semvec/pypi-packages huggingface dataset:
stGACN 0.1
add douyin effect to image

# stGACN

[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

`stGACN` is a Python package for quantitative characterization and interpretation of rare spatial heterogeneity from spatial transcriptomics data. 

### Installation
Users need to create an environment and install stGACN by following procedures:
```
conda create -n stGACN_env python=3.8
conda activate stGACN_env
pip install -r requirements.txt
pip install stGACN
```
### Usage
Input data of stGACN :
- The input files include various data formats, with `h5ad` being a representative example containing spatial transcriptomics data with spatial coordinates stored in `.obsm[‘spatial’]`.

```
import scanpy as sc
file_path = '/home/user/data/spatial_data.h5ad'
adata = sc.read(file_path)
```

The definition and training step of stGACN are carried out as follows:
```
# model definition  
model = stGACN.stGACN(adata,device=device)
# model training
adata = model.train()
```

Subsequently, clustering analysis can be performed using algorithms such as `mclust` or `leiden`.

**Note:** Before using the `mclust` algorithm, make sure to install the `mclust` package in R and configure `os.environ['R_HOME']` with the correct path.

```
# Apply clustering algorithm
from stGACN.utils import clustering
clustering(adata, n_clusters)
```

## Tutorial
Please see the Jupyter notebook in the **Tutorial** folder. It includes several tutorials, providing examples across different species, sequencing technologies, and diseases.

## License
This project is covered under the **MIT License**.
mera-visualizer 1.1.1
A visualizer to see which node runs on SAKURA device.
Requires Python: >=3.6

# mera-visualizer

This visualizer allows you to see which node runs on which device (e.g., SAKURA-II, CPU).

# Installation

Note: We recommend using a virtual environment (conda or venv) for it.

## From `pip` Repository:

```
pip install mera-visualizer
```
You can specify the version `mera-visualizer==1.0.0`.

## From Source: 
Clone the repository and run 
```
pip install -e .
```

<hr>

# Running the Visualizer 

```
mera-visualizer --port=5566
```

This will run a server at `5566` port. 

Visit `http://127.0.0.1:5566` and upload the `model_subgraphs.json` file.




> Note: The `model_subgraphs.json` is generated during the compilation of the model using MERA. If your deployment directory name is `./deploy_vit-small/`, the JSON file might be located at `./deploy_vit-small/mera_deployment/build/InterpreterHwBf16/model_subgraphs.json`.
dendroplot 0.7.0
Helper tasks for astrodendro

# dendroplot
Python analysis scripts for applying dendrograms to LMC ALMA data, and visualizing the results.  Still under active development, but currently available are the following:

`from dendroplot import run_dendro`

`from dendroplot.analysis import calc_phys_props, find_clusters, get_response_width`

`from dendroplot.lte import lte, add_ltemass`

`from dendroplot.plotting import pltprops, colorcode, comp_props`

To get started, please see the sample notebook in `example` and the various scripts in `published`.

## Required packages

- [numpy](https://numpy.org)
- [matplotlib](https://matplotlib.org)
- [astropy](https://astropy.org)
- [astrodendro](https://github.com/tonywong94/astrodendro) (fork by Tony Wong)
- [scimes](https://github.com/tonywong94/SCIMES) (fork by Tony Wong)

## Installation

Install the package directly from PyPI (https://pypi.org/project/dendroplot/) using

    pip install dendroplot

or, if you prefer to keep the latest source code handy, by cloning this Github repository and running

    pip install .

in the directory containing `setup.py`.
PlvLogger 0.166
add Singleton
Requires Python: >=3.10

## PlvLogger: Простая библиотека логирования для Python

PlvLogger - это простая библиотека логирования для Python, разработанная для обеспечения простой настройки и конфигурации логирования в файлы с возможностью их ротации.

### Установка

Вы можете установить PlvLogger с помощью pip:

```bash
pip install PlvLogger
```

### Использование
```
from PlvLogger import Logger

# Создание экземпляра логгера
logger = Logger(logger_name="my_logger", type_log="w", when="D")

# Логирование сообщения с предупреждением
logger.logger.warning("Это сообщение с предупреждением")

# Логирование информационного сообщения
logger.logger.info("Это информационное сообщение")

#Дополнительные параметры для ротации логов:)-
# - 'S': Ротация логов каждые N секунд.
# - 'M': Ротация логов каждые N минут.
# - 'H': Ротация логов каждые N часов.
# - 'D': Ротация логов каждые N дней. По умолчанию ротация происходит в полночь.
# - 'W0'-'W6': Ротация логов в определенный день недели &#40;'W0' - понедельник, 'W1' - вторник, и так далее до 'W6' - воскресенье&#41;. Время ротации также по умолчанию установлено на полночь.
```

### Пример
```
# Создание экземпляра логгера с ротацией логов каждые 2 часа
logger = Logger(logger_name="my_logger", type_log="w", when="H", interval=2)
```
qube_nic 0.3.0
NIC tool for flashing NICs
Requires Python: <4.0,>=3.11

# NIC Flashing Tool
This tool is used to flash the NIC firmware on Qube devices.

## Build
Note that you have to build the project once for each operating system you want to support.
```
pex esptool rich -o nic.pex -e flasher -D .
```

## Development
Install `poetry`, install dependencies and run the tool:

```
pip install poetry
poetry install
poetry run python flasher.py
```