update ans

examples/atlas/sc_similarity_examples/example_usage_anndata.py

@@ -12,6 +12,7 @@
 from torch.utils.data import TensorDataset
 
 from dance.atlas.sc_similarity.anndata_similarity import AnnDataSimilarity, get_anndata
+from dance.settings import DANCEDIR, METADIR
 from dance.utils import set_seed
 
 # target_files = [
@@ -25,8 +26,8 @@
 #     "eeacb0c1-2217-4cf6-b8ce-1f0fedf1b569"
 # ]
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument("--tissue", type=str, default="blood")
-parser.add_argument("--data_dir", default="../../tuning/temp_data")
+parser.add_argument("--tissue", type=str, default="heart")
+parser.add_argument("--data_dir", default=DANCEDIR / f"examples/tuning/temp_data")
 args = parser.parse_args()
 
 data_dir = args.data_dir
@@ -35,8 +36,8 @@
 tissue = args.tissue
 # conf_data = pd.read_csv(f"results/{tissue}_result.csv", index_col=0)
 conf_data = pd.read_excel("Cell Type Annotation Atlas.xlsx", sheet_name=tissue)
-target_files = list(conf_data[conf_data["queryed"] == False]["dataset_id"])
-source_files = list(conf_data[conf_data["queryed"] == True]["dataset_id"])
+atlas_datasets = list(conf_data[conf_data["queryed"] == False]["dataset_id"])
+query_datasets = list(conf_data[conf_data["queryed"] == True]["dataset_id"])
 
 
 class CustomEncoder(json.JSONEncoder):
@@ -117,46 +118,74 @@ def run_test_case(source_file):
 
     """
     ans = {}
-    for target_file in target_files:
+    source_data = get_anndata(train_dataset=[f"{source_file}"], data_dir=data_dir, tissue=tissue.capitalize())
+
+    for target_file in atlas_datasets:
         # source_data=sc.read_h5ad(f"{data_root}/{source_file}.h5ad")
         # target_data=sc.read_h5ad(f"{data_root}/{target_file}.h5ad")
-        source_data = get_anndata(train_dataset=[f"{source_file}"], data_dir=data_dir)
-        target_data = get_anndata(train_dataset=[f"{target_file}"], data_dir=data_dir)
+        target_data = get_anndata(train_dataset=[f"{target_file}"], data_dir=data_dir, tissue=tissue.capitalize())
 
         # Initialize similarity calculator with multiple metrics
         similarity_calculator = AnnDataSimilarity(adata1=source_data, adata2=target_data, sample_size=10,
                                                   init_random_state=42, n_runs=1,
                                                   ground_truth_conf_path="Cell Type Annotation Atlas.xlsx",
-                                                  adata1_name=source_file, adata2_name=target_file)
+                                                  adata1_name=source_file, adata2_name=target_file, tissue=tissue)
 
         # Calculate similarity using multiple methods
         ans[target_file] = similarity_calculator.get_similarity_matrix_A2B(methods=[
-            "wasserstein", "Hausdorff", "chamfer", "energy", "sinkhorn2", "bures", "spectral", "common_genes_num",
-            "ground_truth", "mmd", "metadata_sim"
+            "wasserstein",
+            "Hausdorff",
+            "chamfer",
+            "energy",
+            "sinkhorn2",
+            "bures",
+            "spectral",
+            "common_genes_num",
+            # "ground_truth",
+            "mmd",
+            "metadata_sim"
         ])
 
     # Convert results to DataFrame and save
     ans = pd.DataFrame(ans)
-    ans.to_csv(f'sim_{source_file}.csv')
+    ans_to_path = f'sims/{tissue}/sim_{source_file}.csv'
+    os.makedirs(os.path.dirname(ans_to_path), exist_ok=True)
+    ans.to_csv(ans_to_path)
     return ans
 
 
-query_data = os.listdir(file_root / "query_data")
-with pd.ExcelWriter(file_root / f"{tissue}_similarity.xlsx", engine='openpyxl') as writer:
-    for source_file in source_files:
-        query_ans = [
-            pd.read_csv(file_root / "query_data" / element, index_col=0) for element in query_data
-            if element.split("_")[-3] == source_file
-        ]
-        ans = run_test_case(source_file)
-        merged_df = pd.concat(query_ans + [ans], join='inner')
-        try:
+start = False
+query_data = os.listdir(file_root / "in_atlas_datas" / f"{tissue}")
+excel_path = file_root / f"{tissue}_similarity.xlsx"
+# with pd.ExcelWriter(file_root / f"{tissue}_similarity.xlsx", engine='openpyxl') as writer:
+for source_file in query_datasets:
+    # if source_file[:4]=='c777':
+    #     start=True
+    # if not start:
+    #     continue
+    query_ans = pd.concat([
+        pd.read_csv(file_root / "in_atlas_datas" / f"{tissue}" / element, index_col=0) for element in query_data
+        if element.split("_")[-3] == source_file
+    ])
+    rename_dict = {col: col.replace('_from_cache', '') for col in query_ans.columns if '_from_cache' in col}
+    query_ans = query_ans.rename(columns=rename_dict)
+    ans = run_test_case(source_file)
+    merged_df = pd.concat([query_ans, ans], join='inner')
+    if os.path.exists(excel_path):
+        excel = pd.ExcelFile(excel_path, engine='openpyxl')
+        if source_file[:4] in excel.sheet_names:
             # 尝试读取指定的分表
-            existing_df = pd.read_excel(file_root / f"{tissue}_similarity.xlsx", sheet_name=source_file[:4])
+            existing_df = pd.read_excel(file_root / f"{tissue}_similarity.xlsx", sheet_name=source_file[:4],
+                                        engine="openpyxl", index_col=0)
             # 找出在新数据框中存在但在现有表格中不存在的行
             merged_df = pd.concat([existing_df, merged_df])
-            merged_df = merged_df.drop_duplicates(keep='first')
-            # 使用 ExcelWriter 更新特定分表
-            merged_df.to_excel(writer, sheet_name=source_file[:4], index=False)
-        except ValueError:
-            merged_df.to_excel(writer, sheet_name=source_file[:4], index=True)
+            merged_df = merged_df.drop_duplicates(subset=merged_df.index.name, keep='last')
+        excel.close()
+    if os.path.exists(excel_path):
+        mode = 'a'
+        if_sheet_exists = "replace"
+    else:
+        mode = 'w'
+        if_sheet_exists = None
+    with pd.ExcelWriter(excel_path, engine='openpyxl', mode=mode, if_sheet_exists=if_sheet_exists) as writer:
+        merged_df.to_excel(writer, sheet_name=source_file[:4])