semvec/pypi-packages huggingface dataset:
mes-courriels 0.0.5
A Python mail connector library
License: MIT
Requires Python: ==3.12.*

# mail_connector

Gmail connector for Python — read, modify, and organize emails via OAuth2.

## Installation

```bash
pip install mes-courriels
```

## Setup

1. Create a Google Cloud project and enable the Gmail API
2. Create an OAuth2 Client ID (type Desktop)
3. Copy `.env.example` to `.env` and fill in `GMAIL_CLIENT_ID` and `GMAIL_CLIENT_SECRET`
4. Run the auth script to get a refresh token:

```bash
uv run python scripts/auth.py
```

## Usage

```python
from mail_connector import GmailConnector

gmail = GmailConnector()

gmail.list_labels()
gmail.list_messages(query="from:alice@example.com")
gmail.get_message("msg_id")

gmail.mark_as_read("msg_id")
gmail.mark_as_unread("msg_id")
gmail.archive("msg_id")
gmail.trash("msg_id")
gmail.modify_message("msg_id", add_label_ids=["STARRED"])
```

### CLI

```bash
mes-courriels alice@example.com              # 10 derniers messages
mes-courriels alice@example.com -n 3         # les 3 derniers
mes-courriels alice@example.com -w           # surveiller en continu (60s)
mes-courriels alice@example.com -w -i 30     # surveiller toutes les 30s
```

## Development

- Python >= 3.12, [uv](https://docs.astral.sh/uv/)

```bash
uv sync
uv run pytest
```
gamsapi 53.1.0
GAMS Python API
License: MIT
Requires Python: >=3.10


<div align="center">
  <img src="https://www.gams.com/img/gams_logo.svg"><br>
</div>

-----------------

# gamsapi: powerful Python toolkit to manage GAMS (i.e., sparse) data and control GAMS solves

## What is it?

**gamsapi** is a Python package that includes submodules to control GAMS, manipulate and
transfer data to/from the GAMS modeling system (through GDX files or in-memory objects).
This functionality is available from a variety of different Python interfaces including
standard Python scripts and Jupyter Notebooks. We strive to make it as **simple** as
possible for users to generate, debug, customize, and ultimately use data to solve
optimization problems -- all while maintaining high performance.


## Main Features
Here are just a few of the things that **gamsapi** does well:

  - Seamlessly integrates GAMS data requirements into standard data pipelines (i.e., Pandas, Numpy)
  - Link and harmonize data sets across different symbols
  - Clean/debug data **before** it enters the modeling environment
  - Customize the look and feel of the data (i.e., labeling conventions)
  - Bring data to GAMS from a variety of different starting points
  - Send model output to a variety of different data endpoints (SQL, CSV, Excel, etc.)
  - Automatic data reshaping and standardization -- will work to translate your data formats into the Pandas DataFrame standard
  - Control GAMS model solves and model specification

## Where to get it
The source code is currently available with any typical [GAMS system](https://www.gams.com/download/).
No license is needed in order to use **gamsapi**.  A license is necessary in order to solve GAMS models.

A free [demo license](https://www.gams.com/try_gams/) is available!

## Dependencies
Installing **gamsapi** will not install any third-party dependencies, as such, it only contains basic functionality.
Users should modify this base installation by choosing **extras** to install -- extras are described in the [documentation](https://www.gams.com/latest/docs/API_PY_GETTING_STARTED.html#PY_PIP_INSTALL_BDIST).

```sh
# from PyPI (with extra "transfer")
pip install gamsapi[transfer]
```

```sh
# from PyPI (with extras "transfer" and "magic")
pip install gamsapi[transfer,magic]
```

```sh
# from PyPI (include all dependencies)
pip install gamsapi[all]
```

## Documentation
The official documentation is hosted on [gams.com](https://www.gams.com/latest/docs/API_PY_GETTING_STARTED.html).

## Getting Help

For usage questions, the best place to go to is [GAMS](https://www.gams.com/latest/docs/API_PY_GETTING_STARTED.html).
General questions and discussions can also take place on the [GAMS World Forum](https://forum.gamsworld.org).

## Discussion and Development
If you have a design request or concern, please write to support@gams.com.
todoosy 0.3.4
Todoosy - Markdown-based todo system parser, formatter, linter, and query engine
License: MIT
Requires Python: >=3.10

# Todoosy Python Library

Python implementation of the Todoosy format parser, formatter, linter, and query engine.

## Installation

```bash
pip install -e .
```

For development:
```bash
pip install -e ".[dev]"
```

## Usage

```python
from todoosy import parse, format, lint, query_upcoming, query_misc, parse_scheme

# Parse a document
result = parse('''
# Work

- Task (due 2026-01-15 p1 2h)

# Misc
''')

# Access the AST
for item in result.ast.items:
    print(f"{item.title_text}: due={item.metadata.due}")

# Format a document
formatted = format(input_text)

# Lint a document
warnings = lint(input_text)
for w in warnings.warnings:
    print(f"{w.code}: {w.message}")

# Query upcoming items
scheme = parse_scheme(scheme_text)
upcoming = query_upcoming(input_text, scheme)
for item in upcoming.items:
    print(f"{item.path}: {item.due}")

# Query misc items
misc = query_misc(input_text)
for item in misc.items:
    print(item.title_text)
```

## Running Tests

```bash
pytest
```