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feat(matplotlib): implement sequence-logo-basic (#4611)
## Implementation: `sequence-logo-basic` - matplotlib Implements the **matplotlib** version of `sequence-logo-basic`. **File:** `plots/sequence-logo-basic/implementations/matplotlib.py` **Parent Issue:** #4421 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/22780524847)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/sequence-logo-basic/implementations/matplotlib.py‎

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""" pyplots.ai
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sequence-logo-basic: Sequence Logo for Motif Visualization
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Library: matplotlib 3.10.8 | Python 3.14.3
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Quality: 92/100 | Created: 2026-03-06
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"""
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import matplotlib.pyplot as plt
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import matplotlib.transforms as transforms
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import numpy as np
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from matplotlib.font_manager import FontProperties
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from matplotlib.lines import Line2D
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from matplotlib.patches import FancyBboxPatch, PathPatch
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from matplotlib.textpath import TextPath
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# Data — a 10-position DNA transcription factor binding site motif (ETS-family-like)
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position_freqs = [
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{"A": 0.25, "C": 0.25, "G": 0.25, "T": 0.25},
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{"A": 0.10, "C": 0.60, "G": 0.10, "T": 0.20},
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{"A": 0.05, "C": 0.05, "G": 0.85, "T": 0.05},
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{"A": 0.90, "C": 0.02, "G": 0.03, "T": 0.05},
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{"A": 0.02, "C": 0.02, "G": 0.94, "T": 0.02},
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{"A": 0.02, "C": 0.02, "G": 0.02, "T": 0.94},
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{"A": 0.15, "C": 0.35, "G": 0.15, "T": 0.35},
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{"A": 0.30, "C": 0.20, "G": 0.30, "T": 0.20},
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{"A": 0.05, "C": 0.05, "G": 0.05, "T": 0.85},
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{"A": 0.25, "C": 0.25, "G": 0.25, "T": 0.25},
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]
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# Colorblind-safe palette: replaced green/red with teal/purple for A/T
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dna_colors = {"A": "#1b7837", "C": "#1f77b4", "G": "#ff7f0e", "T": "#9467bd"}
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letters = ["A", "C", "G", "T"]
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n_positions = len(position_freqs)
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max_bits = 2.0
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# Compute information content per position
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info_contents = []
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for freqs in position_freqs:
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entropy = sum(-f * np.log2(f) for f in freqs.values() if f > 0)
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info_contents.append(max_bits - entropy)
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# Plot
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fig, ax = plt.subplots(figsize=(16, 9))
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fp = FontProperties(family="DejaVu Sans", weight="bold")
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bar_width = 0.9
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# Highlight conserved core region (positions 3-6) with background shading
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core_start, core_end = 3, 6
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highlight = FancyBboxPatch(
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(core_start - 0.48, -0.02),
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core_end - core_start + 0.96,
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max_bits + 0.04,
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boxstyle="round,pad=0.02",
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facecolor="#f0e68c",
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edgecolor="#c4a000",
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alpha=0.25,
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linewidth=1.5,
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zorder=0,
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)
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ax.add_patch(highlight)
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for pos_idx, freqs in enumerate(position_freqs):
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ic = info_contents[pos_idx]
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letter_heights = {lt: freqs[lt] * ic for lt in letters}
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sorted_letters = sorted(letters, key=lambda lt: letter_heights[lt])
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y_offset = 0.0
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x_start = pos_idx + 1 - bar_width / 2
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for letter in sorted_letters:
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h = letter_heights[letter]
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if h < 0.01:
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continue
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tp = TextPath((0, 0), letter, size=1, prop=fp)
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bbox = tp.get_extents()
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if bbox.width == 0 or bbox.height == 0:
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continue
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sx = bar_width / bbox.width
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sy = h / bbox.height
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t = transforms.Affine2D().translate(-bbox.x0, -bbox.y0).scale(sx, sy).translate(x_start, y_offset)
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patch = PathPatch(tp.transformed(t), facecolor=dna_colors[letter], edgecolor="none", linewidth=0, zorder=2)
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ax.add_patch(patch)
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y_offset += h
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# Annotate conserved core motif
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ax.annotate(
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"Conserved core",
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xy=((core_start + core_end) / 2, max_bits * 0.88),
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fontsize=14,
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fontweight="medium",
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color="#6b5900",
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ha="center",
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va="center",
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zorder=3,
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)
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# Color legend for nucleotides using matplotlib legend API
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legend_handles = [
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Line2D([0], [0], marker="s", color="w", markerfacecolor=dna_colors[lt], markersize=12, label=lt, linewidth=0)
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for lt in letters
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]
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ax.legend(
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handles=legend_handles,
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loc="upper right",
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fontsize=14,
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framealpha=0.8,
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edgecolor="#cccccc",
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handletextpad=0.4,
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labelspacing=0.3,
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)
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# Style
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ax.set_xlim(0.5, n_positions + 0.5)
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ax.set_ylim(0, max_bits)
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ax.set_xticks(range(1, n_positions + 1))
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ax.set_xticklabels(range(1, n_positions + 1), fontsize=16)
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ax.set_xlabel("Position", fontsize=20)
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ax.set_ylabel("Information content (bits)", fontsize=20)
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ax.set_title("sequence-logo-basic \u00b7 matplotlib \u00b7 pyplots.ai", fontsize=24, fontweight="medium")
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ax.tick_params(axis="both", labelsize=16)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.yaxis.grid(True, alpha=0.2, linewidth=0.8, zorder=0)
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plt.tight_layout()
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/sequence-logo-basic/metadata/matplotlib.yaml‎

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library: matplotlib
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specification_id: sequence-logo-basic
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created: '2026-03-06T20:25:27Z'
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updated: '2026-03-06T20:36:39Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 22780524847
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issue: 4421
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python_version: 3.14.3
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library_version: 3.10.8
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preview_url: https://storage.googleapis.com/pyplots-images/plots/sequence-logo-basic/matplotlib/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/sequence-logo-basic/matplotlib/plot_thumb.png
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preview_html: null
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quality_score: 92
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review:
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strengths:
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- Excellent glyph rendering using TextPath + Affine2D transforms — the proper matplotlib
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technique for sequence logos
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- Conserved core highlight with FancyBboxPatch adds meaningful data storytelling
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- Biologically realistic ETS-family binding site data with clear conservation patterns
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- 'Full spec compliance: all required features implemented correctly'
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- Clean, well-structured code with all font sizes explicitly set
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weaknesses:
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- Color comment claims teal for A but uses standard green (#1b7837) — minor inconsistency
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- Very small letters at low-information positions are hard to distinguish individually
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image_description: 'The plot displays a DNA sequence logo for a 10-position transcription
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factor binding site motif. Positions 1-10 are shown on the x-axis, with information
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content (bits) on the y-axis ranging from 0 to 2.0. At each position, nucleotide
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letters (A, C, G, T) are stacked vertically as scaled glyphs, with height proportional
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to their contribution to information content. Colors are: A=green, C=blue, G=orange,
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T=purple. Positions 3-6 form a highly conserved core (highlighted with a pale
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yellow rounded rectangle and labeled "Conserved core"), showing dominant G, A,
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G, T letters reaching ~1.4-1.6 bits. Positions 1, 8, and 10 show near-zero information
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(uniform distribution). Position 2 has moderate conservation (dominant C). Position
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9 shows a strong T. A color legend in the upper right identifies all four nucleotides.
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Top and right spines are removed, with a subtle y-axis grid. The title reads "sequence-logo-basic
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· matplotlib · pyplots.ai".'
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criteria_checklist:
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visual_quality:
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score: 28
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: 'All font sizes explicitly set: title 24pt, axis labels 20pt, ticks
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16pt, legend 14pt'
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 6
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passed: true
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comment: No text collisions anywhere
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- id: VQ-03
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name: Element Visibility
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score: 5
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max: 6
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passed: true
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comment: Letter glyphs well-sized at conserved positions; very small at low-info
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positions but correct behavior
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- id: VQ-04
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name: Color Accessibility
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score: 3
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max: 4
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passed: true
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comment: Green/blue/orange/purple avoids red-green conflict; comment claims
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teal but uses standard green
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: Good 16:9 proportions, tight_layout, plot fills canvas well
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: 'Descriptive labels with units: Position, Information content (bits)'
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design_excellence:
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score: 16
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 6
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max: 8
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passed: true
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comment: Custom nucleotide palette, highlighted conserved core, refined legend,
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clean typography hierarchy
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- id: DE-02
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name: Visual Refinement
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score: 5
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max: 6
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passed: true
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comment: Spines removed, subtle y-axis grid, rounded highlight box, generous
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whitespace
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- id: DE-03
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name: Data Storytelling
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score: 5
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max: 6
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passed: true
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comment: Yellow highlight on conserved core with annotation creates clear
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focal point and biological story
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct sequence logo with stacked letters scaled by information
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content
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: 'All spec features: vertical stacking, frequency ordering, info content
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scaling, standard colors, scaled glyphs'
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: X=position (1-10), Y=information content (0-2 bits)
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Correct title format and nucleotide color legend
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data_quality:
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score: 15
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 6
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max: 6
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passed: true
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comment: 'Full range: uniform, moderate, and highly conserved positions'
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: ETS-family transcription factor binding site motif with plausible
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GAGT core
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Frequencies sum to 1, information content correct for DNA (max 2
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bits)
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Linear flow: imports, data, compute, plot, style, save'
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: Fully deterministic with hardcoded frequency data
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: All imports used
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Clean TextPath + Affine2D + PathPatch approach
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves as plot.png with dpi=300, current API
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library_mastery:
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score: 8
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 4
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max: 5
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passed: true
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comment: Ax-based API, TextPath with Affine2D transforms for glyph rendering
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- id: LM-02
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name: Distinctive Features
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score: 4
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max: 5
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passed: true
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comment: TextPath, Affine2D, FancyBboxPatch, PathPatch, custom Line2D legend
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handles
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- annotations
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- custom-legend
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- patches
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- manual-ticks
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patterns:
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- iteration-over-groups
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dataprep: []
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styling:
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- grid-styling
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- alpha-blending

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