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Exploring netCDF files with Python

First, we import the needed things from IPython, NumPy, Matplotlib and SciPy all at once with pylab.

This can be done individually with "import modulename", "import modulename as alias" or import only the desired functions "from modulename import function".





In [1]:



    


%pylab inline



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [2]:



    


from netCDF4 import Dataset



    











In [3]:



    


f = Dataset("http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC/"
            +".ERSST/.version4/.sst/dods")
f



    


















    
Out[3]:








<type 'netCDF4._netCDF4.Dataset'>
root group (NETCDF3_CLASSIC data model, file format UNDEFINED):
    Conventions: IRIDL
    dimensions(sizes): T(1941), X(180), Y(89), zlev(1)
    variables(dimensions): float32 Y(Y), float32 zlev(zlev), float32 X(X), float32 T(T), int16 sst(T,zlev,Y,X)
    groups: 

















In [4]:



    


temp = f.variables['sst']
temp



    


















    
Out[4]:








<type 'netCDF4._netCDF4.Variable'>
int16 sst(T, zlev, Y, X)
    pointwidth: 1.0
    missing_value: -999
    scale_factor: 0.01
    units: Celsius_scale
    valid_min: -300
    long_name: Extended reconstructed sea surface temperature
    valid_max: 4500
    standard_name: sea_surface_temperature
    iridl:hasSemantics: iridl:SeaSurfaceTemperature
    expires: 1446422400
unlimited dimensions: 
current shape = (1941, 1, 89, 180)
filling off

















In [5]:



    


!cd /Users/lsiqueira/Desktop/python_lunchbytes
!ls -lh ersst_v4.nc
ifile = 'ersst_v4.nc'



    


















    






-rw-r-----@ 1 lsiqueira  staff    59M Oct 25 17:28 ersst_v4.nc

















In [6]:



    


f = Dataset(ifile)
temp = f.variables['sst'][:].squeeze()
lats = f.variables['Y'][:]
lons = f.variables['X'][:]
f.close()



    











In [7]:



    


shape(temp)



    


















    
Out[7]:








(1941, 89, 180)

















In [8]:



    


%%timeit
f = Dataset(ifile);
temp = f.variables['sst'][:].squeeze().mean(axis=0)



    


















    






1 loops, best of 3: 638 ms per loop

















In [9]:



    


f = Dataset(ifile)
temp = f.variables['sst'][:].squeeze().mean(axis=0)
figure(figsize=(13, 4))
imshow(flipud(temp), cmap='jet')
title('Time mean ERSST V4 1854-2015')
colorbar(label='degrees $^o$C', extend='both');



    


















    






/Users/lsiqueira/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):










    
























In [10]:



    


from mpl_toolkits.basemap import Basemap

figure(figsize=(13, 4))
m = Basemap(projection='cyl', resolution='c', llcrnrlon=0, llcrnrlat=-90,
            urcrnrlon=360, urcrnrlat=90)
x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1, 30, 64)
cs = m.pcolormesh(x,y,temp, shading='flat', cmap=plt.cm.jet)
m.drawcoastlines()
m.drawparallels(arange(-90.,90.,30.), labels=[1,0,0,0], fontsize=14)
m.drawmeridians(arange(0.,360.,60.), labels=[0,0,0,1], fontsize=14)
m.fillcontinents()
colorbar(cs, label='$^o$C', extend='both')
plt.title('Time Mean ERSST V4 1854-2015 from NumPy', fontsize=16)



    


















    
Out[10]:








<matplotlib.text.Text at 0x132a1c750>

Install CDO Python bindings to use embeded CDO commands with "pip install cdo"





In [11]:



    


from cdo import *



    











In [12]:



    


cdo = Cdo()



    











In [13]:



    


cdo.sinfon(input=ifile)



    


















    
Out[13]:








[u'File format: netCDF',
 u'-1 : Institut Source   Ttype    Levels Num  Gridsize Num Dtype : Parameter name',
 u'1 : unknown  unknown  instant       1   1     16020   1  I16  : sst',
 u'Grid coordinates :',
 u'1 : lonlat       > size      : dim = 16020  nx = 180  ny = 89',
 u'X         : first = 0  last = 358  inc = 2  degree_east  circular',
 u'Y         : first = -88  last = 88  inc = 2  degree_north',
 u'Vertical coordinates :',
 u'1 : generic            meters : 0',
 u'Time coordinate :  1941 steps',
 u'RefTime =  1960-01-01 00:00:00  Units = months  Calendar = 360_day',
 u'YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss',
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 u'2004-05-16 00:00:00  2004-06-16 00:00:00  2004-07-16 00:00:00  2004-08-16 00:00:00',
 u'2004-09-16 00:00:00  2004-10-16 00:00:00  2004-11-16 00:00:00  2004-12-16 00:00:00',
 u'2005-01-16 00:00:00  2005-02-16 00:00:00  2005-03-16 00:00:00  2005-04-16 00:00:00',
 u'2005-05-16 00:00:00  2005-06-16 00:00:00  2005-07-16 00:00:00  2005-08-16 00:00:00',
 u'2005-09-16 00:00:00  2005-10-16 00:00:00  2005-11-16 00:00:00  2005-12-16 00:00:00',
 u'2006-01-16 00:00:00  2006-02-16 00:00:00  2006-03-16 00:00:00  2006-04-16 00:00:00',
 u'2006-05-16 00:00:00  2006-06-16 00:00:00  2006-07-16 00:00:00  2006-08-16 00:00:00',
 u'2006-09-16 00:00:00  2006-10-16 00:00:00  2006-11-16 00:00:00  2006-12-16 00:00:00',
 u'2007-01-16 00:00:00  2007-02-16 00:00:00  2007-03-16 00:00:00  2007-04-16 00:00:00',
 u'2007-05-16 00:00:00  2007-06-16 00:00:00  2007-07-16 00:00:00  2007-08-16 00:00:00',
 u'2007-09-16 00:00:00  2007-10-16 00:00:00  2007-11-16 00:00:00  2007-12-16 00:00:00',
 u'2008-01-16 00:00:00  2008-02-16 00:00:00  2008-03-16 00:00:00  2008-04-16 00:00:00',
 u'2008-05-16 00:00:00  2008-06-16 00:00:00  2008-07-16 00:00:00  2008-08-16 00:00:00',
 u'2008-09-16 00:00:00  2008-10-16 00:00:00  2008-11-16 00:00:00  2008-12-16 00:00:00',
 u'2009-01-16 00:00:00  2009-02-16 00:00:00  2009-03-16 00:00:00  2009-04-16 00:00:00',
 u'2009-05-16 00:00:00  2009-06-16 00:00:00  2009-07-16 00:00:00  2009-08-16 00:00:00',
 u'2009-09-16 00:00:00  2009-10-16 00:00:00  2009-11-16 00:00:00  2009-12-16 00:00:00',
 u'2010-01-16 00:00:00  2010-02-16 00:00:00  2010-03-16 00:00:00  2010-04-16 00:00:00',
 u'2010-05-16 00:00:00  2010-06-16 00:00:00  2010-07-16 00:00:00  2010-08-16 00:00:00',
 u'2010-09-16 00:00:00  2010-10-16 00:00:00  2010-11-16 00:00:00  2010-12-16 00:00:00',
 u'2011-01-16 00:00:00  2011-02-16 00:00:00  2011-03-16 00:00:00  2011-04-16 00:00:00',
 u'2011-05-16 00:00:00  2011-06-16 00:00:00  2011-07-16 00:00:00  2011-08-16 00:00:00',
 u'2011-09-16 00:00:00  2011-10-16 00:00:00  2011-11-16 00:00:00  2011-12-16 00:00:00',
 u'2012-01-16 00:00:00  2012-02-16 00:00:00  2012-03-16 00:00:00  2012-04-16 00:00:00',
 u'2012-05-16 00:00:00  2012-06-16 00:00:00  2012-07-16 00:00:00  2012-08-16 00:00:00',
 u'2012-09-16 00:00:00  2012-10-16 00:00:00  2012-11-16 00:00:00  2012-12-16 00:00:00',
 u'2013-01-16 00:00:00  2013-02-16 00:00:00  2013-03-16 00:00:00  2013-04-16 00:00:00',
 u'2013-05-16 00:00:00  2013-06-16 00:00:00  2013-07-16 00:00:00  2013-08-16 00:00:00',
 u'2013-09-16 00:00:00  2013-10-16 00:00:00  2013-11-16 00:00:00  2013-12-16 00:00:00',
 u'2014-01-16 00:00:00  2014-02-16 00:00:00  2014-03-16 00:00:00  2014-04-16 00:00:00',
 u'2014-05-16 00:00:00  2014-06-16 00:00:00  2014-07-16 00:00:00  2014-08-16 00:00:00',
 u'2014-09-16 00:00:00  2014-10-16 00:00:00  2014-11-16 00:00:00  2014-12-16 00:00:00',
 u'2015-01-16 00:00:00  2015-02-16 00:00:00  2015-03-16 00:00:00  2015-04-16 00:00:00',
 u'2015-05-16 00:00:00  2015-06-16 00:00:00  2015-07-16 00:00:00  2015-08-16 00:00:00',
 u'2015-09-16 00:00:00']

Compute the field time mean value using CDO and return it as a numpy array without writing nc file to disk.





In [14]:



    


%%timeit
cdo.timmean(input=ifile,returnCdf=True).variables['sst'][:]



    


















    






1 loops, best of 3: 1.24 s per loop

















In [15]:



    


tavg_cdo = cdo.timmean(input=ifile,returnCdf=True).variables['sst'][:] 

figure(figsize=(13, 4))
m = Basemap(projection='cyl', resolution='c', llcrnrlon=0, llcrnrlat=-90,
            urcrnrlon=360, urcrnrlat=90)
x, y = m(*np.meshgrid(lons, lats))
clevs = np.linspace(-1, 30, 64)
cs = m.pcolormesh(x,y,tavg_cdo.squeeze(), shading='flat', cmap=plt.cm.jet)
m.drawcoastlines()
m.drawparallels((-90, -60, -30, 0, 30, 60, 90), labels=[1,0,0,0], fontsize=14)
m.drawmeridians((0, 60, 120, 180, 240, 300, 360), labels=[0,0,0,1], fontsize=14)
m.fillcontinents()
colorbar(cs, label='degrees $^o$C', extend='both')
plt.title('Time Mean ERSST V4 1854-2015 from CDO ', fontsize=16)



    


















    
Out[15]:








<matplotlib.text.Text at 0x13137fa10>










    
























In [16]:



    


diff = tavg_cdo.squeeze() - temp
diff.max()



    


















    
Out[16]:








0.0050185128378537058

















In [17]:



    


ifile = '20101221-JPL-L4UHfnd-GLOB-v01-fv04-MUR.nc'
f = Dataset(ifile)
temp = f.variables['analysed_sst']
temp



    


















    
Out[17]:








<type 'netCDF4._netCDF4.Variable'>
int16 analysed_sst(time, lat, lon)
    long_name: analysed sea surface temperature
    standard_name: sea_surface_foundation_temperature
    units: kelvin
    _FillValue: -32768
    add_offset: 298.15
    scale_factor: 0.001
    valid_min: -32767
    valid_max: 32767
unlimited dimensions: 
current shape = (1, 5097, 9102)
filling off

















In [18]:



    


temp = f.variables['analysed_sst'][:].squeeze()
lats = f.variables['lat'][:]
lons = f.variables['lon'][:]
f.close()



    











In [19]:



    


from matplotlib.patches import Polygon
from mpl_toolkits.axes_grid1.inset_locator import inset_axes


def make_map(llcrnrlon=-100, urcrnrlon=-50, llcrnrlat=15, urcrnrlat=50,
             projection='cyl', resolution='l', figsize=(10, 3.5), inset=True):
    
    m = Basemap(llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
                llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat,
                projection=projection, resolution=resolution)
    
    fig, ax = plt.subplots(figsize=figsize)
    m.drawcoastlines()
    m.bluemarble()
    meridians = np.arange(llcrnrlon, urcrnrlon + 2, 10)
    parallels = np.arange(llcrnrlat, urcrnrlat + 1, 10)
    m.drawparallels(parallels, linewidth=0, labels=[1, 0, 0, 0], fontsize=20)
    m.drawmeridians(meridians, linewidth=0, labels=[0, 0, 0, 1], fontsize=20)
    m.llcrnrlon = llcrnrlon
    m.urcrnrlon = urcrnrlon
    m.llcrnrlat = llcrnrlat
    m.urcrnrlat = urcrnrlat
    m.ax = ax

    if inset:
        axin = inset_axes(m.ax, width="45%", height="45%", loc=4)
        # Global inset map.
        inmap = Basemap(projection='ortho', lon_0=-60, lat_0=35,
                        ax=axin, anchor='NE')
        inmap.drawcountries(color='white')
        inmap.fillcontinents(color='gray')
        bx, by = inmap(m.boundarylons, m.boundarylats)
        xy = list(zip(bx, by))
        mapboundary = Polygon(xy, edgecolor='k', linewidth=1, fill=False)
        inmap.ax.add_patch(mapboundary)
    return fig, m



    











In [20]:



    


x, y = m(*np.meshgrid(lons, lats))
fig, m = make_map(llcrnrlon=-100, urcrnrlon=-40, llcrnrlat=19.5,
                  urcrnrlat=50, figsize=(15, 5))
clevs = np.linspace(0, 28, 128)
cs = m.contourf(x, y, temp-273.15, clevs, latlon=True, animated =True)
csc = m.contour(x, y, temp-273.15, colors='k',
                latlon=True, alpha=0.5)
_ = m.ax.clabel(csc, fmt='%1.0f $^o$C', fontsize=8, inline=1)



    


















    






/Users/lsiqueira/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:650: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors_original != str('face'):
/Users/lsiqueira/anaconda/lib/python2.7/site-packages/matplotlib/text.py:52: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  if rotation in ('horizontal', None):
/Users/lsiqueira/anaconda/lib/python2.7/site-packages/matplotlib/text.py:54: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  elif rotation == 'vertical':

Time Series analysis with Pandas (statistical package)





In [21]:



    


import pandas as pd
import numpy as np



    











In [22]:



    


pd.set_option('max_rows',15)



    











In [23]:



    


!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii
!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii



    


















    






--2015-10-28 17:16:31--  http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii
Resolving www.cpc.ncep.noaa.gov... 140.90.101.63
Connecting to www.cpc.ncep.noaa.gov|140.90.101.63|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 19725 (19K) [text/plain]
Saving to: 'monthly.ao.index.b50.current.ascii.2'

100%[======================================>] 19,725      12.0KB/s   in 1.6s   

2015-10-28 17:16:34 (12.0 KB/s) - 'monthly.ao.index.b50.current.ascii.2' saved [19725/19725]

--2015-10-28 17:16:34--  http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii
Resolving www.cpc.ncep.noaa.gov... 140.90.101.63
Connecting to www.cpc.ncep.noaa.gov|140.90.101.63|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 18936 (18K) [text/plain]
Saving to: 'norm.nao.monthly.b5001.current.ascii.2'

100%[======================================>] 18,936      23.3KB/s   in 0.8s   

2015-10-28 17:16:35 (23.3 KB/s) - 'norm.nao.monthly.b5001.current.ascii.2' saved [18936/18936]


















In [24]:



    


ao = np.loadtxt('monthly.ao.index.b50.current.ascii')
ao



    


















    
Out[24]:








array([[  1.95000000e+03,   1.00000000e+00,  -6.03100000e-02],
       [  1.95000000e+03,   2.00000000e+00,   6.26810000e-01],
       [  1.95000000e+03,   3.00000000e+00,  -8.12750000e-03],
       ..., 
       [  2.01500000e+03,   7.00000000e+00,  -1.10790000e+00],
       [  2.01500000e+03,   8.00000000e+00,  -6.89020000e-01],
       [  2.01500000e+03,   9.00000000e+00,  -1.64510000e-01]])

















In [25]:



    


dates = pd.date_range('1950-01', '2015-10', freq='M')
AO = pd.Series(ao[:,2], index=dates)
AO



    


















    
Out[25]:








1950-01-31   -0.060310
1950-02-28    0.626810
1950-03-31   -0.008127
1950-04-30    0.555100
1950-05-31    0.071577
1950-06-30    0.538570
1950-07-31   -0.802480
                ...   
2015-03-31    1.837400
2015-04-30    1.215700
2015-05-31    0.762760
2015-06-30    0.427040
2015-07-31   -1.107900
2015-08-31   -0.689020
2015-09-30   -0.164510
Freq: M, dtype: float64

















In [26]:



    


!head norm.nao.monthly.b5001.current.ascii



    


















    






 1950    1  0.92000E+00
 1950    2  0.40000E+00
 1950    3 -0.36000E+00
 1950    4  0.73000E+00
 1950    5 -0.59000E+00
 1950    6 -0.60000E-01
 1950    7 -0.12600E+01
 1950    8 -0.50000E-01
 1950    9  0.25000E+00
 1950   10  0.85000E+00

















In [27]:



    


NAO = pd.read_table('norm.nao.monthly.b5001.current.ascii', sep='\s*', \
              parse_dates={'dates':[0, 1]}, header=None, index_col=0, squeeze=True, engine='python' )
  • first argument is obviously the file name
  • '\s*' - regular expression, that describe separator.
  • parse_dates - combine columns 0 and 1, convert resulting column to dates and give it the name "dates"
  • header - don't use 0 row as header
  • index_col - make column 0 (this will be already result of the parse_dates parsing)
  • squeeze - create Series instead of DataFrame.




In [28]:



    


NAO



    


















    
Out[28]:








dates
1950-01-01    0.920000
1950-02-01    0.400000
1950-03-01   -0.360000
1950-04-01    0.730000
1950-05-01   -0.590000
1950-06-01   -0.060000
1950-07-01   -1.260000
                ...   
2015-03-01    1.449700
2015-04-01    0.725350
2015-05-01    0.145520
2015-06-01   -0.066822
2015-07-01   -3.179000
2015-08-01   -0.760360
2015-09-01   -0.648460
Name: 2, dtype: float64

















In [29]:



    


aonao = pd.DataFrame({'AO':AO, 'NAO':NAO})



    











In [30]:



    


aonao.head(10)



    


















    
Out[30]:










  
    

      
      AO
      NAO
    

  
  
    

      1950-01-01
      NaN
      0.92
    

    

      1950-01-31
      -0.060310
      NaN
    

    

      1950-02-01
      NaN
      0.40
    

    

      1950-02-28
      0.626810
      NaN
    

    

      1950-03-01
      NaN
      -0.36
    

    

      1950-03-31
      -0.008127
      NaN
    

    

      1950-04-01
      NaN
      0.73
    

    

      1950-04-30
      0.555100
      NaN
    

    

      1950-05-01
      NaN
      -0.59
    

    

      1950-05-31
      0.071577
      NaN
    

  




















In [31]:



    


aonao = pd.DataFrame({'AO':AO.to_period(freq='M'),
                      'NAO':NAO.to_period(freq='M')} )



    











In [32]:



    


aonao.head(10)



    


















    
Out[32]:










  
    

      
      AO
      NAO
    

  
  
    

      1950-01
      -0.060310
      0.92
    

    

      1950-02
      0.626810
      0.40
    

    

      1950-03
      -0.008127
      -0.36
    

    

      1950-04
      0.555100
      0.73
    

    

      1950-05
      0.071577
      -0.59
    

    

      1950-06
      0.538570
      -0.06
    

    

      1950-07
      -0.802480
      -1.26
    

    

      1950-08
      -0.851010
      -0.05
    

    

      1950-09
      0.357970
      0.25
    

    

      1950-10
      -0.378900
      0.85
    

  




















In [33]:



    


aonao['2013-12':'2014-05']



    


















    
Out[33]:










  
    

      
      AO
      NAO
    

  
  
    

      2013-12
      1.474900
      0.94566
    

    

      2014-01
      -0.968760
      0.29026
    

    

      2014-02
      0.043775
      1.33520
    

    

      2014-03
      1.205800
      0.79827
    

    

      2014-04
      0.971970
      0.30522
    

    

      2014-05
      0.464210
      -0.92250
    

  




















In [34]:



    


NAO[NAO > 0]



    


















    
Out[34]:








dates
1950-01-01    0.92000
1950-02-01    0.40000
1950-04-01    0.73000
1950-09-01    0.25000
1950-10-01    0.85000
1951-01-01    0.08000
1951-02-01    0.70000
               ...   
2014-11-01    0.67797
2014-12-01    1.85750
2015-01-01    1.78870
2015-02-01    1.32280
2015-03-01    1.44970
2015-04-01    0.72535
2015-05-01    0.14552
Name: 2, dtype: float64

Get everything at once





In [35]:



    


aonao.describe()



    


















    
Out[35]:










  
    

      
      AO
      NAO
    

  
  
    

      count
      789.000000
      789.000000
    

    

      mean
      -0.121171
      -0.024088
    

    

      std
      1.006019
      1.009317
    

    

      min
      -4.265700
      -3.180000
    

    

      25%
      -0.668270
      -0.760360
    

    

      50%
      -0.047510
      0.010000
    

    

      75%
      0.475080
      0.680000
    

    

      max
      3.495300
      3.040000

By the way getting correlation coefficients among all members of the Data Frame is as simple as:





In [36]:



    


aonao.corr()



    


















    
Out[36]:










  
    

      
      AO
      NAO
    

  
  
    

      AO
      1.000000
      0.611348
    

    

      NAO
      0.611348
      1.000000
    

  




















In [37]:



    


AO_mm = AO.resample("A")
AO.plot(style='k-', alpha = 0.3)
AO_mm.plot(style='b--', linewidth =2)



    


















    
Out[37]:








<matplotlib.axes._subplots.AxesSubplot at 0x12d58ad10>










    
























In [38]:



    


aonao['mon'] = aonao.index.month
aonao



    


















    
Out[38]:










  
    

      
      AO
      NAO
      mon
    

  
  
    

      1950-01
      -0.060310
      0.920000
      1
    

    

      1950-02
      0.626810
      0.400000
      2
    

    

      1950-03
      -0.008127
      -0.360000
      3
    

    

      1950-04
      0.555100
      0.730000
      4
    

    

      1950-05
      0.071577
      -0.590000
      5
    

    

      1950-06
      0.538570
      -0.060000
      6
    

    

      1950-07
      -0.802480
      -1.260000
      7
    

    

      ...
      ...
      ...
      ...
    

    

      2015-03
      1.837400
      1.449700
      3
    

    

      2015-04
      1.215700
      0.725350
      4
    

    

      2015-05
      0.762760
      0.145520
      5
    

    

      2015-06
      0.427040
      -0.066822
      6
    

    

      2015-07
      -1.107900
      -3.179000
      7
    

    

      2015-08
      -0.689020
      -0.760360
      8
    

    

      2015-09
      -0.164510
      -0.648460
      9
    

  



789 rows × 3 columns



















In [39]:



    


monmean = aonao['1950':'2013'].groupby('mon').aggregate(mean)
monmean.plot(kind='bar')



    


















    
Out[39]:








<matplotlib.axes._subplots.AxesSubplot at 0x12d58a610>

In order to see what is going on there it is useful to look at the box plots for every month:





In [40]:



    


ax = aonao.boxplot(column=['AO'], by='mon')



    


















    
























In [41]:



    


ax = aonao.boxplot(column=['NAO'], by='mon')

While NAO show more or less uniform spread, AO have pronounced seasonal variations, with largest spread during winter months.





In [42]:



    


pd.rolling_mean(aonao[['AO','NAO']][aonao.mon==2], window=10).plot()



    


















    
Out[42]:








<matplotlib.axes._subplots.AxesSubplot at 0x12ec31a10>

F2Py - Use the Fortran performance within Python





In [43]:



    


%install_ext https://raw.github.com/mgaitan/fortran_magic/master/fortranmagic.py



    


















    






Installed fortranmagic.py. To use it, type:
  %load_ext fortranmagic










    






/Users/lsiqueira/anaconda/lib/python2.7/site-packages/IPython/core/magics/extension.py:47: UserWarning: %install_ext` is deprecated, please distribute your extension(s)as a python packages.
  "as a python packages.", UserWarning)

















In [44]:



    


%load_ext fortranmagic



    


















    






/Users/lsiqueira/anaconda/lib/python2.7/site-packages/IPython/utils/path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")










    



























In [45]:



    


%%fortran

subroutine old_sin(x, y)
    real, intent(in) :: x
    real, intent(out) :: y

    y = sin(x)

end subroutine old_sin



    











In [46]:



    


%timeit old_sin(1.0)



    


















    






The slowest run took 30.12 times longer than the fastest. This could mean that an intermediate result is being cached 
10000000 loops, best of 3: 135 ns per loop

















In [47]:



    


old_sin(30.* np.pi / 180.)



    


















    
Out[47]:








0.5

Lets use Numpy version





In [48]:



    


import numpy as np
%timeit np.sin(30.* np.pi / 180.)



    


















    






The slowest run took 28.77 times longer than the fastest. This could mean that an intermediate result is being cached 
1000000 loops, best of 3: 796 ns per loop

















In [49]:



    


single(np.sin(30.* np.pi / 180.))



    


















    
Out[49]:








0.5

Even got python documentation for old_sin





In [50]:



    


#old_sin?
np.sin?

Good old Fortran!





In [51]:



    


from IPython.display import Image

Image("http://s18.postimg.org/xoghfy291/Olde_Fortran_Malt_Liquor.jpg")



    


















    
Out[51]:

Content source: milancurcic/lunch-bytes

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