semvec/pypi-packages huggingface dataset:
313 3.1.3
reaction-Telegram-Post By : t.me/goldenxpris.
Requires Python: >=3.6

# r - Gmail Temporary API Client

This Python library allows you to reaction post telegram .

## Installation

To install this package , simply :
```bash
pip install r
```

run - (1) :
```python
import r
r.reaction(link='https://t.me/').start
```
run - (2) :
```python
import r
r.reaction(link='https://t.me/')['while=True'].start
```
run - (3) :
```python
import r
r.reaction(link='https://t.me/')['while=True']['threading=5'].start
```
pyfj 0.1.2
A tool for creating django-ninja projects
Requires Python: >=3.10

# PyFJ

A tool for creating django-ninja projects, based on the template: [django_ninja_template](https://github.com/olivetree123/django_ninja_template)

## Install

Recommended installation using pipx:
```bash
pipx install pyfj
```

You can also install using pip:
```bash
pip install pyfj
```

Or using poetry:

```bash
poetry add pyfj
```

## Usage

After installation, you can use the following commands:

### 1. Create a new project

```bash
# Interactive mode
pyfj create

# Directly specify the name
pyfj create --name=myproject
```

### 2. Rename a project

```bash
# Interactive mode
pyfj rename

# Directly specify the names
pyfj rename --name=old_project_name --new_name=new_project_name
```

### 3. View help

```bash
pyfj --help
pyfj create --help
pyfj rename --help
```

## Features

1. Create new projects - Create a project from the Django Ninja template
2. Rename projects - Rename an existing project, including file contents and directory names

## Requirements

- Python 3.8+
- Git (only required for creating new projects)

## Development

```bash
# Clone the repository
git clone https://github.com/olivetree123/pyfj.git
cd pyfj

# Install dependencies
poetry install

# Install in development mode
poetry install -e .

# Or directly run the test script
python test.py
```
numoptml 0.1.1
Численные методы оптимизации для машинного обучения и автоматизации ИИ
Requires Python: >=3.7

# numoptml

**numoptml** — это лёгкая и понятная Python-библиотека численных методов оптимизации, разработанная с нуля для образовательных, исследовательских и экспериментальных целей в области машинного обучения и автоматизации ИИ.

Она идеально подходит для студентов, инженеров и исследователей, которым важна прозрачность алгоритмов и контроль над процессом оптимизации.

## 🚀 Основные возможности

- Градиентный спуск (Gradient Descent)
- Метод Ньютона (Newton's Method)
- Поддержка подбора длины шага (Backtracking Line Search)
- Подходит для интеграции в AutoML, обучение моделей, RL-агентов

## 🔧 Установка

```bash
pip install numoptml
```

## 📌 Примеры использования

### Gradient Descent

```python
from numoptml.optimizers.gradient_descent import gradient_descent
import numpy as np

f = lambda x: (x[0] - 3)**2
grad = lambda x: np.array([2 * (x[0] - 3)])
x0 = [0.0]

res = gradient_descent(f, grad, x0, lr=0.1)
print(res)  # -> [3.]
```

### Newton's Method

```python
from numoptml.optimizers.newton_method import newton_method
import numpy as np

f = lambda x: (x[0] - 3)**2
grad = lambda x: np.array([2 * (x[0] - 3)])
hess = lambda x: np.array([[2]])
x0 = [0.0]

res = newton_method(f, grad, hess, x0)
print(res)  # -> [3.]
```

### Backtracking Line Search

```python
from numoptml.utils.line_search import backtracking_line_search
import numpy as np

f = lambda x: (x[0] - 3)**2
grad = lambda x: np.array([2 * (x[0] - 3)])
x = np.array([0.0])
direction = grad(x)

alpha = backtracking_line_search(f, grad, x, direction)
print(alpha)
```

## 🧠 Использование в ИИ

- Минимизация функции ошибки при обучении моделей
- AutoML: подбор гиперпараметров
- Оптимизация политик RL-агентов
- Edge AI: сжатие и оптимизация моделей
- Использование в MLOps пайплайнах

## 🗂 Структура проекта

- `optimizers/`: алгоритмы градиентного спуска и Ньютона
- `utils/`: утилиты (линейный поиск и т.д.)
- `examples/`: примеры
- `tests/`: автоматические тесты

## 📄 Лицензия

MIT — свободно используйте и модифицируйте.
celltypemark 0.3.0
cell type annotation with scRNA sequencing data using any marker gene sets
Requires Python: >=3.10

# CellTypeMark

CellTypeMark is a Python package for cell type annotation in single-cell RNA sequencing data using marker genes. It provides tools for scoring and annotating cell types based on predefined marker gene sets.

## Features

- Score cell types using marker genes (using scanpy score_genes)
- Automatic cell type annotation based on marker gene expression
- Visualization of cell type annotations
- Integration with Scanpy and AnnData objects
- Support for custom marker gene sets

## Installation

```bash
pip install celltypemark
```

## Usage

```python
import scanpy as sc
import celltypemark as ctm

# Load your AnnData object
adata = sc.read_h5ad("your_data.h5ad")

# Score cell types using default marker genes
adata = ctm.score(adata, ctm.marker_genes)

# Score cell types using specified marker genes
## Example, Tabula_Muris
from pathlib import Path
resource_path = Path("your gene marker file")
marker_genes = ctm.load_resource(resource_path, resource_url='https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=Tabula_Muris')
# For using marker sets from enrichr, uppercase var_names
adata.var_names = adata.var_names.str.upper()
adata = ctm.score(adata, marker_genes)

# Annotate cell types
adata = ctm.mark(adata, ctm.marker_genes, by='leiden', save='results', plot=True)
```

### Output

Add in the adata.obs:
- the scores of the keys in the gene sets for each observation
- the predicted key for each observation
- (if by) the predicted key for each by group, e.g., leiden
- (if save) save the scores of the keys for each by group in a txt file and the heatmap of the data under cellmarkoutput/

## Dependencies

- scanpy

## License

This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Citation

If you use CellTypeMark in your research, please cite:

```
@software{celltypemark2024,
  author = {brainfo},
  title = {CellTypeMark: A Python package for cell type annotation in single-cell RNA sequencing data},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/yourusername/celltypemark}
}
```

---

Copyright (c) 2024

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.