pymaid.CatmaidNeuronList¶

class pymaid.CatmaidNeuronList(x, make_copy=False, **kwargs)[source]¶

Compilation of CatmaidNeuron that allow quick access to neurons’ attributes/functions. They are designed to work in many ways much like a pandas.DataFrames by, for example, supporting .iloc[ ], .itertuples(), .empty or .copy().

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

The easiest way to get a CatmaidNeuronList is by using get_neuron() (see examples).

skeleton_id¶

Type:: array of str

name¶

Type:: array of str

nodes¶

Merged node table.

Type:: pandas.DataFrame

connectors¶

Merged connector table. This also works for presynapses, postsynapses and gap_junctions.

Type:: pandas.DataFrame

tags¶

Node tags.

Type:: np.array of dict

annotations¶

Type:: np.array of list

partners¶

Connectivity table for these neurons.

Type:: pd.DataFrame

graph¶

Type:: np.array of networkx graph objects

igraph¶

Type:: np.array of igraph graph objects

review_status¶

Type:: np.array of int

n_connectors¶

Type:: np.array of int

n_presynapses¶

Type:: np.array of int

n_postsynapses¶

Type:: np.array of int

n_branch_nodes¶

Type:: np.array of int

n_end_nodes¶

Type:: np.array of int

n_open_ends¶

Type:: np.array of int

cable_length¶

Cable lengths in micrometers [um].

Type:: np.array of float

soma¶

Type:: np.array of node IDs

root¶

Type:: np.array of node IDs

n_cores¶

Number of cores to use. Default os.cpu_count()-1.

Type:: int

use_threading¶

If True, will use parallel threads. Should be slightly up to a lot faster depending on the numbers of cores. Switch off if you experience performance issues.

Type:: bool (default=True)

Examples

>>> # Initialize with just a Skeleton ID
>>> nl = pymaid.CatmaidNeuronList([123456, 45677])
>>> # Retrieve review status from server on-demand
>>> nl.review_status
array([90, 23])
>>> # Initialize with skeleton data
>>> nl = pymaid.get_neuron([123456, 45677])
>>> # Get annotations from server
>>> nl.annotations
[['annotation1', 'annotation2'], ['annotation3', 'annotation4']]
>>> Index using node count
>>> subset = nl[nl.n_nodes > 6000]
>>> # Get neuron by its skeleton ID
>>> n = nl.skid[123456]
>>> # Index by multiple skeleton ID
>>> subset = nl[['123456', '45677']]
>>> # Index by neuron name
>>> subset = nl['name1']
>>> # Index using annotation
>>> subset = nl['annotation:uPN right']
>>> # Concatenate lists
>>> nl += pymaid.get_neuron([912345])

__init__(x, make_copy=False, **kwargs)[source]¶

Methods

`__init__`(x[, make_copy])
`append`(v)	Add neuron(s) to this list.
`apply`(func, *[, parallel, n_cores, ...])	Apply function across all neurons in this NeuronList.
`copy`(**kwargs)	Make copy of this neuronlist.
`from_selection`(fname)	Generate NeuronList from CATMAID JSON file.
`get_annotations`([skip_existing])	Get/update annotations for neurons.
`get_names`([skip_existing])	Get/update neuron names.
`get_review`([skip_existing])	Get/update review status.
`get_skeletons`([skip_existing])	Fill in/update skeleton data of neurons.
`has_annotation`(x[, intersect, partial, ...])	Filter neurons by their annotations.
`head`([N])	Return summary for top N neurons.
`itertuples`()	Helper to mimic `pandas.DataFrame.itertuples()`.
`mean`()	Return mean numeric and boolean values over all neurons.
`memory_usage`([deep, estimate, sample])	Return estimated size in memory of this NeuronList.
`plot2d`(**kwargs)	Plot neuron in 2D using `plot2d()`.
`plot3d`(**kwargs)	Plot neuron in 3D using `plot3d()`.
`reload`()	Update neuron skeletons from server.
`remove_duplicates`([key, inplace])	Remove duplicate neurons from list.
`sample`([N])	Return random subset of neurons.
`set_neuron_attributes`(x, name[, register, na])	Set attributes of neurons contained in the NeuronList.
`set_remote_instance`([remote_instance, ...])	Assign remote_instance to all neurons.
`sort_values`(key[, ascending])	Sort neurons by given key.
`sum`()	Return sum numeric and boolean values over all neurons.
`summary`([N, add_props])	Get summary over all neurons in this NeuronList.
`tail`([N])	Return summary for bottom N neurons.
`to_dataframe`()	Turn this CatmaidneuronList into a pandas DataFrame.
`to_selection`([save_to])	Generate JSON file which can be loaded in CATMAID selection tables.
`unmix`()	Split into NeuronLists of the same neuron type.

Attributes

`bbox`	Bounding box across all neurons in the list.
`empty`	Return True if NeuronList is empty.
`is_degenerated`	Return True if contains neurons with non-unique IDs.
`is_mixed`	Return True if contains more than one type of neuron.
`neurons`	Neurons contained in this NeuronList.
`shape`	Shape of NeuronList (N, ).
`types`	Return neuron types present in this list.
`cable_length`
`soma`
`root`
`graph`
`igraph`