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 (https://github.com/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="../sed/config/mpes_example_config.yaml", metadata=metadata, collect_metadata=True)
Configuration loaded from: [/home/runner/work/sed/sed/docs/sed/config/mpes_example_config.yaml]
Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
Default config loaded from: [/home/runner/work/sed/sed/sed/config/default.yaml]
Gathering metadata from different locations
Collecting time stamps...
Collecting file metadata...
Collecting data from the EPICS archive...
[5]:
# Apply jittering to X, Y, t, ADC columns.
sp.add_jitter()
[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)
[8]:
# Apply stored momentum correction
sp.apply_momentum_correction()
Calculating inverse deformation field, this might take a moment...
[9]:
# Apply stored config momentum calibration
sp.apply_momentum_calibration()
[10]:
# Apply stored config energy correction
sp.apply_energy_correction()
[11]:
# Apply stored config energy calibration
sp.append_energy_axis()
[12]:
# Apply delay calibration
delay_range = (-500, 1500)
sp.calibrate_delay_axis(delay_range=delay_range, preview=True)
X Y t ADC Xm \
0 0.496713 0.496713 0.496713 0.496713 -23.163131
1 364.778513 1001.778513 70100.778513 6316.778513 353.266078
2 760.893553 817.893553 75614.893553 6315.893553 791.891026
3 691.679172 970.679172 66454.679172 6316.679172 713.967095
4 670.601283 711.601283 73025.601283 6316.601283 696.467231
5 298.825922 1163.825922 68458.825922 6315.825922 280.491043
6 571.412044 665.412044 73903.412044 6316.412044 588.925290
7 822.120132 545.120132 72632.120132 6318.120132 846.817018
8 817.774699 415.774699 72421.774699 6316.774699 835.746666
9 1005.645877 666.645877 72801.645877 6316.645877 1037.433311
Ym kx ky tm energy delay
0 97.401409 -2.122203 -1.798803 -47.685076 -25.060083 -660.169886
1 1034.444847 -1.112476 0.714707 70083.764727 -9.287763 1471.908359
2 838.557826 0.064085 0.189263 75614.015542 -16.576542 1471.609638
3 983.615815 -0.144937 0.578364 66449.007359 -0.844463 1471.874826
4 741.212070 -0.191878 -0.071856 73025.207923 -13.730282 1471.848534
5 1187.373928 -1.307686 1.124922 68432.312540 -5.971089 1471.586809
6 703.078748 -0.480347 -0.174144 73900.515861 -14.783544 1471.784656
7 587.004306 0.211418 -0.485500 72627.969092 -13.216894 1472.361226
8 466.884194 0.181723 -0.807708 72412.112095 -12.927941 1471.907071
9 707.627545 0.722724 -0.161942 72794.176817 -13.434540 1471.863587
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:
• ../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>
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