DisSModel 🌍

"Science should not need to be rewritten to go into production."
(A ciência não deve ser reescrita para ir para a produção.)

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📖 Research Trajectory

DisSModel did not emerge from a blank slate. It is the current expression of a research agenda that began in 2001 with an undergraduate thesis on geographic data interoperability using XML and open standards — a time when the central question was already forming:

How can geospatial models be built so that others can understand, reuse, and trust them?

Period	Project	Contribution to the Agenda
2001–2002	Terra Translator (XML, ontologies)	Foundation: geographic data needs semantics and open standards
2005	TerraHS (Haskell + GIS)	Vision: scientific models as verifiable, executable artifacts
2007–2010	TerraME / LuccME (INPE)	Maturity: spatially explicit dynamic models as scientific objects
2015–2024	DbCells, Linked Data, QGIS plugins	Infrastructure: reproducibility demands rich metadata and federated access
2024–2026	DisSModel (Python, FAIR, cloud-native)	Synthesis: same code runs from Jupyter to distributed cluster

Three principles unite this trajectory:

1. 🔓 Openness as method — open source and open data as conditions for scientific validation.
2. 🧩 Interoperability as architecture — systems designed to communicate, avoiding silos.
3. ♻️ Reproducibility as requirement — publishing conditions for re-execution, not just results.

DisSModel is the synthesis: a Python-native, FAIR-aligned, cloud-ready simulation framework where the same scientific code runs unchanged from a Jupyter notebook to a distributed production cluster.

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🎯 About

DisSModel is a modular Python framework for spatially explicit dynamic simulation models. Developed by the LambdaGeo group at the Federal University of Maranhão (UFMA), it provides the simulation layer that connects domain models (LUCC, coastal dynamics) to a reproducible execution environment.

INPE / TerraME Ecosystem	LambdaGeo Ecosystem	Role
TerraME	dissmodel	Generic framework for dynamic spatial modeling
LUCCME	DisSLUCC	LUCC domain models built on dissmodel
—	coastal-dynamics	Coastal domain models built on dissmodel
TerraLib	geopandas / rasterio	Geographic data handling

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🌟 Key Features

• Dual substrate — same model logic runs on vector (GeoDataFrame) and raster (RasterBackend/NumPy).
• Lightweight scheduler — pure-Python time-stepped engine; models auto-register at instantiation and receive clock ticks via setup / pre_execute / execute / post_execute lifecycle hooks.
• Executor pattern — strict separation between science (models) and infrastructure (I/O, CLI, reproducible execution).
• Experiment tracking — every run generates an immutable ExperimentRecord with SHA-256 checksums, TOML snapshot, and full provenance.
• Storage-agnostic I/O — dissmodel.io handles local paths and s3:// URIs transparently.
• Cloud-ready — deploy via Docker, FastAPI, and Redis without changing model code.

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🏗 Architecture

┌──────────────────────────────────────────────────────────┐
│  Science Layer  (Model)                                  │
│  FloodModel, AllocationClueLike, MangroveModel, ...      │
│  → only knows math, geometry and time                    │
├──────────────────────────────────────────────────────────┤
│  Infrastructure Layer  (ModelExecutor)                   │
│  CoastalRasterExecutor, LUCCVectorExecutor, ...          │
│  → only knows URIs, local/S3, column_map, parameters     │
├──────────────────────────────────────────────────────────┤
│  Core modules                                            │
│  dissmodel.core      — Environment, Model, SpatialModel  │
│  dissmodel.geo       — RasterBackend, neighborhoods      │
│  dissmodel.executor  — ModelExecutor ABC, ExperimentRecord│
│  dissmodel.io        — load_dataset / save_dataset       │
│  dissmodel.visualization — Map, RasterMap, Chart         │
└──────────────────────────────────────────────────────────┘

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🚀 Quick Start

1. Install

pip install dissmodel

# Or latest development version
pip install "git+https://github.com/DisSModel/dissmodel.git@main"

2. Write a Model

# forest_fire_model.py
from dissmodel.core import Environment, SpatialModel

class ForestFireModel(SpatialModel):
    def setup(self, prob_spread=0.3):
        self.prob_spread = prob_spread

    def execute(self):
        # Called every step — only math here, no I/O
        burning = self.gdf["state"] == "burning"
        # ... apply spread logic ...

env = Environment(end_time=50)
ForestFireModel(gdf=gdf, prob_spread=0.4)
env.run()

3. Wrap an Executor (for CLI + Provenance)

# my_executor.py
from dissmodel.executor import ExperimentRecord, ModelExecutor
from dissmodel.executor.cli import run_cli
from dissmodel.io import load_dataset, save_dataset

class ForestFireExecutor(ModelExecutor):
    name = "forest_fire"

    def load(self, record: ExperimentRecord):
        gdf, checksum = load_dataset(record.source.uri)
        record.source.checksum = checksum
        return gdf

    def run(self, data, record: ExperimentRecord):
        from dissmodel.core import Environment
        env = Environment(end_time=record.parameters.get("end_time", 50))
        ForestFireModel(gdf=data, **record.parameters)
        env.run()
        return data

    def save(self, result, record: ExperimentRecord) -> ExperimentRecord:
        uri = record.output_path or "output.gpkg"
        checksum = save_dataset(result, uri)
        record.output_path = uri
        record.output_sha256 = checksum
        record.status = "completed"
        return record

if __name__ == "__main__":
    run_cli(ForestFireExecutor)

4. Run via CLI

# Execute a simulation
python my_executor.py run \
  --input data/forest.gpkg \
  --output data/result.gpkg \
  --param end_time=50 \
  --toml model.toml

# Validate data contract without running
python my_executor.py validate --input data/forest.gpkg

# Show resolved parameters
python my_executor.py show --toml model.toml

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📦 ExperimentRecord: Reproducibility by Design

Every run produces an immutable provenance record:

{
  "experiment_id": "abc123",
  "model_commit": "a3f9c12",
  "code_version": "0.5.0",
  "resolved_spec": { "...TOML snapshot..." },
  "source": { "uri": "s3://...", "checksum": "e3b0c44..." },
  "artifacts": { "output": "sha256...", "profiling": "sha256..." },
  "metrics": { "time_run_sec": 2.15, "time_total_sec": 2.34 },
  "status": "completed"
}

Reproduce any past experiment exactly:

curl -X POST http://localhost:8000/experiments/abc123/reproduce \
  -H "X-API-Key: chave-sergio"

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📊 Performance Telemetry

Every run via the executor lifecycle generates a profiling_{id}.md alongside the output:

Phase	Time (s)	% Total	Memory Peak (MB)	I/O Ops
Validate	0.000	0.0%	142	0
Load	0.306	14.7%	387	12 (read)
Run	1.025	49.4%	521	0
Save	0.746	35.9%	498	8 (write)
Total	2.077	100%	521	20

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🧩 Ecosystem: Models & Examples

DisSModel is a core framework. To maintain a clean and specialized environment, all simulation models and implementation examples are hosted in separate repositories within the DisSModel ecosystem.

🔬 Specialized Model Libraries

Repository	Description	Install
DisSModel-CA	Classic Cellular Automata (Game of Life, Forest Fire, Growth)	pip install dissmodel-ca
DisSModel-SysDyn	System Dynamics (SIR, Predator-Prey, Lorenz)	pip install dissmodel-sysdyn
coastal-dynamics	Coastal flooding and mangrove succession models	pip install coastal-dynamics
DisSLUCC	Land Use and Cover Change models (CLUE-inspired)	pip install disslucc

🛠 Implementation Templates

Each repository demonstrates how to:

1. Define a Model: Using SpatialModel and Environment.
2. Wrap an Executor: Using ModelExecutor for I/O and provenance.
3. Deploy: Running via CLI or API.

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📚 Documentation

• 📘 User Guide: https://dissmodel.github.io/dissmodel/
• 🧪 API Reference: https://dissmodel.github.io/dissmodel/api/
• 🎓 Tutorials: https://dissmodel.github.io/dissmodel/tutorials/

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🤝 Contributing

Contributions are welcome! Please read our Contributing Guidelines and Code of Conduct before submitting a pull request.

• 🐛 Report bugs → GitHub Issues
• 💡 Request features → GitHub Discussions
• 📝 Improve docs → Fork, edit, and submit a PR

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🎓 Citation

@software{dissmodel2026,
  author = {Costa, Sérgio and Santos Junior, Nerval},
  title = {DisSModel: A Discrete Spatial Modeling Framework for Python},
  year = {2026},
  publisher = {LambdaGeo, Federal University of Maranhão (UFMA)},
  url = {https://github.com/DisSModel/dissmodel},
  version = {0.5.0}
}

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⚖️ License

MIT © DisSModel — UFMA
See LICENSE for details.