From 6d51fd4b08a00f402403b83a530bb1a30df6b53e Mon Sep 17 00:00:00 2001
From: Mats Veldhuizen <mats.veldhuizen@knmi.nl>
Date: Wed, 2 Oct 2024 17:44:15 +0200
Subject: [PATCH] fix issue with precip_models_cascade

---
 pysteps/blending/steps.py | 50 +++++++++++++++++++--------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/pysteps/blending/steps.py b/pysteps/blending/steps.py
index dba88706..71e769ad 100644
--- a/pysteps/blending/steps.py
+++ b/pysteps/blending/steps.py
@@ -645,33 +645,33 @@ def forecast(
         # 2.3.3 If zero_precip_radar, make sure that precip_cascade does not contain
         # only nans or infs. If so, fill it with the zero value.
         if zero_precip_radar:
-            if precip_models_cascade is not None:
-                precip_cascade[~np.isfinite(precip_cascade)] = np.nanmin(
-                    precip_models_cascade
-                )
-            else:
-                done = False
-                for t in timesteps:
-                    if done:
-                        break
-                    for j in range(precip_models.shape[0]):
-                        if not blending.utils.check_norain(
-                            precip_models, precip_thr, norain_thr
-                        ):
-                            precip_models_cascade_temp = decompositor(
-                                precip_models[j, t, :, :],
-                                bp_filter=bp_filter,
-                                fft_method=fft,
-                                output_domain=domain,
-                                normalize=True,
-                                compute_stats=True,
-                                compact_output=True,
-                            )["cascade_levels"]
+            done = False
+            for t in timesteps:
+                if done:
+                    break
+                for j in range(precip_models.shape[0]):
+                    if not blending.utils.check_norain(
+                        precip_models, precip_thr, norain_thr
+                    ):
+                        if precip_models_cascade is not None:
                             precip_cascade[~np.isfinite(precip_cascade)] = np.nanmin(
-                                precip_models_cascade_temp
+                                precip_models_cascade[j, t]
                             )
-                            done = True
-                            break
+                            continue
+                        precip_models_cascade_temp = decompositor(
+                            precip_models[j, t, :, :],
+                            bp_filter=bp_filter,
+                            fft_method=fft,
+                            output_domain=domain,
+                            normalize=True,
+                            compute_stats=True,
+                            compact_output=True,
+                        )["cascade_levels"]
+                        precip_cascade[~np.isfinite(precip_cascade)] = np.nanmin(
+                            precip_models_cascade_temp
+                        )
+                        done = True
+                        break
 
         # 2.3.5 If zero_precip_radar is True, only use the velocity field of the NWP
         # forecast. I.e., velocity (radar) equals velocity_model at the first time