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example use of NXWriter #697

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Aug 12, 2022
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example use of NXWriter #697

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5fd3710
DOC #637 start the new document as a notebook
prjemian Aug 12, 2022
7f962e0
Merge branch 'main' into 637-doc-NXWriter-use
prjemian Aug 12, 2022
555312b
MNT #637 ignore HDF5 files produced locally
prjemian Aug 12, 2022
bb12da7
DOC #637 functional content - needs words
prjemian Aug 12, 2022
67db93c
DOC #637 baselines & monitors
prjemian Aug 12, 2022
f07e39a
MNT #637 flag silence warning about missing items
prjemian Aug 12, 2022
a539405
DOC #637 silence noisy warnings
prjemian Aug 12, 2022
f3861d3
DOC #637 view the data
prjemian Aug 12, 2022
f5b01fd
DOC #637
prjemian Aug 12, 2022
eaec7f1
DOC #637 re-run the notebook
prjemian Aug 12, 2022
58c1b08
DOC #637 more links, re-run
prjemian Aug 12, 2022
f3fe672
DOC #637 text edits
prjemian Aug 12, 2022
9e4c33e
DOC #637 link anchor
prjemian Aug 12, 2022
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DOC #637 more links, re-run
@prjemian
prjemian committed Aug 12, 2022
commit 58c1b08ad4e912bb9ca3bfafe74aa8d513593b4b
60 changes: 30 additions & 30 deletions docs/source/examples/_nxwriter.ipynb
View file
Original file line number Diff line number Diff line change
@@ -8,7 +8,7 @@
"\n",
"**Objective**\n",
"\n",
"Demonstrate use of `apstools.callbacks.NXWriter` with [Bluesky](https://blueskyproject.io/bluesky) data acquisition and then use export data from [databroker](https://blueskyproject.io/databroker). The `NXWriter` records data from a Bluesky measurement [run](https://blueskyproject.io/bluesky/multi_run_plans.html#definition-of-a-run) in a [NeXus](https://manual.nexusformat.org/user_manual.html) [HDF5](https://www.hdfgroup.org/solutions/hdf5) data file.\n",
"Demonstrate use of [NXWriter](https://bcda-aps.github.io/apstools/api/_filewriters.html?highlight=nxwriter#nxwriter) (from [apstools.callbacks](https://bcda-aps.github.io/apstools/api/_filewriters.html#apstools.callbacks.nexus_writer.NXWriter)) with [Bluesky](https://blueskyproject.io/bluesky) data acquisition. The, the `NXWriter` is used to export data from [databroker](https://blueskyproject.io/databroker). The `NXWriter` records data from a Bluesky measurement [run](https://blueskyproject.io/bluesky/multi_run_plans.html#definition-of-a-run) in a [NeXus](https://manual.nexusformat.org/user_manual.html) [HDF5](https://www.hdfgroup.org/solutions/hdf5) data file.\n",
"\n",
"**Contents**\n",
"\n",
@@ -140,19 +140,19 @@
"text": [
"\n",
"\n",
"Transient Scan ID: 1 Time: 2022-08-12 17:15:49\n",
"Persistent Unique Scan ID: '50d9c254-6725-4e1d-8624-18e319afc027'\n",
"Transient Scan ID: 1 Time: 2022-08-12 17:22:32\n",
"Persistent Unique Scan ID: '6ee58390-7c04-4ebe-89cd-3db6f6c22be7'\n",
"New stream: 'primary'\n",
"+-----------+------------+------------+------------+\n",
"| seq_num | time | motor | sensor |\n",
"+-----------+------------+------------+------------+\n",
"| 1 | 17:15:50.5 | -0.50000 | 0.30529 |\n",
"| 2 | 17:15:51.0 | -0.25000 | 0.49244 |\n",
"| 3 | 17:15:51.5 | 0.00000 | 0.47031 |\n",
"| 4 | 17:15:52.0 | 0.25000 | 0.52802 |\n",
"| 5 | 17:15:52.5 | 0.50000 | 0.95268 |\n",
"| 1 | 17:22:34.3 | -0.50000 | 0.25014 |\n",
"| 2 | 17:22:34.8 | -0.25000 | 0.05269 |\n",
"| 3 | 17:22:35.3 | 0.00000 | 0.49683 |\n",
"| 4 | 17:22:35.8 | 0.25000 | 0.46299 |\n",
"| 5 | 17:22:36.3 | 0.50000 | 0.54023 |\n",
"+-----------+------------+------------+------------+\n",
"generator scan ['50d9c254'] (scan num: 1)\n",
"generator scan ['6ee58390'] (scan num: 1)\n",
"\n",
"\n",
"\n",
@@ -161,7 +161,7 @@
},
{
"data": {
"image/png": 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",
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",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
@@ -201,20 +201,20 @@
" @creator = \"NXWriter\"\n",
" @default = \"entry\"\n",
" @file_name = \"/tmp/nxwriter.h5\"\n",
" @file_time = \"2022-08-12T17:15:52.718749\"\n",
" @file_time = \"2022-08-12T17:22:36.515428\"\n",
" @h5py_version = \"3.7.0\"\n",
" entry:NXentry\n",
" @NX_class = \"NXentry\"\n",
" @default = \"data\"\n",
" @target = \"/entry\"\n",
" duration:NX_FLOAT64[] = \n",
" @units = \"s\"\n",
" end_time:NX_CHAR = b'2022-08-12T17:15:52.566333'\n",
" end_time:NX_CHAR = b'2022-08-12T17:22:36.354681'\n",
" entry_identifier --> /entry/instrument/bluesky/metadata/run_start_uid\n",
" plan_name --> /entry/instrument/bluesky/metadata/plan_name\n",
" program_name:NX_CHAR = b'bluesky'\n",
" start_time:NX_CHAR = b'2022-08-12T17:15:49.194144'\n",
" title:NX_CHAR = b'scan-S0001-50d9c25'\n",
" start_time:NX_CHAR = b'2022-08-12T17:22:32.997827'\n",
" title:NX_CHAR = b'scan-S0001-6ee5839'\n",
" data:NXdata\n",
" @NX_class = \"NXdata\"\n",
" @axes = [\"motor\"]\n",
@@ -248,7 +248,7 @@
" @target = \"/entry/instrument/bluesky/metadata/num_intervals\"\n",
" num_points:NX_INT64[] = \n",
" @target = \"/entry/instrument/bluesky/metadata/num_points\"\n",
" plan_args:NX_CHAR = b\"args:\\n- EpicsMotor(prefix='gp:m10', name='motor', settle_time=0.0, timeout=None, read_attrs=['user_readback',\\n 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity',\\n 'acceleration', 'motor_egu'])\\n- -0.5\\n- 0.5\\ndetectors:\\n- EpicsSignalRO(read_pv='gp:userCalc10.VAL', name='sensor', timestamp=1660342548.921025,\\n auto_monitor=False, string=False)\\nnum: 5\\nper_step: None\\n\"\n",
" plan_args:NX_CHAR = b\"args:\\n- EpicsMotor(prefix='gp:m10', name='motor', settle_time=0.0, timeout=None, read_attrs=['user_readback',\\n 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity',\\n 'acceleration', 'motor_egu'])\\n- -0.5\\n- 0.5\\ndetectors:\\n- EpicsSignalRO(read_pv='gp:userCalc10.VAL', name='sensor', timestamp=1660342952.621059,\\n auto_monitor=False, string=False)\\nnum: 5\\nper_step: None\\n\"\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_args\"\n",
" @text_format = \"yaml\"\n",
" plan_name:NX_CHAR = b'scan'\n",
@@ -262,7 +262,7 @@
" @target = \"/entry/instrument/bluesky/metadata/plan_pattern_module\"\n",
" plan_type:NX_CHAR = b'generator'\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_type\"\n",
" run_start_uid:NX_CHAR = b'50d9c254-6725-4e1d-8624-18e319afc027'\n",
" run_start_uid:NX_CHAR = b'6ee58390-7c04-4ebe-89cd-3db6f6c22be7'\n",
" @long_name = \"bluesky run uid\"\n",
" @target = \"/entry/instrument/bluesky/metadata/run_start_uid\"\n",
" versions:NX_CHAR = b'bluesky: 1.8.3\\nophyd: 1.6.4\\n'\n",
@@ -274,21 +274,21 @@
" primary:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary\"\n",
" @uid = \"76eb6977-ac17-4611-90c1-5b0820010d8b\"\n",
" @uid = \"79e7b8ba-889e-405e-9382-707032a6eb87\"\n",
" motor:NXdata\n",
" @NX_class = \"NXdata\"\n",
" @axes = [\"time\"]\n",
" @signal = \"value\"\n",
" @signal_type = \"positioner\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor\"\n",
" EPOCH:NX_FLOAT64[5] = [1660342550.317355, 1660342551.018562, 1660342551.519468, 1660342552.020329, 1660342552.521305]\n",
" EPOCH:NX_FLOAT64[5] = [1660342954.124779, 1660342954.825976, 1660342955.327095, 1660342955.827946, 1660342956.328779]\n",
" @long_name = \"epoch time (s)\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor/EPOCH\"\n",
" @units = \"s\"\n",
" time:NX_FLOAT64[5] = [0.0, 0.701207160949707, 1.202113151550293, 1.7029740810394287, 2.2039501667022705]\n",
" time:NX_FLOAT64[5] = [0.0, 0.7011969089508057, 1.2023160457611084, 1.7031669616699219, 2.2039999961853027]\n",
" @long_name = \"time since first data (s)\"\n",
" @start_time = 1660342550.317355\n",
" @start_time_iso = \"2022-08-12T17:15:50.317355\"\n",
" @start_time = 1660342954.124779\n",
" @start_time_iso = \"2022-08-12T17:22:34.124779\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor/time\"\n",
" @units = \"s\"\n",
" value:NX_FLOAT64[5] = [-0.5, -0.25, 0.0, 0.25, 0.5]\n",
@@ -306,14 +306,14 @@
" @signal = \"value\"\n",
" @signal_type = \"other\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint\"\n",
" EPOCH:NX_FLOAT64[5] = [1660342549.199537, 1660342550.551395, 1660342551.048592, 1660342551.579511, 1660342552.09023]\n",
" EPOCH:NX_FLOAT64[5] = [1660342953.010436, 1660342954.336149, 1660342954.877628, 1660342955.365899, 1660342955.87184]\n",
" @long_name = \"epoch time (s)\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint/EPOCH\"\n",
" @units = \"s\"\n",
" time:NX_FLOAT64[5] = [0.0, 1.3518579006195068, 1.8490550518035889, 2.379973888397217, 2.890692949295044]\n",
" time:NX_FLOAT64[5] = [0.0, 1.3257129192352295, 1.867192029953003, 2.3554630279541016, 2.8614039421081543]\n",
" @long_name = \"time since first data (s)\"\n",
" @start_time = 1660342549.199537\n",
" @start_time_iso = \"2022-08-12T17:15:49.199537\"\n",
" @start_time = 1660342953.010436\n",
" @start_time_iso = \"2022-08-12T17:22:33.010436\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint/time\"\n",
" @units = \"s\"\n",
" value:NX_FLOAT64[5] = [-0.5, -0.25, 0.0, 0.25, 0.5]\n",
@@ -331,17 +331,17 @@
" @signal = \"value\"\n",
" @signal_type = \"detector\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor\"\n",
" EPOCH:NX_FLOAT64[5] = [1660342550.421098, 1660342551.021047, 1660342551.521101, 1660342552.021052, 1660342552.521091]\n",
" EPOCH:NX_FLOAT64[5] = [1660342954.221125, 1660342954.821086, 1660342955.321043, 1660342955.821055, 1660342956.321118]\n",
" @long_name = \"epoch time (s)\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor/EPOCH\"\n",
" @units = \"s\"\n",
" time:NX_FLOAT64[5] = [0.0, 0.5999491214752197, 1.1000030040740967, 1.5999538898468018, 2.0999929904937744]\n",
" time:NX_FLOAT64[5] = [0.0, 0.5999610424041748, 1.0999181270599365, 1.5999300479888916, 2.0999932289123535]\n",
" @long_name = \"time since first data (s)\"\n",
" @start_time = 1660342550.421098\n",
" @start_time_iso = \"2022-08-12T17:15:50.421098\"\n",
" @start_time = 1660342954.221125\n",
" @start_time_iso = \"2022-08-12T17:22:34.221125\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor/time\"\n",
" @units = \"s\"\n",
" value:NX_FLOAT64[5] = [0.30528725108720534, 0.49243915465018695, 0.4703135728999771, 0.528023193713283, 0.9526817730983443]\n",
" value:NX_FLOAT64[5] = [0.25014114595254444, 0.052689402609292744, 0.4968337529564355, 0.46298924238956285, 0.5402304112306401]\n",
" @long_name = \"sensor\"\n",
" @lower_ctrl_limit = 0.0\n",
" @precision = 5\n",