You've already forked coreutils-tracking
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https://github.com/uutils/coreutils-tracking.git
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Sylvestre Ledru
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
# This file is part of the uutils coreutils package.
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#
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# For the full copyright and license information, please view the LICENSE
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# file that was distributed with this source code.
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import sys
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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from graph_common import (COLORS, setup_theme, apply_smoothing, style_axes,
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add_title, style_legend, add_reference_lines)
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if len(sys.argv) <= 2:
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print("graph.py: <json file> <title>")
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sys.exit()
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d = pd.read_json(sys.argv[1], orient="index")
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df = pd.DataFrame(d)
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title = sys.argv[2]
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df.columns.names = ["date"]
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df.index = pd.to_datetime(df.index, utc=True)
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print(df)
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# Set up modern theme
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setup_theme()
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# Create figure with better proportions and higher DPI
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fig, ax = plt.subplots(figsize=(18, 9), dpi=100)
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# Prepare data for plotting - melt to long format for Seaborn
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plot_columns = ['total', 'pass', 'fail']
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if "error" in df.columns and df['error'].notna().any():
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plot_columns.append('error')
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plot_columns.append('skip')
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df_plot = df[plot_columns].copy()
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df_plot = df_plot.reset_index()
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df_plot.rename(columns={df_plot.columns[0]: 'date'}, inplace=True)
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df_plot_long = df_plot.melt(id_vars='date', var_name='metric', value_name='count')
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# Convert string values to numeric
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df_plot_long['count'] = pd.to_numeric(df_plot_long['count'], errors='coerce')
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# Apply smoothing using rolling average (window of 15 for smoother lines)
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df_plot_long['count_smooth'] = apply_smoothing(df_plot_long, 'metric', 'count')
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# Use color palette from common module
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palette = {k: COLORS[k] for k in ['total', 'pass', 'fail', 'error', 'skip']}
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# Add gradient-like area fills first (behind lines)
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for metric in ['total', 'pass', 'fail']:
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if metric in df_plot.columns:
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ax.fill_between(df_plot['date'], 0, df_plot[metric],
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alpha=0.18, color=palette[metric], zorder=1, linewidth=0)
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# Use Seaborn's lineplot with enhanced styling and smoothed data
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sns.lineplot(
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data=df_plot_long,
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x='date',
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y='count_smooth',
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hue='metric',
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palette=palette,
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linewidth=3.5,
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ax=ax,
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markers=False, # Disable markers for smoother look
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dashes=False,
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alpha=1,
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zorder=3
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)
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# Add title and subtitle
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add_title(ax, f'uutils coreutils — {title} Test Suite Results',
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'Tracking test results over time to measure progress and compatibility')
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# Style axes with labels and grid
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style_axes(ax, xlabel='Date', ylabel='Number of Tests')
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# Add reference lines
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y_max = df_plot_long['count_smooth'].max()
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add_reference_lines(ax, y_max)
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# Style legend
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handles, labels = ax.get_legend_handles_labels()
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labels = [label.capitalize() for label in labels]
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style_legend(ax, handles, labels, ncol=len(plot_columns), loc='upper left')
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# Tight layout
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plt.tight_layout()
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# Save with high quality and optimized settings
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plt.savefig(f"{title.lower()}-results.svg", format="svg", dpi=300,
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bbox_inches="tight", facecolor='white', edgecolor='none',
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metadata={'Creator': 'uutils coreutils tracking', 'Title': f'{title} Test Suite Results'})
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