fix(altair): address review feedback for line-retention-cohort

Attempt 2/3 - fixes based on AI review

Author: github-actions[bot]

plots/line-retention-cohort/implementations/altair.py

@@ -1,4 +1,4 @@
-""" pyplots.ai
+"""pyplots.ai
 line-retention-cohort: User Retention Curve by Cohort
 Library: altair 6.0.0 | Python 3.14.3
 Quality: 86/100 | Created: 2026-03-16
@@ -13,104 +13,115 @@
 np.random.seed(42)
 
 cohorts = {
-    "Jan 2025": {"size": 1245, "half_life": 3.5, "order": 0},
-    "Feb 2025": {"size": 1102, "half_life": 4.0, "order": 1},
-    "Mar 2025": {"size": 1380, "half_life": 4.8, "order": 2},
-    "Apr 2025": {"size": 1510, "half_life": 5.5, "order": 3},
-    "May 2025": {"size": 1423, "half_life": 6.2, "order": 4},
+    "Jan 2025": {"size": 1245, "half_life": 3.5},
+    "Feb 2025": {"size": 1102, "half_life": 4.0},
+    "Mar 2025": {"size": 1380, "half_life": 4.8},
+    "Apr 2025": {"size": 1510, "half_life": 5.5},
+    "May 2025": {"size": 1423, "half_life": 6.2},
 }
 
 weeks = np.arange(0, 13)
 rows = []
-for cohort_label, info in cohorts.items():
+for i, (cohort_label, info) in enumerate(cohorts.items()):
     retention = 100 * np.exp(-weeks / info["half_life"])
     noise = np.concatenate([[0], np.cumsum(np.random.randn(12) * 1.5)])
     retention = np.clip(retention + noise, 5, 100)
     retention[0] = 100.0
     legend_label = f"{cohort_label} (n={info['size']:,})"
     for w, r in zip(weeks, retention, strict=True):
-        rows.append({"Week": w, "Retention (%)": round(r, 1), "Cohort": legend_label, "order": info["order"]})
+        rows.append({"Week": w, "Retention (%)": round(r, 1), "Cohort": legend_label, "order": i})
 
 df = pd.DataFrame(rows)
 
-# Colorblind-safe palette: blue → teal → amber → orange → deep blue
-# Avoids red-green distinction, uses luminance + hue variation
+# Colorblind-safe palette (Tol-inspired)
+cohort_labels = list(df["Cohort"].unique())
 colors = ["#88CCEE", "#44AA99", "#DDCC77", "#CC6677", "#332288"]
-cohort_labels = [f"{c} (n={info['size']:,})" for c, info in cohorts.items()]
-
-# Graduated opacity and stroke width: older cohorts fade, newer ones pop
-opacity_map = {0: 0.35, 1: 0.50, 2: 0.65, 3: 0.80, 4: 1.0}
-width_map = {0: 1.5, 1: 2.0, 2: 2.5, 3: 3.0, 4: 4.0}
+order_domain = list(range(5))
+opacity_range = [0.45, 0.59, 0.73, 0.87, 1.0]
+width_range = [1.8, 2.4, 3.0, 3.6, 4.2]
+size_range = [60, 90, 120, 150, 180]
 
 # Interactive highlight on hover
 highlight = alt.selection_point(fields=["Cohort"], on="pointerover", empty=False)
 
-# Build per-cohort line + point layers for graduated styling
-layers = []
-for cohort_label, info in cohorts.items():
-    label = f"{cohort_label} (n={info['size']:,})"
-    idx = info["order"]
-    cohort_df = df[df["Cohort"] == label]
-
-    line = (
-        alt.Chart(cohort_df)
-        .mark_line(strokeWidth=width_map[idx], opacity=opacity_map[idx])
-        .encode(
-            x=alt.X(
-                "Week:Q",
-                title="Weeks Since Signup",
-                scale=alt.Scale(domain=[0, 12]),
-                axis=alt.Axis(labelFontSize=18, titleFontSize=22, tickMinStep=1),
-            ),
-            y=alt.Y(
-                "Retention (%):Q",
-                title="Retention (%)",
-                scale=alt.Scale(domain=[0, 100]),
-                axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".0f"),
-            ),
-            color=alt.Color(
-                "Cohort:N",
-                scale=alt.Scale(domain=cohort_labels, range=colors),
-                legend=alt.Legend(
-                    title="Cohort", titleFontSize=18, labelFontSize=15, symbolStrokeWidth=4, symbolSize=200
-                ),
-            ),
-            strokeWidth=alt.condition(highlight, alt.value(6), alt.value(width_map[idx])),
-            opacity=alt.condition(highlight, alt.value(1.0), alt.value(opacity_map[idx])),
-            tooltip=["Cohort:N", "Week:Q", "Retention (%):Q"],
-        )
-        .add_params(highlight)
-    )
-
-    point = (
-        alt.Chart(cohort_df)
-        .mark_point(filled=True, size=60 + idx * 30)
-        .encode(
-            x="Week:Q",
-            y="Retention (%):Q",
-            color=alt.Color("Cohort:N", scale=alt.Scale(domain=cohort_labels, range=colors), legend=None),
-            opacity=alt.condition(highlight, alt.value(1.0), alt.value(opacity_map[idx])),
-            size=alt.condition(highlight, alt.value(200), alt.value(60 + idx * 30)),
-            tooltip=["Cohort:N", "Week:Q", "Retention (%):Q"],
-        )
-    )
-
-    layers.extend([line, point])
-
 # Reference line at 20% retention threshold
 threshold_df = pd.DataFrame({"y": [20]})
 threshold = alt.Chart(threshold_df).mark_rule(strokeDash=[8, 6], strokeWidth=2, color="#666666").encode(y="y:Q")
-
-# Threshold label
 threshold_label = (
     alt.Chart(threshold_df)
     .mark_text(text="20% Target", align="left", dx=5, dy=-12, fontSize=16, fontWeight="bold", color="#666666")
     .encode(x=alt.value(20), y="y:Q")
 )
 
+# Shared axis encodings
+x_enc = alt.X(
+    "Week:Q",
+    title="Weeks Since Signup",
+    scale=alt.Scale(domain=[0, 12]),
+    axis=alt.Axis(labelFontSize=18, titleFontSize=22, tickMinStep=1),
+)
+y_enc = alt.Y(
+    "Retention (%):Q",
+    title="Retention (%)",
+    scale=alt.Scale(domain=[0, 100]),
+    axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".0f"),
+)
+color_enc = alt.Color(
+    "Cohort:N",
+    scale=alt.Scale(domain=cohort_labels, range=colors),
+    sort=cohort_labels,
+    legend=alt.Legend(title="Cohort", titleFontSize=18, labelFontSize=16, symbolStrokeWidth=4, symbolSize=200),
+)
+
+# Lines — single Chart, graduated styling via order-based scales
+lines = (
+    alt.Chart(df)
+    .mark_line()
+    .encode(
+        x=x_enc,
+        y=y_enc,
+        color=color_enc,
+        strokeWidth=alt.condition(
+            highlight,
+            alt.value(6),
+            alt.StrokeWidth("order:O", scale=alt.Scale(domain=order_domain, range=width_range), legend=None),
+        ),
+        opacity=alt.condition(
+            highlight,
+            alt.value(1.0),
+            alt.Opacity("order:O", scale=alt.Scale(domain=order_domain, range=opacity_range), legend=None),
+        ),
+        detail="Cohort:N",
+        tooltip=["Cohort:N", "Week:Q", "Retention (%):Q"],
+    )
+    .add_params(highlight)
+)
+
+# Points
+points = (
+    alt.Chart(df)
+    .mark_point(filled=True)
+    .encode(
+        x="Week:Q",
+        y="Retention (%):Q",
+        color=alt.Color("Cohort:N", scale=alt.Scale(domain=cohort_labels, range=colors), legend=None),
+        opacity=alt.condition(
+            highlight,
+            alt.value(1.0),
+            alt.Opacity("order:O", scale=alt.Scale(domain=order_domain, range=opacity_range), legend=None),
+        ),
+        size=alt.condition(
+            highlight,
+            alt.value(200),
+            alt.Size("order:O", scale=alt.Scale(domain=order_domain, range=size_range), legend=None),
+        ),
+        tooltip=["Cohort:N", "Week:Q", "Retention (%):Q"],
+    )
+)
+
 # Combine layers
 chart = (
-    alt.layer(threshold, threshold_label, *layers)
+    alt.layer(threshold, threshold_label, lines, points)
     .properties(
         width=1600,
         height=900,
@@ -126,7 +137,7 @@
     .configure_axis(
         gridColor="#D0D0D0",
         gridOpacity=0.3,
-        domainColor="#888888",
+        domainWidth=0,
         tickColor="#888888",
         labelColor="#333333",
         titleColor="#222222",