import numpy as np import numpy.testing as nptest from numpy.testing import assert_equal import pytest from scipy import stats import statsmodels.api as sm from statsmodels.compat.python import PYTHON_IMPL_WASM from statsmodels.graphics import gofplots from statsmodels.graphics.gofplots import ( ProbPlot, qqline, qqplot, qqplot_2samples, ) from statsmodels.graphics.utils import _import_mpl class BaseProbplotMixin: def setup_method(self): try: import matplotlib.pyplot as plt self.fig, self.ax = plt.subplots() except ImportError: pass self.other_array = np.random.normal(size=self.prbplt.data.shape) self.other_prbplot = ProbPlot(self.other_array) self.plot_options = dict( marker="d", markerfacecolor="cornflowerblue", markeredgecolor="white", alpha=0.5, ) @pytest.mark.matplotlib def test_qqplot(self, close_figures): self.prbplt.qqplot(ax=self.ax, line=self.line, **self.plot_options) @pytest.mark.matplotlib def test_ppplot(self, close_figures): self.prbplt.ppplot(ax=self.ax, line=self.line) @pytest.mark.matplotlib def test_probplot(self, close_figures): self.prbplt.probplot(ax=self.ax, line=self.line, **self.plot_options) @pytest.mark.matplotlib def test_probplot_exceed(self, close_figures): self.prbplt.probplot( ax=self.ax, exceed=True, line=self.line, **self.plot_options ) @pytest.mark.matplotlib def test_qqplot_other_array(self, close_figures): self.prbplt.qqplot( ax=self.ax, line=self.line, other=self.other_array, **self.plot_options, ) @pytest.mark.matplotlib def test_ppplot_other_array(self, close_figures): self.prbplt.ppplot( ax=self.ax, line=self.line, other=self.other_array, **self.plot_options, ) @pytest.mark.xfail(strict=True) @pytest.mark.matplotlib @pytest.mark.skipif( PYTHON_IMPL_WASM, reason="Matplotlib uses different backend in WASM/Pyodide" ) def test_probplot_other_array(self, close_figures): self.prbplt.probplot( ax=self.ax, line=self.line, other=self.other_array, **self.plot_options, ) @pytest.mark.matplotlib def test_qqplot_other_prbplt(self, close_figures): self.prbplt.qqplot( ax=self.ax, line=self.line, other=self.other_prbplot, **self.plot_options, ) @pytest.mark.matplotlib def test_ppplot_other_prbplt(self, close_figures): self.prbplt.ppplot( ax=self.ax, line=self.line, other=self.other_prbplot, **self.plot_options, ) @pytest.mark.xfail(strict=True) @pytest.mark.matplotlib @pytest.mark.skipif( PYTHON_IMPL_WASM, reason="Matplotlib uses different backend in WASM/Pyodide" ) def test_probplot_other_prbplt(self, close_figures): self.prbplt.probplot( ax=self.ax, line=self.line, other=self.other_prbplot, **self.plot_options, ) @pytest.mark.matplotlib def test_qqplot_custom_labels(self, close_figures): self.prbplt.qqplot( ax=self.ax, line=self.line, xlabel="Custom X-Label", ylabel="Custom Y-Label", **self.plot_options, ) @pytest.mark.matplotlib def test_ppplot_custom_labels(self, close_figures): self.prbplt.ppplot( ax=self.ax, line=self.line, xlabel="Custom X-Label", ylabel="Custom Y-Label", **self.plot_options, ) @pytest.mark.matplotlib def test_probplot_custom_labels(self, close_figures): self.prbplt.probplot( ax=self.ax, line=self.line, xlabel="Custom X-Label", ylabel="Custom Y-Label", **self.plot_options, ) @pytest.mark.matplotlib def test_qqplot_pltkwargs(self, close_figures): self.prbplt.qqplot( ax=self.ax, line=self.line, marker="d", markerfacecolor="cornflowerblue", markeredgecolor="white", alpha=0.5, ) @pytest.mark.matplotlib def test_ppplot_pltkwargs(self, close_figures): self.prbplt.ppplot( ax=self.ax, line=self.line, marker="d", markerfacecolor="cornflowerblue", markeredgecolor="white", alpha=0.5, ) @pytest.mark.matplotlib def test_probplot_pltkwargs(self, close_figures): self.prbplt.probplot( ax=self.ax, line=self.line, marker="d", markerfacecolor="cornflowerblue", markeredgecolor="white", alpha=0.5, ) def test_fit_params(self): assert self.prbplt.fit_params[-2] == self.prbplt.loc assert self.prbplt.fit_params[-1] == self.prbplt.scale @pytest.mark.skipif( PYTHON_IMPL_WASM, reason="Matplotlib uses different backend in WASM/Pyodide" ) class TestProbPlotLongelyNoFit(BaseProbplotMixin): def setup_method(self): np.random.seed(5) self.data = sm.datasets.longley.load() self.data.exog = sm.add_constant(self.data.exog, prepend=False) self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit() self.prbplt = ProbPlot( self.mod_fit.resid, dist=stats.t, distargs=(4,), fit=False ) self.line = "r" super().setup_method() class TestProbPlotLongelyWithFit(BaseProbplotMixin): def setup_method(self): np.random.seed(5) self.data = sm.datasets.longley.load() self.data.exog = sm.add_constant(self.data.exog, prepend=False) self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit() self.prbplt = ProbPlot( self.mod_fit.resid, dist=stats.t, distargs=(4,), fit=True ) self.line = "r" super().setup_method() class TestProbPlotRandomNormalMinimal(BaseProbplotMixin): def setup_method(self): np.random.seed(5) self.data = np.random.normal(loc=8.25, scale=3.25, size=37) self.prbplt = ProbPlot(self.data) self.line = None super().setup_method() class TestProbPlotRandomNormalWithFit(BaseProbplotMixin): def setup_method(self): np.random.seed(5) self.data = np.random.normal(loc=8.25, scale=3.25, size=37) self.prbplt = ProbPlot(self.data, fit=True) self.line = "q" super().setup_method() class TestProbPlotRandomNormalFullDist(BaseProbplotMixin): def setup_method(self): np.random.seed(5) self.data = np.random.normal(loc=8.25, scale=3.25, size=37) self.prbplt = ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0)) self.line = "45" super().setup_method() def test_loc_set(self): assert self.prbplt.loc == 8.5 def test_scale_set(self): assert self.prbplt.scale == 3.0 def test_exceptions(self): with pytest.raises(ValueError): ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), fit=True) with pytest.raises(ValueError): ProbPlot( self.data, dist=stats.norm(loc=8.5, scale=3.0), distargs=(8.5, 3.0), ) with pytest.raises(ValueError): ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), loc=8.5) with pytest.raises(ValueError): ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), scale=3.0) class TestCompareSamplesDifferentSize: def setup_method(self): np.random.seed(5) self.data1 = ProbPlot(np.random.normal(loc=8.25, scale=3.25, size=37)) self.data2 = ProbPlot(np.random.normal(loc=8.25, scale=3.25, size=55)) @pytest.mark.matplotlib def test_qqplot(self, close_figures): self.data1.qqplot(other=self.data2) with pytest.raises(ValueError): self.data2.qqplot(other=self.data1) @pytest.mark.matplotlib def test_ppplot(self, close_figures): self.data1.ppplot(other=self.data2) self.data2.ppplot(other=self.data1) class TestProbPlotRandomNormalLocScaleDist(BaseProbplotMixin): def setup_method(self): np.random.seed(5) self.data = np.random.normal(loc=8.25, scale=3.25, size=37) self.prbplt = ProbPlot(self.data, loc=8, scale=3) self.line = "45" super().setup_method() def test_loc_set(self): assert self.prbplt.loc == 8 def test_scale_set(self): assert self.prbplt.scale == 3 def test_loc_set_in_dist(self): assert self.prbplt.dist.mean() == 8.0 def test_scale_set_in_dist(self): assert self.prbplt.dist.var() == 9.0 class TestTopLevel: def setup_method(self): self.data = sm.datasets.longley.load() self.data.exog = sm.add_constant(self.data.exog, prepend=False) self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit() self.res = self.mod_fit.resid self.prbplt = ProbPlot(self.mod_fit.resid, dist=stats.t, distargs=(4,)) self.other_array = np.random.normal(size=self.prbplt.data.shape) self.other_prbplot = ProbPlot(self.other_array) @pytest.mark.matplotlib def test_qqplot(self, close_figures): qqplot(self.res, line="r") @pytest.mark.matplotlib def test_qqplot_pltkwargs(self, close_figures): qqplot( self.res, line="r", marker="d", markerfacecolor="cornflowerblue", markeredgecolor="white", alpha=0.5, ) @pytest.mark.matplotlib def test_qqplot_2samples_prob_plot_objects(self, close_figures): # also tests all valuesg 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) def test_invalid_dist_config(close_figures): # GH 4226 np.random.seed(5) data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog, prepend=False) mod_fit = sm.OLS(data.endog, data.exog).fit() with pytest.raises(TypeError, match=r"dist\(0, 1, 4, loc=0, scale=1\)"): ProbPlot(mod_fit.resid, stats.t, distargs=(0, 1, 4)) @pytest.mark.matplotlib def test_qqplot_unequal(): rs = np.random.RandomState(0) data1 = rs.standard_normal(100) data2 = rs.standard_normal(200) fig1 = qqplot_2samples(data1, data2) fig2 = qqplot_2samples(data2, data1) x1, y1 = fig1.get_axes()[0].get_children()[0].get_data() x2, y2 = fig2.get_axes()[0].get_children()[0].get_data() np.testing.assert_allclose(x1, x2) np.testing.assert_allclose(y1, y2) numobj1 = len(fig1.get_axes()[0].get_children()) numobj2 = len(fig2.get_axes()[0].get_children()) assert numobj1 == numobj2 @pytest.mark.matplotlib def test_qqplot(self, close_figures): qqplot(self.res, line="r") @pytest.mark.matplotlib 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