mirror of
https://github.com/sharkdp/hyperfine.git
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125 lines
2.8 KiB
Python
Executable File
125 lines
2.8 KiB
Python
Executable File
#!/usr/bin/env python
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "matplotlib",
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# "pyqt6",
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# "numpy",
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# ]
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# ///
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"""This program shows `hyperfine` benchmark results as a histogram."""
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import argparse
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import matplotlib.pyplot as plt
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import numpy as np
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from plot_utils import METRICS, add_metric_argument, load_results
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parser = argparse.ArgumentParser(description=__doc__)
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add_metric_argument(parser)
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parser.add_argument("file", help="JSON file with benchmark results")
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parser.add_argument("--title", help="Plot title")
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parser.add_argument(
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"--labels", help="Comma-separated list of entries for the plot legend"
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)
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parser.add_argument("--bins", help="Number of bins (default: auto)")
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parser.add_argument(
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"--legend-location",
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help="Location of the legend on plot (default: upper center)",
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choices=[
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"upper center",
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"lower center",
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"right",
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"left",
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"best",
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"upper left",
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"upper right",
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"lower left",
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"lower right",
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"center left",
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"center right",
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"center",
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],
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default="upper center",
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)
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parser.add_argument(
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"--type", help="Type of histogram (*bar*, barstacked, step, stepfilled)"
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)
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parser.add_argument("-o", "--output", help="Save image to the given filename.")
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parser.add_argument(
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"--min",
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"--t-min",
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dest="value_min",
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type=float,
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help="Minimum metric value to display",
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)
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parser.add_argument(
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"--max",
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"--t-max",
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dest="value_max",
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type=float,
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help="Maximum metric value to display",
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)
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parser.add_argument(
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"--log-count",
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help="Use a logarithmic y-axis for the event count",
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action="store_true",
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)
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args = parser.parse_args()
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metric, [results] = load_results(parser, [args.file], args.metric)
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metric_info = METRICS[metric]
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if args.labels:
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labels = args.labels.split(",")
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else:
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labels = [b.get("name", b["command"]) for b in results]
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all_values = [
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[m[metric]["value"] / metric_info.scale for m in b["measurements"]] for b in results
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]
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value_min = (
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args.value_min
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if args.value_min is not None
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else np.min(list(map(np.min, all_values)))
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)
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value_max = (
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args.value_max
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if args.value_max is not None
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else np.max(list(map(np.max, all_values)))
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)
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bins = int(args.bins) if args.bins else "auto"
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histtype = args.type if args.type else "bar"
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plt.figure(figsize=(10, 5))
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plt.hist(
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all_values,
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label=labels,
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bins=bins,
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histtype=histtype,
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range=(value_min, value_max),
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)
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plt.legend(
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loc=args.legend_location,
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fancybox=True,
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shadow=True,
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prop={"size": 10, "family": ["Source Code Pro", "Fira Mono", "Courier New"]},
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)
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plt.xlabel(metric_info.axis_label)
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if args.title:
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plt.title(args.title)
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if args.log_count:
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plt.yscale("log")
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else:
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plt.ylim(0, None)
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if args.output:
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plt.savefig(args.output, dpi=600)
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else:
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plt.show()
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