Merge pull request #166 from navis-org/read_nrrd

Various improvements

docs/api.md

@@ -124,9 +124,12 @@ In addition, a [`navis.TreeNeuron`][] has a range of different properties:
 
 | Method | Description |
 |--------|-------------|
+| [`TreeNeuron.adjacency_matrix`][navis.TreeNeuron.adjacency_matrix] | {{ autosummary("navis.TreeNeuron.adjacency_matrix") }} |
 | [`TreeNeuron.cable_length`][navis.TreeNeuron.cable_length] | {{ autosummary("navis.TreeNeuron.cable_length") }} |
 | [`TreeNeuron.cycles`][navis.TreeNeuron.cycles] | {{ autosummary("navis.TreeNeuron.cycles") }} |
 | [`TreeNeuron.downsample`][navis.TreeNeuron.downsample] | {{ autosummary("navis.TreeNeuron.downsample") }} |
+| [`TreeNeuron.edges`][navis.TreeNeuron.edges] | {{ autosummary("navis.TreeNeuron.edges") }} |
+| [`TreeNeuron.edge_coords`][navis.TreeNeuron.edge_coords] | {{ autosummary("navis.TreeNeuron.edge_coords") }} |
 | [`TreeNeuron.igraph`][navis.TreeNeuron.igraph] | {{ autosummary("navis.TreeNeuron.igraph") }} |
 | [`TreeNeuron.is_tree`][navis.TreeNeuron.is_tree] | {{ autosummary("navis.TreeNeuron.is_tree") }} |
 | [`TreeNeuron.n_branches`][navis.TreeNeuron.n_branches] | {{ autosummary("navis.TreeNeuron.n_branches") }} |
@@ -140,9 +143,22 @@ In addition, a [`navis.TreeNeuron`][] has a range of different properties:
 | [`TreeNeuron.simple`][navis.TreeNeuron.simple] | {{ autosummary("navis.TreeNeuron.simple") }} |
 | [`TreeNeuron.soma_pos`][navis.TreeNeuron.soma_pos] | {{ autosummary("navis.TreeNeuron.soma_pos") }} |
 | [`TreeNeuron.subtrees`][navis.TreeNeuron.subtrees] | {{ autosummary("navis.TreeNeuron.subtrees") }} |
+| [`TreeNeuron.vertices`][navis.TreeNeuron.vertices] | {{ autosummary("navis.TreeNeuron.vertices") }} |
 | [`TreeNeuron.volume`][navis.TreeNeuron.volume] | {{ autosummary("navis.TreeNeuron.volume") }} |
 
 
+#### Skeleton utility functions
+
+| Function | Description |
+|----------|-------------|
+| [`navis.rewire_skeleton()`][navis.rewire_skeleton] | {{ autosummary("navis.rewire_skeleton") }} |
+| [`navis.insert_nodes()`][navis.insert_nodes] | {{ autosummary("navis.insert_nodes") }} |
+| [`navis.remove_nodes()`][navis.remove_nodes] | {{ autosummary("navis.remove_nodes") }} |
+| [`navis.graph.simplify_graph()`][navis.graph.simplify_graph] | {{ autosummary("navis.graph.simplify_graph") }} |
+| [`navis.graph.skeleton_adjacency_matrix()`][navis.graph.skeleton_adjacency_matrix] | {{ autosummary("navis.graph.skeleton_adjacency_matrix") }} |
+
+
+
 ### Mesh neurons
 
 Properties specific to [`navis.MeshNeuron`][]:
@@ -173,10 +189,16 @@ These are methods and properties specific to [VoxelNeurons][navis.VoxelNeuron]:
 
 | Property | Description |
 |------|------|
+| [`VoxelNeuron.density`][navis.VoxelNeuron.density] | {{ autosummary("navis.VoxelNeuron.density") }} |
 | [`VoxelNeuron.grid`][navis.VoxelNeuron.grid] | {{ autosummary("navis.VoxelNeuron.grid") }} |
-| [`VoxelNeuron.voxels`][navis.VoxelNeuron.voxels] | {{ autosummary("navis.VoxelNeuron.voxels") }} |
+| [`VoxelNeuron.max`][navis.VoxelNeuron.max] | {{ autosummary("navis.VoxelNeuron.max") }} |
+| [`VoxelNeuron.min`][navis.VoxelNeuron.min] | {{ autosummary("navis.VoxelNeuron.min") }} |
+| [`VoxelNeuron.nnz`][navis.VoxelNeuron.nnz] | {{ autosummary("navis.VoxelNeuron.nnz") }} |
+| [`VoxelNeuron.offset`][navis.VoxelNeuron.offset] | {{ autosummary("navis.VoxelNeuron.offset") }} |
 | [`VoxelNeuron.shape`][navis.VoxelNeuron.shape] | {{ autosummary("navis.VoxelNeuron.shape") }} |
 | [`VoxelNeuron.strip()`][navis.VoxelNeuron.strip] | {{ autosummary("navis.VoxelNeuron.strip") }} |
+| [`VoxelNeuron.threshold()`][navis.VoxelNeuron.threshold] | {{ autosummary("navis.VoxelNeuron.threshold") }} |
+| [`VoxelNeuron.voxels`][navis.VoxelNeuron.voxels] | {{ autosummary("navis.VoxelNeuron.voxels") }} |
 
 
 ### Dotprops
@@ -207,6 +229,8 @@ These functions will let you convert between neuron types:
 | [`navis.conversion.voxels2mesh()`][navis.conversion.voxels2mesh] | {{ autosummary("navis.conversion.voxels2mesh") }} |
 | [`navis.conversion.tree2meshneuron()`][navis.conversion.tree2meshneuron] | {{ autosummary("navis.conversion.tree2meshneuron") }} |
 
+See also [Utility](#utility) for functions to convert to/from basic data types.
+
 ### NeuronList methods
 
 [`NeuronLists`][navis.NeuronList] let you access all the properties and methods of the neurons
@@ -346,6 +370,7 @@ Functions to edit morphology:
 | [`navis.smooth_skeleton()`][navis.smooth_skeleton] | {{ autosummary("navis.smooth_skeleton") }} |
 | [`navis.smooth_mesh()`][navis.smooth_mesh] | {{ autosummary("navis.smooth_mesh") }} |
 | [`navis.smooth_voxels()`][navis.smooth_voxels] | {{ autosummary("navis.smooth_voxels") }} |
+| [`navis.thin_voxels()`][navis.thin_voxels] | {{ autosummary("navis.thin_voxels") }} |
 
 
 ### Resampling
@@ -508,22 +533,6 @@ Collection of functions to work with graphs and adjacency matrices.
 |----------|-------------|
 | [`navis.NeuronConnector`][] | {{ autosummary("navis.NeuronConnector") }} |
 
-### Graphs
-
-Functions to convert between neurons graph representation (networkx or iGraph).
-
-| Function | Description |
-|----------|-------------|
-| [`navis.neuron2nx()`][navis.neuron2nx] | {{ autosummary("navis.neuron2nx") }} |
-| [`navis.neuron2igraph()`][navis.neuron2igraph] | {{ autosummary("navis.neuron2igraph") }} |
-| [`navis.neuron2KDTree()`][navis.neuron2KDTree] | {{ autosummary("navis.neuron2KDTree") }} |
-| [`navis.network2nx()`][navis.network2nx] | {{ autosummary("navis.network2nx") }} |
-| [`navis.network2igraph()`][navis.network2igraph] | {{ autosummary("navis.network2igraph") }} |
-| [`navis.rewire_skeleton()`][navis.rewire_skeleton] | {{ autosummary("navis.rewire_skeleton") }} |
-| [`navis.insert_nodes()`][navis.insert_nodes] | {{ autosummary("navis.insert_nodes") }} |
-| [`navis.remove_nodes()`][navis.remove_nodes] | {{ autosummary("navis.remove_nodes") }} |
-| [`navis.graph.simplify_graph()`][navis.graph.simplify_graph] | {{ autosummary("navis.graph.simplify_graph") }} |
-
 ### Connectivity metrics
 
 Functions to analyse/cluster neurons based on connectivity.
@@ -581,6 +590,21 @@ Various utility functions.
 | [`navis.example_neurons()`][navis.example_neurons] | {{ autosummary("navis.example_neurons") }} |
 | [`navis.example_volume()`][navis.example_volume] | {{ autosummary("navis.example_volume") }} |
 
+### Conversion
+
+Functions to convert between data types.
+
+| Function | Description |
+|----------|-------------|
+| [`navis.neuron2nx()`][navis.neuron2nx] | {{ autosummary("navis.neuron2nx") }} |
+| [`navis.neuron2igraph()`][navis.neuron2igraph] | {{ autosummary("navis.neuron2igraph") }} |
+| [`navis.neuron2KDTree()`][navis.neuron2KDTree] | {{ autosummary("navis.neuron2KDTree") }} |
+| [`navis.neuron2tangents()`][navis.neuron2tangents] | {{ autosummary("navis.neuron2tangents") }} |
+| [`navis.network2nx()`][navis.network2nx] | {{ autosummary("navis.network2nx") }} |
+| [`navis.network2igraph()`][navis.network2igraph] | {{ autosummary("navis.network2igraph") }} |
+| [`navis.nx2neuron()`][navis.nx2neuron] | {{ autosummary("navis.nx2neuron") }} |
+| [`navis.edges2neuron()`][navis.edges2neuron] | {{ autosummary("navis.edges2neuron") }} |
+
 ## Network Models
 
 {{ navis }} comes with a simple network traversal model (used in [Schlegel, Bates et al., 2021](https://elifesciences.org/articles/66018)).

docs/examples/0_io/tutorial_io_00_skeletons.py

@@ -16,6 +16,9 @@
     (e.g. the MICrONS, neuromorpho, Virtual Fly Brain or Janelia hemibrain datasets).
     These are covered in separate [tutorials](../../gallery).
 
+    If you have light-level microscopy data, you might also be interested in the
+    tutorial on [skeletons from light-level data](../zzz_tutorial_io_05_skeletonize).
+
 ## From SWC files
 
 SWC is a common format for storing neuron skeletons. Thus {{ navis }} provides functions to both
@@ -182,8 +185,4 @@
 #
 # Please also keep in mind that you can also convert one neuron type into another - for example by skeletonizing [`MeshNeurons`][navis.MeshNeuron]
 # (see also the API reference on [neuron conversion](../../../api.md#converting-between-types)).
-#
-# If you have light-level microscopy data, you might also be interested in the
-# [tutorial on skeletons from light-level data](../zzz_tutorial_io_05_skeletonize).
-
 

docs/examples/0_io/tutorial_io_02_dotprops.py

@@ -2,34 +2,98 @@
 Dotprops
 ========
 
-This tutorial will show you have to work with Dotprops.
+This tutorial will show you how to load/create Dotprops.
 
 [`navis.Dotprops`][] are point clouds with associated principal vectors which are mostly used for
 NBLASTing. They are typically derivatives of skeletons or meshes but you can load them straight from
-confocal data using [`navis.read_nrrd`][]:
+e.g. confocal image stacks using [`navis.read_nrrd`][] or [`navis.read_tiff`][].
+
+![dotprops](../../../../_static/dotprops.png)
 """
 
 # %%
 import navis
+import matplotlib.pyplot as plt
 
 # %%
 # ## From image data
 #
-# For this example I downloaded one of Janelia's Fly Light confocal stacks ([link](https://splitgal4.janelia.org/))
-# and converted it to NRRD format using [ImageJ](https://imagej.net/ij/).
+# For this example we will use a stack from [Janelia's split Gal4 collection](https://splitgal4.janelia.org/).
+# This `LH2094` line is also available from [Virtual Fly Brain](https://v2.virtualflybrain.org/org.geppetto.frontend/geppetto?id=VFB_00102926&i=VFB_00101567,VFB_00102926)
+# where, conveniently, they can be downloaded in NRRD format which we can directly read into {{ navis }}.
+#
+# Let's do this step-by-step first:
+
+# Load raw NRRD image
+im, header = navis.read_nrrd(
+    "https://v2.virtualflybrain.org/data/VFB/i/0010/2926/VFB_00101567/volume.nrrd",
+    output="raw"
+)
+
+# Plot a maximum projection
+max_proj = im.max(axis=2)
+plt.imshow(
+    max_proj.T,
+    extent=(0, int(0.5189 * 1210), (0.5189 * 566), 0),  # extent is calculated from the spacing (see `header`) times the no of x/y pixels
+    cmap='Greys_r',
+    vmax=10  # make it really bright so we can see neurons + outline of the brain
+    )
+
+# %%
+# At this point we could threshold the image, extract above-threshold voxels and convert them to a Dotprops object.
+# However, the easier option is to use [`navis.read_nrrd`][] with the `output="dotprops"` parameter:
+
+dp = navis.read_nrrd(
+    "https://v2.virtualflybrain.org/data/VFB/i/0010/2926/VFB_00101567/volume.nrrd",
+    output="dotprops",
+    threshold=5,  # threshold to determine which voxels are used for the dotprops
+    thin=True,   # see note below on this parameter!
+    k=10  # number of neighbours to consider when calculating the tangent vector
+)
+
+# %%
+# !!! note "Thinning"
+#     In the above [`read_nrrd`][navis.read_nrrd] call we used `thin=True`. This is a post-processing step that
+#     thins the image to a single pixel width. This will produce "cleaner" dotprops but can also remove denser
+#     neurites thus emphasizing the backbone of the neuron. This option requires the `scikit-image` package:
+#
+#     ```bash
+#     pip install scikit-image
+#     ```
 #
-# Load NRRD file into Dotprops instead of VoxelNeuron:
-# ```python
-# dp = navis.read_nrrd(
-#    "~/Downloads/JRC_SS86025_JRC_SS86025-20211112_49_B6.nrrd",
-#    output="dotprops",
-#    threshold=3000,
-# )
-# ```
+#  Let's overlay the dotprops on the maximum projection:
+
+fig, ax = plt.subplots()
+ax.imshow(
+    max_proj.T,
+    extent=(0, int(0.5189 * 1210), (0.5189 * 566), 0),
+    cmap='Greys_r',
+    vmax=10
+    )
+navis.plot2d(dp, ax=ax, view=("x", "-y"), method="2d", color="r", linewidth=1.5)
+
+# %%
+# This looks pretty good but we have a bit of little fluff around the brain which we may want to get rid off:
+
+# Drop everything but the two largest connected components
+dp = navis.drop_fluff(dp, n_largest=2)
+
+# Plot again
+fig, ax = plt.subplots()
+ax.imshow(
+    max_proj.T,
+    extent=(0, int(0.5189 * 1210), (0.5189 * 566), 0),
+    cmap='Greys_r',
+    vmax=10
+    )
+navis.plot2d(dp, ax=ax, view=("x", "-y"), method="2d", color="r", linewidth=1.5)
 
 # %%
 # !!! note
-#     Note the threshold parameter? It determines which voxels (by brightness) are used and which are ignored!
+#     To extract the connected components, [`navis.drop_fluff`][] treats all pairs of points within a certain distance
+#     as connected. The distance is determined by the `dp_dist` parameter which defaults to 5 x the average distance
+#     between points. This is a good value ehre but you may need adjust it for your data.
+#
 #
 # ## From other neurons
 #