feat(plotnine): implement forest-basic

plots/forest-basic/implementations/plotnine.py

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+""" pyplots.ai
+forest-basic: Meta-Analysis Forest Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-27
+"""
+
+import pandas as pd
+from plotnine import (
+    aes,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_errorbarh,
+    geom_point,
+    geom_polygon,
+    geom_text,
+    geom_vline,
+    ggplot,
+    labs,
+    scale_size_identity,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+)
+
+
+# Data: Meta-analysis of RCTs comparing treatment vs control
+studies = pd.DataFrame(
+    {
+        "study": [
+            "Smith 2018",
+            "Johnson 2019",
+            "Williams 2019",
+            "Brown 2020",
+            "Davis 2020",
+            "Miller 2021",
+            "Wilson 2021",
+            "Moore 2022",
+            "Taylor 2022",
+            "Anderson 2023",
+        ],
+        "effect_size": [-0.45, -0.22, -0.38, -0.15, -0.52, -0.31, -0.08, -0.41, -0.25, -0.35],
+        "ci_lower": [-0.72, -0.48, -0.61, -0.42, -0.81, -0.55, -0.35, -0.68, -0.51, -0.58],
+        "ci_upper": [-0.18, 0.04, -0.15, 0.12, -0.23, -0.07, 0.19, -0.14, 0.01, -0.12],
+        "weight": [9.8, 11.2, 10.5, 8.7, 7.3, 10.9, 9.1, 8.4, 11.8, 12.3],
+    }
+)
+
+# Calculate pooled estimate (weighted mean)
+pooled_effect = (studies["effect_size"] * studies["weight"]).sum() / studies["weight"].sum()
+pooled_se = 0.08  # Simplified standard error for pooled estimate
+pooled_lower = pooled_effect - 1.96 * pooled_se
+pooled_upper = pooled_effect + 1.96 * pooled_se
+
+# Create y positions (studies listed top to bottom, pooled at bottom)
+studies["y_pos"] = range(len(studies), 0, -1)
+
+# Scale marker sizes for visibility (based on weight, scaled for canvas)
+studies["marker_size"] = studies["weight"] * 0.5
+
+# Create diamond for pooled estimate
+diamond_y = 0
+diamond = pd.DataFrame(
+    {
+        "x": [pooled_lower, pooled_effect, pooled_upper, pooled_effect],
+        "y": [diamond_y, diamond_y + 0.3, diamond_y, diamond_y - 0.3],
+    }
+)
+
+# Create label data for study names and effect sizes
+studies["label"] = (
+    studies["effect_size"].round(2).astype(str)
+    + " ["
+    + studies["ci_lower"].round(2).astype(str)
+    + ", "
+    + studies["ci_upper"].round(2).astype(str)
+    + "]"
+)
+
+# Fixed positions for text columns
+x_left = -1.4  # Left column for study names
+x_right = 0.55  # Right column for effect sizes
+
+# Add fixed positions to dataframe
+studies["x_left"] = x_left
+studies["x_right"] = x_right
+
+# Pooled estimate label data
+pooled_label_left = pd.DataFrame({"x": [x_left], "y": [diamond_y], "label": ["Pooled"]})
+pooled_label_right = pd.DataFrame(
+    {"x": [x_right], "y": [diamond_y], "label": [f"{pooled_effect:.2f} [{pooled_lower:.2f}, {pooled_upper:.2f}]"]}
+)
+
+# Plot
+plot = (
+    ggplot()
+    # Reference line at null effect (0 for mean difference)
+    + geom_vline(xintercept=0, linetype="dashed", color="#888888", size=1)
+    # Error bars (confidence intervals)
+    + geom_errorbarh(
+        aes(y="y_pos", xmin="ci_lower", xmax="ci_upper"), data=studies, height=0.25, size=1.2, color="#306998"
+    )
+    # Point estimates (sized by weight)
+    + geom_point(aes(x="effect_size", y="y_pos", size="marker_size"), data=studies, color="#306998", fill="#306998")
+    + scale_size_identity()
+    # Pooled estimate diamond
+    + geom_polygon(aes(x="x", y="y"), data=diamond, fill="#FFD43B", color="#306998", size=1.2)
+    # Study labels at fixed left position
+    + geom_text(aes(x="x_left", y="y_pos", label="study"), data=studies, ha="left", size=12, color="#333333")
+    # Effect size labels at fixed right position
+    + geom_text(aes(x="x_right", y="y_pos", label="label"), data=studies, ha="left", size=10, color="#333333")
+    # Pooled label
+    + geom_text(
+        aes(x="x", y="y", label="label"), data=pooled_label_left, ha="left", size=12, fontweight="bold", color="#333333"
+    )
+    + geom_text(
+        aes(x="x", y="y", label="label"),
+        data=pooled_label_right,
+        ha="left",
+        size=10,
+        fontweight="bold",
+        color="#333333",
+    )
+    # Labels and theme
+    + labs(x="Mean Difference (Treatment - Control)", y="", title="forest-basic · plotnine · pyplots.ai")
+    + scale_x_continuous(breaks=[-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4], limits=(-1.5, 1.3))
+    + scale_y_continuous(breaks=[], limits=(-1, 11.5))
+    + theme(
+        figure_size=(16, 9),
+        panel_background=element_rect(fill="white"),
+        plot_background=element_rect(fill="white"),
+        panel_grid_major_x=element_line(color="#EEEEEE", size=0.5),
+        panel_grid_major_y=element_blank(),
+        panel_grid_minor=element_blank(),
+        axis_text_x=element_text(size=16, color="#333333"),
+        axis_text_y=element_blank(),
+        axis_title_x=element_text(size=20, color="#333333"),
+        axis_title_y=element_blank(),
+        plot_title=element_text(size=24, ha="center", color="#333333"),
+        axis_ticks_major_y=element_blank(),
+        legend_position="none",
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/forest-basic/metadata/plotnine.yaml

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+library: plotnine
+specification_id: forest-basic
+created: '2025-12-27T19:23:19Z'
+updated: '2025-12-27T19:32:52Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543282765
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/forest-basic/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/forest-basic/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of forest plot structure with clear visual hierarchy
+  - Proper diamond shape for pooled estimate using geom_polygon
+  - Study labels and effect size values clearly displayed on left and right sides
+  - Good color choices with Python blue (#306998) and yellow accent (#FFD43B)
+  - Appropriate sizing for all text elements following quality guidelines
+  - Clean, readable code structure following KISS principles
+  - Realistic meta-analysis data context with meaningful study names
+  weaknesses:
+  - All individual study effects are negative; including some positive effects would
+    better demonstrate the plot ability to show heterogeneous results
+  - Grid lines only on x-axis (though appropriate for forest plots, full grid could
+    be considered)