From f24b53ddaaa51f2a5f2bb7644f62517b9a8a3ae6 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sat, 19 Aug 2023 02:10:04 +0000 Subject: [PATCH] build based on cea9501 --- dev/index.html | 2 +- dev/lib/internals/Manifests/index.html | 2 +- dev/lib/internals/NodeNets/index.html | 2 +- dev/lib/internals/PointArrays/index.html | 2 +- dev/lib/internals/index.html | 2 +- dev/lib/public/index.html | 2 +- dev/man/contributing/index.html | 2 +- dev/man/examples/index.html | 2 +- dev/man/getting_started/index.html | 2 +- dev/man/references/index.html | 2 +- dev/search/index.html | 2 +- 11 files changed, 11 insertions(+), 11 deletions(-) diff --git a/dev/index.html b/dev/index.html index 72ddfbea..d060d235 100644 --- a/dev/index.html +++ b/dev/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
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RealNeuralNetworks.jl

A Skeletonization, morphological and connectivity analysis tool.

Features

  • Skeletonization using TEASAR algorithm.
  • Skeleton IO with format of SWC and neuroglancer precomputed.
  • A lot of morphological analysis functions.
  • Synaptic distribution analysis in/between neurons.
  • Neuron connectivity analysis.

The Getting Started provides a tutorial explaning how to get started.

Some examples of packages could be found on the Examples page.

See the Index for the complete list of functions and types.

Manual Outline

Library Outline

@index Pages = ["lib/public.md"] ```

+
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RealNeuralNetworks.jl

A Skeletonization, morphological and connectivity analysis tool.

Features

  • Skeletonization using TEASAR algorithm.
  • Skeleton IO with format of SWC and neuroglancer precomputed.
  • A lot of morphological analysis functions.
  • Synaptic distribution analysis in/between neurons.
  • Neuron connectivity analysis.

The Getting Started provides a tutorial explaning how to get started.

Some examples of packages could be found on the Examples page.

See the Index for the complete list of functions and types.

Manual Outline

Library Outline

@index Pages = ["lib/public.md"] ```

diff --git a/dev/lib/internals/Manifests/index.html b/dev/lib/internals/Manifests/index.html index 49b7de20..c619ece5 100644 --- a/dev/lib/internals/Manifests/index.html +++ b/dev/lib/internals/Manifests/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
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diff --git a/dev/lib/internals/NodeNets/index.html b/dev/lib/internals/NodeNets/index.html index 732a482c..6253e763 100644 --- a/dev/lib/internals/NodeNets/index.html +++ b/dev/lib/internals/NodeNets/index.html @@ -8,4 +8,4 @@ path_edges: the index pairs of nodeNet in the point_array

In the pathnodes, the nodes were encoded as the index of the pointarray. Since we do not need pointarray anymore, which could be pretty big, we'll only reserve the nodeNet coordinates and let the gc release the pointarray. the same applys to path_edges

source
RealNeuralNetworks.NodeNets.find_closest_node_id — Method
find_closest_node_id(self::NodeNet{T}, point::NTuple{3,T}) where T

look for the id of the closest node

source
RealNeuralNetworks.NodeNets.find_new_root_node_V1 — Method
find_new_root_node( points )

Extracts the point with the lowest linear index from the Array of passed points

source
RealNeuralNetworks.NodeNets.find_new_root_node_id — Method
find_new_root_node( dbf )

Extracts the point with the largest dbf from the Array of passed points

source
RealNeuralNetworks.NodeNets.get_connectivity_matrix — Method
get_connectivity_matrix(edges::Vector)

construct sparse connectivity matrix accordint to the edges

source
RealNeuralNetworks.NodeNets.get_edge_num — Method

the connectivity matrix is symmetric, so the connection is undirected

source
RealNeuralNetworks.NodeNets.get_neuroglancer_precomputed — Method

get binary buffer formatted as neuroglancer nodeNet.

Binary format

UInt32: number of vertex
 UInt32: number of edges
 Array{Float32,2}: Nx3 array, xyz coordinates of vertex
-Array{UInt32,2}: Mx2 arrray, node index pair of edges

reference: https://github.com/seung-lab/neuroglancer/wiki/Skeletons

source
RealNeuralNetworks.NodeNets.get_node_array — Method
get_node_array(self::NodeNet)

Return: nodeArray::Array{T,2}: size is (nd, np), nd is the dimention of each node, np is the number of nodes

source
RealNeuralNetworks.NodeNets.get_num_segment_point — Method
get_segment_point_num(self::NodeNet)

get number of branching points

source
RealNeuralNetworks.NodeNets.get_num_segmentes — Method

assume that the graph is acyclic, no loop.

source
RealNeuralNetworks.NodeNets.get_sholl_number — Method
get_sholl_number(self::NodeNet, radius::AbstractFloat)

get the number of points which is in neurite and incounters with a sphere centered on root node

source
RealNeuralNetworks.NodeNets.local_max_multiplicative_penalty — Method
local_max_multiplicative_penalty( weights, dbf, G )

Returns a version of the edge weights, modified by the DBF-based penalty

w = w * (1 - DBFdest/DBFstar)

Where DBFstar is the maximum DBF value of an outwards neighbor for each node

source
RealNeuralNetworks.NodeNets.make_neighbor_graph — Method
make_neighbor_graph( points )

Creates a LightGraphs graph from a point cloud, such that each point has a node (indexed in the same order as the rows), and each node is connected to its neighbors (by 26-connectivity).

Also returns a sparse matrix of weights for these edges equal to the euclidean distance between the indices of each point. These can be weighted or modified easily upon return.

source
RealNeuralNetworks.NodeNets.nodes_within_radius — Method
nodes_within_radius( sub, sub2node, r, max_dims )

Identifies the node indices within the subscript

source
RealNeuralNetworks.NodeNets.remove_path_from_rns! — Function
remove_path_from_rns!( reachableNodeList, path, points, sub2node, dbf, max_dims, scale_param, const_param)

Identifies the nodes to remove from the reachable nodes within the graph. Probably the ugliest function here, and should be optimized later

TO OPTIMIZE

source
RealNeuralNetworks.NodeNets.save — Method
save(self::NodeNet, cellId::UInt32, d_json::Associative, d_bin::Associative)

save nodeNet in google cloud storage for neuroglancer visualization the format is the same with meshes

source
RealNeuralNetworks.NodeNets.save_edges — Method

save binary file of point pairs used in neuroglancer python interface to visualize the nodeNet

source
RealNeuralNetworks.NodeNets.translate_to_origin! — Method
translate_to_origin!( points::Matrix{T} )

Normalize the point dimensions by subtracting the min across each dimension. This step isn't extremely necessary, but might be useful for compatibility with the MATLAB code. record the offset and add it back after building the nodeNet

source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method
compute_DBF( pointCloud )

Returns an array of DBF values for the point cloud. Currently creates a binary image, and runs bwd2 on it, though ideally we'd get rid of the need for an explicit bin_im

source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method

use segmentation to get binary image to save memory usage

source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method
compute_DBF( bin_im )
source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method

compute Distance from Boundary Field (DBF) based on point cloud and the boundary points

WARN: this function do not work correctly!

source
RealNeuralNetworks.NodeNets.DBFs.create_binary_image — Method
create_binary_image( seg, obj_id )

Creates a boolean volume where the non-segment indices map to true, while the segment indices map to false

source
RealNeuralNetworks.NodeNets.DBFs.create_binary_image — Method
create_binary_image( pointCloud )

Creates a boolean volume where the non-segment indices map to true, while the segment indices map to false.

source
RealNeuralNetworks.NodeNets.DBFs.distance_transform — Method
distance_transform( d::AbstractArray{T,N}, voxelSize::Vector{Float32}=ones(Float32, N) )

Returns a euclidean distance transformation of the mask provided by d. The return value will be a volume of the same size as d where the value at each index corresponds to the distance between that location and the nearest location for which d > 0.

source
RealNeuralNetworks.NodeNets.DBFs.extract_dbf_values — Method
extract_dbf_values( dbf_image, pointCloud )

Takes an array where rows indicate subscripts, and extracts the values within a volume at those subscripts (in row order)

source
RealNeuralNetworks.NodeNets.DBFs.fill_f0! — Method

Fills an n-dimensional volume with initial states for edt transformation, inf for non-feature voxels, and 0 for feature voxels

source
RealNeuralNetworks.NodeNets.DBFs.fv_isfurther — Method

Getting too tired to document these next few, but will be worth it if it works

source
RealNeuralNetworks.NodeNets.DBFs.remove_euclidean_distance_transform — Method
remove_euclidean_distance_transform
source
RealNeuralNetworks.NodeNets.DBFs.row_voronoi_edt! — Method

Performs the edt over a specific row in the volume, following the first dimension

source
RealNeuralNetworks.NodeNets.DBFs.vol_voronoi_edt! — Method

Performs the edt transformation along the first dimension of the N-dimensional volume

source
+Array{UInt32,2}: Mx2 arrray, node index pair of edges

reference: https://github.com/seung-lab/neuroglancer/wiki/Skeletons

source
RealNeuralNetworks.NodeNets.get_node_array — Method
get_node_array(self::NodeNet)

Return: nodeArray::Array{T,2}: size is (nd, np), nd is the dimention of each node, np is the number of nodes

source
RealNeuralNetworks.NodeNets.get_num_segment_point — Method
get_segment_point_num(self::NodeNet)

get number of branching points

source
RealNeuralNetworks.NodeNets.get_num_segmentes — Method

assume that the graph is acyclic, no loop.

source
RealNeuralNetworks.NodeNets.get_sholl_number — Method
get_sholl_number(self::NodeNet, radius::AbstractFloat)

get the number of points which is in neurite and incounters with a sphere centered on root node

source
RealNeuralNetworks.NodeNets.local_max_multiplicative_penalty — Method
local_max_multiplicative_penalty( weights, dbf, G )

Returns a version of the edge weights, modified by the DBF-based penalty

w = w * (1 - DBFdest/DBFstar)

Where DBFstar is the maximum DBF value of an outwards neighbor for each node

source
RealNeuralNetworks.NodeNets.make_neighbor_graph — Method
make_neighbor_graph( points )

Creates a LightGraphs graph from a point cloud, such that each point has a node (indexed in the same order as the rows), and each node is connected to its neighbors (by 26-connectivity).

Also returns a sparse matrix of weights for these edges equal to the euclidean distance between the indices of each point. These can be weighted or modified easily upon return.

source
RealNeuralNetworks.NodeNets.nodes_within_radius — Method
nodes_within_radius( sub, sub2node, r, max_dims )

Identifies the node indices within the subscript

source
RealNeuralNetworks.NodeNets.remove_path_from_rns! — Function
remove_path_from_rns!( reachableNodeList, path, points, sub2node, dbf, max_dims, scale_param, const_param)

Identifies the nodes to remove from the reachable nodes within the graph. Probably the ugliest function here, and should be optimized later

TO OPTIMIZE

source
RealNeuralNetworks.NodeNets.save — Method
save(self::NodeNet, cellId::UInt32, d_json::Associative, d_bin::Associative)

save nodeNet in google cloud storage for neuroglancer visualization the format is the same with meshes

source
RealNeuralNetworks.NodeNets.save_edges — Method

save binary file of point pairs used in neuroglancer python interface to visualize the nodeNet

source
RealNeuralNetworks.NodeNets.translate_to_origin! — Method
translate_to_origin!( points::Matrix{T} )

Normalize the point dimensions by subtracting the min across each dimension. This step isn't extremely necessary, but might be useful for compatibility with the MATLAB code. record the offset and add it back after building the nodeNet

source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method
compute_DBF( pointCloud )

Returns an array of DBF values for the point cloud. Currently creates a binary image, and runs bwd2 on it, though ideally we'd get rid of the need for an explicit bin_im

source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method

use segmentation to get binary image to save memory usage

source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method
compute_DBF( bin_im )
source
RealNeuralNetworks.NodeNets.DBFs.compute_DBF — Method

compute Distance from Boundary Field (DBF) based on point cloud and the boundary points

WARN: this function do not work correctly!

source
RealNeuralNetworks.NodeNets.DBFs.create_binary_image — Method
create_binary_image( seg, obj_id )

Creates a boolean volume where the non-segment indices map to true, while the segment indices map to false

source
RealNeuralNetworks.NodeNets.DBFs.create_binary_image — Method
create_binary_image( pointCloud )

Creates a boolean volume where the non-segment indices map to true, while the segment indices map to false.

source
RealNeuralNetworks.NodeNets.DBFs.distance_transform — Method
distance_transform( d::AbstractArray{T,N}, voxelSize::Vector{Float32}=ones(Float32, N) )

Returns a euclidean distance transformation of the mask provided by d. The return value will be a volume of the same size as d where the value at each index corresponds to the distance between that location and the nearest location for which d > 0.

source
RealNeuralNetworks.NodeNets.DBFs.extract_dbf_values — Method
extract_dbf_values( dbf_image, pointCloud )

Takes an array where rows indicate subscripts, and extracts the values within a volume at those subscripts (in row order)

source
RealNeuralNetworks.NodeNets.DBFs.fill_f0! — Method

Fills an n-dimensional volume with initial states for edt transformation, inf for non-feature voxels, and 0 for feature voxels

source
RealNeuralNetworks.NodeNets.DBFs.fv_isfurther — Method

Getting too tired to document these next few, but will be worth it if it works

source
RealNeuralNetworks.NodeNets.DBFs.remove_euclidean_distance_transform — Method
remove_euclidean_distance_transform
source
RealNeuralNetworks.NodeNets.DBFs.row_voronoi_edt! — Method

Performs the edt over a specific row in the volume, following the first dimension

source
RealNeuralNetworks.NodeNets.DBFs.vol_voronoi_edt! — Method

Performs the edt transformation along the first dimension of the N-dimensional volume

source
diff --git a/dev/lib/internals/PointArrays/index.html b/dev/lib/internals/PointArrays/index.html index 24667612..17c80e53 100644 --- a/dev/lib/internals/PointArrays/index.html +++ b/dev/lib/internals/PointArrays/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
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PointArrays

+
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PointArrays

diff --git a/dev/lib/internals/index.html b/dev/lib/internals/index.html index 76dc2dd5..e7a67a6c 100644 --- a/dev/lib/internals/index.html +++ b/dev/lib/internals/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
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Internal Documentation

This page lists all the documented internals of the RealNeuralNetworks module and submodules.

Contents

Index

A list of all internal documentation sorted by module.

+
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Internal Documentation

This page lists all the documented internals of the RealNeuralNetworks module and submodules.

Contents

Index

A list of all internal documentation sorted by module.

diff --git a/dev/lib/public/index.html b/dev/lib/public/index.html index 09095ac7..ae7e3eea 100644 --- a/dev/lib/public/index.html +++ b/dev/lib/public/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
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Public Documentation

Documentation for RealNeuralNetworks.jl's public interface.

See Internal Documentation for internal package docs covering all submodules.

Contents

Index

    Public Interface

    Missing docstring.

    Missing docstring for RealNeuralNetworks. Check Documenter's build log for details.

    Missing docstring.

    Missing docstring for Neurons. Check Documenter's build log for details.

    Missing docstring.

    Missing docstring for Segments. Check Documenter's build log for details.

    Missing docstring.

    Missing docstring for Synapses. Check Documenter's build log for details.

    Missing docstring.

    Missing docstring for SWCs. Check Documenter's build log for details.

    Missing docstring.

    Missing docstring for NeuralNets. Check Documenter's build log for details.

    Missing docstring.

    Missing docstring for SynapseTables. Check Documenter's build log for details.

    +
    Edit on GitHub

    Public Documentation

    Documentation for RealNeuralNetworks.jl's public interface.

    See Internal Documentation for internal package docs covering all submodules.

    Contents

    Index

      Public Interface

      Missing docstring.

      Missing docstring for RealNeuralNetworks. Check Documenter's build log for details.

      Missing docstring.

      Missing docstring for Neurons. Check Documenter's build log for details.

      Missing docstring.

      Missing docstring for Segments. Check Documenter's build log for details.

      Missing docstring.

      Missing docstring for Synapses. Check Documenter's build log for details.

      Missing docstring.

      Missing docstring for SWCs. Check Documenter's build log for details.

      Missing docstring.

      Missing docstring for NeuralNets. Check Documenter's build log for details.

      Missing docstring.

      Missing docstring for SynapseTables. Check Documenter's build log for details.

      diff --git a/dev/man/contributing/index.html b/dev/man/contributing/index.html index acd67a4b..e051851e 100644 --- a/dev/man/contributing/index.html +++ b/dev/man/contributing/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
      Edit on GitHub

      Please fork and create pull requests in github.

      +
      Edit on GitHub

      Please fork and create pull requests in github.

      diff --git a/dev/man/examples/index.html b/dev/man/examples/index.html index 0631f30c..4cd1c116 100644 --- a/dev/man/examples/index.html +++ b/dev/man/examples/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
      Edit on GitHub
      +
      Edit on GitHub
      diff --git a/dev/man/getting_started/index.html b/dev/man/getting_started/index.html index 31ca788b..0be64e65 100644 --- a/dev/man/getting_started/index.html +++ b/dev/man/getting_started/index.html @@ -11,4 +11,4 @@ nodeNet = NodeNet(seg::Array{UInt32,3}; obj_id = convert(UInt32,77605)) neuron = Neuron( nodeNet ) swc = SWC(neuron) -SWCs.save(swc, tempname()*".swc") +SWCs.save(swc, tempname()*".swc") diff --git a/dev/man/references/index.html b/dev/man/references/index.html index 865ea45e..8363b5dd 100644 --- a/dev/man/references/index.html +++ b/dev/man/references/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -
      Edit on GitHub
      1. Sümbül U, Song S, McCulloch K, Becker M, Lin B, Sanes JR, Masland RH, Seung HS. A genetic and computational approach to structurally classify neuronal types. Nature communications. 2014 Mar 24;5:3512. link
      2. Schierwagen A, Villmann T, Alpár A, Gärtner U. Cluster analysis of cortical pyramidal neurons using som. InIAPR Workshop on Artificial Neural Networks in Pattern Recognition 2010 Apr 11 (pp. 120-130). Springer, Berlin, Heidelberg.
      3. Cuntz H, Forstner F, Borst A, H\äusser M. The TREES Toolbox—Probing the Basis of Axonal and Dendritic Segmenting. Neuroinformatics. 2011;1–6.
      4. Schierwagen A. Neuronal morphology: Shape characteristics and models. Neurophysiology. 2008;40(4):310–315.
      5. Uylings HB., van Pelt J. Measures for quantifying dendritic arborizations. Network: Computation in Neural Systems. 2002;13(3):397–414. a good review paper
      6. Wanner AA, Genoud C, Masudi T, Siksou L, Friedrich RW. Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb. Nature neuroscience. 2016 Jun 1;19(6):816-25. also have clustering methods
      7. Sato, M., I. Bitter, M. A. Bender, A. E. Kaufman, and M. Nakajima. “TEASAR: Tree-Structure Extraction Algorithm for Accurate and Robust Skeletons.” In Proceedings the Eighth Pacific Conference on Computer Graphics and Applications, 281–449, 2000. doi:10.1109/PCCGA.2000.883951.
      +
      Edit on GitHub
      1. Sümbül U, Song S, McCulloch K, Becker M, Lin B, Sanes JR, Masland RH, Seung HS. A genetic and computational approach to structurally classify neuronal types. Nature communications. 2014 Mar 24;5:3512. link
      2. Schierwagen A, Villmann T, Alpár A, Gärtner U. Cluster analysis of cortical pyramidal neurons using som. InIAPR Workshop on Artificial Neural Networks in Pattern Recognition 2010 Apr 11 (pp. 120-130). Springer, Berlin, Heidelberg.
      3. Cuntz H, Forstner F, Borst A, H\äusser M. The TREES Toolbox—Probing the Basis of Axonal and Dendritic Segmenting. Neuroinformatics. 2011;1–6.
      4. Schierwagen A. Neuronal morphology: Shape characteristics and models. Neurophysiology. 2008;40(4):310–315.
      5. Uylings HB., van Pelt J. Measures for quantifying dendritic arborizations. Network: Computation in Neural Systems. 2002;13(3):397–414. a good review paper
      6. Wanner AA, Genoud C, Masudi T, Siksou L, Friedrich RW. Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb. Nature neuroscience. 2016 Jun 1;19(6):816-25. also have clustering methods
      7. Sato, M., I. Bitter, M. A. Bender, A. E. Kaufman, and M. Nakajima. “TEASAR: Tree-Structure Extraction Algorithm for Accurate and Robust Skeletons.” In Proceedings the Eighth Pacific Conference on Computer Graphics and Applications, 281–449, 2000. doi:10.1109/PCCGA.2000.883951.
      diff --git a/dev/search/index.html b/dev/search/index.html index bbd18da1..b8b6e6b5 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -3,4 +3,4 @@ function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-136089579-2', {'page_path': location.pathname + location.search + location.hash}); -

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