This shows you the differences between two versions of the page.
Both sides previous revision Previous revision | Next revision Both sides next revision | ||
cs401r_w2016:lab8 [2018/03/12 22:27] sadler [Description:] |
cs401r_w2016:lab8 [2018/03/12 22:32] sadler [Hints:] |
||
---|---|---|---|
Line 115: | Line 115: | ||
The combination of these two things allowed me to experiment with my particle filter in real-time. Note that you are not required to produce any sort of visualization over time, just a single plot at the end of running your code. The use of ''pyqtgraph'' is strictly for the work you will do debugging your particle filter. | The combination of these two things allowed me to experiment with my particle filter in real-time. Note that you are not required to produce any sort of visualization over time, just a single plot at the end of running your code. The use of ''pyqtgraph'' is strictly for the work you will do debugging your particle filter. | ||
+ | |||
+ | Or if you want to use matplotLib which is better for Jupyter Notebook but slower, here is some code for plotting at each iteration: | ||
+ | <code python> | ||
+ | import matplotlib.pyplot | ||
+ | import numpy | ||
+ | from IPython.display import clear_output | ||
+ | |||
+ | def draw(particles, true_values, predicted_values, t): | ||
+ | clear_output(wait=True) | ||
+ | show_map( room_map ) | ||
+ | plt.scatter(particles[0,:] , particles[1,:], c="yellow") | ||
+ | plt.plot(true_values[0,:t], true_values[1,:t] , c= "green", label="Actual Position") | ||
+ | plt.plot(predicted_values[0,:t], predicted_values[1,:t], c= "red", label="Estimated Position") | ||
+ | plt.legend(loc=1) | ||
+ | plt.show() | ||
+ | </code> |