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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, LogFormatterSciNotation

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(extensions=['png'])

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")

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')

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)

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)