Convert ARPES angle-space data to momentum-space and visualize with ERLabPy.
npx skills add https://github.com/kmnhan/erlabpy --skill arpes-analysis
Please help me install this Agent Skill. Skill: arpes-analysis Source: https://github.com/kmnhan/erlabpy/tree/main/skills/arpes-analysis Command: npx skills add https://github.com/kmnhan/erlabpy --skill arpes-analysis
ARPES data analysis can be complex and time-consuming; this Skill provides patterns and workflows for using ERLabPy to convert angle-space data to momentum-space and visualize results.
Load a sample ARPES dataset, run the momentum-space conversion with data.kspace.convert(), and visualize the resulting map with a plotting utility (e.g., eplt.plot_array).
High-intent search queries and answers about installing and using this skill.
You can convert ARPES angle-space data to momentum-space using ERLabPy's data.kspace.convert() function, which transforms angle coordinates into k-space maps for further analysis. The workflow requires loading your dataset first, then applying the conversion.
K-space conversion maps angle-resolved photoemission data from angle coordinates to momentum-space, allowing researchers to visualize electronic band structure. You need it when analyzing ARPES data to interpret physical properties in momentum rather than angular units.
Yes, this workflow supports loading ARPES data from Igor Pro formats and preparing it for analysis using xarray and numpy. The loaded data can then be transformed and visualized using ERLabPy's plotting utilities.
Yes, erlabpy is required for momentum conversion and visualization. It provides plotting utilities like eplt.plot_array that work with xarray datasets to generate publication-quality k-space maps from your ARPES data.
The best way to visualize k-space maps is using ERLabPy's plotting utilities such as eplt.plot_array after converting your data with data.kspace.convert(). This produces informative publication-quality plots from your transformed xarray datasets.
ARPES momentum conversion requires the erlabpy package along with standard Python data science tools including xarray, numpy, and matplotlib. These handle data loading, transformation, and visualization of the resulting k-space datasets.
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