mirror of
https://github.com/lllin000/PaperForge.git
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274 lines
9.4 KiB
Python
274 lines
9.4 KiB
Python
from __future__ import annotations
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def test_normalize_strict_inventory_maps_bucket_variants_to_common_fields() -> None:
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from paperforge.worker.ocr_figure_reader import _normalize_strict_figure_inventory
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strict_inventory = {
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"matched_figures": [
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{
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"figure_number": 6,
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"block_id": 15,
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"text": "Fig. 6 The figure represents...",
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"matched_assets": [{"block_id": 40, "bbox": [1, 2, 3, 4]}],
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"match_score": 0.91,
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}
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],
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"ambiguous_figures": [
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{
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"figure_number": 3,
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"legend_block_id": 9,
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"text": "FIGURE 3 | Histological evaluation...",
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"candidates": [{"asset_block_id": 10, "match_score": 0.51}, {"asset_block_id": 11, "match_score": 0.49}],
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}
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],
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"unmatched_legends": [
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{
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"block_id": 21,
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"text": "FIGURE 2 | Treadmill exercise protocols...",
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"figure_number": 2,
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}
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],
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"unresolved_clusters": [
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{
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"page": 7,
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"media_block_ids": [30, 31],
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}
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],
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}
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structured_blocks = [
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{"block_id": 21, "marker_signature": {"type": "figure_number"}, "zone": "display_zone", "style_family": "legend_like"}
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]
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normalized = _normalize_strict_figure_inventory(strict_inventory, structured_blocks)
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assert normalized["matched_figures"][0]["legend_block_id"] == 15
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assert normalized["matched_figures"][0]["caption_text"] == "Fig. 6 The figure represents..."
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assert normalized["matched_figures"][0]["asset_block_ids"] == [40]
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assert normalized["ambiguous_figures"][0]["candidate_asset_ids"] == [10, 11]
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assert normalized["unmatched_legends"][0]["legend_block_id"] == 21
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assert normalized["unresolved_clusters"][0]["asset_block_ids"] == [30, 31]
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def test_reader_figure_preserves_separate_reader_and_strict_status() -> None:
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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normalized_inventory = {
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"matched_figures": [],
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"held_figures": [],
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"ambiguous_figures": [
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{
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"figure_number": 3,
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"legend_block_id": 9,
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"caption_text": "FIGURE 3 | Histological evaluation...",
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"candidate_asset_ids": [10, 11],
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"strict_status": "ambiguous",
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"marker_type": "figure_number",
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}
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],
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"unmatched_legends": [],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(normalized_inventory, structured_blocks=[])
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rf = result["reader_figures"][0]
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assert rf["reader_status"] == "GROUPED_APPROXIMATE"
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assert rf["strict_status"] == "ambiguous"
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assert rf["strict_source"] == "ambiguous_figures"
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def test_reader_figure_id_uses_first_asset_id_for_visual_group_when_figure_number_missing() -> None:
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from paperforge.worker.ocr_figure_reader import _stable_reader_figure_id
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assert _stable_reader_figure_id(None, page=7, first_asset_block_id=31, ordinal=2) == "visual_group_7_31_reader"
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def test_coverage_total_counts_deduplicated_eligible_inputs() -> None:
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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strict_inventory = {
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"ambiguous_figures": [
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{
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"figure_number": 2,
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"legend_block_id": 21,
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"caption_text": "FIGURE 2 | Treadmill exercise protocols...",
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"candidate_asset_ids": [30, 31],
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"marker_type": "figure_number",
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}
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],
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"unmatched_legends": [
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{
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"figure_number": 2,
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"legend_block_id": 21,
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"caption_text": "FIGURE 2 | Treadmill exercise protocols...",
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"marker_type": "figure_number",
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}
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],
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"matched_figures": [],
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"held_figures": [],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(strict_inventory, structured_blocks=[])
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assert result["reader_coverage"]["total"] == 1
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def test_ambiguous_without_formal_legend_does_not_enter_reader_layer() -> None:
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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strict_inventory = {
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"matched_figures": [],
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"held_figures": [],
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"ambiguous_figures": [
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{
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"legend_block_id": 50,
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"caption_text": "Figure 2 shows the progression...",
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"candidate_asset_ids": [70],
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"marker_type": "figure_number",
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"inline_mention": True,
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}
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],
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"unmatched_legends": [],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(strict_inventory, structured_blocks=[])
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assert result["reader_figures"] == []
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assert result["reader_coverage"]["total"] == 0
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def test_grouped_approximate_requires_visual_candidates() -> None:
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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strict_inventory = {
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"matched_figures": [],
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"held_figures": [],
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"ambiguous_figures": [
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{
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"figure_number": 4,
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"legend_block_id": 44,
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"caption_text": "FIGURE 4 | Immunohistochemical staining...",
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"candidate_asset_ids": [],
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"marker_type": "figure_number",
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}
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],
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"unmatched_legends": [],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(strict_inventory, structured_blocks=[])
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assert result["reader_figures"][0]["reader_status"] != "GROUPED_APPROXIMATE"
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def test_reader_hold_does_not_default_to_caption_consumption() -> None:
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from paperforge.worker.ocr_figure_reader import _materialize_hold_outcome
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hold = _materialize_hold_outcome(
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legend_block_id=80,
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caption_text="weak fragment",
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page=10,
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candidate_asset_ids=[],
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hold_visibility="audit_hold",
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)
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assert hold["consumed_caption_block_ids"] == []
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assert hold["debug_refs"]["hold_visibility"] == "audit_hold"
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def test_legend_only_consumes_caption_when_rendered() -> None:
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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strict_inventory = {
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"matched_figures": [],
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"held_figures": [],
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"ambiguous_figures": [],
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"unmatched_legends": [
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{
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"figure_number": 2,
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"legend_block_id": 21,
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"caption_text": "FIGURE 2 | Treadmill exercise protocols...",
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"marker_type": "figure_number",
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}
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],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(strict_inventory, structured_blocks=[])
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assert result["consumed_caption_block_ids"] == [21]
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assert result["reader_figures"][0]["reader_status"] == "LEGEND_ONLY"
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def test_reader_sequence_match_promoted_figure_gets_proper_status() -> None:
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"""A figure promoted via sequence_match must get SEQUENCE_MATCH reader_status."""
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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strict_inventory = {
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"matched_figures": [
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{
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"figure_number": 2,
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"legend_block_id": 12,
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"text": "Figure 2. Histological analysis.",
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"matched_assets": [],
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"match_score": {"score": 0.0, "decision": "sequence_match", "evidence": ["sequence_promotion"]},
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"flags": ["sequence_match"],
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"strict_status": "sequence_match",
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"marker_type": "figure_number",
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}
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],
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"held_figures": [],
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"ambiguous_figures": [],
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"unmatched_legends": [],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(strict_inventory, structured_blocks=[])
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assert len(result["reader_figures"]) == 1
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rf = result["reader_figures"][0]
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assert rf["reader_status"] == "SEQUENCE_MATCH", (
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f"Expected SEQUENCE_MATCH, got {rf['reader_status']}"
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)
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assert rf["strict_status"] == "sequence_match"
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assert rf["strict_source"] == "matched_figures"
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def test_reader_payload_coverage_accounted_matches_reader_figures() -> None:
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from paperforge.worker.ocr_figure_reader import synthesize_reader_figures
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strict_inventory = {
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"matched_figures": [
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{
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"figure_number": 1,
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"legend_block_id": 5,
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"text": "Fig. 1 Overview of the system...",
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"matched_assets": [{"block_id": 10, "bbox": [1, 2, 3, 4]}],
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"match_score": 0.91,
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"marker_type": "figure_number",
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}
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],
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"held_figures": [],
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"ambiguous_figures": [],
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"unmatched_legends": [
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{
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"figure_number": 2,
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"legend_block_id": 21,
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"text": "FIGURE 2 | Treadmill exercise protocols...",
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"marker_type": "figure_number",
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}
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],
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"unresolved_clusters": [],
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}
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result = synthesize_reader_figures(strict_inventory, structured_blocks=[])
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assert result["reader_coverage"]["total"] == 2
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assert result["reader_coverage"]["accounted"] == 2
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assert result["reader_coverage"]["gap_count"] == 0
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assert result["reader_coverage"]["ratio"] == 1.0
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assert set(result["consumed_caption_block_ids"]) == {5, 21}
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