proptest: Indexing rules match PandasIndex

pyproject.toml

@@ -30,7 +30,7 @@ dependencies = [
     "pandas>=2",
     "numpy>=2",
     "xproj>=0.2.0",
-    "xarray>=2025",
+    "xarray @ git+https://github.com/dcherian/xarray.git@fix-coord-transform-indexing",
 ]
 dynamic=["version"]
 
@@ -75,6 +75,9 @@ docs = [
 [tool.hatch]
 version.source = "vcs"
 
+[tool.hatch.metadata]
+allow-direct-references = true
+
 [tool.hatch.build]
 hooks.vcs.version-file = "src/rasterix/_version.py"
 

src/rasterix/raster_index.py

@@ -310,17 +310,20 @@ def isel(  # type: ignore[override]
     def sel(self, labels, method=None, tolerance=None):
         coord_name = self.axis_transform.coord_name
         label = labels[coord_name]
-
+        transform = self.axis_transform
         if isinstance(label, slice):
-            if label.start is None:
-                label = slice(0, label.stop, label.step)
+            label = slice(
+                label.start or transform.forward({coord_name: 0})[coord_name],
+                label.stop or transform.forward({coord_name: transform.size})[coord_name],
+                label.step,
+            )
             if label.step is None:
                 # continuous interval slice indexing (preserves the index)
                 pos = self.transform.reverse({coord_name: np.array([label.start, label.stop])})
                 # np.round rounds to even, this way we round upwards
                 pos = np.floor(pos[self.dim] + 0.5).astype("int")
                 new_start = max(pos[0], 0)
-                new_stop = min(pos[1], self.axis_transform.size)
+                new_stop = min(pos[1] + 1, self.axis_transform.size)
                 return IndexSelResult({self.dim: slice(new_start, new_stop)})
             else:
                 # otherwise convert to basic (array) indexing

src/rasterix/strategies.py

@@ -0,0 +1,165 @@
+"""Hypothesis strategies for generating label-based indexers."""
+
+from collections.abc import Hashable
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+from hypothesis import note
+from hypothesis import strategies as st
+from xarray.core.indexes import Indexes
+from xarray.testing.strategies import (
+    basic_indexers,
+    outer_array_indexers,
+    vectorized_indexers,
+)
+
+
+def pos_to_label_indexer(idx: pd.Index, idxr: int | slice | np.ndarray, *, use_scalar: bool = True) -> Any:
+    """Convert a positional indexer to a label-based indexer.
+
+    Parameters
+    ----------
+    idx : pd.Index
+        The pandas Index to use for label lookup.
+    idxr : int | slice | np.ndarray
+        The positional indexer (integer, slice, or array of integers).
+    use_scalar : bool, optional
+        If True, attempt to convert scalar values to Python scalars. Default is True.
+
+    Returns
+    -------
+    Any
+        The label-based indexer (scalar, slice, or array of labels).
+    """
+    if isinstance(idxr, slice):
+        return slice(
+            None if idxr.start is None else idx[idxr.start],
+            # FIXME: This will never go past the label range
+            None if idxr.stop is None else idx[min(idxr.stop, idx.size - 1)],
+        )
+    elif isinstance(idxr, np.ndarray):
+        # Convert array of position indices to array of label values
+        return idx[idxr].values
+    else:
+        val = idx[idxr]
+        if use_scalar:
+            try:
+                # pass python scalars occasionally
+                val = val.item()
+            except Exception:
+                note(f"casting {val!r} to item() failed")
+                pass
+        return val
+
+
+@st.composite
+def basic_label_indexers(draw, /, *, indexes: Indexes) -> dict[Hashable, float | slice]:
+    """Generate label-based indexers by converting position indexers to labels.
+
+    This works in label space by using the coordinate Index values.
+
+    Parameters
+    ----------
+    draw : callable
+        The Hypothesis draw function (automatically provided by @st.composite).
+    indexes : Indexes
+        Dictionary mapping dimension names to their associated indexes
+
+    Returns
+    -------
+    dict[Hashable, float | slice]
+        Label-based indexers as a dict with keys from sizes.keys().
+        Values are either float (for scalar labels) or slice (for label ranges).
+    """
+    idxs = indexes.get_unique()
+    assert all(isinstance(idx, xr.indexes.PandasIndex) for idx in idxs)
+
+    # FIXME: this should be indexes.sizes!
+    sizes = indexes.dims
+
+    pos_indexer = draw(basic_indexers(sizes=sizes))
+    pdindexes = indexes.to_pandas_indexes()
+
+    label_indexer = {
+        dim: pos_to_label_indexer(pdindexes[dim], idx, use_scalar=draw(st.booleans()))
+        for dim, idx in pos_indexer.items()
+    }
+    return label_indexer
+
+
+@st.composite
+def outer_array_label_indexers(draw, /, *, indexes: Indexes) -> dict[Hashable, np.ndarray]:
+    """Generate label-based outer array indexers by converting position indexers to labels.
+
+    This works in label space by using the coordinate Index values.
+
+    Parameters
+    ----------
+    draw : callable
+        The Hypothesis draw function (automatically provided by @st.composite).
+    indexes : Indexes
+        Dictionary mapping dimension names to their associated indexes
+
+    Returns
+    -------
+    dict[Hashable, np.ndarray]
+        Label-based indexers as a dict with keys from indexes.
+        Values are numpy arrays of label values for each dimension.
+    """
+    idxs = indexes.get_unique()
+    assert all(isinstance(idx, xr.indexes.PandasIndex) for idx in idxs)
+
+    # FIXME: this should be indexes.sizes!
+    sizes = indexes.dims
+
+    pos_indexer = draw(outer_array_indexers(sizes=sizes))
+    pdindexes = indexes.to_pandas_indexes()
+
+    label_indexer = {
+        dim: pos_to_label_indexer(pdindexes[dim], idx, use_scalar=False) for dim, idx in pos_indexer.items()
+    }
+    return label_indexer
+
+
+@st.composite
+def vectorized_label_indexers(draw, /, *, indexes: Indexes, **kwargs) -> dict[Hashable, xr.DataArray]:
+    """Generate label-based vectorized indexers by converting position indexers to labels.
+
+    This works in label space by using the coordinate Index values.
+
+    Parameters
+    ----------
+    draw : callable
+        The Hypothesis draw function (automatically provided by @st.composite).
+    indexes : Indexes
+        Dictionary mapping dimension names to their associated indexes
+    **kwargs : dict
+        Additional keyword arguments to pass to vectorized_indexers
+
+    Returns
+    -------
+    dict[Hashable, xr.DataArray]
+        Label-based indexers as a dict with keys from indexes.
+        Values are DataArrays of label values for each dimension.
+    """
+    idxs = indexes.get_unique()
+    assert all(isinstance(idx, xr.indexes.PandasIndex) for idx in idxs)
+
+    # FIXME: this should be indexes.sizes!
+    sizes = indexes.dims
+
+    pos_indexer = draw(vectorized_indexers(sizes=sizes, **kwargs))
+    pdindexes = indexes.to_pandas_indexes()
+
+    label_indexer = {}
+    for dim, idx_array in pos_indexer.items():
+        # Convert each position in the array to its corresponding label
+        # Flatten, index, then reshape back to original shape
+        flat_indices = idx_array.values.ravel()
+        flat_labels = pdindexes[dim][flat_indices].values
+        label_values = flat_labels.reshape(idx_array.shape)
+        label_indexer[dim] = xr.DataArray(label_values, dims=idx_array.dims)
+
+    return label_indexer