Put cube data on the x axis if plotting just a cube against a vertical or y coordinate

docs/iris/gallery_code/oceanography/plot_atlantic_profiles.py

@@ -39,17 +39,15 @@ def main():
     theta_1000m = theta.extract(depth_cons & lon_cons & lat_cons)
     salinity_1000m = salinity.extract(depth_cons & lon_cons & lat_cons)
 
-    # Plot these profiles on the same set of axes. In each case we call plot
-    # with two arguments, the cube followed by the depth coordinate. Putting
-    # them in this order places the depth coordinate on the y-axis.
+    # Plot these profiles on the same set of axes. Depth is automatically
+    # recognised as a vertical coordinate and placed on the y-axis.
     # The first plot is in the default axes. We'll use the same color for the
     # curve and its axes/tick labels.
     plt.figure(figsize=(5, 6))
     temperature_color = (0.3, 0.4, 0.5)
     ax1 = plt.gca()
     iplt.plot(
         theta_1000m,
-        theta_1000m.coord("depth"),
         linewidth=2,
         color=temperature_color,
         alpha=0.75,
@@ -65,7 +63,6 @@ def main():
     ax2 = plt.gca().twiny()
     iplt.plot(
         salinity_1000m,
-        salinity_1000m.coord("depth"),
         linewidth=2,
         color=salinity_color,
         alpha=0.75,

docs/iris/src/userguide/regridding_plots/interpolate_column.py

@@ -1,4 +1,3 @@
-import iris
 import iris.quickplot as qplt
 import iris.analysis
 import matplotlib.pyplot as plt
@@ -12,8 +11,13 @@
 
 # Interpolate the "perfect" linear interpolation. Really this is just
 # a high number of interpolation points, in this case 1000 of them.
-altitude_points = [("altitude", np.linspace(400, 1250, 1000))]
-scheme = iris.analysis.Linear(extrapolation_mode="mask")
+altitude_points = [
+    (
+        "altitude",
+        np.linspace(min(alt_coord.points), max(alt_coord.points), 1000),
+    )
+]
+scheme = iris.analysis.Linear()
 linear_column = column.interpolate(altitude_points, scheme)
 
 # Now interpolate the data onto 10 evenly spaced altitude levels,
@@ -27,7 +31,6 @@
 # Plot the black markers for the original data.
 qplt.plot(
     column,
-    column.coord("altitude"),
     marker="o",
     color="black",
     linestyle="",
@@ -39,7 +42,6 @@
 # Plot the gray line to display the linear interpolation.
 qplt.plot(
     linear_column,
-    linear_column.coord("altitude"),
     color="gray",
     label="Linear interpolation",
     zorder=0,
@@ -48,7 +50,6 @@
 # Plot the red markers for the new data.
 qplt.plot(
     new_column,
-    new_column.coord("altitude"),
     marker="D",
     color="red",
     linestyle="",

docs/iris/src/whatsnew/latest.rst

@@ -16,7 +16,11 @@ This document explains the changes made to Iris for this release
 ✨ Features
 ===========
 
-* N/A
+* `@pelson`_ and `@trexfeathers`_ enhanced :meth:iris.plot.plot and
+  :meth:iris.quickplot.plot to automatically place the cube on the x axis if
+  the primary coordinate being plotted against is a vertical coordinate. E.g.
+  ``iris.plot.plot(z_cube)`` will produce a z-vs-phenomenon plot, where before
+  it would have produced a phenomenon-vs-z plot. (:pull:`3906`)
 
 
 🐛 Bugs Fixed
@@ -56,5 +60,8 @@ This document explains the changes made to Iris for this release
 
 * N/A
 
+
+.. _@pelson: https://github.com/pelson
+.. _@trexfeathers: https://github.com/trexfeathers
 .. _@gcaria: https://github.com/gcaria
 .. _@rcomer: https://github.com/rcomer

lib/iris/plot.py

@@ -665,7 +665,20 @@ def _get_plot_objects(args):
         # single argument
         v_object = args[0]
         u_object = _u_object_from_v_object(v_object)
+
         u, v = _uv_from_u_object_v_object(u_object, args[0])
+
+        # If a single cube argument, and the associated dimension coordinate
+        # is vertical-like, put the coordinate on the y axis, and the data o
+        # the x.
+        if (
+            isinstance(v_object, iris.cube.Cube)
+            and isinstance(u_object, iris.coords.Coord)
+            and iris.util.guess_coord_axis(u_object) in ["Y", "Z"]
+        ):
+            u_object, v_object = v_object, u_object
+            u, v = v, u
+
         args = args[1:]
     return u_object, v_object, u, v, args
 

lib/iris/quickplot.py

@@ -138,12 +138,8 @@ def _get_titles(u_object, v_object):
 
 
 def _label_1d_plot(*args, **kwargs):
-    if len(args) > 1 and isinstance(
-        args[1], (iris.cube.Cube, iris.coords.Coord)
-    ):
-        xlabel, ylabel, title = _get_titles(*args[:2])
-    else:
-        xlabel, ylabel, title = _get_titles(None, args[0])
+    u_obj, v_obj, _, _, _ = iplt._get_plot_objects(args)
+    xlabel, ylabel, title = _get_titles(u_obj, v_obj)
 
     axes = kwargs.pop("axes", None)
 

lib/iris/tests/results/imagerepo.json

@@ -288,9 +288,7 @@
         "https://scitools.github.io/test-iris-imagehash/images/v4/8ff897066a01f0f2f818ee1eb007ca41853e3b81c57e36a991fe2ca9725e29ed.png"
     ],
     "iris.tests.test_plot.Test1dPlotMultiArgs.test_cube.0": [
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8ffac1547a0792546c179db7f1254f6d945b7392841678e895017e3e91c17a0f.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8ff8c1fa7a05b4ea6c059d2ff1494e4b90f26304846d78d1872a6cfc938b2e3e.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8ff8c1fa7a05b4fa6c059d2ef1494e4b90f26304847d78c1872a6cfc938b2e3e.png"
+        "https://scitools.github.io/test-iris-imagehash/images/v4/8fffc1dc7e019c70f001b70ee4386de1814e7938837b6a7f84d07c9f15b02f21.png"
     ],
     "iris.tests.test_plot.Test1dPlotMultiArgs.test_cube_coord.0": [
         "https://scitools.github.io/test-iris-imagehash/images/v4/83fec1ff7e0098757103a71ce4506dc3d11e7b20d2477ec094857db895217f6a.png",
@@ -323,8 +321,7 @@
         "https://scitools.github.io/test-iris-imagehash/images/v4/87ffb79e7f0060d8303fcd1eb007d801c52699e18d769e2199e60ce1da5629ed.png"
     ],
     "iris.tests.test_plot.Test1dQuickplotPlotMultiArgs.test_cube.0": [
-        "https://scitools.github.io/test-iris-imagehash/images/v4/83ffc1dc7e00b0dc66179d95f127cfc9d44959ba846658e891075a3e99415a2f.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/a3ffc1d87e00b49964179d28f16bce4b98724b268c6d58e1972e4874998b2e7e.png"
+        "https://scitools.github.io/test-iris-imagehash/images/v4/a3ffc1de7e009c7030019786f438cde3810fd93c9b734a778ce47c9799b02731.png"
     ],
     "iris.tests.test_plot.Test1dQuickplotPlotMultiArgs.test_cube_coord.0": [
         "https://scitools.github.io/test-iris-imagehash/images/v4/83fec1ff7f90987720029f1ef458cd43811cdb60d647de609485ddb899215f62.png",
@@ -649,15 +646,10 @@
         "https://scitools.github.io/test-iris-imagehash/images/v4/8bfe956b7c01c2f26300929dfc1e3c6690736f91817e3b0c84be6be5d1603ed1.png"
     ],
     "iris.tests.test_plot.TestPlot.test_y.0": [
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8fe896266f068d873b83cb71e435725cd07c607ad07e70fcd0007a7881fe7ab8.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8fe896066f068d873b83cb71e435725cd07c607ad07c70fcd0007af881fe7bb8.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8fe896366f0f8d93398bcb71e435f24ed074646ed07670acf010726d81f2798c.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/aff8946c7a14c99fb193d263e42432d8d00c2d27944a3f8dc5223ef703ff6b90.png"
+        "https://scitools.github.io/test-iris-imagehash/images/v4/8ff99c067e01e7166101c9c6b04396b5cd4e2f0993163de9c4fe7b79207e36a1.png"
     ],
     "iris.tests.test_plot.TestPlot.test_z.0": [
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8ffac1547a0792546c179db7f1254f6d945b7392841678e895017e3e91c17a0f.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8ff8c1fa7a05b4ea6c059d2ff1494e4b90f26304846d78d1872a6cfc938b2e3e.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/8ff8c1fa7a05b4fa6c059d2ef1494e4b90f26304847d78c1872a6cfc938b2e3e.png"
+        "https://scitools.github.io/test-iris-imagehash/images/v4/8fffc1dc7e019c70f001b70ee4386de1814e7938837b6a7f84d07c9f15b02f21.png"
     ],
     "iris.tests.test_plot.TestPlotCitation.test.0": [
         "https://scitools.github.io/test-iris-imagehash/images/v4/abf895067a1d9506f811783585437abd85426ab995067af9f00687f96afe87c8.png",
@@ -836,14 +828,10 @@
         "https://scitools.github.io/test-iris-imagehash/images/v4/82ff950b7f81c0d6620199bcfc5e986695734da1816e1b2c85be2b65d96276d1.png"
     ],
     "iris.tests.test_plot.TestQuickplotPlot.test_y.0": [
-        "https://scitools.github.io/test-iris-imagehash/images/v4/a7ffb6067f008d87339bc973e435d86ef034c87ad07c586cd001da69897e5838.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/a7ffb6067f008d87339bc973e435d86ef034c87ad07cd86cd001da68897e58a8.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/a7efb6367f008d97338fc973e435d86ef030c86ed070d86cd030d86d89f0d82c.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/a2fbb46e7f10c99f2013d863e46498dcd06c0d2798421fa5dd221e7789ff6f10.png"
+        "https://scitools.github.io/test-iris-imagehash/images/v4/a3f9bc067e01c6166009c9c6b5439ee5cd4e0d2993361de9ccf65b79887636a9.png"
     ],
     "iris.tests.test_plot.TestQuickplotPlot.test_z.0": [
-        "https://scitools.github.io/test-iris-imagehash/images/v4/83ffc1dc7e00b0dc66179d95f127cfc9d44959ba846658e891075a3e99415a2f.png",
-        "https://scitools.github.io/test-iris-imagehash/images/v4/a3ffc1d87e00b49964179d28f16bce4b98724b268c6d58e1972e4874998b2e7e.png"
+        "https://scitools.github.io/test-iris-imagehash/images/v4/a3ffc1de7e009c7030019786f438cde3810fd93c9b734a778ce47c9799b02731.png"
     ],
     "iris.tests.test_plot.TestSimple.test_bounds.0": [
         "https://scitools.github.io/test-iris-imagehash/images/v4/ea856a85954a957ac17e954ac17a9c3e956ac07e3e80c07f3e857aa5c27d3f80.png"

lib/iris/tests/unit/quickplot/test_plot.py

@@ -8,6 +8,7 @@
 # Import iris.tests first so that some things can be initialised before
 # importing anything else.
 import iris.tests as tests
+from iris.tests.stock import simple_1d
 from iris.tests.unit.plot import TestGraphicStringCoord
 
 if tests.MPL_AVAILABLE:
@@ -29,5 +30,29 @@ def test_xaxis_labels(self):
         self.assertBoundsTickLabels("xaxis")
 
 
+class TestAxisLabels(tests.GraphicsTest):
+    def test_xy_cube(self):
+        c = simple_1d()
+        qplt.plot(c)
+        ax = qplt.plt.gca()
+        x = ax.xaxis.get_label().get_text()
+        self.assertEqual(x, "Foo")
+        y = ax.yaxis.get_label().get_text()
+        self.assertEqual(y, "Thingness")
+
+    def test_yx_cube(self):
+        c = simple_1d()
+        c.transpose()
+        # Making the cube a vertical coordinate should change the default
+        # orientation of the plot.
+        c.coord("foo").attributes["positive"] = "up"
+        qplt.plot(c)
+        ax = qplt.plt.gca()
+        x = ax.xaxis.get_label().get_text()
+        self.assertEqual(x, "Thingness")
+        y = ax.yaxis.get_label().get_text()
+        self.assertEqual(y, "Foo")
+
+
 if __name__ == "__main__":
     tests.main()