semvec/pypi-packages huggingface dataset:
discrete-optimization 0.7.0
Discrete optimization library
License: MIT
Requires Python: >=3.9

# Discrete Optimization

Discrete Optimization is a python library to ease the definition and re-use of discrete optimization problems and solvers.
It has been initially developed in the frame of [scikit-decide](https://github.com/airbus/scikit-decide) for scheduling.
The code base starting to be big, the repository has now been splitted in two separate ones.

The library contains a range of existing solvers already implemented such as:
* greedy methods
* local search (Hill Climber, Simulated Annealing)
* metaheuristics (Genetic Algorithms, NSGA)
* linear programming
* constraint programming
* domain independent dynamic programming
* hybrid methods (CP and LP based Large Neighborhood Search)

The library also contains implementation of several classic discrete optimization problems:
* Travelling Salesman Problem (TSP)
* Knapsack Problem (KP)
* Vehicle Routing Problem (VRP)
* Facility Location Problem (FLP)
* Resource Constrained Project Scheduling Problem (RCPSP) and its variants (MRCPSP, MSRCPSP)
* Graph Colouring Problem (GCP)
* Maximum independent set (MIS)
* Job shop scheduling problem (JSP) and its flexible variant (FJSP)

In addition, the library contains functionalities to enable robust optimization
through different scenario handling mechanisms) and multi-objective optimization
(aggregation of objectives, Pareto optimization, MO post-processing).

We thank awesome optimization library or modeling language from different communities that are widely used through our library :

notably [ortools](https://github.com/google/or-tools), [minizinc](https://www.minizinc.org/), [deap](https://deap.readthedocs.io/en/master/), [didp](https://didp.ai/), [gurobi](https://www.gurobi.com/)


## Installation

Quick version:
```shell
pip install discrete-optimization
```
For more details, see the [online documentation](https://airbus.github.io/discrete-optimization/master/install).

## Documentation

The latest documentation is available [online](https://airbus.github.io/discrete-optimization).

## Examples

Some educational notebooks are available in `notebooks/` folder.
Links to launch them online with [binder](https://mybinder.org/) are provided in the
[Notebooks section](https://airbus.github.io/discrete-optimization/master/notebooks) of the online documentation.

More examples can be found as Python scripts in the `examples/` folder, using the different features of
the library and showing how to instantiate different problem instances and solvers.

## Contributing

See more about how to contribute in the [online documentation](https://airbus.github.io/discrete-optimization/master/contribute).


## License

This software is under the MIT License that can be found in the [LICENSE](./LICENSE) file at the root of the repository.

Some minzinc models have been adapted from files coming from
- https://github.com/MiniZinc/minizinc-benchmarks under the same [license](https://github.com/MiniZinc/minizinc-benchmarks/blob/master/LICENSE),
- https://github.com/youngkd/MSPSP-InstLib for which we have the written authorization of the author.
aiogram-telemetry 0.2.3
Telemetry and observability helpers for aiogram 3 bots.
License: MIT
Requires Python: >=3.10
Keywords: aiogram, telemetry, prometheus, redis, observability

# aiogram-telemetry

**aiogram-telemetry** is a lightweight observability layer for [aiogram 3.x](https://docs.aiogram.dev) bots.  
It automatically tracks handler performance, errors, and custom metrics across Redis, Prometheus, and optional PostgreSQL backends.

---

## 🚀 Features

- 📊 **Automatic Metrics Collection** — track message/update counts, handler execution times, and errors.  
- ⚙️ **Zero-Boilerplate Setup** — plug it in once; no code changes to your existing handlers.  
- 🔌 **Pluggable Backends** — supports Redis (for aggregation), Prometheus (for export), and optional PostgreSQL (for persistence).  
- 🧩 **Async & Non-Blocking** — built fully on asyncio to integrate seamlessly with aiogram 3.x.  
- 🪶 **Lightweight** — minimal overhead and no extra dependencies beyond aiogram and redis.

---

## 🧠 Installation

```bash
pip install aiogram-telemetry
# optional PostgreSQL support
pip install aiogram-telemetry[postgres]