Retrieve geological data for geothermal systems from coordinates in Python!
Project description
GeoRetriever 🌋🐕
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 package, GeoRetriever is supported by dask and 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| global | ✔️|
| Surface Temperature | ERA5 | global | ✔️ |
| Soil Temperature | 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
University of Edinburgh, Alan Turing Institute
Big thanks to the team an TU Berlin for atlite, the package this library is leaning on.
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