Fig_26.ipynb

Kernel: Python 3 (system-wide)

import os, csv
import numpy as np
import matplotlib.pyplot as plt

from scipy import signal

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

data_file = "../Experimental_Dataset/with_flow/5cm_water_depth/acoustic_doplar_velocimiter/adv_data.txt"

headers = open(data_file).readline()[len('# '):]
data = np.loadtxt(data_file)

def loc_from_header(header):
    ''' find location data from header '''
    x_part, y_part, z_part, u_part = header.split('_')
    # from string to integer mm
    x_int = int(x_part[len('x='):-len('mm')])
    y_int = int(y_part[len('y='):-len('mm')])
    z_int = int(z_part[len('z='):-len('mm')])
    # u_part already correct
    return x_int, y_int, z_int, u_part

time = data[:,0]
data_dict = {}
# skip time
for col, header in enumerate(headers.split()[1:]):
    col +=1 # need to start at 1
    x_int, y_int, z_int, u_part = loc_from_header(header)
    try:
        data_dict[f"{x_int},{y_int},{z_int}"][u_part] = data[:,col].copy()
    except KeyError:
        data_dict[f"{x_int},{y_int},{z_int}"] = {}
        data_dict[f"{x_int},{y_int},{z_int}"][u_part] = data[:,col].copy()

header_parts = [ loc_from_header(header) for header in headers.split()[1:] ]
        
x_y_pairs = set([ f"{x_int},{y_int}" for x_int,y_int,_, _ in header_parts ])
print(x_y_pairs)
z_vals = set([ z_int for _, _,z_int, _ in header_parts ])
print(z_vals)
u_vals = set([ u_part for _, _, _,u_part in header_parts ])
print(u_vals)

x_parts = [ loc.split(',')[0] for loc in sorted(x_y_pairs) ]
y_parts = [ loc.split(',')[1] for loc in sorted(x_y_pairs) ]
x = np.array([int(xp) for xp in x_parts],dtype=int)
y = np.array([int(yp) for yp in y_parts],dtype=int)
#print(x,y)
#print(x.shape)

h, g, b = 50, 9810, 230
c = np.sqrt(g*h)

{'1000,0', '-500,0', '0,0', '500,0', '-1000,114', '-50,0', '-1000,0', '-1500,0', '-50,114', '-500,114', '-2000,0'}
{32, 19, 7}
{'w', 'v', 'u'}

loc = '-50,114'

fig, axes = plt.subplots(figsize=(3,1.5))


for z_val in z_vals:
    c = np.sqrt(9810*50)
    
    u = np.nanmean(data_dict[f'{loc},{z_val}']['u'].copy())
    v = np.nanmean(data_dict[f'{loc},{z_val}']['v'].copy())
    
    plt.quiver(-50/h,114/h,u/c,v/c,
               units='xy',angles='xy',scale_units='xy',scale=0.01,
               label=fr'$z={z_val/50:.2f}$')

    
plt.hlines( 115/h,xmin=5,xmax=0,color='k')
plt.hlines(-115/h,xmin=5,xmax=0,color='k')
#plt.hlines(5.65,xmin=-10,xmax=0,color='k')
plt.vlines(   0,ymin=115/h,ymax=300/h,color='k')
plt.vlines(   0,ymin=-115/h,ymax=-300/h,color='k')

plt.axis('equal')
plt.ylim(1,3)
plt.xlim(-2,0.1)
plt.xlabel(r'$x$')
plt.ylabel(r'$y$')

plt.legend([fr'$z={z_val/50:.2f}$' for z_val in z_vals],loc='lower right')
#plt.tight_layout()

plt.savefig('Fig_26_c.pdf')
plt.show()
plt.close()