Fixes to callback plots (ecmwf#182)

* Lower bound delta lat in power spectrum plot and align input color map for
precip plots

CHANGELOG.md

@@ -13,6 +13,7 @@ Keep it human-readable, your future self will thank you!
 - Not update NaN-weight-mask for loss function when using remapper and no imputer [#178](https://github.com/ecmwf/anemoi-training/pull/178)
 - Dont crash when using the profiler if certain env vars arent set [#180](https://github.com/ecmwf/anemoi-training/pull/180)
 - Remove saving of metadata to training checkpoint [#57](https://github.com/ecmwf/anemoi-training/pull/190)
+- Fixes to callback plots [#182] (power spectrum large numpy array error + precip cmap for cases where precip is prognostic).
 
 ### Added
 - Introduce variable to configure: transfer_learning -> bool, True if loading checkpoint in a transfer learning setting.

src/anemoi/training/config/diagnostics/plot/detailed.yaml

@@ -44,6 +44,7 @@ callbacks:
 
   - _target_: anemoi.training.diagnostics.callbacks.plot.PlotSpectrum
     # every_n_batches: 100 # Override for batch frequency
+    # min_delta: 0.01 # Minimum distance between two consecutive points
     sample_idx: ${diagnostics.plot.sample_idx}
     parameters:
     - z_500

src/anemoi/training/diagnostics/callbacks/plot.py

@@ -1018,6 +1018,7 @@ def __init__(
         config: OmegaConf,
         sample_idx: int,
         parameters: list[str],
+        min_delta: float | None = None,
         every_n_batches: int | None = None,
     ) -> None:
         """Initialise the PlotSpectrum callback.
@@ -1036,6 +1037,7 @@ def __init__(
         super().__init__(config, every_n_batches=every_n_batches)
         self.sample_idx = sample_idx
         self.parameters = parameters
+        self.min_delta = min_delta
 
     @rank_zero_only
     def _plot(
@@ -1070,6 +1072,7 @@ def _plot(
                 data[0, ...].squeeze(),
                 data[rollout_step + 1, ...].squeeze(),
                 output_tensor[rollout_step, ...],
+                min_delta=self.min_delta,
             )
 
             self._output_figure(

src/anemoi/training/diagnostics/plots.py

@@ -138,6 +138,7 @@ def plot_power_spectrum(
     x: np.ndarray,
     y_true: np.ndarray,
     y_pred: np.ndarray,
+    min_delta: float | None = None,
 ) -> Figure:
     """Plots power spectrum.
 
@@ -156,13 +157,16 @@ def plot_power_spectrum(
         Expected data of shape (lat*lon, nvar*level)
     y_pred : np.ndarray
         Predicted data of shape (lat*lon, nvar*level)
+    min_delta: float, optional
+        Minimum distance between lat/lon points, if None defaulted to 1km
 
     Returns
     -------
     Figure
         The figure object handle.
 
     """
+    min_delta = min_delta or 0.0003
     n_plots_x, n_plots_y = len(parameters), 1
 
     figsize = (n_plots_y * 4, n_plots_x * 3)
@@ -177,9 +181,17 @@ def plot_power_spectrum(
     # Calculate delta_lat on the projected grid
     delta_lat = abs(np.diff(pc_lat))
     non_zero_delta_lat = delta_lat[delta_lat != 0]
+    min_delta_lat = np.min(abs(non_zero_delta_lat))
+
+    if min_delta_lat < min_delta:
+        LOGGER.warning(
+            "Min. distance between lat/lon points is < specified minimum distance. Defaulting to min_delta=%s.",
+            min_delta,
+        )
+        min_delta_lat = min_delta
 
     # Define a regular grid for interpolation
-    n_pix_lat = int(np.floor(abs(pc_lat.max() - pc_lat.min()) / abs(np.min(non_zero_delta_lat))))
+    n_pix_lat = int(np.floor(abs(pc_lat.max() - pc_lat.min()) / min_delta_lat))
     n_pix_lon = (n_pix_lat - 1) * 2 + 1  # 2*lmax + 1
     regular_pc_lon = np.linspace(pc_lon.min(), pc_lon.max(), n_pix_lon)
     regular_pc_lat = np.linspace(pc_lat.min(), pc_lat.max(), n_pix_lat)
@@ -313,14 +325,14 @@ def plot_histogram(
             # enforce the same binning for both histograms
             bin_min = min(np.nanmin(yt_xt), np.nanmin(yp_xt))
             bin_max = max(np.nanmax(yt_xt), np.nanmax(yp_xt))
-            hist_yt, bins_yt = np.histogram(yt_xt[~np.isnan(yt_xt)], bins=100, range=[bin_min, bin_max])
-            hist_yp, bins_yp = np.histogram(yp_xt[~np.isnan(yp_xt)], bins=100, range=[bin_min, bin_max])
+            hist_yt, bins_yt = np.histogram(yt_xt[~np.isnan(yt_xt)], bins=100, density=True, range=[bin_min, bin_max])
+            hist_yp, bins_yp = np.histogram(yp_xt[~np.isnan(yp_xt)], bins=100, density=True, range=[bin_min, bin_max])
         else:
             # enforce the same binning for both histograms
             bin_min = min(np.nanmin(yt), np.nanmin(yp))
             bin_max = max(np.nanmax(yt), np.nanmax(yp))
-            hist_yt, bins_yt = np.histogram(yt[~np.isnan(yt)], bins=100, range=[bin_min, bin_max])
-            hist_yp, bins_yp = np.histogram(yp[~np.isnan(yp)], bins=100, range=[bin_min, bin_max])
+            hist_yt, bins_yt = np.histogram(yt[~np.isnan(yt)], bins=100, density=True, range=[bin_min, bin_max])
+            hist_yp, bins_yp = np.histogram(yp[~np.isnan(yp)], bins=100, density=True, range=[bin_min, bin_max])
 
         # Visualization trick for tp
         if variable_name in precip_and_related_fields:
@@ -623,6 +635,51 @@ def error_plot_in_degrees(array1: np.ndarray, array2: np.ndarray) -> np.ndarray:
                 title=f"{vname} persist err: {np.nanmean(np.abs(err_plot)):.{4}f} deg.",
                 datashader=datashader,
             )
+        elif vname in precip_and_related_fields:
+            # Create a custom colormap for precipitation
+            nws_precip_colors = cmap_precip
+            precip_colormap = ListedColormap(nws_precip_colors)
+
+            # Defining the actual precipitation accumulation levels in mm
+            cummulation_lvls = clevels
+            norm = BoundaryNorm(cummulation_lvls, len(cummulation_lvls) + 1)
+
+            # converting to mm from m
+            input_ *= 1000.0
+            truth *= 1000.0
+            pred *= 1000.0
+            single_plot(
+                fig,
+                ax[0],
+                lon=lon,
+                lat=lat,
+                data=input_,
+                cmap=precip_colormap,
+                title=f"{vname} input",
+                datashader=datashader,
+            )
+            single_plot(
+                fig,
+                ax[4],
+                lon=lon,
+                lat=lat,
+                data=pred - input_,
+                cmap="bwr",
+                norm=TwoSlopeNorm(vcenter=0.0),
+                title=f"{vname} increment [pred - input]",
+                datashader=datashader,
+            )
+            single_plot(
+                fig,
+                ax[5],
+                lon=lon,
+                lat=lat,
+                data=truth - input_,
+                cmap="bwr",
+                norm=TwoSlopeNorm(vcenter=0.0),
+                title=f"{vname} persist err",
+                datashader=datashader,
+            )
         else:
             single_plot(fig, ax[0], lon, lat, input_, norm=norm, title=f"{vname} input", datashader=datashader)
             single_plot(