Add disp_profile sampling for sparklines

app/utils/data_loader.py

@@ -91,6 +91,7 @@ def load_prediction_index(pred_dir: str) -> pd.DataFrame:
                             "mean_disp_m0": v.get("mean_disp", 0.0),
                             "max_disp_m0": v.get("max_disp", 0.0),
                             "top_residue": -1,
+                            "disp_profile": v.get("disp_profile", []),
                         })
                 else:
                     # No index.json or empty — scan zip for _mode_0.txt files
@@ -109,6 +110,7 @@ def load_prediction_index(pred_dir: str) -> pd.DataFrame:
                                 "mean_disp_m0": float(mag.mean()),
                                 "max_disp_m0": float(mag.max()),
                                 "top_residue": int(np.argmax(mag)) + 1,
+                                "disp_profile": mag[::max(1, len(mag)//20)].tolist(),
                             })
                         except Exception:
                             continue
@@ -136,12 +138,13 @@ def load_prediction_index(pred_dir: str) -> pd.DataFrame:
                     "mean_disp_m0": float(mag.mean()),
                     "max_disp_m0": float(mag.max()),
                     "top_residue": int(np.argmax(mag)) + 1,
+                    "disp_profile": mag[::max(1, len(mag)//20)].tolist(),
                 })
             except Exception:
                 continue
 
     if not rows:
-        return pd.DataFrame(columns=["name", "seq_len", "n_modes", "mean_disp_m0", "max_disp_m0", "top_residue"])
+        return pd.DataFrame(columns=["name", "seq_len", "n_modes", "mean_disp_m0", "max_disp_m0", "top_residue", "disp_profile"])
     return pd.DataFrame(rows).sort_values("name").reset_index(drop=True)