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Street noise quietness notebook

0. Initialization

0.1. Load required libraries

import os
import topogenesis as tg
import pyvista as pv
import trimesh as tm
import numpy as np
import networkx as nx
import scipy as sp
np.random.seed(0)

0.2 Load availability lattice and meshes

# loading the lattice from csv
lattice_path = os.path.relpath('../data/solar_envelope_324.csv')
avail_lattice = tg.lattice_from_csv(lattice_path)
init_avail_lattice = tg.to_lattice(np.copy(avail_lattice), avail_lattice)

#Load context mesh from csv
context_path = os.path.relpath("../data/immediate_context.obj")
street_50_path = os.path.relpath("../data/street_noise_50db_3.obj") 
street_70_path = os.path.relpath("../data/street_noise_70db_4.obj") 

# load the mesh from file
context_mesh = tm.load(context_path)
street_50_mesh = tm.load(street_50_path)
street_70_mesh = tm.load(street_70_path)

# construct full latice
full_lattice = avail_lattice * 0 + 1

0.3 visualize

pv.set_plot_theme("document")

# 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

# Visualize the mesh using pyvista plotter
#######

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

# fast visualization of the lattice
avail_lattice.fast_vis(p)

# adding the meshes
p.add_mesh(tri_to_pv(context_mesh), opacity=0.1, style='wireframe')
p.add_mesh(tri_to_pv(street_50_mesh), color="ffd571", lighting = False, style='wireframe', line_width = 5)
p.add_mesh(tri_to_pv(street_70_mesh), color="ff7b7b", lighting = False, style='wireframe', line_width = 5)


# plotting
cpos = [(785.8704805788776, 708.4540755788776, 741.8613927288776),
 (65.08283250000001, -12.333572500000002, 21.07374465),
 (0.0, 0.0, 1.0)]
p.camera_position = cpos
p.window_size = 2000, 2000
p.show(use_ipyvtk=True)
# p.screenshot("noise_1")
print(p.camera_position)

1.1. 1st street - Distances between voxels and mesh

#Get voxel centers as points
vox_centroids = full_lattice.centroids
vox_ind = np.array(np.where(full_lattice==1)).T

# Noise level in first street
noise_base_1 = 50.0

#calculate closest distance from each voxel to point
dist_latice = avail_lattice * 0.0

for cen, ind in zip (vox_centroids, vox_ind):

    closest, distance, triangle_id = tm.proximity.closest_point(street_50_mesh, np.array([cen]))
    distance=np.round_(distance, 2)
    distance+= 0.01
    dist_latice[tuple(ind)] = distance

# computing the noise lattice from dist lattice
noise_latice_50 = noise_base_1 - 20 * np.log10(dist_latice) - 8

# initializing the sum lattice of noise
sum_noise_lats = avail_lattice * 0.0

# summing
sum_noise_lats += np.power(10, noise_latice_50 / 10.0)

1.2. 2nd street - Distances between voxels and mesh

#Get voxel centers as points / done with "_2" for troubleshooting
vox_centroids_2 = full_lattice.centroids
vox_ind_2 = np.array(np.where(full_lattice==1)).T

# Noise level in second street
noise_base_2 = 70.0

#calculate closest distance from each voxel to point
dist_latice_2 = avail_lattice * 0.0

for cen_2, ind_2 in zip (vox_centroids_2, vox_ind_2):

    closest, distance_2, triangle_id = tm.proximity.closest_point(street_70_mesh, np.array([cen_2]))
    distance_2+= 0.01
    distance_2=np.round_(distance_2, 2)
    dist_latice_2[tuple(ind_2)] = distance_2

# computing the noise lattice from dist lattice
noise_latice_70 = noise_base_2 - 20 * np.log10(dist_latice_2) -8

# summing
sum_noise_lats += np.power(10, noise_latice_70 / 10.0)

# computing the final aggregation
agg_noise_lats = 10 * np.log10(sum_noise_lats)

1.3. Visulization

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

# adding the meshes
p.add_mesh(tri_to_pv(context_mesh), opacity=0.1, style='wireframe')
p.add_mesh(tri_to_pv(street_50_mesh), color="ffd571", lighting = False, style='wireframe', line_width = 5)
p.add_mesh(tri_to_pv(street_70_mesh), color="ff7b7b", lighting = False, style='wireframe', line_width = 5)

vis_lattice = agg_noise_lats * avail_lattice

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

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

# Add the data values to the cell data
grid.point_arrays["Street noise in dB"] = vis_lattice.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])
p.add_volume(grid, cmap="coolwarm" , clim=[20, 40], opacity=opacity, shade=False)

# plotting
cpos = [(785.8704805788776, 708.4540755788776, 741.8613927288776),
 (65.08283250000001, -12.333572500000002, 21.07374465),
 (0.0, 0.0, 1.0)]
p.camera_position = cpos
p.window_size = 2000, 2000
p.show(use_ipyvtk=True)
# p.screenshot("noise_2")
print(p.camera_position)

2.1. Mapping the values

# mapping the values from (0, max) to (1, 0)

max_noise = np.max(agg_noise_lats)
quietness_lattice = (1- agg_noise_lats / max_noise) * avail_lattice

2.2. Visualize noise value field

# 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

# load the mesh from file
context_path = os.path.relpath('../data/immediate_context.obj')
context_mesh = tm.load(context_path)

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

p.add_mesh(tri_to_pv(context_mesh), opacity=0.1, style='wireframe')
p.add_mesh(tri_to_pv(street_50_mesh), color="ffd571", lighting = False, style='wireframe', line_width = 5)
p.add_mesh(tri_to_pv(street_70_mesh), color="ff7b7b", lighting = False, style='wireframe', line_width = 5)

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

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

# Add the data values to the cell data
grid.point_arrays["Quietness from street noise"] = quietness_lattice.flatten(order="F")  # Flatten the Lattice

# adding the volume
opacity = np.array([0.0,0.6,0.6,0.6,0.6,0.6,0.6])
p.add_volume(grid, cmap="coolwarm", clim=[0.5, 1.0] ,opacity=opacity)

# plotting
cpos = [(785.8704805788776, 708.4540755788776, 741.8613927288776),
 (65.08283250000001, -12.333572500000002, 21.07374465),
 (0.0, 0.0, 1.0)]
p.camera_position = cpos
p.window_size = 2000, 2000
p.show(use_ipyvtk=True)
# p.screenshot("noise_3")
print(p.camera_position)

3.1. Save Quietness from street noise lattice to CSV

# save the interest voxel access lattice

csv_path = os.path.relpath('../data/project_data/quietness_from_street_noise_324.csv')
quietness_lattice.to_csv(csv_path)