crantpy.viz package

crantpy.viz package#

Submodules#

Module contents#

crantpy.viz.chunks_to_nm(xyz_ch, vol, voxel_resolution=[4, 4, 40])[source]#

Map a chunk location to Euclidean space. CV workaround Implemented here after Giacomo’s suggestion

Parameters:

  • xyz_ch (array-like) – (N, 3) array of chunk indices.

  • vol (cloudvolume.CloudVolume) – CloudVolume object associated with the chunked space.

  • voxel_resolution (list, optional) – Voxel resolution.

Returns:

(N, 3) array of spatial points.

Return type:

np.array

crantpy.viz.configure_urllib3_warning_suppression(enable=None)[source]#

Enable suppression of known cosmetic urllib3 warnings.

Trade-offs: Hiding warnings can make it harder to notice real connectivity issues. When enabled, only the specific connection pool message is filtered; other warnings remain visible. Does not call urllib3.disable_warnings().

Control via enable or environment variable CRANTPY_SUPPRESS_URLLIB3_WARNINGS.

Returns True if suppression is enabled, False otherwise.

Parameters:

enable (bool | None)

Return type:

bool

crantpy.viz.detect_soma_mesh(mesh)[source]#

Try detecting the soma based on vertex clusters.

Identifies dense vertex clusters that likely represent the soma.

Parameters:

mesh (trimesh.Trimesh) – Coordinates in nanometers. Mesh must not be downsampled for accurate detection.

Returns:

Array of vertex indices that belong to the detected soma region. Returns empty array if no soma is detected.

Return type:

np.ndarray

crantpy.viz.detect_soma_skeleton(s, min_rad=800, N=3)[source]#

Try detecting the soma based on radii.

Looks for consecutive nodes with large radii to identify soma. Includes additional checks to ensure the skeleton is valid.

Parameters:

  • s (navis.TreeNeuron) – The skeleton to analyze for soma detection.

  • min_rad (int, default 800) – Minimum radius for a node to be considered a soma candidate (in nm).

  • N (int, default 3) – Number of consecutive nodes with radius > min_rad needed to consider them soma candidates.

Returns:

Node ID of the detected soma, or None if no soma found.

Return type:

int or None

crantpy.viz.divide_local_neighbourhood(mesh, radius)[source]#

Divide the mesh into locally connected patches of a given size (overlapping).

Parameters:

  • mesh (trimesh.Trimesh) – The mesh to divide.

  • radius (float) – The radius (in mesh units) for local neighborhoods.

Returns:

Each set contains vertex indices belonging to a local patch.

Return type:

list of sets

crantpy.viz.get_skeletons(root_ids, dataset='latest', progress=True, omit_failures=None, max_threads=6, **kwargs)[source]#

Fetch skeletons for multiple neurons.

Tries to get precomputed skeletons first, then falls back to on-demand skeletonization if needed. if id more than one root_id, it will use the parallel skeletonization function.

Parameters:

  • root_ids (list of int or np.ndarray) – Root IDs of neurons to fetch skeletons for.

  • dataset (str, default 'latest') – Dataset to query against.

  • progress (bool, default True) – Show progress during fetching.

  • omit_failures (bool, optional) – None: raise exception on failures True: skip failed neurons False: return empty TreeNeuron for failed cases

  • max_threads (int, default 6) – Number of parallel threads for fetching skeletons.

  • **kwargs – Additional arguments passed to skeletonization if needed.

Returns:

List of successfully fetched/generated skeletons.

Return type:

navis.NeuronList

crantpy.viz.get_soma_from_annotations(root_id, client, dataset=None)[source]#

Try to get soma location from nucleus annotations.

Parameters:

  • root_id (int) – Root ID of the neuron to get soma information for.

  • client (CAVEclient) – CAVE client for data access.

  • dataset (str, optional) – Dataset identifier (handled by decorators if not provided).

Returns:

(x, y, z) coordinates of the soma in nanometers, or None if not found.

Return type:

tuple or None

crantpy.viz.skeletonize_neuron(client, root_id, shave_skeleton=True, remove_soma_hairball=False, assert_id_match=False, threads=2, save_to=None, progress=True, use_pcg_skel=False, **kwargs)[source]#

Skeletonize a neuron the main function.

Parameters:

  • client (CAVEclient) – CAVE client for data access.

  • root_id (int) – Root ID of the neuron to skeletonize.

  • shave_skeleton (bool, default True) – Remove small protrusions and bristles from skeleton (from my understanding).

  • remove_soma_hairball (bool, default False) – Remove the hairball mesh from the soma

  • assert_id_match (bool, default False) – Verify skeleton nodes map to correct segment ID.

  • threads (int, default 2) – Number of parallel threads for mesh processing.

  • save_to (str, optional) – Save skeleton as SWC file to this path.

  • progress (bool, default True) – Show progress bars during processing.

  • use_pcg_skel (bool, default False) – Try pcg_skel first before skeletor (CAVE-client skeletonization).

  • **kwargs – Additional arguments for skeletonization algorithms.

Returns:

  • navis.TreeNeuron – The skeletonized neuron.

  • # TODOs from fafbseg

  • # - Use synapse locations as constraints

  • # - Mesh preprocessing options

  • # - Chunked skeletonization for large meshes

  • # - Use soma annotations from external sources

  • # - Better error handling/logging

  • # - Allow user-supplied soma location/radius

  • # - Option to return intermediate results

  • # - Support more skeletonization algorithms

  • # - Merge disconnected skeletons

  • # - Custom node/edge attributes

Return type:

navis.TreeNeuron | navis.NeuronList

crantpy.viz.skeletonize_neurons_parallel(client, root_ids, n_cores=None, progress=True, color_map=None, **kwargs)[source]#

Skeletonize multiple neurons in parallel.

Parameters:

  • client (CAVEclient) – CAVE client for data access.

  • root_ids (list of int or np.ndarray) – Root IDs of neurons to skeletonize.

  • n_cores (int, optional) – Number of cores to use. If None, uses half of available cores.

  • progress (bool, default True) – Show progress bars during processing.

  • color_map (str, optional) – Generate colors for each neuron using this colormap. Returns tuple of (neurons, colors) instead of just neurons.

  • **kwargs – Additional arguments passed to skeletonize_neuron.

Returns:

NeuronList of skeletonized neurons, or tuple of (NeuronList, colors) if color_map is specified.

Return type:

navis.NeuronList or tuple

crantpy.viz.suppress_urllib3_connectionpool_warnings()[source]#

Context manager to temporarily suppress urllib3 connectionpool messages.

Only filters the specific “Connection pool is full, discarding connection” warnings while inside the context.

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