Metadata-Version: 2.1
Name: georetriever
Version: 0.1.0
Summary: Retrieve geological data for geothermal systems from coordinates in Python!
Home-page: UNKNOWN
Author: Lukas Franken, Hui Ben, Heather Kennedy, Nikolaos Reppas
Author-email: <lukas.franken@ed.ac.uk>
License: UNKNOWN
Keywords: python,geo,geological,heatflow,thermal,lithography,conductivity
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE
License-File: AUTHORS.rst


## GeoRetriever 🌋🐕



[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)





##### A Python package for array-based retrieval of geological data. In a second step (available in a future version), the data can be converted to localized thermal energy system suitability.



Based on the tools used in the great [atlite](https://github.com/PyPSA/atlite) package, GeoRetriever is supported by [dask](https://github.com/dask/dask) and [xarray](https://github.com/pydata/xarray) for parallelized requests of data chunks.

When completed, the package will be able to retrieve all data relevant the following thermal systems:



- Borehole Thermal Energy Storage (BTES)

- Aquifer Thermal Energy Storage (ATES)

- Shallow Ground Source Heat Pumps (CSHP)

- Water Source Heat Pumps (WSHP)

- Pit Thermal Energy Storage (PTES)

- Mine Thermal Energy Storage (MTES)



The library is under development with more features being added in the future. This is a promise - I need this for my PhD 🧗🏼.



| Feature | Data Source | Coverage | Implemented |

|---------|-------------|----------|-------------|

| Lithology | [Macrostrat](https://macrostrat.org/)| global | ✔️|

| Surface Temperature | [ERA5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) | global | ✔️ |

| Soil Temperature | [ERA5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) | global | ✔️ |

| Aquifer Presence |  | | ❌ |

| Soil Type |  | | ❌ |



### Installation



The package runs on `Python>=3.6` and is available from `pypi` via

```

pip install georetriever

```



### Example



The package works through the `GeoCutout` object. During its initialization, the  spatial and temporal scale of the data is defined. Coordinates are in `(lon, lat)`. The actual retrieval of data starts when the `prepare()`, which takes the features of interest are passed method is called.

For instance:

```

from georetriever import GeoCutout



x = slice(-1, 1)

y = slice(50, 52)

dx = 0.05

dy = 0.05

time = "2019-01-01"

dt = "h"



geocutout = GeoCutout(

    x=x,

    y=y,

    dx=dx,

    dy=dy,

    time=time,

    dt=dt,

)



geocutout.prepare(features=["soil temperature", "lithology"])



print(geocutout.data)

```



### Authors and Contact



__Lukas Franken__ - [lukas.franken@ed.ac.uk](lukas.franken@ed.ac.uk)

University of Edinburgh, Alan Turing Institute



Big thanks to the team an __TU Berlin__ for [atlite](https://github.com/pypsa/atlite), the package this library is leaning on.

