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

In [1]:
%matplotlib inline
import netCDF4
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib import colors
from mpl_toolkits.basemap import Basemap

Data reading

The data file is located in the datafiles directory.

In [2]:
datadir = './datafiles/'
datafile = ''

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 [3]:
with netCDF4.Dataset(datadir + datafile) as nc:
    time0 = nc.variables['TIME'][:]
    time0_units = nc.variables['TIME'].units
    sealevel = nc.variables['SLEV'][:]
    sealevel_units = nc.variables['SLEV'].units

Variable units and dimension

In [4]:
print('Sea level units = %s' %sealevel_units)

Sea level units = m

Let's have a look at the dimension of the array

In [5]:

(30520, 1)

The first number corresponds to the time and the second to the depth.

Basic plot

For a time series, we simply use the plot function of matplotlib.
The 1st line change the font size to 16 (see matplotlib.RcParams).

In [6]:
rcParams.update({'font.size': 16})
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
plt.plot(time0, sealevel, 'k-')

As we plotted all the values, regardless of the quality flags, the result is not meaningful.

Select data according to Quality Flag

We have to load the corresponding variables:

In [7]:
with netCDF4.Dataset(datadir + datafile) as nc:
    sealevel_QC = nc.variables['SLEV_QC'][:]

and we keep only the sea level values with a flag equal to 1.
To do so, we use the masked arrays module.

In [8]:
sealevel =!=1, sealevel)

Let's check the plot again:

In [9]:
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
plt.plot(time0, sealevel, 'k-')

Still bad. It seems the QF don't allow us to filter out the data.
Let's have a closer look at it:

In [10]:
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
plt.plot(time0, sealevel_QC, 'ko')
plt.ylabel('Quality flags')

The values are either 1 (good data) or 9 (missing values), never a value indicating suspect or bad data.

A possible solution is to keep only sea level measurements lower than, let's say, 3 meters.

In [11]:
sealevel =, -3., 3.)

In [12]:
fig = plt.figure(figsize=(15,8))
ax = plt.subplot(111)
plt.plot(time0, sealevel, 'ko-', lw=0.2, ms=1)

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.

In [13]:
from netCDF4 import num2date
dates = num2date(time0, units=time0_units)
print dates[:5]

[datetime.datetime(2011, 7, 4, 1, 0) datetime.datetime(2011, 7, 4, 2, 0)
 datetime.datetime(2011, 7, 4, 3, 0) datetime.datetime(2011, 7, 4, 4, 0)
 datetime.datetime(2011, 7, 4, 5, 0)]

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 [15]:
fig = plt.figure(figsize=(15,8))
ax = plt.subplot(111)
plt.plot(dates, sealevel, 'ko-', lw=0.2, ms=1)
plt.title('Sea level at station HoekVanHolland')
plt.savefig('NO_TS_MO_HoekVanHollandTG.png', dpi=300)

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