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.
metadata['instrument']['temporal_resolution'] = 35.
#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/3.10.18/x64/lib/python3.10/site-packages/sed/config/default.yaml]
WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
WARNING - No valid token provided for elabFTW. Fetching elabFTW metadata will be skipped.
INFO - Collecting data from the EPICS archive...
WARNING - Fetching elabFTW metadata only supported for loading from "runs"
[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 08/27/2025, 16:18:03
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 08/27/2025, 16:18:10
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 08/27/2025, 16:18:10
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 08/27/2025, 16:18:19
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 0.267880 0.267880 0.267880 0.267880 -23.408814
1 364.720189 1001.720189 70100.720189 6316.720189 353.203060
2 761.167040 818.167040 75615.167040 6316.167040 792.174252
3 691.989676 970.989676 66454.989676 6316.989676 714.295476
4 671.465118 712.465118 73026.465118 6317.465118 697.398469
5 299.488467 1164.488467 68459.488467 6316.488467 281.203089
6 570.522442 664.522442 73902.522442 6315.522442 587.961993
7 821.808151 544.808151 72631.808151 6317.808151 846.489366
8 818.348186 416.348186 72422.348186 6317.348186 836.335642
9 1005.837419 666.837419 72801.837419 6316.837419 1037.636421
Ym kx ky tm energy delay
0 97.193646 -2.122862 -1.799360 -47.927297 -8.260139 -660.247129
1 1034.394652 -1.112645 0.714573 70083.705683 7.512343 1471.888671
2 838.811777 0.064845 0.189944 75614.282509 0.223203 1471.701955
3 983.891980 -0.144056 0.579105 66449.306633 15.954646 1471.979637
4 741.986353 -0.189380 -0.069779 73026.085221 3.068609 1472.140124
5 1187.949010 -1.305776 1.126464 68432.974869 10.827429 1471.810453
6 702.292619 -0.482931 -0.176252 73899.588219 2.017519 1471.484369
7 586.706208 0.210539 -0.486300 72627.651733 3.583525 1472.255916
8 467.435947 0.183302 -0.806228 72412.706478 3.871253 1472.100654
9 707.814761 0.723268 -0.161440 72794.361415 3.365221 1471.928243
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>
[ ]: