Gallery 7: Miscellaneous

This page shows off a set of miscellaneous plots.

This notebook is used in combination with the TUFLOW FV Python Toolbox (tfv) package.

from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import tfv.xarray
import xarray as xr

Open TUFLOW FV Model Result

model_folder = Path('../../data')

domain_file = 'HYD_002.nc'
profile_file = 'HYD_002_time_series.nc'

fv = xr.open_dataset(model_folder / domain_file, decode_times=False).tfv
ts = xr.open_dataset(model_folder / profile_file, decode_times=False).tfv

Hovmoller Plot

A hovmoller plot is a type of 2D Timeseries, which shows you time along the x-axis and a vertical variable along the y-axis. You can draw them from a profile timeseries result file (fastest) or from a domain result.

We’ll show both below.

# We'll borrow the locations from the timeseries file.
locations = ts.locations
locations

{'Point_1': [np.float64(159.07547357974093), np.float64(-31.363570800985343)],
 'Point_2': [np.float64(159.08421845507237), np.float64(-31.37241113710583)],
 'Point_3': [np.float64(159.0904215514381), np.float64(-31.381551067375685)],
 'Point_4': [np.float64(159.1002274977631), np.float64(-31.394967859607366)],
 'Point_5': [np.float64(159.1154660170632), np.float64(-31.40318039451822)],
 'Point_6': [np.float64(159.12657031830057), np.float64(-31.41063523347031)],
 'Point_7': [np.float64(159.1205401938322), np.float64(-31.416303583060728)],
 'Point_8': [np.float64(159.11767817266238), np.float64(-31.42373017624952)],
 'Point_9': [np.float64(159.12763707209098), np.float64(-31.440384244441056)]}

Using the Profile file

variable = 'SAL'
location = 'Point_4'

# We define the x-axis limits using a time slicer. You choose the start and end.
time_limits = slice('2011-05-01', '2011-05-07')

fig, ax = plt.subplots()

hov = ts.plot_hovmoller('Point_4', variable, ax=ax, time_limits=time_limits)
fig.autofmt_xdate()

fig.colorbar(hov, label='Salinity (PSU)')

plt.show()

Using the Domain file

variable = 'TEMP'
location = 'Point_2'

# We define the x-axis limits using a time slicer. You choose the start and end.
time_limits = slice('2011-05-01', '2011-05-07')

fig, ax = plt.subplots()

hov = fv.plot_hovmoller(locations[location], variable, ax=ax, time_limits=time_limits)
fig.autofmt_xdate()

fig.colorbar(hov, label='Temperature (degC)')

plt.show()

Using Different Shading Options

variable = 'TEMP'
location = 'Point_6'
clims = (10, 22)
clevels = np.arange(10, 22.5, 0.5) # For contour only! 

# We define the x-axis limits using a time slicer. You choose the start and end.
time_limits = slice('2011-05-01', '2011-05-07')

fig, axes = plt.subplots(ncols=3, figsize=(10, 4), constrained_layout=True)

shading_options = ['contour', 'interp', 'patch']

for i, ax in enumerate(axes):
    shading = shading_options[i]
    ax.set_title(shading)
    ax.set_xticklabels([]) # Hide the xlabels for neatness 

    if shading != 'contour':
        hov = fv.plot_hovmoller(locations[location], variable, ax=ax, time_limits=time_limits, shading=shading, cmap='turbo', clim=clims)
    else:
        hov = fv.plot_hovmoller(locations[location], variable, ax=ax, time_limits=time_limits, shading=shading, cmap='turbo', levels=clevels)

fig.colorbar(hov, ax=axes, orientation='horizontal', aspect=40, label='Temperature (degC)')
fig.autofmt_xdate()
plt.show()

This concludes the example.