scverse / scverse/napari-spatialdata
Cannot set custom colors for categorical variables
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 90
- Forks
- 25
- Avg merge
- 22m
- Merged PRs (30d)
- 1
Description
Hi!
I am testing napari-spatialdata on a public Nanostring Cosmx dataset (https://kero.hgc.jp/Breast_Cancer_Spatial.html), and I would like to set a custom color for each label of a specific categorical variable, i.e., the cell types obtained with an external tool. I tried to manually insert a dictionary in the uns slot of the anndata object used as 'table' in the spatialdata object, in the format {<label_name1>:<hex_code1>, <label_name2>:<hex_code2>, ..... }. However, after I load the spatialdata object on napari and I select the desired annotation in the 'observations' panel on the right, an error message pops up, and the annotation is not visualized at all. Here is the full error:
######################################################################################################
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/site-packages/napari_spatialdata/_widgets.py:65, in ListWidget.__init__.<locals>.<lambda>(item=<PyQt5.QtWidgets.QListWidgetItem object>)
62 self._unique = unique
63 self._viewer = viewer
---> 65 self.itemDoubleClicked.connect(lambda item: self._onAction((item.text(),)))
self = <napari_spatialdata._widgets.AListWidget object at 0x7f989cf30680>
item = <PyQt5.QtWidgets.QListWidgetItem object at 0x7f98dff132f0>
66 self.enterPressed.connect(self._onAction)
67 self.indexChanged.connect(self._onAction)
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/site-packages/napari_spatialdata/_widgets.py:142, in AListWidget._onAction(self=<napari_spatialdata._widgets.AListWidget object>, items=('InSituType_Simple',))
139 features["index"] = index
140 self.model.layer.features = features
--> 142 properties = self._get_points_properties(vec, key=item, layer=self.model.layer)
item = 'InSituType_Simple'
vec = cell_ID
1 BC cells
2 BC cells
3 Mix BC cells TAMs
4 Mix BC cells TAMs
5 BC cells
...
1850 Blood ECs
1851 Mast cells
1852 Myoepithelial cells
1853 Mix BC cells TAMs
1854 CAFs
Name: InSituType_Simple, Length: 1832, dtype: category
Categories (12, object): ['BC cells', 'Blood ECs', 'CAFs', 'DCs', ..., 'NK cells', 'Plasma cells',
'T cells', 'TAMs']
self = <napari_spatialdata._widgets.AListWidget object at 0x7f989cf30680>
143 self.model.color_by = "" if self.model.system_name is None else item
144 if isinstance(self.model.layer, (Points, Shapes)):
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/functools.py:946, in singledispatchmethod.__get__.<locals>._method(*args=(cell_ID
1 BC cells
2 ...ls',
'T cells', 'TAMs'],), **kwargs={'key': 'InSituType_Simple', 'layer': <Labels layer '11_labels'>})
944 def _method(*args, **kwargs):
945 method = self.dispatcher.dispatch(args[0].__class__)
--> 946 return method.__get__(obj, cls)(*args, **kwargs)
method = <function AListWidget._ at 0x7f98df9387c0>
obj = <napari_spatialdata._widgets.AListWidget object at 0x7f989cf30680>
cls = <class 'napari_spatialdata._widgets.AListWidget'>
args = (cell_ID
1 BC cells
2 BC cells
3 Mix BC cells TAMs
4 Mix BC cells TAMs
5 BC cells
...
1850 Blood ECs
1851 Mast cells
1852 Myoepithelial cells
1853 Mix BC cells TAMs
1854 CAFs
Name: InSituType_Simple, Length: 1832, dtype: category
Categories (12, object): ['BC cells', 'Blood ECs', 'CAFs', 'DCs', ..., 'NK cells', 'Plasma cells',
'T cells', 'TAMs'],)
kwargs = {'key': 'InSituType_Simple', 'layer': <Labels layer '11_labels' at 0x7f989d46d520>}
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/site-packages/napari_spatialdata/_widgets.py:248, in AListWidget._(self=<napari_spatialdata._widgets.AListWidget object>, vec=cell_ID
1 BC cells
2 ...ls',
'T cells', 'TAMs'], **kwargs={'key': 'InSituType_Simple'})
245 else:
246 merge_df = pd.merge(element_indices, vec, left_on="element_indices", right_index=True, how="left")
--> 248 merge_df["color"] = merge_df[[vec.name](http://vec.name/)].map(color_dict)
merge_df = element_indices InSituType_Simple
0 1 BC cells
1 2 BC cells
2 3 Mix BC cells TAMs
3 4 Mix BC cells TAMs
4 5 BC cells
... ... ...
1849 1850 Blood ECs
1850 1851 Mast cells
1851 1852 Myoepithelial cells
1852 1853 Mix BC cells TAMs
1853 1854 CAFs
[1854 rows x 2 columns]
color_dict = array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7', 'Mix BC cells TAMs': '#800080', 'Myoepithelial cells': '#daa520', 'Blood ECs': '#efd594', 'Mural cells': '#a77e18', 'T cells': '#001b00', 'Plasma cells': '#ff728a', 'TAMs': '#0000ff', 'DCs': '#000037', 'NK cells': '#003400', 'Mast cells': '#add8e6'}],
dtype=object)
vec = cell_ID
1 BC cells
2 BC cells
3 Mix BC cells TAMs
4 Mix BC cells TAMs
5 BC cells
...
1850 Blood ECs
1851 Mast cells
1852 Myoepithelial cells
1853 Mix BC cells TAMs
1854 CAFs
Name: InSituType_Simple, Length: 1832, dtype: category
Categories (12, object): ['BC cells', 'Blood ECs', 'CAFs', 'DCs', ..., 'NK cells', 'Plasma cells',
'T cells', 'TAMs']
249 if layer is not None and isinstance(layer, Labels):
250 index_color_mapping = dict(zip(merge_df["element_indices"], merge_df["color"]))
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/site-packages/pandas/core/series.py:4700, in Series.map(self=0 BC cells
1 B...ls',
'T cells', 'TAMs'], arg=array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7'...', 'Mast cells': '#add8e6'}],
dtype=object), na_action=None)
4620 def map(
4621 self,
4622 arg: Callable | Mapping | Series,
4623 na_action: Literal["ignore"] | None = None,
4624 ) -> Series:
4625 """
4626 Map values of Series according to an input mapping or function.
4627
(...)
4698 dtype: object
4699 """
-> 4700 new_values = self._map_values(arg, na_action=na_action)
arg = array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7', 'Mix BC cells TAMs': '#800080', 'Myoepithelial cells': '#daa520', 'Blood ECs': '#efd594', 'Mural cells': '#a77e18', 'T cells': '#001b00', 'Plasma cells': '#ff728a', 'TAMs': '#0000ff', 'DCs': '#000037', 'NK cells': '#003400', 'Mast cells': '#add8e6'}],
dtype=object)
self = 0 BC cells
1 BC cells
2 Mix BC cells TAMs
3 Mix BC cells TAMs
4 BC cells
...
1849 Blood ECs
1850 Mast cells
1851 Myoepithelial cells
1852 Mix BC cells TAMs
1853 CAFs
Name: InSituType_Simple, Length: 1854, dtype: category
Categories (12, object): ['BC cells', 'Blood ECs', 'CAFs', 'DCs', ..., 'NK cells', 'Plasma cells',
'T cells', 'TAMs']
na_action = None
4701 return self._constructor(new_values, index=self.index, copy=False).__finalize__(
4702 self, method="map"
4703 )
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/site-packages/pandas/core/base.py:919, in IndexOpsMixin._map_values(self=0 BC cells
1 B...ls',
'T cells', 'TAMs'], mapper=array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7'...', 'Mast cells': '#add8e6'}],
dtype=object), na_action=None, convert=True)
916 arr = self._values
918 if isinstance(arr, ExtensionArray):
--> 919 return arr.map(mapper, na_action=na_action)
arr = ['BC cells', 'BC cells', 'Mix BC cells TAMs', 'Mix BC cells TAMs', 'BC cells', ..., 'Blood ECs', 'Mast cells', 'Myoepithelial cells', 'Mix BC cells TAMs', 'CAFs']
Length: 1854
Categories (12, object): ['BC cells', 'Blood ECs', 'CAFs', 'DCs', ..., 'NK cells', 'Plasma cells',
'T cells', 'TAMs']
mapper = array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7', 'Mix BC cells TAMs': '#800080', 'Myoepithelial cells': '#daa520', 'Blood ECs': '#efd594', 'Mural cells': '#a77e18', 'T cells': '#001b00', 'Plasma cells': '#ff728a', 'TAMs': '#0000ff', 'DCs': '#000037', 'NK cells': '#003400', 'Mast cells': '#add8e6'}],
dtype=object)
na_action = None
921 return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
File ~/anaconda3/envs/SpatialData_prova/lib/python3.12/site-packages/pandas/core/arrays/categorical.py:1555, in Categorical.map(self=['BC cells', 'BC cells', 'Mix BC cells TAMs', 'M...ls',
'T cells', 'TAMs'], mapper=array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7'...', 'Mast cells': '#add8e6'}],
dtype=object), na_action=None)
1545 warnings.warn(
1546 "The default value of 'ignore' for the `na_action` parameter in "
1547 "pandas.Categorical.map is deprecated and will be "
(...)
1551 stacklevel=find_stack_level(),
1552 )
1553 na_action = "ignore"
-> 1555 assert callable(mapper) or is_dict_like(mapper)
mapper = array([{'BC cells': '#ff4500', 'CAFs': '#f7e9c7', 'Mix BC cells TAMs': '#800080', 'Myoepithelial cells': '#daa520', 'Blood ECs': '#efd594', 'Mural cells': '#a77e18', 'T cells': '#001b00', 'Plasma cells': '#ff728a', 'TAMs': '#0000ff', 'DCs': '#000037', 'NK cells': '#003400', 'Mast cells': '#add8e6'}],
dtype=object)
1557 new_categories = self.categories.map(mapper)
1559 has_nans = np.any(self._codes == -1)
AssertionError:
######################################################################################################
I have no clue about this, especially considering that a student of mine previously succeeded in setting custom colors with an older version of napari-spatialdata. Currently I am using the 0.5.4.dev2+gf84b79b version of napari-spatialdata on napari 0.5.4 in python 3.12.3 on an ubuntu machine.
I am still a beginner with python, can anyone give me any advice on how to solve this?
Thank you in advance!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Reproduce the categorical-color selection using the AnnData table and custom colors described in the issue. Start at napari_spatialdata/_widgets.py:248, where the traceback shows the color mapping reaches pandas.Series.map, and inspect how the mapping is obtained from the categorical annotation. Done means selecting the annotation renders the labels with their requested custom colors without the AssertionError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100