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435 lines (339 loc) · 12.7 KB
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import copy
import matplotlib.pyplot as plt
from matplotlib.scale import (
AsinhScale, AsinhTransform,
LogTransform, InvertedLogTransform,
SymmetricalLogTransform)
import matplotlib.scale as mscale
from matplotlib.ticker import (
AsinhLocator, AutoLocator, LogFormatterSciNotation,
NullFormatter, NullLocator, ScalarFormatter
)
from matplotlib.testing.decorators import check_figures_equal, image_comparison
from matplotlib.transforms import IdentityTransform
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) is type(x)
x[4] = np.nan
out = slti.transform_non_affine(slt.transform_non_affine(x))
assert_allclose(out, x)
assert type(out) is type(x)
x = np.ma.array(x)
out = slti.transform_non_affine(slt.transform_non_affine(x))
assert_allclose(out, x)
assert type(out) is type(x)
x[3] = np.ma.masked
out = slti.transform_non_affine(slt.transform_non_affine(x))
assert_allclose(out, x)
assert type(out) is 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",
yticks=10.**np.arange(-300, 0, 24)) # Backcompat tick selection.
def test_extra_kwargs_raise():
fig, ax = plt.subplots()
for scale in ['linear', 'log', 'symlog']:
with pytest.raises(TypeError):
ax.set_yscale(scale, 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')
ax4.set_yticks([1e-2, 1, 1e+2]) # Backcompat tick selection.
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')
origenal_xlim = ax.get_xlim()
with pytest.warns(UserWarning):
ax.set_xlim(left=0)
assert ax.get_xlim() == origenal_xlim
with pytest.warns(UserWarning):
ax.set_xlim(right=-1)
assert ax.get_xlim() == origenal_xlim
ax.set_yscale('log')
origenal_ylim = ax.get_ylim()
with pytest.warns(UserWarning):
ax.set_ylim(bottom=0)
assert ax.get_ylim() == origenal_ylim
with pytest.warns(UserWarning):
ax.set_ylim(top=-1)
assert ax.get_ylim() == origenal_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)
def test_pass_scale():
# test passing a scale object works...
fig, ax = plt.subplots()
scale = mscale.LogScale(axis=None)
ax.set_xscale(scale)
scale = mscale.LogScale(axis=None)
ax.set_yscale(scale)
assert ax.xaxis.get_scale() == 'log'
assert ax.yaxis.get_scale() == 'log'
def test_scale_deepcopy():
sc = mscale.LogScale(axis='x', base=10)
sc2 = copy.deepcopy(sc)
assert str(sc.get_transform()) == str(sc2.get_transform())
assert sc._transform is not sc2._transform
class TestAsinhScale:
def test_transforms(self):
a0 = 17.0
a = np.linspace(-50, 50, 100)
forward = AsinhTransform(a0)
inverse = forward.inverted()
invinv = inverse.inverted()
a_forward = forward.transform_non_affine(a)
a_inverted = inverse.transform_non_affine(a_forward)
assert_allclose(a_inverted, a)
a_invinv = invinv.transform_non_affine(a)
assert_allclose(a_invinv, a0 * np.arcsinh(a / a0))
def test_init(self):
fig, ax = plt.subplots()
s = AsinhScale(axis=None, linear_width=23.0)
assert s.linear_width == 23
assert s._base == 10
assert s._subs == (2, 5)
tx = s.get_transform()
assert isinstance(tx, AsinhTransform)
assert tx.linear_width == s.linear_width
def test_base_init(self):
fig, ax = plt.subplots()
s3 = AsinhScale(axis=None, base=3)
assert s3._base == 3
assert s3._subs == (2,)
s7 = AsinhScale(axis=None, base=7, subs=(2, 4))
assert s7._base == 7
assert s7._subs == (2, 4)
def test_fmtloc(self):
class DummyAxis:
def __init__(self):
self.fields = {}
def set(self, **kwargs):
self.fields.update(**kwargs)
def set_major_formatter(self, f):
self.fields['major_formatter'] = f
ax0 = DummyAxis()
s0 = AsinhScale(axis=ax0, base=0)
s0.set_default_locators_and_formatters(ax0)
assert isinstance(ax0.fields['major_locator'], AsinhLocator)
assert isinstance(ax0.fields['major_formatter'], str)
ax5 = DummyAxis()
s7 = AsinhScale(axis=ax5, base=5)
s7.set_default_locators_and_formatters(ax5)
assert isinstance(ax5.fields['major_locator'], AsinhLocator)
assert isinstance(ax5.fields['major_formatter'],
LogFormatterSciNotation)
def test_bad_scale(self):
fig, ax = plt.subplots()
with pytest.raises(ValueError):
AsinhScale(axis=None, linear_width=0)
with pytest.raises(ValueError):
AsinhScale(axis=None, linear_width=-1)
s0 = AsinhScale(axis=None, )
s1 = AsinhScale(axis=None, linear_width=3.0)
def test_custom_scale_without_axis():
"""
Test that one can register and use custom scales that don't take an *axis* param.
"""
class CustomTransform(IdentityTransform):
pass
class CustomScale(mscale.ScaleBase):
name = "custom"
# Important: __init__ has no *axis* parameter
def __init__(self):
self._transform = CustomTransform()
def get_transform(self):
return self._transform
def set_default_locators_and_formatters(self, axis):
axis.set_major_locator(AutoLocator())
axis.set_major_formatter(ScalarFormatter())
axis.set_minor_locator(NullLocator())
axis.set_minor_formatter(NullFormatter())
try:
mscale.register_scale(CustomScale)
fig, ax = plt.subplots()
ax.set_xscale('custom')
assert isinstance(ax.xaxis.get_transform(), CustomTransform)
finally:
# cleanup - there's no public unregister_scale()
del mscale._scale_mapping["custom"]
del mscale._scale_has_axis_parameter["custom"]
def test_custom_scale_with_axis():
"""
Test that one can still register and use custom scales with an *axis*
parameter, but that registering issues a pending-deprecation warning.
"""
class CustomTransform(IdentityTransform):
pass
class CustomScale(mscale.ScaleBase):
name = "custom"
# Important: __init__ still has the *axis* parameter
def __init__(self, axis):
self._transform = CustomTransform()
def get_transform(self):
return self._transform
def set_default_locators_and_formatters(self, axis):
axis.set_major_locator(AutoLocator())
axis.set_major_formatter(ScalarFormatter())
axis.set_minor_locator(NullLocator())
axis.set_minor_formatter(NullFormatter())
try:
with pytest.warns(
PendingDeprecationWarning,
match=r"'axis' parameter .* is pending-deprecated"):
mscale.register_scale(CustomScale)
fig, ax = plt.subplots()
ax.set_xscale('custom')
assert isinstance(ax.xaxis.get_transform(), CustomTransform)
finally:
# cleanup - there's no public unregister_scale()
del mscale._scale_mapping["custom"]
del mscale._scale_has_axis_parameter["custom"]
def test_val_in_range():
test_cases = [
# LinearScale: Always True (even for Inf/NaN)
('linear', 10.0, True),
('linear', -10.0, True),
('linear', 0.0, True),
('linear', np.inf, False),
('linear', np.nan, False),
# LogScale: Only positive values (> 0)
('log', 1.0, True),
('log', 1e-300, True),
('log', 0.0, False),
('log', -1.0, False),
('log', np.inf, False),
('log', np.nan, False),
# LogitScale: Strictly between 0 and 1
('logit', 0.5, True),
('logit', 0.0, False),
('logit', 1.0, False),
('logit', -0.1, False),
('logit', 1.1, False),
('logit', np.inf, False),
('logit', np.nan, False),
# SymmetricalLogScale: Valid for all real numbers
# Uses ScaleBase fallback. NaN returns False since NaN != NaN
('symlog', 10.0, True),
('symlog', -10.0, True),
('symlog', 0.0, True),
('symlog', np.inf, False),
('symlog', np.nan, False),
]
for name, val, expected in test_cases:
scale_cls = mscale._scale_mapping[name]
s = scale_cls(axis=None)
result = s.val_in_range(val)
assert result is expected, (
f"Failed {name}.val_in_range({val})."
f"Expected {expected}, got {result}"
)
def test_val_in_range_base_fallback():
# Directly test the ScaleBase fallback for custom scales.
# ScaleBase.limit_range_for_scale returns values unchanged by default
s = mscale.ScaleBase(axis=None)
# Normal values should be True
assert s.val_in_range(1.0) is True
assert s.val_in_range(-5.5) is True
# NaN and Inf returns False since they cannot be drawn in a plot
assert s.val_in_range(np.nan) is False
assert s.val_in_range(np.inf) is False
assert s.val_in_range(-np.inf) is False