- Make sure you have a Python installation on your system
- Install
vscode
andgithub
apps. - Install
uv
package manager (e.g.pip install uv
). - Clone repo.
- Run
uv sync
from the directory wherepyproject.toml
in located to install.venv
and packages. - Select
.venv
Python environment. - FYI: Recommended settings and extensions are included in the repo. Proceed if prompted to install extensions.
- Develop and commit to Github often!
See the demo notebook file at /demo.py.
Also, a test with GBG data is found in /solweig_gbg_test.py
The demo and the test uses the datasets included in the tests folder
The code reproduced in the umep
folder is adapted from the original GPLv3-licensed code by Fredrik Lindberg, Ting Sun, Sue Grimmond, Yihao Tang, Nils Wallenberg.
The original code has been modified to work without QGIS to facilitate Python workflows.
The original code can be found at: UMEP-processing.
This modified code is licensed under the GNU General Public License v3.0.
See the LICENSE file for details.
Please give all credit for UMEP code to the original authors and cite accordingly.
© Copyright 2018 - 2020, Fredrik Lindberg, Ting Sun, Sue Grimmond, Yihao Tang, Nils Wallenberg.
Lindberg F, Grimmond CSB, Gabey A, Huang B, Kent CW, Sun T, Theeuwes N, Järvi L, Ward H, Capel- Timms I, Chang YY, Jonsson P, Krave N, Liu D, Meyer D, Olofson F, Tan JG, Wästberg D, Xue L, Zhang Z (2018) Urban Multi-scale Environmental Predictor (UMEP) - An integrated tool for city-based climate services. Environmental Modelling and Software.99, 70-87 https://doi.org/10.1016/j.envsoft.2017.09.020
Two seprated demo dataset are included
Copernicus
https://walkable.cityofathens.gr/home
http://gis.cityofathens.gr/layers/athens_geonode_data:geonode:c40solarmap
Standard dataset used in tutorials (https://umep-docs.readthedocs.io/en/latest/Tutorials.html)