sparse-ir-tutorial-v2
SpM-lab / sparse-ir-tutorial-v2
[Tutorial] Current tutorials for sparse-ir (Python) and SparseIR.jl (Julia) v2: notebooks on DMFT, FLEX, GW, TPSC, the Eliashberg equation, analytic continuation and more
7 Open issues / PRsUpdated: 2026-10-02
Overview
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README
sparse-ir-tutorial
Author guideline
- In Markdown cells, use the
$$,$$ $$environments for equations. - References are listed in the markdown cell at the bottom of each notebook.
- Remove all outputs in a jupyter notebook.
Build html files
Install uv and run the following commands to build html files:
make build
References
You can add references to references.bib.
Source: https://github.com/SpM-lab/sparse-ir-tutorial-v2 README
Richness
Richness = how many of docs / examples / tests are in place, shown as 0–3 stars (1 star = 1 item; the checklist below is the breakdown). Software listed by hand (without repository data) is not assessed and is left out when filtering by a richness of 1 or more.
- Yes: README
- Yes: LICENSE
- No: docs
- No: examples
- No: tests
- No: CITATION
- No: release
Information
- Primary language
- Jupyter Notebook
- Languages
- Jupyter Notebook, Fortran, Julia, TeX, Makefile, Shell
- License
- MIT
- Provider
- github
- Documentation
- https://spm-lab.github.io/sparse-ir-tutorial-v2/