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Sep 29, 2021
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Compress comments in make_image. #21213

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101 changes: 39 additions & 62 deletions lib/matplotlib/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -402,43 +402,34 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
# have to resample to the correct number of pixels

# TODO slice input array first
inp_dtype = A.dtype
a_min = A.min()
a_max = A.max()
# figure out the type we should scale to. For floats,
# leave as is. For integers cast to an appropriate-sized
# float. Small integers get smaller floats in an attempt
# to keep the memory footprint reasonable.
if a_min is np.ma.masked:
# all masked, so values don't matter
if a_min is np.ma.masked: # All masked; values don't matter.
a_min, a_max = np.int32(0), np.int32(1)
if inp_dtype.kind == 'f':
if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
scaled_dtype = np.dtype(
np.float64 if A.dtype.itemsize > 4 else np.float32)
if scaled_dtype.itemsize < A.dtype.itemsize:
_api.warn_external(
f"Casting input data from {A.dtype} to "
f"{scaled_dtype} for imshow")
else:
# probably an integer of some type.
_api.warn_external(f"Casting input data from {A.dtype}"
f" to {scaled_dtype} for imshow.")
else: # Int dtype, likely.
# Scale to appropriately sized float: use float32 if the
# dynamic range is small, to limit the memory footprint.
da = a_max.astype(np.float64) - a_min.astype(np.float64)
# give more breathing room if a big dynamic range
scaled_dtype = np.float64 if da > 1e8 else np.float32

# scale the input data to [.1, .9]. The Agg
# interpolators clip to [0, 1] internally, use a
# smaller input scale to identify which of the
# interpolated points need to be should be flagged as
# over / under.
# This may introduce numeric instabilities in very broadly
# scaled data
# Scale the input data to [.1, .9]. The Agg interpolators clip
# to [0, 1] internally, and we use a smaller input scale to
# identify the interpolated points that need to be flagged as
# over/under. This may introduce numeric instabilities in very
# broadly scaled data.

# Always copy, and don't allow array subtypes.
A_scaled = np.array(A, dtype=scaled_dtype)
# clip scaled data around norm if necessary.
# This is necessary for big numbers at the edge of
# float64's ability to represent changes. Applying
# a norm first would be good, but ruins the interpolation
# of over numbers.
# Clip scaled data around norm if necessary. This is necessary
# for big numbers at the edge of float64's ability to represent
# changes. Applying a norm first would be good, but ruins the
# interpolation of over numbers.
self.norm.autoscale_None(A)
dv = np.float64(self.norm.vmax) - np.float64(self.norm.vmin)
vmid = np.float64(self.norm.vmin) + dv / 2
Expand All @@ -456,30 +447,25 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
if newmax is not None or newmin is not None:
np.clip(A_scaled, newmin, newmax, out=A_scaled)

# used to rescale the raw data to [offset, 1-offset]
# so that the resampling code will run cleanly. Using
# dyadic numbers here could reduce the error, but
# would not full eliminate it and breaks a number of
# tests (due to the slightly different error bouncing
# some pixels across a boundary in the (very
# Rescale the raw data to [offset, 1-offset] so that the
# resampling code will run cleanly. Using dyadic numbers here
# could reduce the error, but would not fully eliminate it and
# breaks a number of tests (due to the slightly different
# error bouncing some pixels across a boundary in the (very
# quantized) colormapping step).
offset = .1
frac = .8
# we need to run the vmin/vmax through the same rescaling
# that we run the raw data through because there are small
# errors in the round-trip due to float precision. If we
# do not run the vmin/vmax through the same pipeline we can
# have values close or equal to the boundaries end up on the
# wrong side.
# Run vmin/vmax through the same rescaling as the raw data;
# otherwise, data values close or equal to the boundaries can
# end up on the wrong side due to floating point error.
vmin, vmax = self.norm.vmin, self.norm.vmax
if vmin is np.ma.masked:
vmin, vmax = a_min, a_max
vrange = np.array([vmin, vmax], dtype=scaled_dtype)

A_scaled -= a_min
vrange -= a_min
# a_min and a_max might be ndarray subclasses so use
# item to avoid errors
# .item() handles a_min/a_max being ndarray subclasses.
a_min = a_min.astype(scaled_dtype).item()
a_max = a_max.astype(scaled_dtype).item()

Expand All @@ -490,13 +476,11 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
vrange += offset
# resample the input data to the correct resolution and shape
A_resampled = _resample(self, A_scaled, out_shape, t)
# done with A_scaled now, remove from namespace to be sure!
del A_scaled
# un-scale the resampled data to approximately the
# original range things that interpolated to above /
# below the original min/max will still be above /
# below, but possibly clipped in the case of higher order
# interpolation + drastically changing data.
del A_scaled # Make sure we don't use A_scaled anymore!
# Un-scale the resampled data to approximately the original
# range. Things that interpolated to outside the original range
# will still be outside, but possibly clipped in the case of
# higher order interpolation + drastically changing data.
A_resampled -= offset
vrange -= offset
if a_min != a_max:
Expand All @@ -514,8 +498,7 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
# we always have to interpolate the mask to account for
# non-affine transformations
out_alpha = _resample(self, mask, out_shape, t, resample=True)
# done with the mask now, delete from namespace to be sure!
del mask
del mask # Make sure we don't use mask anymore!
# Agg updates out_alpha in place. If the pixel has no image
# data it will not be updated (and still be 0 as we initialized
# it), if input data that would go into that output pixel than
Expand All @@ -539,12 +522,9 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
s_vmin = np.finfo(scaled_dtype).eps
# Block the norm from sending an update signal during the
# temporary vmin/vmax change
with self.norm.callbacks.blocked():
with cbook._setattr_cm(self.norm,
vmin=s_vmin,
vmax=s_vmax,
):
output = self.norm(resampled_masked)
with self.norm.callbacks.blocked(), \
cbook._setattr_cm(self.norm, vmin=s_vmin, vmax=s_vmax):
output = self.norm(resampled_masked)
else:
if A.ndim == 2: # _interpolation_stage == 'rgba'
self.norm.autoscale_None(A)
Expand All @@ -558,8 +538,7 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)
output[..., 3] = output_alpha # recombine rgb and alpha

# at this point output is either a 2D array of normed data
# (of int or float)
# output is now either a 2D array of normed (int or float) data
# or an RGBA array of re-sampled input
output = self.to_rgba(output, bytes=True, norm=False)
# output is now a correctly sized RGBA array of uint8
Expand All @@ -568,17 +547,15 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
if A.ndim == 2:
alpha = self._get_scalar_alpha()
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * out_alpha * alpha,
np.uint8)
alpha_channel[:] = ( # Assignment will cast to uint8.
alpha_channel.astype(np.float32) * out_alpha * alpha)

else:
if self._imcache is None:
self._imcache = self.to_rgba(A, bytes=True, norm=(A.ndim == 2))
output = self._imcache

# Subset the input image to only the part that will be
# displayed
# Subset the input image to only the part that will be displayed.
subset = TransformedBbox(clip_bbox, t0.inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
Expand Down