update some comments and fix bug.

stereo/algorithm/get_niche.py

@@ -57,6 +57,8 @@ def main(
             data_result.tl.result.set_item_callback = None
 
         for res_key in data_result.tl.key_record['pca']:
+            if data_result.tl.result[res_key].shape[0] == data_result.shape[0]:
+                continue
             data_result.tl.result[res_key] = data_result.tl.result[res_key][
                 np.isin(data_full.cell_names, cell_list)].copy()
         if cluster_res_key in data_result.cells:

stereo/algorithm/time_series_analysis.py

@@ -19,16 +19,14 @@
 
 class TimeSeriesAnalysis(AlgorithmBase):
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
     def main(
             self,
             run_method=RunMethodType.tvg_marker.value,
             use_col=UseColType.timepoint.value,
             branch=None,
             p_val_combination=PValCombinationType.fdr.value,
-            cluster_number=6
+            cluster_number=6,
+            **kwargs
     ):
         """
         :param run_method: the model type when the algorithm is run, default = `tvg_marker`.
@@ -39,6 +37,17 @@ def main(
         :param p_val_combination: p_value combination method to use, choosing from ['fisher', 'mean', 'FDR']
         :param cluster_number: number of cluster
 
+        The parameters below only for `other` run method.
+
+        :param spatial_weight: the weight to combine spatial feature
+        :param n_spatial_feature: n top features to combine of spatial feature
+        :param temporal_mean_threshold: filter out genes of which mean absolute temporal feature <= temporal_mean_threshold # noqa
+        :param temporal_top_threshold: filter out genes of which top absolute temporal feature < temporal_top_threshold
+        :param Epsilon: max value to finish iteration
+        :param w_size: window size to rasterizing spatial expression, see also data.tl.gene_spatial_feature
+        :param use_col: the col in obs representing celltype or clustering
+        :param branch: celltypes order in use_col
+
         """
         if run_method == RunMethodType.tvg_marker.value:
             self.TVG_marker(
@@ -47,7 +56,7 @@ def main(
                 p_val_combination=p_val_combination
             )
         else:
-            self.fuzzy_C_gene_pattern_cluster(cluster_number)
+            self.fuzzy_C_gene_pattern_cluster(cluster_number, **kwargs)
 
     def TVG_marker(self, use_col, branch, p_val_combination=PValCombinationType.fisher.value):
         """

stereo/tools/cell_correct.py

@@ -289,21 +289,17 @@ def cell_correct(out_dir: str,
         * GEM and raw CGEF (not have been corrected)
         * BGEF and raw CGEF (not have been corrected)
 
-    :param out_dir: the path to save intermediate result, like mask (if generate from ssDNA image),
-        BGEF (generate from GEM), CGEF (generate from GEM and mask), etc. and final corrected result.
+    :param out_dir: the path to save intermediate result, like mask (if generated from ssDNA image),
+        BGEF (generated from GEM), CGEF (generated from GEM and mask), etc. and final corrected result.
     :param threshold: threshold size, default to 20.
     :param gem_path: the path to GEM file.
     :param bgef_path: the path to BGEF file.
     :param raw_cgef_path: the path to CGEF file which not has been corrected.
     :param mask_path: the path to mask file.
     :param process_count: the count of the processes or threads will be started when correct cells, defaults to None
-                by default, it will be set to 10 when `fast` is set to False and will be set to 1 when `fast` is set to v1 or v2.
+                by default, it will be set to 10 when `method` is set to 'GMM' and will be set to 1 when `method` is set to 'FAST' or 'EDM'.
                 if it is set to -1, all of the cores will be used.
 	:param only_save_result: if `True`, only save result to disk; if `False`, return an StereoExpData object.
-    :param fast: specify the version of algorithm, available values include [False, v1, v2], defaults to v2.
-                    False: the oldest and slowest version, it will uses multiprocessing if set `process_count` to more than 1.
-                    v1: the first fast version, it olny uses single process and single threading.
-                    v2: default and recommended algorithm, the latest fast version, faster and more accurate than v1, it will uses multithreading if set `process_count` to more than 1.
     :param method: correct in different method if `method` is set, otherwise `EDM`.
     :param distance: outspread distance based on cellular contour of cell segmentation image, in pixels, only available for v2 algorithm.