fix(altair): address review feedback for heatmap-basic

Attempt 2/3 - fixes based on AI review

Author: github-actions[bot]

plots/heatmap-basic/implementations/altair.py

@@ -1,4 +1,4 @@
-""" pyplots.ai
+"""pyplots.ai
 heatmap-basic: Basic Heatmap
 Library: altair 6.0.0 | Python 3.14.3
 Quality: 87/100 | Updated: 2026-02-15
@@ -9,7 +9,7 @@
 import pandas as pd
 
 
-# Data - correlation matrix with realistic variables
+# Data - correlation matrix with realistic weather variables
 np.random.seed(42)
 variables = [
     "Temperature",
@@ -28,23 +28,23 @@
 # Inject realistic correlations with stronger relationships
 raw[:, 1] += raw[:, 0] * 0.6  # Humidity ~ Temperature
 raw[:, 5] += raw[:, 1] * 0.7  # Cloud Cover ~ Humidity
-raw[:, 6] += raw[:, 5] * 0.65  # Precipitation ~ Cloud Cover (stronger)
-raw[:, 4] -= raw[:, 5] * 0.9  # Visibility inversely ~ Cloud Cover (stronger)
-raw[:, 7] -= raw[:, 5] * 0.7  # UV Index inversely ~ Cloud Cover (stronger)
+raw[:, 6] += raw[:, 5] * 0.65  # Precipitation ~ Cloud Cover
+raw[:, 4] -= raw[:, 5] * 0.9  # Visibility inversely ~ Cloud Cover
+raw[:, 7] -= raw[:, 5] * 0.7  # UV Index inversely ~ Cloud Cover
 raw[:, 7] += raw[:, 0] * 0.5  # UV Index ~ Temperature
-raw[:, 3] -= raw[:, 0] * 0.4  # Pressure inversely ~ Temperature (stronger)
+raw[:, 3] -= raw[:, 0] * 0.4  # Pressure inversely ~ Temperature
 raw[:, 2] += raw[:, 3] * 0.3  # Wind Speed ~ Pressure
 
 corr = np.corrcoef(raw.T)
 
-# Build long-form dataframe
-records = []
-for i, row_var in enumerate(variables):
-    for j, col_var in enumerate(variables):
-        val = round(corr[i, j], 2)
-        records.append({"x": col_var, "y": row_var, "value": val, "abs_value": abs(val)})
-
-df = pd.DataFrame(records)
+# Build long-form dataframe using list comprehension
+df = pd.DataFrame(
+    [
+        {"Row": row_var, "Column": col_var, "value": round(corr[i, j], 2)}
+        for i, row_var in enumerate(variables)
+        for j, col_var in enumerate(variables)
+    ]
+)
 
 # Axis ordering
 axis_order = list(variables)
@@ -55,81 +55,77 @@
     .mark_rect(stroke="#ffffff", strokeWidth=1.5, cornerRadius=2)
     .encode(
         x=alt.X(
-            "x:N",
-            title="Weather Variable",
-            sort=axis_order,
-            axis=alt.Axis(
-                labelFontSize=16, labelAngle=-30, orient="top", titleFontSize=20, titlePadding=12, labelPadding=8
-            ),
-        ),
-        y=alt.Y(
-            "y:N",
-            title="Weather Variable",
+            "Column:N",
+            title=None,
             sort=axis_order,
-            axis=alt.Axis(labelFontSize=16, titleFontSize=20, titlePadding=12, labelPadding=8),
+            axis=alt.Axis(labelFontSize=15, labelAngle=-35, orient="top", labelPadding=6),
         ),
+        y=alt.Y("Row:N", title=None, sort=axis_order, axis=alt.Axis(labelFontSize=15, labelPadding=6)),
         color=alt.Color(
             "value:Q",
             scale=alt.Scale(scheme="blueorange", domain=[-1, 1], domainMid=0),
             legend=alt.Legend(
                 title="Correlation",
-                titleFontSize=18,
-                labelFontSize=16,
-                gradientLength=350,
-                gradientThickness=18,
-                titlePadding=8,
-                offset=12,
+                titleFontSize=16,
+                labelFontSize=14,
+                gradientLength=300,
+                gradientThickness=16,
+                titlePadding=6,
+                offset=8,
+                direction="vertical",
             ),
         ),
         tooltip=[
-            alt.Tooltip("x:N", title="Variable X"),
-            alt.Tooltip("y:N", title="Variable Y"),
+            alt.Tooltip("Column:N", title="Column"),
+            alt.Tooltip("Row:N", title="Row"),
             alt.Tooltip("value:Q", title="Correlation", format=".2f"),
         ],
     )
 )
 
 # Highlight cells with strong correlations using thicker borders
 highlight = (
-    alt.Chart(df[df["abs_value"] >= 0.7])
-    .mark_rect(stroke="#333333", strokeWidth=2.5, filled=False, cornerRadius=2)
-    .encode(x=alt.X("x:N", sort=axis_order), y=alt.Y("y:N", sort=axis_order))
+    alt.Chart(df)
+    .transform_filter((alt.datum.value >= 0.7) | (alt.datum.value <= -0.7))
+    .mark_rect(stroke="#2a2a2a", strokeWidth=2.5, filled=False, cornerRadius=2)
+    .encode(x=alt.X("Column:N", sort=axis_order), y=alt.Y("Row:N", sort=axis_order))
 )
 
 # Text annotations with adaptive color
 text = (
     alt.Chart(df)
-    .mark_text(fontSize=16, fontWeight="bold")
+    .mark_text(fontSize=15, fontWeight="bold")
     .encode(
-        x=alt.X("x:N", sort=axis_order),
-        y=alt.Y("y:N", sort=axis_order),
+        x=alt.X("Column:N", sort=axis_order),
+        y=alt.Y("Row:N", sort=axis_order),
         text=alt.Text("value:Q", format=".2f"),
         color=alt.when((alt.datum.value > 0.55) | (alt.datum.value < -0.55))
-        .then(alt.value("white"))
+        .then(alt.value("#ffffff"))
         .otherwise(alt.value("#333333")),
     )
 )
 
-# Combine and configure
+# Combine layers and configure
 chart = (
     (heatmap + highlight + text)
     .properties(
-        width=1200,
-        height=1200,
+        width=700,
+        height=730,
         title=alt.Title(
             "heatmap-basic · altair · pyplots.ai",
             subtitle="Pairwise Pearson correlation coefficients for 8 weather metrics",
-            fontSize=28,
-            subtitleFontSize=18,
+            fontSize=26,
+            subtitleFontSize=16,
             subtitleColor="#666666",
             anchor="start",
-            offset=20,
+            offset=16,
         ),
+        padding={"left": 10, "right": 10, "top": 10, "bottom": 10},
     )
     .configure_axis(grid=False)
     .configure_view(strokeWidth=0)
 )
 
 # Save
-chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.png", scale_factor=4.0)
 chart.save("plot.html")