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import plotly.graph_objects as go
from plotly.subplots import make_subplots
import hashlib
def get_color(name):
hash_object = hashlib.md5(name.encode())
color_index = int(hash_object.hexdigest(), 16) % 360
return f'hsl({color_index}, 100%, 50%)'
def plot_results(results, filename):
fig = make_subplots(rows=3, cols=1)
for i, (coro_name, x_values) in enumerate(results.items(), start=1):
x_list = []
y_latency_list = []
y_throughput_list = []
y_max_latency_list = []
for speedup, experiments in x_values.items():
for experiment in experiments:
completed_callbacks = experiment["latency"]
virtual_run_time = experiment["virtual_run_time"][0]
x_list.append(speedup * 100)
num_callbacks = len(completed_callbacks)
# handle average latency graph
if num_callbacks > 0:
total_wait = sum([cb[1] for cb in completed_callbacks])
max_wait = max([cb[1] for cb in completed_callbacks])
latency = total_wait / num_callbacks
y_max_latency_list.append(max_wait)
y_latency_list.append(latency)
else:
y_latency_list.append(0)
# handle throughput graph
throughput = num_callbacks / virtual_run_time
y_throughput_list.append(throughput)
fig.add_trace(go.Scatter(
x=x_list,
y=y_latency_list,
mode='markers',
name=coro_name,
marker=dict(color=get_color(coro_name)),
showlegend=True,
), row=1, col=1)
fig.add_trace(go.Scatter(
x=x_list,
y=y_throughput_list,
mode='markers',
name=coro_name,
marker=dict(color=get_color(coro_name)),
showlegend=False,
), row=2, col=1)
fig.add_trace(go.Scatter(
x=x_list,
y=y_max_latency_list,
mode='markers',
name=coro_name,
marker=dict(color=get_color(coro_name)),
showlegend=False,
), row=3, col=1)
fig.update_xaxes(title_text="Speedup (% optimized away)", row=3, col=1)
fig.update_yaxes(title_text="Average Handle Latency (seconds)", row=1, col=1)
fig.update_yaxes(title_text="Throughput (callbacks per second)", row=2, col=1)
fig.update_yaxes(title_text="Maximum Handle Latency (seconds)", row=3, col=1)
fig.write_html(filename)
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