diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/404.html b/404.html new file mode 100644 index 0000000..f0781f5 --- /dev/null +++ b/404.html @@ -0,0 +1 @@ +
There is an experimental implementation but this is very much still work in progress. In particular, we still need to:
navis
At this point, there is only a single function for a simple all-by-all NBLAST:
import fastcore
+
+import numpy as np
+
+from collections import namedtuple
+
+# fastcore expects dotprops to be named tuple
+Dotprop = namedtuple("Dotprop", ["points", "vect"])
+
+# Generate 10 random dotprops
+dps = [
+ Dotprop(
+ np.random.rand(100, 3),
+ np.random.rand(100, 3)
+ )
+ for _ in range(10)
+ ]
+
+scores = fastcore.nblast_allbyall(dps)
+
navis_fastcore.connected_components(node_ids, parent_ids)
Get the connected components for this neuron.
PARAMETER | DESCRIPTION |
---|---|
node_ids |
TYPE: |
parent_ids |
TYPE: |
RETURNS | DESCRIPTION |
---|---|
cc | For each node the node ID of its root (= connected component ID). TYPE: |
Examples:
Fully connected neuron:
>>> import navis_fastcore as fastcore
+>>> import numpy as np
+>>> node_ids = np.arange(7)
+>>> parent_ids = np.array([-1, 0, 1, 2, 1, 4, 5])
+>>> fastcore.connected_components(node_ids, parent_ids)
+array([0, 0, 0, 0, 0, 0, 0])
+
Introduce a break:
navis_fastcore.synapse_flow_centrality(node_ids, parent_ids, presynapses, postsynapses, mode='sum')
Calculate synapse flow centrality for this neuron.
Please note that this implementation currently produces slightly different results than the implementation in navis. I'm not sure why that is but the differences seem to be negligible.
PARAMETER | DESCRIPTION |
---|---|
node_ids |
TYPE: |
parent_ids |
TYPE: |
presynapses |
TYPE: |
postsynapses |
TYPE: |
mode |
TYPE: |
RETURNS | DESCRIPTION |
---|---|
cc | Synapse flow centrality for each node. TYPE: |
navis_fastcore.geodesic_matrix(node_ids, parent_ids, directed=False, sources=None, targets=None, weights=None)
Calculate geodesic ("along-the-arbor") distances.
Under-the-hood, this uses two different implementations depending on whether a full all-by-all or a partial (via sources
/targets
) matrix is requested. The partial implementation is faster and more memory efficient for small-ish subsets of nodes. However, for subsets that include a large portion of the nodes, it may be faster to calculate the full matrix and then subset it.
PARAMETER | DESCRIPTION |
---|---|
node_ids |
TYPE: |
parent_ids |
TYPE: |
directed |
TYPE: |
sources |
TYPE: |
targets |
TYPE: |
weights |
TYPE: |
RETURNS | DESCRIPTION |
---|---|
matrix | Geodesic distances. Unreachable nodes are set to -1. TYPE: |
Examples:
>>> import navis_fastcore as fastcore
+>>> import numpy as np
+>>> node_ids = np.arange(7)
+>>> parent_ids = np.array([-1, 0, 1, 2, 1, 4, 5])
+>>> fastcore.geodesic_matrix(node_ids, parent_ids)
+array([[0., 1., 2., 3., 2., 3., 4.],
+ [1., 0., 1., 2., 1., 2., 3.],
+ [2., 1., 0., 1., 2., 3., 4.],
+ [3., 2., 1., 0., 3., 4., 5.],
+ [2., 1., 2., 3., 0., 1., 2.],
+ [3., 2., 3., 4., 1., 0., 1.],
+ [4., 3., 4., 5., 2., 1., 0.]], dtype=float32)
+>>> fastcore.geodesic_matrix(
+... node_ids, parent_ids,
+... sources=[0, 1], targets=[5, 6]
+... )
+array([[3., 4.],
+ [2., 3.]], dtype=float32)
+
Neurons can be represented as centerline skeletons which themselves are rooted trees or "directed acyclic graphs" (DAG) where each node has at most a single parent (root nodes will have no parents).
Rooted trees have two huge advantages over general graphs:
First, they are super compact and can, at the minimum, be represented by just a single vector of parent indices (with root nodes having negative indices).
Second, they are much easier/faster to traverse because we can make certain assumptions that we can't for general graphs. For example, we know that there is only ever (at most) a single possible path between any pair of nodes.
While networkx
has some DAG-specific functions they don't implement anything related to graph traversal.
The Python bindings for navis-fastcore
currently cover the following functions:
fastcore.geodesic_matrix
: calculate geodesic ("along-the-arbor") distances either between all pairs of nodes or between specific sources and targetsfastcore.connected_components
: generate the connected componentsfastcore.synapse_flow_centrality
: calculate synapse flow centrality (Schneider-Mizell, eLife, 2016)fastcore.generate_segments
: break the neuron into linear segmentsfastcore.segment_coords
: generate coordinates per lineaer segment (useful for plotting)These functions
navis_fastcore.generate_segments(node_ids, parent_ids, weights=None)
Generate linear segments maximizing segment lengths.
PARAMETER | DESCRIPTION |
---|---|
node_ids |
TYPE: |
parent_ids |
TYPE: |
weights |
TYPE: |
RETURNS | DESCRIPTION |
---|---|
segments | Segments as list of arrays, sorted from longest to shortest. Each segment starts with a leaf and stops with a branch point or root node. TYPE: |
Examples:
navis_fastcore.segment_coords(node_ids, parent_ids, coords, weights=None, node_colors=None)
Generate coordinates for linear segments.
This is useful for plotting the skeleton of a neuron.
PARAMETER | DESCRIPTION |
---|---|
node_ids |
TYPE: |
parent_ids |
TYPE: |
coords |
TYPE: |
node_colors |
TYPE: |
RETURNS | DESCRIPTION |
---|---|
seg_coords | TYPE: |
colors | If TYPE: |
Examples:
>>> import navis_fastcore as fastcore
+>>> import numpy as np
+>>> node_ids = np.arange(7)
+>>> parent_ids = np.array([-1, 0, 1, 2, 1, 4, 5])
+>>> coords = np.random.rand(7, 3)
+>>> fastcore.segment_coords(node_ids, parent_ids, coords)
+[array([[5.30713899e-01, 8.26450947e-01, 2.46805326e-01],
+ [1.54144332e-04, 9.07823578e-01, 3.20199043e-01],
+ [6.64580597e-01, 3.23724555e-01, 3.18361918e-01],
+ [7.16579499e-01, 8.65568868e-02, 7.15686948e-01],
+ [5.94874740e-01, 5.95528161e-01, 8.14234930e-01]]),
+array([[0.47814894, 0.84468164, 0.2765942 ],
+ [0.21748528, 0.36673489, 0.81449368],
+ [0.7165795 , 0.08655689, 0.71568695]])]
+
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