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Commite2ce142

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Compress comments in make_image.
make_image is a huge function; shorten it (in physical screen space) abit by rewriting/rewrapping some comments, and a few small stylisticchanges.
1 parenta6e01b5 commite2ce142

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1 file changed

+39
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‎lib/matplotlib/image.py

Lines changed: 39 additions & 62 deletions
Original file line numberDiff line numberDiff line change
@@ -402,43 +402,34 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
402402
# have to resample to the correct number of pixels
403403

404404
# TODO slice input array first
405-
inp_dtype=A.dtype
406405
a_min=A.min()
407406
a_max=A.max()
408-
# figure out the type we should scale to. For floats,
409-
# leave as is. For integers cast to an appropriate-sized
410-
# float. Small integers get smaller floats in an attempt
411-
# to keep the memory footprint reasonable.
412-
ifa_minisnp.ma.masked:
413-
# all masked, so values don't matter
407+
ifa_minisnp.ma.masked:# All masked; values don't matter.
414408
a_min,a_max=np.int32(0),np.int32(1)
415-
ifinp_dtype.kind=='f':
409+
ifA.dtype.kind=='f':# Float dtype: scale to same dtype.
416410
scaled_dtype=np.dtype(
417411
np.float64ifA.dtype.itemsize>4elsenp.float32)
418412
ifscaled_dtype.itemsize<A.dtype.itemsize:
419-
_api.warn_external(
420-
f"Casting input data from{A.dtype} to "
421-
f"{scaled_dtype} for imshow")
422-
else:
423-
#probably an integer of some type.
413+
_api.warn_external(f"Casting input data from{A.dtype}"
414+
f" to{scaled_dtype} for imshow.")
415+
else:# Int dtype, likely.
416+
# Scale to appropriately sized float: use float32 if the
417+
#dynamic range is small, to limit the memory footprint.
424418
da=a_max.astype(np.float64)-a_min.astype(np.float64)
425-
# give more breathing room if a big dynamic range
426419
scaled_dtype=np.float64ifda>1e8elsenp.float32
427420

428-
# scale the input data to [.1, .9]. The Agg
429-
# interpolators clip to [0, 1] internally, use a
430-
# smaller input scale to identify which of the
431-
# interpolated points need to be should be flagged as
432-
# over / under.
433-
# This may introduce numeric instabilities in very broadly
434-
# scaled data
421+
# Scale the input data to [.1, .9]. The Agg interpolators clip
422+
# to [0, 1] internally, and we use a smaller input scale to
423+
# identify the interpolated points that need to be flagged as
424+
# over/under. This may introduce numeric instabilities in very
425+
# broadly scaled data.
426+
435427
# Always copy, and don't allow array subtypes.
436428
A_scaled=np.array(A,dtype=scaled_dtype)
437-
# clip scaled data around norm if necessary.
438-
# This is necessary for big numbers at the edge of
439-
# float64's ability to represent changes. Applying
440-
# a norm first would be good, but ruins the interpolation
441-
# of over numbers.
429+
# Clip scaled data around norm if necessary. This is necessary
430+
# for big numbers at the edge of float64's ability to represent
431+
# changes. Applying a norm first would be good, but ruins the
432+
# interpolation of over numbers.
442433
self.norm.autoscale_None(A)
443434
dv=np.float64(self.norm.vmax)-np.float64(self.norm.vmin)
444435
vmid=np.float64(self.norm.vmin)+dv/2
@@ -456,30 +447,25 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
456447
ifnewmaxisnotNoneornewminisnotNone:
457448
np.clip(A_scaled,newmin,newmax,out=A_scaled)
458449

459-
# used to rescale the raw data to [offset, 1-offset]
460-
# so that the resampling code will run cleanly. Using
461-
# dyadic numbers here could reduce the error, but
462-
# would not full eliminate it and breaks a number of
463-
# tests (due to the slightly different error bouncing
464-
# some pixels across a boundary in the (very
450+
# Rescale the raw data to [offset, 1-offset] so that the
451+
# resampling code will run cleanly. Using dyadic numbers here
452+
# could reduce the error, but would not full eliminate it and
453+
# breaks a number of tests (due to the slightly different
454+
# error bouncing some pixels across a boundary in the (very
465455
# quantized) colormapping step).
466456
offset=.1
467457
frac=.8
468-
# we need to run the vmin/vmax through the same rescaling
469-
# that we run the raw data through because there are small
470-
# errors in the round-trip due to float precision. If we
471-
# do not run the vmin/vmax through the same pipeline we can
472-
# have values close or equal to the boundaries end up on the
473-
# wrong side.
458+
# Run vmin/vmax through the same rescaling as the raw data;
459+
# otherwise, data values close or equal to the boundaries can
460+
# end up on the wrong side due to floating point error.
474461
vmin,vmax=self.norm.vmin,self.norm.vmax
475462
ifvminisnp.ma.masked:
476463
vmin,vmax=a_min,a_max
477464
vrange=np.array([vmin,vmax],dtype=scaled_dtype)
478465

479466
A_scaled-=a_min
480467
vrange-=a_min
481-
# a_min and a_max might be ndarray subclasses so use
482-
# item to avoid errors
468+
# .item() handles a_min/a_max being ndarray subclasses.
483469
a_min=a_min.astype(scaled_dtype).item()
484470
a_max=a_max.astype(scaled_dtype).item()
485471

@@ -490,13 +476,11 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
490476
vrange+=offset
491477
# resample the input data to the correct resolution and shape
492478
A_resampled=_resample(self,A_scaled,out_shape,t)
493-
# done with A_scaled now, remove from namespace to be sure!
494-
delA_scaled
495-
# un-scale the resampled data to approximately the
496-
# original range things that interpolated to above /
497-
# below the original min/max will still be above /
498-
# below, but possibly clipped in the case of higher order
499-
# interpolation + drastically changing data.
479+
delA_scaled# Make sure we don't use A_scaled anymore!
480+
# Un-scale the resampled data to approximately the original
481+
# range. Things that interpolated to outside the original range
482+
# will still be outside, but possibly clipped in the case of
483+
# higher order interpolation + drastically changing data.
500484
A_resampled-=offset
501485
vrange-=offset
502486
ifa_min!=a_max:
@@ -514,8 +498,7 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
514498
# we always have to interpolate the mask to account for
515499
# non-affine transformations
516500
out_alpha=_resample(self,mask,out_shape,t,resample=True)
517-
# done with the mask now, delete from namespace to be sure!
518-
delmask
501+
delmask# Make sure we don't use mask anymore!
519502
# Agg updates out_alpha in place. If the pixel has no image
520503
# data it will not be updated (and still be 0 as we initialized
521504
# it), if input data that would go into that output pixel than
@@ -539,12 +522,9 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
539522
s_vmin=np.finfo(scaled_dtype).eps
540523
# Block the norm from sending an update signal during the
541524
# temporary vmin/vmax change
542-
withself.norm.callbacks.blocked():
543-
withcbook._setattr_cm(self.norm,
544-
vmin=s_vmin,
545-
vmax=s_vmax,
546-
):
547-
output=self.norm(resampled_masked)
525+
withself.norm.callbacks.blocked(), \
526+
cbook._setattr_cm(self.norm,vmin=s_vmin,vmax=s_vmax):
527+
output=self.norm(resampled_masked)
548528
else:
549529
ifA.ndim==2:# _interpolation_stage == 'rgba'
550530
self.norm.autoscale_None(A)
@@ -558,8 +538,7 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
558538
self,_rgb_to_rgba(A[..., :3]),out_shape,t,alpha=alpha)
559539
output[...,3]=output_alpha# recombine rgb and alpha
560540

561-
# at this point output is either a 2D array of normed data
562-
# (of int or float)
541+
# output is now either a 2D array of normed (int or float) data
563542
# or an RGBA array of re-sampled input
564543
output=self.to_rgba(output,bytes=True,norm=False)
565544
# output is now a correctly sized RGBA array of uint8
@@ -568,17 +547,15 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
568547
ifA.ndim==2:
569548
alpha=self._get_scalar_alpha()
570549
alpha_channel=output[:, :,3]
571-
alpha_channel[:]=np.asarray(
572-
np.asarray(alpha_channel,np.float32)*out_alpha*alpha,
573-
np.uint8)
550+
alpha_channel[:]= (
551+
alpha_channel.astype(np.float32)*out_alpha*alpha)
574552

575553
else:
576554
ifself._imcacheisNone:
577555
self._imcache=self.to_rgba(A,bytes=True,norm=(A.ndim==2))
578556
output=self._imcache
579557

580-
# Subset the input image to only the part that will be
581-
# displayed
558+
# Subset the input image to only the part that will be displayed.
582559
subset=TransformedBbox(clip_bbox,t0.inverted()).frozen()
583560
output=output[
584561
int(max(subset.ymin,0)):

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