Jean-Gabriel Gill-Couture
fd8f643a8f
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110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
from dash import Dash, dcc, html, Input, Output
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import plotly.graph_objects as go
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import pandas as pd
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# Load the CSV data
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df = pd.read_csv("iobench.csv") # Replace with the actual file path
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# Initialize Dash app
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app = Dash(__name__)
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# Layout
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app.layout = html.Div(
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[
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html.H1("IOBench Results Viewer", style={"textAlign": "center"}),
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# Filters
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html.Div(
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[
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html.Label("Filter by Label:"),
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dcc.Dropdown(
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id="label-filter",
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options=[{"label": label, "value": label} for label in df["label"].unique()],
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value=df["label"].unique().tolist(),
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multi=True,
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),
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html.Label("Filter by Test Name:"),
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dcc.Dropdown(
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id="test-filter",
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options=[{"label": test, "value": test} for test in df["test_name"].unique()],
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value=df["test_name"].unique().tolist(),
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multi=True,
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),
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],
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style={"width": "25%", "display": "inline-block", "verticalAlign": "top", "padding": "10px"},
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),
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# Graphs
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html.Div(
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[
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dcc.Graph(id="throughput-graph"),
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dcc.Graph(id="latency-graph"),
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],
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style={"width": "70%", "display": "inline-block", "padding": "10px"},
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),
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]
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)
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# Callbacks
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@app.callback(
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[Output("throughput-graph", "figure"), Output("latency-graph", "figure")],
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[Input("label-filter", "value"), Input("test-filter", "value")],
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)
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def update_graphs(selected_labels, selected_tests):
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# Filter data
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filtered_df = df[df["label"].isin(selected_labels) & df["test_name"].isin(selected_tests)]
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# Throughput Graph
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throughput_fig = go.Figure()
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for label in filtered_df["label"].unique():
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subset = filtered_df[filtered_df["label"] == label]
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throughput_fig.add_trace(
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go.Bar(
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x=subset["test_name"],
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y=subset["iops"],
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name=f"{label} - IOPS",
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)
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)
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throughput_fig.add_trace(
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go.Bar(
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x=subset["test_name"],
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y=subset["bandwidth_kibps"],
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name=f"{label} - Bandwidth (KiB/s)",
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)
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)
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throughput_fig.update_layout(
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title="Throughput (IOPS and Bandwidth)",
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xaxis_title="Test Name",
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yaxis_title="Value",
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barmode="group",
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)
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# Latency Graph
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latency_fig = go.Figure()
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for label in filtered_df["label"].unique():
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subset = filtered_df[filtered_df["label"] == label]
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latency_fig.add_trace(
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go.Scatter(
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x=subset["test_name"],
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y=subset["latency_mean_ms"],
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mode="markers+lines",
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name=f"{label} - Latency Mean (ms)",
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error_y=dict(
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type="data",
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array=subset["latency_stddev_ms"],
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visible=True,
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),
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)
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)
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latency_fig.update_layout(
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title="Latency with Standard Deviation",
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xaxis_title="Test Name",
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yaxis_title="Latency (ms)",
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)
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return throughput_fig, latency_fig
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if __name__ == "__main__":
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app.run_server(debug=True)
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