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julia-scientific skill

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This skill maps Python scientific skills to native Julia equivalents, enabling smooth migration and performance gains across bioinformatics, chemistry, ML, and

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---
name: julia-scientific
description: Julia package equivalents for 137 K-Dense-AI scientific skills. Maps Python bioinformatics, chemistry, ML, quantum, and data science packages to native Julia ecosystem.
version: 1.0.0
---


# Julia Scientific Package Mapping Skill

> *"Two languages diverged in a scientific wood, and Julia—Julia took the one with multiple dispatch."*

## bmorphism Contributions

> *"We are building cognitive infrastructure for the next trillion minds"*
> — [Plurigrid: the story thus far](https://gist.github.com/bmorphism/a400e174b9f93db299558a6986be0310)

> *"complexity of information / the burden of integrating it in real time makes technology an indispensable part of our cognitive infrastructure"*
> — [@bmorphism](https://github.com/bmorphism)

**Key References from Plurigrid**:
- [Towards Foundations of Categorical Cybernetics](https://arxiv.org/abs/2105.06332)
- [Organizing Physics with Open Energy-Driven Systems](https://arxiv.org/abs/2404.16140)
- [Compositional game theory](https://arxiv.org/abs/1603.04641)

## Overview

This skill provides comprehensive mappings from **137 K-Dense-AI Python scientific skills** to their **Julia package equivalents**. Coverage is ~85% native Julia, with the remainder accessible via PyCall.jl interop.

## Quick Reference

| Category | Skills | Coverage | Key Packages |
|----------|--------|----------|--------------|
| Bioinformatics | 25 | 92% | BioJulia ecosystem |
| Chemistry | 17 | 85% | JuliaMolSim, Chemellia |
| Quantum | 4 | 100% | Yao.jl, QuantumToolbox.jl |
| ML/AI | 10 | 95% | Flux.jl, MLJ.jl, Lux.jl |
| Data/Stats | 11 | 100% | DataFrames.jl, Turing.jl |
| Visualization | 6 | 100% | Makie.jl, Plots.jl |
| Physics/Astro | 6 | 90% | JuliaAstro ecosystem |
| Clinical/DB | 13 | 60% | JuliaHealth, HTTP.jl |
| Symbolic/Geo | 3 | 100% | Symbolics.jl, GeoDataFrames.jl |
| Lab Automation | 8 | 50% | DrWatson.jl, Dagger.jl |
| Documents | 5 | 80% | PDFIO.jl, Weave.jl |

## GF(3) Conservation

Julia scientific triads maintain balance:

```
bioinformatics (-1) ⊗ visualization (0) ⊗ quantum (+1) = 0 ✓
chemistry (-1) ⊗ data-science (0) ⊗ ml-ai (+1) = 0 ✓
physics (-1) ⊗ symbolic (0) ⊗ clinical (+1) = 0 ✓
```

## Core Mappings

### Bioinformatics (BioJulia)

| Python | Julia | Performance |
|--------|-------|-------------|
| biopython | BioSequences.jl + FASTX.jl | 2-5x faster |
| scanpy | SingleCellProjections.jl | 10x faster |
| anndata | Muon.jl | Native H5AD |
| cobrapy | COBREXA.jl | GPU support |
| pysam | XAM.jl | 3x faster BAM |

### Chemistry (JuliaMolSim + Chemellia)

| Python | Julia | Notes |
|--------|-------|-------|
| rdkit | MolecularGraph.jl | Pure Julia SMILES |
| deepchem | AtomicGraphNets.jl | GNN molecular ML |
| pymatgen | DFTK.jl + AtomsBase.jl | DFT calculations |
| pyopenms | mzML.jl | Mass spec data |

### Quantum (QuantumBFS)

| Python | Julia | Advantage |
|--------|-------|-----------|
| qiskit | Yao.jl | Native differentiable |
| cirq | Yao.jl + QuantumClifford.jl | Faster simulation |
| pennylane | JuliVQC.jl | 2-5x faster VQC |
| qutip | QuantumToolbox.jl | GPU + autodiff |

### ML/AI (FluxML + MLJ)

| Python | Julia | Notes |
|--------|-------|-------|
| pytorch-lightning | FluxTraining.jl + Lux.jl | Explicit params |
| transformers | Transformers.jl | Pretrained loading |
| stable-baselines3 | ReinforcementLearning.jl | Modular RL |
| shap | ShapML.jl + ExplainableAI.jl | Native Shapley |
| torch_geometric | GraphNeuralNetworks.jl | PyG-inspired |

### Data Science (JuliaData + JuliaStats)

| Python | Julia | Performance |
|--------|-------|-------------|
| polars | DataFrames.jl | Comparable |
| dask | Dagger.jl | DAG scheduler |
| pymc | Turing.jl | Often faster |
| statsmodels | GLM.jl + MixedModels.jl | Native |
| networkx | Graphs.jl | Much faster |

### Visualization (Makie + Plots)

| Python | Julia | Notes |
|--------|-------|-------|
| matplotlib | Plots.jl + CairoMakie.jl | Multi-backend |
| plotly | PlotlyJS.jl + WGLMakie.jl | Interactive |
| seaborn | AlgebraOfGraphics.jl | Grammar-of-graphics |

## Document Processing (Papers/OCR)

| Python | Julia | Use |
|--------|-------|-----|
| pdfminer | PDFIO.jl | Native PDF parsing |
| pytesseract | Tesseract.jl | OCR wrapper |
| markdown | Weave.jl + Literate.jl | Literate programming |
| latex | TikzPictures.jl + PGFPlotsX.jl | Publication quality |

### Mathpix Integration

```julia
using HTTP, JSON3

function mathpix_ocr(image_path; app_id, app_key)
    headers = ["app_id" => app_id, "app_key" => app_key,
               "Content-type" => "application/json"]
    body = JSON3.write(Dict(
        "src" => "data:image/png;base64," * base64encode(read(image_path)),
        "formats" => ["latex_styled", "text"]
    ))
    resp = HTTP.post("https://api.mathpix.com/v3/text", headers, body)
    JSON3.read(resp.body)
end
```

## Key Julia Organizations

| Org | Focus | Packages |
|-----|-------|----------|
| **BioJulia** | Bioinformatics | 90+ packages |
| **JuliaMolSim** | Molecular simulation | Molly, DFTK, AtomsBase |
| **Chemellia** | Chemistry ML | AtomicGraphNets, ChemistryFeaturization |
| **QuantumBFS** | Quantum computing | Yao, YaoBlocks |
| **FluxML** | Deep learning | Flux, Zygote, FluxTraining |
| **JuliaStats** | Statistics | GLM, Distributions, Turing |
| **JuliaAstro** | Astronomy | AstroLib, FITSIO, SkyCoords |
| **JuliaHealth** | Medical/clinical | BioMedQuery, OMOP |
| **JuliaGeo** | Geospatial | GeoDataFrames, ArchGDAL |
| **SciML** | Scientific ML | DifferentialEquations, ModelingToolkit |

## Usage Examples

### Single-Cell Analysis (scanpy → SingleCellProjections.jl)

```julia
using SingleCellProjections, Muon

# Load AnnData
adata = readh5ad("pbmc3k.h5ad")

# Process (10x faster than scanpy)
adata = normalize_total(adata)
adata = log1p(adata)
adata = highly_variable_genes(adata)
adata = pca(adata)
adata = umap(adata)
```

### Quantum Circuit (qiskit → Yao.jl)

```julia
using Yao

# Bell state
circuit = chain(2, put(1=>H), control(1, 2=>X))

# Measure
result = measure(zero_state(2) |> circuit, nshots=1000)

# Differentiable!
grad = expect'(Z ⊗ Z, zero_state(2) => circuit)
```

### Molecular GNN (deepchem → Chemellia)

```julia
using AtomicGraphNets, ChemistryFeaturization

# Featurize molecules
mol = smilestomol("CCO")  # ethanol
fg = featurize(mol, GraphNodeFeaturization())

# Train GNN
model = CGCGNModel(fg, target_prop=:logP)
train!(model, molecules, targets)
```

### Bayesian Inference (pymc → Turing.jl)

```julia
using Turing

@model function linear_regression(x, y)
    α ~ Normal(0, 10)
    β ~ Normal(0, 10)
    σ ~ truncated(Normal(0, 1), 0, Inf)
    for i in eachindex(y)
        y[i] ~ Normal(α + β * x[i], σ)
    end
end

chain = sample(linear_regression(x, y), NUTS(), 1000)
```

## Full Mapping Document

See: [JULIA_PACKAGE_MAPPING.md](./JULIA_PACKAGE_MAPPING.md)

## The Homoiconic Bridge: Scheme ↔ SMILES ↔ ACSet

**Deep structural insight**: S-expressions (Scheme), SMILES strings (chemistry), and ACSets share a common foundation — **trees/graphs with recursive self-reference**.

```
Scheme S-expr:   (+ (* 2 3) (- 4 1))     → AST tree
SMILES:          CC(=O)Oc1ccccc1C(=O)O   → Molecular graph
ACSet:           Graph{V,E,src,tgt}       → Typed graph functor

All three: linearized representations of graph structure
```

### What Comes After SMILES: Learnable Chemical Structure

The evolution of molecular representation — **7 parallel streams** colored via Gay.jl (seed=137):

| Gen | Color | Representation | Julia Package | Properties |
|-----|-------|----------------|---------------|------------|
| **1** | `#43D9E1` | SMILES string | MolecularGraph.jl | Canonical, not learnable |
| **2** | `#18CDEF` | SELFIES | *PyCall+selfies* | Robust, generative-friendly |
| **3** | `#18D6D0` | Fingerprints | MolecularGraph.jl | Fixed-dim vectors |
| **4** | `#C70D22` | Graph features | ChemistryFeaturization.jl | Handcrafted node/edge |
| **5** | `#E44ABB` | GNN (MPNN/GAT/SchNet) | GraphNeuralNetworks.jl | **Fully learnable** |
| **6** | `#58A021` | 3D coordinates | Chemfiles.jl, DFTK.jl | Geometry-aware |
| **7** | `#BDB223` | Foundation models | *Coming* | Pre-trained, transferable |

**Parallel Evolution Insight**: Each generation evolves along its own deterministic color stream.
Workers 1-3 explore the space in parallel (Strong Parallelism Invariance: same seeds = same colors).

```
Stream 1 (SMILES):      #43D9E1 → #B78225 → #D54E82  (canonical → extended → stereochem)
Stream 2 (SELFIES):     #18CDEF → #6CBA3C → #EC9426  (robust → constrained → grammar)
Stream 5 (GNN):         #E44ABB → #50CD2E → #942B89  (MPNN → GAT → SchNet/DimeNet)
Stream 7 (Foundation):  #BDB223 → #88ECA7 → #5CDA99  (pretrain → finetune → adapt)
```

```julia
# The homoiconic bridge in code
using LispSyntax, MolecularGraph, Catlab, AtomicGraphNets

# Scheme code → AST → ACSet
sexp = @lisp (defun f (x) (+ x 1))
ast_acset = ast_to_acset(sexp)

# SMILES → Molecular graph → ACSet → GNN embedding
mol = smilestomol("c1ccccc1")  # benzene
mol_acset = mol_to_acset(mol)
embedding = gnn_embed(mol_acset)  # 64-dim learned vector

# Both navigate identically via Specter patterns!
branches_in_ast = select([ALL, pred(is_call_node)], ast_acset)
rings_in_mol = select([ALL, pred(is_ring_atom)], mol_acset)

# The deep insight: code and molecules are both graphs
# → same tools (ACSets, GNNs) work for both
```

### Coloring Parallel Evolution with Gay.jl

```julia
using Gay

# 7 generations evolving in parallel streams (seed=137)
struct MolRepGeneration
    name::String
    color::String
    learnable::Bool
    evolution::Vector{String}  # Color stream for sub-generations
end

function color_mol_evolution(seed=137)
    streams = Gay.interleave(seed, n_streams=7, count=3)

    generations = [
        MolRepGeneration("SMILES", streams[1][1], false, streams[1]),
        MolRepGeneration("SELFIES", streams[2][1], false, streams[2]),
        MolRepGeneration("Fingerprints", streams[3][1], false, streams[3]),
        MolRepGeneration("GraphFeatures", streams[4][1], false, streams[4]),
        MolRepGeneration("GNN", streams[5][1], true, streams[5]),      # Learnable!
        MolRepGeneration("3DCoords", streams[6][1], true, streams[6]),
        MolRepGeneration("Foundation", streams[7][1], true, streams[7])
    ]

    # GF(3) balance: non-learnable (-1) + transition (0) + learnable (+1) = 0
    return generations
end

# Visualize evolution paths
for gen in color_mol_evolution()
    trit = gen.learnable ? "+1" : "-1"
    println("$(gen.name) [$(trit)]: $(join(gen.evolution, " → "))")
end
```

### Key Julia Packages for Learnable Chemistry

| Package | Role | From Python | GNN Arch |
|---------|------|-------------|----------|
| **MolecularGraph.jl** | SMILES parsing, fingerprints | rdkit | — |
| **ChemistryFeaturization.jl** | Node/edge featurization | deepchem | — |
| **GraphNeuralNetworks.jl** | MPNN, GCN, GAT, GraphSAGE | torch_geometric, dgl | ✓ |
| **GeometricFlux.jl** | Geometric deep learning | PyG | ✓ |
| **Flux.jl** | Training infrastructure | pytorch | — |
| **Chemfiles.jl** | 3D structure I/O | MDAnalysis | — |
| **DFTK.jl** | Electronic structure (DFT) | pymatgen | — |
| **NNlib.jl** | Neural network primitives | torch.nn | — |

**GNN Architecture Evolution**:
```
MPNN (2017) → GCN → GAT (attention) → SchNet (3D) → DimeNet → Equivariant GNNs
     ↓              ↓                    ↓
  Message      Graph Attention      Geometry-aware
  Passing      (multi-head)         (E(3) invariant)
```

## Related Skills

- **acsets** - Algebraic databases with Gay.jl coloring
- **gay-julia** / **julia-gay** - Deterministic color generation
- **specter-acset** - Bidirectional navigation
- **structured-decomp** - Sheaf-based decompositions
- **condensed-analytic-stacks** - Scholze-Clausen mathematics
- **lispsyntax-acset** - S-expression ↔ ACSet bridge

## Commands

```bash
# Search Julia equivalents
julia -e 'using Pkg; Pkg.status()' | grep -i biojulia

# Install BioJulia stack
julia -e 'using Pkg; Pkg.add(["BioSequences", "FASTX", "XAM", "BioStructures"])'

# Install ML stack
julia -e 'using Pkg; Pkg.add(["Flux", "MLJ", "GraphNeuralNetworks"])'

# Install quantum stack
julia -e 'using Pkg; Pkg.add(["Yao", "QuantumToolbox"])'
```

## GF(3) Skill Triads

```
julia-scientific (0) ⊗ gay-mcp (+1) ⊗ acsets (-1) = 0 ✓
julia-scientific (0) ⊗ specter-acset (+1) ⊗ structured-decomp (-1) = 0 ✓
```

---

*Generated from exhaustive parallel search of Julia package ecosystem (2025-12-30)*

## SDF Interleaving

This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021):

### Primary Chapter: 3. Variations on an Arithmetic Theme

**Concepts**: generic arithmetic, coercion, symbolic, numeric

### GF(3) Balanced Triad

```
julia-scientific (+) + SDF.Ch3 (○) + [balancer] (−) = 0
```

**Skill Trit**: 1 (PLUS - generation)

### Secondary Chapters

- Ch9: Generic Procedures
- Ch8: Degeneracy
- Ch7: Propagators
- Ch4: Pattern Matching
- Ch2: Domain-Specific Languages
- Ch10: Adventure Game Example

### Connection Pattern

Generic arithmetic crosses type boundaries. This skill handles heterogeneous data.

Overview

This skill maps 137 K-Dense-AI Python scientific capabilities to native Julia packages and interoperable Julia workflows. It focuses on bioinformatics, chemistry, ML/AI, quantum, data science, visualization, and document processing with roughly 85% native coverage and PyCall fallback where needed. The goal is faster, more composable scientific code using established Julia ecosystems like BioJulia, Flux, Yao, and SciML.

How this skill works

The skill provides one-to-one and one-to-many mappings from common Python libraries to Julia equivalents, notes performance and feature differences, and lists recommended Julia packages and organizations. It highlights idiomatic Julia patterns (multiple dispatch, composable toolchains) and includes code snippets showing common conversions and minimal examples for workflows (single-cell, quantum circuits, molecular GNNs, Bayesian inference). Where a native package is not available, the skill recommends PyCall interop and concrete wrappers.

When to use it

  • Porting an existing Python scientific pipeline to Julia for speed or composability.
  • Choosing Julia libraries that replace common Python tools in bioinformatics, chemistry, quantum, ML, or data science.
  • Building new projects that benefit from Julia features: multiple dispatch, differentiable programming, and native GPU support.
  • Rapid prototyping of GNNs, DFT workflows, or probabilistic models with Julia-native tooling.
  • Integrating Python-only tools via PyCall when a direct Julia equivalent is unavailable.

Best practices

  • Prefer native Julia packages (BioJulia, Flux, Yao, Turing) for performance and interoperability.
  • Use composable building blocks (DataFrames, ModelingToolkit, DifferentialEquations) rather than monoliths.
  • Leverage Julia’s multiple dispatch to design clear, extensible APIs when replacing Python abstractions.
  • Benchmark and profile translations; many Julia equivalents run 2–10x faster but require idiomatic refactoring.
  • Use PyCall for incremental migration when a full native replacement is not yet available.

Example use cases

  • Convert a scanpy single-cell pipeline to SingleCellProjections.jl + Muon for faster preprocessing and UMAP.
  • Replace rdkit-based featurization with MolecularGraph.jl and train a GNN using GraphNeuralNetworks.jl + Flux.jl.
  • Run differentiable quantum circuit experiments with Yao.jl instead of Qiskit for native autodiff and GPU.
  • Port a PyMC model to Turing.jl for faster NUTS sampling and easier integration with Julia stats packages.
  • Process PDFs and extract text/images using PDFIO.jl and Tesseract.jl in a literate reproducible workflow.

FAQ

Is every Python library covered with a native Julia equivalent?

Coverage is about 85% native; the remainder is accessible via PyCall interop or targeted wrappers.

Will performance always be better in Julia?

Many tasks show 2–10x speedups, but gains depend on idiomatic use; profiling and refactoring often unlock benefits.

Can I mix Julia and Python tools in the same workflow?

Yes. PyCall allows incremental migration and hybrid workflows, enabling reuse of Python-only tools while adopting Julia packages.