semvec/pypi-packages huggingface dataset:
eedl 2023.9.25
Code to export arbitrarily large areas of data from Earth Engine to local storage and optionally process data locally afterward
Requires Python: >=3.8

![EEDL Logo](docs/source/_static/logo/logo_black.png)

# Earth Engine Downloader

EEDL is a Python package that makes downloading and processing of bulk data from Earth Engine feasible and simple.
Current support includes individual image exports, as well as a helper class that will iterate through items in a
filtered ImageCollection and export them all iteratively. 

Many existing workflows exist for downloading areas small enough to fit into a single tile, but this tool
uses Earth Engine's functionality to tile larger and full resolution exports, then download the pieces and
reassemble them, with optional further processing the data using an arbitrary function (zonal statistics tools are included).

Earth Engine's export quotas still apply, especially for EECUs. For academic accounts, they are frequently generous - we have
not tested them on a commercial account.

## Installation
The package is still in development and we have not yet published to PyPI (pip) or conda, but have built infrastructure
for both. Current installation is to download this repository then run `python setup.py install`

EEDL is tested on Python 3.8-3.11 on Windows and Linux with both standard CPython and Anaconda distributions. EEDL is pure
Python, but depends on GDAL, which has numerous compiled C++ dependencies.

### Windows
Windows users may want to use Anaconda, or [see this writeup about installing GDAL and other spatial packages on Windows](https://github.com/nickrsan/spatial_resources/edit/main/installing_spatial_python_windows.md).

### Linux
Linux users should follow the [GDAL
installation guide](https://pypi.org/project/GDAL/) and 1) Ensure that the gdal-bin and gdal-dev packages are installed and 2) The gdal version they install
for Python matches the gdal version of the system packages (`ogrinfo --version`). We don't pin a version of GDAL to allow
for this workflow. Further details in the GDAL documentation

## Documentation
Documentation is under development at https://eedl.readthedocs.io. API documentation is most complete, but noisy right
now. We are working on additional details to enable full use of the package.

## Licensing
Licensing is still in progress with the University of California, but we are aiming for a permissive license. More to come.

## Authors
EEDL has been built by Nick Santos and Adam Crawford as part of the [Secure Water Future](https://securewaterfuture.net) project. This work is supported
by Agriculture and Food Research Initiative Competitive Grant no. 
2021-69012-35916 from the USDA National Institute of Food and Agriculture. EEDL was built in support of [Water3D](https://waterdecisions.app)
datanexus 0.0.8
A dataset module for your projects

# DataNexus

DataNexus is a simple to use Python module that you can use in your projects to get transcripts, datasets, etc.
The module also allows you to extract character lines from transcripts witch makes it easyer for you to be able to do finetunning of a GPT2 model as an example.

## Key feactures
- Downloading of Datasets and Transcripts
- Extract Characters from Transcripts


## Installation
To get started:
```
pip install datanexus
```



## Usage

⚠️ | Full documenation link to come in the future and the code may be unstable as in testing!

### Downloading of Datasets/Transcripts
```py
from datanexus import datanexus

datanexus = datanexus()

datasets = datanexus.download_dataset('ironman.txt')
print(datasets)
```


### Extract character's from transcripts
You will need to create a folder called `Models` to sucessfully extract the character information
```py
from datanexus import datanexus

character = datanexus.save_character('ironman.txt', 'Tony Stark:', 'Tony.txt')
print(character)
```
## Showing all of the possible datasets
This function will show all of the possible datasets that is usable
```py
from datanexus import datanexus

datasets = datanexus.possible_models()

for dataset in datasets:
    print(dataset)
```


## Support
If you have any question or any issues then feel free to create an issue on [Github](https://github.com/Ethan-Barr/DataNexus). 

Feel free to join [The Workshop](https://discord.gg/sm2ZdxvU6y) discord server and send me a ping (`_Ethan_`)