Plots the NINO Sea Surface Temperature indices (data from the Bureau of Meteorology) and the real-time Southern Oscillation Index (SOI) from LongPaddock

In [27]:

    

%matplotlib inline


    

In [28]:

    

import os, sys

import pandas as pd
from datetime import datetime, timedelta
from cStringIO import StringIO
import requests

import matplotlib as mpl
from matplotlib import pyplot as plt

from IPython.display import Image


    

In [29]:

    

proxies = {}
# proxies['http'] = 'url:port'


    

In [30]:

    

dpath = os.path.join(os.environ['HOME'], 'operational/ICU/indices/figures')


    

In [31]:

    

today = datetime.utcnow() - timedelta(15)


    

In [32]:

    

url = 'http://www.longpaddock.qld.gov.au/seasonalclimateoutlook/southernoscillationindex/soidatafiles/DailySOI1933-1992Base.txt'


    

In [33]:

    

r = requests.get(url, proxies=proxies)


    

In [34]:

    

soi = pd.read_table(StringIO(r.content), sep='\s*', engine='python')


    

In [35]:

    

index = [datetime(year,1,1) + timedelta(day-1) for year, day in soi.loc[:,['Year','Day']].values]


    

In [36]:

    

soi.index = index


    

In [37]:

    

soi = soi.loc[:,['SOI']]


    

In [38]:

    

soi.head()


    

    
Out[38]:






  

    

      

      
SOI
    
  
  

    

      
1999-01-01
      
14.02
    
    

      
1999-01-02
      
18.79
    
    

      
1999-01-03
      
6.35
    
    

      
1999-01-04
      
2.48
    
    

      
1999-01-05
      
7.65
    
  

In [39]:

    

soi['soirm1'] = pd.rolling_mean(soi.SOI, 30)
soi['soirm3'] = pd.rolling_mean(soi.SOI, 90)


    

In [40]:

    

soi = soi.ix['2013':]


    

In [41]:

    

soi.tail()


    

    
Out[41]:






  

    

      

      
SOI
      
soirm1
      
soirm3
    
  
  

    

      
2015-04-03
      
2.79
      
-12.117333
      
-6.304222
    
    

      
2015-04-04
      
9.53
      
-11.616000
      
-6.183000
    
    

      
2015-04-05
      
7.72
      
-11.160333
      
-6.128444
    
    

      
2015-04-06
      
-2.63
      
-10.658667
      
-6.213222
    
    

      
2015-04-07
      
-26.54
      
-10.710333
      
-6.482667
    
  

In [42]:

    

from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
years   = YearLocator()
months  = MonthLocator()
mFMT = DateFormatter('%b')
yFMT = DateFormatter('\n\n%Y')


    

In [43]:

    

mpl.rcParams['xtick.labelsize'] = 12
mpl.rcParams['ytick.labelsize'] = 12
mpl.rcParams['axes.titlesize'] = 14
mpl.rcParams['xtick.direction'] = 'out'
mpl.rcParams['ytick.direction'] = 'out'
mpl.rcParams['xtick.major.size'] = 5
mpl.rcParams['ytick.major.size'] = 5
mpl.rcParams['xtick.minor.size'] = 2


    

In [44]:

    

f, ax = plt.subplots(figsize=(10,8))

ax.fill_between(soi.index, soi.soirm1, 0, (soi.soirm1 >= 0), color='b', alpha=0.7, interpolate=True)
ax.fill_between(soi.index, soi.soirm1, 0, (soi.soirm1 < 0), color='r', alpha=0.7, interpolate=True)

ax.plot(soi.index, soi.soirm1, c='k')

ax.plot(soi.index, soi.soirm3, c='w', lw=2.5)
ax.plot(soi.index, soi.soirm3, c='0.5', lw=2, label='90 days running mean')
ax.legend()

ax.axhline(0, color='k')
ax.set_ylim(-30,30)
ax.grid(linestyle='--')

ax.xaxis.set_minor_locator(months)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_minor_formatter(mFMT)
ax.xaxis.set_major_formatter(yFMT)

[label.set_fontsize(13) for label in ax.get_xminorticklabels()]
[label.set_rotation(90) for label in ax.get_xminorticklabels()]
[label.set_fontsize(18) for label in ax.get_xmajorticklabels()]
[label.set_fontsize(18) for label in ax.get_ymajorticklabels()]

ax.set_title("Southern Oscillation Index (30 days running mean)\
\n ending {0:}: latest 30 days (90 days) value = {1:<4.2f} ({2:<4.2f})".\
             format(soi.index[-1].strftime("%Y-%m-%d"), soi.iloc[-1,1], soi.iloc[-1,2]))

f.savefig(os.path.join(dpath, 'SOI_LP_realtime_plot.png'), dpi=200)


    

In [45]:

    

Image(url='http://www1.ncdc.noaa.gov/pub/data/cmb/teleconnections/nino-regions.gif')


    

    
Out[45]:

In [46]:

    

for nino in ["3.4", "3", "4"]:
    
    print("processing NINO{}".format(nino))

    url = "http://www.bom.gov.au/climate/enso/nino_%s.txt" % (nino)
    r = requests.get(url, proxies=proxies)
    data = pd.read_table(StringIO(r.content), sep=',', header=None, index_col=1, \
                         parse_dates=True, names=['iDate','SST'])
    data = data.ix['2013':]
    lastmonth = data.loc[today.strftime("%Y-%m"),'SST'].mean()
    
    f, ax = plt.subplots(figsize=(10, 8))

    ax.fill_between(data.index, data.SST, 0, (data.SST >= 0), color='r', alpha=0.7, interpolate=True)
    ax.fill_between(data.index, data.SST, 0, (data.SST < 0), color='b', alpha=0.7, interpolate=True)

    ax.plot(data.index, data.SST, c='k')

    ax.axhline(0, color='k')
    ax.set_ylim(-2,2)
    ax.grid(linestyle='--')

    ax.xaxis.set_minor_locator(months)
    ax.xaxis.set_major_locator(years)
    ax.xaxis.set_minor_formatter(mFMT)
    ax.xaxis.set_major_formatter(yFMT)

    [label.set_fontsize(13) for label in ax.get_xminorticklabels()]
    [label.set_rotation(90) for label in ax.get_xminorticklabels()]
    [label.set_fontsize(18) for label in ax.get_xmajorticklabels()]
    [label.set_fontsize(18) for label in ax.get_ymajorticklabels()]

    ax.set_title("NINO {} (weekly) ending {}\nlatest weekly / monthly values: {:<4.2f} / {:<4.2f}"\
                 .format(nino, data.index[-1].strftime("%Y-%B-%d"), data.iloc[-1,-1], lastmonth))
    
    f.savefig(os.path.join(dpath, 'NINO{}_realtime_plot.png'.format(nino)))


    

    




processing NINO3.4
processing NINO3
processing NINO4






    













    













    

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