apply ruff

Tools/ML.py

@@ -28,7 +28,7 @@ def collect_data(
 
     # extract data
     extr_var = extract_X.var(packdata, ipft)
-    
+
     # extract PFT map
     pft_ny = extract_X.pft(packdata, PFT_mask_lai, ipft)
     pft_ny = np.resize(pft_ny, (*extr_var.shape[:-1], 1))
@@ -56,12 +56,14 @@ def combine_data(frames, keys):
             raise ValueError("DataFrames have different columns")
     check_same = {}
     for col in columns:
-        check_same[col] = all((frame[col] == frames[0][col]).dropna().all() for frame in frames)
-    same_cols = [col for col, same in check_same.items() if same or col == 'pft']
+        check_same[col] = all(
+            (frame[col] == frames[0][col]).dropna().all() for frame in frames
+        )
+    same_cols = [col for col, same in check_same.items() if same or col == "pft"]
     df = pd.concat([df.drop(columns=same_cols) for df in frames], keys=keys, axis=1)
     df.columns = [f"{c}_{k}" for k, c in df.columns]
     df = pd.concat([df, frames[0][same_cols]], axis=1)
-    df = df.drop(columns=['pft']).dropna()
+    df = df.drop(columns=["pft"]).dropna()
     return df
 
 
@@ -86,8 +88,8 @@ def MLmap_multidim(
         col_type = "None"
         type_val = "None"
         combineXY = combine_XY
-        
-    Y = combineXY.filter(regex='^Y_')
+
+    Y = combineXY.filter(regex="^Y_")
     X = combineXY.drop(columns=Y.columns)
 
     # combine_XY=pd.get_dummies(combine_XY) # one-hot encoded
@@ -142,8 +144,20 @@ def MLmap_multidim(
     return MLeval.evaluation_map(Global_Predicted_Y_map, Y, PFT_mask)
 
 
-def plot_eval_results(Global_Predicted_Y_map, ipool, pool_map, combineXY, predY_train, varname, ind, ii, ipft, PFT_mask, resultpath, logfile):
-
+def plot_eval_results(
+    Global_Predicted_Y_map,
+    ipool,
+    pool_map,
+    combineXY,
+    predY_train,
+    varname,
+    ind,
+    ii,
+    ipft,
+    PFT_mask,
+    resultpath,
+    logfile,
+):
     # evaluation
     R2, RMSE, slope, reMSE, dNRMSE, sNRMSE, iNRMSE, f_SB, f_SDSD, f_LSC = (
         MLeval.evaluation_map(Global_Predicted_Y_map, pool_map, ipft, PFT_mask)
@@ -257,29 +271,31 @@ def MLloop(
                         if ipft in ii["skip_loop"]["pft"]:
                             continue
 
-                        dim_ind, = zip(ii["dim_loop"], ind)
-
-                        comb_ds[ipool].append((
-                            collect_data(
-                                packdata,
-                                ivar,
-                                ipool,
-                                PFT_mask_lai,
-                                ipft,
-                                varname,
-                                ind,
-                                ii,
-                                labx,
-                                varlist,
-                                logfile,
-                            ),
-                            f"{varname}_{dim_ind[0]}_{dim_ind[1]}"
-                        ))
+                        (dim_ind,) = zip(ii["dim_loop"], ind)
+
+                        comb_ds[ipool].append(
+                            (
+                                collect_data(
+                                    packdata,
+                                    ivar,
+                                    ipool,
+                                    PFT_mask_lai,
+                                    ipft,
+                                    varname,
+                                    ind,
+                                    ii,
+                                    labx,
+                                    varlist,
+                                    logfile,
+                                ),
+                                f"{varname}_{dim_ind[0]}_{dim_ind[1]}",
+                            )
+                        )
                         break
 
                     # close&save netCDF file
                     restnc.close()
-                    
+
                     if len(comb_ds[ipool]) > 3:
                         break
 
@@ -288,7 +304,7 @@ def MLloop(
     for ipool, vals in comb_ds.items():
         df = combine_data(*zip(*vals))
         df.to_csv(f"{resultpath}/{ipool}.csv")
-    
+
         res = MLmap_multidim(
             packdata,
             df,
@@ -302,5 +318,5 @@ def MLloop(
             missVal,
         )
         results.append(res)
-    
+
     return pd.concat(results, keys=comb_ds.keys(), names=["component"])

Tools/extract_X.py

@@ -55,4 +55,4 @@ def var(packdata, ipft):
         extr_var.append(extracted_var.reshape(-1, len(packdata.Nlat), 1))
     com_shape = max(map(np.shape, extr_var))
     extr_var = [np.resize(a, com_shape) for a in extr_var]
-    return np.concatenate(extr_var, axis=-1)
+    return np.concatenate(extr_var, axis=-1)

Tools/readvar.py

@@ -88,7 +88,9 @@ def readvar(varlist, config, logfile):
     # packdata.Tamp = packdata.Tmax - packdata.Tmin
 
     # 0.1.2 Other climatic variables (Rainf,Snowf,Qair,Psurf,SWdown,LWdown)
-    packdata.update((k, (["year", "month", "lat", "lon"], adict[f"MY{k}"])) for k in varname_clim)
+    packdata.update(
+        (k, (["year", "month", "lat", "lon"], adict[f"MY{k}"])) for k in varname_clim
+    )
     # for index in range(len(varname_clim)):
     #     if varname_clim[index] == "Tair":
     #         continue
@@ -167,12 +169,12 @@ def readvar(varlist, config, logfile):
                     da[da == predvar[ipred]["missing_value"]] = np.nan
                 if isinstance(da, np.ma.masked_array):
                     da = da.filled(np.nan)
-                packdata[rename[ivar]] = (["veget", "lat", "lon"][-da.ndim:], da)
+                packdata[rename[ivar]] = (["veget", "lat", "lon"][-da.ndim :], da)
 
     ds = xarray.Dataset(packdata)
 
     # 0.3 Interactions between variables
-    ds['interx'] = ds.Tair * ds.Rainf
+    ds["interx"] = ds.Tair * ds.Rainf
     # packdata.interx2 = packdata.Temp_GS * packdata.Pre_GS
 
     ds.attrs.update(

Tools/train.py

@@ -39,17 +39,17 @@
 ##@retval      predY                  predicted Y
 def training_BAT(X, Y, logfile, loocv):
     print("Data shapes: ", X.shape, Y.shape)
-    
+
     # run the KMeans algorithm to find the cluster centers, and resample the data
     mod = KMeans(n_clusters=3)
     lab = mod.fit_predict(Y)
     count = Counter(lab)
     check.display("Counter(lab):" + str(count), logfile)
     over_samples = SMOTE()
-    over_samples_X, over_samples_y = over_samples.fit_resample(pd.concat([X, Y], axis=1), lab)
-    check.display(
-        "Counter(over_samples_y):" + str(Counter(over_samples_y)), logfile
+    over_samples_X, over_samples_y = over_samples.fit_resample(
+        pd.concat([X, Y], axis=1), lab
     )
+    check.display("Counter(over_samples_y):" + str(Counter(over_samples_y)), logfile)
     X = over_samples_X[X.columns]
     Y = over_samples_X[Y.columns]
     print("Data shapes after resampling: ", X.shape, Y.shape)
@@ -62,10 +62,15 @@ def training_BAT(X, Y, logfile, loocv):
     #     optimizer=optim.Adam,
     #     # device="cuda",
     # )
-    model = MLPRegressor(hidden_layer_sizes=(64, 64), max_iter=100, 
-                         learning_rate='invscaling', learning_rate_init=0.1, verbose=True)
+    model = MLPRegressor(
+        hidden_layer_sizes=(64, 64),
+        max_iter=100,
+        learning_rate="invscaling",
+        learning_rate_init=0.1,
+        verbose=True,
+    )
 
     model.fit(X, Y)
     predY = model.predict(X)
-    
+
     return model, predY