In [1]:
skids = pymaid.get_neurons_in_volume('AL_L')
nl = pymaid.get_neurons(skids)
nl.head()
Out[1]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
neuron 4562948
4562947
2
0
0
1
1
3.945403
NA
False
1
neuron 7094282 MWP Hogeweg
7094281
4267
64
56
57
42
1537.999647
NA
True
2
neuron 8216644 NS
8216643
5934
140
163
166
141
2330.199132
NA
False
3
Multiglomerular PN mALT 57431 IJA ECM
57430
5436
205
101
106
87
1389.803214
NA
True
4
aSP-g tract 6725720 KMS
6725719
2251
29
79
84
38
842.488107
NA
True
In [2]:
subset = nl[nl.n_nodes > 6000]
subset.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
Multiglomerular bilateral PN mALT 57435 LK
57434
8582
155
161
168
82
1415.474701
NA
True
1
PBG5-EBw-gall (right) neuron 341414 EWN AW
4210786
29397
3107
1886
1955
0
4875.387524
NA
True
2
Multiglomerular PN mALT bilateral 57476 IJA
57475
6737
174
192
196
63
1236.637869
NA
True
3
putative OA mALT 57480 GA FML
57479
8558
226
202
207
116
1965.510890
NA
True
4
PN glomerulus VL1 57500 ML
57499
6551
530
303
322
111
1854.932978
NA
True
In [3]:
subset = nl[nl.soma != None]
subset.head()
Out[3]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
neuron 7094282 MWP Hogeweg
7094281
4267
64
56
57
42
1537.999647
NA
True
1
Multiglomerular PN mALT 57431 IJA ECM
57430
5436
205
101
106
87
1389.803214
NA
True
2
aSP-g tract 6725720 KMS
6725719
2251
29
79
84
38
842.488107
NA
True
3
Multiglomerular bilateral PN mALT 57435 LK
57434
8582
155
161
168
82
1415.474701
NA
True
4
PBG5-EBw-gall (right) neuron 341414 EWN AW
4210786
29397
3107
1886
1955
0
4875.387524
NA
True
In [4]:
subset = nl.skid['57499']
subset
Out[4]:
type
<class 'pymaid.core.CatmaidNeuron'>
neuron_name
PN glomerulus VL1 57500 ML
skeleton_id
57499
n_nodes
6551
n_connectors
530
n_branch_nodes
303
n_end_nodes
322
n_open_ends
111
cable_length
1854.93
review_status
NA
soma
3247378
In [5]:
subset = nl.skid [[57499, 57479]]
subset
Out[5]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
putative OA mALT 57480 GA FML
57479
8558
226
202
207
116
1965.510890
NA
True
1
PN glomerulus VL1 57500 ML
57499
6551
530
303
322
111
1854.932978
NA
True
In [6]:
subset = nl['PN glomerulus VL1 57500 ML']
subset
Out[6]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
PN glomerulus VL1 57500 ML
57499
6551
530
303
322
111
1854.932978
NA
True
In [7]:
subset = nl.has_annotation('LH_DONE', intersect=False)
subset.head()
Out[7]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
PN glomerulus VL1 57500 ML
57499
6551
530
303
322
111
1854.932978
NA
True
1
Multiglomerular PN mALT 57537 LK-NM
57536
14654
1233
850
906
278
3193.380472
NA
True
2
PN glomerulus VL1 73938 LK
73937
5273
452
259
274
119
1610.767540
NA
True
3
AL.L(DA1) -{mALT}-> CAL.L-LH.L 2319458 PN036 D...
2319457
10209
964
431
458
90
1747.511710
NA
True
4
PN glomerulus DA2 2467660 RJVR
2467659
2855
266
54
56
8
726.656270
NA
True
In [8]:
subset = nl.has_annotation('~LH_DONE', intersect=False)
subset.head()
Out[8]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
neuron 4562948
4562947
2
0
0
1
1
3.945403
NA
False
1
neuron 7094282 MWP Hogeweg
7094281
4267
64
56
57
42
1537.999647
NA
True
2
neuron 8216644 NS
8216643
5934
140
163
166
141
2330.199132
NA
False
3
Multiglomerular PN mALT 57431 IJA ECM
57430
5436
205
101
106
87
1389.803214
NA
True
4
aSP-g tract 6725720 KMS
6725719
2251
29
79
84
38
842.488107
NA
True
In [9]:
subset = nl.has_annotation(['LH_DONE', 'glomerulus DL1'], intersect=False)
subset.head()
Out[9]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
PN glomerulus VL1 57500 ML
57499
6551
530
303
322
111
1854.932978
NA
True
1
Multiglomerular PN mALT 57537 LK-NM
57536
14654
1233
850
906
278
3193.380472
NA
True
2
PN glomerulus VL1 73938 LK
73937
5273
452
259
274
119
1610.767540
NA
True
3
AL.L(DA1) -{mALT}-> CAL.L-LH.L 2319458 PN036 D...
2319457
10209
964
431
458
90
1747.511710
NA
True
4
PN glomerulus DA2 2467660 RJVR
2467659
2855
266
54
56
8
726.656270
NA
True
In [10]:
subset = nl.has_annotation(['LH_DONE', 'glomerulus DL1'], intersect=True)
subset.head()
Out[10]:
neuron_name
skeleton_id
n_nodes
n_connectors
n_branch_nodes
n_end_nodes
open_ends
cable_length
review_status
soma
0
PN glomerulus DL1 5269365 GSXEJ
5269364
3477
893
167
181
58
1228.339971
NA
True
1
PN glomerulus DL1 5305038 ARJ
5305037
2818
898
128
138
15
1050.094319
NA
True
Content source: schlegelp/pymaid
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