semvec/pypi-packages huggingface dataset:
py-logger-alert 0.2.1
Um monitor de logs automático via Telegram para robôs Python
Requires Python: >=3.7

# py-logger-alert 🚀

Uma biblioteca leve e eficiente para monitoramento de scripts e bots em Python.

O **py-logger-alert** integra o sistema de `logging` padrão do Python com o **Telegram**, permitindo receber **alertas de erros críticos em tempo real**, enquanto mantém um **histórico completo de logs em arquivos locais**.

Ideal para quem roda bots, automações, robôs de trading ou scripts agendados e precisa saber imediatamente quando algo dá errado.

---

## ✨ Principais Funcionalidades

- 📢 **Notificações em Tempo Real**  
  Envia logs de nível `ERROR` e `CRITICAL` diretamente para o Telegram.

- ⚙️ **Configuração Simplificada**  
  Suporte nativo a variáveis de ambiente via arquivo `.env`.

- 🧾 **Formatação Amigável**  
  Mensagens formatadas em blocos de código, facilitando leitura de tracebacks.

- 🔐 **Segurança**  
  Truncamento automático de mensagens longas para respeitar os limites da API do Telegram.

---

## 🛠 Instalação

```bash
pip install py-logger-alert
```

---

## ⚙️ Configuração do Telegram

### 🤖 Criando o Bot (BotFather)

1. Abra o Telegram e procure por **@BotFather**
2. Envie o comando:
```
/start
```
3. Crie o bot:
```
/newbot
```
4. Defina o nome e o username (termina com `bot`)
5. Copie o **TOKEN** retornado

---

### 🆔 Obtendo o Chat ID

1. Procure por **@userinfobot**
2. Envie:
```
/start
```
3. Copie o número retornado (Chat ID)

---

## 📁 Arquivo .env

```env
TELEGRAM_TOKEN=seu_token_aqui
TELEGRAM_CHAT_ID=seu_chat_id_aqui
```

---

## 🚀 Como Usar

```python
from py_logger_alert import setup_monitoring
import logging

setup_monitoring(
    nome_robo="Robô Trader v1",
    log_filename="logs_bot"
)

logging.info("Sistema iniciado com sucesso.")

try:
    10 / 0
except Exception as e:
    logging.error(f"Erro crítico detectado: {e}", exc_info=True)
```

---

## 📜 Licença

MIT License

---

## 👨‍💻 Autor

Vitor Marinheiro  
https://github.com/vitormarinheiro1  
https://github.com/vitormarinheiro1/py-logger-alert
kinematic-tracker 18.0.4
Tracker using kinematic Kalman filters.
Requires Python: >=3.10

# Kinematic Tracker

📖 Live docs: https://kovalp.github.io/kinematic-tracker-docs/

This tracker is based on kinematic Kalman filters. The system state can consist of multiple parts,
each defined by its *kinematic order* and *dimensionality*. For example, to track cuboids
aligned with Cartesian axes, we might decide for a constant-acceleration motion model
for Cartesian coordinates and a constant-position motion model for the sizes of the cuboids.
This choice encoded by `orders = [3, 1]`. The dimensionality of both parts (coordinates and sizes)
is `3`. Therefore `dimensions = [3, 3]` in our example.

The state of the cuboids aligned with Cartesian axis will have 12 variables.
Internally, a block-diagonal states are used, i.e.

```state = p_x, v_x, a_x, p_y, v_y, a_y, p_z, v_z, a_z, s_x, s_y, s_z```

To simplify the input/output processing, one might find attractive a derivative-sorted order

```state = p_x, p_y, p_z, s_x, s_y, s_z, v_x, v_y, v_z, a_x, a_y, a_z```

This package provides utility classes for such conversion (block-diagonal ↔ derivative sorted).

## Process noise models

The tracker supports various process noise models, including a custom adaptive process noise model
based on a white-noise assumption.

## Association metrics

A number of association metrics are implemented. Some of the metrics are experimental.

  - Mahalanobis with a process-measurement noise sum covariance (HPH + R) -- **default**.
  - 3D axes-aligned GIoU (volumetric GIoU for cuboids).
  - Mahalanobis with size-modulated covariance (experimental)
  - A dimension-averaged GIoU (experimental)

## Installation

```shell
pip install kinematic-tracker[gui]
```

## Usage

Simple examples are available in the `test/tutorials` folder.
The `test` folder is available in the source package 

```shell
pip download kinematic-tracker --no-binary :all: --no-deps
```

## Use the kinematic-tracker as dependency

```shell
uv add kinematic-tracker --extra headless
```

If you happen to need GUI-enabled version of OpenCV, then 

```shell
uv add kinematic-tracker --extra gui
```
codebases 0.1.0
Add your description here
Requires Python: >=3.11

# codebases

Initial PyPI package scaffold for `codebases`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
pyuino 0.0.6
LLM Japanese Kana-Kanji convetor
Requires Python: >=3.10

# pyuino
pyuino は LLM の仕組みを利用したかな漢字変換です  
厳密には Qwen3 モデルがベースになっていますが、入出力が変わっているため llama.cpp 等では **動かない** ことを確認済みです  

まだお試し版です。まともに動かないです。  

### インストール
PyPi からインストールできます  
```shell
pip install pyuino
```

### モデル・辞書ファイルの準備
下記の場所からモデルファイル及び辞書ファイルをダウンロードしてください
https://www.dropbox.com/scl/fo/03sverk4gsj3l8qmx9ltw/ACueNBsN8EwwSYP18v2a1lQ?rlkey=99ki15e75q3cx9ddmotzgcpah&st=2n8rooyz&dl=0

* config.json
* model.safetensors
* yuino_dict.pb

ダウンロードしたファイルは `YuinoLM` 下に配置してください  

### ToyBox の起動
ToyBox は pyuino のデモアプリケーションです  
かなを入力すると、変換後のかな漢字を返します  

```shell
$ pyuino-toybox
--Yuino TOY-BOX--
かな > はこねおんせんへようこそ
0.5473639369010925 ['[CLS]', '羽']
0.5501382946968079 ['[CLS]', '筐']
0.6909381747245789 ['[CLS]', 'はこね']
1.3568682670593262 ['[CLS]', 'はこね', 'お']
1.3223243355751038 ['[CLS]', '筐', 'ネオン']
1.4771115183830261 ['[CLS]', 'はこね', 'オンセ']
0.6929624676704407 ['[CLS]', '箱根温泉']
1.1179965436458588 ['[CLS]', '箱根温泉', 'へ']
1.5004573464393616 ['[CLS]', '箱根温泉', 'へよ']
1.525723159313202 ['[CLS]', '箱根温泉', 'へよう']
1.8744302093982697 ['[CLS]', '箱根温泉', 'へ', '楊子']
1.8722382485866547 ['[CLS]', '箱根温泉', 'へ', 'ようこそ']
箱根温泉へようこそ : 0.990405 msec
漢字: 箱根温泉へようこそ
```

### サーバーの起動
現在 IBus-Anya を利用することを想定します  
IBus-Anya 起動前に、下記コマンドでサーバー側を起動してください

```shell
docker run -d -p 30055:30055 -v $HOME/.local/share/yuino:/opt/pyuino/YuinoLM ghcr.io/yuino-im/pyuino -m /opt/pyuino/YuinoLM
```

### 使用モデル・データセット
Yuino では下記のモデル、及びデータセットを使用して学習しました  
（ありがとうございます！！）  

#### 辞書
SudachiDict (WorksApplications)  
https://github.com/WorksApplications/SudachiDict

#### LLMトークナイザ
LINE DistilBERT Japanese (LINE Corporation)  
https://huggingface.co/line-corporation/line-distilbert-base-japanese

#### データセット
CC100(Japanese)  
https://huggingface.co/datasets/range3/cc100-ja