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

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Merged
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 number Diff line number Diff 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 Down Expand 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 Down Expand 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 Down Expand 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 Down Expand 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 Down Expand 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 number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@
LinearSegmentedColormap,
ListedColormap,
Normalize,
TwoSlopeNorm,
to_rgba,
)
from matplotlib.figure import Figure
Expand Down Expand 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
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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.

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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.

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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.

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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.

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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 Down Expand 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,
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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?

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

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

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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).

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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?

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

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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)
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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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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):
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]
norm = Normalize(vmin=0, vmax=5, clip=True)
Expand Down Expand 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 Down Expand 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 Down Expand 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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