def generate_points(subset=0.02):
"""A helper to make a 3D NumPy array of points (n_points by 3)"""
dataset = examples.download_lidar()
ids = np.random.randint(low=0,
high=dataset.n_points - 1,
size=int(dataset.n_points * subset))
return dataset.points[ids]
def get_example_point_cloud(decimateFactor = 0.05):
""" Create numpy array of points from PyVista LiDAR example """
# Get PyVista Lidar Example Data
print("Downloading PyVista LiDAR Example data ...")
dataset = examples.download_lidar()
print(f"Downloading complete. Downloaded {dataset.n_points} points")
print(f"Data type {type(dataset)}")
# Get random points from the dataset
pointIds = np.random.randint(low=0, high=dataset.n_points-1, size=int(dataset.n_points * decimateFactor) )
print(f"Number of points after decimation: {len(pointIds)}")
return dataset.points[pointIds]
def test_download_lidar():
data = examples.download_lidar()
assert data.n_cells
def lidar():
return mesh.load_mesh(examples.download_lidar())
# No eye-dome lighting
p.subplot(0, 1)
p.add_mesh(nefertiti, color=True)
p.add_text("No Eye-Dome Lighting", font_size=24)
p.camera_position = [-1, -1, 0.2]
p.show()
###############################################################################
# Point Cloud
# +++++++++++
#
# When plotting a simple point cloud, it can be difficult to perceive depth.
# Take this Lidar point cloud for example:
point_cloud = examples.download_lidar()
###############################################################################
# And now plot this point cloud as-is:
# Plot a typical point cloud with no EDL
p = pv.Plotter()
p.add_mesh(point_cloud, color="tan", point_size=5)
p.show()
###############################################################################
# We can improve the depth mapping by enabling eye dome lighting on the
# renderer. Reference :func:`pyvista.Renderer.enable_eye_dome_lighting`.
# Plot with EDL
p = pv.Plotter()
def __init__(self):
self._example_data = examples.download_lidar()
_ExampleLoader.__init__(self)
import dash
import dash_vtk
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output, State
import numpy as np
import pyvista as pv
from pyvista import examples
np.random.seed(42)
# Get point cloud data from PyVista
dataset = examples.download_lidar()
subset = 0.2
selection = np.random.randint(low=0,
high=dataset.n_points - 1,
size=int(dataset.n_points * subset))
points = dataset.points[selection]
xyz = points.ravel()
elevation = points[:, -1].ravel()
min_elevation = np.amin(elevation)
max_elevation = np.amax(elevation)
print(f"Number of points: {points.shape}")
print(f"Elevation range: [{min_elevation}, {max_elevation}]")
# Setup VTK rendering of PointCloud
app = dash.Dash(__name__)
server = app.server
vtk_view = dash_vtk.View([
