class VolumetricAPI(Paths):
"""
This class takes care of downloading, analysing and rendering data from:
"High-resolution data-driven model of the mouse connectome ", Knox et al 2018.
[https://www.mitpressjournals.org/doi/full/10.1162/netn_a_00066].
These data can be used to look at spatialised projection strength with sub-region (100um) resolution.
e.g. to look at where in region B are the projections from region A, you can use this class.
To download the data, this class uses code from: https://github.com/AllenInstitute/mouse_connectivity_models.
"""
voxel_size = 100
projections = {}
mapped_projections = {}
hemispheres = dict(left=1, right=2, both=3)
def __init__(self,
base_dir=None,
add_root=True,
use_cache=True,
scene_kwargs={},
**kwargs):
"""
Initialise the class instance to get a few useful paths and variables.
:param base_dir: str, path to base directory in which all of brainrender data are stored.
Pass only if you want to use a different one from what's default.
:param add_root: bool, if True the root mesh is added to the rendered scene
:param use_cache: if true data are loaded from a cache to speed things up.
Useful to set it to false to help debugging.
:param scene_kwargs: dict, params passed to the instance of Scene associated with this class
"""
Paths.__init__(self, base_dir=base_dir, **kwargs)
# Get MCM cache
cache_path = os.path.join(self.mouse_connectivity_volumetric,
'voxel_model_manifest.json')
if not os.path.isfile(cache_path):
if not connected_to_internet():
raise ValueError(
"The first time you use this class it will need to download some data, but it seems that you're not connected to the internet."
)
print(
"Downloading volumetric data. This will take several minutes but it only needs to be done once."
)
self.cache = VoxelModelCache(manifest_file=cache_path)
self.voxel_array = None
self.target_coords, self.source_coords = None, None
# Get projection cache paths
self.data_cache = self.mouse_connectivity_volumetric_cache
self.data_cache_projections = os.path.join(self.data_cache,
"projections")
self.data_cache_targets = os.path.join(self.data_cache, "targets")
self.data_cache_sources = os.path.join(self.data_cache, "sources")
for fold in [
self.data_cache_projections, self.data_cache_targets,
self.data_cache_sources
]:
if not os.path.isdir(fold):
os.mkdir(fold)
# Get structures tree
self.structure_tree = self.cache.get_structure_tree()
# Get scene
self.scene = Scene(add_root=add_root, **scene_kwargs)
# Other vars
self.use_cache = use_cache
def __getattr__(self, attr):
__dict__ = super(VolumetricAPI, self).__getattribute__('__dict__')
try:
return __dict__['scene'].__getattribute__(attr)
except AttributeError as e:
raise AttributeError(
f"Could not attribute {attr} for class VolumetricAPI:\n{e}")
# ---------------------------------------------------------------------------- #
# UTILS #
# ---------------------------------------------------------------------------- #
# ------------------------- Interaction with mcmodels ------------------------ #
def _get_structure_id(self, struct):
" Get the ID of a structure (or list of structures) given it's acronym"
if not isinstance(struct, (list, tuple)):
struct = [struct]
return [
self.structure_tree.get_structures_by_acronym([s])[0]["id"]
for s in struct
]
def _load_voxel_data(self):
"Load the VoxelData array from Knox et al 2018"
if self.voxel_array is None:
# Get VoxelArray
weights_file = os.path.join(self.mouse_connectivity_volumetric,
'voxel_model', 'weights')
nodes_file = os.path.join(self.mouse_connectivity_volumetric,
'voxel_model', 'nodes')
# Try to load from numpy
if os.path.isfile(weights_file + '.npy.gz'):
weights = load_npy_from_gz(weights_file + '.npy.gz')
nodes = load_npy_from_gz(nodes_file + '.npy.gz')
# Create array
self.voxel_array = VoxelConnectivityArray(weights, nodes)
# Get target and source masks
self.source_mask = self.cache.get_source_mask()
self.target_mask = self.cache.get_target_mask()
else:
print("Loading voxel data, might take a few minutes.")
# load from standard cache
self.voxel_array, self.source_mask, self.target_mask = self.cache.get_voxel_connectivity_array(
)
# save to npy
save_npy_to_gz(weights_file + '.npy.gz',
self.voxel_array.weights)
save_npy_to_gz(nodes_file + '.npy.gz', self.voxel_array.nodes)
def _get_coordinates_from_voxel_id(self, p0, as_source=True):
"""
Takes the index of a voxel and returns the 3D coordinates in reference space.
The index number should be extracted with either a source_mask or a target_mask.
If target_mask wa used set as_source as False.
:param p0: int
"""
if self.voxel_array is None:
self._load_voxel_data()
if as_source:
return self.source_mask.coordinates[p0] * self.voxel_size
else:
return self.target_mask.coordinates[p0] * self.voxel_size
def _get_mask_coords(self, as_source):
if as_source:
if self.source_coords is None:
coordinates = self.source_mask.coordinates * self.voxel_size
self.source_coords = coordinates
else:
coordinates = self.source_coords
else:
if self.target_coords is None:
coordinates = self.target_mask.coordinates * self.voxel_size
self.target_coords = coordinates
else:
coordinates = self.target_coords
return coordinates
def _get_voxel_id_from_coordinates(self, p0, as_source=True):
if self.voxel_array is None:
self._load_voxel_data()
# Get the brain region from the coordinates
coordinates = self._get_mask_coords(as_source)
# Get the position of p0 in the coordinates volumetric array
p0 = np.int64([round(p, -2) for p in p0])
try:
x_idx = (np.abs(coordinates[:, 0] - p0[0])).argmin()
y_idx = (np.abs(coordinates[:, 1] - p0[1])).argmin()
z_idx = (np.abs(coordinates[:, 2] - p0[2])).argmin()
p0_idx = [x_idx, y_idx, z_idx]
except:
raise ValueError(
f"Could not find the voxe corresponding to the point given: {p0}"
)
return p0_idx[0]
# ----------------------------------- Cache ---------------------------------- #
def _get_cache_filename(self, tgt, what):
"""Data are cached according to a naming convention, this function gets the name for an object
according to the convention"""
if what == 'projection':
fld = self.data_cache_projections
elif what == 'source':
fld = self.data_cache_sources
elif what == 'target':
fld = self.data_cache_targets
else:
raise ValueError(
f'Error while getting cached data file name.\n' +
f'What was {what} but should be projection/source/target/actor.'
)
name = ''.join([str(i) for i in tgt])
path = os.path.join(fld, name + '.npy.gz')
return name, path, os.path.isfile(path)
def _get_from_cache(self, tgt, what):
""" tries to load objects from cached data, if they exist"""
if not self.use_cache:
return None
name, cache_path, cache_exists = self._get_cache_filename(tgt, what)
if not cache_exists:
return None
else:
return load_npy_from_gz(cache_path)
def save_to_cache(self, tgt, what, obj):
""" Saves data to cache to avoid loading thema again in the future"""
name, cache_path, _ = self._get_cache_filename(tgt, what)
save_npy_to_gz(cache_path, obj)
# ---------------------------------------------------------------------------- #
# PREPROCESSING #
# ---------------------------------------------------------------------------- #
# ------------------------- Sources and targets masks ------------------------ #
def get_source(self, source, hemisphere='both'):
"""
Loads the mask for a source structure
:param source: str or list of str with acronym of source regions
:param hemisphere: str, ['both', 'left', 'right']. Which hemisphere to consider.
"""
if not isinstance(source, (list, tuple)):
source = [source]
self.source = self._get_from_cache(source, 'source')
if self.source is None:
self._load_voxel_data()
source_ids = self._get_structure_id(source)
self.source = self.source_mask.get_structure_indices(
structure_ids=source_ids,
hemisphere_id=self.hemispheres[hemisphere])
self.save_to_cache(source, 'source', self.source)
return self.source
def get_target_mask(self, target, hemisphere):
"""returns a 'key' array and a mask object
used to transform projection data from linear arrays to 3D volumes.
"""
target_ids = self._get_structure_id(target)
self.tgt_mask = Mask.from_cache(
self.cache,
structure_ids=target_ids,
hemisphere_id=self.hemispheres[hemisphere])
def get_target(self, target, hemisphere='both'):
"""
Loads the mask for a target structure.
:param target: str or list of str with acronym of target regions
:param hemisphere: str, ['both', 'left', 'right']. Which hemisphere to consider.
"""
if not isinstance(target, (list, tuple)):
target = [target]
if hemisphere != 'both':
cache_name = target + [hemisphere]
else:
cache_name = target
self.target = self._get_from_cache(cache_name, 'target')
if self.target is None:
self._load_voxel_data()
target_ids = self._get_structure_id(target)
self.target = self.target_mask.get_structure_indices(
structure_ids=target_ids,
hemisphere_id=self.hemispheres[hemisphere])
self.save_to_cache(cache_name, 'target', self.target)
return self.target
# -------------------------------- Projections ------------------------------- #
def get_projection(self,
source,
target,
name,
hemisphere='both',
projection_mode='mean',
mode='target'):
"""
Gets the spatialised projection intensity from a source to a target.
:param source: str or list of str with acronym of source regions
:param target: str or list of str with acronym of target regions
:param name: str, name of the projection
:param projection_mode: str, if 'mean' the data from different experiments are averaged,
if 'max' the highest value is taken.
:param mode: str. If 'target' the spatialised projection strength in the target structures is returned, usefule
to see where source projects to in target. Otherwise if 'source' the spatialised projection strength in
the source structure is return. Useful to see which part of source projects to target.
:return: 1D numpy array with mean projection from source to target voxels
"""
if mode == 'target':
self.get_target_mask(target, hemisphere)
elif mode == 'source':
self.get_target_mask(source, 'right')
else:
raise ValueError(
f'Invalide mode: {mode}. Should be either source or target.')
cache_name = sorted(source) + ['_'] + sorted(target) + [
f'_{projection_mode}_{mode}'
]
if hemisphere != 'both':
cache_name += [hemisphere]
proj = self._get_from_cache(cache_name, 'projection')
if proj is None:
source_idx = self.get_source(source, hemisphere)
target_idx = self.get_target(target, hemisphere)
self._load_voxel_data()
projection = self.voxel_array[source_idx, target_idx]
if mode == 'target':
axis = 0
elif mode == 'source':
axis = 1
else:
raise ValueError(
f'Invalide mode: {mode}. Should be either source or target.'
)
if projection_mode == 'mean':
proj = np.mean(projection, axis=axis)
elif projection_mode == 'max':
proj = np.max(projection, axis=axis)
else:
raise ValueError(
f'Projection mode {projection_mode} not recognized.\n' +
'Should be one of: ["mean", "max"].')
# Save to cache
self.save_to_cache(cache_name, 'projection', proj)
self.projections[name] = proj
return proj
def get_mapped_projection(self, source, target, name, **kwargs):
"""
Gets the spatialised projection intensity from a source to a target, but as
a mapped volume instead of a linear array.
:param source: str or list of str with acronym of source regions
:param target: str or list of str with acronym of target regions
:param name: str, name of the projection
:return: 3D numpy array with projectino intensity
"""
projection = self.get_projection(source, target, name, **kwargs)
mapped_projection = self.tgt_mask.map_masked_to_annotation(projection)
self.mapped_projections[name] = mapped_projection
return mapped_projection
def get_mapped_projection_to_point(self,
p0,
restrict_to=None,
restrict_to_hemisphere='both'):
"""
Gets projection intensity from all voxels to the voxel corresponding to a point of interest
"""
cache_name = f'proj_to_{p0[0]}_{p0[1]}_{p0[1]}'
if restrict_to is not None:
cache_name += f'_{restrict_to}'
proj = self._get_from_cache(cache_name, 'projection')
if proj is None:
p0idx = self._get_voxel_id_from_coordinates(p0, as_source=False)
if restrict_to is not None:
source_idx = self.get_source(restrict_to,
restrict_to_hemisphere)
proj = self.voxel_array[source_idx, p0idx]
self.get_target_mask(restrict_to, restrict_to_hemisphere)
mapped_projection = self.tgt_mask.map_masked_to_annotation(
proj)
else:
proj = self.voxel_array[:, p0idx]
mapped_projection = self.source_mask.map_masked_to_annotation(
proj)
self.save_to_cache(cache_name, 'projection', mapped_projection)
return mapped_projection
else:
return proj
def get_mapped_projection_from_point(self,
p0,
restrict_to=None,
restrict_to_hemisphere='both'):
"""
Gets projection intensity from all voxels to the voxel corresponding to a point of interest
"""
if self.get_hemispere_from_point(p0) == 'left':
raise ValueError(
f'The point passed [{p0}] is in the left hemisphere,' +
' but "projection from point" only works from the right hemisphere.'
)
cache_name = f'proj_from_{p0[0]}_{p0[1]}_{p0[1]}'
if restrict_to is not None:
cache_name += f'_{restrict_to}'
proj = self._get_from_cache(cache_name, 'projection')
if proj is None:
p0idx = self._get_voxel_id_from_coordinates(p0, as_source=True)
if restrict_to is not None:
target_idx = self.get_target(restrict_to,
restrict_to_hemisphere)
proj = self.voxel_array[p0idx, target_idx]
self.get_target_mask(restrict_to, restrict_to_hemisphere)
mapped_projection = self.tgt_mask.map_masked_to_annotation(
proj)
else:
proj = self.voxel_array[p0idx, :]
mapped_projection = self.target_mask.map_masked_to_annotation(
proj)
self.save_to_cache(cache_name, 'projection', mapped_projection)
return mapped_projection
else:
return proj
# ---------------------------------------------------------------------------- #
# RENDERING #
# ---------------------------------------------------------------------------- #
def add_mapped_projection(self,
source,
target,
actor_kwargs={},
render_source_region=False,
render_target_region=False,
regions_kwargs={},
**kwargs):
"""
Gets the spatialised projection intensity from a source to a target
and renders it as a vtkplotter lego visualisation.
:param source: str or list of str with acronym of source regions
:param target: str or list of str with acronym of target regions
:param render_source_region: bool, if true a wireframe mesh of source regions is rendered
:param render_target_region: bool, if true a wireframe mesh of target regions is rendered
:param regions_kwargs: pass options to specify how brain regions should look like
:param kwargs: kwargs can be used to control how the rendered object looks like.
Look at the arguments of 'add_volume' to see what arguments are available.
"""
# Get projection data
if not isinstance(source, list): source = [source]
if not isinstance(target, list): target = [target]
name = ''.join(source) + '_'.join(target)
mapped_projection = self.get_mapped_projection(source, target, name,
**kwargs)
lego_actor = self.add_volume(mapped_projection, **actor_kwargs)
# Render relevant regions meshes
if render_source_region or render_target_region:
wireframe = regions_kwargs.pop('wireframe', True)
use_original_color = regions_kwargs.pop('use_original_color', True)
if render_source_region:
self.scene.add_brain_regions(
source,
use_original_color=use_original_color,
wireframe=wireframe,
**regions_kwargs)
if render_target_region:
self.scene.add_brain_regions(
target,
use_original_color=use_original_color,
wireframe=wireframe,
**regions_kwargs)
return lego_actor
def add_mapped_projection_to_point(self,
p0,
show_point=True,
show_point_region=False,
show_crosshair=True,
crosshair_kwargs={},
point_region_kwargs={},
point_kwargs={},
from_point=False,
**kwargs):
if not isinstance(p0, (list, tuple, np.ndarray)):
raise ValueError(
"point passed should be a list or a 1d array, not: {p0}")
restrict_to = kwargs.pop('restrict_to', None)
restrict_to_hemisphere = kwargs.pop('restrict_to_hemisphere', 'both')
if not from_point:
projection = self.get_mapped_projection_to_point(
p0,
restrict_to=restrict_to,
restrict_to_hemisphere=restrict_to_hemisphere)
else:
projection = self.get_mapped_projection_from_point(
p0,
restrict_to=restrict_to,
restrict_to_hemisphere=restrict_to_hemisphere)
lego_actor = self.add_volume(projection, **kwargs)
if show_point:
color = point_kwargs.pop('color', 'salmon')
radius = point_kwargs.pop('radius', 50)
alpha = point_kwargs.pop('alpha', 1)
if not show_crosshair:
self.scene.add_sphere_at_point(p0,
color=color,
radius=radius,
alpha=alpha,
**point_kwargs)
else:
ml = crosshair_kwargs.pop('ml', True)
dv = crosshair_kwargs.pop('dv', True)
ap = crosshair_kwargs.pop('ap', True)
self.scene.add_crosshair_at_point(p0,
ml=ml,
dv=dv,
ap=ap,
line_kwargs=crosshair_kwargs,
point_kwargs={
'color': color,
'radius': radius,
'alpha': alpha
})
if show_point_region:
use_original_color = point_region_kwargs.pop(
'use_original_color', False)
alpha = point_region_kwargs.pop('alpha', 0.3)
region = self.scene.get_structure_from_coordinates(p0)
self.scene.add_brain_regions([region],
use_original_color=use_original_color,
alpha=alpha,
**point_region_kwargs)
return lego_actor
def add_mapped_projection_from_point(self, *args, **kwargs):
return self.add_mapped_projection_to_point(*args,
**kwargs,
from_point=True)
def add_volume(self,
volume,
cmap='afmhot_r',
alpha=1,
add_colorbar=True,
**kwargs):
"""
Renders intensitdata from a 3D numpy array as a lego volumetric actor.
:param volume: np 3D array with number of dimensions = those of the 100um reference space.
:param cmap: str with name of colormap to use
:param alpha: float, transparency
:param add_colorbar: if True a colorbar is added to show the values of the colormap
"""
# Parse kwargs
line_width = kwargs.pop('line_width', 1)
if cmap == 'random' or not cmap or cmap is None:
cmap = get_random_colormap()
# Get vmin and vmax threshold for visualisation
vmin = kwargs.pop('vmin', 0.000001)
vmax = kwargs.pop('vmax', np.nanmax(volume))
# Check values
if np.max(volume) > vmax:
print(
"While rendering mapped projection some of the values are above the vmax threshold."
+ "They will not be displayed." +
f" vmax was {vmax} but found value {round(np.max(volume), 5)}."
)
if vmin > vmax:
raise ValueError(
f'The vmin threhsold [{vmin}] cannot be larger than the vmax threshold [{vmax}'
)
if vmin < 0: vmin = 0
# Get 'lego' actor
vol = Volume(volume)
lego = vol.legosurface(vmin=vmin, vmax=vmax, cmap=cmap)
# Scale and color actor
lego.alpha(alpha).lw(line_width).scale(self.voxel_size)
lego.cmap = cmap
# Add colorbar
if add_colorbar:
lego.addScalarBar(vmin=vmin,
vmax=vmax,
horizontal=1,
c='k',
pos=(0.05, 0.05),
titleFontSize=40)
# Add to scene
actor = self.scene.add_vtkactor(lego)
return actor
from brainrender.scene import Scene
from brainrender.colors import makePalette
from vtkplotter import Text
# Crate a scene
scene = Scene(add_root=False, display_inset=False, use_default_key_bindings=False)
# Text to add
s = 'BRAINRENDER'
# Specify a color for each letter
colors = makePalette('salmon', 'powderblue', N=len(s))
x = 0 # use to specify the position of each letter
# Add letters one at the time to color them individually
for n, letter in enumerate('BRAINRENDER'):
if 'I' == letter or 'N' == letter and n <5: # make the spacing right
x += .6
else:
x += 1
# Add letter and silhouette to the scne
act = Text(letter, depth=.5, c=colors[n], pos=(x, 0, 0), justify='centered')
sil = act.silhouette().lw(3).color('k')
scene.add_vtkactor(act, sil)
scene.render()
from brainrender.scene import Scene
scene = Scene()
# Load skull from file
skull = scene.add_from_file('Examples/example_files/skull.stl')
skull.c('ivory').alpha(1)
# Align skull and brain (scene.root)
skull_com = skull.centerOfMass()
root_com = scene.root.centerOfMass()
skull.origin(skull.centerOfMass())
skull.rotateY(90).rotateX(180)
skull.x(root_com[0] - skull_com[0])
skull.y(root_com[1] - skull_com[1])
skull.z(root_com[2] - skull_com[2])
skull.x(3500)
skull.rotateZ(-25)
skull.y(7800)
skull.scale([1300, 1500, 1200])
# Cut skull actor to show brain inside
scene.cut_actors_with_plane('sagittal', actors=skull)
# Improve looks
s1 = scene.root.silhouette().lw(3).c('k')
s2 = skull.silhouette().lw(3).c('k')
scene.add_vtkactor(s1, s2)
scene.render()
class AllenMorphology(Paths):
""" Handles the download and visualisation of neuronal morphology data from the Allen database. """
def __init__(self, *args, scene_kwargs={}, **kwargs):
"""
Initialise API interaction and fetch metadata of neurons in the Allen Database.
"""
if not connected_to_internet():
raise ConnectionError(
"You will need to be connected to the internet to use the AllenMorphology class"
)
Paths.__init__(self, *args, **kwargs)
self.scene = Scene(add_root=False, display_inset=False, **scene_kwargs)
# Create a Cache for the Cell Types Cache API
self.ctc = CellTypesCache(
manifest_file=os.path.join(self.morphology_allen, 'manifest.json'))
# Get a list of cell metadata for neurons with reconstructions, download if necessary
self.neurons = pd.DataFrame(
self.ctc.get_cells(species=[CellTypesApi.MOUSE],
require_reconstruction=True))
self.n_neurons = len(self.neurons)
if not self.n_neurons:
raise ValueError(
"Something went wrong and couldn't get neurons metadata from Allen"
)
self.downloaded_neurons = self.get_downloaded_neurons()
def get_downloaded_neurons(self):
"""
Get's the path to files of downloaded neurons
"""
return [
os.path.join(self.morphology_allen, f)
for f in os.listdir(self.morphology_allen) if ".swc" in f
]
def download_neurons(self, ids):
"""
Download neurons
:param ids: list of integers with neurons IDs
"""
if isinstance(ids, np.ndarray):
ids = list(ids)
if not isinstance(ids, (list)): ids = [ids]
neurons = []
for neuron_id in ids:
neuron_file = os.path.join(self.morphology_allen,
"{}.swc".format(neuron_id))
neurons.append(
self.ctc.get_reconstruction(neuron_id, file_name=neuron_file))
return neurons
def parse_neurons_swc_allen(self, morphology, color='blackboard', alpha=1):
"""
SWC parser for Allen neuron's morphology data, they're a bit different from the Mouse Light SWC
:param morphology: data with morphology
:param neuron_number: int, number of the neuron being rendered.
"""
# Create soma actor
radius = 1
neuron_actors = [
shapes.Sphere(pos=get_coords(morphology.soma)[::-1],
c=color,
r=radius * 3)
]
# loop over trees
for tree in morphology._tree_list:
tree = pd.DataFrame(tree)
branching_points = [
t.id for i, t in tree.iterrows()
if len(t.children) > 2 and t.id < len(tree)
]
branch_starts = []
for bp in branching_points:
branch_starts.extend(tree.iloc[bp].children)
for bp in branch_starts:
parent = tree.iloc[tree.iloc[bp].parent]
branch = [(parent.x, parent.y, parent.z)]
point = tree.iloc[bp]
while True:
branch.append((point.x, point.y, point.z))
if not point.children:
break
else:
try:
point = tree.iloc[point.children[0]]
except:
break
# Create actor
neuron_actors.append(
shapes.Tube(branch, r=radius, c='red', alpha=1, res=24))
actor = merge(*neuron_actors)
actor.color(color)
actor.alpha(alpha)
return actor
# # Todo load/save neurons??
def load_save_neuron(self, neuron_file, neuron=None):
neuron_name = os.path.split(neuron_file)[-1].split('.swc')[0]
savepath = os.path.join(self.morphology_cache, neuron_name + '.vtk')
if neuron is None and os.path.isfile(savepath):
return load(savepath)
elif neuron is None:
return None
elif neuron is not None:
neuron.write(savepath)
def parse_neuron_swc(self,
filepath,
color='blackboard',
alpha=1,
radius_multiplier=.1,
overwrite=False):
"""
Given an swc file, render the neuron
:param filepath: str with path to swc file
:param neuron_number: numnber of neuron being rendered
"""
# See if we rendered this neuron already
if not overwrite:
loaded = self.load_save_neuron(filepath)
if loaded is not None:
return loaded.color(color)
print(f"Parsing swc file: {filepath}")
# details on swc files: http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html
_sample = namedtuple("sample", "sampleN structureID x y z r parent"
) # sampleN structureID x y z r parent
if not os.path.isfile(filepath) or not ".swc" in filepath.lower():
raise ValueError("unrecognized file path: {}".format(filepath))
try:
return self.parse_neurons_swc_allen(filepath)
except:
pass # the .swc file fas not generate with by allen
f = open(filepath)
content = f.readlines()
f.close()
content = [
sample.replace("\n", "") for sample in content if sample[0] != '#'
]
content = [sample for sample in content if len(sample) > 3]
# crate empty dicts for soma axon and dendrites
data = dict(id=[],
parentNumber=[],
radius=[],
sampleNumber=[],
x=[],
y=[],
z=[])
# start looping around samples
for sample in content:
s = _sample(
*[float(samp) for samp in sample.lstrip().rstrip().split(" ")])
# append data to dictionary
data['id'] = s.structureID
data['parentNumber'].append(int(s.parent))
data['radius'].append(s.r)
data['x'].append(s.x)
data['y'].append(s.y)
data['z'].append(s.z)
data['sampleNumber'].append(int(s.sampleN))
# Get branches and soma
print(" reconstructing neurites trees")
data = pd.DataFrame(data)
radius = data['radius'].values[0] * radius_multiplier
soma = data.iloc[0]
soma = shapes.Sphere(pos=[soma.x, soma.y, soma.z],
c=color,
r=radius * 4)
neuron_actors = [soma]
branches_end, branches_start = [], [] # Get branches start and end
for parent in data.parentNumber.values:
sons = data.loc[data.parentNumber == parent]
if len(sons) > 1:
branches_end.append(parent)
for i, son in sons.iterrows():
branches_start.append(son.sampleNumber)
print(" creating actors")
for start in branches_start:
node = data.loc[data.sampleNumber == start]
parent = data.loc[data.sampleNumber == node.parentNumber.values[0]]
branch = [(parent.x.values[0], parent.y.values[0],
parent.z.values[0])]
while True:
branch.append(
(node.x.values[0], node.y.values[0], node.z.values[0]))
node = data.loc[data.parentNumber ==
node.sampleNumber.values[0]]
if not len(node): break
if node.sampleNumber.values[0] in branches_end:
branch.append(
(node.x.values[0], node.y.values[0], node.z.values[0]))
break
neuron_actors.append(
shapes.Tube(branch, r=radius, c='red', alpha=1, res=24))
# Merge actors and save
actor = merge(*neuron_actors)
actor.color(color)
actor.alpha(alpha)
self.load_save_neuron(filepath, neuron=actor)
return actor
def add_neuron(self,
neuron,
shadow_axis=None,
shadow_offset=-20,
**kwargs):
if isinstance(neuron, list):
neurons = neuron
else:
if isinstance(neuron, str):
if os.path.isdir(neuron):
neurons = listdir(neuron)
else:
neurons = [neuron]
actors = []
for neuron in neurons:
if isinstance(neuron, str):
neuron = self.parse_neuron_swc(neuron, **kwargs)
elif isinstance(neuron, Morphology):
neuron = self.parse_neurons_swc_allen(neuron, **kwargs)
actor = self.scene.add_vtkactor(neuron)
# scals = actor.points()[:, 1]
# alphas = np.linspace(0.82, .83, 250)
# actor.pointColors(scals, alpha=alphas, cmap="Greens_r")
# actor.points()[:, 0] += np.random.normal(0, 2000)
# actor.points()[:, 2] += np.random.normal(0, 2000)
if shadow_axis == 'x':
actor.addShadow(x=shadow_offset)
elif shadow_axis == 'y':
actor.addShadow(y=shadow_offset)
elif shadow_axis == 'z':
actor.addShadow(z=shadow_offset)
actors.append(neuron)
return actors
def render(self, **kwargs):
self.scene.render(**kwargs)
"""
This example shows how to create a scene that has a cartoony look
(good for schematics and illustrations)
"""
import brainrender
brainrender.SHADER_STYLE = 'cartoon' # gives actors a flat shading
from brainrender.scene import Scene
scene = Scene()
root = scene.actors['root']
th = scene.add_brain_regions('TH', alpha=.5)
# Create a black line around each actor
sil = root.silhouette().lw(1).c('k')
sil2 = th.silhouette().lw(3).c('k')
scene.add_vtkactor(sil, sil2) # add the silhouette meshses to scene
scene.render()
brainrender.SHADER_STYLE = 'cartoon'
from brainrender.scene import Scene
import numpy as np
from vtkplotter import Plane
scene = Scene(use_default_key_bindings=True)
# Add some actors
root = scene.actors['root']
th = scene.add_brain_regions(['STR', 'TH'], alpha=.5)
# Specify position, size and orientation of the plane
pos = scene.atlas._root_midpoint
sx, sy = 15000, 15000
norm = [0, 1, 1]
plane = scene.atlas.get_plane_at_point(pos, norm, sx, sy, color='lightblue')
# Cut
scene.cut_actors_with_plane(
plane,
close_actors=
False, # set close_actors to True close the holes left by cutting
showplane=True,
actors=scene.actors['root'])
sil = root.silhouette().lw(1).c('k')
scene.add_vtkactor(sil)
scene.render(camera='top')
atlas=Celegans, # Pass the custom atlas class to scene.
display_inset=False,
atlas_kwargs=dict(
data_folder=data_folder
), # use this to pass keyword arguments to the Atlas class
title="Whole connectome")
# Exclude some neurons we don't want to render
metadata = scene.atlas.neurons_metadata
neurons = metadata.loc[(metadata.type != 'nonvalid')
& (metadata.type != 'other')]
# Add each neuron with the corresponding outline
scene.add_neurons(list(neurons.neuron.values))
for neuron in scene.actors['neurons']:
scene.add_vtkactor(neuron.silhouette().lw(3).c('k'))
# Render
scene.render()
scene.close()
# ------------------------- Scene 2: showing synapses ------------------------ #
scene = Scene(
add_root=False,
atlas=Celegans, # Pass the custom atlas class to scene.
display_inset=False,
atlas_kwargs=dict(
data_folder=data_folder
), # use this to pass keyword arguments to the Atlas class
title="Synapses")
# scene.render(interactive=False,camera=bespoke_camera, zoom=1)
# scene.take_screenshot()
# time.sleep(1)
# angle 2
# bespoke_camera = dict(
# position = [-1, -.5, 1.8] ,
# focal = [0, 0, 0],
# viewup = [0, -1, 0],
# distance = 9522.144,
# clipping = [5892.778, 14113.736],
# )
# scene.render(interactive=False,camera=bespoke_camera, zoom=1)
# scene.take_screenshot()
# time.sleep(1)
scene.add_vtkactor(MOp_region)
# # angle 1
# bespoke_camera = dict(
# position = [-1, -.3, .7] ,
# focal = [0, 0, 0],
# viewup = [0, -1, 0],
# distance = 9522.144,
# clipping = [5892.778, 14113.736],
# )
# scene.render(interactive=False,camera=bespoke_camera, zoom=1)
# scene.take_screenshot()
# time.sleep(1)
# angle 2
bespoke_camera = dict(
position=[-1, -.5, 1.8],
iso20 = load_pickle(iso20path)
print('All data to 20')
proj_data = iso20.transform(np.sqrt(rates))
print('All data to 3')
proj_data = iso_instance.transform(proj_data)
starts = proj_data[1:, :]
ends = proj_data[:-1, :]
lines = Lines(starts, endPoints=ends)
coords = pd.DataFrame(
dict(x=proj_data[:, 0], y=proj_data[:, 1], z=proj_data[:, 2]))
isoscene = Scene(add_root=False, display_inset=False, title='isomap')
isoscene.add_cells(coords, radius=0.1, color='salmon', res=24)
isoscene.add_vtkactor(lines)
isoscene.render()
isoscene.close()
# %%
# ----------------------------------- UMAP ----------------------------------- #
print('Umap')
_umap_params = dict(
n_neighbors=5, # higher values favour global vs local structure
n_components=3,
min_dist=
0.1, # min distance between point in low D embedding space. Low vals favour clustering
)
if not load_umap:
umapper = umap.UMAP(**_umap_params)
"""
This example shows how to cut actors in the scene using a plane
oriented along the sagittal axis
"""
import brainrender
brainrender.SHADER_STYLE = 'cartoon'
from brainrender.scene import Scene
scene = Scene()
# Add some actors
root = scene.actors['root']
th = scene.add_brain_regions(['STR', 'TH'], alpha=.5)
# Cut with plane
scene.cut_actors_with_plane('sagittal', showplane=False) # Set showplane to True if you want to see the plane location
# Add a silhouette around each actor to emphasize the cut location
sil = root.silhouette().lw(1).c('k')
sil2 = [act.silhouette().lw(3).c('k') for act in th]
scene.add_vtkactor(sil, *sil2)
scene.render(camera='top')
