cs401r_w2016:lab8
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cs401r_w2016:lab8 [2018/03/12 22:27] sadler [Description:] |
cs401r_w2016:lab8 [2021/06/30 23:42] (current) |
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| 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+1], true_values[1,:t+1] , c= "green", label="Actual Position") | ||
| + | plt.plot(predicted_values[0,:t+1], predicted_values[1,:t+1], c= "red", label="Estimated Position") | ||
| + | plt.legend(loc=1) | ||
| + | plt.show() | ||
| + | </code> | ||
cs401r_w2016/lab8.1520893645.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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