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2d array of voxels or a 3d voxel grid. | - `.voxels`: `(N, 3)` array of voxels
- `.values`: `(N, )` array of values (i.e. intensity)
- `.grid`: `(N, M, K)` 3D voxelgrid | | [`navis.Dotprops`][] | A cloud of points, each with an
associated local vector. | - `.points`: `(N, 3)` array of point coordinates
- `.vect`: `(N, 3)` array of normalized vectors | -Note that some functions in {{ navis }} will work on some but not all neuron types: -checkout this [table](../../api.md#neuron-types-and-functions) in the [API](../../api.md) -reference for details. If need be, {{ navis }} also offers ways to convert between the +Note that functions in {{ navis }} may only work on a subset of neuron types: +check out this [table](../../api.md#neuron-types-and-functions) in the [API](../../api.md) +reference for details. If necessary, {{ navis }} can help you convert between the different neuron types (see further [below](#converting-neuron-types))! !!! important - In this guide we introduce the different neuron types using canned data bundled with {{ navis }}. + In this guide we introduce the different neuron types using data bundled with {{ navis }}. To learn how to load your own neurons into {{ navis }} please see the tutorials on [Import/Export](../../gallery#import-export). @@ -34,8 +35,10 @@ This format is commonly used to describe a neuron's topology and often shared using [SWC](http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html) files. -A [`navis.TreeNeuron`][] is typically constructed from an SWC file (see [`navis.read_swc`][]) -but you can also use a `pandas.DataFrame` or a `networkx.DiGraph`. +![skeleton](../../../_static/skeleton.png) + +A [`navis.TreeNeuron`][] is typically loaded from an SWC file via [`navis.read_swc`][] +but you can also constructed one yourself from e.g. `pandas.DataFrame` or a `networkx.DiGraph`. See the [skeleton I/O](../local_data_skels_tut.md) tutorial for details. {{ navis }} ships with a couple example *Drosophila* neurons from the Janelia hemibrain project published @@ -63,6 +66,8 @@ # # [`MeshNeurons`][navis.MeshNeuron] consist of vertices and faces, and are a typical output of e.g. image segmentation. # +# ![mesh](../../../_static/mesh.png) +# # A [`navis.MeshNeuron`][] can be constructed from any object that has `.vertices` and `.faces` properties, a # dictionary of `vertices` and `faces` or a file that can be parsed by `trimesh.load`. # See the [mesh I/O](../local_data_meshes_tut.md) tutorial for details. @@ -84,8 +89,12 @@ # # [`Dotprops`][navis.Dotprops] represent neurons as point clouds where each point is associated with a vector # describing the local orientation. This simple representation often comes from e.g. light-level data -# or as direvative of skeletons/meshes (see [`navis.make_dotprops`][]). Dotprops are used e.g. for -# [NBLAST](../nblast_intro.md). See the [dotprops I/O](../local_data_dotprops_tut) tutorial for details. +# or as direvative of skeletons/meshes (see [`navis.make_dotprops`][]). +# +# ![dotprops](../../../_static/dotprops.png) +# +# Dotprops are used e.g. for [NBLAST](../nblast_intro.md). See the [dotprops I/O](../local_data_dotprops_tut) +# tutorial for details. # # [`navis.Dotprops`][] consist of `.points` and associated `.vect` (vectors). They are typically # created from other types of neurons using [`navis.make_dotprops`][]:

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