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{
"cells": [
{
"cell_type": "markdown",
"id": "b3954c58",
"metadata": {},
"source": [
"# How to extract digitizer peaks with the SCS Toolbox"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "df9acef3-8450-457f-8d8f-f2674e8fdb05",
"metadata": {},
"source": [
"## Workflow during a beamtime\n",
"\n",
"1. Record first data with signal on digitizer.\n",
"2. Find peak integration parameters using `check_peak_params()`.\n",
"3. Update the new parameters in DAMNIT, so that for each new run, automatic processing of each run is performed and saved in `usr/processed_runs` folder. As long as the right bunch pattern is selected for peak extraction, there is no need to care about the number of pulses / period, as they will be adjusted to match the bunch pattern of each run.\n",
"4. For analysis, load digitizer data using `load_processed_peaks()` or `load_all_processed_peaks()`. This is much faster than loading the raw traces and re-performing peak integration.\n",
"5. Checking the integration parameters used for the processed data can be done via `check_processed_peaks_params()`."
]
},
{
"cell_type": "markdown",
"id": "fdcbef95-b471-42a2-b62f-be2646fca9e5",
"metadata": {},
"source": [
"## Peak-integration parameters"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e8d99d6e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cupy is not installed in this environment, no access to the GPU\n"
]
}
],
"source": [
"import toolbox_scs as tb\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.constrained_layout.use'] = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extracting peaks from a raw trace is done using the `get_digitizer_peaks()` function of the SCS Toolbox, by integration over the area of the peak and subtraction of a baseline. For one peak, the parameters `pulseStart` and `pulseStop` (sample numbers in the raw trace) define the integration region and `baseStart` and `baseStop` define the baseline region. In most cases, the pulse pattern is regular and there are `npulses` separated by a `period`. The peak extraction is repeated for each peak, with `pulseStart`, `pulseStop`, `baseStart` and `baseStop` shifted by the period.\n",
"\n",
"An example of integration parameters:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f7050acb-d48b-4802-96b8-65ff3f43ed8d",
"metadata": {},
"outputs": [],
"source": [
"params = {'pulseStart': 100,\n",
" 'pulseStop': 120,\n",
" 'baseStart': 80,\n",
" 'baseStop': 99,\n",
" 'period': 96,\n",
" 'npulses': 25}"
]
},
{
"cell_type": "markdown",
"id": "ad30515c-b30a-458c-a30d-1f65d6b33138",
"metadata": {},
"source": [
"If the pattern is not regular, a list of starting positions can be provided to `pulseStart`, while `pulseStop`, `baseStart`, `baseStop` remain integers and relate to the first peak only. In such case, `period` does not have a meaning and `npulses` is equal to `len(pulseStart)`:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2cb8ce33-a39f-48b7-bb25-7090bfea4238",
"params = {'pulseStart': [100, 200, 500, 600, 900, 1000, 2000, 10000, 15500],\n",
" 'period': 0,\n",
" 'pulseStop': 110,\n",
" 'baseStop': 99,\n",
" 'baseStart': 90,\n",
" 'npulses': 9}"
]
},
{
"cell_type": "markdown",
"id": "bc07d359-aee0-412b-8e7a-695ae01947c3",
"metadata": {},
"source": [
"Let's assume that we are interested in the APD signal on diode 8 looking at the FEL, which corresponds to Ch9 of Fast ADC 2 (mnemonic `FastADC2_9raw`). We can check how the peak-finding algorithm performs by using `tb.check_peak_parameters()` and inspecting the found regions of integration. This shows a plot with the first and last pulses identified by the peak-finding algorithm and displays the region of integration and the region for baseline subtraction. The function returns a dictionnary `good_params` that has all parameters necessary to perform the trapezoidal integration over the digitizer trace.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "946601a8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bunch pattern sase3: 400 pulses, 96 samples between two pulses\n",
"Auto-find peak parameters: 400 pulses, 96 samples between two pulses\n"
]
},
{
"data": {
"text/plain": [
"{'pulseStart': 6763,\n",
" 'period': 96,\n",
" 'pulseStop': 6770,\n",
" 'baseStop': 6762,\n",
" 'baseStart': 6757,\n",
" 'npulses': 400}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"<Figure size 600x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"proposal, runNB = 2956, 13\n",
"good_params = tb.check_peak_params(proposal, runNB, 'FastADC2_9raw', bunchPattern='sase3')\n",
"good_params"
]
},
{
"cell_type": "markdown",
"id": "f0e4fcef-4188-47f4-ae8d-6f9656dbdc12",
"metadata": {},
"source": [
"## Extracting peaks"
]
},
{
"cell_type": "markdown",
"id": "137414e9-b2de-4fe9-8182-808790623af2",
"metadata": {},
"source": [
"The integration parameters are either user-provided or automatically computed by a peak-finding algorithm in `get_digitizer_peaks()` and `check_peak_params()`. The bunch pattern, when provided, is used to determine the parameters or to check consistency with user-provided parameters, and to align the pulse ID. The minimum required inputs to extract peaks are:\n",
"* the `bunchPattern` source ('sase3' if the device is looking at the FEL or 'scs_ppl' if the device is looking at the PP laser), leaving `integParams=None` to let the peak-finding algorithm operate.\n",
"* or `integParams` dict including `pulseStart`, `pulseStop`, `baseStart`, `baseStop`, `period` and `npulses` keys.\n",
"In most cases, automatic peak finding provides good integration parameters. If it fails, or if we want to define fixed parameters to consistently analyze a series of runs, it is necessary to provide the parameters via `integParams`.\n",
"If both the bunch pattern and the integration parameters are provided, the `period` and `npulses` of the user-provided parameters (`integParams`) will be overriden (with a warning in case of mismatch), except if `pulseStart` is a list (e.g. case of irregular patterns).\n"
]
},
{
"cell_type": "markdown",
"id": "8a4fe745-9541-4ec4-aae4-ce02d47f6179",
"metadata": {},
"source": [
"Once the parameters are found, we can extract the peaks using `get_digitizer_peaks()`, with `bunchPattern='sase3'`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5653df96",
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 25MB\n",
"Dimensions: (trainId: 7898, sa3_pId: 400)\n",
" * trainId (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
" * sa3_pId (sa3_pId) int32 2kB 772 776 780 784 ... 2356 2360 2364 2368\n",
" FastADC2_9peaks (trainId, sa3_pId) float64 25MB -1.04e+04 ... -1.866e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-097afa33-b6be-43a8-993a-700569942b54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-097afa33-b6be-43a8-993a-700569942b54' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 7898</li><li><span class='xr-has-index'>sa3_pId</span>: 400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3796698d-e3a5-44e7-b301-400249a18195' class='xr-section-summary-in' type='checkbox' checked><label for='section-3796698d-e3a5-44e7-b301-400249a18195' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1501374970 ... 1501382869</div><input id='attrs-36859df5-b0cd-4068-951c-b994ee21191d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36859df5-b0cd-4068-951c-b994ee21191d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e64e14-c38b-4a1f-a687-b5a9e8535bd8' class='xr-var-data-in' type='checkbox'><label for='data-65e64e14-c38b-4a1f-a687-b5a9e8535bd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1501374970, 1501374971, 1501374972, ..., 1501382867, 1501382868,\n",
" 1501382869], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sa3_pId</span></div><div class='xr-var-dims'>(sa3_pId)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>772 776 780 784 ... 2360 2364 2368</div><input id='attrs-afd647ef-f489-4b41-942b-6564c130ae94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afd647ef-f489-4b41-942b-6564c130ae94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e7c061d-9b7e-46c3-96cd-5f86c1f7a602' class='xr-var-data-in' type='checkbox'><label for='data-7e7c061d-9b7e-46c3-96cd-5f86c1f7a602' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 772, 776, 780, ..., 2360, 2364, 2368], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fd49acd-9d02-45a5-aa69-b7ebdcdfc521' class='xr-section-summary-in' type='checkbox' checked><label for='section-6fd49acd-9d02-45a5-aa69-b7ebdcdfc521' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FastADC2_9peaks</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.04e+04 -2.104e+04 ... -1.866e+03</div><input id='attrs-edb92b9e-9d25-45be-acff-5e0f3e5fc0d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edb92b9e-9d25-45be-acff-5e0f3e5fc0d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-808f8990-0059-41bb-95fe-be4e74805611' class='xr-var-data-in' type='checkbox'><label for='data-808f8990-0059-41bb-95fe-be4e74805611' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FastADC2_9peaks_pulseStart :</span></dt><dd>6763</dd><dt><span>FastADC2_9peaks_period :</span></dt><dd>96</dd><dt><span>FastADC2_9peaks_pulseStop :</span></dt><dd>6770</dd><dt><span>FastADC2_9peaks_baseStop :</span></dt><dd>6762</dd><dt><span>FastADC2_9peaks_baseStart :</span></dt><dd>6757</dd><dt><span>FastADC2_9peaks_npulses :</span></dt><dd>400</dd></dl></div><div class='xr-var-data'><pre>array([[-10396.5, -21040.5, -11618. , ..., -2953. , -14307.5, -9120. ],\n",
" [-14979.5, -3093.5, -8993. , ..., -5679.5, -4255. , -25531. ],\n",
" [-22282. , -2814. , -21852.5, ..., -14205.5, -8499.5, -13072. ],\n",
" [ -607.5, 99. , -660. , ..., -1162.5, -1217. , -808. ],\n",
" [ -999.5, -681. , -327. , ..., -1800. , -935. , -660. ],\n",
" [ -1075. , -1200. , -358. , ..., -753. , -864. , -1866.5]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a7a1f7b-5686-49e4-a5a8-258eda47845c' class='xr-section-summary-in' type='checkbox' ><label for='section-5a7a1f7b-5686-49e4-a5a8-258eda47845c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>trainId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ce56fb5e-fa05-4c35-8905-94328d5b73b8' class='xr-index-data-in' type='checkbox'/><label for='index-ce56fb5e-fa05-4c35-8905-94328d5b73b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1501374970, 1501374971, 1501374972, 1501374973, 1501374974, 1501374975,\n",
" 1501374976, 1501374977, 1501374978, 1501374979,\n",
" ...\n",
" 1501382860, 1501382861, 1501382862, 1501382863, 1501382864, 1501382865,\n",
" 1501382866, 1501382867, 1501382868, 1501382869],\n",
" dtype='uint64', name='trainId', length=7898))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sa3_pId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-06172524-357f-409c-aa9b-1af2b385f49f' class='xr-index-data-in' type='checkbox'/><label for='index-06172524-357f-409c-aa9b-1af2b385f49f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 772, 776, 780, 784, 788, 792, 796, 800, 804, 808,\n",
" ...\n",
" 2332, 2336, 2340, 2344, 2348, 2352, 2356, 2360, 2364, 2368],\n",
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],
"text/plain": [
"<xarray.Dataset> Size: 25MB\n",
"Dimensions: (trainId: 7898, sa3_pId: 400)\n",
" * trainId (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
" * sa3_pId (sa3_pId) int32 2kB 772 776 780 784 ... 2356 2360 2364 2368\n",
" FastADC2_9peaks (trainId, sa3_pId) float64 25MB -1.04e+04 ... -1.866e+03"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peaks = tb.get_digitizer_peaks(proposal, runNB, 'FastADC2_9raw', integParams=good_params, bunchPattern='sase3')\n",
"peaks"
"attachments": {},
"cell_type": "markdown",
"id": "1dbeb1e3-0126-4307-bdde-6d541fecbdc7",
"metadata": {},
"source": [
"We see here that `peaks` has a variable `FastADC2_9peaks` which has dimensions `['trainId', 'sa3_pId']`, which is exactly what we wanted: the raw traces were automatically transformed into vectors of length `sa3_pId`.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "8ab2e1fe",
"metadata": {},
"source": [
"Note that we could also have ommitted the parameter `integParams` in `get_digitizer_peaks()` to force the automatic peak finding algorithm:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2089bc40-54bf-4dc6-b4b9-97ac6fc6ae82",
"peaks = tb.get_digitizer_peaks(proposal, runNB, 'FastADC2_9raw', bunchPattern='sase3')"
]
},
{
"cell_type": "markdown",
"id": "9aa9fca4",
"metadata": {},
"source": [
"## If the peak-finding algorithm fails\n",
"\n",
"\n",
"The best strategy is to inspect the raw trace with `get_dig_avg_trace()` or use `check_peak_params()` with `show_all=True` and determine the regions of integration manually. Once the integration parameter dictionnary is created, one can feed it to the `integParams` argument in `get_digitizer_peaks()`."
]
},
{
"cell_type": "markdown",
"id": "3d0d64a0",
"metadata": {},
"source": [
"## Save / load processed peaks\n",
"\n",
"If we have found good integration parameters, it is worth saving the integrated peaks as processed data. This can be done by selecting `save=True` in `get_digitizer_peaks()`. The location can be chosen by `subdir`, by default it goes to the `usr/processed_runs` folder of the proposal."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2d9bd039-b025-4be5-86cd-3ca5c542801c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"saved data into /gpfs/exfel/exp/SCS/202202/p002956/usr/processed_runs/r0013/r0013-digitizers-data.h5.\n"
}
],
"source": [
"peaks = tb.get_digitizer_peaks(proposal, runNB, 'FastADC2_9raw', integParams=good_params, bunchPattern='sase3',\n",
]
},
{
"cell_type": "markdown",
"id": "e2b80062-fcf9-4d20-a775-1da949f925eb",
"metadata": {},
"source": [
"To load the processed data:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b378684c-527d-480b-909a-4b5e1ffaf734",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'FastADC2_9peaks' (trainId: 7898, sa3_pId: 400)> Size: 25MB\n",
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" FastADC2_9peaks_baseStop: 6762\n",
" FastADC2_9peaks_baseStart: 6757\n",
" FastADC2_9peaks_npulses: 400</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'FastADC2_9peaks'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 7898</li><li><span class='xr-has-index'>sa3_pId</span>: 400</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d0168b73-5618-4bb5-a3bf-e0fe1ed8a0b7' class='xr-array-in' type='checkbox' checked><label for='section-d0168b73-5618-4bb5-a3bf-e0fe1ed8a0b7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.04e+04 -2.104e+04 -1.162e+04 ... -753.0 -864.0 -1.866e+03</span></div><div class='xr-array-data'><pre>array([[-10396.5, -21040.5, -11618. , ..., -2953. , -14307.5, -9120. ],\n",
" [-14979.5, -3093.5, -8993. , ..., -5679.5, -4255. , -25531. ],\n",
" [-22282. , -2814. , -21852.5, ..., -14205.5, -8499.5, -13072. ],\n",
" [ -607.5, 99. , -660. , ..., -1162.5, -1217. , -808. ],\n",
" [ -999.5, -681. , -327. , ..., -1800. , -935. , -660. ],\n",
" [ -1075. , -1200. , -358. , ..., -753. , -864. , -1866.5]])</pre></div></div></li><li class='xr-section-item'><input id='section-64660ba7-c249-43c5-8991-5c8b867cf8eb' class='xr-section-summary-in' type='checkbox' checked><label for='section-64660ba7-c249-43c5-8991-5c8b867cf8eb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1501374970 ... 1501382869</div><input id='attrs-66835e1b-6c09-4e00-8aac-4116a48ed38c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66835e1b-6c09-4e00-8aac-4116a48ed38c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd72ccc-31e0-4767-ada6-a111c3b257de' class='xr-var-data-in' type='checkbox'><label for='data-4dd72ccc-31e0-4767-ada6-a111c3b257de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1501374970, 1501374971, 1501374972, ..., 1501382867, 1501382868,\n",
" 1501382869], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sa3_pId</span></div><div class='xr-var-dims'>(sa3_pId)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>772 776 780 784 ... 2360 2364 2368</div><input id='attrs-d465c711-fc22-4261-b132-1247c82b5e0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d465c711-fc22-4261-b132-1247c82b5e0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5240c0ff-176b-4c44-85a8-da680348bb80' class='xr-var-data-in' type='checkbox'><label for='data-5240c0ff-176b-4c44-85a8-da680348bb80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 772, 776, 780, ..., 2360, 2364, 2368], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3754fa5-68a5-4bd6-9ca9-cd58ab6b5258' class='xr-section-summary-in' type='checkbox' ><label for='section-e3754fa5-68a5-4bd6-9ca9-cd58ab6b5258' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>trainId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-25b3cf42-d09c-4a7c-b28b-321fdbcd1357' class='xr-index-data-in' type='checkbox'/><label for='index-25b3cf42-d09c-4a7c-b28b-321fdbcd1357' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1501374970, 1501374971, 1501374972, 1501374973, 1501374974, 1501374975,\n",
" 1501374976, 1501374977, 1501374978, 1501374979,\n",
" ...\n",
" 1501382860, 1501382861, 1501382862, 1501382863, 1501382864, 1501382865,\n",
" 1501382866, 1501382867, 1501382868, 1501382869],\n",
" dtype='uint64', name='trainId', length=7898))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sa3_pId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fb33ff43-40a8-42ad-ab06-22c514044256' class='xr-index-data-in' type='checkbox'/><label for='index-fb33ff43-40a8-42ad-ab06-22c514044256' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 772, 776, 780, 784, 788, 792, 796, 800, 804, 808,\n",
" ...\n",
" 2332, 2336, 2340, 2344, 2348, 2352, 2356, 2360, 2364, 2368],\n",
" dtype='int32', name='sa3_pId', length=400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-828d2305-9908-466b-b924-7f8af8ff8e19' class='xr-section-summary-in' type='checkbox' checked><label for='section-828d2305-9908-466b-b924-7f8af8ff8e19' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>FastADC2_9peaks_pulseStart :</span></dt><dd>6763</dd><dt><span>FastADC2_9peaks_period :</span></dt><dd>96</dd><dt><span>FastADC2_9peaks_pulseStop :</span></dt><dd>6770</dd><dt><span>FastADC2_9peaks_baseStop :</span></dt><dd>6762</dd><dt><span>FastADC2_9peaks_baseStart :</span></dt><dd>6757</dd><dt><span>FastADC2_9peaks_npulses :</span></dt><dd>400</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'FastADC2_9peaks' (trainId: 7898, sa3_pId: 400)> Size: 25MB\n",
"array([[-10396.5, -21040.5, -11618. , ..., -2953. , -14307.5, -9120. ],\n",
" [-14979.5, -3093.5, -8993. , ..., -5679.5, -4255. , -25531. ],\n",
" [-22282. , -2814. , -21852.5, ..., -14205.5, -8499.5, -13072. ],\n",
" ...,\n",
" [ -607.5, 99. , -660. , ..., -1162.5, -1217. , -808. ],\n",
" [ -999.5, -681. , -327. , ..., -1800. , -935. , -660. ],\n",
" [ -1075. , -1200. , -358. , ..., -753. , -864. , -1866.5]])\n",
" * trainId (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
" * sa3_pId (sa3_pId) int32 2kB 772 776 780 784 788 ... 2356 2360 2364 2368\n",
" FastADC2_9peaks_pulseStart: 6763\n",
" FastADC2_9peaks_period: 96\n",
" FastADC2_9peaks_pulseStop: 6770\n",
" FastADC2_9peaks_baseStop: 6762\n",
" FastADC2_9peaks_baseStart: 6757\n",
" FastADC2_9peaks_npulses: 400"
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tb.load_processed_peaks(proposal, runNB, 'FastADC2_9peaks')"
]
},
{
"cell_type": "markdown",
"id": "bd711480-f2c8-4326-94bd-51a7dcbcfd1e",
"metadata": {},
"source": [
"Note that the attributes are the peak integration parameters used for peak extraction.\n",
"\n",
"It is also possible to load the entire dataset containing the peaks and average traces of all the processed sources by ommitting the `mnemonic` argument:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "86045cd5-ea54-478a-9b61-4afc2586f000",
"metadata": {},
"outputs": [
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".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
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"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
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"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
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"\n",
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"\n",
".xr-attrs dt,\n",
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1482
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" padding: 0;\n",
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"\n",
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"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
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"\n",
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"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 26MB\n",
"Dimensions: (trainId: 7898, sa3_pId: 400, sampleId: 100000)\n",
" * trainId (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
" * sa3_pId (sa3_pId) int32 2kB 772 776 780 784 ... 2356 2360 2364 2368\n",
"Dimensions without coordinates: sampleId\n",
"Data variables:\n",
" FastADC2_9peaks (trainId, sa3_pId) float64 25MB -1.04e+04 ... -1.866e+03\n",
" FastADC2_9avg (sampleId) float64 800kB -411.8 -412.0 ... -411.3 -411.2\n",
" FastADC2_9peaks_pulseStart: 6763\n",
" FastADC2_9peaks_period: 96\n",
" FastADC2_9peaks_pulseStop: 6770\n",
" FastADC2_9peaks_baseStop: 6762\n",
" FastADC2_9peaks_baseStart: 6757\n",
" FastADC2_9peaks_npulses: 400</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-568b2b2e-473b-401e-a700-e22ad971a6a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-568b2b2e-473b-401e-a700-e22ad971a6a8' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 7898</li><li><span class='xr-has-index'>sa3_pId</span>: 400</li><li><span>sampleId</span>: 100000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43b990a6-f9aa-4453-b477-67a72a17e8ed' class='xr-section-summary-in' type='checkbox' checked><label for='section-43b990a6-f9aa-4453-b477-67a72a17e8ed' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1501374970 ... 1501382869</div><input id='attrs-65fecb72-935b-453b-982c-2d4ee918f12d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65fecb72-935b-453b-982c-2d4ee918f12d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-159839fb-653c-42fb-94ff-2aac5ef32d35' class='xr-var-data-in' type='checkbox'><label for='data-159839fb-653c-42fb-94ff-2aac5ef32d35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1501374970, 1501374971, 1501374972, ..., 1501382867, 1501382868,\n",
" 1501382869], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sa3_pId</span></div><div class='xr-var-dims'>(sa3_pId)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>772 776 780 784 ... 2360 2364 2368</div><input id='attrs-e4995142-04e5-499a-a599-70a29ab9d98d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4995142-04e5-499a-a599-70a29ab9d98d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c2afd69-2175-45f2-911b-abbda691762e' class='xr-var-data-in' type='checkbox'><label for='data-9c2afd69-2175-45f2-911b-abbda691762e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 772, 776, 780, ..., 2360, 2364, 2368], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f797cb0-e651-44ed-ac1a-2c55060bce3e' class='xr-section-summary-in' type='checkbox' checked><label for='section-5f797cb0-e651-44ed-ac1a-2c55060bce3e' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>FastADC2_9peaks</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.04e+04 -2.104e+04 ... -1.866e+03</div><input id='attrs-5aedc4ce-cee4-4997-a70a-c4161d7b9d26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aedc4ce-cee4-4997-a70a-c4161d7b9d26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1be3ec80-7d0c-44e4-91e3-17711d5b0ec0' class='xr-var-data-in' type='checkbox'><label for='data-1be3ec80-7d0c-44e4-91e3-17711d5b0ec0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FastADC2_9peaks_pulseStart :</span></dt><dd>6763</dd><dt><span>FastADC2_9peaks_period :</span></dt><dd>96</dd><dt><span>FastADC2_9peaks_pulseStop :</span></dt><dd>6770</dd><dt><span>FastADC2_9peaks_baseStop :</span></dt><dd>6762</dd><dt><span>FastADC2_9peaks_baseStart :</span></dt><dd>6757</dd><dt><span>FastADC2_9peaks_npulses :</span></dt><dd>400</dd></dl></div><div class='xr-var-data'><pre>array([[-10396.5, -21040.5, -11618. , ..., -2953. , -14307.5, -9120. ],\n",
" [-14979.5, -3093.5, -8993. , ..., -5679.5, -4255. , -25531. ],\n",
" [-22282. , -2814. , -21852.5, ..., -14205.5, -8499.5, -13072. ],\n",
" [ -607.5, 99. , -660. , ..., -1162.5, -1217. , -808. ],\n",
" [ -999.5, -681. , -327. , ..., -1800. , -935. , -660. ],\n",
" [ -1075. , -1200. , -358. , ..., -753. , -864. , -1866.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC2_9avg</span></div><div class='xr-var-dims'>(sampleId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-411.8 -412.0 ... -411.3 -411.2</div><input id='attrs-09012fb4-fe41-4dd9-ad3c-0bee82f6e05d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09012fb4-fe41-4dd9-ad3c-0bee82f6e05d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c748613-49c9-4ecf-a0dd-1fe33013d997' class='xr-var-data-in' type='checkbox'><label for='data-1c748613-49c9-4ecf-a0dd-1fe33013d997' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-411.77931122, -411.96657382, -411.46530767, ..., -411.40516586,\n",
" -411.26728286, -411.16624462])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b75963e-4603-4492-87c8-1a0d3c1f040b' class='xr-section-summary-in' type='checkbox' ><label for='section-0b75963e-4603-4492-87c8-1a0d3c1f040b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>trainId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f76cdbef-cba9-45bf-8135-7ad7243a305a' class='xr-index-data-in' type='checkbox'/><label for='index-f76cdbef-cba9-45bf-8135-7ad7243a305a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1501374970, 1501374971, 1501374972, 1501374973, 1501374974, 1501374975,\n",
" 1501374976, 1501374977, 1501374978, 1501374979,\n",
" ...\n",
" 1501382860, 1501382861, 1501382862, 1501382863, 1501382864, 1501382865,\n",
" 1501382866, 1501382867, 1501382868, 1501382869],\n",
" dtype='uint64', name='trainId', length=7898))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sa3_pId</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3c4258f0-317f-478e-ab72-fbdd2fd43a10' class='xr-index-data-in' type='checkbox'/><label for='index-3c4258f0-317f-478e-ab72-fbdd2fd43a10' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 772, 776, 780, 784, 788, 792, 796, 800, 804, 808,\n",
" ...\n",
" 2332, 2336, 2340, 2344, 2348, 2352, 2356, 2360, 2364, 2368],\n",
" dtype='int32', name='sa3_pId', length=400))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44033f01-fce1-4023-9937-a5976b2b460c' class='xr-section-summary-in' type='checkbox' checked><label for='section-44033f01-fce1-4023-9937-a5976b2b460c' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>FastADC2_9peaks_pulseStart :</span></dt><dd>6763</dd><dt><span>FastADC2_9peaks_period :</span></dt><dd>96</dd><dt><span>FastADC2_9peaks_pulseStop :</span></dt><dd>6770</dd><dt><span>FastADC2_9peaks_baseStop :</span></dt><dd>6762</dd><dt><span>FastADC2_9peaks_baseStart :</span></dt><dd>6757</dd><dt><span>FastADC2_9peaks_npulses :</span></dt><dd>400</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 26MB\n",
"Dimensions: (trainId: 7898, sa3_pId: 400, sampleId: 100000)\n",
" * trainId (trainId) uint64 63kB 1501374970 1501374971 ... 1501382869\n",
" * sa3_pId (sa3_pId) int32 2kB 772 776 780 784 ... 2356 2360 2364 2368\n",
"Dimensions without coordinates: sampleId\n",
"Data variables:\n",
" FastADC2_9peaks (trainId, sa3_pId) float64 25MB -1.04e+04 ... -1.866e+03\n",
" FastADC2_9avg (sampleId) float64 800kB -411.8 -412.0 ... -411.3 -411.2\n",
" FastADC2_9peaks_pulseStart: 6763\n",
" FastADC2_9peaks_period: 96\n",
" FastADC2_9peaks_pulseStop: 6770\n",
" FastADC2_9peaks_baseStop: 6762\n",
" FastADC2_9peaks_baseStart: 6757\n",
" FastADC2_9peaks_npulses: 400"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tb.load_processed_peaks(proposal, runNB)"
]
},
{
"cell_type": "markdown",
"id": "c1bcdc1c-b055-4da4-bce0-3fdf0e98cb56",
"metadata": {},
"source": [
"It is also possible to check the integration parameters that were used for peak extraction:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fd2b39dd-1a7f-4163-91f2-7d0bb0bda111",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'pulseStart': 6763,\n",
" 'period': 96,\n",
" 'pulseStop': 6770,\n",
" 'baseStop': 6762,\n",
" 'baseStart': 6757,\n",
" 'npulses': 400}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"<Figure size 600x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tb.check_processed_peak_params(proposal, runNB, 'FastADC2_9peaks')"
]
},
{
"cell_type": "markdown",
"id": "b783d3fb-9161-4dd1-b4fc-342ad57a83b3",
"metadata": {},
"source": [
"## Example of irregular PPL pattern"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "916b1bfd-6279-47f7-a30a-d8390c023e6d",
1635
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bunch pattern scs_ppl: Not a regular pattern. 192 pulses, pulse_ids=[ 0 4 8 12 16 20 24 28 32 36 40 44 48 52\n",
" 56 60 64 68 72 76 80 84 88 92 192 196 200 204\n",
" 208 212 216 220 224 228 232 236 240 244 248 252 256 260\n",
" 264 268 272 276 280 284 384 388 392 396 400 404 408 412\n",
" 416 420 424 428 432 436 440 444 448 452 456 460 464 468\n",
" 472 476 576 580 584 588 592 596 600 604 608 612 616 620\n",
" 624 628 632 636 640 644 648 652 656 660 664 668 768 772\n",
" 776 780 784 788 792 796 800 804 808 812 816 820 824 828\n",
" 832 836 840 844 848 852 856 860 960 964 968 972 976 980\n",
" 984 988 992 996 1000 1004 1008 1012 1016 1020 1024 1028 1032 1036\n",
" 1040 1044 1048 1052 1152 1156 1160 1164 1168 1172 1176 1180 1184 1188\n",
" 1192 1196 1200 1204 1208 1212 1216 1220 1224 1228 1232 1236 1240 1244\n",
" 1344 1348 1352 1356 1360 1364 1368 1372 1376 1380 1384 1388 1392 1396\n",
" 1400 1404 1408 1412 1416 1420 1424 1428 1432 1436].\n",
"Auto-find peak parameters: Not a regular pattern. 192 pulses, pulse_ids=[ 0 4 8 12 16 20 24 28 32 36 40 44 48 52\n",
" 56 60 64 68 72 76 80 84 88 92 192 196 200 204\n",
" 208 212 216 220 224 228 232 236 240 244 248 252 256 260\n",
" 264 268 272 276 280 284 384 388 392 396 400 404 408 412\n",
" 416 420 424 428 432 436 440 444 448 452 456 460 464 468\n",
" 472 476 576 580 584 588 592 596 600 604 608 612 616 620\n",
" 624 628 632 636 640 644 648 652 656 660 664 668 768 772\n",
" 776 780 784 788 792 796 800 804 808 812 816 820 824 828\n",
" 832 836 840 844 848 852 856 860 960 964 968 972 976 980\n",
" 984 988 992 996 1000 1004 1008 1012 1016 1020 1024 1028 1032 1036\n",
" 1040 1044 1048 1052 1152 1156 1160 1164 1168 1172 1176 1180 1184 1188\n",
" 1192 1196 1200 1204 1208 1212 1216 1220 1224 1228 1232 1236 1240 1244\n",
" 1344 1348 1352 1356 1360 1364 1368 1372 1376 1380 1384 1388 1392 1396\n",
" 1400 1404 1408 1412 1416 1420 1424 1428 1432 1436].\n"
]
},
{
"data": {
"image/png": 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",
"<Figure size 600x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"# irregular pattern\n",
"proposal, runNB, field = 8716, 28, 'I0_ILHraw'\n",
"params = tb.check_peak_params(proposal, runNB, field,\n",
" bunchPattern='scs_ppl', show_all=True)"
},
{
"cell_type": "code",
"execution_count": null,
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