update(heatmap-basic): plotnine — comprehensive quality review

plots/heatmap-basic/implementations/plotnine.py

@@ -1,75 +1,98 @@
 """ pyplots.ai
 heatmap-basic: Basic Heatmap
-Library: plotnine 0.15.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: 92/100 | Updated: 2026-02-16
 """
 
 import numpy as np
 import pandas as pd
 from plotnine import (
     aes,
     element_blank,
+    element_rect,
     element_text,
     geom_text,
     geom_tile,
     ggplot,
     labs,
+    scale_color_identity,
     scale_fill_gradient2,
+    scale_x_discrete,
+    scale_y_discrete,
     theme,
     theme_minimal,
 )
 
 
-# Data - 8x8 matrix with meaningful patterns (performance metrics by region and quarter)
+# Data - 8x8 matrix: quarterly growth rates (%) by department
 np.random.seed(42)
-rows = ["Region A", "Region B", "Region C", "Region D", "Region E", "Region F", "Region G", "Region H"]
-cols = ["Q1 2023", "Q2 2023", "Q3 2023", "Q4 2023", "Q1 2024", "Q2 2024", "Q3 2024", "Q4 2024"]
+departments = ["Engineering", "Marketing", "Sales", "Finance", "Operations", "HR", "Research", "Support"]
+quarters = ["Q1 '23", "Q2 '23", "Q3 '23", "Q4 '23", "Q1 '24", "Q2 '24", "Q3 '24", "Q4 '24"]
 
-# Create data with a trend and variation
-base_values = np.linspace(-30, 40, 8)  # Trend across columns
-row_effects = np.random.uniform(-10, 10, 8)  # Row-specific offsets
-values = np.zeros((8, 8))
-for i in range(8):
-    for j in range(8):
-        values[i, j] = base_values[j] + row_effects[i] + np.random.uniform(-8, 8)
+# Vectorized growth rates with recovery trend and departmental variation
+base_trend = np.linspace(-18, 22, 8)
+dept_offsets = np.array([-6, 10, 14, -3, 4, -10, 8, -5])
+values = np.round(base_trend[np.newaxis, :] + dept_offsets[:, np.newaxis] + np.random.normal(0, 4, (8, 8)), 1)
 
-# Create long-form DataFrame for plotnine
-data = []
-for i, row in enumerate(rows):
-    for j, col in enumerate(cols):
-        data.append({"x": col, "y": row, "value": round(values[i, j], 1)})
+# Inject distinctive extreme values for storytelling focal points
+values[5, 0] = -32.5  # HR deep crisis in Q1 '23
+values[2, 7] = 38.2  # Sales strong recovery in Q4 '24
+values[6, 6] = 33.7  # Research surge in Q3 '24
 
-df = pd.DataFrame(data)
+# Build long-form DataFrame via meshgrid indexing
+dept_idx, qtr_idx = np.meshgrid(np.arange(8), np.arange(8), indexing="ij")
+df = pd.DataFrame(
+    {
+        "Department": pd.Categorical(
+            [departments[i] for i in dept_idx.ravel()], categories=departments[::-1], ordered=True
+        ),
+        "Quarter": pd.Categorical([quarters[j] for j in qtr_idx.ravel()], categories=quarters, ordered=True),
+        "Growth (%)": values.ravel(),
+    }
+)
+
+# Conditional text color: white on dark blue, dark gray on mid, dark brown on gold
+df["text_color"] = np.where(df["Growth (%)"] < -12, "white", np.where(df["Growth (%)"] < 18, "#3a3a3a", "#4a2e00"))
 
-# Preserve ordering
-df["x"] = pd.Categorical(df["x"], categories=cols, ordered=True)
-df["y"] = pd.Categorical(df["y"], categories=rows[::-1], ordered=True)  # Reverse for top-to-bottom
+# Signed annotation labels
+df["label"] = [f"{v:+.1f}" for v in df["Growth (%)"]]
 
 # Plot
 plot = (
-    ggplot(df, aes(x="x", y="y", fill="value"))
-    + geom_tile(color="white", size=0.5)
-    + geom_text(aes(label="value"), size=12, color="black")
+    ggplot(df, aes(x="Quarter", y="Department"))
+    + geom_tile(aes(fill="Growth (%)"), color="white", size=1.2)
+    + geom_text(aes(label="label", color="text_color"), size=10, fontweight="bold", show_legend=False)
     + scale_fill_gradient2(
-        low="#306998",  # Python Blue for negative
-        mid="white",
-        high="#FFD43B",  # Python Yellow for positive
-        midpoint=0,
-        name="Value",
+        low="#14405e", mid="#ede8e3", high="#c47d00", midpoint=0, name="Growth (%)", limits=(-35, 40)
+    )
+    + scale_color_identity()
+    + scale_x_discrete(expand=(0, 0.5))
+    + scale_y_discrete(expand=(0, 0.5))
+    + labs(
+        x="Quarter",
+        y="Department",
+        title="Quarterly Growth by Department · heatmap-basic · plotnine · pyplots.ai",
+        subtitle="Year-over-year growth rate (%) across departments, Q1 2023 – Q4 2024",
     )
-    + labs(x="Time Period", y="Region", title="heatmap-basic · plotnine · pyplots.ai")
     + theme_minimal()
     + theme(
         figure_size=(16, 9),
-        text=element_text(size=14),
-        axis_title=element_text(size=20),
-        axis_text_x=element_text(size=14, rotation=45, ha="right"),
-        axis_text_y=element_text(size=16),
-        plot_title=element_text(size=24),
-        legend_title=element_text(size=16),
+        text=element_text(family="sans-serif"),
+        plot_title=element_text(size=24, ha="center", weight="bold", margin={"b": 2}),
+        plot_subtitle=element_text(size=16, ha="center", color="#555555", margin={"b": 8}),
+        axis_title_x=element_text(size=20, margin={"t": 10}),
+        axis_title_y=element_text(size=20, margin={"r": 8}),
+        axis_text_x=element_text(size=16, rotation=45, ha="right", margin={"t": 4}),
+        axis_text_y=element_text(size=16, ha="right", margin={"r": 4}),
+        legend_title=element_text(size=16, weight="bold"),
         legend_text=element_text(size=14),
+        legend_position="right",
+        legend_key_height=40,
         panel_grid_major=element_blank(),
         panel_grid_minor=element_blank(),
+        panel_background=element_rect(fill="white", color="none"),
+        plot_background=element_rect(fill="#f7f7f7", color="none"),
+        plot_margin=0.02,
     )
 )