Adapt to change in io interface

Author: dnerini

examples/cascade_decomposition.py

@@ -58,7 +58,7 @@
                                   ds.fn_ext, ds.timestep, 0, 0)
 
 ## read radar field files
-importer = stp.io.get_method(ds.importer)
+importer = stp.io.get_method(ds.importer, type="importer")
 R, _, metadata = stp.io.read_timeseries(input_files, importer, **ds.importer_kwargs)
 R = R.squeeze() # since this contains just one frame
 Rmask = np.isnan(R)

examples/ensemble_verification.py

@@ -188,7 +188,7 @@ def cond_pars(pars):
 
 
             ## read radar field files
-            importer    = stp.io.get_method(ds.importer)
+            importer    = stp.io.get_method(ds.importer, type="importer")
             R, _, metadata = stp.io.read_timeseries(input_files, importer, **ds.importer_kwargs)
             metadata0 = metadata.copy()
             metadata0["shape"] = R.shape[1:]
@@ -296,7 +296,7 @@ def export(X):
                                           ds.fn_ext, ds.timestep, 0, p["n_lead_times"])
 
         ## read radar field files
-        importer = stp.io.get_method(ds.importer)
+        importer = stp.io.get_method(ds.importer, type="importer")
         R_obs, _, metadata_obs = stp.io.read_timeseries(input_files, importer, **ds.importer_kwargs)
         R_obs = R_obs[1:,:,:]
         metadata_obs["timestamps"] = metadata_obs["timestamps"][1:]
@@ -338,20 +338,7 @@ def export(X):
         for i,lt in enumerate(p["v_leadtimes"]):
 
             idlt = leadtimes == lt
-            
-            if verification["doplot"]:
-                ## plot observation
-                fig = plt.figure()
-                im = stp.plt.plot_precip_field(R_obs[idlt,:,:].squeeze())
-                plt.savefig(os.path.join(path_to_nwc, "%s_R_obs_%03d_%03d.png" % (startdate.strftime("%Y%m%d%H%M"), lt, p["v_accu"])))
-                plt.close()        
-                
-                ## plot forecast
-                fig = plt.figure()
-                im = stp.plt.plot_precip_field(R_fct[0, idlt, :, :].squeeze())
-                plt.savefig(os.path.join(path_to_nwc, "%s_R_fct_%03d_%03d.png" % (startdate.strftime("%Y%m%d%H%M"), lt, p["v_accu"])))
-                plt.close()
-            
+                        
             ## rank histogram
             R_fct_ = np.vstack([R_fct[j, idlt, :, :].flatten() for j in range(p["n_ens_members"])]).T
             stp.verification.ensscores.rankhist_accum(rankhists[lt], 

examples/noise_generators.py

@@ -59,7 +59,7 @@
                                   ds.fn_ext, ds.timestep, 0, 0)
 
 ## read radar field files
-importer = stp.io.get_method(ds.importer)
+importer = stp.io.get_method(ds.importer, type="importer")
 R, _, metadata = stp.io.read_timeseries(input_files, importer, **ds.importer_kwargs)
 R = R.squeeze() # since this contains just one frame
 Rmask = np.isnan(R)

examples/run_deterministic_nowcast.py

@@ -65,7 +65,7 @@
                                  ds.fn_ext, ds.timestep, n_prvs_times, 0)
 
 ## read radar field files
-importer = stp.io.get_method(ds.importer)
+importer = stp.io.get_method(ds.importer, type="importer")
 R, _, metadata = stp.io.read_timeseries(input_files, importer, **ds.importer_kwargs)
 Rmask = np.isnan(R)
 print("The data array has size [nleadtimes,nrows,ncols] =", R.shape)
@@ -101,12 +101,6 @@
 R_fct, _ = transformer(R_fct, metadata, inverse=True)
 R, metadata = transformer(R, metadata, inverse=True)
 
-Rplot=R.copy()
-Rplot[Rmask]=np.nan
-stp.plt.plot_precip_field(Rplot[-1,:,:], False, metadata)
-stp.plt.quiver(UV, metadata)
-plt.show()
-
 ## plot the nowcast...
 R[Rmask] = np.nan # reapply radar mask
 stp.plt.animate(R, nloops=2, timestamps=metadata["timestamps"],

examples/run_ensemble_nowcast.py

@@ -75,7 +75,7 @@
                                   ds.fn_ext, ds.timestep, n_prvs_times, 0)
 
 ## read radar field files
-importer = stp.io.get_method(ds.importer)
+importer = stp.io.get_method(ds.importer, type="importer")
 R, _, metadata = stp.io.read_timeseries(input_files, importer, **ds.importer_kwargs)
 Rmask = np.isnan(R)