From b955305f5cdc12604b48a1c44556d5ac27395b85 Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:06:53 +0200
Subject: [PATCH 1/7] chore(palette): migrate plotly impls from Okabe-Ito to
 imprint
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Positional hex replacement across 139 plotly impls with light+dark
renders, moving them from Okabe-Ito + variant-D to imprint. Mirrors
the same pattern as #7776 / #7779 / #7780.

Proactive semantic-anchor fixes for 13 plotly impls where slot-1
(now lavender) or slot-5 (now red) carried explicit meaning:

- bar-diverging          NEGATIVE_COLOR → red
- candlestick-volume     COLOR_DOWN     → red
- gauge-basic            ZONE_LOW/MID   → red/amber traffic-light
- indicator-ema          GOLDEN_CLR     → amber (was ochre after swap)
- indicator-macd         SIGNAL_COLOR   → ochre (categorical contrast)
- indicator-rsi          overbought 70  → red (line + label)
- kagi-basic             YIN_COLOR      → red
- ohlc-bar               DOWN_COLOR     → red
- point-and-figure-basic COLOR_O        → red
- renko-basic            BEARISH        → red
- shap-waterfall         POS_COLOR      → red (vs blue NEG)
- slope-basic            COLOR_DOWN     → red
- waterfall-basic        COLOR_NEGATIVE → red

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          | 18 +++++-----
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          | 22 ++++++------
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          | 12 +++----
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 12 +++----
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          | 22 ++++++------
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          | 18 +++++-----
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 12 +++----
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  8 ++---
 .../ohlc-bar/implementations/python/plotly.py |  2 +-
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 34 +++++++++---------
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../qq-basic/implementations/python/plotly.py |  2 +-
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 16 ++++-----
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  8 ++---
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          | 12 +++----
 .../implementations/python/plotly.py          | 36 +++++++++----------
 .../implementations/python/plotly.py          | 12 +++----
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          | 20 +++++------
 .../implementations/python/plotly.py          | 10 +++---
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 14 ++++----
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  6 ++--
 .../implementations/python/plotly.py          | 14 ++++----
 .../implementations/python/plotly.py          |  4 +--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 16 ++++-----
 139 files changed, 405 insertions(+), 405 deletions(-)

diff --git a/plots/alluvial-basic/implementations/python/plotly.py b/plots/alluvial-basic/implementations/python/plotly.py
index 4d9797424b..5a208a79b1 100644
--- a/plots/alluvial-basic/implementations/python/plotly.py
+++ b/plots/alluvial-basic/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Browser market share evolution 2015-2024
 # Shows how different browsers have gained/lost market share over time
@@ -27,7 +27,7 @@
 
 # Node labels and colors
 node_labels = categories * 4
-category_colors = {cat: OKABE_ITO[i] for i, cat in enumerate(categories)}
+category_colors = {cat: IMPRINT[i] for i, cat in enumerate(categories)}
 node_colors = [category_colors[cat] for cat in node_labels]
 
 # X positions: 4 time points evenly spaced
@@ -85,7 +85,7 @@
     targets.append(tgt_idx)
     values.append(value)
     # Use source category color with transparency
-    base_color = OKABE_ITO[src_cat]
+    base_color = IMPRINT[src_cat]
     r = int(base_color[1:3], 16)
     g = int(base_color[3:5], 16)
     b = int(base_color[5:7], 16)
diff --git a/plots/andrews-curves/implementations/python/plotly.py b/plots/andrews-curves/implementations/python/plotly.py
index 97b4d396ca..8233a49580 100644
--- a/plots/andrews-curves/implementations/python/plotly.py
+++ b/plots/andrews-curves/implementations/python/plotly.py
@@ -18,8 +18,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
-BRAND = OKABE_ITO[0]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
+BRAND = IMPRINT[0]
 
 # Data
 iris = load_iris()
@@ -57,7 +57,7 @@
                 x=t,
                 y=curve,
                 mode="lines",
-                line=dict(color=OKABE_ITO[species_idx], width=2),
+                line=dict(color=IMPRINT[species_idx], width=2),
                 opacity=0.4,
                 name=species_names[species_idx],
                 legendgroup=species_names[species_idx],
diff --git a/plots/area-cumulative-flow/implementations/python/plotly.py b/plots/area-cumulative-flow/implementations/python/plotly.py
index 6d599ca445..5d4b4f746c 100644
--- a/plots/area-cumulative-flow/implementations/python/plotly.py
+++ b/plots/area-cumulative-flow/implementations/python/plotly.py
@@ -22,12 +22,12 @@
 # Okabe-Ito palette — first series is always #009E73
 FILL_COLORS = [
     "rgba(0,158,115,0.80)",  # #009E73 Done
-    "rgba(213,94,0,0.80)",  # #D55E00 Testing
-    "rgba(0,114,178,0.80)",  # #0072B2 Development
-    "rgba(204,121,167,0.80)",  # #CC79A7 Analysis
-    "rgba(230,159,0,0.80)",  # #E69F00 Backlog
+    "rgba(213,94,0,0.80)",  # #C475FD Testing
+    "rgba(0,114,178,0.80)",  # #4467A3 Development
+    "rgba(204,121,167,0.80)",  # #BD8233 Analysis
+    "rgba(230,159,0,0.80)",  # #AE3030 Backlog
 ]
-LINE_COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+LINE_COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data — Kanban board for a software team over 90 days
 np.random.seed(42)
diff --git a/plots/area-stacked-confidence/implementations/python/plotly.py b/plots/area-stacked-confidence/implementations/python/plotly.py
index 0db561b827..580fa583c6 100644
--- a/plots/area-stacked-confidence/implementations/python/plotly.py
+++ b/plots/area-stacked-confidence/implementations/python/plotly.py
@@ -21,8 +21,8 @@
 
 # Okabe-Ito palette (positions 1-3 for three series)
 HYDRO_COLOR = "#009E73"  # Okabe-Ito position 1 (brand green)
-WIND_COLOR = "#D55E00"  # Okabe-Ito position 2 (vermillion)
-SOLAR_COLOR = "#0072B2"  # Okabe-Ito position 3 (blue)
+WIND_COLOR = "#C475FD"  # Okabe-Ito position 2 (vermillion)
+SOLAR_COLOR = "#4467A3"  # Okabe-Ito position 3 (blue)
 
 # Data - Quarterly energy consumption forecast by source with uncertainty bands
 np.random.seed(42)
diff --git a/plots/area-stacked/implementations/python/plotly.py b/plots/area-stacked/implementations/python/plotly.py
index b11e444c7d..7e104e44c1 100644
--- a/plots/area-stacked/implementations/python/plotly.py
+++ b/plots/area-stacked/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is always brand green)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Monthly revenue by product category over 2 years
 np.random.seed(42)
@@ -48,9 +48,9 @@
         y=software,
         name="Software",
         mode="lines",
-        line=dict(width=0.5, color=OKABE_ITO[0]),
+        line=dict(width=0.5, color=IMPRINT[0]),
         fill="tozeroy",
-        fillcolor=OKABE_ITO[0],
+        fillcolor=IMPRINT[0],
         stackgroup="one",
         hovertemplate="<b>Software</b><br>%{x|%b %Y}<br>Revenue: $%{y:,.0f}<extra></extra>",
     )
@@ -62,9 +62,9 @@
         y=services,
         name="Services",
         mode="lines",
-        line=dict(width=0.5, color=OKABE_ITO[1]),
+        line=dict(width=0.5, color=IMPRINT[1]),
         fill="tonexty",
-        fillcolor=OKABE_ITO[1],
+        fillcolor=IMPRINT[1],
         stackgroup="one",
         hovertemplate="<b>Services</b><br>%{x|%b %Y}<br>Revenue: $%{y:,.0f}<extra></extra>",
     )
@@ -76,9 +76,9 @@
         y=enterprise,
         name="Enterprise",
         mode="lines",
-        line=dict(width=0.5, color=OKABE_ITO[2]),
+        line=dict(width=0.5, color=IMPRINT[2]),
         fill="tonexty",
-        fillcolor=OKABE_ITO[2],
+        fillcolor=IMPRINT[2],
         stackgroup="one",
         hovertemplate="<b>Enterprise</b><br>%{x|%b %Y}<br>Revenue: $%{y:,.0f}<extra></extra>",
     )
@@ -90,9 +90,9 @@
         y=consulting,
         name="Consulting",
         mode="lines",
-        line=dict(width=0.5, color=OKABE_ITO[3]),
+        line=dict(width=0.5, color=IMPRINT[3]),
         fill="tonexty",
-        fillcolor=OKABE_ITO[3],
+        fillcolor=IMPRINT[3],
         stackgroup="one",
         hovertemplate="<b>Consulting</b><br>%{x|%b %Y}<br>Revenue: $%{y:,.0f}<extra></extra>",
     )
diff --git a/plots/bar-3d-categorical/implementations/python/plotly.py b/plots/bar-3d-categorical/implementations/python/plotly.py
index cbc0d39b5d..88dfc7bdc7 100644
--- a/plots/bar-3d-categorical/implementations/python/plotly.py
+++ b/plots/bar-3d-categorical/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID_COLOR = "#D0CEC7" if THEME == "light" else "#2D2D2A"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data
 np.random.seed(42)
@@ -64,7 +64,7 @@
             i=ii,
             j=jj,
             k=kk,
-            color=OKABE_ITO[p_idx],
+            color=IMPRINT[p_idx],
             opacity=1.0,
             name=prod,
             showlegend=True,
diff --git a/plots/bar-diverging/implementations/python/plotly.py b/plots/bar-diverging/implementations/python/plotly.py
index d74de32fc4..0622a9c626 100644
--- a/plots/bar-diverging/implementations/python/plotly.py
+++ b/plots/bar-diverging/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 
 # Okabe-Ito palette: position 1 (green) for positive, position 2 (orange) for negative
 POSITIVE_COLOR = "#009E73"
-NEGATIVE_COLOR = "#D55E00"
+NEGATIVE_COLOR = "#AE3030"  # imprint red — semantic negative
 
 # Data: Customer satisfaction survey results by department
 # Scores range from -100 (very dissatisfied) to +100 (very satisfied)
diff --git a/plots/bar-drilldown/implementations/python/plotly.py b/plots/bar-drilldown/implementations/python/plotly.py
index 96f80a38dc..161a200c3b 100644
--- a/plots/bar-drilldown/implementations/python/plotly.py
+++ b/plots/bar-drilldown/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # anyplot palette — positions 1–4 for 4 quarters
-PALETTE = ["#009E73", "#9418DB", "#B71D27", "#16B8F3"]
+PALETTE = ["#009E73", "#C475FD", "#AE3030", "#4467A3"]
 
 # Data: 2024 Retail Sales — quarter → month two-level hierarchy
 QUARTERS = [
diff --git a/plots/bar-grouped/implementations/python/plotly.py b/plots/bar-grouped/implementations/python/plotly.py
index 39d3b52d17..4c704b3e28 100644
--- a/plots/bar-grouped/implementations/python/plotly.py
+++ b/plots/bar-grouped/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 GRID = "rgba(26,26,23,0.20)" if THEME == "light" else "rgba(240,239,232,0.20)"
 
 # Okabe-Ito palette
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Quarterly revenue by product line
 categories = ["Q1", "Q2", "Q3", "Q4"]
diff --git a/plots/bar-race-animated/implementations/python/plotly.py b/plots/bar-race-animated/implementations/python/plotly.py
index cf89e8cd18..3b3814643e 100644
--- a/plots/bar-race-animated/implementations/python/plotly.py
+++ b/plots/bar-race-animated/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.25)" if THEME == "light" else "rgba(240,239,232,0.25)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data: Major economy GDP rankings (approximate, in trillion USD, 1995–2023)
 np.random.seed(42)
@@ -27,7 +27,7 @@
 years = list(range(1995, 2024))
 
 base_gdp = {"USA": 7.70, "China": 0.73, "Japan": 5.45, "Germany": 2.60, "UK": 1.28, "France": 1.60, "Brazil": 0.77}
-color_map = dict(zip(countries, OKABE_ITO))
+color_map = dict(zip(countries, IMPRINT))
 
 data_rows = []
 for country in countries:
diff --git a/plots/bar-spine/implementations/python/plotly.py b/plots/bar-spine/implementations/python/plotly.py
index 2001eba76c..dd39944004 100644
--- a/plots/bar-spine/implementations/python/plotly.py
+++ b/plots/bar-spine/implementations/python/plotly.py
@@ -42,7 +42,7 @@
 outcome_props = {k: [cnt / m for cnt, m in zip(v, channel_sizes)] for k, v in outcome_counts.items()}
 
 # Okabe-Ito palette: Active=green(1), On Trial=orange(5), Cancelled=vermillion(2)
-COLORS = {"Active": "#009E73", "On Trial": "#E69F00", "Cancelled": "#D55E00"}
+COLORS = {"Active": "#009E73", "On Trial": "#AE3030", "Cancelled": "#C475FD"}
 
 # Plot
 fig = go.Figure()
diff --git a/plots/bar-stacked-labeled/implementations/python/plotly.py b/plots/bar-stacked-labeled/implementations/python/plotly.py
index 4127138448..2d15bf2adc 100644
--- a/plots/bar-stacked-labeled/implementations/python/plotly.py
+++ b/plots/bar-stacked-labeled/implementations/python/plotly.py
@@ -22,7 +22,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is ALWAYS #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Quarterly revenue by product category
 quarters = ["Q1 2024", "Q2 2024", "Q3 2024", "Q4 2024", "Q1 2025"]
@@ -43,7 +43,7 @@
 fig = go.Figure()
 
 # Add stacked bars for each product
-for product, color in zip(products, OKABE_ITO, strict=True):
+for product, color in zip(products, IMPRINT, strict=True):
     fig.add_trace(
         go.Bar(
             name=product,
diff --git a/plots/bar-stacked-percent/implementations/python/plotly.py b/plots/bar-stacked-percent/implementations/python/plotly.py
index a2c9896e56..27e0a23f27 100644
--- a/plots/bar-stacked-percent/implementations/python/plotly.py
+++ b/plots/bar-stacked-percent/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette - first color is always #009E73
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data: Smartphone OS market share across world regions
 categories = ["North America", "South America", "Europe", "Africa", "Asia"]
@@ -45,7 +45,7 @@
             name=component,
             x=categories,
             y=df[component].values,
-            marker=dict(color=OKABE_ITO[i], line=dict(width=0)),
+            marker=dict(color=IMPRINT[i], line=dict(width=0)),
             text=[f"{v:.0f}%" for v in df[component].values],
             textposition="inside",
             textfont=dict(size=16, color="white"),
diff --git a/plots/bar-stacked/implementations/python/plotly.py b/plots/bar-stacked/implementations/python/plotly.py
index 3ea9816e23..69d35936a4 100644
--- a/plots/bar-stacked/implementations/python/plotly.py
+++ b/plots/bar-stacked/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID_SUBTLE = "rgba(26,26,23,0.04)" if THEME == "light" else "rgba(240,239,232,0.04)"
 
 # Okabe-Ito palette (first series always #009E73)
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Quarterly revenue by product category
 quarters = ["Q1 2024", "Q2 2024", "Q3 2024", "Q4 2024"]
diff --git a/plots/biplot-pca/implementations/python/plotly.py b/plots/biplot-pca/implementations/python/plotly.py
index 91a943648e..4214e9c502 100644
--- a/plots/biplot-pca/implementations/python/plotly.py
+++ b/plots/biplot-pca/implementations/python/plotly.py
@@ -22,7 +22,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Load data
 iris = load_iris()
@@ -52,7 +52,7 @@
             x=scores[mask, 0],
             y=scores[mask, 1],
             mode="markers",
-            marker={"size": 14, "color": OKABE_ITO[i], "opacity": 0.8, "line": {"width": 1, "color": PAGE_BG}},
+            marker={"size": 14, "color": IMPRINT[i], "opacity": 0.8, "line": {"width": 1, "color": PAGE_BG}},
             name=target.capitalize(),
             legendgroup=target,
         )
diff --git a/plots/bland-altman-basic/implementations/python/plotly.py b/plots/bland-altman-basic/implementations/python/plotly.py
index 0871ee1ee8..c3690af323 100644
--- a/plots/bland-altman-basic/implementations/python/plotly.py
+++ b/plots/bland-altman-basic/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - Glucose meter readings comparison (medical device validation)
 np.random.seed(73)
diff --git a/plots/box-grouped/implementations/python/plotly.py b/plots/box-grouped/implementations/python/plotly.py
index de03b6750d..d6a0e9593c 100644
--- a/plots/box-grouped/implementations/python/plotly.py
+++ b/plots/box-grouped/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Employee performance scores by department and experience level
 np.random.seed(42)
@@ -73,7 +73,7 @@
             x=x_vals,
             y=y_vals,
             name=subcat,
-            marker_color=OKABE_ITO[i],
+            marker_color=IMPRINT[i],
             boxmean=False,
             line={"width": 2},
             marker={"size": 8, "opacity": 0.7},
diff --git a/plots/box-horizontal/implementations/python/plotly.py b/plots/box-horizontal/implementations/python/plotly.py
index d1f60df922..6e795a207a 100644
--- a/plots/box-horizontal/implementations/python/plotly.py
+++ b/plots/box-horizontal/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data - Response times (ms) by service type
 np.random.seed(42)
@@ -64,9 +64,9 @@
             x=service_data,
             name=service,
             orientation="h",
-            marker=dict(color=OKABE_ITO[i % len(OKABE_ITO)], size=8, outliercolor=OKABE_ITO[i % len(OKABE_ITO)]),
-            line=dict(color=OKABE_ITO[i % len(OKABE_ITO)], width=2),
-            fillcolor=OKABE_ITO[i % len(OKABE_ITO)],
+            marker=dict(color=IMPRINT[i % len(IMPRINT)], size=8, outliercolor=IMPRINT[i % len(IMPRINT)]),
+            line=dict(color=IMPRINT[i % len(IMPRINT)], width=2),
+            fillcolor=IMPRINT[i % len(IMPRINT)],
             opacity=0.7,
             boxmean=False,
             hovertemplate="<b>%{name}</b><br>Value: %{x:.1f} ms<extra></extra>",
diff --git a/plots/box-notched/implementations/python/plotly.py b/plots/box-notched/implementations/python/plotly.py
index 08d2bcac99..eff7ba1d72 100644
--- a/plots/box-notched/implementations/python/plotly.py
+++ b/plots/box-notched/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Employee salary distributions across departments
 np.random.seed(42)
@@ -48,9 +48,9 @@
             name=dept,
             boxpoints="outliers",
             notched=True,
-            marker={"color": OKABE_ITO[i], "size": 10, "opacity": 0.7},
+            marker={"color": IMPRINT[i], "size": 10, "opacity": 0.7},
             line={"width": 2},
-            fillcolor=OKABE_ITO[i],
+            fillcolor=IMPRINT[i],
             opacity=0.7,
         )
     )
diff --git a/plots/bubble-map-geographic/implementations/python/plotly.py b/plots/bubble-map-geographic/implementations/python/plotly.py
index 0352bf807c..875145f2c6 100644
--- a/plots/bubble-map-geographic/implementations/python/plotly.py
+++ b/plots/bubble-map-geographic/implementations/python/plotly.py
@@ -26,11 +26,11 @@
 # Okabe-Ito palette for regions (canonical order, first = #009E73)
 REGION_COLORS = {
     "Asia": "#009E73",
-    "Europe": "#D55E00",
-    "North America": "#0072B2",
-    "South America": "#CC79A7",
-    "Africa": "#E69F00",
-    "Oceania": "#56B4E9",
+    "Europe": "#C475FD",
+    "North America": "#4467A3",
+    "South America": "#BD8233",
+    "Africa": "#AE3030",
+    "Oceania": "#2ABCCD",
 }
 REGION_ORDER = ["Asia", "Europe", "North America", "South America", "Africa", "Oceania"]
 
diff --git a/plots/calibration-curve/implementations/python/plotly.py b/plots/calibration-curve/implementations/python/plotly.py
index af0a95634d..09e044ff2a 100644
--- a/plots/calibration-curve/implementations/python/plotly.py
+++ b/plots/calibration-curve/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND = "#009E73"
-SECONDARY = "#D55E00"
+SECONDARY = "#C475FD"
 NEUTRAL = "#1A1A1A" if THEME == "light" else "#E8E8E0"
 
 # Data - simulate predictions from two classifiers
diff --git a/plots/candlestick-volume/implementations/python/plotly.py b/plots/candlestick-volume/implementations/python/plotly.py
index d200a57187..d2a28db79f 100644
--- a/plots/candlestick-volume/implementations/python/plotly.py
+++ b/plots/candlestick-volume/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 COLOR_UP = "#009E73"  # Okabe-Ito position 1 (brand green)
-COLOR_DOWN = "#D55E00"  # Okabe-Ito position 2 (vermillion)
+COLOR_DOWN = "#AE3030"  # imprint red — down days
 
 # Data - Generate 60 trading days of realistic OHLC + volume data
 np.random.seed(42)
diff --git a/plots/cat-box-strip/implementations/python/plotly.py b/plots/cat-box-strip/implementations/python/plotly.py
index f7a0d64bf1..8e697e72c4 100644
--- a/plots/cat-box-strip/implementations/python/plotly.py
+++ b/plots/cat-box-strip/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND = "#009E73"  # Okabe-Ito position 1
-SERIES2 = "#D55E00"  # Okabe-Ito position 2
+SERIES2 = "#C475FD"  # Okabe-Ito position 2
 
 # Data - Exam scores across different study methods
 np.random.seed(42)
diff --git a/plots/chernoff-basic/implementations/python/plotly.py b/plots/chernoff-basic/implementations/python/plotly.py
index 9670edf584..408f547a92 100644
--- a/plots/chernoff-basic/implementations/python/plotly.py
+++ b/plots/chernoff-basic/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - use Iris dataset with 4 measurements per flower
 np.random.seed(42)
@@ -75,7 +75,7 @@
     cx = col * spacing + spacing / 2
     cy = (n_rows - 1 - row) * spacing + spacing / 2
 
-    color = OKABE_ITO[species]
+    color = IMPRINT[species]
 
     # Feature mapping with increased variation:
     #   - sepal_length (data[0]) -> face width
@@ -209,7 +209,7 @@
 fig.add_trace(go.Scatter(x=[0], y=[0], mode="markers", marker=dict(opacity=0), showlegend=False))
 
 # Add legend entries for species
-for i, (name, color) in enumerate(zip(target_names, OKABE_ITO)):
+for i, (name, color) in enumerate(zip(target_names, IMPRINT)):
     fig.add_trace(
         go.Scatter(
             x=[None],
diff --git a/plots/circlepacking-basic/implementations/python/plotly.py b/plots/circlepacking-basic/implementations/python/plotly.py
index d530add291..1c6e3cdb31 100644
--- a/plots/circlepacking-basic/implementations/python/plotly.py
+++ b/plots/circlepacking-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 np.random.seed(42)
 
@@ -102,7 +102,7 @@
     cx, cy = asset_info["x"], asset_info["y"]
     asset_radius = asset_info["r"]
     subcats = asset_info["subcats"]
-    asset_color = OKABE_ITO[idx % len(OKABE_ITO)]
+    asset_color = IMPRINT[idx % len(IMPRINT)]
 
     all_circles.append(
         {
@@ -313,7 +313,7 @@
             x=[None],
             y=[None],
             mode="markers",
-            marker={"size": 16, "color": OKABE_ITO[idx % len(OKABE_ITO)], "line": {"color": INK_SOFT, "width": 2}},
+            marker={"size": 16, "color": IMPRINT[idx % len(IMPRINT)], "line": {"color": INK_SOFT, "width": 2}},
             name=name,
             showlegend=True,
         )
diff --git a/plots/circos-basic/implementations/python/plotly.py b/plots/circos-basic/implementations/python/plotly.py
index 88898d9004..47281797c8 100644
--- a/plots/circos-basic/implementations/python/plotly.py
+++ b/plots/circos-basic/implementations/python/plotly.py
@@ -30,14 +30,14 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette with first series as brand
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
-    "#E69F00",  # orange
-    "#56B4E9",  # sky blue
-    "#F0E442",  # yellow
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
+    "#AE3030",  # orange
+    "#2ABCCD",  # sky blue
+    "#954477",  # yellow
     "#1A1A1A" if THEME == "light" else "#E8E8E0",  # neutral
 ]
 
@@ -106,7 +106,7 @@
             x=x_arc,
             y=y_arc,
             fill="toself",
-            fillcolor=OKABE_ITO[i],
+            fillcolor=IMPRINT[i],
             line={"color": INK_SOFT, "width": 1},
             name=segments[i],
             hoverinfo="name",
@@ -196,8 +196,8 @@
             y_ribbon = np.concatenate([y_src, curve1_y, y_tgt, curve2_y, [y_src[0]]])
 
             # Blend colors inline
-            c1 = OKABE_ITO[i]
-            c2 = OKABE_ITO[j]
+            c1 = IMPRINT[i]
+            c2 = IMPRINT[j]
             r1, g1, b1 = int(c1[1:3], 16), int(c1[3:5], 16), int(c1[5:7], 16)
             r2, g2, b2 = int(c2[1:3], 16), int(c2[3:5], 16), int(c2[5:7], 16)
             r = int(r1 * 0.5 + r2 * 0.5)
@@ -244,7 +244,7 @@
             x=x_bar,
             y=y_bar,
             fill="toself",
-            fillcolor=OKABE_ITO[i],
+            fillcolor=IMPRINT[i],
             opacity=0.6,
             line={"color": INK_SOFT, "width": 0.5},
             hoverinfo="text",
diff --git a/plots/contour-decision-boundary/implementations/python/plotly.py b/plots/contour-decision-boundary/implementations/python/plotly.py
index 11777d8447..e13215baad 100644
--- a/plots/contour-decision-boundary/implementations/python/plotly.py
+++ b/plots/contour-decision-boundary/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Generate moon-shaped classification data
 np.random.seed(42)
@@ -52,7 +52,7 @@
         x=np.linspace(x_min, x_max, 150),
         y=np.linspace(y_min, y_max, 150),
         z=Z_prob,
-        colorscale=[[0, OKABE_ITO[0]], [1, OKABE_ITO[1]]],
+        colorscale=[[0, IMPRINT[0]], [1, IMPRINT[1]]],
         opacity=0.4,
         showscale=True,
         colorbar=dict(
@@ -90,7 +90,7 @@
         x=X_class0[:, 0],
         y=X_class0[:, 1],
         mode="markers",
-        marker=dict(size=14, color=OKABE_ITO[0], line=dict(color=PAGE_BG, width=2), symbol="circle"),
+        marker=dict(size=14, color=IMPRINT[0], line=dict(color=PAGE_BG, width=2), symbol="circle"),
         name="Class 0",
         hovertemplate="Feature 1: %{x:.2f}<br>Feature 2: %{y:.2f}<br>Class: 0<extra></extra>",
     )
@@ -102,7 +102,7 @@
         x=X_class1[:, 0],
         y=X_class1[:, 1],
         mode="markers",
-        marker=dict(size=14, color=OKABE_ITO[1], line=dict(color=PAGE_BG, width=2), symbol="diamond"),
+        marker=dict(size=14, color=IMPRINT[1], line=dict(color=PAGE_BG, width=2), symbol="diamond"),
         name="Class 1",
         hovertemplate="Feature 1: %{x:.2f}<br>Feature 2: %{y:.2f}<br>Class: 1<extra></extra>",
     )
diff --git a/plots/contour-density/implementations/python/plotly.py b/plots/contour-density/implementations/python/plotly.py
index 1b36a2c678..5d97f2b73e 100644
--- a/plots/contour-density/implementations/python/plotly.py
+++ b/plots/contour-density/implementations/python/plotly.py
@@ -44,7 +44,7 @@
 colorscale = [
     [0, "rgba(255,255,255,0)"],  # Transparent at low density
     [0.2, "#FDB462"],  # Light orange (visible on both themes)
-    [0.5, "#0072B2"],  # Blue (Okabe-Ito position 3)
+    [0.5, "#4467A3"],  # Blue (Okabe-Ito position 3)
     [1, "#005073"],  # Dark blue (increased contrast)
 ]
 
diff --git a/plots/dashboard-synchronized-crosshair/implementations/python/plotly.py b/plots/dashboard-synchronized-crosshair/implementations/python/plotly.py
index a5ccad66cf..1582065e40 100644
--- a/plots/dashboard-synchronized-crosshair/implementations/python/plotly.py
+++ b/plots/dashboard-synchronized-crosshair/implementations/python/plotly.py
@@ -22,8 +22,8 @@
 
 # anyplot palette
 PRICE_COLOR = "#009E73"  # position 1 - green
-VOLUME_COLOR = "#9418DB"  # position 2 - purple
-RSI_COLOR = "#16B8F3"  # position 4 - sky blue
+VOLUME_COLOR = "#C475FD"  # position 2 - purple
+RSI_COLOR = "#4467A3"  # position 4 - sky blue
 
 # Data - Stock data: price, volume, and 14-period RSI over 200 trading days
 np.random.seed(42)
@@ -104,7 +104,7 @@
 )
 
 # RSI reference lines — distinct dash patterns for colorblind accessibility
-fig.add_hline(y=70, line_dash="dash", line_color="#B71D27", line_width=1.5, row=3, col=1)
+fig.add_hline(y=70, line_dash="dash", line_color="#AE3030", line_width=1.5, row=3, col=1)
 fig.add_hline(y=30, line_dash="dot", line_color="#009E73", line_width=1.5, row=3, col=1)
 
 # Style
diff --git a/plots/dendrogram-radial/implementations/python/plotly.py b/plots/dendrogram-radial/implementations/python/plotly.py
index 779c2124d0..fb0ff2570c 100644
--- a/plots/dendrogram-radial/implementations/python/plotly.py
+++ b/plots/dendrogram-radial/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 SPECIES = ["Setosa", "Versicolor", "Virginica"]
 
 # Data: 12 samples per iris species (36 leaves total)
@@ -82,7 +82,7 @@
 def branch_color(node_id):
     sp = node_species[node_id]
     if len(sp) == 1:
-        return OKABE_ITO[next(iter(sp))]
+        return IMPRINT[next(iter(sp))]
     return INK_SOFT
 
 
@@ -142,7 +142,7 @@ def branch_color(node_id):
         x=np.cos(leaf_rad),
         y=np.sin(leaf_rad),
         mode="markers",
-        marker={"color": [OKABE_ITO[y[i]] for i in range(n_leaves)], "size": 10, "line": {"color": PAGE_BG, "width": 1.5}},
+        marker={"color": [IMPRINT[y[i]] for i in range(n_leaves)], "size": 10, "line": {"color": PAGE_BG, "width": 1.5}},
         showlegend=False,
         hovertext=[f"{labels[i]} · {SPECIES[y[i]]}" for i in range(n_leaves)],
         hoverinfo="text",
@@ -156,8 +156,8 @@ def branch_color(node_id):
             x=[None],
             y=[None],
             mode="markers+lines",
-            marker={"color": OKABE_ITO[idx], "size": 12},
-            line={"color": OKABE_ITO[idx], "width": 3},
+            marker={"color": IMPRINT[idx], "size": 12},
+            line={"color": IMPRINT[idx], "width": 3},
             name=species,
             showlegend=True,
         )
@@ -179,7 +179,7 @@ def branch_color(node_id):
             "x": lx,
             "y": ly,
             "text": labels[leaf],
-            "font": {"size": 10, "color": OKABE_ITO[y[leaf]]},
+            "font": {"size": 10, "color": IMPRINT[y[leaf]]},
             "showarrow": False,
             "xanchor": xanchor,
             "yanchor": yanchor,
diff --git a/plots/diagnostic-regression-panel/implementations/python/plotly.py b/plots/diagnostic-regression-panel/implementations/python/plotly.py
index a3fb7e97bc..140376a4b4 100644
--- a/plots/diagnostic-regression-panel/implementations/python/plotly.py
+++ b/plots/diagnostic-regression-panel/implementations/python/plotly.py
@@ -21,8 +21,8 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND = "#009E73"  # Okabe-Ito position 1 — scatter points
-C2 = "#D55E00"  # Okabe-Ito position 2 — LOWESS smoother lines
-C4 = "#CC79A7"  # Okabe-Ito position 4 — Cook's distance contours
+C2 = "#C475FD"  # Okabe-Ito position 2 — LOWESS smoother lines
+C4 = "#BD8233"  # Okabe-Ito position 4 — Cook's distance contours
 
 # Data — apartment rental price regression with synthetic data
 np.random.seed(42)
diff --git a/plots/donut-nested/implementations/python/plotly.py b/plots/donut-nested/implementations/python/plotly.py
index cccf99d308..a59eb09de9 100644
--- a/plots/donut-nested/implementations/python/plotly.py
+++ b/plots/donut-nested/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito categorical palette (canonical order, positions 1-4 for inner ring)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 
 def lighten_hex(hex_color, factor=0.4):
@@ -33,7 +33,7 @@ def lighten_hex(hex_color, factor=0.4):
 # Data - Company budget allocation by department and expense category
 departments = ["Engineering", "Marketing", "Sales", "Operations"]
 dept_values = [45, 25, 18, 12]  # Millions
-dept_colors = OKABE_ITO
+dept_colors = IMPRINT
 
 # Child categories (outer ring) with consistent color families (lighter shades)
 categories = [
diff --git a/plots/dot-matrix-proportional/implementations/python/plotly.py b/plots/dot-matrix-proportional/implementations/python/plotly.py
index 68f275cf98..aa6f2ee983 100644
--- a/plots/dot-matrix-proportional/implementations/python/plotly.py
+++ b/plots/dot-matrix-proportional/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 # Data: city commute survey, 100 respondents
 categories = ["Public Transit", "Car", "Cycling", "Work from Home"]
 counts = [42, 31, 15, 12]
-colors = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+colors = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 total = sum(counts)  # 100
 grid_cols = 10
 grid_rows = total // grid_cols  # 10
diff --git a/plots/drawdown-basic/implementations/python/plotly.py b/plots/drawdown-basic/implementations/python/plotly.py
index 71bbf5165c..e37ac3fd60 100644
--- a/plots/drawdown-basic/implementations/python/plotly.py
+++ b/plots/drawdown-basic/implementations/python/plotly.py
@@ -24,7 +24,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-LOSS_RED = "#B71D27"  # anyplot palette position 3 — semantic: loss/drawdown
+LOSS_RED = "#AE3030"  # anyplot palette position 3 — semantic: loss/drawdown
 RECOVERY_GREEN = "#009E73"  # anyplot palette position 1 — semantic: recovery/new high
 
 # Data — simulated stock price with realistic volatility
diff --git a/plots/dumbbell-basic/implementations/python/plotly.py b/plots/dumbbell-basic/implementations/python/plotly.py
index 738eadd920..eddc52e331 100644
--- a/plots/dumbbell-basic/implementations/python/plotly.py
+++ b/plots/dumbbell-basic/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 
 # Okabe-Ito palette (theme-independent data colors)
 BEFORE_COLOR = "#009E73"  # position 1 — first categorical series
-AFTER_COLOR = "#D55E00"  # position 2 — second series
+AFTER_COLOR = "#C475FD"  # position 2 — second series
 
 # Data — Employee satisfaction scores before and after policy changes.
 # Deterministic, hand-picked values (no RNG): one department (Legal) shows a
diff --git a/plots/elbow-curve/implementations/python/plotly.py b/plots/elbow-curve/implementations/python/plotly.py
index 008105f478..3b8eb9355c 100644
--- a/plots/elbow-curve/implementations/python/plotly.py
+++ b/plots/elbow-curve/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - simulate K-means inertia values for k=1 to k=12
 np.random.seed(42)
diff --git a/plots/frequency-polygon-basic/implementations/python/plotly.py b/plots/frequency-polygon-basic/implementations/python/plotly.py
index e3b91f8959..740603a57c 100644
--- a/plots/frequency-polygon-basic/implementations/python/plotly.py
+++ b/plots/frequency-polygon-basic/implementations/python/plotly.py
@@ -20,8 +20,8 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Okabe-Ito position 1 — ALWAYS first series
-OKABE_VERMILLION = "#D55E00"  # Okabe-Ito position 2
-OKABE_BLUE = "#0072B2"  # Okabe-Ito position 3
+OKABE_VERMILLION = "#C475FD"  # Okabe-Ito position 2
+OKABE_BLUE = "#4467A3"  # Okabe-Ito position 3
 
 # Data - Reaction times (ms) for three experimental conditions
 np.random.seed(42)
diff --git a/plots/frontier-efficient/implementations/python/plotly.py b/plots/frontier-efficient/implementations/python/plotly.py
index cc8b4e6567..f5ebae0b17 100644
--- a/plots/frontier-efficient/implementations/python/plotly.py
+++ b/plots/frontier-efficient/implementations/python/plotly.py
@@ -25,9 +25,9 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"
-SECONDARY = "#D55E00"
-TERTIARY = "#0072B2"
-ACCENT_1 = "#CC79A7"
+SECONDARY = "#C475FD"
+TERTIARY = "#4467A3"
+ACCENT_1 = "#BD8233"
 
 # Data - Generate random portfolios and efficient frontier
 np.random.seed(42)
diff --git a/plots/funnel-basic/implementations/python/plotly.py b/plots/funnel-basic/implementations/python/plotly.py
index ab81b728ae..40320a496c 100644
--- a/plots/funnel-basic/implementations/python/plotly.py
+++ b/plots/funnel-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito categorical palette (first series is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Sales funnel example
 stages = ["Awareness", "Interest", "Consideration", "Intent", "Purchase"]
@@ -31,7 +31,7 @@
         textposition="inside",
         textinfo="value+percent initial",
         textfont={"size": 22, "color": "#FAF8F1"},
-        marker={"color": OKABE_ITO, "line": {"width": 2, "color": PAGE_BG}},
+        marker={"color": IMPRINT, "line": {"width": 2, "color": PAGE_BG}},
         connector={"line": {"color": INK_SOFT, "width": 1, "dash": "dot"}},
     )
 )
diff --git a/plots/gain-curve/implementations/python/plotly.py b/plots/gain-curve/implementations/python/plotly.py
index 7f1e35c446..0b31aef5d6 100644
--- a/plots/gain-curve/implementations/python/plotly.py
+++ b/plots/gain-curve/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 # Okabe-Ito palette
 BRAND = "#009E73"  # First series — always
 BASELINE = "#888888"  # Neutral for reference
-PERFECT = "#E69F00"  # Orange for perfect model
+PERFECT = "#AE3030"  # Orange for perfect model
 
 # Data - Customer response model evaluation
 np.random.seed(42)
diff --git a/plots/gantt-basic/implementations/python/plotly.py b/plots/gantt-basic/implementations/python/plotly.py
index 37f5cb45c0..2ca04aecbe 100644
--- a/plots/gantt-basic/implementations/python/plotly.py
+++ b/plots/gantt-basic/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Software Development Project Timeline
 tasks = [
@@ -47,11 +47,11 @@
 
 # Color map using Okabe-Ito palette
 color_map = {
-    "Planning": OKABE_ITO[0],
-    "Design": OKABE_ITO[1],
-    "Development": OKABE_ITO[2],
-    "Testing": OKABE_ITO[3],
-    "Deployment": OKABE_ITO[4],
+    "Planning": IMPRINT[0],
+    "Design": IMPRINT[1],
+    "Development": IMPRINT[2],
+    "Testing": IMPRINT[3],
+    "Deployment": IMPRINT[4],
 }
 
 # Create Gantt chart using timeline
diff --git a/plots/gauge-basic/implementations/python/plotly.py b/plots/gauge-basic/implementations/python/plotly.py
index a6b0fa5c41..92a253f9de 100644
--- a/plots/gauge-basic/implementations/python/plotly.py
+++ b/plots/gauge-basic/implementations/python/plotly.py
@@ -18,9 +18,9 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito colorblind-safe stand-ins for the red/amber/green convention
-ZONE_LOW = "#D55E00"  # vermillion (bad)
-ZONE_MID = "#E69F00"  # orange (caution)
-ZONE_HIGH = "#009E73"  # bluish green — Okabe-Ito brand (good)
+ZONE_LOW = "#AE3030"  # imprint red — bad
+ZONE_MID = "#DDCC77"  # imprint amber — caution
+ZONE_HIGH = "#009E73"  # imprint green — good
 
 # Data — Sales target achievement for the quarter
 value = 72
diff --git a/plots/hierarchy-toggle-view/implementations/python/plotly.py b/plots/hierarchy-toggle-view/implementations/python/plotly.py
index bb9bc13628..5d63225c3c 100644
--- a/plots/hierarchy-toggle-view/implementations/python/plotly.py
+++ b/plots/hierarchy-toggle-view/implementations/python/plotly.py
@@ -52,7 +52,7 @@
     values.append(team_budget)
 
 # Okabe-Ito palette for departments (positions 1–4)
-dept_colors = {"Engineering": "#009E73", "Marketing": "#D55E00", "Sales": "#0072B2", "Operations": "#CC79A7"}
+dept_colors = {"Engineering": "#009E73", "Marketing": "#C475FD", "Sales": "#4467A3", "Operations": "#BD8233"}
 
 colors = []
 for i, label in enumerate(labels):
diff --git a/plots/histogram-density/implementations/python/plotly.py b/plots/histogram-density/implementations/python/plotly.py
index c5a8867f7c..7a451a36d2 100644
--- a/plots/histogram-density/implementations/python/plotly.py
+++ b/plots/histogram-density/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"  # Okabe-Ito position 1
-OKABE_ITO_2 = "#D55E00"  # Okabe-Ito position 2 for KDE curve
+OKABE_ITO_2 = "#C475FD"  # Okabe-Ito position 2 for KDE curve
 
 # Data - simulated test scores with a more pronounced bimodal distribution
 np.random.seed(42)
diff --git a/plots/histogram-kde/implementations/python/plotly.py b/plots/histogram-kde/implementations/python/plotly.py
index 04e3f2e10a..d9e9028e03 100644
--- a/plots/histogram-kde/implementations/python/plotly.py
+++ b/plots/histogram-kde/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"  # Okabe-Ito position 1 — first series
-KDE_COLOR = "#D55E00"  # Okabe-Ito position 2
+KDE_COLOR = "#C475FD"  # Okabe-Ito position 2
 
 # Data - Stock returns simulation with realistic distribution
 np.random.seed(42)
diff --git a/plots/histogram-overlapping/implementations/python/plotly.py b/plots/histogram-overlapping/implementations/python/plotly.py
index 43918277b6..f9afd7911a 100644
--- a/plots/histogram-overlapping/implementations/python/plotly.py
+++ b/plots/histogram-overlapping/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00"]
+IMPRINT = ["#009E73", "#C475FD"]
 
 # Data - heights by gender (realistic scenario showing overlapping distributions)
 np.random.seed(42)
@@ -32,7 +32,7 @@
     go.Histogram(
         x=male_heights,
         name="Male",
-        marker=dict(color=OKABE_ITO[0], line=dict(color=INK_SOFT, width=1)),
+        marker=dict(color=IMPRINT[0], line=dict(color=INK_SOFT, width=1)),
         opacity=0.5,
         xbins=dict(size=3),
     )
@@ -42,7 +42,7 @@
     go.Histogram(
         x=female_heights,
         name="Female",
-        marker=dict(color=OKABE_ITO[1], line=dict(color=INK_SOFT, width=1)),
+        marker=dict(color=IMPRINT[1], line=dict(color=INK_SOFT, width=1)),
         opacity=0.5,
         xbins=dict(size=3),
     )
diff --git a/plots/histogram-returns-distribution/implementations/python/plotly.py b/plots/histogram-returns-distribution/implementations/python/plotly.py
index b86c4ac88b..f3e9015036 100644
--- a/plots/histogram-returns-distribution/implementations/python/plotly.py
+++ b/plots/histogram-returns-distribution/implementations/python/plotly.py
@@ -21,8 +21,8 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # position 1 — normal return bars
-VERMILLION = "#D55E00"  # position 2 — tail region bars
-BLUE = "#0072B2"  # position 3 — normal distribution curve
+VERMILLION = "#C475FD"  # position 2 — tail region bars
+BLUE = "#4467A3"  # position 3 — normal distribution curve
 
 # Data - Simulated daily stock returns (504 trading days / 2 years)
 np.random.seed(42)
diff --git a/plots/histogram-stacked/implementations/python/plotly.py b/plots/histogram-stacked/implementations/python/plotly.py
index 34755875a6..4fd13657b1 100644
--- a/plots/histogram-stacked/implementations/python/plotly.py
+++ b/plots/histogram-stacked/implementations/python/plotly.py
@@ -27,7 +27,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is ALWAYS #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Product weights from three production lines
 np.random.seed(42)
@@ -59,7 +59,7 @@
         x=bin_centers,
         y=hist_a,
         name="Line A",
-        marker_color=OKABE_ITO[0],
+        marker_color=IMPRINT[0],
         width=bin_width * 0.9,
         hovertemplate="<b>Line A</b><br>Weight: %{x:.1f}g<br>Count: %{y}<extra></extra>",
     )
@@ -70,7 +70,7 @@
         x=bin_centers,
         y=hist_b,
         name="Line B",
-        marker_color=OKABE_ITO[1],
+        marker_color=IMPRINT[1],
         width=bin_width * 0.9,
         hovertemplate="<b>Line B</b><br>Weight: %{x:.1f}g<br>Count: %{y}<extra></extra>",
     )
@@ -81,7 +81,7 @@
         x=bin_centers,
         y=hist_c,
         name="Line C",
-        marker_color=OKABE_ITO[2],
+        marker_color=IMPRINT[2],
         width=bin_width * 0.9,
         hovertemplate="<b>Line C</b><br>Weight: %{x:.1f}g<br>Count: %{y}<extra></extra>",
     )
diff --git a/plots/hive-basic/implementations/python/plotly.py b/plots/hive-basic/implementations/python/plotly.py
index 72c9e2b85b..2869253bb3 100644
--- a/plots/hive-basic/implementations/python/plotly.py
+++ b/plots/hive-basic/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito for the 3 axes (positions 1, 2, 3)
-AXIS_COLORS = {"core": "#009E73", "utility": "#D55E00", "interface": "#0072B2"}
+AXIS_COLORS = {"core": "#009E73", "utility": "#C475FD", "interface": "#4467A3"}
 
 # Data: Software module dependency network
 np.random.seed(42)
diff --git a/plots/icicle-basic/implementations/python/plotly.py b/plots/icicle-basic/implementations/python/plotly.py
index 14c55406c8..b54d946bf8 100644
--- a/plots/icicle-basic/implementations/python/plotly.py
+++ b/plots/icicle-basic/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - File system hierarchy
 data = {
@@ -77,11 +77,11 @@ def get_node_level(node, df):
     if level == 0:
         colors.append(BRAND)
     elif level == 1:
-        colors.append(OKABE_ITO[1])
+        colors.append(IMPRINT[1])
     elif level == 2:
-        colors.append(OKABE_ITO[2])
+        colors.append(IMPRINT[2])
     else:
-        colors.append(OKABE_ITO[3])
+        colors.append(IMPRINT[3])
 
 # Create icicle chart
 fig = go.Figure(
diff --git a/plots/indicator-bollinger/implementations/python/plotly.py b/plots/indicator-bollinger/implementations/python/plotly.py
index 81318e6e30..0104f3a0f6 100644
--- a/plots/indicator-bollinger/implementations/python/plotly.py
+++ b/plots/indicator-bollinger/implementations/python/plotly.py
@@ -31,13 +31,13 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND = "#009E73"  # Okabe-Ito position 1 — close price
-OKABE_ITO = [
-    "#D55E00",  # position 2 — upper/lower bands
-    "#0072B2",  # position 3 — SMA
-    "#CC79A7",
-    "#E69F00",
-    "#56B4E9",
-    "#F0E442",
+IMPRINT = [
+    "#C475FD",  # position 2 — upper/lower bands
+    "#4467A3",  # position 3 — SMA
+    "#BD8233",
+    "#AE3030",
+    "#2ABCCD",
+    "#954477",
 ]
 
 # Data - Generate realistic stock price data with Bollinger Bands
@@ -78,7 +78,7 @@
         mode="lines",
         line={"width": 0},
         fill="tonexty",
-        fillcolor=f"rgba({int(OKABE_ITO[0][1:3], 16)}, {int(OKABE_ITO[0][3:5], 16)}, {int(OKABE_ITO[0][5:7], 16)}, 0.15)",
+        fillcolor=f"rgba({int(IMPRINT[0][1:3], 16)}, {int(IMPRINT[0][3:5], 16)}, {int(IMPRINT[0][5:7], 16)}, 0.15)",
         name="Bollinger Bands (2σ)",
         hoverinfo="skip",
     )
@@ -90,7 +90,7 @@
         x=df["date"],
         y=df["upper_band"],
         mode="lines",
-        line={"color": OKABE_ITO[0], "width": 2, "dash": "solid"},
+        line={"color": IMPRINT[0], "width": 2, "dash": "solid"},
         name="Upper Band (+2σ)",
         hovertemplate="Upper: $%{y:.2f}<extra></extra>",
     )
@@ -102,7 +102,7 @@
         x=df["date"],
         y=df["lower_band"],
         mode="lines",
-        line={"color": OKABE_ITO[0], "width": 2, "dash": "solid"},
+        line={"color": IMPRINT[0], "width": 2, "dash": "solid"},
         name="Lower Band (-2σ)",
         hovertemplate="Lower: $%{y:.2f}<extra></extra>",
     )
@@ -114,7 +114,7 @@
         x=df["date"],
         y=df["sma"],
         mode="lines",
-        line={"color": OKABE_ITO[1], "width": 3, "dash": "dash"},
+        line={"color": IMPRINT[1], "width": 3, "dash": "dash"},
         name="20-day SMA",
         hovertemplate="SMA: $%{y:.2f}<extra></extra>",
     )
diff --git a/plots/indicator-ema/implementations/python/plotly.py b/plots/indicator-ema/implementations/python/plotly.py
index bc51ff1e2f..d32a498f7c 100644
--- a/plots/indicator-ema/implementations/python/plotly.py
+++ b/plots/indicator-ema/implementations/python/plotly.py
@@ -21,10 +21,10 @@
 
 # Okabe-Ito palette — positions 1-5 in canonical order
 BRAND = "#009E73"  # price line (first series)
-EMA_SHORT = "#D55E00"  # EMA 12
-EMA_LONG = "#0072B2"  # EMA 26
-GOLDEN_CLR = "#CC79A7"  # golden cross marker
-DEATH_CLR = "#E69F00"  # death cross marker
+EMA_SHORT = "#C475FD"  # EMA 12
+EMA_LONG = "#4467A3"  # EMA 26
+GOLDEN_CLR = "#DDCC77"  # imprint amber — golden cross marker
+DEATH_CLR = "#AE3030"  # death cross marker
 
 # Data — realistic stock price with trend, consolidation, and recovery
 np.random.seed(42)
diff --git a/plots/indicator-macd/implementations/python/plotly.py b/plots/indicator-macd/implementations/python/plotly.py
index f1e9b30791..b0de1fec78 100644
--- a/plots/indicator-macd/implementations/python/plotly.py
+++ b/plots/indicator-macd/implementations/python/plotly.py
@@ -22,7 +22,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # MACD
-SIGNAL_COLOR = "#D55E00"  # Signal line
+SIGNAL_COLOR = "#BD8233"  # imprint ochre — signal line (categorical contrast)
 
 # Data - Generate realistic stock price data and calculate MACD
 np.random.seed(42)
@@ -52,7 +52,7 @@
 # Create histogram colors using diverging approach
 hist_positive = histogram >= 0
 hist_colors = [
-    "#0072B2" if val else "#CC79A7"  # Blue for positive, purple for negative
+    "#4467A3" if val else "#BD8233"  # Blue for positive, purple for negative
     for val in hist_positive
 ]
 
diff --git a/plots/indicator-rsi/implementations/python/plotly.py b/plots/indicator-rsi/implementations/python/plotly.py
index 25eb9c1618..329c49973f 100644
--- a/plots/indicator-rsi/implementations/python/plotly.py
+++ b/plots/indicator-rsi/implementations/python/plotly.py
@@ -86,9 +86,9 @@
 fig.add_trace(go.Scatter(x=dates, y=rsi, mode="lines", name="RSI (14)", line={"color": BRAND, "width": 3}))
 
 # Add reference lines
-fig.add_hline(y=70, line_dash="dash", line_color="#D55E00", line_width=2)
+fig.add_hline(y=70, line_dash="dash", line_color="#AE3030", line_width=2)  # imprint red overbought
 fig.add_hline(y=50, line_dash="dot", line_color=INK_SOFT, line_width=1.5)
-fig.add_hline(y=30, line_dash="dash", line_color="#0072B2", line_width=2)
+fig.add_hline(y=30, line_dash="dash", line_color="#4467A3", line_width=2)
 
 # Annotations for threshold lines
 fig.add_annotation(
@@ -98,7 +98,7 @@
     showarrow=False,
     xanchor="left",
     xshift=10,
-    font={"size": 16, "color": "#D55E00"},
+    font={"size": 16, "color": "#AE3030"},
 )
 fig.add_annotation(
     x=dates[-1],
@@ -116,7 +116,7 @@
     showarrow=False,
     xanchor="left",
     xshift=10,
-    font={"size": 16, "color": "#0072B2"},
+    font={"size": 16, "color": "#4467A3"},
 )
 
 # Layout
diff --git a/plots/indicator-sma/implementations/python/plotly.py b/plots/indicator-sma/implementations/python/plotly.py
index db3a253620..5620a74f35 100644
--- a/plots/indicator-sma/implementations/python/plotly.py
+++ b/plots/indicator-sma/implementations/python/plotly.py
@@ -21,9 +21,9 @@
 
 # Okabe-Ito palette — positions 1–4
 PRICE_COLOR = "#009E73"  # position 1 — first series (close price)
-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
 np.random.seed(42)
diff --git a/plots/kagi-basic/implementations/python/plotly.py b/plots/kagi-basic/implementations/python/plotly.py
index 75d0853514..2511bc866a 100644
--- a/plots/kagi-basic/implementations/python/plotly.py
+++ b/plots/kagi-basic/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette: green for yang (bullish), vermillion for yin (bearish)
 YANG_COLOR = "#009E73"  # Okabe-Ito position 1 - green
-YIN_COLOR = "#D55E00"  # Okabe-Ito position 2 - vermillion
+YIN_COLOR = "#AE3030"  # imprint red — bearish
 
 # Generate sample price data designed to demonstrate multiple yang/yin transitions
 np.random.seed(42)
diff --git a/plots/learning-curve-basic/implementations/python/plotly.py b/plots/learning-curve-basic/implementations/python/plotly.py
index ffaaab9c99..ec11ccef69 100644
--- a/plots/learning-curve-basic/implementations/python/plotly.py
+++ b/plots/learning-curve-basic/implementations/python/plotly.py
@@ -31,7 +31,7 @@
 
 # Okabe-Ito palette (first series always #009E73)
 TRAIN_COLOR = "#009E73"  # bluish green
-VAL_COLOR = "#D55E00"  # vermillion
+VAL_COLOR = "#C475FD"  # vermillion
 
 # Data - Simulate learning curve data from cross-validation
 np.random.seed(42)
diff --git a/plots/lift-curve/implementations/python/plotly.py b/plots/lift-curve/implementations/python/plotly.py
index 8ccaf6d8a2..5922313e57 100644
--- a/plots/lift-curve/implementations/python/plotly.py
+++ b/plots/lift-curve/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # First series, always
-ACCENT = "#D55E00"  # Second series
+ACCENT = "#C475FD"  # Second series
 
 # Data: Simulate customer response model predictions
 np.random.seed(42)
diff --git a/plots/line-3d-trajectory/implementations/python/plotly.py b/plots/line-3d-trajectory/implementations/python/plotly.py
index 431cc32f77..0f66ace2ec 100644
--- a/plots/line-3d-trajectory/implementations/python/plotly.py
+++ b/plots/line-3d-trajectory/implementations/python/plotly.py
@@ -25,7 +25,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data: Multiple 3D helix trajectories with different periods and amplitudes
 np.random.seed(42)
@@ -39,7 +39,7 @@
 x1 = 5 * np.cos(t1)
 y1 = 5 * np.sin(t1)
 z1 = 3 * t1 / (8 * np.pi)
-trajectories.append((x1, y1, z1, OKABE_ITO[0]))
+trajectories.append((x1, y1, z1, IMPRINT[0]))
 labels.append("Standard Helix")
 
 # Helix 2: Compressed helix (faster rise, tighter spiral)
@@ -47,7 +47,7 @@
 x2 = 3.5 * np.cos(2 * t2)
 y2 = 3.5 * np.sin(2 * t2)
 z2 = 5 * t2 / (6 * np.pi)
-trajectories.append((x2, y2, z2, OKABE_ITO[1]))
+trajectories.append((x2, y2, z2, IMPRINT[1]))
 labels.append("Compressed Helix")
 
 # Helix 3: Expanding helix (amplitude increases with height)
@@ -56,7 +56,7 @@
 x3 = amplitude * np.cos(t3)
 y3 = amplitude * np.sin(t3)
 z3 = 6 * t3 / (4 * np.pi)
-trajectories.append((x3, y3, z3, OKABE_ITO[2]))
+trajectories.append((x3, y3, z3, IMPRINT[2]))
 labels.append("Expanding Helix")
 
 # Create 3D line plot with multiple trajectories
diff --git a/plots/line-loss-training/implementations/python/plotly.py b/plots/line-loss-training/implementations/python/plotly.py
index 2477bc3f90..9625ca796e 100644
--- a/plots/line-loss-training/implementations/python/plotly.py
+++ b/plots/line-loss-training/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette
 TRAIN_COLOR = "#009E73"  # Position 1 - bluish green (brand)
-VAL_COLOR = "#D55E00"  # Position 2 - vermillion
+VAL_COLOR = "#C475FD"  # Position 2 - vermillion
 
 # Data - Simulated neural network training with different trajectory
 np.random.seed(42)
diff --git a/plots/line-markers/implementations/python/plotly.py b/plots/line-markers/implementations/python/plotly.py
index 4a6df00637..4d4f5d2257 100644
--- a/plots/line-markers/implementations/python/plotly.py
+++ b/plots/line-markers/implementations/python/plotly.py
@@ -20,8 +20,8 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"
-COLOR_2 = "#D55E00"
-COLOR_3 = "#0072B2"
+COLOR_2 = "#C475FD"
+COLOR_3 = "#4467A3"
 
 # Data - experimental temperature readings over time
 np.random.seed(42)
diff --git a/plots/line-multi/implementations/python/plotly.py b/plots/line-multi/implementations/python/plotly.py
index 14a6f7cd80..688b72f262 100644
--- a/plots/line-multi/implementations/python/plotly.py
+++ b/plots/line-multi/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (positions 1-4 for 4 series)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Monthly sales (units) for 4 product lines over 12 months
 np.random.seed(42)
@@ -41,7 +41,7 @@
         y=electronics,
         name="Electronics",
         mode="lines+markers",
-        line=dict(color=OKABE_ITO[0], width=4),
+        line=dict(color=IMPRINT[0], width=4),
         marker=dict(size=12, symbol="circle"),
     )
 )
@@ -52,7 +52,7 @@
         y=clothing,
         name="Clothing",
         mode="lines+markers",
-        line=dict(color=OKABE_ITO[1], width=4),
+        line=dict(color=IMPRINT[1], width=4),
         marker=dict(size=12, symbol="square"),
     )
 )
@@ -63,7 +63,7 @@
         y=home_garden,
         name="Home & Garden",
         mode="lines+markers",
-        line=dict(color=OKABE_ITO[2], width=4, dash="dash"),
+        line=dict(color=IMPRINT[2], width=4, dash="dash"),
         marker=dict(size=12, symbol="diamond"),
     )
 )
@@ -74,7 +74,7 @@
         y=sports,
         name="Sports",
         mode="lines+markers",
-        line=dict(color=OKABE_ITO[3], width=4, dash="dot"),
+        line=dict(color=IMPRINT[3], width=4, dash="dot"),
         marker=dict(size=12, symbol="triangle-up"),
     )
 )
diff --git a/plots/line-stock-comparison/implementations/python/plotly.py b/plots/line-stock-comparison/implementations/python/plotly.py
index 8ebb3c9647..01b5490f38 100644
--- a/plots/line-stock-comparison/implementations/python/plotly.py
+++ b/plots/line-stock-comparison/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 SELECTOR_ACTIVE = "rgba(26,26,23,0.12)" if THEME == "light" else "rgba(240,239,232,0.12)"
 
-ANYPLOT_PALETTE = ["#009E73", "#9418DB", "#B71D27", "#16B8F3"]
+IMPRINT = ["#009E73", "#C475FD", "#AE3030", "#4467A3"]
 
 # Data - Generate synthetic stock price data for 4 companies
 np.random.seed(42)
@@ -58,7 +58,7 @@
             y=df_rebased[symbol],
             mode="lines",
             name=symbol,
-            line={"width": 2.5, "color": ANYPLOT_PALETTE[i]},
+            line={"width": 2.5, "color": IMPRINT[i]},
             hovertemplate=f"{symbol}<br>Date: %{{x|%Y-%m-%d}}<br>Value: %{{y:.1f}}<extra></extra>",
         )
     )
@@ -87,7 +87,7 @@
         showarrow=False,
         xanchor="left",
         yanchor="middle",
-        font={"size": 9, "color": ANYPLOT_PALETTE[i]},
+        font={"size": 9, "color": IMPRINT[i]},
     )
 
 # Layout
diff --git a/plots/line-styled/implementations/python/plotly.py b/plots/line-styled/implementations/python/plotly.py
index 00fa32ff23..53a01370d7 100644
--- a/plots/line-styled/implementations/python/plotly.py
+++ b/plots/line-styled/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
 
 # Okabe-Ito colors (position 1 is always brand green)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Temperature readings from different environments over 24 hours
 np.random.seed(42)
@@ -41,7 +41,7 @@
         y=sensor_a,
         mode="lines",
         name="Outdoor Sensor",
-        line=dict(dash="solid", width=4, color=OKABE_ITO[0]),
+        line=dict(dash="solid", width=4, color=IMPRINT[0]),
         hovertemplate="<b>Outdoor</b><br>Hour: %{x}<br>Temp: %{y:.1f}°C<extra></extra>",
     )
 )
@@ -52,7 +52,7 @@
         y=sensor_b,
         mode="lines",
         name="Indoor Sensor",
-        line=dict(dash="dash", width=4, color=OKABE_ITO[1]),
+        line=dict(dash="dash", width=4, color=IMPRINT[1]),
         hovertemplate="<b>Indoor</b><br>Hour: %{x}<br>Temp: %{y:.1f}°C<extra></extra>",
     )
 )
@@ -63,7 +63,7 @@
         y=sensor_c,
         mode="lines",
         name="Greenhouse Sensor",
-        line=dict(dash="dot", width=4, color=OKABE_ITO[2]),
+        line=dict(dash="dot", width=4, color=IMPRINT[2]),
         hovertemplate="<b>Greenhouse</b><br>Hour: %{x}<br>Temp: %{y:.1f}°C<extra></extra>",
     )
 )
@@ -74,7 +74,7 @@
         y=sensor_d,
         mode="lines",
         name="Storage Sensor",
-        line=dict(dash="dashdot", width=4, color=OKABE_ITO[3]),
+        line=dict(dash="dashdot", width=4, color=IMPRINT[3]),
         hovertemplate="<b>Storage</b><br>Hour: %{x}<br>Temp: %{y:.1f}°C<extra></extra>",
     )
 )
diff --git a/plots/line-timeseries-rolling/implementations/python/plotly.py b/plots/line-timeseries-rolling/implementations/python/plotly.py
index 73229904cf..f5400179ac 100644
--- a/plots/line-timeseries-rolling/implementations/python/plotly.py
+++ b/plots/line-timeseries-rolling/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Raw data (first series)
-ACCENT = "#D55E00"  # Rolling average
+ACCENT = "#C475FD"  # Rolling average
 
 # Data - Daily temperature readings with noise
 np.random.seed(42)
diff --git a/plots/logistic-regression/implementations/python/plotly.py b/plots/logistic-regression/implementations/python/plotly.py
index e43888010e..32a6ac8f47 100644
--- a/plots/logistic-regression/implementations/python/plotly.py
+++ b/plots/logistic-regression/implementations/python/plotly.py
@@ -25,7 +25,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # First series - bluish green
-ACCENT = "#D55E00"  # Second series - vermillion
+ACCENT = "#C475FD"  # Second series - vermillion
 
 # Data - medical biomarker vs disease diagnosis
 np.random.seed(42)
diff --git a/plots/lollipop-grouped/implementations/python/plotly.py b/plots/lollipop-grouped/implementations/python/plotly.py
index d268bfc35d..7b71e235cc 100644
--- a/plots/lollipop-grouped/implementations/python/plotly.py
+++ b/plots/lollipop-grouped/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Quarterly revenue by product line across regions (in millions $)
 np.random.seed(42)
@@ -44,7 +44,7 @@
 x_base = np.arange(n_categories)
 
 # Add stems and markers for each series
-for i, (series, color) in enumerate(zip(series_names, OKABE_ITO, strict=False)):
+for i, (series, color) in enumerate(zip(series_names, IMPRINT, strict=False)):
     # Calculate x positions with offset for grouping
     offset = (i - (n_series - 1) / 2) * bar_width
     x_positions = x_base + offset
diff --git a/plots/manhattan-gwas/implementations/python/plotly.py b/plots/manhattan-gwas/implementations/python/plotly.py
index b8177060e0..0f9db4f719 100644
--- a/plots/manhattan-gwas/implementations/python/plotly.py
+++ b/plots/manhattan-gwas/implementations/python/plotly.py
@@ -30,19 +30,19 @@
 GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
 
 # Okabe-Ito palette
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
-    "#E69F00",  # orange
-    "#56B4E9",  # sky blue
-    "#F0E442",  # yellow
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
+    "#AE3030",  # orange
+    "#2ABCCD",  # sky blue
+    "#954477",  # yellow
 ]
 
 # Threshold line colors (theme-adaptive)
 THRESHOLD_COLOR = INK_MUTED
-HIGHLIGHT_COLOR = OKABE_ITO[1]  # vermillion for significant SNPs
+HIGHLIGHT_COLOR = IMPRINT[1]  # vermillion for significant SNPs
 
 # Data - Simulated GWAS results
 np.random.seed(42)
@@ -119,7 +119,7 @@
 
 # Alternating chromosome colors (Okabe-Ito positions 1 and 2)
 def get_chr_color(chrom_num):
-    return OKABE_ITO[0] if int(chrom_num) % 2 == 1 else OKABE_ITO[1]
+    return IMPRINT[0] if int(chrom_num) % 2 == 1 else IMPRINT[1]
 
 
 # Create figure
diff --git a/plots/map-drilldown-geographic/implementations/python/plotly.py b/plots/map-drilldown-geographic/implementations/python/plotly.py
index e28400d4b5..a85b686a2b 100644
--- a/plots/map-drilldown-geographic/implementations/python/plotly.py
+++ b/plots/map-drilldown-geographic/implementations/python/plotly.py
@@ -271,7 +271,7 @@
     ),
     (
         sorted_by_total[-1],
-        "#B71D27",
+        "#AE3030",
         "triangle-down",
         12,
         f"▼ Low: {us_states_data[sorted_by_total[-1]]['abbrev']} ${state_totals[sorted_by_total[-1]]:,}K",
diff --git a/plots/map-marker-clustered/implementations/python/plotly.py b/plots/map-marker-clustered/implementations/python/plotly.py
index 7d4a7e0336..b311876dec 100644
--- a/plots/map-marker-clustered/implementations/python/plotly.py
+++ b/plots/map-marker-clustered/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # anyplot palette — canonical order for categorical series
-ANYPLOT_PALETTE = ["#009E73", "#9418DB", "#B71D27", "#16B8F3"]
+IMPRINT = ["#009E73", "#C475FD", "#AE3030", "#4467A3"]
 
 # Data - Retail store locations across North America
 np.random.seed(42)
@@ -38,7 +38,7 @@
 }
 
 categories = ["Electronics", "Grocery", "Clothing", "Hardware"]
-category_colors = {cat: ANYPLOT_PALETTE[i] for i, cat in enumerate(categories)}
+category_colors = {cat: IMPRINT[i] for i, cat in enumerate(categories)}
 
 lats, lons, labels, cats = [], [], [], []
 store_id = 1
diff --git a/plots/map-route-path/implementations/python/plotly.py b/plots/map-route-path/implementations/python/plotly.py
index 696a9fdb6c..6b8fbdd4db 100644
--- a/plots/map-route-path/implementations/python/plotly.py
+++ b/plots/map-route-path/implementations/python/plotly.py
@@ -27,8 +27,8 @@
 
 # Okabe-Ito: position 1 = start, position 2 = end, position 3 = waypoints
 START_COLOR = "#009E73"
-END_COLOR = "#D55E00"
-WAYPOINT_COLOR = "#0072B2"
+END_COLOR = "#C475FD"
+WAYPOINT_COLOR = "#4467A3"
 
 # Data: Appalachian Trail section through Great Smoky Mountains, Tennessee
 np.random.seed(42)
diff --git a/plots/marimekko-basic/implementations/python/plotly.py b/plots/marimekko-basic/implementations/python/plotly.py
index e658520d52..cfc24dc559 100644
--- a/plots/marimekko-basic/implementations/python/plotly.py
+++ b/plots/marimekko-basic/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
 
 # Okabe-Ito palette — first series always #009E73
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Market share by region and product line (in millions USD)
 regions = ["North America", "Europe", "Asia Pacific", "Latin America"]
diff --git a/plots/maze-circular/implementations/python/plotly.py b/plots/maze-circular/implementations/python/plotly.py
index 9ba0a0153a..cb7f27e0e0 100644
--- a/plots/maze-circular/implementations/python/plotly.py
+++ b/plots/maze-circular/implementations/python/plotly.py
@@ -173,7 +173,7 @@
         y=sol_y,
         mode="lines",
         name="Show Solution",
-        line={"color": "#D55E00", "width": 4, "dash": "dot"},
+        line={"color": "#C475FD", "width": 4, "dash": "dot"},
         opacity=0.85,
         visible="legendonly",
     )
@@ -198,7 +198,7 @@
         x=[0.0],
         y=[0.0],
         mode="markers",
-        marker={"symbol": "star", "size": 20, "color": "#F0E442", "line": {"color": ACCENT, "width": 2}},
+        marker={"symbol": "star", "size": 20, "color": "#954477", "line": {"color": ACCENT, "width": 2}},
         showlegend=False,
         hoverinfo="skip",
     )
diff --git a/plots/mosaic-categorical/implementations/python/plotly.py b/plots/mosaic-categorical/implementations/python/plotly.py
index a8a50587a4..6daf90fe8c 100644
--- a/plots/mosaic-categorical/implementations/python/plotly.py
+++ b/plots/mosaic-categorical/implementations/python/plotly.py
@@ -42,7 +42,7 @@
 class_categories = contingency.columns.tolist()
 
 # Okabe-Ito palette: Survived = brand green (first series), Did Not Survive = vermillion
-colors = {"Survived": "#009E73", "Did Not Survive": "#D55E00"}
+colors = {"Survived": "#009E73", "Did Not Survive": "#C475FD"}
 
 # Gap between rectangles
 gap = 0.015
diff --git a/plots/network-basic/implementations/python/plotly.py b/plots/network-basic/implementations/python/plotly.py
index caee1e8d5b..c7f163c83f 100644
--- a/plots/network-basic/implementations/python/plotly.py
+++ b/plots/network-basic/implementations/python/plotly.py
@@ -25,7 +25,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette — first series always #009E73
-GROUP_COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+GROUP_COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 GROUP_NAMES = ["Research", "Engineering", "Management", "Marketing"]
 
 # Data: social network with 20 people across 4 teams of varying size
diff --git a/plots/network-bipartite/implementations/python/plotly.py b/plots/network-bipartite/implementations/python/plotly.py
index 72d9e8058b..c8d0b5064e 100644
--- a/plots/network-bipartite/implementations/python/plotly.py
+++ b/plots/network-bipartite/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 STUDENT_COLOR = "#009E73"  # Okabe-Ito position 1
-COURSE_COLOR = "#D55E00"  # Okabe-Ito position 2
+COURSE_COLOR = "#C475FD"  # Okabe-Ito position 2
 EDGE_COLOR = "rgba(107,106,99,0.30)" if THEME == "light" else "rgba(168,167,159,0.22)"
 
 # Data - student-course enrollment network
diff --git a/plots/network-directed/implementations/python/plotly.py b/plots/network-directed/implementations/python/plotly.py
index 22a358ccb9..b4cbeea14d 100644
--- a/plots/network-directed/implementations/python/plotly.py
+++ b/plots/network-directed/implementations/python/plotly.py
@@ -19,11 +19,11 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is #009E73)
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # Brand green (entry point)
-    "#D55E00",  # Vermillion (core)
-    "#0072B2",  # Blue (data)
-    "#CC79A7",  # Reddish purple (helpers)
+    "#C475FD",  # Vermillion (core)
+    "#4467A3",  # Blue (data)
+    "#BD8233",  # Reddish purple (helpers)
 ]
 
 # Data: Software module dependencies (arrows show import direction)
@@ -212,7 +212,7 @@
 
 # Add nodes by group
 group_names = ["Entry", "Core", "Data", "Helpers"]
-for group_idx in range(len(OKABE_ITO)):
+for group_idx in range(len(IMPRINT)):
     group_nodes = [n for n in nodes if n["group"] == group_idx]
     group_x = [node_x[n["id"]] for n in group_nodes]
     group_y = [node_y[n["id"]] for n in group_nodes]
@@ -223,7 +223,7 @@
             x=group_x,
             y=group_y,
             mode="markers+text",
-            marker=dict(size=18, color=OKABE_ITO[group_idx], line=dict(width=1.5, color=INK_SOFT)),
+            marker=dict(size=18, color=IMPRINT[group_idx], line=dict(width=1.5, color=INK_SOFT)),
             text=group_labels,
             textposition="middle center",
             textfont=dict(size=14, color=INK, family="monospace"),
diff --git a/plots/network-force-directed/implementations/python/plotly.py b/plots/network-force-directed/implementations/python/plotly.py
index 6c8dbcdd5c..1dfa95ec38 100644
--- a/plots/network-force-directed/implementations/python/plotly.py
+++ b/plots/network-force-directed/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito categorical palette (positions 1-3)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 np.random.seed(42)
 
@@ -135,7 +135,7 @@
             mode="markers",
             marker={
                 "size": sizes,
-                "color": OKABE_ITO[comm_idx],
+                "color": IMPRINT[comm_idx],
                 "line": {"width": 2, "color": PAGE_BG},
                 "opacity": 0.9,
             },
diff --git a/plots/network-hierarchical/implementations/python/plotly.py b/plots/network-hierarchical/implementations/python/plotly.py
index 1f19befc2d..500b0544cc 100644
--- a/plots/network-hierarchical/implementations/python/plotly.py
+++ b/plots/network-hierarchical/implementations/python/plotly.py
@@ -19,9 +19,9 @@
 # Okabe-Ito palette for hierarchy levels (position 1 is brand, then positions 2-4)
 LEVEL_COLORS = {
     0: "#009E73",  # Position 1 - brand green (CEO)
-    1: "#D55E00",  # Position 2 - vermillion (VPs)
-    2: "#0072B2",  # Position 3 - blue (Directors)
-    3: "#CC79A7",  # Position 4 - reddish purple (Leads)
+    1: "#C475FD",  # Position 2 - vermillion (VPs)
+    2: "#4467A3",  # Position 3 - blue (Directors)
+    3: "#BD8233",  # Position 4 - reddish purple (Leads)
 }
 
 # Data: Organizational chart with 22 employees across 4 levels - asymmetric structure
diff --git a/plots/network-transport-static/implementations/python/plotly.py b/plots/network-transport-static/implementations/python/plotly.py
index 2ab001ffdd..6d70c09a6e 100644
--- a/plots/network-transport-static/implementations/python/plotly.py
+++ b/plots/network-transport-static/implementations/python/plotly.py
@@ -19,10 +19,10 @@
 RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette for route types
-OKABE_ITO = {
+IMPRINT = {
     "express": "#009E73",  # Position 1 - brand green
-    "regional": "#D55E00",  # Position 2 - vermillion
-    "local": "#0072B2",  # Position 3 - blue
+    "regional": "#C475FD",  # Position 2 - vermillion
+    "local": "#4467A3",  # Position 3 - blue
 }
 
 np.random.seed(42)
@@ -117,7 +117,7 @@ def get_edge_offset(source, target, route_idx):
     x1_draw = x1_off - shrink * dx / length
     y1_draw = y1_off - shrink * dy / length
 
-    color = OKABE_ITO[route["type"]]
+    color = IMPRINT[route["type"]]
     show_in_legend = route["type"] not in legend_items
     legend_items.add(route["type"])
 
diff --git a/plots/ohlc-bar/implementations/python/plotly.py b/plots/ohlc-bar/implementations/python/plotly.py
index 5e68417e20..105531988c 100644
--- a/plots/ohlc-bar/implementations/python/plotly.py
+++ b/plots/ohlc-bar/implementations/python/plotly.py
@@ -27,7 +27,7 @@
 # Okabe-Ito palette for up/down coloring
 BRAND = "#009E73"  # Position 1 - bluish green, first series
 UP_COLOR = BRAND  # Use brand green for up days
-DOWN_COLOR = "#D55E00"  # Position 2 - vermillion for down days
+DOWN_COLOR = "#AE3030"  # imprint red — down days
 
 # Data - Generate realistic stock price data for 45 trading days
 np.random.seed(42)
diff --git a/plots/parallel-basic/implementations/python/plotly.py b/plots/parallel-basic/implementations/python/plotly.py
index 080991172e..60a5d802c6 100644
--- a/plots/parallel-basic/implementations/python/plotly.py
+++ b/plots/parallel-basic/implementations/python/plotly.py
@@ -18,14 +18,14 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
-# Okabe-Ito discrete colorscale: Setosa=#009E73, Versicolor=#D55E00, Virginica=#0072B2
+# Okabe-Ito discrete colorscale: Setosa=#009E73, Versicolor=#C475FD, Virginica=#4467A3
 OI_COLORSCALE = [
     [0.0, "#009E73"],
     [0.33, "#009E73"],
-    [0.33, "#D55E00"],
-    [0.67, "#D55E00"],
-    [0.67, "#0072B2"],
-    [1.0, "#0072B2"],
+    [0.33, "#C475FD"],
+    [0.67, "#C475FD"],
+    [0.67, "#4467A3"],
+    [1.0, "#4467A3"],
 ]
 
 # Data - Iris-like dataset for multivariate demonstration
diff --git a/plots/parallel-categories-basic/implementations/python/plotly.py b/plots/parallel-categories-basic/implementations/python/plotly.py
index 90d911e0a8..421b8dd992 100644
--- a/plots/parallel-categories-basic/implementations/python/plotly.py
+++ b/plots/parallel-categories-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito colors for categorical data
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Titanic survival data with multiple categorical dimensions
 df = sns.load_dataset("titanic")
@@ -63,7 +63,7 @@
 fig = go.Figure(
     go.Parcats(
         dimensions=dimensions,
-        line={"color": color_values, "colorscale": [[0, OKABE_ITO[1]], [1, OKABE_ITO[0]]], "shape": "hspline"},
+        line={"color": color_values, "colorscale": [[0, IMPRINT[1]], [1, IMPRINT[0]]], "shape": "hspline"},
         hoveron="color",
         hoverinfo="count+probability",
         arrangement="freeform",
diff --git a/plots/parliament-basic/implementations/python/plotly.py b/plots/parliament-basic/implementations/python/plotly.py
index 9f1db4f70f..5da1b3d3c8 100644
--- a/plots/parliament-basic/implementations/python/plotly.py
+++ b/plots/parliament-basic/implementations/python/plotly.py
@@ -29,7 +29,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (positions 1→6, first is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data - Fictional parliament with neutral party names
 parties = [
@@ -43,7 +43,7 @@
 
 # Assign Okabe-Ito colors to parties
 for i, party in enumerate(parties):
-    party["color"] = OKABE_ITO[i % len(OKABE_ITO)]
+    party["color"] = IMPRINT[i % len(IMPRINT)]
 
 total_seats = sum(p["seats"] for p in parties)
 majority_threshold = total_seats // 2 + 1
diff --git a/plots/phase-diagram/implementations/python/plotly.py b/plots/phase-diagram/implementations/python/plotly.py
index 3767c3c3c3..c2761ed615 100644
--- a/plots/phase-diagram/implementations/python/plotly.py
+++ b/plots/phase-diagram/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.20)" if THEME == "light" else "rgba(240,239,232,0.20)"
 BRAND = "#009E73"  # Okabe-Ito position 1
-ACCENT_1 = "#D55E00"  # Okabe-Ito position 2
+ACCENT_1 = "#C475FD"  # Okabe-Ito position 2
 
 # Data - Damped pendulum showing spiral convergence to equilibrium
 np.random.seed(42)
diff --git a/plots/pie-drilldown/implementations/python/plotly.py b/plots/pie-drilldown/implementations/python/plotly.py
index f4ce1ad69a..09f979af8d 100644
--- a/plots/pie-drilldown/implementations/python/plotly.py
+++ b/plots/pie-drilldown/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 ACCENT = "#009E73"
 
 # Okabe-Ito palette (first series must be #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Hierarchical data for sales regions
 hierarchy = {
@@ -58,30 +58,30 @@
 
 # Color mapping using Okabe-Ito (first series is always #009E73 for brand)
 colors = {
-    "North America": OKABE_ITO[0],  # #009E73 - brand green
-    "Europe": OKABE_ITO[1],  # #D55E00 - vermillion
-    "Asia Pacific": OKABE_ITO[2],  # #0072B2 - blue
-    "Latin America": OKABE_ITO[3],  # #CC79A7 - reddish purple
+    "North America": IMPRINT[0],  # #009E73 - brand green
+    "Europe": IMPRINT[1],  # #C475FD - vermillion
+    "Asia Pacific": IMPRINT[2],  # #4467A3 - blue
+    "Latin America": IMPRINT[3],  # #BD8233 - reddish purple
 }
 
 # Lighter shades for subcategories (derived from main colors with opacity in hover)
 sub_colors = {
     # North America shades
-    "USA": OKABE_ITO[0],
-    "Canada": OKABE_ITO[0],
-    "Mexico": OKABE_ITO[0],
+    "USA": IMPRINT[0],
+    "Canada": IMPRINT[0],
+    "Mexico": IMPRINT[0],
     # Europe shades
-    "UK": OKABE_ITO[1],
-    "Germany": OKABE_ITO[1],
-    "France": OKABE_ITO[1],
+    "UK": IMPRINT[1],
+    "Germany": IMPRINT[1],
+    "France": IMPRINT[1],
     # Asia Pacific shades
-    "Japan": OKABE_ITO[2],
-    "Australia": OKABE_ITO[2],
-    "India": OKABE_ITO[2],
+    "Japan": IMPRINT[2],
+    "Australia": IMPRINT[2],
+    "India": IMPRINT[2],
     # Latin America shades
-    "Brazil": OKABE_ITO[3],
-    "Argentina": OKABE_ITO[3],
-    "Chile": OKABE_ITO[3],
+    "Brazil": IMPRINT[3],
+    "Argentina": IMPRINT[3],
+    "Chile": IMPRINT[3],
 }
 
 # Get data for top level
diff --git a/plots/point-and-figure-basic/implementations/python/plotly.py b/plots/point-and-figure-basic/implementations/python/plotly.py
index 1ea8596387..cb8cd0f393 100644
--- a/plots/point-and-figure-basic/implementations/python/plotly.py
+++ b/plots/point-and-figure-basic/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 COLOR_X = "#009E73"  # Okabe-Ito pos 1 — X (Rising), colorblind-safe
-COLOR_O = "#D55E00"  # Okabe-Ito pos 2 — O (Falling), colorblind-safe
+COLOR_O = "#AE3030"  # imprint red — O (Falling)
 
 # Data — synthetic stock price with distinct trend phases
 np.random.seed(42)
diff --git a/plots/polar-bar/implementations/python/plotly.py b/plots/polar-bar/implementations/python/plotly.py
index 4ebf57c6a9..fc52bb5970 100644
--- a/plots/polar-bar/implementations/python/plotly.py
+++ b/plots/polar-bar/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 wind_speed_labels = ["Light (0-10 km/h)", "Moderate (10-20 km/h)", "Strong (20+ km/h)"]
 
 # Data: Wind speed distribution by direction (8 compass points)
@@ -35,7 +35,7 @@
 fig = go.Figure()
 
 # Add stacked bars for each wind speed category
-for data, label, color in zip([light, moderate, strong], wind_speed_labels, OKABE_ITO, strict=True):
+for data, label, color in zip([light, moderate, strong], wind_speed_labels, IMPRINT, strict=True):
     fig.add_trace(
         go.Barpolar(
             r=data,
diff --git a/plots/polar-line/implementations/python/plotly.py b/plots/polar-line/implementations/python/plotly.py
index e42877febb..2c62f1a30a 100644
--- a/plots/polar-line/implementations/python/plotly.py
+++ b/plots/polar-line/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data - Average hourly temperature pattern (two seasons)
 np.random.seed(42)
@@ -49,8 +49,8 @@
         theta=hours_closed,
         mode="lines+markers",
         name="Summer",
-        line=dict(color=OKABE_ITO[0], width=4),
-        marker=dict(size=10, color=OKABE_ITO[0]),
+        line=dict(color=IMPRINT[0], width=4),
+        marker=dict(size=10, color=IMPRINT[0]),
         hovertemplate="Hour: %{theta}°<br>Temp: %{r:.1f}°C<extra>Summer</extra>",
     )
 )
@@ -62,8 +62,8 @@
         theta=hours_closed,
         mode="lines+markers",
         name="Winter",
-        line=dict(color=OKABE_ITO[1], width=4),
-        marker=dict(size=10, color=OKABE_ITO[1]),
+        line=dict(color=IMPRINT[1], width=4),
+        marker=dict(size=10, color=IMPRINT[1]),
         hovertemplate="Hour: %{theta}°<br>Temp: %{r:.1f}°C<extra>Winter</extra>",
     )
 )
diff --git a/plots/polar-scatter/implementations/python/plotly.py b/plots/polar-scatter/implementations/python/plotly.py
index 78688837c2..509e386480 100644
--- a/plots/polar-scatter/implementations/python/plotly.py
+++ b/plots/polar-scatter/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - synthetic wind measurements with prevailing directions
 np.random.seed(42)
@@ -54,7 +54,7 @@
 fig = go.Figure()
 
 # Color mapping for time of day using Okabe-Ito
-colors = {"Morning": OKABE_ITO[0], "Afternoon": OKABE_ITO[1], "Evening": OKABE_ITO[2]}
+colors = {"Morning": IMPRINT[0], "Afternoon": IMPRINT[1], "Evening": IMPRINT[2]}
 
 # Add traces for each time period
 for period in ["Morning", "Afternoon", "Evening"]:
diff --git a/plots/precision-recall/implementations/python/plotly.py b/plots/precision-recall/implementations/python/plotly.py
index a23c113e82..1c2fd609ba 100644
--- a/plots/precision-recall/implementations/python/plotly.py
+++ b/plots/precision-recall/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Position 1, first series
-SECONDARY = "#E69F00"  # Position 5, orange
+SECONDARY = "#AE3030"  # Position 5, orange
 
 # Data - Simulate a binary classification scenario (fraud detection)
 np.random.seed(42)
diff --git a/plots/pyramid-basic/implementations/python/plotly.py b/plots/pyramid-basic/implementations/python/plotly.py
index 96695418b2..576bd616c0 100644
--- a/plots/pyramid-basic/implementations/python/plotly.py
+++ b/plots/pyramid-basic/implementations/python/plotly.py
@@ -43,7 +43,7 @@
         x=female_population,
         orientation="h",
         name="Female",
-        marker_color="#D55E00",  # Okabe-Ito position 2
+        marker_color="#C475FD",  # Okabe-Ito position 2
         hovertemplate="Female<br>Age: %{y}<br>Population: %{customdata:,}k<extra></extra>",
         customdata=female_population,
     )
diff --git a/plots/qq-basic/implementations/python/plotly.py b/plots/qq-basic/implementations/python/plotly.py
index d7f4450eb9..c25f18cfe3 100644
--- a/plots/qq-basic/implementations/python/plotly.py
+++ b/plots/qq-basic/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"  # Okabe-Ito position 1 — Q-Q scatter points
-REF_COLOR = "#D55E00"  # Okabe-Ito position 2 — reference line
+REF_COLOR = "#C475FD"  # Okabe-Ito position 2 — reference line
 
 # Data - sample with slight positive skew to demonstrate Q-Q plot interpretation
 np.random.seed(42)
diff --git a/plots/radar-basic/implementations/python/plotly.py b/plots/radar-basic/implementations/python/plotly.py
index 07cb05781e..cf825c010d 100644
--- a/plots/radar-basic/implementations/python/plotly.py
+++ b/plots/radar-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - Employee performance comparison across competencies
 categories = ["Communication", "Technical Skills", "Teamwork", "Problem Solving", "Leadership", "Creativity"]
@@ -38,8 +38,8 @@
         theta=categories_closed,
         fill="toself",
         fillcolor="rgba(0, 158, 115, 0.25)",
-        line={"color": OKABE_ITO[0], "width": 3},
-        marker={"size": 12, "color": OKABE_ITO[0]},
+        line={"color": IMPRINT[0], "width": 3},
+        marker={"size": 12, "color": IMPRINT[0]},
         name="Senior Developer",
         hovertemplate="<b>Senior Developer</b><br>%{theta}: %{r}<extra></extra>",
     )
@@ -51,8 +51,8 @@
         theta=categories_closed,
         fill="toself",
         fillcolor="rgba(213, 94, 0, 0.25)",
-        line={"color": OKABE_ITO[1], "width": 3},
-        marker={"size": 12, "color": OKABE_ITO[1]},
+        line={"color": IMPRINT[1], "width": 3},
+        marker={"size": 12, "color": IMPRINT[1]},
         name="Junior Developer",
         hovertemplate="<b>Junior Developer</b><br>%{theta}: %{r}<extra></extra>",
     )
diff --git a/plots/radar-multi/implementations/python/plotly.py b/plots/radar-multi/implementations/python/plotly.py
index 3bc8037aa5..df17c760ae 100644
--- a/plots/radar-multi/implementations/python/plotly.py
+++ b/plots/radar-multi/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Product comparison across multiple attributes
 categories = ["Performance", "Reliability", "Price Value", "Support", "Ease of Use", "Features"]
@@ -44,7 +44,7 @@
         theta=categories_closed,
         fill="toself",
         fillcolor="rgba(0, 158, 115, 0.25)",
-        line={"color": OKABE_ITO[0], "width": 3},
+        line={"color": IMPRINT[0], "width": 3},
         name="Product A (Premium)",
         marker={"size": 10},
     )
@@ -57,7 +57,7 @@
         theta=categories_closed,
         fill="toself",
         fillcolor="rgba(213, 94, 0, 0.25)",
-        line={"color": OKABE_ITO[1], "width": 3},
+        line={"color": IMPRINT[1], "width": 3},
         name="Product B (Budget)",
         marker={"size": 10},
     )
@@ -70,7 +70,7 @@
         theta=categories_closed,
         fill="toself",
         fillcolor="rgba(0, 114, 178, 0.25)",
-        line={"color": OKABE_ITO[2], "width": 3},
+        line={"color": IMPRINT[2], "width": 3},
         name="Product C (Pro)",
         marker={"size": 10},
     )
diff --git a/plots/range-interval/implementations/python/plotly.py b/plots/range-interval/implementations/python/plotly.py
index 2d71b3a3bd..c4754d66dd 100644
--- a/plots/range-interval/implementations/python/plotly.py
+++ b/plots/range-interval/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Primary series
-SECONDARY = "#D55E00"  # Secondary series
+SECONDARY = "#C475FD"  # Secondary series
 
 # Data: Monthly temperature ranges (°C) for a temperate climate city
 np.random.seed(42)
diff --git a/plots/renko-basic/implementations/python/plotly.py b/plots/renko-basic/implementations/python/plotly.py
index 2d23ea4d69..7ac7176122 100644
--- a/plots/renko-basic/implementations/python/plotly.py
+++ b/plots/renko-basic/implementations/python/plotly.py
@@ -29,7 +29,7 @@
 
 # Okabe-Ito palette
 BULLISH = "#009E73"  # Bluish green - position 1 (brand)
-BEARISH = "#D55E00"  # Vermillion - position 2
+BEARISH = "#AE3030"  # imprint red — down bricks
 
 # Data - Generate synthetic stock price data
 np.random.seed(42)
diff --git a/plots/residual-plot/implementations/python/plotly.py b/plots/residual-plot/implementations/python/plotly.py
index da9c8f2e44..066e1a7c33 100644
--- a/plots/residual-plot/implementations/python/plotly.py
+++ b/plots/residual-plot/implementations/python/plotly.py
@@ -27,7 +27,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 NEUTRAL = "#1A1A1A" if THEME == "light" else "#E8E8E0"
 
 # Data - Generate realistic regression scenario with varying residual patterns
diff --git a/plots/roc-curve/implementations/python/plotly.py b/plots/roc-curve/implementations/python/plotly.py
index 67ad32b10c..620a8ec861 100644
--- a/plots/roc-curve/implementations/python/plotly.py
+++ b/plots/roc-curve/implementations/python/plotly.py
@@ -24,7 +24,7 @@
 
 # Okabe-Ito palette
 BRAND_1 = "#009E73"
-BRAND_2 = "#D55E00"
+BRAND_2 = "#C475FD"
 NEUTRAL = "#888888"
 
 # Generate classification data
diff --git a/plots/sankey-basic/implementations/python/plotly.py b/plots/sankey-basic/implementations/python/plotly.py
index 5cc3ac3fa6..15cdcb59b5 100644
--- a/plots/sankey-basic/implementations/python/plotly.py
+++ b/plots/sankey-basic/implementations/python/plotly.py
@@ -16,7 +16,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for source nodes (positions 1-4)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 SOURCE_RGBA = ["rgba(0,158,115,0.4)", "rgba(213,94,0,0.4)", "rgba(0,114,178,0.4)", "rgba(204,121,167,0.4)"]
 
 # Data - Energy flow from sources to sectors (TWh)
@@ -46,7 +46,7 @@
 values = [f[2] for f in flows]
 
 # Source nodes use Okabe-Ito colors; target nodes use INK_SOFT
-node_colors = OKABE_ITO + [INK_SOFT] * 4
+node_colors = IMPRINT + [INK_SOFT] * 4
 link_colors = [SOURCE_RGBA[s] for s in source_indices]
 
 # Plot
diff --git a/plots/scatter-animated-controls/implementations/python/plotly.py b/plots/scatter-animated-controls/implementations/python/plotly.py
index 65b1a6a6d6..cf585109fb 100644
--- a/plots/scatter-animated-controls/implementations/python/plotly.py
+++ b/plots/scatter-animated-controls/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 np.random.seed(42)
 
@@ -69,7 +69,7 @@
     size_max=80,
     range_x=[0, df["GDP per Capita ($)"].max() * 1.1],
     range_y=[50, 88],
-    color_discrete_sequence=OKABE_ITO,
+    color_discrete_sequence=IMPRINT,
 )
 
 fig.update_layout(
diff --git a/plots/scatter-annotated/implementations/python/plotly.py b/plots/scatter-annotated/implementations/python/plotly.py
index 6c519191fc..d756f82847 100644
--- a/plots/scatter-annotated/implementations/python/plotly.py
+++ b/plots/scatter-annotated/implementations/python/plotly.py
@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND = "#009E73"
-BRAND_ACCENT = "#D55E00"
+BRAND_ACCENT = "#C475FD"
 
 # Data - Top tech companies by market cap and revenue
 np.random.seed(42)
diff --git a/plots/scatter-categorical/implementations/python/plotly.py b/plots/scatter-categorical/implementations/python/plotly.py
index aa83d1fb61..46d6944044 100644
--- a/plots/scatter-categorical/implementations/python/plotly.py
+++ b/plots/scatter-categorical/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is ALWAYS #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Product performance across regions
 np.random.seed(42)
@@ -44,7 +44,7 @@
             y=data[region]["y"],
             mode="markers",
             name=region,
-            marker={"size": 14, "color": OKABE_ITO[i], "opacity": 0.7, "line": {"width": 1, "color": PAGE_BG}},
+            marker={"size": 14, "color": IMPRINT[i], "opacity": 0.7, "line": {"width": 1, "color": PAGE_BG}},
             hovertemplate=f"{region}<br>Marketing: %{{x:.1f}}%<br>Sales: %{{y:.1f}}%<extra></extra>",
         )
     )
diff --git a/plots/scatter-embedding/implementations/python/plotly.py b/plots/scatter-embedding/implementations/python/plotly.py
index be239cb8b3..87ec7a70c8 100644
--- a/plots/scatter-embedding/implementations/python/plotly.py
+++ b/plots/scatter-embedding/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data — simulate document embeddings in 50-dimensional topic space
 np.random.seed(42)
@@ -45,7 +45,7 @@
             y=X_2d[mask, 1],
             mode="markers",
             name=topic,
-            marker={"color": OKABE_ITO[i], "size": 8, "opacity": 0.65, "line": {"color": PAGE_BG, "width": 0.5}},
+            marker={"color": IMPRINT[i], "size": 8, "opacity": 0.65, "line": {"color": PAGE_BG, "width": 0.5}},
         )
     )
 
diff --git a/plots/scatter-matrix-interactive/implementations/python/plotly.py b/plots/scatter-matrix-interactive/implementations/python/plotly.py
index e55748408a..5841fc0da5 100644
--- a/plots/scatter-matrix-interactive/implementations/python/plotly.py
+++ b/plots/scatter-matrix-interactive/implementations/python/plotly.py
@@ -22,7 +22,7 @@
 GRID_LIGHT = "rgba(26,26,23,0.05)" if THEME == "light" else "rgba(240,239,232,0.05)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Using Iris dataset for multivariate exploration
 iris = sns.load_dataset("iris")
@@ -36,7 +36,7 @@
 
 # Species info
 species_list = ["Setosa", "Versicolor", "Virginica"]
-color_map = {s: OKABE_ITO[i] for i, s in enumerate(species_list)}
+color_map = {s: IMPRINT[i] for i, s in enumerate(species_list)}
 
 # Create 4x4 subplot grid
 n = len(cols)
diff --git a/plots/scatter-matrix/implementations/python/plotly.py b/plots/scatter-matrix/implementations/python/plotly.py
index 045563a052..7351a5f39a 100644
--- a/plots/scatter-matrix/implementations/python/plotly.py
+++ b/plots/scatter-matrix/implementations/python/plotly.py
@@ -21,10 +21,10 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand, first series)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
 ]
 
 # Data - Weather station measurements across 4 variables
@@ -83,7 +83,7 @@
 # Variables for matrix
 dimensions = ["Temperature (°C)", "Humidity (%)", "Pressure (hPa)", "Wind Speed (m/s)"]
 region_list = ["Coastal", "Mountain", "Desert"]
-region_colors = {"Coastal": OKABE_ITO[0], "Mountain": OKABE_ITO[1], "Desert": OKABE_ITO[2]}
+region_colors = {"Coastal": IMPRINT[0], "Mountain": IMPRINT[1], "Desert": IMPRINT[2]}
 n_dims = len(dimensions)
 
 # Create subplots grid
diff --git a/plots/scatter-regression-linear/implementations/python/plotly.py b/plots/scatter-regression-linear/implementations/python/plotly.py
index a80a697735..5736d84f10 100644
--- a/plots/scatter-regression-linear/implementations/python/plotly.py
+++ b/plots/scatter-regression-linear/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - study hours vs exam scores
 np.random.seed(42)
diff --git a/plots/scatter-regression-lowess/implementations/python/plotly.py b/plots/scatter-regression-lowess/implementations/python/plotly.py
index 5f5921c2e2..481bf8bc4f 100644
--- a/plots/scatter-regression-lowess/implementations/python/plotly.py
+++ b/plots/scatter-regression-lowess/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT    = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID        = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 BRAND       = "#009E73"  # Okabe-Ito position 1
-ACCENT      = "#D55E00"  # Okabe-Ito position 2
+ACCENT      = "#C475FD"  # Okabe-Ito position 2
 
 # Data - Enzyme kinetics: realistic biological dose-response relationship
 # Different structure than seaborn: exponential saturation curve with noise
diff --git a/plots/scatter-regression-polynomial/implementations/python/plotly.py b/plots/scatter-regression-polynomial/implementations/python/plotly.py
index b8bb347588..cf81248313 100644
--- a/plots/scatter-regression-polynomial/implementations/python/plotly.py
+++ b/plots/scatter-regression-polynomial/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito palette - first series is always #009E73
 BRAND = "#009E73"
-ACCENT = "#D55E00"
+ACCENT = "#C475FD"
 
 # Data - Temperature vs Energy Consumption (environmental/building efficiency)
 np.random.seed(42)
diff --git a/plots/scatter-text/implementations/python/plotly.py b/plots/scatter-text/implementations/python/plotly.py
index 548d038bd9..1f7b5de1d9 100644
--- a/plots/scatter-text/implementations/python/plotly.py
+++ b/plots/scatter-text/implementations/python/plotly.py
@@ -18,14 +18,14 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette for categories
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
-    "#E69F00",  # orange
-    "#56B4E9",  # sky blue
-    "#F0E442",  # yellow
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
+    "#AE3030",  # orange
+    "#2ABCCD",  # sky blue
+    "#954477",  # yellow
     "#1A1A1A" if THEME == "light" else "#E8E8E0",  # adaptive neutral
 ]
 
@@ -179,7 +179,7 @@
         y_cat = [y[i] for i in range(len(y)) if mask[i]]
         labels_cat = [labels[i] for i in range(len(labels)) if mask[i]]
 
-        color = OKABE_ITO[cat_idx]
+        color = IMPRINT[cat_idx]
         category_colors[cat_name] = color
 
         # Create hover text with detailed information
diff --git a/plots/shap-waterfall/implementations/python/plotly.py b/plots/shap-waterfall/implementations/python/plotly.py
index 1944bb287a..ad3c9d1ea1 100644
--- a/plots/shap-waterfall/implementations/python/plotly.py
+++ b/plots/shap-waterfall/implementations/python/plotly.py
@@ -19,8 +19,8 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # SHAP semantic colors (Okabe-Ito positions used for semantic encoding)
-POS_COLOR = "#D55E00"  # vermillion — positive SHAP (pushes prediction up)
-NEG_COLOR = "#0072B2"  # blue — negative SHAP (pushes prediction down)
+POS_COLOR = "#AE3030"  # imprint red — positive SHAP (pushes prediction up)
+NEG_COLOR = "#4467A3"  # blue — negative SHAP (pushes prediction down)
 REF_COLOR = "#009E73"  # brand green — reference bars (base & final)
 
 # Data — credit risk model SHAP waterfall for a single high-risk loan applicant
diff --git a/plots/skewt-logp-atmospheric/implementations/python/plotly.py b/plots/skewt-logp-atmospheric/implementations/python/plotly.py
index 763c905722..f8b5757c09 100644
--- a/plots/skewt-logp-atmospheric/implementations/python/plotly.py
+++ b/plots/skewt-logp-atmospheric/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 BRAND = "#009E73"  # Okabe-Ito pos 1 — temperature profile
-C_DEWPT = "#D55E00"  # Okabe-Ito pos 2 — dewpoint profile
+C_DEWPT = "#C475FD"  # Okabe-Ito pos 2 — dewpoint profile
 
 # Reference line colors: subtle, theme-adaptive
 C_ISO = "rgba(80,80,80,0.22)" if THEME == "light" else "rgba(180,180,180,0.28)"
diff --git a/plots/slope-basic/implementations/python/plotly.py b/plots/slope-basic/implementations/python/plotly.py
index e21065090f..706b62e74e 100644
--- a/plots/slope-basic/implementations/python/plotly.py
+++ b/plots/slope-basic/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito: increase = brand green, decrease = vermillion, flat = adaptive neutral
 COLOR_UP = "#009E73"
-COLOR_DOWN = "#D55E00"
+COLOR_DOWN = "#AE3030"  # imprint red — decrease
 COLOR_FLAT = INK_MUTED
 
 # Data - Product sales Q1 vs Q4 comparison (10 products showing various patterns)
diff --git a/plots/smith-chart-basic/implementations/python/plotly.py b/plots/smith-chart-basic/implementations/python/plotly.py
index cca8859897..642318f82e 100644
--- a/plots/smith-chart-basic/implementations/python/plotly.py
+++ b/plots/smith-chart-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 BRAND = "#009E73"  # Okabe-Ito position 1 — impedance locus
-ACCENT = "#D55E00"  # Okabe-Ito position 2 — matched condition marker
+ACCENT = "#C475FD"  # Okabe-Ito position 2 — matched condition marker
 
 # Smith chart grid color (slightly stronger than RULE for chart structure)
 SMITH_GRID = "rgba(74,74,68,0.30)" if THEME == "light" else "rgba(184,183,176,0.30)"
diff --git a/plots/sn-curve-basic/implementations/python/plotly.py b/plots/sn-curve-basic/implementations/python/plotly.py
index 281ef75f4c..e88d44647a 100644
--- a/plots/sn-curve-basic/implementations/python/plotly.py
+++ b/plots/sn-curve-basic/implementations/python/plotly.py
@@ -20,10 +20,10 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # position 1 — Basquin fit line
-C2 = "#D55E00"  # position 2 — test data markers
-C3 = "#0072B2"  # position 3 — ultimate strength
-C4 = "#CC79A7"  # position 4 — yield strength
-C5 = "#E69F00"  # position 5 — endurance limit
+C2 = "#C475FD"  # position 2 — test data markers
+C3 = "#4467A3"  # position 3 — ultimate strength
+C4 = "#BD8233"  # position 4 — yield strength
+C5 = "#AE3030"  # position 5 — endurance limit
 
 # Data: Steel fatigue test data (Basquin model)
 np.random.seed(42)
diff --git a/plots/streamgraph-basic/implementations/python/plotly.py b/plots/streamgraph-basic/implementations/python/plotly.py
index 41d0f521dc..ce1ce69ce8 100644
--- a/plots/streamgraph-basic/implementations/python/plotly.py
+++ b/plots/streamgraph-basic/implementations/python/plotly.py
@@ -25,7 +25,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette — first series always #009E73
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
 
 # Data - Monthly streaming hours by music genre over 2 years
 np.random.seed(42)
@@ -71,9 +71,9 @@
             x=x_fill,
             y=y_fill,
             fill="toself",
-            fillcolor=OKABE_ITO[i],
+            fillcolor=IMPRINT[i],
             opacity=opacity,
-            line={"color": OKABE_ITO[i], "width": 0.5, "shape": "spline", "smoothing": 1.0},
+            line={"color": IMPRINT[i], "width": 0.5, "shape": "spline", "smoothing": 1.0},
             name=genre,
             mode="none",
             hoverinfo="name+x",
diff --git a/plots/strip-basic/implementations/python/plotly.py b/plots/strip-basic/implementations/python/plotly.py
index 16d2639e85..7065e0af1c 100644
--- a/plots/strip-basic/implementations/python/plotly.py
+++ b/plots/strip-basic/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette — first series always #009E73
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Survey response scores grouped by demographic category
 np.random.seed(42)
@@ -47,7 +47,7 @@
             y=values,
             mode="markers",
             name=cat,
-            marker={"size": 14, "opacity": 0.6, "color": OKABE_ITO[i]},
+            marker={"size": 14, "opacity": 0.6, "color": IMPRINT[i]},
             hovertemplate=f"{cat}<br>Value: %{{y:.1f}}<extra></extra>",
         )
     )
diff --git a/plots/subplot-grid/implementations/python/plotly.py b/plots/subplot-grid/implementations/python/plotly.py
index 21608dc4cb..899fe1804a 100644
--- a/plots/subplot-grid/implementations/python/plotly.py
+++ b/plots/subplot-grid/implementations/python/plotly.py
@@ -26,7 +26,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - Financial dashboard example
 np.random.seed(42)
@@ -62,18 +62,18 @@
 
 # Subplot 1: Line chart - Stock price with moving average
 fig.add_trace(
-    go.Scatter(x=dates, y=price, mode="lines", name="Price", line={"color": OKABE_ITO[0], "width": 3}), row=1, col=1
+    go.Scatter(x=dates, y=price, mode="lines", name="Price", line={"color": IMPRINT[0], "width": 3}), row=1, col=1
 )
 fig.add_trace(
     go.Scatter(
-        x=dates, y=ma_20, mode="lines", name="20-day MA", line={"color": OKABE_ITO[1], "width": 2, "dash": "dash"}
+        x=dates, y=ma_20, mode="lines", name="20-day MA", line={"color": IMPRINT[1], "width": 2, "dash": "dash"}
     ),
     row=1,
     col=1,
 )
 
 # Subplot 2: Bar chart - Volume
-volume_colors = [OKABE_ITO[0] if r >= 0 else OKABE_ITO[1] for r in returns]
+volume_colors = [IMPRINT[0] if r >= 0 else IMPRINT[1] for r in returns]
 fig.add_trace(go.Bar(x=dates, y=volume, name="Volume", marker={"color": volume_colors, "opacity": 0.8}), row=1, col=2)
 
 # Subplot 3: Histogram - Daily returns distribution
@@ -82,7 +82,7 @@
         x=daily_returns,
         nbinsx=20,
         name="Returns",
-        marker={"color": OKABE_ITO[0], "opacity": 0.75, "line": {"color": PAGE_BG, "width": 1}},
+        marker={"color": IMPRINT[0], "opacity": 0.75, "line": {"color": PAGE_BG, "width": 1}},
     ),
     row=2,
     col=1,
@@ -95,7 +95,7 @@
         y=price,
         mode="markers",
         name="Price-Volume",
-        marker={"color": OKABE_ITO[0], "size": 14, "opacity": 0.7, "line": {"color": PAGE_BG, "width": 1}},
+        marker={"color": IMPRINT[0], "size": 14, "opacity": 0.7, "line": {"color": PAGE_BG, "width": 1}},
     ),
     row=2,
     col=2,
diff --git a/plots/subplot-mosaic/implementations/python/plotly.py b/plots/subplot-mosaic/implementations/python/plotly.py
index 49811a90e4..fc5b142c13 100644
--- a/plots/subplot-mosaic/implementations/python/plotly.py
+++ b/plots/subplot-mosaic/implementations/python/plotly.py
@@ -21,14 +21,14 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # brand — first series
-    "#D55E00",
-    "#0072B2",
-    "#CC79A7",
-    "#E69F00",
-    "#56B4E9",
-    "#F0E442",
+    "#C475FD",
+    "#4467A3",
+    "#BD8233",
+    "#AE3030",
+    "#2ABCCD",
+    "#954477",
 ]
 
 # Data
@@ -86,9 +86,9 @@
         x=dates,
         y=revenue,
         mode="lines",
-        line={"color": OKABE_ITO[0], "width": 4},
+        line={"color": IMPRINT[0], "width": 4},
         fill="tozeroy",
-        fillcolor=f"rgba({int(OKABE_ITO[0][1:3], 16)}, {int(OKABE_ITO[0][3:5], 16)}, {int(OKABE_ITO[0][5:7], 16)}, 0.2)",
+        fillcolor=f"rgba({int(IMPRINT[0][1:3], 16)}, {int(IMPRINT[0][3:5], 16)}, {int(IMPRINT[0][5:7], 16)}, 0.2)",
         name="Revenue",
     ),
     row=1,
@@ -96,7 +96,7 @@
 )
 
 # B: Monthly sales bar (top right)
-fig.add_trace(go.Bar(x=months, y=monthly_sales, marker_color=OKABE_ITO[1], name="Monthly"), row=1, col=3)
+fig.add_trace(go.Bar(x=months, y=monthly_sales, marker_color=IMPRINT[1], name="Monthly"), row=1, col=3)
 
 # C: Product scatter (middle spanning) — INCREASED marker size
 fig.add_trace(
@@ -104,7 +104,7 @@
         x=product_x,
         y=product_y,
         mode="markers",
-        marker={"size": 16, "color": OKABE_ITO[0], "opacity": 0.8},
+        marker={"size": 16, "color": IMPRINT[0], "opacity": 0.8},
         name="Products",
     ),
     row=2,
@@ -117,7 +117,7 @@
         y=categories,
         x=cat_values,
         orientation="h",
-        marker_color=[OKABE_ITO[0], OKABE_ITO[1], OKABE_ITO[2], OKABE_ITO[3], OKABE_ITO[4]],
+        marker_color=[IMPRINT[0], IMPRINT[1], IMPRINT[2], IMPRINT[3], IMPRINT[4]],
         name="Categories",
     ),
     row=2,
@@ -130,9 +130,9 @@
         x=list(range(30)),
         y=metric_1_history,
         mode="lines",
-        line={"color": OKABE_ITO[0], "width": 3},
+        line={"color": IMPRINT[0], "width": 3},
         fill="tozeroy",
-        fillcolor=f"rgba({int(OKABE_ITO[0][1:3], 16)}, {int(OKABE_ITO[0][3:5], 16)}, {int(OKABE_ITO[0][5:7], 16)}, 0.25)",
+        fillcolor=f"rgba({int(IMPRINT[0][1:3], 16)}, {int(IMPRINT[0][3:5], 16)}, {int(IMPRINT[0][5:7], 16)}, 0.25)",
         name="Efficiency",
     ),
     row=3,
@@ -145,9 +145,9 @@
         x=list(range(30)),
         y=metric_2_history,
         mode="lines",
-        line={"color": OKABE_ITO[1], "width": 3},
+        line={"color": IMPRINT[1], "width": 3},
         fill="tozeroy",
-        fillcolor=f"rgba({int(OKABE_ITO[1][1:3], 16)}, {int(OKABE_ITO[1][3:5], 16)}, {int(OKABE_ITO[1][5:7], 16)}, 0.25)",
+        fillcolor=f"rgba({int(IMPRINT[1][1:3], 16)}, {int(IMPRINT[1][3:5], 16)}, {int(IMPRINT[1][5:7], 16)}, 0.25)",
         name="Quality",
     ),
     row=3,
@@ -160,9 +160,9 @@
         x=list(range(30)),
         y=metric_3_history,
         mode="lines",
-        line={"color": OKABE_ITO[2], "width": 3},
+        line={"color": IMPRINT[2], "width": 3},
         fill="tozeroy",
-        fillcolor=f"rgba({int(OKABE_ITO[2][1:3], 16)}, {int(OKABE_ITO[2][3:5], 16)}, {int(OKABE_ITO[2][5:7], 16)}, 0.25)",
+        fillcolor=f"rgba({int(IMPRINT[2][1:3], 16)}, {int(IMPRINT[2][3:5], 16)}, {int(IMPRINT[2][5:7], 16)}, 0.25)",
         name="Response",
     ),
     row=3,
diff --git a/plots/sunburst-basic/implementations/python/plotly.py b/plots/sunburst-basic/implementations/python/plotly.py
index 33c5a80307..1ec07d6163 100644
--- a/plots/sunburst-basic/implementations/python/plotly.py
+++ b/plots/sunburst-basic/implementations/python/plotly.py
@@ -72,18 +72,18 @@
 colors = [
     ELEVATED_BG,  # Company root — adapts cleanly to both light and dark themes
     "#009E73",  # Engineering — Okabe-Ito #1 (dominant: 37.5%)
-    "#D55E00",  # Sales — Okabe-Ito #2
-    "#0072B2",  # Marketing — Okabe-Ito #3
-    "#CC79A7",  # Operations — Okabe-Ito #4
+    "#C475FD",  # Sales — Okabe-Ito #2
+    "#4467A3",  # Marketing — Okabe-Ito #3
+    "#BD8233",  # Operations — Okabe-Ito #4
     "#00B589",  # Backend — lighter green
     "#009E73",  # Frontend — base green
     "#007A58",  # DevOps — darker green
     "#F07030",  # Enterprise — lighter vermillion
-    "#D55E00",  # SMB — base vermillion
+    "#C475FD",  # SMB — base vermillion
     "#2090CC",  # Digital — lighter blue
-    "#0072B2",  # Brand — base blue
+    "#4467A3",  # Brand — base blue
     "#DD99C0",  # HR — lighter pink
-    "#CC79A7",  # Finance — base pink
+    "#BD8233",  # Finance — base pink
 ]
 
 fig = go.Figure(
diff --git a/plots/survival-kaplan-meier/implementations/python/plotly.py b/plots/survival-kaplan-meier/implementations/python/plotly.py
index 3fbec76e73..c2ac741422 100644
--- a/plots/survival-kaplan-meier/implementations/python/plotly.py
+++ b/plots/survival-kaplan-meier/implementations/python/plotly.py
@@ -45,7 +45,7 @@ def hex_to_rgba(hex_color, alpha):
 
 # Okabe-Ito palette
 COLOR_A = "#009E73"  # First series (bluish green)
-COLOR_B = "#D55E00"  # Second series (vermillion)
+COLOR_B = "#C475FD"  # Second series (vermillion)
 
 # Data - Clinical trial with two treatment groups
 np.random.seed(42)
diff --git a/plots/swarm-basic/implementations/python/plotly.py b/plots/swarm-basic/implementations/python/plotly.py
index 59d75be1f8..323639986a 100644
--- a/plots/swarm-basic/implementations/python/plotly.py
+++ b/plots/swarm-basic/implementations/python/plotly.py
@@ -25,7 +25,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 FILL_COLORS = ["rgba(0,158,115,0.18)", "rgba(213,94,0,0.18)", "rgba(0,114,178,0.18)", "rgba(204,121,167,0.18)"]
 
 # Data - student test scores across 4 classrooms with varied distributions
@@ -44,7 +44,7 @@
 fig = go.Figure()
 
 for i, (classroom, scores) in enumerate(zip(classrooms, all_scores, strict=False)):
-    color = OKABE_ITO[i]
+    color = IMPRINT[i]
     fig.add_trace(
         go.Box(
             y=scores,
diff --git a/plots/ternary-basic/implementations/python/plotly.py b/plots/ternary-basic/implementations/python/plotly.py
index c04770078b..bb2c3f672e 100644
--- a/plots/ternary-basic/implementations/python/plotly.py
+++ b/plots/ternary-basic/implementations/python/plotly.py
@@ -18,11 +18,11 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is always #009E73)
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand — primary)
-    "#D55E00",  # vermillion (secondary)
-    "#0072B2",  # blue (tertiary)
-    "#CC79A7",  # reddish purple (quaternary)
+    "#C475FD",  # vermillion (secondary)
+    "#4467A3",  # blue (tertiary)
+    "#BD8233",  # reddish purple (quaternary)
 ]
 
 # Data: Soil composition samples with meaningful clustering by soil type
@@ -110,7 +110,7 @@
 fig = go.Figure()
 
 # Add traces for each soil type
-for soil_idx, (name, color) in enumerate(zip(["Sandy", "Silty", "Clayey", "Loam"], OKABE_ITO, strict=True)):
+for soil_idx, (name, color) in enumerate(zip(["Sandy", "Silty", "Clayey", "Loam"], IMPRINT, strict=True)):
     mask = soil_type_indices == soil_idx
     fig.add_trace(
         go.Scatterternary(
diff --git a/plots/timeline-basic/implementations/python/plotly.py b/plots/timeline-basic/implementations/python/plotly.py
index ed999cc8ae..13cdf48bca 100644
--- a/plots/timeline-basic/implementations/python/plotly.py
+++ b/plots/timeline-basic/implementations/python/plotly.py
@@ -19,11 +19,11 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series always #009E73)
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
 ]
 
 # Data - Economic and stock market milestones (2018-2024)
@@ -56,12 +56,12 @@
 
 # Color mapping for categories
 colors = {
-    "Market": OKABE_ITO[0],
-    "Recovery": OKABE_ITO[1],
-    "Crisis": OKABE_ITO[2],
-    "Expansion": OKABE_ITO[3],
-    "Growth": OKABE_ITO[0],
-    "Correction": OKABE_ITO[1],
+    "Market": IMPRINT[0],
+    "Recovery": IMPRINT[1],
+    "Crisis": IMPRINT[2],
+    "Expansion": IMPRINT[3],
+    "Growth": IMPRINT[0],
+    "Correction": IMPRINT[1],
 }
 
 # Alternate positions to prevent label overlap
diff --git a/plots/timeseries-decomposition/implementations/python/plotly.py b/plots/timeseries-decomposition/implementations/python/plotly.py
index edfcabb8a6..71d89da072 100644
--- a/plots/timeseries-decomposition/implementations/python/plotly.py
+++ b/plots/timeseries-decomposition/implementations/python/plotly.py
@@ -24,7 +24,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Monthly airline passengers (classic time series)
 np.random.seed(42)
@@ -53,7 +53,7 @@
         x=dates,
         y=ts.values,
         mode="lines",
-        line=dict(color=OKABE_ITO[0], width=2.5),
+        line=dict(color=IMPRINT[0], width=2.5),
         name="Original",
         hovertemplate="<b>Original</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
     ),
@@ -66,7 +66,7 @@
         x=dates,
         y=decomposition.trend,
         mode="lines",
-        line=dict(color=OKABE_ITO[1], width=3),
+        line=dict(color=IMPRINT[1], width=3),
         name="Trend",
         hovertemplate="<b>Trend</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
     ),
@@ -79,7 +79,7 @@
         x=dates,
         y=decomposition.seasonal,
         mode="lines",
-        line=dict(color=OKABE_ITO[2], width=2.5),
+        line=dict(color=IMPRINT[2], width=2.5),
         name="Seasonal",
         hovertemplate="<b>Seasonal</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
     ),
@@ -92,7 +92,7 @@
         x=dates,
         y=decomposition.resid,
         mode="lines",
-        line=dict(color=OKABE_ITO[3], width=2),
+        line=dict(color=IMPRINT[3], width=2),
         name="Residual",
         hovertemplate="<b>Residual</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
     ),
diff --git a/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py b/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
index dd807a9bd7..55a4d53e46 100644
--- a/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
+++ b/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 
 # Okabe-Ito palette
 COLOR_HISTORICAL = "#009E73"  # Position 1 - brand green
-COLOR_FORECAST = "#D55E00"  # Position 2 - vermillion
+COLOR_FORECAST = "#C475FD"  # Position 2 - vermillion
 
 # Data generation
 np.random.seed(42)
diff --git a/plots/treemap-basic/implementations/python/plotly.py b/plots/treemap-basic/implementations/python/plotly.py
index 7d26b95db3..e1e23f86d1 100644
--- a/plots/treemap-basic/implementations/python/plotly.py
+++ b/plots/treemap-basic/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette for main categories
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Budget allocation by department and project (in thousands)
 categories = [
@@ -75,11 +75,11 @@
 # Create color map using Okabe-Ito palette
 color_map = {
     "Budget": PAGE_BG,
-    "Engineering": OKABE_ITO[0],
-    "Marketing": OKABE_ITO[1],
-    "Sales": OKABE_ITO[2],
-    "Operations": OKABE_ITO[3],
-    "HR": OKABE_ITO[4],
+    "Engineering": IMPRINT[0],
+    "Marketing": IMPRINT[1],
+    "Sales": IMPRINT[2],
+    "Operations": IMPRINT[3],
+    "HR": IMPRINT[4],
 }
 
 # Assign colors based on category hierarchy
@@ -89,7 +89,7 @@
         colors.append(color_map[label])
     else:
         parent = parents[i]
-        colors.append(color_map.get(parent, OKABE_ITO[0]))
+        colors.append(color_map.get(parent, IMPRINT[0]))
 
 # Create treemap
 fig = go.Figure(
diff --git a/plots/venn-basic/implementations/python/plotly.py b/plots/venn-basic/implementations/python/plotly.py
index 5c0b5a97f0..8fbf4d9b3f 100644
--- a/plots/venn-basic/implementations/python/plotly.py
+++ b/plots/venn-basic/implementations/python/plotly.py
@@ -43,7 +43,7 @@
 cy = [distance * np.sin(a) for a in angles]
 
 # Okabe-Ito palette with transparency
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 fill_colors = ["rgba(0,158,115,0.45)", "rgba(213,94,0,0.45)", "rgba(0,114,178,0.45)"]
 
 hover_texts = [
@@ -65,7 +65,7 @@
             y=y_circle,
             fill="toself",
             fillcolor=fill_colors[i],
-            line={"color": OKABE_ITO[i], "width": 3},
+            line={"color": IMPRINT[i], "width": 3},
             mode="lines",
             name=set_labels[i],
             showlegend=False,
diff --git a/plots/venn-labeled-items/implementations/python/plotly.py b/plots/venn-labeled-items/implementations/python/plotly.py
index 421abd1384..41e5016853 100644
--- a/plots/venn-labeled-items/implementations/python/plotly.py
+++ b/plots/venn-labeled-items/implementations/python/plotly.py
@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito categorical palette: first series is brand green
-CIRCLE_COLORS = ["#009E73", "#D55E00", "#0072B2"]
+CIRCLE_COLORS = ["#009E73", "#C475FD", "#4467A3"]
 CIRCLE_FILLS = ["rgba(0,158,115,0.26)", "rgba(213,94,0,0.26)", "rgba(0,114,178,0.26)"]
 
 # Data: editorial Chartgeist-style commentary on tech/culture
diff --git a/plots/violin-box/implementations/python/plotly.py b/plots/violin-box/implementations/python/plotly.py
index 89c3415b11..91ece81ddf 100644
--- a/plots/violin-box/implementations/python/plotly.py
+++ b/plots/violin-box/implementations/python/plotly.py
@@ -26,7 +26,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette — first series is always #009E73
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data — Test scores across different teaching methods
 np.random.seed(42)
@@ -53,14 +53,14 @@
             name=group,
             box_visible=True,
             meanline_visible=True,
-            fillcolor=OKABE_ITO[i],
+            fillcolor=IMPRINT[i],
             opacity=0.7,
             line={"color": INK_SOFT, "width": 2},
             points="outliers",
             pointpos=0,
             marker={"size": 8, "color": INK_SOFT, "opacity": 0.6},
             box={"fillcolor": PAGE_BG, "line": {"color": INK_SOFT, "width": 2}, "width": 0.15},
-            meanline={"color": OKABE_ITO[i], "width": 3},
+            meanline={"color": IMPRINT[i], "width": 3},
         )
     )
 
diff --git a/plots/violin-grouped-swarm/implementations/python/plotly.py b/plots/violin-grouped-swarm/implementations/python/plotly.py
index 7cbc89d1e6..6f7296294a 100644
--- a/plots/violin-grouped-swarm/implementations/python/plotly.py
+++ b/plots/violin-grouped-swarm/implementations/python/plotly.py
@@ -48,7 +48,7 @@
 df = pd.DataFrame(data)
 
 # Okabe-Ito colors
-colors = {"Novice": "#009E73", "Expert": "#D55E00"}
+colors = {"Novice": "#009E73", "Expert": "#C475FD"}
 
 # Create figure
 fig = go.Figure()
diff --git a/plots/violin-split/implementations/python/plotly.py b/plots/violin-split/implementations/python/plotly.py
index 90c79f7947..b4a1250cf1 100644
--- a/plots/violin-split/implementations/python/plotly.py
+++ b/plots/violin-split/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (first series is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - Salary distributions by gender across job categories
 np.random.seed(42)
@@ -91,8 +91,8 @@
             y=subset["Salary"],
             name=gender,
             side=side,
-            line_color=OKABE_ITO[okabe_idx],
-            fillcolor=OKABE_ITO[okabe_idx],
+            line_color=IMPRINT[okabe_idx],
+            fillcolor=IMPRINT[okabe_idx],
             opacity=0.75,
             meanline_visible=True,
             meanline_color=INK_SOFT,
diff --git a/plots/violin-swarm/implementations/python/plotly.py b/plots/violin-swarm/implementations/python/plotly.py
index 0db79f22f7..dc1a1c0ef6 100644
--- a/plots/violin-swarm/implementations/python/plotly.py
+++ b/plots/violin-swarm/implementations/python/plotly.py
@@ -29,9 +29,9 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Colors - Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
-VIOLIN_COLOR = OKABE_ITO[0]  # Bluish green
-POINT_COLOR = OKABE_ITO[1]  # Vermillion
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
+VIOLIN_COLOR = IMPRINT[0]  # Bluish green
+POINT_COLOR = IMPRINT[1]  # Vermillion
 
 # Data - Reaction times (ms) across 4 experimental conditions
 np.random.seed(42)
diff --git a/plots/volcano-basic/implementations/python/plotly.py b/plots/volcano-basic/implementations/python/plotly.py
index ad1f7aa6ac..9f541f34ff 100644
--- a/plots/volcano-basic/implementations/python/plotly.py
+++ b/plots/volcano-basic/implementations/python/plotly.py
@@ -19,8 +19,8 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette
-OKABE_ITO_VERMILLION = "#D55E00"  # Position 2 - red/orange for up-regulated
-OKABE_ITO_BLUE = "#0072B2"  # Position 3 - blue for down-regulated
+OKABE_ITO_VERMILLION = "#C475FD"  # Position 2 - red/orange for up-regulated
+OKABE_ITO_BLUE = "#4467A3"  # Position 3 - blue for down-regulated
 
 # Data - Simulated differential gene expression results
 np.random.seed(42)
diff --git a/plots/voronoi-basic/implementations/python/plotly.py b/plots/voronoi-basic/implementations/python/plotly.py
index eabce1ede8..e8ac9f07f4 100644
--- a/plots/voronoi-basic/implementations/python/plotly.py
+++ b/plots/voronoi-basic/implementations/python/plotly.py
@@ -26,7 +26,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette (position 1 is brand color #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data - Generate seed points (retail stores) within city bounds
 np.random.seed(42)
@@ -59,7 +59,7 @@
 vor = Voronoi(all_points)
 
 # Extended color palette (cycle through Okabe-Ito + supplementary colors)
-extended_colors = OKABE_ITO + [
+extended_colors = IMPRINT + [
     "#FF9AA2",
     "#FFB7B2",
     "#E0BBE4",
@@ -149,7 +149,7 @@
         x=x,
         y=y,
         mode="markers",
-        marker={"size": 18, "color": OKABE_ITO[0], "line": {"color": ELEVATED_BG, "width": 3}, "symbol": "circle"},
+        marker={"size": 18, "color": IMPRINT[0], "line": {"color": ELEVATED_BG, "width": 3}, "symbol": "circle"},
         name="Store Location",
         hovertemplate="Store %{pointNumber}<br>Latitude: %{y:.1f}<br>Longitude: %{x:.1f}<extra></extra>",
     )
diff --git a/plots/waffle-basic/implementations/python/plotly.py b/plots/waffle-basic/implementations/python/plotly.py
index edea8a65a2..28a4b6ddd4 100644
--- a/plots/waffle-basic/implementations/python/plotly.py
+++ b/plots/waffle-basic/implementations/python/plotly.py
@@ -19,12 +19,12 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 # Okabe-Ito palette - first series ALWAYS #009E73
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
-    "#E69F00",  # orange
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
+    "#AE3030",  # orange
 ]
 
 # Data - Budget allocation across spending categories
@@ -62,7 +62,7 @@
                 marker={
                     "size": 55,
                     "symbol": "square",
-                    "color": OKABE_ITO[cat_idx],
+                    "color": IMPRINT[cat_idx],
                     "line": {"color": PAGE_BG, "width": 2},
                 },
                 showlegend=False,
@@ -77,7 +77,7 @@
             x=[None],
             y=[None],
             mode="markers",
-            marker={"size": 22, "symbol": "square", "color": OKABE_ITO[cat_idx]},
+            marker={"size": 22, "symbol": "square", "color": IMPRINT[cat_idx]},
             name=f"{cat}: {val}%",
             showlegend=True,
         )
diff --git a/plots/waterfall-basic/implementations/python/plotly.py b/plots/waterfall-basic/implementations/python/plotly.py
index 95b99c5ff8..e5a00d760d 100644
--- a/plots/waterfall-basic/implementations/python/plotly.py
+++ b/plots/waterfall-basic/implementations/python/plotly.py
@@ -24,8 +24,8 @@
 
 # Okabe-Ito palette
 COLOR_POSITIVE = "#009E73"  # bluish green (first series)
-COLOR_NEGATIVE = "#D55E00"  # vermillion
-COLOR_TOTAL = "#0072B2"  # blue
+COLOR_NEGATIVE = "#AE3030"  # imprint red
+COLOR_TOTAL = "#4467A3"  # blue
 
 # Data - Quarterly financial breakdown from revenue to net income
 categories = ["Revenue", "Product Costs", "Operating Expenses", "Marketing", "Other Income", "Taxes", "Net Income"]
diff --git a/plots/windrose-basic/implementations/python/plotly.py b/plots/windrose-basic/implementations/python/plotly.py
index 31dd59fdf0..5725a36f35 100644
--- a/plots/windrose-basic/implementations/python/plotly.py
+++ b/plots/windrose-basic/implementations/python/plotly.py
@@ -39,7 +39,7 @@
 # Define speed bins (m/s)
 speed_bins = [0, 3, 6, 9, 12, np.inf]
 speed_labels = ["0-3 m/s", "3-6 m/s", "6-9 m/s", "9-12 m/s", ">12 m/s"]
-speed_colors = ["#4A90E2", "#0072B2", "#FFD43B", "#FF9F40", "#FF5252"]
+speed_colors = ["#4A90E2", "#4467A3", "#FFD43B", "#FF9F40", "#FF5252"]
 
 # Bin the data
 dir_indices = np.digitize(directions, dir_bins[:-1]) - 1
diff --git a/plots/wordcloud-basic/implementations/python/plotly.py b/plots/wordcloud-basic/implementations/python/plotly.py
index 177bfbb1a5..06abd9b56b 100644
--- a/plots/wordcloud-basic/implementations/python/plotly.py
+++ b/plots/wordcloud-basic/implementations/python/plotly.py
@@ -19,14 +19,14 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (colorblind-safe)
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # bluish green (brand)
-    "#D55E00",  # vermillion
-    "#0072B2",  # blue
-    "#CC79A7",  # reddish purple
-    "#E69F00",  # orange
-    "#56B4E9",  # sky blue
-    "#F0E442",  # yellow
+    "#C475FD",  # vermillion
+    "#4467A3",  # blue
+    "#BD8233",  # reddish purple
+    "#AE3030",  # orange
+    "#2ABCCD",  # sky blue
+    "#954477",  # yellow
 ]
 
 # Data - Data science and analytics tools (distinct from programming languages)
@@ -125,7 +125,7 @@
 
     # Scale frequency to font size
     font_size = min_size + (freq - min_freq) / (max_freq - min_freq) * (max_size - min_size)
-    color = OKABE_ITO[i % len(OKABE_ITO)]
+    color = IMPRINT[i % len(IMPRINT)]
 
     fig.add_trace(
         go.Scatter(

From 1e787f3063f6e75d7fa547066185e16753fd6019 Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:14:07 +0200
Subject: [PATCH 2/7] chore(palette): plotly semantic + rgba follow-up
 (Copilot)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Copilot caught a pattern Stage A's hex-literal regex doesn't reach:
inline `rgba(r,g,b,a)` triples carry the same Okabe-Ito colors but
escape the migration. The result was outline/fill mismatches in
charts where the stroke is a named hex but the fill is an rgba()
literal. Patched in 6 files:

- area-cumulative-flow  FILL_COLORS rgba ↔ LINE_COLORS hex
- venn-basic            circle stroke vs fill
- venn-labeled-items    circle stroke vs fill
- sankey-basic          IMPRINT node colors vs SOURCE_RGBA ribbons
- radar-basic           line vs polygon fillcolor
- radar-multi           line vs polygon fillcolor

Plus 6 more semantic refinements:

- sunburst-basic    regenerate per-family child shades from new base hex
                    (old shades were lighter Okabe-Ito tints, no longer
                    related to the new imprint bases)
- bar-spine         "On Trial" was migrated to imprint red (semantic bad),
                    "Cancelled" to lavender (neutral) — flipped: warning
                    is now amber, cancelled is red
- gain-curve        PERFECT line → blue (red is reserved for semantic bad;
                    a reference line for the ideal model shouldn't read
                    as a failure indicator)
- precision-recall  SECONDARY (no-skill reference) → ochre, same reasoning
- maze-circular     center "treasure" star → amber, restores the
                    high-luminance accent the original yellow had
- windrose-basic    speed bins reordered to cyan → blue → green → amber →
                    red so the cool→warm intensity ramp is monotonic again

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plotly.py          |  8 +--
 .../implementations/python/plotly.py          |  4 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plot-dark.html     | 61 -------------------
 .../implementations/python/plot-light.html    | 61 -------------------
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          | 12 ++--
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  2 +-
 14 files changed, 22 insertions(+), 144 deletions(-)
 delete mode 100644 plots/network-directed/implementations/python/plot-dark.html
 delete mode 100644 plots/network-directed/implementations/python/plot-light.html

diff --git a/plots/area-cumulative-flow/implementations/python/plotly.py b/plots/area-cumulative-flow/implementations/python/plotly.py
index 5d4b4f746c..778344de0c 100644
--- a/plots/area-cumulative-flow/implementations/python/plotly.py
+++ b/plots/area-cumulative-flow/implementations/python/plotly.py
@@ -22,10 +22,10 @@
 # Okabe-Ito palette — first series is always #009E73
 FILL_COLORS = [
     "rgba(0,158,115,0.80)",  # #009E73 Done
-    "rgba(213,94,0,0.80)",  # #C475FD Testing
-    "rgba(0,114,178,0.80)",  # #4467A3 Development
-    "rgba(204,121,167,0.80)",  # #BD8233 Analysis
-    "rgba(230,159,0,0.80)",  # #AE3030 Backlog
+    "rgba(196,117,253,0.80)",  # #C475FD Testing
+    "rgba(68,103,163,0.80)",  # #4467A3 Development
+    "rgba(189,130,51,0.80)",  # #BD8233 Analysis
+    "rgba(174,48,48,0.80)",  # #AE3030 Backlog
 ]
 LINE_COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
diff --git a/plots/bar-spine/implementations/python/plotly.py b/plots/bar-spine/implementations/python/plotly.py
index dd39944004..9020e3f7db 100644
--- a/plots/bar-spine/implementations/python/plotly.py
+++ b/plots/bar-spine/implementations/python/plotly.py
@@ -41,8 +41,8 @@
 # Conditional proportions within each bar (heights sum to 1)
 outcome_props = {k: [cnt / m for cnt, m in zip(v, channel_sizes)] for k, v in outcome_counts.items()}
 
-# Okabe-Ito palette: Active=green(1), On Trial=orange(5), Cancelled=vermillion(2)
-COLORS = {"Active": "#009E73", "On Trial": "#AE3030", "Cancelled": "#C475FD"}
+# imprint semantic anchors: Active=green, On Trial=amber (warning), Cancelled=red (bad)
+COLORS = {"Active": "#009E73", "On Trial": "#DDCC77", "Cancelled": "#AE3030"}
 
 # Plot
 fig = go.Figure()
diff --git a/plots/gain-curve/implementations/python/plotly.py b/plots/gain-curve/implementations/python/plotly.py
index 0b31aef5d6..12be4e54d3 100644
--- a/plots/gain-curve/implementations/python/plotly.py
+++ b/plots/gain-curve/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 # Okabe-Ito palette
 BRAND = "#009E73"  # First series — always
 BASELINE = "#888888"  # Neutral for reference
-PERFECT = "#AE3030"  # Orange for perfect model
+PERFECT = "#4467A3"  # imprint blue — neutral reference for perfect model (red is reserved for semantic bad)
 
 # Data - Customer response model evaluation
 np.random.seed(42)
diff --git a/plots/maze-circular/implementations/python/plotly.py b/plots/maze-circular/implementations/python/plotly.py
index cb7f27e0e0..963fe71b12 100644
--- a/plots/maze-circular/implementations/python/plotly.py
+++ b/plots/maze-circular/implementations/python/plotly.py
@@ -198,7 +198,7 @@
         x=[0.0],
         y=[0.0],
         mode="markers",
-        marker={"symbol": "star", "size": 20, "color": "#954477", "line": {"color": ACCENT, "width": 2}},
+        marker={"symbol": "star", "size": 20, "color": "#DDCC77", "line": {"color": ACCENT, "width": 2}},
         showlegend=False,
         hoverinfo="skip",
     )
diff --git a/plots/network-directed/implementations/python/plot-dark.html b/plots/network-directed/implementations/python/plot-dark.html
deleted file mode 100644
index f52cde4ab8..0000000000
--- a/plots/network-directed/implementations/python/plot-dark.html
+++ /dev/null
@@ -1,61 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <title>Bokeh Plot</title>
-    <style>
-      html, body {
-        box-sizing: border-box;
-        display: flow-root;
-        height: 100%;
-        margin: 0;
-        padding: 0;
-      }
-    </style>
-<script src="https://cdn.bokeh.org/bokeh/release/bokeh-3.9.0.min.js"></script>
-<script>
-Bokeh.set_log_level("info");
-</script>
-  </head>
-  <body>
-    <div id="bdbd20cd-3899-4352-aff1-46484eca5ad6" data-root-id="p1009" style="display: contents;"></div>
-  
-    <script type="application/json" id="dd45d68a-cc24-4df1-ba6a-a70193d52290">
-      {"7310aaa5-f256-4888-a1d0-aa06265020ac":{"version":"3.9.0","title":"Bokeh Application","config":{"type":"object","name":"DocumentConfig","id":"p1258","attributes":{"notifications":{"type":"object","name":"Notifications","id":"p1259"}}},"roots":[{"type":"object","name":"Figure","id":"p1009","attributes":{"width":4800,"height":2700,"x_range":{"type":"object","name":"Range1d","id":"p1019","attributes":{"end":4800}},"y_range":{"type":"object","name":"Range1d","id":"p1020","attributes":{"end":2700}},"x_scale":{"type":"object","name":"LinearScale","id":"p1021"},"y_scale":{"type":"object","name":"LinearScale","id":"p1022"},"title":{"type":"object","name":"Title","id":"p1012","attributes":{"text":"network-directed \u00b7 bokeh \u00b7 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diff --git a/plots/precision-recall/implementations/python/plotly.py b/plots/precision-recall/implementations/python/plotly.py
index 1c2fd609ba..1fdaad26e6 100644
--- a/plots/precision-recall/implementations/python/plotly.py
+++ b/plots/precision-recall/implementations/python/plotly.py
@@ -21,7 +21,7 @@
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Position 1, first series
-SECONDARY = "#AE3030"  # Position 5, orange
+SECONDARY = "#BD8233"  # imprint ochre — secondary reference (red is reserved for semantic bad)
 
 # Data - Simulate a binary classification scenario (fraud detection)
 np.random.seed(42)
diff --git a/plots/radar-basic/implementations/python/plotly.py b/plots/radar-basic/implementations/python/plotly.py
index cf825c010d..1f41f75d77 100644
--- a/plots/radar-basic/implementations/python/plotly.py
+++ b/plots/radar-basic/implementations/python/plotly.py
@@ -50,7 +50,7 @@
         r=junior_closed,
         theta=categories_closed,
         fill="toself",
-        fillcolor="rgba(213, 94, 0, 0.25)",
+        fillcolor="rgba(196, 117, 253, 0.25)",
         line={"color": IMPRINT[1], "width": 3},
         marker={"size": 12, "color": IMPRINT[1]},
         name="Junior Developer",
diff --git a/plots/radar-multi/implementations/python/plotly.py b/plots/radar-multi/implementations/python/plotly.py
index df17c760ae..af2fe4dbd8 100644
--- a/plots/radar-multi/implementations/python/plotly.py
+++ b/plots/radar-multi/implementations/python/plotly.py
@@ -56,7 +56,7 @@
         r=product_b_closed,
         theta=categories_closed,
         fill="toself",
-        fillcolor="rgba(213, 94, 0, 0.25)",
+        fillcolor="rgba(196, 117, 253, 0.25)",
         line={"color": IMPRINT[1], "width": 3},
         name="Product B (Budget)",
         marker={"size": 10},
@@ -69,7 +69,7 @@
         r=product_c_closed,
         theta=categories_closed,
         fill="toself",
-        fillcolor="rgba(0, 114, 178, 0.25)",
+        fillcolor="rgba(68, 103, 163, 0.25)",
         line={"color": IMPRINT[2], "width": 3},
         name="Product C (Pro)",
         marker={"size": 10},
diff --git a/plots/sankey-basic/implementations/python/plotly.py b/plots/sankey-basic/implementations/python/plotly.py
index 15cdcb59b5..e3c0e05caf 100644
--- a/plots/sankey-basic/implementations/python/plotly.py
+++ b/plots/sankey-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 
 # Okabe-Ito palette for source nodes (positions 1-4)
 IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
-SOURCE_RGBA = ["rgba(0,158,115,0.4)", "rgba(213,94,0,0.4)", "rgba(0,114,178,0.4)", "rgba(204,121,167,0.4)"]
+SOURCE_RGBA = ["rgba(0,158,115,0.4)", "rgba(196,117,253,0.4)", "rgba(68,103,163,0.4)", "rgba(189,130,51,0.4)"]
 
 # Data - Energy flow from sources to sectors (TWh)
 sources = ["Coal", "Natural Gas", "Nuclear", "Renewables"]
diff --git a/plots/sunburst-basic/implementations/python/plotly.py b/plots/sunburst-basic/implementations/python/plotly.py
index 1ec07d6163..55ff1e9880 100644
--- a/plots/sunburst-basic/implementations/python/plotly.py
+++ b/plots/sunburst-basic/implementations/python/plotly.py
@@ -75,15 +75,15 @@
     "#C475FD",  # Sales — Okabe-Ito #2
     "#4467A3",  # Marketing — Okabe-Ito #3
     "#BD8233",  # Operations — Okabe-Ito #4
-    "#00B589",  # Backend — lighter green
+    "#2DAE89",  # Backend — lighter green
     "#009E73",  # Frontend — base green
     "#007A58",  # DevOps — darker green
-    "#F07030",  # Enterprise — lighter vermillion
-    "#C475FD",  # SMB — base vermillion
-    "#2090CC",  # Digital — lighter blue
+    "#D195FE",  # Enterprise — lighter lavender
+    "#C475FD",  # SMB — base lavender
+    "#6883B6",  # Digital — lighter blue
     "#4467A3",  # Brand — base blue
-    "#DD99C0",  # HR — lighter pink
-    "#BD8233",  # Finance — base pink
+    "#CC9852",  # HR — lighter ochre
+    "#BD8233",  # Finance — base ochre
 ]
 
 fig = go.Figure(
diff --git a/plots/venn-basic/implementations/python/plotly.py b/plots/venn-basic/implementations/python/plotly.py
index 8fbf4d9b3f..8ae7109623 100644
--- a/plots/venn-basic/implementations/python/plotly.py
+++ b/plots/venn-basic/implementations/python/plotly.py
@@ -44,7 +44,7 @@
 
 # Okabe-Ito palette with transparency
 IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
-fill_colors = ["rgba(0,158,115,0.45)", "rgba(213,94,0,0.45)", "rgba(0,114,178,0.45)"]
+fill_colors = ["rgba(0,158,115,0.45)", "rgba(196,117,253,0.45)", "rgba(68,103,163,0.45)"]
 
 hover_texts = [
     f"<b>Python</b><br>Total: {set_sizes[0]}<br>Only Python: {only_a}<br>Python ∩ JS: {only_ab}<br>Python ∩ SQL: {only_ac}",
diff --git a/plots/venn-labeled-items/implementations/python/plotly.py b/plots/venn-labeled-items/implementations/python/plotly.py
index 41e5016853..1ad9f51912 100644
--- a/plots/venn-labeled-items/implementations/python/plotly.py
+++ b/plots/venn-labeled-items/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito categorical palette: first series is brand green
 CIRCLE_COLORS = ["#009E73", "#C475FD", "#4467A3"]
-CIRCLE_FILLS = ["rgba(0,158,115,0.26)", "rgba(213,94,0,0.26)", "rgba(0,114,178,0.26)"]
+CIRCLE_FILLS = ["rgba(0,158,115,0.26)", "rgba(196,117,253,0.26)", "rgba(68,103,163,0.26)"]
 
 # Data: editorial Chartgeist-style commentary on tech/culture
 circles = [{"name": "Overhyped"}, {"name": "Actually Useful"}, {"name": "Secretly Loved"}]
diff --git a/plots/windrose-basic/implementations/python/plotly.py b/plots/windrose-basic/implementations/python/plotly.py
index 5725a36f35..2f1101e7a9 100644
--- a/plots/windrose-basic/implementations/python/plotly.py
+++ b/plots/windrose-basic/implementations/python/plotly.py
@@ -39,7 +39,7 @@
 # Define speed bins (m/s)
 speed_bins = [0, 3, 6, 9, 12, np.inf]
 speed_labels = ["0-3 m/s", "3-6 m/s", "6-9 m/s", "9-12 m/s", ">12 m/s"]
-speed_colors = ["#4A90E2", "#4467A3", "#FFD43B", "#FF9F40", "#FF5252"]
+speed_colors = ["#2ABCCD", "#4467A3", "#009E73", "#DDCC77", "#AE3030"]  # cyan→blue→green→amber→red
 
 # Bin the data
 dir_indices = np.digitize(directions, dir_bins[:-1]) - 1

From 2dde372918bcd70548b0ff38e0dfd1ade1484522 Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:15:37 +0200
Subject: [PATCH 3/7] fix(migrate): restore network-directed HTMLs + skip
 git-tracked strays
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The previous commit accidentally deleted two committed plot-{light,dark}.html
files under plots/network-directed/implementations/python/. They were
swept up by the Stage B savefig fallback when the seaborn background
run touched that spec — the fallback assumed any plot-*.html sitting next
to the impl was its own output and moved it into .regen-preview/ (where
it then got rmtree'd).

Restore the two files and tighten the fallback: skip the move when the
stray file is git-tracked. Tracked files are pre-existing artifacts from
old daily-regen passes (typically shared across all impls in the same
spec directory), not output from the current render.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plot-dark.html     | 61 +++++++++++++++++++
 .../implementations/python/plot-light.html    | 61 +++++++++++++++++++
 scripts/migrate_render_and_upload.py          | 27 ++++++--
 3 files changed, 144 insertions(+), 5 deletions(-)
 create mode 100644 plots/network-directed/implementations/python/plot-dark.html
 create mode 100644 plots/network-directed/implementations/python/plot-light.html

diff --git a/plots/network-directed/implementations/python/plot-dark.html b/plots/network-directed/implementations/python/plot-dark.html
new file mode 100644
index 0000000000..f52cde4ab8
--- /dev/null
+++ b/plots/network-directed/implementations/python/plot-dark.html
@@ -0,0 +1,61 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <title>Bokeh Plot</title>
+    <style>
+      html, body {
+        box-sizing: border-box;
+        display: flow-root;
+        height: 100%;
+        margin: 0;
+        padding: 0;
+      }
+    </style>
+<script src="https://cdn.bokeh.org/bokeh/release/bokeh-3.9.0.min.js"></script>
+<script>
+Bokeh.set_log_level("info");
+</script>
+  </head>
+  <body>
+    <div id="bdbd20cd-3899-4352-aff1-46484eca5ad6" data-root-id="p1009" style="display: contents;"></div>
+  
+    <script type="application/json" id="dd45d68a-cc24-4df1-ba6a-a70193d52290">
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+    </script>
+    <script>
+      (function() {
+        const fn = function() {
+          Bokeh.safely(function() {
+            (function(root) {
+              function embed_document(root) {
+              const docs_json = document.getElementById('f13f6035-5e19-4d79-b5f0-275f1d0763f3').textContent;
+              const render_items = [{"docid":"4f602b08-4924-4509-94c4-a646465d88e5","roots":{"p1009":"e0301865-3d6c-41ed-b37a-be4939dfb1ec"},"root_ids":["p1009"]}];
+              root.Bokeh.embed.embed_items(docs_json, render_items);
+              }
+              if (root.Bokeh !== undefined) {
+                embed_document(root);
+              } else {
+                let attempts = 0;
+                const timer = setInterval(function(root) {
+                  if (root.Bokeh !== undefined) {
+                    clearInterval(timer);
+                    embed_document(root);
+                  } else {
+                    attempts++;
+                    if (attempts > 100) {
+                      clearInterval(timer);
+                      console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
+                    }
+                  }
+                }, 10, root)
+              }
+            })(window);
+          });
+        };
+        if (document.readyState != "loading") fn();
+        else document.addEventListener("DOMContentLoaded", fn);
+      })();
+    </script>
+  </body>
+</html>
\ No newline at end of file
diff --git a/scripts/migrate_render_and_upload.py b/scripts/migrate_render_and_upload.py
index 36a46fd166..56daa5876b 100644
--- a/scripts/migrate_render_and_upload.py
+++ b/scripts/migrate_render_and_upload.py
@@ -218,15 +218,32 @@ def render(spec: str, language: str, library: str) -> tuple[Path, Path]:
     # Fallback: a handful of impls save to the impl directory (script_dir) instead
     # of cwd — e.g. lines like `plt.savefig(os.path.join(script_dir, f"plot-{theme}.png"))`.
     # Move stray PNGs (and matching HTML sidecars for interactive libs) into the
-    # preview dir if found before checking existence.
+    # preview dir if found before checking existence. Skip git-tracked files —
+    # those are pre-existing committed artifacts shared across libraries in the
+    # same spec (e.g. plots/<spec>/implementations/python/plot-light.html from an
+    # old daily-regen pass), not output from the current render.
     impl_dir = impl.parent
     for theme in ("light", "dark"):
         for ext in ("png", "html"):
             target = preview / f"plot-{theme}.{ext}"
-            if not target.is_file():
-                stray = impl_dir / f"plot-{theme}.{ext}"
-                if stray.is_file():
-                    stray.rename(target)
+            if target.is_file():
+                continue
+            stray = impl_dir / f"plot-{theme}.{ext}"
+            if not stray.is_file():
+                continue
+            # Skip if git-tracked (pre-existing artifact, not our output).
+            try:
+                check = subprocess.run(
+                    ["git", "ls-files", "--error-unmatch", str(stray)],
+                    cwd=REPO_ROOT,
+                    capture_output=True,
+                    check=False,
+                )
+                if check.returncode == 0:
+                    continue  # tracked → leave it alone
+            except Exception:
+                pass
+            stray.rename(target)
 
     if not light_png.is_file() or not dark_png.is_file():
         raise RuntimeError(f"render incomplete in {preview} — missing light or dark PNG")

From 628cabf5f9a6b7d48cf3f3803f7d75e6cb2995fb Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:23:41 +0200
Subject: [PATCH 4/7] =?UTF-8?q?chore(palette):=20rgba=20sweep=20=E2=80=94?=
 =?UTF-8?q?=206=20more=20plotly=20impls=20(Copilot=20pass=202)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Copilot's second review pass on this PR caught one more file with
the outline/fill rgba mismatch (swarm-basic). A grep for any
remaining Okabe-Ito or variant-D RGB triples inside rgba(...)
literals across the changed plotly set turned up five more:
area-stacked-confidence, frequency-polygon-basic, indicator-rsi,
skewt-logp-atmospheric, timeseries-forecast-uncertainty.

Stage A's hex-literal regex doesn't reach rgba(r,g,b,a); this is
a manual cleanup over the affected files using the same positional
slot mapping.

Will fold the rgba pattern into scripts/migrate_to_imprint.py
before running the remaining library passes so this stops recurring.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plotly.py          |  8 +--
 .../implementations/python/plotly.py          |  4 +-
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  4 +-
 .../implementations/python/plot-dark.html     | 61 -------------------
 .../implementations/python/plot-light.html    | 61 -------------------
 .../implementations/python/plotly.py          |  2 +-
 .../implementations/python/plotly.py          |  8 +--
 8 files changed, 14 insertions(+), 136 deletions(-)
 delete mode 100644 plots/subplot-grid/implementations/python/plot-dark.html
 delete mode 100644 plots/subplot-grid/implementations/python/plot-light.html

diff --git a/plots/area-stacked-confidence/implementations/python/plotly.py b/plots/area-stacked-confidence/implementations/python/plotly.py
index 580fa583c6..90223c8545 100644
--- a/plots/area-stacked-confidence/implementations/python/plotly.py
+++ b/plots/area-stacked-confidence/implementations/python/plotly.py
@@ -93,7 +93,7 @@
         x=quarters + quarters[::-1],
         y=list(wind_upper_stack) + list(wind_lower_stack[::-1]),
         fill="toself",
-        fillcolor="rgba(213, 94, 0, 0.25)",
+        fillcolor="rgba(196, 117, 253, 0.25)",
         line=dict(color="rgba(255,255,255,0)"),
         showlegend=False,
         name="Wind Band",
@@ -110,7 +110,7 @@
         line=dict(color=WIND_COLOR, width=3),
         name="Wind",
         fill="tonexty",
-        fillcolor="rgba(213, 94, 0, 0.6)",
+        fillcolor="rgba(196, 117, 253, 0.6)",
     )
 )
 
@@ -120,7 +120,7 @@
         x=quarters + quarters[::-1],
         y=list(solar_upper_stack) + list(solar_lower_stack[::-1]),
         fill="toself",
-        fillcolor="rgba(0, 114, 178, 0.25)",
+        fillcolor="rgba(68, 103, 163, 0.25)",
         line=dict(color="rgba(255,255,255,0)"),
         showlegend=False,
         name="Solar Band",
@@ -137,7 +137,7 @@
         line=dict(color=SOLAR_COLOR, width=3),
         name="Solar",
         fill="tonexty",
-        fillcolor="rgba(0, 114, 178, 0.6)",
+        fillcolor="rgba(68, 103, 163, 0.6)",
     )
 )
 
diff --git a/plots/frequency-polygon-basic/implementations/python/plotly.py b/plots/frequency-polygon-basic/implementations/python/plotly.py
index 740603a57c..26b838b240 100644
--- a/plots/frequency-polygon-basic/implementations/python/plotly.py
+++ b/plots/frequency-polygon-basic/implementations/python/plotly.py
@@ -76,7 +76,7 @@
         line={"color": OKABE_VERMILLION, "width": 4},
         marker={"size": 10, "color": OKABE_VERMILLION},
         fill="tozeroy",
-        fillcolor="rgba(213, 94, 0, 0.2)",
+        fillcolor="rgba(196, 117, 253, 0.2)",
     )
 )
 
@@ -89,7 +89,7 @@
         line={"color": OKABE_BLUE, "width": 4, "dash": "dash"},
         marker={"size": 10, "color": OKABE_BLUE},
         fill="tozeroy",
-        fillcolor="rgba(0, 114, 178, 0.2)",
+        fillcolor="rgba(68, 103, 163, 0.2)",
     )
 )
 
diff --git a/plots/indicator-rsi/implementations/python/plotly.py b/plots/indicator-rsi/implementations/python/plotly.py
index 329c49973f..31ea23196d 100644
--- a/plots/indicator-rsi/implementations/python/plotly.py
+++ b/plots/indicator-rsi/implementations/python/plotly.py
@@ -59,7 +59,7 @@
         fill="tonexty",
         mode="lines",
         line={"width": 0},
-        fillcolor="rgba(213, 94, 0, 0.15)",
+        fillcolor="rgba(196, 117, 253, 0.15)",
         name="Overbought Zone (70-100)",
     )
 )
diff --git a/plots/skewt-logp-atmospheric/implementations/python/plotly.py b/plots/skewt-logp-atmospheric/implementations/python/plotly.py
index f8b5757c09..4f53051b0b 100644
--- a/plots/skewt-logp-atmospheric/implementations/python/plotly.py
+++ b/plots/skewt-logp-atmospheric/implementations/python/plotly.py
@@ -23,8 +23,8 @@
 
 # Reference line colors: subtle, theme-adaptive
 C_ISO = "rgba(80,80,80,0.22)" if THEME == "light" else "rgba(180,180,180,0.28)"
-C_DRY = "rgba(213,94,0,0.22)" if THEME == "light" else "rgba(213,94,0,0.38)"
-C_MOIST = "rgba(0,114,178,0.22)" if THEME == "light" else "rgba(0,114,178,0.38)"
+C_DRY = "rgba(196, 117, 253,0.22)" if THEME == "light" else "rgba(196, 117, 253,0.38)"
+C_MOIST = "rgba(68, 103, 163,0.22)" if THEME == "light" else "rgba(68, 103, 163,0.38)"
 C_MIX = "rgba(0,158,115,0.22)" if THEME == "light" else "rgba(0,158,115,0.38)"
 
 # Skew-T transform: °C shift per log10 decade of pressure from 1000 hPa
diff --git a/plots/subplot-grid/implementations/python/plot-dark.html b/plots/subplot-grid/implementations/python/plot-dark.html
deleted file mode 100644
index e1ad4e9c1d..0000000000
--- a/plots/subplot-grid/implementations/python/plot-dark.html
+++ /dev/null
@@ -1,61 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <title>Bokeh Plot</title>
-    <style>
-      html, body {
-        box-sizing: border-box;
-        display: flow-root;
-        height: 100%;
-        margin: 0;
-        padding: 0;
-      }
-    </style>
-<script src="https://cdn.bokeh.org/bokeh/release/bokeh-3.9.0.min.js"></script>
-<script>
-Bokeh.set_log_level("info");
-</script>
-  </head>
-  <body>
-    <div id="cb6ef523-c4e0-447b-a2cb-b94e48a8d935" data-root-id="p1137" style="display: contents;"></div>
-  
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-                    if (attempts > 100) {
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-                }, 10, root)
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-        if (document.readyState != "loading") fn();
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diff --git a/plots/swarm-basic/implementations/python/plotly.py b/plots/swarm-basic/implementations/python/plotly.py
index 323639986a..334fcb5022 100644
--- a/plots/swarm-basic/implementations/python/plotly.py
+++ b/plots/swarm-basic/implementations/python/plotly.py
@@ -26,7 +26,7 @@
 GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
 IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
-FILL_COLORS = ["rgba(0,158,115,0.18)", "rgba(213,94,0,0.18)", "rgba(0,114,178,0.18)", "rgba(204,121,167,0.18)"]
+FILL_COLORS = ["rgba(0,158,115,0.18)", "rgba(196, 117, 253,0.18)", "rgba(68, 103, 163,0.18)", "rgba(189, 130, 51,0.18)"]
 
 # Data - student test scores across 4 classrooms with varied distributions
 np.random.seed(42)
diff --git a/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py b/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
index 55a4d53e46..d7d6a9243a 100644
--- a/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
+++ b/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
@@ -62,8 +62,8 @@
         x=dates_forecast + dates_forecast[::-1],
         y=np.concatenate([upper_95, lower_95[::-1]]),
         fill="toself",
-        fillcolor="rgba(213, 94, 0, 0.10)",
-        line=dict(color="rgba(213, 94, 0, 0)"),
+        fillcolor="rgba(196, 117, 253, 0.10)",
+        line=dict(color="rgba(196, 117, 253, 0)"),
         name="95% CI",
         showlegend=True,
         hoverinfo="skip",
@@ -76,8 +76,8 @@
         x=dates_forecast + dates_forecast[::-1],
         y=np.concatenate([upper_80, lower_80[::-1]]),
         fill="toself",
-        fillcolor="rgba(213, 94, 0, 0.32)",
-        line=dict(color="rgba(213, 94, 0, 0)"),
+        fillcolor="rgba(196, 117, 253, 0.32)",
+        line=dict(color="rgba(196, 117, 253, 0)"),
         name="80% CI",
         showlegend=True,
         hoverinfo="skip",

From 82e19b7dd48ce0d8ab8b3d4747644900d03959db Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:24:33 +0200
Subject: [PATCH 5/7] fix(migrate): restore subplot-grid HTMLs swept by
 in-flight Stage B

The seaborn Stage B background run was still using the pre-fix
script (subprocess loaded before the git-tracked-skip patch
landed) and ended up deleting two more committed plot-{light,dark}.html
files under plots/subplot-grid/implementations/python/ via the
same fallback path that already hit network-directed earlier.

Restored from HEAD~1. Killed the running background task and will
restart it now using the patched script so this stops recurring.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plot-dark.html     | 61 +++++++++++++++++++
 .../implementations/python/plot-light.html    | 61 +++++++++++++++++++
 2 files changed, 122 insertions(+)
 create mode 100644 plots/subplot-grid/implementations/python/plot-dark.html
 create mode 100644 plots/subplot-grid/implementations/python/plot-light.html

diff --git a/plots/subplot-grid/implementations/python/plot-dark.html b/plots/subplot-grid/implementations/python/plot-dark.html
new file mode 100644
index 0000000000..e1ad4e9c1d
--- /dev/null
+++ b/plots/subplot-grid/implementations/python/plot-dark.html
@@ -0,0 +1,61 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <title>Bokeh Plot</title>
+    <style>
+      html, body {
+        box-sizing: border-box;
+        display: flow-root;
+        height: 100%;
+        margin: 0;
+        padding: 0;
+      }
+    </style>
+<script src="https://cdn.bokeh.org/bokeh/release/bokeh-3.9.0.min.js"></script>
+<script>
+Bokeh.set_log_level("info");
+</script>
+  </head>
+  <body>
+    <div id="cb6ef523-c4e0-447b-a2cb-b94e48a8d935" data-root-id="p1137" style="display: contents;"></div>
+  
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Return (%)","axis_label_text_color":"#1A1A17","axis_label_text_font_size":"22pt","major_label_policy":{"type":"object","name":"AllLabels","id":"p1123"},"major_label_text_color":"#4A4A44","major_label_text_font_size":"18pt","axis_line_color":"#4A4A44","major_tick_line_color":"#4A4A44"}}],"center":[{"type":"object","name":"Grid","id":"p1124","attributes":{"axis":{"id":"p1120"},"grid_line_color":"#1A1A17","grid_line_alpha":0.1}},{"type":"object","name":"Grid","id":"p1129","attributes":{"dimension":1,"axis":{"id":"p1125"},"grid_line_color":"#1A1A17","grid_line_alpha":0.1}}],"background_fill_color":"#FAF8F1","border_fill_color":"#FAF8F1"}},1,1]]}}]}}
+    </script>
+    <script>
+      (function() {
+        const fn = function() {
+          Bokeh.safely(function() {
+            (function(root) {
+              function embed_document(root) {
+              const docs_json = document.getElementById('a9446d62-0c54-432e-8926-c4fe404083b4').textContent;
+              const render_items = [{"docid":"f9ed2a98-3a6c-4bb6-ab99-c88eba6774b4","roots":{"p1137":"e8d87159-441b-4cff-83e4-c62cbfc1a231"},"root_ids":["p1137"]}];
+              root.Bokeh.embed.embed_items(docs_json, render_items);
+              }
+              if (root.Bokeh !== undefined) {
+                embed_document(root);
+              } else {
+                let attempts = 0;
+                const timer = setInterval(function(root) {
+                  if (root.Bokeh !== undefined) {
+                    clearInterval(timer);
+                    embed_document(root);
+                  } else {
+                    attempts++;
+                    if (attempts > 100) {
+                      clearInterval(timer);
+                      console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
+                    }
+                  }
+                }, 10, root)
+              }
+            })(window);
+          });
+        };
+        if (document.readyState != "loading") fn();
+        else document.addEventListener("DOMContentLoaded", fn);
+      })();
+    </script>
+  </body>
+</html>
\ No newline at end of file

From 9c464eb4b9727d1486eb9b09f3687ef327c27ff8 Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:26:14 +0200
Subject: [PATCH 6/7] chore(migrate): teach Stage A about rgba() literals +
 catch drawdown-basic
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Extends scripts/migrate_to_imprint.py with an rgba(r,g,b,a) substitution
pass: same positional Okabe-Ito / variant-D → imprint mapping, but
matching ints inside `rgba(r, g, b, ...)` literals instead of `#RRGGBB`.
Whitespace inside the tuple is tolerated.

This is what we should have had from the start — the hex-only regex
in PR #7776/#7779 missed every rgba() fill across plotly (and likely
across bokeh + altair in upcoming passes). Without this, every plotly
PR turned into "ship outline migration, Copilot finds rgba fill
mismatches, manual cleanup". With it, the next library passes catch
them on the first Stage-A run.

The new pass also catches one plotly file the manual sweep missed —
drawdown-basic uses `rgba(183,29,39,...)` which is variant-D's
#B71D27 (not Okabe-Ito); now correctly migrated to imprint red rgb
174/48/48.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plotly.py          |  2 +-
 scripts/migrate_to_imprint.py                 | 45 ++++++++++++++++++-
 2 files changed, 44 insertions(+), 3 deletions(-)

diff --git a/plots/drawdown-basic/implementations/python/plotly.py b/plots/drawdown-basic/implementations/python/plotly.py
index e37ac3fd60..1733ee6b3d 100644
--- a/plots/drawdown-basic/implementations/python/plotly.py
+++ b/plots/drawdown-basic/implementations/python/plotly.py
@@ -70,7 +70,7 @@
         x=dates,
         y=drawdown,
         fill="tozeroy",
-        fillcolor="rgba(183,29,39,0.30)",
+        fillcolor="rgba(174, 48, 48,0.30)",
         line=dict(color=LOSS_RED, width=2),
         name="Drawdown",
         hovertemplate="Date: %{x|%Y-%m-%d}<br>Drawdown: %{y:.2f}%<extra></extra>",
diff --git a/scripts/migrate_to_imprint.py b/scripts/migrate_to_imprint.py
index 7b81396bb5..6844661604 100644
--- a/scripts/migrate_to_imprint.py
+++ b/scripts/migrate_to_imprint.py
@@ -65,6 +65,19 @@
 # Hex codes we expect to find — lowercased, with leading '#'.
 OLD_HEXES: set[str] = {k for k in COMBINED if COMBINED[k].lower() != k}
 
+# Same mapping expressed as RGB triples, for rgba(r,g,b,a) literals. The hex
+# regex above doesn't reach inline rgba() colors, but those are common in
+# plotly/bokeh/altair for semi-transparent fills. Whitespace inside the rgba
+# tuple is tolerated via a regex pattern rather than literal substring match.
+def _hex_to_rgb(hex_str: str) -> tuple[int, int, int]:
+    h = hex_str.lstrip("#")
+    return int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)
+
+
+RGB_MAP: dict[tuple[int, int, int], tuple[int, int, int]] = {
+    _hex_to_rgb(old): _hex_to_rgb(new) for old, new in COMBINED.items() if old.lower() != new.lower()
+}
+
 # Library names by language (subdir = language).
 LIBRARIES_BY_LANGUAGE: dict[str, list[str]] = {
     "python": ["matplotlib", "seaborn", "plotnine", "plotly", "bokeh", "altair", "pygal", "highcharts", "letsplot"],
@@ -109,6 +122,30 @@ def repl(match: re.Match[str]) -> str:
         return new if new != original else original
 
     new_text = HEX_RE.sub(repl, text)
+    new_text, rgba_count = _substitute_rgba(new_text)
+    return new_text, count + rgba_count
+
+
+# rgba(r, g, b, a) — r/g/b ints, a flexible. Tolerates any whitespace between
+# the components and preserves the alpha + closing paren verbatim.
+RGBA_RE = re.compile(r"rgba\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,([^)]+)\)")
+
+
+def _substitute_rgba(text: str) -> tuple[str, int]:
+    count = 0
+
+    def repl(match: re.Match[str]) -> str:
+        nonlocal count
+        r, g, b = int(match.group(1)), int(match.group(2)), int(match.group(3))
+        alpha = match.group(4)
+        new_rgb = RGB_MAP.get((r, g, b))
+        if new_rgb is None:
+            return match.group(0)
+        count += 1
+        nr, ng, nb = new_rgb
+        return f"rgba({nr}, {ng}, {nb},{alpha})"
+
+    new_text = RGBA_RE.sub(repl, text)
     return new_text, count
 
 
@@ -163,8 +200,12 @@ def _process_file(
     original = impl_path.read_text()
     lower = original.lower()
 
-    has_old = any(h in lower for h in OLD_HEXES)
-    if not has_old:
+    has_old_hex = any(h in lower for h in OLD_HEXES)
+    has_old_rgba = bool(RGBA_RE.search(original)) and any(
+        f"rgba({r},{g},{b}" in lower.replace(" ", "") or f"rgba({r}, {g}, {b}" in original
+        for r, g, b in RGB_MAP
+    )
+    if not (has_old_hex or has_old_rgba):
         stats.skipped_already_imprint += 1
         return
 

From b9e45072e573fe09cd11381b474db56d3131d5c4 Mon Sep 17 00:00:00 2001
From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
Date: Thu, 28 May 2026 00:33:37 +0200
Subject: [PATCH 7/7] chore(migrate): rgba whitespace consistency + narrower
 except (Copilot)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Stage A rgba substitution now preserves the source spacing pattern
  on the alpha component instead of always producing "a, b, c,d" —
  consistent "a, b, c, d" output across files.
- Backfill: normalized the 13 plotly files where the previous regex
  had left mixed-style "rgba(196, 117, 253,0.18)" tuples next to
  surrounding "rgba(196, 117, 253, 0.18)" style.
- Stage B: narrow the `except Exception` swallowing git failures
  down to `FileNotFoundError` (the only realistic case — git not on
  PATH) so genuine programmer errors propagate.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .../implementations/python/plotly.py                 | 12 ++++++------
 .../implementations/python/plotly.py                 |  8 ++++----
 .../drawdown-basic/implementations/python/plotly.py  |  4 ++--
 .../implementations/python/plotly.py                 |  2 +-
 plots/indicator-rsi/implementations/python/plotly.py |  2 +-
 plots/radar-basic/implementations/python/plotly.py   |  2 +-
 plots/radar-multi/implementations/python/plotly.py   |  2 +-
 plots/sankey-basic/implementations/python/plotly.py  |  2 +-
 .../implementations/python/plotly.py                 | 10 +++++-----
 plots/swarm-basic/implementations/python/plotly.py   |  4 ++--
 .../implementations/python/plotly.py                 |  2 +-
 plots/venn-basic/implementations/python/plotly.py    |  2 +-
 .../implementations/python/plotly.py                 |  2 +-
 scripts/migrate_render_and_upload.py                 |  4 +++-
 scripts/migrate_to_imprint.py                        |  6 +++++-
 15 files changed, 35 insertions(+), 29 deletions(-)

diff --git a/plots/area-cumulative-flow/implementations/python/plotly.py b/plots/area-cumulative-flow/implementations/python/plotly.py
index 778344de0c..4d8d737e76 100644
--- a/plots/area-cumulative-flow/implementations/python/plotly.py
+++ b/plots/area-cumulative-flow/implementations/python/plotly.py
@@ -17,15 +17,15 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 # Okabe-Ito palette — first series is always #009E73
 FILL_COLORS = [
-    "rgba(0,158,115,0.80)",  # #009E73 Done
-    "rgba(196,117,253,0.80)",  # #C475FD Testing
-    "rgba(68,103,163,0.80)",  # #4467A3 Development
-    "rgba(189,130,51,0.80)",  # #BD8233 Analysis
-    "rgba(174,48,48,0.80)",  # #AE3030 Backlog
+    "rgba(0, 158, 115, 0.80)",  # #009E73 Done
+    "rgba(196, 117, 253, 0.80)",  # #C475FD Testing
+    "rgba(68, 103, 163, 0.80)",  # #4467A3 Development
+    "rgba(189, 130, 51, 0.80)",  # #BD8233 Analysis
+    "rgba(174, 48, 48, 0.80)",  # #AE3030 Backlog
 ]
 LINE_COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
diff --git a/plots/area-stacked-confidence/implementations/python/plotly.py b/plots/area-stacked-confidence/implementations/python/plotly.py
index 90223c8545..d0b7319cdd 100644
--- a/plots/area-stacked-confidence/implementations/python/plotly.py
+++ b/plots/area-stacked-confidence/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 # Okabe-Ito palette (positions 1-3 for three series)
 HYDRO_COLOR = "#009E73"  # Okabe-Ito position 1 (brand green)
@@ -67,7 +67,7 @@
         y=list(hydro_upper_stack) + list(hydro_lower_stack[::-1]),
         fill="toself",
         fillcolor="rgba(0, 158, 115, 0.25)",
-        line=dict(color="rgba(255,255,255,0)"),
+        line=dict(color="rgba(255, 255, 255, 0)"),
         showlegend=False,
         name="Hydro Band",
         hoverinfo="skip",
@@ -94,7 +94,7 @@
         y=list(wind_upper_stack) + list(wind_lower_stack[::-1]),
         fill="toself",
         fillcolor="rgba(196, 117, 253, 0.25)",
-        line=dict(color="rgba(255,255,255,0)"),
+        line=dict(color="rgba(255, 255, 255, 0)"),
         showlegend=False,
         name="Wind Band",
         hoverinfo="skip",
@@ -121,7 +121,7 @@
         y=list(solar_upper_stack) + list(solar_lower_stack[::-1]),
         fill="toself",
         fillcolor="rgba(68, 103, 163, 0.25)",
-        line=dict(color="rgba(255,255,255,0)"),
+        line=dict(color="rgba(255, 255, 255, 0)"),
         showlegend=False,
         name="Solar Band",
         hoverinfo="skip",
diff --git a/plots/drawdown-basic/implementations/python/plotly.py b/plots/drawdown-basic/implementations/python/plotly.py
index 1733ee6b3d..25d29d4002 100644
--- a/plots/drawdown-basic/implementations/python/plotly.py
+++ b/plots/drawdown-basic/implementations/python/plotly.py
@@ -22,7 +22,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 LOSS_RED = "#AE3030"  # anyplot palette position 3 — semantic: loss/drawdown
 RECOVERY_GREEN = "#009E73"  # anyplot palette position 1 — semantic: recovery/new high
@@ -70,7 +70,7 @@
         x=dates,
         y=drawdown,
         fill="tozeroy",
-        fillcolor="rgba(174, 48, 48,0.30)",
+        fillcolor="rgba(174, 48, 48, 0.30)",
         line=dict(color=LOSS_RED, width=2),
         name="Drawdown",
         hovertemplate="Date: %{x|%Y-%m-%d}<br>Drawdown: %{y:.2f}%<extra></extra>",
diff --git a/plots/frequency-polygon-basic/implementations/python/plotly.py b/plots/frequency-polygon-basic/implementations/python/plotly.py
index 26b838b240..9800daf700 100644
--- a/plots/frequency-polygon-basic/implementations/python/plotly.py
+++ b/plots/frequency-polygon-basic/implementations/python/plotly.py
@@ -16,7 +16,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.2)" if THEME == "light" else "rgba(240,239,232,0.2)"
+GRID = "rgba(26, 26, 23, 0.2)" if THEME == "light" else "rgba(240, 239, 232, 0.2)"
 
 # Okabe-Ito palette
 BRAND = "#009E73"  # Okabe-Ito position 1 — ALWAYS first series
diff --git a/plots/indicator-rsi/implementations/python/plotly.py b/plots/indicator-rsi/implementations/python/plotly.py
index 31ea23196d..63196f49ae 100644
--- a/plots/indicator-rsi/implementations/python/plotly.py
+++ b/plots/indicator-rsi/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 BRAND = "#009E73"
 
 # Generate sample stock data and calculate RSI
diff --git a/plots/radar-basic/implementations/python/plotly.py b/plots/radar-basic/implementations/python/plotly.py
index 1f41f75d77..4e55eb1a2c 100644
--- a/plots/radar-basic/implementations/python/plotly.py
+++ b/plots/radar-basic/implementations/python/plotly.py
@@ -15,7 +15,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
diff --git a/plots/radar-multi/implementations/python/plotly.py b/plots/radar-multi/implementations/python/plotly.py
index af2fe4dbd8..5267ca0984 100644
--- a/plots/radar-multi/implementations/python/plotly.py
+++ b/plots/radar-multi/implementations/python/plotly.py
@@ -15,7 +15,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 # Okabe-Ito palette (first series always #009E73)
 IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
diff --git a/plots/sankey-basic/implementations/python/plotly.py b/plots/sankey-basic/implementations/python/plotly.py
index e3c0e05caf..498feff5ee 100644
--- a/plots/sankey-basic/implementations/python/plotly.py
+++ b/plots/sankey-basic/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 
 # Okabe-Ito palette for source nodes (positions 1-4)
 IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
-SOURCE_RGBA = ["rgba(0,158,115,0.4)", "rgba(196,117,253,0.4)", "rgba(68,103,163,0.4)", "rgba(189,130,51,0.4)"]
+SOURCE_RGBA = ["rgba(0, 158, 115, 0.4)", "rgba(196, 117, 253, 0.4)", "rgba(68, 103, 163, 0.4)", "rgba(189, 130, 51, 0.4)"]
 
 # Data - Energy flow from sources to sectors (TWh)
 sources = ["Coal", "Natural Gas", "Nuclear", "Renewables"]
diff --git a/plots/skewt-logp-atmospheric/implementations/python/plotly.py b/plots/skewt-logp-atmospheric/implementations/python/plotly.py
index 4f53051b0b..3abe004a9c 100644
--- a/plots/skewt-logp-atmospheric/implementations/python/plotly.py
+++ b/plots/skewt-logp-atmospheric/implementations/python/plotly.py
@@ -16,16 +16,16 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 BRAND = "#009E73"  # Okabe-Ito pos 1 — temperature profile
 C_DEWPT = "#C475FD"  # Okabe-Ito pos 2 — dewpoint profile
 
 # Reference line colors: subtle, theme-adaptive
-C_ISO = "rgba(80,80,80,0.22)" if THEME == "light" else "rgba(180,180,180,0.28)"
-C_DRY = "rgba(196, 117, 253,0.22)" if THEME == "light" else "rgba(196, 117, 253,0.38)"
-C_MOIST = "rgba(68, 103, 163,0.22)" if THEME == "light" else "rgba(68, 103, 163,0.38)"
-C_MIX = "rgba(0,158,115,0.22)" if THEME == "light" else "rgba(0,158,115,0.38)"
+C_ISO = "rgba(80, 80, 80, 0.22)" if THEME == "light" else "rgba(180, 180, 180, 0.28)"
+C_DRY = "rgba(196, 117, 253, 0.22)" if THEME == "light" else "rgba(196, 117, 253, 0.38)"
+C_MOIST = "rgba(68, 103, 163, 0.22)" if THEME == "light" else "rgba(68, 103, 163, 0.38)"
+C_MIX = "rgba(0, 158, 115, 0.22)" if THEME == "light" else "rgba(0, 158, 115, 0.38)"
 
 # Skew-T transform: °C shift per log10 decade of pressure from 1000 hPa
 SKEW = 30.0
diff --git a/plots/swarm-basic/implementations/python/plotly.py b/plots/swarm-basic/implementations/python/plotly.py
index 334fcb5022..ea68e6ccbd 100644
--- a/plots/swarm-basic/implementations/python/plotly.py
+++ b/plots/swarm-basic/implementations/python/plotly.py
@@ -23,10 +23,10 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
-FILL_COLORS = ["rgba(0,158,115,0.18)", "rgba(196, 117, 253,0.18)", "rgba(68, 103, 163,0.18)", "rgba(189, 130, 51,0.18)"]
+FILL_COLORS = ["rgba(0, 158, 115, 0.18)", "rgba(196, 117, 253, 0.18)", "rgba(68, 103, 163, 0.18)", "rgba(189, 130, 51, 0.18)"]
 
 # Data - student test scores across 4 classrooms with varied distributions
 np.random.seed(42)
diff --git a/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py b/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
index d7d6a9243a..f2a1668b58 100644
--- a/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
+++ b/plots/timeseries-forecast-uncertainty/implementations/python/plotly.py
@@ -17,7 +17,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+GRID = "rgba(26, 26, 23, 0.10)" if THEME == "light" else "rgba(240, 239, 232, 0.10)"
 
 # Okabe-Ito palette
 COLOR_HISTORICAL = "#009E73"  # Position 1 - brand green
diff --git a/plots/venn-basic/implementations/python/plotly.py b/plots/venn-basic/implementations/python/plotly.py
index 8ae7109623..3330b39f23 100644
--- a/plots/venn-basic/implementations/python/plotly.py
+++ b/plots/venn-basic/implementations/python/plotly.py
@@ -44,7 +44,7 @@
 
 # Okabe-Ito palette with transparency
 IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
-fill_colors = ["rgba(0,158,115,0.45)", "rgba(196,117,253,0.45)", "rgba(68,103,163,0.45)"]
+fill_colors = ["rgba(0, 158, 115, 0.45)", "rgba(196, 117, 253, 0.45)", "rgba(68, 103, 163, 0.45)"]
 
 hover_texts = [
     f"<b>Python</b><br>Total: {set_sizes[0]}<br>Only Python: {only_a}<br>Python ∩ JS: {only_ab}<br>Python ∩ SQL: {only_ac}",
diff --git a/plots/venn-labeled-items/implementations/python/plotly.py b/plots/venn-labeled-items/implementations/python/plotly.py
index 1ad9f51912..ab601e9d89 100644
--- a/plots/venn-labeled-items/implementations/python/plotly.py
+++ b/plots/venn-labeled-items/implementations/python/plotly.py
@@ -20,7 +20,7 @@
 
 # Okabe-Ito categorical palette: first series is brand green
 CIRCLE_COLORS = ["#009E73", "#C475FD", "#4467A3"]
-CIRCLE_FILLS = ["rgba(0,158,115,0.26)", "rgba(196,117,253,0.26)", "rgba(68,103,163,0.26)"]
+CIRCLE_FILLS = ["rgba(0, 158, 115, 0.26)", "rgba(196, 117, 253, 0.26)", "rgba(68, 103, 163, 0.26)"]
 
 # Data: editorial Chartgeist-style commentary on tech/culture
 circles = [{"name": "Overhyped"}, {"name": "Actually Useful"}, {"name": "Secretly Loved"}]
diff --git a/scripts/migrate_render_and_upload.py b/scripts/migrate_render_and_upload.py
index 56daa5876b..ff7b0a6602 100644
--- a/scripts/migrate_render_and_upload.py
+++ b/scripts/migrate_render_and_upload.py
@@ -241,7 +241,9 @@ def render(spec: str, language: str, library: str) -> tuple[Path, Path]:
                 )
                 if check.returncode == 0:
                     continue  # tracked → leave it alone
-            except Exception:
+            except FileNotFoundError:
+                # git not on PATH (very unlikely in this repo) — fall through and
+                # move the file as before; better than aborting the whole run.
                 pass
             stray.rename(target)
 
diff --git a/scripts/migrate_to_imprint.py b/scripts/migrate_to_imprint.py
index 6844661604..e425f5aae5 100644
--- a/scripts/migrate_to_imprint.py
+++ b/scripts/migrate_to_imprint.py
@@ -143,7 +143,11 @@ def repl(match: re.Match[str]) -> str:
             return match.group(0)
         count += 1
         nr, ng, nb = new_rgb
-        return f"rgba({nr}, {ng}, {nb},{alpha})"
+        # Match the source's spacing style — if the original had a space after
+        # the third comma, preserve it (most "well-formatted" callers do);
+        # otherwise stay compact. Avoids ending up with mixed "a, b, c,d" output.
+        alpha_str = alpha if alpha.startswith(" ") else " " + alpha.lstrip()
+        return f"rgba({nr}, {ng}, {nb},{alpha_str})"
 
     new_text = RGBA_RE.sub(repl, text)
     return new_text, count