navis.TreeNeuron¶

class navis.TreeNeuron(x, units=None, **metadata)[source]¶

Neuron represented as hierarchical tree (i.e. a skeleton).

Parameters:

x (Union[DataFrame, BufferedIOBase, str, TreeNeuron, DiGraph]) –
Data to construct neuron from:
- pandas.DataFrame is expected to be SWC table
- pandas.Series is expected to have a DataFrame as .nodes - additional properties will be attached as meta data
- str filepath is passed to navis.read_swc()
- BufferedIOBase e.g. from open(filename)
- networkx.DiGraph parsed by navis.nx2neuron
- None will initialize an empty neuron
- skeletor.Skeleton
- TreeNeuron - in this case we will try to copy every attribute
units (str | pint.Units | pint.Quantity) – Units for coordinates. Defaults to None (dimensionless). Strings must be parsable by pint: e.g. “nm”, “um”, “micrometer” or “8 nanometers”.
**metadata – Any additional data to attach to neuron.

Initialize Skeleton Neuron.

__init__(x, units=None, **metadata)[source]¶: Initialize Skeleton Neuron.

Methods

`__init__`(x[, units])	Initialize Skeleton Neuron.
`cell_body_fiber`([reroot_soma, inplace])	Prune neuron to its cell body fiber.
`convert_units`(to[, inplace])	Convert coordinates to different unit.
`copy`([deepcopy])	Return a copy of the neuron.
`downsample`([factor, inplace])	Downsample the neuron by given factor.
`get_graph_nx`()	Calculate and return networkX representation of neuron.
`get_igraph`()	Calculate and return iGraph representation of neuron.
`map_units`(units[, on_error])	Convert units to match neuron space.
`memory_usage`([deep, estimate])	Return estimated memory usage of this neuron.
`plot2d`(**kwargs)	Plot neuron using `navis.plot2d()`.
`plot3d`(**kwargs)	Plot neuron using `navis.plot3d()`.
`prune_at_depth`(depth[, source, inplace])	Prune all neurites past a given distance from a source.
`prune_by_longest_neurite`([n, reroot_soma, ...])	Prune neuron down to the longest neurite.
`prune_by_strahler`(to_prune[, inplace])	Prune neuron based on Strahler order.
`prune_by_volume`(v[, mode, ...])	Prune neuron by intersection with given volume(s).
`prune_distal_to`(node[, inplace])	Cut off nodes distal to given nodes.
`prune_proximal_to`(node[, inplace])	Remove nodes proximal to given node.
`prune_twigs`(size[, inplace, recursive])	Prune terminal twigs under a given size.
`reload`([inplace])	Reload neuron.
`reroot`(new_root[, inplace])	Reroot neuron to given node ID or node tag.
`resample`(resample_to[, inplace])	Resample neuron to given resolution.
`snap`(locs[, to])	Snap xyz location(s) to closest node or synapse.
`summary`([add_props])	Get a summary of this neuron.
`to_swc`([filename])	Generate SWC file from this neuron.

Attributes

`CORE_DATA`	Core data table(s) used to calculate hash
`EQ_ATTRIBUTES`	Attributes to be used when comparing two neurons.
`SUMMARY_PROPS`	Attributes used for neuron summary
`TEMP_ATTR`	Temporary attributes that need to be regenerated when data changes.
`bbox`	Bounding box (includes connectors).
`branch_points`	Branch node table.
`cable_length`	Cable length.
`connectors`	Connector table.
`core_md5`	MD5 checksum of core data.
`cycles`	Cycles in neuron if any.
`datatables`	Names of all DataFrames attached to this neuron.
`edge_coords`	Coordinates of edges between nodes.
`edges`	Edges between nodes.
`ends`	End node table (same as leafs).
`extents`	Extents of neuron in x/y/z direction (includes connectors).
`geodesic_matrix`	Matrix with geodesic (along-the-arbor) distance between nodes.
`graph`	Networkx Graph representation of this neuron.
`id`	Hashable ID.
`igraph`	iGraph representation of this neuron.
`is_isometric`	Test if neuron is isometric.
`is_locked`	Test if neuron is locked.
`is_stale`	Test if temporary attributes might be outdated.
`is_tree`	Whether neuron is a tree.
`label`	Label (e.g.
`leafs`	Leaf node table.
`n_branches`	Number of branch points.
`n_skeletons`	Return number of seperate skeletons in this neuron.
`n_trees`	Count number of connected trees in this neuron.
`name`	Neuron name.
`nodes`	Node table.
`postsynapses`	Table with postsynapses.
`presynapses`	Table with presynapses.
`root`	Root node(s).
`sampling_resolution`	Average cable length between 2 nodes.
`segments`	Neuron broken down into linear segments (see also .small_segments).
`simple`	Return simple neuron representation.
`small_segments`	Neuron broken down into small linear segments (see also .segments).
`soma`	Search for soma and return node ID(s).
`soma_detection_label`	Label for soma detection.
`soma_detection_radius`	Minimum radius for soma detection.
`soma_pos`	Search for soma and return its position.
`soma_radius`	Soma radius (e.g.
`subtrees`	List of subtrees.
`tags`
`type`	Neuron type.
`units`	Units for coordinate space.
`units_xyz`	Units for coordinate space.
`volume`	Radius-based volume.
`n_leafs`