Demonstration of the conversion pipeline using time-resolved ARPES data stored on Zenodo

In this example, we pull some time-resolved ARPES data from Zenodo, and load it into the sed package using functions of the mpes package. Then, we run a conversion pipeline on it, containing steps for visualizing the channels, correcting image distortions, calibrating the momentum space, correcting for energy distortions and calibrating the energy axis. Finally, the data are binned in calibrated axes. For performance reasons, best store the data on a locally attached storage (no network drive). This can also be achieved transparently using the included MirrorUtil class.

%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import sed
from sed.dataset import dataset

%matplotlib widget

Load Data

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, caldir = dataset.subdirs # scandir contains the data, caldir 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.

# create sed processor using the config file:
sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={}, verbose=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.

# Apply jittering to X, Y, t, ADC columns.
# Columns are defined in the config, or can be provided as list.
sp.add_jitter()

INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].

# Plot of the count rate through the scan
rate, secs = sp.loader.get_count_rate(range(100))
plt.plot(secs, rate)

[<matplotlib.lines.Line2D at 0x7fbff8b313c0>]

# The time elapsed in the scan
sp.loader.get_elapsed_time()

2588.4949999999994

# Inspect data in dataframe Columns:
# axes = ['X', 'Y', 't', 'ADC']
# bins = [100, 100, 100, 100]
# ranges = [(0, 1800), (0, 1800), (130000, 140000), (0, 9000)]
# sp.view_event_histogram(dfpid=1, axes=axes, bins=bins, ranges=ranges)
sp.view_event_histogram(dfpid=2)

Distortion correction and Momentum Calibration workflow

Distortion correction

1. step:

Bin and load part of the dataframe in detector coordinates, and choose energy plane where high-symmetry points can well be identified. Either use the interactive tool, or pre-select the range:

#sp.bin_and_load_momentum_calibration(df_partitions=20, plane=170)
sp.bin_and_load_momentum_calibration(df_partitions=100, plane=33, width=10, apply=True)

2. Step:

Next, we select a number of features corresponding to the rotational symmetry of the material, plus the center. These can either be auto-detected (for well-isolated points), or provided as a list (these can be read-off the graph in the cell above). These are then symmetrized according to the rotational symmetry, and a spline-warping correction for the x/y coordinates is calculated, which corrects for any geometric distortions from the perfect n-fold rotational symmetry.

#features = np.array([[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]])
#sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)
# Manual selection: Use a GUI tool to select peaks:
#sp.define_features(rotation_symmetry=6, include_center=True)
# Autodetect: Uses the DAOStarFinder routine to locate maxima.
# Parameters are:
#   fwhm: Full-width at half maximum of peaks.
#   sigma: Number of standard deviations above the mean value of the image peaks must have.
#   sigma_radius: number of standard deviations around a peak that peaks are fitted
sp.define_features(rotation_symmetry=6, auto_detect=True, include_center=True, fwhm=10, sigma=12, sigma_radius=4, apply=True)

3. Step:

Generate nonlinear correction using splinewarp algorithm. If no landmarks have been defined in previous step, default parameters from the config are used

# Option whether a central point shall be fixed in the determination fo the correction
sp.generate_splinewarp(include_center=True)

INFO - Calculated thin spline correction based on the following landmarks:
pouter_ord: [[203.00551114 342.99261194]
 [299.87829082 346.20443639]
 [350.95807186 244.79003071]
 [305.65101784 150.21902049]
 [199.531846   152.78169434]
 [153.40577201 243.0451876 ]]
pcent: (249.22575592166083, 249.24844555078755)

Optional (Step 3a):

Save distortion correction parameters to configuration file in current data folder:

# Save generated distortion correction parameters for later reuse
sp.save_splinewarp()

INFO - Saved momentum correction parameters to "sed_config.yaml".

4. Step:

To adjust scaling, position and orientation of the corrected momentum space image, you can apply further affine transformations to the distortion correction field. Here, first a potential scaling is applied, next a translation, and finally a rotation around the center of the image (defined via the config). One can either use an interactive tool, or provide the adjusted values and apply them directly.

#sp.pose_adjustment(xtrans=14, ytrans=18, angle=2)
sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True)

INFO - Applied translation with (xtrans=8.0, ytrans=7.0).
INFO - Applied rotation with angle=-4.0.

5. Step:

Finally, the momentum correction is applied to the dataframe, and corresponding meta data are stored

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

Momentum calibration workflow

1. Step:

First, the momentum scaling needs to be calibrated. Either, one can provide the coordinates of one point outside the center, and provide its distance to the Brillouin zone center (which is assumed to be located in the center of the image), one can specify two points on the image and their distance (where the 2nd point marks the BZ center),or one can provide absolute k-coordinates of two distinct momentum points.

If no points are provided, an interactive tool is created. Here, left mouse click selects the off-center point (brillouin_zone_centered=True) or toggle-selects the off-center and center point.

k_distance = 2/np.sqrt(3)*np.pi/3.28 # k-distance of the K-point in a hexagonal Brillouin zone
#sp.calibrate_momentum_axes(k_distance = k_distance)
point_a = [308, 345]
sp.calibrate_momentum_axes(point_a=point_a, k_distance = k_distance, apply=True)
#point_b = [247, 249]
#sp.calibrate_momentum_axes(point_a=point_a, point_b = point_b, k_coord_a = [.5, 1.1], k_coord_b = [0, 0], equiscale=False)

Optional (Step 1a):

Save momentum calibration parameters to configuration file in current data folder:

# Save generated momentum calibration parameters for later reuse
sp.save_momentum_calibration()

INFO - Saved momentum calibration parameters to sed_config.yaml

2. Step:

Now, the distortion correction and momentum calibration needs to be applied to the dataframe.

sp.apply_momentum_calibration()

INFO - Adding kx/ky columns to dataframe:
INFO - Using momentum calibration parameters generated on 08/29/2025, 09:05:16
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

Energy Correction and Calibration workflow

Energy Correction (optional)

The purpose of the energy correction is to correct for any momentum-dependent distortion of the energy axis, e.g. from geometric effects in the flight tube, or from space charge

1st step:

Here, one can select the functional form to be used, and adjust its parameters. The binned data used for the momentum calibration is plotted around the Fermi energy (defined by tof_fermi), and the correction function is plotted ontop. Possible correction functions are: “spherical” (parameter: diameter), “Lorentzian” (parameter: gamma), “Gaussian” (parameter: sigma), and “Lorentzian_asymmetric” (parameters: gamma, amplitude2, gamma2).

One can either use an interactive alignment tool, or provide parameters directly.

#sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200)
sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200, apply=True)

Optional (Step 1a):

Save energy correction parameters to configuration file in current data folder:

# Save generated energy correction parameters for later reuse
sp.save_energy_correction()

INFO - Saved energy correction parameters to sed_config.yaml

2. Step

After adjustment, the energy correction is directly applied to the TOF axis.

sp.apply_energy_correction()

INFO - Applying energy correction to dataframe...
INFO - Using energy correction parameters generated on 08/29/2025, 09:05:17
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

Energy calibration

For calibrating the energy axis, a set of data taken at different bias voltages around the value where the measurement was taken is required.

1. Step:

In a first step, the data are loaded, binned along the TOF dimension, and normalized. The used bias voltages can be either provided, or read from attributes in the source files if present.

# Load energy calibration EDCs
energycalfolder = caldir
scans = np.arange(1,12)
voltages = np.arange(12,23,1)
files = [energycalfolder + r'/Scan' + str(num).zfill(3) + '_' + str(num+11) + '.h5' for num in scans]
sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 75000)])

WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.

2. Step:

Next, the same peak or feature needs to be selected in each curve. For this, one needs to define “ranges” for each curve, within which the peak of interest is located. One can either provide these ranges manually, or provide one range for a “reference” curve, and infer the ranges for the other curves using a dynamic time warping algorithm.

# Option 1 = specify the ranges containing a common feature (e.g an equivalent peak) for all bias scans
# rg = [(129031.03103103103, 129621.62162162163), (129541.54154154155, 130142.14214214214), (130062.06206206206, 130662.66266266267), (130612.61261261262, 131213.21321321322), (131203.20320320321, 131803.8038038038), (131793.7937937938, 132384.38438438438), (132434.43443443443, 133045.04504504506), (133105.10510510512, 133715.71571571572), (133805.8058058058, 134436.43643643643), (134546.54654654654, 135197.1971971972)]
# sp.find_bias_peaks(ranges=rg, infer_others=False)
# Option 2 = specify the range for one curve and infer the others
# This will open an interactive tool to select the correct ranges for the curves.
# IMPORTANT: Don't choose the range too narrow about a peak, and choose a refid
# somewhere in the middle or towards larger biases!
rg = (66100, 67000)
sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)

INFO - Use feature ranges: [(64638.0, 65386.0), (64913.0, 65683.0), (65188.0, 65991.0), (65474.0, 66310.0), (65782.0, 66651.0), (66101.0, 67003.0), (66442.0, 67388.0), (66794.0, 67795.0), (67190.0, 68213.0), (67575.0, 68664.0), (67993.0, 69148.0)].
INFO - Extracted energy features: [[6.51330000e+04 9.43293095e-01]
 [6.54080000e+04 9.52672958e-01]
 [6.57050000e+04 9.47981834e-01]
 [6.60130000e+04 9.46402431e-01]
 [6.63430000e+04 9.50330198e-01]
 [6.66730000e+04 9.63564813e-01]
 [6.70360000e+04 9.59838033e-01]
 [6.73990000e+04 9.67203319e-01]
 [6.78060000e+04 9.55975950e-01]
 [6.82130000e+04 9.56439197e-01]
 [6.86750000e+04 9.70683038e-01]].

3. Step:

Next, the detected peak positions and bias voltages are used to determine the calibration function. Essentially, the functional Energy(TOF) is being determined by either least-squares fitting of the functional form d^2/(t-t0)2 via lmfit (method: “lmfit”), or by analytically obtaining a polynomial approximation (method: “lstsq” or “lsqr”). The parameter ref_energy is used to define the absolute energy position of the feature used for calibration in the calibrated energy scale. energy_scale can be either “kinetic” (decreasing energy with increasing TOF), or “binding” (increasing energy with increasing TOF).

After calculating the calibration, all traces corrected with the calibration are plotted ontop of each other, and the calibration function (Energy(TOF)) together with the extracted features is being plotted.

# Eref can be used to set the absolute energy (kinetic energy, E-EF, etc.) of the feature used for energy calibration (if known)
Eref=-1.3
# the lmfit method uses a fit of (d/(t-t0))**2 to determine the energy calibration
# limits and starting values for the fitting parameters can be provided as dictionaries
sp.calibrate_energy_axis(
    ref_energy=Eref,
    method="lmfit",
    energy_scale='kinetic',
    d={'value':1.0,'min': .7, 'max':1.2, 'vary':True},
    t0={'value':8e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
    E0={'value': 0., 'min': -100, 'max': 0, 'vary': True},
)

INFO - [[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 43
    # data points      = 11
    # variables        = 3
    chi-square         = 0.00218781
    reduced chi-square = 2.7348e-04
    Akaike info crit   = -87.7502612
    Bayesian info crit = -86.5565754
[[Variables]]
    d:   1.09544523 +/- 0.03646409 (3.33%) (init = 1)
    t0:  7.6073e-07 +/- 7.5361e-09 (0.99%) (init = 8e-07)
    E0: -46.6158341 +/- 0.79487877 (1.71%) (init = 0)
[[Correlations]] (unreported correlations are < 0.100)
    C(d, t0)  = -0.9997
    C(d, E0)  = -0.9988
    C(t0, E0) = +0.9974

Optional (Step 3a):

Save energy calibration parameters to configuration file in current data folder:

# Save generated energy calibration parameters for later reuse
sp.save_energy_calibration()

INFO - Saved energy calibration parameters to "sed_config.yaml".

4. Step:

Finally, the the energy axis is added to the dataframe. Here, the applied bias voltages of the measurement is taken into account to provide the correct energy offset. If the bias cannot be read from the file, it can be provided manually.

sp.append_energy_axis(bias_voltage=16.8)

INFO - Adding energy column to dataframe:
INFO - Using energy calibration parameters generated on 08/29/2025, 09:05:26
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

4. Delay calibration:

The delay axis is calculated from the ADC input column based on the provided delay range. ALternatively, the delay scan range can also be extracted from attributes inside a source file, if present.

sp.dataframe.head()

	X	Y	t	ADC	Xm	Ym	kx	ky	tm	energy
0	0.159313	0.159313	0.159313	0.159313	-13.079549	87.446132	-2.095155	-1.825507	-48.042213	-25.223880
1	364.847990	1001.847990	70100.847990	6316.847990	355.345207	1032.111795	-1.106899	0.708449	70083.835059	-9.314767
2	760.636697	817.636697	75614.636697	6315.636697	791.043606	839.257516	0.061812	0.191140	75613.764784	-16.716894
3	692.072824	971.072824	66455.072824	6317.072824	713.646389	985.076228	-0.145797	0.582282	66449.386770	-0.833464
4	670.821918	711.821918	73025.821918	6316.821918	696.775616	741.212928	-0.191051	-0.071853	73025.432025	-13.816873

#from pathlib import Path
#datafile = "file.h5"
#print(datafile)
#sp.calibrate_delay_axis(datafile=datafile)
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     0.076940     0.076940      0.076940     0.076940   -13.167187
1   365.444260  1002.444260  70101.444260  6317.444260   355.980573
2   760.772320   817.772320  75614.772320  6315.772320   791.184245
3   692.262004   971.262004  66455.262004  6317.262004   713.845421
4   671.021733   712.021733  73026.021733  6317.021733   696.988056
5   298.920345  1163.920345  68458.920345  6315.920345   282.135268
6   571.420840   665.420840  73903.420840  6316.420840   590.422359
7   821.995381   544.995381  72631.995381  6317.995381   848.047638
8   817.531585   415.531585  72421.531585  6316.531585   837.980922
9  1006.140391   667.140391  72802.140391  6317.140391  1040.175610

            Ym        kx        ky            tm     energy        delay
0    87.370295 -2.095390 -1.825710    -48.129403 -25.223902  -660.311581
1  1032.633744 -1.105194  0.709849  70084.438621  -9.315864  1472.133084
2   839.384862  0.062189  0.191482  75613.897191 -16.717023  1471.568716
3   985.247293 -0.145263  0.582741  66449.569087  -0.834007  1472.071562
4   741.394742 -0.190481 -0.071366  73025.634963 -13.817134  1471.990458
5  1185.202212 -1.303276  1.119097  68432.406938  -5.972003  1471.618682
6   701.655109 -0.476331 -0.177962  73900.525032 -14.888385  1471.787625
7   586.842476  0.214718 -0.485934  72627.842195 -13.294638  1472.319116
8   466.056827  0.187716 -0.809928  72411.860093 -13.000968  1471.825007
9   709.294739  0.730079 -0.157470  72794.653382 -13.516588  1472.030512

5. Visualization of calibrated histograms

With all calibrated axes present in the dataframe, we can visualize the corresponding histograms, and determine the respective binning ranges

axes = ['kx', 'ky', 'energy', 'delay']
ranges = [[-3, 3], [-3, 3], [-6, 2], [-600, 1600]]
sp.view_event_histogram(dfpid=1, axes=axes, ranges=ranges)

Define the binning ranges and compute calibrated data volume

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, normalize_to_acquisition_time="delay")

INFO - Calculate normalization histogram for axis 'delay'...

Some visualization:

fig, axs = plt.subplots(4, 1, figsize=(6, 18), constrained_layout=True)
res.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
res.loc[{'kx':slice(-.8, -.5)}].sum(axis=(0,3)).T.plot(ax=axs[1])
res.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
res.loc[{'kx':slice(-.8, -.5), 'energy':slice(.5, 2)}].sum(axis=(0,1)).plot(ax=axs[3])

<matplotlib.collections.QuadMesh at 0x7fbfe8b11e40>

fig, ax = plt.subplots(1,1)
(sp._normalization_histogram*90000).plot(ax=ax)
sp._binned.sum(axis=(0,1,2)).plot(ax=ax)
plt.show()