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Copy pathtest_scale.py
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206 lines (155 loc) · 5.53 KB
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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)