Make kmperpixel and timestep optional keyword arguments

Author: pulkkins

examples/ensemble_verification.py

@@ -240,15 +240,19 @@ def export(X):
             ## start the nowcast
             nwc_method = stp.nowcasts.get_method(p["nwc_method"])
             R_fct = nwc_method(R, UV, p["n_lead_times"], p["n_ens_members"],
-                            p["n_cascade_levels"], metadata["xpixelsize"]/1000, 
-                            ds.timestep, R_thr=metadata["threshold"], extrap_method=p["adv_method"],
-                            decomp_method=p["decomp_method"], bandpass_filter_method=p["bandpass_filter"], 
-                            noise_method=p["noise_method"], noise_stddev_adj=p["noise_adjustment"],
+                            p["n_cascade_levels"], kmperpixel=metadata["xpixelsize"]/1000, 
+                            timestep=ds.timestep, R_thr=metadata["threshold"], 
+                            extrap_method=p["adv_method"], 
+                            decomp_method=p["decomp_method"], 
+                            bandpass_filter_method=p["bandpass_filter"], 
+                            noise_method=p["noise_method"], 
+                            noise_stddev_adj=p["noise_adjustment"],
                             ar_order=p["ar_order"],conditional=p["conditional"], 
-                            use_probmatching=p["prob_matching"], mask_method=p["mask_method"], 
+                            use_probmatching=p["prob_matching"], 
+                            mask_method=p["mask_method"], 
                             use_precip_mask=p["precip_mask"], callback=export, 
                             return_output=False, seed=p["seed"])
-    
+            
             ## save results
             stp.io.close_forecast_file(exporter)
             R_fct = None

examples/run_ensemble_nowcast.py

@@ -108,9 +108,10 @@
 # Perform the nowcast
 nwc_method = stp.nowcasts.get_method(nwc_method)
 R_fct = nwc_method(R, UV, n_lead_times, n_ens_members, 
-                   n_cascade_levels, metadata["xpixelsize"]/1000, 
-                   ds.timestep,  R_thr=metadata["threshold"], extrap_method=adv_method, 
-                   decomp_method=decomp_method, bandpass_filter_method=bandpass_filter, 
+                   n_cascade_levels, kmperpixel=metadata["xpixelsize"]/1000, 
+                   timestep=ds.timestep,  R_thr=metadata["threshold"], 
+                   extrap_method=adv_method, decomp_method=decomp_method, 
+                   bandpass_filter_method=bandpass_filter, 
                    noise_method=noise_method, noise_stddev_adj=adjust_noise, 
                    ar_order=ar_order, conditional=conditional, 
                    use_precip_mask=precip_mask, mask_method=mask_method, 

pysteps/nowcasts/steps.py

@@ -15,8 +15,8 @@
 except ImportError:
     dask_imported = False
 
-def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels, 
-             kmperpixel, timestep, R_thr=None, extrap_method="semilagrangian", 
+def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels, R_thr=None, 
+             kmperpixel=None, timestep=None, extrap_method="semilagrangian", 
              decomp_method="fft", bandpass_filter_method="gaussian", 
              noise_method="nonparametric", noise_stddev_adj=True, ar_order=2, 
              vel_pert_method=None, conditional=False, use_precip_mask=True, 
@@ -41,16 +41,18 @@ def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels,
       The number of ensemble members to generate.
     n_cascade_levels : int
       The number of cascade levels to use.
-    kmperpixel : float
-      Spatial resolution of the input data (kilometers/pixel).
-    timestep : float
-      Time step of the motion vectors (minutes).
     
     Other Parameters
     ----------------
     R_thr : float
       Specifies the threshold value for minimum observable precipitation 
       intensity. Must be set if use_probmatching is True or conditional is True.
+    kmperpixel : float
+      Spatial resolution of the input data (kilometers/pixel). Required if 
+      vel_pert_method is not None or mask_method is 'incremental'.
+    timestep : float
+      Time step of the motion vectors (minutes). Required if vel_pert_method is 
+      not None or mask_method is 'incremental'.
     extrap_method : {'semilagrangian'}
       Name of the extrapolation method to use. See the documentation of 
       pysteps.advection.
@@ -148,15 +150,29 @@ def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels,
     if use_probmatching and R_thr is None:
         raise Exception("use_probmatching=True but R_thr is not set")
 
+    if kmperpixel is None:
+        if vel_pert_method is None:
+            raise Exception("vel_pert_method is set but kmperpixel=None")
+        if mask_method == "incremental":
+            raise Exception("mask_method='incremental' but kmperpixel=None")
+    
+    if timestep is None:
+        if vel_pert_method is None:
+            raise Exception("vel_pert_method is set but timestep=None")
+        if mask_method == "incremental":
+            raise Exception("mask_method='incremental' but timestep=None")
+    
     print("Computing STEPS nowcast:")
     print("------------------------")
     print("")
 
     print("Inputs:")
     print("-------")
     print("input dimensions: %dx%d" % (R.shape[1], R.shape[2]))
-    print("km/pixel:         %g"    % kmperpixel)
-    print("time step:        %d minutes" % timestep)
+    if kmperpixel is not None:
+        print("km/pixel:         %g"    % kmperpixel)
+    if timestep is not None:
+        print("time step:        %d minutes" % timestep)
     print("")
 
     print("Methods:")