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test_plot.py
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3387 lines (2803 loc) · 121 KB
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from __future__ import annotations
import contextlib
import inspect
import math
from collections.abc import Hashable
from copy import copy
from datetime import datetime
from typing import Any, Callable, Literal
import numpy as np
import pandas as pd
import pytest
import xarray as xr
import xarray.plot as xplt
from xarray import DataArray, Dataset
from xarray.core.utils import module_available
from xarray.plot.dataarray_plot import _infer_interval_breaks
from xarray.plot.dataset_plot import _infer_meta_data
from xarray.plot.utils import (
_assert_valid_xy,
_build_discrete_cmap,
_color_palette,
_determine_cmap_params,
_maybe_gca,
get_axis,
label_from_attrs,
)
from xarray.tests import (
assert_array_equal,
assert_equal,
assert_no_warnings,
requires_cartopy,
requires_cftime,
requires_matplotlib,
requires_seaborn,
)
# this should not be imported to test if the automatic lazy import works
has_nc_time_axis = module_available("nc_time_axis")
# import mpl and change the backend before other mpl imports
try:
import matplotlib as mpl
import matplotlib.dates
import matplotlib.pyplot as plt
import mpl_toolkits
except ImportError:
pass
try:
import cartopy
except ImportError:
pass
@contextlib.contextmanager
def figure_context(*args, **kwargs):
"""context manager which autocloses a figure (even if the test failed)"""
try:
yield None
finally:
plt.close("all")
@pytest.fixture(scope="function", autouse=True)
def test_all_figures_closed():
"""meta-test to ensure all figures are closed at the end of a test
Notes: Scope is kept to module (only invoke this function once per test
module) else tests cannot be run in parallel (locally). Disadvantage: only
catches one open figure per run. May still give a false positive if tests
are run in parallel.
"""
yield None
open_figs = len(plt.get_fignums())
if open_figs:
raise RuntimeError(
f"tests did not close all figures ({open_figs} figures open)"
)
@pytest.mark.flaky
@pytest.mark.skip(reason="maybe flaky")
def text_in_fig():
"""
Return the set of all text in the figure
"""
return {t.get_text() for t in plt.gcf().findobj(mpl.text.Text)}
def find_possible_colorbars():
# nb. this function also matches meshes from pcolormesh
return plt.gcf().findobj(mpl.collections.QuadMesh)
def substring_in_axes(substring, ax):
"""
Return True if a substring is found anywhere in an axes
"""
alltxt = {t.get_text() for t in ax.findobj(mpl.text.Text)}
for txt in alltxt:
if substring in txt:
return True
return False
def substring_not_in_axes(substring, ax):
"""
Return True if a substring is not found anywhere in an axes
"""
alltxt = {t.get_text() for t in ax.findobj(mpl.text.Text)}
check = [(substring not in txt) for txt in alltxt]
return all(check)
def property_in_axes_text(property, property_str, target_txt, ax):
"""
Return True if the specified text in an axes
has the property assigned to property_str
"""
alltxt = ax.findobj(mpl.text.Text)
check = []
for t in alltxt:
if t.get_text() == target_txt:
check.append(plt.getp(t, property) == property_str)
return all(check)
def easy_array(shape, start=0, stop=1):
"""
Make an array with desired shape using np.linspace
shape is a tuple like (2, 3)
"""
a = np.linspace(start, stop, num=math.prod(shape))
return a.reshape(shape)
def get_colorbar_label(colorbar):
if colorbar.orientation == "vertical":
return colorbar.ax.get_ylabel()
else:
return colorbar.ax.get_xlabel()
@requires_matplotlib
class PlotTestCase:
@pytest.fixture(autouse=True)
def setup(self):
yield
# Remove all matplotlib figures
plt.close("all")
def pass_in_axis(self, plotmethod, subplot_kw=None):
fig, axs = plt.subplots(ncols=2, subplot_kw=subplot_kw)
plotmethod(ax=axs[0])
assert axs[0].has_data()
@pytest.mark.slow
def imshow_called(self, plotmethod):
plotmethod()
images = plt.gca().findobj(mpl.image.AxesImage)
return len(images) > 0
def contourf_called(self, plotmethod):
plotmethod()
# Compatible with mpl before (PathCollection) and after (QuadContourSet) 3.8
def matchfunc(x):
return isinstance(
x, (mpl.collections.PathCollection, mpl.contour.QuadContourSet)
)
paths = plt.gca().findobj(matchfunc)
return len(paths) > 0
class TestPlot(PlotTestCase):
@pytest.fixture(autouse=True)
def setup_array(self) -> None:
self.darray = DataArray(easy_array((2, 3, 4)))
def test_accessor(self) -> None:
from xarray.plot.accessor import DataArrayPlotAccessor
assert DataArray.plot is DataArrayPlotAccessor
assert isinstance(self.darray.plot, DataArrayPlotAccessor)
def test_label_from_attrs(self) -> None:
da = self.darray.copy()
assert "" == label_from_attrs(da)
da.name = 0
assert "0" == label_from_attrs(da)
da.name = "a"
da.attrs["units"] = "a_units"
da.attrs["long_name"] = "a_long_name"
da.attrs["standard_name"] = "a_standard_name"
assert "a_long_name [a_units]" == label_from_attrs(da)
da.attrs.pop("long_name")
assert "a_standard_name [a_units]" == label_from_attrs(da)
da.attrs.pop("units")
assert "a_standard_name" == label_from_attrs(da)
da.attrs["units"] = "a_units"
da.attrs.pop("standard_name")
assert "a [a_units]" == label_from_attrs(da)
da.attrs.pop("units")
assert "a" == label_from_attrs(da)
# Latex strings can be longer without needing a new line:
long_latex_name = r"$Ra_s = \mathrm{mean}(\epsilon_k) / \mu M^2_\infty$"
da.attrs = dict(long_name=long_latex_name)
assert label_from_attrs(da) == long_latex_name
def test1d(self) -> None:
self.darray[:, 0, 0].plot()
with pytest.raises(ValueError, match=r"x must be one of None, 'dim_0'"):
self.darray[:, 0, 0].plot(x="dim_1")
with pytest.raises(TypeError, match=r"complex128"):
(self.darray[:, 0, 0] + 1j).plot()
def test_1d_bool(self) -> None:
xr.ones_like(self.darray[:, 0, 0], dtype=bool).plot()
def test_1d_x_y_kw(self) -> None:
z = np.arange(10)
da = DataArray(np.cos(z), dims=["z"], coords=[z], name="f")
xy: list[list[None | str]] = [[None, None], [None, "z"], ["z", None]]
f, ax = plt.subplots(3, 1)
for aa, (x, y) in enumerate(xy):
da.plot(x=x, y=y, ax=ax.flat[aa])
with pytest.raises(ValueError, match=r"Cannot specify both"):
da.plot(x="z", y="z")
error_msg = "must be one of None, 'z'"
with pytest.raises(ValueError, match=rf"x {error_msg}"):
da.plot(x="f")
with pytest.raises(ValueError, match=rf"y {error_msg}"):
da.plot(y="f")
def test_multiindex_level_as_coord(self) -> None:
da = xr.DataArray(
np.arange(5),
dims="x",
coords=dict(a=("x", np.arange(5)), b=("x", np.arange(5, 10))),
)
da = da.set_index(x=["a", "b"])
for x in ["a", "b"]:
h = da.plot(x=x)[0]
assert_array_equal(h.get_xdata(), da[x].values)
for y in ["a", "b"]:
h = da.plot(y=y)[0]
assert_array_equal(h.get_ydata(), da[y].values)
# Test for bug in GH issue #2725
def test_infer_line_data(self) -> None:
current = DataArray(
name="I",
data=np.array([5, 8]),
dims=["t"],
coords={
"t": (["t"], np.array([0.1, 0.2])),
"V": (["t"], np.array([100, 200])),
},
)
# Plot current against voltage
line = current.plot.line(x="V")[0]
assert_array_equal(line.get_xdata(), current.coords["V"].values)
# Plot current against time
line = current.plot.line()[0]
assert_array_equal(line.get_xdata(), current.coords["t"].values)
def test_line_plot_along_1d_coord(self) -> None:
# Test for bug in GH #3334
x_coord = xr.DataArray(data=[0.1, 0.2], dims=["x"])
t_coord = xr.DataArray(data=[10, 20], dims=["t"])
da = xr.DataArray(
data=np.array([[0, 1], [5, 9]]),
dims=["x", "t"],
coords={"x": x_coord, "time": t_coord},
)
line = da.plot(x="time", hue="x")[0]
assert_array_equal(line.get_xdata(), da.coords["time"].values)
line = da.plot(y="time", hue="x")[0]
assert_array_equal(line.get_ydata(), da.coords["time"].values)
def test_line_plot_wrong_hue(self) -> None:
da = xr.DataArray(
data=np.array([[0, 1], [5, 9]]),
dims=["x", "t"],
)
with pytest.raises(ValueError, match="hue must be one of"):
da.plot(x="t", hue="wrong_coord")
def test_2d_line(self) -> None:
with pytest.raises(ValueError, match=r"hue"):
self.darray[:, :, 0].plot.line()
self.darray[:, :, 0].plot.line(hue="dim_1")
self.darray[:, :, 0].plot.line(x="dim_1")
self.darray[:, :, 0].plot.line(y="dim_1")
self.darray[:, :, 0].plot.line(x="dim_0", hue="dim_1")
self.darray[:, :, 0].plot.line(y="dim_0", hue="dim_1")
with pytest.raises(ValueError, match=r"Cannot"):
self.darray[:, :, 0].plot.line(x="dim_1", y="dim_0", hue="dim_1")
def test_2d_line_accepts_legend_kw(self) -> None:
self.darray[:, :, 0].plot.line(x="dim_0", add_legend=False)
assert not plt.gca().get_legend()
plt.cla()
self.darray[:, :, 0].plot.line(x="dim_0", add_legend=True)
assert plt.gca().get_legend()
# check whether legend title is set
assert plt.gca().get_legend().get_title().get_text() == "dim_1"
def test_2d_line_accepts_x_kw(self) -> None:
self.darray[:, :, 0].plot.line(x="dim_0")
assert plt.gca().get_xlabel() == "dim_0"
plt.cla()
self.darray[:, :, 0].plot.line(x="dim_1")
assert plt.gca().get_xlabel() == "dim_1"
def test_2d_line_accepts_hue_kw(self) -> None:
self.darray[:, :, 0].plot.line(hue="dim_0")
assert plt.gca().get_legend().get_title().get_text() == "dim_0"
plt.cla()
self.darray[:, :, 0].plot.line(hue="dim_1")
assert plt.gca().get_legend().get_title().get_text() == "dim_1"
def test_2d_coords_line_plot(self) -> None:
lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4))
lon += lat / 10
lat += lon / 10
da = xr.DataArray(
np.arange(20).reshape(4, 5),
dims=["y", "x"],
coords={"lat": (("y", "x"), lat), "lon": (("y", "x"), lon)},
)
with figure_context():
hdl = da.plot.line(x="lon", hue="x")
assert len(hdl) == 5
with figure_context():
hdl = da.plot.line(x="lon", hue="y")
assert len(hdl) == 4
with pytest.raises(ValueError, match="For 2D inputs, hue must be a dimension"):
da.plot.line(x="lon", hue="lat")
def test_2d_coord_line_plot_coords_transpose_invariant(self) -> None:
# checks for bug reported in GH #3933
x = np.arange(10)
y = np.arange(20)
ds = xr.Dataset(coords={"x": x, "y": y})
for z in [ds.y + ds.x, ds.x + ds.y]:
ds = ds.assign_coords(z=z)
ds["v"] = ds.x + ds.y
ds["v"].plot.line(y="z", hue="x")
def test_2d_before_squeeze(self) -> None:
a = DataArray(easy_array((1, 5)))
a.plot()
def test2d_uniform_calls_imshow(self) -> None:
assert self.imshow_called(self.darray[:, :, 0].plot.imshow)
@pytest.mark.slow
def test2d_nonuniform_calls_contourf(self) -> None:
a = self.darray[:, :, 0]
a.coords["dim_1"] = [2, 1, 89]
assert self.contourf_called(a.plot.contourf)
def test2d_1d_2d_coordinates_contourf(self) -> None:
sz = (20, 10)
depth = easy_array(sz)
a = DataArray(
easy_array(sz),
dims=["z", "time"],
coords={"depth": (["z", "time"], depth), "time": np.linspace(0, 1, sz[1])},
)
a.plot.contourf(x="time", y="depth")
a.plot.contourf(x="depth", y="time")
def test2d_1d_2d_coordinates_pcolormesh(self) -> None:
# Test with equal coordinates to catch bug from #5097
sz = 10
y2d, x2d = np.meshgrid(np.arange(sz), np.arange(sz))
a = DataArray(
easy_array((sz, sz)),
dims=["x", "y"],
coords={"x2d": (["x", "y"], x2d), "y2d": (["x", "y"], y2d)},
)
for x, y in [
("x", "y"),
("y", "x"),
("x2d", "y"),
("y", "x2d"),
("x", "y2d"),
("y2d", "x"),
("x2d", "y2d"),
("y2d", "x2d"),
]:
p = a.plot.pcolormesh(x=x, y=y)
v = p.get_paths()[0].vertices
assert isinstance(v, np.ndarray)
# Check all vertices are different, except last vertex which should be the
# same as the first
_, unique_counts = np.unique(v[:-1], axis=0, return_counts=True)
assert np.all(unique_counts == 1)
def test_str_coordinates_pcolormesh(self) -> None:
# test for #6775
x = DataArray(
[[1, 2, 3], [4, 5, 6]],
dims=("a", "b"),
coords={"a": [1, 2], "b": ["a", "b", "c"]},
)
x.plot.pcolormesh()
x.T.plot.pcolormesh()
def test_contourf_cmap_set(self) -> None:
a = DataArray(easy_array((4, 4)), dims=["z", "time"])
cmap_expected = mpl.colormaps["viridis"]
# use copy to ensure cmap is not changed by contourf()
# Set vmin and vmax so that _build_discrete_colormap is called with
# extend='both'. extend is passed to
# mpl.colors.from_levels_and_colors(), which returns a result with
# sensible under and over values if extend='both', but not if
# extend='neither' (but if extend='neither' the under and over values
# would not be used because the data would all be within the plotted
# range)
pl = a.plot.contourf(cmap=copy(cmap_expected), vmin=0.1, vmax=0.9)
# check the set_bad color
cmap = pl.cmap
assert cmap is not None
assert_array_equal(
cmap(np.ma.masked_invalid([np.nan]))[0],
cmap_expected(np.ma.masked_invalid([np.nan]))[0],
)
# check the set_under color
assert cmap(-np.inf) == cmap_expected(-np.inf)
# check the set_over color
assert cmap(np.inf) == cmap_expected(np.inf)
def test_contourf_cmap_set_with_bad_under_over(self) -> None:
a = DataArray(easy_array((4, 4)), dims=["z", "time"])
# make a copy here because we want a local cmap that we will modify.
cmap_expected = copy(mpl.colormaps["viridis"])
cmap_expected.set_bad("w")
# check we actually changed the set_bad color
assert np.all(
cmap_expected(np.ma.masked_invalid([np.nan]))[0]
!= mpl.colormaps["viridis"](np.ma.masked_invalid([np.nan]))[0]
)
cmap_expected.set_under("r")
# check we actually changed the set_under color
assert cmap_expected(-np.inf) != mpl.colormaps["viridis"](-np.inf)
cmap_expected.set_over("g")
# check we actually changed the set_over color
assert cmap_expected(np.inf) != mpl.colormaps["viridis"](-np.inf)
# copy to ensure cmap is not changed by contourf()
pl = a.plot.contourf(cmap=copy(cmap_expected))
cmap = pl.cmap
assert cmap is not None
# check the set_bad color has been kept
assert_array_equal(
cmap(np.ma.masked_invalid([np.nan]))[0],
cmap_expected(np.ma.masked_invalid([np.nan]))[0],
)
# check the set_under color has been kept
assert cmap(-np.inf) == cmap_expected(-np.inf)
# check the set_over color has been kept
assert cmap(np.inf) == cmap_expected(np.inf)
def test3d(self) -> None:
self.darray.plot()
def test_can_pass_in_axis(self) -> None:
self.pass_in_axis(self.darray.plot)
def test__infer_interval_breaks(self) -> None:
assert_array_equal([-0.5, 0.5, 1.5], _infer_interval_breaks([0, 1]))
assert_array_equal(
[-0.5, 0.5, 5.0, 9.5, 10.5], _infer_interval_breaks([0, 1, 9, 10])
)
assert_array_equal(
pd.date_range("20000101", periods=4) - np.timedelta64(12, "h"),
_infer_interval_breaks(pd.date_range("20000101", periods=3)),
)
# make a bounded 2D array that we will center and re-infer
xref, yref = np.meshgrid(np.arange(6), np.arange(5))
cx = (xref[1:, 1:] + xref[:-1, :-1]) / 2
cy = (yref[1:, 1:] + yref[:-1, :-1]) / 2
x = _infer_interval_breaks(cx, axis=1)
x = _infer_interval_breaks(x, axis=0)
y = _infer_interval_breaks(cy, axis=1)
y = _infer_interval_breaks(y, axis=0)
np.testing.assert_allclose(xref, x)
np.testing.assert_allclose(yref, y)
# test that ValueError is raised for non-monotonic 1D inputs
with pytest.raises(ValueError):
_infer_interval_breaks(np.array([0, 2, 1]), check_monotonic=True)
def test__infer_interval_breaks_logscale(self) -> None:
"""
Check if interval breaks are defined in the logspace if scale="log"
"""
# Check for 1d arrays
x = np.logspace(-4, 3, 8)
expected_interval_breaks = 10 ** np.linspace(-4.5, 3.5, 9)
np.testing.assert_allclose(
_infer_interval_breaks(x, scale="log"), expected_interval_breaks
)
# Check for 2d arrays
x = np.logspace(-4, 3, 8)
y = np.linspace(-5, 5, 11)
x, y = np.meshgrid(x, y)
expected_interval_breaks = np.vstack([10 ** np.linspace(-4.5, 3.5, 9)] * 12)
x = _infer_interval_breaks(x, axis=1, scale="log")
x = _infer_interval_breaks(x, axis=0, scale="log")
np.testing.assert_allclose(x, expected_interval_breaks)
def test__infer_interval_breaks_logscale_invalid_coords(self) -> None:
"""
Check error is raised when passing non-positive coordinates with logscale
"""
# Check if error is raised after a zero value in the array
x = np.linspace(0, 5, 6)
with pytest.raises(ValueError):
_infer_interval_breaks(x, scale="log")
# Check if error is raised after negative values in the array
x = np.linspace(-5, 5, 11)
with pytest.raises(ValueError):
_infer_interval_breaks(x, scale="log")
def test_geo_data(self) -> None:
# Regression test for gh2250
# Realistic coordinates taken from the example dataset
lat = np.array(
[
[16.28, 18.48, 19.58, 19.54, 18.35],
[28.07, 30.52, 31.73, 31.68, 30.37],
[39.65, 42.27, 43.56, 43.51, 42.11],
[50.52, 53.22, 54.55, 54.50, 53.06],
]
)
lon = np.array(
[
[-126.13, -113.69, -100.92, -88.04, -75.29],
[-129.27, -115.62, -101.54, -87.32, -73.26],
[-133.10, -118.00, -102.31, -86.42, -70.76],
[-137.85, -120.99, -103.28, -85.28, -67.62],
]
)
data = np.sqrt(lon**2 + lat**2)
da = DataArray(
data,
dims=("y", "x"),
coords={"lon": (("y", "x"), lon), "lat": (("y", "x"), lat)},
)
da.plot(x="lon", y="lat")
ax = plt.gca()
assert ax.has_data()
da.plot(x="lat", y="lon")
ax = plt.gca()
assert ax.has_data()
def test_datetime_dimension(self) -> None:
nrow = 3
ncol = 4
time = pd.date_range("2000-01-01", periods=nrow)
a = DataArray(
easy_array((nrow, ncol)), coords=[("time", time), ("y", range(ncol))]
)
a.plot()
ax = plt.gca()
assert ax.has_data()
@pytest.mark.slow
@pytest.mark.filterwarnings("ignore:tight_layout cannot")
def test_convenient_facetgrid(self) -> None:
a = easy_array((10, 15, 4))
d = DataArray(a, dims=["y", "x", "z"])
d.coords["z"] = list("abcd")
g = d.plot(x="x", y="y", col="z", col_wrap=2, cmap="cool")
assert_array_equal(g.axs.shape, [2, 2])
for ax in g.axs.flat:
assert ax.has_data()
with pytest.raises(ValueError, match=r"[Ff]acet"):
d.plot(x="x", y="y", col="z", ax=plt.gca())
with pytest.raises(ValueError, match=r"[Ff]acet"):
d[0].plot(x="x", y="y", col="z", ax=plt.gca())
@pytest.mark.slow
def test_subplot_kws(self) -> None:
a = easy_array((10, 15, 4))
d = DataArray(a, dims=["y", "x", "z"])
d.coords["z"] = list("abcd")
g = d.plot(
x="x",
y="y",
col="z",
col_wrap=2,
cmap="cool",
subplot_kws=dict(facecolor="r"),
)
for ax in g.axs.flat:
# mpl V2
assert ax.get_facecolor()[0:3] == mpl.colors.to_rgb("r")
@pytest.mark.slow
def test_plot_size(self) -> None:
self.darray[:, 0, 0].plot(figsize=(13, 5))
assert tuple(plt.gcf().get_size_inches()) == (13, 5)
self.darray.plot(figsize=(13, 5))
assert tuple(plt.gcf().get_size_inches()) == (13, 5)
self.darray.plot(size=5)
assert plt.gcf().get_size_inches()[1] == 5
self.darray.plot(size=5, aspect=2)
assert tuple(plt.gcf().get_size_inches()) == (10, 5)
with pytest.raises(ValueError, match=r"cannot provide both"):
self.darray.plot(ax=plt.gca(), figsize=(3, 4))
with pytest.raises(ValueError, match=r"cannot provide both"):
self.darray.plot(size=5, figsize=(3, 4))
with pytest.raises(ValueError, match=r"cannot provide both"):
self.darray.plot(size=5, ax=plt.gca())
with pytest.raises(ValueError, match=r"cannot provide `aspect`"):
self.darray.plot(aspect=1)
@pytest.mark.slow
@pytest.mark.filterwarnings("ignore:tight_layout cannot")
def test_convenient_facetgrid_4d(self) -> None:
a = easy_array((10, 15, 2, 3))
d = DataArray(a, dims=["y", "x", "columns", "rows"])
g = d.plot(x="x", y="y", col="columns", row="rows")
assert_array_equal(g.axs.shape, [3, 2])
for ax in g.axs.flat:
assert ax.has_data()
with pytest.raises(ValueError, match=r"[Ff]acet"):
d.plot(x="x", y="y", col="columns", ax=plt.gca())
def test_coord_with_interval(self) -> None:
"""Test line plot with intervals."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).plot()
def test_coord_with_interval_x(self) -> None:
"""Test line plot with intervals explicitly on x axis."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).plot(x="dim_0_bins")
def test_coord_with_interval_y(self) -> None:
"""Test line plot with intervals explicitly on y axis."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).plot(y="dim_0_bins")
def test_coord_with_interval_xy(self) -> None:
"""Test line plot with intervals on both x and y axes."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).dim_0_bins.plot()
@pytest.mark.parametrize("dim", ("x", "y"))
def test_labels_with_units_with_interval(self, dim) -> None:
"""Test line plot with intervals and a units attribute."""
bins = [-1, 0, 1, 2]
arr = self.darray.groupby_bins("dim_0", bins).mean(...)
arr.dim_0_bins.attrs["units"] = "m"
(mappable,) = arr.plot(**{dim: "dim_0_bins"})
ax = mappable.figure.gca()
actual = getattr(ax, f"get_{dim}label")()
expected = "dim_0_bins_center [m]"
assert actual == expected
def test_multiplot_over_length_one_dim(self) -> None:
a = easy_array((3, 1, 1, 1))
d = DataArray(a, dims=("x", "col", "row", "hue"))
d.plot(col="col")
d.plot(row="row")
d.plot(hue="hue")
class TestPlot1D(PlotTestCase):
@pytest.fixture(autouse=True)
def setUp(self) -> None:
d = [0, 1.1, 0, 2]
self.darray = DataArray(d, coords={"period": range(len(d))}, dims="period")
self.darray.period.attrs["units"] = "s"
def test_xlabel_is_index_name(self) -> None:
self.darray.plot()
assert "period [s]" == plt.gca().get_xlabel()
def test_no_label_name_on_x_axis(self) -> None:
self.darray.plot(y="period")
assert "" == plt.gca().get_xlabel()
def test_no_label_name_on_y_axis(self) -> None:
self.darray.plot()
assert "" == plt.gca().get_ylabel()
def test_ylabel_is_data_name(self) -> None:
self.darray.name = "temperature"
self.darray.attrs["units"] = "degrees_Celsius"
self.darray.plot()
assert "temperature [degrees_Celsius]" == plt.gca().get_ylabel()
def test_xlabel_is_data_name(self) -> None:
self.darray.name = "temperature"
self.darray.attrs["units"] = "degrees_Celsius"
self.darray.plot(y="period")
assert "temperature [degrees_Celsius]" == plt.gca().get_xlabel()
def test_format_string(self) -> None:
self.darray.plot.line("ro")
def test_can_pass_in_axis(self) -> None:
self.pass_in_axis(self.darray.plot.line)
def test_nonnumeric_index(self) -> None:
a = DataArray([1, 2, 3], {"letter": ["a", "b", "c"]}, dims="letter")
a.plot.line()
def test_primitive_returned(self) -> None:
p = self.darray.plot.line()
assert isinstance(p[0], mpl.lines.Line2D)
@pytest.mark.slow
def test_plot_nans(self) -> None:
self.darray[1] = np.nan
self.darray.plot.line()
def test_dates_are_concise(self) -> None:
import matplotlib.dates as mdates
time = pd.date_range("2000-01-01", "2000-01-10")
a = DataArray(np.arange(len(time)), [("t", time)])
a.plot.line()
ax = plt.gca()
assert isinstance(ax.xaxis.get_major_locator(), mdates.AutoDateLocator)
assert isinstance(ax.xaxis.get_major_formatter(), mdates.ConciseDateFormatter)
def test_xyincrease_false_changes_axes(self) -> None:
self.darray.plot.line(xincrease=False, yincrease=False)
xlim = plt.gca().get_xlim()
ylim = plt.gca().get_ylim()
diffs = xlim[1] - xlim[0], ylim[1] - ylim[0]
assert all(x < 0 for x in diffs)
def test_slice_in_title(self) -> None:
self.darray.coords["d"] = 10.009
self.darray.plot.line()
title = plt.gca().get_title()
assert "d = 10.01" == title
def test_slice_in_title_single_item_array(self) -> None:
"""Edge case for data of shape (1, N) or (N, 1)."""
darray = self.darray.expand_dims({"d": np.array([10.009])})
darray.plot.line(x="period")
title = plt.gca().get_title()
assert "d = 10.01" == title
class TestPlotStep(PlotTestCase):
@pytest.fixture(autouse=True)
def setUp(self) -> None:
self.darray = DataArray(easy_array((2, 3, 4)))
def test_step(self) -> None:
hdl = self.darray[0, 0].plot.step()
assert "steps" in hdl[0].get_drawstyle()
@pytest.mark.parametrize("where", ["pre", "post", "mid"])
def test_step_with_where(self, where) -> None:
hdl = self.darray[0, 0].plot.step(where=where)
assert hdl[0].get_drawstyle() == f"steps-{where}"
def test_step_with_hue(self) -> None:
hdl = self.darray[0].plot.step(hue="dim_2")
assert hdl[0].get_drawstyle() == "steps-pre"
@pytest.mark.parametrize("where", ["pre", "post", "mid"])
def test_step_with_hue_and_where(self, where) -> None:
hdl = self.darray[0].plot.step(hue="dim_2", where=where)
assert hdl[0].get_drawstyle() == f"steps-{where}"
def test_drawstyle_steps(self) -> None:
hdl = self.darray[0].plot(hue="dim_2", drawstyle="steps")
assert hdl[0].get_drawstyle() == "steps"
@pytest.mark.parametrize("where", ["pre", "post", "mid"])
def test_drawstyle_steps_with_where(self, where) -> None:
hdl = self.darray[0].plot(hue="dim_2", drawstyle=f"steps-{where}")
assert hdl[0].get_drawstyle() == f"steps-{where}"
def test_coord_with_interval_step(self) -> None:
"""Test step plot with intervals."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).plot.step()
line = plt.gca().lines[0]
assert isinstance(line, mpl.lines.Line2D)
assert len(np.asarray(line.get_xdata())) == ((len(bins) - 1) * 2)
def test_coord_with_interval_step_x(self) -> None:
"""Test step plot with intervals explicitly on x axis."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).plot.step(x="dim_0_bins")
line = plt.gca().lines[0]
assert isinstance(line, mpl.lines.Line2D)
assert len(np.asarray(line.get_xdata())) == ((len(bins) - 1) * 2)
def test_coord_with_interval_step_y(self) -> None:
"""Test step plot with intervals explicitly on y axis."""
bins = [-1, 0, 1, 2]
self.darray.groupby_bins("dim_0", bins).mean(...).plot.step(y="dim_0_bins")
line = plt.gca().lines[0]
assert isinstance(line, mpl.lines.Line2D)
assert len(np.asarray(line.get_xdata())) == ((len(bins) - 1) * 2)
def test_coord_with_interval_step_x_and_y_raises_valueeerror(self) -> None:
"""Test that step plot with intervals both on x and y axes raises an error."""
arr = xr.DataArray(
[pd.Interval(0, 1), pd.Interval(1, 2)],
coords=[("x", [pd.Interval(0, 1), pd.Interval(1, 2)])],
)
with pytest.raises(TypeError, match="intervals against intervals"):
arr.plot.step()
class TestPlotHistogram(PlotTestCase):
@pytest.fixture(autouse=True)
def setUp(self) -> None:
self.darray = DataArray(easy_array((2, 3, 4)))
def test_3d_array(self) -> None:
self.darray.plot.hist()
def test_xlabel_uses_name(self) -> None:
self.darray.name = "testpoints"
self.darray.attrs["units"] = "testunits"
self.darray.plot.hist()
assert "testpoints [testunits]" == plt.gca().get_xlabel()
def test_title_is_histogram(self) -> None:
self.darray.coords["d"] = 10
self.darray.plot.hist()
assert "d = 10" == plt.gca().get_title()
def test_can_pass_in_kwargs(self) -> None:
nbins = 5
self.darray.plot.hist(bins=nbins)
assert nbins == len(plt.gca().patches)
def test_can_pass_in_axis(self) -> None:
self.pass_in_axis(self.darray.plot.hist)
def test_primitive_returned(self) -> None:
n, bins, patches = self.darray.plot.hist()
assert isinstance(n, np.ndarray)
assert isinstance(bins, np.ndarray)
assert isinstance(patches, mpl.container.BarContainer)
assert isinstance(patches[0], mpl.patches.Rectangle)
@pytest.mark.slow
def test_plot_nans(self) -> None:
self.darray[0, 0, 0] = np.nan
self.darray.plot.hist()
def test_hist_coord_with_interval(self) -> None:
(
self.darray.groupby_bins("dim_0", [-1, 0, 1, 2])
.mean(...)
.plot.hist(range=(-1, 2))
)
@requires_matplotlib
class TestDetermineCmapParams:
@pytest.fixture(autouse=True)
def setUp(self) -> None:
self.data = np.linspace(0, 1, num=100)
def test_robust(self) -> None:
cmap_params = _determine_cmap_params(self.data, robust=True)
assert cmap_params["vmin"] == np.percentile(self.data, 2)
assert cmap_params["vmax"] == np.percentile(self.data, 98)
assert cmap_params["cmap"] == "viridis"
assert cmap_params["extend"] == "both"
assert cmap_params["levels"] is None
assert cmap_params["norm"] is None
def test_center(self) -> None:
cmap_params = _determine_cmap_params(self.data, center=0.5)
assert cmap_params["vmax"] - 0.5 == 0.5 - cmap_params["vmin"]
assert cmap_params["cmap"] == "RdBu_r"
assert cmap_params["extend"] == "neither"
assert cmap_params["levels"] is None
assert cmap_params["norm"] is None
def test_cmap_sequential_option(self) -> None:
with xr.set_options(cmap_sequential="magma"):
cmap_params = _determine_cmap_params(self.data)
assert cmap_params["cmap"] == "magma"
def test_cmap_sequential_explicit_option(self) -> None:
with xr.set_options(cmap_sequential=mpl.colormaps["magma"]):
cmap_params = _determine_cmap_params(self.data)
assert cmap_params["cmap"] == mpl.colormaps["magma"]
def test_cmap_divergent_option(self) -> None:
with xr.set_options(cmap_divergent="magma"):
cmap_params = _determine_cmap_params(self.data, center=0.5)
assert cmap_params["cmap"] == "magma"
def test_nan_inf_are_ignored(self) -> None:
cmap_params1 = _determine_cmap_params(self.data)
data = self.data
data[50:55] = np.nan
data[56:60] = np.inf
cmap_params2 = _determine_cmap_params(data)
assert cmap_params1["vmin"] == cmap_params2["vmin"]
assert cmap_params1["vmax"] == cmap_params2["vmax"]
@pytest.mark.slow
def test_integer_levels(self) -> None:
data = self.data + 1
# default is to cover full data range but with no guarantee on Nlevels
for level in np.arange(2, 10, dtype=int):
cmap_params = _determine_cmap_params(data, levels=level)
assert cmap_params["vmin"] is None
assert cmap_params["vmax"] is None
assert cmap_params["norm"].vmin == cmap_params["levels"][0]
assert cmap_params["norm"].vmax == cmap_params["levels"][-1]
assert cmap_params["extend"] == "neither"
# with min max we are more strict
cmap_params = _determine_cmap_params(
data, levels=5, vmin=0, vmax=5, cmap="Blues"
)
assert cmap_params["vmin"] is None