diff --git a/extern/agg24-svn/include/agg_span_image_filter_gray.h b/extern/agg24-svn/include/agg_span_image_filter_gray.h index e2c688e004cb..7ca583af724d 100644 --- a/extern/agg24-svn/include/agg_span_image_filter_gray.h +++ b/extern/agg24-svn/include/agg_span_image_filter_gray.h @@ -397,7 +397,9 @@ namespace agg fg += weight * *fg_ptr; fg >>= image_filter_shift; +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); @@ -491,8 +493,10 @@ namespace agg } fg = color_type::downshift(fg, image_filter_shift); +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg < 0) fg = 0; if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); @@ -593,8 +597,10 @@ namespace agg } fg /= total_weight; +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg < 0) fg = 0; if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); @@ -701,8 +707,10 @@ namespace agg } fg /= total_weight; +#ifndef MPL_DISABLE_AGG_GRAY_CLIPPING if(fg < 0) fg = 0; if(fg > color_type::full_value()) fg = color_type::full_value(); +#endif span->v = (value_type)fg; span->a = color_type::full_value(); diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py index 3b4dd4c75b5d..95994201b94e 100644 --- a/lib/matplotlib/image.py +++ b/lib/matplotlib/image.py @@ -457,93 +457,21 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0, # input data is not going to match the size on the screen so we # have to resample to the correct number of pixels - # TODO slice input array first - a_min = A.min() - a_max = A.max() - if a_min is np.ma.masked: # All masked; values don't matter. - a_min, a_max = np.int32(0), np.int32(1) 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) + scaled_dtype = np.dtype("f8" if A.dtype.itemsize > 4 else "f4") if scaled_dtype.itemsize < A.dtype.itemsize: _api.warn_external(f"Casting input data from {A.dtype}" f" to {scaled_dtype} for imshow.") else: # Int dtype, likely. + # TODO slice input array first # 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) - 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, 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. - self.norm.autoscale_None(A) - dv = np.float64(self.norm.vmax) - np.float64(self.norm.vmin) - vmid = np.float64(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: - np.clip(A_scaled, newmin, newmax, out=A_scaled) - - # 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 - # 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 - # .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() - - if a_min != a_max: - A_scaled /= ((a_max - a_min) / frac) - vrange /= ((a_max - a_min) / frac) - A_scaled += offset - vrange += offset + da = A.max().astype("f8") - A.min().astype("f8") + scaled_dtype = "f8" if da > 1e8 else "f4" + # resample the input data to the correct resolution and shape - A_resampled = _resample(self, A_scaled, out_shape, t) - 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: - A_resampled *= ((a_max - a_min) / frac) - vrange *= ((a_max - a_min) / frac) - A_resampled += a_min - vrange += a_min + A_resampled = _resample(self, A.astype(scaled_dtype), out_shape, t) + # if using NoNorm, cast back to the original datatype if isinstance(self.norm, mcolors.NoNorm): A_resampled = A_resampled.astype(A.dtype) @@ -564,21 +492,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0, # Apply the pixel-by-pixel alpha values if present alpha = self.get_alpha() if alpha is not None and np.ndim(alpha) > 0: - out_alpha *= _resample(self, alpha, out_shape, - t, resample=True) + out_alpha *= _resample(self, alpha, out_shape, t, resample=True) # mask and run through the norm resampled_masked = np.ma.masked_array(A_resampled, out_mask) - # we have re-set the vmin/vmax to account for small errors - # that may have moved input values in/out of range - s_vmin, s_vmax = vrange - if isinstance(self.norm, mcolors.LogNorm) and s_vmin <= 0: - # Don't give 0 or negative values to LogNorm - 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(), \ - cbook._setattr_cm(self.norm, vmin=s_vmin, vmax=s_vmax): - output = self.norm(resampled_masked) + output = self.norm(resampled_masked) else: if A.ndim == 2: # interpolation_stage = 'rgba' self.norm.autoscale_None(A) diff --git a/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png b/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png index 72918a27fbc1..9e68784cff4f 100644 Binary files a/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png and b/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.png differ diff --git a/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.svg b/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.svg index 8123e200c27a..c0385c18467c 100644 --- a/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.svg +++ b/lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.svg @@ -6,11 +6,11 @@ - 2023-04-16T19:34:05.748213 + 2024-04-23T11:45:45.434641 image/svg+xml - Matplotlib v3.8.0.dev855+gc9636b5044.d20230417, https://matplotlib.org/ + Matplotlib v3.9.0.dev1543+gdd88cca65b.d20240423, https://matplotlib.org/ @@ -29,167 +29,167 @@ z " style="fill: #ffffff"/> - + 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" id="image766171a396" transform="scale(1 -1) translate(0 -51.12)" x="57.6" y="-54.994689" width="51.12" height="51.12"/> - 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+ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/lib/matplotlib/tests/baseline_images/test_image/rotate_image.png b/lib/matplotlib/tests/baseline_images/test_image/rotate_image.png index f0edf0225890..31e62e4ed4cb 100644 Binary files a/lib/matplotlib/tests/baseline_images/test_image/rotate_image.png and b/lib/matplotlib/tests/baseline_images/test_image/rotate_image.png differ diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py index 4340be96a38b..dfacfccb3e0e 100644 --- a/lib/matplotlib/tests/test_image.py +++ b/lib/matplotlib/tests/test_image.py @@ -696,7 +696,7 @@ def test_jpeg_alpha(): # If this fails, there will be only one color (all black). If this # is working, we should have all 256 shades of grey represented. num_colors = len(image.getcolors(256)) - assert 175 <= num_colors <= 210 + assert 175 <= num_colors <= 230 # The fully transparent part should be red. corner_pixel = image.getpixel((0, 0)) assert corner_pixel == (254, 0, 0) @@ -1404,8 +1404,7 @@ def test_nonuniform_logscale(): ax.add_image(im) -@image_comparison( - ['rgba_antialias.png'], style='mpl20', remove_text=True, tol=0.01) +@image_comparison(['rgba_antialias.png'], style='mpl20', remove_text=True, tol=0.02) def test_rgba_antialias(): fig, axs = plt.subplots(2, 2, figsize=(3.5, 3.5), sharex=False, sharey=False, constrained_layout=True) diff --git a/lib/matplotlib/tests/test_png.py b/lib/matplotlib/tests/test_png.py index aa7591508a67..9208c31df2bf 100644 --- a/lib/matplotlib/tests/test_png.py +++ b/lib/matplotlib/tests/test_png.py @@ -7,7 +7,7 @@ from matplotlib import cm, pyplot as plt -@image_comparison(['pngsuite.png'], tol=0.03) +@image_comparison(['pngsuite.png'], tol=0.04) def test_pngsuite(): files = sorted( (Path(__file__).parent / "baseline_images/pngsuite").glob("basn*.png")) diff --git a/src/_image_resample.h b/src/_image_resample.h index a6404092ea2d..ddf1a4050325 100644 --- a/src/_image_resample.h +++ b/src/_image_resample.h @@ -3,6 +3,8 @@ #ifndef MPL_RESAMPLE_H #define MPL_RESAMPLE_H +#define MPL_DISABLE_AGG_GRAY_CLIPPING + #include "agg_image_accessors.h" #include "agg_path_storage.h" #include "agg_pixfmt_gray.h"