Gallery 5: Combined Plots

Putting it all together: a sheet, curtain, profile and time series on the same figure.

This notebook is used in combination with the TUFLOW FV Python Toolbox (tfv) package. To follow along on your own computer, please download the demonstration notebooks from the TUFLOW Downloads Page. Look for the TUFLOW FV Python Toolbox download. Installation instructions are provided on our TUFLOW FV Python Toolbox Wiki Page.

import xarray as xr  # We utilise xarray to do all the heavy lifting 
import tfv.xarray
from pathlib import Path # We'll also make use of the `pathlib` module to assist with managing file-paths, although this is entirely optional! 
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

Open TUFLOW FV Model Result

model_folder = Path(r'..\..\data')
model_file = 'HYD_002.nc'

fv = xr.open_dataset(model_folder / model_file, decode_times=False).tfv
# fv  # Uncomment if you'd like to review the dataset

# User input - select variables and location for plotting
polyline = np.loadtxt(model_folder / 'polyline.csv', skiprows=1, delimiter=',')
loc = 'P2'
time = '2011-05-02 12:00'
var = 'SAL'

locs = {
    'P1' : (159.0758, -31.3638),
    'P2' : (159.0845,  -31.3727),
    'P3' : (159.0906,  -31.3814),
    'P4' : (159.1001,  -31.3948),
    'P5' : (159.1154,  -31.4032),
    'P6' : (159.1266,  -31.4105),
    'P7' : (159.1202,  -31.4165),
    'P8' : (159.1178,  -31.4236),
}

# Extract the timeseries and profile data
ts = fv.get_timeseries(['SAL'],locs)
prof = fv.get_profile(locs[loc],variables=['SAL'])

Extracting timeseries, please wait: 100%|███████████████████████████████████████████| 145/145 [00:00<00:00, 290.22it/s]

Plot Multiple Ways

# Set Up Figure
from matplotlib.gridspec import GridSpec
sns.set(style='white', font_scale=0.8)
cspec =  dict(cmap='ocean', clim=(0, 36))

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 8), gridspec_kw={'width_ratios': [1, 3]}, constrained_layout=True)
fv.plot(var, time, ax=ax1, colorbar=False, **cspec)
ax1.plot(polyline[:, 0], polyline[:, 1], lw=1, color='black', ls='--')
ax1.plot(locs['P2'][0], locs['P2'][1], 'o', color='red')
ax1.set_aspect('equal')
ax1.set_title(f'Depth Avg. {var}')

# Plot curtain
fv.plot_curtain(polyline, var, time, ax=ax2, ec='none', **cspec)
ax2.set_title(f'{var} Curtain Profile')

# Plot profile
prof.sel(Time=time).plot.scatter(x='SAL',y='Z', ax=ax3, color='black', lw=1)
zfaces = prof['layerface_Z'].sel(Time=time)
ax3.set_yticks(zfaces)
ax3.grid(axis='y')

# Plot timeseries
ts[var].sel(Location=loc).plot(ax=ax4, color='black', lw=1)
ax4.grid()
ax4.set_ylabel('Depth averaged salinity [PSU]')

fig.suptitle('Salinity Four Ways - A Salty Tale', fontweight='bold', x=0.45, fontsize=16)

Text(0.45, 0.98, 'Salinity Four Ways - A Salty Tale')

This concludes the examples on combined plotting.