gbanyan
150417ffcc
- Add matplotlib>=3.8.0 and seaborn>=0.13.0 to dependencies - Create visualizations.py with 3 plot functions and orchestrator - plot_score_distribution: histogram colored by confidence tier - plot_layer_contributions: bar chart of evidence layer coverage - plot_tier_breakdown: pie chart of tier distribution - Use Agg backend for headless/CLI safety - All plots saved at 300 DPI with proper figure cleanup - 6 tests covering file creation, edge cases, and return values
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
"""Tests for visualization generation."""
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from pathlib import Path
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import polars as pl
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import pytest
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from usher_pipeline.output.visualizations import (
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generate_all_plots,
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plot_layer_contributions,
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plot_score_distribution,
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plot_tier_breakdown,
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)
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@pytest.fixture
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def synthetic_results_df():
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"""Create synthetic scored results DataFrame."""
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return pl.DataFrame({
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"gene_symbol": [f"GENE{i}" for i in range(30)],
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"composite_score": [0.1 + i * 0.03 for i in range(30)],
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"confidence_tier": (
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["HIGH"] * 10 + ["MEDIUM"] * 10 + ["LOW"] * 10
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),
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"gnomad_score": [0.5 if i % 2 == 0 else None for i in range(30)],
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"expression_score": [0.6 if i % 3 == 0 else None for i in range(30)],
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"annotation_score": [0.7 if i % 4 == 0 else None for i in range(30)],
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"localization_score": [0.8 if i % 5 == 0 else None for i in range(30)],
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"animal_model_score": [0.9 if i % 6 == 0 else None for i in range(30)],
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"literature_score": [0.85 if i % 7 == 0 else None for i in range(30)],
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})
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def test_plot_score_distribution_creates_file(synthetic_results_df, tmp_path):
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"""Test that score distribution plot creates a PNG file."""
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output_path = tmp_path / "score_dist.png"
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result = plot_score_distribution(synthetic_results_df, output_path)
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assert result == output_path
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assert output_path.exists()
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assert output_path.stat().st_size > 0
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def test_plot_layer_contributions_creates_file(synthetic_results_df, tmp_path):
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"""Test that layer contributions plot creates a PNG file."""
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output_path = tmp_path / "layer_contrib.png"
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result = plot_layer_contributions(synthetic_results_df, output_path)
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assert result == output_path
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assert output_path.exists()
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assert output_path.stat().st_size > 0
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def test_plot_tier_breakdown_creates_file(synthetic_results_df, tmp_path):
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"""Test that tier breakdown plot creates a PNG file."""
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output_path = tmp_path / "tier_breakdown.png"
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result = plot_tier_breakdown(synthetic_results_df, output_path)
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assert result == output_path
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assert output_path.exists()
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assert output_path.stat().st_size > 0
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def test_generate_all_plots_creates_all_files(synthetic_results_df, tmp_path):
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"""Test that generate_all_plots creates all 3 PNG files."""
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output_dir = tmp_path / "plots"
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plots = generate_all_plots(synthetic_results_df, output_dir)
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# Check all files exist
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assert (output_dir / "score_distribution.png").exists()
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assert (output_dir / "layer_contributions.png").exists()
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assert (output_dir / "tier_breakdown.png").exists()
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def test_generate_all_plots_returns_paths(synthetic_results_df, tmp_path):
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"""Test that generate_all_plots returns dict with 3 entries."""
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output_dir = tmp_path / "plots"
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plots = generate_all_plots(synthetic_results_df, output_dir)
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assert len(plots) == 3
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assert "score_distribution" in plots
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assert "layer_contributions" in plots
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assert "tier_breakdown" in plots
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def test_plots_handle_empty_dataframe(tmp_path):
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"""Test that plots handle empty DataFrames without crashing."""
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empty_df = pl.DataFrame({
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"gene_symbol": [],
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"composite_score": [],
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"confidence_tier": [],
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"gnomad_score": [],
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"expression_score": [],
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"annotation_score": [],
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"localization_score": [],
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"animal_model_score": [],
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"literature_score": [],
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})
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output_dir = tmp_path / "empty_plots"
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# Should not crash
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plots = generate_all_plots(empty_df, output_dir)
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# At minimum, the function should return without error
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# Some plots may succeed (empty plot) or fail gracefully
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assert isinstance(plots, dict)
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