SimulateCutoffDistribution.py

"""
This script uses the HKplus functions to load an existing centerline, run it for a certain number of years, then save the resulting cutoff distribution and bend-by-bend migration rate measurements.

"""
import math
import matplotlib.pyplot as plt
import HKplus as hkp
import numpy as np
import pandas as pd
import os


# Set Variables for centerline and curvature calculation

D = 3.4  # constant width-average channel depth (m)
W = 100  # constant channel width (m)
deltas = W // 2
# spacing of n2des along centerline
Cf = 0.005  # dimensionless Chezy friction factor
kl = 35 / (365 * 24 * 60 * 60.0)  # migration rate constant (m/s)
dt = 0.25 * 365 * 24 * 60 * 60.0  # time step (s)
pad = 100  # number of nodes for periodic boundary
saved_ts = 100  # timeteps between saving centerlines
crdist = 4 * W  # how close banks get before cutoff in m
cut_thresh = 500  # how many cutoffs to simulate, arbitrary if running for time

# Set Variables fror Cutoff nonlocal efects
tau = 8  # e-folding timescale for nonlocal effects in years
decay_rate = dt / (tau * (365 * 24 * 60 * 60.0))
# this is the half-life on nonlocal effects, in units of seconds
bump_scale = 3  # this is the magntiude of nonlocal effects in relative difference


# Set mode for titles
mode = "66"

# Set Result Directory
result_dir = "results/experiments/" + str(mode) + "/"

# Load existing Centerline
filepath = "data/InitialChannel/InitialCL_" + str(mode) + ".csv"

ch = hkp.load_initial_channel(filepath, W, D, deltas)

# Initalize Channel Belt for migration
chb = hkp.ChannelBelt(
    channels=[ch],
    cutoffs=[],
    cl_times=[0],
    cutoff_times=[],
    cutoff_dists=[],
    decay_rate=decay_rate,
    bump_scale=bump_scale,
    cut_thresh=cut_thresh,
    sinuosity=[],
)

# Plot initial centerline
chb.plot_channels()
plt.show()

# Migrate
chb.migrate_cuts(saved_ts, deltas, pad, crdist, Cf, kl, dt)

# Plot resulting Centerline
chb.plot_channels()
plt.show()
# Save Sinuosity time series
times = chb.cl_times
sins = chb.sinuosity
# Save Cutoff Distributions for Clustering Tests #
chb.cutoff_distributions(int(chb.cutoff_times[-1]), result_dir, mode)
plt.title(str(len(chb.cutoff_times)) + " cutoffs")
plt.savefig(
    result_dir + mode + "_" + str(len(chb.cutoff_times)) + "_timevsspace.png",
    bbox_inches="tight",
)
plt.close()


# Save Resulting Centerline
xes = chb.channels[-1].x
yes = chb.channels[-1].y
cl = pd.DataFrame({"x": xes, "y": yes})

cl.to_csv(result_dir + "InitialChannel_result.csv", header=False, index=False)