diff --git a/doc/Loading_data_in_memory.ipynb b/doc/Loading_data_in_memory.ipynb
index 0ed9458a7c87b47466e108247eca75858991ea0a..3e99a99e4647527033b06ae88f730030677cfbb0 100644
--- a/doc/Loading_data_in_memory.ipynb
+++ b/doc/Loading_data_in_memory.ipynb
@@ -75,7 +75,7 @@
{
"data": {
"text/plain": [
- "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'])"
+ "dict_keys(['sase3', 'sase2', 'sase1', 'laser', 'maindump', 'bunchpattern', 'bunchPatternTable', 'npulses_sase3', 'npulses_sase1', 'npulses_laser', 'BAM414', 'BAM1932M', 'BAM1932S', 'nrj', 'nrj_target', 'mono_order', 'M2BEND', 'tpi', 'VSLIT', 'ESLIT', 'HSLIT', 'transmission', 'transmission_setpoint', '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_HAMP_HV', '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_exposure', '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,
@@ -120,22 +120,26 @@
"{'sase3': array([612, 616, 620, ..., 1, 1, 1], dtype=int32),\n",
" 'sase2': array([150, 0, 0, ..., 0, 0, 0], dtype=int32),\n",
" 'sase1': array([610, 674, 738, ..., 1, 1, 1], dtype=int32),\n",
+ " 'laser': array([ 0, 40, 80, ..., 0, 0, 0], dtype=int32),\n",
" 'maindump': array([0, 2, 4, ..., 1, 1, 1], dtype=int32),\n",
" 'bunchpattern': 1,\n",
" 'bunchPatternTable': None,\n",
" 'npulses_sase3': 500,\n",
" 'npulses_sase1': 30,\n",
+ " 'npulses_laser': 22,\n",
" 'BAM414': None,\n",
" 'BAM1932M': None,\n",
" 'BAM1932S': None,\n",
" 'nrj': 927.9717888233587,\n",
" 'nrj_target': 928.0,\n",
+ " 'mono_order': 1,\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_setpoint': 2.0,\n",
" 'transmission_col2': 2.3306329751092547,\n",
" 'GATT_pressure': 0.6412954330444336,\n",
" 'UND': 0.9271398,\n",
@@ -154,6 +158,7 @@
" 'XTD10_slowTrain_SA3': 1668.3716,\n",
" 'SCS_photonFlux': 0.051418982,\n",
" 'SCS_photonFlux_sigma': 0.0027955994,\n",
+ " 'SCS_HAMP_HV': -8.5229,\n",
" 'SCS_XGM': None,\n",
" 'SCS_XGM_sigma': None,\n",
" 'SCS_SA1': None,\n",
@@ -172,6 +177,7 @@
" 'PP800_FocusLens': 131.0,\n",
" 'FFT_FocusLens': 22.336018,\n",
" 'hRIXS_det': None,\n",
+ " 'hRIXS_exposure': 10000.0,\n",
" 'hRIXS_delay': -0.5,\n",
" 'hRIXS_index': 0,\n",
" 'hRIXS_norm': 0.0,\n",
@@ -636,19 +642,17 @@
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
- "Dimensions: (pulse_slot: 2700, sa3_pId: 125, trainId: 3066)\n",
+ "Dimensions: (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",
+ " * trainId (trainId) uint64 520069541 520069542 ... 520072605 520072606\n",
+ " * sa3_pId (sa3_pId) int64 1040 1048 1056 1064 1072 ... 2008 2016 2024 2032\n",
"Data variables:\n",
- " bunchPatternTable (trainId, pulse_slot) uint32 2139945 0 2129961 ... 0 0 0\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",
- " SCS_SA3 (trainId, sa3_pId) float64 2.839e+03 897.9 ... 8.069e+03\n",
+ " nrj (trainId) float64 778.6 778.6 778.5 778.5 ... 783.4 783.4 783.4\n",
+ " MCP3peaks (trainId, sa3_pId) float64 -197.7 -34.67 ... -1.213e+03\n",
+ " SCS_SA3 (trainId, sa3_pId) float32 2838.6826 897.9348 ... 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-e91e321d-a5e0-465a-a6f4-d49d28d411bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e91e321d-a5e0-465a-a6f4-d49d28d411bf' 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-55336812-fe75-427a-ac1f-1d4cdc58f28e' class='xr-section-summary-in' type='checkbox' checked><label for='section-55336812-fe75-427a-ac1f-1d4cdc58f28e' 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-05d2039c-810a-46e1-a38f-1f36d4bb2a8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05d2039c-810a-46e1-a38f-1f36d4bb2a8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b89ee3-7785-4838-86cf-1dba70675a51' class='xr-var-data-in' type='checkbox'><label for='data-f1b89ee3-7785-4838-86cf-1dba70675a51' 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-b2784392-33cc-42a7-aee3-b418b3f52e30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2784392-33cc-42a7-aee3-b418b3f52e30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf93940a-d351-4146-8cd6-c60f0e76b7c1' class='xr-var-data-in' type='checkbox'><label for='data-bf93940a-d351-4146-8cd6-c60f0e76b7c1' 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-74989f5f-0be4-449b-803f-9a0056aecb89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74989f5f-0be4-449b-803f-9a0056aecb89' 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'>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-99327648-08f1-4329-aa2a-fcd0c6d46f47' class='xr-section-summary-in' type='checkbox' checked><label for='section-99327648-08f1-4329-aa2a-fcd0c6d46f47' 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-47b77359-053e-4be2-9f54-ad18db57e4c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b77359-053e-4be2-9f54-ad18db57e4c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59ce29ac-459f-4a85-921b-d359f86d30b5' class='xr-var-data-in' type='checkbox'><label for='data-59ce29ac-459f-4a85-921b-d359f86d30b5' 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-50765c88-a568-4024-9894-8e72afe40ead' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50765c88-a568-4024-9894-8e72afe40ead' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b89896-c2d2-40bf-ade4-38c0fbd2f38e' class='xr-var-data-in' type='checkbox'><label for='data-f4b89896-c2d2-40bf-ade4-38c0fbd2f38e' 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",
@@ -658,15 +662,8 @@
" 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-94489238-ed9f-46a1-84e6-94a0d5d9b4a2' class='xr-section-summary-in' type='checkbox' checked><label for='section-94489238-ed9f-46a1-84e6-94a0d5d9b4a2' 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-2cf62042-cf4b-4f1c-b237-fdbb0aa99a02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cf62042-cf4b-4f1c-b237-fdbb0aa99a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3c693ec-57be-48f5-81d6-e4eb4449d932' class='xr-var-data-in' type='checkbox'><label for='data-a3c693ec-57be-48f5-81d6-e4eb4449d932' 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-c6648b45-7919-4027-a792-423f45fd4e2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6648b45-7919-4027-a792-423f45fd4e2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f00076eb-fd79-417f-ab89-93592e87066b' class='xr-var-data-in' type='checkbox'><label for='data-f00076eb-fd79-417f-ab89-93592e87066b' 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-5613b72d-031a-4cd6-89e9-fad9fc58cbfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5613b72d-031a-4cd6-89e9-fad9fc58cbfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38daf398-9e96-43dc-9855-1b1c6b6f2381' class='xr-var-data-in' type='checkbox'><label for='data-38daf398-9e96-43dc-9855-1b1c6b6f2381' 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",
+ " 2000, 2008, 2016, 2024, 2032])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21892919-8222-4f6d-82dc-3e3bf7a3516b' class='xr-section-summary-in' type='checkbox' checked><label for='section-21892919-8222-4f6d-82dc-3e3bf7a3516b' class='xr-section-summary' >Data variables: <span>(3)</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-aa6b48a6-bce2-4843-a5bb-6bfbbafab4cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa6b48a6-bce2-4843-a5bb-6bfbbafab4cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af204f67-7ed6-4c56-92f5-72d3bff80ebb' class='xr-var-data-in' type='checkbox'><label for='data-af204f67-7ed6-4c56-92f5-72d3bff80ebb' 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-6ddd9669-affa-4245-9bb6-aecd3d6c1c2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ddd9669-affa-4245-9bb6-aecd3d6c1c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc46b69e-3629-442a-9b70-d1c578ed6b50' class='xr-var-data-in' type='checkbox'><label for='data-bc46b69e-3629-442a-9b70-d1c578ed6b50' 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",
@@ -678,32 +675,30 @@
" [-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-2a8005d6-406f-4029-b98f-f6055507426a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a8005d6-406f-4029-b98f-f6055507426a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3402972f-1e6e-4688-ba6a-a23dfbabee56' class='xr-var-data-in' type='checkbox'><label for='data-3402972f-1e6e-4688-ba6a-a23dfbabee56' 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",
- " [ 603.07495117, 4487.36669922, 2917.90380859, ...,\n",
- " 7453.79638672, 11550.72070312, 10727.46191406],\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'>float32</div><div class='xr-var-preview xr-preview'>2838.6826 897.9348 ... 8069.3115</div><input id='attrs-bcf81088-196b-48ed-9810-4f50fc98bae0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcf81088-196b-48ed-9810-4f50fc98bae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1fcb774-2cc6-4712-a136-cc8ff4fe87f5' class='xr-var-data-in' type='checkbox'><label for='data-e1fcb774-2cc6-4712-a136-cc8ff4fe87f5' 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",
- " [ 1868.02709961, 1402.94287109, 1433.98071289, ...,\n",
- " 7914.3984375 , 4954.84765625, 6647.52441406],\n",
- " [ 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-1be04acd-7db3-4bbc-bb4e-de697a76a57b' class='xr-section-summary-in' type='checkbox' checked><label for='section-1be04acd-7db3-4bbc-bb4e-de697a76a57b' 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>"
+ " [ 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-879a5c15-d357-443c-87c6-0f61a4de2456' class='xr-section-summary-in' type='checkbox' checked><label for='section-879a5c15-d357-443c-87c6-0f61a4de2456' 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",
+ "Dimensions: (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",
+ " * trainId (trainId) uint64 520069541 520069542 ... 520072605 520072606\n",
+ " * sa3_pId (sa3_pId) int64 1040 1048 1056 1064 1072 ... 2008 2016 2024 2032\n",
"Data variables:\n",
- " bunchPatternTable (trainId, pulse_slot) uint32 2139945 0 2129961 ... 0 0 0\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",
- " SCS_SA3 (trainId, sa3_pId) float64 2.839e+03 897.9 ... 8.069e+03\n",
+ " nrj (trainId) float64 778.6 778.6 778.5 778.5 ... 783.4 783.4 783.4\n",
+ " MCP3peaks (trainId, sa3_pId) float64 -197.7 -34.67 ... -1.213e+03\n",
+ " SCS_SA3 (trainId, sa3_pId) float32 2838.6826 897.9348 ... 8069.3115\n",
"Attributes:\n",
" runFolder: /gpfs/exfel/exp/SCS/201901/p002212/raw/r0208"
]
@@ -730,7 +725,7 @@
"\n",
"* Dimensions `pulse_slot`, `trainId`, `sa3_pId`\n",
"* Coordinates: `trainId` and `sa3_pId`: the train Id values and the SASE 3 pulse Id values.\n",
- "* Data variables: The loaded data arrays. In this example, nrj is the monochromator energy, in eV, for each train. MCP3peaks is one of the MCPs of the TIM detector, SCS_SA3 is the pulse energy of the SASE 3 pulses measured by the XGM in the SCS hutch. The bunchPatternTable is loaded by default. It is an array of 2700 values per train (the maximum number of pulses at 4.5 MHz provided by the machine) and contains information on how the pulses are distributed among SASE 1, 2, 3, and the various lasers at European XFEL. The `sa3_pId` coordinates are extracted from this table. \n",
+ "* Data variables: The loaded data arrays. In this example, nrj is the monochromator energy, in eV, for each train. MCP3peaks is one of the MCPs of the TIM detector, SCS_SA3 is the pulse energy of the SASE 3 pulses measured by the XGM in the SCS hutch. The bunchPatternTable is loaded if the number of pulses has changed during the run. It is an array of 2700 values per train (the maximum number of pulses at 4.5 MHz provided by the machine) and contains information on how the pulses are distributed among SASE 1, 2, 3, and the various lasers at European XFEL. The `sa3_pId` coordinates are extracted from this table. \n",
"* Attribute `runFolder`, the name of the folder that contains the raw files of the run. It can be accessed via: `data.attrs['runFolder']`."
]
},
@@ -1123,14 +1118,14 @@
" [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-029d8e06-5cb1-4492-8fc2-ddc17e6b3068' class='xr-array-in' type='checkbox' checked><label for='section-029d8e06-5cb1-4492-8fc2-ddc17e6b3068' 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-7d0c7184-25b1-4288-bfd9-47f3e9b4f93a' class='xr-array-in' type='checkbox' checked><label for='section-7d0c7184-25b1-4288-bfd9-47f3e9b4f93a' 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-1ff18e4d-f036-46f0-b0d1-d68ae3440ca2' class='xr-section-summary-in' type='checkbox' checked><label for='section-1ff18e4d-f036-46f0-b0d1-d68ae3440ca2' 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-7f8d26e6-f291-4868-af14-f296b995e019' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f8d26e6-f291-4868-af14-f296b995e019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b20af376-e780-44c6-9c29-51720d2d2a84' class='xr-var-data-in' type='checkbox'><label for='data-b20af376-e780-44c6-9c29-51720d2d2a84' 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-5dbd53ce-73a1-4d51-892f-20562f6aa542' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5dbd53ce-73a1-4d51-892f-20562f6aa542' 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-d123789c-c419-49e2-bf55-ef97bf2d6c34' class='xr-section-summary-in' type='checkbox' checked><label for='section-d123789c-c419-49e2-bf55-ef97bf2d6c34' 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-fc637dab-4e94-4478-8fe2-dd6fc460902d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc637dab-4e94-4478-8fe2-dd6fc460902d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0b67c0b-bef9-42f0-95df-4489f4aed951' class='xr-var-data-in' type='checkbox'><label for='data-a0b67c0b-bef9-42f0-95df-4489f4aed951' 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-a9abdb8f-8036-48fe-bdb2-659be323c556' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9abdb8f-8036-48fe-bdb2-659be323c556' 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",
@@ -1171,7 +1166,7 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x2ba30afa7550>]"
+ "[<matplotlib.lines.Line2D at 0x2b08f97ab748>]"
]
},
"execution_count": 7,
diff --git a/doc/bunch_pattern_decoding.rst b/doc/bunch_pattern_decoding.rst
index 38dc708191c9cf4c32aa60ad6b21f7b0e4dfa697..5538df610ab2863b08af96d57c2a08420b5dadd5 100644
--- a/doc/bunch_pattern_decoding.rst
+++ b/doc/bunch_pattern_decoding.rst
@@ -4,18 +4,17 @@ The data stored in the bunch pattern table (mnemonic *bunchPatternTable*) can be
.. code:: ipython3
import toolbox_scs as tb
- import toolbox_scs.misc as tbm
- proposalNB = 2511
- runNB = 176
+ proposalNB = 2953
+ runNB = 507
- run, data = tb.load(proposalNB, runNB, "bunchPatternTable")
- ppl_mask = tbm.is_ppl(data.bunchPatternTable)
+ run, ds = tb.load(proposalNB, runNB, "bunchPatternTable")
+ ppl_mask = tb.is_ppl(ds["bunchPatternTable"])
ppl_mask is a boolean DataArray of dimensions trainId x 2700, where True values indicate where a laser pulse from the PP laser was triggered.
.. note::
- The position of the PP laser pulses with respect to that of the SASE 3 pulses is arbitrary. The PP laser pattern always starts at pulse Id 0, while that of SASE 3 can vary, depending on the machine parameters.
+ The position of the PP laser pulses with respect to that of the SASE 3 pulses is arbitrary. The PP laser pattern always starts at pulse Id 0, while that of SASE 3 can vary, depending on the machine parameters. One can use the ToolBox function `align_ol_to_fel_pId()` to shift the PP laser pulse Ids to match those of SASE 3.
From this mask, one can obtain the number of pulses per train by summing along the 'pulse_slot' dimension:
@@ -23,3 +22,14 @@ From this mask, one can obtain the number of pulses per train by summing along t
ppl_npulses = ppl_mask.sum(dim='pulse_slot')
+There is also the function `get_sase_pId()` that can be used to directly extract the pulse Ids of a particular location:
+
+.. code:: ipython3
+
+ ppl_pId = tb.get_sase_pId(run, loc='scs_ppl')
+
+This provides a list of the pulse Ids used during a run but does not track changes of number of pulses. For this, the mnemonic `npulses_laser` can be loaded to get the number of pulses in each trainId and the mnemonic `laser` can be loaded to get the pulse Ids of the scs_ppl in each trainId:
+
+.. code:: ipython3
+
+ run, ds = tb.load(proposalNB, runNB, ['laser', 'npulses_laser'])
diff --git a/doc/changelog.rst b/doc/changelog.rst
index 36fe408f9872f82a3f056c6d436ffc1154e3ed6b..cc9779f352ee9d5375cc37cf60a0653a5bd9fbf0 100644
--- a/doc/changelog.rst
+++ b/doc/changelog.rst
@@ -17,6 +17,7 @@ unreleased
- Improved hRIXS class and utilities :mr:`182`
- Documentation on extracting digitizer peaks, clean up of digitizer functions :mr:`215`
- Improved peak-finding algorithm for digitizer traces :mr:`216`, :mr:`227`
+ - Only load bunch pattern table when necessary :mr:`234`
- **New Features**
diff --git a/doc/load.rst b/doc/load.rst
index 6e2df267b427567d54fd67de3acb2275c8addd26..8817c5ffaacb622c979b7c205cf6e27f8d8725d7 100644
--- a/doc/load.rst
+++ b/doc/load.rst
@@ -17,7 +17,7 @@ Loading data in memory is performed as follows:
run, data = tb.load(proposalNr, runNr, fields)
-run is an extra_data dataCollection and data is an xarray Dataset containing all variables listed in fields. For convinience, data also contains the variable bunchPatternTable, which is used by other functions of the ToolBox. All variables are aligned by train Id.
+run is an extra_data dataCollection and data is an xarray Dataset containing all variables listed in fields. All variables are aligned by train Id.
**Option 2**:
@@ -26,11 +26,10 @@ run is an extra_data dataCollection and data is an xarray Dataset containing all
import toolbox_scs as tb
# get entry for single data source defined in mnemonics
- mnemonic = tb.mnemonics["scannerX"]
proposalNr = 2565
runNr = 19
- run, _ = tb.load(proposalNr, runNr)
- data = run.get_array(*mnemonic.values())
+ run = tb.open_run(proposalNr, runNr)
+ data = tb.get_array(run, "scannerX")
run is an extra_data dataCollection and data an xarray dataArray for a single data source.
diff --git a/src/toolbox_scs/constants.py b/src/toolbox_scs/constants.py
index 4cd78b85fba9520b875c68f70bf4ce1b4fc3a37c..27b0304fe2c0a9659b8ae5b97bb13d9854c80470 100644
--- a/src/toolbox_scs/constants.py
+++ b/src/toolbox_scs/constants.py
@@ -14,6 +14,9 @@ mnemonics = {
"sase1": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key': 'sase1.pulseIds.value',
'dim': ['bunchId']},),
+ "laser": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'laser.pulseIds.value',
+ 'dim': ['bunchId']},),
"maindump": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key': 'maindump.pulseIds.value',
'dim': ['bunchId']},),
@@ -33,6 +36,9 @@ mnemonics = {
"npulses_sase1": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key': 'sase1.nPulses.value',
'dim': None},),
+ "npulses_laser": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'laser.nPulses.value',
+ 'dim': None},),
"bunchPatternTable_SA3": (
{'source': 'SA3_BR_UTC/TSYS/TIMESERVER:outputBunchPattern',
'key': 'data.bunchPatternTable',
diff --git a/src/toolbox_scs/detectors/bam_detectors.py b/src/toolbox_scs/detectors/bam_detectors.py
index 24db3ddfa01875f5ca9953eeb743b060d1823058..7cd6fb21e4509609e873ce2cc5b5d5d664f2edec 100644
--- a/src/toolbox_scs/detectors/bam_detectors.py
+++ b/src/toolbox_scs/detectors/bam_detectors.py
@@ -7,14 +7,14 @@
"""
import logging
-
-import numpy as np
import xarray as xr
from ..misc.bunch_pattern_external import is_pulse_at
-from ..mnemonics_machinery import (mnemonics_to_process,
- mnemonics_for_run)
+from ..mnemonics_machinery import mnemonics_for_run
from ..constants import mnemonics as _mnemonics
+from ..misc.bunch_pattern import (npulses_has_changed,
+ get_unique_sase_pId, load_bpt)
+from toolbox_scs.load import get_array
__all__ = [
'get_bam',
@@ -24,7 +24,110 @@ __all__ = [
log = logging.getLogger(__name__)
-def get_bam(run, mnemonics=None, merge_with=None, bunchPattern='sase3'):
+def get_bam(run, mnemonics=None, merge_with=None, bunchPattern='sase3',
+ pulseIds=None):
+ """
+ Load beam arrival monitor (BAM) data and align their pulse ID
+ according to the bunch pattern. Sources can be loaded on the fly
+ via the mnemonics argument, or processed from an existing data set
+ (merge_with).
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the bam data.
+ mnemonics: str or list of str
+ mnemonics for BAM, e.g. "BAM1932M" or ["BAM414", "BAM1932M"].
+ If None, defaults to "BAM1932M" in case no merge_with dataset
+ is provided.
+ merge_with: xarray Dataset
+ If provided, the resulting Dataset will be merged with this
+ one. The BAM variables of merge_with (if any) will also be
+ selected, aligned and merged.
+ bunchPattern: str
+ 'sase1' or 'sase3' or 'scs_ppl', bunch pattern
+ used to extract peaks. The pulse ID dimension will be named
+ 'sa1_pId', 'sa3_pId' or 'ol_pId', respectively.
+ pulseIds: list, 1D array
+ Pulse Ids. If None, they are automatically loaded.
+ Returns
+ -------
+ xarray Dataset with pulse-resolved BAM variables aligned,
+ merged with Dataset *merge_with* if provided.
+
+ Example
+ -------
+ >>> import toolbox_scs as tb
+ >>> run, ds = tb.load(2711, 303, 'BAM1932S')
+ >>> ds['BAM1932S']
+
+ """
+ bam_mnemos = ['BAM4', 'BAM1']
+ m2 = []
+ if mnemonics is not None:
+ mnemonics = [mnemonics] if isinstance(mnemonics, str) else mnemonics
+ for m in mnemonics:
+ if any([(k in m) for k in bam_mnemos]):
+ m2.append(m)
+ if merge_with is not None:
+ in_mw = []
+ for m, da in merge_with.items():
+ if any([(k in m) for k in bam_mnemos]) and 'BAMbunchId' in da.dims:
+ in_mw.append(m)
+ m2 += in_mw
+
+ if len(m2) == 0:
+ log.info('no BAM mnemonics to process. Skipping.')
+ return merge_with
+ mnemonics = list(set(m2))
+ # Prepare the dataset of non-BAM data to merge with
+ if bool(merge_with):
+ ds_mw = merge_with.drop(mnemonics, errors='ignore')
+ else:
+ ds_mw = xr.Dataset()
+
+ ds = xr.Dataset()
+ dim_names = {'sase3': 'sa3_pId', 'sase1': 'sa1_pId',
+ 'scs_ppl': 'ol_pId'}
+ run_mnemonics = mnemonics_for_run(run)
+ if pulseIds is None:
+ npulses_changed = npulses_has_changed(run, bunchPattern,
+ run_mnemonics)
+ if npulses_changed is False:
+ pulseIds = get_unique_sase_pId(run, bunchPattern,
+ run_mnemonics)
+ for m in mnemonics:
+ if merge_with is not None and m in merge_with:
+ da_bam = merge_with[m]
+ else:
+ da_bam = get_array(run, m)
+ da_bam = da_bam.sel(BAMbunchId=slice(0, 5400, 2))
+ if npulses_changed is False:
+ da_bam = da_bam.isel(BAMbunchId=pulseIds)
+ da_bam = da_bam.assign_coords(BAMbunchId=pulseIds)
+ da_bam = da_bam.rename(BAMbunchId=dim_names[bunchPattern])
+ else:
+ # align the pulse Id
+ bpt = load_bpt(run, ds_mw)
+ if bpt is not None and len(ds.variables) > 0:
+ if bpt.name not in ds_mw:
+ log.warning('Number of pulses changed during '
+ 'the run. Loading bunch pattern table.')
+ ds_mw = ds_mw.merge(bpt, join='inner')
+ mask = is_pulse_at(bpt, bunchPattern)
+ mask = mask.rename({'pulse_slot': dim_names[bunchPattern]})
+ da_bam = da_bam.rename(BAMbunchId=dim_names[bunchPattern])
+ da_bam = da_bam.where(mask, drop=True)
+ if run_mnemonics[m]['key'] != 'data.lowChargeArrivalTime':
+ da_bam *= 1e-3
+ ds = ds.merge(da_bam, join='inner')
+ # merge with non-BAM dataset
+ ds = ds_mw.merge(ds, join='inner')
+ return ds
+
+
+'''
+def get_bam_old(run, mnemonics=None, merge_with=None, bunchPattern='sase3'):
"""
Load beam arrival monitor (BAM) data and align their pulse ID
according to the bunch pattern. Sources can be loaded on the fly
@@ -117,6 +220,7 @@ def get_bam(run, mnemonics=None, merge_with=None, bunchPattern='sase3'):
ds = mw_ds.merge(ds, join='inner')
return ds
+'''
def get_bam_params(run, mnemo_or_source='BAM1932S'):
diff --git a/src/toolbox_scs/detectors/xgm.py b/src/toolbox_scs/detectors/xgm.py
index 6882fe30f49819babc56adfdfd1f717af6a764fc..cb85e276881c0489ec54eadf3b89149b46b169df 100644
--- a/src/toolbox_scs/detectors/xgm.py
+++ b/src/toolbox_scs/detectors/xgm.py
@@ -13,8 +13,10 @@ import xarray as xr
import matplotlib.pyplot as plt
from ..misc.bunch_pattern_external import is_sase_1, is_sase_3
-from ..mnemonics_machinery import (mnemonics_to_process,
- mnemonics_for_run)
+from ..misc.bunch_pattern import (npulses_has_changed,
+ get_unique_sase_pId, load_bpt)
+from ..mnemonics_machinery import mnemonics_for_run
+from toolbox_scs.load import get_array
__all__ = [
'calibrate_xgm',
@@ -25,7 +27,160 @@ __all__ = [
log = logging.getLogger(__name__)
-def get_xgm(run, mnemonics=None, merge_with=None, keepAllSase=False,
+def get_xgm(run, mnemonics=None, merge_with=None,
+ indices=slice(0, None), sase3=None):
+ """
+ Load and/or computes XGM data. Sources can be loaded on the
+ fly via the mnemonics argument, or processed from an existing dataset
+ (merge_with). The bunch pattern table is used to assign the pulse
+ id coordinates if the number of pulses has changed during the run.
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the xgm data.
+ mnemonics: str or list of str
+ mnemonics for XGM, e.g. "SCS_SA3" or ["XTD10_XGM", "SCS_XGM"].
+ If None, defaults to "SCS_SA3" in case no merge_with dataset
+ is provided.
+ merge_with: xarray Dataset
+ If provided, the resulting Dataset will be merged with this
+ one. The XGM variables of merge_with (if any) will also be
+ computed and merged.
+ indices: slice, list, 1D array
+ Pulse indices of the XGM array in case bunch pattern is missing.
+ sase3: list, 1D array
+ Pulse Ids of sase3. If None, they are automatically loaded.
+ Returns
+ -------
+ xarray Dataset with pulse-resolved XGM variables aligned,
+ merged with Dataset *merge_with* if provided.
+
+ Example
+ -------
+ >>> import toolbox_scs as tb
+ >>> run, ds = tb.load(2212, 213, 'SCS_SA3')
+ >>> ds['SCS_SA3']
+
+ """
+ xgm_mnemos = ['XTD10_SA', 'XTD10_XGM', 'SCS_SA', 'SCS_XGM']
+ m2 = []
+ if mnemonics is not None:
+ mnemonics = [mnemonics] if isinstance(mnemonics, str) else mnemonics
+ for m in mnemonics:
+ if any([(k in m) for k in xgm_mnemos]):
+ m2.append(m)
+ if merge_with is not None:
+ in_mw = []
+ for m, da in merge_with.items():
+ if any([(k in m) for k in xgm_mnemos]) and 'XGMbunchId' in da.dims:
+ in_mw.append(m)
+ m2 += in_mw
+ if len(m2) == 0:
+ log.info('no XGM mnemonics to process. Skipping.')
+ return merge_with
+ mnemonics = list(set(m2))
+ # Prepare the dataset of non-XGM data to merge with
+ if bool(merge_with):
+ ds_mw = merge_with.drop(mnemonics, errors='ignore')
+ else:
+ ds_mw = xr.Dataset()
+
+ sase1 = None
+ sase1_changed = sase3_changed = False
+ run_mnemonics = mnemonics_for_run(run)
+ ds = xr.Dataset()
+ for m in mnemonics:
+ if merge_with is not None and m in merge_with:
+ da_xgm = merge_with[m]
+ else:
+ da_xgm = get_array(run, m)
+ if sase1 is None and ('XGM' in m or 'SA1' in m):
+ sase1_changed = npulses_has_changed(run, 'sase1',
+ run_mnemonics)
+ if sase3 is None and ('XGM' in m or 'SA3' in m):
+ sase3_changed = npulses_has_changed(run, 'sase3',
+ run_mnemonics)
+ if sase3_changed is False and sase1_changed is False:
+ sase1 = get_unique_sase_pId(run, 'sase1', run_mnemonics)
+ sase3 = get_unique_sase_pId(run, 'sase3', run_mnemonics)
+ ds_xgm = load_xgm_array(run, da_xgm, m, sase1, sase3)
+ else:
+ bpt = load_bpt(run, merge_with)
+ if bpt is not None:
+ ds_xgm = align_xgm_array(da_xgm, bpt)
+ if bpt.name not in ds_mw:
+ log.warning('Number of pulses changed during '
+ 'the run. Loading bunch pattern table.')
+ ds_mw = ds_mw.merge(bpt, join='inner')
+ else:
+ xgm_val = da_xgm.values
+ xgm_val[xgm_val == 1] = np.nan
+ xgm_val[xgm_val == 0] = np.nan
+ da_xgm.values = xgm_val
+ da_xgm = da_xgm.dropna(dim='XGMbunchId', how='all')
+ ds_xgm = da_xgm.fillna(0).sel(XGMbunchId=indices).to_dataset()
+ ds = ds.merge(ds_xgm, join='inner')
+ # merge with non-BAM dataset
+ ds = ds_mw.merge(ds, join='inner')
+ return ds
+
+
+def load_xgm_array(run, xgm, mnemonic, sase1, sase3):
+ """
+ from a raw array xgm, extract and assign pulse Id coordinates
+ when the number of pulses does not change during the run.
+ If 'XGM' in mnemonic, the data is split in two variables
+ 'SA1' and 'SA3'.
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the xgm data.
+ xgm: xarray.DataArray
+ the raw XGM array
+ mnemonic: str
+ the XGM mnemonic
+ sase1: list or 1D array
+ the sase1 pulse ids
+ sase3: list or 1D array
+ the sase3 pulse ids
+
+ Returns
+ -------
+ ds_xgm: xarray.Dataset
+ the dataset containing the aligned XGM variable(s).
+ """
+ xgm_val = xgm.values
+ xgm_val[xgm_val == 1] = np.nan
+ xgm_val[xgm_val == 0] = np.nan
+ xgm.values = xgm_val
+ xgm = xgm.dropna(dim='XGMbunchId', how='all')
+ xgm = xgm.fillna(0)
+ if 'XGM' in mnemonic:
+ sase1_3 = np.sort(np.concatenate([sase1, sase3]))
+ sase1_idx = [np.argwhere(sase1_3 == i)[0][0] for i in sase1]
+ sase3_idx = [np.argwhere(sase1_3 == i)[0][0] for i in sase3]
+ xgm_sa1 = xgm.isel(XGMbunchId=sase1_idx).rename(XGMbunchId='sa1_pId')
+ xgm_sa1 = xgm_sa1.assign_coords(sa1_pId=sase1)
+ xgm_sa1 = xgm_sa1.rename(mnemonic.replace('XGM', 'SA1'))
+ xgm_sa3 = xgm.isel(XGMbunchId=sase3_idx).rename(XGMbunchId='sa3_pId')
+ xgm_sa3 = xgm_sa3.assign_coords(sa3_pId=sase3)
+ xgm_sa3 = xgm_sa3.rename(mnemonic.replace('XGM', 'SA3'))
+ xgm = xr.merge([xgm_sa1, xgm_sa3])
+ elif 'SA1' in mnemonic:
+ xgm = xgm.rename(XGMbunchId='sa1_pId')
+ xgm = xgm.assign_coords(sa1_pId=sase1).rename(mnemonic)
+ xgm = xgm.to_dataset()
+ elif 'SA3' in mnemonic:
+ xgm = xgm.rename(XGMbunchId='sa3_pId')
+ xgm = xgm.assign_coords(sa3_pId=sase3).rename(mnemonic)
+ xgm = xgm.to_dataset()
+ return xgm
+
+
+'''
+def get_xgm_old(run, mnemonics=None, merge_with=None, keepAllSase=False,
indices=slice(0, None)):
"""
Load and/or computes XGM data. Sources can be loaded on the
@@ -103,6 +258,7 @@ def get_xgm(run, mnemonics=None, merge_with=None, keepAllSase=False,
arr = arr.where(arr != 1., drop=True).sel(XGMbunchId=indices)
ds = ds.merge(arr, join='inner')
return ds
+'''
def align_xgm_array(xgm_arr, bpt):
diff --git a/src/toolbox_scs/load.py b/src/toolbox_scs/load.py
index 2673cb40473ebbcdafbe3ca1559839ca9c904c48..6374aa01e6a62138b52d4196888353c68fda0a8c 100644
--- a/src/toolbox_scs/load.py
+++ b/src/toolbox_scs/load.py
@@ -20,6 +20,8 @@ from .constants import mnemonics as _mnemonics
from .mnemonics_machinery import mnemonics_for_run
from .util.exceptions import ToolBoxValueError
import toolbox_scs.detectors as tbdet
+from .misc.bunch_pattern import (npulses_has_changed,
+ get_unique_sase_pId, load_bpt)
__all__ = [
'concatenateRuns',
@@ -55,7 +57,7 @@ def load(proposalNB=None, runNB=None,
laser_bp=None,
):
"""
- Load a run and extract the data. Output is an xarray with aligned
+ Load a run and extract the data. Output is an xarray with aligned
trainIds.
Parameters
@@ -97,9 +99,9 @@ def load(proposalNB=None, runNB=None,
'FastADC3peaks') and aligns the pulse Id according to the fadc_bp bunch
pattern.
extract_fadc2: bool
- If True, extracts the peaks from FastADC2 variables (e.g. 'FastADC2_5raw',
- 'FastADC2_3peaks') and aligns the pulse Id according to the fadc2_bp bunch
- pattern.
+ If True, extracts the peaks from FastADC2 variables (e.g.
+ 'FastADC2_5raw', 'FastADC2_3peaks') and aligns the pulse Id according
+ to the fadc2_bp bunch pattern.
extract_xgm: bool
If True, extracts the values from XGM variables (e.g. 'SCS_SA3',
'XTD10_XGM') and aligns the pulse Id with the sase1 / sase3 bunch
@@ -152,12 +154,18 @@ def load(proposalNB=None, runNB=None,
data_arrays = []
run_mnemonics = mnemonics_for_run(run)
- # load pulse pattern info
- bpt = load_bpt(run, run_mnemonics=run_mnemonics)
- if bpt is None:
- log.warning('Bunch pattern table not found in run. Skipping!')
+ # load pulse pattern info only if number of sase 3 pulses changed
+ sase3 = None
+ if npulses_has_changed(run, run_mnemonics=run_mnemonics) is False:
+ sase3 = get_unique_sase_pId(run, run_mnemonics=run_mnemonics)
else:
- data_arrays.append(bpt)
+ log.warning('Number of pulses changed during the run. '
+ 'Loading bunch pattern table.')
+ bpt = load_bpt(run, run_mnemonics=run_mnemonics)
+ if bpt is None:
+ log.warning('Bunch pattern table not found in run. Skipping!')
+ else:
+ data_arrays.append(bpt)
for f in fields:
if type(f) == dict:
@@ -216,7 +224,7 @@ def load(proposalNB=None, runNB=None,
data = xr.merge(data_arrays, join='inner')
data.attrs['runFolder'] = runFolder
-
+
# backward compatibility with old-defined variables:
if extract_tim is not None:
extract_adq412 = extract_tim
@@ -226,13 +234,14 @@ def load(proposalNB=None, runNB=None,
adq412_bp = tim_bp
if laser_bp is not None:
fadc_bp = laser_bp
-
+
adq412 = [k for k in run_mnemonics if 'MCP' in k and k in data]
if extract_adq412 and len(adq412) > 0:
- data = tbdet.get_digitizer_peaks(run, mnemonics=adq412, merge_with=data,
- bunchPattern=adq412_bp)
+ data = tbdet.get_digitizer_peaks(run, mnemonics=adq412,
+ merge_with=data,
+ bunchPattern=adq412_bp)
- fadc = [k for k in run_mnemonics if ('FastADC' in k)
+ fadc = [k for k in run_mnemonics if ('FastADC' in k)
and ('FastADC2_' not in k) and (k in data)]
if extract_fadc and len(fadc) > 0:
data = tbdet.get_digitizer_peaks(run, mnemonics=fadc, merge_with=data,
@@ -247,7 +256,8 @@ def load(proposalNB=None, runNB=None,
'SCS_SA1', 'SCS_SA1_sigma', 'SCS_SA3', 'SCS_SA3_sigma']
xgm = [k for k in xgm if k in data]
if extract_xgm and len(xgm) > 0:
- data = tbdet.get_xgm(run, mnemonics=xgm, merge_with=data)
+ data = tbdet.get_xgm(run, mnemonics=xgm, merge_with=data,
+ sase3=sase3)
bam = [k for k in run_mnemonics if 'BAM' in k and k in data]
if extract_bam and len(bam) > 0:
@@ -492,20 +502,3 @@ def concatenateRuns(runs):
for k in orderedRuns[0].attrs.keys():
result.attrs[k] = [run.attrs[k] for run in orderedRuns]
return result
-
-
-def load_bpt(run, merge_with=None, run_mnemonics=None):
- if run_mnemonics is None:
- run_mnemonics = mnemonics_for_run(run)
-
- for key in ['bunchPatternTable', 'bunchPatternTable_SA3']:
- if bool(merge_with) and key in merge_with:
- log.debug(f'Using {key} from merge_with dataset.')
- return merge_with[key]
- if key in run_mnemonics:
- bpt = run.get_array(*run_mnemonics[key].values(),
- name='bunchPatternTable')
- log.debug(f'Loaded {key} from DataCollection.')
- return bpt
- log.debug('Could not find bunch pattern table.')
- return None
diff --git a/src/toolbox_scs/misc/bunch_pattern.py b/src/toolbox_scs/misc/bunch_pattern.py
index 90deb34e53e8d58430147d980e29c367a937025f..6431fd1f648b43d5c69ef50cfc3c6c36b2a9e72c 100644
--- a/src/toolbox_scs/misc/bunch_pattern.py
+++ b/src/toolbox_scs/misc/bunch_pattern.py
@@ -1,12 +1,14 @@
# -*- coding: utf-8 -*-
""" Toolbox for SCS.
- Various utilities function to quickly process data measured at the SCS instruments.
+ Various utilities function to quickly process data
+ measured at the SCS instruments.
Copyright (2019) SCS Team.
"""
import os
+import logging
import numpy as np
import xarray as xr
@@ -15,13 +17,172 @@ from extra_data import RunDirectory
# import and hide variable, such that it does not alter namespace.
from ..constants import mnemonics as _mnemonics_bp
+from ..mnemonics_machinery import mnemonics_for_run
+from .bunch_pattern_external import is_pulse_at
__all__ = [
'extractBunchPattern',
+ 'get_sase_pId',
+ 'npulses_has_changed',
'pulsePatternInfo',
- 'repRate'
+ 'repRate',
]
+log = logging.getLogger(__name__)
+
+
+def npulses_has_changed(run, loc='sase3', run_mnemonics=None):
+ """
+ Checks if the number of pulses has changed during the run for
+ a specific location `loc` (='sase1', 'sase3', 'scs_ppl' or 'laser')
+ If the source is not found in the run, returns True.
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the data.
+ loc: str
+ The location where to check: {'sase1', 'sase3', 'scs_ppl'}
+ run_mnemonics: dict
+ the mnemonics for the run (see `menonics_for_run`)
+
+ Returns
+ -------
+ ret: bool
+ True if the number of pulses has changed or the source was not
+ found, False if the number of pulses did not change.
+ """
+ sase_list = ['sase1', 'sase3', 'laser', 'scs_ppl']
+ if loc not in sase_list:
+ raise ValueError(f"Unknow sase location '{loc}'. Expected one in"
+ f"{sase_list}")
+ if run_mnemonics is None:
+ run_mnemonics = mnemonics_for_run(run)
+ if loc == 'scs_ppl':
+ loc = 'laser'
+ if loc not in run_mnemonics:
+ return True
+ if run_mnemonics[loc]['key'] not in run[run_mnemonics[loc]['source']].keys():
+ log.info(f'Mnemonic {loc} not found in run.')
+ return True
+ npulses = run.get_array(*run_mnemonics['npulses_'+loc].values())
+ if len(np.unique(npulses)) == 1:
+ return False
+ return True
+
+
+def get_unique_sase_pId(run, loc='sase3', run_mnemonics=None):
+ """
+ Assuming that the number of pulses did not change during the run,
+ returns the pulse Ids as the run value of the sase mnemonic.
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the data.
+ loc: str
+ The location where to check: {'sase1', 'sase3', 'scs_ppl'}
+ run_mnemonics: dict
+ the mnemonics for the run (see `menonics_for_run`)
+
+ Returns
+ -------
+ pulseIds: np.array
+ the pulse ids at the specified location. Returns None if the
+ mnemonic is not in the run.
+ """
+ if run_mnemonics is None:
+ run_mnemonics = mnemonics_for_run(run)
+ if loc == 'scs_ppl':
+ loc = 'laser'
+ if loc not in run_mnemonics:
+ # bunch pattern not recorded
+ return None
+ npulses = run.get_run_value(run_mnemonics['npulses_'+loc]['source'],
+ run_mnemonics['npulses_'+loc]['key'])
+ pulseIds = run.get_run_value(run_mnemonics[loc]['source'],
+ run_mnemonics[loc]['key'])[:npulses]
+ return pulseIds
+
+
+def get_sase_pId(run, loc='sase3', run_mnemonics=None,
+ bpt=None, merge_with=None):
+ """
+ Returns the pulse Ids of the specified `loc` during a run.
+ If the number of pulses has changed during the run, it loads the
+ bunch pattern table and extract all pulse Ids used.
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the data.
+ loc: str
+ The location where to check: {'sase1', 'sase3', 'scs_ppl'}
+ run_mnemonics: dict
+ the mnemonics for the run (see `menonics_for_run`)
+ bpt: 2D-array
+ The bunch pattern table. Used only if the number of pulses
+ has changed. If None, it is loaded on the fly.
+ merge_with: xarray.Dataset
+ dataset that may contain the bunch pattern table to use in
+ case the number of pulses has changed. If merge_with does
+ not contain the bunch pattern table, it is loaded and added
+ as a variable 'bunchPatternTable' to merge_with.
+
+ Returns
+ -------
+ pulseIds: np.array
+ the pulse ids at the specified location. Returns None if the
+ mnemonic is not in the run.
+ """
+ if npulses_has_changed(run, loc, run_mnemonics) is False:
+ return get_unique_sase_pId(run, loc, run_mnemonics)
+ if bpt is None:
+ bpt = load_bpt(run, merge_with, run_mnemonics)
+ if bpt is not None:
+ mask = is_pulse_at(bpt, loc)
+ return np.unique(np.nonzero(mask.values)[1])
+ return None
+
+
+def load_bpt(run, merge_with=None, run_mnemonics=None):
+ """
+ Load the bunch pattern table. It returns the one contained in
+ merge_with if possible. Or, it adds it to merge_with once it is
+ loaded.
+
+ Parameters
+ ----------
+ run: extra_data.DataCollection
+ DataCollection containing the data.
+ merge_with: xarray.Dataset
+ dataset that may contain the bunch pattern table or to which
+ add the bunch pattern table once loaded.
+ run_mnemonics: dict
+ the mnemonics for the run (see `menonics_for_run`)
+
+ Returns
+ -------
+ bpt: xarray.Dataset
+ the bunch pattern table as specified by the mnemonics
+ 'bunchPatternTable'
+ """
+ if run_mnemonics is None:
+ run_mnemonics = mnemonics_for_run(run)
+ for key in ['bunchPatternTable', 'bunchPatternTable_SA3']:
+ if merge_with is not None and key in merge_with:
+ log.debug(f'Using {key} from merge_with dataset.')
+ return merge_with[key]
+ if key in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics[key].values(),
+ name='bunchPatternTable')
+ log.debug(f'Loaded {key} from DataCollection.')
+ if merge_with is not None:
+ merge_with = merge_with.merge(bpt, join='inner')
+ return bpt
+ log.debug('Could not find bunch pattern table.')
+ return None
+
def extractBunchPattern(bp_table=None, key='sase3', runDir=None):
''' generate the bunch pattern and number of pulses of a source directly from the
@@ -177,13 +338,16 @@ def repRate(data=None, runNB=None, proposalNB=None, key='sase3'):
''' Calculates the pulse repetition rate (in kHz) in sase
according to the bunch pattern and assuming a grid of
4.5 MHz.
+
Inputs:
+ -------
data: xarray Dataset containing pulse pattern, needed if runNB is none
runNB: int or str, run number. Needed if data is None
proposal: int or str, proposal where to find the run. Needed if data is None
key: str in [sase1, sase2, sase3, scs_ppl], source for which the
repetition rate is calculated
Output:
+ -------
f: repetition rate in kHz
'''
if runNB is None and data is None: