Inshore wave data for Narrabeen¶
This notebook is a companion to your practical.
Since March 2016, an archive of inshore wave information has been made available, currently spanning the fixed period of 1979-2014 from the Narrabeen-Collaroy Beach Survey Program.
All data can be downloaded from the Narrabeen data repository: http://narrabeen.wrl.unsw.edu.au.
import requests
import io
import scipy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.dates as mdates
import datetime as dt
from pylab import *
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
%config InlineBackend.figure_format = 'svg'
%matplotlib inline
Loading the dataset¶
After following the instructuions in the practical, you will have already downloaded your profiles as a .csv files (comma-separated values).
Note
CSV is a commonly used data format that opens easily in a range of software such as Notepad, Microsoft Excel, and obviously Python.
The downloaded data can be visualised using Pandas. Here, I have extracted some of wave data in a file located in the pracenv/dataset folder:
dataframe = pd.read_csv('../pracenv/dataset/Inshore_Waves.csv', encoding="utf8")
dataframe
| Profile ID | Date and time (dd/mm/yyyy HH:MM AEST) | Significant wave height (m) | Peak wave period (s) | Direction (degrees TN) | Flag | |
|---|---|---|---|---|---|---|
| 0 | PF1 | 15/01/1979 00:00 | 1.32 | 7.04 | 84 | HIND |
| 1 | PF1 | 15/01/1979 01:00 | 1.31 | 7.14 | 85 | HIND |
| 2 | PF1 | 15/01/1979 02:00 | 1.30 | 7.46 | 86 | HIND |
| 3 | PF1 | 15/01/1979 03:00 | 1.28 | 7.69 | 87 | HIND |
| 4 | PF1 | 15/01/1979 04:00 | 1.25 | 7.81 | 87 | HIND |
| ... | ... | ... | ... | ... | ... | ... |
| 1566840 | PF8 | 14/10/2014 20:00 | 1.13 | 10.27 | 94 | MEAS |
| 1566841 | PF8 | 14/10/2014 21:00 | 1.30 | 10.83 | 95 | MEAS |
| 1566842 | PF8 | 14/10/2014 22:00 | 1.62 | 12.14 | 93 | MEAS |
| 1566843 | PF8 | 14/10/2014 23:00 | 1.57 | 11.45 | 94 | MEAS |
| 1566844 | PF8 | 15/10/2014 00:00 | 1.87 | 12.93 | 92 | MEAS |
1566845 rows × 6 columns
We will redefine the header to make it easier for the rest of the manipulation:
# Manually define names for each column
names = [
"Profile",
"date",
"Hs",
"Tp",
"Dir",
"Flag"
]
df = pd.read_csv('../pracenv/dataset/Inshore_Waves.csv', encoding="utf8", names=names, skiprows=1)
dates = pd.to_datetime(df['date'], format = '%d/%m/%Y %H:%M')
# Append a new column at the end of our iribarren values
df["time"] = dates
df
| Profile | date | Hs | Tp | Dir | Flag | time | |
|---|---|---|---|---|---|---|---|
| 0 | PF1 | 15/01/1979 00:00 | 1.32 | 7.04 | 84 | HIND | 1979-01-15 00:00:00 |
| 1 | PF1 | 15/01/1979 01:00 | 1.31 | 7.14 | 85 | HIND | 1979-01-15 01:00:00 |
| 2 | PF1 | 15/01/1979 02:00 | 1.30 | 7.46 | 86 | HIND | 1979-01-15 02:00:00 |
| 3 | PF1 | 15/01/1979 03:00 | 1.28 | 7.69 | 87 | HIND | 1979-01-15 03:00:00 |
| 4 | PF1 | 15/01/1979 04:00 | 1.25 | 7.81 | 87 | HIND | 1979-01-15 04:00:00 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1566840 | PF8 | 14/10/2014 20:00 | 1.13 | 10.27 | 94 | MEAS | 2014-10-14 20:00:00 |
| 1566841 | PF8 | 14/10/2014 21:00 | 1.30 | 10.83 | 95 | MEAS | 2014-10-14 21:00:00 |
| 1566842 | PF8 | 14/10/2014 22:00 | 1.62 | 12.14 | 93 | MEAS | 2014-10-14 22:00:00 |
| 1566843 | PF8 | 14/10/2014 23:00 | 1.57 | 11.45 | 94 | MEAS | 2014-10-14 23:00:00 |
| 1566844 | PF8 | 15/10/2014 00:00 | 1.87 | 12.93 | 92 | MEAS | 2014-10-15 00:00:00 |
1566845 rows × 7 columns
Plotting significant wave height¶
We will now define a function that will take a specific profile ID PID and a start and end time and will plot the existing inshore wave data, showing significant wave height for the considered period…
def plotHsSeries(PID, start_date, end_date):
'''
This function plot the significant wave height for a specific transect based on specified period of time.
args:
- PID: transect ID
- start_date: initial date requested in year-month-day
- end_date: end date requested in year-month-day
'''
mask = (df['time'] > start_date) & (df['time'] <= end_date)
df_time = df.loc[mask]
MyDateFormatter = DateFormatter('%d-%b')
fig = plt.figure(figsize=(11,4), dpi=160)
ax1 = fig.add_subplot(111)
for k in range(len(PID)):
ids1 = np.where(df_time.Profile==PID[k])[0]
ax1.plot(df_time['time'].iloc[ids1],df_time['Hs'].iloc[ids1],linewidth=1,label=PID[k])
locator = mdates.AutoDateLocator()
ax1.xaxis.set_major_locator(locator)
ax1.xaxis.set_major_formatter(MyDateFormatter)
ax1.grid(True)
setp(gca().get_xticklabels(), rotation=0, horizontalalignment='center')
ax1.set_ylabel('$H_s$ (m)')
ax1.legend()
plt.title('Narrabeen-Collaroy Wave Height from '+str(start_date)+' to '+str(end_date),fontsize=10)
return
We now apply the function to 2 profiles. Here we chose the profile PF1 and PF8:
plotHsSeries(PID=['PF1','PF8'], start_date = '2007-6-1', end_date = '2007-7-1')
Plotting significant wave period¶
Similarly we can plot the inshore wave data, showing peak wave period (Tp)…
def plotTpSeries(PID, start_date, end_date):
'''
This function plot the significant wave period for a specific transect based on specified period of time.
args:
- PID: transect ID
- start_date: initial date requested in year-month-day
- end_date: end date requested in year-month-day
'''
mask = (df['time'] > start_date) & (df['time'] <= end_date)
df_time = df.loc[mask]
MyDateFormatter = DateFormatter('%d-%b')
fig = plt.figure(figsize=(11,4), dpi=160)
ax1 = fig.add_subplot(111)
ids1 = np.where(df_time.Profile==PID)[0]
ax1.plot(df_time['time'].iloc[ids1],df_time['Tp'].iloc[ids1],linewidth=1,label=PID,color='k')
locator = mdates.AutoDateLocator()
ax1.xaxis.set_major_locator(locator)
ax1.xaxis.set_major_formatter(MyDateFormatter)
ax1.grid(True)
setp(gca().get_xticklabels(), rotation=0, horizontalalignment='center')
ax1.set_ylabel('$T_p$ (s)')
ax1.legend()
plt.title('Narrabeen-Collaroy Wave Period from '+str(start_date)+' to '+str(end_date),fontsize=10)
return
plotTpSeries(PID='PF1', start_date = '2007-6-1', end_date = '2007-7-1')
Plotting wave direction¶
or the inshore wave data, showing wave direction…
def plotDirSeries(PID, start_date, end_date):
'''
This function plot the significant wave direction for a specific transect based on specified period of time.
args:
- PID: transect ID
- start_date: initial date requested in year-month-day
- end_date: end date requested in year-month-day
'''
mask = (df['time'] > start_date) & (df['time'] <= end_date)
df_time = df.loc[mask]
MyDateFormatter = DateFormatter('%d-%b')
fig = plt.figure(figsize=(11,4), dpi=160)
ax1 = fig.add_subplot(111)
for k in range(len(PID)):
ids1 = np.where(df_time.Profile==PID[k])[0]
ax1.plot(df_time['time'].iloc[ids1],df_time['Dir'].iloc[ids1],'.',label=PID[k], markersize=2)
locator = mdates.AutoDateLocator()
ax1.xaxis.set_major_locator(locator)
ax1.xaxis.set_major_formatter(MyDateFormatter)
ax1.grid(True)
setp(gca().get_xticklabels(), rotation=0, horizontalalignment='center')
ax1.set_ylabel('Dir ($^o$)')
ax1.legend()
plt.title('Narrabeen-Collaroy Wave Direction from '+str(start_date)+' to '+str(end_date),fontsize=10)
return
plotDirSeries(PID=['PF1','PF8'], start_date = '2007-6-1', end_date = '2007-7-1')
