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>






    

In [85]:

    

len(s_all)


    

    
Out[85]:





17311

In [95]:

    

dist2=np.array([np.dot(f_ones_250[0]-f_ones_250[i],f_ones_250[0]-f_ones_250[i]) for i in range(250)])
df_ones["dist2"]=dist2
dt_sort=df_ones.sort("dist2").drop("dist2",axis=1)
dt_sort.set_index(np.arange(dt_sort.shape[0]),inplace=True)
sorted_f_ones=dt_sort['phi_ones'].values
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)
plt.figure(figsize=(12,12))
plt.imshow(euclid[0:250,0:250])
plt.colorbar()


    

    




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






    










    
Out[95]:





<matplotlib.colorbar.Colorbar at 0x1346657d0>






    

In [99]:

    

plt.figure(figsize=(10,10))
plt.imshow(euclid[0:100,0:100],interpolation='nearest')
plt.colorbar()


    

    
Out[99]:





<matplotlib.colorbar.Colorbar at 0x135c9d690>






    

In [96]:

    

dt_sort["Composition"][0:20]


    

    
Out[96]:





0      (O, Nb, Ag)
1      (F, Ni, Ag)
2      (F, Mg, Ag)
3      (F, Zn, Ag)
4      (O, Nb, Ag)
5       (O, W, Ag)
6      (O, Te, Ag)
7      (Hf, F, Ag)
8      (S, Na, Ag)
9     (Se, Ho, Ag)
10     (O, Te, Ag)
11    (Se, Er, Ag)
12     (O, Ru, Ag)
13     (O, Nb, Ag)
14     (F, Sn, Ag)
15    (As, Nd, Ag)
16      (O, W, Ag)
17     (Ge, O, Ag)
18     (S, Bi, Ag)
19     (F, Ru, Ag)
Name: Composition, dtype: object

In [102]:

    

df_Chi=pd.DataFrame({"phi_Chi":f_Chi_250})
dist2=np.array([np.dot(f_Chi_250[0]-f_Chi_250[i],f_Chi_250[0]-f_Chi_250[i]) for i in range(250)])
df_Chi["dist2"]=dist2
dt_sort=df_Chi.sort("dist2").drop("dist2",axis=1)
dt_sort.set_index(np.arange(dt_sort.shape[0]),inplace=True)
sorted_f_ones=dt_sort['phi_Chi'].values
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)
plt.figure(figsize=(12,12))
plt.imshow(euclid[0:250,0:250])
plt.colorbar()


    

    




/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)






    










    
Out[102]:





<matplotlib.colorbar.Colorbar at 0x193847510>






    

In [103]:

    

from sklearn.cluster import KMeans


    

In [104]:

    

s250=s_all[0:2000]
f_ones_2000=[fp.get_phi(s250[i],obser="ones",rmax=12,delta=0.05) for i in tqdm.tqdm_notebook(range(len(s250)))]


    

In [111]:

    

inertia=np.zeros(50,dtype=float)
for i in tqdm.tqdm_notebook(range(50)):
    Km=KMeans(n_clusters=5+i)
    Km.fit_predict(f_ones_2000)
    inertia[i]=Km.inertia_
plt.plot(inertia)


    

    










    
Out[111]:





[<matplotlib.lines.Line2D at 0x134f6c610>]






    

In [108]:

    

comps=[s250[i].composition for i in range(2000)]
df_ones=pd.DataFrame({"phi_ones":f_ones_2000})
df_ones["cluster"]=clust
df_ones["Composition"]=comps
df_sorted=df_ones.sort("cluster")
sorted_f_ones=df_sorted['phi_ones'].values
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(2000)) for j in range(2000)]).reshape(2000,2000)
plt.figure(figsize=(12,12))
plt.imshow(euclid)
plt.colorbar()


    

    




/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)






    










    
Out[108]:





<matplotlib.colorbar.Colorbar at 0x136da7750>






    

In [136]:

    

Km=KMeans(n_clusters=12)
clust=Km.fit_predict(f_ones_2000)
comps=[s250[i].composition.formula for i in range(2000)]
df_ones=pd.DataFrame({"phi_ones":f_ones_2000})
df_ones["cluster"]=clust
df_ones["Composition"]=comps
df_sorted=df_ones.sort("cluster")
df_sorted.set_index(np.arange(2000))
sorted_f_ones=df_sorted['phi_ones'].values
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(2000)) for j in range(2000)]).reshape(2000,2000)
plt.figure(figsize=(12,12))
plt.imshow(euclid)
plt.colorbar()


    

    




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






    










    
Out[136]:





<matplotlib.colorbar.Colorbar at 0x118fab490>