def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale and color the image `A` from the given
in_bbox (in data space), to the given out_bbox (in pixel
space) clipped to the given clip_bbox (also in pixel space),
and magnified by the magnification factor.
`A` may be a greyscale image (MxN) with a dtype of `float32`,
`float64`, `uint16` or `uint8`, or an RGBA image (MxNx4) with
a dtype of `float32`, `float64`, or `uint8`.
If `unsampled` is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
If `round_to_pixel_border` is True, the output image size will
be rounded to the nearest pixel boundary. This makes the
images align correctly with the axes. It should not be used
in cases where you want exact scaling, however, such as
FigureImage.
Returns the resulting (image, x, y, trans), where (x, y) is
the upper left corner of the result in pixel space, and
`trans` is the affine transformation from the image to pixel
space.
"""
if A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base))
out_height = int(ceil(out_height_base))
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(
1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
created_rgba_mask = False
if A.ndim not in (2, 3):
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
if A.ndim == 2:
A = self.norm(A)
if A.dtype.kind == 'f':
# If the image is greyscale, convert to RGBA and
# use the extra channels for resizing the over,
# under, and bad pixels. This is needed because
# Agg's resampler is very aggressive about
# clipping to [0, 1] and we use out-of-bounds
# values to carry the over/under/bad information
rgba = np.empty((A.shape[0], A.shape[1], 4), dtype=A.dtype)
rgba[..., 0] = A # normalized data
rgba[..., 1] = A < 0 # under data
rgba[..., 2] = A > 1 # over data
rgba[..., 3] = ~A.mask # bad data
A = rgba
output = np.zeros((out_height, out_width, 4),
dtype=A.dtype)
alpha = 1.0
created_rgba_mask = True
else:
# colormap norms that output integers (ex NoNorm
# and BoundaryNorm) to RGBA space before
# interpolating. This is needed due to the
# Agg resampler only working on floats in the
# range [0, 1] and because interpolating indexes
# into an arbitrary LUT may be problematic.
#
# This falls back to interpolating in RGBA space which
# can produce it's own artifacts of colors not in the map
# showing up in the final image.
A = self.cmap(A, alpha=self.get_alpha(), bytes=True)
if not created_rgba_mask:
# Always convert to RGBA, even if only RGB input
if A.shape[2] == 3:
A = _rgb_to_rgba(A)
elif A.shape[2] != 4:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = self.get_alpha()
if alpha is None:
alpha = 1.0
_image.resample(
A, output, t, _interpd_[self.get_interpolation()],
self.get_resample(), alpha,
self.get_filternorm() or 0.0, self.get_filterrad() or 0.0)
if created_rgba_mask:
# Convert back to a masked greyscale array so
# colormapping works correctly
hid_output = output
output = np.ma.masked_array(
hid_output[..., 0], hid_output[..., 3] < 0.5)
# relabel under data
output[hid_output[..., 1] > .5] = -1
# relabel over data
output[hid_output[..., 2] > .5] = 2
output = self.to_rgba(output, bytes=True, norm=False)
# Apply alpha *after* if the input was greyscale without a mask
if A.ndim == 2 or created_rgba_mask:
alpha = self.get_alpha()
if alpha is not None and alpha != 1.0:
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * alpha,
np.uint8)
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 = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale and color the image `A` from the given
in_bbox (in data space), to the given out_bbox (in pixel
space) clipped to the given clip_bbox (also in pixel space),
and magnified by the magnification factor.
`A` may be a greyscale image (MxN) with a dtype of `float32`,
`float64`, `uint16` or `uint8`, or an RGBA image (MxNx4) with
a dtype of `float32`, `float64`, or `uint8`.
If `unsampled` is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
If `round_to_pixel_border` is True, the output image size will
be rounded to the nearest pixel boundary. This makes the
images align correctly with the axes. It should not be used
in cases where you want exact scaling, however, such as
FigureImage.
Returns the resulting (image, x, y, trans), where (x, y) is
the upper left corner of the result in pixel space, and
`trans` is the affine transformation from the image to pixel
space.
"""
if A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or
out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base) + 1)
out_height = int(ceil(out_height_base) + 1)
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(
1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
created_rgba_mask = False
if A.ndim == 2:
A = self.norm(A)
# If the image is greyscale, convert to RGBA with the
# correct alpha channel for resizing
rgba = np.empty((A.shape[0], A.shape[1], 4), dtype=A.dtype)
rgba[..., 0:3] = np.expand_dims(A, 2)
if A.dtype.kind == 'f':
rgba[..., 3] = ~A.mask
else:
rgba[..., 3] = np.where(A.mask, 0, np.iinfo(A.dtype).max)
A = rgba
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = 1.0
created_rgba_mask = True
elif A.ndim == 3:
# Always convert to RGBA, even if only RGB input
if A.shape[2] == 3:
A = _rgb_to_rgba(A)
elif A.shape[2] != 4:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = self.get_alpha()
if alpha is None:
alpha = 1.0
else:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
_image.resample(
A, output, t, _interpd_[self.get_interpolation()],
self.get_resample(), alpha,
self.get_filternorm() or 0.0, self.get_filterrad() or 0.0)
if created_rgba_mask:
# Convert back to a masked greyscale array so
# colormapping works correctly
output = np.ma.masked_array(
output[..., 0], output[..., 3] < 0.5)
output = self.to_rgba(output, bytes=True, norm=False)
# Apply alpha *after* if the input was greyscale without a mask
if A.ndim == 2 or created_rgba_mask:
alpha = self.get_alpha()
if alpha is not None and alpha != 1.0:
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * alpha,
np.uint8)
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 = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale and color the image `A` from the given
in_bbox (in data space), to the given out_bbox (in pixel
space) clipped to the given clip_bbox (also in pixel space),
and magnified by the magnification factor.
`A` may be a greyscale image (MxN) with a dtype of `float32`,
`float64`, `uint16` or `uint8`, or an RGBA image (MxNx4) with
a dtype of `float32`, `float64`, or `uint8`.
If `unsampled` is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
If `round_to_pixel_border` is True, the output image size will
be rounded to the nearest pixel boundary. This makes the
images align correctly with the axes. It should not be used
in cases where you want exact scaling, however, such as
FigureImage.
Returns the resulting (image, x, y, trans), where (x, y) is
the upper left corner of the result in pixel space, and
`trans` is the affine transformation from the image to pixel
space.
"""
if A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base))
out_height = int(ceil(out_height_base))
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(
1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
created_rgba_mask = False
if A.ndim not in (2, 3):
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
if A.ndim == 2:
A = self.norm(A)
if A.dtype.kind == 'f':
# If the image is greyscale, convert to RGBA and
# use the extra channels for resizing the over,
# under, and bad pixels. This is needed because
# Agg's resampler is very aggressive about
# clipping to [0, 1] and we use out-of-bounds
# values to carry the over/under/bad information
rgba = np.empty((A.shape[0], A.shape[1], 4), dtype=A.dtype)
rgba[..., 0] = A # normalized data
# this is to work around spurious warnings coming
# out of masked arrays.
with np.errstate(invalid='ignore'):
rgba[..., 1] = A < 0 # under data
rgba[..., 2] = A > 1 # over data
rgba[..., 3] = ~A.mask # bad data
A = rgba
output = np.zeros((out_height, out_width, 4),
dtype=A.dtype)
alpha = 1.0
created_rgba_mask = True
else:
# colormap norms that output integers (ex NoNorm
# and BoundaryNorm) to RGBA space before
# interpolating. This is needed due to the
# Agg resampler only working on floats in the
# range [0, 1] and because interpolating indexes
# into an arbitrary LUT may be problematic.
#
# This falls back to interpolating in RGBA space which
# can produce it's own artifacts of colors not in the map
# showing up in the final image.
A = self.cmap(A, alpha=self.get_alpha(), bytes=True)
if not created_rgba_mask:
# Always convert to RGBA, even if only RGB input
if A.shape[2] == 3:
A = _rgb_to_rgba(A)
elif A.shape[2] != 4:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = self.get_alpha()
if alpha is None:
alpha = 1.0
_image.resample(
A, output, t, _interpd_[self.get_interpolation()],
self.get_resample(), alpha,
self.get_filternorm() or 0.0, self.get_filterrad() or 0.0)
if created_rgba_mask:
# Convert back to a masked greyscale array so
# colormapping works correctly
hid_output = output
output = np.ma.masked_array(
hid_output[..., 0], hid_output[..., 3] < 0.5)
# relabel under data
output[hid_output[..., 1] > .5] = -1
# relabel over data
output[hid_output[..., 2] > .5] = 2
output = self.to_rgba(output, bytes=True, norm=False)
# Apply alpha *after* if the input was greyscale without a mask
if A.ndim == 2 or created_rgba_mask:
alpha = self.get_alpha()
if alpha is not None and alpha != 1.0:
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * alpha,
np.uint8)
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 = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True):
"""
Normalize, rescale and color the image `A` from the given
in_bbox (in data space), to the given out_bbox (in pixel
space) clipped to the given clip_bbox (also in pixel space),
and magnified by the magnification factor.
`A` may be a greyscale image (MxN) with a dtype of `float32`,
`float64`, `uint16` or `uint8`, or an RGBA image (MxNx4) with
a dtype of `float32`, `float64`, or `uint8`.
If `unsampled` is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.
If `round_to_pixel_border` is True, the output image size will
be rounded to the nearest pixel boundary. This makes the
images align correctly with the axes. It should not be used
in cases where you want exact scaling, however, such as
FigureImage.
Returns the resulting (image, x, y, trans), where (x, y) is
the upper left corner of the result in pixel space, and
`trans` is the affine transformation from the image to pixel
space.
"""
if A is None:
raise RuntimeError('You must first set the image'
' array or the image attribute')
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or
out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base) + 1)
out_height = int(ceil(out_height_base) + 1)
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(
1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
if A.ndim == 2:
A = self.norm(A)
if A.dtype.kind == 'f':
# For floating-point greyscale images, we treat negative
# numbers as transparent.
# TODO: Use np.full when we support Numpy 1.9 as a
# minimum
output = np.empty((out_height, out_width), dtype=A.dtype)
output[...] = -100.0
else:
output = np.zeros((out_height, out_width), dtype=A.dtype)
alpha = 1.0
elif A.ndim == 3:
# Always convert to RGBA, even if only RGB input
if A.shape[2] == 3:
A = _rgb_to_rgba(A)
elif A.shape[2] != 4:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
alpha = self.get_alpha()
if alpha is None:
alpha = 1.0
else:
raise ValueError("Invalid dimensions, got %s" % (A.shape,))
_image.resample(
A, output, t, _interpd_[self.get_interpolation()],
self.get_resample(), alpha,
self.get_filternorm() or 0.0, self.get_filterrad() or 0.0)
output = self.to_rgba(output, bytes=True, norm=False)
# Apply alpha *after* if the input was greyscale
if A.ndim == 2:
alpha = self.get_alpha()
if alpha is not None and alpha != 1.0:
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * alpha,
np.uint8)
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 = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
def _make_image_special(self, A, density, in_bbox, out_bbox, clip_bbox, magnification=1.0,
unsampled=False, round_to_pixel_border=True, densities=None):
"""
This function is a copy of _ImageBase._make_image(*args, **kwargs), but with only the A.ndim == 2 case,
and with the transformation of the alpha channel on top of the normal behaviour with the colormap's RGBA
"""
if A is None:
raise RuntimeError('You must first set the image '
'array or the image attribute')
if A.size == 0:
raise RuntimeError("_make_image must get a non-empty image. "
"Your Artist's draw method must filter before "
"this method is called.")
clipped_bbox = Bbox.intersection(out_bbox, clip_bbox)
if clipped_bbox is None:
return None, 0, 0, None
out_width_base = clipped_bbox.width * magnification
out_height_base = clipped_bbox.height * magnification
if out_width_base == 0 or out_height_base == 0:
return None, 0, 0, None
if self.origin == 'upper':
# Flip the input image using a transform. This avoids the
# problem with flipping the array, which results in a copy
# when it is converted to contiguous in the C wrapper
t0 = Affine2D().translate(0, -A.shape[0]).scale(1, -1)
else:
t0 = IdentityTransform()
t0 += (
Affine2D()
.scale(
in_bbox.width / A.shape[1],
in_bbox.height / A.shape[0])
.translate(in_bbox.x0, in_bbox.y0)
+ self.get_transform())
t = (t0
+ Affine2D().translate(
-clipped_bbox.x0,
-clipped_bbox.y0)
.scale(magnification, magnification))
# So that the image is aligned with the edge of the axes, we want
# to round up the output width to the next integer. This also
# means scaling the transform just slightly to account for the
# extra subpixel.
if (t.is_affine and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
out_width = int(ceil(out_width_base))
out_height = int(ceil(out_height_base))
extra_width = (out_width - out_width_base) / out_width_base
extra_height = (out_height - out_height_base) / out_height_base
t += Affine2D().scale(1.0 + extra_width, 1.0 + extra_height)
else:
out_width = int(out_width_base)
out_height = int(out_height_base)
if not unsampled:
#if A.ndim not in (2, 3):
# raise ValueError("Invalid dimensions, got {}".format(A.shape))
# if we are a 2D array, then we are running through the
# norm + colormap transformation. However, in general the
# input data is not going to match the size on the screen so we
# have to resample to the correct number of pixels
# need to
# 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
a_min, a_max = np.int32(0), np.int32(1)
if inp_dtype.kind == 'f':
scaled_dtype = A.dtype
else:
# probably an integer of some type.
da = a_max.astype(np.float64) - a_min.astype(np.float64)
if da > 1e8:
# give more breathing room if a big dynamic range
scaled_dtype = np.float64
else:
scaled_dtype = 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
A_scaled = np.empty(A.shape, dtype=scaled_dtype)
A_scaled[:] = A
# 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 = 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 avoid errors
a_min = np.asscalar(a_min.astype(scaled_dtype))
a_max = np.asscalar(a_max.astype(scaled_dtype))
if a_min != a_max:
A_scaled /= ((a_max - a_min) / 0.8)
A_scaled += 0.1
A_resampled = np.zeros((out_height, out_width),
dtype=A_scaled.dtype)
# resample the input data to the correct resolution and shape
_image.resample(A_scaled, A_resampled,
t,
_interpd_[self.get_interpolation()],
self.get_resample(), 1.0,
self.get_filternorm() or 0.0,
self.get_filterrad() or 0.0)
#alpha = self.get_alpha()
new_density = np.zeros((out_height, out_width),
dtype=density.dtype)
_image.resample(density, new_density,
t,
_interpd_[self.get_interpolation()],
self.get_resample(), 1.0,
self.get_filternorm() or 0.0,
self.get_filterrad() or 0.0)
# we are 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.
A_resampled -= 0.1
if a_min != a_max:
A_resampled *= ((a_max - a_min) / 0.8)
A_resampled += a_min
# if using NoNorm, cast back to the original datatype
if isinstance(self.norm, mcolors.NoNorm):
A_resampled = A_resampled.astype(A.dtype)
mask = np.empty(A.shape, dtype=np.float32)
if A.mask.shape == A.shape:
# this is the case of a nontrivial mask
mask[:] = np.where(A.mask, np.float32(np.nan),
np.float32(1))
else:
mask[:] = 1
# we always have to interpolate the mask to account for
# non-affine transformations
out_mask = np.zeros((out_height, out_width),
dtype=mask.dtype)
_image.resample(mask, out_mask,
t,
_interpd_[self.get_interpolation()],
True, 1,
self.get_filternorm() or 0.0,
self.get_filterrad() or 0.0)
# we are done with the mask, delete from namespace to be sure!
del mask
# Agg updates the out_mask 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 it will be `nan`, if all
# the input data for a pixel is good it will be 1, and
# if there is _some_ good data in that output pixel it
# will be between [0, 1] (such as a rotated image).
out_alpha = np.array(out_mask)
out_mask = np.isnan(out_mask)
out_alpha[out_mask] = 1
new_alpha = alpha_from_densities(new_density, tresfrac=self.c_alpha_log_tresh, scaleval=self.c_alpha_log_enhance, logscale=self.c_alpha_logdensity)
out_alpha *= new_alpha
# mask and run through the norm
output = self.norm(np.ma.masked_array(A_resampled, out_mask))
# at this point output is either a 2D array of normed data
# (of int or float)
# 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
# Apply alpha *after* if the input was greyscale without a mask
if A.ndim == 2:
# alpha = self.get_alpha()
# if alpha is None:
# alpha = 1
alpha = 1
alpha_channel = output[:, :, 3]
alpha_channel[:] = np.asarray(
np.asarray(alpha_channel, np.float32) * out_alpha * alpha,
np.uint8)
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 = TransformedBbox(
clip_bbox, t0.frozen().inverted()).frozen()
output = output[
int(max(subset.ymin, 0)):
int(min(subset.ymax + 1, output.shape[0])),
int(max(subset.xmin, 0)):
int(min(subset.xmax + 1, output.shape[1]))]
t = Affine2D().translate(
int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
return output, clipped_bbox.x0, clipped_bbox.y0, t
