diff --git a/plots/alluvial-basic/implementations/python/matplotlib.py b/plots/alluvial-basic/implementations/python/matplotlib.py
index 7b432c7ae9..d0ce39bad1 100644
--- a/plots/alluvial-basic/implementations/python/matplotlib.py
+++ b/plots/alluvial-basic/implementations/python/matplotlib.py
@@ -20,11 +20,11 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette — use positions 1→N in order
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # 1: bluish green (brand)
-    "#D55E00",  # 2: vermillion
-    "#0072B2",  # 3: blue
-    "#CC79A7",  # 4: reddish purple
+    "#C475FD",  # 2: vermillion
+    "#4467A3",  # 3: blue
+    "#BD8233",  # 4: reddish purple
 ]
 
 # Data: Voter migration across 4 election cycles
@@ -32,7 +32,7 @@
 
 time_points = ["2012", "2016", "2020", "2024"]
 categories = ["Party A", "Party B", "Party C", "Independent"]
-colors = {cat: OKABE_ITO[i] for i, cat in enumerate(categories)}
+colors = {cat: IMPRINT[i] for i, cat in enumerate(categories)}
 
 # Node values at each time point (thousands of voters)
 node_values = {
diff --git a/plots/andrews-curves/implementations/python/matplotlib.py b/plots/andrews-curves/implementations/python/matplotlib.py
index 02a5c73db4..7b038a731d 100644
--- a/plots/andrews-curves/implementations/python/matplotlib.py
+++ b/plots/andrews-curves/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data
 np.random.seed(42)
@@ -55,11 +55,11 @@
 
 # Plot Andrews curves for each observation
 for i in range(len(curves)):
-    ax.plot(t, curves[i], color=OKABE_ITO[y[i]], alpha=0.4, linewidth=2.5)
+    ax.plot(t, curves[i], color=IMPRINT[y[i]], alpha=0.4, linewidth=2.5)
 
 # Create legend with sample lines
 for idx, species in enumerate(species_names):
-    ax.plot([], [], color=OKABE_ITO[idx], linewidth=3, label=species, alpha=0.4)
+    ax.plot([], [], color=IMPRINT[idx], linewidth=3, label=species, alpha=0.4)
 
 # Style
 ax.set_xlabel("t (radians)", fontsize=20, color=INK)
diff --git a/plots/area-cumulative-flow/implementations/python/matplotlib.py b/plots/area-cumulative-flow/implementations/python/matplotlib.py
index 361264cc47..69e895cbc5 100644
--- a/plots/area-cumulative-flow/implementations/python/matplotlib.py
+++ b/plots/area-cumulative-flow/implementations/python/matplotlib.py
@@ -28,7 +28,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito positions 1-5 (bottom to top: Done → Backlog)
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data: 90-day software Kanban board simulation
 np.random.seed(42)
diff --git a/plots/area-stacked-confidence/implementations/python/matplotlib.py b/plots/area-stacked-confidence/implementations/python/matplotlib.py
index 05a98c744a..52790a87d2 100644
--- a/plots/area-stacked-confidence/implementations/python/matplotlib.py
+++ b/plots/area-stacked-confidence/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: quarterly energy consumption by source with measurement uncertainty
 np.random.seed(42)
@@ -73,34 +73,34 @@
 
 # Plot stacked areas from bottom to top with enhanced styling
 # Solar (bottom layer)
-ax.fill_between(quarters, 0, solar_stack, color=OKABE_ITO[0], alpha=0.85, label="Solar", zorder=3)
-ax.fill_between(quarters, solar_lower, solar_upper, color=OKABE_ITO[0], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, solar_lower, color=OKABE_ITO[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, solar_upper, color=OKABE_ITO[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, 0, solar_stack, color=IMPRINT[0], alpha=0.85, label="Solar", zorder=3)
+ax.fill_between(quarters, solar_lower, solar_upper, color=IMPRINT[0], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, solar_lower, color=IMPRINT[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, solar_upper, color=IMPRINT[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Wind (second layer)
-ax.fill_between(quarters, solar_stack, wind_stack, color=OKABE_ITO[1], alpha=0.85, label="Wind", zorder=3)
-ax.fill_between(quarters, wind_lower, wind_upper, color=OKABE_ITO[1], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, wind_lower, color=OKABE_ITO[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, wind_upper, color=OKABE_ITO[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, solar_stack, wind_stack, color=IMPRINT[1], alpha=0.85, label="Wind", zorder=3)
+ax.fill_between(quarters, wind_lower, wind_upper, color=IMPRINT[1], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, wind_lower, color=IMPRINT[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, wind_upper, color=IMPRINT[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Hydro (third layer)
-ax.fill_between(quarters, wind_stack, hydro_stack, color=OKABE_ITO[2], alpha=0.85, label="Hydro", zorder=3)
-ax.fill_between(quarters, hydro_lower, hydro_upper, color=OKABE_ITO[2], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, hydro_lower, color=OKABE_ITO[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, hydro_upper, color=OKABE_ITO[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, wind_stack, hydro_stack, color=IMPRINT[2], alpha=0.85, label="Hydro", zorder=3)
+ax.fill_between(quarters, hydro_lower, hydro_upper, color=IMPRINT[2], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, hydro_lower, color=IMPRINT[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, hydro_upper, color=IMPRINT[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Natural Gas (top layer)
-ax.fill_between(quarters, hydro_stack, gas_stack, color=OKABE_ITO[3], alpha=0.85, label="Natural Gas", zorder=3)
-ax.fill_between(quarters, gas_lower, gas_upper, color=OKABE_ITO[3], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, gas_lower, color=OKABE_ITO[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, gas_upper, color=OKABE_ITO[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, hydro_stack, gas_stack, color=IMPRINT[3], alpha=0.85, label="Natural Gas", zorder=3)
+ax.fill_between(quarters, gas_lower, gas_upper, color=IMPRINT[3], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, gas_lower, color=IMPRINT[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, gas_upper, color=IMPRINT[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Add prominent center lines for clarity and visual hierarchy
-ax.plot(quarters, solar_stack, color=OKABE_ITO[0], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
-ax.plot(quarters, wind_stack, color=OKABE_ITO[1], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
-ax.plot(quarters, hydro_stack, color=OKABE_ITO[2], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
-ax.plot(quarters, gas_stack, color=OKABE_ITO[3], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, solar_stack, color=IMPRINT[0], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, wind_stack, color=IMPRINT[1], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, hydro_stack, color=IMPRINT[2], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, gas_stack, color=IMPRINT[3], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
 
 # Styling
 ax.set_xlabel("Quarter", fontsize=20, color=INK)
diff --git a/plots/area-stacked-percent/implementations/python/matplotlib.py b/plots/area-stacked-percent/implementations/python/matplotlib.py
index a6f8483430..d72923f825 100644
--- a/plots/area-stacked-percent/implementations/python/matplotlib.py
+++ b/plots/area-stacked-percent/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Renewable energy evolution (2014-2024)
 years = np.arange(2014, 2025)
@@ -41,7 +41,7 @@
 labels = ["Solar", "Wind", "Hydro", "Natural Gas", "Coal"]
 
 # Create stacked area
-ax.stackplot(years, data_percent, labels=labels, colors=OKABE_ITO, alpha=0.85, edgecolor=PAGE_BG, linewidth=1.5)
+ax.stackplot(years, data_percent, labels=labels, colors=IMPRINT, alpha=0.85, edgecolor=PAGE_BG, linewidth=1.5)
 
 # Styling
 ax.set_xlabel("Year (2014–2024)", fontsize=20, color=INK)
diff --git a/plots/area-stacked/implementations/python/matplotlib.py b/plots/area-stacked/implementations/python/matplotlib.py
index 51807968b1..494c6f8ce6 100644
--- a/plots/area-stacked/implementations/python/matplotlib.py
+++ b/plots/area-stacked/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (use positions 1→N)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Monthly website traffic sources over 24 months
 np.random.seed(42)
@@ -44,7 +44,7 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-ax.stackplot(months, data, labels=categories, colors=OKABE_ITO, alpha=0.85)
+ax.stackplot(months, data, labels=categories, colors=IMPRINT, alpha=0.85)
 
 # X-axis formatting (show as months)
 tick_positions = [1, 6, 12, 18, 24]
diff --git a/plots/bar-diverging/implementations/python/matplotlib.py b/plots/bar-diverging/implementations/python/matplotlib.py
index 5eead35e9b..993fdb9cf3 100644
--- a/plots/bar-diverging/implementations/python/matplotlib.py
+++ b/plots/bar-diverging/implementations/python/matplotlib.py
@@ -43,8 +43,8 @@
 values_sorted = values[sorted_indices]
 
 # Create colors using Okabe-Ito palette
-# #009E73 (position 1) for positive, #D55E00 (position 2) for negative
-colors = ["#009E73" if v >= 0 else "#D55E00" for v in values_sorted]
+# #009E73 (position 1) for positive, #C475FD (position 2) for negative
+colors = ["#009E73" if v >= 0 else "#C475FD" for v in values_sorted]
 
 # Plot
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
@@ -87,7 +87,7 @@
 # Add legend
 legend_elements = [
     Patch(facecolor="#009E73", edgecolor=INK_SOFT, label="Positive (Satisfied)"),
-    Patch(facecolor="#D55E00", edgecolor=INK_SOFT, label="Negative (Dissatisfied)"),
+    Patch(facecolor="#C475FD", edgecolor=INK_SOFT, label="Negative (Dissatisfied)"),
 ]
 leg = ax.legend(handles=legend_elements, loc="lower right", fontsize=16)
 if leg:
diff --git a/plots/bar-grouped/implementations/python/matplotlib.py b/plots/bar-grouped/implementations/python/matplotlib.py
index c7dc14dd62..07e5e5e7f4 100644
--- a/plots/bar-grouped/implementations/python/matplotlib.py
+++ b/plots/bar-grouped/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette (position 1 is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Quarterly sales by product line (in thousands USD)
 categories = ["Q1", "Q2", "Q3", "Q4"]
@@ -45,7 +45,7 @@
 max_values_per_category = {cat: max(sales_data[group][i] for group in groups) for i, cat in enumerate(categories)}
 
 bars = []
-for i, (group, color) in enumerate(zip(groups, OKABE_ITO, strict=True)):
+for i, (group, color) in enumerate(zip(groups, IMPRINT, strict=True)):
     bar = ax.bar(
         x + offsets[i], sales_data[group], bar_width, label=group, color=color, edgecolor=INK_SOFT, linewidth=1.5
     )
diff --git a/plots/bar-race-animated/implementations/python/matplotlib.py b/plots/bar-race-animated/implementations/python/matplotlib.py
index b62e2e2922..d8b614f69f 100644
--- a/plots/bar-race-animated/implementations/python/matplotlib.py
+++ b/plots/bar-race-animated/implementations/python/matplotlib.py
@@ -18,11 +18,11 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data: Tech Company Market Cap ($B) — year-end snapshots (2019–2024)
 companies = ["Alphabet", "Amazon", "Apple", "Meta", "Microsoft", "Nvidia", "Tesla"]
-company_colors = {c: OKABE_ITO[i] for i, c in enumerate(companies)}
+company_colors = {c: IMPRINT[i] for i, c in enumerate(companies)}
 
 market_cap = {
     "Alphabet": [920, 1200, 1960, 1140, 1800, 2150],
diff --git a/plots/bar-spine/implementations/python/matplotlib.py b/plots/bar-spine/implementations/python/matplotlib.py
index 553cd74de5..8b8fb38f2b 100644
--- a/plots/bar-spine/implementations/python/matplotlib.py
+++ b/plots/bar-spine/implementations/python/matplotlib.py
@@ -48,7 +48,7 @@
 
 # Colors: Okabe-Ito positions 1 and 2
 COLOR_SURVIVED = "#009E73"
-COLOR_DIED = "#D55E00"
+COLOR_DIED = "#C475FD"
 
 # Plot
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
diff --git a/plots/bar-stacked-labeled/implementations/python/matplotlib.py b/plots/bar-stacked-labeled/implementations/python/matplotlib.py
index ab6c2a01dd..df2ff21efa 100644
--- a/plots/bar-stacked-labeled/implementations/python/matplotlib.py
+++ b/plots/bar-stacked-labeled/implementations/python/matplotlib.py
@@ -26,7 +26,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette (positions 1-4)
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Quarterly revenue by product category (in millions $)
 np.random.seed(42)
diff --git a/plots/bar-stacked-percent/implementations/python/matplotlib.py b/plots/bar-stacked-percent/implementations/python/matplotlib.py
index 97aca6f3db..2fa53eb67d 100644
--- a/plots/bar-stacked-percent/implementations/python/matplotlib.py
+++ b/plots/bar-stacked-percent/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (first series is ALWAYS position 1)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data: Energy mix by country (percentage of total electricity generation)
 categories = ["Germany", "France", "UK", "Spain", "Italy", "Poland"]
@@ -49,7 +49,7 @@
 bottom = np.zeros(len(categories))
 
 # Create stacked bars
-for i, (component, color) in enumerate(zip(components, OKABE_ITO, strict=True)):
+for i, (component, color) in enumerate(zip(components, IMPRINT, strict=True)):
     bars = ax.bar(
         x, percentages[:, i], bar_width, bottom=bottom, label=component, color=color, edgecolor=PAGE_BG, linewidth=1.5
     )
diff --git a/plots/bar-stacked/implementations/python/matplotlib.py b/plots/bar-stacked/implementations/python/matplotlib.py
index dcb98886f7..57807c5273 100644
--- a/plots/bar-stacked/implementations/python/matplotlib.py
+++ b/plots/bar-stacked/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-4)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Quarterly revenue by product category (in millions USD)
 categories = ["Q1", "Q2", "Q3", "Q4"]
@@ -39,7 +39,7 @@
 # Create stacked bars
 bottom = np.zeros(len(categories))
 for i, (product, values) in enumerate(zip(products, [software, hardware, services, support], strict=True)):
-    ax.bar(x, values, bar_width, label=product, bottom=bottom, color=OKABE_ITO[i], edgecolor=PAGE_BG, linewidth=1.5)
+    ax.bar(x, values, bar_width, label=product, bottom=bottom, color=IMPRINT[i], edgecolor=PAGE_BG, linewidth=1.5)
     bottom += values
 
 # Add total labels above each stacked bar
diff --git a/plots/bland-altman-basic/implementations/python/matplotlib.py b/plots/bland-altman-basic/implementations/python/matplotlib.py
index d3be83ea72..8e8fa7caa0 100644
--- a/plots/bland-altman-basic/implementations/python/matplotlib.py
+++ b/plots/bland-altman-basic/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1 — first categorical series
-ACCENT2 = "#D55E00"  # Okabe-Ito position 2 — for limits of agreement lines
+ACCENT2 = "#C475FD"  # Okabe-Ito position 2 — for limits of agreement lines
 
 # Data: Simulated blood pressure readings from two sphygmomanometers
 np.random.seed(42)
diff --git a/plots/box-grouped/implementations/python/matplotlib.py b/plots/box-grouped/implementations/python/matplotlib.py
index 7dd222b850..bb08e43e59 100644
--- a/plots/box-grouped/implementations/python/matplotlib.py
+++ b/plots/box-grouped/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-3 for three subcategories)
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Employee performance scores across departments and experience levels
 np.random.seed(42)
diff --git a/plots/box-notched/implementations/python/matplotlib.py b/plots/box-notched/implementations/python/matplotlib.py
index 46b4a26c34..ce10406774 100644
--- a/plots/box-notched/implementations/python/matplotlib.py
+++ b/plots/box-notched/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette - first series is always brand green
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Test success rates across different test suites
 np.random.seed(42)
@@ -68,7 +68,7 @@
 )
 
 # Apply colors to boxes
-for patch, color in zip(bp["boxes"], OKABE_ITO, strict=True):
+for patch, color in zip(bp["boxes"], IMPRINT, strict=True):
     patch.set_facecolor(color)
     patch.set_alpha(0.75)
     patch.set_edgecolor(INK_SOFT)
diff --git a/plots/calibration-curve/implementations/python/matplotlib.py b/plots/calibration-curve/implementations/python/matplotlib.py
index 707ea96590..722cbfe9be 100644
--- a/plots/calibration-curve/implementations/python/matplotlib.py
+++ b/plots/calibration-curve/implementations/python/matplotlib.py
@@ -18,8 +18,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette - canonical order
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
-BRAND = OKABE_ITO[0]  # #009E73
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
+BRAND = IMPRINT[0]  # #009E73
 
 # Data
 np.random.seed(42)
@@ -97,13 +97,13 @@
 
 # Well-calibrated
 ax1.fill_between(
-    prob_pred_cal, prob_true_cal - prob_std_cal, prob_true_cal + prob_std_cal, alpha=0.2, color=OKABE_ITO[0]
+    prob_pred_cal, prob_true_cal - prob_std_cal, prob_true_cal + prob_std_cal, alpha=0.2, color=IMPRINT[0]
 )
 ax1.plot(
     prob_pred_cal,
     prob_true_cal,
     "o-",
-    color=OKABE_ITO[0],
+    color=IMPRINT[0],
     linewidth=3,
     markersize=10,
     label=f"Well-Calibrated (Brier: {brier_cal:.3f})",
@@ -111,13 +111,13 @@
 
 # Overconfident
 ax1.fill_between(
-    prob_pred_over, prob_true_over - prob_std_over, prob_true_over + prob_std_over, alpha=0.2, color=OKABE_ITO[1]
+    prob_pred_over, prob_true_over - prob_std_over, prob_true_over + prob_std_over, alpha=0.2, color=IMPRINT[1]
 )
 ax1.plot(
     prob_pred_over,
     prob_true_over,
     "s-",
-    color=OKABE_ITO[1],
+    color=IMPRINT[1],
     linewidth=3,
     markersize=10,
     label=f"Overconfident (Brier: {brier_over:.3f})",
@@ -125,13 +125,13 @@
 
 # Underconfident
 ax1.fill_between(
-    prob_pred_under, prob_true_under - prob_std_under, prob_true_under + prob_std_under, alpha=0.2, color=OKABE_ITO[2]
+    prob_pred_under, prob_true_under - prob_std_under, prob_true_under + prob_std_under, alpha=0.2, color=IMPRINT[2]
 )
 ax1.plot(
     prob_pred_under,
     prob_true_under,
     "^-",
-    color=OKABE_ITO[2],
+    color=IMPRINT[2],
     linewidth=3,
     markersize=10,
     label=f"Underconfident (Brier: {brier_under:.3f})",
@@ -159,13 +159,13 @@
 
 # Histogram
 ax2.hist(
-    y_prob_calibrated, bins=20, alpha=0.6, color=OKABE_ITO[0], label="Well-Calibrated", edgecolor=PAGE_BG, linewidth=0.5
+    y_prob_calibrated, bins=20, alpha=0.6, color=IMPRINT[0], label="Well-Calibrated", edgecolor=PAGE_BG, linewidth=0.5
 )
 ax2.hist(
     y_prob_overconfident,
     bins=20,
     alpha=0.6,
-    color=OKABE_ITO[1],
+    color=IMPRINT[1],
     label="Overconfident",
     edgecolor=PAGE_BG,
     linewidth=0.5,
@@ -174,7 +174,7 @@
     y_prob_underconfident,
     bins=20,
     alpha=0.6,
-    color=OKABE_ITO[2],
+    color=IMPRINT[2],
     label="Underconfident",
     edgecolor=PAGE_BG,
     linewidth=0.5,
diff --git a/plots/candlestick-volume/implementations/python/matplotlib.py b/plots/candlestick-volume/implementations/python/matplotlib.py
index 293ed87650..b297b32c35 100644
--- a/plots/candlestick-volume/implementations/python/matplotlib.py
+++ b/plots/candlestick-volume/implementations/python/matplotlib.py
@@ -22,7 +22,7 @@
 
 # Okabe-Ito palette
 UP_COLOR = "#009E73"  # Brand green (up days)
-DOWN_COLOR = "#D55E00"  # Vermillion (down days)
+DOWN_COLOR = "#C475FD"  # Vermillion (down days)
 
 # Data - Generate realistic 60 trading days of OHLC data with volume
 np.random.seed(42)
diff --git a/plots/cat-box-strip/implementations/python/matplotlib.py b/plots/cat-box-strip/implementations/python/matplotlib.py
index c72fde9aee..1748d6fc86 100644
--- a/plots/cat-box-strip/implementations/python/matplotlib.py
+++ b/plots/cat-box-strip/implementations/python/matplotlib.py
@@ -54,7 +54,7 @@
     widths=0.5,
     patch_artist=True,
     boxprops={"facecolor": BRAND, "alpha": 0.4, "linewidth": 2, "edgecolor": INK_SOFT},
-    medianprops={"color": "#E69F00", "linewidth": 3},
+    medianprops={"color": "#AE3030", "linewidth": 3},
     whiskerprops={"color": INK_SOFT, "linewidth": 2},
     capprops={"color": INK_SOFT, "linewidth": 2},
     flierprops={
diff --git a/plots/circlepacking-basic/implementations/python/matplotlib.py b/plots/circlepacking-basic/implementations/python/matplotlib.py
index 9f9b88e284..d3102f0639 100644
--- a/plots/circlepacking-basic/implementations/python/matplotlib.py
+++ b/plots/circlepacking-basic/implementations/python/matplotlib.py
@@ -64,8 +64,8 @@
 # Depth-based colors using Okabe-Ito palette
 depth_colors = {
     0: BRAND,  # Root: brand green
-    1: "#D55E00",  # Departments: vermillion
-    2: "#0072B2",  # Teams: blue
+    1: "#C475FD",  # Departments: vermillion
+    2: "#4467A3",  # Teams: blue
 }
 
 # Circle packing layout
diff --git a/plots/circos-basic/implementations/python/matplotlib.py b/plots/circos-basic/implementations/python/matplotlib.py
index 2123c18438..9437a9f839 100644
--- a/plots/circos-basic/implementations/python/matplotlib.py
+++ b/plots/circos-basic/implementations/python/matplotlib.py
@@ -25,14 +25,14 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-7, plus position 8 for neutral)
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # 1: bluish green (brand)
-    "#D55E00",  # 2: vermillion
-    "#0072B2",  # 3: blue
-    "#CC79A7",  # 4: reddish purple
-    "#E69F00",  # 5: orange
-    "#56B4E9",  # 6: sky blue
-    "#F0E442",  # 7: yellow
+    "#C475FD",  # 2: vermillion
+    "#4467A3",  # 3: blue
+    "#BD8233",  # 4: reddish purple
+    "#AE3030",  # 5: orange
+    "#2ABCCD",  # 6: sky blue
+    "#954477",  # 7: yellow
 ]
 
 # Data: Genomic chromosome interactions
@@ -108,8 +108,8 @@
     x = np.concatenate([x_outer, x_inner])
     y = np.concatenate([y_outer, y_inner])
 
-    color_idx = i % len(OKABE_ITO)
-    ax.fill(x, y, color=OKABE_ITO[color_idx], alpha=0.85, edgecolor=PAGE_BG, linewidth=1.5)
+    color_idx = i % len(IMPRINT)
+    ax.fill(x, y, color=IMPRINT[color_idx], alpha=0.85, edgecolor=PAGE_BG, linewidth=1.5)
 
     # Add segment label
     mid_angle = np.radians((start + end) / 2)
@@ -137,8 +137,8 @@
     x = np.concatenate([x_outer, x_inner])
     y = np.concatenate([y_outer, y_inner])
 
-    color_idx = i % len(OKABE_ITO)
-    ax.fill(x, y, color=OKABE_ITO[color_idx], alpha=0.5, edgecolor="none")
+    color_idx = i % len(IMPRINT)
+    ax.fill(x, y, color=IMPRINT[color_idx], alpha=0.5, edgecolor="none")
 
 # Draw connections (ribbons for synteny blocks)
 max_value = max(c[2] for c in connections)
@@ -191,8 +191,8 @@
     codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY]
 
     path = Path(verts, codes)
-    color_idx = idx1 % len(OKABE_ITO)
-    patch = mpatches.PathPatch(path, facecolor=OKABE_ITO[color_idx], alpha=0.4, edgecolor="none")
+    color_idx = idx1 % len(IMPRINT)
+    patch = mpatches.PathPatch(path, facecolor=IMPRINT[color_idx], alpha=0.4, edgecolor="none")
     ax.add_patch(patch)
 
 # Title
@@ -204,7 +204,7 @@
 
 # Legend (outside the plot)
 legend_elements = [
-    mpatches.Patch(facecolor=OKABE_ITO[i % len(OKABE_ITO)], label=chromosomes[i], alpha=0.85) for i in range(n_chroms)
+    mpatches.Patch(facecolor=IMPRINT[i % len(IMPRINT)], label=chromosomes[i], alpha=0.85) for i in range(n_chroms)
 ]
 leg = ax.legend(
     handles=legend_elements,
diff --git a/plots/contour-decision-boundary/implementations/python/matplotlib.py b/plots/contour-decision-boundary/implementations/python/matplotlib.py
index c93760bbdb..135765043b 100644
--- a/plots/contour-decision-boundary/implementations/python/matplotlib.py
+++ b/plots/contour-decision-boundary/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - generate synthetic two-moon classification data
 np.random.seed(42)
@@ -43,7 +43,7 @@
 ax.set_facecolor(PAGE_BG)
 
 # Plot decision regions with contourf
-ax.contourf(xx, yy, Z, alpha=0.3, colors=[OKABE_ITO[0], OKABE_ITO[1]], levels=[-0.5, 0.5, 1.5])
+ax.contourf(xx, yy, Z, alpha=0.3, colors=[IMPRINT[0], IMPRINT[1]], levels=[-0.5, 0.5, 1.5])
 
 # Add decision boundary line
 ax.contour(xx, yy, Z, colors=INK_SOFT, linewidths=2, levels=[0.5])
@@ -59,7 +59,7 @@
 ax.scatter(
     X[class_0_correct, 0],
     X[class_0_correct, 1],
-    c=OKABE_ITO[0],
+    c=IMPRINT[0],
     s=150,
     alpha=0.9,
     edgecolors=PAGE_BG,
@@ -71,7 +71,7 @@
 ax.scatter(
     X[class_1_correct, 0],
     X[class_1_correct, 1],
-    c=OKABE_ITO[1],
+    c=IMPRINT[1],
     s=150,
     alpha=0.9,
     edgecolors=PAGE_BG,
@@ -89,7 +89,7 @@
         ax.scatter(
             X[class_0_incorrect, 0],
             X[class_0_incorrect, 1],
-            c=OKABE_ITO[0],
+            c=IMPRINT[0],
             s=200,
             alpha=0.9,
             edgecolors=INK_SOFT,
@@ -102,7 +102,7 @@
         ax.scatter(
             X[class_1_incorrect, 0],
             X[class_1_incorrect, 1],
-            c=OKABE_ITO[1],
+            c=IMPRINT[1],
             s=200,
             alpha=0.9,
             edgecolors=INK_SOFT,
diff --git a/plots/dashboard-metrics-tiles/implementations/python/matplotlib.py b/plots/dashboard-metrics-tiles/implementations/python/matplotlib.py
index a3c4d91fd8..41b5277d93 100644
--- a/plots/dashboard-metrics-tiles/implementations/python/matplotlib.py
+++ b/plots/dashboard-metrics-tiles/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Status colors — semantic alarm indicators using Okabe-Ito positions 1, 5, 2
-STATUS_COLORS = {"good": "#009E73", "warning": "#E69F00", "critical": "#D55E00"}
+STATUS_COLORS = {"good": "#009E73", "warning": "#AE3030", "critical": "#C475FD"}
 STATUS_LABELS = {"good": "GOOD", "warning": "WARNING", "critical": "CRITICAL"}
 
 # Data
diff --git a/plots/dashboard-synchronized-crosshair/implementations/python/matplotlib.py b/plots/dashboard-synchronized-crosshair/implementations/python/matplotlib.py
index 1843b877f2..003006ae14 100644
--- a/plots/dashboard-synchronized-crosshair/implementations/python/matplotlib.py
+++ b/plots/dashboard-synchronized-crosshair/implementations/python/matplotlib.py
@@ -26,8 +26,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 BRAND = "#009E73"  # anyplot palette position 1 — always first series
-COLOR_2 = "#9418DB"  # position 2 — RSI line
-COLOR_RED = "#B71D27"  # position 3 — semantic: down / overbought
+COLOR_2 = "#C475FD"  # position 2 — RSI line
+COLOR_RED = "#AE3030"  # position 3 — semantic: down / overbought
 
 # Data — stock-like time series: price, volume, RSI
 np.random.seed(42)
diff --git a/plots/datamatrix-basic/implementations/python/matplotlib.py b/plots/datamatrix-basic/implementations/python/matplotlib.py
index f6a93a5bd2..8b9d3e2a02 100644
--- a/plots/datamatrix-basic/implementations/python/matplotlib.py
+++ b/plots/datamatrix-basic/implementations/python/matplotlib.py
@@ -28,8 +28,8 @@
 
 # Zone colors — Okabe-Ito, theme-constant (data encoding)
 COLOR_FINDER = "#009E73"
-COLOR_TIMING = "#0072B2"
-COLOR_DATA = "#E69F00"
+COLOR_TIMING = "#4467A3"
+COLOR_DATA = "#AE3030"
 
 # Data — 20×20 Data Matrix for electronics component traceability
 np.random.seed(42)
diff --git a/plots/dendrogram-radial/implementations/python/matplotlib.py b/plots/dendrogram-radial/implementations/python/matplotlib.py
index ffaea53a55..9dbd4b1209 100644
--- a/plots/dendrogram-radial/implementations/python/matplotlib.py
+++ b/plots/dendrogram-radial/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 family_names = ["Asteraceae", "Rosaceae", "Fabaceae", "Poaceae", "Lamiaceae"]
 # Abbreviated species labels: As01-As10, Ro01-Ro10, Fa01-Fa10, Po01-Po10, La01-La10
@@ -53,7 +53,7 @@
 
 def link_color_func(node_id):
     c = node_cluster.get(node_id, -1)
-    return OKABE_ITO[c - 1] if c != -1 else INK_SOFT
+    return IMPRINT[c - 1] if c != -1 else INK_SOFT
 
 
 # Compute dendrogram layout with colored links
@@ -63,7 +63,7 @@ def link_color_func(node_id):
 link_colors = dendro["color_list"]
 leaves = dendro["leaves"]
 
-leaf_colors = [OKABE_ITO[cluster_labels[leaf] - 1] for leaf in leaves]
+leaf_colors = [IMPRINT[cluster_labels[leaf] - 1] for leaf in leaves]
 
 # Radial coordinate helpers — inner_r pads the root away from center
 x_max = n_species * 10.0
@@ -141,7 +141,7 @@ def to_xy(x, y):
 
 # Legend
 legend_elements = [
-    Line2D([0], [0], marker="o", linestyle="None", markerfacecolor=OKABE_ITO[i], markersize=14, label=family_names[i])
+    Line2D([0], [0], marker="o", linestyle="None", markerfacecolor=IMPRINT[i], markersize=14, label=family_names[i])
     for i in range(k_families)
 ]
 leg = ax.legend(
diff --git a/plots/donut-basic/implementations/python/matplotlib.py b/plots/donut-basic/implementations/python/matplotlib.py
index c8dc749e3e..8c10754e3c 100644
--- a/plots/donut-basic/implementations/python/matplotlib.py
+++ b/plots/donut-basic/implementations/python/matplotlib.py
@@ -16,7 +16,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (first segment is always the brand green)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Annual budget allocation by department (USD thousands)
 categories = ["Engineering", "Marketing", "Operations", "Sales", "Support"]
@@ -32,7 +32,7 @@
     labels=categories,
     autopct="%1.1f%%",
     startangle=90,
-    colors=OKABE_ITO,
+    colors=IMPRINT,
     wedgeprops={"width": 0.42, "edgecolor": PAGE_BG, "linewidth": 3},
     pctdistance=0.78,
     labeldistance=1.08,
diff --git a/plots/donut-nested/implementations/python/matplotlib.py b/plots/donut-nested/implementations/python/matplotlib.py
index ecd90ff592..47d9505175 100644
--- a/plots/donut-nested/implementations/python/matplotlib.py
+++ b/plots/donut-nested/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-4 for departments)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Budget allocation: departments (inner) and expense categories (outer)
 departments = ["Engineering", "Marketing", "Operations", "Sales"]
@@ -48,11 +48,11 @@
 # Color palette - use Okabe-Ito base with varying opacity per subcategory
 dept_colors = {}
 for i, dept in enumerate(departments):
-    base_color = OKABE_ITO[i]
+    base_color = IMPRINT[i]
     num_cats = len(categories[dept])
     dept_colors[dept] = [base_color] * num_cats
 
-inner_colors = [OKABE_ITO[i] for i in range(len(departments))]
+inner_colors = [IMPRINT[i] for i in range(len(departments))]
 outer_colors = []
 for dept in departments:
     outer_colors.extend(dept_colors[dept])
@@ -102,7 +102,7 @@
 for i, dept in enumerate(departments):
     for cat in categories[dept]:
         legend_elements.append(
-            plt.Rectangle((0, 0), 1, 1, facecolor=OKABE_ITO[i], edgecolor=INK_SOFT, label=f"{dept}: {cat}")
+            plt.Rectangle((0, 0), 1, 1, facecolor=IMPRINT[i], edgecolor=INK_SOFT, label=f"{dept}: {cat}")
         )
 
 ax.legend(
diff --git a/plots/dot-matrix-proportional/implementations/python/matplotlib.py b/plots/dot-matrix-proportional/implementations/python/matplotlib.py
index 3c3de5a87a..c31b8bc947 100644
--- a/plots/dot-matrix-proportional/implementations/python/matplotlib.py
+++ b/plots/dot-matrix-proportional/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: remote work preference survey, n=100 employees
 categories = ["Hybrid", "Fully Remote", "In-Office"]
@@ -35,7 +35,7 @@
 
 xs = [idx % n_cols for idx in range(total)]
 ys = [n_rows - 1 - idx // n_cols for idx in range(total)]
-dot_colors = [OKABE_ITO[c] for c in dot_cats]
+dot_colors = [IMPRINT[c] for c in dot_cats]
 
 # Landscape figure: dot grid occupies a square central column; legend sits below
 fig, ax = plt.subplots(figsize=(16, 10), facecolor=PAGE_BG)
@@ -53,7 +53,7 @@
 
 # Legend — three items in one row, centred below the grid
 handles = [
-    mpatches.Patch(color=OKABE_ITO[i], label=f"{categories[i]}  —  {counts[i]} / {total}")
+    mpatches.Patch(color=IMPRINT[i], label=f"{categories[i]}  —  {counts[i]} / {total}")
     for i in range(len(categories))
 ]
 leg = ax.legend(
diff --git a/plots/drawdown-basic/implementations/python/matplotlib.py b/plots/drawdown-basic/implementations/python/matplotlib.py
index ed1334d67f..c270772799 100644
--- a/plots/drawdown-basic/implementations/python/matplotlib.py
+++ b/plots/drawdown-basic/implementations/python/matplotlib.py
@@ -22,7 +22,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Semantic palette: red for drawdown (loss), green for recovery (gain)
-DRAWDOWN_COLOR = "#B71D27"  # anyplot palette position 3
+DRAWDOWN_COLOR = "#AE3030"  # anyplot palette position 3
 RECOVERY_COLOR = "#009E73"  # anyplot palette position 1
 
 # Data — 2 years of simulated daily portfolio values with multiple drawdown cycles
diff --git a/plots/dumbbell-basic/implementations/python/matplotlib.py b/plots/dumbbell-basic/implementations/python/matplotlib.py
index 51f9431c60..9294f0499f 100644
--- a/plots/dumbbell-basic/implementations/python/matplotlib.py
+++ b/plots/dumbbell-basic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 RULE = (0.10, 0.10, 0.09, 0.18) if THEME == "light" else (0.94, 0.94, 0.91, 0.22)
 
 BEFORE_COLOR = "#009E73"  # Okabe-Ito 1 — first series
-AFTER_COLOR = "#D55E00"  # Okabe-Ito 2
+AFTER_COLOR = "#C475FD"  # Okabe-Ito 2
 
 # Data: Employee satisfaction scores before and after workplace policy changes
 categories = ["Engineering", "Marketing", "Sales", "HR", "Finance", "Operations", "Customer Support", "Product"]
diff --git a/plots/facet-grid/implementations/python/matplotlib.py b/plots/facet-grid/implementations/python/matplotlib.py
index 263af25d8e..7fd74cebdf 100644
--- a/plots/facet-grid/implementations/python/matplotlib.py
+++ b/plots/facet-grid/implementations/python/matplotlib.py
@@ -29,11 +29,11 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette - first series is always #009E73
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # brand green
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
 ]
 
 # Data
@@ -68,7 +68,7 @@
 )
 
 # Color map: regions to Okabe-Ito palette
-region_colors = {region: OKABE_ITO[i] for i, region in enumerate(regions)}
+region_colors = {region: IMPRINT[i] for i, region in enumerate(regions)}
 
 # Create scatter plots in each facet with trend lines
 for i, region in enumerate(regions):
diff --git a/plots/forest-basic/implementations/python/matplotlib.py b/plots/forest-basic/implementations/python/matplotlib.py
index 2160f08763..4e9b54bae9 100644
--- a/plots/forest-basic/implementations/python/matplotlib.py
+++ b/plots/forest-basic/implementations/python/matplotlib.py
@@ -22,7 +22,7 @@
 
 # Okabe-Ito palette
 OKABE_ITO_1 = "#009E73"  # Primary data color (brand green)
-OKABE_ITO_2 = "#D55E00"  # Secondary
+OKABE_ITO_2 = "#C475FD"  # Secondary
 
 # Data: Meta-analysis of RCTs comparing treatment efficacy (standardized mean difference)
 studies = [
diff --git a/plots/frequency-polygon-basic/implementations/python/matplotlib.py b/plots/frequency-polygon-basic/implementations/python/matplotlib.py
index 87cf2f387e..6f71a07880 100644
--- a/plots/frequency-polygon-basic/implementations/python/matplotlib.py
+++ b/plots/frequency-polygon-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-3)
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Heights by age group
 np.random.seed(42)
diff --git a/plots/funnel-basic/implementations/python/matplotlib.py b/plots/funnel-basic/implementations/python/matplotlib.py
index ab6663cf85..253479fe1f 100644
--- a/plots/funnel-basic/implementations/python/matplotlib.py
+++ b/plots/funnel-basic/implementations/python/matplotlib.py
@@ -17,8 +17,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette — first stage is brand green (#009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
-# Light orange #E69F00 needs dark text; pick INK so the label also
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
+# Light orange #AE3030 needs dark text; pick INK so the label also
 # stays legible where it overflows the narrow bottom segment.
 TEXT_ON_FILL = ["white", "white", "white", "white", INK]
 
@@ -48,7 +48,7 @@
     y = np.array([y_top, y_bot])
     x_left = np.array([-w_top / 2, -w_bot / 2])
     x_right = np.array([w_top / 2, w_bot / 2])
-    ax.fill_betweenx(y, x_left, x_right, facecolor=OKABE_ITO[i], edgecolor=PAGE_BG, linewidth=2)
+    ax.fill_betweenx(y, x_left, x_right, facecolor=IMPRINT[i], edgecolor=PAGE_BG, linewidth=2)
 
     y_mid = (y_top + y_bot) / 2
     pct = (values[i] / max_value) * 100
diff --git a/plots/gain-curve/implementations/python/matplotlib.py b/plots/gain-curve/implementations/python/matplotlib.py
index 7e00429386..4bb4ed428d 100644
--- a/plots/gain-curve/implementations/python/matplotlib.py
+++ b/plots/gain-curve/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 BRAND = "#009E73"  # Okabe-Ito position 1
-SECONDARY = "#D55E00"  # Okabe-Ito position 2
+SECONDARY = "#C475FD"  # Okabe-Ito position 2
 NEUTRAL = INK_MUTED
 
 # Data: Generate synthetic classification data (customer response model)
diff --git a/plots/gantt-basic/implementations/python/matplotlib.py b/plots/gantt-basic/implementations/python/matplotlib.py
index 27a81e618a..d56bfe04e6 100644
--- a/plots/gantt-basic/implementations/python/matplotlib.py
+++ b/plots/gantt-basic/implementations/python/matplotlib.py
@@ -21,7 +21,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette for categories
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data - Software Development Project
 tasks = [
@@ -66,10 +66,10 @@
 
 # Category to color mapping using Okabe-Ito palette
 category_colors = {
-    "Planning": OKABE_ITO[0],
-    "Development": OKABE_ITO[1],
-    "Testing": OKABE_ITO[2],
-    "Deployment": OKABE_ITO[3],
+    "Planning": IMPRINT[0],
+    "Development": IMPRINT[1],
+    "Testing": IMPRINT[2],
+    "Deployment": IMPRINT[3],
 }
 
 # Plot
diff --git a/plots/gauge-basic/implementations/python/matplotlib.py b/plots/gauge-basic/implementations/python/matplotlib.py
index 7b386d1ec8..2edd35e0b8 100644
--- a/plots/gauge-basic/implementations/python/matplotlib.py
+++ b/plots/gauge-basic/implementations/python/matplotlib.py
@@ -20,8 +20,8 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito zone colors (colorblind-safe red/yellow/green)
-ZONE_BAD = "#D55E00"  # vermillion
-ZONE_WARN = "#E69F00"  # orange
+ZONE_BAD = "#C475FD"  # vermillion
+ZONE_WARN = "#AE3030"  # orange
 ZONE_GOOD = "#009E73"  # bluish green (brand)
 
 # Data
diff --git a/plots/heatmap-adjacency/implementations/python/matplotlib.py b/plots/heatmap-adjacency/implementations/python/matplotlib.py
index eb0a85fdf7..c6588be0b5 100644
--- a/plots/heatmap-adjacency/implementations/python/matplotlib.py
+++ b/plots/heatmap-adjacency/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-COMM_COLORS = ["#009E73", "#D55E00", "#0072B2"]  # Okabe-Ito positions 1–3
+COMM_COLORS = ["#009E73", "#C475FD", "#4467A3"]  # Okabe-Ito positions 1–3
 
 # Data: research collaboration network with 3 communities
 np.random.seed(42)
diff --git a/plots/heatmap-geographic/implementations/python/matplotlib.py b/plots/heatmap-geographic/implementations/python/matplotlib.py
index c0f110c0d9..60738a3820 100644
--- a/plots/heatmap-geographic/implementations/python/matplotlib.py
+++ b/plots/heatmap-geographic/implementations/python/matplotlib.py
@@ -125,7 +125,7 @@
 )
 
 # Sensor locations (slightly more visible than before)
-ax.scatter(longitudes, latitudes, s=25, c="#0072B2", alpha=0.45, edgecolors="none", label="Sensor Locations")
+ax.scatter(longitudes, latitudes, s=25, c="#4467A3", alpha=0.45, edgecolors="none", label="Sensor Locations")
 
 # Colorbar
 cbar = plt.colorbar(im, ax=ax, shrink=0.8, pad=0.02)
diff --git a/plots/hierarchy-toggle-view/implementations/python/matplotlib.py b/plots/hierarchy-toggle-view/implementations/python/matplotlib.py
index 32b6b950b6..ff764c86db 100644
--- a/plots/hierarchy-toggle-view/implementations/python/matplotlib.py
+++ b/plots/hierarchy-toggle-view/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Corporate Annual Budget ($M)
 budget = {
diff --git a/plots/histogram-cumulative/implementations/python/matplotlib.py b/plots/histogram-cumulative/implementations/python/matplotlib.py
index 533b2a09db..a1d3ebace3 100644
--- a/plots/histogram-cumulative/implementations/python/matplotlib.py
+++ b/plots/histogram-cumulative/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 BRAND = "#009E73"  # Okabe-Ito position 1
-ACCENT = "#D55E00"  # Okabe-Ito position 2 for reference lines
+ACCENT = "#C475FD"  # Okabe-Ito position 2 for reference lines
 
 # Data - exam scores with realistic distribution
 np.random.seed(42)
diff --git a/plots/histogram-density/implementations/python/matplotlib.py b/plots/histogram-density/implementations/python/matplotlib.py
index d8e94a33d5..fc3f7bb65a 100644
--- a/plots/histogram-density/implementations/python/matplotlib.py
+++ b/plots/histogram-density/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # First categorical series
-ACCENT = "#D55E00"  # Second color for overlay
+ACCENT = "#C475FD"  # Second color for overlay
 
 # Data: Generate realistic test score data with a normal distribution
 np.random.seed(42)
diff --git a/plots/histogram-kde/implementations/python/matplotlib.py b/plots/histogram-kde/implementations/python/matplotlib.py
index aef4f0290a..4fb706ce13 100644
--- a/plots/histogram-kde/implementations/python/matplotlib.py
+++ b/plots/histogram-kde/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - simulate stock daily returns (realistic financial data)
 np.random.seed(42)
diff --git a/plots/histogram-overlapping/implementations/python/matplotlib.py b/plots/histogram-overlapping/implementations/python/matplotlib.py
index 1d38842393..81762416a5 100644
--- a/plots/histogram-overlapping/implementations/python/matplotlib.py
+++ b/plots/histogram-overlapping/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito colors (first series is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Comparing salary distributions across three departments
 np.random.seed(42)
@@ -40,9 +40,9 @@
 bins = np.linspace(20000, 150000, 35)
 
 # Plot overlapping histograms with transparency
-ax.hist(engineering, bins=bins, alpha=0.5, label="Engineering", color=OKABE_ITO[0], edgecolor=INK_SOFT, linewidth=1.5)
-ax.hist(marketing, bins=bins, alpha=0.5, label="Marketing", color=OKABE_ITO[1], edgecolor=INK_SOFT, linewidth=1.5)
-ax.hist(sales, bins=bins, alpha=0.5, label="Sales", color=OKABE_ITO[2], edgecolor=INK_SOFT, linewidth=1.5)
+ax.hist(engineering, bins=bins, alpha=0.5, label="Engineering", color=IMPRINT[0], edgecolor=INK_SOFT, linewidth=1.5)
+ax.hist(marketing, bins=bins, alpha=0.5, label="Marketing", color=IMPRINT[1], edgecolor=INK_SOFT, linewidth=1.5)
+ax.hist(sales, bins=bins, alpha=0.5, label="Sales", color=IMPRINT[2], edgecolor=INK_SOFT, linewidth=1.5)
 
 # Labels and styling
 ax.set_xlabel("Annual Salary ($)", fontsize=20, color=INK)
diff --git a/plots/histogram-returns-distribution/implementations/python/matplotlib.py b/plots/histogram-returns-distribution/implementations/python/matplotlib.py
index 579e611214..9905022927 100644
--- a/plots/histogram-returns-distribution/implementations/python/matplotlib.py
+++ b/plots/histogram-returns-distribution/implementations/python/matplotlib.py
@@ -21,8 +21,8 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 BRAND = "#009E73"  # Okabe-Ito pos 1 — main histogram bars
-TAIL_COLOR = "#D55E00"  # Okabe-Ito pos 2 — tail regions
-CURVE_COLOR = "#0072B2"  # Okabe-Ito pos 3 — normal distribution curve
+TAIL_COLOR = "#C475FD"  # Okabe-Ito pos 2 — tail regions
+CURVE_COLOR = "#4467A3"  # Okabe-Ito pos 3 — normal distribution curve
 
 # Data — simulate daily stock returns with slight fat tails (t-distribution df=8)
 np.random.seed(42)
diff --git a/plots/histogram-stacked/implementations/python/matplotlib.py b/plots/histogram-stacked/implementations/python/matplotlib.py
index 3dd2a45ce7..bc6e380c5e 100644
--- a/plots/histogram-stacked/implementations/python/matplotlib.py
+++ b/plots/histogram-stacked/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette - using first 3 colors in canonical order
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Response times (ms) for three different server regions
 np.random.seed(42)
@@ -41,7 +41,7 @@
     [us_east, europe, asia],
     bins=20,
     stacked=True,
-    color=OKABE_ITO,
+    color=IMPRINT,
     label=labels,
     edgecolor=PAGE_BG,
     linewidth=0.5,
diff --git a/plots/histogram-stepwise/implementations/python/matplotlib.py b/plots/histogram-stepwise/implementations/python/matplotlib.py
index eea063e82a..88df5f17b9 100644
--- a/plots/histogram-stepwise/implementations/python/matplotlib.py
+++ b/plots/histogram-stepwise/implementations/python/matplotlib.py
@@ -25,7 +25,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Position 1
-ACCENT = "#D55E00"  # Position 2
+ACCENT = "#C475FD"  # Position 2
 
 # Data - Two distributions with distinct shapes
 np.random.seed(42)
diff --git a/plots/hive-basic/implementations/python/matplotlib.py b/plots/hive-basic/implementations/python/matplotlib.py
index 7963402230..cf7d774d24 100644
--- a/plots/hive-basic/implementations/python/matplotlib.py
+++ b/plots/hive-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for axes (first 3 colors for 3 categories)
-AXIS_COLORS = ["#009E73", "#D55E00", "#0072B2"]
+AXIS_COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Create software module dependency network data
 np.random.seed(42)
diff --git a/plots/horizon-basic/implementations/python/matplotlib.py b/plots/horizon-basic/implementations/python/matplotlib.py
index 59344c0961..a9006b1283 100644
--- a/plots/horizon-basic/implementations/python/matplotlib.py
+++ b/plots/horizon-basic/implementations/python/matplotlib.py
@@ -46,7 +46,7 @@
 
 # Color bases with theme-adaptive styling
 pos_base = mcolors.to_rgb("#009E73")  # Okabe-Ito brand green for positive
-neg_base = mcolors.to_rgb("#D55E00")  # Okabe-Ito vermillion for negative
+neg_base = mcolors.to_rgb("#C475FD")  # Okabe-Ito vermillion for negative
 
 # Create figure
 fig, axes = plt.subplots(n_series, 1, figsize=(16, 9), sharex=True, facecolor=PAGE_BG)
diff --git a/plots/ice-basic/implementations/python/matplotlib.py b/plots/ice-basic/implementations/python/matplotlib.py
index fdb17b9f89..dabc03b87e 100644
--- a/plots/ice-basic/implementations/python/matplotlib.py
+++ b/plots/ice-basic/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 BRAND = "#009E73"  # Okabe-Ito position 1 — ICE individual lines
-ACCENT = "#D55E00"  # Okabe-Ito position 2 — PDP average line
+ACCENT = "#C475FD"  # Okabe-Ito position 2 — PDP average line
 
 # Data: synthetic housing dataset
 np.random.seed(42)
diff --git a/plots/indicator-bollinger/implementations/python/matplotlib.py b/plots/indicator-bollinger/implementations/python/matplotlib.py
index b0b1abdccf..65316420fe 100644
--- a/plots/indicator-bollinger/implementations/python/matplotlib.py
+++ b/plots/indicator-bollinger/implementations/python/matplotlib.py
@@ -20,8 +20,8 @@
 
 # Okabe-Ito palette - first color is always brand green
 BRAND = "#009E73"
-SECONDARY = "#D55E00"
-TERTIARY = "#0072B2"
+SECONDARY = "#C475FD"
+TERTIARY = "#4467A3"
 
 # Data - Generate realistic stock price data with Bollinger Bands
 np.random.seed(42)
diff --git a/plots/indicator-ema/implementations/python/matplotlib.py b/plots/indicator-ema/implementations/python/matplotlib.py
index e291dd5705..e42a954b2d 100644
--- a/plots/indicator-ema/implementations/python/matplotlib.py
+++ b/plots/indicator-ema/implementations/python/matplotlib.py
@@ -22,9 +22,9 @@
 
 # Okabe-Ito colors for EMAs
 EMA_SHORT_COLOR = "#009E73"  # Okabe-Ito pos 1 — 12-day EMA
-EMA_LONG_COLOR = "#D55E00"  # Okabe-Ito pos 2 — 26-day EMA
-GOLDEN_COLOR = "#E69F00"  # Okabe-Ito pos 5 — bullish crossover
-DEATH_COLOR = "#CC79A7"  # Okabe-Ito pos 4 — bearish crossover
+EMA_LONG_COLOR = "#C475FD"  # Okabe-Ito pos 2 — 26-day EMA
+GOLDEN_COLOR = "#AE3030"  # Okabe-Ito pos 5 — bullish crossover
+DEATH_COLOR = "#BD8233"  # Okabe-Ito pos 4 — bearish crossover
 
 # Data
 np.random.seed(42)
diff --git a/plots/indicator-macd/implementations/python/matplotlib.py b/plots/indicator-macd/implementations/python/matplotlib.py
index 7db22ba626..59ef870768 100644
--- a/plots/indicator-macd/implementations/python/matplotlib.py
+++ b/plots/indicator-macd/implementations/python/matplotlib.py
@@ -23,10 +23,10 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-MACD_COLOR = "#0072B2"  # Blue (position 3)
-SIGNAL_COLOR = "#E69F00"  # Orange (position 5)
+MACD_COLOR = "#4467A3"  # Blue (position 3)
+SIGNAL_COLOR = "#AE3030"  # Orange (position 5)
 POSITIVE_COLOR = "#009E73"  # Green (position 1)
-NEGATIVE_COLOR = "#D55E00"  # Red-orange (position 2)
+NEGATIVE_COLOR = "#C475FD"  # Red-orange (position 2)
 
 # Generate synthetic stock price data and calculate MACD
 np.random.seed(42)
diff --git a/plots/indicator-rsi/implementations/python/matplotlib.py b/plots/indicator-rsi/implementations/python/matplotlib.py
index fd75211045..de5904d7bd 100644
--- a/plots/indicator-rsi/implementations/python/matplotlib.py
+++ b/plots/indicator-rsi/implementations/python/matplotlib.py
@@ -66,10 +66,10 @@
 ax.set_facecolor(PAGE_BG)
 
 # Shade overbought zone (70-100)
-ax.fill_between(df["date"], 70, 100, alpha=0.12, color="#D55E00")
+ax.fill_between(df["date"], 70, 100, alpha=0.12, color="#C475FD")
 
 # Shade oversold zone (0-30)
-ax.fill_between(df["date"], 0, 30, alpha=0.12, color="#0072B2")
+ax.fill_between(df["date"], 0, 30, alpha=0.12, color="#4467A3")
 
 # Plot RSI line using brand color
 ax.plot(df["date"], df["rsi"], color=BRAND, linewidth=3, label="RSI (14-period)")
diff --git a/plots/indicator-sma/implementations/python/matplotlib.py b/plots/indicator-sma/implementations/python/matplotlib.py
index 8241f96e32..971f166cab 100644
--- a/plots/indicator-sma/implementations/python/matplotlib.py
+++ b/plots/indicator-sma/implementations/python/matplotlib.py
@@ -21,9 +21,9 @@
 
 # Okabe-Ito palette — canonical order
 PRICE_COLOR = "#009E73"  # position 1 — price line (first series)
-SMA20_COLOR = "#D55E00"  # position 2
-SMA50_COLOR = "#0072B2"  # position 3
-SMA200_COLOR = "#CC79A7"  # position 4
+SMA20_COLOR = "#C475FD"  # position 2
+SMA50_COLOR = "#4467A3"  # position 3
+SMA200_COLOR = "#BD8233"  # position 4
 
 # Data - realistic stock price data with trend and volatility
 np.random.seed(42)
diff --git a/plots/kagi-basic/implementations/python/matplotlib.py b/plots/kagi-basic/implementations/python/matplotlib.py
index f7c8322e0e..e360710845 100644
--- a/plots/kagi-basic/implementations/python/matplotlib.py
+++ b/plots/kagi-basic/implementations/python/matplotlib.py
@@ -21,7 +21,7 @@
 
 # Data colors (Okabe-Ito palette, theme-independent)
 COLOR_YANG = "#009E73"  # Okabe-Ito position 1 (green for bullish)
-COLOR_YIN = "#D55E00"  # Okabe-Ito position 2 (red for bearish)
+COLOR_YIN = "#C475FD"  # Okabe-Ito position 2 (red for bearish)
 
 # Generate synthetic stock price data
 np.random.seed(42)
diff --git a/plots/learning-curve-basic/implementations/python/matplotlib.py b/plots/learning-curve-basic/implementations/python/matplotlib.py
index ac333659b9..40573f3ad9 100644
--- a/plots/learning-curve-basic/implementations/python/matplotlib.py
+++ b/plots/learning-curve-basic/implementations/python/matplotlib.py
@@ -16,7 +16,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - Simulate learning curve for a model
 np.random.seed(42)
diff --git a/plots/lift-curve/implementations/python/matplotlib.py b/plots/lift-curve/implementations/python/matplotlib.py
index d66d1c55fb..806d13afbc 100644
--- a/plots/lift-curve/implementations/python/matplotlib.py
+++ b/plots/lift-curve/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1
-ACCENT = "#E69F00"  # Okabe-Ito position 5 for reference line
+ACCENT = "#AE3030"  # Okabe-Ito position 5 for reference line
 
 # Data - Simulate realistic customer response model predictions
 np.random.seed(42)
diff --git a/plots/line-annotated-events/implementations/python/matplotlib.py b/plots/line-annotated-events/implementations/python/matplotlib.py
index 2081a82907..016ee40f67 100644
--- a/plots/line-annotated-events/implementations/python/matplotlib.py
+++ b/plots/line-annotated-events/implementations/python/matplotlib.py
@@ -44,7 +44,7 @@
 ax.plot(dates, prices, linewidth=3, color=BRAND, label="Stock Price", zorder=2)
 
 # Event markers with alternating heights
-event_color = "#D55E00" if THEME == "light" else "#56B4E9"
+event_color = "#C475FD" if THEME == "light" else "#2ABCCD"
 heights = [0.85, 0.70, 0.85, 0.70, 0.85]
 
 for i, (event_date, event_label) in enumerate(events):
diff --git a/plots/line-confidence/implementations/python/matplotlib.py b/plots/line-confidence/implementations/python/matplotlib.py
index f0b9e90aa9..56b9cc19cf 100644
--- a/plots/line-confidence/implementations/python/matplotlib.py
+++ b/plots/line-confidence/implementations/python/matplotlib.py
@@ -41,8 +41,8 @@
 ax.set_facecolor(PAGE_BG)
 
 # Shaded confidence band (contrasting color with semi-transparent alpha)
-# Use sky blue (#56B4E9) for the band to contrast with green line
-ax.fill_between(days, y_lower, y_upper, alpha=0.25, color="#56B4E9", label="95% Confidence Interval")
+# Use sky blue (#2ABCCD) for the band to contrast with green line
+ax.fill_between(days, y_lower, y_upper, alpha=0.25, color="#2ABCCD", label="95% Confidence Interval")
 
 # Central trend line (prominent, brand green)
 ax.plot(days, y, color="#009E73", linewidth=3, label="Forecast Mean")
diff --git a/plots/line-loss-training/implementations/python/matplotlib.py b/plots/line-loss-training/implementations/python/matplotlib.py
index d4e89f90ea..768fe3c15d 100644
--- a/plots/line-loss-training/implementations/python/matplotlib.py
+++ b/plots/line-loss-training/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 
 # Okabe-Ito colors
 TRAINING_COLOR = "#009E73"
-VALIDATION_COLOR = "#D55E00"
+VALIDATION_COLOR = "#C475FD"
 
 # Data
 np.random.seed(42)
diff --git a/plots/line-multi/implementations/python/matplotlib.py b/plots/line-multi/implementations/python/matplotlib.py
index eaa6b67219..fd1d0a54e0 100644
--- a/plots/line-multi/implementations/python/matplotlib.py
+++ b/plots/line-multi/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Monthly sales (in thousands) for 3 product lines over 12 months
 np.random.seed(42)
@@ -46,7 +46,7 @@
 ax.plot(
     months,
     product_a,
-    color=OKABE_ITO[0],
+    color=IMPRINT[0],
     linewidth=3,
     marker="o",
     markersize=10,
@@ -56,7 +56,7 @@
 ax.plot(
     months,
     product_b,
-    color=OKABE_ITO[1],
+    color=IMPRINT[1],
     linewidth=3,
     marker="s",
     markersize=10,
@@ -66,7 +66,7 @@
 ax.plot(
     months,
     product_c,
-    color=OKABE_ITO[2],
+    color=IMPRINT[2],
     linewidth=3,
     marker="^",
     markersize=10,
diff --git a/plots/line-stepwise/implementations/python/matplotlib.py b/plots/line-stepwise/implementations/python/matplotlib.py
index caaf575f16..941bd9dc32 100644
--- a/plots/line-stepwise/implementations/python/matplotlib.py
+++ b/plots/line-stepwise/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette (first series is always brand green)
 BRAND = "#009E73"
-SECONDARY = "#D55E00"
+SECONDARY = "#C475FD"
 
 # Data: Server capacity levels over monitoring period
 np.random.seed(42)
diff --git a/plots/line-stock-comparison/implementations/python/matplotlib.py b/plots/line-stock-comparison/implementations/python/matplotlib.py
index 4e10bc713e..c4426c0989 100644
--- a/plots/line-stock-comparison/implementations/python/matplotlib.py
+++ b/plots/line-stock-comparison/implementations/python/matplotlib.py
@@ -29,7 +29,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # anyplot palette — canonical order
-ANYPLOT_PALETTE = ["#009E73", "#9418DB", "#B71D27", "#16B8F3"]
+IMPRINT = ["#009E73", "#C475FD", "#AE3030", "#4467A3"]
 
 # Data — simulated daily stock prices for ~1 year (252 trading days)
 np.random.seed(42)
@@ -53,7 +53,7 @@
 fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-for (symbol, values), color in zip(rebased.items(), ANYPLOT_PALETTE, strict=False):
+for (symbol, values), color in zip(rebased.items(), IMPRINT, strict=False):
     # SPY is the benchmark — thicker line for emphasis and visual hierarchy
     lw = 3.0 if symbol == "SPY" else 2.0
     ax.plot(dates, values, label=symbol, color=color, linewidth=lw)
@@ -63,7 +63,7 @@
 
 # Annotate SPY as benchmark to create visual hierarchy and storytelling focal point
 spy_final = rebased["SPY"][-1]
-spy_color = ANYPLOT_PALETTE[3]  # SPY is 4th series (#16B8F3)
+spy_color = IMPRINT[3]  # SPY is 4th series (#4467A3)
 ax.annotate(
     "Benchmark",
     xy=(dates[-1], spy_final),
diff --git a/plots/line-styled/implementations/python/matplotlib.py b/plots/line-styled/implementations/python/matplotlib.py
index 08bb0e2fc0..454ae284d5 100644
--- a/plots/line-styled/implementations/python/matplotlib.py
+++ b/plots/line-styled/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - CPU performance benchmarks over time
 np.random.seed(42)
diff --git a/plots/line-timeseries-rolling/implementations/python/matplotlib.py b/plots/line-timeseries-rolling/implementations/python/matplotlib.py
index 3466ad60c5..38ee72698d 100644
--- a/plots/line-timeseries-rolling/implementations/python/matplotlib.py
+++ b/plots/line-timeseries-rolling/implementations/python/matplotlib.py
@@ -40,7 +40,7 @@
 ax.set_facecolor(PAGE_BG)
 
 # Raw data - thin, semi-transparent line (secondary Okabe-Ito color)
-ax.plot(df["date"], df["temperature"], linewidth=1, alpha=0.4, color="#D55E00", label="Daily Temperature")
+ax.plot(df["date"], df["temperature"], linewidth=1, alpha=0.4, color="#C475FD", label="Daily Temperature")
 
 # Rolling average - prominent smooth line (brand green)
 ax.plot(df["date"], df["rolling_avg"], linewidth=3.5, color="#009E73", label="7-Day Rolling Average")
diff --git a/plots/linked-views-selection/implementations/python/matplotlib.py b/plots/linked-views-selection/implementations/python/matplotlib.py
index c92d1dc889..ec64ec1166 100644
--- a/plots/linked-views-selection/implementations/python/matplotlib.py
+++ b/plots/linked-views-selection/implementations/python/matplotlib.py
@@ -21,8 +21,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
-BRAND = OKABE_ITO[0]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
+BRAND = IMPRINT[0]
 
 # Data
 np.random.seed(42)
diff --git a/plots/logistic-regression/implementations/python/matplotlib.py b/plots/logistic-regression/implementations/python/matplotlib.py
index 7a54a73ea6..17226c3f7b 100644
--- a/plots/logistic-regression/implementations/python/matplotlib.py
+++ b/plots/logistic-regression/implementations/python/matplotlib.py
@@ -25,7 +25,7 @@
 
 # Okabe-Ito palette (first series is brand green)
 BRAND = "#009E73"
-SECONDARY = "#D55E00"
+SECONDARY = "#C475FD"
 
 # Data - Credit risk scoring: probability of loan approval based on credit score
 np.random.seed(42)
diff --git a/plots/manhattan-gwas/implementations/python/matplotlib.py b/plots/manhattan-gwas/implementations/python/matplotlib.py
index 4321149853..beca93a199 100644
--- a/plots/manhattan-gwas/implementations/python/matplotlib.py
+++ b/plots/manhattan-gwas/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 BRAND = "#009E73"
 
 # Okabe-Ito palette for chromosome alternation
-CHROM_COLORS = ["#009E73", "#D55E00"]  # Brand green and vermillion, alternating
+CHROM_COLORS = ["#009E73", "#C475FD"]  # Brand green and vermillion, alternating
 
 # Data - Simulate GWAS results for 22 chromosomes
 np.random.seed(42)
diff --git a/plots/map-drilldown-geographic/implementations/python/matplotlib.py b/plots/map-drilldown-geographic/implementations/python/matplotlib.py
index ab475f5ec7..5d2f75c919 100644
--- a/plots/map-drilldown-geographic/implementations/python/matplotlib.py
+++ b/plots/map-drilldown-geographic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 BOX_TEXT = "#F0EFE8"  # light text on dark anyplot_seq boxes (good contrast on both ends)
-HIGHLIGHT = "#9418DB"  # anyplot palette position 2 — selected-region indicator
+HIGHLIGHT = "#C475FD"  # anyplot palette position 2 — selected-region indicator
 
 # anyplot sequential colormap: green → dark azure (single-polarity continuous)
 anyplot_seq = LinearSegmentedColormap.from_list("anyplot_seq", ["#009E73", "#003D94"])
diff --git a/plots/map-marker-clustered/implementations/python/matplotlib.py b/plots/map-marker-clustered/implementations/python/matplotlib.py
index 8db713226c..913e7f49e9 100644
--- a/plots/map-marker-clustered/implementations/python/matplotlib.py
+++ b/plots/map-marker-clustered/implementations/python/matplotlib.py
@@ -26,7 +26,7 @@
 
 # anyplot categorical palette (positions 1-3)
 cat_names = ["Retail", "Grocery", "Electronics"]
-cat_colors = {"Retail": "#009E73", "Grocery": "#9418DB", "Electronics": "#B71D27"}
+cat_colors = {"Retail": "#009E73", "Grocery": "#C475FD", "Electronics": "#AE3030"}
 
 # Data: European store locations clustered by city
 np.random.seed(42)
diff --git a/plots/map-projections/implementations/python/matplotlib.py b/plots/map-projections/implementations/python/matplotlib.py
index 2959b4a46e..9c9ebea0b5 100644
--- a/plots/map-projections/implementations/python/matplotlib.py
+++ b/plots/map-projections/implementations/python/matplotlib.py
@@ -29,7 +29,7 @@
 # Map colors (theme-adaptive; semantic: land/ocean are not data series)
 LAND_COLOR = "#E8E4D9" if THEME == "light" else "#3A3830"
 OCEAN_COLOR = "#A8D5E5" if THEME == "light" else "#1A3348"
-TISSOT_COLOR = "#B71D27"  # anyplot palette position 3 (red)
+TISSOT_COLOR = "#AE3030"  # anyplot palette position 3 (red)
 
 # Projections to compare
 projections = [
diff --git a/plots/map-route-path/implementations/python/matplotlib.py b/plots/map-route-path/implementations/python/matplotlib.py
index d137ab228f..f94870895e 100644
--- a/plots/map-route-path/implementations/python/matplotlib.py
+++ b/plots/map-route-path/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 
 # Okabe-Ito positions for start/end markers
 START_COLOR = "#009E73"  # position 1 — green
-END_COLOR = "#D55E00"  # position 2 — vermillion
+END_COLOR = "#C475FD"  # position 2 — vermillion
 
 # Data: Simulated hiking trail GPS track (San Francisco coastal path)
 np.random.seed(42)
diff --git a/plots/marimekko-basic/implementations/python/matplotlib.py b/plots/marimekko-basic/implementations/python/matplotlib.py
index fd55caf25b..11b9a317e1 100644
--- a/plots/marimekko-basic/implementations/python/matplotlib.py
+++ b/plots/marimekko-basic/implementations/python/matplotlib.py
@@ -34,8 +34,8 @@
 )
 
 # Okabe-Ito palette (positions 1-4 in canonical order)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
-# Smart label contrast: white on dark segments, ink on lighter #CC79A7
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
+# Smart label contrast: white on dark segments, ink on lighter #BD8233
 label_colors = ["white", "white", "white", INK]
 
 # Calculate bar widths (proportional to column totals)
@@ -48,7 +48,7 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-for i, (product, color, label_color) in enumerate(zip(products, OKABE_ITO, label_colors, strict=True)):
+for i, (product, color, label_color) in enumerate(zip(products, IMPRINT, label_colors, strict=True)):
     heights = values[i] / column_totals
     bottoms = values[:i].sum(axis=0) / column_totals if i > 0 else np.zeros(len(regions))
 
diff --git a/plots/maze-circular/implementations/python/matplotlib.py b/plots/maze-circular/implementations/python/matplotlib.py
index f7d570429d..21dc62588f 100644
--- a/plots/maze-circular/implementations/python/matplotlib.py
+++ b/plots/maze-circular/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 
 GOAL_COLOR = "#009E73"  # Okabe-Ito position 1
-ENTRY_COLOR = "#D55E00"  # Okabe-Ito position 2
+ENTRY_COLOR = "#C475FD"  # Okabe-Ito position 2
 
 # Data — Fibonacci sector progression for naturally varied corridor widths
 np.random.seed(13)
diff --git a/plots/mosaic-categorical/implementations/python/matplotlib.py b/plots/mosaic-categorical/implementations/python/matplotlib.py
index 67a4cbbb86..5f1183c578 100644
--- a/plots/mosaic-categorical/implementations/python/matplotlib.py
+++ b/plots/mosaic-categorical/implementations/python/matplotlib.py
@@ -23,7 +23,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette — first series always #009E73
-CELL_COLORS = {"Survived": "#009E73", "Did Not Survive": "#D55E00"}
+CELL_COLORS = {"Survived": "#009E73", "Did Not Survive": "#C475FD"}
 
 # Data: Titanic passenger survival by class (realistic proportions)
 counts = {
diff --git a/plots/network-basic/implementations/python/matplotlib.py b/plots/network-basic/implementations/python/matplotlib.py
index afb8ad9c49..8e74b6b8d0 100644
--- a/plots/network-basic/implementations/python/matplotlib.py
+++ b/plots/network-basic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for 4 departments
-GROUP_COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+GROUP_COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 GROUP_NAMES = ["Engineering", "Research", "Marketing", "Design"]
 
 # Data: social network of 20 people across 4 company departments
@@ -141,7 +141,7 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-BRIDGE_COLOR = "#E69F00"  # Okabe-Ito amber — distinct cross-department highlight
+BRIDGE_COLOR = "#AE3030"  # Okabe-Ito amber — distinct cross-department highlight
 
 # Draw curved edges using FancyArrowPatch
 for src, tgt in edges:
diff --git a/plots/network-bipartite/implementations/python/matplotlib.py b/plots/network-bipartite/implementations/python/matplotlib.py
index ba48dfc885..aae4138c3a 100644
--- a/plots/network-bipartite/implementations/python/matplotlib.py
+++ b/plots/network-bipartite/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito pos 1 — researchers
-ACCENT = "#D55E00"  # Okabe-Ito pos 2 — papers
+ACCENT = "#C475FD"  # Okabe-Ito pos 2 — papers
 
 # Data — researcher-paper affiliation network (bibliometrics)
 np.random.seed(42)
diff --git a/plots/network-directed/implementations/python/matplotlib.py b/plots/network-directed/implementations/python/matplotlib.py
index e1314931c5..a04bb5b54e 100644
--- a/plots/network-directed/implementations/python/matplotlib.py
+++ b/plots/network-directed/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for group colors (first 4 positions)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Software package dependencies (arrows show import direction)
 np.random.seed(42)
@@ -68,7 +68,7 @@
 ]
 
 # Group colors (Okabe-Ito)
-group_colors = {"core": OKABE_ITO[0], "api": OKABE_ITO[1], "utils": OKABE_ITO[2], "tests": OKABE_ITO[3]}
+group_colors = {"core": IMPRINT[0], "api": IMPRINT[1], "utils": IMPRINT[2], "tests": IMPRINT[3]}
 
 # Plot
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
diff --git a/plots/network-force-directed/implementations/python/matplotlib.py b/plots/network-force-directed/implementations/python/matplotlib.py
index f128d701b6..8b91b0bcee 100644
--- a/plots/network-force-directed/implementations/python/matplotlib.py
+++ b/plots/network-force-directed/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette — first series is always #009E73
-COMMUNITY_COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COMMUNITY_COLORS = ["#009E73", "#C475FD", "#4467A3"]
 COMMUNITY_NAMES = ["Engineering", "Marketing", "Sales"]
 
 # Data: a 50-person company social network with 3 departments
diff --git a/plots/network-hierarchical/implementations/python/matplotlib.py b/plots/network-hierarchical/implementations/python/matplotlib.py
index dc244d798c..1baf32322c 100644
--- a/plots/network-hierarchical/implementations/python/matplotlib.py
+++ b/plots/network-hierarchical/implementations/python/matplotlib.py
@@ -25,7 +25,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Software Module Hierarchy (24 nodes, 4 levels)
 np.random.seed(42)
@@ -106,10 +106,10 @@
 
 # Use Okabe-Ito palette for levels
 level_colors = {
-    0: OKABE_ITO[0],  # Brand green (#009E73)
-    1: OKABE_ITO[1],  # Vermillion (#D55E00)
-    2: OKABE_ITO[2],  # Blue (#0072B2)
-    3: OKABE_ITO[3],  # Reddish purple (#CC79A7)
+    0: IMPRINT[0],  # Brand green (#009E73)
+    1: IMPRINT[1],  # Vermillion (#C475FD)
+    2: IMPRINT[2],  # Blue (#4467A3)
+    3: IMPRINT[3],  # Reddish purple (#BD8233)
 }
 level_names = ["Root Module", "Core Modules", "Sub-modules", "Leaf Modules"]
 
diff --git a/plots/network-transport-static/implementations/python/matplotlib.py b/plots/network-transport-static/implementations/python/matplotlib.py
index 935bbd6189..943f17ff34 100644
--- a/plots/network-transport-static/implementations/python/matplotlib.py
+++ b/plots/network-transport-static/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for route types
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Seed for reproducibility
 np.random.seed(42)
@@ -192,7 +192,7 @@
             curve = -0.2
 
     # Get route color from Okabe-Ito palette based on type
-    color = OKABE_ITO[route["type"]]
+    color = IMPRINT[route["type"]]
 
     # Draw arrow
     arrow = mpatches.FancyArrowPatch(
@@ -272,9 +272,9 @@
 
 # Create legend
 legend_elements = [
-    mpatches.Patch(facecolor=OKABE_ITO[0], edgecolor=OKABE_ITO[0], label="Regional Express (RE)"),
-    mpatches.Patch(facecolor=OKABE_ITO[1], edgecolor=OKABE_ITO[1], label="Airport Service (AIR)"),
-    mpatches.Patch(facecolor=OKABE_ITO[2], edgecolor=OKABE_ITO[2], label="S-Bahn (S)"),
+    mpatches.Patch(facecolor=IMPRINT[0], edgecolor=IMPRINT[0], label="Regional Express (RE)"),
+    mpatches.Patch(facecolor=IMPRINT[1], edgecolor=IMPRINT[1], label="Airport Service (AIR)"),
+    mpatches.Patch(facecolor=IMPRINT[2], edgecolor=IMPRINT[2], label="S-Bahn (S)"),
 ]
 leg = ax.legend(handles=legend_elements, loc="upper right", fontsize=16, framealpha=0.95)
 if leg:
diff --git a/plots/network-weighted/implementations/python/matplotlib.py b/plots/network-weighted/implementations/python/matplotlib.py
index c5f208ac25..9efd9340f5 100644
--- a/plots/network-weighted/implementations/python/matplotlib.py
+++ b/plots/network-weighted/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito categorical palette (positions 1-4)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Trade network between countries (billions USD)
 np.random.seed(42)
@@ -128,7 +128,7 @@
 
 # Region names and group mapping
 region_names = ["Americas", "Europe", "Asia", "Oceania"]
-group_colors = {i: OKABE_ITO[i] for i in range(4)}
+group_colors = {i: IMPRINT[i] for i in range(4)}
 
 # Create plot
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
@@ -185,7 +185,7 @@
 # Region legend
 legend_handles = []
 for i, region in enumerate(region_names):
-    handle = ax.scatter([], [], s=400, c=OKABE_ITO[i], edgecolors=PAGE_BG, linewidths=2, label=region)
+    handle = ax.scatter([], [], s=400, c=IMPRINT[i], edgecolors=PAGE_BG, linewidths=2, label=region)
     legend_handles.append(handle)
 
 leg = ax.legend(
diff --git a/plots/ohlc-bar/implementations/python/matplotlib.py b/plots/ohlc-bar/implementations/python/matplotlib.py
index ff0c764bab..9806e37714 100644
--- a/plots/ohlc-bar/implementations/python/matplotlib.py
+++ b/plots/ohlc-bar/implementations/python/matplotlib.py
@@ -22,7 +22,7 @@
 
 # Okabe-Ito palette
 COLOR_UP = "#009E73"  # Brand green for up bars
-COLOR_DOWN = "#D55E00"  # Vermillion for down bars
+COLOR_DOWN = "#C475FD"  # Vermillion for down bars
 
 # Data - Generate 45 trading days of synthetic stock OHLC data
 np.random.seed(42)
diff --git a/plots/parallel-categories-basic/implementations/python/matplotlib.py b/plots/parallel-categories-basic/implementations/python/matplotlib.py
index 9694d7a17e..d13043e92b 100644
--- a/plots/parallel-categories-basic/implementations/python/matplotlib.py
+++ b/plots/parallel-categories-basic/implementations/python/matplotlib.py
@@ -39,7 +39,7 @@
 }
 
 # Okabe-Ito palette for channel colors
-colors = {"Online": "#009E73", "Store": "#D55E00", "Mobile": "#0072B2"}
+colors = {"Online": "#009E73", "Store": "#C475FD", "Mobile": "#4467A3"}
 
 # Create figure
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
diff --git a/plots/parliament-basic/implementations/python/matplotlib.py b/plots/parliament-basic/implementations/python/matplotlib.py
index 792a14ad83..a5d0c26804 100644
--- a/plots/parliament-basic/implementations/python/matplotlib.py
+++ b/plots/parliament-basic/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (canonical order, starting with brand green)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - fictional parliament with 6 parties (400 total seats)
 parties = [
@@ -29,7 +29,7 @@
     "Progressive Front",
 ]
 seats = [98, 87, 76, 65, 48, 26]
-colors = OKABE_ITO[: len(parties)]
+colors = IMPRINT[: len(parties)]
 total_seats = sum(seats)
 
 # Parliament layout parameters
diff --git a/plots/pdp-basic/implementations/python/matplotlib.py b/plots/pdp-basic/implementations/python/matplotlib.py
index b6501e395f..a36e8fa29a 100644
--- a/plots/pdp-basic/implementations/python/matplotlib.py
+++ b/plots/pdp-basic/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1
-ACCENT = "#E69F00"  # Okabe-Ito position 5 for rug plot
+ACCENT = "#AE3030"  # Okabe-Ito position 5 for rug plot
 
 # Data: Train a gradient boosting model and compute partial dependence
 np.random.seed(42)
diff --git a/plots/phase-diagram/implementations/python/matplotlib.py b/plots/phase-diagram/implementations/python/matplotlib.py
index 2f7377683e..dbb65b3763 100644
--- a/plots/phase-diagram/implementations/python/matplotlib.py
+++ b/plots/phase-diagram/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Damped harmonic oscillator: m*x'' + c*x' + k*x = 0
 # Using underdamped solution: x(t) = A*exp(-gamma*t)*cos(omega_d*t + phi)
@@ -50,10 +50,10 @@
 # Plot each trajectory
 for i, (x, v, A, _phi) in enumerate(trajectories):
     # Plot trajectory line
-    ax.plot(x, v, color=OKABE_ITO[i], linewidth=2.5, alpha=0.8, label=f"Trajectory {i + 1} (A={A:.1f})")
+    ax.plot(x, v, color=IMPRINT[i], linewidth=2.5, alpha=0.8, label=f"Trajectory {i + 1} (A={A:.1f})")
 
     # Mark start point with larger marker
-    ax.scatter(x[0], v[0], s=250, color=OKABE_ITO[i], edgecolor=PAGE_BG, linewidth=2, zorder=5, marker="o")
+    ax.scatter(x[0], v[0], s=250, color=IMPRINT[i], edgecolor=PAGE_BG, linewidth=2, zorder=5, marker="o")
 
     # Add arrows to show direction along trajectory
     n_points = len(x)
@@ -68,7 +68,7 @@
                 "",
                 xy=(x[idx + 10], v[idx + 10]),
                 xytext=(x[idx], v[idx]),
-                arrowprops={"arrowstyle": "->", "color": OKABE_ITO[i], "lw": 2.5, "mutation_scale": 20},
+                arrowprops={"arrowstyle": "->", "color": IMPRINT[i], "lw": 2.5, "mutation_scale": 20},
             )
 
 # Mark the equilibrium point (stable fixed point at origin)
diff --git a/plots/point-and-figure-basic/implementations/python/matplotlib.py b/plots/point-and-figure-basic/implementations/python/matplotlib.py
index d7067cbb2a..52ff41bddb 100644
--- a/plots/point-and-figure-basic/implementations/python/matplotlib.py
+++ b/plots/point-and-figure-basic/implementations/python/matplotlib.py
@@ -27,7 +27,7 @@
 
 # Okabe-Ito: green for rising (X), vermillion for falling (O)
 X_COLOR = "#009E73"
-O_COLOR = "#D55E00"
+O_COLOR = "#C475FD"
 
 # Data: synthetic stock price with upward drift
 np.random.seed(42)
diff --git a/plots/polar-bar/implementations/python/matplotlib.py b/plots/polar-bar/implementations/python/matplotlib.py
index 514f033253..7e0d349242 100644
--- a/plots/polar-bar/implementations/python/matplotlib.py
+++ b/plots/polar-bar/implementations/python/matplotlib.py
@@ -19,8 +19,8 @@
 
 # Okabe-Ito palette (positions 1, 2, 3 for three series)
 BRAND = "#009E73"  # Position 1 (bluish green)
-ACCENT1 = "#D55E00"  # Position 2 (vermillion)
-ACCENT2 = "#0072B2"  # Position 3 (blue)
+ACCENT1 = "#C475FD"  # Position 2 (vermillion)
+ACCENT2 = "#4467A3"  # Position 3 (blue)
 
 # Data - Wind direction frequencies (8 compass directions)
 np.random.seed(42)
diff --git a/plots/polar-line/implementations/python/matplotlib.py b/plots/polar-line/implementations/python/matplotlib.py
index 4cfbb9804e..780cbf3d79 100644
--- a/plots/polar-line/implementations/python/matplotlib.py
+++ b/plots/polar-line/implementations/python/matplotlib.py
@@ -22,7 +22,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - Simulating hourly temperature patterns for two seasons
 np.random.seed(42)
diff --git a/plots/polar-scatter/implementations/python/matplotlib.py b/plots/polar-scatter/implementations/python/matplotlib.py
index 846c8d057a..56766b1c45 100644
--- a/plots/polar-scatter/implementations/python/matplotlib.py
+++ b/plots/polar-scatter/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Wind measurement data with prevailing directions
 np.random.seed(42)
diff --git a/plots/qq-basic/implementations/python/matplotlib.py b/plots/qq-basic/implementations/python/matplotlib.py
index a67b4ec28c..6994413dd3 100644
--- a/plots/qq-basic/implementations/python/matplotlib.py
+++ b/plots/qq-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1 — scatter points
-REF_COLOR = "#D55E00"  # Okabe-Ito position 2 — reference line
+REF_COLOR = "#C475FD"  # Okabe-Ito position 2 — reference line
 
 # Data: systolic blood pressure (mmHg) from a mixed patient cohort
 # Bimodal mixture of normotensive (n=75) and hypertensive (n=25) patients
diff --git a/plots/radar-basic/implementations/python/matplotlib.py b/plots/radar-basic/implementations/python/matplotlib.py
index 6c272d8874..e5460ad225 100644
--- a/plots/radar-basic/implementations/python/matplotlib.py
+++ b/plots/radar-basic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 SERIES_1 = "#009E73"  # Okabe-Ito position 1
-SERIES_2 = "#D55E00"  # Okabe-Ito position 2
+SERIES_2 = "#C475FD"  # Okabe-Ito position 2
 
 # Data - Employee performance comparison across six competencies
 categories = ["Communication", "Technical Skills", "Teamwork", "Problem Solving", "Leadership", "Creativity"]
diff --git a/plots/radar-multi/implementations/python/matplotlib.py b/plots/radar-multi/implementations/python/matplotlib.py
index d9d777e8d3..40a0e55ecc 100644
--- a/plots/radar-multi/implementations/python/matplotlib.py
+++ b/plots/radar-multi/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (first three series)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Product comparison across key attributes
 categories = ["Performance", "Battery Life", "Camera", "Display", "Build Quality", "Value"]
@@ -42,8 +42,8 @@
 # Plot each product
 for idx, (product, values) in enumerate(products.items()):
     values_closed = values + values[:1]  # Close the polygon
-    ax.plot(angles, values_closed, "o-", linewidth=3, label=product, color=OKABE_ITO[idx], markersize=10)
-    ax.fill(angles, values_closed, alpha=0.25, color=OKABE_ITO[idx])
+    ax.plot(angles, values_closed, "o-", linewidth=3, label=product, color=IMPRINT[idx], markersize=10)
+    ax.fill(angles, values_closed, alpha=0.25, color=IMPRINT[idx])
 
 # Set category labels at each axis
 ax.set_xticks(angles[:-1])
diff --git a/plots/renko-basic/implementations/python/matplotlib.py b/plots/renko-basic/implementations/python/matplotlib.py
index 1b6487288d..ffbf46806e 100644
--- a/plots/renko-basic/implementations/python/matplotlib.py
+++ b/plots/renko-basic/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette
 BULLISH = "#009E73"  # Green for upward
-BEARISH = "#D55E00"  # Vermillion (reddish) for downward
+BEARISH = "#C475FD"  # Vermillion (reddish) for downward
 
 # Generate synthetic price data
 np.random.seed(42)
diff --git a/plots/roc-curve/implementations/python/matplotlib.py b/plots/roc-curve/implementations/python/matplotlib.py
index a80e37035c..68f381d28f 100644
--- a/plots/roc-curve/implementations/python/matplotlib.py
+++ b/plots/roc-curve/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data
 np.random.seed(42)
@@ -45,14 +45,14 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-ax.plot(fpr1, tpr1, color=OKABE_ITO[0], linewidth=3.5, label=f"Random Forest (AUC = {auc1:.2f})")
-ax.plot(fpr2, tpr2, color=OKABE_ITO[1], linewidth=3.5, label=f"Logistic Regression (AUC = {auc2:.2f})")
-ax.plot(fpr3, tpr3, color=OKABE_ITO[2], linewidth=3.5, label=f"Decision Tree (AUC = {auc3:.2f})")
+ax.plot(fpr1, tpr1, color=IMPRINT[0], linewidth=3.5, label=f"Random Forest (AUC = {auc1:.2f})")
+ax.plot(fpr2, tpr2, color=IMPRINT[1], linewidth=3.5, label=f"Logistic Regression (AUC = {auc2:.2f})")
+ax.plot(fpr3, tpr3, color=IMPRINT[2], linewidth=3.5, label=f"Decision Tree (AUC = {auc3:.2f})")
 
 ax.plot([0, 1], [0, 1], color=INK_SOFT, linewidth=2.5, linestyle="--", label="Random Classifier (AUC = 0.50)")
 
-ax.fill_between(fpr1, tpr1, alpha=0.12, color=OKABE_ITO[0])
-ax.fill_between(fpr2, tpr2, alpha=0.08, color=OKABE_ITO[1])
+ax.fill_between(fpr1, tpr1, alpha=0.12, color=IMPRINT[0])
+ax.fill_between(fpr2, tpr2, alpha=0.08, color=IMPRINT[1])
 
 # Style
 ax.set_xlabel("False Positive Rate", fontsize=20, color=INK)
diff --git a/plots/scatter-animated-controls/implementations/python/matplotlib.py b/plots/scatter-animated-controls/implementations/python/matplotlib.py
index fd6e2e8db5..5a89c1cccb 100644
--- a/plots/scatter-animated-controls/implementations/python/matplotlib.py
+++ b/plots/scatter-animated-controls/implementations/python/matplotlib.py
@@ -16,7 +16,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 np.random.seed(42)
 countries = 15
diff --git a/plots/scatter-categorical/implementations/python/matplotlib.py b/plots/scatter-categorical/implementations/python/matplotlib.py
index 574334fac1..e16a3d5b8f 100644
--- a/plots/scatter-categorical/implementations/python/matplotlib.py
+++ b/plots/scatter-categorical/implementations/python/matplotlib.py
@@ -18,11 +18,11 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (canonical order)
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green — ALWAYS first series
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
 ]
 
 # Data — plant species with distinct petal characteristics
@@ -50,9 +50,9 @@
 
 # Plot each category with distinct colors and markers
 scatter_data = [
-    (species_a_x, species_a_y, OKABE_ITO[0], markers[0]),
-    (species_b_x, species_b_y, OKABE_ITO[1], markers[1]),
-    (species_c_x, species_c_y, OKABE_ITO[2], markers[2]),
+    (species_a_x, species_a_y, IMPRINT[0], markers[0]),
+    (species_b_x, species_b_y, IMPRINT[1], markers[1]),
+    (species_c_x, species_c_y, IMPRINT[2], markers[2]),
 ]
 
 for i, (x, y, color, marker) in enumerate(scatter_data):
diff --git a/plots/scatter-embedding/implementations/python/matplotlib.py b/plots/scatter-embedding/implementations/python/matplotlib.py
index 717bd970b0..4c4d0208f4 100644
--- a/plots/scatter-embedding/implementations/python/matplotlib.py
+++ b/plots/scatter-embedding/implementations/python/matplotlib.py
@@ -23,7 +23,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 CLUSTER_LABELS = ["Finance", "Healthcare", "Technology", "Sports", "Politics", "Science", "Entertainment"]
 
@@ -37,7 +37,7 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-for i, (label, color) in enumerate(zip(CLUSTER_LABELS, OKABE_ITO, strict=False)):
+for i, (label, color) in enumerate(zip(CLUSTER_LABELS, IMPRINT, strict=False)):
     mask = labels == i
     ax.scatter(X_2d[mask, 0], X_2d[mask, 1], c=color, s=90, alpha=0.65, edgecolors=PAGE_BG, linewidth=0.5, label=label)
 
diff --git a/plots/scatter-map-geographic/implementations/python/matplotlib.py b/plots/scatter-map-geographic/implementations/python/matplotlib.py
index 48426e36d5..f350d62ecf 100644
--- a/plots/scatter-map-geographic/implementations/python/matplotlib.py
+++ b/plots/scatter-map-geographic/implementations/python/matplotlib.py
@@ -20,13 +20,13 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette — use positions 1→N in canonical order
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # 1: bluish green (ALWAYS first series)
-    "#D55E00",  # 2: vermillion
-    "#0072B2",  # 3: blue
-    "#CC79A7",  # 4: reddish purple
-    "#E69F00",  # 5: orange
-    "#56B4E9",  # 6: sky blue
+    "#C475FD",  # 2: vermillion
+    "#4467A3",  # 3: blue
+    "#BD8233",  # 4: reddish purple
+    "#AE3030",  # 5: orange
+    "#2ABCCD",  # 6: sky blue
 ]
 
 # Data - Major world cities with population and region
@@ -69,7 +69,7 @@
 
 # Map regions to Okabe-Ito colors
 unique_regions = ["Asia", "South America", "North America", "Africa", "Europe", "Oceania"]
-region_colors = {region: OKABE_ITO[i] for i, region in enumerate(unique_regions)}
+region_colors = {region: IMPRINT[i] for i, region in enumerate(unique_regions)}
 colors = [region_colors[r] for r in regions]
 
 # Scale sizes based on population
@@ -480,7 +480,7 @@
 size_labels = ["10M", "20M", "35M"]
 size_handles = []
 for val, label in zip(size_values, size_labels, strict=True):
-    handle = ax.scatter([], [], c=OKABE_ITO[0], s=val * 22, label=label, edgecolors=PAGE_BG, linewidths=1.5, alpha=0.8)
+    handle = ax.scatter([], [], c=IMPRINT[0], s=val * 22, label=label, edgecolors=PAGE_BG, linewidths=1.5, alpha=0.8)
     size_handles.append(handle)
 
 size_legend = ax.legend(
diff --git a/plots/scatter-matrix-interactive/implementations/python/matplotlib.py b/plots/scatter-matrix-interactive/implementations/python/matplotlib.py
index cd149bf072..bdd2a05d72 100644
--- a/plots/scatter-matrix-interactive/implementations/python/matplotlib.py
+++ b/plots/scatter-matrix-interactive/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette (positions 1-3)
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Iris dataset (4 numeric variables, 150 samples)
 iris = load_iris()
diff --git a/plots/scatter-matrix/implementations/python/matplotlib.py b/plots/scatter-matrix/implementations/python/matplotlib.py
index fbb25ff7ea..32391e546e 100644
--- a/plots/scatter-matrix/implementations/python/matplotlib.py
+++ b/plots/scatter-matrix/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (3 colors for 3 species)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Iris-like flower measurements (4 variables, 3 species)
 np.random.seed(42)
@@ -61,13 +61,13 @@
 
         if i == j:
             # Diagonal: histograms with enhanced visual hierarchy
-            for species_idx, (_species, color) in enumerate(zip(species_names, OKABE_ITO, strict=True)):
+            for species_idx, (_species, color) in enumerate(zip(species_names, IMPRINT, strict=True)):
                 mask = species_indices == species_idx
                 species_data = data_arrays[i][mask]
                 ax.hist(species_data, bins=12, alpha=0.85, color=color, edgecolor=INK_SOFT, linewidth=1.0)
         else:
             # Off-diagonal: scatter plots with enhanced marker definition
-            for species_idx, (_species, color) in enumerate(zip(species_names, OKABE_ITO, strict=True)):
+            for species_idx, (_species, color) in enumerate(zip(species_names, IMPRINT, strict=True)):
                 mask = species_indices == species_idx
                 ax.scatter(
                     data_arrays[j][mask],
@@ -121,7 +121,7 @@
         markeredgecolor=PAGE_BG,
         markeredgewidth=0.5,
     )
-    for species, color in zip(species_names, OKABE_ITO, strict=True)
+    for species, color in zip(species_names, IMPRINT, strict=True)
 ]
 leg = fig.legend(
     handles=legend_elements, loc="center right", fontsize=16, frameon=True, fancybox=False, edgecolor=INK_SOFT
diff --git a/plots/scatter-regression-linear/implementations/python/matplotlib.py b/plots/scatter-regression-linear/implementations/python/matplotlib.py
index f21823de1a..b52d9fc13b 100644
--- a/plots/scatter-regression-linear/implementations/python/matplotlib.py
+++ b/plots/scatter-regression-linear/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1
-SECONDARY = "#D55E00"  # Okabe-Ito position 2 for regression line
+SECONDARY = "#C475FD"  # Okabe-Ito position 2 for regression line
 
 # Data: Study hours vs exam scores (realistic educational context)
 np.random.seed(42)
diff --git a/plots/scatter-regression-lowess/implementations/python/matplotlib.py b/plots/scatter-regression-lowess/implementations/python/matplotlib.py
index d14d61eb99..c4ea2db97e 100644
--- a/plots/scatter-regression-lowess/implementations/python/matplotlib.py
+++ b/plots/scatter-regression-lowess/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - tree height vs age with realistic growth pattern (power law)
 np.random.seed(42)
diff --git a/plots/scatter-regression-polynomial/implementations/python/matplotlib.py b/plots/scatter-regression-polynomial/implementations/python/matplotlib.py
index 65986e5470..b490b90784 100644
--- a/plots/scatter-regression-polynomial/implementations/python/matplotlib.py
+++ b/plots/scatter-regression-polynomial/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1 — ALWAYS first series
-SECONDARY = "#D55E00"  # Okabe-Ito position 2
+SECONDARY = "#C475FD"  # Okabe-Ito position 2
 
 # Data - Modeling diminishing returns (economics example)
 np.random.seed(42)
diff --git a/plots/scatter-text/implementations/python/matplotlib.py b/plots/scatter-text/implementations/python/matplotlib.py
index e2a91d64df..29564ec957 100644
--- a/plots/scatter-text/implementations/python/matplotlib.py
+++ b/plots/scatter-text/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito categorical palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Programming languages positioned by paradigm similarity
 # (simulating dimensionality reduction output)
@@ -160,7 +160,7 @@
     "systems",
 ]
 
-category_to_color = {"dynamic": OKABE_ITO[0], "functional": OKABE_ITO[1], "systems": OKABE_ITO[2], "jvm": OKABE_ITO[3]}
+category_to_color = {"dynamic": IMPRINT[0], "functional": IMPRINT[1], "systems": IMPRINT[2], "jvm": IMPRINT[3]}
 
 color_list = [category_to_color[cat] for cat in categories]
 
@@ -195,10 +195,10 @@
 
 # Legend
 legend_elements = [
-    Patch(facecolor=OKABE_ITO[0], label="Dynamic/Scripting"),
-    Patch(facecolor=OKABE_ITO[1], label="Functional"),
-    Patch(facecolor=OKABE_ITO[2], label="Systems"),
-    Patch(facecolor=OKABE_ITO[3], label="JVM-based"),
+    Patch(facecolor=IMPRINT[0], label="Dynamic/Scripting"),
+    Patch(facecolor=IMPRINT[1], label="Functional"),
+    Patch(facecolor=IMPRINT[2], label="Systems"),
+    Patch(facecolor=IMPRINT[3], label="JVM-based"),
 ]
 leg = ax.legend(handles=legend_elements, loc="upper right", fontsize=16, framealpha=0.9)
 if leg:
diff --git a/plots/shap-waterfall/implementations/python/matplotlib.py b/plots/shap-waterfall/implementations/python/matplotlib.py
index 40f892f427..647f4f082c 100644
--- a/plots/shap-waterfall/implementations/python/matplotlib.py
+++ b/plots/shap-waterfall/implementations/python/matplotlib.py
@@ -20,8 +20,8 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Semantic direction colors — Okabe-Ito positions 2 and 3
-COLOR_POS = "#D55E00"  # vermillion: positive SHAP (pushes prediction up)
-COLOR_NEG = "#0072B2"  # blue: negative SHAP (pushes prediction down)
+COLOR_POS = "#C475FD"  # vermillion: positive SHAP (pushes prediction up)
+COLOR_NEG = "#4467A3"  # blue: negative SHAP (pushes prediction down)
 
 # Data — credit loan approval model, individual applicant explanation
 # Features ordered by absolute SHAP magnitude, largest first (top of chart)
diff --git a/plots/silhouette-basic/implementations/python/matplotlib.py b/plots/silhouette-basic/implementations/python/matplotlib.py
index e9d85f0f60..4934b8e383 100644
--- a/plots/silhouette-basic/implementations/python/matplotlib.py
+++ b/plots/silhouette-basic/implementations/python/matplotlib.py
@@ -21,7 +21,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for clusters
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - use iris dataset for realistic clustering example
 iris = load_iris()
@@ -54,8 +54,8 @@
         np.arange(y_lower, y_upper),
         0,
         ith_cluster_silhouette_values,
-        facecolor=OKABE_ITO[i % len(OKABE_ITO)],
-        edgecolor=OKABE_ITO[i % len(OKABE_ITO)],
+        facecolor=IMPRINT[i % len(IMPRINT)],
+        edgecolor=IMPRINT[i % len(IMPRINT)],
         alpha=0.8,
     )
 
diff --git a/plots/skewt-logp-atmospheric/implementations/python/matplotlib.py b/plots/skewt-logp-atmospheric/implementations/python/matplotlib.py
index 08e977a8ed..f5fa2fd693 100644
--- a/plots/skewt-logp-atmospheric/implementations/python/matplotlib.py
+++ b/plots/skewt-logp-atmospheric/implementations/python/matplotlib.py
@@ -25,9 +25,9 @@
 
 # Okabe-Ito — data series
 TEMP_COLOR = "#009E73"  # temperature — position 1
-DEWPOINT_COLOR = "#D55E00"  # dewpoint — position 2
-CAPE_COLOR = "#E69F00"  # CAPE shading — position 5
-CIN_COLOR = "#0072B2"  # CIN shading — position 3
+DEWPOINT_COLOR = "#C475FD"  # dewpoint — position 2
+CAPE_COLOR = "#AE3030"  # CAPE shading — position 5
+CIN_COLOR = "#4467A3"  # CIN shading — position 3
 
 # Data — simulated mid-latitude radiosonde sounding (surface to upper troposphere)
 np.random.seed(42)
diff --git a/plots/slope-basic/implementations/python/matplotlib.py b/plots/slope-basic/implementations/python/matplotlib.py
index 5ac2eb0c05..a41dbd1077 100644
--- a/plots/slope-basic/implementations/python/matplotlib.py
+++ b/plots/slope-basic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 COLOR_INC = "#009E73"  # Okabe-Ito pos 1 — increase (brand green)
-COLOR_DEC = "#D55E00"  # Okabe-Ito pos 2 — decrease (vermillion)
+COLOR_DEC = "#C475FD"  # Okabe-Ito pos 2 — decrease (vermillion)
 
 # Data
 products = ["Product A", "Product B", "Product C", "Product D", "Product E", "Product F", "Product G", "Product H"]
diff --git a/plots/smith-chart-basic/implementations/python/matplotlib.py b/plots/smith-chart-basic/implementations/python/matplotlib.py
index c77077b1f2..4fa0ecee87 100644
--- a/plots/smith-chart-basic/implementations/python/matplotlib.py
+++ b/plots/smith-chart-basic/implementations/python/matplotlib.py
@@ -25,8 +25,8 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
-OI_2 = "#D55E00"  # Okabe-Ito position 2 — VSWR circles
-OI_3 = "#0072B2"  # Okabe-Ito position 3 — boundary / matched condition
+OI_2 = "#C475FD"  # Okabe-Ito position 2 — VSWR circles
+OI_3 = "#4467A3"  # Okabe-Ito position 3 — boundary / matched condition
 
 # Data — antenna impedance sweep 1–6 GHz
 Z0 = 50
diff --git a/plots/sn-curve-basic/implementations/python/matplotlib.py b/plots/sn-curve-basic/implementations/python/matplotlib.py
index e452d079c2..27e62537e1 100644
--- a/plots/sn-curve-basic/implementations/python/matplotlib.py
+++ b/plots/sn-curve-basic/implementations/python/matplotlib.py
@@ -20,10 +20,10 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 BRAND = "#009E73"  # Okabe-Ito #1 — test data points
-COLOR_FIT = "#D55E00"  # Okabe-Ito #2 — Basquin fit line
-COLOR_ULT = "#0072B2"  # Okabe-Ito #3 — Ultimate Strength
-COLOR_YLD = "#CC79A7"  # Okabe-Ito #4 — Yield Strength
-COLOR_END = "#E69F00"  # Okabe-Ito #5 — Endurance Limit
+COLOR_FIT = "#C475FD"  # Okabe-Ito #2 — Basquin fit line
+COLOR_ULT = "#4467A3"  # Okabe-Ito #3 — Ultimate Strength
+COLOR_YLD = "#BD8233"  # Okabe-Ito #4 — Yield Strength
+COLOR_END = "#AE3030"  # Okabe-Ito #5 — Endurance Limit
 
 # Data: Simulated fatigue test results for structural steel specimens
 np.random.seed(42)
diff --git a/plots/span-basic/implementations/python/matplotlib.py b/plots/span-basic/implementations/python/matplotlib.py
index ca90f2e7bd..eea99ed567 100644
--- a/plots/span-basic/implementations/python/matplotlib.py
+++ b/plots/span-basic/implementations/python/matplotlib.py
@@ -19,8 +19,8 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 BRAND = "#009E73"  # Okabe-Ito position 1 — first series
-C2 = "#D55E00"  # Okabe-Ito position 2
-C3 = "#0072B2"  # Okabe-Ito position 3
+C2 = "#C475FD"  # Okabe-Ito position 2
+C3 = "#4467A3"  # Okabe-Ito position 3
 
 # Data — stock prices with a simulated recession dip
 np.random.seed(42)
diff --git a/plots/spectrum-basic/implementations/python/matplotlib.py b/plots/spectrum-basic/implementations/python/matplotlib.py
index 300824b486..4c074fda8d 100644
--- a/plots/spectrum-basic/implementations/python/matplotlib.py
+++ b/plots/spectrum-basic/implementations/python/matplotlib.py
@@ -16,7 +16,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1
-ACCENT = "#D55E00"  # Okabe-Ito position 2 for annotations
+ACCENT = "#C475FD"  # Okabe-Ito position 2 for annotations
 
 # Generate synthetic signal with multiple frequency components
 np.random.seed(42)
diff --git a/plots/streamgraph-basic/implementations/python/matplotlib.py b/plots/streamgraph-basic/implementations/python/matplotlib.py
index 8a262e42d5..90e28faa84 100644
--- a/plots/streamgraph-basic/implementations/python/matplotlib.py
+++ b/plots/streamgraph-basic/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 np.random.seed(42)
 
@@ -70,7 +70,7 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-ax.stackplot(months_fine, data_smooth, labels=categories, colors=OKABE_ITO, baseline="wiggle", alpha=0.85)
+ax.stackplot(months_fine, data_smooth, labels=categories, colors=IMPRINT, baseline="wiggle", alpha=0.85)
 
 ax.set_xlabel("Month (Jan 2023 – Dec 2024)", fontsize=20, color=INK)
 ax.set_title("streamgraph-basic · matplotlib · anyplot.ai", fontsize=24, color=INK, fontweight="medium")
diff --git a/plots/strip-basic/implementations/python/matplotlib.py b/plots/strip-basic/implementations/python/matplotlib.py
index e631ed835f..6e19b4525a 100644
--- a/plots/strip-basic/implementations/python/matplotlib.py
+++ b/plots/strip-basic/implementations/python/matplotlib.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data — employee satisfaction scores by department (1–10 scale)
 np.random.seed(42)
@@ -39,7 +39,7 @@
 for i, dept in enumerate(departments):
     scores = dept_scores[dept]
     jitter = np.random.uniform(-0.2, 0.2, len(scores))
-    ax.scatter(i + jitter, scores, s=200, alpha=0.6, color=OKABE_ITO[i], edgecolors=PAGE_BG, linewidth=0.5)
+    ax.scatter(i + jitter, scores, s=200, alpha=0.6, color=IMPRINT[i], edgecolors=PAGE_BG, linewidth=0.5)
 
 # Mean reference lines
 for i, dept in enumerate(departments):
diff --git a/plots/subplot-mosaic/implementations/python/matplotlib.py b/plots/subplot-mosaic/implementations/python/matplotlib.py
index e77011e341..890d6f6e94 100644
--- a/plots/subplot-mosaic/implementations/python/matplotlib.py
+++ b/plots/subplot-mosaic/implementations/python/matplotlib.py
@@ -20,9 +20,9 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Position 1 - always first series
-OI_2 = "#D55E00"
-OI_3 = "#0072B2"
-OI_4 = "#CC79A7"
+OI_2 = "#C475FD"
+OI_3 = "#4467A3"
+OI_4 = "#BD8233"
 
 # Data
 np.random.seed(42)
diff --git a/plots/sunburst-basic/implementations/python/matplotlib.py b/plots/sunburst-basic/implementations/python/matplotlib.py
index bae70288d9..da4ba993a3 100644
--- a/plots/sunburst-basic/implementations/python/matplotlib.py
+++ b/plots/sunburst-basic/implementations/python/matplotlib.py
@@ -16,8 +16,8 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 
 # Okabe-Ito base colors for level-1 departments (positions 1–3)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
-OKABE_RGB = [tuple(int(c[j : j + 2], 16) / 255 for j in (1, 3, 5)) for c in OKABE_ITO]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
+OKABE_RGB = [tuple(int(c[j : j + 2], 16) / 255 for j in (1, 3, 5)) for c in IMPRINT]
 
 # Data: Company budget breakdown ($thousands)
 data = {
@@ -39,7 +39,7 @@
     dept_total = sum(sum(projs.values()) for projs in teams.values())
     level1_names.append(dept)
     level1_values.append(dept_total)
-    level1_colors.append(OKABE_ITO[i])
+    level1_colors.append(IMPRINT[i])
 
     r, g, b = OKABE_RGB[i]
     team_items = list(teams.items())
diff --git a/plots/swarm-basic/implementations/python/matplotlib.py b/plots/swarm-basic/implementations/python/matplotlib.py
index fcca3d32e4..5f72fd0815 100644
--- a/plots/swarm-basic/implementations/python/matplotlib.py
+++ b/plots/swarm-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette — 4 departments
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Employee performance scores by department
 np.random.seed(42)
diff --git a/plots/timeline-basic/implementations/python/matplotlib.py b/plots/timeline-basic/implementations/python/matplotlib.py
index 91bf7fff51..ed3b21f579 100644
--- a/plots/timeline-basic/implementations/python/matplotlib.py
+++ b/plots/timeline-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 np.random.seed(42)
 events = pd.DataFrame(
@@ -50,10 +50,10 @@
 )
 
 category_colors = {
-    "Planning": OKABE_ITO[0],
-    "Development": OKABE_ITO[1],
-    "Testing": OKABE_ITO[2],
-    "Release": OKABE_ITO[3],
+    "Planning": IMPRINT[0],
+    "Development": IMPRINT[1],
+    "Testing": IMPRINT[2],
+    "Release": IMPRINT[3],
 }
 colors = [category_colors[cat] for cat in events["category"]]
 
diff --git a/plots/timeseries-decomposition/implementations/python/matplotlib.py b/plots/timeseries-decomposition/implementations/python/matplotlib.py
index a4aebba575..f07ee4fbae 100644
--- a/plots/timeseries-decomposition/implementations/python/matplotlib.py
+++ b/plots/timeseries-decomposition/implementations/python/matplotlib.py
@@ -20,11 +20,11 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand — first series)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
 ]
 
 # Data - Monthly retail sales over 6 years (72 months = 6 full annual cycles)
@@ -51,7 +51,7 @@
 fig, axes = plt.subplots(4, 1, figsize=(16, 12), sharex=True, facecolor=PAGE_BG)
 
 # Original series
-axes[0].plot(dates, ts.values, color=OKABE_ITO[0], linewidth=2.5)
+axes[0].plot(dates, ts.values, color=IMPRINT[0], linewidth=2.5)
 axes[0].set_facecolor(PAGE_BG)
 axes[0].set_ylabel("Original (Sales USD)", fontsize=20, color=INK)
 axes[0].tick_params(axis="y", labelsize=16, colors=INK_SOFT)
@@ -61,21 +61,21 @@
 )
 
 # Trend component
-axes[1].plot(dates, decomposition.trend, color=OKABE_ITO[1], linewidth=2.5)
+axes[1].plot(dates, decomposition.trend, color=IMPRINT[1], linewidth=2.5)
 axes[1].set_facecolor(PAGE_BG)
 axes[1].set_ylabel("Trend (Sales USD)", fontsize=20, color=INK)
 axes[1].tick_params(axis="y", labelsize=16, colors=INK_SOFT)
 axes[1].grid(True, alpha=0.15, linewidth=0.8, color=INK)
 
 # Seasonal component
-axes[2].plot(dates, decomposition.seasonal, color=OKABE_ITO[2], linewidth=2.5)
+axes[2].plot(dates, decomposition.seasonal, color=IMPRINT[2], linewidth=2.5)
 axes[2].set_facecolor(PAGE_BG)
 axes[2].set_ylabel("Seasonal (Sales USD)", fontsize=20, color=INK)
 axes[2].tick_params(axis="y", labelsize=16, colors=INK_SOFT)
 axes[2].grid(True, alpha=0.15, linewidth=0.8, color=INK)
 
 # Residual component
-axes[3].plot(dates, decomposition.resid, color=OKABE_ITO[3], linewidth=2.5)
+axes[3].plot(dates, decomposition.resid, color=IMPRINT[3], linewidth=2.5)
 axes[3].axhline(y=0, color=INK_SOFT, linestyle="-", linewidth=1, alpha=0.3)
 axes[3].set_facecolor(PAGE_BG)
 axes[3].set_ylabel("Residual (Sales USD)", fontsize=20, color=INK)
diff --git a/plots/timeseries-forecast-uncertainty/implementations/python/matplotlib.py b/plots/timeseries-forecast-uncertainty/implementations/python/matplotlib.py
index 5e00c50691..20e2e785aa 100644
--- a/plots/timeseries-forecast-uncertainty/implementations/python/matplotlib.py
+++ b/plots/timeseries-forecast-uncertainty/implementations/python/matplotlib.py
@@ -22,8 +22,8 @@
 
 # Okabe-Ito — pos 1 (historical), pos 2 (forecast), pos 6 (CI bands)
 HISTORICAL_COLOR = "#009E73"
-FORECAST_COLOR = "#D55E00"
-CI_COLOR = "#56B4E9"
+FORECAST_COLOR = "#C475FD"
+CI_COLOR = "#2ABCCD"
 
 # Data — monthly retail sales, 3-year history + 12-month ARIMA-style forecast
 np.random.seed(42)
diff --git a/plots/treemap-basic/implementations/python/matplotlib.py b/plots/treemap-basic/implementations/python/matplotlib.py
index e6685faa75..a7da603e23 100644
--- a/plots/treemap-basic/implementations/python/matplotlib.py
+++ b/plots/treemap-basic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for categories
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Budget allocation by department and project
 data = [
@@ -44,7 +44,7 @@
 
 # Category to color mapping (using Okabe-Ito palette)
 unique_categories = ["Engineering", "Sales", "Marketing", "Operations", "HR"]
-category_colors = {cat: OKABE_ITO[i % len(OKABE_ITO)] for i, cat in enumerate(unique_categories)}
+category_colors = {cat: IMPRINT[i % len(IMPRINT)] for i, cat in enumerate(unique_categories)}
 
 # Normalize values to fill a 160x90 area (matching figsize aspect ratio)
 total = sum(values)
diff --git a/plots/upset-basic/implementations/python/matplotlib.py b/plots/upset-basic/implementations/python/matplotlib.py
index 0181dd1150..bc6f49de55 100644
--- a/plots/upset-basic/implementations/python/matplotlib.py
+++ b/plots/upset-basic/implementations/python/matplotlib.py
@@ -25,7 +25,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 BRAND = "#009E73"
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data: bug reports spanning software system modules
 np.random.seed(42)
@@ -71,7 +71,7 @@
 ax_sets = fig.add_subplot(gs[1, 0])
 
 # ── Intersection bars (top right) ──────────────────────────────────────────────
-bar_colors = [OKABE_ITO[min(d - 1, len(OKABE_ITO) - 1)] for d in degrees]
+bar_colors = [IMPRINT[min(d - 1, len(IMPRINT) - 1)] for d in degrees]
 ax_bars.bar(range(n_ints), int_counts, color=bar_colors, width=0.65, zorder=2)
 for i, (cnt, _col) in enumerate(zip(int_counts, bar_colors, strict=False)):
     ax_bars.text(
@@ -99,7 +99,7 @@
 ax_bars.spines["left"].set_color(INK_SOFT)
 
 # Degree legend inside intersection bars area
-patches = [mpatches.Patch(color=OKABE_ITO[min(d - 1, 5)], label=f"Degree {d}") for d in sorted(set(degrees))]
+patches = [mpatches.Patch(color=IMPRINT[min(d - 1, 5)], label=f"Degree {d}") for d in sorted(set(degrees))]
 leg = ax_bars.legend(
     handles=patches,
     fontsize=13,
@@ -136,7 +136,7 @@
 
 # Active dots + connecting lines
 for col_j, (sig, deg) in enumerate(zip(int_sigs, degrees, strict=False)):
-    color = OKABE_ITO[min(deg - 1, len(OKABE_ITO) - 1)]
+    color = IMPRINT[min(deg - 1, len(IMPRINT) - 1)]
     active_rows = [i for i, v in enumerate(sig) if v]
     if len(active_rows) > 1:
         ax_matrix.plot(
diff --git a/plots/venn-basic/implementations/python/matplotlib.py b/plots/venn-basic/implementations/python/matplotlib.py
index d5a9d79062..78d0fb5701 100644
--- a/plots/venn-basic/implementations/python/matplotlib.py
+++ b/plots/venn-basic/implementations/python/matplotlib.py
@@ -16,7 +16,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Developer survey - programming language proficiency
 # venn3 expects: (Abc, aBc, ABc, abC, AbC, aBC, ABC)
@@ -38,7 +38,7 @@
     set_labels=("Python", "JavaScript", "SQL"),
     ax=ax,
     alpha=0.6,
-    set_colors=OKABE_ITO,
+    set_colors=IMPRINT,
 )
 
 # Style circle labels (set names)
diff --git a/plots/venn-labeled-items/implementations/python/matplotlib.py b/plots/venn-labeled-items/implementations/python/matplotlib.py
index e897dedd68..8db064c4d8 100644
--- a/plots/venn-labeled-items/implementations/python/matplotlib.py
+++ b/plots/venn-labeled-items/implementations/python/matplotlib.py
@@ -19,8 +19,8 @@
 
 # Okabe-Ito categorical (canonical positions 1, 2, 3)
 COLOR_A = "#009E73"  # brand green — ALWAYS first series
-COLOR_B = "#D55E00"  # vermillion
-COLOR_C = "#0072B2"  # blue
+COLOR_B = "#C475FD"  # vermillion
+COLOR_C = "#4467A3"  # blue
 
 # Data: three Chartgeist-style traits
 RADIUS = 1.5
diff --git a/plots/violin-grouped-swarm/implementations/python/matplotlib.py b/plots/violin-grouped-swarm/implementations/python/matplotlib.py
index b8329e8c4e..3812dc1ebb 100644
--- a/plots/violin-grouped-swarm/implementations/python/matplotlib.py
+++ b/plots/violin-grouped-swarm/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (first two colors for the two groups)
-COLORS = ["#009E73", "#D55E00"]
+COLORS = ["#009E73", "#C475FD"]
 
 # Data - Response times across task types and expertise levels
 np.random.seed(42)
diff --git a/plots/violin-split/implementations/python/matplotlib.py b/plots/violin-split/implementations/python/matplotlib.py
index dd98716294..1d68a9fc52 100644
--- a/plots/violin-split/implementations/python/matplotlib.py
+++ b/plots/violin-split/implementations/python/matplotlib.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette
 COLOR_CONTROL = "#009E73"  # Position 1 - brand green
-COLOR_TREATMENT = "#D55E00"  # Position 2 - vermillion
+COLOR_TREATMENT = "#C475FD"  # Position 2 - vermillion
 
 # Data - Test score distributions for control vs treatment groups across school levels
 np.random.seed(42)
diff --git a/plots/violin-swarm/implementations/python/matplotlib.py b/plots/violin-swarm/implementations/python/matplotlib.py
index f3f77c3d02..1d21e34912 100644
--- a/plots/violin-swarm/implementations/python/matplotlib.py
+++ b/plots/violin-swarm/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 
 # Okabe-Ito palette - first series is always #009E73
 BRAND = "#009E73"  # violin fill
-ACCENT = "#D55E00"  # swarm points
+ACCENT = "#C475FD"  # swarm points
 
 # Data - Reaction times (ms) across 4 experimental conditions
 np.random.seed(42)
diff --git a/plots/volcano-basic/implementations/python/matplotlib.py b/plots/volcano-basic/implementations/python/matplotlib.py
index b9aa7d9923..fc657d9ec8 100644
--- a/plots/volcano-basic/implementations/python/matplotlib.py
+++ b/plots/volcano-basic/implementations/python/matplotlib.py
@@ -17,8 +17,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for significance categories
-COLOR_DOWNREG = "#0072B2"  # Blue
-COLOR_UPREG = "#D55E00"  # Vermillion
+COLOR_DOWNREG = "#4467A3"  # Blue
+COLOR_UPREG = "#C475FD"  # Vermillion
 COLOR_NONSIG = "#888888"  # Neutral gray
 
 # Data - simulated differential expression results
diff --git a/plots/voronoi-basic/implementations/python/matplotlib.py b/plots/voronoi-basic/implementations/python/matplotlib.py
index a768645632..1729271c3f 100644
--- a/plots/voronoi-basic/implementations/python/matplotlib.py
+++ b/plots/voronoi-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for Voronoi regions
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - Generate seed points for Voronoi diagram
 np.random.seed(42)
@@ -64,7 +64,7 @@
     polygon[:, 1] = np.clip(polygon[:, 1], y_min, y_max)
 
     polygons.append(polygon)
-    poly_colors.append(OKABE_ITO[idx % len(OKABE_ITO)])
+    poly_colors.append(IMPRINT[idx % len(IMPRINT)])
 
 # Draw all Voronoi regions at once
 collection = PolyCollection(polygons, facecolors=poly_colors, edgecolors=INK_SOFT, linewidths=2.5, alpha=0.6)
diff --git a/plots/waffle-basic/implementations/python/matplotlib.py b/plots/waffle-basic/implementations/python/matplotlib.py
index 417e141671..71fe4ad68a 100644
--- a/plots/waffle-basic/implementations/python/matplotlib.py
+++ b/plots/waffle-basic/implementations/python/matplotlib.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (canonical order)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Survey responses (sums to 100%)
 categories = ["Strongly Agree", "Agree", "Neutral", "Disagree"]
@@ -53,7 +53,7 @@
             square_size,
             square_size,
             boxstyle="round,pad=0.02,rounding_size=0.1",
-            facecolor=OKABE_ITO[category_idx],
+            facecolor=IMPRINT[category_idx],
             edgecolor=PAGE_BG,
             linewidth=1.5,
         )
@@ -67,7 +67,7 @@
 
 # Create legend with percentage labels
 legend_patches = [
-    mpatches.Patch(color=OKABE_ITO[i], label=f"{categories[i]} ({values[i]}%)") for i in range(len(categories))
+    mpatches.Patch(color=IMPRINT[i], label=f"{categories[i]} ({values[i]}%)") for i in range(len(categories))
 ]
 leg = ax.legend(
     handles=legend_patches, loc="center left", bbox_to_anchor=(1.02, 0.5), fontsize=18, frameon=True, fancybox=True
diff --git a/plots/waterfall-basic/implementations/python/matplotlib.py b/plots/waterfall-basic/implementations/python/matplotlib.py
index e79b5a90d5..dbf16343a5 100644
--- a/plots/waterfall-basic/implementations/python/matplotlib.py
+++ b/plots/waterfall-basic/implementations/python/matplotlib.py
@@ -20,8 +20,8 @@
 
 # Okabe-Ito palette for waterfall categories
 INCREASE_COLOR = "#009E73"  # Brand green (position 1)
-DECREASE_COLOR = "#D55E00"  # Vermillion (position 2)
-TOTAL_COLOR = "#0072B2"  # Blue (position 3)
+DECREASE_COLOR = "#C475FD"  # Vermillion (position 2)
+TOTAL_COLOR = "#4467A3"  # Blue (position 3)
 
 # Data: Project Budget Allocation (from initial budget to final allocation)
 # Different scenario from the original (avoiding Altair match)
diff --git a/plots/windrose-basic/implementations/python/matplotlib.py b/plots/windrose-basic/implementations/python/matplotlib.py
index 9c7ba3b75e..defa09a2fe 100644
--- a/plots/windrose-basic/implementations/python/matplotlib.py
+++ b/plots/windrose-basic/implementations/python/matplotlib.py
@@ -22,7 +22,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for wind speed bins (starting with brand green)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data - Simulated annual wind measurements (8760 hourly readings)
 np.random.seed(42)
@@ -91,7 +91,7 @@
         freq_matrix[:, j],
         width=bar_width,
         bottom=bottoms,
-        color=OKABE_ITO[j],
+        color=IMPRINT[j],
         edgecolor=PAGE_BG,
         linewidth=0.5,
         label=f"{speed_labels[j]} m/s",
diff --git a/plots/wordcloud-basic/implementations/python/matplotlib.py b/plots/wordcloud-basic/implementations/python/matplotlib.py
index c1b2f99e01..60fbcf33d9 100644
--- a/plots/wordcloud-basic/implementations/python/matplotlib.py
+++ b/plots/wordcloud-basic/implementations/python/matplotlib.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - Tech industry survey responses about most valued skills
 word_frequencies = {
@@ -67,7 +67,7 @@
 
 # Create word cloud with Okabe-Ito color palette and proper backgrounds
 def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
-    return OKABE_ITO[hash(word) % len(OKABE_ITO)]
+    return IMPRINT[hash(word) % len(IMPRINT)]
 
 
 wc = WordCloud(
diff --git a/pyproject.toml b/pyproject.toml
index 60cda3bcc8..df40fd1c14 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -150,7 +150,6 @@ ignore = [
 "plots/*/implementations/python/plotnine.py" = ["E402"]
 "plots/*/implementations/python/bokeh.py" = ["E402"]
 "plots/*/implementations/python/seaborn.py" = ["E402"]
-"plots/*/implementations/python/plotly.py" = ["E402"]
 
 [tool.ruff.format]
 quote-style = "double"
diff --git a/scripts/migrate_render_and_upload.py b/scripts/migrate_render_and_upload.py
new file mode 100644
index 0000000000..906db1ba80
--- /dev/null
+++ b/scripts/migrate_render_and_upload.py
@@ -0,0 +1,470 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.13"
+# dependencies = [
+#   "click>=8.1",
+#   "pyyaml>=6.0",
+#   "rich>=13.0",
+# ]
+# ///
+"""Stage B of the imprint migration: re-render light+dark images locally and
+overwrite the GCS production bucket.
+
+For every (spec, language, library) listed in ``.migration-list.json`` (or
+discovered from a freshly-applied Stage A), this script:
+
+1. Renders ``plot-light.png`` and ``plot-dark.png`` in ``.regen-preview/{library}/``
+   by running the impl with ``ANYPLOT_THEME`` set to each theme.
+2. Optimizes each PNG in place and emits responsive variants (400/800/1200 px,
+   PNG + WebP) via ``core.images``.
+3. Uploads the bundle to ``gs://anyplot-images/staging/{spec}/{language}/{library}/``.
+4. Promotes staging → ``gs://anyplot-images/plots/{spec}/{language}/{library}/``
+   with public-read ACL, then deletes staging.
+
+Resumable via ``.migration-progress.json`` — Ctrl-C and re-run to continue from
+where you stopped. Skips items already marked ``done``.
+
+Self-contained: does NOT depend on the regen module, so it works for R and
+Julia impls too (the regen module is Python-only).
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import shutil
+import subprocess
+import sys
+import time
+from dataclasses import dataclass
+from pathlib import Path
+
+import click
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn, TimeElapsedColumn
+
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+PROGRESS_FILE = REPO_ROOT / ".migration-progress.json"
+
+# Use the project's venv directly. The script's own inline-deps venv (click,
+# pyyaml, rich) does not have matplotlib/bokeh/etc., so `uv run python` would
+# pick the wrong interpreter. The project venv is created via `uv sync --extra plotting`.
+PROJECT_PYTHON = REPO_ROOT / ".venv" / "bin" / "python"
+
+GCS_BUCKET = "anyplot-images"
+CACHE_HEADER = "Cache-Control:public, max-age=604800"
+
+FILE_EXT_BY_LANGUAGE: dict[str, str] = {"python": ".py", "r": ".R", "julia": ".jl"}
+
+# Libraries that need a display server (Selenium-driven Chrome for PNG export).
+SELENIUM_LIBS = {"bokeh", "highcharts"}
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Path helpers
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def _impl_path(spec: str, language: str, library: str) -> Path:
+    return REPO_ROOT / "plots" / spec / "implementations" / language / f"{library}{FILE_EXT_BY_LANGUAGE[language]}"
+
+
+def _preview_dir(spec: str, language: str, library: str) -> Path:
+    return REPO_ROOT / "plots" / spec / "implementations" / language / ".regen-preview" / library
+
+
+def _staging_path(spec: str, language: str, library: str) -> str:
+    return f"gs://{GCS_BUCKET}/staging/{spec}/{language}/{library}"
+
+
+def _production_path(spec: str, language: str, library: str) -> str:
+    return f"gs://{GCS_BUCKET}/plots/{spec}/{language}/{library}"
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Pre-flight checks
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+@dataclass
+class PreFlight:
+    python_ok: bool
+    r_ok: bool
+    julia_ok: bool
+    chrome_ok: bool
+    chromedriver_ok: bool
+    gcloud_ok: bool
+    notes: list[str]
+
+
+def _check_command(cmd: list[str]) -> bool:
+    try:
+        subprocess.run(cmd, capture_output=True, check=True, timeout=10)
+        return True
+    except (subprocess.CalledProcessError, FileNotFoundError, subprocess.TimeoutExpired):
+        return False
+
+
+PYTHON_LIB_IMPORTS: dict[str, str] = {
+    "matplotlib": "matplotlib",
+    "seaborn": "seaborn",
+    "plotnine": "plotnine",
+    "plotly": "plotly",
+    "bokeh": "bokeh",
+    "altair": "altair",
+    "pygal": "pygal",
+    "highcharts": "highcharts_core",
+    "letsplot": "lets_plot",
+}
+
+
+def preflight(python_libs: set[str], needs_r: bool, needs_julia: bool, needs_selenium: bool) -> PreFlight:
+    notes: list[str] = []
+
+    if not PROJECT_PYTHON.is_file():
+        python_ok = False
+        notes.append(f"Project venv missing at {PROJECT_PYTHON} → run: uv sync --extra plotting")
+    elif not python_libs:
+        python_ok = True
+    else:
+        modules = sorted({PYTHON_LIB_IMPORTS[lib] for lib in python_libs if lib in PYTHON_LIB_IMPORTS})
+        python_ok = _check_command([str(PROJECT_PYTHON), "-c", "import " + ", ".join(modules)])
+        if not python_ok:
+            notes.append(f"Project venv lacks one of: {', '.join(modules)} → run: uv sync --extra plotting")
+
+    r_ok = _check_command(["R", "--version"]) if needs_r else True
+    if needs_r and not r_ok:
+        notes.append("R not installed → sudo apt-get install r-base r-base-dev, then install ggplot2 + ragg + tidyr + dplyr (see plan)")
+
+    julia_ok = _check_command(["julia", "--version"]) if needs_julia else True
+    if needs_julia and not julia_ok:
+        notes.append("Julia not installed → download from julialang.org, then julia --project=. -e 'using Pkg; Pkg.instantiate()'")
+
+    chrome_ok = True
+    chromedriver_ok = True
+    if needs_selenium:
+        chrome_ok = _check_command(["google-chrome", "--version"]) or _check_command(["chromium", "--version"])
+        chromedriver_ok = _check_command(["chromedriver", "--version"])
+        if not chrome_ok:
+            notes.append("Chrome/Chromium missing (needed for bokeh + highcharts PNG export)")
+        if not chromedriver_ok:
+            notes.append("chromedriver missing (needed for bokeh + highcharts PNG export)")
+
+    gcloud_ok = _check_command(["gcloud", "auth", "list", "--format=value(account)"])
+    if not gcloud_ok:
+        notes.append("gcloud not authenticated → gcloud auth login")
+
+    return PreFlight(
+        python_ok=python_ok,
+        r_ok=r_ok,
+        julia_ok=julia_ok,
+        chrome_ok=chrome_ok,
+        chromedriver_ok=chromedriver_ok,
+        gcloud_ok=gcloud_ok,
+        notes=notes,
+    )
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Render
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def _render_one_theme(impl: Path, preview: Path, language: str, theme: str) -> None:
+    env = os.environ.copy()
+    env["ANYPLOT_THEME"] = theme
+    env["MPLBACKEND"] = "Agg"
+
+    if language == "python":
+        cmd = [str(PROJECT_PYTHON), "-P", str(impl.resolve())]
+    elif language == "r":
+        cmd = ["Rscript", str(impl.resolve())]
+    elif language == "julia":
+        cmd = ["julia", f"--project={REPO_ROOT}", str(impl.resolve())]
+    else:
+        raise ValueError(f"Unsupported language: {language}")
+
+    # xvfb-run gives bokeh/highcharts a display when Chrome is headless-only.
+    try:
+        subprocess.run(["xvfb-run", *cmd], cwd=preview, env=env, check=True, capture_output=True, timeout=180)
+    except FileNotFoundError:
+        subprocess.run(cmd, cwd=preview, env=env, check=True, capture_output=True, timeout=180)
+
+
+def render(spec: str, language: str, library: str) -> tuple[Path, Path]:
+    impl = _impl_path(spec, language, library)
+    if not impl.is_file():
+        raise FileNotFoundError(f"impl not found: {impl}")
+
+    preview = _preview_dir(spec, language, library)
+    if preview.exists():
+        shutil.rmtree(preview)
+    preview.mkdir(parents=True)
+
+    for theme in ("light", "dark"):
+        _render_one_theme(impl, preview, language, theme)
+
+    light_png = preview / "plot-light.png"
+    dark_png = preview / "plot-dark.png"
+    if not light_png.is_file() or not dark_png.is_file():
+        raise RuntimeError(f"render incomplete in {preview} — missing light or dark PNG")
+    return light_png, dark_png
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Optimize + responsive variants
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def _process_image(png: Path) -> None:
+    subprocess.run(
+        [str(PROJECT_PYTHON), "-m", "core.images", "process", str(png), str(png)],
+        cwd=REPO_ROOT,
+        check=True,
+        capture_output=True,
+    )
+    subprocess.run(
+        [str(PROJECT_PYTHON), "-m", "core.images", "responsive", str(png), str(png.parent) + "/"],
+        cwd=REPO_ROOT,
+        check=True,
+        capture_output=True,
+    )
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# GCS upload + promote
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def _gsutil(args: list[str], check: bool = True) -> subprocess.CompletedProcess:
+    return subprocess.run(["gsutil", *args], check=check, capture_output=True, text=True)
+
+
+def upload_to_staging(preview: Path, staging: str) -> None:
+    targets: list[Path] = []
+    for pat in ("plot-light*.png", "plot-light*.webp", "plot-dark*.png", "plot-dark*.webp"):
+        targets.extend(sorted(preview.glob(pat)))
+    if not targets:
+        raise FileNotFoundError(f"no rendered images in {preview}")
+    _gsutil(["-m", "-h", CACHE_HEADER, "cp", *(str(p) for p in targets), f"{staging}/"])
+    _gsutil(["-m", "acl", "ch", "-u", "AllUsers:R", f"{staging}/plot-light*", f"{staging}/plot-dark*"], check=False)
+
+    for theme in ("light", "dark"):
+        html = preview / f"plot-{theme}.html"
+        if html.is_file():
+            _gsutil(["-h", CACHE_HEADER, "cp", str(html), f"{staging}/plot-{theme}.html"])
+            _gsutil(["acl", "ch", "-u", "AllUsers:R", f"{staging}/plot-{theme}.html"], check=False)
+
+
+def promote_to_production(staging: str, production: str) -> None:
+    # Copy then make public; finally clean up staging.
+    _gsutil(["-m", "-h", CACHE_HEADER, "cp", "-r", f"{staging}/*", f"{production}/"])
+    _gsutil(["-m", "acl", "ch", "-r", "-u", "AllUsers:R", f"{production}/"], check=False)
+    _gsutil(["-m", "rm", "-r", f"{staging}/"], check=False)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Progress persistence
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def _load_progress() -> dict[str, str]:
+    if PROGRESS_FILE.is_file():
+        return json.loads(PROGRESS_FILE.read_text())
+    return {}
+
+
+def _save_progress(progress: dict[str, str]) -> None:
+    PROGRESS_FILE.write_text(json.dumps(progress, indent=2, sort_keys=True))
+
+
+def _key(spec: str, language: str, library: str) -> str:
+    return f"{spec}/{language}/{library}"
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Target discovery — files that were just changed by Stage A
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def _discover_targets_from_git(library_filter: str) -> list[tuple[str, str, str]]:
+    """Find (spec, language, library) from git's working tree — files modified vs HEAD."""
+    out = subprocess.run(
+        ["git", "diff", "--name-only", "HEAD"],
+        cwd=REPO_ROOT,
+        capture_output=True,
+        text=True,
+        check=True,
+    )
+    targets: list[tuple[str, str, str]] = []
+    for line in out.stdout.splitlines():
+        # Expected: plots/{spec}/implementations/{language}/{library}.{ext}
+        parts = line.split("/")
+        if len(parts) != 5 or parts[0] != "plots" or parts[2] != "implementations":
+            continue
+        spec = parts[1]
+        language = parts[3]
+        if language not in FILE_EXT_BY_LANGUAGE:
+            continue
+        library = Path(parts[4]).stem
+        if library_filter != "all" and library != library_filter:
+            continue
+        targets.append((spec, language, library))
+    return targets
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Per-target driver
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+def process_one(spec: str, language: str, library: str, *, dry_run: bool, console: Console) -> str:
+    """Returns one of: done | error: <msg> | skipped (dry-run)."""
+    preview = _preview_dir(spec, language, library)
+
+    try:
+        light, dark = render(spec, language, library)
+    except Exception as e:
+        return f"error: render failed: {type(e).__name__}: {e}"
+
+    if dry_run:
+        console.print(f"  [dim]dry-run[/dim] rendered {light.relative_to(REPO_ROOT)} + {dark.relative_to(REPO_ROOT)}")
+        return "skipped (dry-run)"
+
+    try:
+        _process_image(light)
+        _process_image(dark)
+    except subprocess.CalledProcessError as e:
+        return f"error: image processing failed: {e}"
+
+    staging = _staging_path(spec, language, library)
+    production = _production_path(spec, language, library)
+    try:
+        upload_to_staging(preview, staging)
+        promote_to_production(staging, production)
+    except subprocess.CalledProcessError as e:
+        return f"error: GCS upload failed: {e}"
+
+    # Clean preview dir to save disk over a 1000+ file run.
+    shutil.rmtree(preview, ignore_errors=True)
+    return "done"
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# CLI
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+@click.command()
+@click.option(
+    "--library",
+    default="all",
+    help="Process one library or 'all'. Filters discovered targets.",
+)
+@click.option("--spec", default=None, help="Restrict to one spec (for testing).")
+@click.option(
+    "--check-only",
+    is_flag=True,
+    help="Run pre-flight checks and exit. Does not render or upload.",
+)
+@click.option(
+    "--dry-run",
+    is_flag=True,
+    help="Render locally but do NOT optimize, upload, or promote.",
+)
+@click.option(
+    "--skip-on-error",
+    is_flag=True,
+    help="Continue past per-target failures instead of stopping.",
+)
+@click.option(
+    "--resume",
+    is_flag=True,
+    help="Skip items already marked 'done' in .migration-progress.json.",
+)
+def main(library: str, spec: str | None, check_only: bool, dry_run: bool, skip_on_error: bool, resume: bool) -> None:
+    """Re-render and upload light/dark images for migrated impls."""
+    console = Console()
+
+    # Discover targets first so we know which langs to pre-flight.
+    targets = _discover_targets_from_git(library)
+    if spec:
+        targets = [t for t in targets if t[0] == spec]
+
+    if not targets and not check_only:
+        console.print("[yellow]No modified impl files in working tree.[/yellow] "
+                      "Apply Stage A first (or pass --spec for one-off testing).")
+        if not spec:
+            sys.exit(1)
+        # If --spec given, fall through with a synthetic target.
+        # Spec given but not changed → run a single target inferred from library filter.
+        if library != "all":
+            for lang, libs in (("python", ["matplotlib", "seaborn", "plotnine", "plotly", "bokeh", "altair", "pygal", "highcharts", "letsplot"]),
+                                ("r", ["ggplot2"]),
+                                ("julia", ["makie"])):
+                if library in libs:
+                    targets = [(spec, lang, library)]
+                    break
+
+    needs_r = any(t[1] == "r" for t in targets)
+    needs_julia = any(t[1] == "julia" for t in targets)
+    needs_selenium = any(t[2] in SELENIUM_LIBS for t in targets)
+    python_libs = {t[2] for t in targets if t[1] == "python"}
+
+    pf = preflight(python_libs=python_libs, needs_r=needs_r, needs_julia=needs_julia, needs_selenium=needs_selenium)
+    if pf.notes:
+        console.print("[bold]Pre-flight checks:[/bold]")
+        for n in pf.notes:
+            console.print(f"  [red]✗[/red] {n}")
+        if check_only or not (pf.python_ok and pf.r_ok and pf.julia_ok and pf.gcloud_ok):
+            console.print()
+            console.print("[red]Fix the pre-flight issues above before continuing.[/red]")
+            sys.exit(2 if not check_only else 0)
+    else:
+        console.print("[green]All pre-flight checks passed.[/green]")
+        if check_only:
+            sys.exit(0)
+
+    progress_data = _load_progress() if resume else {}
+    progress_data.setdefault("__started__", time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()))
+
+    console.print(f"\nProcessing {len(targets)} target(s) "
+                  f"(library={library}, spec={spec or 'all'}, dry_run={dry_run})")
+
+    failures: list[tuple[str, str]] = []
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("{task.description}"),
+        TimeElapsedColumn(),
+        console=console,
+        transient=False,
+    ) as bar:
+        task = bar.add_task("Migrating", total=len(targets))
+        for spec_id, language, lib in targets:
+            key = _key(spec_id, language, lib)
+            if resume and progress_data.get(key) == "done":
+                bar.advance(task)
+                continue
+            bar.update(task, description=f"Migrating {key}")
+            result = process_one(spec_id, language, lib, dry_run=dry_run, console=console)
+            progress_data[key] = result
+            _save_progress(progress_data)
+            if result.startswith("error"):
+                failures.append((key, result))
+                if not skip_on_error:
+                    console.print(f"\n[red]Aborting on first error: {key} → {result}[/red]")
+                    console.print("Re-run with --skip-on-error to continue past failures.")
+                    sys.exit(1)
+            bar.advance(task)
+
+    console.print(f"\n[bold]Done.[/bold] {len(targets) - len(failures)} succeeded, {len(failures)} failed.")
+    if failures:
+        console.print("[yellow]Failures:[/yellow]")
+        for k, msg in failures[:20]:
+            console.print(f"  • {k}: {msg}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/migrate_to_imprint.py b/scripts/migrate_to_imprint.py
new file mode 100644
index 0000000000..49e321cc1c
--- /dev/null
+++ b/scripts/migrate_to_imprint.py
@@ -0,0 +1,298 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.13"
+# dependencies = [
+#   "click>=8.1",
+#   "pyyaml>=6.0",
+#   "rich>=13.0",
+# ]
+# ///
+"""One-shot positional hex migration: Okabe-Ito / variant-D → imprint.
+
+Walks every existing impl file under ``plots/*/implementations/{python,r,julia}/``,
+keeps only those whose metadata YAML has BOTH ``preview_url_light`` and
+``preview_url_dark``, and rewrites old palette hex literals in place using
+a positional slot-to-slot mapping. Idempotent: files that already contain
+only imprint hex are left untouched.
+
+This is intentionally NOT integrated with the regen module — it's a
+self-contained one-time fix that bypasses the AI review cycle to unify
+the homepage palette quickly.
+"""
+
+from __future__ import annotations
+
+import re
+import sys
+from collections import Counter, defaultdict
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import click
+import yaml
+from rich.console import Console
+from rich.table import Table
+
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+
+# Positional slot mappings. Order = slot index in the old palette.
+# Keys are lowercased so the regex match (also lowercased) hits them.
+OKABE_TO_IMPRINT: dict[str, str] = {
+    "#009e73": "#009E73",
+    "#d55e00": "#C475FD",
+    "#0072b2": "#4467A3",
+    "#cc79a7": "#BD8233",
+    "#e69f00": "#AE3030",
+    "#56b4e9": "#2ABCCD",
+    "#f0e442": "#954477",
+}
+
+VARIANT_D_TO_IMPRINT: dict[str, str] = {
+    "#009e73": "#009E73",
+    "#9418db": "#C475FD",
+    "#b71d27": "#AE3030",
+    "#16b8f3": "#4467A3",
+    "#99b314": "#99B314",
+    "#d359a7": "#954477",
+    "#ba843e": "#BD8233",
+}
+
+# Combined map. Both palettes share #009E73, and Okabe doesn't include the
+# variant-D hex codes (and vice versa), so they merge cleanly.
+COMBINED: dict[str, str] = {**OKABE_TO_IMPRINT, **VARIANT_D_TO_IMPRINT}
+
+# Hex codes we expect to find — lowercased, with leading '#'.
+OLD_HEXES: set[str] = {k for k in COMBINED if COMBINED[k].lower() != k}
+
+# Library names by language (subdir = language).
+LIBRARIES_BY_LANGUAGE: dict[str, list[str]] = {
+    "python": ["matplotlib", "seaborn", "plotnine", "plotly", "bokeh", "altair", "pygal", "highcharts", "letsplot"],
+    "r": ["ggplot2"],
+    "julia": ["makie"],
+}
+FILE_EXT_BY_LANGUAGE: dict[str, str] = {"python": ".py", "r": ".R", "julia": ".jl"}
+
+
+@dataclass
+class Stats:
+    changed: int = 0
+    skipped_already_imprint: int = 0
+    skipped_no_dark: int = 0
+    skipped_no_metadata: int = 0
+    warnings: list[str] = field(default_factory=list)
+    changed_files: list[Path] = field(default_factory=list)
+
+
+# Match any #RRGGBB literal (3-byte form). The substitution callback decides
+# whether to replace based on the lowercased value.
+HEX_RE = re.compile(r"#[0-9A-Fa-f]{6}")
+
+
+def _substitute(text: str) -> tuple[str, int]:
+    """Apply combined positional map. Returns (new_text, replacement_count)."""
+    count = 0
+
+    def repl(match: re.Match[str]) -> str:
+        nonlocal count
+        original = match.group(0)
+        lower = original.lower()
+        new = COMBINED.get(lower)
+        if new is None or new == original:
+            return original
+        # Only count a real change (don't count #009E73 → #009E73 no-ops).
+        if new.lower() != lower:
+            count += 1
+            return new
+        # Same hex but with different case — normalize case anyway, but don't
+        # count as a substantive change.
+        return new if new != original else original
+
+    new_text = HEX_RE.sub(repl, text)
+    return new_text, count
+
+
+def _rename_palette_constant(text: str) -> str:
+    """Rename common old palette variable names to IMPRINT (Python, R, Julia).
+
+    Only renames identifiers that look like a standalone palette constant
+    (whole-word match, all-caps name). Conservative — does not touch
+    inline comments or string literals.
+    """
+    # Whole-word, ASCII identifier boundary on both sides.
+    for old in ("OKABE_ITO", "ANYPLOT_PALETTE"):
+        text = re.sub(rf"\b{old}\b", "IMPRINT", text)
+    return text
+
+
+def _metadata_path(spec_dir: Path, language: str, library: str) -> Path:
+    return spec_dir / "metadata" / language / f"{library}.yaml"
+
+
+def _impl_path(spec_dir: Path, language: str, library: str) -> Path:
+    return spec_dir / "implementations" / language / f"{library}{FILE_EXT_BY_LANGUAGE[language]}"
+
+
+def _has_dual_theme(meta_path: Path) -> bool:
+    """True iff metadata YAML has both preview_url_light AND preview_url_dark non-empty."""
+    if not meta_path.is_file():
+        return False
+    try:
+        data = yaml.safe_load(meta_path.read_text()) or {}
+    except yaml.YAMLError:
+        return False
+    return bool(data.get("preview_url_light")) and bool(data.get("preview_url_dark"))
+
+
+def _process_file(
+    impl_path: Path,
+    meta_path: Path,
+    *,
+    dry_run: bool,
+    stats: Stats,
+) -> None:
+    if not _has_dual_theme(meta_path):
+        # No dark variant on record → skip per scope rule.
+        if not meta_path.is_file():
+            stats.skipped_no_metadata += 1
+        else:
+            stats.skipped_no_dark += 1
+        return
+
+    original = impl_path.read_text()
+    lower = original.lower()
+
+    has_old = any(h in lower for h in OLD_HEXES)
+    if not has_old:
+        stats.skipped_already_imprint += 1
+        return
+
+    # Detect mixed-palette case (file has hex from BOTH old palettes).
+    okabe_only = OLD_HEXES & set(OKABE_TO_IMPRINT) - {"#009e73"}
+    variant_only = OLD_HEXES & set(VARIANT_D_TO_IMPRINT) - {"#009e73", "#99b314"}
+    has_okabe = any(h in lower for h in okabe_only)
+    has_variant = any(h in lower for h in variant_only)
+    if has_okabe and has_variant:
+        stats.warnings.append(f"mixed palettes (Okabe + variant-D) in {impl_path.relative_to(REPO_ROOT)}")
+
+    new_text, count = _substitute(original)
+    new_text = _rename_palette_constant(new_text)
+
+    if new_text == original:
+        # Should not happen given has_old==True, but guard anyway.
+        stats.skipped_already_imprint += 1
+        return
+
+    if not dry_run:
+        impl_path.write_text(new_text)
+    stats.changed += 1
+    stats.changed_files.append(impl_path)
+
+
+def _iter_targets(
+    library_filter: str,
+    spec_filter: str | None,
+) -> list[tuple[Path, str, str]]:
+    """Yield (spec_dir, language, library) for every existing impl matching filters."""
+    plots_root = REPO_ROOT / "plots"
+    if not plots_root.is_dir():
+        raise click.ClickException(f"plots/ not found at {plots_root}")
+
+    targets: list[tuple[Path, str, str]] = []
+    spec_dirs = sorted(plots_root.iterdir())
+    for spec_dir in spec_dirs:
+        if not spec_dir.is_dir():
+            continue
+        if spec_filter and spec_dir.name != spec_filter:
+            continue
+        for language, libs in LIBRARIES_BY_LANGUAGE.items():
+            for library in libs:
+                if library_filter != "all" and library != library_filter:
+                    continue
+                impl = _impl_path(spec_dir, language, library)
+                if impl.is_file():
+                    targets.append((spec_dir, language, library))
+    return targets
+
+
+def _summarize(stats_by_lib: dict[str, Stats], console: Console, dry_run: bool) -> None:
+    table = Table(title=("DRY RUN — no files written" if dry_run else "Migration applied"))
+    table.add_column("Library", style="bold")
+    table.add_column("Changed", justify="right", style="green")
+    table.add_column("Already imprint", justify="right", style="cyan")
+    table.add_column("No dark variant", justify="right", style="yellow")
+    table.add_column("No metadata", justify="right", style="red")
+
+    totals = Counter()
+    for lib in sorted(stats_by_lib):
+        s = stats_by_lib[lib]
+        table.add_row(
+            lib,
+            str(s.changed),
+            str(s.skipped_already_imprint),
+            str(s.skipped_no_dark),
+            str(s.skipped_no_metadata),
+        )
+        totals["changed"] += s.changed
+        totals["skipped_already_imprint"] += s.skipped_already_imprint
+        totals["skipped_no_dark"] += s.skipped_no_dark
+        totals["skipped_no_metadata"] += s.skipped_no_metadata
+    table.add_section()
+    table.add_row(
+        "[bold]TOTAL[/bold]",
+        f"[bold]{totals['changed']}[/bold]",
+        str(totals["skipped_already_imprint"]),
+        str(totals["skipped_no_dark"]),
+        str(totals["skipped_no_metadata"]),
+    )
+    console.print(table)
+
+    warns = [w for s in stats_by_lib.values() for w in s.warnings]
+    if warns:
+        console.print(f"\n[yellow]Warnings ({len(warns)}):[/yellow]")
+        for w in warns[:20]:
+            console.print(f"  • {w}")
+        if len(warns) > 20:
+            console.print(f"  … and {len(warns) - 20} more")
+
+
+@click.command()
+@click.option(
+    "--library",
+    default="all",
+    help="Restrict to one library (matplotlib, seaborn, ..., ggplot2, makie) or 'all'.",
+)
+@click.option("--spec", default=None, help="Restrict to one spec id (for testing).")
+@click.option("--dry-run", is_flag=True, help="Report what would change; do not write files.")
+@click.option("--list-changed", is_flag=True, help="After summary, list every changed file path (full report).")
+def main(library: str, spec: str | None, dry_run: bool, list_changed: bool) -> None:
+    """Migrate Okabe-Ito / variant-D hex codes to imprint, in place."""
+    console = Console(stderr=True)
+
+    valid_libs = {lib for libs in LIBRARIES_BY_LANGUAGE.values() for lib in libs} | {"all"}
+    if library not in valid_libs:
+        raise click.ClickException(f"Unknown library: {library}. Valid: {sorted(valid_libs)}")
+
+    targets = _iter_targets(library, spec)
+    if not targets:
+        console.print(f"[red]No targets matched library={library} spec={spec}[/red]")
+        sys.exit(1)
+
+    console.print(f"Scanning {len(targets)} impl files (library={library}, spec={spec or 'all'})…")
+
+    stats_by_lib: dict[str, Stats] = defaultdict(Stats)
+    for spec_dir, language, lib in targets:
+        impl = _impl_path(spec_dir, language, lib)
+        meta = _metadata_path(spec_dir, language, lib)
+        _process_file(impl, meta, dry_run=dry_run, stats=stats_by_lib[lib])
+
+    _summarize(stats_by_lib, console, dry_run=dry_run)
+
+    if list_changed:
+        all_changed = [p for s in stats_by_lib.values() for p in s.changed_files]
+        for p in sorted(all_changed):
+            print(p.relative_to(REPO_ROOT))
+
+
+if __name__ == "__main__":
+    main()