Scientific Computing
- 161 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Implement numerics, simulation, stats, and reproducible notebooks for research codebases using Python scientific stacks.
About
scientific-computing guides Claude through building robust Python scientific software: array programming, statistical routines, simulation loops, and reproducible pipelines. It emphasizes correct dtypes, vectorization, testing numerical edge cases, and packaging results so research code can graduate from notebooks into maintainable backend modules and CLI tools.
- NumPy/SciPy-style numerical workflows
- Reproducible experiment structure
- Performance-aware vectorization patterns
- Data pipeline and validation hooks
- ltk guidance for research engineering
Scientific Computing by the numbers
- 161 all-time installs (skills.sh)
- Ranked #720 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 161 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Implement numerics, simulation, stats, and reproducible notebooks for research codebases using Python scientific stacks.
Files
Scientific Computing
Domain-specific Python libraries for scientific applications.
Libraries
| Library | Domain | Purpose |
|---|---|---|
| AstroPy | Astronomy | Coordinates, units, FITS files |
| BioPython | Bioinformatics | Sequences, BLAST, PDB |
| SymPy | Mathematics | Symbolic computation |
| Statsmodels | Statistics | Statistical modeling, tests |
---
AstroPy
Astronomy and astrophysics computations.
Key capabilities:
- Units: Physical unit handling with automatic conversion
- Coordinates: Celestial coordinate systems (ICRS, galactic, etc.)
- Time: Astronomical time scales (UTC, TAI, Julian dates)
- FITS: Read/write FITS astronomical data format
Key concept: Unit-aware calculations prevent errors from unit mismatches.
---
BioPython
Bioinformatics - sequences, structures, databases.
Key capabilities:
- Sequences: DNA/RNA/protein manipulation, translation, complement
- File parsing: FASTA, GenBank, PDB formats
- BLAST: Local and remote sequence alignment
- NCBI Entrez: Database access (nucleotide, protein, taxonomy)
Key concept: SeqIO for reading any sequence format, Seq for sequence operations.
---
SymPy
Symbolic mathematics - algebra, calculus, equation solving.
Key capabilities:
- Algebra: Solve equations, simplify, expand, factor
- Calculus: Derivatives, integrals, limits, series
- Linear algebra: Matrix operations, eigenvalues
- Printing: LaTeX output for documentation
Key concept: Work with symbols, not numbers. Get exact answers, not approximations.
---
Statsmodels
Statistical modeling with R-like formula interface.
Key capabilities:
- Regression: OLS, logistic, generalized linear models
- Time series: ARIMA, VAR, state space models
- Statistical tests: t-tests, ANOVA, diagnostics
- Formula API: R-style formulas (
y ~ x1 + x2)
Key concept: model.summary() gives comprehensive statistical output like R.
---
Decision Guide
| Domain | Library |
|---|---|
| Astronomy/astrophysics | AstroPy |
| Biology/genetics | BioPython |
| Symbolic math | SymPy |
| Statistical analysis | Statsmodels |
| Numerical computing | NumPy, SciPy |
| Data manipulation | Pandas |
Resources
- AstroPy: <https://docs.astropy.org>
- BioPython: <https://biopython.org/docs/>
- SymPy: <https://docs.sympy.org>
- Statsmodels: <https://www.statsmodels.org>
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