Fixed plotting.

pes_to_spec/test/offline_analysis.py

@@ -300,7 +300,7 @@ def main():
         # chi2 w.r.t XGM intensity
         erange = spec_raw_pe[0,-1] - spec_raw_pe[0,0]
         de = (spec_raw_pe[0,1] - spec_raw_pe[0,0])
-        chi2 = np.sum((spec_smooth[:, np.newaxis] - spec_pred["expected"])**2/(spec_pred["total_unc"]**2), axis=1)
+        chi2 = np.sum((spec_smooth[:, np.newaxis, :] - spec_pred["expected"])**2/(spec_pred["total_unc"]**2), axis=(-1, -2))
         ndof = float(spec_smooth.shape[1]) - 1.0
         fig = plt.figure(figsize=(12, 8))
         gs = GridSpec(1, 1)
@@ -369,7 +369,7 @@ def main():
         plt.close(fig)
 
         # rmse
-        rmse = np.sqrt(np.mean((spec_smooth[:, np.newaxis] - spec_pred["expected"])**2, axis=1))
+        rmse = np.sqrt(np.mean((spec_smooth[:, np.newaxis, :] - spec_pred["expected"])**2, axis=(-1, -2)))
         fig = plt.figure(figsize=(12, 8))
         gs = GridSpec(1, 1)
         ax = fig.add_subplot(gs[0, 0])
@@ -418,7 +418,7 @@ def main():
         fig = plt.figure(figsize=(12, 8))
         gs = GridSpec(1, 1)
         ax = fig.add_subplot(gs[0, 0])
-        sns.regplot(x=np.sum(spec_raw_int, axis=1)*de, y=np.sum(spec_pred["expected"], axis=1)*de, color='r', robust=True, ax=ax)
+        sns.regplot(x=np.sum(spec_raw_int, axis=-1)*de, y=np.sum(spec_pred["expected"], axis=(-1, -2))*de, color='r', robust=True, ax=ax)
         ax.set(title=f"",
                xlabel="SPEC (raw) integral",
                ylabel="Predicted integral",
@@ -429,7 +429,7 @@ def main():
         fig = plt.figure(figsize=(12, 8))
         gs = GridSpec(1, 1)
         ax = fig.add_subplot(gs[0, 0])
-        sns.regplot(x=np.sum(spec_pred["expected"], axis=1)*de, y=xgm_flux[:,0], color='r', robust=True, ax=ax)
+        sns.regplot(x=np.sum(spec_pred["expected"], axis=(-1, -2))*de, y=xgm_flux[:,0], color='r', robust=True, ax=ax)
         ax.set(title=f"",
                xlabel="Predicted integral",
                ylabel="XGM intensity [uJ]",
@@ -445,7 +445,7 @@ def main():
     for tid in test_tids:
         idx = np.where(tid==tids)[0][0]
         plot_result(os.path.join(args.directory, f"test_{tid}.png"),
-                   {k: item[idx, ...] if k != "pca"
+                   {k: item[idx, 0, ...] if k != "pca"
                        else item[0, ...]
                        for k, item in spec_pred.items()},
                     spec_smooth[idx, :] if showSpec else None,