Fix evaluation example visualisation plots

neural_lam/datastore/base.py

@@ -295,8 +295,13 @@ def get_xy_extent(self, category: str) -> List[float]:
             The extent of the x, y coordinates.
 
         """
-        xy = self.get_xy(category, stacked=False)
-        extent = [xy[0].min(), xy[0].max(), xy[1].min(), xy[1].max()]
+        xy = self.get_xy(category, stacked=True)
+        extent = [
+            xy[:, 0].min(),
+            xy[:, 0].max(),
+            xy[:, 1].min(),
+            xy[:, 1].max(),
+        ]
         return [float(v) for v in extent]
 
     @property

neural_lam/datastore/mdp.py

@@ -1,4 +1,5 @@
 # Standard library
+import copy
 import warnings
 from functools import cached_property
 from pathlib import Path
@@ -394,7 +395,9 @@ def coords_projection(self) -> ccrs.Projection:
 
         class_name = projection_info["class_name"]
         ProjectionClass = getattr(ccrs, class_name)
-        kwargs = projection_info["kwargs"]
+        # need to copy otherwise we modify the dict stored in the dataclass
+        # in-place
+        kwargs = copy.deepcopy(projection_info["kwargs"])
 
         globe_kwargs = kwargs.pop("globe", {})
         if len(globe_kwargs) > 0:

neural_lam/models/ar_model.py

@@ -1,18 +1,21 @@
 # Standard library
 import os
+from typing import List, Union
 
 # Third-party
 import matplotlib.pyplot as plt
 import numpy as np
 import pytorch_lightning as pl
 import torch
 import wandb
+import xarray as xr
 
 # Local
 from .. import metrics, vis
 from ..config import NeuralLAMConfig
 from ..datastore import BaseDatastore
 from ..loss_weighting import get_state_feature_weighting
+from ..weather_dataset import WeatherDataset
 
 
 class ARModel(pl.LightningModule):
@@ -147,6 +150,42 @@ def __init__(
         # For storing spatial loss maps during evaluation
         self.spatial_loss_maps = []
 
+    def _create_dataarray_from_tensor(
+        self,
+        tensor: torch.Tensor,
+        time: Union[int, List[int]],
+        split: str,
+        category: str,
+    ) -> xr.DataArray:
+        """
+        Create an `xr.DataArray` from a tensor, with the correct dimensions and
+        coordinates to match the datastore used by the model. This function in
+        in effect is the inverse of what is returned by
+        `WeatherDataset.__getitem__`.
+
+        Parameters
+        ----------
+        tensor : torch.Tensor
+            The tensor to convert to a `xr.DataArray` with dimensions [time,
+            grid_index, feature]
+        time : Union[int,List[int]]
+            The time index or indices for the data, given as integers or a list
+            of integers representing epoch time in nanoseconds.
+        split : str
+            The split of the data, either 'train', 'val', or 'test'
+        category : str
+            The category of the data, either 'state' or 'forcing'
+        """
+        # TODO: creating an instance of WeatherDataset here on every call is
+        # not how this should be done but whether WeatherDataset should be
+        # provided to ARModel or where to put plotting still needs discussion
+        weather_dataset = WeatherDataset(datastore=self._datastore, split=split)
+        time = np.array(time, dtype="datetime64[ns]")
+        da = weather_dataset.create_dataarray_from_tensor(
+            tensor=tensor, time=time, category=category
+        )
+        return da
+
     def configure_optimizers(self):
         opt = torch.optim.AdamW(
             self.parameters(), lr=self.args.lr, betas=(0.9, 0.95)
@@ -406,10 +445,13 @@ def test_step(self, batch, batch_idx):
             )
 
             self.plot_examples(
-                batch, n_additional_examples, prediction=prediction
+                batch,
+                n_additional_examples,
+                prediction=prediction,
+                split="test",
             )
 
-    def plot_examples(self, batch, n_examples, prediction=None):
+    def plot_examples(self, batch, n_examples, split, prediction=None):
         """
         Plot the first n_examples forecasts from batch
 
@@ -422,18 +464,34 @@ def plot_examples(self, batch, n_examples, prediction=None):
             prediction, target, _, _ = self.common_step(batch)
 
         target = batch[1]
+        time = batch[3]
 
         # Rescale to original data scale
         prediction_rescaled = prediction * self.state_std + self.state_mean
         target_rescaled = target * self.state_std + self.state_mean
 
         # Iterate over the examples
-        for pred_slice, target_slice in zip(
-            prediction_rescaled[:n_examples], target_rescaled[:n_examples]
+        for pred_slice, target_slice, time_slice in zip(
+            prediction_rescaled[:n_examples],
+            target_rescaled[:n_examples],
+            time[:n_examples],
         ):
             # Each slice is (pred_steps, num_grid_nodes, d_f)
             self.plotted_examples += 1  # Increment already here
 
+            da_prediction = self._create_dataarray_from_tensor(
+                tensor=pred_slice,
+                time=time_slice,
+                split=split,
+                category="state",
+            ).unstack("grid_index")
+            da_target = self._create_dataarray_from_tensor(
+                tensor=target_slice,
+                time=time_slice,
+                split=split,
+                category="state",
+            ).unstack("grid_index")
+
             var_vmin = (
                 torch.minimum(
                     pred_slice.flatten(0, 1).min(dim=0)[0],
@@ -453,18 +511,18 @@ def plot_examples(self, batch, n_examples, prediction=None):
             var_vranges = list(zip(var_vmin, var_vmax))
 
             # Iterate over prediction horizon time steps
-            for t_i, (pred_t, target_t) in enumerate(
-                zip(pred_slice, target_slice), start=1
-            ):
+            for t_i, _ in enumerate(zip(pred_slice, target_slice), start=1):
                 # Create one figure per variable at this time step
                 var_figs = [
                     vis.plot_prediction(
-                        pred=pred_t[:, var_i],
-                        target=target_t[:, var_i],
                         datastore=self._datastore,
                         title=f"{var_name} ({var_unit}), "
                         f"t={t_i} ({self._datastore.step_length * t_i} h)",
                         vrange=var_vrange,
+                        da_prediction=da_prediction.isel(
+                            state_feature=var_i
+                        ).squeeze(),
+                        da_target=da_target.isel(state_feature=var_i).squeeze(),
                     )
                     for var_i, (var_name, var_unit, var_vrange) in enumerate(
                         zip(
@@ -476,6 +534,7 @@ def plot_examples(self, batch, n_examples, prediction=None):
                 ]
 
                 example_i = self.plotted_examples
+
                 wandb.log(
                     {
                         f"{var_name}_example_{example_i}": wandb.Image(fig)

neural_lam/vis.py

@@ -65,9 +65,9 @@ def plot_error_map(errors, datastore: BaseRegularGridDatastore, title=None):
 
 @matplotlib.rc_context(utils.fractional_plot_bundle(1))
 def plot_prediction(
-    pred,
-    target,
     datastore: BaseRegularGridDatastore,
+    da_prediction=None,
+    da_target=None,
     title=None,
     vrange=None,
 ):
@@ -79,19 +79,17 @@ def plot_prediction(
     """
     # Get common scale for values
     if vrange is None:
-        vmin = min(vals.min().cpu().item() for vals in (pred, target))
-        vmax = max(vals.max().cpu().item() for vals in (pred, target))
+        vmin = min(da_prediction.min(), da_target.min())
+        vmax = max(da_prediction.max(), da_target.max())
     else:
         vmin, vmax = vrange
 
     extent = datastore.get_xy_extent("state")
 
     # Set up masking of border region
     da_mask = datastore.unstack_grid_coords(datastore.boundary_mask)
-    mask_reshaped = da_mask.values
-    pixel_alpha = (
-        mask_reshaped.clamp(0.7, 1).cpu().numpy()
-    )  # Faded border region
+    mask_values = np.invert(da_mask.values.astype(bool)).astype(float)
+    pixel_alpha = mask_values.clip(0.7, 1)  # Faded border region
 
     fig, axes = plt.subplots(
         1,
@@ -101,28 +99,23 @@ def plot_prediction(
     )
 
     # Plot pred and target
-    for ax, data in zip(axes, (target, pred)):
+    for ax, da in zip(axes, (da_target, da_prediction)):
         ax.coastlines()  # Add coastline outlines
-        data_grid = (
-            data.reshape(list(datastore.grid_shape_state.values.values()))
-            .cpu()
-            .numpy()
-        )
-        im = ax.imshow(
-            data_grid,
+        da.plot.imshow(
+            ax=ax,
             origin="lower",
+            x="x",
             extent=extent,
-            alpha=pixel_alpha,
+            alpha=pixel_alpha.T,
             vmin=vmin,
             vmax=vmax,
             cmap="plasma",
+            transform=datastore.coords_projection,
         )
 
     # Ticks and labels
     axes[0].set_title("Ground Truth", size=15)
     axes[1].set_title("Prediction", size=15)
-    cbar = fig.colorbar(im, aspect=30)
-    cbar.ax.tick_params(labelsize=10)
 
     if title:
         fig.suptitle(title, size=20)
@@ -150,9 +143,7 @@ def plot_spatial_error(
     # Set up masking of border region
     da_mask = datastore.unstack_grid_coords(datastore.boundary_mask)
     mask_reshaped = da_mask.values
-    pixel_alpha = (
-        mask_reshaped.clamp(0.7, 1).cpu().numpy()
-    )  # Faded border region
+    pixel_alpha = mask_reshaped.clip(0.7, 1)  # Faded border region
 
     fig, ax = plt.subplots(
         figsize=(5, 4.8),
@@ -161,8 +152,10 @@ def plot_spatial_error(
 
     ax.coastlines()  # Add coastline outlines
     error_grid = (
-        error.reshape(list(datastore.grid_shape_state.values.values()))
-        .cpu()
+        error.reshape(
+            [datastore.grid_shape_state.x, datastore.grid_shape_state.y]
+        )
+        .T.cpu()
         .numpy()
     )