In [1]:

    

import pymaid
import matplotlib.pyplot as plt

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

nl = pymaid.get_neurons('annotation:glomerulus DA1 right excitatory')

# Access single attribute: e.g. cable lengths [um]
nl.cable_length


    

    




INFO  : Global CATMAID instance set. (pymaid.fetch)
INFO  : Looking for Annotation(s): glomerulus DA1 right excitatory (pymaid.fetch)
INFO  : Found 8 skeletons with matching annotation(s) (pymaid.fetch)






    
Out[1]:





array([1591.51982146, 1182.10245803, 1035.09925403, 1113.15667872,
       1215.92059396, 1182.6479815 , 1059.72905251, 1109.88615456])

In [2]:

    

# .. or get a full summary as pandas DataFrame
df = nl.summary()
df.head()


    

    
Out[2]:







  

    

      

      
neuron_name
      
skeleton_id
      
n_nodes
      
n_connectors
      
n_branch_nodes
      
n_end_nodes
      
open_ends
      
cable_length
      
review_status
      
soma
    
  
  

    

      
0
      
PN glomerulus DA1 27296 BH
      
27295
      
9969
      
463
      
211
      
218
      
58
      
1590.676589
      
NA
      
True
    
    

      
1
      
PN glomerulus DA1 57312 LK
      
57311
      
4874
      
421
      
156
      
163
      
105
      
1180.597489
      
NA
      
True
    
    

      
2
      
PN glomerulus DA1 57324 LK JSL
      
57323
      
4585
      
434
      
120
      
127
      
59
      
1035.076857
      
NA
      
True
    
    

      
3
      
PN glomerulus DA1 57354 GA
      
57353
      
4895
      
324
      
90
      
95
      
52
      
1113.156676
      
NA
      
True
    
    

      
4
      
PN glomerulus DA1 57382 ML
      
57381
      
7727
      
357
      
153
      
162
      
71
      
1215.920594
      
NA
      
True
    
  

In [3]:

    

# Reroot a single neuron to its soma
nl[0].soma


    

    
Out[3]:





3005291

In [4]:

    

# .soma returns the treenode ID of the soma (if existing) and can be used to reroot
nl[0].reroot(nl[0].soma)


    

In [5]:

    

# You can also perform this operation on the entire CatmaidNeuronList
nl.reroot( nl.soma )


    

In [6]:

    

# Downsample by "skipping" N nodes (here: 10) 
nl_downsampled = nl.downsample( 10, inplace=False )
# More elaborate: resample to given resolution in nanometers (here: 1000nm = 1um)
nl_resampled = nl.resample( 1000, inplace=False )


    

In [7]:

    

import matplotlib.pyplot as plt

# Plot an original neuron first
fig, ax = nl[0].plot2d(color='red')

# Shift the downsampled and resampled versions slightly and plot
n_ds = nl_downsampled[0].copy()
n_rs = nl_resampled[0].copy()

n_ds.nodes.x += 10000
n_rs.nodes.x += 20000

fig, ax = n_ds.plot2d(color='blue', ax=ax)
fig, ax = n_rs.plot2d(color='green', ax=ax)

plt.show()


    

In [8]:

    

# Cut a neuron in two using either a treenode ID or (in this case) a node tag
distal, proximal = pymaid.cut_neuron( nl[0], cut_node='SCHLEGEL_LH' )
# Plot neuron fragments
fig, ax = distal.plot2d(color='red', method='2d', connectors=False)
fig, ax = proximal.plot2d(color='blue', method='2d', connectors=False, ax=ax)

# Annotate cut point
cut_coords = distal.nodes.set_index('treenode_id').loc[ distal.root, ['x','y'] ].values[0]
ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), 
            xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',
            arrowprops=dict(facecolor='black', shrink=0.01, width=1),
            )

plt.show()


    

In [9]:

    

n = nl[0].prune_distal_to('SCHLEGEL_LH', inplace=False)

# Plot original neuron in black
fig, ax = nl[0].plot2d(color='black', method='2d', connectors=False, linestyle=(0, (5, 10)))

# Plot pruned neuron in red
fig, ax = n.plot2d(color='red', method='2d', connectors=False, ax=ax)

# Annotate cut point
ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), 
            xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',
            arrowprops=dict(facecolor='black', shrink=0.01, width=1),
            )

plt.show()


    

In [10]:

    

# To undo, simply reload the neuron from server
nl[0].reload()


    

In [11]:

    

# These operations can also be performed on a collection of neurons
n = nl[:5].prune_distal_to('SCHLEGEL_LH', inplace=False)

# Plot original neurons in black
fig, ax = nl[:5].plot2d(color='black', method='2d', connectors=False, linestyle=(0, (5, 10)))

# Plot pruned neurons in red
fig, ax = n.plot2d(color='red', method='2d', connectors=False, ax=ax)

# Annotate cut point
ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), 
            xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',
            arrowprops=dict(facecolor='black', shrink=0.01, width=1),
            )

plt.show()


    

In [12]:

    

# Again, let's undo
nl.reload()
nl.reroot(nl.soma)


    

In [13]:

    

# Something more sophisticated: pruning by strahler index
n = nl[:5].prune_by_strahler( to_prune = [1,2,3], inplace=False )

# Plot original neurons in black
fig, ax = nl[:5].plot2d(color='black', method='2d', connectors=False)

# Plot pruned neurons in red
fig, ax = n.plot2d(color='red', method='2d', connectors=False, ax=ax, linewidth=1.5)

# Annotate cut point
ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), 
            xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',
            arrowprops=dict(facecolor='black', shrink=0.01, width=1),
            )

plt.show()


    

In [14]:

    

# Again, let's undo
nl.reload()


    

In [15]:

    

# Get a volume
lh = pymaid.get_volume('LH_R')

# Prune by volume
nl_lh = nl.prune_by_volume(lh, inplace=False)

#nl_lh.summary().head()


    

In [16]:

    

# Set color of volume
lh['color'] = (250,250,250,.2)

# Plot neurons that have some cable left and the volume
fig, ax = pymaid.plot2d([nl_lh[nl_lh.cable_length > 10], lh], 
                        method='3d', 
                        connectors=False, 
                        linewidth=2)

ax.dist=6
plt.show()