illustrates the use of the indices class

In [1]:

    

%matplotlib inline
import matplotlib.pyplot as plt


    

In [2]:

    

import sys


    

In [3]:

    

sys.path.append('../')


    

In [4]:

    

from paleopy import proxy 
from paleopy import analogs
from paleopy.plotting import indices


    

In [5]:

    

djsons = '../jsons/'
pjsons = '../jsons/proxies'


    

In [7]:

    

p = proxy(sitename='Rarotonga', \
          lon = -159.82, \
          lat = -21.23, \
          djsons = djsons, \
          pjsons = pjsons, \
          pfname = 'Rarotonga.json', \
          dataset = 'ersst', \
          variable ='sst', \
          measurement ='delta O18', \
          dating_convention = 'absolute', \
          calendar = 'gregorian',\
          chronology = 'historic', \
          season = 'DJF', \
          value = 0.6, \
          calc_anoms = 1, \
          detrend = 1)


    

In [8]:

    

p.find_analogs()


    

In [9]:

    

p.analog_years


    

    
Out[9]:





array([1982, 1997, 1999, 2000, 2001, 2006, 2009], dtype=int32)

In [10]:

    

p.analogs


    

    
Out[10]:






  

    

      

      
d_anomalies
      
cat
      
weights
    
    

      
time
      

      

      

    
  
  

    

      
1982-02-15
      
0.638992
      
WA
      
0.156455
    
    

      
1997-02-15
      
0.747619
      
WA
      
0.128005
    
    

      
1999-02-15
      
0.736547
      
WA
      
0.130905
    
    

      
2000-02-15
      
0.612678
      
WA
      
0.163346
    
    

      
2001-02-15
      
0.565475
      
WA
      
0.157625
    
    

      
2006-02-15
      
0.429462
      
WA
      
0.122003
    
    

      
2009-02-15
      
0.504521
      
WA
      
0.141661
    
  

In [11]:

    

f = p.plot_season_ts()


    

In [12]:

    

p.proxy_repr(pprint=True)


    

    




{
sitename:Rarotonga
proxy_type:None
measurement:delta O18
dating_convention:absolute
calendar:gregorian
chronology:historic
coords:(200.18, -21.23)
aspect:None
elevation:None
season:DJF
dataset:ersst
variable:sst
calc_anoms:True
detrend:True
value:0.6
climatology:(1981, 2010)
period:(1979, 2014)
extracted_coords:[200.0, -22.0]
distance_point:87.56368858840081
trend_params:{'slope': 0.030535947029886677, 'intercept': -60.965016377755163}
category:WA
analog_years:[1982, 1997, 1999, 2000, 2001, 2006, 2009]
weights:[0.15645480307020584, 0.12800536294956205, 0.13090508326029401, 0.16334634614636281, 0.15762466128135916, 0.1220029495538857, 0.1416607937383304]
}

In [13]:

    

indice = indices(p)


    

In [14]:

    

indice.composite()


    

In [15]:

    

indice.compos.std()


    

    
Out[15]:





NINO34    0.555699
SAM       1.001978
IOD       0.760739
SOI       0.372481
dtype: float64

In [16]:

    

f = indices(p).plot()


    

In [18]:

    

from paleopy import ensemble


    

In [19]:

    

djsons = '../jsons/'
pjsons = '../jsons/proxies'


    

In [20]:

    

ens = ensemble(djsons=djsons, pjsons=pjsons, season='DJF')


    

In [21]:

    

f = indices(ens).plot()


    

In [29]:

    

obj = indices(p)


    

In [30]:

    

obj.composite()


    

In [31]:

    

obj.compos


    

    
Out[31]:






  

    

      

      
NINO34
      
SAM
      
IOD
      
SOI
    
  
  

    

      
1982-02-01
      
0.032729
      
1.001330
      
0.544804
      
0.471084
    
    

      
1997-02-01
      
-0.365314
      
0.124978
      
-0.181042
      
0.813785
    
    

      
1999-02-01
      
-1.375472
      
1.252810
      
-0.907732
      
1.347353
    
    

      
2000-02-01
      
-1.569476
      
1.533521
      
0.434085
      
1.066006
    
    

      
2001-02-01
      
-0.589422
      
-0.816528
      
1.131153
      
0.970974
    
    

      
2006-02-01
      
-0.719873
      
-1.017501
      
-0.851495
      
0.415971
    
    

      
2009-02-01
      
-0.696459
      
0.691977
      
0.418646
      
1.319047
    
  

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