Qvalue contact list, compute_stat summaries, and plot padding fixes

CHANGELOG.md

@@ -6,7 +6,12 @@ and the project adheres to [Semantic Versioning](https://semver.org/).
 
 ## [Unreleased]
 ### Added
-- (Add upcoming features here.)
+- **Fraction of Native Contacts (Q-Value) Analysis**: New `qvalue` module implementing the Best-Hummer-Eaton Q metric for measuring protein folding state and native structure retention.
+  - User-configurable parameters: `reference_frame`, `beta_const`, `lambda_const`, `native_cutoff`
+  - Automatic native contact identification and metadata reporting
+  - Integration with multi-analysis orchestrator and slide generation
+  - Full CLI support with `fastmda qvalue` command
+- New features since `v1.0.0`.
 ### Changed
 - (Add behavior/CLI/API changes here.)
 ### Fixed

docs/source/analysis/qvalue.rst

@@ -117,6 +117,7 @@ Outputs
 
 - ``qvalue.dat`` — Q-values (one per frame, range [0, 1])
 - ``qvalue_stats.dat`` — mean and standard deviation when ``compute_stat=True``
+- ``qvalue_contact_list.dat`` — residue index pairs (1-based) used as native contacts
 - ``qvalue.png`` — Line plot of Q-value vs. frame with metadata annotation
 - ``qvalue_metadata.json`` — Complete metadata including:
 

src/fastmdanalysis/analysis/qvalue.py

@@ -236,6 +236,14 @@ def run(self) -> Dict[str, np.ndarray]:
             r = md.compute_distances(self.traj, native_contacts)  # shape (N_frames, N_contacts)
             r0 = md.compute_distances(self.traj[self.reference_frame], native_contacts)[0]  # shape (N_contacts,)
 
+            # Build residue-pair contact list (1-based residue indices)
+            contact_pairs = []
+            for atom_i, atom_j in native_contacts:
+                res_i = self.traj.topology.atom(atom_i).residue.index + 1
+                res_j = self.traj.topology.atom(atom_j).residue.index + 1
+                contact_pairs.append((res_i, res_j))
+            contact_pairs = np.asarray(contact_pairs, dtype=int)
+
             # Compute Q-values using Best-Hummer-Eaton formula
             logger.debug("Computing Q-values...")
             q_values = np.mean(
@@ -244,7 +252,10 @@ def run(self) -> Dict[str, np.ndarray]:
             )
 
             self.data = np.asarray(q_values, dtype=float).reshape(-1, 1)
-            self.results = {"qvalue": self.data}
+            self.results = {
+                "qvalue": self.data,
+                "contact_list": contact_pairs,
+            }
 
             if self.compute_stat:
                 mean_val = float(np.nanmean(q_values))
@@ -256,10 +267,15 @@ def run(self) -> Dict[str, np.ndarray]:
                     header="mean std",
                     fmt="%.6f",
                 )
-
             # Save data
             logger.info("Saving Q-value data...")
             self._save_data(self.data, "qvalue", header="qvalue", fmt="%.6f")
+            self._save_data(
+                contact_pairs,
+                "qvalue_contact_list",
+                header="residue_i residue_j (1-based)",
+                fmt="%d",
+            )
 
             # Save metadata
             self._save_metadata()
@@ -341,7 +357,6 @@ def plot(self, data: Optional[np.ndarray] = None, **kwargs):
         line_kwargs = {"linestyle": linestyle, "marker": marker}
         if color is not None:
             line_kwargs["color"] = color
-
         ax.plot(x, y, **line_kwargs)
         if self.compute_stat:
             mean_val = float(np.nanmean(y))

tests/test_qvalue.py

@@ -42,6 +42,10 @@ def test_qvalue_metadata(fastmda, tmp_path):
     metadata_path = output_dir / "qvalue_metadata.json"
     assert metadata_path.exists()
 
+    # Contact list exists
+    contact_path = output_dir / "qvalue_contact_list.dat"
+    assert contact_path.exists()
+
     # Load and validate metadata
     with open(metadata_path, "r") as f:
         metadata = json.load(f)