semvec/pypi-packages huggingface dataset:
fast_pyworld3 1.0.0
A high-performance Python implementation of the World3 model from Limits to Growth (2004). 20x faster than PyWorld3-03.
Requires Python: >=3.13
Keywords: world3, limits-to-growth, simulation, system-dynamics, modeling

# fast_pyworld3

A high-performance Python implementation of the World3 model from "Limits to Growth: The 30-Year Update" (2004). Resolves circular dependency issues for faster simulation.

## Installation

```bash
pip install fast_pyworld3
```

## Usage

```python
from fast_pyworld3 import World3

world3 = World3()
world3.run()
results = world3.get_results()
```

```python
from fast_pyworld3.core import World3, World3Config
config = World3Config(year_max=2050, dt=0.25)
world3 = World3(config)
world3.init_constants(nri=1.5e12)  # custom
world3.init_variables()
world3.set_table_functions()
world3.set_delay_functions()
world3.run()

```


## Simulation Sectors

- Population
- Capital (Industrial/Service)
- Agriculture
- Pollution (Persistent)
- Resources (Nonrenewable)

## Benchmark

**20x faster** than PyWorld3-03 with identical results.

| Metric  | PyWorld3-03 | fast_pyworld3 |
| ------- | ----------- | ------------- |
| Mean    | 1903.91 ms  | 94.24 ms      |
| Speedup | -           | **20.20x**    |

### Validation

| Variable          | Correlation | Mean Rel Err | Max Rel Err |
| ----------------- | ----------- | ------------ | ----------- |
| Total Population  | 1.000000    | 0.0000%      | 0.0000%     |
| Industrial Output | 1.000000    | 0.0000%      | 0.0000%     |
| Food Per Capita   | 1.000000    | 0.0000%      | 0.0000%     |
| Pollution Index   | 1.000000    | 0.0000%      | 0.0000%     |
| Natural Resources | 1.000000    | 0.0000%      | 0.0000%     |

### Time Step Scaling

| dt    | Steps | PyWorld3-03 | fast_pyworld3 | Speedup |
| ----- | ----- | ----------- | ------------- | ------- |
| 1.000 | 201   | 978.25 ms   | 50.67 ms      | 19.31x  |
| 0.500 | 401   | 1915.30 ms  | 92.16 ms      | 20.78x  |
| 0.250 | 801   | 3840.44 ms  | 190.58 ms     | 20.15x  |
| 0.125 | 1601  | 7794.03 ms  | 381.27 ms     | 20.44x  |

## Requirements

- Python >= 3.13
- numpy, scipy, matplotlib

## License

CeCILL v2.1
teachbooks 0.2.4
TeachBooks wrapper around JupyterBooks
Requires Python: >=3.10

# TeachBooks Package: Jupyter Book Wrapper

This Python package is primarily a wrapper around the Jupyter Book package and is designed to facilitate usage of the platform in educational contexts. In this case "wrapper" refers to the CLI usage: CLI commands generally invoke `jupyter-book` commands internally; the `jupyter-book` package is _not_ distributed within the `teachbooks` package.

The source code and function of the package is documented at [teachbooks.readthedocs.io](https://teachbooks.readthedocs.io). Visit the TeachBooks [website](https://teachbooks.io) and [dedicated Manual page](https://teachbooks.io/manual/features/overview.html#teachbooks-python-package) to learn more about how this package is used in an educational context.

## Updates and Improvements

Expect frequent updates to the package as patches and minor releases until further notice. We expect to release `v1.0.0` in Spring, 2025. Update the package in your local environment using using `pip install --upgrade teachbooks`.

Contributions are ideally made via a fork and pull request to the (default) `develop` branch (see the project [Documentation](https://teachbooks.readthedocs.io) for detailed instructions).

## Documentation Website

The documentation for this package is built using Sphinx and @pradyunsg's Furo; use the [Furo documentation](https://pradyunsg.me/furo/#) as a reference when updating the documentation site.

The Read the Docs website [teachbooks.readthedocs.io](https://teachbooks.readthedocs.io) maintains documentation for each tagged release beginning with `v0.2.0`. The documentation website is also deployed from GitHub Pages from the `stable` branch and can be accessed at [teachbooks.io/TeachBooks/](https://teachbooks.io/TeachBooks/). This should remain identical to the "latest" (default) Read the Docs documentation page as long as the most recent tagged release is on branch `stable`.

## Acknowledgements

This package received financial support from the Civil Engineering and Geosciences faculty at Delft University of Technology in the Netherlands via Education Innovation Projects, [MUDE](https://mude.citg.tudelft.nl) and direct financial support of Jupyter Book applications in education by the CEG faculty. The project also received funding from the TU Delft Library at the end of 2024. Bart Schilperoort of the Netherlands eScience Center implemented the external contents module and also provided critical advice on the journey to `v1.0.0`.

The first version of this package was created and released by Caspar Jungbacker in Spring, 2024 and has since been primarily maintained by a variety of TeachBooks contributors.
arglib 0.1.11
Build a general-purpose, batteries-included Python library for creating, importing, analyzing, and reasoning over argument graphs derived from text and multimodal evidence.
Requires Python: >=3.11

# ArgLib

[![PyPI](https://img.shields.io/pypi/v/arglib)](https://pypi.org/project/arglib/)
[![Python](https://img.shields.io/pypi/pyversions/arglib)](https://pypi.org/project/arglib/)
[![CI](https://github.com/vasanthsarathy/arglib/actions/workflows/ci.yml/badge.svg)](https://github.com/vasanthsarathy/arglib/actions/workflows/ci.yml)
[![Docs](https://github.com/vasanthsarathy/arglib/actions/workflows/docs.yml/badge.svg)](https://vasanthsarathy.github.io/arglib/)
[![License](https://img.shields.io/github/license/vasanthsarathy/arglib)](LICENSE)

ArgLib is a batteries-included Python library for creating, importing, analyzing, and reasoning over argument graphs derived from text and multimodal evidence.

## Highlights
- Canonical `ArgumentGraph` model with provenance-aware nodes and relations.
- Warrant-gated scoring with claim, warrant, and gate scores.
- Axiom flags to seed manual scores with optional influence locking.
- Diagnostics for cycles, components, reachability, and degree stats.
- JSON IO with schema validation and Graphviz DOT export.
- CLI tools for DOT, diagnostics, and validation.
- Argument bundles for higher-level reasoning and credibility propagation scoring.
- Evidence cards and supporting documents for evidence pipelines.
- Deterministic evidence scoring and edge validation helpers (LLM adapters planned).

## Install
```bash
python -m pip install arglib
```

## Quickstart
```python
from arglib.core import ArgumentGraph
from arglib.reasoning import compute_credibility

graph = ArgumentGraph.new(title="Parks")
c1 = graph.add_claim("Green spaces reduce urban heat.", type="fact")
c2 = graph.add_claim("Cities should fund parks.", type="policy")
graph.add_support(c1, c2, rationale="Cooling improves health", gate_mode="OR")

credibility = compute_credibility(graph)
scores = credibility.final_scores
```

## Evidence and scoring
```python
from arglib.ai import score_evidence, validate_edges

scores = score_evidence(graph)
edge_report = validate_edges(graph)
```

## Axioms
```python
claim = graph.add_claim("Assume baseline demand holds.", is_axiom=True, score=0.6)
warrant = graph.add_warrant("This baseline is reliable.", is_axiom=True, score=0.7)
graph.units[claim].ignore_influence = True
```

## Bundles and credibility propagation
```python
from arglib.reasoning import compute_credibility

bundle = graph.define_argument([c1, c2], bundle_id="arg-1")
cred = compute_credibility(graph)
```

## CLI examples
```bash
arglib dot path/to/graph.json
arglib diagnostics path/to/graph.json --validate
arglib validate path/to/graph.json
```

## Development
This repo uses `uv` for dependency management.
```bash
uv sync
scripts/check.sh
```

## Documentation
Full docs and guides are available at https://vasanthsarathy.github.io/arglib/.