[EJP] plot batimetry with colormap from Alba and threed exemples

bluemath_tk/core/plotting/plot_bathymetry.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import rasterio
+import cartopy.crs as ccrs
+import xarray as xr
+from rasterio.transform import from_bounds
+from bluemath_tk.core.plotting.colors import hex_colors_land, hex_colors_water
+from bluemath_tk.core.plotting.utils import join_colormaps
+
+def plot_bathymetry(rasters, figsize=(10, 8), cbar=False, ax=None):
+    """
+    Plot a bathymetry map from either a raster file or a NetCDF dataset.
+
+    Parameters
+    ----------
+    rasters : str or xarray.Dataset
+        Either a path to a raster file readable by rasterio or an xarray Dataset.
+    figsize : tuple of float, optional
+        Figure size in inches, by default ``(10, 8)``.
+    cbar : bool, optional
+        If ``True``, display a colorbar.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure
+        The generated figure.
+    ax : matplotlib.axes._subplots.AxesSubplot
+        The map axis with ``PlateCarree`` projection.
+
+    Examples
+    --------
+    >>> import xarray as xr
+    >>> ds = xr.open_dataset("GEBCO_sample.nc")
+    >>> fig, ax = plot_bathymetry(ds)
+
+    >>> fig, ax = plot_bathymetry("path/to/raster.tif")
+    """
+    if isinstance(rasters, str):
+        data = []
+        with rasterio.open(rasters) as src:
+            raster_data = src.read(1)
+            no_data_value = src.nodata
+            if no_data_value is not None:
+                raster_data = np.ma.masked_equal(raster_data, no_data_value)
+            data.append(raster_data)
+            transform = src.transform
+        height, width = data[0].shape
+
+    elif isinstance(rasters, xr.Dataset):
+        data, transform, height, width = nc_to_raster_like(rasters, var_name=None)
+    else:
+        raise TypeError("Input must be a raster path or an xarray Dataset.")
+
+    cols, rows = np.meshgrid(np.arange(width), np.arange(height))
+    xs, ys = rasterio.transform.xy(transform, rows, cols)
+
+    vmin = np.nanmin(data[0])
+    vmax = np.nanmax(data[0])
+
+    cmap, norm = join_colormaps(
+        cmap1=hex_colors_water,
+        cmap2=hex_colors_land,
+        value_range1=(vmin, 0.0),
+        value_range2=(0.0, vmax),
+        name="raster_cmap",
+    )
+
+    if ax is None:
+        fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}, figsize=figsize)
+    else:
+        fig = ax.figure
+    im = ax.imshow(
+        data[0],
+        cmap=cmap,
+        norm=norm,
+        extent=(np.min(xs), np.max(xs), np.min(ys), np.max(ys)),
+    )
+    if cbar:
+        plt.colorbar(im, ax=ax, orientation="vertical", label="Elevation (m)")
+
+    return fig, ax
+
+def find_main_data_variable(ds):
+    """
+    Find the first variable in the dataset that depends on the coordinate axes.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        Input dataset.
+
+    Returns
+    -------
+    str or None
+        Name of the main data variable if found, otherwise ``None``.
+    """
+    coord_names = list(ds.coords)
+    for var_name, var in ds.data_vars.items():
+        var_dims = set(var.dims)
+        if all(c in var_dims for c in coord_names):
+            return var_name
+
+    best_var = None
+    best_score = -1
+    for var_name, var in ds.data_vars.items():
+        score = len(set(var.dims).intersection(coord_names))
+        if score > best_score:
+            best_score = score
+            best_var = var_name
+    return best_var
+
+
+def nc_to_raster_like(ds, var_name=None):
+    """
+    Convert an xarray Dataset to a raster-like structure equivalent to a rasterio read.
+
+    This function extracts the main data variable, determines the coordinate
+    system (lon/lat), flips the data if needed (north-south inversion),
+    and builds the affine transform.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        Input dataset.
+    var_name : str, optional
+        Variable name to extract. If ``None``, the first variable depending
+        on coordinates will be automatically detected.
+
+    Returns
+    -------
+    data : list of numpy.ndarray
+        List containing the raster-like array.
+    transform : affine.Affine
+        Affine transform equivalent to rasterio geotransform.
+    height : int
+        Number of rows.
+    width : int
+        Number of columns.
+
+    Raises
+    ------
+    ValueError
+        If no suitable variable or coordinate system is found.
+
+    Examples
+    --------
+    >>> import xarray as xr
+    >>> ds = xr.open_dataset("GEBCO_sample.nc")
+    >>> data, transform, height, width = nc_to_raster_like(ds)
+    """
+    if var_name is None:
+        var_name = find_main_data_variable(ds)
+        if var_name is None:
+            raise ValueError("No variable found depending on lat/lon coordinates.")
+
+    da = ds[var_name]
+    raster_data = da.values
+    if np.isnan(raster_data).any():
+        raster_data = np.ma.masked_invalid(raster_data)
+
+    coords_and_vars = list(ds.coords)
+    lon_name = next((n for n in coords_and_vars if "lon" in n.lower()), None)
+    lat_name = next((n for n in coords_and_vars if "lat" in n.lower()), None)
+    if lon_name is None or lat_name is None:
+        raise ValueError("Could not detect latitude/longitude coordinates.")
+
+    lon = ds[lon_name].values
+    lat = ds[lat_name].values
+    lon_min, lon_max = lon.min(), lon.max()
+    lat_min, lat_max = lat.min(), lat.max()
+    width = len(lon)
+    height = len(lat)
+
+    if lat[0] < lat[-1]:
+        raster_data = raster_data[::-1, :]
+        lat_min, lat_max = lat_max, lat_min
+
+    transform = from_bounds(lon_min, lat_min, lon_max, lat_max, width, height)
+    data = [raster_data]
+
+    return data, transform, height, width
+
+
+# =======================
+# Example usage
+# =======================
+if __name__ == "__main__":
+    GEBCO = xr.open_dataset(
+        "https://dap.ceda.ac.uk/thredds/dodsC/bodc/gebco/global/gebco_2025/ice_surface_elevation/netcdf/GEBCO_2025.nc"
+    )
+    GEBCO_sel = GEBCO.sel(lon=slice(-4, -3), lat=slice(43, 44))
+
+    EMODnet = xr.open_dataset(
+        "https://geoocean.sci.unican.es/thredds/dodsC/geoocean/emodnet-bathy-2024"
+    )
+    EMODnet_sel = EMODnet.sel(lon=slice(-4, -3), lat=slice(43, 44))
+
+    fig, axes = plt.subplots(
+        1, 2,
+        subplot_kw={"projection": ccrs.PlateCarree()},
+        figsize=(14, 6)
+    )
+
+    plot_bathymetry(GEBCO_sel, ax=axes[0])
+    axes[0].set_title("GEBCO 2025 Bathymetry")
+
+    plot_bathymetry(EMODnet_sel, ax=axes[1])
+    axes[1].set_title("EMODnet 2024 Bathymetry")
+
+    # For raster files:
+    # plot_bathymetry("path/to/raster.tif", cbar=True)