move xarray functions to xrcompat (#35)

* move xarray functions to xrcompat

* fix tests

* Update mplotutils/xrcompat.py

* Update mplotutils/xrcompat.py

* Update mplotutils/xrcompat.py

Author: mathause

mplotutils/__init__.py

@@ -4,6 +4,7 @@
 from .cartopy_utils import *
 from .colorbar_utils import *
 from .mpl_utils import *
+from .xrcompat import *
 
 try:
     from importlib.metadata import version as _get_version

mplotutils/cartopy_utils.py

@@ -1,5 +1,3 @@
-import warnings
-
 import cartopy.crs as ccrs
 import matplotlib.pyplot as plt
 import numpy as np
@@ -32,98 +30,6 @@ def sample_data_map(nlons, nlats):
     return lon, lat, data
 
 
-# =============================================================================
-
-
-# from xarray
-def infer_interval_breaks(x, y, clip=False):
-    """find edges of gridcells, given their centers"""
-
-    # TODO: require cartopy >= 0.21 when removing this function
-    warnings.warn(
-        "It's no longer necessary to compute the edges of the array. This is now done"
-        "in matplotlib. This function will be removed from mplotutils in a future "
-        "version.",
-        FutureWarning,
-    )
-
-    if len(x.shape) == 1:
-        x = _infer_interval_breaks(x)
-        y = _infer_interval_breaks(y)
-    else:
-        # we have to infer the intervals on both axes
-        x = _infer_interval_breaks(x, axis=1)
-        x = _infer_interval_breaks(x, axis=0)
-        y = _infer_interval_breaks(y, axis=1)
-        y = _infer_interval_breaks(y, axis=0)
-
-    if clip:
-        y = np.clip(y, -90, 90)
-
-    return x, y
-
-
-# from xarray
-def _infer_interval_breaks(coord, axis=0):
-    """
-    >>> _infer_interval_breaks(np.arange(5))
-    array([-0.5,  0.5,  1.5,  2.5,  3.5,  4.5])
-    >>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)
-    array([[-0.5,  0.5,  1.5],
-           [ 2.5,  3.5,  4.5]])
-    """
-
-    if not _is_monotonic(coord, axis=axis):
-        raise ValueError(
-            "The input coordinate is not sorted in increasing "
-            "order along axis %d. This can lead to unexpected "
-            "results. Consider calling the `sortby` method on "
-            "the input DataArray. To plot data with categorical "
-            "axes, consider using the `heatmap` function from "
-            "the `seaborn` statistical plotting library." % axis
-        )
-
-    coord = np.asarray(coord)
-    deltas = 0.5 * np.diff(coord, axis=axis)
-    if deltas.size == 0:
-        deltas = np.array(0.0)
-    first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis)
-    last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis)
-    trim_last = tuple(
-        slice(None, -1) if n == axis else slice(None) for n in range(coord.ndim)
-    )
-    return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis)
-
-
-# from xarray
-def _is_monotonic(coord, axis=0):
-    """
-    >>> _is_monotonic(np.array([0, 1, 2]))
-    True
-    >>> _is_monotonic(np.array([2, 1, 0]))
-    True
-    >>> _is_monotonic(np.array([0, 2, 1]))
-    False
-    """
-    coord = np.asarray(coord)
-
-    if coord.shape[axis] < 3:
-        return True
-    else:
-        n = coord.shape[axis]
-        delta_pos = coord.take(np.arange(1, n), axis=axis) >= coord.take(
-            np.arange(0, n - 1), axis=axis
-        )
-        delta_neg = coord.take(np.arange(1, n), axis=axis) <= coord.take(
-            np.arange(0, n - 1), axis=axis
-        )
-
-    return np.all(delta_pos) or np.all(delta_neg)
-
-
-# =============================================================================
-
-
 def cyclic_dataarray(obj, coord="lon"):
     """Add a cyclic coordinate point to a DataArray or Dataset along a dimension.
 

mplotutils/tests/test_infer_interval_breaks.py

@@ -2,7 +2,7 @@
 import pytest
 from numpy.testing import assert_array_equal  # noqa: F401
 
-from mplotutils.cartopy_utils import _infer_interval_breaks, infer_interval_breaks
+from mplotutils.xrcompat import _infer_interval_breaks, infer_interval_breaks
 
 
 def test_infer_interval_breaks_warns():

mplotutils/xrcompat.py

@@ -0,0 +1,89 @@
+# code vendored from xarray under the conditions of their license
+# see licenses/XARRAY_LICENSE
+
+import warnings
+
+import numpy as np
+
+
+def infer_interval_breaks(x, y, clip=False):
+    """find edges of gridcells, given their centers"""
+
+    # TODO: require cartopy >= 0.21 when removing this function
+    warnings.warn(
+        "It's no longer necessary to compute the edges of the array. This is now done"
+        "in matplotlib. This function will be removed from mplotutils in a future "
+        "version.",
+        FutureWarning,
+    )
+
+    if len(x.shape) == 1:
+        x = _infer_interval_breaks(x)
+        y = _infer_interval_breaks(y)
+    else:
+        # we have to infer the intervals on both axes
+        x = _infer_interval_breaks(x, axis=1)
+        x = _infer_interval_breaks(x, axis=0)
+        y = _infer_interval_breaks(y, axis=1)
+        y = _infer_interval_breaks(y, axis=0)
+
+    if clip:
+        y = np.clip(y, -90, 90)
+
+    return x, y
+
+
+def _infer_interval_breaks(coord, axis=0):
+    """
+    >>> _infer_interval_breaks(np.arange(5))
+    array([-0.5,  0.5,  1.5,  2.5,  3.5,  4.5])
+    >>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)
+    array([[-0.5,  0.5,  1.5],
+           [ 2.5,  3.5,  4.5]])
+    """
+
+    if not _is_monotonic(coord, axis=axis):
+        raise ValueError(
+            "The input coordinate is not sorted in increasing "
+            "order along axis %d. This can lead to unexpected "
+            "results. Consider calling the `sortby` method on "
+            "the input DataArray. To plot data with categorical "
+            "axes, consider using the `heatmap` function from "
+            "the `seaborn` statistical plotting library." % axis
+        )
+
+    coord = np.asarray(coord)
+    deltas = 0.5 * np.diff(coord, axis=axis)
+    if deltas.size == 0:
+        deltas = np.array(0.0)
+    first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis)
+    last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis)
+    trim_last = tuple(
+        slice(None, -1) if n == axis else slice(None) for n in range(coord.ndim)
+    )
+    return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis)
+
+
+def _is_monotonic(coord, axis=0):
+    """
+    >>> _is_monotonic(np.array([0, 1, 2]))
+    True
+    >>> _is_monotonic(np.array([2, 1, 0]))
+    True
+    >>> _is_monotonic(np.array([0, 2, 1]))
+    False
+    """
+    coord = np.asarray(coord)
+
+    if coord.shape[axis] < 3:
+        return True
+    else:
+        n = coord.shape[axis]
+        delta_pos = coord.take(np.arange(1, n), axis=axis) >= coord.take(
+            np.arange(0, n - 1), axis=axis
+        )
+        delta_neg = coord.take(np.arange(1, n), axis=axis) <= coord.take(
+            np.arange(0, n - 1), axis=axis
+        )
+
+    return np.all(delta_pos) or np.all(delta_neg)