From 22486559a22204f7b9a6e3cfa98d3e1428fadcc8 Mon Sep 17 00:00:00 2001
From: anaismoller <anaismoller@gmail.com>
Date: Wed, 19 Oct 2022 15:24:17 +1100
Subject: [PATCH] plot_lcs exception 1 point + peak

---
 supernnova/visualization/early_prediction.py | 111 ++++++++++---------
 1 file changed, 59 insertions(+), 52 deletions(-)

diff --git a/supernnova/visualization/early_prediction.py b/supernnova/visualization/early_prediction.py
index 53c4367..3e23c26 100644
--- a/supernnova/visualization/early_prediction.py
+++ b/supernnova/visualization/early_prediction.py
@@ -159,7 +159,7 @@ def plot_predictions(
         OOD is None
         and not settings.data_testing
         and arr_time.min() < peak_MJD
-        and peak_MJD > arr_time.max()
+        and peak_MJD < arr_time.max()
     ):
         ax.plot([peak_MJD, peak_MJD], [0, 1], "k--", label="Peak MJD")
     ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
@@ -283,60 +283,67 @@ def make_early_prediction(settings, nb_lcs=1, do_gifs=False):
                     settings, dict_rnn, X, target, OOD=OOD
                 )
             # X here has been normalized. We unnormalize X
-            X_unnormed = tu.unnormalize_arr(X_normed, settings)
-            # Check we do recover X_ori when OOD is None
-            if OOD is None and "cosmo" not in settings.norm:
-                # check if normalization converges
-                # using clipping in case of min<model_min
-                X_clip = X_ori.copy()
-                X_clip = np.clip(
-                    X_clip[:, settings.idx_features_to_normalize],
-                    settings.arr_norm[:, 0],
-                    np.inf,
-                )
-                X_ori[:, settings.idx_features_to_normalize] = X_clip
-                assert np.all(
-                    np.all(np.isclose(np.ravel(X_ori), np.ravel(X_unnormed), atol=1e-1))
+            try:
+                X_unnormed = tu.unnormalize_arr(X_normed, settings)
+
+                # Check we do recover X_ori when OOD is None
+                if OOD is None and "cosmo" not in settings.norm:
+                    # check if normalization converges
+                    # using clipping in case of min<model_min
+                    X_clip = X_ori.copy()
+                    X_clip = np.clip(
+                        X_clip[:, settings.idx_features_to_normalize],
+                        settings.arr_norm[:, 0],
+                        np.inf,
+                    )
+                    X_ori[:, settings.idx_features_to_normalize] = X_clip
+                    assert np.all(
+                        np.all(
+                            np.isclose(np.ravel(X_ori), np.ravel(X_unnormed), atol=1e-1)
+                        )
+                    )
+
+                # TODO: IMPROVE
+                df_temp = pd.DataFrame(data=X_unnormed, columns=features)
+                arr_time = np.cumsum(df_temp.delta_time.values)
+                df_temp["time"] = arr_time
+                for flt in settings.list_filters:
+                    non_zero = np.where(
+                        ~np.isclose(df_temp[f"FLUXCAL_{flt}"].values, 0, atol=1e-2)
+                    )[0]
+                    d_plot[flt]["FLUXCAL"] = df_temp[f"FLUXCAL_{flt}"].values[non_zero]
+                    d_plot[flt]["FLUXCALERR"] = df_temp[f"FLUXCALERR_{flt}"].values[
+                        non_zero
+                    ]
+                    d_plot[flt]["MJD"] = arr_time[non_zero]
+                plot_predictions(
+                    settings,
+                    d_plot,
+                    SNID,
+                    redshift,
+                    peak_MJD,
+                    target,
+                    arr_time,
+                    d_pred,
+                    OOD,
                 )
 
-            # TODO: IMPROVE
-            df_temp = pd.DataFrame(data=X_unnormed, columns=features)
-            arr_time = np.cumsum(df_temp.delta_time.values)
-            df_temp["time"] = arr_time
-            for flt in settings.list_filters:
-                non_zero = np.where(
-                    ~np.isclose(df_temp[f"FLUXCAL_{flt}"].values, 0, atol=1e-2)
-                )[0]
-                d_plot[flt]["FLUXCAL"] = df_temp[f"FLUXCAL_{flt}"].values[non_zero]
-                d_plot[flt]["FLUXCALERR"] = df_temp[f"FLUXCALERR_{flt}"].values[
-                    non_zero
-                ]
-                d_plot[flt]["MJD"] = arr_time[non_zero]
-            plot_predictions(
-                settings,
-                d_plot,
-                SNID,
-                redshift,
-                peak_MJD,
-                target,
-                arr_time,
-                d_pred,
-                OOD,
-            )
+                # use to create GIFs
+                if not OOD:
+                    if do_gifs:
+                        plot_gif(
+                            settings,
+                            df_temp,
+                            SNID,
+                            redshift,
+                            peak_MJD,
+                            target,
+                            arr_time,
+                            d_pred,
+                        )
+            except Exception:
+                lu.print_red(f"SNID {SNID} only has {len(X)} measurement, not plotting")
 
-            # use to create GIFs
-            if not OOD:
-                if do_gifs:
-                    plot_gif(
-                        settings,
-                        df_temp,
-                        SNID,
-                        redshift,
-                        peak_MJD,
-                        target,
-                        arr_time,
-                        d_pred,
-                    )
     lu.print_green("Finished plotting lightcurves and predictions ")