CDIPpy Example - Compendium Plot¶

The following example runs an application of the CDIPpy python library to create plots of significant wave height (Hs), peak period (Tp) and peak direction (Dp) for a single station for a given month on 3 separate plots. The format of this plot is consistent with the original USACE wave parameters plot known as the wave compendium plot. Plotting all 3 parameters simultaneously gives a concise display of the general wave environment for the month.

read in CDIP station metadata,
access wave data for time period of interest,
plot timeseries,

Import Libraries¶

Start by importing the necessary python packages and CDIPPY module

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import time
import calendar

# CDIP imports
import cdippy
from cdippy.stndata import StnData
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import time
import calendar

# CDIP imports
import cdippy
from cdippy.stndata import StnData

Initialize CDIPpy input parameters¶

Supply CDIP station id
Start date (YYYYMMDDHH)
End date
Station parameters

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##- Initialize station id, start/end date, and parameters
stn = '100p1'
sdate = '20210201040201'
edate = '20220601040201'
params = ['waveHs','waveTp','waveDp']
##- Initialize station id, start/end date, and parameters
stn = '100p1'
sdate = '20210201040201'
edate = '20220601040201'
params = ['waveHs','waveTp','waveDp']

Data Access¶

Use cdippy.stndata function StnData(stn)
Returns StnData object
Access station metadata about individual stations deployments

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##- Get Station Dataset object
stn_data = StnData(stn)

##- Get metadata (i.e. information about individual deployments)
meta = stn_data.get_stn_meta()
stn_name = meta['metaStationName']
print(stn_name)
##- Get Station Dataset object
stn_data = StnData(stn)

##- Get metadata (i.e. information about individual deployments)
meta = stn_data.get_stn_meta()
stn_name = meta['metaStationName']
print(stn_name)

TORREY PINES OUTER, CA BUOY - 100p1

Grab data using 'get_series' function¶

Will return a dictionary of arrays for each parameter as keys

stn_data.get_series(start, end, params)

start: start datetime (datetime obj)
end: end datetime (datetime obj)
params: list of parameters

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##- Use CDIPPY to convert input start/end date strings to datetime objects
start = cdippy.utils.utils.cdip_datetime(sdate)
end = cdippy.utils.utils.cdip_datetime(edate)

##- Grab data using 'get_series' function
data = stn_data.get_series(start, end, params)
data.keys()
##- Use CDIPPY to convert input start/end date strings to datetime objects
start = cdippy.utils.utils.cdip_datetime(sdate)
end = cdippy.utils.utils.cdip_datetime(edate)

##- Grab data using 'get_series' function
data = stn_data.get_series(start, end, params)
data.keys()

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dict_keys(['waveDp', 'waveTime', 'waveTp', 'waveHs'])

Convert waveTimes to Datetime object¶

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## Convert wave times to datetime objects (currently integers)
##- Convert times to datetime objects
wT = [cdippy.utils.utils.timestamp_to_datetime(x) for x in data['waveTime']]
## Convert wave times to datetime objects (currently integers)
##- Convert times to datetime objects
wT = [cdippy.utils.utils.timestamp_to_datetime(x) for x in data['waveTime']]

Create a Pandas Dataframe indexed by time¶

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## Create pandas dataframe from data
df = pd.DataFrame({'datetime': wT, 'Hs': data['waveHs'].data})
df.set_index('datetime', inplace=True)
# Add a "Month" column for grouping
df['Month'] = df.index.month
df.head()
## Create pandas dataframe from data
df = pd.DataFrame({'datetime': wT, 'Hs': data['waveHs'].data})
df.set_index('datetime', inplace=True)
# Add a "Month" column for grouping
df['Month'] = df.index.month
df.head()

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	Hs	Month
datetime
2021-02-01 04:30:00	0.65	2
2021-02-01 05:00:00	0.64	2
2021-02-01 05:30:00	0.66	2
2021-02-01 06:00:00	0.65	2
2021-02-01 06:30:00	0.68	2

Create Compendium plot¶

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# Crete figure and specify subplot orientation (3 rows, 1 column), shared x-axis, and figure size
f, (pHs, pTp, pDp) = plt.subplots(3, 1, sharex=True, figsize=(15,10)) 

# Create 3 stacked subplots for three PARAMETERS (Hs, Tp, Dp)
pHs.plot(wT,data['waveHs'],'b', linewidth="0.5")
pTp.plot(wT,data['waveTp'],'b', linewidth="0.5")
pDp.scatter(wT,data['waveDp'],color='blue',s=3) # Plot Dp variable as a scatterplot, rather than line

# Set Titles
plt.suptitle(stn_name, fontsize=30, y=0.99)
plt.title(start.strftime("%Y%m%d")+'-'+end.strftime("%Y%m%d"), fontsize=20, y=3.45)

# Set tick parameters
#pHs.set_xticklabels(['1','6','11','16','21','26','31']) 
pHs.tick_params(axis='y', which='major', labelsize=12, right='off')
pHs.tick_params(axis='x', which='major', labelsize=12, top='off')

# Make a second y-axis for the Hs plot, to show values in both meters and feet
pHs2 = pHs.twinx()

# Set y-axis limits for each plot
pHs.set_ylim(0,8)
pHs2.set_ylim(0,25)
pTp.set_ylim(0,28)
pDp.set_ylim(0,360)

# Label each y-axis
pHs.set_ylabel('Hs, m', fontsize=18)
pHs2.set_ylabel('Hs, ft', fontsize=18)
pTp.set_ylabel('Tp, s', fontsize=18)
pDp.set_ylabel('Dp, deg', fontsize=18)

# Plot dashed gridlines
pHs.grid(linestyle='--')
pTp.grid(linestyle='--')
pDp.grid(linestyle='--')
# Crete figure and specify subplot orientation (3 rows, 1 column), shared x-axis, and figure size
f, (pHs, pTp, pDp) = plt.subplots(3, 1, sharex=True, figsize=(15,10)) 

# Create 3 stacked subplots for three PARAMETERS (Hs, Tp, Dp)
pHs.plot(wT,data['waveHs'],'b', linewidth="0.5")
pTp.plot(wT,data['waveTp'],'b', linewidth="0.5")
pDp.scatter(wT,data['waveDp'],color='blue',s=3) # Plot Dp variable as a scatterplot, rather than line

# Set Titles
plt.suptitle(stn_name, fontsize=30, y=0.99)
plt.title(start.strftime("%Y%m%d")+'-'+end.strftime("%Y%m%d"), fontsize=20, y=3.45)

# Set tick parameters
#pHs.set_xticklabels(['1','6','11','16','21','26','31']) 
pHs.tick_params(axis='y', which='major', labelsize=12, right='off')
pHs.tick_params(axis='x', which='major', labelsize=12, top='off')

# Make a second y-axis for the Hs plot, to show values in both meters and feet
pHs2 = pHs.twinx()

# Set y-axis limits for each plot
pHs.set_ylim(0,8)
pHs2.set_ylim(0,25)
pTp.set_ylim(0,28)
pDp.set_ylim(0,360)

# Label each y-axis
pHs.set_ylabel('Hs, m', fontsize=18)
pHs2.set_ylabel('Hs, ft', fontsize=18)
pTp.set_ylabel('Tp, s', fontsize=18)
pDp.set_ylabel('Dp, deg', fontsize=18)

# Plot dashed gridlines
pHs.grid(linestyle='--')
pTp.grid(linestyle='--')
pDp.grid(linestyle='--')

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