<jupyter_start><jupyter_text>Compass heading<jupyter_code>plt.plot(standardized_time_1, compass_heading_1, label='China')
plt.plot(standardized_time_2, compass_heading_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('Heading [degree]', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>Temperature<jupyter_code>plt.plot(standardized_time_1, temp_1, label='China')
plt.plot(standardized_time_2, temp_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('Temperature [degree]', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>PH<jupyter_code>plt.plot(standardized_time_1, PH_1, label='China')
plt.plot(standardized_time_2, PH_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('PH', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>Conductivity* around time 1000, catabot hit another boat at China<jupyter_code>plt.plot(standardized_time_1, cond_1, label='China')
plt.plot(standardized_time_2, cond_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('Conductivity', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>Chlorophyll * around time 1000, catabot hit another boat at China<jupyter_code>plt.plot(standardized_time_1, chlorophyll_1, label='China')
plt.plot(standardized_time_2, chlorophyll_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('chlorophyll [RFU]', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>ODO<jupyter_code>plt.plot(standardized_time_1, ODO_1, label='China')
plt.plot(standardized_time_2, ODO_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('ODO [mg/L]', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>Sonar depth<jupyter_code>plt.plot(standardized_time_1, sonar_1, label='China')
plt.plot(standardized_time_2, sonar_2, label='Auburn')

plt.xlabel('Time [sec]', fontsize=16)
plt.ylabel('sonar [m]', fontsize=16)
plt.legend()
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16220095693779904, 'fraction_numerical': 0.03062200956937799, 'mean_word_length': 11.987577639751553, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 29, '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': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '66477', 'license': 'MIT', 'n_tokens_mistral': 927, 'n_tokens_neox': 842, 'n_words': 110, 'path': 'Jupyter_notebook/20200915-Comparison (China-Auburn).ipynb', 'repo_name': 'dartmouthrobotics/epscor_asv_data_analysis', 'size': '2090'}