Gold Coast wave monitoring¶
Measured and derived wave parameters from data collected by a wave monitoring buoy anchored at the Gold Coast. For more information please refer to www.qld.gov.au/waves.
The dataset is available from the Gold Coast wave monitoring website. On this page you will find at the bottom right a Download data button, which provides a link to a series of dataset.
Field names:
Hs- Significant wave height, an average of the highest third of the waves in a record (26.6 minute recording period).Hmax- The maximum wave height in the record.Tz- The zero upcrossing wave period.Tp- The peak energy wave period.Peak Direction- Direction (related to true north) from which the peak period waves are coming from.SST- Approximation of sea surface temperature.
%matplotlib inline
import io
import requests
from pylab import *
import netCDF4
import datetime as dt
import numpy as np
import pandas as pd
import matplotlib.dates as mdates
from pylab import rcParams
import warnings
warnings.filterwarnings('ignore')
%config InlineBackend.figure_format = 'svg'
Getting the dataset¶
From the website you can either download a CSV containing the data or directly get it from the provided url.
# Get the URL link
url = "https://www.data.qld.gov.au/dataset/d656d418-31b1-41fe-aae2-3d8a12588711/resource/73b4e42a-f3e7-4632-8b8e-d52205899048/download/gold-coast_2020-01-01t00_00-2020-12-31t23_30.csv"
# Open it with Pandas Library
s = requests.get(url).content
data_df = pd.read_csv(io.StringIO(s.decode('utf-8')))
We can visualise what is in the dataset by doing:
data_df
| Date/Time | Hs | Hmax | Tz | Tp | Peak Direction | SST | |
|---|---|---|---|---|---|---|---|
| 0 | 2020-01-01 00:00:00 | 1.490 | 2.48 | 7.339 | 12.174 | 78.08 | 25.35 |
| 1 | 2020-01-01 00:30:00 | 1.515 | 2.56 | 7.106 | 13.074 | 79.08 | 25.40 |
| 2 | 2020-01-01 01:00:00 | 1.533 | 2.42 | 7.274 | 13.406 | 79.08 | 25.35 |
| 3 | 2020-01-01 01:30:00 | 1.501 | 2.59 | 6.888 | 12.242 | 81.08 | 25.35 |
| 4 | 2020-01-01 02:00:00 | 1.499 | 2.72 | 7.255 | 12.920 | 84.08 | 25.35 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 17563 | 2020-12-31 21:30:00 | 1.295 | 2.21 | 4.809 | 6.192 | 103.24 | 23.90 |
| 17564 | 2020-12-31 22:00:00 | 1.190 | 1.85 | 4.705 | 6.826 | 106.24 | 23.90 |
| 17565 | 2020-12-31 22:30:00 | 1.144 | 1.89 | 4.867 | 5.997 | 115.24 | 23.90 |
| 17566 | 2020-12-31 23:00:00 | 1.215 | 2.22 | 5.119 | 6.973 | 82.24 | 23.95 |
| 17567 | 2020-12-31 23:30:00 | 1.210 | 2.39 | 5.040 | 7.491 | 101.24 | 24.00 |
17568 rows × 7 columns
First let’s define the Date/Time format:
dates = pd.to_datetime(data_df['Date/Time'], format = '%Y-%m-%d %H:%M:%S')
Plotting it¶
We can now plot the data, for example we will plot the significant wave height Hs over time:
# Figure dimension
fig = plt.figure(figsize=(10,4))
ax = fig.add_subplot(111)
# Hs over time is specified here
ax.plot(dates,data_df['Hs'], c='k', linewidth=1)
# Decorators for the plot
locator = mdates.AutoDateLocator()
ax.grid(True)
setp(gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# Axis labels
plt.title('Offshore Gold Coast Buoy Data 2020')
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%b-%d'))
ax.set_ylabel('meters')
fig.show()
It seems that there are some weird data on this graph… obviously we don’t expect to get negative values for Hs…
Dealing with nodata¶
Let’s find out these values:
data_df['Hs'].min()
-99.9
Ok, so we have no data values which are specified as -99.9.
Instead of giving them a default values we will define them as NaN directly.
# We could use that data_df = data_df.replace(-99.9,np.NaN) or the following
data_df['Hs'] = data_df['Hs'].replace(data_df['Hs'].min(),np.NaN)
Let’s plot the dataset now…
# Figure dimension
fig = plt.figure(figsize=(10,4))
ax = fig.add_subplot(111)
# Hs over time is specified here
ax.plot(dates,data_df['Hs'], linewidth=1)
# Decorators for the plot
locator = mdates.AutoDateLocator()
ax.grid(True)
setp(gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# Axis labels
plt.title('Offshore Gold Coast Buoy Data 2020')
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%b-%d'))
ax.set_ylabel('meters')
fig.show()
Look’s better now! You can try to plot one of the other parameters:
Hmax- The maximum wave height in the record.Tz- The zero upcrossing wave period.Tp- The peak energy wave period.SST- Approximation of sea surface temperature.