def test_mouselight():
from brainrender.Utils.MouseLightAPI.mouselight_api import MouseLightAPI
from brainrender.Utils.MouseLightAPI.mouselight_info import mouselight_api_info, mouselight_fetch_neurons_metadata
# Fetch metadata for neurons with some in the secondary motor cortex
neurons_metadata = mouselight_fetch_neurons_metadata(
filterby='soma', filter_regions=['MOs'])
# Then we can download the files and save them as a .json file
ml_api = MouseLightAPI()
neurons_files = ml_api.download_neurons(
neurons_metadata[:2]
) # just saving the first couple neurons to speed things up
# Show neurons and ZI in the same scene:
scene = Scene()
scene.add_neurons(
neurons_files,
soma_color='orangered',
dendrites_color='orangered',
axon_color='darkseagreen',
neurite_radius=8
) # add_neurons takes a lot of arguments to specify how the neurons should look
# make sure to check the source code to see all available optionsq
scene.add_brain_regions(['MOs'], alpha=0.15)
scene.render(camera='coronal')
def test_custom_video():
from brainrender.animation.video import CustomVideoMaker
# --------------------------------- Variables -------------------------------- #
N_FRAMES = 20
# Variables to specify camera position at each frame
zoom = np.linspace(1, 1.35, N_FRAMES)
frac = np.zeros_like(
zoom
) # for camera transition, interpolation value between cameras
frac[:10] = np.linspace(0, 1, 10)
frac[10:] = np.linspace(1, 0, len(frac[10:]))
# ------------------------------- Create scene ------------------------------- #
scene = Scene(display_inset=True, use_default_key_bindings=True)
filepaths, data = scene.atlas.download_streamlines_for_region("TH")
scene.add_brain_regions(["TH"], alpha=0.2)
# Create new cameras
cam1 = buildcam(sagittal_camera)
cam2 = buildcam(top_camera)
cam3 = buildcam(
dict(
position=[1862.135, -4020.792, -36292.348],
focal=[6587.835, 3849.085, 5688.164],
viewup=[0.185, -0.97, 0.161],
distance=42972.44,
clipping=[29629.503, 59872.10],
)
)
# Iniziale camera position
scene.plotter.moveCamera(cam1, cam2, frac[0])
# ------------------------------- Create frames ------------------------------ #
def frame_maker(scene=None, video=None, videomaker=None):
for step in track(
np.arange(N_FRAMES),
total=N_FRAMES,
description="Generating frames...",
):
# Move scene camera between 3 cameras
if step < 150:
scene.plotter.moveCamera(cam1, cam2, frac[step])
else:
scene.plotter.moveCamera(cam3, cam2, frac[step])
# Add frame to video
scene.render(zoom=zoom[step], interactive=False, video=True)
video.addFrame()
return video
# ---------------------------------------------------------------------------- #
# Video maker #
# ---------------------------------------------------------------------------- #
vm = CustomVideoMaker(scene, save_name="streamlines_animation")
vm.make_video(frame_maker)
def BrainRegionsScene():
scene = Scene()
scene.add_brain_regions(['TH', 'VP'], use_original_color=True, alpha=1)
act = scene.actors['regions']['TH']
scene.edit_actors([act], wireframe=True)
scene.render()
def test_regions():
scene = Scene(camera=coronal_camera)
regions = ["MOs", "VISp", "ZI"]
scene.add_brain_regions(regions, colors="green")
ca1 = scene.add_brain_regions("CA1", add_labels=True)
ca1.alpha(0.2)
print(ca1)
scene.close()
def test_streamlines():
streamlines_api = StreamlinesAPI()
streamlines_files, data = streamlines_api.download_streamlines_for_region("PAG")
scene = Scene()
scene.add_streamlines(data[3], color="powderblue", show_injection_site=False, alpha=.3, radius=10)
scene.add_brain_regions(['PAG'], use_original_color=False, colors='powderblue', alpha=.9)
mos = scene.actors['regions']['PAG']
scene.edit_actors([mos], wireframe=True)
def CartoonStyleScene():
if brainrender.SHADER_STYLE != 'cartoon':
raise ValueError('Set cartoon style at imports')
scene = Scene(camera='coronal', add_root=False)
scene.add_brain_regions(['PAG', 'SCm', 'SCs'],
use_original_color=True,
alpha=1)
# scene.add_brain_regions(['VISl', 'VISpl', 'VISpm', 'VISam', 'VISal', 'VISa'], use_original_color=True, alpha=.4)
scene.render()
def NeuronsScene(show_regions = False):
scene = Scene()
fl = 'Examples/example_files/one_neuron.json'
scene.add_neurons(fl, soma_color='darkseagreen', force_to_hemisphere="right",)
if show_regions:
scene.add_brain_regions(['ZI', 'PAG', 'MRN', 'NPC', "VTA", "STN", "PPT", "SCm", "HY"],
use_original_color=True, alpha=.5)
set_camera(scene)
scene.render()
def ConnectivityScene():
scene = Scene()
p0 = scene.get_region_CenterOfMass("ZI")
# Then we se these coordinates to get tractography data, note: any set of X,Y,Z coordinates would do.
tract = aba.get_projection_tracts_to_target(p0=p0)
scene.add_tractography(tract, display_injection_structure=False, color_by="region",
display_injection_volume=True, others_alpha=.25)
scene.add_brain_regions(['ZI'], colors="ivory", alpha=1)
set_camera(scene)
scene.render()
def test_streamlines():
scene = Scene()
filepaths, data = scene.atlas.download_streamlines_for_region("CA1")
scene.add_brain_regions(['CA1'], use_original_color=True, alpha=.2)
scene.add_streamlines(data,
color="darkseagreen",
show_injection_site=False)
scene.render(camera='sagittal', zoom=1, interactive=False)
scene.close()
def NeuronsScene3():
scene = Scene()
neurons_metadata = mouselight_fetch_neurons_metadata(filterby='soma', filter_regions=['VAL'])
neurons_files = mlapi.download_neurons(neurons_metadata[2:6])
scene.add_neurons(neurons_files, soma_color='deepskyblue', force_to_hemisphere="right")
scene.add_brain_regions(['VAL'], use_original_color=False, colors='palegreen', alpha=.9)
mos = scene.actors['regions']['VAL']
scene.edit_actors([mos], wireframe=True)
streamlines_files, data = streamlines_api.download_streamlines_for_region("VAL")
scene.add_streamlines(data[:1], color="palegreen", show_injection_site=False, alpha=.2, radius=10)
set_camera(scene)
scene.render()
def ElectrodesArrayScene():
scene = Scene(add_root=False, camera='sagittal')
z_offset = -1500
scene.add_brain_regions(['VAL'], use_original_color=True, alpha=.5)
scene.add_brain_regions(['TH'],
use_original_color=True,
alpha=.5,
wireframe=True)
# scene.add_optic_cannula('VAL')
# for x_offset in [-200, -500, -800, -1100]:
# scene.add_optic_cannula('VAL', z_offset=z_offset, x_offset=x_offset, alpha=1,
# radius=50, y_offset=-500, color='blackboard')
scene.render()
def test_streamlines():
from brainrender.Utils.parsers.streamlines import StreamlinesAPI
# Download streamlines data for injections in the CA1 field of the hippocampus
streamlines_api = StreamlinesAPI()
filepaths, data = streamlines_api.download_streamlines_for_region("CA1")
# Start by creating a scene
scene = Scene()
scene.add_brain_regions(['CA1'], use_original_color=True, alpha=.2)
# you can pass either the filepaths or the data
scene.add_streamlines(data, color="darkseagreen", show_injection_site=False)
scene.render(interactive=False, camera='sagittal', zoom=1)
scene.close()
def test_tractography():
from brainrender.Utils.ABA.connectome import ABA
# Create a scene
scene = Scene()
# Get the center of mass of the region of interest
p0 = scene.get_region_CenterOfMass("ZI")
# Get projections to that point
analyzer = ABA()
tract = analyzer.get_projection_tracts_to_target(p0=p0)
# Add the brain regions and the projections to it
scene.add_brain_regions(['ZI'], alpha=.4, use_original_color=True)
scene.add_tractography(tract, display_injection_structure=False, color_by="region")
scene.render(interactive=False, )
scene.close()
def test_scene_creation_brainglobe():
scene = Scene(atlas="allen_mouse_25um")
try:
scene.add_brain_regions("TH")
except:
raise ValueError
try:
scene.add_streamlines
except:
raise ValueError
scene = Scene(atlas="allen_human_500um")
try:
scene.add_brain_regions("TH")
except:
raise ValueError
def StreamlinesScene2():
scene = Scene()
streamlines_files, data = streamlines_api.download_streamlines_for_region("VAL")
scene.add_streamlines(data, color="palegreen", show_injection_site=False, alpha=.3, radius=10)
streamlines_files, data = streamlines_api.download_streamlines_for_region("VM")
scene.add_streamlines(data, color="palevioletred", show_injection_site=False, alpha=.3, radius=10)
scene.add_brain_regions(['VAL'], use_original_color=False, colors='palegreen', alpha=.9, hemisphere='right')
mos = scene.actors['regions']['VAL']
scene.edit_actors([mos], wireframe=True)
scene.add_brain_regions(['VM'], use_original_color=False, colors='palevioletred', alpha=.9, hemisphere='right')
mos = scene.actors['regions']['VM']
scene.edit_actors([mos], wireframe=True)
set_camera(scene)
scene.render()
def test_labelled_cells():
# Create a scene
scene = Scene() # specify that you want a view from the top
# Gerate the coordinates of N cells across 3 regions
regions = ["MOs", "VISp", "ZI"]
N = 1000 # getting 1k cells per region, but brainrender can deal with >1M cells easily.
# Render regions
scene.add_brain_regions(regions, alpha=.2)
# Get fake cell coordinates
cells = [] # to store x,y,z coordinates
for region in regions:
region_cells = scene.get_n_random_points_in_region(region=region, N=N)
cells.extend(region_cells)
x, y, z = [c[0] for c in cells], [c[1]
for c in cells], [c[2] for c in cells]
cells = pd.DataFrame(dict(x=x, y=y, z=z))
# Add cells
scene.add_cells(cells, color='darkseagreen', res=12, radius=25)
def main(regions, atlas=None, cartoon=False, debug=False, file=None):
# Set look
if cartoon:
brainrender.SHADER_STYLE = "cartoon"
# Create scene
scene = Scene(atlas=atlas)
# Add brain regions
if regions is not None and len(regions) > 0:
acts = scene.add_brain_regions(list(regions))
# Add silhouettes
if cartoon:
if isinstance(acts, list):
scene.add_silhouette(*acts)
else:
scene.add_silhouette(acts)
# Add data from file
if file is not None:
if file.endswith(".h5"):
scene.add_cells_from_file(file)
else:
try:
scene.add_from_file(file)
except Exception as e:
raise ValueError(
f"Failed to load data from file onto scene: {file}\n{e}"
)
# If debug set interactive = Off and close scene
if not debug:
interactive = True
else:
interactive = False
# Render and close
scene.render(interactive=interactive)
if debug:
scene.close()
# Variables to specify camera position at each frame
zoom = np.linspace(1, 1.5, N_FRAMES)
frac = np.zeros_like(
zoom) # for camera transition, interpolation value between cameras
frac[:150] = np.linspace(0, 1, 150)
frac[150:] = np.linspace(1, 0, len(frac[150:]))
# ------------------------------- Create scene ------------------------------- #
scene = Scene(display_inset=True, use_default_key_bindings=True)
root = scene.actors['root']
filepaths, data = scene.atlas.download_streamlines_for_region("TH")
scene.add_streamlines(data, color="darkseagreen", show_injection_site=False)
scene.add_brain_regions(['TH'], alpha=.2)
# Make all streamlines background
for mesh in scene.actors['tracts']:
mesh.alpha(minalpha)
mesh.color(darkcolor)
# Create new cameras
cam1 = buildcam(sagittal_camera)
cam2 = buildcam(top_camera)
cam3 = buildcam(
dict(
position=[1862.135, -4020.792, -36292.348],
focal=[6587.835, 3849.085, 5688.164],
import brainrender # brainrender.SHADER_STYLE = 'cartoon' from brainrender.scene import Scene scene = Scene() scene.add_brain_regions(['VISp'], alpha=.5, use_original_color=True) scene.add_brain_regions(['VISal'], alpha=.5, use_original_color=True) scene.add_brain_regions(['LP'], alpha=.5, use_original_color=True) scene.add_brain_regions(['LGd'], alpha=.5, use_original_color=True) scene.add_brain_regions(['SCs'], alpha=.5, use_original_color=True) scene.add_brain_regions(['scwm'], alpha=.15, use_original_color=True) scene.render()
"""
This tutorial shows you how to render efferent mesoscale connectivity
data from the Allen mouse connectome project as streamlines coloring
each injection's streamline individually.
"""
from brainrender.scene import Scene
from brainrender.colors import makePalette
# Start by creating a scene with the allen brain atlas atlas
scene = Scene(title="streamlines")
# Download streamlines data for injections in the CA1 field of the hippocampus
filepaths, data = scene.atlas.download_streamlines_for_region("CA1")
scene.add_brain_regions(["CA1"], use_original_color=True, alpha=0.2)
# you can pass either the filepaths or the data
colors = makePalette(len(data), "salmon", "lightgreen")
scene.add_streamlines(data, color=colors, show_injection_site=False)
scene.render(camera="sagittal", zoom=1)
"""
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()
"""
This tutorial shows how download and rendered afferent mesoscale projection data
using the AllenBrainAtlas (ABA) and Scene classes
"""
import brainrender
brainrender.SHADER_STYLE = 'cartoon'
from brainrender.scene import Scene
from brainrender.atlases.aba import ABA
# Create a scene
scene = Scene()
# Get the center of mass of the region of interest
p0 = scene.get_region_CenterOfMass("ZI")
# Get projections to that point
analyzer = ABA()
tract = analyzer.get_projection_tracts_to_target(p0=p0)
# Add the brain regions and the projections to it
scene.add_brain_regions(['ZI'], alpha=.4, use_original_color=True)
scene.add_tractography(tract, display_injection_structure=False, color_by="region")
scene.render()
"""
This example shows how to measure and display
the distance between two points.
"""
from brainrender.scene import Scene
import brainrender
from brainrender import ruler
brainrender.SHOW_AXES = True
# Create Scene
scene = Scene()
# add brain regions
mos, hy = scene.add_brain_regions(["MOs", "HY"], alpha=0.2)
mos.wireframe()
hy.wireframe()
# Get center of mass of the two regions
p1 = scene.atlas.get_region_CenterOfMass("MOs")
p2 = scene.atlas.get_region_CenterOfMass("HY")
# Use the ruler class to display the distance between the two points
"""
Brainrender units are in micrometers. To display the distance
measure instead we will divide by a factor of 1000 using
the unit_scale argument.
"""
# Add a ruler form the brain surface
import pandas as pd
import brainrender
brainrender.SHADER_STYLE = 'cartoon'
from brainrender.scene import Scene
# Create a scene
scene = Scene() # specify that you want a view from the top
# Gerate the coordinates of N cells across 3 regions
regions = ["MOs", "VISp", "ZI"]
N = 1000 # getting 1k cells per region, but brainrender can deal with >1M cells easily.
# Render regions
scene.add_brain_regions(regions, alpha=.2)
# Get fake cell coordinates
cells = [] # to store x,y,z coordinates
for region in regions:
region_cells = scene.get_n_random_points_in_region(region=region, N=N)
cells.extend(region_cells)
x, y, z = [c[0] for c in cells], [c[1] for c in cells], [c[2] for c in cells]
cells = pd.DataFrame(dict(
x=x, y=y, z=z)) # ! <- coordinates should be stared as a pandas dataframe
# Add cells
scene.add_cells(cells, color='darkseagreen', res=12, radius=25)
# render
scene.render()
"""
This tutorial shows how to render a cross hair at a specific location in the scene
"""
from brainrender.scene import Scene
scene = Scene()
scene.add_brain_regions('TH', use_original_color=False, alpha=.4)
# Add a point in the right hemisphere
point = scene.atlas.get_region_CenterOfMass('TH')
scene.add_crosshair_at_point(
point,
ap=False, # show only lines on the medio-lateral and dorso-ventral axes
point_kwargs={
'color': 'salmon'
} # specify how the point at the center of the crosshair looks like
)
# Add a point in the left hemisphere
point = scene.atlas.get_region_CenterOfMass('TH', hemisphere='left')
scene.add_crosshair_at_point(
point,
ap=False, # show only lines on the medio-lateral and dorso-ventral axes
point_kwargs={
'color': 'darkseagreen'
} # specify how the point at the center of the crosshair looks like
)
scene.render()
You can also visualize these data interactively at: https://insectbraindb.org/app/three;controls=true;mode=species
"""
# Create a brainrender scene with a custom Atlas class
"""
By passing a custom Atlas class (instance of brainrender.atlases.base Atlas class)
to Scene, Scene will use the atlas class' methods to fetch data and crate actors.
"""
scene = Scene(
atlas=IBDB, # specify that we are using the insects brains databse atlas
atlas_kwargs=dict(species='Schistocerca gregaria'
) # Specify which insect species' brain to use
)
# You can use print(scene.atlas.species_info) to see a list of available species
# Print a list of all the brain structures available for this species' brain
print(scene.atlas.structures)
# Add some brain regions in the mushroom body to the rendering
central_complex = [
'CBU-S2', 'CBU-S1', 'CBU-S3', 'NO-S3_left', 'NO-S2_left', 'NO-S2_right',
'NO-S3_right', 'NO_S1_left', 'NO-S1_right', 'NO-S4_left', 'NO-S4_right',
'CBL', 'PB'
]
scene.add_brain_regions(central_complex, alpha=1)
scene.render()
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
def create_scene(default_structures):
scene = Scene(add_root=True)
for structure in default_structures:
scene.add_brain_regions([structure], use_original_color=True)
return scene
passing the downloaded files to `scene.add_neurons`.
"""
import brainrender
brainrender.USE_MORPHOLOGY_CACHE = True
from brainrender.scene import Scene
from brainrender.Utils.MouseLightAPI.mouselight_api import MouseLightAPI
from brainrender.Utils.MouseLightAPI.mouselight_info import mouselight_api_info, mouselight_fetch_neurons_metadata
# Fetch metadata for neurons with some in the secondary motor cortex
neurons_metadata = mouselight_fetch_neurons_metadata(filterby='soma',
filter_regions=['MOs'])
# Then we can download the files and save them as a .json file
ml_api = MouseLightAPI()
neurons_files = ml_api.download_neurons(
neurons_metadata[:2]
) # just saving the first couple neurons to speed things up
# Show neurons and ZI in the same scene:
scene = Scene()
scene.add_neurons(
neurons_files,
soma_color='orangered',
dendrites_color='orangered',
axon_color='darkseagreen',
neurite_radius=8
) # add_neurons takes a lot of arguments to specify how the neurons should look
# make sure to check the source code to see all available optionsq
scene.add_brain_regions(['MOs'], alpha=0.15)
scene.render(camera='coronal')
"""
This tutorial shows how to create and render a brainrender scene with some brain regions
"""
import brainrender
brainrender.SHADER_STYLE = 'cartoon'
from brainrender.scene import Scene
# Create a scene
scene = Scene()
# Add the whole thalamus in gray
scene.add_brain_regions(['TH'], alpha=.15)
# Add VAL nucleus in wireframe style with the allen color
scene.add_brain_regions(['VAL'], use_original_color=True, wireframe=True)
scene.render()
