Now raising an error if color or table doesn't exist (#515)

Author: timtreis

src/spatialdata_plot/_logging.py

@@ -1,6 +1,13 @@
 # from https://github.com/scverse/spatialdata/blob/main/src/spatialdata/_logging.py
 
 import logging
+import re
+from collections.abc import Iterator
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:  # pragma: no cover
+    from _pytest.logging import LogCaptureFixture
 
 
 def _setup_logger() -> "logging.Logger":
@@ -21,3 +28,44 @@ def _setup_logger() -> "logging.Logger":
 
 
 logger = _setup_logger()
+
+
+@contextmanager
+def logger_warns(
+    caplog: "LogCaptureFixture",
+    logger: logging.Logger,
+    match: str | None = None,
+    level: int = logging.WARNING,
+) -> Iterator[None]:
+    """
+    Context manager similar to pytest.warns, but for logging.Logger.
+
+    Usage:
+        with logger_warns(caplog, logger, match="Found 1 NaN"):
+            call_code_that_logs()
+    """
+    # Store initial record count to only check new records
+    initial_record_count = len(caplog.records)
+
+    # Add caplog's handler directly to the logger to capture logs even if propagate=False
+    handler = caplog.handler
+    logger.addHandler(handler)
+    original_level = logger.level
+    logger.setLevel(level)
+
+    # Use caplog.at_level to ensure proper capture setup
+    with caplog.at_level(level, logger=logger.name):
+        try:
+            yield
+        finally:
+            logger.removeHandler(handler)
+            logger.setLevel(original_level)
+
+    # Only check records that were added during this context
+    records = [r for r in caplog.records[initial_record_count:] if r.levelno >= level]
+
+    if match is not None:
+        pattern = re.compile(match)
+        if not any(pattern.search(r.getMessage()) for r in records):
+            msgs = [r.getMessage() for r in records]
+            raise AssertionError(f"Did not find log matching {match!r} in records: {msgs!r}")

src/spatialdata_plot/pl/render.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import warnings
 from collections import abc
 from copy import copy
 
@@ -49,7 +48,6 @@
     _get_extent_and_range_for_datashader_canvas,
     _get_linear_colormap,
     _hex_no_alpha,
-    _is_coercable_to_float,
     _map_color_seg,
     _maybe_set_colors,
     _mpl_ax_contains_elements,
@@ -94,20 +92,7 @@ def _render_shapes(
         )
         sdata_filt[table_name] = table = joined_table
 
-    if (
-        col_for_color is not None
-        and table_name is not None
-        and col_for_color in sdata_filt[table_name].obs.columns
-        and (color_col := sdata_filt[table_name].obs[col_for_color]).dtype == "O"
-        and not _is_coercable_to_float(color_col)
-    ):
-        warnings.warn(
-            f"Converting copy of '{col_for_color}' column to categorical dtype for categorical plotting. "
-            f"Consider converting before plotting.",
-            UserWarning,
-            stacklevel=2,
-        )
-        sdata_filt[table_name].obs[col_for_color] = sdata_filt[table_name].obs[col_for_color].astype("category")
+    shapes = sdata_filt[element]
 
     # get color vector (categorical or continuous)
     color_source_vector, color_vector, _ = _set_color_source_vec(
@@ -121,6 +106,7 @@ def _render_shapes(
         cmap_params=render_params.cmap_params,
         table_name=table_name,
         table_layer=table_layer,
+        coordinate_system=coordinate_system,
     )
 
     values_are_categorical = color_source_vector is not None
@@ -144,12 +130,25 @@ def _render_shapes(
 
     # continuous case: leave NaNs as NaNs; utils maps them to na_color during draw
     if color_source_vector is None and not values_are_categorical:
-        color_vector = np.asarray(color_vector, dtype=float)
-        if np.isnan(color_vector).any():
-            nan_count = int(np.isnan(color_vector).sum())
-            msg = f"Found {nan_count} NaN values in color data. These observations will be colored with the 'na_color'."
-            warnings.warn(msg, UserWarning, stacklevel=2)
-            logger.warning(msg)
+        _series = color_vector if isinstance(color_vector, pd.Series) else pd.Series(color_vector)
+
+        try:
+            color_vector = np.asarray(_series, dtype=float)
+        except (TypeError, ValueError):
+            nan_count = int(_series.isna().sum())
+            if nan_count:
+                logger.warning(
+                    f"Found {nan_count} NaN values in color data. "
+                    "These observations will be colored with the 'na_color'."
+                )
+            color_vector = _series.to_numpy()
+        else:
+            if np.isnan(color_vector).any():
+                nan_count = int(np.isnan(color_vector).sum())
+                logger.warning(
+                    f"Found {nan_count} NaN values in color data. "
+                    "These observations will be colored with the 'na_color'."
+                )
 
     # Using dict.fromkeys here since set returns in arbitrary order
     # remove the color of NaN values, else it might be assigned to a category
@@ -476,10 +475,33 @@ def _render_shapes(
     if not values_are_categorical:
         vmin = render_params.cmap_params.norm.vmin
         vmax = render_params.cmap_params.norm.vmax
-        if vmin is None:
-            vmin = float(np.nanmin(color_vector))
-        if vmax is None:
-            vmax = float(np.nanmax(color_vector))
+        if vmin is None or vmax is None:
+            # Extract numeric values only (filter out strings and other non-numeric types)
+            if isinstance(color_vector, np.ndarray):
+                if np.issubdtype(color_vector.dtype, np.number):
+                    # Already numeric, can use directly
+                    numeric_values = color_vector
+                else:
+                    # Mixed types - extract only numeric values using pandas
+                    numeric_values = pd.to_numeric(color_vector, errors="coerce")
+                    numeric_values = numeric_values[np.isfinite(numeric_values)]
+                if len(numeric_values) > 0:
+                    if vmin is None:
+                        vmin = float(np.nanmin(numeric_values))
+                    if vmax is None:
+                        vmax = float(np.nanmax(numeric_values))
+                else:
+                    # No numeric values found, use defaults
+                    if vmin is None:
+                        vmin = 0.0
+                    if vmax is None:
+                        vmax = 1.0
+            else:
+                # Not a numpy array, use defaults
+                if vmin is None:
+                    vmin = 0.0
+                if vmax is None:
+                    vmax = 1.0
         _cax.set_clim(vmin=vmin, vmax=vmax)
 
     if (
@@ -541,31 +563,16 @@ def _render_points(
     coords = ["x", "y"]
 
     if table_name is not None and col_for_color not in points.columns:
-        warnings.warn(
+        logger.warning(
             f"Annotating points with {col_for_color} which is stored in the table `{table_name}`. "
-            f"To improve performance, it is advisable to store point annotations directly in the .parquet file.",
-            UserWarning,
-            stacklevel=2,
+            f"To improve performance, it is advisable to store point annotations directly in the .parquet file."
         )
 
     if col_for_color is None or (
         table_name is not None
         and (col_for_color in sdata_filt[table_name].obs.columns or col_for_color in sdata_filt[table_name].var_names)
     ):
         points = points[coords].compute()
-        if (
-            col_for_color
-            and col_for_color in sdata_filt[table_name].obs.columns
-            and (color_col := sdata_filt[table_name].obs[col_for_color]).dtype == "O"
-            and not _is_coercable_to_float(color_col)
-        ):
-            warnings.warn(
-                f"Converting copy of '{col_for_color}' column to categorical dtype for categorical "
-                f"plotting. Consider converting before plotting.",
-                UserWarning,
-                stacklevel=2,
-            )
-            sdata_filt[table_name].obs[col_for_color] = sdata_filt[table_name].obs[col_for_color].astype("category")
     else:
         coords += [col_for_color]
         points = points[coords].compute()
@@ -683,6 +690,7 @@ def _render_points(
         alpha=render_params.alpha,
         table_name=table_name,
         render_type="points",
+        coordinate_system=coordinate_system,
     )
 
     if added_color_from_table and col_for_color is not None:
@@ -1219,6 +1227,7 @@ def _render_labels(
         cmap_params=render_params.cmap_params,
         table_name=table_name,
         table_layer=table_layer,
+        coordinate_system=coordinate_system,
     )
 
     # rasterize could have removed labels from label