Pandas

   1 import numpy as np
   2 import pandas as p
   3 import Nio
   4 
   5 nc1 = Nio.open_file('10147-precip.nc') # hamburg
   6 nc2 = Nio.open_file('10015-precip.nc') # helgoland
   7 
   8 time1 = nc1.variables['time'][:]
   9 time2 = nc2.variables['time'][:]
  10 
  11 rain1 = nc1.variables['rainfall_rate_hour'][:]
  12 rain2 = nc2.variables['rainfall_rate_hour'][:]
  13 
  14 
  15 # plot data 
  16 plot(rain1, 'g', rain2, 'b')
  17 
  18 # Timestamps shall be python dates
  19 dates1 = num2date(epoch2num(time1))
  20 dates2 = num2date(epoch2num(time2))
  21 
  22 # Indexed arrays - p.Series
  23 ds1 = p.Series(rain1, index = dates1)
  24 ds2 = p.Series(rain2, index = dates2)
  25 
  26 # Pandas is using numpy.na representation of not-a-number,
  27 # while Nio returns masked arrays
  28 # Many basic array operations are valid for pandas Series
  29 ds1 = np.where(ds1<0, nan, ds1)
  30 ds2 = np.where(ds2<0, nan, ds2)
  31 
  32 # built-in plotting functions
  33 ds1.plot()
  34 ds2.plot()
  35 
  36 # newer pandas version can drop NaN's, 
  37 # current one can only fill, 
  38 # otherwise drop by hand (hint: nan is not equal to nan :)
  39 ds1=ds1[ds1==ds1]
  40 ds2=ds2[ds2==ds2]