Last updated
## 路径改成自己本地的文件路径
df_target_label = pd.read_csv(u'YOUR FILE PATH/graph_raw_label.csv')
df_target_relation = pd.read_csv(u'YOUR FILE PATH/graph_relation.csv')
##取所有address list用于请求API
address_list=list(df_target_label.address.values)
balance_list=[]
print(address_list)while len(address_list)>0:
for address in address_list:
api_key = "api_key"
try:
response = requests.get(
"https://api.etherscan.io/api?module=account&action=balance&address=" + address + "&tag=latest&apikey=" + api_key
)
# Parse the JSON-formatted response
response_json = json.loads(response.text)
# Get the balance information from the response
eth_balance = response_json["result"]
eth_balance= int(eth_balance)/(1E18)
balance_list.append((address,eth_balance))
address_list.remove(address)
time.sleep(1)
print(eth_balance)
except:
print('Error')
print('List Length:'+str(len(address_list)))
df_balance = pd.DataFrame(balance_list, columns=['address', 'Balance'])
df_target_label=df_target_label.merge(df_balance,left_on=['address'],right_on=['address'],how='left')
print('end')##定义一个函数根据值的大小去返回不同的标签，类似于SQL里的case when
def get_balance_level(x):
if x ==0 :
output = 'Small'
elif x > 0 and x<1000:
output = 'Medium'
elif x > 1000 and x<10000:
output = 'Large'
else:
output = 'Huge'
return output
df_target_label['Balance_level'] = df_target_label['Balance'].round(2).apply(lambda x: get_balance_level(x))
df_target_label['Balance'] = df_target_label['Balance'].round(2).astype('string')
df_target_label['label'] =df_target_label['label']+' | '+ df_target_label['Balance'] +' ETH' def drew_graph(df_target_relation,df_target_label):
def add_node_base_data(df_target_relation):
df_target_relation = df_target_relation
node_list = list(set(df_target_relation['from_address'].to_list()+df_target_relation['to_address'].to_list()))
edges = list(set(df_target_relation.apply(lambda x: (x.from_address, x.to_address), axis=1).to_list()))
G.add_nodes_from(node_list)
G.add_edges_from(edges)
return node_list,edges
def add_node_attributes(df_target_label,df_key_list,df_vlaue_list,color_list):
for node, (n,p) in zip(G.nodes(), pos.items()):
G.nodes[node]['pos'] = p
G.nodes[node]['color'] = '#614433'
for id,label,layer_type,Balance_level in list(set(df_target_label.apply(lambda x: (x.address, x.label, x.level_type,x.Balance_level), axis=1).to_list())):
if node==id:
G.nodes[node]['label']=label
if Balance_level=='Large':
G.nodes[node]['size']=40
elif Balance_level=='Medium':
G.nodes[node]['size']=20
elif Balance_level=='Small':
G.nodes[node]['size']=10
elif Balance_level=='Huge':
G.nodes[node]['size']=80
for x,y,z in zip(df_key_list,df_vlaue_list,color_list):
target_list = df_target_label[df_target_label[x]==y]['address'].values.tolist()
if len(target_list)>0:
for id in target_list:
if id==node and G.nodes[node]['color']=='#614433':
G.nodes[node]['color'] = z
###############画出所有的边
def get_edge_trace(G):
xtext=[]
ytext=[]
edge_x = []
edge_y = []
for edge in G.edges():
x0, y0 = G.nodes[edge[0]]['pos']
x1, y1 = G.nodes[edge[1]]['pos']
xtext.append((x0+x1)/2)
ytext.append((y0+y1)/2)
edge_x.append(x0)
edge_x.append(x1)
edge_x.append(None)
edge_y.append(y0)
edge_y.append(y1)
edge_y.append(None)
xtext.append((x0+x1)/2)
ytext.append((y0+y1)/2)
edge_trace = go.Scatter(
x=edge_x, y=edge_y,
line=dict(width=0.5, color='#333'),
hoverinfo='none',
mode='lines')
eweights_trace = go.Scatter(x=xtext,y= ytext, mode='text',
marker_size=0.5,
text=[0.45, 0.7, 0.34],
textposition='top center',
hovertemplate='weight: %{text}<extra></extra>')
return edge_trace, eweights_trace
def get_node_trace(G):
node_x = []
node_y = []
for node in G.nodes():
x, y = G.nodes[node]['pos']
node_x.append(x)
node_y.append(y)
node_trace = go.Scatter(
x=node_x, y=node_y,
mode='markers',
hoverinfo='text',
marker=dict(
color=[],
colorscale = px.colors.qualitative.Plotly,
size=10,
line_width=0))
return node_trace
###############定义Graph
G = nx.Graph()
###############给Graph添加Node以及Edge
node_list = add_node_base_data(df_target_relation)[0]
edges = add_node_base_data(df_target_relation)[1]
# eweights_trace = add_node_base_data(df_target_relation)[1]
###############选择layout并得到相关node的pos
pos = nx.fruchterman_reingold_layout(G)
df_key_list = [ 'level_type' ,'account_type' , 'account_type' , 'account_type' ]
df_vlaue_list = [ 'Core' , 'EOA' , 'Cex Address' , 'Contract Address']
color_list = [ '#109947' ,'#0031DE' , '#F7F022' , '#E831D6' ]
###############给node添加label,Size,color属性
add_node_attributes(df_target_label,df_key_list,df_vlaue_list,color_list)
edge_trace, eweights_trace = get_edge_trace(G)
node_trace = get_node_trace(G)
###############定义color的规则
###############将node_text，node_size，node_color写入list
node_text = []
node_size = []
node_color = []
for node in G.nodes():
x = G.nodes[node]['label']
y = G.nodes[node]['size']
z = G.nodes[node]['color']
node_text.append(x)
node_size.append(y)
node_color.append(z)
# 依据设置label，size，color
node_trace.marker.color = node_color
node_trace.marker.size =node_size
node_trace.text = node_text
fig_target_id=go.Figure()
fig_target_id.add_trace(edge_trace)
fig_target_id.add_trace(node_trace)
fig_target_id.update_layout(
height=1000,
width=1000,
xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
showlegend=False,
hovermode='closest',
)
return fig_target_id
fig =drew_graph(df_target_relation,df_target_label)
fig.show()
fig.write_html(u'YOUR FILE PATH/FTX_Accounts_Drainer.html')
print('end')