feat(plotnine): implement ridgeline-basic

Author: github-actions[bot]

plots/ridgeline-basic/implementations/plotnine.py

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+"""
+ridgeline-basic: Ridgeline Plot
+Library: plotnine
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_blank,
+    element_line,
+    element_text,
+    facet_wrap,
+    geom_density,
+    ggplot,
+    labs,
+    scale_fill_manual,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Monthly temperature readings
+np.random.seed(42)
+months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+n_per_month = 100
+
+# Generate temperature data with seasonal pattern
+data_list = []
+base_temps = [2, 4, 8, 12, 17, 21, 24, 23, 19, 13, 7, 3]  # Typical seasonal pattern
+
+for i, month in enumerate(months):
+    temps = np.random.normal(base_temps[i], 3, n_per_month)
+    data_list.append(pd.DataFrame({"month": month, "temperature": temps}))
+
+data = pd.concat(data_list, ignore_index=True)
+
+# Convert month to ordered categorical (reversed for ridgeline stacking - Dec at top)
+data["month"] = pd.Categorical(data["month"], categories=months[::-1], ordered=True)
+
+# Create gradient colors from cool to warm (matching seasonal pattern)
+colors = {
+    "Jan": "#306998",
+    "Feb": "#3B7AAD",
+    "Mar": "#4D8BC2",
+    "Apr": "#5F9CD7",
+    "May": "#71ADEC",
+    "Jun": "#FFD43B",
+    "Jul": "#F97316",
+    "Aug": "#DC2626",
+    "Sep": "#F97316",
+    "Oct": "#FFD43B",
+    "Nov": "#71ADEC",
+    "Dec": "#306998",
+}
+
+# Create ridgeline plot using facet_wrap for vertical stacking
+plot = (
+    ggplot(data, aes(x="temperature", fill="month"))
+    + geom_density(alpha=0.7, color="white", size=0.5)
+    + facet_wrap("~month", ncol=1, scales="free_y")
+    + scale_fill_manual(values=colors)
+    + labs(x="Temperature (\u00b0C)", y="", title="Monthly Temperature Distribution")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=20),
+        axis_title_x=element_text(size=20),
+        axis_text_x=element_text(size=16),
+        axis_text_y=element_blank(),
+        axis_ticks_major_y=element_blank(),
+        strip_text=element_text(size=14),
+        strip_background=element_blank(),
+        legend_position="none",
+        panel_spacing_y=-0.3,
+        panel_grid=element_blank(),
+        axis_line_x=element_line(color="#333333", size=0.5),
+    )
+    + scale_y_continuous(expand=(0, 0))
+)
+
+# Save
+plot.save("plot.png", dpi=300)