Repository Analysis

marceloprates/prettymaps

Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely

18.8 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of marceloprates/prettymaps, a Python project with 12,308 GitHub stars. SynthScan v2.0 examined 5,032 lines of code across 35 source files, recording 66 pattern matches distributed across 8 syntactic categories. The overall adjusted score of 18.8 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

18.8
Adjusted Score
18.8
Raw Score
100%
Time Factor
2026-07-30
Last Push
12.3K
Stars
Python
Language
5.0K
Lines of Code
35
Files
66
Pattern Hits
2026-08-02
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 16LOW 50

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 66 distinct pattern matches across 8 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators14 hits · 42 pts
SeverityFileLineSnippetContext
MEDIUMprettymaps/draw.py70# =============================================================================COMMENT
MEDIUMprettymaps/draw.py72# =============================================================================COMMENT
MEDIUMprettymaps/draw.py170# =============================================================================COMMENT
MEDIUMprettymaps/draw.py172# =============================================================================COMMENT
MEDIUMprettymaps/draw.py646# =============================================================================COMMENT
MEDIUMprettymaps/draw.py648# =============================================================================COMMENT
MEDIUMprettymaps/draw.py697# =============================================================================COMMENT
MEDIUMprettymaps/draw.py699# =============================================================================COMMENT
MEDIUMprettymaps/draw.py904# =============================================================================COMMENT
MEDIUMprettymaps/draw.py906# =============================================================================COMMENT
MEDIUMprettymaps/draw.py974# =============================================================================COMMENT
MEDIUMprettymaps/draw.py976# =============================================================================COMMENT
MEDIUMprettymaps/draw.py1101# =============================================================================COMMENT
MEDIUMprettymaps/draw.py1103# =============================================================================COMMENT
Unused Imports19 hits · 19 pts
SeverityFileLineSnippetContext
LOWprettymaps/fetch.py24CODE
LOWprettymaps/fetch.py24CODE
LOWprettymaps/fetch.py37CODE
LOWprettymaps/fetch.py39CODE
LOWprettymaps/fetch.py48CODE
LOWprettymaps/fetch.py50CODE
LOWprettymaps/fetch.py51CODE
LOWprettymaps/fetch.py52CODE
LOWprettymaps/fetch.py53CODE
LOWprettymaps/__init__.py1CODE
LOWprettymaps/__init__.py1CODE
LOWprettymaps/__init__.py1CODE
LOWprettymaps/__init__.py1CODE
LOWprettymaps/__init__.py1CODE
LOWprettymaps/__init__.py1CODE
LOWprettymaps/draw.py53CODE
LOWtests/test_gpx.py4CODE
LOWtests/test.py1CODE
LOWtests/test.py4CODE
Hyper-Verbose Identifiers12 hits · 10 pts
SeverityFileLineSnippetContext
LOWtests/test_gpx.py61def test_read_track_missing_geometry(tmp_path):STRING
LOWtests/test_gpx.py67def test_read_track_multiple_files(tmp_path):STRING
LOWtests/test_gpx.py95def test_plot_gpx_query_autoframes_and_draws(monkeypatch, tmp_path):STRING
LOWtests/test_gpx.py116def test_plot_gpx_param_overlays_without_reframing(monkeypatch, tmp_path):CODE
LOWtests/test_gpx.py133def test_plot_gpx_custom_style(monkeypatch, tmp_path):CODE
LOWtests/test.py27def test_create_and_read_preset(tmp_path):CODE
LOWtests/test.py81def test_plot_custom_layers_style(monkeypatch):CODE
LOWtests/test.py154def test_plot_circle_radius_dilate(monkeypatch):CODE
LOWtests/test.py184def test_multiplot_custom_subplots(monkeypatch):CODE
LOWtests/test.py236def test_plot_transform_params(monkeypatch):CODE
LOWtests/test.py249def test_plot_show_true_false(monkeypatch):CODE
LOWtests/test.py290def test_plot_adjust_aspect_ratio(monkeypatch):CODE
Deep Nesting9 hits · 8 pts
SeverityFileLineSnippetContext
LOWprettymaps/fetch.py522CODE
LOWprettymaps/fetch.py572CODE
LOWprettymaps/fetch.py536CODE
LOWprettymaps/gpx.py30CODE
LOWprettymaps/gpx.py47CODE
LOWprettymaps/draw.py175CODE
LOWprettymaps/draw.py296CODE
LOWprettymaps/draw.py515CODE
LOWprettymaps/draw.py188CODE
Excessive Try-Catch Wrapping6 hits · 8 pts
SeverityFileLineSnippetContext
LOWprettymaps/fetch.py469 except Exception as e:STRING
MEDIUMprettymaps/fetch.py470 # print(f"Error fetching {layer}: {e}")STRING
LOWprettymaps/fetch.py609 except Exception as e:CODE
LOWprettymaps/fetch.py692 except Exception as e:CODE
MEDIUMprettymaps/fetch.py693 # print(f"Error fetching {layer}: {e}")COMMENT
LOWprettymaps/draw.py579 except Exception as e:CODE
Redundant / Tautological Comments4 hits · 5 pts
SeverityFileLineSnippetContext
LOWapp.py18# Set Streamlit to use the wide layoutCOMMENT
LOWprettymaps/fetch.py289 # Check if the gdf is cachedSTRING
LOWprettymaps/fetch.py374 # Check if the gdf is cachedCOMMENT
LOWtests/test_app_runs.py16 # Check if the process is still running (should be)COMMENT
Modern Structural Boilerplate1 hit · 1 pts
SeverityFileLineSnippetContext
LOWprettymaps/gpx.py22__all__ = ["read_track", "is_track_file", "track_center", "track_radius"]CODE
AI Structural Patterns1 hit · 1 pts
SeverityFileLineSnippetContext
LOWprettymaps/draw.py1106CODE