semvec/pypi-packages huggingface dataset:
imagecorruptions 1.1.2
This package provides a set of image corruptions.

# imagecorruptions
This package provides a set of corruptions that can be applied to images in order to benchmark the robustness of neural networks. These corruptions are not meant to be used as training data augmentation but rather to test the networks against unseen perturbations. For more information have a look at the paper on the original corruption package by Hendrycks and Dietterich: [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://arxiv.org/abs/1807.01697).

![image corruptions](https://raw.githubusercontent.com/bethgelab/imagecorruptions/master/assets/corruptions_sev_3.png?token=ACY4L7YQWNOLTMRRO53U6FS5G3UF6)

## Installation and Usage
This package is pip installable via `pip3 install imagecorruptions`. An example of how to use the corruption function is given below:
```python
from imagecorruptions import corrupt
...
corrupted_image = corrupt(image, corruption_name='gaussian_blur', severity=1)
...
```
Looping over all available corruptions can be done either by name or by index:
```python
# via name
from imagecorruptions import get_corruption_names
for corruption in get_corruption_names():
    for severity in range(5):
        corrupted = corrupt(image, corruption_name=corruption, severity=severity+1)
        ...

# via number:
for i in range(15):
    for severity in range(5):
        corrupted = corrupt(image, corruption_number=i, severity=severity+1)
        ...
```

Note that the first 15 image corruptions are the common corruptions (the ones you get via `get_corruption_names()`). If you really wish to use these as data augmentation, there exist four additional validation corruptions which can be accessed via `get_corruption_names('validation')` which should then be used to test the corruption robustness of the trained model.

## Citation

If you use our code or the imagecorruptions package, please consider citing:
```
@article{michaelis2019dragon,
  title={Benchmarking Robustness in Object Detection: 
    Autonomous Driving when Winter is Coming},
  author={Michaelis, Claudio and Mitzkus, Benjamin and 
    Geirhos, Robert and Rusak, Evgenia and 
    Bringmann, Oliver and Ecker, Alexander S. and 
    Bethge, Matthias and Brendel, Wieland},
  journal={arXiv preprint arXiv:1907.07484},
  year={2019}
}
```

## Credit and Changelog
This package is an extension of the image corruption functions provided by Dan Hendrycks in the repository  [corruptions](https://github.com/hendrycks/robustness). The image corruptions implemented by Hendrycks are generalized to work on images with arbitrary image dimensions and aspect ratios aswell as on grayscale images. We furthermore removed the dependency to `libmagickwand` and the python api `Wand` and reimplemented the `motion_blur` in python.
dacot 2.0.0
DataCOVID Transformer

# dacot: DataCOVID Transformer

`dacot` genera nuevos datasets a partir de los datos disponibles en los
[Estudios de movilidad a partir de la telefonía móvil"](https://www.ine.es/experimental/movilidad/experimental_em.htm)
realizados por el Instituto Nacional de Estadística. Se recomienda leer y
entender la información suministrada por el INE para entender el alcance de
dichos estudios.

Cada ejecución de `dacot` genera un nuevo directorio dentro de `data/output/`
con el formato `output_ESTUDIO_YYYYMMDD-VERSION` donde:

 - `ESTUDIO` se corresponde al estudio correspondiente (`em2` o `em3`)
 - `YYYY` se corresponde al año (2020)
 - `MM` se corresponde al mes (10)
 - `DD` se corresponde al día (01)
 - `VERSION` se corresponde a la versión del programa utilizada.

Dentro de este directorio se generan directorios adicionales, con el formato
`YYYY-MM-DD` correspondientes a los datos del INE. Dentro de cada uno de éstos
se encuentran dos directorios adicionales:

 - `original`: contiene los ficheros originales del INE, sin modificaciones.
 - `province flux`: contiene los datasets generados con información de
   provincia, con los siguientes ficheros:
     - `flux.csv`: Flujos (> 15 personas) con información provincial.
     - `flux-inter.csv`: Flujos (> 15 personas) interprovinciales.
     - `flux-intra.csv`: Flujos (> 15 personas) intraprovinciales.

Asimismo, estos ficheros se concatenan y se guardan el el directorio de salida.

## Instalación

Se recomienda utilizar un `virtualenv`:

    virtualenv --python python3 .venv
    source .venv/bin/activate
    pip install .

## Uso

Una vez instalado se puede ejecutar el programa `dacot`:

    $ dacot em2
    dacot version 0.0.1.dev1
    Checking directories...

                     Base path: data
        INE data download path: data/raw/datos_disponibles_20201021-0.0.1.dev1.zip
             Interim data path: data/interim
         Output directory path: data/output/output_20201021-0.0.1.dev1
    (...)

O bien, sin instalarlo:

    $ python dacot/run.py

# Acknowledgements

Este programa forma parte del proyecto [distancia
COVID](https://distancia-covid.csic.es/) CSICCOV19-039, que ha sido posible
gracias al apoyo del [CSIC](https:www.csic.es) y de [Aena](https://aena.es).