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Make sure Locators always return a tick out of bounds #25225

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6cdacc7
Make MaxNLocator return ticks out of bounds
dstansby Feb 15, 2023
07f766b
Make LinearLocator always return a tick out of bounds
dstansby Feb 15, 2023
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22 changes: 13 additions & 9 deletions lib/matplotlib/tests/test_ticker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,11 +15,13 @@

class TestMaxNLocator:
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),
# vmin, vmax, expected
(20, 100, np.array([0, 20., 40., 60., 80., 100., 120])),
(0.001, 0.0001, np.array([0., 0.0002, 0.0004, 0.0006, 0.0008, 0.001, 0.0012])),
(-1e15, 1e15,
np.array([-1.5e15, -1.0e+15, -5.0e+14, 0e+00, 5e+14, 1.0e+15, 1.5e15])),
(0, 0.85e-50, np.arange(-1, 6) * 2e-51),
(-0.85e-50, 0, np.arange(-5, 2) * 2e-51),
]

integer_data = [
Expand All @@ -42,7 +44,7 @@ def test_integer(self, vmin, vmax, steps, expected):
class TestLinearLocator:
def test_basic(self):
loc = mticker.LinearLocator(numticks=3)
test_value = np.array([-0.8, -0.3, 0.2])
test_value = np.array([-1.3, -0.8, -0.3, 0.2, 0.7])
assert_almost_equal(loc.tick_values(-0.8, 0.2), test_value)

def test_set_params(self):
Expand Down Expand Up @@ -161,15 +163,15 @@ def test_number_of_minor_ticks(
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],
9.50e-14, 1.05e-13, 1.10e-13, 1.15e-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],
1.38e-06, 1.39e-06, 1.41e-06, 1.42e-06, 1.43e-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],
Expand Down Expand Up @@ -336,7 +338,9 @@ def test_nbins_major(self, lims):
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
ticks = loc.tick_values(*lims)
ticks_in_bounds = ticks[(ticks < lims[0]) & (ticks > lims[1])]
assert len(ticks_in_bounds) <= nbins + 1

@pytest.mark.parametrize(
"lims, expected_low_ticks",
Expand Down
25 changes: 22 additions & 3 deletions lib/matplotlib/ticker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1827,7 +1827,12 @@ def tick_values(self, vmin, vmax):

if self.numticks == 0:
return []
ticklocs = np.linspace(vmin, vmax, self.numticks)
ticklocs, step = np.linspace(vmin, vmax, self.numticks, retstep=True)

# Extend so there is a single tick out of bounds
ticklocs = np.concatenate(
([ticklocs[0] - step], ticklocs, [ticklocs[-1] + step])
)

return self.raise_if_exceeds(ticklocs)

Expand Down Expand Up @@ -1948,13 +1953,27 @@ def le(self, x):
return d + 1
return d

def lt(self, x):
"""Return the largest integer n: n*step < x."""
d, m = divmod(x, self.step)
if self.closeto(m / self.step, 0):
return d - 1
return d

def ge(self, x):
"""Return the smallest n: n*step >= x."""
d, m = divmod(x, self.step)
if self.closeto(m / self.step, 0):
return d
return d + 1

def gt(self, x):
"""Return the smallest integer n: n*step > x."""
d, m = divmod(x, self.step)
if self.closeto(m / self.step, 1):
return d + 2
return d + 1


class MaxNLocator(Locator):
"""
Expand Down Expand Up @@ -2125,8 +2144,8 @@ def _raw_ticks(self, vmin, vmax):
# The edge ticks beyond vmin and/or vmax are needed for the
# "round_numbers" autolimit mode.
edge = _Edge_integer(step, offset)
low = edge.le(_vmin - best_vmin)
high = edge.ge(_vmax - best_vmin)
low = edge.lt(_vmin - best_vmin)
high = edge.gt(_vmax - best_vmin)
ticks = np.arange(low, high + 1) * step + best_vmin
# Count only the ticks that will be displayed.
nticks = ((ticks <= _vmax) & (ticks >= _vmin)).sum()
Expand Down