Wip/typology (#6)

* enhancements

* wip typology

* add idea for pycharm

---------

Co-authored-by: floriscalkoen <floris_calkoen@hotmail.com>

.gitignore

@@ -172,3 +172,5 @@ logs/*
 .vscode
 
 src/coastpy/_version.py
+
+.idea

src/coastpy/geo/ops.py

@@ -425,15 +425,15 @@ def generate_offset_line(line: LineString, offset: float) -> LineString:
     return line.offset_curve(offset) if offset != 0 else line
 
 
-def determine_rotation_angle(
+def get_rotation_angle(
     pt1: Point | tuple[float, float],
     pt2: Point | tuple[float, float],
     target_axis: Literal[
         "closest", "vertical", "horizontal", "horizontal-right-aligned"
     ] = "closest",
 ) -> float | None:
     """
-    Determines the correct rotation angle to align a transect with a specified axis.
+    Computes the correct rotation angle to align with a specified axis.
 
     Args:
         pt1 (Union[Point, Tuple[float, float]]): The starting point of the transect.
@@ -479,6 +479,7 @@ def determine_rotation_angle(
             (270, 360): lambda b: b - 270,
         }
 
+    # TODO: rename to landward right
     elif target_axis == "horizontal-right-aligned":
         angle_rotations = {
             (0, 90): lambda b: 90 + b,

src/coastpy/utils/xarray.py

@@ -1,7 +1,6 @@
 import warnings
 
 import numpy as np
-import rasterio
 import xarray as xr
 from affine import Affine
 from rasterio.enums import Resampling
@@ -171,69 +170,71 @@ def interpolate_raster(
     ds: xr.Dataset,
     y_shape: int,
     x_shape: int,
-    resampling: rasterio.enums.Resampling = rasterio.enums.Resampling.nearest,
+    resampling: Resampling,
 ) -> xr.Dataset:
     """
-    Interpolates a given raster (xarray Dataset) to a specified resolution using the provided method.
+    Interpolates a given raster (xarray Dataset) to a specified shape using Rasterio resampling methods.
 
     Args:
         ds (xr.Dataset): The input raster to interpolate.
-        y_shape (int): Desired number of grid points along y dimension.
-        x_shape (int): Desired number of grid points along x dimension.
+        y_shape (int): Desired number of grid points along the y dimension.
+        x_shape (int): Desired number of grid points along the x dimension.
         resampling: rasterio.enums.Resampling: The interpolation method to use.
 
     Returns:
-        xr.Dataset: Interpolated raster without geospatial metadata.
-
-    Example:
-        >>> ds = xr.Dataset(data_vars={"var": (("x", "y"), np.random.randn(10, 10))},
-        ...                 coords={"x": np.linspace(0, 9, 10), "y": np.linspace(0, 9, 10)})
-        >>> interpolated_ds = interpolate_raster(ds, 20, 20)
-        >>> print(interpolated_ds.dims)
-        {'x': 20, 'y': 20}
+        xr.Dataset: Interpolated raster with updated geospatial metadata.
     """
 
-    # swap dims if y is longer than x
-    if ds.dims["x"] < ds.dims["y"]:
-        y_shape, x_shape = x_shape, y_shape
+    # Compute the target transformation based on the desired shape
 
-    # this will be the new grid
-    new_y = np.linspace(ds.y.min(), ds.y.max(), y_shape)
-    new_x = np.linspace(ds.x.min(), ds.x.max(), x_shape)
+    transform = ds.rio.transform()
 
-    interpolated = ds.interp(y=new_y, x=new_x, method=resampling)
+    # Define the target transform for the new resolution
+    target_transform = transform * transform.scale(
+        (ds.sizes["x"] / x_shape), (ds.sizes["y"] / y_shape)
+    )
 
-    # add new coords because the old ones are now two dimensional
-    interpolated = interpolated.assign_coords(y=range(y_shape), x=range(x_shape))
+    # Create a template for the new shape
+    out_shape = (
+        (ds.sizes["band"], y_shape, x_shape)
+        if "band" in ds.dims
+        else (y_shape, x_shape)
+    )
 
-    # the transformation matrix can be computed by scaling the src one
-    src_dims = ds.dims
-    src_transform = ds.rio.transform()
-    x_scale = src_dims["x"] / x_shape
-    y_scale = src_dims["y"] / y_shape
+    # Reproject the dataset to the new shape using rasterio
+    interpolated = ds.rio.reproject(
+        ds.rio.crs,
+        shape=out_shape,
+        transform=target_transform,
+        resampling=resampling,
+        nodata=np.nan,
+    )
 
-    # write new transformation matrix to the interplated dataset
-    dst_transform = src_transform * Affine.scale(x_scale, y_scale)
-    interpolated = interpolated.rio.write_transform(dst_transform)
+    interpolated = interpolated.rio.write_transform(target_transform)
 
     return interpolated
 
 
+import xarray as xr
+
+
 def trim_outer_nans(
     data: xr.DataArray | xr.Dataset,
-    crop_size: int = 1,
     nodata: float | int | None = None,
+    crop_size: int = 0,
 ) -> xr.DataArray | xr.Dataset:
     """
     Trim the outer nodata or NaN values from an xarray DataArray or Dataset, returning a bounding box around the data.
 
     Args:
         data (xr.DataArray | xr.Dataset): Input DataArray or Dataset with potential outer NaN or nodata values.
-        crop_size (int): The number of pixels to crop from the outer edges of the data. Defaults to 1.
         nodata (float | int | None): Optional no-data value to use for trimming. Defaults to None, which uses NaN.
+        crop_size (int, optional): The number of pixels to crop from the outer edges of the data after trimming.
+                                   Defaults to 0 (no additional cropping).
 
     Returns:
-        (xr.DataArray | xr.Dataset): A DataArray or Dataset trimmed of its outer NaN or nodata values.
+        (xr.DataArray | xr.Dataset): A DataArray or Dataset trimmed of its outer NaN or nodata values, with optional
+                                     additional cropping applied.
     """
 
     # Determine the representative DataArray for NaN or nodata calculation
@@ -251,18 +252,24 @@ def trim_outer_nans(
     if not y_valid.size or not x_valid.size:
         return data
 
-    # Compute bounding indices (adjusting by the crop_size in pixels as before)
-    y_min, y_max = y_valid.min() + crop_size, y_valid.max() - crop_size
-    x_min, x_max = x_valid.min() + crop_size, x_valid.max() - crop_size
+    # Compute bounding indices with optional additional cropping
+    y_min, y_max = (
+        max(y_valid.min() + crop_size, 0),
+        min(y_valid.max() - crop_size, ref_data_array.shape[0] - 1),
+    )
+    x_min, x_max = (
+        max(x_valid.min() + crop_size, 0),
+        min(x_valid.max() - crop_size, ref_data_array.shape[1] - 1),
+    )
 
-    # In Python slicing is end exclusive, so we need to add 1 to the max indices
+    # In Python slicing is end-exclusive, so we need to add 1 to the max indices
     trimmed_data = data.isel(y=slice(y_min, y_max + 1), x=slice(x_min, x_max + 1))
 
-    # Adjust the x,y offsets, also taking into account the rotation
+    # Adjust the x,y offsets, taking into account the rotation and translation
     new_c = transform.c + x_min * transform.a + y_min * transform.b
     new_f = transform.f + x_min * transform.d + y_min * transform.e
 
-    # Make the new transformation matrix and write to the array
+    # Create the new transformation matrix
     new_transform = Affine(
         transform.a,
         transform.b,
@@ -271,6 +278,8 @@ def trim_outer_nans(
         transform.e,
         new_f,
     )
+
+    # Apply the new transformation to the trimmed data
     trimmed_data = trimmed_data.rio.write_transform(new_transform)
 
     return trimmed_data