xarray/xarray/tests/test_plot.py at main · bhemmer/xarray · GitHub

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import contextlib

import inspect

from copy import copy

from datetime import datetime

from typing import Any, Dict, Union

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.plot.dataset_plot import _infer_meta_data

from xarray.plot.plot import _infer_interval_breaks

from xarray.plot.utils import (

_build_discrete_cmap,

_color_palette,

_determine_cmap_params,

_maybe_gca,

get_axis,

label_from_attrs,

)

from . import (

assert_array_equal,

assert_equal,

has_nc_time_axis,

requires_cartopy,

requires_cftime,

requires_matplotlib,

requires_nc_time_axis,

requires_seaborn,

)

# import mpl and change the backend before other mpl imports

try:

import matplotlib as mpl

import matplotlib.pyplot as plt

import mpl_toolkits # type: ignore

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 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=np.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, axes = plt.subplots(ncols=2, subplot_kw=subplot_kw)

plotmethod(ax=axes[0])

assert axes[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()

paths = plt.gca().findobj(mpl.collections.PathCollection)

return len(paths) > 0

class TestPlot(PlotTestCase):

@pytest.fixture(autouse=True)

def setup_array(self):

self.darray = DataArray(easy_array((2, 3, 4)))

def test_accessor(self):

from ..plot.plot import _PlotMethods

assert DataArray.plot is _PlotMethods

assert isinstance(self.darray.plot, _PlotMethods)

def test_label_from_attrs(self):

da = self.darray.copy()

assert "" == 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):

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):

xr.ones_like(self.darray[:, 0, 0], dtype=bool).plot()

def test_1d_x_y_kw(self):

z = np.arange(10)

da = DataArray(np.cos(z), dims=["z"], coords=[z], name="f")

xy = [[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):

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):

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):

# 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):

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):

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):

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):

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):

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):

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):

# 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):

a = DataArray(easy_array((1, 5)))

a.plot()

def test2d_uniform_calls_imshow(self):

assert self.imshow_called(self.darray[:, :, 0].plot.imshow)

@pytest.mark.slow

def test2d_nonuniform_calls_contourf(self):

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):

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):

# 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

# 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_contourf_cmap_set(self):

a = DataArray(easy_array((4, 4)), dims=["z", "time"])

cmap = mpl.cm.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), vmin=0.1, vmax=0.9)

# check the set_bad color

assert_array_equal(

pl.cmap(np.ma.masked_invalid([np.nan]))[0],

cmap(np.ma.masked_invalid([np.nan]))[0],

)

# check the set_under color

assert pl.cmap(-np.inf) == cmap(-np.inf)

# check the set_over color

assert pl.cmap(np.inf) == cmap(np.inf)

def test_contourf_cmap_set_with_bad_under_over(self):

a = DataArray(easy_array((4, 4)), dims=["z", "time"])

# make a copy here because we want a local cmap that we will modify.

cmap = copy(mpl.cm.viridis)

cmap.set_bad("w")

# check we actually changed the set_bad color

assert np.all(

cmap(np.ma.masked_invalid([np.nan]))[0]

!= mpl.cm.viridis(np.ma.masked_invalid([np.nan]))[0]

)

cmap.set_under("r")

# check we actually changed the set_under color

assert cmap(-np.inf) != mpl.cm.viridis(-np.inf)

cmap.set_over("g")

# check we actually changed the set_over color

assert cmap(np.inf) != mpl.cm.viridis(-np.inf)

# copy to ensure cmap is not changed by contourf()

pl = a.plot.contourf(cmap=copy(cmap))

# check the set_bad color has been kept

assert_array_equal(

pl.cmap(np.ma.masked_invalid([np.nan]))[0],

cmap(np.ma.masked_invalid([np.nan]))[0],

)

# check the set_under color has been kept

assert pl.cmap(-np.inf) == cmap(-np.inf)

# check the set_over color has been kept

assert pl.cmap(np.inf) == cmap(np.inf)

def test3d(self):

self.darray.plot()

def test_can_pass_in_axis(self):

self.pass_in_axis(self.darray.plot)

def test__infer_interval_breaks(self):

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):

"""

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):

"""

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 nagative 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):

# 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):

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):

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.axes.shape, [2, 2])

for ax in g.axes.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):

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.axes.flat:

# mpl V2

assert ax.get_facecolor()[0:3] == mpl.colors.to_rgb("r")

@pytest.mark.slow

def test_plot_size(self):

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):

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.axes.shape, [3, 2])

for ax in g.axes.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):

"""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):

"""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):

"""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):

"""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):

"""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

class TestPlot1D(PlotTestCase):

@pytest.fixture(autouse=True)

def setUp(self):

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):

self.darray.plot()

assert "period [s]" == plt.gca().get_xlabel()

def test_no_label_name_on_x_axis(self):

self.darray.plot(y="period")

assert "" == plt.gca().get_xlabel()

def test_no_label_name_on_y_axis(self):

self.darray.plot()

assert "" == plt.gca().get_ylabel()

def test_ylabel_is_data_name(self):

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):

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):

self.darray.plot.line("ro")

def test_can_pass_in_axis(self):

self.pass_in_axis(self.darray.plot.line)

def test_nonnumeric_index(self):

a = DataArray([1, 2, 3], {"letter": ["a", "b", "c"]}, dims="letter")

a.plot.line()

def test_primitive_returned(self):

p = self.darray.plot.line()

assert isinstance(p[0], mpl.lines.Line2D)

@pytest.mark.slow

def test_plot_nans(self):

self.darray[1] = np.nan

self.darray.plot.line()

def test_x_ticks_are_rotated_for_time(self):

time = pd.date_range("2000-01-01", "2000-01-10")

a = DataArray(np.arange(len(time)), [("t", time)])

a.plot.line()

rotation = plt.gca().get_xticklabels()[0].get_rotation()

assert rotation != 0

def test_xyincrease_false_changes_axes(self):

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):

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):

"""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):

self.darray = DataArray(easy_array((2, 3, 4)))

def test_step(self):

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):

hdl = self.darray[0, 0].plot.step(where=where)

assert hdl[0].get_drawstyle() == f"steps-{where}"

def test_coord_with_interval_step(self):

"""Test step plot with intervals."""

bins = [-1, 0, 1, 2]

self.darray.groupby_bins("dim_0", bins).mean(...).plot.step()

assert len(plt.gca().lines[0].get_xdata()) == ((len(bins) - 1) * 2)

def test_coord_with_interval_step_x(self):

"""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")

assert len(plt.gca().lines[0].get_xdata()) == ((len(bins) - 1) * 2)

def test_coord_with_interval_step_y(self):

"""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")

assert len(plt.gca().lines[0].get_xdata()) == ((len(bins) - 1) * 2)

class TestPlotHistogram(PlotTestCase):

@pytest.fixture(autouse=True)

def setUp(self):

self.darray = DataArray(easy_array((2, 3, 4)))

def test_3d_array(self):

self.darray.plot.hist()

def test_xlabel_uses_name(self):

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):

self.darray.coords["d"] = 10

self.darray.plot.hist()

assert "d = 10" == plt.gca().get_title()

def test_can_pass_in_kwargs(self):

nbins = 5

self.darray.plot.hist(bins=nbins)

assert nbins == len(plt.gca().patches)

def test_can_pass_in_axis(self):

self.pass_in_axis(self.darray.plot.hist)

def test_primitive_returned(self):

h = self.darray.plot.hist()

assert isinstance(h[-1][0], mpl.patches.Rectangle)

@pytest.mark.slow

def test_plot_nans(self):

self.darray[0, 0, 0] = np.nan

self.darray.plot.hist()

def test_hist_coord_with_interval(self):

(

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):

self.data = np.linspace(0, 1, num=100)

def test_robust(self):

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):

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):

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):

with xr.set_options(cmap_sequential=mpl.cm.magma):

cmap_params = _determine_cmap_params(self.data)

assert cmap_params["cmap"] == mpl.cm.magma

def test_cmap_divergent_option(self):

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):

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):

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

assert cmap_params["vmax"] is None

assert cmap_params["norm"].vmin == 0

assert cmap_params["norm"].vmax == 5

assert cmap_params["norm"].vmin == cmap_params["levels"][0]

assert cmap_params["norm"].vmax == cmap_params["levels"][-1]

assert cmap_params["cmap"].name == "Blues"

assert cmap_params["extend"] == "neither"

assert cmap_params["cmap"].N == 4

assert cmap_params["norm"].N == 5

cmap_params = _determine_cmap_params(data, levels=5, vmin=0.5, vmax=1.5)

assert cmap_params["cmap"].name == "viridis"

assert cmap_params["extend"] == "max"

cmap_params = _determine_cmap_params(data, levels=5, vmin=1.5)

assert cmap_params["cmap"].name == "viridis"

assert cmap_params["extend"] == "min"

cmap_params = _determine_cmap_params(data, levels=5, vmin=1.3, vmax=1.5)

assert cmap_params["cmap"].name == "viridis"

assert cmap_params["extend"] == "both"

def test_list_levels(self):

data = self.data + 1

orig_levels = [0, 1, 2, 3, 4, 5]

# vmin and vmax should be ignored if levels are explicitly provided

cmap_params = _determine_cmap_params(data, levels=orig_levels, vmin=0, vmax=3)

assert cmap_params["vmin"] is None

assert cmap_params["vmax"] is None

assert cmap_params["norm"].vmin == 0

assert cmap_params["norm"].vmax == 5

assert cmap_params["cmap"].N == 5

assert cmap_params["norm"].N == 6

for wrap_levels in [list, np.array, pd.Index, DataArray]:

cmap_params = _determine_cmap_params(data, levels=wrap_levels(orig_levels))

assert_array_equal(cmap_params["levels"], orig_levels)

def test_divergentcontrol(self):

neg = self.data - 0.1

pos = self.data

# Default with positive data will be a normal cmap

cmap_params = _determine_cmap_params(pos)

assert cmap_params["vmin"] == 0

assert cmap_params["vmax"] == 1

assert cmap_params["cmap"] == "viridis"

# Default with negative data will be a divergent cmap

cmap_params = _determine_cmap_params(neg)

assert cmap_params["vmin"] == -0.9

assert cmap_params["vmax"] == 0.9

assert cmap_params["cmap"] == "RdBu_r"

# Setting vmin or vmax should prevent this only if center is false

cmap_params = _determine_cmap_params(neg, vmin=-0.1, center=False)

assert cmap_params["vmin"] == -0.1

assert cmap_params["vmax"] == 0.9

assert cmap_params["cmap"] == "viridis"

cmap_params = _determine_cmap_params(neg, vmax=0.5, center=False)

assert cmap_params["vmin"] == -0.1

assert cmap_params["vmax"] == 0.5

assert cmap_params["cmap"] == "viridis"

# Setting center=False too

cmap_params = _determine_cmap_params(neg, center=False)

assert cmap_params["vmin"] == -0.1

assert cmap_params["vmax"] == 0.9

assert cmap_params["cmap"] == "viridis"

# However, I should still be able to set center and have a div cmap

cmap_params = _determine_cmap_params(neg, center=0)

assert cmap_params["vmin"] == -0.9

assert cmap_params["vmax"] == 0.9

assert cmap_params["cmap"] == "RdBu_r"

# Setting vmin or vmax alone will force symmetric bounds around center

cmap_params = _determine_cmap_params(neg, vmin=-0.1)

assert cmap_params["vmin"] == -0.1

assert cmap_params["vmax"] == 0.1

assert cmap_params["cmap"] == "RdBu_r"

cmap_params = _determine_cmap_params(neg, vmax=0.5)

assert cmap_params["vmin"] == -0.5

assert cmap_params["vmax"] == 0.5

assert cmap_params["cmap"] == "RdBu_r"

cmap_params = _determine_cmap_params(neg, vmax=0.6, center=0.1)

assert cmap_params["vmin"] == -0.4

assert cmap_params["vmax"] == 0.6

assert cmap_params["cmap"] == "RdBu_r"

# But this is only true if vmin or vmax are negative

cmap_params = _determine_cmap_params(pos, vmin=-0.1)

assert cmap_params["vmin"] == -0.1

View remainder of file in raw view

xarray/xarray/tests/test_plot.py at main · bhemmer/xarray · GitHub

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