The objective of this notebook is to show how to read and plot data from a mooring (time series).



In [30]:

    
%matplotlib inline
import netCDF4
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.basemap import Basemap

Data reading

The data file is located in the datafiles directory.



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datadir = './datafiles/'
datafile = 'GL_TS_MO_62164.nc'

As the platform is fixed, we will work on time series.
We will read the time and the sea water temperature variables, as well as their respective units.



In [54]:

    
with netCDF4.Dataset(datadir + datafile) as nc:
    time0 = nc.variables['TIME'][:]
    time0_units = nc.variables['TIME'].units
    temperature = nc.variables['TEMP'][:]
    temperature_units = nc.variables['TEMP'].units



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print ('Temperature units = %s' %temperature_units)









    



Temperature units = degree_Celsius

Basic plot

For a time series, we simply use the plot function of matplotlib.
Also, we set the font size to 16:



In [57]:

    
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
plt.plot(time0, temperature, 'k-')
plt.xlabel(time0_units)
plt.ylabel(temperature_units)
plt.show()

The units set for the time is maybe not the easiest to read.
However the netCDF4 module offers easy solutions to properly convert the time.

Converting time units

NetCDF4 provides the function num2date to convert the time vector into dates.
http://unidata.github.io/netcdf4-python/#section7



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from netCDF4 import num2date
dates = num2date(time0, units=time0_units)
print dates[:5]









    



[datetime.datetime(2007, 5, 1, 0, 0) datetime.datetime(2007, 5, 1, 1, 0)
 datetime.datetime(2007, 5, 1, 2, 0) datetime.datetime(2007, 5, 1, 3, 0)
 datetime.datetime(2007, 5, 1, 4, 0)]

The dates contains datetime objects.

We also extract the platform name from the file:



In [62]:

    
with netCDF4.Dataset(datadir + datafile) as nc:
    platform_name = nc.platform_name

Finally, to avoid to have the overlap of the date ticklabels, we use the autofmt_xdate function.
Everything is in place to create the improved plot.



In [63]:

    
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
plt.plot(dates, temperature, 'k-')
plt.ylabel(temperature_units)
fig.autofmt_xdate()
plt.title('Temperature at ' + platform_name)
plt.show()



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