Feature: Time to First Result (TTFR) Estimator (foundation)

backend/demo_ttfr.py

@@ -0,0 +1,60 @@
+from ttfr_estimator import estimate_ttfr
+
+print("=" * 60)
+print("TTFR Estimator Demo")
+print("=" * 60)
+
+test_cases = [
+    {
+        "name": "OpenNeuro fMRI Dataset",
+        "datasource_id": "scr_005031_openneuro",
+        "metadata": {"description": "Large-scale fMRI study with 200 participants investigating cognitive control"},
+        "content": "BIDS-formatted neuroimaging data"
+    },
+    {
+        "name": "DANDI Electrophysiology",
+        "datasource_id": "scr_017571_dandi",
+        "metadata": {"description": "Multi-electrode array recordings from mouse visual cortex"},
+        "content": "NWB format neural recordings"
+    },
+    {
+        "name": "Multimodal Dataset",
+        "datasource_id": None,
+        "metadata": {"description": "Combined fMRI and electrophysiology study of sensory processing"},
+        "content": "Multimodal neuroimaging and neural recordings"
+    },
+    {
+        "name": "Simple Morphology Database",
+        "datasource_id": "scr_002145_neuromorpho_modelimage",
+        "metadata": {"description": "Curated collection of neuron morphology reconstructions"},
+        "content": "SWC format morphology files"
+    }
+]
+
+for case in test_cases:
+    print(f"\n{'─' * 60}")
+    print(f"Dataset: {case['name']}")
+    print(f"{'─' * 60}")
+
+    estimate = estimate_ttfr(
+        datasource_id=case["datasource_id"],
+        metadata=case["metadata"],
+        content=case["content"]
+    )
+
+    print(f"\nTime to First Result: {estimate.total}")
+    print(f"\nBreakdown:")
+    print(f"  - Access & setup: {estimate.access_setup}")
+    print(f"  - Preprocessing: {estimate.preprocessing}")
+    print(f"  - First output: {estimate.first_output}")
+    print(f"\nAssumptions:")
+    for assumption in estimate.assumptions:
+        print(f" - {assumption}")
+
+    print(f"\nJSON format:")
+    import json
+    print(json.dumps(estimate.to_dict(), indent=2))
+
+print(f"\n{'=' * 60}")
+print("Demo Complete!")
+print("=" * 60)

backend/test_ttfr_estimator.py

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+import pytest
+from ttfr_estimator import (
+    estimate_ttfr,
+    AccessType,
+    ModalityComplexity,
+    FormatType,
+    infer_modality_from_keywords,
+    detect_multimodal,
+    assess_documentation_quality
+)
+
+
+class TestModalityInference:
+    def test_infer_low_complexity(self):
+        assert infer_modality_from_keywords("computational model simulation") == ModalityComplexity.LOW
+        assert infer_modality_from_keywords("neuron morphology database") == ModalityComplexity.LOW
+
+    def test_infer_medium_complexity(self):
+        assert infer_modality_from_keywords("microscopy images") == ModalityComplexity.MEDIUM
+        assert infer_modality_from_keywords("electrophysiology recordings") == ModalityComplexity.MEDIUM
+
+    def test_infer_high_complexity(self):
+        assert infer_modality_from_keywords("fMRI BOLD imaging") == ModalityComplexity.HIGH
+        assert infer_modality_from_keywords("MRI brain scans") == ModalityComplexity.HIGH
+
+    def test_infer_very_high_complexity(self):
+        assert infer_modality_from_keywords("multimodal neuroimaging dataset") == ModalityComplexity.VERY_HIGH
+
+    def test_empty_input(self):
+        assert infer_modality_from_keywords("") == ModalityComplexity.MEDIUM
+        assert infer_modality_from_keywords(None) == ModalityComplexity.MEDIUM
+
+
+class TestMultimodalDetection:
+    def test_detect_multimodal_positive(self):
+        assert detect_multimodal("multimodal imaging and electrophysiology") == True
+        assert detect_multimodal("combined MRI and PET") == True
+        assert detect_multimodal("fMRI and behavioral data") == True
+
+    def test_detect_multimodal_negative(self):
+        assert detect_multimodal("fMRI imaging only") == False
+        assert detect_multimodal("microscopy images") == False
+
+    def test_empty_input(self):
+        assert detect_multimodal("") == False
+        assert detect_multimodal(None) == False
+
+
+class TestDocumentationQuality:
+    def test_high_quality(self):
+        metadata = {
+            "description": "A" * 150,
+            "authors": ["Author 1"],
+            "license": "MIT",
+            "readme": "README.md"
+        }
+        assert assess_documentation_quality(metadata) == "high"
+
+    def test_medium_quality(self):
+        metadata = {
+            "description": "A" * 150,
+            "license": "MIT"
+        }
+        assert assess_documentation_quality(metadata) == "medium"
+
+    def test_low_quality(self):
+        metadata = {
+            "description": "Short"
+        }
+        assert assess_documentation_quality(metadata) == "low"
+
+    def test_empty_metadata(self):
+        assert assess_documentation_quality({}) == "low"
+
+
+class TestTTFREstimation:
+    def test_openneuro_dataset(self):
+        estimate = estimate_ttfr(
+            datasource_id="scr_005031_openneuro",
+            metadata={"description": "fMRI study with 100 subjects"},
+            content="BIDS-formatted neuroimaging data"
+        )
+
+        assert estimate.total.min_days >= 0
+        assert estimate.total.max_days > estimate.total.min_days
+        assert "scr_005031_openneuro" in estimate.assumptions[0]
+
+    def test_dandi_dataset(self):
+        estimate = estimate_ttfr(
+            datasource_id="scr_017571_dandi",
+            metadata={"description": "Electrophysiology recordings from mouse cortex"},
+            content="NWB format neural recordings"
+        )
+
+        assert estimate.total.min_days >= 0
+        assert estimate.access_setup.min_days == 0.0
+
+    def test_unknown_datasource_simple(self):
+        estimate = estimate_ttfr(
+            metadata={"description": "Simple morphology database"},
+            content="Neuron morphology data"
+        )
+
+        assert estimate.total.min_days >= 0
+        assert "general estimates" in estimate.assumptions[0].lower()
+
+    def test_multimodal_dataset(self):
+        estimate = estimate_ttfr(
+            metadata={"description": "Combined fMRI and electrophysiology study"},
+            content="Multimodal neuroimaging and neural recordings"
+        )
+
+        assert any("multimodal" in a.lower() for a in estimate.assumptions)
+        assert estimate.preprocessing.max_days > 2.0
+
+    def test_approval_required(self):
+        estimate = estimate_ttfr(
+            datasource_id="scr_005069_brainminds"
+        )
+
+        assert estimate.access_setup.min_days >= 1.0
+
+    def test_to_dict_conversion(self):
+        estimate = estimate_ttfr(datasource_id="scr_007271_modeldb_models")
+        result = estimate.to_dict()
+
+        assert "total" in result
+        assert "breakdown" in result
+        assert "assumptions" in result
+        assert "display" in result["total"]
+        assert isinstance(result["assumptions"], list)
+
+
+class TestTimeRangeFormatting:
+    def test_format_hours(self):
+        estimate = estimate_ttfr(
+            datasource_id="scr_002145_neuromorpho_modelimage"
+        )
+
+        total_str = str(estimate.total)
+        assert "days" in total_str or "hours" in total_str
+
+    def test_format_consistency(self):
+        estimate = estimate_ttfr(
+            datasource_id="scr_005031_openneuro"
+        )
+
+        result_dict = estimate.to_dict()
+        assert result_dict["total"]["display"] == str(estimate.total)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])