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- Extracted a function to load the metrics file, which already separates the different proof stages - Use it in `openvm/scripts/plot_trace_cells.py`
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
#!/usr/bin/env python3
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import pandas as pd
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import matplotlib.pyplot as plt
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import argparse
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from metrics_utils import load_metrics_dataframes
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def autopct_with_billions(pct, total):
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val = pct * total / 100
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return f'{pct:.1f}%\n{val/1e9:.2f}B'
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def main(metrics_path, output_path=None, subtitle=None):
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# Load only the app dataframe
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app, _, _ = load_metrics_dataframes(metrics_path)
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# Get total cells from app dataframe
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total_cells_df = app[app["metric"] == "total_cells"]
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total_cells = pd.to_numeric(total_cells_df["value"]).sum()
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print(f"Total cells: {total_cells/1e9:.2f}B")
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# Get cell entries from app dataframe
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cells_df = app[app["metric"] == "cells"].copy()
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cells_df["segment"] = pd.to_numeric(cells_df["segment"].fillna(0))
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cells_df["cells"] = pd.to_numeric(cells_df["value"])
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# Create dataframe with required columns
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df = cells_df[["segment", "air_name", "cells"]]
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# Group and threshold
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cells_by_air = df.groupby('air_name')['cells'].sum().sort_values(ascending=False)
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print("Cells by AIR:")
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print(cells_by_air)
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# Sanity check: #cells should match total_cells
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assert total_cells == cells_by_air.sum()
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threshold_ratio = 0.015
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threshold = threshold_ratio * cells_by_air.sum()
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large = cells_by_air[cells_by_air >= threshold]
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small = cells_by_air[cells_by_air < threshold]
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if not small.empty:
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large['Other'] = small.sum()
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_, ax = plt.subplots(figsize=(7.5, 7.5))
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plot_title = "Trace cells by AIR" if subtitle is None else f"Trace cells by AIR ({subtitle})"
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ax.set_title(plot_title)
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total = large.sum()
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colors = plt.get_cmap("tab20")(range(len(large)))
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def autopct_filtered(pct):
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return autopct_with_billions(pct, total) if pct > 5 else ''
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wedges, _, _ = ax.pie(
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large,
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autopct=autopct_filtered,
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startangle=90,
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colors=colors
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)
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percentages = 100 * large / total
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legend_labels = [f"{percent:.1f}% - {label}" for label, percent in zip(large.index, percentages)]
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ax.legend(
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wedges,
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legend_labels,
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title="AIRs",
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loc="upper center",
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bbox_to_anchor=(0.5, 0),
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ncol=1,
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fontsize='small',
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title_fontsize='medium',
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frameon=False
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)
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plt.ylabel('')
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plt.tight_layout(pad=5.0)
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if output_path:
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print(f"Saving plot to {output_path}")
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plt.savefig(output_path, bbox_inches="tight")
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else:
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plt.show()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Visualize AIR cell metrics from a JSON file.")
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parser.add_argument("metrics_path", help="Path to the metrics.json file")
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parser.add_argument("-o", "--output", help="Optional path to save the output image")
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parser.add_argument("-s", "--subtitle", help="Optional subtitle for the plot")
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args = parser.parse_args()
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main(args.metrics_path, args.output, args.subtitle) |