Using the grid as point service, extracting a time series closest to a specified lon,lat location.
At first we use the NetCDF Subset Web Form below to construct the query, but then reuse the URL generated by the form directly in the Python code below. In this way different time periods, depths or variables may be extracted without returning to the form, and analyzed and visualized in Python without saving and loading CSV files.
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import pandas as pd
import time
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var = 'temp,salt'
lat = 43.7148
lon = -69.3578
#start = '2012-05-01T00:00:00Z'
start = '2014-08-24T00:00:00Z'
stop = '2014-08-27T00:00:00Z'
level = 2
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url='http://geoport.whoi.edu/thredds/ncss/grid/coawst_4/use/fmrc/coawst_4_use_best.ncd?\
var=%s&latitude=%f&longitude=%f&time_start=%s&time_end=%s&\
vertCoord=%d&accept=csv' % (var,lat,lon,start,stop,level)
print(url)
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# Extract CSV data directly into Pandas DataFrame (and time how long it takes to extract the data)
time0=time.time()
df = pd.read_csv(url,index_col='date',parse_dates=True) # skip the units row
print('Elapsed time=%d seconds' % (time.time()-time0))
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# Make a new DataFrame with just the last two variables (temp & salt)
df2=df.ix[:,-2:]
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# Plotting time series in Pandas is easy
df.ix[:,-1].plot(figsize(12,4),legend=True)
df.ix[:,-2].plot(secondary_y=True,legend=True);
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# take a look at the first few values
df.ix[:,-3:].head(5)
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# calculate the maximum
df2.max()
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