diff --git a/doc/Loading_data_in_memory.ipynb b/doc/Loading_data_in_memory.ipynb
index 0b9fabebedcf2f4e36eb27c763c9abafae1c3e4e..b9050c73ea7a5b10b2000e625640846f29d82ee0 100644
--- a/doc/Loading_data_in_memory.ipynb
+++ b/doc/Loading_data_in_memory.ipynb
@@ -60,10 +60,10 @@
"outputs": [],
"source": [
"# providing the proposal and run numbers\n",
- "run_mnemonics = tb.mnemonics_for_run(2212, 213)\n",
+ "run_mnemonics = tb.mnemonics_for_run(3485, 52)\n",
"\n",
"# alternative, providing the DataCollection as input argument\n",
- "run = tb.open_run(2212, 213)\n",
+ "run = tb.open_run(3485, 52)\n",
"run_mnemonics = tb.mnemonics_for_run(run)"
]
},
@@ -75,7 +75,7 @@
{
"data": {
"text/plain": [
- "dict_keys(['sase3', 'sase2', 'sase1', 'maindump', 'bunchpattern', 'bunchPatternTable', 'npulses_sase3', 'npulses_sase1', 'nrj', 'XTD10_photonFlux', 'XTD10_photonFlux_sigma', 'XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3', 'XTD10_SA3_sigma', 'XTD10_SA1', 'XTD10_SA1_sigma', 'XTD10_slowTrain', 'XTD10_slowTrain_SA1', 'XTD10_slowTrain_SA3', 'SCS_photonFlux', 'SCS_photonFlux_sigma', 'SCS_XGM', 'SCS_XGM_sigma', 'SCS_SA1', 'SCS_SA1_sigma', 'SCS_SA3', 'SCS_SA3_sigma', 'SCS_slowTrain', 'SCS_slowTrain_SA1', 'SCS_slowTrain_SA3', 'AFS_FocusLens', 'PP800_PhaseShifter', 'PP800_DelayLine', 'PP800_HalfWP', 'PP800_FocusLens', 'PP800_TeleLens', 'MCP1apd', 'MCP1raw', 'MCP2apd', 'MCP2raw', 'MCP3apd', 'MCP3raw', 'MCP4apd', 'MCP4raw', 'FastADC0peaks', 'FastADC0raw', 'FastADC1peaks', 'FastADC1raw', 'FastADC2peaks', 'FastADC2raw', 'FastADC3peaks', 'FastADC3raw', 'FastADC4peaks', 'FastADC4raw', 'FastADC5peaks', 'FastADC5raw', 'FastADC6peaks', 'FastADC6raw', 'FastADC7peaks', 'FastADC7raw', 'FastADC8peaks', 'FastADC8raw', 'FastADC9peaks', 'FastADC9raw'])"
+ "dict_keys(['sase3', 'sase2', 'sase1', 'maindump', 'bunchpattern', 'bunchPatternTable', 'npulses_sase3', 'npulses_sase1', 'BAM414', 'BAM1932M', 'BAM1932S', 'nrj', 'nrj_target', 'M2BEND', 'tpi', 'VSLIT', 'ESLIT', 'HSLIT', 'transmission', 'transmission_col2', 'GATT_pressure', 'UND', 'UND2', 'UND3', 'XTD10_photonFlux', 'XTD10_photonFlux_sigma', 'XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3', 'XTD10_SA3_sigma', 'XTD10_SA1', 'XTD10_SA1_sigma', 'XTD10_slowTrain', 'XTD10_slowTrain_SA1', 'XTD10_slowTrain_SA3', 'SCS_photonFlux', 'SCS_photonFlux_sigma', 'SCS_XGM', 'SCS_XGM_sigma', 'SCS_SA1', 'SCS_SA1_sigma', 'SCS_SA3', 'SCS_SA3_sigma', 'SCS_slowTrain', 'SCS_slowTrain_SA1', 'SCS_slowTrain_SA3', 'AFS_DelayLine', 'AFS_FocusLens', 'PP800_PhaseShifter', 'PP800_SynchDelayLine', 'PP800_DelayLine', 'PP800_HalfWP', 'PP800_FocusLens', 'FFT_FocusLens', 'hRIXS_det', 'hRIXS_delay', 'hRIXS_index', 'hRIXS_norm', 'hRIXS_ABB', 'hRIXS_ABL', 'hRIXS_ABR', 'hRIXS_ABT', 'hRIXS_DRX', 'hRIXS_DTY1', 'hRIXS_DTZ', 'hRIXS_GMX', 'hRIXS_GRX', 'hRIXS_GTLY', 'hRIXS_GTRY', 'hRIXS_GTX', 'hRIXS_GTZ', 'XRD_DRY', 'XRD_SRX', 'XRD_SRY', 'XRD_SRZ', 'XRD_STX', 'XRD_STY', 'XRD_STZ', 'XRD_SXT1Y', 'XRD_SXT2Y', 'XRD_SXTX', 'XRD_SXTZ', 'FastADC0peaks', 'FastADC0raw', 'FastADC1peaks', 'FastADC1raw', 'FastADC2peaks', 'FastADC2raw', 'FastADC3peaks', 'FastADC3raw', 'FastADC4peaks', 'FastADC4raw', 'FastADC5peaks', 'FastADC5raw', 'FastADC6peaks', 'FastADC6raw', 'FastADC7peaks', 'FastADC7raw', 'FastADC8peaks', 'FastADC8raw', 'FastADC9peaks', 'FastADC9raw', 'FastADC2_0peaks', 'FastADC2_0raw', 'FastADC2_1peaks', 'FastADC2_1raw', 'FastADC2_2peaks', 'FastADC2_2raw', 'FastADC2_3peaks', 'FastADC2_3raw', 'FastADC2_4peaks', 'FastADC2_4raw', 'FastADC2_5peaks', 'FastADC2_5raw', 'FastADC2_6peaks', 'FastADC2_6raw', 'FastADC2_7peaks', 'FastADC2_7raw', 'FastADC2_8peaks', 'FastADC2_8raw', 'FastADC2_9peaks', 'FastADC2_9raw'])"
]
},
"execution_count": 3,
@@ -98,6 +98,109 @@
"</div>"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is possible to extract the \"run value\" (see [get_run_value()](https://extra-data.readthedocs.io/en/latest/reading_files.html#extra_data.DataCollection.get_run_value) for details) of a source/key combination by using the parameter `get_run_values=True` in `mnemonics_for_run()`.\n",
+ "\n",
+ "This is a convenient way of quickly checking the values of the most relevant parameters of a run, like the opening of the exit slit of the monochromator ('ESLIT' im mm) or the transmission of the gas attenuator ('transmission' in %) without loading the full data, which would take much more time and require large memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sase3 [612 616 620 ... 1 1 1]\n",
+ "sase2 [150 0 0 ... 0 0 0]\n",
+ "sase1 [610 674 738 ... 1 1 1]\n",
+ "maindump [0 2 4 ... 1 1 1]\n",
+ "bunchpattern 1\n",
+ "npulses_sase3 500\n",
+ "npulses_sase1 30\n",
+ "nrj 927.9717888233587\n",
+ "nrj_target 928.0\n",
+ "M2BEND 116.0004793503568\n",
+ "tpi 1\n",
+ "VSLIT 2.148199999999999\n",
+ "ESLIT 0.10432264111327783\n",
+ "HSLIT 31.00000573730469\n",
+ "transmission 1.1666694088238525\n",
+ "transmission_col2 2.3306329751092547\n",
+ "GATT_pressure 0.6412954330444336\n",
+ "UND 0.9271398\n",
+ "UND2 0.5390185\n",
+ "UND3 0.9\n",
+ "XTD10_photonFlux 1561.6473\n",
+ "XTD10_photonFlux_sigma 71.602005\n",
+ "XTD10_slowTrain 1574.1066\n",
+ "XTD10_slowTrain_SA1 3.0236197\n",
+ "XTD10_slowTrain_SA3 1668.3716\n",
+ "SCS_photonFlux 0.051418982\n",
+ "SCS_photonFlux_sigma 0.0027955994\n",
+ "SCS_slowTrain 0.13026054\n",
+ "SCS_slowTrain_SA1 -0.50622654\n",
+ "SCS_slowTrain_SA3 0.16844976\n",
+ "AFS_DelayLine 240.84901428222656\n",
+ "AFS_FocusLens 131.0\n",
+ "PP800_PhaseShifter -3936.0\n",
+ "PP800_SynchDelayLine -825.388\n",
+ "PP800_DelayLine 240.84901428222656\n",
+ "PP800_HalfWP 7.0893707\n",
+ "PP800_FocusLens 131.0\n",
+ "FFT_FocusLens 22.336018\n",
+ "hRIXS_delay -0.5\n",
+ "hRIXS_index 0\n",
+ "hRIXS_norm 0.0\n",
+ "hRIXS_ABB 0.0\n",
+ "hRIXS_ABL 21.564609375\n",
+ "hRIXS_ABR 0.0\n",
+ "hRIXS_ABT 0.0\n",
+ "hRIXS_DRX -5.2644210820501485\n",
+ "hRIXS_DTY1 240.3821333740234\n",
+ "hRIXS_DTZ 4382.85261953125\n",
+ "hRIXS_GMX 208862.66475\n",
+ "hRIXS_GRX 1.6500045224951094\n",
+ "hRIXS_GTLY -0.4431999999999334\n",
+ "hRIXS_GTRY -0.5559499999999389\n",
+ "hRIXS_GTX 59.27243333333334\n",
+ "hRIXS_GTZ 1774.0199662109371\n",
+ "XRD_DRY 123.662302995\n",
+ "XRD_SRX -1.8002418199998829\n",
+ "XRD_SRY 25.37062886099997\n",
+ "XRD_SRZ 1.2223084440011007\n",
+ "XRD_STX -6.502829999999449\n",
+ "XRD_STY 0.6200250000001688\n",
+ "XRD_STZ -2.2999949999993987\n",
+ "XRD_SXT1Y 1.3053499999999758\n",
+ "XRD_SXT2Y 1.2957000000000107\n",
+ "XRD_SXTX 1.3077499999999418\n",
+ "XRD_SXTZ 4.061200000001918\n"
+ ]
+ }
+ ],
+ "source": [
+ "run_mnemonics = tb.mnemonics_for_run(run, get_run_values=True)\n",
+ "for m in run_mnemonics:\n",
+ " if run_mnemonics[m]['value'] is not None:\n",
+ " print(m, run_mnemonics[m]['value'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\">\n",
+ "\n",
+ "The run value of a source/key combination is stored at the beginning of the run. It DOES NOT show nor it checks the variations of the variable within the run and can only be representative if the value has not changed. The full check can be done with [as_single_value()](https://extra-data.readthedocs.io/en/latest/reading_files.html#extra_data.KeyData.as_single_value) function or using the `load` function described below.\n",
+ "</div>"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -116,7 +219,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -484,8 +587,8 @@
" MCP3peaks (trainId, sa3_pId) float64 -197.7 -34.67 ... -1.213e+03\n",
" SCS_SA3 (trainId, sa3_pId) float64 2.839e+03 897.9 ... 8.069e+03\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-9fd6588a-6d8d-4ffa-a5e4-0a30f40ae81c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fd6588a-6d8d-4ffa-a5e4-0a30f40ae81c' 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-a1ac8cc9-d3d1-4ed7-bba1-cdb4caedd52d' class='xr-section-summary-in' type='checkbox' checked><label for='section-a1ac8cc9-d3d1-4ed7-bba1-cdb4caedd52d' 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-475c6f77-b69a-44ef-a4dd-2d922e44e021' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-475c6f77-b69a-44ef-a4dd-2d922e44e021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06fc8160-10a7-4209-9546-cfbbef37bd5a' class='xr-var-data-in' type='checkbox'><label for='data-06fc8160-10a7-4209-9546-cfbbef37bd5a' 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-0894ec57-d93d-4630-8b77-31568e72e9cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0894ec57-d93d-4630-8b77-31568e72e9cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48cd168f-2ab1-4acb-862a-00879f2a260e' class='xr-var-data-in' type='checkbox'><label for='data-48cd168f-2ab1-4acb-862a-00879f2a260e' 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",
+ " 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-f4f29bd1-b35e-44ae-aa43-79299b9adb8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4f29bd1-b35e-44ae-aa43-79299b9adb8c' 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-b34a7208-e648-45e5-818c-41161bdb7d6b' class='xr-section-summary-in' type='checkbox' checked><label for='section-b34a7208-e648-45e5-818c-41161bdb7d6b' 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-326bb044-ccc9-4df2-8d21-5a391d864de1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-326bb044-ccc9-4df2-8d21-5a391d864de1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c436ca4-f310-4e5a-a04b-906a7c4c0575' class='xr-var-data-in' type='checkbox'><label for='data-5c436ca4-f310-4e5a-a04b-906a7c4c0575' 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-39605941-9a5c-4a5d-8cff-d1db2116f221' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39605941-9a5c-4a5d-8cff-d1db2116f221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3fc06a-eb43-458a-bf36-26c58bd96ca8' class='xr-var-data-in' type='checkbox'><label for='data-5c3fc06a-eb43-458a-bf36-26c58bd96ca8' 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",
@@ -495,15 +598,15 @@
" 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-68742532-f169-49c7-982d-3b5ef2962fac' class='xr-section-summary-in' type='checkbox' checked><label for='section-68742532-f169-49c7-982d-3b5ef2962fac' 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>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-d9140a9e-19f5-4d93-a8d0-b80fe4ca26e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9140a9e-19f5-4d93-a8d0-b80fe4ca26e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb6e5207-2903-4718-894f-7f4d597bc8ba' class='xr-var-data-in' type='checkbox'><label for='data-bb6e5207-2903-4718-894f-7f4d597bc8ba' 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",
+ " 2000, 2008, 2016, 2024, 2032])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4da57330-9074-4ee3-92c7-ea41d3ee0065' class='xr-section-summary-in' type='checkbox' checked><label for='section-4da57330-9074-4ee3-92c7-ea41d3ee0065' 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>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-1518c609-0ae2-4d11-bec5-d59c62bdf1c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1518c609-0ae2-4d11-bec5-d59c62bdf1c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1ea5b42-63b9-45a8-9b9d-64488e81029c' class='xr-var-data-in' type='checkbox'><label for='data-d1ea5b42-63b9-45a8-9b9d-64488e81029c' 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>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-d6e0b5fc-1319-4d95-99a1-54c9ddd6c5b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6e0b5fc-1319-4d95-99a1-54c9ddd6c5b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af94d504-7788-4754-a6a5-055c106af92c' class='xr-var-data-in' type='checkbox'><label for='data-af94d504-7788-4754-a6a5-055c106af92c' 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-775ac114-f6f1-4892-a739-0df13a7db2fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-775ac114-f6f1-4892-a739-0df13a7db2fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56907357-ebc7-4dbf-bd9d-61154a108cba' class='xr-var-data-in' type='checkbox'><label for='data-56907357-ebc7-4dbf-bd9d-61154a108cba' 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",
+ " dtype=uint32)</pre></div></li><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-71ee18a1-7bfa-482d-b1e7-75b771abd06b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71ee18a1-7bfa-482d-b1e7-75b771abd06b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a0dfd5f-969c-4525-903b-1b1eba8aaa6c' class='xr-var-data-in' type='checkbox'><label for='data-4a0dfd5f-969c-4525-903b-1b1eba8aaa6c' 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-c2f8e12f-d23f-4af2-8658-98673006d9b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2f8e12f-d23f-4af2-8658-98673006d9b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a2cc45a-5556-470a-8525-4f5599a1d9bb' class='xr-var-data-in' type='checkbox'><label for='data-0a2cc45a-5556-470a-8525-4f5599a1d9bb' 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",
@@ -515,7 +618,7 @@
" [-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>SCS_SA3</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'>2.839e+03 897.9 ... 8.069e+03</div><input id='attrs-790807f1-7273-47dd-a373-807d4a90b140' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-790807f1-7273-47dd-a373-807d4a90b140' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e973abe3-f710-4b2f-bba9-0df5ecd36ea7' class='xr-var-data-in' type='checkbox'><label for='data-e973abe3-f710-4b2f-bba9-0df5ecd36ea7' 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([[ 2838.68261719, 897.93481445, 1270.12817383, ...,\n",
+ " -9.43666667e+02, -2.86800000e+03, -1.21266667e+03]])</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'>float64</div><div class='xr-var-preview xr-preview'>2.839e+03 897.9 ... 8.069e+03</div><input id='attrs-4df6c63c-9191-404d-a7dc-1aa9bf94229f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4df6c63c-9191-404d-a7dc-1aa9bf94229f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc8a9aec-25d4-4600-9708-a740c489dd65' class='xr-var-data-in' type='checkbox'><label for='data-cc8a9aec-25d4-4600-9708-a740c489dd65' 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([[ 2838.68261719, 897.93481445, 1270.12817383, ...,\n",
" 33158.98046875, 19836.09570312, 27724.03515625],\n",
" [ 2088.77197266, 861.36578369, 3565.16918945, ...,\n",
" 16303.64941406, 12787.91503906, 6092.00097656],\n",
@@ -527,7 +630,7 @@
" [ 3646.75048828, 2033.26245117, 569.56018066, ...,\n",
" 9144.62402344, 7623.27978516, 4444.45361328],\n",
" [ 708.95373535, 1963.84277344, 912.64025879, ...,\n",
- " 5079.55371094, 12632.79003906, 8069.31152344]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d21710e-2503-447e-806b-3508c0edac69' class='xr-section-summary-in' type='checkbox' checked><label for='section-2d21710e-2503-447e-806b-3508c0edac69' 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>"
+ " 5079.55371094, 12632.79003906, 8069.31152344]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa1335c2-bd8b-49fe-aca0-ecbe191f1ce7' class='xr-section-summary-in' type='checkbox' checked><label for='section-aa1335c2-bd8b-49fe-aca0-ecbe191f1ce7' 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",
@@ -545,7 +648,7 @@
" runFolder: /gpfs/exfel/exp/SCS/201901/p002212/raw/r0208"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -555,6 +658,7 @@
"runNB = 208\n",
"fields = ['SCS_SA3', 'MCP3apd', 'nrj']\n",
"run, data = tb.load(proposalNB, runNB, fields)\n",
+ "run_mnemonics = tb.mnemonics_for_run(run)\n",
"data"
]
},
@@ -593,7 +697,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -959,14 +1063,14 @@
" [1499, 1502, 1508, ..., 1508, 1502, 1500]], dtype=int16)\n",
"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-04c49329-5850-46c0-9b41-0c15f47e6817' class='xr-array-in' type='checkbox' checked><label for='section-04c49329-5850-46c0-9b41-0c15f47e6817' 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",
+ "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-41b7e677-ef5b-4525-a0d4-f8a4ad5b7d51' class='xr-array-in' type='checkbox' checked><label for='section-41b7e677-ef5b-4525-a0d4-f8a4ad5b7d51' 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-3f04f9f4-e464-4d1c-848c-edcb6b3c2f8a' class='xr-section-summary-in' type='checkbox' checked><label for='section-3f04f9f4-e464-4d1c-848c-edcb6b3c2f8a' 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-3247f641-0199-4d4a-ab67-429c74de456e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3247f641-0199-4d4a-ab67-429c74de456e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cee87eb8-5a66-4ece-8bc9-b18d39370c4d' class='xr-var-data-in' type='checkbox'><label for='data-cee87eb8-5a66-4ece-8bc9-b18d39370c4d' 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-e03d72bc-82e3-43aa-af0a-c4d45c357d79' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e03d72bc-82e3-43aa-af0a-c4d45c357d79' 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>"
+ " [1499, 1502, 1508, ..., 1508, 1502, 1500]], dtype=int16)</pre></div></div></li><li class='xr-section-item'><input id='section-8ee67c60-089f-4d3e-8d92-13692715bb4f' class='xr-section-summary-in' type='checkbox' checked><label for='section-8ee67c60-089f-4d3e-8d92-13692715bb4f' 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-c73ffafe-8f91-4c80-a6b4-466b1df013de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c73ffafe-8f91-4c80-a6b4-466b1df013de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab9f245d-8166-413a-9454-f2511e7c8076' class='xr-var-data-in' type='checkbox'><label for='data-ab9f245d-8166-413a-9454-f2511e7c8076' 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-d0ebbe78-afae-477e-9a64-3a2aa40d5735' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d0ebbe78-afae-477e-9a64-3a2aa40d5735' 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",
@@ -982,7 +1086,7 @@
"Dimensions without coordinates: samplesId"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -1001,22 +1105,22 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x2b3d82127c88>]"
+ "[<matplotlib.lines.Line2D at 0x2ab5a7ab3e80>]"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1034,7 +1138,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "xfel",
+ "display_name": "xfel (Python 3.7)",
"language": "python",
"name": "xfel"
},