API Documentation¶
Data structures¶
File contains:
B. Torben-Nielsen (from legacy code). Daniele Linaro contributed the iterators in STree2.
- class btmorph.btstructs2.P3D2(xyz, radius, type=7)[source]¶
Bases: object
Basic container to represent and store 3D information
Constructor.
Parameters: xyz : numpy.array
3D location
radius : float
type : int
Type asscoiated with the segment according to SWC standards
- class btmorph.btstructs2.SNode2(index)[source]¶
Bases: object
Simple Node for use with a simple Tree (STree)
By design, the “content” should be a dictionary. (2013-03-08)
Attributes
children Return the children nodes of this one (if any) content Return the content dict of a SNode2 index Return the index of this node parent Return the parent node of this one. Methods
add_child(child_node) add a child to the children list of a given node get_children() Return the children nodes of this one (if any) get_content() Return the content dict of a SNode2 get_index() Return the index of this node get_parent() Return the parent node of this one. make_empty() Clear the node. remove_child(child) Remove a child node from the list of children of a specific node set_children(children) Set the children nodes of this one set_content(content) Set the content of a node. set_index(index) Set the unqiue name of a node set_parent(parent) Set the parent node of a given other node Constructor.
Parameters: index : int
Index, unique name of the SNode2
Attributes
children Return the children nodes of this one (if any) content Return the content dict of a SNode2 index Return the index of this node parent Return the parent node of this one. Methods
add_child(child_node) add a child to the children list of a given node get_children() Return the children nodes of this one (if any) get_content() Return the content dict of a SNode2 get_index() Return the index of this node get_parent() Return the parent node of this one. make_empty() Clear the node. remove_child(child) Remove a child node from the list of children of a specific node set_children(children) Set the children nodes of this one set_content(content) Set the content of a node. set_index(index) Set the unqiue name of a node set_parent(parent) Set the parent node of a given other node - add_child(child_node)[source]¶
add a child to the children list of a given node
Parameters: node : SNode2
- children¶
Return the children nodes of this one (if any)
Returns: children : list SNode2
In case of a leaf an empty list is returned
- content¶
Return the content dict of a SNode2
Returns: parent : SNode2
In case of the root, None is returned.Otherwise a SNode2 is returned
- get_children()[source]¶
Return the children nodes of this one (if any)
Returns: children : list SNode2
In case of a leaf an empty list is returned
- get_content()[source]¶
Return the content dict of a SNode2
Returns: parent : SNode2
In case of the root, None is returned.Otherwise a SNode2 is returned
- get_parent()[source]¶
Return the parent node of this one.
Returns: parent : SNode2
In case of the root, None is returned.Otherwise a SNode2 is returned
- index¶
Return the index of this node
Returns: index : int
- make_empty()[source]¶
Clear the node. Unclear why I ever implemented this. Probably to cover up some failed garbage collection
- parent¶
Return the parent node of this one.
Returns: parent : SNode2
In case of the root, None is returned.Otherwise a SNode2 is returned
- remove_child(child)[source]¶
Remove a child node from the list of children of a specific node
Parameters: node : SNode2
If the child doesn’t exist, you get into problems.
- set_children(children)[source]¶
Set the children nodes of this one
Parameters: children: list :class:`SNode2`
- class btmorph.btstructs2.STree2[source]¶
Bases: object
Simple tree for use with a simple Node (SNode2).
While the class is designed to contain binary trees (for neuronal morphologies) the number of children is not limited. As such, this is a generic implementation of a tree structure as a linked list.
Attributes
root Obtain the root node Methods
add_node_with_parent(node, parent) Add a node to the tree under a specific parent node degree_of_node(node) Get the degree of a given node. get_node_in_subtree(index, fake_root) Get a node with a specific name in a the subtree rooted at fake_root. get_node_with_index(index) Get a node with a specific name. get_nodes() Obtain a list of all nodes int the tree get_root() Obtain the root node get_sub_tree(fake_root) Obtain the subtree starting from the given node is_leaf(node) Check whether a node is a leaf node, i.e., a node without children is_root(node) Check whether a node is the root node order_of_node(node) Get the order of a given node. path_between_nodes(from_node, to_node) Find the path between two nodes. path_to_root(node) Find and return the path between a node and the root. read_SWC_tree_from_file(file_n[, types]) Non-specific for a tree. remove_node(node) Remove a node from the tree set_root(node) Set the root node of the tree write_SWC_tree_to_file(file_n) Non-specific for a tree. Default constructor. No arguments are passed.
Attributes
root Obtain the root node Methods
add_node_with_parent(node, parent) Add a node to the tree under a specific parent node degree_of_node(node) Get the degree of a given node. get_node_in_subtree(index, fake_root) Get a node with a specific name in a the subtree rooted at fake_root. get_node_with_index(index) Get a node with a specific name. get_nodes() Obtain a list of all nodes int the tree get_root() Obtain the root node get_sub_tree(fake_root) Obtain the subtree starting from the given node is_leaf(node) Check whether a node is a leaf node, i.e., a node without children is_root(node) Check whether a node is the root node order_of_node(node) Get the order of a given node. path_between_nodes(from_node, to_node) Find the path between two nodes. path_to_root(node) Find and return the path between a node and the root. read_SWC_tree_from_file(file_n[, types]) Non-specific for a tree. remove_node(node) Remove a node from the tree set_root(node) Set the root node of the tree write_SWC_tree_to_file(file_n) Non-specific for a tree. - add_node_with_parent(node, parent)[source]¶
Add a node to the tree under a specific parent node
Parameters: node : SNode2
node to be added
parent : SNode2
parent node of the newly added node
- degree_of_node(node)[source]¶
Get the degree of a given node. The degree is defined as the number of leaf nodes in the subtree rooted at this node.
Parameters: node : SNode2
Node of which the degree is to be computed.
Returns: degree : int
- get_node_in_subtree(index, fake_root)[source]¶
Get a node with a specific name in a the subtree rooted at fake_root. The name is always an integer
Parameters: index : int
Name of the node to be found
fake_root: :class:`SNode2`
Root node of the subtree in which the node with a given index is searched for
Returns: node : SNode2
Node with the specific index
- get_node_with_index(index)[source]¶
Get a node with a specific name. The name is always an integer
Parameters: index : int
Name of the node to be found
Returns: node : SNode2
Node with the specific index
- get_sub_tree(fake_root)[source]¶
Obtain the subtree starting from the given node
Parameters: fake_root : SNode2
Node which becomes the new root of the subtree
Returns: sub_tree : STree2
New tree with the node from the first argument as root node
- is_leaf(node)[source]¶
Check whether a node is a leaf node, i.e., a node without children
Returns: is_leaf : boolean
True is the queried node is a leaf, False otherwise
- is_root(node)[source]¶
Check whether a node is the root node
Returns: is_root : boolean
True is the queried node is the root, False otherwise
- order_of_node(node)[source]¶
Get the order of a given node. The order or centrifugal order is defined as 0 for the root and increased with any bifurcation. Hence, a node with 2 branch points on the shortest path between that node and the root has order 2.
Parameters: node : SNode2
Node of which the order is to be computed.
Returns: order : int
- path_between_nodes(from_node, to_node)[source]¶
Find the path between two nodes. The from_node needs to be of higher order than the to_node. In case there is no path between the nodes, the path from the from_node to the soma is given.
Parameters: from_node : SNode2
to_node : SNode2
- path_to_root(node)[source]¶
Find and return the path between a node and the root.
Parameters: node : SNode2
Node at which the path starts
Returns: path : list of SNode2
list of SNode2 with the provided node and the root as first and last entry, respectively.
- read_SWC_tree_from_file(file_n, types=[1, 2, 3, 4, 5, 6, 7, 8, 9])[source]¶
Non-specific for a tree. Read and load a morphology from an SWC file and parse it into an STree2 object.
On the NeuroMorpho.org website, 5 types of somadescriptions are considered (http://neuromorpho.org/neuroMorpho/SomaFormat.html). The “3-point soma” is the standard and most files are converted to this format during a curation step. btmorph follows this default specificationand the internal structure of btmorph implements the 3-point soma.
However, two other options to describe the soma are still allowed and available, namely: - soma absent: btmorph adds a 3-point soma in between of [TO DEFINE/TODO] - multiple cylinder: [TO DEFINE/TODO]
Parameters: file_n : str
name of the file to open
Morphometrics¶
- class btmorph.btstats.BTStats(tree)[source]¶
Bases: object
Compute morphometric features and statistics of a single morphology
Assume the “3 point” soma of the curated NeuroMorpho format. (website)
- Torben-Nielsen (legacy code)
Methods
approx_soma() Scalar, global morphometric bifurcation_angle_vec(node[, where]) Vector, local morphometric bifurcation_rall_ratio_classic(node[, where]) Vector, local morphometric bifurcation_ralls_power_brute(node[, where, ...]) Vector, local morphometric bifurcation_ralls_power_fmin(node[, where]) Vector, local morphometric bifurcation_sibling_ratio(node[, where]) Vector, local morphometric degree_of_node(node) Degree of a node. frac_dim_lac([vg]) Compute both lacunarity and fractal dimension Calculates lacunarity based on standard fixed grid box counting method with coef. fractal_dimension_box_counting_core(vg) Calculates fractal dimension of the given voxel grid by this formula: fractal_dimension_lacunarity(voxelSize) Calculate both lacunarity and fractal dimension of a tree. get_Euclidean_length_to_root(from_node) euclidean length between the from_node and the root get_diameters() Vector, local morphometric get_pathlength_to_root(from_node) Length of the path between from_node to the root. get_points_of_interest() Get lists containting the “points of interest”, i.e., soma points, bifurcation points and end/terminal points. get_segment_Euclidean_length(to_node) Euclidean length to the incoming segment. get_segment_pathlength(to_node) Vector, local morphometric. global_horton_strahler() Calculate Horton-Strahler number at the root lacunarity_box_counting_core(vg) Calculate lacunarity based on standard fixed grid box counting method with coef. local_horton_strahler(node) We assign Horton-Strahler number to all nodes of a tree, in bottom-up order, as follows: no_bifurcations() Scalar, global morphometric no_stems() Scalar, global morphometric no_terminals() Scalar, global morphometric order_of_node(node) Order of a node. partition_asymmetry(node) Vector, local morphometric pca(A) performs principal components analysis total_dimension() Scalar, global morphometric Overall dimension of the morphology total_dimensions_verbose() Scalar, global morphometric total_length() Scalar, global morphometric total_surface() Scalar, global morphometric total_volume() Scalar, global morphometric Constructor.
Parameters: tree : STree2
Neuronal tree for which to compute morphometrics
Methods
approx_soma() Scalar, global morphometric bifurcation_angle_vec(node[, where]) Vector, local morphometric bifurcation_rall_ratio_classic(node[, where]) Vector, local morphometric bifurcation_ralls_power_brute(node[, where, ...]) Vector, local morphometric bifurcation_ralls_power_fmin(node[, where]) Vector, local morphometric bifurcation_sibling_ratio(node[, where]) Vector, local morphometric degree_of_node(node) Degree of a node. frac_dim_lac([vg]) Compute both lacunarity and fractal dimension Calculates lacunarity based on standard fixed grid box counting method with coef. fractal_dimension_box_counting_core(vg) Calculates fractal dimension of the given voxel grid by this formula: fractal_dimension_lacunarity(voxelSize) Calculate both lacunarity and fractal dimension of a tree. get_Euclidean_length_to_root(from_node) euclidean length between the from_node and the root get_diameters() Vector, local morphometric get_pathlength_to_root(from_node) Length of the path between from_node to the root. get_points_of_interest() Get lists containting the “points of interest”, i.e., soma points, bifurcation points and end/terminal points. get_segment_Euclidean_length(to_node) Euclidean length to the incoming segment. get_segment_pathlength(to_node) Vector, local morphometric. global_horton_strahler() Calculate Horton-Strahler number at the root lacunarity_box_counting_core(vg) Calculate lacunarity based on standard fixed grid box counting method with coef. local_horton_strahler(node) We assign Horton-Strahler number to all nodes of a tree, in bottom-up order, as follows: no_bifurcations() Scalar, global morphometric no_stems() Scalar, global morphometric no_terminals() Scalar, global morphometric order_of_node(node) Order of a node. partition_asymmetry(node) Vector, local morphometric pca(A) performs principal components analysis total_dimension() Scalar, global morphometric Overall dimension of the morphology total_dimensions_verbose() Scalar, global morphometric total_length() Scalar, global morphometric total_surface() Scalar, global morphometric total_volume() Scalar, global morphometric - approx_soma()[source]¶
Scalar, global morphometric
By NeuroMorpho.org convention: soma surface ~ 4*pi*r^2, where r is the abs(y_value) of point 2 and 3 in the SWC file
Returns: surface : float
soma surface in micron squared
- bifurcation_angle_vec(node, where='local')[source]¶
Vector, local morphometric
Only to be computed at branch points (_bif_points). Computes the angle between the two daughter branches in the plane defined by the parent and the two daughters.
cos alpha = \((a \dot b) / (|a||b|)\)
Parameters: node : btmorph.btstructs2.SNode2
where : string
either “local” or “remote”. “Local” uses the immediate daughter points while “remote” uses the point just before the next bifurcation or terminal point.
Returns: angle : float
Angle in degrees
- bifurcation_rall_ratio_classic(node, where='local')[source]¶
Vector, local morphometric
The ratio \(\frac{ {d_1}^p + {d_2}^p }{D^p}\) computed with \(p=1.5\)
Parameters: node : btmorph.btstructs2.SNode2
where : string
either ‘local or ‘remote’. ‘Local’ uses the immediate daughter points while “remote” uses the point just before the next bifurcation or terminal point.
Returns: rr : float
Approximation of Rall’s ratio
- bifurcation_ralls_power_brute(node, where='local', min_v=0, max_v=5, steps=1000)[source]¶
Vector, local morphometric
Approximation of Rall’s ratio. \(D^p = {d_1}^p + {d_2}^p\), p is approximated by brute-force checking the interval [0,5] in 1000 steps (by default, but the exact search dimensions can be specified by keyworded arguments.
Parameters: node : btmorph.btstructs2.SNode2
where : string
either ‘local or ‘remote’. ‘Local’ uses the immediate daughter points while “remote” uses the point just before the next bifurcation or terminal point.
Returns: rr : float
Approximation of Rall’s power, p
- bifurcation_ralls_power_fmin(node, where='local')[source]¶
Vector, local morphometric
Approximation of Rall’s ratio using scipy.optimize.fmin. The error function is \(F={D_{d1}}^n+{D_{d2}}^n-{D_p}^n\)
Parameters: node : btmorph.btstructs2.SNode2
where : string
either “local” or “remote”. “Local” uses the immediate daughter points while “remote” uses the point just before the next bifurcation or terminal point.
Returns: rr : float
Appriximation of Rall’s ratio
- bifurcation_sibling_ratio(node, where='local')[source]¶
Vector, local morphometric
Ratio between the diameters of two siblings.
Parameters: node : btmorph.btstructs2.SNode2
where : string
Toggle ‘local’ or ‘remote’
Returns: result : float
Ratio between the diameter of two siblings
- degree_of_node(node)[source]¶
Degree of a node. (The number of leaf node in the subtree mounted at the provided node)
Parameters: node : btmorph.btstructs2.SNode2
Returns: degree : float
degree of the subtree rooted at node
- frac_dim_lac(vg=None)[source]¶
Compute both lacunarity and fractal dimension Calculates lacunarity based on standard fixed grid box counting method with coef. of variation See wikipedia for more information: http://en.wikipedia.org/wiki/Lacunarity#equation_1 Note: here we ignore orientations (all boxes start from (0,0,0)) and box sizes are always power of two Calculates fractal dimension of the given voxel grid by this formula: D = lim e -> 0 of (log(Ne)/log(e)) http://rsbweb.nih.gov/ij/plugins/fraclac/FLHelp/Glossary.htm#db
Parameters: vg : btmorph.btstructs2.VoxelGrid
Ready to use voxel grid
Returns: lacunarity, fractal_dimension : tuple
- fractal_dimension_box_counting_core(vg)[source]¶
Calculates fractal dimension of the given voxel grid by this formula: D = lim e -> 0 of (log(Ne)/log(e)) http://rsbweb.nih.gov/ij/plugins/fraclac/FLHelp/Glossary.htm#db
- fractal_dimension_lacunarity(voxelSize)[source]¶
Calculate both lacunarity and fractal dimension of a tree. Faster than calling fractal_dim_box_counting and lacunarity_standard separately
Parameters: voxelSize : number
Desired voxel size, affects resolution. Both measures use voxelization of the 3D tree for calculations
Returns: (lacunarity, fractal_dimension)
- get_Euclidean_length_to_root(from_node)[source]¶
euclidean length between the from_node and the root
Parameters: from_node : btmorph.btstructs2.SNode2
Returns: length : float
length of the path between the soma and the provided node
- get_diameters()[source]¶
Vector, local morphometric
Get the diameters of all points in the morphology
- get_pathlength_to_root(from_node)[source]¶
Length of the path between from_node to the root. another branching point
Parameters: from_node : btmorph.btstructs2.SNode2
Returns: length : float
length of the path between the soma and the provided node
- get_points_of_interest()[source]¶
Get lists containting the “points of interest”, i.e., soma points, bifurcation points and end/terminal points.
Returns: soma_points : list
bif_points : list
end_points : list
- get_segment_Euclidean_length(to_node)[source]¶
Euclidean length to the incoming segment. Between this node and the soma or another branching point
Parameters: from_node : btmorph.btstructs2.SNode2
Returns: length : float
Euclidean distance to provided node (from soma or first branch point with lower order)
- get_segment_pathlength(to_node)[source]¶
Vector, local morphometric.
Length of the incoming segment. Between this node and the soma or another branching point. A path is defined as a stretch between the soma and a bifurcation point, between bifurcation points, or in between of a bifurcation point and a terminal point
Parameters: to_node : btmorph.btstructs2.SNode2
Node to which the measurement is taken
Returns: length : float
length of the incoming path in micron
- global_horton_strahler()[source]¶
Calculate Horton-Strahler number at the root See local_horton_strahler()
Returns: Horton-Strahler number at the root
- lacunarity_box_counting_core(vg)[source]¶
Calculate lacunarity based on standard fixed grid box counting method with coef. of variation See wikipedia for more information: http://en.wikipedia.org/wiki/Lacunarity#equation_1 Note: here we ignore orientations (all boxes start from (0,0,0)) and box sizes are always power of two
Parameters: vg : btmorph.btstructs2.VoxelGrid
Ready to use voxel grid
Returns: lacunarity : float
- local_horton_strahler(node)[source]¶
We assign Horton-Strahler number to all nodes of a tree, in bottom-up order, as follows:
If the node is a leaf (has no children), its Strahler number is one. If the node has one child with Strahler number i, and all other children have Strahler numbers less than i, then the Strahler number of the node is i again. If the node has two or more children with Strahler number i, and no children with greater number, then the Strahler number of the node is i + 1. *If the node has only one child, the Strahler number of the node equals to the Strahler number of the child The Strahler number of a tree is the number of its root node.
See wikipedia for more information: http://en.wikipedia.org/wiki/Strahler_number
- node : btmorph.btstructs2.SNode2
- Node of interest
- hs : int
- The Horton-Strahler number (Strahler number) of the node
- no_bifurcations()[source]¶
Scalar, global morphometric
Count the number of bifurcations points in a complete moprhology
Returns: no_bifurcations : int
number of bifurcation
- no_stems()[source]¶
Scalar, global morphometric
Count the number of stems in a complete moprhology (except the three point soma from the Neuromoprho.org standard)
Returns: no_stems : int
number of stems
- no_terminals()[source]¶
Scalar, global morphometric
Count the number of temrinal points in a complete moprhology
Returns: no_terminals : int
number of terminals
- order_of_node(node)[source]¶
Order of a node. (Going centrifugally away from the soma, the order increases with 1 each time a bifurcation point is passed)
Parameters: node : btmorph.btstructs2.SNode2
Returns: order : float
order of the subtree rooted at node
- partition_asymmetry(node)[source]¶
Vector, local morphometric
Compute the partition asymmetry for a given node.
Parameters: node : btmorph.btstructs2.SNode2
Returns: partition_asymmetry : float
partition asymmetry of the subtree rooted at node (according to vanpelt and schierwagen 199x)
- pca(A)[source]¶
- performs principal components analysis
(PCA) on the n-by-p data matrix A Rows of A correspond to observations, columns to variables.
- Returns :
- coeff :
is a p-by-p matrix, each column containing coefficients for one principal component.
score :the principal component scores; that is, the representation of A in the principal component space. Rows of SCORE correspond to observations, columns to components.
latent :a vector containing the eigenvalues of the covariance matrix of A. source: http://glowingpython.blogspot.jp/2011/07/principal-component-analysis-with-numpy.html
- total_dimension()[source]¶
Scalar, global morphometric Overall dimension of the morphology
Returns: dx : float
x-dimension
dy : float
y-dimension
dz : float
z-dimension
- total_dimensions_verbose()[source]¶
Scalar, global morphometric
Overall dimension of the whole moprhology. (No translation of the moprhology according to arbitrary axes.)
Returns: dx : float
x-dimension
dy : float
y-dimension
dz : float
z-dimension
data : list
minX,maxX,minY,maxY,minZ,maxZ
- total_length()[source]¶
Scalar, global morphometric
Calculate the total length of a complete morphology
Returns: total_length : float
total length in micron
Visualization¶
Basic visualization of neurite morphologies using matplotlib.
Usage is restricted to morphologies in the sWC format with the three-point soma standard
- Torben-Nielsen
- btmorph.btviz.pca_project_tree(tree)[source]¶
Returns a tree which is a projection of the original tree on the plane of most variance
Parameters: tree : btmorph.btstructs2.STree2
A tree
Returns: tree : btmorph.btstructs2.STree2
New flattened tree
- btmorph.btviz.plot_2D_SWC(file_name='P20-DEV139.CNG.swc', cs=None, synapses=None, syn_cs='ro', outN=None, align=True, offset=None, show_axis=False, depth='Y', filter=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], show_radius=True, bar_L=None, bar=[50, 50, 50], new_fig=True, color_scheme='default')[source]¶
2D matplotlib plot of a neuronal moprhology. Projection can be in XY and XZ. The SWC has to be formatted with a “three point soma”. Colors can be provided
Parameters: file_name : string
File name of the SWC file to plots
cs : list of float
Raw values that will be plotted on the morphology according to a colormap
synapses : list of int
Indices of the compartments where synapses (= small circles) should be drawn
syn_c : string
Color of the synapses (‘r’,’g’, ‘b’, ...). String follows matplotlib conventions. You can include the marker style as well. Default syn_c=’ro’
outN : string
File name of the output file. Extension of this file sets the file type
align : boolean
Translate the figure so that the soma is on the origin [0,0,0].
offset : list on float
List of length 3 with X,Y and Z shift of the morphology to be plotted. Not to be used in conjunction with the “align” option
show_axis : boolean
whether or not to draw the axis
filter : list
List of integers indicating the SWC types to be included (1:soma, 2:axon, 3:basal,4:apical,...). By default, all SWC types are included
show_radius : boolean
Default “True” plots the diameter; “False” will render a wire plot.
bar_L : float
Add a scale bar to the plot. Currently only works with align=True
bar : list of real
When the axis are shown (show_axis=True), ticks will be plotted according to this list. List contains 3 values for X, Y and Z ticks. Default [50,50,50]
depth : string
Default “Y” means that X represents the superficial to deep axis. Otherwise, use “Z” to conform the mathematical standard of having the Z axis.
new_fig : boolean
True if matplotlib has to plot in a new figure. False, if otherwise.
color_scheme : string
Set the color scheme used for background and neurite types. Default default. Other option neuromorpho
Notes
If the soma is not located at [0,0,0], the scale bar (bar_L) and the ticks (bar) might not work as expected
- btmorph.btviz.plot_3D_SWC(file_name='P20-DEV139.CNG.swc', cs=None, synapses=None, syn_cs=None, outN=None, offset=None, align=True, filter=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])[source]¶
3D matplotlib plot of a neuronal morphology. The SWC has to be formatted with a “three point soma”. Colors can be provided and synapse location marked
Parameters: file_name : string
File name of the SWC file to plots
cs : list of float
Raw values that will be plotted on the morphology according to a colormap
synapses : list of int
Indices of the compartments where synapses (= small circles) should be drawn
syn_cs : string
Color of the synapses (‘r’,’g’, ‘b’, ...)
outN : string
File name of the output file. Extension of this file sets the file type
offset : list on float
List of length 3 with X,Y and Z shift of the morphology to be plotted. Not to be used in conjunction with the “align” option
show_axis : boolean
whether or not to draw the axis
filter : list
List of integers indicating the SWC types to be included (1:soma, 2:axon, 3:basal,4:apical,...). By default, all SWC types are included
- btmorph.btviz.plot_dendrogram(file_name, transform='plain', shift=0, c='k', radius=True, rm=20000.0, ra=200, outN=None)[source]¶
Generate a dendrogram from an SWC file. The SWC has to be formatted with a “three point soma”
Parameters: file_name : string
File name of the SWC file to plots
transform : string
Either ‘plain’ or ‘lambda’. Plain means no transform while ‘lambda’ performs an elecotrtonic transform
shift : float
Offset in the x-direction
c : string
Color (‘r’,’g’, ‘b’, ...)
radius : boolean
Plot a wire (False) dendrogram or one with the thickness of the processes (True)
rm : float
Membrane resistance. Only needed when transform = ‘lambda’
rm : float
Axial resistance. Only needed when transform = ‘lambda’
outN : string
File name of the output file. Extension of this file sets the file type
- btmorph.btviz.true_2D_projections(file_name='P20-DEV139.CNG.swc', align=True, outN=None, bar=None, depth='Z')[source]¶
Three 2D projections
Parameters: file_name : string
File name of the SWC file to plots
depth : string
Set which axis represents “depth”. In experimental work, the Z-axis is depth (as in my PPNeurMorphC) but in NeuroMorpho the Y-axis is the depth. (Depth is used to denote the axis from deep to superficial)
align : boolean
Translate the figure so that the soma is on the origin [0,0,0].
outN : string
File name of the output file. Extension of this file sets the file type
bar : list of int or real
Three values to set the thicks and marks on the plot in X,Y and Z-dimension
depth : string
Set the axis representing the depth (= axis from superficial to deep). In most SWC files this is ‘Y’. The other option is ‘Z’, that is more consistent with the usual Cartesian coordinate systems
- btmorph.population_density_plots.population_2D_density_projections(destination='.', filter='*.swc', outN=None, depth='Z', limits=None, precision=[10, 10, 10])[source]¶
Plot a pseudo-3D heat-map of all neurites in a population by means of three 2D plots. A population can be sepcified by setting the destination and filter arguments.
Parameters: destination : string
Set the directory in which the population is located. Default ”.” sets the current working directory.
filter : string
outN : string
File name to save the figure to. Default is None and the figure will not be saved by default.
depth : string
Indicate the axis from superficial to deep. In most files this is “Y”. Default, however, is set to the mathematical standard “Z”.
precision : list of ints
Set the bin size of the heatmap. Default [10,10,10] (in micron)
- btmorph.population_density_plots.population_density_projection(destination='.', filter='*.swc', outN=None, depth='Z', precision=[10, 10, 10])[source]¶
Plot a 2D heat-map of all neurites in a population. A population can be sepcified by setting the destination and filter arguments. Parameters ———- destination : string
Set the directory in which the population is located. Default ”.” sets the current working directory.- filter : string
- Filter the file-names to constrain the population. Default “*.swc” will select all SWC files in one directory. The filter uses the glob module so make sure glob understands your filter!
- outN : string
- File name to save the figure to. Default is None and the figure will not be saved by default.
- depth : string
- Indicate the axis from superficial to deep. In most files this is “Y”. Default, however, is set to the mathematical standard “Z”.
- precision : list of ints
- Set the bin size of the heatmap. Default [10,10,10] (in micron)
Wrappers / Tools¶
- btmorph.tools.analyze_1D_population.perform_1D_population_analysis(destination, filter='*.swc', depth='Y', bar=[200, 200, 200], post_name=None, max_n=None)[source]¶
Wrapper function to perform a complete analysis of a population of neuronal morphologies stored in SWC format (and three-point soma).
Computes the following features:
- # bifurcations
- # terminals
- # stems
- total length
- total volume
- total surface
- max centrifugal order
- inter bifurcation length
- terminal segment length
- euclidean distance between terminal tips and soma
- path legth between terminal tips and soma
For each feature a list is created with all values collected from all morphologies.
These vectors are saved as python Pickle objects.
At the same time a histogram is generated to display the data.
Parameters: destination : string
string with the location of where to find the SWC files.
filter : string
depth : string
Dimension that indicates “depth”/distance from top. Default is “Y”; NeuroMac generated files use “Z”.
bar : array of float
Include a scale-bar with the specified dimensions
max_n : int
To perform the analysis on a subset of morphologies specify a number. Default is None and all morphologies will be taken into account.
post_name : string
string appended to the file name when saving. Default None
- btmorph.tools.analyze_2D_per_neuron.perform_2D_analysis(destination, filter='*.swc', max_n=None)[source]¶
Wrapper function to perform an analysis of the vector features of one neuronal morphology (in the SWC format and with 3-point soma)
For both the terminal points and the branching points the following features are recorded
- Order of the node
- degree of the node
- Euclidean distance to the soma
- path length to the soma
- pathlength of the segment (coming in to a node)
- Euclidean distance of the segment (coming in the a node)
- branch angle amplitude [branch points only]
Two text files are generated, for terminal and branching points. Each row corresponds to a node (point) and the six columns correspond to the features above.
Parameters: destination : string
string with the location of where to find the SWC files.
- btmorph.tools.filter_and_save_swc.filter_and_save_SWC(destination, filter, types=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], prefix='_filtered')[source]¶
Removes points from a SWC structure and saves the new SWC to a file.
Can be used to remove unwanted structures that are identifiable by the type-field in the SWC description. Specification of (standard) SWC type fields can be found here.
To select the basal dendrites only, use the argument types=[1,3]: 1 to select the soma and 3 for the basal dendrites themselves.
Parameters: destination : string
string with the location of where to find the SWC files.
types : list of int
types that are to be remained in the SWC file.