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"source": [
"# Example 1: SpecsAnalyzer conversion\n",
"This is an example showcasing the conversion of Phoibos analyzer data with SpecsAnalyzer\n",
"\n",
"The image is loaded from a text file, and the conversion into xarrays with calibrated dimensions is demonstrated for different modes of operation "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"from specsanalyzer import SpecsAnalyzer\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib widget"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image conversion\n",
"create specsanalyzer instance from config file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spa = SpecsAnalyzer(config=\"../tests/data/dataEPFL/config/config.yaml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"convert single image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lens_mode = \"WideAngleMode\"\n",
"kinetic_energy = 35.000000\n",
"pass_energy = 35.000000\n",
"work_function = 4.3\n",
"binning = 4\n",
"\n",
"raw_image_name = \"../tests/data/dataEPFL/R9132/Data9132_RAWDATA.tsv\"\n",
"with open(raw_image_name) as file:\n",
" tsv_data = np.loadtxt(file, delimiter=\"\\t\")\n",
"\n",
"res_xarray = spa.convert_image(\n",
" tsv_data,\n",
" lens_mode,\n",
" kinetic_energy,\n",
" pass_energy,\n",
" work_function,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res_xarray.dims"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"res_xarray.plot()"
]
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conversion parameters are stored in the attributes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res_xarray.attrs[\"conversion_parameters\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adjusting offsets and angle\n",
"image rotation angle and center offsets can be adjusted by keyword arguments, or from the config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res_xarray = spa.convert_image(\n",
" tsv_data,\n",
" lens_mode,\n",
" kinetic_energy,\n",
" pass_energy,\n",
" work_function,\n",
" rotation_angle=2,\n",
" angle_offset_px=-3,\n",
")\n",
"plt.figure()\n",
"res_xarray.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Removal of mesh artefact\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spa = SpecsAnalyzer(config=\"../tests/data/dataEPFL/config/config_filterON.yaml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res_xarray = spa.convert_image(\n",
" tsv_data,\n",
" lens_mode,\n",
" kinetic_energy,\n",
" pass_energy,\n",
" work_function,\n",
")\n",
"plt.figure()\n",
"res_xarray.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, one can use the interactive fft tool to optimize the fft peak positions of the grid."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spa.fft_tool(tsv_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The peak parameters are stored in the config dict which can be passed as kwds to the convert_image function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fft_filter_peaks = spa.config['fft_filter_peaks']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res_xarray = spa.convert_image(\n",
" tsv_data,\n",
" lens_mode,\n",
" kinetic_energy,\n",
" pass_energy,\n",
" work_function,\n",
" apply_fft_filter=True,\n",
" fft_filter_peaks=fft_filter_peaks\n",
")\n",
"plt.figure()\n",
"res_xarray.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conversion into spatially resolved modes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lens_mode = \"HighMagnification2\"\n",
"res_xarray = spa.convert_image(\n",
" tsv_data,\n",
" lens_mode,\n",
" kinetic_energy,\n",
" pass_energy,\n",
" work_function,\n",
")\n",
"plt.figure()\n",
"res_xarray.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conversion using conversion_parameters dict\n",
"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:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spa = SpecsAnalyzer() # Using default config w/o calib2d file\n",
"\n",
"conversion_parameters = {\n",
" 'apply_fft_filter': False,\n",
" 'binning': 4,\n",
" 'rotation_angle': 0,\n",
" 'lens_mode': 'WideAngleMode',\n",
" 'kinetic_energy': 35.0,\n",
" 'pass_energy': 35.0,\n",
" 'work_function': 4.3,\n",
" 'a_inner': 15.0,\n",
" 'da_matrix': np.array(\n",
" [[ 7.19828571e-01, 7.53542857e-01, 7.59685714e-01],\n",
" [-1.36678571e-03, 5.85771429e-02, 1.34014286e-01],\n",
" [-1.37997143e-02, -5.04428571e-02, -9.16571429e-02],\n",
" [-4.20521429e-04, 9.74571429e-03, 1.95942857e-02]]),\n",
" 'retardation_ratio': 0.8771428571428571,\n",
" 'source': 'interpolated as 0.2857142857142865*WideAngleMode@0.82 + 0.7142857142857135*WideAngleMode@0.9',\n",
" 'dims': ['Angle', 'Ekin'],\n",
" 'e_shift': np.array([-0.05, 0. , 0.05]),\n",
" 'de1': [0.0033],\n",
" 'e_range': [-0.066, 0.066],\n",
" 'a_range': [-15.0, 15.0],\n",
" 'pixel_size': 0.0258,\n",
" 'magnification': 4.54,\n",
" 'angle_offset_px': -2,\n",
" 'energy_offset_px': 0\n",
"}\n",
"\n",
"res_xarray = spa.convert_image(\n",
" tsv_data,\n",
" lens_mode,\n",
" kinetic_energy,\n",
" pass_energy,\n",
" work_function,\n",
" conversion_parameters=conversion_parameters,\n",
")\n",
"plt.figure()\n",
"res_xarray.plot()"
]
}
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