12 support non latlon grib grids (#16)

* Adds support for non-lat-lon GRIB data points

* Re-enable automatic legend titles with proper fallback if no metadata found

* QA tweaks

* Rollback version of micromamba used with unit tests to v12

* Testing latest micromamba

* Migrate to new setup-micromamba

.github/workflows/ci.yml

@@ -46,13 +46,12 @@ jobs:
       with:
         ref: ${{ github.event.pull_request.head.sha || github.ref }}
     - name: Install Conda environment with Micromamba
-      uses: mamba-org/provision-with-micromamba@v14
+      uses: mamba-org/setup-micromamba@v1
       with:
         environment-file: environment.yml
         environment-name: DEVELOP
-        channels: conda-forge
-        cache-env: true
-        extra-specs: |
+        cache-environment: true
+        create-args: >-
           python=3.10
     - name: Install package
       run: |
@@ -140,14 +139,13 @@ jobs:
       with:
         ref: ${{ github.event.pull_request.head.sha || github.ref }}
     - name: Install Conda environment with Micromamba
-      uses: mamba-org/provision-with-micromamba@v12
+      uses: mamba-org/setup-micromamba@v1
       with:
         environment-file: environment${{ matrix.extra }}.yml
         environment-name: DEVELOP${{ matrix.extra }}
-        channels: conda-forge
-        cache-env: true
-        cache-env-key: ubuntu-latest-${{ matrix.python-version }}${{ matrix.extra }}.
-        extra-specs: |
+        cache-environment: true
+        cache-environment-key: ubuntu-latest-${{ matrix.python-version }}${{ matrix.extra }}.
+        create-args: >-
           python=${{matrix.python-version }}
     - name: Install package
       run: |

src/earthkit/plots/components/subplots.py

@@ -878,7 +878,7 @@ def legend(self, style=None, location=None, **kwargs):
             dummy = [[1, 2], [3, 4]]
             mappable = self.contourf(x=dummy, y=dummy, z=dummy, style=style)
             layer = Layer(single.SingleSource(), mappable, self, style)
-            legend = layer.style.legend(layer, label=kwargs.pop("label", ""), **kwargs)
+            legend = layer.style.legend(layer, **kwargs)
             legends.append(legend)
         else:
             for i, layer in enumerate(self.distinct_legend_layers):

src/earthkit/plots/geo/grids.py

@@ -21,59 +21,109 @@
     _NO_SCIPY = True
 
 
-def is_structured(lat, lon, tol=1e-5):
+def is_structured(x, y, tol=1e-5):
+    """
+    Determines whether the x and y points form a structured grid.
+
+    This function checks if the x and y coordinate arrays represent a structured
+    grid, i.e., a grid with consistent spacing between points. The function supports
+    1D arrays (representing coordinates of a grid) and 2D arrays (representing the
+    actual grid coordinates) of x and y.
+
+    Parameters
+    ----------
+    x : array_like
+        A 1D or 2D array of x-coordinates. For example, this can be longitude or
+        the x-coordinate in a Cartesian grid.
+    y : array_like
+        A 1D or 2D array of y-coordinates. For example, this can be latitude or
+        the y-coordinate in a Cartesian grid.
+    tol : float, optional
+        Tolerance for floating-point comparison to account for numerical precision
+        errors when checking spacing consistency. The default is 1e-5.
+
+    Returns
+    -------
+    bool
+        True if the data represents a structured grid, i.e., the spacing between
+        consecutive points in both x and y is consistent. False otherwise.
     """
-    Determines whether the latitude and longitude points form a structured grid.
 
-    Parameters:
-    - lat: A 1D or 2D array of latitude points.
-    - lon: A 1D or 2D array of longitude points.
-    - tol: Tolerance for floating-point comparison (default 1e-5).
+    x = np.asarray(x)
+    y = np.asarray(y)
 
-    Returns:
-    - True if the data is structured (grid), False if it's unstructured.
-    """
+    # If both x and y are 1D arrays, ensure they can form a grid
+    if x.ndim == 1 and y.ndim == 1:
+        # Check if the number of points match (can form a meshgrid)
+        if len(x) * len(y) != x.size * y.size:
+            return False
+
+        # Check consistent spacing in x and y
+        x_diff = np.diff(x)
+        y_diff = np.diff(y)
 
-    lat = np.asarray(lat)
-    lon = np.asarray(lon)
+        x_spacing_consistent = np.all(np.abs(x_diff - x_diff[0]) < tol)
+        y_spacing_consistent = np.all(np.abs(y_diff - y_diff[0]) < tol)
 
-    # Check if there are consistent spacing in latitudes and longitudes
-    unique_lat = np.unique(lat)
-    unique_lon = np.unique(lon)
+        return x_spacing_consistent and y_spacing_consistent
 
-    # Structured grid condition: the number of unique lat/lon values should multiply to the number of total points
-    if len(unique_lat) * len(unique_lon) == len(lat) * len(lon):
-        # Now check if the spacing is consistent
-        lat_diff = np.diff(unique_lat)
-        lon_diff = np.diff(unique_lon)
+    # If x and y are 2D arrays, verify they are structured as a grid
+    elif x.ndim == 2 and y.ndim == 2:
+        # Check if rows of x and y have consistent spacing along the grid lines
+        # x should vary only along one axis, y along the other axis
 
-        # Check if lat/lon differences are consistent
-        lat_spacing_consistent = np.all(np.abs(lat_diff - lat_diff[0]) < tol)
-        lon_spacing_consistent = np.all(np.abs(lon_diff - lon_diff[0]) < tol)
+        x_rows_consistent = np.all(
+            np.abs(np.diff(x, axis=1) - np.diff(x, axis=1)[:, 0:1]) < tol
+        )
+        y_columns_consistent = np.all(
+            np.abs(np.diff(y, axis=0) - np.diff(y, axis=0)[0:1, :]) < tol
+        )
 
-        return lat_spacing_consistent and lon_spacing_consistent
+        return x_rows_consistent and y_columns_consistent
 
-    # If the product of unique lat/lon values doesn't match total points, it's unstructured
-    return False
+    else:
+        # Invalid input, dimensions of x and y must match (either both 1D or both 2D)
+        return False
 
 
 def interpolate_unstructured(x, y, z, resolution=1000, method="linear"):
     """
-    Interpolates unstructured data to a structured grid, handling NaNs in z-values
-    and preventing interpolation across large gaps.
-
-    Parameters:
-    - x: 1D array of x-coordinates.
-    - y: 1D array of y-coordinates.
-    - z: 1D array of z values.
-    - resolution: The number of points along each axis for the structured grid.
-    - method: Interpolation method ('linear', 'nearest', 'cubic').
-    - gap_threshold: The distance threshold beyond which interpolation is not performed (set to NaN).
-
-    Returns:
-    - grid_x: 2D grid of x-coordinates.
-    - grid_y: 2D grid of y-coordinates.
-    - grid_z: 2D grid of interpolated z-values, with NaNs in large gap regions.
+    Interpolate unstructured data to a structured grid.
+
+    This function takes unstructured (scattered) data points and interpolates them
+    to a structured grid, handling NaN values in `z` and providing options for
+    different interpolation methods. It creates a regular grid based on the given
+    resolution and interpolates the z-values from the unstructured points onto this grid.
+
+    Parameters
+    ----------
+    x : array_like
+        1D array of x-coordinates.
+    y : array_like
+        1D array of y-coordinates.
+    z : array_like
+        1D array of z-values at each (x, y) point.
+    resolution : int, optional
+        The number of points along each axis for the structured grid.
+        Default is 1000.
+    method : {'linear', 'nearest', 'cubic'}, optional
+        The interpolation method to use. Default is 'linear'.
+        The methods supported are:
+
+        - 'linear': Linear interpolation between points.
+        - 'nearest': Nearest-neighbor interpolation.
+        - 'cubic': Cubic interpolation, which may produce smoother results.
+
+    Returns
+    -------
+    grid_x : ndarray
+        2D array representing the x-coordinates of the structured grid.
+    grid_y : ndarray
+        2D array representing the y-coordinates of the structured grid.
+    grid_z : ndarray
+        2D array of interpolated z-values at the grid points. NaNs may be
+        present in regions where interpolation was not possible (e.g., due to
+        large gaps in the data).
     """
     if _NO_SCIPY:
         raise ImportError(

src/earthkit/plots/sources/earthkit.py

@@ -133,9 +133,17 @@ def extract_xy(self):
                 )
             points = get_points(1)
         else:
-            points = self.data.to_points(flatten=False)
-        x = points["x"]
-        y = points["y"]
+            try:
+                points = self.data.to_points(flatten=False)
+                x = points["x"]
+                y = points["y"]
+            except ValueError:
+                latlon = self.data.to_latlon(flatten=False)
+                lat = latlon["lat"]
+                lon = latlon["lon"]
+                transformed = self.crs.transform_points(ccrs.PlateCarree(), lon, lat)
+                x = transformed[:, :, 0]
+                y = transformed[:, :, 1]
         return x, y
 
     def extract_x(self):

src/earthkit/plots/styles/legends.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-DEFAULT_LEGEND_LABEL = ""  # {variable_name} ({units})"
+DEFAULT_LEGEND_LABEL = "{variable_name} ({units})"
 
 _DISJOINT_LEGEND_LOCATIONS = {
     "bottom": {
@@ -55,7 +55,10 @@ def colorbar(layer, *args, shrink=0.8, aspect=35, ax=None, **kwargs):
         Any keyword arguments accepted by `matplotlib.figures.Figure.colorbar`.
     """
     label = kwargs.pop("label", DEFAULT_LEGEND_LABEL)
-    label = layer.format_string(label)
+    try:
+        label = layer.format_string(label)
+    except (AttributeError, ValueError, KeyError):
+        label = ""
 
     kwargs = {**layer.style._legend_kwargs, **kwargs}
     kwargs.setdefault("format", lambda x, _: f"{x:g}")