1. Data manipulation with Numpy
Francesco Benincasa – 07/02/2013

2. 1. Basics
● N-dimensional arrays (ndarray) creation:
● From another python structure (tuple, list, …)
● Numpy funcions (arange, zeros, empty, …)
● From text (or csv) files (loadtxt, genfromtxt, ...)

3. 2a. Array objects
● Indexing
>> x=np.arange(10).reshape(2,5)
>> x[1][2]=x[1,2]
>> x.repeat(2).reshape(10,2)
● Slicing
>> x[0,0:5:2]
array([0, 2, 4])
>> x[:,0]
array([0, 5])

4. 2b. Array objects
● Masks
>> x[x>5]
array([6, 7, 8, 9])
>> print np.ma.masked_where(x<5, x)
[[-- -- -- -- –][5 6 7 8 9]]
● Ignoring extreme values
>> np.ma.masked_outside(x,1,8)
masked_array(data =
[[-- 1 2 3 4]
[5 6 7 8 --]],
mask =
[[ True False False False False]
[False False False False True]],
fill_value = 999999)

5. 3. NetCDF files
● Pupynere: available into scipy library or stand-alone
● Read/Write
● Handle
● Variables
● Dimensions
● Attributes
(example1)

6. Visualization – 4. Maps + Contours
● Read NetCDF data
>> sc = f.variables['SCONC_DUST'].getValue()
● Create grid:
>> x, y = meshgrid(lons, lats)
● Draw contour for each timestep:
>> for i in range(sc.shape[0]):
>> m.contourf(x, y, sc[i], cmap=cm.YlOrBr)
(example 2)

7. References
● Numpy reference:
http://docs.scipy.org/doc/numpy/reference/
● NetCDF APIs:
https://bitbucket.org/robertodealmeida/pupynere/
http://gfesuite.noaa.gov/developer/netCDFPythonInterface.html
● Matplotlib (docs, examples …):
http://matplotlib.sourceforge.net/
Thank you!