In [1]:

    

import fingerprint as fp


    

    




/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

In [2]:

    

s_all=fp.read_pickle("struct_all.pickle")


    

In [4]:

    

import pandas as pd
import tqdm


    

In [5]:

    

s250=s_all[0:250]
f_ones_250=[fp.get_phi(s250[i],obser="ones",rmax=12,delta=0.05) for i in tqdm.tqdm_notebook(range(len(s250)))]
f_Z_250=[fp.get_phi(s250[i],obser="Z",rmax=12,delta=0.05) for i in tqdm.tqdm_notebook(range(len(s250)))]
f_Chi_250=[fp.get_phi(s250[i],obser="Chi",rmax=12,delta=0.05) for i in tqdm.tqdm_notebook(range(len(s250)))]


    

In [6]:

    

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np


    

In [12]:

    

plt.figure(figsize=(10,10))
r=np.linspace(0.05,12,(12/0.05))
for i in range(2):
    lab=s250[i].composition
    plt.plot(r,f_Z_250[i],label=lab)
plt.legend()


    

    
Out[12]:





<matplotlib.legend.Legend at 0x12830d1d0>






    

In [14]:

    

df_ones=pd.DataFrame(f_ones_250)


    

In [15]:

    

metric_ones=np.array([np.dot(f_ones_250[i],f_ones_250[j]) for i in range(250) for j in range(250)]).reshape(250,250)


    

In [16]:

    

metric_Z=np.array([np.dot(f_ones_250[i],f_ones_250[j]) for i in range(250) for j in range(250)]).reshape(250,250)


    

In [17]:

    

dist=np.array([np.dot(f_ones_250[0],f_ones_250[i]) for i in range(250)])


    

In [18]:

    

df_ones=pd.DataFrame({"phi_ones":f_ones_250})


    

In [19]:

    

df_ones["dist"]=dist

dt_sort=df_ones.sort("dist").drop("dist",axis=1)


    

    




/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  app.launch_new_instance()

In [20]:

    

comps=[s250[i].composition for i in range(250)]


    

In [21]:

    

df_ones["Composition"]=comps


    

In [22]:

    

dt_sort=df_ones.sort("dist",ascending=False)


    

    




/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  if __name__ == '__main__':

In [23]:

    

dt_sort.set_index(np.arange(dt_sort.shape[0]),inplace=True)


    

In [24]:

    

for i in range(5):
    print dt_sort["Composition"][i]


    

    




Nb1 Ag1 O3
Ni1 Ag1 F3
Mg1 Ag1 F3
Zn1 Ag1 F3
S2 Bi1 Ag1

In [25]:

    

sorted_f_ones=dt_sort['phi_ones'].values


    

In [26]:

    

metric_ones=np.array([np.dot(sorted_f_ones[i],sorted_f_ones[j]) for i in range(250) for j in range(250)]).reshape(250,250)


    

In [27]:

    

#for i in range(250):    
#    metric_ones[i][i]=0
plt.figure(figsize=(15,15))
plt.imshow(metric_ones[0:250,0:250])
plt.colorbar()


    

    
Out[27]:





<matplotlib.colorbar.Colorbar at 0x11e4e2950>






    

In [73]:

    

import sklearn


    

In [74]:

    

from sklearn.metrics.pairwise import euclidean_distances


    

In [79]:

    

euclid=np.array([np.sqrt(np.dot(sorted_f_ones[i],sorted_f_ones[i])+np.dot(sorted_f_ones[j],sorted_f_ones[j])-2*np.dot(sorted_f_ones[i],sorted_f_ones[j])) for i in tqdm.tqdm_notebook(range(250)) for j in range(250)]).reshape(250,250)


    

In [82]:

    

plt.figure(figsize=(15,15))
plt.imshow(euclid[0:250,0:250])
plt.colorbar()


    

    
Out[82]:





<matplotlib.colorbar.Colorbar at 0x11a183790>