[DRAFT] unfinished attempt to fix logging

config/evaluate/diffusion_overfitting.yaml

@@ -0,0 +1,52 @@
+#optional: if commented out all is taken care of by the default settings
+# NB. global options apply to all run_ids 
+#global_plotting_options:
+#  image_format : "png" #options: "png", "pdf", "svg", "eps", "jpg" ..
+#  dpi_val : 300
+#  ERA5:
+#    marker_size: 2
+#    scale_marker_size: 1
+#    marker: "o"
+#    # alpha: 0.5
+#    2t: 
+#      vmin: 250
+#      vmax: 300
+#    10u:
+#      vmin: -40
+#      vmax: 40
+
+evaluation:
+  metrics  : ["rmse", "mae"]
+  regions: ["global", "nhem"]
+  summary_plots : true
+  summary_dir: "./plots/"
+  plot_ensemble: "members" #supported: false, "std", "minmax", "members"
+  plot_score_maps: false #plot scores on a 2D maps. it slows down score computation 
+  print_summary: false #print out score values on screen. it can be verbose
+  log_scale: false
+  add_grid: false
+  score_cards: false
+  num_processes: 0  #options: int, "auto", 0 means no parallelism (default)
+
+run_ids :
+  vpqg83lz:
+    label: "overfit model vpqg83lz"
+    results_base_dir : "./results/"
+    mini_epoch: 0
+    rank: 0
+    streams:
+      ERA5:
+        channels: ["2t", "10u"] #, "10v", "z_500", "t_850", "u_850", "v_850", "q_850", ]
+        evaluation:
+          forecast_step: "all"
+          sample: "all"
+          ensemble: "all" #supported: "all", "mean", [0,1,2]
+        plotting:
+          sample: [1]
+          forecast_step: [1]
+          ensemble: "all" #supported: "all", "mean", [0,1,2]
+          plot_maps: true
+          plot_histograms: true
+          plot_animations: true
+
+

src/weathergen/train/trainer.py

@@ -427,22 +427,27 @@ def _prepare_logging(
         ]
 
         # assert len(targets_rt) == len(preds) and len(preds) == len(self.cf.streams)
-        fsteps = len(targets_rt)
+
+        preds = preds[0]
         preds_all: list[list[list[NDArray]]] = [
-            [[] for _ in self.cf.streams] for _ in range(fsteps)
+            [[] for _ in self.cf.streams] for _ in forecast_range
         ]
         targets_all: list[list[list[NDArray]]] = [
-            [[] for _ in self.cf.streams] for _ in range(fsteps)
+            [[] for _ in self.cf.streams] for _ in forecast_range
         ]
-        targets_lens: list[list[list[int]]] = [[[] for _ in self.cf.streams] for _ in range(fsteps)]
+        targets_lens: list[list[list[int]]] = [[[] for _ in self.cf.streams] for _ in forecast_range]
 
         # TODO: iterate over batches here in future, and change loop order to batch, stream, fstep
-        for fstep in range(len(targets_rt)):
+        print(f'forecast range is {forecast_range}')
+        print(f'range of targets_rt is {range(len(targets_rt))}')
+        for fstep in forecast_range:
+            print(f'Processing forecast step {fstep}...')
+            print(len(preds.physical))
             if len(preds.physical[fstep]) == 0:
                 continue
 
             for i_strm, target in enumerate(targets_rt[fstep]):
-                pred = preds[fstep][i_strm]
+                pred = preds.physical[fstep][i_strm]
                 idxs_inv = idxs_inv_rt[fstep][i_strm]
 
                 if not (target.shape[0] > 0 and pred.shape[0] > 0):
@@ -838,7 +843,11 @@ def validate(self, mini_epoch):
                     # log output
                     if bidx < cf.log_validation:
                         # TODO: Move _prepare_logging into write_validation by passing streams_data
-                        streams_data: list[list[StreamData]] = batch[0]
+                        streams_data: list[list[StreamData]] = batch[0][0]
+                        for i in streams_data:
+                            print(f"unpacking {i}")
+                            for f in i:
+                                print(f)
                         (
                             preds_all,
                             targets_all,