Calculate ground proximity and pent house factor

0. Initialization

Importing all necessary libraries and specifying the inputs

import os
import topogenesis as tg
import pyvista as pv
import trimesh as tm
import numpy as np

# convert mesh to pv_mesh
def tri_to_pv(tri_mesh):
    faces = np.pad(tri_mesh.faces, ((0, 0),(1,0)), 'constant', constant_values=3)
    pv_mesh = pv.PolyData(tri_mesh.vertices, faces)
    return pv_mesh

envelope_path = os.path.relpath('../data/meshes/compulsory_envelope.obj')
context_path = os.path.relpath('../data/meshes/immediate_context.obj')

# load the mesh from file
envelope_mesh = tm.load(envelope_path)
context_mesh = tm.load(context_path)

# Check if the mesh is watertight
print(envelope_mesh.is_watertight)
print(context_mesh.is_watertight)

# initiating the plotter
p = pv.Plotter(notebook=True)

# adding the meshes
p.add_mesh(tri_to_pv(envelope_mesh), color='#abd8ff')
p.add_mesh(tri_to_pv(context_mesh), color='#aaaaaa')

# plotting
p.show()

# load lattices from before
lattice_path = os.path.relpath('../data/meshes/voxelized_envelope_highres.csv')
envelope_lattice = tg.lattice_from_csv(lattice_path)

Visaulization of voxels

# initiating the plotter
p = pv.Plotter(notebook=True)

# adding the meshes
envelope_lattice.fast_vis(p)
p.add_mesh(tri_to_pv(context_mesh), color='#aaaaaa')

# plotting
p.show()

name

Calculate ground proximity

ground_proximity = envelope_lattice.astype(int)
ground_proximity[ground_proximity == 1] = envelope_lattice.centroids.flatten()[2::3] / envelope_lattice.unit[2] - 1
ground_proximity = ground_proximity.astype(int)
ground_proximity

Calculate Penthouse factor

# set the lattice to be visualized
l = ground_proximity
# remapping
l = (l - l.min()) / (l.max()-l.min())

ground_proximity_norm_inverse=(1-l)*envelope_lattice
ground_proximity_norm_inverse

Save Ground proximity and Penthouse factor to CSV

ground_proximity_norm_inverse.to_csv('../data/fields/proximity_to_ground_floor.csv')

pent_house_factor = (1 - ground_proximity_norm_inverse) * envelope_lattice
pent_house_factor.to_csv('../data/fields/penthouse_factor.csv')

Initialize visualisations

# set the lattice to be visualized
l = ground_proximity_norm_inverse

# initiating the plotter
p = pv.Plotter(notebook=True)

# Create the spatial reference
grid = pv.UniformGrid()

# Set the grid dimensions: shape because we want to inject our values
grid.dimensions = l.shape
# The bottom left corner of the data set
grid.origin = l.minbound
# These are the cell sizes along each axis
grid.spacing = l.unit

# Add the data values to the cell data
grid.point_arrays["Street Access"] = l.flatten(order="F")  # Flatten the Lattice

# adding the volume
opacity = np.array([0,0.6,0.6,0.6,0.6,0.6,0.6]) * 1.5
p.add_volume(grid, cmap="coolwarm", opacity=opacity, shade=True, show_scalar_bar=False)

# plotting
p.show(use_ipyvtk=True)

# set the lattice to be visualized
l = ground_proximity
# remapping
l = (l - l.min()) / (l.max()-l.min())

# initiating the plotter
p = pv.Plotter(notebook=True)

# Create the spatial reference
grid = pv.UniformGrid()

# Set the grid dimensions: shape because we want to inject our values
grid.dimensions = l.shape
# The bottom left corner of the data set
grid.origin = l.minbound
# These are the cell sizes along each axis
grid.spacing = l.unit

# Add the data values to the cell data
grid.point_arrays["Street Access"] = l.flatten(order="F")  # Flatten the Lattice

# adding the volume
opacity = np.array([0,0.6,0.6,0.6,0.6,0.6,0.6]) * 1.5
p.add_volume(grid, cmap="coolwarm", opacity=opacity, shade=True, show_scalar_bar=False)

# plotting
p.show(use_ipyvtk=True)