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Systems","sub_category":"Energy System Modeling Frameworks","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# fluxopt\n\nEnergy system optimization with [linopy](https://github.com/PyPSA/linopy) — detailed dispatch, scaled to multi period planning.\n\n[![PyPI](https://img.shields.io/pypi/v/fluxopt)](https://pypi.org/project/fluxopt/)\n[![Downloads](https://img.shields.io/pypi/dm/fluxopt)](https://pypi.org/project/fluxopt/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)\n[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\n\n\u003e **Early development** — the API may change between releases.\n\u003e Planned features and progress are tracked in [Issues](https://github.com/FBumann/fluxopt/issues).\n\n## Installation\n\n```bash\npip install fluxopt\n```\n\nIncludes the [HiGHS](https://highs.dev/) solver out of the box.\n\n## Quick Start\n\n\u003c!--quickstart-start--\u003e\n```python\n# A gas boiler covers a heat demand, minimizing fuel cost\nfrom datetime import datetime\nfrom fluxopt import Carrier, Converter, Effect, Flow, Port, optimize\n\nresult = optimize(\n    timesteps=[datetime(2024, 1, 1, h) for h in range(4)],\n    carriers=[Carrier(id='gas'), Carrier(id='heat')],\n    effects=[Effect(id='cost')],\n    ports=[\n        Port(id='grid', imports=[Flow(carrier='gas', size=500, effects_per_flow_hour={'cost': 0.04})]),\n        Port(id='demand', exports=[Flow(carrier='heat', size=100, fixed_relative_profile=[0.4, 0.7, 0.5, 0.6])]),\n    ],\n    converters=[\n        Converter.boiler(\n            'boiler',\n            thermal_efficiency=0.9,\n            fuel_flow=Flow(carrier='gas', size=300),\n            thermal_flow=Flow(carrier='heat', size=200),\n        )\n    ],\n    objective='cost',\n)\n\nprint(f'Total cost: {result.objective:.2f}')\nprint(result.flow_rates)\n```\n\u003c!--quickstart-end--\u003e\n\n## One API, four levels of control\n\nEvery level returns the same `Result`; each one only adds control — pick the\nlowest rung that does the job.\n\n**1. One-shot** — `optimize(...)` as above. Elements in, `Result` out, with\nfail-fast validation of ids and references.\n\n**2. Declarative** — gather the same arguments into a reusable, serializable\nsystem. Time series can stay out of the structure as `ProfileRef`s and be\nsupplied at solve time via `profiles`:\n\n```python\nspec = fx.FlowSystem.from_yaml('system.yaml')  # or FlowSystem(...) in Python\nresult = spec.optimize(profiles={'load': demand_ds})\nspec.to_yaml('system.yaml')  # round-trips\n```\n\n**3. Inspectable** — materialize the solver model without solving, inspect or\nextend the underlying linopy model, retarget the objective, then solve:\n\n```python\nmodel = spec.build_model(profiles={'load': demand_ds})  # unbuilt FlowSystemModel\nmodel.build()\nmodel.m.add_constraints(...)  # full linopy access\nresult = model.solve()\n\nmodel.objective = {'cost': 1, 'co2': 50}  # retarget…\nmodel.build()  # …and rebuild\n```\n\nFor a one-off tweak, stay on level 1/2 and pass\n`customize=lambda m: m.m.add_constraints(...)` instead.\n\n**4. Data-level** — build or load the xarray `ModelData` yourself and edit it\nbefore modeling:\n\n```python\ndata = fx.ModelData.build(...)  # or ModelData.from_netcdf(path)\ndata.flows.fixed_profile.loc[{'flow': 'demand(heat)'}] = 0.7\nresult = fx.FlowSystemModel(data, objective='cost').optimize()\n```\n\nResults close the loop: `result.flow_rates`, `result.effect_totals`,\n`result.stats` (KPIs, effect contributions), `result.plot`, netCDF round-trip,\nand `result.data` — the exact `ModelData` the solution came from.\n\n## Roadmap\n\nfluxopt is evolving into a family of packages with a lean core and optional companions:\n\n```\n                          ┌──────────────┐\n                          │   fluxopt    │  core: model building, solving, results, IO\n                          └──────┬───────┘\n        ┌──────────────┬─────────┼──────────────┬──────────────┐\n        │              │         │              │              │\n fluxopt-plot   fluxopt-yaml  fluxopt-tsam  fluxopt-marimo  (examples)\n   plotting      YAML+CSV    time series    interactive     cross-package\n   (plotly)       loader     aggregation       apps          notebooks\n```\n\nCompanion packages depend on core — core has no knowledge of companions.\n\n### Companion packages\n\n| Package | Role | Versioning · Tier | `fluxopt` pin | Status |\n|---------|------|-------------------|---------------|--------|\n| `fluxopt-plot` | Result visualization (Plotly) | Semver · Experimental — method signatures may change | Tight (`\u003e=A.B,\u003cA.C`), validated per release | Scaffolded — [docs](https://fbumann.github.io/fluxopt-plot/latest/) · [#51](https://github.com/FBumann/fluxopt/issues/51) |\n| `fluxopt-yaml` | Declarative model loader (YAML + CSV → `Element`s) | Semver · Experimental — YAML schema may change | Tight (`\u003e=A.B,\u003cA.C`), validated per release | Scaffolded — [docs](https://fbumann.github.io/fluxopt-yaml/latest/) · [#52](https://github.com/FBumann/fluxopt/issues/52) |\n| `fluxopt-tsam` | Time series aggregation — input pre-processing, possibly result disaggregation | Semver · Experimental — round-trip schema may evolve | **Undecided** — depends on whether representative-period primitives live in core (→ loose) or in this package (→ tight) | Planned |\n| `fluxopt-marimo` | Interactive exploration \u0026 dashboards (marimo apps) | CalVer (`YYYY.MM.PATCH`) · Experimental — apps are templates | Tight (`\u003e=A.B,\u003cA.C`), validated per release | Planned |\n\nTight-pinned companions release on every `fluxopt` minor; validation is\nautomated via scheduled CI. `fluxopt-tsam`'s pin policy is blocked on an\narchitectural decision — if representative-period primitives live in core, tsam stays\na thin adapter (loose pin); if they live in tsam, the package owns deep\nround-trip behavior (tight pin).\n\n### Milestones\n\nCross-cutting work not tied to a single companion package:\n\n| Milestone | Description | Status | Issue |\n|-----------|-------------|--------|-------|\n| `Result.stats` accessor | Cached xarray properties for post-processing | Planned | [#49](https://github.com/FBumann/fluxopt/issues/49) |\n| `.plot` stub on `Result` | Discoverable property, helpful error if plot package absent | Planned | [#50](https://github.com/FBumann/fluxopt/issues/50) |\n| ReadTheDocs migration | Automatic versioned docs from git tags | Planned | [#53](https://github.com/FBumann/fluxopt/issues/53) |\n| Remove plotly from core | Keep core lean — plotting deps in `fluxopt-plot` only | Planned | [#54](https://github.com/FBumann/fluxopt/issues/54) |\n\n### Stability Tiers\n\n| Component | Tier | Policy |\n|-----------|------|--------|\n| Core modeling API | **Stable** | Semver. Deprecation warnings before removal. |\n| Stats accessor | **Semi-stable** | Breaking changes allowed between minor versions with changelog entry. |\n\nCompanion packages have their own stability policies — see the table above.\n\nSee [#47](https://github.com/FBumann/fluxopt/issues/47) for the full architecture discussion.\n\n## Development\n\nRequires [uv](https://docs.astral.sh/uv/) and Python \u003e= 3.12.\n\n```bash\nuv sync --group dev      # Install deps\nuv run pytest -v         # Run tests\nuv run ruff check .      # Lint\nuv run ruff format .     # Format\n```\n\n## License\n\nMIT\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/357992","html_url":"https://ost.ecosyste.ms/projects/357992"}