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https://github.com/nismod/snail
spatial networks impact assessment library 🐌
https://github.com/nismod/snail
Last synced: 16 days ago
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spatial networks impact assessment library 🐌
- Host: GitHub
- URL: https://github.com/nismod/snail
- Owner: nismod
- License: mit
- Created: 2020-09-04T08:58:20.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-24T14:48:33.000Z (3 months ago)
- Last Synced: 2024-10-29T05:07:55.573Z (about 1 month ago)
- Language: Python
- Homepage: https://nismod.github.io/snail/
- Size: 3.56 MB
- Stars: 9
- Watchers: 4
- Forks: 1
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-starred - nismod/snail - spatial networks impact assessment library 🐌 (others)
README
[](https://pypi.org/project/nismod-snail/)
[](https://github.com/nismod/snail/actions/workflows/build.yml)
[](https://opensource.org/licenses/MIT)
# 🤔 What is this?
This is a Python package to help with analysis of the potential impacts of
climate hazards and other perils on infrastructure networks.
## Installation
Install using pip:
pip install nismod-snail
This should bring all dependencies with it. If any of these cause difficulties,
try using a [conda](https://docs.conda.io/en/latest/miniconda.html) environment:
conda env create -n snail_env \
python=3.11 geopandas shapely rasterio python-igraph
conda activate snail_env
pip install nismod-snail
If all worked okay, you should be able to run python and import snail:
$ python
>>> import snail
>>> help(snail)
Help on package snail:
NAME
snail - snail - the spatial networks impact assessment library
## Using the `snail` command
Once installed, you can use `snail` directly from the command line.
Split features on a grid defined by its transform, width and height:
```bash
snail split \
--features input.shp \
--transform 1 0 -180 0 -1 90 \
--width 360 \
--height 180 \
--output split.gpkg
```
Split features on a grid defined by a GeoTIFF, optionally adding the values from each raster band to each split feature as a new attribute:
```bash
snail split \
--features lines.geojson \
--raster gridded_data.tif \
--attribute \
--output split_lines_with_raster_values.geojson
```
Split multiple vector feature files along the grids defined by multiple raster files, attributing all raster values:
```bash
snail process -fs features.csv -rs rasters.csv
```
Where at a minimum, each CSV has a column `path` with the path to each file.
### Transform
A note on `transform` - these six numbers define the transform from `i,j` cell index (column/row) coordinates in the rectangular grid to `x,y` geographic coordinates, in the coordinate reference system of the input and output files. They effectively form the first two rows of a 3x3 matrix:
```
| x | | a b c | | i |
| y | = | d e f | | j |
| 1 | | 0 0 1 | | 1 |
```
In cases without shear or rotation, `a` and `e` define scaling or grid cell size, while `c` and `f` define the offset or grid upper-left corner:
```
| x_scale 0 x_offset |
| 0 y_scale y_offset |
| 0 0 1 |
```
See [`rasterio/affine`](https://github.com/rasterio/affine#usage) and [GDAL Raster Data Model](https://gdal.org/user/raster_data_model.html#affine-geotransform) for more documentation.
## Development
Clone this repository using [GitHub Desktop](https://desktop.github.com/) or on
the command line:
git clone [email protected]:nismod/snail.git
Change directory into the root of the project:
cd snail
To create and activate a conda environment with snail's dependencies installed:
conda env create -f environment.yml
conda activate snail-dev
Run this to install the source code as a package:
pip install .
If you're working on snail itself, install it as "editable" along with test and
development packages:
pip install -e .[dev]
Run tests using [pytest](https://docs.pytest.org/en/latest/) and
[pytest-cov](https://pytest-cov.readthedocs.io) to check coverage:
pytest --cov=snail --cov-report=term-missing
Run a formatter ([black](https://github.com/psf/black)) to fix code
formatting:
black src/snail
When working on the tutorial notebooks, it is recommended to install and
configure [nbstripout](https://github.com/kynan/nbstripout) so data and outputs
are not committed in the notebook files:
nbstripout --install
### C++ library
The C++ library in `extension/src` contains the core routines to find intersections of
lines with raster grids.
Before working on the C++ library, fetch source code for Catch2 unit testing
library (this is included as a git submodule):
git submodule update --init --recursive
Build the library and run tests:
cmake -Bbuild ./extension
cmake --build build/
./build/run_tests
Run code style auto-formatting:
clang-format -i extension/src/*.{cpp,hpp}
Run lints and checks:
clang-tidy --checks 'cppcoreguidelines-*' extension/src/*.{cpp,hpp}
This may need some includes for `pybind11` - which will vary depending on your
python installation. For example, with python via miniconda:
clang-tidy --checks 'cppcoreguidelines-*' extension/src/* -- \
-I/home/username/miniconda3/include/python3.11/ \
-I./pybind11/include/
Or with C++ headers installed on a Linux machine:
clang-tidy --checks 'cppcoreguidelines-*' extension/src/* -- \
-std=c++14 \
-I/usr/include/x86_64-linux-gnu/c++/11 \
-I/usr/include/c++/11 \
-I{$PWD}/extension/extern/pybind11/include \
-I/usr/include/python3.10
### Integration of C++ and Python using pybind11
The `snail.core.intersections` module is built using `pybind11` with
`scikit-build-core` (see [docs](https://scikit-build-core.readthedocs.io/en/latest/))
- `extension/src/intersections.cpp` defines the module interface using the
`PYBIND11_MODULE` macro
- `pyproject.toml` defines the build requirements for snail, which includes
pybind11 and scikit-build-core
## Acknowledgments
> MIT License
>
> Copyright (c) 2020-23 Tom Russell and all [snail contributors](https://github.com/nismod/snail/graphs/contributors)
This library is developed by researchers in the [Oxford Programme for Sustainable
Infrastructure Systems](https://opsis.eci.ox.ac.uk/) at the University of Oxford,
funded by multiple research projects.
This research received funding from the FCDO Climate Compatible Growth Programme.
The views expressed here do not necessarily reflect the UK government's official
policies.