performance fix and black complaince

pysteps/blending/steps.py

@@ -57,6 +57,8 @@
 from pysteps.timeseries import autoregression, correlation
 from pysteps import blending
 
+from copy import deepcopy
+
 try:
     import dask
 
@@ -694,15 +696,11 @@ def forecast(
         # 5. Repeat precip_cascade for n ensemble members
         # First, discard all except the p-1 last cascades because they are not needed
         # for the AR(p) model
-        precip_cascade = [
-            precip_cascade[i][-ar_order:] for i in range(n_cascade_levels)
-        ]
 
-        precip_cascade = [
-            [precip_cascade[j].copy() for j in range(n_cascade_levels)]
-            for i in range(n_ens_members)
-        ]
-        precip_cascade = np.stack(precip_cascade)
+        precip_cascade = np.stack(
+            [[precip_cascade[i][-ar_order:].copy() for i in range(n_cascade_levels)]]
+            * n_ens_members
+        )
 
         # 6. Initialize all the random generators and prepare for the forecast loop
         randgen_prec, vps, generate_vel_noise = _init_random_generators(
@@ -773,8 +771,10 @@ def forecast(
             timestep_type = "list"
 
         extrap_kwargs["return_displacement"] = True
-        forecast_prev = precip_cascade
-        noise_prev = noise_cascade
+
+        forecast_prev = deepcopy(precip_cascade)
+        noise_prev = deepcopy(noise_cascade)
+
         t_prev = [0.0 for j in range(n_ens_members)]
         t_total = [0.0 for j in range(n_ens_members)]
 

pysteps/blending/utils.py

@@ -470,7 +470,7 @@ def load_NWP(input_nc_path_decomp, input_path_velocities, start_time, n_timestep
     decomp_dict = {
         "domain": ncf_decomp.domain,
         "normalized": bool(ncf_decomp.normalized),
-        "compact_output": bool(ncf_decomp.compact_output)
+        "compact_output": bool(ncf_decomp.compact_output),
     }
 
     # Convert the start time and the timestep to datetime64 and timedelta64 type
@@ -512,7 +512,9 @@ def load_NWP(input_nc_path_decomp, input_path_velocities, start_time, n_timestep
     stds = ncf_decomp.variables["stds"][start_i:end_i, :]
 
     for i in range(n_timesteps + 1):
-        decomp_dict["cascade_levels"] = np.ma.filled(pr_decomposed[i], fill_value=np.nan)
+        decomp_dict["cascade_levels"] = np.ma.filled(
+            pr_decomposed[i], fill_value=np.nan
+        )
         decomp_dict["means"] = np.ma.filled(means[i], fill_value=np.nan)
         decomp_dict["stds"] = np.ma.filled(stds[i], fill_value=np.nan)
 

pysteps/extrapolation/semilagrangian.py

@@ -173,7 +173,7 @@ def extrapolate(
 
     if xy_coords is None:
         x_values, y_values = np.meshgrid(
-            np.arange(velocity.shape[2]), np.arange(velocity.shape[1])
+            np.arange(velocity.shape[2]), np.arange(velocity.shape[1]), copy=False
         )
 
         xy_coords = np.stack([x_values, y_values])