Bachelor-Thesis_Furkan_Karakocaoglu/testumgebung/CrowdModelling/Assets/Depictions_Years/Scripts/Python/Heatmap_3D.py

import UnityEngine as ue
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
# import seaborn as sns
# import matplotlib.patches as patches
# import matplotlib.colors as colors
# from sklearn import preprocessing
# from pylab import *
# from matplotlib import style
from matplotlib.colors import LinearSegmentedColormap 
from mpl_toolkits.mplot3d import Axes3D


WIDTH = int(70)
HEIGHT = int(35)
OBSTACLE_PATH = "Assets/Data_image/obstacle.pkl"
POSITION_PATH = ue.Application.dataPath + "/Data_position/80/Walk4.csv"
HEATMAP_PATH = "Assets/Data_image/80/heatmap3D4.png"

# 1. Get position data from csv file
data = pd.read_csv(POSITION_PATH, sep=';', usecols=["Position x", "Position z"], decimal=',', dtype={'Position x': float, 'Position z': float})
data = data.round(0)

# 2. Group by positions and count appearance
data_count = data.groupby(['Position x', 'Position z']).size().reset_index(name='counts')

# 3. Assign x, y, z, width, depth, height
x = data_count["Position x"].tolist()
y = data_count["Position z"].tolist()
z = np.zeros_like(len(x))
dz = data_count["counts"].tolist() # Change

# 4. Create figure and axes
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 5.1 Create custom colormap Day1, Day2, Day3
cmap = LinearSegmentedColormap.from_list(name='day1', colors=[(0.40,0.76,0.65), (0.11,0.62,0.47)])
# cmap = LinearSegmentedColormap.from_list(name='day2', colors=['grey', (0.99,0.55,0.38), (0.85,0.37,0.01)])
# cmap = LinearSegmentedColormap.from_list(name='day3', colors=['grey', (0.55,0.63,0.80), (0.46,0.44,0.70)])

# 5.2 Initialize array for coloring the bars
dz_array = np.array(data_count['counts'])
fracs = dz_array.astype(float) / dz_array.max()
color_values = cmap(fracs.tolist())

# 6. Create the bars
# for i in range(len(x)):
#     img = ax.bar3d(x[i], y[i], z, 1, 1, dz[i], color=color_values[i], shade=False)
img = ax.bar3d(x, y, z, 1, 1, dz, color=color_values, shade=False)

# 7. Create Colorbar
color_map = cm.ScalarMappable(cmap=cmap)
color_map.set_array(dz)
fig.colorbar(color_map)

# 8. 
plt.show()

# 10. Save 3D Heatmap
# heatmap.get_figure().savefig(HEATMAP_PATH, transparent=True)
fig.savefig(HEATMAP_PATH, transparent=True)