Update all EMCPy hashes

requirements-github.txt

@@ -12,4 +12,4 @@ bokeh<3.6.0,>=3.5.0
 geopandas>=0.13.2
 geoviews>=1.10.0
 nbsite>=0.8.1
-git+https://github.com/NOAA-EMC/emcpy.git@1764011794f84c76488b0876e8b577d6af74df20#egg=emcpy
+git+https://github.com/NOAA-EMC/emcpy.git@7794574611e760475d61eb5d9458af2d3d2191d8#egg=emcpy

requirements.txt

@@ -10,7 +10,7 @@ pandas
 numpy
 
 # Additional packages
-git+https://github.com/NOAA-EMC/emcpy.git@92aa62f34a1f413d8cb1646bca0e81f267b61365#egg=emcpy
+git+https://github.com/NOAA-EMC/emcpy.git@7794574611e760475d61eb5d9458af2d3d2191d8#egg=emcpy
 scikit-learn
 seaborn
 hvplot

requirements_sles15.txt

@@ -18,7 +18,7 @@ contourpy
 msgpack>=1.0.0
 
 # Additional packages
-git+https://github.com/NOAA-EMC/emcpy.git@92aa62f34a1f413d8cb1646bca0e81f267b61365#egg=emcpy
+git+https://github.com/NOAA-EMC/emcpy.git@7794574611e760475d61eb5d9458af2d3d2191d8#egg=emcpy
 scikit-learn
 seaborn
 hvplot

src/eva/plotting/batch/emcpy/diagnostics/emcpy_map_gridded.py

@@ -3,6 +3,7 @@
 from eva.utilities.utils import get_schema, update_object
 import emcpy.plots.map_plots
 import os
+import numpy as np
 
 from eva.plotting.batch.base.diagnostics.map_gridded import MapGridded
 
@@ -11,32 +12,143 @@
 
 class EmcpyMapGridded(MapGridded):
     """
-    EmcpyMapGridded class is a subclass of the MapGridded class, tailored for
-    configuring and plotting gridded map visualizations using the emcpy library.
+    EMCPy backend for gridded maps.
+    Option A: if latitude/longitude are 1-D centers, convert them to 2-D center grids
+    with np.meshgrid and reduce data to a single 2-D level before plotting.
+    """
 
-    Attributes:
-        Inherits attributes from the MapGridded class.
+    def _to_2d_centers(self, latvar, lonvar, datavar):
+        """
+        Normalize inputs for EMCPy MapGridded by ensuring 2-D lat/lon and 2-D data.
+        Handles:
+          - 3-D curvilinear lat/lon with a 'tile' dim: selects one tile (tile_index, default 0)
+          - 2-D curvilinear lat/lon: pass-through, squeeze data to 2-D
+          - 1-D centers: meshgrid to 2-D and orient data to (Nlat, Nlon)
+        """
+        lat = np.asarray(latvar)
+        lon = np.asarray(lonvar)
+        A = np.asarray(datavar)
 
-    Methods:
-        configure_plot(): Configures the plotting settings for the gridded map.
-    """
+        # --- CASE 1: 3-D curvilinear (lon, lat, tile) → choose one tile to get 2-D ---
+        if lat.ndim == 3 and lon.ndim == 3 and lat.shape == lon.shape:
+            tile_count = lat.shape[2]
+            tile_idx = int(self.config.get("tile_index", 0))
+            if not (0 <= tile_idx < tile_count):
+                raise ValueError(f"tile_index {tile_idx} out of range [0,{tile_count-1}]")
+
+            # slice lat/lon to 2-D for this tile
+            lat2d = lat[:, :, tile_idx]
+            lon2d = lon[:, :, tile_idx]
+
+            # slice data on its tile axis (if present)
+            A2 = A
+            if A2.ndim >= 3:
+                # find axis whose size == tile_count
+                tile_axis = next((i for i, s in enumerate(A2.shape) if s == tile_count), None)
+                if tile_axis is not None:
+                    A2 = np.take(A2, tile_idx, axis=tile_axis)
+
+            # if any extra leading level/ensemble dim remains, pick first or configured
+            if A2.ndim == 3:
+                lev_idx = int(self.config.get("level_index", 0))
+                # choose an axis that is not lat or lon sized
+                lat_size, lon_size = lat2d.shape
+                # bring to (lat, lon, extra) or (lon, lat, extra)
+                if A2.shape[:2] == (lat_size, lon_size):
+                    A2 = A2[:, :, lev_idx]
+                elif A2.shape[:2] == (lon_size, lat_size):
+                    A2 = A2[:, :, lev_idx].T
+                else:
+                    # try matching by transpose
+                    if A2.transpose(1, 0, 2).shape[:2] == (lat_size, lon_size):
+                        A2 = A2.transpose(1, 0, 2)[:, :, lev_idx]
+                    else:
+                        # last resort: squeeze to 2-D
+                        A2 = np.squeeze(A2)
+
+            A2 = np.squeeze(A2)
+            # Ensure shapes match lat2d/lon2d
+            if A2.shape != lat2d.shape:
+                if A2.T.shape == lat2d.shape:
+                    A2 = A2.T
+                else:
+                    raise ValueError(f"Data shape {A2.shape} incompatible "
+                                     f"with tile lat/lon {lat2d.shape}")
+
+            return lat2d, lon2d, A2
+
+        # --- CASE 2: 2-D curvilinear lat/lon: pass-through, squeeze data to 2-D ---
+        if lat.ndim == 2 and lon.ndim == 2:
+            A2 = A
+            if A2.ndim == 3:
+                lev_idx = int(self.config.get("level_index", 0))
+                # assume first axis is level/extra
+                A2 = A2[lev_idx, ...]
+            return lat, lon, np.squeeze(A2)
+
+        # --- CASE 3: 1-D centers → build 2-D centers via meshgrid ---
+        if lat.ndim == 1 and lon.ndim == 1:
+            lat1d = lat.squeeze()
+            lon1d = lon.squeeze()
+            A2 = A
+            if A2.ndim == 3:
+                # identify lat/lon axes by matching sizes and pick level_index
+                shape = A2.shape
+                lat_axis = next((i for i, s in enumerate(shape) if s == lat1d.size), None)
+                lon_axis = next((i for i, s in enumerate(shape) if s == lon1d.size), None)
+                if lat_axis is not None and lon_axis is not None:
+                    axes = (lat_axis, lon_axis)
+                    extra = [
+                        i for i in range(A2.ndim)
+                        if i not in axes
+                    ]
+                    order = [*axes, *extra]
+                    A2 = np.transpose(A2, order)
+                    lev_idx = int(self.config.get("level_index", 0))
+                    if A2.ndim == 3:
+                        A2 = A2[:, :, lev_idx]
+                else:
+                    lev_idx = int(self.config.get("level_index", 0))
+                    A2 = A2[lev_idx, ...]
+            A2 = np.squeeze(A2)
+
+            # orient to (Nlat, Nlon)
+            if A2.shape != (lat1d.size, lon1d.size):
+                if A2.T.shape == (lat1d.size, lon1d.size):
+                    A2 = A2.T
+                else:
+                    raise ValueError(
+                        f"Data shape {A2.shape} incompatible with "
+                        f"lat {lat1d.size} / lon {lon1d.size}"
+                    )
+
+            LAT2D, LON2D = np.meshgrid(lat1d, lon1d, indexing="ij")
+            return LAT2D, LON2D, A2
+
+        # Otherwise unsupported
+        raise ValueError(f"Expected 1-D or 2-D lat/lon; got lat {lat.shape}, lon {lon.shape}")
 
     def configure_plot(self):
         """
         Configures the plotting settings for the gridded map.
-
         Returns:
-            plotobj: The configured plot object for emcpy gridded maps.
+            plotobj: The configured plot object for EMCPy gridded maps.
         """
+        # Convert to 2-D centers + 2-D data if needed
+        lat2d, lon2d, data2d = self._to_2d_centers(self.latvar, self.lonvar, self.datavar)
+
+        # Create EMCPy MapGridded object
+        self.plotobj = emcpy.plots.map_plots.MapGridded(lat2d, lon2d, data2d)
 
-        # create declarative plotting MapGridded object
-        self.plotobj = emcpy.plots.map_plots.MapGridded(self.latvar, self.lonvar, self.datavar)
-        # get defaults from schema
-        layer_schema = self.config.get('schema', os.path.join(return_eva_path(), 'plotting',
-                                       'batch', 'emcpy', 'defaults', 'map_gridded.yaml'))
+        # Apply schema defaults/overrides
+        layer_schema = self.config.get(
+            "schema",
+            os.path.join(
+                return_eva_path(), "plotting", "batch", "emcpy", "defaults", "map_gridded.yaml"
+            ),
+        )
         new_config = get_schema(layer_schema, self.config, self.logger)
-        delvars = ['longitude', 'latitude', 'data', 'type', 'schema']
-        for d in delvars:
+        for d in ["longitude", "latitude", "data", "type", "schema", "level_index"]:
             new_config.pop(d, None)
         self.plotobj = update_object(self.plotobj, new_config, self.logger)
         return self.plotobj