689 lines
21 KiB
Python
689 lines
21 KiB
Python
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import numpy as np
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import numpy.testing as nptest
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from numpy.testing import assert_equal
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import pytest
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from scipy import stats
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import statsmodels.api as sm
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from statsmodels.graphics import gofplots
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from statsmodels.graphics.gofplots import (
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ProbPlot,
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qqline,
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qqplot,
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qqplot_2samples,
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)
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from statsmodels.graphics.utils import _import_mpl
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class BaseProbplotMixin:
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def setup_method(self):
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try:
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import matplotlib.pyplot as plt
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self.fig, self.ax = plt.subplots()
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except ImportError:
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pass
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self.other_array = np.random.normal(size=self.prbplt.data.shape)
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self.other_prbplot = ProbPlot(self.other_array)
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self.plot_options = dict(
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marker="d",
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markerfacecolor="cornflowerblue",
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markeredgecolor="white",
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alpha=0.5,
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)
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@pytest.mark.matplotlib
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def test_qqplot(self, close_figures):
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self.prbplt.qqplot(ax=self.ax, line=self.line, **self.plot_options)
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@pytest.mark.matplotlib
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def test_ppplot(self, close_figures):
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self.prbplt.ppplot(ax=self.ax, line=self.line)
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@pytest.mark.matplotlib
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def test_probplot(self, close_figures):
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self.prbplt.probplot(ax=self.ax, line=self.line, **self.plot_options)
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@pytest.mark.matplotlib
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def test_probplot_exceed(self, close_figures):
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self.prbplt.probplot(
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ax=self.ax, exceed=True, line=self.line, **self.plot_options
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)
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@pytest.mark.matplotlib
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def test_qqplot_other_array(self, close_figures):
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self.prbplt.qqplot(
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ax=self.ax,
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line=self.line,
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other=self.other_array,
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_ppplot_other_array(self, close_figures):
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self.prbplt.ppplot(
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ax=self.ax,
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line=self.line,
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other=self.other_array,
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**self.plot_options,
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)
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@pytest.mark.xfail(strict=True)
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@pytest.mark.matplotlib
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def test_probplot_other_array(self, close_figures):
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self.prbplt.probplot(
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ax=self.ax,
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line=self.line,
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other=self.other_array,
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_qqplot_other_prbplt(self, close_figures):
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self.prbplt.qqplot(
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ax=self.ax,
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line=self.line,
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other=self.other_prbplot,
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_ppplot_other_prbplt(self, close_figures):
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self.prbplt.ppplot(
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ax=self.ax,
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line=self.line,
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other=self.other_prbplot,
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**self.plot_options,
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)
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@pytest.mark.xfail(strict=True)
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@pytest.mark.matplotlib
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def test_probplot_other_prbplt(self, close_figures):
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self.prbplt.probplot(
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ax=self.ax,
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line=self.line,
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other=self.other_prbplot,
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_qqplot_custom_labels(self, close_figures):
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self.prbplt.qqplot(
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ax=self.ax,
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line=self.line,
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xlabel="Custom X-Label",
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ylabel="Custom Y-Label",
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_ppplot_custom_labels(self, close_figures):
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self.prbplt.ppplot(
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ax=self.ax,
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line=self.line,
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xlabel="Custom X-Label",
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ylabel="Custom Y-Label",
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_probplot_custom_labels(self, close_figures):
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self.prbplt.probplot(
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ax=self.ax,
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line=self.line,
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xlabel="Custom X-Label",
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ylabel="Custom Y-Label",
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**self.plot_options,
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)
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@pytest.mark.matplotlib
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def test_qqplot_pltkwargs(self, close_figures):
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self.prbplt.qqplot(
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ax=self.ax,
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line=self.line,
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marker="d",
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markerfacecolor="cornflowerblue",
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markeredgecolor="white",
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alpha=0.5,
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)
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@pytest.mark.matplotlib
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def test_ppplot_pltkwargs(self, close_figures):
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self.prbplt.ppplot(
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ax=self.ax,
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line=self.line,
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marker="d",
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markerfacecolor="cornflowerblue",
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markeredgecolor="white",
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alpha=0.5,
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)
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@pytest.mark.matplotlib
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def test_probplot_pltkwargs(self, close_figures):
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self.prbplt.probplot(
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ax=self.ax,
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line=self.line,
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marker="d",
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markerfacecolor="cornflowerblue",
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markeredgecolor="white",
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alpha=0.5,
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)
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def test_fit_params(self):
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assert self.prbplt.fit_params[-2] == self.prbplt.loc
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assert self.prbplt.fit_params[-1] == self.prbplt.scale
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class TestProbPlotLongelyNoFit(BaseProbplotMixin):
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def setup_method(self):
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np.random.seed(5)
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self.data = sm.datasets.longley.load()
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self.data.exog = sm.add_constant(self.data.exog, prepend=False)
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self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit()
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self.prbplt = ProbPlot(
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self.mod_fit.resid, dist=stats.t, distargs=(4,), fit=False
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)
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self.line = "r"
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super().setup_method()
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class TestProbPlotLongelyWithFit(BaseProbplotMixin):
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def setup_method(self):
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np.random.seed(5)
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self.data = sm.datasets.longley.load()
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self.data.exog = sm.add_constant(self.data.exog, prepend=False)
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self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit()
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self.prbplt = ProbPlot(
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self.mod_fit.resid, dist=stats.t, distargs=(4,), fit=True
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)
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self.line = "r"
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super().setup_method()
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class TestProbPlotRandomNormalMinimal(BaseProbplotMixin):
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def setup_method(self):
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np.random.seed(5)
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self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
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self.prbplt = ProbPlot(self.data)
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self.line = None
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super().setup_method()
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class TestProbPlotRandomNormalWithFit(BaseProbplotMixin):
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def setup_method(self):
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np.random.seed(5)
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self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
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self.prbplt = ProbPlot(self.data, fit=True)
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self.line = "q"
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super().setup_method()
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class TestProbPlotRandomNormalFullDist(BaseProbplotMixin):
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def setup_method(self):
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np.random.seed(5)
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self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
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self.prbplt = ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0))
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self.line = "45"
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super().setup_method()
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def test_loc_set(self):
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assert self.prbplt.loc == 8.5
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def test_scale_set(self):
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assert self.prbplt.scale == 3.0
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def test_exceptions(self):
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with pytest.raises(ValueError):
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ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), fit=True)
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with pytest.raises(ValueError):
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ProbPlot(
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self.data,
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dist=stats.norm(loc=8.5, scale=3.0),
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distargs=(8.5, 3.0),
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)
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with pytest.raises(ValueError):
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ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), loc=8.5)
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with pytest.raises(ValueError):
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ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), scale=3.0)
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class TestCompareSamplesDifferentSize:
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def setup_method(self):
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np.random.seed(5)
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self.data1 = ProbPlot(np.random.normal(loc=8.25, scale=3.25, size=37))
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self.data2 = ProbPlot(np.random.normal(loc=8.25, scale=3.25, size=55))
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@pytest.mark.matplotlib
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def test_qqplot(self, close_figures):
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self.data1.qqplot(other=self.data2)
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with pytest.raises(ValueError):
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self.data2.qqplot(other=self.data1)
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@pytest.mark.matplotlib
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def test_ppplot(self, close_figures):
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self.data1.ppplot(other=self.data2)
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self.data2.ppplot(other=self.data1)
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class TestProbPlotRandomNormalLocScaleDist(BaseProbplotMixin):
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def setup_method(self):
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np.random.seed(5)
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self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
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self.prbplt = ProbPlot(self.data, loc=8, scale=3)
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self.line = "45"
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super().setup_method()
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def test_loc_set(self):
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assert self.prbplt.loc == 8
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def test_scale_set(self):
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assert self.prbplt.scale == 3
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def test_loc_set_in_dist(self):
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assert self.prbplt.dist.mean() == 8.0
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def test_scale_set_in_dist(self):
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assert self.prbplt.dist.var() == 9.0
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class TestTopLevel:
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def setup_method(self):
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self.data = sm.datasets.longley.load()
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self.data.exog = sm.add_constant(self.data.exog, prepend=False)
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self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit()
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self.res = self.mod_fit.resid
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self.prbplt = ProbPlot(self.mod_fit.resid, dist=stats.t, distargs=(4,))
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self.other_array = np.random.normal(size=self.prbplt.data.shape)
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self.other_prbplot = ProbPlot(self.other_array)
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@pytest.mark.matplotlib
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def test_qqplot(self, close_figures):
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qqplot(self.res, line="r")
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@pytest.mark.matplotlib
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def test_qqplot_pltkwargs(self, close_figures):
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qqplot(
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self.res,
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line="r",
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marker="d",
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markerfacecolor="cornflowerblue",
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markeredgecolor="white",
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alpha=0.5,
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)
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@pytest.mark.matplotlib
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def test_qqplot_2samples_prob_plot_objects(self, close_figures):
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# also tests all valuesg for line
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for line in ["r", "q", "45", "s"]:
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# test with `ProbPlot` instances
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qqplot_2samples(self.prbplt, self.other_prbplot, line=line)
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@pytest.mark.matplotlib
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def test_qqplot_2samples_arrays(self, close_figures):
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# also tests all values for line
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for line in ["r", "q", "45", "s"]:
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# test with arrays
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qqplot_2samples(self.res, self.other_array, line=line)
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def test_invalid_dist_config(close_figures):
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# GH 4226
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np.random.seed(5)
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data = sm.datasets.longley.load()
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data.exog = sm.add_constant(data.exog, prepend=False)
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mod_fit = sm.OLS(data.endog, data.exog).fit()
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with pytest.raises(TypeError, match=r"dist\(0, 1, 4, loc=0, scale=1\)"):
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ProbPlot(mod_fit.resid, stats.t, distargs=(0, 1, 4))
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@pytest.mark.matplotlib
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def test_qqplot_unequal():
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rs = np.random.RandomState(0)
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data1 = rs.standard_normal(100)
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data2 = rs.standard_normal(200)
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fig1 = qqplot_2samples(data1, data2)
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fig2 = qqplot_2samples(data2, data1)
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x1, y1 = fig1.get_axes()[0].get_children()[0].get_data()
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x2, y2 = fig2.get_axes()[0].get_children()[0].get_data()
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np.testing.assert_allclose(x1, x2)
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np.testing.assert_allclose(y1, y2)
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numobj1 = len(fig1.get_axes()[0].get_children())
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numobj2 = len(fig2.get_axes()[0].get_children())
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assert numobj1 == numobj2
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@pytest.mark.matplotlib
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def test_qqplot(self, close_figures):
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qqplot(self.res, line="r")
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@pytest.mark.matplotlib
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|
|
def test_qqplot_2samples_prob_plot_obj(self, close_figures):
|
||
|
|
# also tests all values for line
|
||
|
|
for line in ["r", "q", "45", "s"]:
|
||
|
|
# test with `ProbPlot` instances
|
||
|
|
qqplot_2samples(self.prbplt, self.other_prbplot, line=line)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_qqplot_2samples_arrays(self, close_figures):
|
||
|
|
# also tests all values for line
|
||
|
|
for line in ["r", "q", "45", "s"]:
|
||
|
|
# test with arrays
|
||
|
|
qqplot_2samples(self.res, self.other_array, line=line)
|
||
|
|
|
||
|
|
|
||
|
|
class TestCheckDist:
|
||
|
|
def test_good(self):
|
||
|
|
gofplots._check_for(stats.norm, "ppf")
|
||
|
|
gofplots._check_for(stats.norm, "cdf")
|
||
|
|
|
||
|
|
def test_bad(self):
|
||
|
|
with pytest.raises(AttributeError):
|
||
|
|
gofplots._check_for("junk", "ppf")
|
||
|
|
with pytest.raises(AttributeError):
|
||
|
|
gofplots._check_for("junk", "cdf")
|
||
|
|
|
||
|
|
|
||
|
|
class TestDoPlot:
|
||
|
|
def setup_method(self):
|
||
|
|
try:
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
self.fig, self.ax = plt.subplots()
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
self.x = [0.2, 0.6, 2.0, 4.5, 10.0, 50.0, 83.0, 99.1, 99.7]
|
||
|
|
self.y = [1.2, 1.4, 1.7, 2.1, 3.2, 3.7, 4.5, 5.1, 6.3]
|
||
|
|
self.full_options = {
|
||
|
|
"marker": "s",
|
||
|
|
"markerfacecolor": "cornflowerblue",
|
||
|
|
"markeredgecolor": "firebrick",
|
||
|
|
"markeredgewidth": 1.25,
|
||
|
|
"linestyle": "--",
|
||
|
|
}
|
||
|
|
self.step_options = {"linestyle": "-", "where": "mid"}
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_baseline(self, close_figures):
|
||
|
|
plt = _import_mpl()
|
||
|
|
fig, ax = gofplots._do_plot(self.x, self.y)
|
||
|
|
assert isinstance(fig, plt.Figure)
|
||
|
|
assert isinstance(ax, plt.Axes)
|
||
|
|
assert self.fig is not fig
|
||
|
|
assert self.ax is not ax
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_with_ax(self, close_figures):
|
||
|
|
plt = _import_mpl()
|
||
|
|
fig, ax = gofplots._do_plot(self.x, self.y, ax=self.ax)
|
||
|
|
assert isinstance(fig, plt.Figure)
|
||
|
|
assert isinstance(ax, plt.Axes)
|
||
|
|
assert self.fig is fig
|
||
|
|
assert self.ax is ax
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_plot_full_options(self, close_figures):
|
||
|
|
gofplots._do_plot(
|
||
|
|
self.x,
|
||
|
|
self.y,
|
||
|
|
ax=self.ax,
|
||
|
|
step=False,
|
||
|
|
**self.full_options,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_step_baseline(self, close_figures):
|
||
|
|
gofplots._do_plot(
|
||
|
|
self.x,
|
||
|
|
self.y,
|
||
|
|
ax=self.ax,
|
||
|
|
step=True,
|
||
|
|
**self.step_options,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_step_full_options(self, close_figures):
|
||
|
|
gofplots._do_plot(
|
||
|
|
self.x,
|
||
|
|
self.y,
|
||
|
|
ax=self.ax,
|
||
|
|
step=True,
|
||
|
|
**self.full_options,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_plot_qq_line(self, close_figures):
|
||
|
|
gofplots._do_plot(self.x, self.y, ax=self.ax, line="r")
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_step_qq_line(self, close_figures):
|
||
|
|
gofplots._do_plot(self.x, self.y, ax=self.ax, step=True, line="r")
|
||
|
|
|
||
|
|
|
||
|
|
class TestQQLine:
|
||
|
|
def setup_method(self):
|
||
|
|
np.random.seed(0)
|
||
|
|
self.x = np.sort(np.random.normal(loc=2.9, scale=1.2, size=37))
|
||
|
|
self.y = np.sort(np.random.normal(loc=3.0, scale=1.1, size=37))
|
||
|
|
try:
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
self.fig, self.ax = plt.subplots()
|
||
|
|
self.ax.plot(self.x, self.y, "ko")
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
self.lineoptions = {
|
||
|
|
"linewidth": 2,
|
||
|
|
"dashes": (10, 1, 3, 4),
|
||
|
|
"color": "green",
|
||
|
|
}
|
||
|
|
self.fmt = "bo-"
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_badline(self):
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
qqline(self.ax, "junk")
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_non45_no_x(self, close_figures):
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
qqline(self.ax, "s", y=self.y)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_non45_no_y(self, close_figures):
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
qqline(self.ax, "s", x=self.x)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_non45_no_x_no_y(self, close_figures):
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
qqline(self.ax, "s")
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_45(self, close_figures):
|
||
|
|
nchildren = len(self.ax.get_children())
|
||
|
|
qqline(self.ax, "45")
|
||
|
|
assert len(self.ax.get_children()) > nchildren
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_45_fmt(self, close_figures):
|
||
|
|
qqline(self.ax, "45", fmt=self.fmt)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_45_fmt_lineoptions(self, close_figures):
|
||
|
|
qqline(self.ax, "45", fmt=self.fmt, **self.lineoptions)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_r(self, close_figures):
|
||
|
|
nchildren = len(self.ax.get_children())
|
||
|
|
qqline(self.ax, "r", x=self.x, y=self.y)
|
||
|
|
assert len(self.ax.get_children()) > nchildren
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_r_fmt(self, close_figures):
|
||
|
|
qqline(self.ax, "r", x=self.x, y=self.y, fmt=self.fmt)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_r_fmt_lineoptions(self, close_figures):
|
||
|
|
qqline(
|
||
|
|
self.ax, "r", x=self.x, y=self.y, fmt=self.fmt, **self.lineoptions
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_s(self, close_figures):
|
||
|
|
nchildren = len(self.ax.get_children())
|
||
|
|
qqline(self.ax, "s", x=self.x, y=self.y)
|
||
|
|
assert len(self.ax.get_children()) > nchildren
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_s_fmt(self, close_figures):
|
||
|
|
qqline(self.ax, "s", x=self.x, y=self.y, fmt=self.fmt)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_s_fmt_lineoptions(self, close_figures):
|
||
|
|
qqline(
|
||
|
|
self.ax, "s", x=self.x, y=self.y, fmt=self.fmt, **self.lineoptions
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_q(self, close_figures):
|
||
|
|
nchildren = len(self.ax.get_children())
|
||
|
|
qqline(self.ax, "q", dist=stats.norm, x=self.x, y=self.y)
|
||
|
|
assert len(self.ax.get_children()) > nchildren
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_q_fmt(self, close_figures):
|
||
|
|
qqline(self.ax, "q", dist=stats.norm, x=self.x, y=self.y, fmt=self.fmt)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_q_fmt_lineoptions(self, close_figures):
|
||
|
|
qqline(
|
||
|
|
self.ax,
|
||
|
|
"q",
|
||
|
|
dist=stats.norm,
|
||
|
|
x=self.x,
|
||
|
|
y=self.y,
|
||
|
|
fmt=self.fmt,
|
||
|
|
**self.lineoptions,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class TestPlottingPosition:
|
||
|
|
def setup_method(self):
|
||
|
|
self.N = 13
|
||
|
|
self.data = np.arange(self.N)
|
||
|
|
|
||
|
|
def do_test(self, alpha, beta):
|
||
|
|
smpp = gofplots.plotting_pos(self.N, a=alpha, b=beta)
|
||
|
|
sppp = stats.mstats.plotting_positions(
|
||
|
|
self.data, alpha=alpha, beta=beta
|
||
|
|
)
|
||
|
|
|
||
|
|
nptest.assert_array_almost_equal(smpp, sppp, decimal=5)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_weibull(self, close_figures):
|
||
|
|
self.do_test(0, 0)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_lininterp(self, close_figures):
|
||
|
|
self.do_test(0, 1)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_piecewise(self, close_figures):
|
||
|
|
self.do_test(0.5, 0.5)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_approx_med_unbiased(self, close_figures):
|
||
|
|
self.do_test(1.0 / 3.0, 1.0 / 3.0)
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_cunnane(self, close_figures):
|
||
|
|
self.do_test(0.4, 0.4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_param_unpacking():
|
||
|
|
expected = np.array([2.0, 3, 0, 1])
|
||
|
|
pp = ProbPlot(np.empty(100), dist=stats.beta(2, 3))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(2, b=3))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(a=2, b=3))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
|
||
|
|
expected = np.array([2.0, 3, 4, 1])
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(2, 3, 4))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(a=2, b=3, loc=4))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
|
||
|
|
expected = np.array([2.0, 3, 4, 5])
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(2, 3, 4, 5))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(2, 3, 4, scale=5))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(2, 3, loc=4, scale=5))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(2, b=3, loc=4, scale=5))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
pp = ProbPlot(np.empty(100), stats.beta(a=2, b=3, loc=4, scale=5))
|
||
|
|
assert_equal(pp.fit_params, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
@pytest.mark.parametrize("labels", [{}, {"xlabel": "X", "ylabel": "Y"}])
|
||
|
|
@pytest.mark.parametrize("x_size", [30, 50])
|
||
|
|
@pytest.mark.parametrize("y_size", [30, 50])
|
||
|
|
@pytest.mark.parametrize("line", [None, "45", "s", "r", "q"])
|
||
|
|
def test_correct_labels(
|
||
|
|
close_figures, reset_randomstate, line, x_size, y_size, labels
|
||
|
|
):
|
||
|
|
rs = np.random.RandomState(9876554)
|
||
|
|
x = rs.normal(loc=0, scale=0.1, size=x_size)
|
||
|
|
y = rs.standard_t(3, size=y_size)
|
||
|
|
pp_x = sm.ProbPlot(x)
|
||
|
|
pp_y = sm.ProbPlot(y)
|
||
|
|
fig = qqplot_2samples(pp_x, pp_y, line=line, **labels)
|
||
|
|
ax = fig.get_axes()[0]
|
||
|
|
x_label = ax.get_xlabel()
|
||
|
|
y_label = ax.get_ylabel()
|
||
|
|
if x_size < y_size:
|
||
|
|
if not labels:
|
||
|
|
assert "2nd" in x_label
|
||
|
|
assert "1st" in y_label
|
||
|
|
else:
|
||
|
|
assert "Y" in x_label
|
||
|
|
assert "X" in y_label
|
||
|
|
else:
|
||
|
|
if not labels:
|
||
|
|
assert "1st" in x_label
|
||
|
|
assert "2nd" in y_label
|
||
|
|
else:
|
||
|
|
assert "X" in x_label
|
||
|
|
assert "Y" in y_label
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.matplotlib
|
||
|
|
def test_axis_order(close_figures):
|
||
|
|
xx = np.random.normal(10, 1, (100,))
|
||
|
|
xy = np.random.normal(1, 0.01, (100,))
|
||
|
|
fig = qqplot_2samples(xx, xy, "x", "y")
|
||
|
|
ax = fig.get_axes()[0]
|
||
|
|
y_range = np.diff(ax.get_ylim())[0]
|
||
|
|
x_range = np.diff(ax.get_xlim())[0]
|
||
|
|
assert y_range < x_range
|
||
|
|
|
||
|
|
xx_long = np.random.normal(10, 1, (1000,))
|
||
|
|
fig = qqplot_2samples(xx_long, xy, "x", "y")
|
||
|
|
ax = fig.get_axes()[0]
|
||
|
|
y_range = np.diff(ax.get_ylim())[0]
|
||
|
|
x_range = np.diff(ax.get_xlim())[0]
|
||
|
|
assert y_range < x_range
|
||
|
|
|
||
|
|
xy_long = np.random.normal(1, 0.01, (1000,))
|
||
|
|
fig = qqplot_2samples(xx, xy_long, "x", "y")
|
||
|
|
ax = fig.get_axes()[0]
|
||
|
|
y_range = np.diff(ax.get_ylim())[0]
|
||
|
|
x_range = np.diff(ax.get_xlim())[0]
|
||
|
|
assert x_range < y_range
|