<jupyter_start><jupyter_text>Mustang<jupyter_code>mustang = pd.read_csv('in/mustang_release_v1.0beta.csv', delimiter=',')
mustang[SUBMISSION_TIME] = mustang['submit_time'].apply(lambda x: safe_parse_date(x))
mustang[START_TIME] = mustang['start_time'].apply(lambda x: safe_parse_date(x))
mustang[END_TIME] = mustang['end_time'].apply(lambda x: safe_parse_date(x))
mustang[JOB_TYPE] = 0
mustang[SOURCE] = 0
mustang[SERVER_TYPE] = 0
mustang[LOCATION] = 0
mustang = mustang[NAMES + ['node_count']].dropna(axis='rows')
mustang = mustang.sort_values(by=SUBMISSION_TIME)<jupyter_output><empty_output><jupyter_text>Select jobs that are processed within one time slot.<jupyter_code># mustang = mustang[mustang[END_TIME] - mustang[START_TIME] <= TIME_SLOT_LENGTH]<jupyter_output><empty_output><jupyter_text>Repeat jobs once for each node they were assigned to.<jupyter_code>raw_mustang = mustang.loc[mustang.index.repeat(mustang['node_count'])]
raw_mustang[NAMES].to_csv('out/mustang.csv', index=False)<jupyter_output><empty_output><jupyter_text>Trinity<jupyter_code>trinity = pd.read_csv('in/trinity_formatted_release_v1.0beta.csv', delimiter=',')
trinity[SUBMISSION_TIME] = trinity['submit_time'].apply(lambda x: safe_parse_date(x))
trinity[START_TIME] = trinity['start_time'].apply(lambda x: safe_parse_date(x))
trinity[END_TIME] = trinity['end_time'].apply(lambda x: safe_parse_date(x))
trinity[JOB_TYPE] = 0
trinity[SOURCE] = 0
trinity[SERVER_TYPE] = 0
trinity[LOCATION] = 0
trinity = trinity[NAMES + ['node_count']].dropna(axis='rows')
trinity = trinity.sort_values(by=SUBMISSION_TIME)<jupyter_output><empty_output><jupyter_text>Repeat jobs once for each node they were assigned to.<jupyter_code>raw_trinity = trinity.loc[trinity.index.repeat(trinity['node_count'])]
raw_trinity[NAMES].to_csv('out/trinity.csv', index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1458670988654781, 'fraction_numerical': 0.006482982171799027, 'mean_word_length': 11.18421052631579, 'pattern_counts': {'":': 0, '<': 22, '<?xml version=': 0, '>': 21, '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': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '202260', 'license': 'MIT', 'n_tokens_mistral': 713, 'n_tokens_neox': 704, 'n_words': 91, 'path': 'analysis/traces/los_alamos/parsing.ipynb', 'repo_name': 'jonhue/bachelors-thesis', 'size': '1851'}