8000 Disable clipping in Agg resamplers. · matplotlib/matplotlib@b531271 · GitHub
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anntzeranntzer
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Disable clipping in Agg resamplers.
I chose to add macro guards directly in the agg source as that seemed easier than copy-pasting the whole code. I chose to bump the tolerance on test_rgba_antialias (as that's also getting bumped by the interpolation_stage antialias PR) and to test_pngsuite (which is not particularly relevant), and to update the other baselines.
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‎extern/agg24-svn/include/agg_span_image_filter_gray.h

Lines changed: 8 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -397,7 +397,9 @@ namespace agg
397397
fg += weight * *fg_ptr;
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fg >>= image_filter_shift;
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#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING
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if(fg > color_type::full_value()) fg = color_type::full_value();
402+
#endif
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402404
span->v = (value_type)fg;
403405
span->a = color_type::full_value();
@@ -491,8 +493,10 @@ namespace agg
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}
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493495
fg = color_type::downshift(fg, image_filter_shift);
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#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING
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if(fg < 0) fg = 0;
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if(fg > color_type::full_value()) fg = color_type::full_value();
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#endif
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span->v = (value_type)fg;
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span->a = color_type::full_value();
498502

@@ -593,8 +597,10 @@ namespace agg
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}
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fg /= total_weight;
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#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING
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if(fg < 0) fg = 0;
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if(fg > color_type::full_value()) fg = color_type::full_value();
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#endif
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span->v = (value_type)fg;
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span->a = color_type::full_value();
@@ -701,8 +707,10 @@ namespace agg
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}
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fg /= total_weight;
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#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING
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if(fg < 0) fg = 0;
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if(fg > color_type::full_value()) fg = color_type::full_value();
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#endif
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span->v = (value_type)fg;
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span->a = color_type::full_value();

‎lib/matplotlib/image.py

Lines changed: 9 additions & 92 deletions
Original file line numberDiff line numberDiff line change
@@ -427,93 +427,21 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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# input data is not going to match the size on the screen so we
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# have to resample to the correct number of pixels
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430-
# TODO slice input array first
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a_min = A.min()
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a_max = A.max()
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if a_min is np.ma.masked: # All masked; values don't matter.
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a_min, a_max = np.int32(0), np.int32(1)
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if A.dtype.kind == 'f': # Float dtype: scale to same dtype.
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scaled_dtype = np.dtype(
437-
np.float64 if A.dtype.itemsize > 4 else np.float32)
431+
scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4")
438432
if scaled_dtype.itemsize < A.dtype.itemsize:
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_api.warn_external(f"Casting input data from {A.dtype}"
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f" to {scaled_dtype} for imshow.")
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else: # Int dtype, likely.
436+
# TODO slice input array first
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# Scale to appropriately sized float: use float32 if the
443438
# dynamic range is small, to limit the memory footprint.
444-
da = a_max.astype(np.float64) - a_min.astype(np.float64)
445-
scaled_dtype = np.float64 if da > 1e8 else np.float32
446-
447-
# Scale the input data to [.1, .9]. The Agg interpolators clip
448-
# to [0, 1] internally, and we use a smaller input scale to
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# identify the interpolated points that need to be flagged as
450-
# over/under. This may introduce numeric instabilities in very
451-
# broadly scaled data.
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# Always copy, and don't allow array subtypes.
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A_scaled = np.array(A, dtype=scaled_dtype)
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# Clip scaled data around norm if necessary. This is necessary
456-
# for big numbers at the edge of float64's ability to represent
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# changes. Applying a norm first would be good, but ruins the
458-
# interpolation of over numbers.
459-
self.norm.autoscale_None(A)
460-
dv = np.float64(self.norm.vmax) - np.float64(self.norm.vmin)
461-
vmid = np.float64(self.norm.vmin) + dv / 2
462-
fact = 1e7 if scaled_dtype == np.float64 else 1e4
463-
newmin = vmid - dv * fact
464-
if newmin < a_min:
465-
newmin = None
466-
else:
467-
a_min = np.float64(newmin)
468-
newmax = vmid + dv * fact
469-
if newmax > a_max:
470-
newmax = None
471-
else:
472-
a_max = np.float64(newmax)
473-
if newmax is not None or newmin is not None:
474-
np.clip(A_scaled, newmin, newmax, out=A_scaled)
475-
476-
# Rescale the raw data to [offset, 1-offset] so that the
477-
# resampling code will run cleanly. Using dyadic numbers here
478-
# could reduce the error, but would not fully eliminate it and
479-
# breaks a number of tests (due to the slightly different
480-
# error bouncing some pixels across a boundary in the (very
481-
# quantized) colormapping step).
482-
offset = .1
483-
frac = .8
484-
# Run vmin/vmax through the same rescaling as the raw data;
485-
# otherwise, data values close or equal to the boundaries can
486-
# end up on the wrong side due to floating point error.
487-
vmin, vmax = self.norm.vmin, self.norm.vmax
488-
if vmin is np.ma.masked:
489-
vmin, vmax = a_min, a_max
490-
vrange = np.array([vmin, vmax], dtype=scaled_dtype)
491-
492-
A_scaled -= a_min
493-
vrange -= a_min
494-
# .item() handles a_min/a_max being ndarray subclasses.
495-
a_min = a_min.astype(scaled_dtype).item()
496-
a_max = a_max.astype(scaled_dtype).item()
497-
498-
if a_min != a_max:
499-
A_scaled /= ((a_max - a_min) / frac)
500-
vrange /= ((a_max - a_min) / frac)
501-
A_scaled += offset
502-
vrange += offset
439+
da = A.max().astype("f8") - A.min().astype("f8")
440+
scaled_dtype = "f8" if da > 1e8 else "f4"
441+
503442
# resample the input data to the correct resolution and shape
504-
A_resampled = _resample(self, A_scaled, out_shape, t)
505-
del A_scaled # Make sure we don't use A_scaled anymore!
506-
# Un-scale the resampled data to approximately the original
507-
# range. Things that interpolated to outside the original range
508-
# will still be outside, but possibly clipped in the case of
509-
# higher order interpolation + drastically changing data.
510-
A_resampled -= offset
511-
vrange -= offset
512-
if a_min != a_max:
513-
A_resampled *= ((a_max - a_min) / frac)
514-
vrange *= ((a_max - a_min) / frac)
515-
A_resampled += a_min
516-
vrange += a_min
443+
A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t)
444+
517445
# if using NoNorm, cast back to the original datatype
518446
if isinstance(self.norm, mcolors.NoNorm):
519447
A_resampled = A_resampled.astype(A.dtype)
@@ -536,21 +464,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
536464
# Apply the pixel-by-pixel alpha values if present
537465
alpha = self.get_alpha()
538466
if alpha is not None and np.ndim(alpha) > 0:
539-
out_alpha *= _resample(self, alpha, out_shape,
540-
t, resample=True)
467+
out_alpha *= _resample(self, alpha, out_shape, t, resample=True)
541468
# mask and run through the norm
542469
resampled_masked = np.ma.masked_array(A_resampled, out_mask)
543-
# we have re-set the vmin/vmax to account for small errors
544-
# that may have moved input values in/out of range
545-
s_vmin, s_vmax = vrange
546-
if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0:
547-
# Don't give 0 or negative values to LogNorm
548-
s_vmin = np.finfo(scaled_dtype).eps
549-
# Block the norm from sending an update signal during the
550-
# temporary vmin/vmax change
551-
with self.norm.callbacks.blocked(), \
552-
cbook._setattr_cm(self.norm, vmin=s_vmin, vmax=s_vmax):
553-
output = self.norm(resampled_masked)
470+
output = self.norm(resampled_masked)
554471
else:
555472
if A.ndim == 2: # _interpolation_stage == 'rgba'
556473
self.norm.autoscale_None(A)

‎lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png

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