(add)添加dume get token info

src/code/00000_test_dexscreener_playwright_chrome.py

@@ -19,7 +19,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 
 def makedirpath(   folder):

src/code/00000_test_dexscreener_playwright_edge.py

@@ -19,7 +19,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 
 def makedirpath(   folder):

src/code/2003_check_dalao_tokens_ispump.py

@@ -17,7 +17,7 @@ print = timestamped_print
 print('\n'*5)
 
  
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 def makescreenurl( urlname,urlparam):
     _url = urlname + urlparam

src/code/2004_gmgn_handle_ori_pnl.py

@@ -13,7 +13,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 baseclass.makedirpath(  baseclass.dalao_pnl_gmgn_path)
 baseclass.makedirpath(baseclass.dalao_total_ana_gmgn_path)

src/code/3002_hanle_dalao_ori_pnl_gmgn_consider_0profit.py

@@ -17,7 +17,7 @@ print = timestamped_print
 print('\n'*5)
 
  
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 def makescreenurl( urlname,urlparam):
     _url = urlname + urlparam

src/code/3003_analysis_dalao_pnl_gmgn_consider_0profit.py

@@ -14,7 +14,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 baseclass.makedirpath(  baseclass.dalao_pnl_gmgn_path)
 baseclass.makedirpath(baseclass.dalao_total_ana_gmgn_path)

src/code/4000_getATA_for_analysisToken.py

@@ -14,7 +14,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
  
 
 def makescreenurl( urlname,urlparam):

src/code/4001_get_solscan_url.py

@@ -14,7 +14,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
  
 # def makescreenurl( urlname,urlparam):
 #     _url = urlname + urlparam

src/code/50004_修改节点悬停信息.py

@@ -0,0 +1,50 @@
+import dash
+from dash import dcc, html
+import plotly.graph_objects as go
+
+# 假设你的节点标签是长度为30的字符串
+labels = [f"Label{i:030}" for i in range(1, 8)]  # 生成示例标签
+
+# 数据准备
+data = {
+    'source': [0, 1, 2, 0],
+    'target': [3, 4, 5, 6],
+    'value': [10, 15, 5, 20]
+}
+
+# 创建Sankey图
+def create_sankey():
+    fig = go.Figure(data=[go.Sankey(
+        node=dict(
+            label=[label[:5] + "..." for label in labels],  # 显示前5个字符加省略号
+            pad=15,
+            thickness=20,
+            line=dict(color="black", width=0.5),
+            hoverinfo="all",  # 在悬停时显示标签和值
+            customdata=labels , # 添加完整的标签作为自定义数据
+            hovertemplate = '%{label}<br>Full Label: %{customdata}<br>Value: %{value}<br>',
+        ),
+        link=dict(
+            source=data['source'],
+            target=data['target'],
+            value=data['value'],
+        )
+    )])
+
+    # 设置悬停模板以显示完整的节点标签
+    # fig['data'][0]['node']['hovertemplate'] = '%{label}<br>Full Label: %{customdata}<br>Value: %{value}<br>'
+
+    fig.update_layout(title_text="Sankey Diagram with Abbreviated Node Labels", font_size=10)
+    return fig
+
+# 创建 Dash 应用
+app = dash.Dash(__name__)
+
+# 定义应用布局
+app.layout = html.Div([
+    dcc.Graph(id='sankey', figure=create_sankey())
+])
+
+# 运行应用
+if __name__ == '__main__':
+    app.run_server(debug=True)

src/code/50004_修改边信息.py

@@ -0,0 +1,38 @@
+import plotly.graph_objects as go
+
+# 假设这是你的 DataFrame
+import pandas as pd
+df = pd.DataFrame({
+    'from': [0, 1, 2, 0],
+    'to': [3, 4, 5, 6],
+    'amount': [10, 15, 5, 20],
+    'counts': [2, 3, 1, 4]
+})
+
+# labels 和节点数量
+labels = list(set(df['from']).union(set(df['to'])))
+source = df['from']
+target = df['to']
+value = df['amount']  # 或者 df['counts']，取决于你想展示哪个值
+
+# 创建 Sankey 图
+fig = go.Figure(data=[go.Sankey(
+    node=dict(
+        label=labels,
+        pad=15,
+        thickness=20,
+        line=dict(color="black", width=0.5),
+    ),
+    link=dict(
+        source=source,
+        target=target,
+        value=value,
+        hovertemplate='Amount: %{value}<br>From: %{source.label}<br>To: %{target.label}<extra></extra>'  # 自定义悬停模板
+    )
+)])
+
+# 更新布局
+fig.update_layout(title_text="Custom Hover Information for Sankey Diagram", font_size=10)
+
+# 显示图形
+fig.show()

src/code/5000_get_one_token_relation_graph_query_dune.py

@@ -0,0 +1,73 @@
+
+
+from base_class import BaseVariableFunction
+from base_class import *
+baseclass = BaseVariableFunction(__file__)
+
+old_print = print
+ 
+
+def timestamped_print(*args, **kwargs):
+    old_print(datetime.datetime.utcnow().replace(
+        microsecond=0), *args, **kwargs)
+
+ 
+
+print = timestamped_print
+print('\n'*5)
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
+
+def  get_one_token_relation_graph_query_dune():
+    global excutionid , dune_api
+    
+    
+    file_name = f"one_token_relation_graph_{excutionid}.csv"
+    file_path = baseclass.dune_excution_path /file_name
+    if file_path.exists():
+        print(f"file_name= {file_name} already exists, skip download")
+        return
+    
+    url = f"https://api.dune.com/api/v1/execution/{excutionid}/results/csv"
+    headers = {"X-DUNE-API-KEY": dune_api}
+    response = requests.request("GET", url, headers=headers)
+    response_text = response.text
+    print(response_text)
+
+    # print(response.text)
+    with open(file_path, "w", encoding="utf-8") as file:
+        # 将 response.text 写入文件
+        file.write(response_text)
+
+
+    return
+
+
+
+
+
+dune_api = 'W7zTOtVucO0CepixObF7MgGGTzqUYqbM'
+#  arr_duneapi=['W7zTOtVucO0CepixObF7MgGGTzqUYqbM']
+
+excutionid ='01JBXRQNBWHRQ0RT66W4AC1QFE'
+
+# Query	one_token_relation_graph
+# Execution Id	01JBXRQNBWHRQ0RT66W4AC1QFE
+# Execution time
+# 6.4670000076293945 seconds
+# Wall time
+# 9.043000221252441 seconds
+# Analysis time
+# 2.575000047683716 seconds
+# CPU time
+# 440.3890075683594 seconds
+# Queued time
+# 0 seconds
+# Physical input	2074654689 rows (86.1 GB)
+# Output	214 rows (44.7 KB)
+# Peak task total memory	837.2 MB
+
+get_one_token_relation_graph_query_dune()
+
+
+print(f"{'{:<6}'.format('END')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
+

src/code/5001_get_dalao_relationship.py

@@ -0,0 +1,125 @@
+
+
+
+
+from base_class import BaseVariableFunction
+from base_class import *
+baseclass = BaseVariableFunction(__file__)
+
+old_print = print
+ 
+
+def timestamped_print(*args, **kwargs):
+    old_print(datetime.datetime.utcnow().replace(
+        microsecond=0), *args, **kwargs)
+
+ 
+
+print = timestamped_print
+print('\n'*5)
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
+
+
+ 
+ 
+ 
+# 获取输入节点相关的图
+def get_connected_edges(df, str_dalaoaddress, max_connections=6):
+    # 初始化结果列表
+    result = []
+    # 初始化待检查的节点集合
+    to_check = {str_dalaoaddress}
+    # 初始化已检查的节点集合
+    checked = set()
+    # 初始化连接数字典
+    connection_counts = {node: 0 for node in df['from_owner'].tolist() + df['to_owner'].tolist()}
+    
+    while to_check:
+        # 取出一个节点
+        node = to_check.pop()
+        # 添加到已检查集合
+        checked.add(node)
+        
+        # 如果节点连接数超过最大限制，则跳过
+        if connection_counts[node] > max_connections:
+            continue
+        
+        # 找到所有从这个节点出发的边
+        from_edges = df[df['from_owner'] == node]
+        # 找到所有进入这个节点的边
+        to_edges = df[df['to_owner'] == node]
+        
+        # 更新连接数
+        connection_counts[node] += len(from_edges) + len(to_edges)
+        
+        # 如果连接数超过最大限制，则跳过
+        if connection_counts[node] > max_connections:
+            continue
+        
+        # 将这些边添加到结果列表
+        result.extend(from_edges.index.tolist())
+        result.extend(to_edges.index.tolist())
+        
+        # 将所有连接的节点添加到待检查集合
+        for edge in from_edges['to_owner'].tolist() + to_edges['from_owner'].tolist():
+            if edge not in checked:
+                to_check.add(edge)
+    
+    # 返回与起始节点连接的所有边的索引
+    connected_edges_indices = result
+    connected_df = df.loc[connected_edges_indices]
+    connected_df=connected_df.drop_duplicates().reset_index(drop=True) 
+    return connected_df
+
+excutionid ='01JBXRQNBWHRQ0RT66W4AC1QFE'
+
+
+file_name = f"one_token_relation_graph_{excutionid}.csv"
+obj_excutionid_2_tokeninfo = None
+obj_tokeninfo = None
+
+with open(baseclass.dune_excution_path/"obj_excutionid_2_tokeninfo.json",mode='r',encoding='utf8') as f:
+    obj_excutionid_2_tokeninfo = json.load(f)
+obj_tokeninfo = obj_excutionid_2_tokeninfo[excutionid]
+
+ 
+str_tokenaddress = obj_tokeninfo["tokenaddress"]
+str_tokenname = obj_tokeninfo["tokenname"]
+# df =pd.read_excel(baseclass.librarydata_path/"test.xlsx",dtype=object)
+df =pd.read_csv(baseclass.dune_excution_path/file_name , dtype=object)
+
+
+str_dalaoaddress ='EeS7DBots7zF7JA4iCPun1uA779aXCPCBhe4kGkuCuRA'
+df = get_connected_edges(df, str_dalaoaddress)
+df =df.astype(
+   { "owner_count":int,
+    }
+)
+
+    # def makeurl( self,url,url_show):
+    #     return '=HYPERLINK("{}","{}")'.format(url, "url_"+url_show)
+    
+ 
+
+df["gmgn_from"] = df.apply(lambda ser: baseclass.makeurl(
+   f"https://gmgn.ai/sol/token/{str_tokenaddress}?maker={ser['from_owner']}" , f"gmgn_{ser['from_owner'][0:6]}" ), axis=1)
+
+df["gmgn_to"] = df.apply(lambda ser: baseclass.makeurl(
+   f"https://gmgn.ai/sol/token/{str_tokenaddress}?maker={ser['to_owner']}" , f"gmgn_{ser['to_owner'][0:6]}" ), axis=1)
+ 
+
+ 
+    
+df["url_from_ata"] = df.apply(lambda ser: baseclass.makeurl(
+   f"https://solscan.io/account/{ser['from_token_account']}" , f"sol_{ser['from_token_account'][0:6]}" ), axis=1)
+
+df["url_to_ata"] = df.apply(lambda ser: baseclass.makeurl(
+   f"https://solscan.io/account/{ser['to_token_account']}" , f"sol_{ser['to_token_account'][0:6]}" ), axis=1)
+ 
+
+df =df.sort_values(by=['from_owner']).reset_index(drop=True)
+baseclass.makedirpath(baseclass.dune_token_graph_path/ str_tokenaddress)
+ 
+df.to_excel(baseclass.dune_token_graph_path/ str_tokenaddress/f"{str_dalaoaddress}.xlsx",index=False)
+ 
+

src/code/5002_draw_sankey.py

@@ -0,0 +1,103 @@
+ 
+
+import dash
+from dash import dcc
+from dash import html
+import plotly.graph_objects as go
+
+
+from base_class import BaseVariableFunction
+from base_class import *
+baseclass = BaseVariableFunction(__file__)
+
+old_print = print
+ 
+
+def timestamped_print(*args, **kwargs):
+    old_print(datetime.datetime.utcnow().replace(
+        microsecond=0), *args, **kwargs)
+
+ 
+
+print = timestamped_print
+print('\n'*5)
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
+
+ 
+ 
+str_tokenaddress="4GULMPKBJLruChBZWksZzukAg1AjSCmCTMn9ny2Xpump"
+str_dalaoaddress ='EeS7DBots7zF7JA4iCPun1uA779aXCPCBhe4kGkuCuRA'
+ 
+# str_dalaoaddress ='total'
+
+df = pd.read_excel(baseclass.dune_token_graph_path/ str_tokenaddress/f"{str_dalaoaddress}.xlsx", dtype=object)
+
+df =df[["amount" ,"from_owner",	"to_owner" , "owner_count"]].reset_index(drop=True)
+df =df.astype({
+    "amount":float,
+    "owner_count":int
+    
+})
+def merge_gdf_has_mul_ata(gdf):
+    if len(gdf)==1:
+        return gdf
+    
+    gdf["amount"] = gdf["amount"].sum()
+    gdf["owner_count"] = gdf["owner_count"].sum()
+    gdf =gdf.drop_duplicates()
+    return gdf
+
+df = df.groupby(by=["from_owner","to_owner"], group_keys=False).apply(
+    lambda gdf: merge_gdf_has_mul_ata(gdf))
+
+ 
+
+# 创建 Sankey 图
+def create_sankey(df):
+    labels = list(set(df['from_owner'].tolist() + df['to_owner'].tolist()))
+  
+    # 获取source和target的索引
+    source_indices  = [labels.index(src) for src in df['from_owner'].tolist()]
+    target_indices  = [labels.index(tgt) for tgt in df['to_owner'].tolist()]
+
+    fig = go.Figure(data=[go.Sankey(
+    node=dict(
+               pad=20,
+        thickness=15,
+        line=dict(color="black", width=0.5),
+        label =[ item[0:6] for item in labels],
+             hoverinfo="all",  # 在悬停时显示标签和值
+             customdata= [{"dalao_address":label,"count":99} for  label in labels] , # 添加完整的标签作为自定义数据
+             hovertemplate = '%{label}<br>node: %{customdata.dalao_address}  <br>Value: %{value}<br>',
+
+    ),
+    link=dict(
+          source = source_indices ,
+          target =target_indices,
+          value = df['amount'].tolist(),
+          label  = labels,
+        hovertemplate='Amount: %{value}<br>From: %{source.label}<br>To: %{target.label}<extra></extra>'  # 自定义悬停模板
+       
+   
+    ))])
+    # fig.update_layout(
+    #     width=2000,  # 增加图表宽度
+    #     height=1200,  # 增加图表高度
+    #     font=dict(size=10, color="black")  # 调整字体大小和颜色
+    # )
+
+
+    return fig
+
+# 初始化 Dash 应用
+app = dash.Dash(__name__)
+fig = create_sankey(df)
+ 
+# 定义应用布局
+app.layout = html.Div([
+    dcc.Graph(figure=fig)
+])
+
+# 运行应用
+if __name__ == '__main__':
+    app.run_server(debug=True)

src/code/5005.py

@@ -0,0 +1,91 @@
+import networkx as nx
+import plotly.graph_objects as go
+
+# 创建一个空的Graph
+G = nx.DiGraph()
+
+# 添加节点和边
+G.add_edge('A', 'B', weight=1)
+G.add_edge('A', 'C', weight=2)
+G.add_edge('B', 'C', weight=3)
+G.add_edge('C', 'D', weight=4)
+
+# 使用spring布局获取节点位置
+pos = nx.spring_layout(G)
+
+# 创建一个节点列表和边列表，用于绘图
+edge_x = []
+edge_y = []
+for edge in G.edges():
+    x0, y0 = pos[edge[0]]
+    x1, y1 = pos[edge[1]]
+    edge_x.append(x0)
+    edge_x.append(x1)
+    edge_x.append(None)  # None 表示断开，用于绘制多条线段
+    edge_y.append(y0)
+    edge_y.append(y1)
+    edge_y.append(None)
+
+# 创建节点数据
+node_x = [pos[node][0] for node in G.nodes()]
+node_y = [pos[node][1] for node in G.nodes()]
+
+# 创建节点和边轨迹
+edge_trace = go.Scatter(
+    x=edge_x, y=edge_y,
+    line=dict(width=0.5, color='black'),
+    hoverinfo='none',
+    mode='lines')
+
+node_trace = go.Scatter(
+    x=node_x, y=node_y,
+    text=[],
+    mode='markers',
+    hoverinfo='text',
+    marker=dict(
+        showscale=True,
+        # colorscale options
+        #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |
+        #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |
+        #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |
+        colorscale='YlGnBu',
+        reversescale=True,
+        color=[],
+        size=10,
+        colorbar=dict(
+            thickness=15,
+            title='Node Connections',
+            xanchor='left',
+            titleside='right'
+        ),
+        line_width=2))
+
+# 更新节点颜色和大小
+node_adjacencies = []
+node_text = []
+for node, adjacencies in enumerate(G.adjacency()):
+    node_adjacencies.append(len(adjacencies[1]))
+    node_text.append('# of connections: '+str(len(adjacencies[1])))
+
+node_trace.marker.color = node_adjacencies
+node_trace.text = node_text
+
+# 创建图布局
+fig = go.Figure(data=[edge_trace, node_trace],
+             layout=go.Layout(
+                title='Interactive Network Graph',
+                titlefont_size=16,
+                showlegend=False,
+                hovermode='closest',
+                margin=dict(b=20,l=5,r=5,t=40),
+                annotations=[ dict(
+                    text="Python code: <a href='https://plotly.com/'> Plotly</a>",
+                    showarrow=False,
+                    xref="paper", yref="paper",
+                    x=0.005, y=-0.002 ) ],
+                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
+                )
+
+# 显示图形
+fig.show()

src/code/base_class.py

@@ -53,6 +53,11 @@ class BaseVariableFunction:
         self.dalao_ori_tran_defined_path = self.librarydata_path/"dalao_ori_tran_defined/"
         self.dalao_ori_tran_gmgnOrdex_path = self.librarydata_path/"dalao_ori_tran_gmgnOrdex/"
         self.dalao_tran_gmgnOrdex_path = self.librarydata_path/"dalao_tran_gmgnOrdex/"
+        self.dune_excution_path = self.librarydata_path/"dune_excution/"
+        self.dune_token_graph_path = self.librarydata_path/"dune_token_graph/"
+        
+        
+        
         
         
         
@@ -142,7 +147,7 @@ class BaseVariableFunction:
             "Fm3DL8T4whBCk5yHLUTG6HGRbrzm3rsDkaRPF4GVVRuC",
         ]
         
-        self.duneapi=['jLBrrvAv9Wlt3eAwuj28cCpSBUdZqcXB']
+        self.arr_duneapi=['W7zTOtVucO0CepixObF7MgGGTzqUYqbM']
 
 
 
@@ -380,3 +385,5 @@ class BaseVariableFunction:
     def makescreenurl( self,urlname,urlparam):
         _url = urlname + urlparam
         return '=HYPERLINK("{}","{}")'.format(_url, "url_"+urlparam)
+    def makeurl( self,url,url_show):
+        return '=HYPERLINK("{}","{}")'.format(url,  url_show)

src/code/tempCodeRunnerFile.py

@@ -1 +1 @@
-0
+

src/code/to_mysql.py

@@ -1,14 +1,14 @@
 import pandas as pd
 from sqlalchemy import create_engine
 
-# from base_class import BaseVariableFunction
-# from base_class import *
-# baseclass = BaseVariableFunction(__file__)
+from base_class import BaseVariableFunction
+from base_class import *
+baseclass = BaseVariableFunction(__file__)
 
 # 数据库连接信息
 username = 'root'
-# password = '123456'
-password = '123456abc'
+password = '123456'
+# password = '123456abc'
 
 host = 'localhost'
 database = 'mine_01'
@@ -25,8 +25,8 @@ print(con)
 #     'column1': [1, 2, 3],
 #     'column2': ['a', 'b', 'c']
 # })
-# df = pd.read_excel(baseclass.library_path/f"a_case.xlsx",dtype =object)
-df = pd.read_excel (f"a_case.xlsx",dtype =object)
+df = pd.read_excel(baseclass.library_path/f"a_case.xlsx",dtype =object)
+# df = pd.read_excel (f"a_case.xlsx",dtype =object)
 df  = df.astype({
     "time":int,
 })

src/code/zzzzzzz.py

@@ -0,0 +1,63 @@
+import pandas as pd
+
+# 假设这是你的DataFrame
+df = pd.DataFrame({
+    'from': ['A', 'B', 'C', 'A', 'D', 'E', 'G', 'H', 'A', 'A'],
+    'to': ['B', 'C', 'A', 'D', 'E', 'H', 'I', 'J', 'F', 'G']
+})
+
+def get_connected_edges(df, start_node, max_connections=6):
+    # 初始化结果列表
+    result = []
+    # 初始化待检查的节点集合，并设置起始节点的深度为0
+    to_check = {start_node: 0}
+    # 初始化已检查的节点集合
+    checked = set()
+    # 初始化连接数字典
+    connection_counts = {node: 0 for node in df['from'].tolist() + df['to'].tolist()}
+    
+    while to_check:
+        # 取出一个节点及其深度
+        node, depth = to_check.popitem()
+        # 添加到已检查集合
+        checked.add(node)
+        
+        # 如果节点连接数超过最大限制，则跳过
+        if connection_counts[node] > max_connections:
+            continue
+        
+        # 找到所有从这个节点出发的边
+        from_edges = df[df['from'] == node]
+        # 找到所有进入这个节点的边
+        to_edges = df[df['to'] == node]
+        
+        # 更新连接数
+        connection_counts[node] += len(from_edges) + len(to_edges)
+        
+        # 如果连接数超过最大限制，则跳过
+        if connection_counts[node] > max_connections:
+            continue
+        
+        # 将这些边添加到结果列表，并记录深度
+        for index in from_edges.index.tolist() + to_edges.index.tolist():
+            result.append({'index': index, 'depth': depth})
+        
+        # 将所有连接的节点添加到待检查集合，并更新深度
+        for edge in from_edges['to'].tolist() + to_edges['from'].tolist():
+            if edge not in checked:
+                to_check[edge] = depth + 1
+    
+    # 将结果列表转换为DataFrame
+    result_df = pd.DataFrame(result)
+    print(result_df)
+    # 使用找到的索引来筛选DataFrame，并添加深度列
+    connected_edges_df = df.loc[result_df['index']].assign(depth=result_df['depth'])
+    print(connected_edges_df)
+    
+    return connected_edges_df
+
+# 假设我们要找到与节点 'A' 相关的所有边
+start_node = 'A'
+connected_edges_df = get_connected_edges(df, start_node)
+
+# print(connected_edges_df)

src/code/zzzzzzzzzzzz_test_defined_playwright_chrome.py

@@ -19,7 +19,7 @@ def timestamped_print(*args, **kwargs):
 
 print = timestamped_print
 print('\n'*5)
-print(f"{'{:<6}'.format('enter')}  ----------------NOTE-----------NOTE---------------")
+print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")
 
 
 def makedirpath(   folder):

src/library/读书笔记/K线.txt

@@ -0,0 +1,9 @@
+价格形态（技术形态）：股票价格在一段时间内形成的各种形状
+三角形 双头型 
+反转形态 持续形态
+反转形态有：头肩顶和头肩底，双顶和双底，弧顶和圆弧底
+持续形态：三角形，矩形，旗形
+矩形振荡期间的上下宽度就是突破后的涨跌幅度
+三角形最宽的垂直距离是突破后的涨跌幅度
+
+反转形态的前提是趋势的存在，反转形态一旦确立，最小和最大价格目标位都会出现

src/librarydata/dune_excution/obj_excutionid_2_tokeninfo.json

@@ -0,0 +1,7 @@
+{
+    "01JBXRQNBWHRQ0RT66W4AC1QFE": {
+        "tokenaddress": "4GULMPKBJLruChBZWksZzukAg1AjSCmCTMn9ny2Xpump",
+        "decimals": 6,
+        "tokenname": "Ouuu"
+    }
+}

src/librarydata/dune_excution/one_token_relation_graph_01JBXRQNBWHRQ0RT66W4AC1QFE.csv

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src/librarydata/dune_token_graph/4GULMPKBJLruChBZWksZzukAg1AjSCmCTMn9ny2Xpump/total.csv

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