From 8c7e568c25da551bb19264f9c00748c4d1886753 Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Tue, 4 May 2021 21:38:21 +0200
Subject: [PATCH] Add mnemonic machinery to take into account mnemonics
versions
---
doc/Loading_data_in_memory.ipynb | 65 +-
src/toolbox_scs/__init__.py | 4 +-
src/toolbox_scs/constants.py | 718 +++++++++++----------
src/toolbox_scs/detectors/bam_detectors.py | 19 +-
src/toolbox_scs/detectors/digitizers.py | 42 +-
src/toolbox_scs/detectors/xgm.py | 26 +-
src/toolbox_scs/load.py | 157 ++---
src/toolbox_scs/mnemonics_machinery.py | 145 +++++
8 files changed, 657 insertions(+), 519 deletions(-)
create mode 100644 src/toolbox_scs/mnemonics_machinery.py
diff --git a/doc/Loading_data_in_memory.ipynb b/doc/Loading_data_in_memory.ipynb
index b553347..20b40d9 100644
--- a/doc/Loading_data_in_memory.ipynb
+++ b/doc/Loading_data_in_memory.ipynb
@@ -31,9 +31,9 @@
{
"data": {
"text/plain": [
- "{'source': 'SCS_BLU_XGM/XGM/DOOCS:output',\n",
- " 'key': 'data.intensityTD',\n",
- " 'dim': ['XGMbunchId']}"
+ "({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',\n",
+ " 'key': 'data.intensityTD',\n",
+ " 'dim': ['XGMbunchId']},)"
]
},
"execution_count": 1,
@@ -50,7 +50,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The available mnemonics can be display with: `print(list(tb.mnemonics.keys()))`."
+ "The list of available mnemonics can vary from run to run, depending on what sources were recorded. The function `mnemonics_for_run` returns the mnemonics that correspond to actual data sources in a run (`extra_data` `DataCollection`):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run, _ = tb.load(2212, 213)\n",
+ "run_mnemonics = tb.mnemonics_for_run(run)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\">\n",
+ "\n",
+ "The mnemonics are by no means an exhaustive list of the contents of a run, but rather convenience shortcuts to the mostly used data sources at SCS. Please refer to the [extra_data](https://extra-data.readthedocs.io/en/latest/) package to access the full list of data sources present in a run.\n",
+ "\n",
+ "</div>"
]
},
{
@@ -71,7 +92,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -439,8 +460,8 @@
" 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-d190895c-429f-49c5-b6d3-a746eeba5ee7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d190895c-429f-49c5-b6d3-a746eeba5ee7' 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-4b85fc5e-de13-43f7-963b-b77286fd62b5' class='xr-section-summary-in' type='checkbox' checked><label for='section-4b85fc5e-de13-43f7-963b-b77286fd62b5' 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-07fa6d2e-5dd2-4fed-9bc9-4bddb9616c4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07fa6d2e-5dd2-4fed-9bc9-4bddb9616c4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b328c4e-f541-4092-8778-ce46b92ce7e0' class='xr-var-data-in' type='checkbox'><label for='data-3b328c4e-f541-4092-8778-ce46b92ce7e0' 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-7e7b0279-e00c-4e72-b9b9-cf39b39185ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e7b0279-e00c-4e72-b9b9-cf39b39185ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4074543-0528-49db-8695-cd0f8b95bab2' class='xr-var-data-in' type='checkbox'><label for='data-e4074543-0528-49db-8695-cd0f8b95bab2' 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-5749d5d4-4669-43a9-bf34-bd10d1445694' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5749d5d4-4669-43a9-bf34-bd10d1445694' 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-abec55c2-d5c0-4e22-82ba-37ea68bd1b34' class='xr-section-summary-in' type='checkbox' checked><label for='section-abec55c2-d5c0-4e22-82ba-37ea68bd1b34' 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-e9c66ef0-ab66-4116-b9ab-c958ace198d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9c66ef0-ab66-4116-b9ab-c958ace198d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eef747e7-31e0-4cbc-9417-41afa22ac2ab' class='xr-var-data-in' type='checkbox'><label for='data-eef747e7-31e0-4cbc-9417-41afa22ac2ab' 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-375b6fcb-7273-4f21-9288-68533d2a75d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-375b6fcb-7273-4f21-9288-68533d2a75d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd1c96a-68b6-40d8-8e0f-bf7e75dbbd77' class='xr-var-data-in' type='checkbox'><label for='data-4dd1c96a-68b6-40d8-8e0f-bf7e75dbbd77' 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",
@@ -450,15 +471,15 @@
" 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-0744a4dc-6a08-4bd6-a4ec-28ab10915d87' class='xr-section-summary-in' type='checkbox' checked><label for='section-0744a4dc-6a08-4bd6-a4ec-28ab10915d87' 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-4a316316-f1a1-4c1c-9e40-6be175d58c3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a316316-f1a1-4c1c-9e40-6be175d58c3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3998e9f3-e48a-4d1b-80f6-3cdc00fab4c8' class='xr-var-data-in' type='checkbox'><label for='data-3998e9f3-e48a-4d1b-80f6-3cdc00fab4c8' 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-2bf2a4bf-5efc-4328-90f8-6434c99e0d82' class='xr-section-summary-in' type='checkbox' checked><label for='section-2bf2a4bf-5efc-4328-90f8-6434c99e0d82' 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-c6f3163a-6ff8-46d0-896d-84f4a30a1274' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6f3163a-6ff8-46d0-896d-84f4a30a1274' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f42782b-c07b-491c-aab0-bad6d85b08a5' class='xr-var-data-in' type='checkbox'><label for='data-9f42782b-c07b-491c-aab0-bad6d85b08a5' 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-3133f05d-246a-498b-bbd1-a695804ca7aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3133f05d-246a-498b-bbd1-a695804ca7aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a408860-cf45-47b6-8d6c-bd86b50714b4' class='xr-var-data-in' type='checkbox'><label for='data-8a408860-cf45-47b6-8d6c-bd86b50714b4' 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-5734ac8d-335a-4815-9ee2-78fb4203170e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5734ac8d-335a-4815-9ee2-78fb4203170e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae539ba8-cec2-4f31-b1b7-2c3813777b7c' class='xr-var-data-in' type='checkbox'><label for='data-ae539ba8-cec2-4f31-b1b7-2c3813777b7c' 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-160a6c0f-b0f4-436b-a1d7-e391ce9be176' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-160a6c0f-b0f4-436b-a1d7-e391ce9be176' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7477817-0648-4b12-b543-4468c4bb0021' class='xr-var-data-in' type='checkbox'><label for='data-d7477817-0648-4b12-b543-4468c4bb0021' 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-aa041bc0-149c-49c6-b91a-5b1d2601beeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa041bc0-149c-49c6-b91a-5b1d2601beeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c711b983-98a1-4331-a8f6-cf6d597093fa' class='xr-var-data-in' type='checkbox'><label for='data-c711b983-98a1-4331-a8f6-cf6d597093fa' 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",
@@ -470,7 +491,7 @@
" [-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-8d82e392-da97-41ca-83db-d29849d6dd1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d82e392-da97-41ca-83db-d29849d6dd1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49ac9135-f9d9-43a0-95da-0a48aff881c3' class='xr-var-data-in' type='checkbox'><label for='data-49ac9135-f9d9-43a0-95da-0a48aff881c3' 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-a2a05ecd-eb49-4e87-9483-3a5bd014bcf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2a05ecd-eb49-4e87-9483-3a5bd014bcf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a20bc7cc-3de5-4cfd-ae37-0436989b734f' class='xr-var-data-in' type='checkbox'><label for='data-a20bc7cc-3de5-4cfd-ae37-0436989b734f' 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",
@@ -482,7 +503,7 @@
" [ 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-7ad05c3b-49ee-497e-9b89-5cd235a25de5' class='xr-section-summary-in' type='checkbox' checked><label for='section-7ad05c3b-49ee-497e-9b89-5cd235a25de5' 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-cda1897f-d27d-40dd-a0fb-5ca7d116fb6b' class='xr-section-summary-in' type='checkbox' checked><label for='section-cda1897f-d27d-40dd-a0fb-5ca7d116fb6b' 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",
@@ -500,7 +521,7 @@
" runFolder: /gpfs/exfel/exp/SCS/201901/p002212/raw/r0208"
]
},
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -548,7 +569,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -914,14 +935,14 @@
" [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-797922ee-1be5-4715-99a3-b1e48d2a750d' class='xr-array-in' type='checkbox' checked><label for='section-797922ee-1be5-4715-99a3-b1e48d2a750d' 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-0d8dea0c-7010-4a3f-82b7-4dc59ecb6bb5' class='xr-array-in' type='checkbox' checked><label for='section-0d8dea0c-7010-4a3f-82b7-4dc59ecb6bb5' 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-8a9e5391-7fe8-4d6a-9deb-c98f7b93332a' class='xr-section-summary-in' type='checkbox' checked><label for='section-8a9e5391-7fe8-4d6a-9deb-c98f7b93332a' 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-9435069b-6d74-409c-aed3-ac5294cce5f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9435069b-6d74-409c-aed3-ac5294cce5f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10417c8c-1a86-44b8-9e3b-d79d616b5eff' class='xr-var-data-in' type='checkbox'><label for='data-10417c8c-1a86-44b8-9e3b-d79d616b5eff' 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-4020eff5-2cf5-4308-9874-0461d72a2239' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4020eff5-2cf5-4308-9874-0461d72a2239' 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-f974c2c1-cd6d-4d6f-929f-17f8ffe396ca' class='xr-section-summary-in' type='checkbox' checked><label for='section-f974c2c1-cd6d-4d6f-929f-17f8ffe396ca' 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-adb662e7-d59a-4dcc-87bf-b4cb24492252' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adb662e7-d59a-4dcc-87bf-b4cb24492252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a9f5877-8c70-4e0e-90f0-f06830c55b69' class='xr-var-data-in' type='checkbox'><label for='data-6a9f5877-8c70-4e0e-90f0-f06830c55b69' 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-c90f8620-2a1d-4d41-9738-96bc8a4f4bc7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c90f8620-2a1d-4d41-9738-96bc8a4f4bc7' 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",
@@ -937,13 +958,13 @@
"Dimensions without coordinates: samplesId"
]
},
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "raw_traces = run.get_array(*tb.mnemonics['MCP2raw'].values())\n",
+ "raw_traces = run.get_array(*run_mnemonics['MCP2raw'].values())\n",
"raw_traces"
]
},
@@ -956,16 +977,16 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x2ae2a8e23f60>]"
+ "[<matplotlib.lines.Line2D at 0x2aef559eed68>]"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
diff --git a/src/toolbox_scs/__init__.py b/src/toolbox_scs/__init__.py
index 47d2252..f1acd89 100644
--- a/src/toolbox_scs/__init__.py
+++ b/src/toolbox_scs/__init__.py
@@ -1,6 +1,6 @@
from .load import (load, concatenateRuns, get_array, run_by_path)
-
from .constants import mnemonics
+from .mnemonics_machinery import mnemonics_for_run
__all__ = (
# functions
@@ -8,6 +8,7 @@ __all__ = (
"concatenateRuns",
"get_array",
"run_by_path",
+ "mnemonics_for_run",
# Classes
# Variables
"mnemonics",
@@ -22,6 +23,7 @@ __all__ = (
clean_ns = [
# filenames
'constants',
+ 'mnemonics_machinery'
# folders
'misc',
'util',
diff --git a/src/toolbox_scs/constants.py b/src/toolbox_scs/constants.py
index 72eee76..b4ad8c0 100644
--- a/src/toolbox_scs/constants.py
+++ b/src/toolbox_scs/constants.py
@@ -1,281 +1,303 @@
mnemonics = {
# Machine
- "sase3": {'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
- 'key': 'sase3.pulseIds.value',
- 'dim': ['bunchId']},
- "sase2": {'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
- 'key': 'sase2.pulseIds.value',
- 'dim': ['bunchId']},
- "sase1": {'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
- 'key': 'sase1.pulseIds.value',
- 'dim': ['bunchId']},
- "maindump": {'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
- 'key': 'maindump.pulseIds.value',
- 'dim': ['bunchId']},
- "bunchpattern": {'source': 'SCS_RR_UTC/TSYS/TIMESERVER',
- 'key': 'readBunchPatternTable.value',
- 'dim': None},
- "bunchPatternTable": {'source': 'SCS_RR_UTC/TSYS/TIMESERVER',
- 'key': 'bunchPatternTable.value',
- 'dim': ['pulse_slot']},
- "npulses_sase3": {'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
- 'key': 'sase3.nPulses.value',
- 'dim': None},
- "npulses_sase1": {'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
- 'key': 'sase1.nPulses.value',
- 'dim': None},
- "bunchPatternTable_SA3": {
- 'source': 'SA3_BR_UTC/TSYS/TIMESERVER:outputBunchPattern',
- 'key': 'data.bunchPatternTable',
- 'dim': ['pulse_slot']},
+ "sase3": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'sase3.pulseIds.value',
+ 'dim': ['bunchId']},),
+ "sase2": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'sase2.pulseIds.value',
+ 'dim': ['bunchId']},),
+ "sase1": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'sase1.pulseIds.value',
+ 'dim': ['bunchId']},),
+ "maindump": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'maindump.pulseIds.value',
+ 'dim': ['bunchId']},),
+ "bunchpattern": ({'source': 'SCS_RR_UTC/TSYS/TIMESERVER',
+ 'key': 'readBunchPatternTable.value',
+ 'dim': None},),
+ "bunchPatternTable": (
+ {'source': 'SCS_RR_UTC/TSYS/TIMESERVER:outputBunchPattern',
+ 'key': 'data.bunchPatternTable',
+ 'dim': ['pulse_slot']},
+ {'source': 'SCS_RR_UTC/TSYS/TIMESERVER',
+ 'key': 'bunchPatternTable.value',
+ 'dim': ['pulse_slot']}),
+ "npulses_sase3": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'sase3.nPulses.value',
+ 'dim': None},),
+ "npulses_sase1": ({'source': 'SCS_RR_UTC/MDL/BUNCH_DECODER',
+ 'key': 'sase1.nPulses.value',
+ 'dim': None},),
+ "bunchPatternTable_SA3": (
+ {'source': 'SA3_BR_UTC/TSYS/TIMESERVER:outputBunchPattern',
+ 'key': 'data.bunchPatternTable',
+ 'dim': ['pulse_slot']},),
# Bunch Arrival Monitors
- "BAM414": {'source': 'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
- 'key': 'data.lowChargeArrivalTime',
- 'dim': ['BAMbunchId']},
- "BAM1932M": {'source': 'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
- 'key': 'data.lowChargeArrivalTime',
- 'dim': ['BAMbunchId']},
- "BAM1932S": {'source': 'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
- 'key': 'data.lowChargeArrivalTime',
- 'dim': ['BAMbunchId']},
+ "BAM414": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
+ 'key': 'data.lowChargeArrivalTime',
+ 'dim': ['BAMbunchId']},),
+ "BAM1932M": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
+ 'key': 'data.lowChargeArrivalTime',
+ 'dim': ['BAMbunchId']},),
+ "BAM1932S": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
+ 'key': 'data.lowChargeArrivalTime',
+ 'dim': ['BAMbunchId']},),
# SA3
- "nrj": {'source': 'SA3_XTD10_MONO/MDL/PHOTON_ENERGY',
- 'key': 'actualEnergy.value',
- 'dim': None},
+ "nrj": ({'source': 'SA3_XTD10_MONO/MDL/PHOTON_ENERGY',
+ 'key': 'actualEnergy.value',
+ 'dim': None},),
- "M2BEND": {'source': 'SA3_XTD10_MIRR-2/MOTOR/BENDER',
- 'key': 'actualPosition.value',
- 'dim': None},
- "VSLIT": {'source': 'SA3_XTD10_VSLIT/MDL/BLADE',
- 'key': 'actualGap.value',
- 'dim': None},
- "ESLIT": {'source': 'SCS_XTD10_ESLIT/MDL/MAIN',
- 'key': 'actualGap.value',
- 'dim': None},
- "HSLIT": {'source': 'SCS_XTD10_HSLIT/MDL/BLADE',
- 'key': 'actualGap.value',
- 'dim': None},
- "transmission": {'source': 'SA3_XTD10_GATT/MDL/GATT_TRANSMISSION_MONITOR',
- 'key': 'Estimated_Tr.value',
- 'dim': None},
- "GATT_pressure": {'source': 'P_GATT',
- 'key': 'value.value',
- 'dim': None},
- "navitar": {'source': 'SCS_XTD10_IMGES/CAM/BEAMVIEW_NAVITAR:daqOutput',
- 'key': 'data.image.pixels',
- 'dim': ['x', 'y']},
- "UND": {'source': 'SA3_XTD10_UND/DOOCS/PHOTON_ENERGY',
- 'key': 'actualPosition.value',
- 'dim': None},
- # DPS imagers
- "DPS2CAM2": {'source': 'SCS_BLU_DPS-2/CAM/IMAGER2CAMERA:daqOutput',
+ "M2BEND": ({'source': 'SA3_XTD10_MIRR-2/MOTOR/BENDER',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "VSLIT": ({'source': 'SA3_XTD10_VSLIT/MDL/BLADE',
+ 'key': 'actualGap.value',
+ 'dim': None},),
+ "ESLIT": ({'source': 'SCS_XTD10_ESLIT/MDL/MAIN',
+ 'key': 'actualGap.value',
+ 'dim': None},),
+ "HSLIT": ({'source': 'SCS_XTD10_HSLIT/MDL/BLADE',
+ 'key': 'actualGap.value',
+ 'dim': None},),
+ "transmission": ({'source': 'SA3_XTD10_GATT/MDL/GATT_TRANSMISSION_MONITOR',
+ 'key': 'Estimated_Tr.value',
+ 'dim': None},),
+ "GATT_pressure": ({'source': 'P_GATT',
+ 'key': 'value.value',
+ 'dim': None},),
+ "navitar": ({'source': 'SCS_XTD10_IMGES/CAM/BEAMVIEW_NAVITAR:daqOutput',
'key': 'data.image.pixels',
- 'dim': ['dps2cam2_y', 'dps2cam2_x']},
+ 'dim': ['x', 'y']},),
+ "UND": ({'source': 'SA3_XTD10_UND/DOOCS/PHOTON_ENERGY',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ # DPS imagers
+ "DPS1CAM2": ({'source': 'SCS_BLU_DPS-1/CAM/IMAGER2CAMERA:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['dps1cam2_y', 'dps1cam2_x']},),
+ "DPS2CAM2": ({'source': 'SCS_BLU_DPS-2/CAM/IMAGER2CAMERA:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['dps2cam2_y', 'dps2cam2_x']},),
# XTD10 XGM
# keithley
- "XTD10_photonFlux": {'source': 'SA3_XTD10_XGM/XGM/DOOCS',
- 'key': 'pulseEnergy.photonFlux.value',
- 'dim': None},
- "XTD10_photonFlux_sigma": {'source': 'SA3_XTD10_XGM/XGM/DOOCS',
- 'key': 'pulseEnergy.photonFluxSigma.value',
- 'dim': None},
+ "XTD10_photonFlux": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS',
+ 'key': 'pulseEnergy.photonFlux.value',
+ 'dim': None},),
+ "XTD10_photonFlux_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS',
+ 'key': 'pulseEnergy.photonFluxSigma.value',
+ 'dim': None},),
# ADC
- "XTD10_XGM": {'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
- 'key': 'data.intensityTD',
- 'dim': ['XGMbunchId']},
- "XTD10_XGM_sigma": {'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySigmaTD',
- 'dim': ['XGMbunchId']},
- "XTD10_SA3": {'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa3TD',
- 'dim': ['XGMbunchId']},
- "XTD10_SA3_sigma": {'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa3SigmaTD',
- 'dim': ['XGMbunchId']},
- "XTD10_SA1": {'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa1TD',
- 'dim': ['XGMbunchId']},
- "XTD10_SA1_sigma": {'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa1SigmaTD',
- 'dim': ['XGMbunchId']},
+ "XTD10_XGM": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensityTD',
+ 'dim': ['XGMbunchId']},),
+ "XTD10_XGM_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySigmaTD',
+ 'dim': ['XGMbunchId']},),
+ "XTD10_SA3": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa3TD',
+ 'dim': ['XGMbunchId']},),
+ "XTD10_SA3_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa3SigmaTD',
+ 'dim': ['XGMbunchId']},),
+ "XTD10_SA1": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa1TD',
+ 'dim': ['XGMbunchId']},),
+ "XTD10_SA1_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa1SigmaTD',
+ 'dim': ['XGMbunchId']},),
# low pass averaged ADC
- "XTD10_slowTrain": {'source': 'SA3_XTD10_XGM/XGM/DOOCS',
- 'key': 'controlData.slowTrain.value',
- 'dim': None},
- "XTD10_slowTrain_SA1": {'source': 'SA3_XTD10_XGM/XGM/DOOCS',
- 'key': 'controlData.slowTrainSa1.value',
- 'dim': None},
- "XTD10_slowTrain_SA3": {'source': 'SA3_XTD10_XGM/XGM/DOOCS',
- 'key': 'controlData.slowTrainSa3.value',
- 'dim': None},
+ "XTD10_slowTrain": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS',
+ 'key': 'controlData.slowTrain.value',
+ 'dim': None},),
+ "XTD10_slowTrain_SA1": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS',
+ 'key': 'controlData.slowTrainSa1.value',
+ 'dim': None},),
+ "XTD10_slowTrain_SA3": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS',
+ 'key': 'controlData.slowTrainSa3.value',
+ 'dim': None},),
# SCS XGM
# keithley
- "SCS_photonFlux": {'source': 'SCS_BLU_XGM/XGM/DOOCS',
- 'key': 'pulseEnergy.photonFlux.value',
- 'dim': None},
- "SCS_photonFlux_sigma": {'source': 'SCS_BLU_XGM/XGM/DOOCS',
- 'key': 'pulseEnergy.photonFluxSigma.value',
- 'dim': None},
+ "SCS_photonFlux": ({'source': 'SCS_BLU_XGM/XGM/DOOCS',
+ 'key': 'pulseEnergy.photonFlux.value',
+ 'dim': None},),
+ "SCS_photonFlux_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS',
+ 'key': 'pulseEnergy.photonFluxSigma.value',
+ 'dim': None},),
# ADC
- "SCS_XGM": {'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
- 'key': 'data.intensityTD',
- 'dim': ['XGMbunchId']},
- "SCS_XGM_sigma": {'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySigmaTD',
- 'dim': ['XGMbunchId']},
- "SCS_SA1": {'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa1TD',
- 'dim': ['XGMbunchId']},
- "SCS_SA1_sigma": {'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa1SigmaTD',
- 'dim': ['XGMbunchId']},
- "SCS_SA3": {'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa3TD',
- 'dim': ['XGMbunchId']},
- "SCS_SA3_sigma": {'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
- 'key': 'data.intensitySa3SigmaTD',
- 'dim': ['XGMbunchId']},
+ "SCS_XGM": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensityTD',
+ 'dim': ['XGMbunchId']},),
+ "SCS_XGM_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySigmaTD',
+ 'dim': ['XGMbunchId']},),
+ "SCS_SA1": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa1TD',
+ 'dim': ['XGMbunchId']},),
+ "SCS_SA1_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa1SigmaTD',
+ 'dim': ['XGMbunchId']},),
+ "SCS_SA3": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa3TD',
+ 'dim': ['XGMbunchId']},),
+ "SCS_SA3_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
+ 'key': 'data.intensitySa3SigmaTD',
+ 'dim': ['XGMbunchId']},),
# low pass averaged ADC
- "SCS_slowTrain": {'source': 'SCS_BLU_XGM/XGM/DOOCS',
- 'key': 'controlData.slowTrain.value',
- 'dim': None},
- "SCS_slowTrain_SA1": {'source': 'SCS_BLU_XGM/XGM/DOOCS',
- 'key': 'controlData.slowTrainSa1.value',
- 'dim': None},
- "SCS_slowTrain_SA3": {'source': 'SCS_BLU_XGM/XGM/DOOCS',
- 'key': 'controlData.slowTrainSa3.value',
- 'dim': None},
+ "SCS_slowTrain": ({'source': 'SCS_BLU_XGM/XGM/DOOCS',
+ 'key': 'controlData.slowTrain.value',
+ 'dim': None},),
+ "SCS_slowTrain_SA1": ({'source': 'SCS_BLU_XGM/XGM/DOOCS',
+ 'key': 'controlData.slowTrainSa1.value',
+ 'dim': None},),
+ "SCS_slowTrain_SA3": ({'source': 'SCS_BLU_XGM/XGM/DOOCS',
+ 'key': 'controlData.slowTrainSa3.value',
+ 'dim': None},),
# KBS
- "HFM_CAPB": {'source': 'SCS_KBS_HFM/ASENS/CAPB',
- 'key': 'value.value',
- 'dim': None},
- "HFM_CAPF": {'source': 'SCS_KBS_HFM/ASENS/CAPF',
- 'key': 'value.value',
- 'dim': None},
- "HFM_CAPM": {'source': 'SCS_KBS_HFM/ASENS/CAPM',
- 'key': 'value.value',
- 'dim': None},
- "HFM_BENDERB": {'source': 'SCS_KBS_HFM/MOTOR/BENDERB',
- 'key': 'encoderPosition.value',
- 'dim': None},
- "HFM_BENDERF": {'source': 'SCS_KBS_HFM/MOTOR/BENDERF',
- 'key': 'encoderPosition.value',
- 'dim': None},
- "VFM_CAPB": {'source': 'SCS_KBS_VFM/ASENS/CAPB',
- 'key': 'value.value',
- 'dim': None},
- "VFM_CAPF": {'source': 'SCS_KBS_VFM/ASENS/CAPF',
- 'key': 'value.value',
- 'dim': None},
- "VFM_CAPM": {'source': 'SCS_KBS_VFM/ASENS/CAPM',
- 'key': 'value.value',
- 'dim': None},
- "VFM_BENDERB": {'source': 'SCS_KBS_VFM/MOTOR/BENDERB',
- 'key': 'encoderPosition.value',
- 'dim': None},
- "VFM_BENDERF": {'source': 'SCS_KBS_VFM/MOTOR/BENDERF',
- 'key': 'encoderPosition.value',
- 'dim': None},
+ "HFM_CAPB": ({'source': 'SCS_KBS_HFM/ASENS/CAPB',
+ 'key': 'value.value',
+ 'dim': None},),
+ "HFM_CAPF": ({'source': 'SCS_KBS_HFM/ASENS/CAPF',
+ 'key': 'value.value',
+ 'dim': None},),
+ "HFM_CAPM": ({'source': 'SCS_KBS_HFM/ASENS/CAPM',
+ 'key': 'value.value',
+ 'dim': None},),
+ "HFM_BENDERB": ({'source': 'SCS_KBS_HFM/MOTOR/BENDERB',
+ 'key': 'encoderPosition.value',
+ 'dim': None},),
+ "HFM_BENDERF": ({'source': 'SCS_KBS_HFM/MOTOR/BENDERF',
+ 'key': 'encoderPosition.value',
+ 'dim': None},),
+ "VFM_CAPB": ({'source': 'SCS_KBS_VFM/ASENS/CAPB',
+ 'key': 'value.value',
+ 'dim': None},),
+ "VFM_CAPF": ({'source': 'SCS_KBS_VFM/ASENS/CAPF',
+ 'key': 'value.value',
+ 'dim': None},),
+ "VFM_CAPM": ({'source': 'SCS_KBS_VFM/ASENS/CAPM',
+ 'key': 'value.value',
+ 'dim': None},),
+ "VFM_BENDERB": ({'source': 'SCS_KBS_VFM/MOTOR/BENDERB',
+ 'key': 'encoderPosition.value',
+ 'dim': None},),
+ "VFM_BENDERF": ({'source': 'SCS_KBS_VFM/MOTOR/BENDERF',
+ 'key': 'encoderPosition.value',
+ 'dim': None},),
# AFS LASER
- "AFS_PhaseShifter": {'source': 'SCS_ILH_LAS/PHASESHIFTER/DOOCS',
- 'key': 'actualPosition.value',
- 'dim': None},
- "AFS_DelayLine": {'source': 'SCS_ILH_LAS/MOTOR/LT3',
- 'key': 'actualPosition.value',
- 'dim': None},
- "AFS_HalfWP": {'source': 'SCS_ILH_LAS/MOTOR/ROT_OPA_BWP1',
- 'key': 'actualPosition.value',
- 'dim': None},
- "AFS_FocusLens": {'source': 'SCS_ILH_LAS/MOTOR/LT_SPARE1',
- 'key': 'actualPosition.value',
- 'dim': None},
+ "AFS_PhaseShifter": ({'source': 'SCS_ILH_LAS/PHASESHIFTER/DOOCS',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "AFS_DelayLine": ({'source': 'SCS_ILH_LAS/MOTOR/LT3',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "AFS_HalfWP": ({'source': 'SCS_ILH_LAS/MOTOR/ROT_OPA_BWP1',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "AFS_FocusLens": ({'source': 'SCS_ILH_LAS/MOTOR/LT_SPARE1',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
# 2nd lens of telescope
- "AFS_TeleLens": {'source': 'SCS_ILH_LAS/MOTOR/LT2',
- 'key': 'actualPosition.value',
- 'dim': None},
+ "AFS_TeleLens": ({'source': 'SCS_ILH_LAS/MOTOR/LT2',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
# PP LASER 800 nm path
- "PP800_PhaseShifter": {'source': 'SCS_ILH_LAS/DOOCS/PP800_PHASESHIFTER',
- 'key': 'actualPosition.value',
- 'dim': None},
- "PP800_SynchDelayLine": {'source': 'SCS_ILH_LAS/DOOCS/PPL_OPT_DELAY',
- 'key': 'actualPosition.value',
- 'dim': None},
- "PP800_DelayLine": {'source': 'SCS_ILH_LAS/MOTOR/LT3',
+ "PP800_PhaseShifter": ({'source': 'SCS_ILH_LAS/DOOCS/PP800_PHASESHIFTER',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "PP800_SynchDelayLine": ({'source': 'SCS_ILH_LAS/DOOCS/PPL_OPT_DELAY',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "PP800_DelayLine": ({'source': 'SCS_ILH_LAS/MOTOR/LT3',
+ 'key': 'actualPosition.value',
+ 'dim': None},
+ {'source': 'SCS_ILH_LAS/MOTOR/LT3',
+ 'key': 'AActualPosition.value',
+ 'dim': None},),
+ "PP800_HalfWP": ({'source': 'SCS_ILH_LAS/MOTOR/ROT8WP1',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "PP800_FocusLens": ({'source': 'SCS_ILH_LAS/MOTOR/LT_SPARE1',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ # 1st lens of telescope (setup of August 2019)
+ "PP800_TeleLens": ({'source': 'SCS_ILH_LAS/MOTOR/LT7',
'key': 'actualPosition.value',
- 'dim': None},
- "PP800_HalfWP": {'source': 'SCS_ILH_LAS/MOTOR/ROT8WP1',
+ 'dim': None},),
+ "ILH_8CAM1": ({'source': 'SCS_ILH_LAS/CAM/8CAM1:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['8cam1_y', '8cam1_x']},),
+ "ILH_10CAM1": ({'source': 'SCS_ILH_LAS/CAM/10CAM1:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['10cam1_y', '10cam1_x']},),
+ "ILH_PIDelay": ({'source': 'SCS_ILH_LAS/MOTOR/PIDELAY',
'key': 'actualPosition.value',
- 'dim': None},
- "PP800_FocusLens": {'source': 'SCS_ILH_LAS/MOTOR/LT_SPARE1',
- 'key': 'actualPosition.value',
- 'dim': None},
- # 1st lens of telescope (setup of August 2019)
- "PP800_TeleLens": {'source': 'SCS_ILH_LAS/MOTOR/LT7',
- 'key': 'actualPosition.value',
- 'dim': None},
- "ILH_8CAM1": {'source': 'SCS_ILH_LAS/CAM/8CAM1:daqOutput',
- 'key': 'data.image.pixels',
- 'dim': ['8cam1_y', '8cam1_x']},
+ 'dim': None},),
# GPC
- "GPC_EOS_DelayLine": {'source': 'SCS_CDIDET_GRD/MOTOR/IMAGER',
- 'key': 'actualPosition.value',
- 'dim': None},
- "GPC_X": {'source': 'SCS_GPC_MOV/MOTOR/X',
- 'key': 'actualPosition.value',
- 'dim': None},
- "GPC_Y": {'source': 'SCS_GPC_MOV/MOTOR/Y',
- 'key': 'actualPosition.value',
- 'dim': None},
- "GPC_THETA": {'source': 'SCS_GPC_MOV/MOTOR/THETA',
- 'key': 'actualPosition.value',
- 'dim': None},
- "GPC_THETAMAG": {'source': 'SCS_GPC_MOV/MOTOR/THETAMAG',
- 'key': 'actualPosition.value',
- 'dim': None},
+ "GPC_EOS_DelayLine": ({'source': 'SCS_CDIDET_GRD/MOTOR/IMAGER',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "GPC_X": ({'source': 'SCS_GPC_MOV/MOTOR/X',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "GPC_Y": ({'source': 'SCS_GPC_MOV/MOTOR/Y',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "GPC_THETA": ({'source': 'SCS_GPC_MOV/MOTOR/THETA',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "GPC_THETAMAG": ({'source': 'SCS_GPC_MOV/MOTOR/THETAMAG',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
# FFT
- "scannerX": {'source': 'SCS_CDIFFT_SAM/LMOTOR/SCANNERX',
- 'key': 'actualPosition.value',
- 'dim': None},
- "scannerY": {'source': 'SCS_CDIFFT_SAM/MOTOR/SCANNERY',
- 'key': 'actualPosition.value',
- 'dim': None},
- "scannerY_enc": {'source': 'SCS_CDIFFT_SAM/ENC/SCANNERY',
- 'key': 'value.value',
- 'dim': None},
- "SAM-Z": {'source': 'SCS_CDIFFT_MOV/ENC/SAM_Z',
- 'key': 'value.value',
- 'dim': None},
- "magnet": {'source': 'SCS_CDIFFT_MAG/ASENS/CURRENT',
+ "scannerX": ({'source': 'SCS_CDIFFT_SAM/LMOTOR/SCANNERX',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "scannerY": ({'source': 'SCS_CDIFFT_SAM/MOTOR/SCANNERY',
+ 'key': 'actualPosition.value',
+ 'dim': None},),
+ "scannerY_enc": ({'source': 'SCS_CDIFFT_SAM/ENC/SCANNERY',
+ 'key': 'value.value',
+ 'dim': None},),
+ "SAM-Z": ({'source': 'SCS_CDIFFT_MOV/ENC/SAM_Z',
'key': 'value.value',
- 'dim': None},
- "magnet_old": {'source': 'SCS_CDIFFT_MAG/SUPPLY/CURRENT',
- 'key': 'actualCurrent.value',
- 'dim': None},
- "Vertical_FDM": {'source': 'SCS_CDIFFT_LDM/CAM/CAMERA1A:daqOutput',
+ 'dim': None},),
+ "magnet": ({'source': 'SCS_CDIFFT_MAG/ASENS/PSU_CMON',
+ 'key': 'value.value',
+ 'dim': None},
+ {'source': 'SCS_CDIFFT_MAG/ASENS/CURRENT',
+ 'key': 'value.value',
+ 'dim': None},
+ {'source': 'SCS_CDIFFT_MAG/SUPPLY/CURRENT',
+ 'key': 'actualCurrent.value',
+ 'dim': None}),
+ "Vertical_FDM": ({'source': 'SCS_CDIFFT_LDM/CAM/CAMERA1A:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['vfdm_y', 'vfdm_x']},),
+ "LLC_webcam2": ({'source': 'SCS_CDILLC_VAC/CAM/WEBCAMERA2:daqOutput',
'key': 'data.image.pixels',
- 'dim': ['vfdm_y', 'vfdm_x']},
+ 'dim': ['llc2_y', 'llc2_x']},),
# FastCCD, if in raw folder, raw images
# if in proc folder, dark substracted and relative gain corrected
- "fastccd": {'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
- 'key': 'data.image.pixels',
- 'dim': ['x', 'y']},
+ "fastccd": ({'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['x', 'y']},),
# FastCCD with common mode correction
- "fastccd_cm": {'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
- 'key': 'data.image.pixels_cm',
- 'dim': ['x', 'y']},
+ "fastccd_cm": ({'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+ 'key': 'data.image.pixels_cm',
+ 'dim': ['x', 'y']},),
# FastCCD charge split correction in very low photon count regime
- "fastccd_classified": {'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
- 'key': 'data.image.pixels_classified',
- 'dim': ['x', 'y']},
+ "fastccd_classified": ({'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+ 'key': 'data.image.pixels_classified',
+ 'dim': ['x', 'y']},),
# FastCCD event multiplicity from the charge split correction:
# 0: no events
# 100, 101: single events
@@ -284,116 +306,126 @@ mnemonics = {
# 400-403: charge split into four pixels in four different orientations
# 1000: charge in more than four neighboring pixels. Cannot be produced
# by a single photon alone.
- "fastccd_patterns": {'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
- 'key': 'data.image.patterns',
- 'dim': ['x', 'y']},
+ "fastccd_patterns": ({'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+ 'key': 'data.image.patterns',
+ 'dim': ['x', 'y']},),
# FastCCD gain map, 0 high gain, 1 medium gain, 2 low gain
- "fastccd_gain": {'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
- 'key': 'data.image.gain',
- 'dim': ['x', 'y']},
+ "fastccd_gain": ({'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+ 'key': 'data.image.gain',
+ 'dim': ['x', 'y']},),
# FastCCD mask, bad pixel map to be ignored if > 0
- "fastccd_mask": {'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
- 'key': 'data.image.mask',
- 'dim': ['x', 'y']},
+ "fastccd_mask": ({'source': 'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
+ 'key': 'data.image.mask',
+ 'dim': ['x', 'y']},),
# TIM
- "MCP1apd": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_D.apd.pulseIntegral',
- 'dim': ['apdId']},
- "MCP1raw": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_D.raw.samples',
- 'dim': ['samplesId']},
- "MCP2apd": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_C.apd.pulseIntegral',
- 'dim': ['apdId']},
- "MCP2raw": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_C.raw.samples',
- 'dim': ['samplesId']},
- "MCP3apd": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_B.apd.pulseIntegral',
- 'dim': ['apdId']},
- "MCP3raw": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_B.raw.samples',
- 'dim': ['samplesId']},
- "MCP4apd": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_A.apd.pulseIntegral',
- 'dim': ['apdId']},
- "MCP4raw": {'source': 'SCS_UTC1_ADQ/ADC/1:network',
- 'key': 'digitizers.channel_1_A.raw.samples',
- 'dim': ['samplesId']},
+ "MCP1apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_D.apd.pulseIntegral',
+ 'dim': ['apdId']},),
+ "MCP1raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_D.raw.samples',
+ 'dim': ['samplesId']},),
+ "MCP2apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_C.apd.pulseIntegral',
+ 'dim': ['apdId']},),
+ "MCP2raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_C.raw.samples',
+ 'dim': ['samplesId']},),
+ "MCP3apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_B.apd.pulseIntegral',
+ 'dim': ['apdId']},),
+ "MCP3raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_B.raw.samples',
+ 'dim': ['samplesId']},),
+ "MCP4apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_A.apd.pulseIntegral',
+ 'dim': ['apdId']},),
+ "MCP4raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_A.raw.samples',
+ 'dim': ['samplesId']},),
# FastADC
- "FastADC0peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC0raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC1peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC1raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC2peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC2raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC3peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC3raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC4peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC4raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC5peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC5raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC6peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC6raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC7peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC7raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC8peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC8raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
- "FastADC9peaks": {'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
- 'key': 'data.peaks',
- 'dim': ['peakId']},
- "FastADC9raw": {'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
- 'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},
+ "FastADC0peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC0raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC1peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC1raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC2peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC2raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC3peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC3raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC4peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC4raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC5peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC5raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC6peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC6raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC7peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC7raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC8peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC8raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
+ "FastADC9peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
+ 'key': 'data.peaks',
+ 'dim': ['peakId']},),
+ "FastADC9raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc_samplesId']},),
# KARABACON
- "KARABACON": {'source': 'SCS_DAQ_SCAN/MDL/KARABACON',
- 'key': 'actualStep.value',
- 'dim': None},
+ "KARABACON": ({'source': 'SCS_DAQ_SCAN/MDL/KARABACON',
+ 'key': 'actualStep.value',
+ 'dim': None},),
# GOTTHARD
- "Gotthard1": {'source': 'SCS_PAM_XOX/DET/GOTTHARD_RECEIVER1:daqOutput',
- 'key': 'data.adc',
- 'dim': ['gott_pId', 'pixelId']},
- "Gotthard2": {'source': 'SCS_PAM_XOX/DET/GOTTHARD_RECEIVER2:daqOutput',
- 'key': 'data.adc',
- 'dim': ['gott_pId', 'pixelId']}
+ "Gotthard1": ({'source': 'SCS_PAM_XOX/DET/GOTTHARD_RECEIVER1:daqOutput',
+ 'key': 'data.adc',
+ 'dim': ['gott_pId', 'pixelId']},),
+ "Gotthard2": ({'source': 'SCS_PAM_XOX/DET/GOTTHARD_RECEIVER2:daqOutput',
+ 'key': 'data.adc',
+ 'dim': ['gott_pId', 'pixelId']},),
+
+ # BASLER RACER
+ "Racer1": ({'source': 'SCS_PAM_XOX/CAM/RACER1:daqOutput',
+ 'key': 'data.image.pixels',
+ 'dim': ['racer1_pId', 'racer1_x']},),
+
+ # BBO2 RY (interferometric timing tool, Nov 2020, p2711)
+ "BBO_RY": ({'source': 'SCS_CDIDET_GRD/MOTOR/IMAGER',
+ 'key': 'actualPosition.value',
+ 'dim': None},)
}
diff --git a/src/toolbox_scs/detectors/bam_detectors.py b/src/toolbox_scs/detectors/bam_detectors.py
index 4b0d77f..12d6ef1 100644
--- a/src/toolbox_scs/detectors/bam_detectors.py
+++ b/src/toolbox_scs/detectors/bam_detectors.py
@@ -11,9 +11,9 @@ import logging
import numpy as np
import xarray as xr
-from ..constants import mnemonics as _mnemonics
from ..misc.bunch_pattern_external import is_pulse_at
-import toolbox_scs.load as tbload
+from ..mnemonics_machinery import (mnemonics_to_process,
+ mnemonics_for_run)
log = logging.getLogger(__name__)
@@ -56,7 +56,7 @@ def get_bam(run, mnemonics=None, merge_with=None, bunchPattern='sase3'):
"""
# get the list of mnemonics to process
- mnemonics = tbload.mnemonics_to_process(mnemonics, merge_with, 'BAM')
+ mnemonics = mnemonics_to_process(mnemonics, merge_with, 'BAM')
if len(mnemonics) == 0:
log.info('No array with unaligned BAM data to extract. Skipping.')
@@ -70,25 +70,26 @@ def get_bam(run, mnemonics=None, merge_with=None, bunchPattern='sase3'):
else:
mw_ds = xr.Dataset()
+ run_mnemonics = mnemonics_for_run(run)
# extract the XFEL pulses: slice(0,5400,2)
roi = np.s_[:5400:2]
ds = xr.Dataset()
for m in mnemonics:
if bool(merge_with) and m in merge_with:
- val = merge_with[m].isel({_mnemonics[m]['dim'][0]: roi})
+ val = merge_with[m].isel({run_mnemonics[m]['dim'][0]: roi})
log.debug(f'Using {m} from merge_with dataset.')
else:
- val = run.get_array(*_mnemonics[m].values(), roi=roi, name=m)
+ val = run.get_array(*run_mnemonics[m].values(), roi=roi, name=m)
log.debug(f'Loading {m} from DataCollection.')
- val[_mnemonics[m]['dim'][0]] = np.arange(2700)
+ val[run_mnemonics[m]['dim'][0]] = np.arange(2700)
ds = ds.merge(val, join='inner')
# check if bunch pattern table exists
if bool(merge_with) and 'bunchPatternTable' in merge_with:
bpt = merge_with['bunchPatternTable']
log.debug('Using bpt from merge_with dataset.')
- elif _mnemonics['bunchPatternTable']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
+ elif 'bunchPatternTable' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable'].values())
log.debug('Loaded bpt from extra_data run.')
else:
bpt = None
@@ -99,7 +100,7 @@ def get_bam(run, mnemonics=None, merge_with=None, bunchPattern='sase3'):
'scs_ppl': 'ol_pId'}
mask = is_pulse_at(bpt, bunchPattern)
mask = mask.rename({'pulse_slot': dim_names[bunchPattern]})
- ds = ds.rename({_mnemonics['BAM1932M']['dim'][0]:
+ ds = ds.rename({run_mnemonics['BAM1932M']['dim'][0]:
dim_names[bunchPattern]})
ds = ds.where(mask, drop=True)
diff --git a/src/toolbox_scs/detectors/digitizers.py b/src/toolbox_scs/detectors/digitizers.py
index d412cbf..b677323 100644
--- a/src/toolbox_scs/detectors/digitizers.py
+++ b/src/toolbox_scs/detectors/digitizers.py
@@ -13,10 +13,10 @@ import xarray as xr
import matplotlib.pyplot as plt
from scipy.signal import find_peaks
-from ..constants import mnemonics as _mnemonics
from ..misc.bunch_pattern_external import is_pulse_at
from ..util.exceptions import ToolBoxValueError
-import toolbox_scs.load as tbload
+from ..mnemonics_machinery import (mnemonics_to_process,
+ mnemonics_for_run)
log = logging.getLogger(__name__)
@@ -223,14 +223,15 @@ def get_peaks(run,
'are required.')
# Load data
arr = None
+ run_mnemonics = mnemonics_for_run(run)
if data is None:
log.debug(f'Loading array from DataCollection with {source}, {key}')
arr = run.get_array(source, key)
if isinstance(data, str):
log.debug(f'Loading array from mnemonic {data}')
- arr = run.get_array(*_mnemonics[data].values())
- source = _mnemonics[data]['source']
- key = _mnemonics[data]['key']
+ arr = run.get_array(*run_mnemonics[data].values())
+ source = run_mnemonics[data]['source']
+ key = run_mnemonics[data]['key']
if arr is None:
log.debug('Using array provided in data argument.')
if source is None or key is None:
@@ -244,8 +245,8 @@ def get_peaks(run,
bpt = run.get_array(bunchPattern['source'], bunchPattern['key'],
extra_dims=['pulse_slot'])
pattern = bunchPattern['pattern']
- if _mnemonics['bunchPatternTable']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
+ if 'bunchPatternTable' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable'].values())
pattern = bunchPattern
else:
pattern = bunchPattern
@@ -375,8 +376,9 @@ def channel_peak_params(run, source, key=None, digitizer=None,
-------
dict with peak integration parameters.
"""
- if source in _mnemonics:
- m = _mnemonics[source]
+ run_mnemonics = mnemonics_for_run(run)
+ if source in run_mnemonics:
+ m = run_mnemonics[source]
source = m['source']
key = m['key']
if key is not None:
@@ -510,7 +512,8 @@ def get_peak_params(run, mnemonic, raw_trace=None, ntrains=200):
Only used if raw_trace is None. Number of trains used to
calculate the average raw trace of the corresponding channel.
"""
- if mnemonic not in _mnemonics:
+ run_mnemonics = mnemonics_for_run(run)
+ if mnemonic not in run_mnemonics:
raise ToolBoxValueError("mnemonic must be a ToolBox mnemonics")
if "raw" not in mnemonic:
mnemo_raw = mnemonic.replace('peaks', 'raw').replace('apd', 'raw')
@@ -524,7 +527,7 @@ def get_peak_params(run, mnemonic, raw_trace=None, ntrains=200):
title = 'Auto-find peak params'
if raw_trace is None:
sel = run.select_trains(np.s_[:ntrains])
- raw_trace = sel.get_array(*_mnemonics[mnemo_raw].values())
+ raw_trace = sel.get_array(*run_mnemonics[mnemo_raw].values())
raw_trace = raw_trace.mean(dim='trainId')
params = find_integ_params(raw_trace, min_distance=min_distance)
log.debug(f'{title} for {mnemonic}: {params}')
@@ -569,6 +572,7 @@ def check_peak_params(run, mnemonic, raw_trace=None, ntrains=200, params=None,
-------
dictionnary of peak integration parameters
"""
+ run_mnemonics = mnemonics_for_run(run)
if "raw" in mnemonic:
mnemo_raw = mnemonic
title = 'Auto-find peak params'
@@ -577,7 +581,7 @@ def check_peak_params(run, mnemonic, raw_trace=None, ntrains=200, params=None,
title = 'Digitizer peak params'
if raw_trace is None:
sel = run.select_trains(np.s_[:ntrains])
- raw_trace = sel.get_array(*_mnemonics[mnemo_raw].values())
+ raw_trace = sel.get_array(*run_mnemonics[mnemo_raw].values())
raw_trace = raw_trace.mean(dim='trainId')
if params is None:
params = get_peak_params(run, mnemonic, raw_trace)
@@ -586,11 +590,10 @@ def check_peak_params(run, mnemonic, raw_trace=None, ntrains=200, params=None,
if not plot:
return params
min_distance = 24 if "FastADC" in mnemonic else 440
- if _mnemonics['bunchPatternTable']['source'] in sel.all_sources \
- and bunchPattern != 'None':
+ if 'bunchPatternTable' in run_mnemonics and bunchPattern != 'None':
sel = run.select_trains(np.s_[:ntrains])
bp_params = {}
- bpt = sel.get_array(*_mnemonics['bunchPatternTable'].values())
+ bpt = sel.get_array(*run_mnemonics['bunchPatternTable'].values())
mask = is_pulse_at(bpt, bunchPattern)
pid = np.sort(np.unique(np.where(mask)[1]))
bp_params['npulses'] = len(pid)
@@ -816,7 +819,7 @@ def get_digitizer_peaks(run, mnemonics, digitizer, merge_with=None,
# get the list of mnemonics to process
def to_processed_name(name):
return name.replace('raw', 'peaks').replace('apd', 'peaks')
- mnemonics = tbload.mnemonics_to_process(mnemonics, merge_with,
+ mnemonics = mnemonics_to_process(mnemonics, merge_with,
digitizer, to_processed_name)
if len(mnemonics) == 0:
@@ -826,12 +829,13 @@ def get_digitizer_peaks(run, mnemonics, digitizer, merge_with=None,
else:
log.info(f'Extracting {digitizer} peaks from {mnemonics}.')
+ run_mnemonics = mnemonics_for_run(run)
# check if bunch pattern table exists
if bool(merge_with) and 'bunchPatternTable' in merge_with:
bpt = merge_with['bunchPatternTable']
log.debug('Using bpt from merge_with dataset.')
- elif _mnemonics['bunchPatternTable']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
+ elif 'bunchPatternTable' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable'].values())
log.debug('Loaded bpt from DataCollection.')
else:
bpt = None
@@ -846,7 +850,7 @@ def get_digitizer_peaks(run, mnemonics, digitizer, merge_with=None,
vals = []
for k in mnemonics:
useRaw = True if 'raw' in k else False
- m = _mnemonics[k]
+ m = run_mnemonics[k]
if bool(merge_with) and k in merge_with:
data = merge_with[k]
else:
diff --git a/src/toolbox_scs/detectors/xgm.py b/src/toolbox_scs/detectors/xgm.py
index bed5941..cdb84dc 100644
--- a/src/toolbox_scs/detectors/xgm.py
+++ b/src/toolbox_scs/detectors/xgm.py
@@ -12,9 +12,9 @@ import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
-from ..constants import mnemonics as _mnemonics
from ..misc.bunch_pattern_external import is_sase_1, is_sase_3
-import toolbox_scs.load as tbload
+from ..mnemonics_machinery import (mnemonics_to_process,
+ mnemonics_for_run)
log = logging.getLogger(__name__)
@@ -59,7 +59,7 @@ def get_xgm(run, mnemonics=None, merge_with=None, keepAllSase=False,
"""
# get the list of mnemonics to process
- mnemonics = tbload.mnemonics_to_process(mnemonics, merge_with, 'XGM')
+ mnemonics = mnemonics_to_process(mnemonics, merge_with, 'XGM')
if len(mnemonics) == 0:
log.info('No array with unaligned XGM peaks to extract. Skipping.')
@@ -73,12 +73,13 @@ def get_xgm(run, mnemonics=None, merge_with=None, keepAllSase=False,
else:
ds = xr.Dataset()
+ run_mnemonics = mnemonics_for_run(run)
# check if bunch pattern table exists
if bool(merge_with) and 'bunchPatternTable' in merge_with:
bpt = merge_with['bunchPatternTable']
log.debug('Using bpt from merge_with dataset.')
- elif _mnemonics['bunchPatternTable']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
+ elif 'bunchPatternTable' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable'].values())
log.debug('Loaded bpt from DataCollection.')
else:
bpt = None
@@ -89,7 +90,7 @@ def get_xgm(run, mnemonics=None, merge_with=None, keepAllSase=False,
arr = merge_with[m]
log.debug(f'Using {m} from merge_with dataset.')
else:
- arr = run.get_array(*_mnemonics[m].values(), name=m)
+ arr = run.get_array(*run_mnemonics[m].values(), name=m)
log.debug(f'Loading {m} from DataCollection.')
if bpt is not None:
arr = align_xgm_array(arr, bpt)
@@ -226,13 +227,14 @@ def calibrate_xgm(run, data, xgm='SCS', plot=False):
>>> ds['SCS_SA3_uJ'] = F * ds['SCS_SA3']
"""
+ run_mnemonics = mnemonics_for_run(run)
+ # check if bunch pattern table exists
if 'bunchPatternTable' in data:
bpt = data['bunchPatternTable']
- log.debug('Using bpt from provided dataset.')
- elif _mnemonics['bunchPatternTable']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
- elif _mnemonics['bunchPatternTable_SA3']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable_SA3'].values())
+ elif 'bunchPatternTable' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable'].values())
+ elif 'bunchPatternTable_SA3' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable_SA3'].values())
else:
raise ValueError('Bunch pattern missing. Cannot calibrate XGM.')
mask_sa3 = is_sase_3(bpt.sel(trainId=data.trainId))
@@ -254,7 +256,7 @@ def calibrate_xgm(run, data, xgm='SCS', plot=False):
if pflux_key in data:
pflux = data[pflux_key]
else:
- pflux = run.get_array(*_mnemonics[pflux_key].values())
+ pflux = run.get_array(*run_mnemonics[pflux_key].values())
pflux = pflux.sel(trainId=data.trainId)
pflux_sa3 = (npulses_sa1 + npulses_sa3) * pflux / npulses_sa3
avg_fast = data[f'{xgm}_SA3'].rolling(trainId=200).mean().mean(axis=1)
diff --git a/src/toolbox_scs/load.py b/src/toolbox_scs/load.py
index 19021e7..5ebac8b 100644
--- a/src/toolbox_scs/load.py
+++ b/src/toolbox_scs/load.py
@@ -15,6 +15,7 @@ import xarray as xr
import extra_data as ed
from .constants import mnemonics as _mnemonics
+from .mnemonics_machinery import mnemonics_for_run
from .util.exceptions import ToolBoxValueError, ToolBoxPathError
from .util.data_access import find_run_dir
import toolbox_scs.detectors as tbdet
@@ -75,17 +76,21 @@ def load(proposalNB=None, runNB=None,
'MCP3apd') and aligns the pulse Id with the sase3 bunch pattern.
extract_laser: bool
If True, extracts the peaks from FastADC variables (e.g. 'FastADC5raw',
- 'FastADC3peaks') and aligns the pulse Id with the PP laser bunch pattern.
+ 'FastADC3peaks') and aligns the pulse Id with the PP laser 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 pattern.
+ 'XTD10_XGM') and aligns the pulse Id with the sase1 / sase3 bunch
+ pattern.
extract_bam: bool
If True, extracts the values from BAM variables (e.g. 'BAM1932M')
and aligns the pulse Id with the sase3 bunch pattern.
tim_bp: str
- bunch pattern used to extract the TIM pulses. Ignored if extract_tim=False.
+ bunch pattern used to extract the TIM pulses.
+ Ignored if extract_tim=False.
laser_bp: str
- bunch pattern used to extract the TIM pulses. Ignored if extract_tim=False.
+ bunch pattern used to extract the TIM pulses.
+ Ignored if extract_tim=False.
Returns
-------
@@ -116,23 +121,22 @@ def load(proposalNB=None, runNB=None,
print('Loading data from {}'.format(runFolder))
run.info()
- keys = []
- vals = []
+ data_arrays = []
+ run_mnemonics = mnemonics_for_run(run)
# load pulse pattern info
- if _mnemonics['bunchPatternTable']['source'] in run.all_sources:
- bpt = run.get_array(*_mnemonics['bunchPatternTable'].values())
- keys.append("bunchPatternTable")
- vals.append(bpt)
- elif _mnemonics['bunchPatternTable_SA3']['source'] in run.all_sources:
+ if 'bunchPatternTable' in run_mnemonics:
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable'].values(),
+ name='bunchPatternTable')
+ data_arrays.append(bpt)
+ elif 'bunchPatternTable_SA3' in run_mnemonics:
log.info('Did not find SCS bunch pattern table but found the SA3 one.')
- bpt = run.get_array(*_mnemonics['bunchPatternTable_SA3'].values())
- keys.append("bunchPatternTable")
- vals.append(bpt)
+ bpt = run.get_array(*run_mnemonics['bunchPatternTable_SA3'].values(),
+ name='bunchPatternTable')
+ data_arrays.append(bpt)
else:
- log.warning('Source {} and {} not found in run. Skipping!'.format(
- _mnemonics['bunchPatternTable']['source'],
- _mnemonics['bunchPatternTable_SA3']['source'],))
+ log.warning('Bunch pattern table not found in run. Skipping!')
+
for f in fields:
if type(f) == dict:
# extracting mnemomic defined on the spot
@@ -143,13 +147,18 @@ def load(proposalNB=None, runNB=None,
v = f[k]
else:
# extracting mnemomic from the table
- if f in _mnemonics:
- v = _mnemonics[f]
+ if f in run_mnemonics:
+ v = run_mnemonics[f]
k = f
else:
- print(f'Unknow mnemonic "{f}". Skipping!')
+ if f in _mnemonics:
+ log.warning(f'Mnemonic "{f}" not found in run. Skipping!')
+ print(f'Mnemonic "{f}" not found in run. Skipping!')
+ else:
+ log.warning(f'Unknow mnemonic "{f}". Skipping!')
+ print(f'Unknow mnemonic "{f}". Skipping!')
continue
- if k in keys:
+ if k in [d.name for d in data_arrays]:
continue # already loaded, skip
if display:
print(f'Loading {k}')
@@ -159,53 +168,51 @@ def load(proposalNB=None, runNB=None,
continue
if k not in rois:
# no ROIs selection, we read everything
- arr = run.get_array(*v.values())
+ arr = run.get_array(*v.values(), name=k)
if len(arr) == 0:
log.warning(f'Empty array for {f}: {v["source"]}, {v["key"]}. '
'Skipping!')
print(f'Empty array for {f}: {v["source"]}, {v["key"]}. '
'Skipping!')
continue
- vals.append(arr)
- keys.append(k)
+ data_arrays.append(arr)
else:
# ROIs selection, for each ROI we select a region of the data and
# save it with new name and dimensions
for nk, nv in rois[k].items():
arr = run.get_array(v['source'], v['key'],
extra_dims=nv['dim'],
- roi=nv['roi'])
+ roi=nv['roi'],
+ name=nk)
if len(arr) == 0:
- log.warning(f'Empty array for {f}: {v["source"]}, {v["key"]}. '
- 'Skipping!')
+ log.warning(f'Empty array for {f}: {v["source"]}, '
+ f'{v["key"]}. Skipping!')
print(f'Empty array for {f}: {v["source"]}, {v["key"]}. '
'Skipping!')
continue
- vals.append(arr)
- keys.append(nk)
- aligned_vals = xr.align(*vals, join='inner')
- data = dict(zip(keys, aligned_vals))
- data = xr.Dataset(data)
+ data_arrays.append(arr)
+
+ data = xr.merge(data_arrays, join='inner')
data.attrs['runFolder'] = runFolder
- tim = [k for k in _mnemonics if 'MCP' in k and k in data]
+ tim = [k for k in run_mnemonics if 'MCP' in k and k in data]
if extract_tim and len(tim) > 0:
data = tbdet.get_tim_peaks(run, mnemonics=tim, merge_with=data,
bunchPattern=tim_bp)
- laser = [k for k in _mnemonics if 'FastADC' in k and k in data]
+ laser = [k for k in run_mnemonics if 'FastADC' in k and k in data]
if extract_laser and len(laser) > 0:
data = tbdet.get_laser_peaks(run, mnemonics=laser, merge_with=data,
bunchPattern=laser_bp)
- xgm = ['XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3', 'XTD10_SA3_sigma',
- 'XTD10_SA1', 'XTD10_SA1_sigma', 'SCS_XGM', 'SCS_XGM_sigma',
+ xgm = ['XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3', 'XTD10_SA3_sigma',
+ 'XTD10_SA1', 'XTD10_SA1_sigma', 'SCS_XGM', 'SCS_XGM_sigma',
'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)
- bam = [k for k in _mnemonics if 'BAM' in k and k in data]
+ bam = [k for k in run_mnemonics if 'BAM' in k and k in data]
if extract_bam and len(bam) > 0:
data = tbdet.get_bam(run, mnemonics=bam, merge_with=data)
@@ -314,79 +321,3 @@ def get_array(run, mnemonic_key=None, stepsize=None):
raise
return data
-
-
-def mnemonics_to_process(mnemo_list, merge_with, detector, func=None):
- """
- Finds the list of mnemonics, within mnemo_list and merge_with, that
- correspond to arrays that are not yet loaded and/or processed by a
- detector function. Removes the mnemonics of the already processed
- arrays from the list.
-
- Parameters
- ----------
- mnemo_list: str or list of str
- ToolBox mnemonics of pulse-resolved detector arrays
- merge_with: xarray Dataset
- Dataset that may contain non-processed arrays
- detector: str
- One in {'ADQ412', 'FastADC', 'XGM', 'BAM'}
- func: function
- function that takes one argument, an unprocessed mnemonic string,
- and converts it into a processed one, i.e. from 'MCP2apd' to
- 'MCP2peaks'. If None, the function returns the input mnemonic.
-
- Returns
- -------
- mnemonics: list of str
- the mnemonics to process
- """
- if func is None:
- def func(x):
- return x
-
- if detector == 'BAM':
- det_mnemos = [m for m in _mnemonics if 'BAM' in m]
- default_mnemo = 'BAM1932M'
- default_processed = 'BAM1932M'
- if detector == 'XGM':
- det_mnemos = ['XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3',
- 'XTD10_SA3_sigma', 'XTD10_SA1', 'XTD10_SA1_sigma',
- 'SCS_XGM', 'SCS_XGM_sigma', 'SCS_SA1', 'SCS_SA1_sigma',
- 'SCS_SA3', 'SCS_SA3_sigma']
- default_mnemo = 'SCS_SA3'
- default_processed = 'SCS_SA3'
- if detector == 'ADQ412':
- det_mnemos = [m for m in _mnemonics if 'MCP' in m]
- default_mnemo = 'MCP2apd'
- default_processed = 'MCP2peaks'
- if detector == 'FastADC':
- det_mnemos = [m for m in _mnemonics if 'FastADC' in m]
- default_mnemo = 'FastADC5raw'
- default_processed = 'FastADC5peaks'
-
- dig_dims = list(set([_mnemonics[m]['dim'][0] for m in det_mnemos]))
- processed_mnemos = list(set([func(m) for m in det_mnemos]))
-
- # create a list of mnemonics to process from the provided mnemonics and
- # merge_with Dataset
- mw_mnemos = []
- mw_processed = []
- if bool(merge_with):
- mw_mnemos = [m for m in merge_with if m in det_mnemos and
- any(dim in merge_with[m].dims for dim in dig_dims)]
- mw_processed = [m for m in merge_with if m in processed_mnemos and
- any(dim in merge_with[m].dims for dim in dig_dims)
- is False]
- if mnemo_list is None:
- mnemonics = []
- if len(mw_mnemos) == 0 and default_processed not in mw_processed:
- mnemonics = [default_mnemo]
- else:
- mnemonics = [mnemo_list] if isinstance(mnemo_list, str) else mnemo_list
- mnemonics = list(set(mnemonics + mw_mnemos))
- for m in mnemonics[:]:
- if func(m) in mw_processed:
- mnemonics.remove(m)
-
- return mnemonics
diff --git a/src/toolbox_scs/mnemonics_machinery.py b/src/toolbox_scs/mnemonics_machinery.py
new file mode 100644
index 0000000..dac8d1a
--- /dev/null
+++ b/src/toolbox_scs/mnemonics_machinery.py
@@ -0,0 +1,145 @@
+""" Handling ToolBox mnemonics sub-routines
+
+ Copyright (2021) SCS Team.
+
+ (contributions preferrably comply with pep8 code structure
+ guidelines.)
+"""
+
+import logging
+
+from .constants import mnemonics as _mnemonics
+
+log = logging.getLogger(__name__)
+
+
+def mnemonics_for_run(run):
+ """
+ Returns the availble ToolBox mnemonics for a give extra_data
+ DataCollection
+
+ Parameters
+ ----------
+ run: extra_data DataCollection
+ The run to check for mnemonics
+
+ Returns
+ -------
+ mnemonics: dict
+ The dictionnary of mnemonics that are available in the run.
+
+ Example
+ -------
+ >>> import toolbox_scs as tb
+ >>> run, _ = tb.load(2212, 213)
+ >>> tb.mnemonics_for_run(run)
+ """
+ result = {}
+ for m in _mnemonics:
+ version = mnemo_version_index(run, m)
+ if version != -1:
+ result[m] = _mnemonics[m][version]
+ return result
+
+
+def mnemo_version_index(run, mnemonic):
+ """
+ Given a mnemonic and a DataCollection, checks the existence of the
+ mnemonic versions within the tuple value of the mnemonic and returns the
+ valid version index.
+
+ Parameters
+ ----------
+ run: extra_data DataCollection
+ The run that contains the data source corresponding to the mnemonic
+ menmonic: str
+ A ToolBox mnemonic
+
+ Returns
+ -------
+ index: int
+ The index of the tuple. If no valid version is found, returns -1.
+
+ """
+ for i, v in enumerate(_mnemonics[mnemonic]):
+ if (v['source'] in run.all_sources and
+ v['key'] in run.keys_for_source(v['source'])):
+ log.debug(f'Found version {i} for mnemonic "{mnemonic}": {v}')
+ return i
+ return -1
+
+
+def mnemonics_to_process(mnemo_list, merge_with, detector, func=None):
+ """
+ Finds the list of mnemonics, within mnemo_list and merge_with, that
+ correspond to arrays that are not yet loaded and/or processed by a
+ detector function. Removes the mnemonics of the already processed
+ arrays from the list.
+
+ Parameters
+ ----------
+ mnemo_list: str or list of str
+ ToolBox mnemonics of pulse-resolved detector arrays
+ merge_with: xarray Dataset
+ Dataset that may contain non-processed arrays
+ detector: str
+ One in {'ADQ412', 'FastADC', 'XGM', 'BAM'}
+ func: function
+ function that takes one argument, an unprocessed mnemonic string,
+ and converts it into a processed one, i.e. from 'MCP2apd' to
+ 'MCP2peaks'. If None, the function returns the input mnemonic.
+
+ Returns
+ -------
+ mnemonics: list of str
+ the mnemonics to process
+ """
+ if func is None:
+ def func(x):
+ return x
+
+ if detector == 'BAM':
+ det_mnemos = [m for m in _mnemonics if 'BAM' in m]
+ default_mnemo = 'BAM1932M'
+ default_processed = 'BAM1932M'
+ if detector == 'XGM':
+ det_mnemos = ['XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3',
+ 'XTD10_SA3_sigma', 'XTD10_SA1', 'XTD10_SA1_sigma',
+ 'SCS_XGM', 'SCS_XGM_sigma', 'SCS_SA1', 'SCS_SA1_sigma',
+ 'SCS_SA3', 'SCS_SA3_sigma']
+ default_mnemo = 'SCS_SA3'
+ default_processed = 'SCS_SA3'
+ if detector == 'ADQ412':
+ det_mnemos = [m for m in _mnemonics if 'MCP' in m]
+ default_mnemo = 'MCP2apd'
+ default_processed = 'MCP2peaks'
+ if detector == 'FastADC':
+ det_mnemos = [m for m in _mnemonics if 'FastADC' in m]
+ default_mnemo = 'FastADC5raw'
+ default_processed = 'FastADC5peaks'
+
+ dig_dims = list(set([_mnemonics[m][0]['dim'][0] for m in det_mnemos]))
+ processed_mnemos = list(set([func(m) for m in det_mnemos]))
+
+ # create a list of mnemonics to process from the provided mnemonics and
+ # merge_with Dataset
+ mw_mnemos = []
+ mw_processed = []
+ if bool(merge_with):
+ mw_mnemos = [m for m in merge_with if m in det_mnemos and
+ any(dim in merge_with[m].dims for dim in dig_dims)]
+ mw_processed = [m for m in merge_with if m in processed_mnemos and
+ any(dim in merge_with[m].dims for dim in dig_dims)
+ is False]
+ if mnemo_list is None:
+ mnemonics = []
+ if len(mw_mnemos) == 0 and default_processed not in mw_processed:
+ mnemonics = [default_mnemo]
+ else:
+ mnemonics = [mnemo_list] if isinstance(mnemo_list, str) else mnemo_list
+ mnemonics = list(set(mnemonics + mw_mnemos))
+ for m in mnemonics[:]:
+ if func(m) in mw_processed:
+ mnemonics.remove(m)
+
+ return mnemonics
--
GitLab