basic_data = [

(20, 100, np.array([20., 40., 60., 80., 100.])),

(0.001, 0.0001, np.array([0., 0.0002, 0.0004, 0.0006, 0.0008, 0.001])),

(-1e15, 1e15, np.array([-1.0e+15, -5.0e+14, 0e+00, 5e+14, 1.0e+15])),

(0, 0.85e-50, np.arange(6) * 2e-51),

(-0.85e-50, 0, np.arange(-5, 1) * 2e-51),

]

integer_data = [

(-0.1, 1.1, None, np.array([-1, 0, 1, 2])),

(-0.1, 0.95, None, np.array([-0.25, 0, 0.25, 0.5, 0.75, 1.0])),

(1, 55, [1, 1.5, 5, 6, 10], np.array([0, 15, 30, 45, 60])),

]

@pytest.mark.parametrize('vmin, vmax, expected', basic_data)

def test_basic(self, vmin, vmax, expected):

loc = mticker.MaxNLocator(nbins=5)

assert_almost_equal(loc.tick_values(vmin, vmax), expected)

@pytest.mark.parametrize('vmin, vmax, steps, expected', integer_data)

def test_integer(self, vmin, vmax, steps, expected):

loc = mticker.MaxNLocator(nbins=5, integer=True, steps=steps)

assert_almost_equal(loc.tick_values(vmin, vmax), expected)

@pytest.mark.parametrize('kwargs, errortype, match', [

def test_errors(self, kwargs, errortype, match):

with pytest.raises(errortype, match=match):

mticker.MaxNLocator(**kwargs)

@pytest.mark.parametrize('steps, result', [

def test_padding(self, steps, result):

loc = mticker.MaxNLocator(steps=steps)

def test_basic(self):

loc = mticker.LinearLocator(numticks=3)

test_value = np.array([-0.8, -0.3, 0.2])

assert_almost_equal(loc.tick_values(-0.8, 0.2), test_value)

def test_zero_numticks(self):

loc = mticker.LinearLocator(numticks=0)

loc.tick_values(-0.8, 0.2) == []

def test_set_params(self):

"""

loc = mticker.LinearLocator(numticks=2)

loc.set_params(numticks=8, presets={(0, 1): []})

assert loc.numticks == 8

assert loc.presets == {(0, 1): []}

def test_presets(self):

loc = mticker.LinearLocator(presets={(1, 2): [1, 1.25, 1.75],

(0, 2): [0.5, 1.5]})

assert loc.tick_values(1, 2) == [1, 1.25, 1.75]

assert loc.tick_values(2, 1) == [1, 1.25, 1.75]

assert loc.tick_values(0, 2) == [0.5, 1.5]

assert loc.tick_values(0.0, 2.0) == [0.5, 1.5]

def test_basic(self):

loc = mticker.MultipleLocator(base=3.147)

test_value = np.array([-9.441, -6.294, -3.147, 0., 3.147, 6.294,

9.441, 12.588])

assert_almost_equal(loc.tick_values(-7, 10), test_value)

def test_basic_with_offset(self):

loc = mticker.MultipleLocator(base=3.147, offset=1.2)

test_value = np.array([-8.241, -5.094, -1.947, 1.2, 4.347, 7.494,

10.641])

assert_almost_equal(loc.tick_values(-7, 10), test_value)

def test_view_limits(self):

"""

with mpl.rc_context({'axes.autolimit_mode': 'data'}):

loc = mticker.MultipleLocator(base=3.147)

assert_almost_equal(loc.view_limits(-5, 5), (-5, 5))

def test_view_limits_round_numbers(self):

"""

with mpl.rc_context({'axes.autolimit_mode': 'round_numbers'}):

loc = mticker.MultipleLocator(base=3.147)

assert_almost_equal(loc.view_limits(-4, 4), (-6.294, 6.294))

def test_view_limits_round_numbers_with_offset(self):

"""

with mpl.rc_context({'axes.autolimit_mode': 'round_numbers'}):

loc = mticker.MultipleLocator(base=3.147, offset=1.3)

assert_almost_equal(loc.view_limits(-4, 4), (-4.994, 4.447))

def test_view_limits_single_bin(self):

"""

with mpl.rc_context({'axes.autolimit_mode': 'round_numbers'}):

loc = mticker.MaxNLocator(nbins=1)

assert_almost_equal(loc.view_limits(-2.3, 2.3), (-4, 4))

def test_set_params(self):

"""

mult = mticker.MultipleLocator(base=0.7)

mult.set_params(base=1.7)

assert mult._edge.step == 1.7

mult.set_params(offset=3)

def test_basic(self):

fig, ax = plt.subplots()

ax.set_xlim(0, 1.39)

ax.minorticks_on()

test_value = np.array([0.05, 0.1, 0.15, 0.25, 0.3, 0.35, 0.45,

0.5, 0.55, 0.65, 0.7, 0.75, 0.85, 0.9,

0.95, 1.05, 1.1, 1.15, 1.25, 1.3, 1.35])

assert_almost_equal(ax.xaxis.get_ticklocs(minor=True), test_value)

# NB: the following values are assuming that *xlim* is [0, 5]

params = [

(0, 0), # no major tick => no minor tick either

(1, 0) # a single major tick => no minor tick

]

def test_first_and_last_minorticks(self):

"""

# This test is related to issue #22331

fig, ax = plt.subplots()

ax.set_xlim(-1.9, 1.9)

ax.xaxis.set_minor_locator(mticker.AutoMinorLocator())

test_value = np.array([-1.9, -1.8, -1.7, -1.6, -1.4, -1.3, -1.2, -1.1,

-0.9, -0.8, -0.7, -0.6, -0.4, -0.3, -0.2, -0.1,

0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9, 1.1,

1.2, 1.3, 1.4, 1.6, 1.7, 1.8, 1.9])

assert_almost_equal(ax.xaxis.get_ticklocs(minor=True), test_value)

ax.set_xlim(-5, 5)

test_value = np.array([-5.0, -4.5, -3.5, -3.0, -2.5, -1.5, -1.0, -0.5,

0.5, 1.0, 1.5, 2.5, 3.0, 3.5, 4.5, 5.0])

assert_almost_equal(ax.xaxis.get_ticklocs(minor=True), test_value)

@pytest.mark.parametrize('nb_majorticks, expected_nb_minorticks', params)

def test_low_number_of_majorticks(

self, nb_majorticks, expected_nb_minorticks):

# This test is related to issue #8804

fig, ax = plt.subplots()

xlims = (0, 5) # easier to test the different code paths

ax.set_xlim(*xlims)

ax.set_xticks(np.linspace(xlims[0], xlims[1], nb_majorticks))

ax.minorticks_on()

ax.xaxis.set_minor_locator(mticker.AutoMinorLocator())

assert len(ax.xaxis.get_minorticklocs()) == expected_nb_minorticks

majorstep_minordivisions = [(1, 5),

(2, 4),

(2.5, 5),

(5, 5),

(10, 5)]

# This test is meant to verify the parameterization for

# test_number_of_minor_ticks

def test_using_all_default_major_steps(self):

with mpl.rc_context({'_internal.classic_mode': False}):

majorsteps = [x[0] for x in self.majorstep_minordivisions]

np.testing.assert_allclose(majorsteps,

mticker.AutoLocator()._steps)

@pytest.mark.parametrize('major_step, expected_nb_minordivisions',

def test_number_of_minor_ticks(

self, major_step, expected_nb_minordivisions):

fig, ax = plt.subplots()

xlims = (0, major_step)

ax.set_xlim(*xlims)

ax.set_xticks(xlims)

ax.minorticks_on()

ax.xaxis.set_minor_locator(mticker.AutoMinorLocator())

nb_minor_divisions = len(ax.xaxis.get_minorticklocs()) + 1

assert nb_minor_divisions == expected_nb_minordivisions

limits = [(0, 1.39), (0, 0.139),

(0, 0.11e-19), (0, 0.112e-12),

(-2.0e-07, -3.3e-08), (1.20e-06, 1.42e-06),

(-1.34e-06, -1.44e-06), (-8.76e-07, -1.51e-06)]

reference = [

[0.05, 0.1, 0.15, 0.25, 0.3, 0.35, 0.45, 0.5, 0.55, 0.65, 0.7,

0.75, 0.85, 0.9, 0.95, 1.05, 1.1, 1.15, 1.25, 1.3, 1.35],

[0.005, 0.01, 0.015, 0.025, 0.03, 0.035, 0.045, 0.05, 0.055, 0.065,

0.07, 0.075, 0.085, 0.09, 0.095, 0.105, 0.11, 0.115, 0.125, 0.13,

0.135],

[5.00e-22, 1.00e-21, 1.50e-21, 2.50e-21, 3.00e-21, 3.50e-21, 4.50e-21,

5.00e-21, 5.50e-21, 6.50e-21, 7.00e-21, 7.50e-21, 8.50e-21, 9.00e-21,

9.50e-21, 1.05e-20, 1.10e-20],

[5.00e-15, 1.00e-14, 1.50e-14, 2.50e-14, 3.00e-14, 3.50e-14, 4.50e-14,

5.00e-14, 5.50e-14, 6.50e-14, 7.00e-14, 7.50e-14, 8.50e-14, 9.00e-14,

9.50e-14, 1.05e-13, 1.10e-13],

[-1.95e-07, -1.90e-07, -1.85e-07, -1.75e-07, -1.70e-07, -1.65e-07,

-1.55e-07, -1.50e-07, -1.45e-07, -1.35e-07, -1.30e-07, -1.25e-07,

-1.15e-07, -1.10e-07, -1.05e-07, -9.50e-08, -9.00e-08, -8.50e-08,

-7.50e-08, -7.00e-08, -6.50e-08, -5.50e-08, -5.00e-08, -4.50e-08,

-3.50e-08],

[1.21e-06, 1.22e-06, 1.23e-06, 1.24e-06, 1.26e-06, 1.27e-06, 1.28e-06,

1.29e-06, 1.31e-06, 1.32e-06, 1.33e-06, 1.34e-06, 1.36e-06, 1.37e-06,

1.38e-06, 1.39e-06, 1.41e-06, 1.42e-06],

[-1.435e-06, -1.430e-06, -1.425e-06, -1.415e-06, -1.410e-06,

-1.405e-06, -1.395e-06, -1.390e-06, -1.385e-06, -1.375e-06,

-1.370e-06, -1.365e-06, -1.355e-06, -1.350e-06, -1.345e-06],

[-1.48e-06, -1.46e-06, -1.44e-06, -1.42e-06, -1.38e-06, -1.36e-06,

-1.34e-06, -1.32e-06, -1.28e-06, -1.26e-06, -1.24e-06, -1.22e-06,

-1.18e-06, -1.16e-06, -1.14e-06, -1.12e-06, -1.08e-06, -1.06e-06,

-1.04e-06, -1.02e-06, -9.80e-07, -9.60e-07, -9.40e-07, -9.20e-07,

-8.80e-07]]

additional_data = list(zip(limits, reference))

@pytest.mark.parametrize('lim, ref', additional_data)

def test_additional(self, lim, ref):

fig, ax = plt.subplots()

ax.minorticks_on()

ax.grid(True, 'minor', 'y', linewidth=1)

ax.grid(True, 'major', color='k', linewidth=1)

ax.set_ylim(lim)

assert_almost_equal(ax.yaxis.get_ticklocs(minor=True), ref)

@pytest.mark.parametrize('use_rcparam', [False, True])

@pytest.mark.parametrize(

def test_number_of_minor_ticks_auto(self, lim, ref, use_rcparam):

if use_rcparam:

context = {'xtick.minor.ndivs': 'auto', 'ytick.minor.ndivs': 'auto'}

kwargs = {}

else:

context = {}

kwargs = {'n': 'auto'}

with mpl.rc_context(context):

fig, ax = plt.subplots()

ax.set_xlim(*lim)

ax.set_ylim(*lim)

ax.xaxis.set_minor_locator(mticker.AutoMinorLocator(**kwargs))

ax.yaxis.set_minor_locator(mticker.AutoMinorLocator(**kwargs))

assert_almost_equal(ax.xaxis.get_ticklocs(minor=True), ref)

assert_almost_equal(ax.yaxis.get_ticklocs(minor=True), ref)

@pytest.mark.parametrize('use_rcparam', [False, True])

@pytest.mark.parametrize(

def test_number_of_minor_ticks_int(self, n, lim, ref, use_rcparam):

if use_rcparam:

context = {'xtick.minor.ndivs': n, 'ytick.minor.ndivs': n}

kwargs = {}

else:

context = {}

kwargs = {'n': n}

with mpl.rc_context(context):

fig, ax = plt.subplots()

ax.set_xlim(*lim)

ax.set_ylim(*lim)

ax.xaxis.set_major_locator(mticker.MultipleLocator(1))

ax.xaxis.set_minor_locator(mticker.AutoMinorLocator(**kwargs))

ax.yaxis.set_major_locator(mticker.MultipleLocator(1))

ax.yaxis.set_minor_locator(mticker.AutoMinorLocator(**kwargs))

assert_almost_equal(ax.xaxis.get_ticklocs(minor=True), ref)

def test_basic(self):

loc = mticker.LogLocator(numticks=5)

with pytest.raises(ValueError):

loc.tick_values(0, 1000)

test_value = np.array([1.00000000e-05, 1.00000000e-03, 1.00000000e-01,

1.00000000e+01, 1.00000000e+03, 1.00000000e+05,

1.00000000e+07, 1.000000000e+09])

assert_almost_equal(loc.tick_values(0.001, 1.1e5), test_value)

loc = mticker.LogLocator(base=2)

test_value = np.array([0.5, 1., 2., 4., 8., 16., 32., 64., 128., 256.])

assert_almost_equal(loc.tick_values(1, 100), test_value)

def test_polar_axes(self):

"""

fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})

ax.set_yscale('log')

ax.set_ylim(1, 100)

assert_array_equal(ax.get_yticks(), [10, 100, 1000])

def test_switch_to_autolocator(self):

loc = mticker.LogLocator(subs="all")

assert_array_equal(loc.tick_values(0.45, 0.55),

[0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56])

# check that we *skip* 1.0, and 10, because this is a minor locator

loc = mticker.LogLocator(subs=np.arange(2, 10))

assert 1.0 not in loc.tick_values(0.9, 20.)

assert 10.0 not in loc.tick_values(0.9, 20.)

def test_set_params(self):

"""

loc = mticker.LogLocator()

loc.set_params(numticks=7, subs=[2.0], base=4)

assert loc.numticks == 7

assert loc._base == 4

assert list(loc._subs) == [2.0]

def test_tick_values_correct(self):

ll = mticker.LogLocator(subs=(1, 2, 5))

test_value = np.array([1.e-01, 2.e-01, 5.e-01, 1.e+00, 2.e+00, 5.e+00,

1.e+01, 2.e+01, 5.e+01, 1.e+02, 2.e+02, 5.e+02,

1.e+03, 2.e+03, 5.e+03, 1.e+04, 2.e+04, 5.e+04,

1.e+05, 2.e+05, 5.e+05, 1.e+06, 2.e+06, 5.e+06,

1.e+07, 2.e+07, 5.e+07, 1.e+08, 2.e+08, 5.e+08])

assert_almost_equal(ll.tick_values(1, 1e7), test_value)

def test_tick_values_not_empty(self):

mpl.rcParams['_internal.classic_mode'] = False

ll = mticker.LogLocator(subs=(1, 2, 5))

test_value = np.array([1.e-01, 2.e-01, 5.e-01, 1.e+00, 2.e+00, 5.e+00,

1.e+01, 2.e+01, 5.e+01, 1.e+02, 2.e+02, 5.e+02,

1.e+03, 2.e+03, 5.e+03, 1.e+04, 2.e+04, 5.e+04,

1.e+05, 2.e+05, 5.e+05, 1.e+06, 2.e+06, 5.e+06,

1.e+07, 2.e+07, 5.e+07, 1.e+08, 2.e+08, 5.e+08,

1.e+09, 2.e+09, 5.e+09])

assert_almost_equal(ll.tick_values(1, 1e8), test_value)

def test_multiple_shared_axes(self):

rng = np.random.default_rng(19680801)

dummy_data = [rng.normal(size=100), [], []]

fig, axes = plt.subplots(len(dummy_data), sharex=True, sharey=True)

for ax, data in zip(axes.flatten(), dummy_data):

ax.hist(data, bins=10)

ax.set_yscale('log', nonpositive='clip')

for ax in axes.flatten():

assert all(ax.get_yticks() == axes[0].get_yticks())

def test_set_params(self):

"""

loc = mticker.NullLocator()

with pytest.warns(UserWarning):

@staticmethod

def isclose(x, y):

return (np.isclose(-np.log(1/x-1), -np.log(1/y-1))

if 0 < x < 1 and 0 < y < 1 else False)

@staticmethod

def assert_almost_equal(x, y):

ax = np.array(x)

ay = np.array(y)

assert np.all(ax > 0) and np.all(ax < 1)

assert np.all(ay > 0) and np.all(ay < 1)

lx = -np.log(1/ax-1)

ly = -np.log(1/ay-1)

ref_basic_limits = [

(5e-2, 1 - 5e-2),

(5e-3, 1 - 5e-3),

(5e-4, 1 - 5e-4),

(5e-5, 1 - 5e-5),

(5e-6, 1 - 5e-6),

(5e-7, 1 - 5e-7),

(5e-8, 1 - 5e-8),

(5e-9, 1 - 5e-9),

]

ref_basic_major_ticks = [

1 / (10 ** np.arange(1, 3)),

1 / (10 ** np.arange(1, 4)),

1 / (10 ** np.arange(1, 5)),

1 / (10 ** np.arange(1, 6)),

1 / (10 ** np.arange(1, 7)),

1 / (10 ** np.arange(1, 8)),

1 / (10 ** np.arange(1, 9)),

1 / (10 ** np.arange(1, 10)),

]

ref_maxn_limits = [(0.4, 0.6), (5e-2, 2e-1), (1 - 2e-1, 1 - 5e-2)]

@pytest.mark.parametrize(

def test_basic_major(self, lims, expected_low_ticks):

"""

expected_ticks = sorted(

[*expected_low_ticks, 0.5, *(1 - expected_low_ticks)]

)

loc = mticker.LogitLocator(nbins=100)

_LogitHelper.assert_almost_equal(

loc.tick_values(*lims),

expected_ticks

)

@pytest.mark.parametrize("lims", ref_maxn_limits)

def test_maxn_major(self, lims):

"""

loc = mticker.LogitLocator(nbins=100)

maxn_loc = mticker.MaxNLocator(nbins=100, steps=[1, 2, 5, 10])

for nbins in (4, 8, 16):

loc.set_params(nbins=nbins)

maxn_loc.set_params(nbins=nbins)

ticks = loc.tick_values(*lims)

maxn_ticks = maxn_loc.tick_values(*lims)

assert ticks.shape == maxn_ticks.shape

assert (ticks == maxn_ticks).all()

@pytest.mark.parametrize("lims", ref_basic_limits + ref_maxn_limits)

def test_nbins_major(self, lims):

"""

basic_needed = int(-np.floor(np.log10(lims[0]))) * 2 + 1

loc = mticker.LogitLocator(nbins=100)

for nbins in range(basic_needed, 2, -1):

loc.set_params(nbins=nbins)

assert len(loc.tick_values(*lims)) <= nbins + 2

@pytest.mark.parametrize(

def test_minor(self, lims, expected_low_ticks):

"""

expected_ticks = sorted(

[*expected_low_ticks, 0.5, *(1 - expected_low_ticks)]

)

basic_needed = len(expected_ticks)

loc = mticker.LogitLocator(nbins=100)

minor_loc = mticker.LogitLocator(nbins=100, minor=True)

for nbins in range(basic_needed, 2, -1):

loc.set_params(nbins=nbins)

minor_loc.set_params(nbins=nbins)

major_ticks = loc.tick_values(*lims)

minor_ticks = minor_loc.tick_values(*lims)

if len(major_ticks) >= len(expected_ticks):

# no subsample, we must have a lot of minors ticks

assert (len(major_ticks) - 1) * 5 < len(minor_ticks)

else:

# subsample

_LogitHelper.assert_almost_equal(

sorted([*major_ticks, *minor_ticks]), expected_ticks)

def test_minor_attr(self):

loc = mticker.LogitLocator(nbins=100)

assert not loc.minor

loc.minor = True

assert loc.minor

loc.set_params(minor=False)

assert not loc.minor

acceptable_vmin_vmax = [

*(2.5 ** np.arange(-3, 0)),

*(1 - 2.5 ** np.arange(-3, 0)),

]

@pytest.mark.parametrize(

def test_nonsingular_ok(self, lims):

"""

loc = mticker.LogitLocator()

lims2 = loc.nonsingular(*lims)

assert sorted(lims) == sorted(lims2)

@pytest.mark.parametrize("okval", acceptable_vmin_vmax)

def test_nonsingular_nok(self, okval):

"""

loc = mticker.LogitLocator()

vmin, vmax = (-1, okval)

vmin2, vmax2 = loc.nonsingular(vmin, vmax)

assert vmax2 == vmax

assert 0 < vmin2 < vmax2

vmin, vmax = (okval, 2)

vmin2, vmax2 = loc.nonsingular(vmin, vmax)

assert vmin2 == vmin

def test_set_params(self):

"""

fixed = mticker.FixedLocator(range(0, 24), nbins=5)

fixed.set_params(nbins=7)

def test_set_params(self):

"""

index = mticker.IndexLocator(base=3, offset=4)

index.set_params(base=7, offset=7)

assert index._base == 7

def test_set_params(self):

"""

sym = mticker.SymmetricalLogLocator(base=10, linthresh=1)

sym.set_params(subs=[2.0], numticks=8)

assert sym._subs == [2.0]

assert sym.numticks == 8

@pytest.mark.parametrize(

def test_values(self, vmin, vmax, expected):

# https://github.com/matplotlib/matplotlib/issues/25945

sym = mticker.SymmetricalLogLocator(base=10, linthresh=1)

ticks = sym.tick_values(vmin=vmin, vmax=vmax)

assert_array_equal(ticks, expected)

def test_subs(self):

sym = mticker.SymmetricalLogLocator(base=10, linthresh=1, subs=[2.0, 4.0])

sym.create_dummy_axis()

sym.axis.set_view_interval(-10, 10)

assert_array_equal(sym(), [-20, -40, -2, -4, 0, 2, 4, 20, 40])

def test_extending(self):

sym = mticker.SymmetricalLogLocator(base=10, linthresh=1)

sym.create_dummy_axis()

sym.axis.set_view_interval(8, 9)

assert (sym() == [1.0]).all()

sym.axis.set_view_interval(8, 12)

assert (sym() == [1.0, 10.0]).all()

assert sym.view_limits(10, 10) == (1, 100)

assert sym.view_limits(-10, -10) == (-100, -1)

def test_init(self):

lctr = mticker.AsinhLocator(linear_width=2.718, numticks=19)

assert lctr.linear_width == 2.718

assert lctr.numticks == 19

assert lctr.base == 10

def test_set_params(self):

lctr = mticker.AsinhLocator(linear_width=5,

numticks=17, symthresh=0.125,

base=4, subs=(2.5, 3.25))

assert lctr.numticks == 17

assert lctr.symthresh == 0.125

assert lctr.base == 4

assert lctr.subs == (2.5, 3.25)

lctr.set_params(numticks=23)

assert lctr.numticks == 23

lctr.set_params(None)

assert lctr.numticks == 23

lctr.set_params(symthresh=0.5)

assert lctr.symthresh == 0.5

lctr.set_params(symthresh=None)

assert lctr.symthresh == 0.5

lctr.set_params(base=7)

assert lctr.base == 7

lctr.set_params(base=None)

assert lctr.base == 7

lctr.set_params(subs=(2, 4.125))

assert lctr.subs == (2, 4.125)

lctr.set_params(subs=None)

assert lctr.subs == (2, 4.125)

lctr.set_params(subs=[])

assert lctr.subs is None

def test_linear_values(self):

lctr = mticker.AsinhLocator(linear_width=100, numticks=11, base=0)

assert_almost_equal(lctr.tick_values(-1, 1),

np.arange(-1, 1.01, 0.2))

assert_almost_equal(lctr.tick_values(-0.1, 0.1),

np.arange(-0.1, 0.101, 0.02))

assert_almost_equal(lctr.tick_values(-0.01, 0.01),

np.arange(-0.01, 0.0101, 0.002))

def test_wide_values(self):

lctr = mticker.AsinhLocator(linear_width=0.1, numticks=11, base=0)

assert_almost_equal(lctr.tick_values(-100, 100),

[-100, -20, -5, -1, -0.2,

0, 0.2, 1, 5, 20, 100])

assert_almost_equal(lctr.tick_values(-1000, 1000),

[-1000, -100, -20, -3, -0.4,

0, 0.4, 3, 20, 100, 1000])

def test_near_zero(self):

"""Check that manually injected zero will supersede nearby tick"""

lctr = mticker.AsinhLocator(linear_width=100, numticks=3, base=0)

assert_almost_equal(lctr.tick_values(-1.1, 0.9), [-1.0, 0.0, 0.9])

def test_fallback(self):

lctr = mticker.AsinhLocator(1.0, numticks=11)

assert_almost_equal(lctr.tick_values(101, 102),

np.arange(101, 102.01, 0.1))

def test_symmetrizing(self):

lctr = mticker.AsinhLocator(linear_width=1, numticks=3,

symthresh=0.25, base=0)

lctr.create_dummy_axis()

lctr.axis.set_view_interval(-1, 2)

assert_almost_equal(lctr(), [-1, 0, 2])

lctr.axis.set_view_interval(-1, 0.9)

assert_almost_equal(lctr(), [-1, 0, 1])

lctr.axis.set_view_interval(-0.85, 1.05)

assert_almost_equal(lctr(), [-1, 0, 1])

lctr.axis.set_view_interval(1, 1.1)

assert_almost_equal(lctr(), [1, 1.05, 1.1])

def test_base_rounding(self):

lctr10 = mticker.AsinhLocator(linear_width=1, numticks=8,

base=10, subs=(1, 3, 5))

assert_almost_equal(lctr10.tick_values(-110, 110),

[-500, -300, -100, -50, -30, -10, -5, -3, -1,

-0.5, -0.3, -0.1, 0, 0.1, 0.3, 0.5,

1, 3, 5, 10, 30, 50, 100, 300, 500])

lctr5 = mticker.AsinhLocator(linear_width=1, numticks=20, base=5)

assert_almost_equal(lctr5.tick_values(-1050, 1050),

[-625, -125, -25, -5, -1, -0.2, 0,

offset_data = [

(123, 189, 0),

(-189, -123, 0),

(12341, 12349, 12340),

(-12349, -12341, -12340),

(99999.5, 100010.5, 100000),

(-100010.5, -99999.5, -100000),

(99990.5, 100000.5, 100000),

(-100000.5, -99990.5, -100000),

(1233999, 1234001, 1234000),

(-1234001, -1233999, -1234000),

(1, 1, 1),

(123, 123, 0),

# Test cases courtesy of @WeatherGod

(.4538, .4578, .45),

(3789.12, 3783.1, 3780),

(45124.3, 45831.75, 45000),

(0.000721, 0.0007243, 0.00072),

(12592.82, 12591.43, 12590),

(9., 12., 0),

(900., 1200., 0),

(1900., 1200., 0),

(0.99, 1.01, 1),

(9.99, 10.01, 10),

(99.99, 100.01, 100),

(5.99, 6.01, 6),

(15.99, 16.01, 16),

(-0.452, 0.492, 0),

(-0.492, 0.492, 0),

(12331.4, 12350.5, 12300),

(-12335.3, 12335.3, 0),

]

use_offset_data = [True, False]

useMathText_data = [True, False]

# (sci_type, scilimits, lim, orderOfMag, fewticks)

scilimits_data = [

(False, (0, 0), (10.0, 20.0), 0, False),

(True, (-2, 2), (-10, 20), 0, False),

(True, (-2, 2), (-20, 10), 0, False),

(True, (-2, 2), (-110, 120), 2, False),

(True, (-2, 2), (-120, 110), 2, False),

(True, (-2, 2), (-.001, 0.002), -3, False),

(True, (-7, 7), (0.18e10, 0.83e10), 9, True),

(True, (0, 0), (-1e5, 1e5), 5, False),

(True, (6, 6), (-1e5, 1e5), 6, False),

]

cursor_data = [

[0., "0.000"],

[0.0123, "0.012"],

[0.123, "0.123"],

[1.23, "1.230"],

[12.3, "12.300"],

]

format_data = [

(.1, "1e-1"),

(.11, "1.1e-1"),

(1e8, "1e8"),

(1.1e8, "1.1e8"),

]

@pytest.mark.parametrize('unicode_minus, result',

def test_unicode_minus(self, unicode_minus, result):

mpl.rcParams['axes.unicode_minus'] = unicode_minus

assert (

plt.gca().xaxis.get_major_formatter().format_data_short(-1).strip()

== result)

@pytest.mark.parametrize('left, right, offset', offset_data)

def test_offset_value(self, left, right, offset):

fig, ax = plt.subplots()

formatter = ax.xaxis.get_major_formatter()

with (pytest.warns(UserWarning, match='Attempting to set identical')

if left == right else nullcontext()):

ax.set_xlim(left, right)

ax.xaxis._update_ticks()

assert formatter.offset == offset

with (pytest.warns(UserWarning, match='Attempting to set identical')

if left == right else nullcontext()):

ax.set_xlim(right, left)

ax.xaxis._update_ticks()

assert formatter.offset == offset

@pytest.mark.parametrize('use_offset', use_offset_data)

def test_use_offset(self, use_offset):

with mpl.rc_context({'axes.formatter.useoffset': use_offset}):

tmp_form = mticker.ScalarFormatter()

assert use_offset == tmp_form.get_useOffset()

assert tmp_form.offset == 0

@pytest.mark.parametrize('use_math_text', useMathText_data)

def test_useMathText(self, use_math_text):

with mpl.rc_context({'axes.formatter.use_mathtext': use_math_text}):

tmp_form = mticker.ScalarFormatter()

assert use_math_text == tmp_form.get_useMathText()

def test_set_use_offset_float(self):

tmp_form = mticker.ScalarFormatter()

tmp_form.set_useOffset(0.5)

assert not tmp_form.get_useOffset()

assert tmp_form.offset == 0.5

def test_use_locale(self):

conv = locale.localeconv()

sep = conv['thousands_sep']

if not sep or conv['grouping'][-1:] in ([], [locale.CHAR_MAX]):

pytest.skip('Locale does not apply grouping') # pragma: no cover

with mpl.rc_context({'axes.formatter.use_locale': True}):

tmp_form = mticker.ScalarFormatter()

assert tmp_form.get_useLocale()

tmp_form.create_dummy_axis()

tmp_form.axis.set_data_interval(0, 10)

tmp_form.set_locs([1, 2, 3])