Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
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436 lines
15 KiB
436 lines
15 KiB
4 years ago
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""" Test cases for misc plot functions """
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import numpy as np
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from numpy import random
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from numpy.random import randn
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import pytest
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import pandas.util._test_decorators as td
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from pandas import DataFrame, Series
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import pandas._testing as tm
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from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
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import pandas.plotting as plotting
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@td.skip_if_mpl
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def test_import_error_message():
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# GH-19810
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df = DataFrame({"A": [1, 2]})
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with pytest.raises(ImportError, match="matplotlib is required for plotting"):
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df.plot()
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def test_get_accessor_args():
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func = plotting._core.PlotAccessor._get_call_args
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msg = "Called plot accessor for type list, expected Series or DataFrame"
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with pytest.raises(TypeError, match=msg):
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func(backend_name="", data=[], args=[], kwargs={})
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msg = "should not be called with positional arguments"
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with pytest.raises(TypeError, match=msg):
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func(backend_name="", data=Series(dtype=object), args=["line", None], kwargs={})
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x, y, kind, kwargs = func(
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backend_name="",
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data=DataFrame(),
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args=["x"],
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kwargs={"y": "y", "kind": "bar", "grid": False},
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)
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assert x == "x"
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assert y == "y"
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assert kind == "bar"
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assert kwargs == {"grid": False}
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x, y, kind, kwargs = func(
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backend_name="pandas.plotting._matplotlib",
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data=Series(dtype=object),
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args=[],
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kwargs={},
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)
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assert x is None
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assert y is None
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assert kind == "line"
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assert len(kwargs) == 24
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@td.skip_if_no_mpl
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class TestSeriesPlots(TestPlotBase):
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def setup_method(self, method):
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TestPlotBase.setup_method(self, method)
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import matplotlib as mpl
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mpl.rcdefaults()
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self.ts = tm.makeTimeSeries()
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self.ts.name = "ts"
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@pytest.mark.slow
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def test_autocorrelation_plot(self):
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from pandas.plotting import autocorrelation_plot
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_check_plot_works(autocorrelation_plot, series=self.ts)
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_check_plot_works(autocorrelation_plot, series=self.ts.values)
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ax = autocorrelation_plot(self.ts, label="Test")
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self._check_legend_labels(ax, labels=["Test"])
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@pytest.mark.slow
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def test_lag_plot(self):
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from pandas.plotting import lag_plot
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_check_plot_works(lag_plot, series=self.ts)
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_check_plot_works(lag_plot, series=self.ts, lag=5)
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@pytest.mark.slow
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def test_bootstrap_plot(self):
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from pandas.plotting import bootstrap_plot
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_check_plot_works(bootstrap_plot, series=self.ts, size=10)
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@td.skip_if_no_mpl
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class TestDataFramePlots(TestPlotBase):
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@td.skip_if_no_scipy
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def test_scatter_matrix_axis(self):
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from pandas.plotting._matplotlib.compat import _mpl_ge_3_0_0
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scatter_matrix = plotting.scatter_matrix
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with tm.RNGContext(42):
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df = DataFrame(randn(100, 3))
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# we are plotting multiples on a sub-plot
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with tm.assert_produces_warning(
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UserWarning, raise_on_extra_warnings=_mpl_ge_3_0_0()
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):
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axes = _check_plot_works(
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scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1
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)
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axes0_labels = axes[0][0].yaxis.get_majorticklabels()
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# GH 5662
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expected = ["-2", "0", "2"]
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self._check_text_labels(axes0_labels, expected)
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self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
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df[0] = (df[0] - 2) / 3
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# we are plotting multiples on a sub-plot
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with tm.assert_produces_warning(UserWarning):
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axes = _check_plot_works(
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scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1
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)
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axes0_labels = axes[0][0].yaxis.get_majorticklabels()
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expected = ["-1.0", "-0.5", "0.0"]
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self._check_text_labels(axes0_labels, expected)
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self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
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@pytest.mark.slow
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def test_andrews_curves(self, iris):
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from matplotlib import cm
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from pandas.plotting import andrews_curves
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df = iris
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_check_plot_works(andrews_curves, frame=df, class_column="Name")
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rgba = ("#556270", "#4ECDC4", "#C7F464")
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ax = _check_plot_works(
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andrews_curves, frame=df, class_column="Name", color=rgba
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)
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self._check_colors(
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ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
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)
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cnames = ["dodgerblue", "aquamarine", "seagreen"]
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ax = _check_plot_works(
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andrews_curves, frame=df, class_column="Name", color=cnames
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)
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self._check_colors(
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ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
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)
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ax = _check_plot_works(
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andrews_curves, frame=df, class_column="Name", colormap=cm.jet
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)
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cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
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self._check_colors(
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ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
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)
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length = 10
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df = DataFrame(
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{
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"A": random.rand(length),
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"B": random.rand(length),
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"C": random.rand(length),
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"Name": ["A"] * length,
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}
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)
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_check_plot_works(andrews_curves, frame=df, class_column="Name")
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rgba = ("#556270", "#4ECDC4", "#C7F464")
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ax = _check_plot_works(
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andrews_curves, frame=df, class_column="Name", color=rgba
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)
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self._check_colors(
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ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
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)
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cnames = ["dodgerblue", "aquamarine", "seagreen"]
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ax = _check_plot_works(
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andrews_curves, frame=df, class_column="Name", color=cnames
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)
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self._check_colors(
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ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
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)
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ax = _check_plot_works(
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andrews_curves, frame=df, class_column="Name", colormap=cm.jet
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)
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cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
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self._check_colors(
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ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
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)
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colors = ["b", "g", "r"]
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df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
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ax = andrews_curves(df, "Name", color=colors)
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handles, labels = ax.get_legend_handles_labels()
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self._check_colors(handles, linecolors=colors)
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@pytest.mark.slow
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def test_parallel_coordinates(self, iris):
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from matplotlib import cm
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from pandas.plotting import parallel_coordinates
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df = iris
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ax = _check_plot_works(parallel_coordinates, frame=df, class_column="Name")
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nlines = len(ax.get_lines())
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nxticks = len(ax.xaxis.get_ticklabels())
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rgba = ("#556270", "#4ECDC4", "#C7F464")
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ax = _check_plot_works(
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parallel_coordinates, frame=df, class_column="Name", color=rgba
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)
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self._check_colors(
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ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
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)
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cnames = ["dodgerblue", "aquamarine", "seagreen"]
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ax = _check_plot_works(
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parallel_coordinates, frame=df, class_column="Name", color=cnames
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)
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self._check_colors(
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ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
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)
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ax = _check_plot_works(
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parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet
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)
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cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
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self._check_colors(
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ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
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)
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ax = _check_plot_works(
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parallel_coordinates, frame=df, class_column="Name", axvlines=False
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)
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assert len(ax.get_lines()) == (nlines - nxticks)
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colors = ["b", "g", "r"]
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df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
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ax = parallel_coordinates(df, "Name", color=colors)
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handles, labels = ax.get_legend_handles_labels()
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self._check_colors(handles, linecolors=colors)
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# not sure if this is indicative of a problem
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@pytest.mark.filterwarnings("ignore:Attempting to set:UserWarning")
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def test_parallel_coordinates_with_sorted_labels(self):
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""" For #15908 """
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from pandas.plotting import parallel_coordinates
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df = DataFrame(
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{
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"feat": list(range(30)),
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"class": [2 for _ in range(10)]
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+ [3 for _ in range(10)]
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+ [1 for _ in range(10)],
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}
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)
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ax = parallel_coordinates(df, "class", sort_labels=True)
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polylines, labels = ax.get_legend_handles_labels()
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color_label_tuples = zip(
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[polyline.get_color() for polyline in polylines], labels
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)
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ordered_color_label_tuples = sorted(color_label_tuples, key=lambda x: x[1])
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prev_next_tupels = zip(
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list(ordered_color_label_tuples[0:-1]), list(ordered_color_label_tuples[1:])
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)
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for prev, nxt in prev_next_tupels:
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# labels and colors are ordered strictly increasing
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assert prev[1] < nxt[1] and prev[0] < nxt[0]
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@pytest.mark.slow
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def test_radviz(self, iris):
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from matplotlib import cm
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from pandas.plotting import radviz
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df = iris
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_check_plot_works(radviz, frame=df, class_column="Name")
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rgba = ("#556270", "#4ECDC4", "#C7F464")
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ax = _check_plot_works(radviz, frame=df, class_column="Name", color=rgba)
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# skip Circle drawn as ticks
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patches = [p for p in ax.patches[:20] if p.get_label() != ""]
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self._check_colors(patches[:10], facecolors=rgba, mapping=df["Name"][:10])
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cnames = ["dodgerblue", "aquamarine", "seagreen"]
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_check_plot_works(radviz, frame=df, class_column="Name", color=cnames)
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patches = [p for p in ax.patches[:20] if p.get_label() != ""]
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self._check_colors(patches, facecolors=cnames, mapping=df["Name"][:10])
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_check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet)
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cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
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patches = [p for p in ax.patches[:20] if p.get_label() != ""]
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self._check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10])
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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]]
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df = DataFrame(
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{"A": [1, 2, 3], "B": [2, 1, 3], "C": [3, 2, 1], "Name": ["b", "g", "r"]}
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)
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ax = radviz(df, "Name", color=colors)
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handles, labels = ax.get_legend_handles_labels()
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self._check_colors(handles, facecolors=colors)
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@pytest.mark.slow
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def test_subplot_titles(self, iris):
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df = iris.drop("Name", axis=1).head()
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# Use the column names as the subplot titles
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title = list(df.columns)
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# Case len(title) == len(df)
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plot = df.plot(subplots=True, title=title)
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assert [p.get_title() for p in plot] == title
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# Case len(title) > len(df)
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msg = (
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"The length of `title` must equal the number of columns if "
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"using `title` of type `list` and `subplots=True`"
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)
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with pytest.raises(ValueError, match=msg):
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df.plot(subplots=True, title=title + ["kittens > puppies"])
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# Case len(title) < len(df)
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with pytest.raises(ValueError, match=msg):
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df.plot(subplots=True, title=title[:2])
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# Case subplots=False and title is of type list
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msg = (
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"Using `title` of type `list` is not supported unless "
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"`subplots=True` is passed"
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)
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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))
|