def __init__(
self,
data,
radius=10,
color="salmon",
alpha=1,
show_injection=True,
name=None,
):
"""
Turns streamlines data to a mesh.
:param data: pd.DataFrame with streamlines points data
:param radius: float. Radius of the Tube mesh used to render streamlines
:param color: str, name of the color to be used
:param alpha: float, transparancy
:param name: str, name of the actor.
:param show_injection: bool. If true spheres mark the injection sites
"""
logger.debug(f"Creating a streamlines actor")
if isinstance(data, (str, Path)):
data = pd.read_json(data)
elif not isinstance(data, pd.DataFrame):
raise TypeError("Input data should be a dataframe")
self.radius = radius
mesh = (
self._make_mesh(data, show_injection=show_injection)
.c(color)
.alpha(alpha)
.clean()
)
name = name or "Streamlines"
Actor.__init__(self, mesh, name=name, br_class="Streamliness")
def __init__(self, data, name=None, colors="salmon", alpha=1, radius=20):
"""
Creates an actor representing multiple points (more efficient than
creating many Point instances).
:param data: np.ndarray, Nx3 array or path to .npy file with coords data
:param radius: float
:param color: str, or list of str with color names or hex codes
:param alpha: float
:param name: str, actor name
"""
PointsBase.__init__(self)
self.radius = radius
self.colors = colors
self.alpha = alpha
self.name = name
if isinstance(data, np.ndarray):
mesh = self._from_numpy(data)
elif isinstance(data, (str, Path)):
mesh = self._from_file(data)
else: # pragma: no cover
raise TypeError( # pragma: no cover
f"Input data should be either a numpy array or a file path, not: {_class_name(data)}" # pragma: no cover
) # pragma: no cover
Actor.__init__(self, mesh, name=self.name, br_class="Points")
def __init__(self, pos, root, color="powderblue", alpha=1, radius=350):
"""
Cylinder class creates a cylinder mesh between a given
point and the brain's surface.
:param pos: list, np.array of ap, dv, ml coordinates.
If an actor is passed, get's the center of mass instead
:param root: brain root Actor or mesh object
:param color: str, color
:param alpha: float
:param radius: float
"""
# Get pos
if isinstance(pos, Mesh):
pos = pos.points().mean(axis=0)
elif isinstance(pos, Actor):
pos = pos.center
logger.debug(f"Creating Cylinder actor at: {pos}")
# Get point at top of cylinder
top = pos.copy()
top[1] = root.bounds()[2] - 500
# Create mesh and Actor
mesh = shapes.Cylinder(pos=[top, pos], c=color, r=radius, alpha=alpha)
Actor.__init__(self, mesh, name="Cylinder", br_class="Cylinder")
def __init__(self, *args, ROIs=None, **kwargs):
self.ROIs = ROIs or [
(0, self.length),
]
ProbeGeometry.__init__(self, *args, **kwargs)
Actor.__init__(self, self.get_mesh(), name="Probe", br_class="Probe")
def __init__(self,
data,
name=None,
dims=(40, 40, 40),
radius=None,
**kwargs):
"""
Creates a Volume actor showing the 3d density of a set
of points.
:param data: np.ndarray, Nx3 array with cell coordinates
from vedo:
Generate a density field from a point cloud. Input can also be a set of 3D coordinates.
Output is a ``Volume``.
The local neighborhood is specified as the `radius` around each sample position (each voxel).
The density is expressed as the number of counts in the radius search.
:param int,list dims: numer of voxels in x, y and z of the output Volume.
"""
volume = vPoints(data).density(dims=dims, radius=radius,
**kwargs) # returns a vedo Volume
Actor.__init__(self, volume, name=name, br_class="density")
def __init__(self,
data,
name=None,
dims=(40, 40, 40),
radius=None,
**kwargs):
"""
Creates a Volume actor showing the 3d density of a set
of points.
:param data: np.ndarray, Nx3 array with cell coordinates
from vedo:
Generate a density field from a point cloud. Input can also be a set of 3D coordinates.
Output is a ``Volume``.
The local neighborhood is specified as the `radius` around each sample position (each voxel).
The density is expressed as the number of counts in the radius search.
:param int,list dims: numer of voxels in x, y and z of the output Volume.
"""
logger.debug("Creating a PointsDensity actor")
# flip coordinates on XY axis to match brainrender coordinates system
# data[:, 2] = -data[:, 2]
# create volume and then actor
volume = (vPoints(data).density(dims=dims, radius=radius,
**kwargs).c("Dark2").alpha([0, 0.9
]).mode(1)
) # returns a vedo Volume
volume.mirror("z")
Actor.__init__(self, volume, name=name, br_class="density")
def __init__(
self,
griddata,
voxel_size=1,
cmap="bwr",
min_quantile=None,
min_value=None,
name=None,
br_class=None,
as_surface=True,
**volume_kwargs,
):
"""
Takes a 3d numpy array with volumetric data
and returns an Actor with mesh: vedo.Volume.isosurface or a vedo.Volume.
BY default the volume is represented as a surface
To extract the surface:
The isosurface needs a lower bound threshold, this can be
either a user defined hard value (min_value) or the value
corresponding to some percentile of the grid data.
:param griddata: np.ndarray, 3d array with grid data
:param voxel_size: int, size of each voxel in microns
:param min_quantile: float, percentile for threshold
:param min_value: float, value for threshold
:param cmap: str, name of colormap to use
:param as_surface, bool. default True. If True
a surface mesh is returned instead of the whole volume
:param volume_kwargs: keyword arguments for vedo's Volume class
"""
# Create mesh
color = volume_kwargs.pop("c", "viridis")
if isinstance(griddata, np.ndarray):
# create volume from data
mesh = VedoVolume(
griddata,
spacing=[voxel_size, voxel_size, voxel_size],
c=color,
**volume_kwargs,
)
else:
mesh = griddata # assume a vedo Volume was passed
if as_surface:
# Get threshold
if min_quantile is None and min_value is None:
th = 0
elif min_value is not None:
th = min_value
else:
th = np.percentile(griddata.ravel(), min_quantile)
mesh = mesh.legosurface(vmin=th, cmap=cmap)
Actor.__init__(self,
mesh,
name=name or "Volume",
br_class=br_class or "Volume")
def __init__(self, data, radius=10, color="salmon", alpha=1, name=None):
"""
Turns streamlines data to a mesh.
:param data: pd.DataFrame with streamlines points data
:param radius: float. Radius of the Tube mesh used to render streamlines
:param color: str, name of the color to be used
:param alpha: float, transparancy
:param name: str, name of the actor.
"""
if not isinstance(data, pd.DataFrame):
raise TypeError("Input data should be a dataframe")
self.radius = radius
mesh = self._make_mesh(data).c(color).alpha(alpha)
name = name or "Streamlines"
Actor.__init__(self, mesh, name=name, br_class="Streamliness")
def __init__(self,
pos,
radius=100,
color="blackboard",
alpha=1,
res=25,
name=None):
"""
Creates an actor representing a single point
:param pos: list or np.ndarray with coordinates
:param radius: float
:param color: str,
:param alpha: float
:param res: int, resolution of mesh
:param name: str, actor name
"""
mesh = Sphere(pos=pos, r=radius, c=color, alpha=alpha, res=res)
name = name or "Point"
Actor.__init__(self, mesh, name=name, br_class="Point")
def __init__(
self,
neuron,
color=None,
alpha=1,
neurite_radius=8,
soma_radius=15,
name=None,
):
"""
Creates an Actor representing a single neuron's morphology
:param neuron: path to .swc file, Mesh, Actor or Neuron from morphapi.morphology
:param alpha: float
:param color: str,
:param neuron_radius: float, radius of axon/dendrites
:param soma_radius: float, radius of soma
:param name: str, actor name
"""
logger.debug(f"Creating a Neuron actor")
if color is None:
color = "blackboard"
alpha = alpha
self.neurite_radius = neurite_radius
self.soma_radius = soma_radius
self.name = None
if isinstance(neuron, (str, Path)):
mesh = self._from_file(neuron)
elif isinstance(neuron, (Mesh)):
mesh = neuron
elif isinstance(neuron, Actor):
mesh = neuron.mesh
elif isinstance(neuron, MorphoNeuron):
mesh = self._from_morphapi_neuron(neuron)
else:
raise ValueError(
f'Argument "neuron" is not in a recognized format: {_class_name(neuron)}'
)
Actor.__init__(self, mesh, name=self.name, br_class="Neuron")
self.mesh.c(color).alpha(alpha)