<jupyter_start><jupyter_text>Read csv file generated by UKF run containing NIS values for laser and radar measurement. Separate them by sensor type.<jupyter_code>df = pd.read_csv('./build/nis_out.csv', index_col=0)
laser_nis = df[df['Sensor Type (L/R)'] == 'L']['NIS']
radar_nis = df[df['Sensor Type (L/R)'] == 'R']['NIS']<jupyter_output><empty_output><jupyter_text>What does the 95th percentile NIS value look like?<jupyter_code>print("Laser NIS 95th percentile: %.2f" % laser_nis.quantile(0.95))
print("Radar NIS 95th percentile: %.2f" % radar_nis.quantile(0.95))<jupyter_output>Laser NIS 95th percentile: 4.94
Radar NIS 95th percentile: 7.48<jupyter_text>Now plot NIS values<jupyter_code>plt.figure(figsize=(15,5))

# Plot Lidar NIS
plt.subplot(121)
plt.plot(laser_nis)
plt.title('Lidar')
plt.ylabel('NIS')
plt.xlabel('Timestamp (us)')
plt.ylim([0, 20]);
plt.axhline(y=7.8, linewidth=1, color='r', label='NIS=7.8');
plt.legend()


# Plot Radar NIS
plt.subplot(122)
plt.plot(radar_nis)
plt.title('Radar')
plt.ylabel('NIS')
plt.xlabel('Timestamp (us)')
plt.ylim([0, 20]);
plt.axhline(y=7.8, linewidth=1, color='r', label='NIS=7.8');
plt.legend()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18197278911564627, 'fraction_numerical': 0.04251700680272109, 'mean_word_length': 7.117241379310345, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '274818', 'license': 'MIT', 'n_tokens_mistral': 539, 'n_tokens_neox': 488, 'n_words': 95, 'path': 'PlotNIS.ipynb', 'repo_name': 'rajatsharma01/CarND-Unscented-Kalman-Filter-Project', 'size': '1176'}