scarplet is a Python package for applying template matching techniques to digital elevation data, in particular for detecting and measuring the maturity of fault scarps and other landforms [0, 1].
It is intended for earth scientists who want to apply diffusion dating methods to or extract landforms from large datasets. The scarplet API can be used to estimate the height and relative age of a landform or identify DEM pixels based on their fit to a landform template.
It was designed with two main goals:
- Allow contributors to define template functions for their problem area of interest
- Make it straightforward to apply these methods to large datasets by parallelizing/distrbuting computation using multiprocessing, dask, or other tools [2]
scarplet can be installed using conda or pip. It is developed for Python 3.4+ and currently works on Linux and Mac OS X.
conda install scarplet -c conda-forgeOr, to manually install the latest version from github:
git clone https://github.com/rmsare/scarplet
cd scarplet
conda install --file=requirements.txt -c conda-forge
python setup.py developThe main dependencies are numpy, scipy, numexpr, pyfftw (which requires LibFFTW3) and rasterio/GDAL.
Example notebooks can be found in the docs folder or website and sample datasets can be loaded using the datasets submodule.
This example uses a scarp template based on a diffusion model of scarp degradation [0] to identify scarp-like landforms along the San Andreas Fault near Wallace Creek, CA.
import numpy as np
import scarplet as sl
from scarplet.datasets import load_carrizo
from scarplet.WindowedTemplate import Scarp
params = {'scale': 100,
'age': 10,
'ang_min': -10 * np.pi / 2,
'ang_max': 10 * np.pi / 2
}
data = load_carrizo()
res = sl.match(data, Scarp, **params)
sl.plot_results(data, res)
To illustrate template function flexibility, this example uses a Channel template similar to the Ricker wavelet [3] to extract part of a channel network. This is example uses a moderate resolution SRTM data tile. In general, for high resolution data like lidar, there are more robust alternatives for channel network extraction or channel head identification [4, 5].
import numpy as np
import scarplet as sl
from scarplet.datasets import load_grandcanyon
from scarplet.WindowedTemplate import Channel
params = {'scale': 10,
'age': 0.1,
'ang_min': -np.pi / 2,
'ang_max': np.pi / 2
}
data = load_grandcanyon()
res = sl.match(data, Channel, **params)
sl.plot_results(data, res)
There are also example notebooks and an API reference in the documentation.
Read the documentation for example use cases, an API reference, and more. They are hosted at scarplet.readthedocs.io.
Bug reports are much appreciated. Please open an issue with the bug label,
and provide a minimal example illustrating the problem.
Feel free to suggest new features in an issue with the new-feature label.
Don't hestitate to file an issue; I would be happy to discuss extensions or help to build a new feature.
If you would like to add a feature or fix a bug, please fork the repository, create a feature branch, and submit a PR and reference any relevant issues. There are nice guides to contributing with GitHub here and here. Please include tests where appropriate and check that the test suite passes (a Travis build or pytest scarplet/tests) before submitting.
Please open an issue with your question.
[0] Hanks, T.C., 2000. The age of scarplike landforms from diffusion‐equation analysis. Quaternary Geochronology, 4, pp. 313-338. doi
[1] Hilley, G.E., DeLong, S., Prentice, C., Blisniuk, K. and Arrowsmith, J.R., 2010. Morphologic dating of fault scarps using airborne laser swath mapping (ALSM) data. Geophysical Research Letters, 37(4). doi
[2] Sare, R, Hilley, G. E., and DeLong, S. B., 2019, Regional scale detection of fault scarps and other tectonic landforms: Examples from Northern California, Journal of Geophysical Research: Solid Earth. doi
[3] Lashermes, B., Foufoula‐Georgiou, E., and Dietrich, W. E., 2007, Channel network extraction from high resolution topography using wavelets. Geophysical Research Letters, 34(23). doi
[4] Passalacqua, P., Tarolli, P., and Foufoula‐Georgiou, E., 2010, Testing space‐scale methodologies for automatic geomorphic feature extraction from lidar in a complex mountainous landscape. Water Resources Research, 46(11). doi
[5] Clubb, F. J., Mudd, S. M., Milodowski, D. T., Hurst, M. D., and Slater, L. J., 2014, Objective extraction of channel heads from high‐resolution topographic data. Water Resources Research, 50(5). doi
This work is licensed under the MIT License (see LICENSE).