do some updates

stereo/io/reader.py

@@ -48,7 +48,7 @@ def read_gem(
         bin_type: str = "bins",
         bin_size: int = 100,
         is_sparse: bool = True,
-        bin_coord_offset: bool = False,
+        center_coordinates: bool = False,
         gene_name_index: bool = False
 ):
     """
@@ -66,9 +66,9 @@ def read_gem(
         the size of bin to merge, when `bin_type` is set to `'bins'`.
     is_sparse
         the expression matrix is sparse matrix, if `True`, otherwise `np.ndarray`.
-    bin_coord_offset
-        if set it to True, the coordinates of bins are calculated as
-        ((gene_coordinates - min_coordinates) // bin_size) * bin_size + min_coordinates + bin_size/2
+    center_coordinates
+        if set it to True, the coordinate of each bin will be the center of the bin,
+        otherwise, the coordinate of each bin will be the left-top corner of the bin.
     gene_name_index
         In a v0.1 gem file, the column geneID is the gene name actually, but in a v0.2,
         geneID just a ID for genes and there is an additional column called geneName where is the gene name,
@@ -106,7 +106,7 @@ def read_gem(
     if data.bin_type == 'cell_bins':
         gdf = parse_cell_bin_coor(df)
     else:
-        if bin_coord_offset:
+        if center_coordinates:
             df = parse_bin_coor(df, bin_size)
         else:
             df = parse_bin_coor_no_offset(df, bin_size)
@@ -153,7 +153,7 @@ def read_gem(
         'maxExp': data.exp_matrix.max(),  # noqa
         'resolution': resolution,
     }
-    data.bin_coord_offset = bin_coord_offset
+    data.center_coordinates = center_coordinates
     logger.info(f'the martrix has {data.cell_names.size} cells, and {data.gene_names.size} genes.')
     return data
 
@@ -732,6 +732,7 @@ def read_h5ad(
         flavor: str = 'scanpy',
         bin_type: str = None,
         bin_size: int = None,
+        spatial_key: str = 'spatial',
         **kwargs
 ) -> Union[StereoExpData, AnnBasedStereoExpData]:
     """
@@ -742,20 +743,22 @@ def read_h5ad(
     file_path
         the path of the h5ad file.
     anndata
-        the object of AnnData which to be loaded, only available while `flavor` is `'scanpy'`.
+        the object of AnnData to be loaded, only available while `flavor` is `'scanpy'`.
         `file_path` and `anndata` only can input one of them.
     flavor
         the format of the h5ad file, defaults to `'scanpy'`.
         `scanpy`: AnnData format of scanpy
-        `stereopy`: h5ad format of stereo
+        `stereopy`: h5 format of stereo
     bin_type
-        the bin type includes `'bins'` or `'cell_bins'`.
+        the bin type includes `'bins'` and `'cell_bins'`.
     bin_size
         the size of bin to merge, when `bin_type` is set to `'bins'`.
+    spatial_key
+        the key of spatial information in AnnData.obsm, default to `'spatial'`.
+        Only available while `flavor` is `'scanpy'`.
     Returns
     ---------------
-    An object of StereoExpData while `flavor` is `'stereopy'` or an object of AnnBasedStereoExpData while `flavor` is
-    `'scanpy'`
+    An object of StereoExpData while `flavor` is `'stereopy'` or an object of AnnBasedStereoExpData while `flavor` is `'scanpy'`
 
     """
     flavor = flavor.lower()
@@ -775,7 +778,7 @@ def read_h5ad(
         if file_path is not None and anndata is not None:
             raise Exception("'file_path' and 'anndata' only can input one of them")
         return AnnBasedStereoExpData(h5ad_file_path=file_path, based_ann_data=anndata, bin_type=bin_type,
-                                     bin_size=bin_size, **kwargs)
+                                     bin_size=bin_size, spatial_key=spatial_key, **kwargs)
     else:
         raise ValueError("Invalid value for 'flavor'")
 

stereo/plots/interact_plot/interactive_scatter.py

@@ -165,7 +165,7 @@ def generate_gem_file(
 
         if selected_areas.bin_type == 'bins':
             @nb.njit(cache=True, nogil=True, parallel=True)
-            def __get_filtering_flag(data, bin_size, position, bin_coord_offset, num_threads):
+            def __get_filtering_flag(data, bin_size, position, center_coordinates, num_threads):
                 num_threads = min(position.shape[0], num_threads)
                 num_per_thread = position.shape[0] // num_threads
                 num_left = position.shape[0] % num_threads
@@ -183,7 +183,7 @@ def __get_filtering_flag(data, bin_size, position, bin_coord_offset, num_threads
                     end = interval[i + 1]
                     for j in range(start, end):
                         x_start, y_start = position[j]
-                        if bin_coord_offset:
+                        if center_coordinates:
                             x_start -= bin_size // 2
                             y_start -= bin_size // 2
                         x_end = x_start + bin_size
@@ -198,7 +198,7 @@ def __get_filtering_flag(data, bin_size, position, bin_coord_offset, num_threads
                 original_gem_df[['x', 'y', 'UMICount']].to_numpy(),
                 selected_areas.bin_size,
                 selected_areas.position,
-                selected_areas.bin_coord_offset,
+                selected_areas.center_coordinates,
                 nb.get_num_threads(),
             )
             selected_gem_df = original_gem_df[flag]

stereo/plots/interact_plot/poly_selection.py

@@ -209,7 +209,7 @@ def generate_gem_file(
 
         if selected_areas.bin_type == 'bins':
             @nb.njit(cache=True, nogil=True, parallel=True)
-            def __get_filtering_flag(data, bin_size, position, bin_coord_offset, num_threads, drop):
+            def __get_filtering_flag(data, bin_size, position, center_coordinates, num_threads, drop):
                 num_threads = min(position.shape[0], num_threads)
                 num_per_thread = position.shape[0] // num_threads
                 num_left = position.shape[0] % num_threads
@@ -227,7 +227,7 @@ def __get_filtering_flag(data, bin_size, position, bin_coord_offset, num_threads
                     end = interval[i + 1]
                     for j in range(start, end):
                         x_start, y_start = position[j]
-                        if bin_coord_offset:
+                        if center_coordinates:
                             x_start -= bin_size // 2
                             y_start -= bin_size // 2
                         x_end = x_start + bin_size
@@ -242,7 +242,7 @@ def __get_filtering_flag(data, bin_size, position, bin_coord_offset, num_threads
                 original_gem_df[['x', 'y', 'UMICount']].to_numpy(),
                 selected_areas.bin_size,
                 selected_areas.position,
-                selected_areas.bin_coord_offset,
+                selected_areas.center_coordinates,
                 nb.get_num_threads(),
                 drop
             )

stereo/utils/data_helper.py

@@ -321,10 +321,12 @@ def __merge_for_stereo_exp_data(
         new_data.sn[str(batch)] = data.sn
         if i == 0:
             new_data.exp_matrix = data.exp_matrix.copy()
-            new_data.cells = Cell(cell_name=cell_names, cell_border=data.cells.cell_border, batch=data.cells.batch)
-            new_data.genes = Gene(gene_name=data.gene_names)
-            new_data.cells._obs = data.cells._obs.copy(deep=True)
-            new_data.cells._obs.index = cell_names
+            # new_data.cells = Cell(cell_name=cell_names, cell_border=data.cells.cell_border, batch=data.cells.batch)
+            # new_data.genes = Gene(gene_name=data.gene_names)
+            # new_data.cells._obs = data.cells._obs.copy(deep=True)
+            # new_data.cells._obs.index = cell_names
+            new_data.cells = Cell(obs=data.cells._obs.copy(deep=True), cell_border=data.cells.cell_border, batch=data.cells.batch)
+            new_data.genes = Gene(var=data.genes._var.copy(deep=True))
             new_data.position = data.position
             if data.position_z is None:
                 new_data.position_z = np.repeat([[0]], repeats=data.position.shape[0], axis=0).astype(
@@ -337,7 +339,7 @@ def __merge_for_stereo_exp_data(
             new_data.offset_y = data.offset_y
             new_data.attr = data.attr
         else:
-            current_obs = data.cells._obs.copy()
+            current_obs = data.cells._obs.copy(deep=True)
             current_obs.index = cell_names
             new_data.cells._obs = pd.concat([new_data.cells._obs, current_obs])
             if new_data.cell_borders is not None and data.cell_borders is not None: