semvec/pypi-packages huggingface dataset:
pogodoc 0.5.8
Python SDK for Pogodoc
Requires Python: >=3.12

## Pogodoc Python SDK

The Pogodoc Python SDK enables developers to seamlessly generate documents and manage templates using Pogodoc’s API.

### Installation

To install the Python SDK, just execute the following command

```bash
$ pip install pogodoc
```

### Setup

To use the SDK you will need an API key which can be obtained from the [Pogodoc Dashboard](https://app.pogodoc.com)

### Example

```py
from pogodoc import PogodocClient, RenderConfig

def main():
    client = PogodocClient(
        token="YOUR_POGODOC_API_TOKEN",
    )

    response = client.generate_document(
        template_id = "your-template-id",
        data = {"name": "John Doe"},
        render_config = RenderConfig(
            type = "html",
            target = "pdf",
            format_opts = InitializeRenderJobRequestFormatOpts(
                from_page = 1,
            ),
        ),
    )

    print("Generated document url:\n", response.output.data.url)

if __name__ == "__main__":
    main()

```

### License

MIT License
django-db-s3-backup 0.1.3
Django database backup with S3 storage and scheduling
Requires Python: >=3.7
Keywords: django database backup s3_access

# Django DB S3 Backup

A robust Django library for automated PostgreSQL database backups with local and S3 storage support, featuring scheduled backups and restoration capabilities.

## Features

- 🗄️ Automated database backups
- ⏰ Scheduled backups using cron syntax
- 💾 Local backup storage with rotation
- ☁️ S3 backup storage with rotation
- 🔄 Easy restoration process
- 🔒 Secure credential management

## Installation

```bash
pip install django-db-s3-backup



## Configuration
#Add to your INSTALLED_APPS
INSTALLED_APPS = [
    ...
    'django_db_s3_backup.apps.DBS3BackupConfig',
    'django_apscheduler', 
    ...
]


#Database Configuration
# Optional - falls back to default Django DB settings if not specified
DB_BACKUP_HOST = 'backup.db.example.com'
DB_BACKUP_PORT = '5432'
DB_BACKUP_NAME = 'backup_db'
DB_BACKUP_USER = 'backup_user'
DB_BACKUP_PASSWORD = 'securepassword'


#Local Storage
DB_BACKUP_LOCAL_DIR = os.path.join(BASE_DIR, 'db_backups')  # Local backup directory
DB_BACKUP_MAX_LOCAL = 5  # Max local backups to keep


#S3 Storage

DB_BACKUP_S3_ENABLED = True  # Set False to disable S3
DB_BACKUP_S3_BUCKET_NAME = 'your-backup-bucket'
DB_BACKUP_S3_ACCESS_KEY = 'your-access-key'  # Consider using env vars
DB_BACKUP_S3_SECRET_KEY = 'your-secret-key'  # Consider using env vars
DB_BACKUP_S3_DIR = 'backups'  # S3 path prefix
DB_BACKUP_MAX_S3 = 30  # Max S3 backups to keep



#Scheduling
DB_BACKUP_SCHEDULE_ENABLED = True  # Enable scheduled backups, False by default
DB_BACKUP_SCHEDULE_CRON = '0 2 * * *'  # 2 AM daily (cron syntax)
automato 0.1.1
Implementation of the AuToMATo clustering algorithm.
Requires Python: >=3.10

Implementation of the AuToMATo clustering algorithm introduced in [<em>AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm</em>](https://arxiv.org/abs/2408.06958).

---

__Example of running AuToMATo__

```
>>> from automato import Automato
>>> from sklearn.datasets import make_blobs
>>> X, y = make_blobs(centers=2, random_state=42)
>>> aut = Automato(random_state=42).fit(X)
>>> aut.n_clusters_
2
>>> (aut.labels_ == y).all()
True
```

---

__Installation and requirements__

AuToMATo can be installed via `pip` by running `pip install -U automato`.

Required Python dependencies are specified in `pyproject.toml`. Provided that `uv` is installed, these dependencies can be installed by running `uv pip install -r pyproject.toml`. The environment specified in `uv.lock` can be recreated by running `uv sync`.

---

__Installing AuToMATo from PyPI for `uv` users__

```
$ uv init
$ uv add automato
$ uv run python
>>> from automato import Automato
>>> ...
```
litsense-wrapper 0.1.1
Lightweight Python wrapper for the NCBI LitSense2 API
Requires Python: >=3.9
Keywords: litsense, ncbi, biomedical, nlp, api

### LitSense-Wrapper

Python wrapper for the NCBI LitSense2 API. LitSense2 (an update of LitSense) is a NCBI/NLM system for sentence- and paragraph-level semantic search across PubMed abstracts and the PMC Open Access subset, returning the most relevant sentences or passages for a query. It is useful for rapid evidence attribution, biocuration, and comparing new findings with existing literature. Learn more on the [NCBI LitSense2 page](https://www.ncbi.nlm.nih.gov/research/litsense2/).

#### Installation

```bash
pip install litsense-wrapper
```

#### Usage

```python
from litsense_wrapper import LitSense_API

engine = LitSense_API()
results = engine.retrieve(query_str='COVID-19 mechanism', limit=2)

for result in results:
    print(result.text)
```

#### Notes
- Modes supported: `passages` (default) and `sentences`.
- Optional `min_score` to filters results based on similarity scores
pmg 1.0.6
A toolbox full of handy little helper functions and utilities.
Requires Python: >=3.9
Keywords: tools, utils

# PMG Toolbox

This package contains functions I've found myself using over and over in various projects. If you find them useful, please go ahead and use them. Much appreciated if you let me know. If you find better ways of accomplishing the same task (or spot a bug!), please let me know even more... I'm always eager to learn.

This is an ecclectic collection of functions. I've worked on several projects using LaTeX and SQLite, which is why there are specific helper functions for these technologies. One of the main goals of the `pmg` package is to help me ensure consistency in how I solve problems, especially when working with technologies that aren't used every day.
cesnet-models 0.4.1
Pre-trained neural networks for encrypted traffic classification
Requires Python: >=3.10
Keywords: traffic classification, deep learning, models

<p align="center">
    <img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/models.svg" width="450">
</p>

[![](https://img.shields.io/badge/license-BSD-blue.svg)](https://github.com/CESNET/cesnet-models/blob/main/LICENCE)
[![](https://img.shields.io/badge/docs-cesnet--models-blue.svg)](https://cesnet.github.io/cesnet-models/)
[![](https://img.shields.io/badge/python->=3.10-blue.svg)](https://pypi.org/project/cesnet-models/)
[![](https://img.shields.io/pypi/v/cesnet-models)](https://pypi.org/project/cesnet-models/)


The goal of this project is to provide neural network architectures for traffic classification and their pre-trained weights.

The package provides two network architectures, 30pktTCNET and Multi-modal CESNET v2, both visualized in the following pictures. See the [getting started](https://cesnet.github.io/cesnet-models/getting_started/) page and [models](https://cesnet.github.io/cesnet-models/reference_models/) reference for more information.

:frog: :frog: See a related project [CESNET DataZoo](https://github.com/CESNET/cesnet-datazoo) providing large TLS and QUIC traffic datasets. :frog: :frog:

:notebook: :notebook: Example Jupyter notebooks are included in a separate [CESNET Traffic Classification Examples](https://github.com/CESNET/cesnet-tcexamples) repo. :notebook: :notebook:

### 30pktTCNET
<p align="center">
    <img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/30pktTCNET.png" width="800">
</p>

### Multi-modal CESNET v2
<p align="center">
    <img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/model-mm-cesnet-v2.png" width="400">
</p>

## Installation

Install the package from pip with:

```bash
pip install cesnet-models
```

or for editable install with:

```bash
pip install -e git+https://github.com/CESNET/cesnet-models
```

## Papers

Models from the following papers are included:

* [Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success](https://doi.org/10.48550/arXiv.2502.12930) <br>
Jan Luxemburk, Karel Hynek, Richard Plný, and Tomáš Čejka <br>
arXiv preprint, 2025

* [Encrypted traffic classification: the QUIC case](https://doi.org/10.23919/TMA58422.2023.10199052) <br>
Jan Luxemburk and Karel Hynek <br>
2023 7th Network Traffic Measurement and Analysis Conference (TMA)

* [Fine-grained TLS services classification with reject option](https://doi.org/10.1016/j.comnet.2022.109467) <br>
Jan Luxemburk and Tomáš Čejka <br>
Computer Networks, 2023