Skip auto reverse axis for weird dependent coords

Author: lukelbd

proplot/axes/plot.py

@@ -1911,30 +1911,22 @@ def _parse_1d_format(
                 if axis.isDefault_label:
                     kw_format[sy + 'label'] = title
 
-        # Convert string-type coordinates
+        # Convert string-type coordinates to indices
         # NOTE: This should even allow qualitative string input to hist()
         if autox:
             x, kw_format = inputs._meta_coords(x, which=sx, **kw_format)
         if autoy:
             *ys, kw_format = inputs._meta_coords(*ys, which=sy, **kw_format)
-        descending = (
-            autox
-            and autoreverse
-            and x.ndim == 1
-            and x.size > 1
-            and np.all(np.sign(np.diff(inputs._to_numpy_array(x))) == -1)
-        )
-        if descending and getattr(self, f'get_autoscale{sx}_on')():
-            kw_format[sx + 'reverse'] = True
-
-        # Apply formatting
-        if kw_format:
-            self.format(**kw_format)
+        if autox and autoreverse and inputs._is_descending(x):
+            if getattr(self, f'get_autoscale{sx}_on')():
+                kw_format[sx + 'reverse'] = True
 
-        # Finally strip metadata
+        # Finally apply formatting and strip metadata
         # WARNING: Most methods that accept 2D arrays use columns of data, but when
         # pandas DataFrame specifically is passed to hist, boxplot, or violinplot, rows
         # of data assumed! Converting to ndarray necessary.
+        if kw_format:
+            self.format(**kw_format)
         ys = tuple(map(inputs._to_numpy_array, ys))
         if x is not None:  # pie() and hist()
             x = inputs._to_numpy_array(x)
@@ -2004,14 +1996,9 @@ def _parse_2d_format(
             x, kw_format = inputs._meta_coords(x, which='x', **kw_format)
             y, kw_format = inputs._meta_coords(y, which='y', **kw_format)
             for s, d in zip('xy', (x, y)):
-                descending = (
-                    autoreverse
-                    and d.ndim == 1
-                    and d.size > 1
-                    and np.all(np.sign(np.diff(inputs._to_numpy_array(d))) == -1)
-                )
-                if descending and getattr(self, f'get_autoscale{s}_on')():
-                    kw_format[s + 'reverse'] = True
+                if autoreverse and inputs._is_descending(d):
+                    if getattr(self, f'get_autoscale{s}_on')():
+                        kw_format[s + 'reverse'] = True
 
             # Apply formatting
             if kw_format:

proplot/colors.py

@@ -30,7 +30,14 @@
 
 from .config import rc
 from .internals import ic  # noqa: F401
-from .internals import _kwargs_to_args, _not_none, _pop_props, docstring, warnings
+from .internals import (
+    _kwargs_to_args,
+    _not_none,
+    _pop_props,
+    docstring,
+    inputs,
+    warnings,
+)
 from .utils import set_alpha, to_hex, to_rgb, to_rgba, to_xyz, to_xyza
 
 __all__ = [
@@ -2305,12 +2312,12 @@ def _sanitize_levels(levels):
     """
     Ensure the levels are monotonic. If they are descending, reverse them.
     """
-    levels = np.atleast_1d(levels)
+    levels = inputs._to_numpy_array(levels)
     if levels.ndim != 1 or levels.size < 2:
         raise ValueError(f'Levels {levels} must be a 1D array with size >= 2.')
     if isinstance(levels, ma.core.MaskedArray):
         levels = levels.filled(np.nan)
-    if not np.all(np.isfinite(levels)):
+    if not np.all(np.isfinite(levels)) or not inputs._is_numeric(levels):
         raise ValueError(f'Levels {levels} contain invalid values.')
     diffs = np.sign(np.diff(levels))
     if np.all(diffs == 1):

proplot/internals/inputs.py

@@ -88,6 +88,21 @@ def _is_categorical(data):
     )
 
 
+def _is_descending(data):
+    """
+    Test whether the input data is descending. This is used for auto axis reversal.
+    """
+    # NOTE: Want this to work with e.g. datetime axes but fail for strange
+    # mixed types so filter against object dtypes rather than using _is_numeric
+    data = _to_numpy_array(data)
+    return (
+        data.ndim == 1
+        and data.size > 1
+        and data.dtype != 'O'
+        and np.all(np.sign(np.diff(data)) == -1)
+    )
+
+
 def _to_duck_array(data, strip_units=False):
     """
     Convert arbitrary input to duck array. Preserve array containers with metadata.