# This file is part of the uutils coreutils package.
#
# For the full copyright and license information, please view the LICENSE
# file that was distributed with this source code.

import sys

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

from graph_common import (
    COLORS,
    setup_theme,
    apply_smoothing,
    style_axes,
    add_title,
    style_legend,
)

d = pd.read_json(sys.argv[1], orient="index")
df = pd.DataFrame(d)

df.columns.names = ["date"]
df.index = pd.to_datetime(df.index, utc=True, format="mixed")

print(df)

# Set up modern theme
setup_theme()

# Create figure with better proportions and higher DPI
fig, ax = plt.subplots(figsize=(18, 9), dpi=100)

# Prepare data for Seaborn - melt to long format
df_plot = df[["size", "multisize"]].copy()
df_plot = df_plot.reset_index()
df_plot.columns = ["date", "size", "multisize"]
df_plot_long = df_plot.melt(
    id_vars="date", var_name="binary_type", value_name="size_kb"
)

# Convert to numeric
df_plot_long["size_kb"] = pd.to_numeric(df_plot_long["size_kb"], errors="coerce")

# Apply smoothing using rolling average
df_plot_long["size_kb_smooth"] = apply_smoothing(df_plot_long, "binary_type", "size_kb")

# Use color palette from common module
palette = {"size": COLORS["size"], "multisize": COLORS["multisize"]}

# Add gradient-like area fills first
for col, color in palette.items():
    if col in df_plot.columns:
        ax.fill_between(
            df_plot["date"],
            0,
            df_plot[col],
            alpha=0.2,
            color=color,
            zorder=1,
            linewidth=0,
        )

# Use Seaborn's lineplot with enhanced styling and smoothed data
sns.lineplot(
    data=df_plot_long,
    x="date",
    y="size_kb_smooth",
    hue="binary_type",
    palette=palette,
    linewidth=4,
    ax=ax,
    markers=False,  # Disable markers for smoother look
    dashes=False,
    alpha=1,
    zorder=3,
)

# Add title and subtitle
add_title(
    ax,
    "uutils coreutils — Binary Size Evolution",
    "Tracking binary size optimization and comparing build strategies",
)

# Style axes with labels and grid
style_axes(ax, xlabel="Date", ylabel="Size (kilobytes)")

# Style legend
handles, labels = ax.get_legend_handles_labels()
label_map = {"size": "Multiple Binaries", "multisize": "Multicall Binary (Optimized)"}
labels = [label_map.get(label, label) for label in labels]
style_legend(ax, handles, labels, ncol=1, loc="upper left")

# Tight layout
plt.tight_layout()

# Save with high quality
plt.savefig(
    "size-results.svg",
    format="svg",
    dpi=300,
    bbox_inches="tight",
    facecolor="white",
    edgecolor="none",
    metadata={"Creator": "uutils coreutils tracking", "Title": "Binary Size Evolution"},
)