Fix clims when plotting shapes element annotations with matplotlib rendering#368

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Merged
melonora merged 11 commits into from
Oct 10, 2024
Merged

Fix clims when plotting shapes element annotations with matplotlib rendering #368

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0744fc4
fix clims for shapes
melonora Oct 3, 2024
17b9753
add test for set shape clims
melonora Oct 3, 2024
31cfc57
silence warning
melonora Oct 4, 2024
bff962e
silence warnings
melonora Oct 4, 2024
a89a73e
fix tests
melonora Oct 4, 2024
440100a
remove unnecessary list multiplication
melonora Oct 4, 2024
fcf1549
remove wrong argument and list multiplications
melonora Oct 4, 2024
e652ba2
remove unused arguments
melonora Oct 4, 2024
3f621df
set clip to false
melonora Oct 9, 2024
96acdc6
add deprecation warning
melonora Oct 9, 2024
16a5a38
add deprecation warning at all render functions
melonora Oct 10, 2024
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32 changes: 28 additions & 4 deletions src/spatialdata_plot/pl/basic.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
sd.SpatialData
The modified SpatialData object with the rendered shapes.
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_shape_render_params(
self._sdata,
element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
cmap=cmap,
norm=norm,
na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
sd.SpatialData
The modified SpatialData object with the rendered shapes.
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_points_render_params(
self._sdata,
element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
cmap=cmap,
norm=norm,
na_color=param_values["na_color"], # type: ignore[arg-type]
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
sd.SpatialData
The SpatialData object with the rendered images.
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_image_render_params(
self._sdata,
element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
cmap=c,
norm=norm,
na_color=param_values["na_color"],
**kwargs,
)
for c in cmap
]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
-------
None
"""
# TODO add Normalize object in tutorial notebook and point to that notebook here
if "vmin" in kwargs or "vmax" in kwargs:
warnings.warn(
"`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
DeprecationWarning,
stacklevel=2,
)
params_dict = _validate_label_render_params(
self._sdata,
element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
cmap=cmap,
norm=norm,
na_color=param_values["na_color"], # type: ignore[arg-type]
**kwargs,
)
sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
element=element,
Expand Down
18 changes: 4 additions & 14 deletions src/spatialdata_plot/pl/utils.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
LinearSegmentedColormap,
ListedColormap,
Normalize,
TwoSlopeNorm,
to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
c = cmap(c)
else:
try:
norm = colors.Normalize(vmin=min(c), vmax=max(c))
norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm

@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.

@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.

@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.

@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.

@melonoramelonoraOct 9, 2024

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default is set to False now

except ValueError as e:
raise ValueError(
"Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
c = cmap(c)
else:
try:
norm = colors.Normalize(vmin=min(c), vmax=max(c))
norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
except ValueError as e:
raise ValueError(
"Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
cmap: Colormap | str | None = None,
norm: Normalize | None = None,
na_color: ColorLike | None = None,
vmin: float | None = None,

@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?

@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called

@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?

@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).

@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?

@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters

@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs

vmax: float | None = None,
vcenter: float | None = None,
**kwargs: Any,
) -> CmapParams:
# TODO: check refactoring norm out here as it gets overwritten later
cmap_is_default = cmap is None
if cmap is None:
cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
cmap = copy(cmap)

if norm is None:
norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
elif isinstance(norm, Normalize) or not norm:
pass # TODO
elif vcenter is None:
norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
else:
norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)

@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.

norm = Normalize(vmin=None, vmax=None, clip=False)

na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
cmap.set_bad(na_color)
Expand Down
Binary file modifiedtests/_images/Labels_can_render_given_scale_of_multiscale_labels.png
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33 changes: 23 additions & 10 deletions tests/pl/test_render_shapes.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
blob = deepcopy(sdata_blobs)
blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
blob["table"].obs["region"] = "blobs_polygons"
blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
_, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
sdata_blobs["table"].obs["region"] = "blobs_polygons"
sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
)

def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
sdata_blobs["table"].obs["region"] = "blobs_polygons"
sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
).pl.show()

def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
sdata_blobs["table"].obs["region"] = "blobs_polygons"
sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
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sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
LucaMarconato marked this conversation as resolved.
sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
sdata_blobs["table"].obs["region"] = "blobs_circles"
new_table = sdata_blobs["table"][:5]
new_table = sdata_blobs["table"][:5].copy()
new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
sdata_blobs["table"].obs["region"] = "blobs_circles"
new_table = sdata_blobs["table"][:10]
new_table = sdata_blobs["table"][:10].copy()
new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
sdata_blobs["table"].obs["region"] = "blobs_circles"
new_table = sdata_blobs["table"][:10]
new_table = sdata_blobs["table"][:10].copy()
new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
sdata_blobs["table"].obs["region"] = "blobs_polygons"
sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
table_shapes = sdata_blobs["table"][:5].copy()
table_shapes.obs.instance_id = list(range(5))
table_shapes.obs["region"] = "blobs_circles"
table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
sdata_blobs["new_table"] = table_shapes

norm = Normalize(vmin=20, vmax=40, clip=True)
sdata_blobs.pl.render_shapes(
"blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
).pl.show()
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Fix clims when plotting shapes element annotations with matplotlib rendering#368

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 Merged

melonora
merged 11 commits into

from

Oct 10, 2024
 Merged


Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
Comment thread
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 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
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 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()

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injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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Fix clims when plotting shapes element annotations with matplotlib rendering#368

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 Merged

melonora
merged 11 commits into

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Oct 10, 2024
 Merged


Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
Comment thread
LucaMarconato marked this conversation as resolved.

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
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 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()

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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix clims when plotting shapes element annotations with matplotlib rendering#368

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 Merged

melonora
merged 11 commits into

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Oct 10, 2024
 Merged


Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
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 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
LucaMarconato marked this conversation as resolved.

 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Fix clims when plotting shapes element annotations with matplotlib rendering#368

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melonora
merged 11 commits into

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Oct 10, 2024
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Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
Comment thread
LucaMarconato marked this conversation as resolved.

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
LucaMarconato marked this conversation as resolved.

 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Fix clims when plotting shapes element annotations with matplotlib rendering#368

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 Merged

melonora
merged 11 commits into

from

Oct 10, 2024
 Merged


Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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Commits

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11 commits


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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
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 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
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 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()

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Fix clims when plotting shapes element annotations with matplotlib rendering#368

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 Merged

melonora
merged 11 commits into

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Oct 10, 2024
 Merged


Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
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 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
Comment thread
LucaMarconato marked this conversation as resolved.

 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Fix clims when plotting shapes element annotations with matplotlib rendering#368

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 Merged

melonora
merged 11 commits into

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Oct 10, 2024
 Merged


Fix clims when plotting shapes element annotations with matplotlib rendering
#368
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0744fc4

fix clims for shapes


melonora Oct 3, 2024
17b9753

add test for set shape clims


melonora Oct 3, 2024
31cfc57

silence warning


melonora Oct 4, 2024
bff962e

silence warnings


melonora Oct 4, 2024
a89a73e

fix tests


melonora Oct 4, 2024
440100a

remove unnecessary list multiplication


melonora Oct 4, 2024
fcf1549

remove wrong argument and list multiplications


melonora Oct 4, 2024
e652ba2

remove unused arguments


melonora Oct 4, 2024
3f621df

set clip to false


melonora Oct 9, 2024
96acdc6

add deprecation warning


melonora Oct 9, 2024
16a5a38

add deprecation warning at all render functions


melonora Oct 10, 2024

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32 changes: 28 additions & 4 deletions
src/spatialdata_plot/pl/basic.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,6 +241,13 @@ def render_shapes(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_shape_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -269,7 +276,6 @@ def render_shapes(
 cmap=cmap,
 norm=norm,
 na_color=params_dict[element]["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_shapes"] = ShapesRenderParams(
 element=element,
Expand DownExpand Up@@ -363,6 +369,13 @@ def render_points(
 sd.SpatialData
 The modified SpatialData object with the rendered shapes.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_points_render_params(
 self._sdata,
 element=element,
Expand DownExpand Up@@ -392,7 +405,6 @@ def render_points(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_points"] = PointsRenderParams(
 element=element,
Expand DownExpand Up@@ -473,6 +485,13 @@ def render_images(
 sd.SpatialData
 The SpatialData object with the rendered images.
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_image_render_params(
 self._sdata,
 element=element,
Expand All@@ -498,7 +517,6 @@ def render_images(
 cmap=c,
 norm=norm,
 na_color=param_values["na_color"],
 **kwargs,
 )
 for c in cmap
 ]
Expand DownExpand Up@@ -598,6 +616,13 @@ def render_labels(
 -------
 None
 """
 # TODO add Normalize object in tutorial notebook and point to that notebook here
 if "vmin" in kwargs or "vmax" in kwargs:
 warnings.warn(
 "`vmin` and `vmax` are deprecated. Pass matplotlib `Normalize` object to norm instead.",
 DeprecationWarning,
 stacklevel=2,
 )
 params_dict = _validate_label_render_params(
 self._sdata,
 element=element,
Expand All@@ -623,7 +648,6 @@ def render_labels(
 cmap=cmap,
 norm=norm,
 na_color=param_values["na_color"], # type: ignore[arg-type]
 **kwargs,
 )
 sdata.plotting_tree[f"{n_steps+1}_render_labels"] = LabelsRenderParams(
 element=element,
Expand Down

18 changes: 4 additions & 14 deletions
src/spatialdata_plot/pl/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -32,7 +32,6 @@
 LinearSegmentedColormap,
 ListedColormap,
 Normalize,
 TwoSlopeNorm,
 to_rgba,
)
from matplotlib.figure import Figure
Expand DownExpand Up@@ -339,7 +338,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
@LucaMarconatoLucaMarconatoOct 4, 2024

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In this function Normalize() is initialized without clip, while in _prepare_cmap_norm() the default is to set clip=True. I would choose one of the two as our default choice. The user will be able to specify clip, vcenter, etc by passing a norm object directly.
@melonoramelonoraOct 4, 2024

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hmm let me double check that if we don't pass norm as user, whether ultimately the norm is always created anyway, then we can get rid of normalize instance initiated here.
@melonoramelonoraOct 4, 2024

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ok there is code left over of when vmin and vmax were removed. Not certain whether to address this in a different PR.
@LucaMarconatoLucaMarconatoOct 9, 2024

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I'd address the choice of the value of clip in this PR please, because it's easy to forget about this in a new PR.
@melonoramelonoraOct 9, 2024

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default is set to False now
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand All@@ -353,7 +352,7 @@ def _get_collection_shape(
 c = cmap(c)
 else:
 try:
 norm = colors.Normalize(vmin=min(c), vmax=max(c))
 norm = colors.Normalize(vmin=min(c), vmax=max(c)) if norm is None else norm
 except ValueError as e:
 raise ValueError(
 "Could not convert values in the `color` column to float, if `color` column represents"
Expand DownExpand Up@@ -491,11 +490,8 @@ def _prepare_cmap_norm(
 cmap: Colormap | str | None = None,
 norm: Normalize | None = None,
 na_color: ColorLike | None = None,
 vmin: float | None = None,
@LucaMarconatoLucaMarconatoOct 9, 2024

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@timtreis I don't remember the outcome of the discussion with the user that reported this. Is this the way to go (=letting users only use norm and not vmin, vmax) or shall we remove vcenter only and keep vmin, vmax and use them to initialize the default Normalize object?
@melonoramelonoraOct 9, 2024

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In the discussion it was stated that vmin and vmax are removed. This function is only internally called
@melonoramelonoraOct 9, 2024

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So we agree on clip 'True' by default if user does not provide normaloze object?
@LucaMarconatoLucaMarconatoOct 9, 2024

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If vmin and vmax are not exposed to the user (and hence they are None), then clip will have no effect because when exposed vmin, vmax are None, the data limits are used. So I would keep the default clip to be False (which is matplotlib's default).
@LucaMarconatoLucaMarconatoOct 9, 2024

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One thing, if vmin, vmax are removed from pl.render_shapes(), we should throw an informative exception or deprecation warning, explaining to the user that norm should be used instead. Could you add that please?
@melonoramelonoraOct 9, 2024

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They have not been removed in this PR though and the public functions thus already did not contain these parameters
@melonoramelonoraOct 9, 2024

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I did add a deprecation warning in case of the arguments being passed as kwargs
 vmax: float | None = None,
 vcenter: float | None = None,
 **kwargs: Any,
) -> CmapParams:
 # TODO: check refactoring norm out here as it gets overwritten later
 cmap_is_default = cmap is None
 if cmap is None:
 cmap = rcParams["image.cmap"]
Expand All@@ -505,13 +501,7 @@ def _prepare_cmap_norm(
 cmap = copy(cmap)

 if norm is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 elif isinstance(norm, Normalize) or not norm:
 pass # TODO
 elif vcenter is None:
 norm = Normalize(vmin=vmin, vmax=vmax, clip=True)
 else:
 norm = TwoSlopeNorm(vmin=vmin, vmax=vmax, vcenter=vcenter)
@LucaMarconatoLucaMarconatoOct 4, 2024

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Now that vcenter is removed, it should be removed also from the function signature. Also, kwargs is in the signature but not used, so I would remove it.
 norm = Normalize(vmin=None, vmax=None, clip=False)

 na_color, na_color_modified_by_user = _sanitise_na_color(na_color)
 cmap.set_bad(na_color)
Expand Down
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33 changes: 23 additions & 10 deletions
tests/pl/test_render_shapes.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -97,7 +97,7 @@ def _make_multi():

 def test_plot_can_color_from_geodataframe(self, sdata_blobs: SpatialData):
 blob = deepcopy(sdata_blobs)
 blob["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 blob["table"].obs["region"] = "blobs_polygons"
 blob["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 blob.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 blob.pl.render_shapes(
Expand All@@ -111,7 +111,7 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
 def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")

 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -125,7 +125,7 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
 )

 def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = "c1"
 sdata_blobs.shapes["blobs_polygons"].iloc[3:5, 1] = "c2"
Expand All@@ -138,13 +138,13 @@ def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
 ).pl.show()

 def test_plot_colorbar_respects_input_limits(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups=["c1"]).pl.show()
 sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show()

 def test_plot_colorbar_can_be_normalised(self, sdata_blobs: SpatialData):
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 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
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 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["cluster"] = [1, 2, 3, 5, 20]
 norm = Normalize(vmin=0, vmax=5, clip=True)
Expand DownExpand Up@@ -186,7 +186,7 @@ def test_plot_can_plot_with_annotation_despite_random_shuffling(self, sdata_blob

 def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:5]
 new_table = sdata_blobs["table"][:5].copy()
 new_table.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 new_table.obs["instance_id"] = np.array(range(5))

Expand DownExpand Up@@ -214,7 +214,7 @@ def test_plot_can_plot_queried_with_annotation_despite_random_shuffling(self, sd

 def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand All@@ -230,7 +230,7 @@ def test_plot_can_color_two_shapes_elements_by_annotation(self, sdata_blobs: Spa

 def test_plot_can_color_two_queried_shapes_elements_by_annotation(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = "blobs_circles"
 new_table = sdata_blobs["table"][:10]
 new_table = sdata_blobs["table"][:10].copy()
 new_table.uns["spatialdata_attrs"]["region"] = ["blobs_circles", "blobs_polygons"]
 new_table.obs["instance_id"] = np.concatenate((np.array(range(5)), np.array(range(5))))

Expand DownExpand Up@@ -312,7 +312,20 @@ def test_plot_datashader_can_color_by_category(self, sdata_blobs: SpatialData):
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="category", method="datashader").pl.show()

 def test_plot_datashader_can_color_by_value(self, sdata_blobs: SpatialData):
 sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs
 sdata_blobs["table"].obs["region"] = "blobs_polygons"
 sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_polygons"
 sdata_blobs.shapes["blobs_polygons"]["value"] = [1, 10, 1, 20, 1]
 sdata_blobs.pl.render_shapes(element="blobs_polygons", color="value", method="datashader").pl.show()

 def test_plot_can_set_clims_clip(self, sdata_blobs: SpatialData):
 table_shapes = sdata_blobs["table"][:5].copy()
 table_shapes.obs.instance_id = list(range(5))
 table_shapes.obs["region"] = "blobs_circles"
 table_shapes.obs["dummy_gene_expression"] = [i * 10 for i in range(5)]
 table_shapes.uns["spatialdata_attrs"]["region"] = "blobs_circles"
 sdata_blobs["new_table"] = table_shapes

 norm = Normalize(vmin=20, vmax=40, clip=True)
 sdata_blobs.pl.render_shapes(
 "blobs_circles", color="dummy_gene_expression", norm=norm, table_name="new_table"
 ).pl.show()