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Factor out common checks for set_data in various Image subclasses. #26070

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merged 1 commit into from
Jun 8, 2023
Merged

Factor out common checks for set_data in various Image subclasses. #26070

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100 changes: 38 additions & 62 deletions lib/matplotlib/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -688,50 +688,50 @@ def write_png(self, fname):
bytes=True, norm=True)
PIL.Image.fromarray(im).save(fname, format="png")

def set_data(self, A):
@staticmethod
def _normalize_image_array(A):
"""
Set the image array.

Note that this function does *not* update the normalization used.

Parameters
----------
A : array-like or `PIL.Image.Image`
Check validity of image-like input *A* and normalize it to a format suitable for
Image subclasses.
"""
if isinstance(A, PIL.Image.Image):
A = pil_to_array(A) # Needed e.g. to apply png palette.
self._A = cbook.safe_masked_invalid(A, copy=True)

if (self._A.dtype != np.uint8 and
not np.can_cast(self._A.dtype, float, "same_kind")):
raise TypeError(f"Image data of dtype {self._A.dtype} cannot be "
"converted to float")

if self._A.ndim == 3 and self._A.shape[-1] == 1:
# If just one dimension assume scalar and apply colormap
self._A = self._A[:, :, 0]

if not (self._A.ndim == 2
or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):
raise TypeError(f"Invalid shape {self._A.shape} for image data")

if self._A.ndim == 3:
A = cbook.safe_masked_invalid(A, copy=True)
if A.dtype != np.uint8 and not np.can_cast(A.dtype, float, "same_kind"):
raise TypeError(f"Image data of dtype {A.dtype} cannot be "
f"converted to float")
if A.ndim == 3 and A.shape[-1] == 1:
A = A.squeeze(-1) # If just (M, N, 1), assume scalar and apply colormap.
if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in [3, 4]):
raise TypeError(f"Invalid shape {A.shape} for image data")
if A.ndim == 3:
# If the input data has values outside the valid range (after
# normalisation), we issue a warning and then clip X to the bounds
# - otherwise casting wraps extreme values, hiding outliers and
# making reliable interpretation impossible.
high = 255 if np.issubdtype(self._A.dtype, np.integer) else 1
if self._A.min() < 0 or high < self._A.max():
high = 255 if np.issubdtype(A.dtype, np.integer) else 1
if A.min() < 0 or high < A.max():
_log.warning(
'Clipping input data to the valid range for imshow with '
'RGB data ([0..1] for floats or [0..255] for integers).'
)
self._A = np.clip(self._A, 0, high)
A = np.clip(A, 0, high)
# Cast unsupported integer types to uint8
if self._A.dtype != np.uint8 and np.issubdtype(self._A.dtype,
np.integer):
self._A = self._A.astype(np.uint8)
if A.dtype != np.uint8 and np.issubdtype(A.dtype, np.integer):
A = A.astype(np.uint8)
return A

def set_data(self, A):
"""
Set the image array.

Note that this function does *not* update the normalization used.

Parameters
----------
A : array-like or `PIL.Image.Image`
"""
if isinstance(A, PIL.Image.Image):
A = pil_to_array(A) # Needed e.g. to apply png palette.
self._A = self._normalize_image_array(A)
self._imcache = None
self.stale = True

Expand Down Expand Up @@ -1149,23 +1149,15 @@ def set_data(self, x, y, A):
(M, N) `~numpy.ndarray` or masked array of values to be
colormapped, or (M, N, 3) RGB array, or (M, N, 4) RGBA array.
"""
A = self._normalize_image_array(A)
x = np.array(x, np.float32)
y = np.array(y, np.float32)
A = cbook.safe_masked_invalid(A, copy=True)
if not (x.ndim == y.ndim == 1 and A.shape[0:2] == y.shape + x.shape):
if not (x.ndim == y.ndim == 1 and A.shape[:2] == y.shape + x.shape):
raise TypeError("Axes don't match array shape")
if A.ndim not in [2, 3]:
raise TypeError("Can only plot 2D or 3D data")
if A.ndim == 3 and A.shape[2] not in [1, 3, 4]:
raise TypeError("3D arrays must have three (RGB) "
"or four (RGBA) color components")
if A.ndim == 3 and A.shape[2] == 1:
A = A.squeeze(axis=-1)
self._A = A
self._Ax = x
self._Ay = y
self._imcache = None

self.stale = True

def set_array(self, *args):
Expand Down Expand Up @@ -1307,36 +1299,20 @@ def set_data(self, x, y, A):
- (M, N, 3): RGB array
- (M, N, 4): RGBA array
"""
A = cbook.safe_masked_invalid(A, copy=True)
if x is None:
x = np.arange(0, A.shape[1]+1, dtype=np.float64)
else:
x = np.array(x, np.float64).ravel()
if y is None:
y = np.arange(0, A.shape[0]+1, dtype=np.float64)
else:
y = np.array(y, np.float64).ravel()

if A.shape[:2] != (y.size-1, x.size-1):
A = self._normalize_image_array(A)
x = np.arange(0., A.shape[1] + 1) if x is None else np.array(x, float).ravel()
y = np.arange(0., A.shape[0] + 1) if y is None else np.array(y, float).ravel()
if A.shape[:2] != (y.size - 1, x.size - 1):
raise ValueError(
"Axes don't match array shape. Got %s, expected %s." %
(A.shape[:2], (y.size - 1, x.size - 1)))
if A.ndim not in [2, 3]:
raise ValueError("A must be 2D or 3D")
if A.ndim == 3:
if A.shape[2] == 1:
A = A.squeeze(axis=-1)
elif A.shape[2] not in [3, 4]:
raise ValueError("3D arrays must have RGB or RGBA as last dim")

# For efficient cursor readout, ensure x and y are increasing.
if x[-1] < x[0]:
x = x[::-1]
A = A[:, ::-1]
if y[-1] < y[0]:
y = y[::-1]
A = A[::-1]

self._A = A
self._Ax = x
self._Ay = y
Expand Down