus Major Radius [m]")
ax.axhline(data.mean(), color="k", label="Averaged duration")
ax.legend()
title = (
f"Shadow TES simulation\n"
f"RE(bp.scan([bsr], toroid.r_maj, "
f"{', '.join([str(x) for x in scan_range])}))"
)
ax.set_title(title)
plt.show()
