semvec/pypi-packages huggingface dataset:
benchmarkme 0.1.0a2
Ergonomic interface to Python's built-in timeit.
Requires Python: >=3.12

[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fhunterdsp%2Fbenchmarkme%2Frefs%2Fheads%2Fmain%2Fpyproject.toml)
![PyPI - Implementation](https://img.shields.io/pypi/implementation/benchmarkme)
![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/hunterdsp/benchmarkme/python-publish.yml)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/benchmarkme)
![GitHub License](https://img.shields.io/github/license/hunterdsp/benchmarkme)

# benchmarkme

Ergonomic Python interface to the built-in [`timeit`](
    https://docs.python.org/3/library/timeit.html) utility for quick-look
profiling.

## Out-Of-The-Box Features

- Python API with enhanced reporting
- Yields configuration, best & worst times and processing rates
- Default autoranging
- Automatic looping & repeat
- Customizable environment

## Usage

Minimal example

```{console=python}
>>> from time import sleep
>>> from benchmarkme import BenchMark
>>> b = BenchMark(f=sleep, fargs=[0.2,])
>>> b() # call the instance directly to run the main utility
Benchmark Results
-----------------
* Ran 1 trials 5 times.
* Time of one independent trial: 200 msec
* Best time of subsequent trials: 200 msec
* Worst time of subsequent trials: 200 msec
* Best speed: 5 items / sec
* Worst speed: 4.99 items / sec

```

Provide the items processed to get rate in the desired units (from
`benchmarkme/examples/basic.py`)

```{script}
import math
from benchmarkme import BenchMark

def fmath(x: list) -> None:
    """Calculate somethig complex-looking to time."""        
    return [math.log(math.acos(item / 1e5)) ** 2 / 1733 for item in x]

items = 100000
bmrk_math = BenchMark(
    f=fmath, 
    fargs=[[*range(items)],], 
    item_units='numbers', 
    items_processed=items
)

if __name__ == "__main__":
    bmrk_math()
```

You'll get something like the following, i.e. the function computes around 10
million numbers per second on this machine.

```{console=bash}
Benchmark Results
-----------------
* Ran 50 trials 5 times.
* Time of one independent trial: 8.92 msec
* Best time of subsequent trials: 8.89 msec
* Worst time of subsequent trials: 12.1 msec
* Best speed: 11.2 Mnumbers / sec
* Worst speed: 8.28 Mnumbers / sec
```
practica-hack4u 0.1.0
Una biblioteca para consultar algunos cursos de hack4u.

## Cursos disponibles:
    - Introducción a Linux [15 horas]
    - Personalización de Linux [3 horas]
    - Python ofensivo [35 horas]
    - Introducción al hacking [53 horas]

## Instalación

Instala el paquete usando 'pip3':

python3
pip3 install hack4u
ahorn-loader 0.4.2
Library and command-line application to interact with datasets in the Aachen Higher-Order Repository of Networks.
Requires Python: >=3.12

# `ahorn-loader`

Library and command-line application to interact with datasets in [AHORN](https://ahorn.rwth-aachen.de/).

<div align="center">

[![Python](https://img.shields.io/badge/python-3.12+-blue)](https://www.python.org/)
[![license](https://badgen.net/github/license/netsci-rwth/ahorn-loader)](https://github.com/netsci-rwth/ahorn-loader/blob/main/LICENSE)

</div>

## Usage

`ahorn-loader` is both a command-line application and a Python package to interact with the AHORN repository for higher-order datasets.

### Command-Line Usage

To install and use `ahorn-loader` from the command line, you can run the following command:

```bash
uvx ahorn-loader [command] [args]
```

Commands include:

- `ls`: List available datasets in AHORN.
- `download`: Download a dataset from AHORN.
- `validate`: Validate a specific dataset file (e.g., before adding it to AHORN).

To get a full help of available commands and options, run `ahorn-loader --help`.

### Python Package Usage

To use `ahorn-loader` as a Python package, you can install it via `pip` (or some other package manager of your choice):

```bash
pip install ahorn-loader
```

Then, you can use it in your Python scripts:

```python
import ahorn_loader

# Download a dataset:
ahorn_loader.download_dataset("dataset_name", "target_path")

# Download and read a dataset:
# The dataset will be stored in your system's cache. For a more permanent storage
# location, use `ahorn_loader.download_dataset` instead.
with ahorn_loader.read_dataset("dataset_name") as dataset:
    for line in dataset:
        ...
```

## Funding

<img align="right" width="200" src="https://raw.githubusercontent.com/netsci-rwth/ahorn/main/public/images/erc_logo.png">

Funded by the European Union (ERC, HIGH-HOPeS, 101039827).
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency.
Neither the European Union nor the granting authority can be held responsible for them.