Source code for navis.core.skeleton

#    This script is part of navis (http://www.github.com/navis-org/navis).
#    Copyright (C) 2018 Philipp Schlegel
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.

import copy
import functools
import numbers
import pint
import types
import warnings

import networkx as nx
import numpy as np
import pandas as pd
import skeletor as sk

from io import BufferedIOBase

from typing import Union, Callable, List, Sequence, Optional, Dict, overload
from typing_extensions import Literal

from .. import graph, morpho, utils, config, core, sampling, intersection
from .. import io  # type: ignore # double import

from .base import BaseNeuron
from .core_utils import temp_property

try:
    import xxhash
except ImportError:
    xxhash = None

__all__ = ['TreeNeuron']

# Set up logging
logger = config.get_logger(__name__)

# This is to prevent pint to throw a warning about numpy integration
with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    pint.Quantity([])


def requires_nodes(func):
    """Return ``None`` if neuron has no nodes."""
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        self = args[0]
        # Return 0
        if isinstance(self.nodes, str) and self.nodes == 'NA':
            return 'NA'
        if not isinstance(self.nodes, pd.DataFrame):
            return None
        return func(*args, **kwargs)
    return wrapper


[docs] class TreeNeuron(BaseNeuron): """Neuron represented as hierarchical tree (i.e. a skeleton). Parameters ---------- x Data to construct neuron from: - ``pandas.DataFrame`` is expected to be SWC table - ``pandas.Series`` is expected to have a DataFrame as ``.nodes`` - additional properties will be attached as meta data - ``str`` filepath is passed to :func:`navis.read_swc` - ``BufferedIOBase`` e.g. from ``open(filename)`` - ``networkx.DiGraph`` parsed by `navis.nx2neuron` - ``None`` will initialize an empty neuron - ``skeletor.Skeleton`` - ``TreeNeuron`` - in this case we will try to copy every attribute 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. """ nodes: pd.DataFrame graph: 'nx.DiGraph' igraph: 'igraph.Graph' # type: ignore # doesn't know iGraph n_branches: int n_leafs: int cable_length: Union[int, float] segments: List[list] small_segments: List[list] root: np.ndarray soma: Optional[Union[int, str]] soma_pos: Optional[Sequence] #: Minimum radius for soma detection. Set to ``None`` if no tag needed. #: Default = 1 micron soma_detection_radius: Union[float, int, pint.Quantity] = 1 * config.ureg.um #: Label for soma detection. Set to ``None`` if no tag needed. Default = 1. soma_detection_label: Union[float, int, str] = 1 #: Soma radius (e.g. for plotting). If string, must be column in nodes #: table. Default = 'radius'. soma_radius: Union[float, int, str] = 'radius' # Set default function for soma finding. Default = :func:`navis.morpho.find_soma` _soma: Union[Callable[['TreeNeuron'], Sequence[int]], int] = morpho.find_soma tags: Optional[Dict[str, List[int]]] = None #: Attributes to be used when comparing two neurons. EQ_ATTRIBUTES = ['n_nodes', 'n_connectors', 'soma', 'root', 'n_branches', 'n_leafs', 'cable_length', 'name'] #: Temporary attributes that need to be regenerated when data changes. TEMP_ATTR = ['_igraph', '_graph_nx', '_segments', '_small_segments', '_geodesic_matrix', 'centrality_method', '_simple', '_cable_length', '_memory_usage'] #: Attributes used for neuron summary SUMMARY_PROPS = ['type', 'name', 'n_nodes', 'n_connectors', 'n_branches', 'n_leafs', 'cable_length', 'soma', 'units'] #: Core data table(s) used to calculate hash CORE_DATA = ['nodes:node_id,parent_id,x,y,z']
[docs] def __init__(self, x: Union[pd.DataFrame, BufferedIOBase, str, 'TreeNeuron', nx.DiGraph], units: Union[pint.Unit, str] = None, **metadata ): """Initialize Skeleton Neuron.""" super().__init__() # Lock neuron during construction self._lock = 1 if isinstance(x, pd.DataFrame): self.nodes = x elif isinstance(x, pd.Series): if not hasattr(x, 'nodes'): raise ValueError('pandas.Series must have `nodes` entry.') elif not isinstance(x.nodes, pd.DataFrame): raise TypeError(f'Nodes must be pandas DataFrame, got "{type(x.nodes)}"') self.nodes = x.nodes metadata.update(x.to_dict()) elif isinstance(x, nx.Graph): self.nodes = graph.nx2neuron(x).nodes elif isinstance(x, BufferedIOBase) or isinstance(x, str): x = io.read_swc(x) # type: ignore self.__dict__.update(x.__dict__) elif isinstance(x, sk.Skeleton): self.nodes = x.swc.copy() self.vertex_map = x.mesh_map elif isinstance(x, TreeNeuron): self.__dict__.update(x.copy().__dict__) # Try to copy every attribute for at in self.__dict__: try: setattr(self, at, copy.copy(getattr(self, at))) except BaseException: logger.warning(f'Unable to deep-copy attribute "{at}"') elif isinstance(x, type(None)): # This is a essentially an empty neuron pass else: raise utils.ConstructionError(f'Unable to construct TreeNeuron from "{type(x)}"') for k, v in metadata.items(): try: setattr(self, k, v) except AttributeError: raise AttributeError(f"Unable to set neuron's `{k}` attribute.") self.units = units self._current_md5 = self.core_md5 self._lock = 0
def __getattr__(self, key): """We will use this magic method to calculate some attributes on-demand.""" # Note that we're mixing @property and __getattr__ which causes problems: # if a @property raises an Exception, Python falls back to __getattr__ # and traceback is lost! # Last ditch effort - maybe the base class knows the key? return super().__getattr__(key) def __truediv__(self, other, copy=True): """Implement division for coordinates (nodes, connectors).""" if isinstance(other, numbers.Number) or utils.is_iterable(other): if utils.is_iterable(other): # If divisor is isotropic use only single value if len(set(other)) == 1: other == other[0] elif len(other) != 4: raise ValueError('Division by list/array requires 4 ' 'divisors for x/y/z and radius - ' f'got {len(other)}') # If a number, consider this an offset for coordinates n = self.copy() if copy else self n.nodes.loc[:, ['x', 'y', 'z', 'radius']] /= other # At this point we can ditch any 4th unit if utils.is_iterable(other): other = other[:3] if n.has_connectors: n.connectors.loc[:, ['x', 'y', 'z']] /= other if hasattr(n, 'soma_radius'): if isinstance(n.soma_radius, numbers.Number): n.soma_radius /= other # Convert units # Note: .to_compact() throws a RuntimeWarning and returns unchanged # values when `units` is a iterable with warnings.catch_warnings(): warnings.simplefilter("ignore") n.units = (n.units * other).to_compact() n._clear_temp_attr(exclude=['classify_nodes']) return n return NotImplemented def __mul__(self, other, copy=True): """Implement multiplication for coordinates (nodes, connectors).""" if isinstance(other, numbers.Number) or utils.is_iterable(other): if utils.is_iterable(other): # If multiplicator is isotropic use only single value if len(set(other)) == 1: other == other[0] elif len(other) != 4: raise ValueError('Multiplication by list/array requires 4' 'multipliers for x/y/z and radius - ' f'got {len(other)}') # If a number, consider this an offset for coordinates n = self.copy() if copy else self n.nodes.loc[:, ['x', 'y', 'z', 'radius']] *= other # At this point we can ditch any 4th unit if utils.is_iterable(other): other = other[:3] if n.has_connectors: n.connectors.loc[:, ['x', 'y', 'z']] *= other if hasattr(n, 'soma_radius'): if isinstance(n.soma_radius, numbers.Number): n.soma_radius *= other # Convert units # Note: .to_compact() throws a RuntimeWarning and returns unchanged # values when `units` is a iterable with warnings.catch_warnings(): warnings.simplefilter("ignore") n.units = (n.units / other).to_compact() n._clear_temp_attr(exclude=['classify_nodes']) return n return NotImplemented def __getstate__(self): """Get state (used e.g. for pickling).""" state = {k: v for k, v in self.__dict__.items() if not callable(v)} # Pickling the graphs actually takes longer than regenerating them # from scratch if '_graph_nx' in state: _ = state.pop('_graph_nx') if '_igraph' in state: _ = state.pop('_igraph') return state @property @requires_nodes def edges(self) -> np.ndarray: """Edges between nodes. See Also -------- edge_coords Same but with x/y/z coordinates instead of node IDs. """ not_root = self.nodes[self.nodes.parent_id >= 0] return not_root[['node_id', 'parent_id']].values @property def edge_coords(self) -> np.ndarray: """Coordinates of edges between nodes. See Also -------- edges Same but with node IDs instead of x/y/z coordinates. """ locs = self.nodes.set_index('node_id')[['x', 'y', 'z']] edges = self.edges edges_co = np.zeros((edges.shape[0], 2, 3)) edges_co[:, 0, :] = locs.loc[edges[:, 0]].values edges_co[:, 1, :] = locs.loc[edges[:, 1]].values return edges_co @temp_property def igraph(self) -> 'igraph.Graph': """iGraph representation of this neuron.""" # If igraph does not exist, create and return if not hasattr(self, '_igraph'): # This also sets the attribute return self.get_igraph() return self._igraph @temp_property def graph(self) -> nx.DiGraph: """Networkx Graph representation of this neuron.""" # If graph does not exist, create and return if not hasattr(self, '_graph_nx'): # This also sets the attribute return self.get_graph_nx() return self._graph_nx @temp_property def geodesic_matrix(self): """Matrix with geodesic (along-the-arbor) distance between nodes.""" # If matrix has not yet been generated or needs update if not hasattr(self, '_geodesic_matrix'): # (Re-)generate matrix self._geodesic_matrix = graph.geodesic_matrix(self) return self._geodesic_matrix @property @requires_nodes def leafs(self) -> pd.DataFrame: """Leaf node table.""" return self.nodes[self.nodes['type'] == 'end'] @property @requires_nodes def ends(self): """End node table (same as leafs).""" return self.leafs @property @requires_nodes def branch_points(self): """Branch node table.""" return self.nodes[self.nodes['type'] == 'branch'] @property def nodes(self) -> pd.DataFrame: """Node table.""" return self._get_nodes() def _get_nodes(self) -> pd.DataFrame: # Redefine this function in subclass to change how nodes are retrieved return self._nodes @nodes.setter def nodes(self, v): """Validate and set node table.""" # We are refering to an extra function to facilitate subclassing: # Redefine _set_nodes() to not break property self._set_nodes(v) def _set_nodes(self, v): # Redefine this function in subclass to change validation self._nodes = utils.validate_table(v, required=[('node_id', 'rowId', 'node', 'treenode_id', 'PointNo'), ('parent_id', 'link', 'parent', 'Parent'), ('x', 'X'), ('y', 'Y'), ('z', 'Z')], rename=True, optional={('radius', 'W'): 0}, restrict=False) graph.classify_nodes(self) @property def n_trees(self) -> int: """Count number of connected trees in this neuron.""" return len(self.subtrees) @property def is_tree(self) -> bool: """Whether neuron is a tree. Also returns True if neuron consists of multiple separate trees! See also -------- networkx.is_forest() Function used to test whether neuron is a tree. :attr:`TreeNeuron.cycles` If your neuron is not a tree, this will help you identify cycles. """ return nx.is_forest(self.graph) @property def subtrees(self) -> List[List[int]]: """List of subtrees. Sorted by size as sets of node IDs.""" return sorted(graph._connected_components(self), key=lambda x: -len(x)) @property def connectors(self) -> pd.DataFrame: """Connector table. If none, will return ``None``.""" return self._get_connectors() def _get_connectors(self) -> pd.DataFrame: # Redefine this function in subclass to change how nodes are retrieved return getattr(self, '_connectors', None) @connectors.setter def connectors(self, v): """Validate and set connector table.""" # We are refering to an extra function to facilitate subclassing: # Redefine _set_connectors() to not break property self._set_connectors(v) def _set_connectors(self, v): # Redefine this function in subclass to change validation if isinstance(v, type(None)): self._connectors = None else: self._connectors = utils.validate_table(v, required=[('connector_id', 'id'), ('node_id', 'rowId', 'node', 'treenode_id'), ('x', 'X'), ('y', 'Y'), ('z', 'Z'), ('type', 'relation', 'label', 'prepost')], rename=True, restrict=False) @property @requires_nodes def cycles(self) -> Optional[List[int]]: """Cycles in neuron (if any). See also -------- networkx.find_cycles() Function used to find cycles. """ try: c = nx.find_cycle(self.graph, source=self.nodes[self.nodes.type == 'end'].node_id.values) return c except nx.exception.NetworkXNoCycle: return None except BaseException: raise @property def simple(self) -> 'TreeNeuron': """Simplified representation consisting only of root, branch points and leafs.""" if not hasattr(self, '_simple'): self._simple = self.downsample(float('inf'), inplace=False) return self._simple @property def soma(self) -> Optional[Union[str, int]]: """Search for soma and return node ID(s). ``None`` if no soma. You can assign either a function that accepts a TreeNeuron as input or a fix value. The default is :func:`navis.utils.find_soma`. """ if callable(self._soma): soma = self._soma.__call__() # type: ignore # say int not callable else: soma = self._soma # Sanity check to make sure that the soma node actually exists if isinstance(soma, type(None)): # Return immmediately without expensive checks return soma elif utils.is_iterable(soma): if all(pd.isnull(soma)): soma = None elif not any(self.nodes.node_id.isin(soma)): logger.warning(f'Soma(s) {soma} not found in node table.') soma = None else: if soma not in self.nodes.node_id.values: logger.warning(f'Soma {soma} not found in node table.') soma = None return soma @soma.setter def soma(self, value: Union[Callable, int, None]) -> None: """Set soma.""" if hasattr(value, '__call__'): self._soma = types.MethodType(value, self) elif isinstance(value, type(None)): self._soma = None elif isinstance(value, bool) and not value: self._soma = None else: if value in self.nodes.node_id.values: self._soma = value else: raise ValueError('Soma must be function, None or a valid node ID.') @property def soma_pos(self) -> Optional[Sequence]: """Search for soma and return its position. Returns ``None`` if no soma. You can also use this to assign a soma by position in which case it will snap to the closest node. """ # Sanity check to make sure that the soma node actually exists soma = self.soma if isinstance(soma, type(None)): return None elif utils.is_iterable(soma): if all(pd.isnull(soma)): return None else: soma = utils.make_iterable(soma) return self.nodes.loc[self.nodes.node_id.isin(soma), ['x', 'y', 'z']].values @soma_pos.setter def soma_pos(self, value: Sequence) -> None: """Set soma by position.""" try: value = np.asarray(value).astype(np.float64).reshape(3) except BaseException: raise ValueError(f'Unable to convert soma position "{value}" ' f'to numeric (3, ) numpy array.') # Generate tree id, dist = self.snap(value, to='nodes') # A sanity check if dist > (self.sampling_resolution * 10): logger.warning(f'New soma position for {self.id} is suspiciously ' f'far away from the closest node: {dist}') self.soma = id @property @requires_nodes def root(self) -> Sequence: """Root node(s).""" roots = self.nodes[self.nodes.parent_id < 0].node_id.values return roots @root.setter def root(self, value: Union[int, List[int]]) -> None: """Reroot neuron to given node.""" self.reroot(value, inplace=True) @property def type(self) -> str: """Neuron type.""" return 'navis.TreeNeuron' @property @requires_nodes def n_branches(self) -> Optional[int]: """Number of branch points.""" return self.nodes[self.nodes.type == 'branch'].shape[0] @property @requires_nodes def n_leafs(self) -> Optional[int]: """Number of leaf nodes.""" return self.nodes[self.nodes.type == 'end'].shape[0] @temp_property def cable_length(self) -> Union[int, float]: """Cable length.""" if not hasattr(self, '_cable_length'): # Simply sum up edge weight of all graph edges if config.use_igraph and self.igraph: w = self.igraph.es.get_attribute_values('weight') # type: ignore # doesn't know iGraph else: w = nx.get_edge_attributes(self.graph, 'weight').values() self._cable_length = np.nansum(list(w)) return self._cable_length @property def surface_area(self) -> float: """Radius-based lateral surface area.""" if 'radius' not in self.nodes.columns: raise ValueError(f'Neuron {self.id} does not have radius information') if any(self.nodes.radius < 0): logger.warning(f'Neuron {self.id} has negative radii - area will not be correct.') if any(self.nodes.radius.isnull()): logger.warning(f'Neuron {self.id} has NaN radii - area will not be correct.') # Generate radius dict radii = self.nodes.set_index('node_id').radius.to_dict() # Drop root node(s) not_root = self.nodes.parent_id >= 0 # For each cylinder get the height h = morpho.mmetrics.parent_dist(self, root_dist=0)[not_root] # Radii for top and bottom of tapered cylinder nodes = self.nodes[not_root] r1 = nodes.node_id.map(radii).values r2 = nodes.parent_id.map(radii).values return (np.pi * (r1 + r2) * np.sqrt( (r1-r2)**2 + h**2)).sum() @property def volume(self) -> float: """Radius-based volume.""" if 'radius' not in self.nodes.columns: raise ValueError(f'Neuron {self.id} does not have radius information') if any(self.nodes.radius < 0): logger.warning(f'Neuron {self.id} has negative radii - volume will not be correct.') if any(self.nodes.radius.isnull()): logger.warning(f'Neuron {self.id} has NaN radii - volume will not be correct.') # Generate radius dict radii = self.nodes.set_index('node_id').radius.to_dict() # Drop root node(s) not_root = self.nodes.parent_id >= 0 # For each cylinder get the height h = morpho.mmetrics.parent_dist(self, root_dist=0)[not_root] # Radii for top and bottom of tapered cylinder nodes = self.nodes[not_root] r1 = nodes.node_id.map(radii).values r2 = nodes.parent_id.map(radii).values return (1/3 * np.pi * (r1**2 + r1 * r2 + r2**2) * h).sum() @property def bbox(self) -> np.ndarray: """Bounding box (includes connectors).""" mn = np.min(self.nodes[['x', 'y', 'z']].values, axis=0) mx = np.max(self.nodes[['x', 'y', 'z']].values, axis=0) if self.has_connectors: cn_mn = np.min(self.connectors[['x', 'y', 'z']].values, axis=0) cn_mx = np.max(self.connectors[['x', 'y', 'z']].values, axis=0) mn = np.min(np.vstack((mn, cn_mn)), axis=0) mx = np.max(np.vstack((mx, cn_mx)), axis=0) return np.vstack((mn, mx)).T @property def sampling_resolution(self) -> float: """Average cable length between child -> parent nodes.""" return self.cable_length / self.n_nodes @temp_property def segments(self) -> List[list]: """Neuron broken down into linear segments (see also `.small_segments`).""" # Calculate if required if not hasattr(self, '_segments'): # This also sets the attribute self._segments = self._get_segments(how='length') return self._segments @temp_property def small_segments(self) -> List[list]: """Neuron broken down into small linear segments (see also `.segments`).""" # Calculate if required if not hasattr(self, '_small_segments'): # This also sets the attribute self._small_segments = self._get_segments(how='break') return self._small_segments def _get_segments(self, how: Union[Literal['length'], Literal['break']] = 'length' ) -> List[list]: """Generate segments for neuron.""" if how == 'length': return graph._generate_segments(self) elif how == 'break': return graph._break_segments(self) else: raise ValueError(f'Unknown method: "{how}"') @property def n_skeletons(self) -> int: """Number of seperate skeletons in this neuron.""" return len(self.root) def _clear_temp_attr(self, exclude: list = []) -> None: """Clear temporary attributes.""" super()._clear_temp_attr(exclude=exclude) # Remove temporary node values # temp_node_cols = ['flow_centrality', 'strahler_index', 'SI', 'bending_flow'] # self._nodes.drop(columns=temp_node_cols, errors='ignore', inplace=True) # Remove soma if it was manually assigned and is not present anymore if not callable(self._soma) and not isinstance(self._soma, type(None)): if utils.is_iterable(self._soma): exists = np.isin(self._soma, self.nodes.node_id.values) self._soma = np.asarray(self._soma)[exists] if not np.any(self._soma): self._soma = None elif self._soma not in self.nodes.node_id.values: self.soma = None if 'classify_nodes' not in exclude: # Reclassify nodes graph.classify_nodes(self, inplace=True) def copy(self, deepcopy: bool = False) -> 'TreeNeuron': """Return a copy of the neuron. Parameters ---------- deepcopy : bool, optional If False, ``.graph`` (NetworkX DiGraph) will be returned as view - changes to nodes/edges can progagate back! ``.igraph`` (iGraph) - if available - will always be deepcopied. Returns ------- TreeNeuron """ no_copy = ['_lock'] # Generate new empty neuron x = self.__class__(None) # Populate with this neuron's data x.__dict__.update({k: copy.copy(v) for k, v in self.__dict__.items() if k not in no_copy}) # Copy graphs only if neuron is not stale if not self.is_stale: if '_graph_nx' in self.__dict__: x._graph_nx = self._graph_nx.copy(as_view=deepcopy is not True) if '_igraph' in self.__dict__: if self._igraph is not None: # This is pretty cheap, so we will always make a deep copy x._igraph = self._igraph.copy() else: x._clear_temp_attr() return x
[docs] def get_graph_nx(self) -> nx.DiGraph: """Calculate and return networkX representation of neuron. Once calculated stored as ``.graph``. Call function again to update graph. See Also -------- :func:`navis.neuron2nx` """ self._graph_nx = graph.neuron2nx(self) return self._graph_nx
[docs] def get_igraph(self) -> 'igraph.Graph': # type: ignore """Calculate and return iGraph representation of neuron. Once calculated stored as ``.igraph``. Call function again to update iGraph. Important --------- Returns ``None`` if igraph is not installed! See Also -------- :func:`navis.neuron2igraph` """ self._igraph = graph.neuron2igraph(self, raise_not_installed=False) return self._igraph
@overload def resample(self, resample_to: int, inplace: Literal[False]) -> 'TreeNeuron': ... @overload def resample(self, resample_to: int, inplace: Literal[True]) -> None: ...
[docs] def resample(self, resample_to, inplace=False): """Resample neuron to given resolution. Parameters ---------- resample_to : int Resolution to which to resample the neuron. inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.resample_skeleton` Base function. See for details and examples. """ if inplace: x = self else: x = self.copy(deepcopy=False) sampling.resample_skeleton(x, resample_to, inplace=True) # No need to call this as base function does this for us # x._clear_temp_attr() if not inplace: return x return None
@overload def downsample(self, factor: float, inplace: Literal[False], **kwargs) -> 'TreeNeuron': ... @overload def downsample(self, factor: float, inplace: Literal[True], **kwargs) -> None: ...
[docs] def downsample(self, factor=5, inplace=False, **kwargs): """Downsample the neuron by given factor. Parameters ---------- factor : int, optional Factor by which to downsample the neurons. Default = 5. inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. **kwargs Additional arguments passed to :func:`~navis.downsample_neuron`. See Also -------- :func:`~navis.downsample_neuron` Base function. See for details and examples. """ if inplace: x = self else: x = self.copy(deepcopy=False) sampling.downsample_neuron(x, factor, inplace=True, **kwargs) # Delete outdated attributes x._clear_temp_attr() if not inplace: return x return None
[docs] def reroot(self, new_root: Union[int, str], inplace: bool = False) -> Optional['TreeNeuron']: """Reroot neuron to given node ID or node tag. Parameters ---------- new_root : int | str Either node ID or node tag. inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.reroot_skeleton` Base function. See for details and examples. """ if inplace: x = self else: x = self.copy(deepcopy=False) graph.reroot_skeleton(x, new_root, inplace=True) # Clear temporary attributes is done by morpho.reroot_skeleton() # x._clear_temp_attr() if not inplace: return x return None
[docs] def prune_distal_to(self, node: Union[str, int], inplace: bool = False) -> Optional['TreeNeuron']: """Cut off nodes distal to given nodes. Parameters ---------- node : node ID | node tag Provide either node ID(s) or a unique tag(s) inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.cut_skeleton` Base function. See for details and examples. """ if inplace: x = self else: x = self.copy(deepcopy=False) node = utils.make_iterable(node, force_type=None) for n in node: prox = graph.cut_skeleton(x, n, ret='proximal')[0] # Reinitialise with proximal data x.__init__(prox) # type: ignore # Cannot access "__init__" directly # Remove potential "left over" attributes (happens if we use a copy) x._clear_temp_attr() if not inplace: return x return None
[docs] def prune_proximal_to(self, node: Union[str, int], inplace: bool = False) -> Optional['TreeNeuron']: """Remove nodes proximal to given node. Reroots neuron to cut node. Parameters ---------- node : node_id | node tag Provide either a node ID or a (unique) tag inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.cut_skeleton` Base function. See for details and examples. """ if inplace: x = self else: x = self.copy(deepcopy=False) node = utils.make_iterable(node, force_type=None) for n in node: dist = graph.cut_skeleton(x, n, ret='distal')[0] # Reinitialise with distal data x.__init__(dist) # type: ignore # Cannot access "__init__" directly # Remove potential "left over" attributes (happens if we use a copy) x._clear_temp_attr() # Clear temporary attributes is done by cut_skeleton # x._clear_temp_attr() if not inplace: return x return None
[docs] def prune_by_strahler(self, to_prune: Union[int, List[int], slice], inplace: bool = False) -> Optional['TreeNeuron']: """Prune neuron based on `Strahler order <https://en.wikipedia.org/wiki/Strahler_number>`_. Will reroot neuron to soma if possible. Parameters ---------- to_prune : int | list | range | slice Strahler indices to prune. For example: 1. ``to_prune=1`` removes all leaf branches 2. ``to_prune=[1, 2]`` removes SI 1 and 2 3. ``to_prune=range(1, 4)`` removes SI 1, 2 and 3 4. ``to_prune=slice(1, -1)`` removes everything but the highest SI 5. ``to_prune=slice(-1, None)`` removes only the highest SI inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.prune_by_strahler` This is the base function. See for details and examples. """ if inplace: x = self else: x = self.copy() morpho.prune_by_strahler( x, to_prune=to_prune, reroot_soma=True, inplace=True) # No need to call this as morpho.prune_by_strahler does this already # self._clear_temp_attr() if not inplace: return x return None
[docs] def prune_twigs(self, size: float, inplace: bool = False, recursive: Union[int, bool, float] = False ) -> Optional['TreeNeuron']: """Prune terminal twigs under a given size. Parameters ---------- size : int | float Twigs shorter than this will be pruned. inplace : bool, optional If False, pruning is performed on copy of original neuron which is then returned. recursive : int | bool | "inf", optional If `int` will undergo that many rounds of recursive pruning. Use ``float("inf")`` to prune until no more twigs under the given size are left. See Also -------- :func:`~navis.prune_twigs` This is the base function. See for details and examples. """ if inplace: x = self else: x = self.copy() morpho.prune_twigs(x, size=size, inplace=True) if not inplace: return x return None
def prune_at_depth(self, depth: Union[float, int], source: Optional[int] = None, inplace: bool = False ) -> Optional['TreeNeuron']: """Prune all neurites past a given distance from a source. Parameters ---------- x : TreeNeuron | NeuronList depth : int | float Distance from source at which to start pruning. source : int, optional Source node for depth calculation. If ``None``, will use root. If ``x`` is a list of neurons then must provide a source for each neuron. inplace : bool, optional If False, pruning is performed on copy of original neuron which is then returned. Returns ------- TreeNeuron/List Pruned neuron(s). See Also -------- :func:`~navis.prune_at_depth` This is the base function. See for details and examples. """ if inplace: x = self else: x = self.copy() morpho.prune_at_depth(x, depth=depth, source=source, inplace=True) if not inplace: return x return None
[docs] def cell_body_fiber(self, reroot_soma: bool = True, inplace: bool = False, ) -> Optional['TreeNeuron']: """Prune neuron to its cell body fiber. Parameters ---------- reroot_soma : bool, optional If True, will reroot to soma. inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.cell_body_fiber` This is the base function. See for details and examples. """ if inplace: x = self else: x = self.copy() morpho.cell_body_fiber(x, inplace=True, reroot_soma=reroot_soma) # Clear temporary attributes x._clear_temp_attr() if not inplace: return x return None
[docs] def prune_by_longest_neurite(self, n: int = 1, reroot_soma: bool = False, inplace: bool = False, ) -> Optional['TreeNeuron']: """Prune neuron down to the longest neurite. Parameters ---------- n : int, optional Number of longest neurites to preserve. reroot_soma : bool, optional If True, will reroot to soma before pruning. inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.longest_neurite` This is the base function. See for details and examples. """ if inplace: x = self else: x = self.copy() graph.longest_neurite( x, n, inplace=True, reroot_soma=reroot_soma) # Clear temporary attributes x._clear_temp_attr() if not inplace: return x return None
[docs] def prune_by_volume(self, v: Union[core.Volume, List[core.Volume], Dict[str, core.Volume]], mode: Union[Literal['IN'], Literal['OUT']] = 'IN', prevent_fragments: bool = False, inplace: bool = False ) -> Optional['TreeNeuron']: """Prune neuron by intersection with given volume(s). Parameters ---------- v : str | navis.Volume | list of either Volume(s) to check for intersection mode : 'IN' | 'OUT', optional If 'IN', parts of the neuron inside the volume are kept. prevent_fragments : bool, optional If True, will add nodes to ``subset`` required to keep neuron from fragmenting. inplace : bool, optional If True, operation will be performed on itself. If False, operation is performed on copy which is then returned. See Also -------- :func:`~navis.in_volume` Base function. See for details and examples. """ if inplace: x = self else: x = self.copy() intersection.in_volume(x, v, inplace=True, prevent_fragments=prevent_fragments, mode=mode) # Clear temporary attributes # x._clear_temp_attr() if not inplace: return x return None
def to_swc(self, filename: Optional[str] = None, **kwargs) -> None: """Generate SWC file from this neuron. Parameters ---------- filename : str | None, optional If ``None``, will use "neuron_{id}.swc". kwargs Additional arguments passed to :func:`~navis.write_swc`. Returns ------- Nothing See Also -------- :func:`~navis.write_swc` See this function for further details. """ return io.write_swc(self, filename, **kwargs) # type: ignore # double import of "io"
[docs] def reload(self, inplace: bool = False, ) -> Optional['TreeNeuron']: """Reload neuron. Must have filepath as ``.origin`` as attribute. Returns ------- TreeNeuron If ``inplace=False``. """ if not hasattr(self, 'origin'): raise AttributeError('To reload TreeNeuron must have `.origin` ' 'attribute') if self.origin in ('DataFrame', 'string'): raise ValueError('Unable to reload TreeNeuron: it appears to have ' 'been created from string or DataFrame.') kwargs = {} if hasattr(self, 'soma_label'): kwargs['soma_label'] = self.soma_label if hasattr(self, 'connector_labels'): kwargs['connector_labels'] = self.connector_labels x = io.read_swc(self.origin, **kwargs) if inplace: self.__dict__.update(x.__dict__) self._clear_temp_attr() else: # This makes sure that we keep any additional data stored after # this neuron has been loaded x2 = self.copy() x2.__dict__.update(x.__dict__) x2._clear_temp_attr() return x
[docs] def snap(self, locs, to='nodes'): """Snap xyz location(s) to closest node or synapse. Parameters ---------- locs : (N, 3) array | (3, ) array Either single or multiple XYZ locations. to : "nodes" | "connectors" Whether to snap to nodes or connectors. Returns ------- id : int | list of int ID(s) of the closest node/connector. dist : float | list of float Distance(s) to the closest node/connector. Examples -------- >>> import navis >>> n = navis.example_neurons(1) >>> id, dist = n.snap([0, 0, 0]) >>> id 1124 """ locs = np.asarray(locs).astype(np.float64) is_single = (locs.ndim == 1 and len(locs) == 3) is_multi = (locs.ndim == 2 and locs.shape[1] == 3) if not is_single and not is_multi: raise ValueError('Expected a single (x, y, z) location or a ' '(N, 3) array of multiple locations') if to not in ['nodes', 'connectors']: raise ValueError('`to` must be "nodes" or "connectors", ' f'got {to}') # Generate tree tree = graph.neuron2KDTree(self, data=to) # Find the closest node dist, ix = tree.query(locs) if to == 'nodes': id = self.nodes.node_id.values[ix] else: id = self.connectors.connector_id.values[ix] return id, dist