Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/matplotlib/tests/test_scale.py

@@ -0,0 +1,206 @@
+from matplotlib.cbook import MatplotlibDeprecationWarning
+import matplotlib.pyplot as plt
+from matplotlib.scale import (
+    LogTransform, InvertedLogTransform,
+    SymmetricalLogTransform)
+from matplotlib.testing.decorators import check_figures_equal, image_comparison
+
+import numpy as np
+from numpy.testing import assert_allclose
+import io
+import pytest
+
+
+@check_figures_equal()
+def test_log_scales(fig_test, fig_ref):
+    ax_test = fig_test.add_subplot(122, yscale='log', xscale='symlog')
+    ax_test.axvline(24.1)
+    ax_test.axhline(24.1)
+    xlim = ax_test.get_xlim()
+    ylim = ax_test.get_ylim()
+    ax_ref = fig_ref.add_subplot(122, yscale='log', xscale='symlog')
+    ax_ref.set(xlim=xlim, ylim=ylim)
+    ax_ref.plot([24.1, 24.1], ylim, 'b')
+    ax_ref.plot(xlim, [24.1, 24.1], 'b')
+
+
+def test_symlog_mask_nan():
+    # Use a transform round-trip to verify that the forward and inverse
+    # transforms work, and that they respect nans and/or masking.
+    slt = SymmetricalLogTransform(10, 2, 1)
+    slti = slt.inverted()
+
+    x = np.arange(-1.5, 5, 0.5)
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+    x[4] = np.nan
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+    x = np.ma.array(x)
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+    x[3] = np.ma.masked
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+
+@image_comparison(['logit_scales.png'], remove_text=True)
+def test_logit_scales():
+    fig, ax = plt.subplots()
+
+    # Typical extinction curve for logit
+    x = np.array([0.001, 0.003, 0.01, 0.03, 0.1, 0.2, 0.3, 0.4, 0.5,
+                  0.6, 0.7, 0.8, 0.9, 0.97, 0.99, 0.997, 0.999])
+    y = 1.0 / x
+
+    ax.plot(x, y)
+    ax.set_xscale('logit')
+    ax.grid(True)
+    bbox = ax.get_tightbbox(fig.canvas.get_renderer())
+    assert np.isfinite(bbox.x0)
+    assert np.isfinite(bbox.y0)
+
+
+def test_log_scatter():
+    """Issue #1799"""
+    fig, ax = plt.subplots(1)
+
+    x = np.arange(10)
+    y = np.arange(10) - 1
+
+    ax.scatter(x, y)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format='pdf')
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format='eps')
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format='svg')
+
+
+def test_logscale_subs():
+    fig, ax = plt.subplots()
+    ax.set_yscale('log', subs=np.array([2, 3, 4]))
+    # force draw
+    fig.canvas.draw()
+
+
+@image_comparison(['logscale_mask.png'], remove_text=True)
+def test_logscale_mask():
+    # Check that zero values are masked correctly on log scales.
+    # See github issue 8045
+    xs = np.linspace(0, 50, 1001)
+
+    fig, ax = plt.subplots()
+    ax.plot(np.exp(-xs**2))
+    fig.canvas.draw()
+    ax.set(yscale="log")
+
+
+def test_extra_kwargs_raise_or_warn():
+    fig, ax = plt.subplots()
+
+    with pytest.warns(MatplotlibDeprecationWarning):
+        ax.set_yscale('linear', foo='mask')
+
+    with pytest.raises(TypeError):
+        ax.set_yscale('log', foo='mask')
+
+    with pytest.warns(MatplotlibDeprecationWarning):
+        ax.set_yscale('symlog', foo='mask')
+
+
+def test_logscale_invert_transform():
+    fig, ax = plt.subplots()
+    ax.set_yscale('log')
+    # get transformation from data to axes
+    tform = (ax.transAxes + ax.transData.inverted()).inverted()
+
+    # direct test of log transform inversion
+    inverted_transform = LogTransform(base=2).inverted()
+    assert isinstance(inverted_transform, InvertedLogTransform)
+    assert inverted_transform.base == 2
+
+
+def test_logscale_transform_repr():
+    fig, ax = plt.subplots()
+    ax.set_yscale('log')
+    repr(ax.transData)
+    repr(LogTransform(10, nonpositive='clip'))
+
+
+@image_comparison(['logscale_nonpos_values.png'],
+                  remove_text=True, tol=0.02, style='mpl20')
+def test_logscale_nonpos_values():
+    np.random.seed(19680801)
+    xs = np.random.normal(size=int(1e3))
+    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
+    ax1.hist(xs, range=(-5, 5), bins=10)
+    ax1.set_yscale('log')
+    ax2.hist(xs, range=(-5, 5), bins=10)
+    ax2.set_yscale('log', nonpositive='mask')
+
+    xdata = np.arange(0, 10, 0.01)
+    ydata = np.exp(-xdata)
+    edata = 0.2*(10-xdata)*np.cos(5*xdata)*np.exp(-xdata)
+
+    ax3.fill_between(xdata, ydata - edata, ydata + edata)
+    ax3.set_yscale('log')
+
+    x = np.logspace(-1, 1)
+    y = x ** 3
+    yerr = x**2
+    ax4.errorbar(x, y, yerr=yerr)
+
+    ax4.set_yscale('log')
+    ax4.set_xscale('log')
+
+
+def test_invalid_log_lims():
+    # Check that invalid log scale limits are ignored
+    fig, ax = plt.subplots()
+    ax.scatter(range(0, 4), range(0, 4))
+
+    ax.set_xscale('log')
+    original_xlim = ax.get_xlim()
+    with pytest.warns(UserWarning):
+        ax.set_xlim(left=0)
+    assert ax.get_xlim() == original_xlim
+    with pytest.warns(UserWarning):
+        ax.set_xlim(right=-1)
+    assert ax.get_xlim() == original_xlim
+
+    ax.set_yscale('log')
+    original_ylim = ax.get_ylim()
+    with pytest.warns(UserWarning):
+        ax.set_ylim(bottom=0)
+    assert ax.get_ylim() == original_ylim
+    with pytest.warns(UserWarning):
+        ax.set_ylim(top=-1)
+    assert ax.get_ylim() == original_ylim
+
+
+@image_comparison(['function_scales.png'], remove_text=True, style='mpl20')
+def test_function_scale():
+    def inverse(x):
+        return x**2
+
+    def forward(x):
+        return x**(1/2)
+
+    fig, ax = plt.subplots()
+
+    x = np.arange(1, 1000)
+
+    ax.plot(x, x)
+    ax.set_xscale('function', functions=(forward, inverse))
+    ax.set_xlim(1, 1000)