navis.Dotprops¶

class navis.Dotprops(points, k, vect=None, alpha=None, units=None, **metadata)[source]¶

Neuron represented as points + local vectors.

Dotprops consist of points with x/y/z coordinates, a tangent vector and an alpha value describing the immediate neighbourhood (see also references).

Typically constructed using navis.make_dotprops().

References

Masse N.Y., Cachero S., Ostrovsky A., and Jefferis G.S.X.E. (2012). A mutual information approach to automate identification of neuronal clusters in Drosophila brain images. Frontiers in Neuroinformatics 6 (00021). doi: 10.3389/fninf.2012.00021

Parameters:

points (numpy array) – (N, 3) array of x/y/z coordinates.
k (int, optional) – Number of nearest neighbors for tangent vector calculation. This can be None or 0 but then vectors must be provided on initialization and can subsequently not be re-calculated. Typical values here are k=20 for dense (e.g. from light level data) and k=5 for sparse (e.g. from skeletons) point clouds.
vect (numpy array, optional) – (N, 3) array of vectors. If not provided will recalculate both vect and alpha using k.
alpha (numpy array, optional) – (N, ) array of alpha values. If not provided will recalculate both alpha and vect using k.
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 Dotprops Neuron.

__init__(points, k, vect=None, alpha=None, units=None, **metadata)[source]¶: Initialize Dotprops Neuron.

Methods

`__init__`(points, k[, vect, alpha, units])	Initialize Dotprops Neuron.
`convert_units`(to[, inplace])	Convert coordinates to different unit.
`copy`()	Return a copy of the dotprops.
`dist_dots`(other[, alpha, distance_upper_bound])	Query this Dotprops against another.
`downsample`([factor, inplace])	Downsample the neuron by given factor.
`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()`.
`recalculate_tangents`(k[, inplace])	Recalculate tangent vectors and alpha with a new `k`.
`snap`(locs[, to])	Snap xyz location(s) to closest point or synapse.
`summary`([add_props])	Get a summary of this neuron.
`to_skeleton`([scale_vec])	Turn dotprops into a skeleton.

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 clearing when neuron data changes
`alpha`	Alpha value for tangent vectors.
`bbox`	Bounding box (includes connectors).
`connectors`	Connector table.
`core_md5`	MD5 checksum of core data.
`datatables`	Names of all DataFrames attached to this neuron.
`extents`	Extents of neuron in x/y/z direction (includes connectors).
`id`	Hashable ID.
`is_isometric`	Test if neuron is isometric.
`is_locked`	Test if neuron is locked.
`is_stale`	Test if temporary attributes might be outdated.
`kdtree`	KDTree for points.
`label`	Label (e.g.
`name`	Neuron name.
`points`	Center of tangent vectors.
`postsynapses`	Table with postsynapses.
`presynapses`	Table with presynapses.
`sampling_resolution`	Mean distance between points.
`soma`	Index of soma point.
`type`	Neuron type.
`units`	Units for coordinate space.
`units_xyz`	Units for coordinate space.
`vect`	Tangent vectors.
`k`