Example 1: SpecsAnalyzer conversion

This is an example showcasing the conversion of Phoibos analyzer data with SpecsAnalyzer

The image is loaded from a text file, and the conversion into xarrays with calibrated dimensions is demonstrated for different modes of operation

%load_ext autoreload
%autoreload 2
from specsanalyzer import SpecsAnalyzer
import matplotlib.pyplot as plt
import numpy as np
%matplotlib widget

Image conversion

create specsanalyzer instance from config file

spa = SpecsAnalyzer(config="../tests/data/dataEPFL/config/config.yaml")

Configuration loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/tests/data/dataEPFL/config/config.yaml]
Default config loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/specsanalyzer/config/default.yaml]

convert single image

lens_mode = "WideAngleMode"
kinetic_energy = 35.000000
pass_energy = 35.000000
work_function = 4.3
binning = 4

raw_image_name = "../tests/data/dataEPFL/R9132/Data9132_RAWDATA.tsv"
with open(raw_image_name) as file:
    tsv_data = np.loadtxt(file, delimiter="\t")

res_xarray = spa.convert_image(
    tsv_data,
    lens_mode,
    kinetic_energy,
    pass_energy,
    work_function,
)

res_xarray.dims

('Angle', 'Ekin')

plt.figure()
res_xarray.plot()

<matplotlib.collections.QuadMesh at 0x7fecb213b940>

Conversion parameters are stored in the attributes

res_xarray.attrs["conversion_parameters"]

{'apply_fft_filter': False,
 'binning': 4,
 'rotation_angle': 0,
 'lens_mode': 'WideAngleMode',
 'kinetic_energy': 35.0,
 'pass_energy': 35.0,
 'work_function': 4.3,
 'a_inner': 15.0,
 'da_matrix': array([[ 7.19828571e-01,  7.53542857e-01,  7.59685714e-01],
        [-1.36678571e-03,  5.85771429e-02,  1.34014286e-01],
        [-1.37997143e-02, -5.04428571e-02, -9.16571429e-02],
        [-4.20521429e-04,  9.74571429e-03,  1.95942857e-02]]),
 'retardation_ratio': 0.8771428571428571,
 'source': 'interpolated as 0.2857142857142865*WideAngleMode@0.82 + 0.7142857142857135*WideAngleMode@0.9',
 'dims': ['Angle', 'Ekin'],
 'e_shift': array([-0.05,  0.  ,  0.05]),
 'de1': [0.0033],
 'e_range': [-0.066, 0.066],
 'a_range': [-15.0, 15.0],
 'pixel_size': 0.0258,
 'magnification': 4.54,
 'angle_offset_px': -2,
 'energy_offset_px': 0}

Adjusting offsets and angle

image rotation angle and center offsets can be adjusted by keyword arguments, or from the config.

res_xarray = spa.convert_image(
    tsv_data,
    lens_mode,
    kinetic_energy,
    pass_energy,
    work_function,
    rotation_angle=2,
    angle_offset_px=-3,
)
plt.figure()
res_xarray.plot()

<matplotlib.collections.QuadMesh at 0x7fecc37a4220>

Removal of mesh artefact

The mesh in front of the MCP introduces some visual artifacts. These can be mitigated by applying a Fourier filter approach, with Peaks in the Fourier plane to remove defined in the config file.

spa = SpecsAnalyzer(config="../tests/data/dataEPFL/config/config_filterON.yaml")

Configuration loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/tests/data/dataEPFL/config/config_filterON.yaml]
Default config loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/specsanalyzer/config/default.yaml]

res_xarray = spa.convert_image(
    tsv_data,
    lens_mode,
    kinetic_energy,
    pass_energy,
    work_function,
)
plt.figure()
res_xarray.plot()

<matplotlib.collections.QuadMesh at 0x7fecb21a3700>

Alternatively, one can use the interactive fft tool to optimize the fft peak positions of the grid.

spa.fft_tool(tsv_data)

The peak parameters are stored in the config dict which can be passed as kwds to the convert_image function

fft_filter_peaks = spa.config['fft_filter_peaks']

res_xarray = spa.convert_image(
    tsv_data,
    lens_mode,
    kinetic_energy,
    pass_energy,
    work_function,
    apply_fft_filter=True,
    fft_filter_peaks=fft_filter_peaks
)
plt.figure()
res_xarray.plot()

<matplotlib.collections.QuadMesh at 0x7feca7e02280>

Conversion into spatially resolved modes

lens_mode = "HighMagnification2"
res_xarray = spa.convert_image(
    tsv_data,
    lens_mode,
    kinetic_energy,
    pass_energy,
    work_function,
)
plt.figure()
res_xarray.plot()

This is a spatial mode, using default HighMagnification2 config

<matplotlib.collections.QuadMesh at 0x7feca7aa8880>

Conversion using conversion_parameters dict

If no valid calib2d file is available, conversion can also be performed by passing the interpolated conversion parameters, as e.g. stored in SPECS .sp2 files:

spa = SpecsAnalyzer() # Using default config w/o calib2d file

conversion_parameters = {
    'apply_fft_filter': False,
    'binning': 4,
    'rotation_angle': 0,
    'lens_mode': 'WideAngleMode',
    'kinetic_energy': 35.0,
    'pass_energy': 35.0,
    'work_function': 4.3,
    'a_inner': 15.0,
    'da_matrix': np.array(
        [[ 7.19828571e-01,  7.53542857e-01,  7.59685714e-01],
        [-1.36678571e-03,  5.85771429e-02,  1.34014286e-01],
        [-1.37997143e-02, -5.04428571e-02, -9.16571429e-02],
        [-4.20521429e-04,  9.74571429e-03,  1.95942857e-02]]),
    'retardation_ratio': 0.8771428571428571,
    'source': 'interpolated as 0.2857142857142865*WideAngleMode@0.82 + 0.7142857142857135*WideAngleMode@0.9',
    'dims': ['Angle', 'Ekin'],
    'e_shift': np.array([-0.05,  0.  ,  0.05]),
    'de1': [0.0033],
    'e_range': [-0.066, 0.066],
    'a_range': [-15.0, 15.0],
    'pixel_size': 0.0258,
    'magnification': 4.54,
    'angle_offset_px': -2,
    'energy_offset_px': 0
}

res_xarray = spa.convert_image(
    tsv_data,
    lens_mode,
    kinetic_energy,
    pass_energy,
    work_function,
    conversion_parameters=conversion_parameters,
)
plt.figure()
res_xarray.plot()

Default config loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/specsanalyzer/config/default.yaml]
No valid calib2 file found. Please provide the path to the calib2d file
corresponding to your detector in the config, or copy into the config
folder of the package! Without valid calib2d file, calibration parameters
have to be provided explicitly.

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