151 lines
4.0 KiB
Python
151 lines
4.0 KiB
Python
from matplotlib.testing.decorators import image_comparison
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import matplotlib.pyplot as plt
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from matplotlib.scale import Log10Transform, InvertedLog10Transform
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import numpy as np
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import io
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import platform
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import pytest
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@image_comparison(baseline_images=['log_scales'], remove_text=True)
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def test_log_scales():
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ax = plt.figure().add_subplot(122, yscale='log', xscale='symlog')
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ax.axvline(24.1)
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ax.axhline(24.1)
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@image_comparison(baseline_images=['logit_scales'], remove_text=True,
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extensions=['png'])
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def test_logit_scales():
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fig, ax = plt.subplots()
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# Typical extinction curve for logit
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x = np.array([0.001, 0.003, 0.01, 0.03, 0.1, 0.2, 0.3, 0.4, 0.5,
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0.6, 0.7, 0.8, 0.9, 0.97, 0.99, 0.997, 0.999])
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y = 1.0 / x
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ax.plot(x, y)
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ax.set_xscale('logit')
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ax.grid(True)
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bbox = ax.get_tightbbox(fig.canvas.get_renderer())
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assert np.isfinite(bbox.x0)
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assert np.isfinite(bbox.y0)
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def test_log_scatter():
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"""Issue #1799"""
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fig, ax = plt.subplots(1)
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x = np.arange(10)
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y = np.arange(10) - 1
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ax.scatter(x, y)
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buf = io.BytesIO()
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fig.savefig(buf, format='pdf')
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buf = io.BytesIO()
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fig.savefig(buf, format='eps')
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buf = io.BytesIO()
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fig.savefig(buf, format='svg')
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def test_logscale_subs():
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fig, ax = plt.subplots()
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ax.set_yscale('log', subsy=np.array([2, 3, 4]))
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# force draw
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fig.canvas.draw()
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@image_comparison(baseline_images=['logscale_mask'], remove_text=True,
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extensions=['png'])
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def test_logscale_mask():
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# Check that zero values are masked correctly on log scales.
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# See github issue 8045
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xs = np.linspace(0, 50, 1001)
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fig, ax = plt.subplots()
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ax.plot(np.exp(-xs**2))
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fig.canvas.draw()
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ax.set(yscale="log")
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def test_extra_kwargs_raise():
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fig, ax = plt.subplots()
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with pytest.raises(ValueError):
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ax.set_yscale('log', nonpos='mask')
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def test_logscale_invert_transform():
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fig, ax = plt.subplots()
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ax.set_yscale('log')
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# get transformation from data to axes
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tform = (ax.transAxes + ax.transData.inverted()).inverted()
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# direct test of log transform inversion
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assert isinstance(Log10Transform().inverted(), InvertedLog10Transform)
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def test_logscale_transform_repr():
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# check that repr of log transform succeeds
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fig, ax = plt.subplots()
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ax.set_yscale('log')
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s = repr(ax.transData)
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# check that repr of log transform succeeds
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s = repr(Log10Transform(nonpos='clip'))
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@image_comparison(baseline_images=['logscale_nonpos_values'], remove_text=True,
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tol={'aarch64': 0.02}.get(platform.machine(), 0.0),
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extensions=['png'], style='mpl20')
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def test_logscale_nonpos_values():
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np.random.seed(19680801)
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xs = np.random.normal(size=int(1e3))
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
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ax1.hist(xs, range=(-5, 5), bins=10)
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ax1.set_yscale('log')
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ax2.hist(xs, range=(-5, 5), bins=10)
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ax2.set_yscale('log', nonposy='mask')
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xdata = np.arange(0, 10, 0.01)
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ydata = np.exp(-xdata)
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edata = 0.2*(10-xdata)*np.cos(5*xdata)*np.exp(-xdata)
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ax3.fill_between(xdata, ydata - edata, ydata + edata)
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ax3.set_yscale('log')
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x = np.logspace(-1, 1)
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y = x ** 3
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yerr = x**2
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ax4.errorbar(x, y, yerr=yerr)
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ax4.set_yscale('log')
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ax4.set_xscale('log')
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def test_invalid_log_lims():
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# Check that invalid log scale limits are ignored
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fig, ax = plt.subplots()
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ax.scatter(range(0, 4), range(0, 4))
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ax.set_xscale('log')
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original_xlim = ax.get_xlim()
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with pytest.warns(UserWarning):
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ax.set_xlim(left=0)
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assert ax.get_xlim() == original_xlim
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with pytest.warns(UserWarning):
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ax.set_xlim(right=-1)
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assert ax.get_xlim() == original_xlim
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ax.set_yscale('log')
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original_ylim = ax.get_ylim()
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with pytest.warns(UserWarning):
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ax.set_ylim(bottom=0)
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assert ax.get_ylim() == original_ylim
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with pytest.warns(UserWarning):
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ax.set_ylim(top=-1)
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assert ax.get_ylim() == original_ylim
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