chore(palette): migrate matplotlib impls from Okabe-Ito to imprint

plots/alluvial-basic/implementations/python/matplotlib.py

@@ -20,19 +20,19 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette — use positions 1→N in order
-OKABE_ITO = [
+IMPRINT = [
     "#009E73",  # 1: bluish green (brand)
-    "#D55E00",  # 2: vermillion
-    "#0072B2",  # 3: blue
-    "#CC79A7",  # 4: reddish purple
+    "#C475FD",  # 2: vermillion
+    "#4467A3",  # 3: blue
+    "#BD8233",  # 4: reddish purple
 ]
 
 # Data: Voter migration across 4 election cycles
 np.random.seed(42)
 
 time_points = ["2012", "2016", "2020", "2024"]
 categories = ["Party A", "Party B", "Party C", "Independent"]
-colors = {cat: OKABE_ITO[i] for i, cat in enumerate(categories)}
+colors = {cat: IMPRINT[i] for i, cat in enumerate(categories)}
 
 # Node values at each time point (thousands of voters)
 node_values = {

plots/andrews-curves/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data
 np.random.seed(42)
@@ -55,11 +55,11 @@
 
 # Plot Andrews curves for each observation
 for i in range(len(curves)):
-    ax.plot(t, curves[i], color=OKABE_ITO[y[i]], alpha=0.4, linewidth=2.5)
+    ax.plot(t, curves[i], color=IMPRINT[y[i]], alpha=0.4, linewidth=2.5)
 
 # Create legend with sample lines
 for idx, species in enumerate(species_names):
-    ax.plot([], [], color=OKABE_ITO[idx], linewidth=3, label=species, alpha=0.4)
+    ax.plot([], [], color=IMPRINT[idx], linewidth=3, label=species, alpha=0.4)
 
 # Style
 ax.set_xlabel("t (radians)", fontsize=20, color=INK)

plots/area-cumulative-flow/implementations/python/matplotlib.py

@@ -28,7 +28,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito positions 1-5 (bottom to top: Done → Backlog)
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data: 90-day software Kanban board simulation
 np.random.seed(42)

plots/area-stacked-confidence/implementations/python/matplotlib.py

@@ -19,7 +19,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: quarterly energy consumption by source with measurement uncertainty
 np.random.seed(42)
@@ -73,34 +73,34 @@
 
 # Plot stacked areas from bottom to top with enhanced styling
 # Solar (bottom layer)
-ax.fill_between(quarters, 0, solar_stack, color=OKABE_ITO[0], alpha=0.85, label="Solar", zorder=3)
-ax.fill_between(quarters, solar_lower, solar_upper, color=OKABE_ITO[0], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, solar_lower, color=OKABE_ITO[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, solar_upper, color=OKABE_ITO[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, 0, solar_stack, color=IMPRINT[0], alpha=0.85, label="Solar", zorder=3)
+ax.fill_between(quarters, solar_lower, solar_upper, color=IMPRINT[0], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, solar_lower, color=IMPRINT[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, solar_upper, color=IMPRINT[0], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Wind (second layer)
-ax.fill_between(quarters, solar_stack, wind_stack, color=OKABE_ITO[1], alpha=0.85, label="Wind", zorder=3)
-ax.fill_between(quarters, wind_lower, wind_upper, color=OKABE_ITO[1], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, wind_lower, color=OKABE_ITO[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, wind_upper, color=OKABE_ITO[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, solar_stack, wind_stack, color=IMPRINT[1], alpha=0.85, label="Wind", zorder=3)
+ax.fill_between(quarters, wind_lower, wind_upper, color=IMPRINT[1], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, wind_lower, color=IMPRINT[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, wind_upper, color=IMPRINT[1], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Hydro (third layer)
-ax.fill_between(quarters, wind_stack, hydro_stack, color=OKABE_ITO[2], alpha=0.85, label="Hydro", zorder=3)
-ax.fill_between(quarters, hydro_lower, hydro_upper, color=OKABE_ITO[2], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, hydro_lower, color=OKABE_ITO[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, hydro_upper, color=OKABE_ITO[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, wind_stack, hydro_stack, color=IMPRINT[2], alpha=0.85, label="Hydro", zorder=3)
+ax.fill_between(quarters, hydro_lower, hydro_upper, color=IMPRINT[2], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, hydro_lower, color=IMPRINT[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, hydro_upper, color=IMPRINT[2], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Natural Gas (top layer)
-ax.fill_between(quarters, hydro_stack, gas_stack, color=OKABE_ITO[3], alpha=0.85, label="Natural Gas", zorder=3)
-ax.fill_between(quarters, gas_lower, gas_upper, color=OKABE_ITO[3], alpha=0.25, linewidth=0, zorder=2)
-ax.plot(quarters, gas_lower, color=OKABE_ITO[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
-ax.plot(quarters, gas_upper, color=OKABE_ITO[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.fill_between(quarters, hydro_stack, gas_stack, color=IMPRINT[3], alpha=0.85, label="Natural Gas", zorder=3)
+ax.fill_between(quarters, gas_lower, gas_upper, color=IMPRINT[3], alpha=0.25, linewidth=0, zorder=2)
+ax.plot(quarters, gas_lower, color=IMPRINT[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
+ax.plot(quarters, gas_upper, color=IMPRINT[3], linewidth=0.8, alpha=0.4, linestyle=":", zorder=1)
 
 # Add prominent center lines for clarity and visual hierarchy
-ax.plot(quarters, solar_stack, color=OKABE_ITO[0], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
-ax.plot(quarters, wind_stack, color=OKABE_ITO[1], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
-ax.plot(quarters, hydro_stack, color=OKABE_ITO[2], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
-ax.plot(quarters, gas_stack, color=OKABE_ITO[3], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, solar_stack, color=IMPRINT[0], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, wind_stack, color=IMPRINT[1], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, hydro_stack, color=IMPRINT[2], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
+ax.plot(quarters, gas_stack, color=IMPRINT[3], linewidth=3, alpha=1.0, zorder=4, solid_capstyle="round")
 
 # Styling
 ax.set_xlabel("Quarter", fontsize=20, color=INK)

plots/area-stacked-percent/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Renewable energy evolution (2014-2024)
 years = np.arange(2014, 2025)
@@ -41,7 +41,7 @@
 labels = ["Solar", "Wind", "Hydro", "Natural Gas", "Coal"]
 
 # Create stacked area
-ax.stackplot(years, data_percent, labels=labels, colors=OKABE_ITO, alpha=0.85, edgecolor=PAGE_BG, linewidth=1.5)
+ax.stackplot(years, data_percent, labels=labels, colors=IMPRINT, alpha=0.85, edgecolor=PAGE_BG, linewidth=1.5)
 
 # Styling
 ax.set_xlabel("Year (2014–2024)", fontsize=20, color=INK)

plots/area-stacked/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (use positions 1→N)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Monthly website traffic sources over 24 months
 np.random.seed(42)
@@ -44,7 +44,7 @@
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-ax.stackplot(months, data, labels=categories, colors=OKABE_ITO, alpha=0.85)
+ax.stackplot(months, data, labels=categories, colors=IMPRINT, alpha=0.85)
 
 # X-axis formatting (show as months)
 tick_positions = [1, 6, 12, 18, 24]

plots/bar-diverging/implementations/python/matplotlib.py

@@ -43,8 +43,8 @@
 values_sorted = values[sorted_indices]
 
 # Create colors using Okabe-Ito palette
-# #009E73 (position 1) for positive, #D55E00 (position 2) for negative
-colors = ["#009E73" if v >= 0 else "#D55E00" for v in values_sorted]
+# #009E73 (position 1) for positive, #C475FD (position 2) for negative
+colors = ["#009E73" if v >= 0 else "#C475FD" for v in values_sorted]
 
 # Plot
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
@@ -87,7 +87,7 @@
 # Add legend
 legend_elements = [
     Patch(facecolor="#009E73", edgecolor=INK_SOFT, label="Positive (Satisfied)"),
-    Patch(facecolor="#D55E00", edgecolor=INK_SOFT, label="Negative (Dissatisfied)"),
+    Patch(facecolor="#C475FD", edgecolor=INK_SOFT, label="Negative (Dissatisfied)"),
 ]
 leg = ax.legend(handles=legend_elements, loc="lower right", fontsize=16)
 if leg:

plots/bar-grouped/implementations/python/matplotlib.py

@@ -19,7 +19,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette (position 1 is always #009E73)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data: Quarterly sales by product line (in thousands USD)
 categories = ["Q1", "Q2", "Q3", "Q4"]
@@ -45,7 +45,7 @@
 max_values_per_category = {cat: max(sales_data[group][i] for group in groups) for i, cat in enumerate(categories)}
 
 bars = []
-for i, (group, color) in enumerate(zip(groups, OKABE_ITO, strict=True)):
+for i, (group, color) in enumerate(zip(groups, IMPRINT, strict=True)):
     bar = ax.bar(
         x + offsets[i], sales_data[group], bar_width, label=group, color=color, edgecolor=INK_SOFT, linewidth=1.5
     )

plots/bar-race-animated/implementations/python/matplotlib.py

@@ -18,11 +18,11 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 
 # Data: Tech Company Market Cap ($B) — year-end snapshots (2019–2024)
 companies = ["Alphabet", "Amazon", "Apple", "Meta", "Microsoft", "Nvidia", "Tesla"]
-company_colors = {c: OKABE_ITO[i] for i, c in enumerate(companies)}
+company_colors = {c: IMPRINT[i] for i, c in enumerate(companies)}
 
 market_cap = {
     "Alphabet": [920, 1200, 1960, 1140, 1800, 2150],

plots/bar-spine/implementations/python/matplotlib.py

@@ -48,7 +48,7 @@
 
 # Colors: Okabe-Ito positions 1 and 2
 COLOR_SURVIVED = "#009E73"
-COLOR_DIED = "#D55E00"
+COLOR_DIED = "#C475FD"
 
 # Plot
 fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)

plots/bar-stacked-labeled/implementations/python/matplotlib.py

@@ -26,7 +26,7 @@
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
 # Okabe-Ito palette (positions 1-4)
-COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+COLORS = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data - Quarterly revenue by product category (in millions $)
 np.random.seed(42)

plots/bar-stacked-percent/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (first series is ALWAYS position 1)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data: Energy mix by country (percentage of total electricity generation)
 categories = ["Germany", "France", "UK", "Spain", "Italy", "Poland"]
@@ -49,7 +49,7 @@
 bottom = np.zeros(len(categories))
 
 # Create stacked bars
-for i, (component, color) in enumerate(zip(components, OKABE_ITO, strict=True)):
+for i, (component, color) in enumerate(zip(components, IMPRINT, strict=True)):
     bars = ax.bar(
         x, percentages[:, i], bar_width, bottom=bottom, label=component, color=color, edgecolor=PAGE_BG, linewidth=1.5
     )

plots/bar-stacked/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-4)
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
 
 # Data: Quarterly revenue by product category (in millions USD)
 categories = ["Q1", "Q2", "Q3", "Q4"]
@@ -39,7 +39,7 @@
 # Create stacked bars
 bottom = np.zeros(len(categories))
 for i, (product, values) in enumerate(zip(products, [software, hardware, services, support], strict=True)):
-    ax.bar(x, values, bar_width, label=product, bottom=bottom, color=OKABE_ITO[i], edgecolor=PAGE_BG, linewidth=1.5)
+    ax.bar(x, values, bar_width, label=product, bottom=bottom, color=IMPRINT[i], edgecolor=PAGE_BG, linewidth=1.5)
     bottom += values
 
 # Add total labels above each stacked bar

plots/bland-altman-basic/implementations/python/matplotlib.py

@@ -17,7 +17,7 @@
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"  # Okabe-Ito position 1 — first categorical series
-ACCENT2 = "#D55E00"  # Okabe-Ito position 2 — for limits of agreement lines
+ACCENT2 = "#C475FD"  # Okabe-Ito position 2 — for limits of agreement lines
 
 # Data: Simulated blood pressure readings from two sphygmomanometers
 np.random.seed(42)

plots/box-grouped/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette (positions 1-3 for three subcategories)
-COLORS = ["#009E73", "#D55E00", "#0072B2"]
+COLORS = ["#009E73", "#C475FD", "#4467A3"]
 
 # Data - Employee performance scores across departments and experience levels
 np.random.seed(42)

plots/box-notched/implementations/python/matplotlib.py

@@ -18,7 +18,7 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette - first series is always brand green
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00"]
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
 
 # Data - Test success rates across different test suites
 np.random.seed(42)
@@ -68,7 +68,7 @@
 )
 
 # Apply colors to boxes
-for patch, color in zip(bp["boxes"], OKABE_ITO, strict=True):
+for patch, color in zip(bp["boxes"], IMPRINT, strict=True):
     patch.set_facecolor(color)
     patch.set_alpha(0.75)
     patch.set_edgecolor(INK_SOFT)

plots/calibration-curve/implementations/python/matplotlib.py

@@ -18,8 +18,8 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Okabe-Ito palette - canonical order
-OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
-BRAND = OKABE_ITO[0]  # #009E73
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
+BRAND = IMPRINT[0]  # #009E73
 
 # Data
 np.random.seed(42)
@@ -97,41 +97,41 @@
 
 # Well-calibrated
 ax1.fill_between(
-    prob_pred_cal, prob_true_cal - prob_std_cal, prob_true_cal + prob_std_cal, alpha=0.2, color=OKABE_ITO[0]
+    prob_pred_cal, prob_true_cal - prob_std_cal, prob_true_cal + prob_std_cal, alpha=0.2, color=IMPRINT[0]
 )
 ax1.plot(
     prob_pred_cal,
     prob_true_cal,
     "o-",
-    color=OKABE_ITO[0],
+    color=IMPRINT[0],
     linewidth=3,
     markersize=10,
     label=f"Well-Calibrated (Brier: {brier_cal:.3f})",
 )
 
 # Overconfident
 ax1.fill_between(
-    prob_pred_over, prob_true_over - prob_std_over, prob_true_over + prob_std_over, alpha=0.2, color=OKABE_ITO[1]
+    prob_pred_over, prob_true_over - prob_std_over, prob_true_over + prob_std_over, alpha=0.2, color=IMPRINT[1]
 )
 ax1.plot(
     prob_pred_over,
     prob_true_over,
     "s-",
-    color=OKABE_ITO[1],
+    color=IMPRINT[1],
     linewidth=3,
     markersize=10,
     label=f"Overconfident (Brier: {brier_over:.3f})",
 )
 
 # Underconfident
 ax1.fill_between(
-    prob_pred_under, prob_true_under - prob_std_under, prob_true_under + prob_std_under, alpha=0.2, color=OKABE_ITO[2]
+    prob_pred_under, prob_true_under - prob_std_under, prob_true_under + prob_std_under, alpha=0.2, color=IMPRINT[2]
 )
 ax1.plot(
     prob_pred_under,
     prob_true_under,
     "^-",
-    color=OKABE_ITO[2],
+    color=IMPRINT[2],
     linewidth=3,
     markersize=10,
     label=f"Underconfident (Brier: {brier_under:.3f})",
@@ -159,13 +159,13 @@
 
 # Histogram
 ax2.hist(
-    y_prob_calibrated, bins=20, alpha=0.6, color=OKABE_ITO[0], label="Well-Calibrated", edgecolor=PAGE_BG, linewidth=0.5
+    y_prob_calibrated, bins=20, alpha=0.6, color=IMPRINT[0], label="Well-Calibrated", edgecolor=PAGE_BG, linewidth=0.5
 )
 ax2.hist(
     y_prob_overconfident,
     bins=20,
     alpha=0.6,
-    color=OKABE_ITO[1],
+    color=IMPRINT[1],
     label="Overconfident",
     edgecolor=PAGE_BG,
     linewidth=0.5,
@@ -174,7 +174,7 @@
     y_prob_underconfident,
     bins=20,
     alpha=0.6,
-    color=OKABE_ITO[2],
+    color=IMPRINT[2],
     label="Underconfident",
     edgecolor=PAGE_BG,
     linewidth=0.5,

plots/candlestick-volume/implementations/python/matplotlib.py

@@ -22,7 +22,7 @@
 
 # Okabe-Ito palette
 UP_COLOR = "#009E73"  # Brand green (up days)
-DOWN_COLOR = "#D55E00"  # Vermillion (down days)
+DOWN_COLOR = "#C475FD"  # Vermillion (down days)
 
 # Data - Generate realistic 60 trading days of OHLC data with volume
 np.random.seed(42)