semvec/pypi-packages huggingface dataset:
windkit 2.0.1
WindKit provides core functionalities for working with wind resource data
Requires Python: >=3.11

# WindKit

[![pipeline status](https://gitlab-internal.windenergy.dtu.dk/ram/software/pywasp/windkit/badges/master/pipeline.svg)](https://gitlab-internal.windenergy.dtu.dk/ram/software/pywasp/windkit/-/pipelines)
[![coverage](https://gitlab-internal.windenergy.dtu.dk/ram/software/pywasp/windkit/badges/master/coverage.svg)](https://gitlab-internal.windenergy.dtu.dk/ram/software/pywasp/windkit/-/pipelines)
[![release](https://gitlab-internal.windenergy.dtu.dk/ram/software/pywasp/windkit/-/badges/release.svg)](https://gitlab-internal.windenergy.dtu.dk/ram/software/pywasp/windkit/-/releases)
[![License: BSD-3-Clause](https://img.shields.io/badge/License-BSD--3--Clause-blue.svg)](LICENSE)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
[![Docs](https://img.shields.io/badge/docs-docs.wasp.dk-blue?logo=readthedocs&logoColor=white)](https://docs.wasp.dk/windkit)

[![PyPI](https://img.shields.io/pypi/v/windkit)](https://pypi.org/project/windkit/)
[![Downloads-pypi](https://img.shields.io/pypi/dm/windkit)](https://pypi.org/project/windkit/)


WindKit provides core functionalities for working with wind resource data, built on top of [xarray](https://xarray.dev/) and [geopandas](https://geopandas.org/).

## Installation

**pip:**
```bash
pip install windkit
```

**conda:**
```bash
conda install -c https://conda.windenergy.dtu.dk/channel/open windkit
```

## Quick Start

```python
import windkit as wk

# Read a binned wind climate
bwc = wk.read_bwc("hovsore.tab")

# Calculate mean wind speed
mean_ws = wk.mean_wind_speed(bwc)
print(f"Mean wind speed: {mean_ws.values:.2f} m/s")

# Fit Weibull distribution and convert to Weibull wind climate
wwc = wk.weibull_fit(bwc)
```

## Features

- **Wind Climate I/O** - Read/write binned, Weibull, generalized, and time-series wind climates (NetCDF, .lib, .tab, .rsf, .wrg)
- **Map I/O** - Read/write elevation, roughness, and landcover maps (raster and vector formats)
- **WAsP Integration** - Read WAsP Workspace files and WAsP Engineering project files
- **Spatial Processing** - Clip, reproject, and interpolate spatial data with full CRS support
- **Wind Statistics** - Calculate mean wind speed, power density, Weibull parameters, and more
- **Plotting** - Interactive plots with Plotly, static plots with Matplotlib
- **Remote Data Access** - Download ERA5 reanalysis data and satellite-derived maps

## Documentation

Full documentation: [docs.wasp.dk/windkit](https://docs.wasp.dk/windkit)

Community forum: [WAsP Python tools](https://www.wasptechnical.dk/forum/forum/19-wasp-python-tools/)

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for development setup and guidelines.

## License

BSD-3-Clause - see [LICENSE](LICENSE) for details.
python-matchit 0.3.0
Bindings for matchit rust library.
Requires Python: >=3.8

**python-matchit** package contains binding for [matchit](https://docs.rs/matchit/latest/matchit/) rust library.

# Installation

```shell
pip install python-matchit
```

# Usage

```python
from matchit import Router
router = Router()
router.insert("/users/{id}", "test_id")

res = router.at("/users/123")

assert res.value == "test_id"
assert res.params == {"id": "123"}

try:
    assert r.at("/noway")
except LookupError:
    print("Not found as expected")
```
lazysdk 0.2.20
基于Python的懒人包

# lazysdk
![](https://img.shields.io/badge/Python-3.8.6-green.svg)


#### 介绍
基于Python3的懒人包
详细文档见：https://gzcaxecc4u.feishu.cn/docx/EaymdTdIQolphcxgD2jcMsjJn6c

#### 软件架构
软件架构说明
- lazysdk.lazyprocess
  - 多进程控制

- lazysdk.lazywebhook
  - webhook推送
  
#### 安装教程
1.  使用pip安装
- 普通方式安装
```shell script
pip3 install lazysdk
```

- 使用阿里镜像加速安装
```shell script
pip3 install lazysdk -i https://mirrors.aliyun.com/pypi/simple
```

#### 使用说明

1. lazyprocess
```python3
import lazysdk

task_list = [1,2,3,4,5,6,7,8]
def process_task(
        task_index, 
        task_info
):
    print(f'task_index:{task_index}, task_info:{task_info}')
    
    
lazysdk.lazyprocess.run(
    task_list=task_list,
    task_function=process_task,
    subprocess_limit=2
)
```

2. lazywebhook
- 目前仅支持企业微信
```python3
from lazysdk import lazywebhook
    
lazywebhook.send_text(
    webhook='webhook url'
)
```


## Links
- PyPI Releases: https://pypi.org/project/lazysdk/
stillsuit 0.1.0
A place to store all that is precious.
Requires Python: >=3.9

# stillsuit

A place to store the buffers

## Example

```bash
e1-056827:offline crh184$ cat merge.sh 
# First stage clustering
stillsuit-merge-reduce \
	--config-schema fake_cbc.yaml \
	--clustering-column network_chisq_weighted_snr \
	--clustering-window 0.1 \
	--db-to-insert-into cluster1.sqlite \
	--verbose \
        --dbs out.sqlite 

# Second stage clustering
stillsuit-merge-reduce \
	--config-schema fake_cbc.yaml \
	--clustering-column likelihood \
	--clustering-window 4.0 \
	--db-to-insert-into cluster2.sqlite \
	--verbose \
        --dbs cluster1.sqlite 
```
pySingleCellNet 0.1.4
Single-cell classification and analysis, optimized for embryonic and fetal development
Requires Python: >=3.9
Keywords: single cell, cell typing, classification, embryo, embryoid, gastruloid

# pySingleCellNet

[![Docs Status](https://readthedocs.org/projects/pysinglecellnet/badge/?version=latest)](https://cahanlab-pysinglecellnet.readthedocs-hosted.com/)


[![GitHub stars](https://img.shields.io/github/stars/CahanLab/PySingleCellNet.svg?style=social&label=Star)](https://github.com/CahanLab/PySingleCellNet/stargazers)

[![PyPI version](https://img.shields.io/pypi/v/pySingleCellNet.svg)](https://pypi.org/project/pySingleCellNet/)

**pySingleCellNet** helps you classify and analyze single-cell RNA-Seq data, …

# PySingleCellNet
### A computational toolkit for the single cell analysis and comparison of embryos and embryo models
PySingleCellNet (PySCN) predicts the 'cell type' of query scRNA-seq data by Random forest multi-class classification. See [Tan & Cahan 2019] for more details. PySCN includes functionality to aid in the analysis of engineered cell populations (i.e. cells derived via directed differentiation of pluripotent stem cells or via direct conversion).

[Tan & Cahan 2019]: https://doi.org/10.1016/j.cels.2019.06.004
[github]: https://github.com/pcahan1/PySingleCellNet
[original version]: https://github.com/pcahan1/PySingleCellNet
mircat-v2 1.0.8
Add your description here
Requires Python: >=3.11

# MirCAT-v2
Mirshahi-Lab CT Analysis Toolkit (MirCAT). Convert dicoms, segment niftis, extract data.  
V1 has been archived. This version is almost a full rewrite of the segmentation section, specifically to incorporate nnUNet models directly into the pipeline.


# NOTES
When using docker -> make sure to add the --ipc=host flag or segmentation will not work