Fig_25.ipynb

Kernel: Python 3 (system-wide)

import os
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

plt.style.use('../jfm.mplstyle')

!find ../Experimental_Dataset -type f -name "elevation.csv.gz" | xargs dirname | sort | grep "undular" | tee "data_directories.txt" > /dev/null

def find_idx_nearest(x,v):
    return np.argmin(np.abs(x-v))

with open("data_directories.txt","r") as data_directories_file:
    data_directories = data_directories_file.read().splitlines()
    
data_directories = sorted([data_dir for data_dir in data_directories if 'y=+00.00cm' in data_dir])

h0, a0, g = 50, 5, 9810
c0 = np.sqrt(g*h0)
t0 = h0/c0

#fig2, ax2 = plt.subplots()

fig, ax = plt.subplots(figsize=(4,2.0),layout='constrained')

linestyles = ['--','-']

for data_dir, linestyle in zip(data_directories,linestyles):
    time  = np.loadtxt(f"{data_dir}/time.csv",delimiter=',',dtype=int)
    space = np.loadtxt(f"{data_dir}/space.csv",delimiter=',',dtype=int)
    elev  = np.loadtxt(f"{data_dir}/elevation.csv",delimiter=',')

    crestnames = sorted([ f"{data_dir}/{name}" for name in os.listdir(data_dir) if "crests" in name ])
    troughnames = sorted([ f"{data_dir}/{name}" for name in os.listdir(data_dir) if "troughs" in name ])

    crestname = [name for name in crestnames if "1st" in name][0]
    troughname = [name for name in troughnames if "4th" in name][0]

    c_t_kwargs = {'delimiter':',','dtype':int,'usecols':(0,)}
    #c_v_kwargs = {'delimiter':',','dtype':float,'usecols':(1,)}

    c_t = np.loadtxt(crestname,**c_t_kwargs)
    t_t = np.loadtxt(troughname,**c_t_kwargs)
    
    x   = (space*0.3)/h0
    t   = (time/125)/t0
    eta = elev/h0

    x_idx = find_idx_nearest(x,20)

    plateau_fit = np.empty((1000,))
    levels = np.linspace(0.1,0.2,1000)
    for i, mwl in enumerate(levels):
        # find the zero up crossings
        eta_new = eta[:,x_idx] - mwl
        # because the first crossing is an up crossing, just take every other
        zero_crossings = np.where(np.diff(np.sign(eta_new)))[0][::2]
        if zero_crossings.size == 0 or zero_crossings.size < 5:
            plateau_fit[i] = np.nan
            continue
            
        t_start = zero_crossings[0]
        t_stop = zero_crossings[4] # we assure there are 5 zeros (index 4)
        plateau_fit[i] = np.mean(eta_new[t_start:t_stop])


    plateau_height = levels[np.nanargmin(np.abs(plateau_fit))]

    ax.plot(t,eta[:,x_idx],color='k',linestyle=linestyle)
    ax.axhline(plateau_height,color='k',linestyle=linestyle,linewidth=0.9)

ax.set_xlim(80,140)
ax.set_ylim(0,0.26)

ax.set_xlabel(r'$t$')
ax.set_ylabel(r'$\eta$')

plt.savefig('Fig_25.pdf')