semvec/pypi-packages huggingface dataset:
stactools-amazonia-1 0.1.3
Amazonia-1 on AWS stactools package
Keywords: stactools,pystac,catalog,STAC,Amazonia-1

# stactools-amazonia-1

[![PyPI](https://img.shields.io/pypi/v/stactools-amazonia-1)](https://pypi.org/project/stactools-amazonia-1/)

- Name: amazonia-1
- Package: `stactools.amazonia_1`
- [stactools-amazonia-1 on PyPI](https://pypi.org/project/stactools-amazonia-1/)
- Owner: @fredliporace
- [Dataset homepage](https://aws.amazon.com/marketplace/pp/prodview-khrlpmr36l66s)
- STAC extensions used:
  - [eo](https://github.com/stac-extensions/eo)
  - [item-assets](https://github.com/stac-extensions/item-assets)
  - [proj](https://github.com/stac-extensions/projection/)
  - [sat](https://github.com/stac-extensions/sat)
  - [view](https://github.com/stac-extensions/view)
- Extra fields:
  - `amazonia:data_type`: Product level: L2 stands for system geometric
  correction (equivalent to Landast L1GS), L4 for ortho.
  - `amazonia:path`: Path in Amazonia-1 reference grid.
  - `amazonia:row`: Row in Amazonia-1 reference grid.
- [Browse the example in human-readable form](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/stactools-packages/amazonia-1/main/examples/collection.json)

Create Amazonia-1 STAC items from INPE's original metadata format,
using assets stored on AWS.

## STAC Examples

- [Collection](examples/collection.json)
- [Item](examples/AMAZONIA_1_WFI_20220810_033_018.json)

## Installation

```shell
pip install stactools-amazonia-1
```

## Command-line Usage

Creating a STAC item from INPE's XML file:

```shell
stac amazonia1 create-item SOURCE DESTINATION
```

Use `stac amazonia1 --help` to see all subcommands and options.

Creating the Amazonia-1 WFI collection:

```shell
stac amazonia1 create-collection DESTINATION
```

Use `stac amazonia1 create-collection --help` to see all subcommands and options.

## Contributing

We use [pre-commit](https://pre-commit.com/) to check any changes.
To set up your development environment:

```shell
pip install -e .
pip install -r requirements-dev.txt
pre-commit install
```

To check all files:

```shell
pre-commit run --all-files
```

To run the tests:

```shell
pytest -vv
```
gan-sampler 1.0.0
Oversampling using GANs
Requires Python: >=3.9

## Contains details of files and dependencies


A GAN based over-sampling package.
Includes implementation of both GAN and WGANGP based over-sampling.
Also imcludes a combined strategy implementation using
traditional over-sampling techniques (SMOTE, Borderline-SMOTE, ADASYN)
and then fine tuning them using GAN and WGANGP.

### utils
Contains utility functions such as data builder for GAN training,
GAN loss functions such as wasserstein loss and sliced wasserstein loss and
generator, discriminator and critic neural network builders.

### gan_optuna 
Optuna objective functions for GAN and WGANGP hyper-parameter tuning.

### dcgan 
Implementation of GAN over-sampling technique.

### dcgan_resample
A pipeline to implement the dcgan over-sampling technique using
random noise as generator input.

### smote_dcgan
A pipeline to implement the two step strategy using traditional
over-sampling techniques (SMOTE, Borderline-SMOTE and ADASYN) as dcgan generator
input.

### wgangp
Implementation of WGANGP over-sampling technique.

### wgangp_resample
A pipeline to implement the wgangp over-sampling technique using
random noise as generator input.

### smote_wgangp
A pipeline to implement the two step strategy using traditional
over-sampling techniques (SMOTE, Borderline-SMOTE and ADASYN) as wgangp generator
input.

### simulators
Example simulations for all the over-sampling technqiues that performs
hyper-parameter tuning for both DCGAN and WGANGP and then resample. Once resampled,
an xgboost model is fit using a random sampling strategy 3 times and average scores
(Precision, Recall, F1-score, G-mean and ROC-AUC score) are calculated.

Each jupyter notebook is an experimental implementation for each dataset in data folder.


### Dependencies:
    - pandas
    - numpy
    - scikit-learn
    - pot
    - imblearn
    - optuna
    - optuna-integration
    - xgboost
    - tensorflow (GPU enabled)
    - tqdm