do some updates

stereo/algorithm/get_niche.py

@@ -117,11 +117,11 @@ def main(
         else:
             raise InvalidNicheMethod(method)
 
-        data_result = filter_cells(data_full, cell_list=cell_list, inplace=inplace)
-        if filter_raw and data_result.raw is not None:
-            filter_cells(data_result.raw, cell_list=cell_list, inplace=True)
-            if isinstance(data_result, AnnBasedStereoExpData):
-                data_result.adata.raw = data_result.raw.adata
+        data_result = filter_cells(data_full, cell_list=cell_list, inplace=inplace, filter_raw=filter_raw)
+        # if filter_raw and data_result.raw is not None:
+        #     filter_cells(data_result.raw, cell_list=cell_list, inplace=True)
+        #     if isinstance(data_result, AnnBasedStereoExpData):
+        #         data_result.adata.raw = data_result.raw.adata
 
 
         return data_result

stereo/plots/plot_collection.py

@@ -422,8 +422,10 @@ def spatial_scatter(
         from .scatter import multi_scatter
         x = self.data.position[:, 0]
         y = self.data.position[:, 1]
-        x_min, x_max = int(x.min()), int(x.max())
-        y_min, y_max = int(y.min()), int(y.max())
+        # x_min, x_max = int(x.min()), int(x.max())
+        # y_min, y_max = int(y.min()), int(y.max())
+        x_min, x_max = x.min(), x.max()
+        y_min, y_max = y.min(), y.max()
         boundary = [x_min, x_max, y_min, y_max]
         marker = 's'
 

stereo/plots/scatter.py

@@ -38,8 +38,10 @@ def _plot_scale(
         boundary: list
 ):
     if boundary is None:
-        min_x, max_x = np.min(x).astype(int), np.max(x).astype(int)
-        min_y, max_y = np.min(y).astype(int), np.max(y).astype(int)
+        # min_x, max_x = np.min(x).astype(int), np.max(x).astype(int)
+        # min_y, max_y = np.min(y).astype(int), np.max(y).astype(int)
+        min_x, max_x = np.min(x), np.max(x)
+        min_y, max_y = np.min(y), np.max(y)
     else:
         min_x, max_x, min_y, max_y = boundary
 
@@ -49,7 +51,8 @@ def _plot_scale(
     if plotting_scale_width is None:
         data_width = max_x - min_x + 1
         data_height = max_y - min_y + 1
-        plotting_scale_width = max(np.ceil(min(data_width, data_height) / 5), 10)
+        # plotting_scale_width = max(np.ceil(min(data_width, data_height) / 5), 10)
+        plotting_scale_width = np.ceil(min(data_width, data_height) / 5)
         highest_num = plotting_scale_width // (10 ** np.log10(plotting_scale_width).astype(int))
         plotting_scale_width = highest_num * (10 ** np.log10(plotting_scale_width).astype(int))
 
@@ -59,7 +62,8 @@ def _plot_scale(
     bin_count = plotting_scale_width // data_bin_offset
 
     # horizontal_end_x = horizontal_start_x + (bin_count - 1) * data_bin_offset
-    horizontal_end_x = horizontal_start_x + plotting_scale_width - 1
+    # horizontal_end_x = horizontal_start_x + plotting_scale_width - 1
+    horizontal_end_x = horizontal_start_x + plotting_scale_width
     horizontal_text_location_x = horizontal_start_x + plotting_scale_width / 2
 
     vertical_x_location = min_x - plotting_scale_height * 2
@@ -70,7 +74,8 @@ def _plot_scale(
         horizontal_y_location = min_y - plotting_scale_height * 2
         vertical_start_y = min_y
         # vertical_end_y = vertical_start_y + (bin_count - 1) * data_bin_offset
-        vertical_end_y = vertical_start_y + plotting_scale_width - 1
+        # vertical_end_y = vertical_start_y + plotting_scale_width - 1
+        vertical_end_y = vertical_start_y + plotting_scale_width
         vertical_text_location_y = vertical_start_y + plotting_scale_width / 2
         vertices = [
             (horizontal_start_x, horizontal_y_location - plotting_scale_height),
@@ -85,7 +90,8 @@ def _plot_scale(
         horizontal_y_location = max_y + plotting_scale_height * 2
         vertical_start_y = max_y
         # vertical_end_y = vertical_start_y - (bin_count - 1) * data_bin_offset
-        vertical_end_y = vertical_start_y - plotting_scale_width + 1
+        # vertical_end_y = vertical_start_y - plotting_scale_width + 1
+        vertical_end_y = vertical_start_y - plotting_scale_width
         vertical_text_location_y = vertical_start_y - plotting_scale_width / 2
         vertices = [
             (horizontal_start_x, horizontal_y_location + plotting_scale_height),

stereo/preprocess/filter.py

@@ -40,7 +40,7 @@ def filter_cells(
         use_raw=True,
         layer=None,
         inplace=True
-    ):
+) -> StereoExpData:
     """
     filter cells based on numbers of genes expressed.
 
@@ -99,7 +99,7 @@ def filter_genes(
         use_raw=True,
         layer=None,
         inplace=True
-):
+) -> StereoExpData:
     """
     filter genes based on the numbers of cells.
 
@@ -156,7 +156,7 @@ def filter_coordinates(
         max_y=None,
         filter_raw=True,
         inplace=True
-):
+) -> StereoExpData:
     """
     filter cells based on the coordinates of cells.
 
@@ -187,44 +187,14 @@ def filter_coordinates(
     data.sub_by_index(cell_index=obs_subset, filter_raw=filter_raw)
     return data
 
-
-# def filter_by_clusters(
-#         data: StereoExpData,
-#         cluster_res: pd.DataFrame,
-#         groups: Union[str, np.ndarray, List[str]],
-#         excluded: bool = False,
-#         inplace: bool = True
-# ) -> Tuple[StereoExpData, pd.DataFrame]:
-#     """_summary_
-
-#     :param data: StereoExpData object.
-#     :param cluster_res: clustering result.
-#     :param groups: the groups in clustering result which will be filtered.
-#     :param inplace: whether inplace the original data or return a new data.
-#     :param excluded: bool type.
-#     :return: StereoExpData object
-#     """
-#     data = data if inplace else copy.deepcopy(data)
-#     all_groups = cluster_res['group']
-#     if isinstance(groups, str):
-#         groups = [groups]
-#     is_in_bool = all_groups.isin(groups).to_numpy()
-#     if excluded:
-#         is_in_bool = ~is_in_bool
-#     data.sub_by_index(cell_index=is_in_bool)
-#     cluster_res = cluster_res[is_in_bool].copy()
-#     cluster_res['group'] = cluster_res['group'].to_numpy()
-#     cluster_res['group'] = cluster_res['group'].astype('category')
-#     return data, cluster_res
-
 def filter_by_clusters(
         data: StereoExpData,
         cluster_res_key: str,
         groups: Union[str, np.ndarray, List[str]],
         excluded: bool = False,
         filter_raw: bool = True,
         inplace: bool = False
-) -> Tuple[StereoExpData, pd.DataFrame]:
+) -> StereoExpData:
     """_summary_
 
     :param data: StereoExpData object.