demo + utils venv

utils/venv/lib/python3.6/site-packages/matplotlib/tests/test_scale.py

@@ -0,0 +1,150 @@
+from matplotlib.testing.decorators import image_comparison
+import matplotlib.pyplot as plt
+from matplotlib.scale import Log10Transform, InvertedLog10Transform
+import numpy as np
+import io
+import platform
+import pytest
+
+
+@image_comparison(baseline_images=['log_scales'], remove_text=True)
+def test_log_scales():
+    ax = plt.figure().add_subplot(122, yscale='log', xscale='symlog')
+
+    ax.axvline(24.1)
+    ax.axhline(24.1)
+
+
+@image_comparison(baseline_images=['logit_scales'], remove_text=True,
+                  extensions=['png'])
+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', subsy=np.array([2, 3, 4]))
+    # force draw
+    fig.canvas.draw()
+
+
+@image_comparison(baseline_images=['logscale_mask'], remove_text=True,
+                  extensions=['png'])
+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():
+    fig, ax = plt.subplots()
+    with pytest.raises(ValueError):
+        ax.set_yscale('log', nonpos='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
+    assert isinstance(Log10Transform().inverted(), InvertedLog10Transform)
+
+
+def test_logscale_transform_repr():
+    # check that repr of log transform succeeds
+    fig, ax = plt.subplots()
+    ax.set_yscale('log')
+    s = repr(ax.transData)
+
+    # check that repr of log transform succeeds
+    s = repr(Log10Transform(nonpos='clip'))
+
+
+@image_comparison(baseline_images=['logscale_nonpos_values'], remove_text=True,
+                  tol={'aarch64': 0.02}.get(platform.machine(), 0.0),
+                  extensions=['png'], 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', nonposy='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