<jupyter_start><jupyter_text>All Messages<jupyter_code>data = pd.read_csv('results.csv')
data["latency (μs)"] = data["latency (ns)"] / 1000
g = sns.barplot("clients", "latency (μs)", data=data, palette="Blues_d")
g.set_title("RTT Message Latency to Single Server (total linearizeability)")
g.set_ylabel("mean latency (μs)")
g.set_xlabel("# concurrent clients")<jupyter_output><empty_output><jupyter_text>Sample of Messages<jupyter_code>data = data.sample(frac=0.1)
data = data[data["latency (μs)"] < 5000.0]
data.describe()
g = sns.boxplot("clients", "latency (μs)", data=data, palette="Blues_d")<jupyter_output><empty_output><jupyter_text>Per-Machine Aggregates<jupyter_code>data = pd.read_csv('throughput.csv')
data.describe()
data['throughput (msg/sec)'] = data['messages'] / (data['latency (ns)'] / 1000000000)
g = sns.barplot("clients", "throughput (msg/sec)", data=data, palette="Blues_d")
g.set_title("Write Throughput to Single Server (total linearizeability)")
g.set_ylabel("throughput (msg/sec)")
g.set_xlabel("# concurrent clients")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1798695246971109, 'fraction_numerical': 0.0195712954333644, 'mean_word_length': 9.037383177570094, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 1, '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': '348705', 'license': 'MIT', 'n_tokens_mistral': 431, 'n_tokens_neox': 401, 'n_words': 69, 'path': 'fixtures/vms/client/results.ipynb', 'repo_name': 'bbengfort/honu', 'size': '1073'}