Howto BF-STREAMS-004: Visualizing Moving 3D Clouds Provided By MLPro
Executable code
## -------------------------------------------------------------------------------------------------
## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
## -- Package : mlpro.bf.examples
## -- Module : howto_bf_streams_004_visualize_moving_clouds3d.py
## -------------------------------------------------------------------------------------------------
## -- History :
## -- yyyy-mm-dd Ver. Auth. Description
## -- 2023-03-23 0.0.0 SP Creation
## -- 2023-03-23 1.0.0 SP First draft implementation
## -- 2023-04-07 1.0.1 SP Corrections to include visualize parameter
## -- 2023-04-08 1.0.2 DA Addded seeding for reproducible behavior
## -------------------------------------------------------------------------------------------------
"""
Ver. 1.0.2 (2023-04-08)
This module demonstrates how to visualize the moving clouds 3D data stream provided by MLPro.
You will learn:
1) How to access MLPro's native moving clouds 3D data stream.
2) How to iterate the instances of the stream.
3) How to access feature data of the stream.
4) How to visualize the data.
"""
# Import the necessary libraries
from mlpro.bf.streams.streams import *
from mlpro.bf.streams.streams.clouds3d_dynamic import StreamMLProDynamicClouds3D
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from mlpro.bf.various import Log
# 0 Prepare Demo/Unit test mode
if __name__ == '__main__':
logging = Log.C_LOG_ALL
visualize = True
else:
logging = Log.C_LOG_NOTHING
visualize = False
if visualize:
# Initialise the stream object using the StreamMLProDynamicClouds3D class
# and creates an iterator my_iter for it.
# The argument pattern can be random, random chain, static and merge.
stream = StreamMLProDynamicClouds3D( p_pattern = 'random',
p_no_clouds = 8,
p_variance = 7.0,
p_velocity = 1.0,
p_logging = logging )
stream.set_random_seed(3)
my_iter = iter(stream)
# Create three empty lists to store x, y and z coordinates of the points.
x = []
y = []
z = []
# Creating figure
fig = plt.figure(figsize = (10, 7))
ax = plt.axes(projection ="3d")
# Iterate over the instances of the stream and store the coordinate values in the empty lists.
for i, curr_instance in enumerate(my_iter):
curr_data = curr_instance.get_feature_data().get_values()
x.append(list(curr_data)[0])
y.append(list(curr_data)[1])
z.append(list(curr_data)[2])
# Function that draws each frame of the animation
def animate(i):
ax.clear()
ax.scatter3D(x[:(i+1)], y[:(i+1)], z[:(i+1)], s=1)
ax.set_xlim([-110,110])
ax.set_ylim([-110,110])
ax.set_zlim([-110,110])
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
# Run the animation
ani = FuncAnimation(fig, animate, frames=1999, interval=100, repeat=False)
plt.show()
Results
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