In [1]:

    

import pymaid

rm = pymaid.CatmaidInstance('server_url', 'api_token', 'http_user', 'http_password')


    

    




INFO  : Global CATMAID instance set. (pymaid)

In [2]:

    

pns = pymaid.get_skids_by_annotation('glomerulus DA1 right excitatory')

cn_table = pymaid.get_partners(pns)
cn_table.head()


    

    




INFO  : Found 8 neurons with matching annotation(s) (pymaid)
INFO  : Fetching connectivity table for 8 neurons (pymaid)
INFO  : Done. Found 95 pre-, 588 postsynaptic and 0 gap junction-connected neurons (pymaid)






    
Out[2]:







  

    

      

      
neuron_name
      
skeleton_id
      
num_nodes
      
relation
      
2863104
      
57381
      
61221
      
57353
      
57323
      
755022
      
27295
      
57311
      
total
    
  
  

    

      
0
      
AV4 [LH.R] 1803749 CH RJVR Vegito - check
      
1803748
      
6322
      
upstream
      
10
      
3
      
8
      
4
      
21
      
17
      
7
      
8
      
78.0
    
    

      
1
      
AV4b1#1 1853423 Accursed  RJVR - check FML
      
1853423
      
10253
      
upstream
      
7
      
6
      
6
      
4
      
12
      
18
      
6
      
8
      
67.0
    
    

      
2
      
AV4 [LH.R] 1095415 ARH RJVR Static
      
1095414
      
3949
      
upstream
      
5
      
2
      
9
      
1
      
9
      
16
      
12
      
9
      
63.0
    
    

      
3
      
AV4 [LH.R] 1911125 FML PS RJVR
      
1911124
      
4327
      
upstream
      
7
      
0
      
4
      
2
      
4
      
4
      
7
      
3
      
31.0
    
    

      
4
      
AV4 [LH.R] 1812366 PS DA1 downstream
      
1812365
      
2839
      
upstream
      
2
      
1
      
1
      
1
      
5
      
8
      
2
      
2
      
22.0
    
  

In [3]:

    

downstream_only = cn_table[cn_table.relation == 'downstream']

strong_only = cn_table[cn_table.total >= 10]

large_only = cn_table[cn_table.num_nodes >= 2000]

connected_to_all = cn_table[cn_table[['755022', '2863104', '27295', '61221', '57353', '57323', '57381', '57311']].min(axis=1) != 0]


    

In [4]:

    

adj_mat = pymaid.adjacency_matrix(pns, downstream_only[downstream_only.total >= 10])
adj_mat.head()


    

    




INFO  : Retrieving and filtering connectivity... (pymaid)
INFO  : Finished! (pymaid)






    
Out[4]:







  

    

      
target_skid
      
1811442
      
1870230
      
1803748
      
488055
      
1853423
      
6762450
      
1812558
      
1095414
      
1911124
      
1796364
      
...
      
515071
      
60445
      
1804938
      
4205169
      
1803929
      
9271871
      
424553
      
55153
      
3828811
      
1415926
    
    

      
source_skid
      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
2863104
      
27.0
      
5.0
      
15.0
      
15.0
      
11.0
      
18.0
      
6.0
      
8.0
      
15.0
      
0.0
      
...
      
0.0
      
2.0
      
0.0
      
10.0
      
2.0
      
0.0
      
0.0
      
10.0
      
0.0
      
0.0
    
    

      
57381
      
4.0
      
28.0
      
7.0
      
0.0
      
3.0
      
0.0
      
3.0
      
1.0
      
2.0
      
36.0
      
...
      
0.0
      
1.0
      
2.0
      
0.0
      
0.0
      
0.0
      
0.0
      
0.0
      
0.0
      
0.0
    
    

      
61221
      
23.0
      
7.0
      
13.0
      
15.0
      
15.0
      
4.0
      
4.0
      
16.0
      
12.0
      
0.0
      
...
      
0.0
      
1.0
      
0.0
      
0.0
      
2.0
      
0.0
      
0.0
      
0.0
      
0.0
      
1.0
    
    

      
57353
      
2.0
      
23.0
      
3.0
      
3.0
      
5.0
      
4.0
      
5.0
      
4.0
      
2.0
      
0.0
      
...
      
0.0
      
5.0
      
0.0
      
0.0
      
1.0
      
10.0
      
10.0
      
0.0
      
10.0
      
0.0
    
    

      
57323
      
20.0
      
7.0
      
22.0
      
11.0
      
19.0
      
15.0
      
18.0
      
9.0
      
10.0
      
26.0
      
...
      
0.0
      
0.0
      
1.0
      
0.0
      
0.0
      
0.0
      
0.0
      
0.0
      
0.0
      
2.0
    
  

5 rows × 148 columns

In [5]:

    

import seaborn as sns
import matplotlib.pyplot as plt

cm = sns.clustermap(adj_mat,
                    cmap='Greys')

plt.show()


    

In [6]:

    

import networkx as nx

# Turn the adjacency matrix into a networkX Graph
g = pymaid.network2nx(adj_mat)

# As example: get the degree centrality for each node
dc = nx.degree_centrality(g)
print('Node centrality for some of the nodes in the network: ', dc['755022'], dc['2863104'], dc['27295'])


    

    




Node centrality for some of the nodes in the network:  0.3548387096774194 0.4258064516129032 0.3419354838709677

In [7]:

    

nx.write_graphml(g, 'graph.graphml')