In [1]:

    

%matplotlib inline


    

In [2]:

    

import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np


    

In [5]:

    

a = np.load('/home/taylorm/mcli/DJF/96z_mslp_meanPa.npy')


    

In [6]:

    

a.shape


    

    
Out[6]:





(3307, 30, 63)

In [7]:

    

a = np.delete(a,[3300,3301],axis=0)


    

In [15]:

    

bench = a[:,18,55]
newarr=np.zeros(1)
for x in range (0,3305):
    if bench[x] <100000:
        if x == 0:
            mean = np.expand_dims(bench[x],axis=0)
            newarr=mean
        else:
            newarr = np.append(newarr,np.expand_dims(bench[x],axis=0),axis=0)
newarr=np.delete(newarr,[0],axis=0)


    

In [16]:

    

shape,loc,scale=stats.genextreme.fit(newarr)


    

In [17]:

    

print shape, loc, scale


    

    




0.881103891071 99530.3539779 414.229099086

In [18]:

    

sns.distplot(newarr)


    

    
Out[18]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f36e5d08a90>






    

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