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Fix axes aspect for non-linear, non-log, possibly mixed-scale axes. #14727

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Jul 18, 2019
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Fix axes aspect for non-linear, non-log, possibly mixed-scale axes. #14727

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8 changes: 8 additions & 0 deletions doc/api/next_api_changes/2019-07-09-AL.rst
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@@ -0,0 +1,8 @@
API changes
```````````

``Axes.get_data_ratio`` now takes the axes scale into account (linear, log,
logit, etc.) before computing the y-to-x ratio. This change allows fixed
aspects to be applied to any combination of x and y scales.

``Axes.get_data_ratio_log`` is deprecated.
104 changes: 35 additions & 69 deletions lib/matplotlib/axes/_base.py
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Expand Up @@ -1396,20 +1396,21 @@ def set_anchor(self, anchor, share=False):

def get_data_ratio(self):
"""
Return the aspect ratio of the raw data.
Return the aspect ratio of the scaled data.

Notes
-----
This method is intended to be overridden by new projection types.
"""
xmin, xmax = self.get_xbound()
ymin, ymax = self.get_ybound()

xsize = max(abs(xmax - xmin), 1e-30)
ysize = max(abs(ymax - ymin), 1e-30)

trf_xmin, trf_xmax = map(
self.xaxis.get_transform().transform, self.get_xbound())
trf_ymin, trf_ymax = map(
self.yaxis.get_transform().transform, self.get_ybound())
xsize = max(abs(trf_xmax - trf_xmin), 1e-30)
ysize = max(abs(trf_ymax - trf_ymin), 1e-30)
return ysize / xsize

@cbook.deprecated("3.2")
def get_data_ratio_log(self):
"""
Return the aspect ratio of the raw data in log scale.
Expand Down Expand Up @@ -1455,99 +1456,70 @@ def apply_aspect(self, position=None):

aspect = self.get_aspect()

if self.name != 'polar':
xscale, yscale = self.get_xscale(), self.get_yscale()
if xscale == "linear" and yscale == "linear":
aspect_scale_mode = "linear"
elif xscale == "log" and yscale == "log":
aspect_scale_mode = "log"
elif ((xscale == "linear" and yscale == "log") or
(xscale == "log" and yscale == "linear")):
if aspect != "auto":
cbook._warn_external(
'aspect is not supported for Axes with xscale=%s, '
'yscale=%s' % (xscale, yscale))
aspect = "auto"
else: # some custom projections have their own scales.
pass
else:
aspect_scale_mode = "linear"

if aspect == 'auto':
self._set_position(position, which='active')
return

if aspect == 'equal':
A = 1
else:
A = aspect
aspect = 1

fig_width, fig_height = self.get_figure().get_size_inches()
fig_aspect = fig_height / fig_width

figW, figH = self.get_figure().get_size_inches()
fig_aspect = figH / figW
if self._adjustable == 'box':
if self in self._twinned_axes:
raise RuntimeError("Adjustable 'box' is not allowed in a"
" twinned Axes. Use 'datalim' instead.")
if aspect_scale_mode == "log":
box_aspect = A * self.get_data_ratio_log()
else:
box_aspect = A * self.get_data_ratio()
raise RuntimeError("Adjustable 'box' is not allowed in a "
"twinned Axes; use 'datalim' instead")
box_aspect = aspect * self.get_data_ratio()
pb = position.frozen()
pb1 = pb.shrunk_to_aspect(box_aspect, pb, fig_aspect)
self._set_position(pb1.anchored(self.get_anchor(), pb), 'active')
return

# reset active to original in case it had been changed
# by prior use of 'box'
self._set_position(position, which='active')

xmin, xmax = self.get_xbound()
ymin, ymax = self.get_ybound()
# self._adjustable == 'datalim'

if aspect_scale_mode == "log":
xmin, xmax = math.log10(xmin), math.log10(xmax)
ymin, ymax = math.log10(ymin), math.log10(ymax)
# reset active to original in case it had been changed by prior use
# of 'box'
self._set_position(position, which='active')

x_trf = self.xaxis.get_transform()
y_trf = self.yaxis.get_transform()
xmin, xmax = map(x_trf.transform, self.get_xbound())
ymin, ymax = map(y_trf.transform, self.get_ybound())
xsize = max(abs(xmax - xmin), 1e-30)
ysize = max(abs(ymax - ymin), 1e-30)

l, b, w, h = position.bounds
box_aspect = fig_aspect * (h / w)
data_ratio = box_aspect / A
data_ratio = box_aspect / aspect

y_expander = (data_ratio * xsize / ysize - 1.0)
y_expander = data_ratio * xsize / ysize - 1
# If y_expander > 0, the dy/dx viewLim ratio needs to increase
if abs(y_expander) < 0.005:
return

if aspect_scale_mode == "log":
dL = self.dataLim
dL_width = math.log10(dL.x1) - math.log10(dL.x0)
dL_height = math.log10(dL.y1) - math.log10(dL.y0)
xr = 1.05 * dL_width
yr = 1.05 * dL_height
else:
dL = self.dataLim
xr = 1.05 * dL.width
yr = 1.05 * dL.height
dL = self.dataLim
x0, x1 = map(x_trf.inverted().transform, dL.intervalx)
y0, y1 = map(y_trf.inverted().transform, dL.intervaly)
xr = 1.05 * (x1 - x0)
yr = 1.05 * (y1 - y0)

xmarg = xsize - xr
ymarg = ysize - yr
Ysize = data_ratio * xsize
Xsize = ysize / data_ratio
Xmarg = Xsize - xr
Ymarg = Ysize - yr
# Setting these targets to, e.g., 0.05*xr does not seem to
# help.
# Setting these targets to, e.g., 0.05*xr does not seem to help.
xm = 0
ym = 0

shared_x = self in self._shared_x_axes
shared_y = self in self._shared_y_axes
# Not sure whether we need this check:
if shared_x and shared_y:
raise RuntimeError("adjustable='datalim' is not allowed when both"
" axes are shared.")
raise RuntimeError("adjustable='datalim' is not allowed when both "
"axes are shared")

# If y is shared, then we are only allowed to change x, etc.
if shared_y:
Expand All @@ -1564,18 +1536,12 @@ def apply_aspect(self, position=None):
yc = 0.5 * (ymin + ymax)
y0 = yc - Ysize / 2.0
y1 = yc + Ysize / 2.0
if aspect_scale_mode == "log":
self.set_ybound((10. ** y0, 10. ** y1))
else:
self.set_ybound((y0, y1))
self.set_ybound(*map(y_trf.inverted().transform, (y0, y1)))
else:
xc = 0.5 * (xmin + xmax)
x0 = xc - Xsize / 2.0
x1 = xc + Xsize / 2.0
if aspect_scale_mode == "log":
self.set_xbound((10. ** x0, 10. ** x1))
else:
self.set_xbound((x0, x1))
self.set_xbound(*map(x_trf.inverted().transform, (x0, x1)))

def axis(self, *args, emit=True, **kwargs):
"""
Expand Down
26 changes: 26 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -6495,3 +6495,29 @@ def test_set_ticks_inverted():
ax.invert_xaxis()
ax.set_xticks([.3, .7])
assert ax.get_xlim() == (1, 0)


def test_aspect_nonlinear_adjustable_box():
fig = plt.figure(figsize=(10, 10)) # Square.

ax = fig.add_subplot()
ax.plot([.4, .6], [.4, .6]) # Set minpos to keep logit happy.
ax.set(xscale="log", xlim=(1, 10),
yscale="logit", ylim=(1/11, 1/1001),
aspect=1, adjustable="box")
ax.margins(0)
pos = fig.transFigure.transform_bbox(ax.get_position())
assert pos.height / pos.width == pytest.approx(2)


def test_aspect_nonlinear_adjustable_datalim():
fig = plt.figure(figsize=(10, 10)) # Square.

ax = fig.add_axes([.1, .1, .8, .8]) # Square.
ax.plot([.4, .6], [.4, .6]) # Set minpos to keep logit happy.
ax.set(xscale="log", xlim=(1, 10),
yscale="logit", ylim=(1/11, 1/1001),
aspect=1, adjustable="datalim")
ax.margins(0)
ax.apply_aspect()
assert ax.get_xlim() == pytest.approx(np.array([1/10, 10]) * np.sqrt(10))
9 changes: 4 additions & 5 deletions lib/matplotlib/tests/test_image.py
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Expand Up @@ -686,15 +686,14 @@ def test_load_from_url():


@image_comparison(['log_scale_image'], remove_text=True)
# The recwarn fixture captures a warning in image_comparison.
def test_log_scale_image(recwarn):
def test_log_scale_image():
Z = np.zeros((10, 10))
Z[::2] = 1

fig, ax = plt.subplots()
ax.imshow(Z, extent=[1, 100, 1, 100], cmap='viridis',
vmax=1, vmin=-1)
ax.set_yscale('log')
ax.imshow(Z, extent=[1, 100, 1, 100], cmap='viridis', vmax=1, vmin=-1,
aspect='auto')
ax.set(yscale='log')


@image_comparison(['rotate_image'], remove_text=True)
Expand Down