semvec/pypi-packages huggingface dataset:
plans 1.1.3
A simulation tool for planning nature-based solutions in watershed conservation projects
Requires Python: >=3.11
Keywords: python, watershed conservation, nature-based solutions, ecosystem services, water resources, hydrology, topmodel, hillslope hydrology

![Style Status](https://github.com/iporepos/plans/actions/workflows/style.yaml/badge.svg)
![Docs Status](https://github.com/iporepos/copyme/actions/workflows/docs.yaml/badge.svg)
![Tests Status](https://github.com/iporepos/plans/actions/workflows/tests.yaml/badge.svg)
![Top Language](https://img.shields.io/github/languages/top/iporepos/copyme)
[![Documentation](https://img.shields.io/badge/docs-online-blue)](https://iporepos.github.io/plans/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17142043.svg)](https://doi.org/10.5281/zenodo.17142043)
[![PyPI Latest Release](https://img.shields.io/pypi/v/plans.svg?label=PyPI)](https://pypi.org/project/plans/)
[![PyPI Downloads](https://img.shields.io/pypi/dm/plans.svg?label=PyPI%20downloads)](
https://pypi.org/project/plans/)

<a logo>
<img src="https://github.com/iporepos/plans-assets/blob/main/docs/figs/logo.jpg?raw=true" height="130" width="130">
</a>


---

# plans

A simulation tool that supports watershed conservation 
projects in planning the expansion of nature-based solutions for water
at the hillslope scale.

<a example2>
<img src="https://github.com/iporepos/plans-assets/blob/main/docs/figs/about.jpg?raw=true">
</a>

> [!NOTE]
> Check out the [documentation website](https://iporepos.github.io/plans/) for more info.

---

## Quick Gallery

Mapping riparian wetlands dynamics during infiltration event:

<a example2>
<img src="https://github.com/iporepos/plans-assets/blob/main/docs/gallery/example2.gif?raw=true">
</a>


---

# Installation

As a Python package in any OS:

```bash
python -m pip install plans
```

As a Windows 11 application:

1. Install Python 3 (add to PATH during installation);
2. Download ``install-plans.ps1`` from the GitHub repository;
3. Run ``install-plans.ps1`` with ``PowerShell``.


> [!NOTE]
> See more details in the [documentation website](https://iporepos.github.io/plans/)


---

# Repository layout

```txt
plans/
  │
  ├── LICENSE
  ├── README.md                     # this file (landing page)
  ├── ...                           # configuration files
  │
  ├── src/                          # source code
  │    └── plans/                   # library
  ├── tests/                        # testing resources
  ├── docs/                         # documentation resources 
  ├── dev/                          # development resources 
  └── examples/                     # learning resources       

```
radt 0.2.26
Resource-Aware Data systems Tracker (radT) for automatically tracking and training machine learning software.

<div align="center">

[![preview](media/logo_rad.png)](#readme)

</div>

# radT

radT (Resource Aware Data science Tracker) is an extension to MLFlow that simplifies the collection and exploration of hardware metrics of machine learning and deep learning applications. Usually, collecting and processing all the required metrics for these workloads is a hassle. In contrast, RADT is easy to deploy and use, with minimal impact on both performance and time investment. The codebase of RADT is documented and easily expandable.

This work has been published at the SIGMOD workshop DEEM 2023: [Data Management and Visualization for Benchmarking
Deep Learning Training Systems](https://itu-dasyalab.github.io/RAD/publication/papers/DEEM2023.pdf)

```sh
pip install radt
```

<!-- - [Documentation](https://radt.readthedocs.io/) -->
- [Examples](https://github.com/Resource-Aware-Data-systems-RAD/radt/tree/master/examples/#readme)
- [radT on Github](https://github.com/Resource-Aware-Data-Systems-RAD/radt)
- [radT on PyPI](https://pypi.org/project/radt/)
scd-cohort-descriptive-cli-140226 0.1.0
CLI for descriptive SCD cohort analyses.
Requires Python: >=3.9

# scd_cohort_descriptive_cli

CLI for descriptive SCD cohort analyses from Arrow/CSV pipeline outputs.

## Names

- Rust crate/package: `scd_cohort_descriptive_cli`
- Binary: `scd_cohort_descriptive_cli`
- Python package (maturin): `scd-cohort-descriptive-cli`

## Run

```bash
cargo run -- --input-dir <input_dir> --output-dir <output_dir>
```

Common flags:

- `--safe-export-dir <dir>`
- `--skip-disclosure-check`
- `--match-ratios 4,3,2`
- `--min-year <year> --max-year <year>`

## Test and Lint

```bash
cargo test
cargo clippy --all-targets -- -W clippy::pedantic -W clippy::nursery
```

## Build Python Binary Wheel

This project uses maturin with `bindings = "bin"` and the root `Cargo.toml`:

```bash
maturin build
```