sandplover.mobility.channel_presence

sandplover.mobility.channel_presence(chmap)

Calculate the normalized channel presence at each pixel location.

Measure the normalized fraction of time a given pixel is channelized, based on method in Liang et al 2016 [1]. This requires providing a 3-D input channel map (t-x-y).

[1]

Liang, Man, Corey Van Dyk, and Paola Passalacqua. “Quantifying the patterns and dynamics of river deltas under conditions of steady forcing and relative sea level rise.” Journal of Geophysical Research: Earth Surface 121, no. 2 (2016): 465-496.

Parameters:

chmap (ndarray) – A t-x-y shaped array with binary channel maps

Returns:

channel_presence – A x-y shaped array with the normalized channel presence values.

Return type:

ndarray

Examples

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from sandplover.mask import ChannelMask
>>> from sandplover.mobility import channel_presence
>>> from sandplover.plot import append_colorbar
>>> from sandplover.sample_data.sample_data import golf

>>> golfcube = golf()
>>> x, y = np.shape(golfcube["eta"][-1, ...])

Calculate channel masks/presence over final 5 timesteps

>>> chmap = np.zeros((5, x, y))  # initialize channel map
>>> for i in np.arange(-5, 0):
...     chmap[i, ...] = ChannelMask(
...         golfcube["eta"][i, ...],
...         golfcube["velocity"][i, ...],
...         elevation_threshold=0,
...         flow_threshold=0,
...     ).mask
...
>>>
>>> fig, ax = plt.subplots(1, 2)
>>> golfcube.quick_show("eta", ax=ax[0])  # final delta
>>> p = ax[1].imshow(channel_presence(chmap), cmap="Blues")
>>> _ = append_colorbar(p, ax[1], label="Channelized Time")

(Source code, png, hires.png)