mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-05 04:55:06 +01:00
436 lines
15 KiB
Python
436 lines
15 KiB
Python
|
""" Test cases for misc plot functions """
|
||
|
|
||
|
import numpy as np
|
||
|
from numpy import random
|
||
|
from numpy.random import randn
|
||
|
import pytest
|
||
|
|
||
|
import pandas.util._test_decorators as td
|
||
|
|
||
|
from pandas import DataFrame, Series
|
||
|
import pandas._testing as tm
|
||
|
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
|
||
|
|
||
|
import pandas.plotting as plotting
|
||
|
|
||
|
|
||
|
@td.skip_if_mpl
|
||
|
def test_import_error_message():
|
||
|
# GH-19810
|
||
|
df = DataFrame({"A": [1, 2]})
|
||
|
|
||
|
with pytest.raises(ImportError, match="matplotlib is required for plotting"):
|
||
|
df.plot()
|
||
|
|
||
|
|
||
|
def test_get_accessor_args():
|
||
|
func = plotting._core.PlotAccessor._get_call_args
|
||
|
|
||
|
msg = "Called plot accessor for type list, expected Series or DataFrame"
|
||
|
with pytest.raises(TypeError, match=msg):
|
||
|
func(backend_name="", data=[], args=[], kwargs={})
|
||
|
|
||
|
msg = "should not be called with positional arguments"
|
||
|
with pytest.raises(TypeError, match=msg):
|
||
|
func(backend_name="", data=Series(dtype=object), args=["line", None], kwargs={})
|
||
|
|
||
|
x, y, kind, kwargs = func(
|
||
|
backend_name="",
|
||
|
data=DataFrame(),
|
||
|
args=["x"],
|
||
|
kwargs={"y": "y", "kind": "bar", "grid": False},
|
||
|
)
|
||
|
assert x == "x"
|
||
|
assert y == "y"
|
||
|
assert kind == "bar"
|
||
|
assert kwargs == {"grid": False}
|
||
|
|
||
|
x, y, kind, kwargs = func(
|
||
|
backend_name="pandas.plotting._matplotlib",
|
||
|
data=Series(dtype=object),
|
||
|
args=[],
|
||
|
kwargs={},
|
||
|
)
|
||
|
assert x is None
|
||
|
assert y is None
|
||
|
assert kind == "line"
|
||
|
assert len(kwargs) == 24
|
||
|
|
||
|
|
||
|
@td.skip_if_no_mpl
|
||
|
class TestSeriesPlots(TestPlotBase):
|
||
|
def setup_method(self, method):
|
||
|
TestPlotBase.setup_method(self, method)
|
||
|
import matplotlib as mpl
|
||
|
|
||
|
mpl.rcdefaults()
|
||
|
|
||
|
self.ts = tm.makeTimeSeries()
|
||
|
self.ts.name = "ts"
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_autocorrelation_plot(self):
|
||
|
from pandas.plotting import autocorrelation_plot
|
||
|
|
||
|
_check_plot_works(autocorrelation_plot, series=self.ts)
|
||
|
_check_plot_works(autocorrelation_plot, series=self.ts.values)
|
||
|
|
||
|
ax = autocorrelation_plot(self.ts, label="Test")
|
||
|
self._check_legend_labels(ax, labels=["Test"])
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_lag_plot(self):
|
||
|
from pandas.plotting import lag_plot
|
||
|
|
||
|
_check_plot_works(lag_plot, series=self.ts)
|
||
|
_check_plot_works(lag_plot, series=self.ts, lag=5)
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_bootstrap_plot(self):
|
||
|
from pandas.plotting import bootstrap_plot
|
||
|
|
||
|
_check_plot_works(bootstrap_plot, series=self.ts, size=10)
|
||
|
|
||
|
|
||
|
@td.skip_if_no_mpl
|
||
|
class TestDataFramePlots(TestPlotBase):
|
||
|
@td.skip_if_no_scipy
|
||
|
def test_scatter_matrix_axis(self):
|
||
|
from pandas.plotting._matplotlib.compat import _mpl_ge_3_0_0
|
||
|
|
||
|
scatter_matrix = plotting.scatter_matrix
|
||
|
|
||
|
with tm.RNGContext(42):
|
||
|
df = DataFrame(randn(100, 3))
|
||
|
|
||
|
# we are plotting multiples on a sub-plot
|
||
|
with tm.assert_produces_warning(
|
||
|
UserWarning, raise_on_extra_warnings=_mpl_ge_3_0_0()
|
||
|
):
|
||
|
axes = _check_plot_works(
|
||
|
scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1
|
||
|
)
|
||
|
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
|
||
|
|
||
|
# GH 5662
|
||
|
expected = ["-2", "0", "2"]
|
||
|
self._check_text_labels(axes0_labels, expected)
|
||
|
self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
|
||
|
|
||
|
df[0] = (df[0] - 2) / 3
|
||
|
|
||
|
# we are plotting multiples on a sub-plot
|
||
|
with tm.assert_produces_warning(UserWarning):
|
||
|
axes = _check_plot_works(
|
||
|
scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1
|
||
|
)
|
||
|
axes0_labels = axes[0][0].yaxis.get_majorticklabels()
|
||
|
expected = ["-1.0", "-0.5", "0.0"]
|
||
|
self._check_text_labels(axes0_labels, expected)
|
||
|
self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_andrews_curves(self, iris):
|
||
|
from matplotlib import cm
|
||
|
|
||
|
from pandas.plotting import andrews_curves
|
||
|
|
||
|
df = iris
|
||
|
|
||
|
_check_plot_works(andrews_curves, frame=df, class_column="Name")
|
||
|
|
||
|
rgba = ("#556270", "#4ECDC4", "#C7F464")
|
||
|
ax = _check_plot_works(
|
||
|
andrews_curves, frame=df, class_column="Name", color=rgba
|
||
|
)
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
cnames = ["dodgerblue", "aquamarine", "seagreen"]
|
||
|
ax = _check_plot_works(
|
||
|
andrews_curves, frame=df, class_column="Name", color=cnames
|
||
|
)
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
ax = _check_plot_works(
|
||
|
andrews_curves, frame=df, class_column="Name", colormap=cm.jet
|
||
|
)
|
||
|
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
length = 10
|
||
|
df = DataFrame(
|
||
|
{
|
||
|
"A": random.rand(length),
|
||
|
"B": random.rand(length),
|
||
|
"C": random.rand(length),
|
||
|
"Name": ["A"] * length,
|
||
|
}
|
||
|
)
|
||
|
|
||
|
_check_plot_works(andrews_curves, frame=df, class_column="Name")
|
||
|
|
||
|
rgba = ("#556270", "#4ECDC4", "#C7F464")
|
||
|
ax = _check_plot_works(
|
||
|
andrews_curves, frame=df, class_column="Name", color=rgba
|
||
|
)
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
cnames = ["dodgerblue", "aquamarine", "seagreen"]
|
||
|
ax = _check_plot_works(
|
||
|
andrews_curves, frame=df, class_column="Name", color=cnames
|
||
|
)
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
ax = _check_plot_works(
|
||
|
andrews_curves, frame=df, class_column="Name", colormap=cm.jet
|
||
|
)
|
||
|
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
colors = ["b", "g", "r"]
|
||
|
df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
|
||
|
ax = andrews_curves(df, "Name", color=colors)
|
||
|
handles, labels = ax.get_legend_handles_labels()
|
||
|
self._check_colors(handles, linecolors=colors)
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_parallel_coordinates(self, iris):
|
||
|
from matplotlib import cm
|
||
|
|
||
|
from pandas.plotting import parallel_coordinates
|
||
|
|
||
|
df = iris
|
||
|
|
||
|
ax = _check_plot_works(parallel_coordinates, frame=df, class_column="Name")
|
||
|
nlines = len(ax.get_lines())
|
||
|
nxticks = len(ax.xaxis.get_ticklabels())
|
||
|
|
||
|
rgba = ("#556270", "#4ECDC4", "#C7F464")
|
||
|
ax = _check_plot_works(
|
||
|
parallel_coordinates, frame=df, class_column="Name", color=rgba
|
||
|
)
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
cnames = ["dodgerblue", "aquamarine", "seagreen"]
|
||
|
ax = _check_plot_works(
|
||
|
parallel_coordinates, frame=df, class_column="Name", color=cnames
|
||
|
)
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
ax = _check_plot_works(
|
||
|
parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet
|
||
|
)
|
||
|
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
|
||
|
self._check_colors(
|
||
|
ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
|
||
|
)
|
||
|
|
||
|
ax = _check_plot_works(
|
||
|
parallel_coordinates, frame=df, class_column="Name", axvlines=False
|
||
|
)
|
||
|
assert len(ax.get_lines()) == (nlines - nxticks)
|
||
|
|
||
|
colors = ["b", "g", "r"]
|
||
|
df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
|
||
|
ax = parallel_coordinates(df, "Name", color=colors)
|
||
|
handles, labels = ax.get_legend_handles_labels()
|
||
|
self._check_colors(handles, linecolors=colors)
|
||
|
|
||
|
# not sure if this is indicative of a problem
|
||
|
@pytest.mark.filterwarnings("ignore:Attempting to set:UserWarning")
|
||
|
def test_parallel_coordinates_with_sorted_labels(self):
|
||
|
""" For #15908 """
|
||
|
from pandas.plotting import parallel_coordinates
|
||
|
|
||
|
df = DataFrame(
|
||
|
{
|
||
|
"feat": list(range(30)),
|
||
|
"class": [2 for _ in range(10)]
|
||
|
+ [3 for _ in range(10)]
|
||
|
+ [1 for _ in range(10)],
|
||
|
}
|
||
|
)
|
||
|
ax = parallel_coordinates(df, "class", sort_labels=True)
|
||
|
polylines, labels = ax.get_legend_handles_labels()
|
||
|
color_label_tuples = zip(
|
||
|
[polyline.get_color() for polyline in polylines], labels
|
||
|
)
|
||
|
ordered_color_label_tuples = sorted(color_label_tuples, key=lambda x: x[1])
|
||
|
prev_next_tupels = zip(
|
||
|
list(ordered_color_label_tuples[0:-1]), list(ordered_color_label_tuples[1:])
|
||
|
)
|
||
|
for prev, nxt in prev_next_tupels:
|
||
|
# labels and colors are ordered strictly increasing
|
||
|
assert prev[1] < nxt[1] and prev[0] < nxt[0]
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_radviz(self, iris):
|
||
|
from matplotlib import cm
|
||
|
|
||
|
from pandas.plotting import radviz
|
||
|
|
||
|
df = iris
|
||
|
_check_plot_works(radviz, frame=df, class_column="Name")
|
||
|
|
||
|
rgba = ("#556270", "#4ECDC4", "#C7F464")
|
||
|
ax = _check_plot_works(radviz, frame=df, class_column="Name", color=rgba)
|
||
|
# skip Circle drawn as ticks
|
||
|
patches = [p for p in ax.patches[:20] if p.get_label() != ""]
|
||
|
self._check_colors(patches[:10], facecolors=rgba, mapping=df["Name"][:10])
|
||
|
|
||
|
cnames = ["dodgerblue", "aquamarine", "seagreen"]
|
||
|
_check_plot_works(radviz, frame=df, class_column="Name", color=cnames)
|
||
|
patches = [p for p in ax.patches[:20] if p.get_label() != ""]
|
||
|
self._check_colors(patches, facecolors=cnames, mapping=df["Name"][:10])
|
||
|
|
||
|
_check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet)
|
||
|
cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
|
||
|
patches = [p for p in ax.patches[:20] if p.get_label() != ""]
|
||
|
self._check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10])
|
||
|
|
||
|
colors = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]]
|
||
|
df = DataFrame(
|
||
|
{"A": [1, 2, 3], "B": [2, 1, 3], "C": [3, 2, 1], "Name": ["b", "g", "r"]}
|
||
|
)
|
||
|
ax = radviz(df, "Name", color=colors)
|
||
|
handles, labels = ax.get_legend_handles_labels()
|
||
|
self._check_colors(handles, facecolors=colors)
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_subplot_titles(self, iris):
|
||
|
df = iris.drop("Name", axis=1).head()
|
||
|
# Use the column names as the subplot titles
|
||
|
title = list(df.columns)
|
||
|
|
||
|
# Case len(title) == len(df)
|
||
|
plot = df.plot(subplots=True, title=title)
|
||
|
assert [p.get_title() for p in plot] == title
|
||
|
|
||
|
# Case len(title) > len(df)
|
||
|
msg = (
|
||
|
"The length of `title` must equal the number of columns if "
|
||
|
"using `title` of type `list` and `subplots=True`"
|
||
|
)
|
||
|
with pytest.raises(ValueError, match=msg):
|
||
|
df.plot(subplots=True, title=title + ["kittens > puppies"])
|
||
|
|
||
|
# Case len(title) < len(df)
|
||
|
with pytest.raises(ValueError, match=msg):
|
||
|
df.plot(subplots=True, title=title[:2])
|
||
|
|
||
|
# Case subplots=False and title is of type list
|
||
|
msg = (
|
||
|
"Using `title` of type `list` is not supported unless "
|
||
|
"`subplots=True` is passed"
|
||
|
)
|
||
|
with pytest.raises(ValueError, match=msg):
|
||
|
df.plot(subplots=False, title=title)
|
||
|
|
||
|
# Case df with 3 numeric columns but layout of (2,2)
|
||
|
plot = df.drop("SepalWidth", axis=1).plot(
|
||
|
subplots=True, layout=(2, 2), title=title[:-1]
|
||
|
)
|
||
|
title_list = [ax.get_title() for sublist in plot for ax in sublist]
|
||
|
assert title_list == title[:3] + [""]
|
||
|
|
||
|
def test_get_standard_colors_random_seed(self):
|
||
|
# GH17525
|
||
|
df = DataFrame(np.zeros((10, 10)))
|
||
|
|
||
|
# Make sure that the random seed isn't reset by _get_standard_colors
|
||
|
plotting.parallel_coordinates(df, 0)
|
||
|
rand1 = random.random()
|
||
|
plotting.parallel_coordinates(df, 0)
|
||
|
rand2 = random.random()
|
||
|
assert rand1 != rand2
|
||
|
|
||
|
# Make sure it produces the same colors every time it's called
|
||
|
from pandas.plotting._matplotlib.style import _get_standard_colors
|
||
|
|
||
|
color1 = _get_standard_colors(1, color_type="random")
|
||
|
color2 = _get_standard_colors(1, color_type="random")
|
||
|
assert color1 == color2
|
||
|
|
||
|
def test_get_standard_colors_default_num_colors(self):
|
||
|
from pandas.plotting._matplotlib.style import _get_standard_colors
|
||
|
|
||
|
# Make sure the default color_types returns the specified amount
|
||
|
color1 = _get_standard_colors(1, color_type="default")
|
||
|
color2 = _get_standard_colors(9, color_type="default")
|
||
|
color3 = _get_standard_colors(20, color_type="default")
|
||
|
assert len(color1) == 1
|
||
|
assert len(color2) == 9
|
||
|
assert len(color3) == 20
|
||
|
|
||
|
def test_plot_single_color(self):
|
||
|
# Example from #20585. All 3 bars should have the same color
|
||
|
df = DataFrame(
|
||
|
{
|
||
|
"account-start": ["2017-02-03", "2017-03-03", "2017-01-01"],
|
||
|
"client": ["Alice Anders", "Bob Baker", "Charlie Chaplin"],
|
||
|
"balance": [-1432.32, 10.43, 30000.00],
|
||
|
"db-id": [1234, 2424, 251],
|
||
|
"proxy-id": [525, 1525, 2542],
|
||
|
"rank": [52, 525, 32],
|
||
|
}
|
||
|
)
|
||
|
ax = df.client.value_counts().plot.bar()
|
||
|
colors = [rect.get_facecolor() for rect in ax.get_children()[0:3]]
|
||
|
assert all(color == colors[0] for color in colors)
|
||
|
|
||
|
def test_get_standard_colors_no_appending(self):
|
||
|
# GH20726
|
||
|
|
||
|
# Make sure not to add more colors so that matplotlib can cycle
|
||
|
# correctly.
|
||
|
from matplotlib import cm
|
||
|
|
||
|
from pandas.plotting._matplotlib.style import _get_standard_colors
|
||
|
|
||
|
color_before = cm.gnuplot(range(5))
|
||
|
color_after = _get_standard_colors(1, color=color_before)
|
||
|
assert len(color_after) == len(color_before)
|
||
|
|
||
|
df = DataFrame(np.random.randn(48, 4), columns=list("ABCD"))
|
||
|
|
||
|
color_list = cm.gnuplot(np.linspace(0, 1, 16))
|
||
|
p = df.A.plot.bar(figsize=(16, 7), color=color_list)
|
||
|
assert p.patches[1].get_facecolor() == p.patches[17].get_facecolor()
|
||
|
|
||
|
@pytest.mark.slow
|
||
|
def test_dictionary_color(self):
|
||
|
# issue-8193
|
||
|
# Test plot color dictionary format
|
||
|
data_files = ["a", "b"]
|
||
|
|
||
|
expected = [(0.5, 0.24, 0.6), (0.3, 0.7, 0.7)]
|
||
|
|
||
|
df1 = DataFrame(np.random.rand(2, 2), columns=data_files)
|
||
|
dic_color = {"b": (0.3, 0.7, 0.7), "a": (0.5, 0.24, 0.6)}
|
||
|
|
||
|
# Bar color test
|
||
|
ax = df1.plot(kind="bar", color=dic_color)
|
||
|
colors = [rect.get_facecolor()[0:-1] for rect in ax.get_children()[0:3:2]]
|
||
|
assert all(color == expected[index] for index, color in enumerate(colors))
|
||
|
|
||
|
# Line color test
|
||
|
ax = df1.plot(kind="line", color=dic_color)
|
||
|
colors = [rect.get_color() for rect in ax.get_lines()[0:2]]
|
||
|
assert all(color == expected[index] for index, color in enumerate(colors))
|