diff --git a/docs/_static/dotprops.png b/docs/_static/dotprops.png new file mode 100644 index 00000000..30486528 Binary files /dev/null and b/docs/_static/dotprops.png differ diff --git a/docs/_static/mesh.png b/docs/_static/mesh.png new file mode 100644 index 00000000..20ef6e19 Binary files /dev/null and b/docs/_static/mesh.png differ diff --git a/docs/_static/skeleton.png b/docs/_static/skeleton.png new file mode 100644 index 00000000..2d3f0e1e Binary files /dev/null and b/docs/_static/skeleton.png differ diff --git a/docs/examples/4_remote/plot_01_remote_cloudvolume.py b/docs/examples/4_remote/plot_01_remote_cloudvolume.py index 8a32ce5f..b4d33cae 100644 --- a/docs/examples/4_remote/plot_01_remote_cloudvolume.py +++ b/docs/examples/4_remote/plot_01_remote_cloudvolume.py @@ -78,3 +78,8 @@ sk = vol.skeleton.get([4335355146, 2913913713, 2137190164, 2268989790], as_navis=True) sk + +# %% +# !!! experiment "Try it out!" +# If you are working a lot with NeuroGlancer and need to e.g. generated or parse URLs, you might want to check out the +# [`nglscenes`](https://github.com/schlegelp/nglscenes) package. diff --git a/docs/examples/plot_01_neurons_intro.py b/docs/examples/plot_01_neurons_intro.py index 8de37438..466f6174 100644 --- a/docs/examples/plot_01_neurons_intro.py +++ b/docs/examples/plot_01_neurons_intro.py @@ -4,11 +4,12 @@ This tutorial will show you the different neuron types and how to work with them. -Depending your data/workflows you will use different ways to represent neurons. -If, for example, you work with light-level data you might end up with point clouds -or skeletons whereas modern connectomes typically provide meshes. +Depending your data/workflows, you will use different representations of neurons. +If, for example, you work with light-level data you might end up extracting point +clouds or neuron skeletons from image stacks. If, on the other hand, you work with +segmented EM data, you will typically work with meshes. -To cater for these different data types neurons in {{ navis }} come in four flavours: +To cater for these different representations, neurons in {{ navis }} come in four flavours: | Neuron type | Description | Core data | |-------------------------|-----------------------------------------------------------------------|-------------------------------------| @@ -17,13 +18,13 @@ | [`navis.VoxelNeuron`][] | An image represented by either a
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`][]: