def draw_shapes(self):
sb = self.source.bounds()
x1, x2, y1, y2, z1, z2 = sb
maxb = max(x2 - x1, y2 - y1)
grid0 = Grid(self.source.centerOfMass(),
sx=maxb,
sy=maxb,
resx=40,
resy=40)
T = self.morphed_source.getTransform()
grid1 = grid0.alpha(0.3).wireframe(0).clone().applyTransform(
T) # warp the grid
arrows = Arrows(self.ptsource, self.pttarget, alpha=0.5, s=3).c("k")
lines = Lines(self.source, self.target).c('db')
mlines = Lines(self.morphed_source, self.target).c('db')
show(grid0,
self.source,
self.target,
lines,
arrows,
__doc__,
at=0,
N=2)
show(grid1,
self.morphed_source,
self.target,
mlines,
f"morphed source (green) vs target (red)\nNDF = {2*self.npts}",
at=1,
interactive=True).close()
for t_interval in range(int(T / output_int)):
for it in range(output_int):
t = t_interval * output_int + it
#print('\r', "t=", t, end='')
#print("t=", t)
take_step(d_X, N)
print('\r', "t=", t, end='')
out_X = d_X.copy_to_host()
pol = arr_pol_to_float3(out_X[:, 3:5])
coords_t.append(out_X[:, :3])
pol_t.append(pol)
#print('\r', "DONE", t, end='')
#######################################################################
from vedo import Box, Spheres, Arrows, show, interactive
world = Box(pos=(1, 1, 0), size=(10, 10, 4), alpha=1).wireframe()
for t in range(len(coords_t)):
cells = Spheres(coords_t[t], c='b', r=0.4)
polarities = Arrows(startPoints=coords_t[t],
endPoints=coords_t[t] + pol_t[t],
c='tomato')
show(world, cells, polarities, interactive=0, viewup='z')
interactive()
def get_neurons_synapses(
self,
scene_store,
neurons,
alpha=1,
pre=False,
post=False,
colorby="synapse_type",
draw_patches=False,
draw_arrows=True,
):
"""
THIS METHODS GETS CALLED BY SCENE, self referes to the instance of Scene not to this class.
Renders neurons and adds them to the scene.
:param neurons: list of names of neurons
:param alpha: float in range 0,1 - neurons transparency
:param pre: bool, if True the presynaptic sites of each neuron are rendered
:param post: bool, if True the postsynaptic sites on each neuron are rendered
:param colorby: str, criteria to use to color the neurons.
Accepts values like synapse_type, type, individual etc.
:param draw_patches: bool, default True. If true dark patches are used to show the location of post synapses
:param draw_arrows: bool, default True. If true arrows are used to show the location of post synapses
"""
col_names = ["x", "z", "y"]
# used to correctly position synapses on .obj files
neurons = self._check_neuron_argument(neurons)
spheres_data, actors = [], []
for neuron in neurons:
if pre:
if colorby == "synapse_type":
color = self.pre_synapses_color
else:
color = self.get_neuron_color(neuron, colorby=colorby)
data = self.synapses_data.loc[self.synapses_data.pre == neuron]
if not len(data):
print(f"No pre- synapses found for neuron {neuron}")
else:
data = data[["x", "y", "z"]]
data["y"] = -data["y"]
spheres_data.append((
data,
dict(
color=color,
verbose=False,
alpha=alpha,
radius=self.synapses_radius,
res=24,
col_names=col_names,
),
))
if post:
if colorby == "synapse_type":
color = self.post_synapses_color
else:
color = self.get_neuron_color(neuron, colorby=colorby)
rows = [
i for i, row in self.synapses_data.iterrows()
if neuron in row.posts
]
data = self.synapses_data.iloc[rows]
if not len(data):
print(f"No post- synapses found for neuron {neuron}")
else:
data = data[["x", "y", "z"]]
data["y"] = -data["y"]
"""
Post synaptic locations are shown as darkening of patches
of a neuron's mesh and or as a 3d arrow point toward the neuron.
"""
spheres_data.append((
data,
dict(
color="black",
verbose=False,
alpha=0,
radius=self.synapses_radius * 4,
res=24,
col_names=col_names,
),
))
# Get mesh points for neuron the synapses belong to
if neuron not in scene_store.keys():
neuron_file = [
f for f in self.neurons_files if neuron in f
][0]
neuron_act = load_mesh_from_file(neuron_file, c=color)
else:
neuron_act, as_skeleton = scene_store[neuron]
# Draw post synapses as dark patches
if draw_patches:
if as_skeleton:
print(
"Can't display post synapses as dark spots when neron is rendered in skeleton mode"
)
else:
# Get faces that are inside the synapses spheres and color them darker
raise NotImplementedError("This needs some fixing")
# neuron_points = neuron_act.cellCenters()
# inside_points = spheres.insidePoints(
# neuron_points, returnIds=True
# )
# n_cells = neuron_act.polydata().GetNumberOfCells()
# scals = np.zeros((n_cells))
# scals[inside_points] = 1
# colors = [
# neuron_act.c()
# if s == 0
# else getColor("blackboard")
# for s in scals
# ]
# neuron_act.cellIndividualColors(colors)
# Draw post synapses as arrow
if draw_arrows:
points1 = [[x, y, z] for x, y, z in zip(
data[col_names[0]].values,
data[col_names[1]].values,
data[col_names[2]].values,
)]
points2 = [neuron_act.closestPoint(p) for p in points1]
# shift point1 to make arrows longer
def dist(p1, p2):
return math.sqrt((p1[0] - p2[0])**2 +
(p1[1] - p2[1])**2 +
(p1[2] - p2[2])**2)
def get_point(p1, p2, d, u):
alpha = (1 / d) * u
x = (1 - alpha) * p1[0] + alpha * p2[0]
y = (1 - alpha) * p1[1] + alpha * p2[1]
z = (1 - alpha) * p1[2] + alpha * p2[2]
return [x, y, z]
dists = [
dist(p1, p2) for p1, p2 in zip(points1, points2)
]
points0 = [
get_point(p1, p2, d, -0.5)
for p1, p2, d in zip(points1, points2, dists)
]
arrows = Arrows(points0,
endPoints=points2,
c=color,
s=4)
# ! aasduaisdbasiudbuia
actors.append(arrows)
return spheres_data, actors
"""Read structured grid data and show
the associated vector and scalar fields
"""
from vedo import load, datadir, Arrows, show
g = load(datadir+'structgrid.vts').printInfo()
coords = g.points()
vects = g.getPointArray('Momentum')/600 # printInfo gives the list
print('numpy array shapes are:', coords.shape, vects.shape)
# build arrows from starting points to endpoints, with colormap
arrows = Arrows(coords-vects, coords+vects, c='hot_r')
g.pointColors('Density').lineWidth(0.1).alpha(0.3)
#g.color('grey').alpha(0.3)
show(g, arrows, __doc__, axes=9) # axes type 9 is a simple box
mesh = trimesh.load(plyfile)
points = mesh.bounding_box_oriented.sample_volume(count=30)
# find the closest point on the mesh to each random point
closest_points, distances, triangle_id = mesh.nearest.on_surface(points)
#print('Distance from point to surface of mesh:\n{}'.format(distances))
# create a PointCloud object out of each (n,3) list of points
cloud_original = trimesh.points.PointCloud(points)
cloud_close = trimesh.points.PointCloud(closest_points)
# create a unique color for each point
cloud_colors = np.array([trimesh.visual.random_color() for i in points])
# set the colors on the random point and its nearest point to be the same
cloud_original.vertices_color = cloud_colors
cloud_close.vertices_color = cloud_colors
arrs = Arrows(cloud_original.vertices, cloud_close.vertices, c='w')
## create a scene containing the mesh and two sets of points
show(mesh,
cloud_original,
cloud_close,
arrs,
__doc__,
bg='bb',
axes=1,
viewup='z').close()
sources = [(5, 8, 5), (8, 5, 5), (5, 2, 5)]
deltas = [(1, 1, 0.2), (1, 0, -0.8), (1, -1, 0.2)]
apos = Points(positions, r=2)
# for p in apos.points(): ####### Uncomment to fix some points.
# if abs(p[2]-5) > 4.999: # differences btw RBF and thinplate
# sources.append(p) # will become much smaller.
# deltas.append(np.zeros(3))
sources = np.array(sources)
deltas = np.array(deltas)
src = Points(sources, c="r", r=12)
trs = Points(sources + deltas, c="v", r=12)
arr = Arrows(sources, sources + deltas)
################################################# Thin Plate Splines
warped = apos.clone().thinPlateSpline(sources, sources + deltas)
warped.alpha(0.4).color("lg").pointSize(10)
allarr = Arrows(apos.points(), warped.points())
set1 = [apos, warped, src, trs, arr, __doc__]
plt1 = show([set1, allarr], N=2, bg='bb') # returns the Plotter class
################################################# RBF
from scipy.interpolate import Rbf
x, y, z = sources[:, 0], sources[:, 1], sources[:, 2]
dx, dy, dz = deltas[:, 0], deltas[:, 1], deltas[:, 2]
end_points = np.array([section.pos_target])
if section.pos2 is not None:
start_points = np.concatenate((start_points, [section.pos2]))
end_points = np.concatenate(([section.pos2], [section.pos_target]))
if section.pos3 is not None:
start_points = np.concatenate((start_points, [section.pos3]))
end_points = np.concatenate(
([section.pos2], [section.pos3], [section.pos_target]))
lines = Lines(startPoints=start_points, endPoints=end_points, c='green', lw=5)
# Add some arrows to represent the vectors at the start and end positions
scalar = 150
arrows = Arrows(startPoints=np.array([section.pos1, section.pos_target]),
endPoints=np.array([
section.pos1 + scalar * section.vec1,
section.pos_target + scalar * section.vec_target
]),
s=0.5,
res=24)
# generate a mesh of the generated section from the survey
# use the 'circle' method to construct a cylinder with constant radius
mesh = we.mesh.WellMesh(
survey=survey,
method='circle',
n_verts=24,
)
# plot the results
we.visual.plot(
[mesh.mesh],
def visualize_log_data():
parser = config_parser()
args = parser.parse_args()
if args.run_dir == "newest":
run_folders = os.listdir('../runs')
if len(run_folders) == 0:
raise ValueError('There is no run in the runs directory')
newest = 0
run_dir = ""
for run_folder in run_folders:
timestamp = os.path.getmtime(os.path.join('../runs', run_folder))
if timestamp > newest:
newest = timestamp
run_dir = os.path.join('../runs', run_folder)
else:
run_dir = args.run_dir
if args.epoch == 0:
try:
filenames = os.listdir(os.path.join(run_dir, 'vedo_data'))
except:
raise ValueError("There seems to be no vedo data generated for the specified run since the path ",
os.path.join(run_dir, 'vedo_data'), ' was not found')
if len(filenames) == 0:
raise ValueError('No epoch in the vedo_data folder')
epoch = 0
for filename in filenames:
file_epoch = int(re.findall(r'\d+', filename)[0])
if file_epoch > epoch:
epoch = file_epoch
else:
epoch = args.epoch
ats = []
images = []
print('Using run ' + run_dir + ' and epoch ' + str(epoch))
for image_index in range(args.number_images):
try:
densities_samples_warps = np.load(
os.path.join(run_dir, 'vedo_data',
"densities_samples_warps_epoch_" + str(epoch) + '_image_' + str(image_index) + '.npz'))
densities, densities_samples, warps, warps_samples = densities_samples_warps['densities'], \
densities_samples_warps['samples_density'], \
densities_samples_warps['warps'], \
densities_samples_warps['samples_warp']
if args.mode == "density":
max_density = np.max(densities)
if max_density == 0:
print('Every density for image ', image_index,
' is 0 so your images are probably white and this visualization has spheres of radius 0')
normalized_densities = densities / max_density
radii = normalized_densities * 0.05
ats.append(image_index)
images.append(Spheres(densities_samples, r=radii, c="lb", res=8))
elif args.mode == "warp":
ats.append(image_index)
images.append([Arrows(warps_samples, warps_samples + warps, s=0.3),
Spheres(warps_samples, r=0.01, res=8)])
except FileNotFoundError as err:
print('Skipping the iteration with image index ', image_index, ' because the file for that image '
'was not found: ', err)
show(images, at=ats, axes=2)
Any point on the mesh close to a source landmark will
be moved to a place close to the corresponding target landmark.
The points in between are interpolated using Bookstein's algorithm."""
from vedo import Grid, merge, Points, Arrows, show
import numpy as np
#np.random.seed(2)
grids = []
for i in range(5):
gr = Grid([0, 0, i / 8], resx=12, resy=12)
grids.append(gr)
mesh = merge(grids) # merge grids into a single object
idxs = np.random.randint(0, mesh.N(), 10) # pick 10 indices
pts = mesh.points()[idxs]
ptsource, pttarget = [], []
for pt in pts:
ptold = pt + np.random.randn(3) * 0.02
ptsource.append(ptold)
ptnew = ptold + [0, 0, np.random.randn(1) * 0.1] # move in z
pttarget.append(ptnew)
warped = mesh.warp(ptsource, pttarget)
warped.color("b4").lc('light blue').wireframe(False).lw(1)
apts = Points(ptsource, r=10, c="r")
arrs = Arrows(ptsource, pttarget, c='k')
show(warped, apts, arrs, __doc__, axes=1, viewup="z").close()
out_X = np.copy(X)
pol = arr_pol_to_float3(out_X[:, 3:5])
coords_t.append(out_X[:, :3])
pol_t.append(pol)
###############################################################################
# View as an interactive time sequence with a slider
max_time = len(coords_t)
vp = Plotter(interactive=False)
coord_acts = []
pol_acts = []
for t in range(len(coords_t)):
coords = Spheres(coords_t[t], c='b', r=0.4).off()
polarities = Arrows(startPoints=coords_t[t],
endPoints=coords_t[t] + pol_t[t],
c='t').off()
vp += [coords, polarities]
coord_acts.append(coords)
pol_acts.append(polarities)
def set_time(widget, event):
new_time = int(floor(widget.GetRepresentation().GetValue()))
for t in range(0, max_time):
if t == new_time:
coord_acts[t].on()
pol_acts[t].on()
else:
coord_acts[t].off()
pol_acts[t].off()
