# hgraph
A functional reactive programming engine with a Python front-end.
This provides a DSL and runtime to support the computation of results over time, featuring
a graph based directed acyclic dependency graph and the concept of time-series properties.
The language is function-based, and promotes composition to extend behaviour.
Here is a simple example:
```python
from hgraph import graph, evaluate_graph, GraphConfiguration, const, debug_print
@graph
def main():
a = const(1)
c = a + 2
debug_print("a + 2", c)
evaluate_graph(main, GraphConfiguration())
```
Results in:
```
[1970-01-01 00:00:00.000385][1970-01-01 00:00:00.000001] a + 2: 3
```
See [this](https://hgraph.readthedocs.io/en/latest/) for more information.
## Development
The project is currently configured to make use of [uv](https://github.com/astral-sh/uv) for dependency management.
Take a look at the website to see how best to install the tool.
Here are some useful commands:
First, create a virtual environment in the project directory:
```bash
uv venv
```
Then use the following command to install the project and its dependencies:
```bash
# Install the project with all dependencies
uv pip install -e .
# Install with optional dependencies
uv pip install -e ".[docs,web,notebook]"
# Install with all optional dependencies
uv pip install -e ".[docs,web,notebook,test]"
```
PyCharm can make use of the virtual environment created by uv to ``setup`` the project.
### Run Tests
```bash
# No Coverage
python -m pytest
```
```bash
# Generate Coverage Report
python -m pytest --cov=hgraph --cov-report=xml
```
## Indexing with Context7 MCP
This repository includes a baseline configuration for Context7 MCP to improve code search and retrieval quality.
- See docs/context7_indexing.md for guidance.
- The root-level context7.yaml config sets sensible include/exclude rules, priorities, and summarization hints.
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"description": "# hgraph\nA functional reactive programming engine with a Python front-end.\n\nThis provides a DSL and runtime to support the computation of results over time, featuring\na graph based directed acyclic dependency graph and the concept of time-series properties.\nThe language is function-based, and promotes composition to extend behaviour.\n\nHere is a simple example:\n\n```python\nfrom hgraph import graph, evaluate_graph, GraphConfiguration, const, debug_print\n\n@graph\ndef main():\n a = const(1)\n c = a + 2\n debug_print(\"a + 2\", c)\n\nevaluate_graph(main, GraphConfiguration())\n```\nResults in:\n```\n[1970-01-01 00:00:00.000385][1970-01-01 00:00:00.000001] a + 2: 3\n```\n\nSee [this](https://hgraph.readthedocs.io/en/latest/) for more information.\n\n## Development\n\nThe project is currently configured to make use of [uv](https://github.com/astral-sh/uv) for dependency management. \nTake a look at the website to see how best to install the tool.\n\nHere are some useful commands:\n\nFirst, create a virtual environment in the project directory:\n\n```bash\nuv venv\n```\n\nThen use the following command to install the project and its dependencies:\n\n```bash\n# Install the project with all dependencies\nuv pip install -e .\n\n# Install with optional dependencies\nuv pip install -e \".[docs,web,notebook]\"\n\n# Install with all optional dependencies\nuv pip install -e \".[docs,web,notebook,test]\"\n```\n\nPyCharm can make use of the virtual environment created by uv to ``setup`` the project.\n\n### Run Tests\n\n```bash\n# No Coverage\npython -m pytest\n```\n\n```bash\n# Generate Coverage Report\npython -m pytest --cov=hgraph --cov-report=xml\n```\n\n\n## Indexing with Context7 MCP\n\nThis repository includes a baseline configuration for Context7 MCP to improve code search and retrieval quality.\n\n- See docs/context7_indexing.md for guidance.\n- The root-level context7.yaml config sets sensible include/exclude rules, priorities, and summarization hints.\n",
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