Includes input energy parameter in the model and adds non-linearities

pes_to_spec/test/offline_analysis.py

@@ -134,7 +134,8 @@ def main():
     """
     parser = argparse.ArgumentParser(prog="offline_analysis", description="Test pes2spec doing an offline analysis of the data.")
     parser.add_argument('-p', '--proposal', type=int, metavar='INT', help='Proposal number', default=2828)
-    parser.add_argument('-r', '--run', type=int, metavar='INT', help='Run number', default=206)
+    parser.add_argument('-r', '--run', type=str, metavar='INT,INT,...', help='Run numbers, comma-separated.', default=206)
+    parser.add_argument('-t', '--test-run', type=int, metavar='INT', help='Run to test', default=None)
     parser.add_argument('-m', '--model', type=str, metavar='FILENAME', default="", help='Model to load. If given, do not train a model and just do inference with this one.')
     parser.add_argument('-d', '--directory', type=str, metavar='DIRECTORY', default=".", help='Where to save the results.')
     parser.add_argument('-S', '--spec', type=str, metavar='NAME', default="SA3_XTD10_SPECT/MDL/SPECTROMETER_SQS_NAVITAR:output", help='SPEC name')
@@ -147,8 +148,20 @@ def main():
     args = parser.parse_args()
 
     print("Opening run ...")
+    runs = args.run.split(',')
+    runs = [int(r) for r in runs]
     # get run
-    run = open_run(proposal=args.proposal, run=args.run)
+    run_list = [open_run(proposal=args.proposal, run=r) for r in runs]
+    run = run_list[0]
+    if len(run_list) > 1:
+        run = run.union(*run_list[1:])
+
+    run_test = run
+    other_run_test = False
+    if "test_run" in args and args.test_run is not None:
+        other_run_test = True
+        run_test = open_run(proposal=args.proposal, run=args.test_run)
+
     #run = RunDirectory("/gpfs/exfel/data/scratch/tmichela/data/r0206")
 
     # ----------------Used in the first tests-------------------------
@@ -186,15 +199,25 @@ def main():
 
     # reserve part of it for the test stage
     train_tids = tids[:-10]
-    test_tids = tids[-10:]
+    if other_run_test:
+        pes_tidt = run_test[pes_name, "digitizers.trainId"].ndarray()
+        xgm_tidt = run_test[xgm_name, "data.trainId"].ndarray()
+        spec_tidt = run_test[spec_name, "data.trainId"].ndarray()
+        test_tids = matching_ids(spec_tidt, pes_tidt, xgm_tidt)
+    else:
+        test_tids = tids
 
     # read the PES data for each channel
     channels = [f"channel_{i}_{l}"
                 for i, l in product(range(1, 5), ["A", "B", "C", "D"])]
     pes_raw = {ch: run[pes_name, f"digitizers.{ch}.raw.samples"].select_trains(by_id[tids]).ndarray()
                for ch in channels}
-    pes_raw_t = {ch: run[pes_name, f"digitizers.{ch}.raw.samples"].select_trains(by_id[test_tids]).ndarray()
-               for ch in channels}
+    pes_raw_t = {ch: run_test[pes_name, f"digitizers.{ch}.raw.samples"].select_trains(by_id[test_tids]).ndarray()
+                   for ch in channels}
+
+    # select test SPEC data
+    spec_raw_pe_t = run_test[spec_name, "data.photonEnergy"].select_trains(by_id[test_tids - spec_offset]).ndarray()
+    spec_raw_int_t = run_test[spec_name, "data.intensityDistribution"].select_trains(by_id[test_tids - spec_offset]).ndarray()
 
     print("Data in memory.")
 
@@ -203,9 +226,9 @@ def main():
     #xgm_pe =  run['SA3_XTD10_XGM/XGM/DOOCS:output', "data.intensitySa3TD"].select_trains(by_id[tids]).ndarray()
     #retvol_raw = run["SA3_XTD10_PES/MDL/DAQ_MPOD", "u212.value"].select_trains(by_id[tids]).ndarray()
     #retvol_raw_timestamp = run["SA3_XTD10_PES/MDL/DAQ_MPOD", "u212.timestamp"].select_trains(by_id[tids]).ndarray()
-    
+
     xgm_flux =  run['SA3_XTD10_XGM/XGM/DOOCS:output', "data.intensitySa3TD"].select_trains(by_id[tids]).ndarray()[:, 0][:, np.newaxis]
-    xgm_flux_t =  run['SA3_XTD10_XGM/XGM/DOOCS:output', "data.intensitySa3TD"].select_trains(by_id[test_tids]).ndarray()[:, 0][:, np.newaxis]
+    xgm_flux_t =  run_test['SA3_XTD10_XGM/XGM/DOOCS:output', "data.intensitySa3TD"].select_trains(by_id[test_tids]).ndarray()[:, 0][:, np.newaxis]
 
     t = list()
     t_names = list()
@@ -213,6 +236,9 @@ def main():
     model = Model(poly=args.poly)
 
     train_idx = np.isin(tids, train_tids) & (xgm_flux[:,0] > args.xgm_cut)
+    # we just need this for training and we need to avoid copying it, which blows up the memoray usage
+    for k in pes_raw.keys():
+        pes_raw[k] = pes_raw[k][train_idx]
 
     model.debug_peak_finding(pes_raw, os.path.join(args.directory, "test_peak_finding.png"))
     if len(args.model) == 0:
@@ -220,10 +246,11 @@ def main():
         start = time_ns()
         w = model.uniformize(xgm_flux[train_idx])
         print("w", np.amin(w), np.amax(w), np.median(w))
-        model.fit({k: v[train_idx, :]
-                   for k, v in pes_raw.items()},
-                   spec_raw_int[train_idx, :],
-                   spec_raw_pe[train_idx, :],
+        model.fit(pes_raw,
+                  #{k: v[train_idx]
+                  #for k, v in pes_raw.items()},
+                   spec_raw_int[train_idx],
+                   spec_raw_pe[train_idx],
                    w,
                    pulse_energy=xgm_flux[train_idx],
                    )
@@ -282,7 +309,7 @@ def main():
     # test
     print("Predict")
     start = time_ns()
-    spec_pred = model.predict(pes_raw, pulse_energy=xgm_flux)
+    spec_pred = model.predict(pes_raw_t, pulse_energy=xgm_flux_t)
     spec_pred["deconvolved"] = model.deconvolve(spec_pred["expected"])
     t += [time_ns() - start]
     t_names += ["Predict"]
@@ -296,17 +323,16 @@ def main():
     showSpec = False
     if len(args.model) == 0:
         showSpec = True
-        spec_smooth = model.preprocess_high_res(spec_raw_int)
+        spec_smooth = model.preprocess_high_res(spec_raw_int_t)
 
         # 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])
+        de = (spec_raw_pe_t[0,1] - spec_raw_pe_t[0,0])
         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)
         ax = fig.add_subplot(gs[0, 0])
-        ax.scatter(chi2/ndof, xgm_flux[:,0], c='r', s=20)
+        ax.scatter(chi2/ndof, xgm_flux_t[:,0], c='r', s=20)
         ax.set(title=f"", #avg(stat unc) = {unc_stat}, avg(pca unc) = {unc_pca}",
                xlabel=r"$\chi^2/$ndof",
                ylabel="XGM intensity [uJ]",
@@ -316,7 +342,7 @@ def main():
         # Manually set the position and relative size of the inset axes within ax1
         ip = InsetPosition(ax, [0.65,0.6,0.35,0.4])
         ax2.set_axes_locator(ip)
-        ax2.scatter(chi2/ndof, xgm_flux[:,0], c='r', s=30)
+        ax2.scatter(chi2/ndof, xgm_flux_t[:,0], c='r', s=30)
         #ax2.scatter(chi2/ndof, np.sum(spec_pred["expected"], axis=1)*de, c='b', s=30)
         #ax2.scatter(chi2/ndof, np.sum(spec_raw_int, axis=1)*de, c='g', s=30)
         ax2.set(title="",
@@ -353,16 +379,16 @@ def main():
         fig = plt.figure(figsize=(12, 8))
         gs = GridSpec(1, 1)
         ax = fig.add_subplot(gs[0, 0])
-        sns.kdeplot(x=xgm_flux[:,0], ax=ax)
+        sns.kdeplot(x=xgm_flux_t[:,0], ax=ax)
         ax.set(title=f"",
                xlabel="XGM intensity [uJ]",
                ylabel="Density [a.u.]",
                )
-        ax.text(0.90, 0.95, fr"$\mu = ${np.mean(xgm_flux[:,0]):.2f}",
+        ax.text(0.90, 0.95, fr"$\mu = ${np.mean(xgm_flux_t[:,0]):.2f}",
                 verticalalignment='top', horizontalalignment='right',
                 transform=ax.transAxes,
                 color='black', fontsize=15)
-        ax.text(0.90, 0.90, fr"$\sigma = ${np.std(xgm_flux[:,0]):.2f}",
+        ax.text(0.90, 0.90, fr"$\sigma = ${np.std(xgm_flux_t[:,0]):.2f}",
                 verticalalignment='top', horizontalalignment='right',
                 transform=ax.transAxes,
                 color='black', fontsize=15)
@@ -374,7 +400,7 @@ def main():
         fig = plt.figure(figsize=(12, 8))
         gs = GridSpec(1, 1)
         ax = fig.add_subplot(gs[0, 0])
-        ax.scatter(rmse, xgm_flux[:,0], c='r', s=30)
+        ax.scatter(rmse, xgm_flux_t[:,0], c='r', s=30)
         ax = plt.gca()
         ax.set(title=f"",
                xlabel=r"Root-mean-squared error",
@@ -407,7 +433,7 @@ 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=xgm_flux[:,0], color='r', robust=True, ax=ax)
+        sns.regplot(x=np.sum(spec_raw_int_t, axis=1)*de, y=xgm_flux_t[:,0], color='r', robust=True, ax=ax)
         ax.set(title=f"",
                xlabel="SPEC (raw) integral",
                ylabel="XGM Intensity [uJ]",
@@ -419,7 +445,7 @@ 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, -2))*de, color='r', robust=True, ax=ax)
+        sns.regplot(x=np.sum(spec_raw_int_t, 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",
@@ -430,7 +456,7 @@ 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, -2))*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_t[:,0], color='r', robust=True, ax=ax)
         ax.set(title=f"",
                xlabel="Predicted integral",
                ylabel="XGM intensity [uJ]",
@@ -443,23 +469,19 @@ def main():
     last -= 10
     pes_to_show = 'channel_1_D'
     # plot
-    for tid in test_tids:
-        idx = np.where(tid==tids)[0][0]
+    for idx in range(len(spec_raw_int_t) - 10, len(spec_raw_int_t)):
+        tid = test_tids[idx]
         plot_result(os.path.join(args.directory, f"test_{tid}.png"),
                    {k: item[idx, 0, ...] if k != "pca"
                        else item[0, ...]
                        for k, item in spec_pred.items()},
                     spec_smooth[idx, :] if showSpec else None,
-                    spec_raw_pe[idx, :] if showSpec else None,
-                    spec_raw_int[idx, :] if showSpec else None,
-                    #pes=-pes_raw[pes_to_show][idx, first:last],
-                    #pes_to_show=pes_to_show.replace('_', ' '),
-                    #pes_bin=np.arange(first, last),
-                    #wiener=model.wiener_filter
+                    spec_raw_pe_t[idx, :] if showSpec else None,
+                    spec_raw_int_t[idx, :] if showSpec else None,
                     )
         for ch in channels:
             plot_pes(os.path.join(args.directory, f"test_pes_{tid}_{ch}.png"),
-                     pes_raw[ch][idx, first:last], first, last)
+                     pes_raw_t[ch][idx, first:last], first, last)
 
 if __name__ == '__main__':
     main()