8000 FIX: image respect norm limits w/ None by jklymak · Pull Request #11047 · matplotlib/matplotlib · GitHub
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FIX: image respect norm limits w/ None #11047

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Merged
merged 3 commits into from
May 14, 2018
Merged

FIX: image respect norm limits w/ None #11047

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ea8f80a
FIX: image respect norm limits w/ None
jklymak Apr 13, 2018
89c3345
FIX: image respect norm limits w/ None
jklymak Apr 13, 2018
8d53a8a
FIX: image respect norm limits w/ None
jklymak May 9, 2018
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35 changes: 18 additions & 17 deletions lib/matplotlib/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -390,26 +390,27 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
# float64's ability to represent changes. Applying
# a norm first would be good, but ruins the interpolation
9FD8 # of over numbers.
if self.norm.vmin is not None and self.norm.vmax is not None:
dv = (np.float64(self.norm.vmax) -
np.float64(self.norm.vmin))
vmid = self.norm.vmin + dv / 2
newmin = vmid - dv * 1.e7
if newmin < a_min:
newmin = None
else:
a_min = np.float64(newmin)
newmax = vmid + dv * 1.e7
if newmax > a_max:
newmax = None
else:
a_max = np.float64(newmax)
if newmax is not None or newmin is not None:
A_scaled = np.clip(A_scaled, newmin, newmax)
self.norm.autoscale_None(A)
dv = (np.float64(self.norm.vmax) -
np.float64(self.norm.vmin))
vmid = self.norm.vmin + dv / 2
fact = 1e7 if scaled_dtype == np.float64 else 1e4
newmin = vmid - dv * fact
if newmin < a_min:
newmin = None
else:
a_min = np.float64(newmin)
newmax = vmid + dv * fact
if newmax > a_max:
newmax = None
else:
a_max = np.float64(newmax)
if newmax is not None or newmin is not None:
A_scaled = np.clip(A_scaled, newmin, newmax)

A_scaled -= a_min
# a_min and a_max might be ndarray subclasses so use
# asscalar to ensure they are scalars to avoid errors
# asscalar to avoid errors
a_min = np.asscalar(a_min.astype(scaled_dtype))
a_max = np.asscalar(a_max.astype(scaled_dtype))

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