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Put cube data on the x axis if plotting just a cube against a vertical or y coordinate #3906

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Merged
rcomer merged 7 commits into from
Dec 16, 2020
Merged

Put cube data on the x axis if plotting just a cube against a vertical or y coordinate #3906

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f4ce7fb
Set 1D plots to put the coordinate on the y-axis where appropriate.
trexfeathers Oct 14, 2020
a1c329e
New target image-hashes for 1D y-axis-coordinate plots.
trexfeathers Oct 14, 2020
c6136d3
Whatsnew entry for 1D y-axis-coordinate plots.
trexfeathers Oct 14, 2020
72f381e
Remove duplicate comment about flipping plot axes.
trexfeathers Dec 14, 2020
54f952a
Use simple_1d stock cube for TestAxisLabels instead of simple_2d.
trexfeathers Dec 15, 2020
808d1af
Only attempt 1D vertical axis swap if coord can be identified.
trexfeathers Dec 16, 2020
fa41a55
Simplify usages of plot() to take advantage of auto 1D vertical axis …
trexfeathers Dec 16, 2020
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7 changes: 2 additions & 5 deletions docs/iris/gallery_code/oceanography/plot_atlantic_profiles.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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,
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13 changes: 7 additions & 6 deletions docs/iris/src/userguide/regridding_plots/interpolate_column.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
import iris
import iris.quickplot as qplt
import iris.analysis
import matplotlib.pyplot as plt
Expand All @@ -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,
Expand All @@ -27,7 +31,6 @@
# Plot the black markers for the original data.
qplt.plot(
column,
column.coord("altitude"),
marker="o",
color="black",
linestyle="",
Expand All @@ -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,
Expand All @@ -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="",
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8 changes: 5 additions & 3 deletions lib/iris/plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -671,9 +671,11 @@ def _get_plot_objects(args):
# 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 iris.util.guess_coord_axis(
u_object
) in ["Y", "Z"]:
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

Expand Down