500 lines
15 KiB
Python
500 lines
15 KiB
Python
import copy
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import matplotlib.pyplot as plt
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from matplotlib.scale import (
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AsinhScale, AsinhTransform,
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FuncScale, LogitScale,
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LogTransform, InvertedLogTransform,
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SymmetricalLogTransform)
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import matplotlib.scale as mscale
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from matplotlib.ticker import (
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AsinhLocator, AutoLocator, LogFormatterSciNotation,
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NullFormatter, NullLocator, ScalarFormatter
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)
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from matplotlib.testing.decorators import check_figures_equal, image_comparison
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from matplotlib.transforms import IdentityTransform
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import numpy as np
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from numpy.testing import assert_allclose
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import io
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import pytest
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def test_optional_axis_signature():
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# There are three types of original signatures possible, and this only tests one
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# example class of each:
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# 1. `axis` without default: LinearScale, FuncScale, FuncScaleLog
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# 2. `axis` with default and more positional parameters: LogitScale
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# 3. `axis` with default and only keyword-only parameters: LogScale, AsinhScale,
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# SymmetricalLogScale
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# Testing with None is sufficient as detection is purely based on the
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# signature structure; no type information is involved.
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axis = None
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# Old signature with axis positionally.
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FuncScale(axis, (lambda x: x, lambda x: x))
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FuncScale(axis, functions=(lambda x: x, lambda x: x))
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LogitScale(axis)
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LogitScale(axis, 'clip')
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LogitScale(axis, nonpositive='clip')
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LogitScale(axis, use_overline=True)
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AsinhScale(axis)
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AsinhScale(axis, linear_width=2)
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AsinhScale(axis, base=3)
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AsinhScale(axis, subs=[2, 6])
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# Old signature with axis as keyword.
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FuncScale(axis=axis, functions=(lambda x: x, lambda x: x))
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LogitScale(axis=axis)
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LogitScale(axis=axis, nonpositive='clip')
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LogitScale(axis=axis, use_overline=True)
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AsinhScale(axis=axis)
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AsinhScale(axis=axis, linear_width=2)
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AsinhScale(axis=axis, base=3)
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AsinhScale(axis=axis, subs=[2, 6])
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# New signature without axis.
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FuncScale((lambda x: x, lambda x: x))
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FuncScale(functions=(lambda x: x, lambda x: x))
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LogitScale()
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LogitScale(nonpositive='clip')
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LogitScale(use_overline=True)
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AsinhScale()
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AsinhScale(linear_width=2)
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AsinhScale(base=3)
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AsinhScale(subs=[2, 6])
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@check_figures_equal()
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def test_log_scales(fig_test, fig_ref):
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ax_test = fig_test.add_subplot(122, yscale='log', xscale='symlog')
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ax_test.axvline(24.1)
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ax_test.axhline(24.1)
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xlim = ax_test.get_xlim()
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ylim = ax_test.get_ylim()
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ax_ref = fig_ref.add_subplot(122, yscale='log', xscale='symlog')
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ax_ref.set(xlim=xlim, ylim=ylim)
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ax_ref.plot([24.1, 24.1], ylim, 'b')
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ax_ref.plot(xlim, [24.1, 24.1], 'b')
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def test_symlog_mask_nan():
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# Use a transform round-trip to verify that the forward and inverse
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# transforms work, and that they respect nans and/or masking.
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slt = SymmetricalLogTransform(10, 2, 1)
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slti = slt.inverted()
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x = np.arange(-1.5, 5, 0.5)
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out = slti.transform_non_affine(slt.transform_non_affine(x))
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assert_allclose(out, x)
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assert type(out) is type(x)
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x[4] = np.nan
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out = slti.transform_non_affine(slt.transform_non_affine(x))
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assert_allclose(out, x)
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assert type(out) is type(x)
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x = np.ma.array(x)
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out = slti.transform_non_affine(slt.transform_non_affine(x))
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assert_allclose(out, x)
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assert type(out) is type(x)
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x[3] = np.ma.masked
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out = slti.transform_non_affine(slt.transform_non_affine(x))
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assert_allclose(out, x)
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assert type(out) is type(x)
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@image_comparison(['logit_scales.png'], remove_text=True, style='_classic_test')
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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', subs=np.array([2, 3, 4]))
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# force draw
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fig.canvas.draw()
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@image_comparison(['logscale_mask.png'], remove_text=True, style='_classic_test')
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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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yticks=10.**np.arange(-300, 0, 24)) # Backcompat tick selection.
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def test_extra_kwargs_raise():
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fig, ax = plt.subplots()
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for scale in ['linear', 'log', 'symlog']:
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with pytest.raises(TypeError):
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ax.set_yscale(scale, foo='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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inverted_transform = LogTransform(base=2).inverted()
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assert isinstance(inverted_transform, InvertedLogTransform)
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assert inverted_transform.base == 2
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def test_logscale_transform_repr():
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fig, ax = plt.subplots()
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ax.set_yscale('log')
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repr(ax.transData)
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repr(LogTransform(10, nonpositive='clip'))
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@image_comparison(['logscale_nonpos_values.png'],
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remove_text=True, tol=0.02, 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', nonpositive='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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ax4.set_yticks([1e-2, 1, 1e+2]) # Backcompat tick selection.
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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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@image_comparison(['function_scales.png'], remove_text=True, style='mpl20')
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def test_function_scale():
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def inverse(x):
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return x**2
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def forward(x):
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return x**(1/2)
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fig, ax = plt.subplots()
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x = np.arange(1, 1000)
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ax.plot(x, x)
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ax.set_xscale('function', functions=(forward, inverse))
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ax.set_xlim(1, 1000)
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def test_pass_scale():
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# test passing a scale object works...
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fig, ax = plt.subplots()
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scale = mscale.LogScale(axis=None)
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ax.set_xscale(scale)
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scale = mscale.LogScale(axis=None)
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ax.set_yscale(scale)
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assert ax.xaxis.get_scale() == 'log'
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assert ax.yaxis.get_scale() == 'log'
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def test_scale_deepcopy():
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sc = mscale.LogScale(axis='x', base=10)
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sc2 = copy.deepcopy(sc)
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assert str(sc.get_transform()) == str(sc2.get_transform())
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assert sc._transform is not sc2._transform
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class TestAsinhScale:
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def test_transforms(self):
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a0 = 17.0
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a = np.linspace(-50, 50, 100)
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forward = AsinhTransform(a0)
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inverse = forward.inverted()
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invinv = inverse.inverted()
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a_forward = forward.transform_non_affine(a)
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a_inverted = inverse.transform_non_affine(a_forward)
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assert_allclose(a_inverted, a)
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a_invinv = invinv.transform_non_affine(a)
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assert_allclose(a_invinv, a0 * np.arcsinh(a / a0))
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def test_init(self):
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fig, ax = plt.subplots()
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s = AsinhScale(axis=None, linear_width=23.0)
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assert s.linear_width == 23
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assert s._base == 10
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assert s._subs == (2, 5)
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tx = s.get_transform()
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assert isinstance(tx, AsinhTransform)
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assert tx.linear_width == s.linear_width
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def test_base_init(self):
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fig, ax = plt.subplots()
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s3 = AsinhScale(axis=None, base=3)
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assert s3._base == 3
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assert s3._subs == (2,)
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s7 = AsinhScale(axis=None, base=7, subs=(2, 4))
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assert s7._base == 7
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assert s7._subs == (2, 4)
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def test_fmtloc(self):
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class DummyAxis:
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def __init__(self):
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self.fields = {}
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def set(self, **kwargs):
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self.fields.update(**kwargs)
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def set_major_formatter(self, f):
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self.fields['major_formatter'] = f
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ax0 = DummyAxis()
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s0 = AsinhScale(axis=ax0, base=0)
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s0.set_default_locators_and_formatters(ax0)
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assert isinstance(ax0.fields['major_locator'], AsinhLocator)
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assert isinstance(ax0.fields['major_formatter'], str)
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ax5 = DummyAxis()
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s7 = AsinhScale(axis=ax5, base=5)
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s7.set_default_locators_and_formatters(ax5)
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assert isinstance(ax5.fields['major_locator'], AsinhLocator)
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assert isinstance(ax5.fields['major_formatter'],
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LogFormatterSciNotation)
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def test_bad_scale(self):
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fig, ax = plt.subplots()
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with pytest.raises(ValueError):
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AsinhScale(axis=None, linear_width=0)
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with pytest.raises(ValueError):
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AsinhScale(axis=None, linear_width=-1)
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s0 = AsinhScale(axis=None, )
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s1 = AsinhScale(axis=None, linear_width=3.0)
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def test_custom_scale_without_axis():
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"""
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Test that one can register and use custom scales that don't take an *axis* param.
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"""
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class CustomTransform(IdentityTransform):
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pass
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class CustomScale(mscale.ScaleBase):
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name = "custom"
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# Important: __init__ has no *axis* parameter
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def __init__(self):
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self._transform = CustomTransform()
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def get_transform(self):
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return self._transform
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def set_default_locators_and_formatters(self, axis):
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axis.set_major_locator(AutoLocator())
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axis.set_major_formatter(ScalarFormatter())
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axis.set_minor_locator(NullLocator())
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axis.set_minor_formatter(NullFormatter())
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try:
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mscale.register_scale(CustomScale)
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fig, ax = plt.subplots()
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ax.set_xscale('custom')
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assert isinstance(ax.xaxis.get_transform(), CustomTransform)
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finally:
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# cleanup - there's no public unregister_scale()
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del mscale._scale_mapping["custom"]
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del mscale._scale_has_axis_parameter["custom"]
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def test_custom_scale_with_axis():
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"""
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Test that one can still register and use custom scales with an *axis*
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parameter, but that registering issues a pending-deprecation warning.
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"""
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class CustomTransform(IdentityTransform):
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pass
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class CustomScale(mscale.ScaleBase):
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name = "custom"
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# Important: __init__ still has the *axis* parameter
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def __init__(self, axis):
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self._transform = CustomTransform()
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def get_transform(self):
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return self._transform
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def set_default_locators_and_formatters(self, axis):
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axis.set_major_locator(AutoLocator())
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axis.set_major_formatter(ScalarFormatter())
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axis.set_minor_locator(NullLocator())
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axis.set_minor_formatter(NullFormatter())
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try:
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with pytest.warns(
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PendingDeprecationWarning,
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match=r"'axis' parameter .* is pending-deprecated"):
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mscale.register_scale(CustomScale)
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fig, ax = plt.subplots()
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ax.set_xscale('custom')
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assert isinstance(ax.xaxis.get_transform(), CustomTransform)
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finally:
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# cleanup - there's no public unregister_scale()
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del mscale._scale_mapping["custom"]
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del mscale._scale_has_axis_parameter["custom"]
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def test_val_in_range():
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test_cases = [
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# LinearScale: Always True (even for Inf/NaN)
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('linear', 10.0, True),
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('linear', -10.0, True),
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('linear', 0.0, True),
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('linear', np.inf, False),
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('linear', np.nan, False),
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# LogScale: Only positive values (> 0)
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('log', 1.0, True),
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('log', 1e-300, True),
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('log', 0.0, False),
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('log', -1.0, False),
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('log', np.inf, False),
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('log', np.nan, False),
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# LogitScale: Strictly between 0 and 1
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('logit', 0.5, True),
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('logit', 0.0, False),
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('logit', 1.0, False),
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('logit', -0.1, False),
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('logit', 1.1, False),
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('logit', np.inf, False),
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('logit', np.nan, False),
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# SymmetricalLogScale: Valid for all real numbers
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# Uses ScaleBase fallback. NaN returns False since NaN != NaN
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('symlog', 10.0, True),
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('symlog', -10.0, True),
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('symlog', 0.0, True),
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('symlog', np.inf, False),
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('symlog', np.nan, False),
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]
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for name, val, expected in test_cases:
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scale_cls = mscale._scale_mapping[name]
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s = scale_cls(axis=None)
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result = s.val_in_range(val)
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assert result is expected, (
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f"Failed {name}.val_in_range({val})."
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f"Expected {expected}, got {result}"
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)
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def test_val_in_range_base_fallback():
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# Directly test the ScaleBase fallback for custom scales.
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# ScaleBase.limit_range_for_scale returns values unchanged by default
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s = mscale.ScaleBase(axis=None)
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# Normal values should be True
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assert s.val_in_range(1.0) is True
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assert s.val_in_range(-5.5) is True
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# NaN and Inf returns False since they cannot be drawn in a plot
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assert s.val_in_range(np.nan) is False
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assert s.val_in_range(np.inf) is False
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assert s.val_in_range(-np.inf) is False
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def test_val_in_range_array():
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# Vectorized: scalar in -> scalar bool, array in -> bool array.
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arr = np.array([0.5, -1.0, 0.0, np.nan, np.inf, 0.25])
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cases = {
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'linear': [True, True, True, False, False, True],
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'log': [True, False, False, False, False, True],
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'symlog': [True, True, True, False, False, True],
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'asinh': [True, True, True, False, False, True],
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'logit': [True, False, False, False, False, True],
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}
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for name, expected in cases.items():
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s = mscale._scale_mapping[name](axis=None)
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np.testing.assert_array_equal(s.val_in_range(arr), expected)
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# 2D shape is preserved.
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out = mscale._scale_mapping['log'](axis=None).val_in_range(
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np.array([[1.0, -1.0], [0.5, np.nan]]))
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np.testing.assert_array_equal(out, [[True, False], [True, False]])
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