Python matplotlib.transforms.BlendedGenericTransform() Examples
The following are 7
code examples of matplotlib.transforms.BlendedGenericTransform().
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Example #1
| Source File: test_transforms.py From neural-network-animation with MIT License | 6 votes |
|
def test_clipping_of_log():
# issue 804
M,L,C = Path.MOVETO, Path.LINETO, Path.CLOSEPOLY
points = [ (0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99) ]
codes = [ M, L, L, L, C ]
path = Path(points, codes)
# something like this happens in plotting logarithmic histograms
trans = BlendedGenericTransform(Affine2D(),
LogScale.Log10Transform('clip'))
tpath = trans.transform_path_non_affine(path)
result = tpath.iter_segments(trans.get_affine(),
clip=(0, 0, 100, 100),
simplify=False)
tpoints, tcodes = list(zip(*result))
# Because y coordinate -99 is outside the clip zone, the first
# line segment is effectively removed. That means that the closepoly
# operation must be replaced by a move to the first point.
assert np.allclose(tcodes, [ M, M, L, L, L ])
Example #2
| Source File: test_transforms.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def test_clipping_of_log():
# issue 804
M, L, C = Path.MOVETO, Path.LINETO, Path.CLOSEPOLY
points = [(0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99)]
codes = [M, L, L, L, C]
path = Path(points, codes)
# something like this happens in plotting logarithmic histograms
trans = mtransforms.BlendedGenericTransform(mtransforms.Affine2D(),
LogScale.Log10Transform('clip'))
tpath = trans.transform_path_non_affine(path)
result = tpath.iter_segments(trans.get_affine(),
clip=(0, 0, 100, 100),
simplify=False)
tpoints, tcodes = zip(*result)
assert_allclose(tcodes, [M, L, L, L, C])
Example #3
| Source File: test_transforms.py From ImageFusion with MIT License | 6 votes |
|
def test_clipping_of_log():
# issue 804
M,L,C = Path.MOVETO, Path.LINETO, Path.CLOSEPOLY
points = [ (0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99) ]
codes = [ M, L, L, L, C ]
path = Path(points, codes)
# something like this happens in plotting logarithmic histograms
trans = BlendedGenericTransform(Affine2D(),
LogScale.Log10Transform('clip'))
tpath = trans.transform_path_non_affine(path)
result = tpath.iter_segments(trans.get_affine(),
clip=(0, 0, 100, 100),
simplify=False)
tpoints, tcodes = list(zip(*result))
# Because y coordinate -99 is outside the clip zone, the first
# line segment is effectively removed. That means that the closepoly
# operation must be replaced by a move to the first point.
assert np.allclose(tcodes, [ M, M, L, L, L ])
Example #4
| Source File: test_transforms.py From coffeegrindsize with MIT License | 6 votes |
|
def test_clipping_of_log():
# issue 804
M, L, C = Path.MOVETO, Path.LINETO, Path.CLOSEPOLY
points = [(0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99)]
codes = [M, L, L, L, C]
path = Path(points, codes)
# something like this happens in plotting logarithmic histograms
trans = mtransforms.BlendedGenericTransform(mtransforms.Affine2D(),
LogScale.Log10Transform('clip'))
tpath = trans.transform_path_non_affine(path)
result = tpath.iter_segments(trans.get_affine(),
clip=(0, 0, 100, 100),
simplify=False)
tpoints, tcodes = zip(*result)
assert_allclose(tcodes, [M, L, L, L, C])
Example #5
| Source File: test_transforms.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_clipping_of_log():
# issue 804
M, L, C = Path.MOVETO, Path.LINETO, Path.CLOSEPOLY
points = [(0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20), (0.2, -99)]
codes = [M, L, L, L, C]
path = Path(points, codes)
# something like this happens in plotting logarithmic histograms
trans = mtransforms.BlendedGenericTransform(mtransforms.Affine2D(),
LogScale.Log10Transform('clip'))
tpath = trans.transform_path_non_affine(path)
result = tpath.iter_segments(trans.get_affine(),
clip=(0, 0, 100, 100),
simplify=False)
tpoints, tcodes = zip(*result)
assert_allclose(tcodes, [M, L, L, L, C])
Example #6
| Source File: exporter.py From mplexporter with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def process_transform(transform, ax=None, data=None, return_trans=False,
force_trans=None):
"""Process the transform and convert data to figure or data coordinates
Parameters
----------
transform : matplotlib Transform object
The transform applied to the data
ax : matplotlib Axes object (optional)
The axes the data is associated with
data : ndarray (optional)
The array of data to be transformed.
return_trans : bool (optional)
If true, return the final transform of the data
force_trans : matplotlib.transform instance (optional)
If supplied, first force the data to this transform
Returns
-------
code : string
Code is either "data", "axes", "figure", or "display", indicating
the type of coordinates output.
transform : matplotlib transform
the transform used to map input data to output data.
Returned only if return_trans is True
new_data : ndarray
Data transformed to match the given coordinate code.
Returned only if data is specified
"""
if isinstance(transform, transforms.BlendedGenericTransform):
warnings.warn("Blended transforms not yet supported. "
"Zoom behavior may not work as expected.")
if force_trans is not None:
if data is not None:
data = (transform - force_trans).transform(data)
transform = force_trans
code = "display"
if ax is not None:
for (c, trans) in [("data", ax.transData),
("axes", ax.transAxes),
("figure", ax.figure.transFigure),
("display", transforms.IdentityTransform())]:
if transform.contains_branch(trans):
code, transform = (c, transform - trans)
break
if data is not None:
if return_trans:
return code, transform.transform(data), transform
else:
return code, transform.transform(data)
else:
if return_trans:
return code, transform
else:
return code
Example #7
| Source File: exporter.py From lddmm-ot with MIT License | 4 votes |
|
def process_transform(transform, ax=None, data=None, return_trans=False,
force_trans=None):
"""Process the transform and convert data to figure or data coordinates
Parameters
----------
transform : matplotlib Transform object
The transform applied to the data
ax : matplotlib Axes object (optional)
The axes the data is associated with
data : ndarray (optional)
The array of data to be transformed.
return_trans : bool (optional)
If true, return the final transform of the data
force_trans : matplotlib.transform instance (optional)
If supplied, first force the data to this transform
Returns
-------
code : string
Code is either "data", "axes", "figure", or "display", indicating
the type of coordinates output.
transform : matplotlib transform
the transform used to map input data to output data.
Returned only if return_trans is True
new_data : ndarray
Data transformed to match the given coordinate code.
Returned only if data is specified
"""
if isinstance(transform, transforms.BlendedGenericTransform):
warnings.warn("Blended transforms not yet supported. "
"Zoom behavior may not work as expected.")
if force_trans is not None:
if data is not None:
data = (transform - force_trans).transform(data)
transform = force_trans
code = "display"
if ax is not None:
for (c, trans) in [("data", ax.transData),
("axes", ax.transAxes),
("figure", ax.figure.transFigure),
("display", transforms.IdentityTransform())]:
if transform.contains_branch(trans):
code, transform = (c, transform - trans)
break
if data is not None:
if return_trans:
return code, transform.transform(data), transform
else:
return code, transform.transform(data)
else:
if return_trans:
return code, transform
else:
return code
