Binning with metadata generation, and storing into a NeXus file#
In this example, we show how to bin the same data used for example 3, but using the values for correction/calibration parameters generated in the example notebook 3, which are locally saved in the file sed_config.yaml. These data and the corresponding (machine and processing) metadata are then stored to a NeXus file following the NXmpes NeXus standard (https://fairmat-experimental.github.io/nexus-fairmat-proposal/9636feecb79bb32b828b1a9804269573256d7696/classes/contributed_definitions/NXmpes.html#nxmpes) using the ‘dataconverter’ of the pynxtools package (FAIRmat-NFDI/pynxtools).
[1]:
%load_ext autoreload
%autoreload 2
import sed
from sed.dataset import dataset
%matplotlib widget
Load Data#
[2]:
dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
data_path = dataset.dir # This is the path to the data
scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
Set 'use_existing' to False if you want to download to a new location.
INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
INFO - WSe2 data is already present.
[3]:
metadata = {}
# manual Meta data. These should ideally come from an Electronic Lab Notebook.
#General
metadata['experiment_summary'] = 'WSe2 XUV NIR pump probe data.'
metadata['entry_title'] = 'Valence Band Dynamics - 800 nm linear s-polarized pump, 0.6 mJ/cm2 absorbed fluence'
metadata['experiment_title'] = 'Valence band dynamics of 2H-WSe2'
#User
# Fill general parameters of NXuser
# TODO: discuss how to deal with multiple users?
metadata['user0'] = {}
metadata['user0']['name'] = 'Julian Maklar'
metadata['user0']['role'] = 'Principal Investigator'
metadata['user0']['affiliation'] = 'Fritz Haber Institute of the Max Planck Society'
metadata['user0']['address'] = 'Faradayweg 4-6, 14195 Berlin'
metadata['user0']['email'] = 'maklar@fhi-berlin.mpg.de'
#NXinstrument
metadata['instrument'] = {}
metadata['instrument']['energy_resolution'] = 140.
#analyzer
metadata['instrument']['analyzer']={}
metadata['instrument']['analyzer']['slow_axes'] = "delay" # the scanned axes
metadata['instrument']['analyzer']['spatial_resolution'] = 10.
metadata['instrument']['analyzer']['energy_resolution'] = 110.
metadata['instrument']['analyzer']['momentum_resolution'] = 0.08
metadata['instrument']['analyzer']['working_distance'] = 4.
metadata['instrument']['analyzer']['lens_mode'] = "6kV_kmodem4.0_30VTOF.sav"
#probe beam
metadata['instrument']['beam']={}
metadata['instrument']['beam']['probe']={}
metadata['instrument']['beam']['probe']['incident_energy'] = 21.7
metadata['instrument']['beam']['probe']['incident_energy_spread'] = 0.11
metadata['instrument']['beam']['probe']['pulse_duration'] = 20.
metadata['instrument']['beam']['probe']['frequency'] = 500.
metadata['instrument']['beam']['probe']['incident_polarization'] = [1, 1, 0, 0] # p pol Stokes vector
metadata['instrument']['beam']['probe']['extent'] = [80., 80.]
#pump beam
metadata['instrument']['beam']['pump']={}
metadata['instrument']['beam']['pump']['incident_energy'] = 1.55
metadata['instrument']['beam']['pump']['incident_energy_spread'] = 0.08
metadata['instrument']['beam']['pump']['pulse_duration'] = 35.
metadata['instrument']['beam']['pump']['frequency'] = 500.
metadata['instrument']['beam']['pump']['incident_polarization'] = [1, -1, 0, 0] # s pol Stokes vector
metadata['instrument']['beam']['pump']['incident_wavelength'] = 800.
metadata['instrument']['beam']['pump']['average_power'] = 300.
metadata['instrument']['beam']['pump']['pulse_energy'] = metadata['instrument']['beam']['pump']['average_power']/metadata['instrument']['beam']['pump']['frequency']#µJ
metadata['instrument']['beam']['pump']['extent'] = [230., 265.]
metadata['instrument']['beam']['pump']['fluence'] = 0.15
#sample
metadata['sample']={}
metadata['sample']['preparation_date'] = '2019-01-13T10:00:00+00:00'
metadata['sample']['preparation_description'] = 'Cleaved'
metadata['sample']['sample_history'] = 'Cleaved'
metadata['sample']['chemical_formula'] = 'WSe2'
metadata['sample']['description'] = 'Sample'
metadata['sample']['name'] = 'WSe2 Single Crystal'
metadata['file'] = {}
metadata['file']["trARPES:Carving:TEMP_RBV"] = 300.
metadata['file']["trARPES:XGS600:PressureAC:P_RD"] = 5.e-11
metadata['file']["KTOF:Lens:Extr:I"] = -0.12877
metadata['file']["KTOF:Lens:UDLD:V"] = 399.99905
metadata['file']["KTOF:Lens:Sample:V"] = 17.19976
metadata['file']["KTOF:Apertures:m1.RBV"] = 3.729931
metadata['file']["KTOF:Apertures:m2.RBV"] = -5.200078
metadata['file']["KTOF:Apertures:m3.RBV"] = -11.000425
# Sample motor positions
metadata['file']['trARPES:Carving:TRX.RBV'] = 7.1900000000000004
metadata['file']['trARPES:Carving:TRY.RBV'] = -6.1700200225439552
metadata['file']['trARPES:Carving:TRZ.RBV'] = 33.4501953125
metadata['file']['trARPES:Carving:THT.RBV'] = 423.30500940561586
metadata['file']['trARPES:Carving:PHI.RBV'] = 0.99931647456264949
metadata['file']['trARPES:Carving:OMG.RBV'] = 11.002500171914066
[4]:
# create sed processor using the config file, and collect the meta data from the files:
sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={}, metadata=metadata, collect_metadata=True)
INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
[5]:
# Apply jittering to X, Y, t, ADC columns.
sp.add_jitter()
INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
[6]:
# Calculate machine-coordinate data for pose adjustment
sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=10, apply=True)
[7]:
# Adjust pose alignment, using stored distortion correction
sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True, use_correction=True)
INFO - No landmarks defined, using momentum correction parameters generated on 03/06/2025, 09:26:41
INFO - Calculated thin spline correction based on the following landmarks:
pouter_ord: [[203.2 341.96]
[299.16 345.32]
[350.25 243.7 ]
[304.38 149.88]
[199.52 152.48]
[154.28 242.27]]
pcent: (248.29, 248.62)
INFO - Applied translation with (xtrans=8.0, ytrans=7.0).
INFO - Applied rotation with angle=-4.0.
[8]:
# Apply stored momentum correction
sp.apply_momentum_correction()
INFO - Adding corrected X/Y columns to dataframe:
Calculating inverse deformation field, this might take a moment...
INFO - Dask DataFrame Structure:
X Y t ADC Xm Ym
npartitions=100
float64 float64 float64 float64 float64 float64
... ... ... ... ... ...
... ... ... ... ... ... ...
... ... ... ... ... ...
... ... ... ... ... ...
Dask Name: apply_dfield, 206 graph layers
[9]:
# Apply stored config momentum calibration
sp.apply_momentum_calibration()
INFO - Adding kx/ky columns to dataframe:
INFO - Using momentum calibration parameters generated on 03/06/2025, 09:26:48
INFO - Dask DataFrame Structure:
X Y t ADC Xm Ym kx ky
npartitions=100
float64 float64 float64 float64 float64 float64 float64 float64
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
Dask Name: assign, 216 graph layers
[10]:
# Apply stored config energy correction
sp.apply_energy_correction()
INFO - Applying energy correction to dataframe...
INFO - Using energy correction parameters generated on 03/06/2025, 09:26:48
INFO - Dask DataFrame Structure:
X Y t ADC Xm Ym kx ky tm
npartitions=100
float64 float64 float64 float64 float64 float64 float64 float64 float64
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ...
Dask Name: assign, 230 graph layers
[11]:
# Apply stored config energy calibration
sp.append_energy_axis(bias_voltage=16.8)
INFO - Adding energy column to dataframe:
INFO - Using energy calibration parameters generated on 03/06/2025, 09:26:57
INFO - Dask DataFrame Structure:
X Y t ADC Xm Ym kx ky tm energy
npartitions=100
float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ...
Dask Name: assign, 243 graph layers
[12]:
# Apply delay calibration
delay_range = (-500, 1500)
sp.calibrate_delay_axis(delay_range=delay_range, preview=True)
INFO - Adding delay column to dataframe:
INFO - Append delay axis using delay_range = [-500, 1500] and adc_range = [475.0, 6400.0]
INFO - X Y t ADC Xm \
0 0.384484 0.384484 0.384484 0.384484 -23.283623
1 365.244567 1002.244567 70101.244567 6317.244567 353.769545
2 761.101929 818.101929 75615.101929 6316.101929 792.106877
3 692.429705 971.429705 66455.429705 6317.429705 714.760810
4 671.413437 712.413437 73026.413437 6317.413437 697.342750
5 298.969298 1163.969298 68458.969298 6315.969298 280.645123
6 570.922107 664.922107 73902.922107 6315.922107 588.394845
7 822.485227 545.485227 72632.485227 6318.485227 847.200367
8 817.521308 415.521308 72421.521308 6316.521308 835.486497
9 1005.828330 666.828330 72801.828330 6316.828330 1037.626783
Ym kx ky tm energy delay
0 97.299514 -2.122526 -1.799076 -47.803871 -8.260110 -660.207769
1 1034.845992 -1.111125 0.715783 70084.236496 7.511388 1472.065676
2 838.751262 0.064664 0.189782 75614.218953 0.223263 1471.679976
3 984.283338 -0.142808 0.580155 66449.730696 15.953383 1472.128171
4 741.940029 -0.189530 -0.069903 73026.032742 3.068676 1472.122679
5 1187.498385 -1.307273 1.125256 68432.455877 10.828590 1471.635206
6 702.645708 -0.481770 -0.175305 73900.005007 2.017041 1471.619277
7 587.353173 0.212446 -0.484564 72628.340437 3.582615 1472.484465
8 466.640482 0.181025 -0.808362 72411.849440 3.872415 1471.821539
9 707.805877 0.723243 -0.161464 72794.352656 3.365232 1471.925175
Compute final data volume#
[13]:
axes = ['kx', 'ky', 'energy', 'delay']
bins = [100, 100, 200, 50]
ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]]
res = sp.compute(bins=bins, axes=axes, ranges=ranges)
[14]:
# save to NXmpes NeXus (including standardized metadata)
sp.save(data_path + "/binned.nxs")
Using mpes reader to convert the given files:
• ../src/sed/config/NXmpes_config.json
The output file generated: /home/runner/work/sed/sed/docs/tutorial/datasets/WSe2/binned.nxs.
[15]:
# Visualization (requires JupyterLab)
from jupyterlab_h5web import H5Web
H5Web(data_path + "/binned.nxs")
[15]:
<jupyterlab_h5web.widget.H5Web object>
[ ]: