navis.interfaces.r.nblast

navis.interfaces.r.nblast(query, target=None, scores='forward', n_cores=0, normalized=True, use_alpha=False, k=5, resample=None)

NBLAST using R’s nat.nblast::nblast.

Please note that NBLAST is optimized for units in microns.

Parameters:

• query (TreeNeuron | NeuronList | Dotprops | nat.neuron) – Query neuron(s) to NBLAST against the targets.

• target (TreeNeuron | NeuronList | Dotprops | nat.neuron | str) –

  Neuron(s) to run the query against. If a str, must be either:

  1. the name of a file in 'flycircuit.datadir'

  2. a path (e.g. '.../gmrdps.rds')

  3. an R file object (e.g. robjects.r("load('.../gmrdps.rds')"))

  4. a URL to load the list from (e.g. 'http://.../gmrdps.rds')

  5. ”flycircuit”

• scores ('forward' | 'mean' | 'min' | 'max') –

  Determines the final scores:

  • ’forward’ (default) returns query->target scores

  • ’mean’ returns the mean of query->target and target->query scores

  • ’min’ returns the minium between query->target and target->query scores

  • ’max’ returns the maximum between query->target and target->query scores

• n_cores (int, optional) – Number of cores to use for nblasting. Default is os.cpu_count() - 2.

• use_alpha (bool, optional) – Emphasizes neurons’ straight parts (backbone) over parts that have lots of branches.

• normalized (bool, optional) – Whether to return normalized NBLAST scores.

• k (int, optional) – Number of nearest neighbors to use for dotprops generation. Only relevant if input data is not already Dotprops.

• resample (int | bool, optional) – Resampling during dotprops generation. A good value is 1 which means if data is in microns (which it should!) it will be resampled to 1 tangent vector per micron. Only relevant if input data is not already Dotprops.

Returns:

scores – Matrix with NBLAST scores. Rows are query neurons, columns are targets.

Return type:

pandas.DataFrame

Examples

NBLAST example neurons against flycircuit DB

>>> import navis
>>> from navis.interfaces import r
>>> nl = navis.example_neurons(n=5)
>>> nl.units
array([8, 8, 8, 8, 8]) <Unit('nanometer')>
>>> # Convert to microns
>>> nl_um = nl / 1000
>>> # Run the nblast
>>> scores = r.nblast(nl_um)