# 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_index()[source]

Return the index of this node

Returns: index : int
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
set_content(content)[source]

Set the content of a node. The content must be a dict

Parameters: content : dict dict with content. For use in btmorph at least a ‘p3d’ entry should be present
set_index(index)[source]

Set the unqiue name of a node

Parameters: index : int
set_parent(parent)[source]

Set the parent node of a given other node

Parameters: node : 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. 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 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 node : SNode2 Node with the specific index
get_nodes()[source]

Obtain a list of all nodes int the tree

Returns: all_nodes : list of SNode2
get_root()[source]

Obtain the root node

Returns: root : SNode2
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 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. 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 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
remove_node(node)[source]

Remove a node from the tree

Parameters: node : SNode2 node to be removed
root

Obtain the root node

Returns: root : SNode2
set_root(node)[source]

Set the root node of the tree

Parameters: node : SNode2 to-be-root node
write_SWC_tree_to_file(file_n)[source]

Non-specific for a tree.

Used to write an SWC file from a morphology stored in this STree2. Output uses the 3-point soma standard.

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)

1. 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: 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. 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: 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. 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: 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. 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: 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. 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: where : string Toggle ‘local’ or ‘remote’ 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: 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 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 (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 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 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 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 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 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: 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: 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
total_surface()[source]

Scalar, global morphometric

Total neurite surface (at least, surface of all neurites excluding the soma. In accordance to the NeuroMorpho / L-Measure standard)

Returns: total_surface : float total surface in micron squared
total_volume()[source]

Scalar, global morphometric

Total neurite volume (at least, surface of all neurites excluding the soma. In accordance to the NeuroMorpho / L-Measure standard)

Returns: total_volume : float total volume in micron cubed

## Visualization¶

Basic visualization of neurite morphologies using matplotlib.

Usage is restricted to morphologies in the sWC format with the three-point soma standard

1. 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: A tree 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 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)
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 filter to select SWC files. Default is “*.swc”. See glob documentation for more advanced filters 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.