pymaid.CatmaidNeuron¶

class pymaid.CatmaidNeuron(x, remote_instance=None, units='nm', **metadata)[source]¶

Catmaid neuron object holding neuron data (nodes, connectors, name, etc) and providing quick access to various PyMaid functions.

CatmaidNeuron can be minimally constructed from just a skeleton ID and a CatmaidInstance. Other parameters (nodes, connectors, neuron name, annotations, etc.) will then be retrieved from the server ‘on-demand’.

The easiest way to construct a CatmaidNeuron is by using get_neuron().

Manually, a complete CatmaidNeuron can be constructed from a pandas DataFrame (df) containing: df.nodes, df.connectors, df.skeleton_id, df.neuron_name, df.tags

Using a CatmaidNeuron to initialise a CatmaidNeuron will automatically make a copy.

skeleton_id¶

This neuron’s skeleton ID.

Type:: str

neuron_name¶

This neuron’s name.

Type:: str

nodes¶

Contains complete node table.

Type:: pandas.DataFrame

connectors¶

Contains complete connector table.

Type:: pandas.DataFrame

presynapses¶

All presynaptic connectors.

Type:: pandas.DataFrame

postsynapses¶

All postsynaptic connectors.

Type:: pandas.DataFrame

gap_junctions¶

All gap junction connectors.

Type:: pandas.DataFrame

date_retrieved¶

Timestamp of data retrieval.

Type:: datetime object

tags¶

Node tags.

Type:: dict

connector_tags¶

Connector tags.

Type:: dict

annotations¶

This neuron’s annotations.

Type:: list

graph¶

Graph representation of this neuron.

Type:: network.DiGraph

igraph¶

iGraph representation of this neuron. Returns None if igraph library not installed.

Type:: igraph.Graph

review_status¶

This neuron’s review status.

Type:: int

n_connectors¶

Total number of synapses.

Type:: int

n_presynapses¶

Total number of presynaptic sites.

Type:: int

n_postsynapses¶

Total number of presynaptic sites.

Type:: int

n_branch_nodes¶

Number of branch nodes.

Type:: int

n_end_nodes¶

Number of end nodes.

Type:: int

n_open_ends¶

Number of open end nodes = leaf nodes that are not tagged with either: ends, not a branch, uncertain end, soma or uncertain continuation.

Type:: int

cable_length¶

Cable length in micrometers [um].

Type:: float

segments¶

Node IDs making up linear segments. Maximizes segment lengths (similar to CATMAID’s review widget).

Type:: list of lists

small_segments¶

Node IDs making up linear segments between end/branch points.

Type:: list of lists

soma¶

Returns None if no soma or ‘NA’ if data not available.

Type:: node ID of soma

root¶

Node ID(s) of root.

Type:: numpy.array

color¶

Color of neuron. Used for e.g. export to json.

Type:: tuple

partners¶

Connectivity table of this neuron.

Type:: pd.DataFrame

Examples

>>> # Initialize a new neuron
>>> n = pymaid.CatmaidNeuron(123456)
>>> # Retrieve node data from server on-demand
>>> n.nodes
CatmaidNeuron - INFO - Retrieving skeleton data...
  node_id  parent_id  creator_id  x  y  z radius confidence
0  ...
>>> # Initialize with skeleton data
>>> n = pymaid.get_neuron(123456)
>>> # Get annotations from server
>>> n.annotations
['annotation1', 'annotation2']
>>> # Force update of annotations
>>> n.get_annotations()

Initialize CatmaidNeuron.

Parameters:

x –
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 is treated as SWC file name
- BufferedIOBase e.g. from open(filename)
- networkx.DiGraph parsed by navis.nx2neuron
remote_instance (CatmaidInstance, optional) – Storing this makes it more convenient to retrieve e.g. neuron annotations, review status, etc. If not provided, will try using global CatmaidInstance.
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.

__init__(x, remote_instance=None, units='nm', **metadata)[source]¶

Initialize CatmaidNeuron.

Parameters:

x –
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 is treated as SWC file name
- BufferedIOBase e.g. from open(filename)
- networkx.DiGraph parsed by navis.nx2neuron
remote_instance (CatmaidInstance, optional) – Storing this makes it more convenient to retrieve e.g. neuron annotations, review status, etc. If not provided, will try using global CatmaidInstance.
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.

Methods

`__init__`(x[, remote_instance, units])	Initialize CatmaidNeuron.
`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 this neuron.
`downsample`([factor, inplace])	Downsample the neuron by given factor.
`get_annotations`([remote_instance])	Retrieve annotations for neuron.
`get_connector_tags`([remote_instance])	Fetch tags on connectors of a neuron.
`get_graph_nx`()	Calculate and return networkX representation of neuron.
`get_igraph`()	Calculate and return iGraph representation of neuron.
`get_name`([remote_instance])	Retrieve/update name of neuron.
`get_partners`([remote_instance])	Get connectivity table for this neuron.
`get_review`([remote_instance])	Get/Update review status for neuron.
`get_skeleton`([remote_instance])	Get/update skeleton data for 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`([remote_instance])	Reload neuron from server.
`reroot`(new_root[, inplace])	Reroot neuron to given node ID or node tag.
`resample`(resample_to[, inplace])	Resample neuron to given resolution.
`set_remote_instance`([remote_instance, ...])	Assign remote_instance to neuron.
`snap`(locs[, to])	Snap xyz location(s) to closest node or synapse.
`summary`([add_props])	Get a summary of this neuron.
`to_dataframe`()	Turn this CatmaidNeuron into a pandas DataFrame with original CATMAID data.
`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.
`annotations`	Neuron annotations.
`bbox`	Bounding box (includes connectors).
`branch_points`	Branch node table.
`cable_length`	Cable length.
`connector_tags`	Connector tags.
`connectors`	Connector table.
`core_md5`	MD5 of core information for the neuron.
`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).
`gap_junctions`	Table with gap junctions.
`geodesic_matrix`	Matrix with geodesic (along-the-arbor) distance between nodes.
`graph`	Networkx Graph representation of this neuron.
`id`	ID of the neuron.
`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. for legends).
`leafs`	Leaf node table.
`n_branches`	Number of branch points.
`n_leafs`	Number of leaf nodes.
`n_skeletons`	Number of seperate skeletons in this neuron.
`n_trees`	Count number of connected trees in this neuron.
`name`	Neuron name.
`neuron_name`	Neuron name (legacy - please use .name instead).
`nodes`	Node table.
`open_ends`	Node IDs of open ends.
`partners`	Get connected partners.
`postsynapses`	Table with postsynapses (filtered from connectors table).
`presynapses`	Table with presynapses (filtered from connectors table).
`review_status`	Review status in percent.
`root`	Root node(s).
`sampling_resolution`	Average cable length between child -> parent nodes.
`segments`	Neuron broken down into linear segments (see also .small_segments).
`simple`	Simplified representation consisting only of root, branch points and leafs.
`skeleton_id`	Skeleton ID.
`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_detection_tag`
`soma_pos`	Search for soma and return its position.
`soma_radius`	Soma radius (e.g. for plotting).
`subtrees`	List of subtrees.
`surface_area`	Radius-based lateral surface area.
`tags`	Node tags.
`type`	Return type.
`units`	Units for coordinate space.
`units_xyz`	Units for coordinate space.
`volume`	Radius-based volume.