Getting started with scarplet

Input data

Currently scarplet handles input data in GeoTiff format. Get a copy of your elevation data as a GeoTiff, and you can load it as

import scarplet as sl
data = sl.load('mydem.tif')

Choosing a template

If you have gaps in your DEM, no data values will automatically be filled. Then you are ready to choose a template and fit it to your data. These are defined as classes in the WindowedTemplate submodule:

Class	Landform	Use
Scarp	Fault scarps, topographic steps	Detecting and morphologic dating of scarp-like landforms
Channel	Confined channels	Extracting channel orientations, valley relief
Crater	Radially symmetric craters	Measuring crater depth and diffusion dating
Ricker	Channels, ridges	Extracting ridge and channel orientations

For example, to use a vertical scarp template, you would import the appropiate template and define a scale and the orientation parameters. In this case, +/- 90 degrees from vertical (y direction) captures all scarp orientations.

import numpy as np
from scarplet.WindowedTemplate import Scarp
params = {'scale': 100,
          'ang_min': -np.pi / 2,
          'ang_max': np.pi / 2
          }

Then, scarplet’s match function will search over all parameters and return the best-fitting height, relative age, and orientation at each DEM pixel.

res = sl.match(data, Scarp, **params)
sl.plot_results(data, res)

Viewing matching results

All results are returned as 4 x height x width arrays of height/amplitude, relative age, orientation, and signal-to-noise-ratio. The easiest way to work with these is to unpack the results and manipulate them as NumPy arrays

import matplotlib.pyplot as plt
amp, age, angle, snr = res

fig, ax = plt.subplots(2, 1)
ax[0].hist(np.log10(age.reshape((-1,))), bins=10)
ax[0].set_xlabel('Morphologic age [m$^2$]')

ax[1].hist(angle.reshape((-1,)) * 180 / np.pi, nbins=19)
ax[1].set_xlabel('Orientation [deg.]')