diff --git a/graph_dit/exp_201/barplog.png b/graph_dit/exp_201/barplog.png
new file mode 100644
index 0000000..f59f79a
Binary files /dev/null and b/graph_dit/exp_201/barplog.png differ
diff --git a/graph_dit/exp_201/main.py b/graph_dit/exp_201/main.py
index 2cad786..378f752 100644
--- a/graph_dit/exp_201/main.py
+++ b/graph_dit/exp_201/main.py
@@ -2,44 +2,45 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 from nas_201_api import NASBench201API as API
-from naswot.score_networks import get_nasbench201_idx_score
-from naswot import datasets as dt
-from naswot import nasspace
+# from naswot.score_networks import get_nasbench201_idx_score
+# from naswot import datasets as dt
+# from naswot import nasspace
 
-class Args():
-    pass
-args = Args()
-args.trainval = True
-args.augtype = 'none'
-args.repeat = 1
-args.score = 'hook_logdet'
-args.sigma = 0.05
-args.nasspace = 'nasbench201'
-args.batch_size = 128
-args.GPU = '0'
-args.dataset = 'cifar10'
-args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
-args.data_loc = '../cifardata/'
-args.seed = 777
-args.init = ''
-args.save_loc = 'results'
-args.save_string = 'naswot'
-args.dropout = False
-args.maxofn = 1
-args.n_samples = 100
-args.n_runs = 500
-args.stem_out_channels = 16
-args.num_stacks = 3
-args.num_modules_per_stack = 3
-args.num_labels = 1
-searchspace = nasspace.get_search_space(args)
-train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
-device = torch.device('cuda:2')
+# class Args():
+#     pass
+# args = Args()
+# args.trainval = True
+# args.augtype = 'none'
+# args.repeat = 1
+# args.score = 'hook_logdet'
+# args.sigma = 0.05
+# args.nasspace = 'nasbench201'
+# args.batch_size = 128
+# args.GPU = '0'
+# args.dataset = 'cifar10'
+# args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+# args.data_loc = '../cifardata/'
+# args.seed = 777
+# args.init = ''
+# args.save_loc = 'results'
+# args.save_string = 'naswot'
+# args.dropout = False
+# args.maxofn = 1
+# args.n_samples = 100
+# args.n_runs = 500
+# args.stem_out_channels = 16
+# args.num_stacks = 3
+# args.num_modules_per_stack = 3
+# args.num_labels = 1
+# searchspace = nasspace.get_search_space(args)
+# train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
+# device = torch.device('cuda:2')
+
+
+source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+api = API(source)
 
 
-         
-# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
-# api = API(source)
 
 
 
@@ -50,8 +51,10 @@ percentages = []
 len_201 = 15625
 
 for i in range(len_201):
-    percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
-    percentages.append(percentage)
+    # percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
+    results = api.query_by_index(i, 'cifar10')
+    result = results[111].get_eval('ori-test')
+    percentages.append(result)
 
 # 定义10%区间
 bins = [i for i in range(0, 101, 10)]