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Ability to scale axis by a fixed factor #10321

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Merged
merged 14 commits into from
May 7, 2018
Merged

Ability to scale axis by a fixed factor #10321

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df16efc
Fixed scaling when lower power limit = upper <> 0
HHest Jan 25, 2018
45e1bf3
Update doc with fixed scaling feature
HHest Jan 25, 2018
d001033
Fix line length and PEP spacing complaints
HHest Jan 25, 2018
b12202a
move fixed-scaling check higher up
HHest Jan 25, 2018
130f257
whats new for fixed-scaling of axis
HHest Jan 25, 2018
482e07a
Test for several ScalarFormatter methods
HHest Jan 26, 2018
38af9db
Fix PEP8 errors
HHest Jan 26, 2018
c23ef3b
Take scilimits = single integer for fixed-scaling
HHest Jan 26, 2018
d2a1769
Update for scilimits=single int
HHest Jan 26, 2018
01fb668
removed trailing space for pep8
HHest Jan 27, 2018
3260e70
Revert the scilimits = single int change
HHest Jan 30, 2018
2188177
Revert scilimits = single int change
HHest Jan 30, 2018
50a9695
Add explanation linking to previous behavior.
HHest Jan 30, 2018
5465a81
Fix reference to y axis
HHest Jan 30, 2018
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10 changes: 10 additions & 0 deletions doc/users/next_whats_new/2018_01_25_scale_axis_by_factor.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,10 @@
Ability to scale axis by a fixed order of magnitude
---------------------------------------------------

To scale an axis by a fixed order of magnitude, set the *scilimits* argument of
``Axes.ticklabel_format`` to the same (non-zero) lower and upper limits. Say to scale
the y axis by a million (1e6), use ``ax.ticklabel_format(style='sci', scilimits=(6, 6), axis='y')``.

The behavior of ``scilimits=(0, 0)`` is unchanged. With this setting, matplotlib will adjust
the order of magnitude depending on the axis values, rather than keeping it fixed. Previously, setting
``scilimits=(m, m)`` was equivalent to setting ``scilimits=(0, 0)``.
2 changes: 2 additions & 0 deletions lib/matplotlib/axes/_base.py
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Expand Up @@ -2672,6 +2672,8 @@ def ticklabel_format(self, **kwargs):
be used for numbers outside the range
10`m`:sup: to 10`n`:sup:.
Use (0,0) to include all numbers.
Use (m,m) where m <> 0 to fix the order
of magnitude to 10`m`:sup:.
*useOffset* [ bool | offset ]; if True,
the offset will be calculated as needed;
if False, no offset will be used; if a
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23 changes: 23 additions & 0 deletions lib/matplotlib/tests/test_ticker.py
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Expand Up @@ -228,6 +228,17 @@ class TestScalarFormatter(object):

use_offset_data = [True, False]

scilimits_data = [
(False, (0, 0), (10.0, 20.0), 0),
(True, (-2, 2), (-10, 20), 0),
(True, (-2, 2), (-20, 10), 0),
(True, (-2, 2), (-110, 120), 2),
(True, (-2, 2), (-120, 110), 2),
(True, (-2, 2), (-.001, 0.002), -3),
(True, (0, 0), (-1e5, 1e5), 5),
(True, (6, 6), (-1e5, 1e5), 6),
]
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can you add a scalar example into here?

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Since we've decided not to take in scalar anymore, this is a moot point..., but just for posterity sake, my intention was to allow scalar input at the ticklabel_format API level, not at this lower level, where the test sits. The scalar input would then be translated to pair of (repeated) values for lower level consumption.


@pytest.mark.parametrize('left, right, offset', offset_data)
def test_offset_value(self, left, right, offset):
fig, ax = plt.subplots()
Expand Down Expand Up @@ -257,6 +268,18 @@ def test_use_offset(self, use_offset):
tmp_form = mticker.ScalarFormatter()
assert use_offset == tmp_form.get_useOffset()

@pytest.mark.parametrize(
'sci_type, scilimits, lim, orderOfMag', scilimits_data)
def test_scilimits(self, sci_type, scilimits, lim, orderOfMag):
tmp_form = mticker.ScalarFormatter()
tmp_form.set_scientific(sci_type)
tmp_form.set_powerlimits(scilimits)
fig, ax = plt.subplots()
ax.yaxis.set_major_formatter(tmp_form)
ax.set_ylim(*lim)
tmp_form.set_locs(ax.yaxis.get_majorticklocs())
assert orderOfMag == tmp_form.orderOfMagnitude


class FakeAxis(object):
"""Allow Formatter to be called without having a "full" plot set up."""
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6 changes: 5 additions & 1 deletion lib/matplotlib/ticker.py
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Expand Up @@ -711,10 +711,14 @@ def _compute_offset(self):
def _set_orderOfMagnitude(self, range):
# if scientific notation is to be used, find the appropriate exponent
# if using an numerical offset, find the exponent after applying the
# offset
# offset. When lower power limit = upper <> 0, use provided exponent.
if not self._scientific:
self.orderOfMagnitude = 0
return
if self._powerlimits[0] == self._powerlimits[1] != 0:
# fixed scaling when lower power limit = upper <> 0.
self.orderOfMagnitude = self._powerlimits[0]
return
locs = np.abs(self.locs)
if self.offset:
oom = math.floor(math.log10(range))
Expand Down