Fig_03.ipynb

Kernel: Python 3 (system-wide)

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms

from matplotlib.colors import LinearSegmentedColormap

from scipy import signal
from scipy.signal import convolve2d
from scipy.stats import linregress
from scipy.special import erf

from itertools import cycle

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

rainbow = np.loadtxt('../rainbow.tsv')

mymap = LinearSegmentedColormap.from_list('rainbow', rainbow)

data_dir = '../Experimental_Dataset/with_flow/5cm_water_depth/particle_image_velocimitry'

x_int = np.loadtxt(f'{data_dir}/velocity_x_interp.txt',dtype=int)
y_int = np.loadtxt(f'{data_dir}/velocity_y_interp.txt',dtype=int)
u = np.loadtxt(f'{data_dir}/velocity_u_interp.txt',dtype="float32")
v = np.loadtxt(f'{data_dir}/velocity_v_interp.txt',dtype="float32")
m = np.loadtxt(f'{data_dir}/velocity_m_interp.txt',dtype="float32")
u_std = np.loadtxt(f'{data_dir}/velocity_u_std_interp.txt',dtype="float32")
v_std = np.loadtxt(f'{data_dir}/velocity_v_std_interp.txt',dtype="float32")
m_std = np.loadtxt(f'{data_dir}/velocity_m_std_interp.txt',dtype="float32")

h, g, b0 = 50, 9810, 229
c = np.sqrt(g*h)

x = (x_int/h)
y = (y_int/h)

u /= c
v /= c
m /= c

u_std /=c
v_std /= c
m_std /= c

N = 5
k = np.ones((N,N))/(N**2)

kwargs = {'mode':'same','boundary':'fill','fillvalue':np.nan}

u_smooth = convolve2d(u,k,**kwargs)
v_smooth = convolve2d(v,k,**kwargs)
m_smooth = convolve2d(m,k,**kwargs)

u_std_smooth = convolve2d(u_std,k,**kwargs)
v_std_smooth = convolve2d(v_std,k,**kwargs)
m_std_smooth = convolve2d(m_std,k,**kwargs)

fig, ax = plt.subplots(figsize=(5.0,2.0))

xlim = (-45,10)
ylim = (-10,10)
line_kwargs = {'color':'k','linewidth':2}

step_x = 125
step_y = 40
mask = np.logical_and(x_int%step_x==0,y_int%step_y==0)

cmap = ax.pcolormesh(x,y,m_std_smooth,cmap=mymap,vmin=0,shading='gouraud')

quiver = ax.quiver(x[mask],y[mask],u_smooth[mask]*c/h,v_smooth[mask]*c/h,
          units='xy',angles='xy',scale_units='xy',scale=0.6,pivot='tail',color='k',
                   width=0.15)

ax.quiver([5],[5],[-0.1*c/h],[0],
           units='xy',angles='xy',scale_units='xy',scale=0.6,pivot='mid',color='k',
           width=0.15)

ax.hlines(  115/h,xmin=600/h,xmax=0 ,**line_kwargs)
ax.hlines( -115/h,xmin=600/h,xmax=0 ,**line_kwargs)
ax.hlines( 1360/h,xmin=0  ,xmax=-2250/h,**line_kwargs)
ax.vlines(       0,ymin= 115/h,ymax= 1360/h,**line_kwargs)
ax.vlines(       0,ymin=-115/h,ymax=-500/h,**line_kwargs)

ax.axis('scaled')
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)

#for ax, label in zip(axes,['(a)','(b)']):
#    ax.text(-0.16, 1.1, label, transform=ax.transAxes, size=7)

#plt.tight_layout()

clb = plt.colorbar(cmap,ax=ax,
            location='right',orientation='vertical',shrink=0.7)
clb.set_ticks([0,0.007,0.014])
#clb.ax.set_title(r'$\sigma(|\vec{u}|)$')
clb.set_label(r'$\sigma(|\vec{u}|)$', labelpad=-30, y=1.2, rotation=0)

ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')

plt.tight_layout()
#plt.savefig('Fig_03.pdf')
plt.savefig('Fig_03.jpg')
plt.show()