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feat(seaborn): implement cartogram-area-distortion #8457

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5f22a3b
feat(seaborn): implement cartogram-area-distortion
github-actions[bot] Jun 8, 2026
fbed57e
chore(seaborn): add metadata for cartogram-area-distortion
github-actions[bot] Jun 8, 2026
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220 changes: 114 additions & 106 deletions plots/cartogram-area-distortion/implementations/python/seaborn.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,32 @@
""" pyplots.ai
"""anyplot.ai
cartogram-area-distortion: Cartogram with Area Distortion by Data Value
Library: seaborn 0.13.2 | Python 3.14.3
Quality: 89/100 | Created: 2026-03-13
Library: seaborn | Python 3.13
Quality: pending | Created: 2026-06-08
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


# Data: US states with population (millions) and grid positions
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Imprint palette — first series always #009E73
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]

# Data: US states with population (millions) and approximate grid positions
np.random.seed(42)

states_data = {
# (row, col, population_millions, region)
"WA": (0, 1, 7.7, "West"),
"MT": (0, 3, 1.1, "West"),
"ND": (0, 5, 0.8, "Midwest"),
Expand Down Expand Up @@ -67,35 +79,56 @@
"HI": (5, 2, 1.4, "West"),
}

# Build dataframe
rows = []
rows_data = []
for state, (r, c, pop, region) in states_data.items():
rows.append({"state": state, "row": r, "col": c, "population": pop, "region": region})
df = pd.DataFrame(rows)

# Set minimum marker size so smallest states remain readable
size_min = 150
size_max = 4200
rows_data.append({"state": state, "row": r, "col": c, "population": pop, "region": region})
df = pd.DataFrame(rows_data)

# Region ordering and seaborn colorblind palette
# Region colors using Imprint palette (West→green, Midwest→lavender, South→blue, Northeast→ochre)
region_order = ["West", "Midwest", "South", "Northeast"]
region_palette = dict(zip(region_order, sns.color_palette("colorblind", n_colors=4), strict=False))
region_palette = dict(zip(region_order, IMPRINT_PALETTE[:4], strict=False))

# Setup theme using seaborn's set_theme with custom rc params
# Apply seaborn theme with theme-adaptive tokens
sns.set_theme(
style="white",
context="talk",
font_scale=1.15,
rc={"figure.facecolor": "#f5f5f5", "axes.facecolor": "#f5f5f5", "font.family": "sans-serif"},
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.15,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)

fig = plt.figure(figsize=(16, 9))
gs = fig.add_gridspec(2, 2, width_ratios=[3.2, 1], height_ratios=[1, 1], wspace=0.08, hspace=0.25)
# Canvas — exactly 3200×1800 px (landscape 16:9)
fig = plt.figure(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
gs = fig.add_gridspec(
2,
2,
width_ratios=[3.0, 1.2],
height_ratios=[1, 1],
wspace=0.12,
hspace=0.32,
left=0.01,
right=0.99,
top=0.89,
bottom=0.04,
)
ax_main = fig.add_subplot(gs[:, 0])
ax_ref = fig.add_subplot(gs[0, 1])
ax_bar = fig.add_subplot(gs[1, 1])

# Main cartogram using sns.scatterplot with size and hue encoding
# Marker sizes calibrated for 3200×1800 canvas
size_min = 20
size_max = 620

# Main cartogram: bubble area ∝ population, color = geographic region
sns.scatterplot(
data=df,
x="col",
Expand All @@ -108,46 +141,51 @@
style="region",
style_order=region_order,
markers=dict.fromkeys(region_order, "s"),
alpha=0.85,
edgecolor="white",
linewidth=1.5,
alpha=0.88,
edgecolor=PAGE_BG,
linewidth=0.5,
ax=ax_main,
)

# Extract region-only handles from auto-generated legend, then reposition as compact horizontal
# Compact region legend — keep only hue (region) handles, remove size handles
handles, labels = ax_main.get_legend_handles_labels()
region_handles = []
region_labels = []
region_handles, region_labels = [], []
seen = set()
for handle, lbl in zip(handles, labels, strict=False):
if lbl in region_order:
handle.set_markersize(12)
handle.set_markeredgecolor("white")
handle.set_markeredgewidth(1)
if lbl in region_order and lbl not in seen:
try:
handle.set_markersize(7)
handle.set_markeredgecolor(PAGE_BG)
handle.set_markeredgewidth(0.4)
except AttributeError:
pass
region_handles.append(handle)
region_labels.append(lbl)
seen.add(lbl)

ax_main.get_legend().remove()
ax_main.legend(
handles=region_handles,
labels=region_labels,
loc="upper center",
fontsize=12,
fontsize=5.5,
title="Region",
title_fontsize=13,
title_fontsize=6,
framealpha=0.9,
edgecolor="#cccccc",
facecolor=ELEVATED_BG,
edgecolor=INK_SOFT,
ncol=4,
bbox_to_anchor=(0.45, 1.02),
borderpad=0.6,
columnspacing=1.0,
handletextpad=0.4,
bbox_to_anchor=(0.42, 1.0),
borderpad=0.4,
columnspacing=0.7,
handletextpad=0.3,
)

# State abbreviation labels on main cartogram
# State abbreviation labels — size scales with population
pop_max = df["population"].max()
for _, row in df.iterrows():
pop_frac = row["population"] / pop_max
fontsize = max(10, min(18, int(11 + pop_frac * 8)))
fontsize = max(4, min(7, int(4 + pop_frac * 3.5)))
ax_main.text(
row["col"],
row["row"] - 0.03,
Expand All @@ -159,53 +197,49 @@
color="white",
zorder=5,
)
# Population label for larger states
if row["population"] >= 5.0:
if row["population"] >= 8.0:
ax_main.text(
row["col"],
row["row"] + 0.22,
f"{row['population']:.0f}M",
ha="center",
va="center",
fontsize=max(8, int(fontsize * 0.6)),
fontsize=max(3.5, int(fontsize * 0.65)),
color="white",
alpha=0.9,
zorder=5,
)

# Style main axes
ax_main.invert_yaxis()
ax_main.set_aspect("equal")
ax_main.set_xlim(-0.8, 13.5)
ax_main.set_ylim(5.8, -0.7)
ax_main.set_xlabel("")
ax_main.set_ylabel("")
ax_main.set_xticks([])
ax_main.set_yticks([])
ax_main.set_facecolor(PAGE_BG)
sns.despine(ax=ax_main, left=True, bottom=True)

# Title - at least 24pt per quality criteria VQ-01
ax_main.set_title(
"US States by Population\ncartogram-area-distortion \u00b7 seaborn \u00b7 pyplots.ai",
fontsize=24,
fontweight="bold",
pad=18,
)
# Title — scale fontsize for long title string
title = "US States by Population · cartogram-area-distortion · python · seaborn · anyplot.ai"
n = len(title)
ratio = 67 / n if n > 67 else 1.0
title_fontsize = max(8, round(12 * ratio))
ax_main.set_title(title, fontsize=title_fontsize, fontweight="medium", color=INK, pad=8)

# Size annotation
ax_main.text(
0.98,
0.02,
"Tile area \u221d state population",
"Tile area ∝ state population",
ha="right",
va="bottom",
fontsize=13,
color="#666666",
fontsize=5.5,
color=INK_MUTED,
fontstyle="italic",
transform=ax_main.transAxes,
)

# --- Reference inset: equal-size tile map using seaborn's scatterplot with hue ---
# Reference inset — equal-area tile map for comparison
sns.scatterplot(
data=df,
x="col",
Expand All @@ -216,58 +250,41 @@
style="region",
style_order=region_order,
markers=dict.fromkeys(region_order, "s"),
s=120,
alpha=0.7,
edgecolor="white",
linewidth=0.8,
s=22,
alpha=0.72,
edgecolor=PAGE_BG,
linewidth=0.3,
legend=False,
ax=ax_ref,
)

# Labels on reference map - increased to 8pt for readability
for _, row in df.iterrows():
ax_ref.text(
row["col"],
row["row"],
row["state"],
ha="center",
va="center",
fontsize=8,
fontsize=3.5,
fontweight="bold",
color="white",
zorder=5,
)

ax_ref.invert_yaxis()
ax_ref.set_aspect("equal")
ax_ref.set_xlim(-0.5, 13.0)
ax_ref.set_ylim(5.8, -0.5)
ax_ref.set_xlabel("")
ax_ref.set_ylabel("")
ax_ref.set_xticks([])
ax_ref.set_yticks([])
ax_ref.set_facecolor(PAGE_BG)
sns.despine(ax=ax_ref, left=True, bottom=True)
ax_ref.set_title("Equal-Area\nReference", fontsize=13, fontweight="bold", pad=8)
ax_ref.set_title("Equal-Area Reference", fontsize=7.5, fontweight="bold", color=INK, pad=4)

# Subtle divider line between main and reference/bar panels
divider_x = 0.74
fig.add_artist(
plt.Line2D(
[divider_x, divider_x],
[0.05, 0.92],
transform=fig.transFigure,
color="#cccccc",
linewidth=1,
linestyle="--",
alpha=0.6,
)
)

# --- Population by region: seaborn barplot with statistical aggregation ---
# Aggregate total population per region - leverages seaborn's categorical plotting
# Regional population totals: horizontal bar chart
region_totals = df.groupby("region", observed=True)["population"].sum().reset_index()
region_totals.columns = ["region", "total_pop"]
region_totals["total_pop"] = region_totals["total_pop"].round(1)

sns.barplot(
data=region_totals,
Expand All @@ -277,34 +294,25 @@
hue_order=region_order,
order=region_order,
palette=region_palette,
edgecolor="white",
linewidth=1.2,
edgecolor=PAGE_BG,
linewidth=0.8,
legend=False,
ax=ax_bar,
saturation=0.85,
)

# Add value labels on bars
for i, (_, rrow) in enumerate(region_totals.set_index("region").reindex(region_order).iterrows()):
ax_bar.text(
rrow["total_pop"] + 0.8,
i,
f"{rrow['total_pop']:.0f}M",
ha="left",
va="center",
fontsize=11,
fontweight="bold",
color="#444444",
)
for i, region_name in enumerate(region_order):
val = region_totals.loc[region_totals["region"] == region_name, "total_pop"].values[0]
ax_bar.text(val + 1.0, i, f"{val:.0f}M", ha="left", va="center", fontsize=6.5, fontweight="bold", color=INK_SOFT)

ax_bar.set_xlabel("Total Population (M)", fontsize=12)
ax_bar.set_ylabel("")
ax_bar.set_title("Regional Totals", fontsize=13, fontweight="bold", pad=8)
ax_bar.tick_params(axis="y", labelsize=11)
ax_bar.tick_params(axis="x", labelsize=10)
ax_bar.set_xlabel("Total Population (M)", fontsize=7.5, color=INK)
ax_bar.set_ylabel("", color=INK)
ax_bar.set_title("Regional Totals", fontsize=8, fontweight="bold", color=INK, pad=4)
ax_bar.tick_params(axis="y", labelsize=7, colors=INK_SOFT)
ax_bar.tick_params(axis="x", labelsize=6.5, colors=INK_SOFT)
ax_bar.set_xlim(0, region_totals["total_pop"].max() * 1.25)
ax_bar.set_facecolor(PAGE_BG)
sns.despine(ax=ax_bar, left=True)
ax_bar.yaxis.grid(False)
ax_bar.xaxis.grid(True, alpha=0.15, linewidth=0.8)
ax_bar.xaxis.grid(True, alpha=0.15, linewidth=0.6, color=INK)

plt.savefig("plot.png", dpi=300, bbox_inches="tight")
# Save — no bbox_inches='tight' per seaborn canvas rules
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
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