diff --git a/doc/Loading_data_in_memory.ipynb b/doc/Loading_data_in_memory.ipynb
index f88e062eeaec1b9ff4525a8b824add5f7cb49811..aaea61bf2674093a802d14b169643511ca8287f6 100644
--- a/doc/Loading_data_in_memory.ipynb
+++ b/doc/Loading_data_in_memory.ipynb
@@ -292,9 +292,440 @@
},
{
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+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (pulse_slot: 2700, sa3_pId: 125, trainId: 3066)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 520069541 520069542 ... 520072606\n",
+ " * sa3_pId (sa3_pId) int64 1040 1048 1056 1064 ... 2016 2024 2032\n",
+ "Dimensions without coordinates: pulse_slot\n",
+ "Data variables:\n",
+ " nrj (trainId) float64 778.6 778.6 778.5 ... 783.4 783.4 783.4\n",
+ " MCP3peaks (trainId, sa3_pId) float64 -197.7 -34.67 ... -1.213e+03\n",
+ " bunchPatternTable (trainId, pulse_slot) uint32 2139945 0 2129961 ... 0 0 0\n",
+ " SCS_SA3 (trainId, sa3_pId) float32 2838.6826 ... 8069.3115\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/201901/p002212/raw/r0208</pre><div class='xr-wrap' hidden><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-7b443b30-243c-41a0-9f1b-2a7e0fa45493' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b443b30-243c-41a0-9f1b-2a7e0fa45493' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>pulse_slot</span>: 2700</li><li><span class='xr-has-index'>sa3_pId</span>: 125</li><li><span class='xr-has-index'>trainId</span>: 3066</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f27ccb8a-f60c-4c85-8fea-5d46ba0caff2' class='xr-section-summary-in' type='checkbox' checked><label for='section-f27ccb8a-f60c-4c85-8fea-5d46ba0caff2' 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'>520069541 520069542 ... 520072606</div><input id='attrs-b2803417-862a-4320-b177-9a83c61d6a6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2803417-862a-4320-b177-9a83c61d6a6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-270a486e-a90f-4c3b-a728-55294ef5c24d' class='xr-var-data-in' type='checkbox'><label for='data-270a486e-a90f-4c3b-a728-55294ef5c24d' 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([520069541, 520069542, 520069543, ..., 520072604, 520072605, 520072606],\n",
+ " 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'>int64</div><div class='xr-var-preview xr-preview'>1040 1048 1056 ... 2016 2024 2032</div><input id='attrs-e5183157-9c1e-4cc3-a26a-1507270db8a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5183157-9c1e-4cc3-a26a-1507270db8a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-369fc1a8-cf9f-42d1-9696-1972aa188934' class='xr-var-data-in' type='checkbox'><label for='data-369fc1a8-cf9f-42d1-9696-1972aa188934' 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([1040, 1048, 1056, 1064, 1072, 1080, 1088, 1096, 1104, 1112, 1120, 1128,\n",
+ " 1136, 1144, 1152, 1160, 1168, 1176, 1184, 1192, 1200, 1208, 1216, 1224,\n",
+ " 1232, 1240, 1248, 1256, 1264, 1272, 1280, 1288, 1296, 1304, 1312, 1320,\n",
+ " 1328, 1336, 1344, 1352, 1360, 1368, 1376, 1384, 1392, 1400, 1408, 1416,\n",
+ " 1424, 1432, 1440, 1448, 1456, 1464, 1472, 1480, 1488, 1496, 1504, 1512,\n",
+ " 1520, 1528, 1536, 1544, 1552, 1560, 1568, 1576, 1584, 1592, 1600, 1608,\n",
+ " 1616, 1624, 1632, 1640, 1648, 1656, 1664, 1672, 1680, 1688, 1696, 1704,\n",
+ " 1712, 1720, 1728, 1736, 1744, 1752, 1760, 1768, 1776, 1784, 1792, 1800,\n",
+ " 1808, 1816, 1824, 1832, 1840, 1848, 1856, 1864, 1872, 1880, 1888, 1896,\n",
+ " 1904, 1912, 1920, 1928, 1936, 1944, 1952, 1960, 1968, 1976, 1984, 1992,\n",
+ " 2000, 2008, 2016, 2024, 2032])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3434caeb-972e-47a8-9f46-4b8610f92daf' class='xr-section-summary-in' type='checkbox' checked><label for='section-3434caeb-972e-47a8-9f46-4b8610f92daf' class='xr-section-summary' >Data variables: <span>(4)</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>nrj</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>778.6 778.6 778.5 ... 783.4 783.4</div><input id='attrs-ecba691a-03ec-4a14-b508-c80036bb2cae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecba691a-03ec-4a14-b508-c80036bb2cae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e5357da-afba-4b6b-8fe2-87e4e3077b59' class='xr-var-data-in' type='checkbox'><label for='data-0e5357da-afba-4b6b-8fe2-87e4e3077b59' 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([778.62824057, 778.55124428, 778.52251822, ..., 783.36562112,\n",
+ " 783.3947057 , 783.37531574])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MCP3peaks</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'>-197.7 -34.67 ... -1.213e+03</div><input id='attrs-42928d54-0c07-4806-8c72-41f94340fe88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42928d54-0c07-4806-8c72-41f94340fe88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be1aacef-7d53-48f9-a0a4-b8b8248b57ad' class='xr-var-data-in' type='checkbox'><label for='data-be1aacef-7d53-48f9-a0a4-b8b8248b57ad' 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([[-1.97666667e+02, -3.46666667e+01, 1.83333333e+01, ...,\n",
+ " -4.95533333e+03, -2.58333333e+03, -3.64000000e+03],\n",
+ " [-1.14000000e+02, -4.00000000e+02, -2.75000000e+02, ...,\n",
+ " -1.89733333e+03, -1.63966667e+03, -9.57000000e+02],\n",
+ " [-5.61000000e+02, -1.00366667e+03, -3.75000000e+02, ...,\n",
+ " -7.72000000e+02, -2.23633333e+03, -1.49466667e+03],\n",
+ " ...,\n",
+ " [-1.51333333e+02, -6.10000000e+01, -4.74333333e+02, ...,\n",
+ " -9.53333333e+02, -9.39000000e+02, -1.34033333e+03],\n",
+ " [-6.08666667e+02, -7.73333333e+01, 2.33333333e+00, ...,\n",
+ " -1.58466667e+03, -9.06333333e+02, -1.04700000e+03],\n",
+ " [-4.16666667e+01, -4.10333333e+02, 5.00000000e+01, ...,\n",
+ " -9.43666667e+02, -2.86800000e+03, -1.21266667e+03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bunchPatternTable</span></div><div class='xr-var-dims'>(trainId, pulse_slot)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>2139945 0 2129961 0 ... 0 0 0 0</div><input id='attrs-ba43929a-151c-418f-ba9e-48fc9aa3033f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba43929a-151c-418f-ba9e-48fc9aa3033f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa84ae0f-d9fb-4c65-baff-c888a7b052c7' class='xr-var-data-in' type='checkbox'><label for='data-fa84ae0f-d9fb-4c65-baff-c888a7b052c7' 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([[2139945, 0, 2129961, ..., 0, 0, 0],\n",
+ " [2141993, 0, 2129961, ..., 0, 0, 0],\n",
+ " [2139945, 0, 2129961, ..., 0, 0, 0],\n",
+ " ...,\n",
+ " [2141993, 0, 2129961, ..., 0, 0, 0],\n",
+ " [2139945, 0, 2129961, ..., 0, 0, 0],\n",
+ " [2141993, 0, 2129961, ..., 0, 0, 0]],\n",
+ " dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCS_SA3</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2838.6826 897.9348 ... 8069.3115</div><input id='attrs-b75ea6ce-ad24-4db2-9b4a-2a9468f8bc51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b75ea6ce-ad24-4db2-9b4a-2a9468f8bc51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcc9ecf1-e020-4c3a-9060-818742731193' class='xr-var-data-in' type='checkbox'><label for='data-fcc9ecf1-e020-4c3a-9060-818742731193' 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([[ 2838.6826 , 897.9348 , 1270.1282 , ..., 33158.98 ,\n",
+ " 19836.096 , 27724.035 ],\n",
+ " [ 2088.772 , 861.3658 , 3565.1692 , ..., 16303.649 ,\n",
+ " 12787.915 , 6092.001 ],\n",
+ " [ 603.07495, 4487.3667 , 2917.9038 , ..., 7453.7964 ,\n",
+ " 11550.721 , 10727.462 ],\n",
+ " ...,\n",
+ " [ 1868.0271 , 1402.9429 , 1433.9807 , ..., 7914.3984 ,\n",
+ " 4954.8477 , 6647.5244 ],\n",
+ " [ 3646.7505 , 2033.2625 , 569.5602 , ..., 9144.624 ,\n",
+ " 7623.28 , 4444.4536 ],\n",
+ " [ 708.95374, 1963.8428 , 912.64026, ..., 5079.5537 ,\n",
+ " 12632.79 , 8069.3115 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a76cdd2-c54c-4828-a3b6-be448931b99b' class='xr-section-summary-in' type='checkbox' checked><label for='section-3a76cdd2-c54c-4828-a3b6-be448931b99b' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>runFolder :</span></dt><dd>/gpfs/exfel/exp/SCS/201901/p002212/raw/r0208</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (pulse_slot: 2700, sa3_pId: 125, trainId: 3066)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 520069541 520069542 ... 520072606\n",
+ " * sa3_pId (sa3_pId) int64 1040 1048 1056 1064 ... 2016 2024 2032\n",
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+ " MCP3peaks (trainId, sa3_pId) float64 -197.7 -34.67 ... -1.213e+03\n",
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+ ]
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+ }
+ ],
"source": [
"proposalNB = 2212\n",
"runNB = 208\n",
@@ -339,9 +770,400 @@
},
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+ "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'SCS_UTC1_ADQ/ADC/1:network.digitizers.channel_1_C.raw.samples' (trainId: 3066, samplesId: 600000)>\n",
+ "array([[1515, 1500, 1507, ..., 1505, 1498, 1500],\n",
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+ "Coordinates:\n",
+ " * trainId (trainId) uint64 520069541 520069542 ... 520072605 520072606\n",
+ "Dimensions without coordinates: samplesId</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'SCS_UTC1_ADQ/ADC/1:network.digitizers.channel_1_C.raw.samples'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 3066</li><li><span>samplesId</span>: 600000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e89ac97a-c2e4-4b1d-99bb-5edfce654d53' class='xr-array-in' type='checkbox' checked><label for='section-e89ac97a-c2e4-4b1d-99bb-5edfce654d53' 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>1515 1500 1507 1506 1497 1505 1510 ... 1506 1508 1513 1508 1502 1500</span></div><div class='xr-array-data'><pre>array([[1515, 1500, 1507, ..., 1505, 1498, 1500],\n",
+ " [1500, 1502, 1498, ..., 1504, 1490, 1499],\n",
+ " [1503, 1508, 1507, ..., 1512, 1500, 1496],\n",
+ " ...,\n",
+ " [1502, 1515, 1517, ..., 1503, 1498, 1509],\n",
+ " [1512, 1511, 1513, ..., 1506, 1504, 1506],\n",
+ " [1499, 1502, 1508, ..., 1508, 1502, 1500]], dtype=int16)</pre></div></div></li><li class='xr-section-item'><input id='section-eabc90ed-d5f6-4169-8fa7-e663893e1b52' class='xr-section-summary-in' type='checkbox' checked><label for='section-eabc90ed-d5f6-4169-8fa7-e663893e1b52' class='xr-section-summary' >Coordinates: <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 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'>520069541 520069542 ... 520072606</div><input id='attrs-28daacc8-e419-423f-bfd3-cbf55767f679' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28daacc8-e419-423f-bfd3-cbf55767f679' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1ee4e2-cbca-48b7-bde1-91b283cca006' class='xr-var-data-in' type='checkbox'><label for='data-ff1ee4e2-cbca-48b7-bde1-91b283cca006' 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([520069541, 520069542, 520069543, ..., 520072604, 520072605, 520072606],\n",
+ " dtype=uint64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ad4ca76-f7f6-4278-bf81-bfe4f41cdfb7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ad4ca76-f7f6-4278-bf81-bfe4f41cdfb7' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray 'SCS_UTC1_ADQ/ADC/1:network.digitizers.channel_1_C.raw.samples' (trainId: 3066, samplesId: 600000)>\n",
+ "array([[1515, 1500, 1507, ..., 1505, 1498, 1500],\n",
+ " [1500, 1502, 1498, ..., 1504, 1490, 1499],\n",
+ " [1503, 1508, 1507, ..., 1512, 1500, 1496],\n",
+ " ...,\n",
+ " [1502, 1515, 1517, ..., 1503, 1498, 1509],\n",
+ " [1512, 1511, 1513, ..., 1506, 1504, 1506],\n",
+ " [1499, 1502, 1508, ..., 1508, 1502, 1500]], dtype=int16)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 520069541 520069542 ... 520072605 520072606\n",
+ "Dimensions without coordinates: samplesId"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"raw_traces = run.get_array(*run_mnemonics['MCP2raw'].values())\n",
"raw_traces"
@@ -356,9 +1178,32 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x2b2ef42ca320>]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"raw_traces.isel(trainId=0).plot()"
]
@@ -367,7 +1212,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Missing trains"
+ "## Missing trains"
]
},
{
@@ -379,9 +1224,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "SCS_SA3: only 85.6% of trains (2122 out of 2479) contain data.\n"
+ ]
+ }
+ ],
"source": [
"fields = ['SCS_HAMP_HV', 'SCS_SA3']\n",
"run, ds = tb.load(5836, 162, fields)"
@@ -396,9 +1249,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'SCS_HAMP_HV': 1.0, 'SCS_SA3': 0.8559903186768858}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"tb.check_data_rate(run, fields)"
]
@@ -412,12 +1276,126 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'sase3': 1.0,\n",
+ " 'sase2': 1.0,\n",
+ " 'sase1': 1.0,\n",
+ " 'laser': 1.0,\n",
+ " 'maindump': 1.0,\n",
+ " 'bunchpattern': 1.0,\n",
+ " 'bunchPatternTable': 1.0,\n",
+ " 'npulses_sase3': 1.0,\n",
+ " 'npulses_sase1': 1.0,\n",
+ " 'npulses_laser': 1.0,\n",
+ " 'BAM414': 0.9693424768051634,\n",
+ " 'BAM1932M': 0.982654296087132,\n",
+ " 'BAM1932S': 0.9669221460266236,\n",
+ " 'DPS2CAM2': 0.0,\n",
+ " 'XTD10_photonFlux': 1.0,\n",
+ " 'XTD10_photonFlux_sigma': 1.0,\n",
+ " 'XTD10_XGM': 0.9983864461476402,\n",
+ " 'XTD10_XGM_sigma': 0.9983864461476402,\n",
+ " 'XTD10_SA3': 0.9983864461476402,\n",
+ " 'XTD10_SA3_sigma': 0.9983864461476402,\n",
+ " 'XTD10_SA1': 0.9983864461476402,\n",
+ " 'XTD10_SA1_sigma': 0.9983864461476402,\n",
+ " 'XTD10_slowTrain': 1.0,\n",
+ " 'XTD10_slowTrain_SA1': 1.0,\n",
+ " 'XTD10_slowTrain_SA3': 1.0,\n",
+ " 'SCS_photonFlux': 1.0,\n",
+ " 'SCS_photonFlux_sigma': 1.0,\n",
+ " 'SCS_HAMP_HV': 1.0,\n",
+ " 'SCS_XGM': 0.8559903186768858,\n",
+ " 'SCS_XGM_sigma': 0.8559903186768858,\n",
+ " 'SCS_SA1': 0.8559903186768858,\n",
+ " 'SCS_SA1_sigma': 0.8559903186768858,\n",
+ " 'SCS_SA3': 0.8559903186768858,\n",
+ " 'SCS_SA3_sigma': 0.8559903186768858,\n",
+ " 'SCS_slowTrain': 1.0,\n",
+ " 'SCS_slowTrain_SA1': 1.0,\n",
+ " 'SCS_slowTrain_SA3': 1.0,\n",
+ " 'AFS_DelayLine': 1.0,\n",
+ " 'AFS_FocusLens': 1.0,\n",
+ " 'PP800_PhaseShifter': 1.0,\n",
+ " 'PP800_SynchDelayLine': 1.0,\n",
+ " 'PP800_DelayLine': 1.0,\n",
+ " 'PP800_HalfWP': 1.0,\n",
+ " 'PP800_FocusLens': 1.0,\n",
+ " 'FFT_FocusLens': 1.0,\n",
+ " 'ZABER110_ODL': 1.0,\n",
+ " 'FastADC0peaks': 0.0,\n",
+ " 'FastADC0raw': 0.0,\n",
+ " 'FastADC1peaks': 0.0,\n",
+ " 'FastADC1raw': 0.0,\n",
+ " 'FastADC2peaks': 0.0,\n",
+ " 'FastADC2raw': 0.0,\n",
+ " 'FastADC3peaks': 1.0,\n",
+ " 'FastADC3raw': 1.0,\n",
+ " 'FastADC4peaks': 0.0,\n",
+ " 'FastADC4raw': 0.0,\n",
+ " 'FastADC5peaks': 1.0,\n",
+ " 'FastADC5raw': 1.0,\n",
+ " 'FastADC6peaks': 0.0,\n",
+ " 'FastADC6raw': 0.0,\n",
+ " 'FastADC7peaks': 0.0,\n",
+ " 'FastADC7raw': 0.0,\n",
+ " 'FastADC8peaks': 0.0,\n",
+ " 'FastADC8raw': 0.0,\n",
+ " 'FastADC9peaks': 1.0,\n",
+ " 'FastADC9raw': 1.0,\n",
+ " 'FastADC2_0peaks': 0.0,\n",
+ " 'FastADC2_0raw': 0.0,\n",
+ " 'FastADC2_1peaks': 0.0,\n",
+ " 'FastADC2_1raw': 0.0,\n",
+ " 'FastADC2_2peaks': 0.0,\n",
+ " 'FastADC2_2raw': 0.0,\n",
+ " 'FastADC2_3peaks': 0.0,\n",
+ " 'FastADC2_3raw': 0.0,\n",
+ " 'FastADC2_4peaks': 0.0,\n",
+ " 'FastADC2_4raw': 0.0,\n",
+ " 'FastADC2_5peaks': 0.0,\n",
+ " 'FastADC2_5raw': 0.0,\n",
+ " 'FastADC2_6peaks': 1.0,\n",
+ " 'FastADC2_6raw': 1.0,\n",
+ " 'FastADC2_7peaks': 1.0,\n",
+ " 'FastADC2_7raw': 1.0,\n",
+ " 'FastADC2_8peaks': 0.0,\n",
+ " 'FastADC2_8raw': 0.0,\n",
+ " 'FastADC2_9peaks': 0.0,\n",
+ " 'FastADC2_9raw': 0.0,\n",
+ " 'Gotthard1': 1.0,\n",
+ " 'Gotthard2': 0.0}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"tb.check_data_rate(run)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There is also an unreleased function from `extra_data`, called `plot_missing_trains()`, to be released in version 1.13.0 (see [here](https://extra-data.readthedocs.io/en/latest/reading_files.html#extra_data.DataCollection.plot_missing_data)) that should shows similar quantities."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {