feat(plotnine): implement scatter-animated-controls (#3084)

## Implementation: `scatter-animated-controls` - plotnine

Implements the **plotnine** version of `scatter-animated-controls`.

**File:** `plots/scatter-animated-controls/implementations/plotnine.py`

**Parent Issue:** #3067

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620303821)*

---------

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/scatter-animated-controls/implementations/plotnine.py

@@ -0,0 +1,120 @@
+""" pyplots.ai
+scatter-animated-controls: Animated Scatter Plot with Play Controls
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    facet_wrap,
+    geom_point,
+    ggplot,
+    labs,
+    scale_color_brewer,
+    scale_size_continuous,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Simulated country metrics over time (Gapminder-style)
+np.random.seed(42)
+
+countries = ["Nordland", "Australis", "Eastoria", "Pacifica", "Centralia", "Westmark"]
+regions = ["Europe", "Americas", "Asia", "Asia", "Africa", "Europe"]
+years = list(range(2000, 2021, 2))  # 11 time points: 2000-2020 every 2 years
+n_countries = len(countries)
+n_years = len(years)
+
+# Generate base values for each country with distinct trajectories
+base_gdp = np.array([25000, 12000, 8000, 3000, 1500, 35000])
+base_life_exp = np.array([76, 72, 65, 58, 52, 79])
+base_population = np.array([10, 45, 200, 130, 90, 25])  # in millions
+
+data = []
+for i, country in enumerate(countries):
+    for j, year in enumerate(years):
+        # Different growth patterns per region
+        if regions[i] == "Asia":
+            gdp_growth = 1 + 0.08 * j + np.random.uniform(-0.02, 0.02)  # Fast growth
+        elif regions[i] == "Africa":
+            gdp_growth = 1 + 0.04 * j + np.random.uniform(-0.03, 0.03)  # Moderate with volatility
+        else:
+            gdp_growth = 1 + 0.025 * j + np.random.uniform(-0.01, 0.01)  # Steady growth
+
+        gdp = base_gdp[i] * gdp_growth
+
+        # Life expectancy increases at different rates
+        life_gain = 0.4 if regions[i] == "Africa" else 0.25
+        life_exp = base_life_exp[i] + life_gain * j + np.random.uniform(-0.3, 0.3)
+
+        # Population growth varies
+        pop_rate = 0.025 if regions[i] in ["Asia", "Africa"] else 0.008
+        pop_growth = 1 + pop_rate * j + np.random.uniform(-0.005, 0.005)
+        population = base_population[i] * pop_growth
+
+        data.append(
+            {
+                "country": country,
+                "region": regions[i],
+                "year": year,
+                "gdp_per_capita": gdp,
+                "life_expectancy": life_exp,
+                "population": population,
+            }
+        )
+
+df = pd.DataFrame(data)
+
+# Create faceted scatter plot showing key time points
+# Select 6 key time points for facets (fills 2x3 grid perfectly)
+key_years = [2000, 2004, 2008, 2012, 2016, 2020]
+df_faceted = df[df["year"].isin(key_years)].copy()
+
+plot = (
+    ggplot(df_faceted, aes(x="gdp_per_capita", y="life_expectancy", color="region", size="population"))
+    + geom_point(alpha=0.85)
+    + facet_wrap("~year", ncol=3)
+    + labs(
+        x="GDP per Capita (USD)",
+        y="Life Expectancy (years)",
+        title="scatter-animated-controls · plotnine · pyplots.ai",
+        color="Region",
+        size="Population (M)",
+    )
+    + scale_color_brewer(type="qual", palette="Dark2")
+    + scale_size_continuous(range=(4, 18), breaks=[25, 100, 200])
+    + scale_x_continuous(breaks=[0, 30000, 60000], labels=["0", "30k", "60k"], limits=(-2000, 65000))
+    + scale_y_continuous(breaks=[55, 65, 75, 85], limits=(50, 88))
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=12),
+        axis_title=element_text(size=18),
+        axis_text=element_text(size=10),
+        axis_text_x=element_text(size=9),
+        plot_title=element_text(size=22, ha="center", weight="bold"),
+        legend_text=element_text(size=12),
+        legend_title=element_text(size=14, weight="bold"),
+        legend_position="right",
+        legend_box_spacing=0.4,
+        strip_text=element_text(size=16, weight="bold"),
+        strip_background=element_rect(fill="#e8e8e8", color="#cccccc", size=0.5),
+        panel_spacing_x=0.15,
+        panel_spacing_y=0.15,
+        panel_grid_major=element_line(color="#dddddd", size=0.4),
+        panel_grid_minor=element_blank(),
+        panel_background=element_rect(fill="#fafafa"),
+    )
+)
+
+# Save plot
+plot.save("plot.png", dpi=300, verbose=False)

plots/scatter-animated-controls/metadata/plotnine.yaml

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+library: plotnine
+specification_id: scatter-animated-controls
+created: '2025-12-31T13:52:08Z'
+updated: '2025-12-31T14:13:12Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620303821
+issue: 3067
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-animated-controls/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-animated-controls/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 90
+review:
+  strengths:
+  - Excellent Gapminder-style data scenario with realistic country trajectories showing
+    different regional growth patterns
+  - Clean faceted layout that effectively communicates temporal evolution without
+    animation
+  - Good use of Dark2 colorblind-safe palette with appropriate alpha for point visibility
+  - Well-structured code following KISS principles with comprehensive theme customization
+  - Proper use of scale_size_continuous with meaningful breaks for population encoding
+  weaknesses:
+  - Size legend shows three solid circles that appear similar in rendering - may not
+    clearly show size differentiation
+  - X-axis tick labels could be slightly larger for better readability at full resolution