Unit agnostic intervals for color-mapped hodographs (#3054)

* Allow plot_colormapped() to accept intervals that are array-like. No longer limited to instances of pint.Quantity.
* Improved documentation for plot_colormapped() about behavior when c and intervals are height data.
* Tests for hodographs where intervals are given with no units, km, or hPa.
* Improved type checking with is_quantity

Author: 23ccozad

src/metpy/plots/skewt.py

@@ -26,7 +26,7 @@
 from ..calc.tools import _delete_masked_points
 from ..interpolate import interpolate_1d
 from ..package_tools import Exporter
-from ..units import concatenate, units
+from ..units import concatenate, is_quantity, units
 
 exporter = Exporter(globals())
 
@@ -890,10 +890,12 @@ def plot_colormapped(self, u, v, c, intervals=None, colors=None, **kwargs):
         variable besides the winds (e.g. heights or pressure levels) and either a colormap to
         color it with or a series of contour intervals and colors to create a colormap and
         norm to control colormapping. The intervals must always be in increasing
-        order. For using custom contour intervals with height data, the function will
-        automatically interpolate to the contour intervals from the height and wind data,
-        as well as convert the input contour intervals from height AGL to MSL to work with the
-        provided heights.
+        order.
+
+        When c and intervals are height data (`pint.Quantity` objects with units of length,
+        such as 'm' or 'km'), the function will automatically interpolate to the contour
+        intervals from the height and wind data, as well as convert the input contour intervals
+        from height AGL to MSL to work with the provided heights.
 
         Parameters
         ----------
@@ -926,7 +928,7 @@ def plot_colormapped(self, u, v, c, intervals=None, colors=None, **kwargs):
         if colors:
             cmap = mcolors.ListedColormap(colors)
             # If we are segmenting by height (a length), interpolate the contour intervals
-            if intervals.check('[length]'):
+            if is_quantity(intervals) and intervals.check('[length]'):
 
                 # Find any intervals not in the data and interpolate them
                 heights_min = np.nanmin(c)
@@ -952,7 +954,8 @@ def plot_colormapped(self, u, v, c, intervals=None, colors=None, **kwargs):
                 c = c.to_base_units()  # TODO: This shouldn't be required!
                 intervals = intervals.to_base_units()
 
-            norm = mcolors.BoundaryNorm(intervals.magnitude, cmap.N)
+            intervals_m = intervals.m if is_quantity(intervals) else intervals
+            norm = mcolors.BoundaryNorm(intervals_m, cmap.N)
             cmap.set_over('none')
             cmap.set_under('none')
             kwargs['cmap'] = cmap

tests/plots/baseline/test_hodograph_plot_colors_with_height_intervals.png

@@ -490,6 +490,54 @@ def test_hodograph_plot_layers_bound_units():
     return fig
 
 
+@pytest.mark.mpl_image_compare(tolerance=0)
+def test_hodograph_plot_colors_with_unitless_intervals():
+    """Test hodograph colored layers when intervals have no units."""
+    fig = plt.figure(figsize=(7, 7))
+    ax = fig.add_subplot(1, 1, 1)
+    hodo = Hodograph(ax, component_range=50)
+    hodo.add_grid(10)
+    u = np.array([0, 6, 26, 32, 48])
+    v = np.array([0, 23, 34, 23, 5])
+    p = np.flip(np.array([900, 750, 600, 450, 250]))
+    intervals = np.flip(np.array([1000, 850, 700, 500, 300, 200]))
+    colors = ['red', 'green', 'yellow', 'blue', 'purple']
+    hodo.plot_colormapped(u, v, p, intervals=intervals, colors=colors)
+    return fig
+
+
+@pytest.mark.mpl_image_compare(tolerance=0)
+def test_hodograph_plot_colors_with_pressure_intervals():
+    """Test hodograph colored layers when intervals are given in units of pressure."""
+    fig = plt.figure(figsize=(7, 7))
+    ax = fig.add_subplot(1, 1, 1)
+    hodo = Hodograph(ax, component_range=50)
+    hodo.add_grid(10)
+    u = np.array([0, 6, 26, 32, 48])
+    v = np.array([0, 23, 34, 23, 5])
+    p = units.Quantity(np.flip(np.array([900, 750, 600, 450, 250])), 'hPa')
+    intervals = units.Quantity(np.flip(np.array([1000, 850, 700, 500, 300, 200])), 'hPa')
+    colors = ['red', 'green', 'yellow', 'blue', 'purple']
+    hodo.plot_colormapped(u, v, p, intervals=intervals, colors=colors)
+    return fig
+
+
+@pytest.mark.mpl_image_compare(tolerance=0)
+def test_hodograph_plot_colors_with_height_intervals():
+    """Test hodograph colored layers when intervals are given in units of height."""
+    fig = plt.figure(figsize=(7, 7))
+    ax = fig.add_subplot(1, 1, 1)
+    hodo = Hodograph(ax, component_range=50)
+    hodo.add_grid(10)
+    u = np.array([0, 6, 26, 32, 48])
+    v = np.array([0, 23, 34, 23, 5])
+    h = units.Quantity(np.array([0.1, 3.5, 5.5, 10.9, 14.0]), 'km')
+    intervals = units.Quantity(np.array([0, 3, 6, 9, 12, 15]), 'km')
+    colors = ['red', 'green', 'yellow', 'blue', 'purple']
+    hodo.plot_colormapped(u, v, h, intervals=intervals, colors=colors)
+    return fig
+
+
 @pytest.mark.mpl_image_compare(tolerance=0, remove_text=True)
 def test_hodograph_plot_arbitrary_layer():
     """Test hodograph colored layers for arbitrary variables without interpolation."""