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prjemian committed Aug 30, 2023
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: 25948815f761a4e4096278e852fd58fe
tags: 645f666f9bcd5a90fca523b33c5a78b7
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863 changes: 863 additions & 0 deletions 1.6.10/_downloads/1910b6fa602cece4a3f661636dfb4c26/pl_excel_scan.ipynb

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29 changes: 29 additions & 0 deletions 1.6.10/_downloads/2b7ae88d0782f503632f7678bbb4ba34/spec3.dat
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#F spec3.dat
#E 1550619537
#D Tue Feb 19 17:38:57 2019
#C BlueSky user = mintadmin host = mint-vm

#S 3 TuneAxis.multi_pass_tune()
#D Tue Feb 19 17:12:19 2019
#C Tue Feb 19 17:12:19 2019. plan_type = generator
#C Tue Feb 19 17:12:19 2019. uid = 37c188c0-4f24-4e9b-b8ab-d610dc8797c5
#MD md = {'activity': 'TuneAxis development and testing', 'peak_model': 'pseudo Voigt', 'peak_scale': 100000.0, 'peak_center': -1.3940973681450914, 'peak_sigma': 0.025534621641250733, 'peak_eta': 0.2993015167776747, 'peak_bkg': 0.0045580721055284755}
#MD pass = 3
#MD pass_max = 6
#MD tune_md = {'width': 0.15625, 'initial_position': -1.3900000000000001, 'time_iso8601': '2019-02-19 17:12:19.684373'}
#MD tune_parameters = {'num': 10, 'width': 0.15625, 'initial_position': -1.3900000000000001, 'peak_choice': 'cen', 'x_axis': 'm1', 'y_axis': 'spvoigt'}
#N 10
#L Epoch_float m1 m1_user_setpoint spvoigt Epoch
0.29555630683898926 -1.47 -1.4681250000000001 4343.664517196899 0
0.47817325592041016 -1.45 -1.450763888888889 12001.373933389279 0
0.6789727210998535 -1.43 -1.4334027777777778 36585.97751958579 1
0.8800690174102783 -1.42 -1.4160416666666669 57093.79924666349 1
1.0813727378845215 -1.4000000000000001 -1.3986805555555557 97090.26002609897 1
1.2834296226501465 -1.3800000000000001 -1.3813194444444445 83559.33671408126 1
1.483093023300171 -1.36 -1.3639583333333334 39939.540804745666 1
1.6826446056365967 -1.35 -1.3465972222222224 23744.262772095804 2
1.8820805549621582 -1.33 -1.3292361111111113 7556.046660537259 2
2.0840554237365723 -1.31 -1.3118750000000001 3291.3575120438236 2
#C Tue Feb 19 17:12:22 2019. num_events_primary = 10
#C Tue Feb 19 17:12:22 2019. num_events_PeakStats = 1
#C Tue Feb 19 17:12:22 2019. exit_status = success
59 changes: 59 additions & 0 deletions 1.6.10/_downloads/42005c025a2db75a3863375d2bb26d64/spec2.dat
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#F spec2.dat
#E 1550619537
#D Tue Feb 19 17:38:57 2019
#C BlueSky user = mintadmin host = mint-vm

#S 1 scan(detectors=['scaler'], num=15, args=['m1', -5, 5], per_step=None)
#D Tue Feb 19 17:11:20 2019
#C Tue Feb 19 17:11:20 2019. plan_type = generator
#C Tue Feb 19 17:11:20 2019. uid = 225eef4b-4a56-427b-b507-e959b216a9f4
#MD motors = ['m1']
#MD num_intervals = 14
#MD num_points = 15
#MD plan_pattern = inner_product
#MD plan_pattern_args = {'num': 15, 'args': ["EpicsMotor(prefix='vm7:m1', name='m1', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])", -5, 5]}
#MD plan_pattern_module = bluesky.plan_patterns
#N 15
#L m1 I0 m1_user_setpoint scaler_time scint Epoch_float Epoch clock
-5.0 2.0 -5.0 0.5 3.0 5.246389150619507 5 5000000.0
-4.29 2.0 -4.285714285714286 0.5 3.0 6.928081274032593 7 5000000.0
-3.5700000000000003 3.0 -3.571428571428571 0.5 2.0 8.540517807006836 9 5000000.0
-2.86 2.0 -2.857142857142857 0.5 3.0 10.122943639755249 10 5000000.0
-2.14 2.0 -2.142857142857143 0.5 2.0 11.731646537780762 12 5000000.0
-1.43 2.0 -1.4285714285714284 0.5 1.0 13.331648111343384 13 5000000.0
-0.71 1.0 -0.7142857142857144 0.5 1.0 14.949140787124634 15 5000000.0
0.0 2.0 0.0 0.5 2.0 16.650282859802246 17 5000000.0
0.71 2.0 0.7142857142857144 0.5 2.0 18.361730813980103 18 5000000.0
1.43 1.0 1.4285714285714288 0.5 2.0 20.046799898147583 20 5000000.0
2.14 2.0 2.1428571428571432 0.5 0.0 21.749138832092285 22 5000000.0
2.86 1.0 2.8571428571428577 0.4 2.0 23.461166858673096 23 4000000.0
3.5700000000000003 2.0 3.571428571428571 0.5 3.0 25.067540168762207 25 5000000.0
4.29 2.0 4.2857142857142865 0.5 2.0 26.777003288269043 27 5000000.0
5.0 3.0 5.0 0.5 2.0 28.478744506835938 28 5000000.0
#C Tue Feb 19 17:11:49 2019. num_events_primary = 15
#C Tue Feb 19 17:11:49 2019. exit_status = success

#S 3 TuneAxis.multi_pass_tune()
#D Tue Feb 19 17:12:19 2019
#C Tue Feb 19 17:12:19 2019. plan_type = generator
#C Tue Feb 19 17:12:19 2019. uid = 37c188c0-4f24-4e9b-b8ab-d610dc8797c5
#MD md = {'activity': 'TuneAxis development and testing', 'peak_model': 'pseudo Voigt', 'peak_scale': 100000.0, 'peak_center': -1.3940973681450914, 'peak_sigma': 0.025534621641250733, 'peak_eta': 0.2993015167776747, 'peak_bkg': 0.0045580721055284755}
#MD pass = 3
#MD pass_max = 6
#MD tune_md = {'width': 0.15625, 'initial_position': -1.3900000000000001, 'time_iso8601': '2019-02-19 17:12:19.684373'}
#MD tune_parameters = {'num': 10, 'width': 0.15625, 'initial_position': -1.3900000000000001, 'peak_choice': 'cen', 'x_axis': 'm1', 'y_axis': 'spvoigt'}
#N 10
#L Epoch_float m1 m1_user_setpoint spvoigt Epoch
0.29555630683898926 -1.47 -1.4681250000000001 4343.664517196899 0
0.47817325592041016 -1.45 -1.450763888888889 12001.373933389279 0
0.6789727210998535 -1.43 -1.4334027777777778 36585.97751958579 1
0.8800690174102783 -1.42 -1.4160416666666669 57093.79924666349 1
1.0813727378845215 -1.4000000000000001 -1.3986805555555557 97090.26002609897 1
1.2834296226501465 -1.3800000000000001 -1.3813194444444445 83559.33671408126 1
1.483093023300171 -1.36 -1.3639583333333334 39939.540804745666 1
1.6826446056365967 -1.35 -1.3465972222222224 23744.262772095804 2
1.8820805549621582 -1.33 -1.3292361111111113 7556.046660537259 2
2.0840554237365723 -1.31 -1.3118750000000001 3291.3575120438236 2
#C Tue Feb 19 17:12:22 2019. num_events_primary = 10
#C Tue Feb 19 17:12:22 2019. num_events_PeakStats = 1
#C Tue Feb 19 17:12:22 2019. exit_status = success
160 changes: 160 additions & 0 deletions 1.6.10/_downloads/625ccc137094c096a34ec8191ab2650c/pl_nscan.ipynb
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# The ``nscan()`` plan -- scan multiple axes together\n",
"\n",
"In this example, we demonstrate the `apstools.plans.nscan()` plan. The \n",
"`nscan()` plan is used to scan two or more axes together, each in equally\n",
"spaced steps, such as a $\\theta$ - $2\\theta$ powder diffractometer scan.\n",
"\n",
"Here, we scan two motors together, each in equally spaced steps. We use an\n",
"[swait](https://htmlpreview.github.io/?https://raw.githubusercontent.com/epics-modules/calc/R3-6-1/documentation/swaitRecord.html) record (part of the *userCalc* support from synApps) as a detector. We\n",
"configure the `swait` record with a calculation (from\n",
"[setup_random_number_swait()](https://bcda-aps.github.io/apstools/latest/_modules/apstools/synApps/swait.html#setup_random_number_swait) in `apstools.synApps`) that computes a noisy (random number) signal. \n",
"\n",
"## Setup\n",
"\n",
"For this demo, we do not need the databroker since we do not plan to review any of this data after collection. We'll display the data during the scan using the *LiveTable()* code."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from apstools.synApps import UserCalcN, setup_random_number_swait\n",
"from apstools.plans import nscan\n",
"from bluesky import RunEngine\n",
"from bluesky.callbacks import LiveTable\n",
"from ophyd import EpicsMotor\n",
"\n",
"RE = RunEngine({})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the prefix for the EPICS IOC that provides the PVs we'll use here."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"IOC = \"gp:\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Connect to our motors and create the *noisy* detector."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"m1 = EpicsMotor(f\"{IOC}m1\", name=\"m1\")\n",
"m2 = EpicsMotor(f\"{IOC}m2\", name=\"m2\")\n",
"noisy = UserCalcN(f\"{IOC}userCalc1\", name=\"noisy\")\n",
"\n",
"m1.wait_for_connection()\n",
"m2.wait_for_connection()\n",
"noisy.wait_for_connection()\n",
"\n",
"# configure the *detector* as a random number generator using a calculation.\n",
"setup_random_number_swait(noisy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scan\n",
"\n",
"Measure the noisy detector while step scanning both the m1 & m2 motors together. We'll move m2 twice as far as m1, like a $\\theta$-$2\\theta$ scan on a diffractometer."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"+-----------+------------+------------+------------+\n",
"| seq_num | time | m1 | m2 |\n",
"+-----------+------------+------------+------------+\n",
"| 1 | 00:22:27.5 | 2.00000 | 4.00000 |\n",
"| 2 | 00:22:28.6 | 1.60000 | 3.20000 |\n",
"| 3 | 00:22:29.7 | 1.20000 | 2.40000 |\n",
"| 4 | 00:22:30.8 | 0.80000 | 1.60000 |\n",
"| 5 | 00:22:31.9 | 0.40000 | 0.80000 |\n",
"| 6 | 00:22:33.0 | 0.00000 | 0.00000 |\n",
"+-----------+------------+------------+------------+\n",
"generator nscan ['fcb9a1ad'] (scan num: 1)\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"('fcb9a1ad-1901-4955-96df-f2079f4edd1c',)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RE(\n",
" nscan([noisy, ], m1, 2, 0, m2, 4, 0, num=6),\n",
" LiveTable([\"m1\", \"m2\", \"noisy_val\"])\n",
" )"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.13 ('base')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:56:21) \n[GCC 10.3.0]"
},
"vscode": {
"interpreter": {
"hash": "f38aef175fb08dfc130a7d9bb9234f0792dc9ad861f95b6c05aedd1b380356e2"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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