feat(seaborn): implement tree-phylogenetic (#3108)

## Implementation: `tree-phylogenetic` - seaborn

Implements the **seaborn** version of `tree-phylogenetic`.

**File:** `plots/tree-phylogenetic/implementations/seaborn.py`

**Parent Issue:** #3070

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620332761)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/tree-phylogenetic/implementations/seaborn.py

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+""" pyplots.ai
+tree-phylogenetic: Phylogenetic Tree Diagram
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from matplotlib.patches import Patch
+from scipy.cluster.hierarchy import dendrogram, linkage
+from scipy.spatial.distance import squareform
+
+
+# Set seaborn style for better aesthetics
+sns.set_theme(style="white")
+
+# Define primate species for phylogenetic tree
+species = ["Human", "Chimpanzee", "Gorilla", "Orangutan", "Gibbon", "Baboon", "Macaque", "Marmoset", "Lemur", "Tarsier"]
+
+# Create evolutionary distance matrix (symmetric)
+# Based on approximate mitochondrial DNA divergence (millions of years ago)
+np.random.seed(42)
+n_species = len(species)
+
+# Base distances representing evolutionary divergence
+# Closer species have smaller distances
+base_distances = np.array(
+    [
+        [0, 6, 9, 14, 18, 25, 25, 35, 55, 58],  # Human
+        [6, 0, 9, 14, 18, 25, 25, 35, 55, 58],  # Chimpanzee
+        [9, 9, 0, 14, 18, 25, 25, 35, 55, 58],  # Gorilla
+        [14, 14, 14, 0, 18, 25, 25, 35, 55, 58],  # Orangutan
+        [18, 18, 18, 18, 0, 25, 25, 35, 55, 58],  # Gibbon
+        [25, 25, 25, 25, 25, 0, 10, 35, 55, 58],  # Baboon
+        [25, 25, 25, 25, 25, 10, 0, 35, 55, 58],  # Macaque
+        [35, 35, 35, 35, 35, 35, 35, 0, 55, 58],  # Marmoset
+        [55, 55, 55, 55, 55, 55, 55, 55, 0, 50],  # Lemur
+        [58, 58, 58, 58, 58, 58, 58, 58, 50, 0],  # Tarsier
+    ]
+)
+
+# Convert distance matrix to condensed form for hierarchical clustering
+condensed_distances = squareform(base_distances)
+
+# Perform hierarchical clustering using UPGMA (average linkage)
+# This is commonly used for phylogenetic analysis
+linkage_matrix = linkage(condensed_distances, method="average")
+
+# Create figure with seaborn styling
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Define colors using Python palette and colorblind-safe colors
+# Assign each species to a clade index for consistent coloring
+clade_colors = {
+    "Human": "#306998",  # Python Blue - Great Apes
+    "Chimpanzee": "#306998",
+    "Gorilla": "#306998",
+    "Orangutan": "#4A90D9",  # Light blue - Asian great ape
+    "Gibbon": "#4A90D9",
+    "Baboon": "#FFD43B",  # Python Yellow - Old World Monkeys
+    "Macaque": "#FFD43B",
+    "Marmoset": "#2ECC71",  # Green - New World Monkey
+    "Lemur": "#E74C3C",  # Red - Prosimians
+    "Tarsier": "#E74C3C",
+}
+
+# Define link colors based on cluster membership
+# Map each leaf to its clade color for dendrogram branches
+leaf_colors = [clade_colors[s] for s in species]
+
+# Create a color mapping function for dendrogram links
+# Links below leaves inherit the leaf color, higher links use a neutral color
+n = len(species)
+
+
+def get_link_color(link_id):
+    """Return color for dendrogram link based on cluster composition."""
+    if link_id < n:
+        return leaf_colors[link_id]
+    # For internal nodes, get the cluster members
+    cluster_idx = int(link_id - n)
+    left_child = int(linkage_matrix[cluster_idx, 0])
+    right_child = int(linkage_matrix[cluster_idx, 1])
+
+    # Get colors of both children
+    left_color = get_link_color(left_child)
+    right_color = get_link_color(right_child)
+
+    # If both children have same color, use that color
+    if left_color == right_color:
+        return left_color
+    # Otherwise use neutral gray for mixed clades
+    return "#808080"
+
+
+# Build link color list for all links in dendrogram
+link_colors = [get_link_color(i + n) for i in range(len(linkage_matrix))]
+
+# Plot dendrogram (phylogenetic tree) with custom clade colors
+dendro = dendrogram(
+    linkage_matrix,
+    labels=species,
+    orientation="left",
+    ax=ax,
+    leaf_font_size=18,
+    link_color_func=lambda k: link_colors[k - n] if k >= n else leaf_colors[k],
+)
+
+# Make branch lines thicker for improved visibility
+for line_collection in ax.collections:
+    line_collection.set_linewidth(3)
+
+# Style the dendrogram with seaborn aesthetics
+ax.set_xlabel("Evolutionary Distance (Million Years)", fontsize=20, fontweight="bold")
+ax.set_title("Primate Evolution · tree-phylogenetic · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
+
+# Adjust tick parameters for readability
+ax.tick_params(axis="x", labelsize=16)
+ax.tick_params(axis="y", labelsize=18)
+
+# Add subtle grid on x-axis only
+ax.grid(axis="x", alpha=0.3, linestyle="--")
+ax.set_axisbelow(True)
+
+# Add scale bar annotation
+ax.annotate(
+    "Scale: branch length = evolutionary distance",
+    xy=(0.98, 0.02),
+    xycoords="axes fraction",
+    fontsize=14,
+    ha="right",
+    va="bottom",
+    style="italic",
+    color="#666666",
+)
+
+# Color the species labels based on clade
+for label in ax.get_yticklabels():
+    species_name = label.get_text()
+    if species_name in clade_colors:
+        label.set_color(clade_colors[species_name])
+        label.set_fontweight("bold")
+
+# Add legend for clades (positioned to avoid overlap)
+legend_elements = [
+    Patch(facecolor="#306998", edgecolor="none", label="Great Apes"),
+    Patch(facecolor="#4A90D9", edgecolor="none", label="Lesser Apes"),
+    Patch(facecolor="#FFD43B", edgecolor="none", label="Old World Monkeys"),
+    Patch(facecolor="#2ECC71", edgecolor="none", label="New World Monkeys"),
+    Patch(facecolor="#E74C3C", edgecolor="none", label="Prosimians"),
+]
+ax.legend(handles=legend_elements, loc="lower left", fontsize=14, title="Clades", title_fontsize=16, framealpha=0.9)
+
+# Remove top and right spines for cleaner look
+sns.despine(ax=ax, top=True, right=True)
+
+# Adjust layout
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/tree-phylogenetic/metadata/seaborn.yaml

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+library: seaborn
+specification_id: tree-phylogenetic
+created: '2025-12-31T13:54:59Z'
+updated: '2025-12-31T14:24:15Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620332761
+issue: 3070
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/tree-phylogenetic/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/tree-phylogenetic/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual presentation with clear clade-based color coding throughout branches
+    and labels
+  - Biologically accurate evolutionary distance matrix for primate species
+  - Professional typography with proper font sizes and weights for all text elements
+  - Thoughtful color palette using distinct, colorblind-safe colors for 5 clades
+  - Clean layout with appropriate use of seaborn aesthetic styling
+  weaknesses:
+  - Contains a helper function (get_link_color) which violates KISS principle - inline
+    logic preferred
+  - Title format includes Primate Evolution prefix before spec-id which deviates from
+    standard format