gauravlochab commited on
Commit
db4f69b
·
1 Parent(s): ef47052

feat: add graph for optimus agent

Browse files
.gitignore ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ hf-dash/
2
+ env/
3
+ debug_scripts/
app.py CHANGED
@@ -215,16 +215,16 @@ def fetch_apr_data_from_db():
215
  logger.info("==== Starting APR data fetch ====")
216
 
217
  try:
218
- # Step 1: Find the Modius agent type
219
- logger.info("Finding Modius agent type")
220
- modius_type = get_agent_type_by_name("Modius")
221
- if not modius_type:
222
- logger.error("Modius agent type not found, using placeholder data")
223
  global_df = pd.DataFrame([])
224
  return global_df
225
 
226
- type_id = modius_type["type_id"]
227
- logger.info(f"Found Modius agent type with ID: {type_id}")
228
 
229
  # Step 2: Find the APR attribute definition
230
  logger.info("Finding APR attribute definition")
@@ -237,22 +237,22 @@ def fetch_apr_data_from_db():
237
  attr_def_id = apr_attr_def["attr_def_id"]
238
  logger.info(f"Found APR attribute definition with ID: {attr_def_id}")
239
 
240
- # Step 3: Get all agents of type Modius
241
- logger.info(f"Getting all agents of type Modius (type_id: {type_id})")
242
- modius_agents = get_agents_by_type(type_id)
243
- if not modius_agents:
244
- logger.error("No agents of type 'Modius' found")
245
  global_df = pd.DataFrame([])
246
  return global_df
247
 
248
- logger.info(f"Found {len(modius_agents)} Modius agents")
249
- logger.debug(f"Modius agents: {[{'agent_id': a['agent_id'], 'agent_name': a['agent_name']} for a in modius_agents]}")
250
 
251
- # Step 4: Fetch all APR values for Modius agents
252
- logger.info(f"Fetching APR values for all Modius agents (attr_def_id: {attr_def_id})")
253
- apr_attributes = get_attribute_values_by_type_and_attr(modius_agents, attr_def_id)
254
  if not apr_attributes:
255
- logger.error("No APR values found for 'Modius' agents")
256
  global_df = pd.DataFrame([])
257
  return global_df
258
 
@@ -267,7 +267,7 @@ def fetch_apr_data_from_db():
267
  data = extract_apr_value(attr)
268
  if data["timestamp"] is not None:
269
  # Get agent name
270
- agent_name = get_agent_name(attr["agent_id"], modius_agents)
271
  # Add agent name to the data
272
  data["agent_name"] = agent_name
273
  # Add is_dummy flag (all real data)
@@ -670,8 +670,8 @@ def generate_apr_visualizations():
670
  )
671
 
672
  # Save as static file for reference
673
- fig.write_html("modius_apr_combined_graph.html")
674
- fig.write_image("modius_apr_combined_graph.png")
675
 
676
  csv_file = None
677
  return fig, csv_file
@@ -715,8 +715,8 @@ def generate_roi_visualizations():
715
  )
716
 
717
  # Save as static file for reference
718
- fig.write_html("modius_roi_graph.html")
719
- fig.write_image("modius_roi_graph.png")
720
 
721
  csv_file = None
722
  return fig, csv_file
@@ -744,8 +744,8 @@ def create_combined_roi_time_series_graph(df):
744
  )
745
  return fig
746
 
747
- # Define fixed start date (February 1, 2025)
748
- fixed_start_date = datetime(2025, 2, 1)
749
  logger.info(f"Using fixed start date for ROI runtime calculation: {fixed_start_date}")
750
 
751
  # Calculate runtime for each agent from fixed start date
@@ -805,7 +805,7 @@ def create_combined_roi_time_series_graph(df):
805
  type="rect",
806
  fillcolor="rgba(230, 243, 255, 0.3)",
807
  line=dict(width=0),
808
- y0=0, y1=100, # Use a fixed positive value (percentage)
809
  x0=min_time, x1=max_time,
810
  layer="below"
811
  )
@@ -815,7 +815,7 @@ def create_combined_roi_time_series_graph(df):
815
  type="rect",
816
  fillcolor="rgba(255, 230, 230, 0.3)",
817
  line=dict(width=0),
818
- y0=-100, y1=0, # Use a fixed negative value (percentage)
819
  x0=min_time, x1=max_time,
820
  layer="below"
821
  )
@@ -986,7 +986,7 @@ def create_combined_roi_time_series_graph(df):
986
  # Update layout with average runtime information in the title
987
  fig.update_layout(
988
  title=dict(
989
- text=f"Modius Agents ROI (over avg. {avg_runtime:.1f} days runtime)",
990
  font=dict(
991
  family="Arial, sans-serif",
992
  size=22,
@@ -1042,27 +1042,25 @@ def create_combined_roi_time_series_graph(df):
1042
  )
1043
  )
1044
 
1045
- # Update y-axis with fixed range of -100% to +100% for ROI
1046
  fig.update_yaxes(
1047
  showgrid=True,
1048
  gridwidth=1,
1049
  gridcolor='rgba(0,0,0,0.1)',
1050
  # Use fixed range instead of autoscaling
1051
- autorange=False, # Disable autoscaling
1052
- range=[-100, 100], # Set fixed range from -100% to +100%
1053
  tickformat=".2f", # Format tick labels with 2 decimal places
1054
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
1055
  title=None # Remove the built-in axis title since we're using annotations
1056
  )
1057
 
1058
- # Update x-axis with better formatting and fixed range
1059
  fig.update_xaxes(
1060
  showgrid=True,
1061
  gridwidth=1,
1062
  gridcolor='rgba(0,0,0,0.1)',
1063
- # Set fixed range with start date and ending at the last valid date
1064
- autorange=False, # Disable autoscaling
1065
- range=[x_start_date, last_valid_date], # Set fixed range from start date to last valid date
1066
  tickformat="%b %d", # Simplified date format without time
1067
  tickangle=-30, # Angle the labels for better readability
1068
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
@@ -1071,11 +1069,11 @@ def create_combined_roi_time_series_graph(df):
1071
 
1072
  try:
1073
  # Save the figure
1074
- graph_file = "modius_roi_graph.html"
1075
  fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1076
 
1077
  # Also save as image for compatibility
1078
- img_file = "modius_roi_graph.png"
1079
  try:
1080
  fig.write_image(img_file)
1081
  logger.info(f"ROI graph saved to {graph_file} and {img_file}")
@@ -1101,6 +1099,44 @@ def create_combined_roi_time_series_graph(df):
1101
  x0=min_time, x1=max_time
1102
  )
1103
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1104
  # Simply plot the average ROI data with moving average
1105
  if not avg_roi_data.empty:
1106
  # Add moving average as a line
@@ -1117,7 +1153,7 @@ def create_combined_roi_time_series_graph(df):
1117
  # Simplified layout with adjusted y-axis range
1118
  simple_fig.update_layout(
1119
  title=dict(
1120
- text="Modius Agents ROI",
1121
  font=dict(
1122
  family="Arial, sans-serif",
1123
  size=22,
@@ -1133,32 +1169,30 @@ def create_combined_roi_time_series_graph(df):
1133
  margin=dict(r=30, l=120, t=40, b=50)
1134
  )
1135
 
1136
- # Update y-axis with fixed range of -100% to +100% for ROI
1137
  simple_fig.update_yaxes(
1138
  showgrid=True,
1139
  gridwidth=1,
1140
  gridcolor='rgba(0,0,0,0.1)',
1141
- autorange=False,
1142
- range=[-100, 100],
1143
  tickformat=".2f",
1144
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"),
1145
  title=None # Remove the built-in axis title since we're using annotations
1146
  )
1147
 
1148
- # Update x-axis with better formatting and fixed range
1149
  simple_fig.update_xaxes(
1150
  showgrid=True,
1151
  gridwidth=1,
1152
  gridcolor='rgba(0,0,0,0.1)',
1153
- autorange=False,
1154
- range=[x_start_date, max_time],
1155
  tickformat="%b %d",
1156
  tickangle=-30,
1157
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold")
1158
  )
1159
 
1160
  # Save the figure
1161
- graph_file = "modius_roi_graph.html"
1162
  simple_fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1163
 
1164
  # Return the simple figure
@@ -1171,7 +1205,7 @@ def save_roi_to_csv(df):
1171
  return None
1172
 
1173
  # Define the CSV file path
1174
- csv_file = "modius_roi_values.csv"
1175
 
1176
  # Save to CSV
1177
  df.to_csv(csv_file, index=False)
@@ -1213,15 +1247,17 @@ def create_time_series_graph_per_agent(df):
1213
  row=row, col=1
1214
  )
1215
 
1216
- # Add background colors
1217
  fig.add_shape(
1218
  type="rect", fillcolor="rgba(230, 243, 255, 0.3)", line=dict(width=0),
1219
- y0=0, y1=1000, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
 
1220
  row=row, col=1, layer="below"
1221
  )
1222
  fig.add_shape(
1223
  type="rect", fillcolor="rgba(255, 230, 230, 0.3)", line=dict(width=0),
1224
- y0=-1000, y1=0, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
 
1225
  row=row, col=1, layer="below"
1226
  )
1227
 
@@ -1301,11 +1337,11 @@ def create_time_series_graph_per_agent(df):
1301
  )
1302
 
1303
  # Save the figure (still useful for reference)
1304
- graph_file = "modius_apr_per_agent_graph.html"
1305
  fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1306
 
1307
  # Also save as image for compatibility
1308
- img_file = "modius_apr_per_agent_graph.png"
1309
  fig.write_image(img_file)
1310
 
1311
  logger.info(f"Per-agent graph saved to {graph_file} and {img_file}")
@@ -1343,8 +1379,8 @@ def create_combined_time_series_graph(df):
1343
  min_time = df['timestamp'].min()
1344
  max_time = df['timestamp'].max()
1345
 
1346
- # Use April 17th, 2025 as the fixed start date for APR graph
1347
- x_start_date = datetime(2025, 4, 17)
1348
 
1349
  # CRITICAL: Log the exact dataframe we're using for plotting to help debug
1350
  logger.info(f"Graph data - shape: {df.shape}, columns: {df.columns}")
@@ -1402,7 +1438,7 @@ def create_combined_time_series_graph(df):
1402
  type="rect",
1403
  fillcolor="rgba(230, 243, 255, 0.3)",
1404
  line=dict(width=0),
1405
- y0=0, y1=100, # Use a fixed positive value
1406
  x0=min_time, x1=max_time,
1407
  layer="below"
1408
  )
@@ -1412,7 +1448,7 @@ def create_combined_time_series_graph(df):
1412
  type="rect",
1413
  fillcolor="rgba(255, 230, 230, 0.3)",
1414
  line=dict(width=0),
1415
- y0=-100, y1=0, # Use a fixed negative value
1416
  x0=min_time, x1=max_time,
1417
  layer="below"
1418
  )
@@ -1719,7 +1755,7 @@ def create_combined_time_series_graph(df):
1719
  # Make chart responsive instead of fixed width
1720
  fig.update_layout(
1721
  title=dict(
1722
- text="Modius Agents",
1723
  font=dict(
1724
  family="Arial, sans-serif",
1725
  size=22,
@@ -1800,27 +1836,25 @@ def create_combined_time_series_graph(df):
1800
  )
1801
  )
1802
 
1803
- # Update y-axis with fixed range of -50 to +100 for psychological effect
1804
  fig.update_yaxes(
1805
  showgrid=True,
1806
  gridwidth=1,
1807
  gridcolor='rgba(0,0,0,0.1)',
1808
  # Use fixed range instead of autoscaling
1809
- autorange=False, # Disable autoscaling
1810
- range=[-50, 100], # Set fixed range from -50 to +100
1811
  tickformat=".2f", # Format tick labels with 2 decimal places
1812
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
1813
  title=None # Remove the built-in axis title since we're using annotations
1814
  )
1815
 
1816
- # Update x-axis with better formatting and fixed range
1817
  fig.update_xaxes(
1818
  showgrid=True,
1819
  gridwidth=1,
1820
  gridcolor='rgba(0,0,0,0.1)',
1821
- # Set fixed range with April 17 as start date and ending at the last valid date
1822
- autorange=False, # Disable autoscaling
1823
- range=[x_start_date, last_valid_date], # Set fixed range from April 17 to last valid date
1824
  tickformat="%b %d", # Simplified date format without time
1825
  tickangle=-30, # Angle the labels for better readability
1826
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
@@ -1834,11 +1868,11 @@ def create_combined_time_series_graph(df):
1834
  write_debug_info(df, fig)
1835
 
1836
  # Save the figure (still useful for reference)
1837
- graph_file = "modius_apr_combined_graph.html"
1838
  fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1839
 
1840
  # Also save as image for compatibility
1841
- img_file = "modius_apr_combined_graph.png"
1842
  try:
1843
  fig.write_image(img_file)
1844
  logger.info(f"Combined graph saved to {graph_file} and {img_file}")
@@ -1965,10 +1999,10 @@ def create_combined_time_series_graph(df):
1965
  )
1966
  )
1967
 
1968
- # Simplified layout with adjusted y-axis range and increased size
1969
  simple_fig.update_layout(
1970
  title=dict(
1971
- text="Modius Agents",
1972
  font=dict(
1973
  family="Arial, sans-serif",
1974
  size=22,
@@ -1979,8 +2013,8 @@ def create_combined_time_series_graph(df):
1979
  xaxis_title=None, # Remove x-axis title to use annotation instead
1980
  yaxis_title=None, # Remove the y-axis title as we'll use annotations instead
1981
  yaxis=dict(
1982
- # No fixed range - let Plotly autoscale
1983
- autorange=True, # Explicitly enable autoscaling
1984
  tickformat=".2f", # Format tick labels with 2 decimal places
1985
  tickfont=dict(size=12) # Larger font for tick labels
1986
  ),
@@ -2040,10 +2074,9 @@ def create_combined_time_series_graph(df):
2040
  )
2041
  )
2042
 
2043
- # Apply fixed range to the x-axis for the fallback graph
2044
  simple_fig.update_xaxes(
2045
- autorange=False, # Disable autoscaling
2046
- range=[x_start_date, max_time], # Set fixed range from April 17
2047
  tickformat="%b %d", # Simplified date format without time
2048
  tickangle=-30,
2049
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
@@ -2076,7 +2109,7 @@ def save_to_csv(df):
2076
  return None
2077
 
2078
  # Define the CSV file path
2079
- csv_file = "modius_apr_values.csv"
2080
 
2081
  # Save to CSV
2082
  df.to_csv(csv_file, index=False)
@@ -2084,7 +2117,7 @@ def save_to_csv(df):
2084
 
2085
  # Also generate a statistics CSV file
2086
  stats_df = generate_statistics_from_data(df)
2087
- stats_csv = "modius_apr_statistics.csv"
2088
  stats_df.to_csv(stats_csv, index=False)
2089
  logger.info(f"Statistics saved to {stats_csv}")
2090
 
@@ -2197,43 +2230,6 @@ def create_active_agents_visualizations():
2197
  )
2198
  return fig
2199
 
2200
- # Comment out the blockchain connection code
2201
- """
2202
- # Load environment variables from .env file
2203
- # RPC URLs
2204
- OPTIMISM_RPC_URL = os.getenv('OPTIMISM_RPC_URL')
2205
- MODE_RPC_URL = os.getenv('MODE_RPC_URL')
2206
-
2207
- # Initialize Web3 instances
2208
- web3_instances = {
2209
- 'optimism': Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL)),
2210
- 'mode': Web3(Web3.HTTPProvider(MODE_RPC_URL))
2211
- }
2212
-
2213
- # Contract addresses for service registries
2214
- contract_addresses = {
2215
- 'optimism': '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44',
2216
- 'mode': '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'
2217
- }
2218
-
2219
- # Load the ABI from the provided JSON file
2220
- with open('./contracts/service_registry_abi.json', 'r') as abi_file:
2221
- contract_abi = json.load(abi_file)
2222
-
2223
- # Create the contract instances
2224
- service_registries = {
2225
- chain_name: web3.eth.contract(address=contract_addresses[chain_name], abi=contract_abi)
2226
- for chain_name, web3 in web3_instances.items()
2227
- }
2228
-
2229
- # Check if connections are successful
2230
- for chain_name, web3_instance in web3_instances.items():
2231
- if not web3_instance.is_connected():
2232
- raise Exception(f"Failed to connect to the {chain_name.capitalize()} network.")
2233
- else:
2234
- print(f"Successfully connected to the {chain_name.capitalize()} network.")
2235
- """
2236
-
2237
  # Dummy blockchain functions to replace the commented ones
2238
  def get_transfers(integrator: str, wallet: str) -> str:
2239
  """Dummy function that returns an empty result"""
@@ -2253,227 +2249,6 @@ def process_transactions_and_agents(data):
2253
 
2254
  # Function to create visualizations based on the metrics
2255
  def create_visualizations():
2256
- """
2257
- # Commenting out the original visualization code temporarily for debugging
2258
- transactions_data = fetch_and_aggregate_transactions()
2259
- df_transactions, df_agents, df_agents_weekly = process_transactions_and_agents(transactions_data)
2260
-
2261
- # Fetch daily value locked data
2262
- df_tvl = pd.read_csv('daily_value_locked.csv')
2263
-
2264
- # Calculate total value locked per chain per day
2265
- df_tvl["total_value_locked_usd"] = df_tvl["amount0_usd"] + df_tvl["amount1_usd"]
2266
- df_tvl_daily = df_tvl.groupby(["date", "chain_name"])["total_value_locked_usd"].sum().reset_index()
2267
- df_tvl_daily['date'] = pd.to_datetime(df_tvl_daily['date'])
2268
-
2269
- # Filter out dates with zero total value locked
2270
- df_tvl_daily = df_tvl_daily[df_tvl_daily["total_value_locked_usd"] > 0]
2271
-
2272
- chain_name_map = {
2273
- "mode": "Mode",
2274
- "base": "Base",
2275
- "ethereum": "Ethereum",
2276
- "optimism": "Optimism"
2277
- }
2278
- df_tvl_daily["chain_name"] = df_tvl_daily["chain_name"].map(chain_name_map)
2279
-
2280
- # Plot total value locked
2281
- fig_tvl = px.bar(
2282
- df_tvl_daily,
2283
- x="date",
2284
- y="total_value_locked_usd",
2285
- color="chain_name",
2286
- opacity=0.7,
2287
- title="Total Volume Invested in Pools in Different Chains Daily",
2288
- labels={"date": "Date","chain_name": "Transaction Chain", "total_value_locked_usd": "Total Volume Invested (USD)"},
2289
- barmode='stack',
2290
- color_discrete_map={
2291
- "Mode": "orange",
2292
- "Base": "purple",
2293
- "Ethereum": "darkgreen",
2294
- "Optimism": "blue"
2295
- }
2296
- )
2297
- fig_tvl.update_layout(
2298
- xaxis_title="Date",
2299
-
2300
- yaxis=dict(tickmode='linear', tick0=0, dtick=4),
2301
- xaxis=dict(
2302
- tickmode='array',
2303
- tickvals=df_tvl_daily['date'],
2304
- ticktext=df_tvl_daily['date'].dt.strftime('%b %d'),
2305
- tickangle=-45,
2306
- ),
2307
- bargap=0.6, # Increase gap between bar groups (0-1)
2308
- bargroupgap=0.1, # Decrease gap between bars in a group (0-1)
2309
- height=600,
2310
- width=1200, # Specify width to prevent bars from being too wide
2311
- showlegend=True,
2312
- template='plotly_white'
2313
- )
2314
- fig_tvl.update_xaxes(tickformat="%b %d")
2315
-
2316
- chain_name_map = {
2317
- 10: "Optimism",
2318
- 8453: "Base",
2319
- 1: "Ethereum",
2320
- 34443: "Mode"
2321
- }
2322
-
2323
- df_transactions["sending_chain"] = df_transactions["sending_chain"].map(chain_name_map)
2324
- df_transactions["receiving_chain"] = df_transactions["receiving_chain"].map(chain_name_map)
2325
-
2326
- df_transactions["sending_chain"] = df_transactions["sending_chain"].astype(str)
2327
- df_transactions["receiving_chain"] = df_transactions["receiving_chain"].astype(str)
2328
- df_transactions['date'] = pd.to_datetime(df_transactions['date'])
2329
- df_transactions["is_swap"] = df_transactions.apply(lambda x: x["sending_chain"] == x["receiving_chain"], axis=1)
2330
-
2331
- swaps_per_chain = df_transactions[df_transactions["is_swap"]].groupby(["date", "sending_chain"]).size().reset_index(name="swap_count")
2332
- fig_swaps_chain = px.bar(
2333
- swaps_per_chain,
2334
- x="date",
2335
- y="swap_count",
2336
- color="sending_chain",
2337
- title="Chain Daily Activity: Swaps",
2338
- labels={"sending_chain": "Transaction Chain", "swap_count": "Daily Swap Nr"},
2339
- barmode="stack",
2340
- opacity=0.7,
2341
- color_discrete_map={
2342
- "Optimism": "blue",
2343
- "Ethereum": "darkgreen",
2344
- "Base": "purple",
2345
- "Mode": "orange"
2346
- }
2347
- )
2348
- fig_swaps_chain.update_layout(
2349
- xaxis_title="Date",
2350
- yaxis_title="Daily Swap Count",
2351
- yaxis=dict(tickmode='linear', tick0=0, dtick=1),
2352
- xaxis=dict(
2353
- tickmode='array',
2354
- tickvals=[d for d in swaps_per_chain['date']],
2355
- ticktext=[d.strftime('%m-%d') for d in swaps_per_chain['date']],
2356
- tickangle=-45,
2357
- ),
2358
- bargap=0.6,
2359
- bargroupgap=0.1,
2360
- height=600,
2361
- width=1200,
2362
- margin=dict(l=50, r=50, t=50, b=50),
2363
- showlegend=True,
2364
- legend=dict(
2365
- yanchor="top",
2366
- y=0.99,
2367
- xanchor="right",
2368
- x=0.99
2369
- ),
2370
- template='plotly_white'
2371
- )
2372
- fig_swaps_chain.update_xaxes(tickformat="%m-%d")
2373
-
2374
- df_transactions["is_bridge"] = df_transactions.apply(lambda x: x["sending_chain"] != x["receiving_chain"], axis=1)
2375
-
2376
- bridges_per_chain = df_transactions[df_transactions["is_bridge"]].groupby(["date", "sending_chain"]).size().reset_index(name="bridge_count")
2377
- fig_bridges_chain = px.bar(
2378
- bridges_per_chain,
2379
- x="date",
2380
- y="bridge_count",
2381
- color="sending_chain",
2382
- title="Chain Daily Activity: Bridges",
2383
- labels={"sending_chain": "Transaction Chain", "bridge_count": "Daily Bridge Nr"},
2384
- barmode="stack",
2385
- opacity=0.7,
2386
- color_discrete_map={
2387
- "Optimism": "blue",
2388
- "Ethereum": "darkgreen",
2389
- "Base": "purple",
2390
- "Mode": "orange"
2391
- }
2392
- )
2393
- fig_bridges_chain.update_layout(
2394
- xaxis_title="Date",
2395
- yaxis_title="Daily Bridge Count",
2396
- yaxis=dict(tickmode='linear', tick0=0, dtick=1),
2397
- xaxis=dict(
2398
- tickmode='array',
2399
- tickvals=[d for d in bridges_per_chain['date']],
2400
- ticktext=[d.strftime('%m-%d') for d in bridges_per_chain['date']],
2401
- tickangle=-45,
2402
- ),
2403
- bargap=0.6,
2404
- bargroupgap=0.1,
2405
- height=600,
2406
- width=1200,
2407
- margin=dict(l=50, r=50, t=50, b=50),
2408
- showlegend=True,
2409
- legend=dict(
2410
- yanchor="top",
2411
- y=0.99,
2412
- xanchor="right",
2413
- x=0.99
2414
- ),
2415
- template='plotly_white'
2416
- )
2417
- fig_bridges_chain.update_xaxes(tickformat="%m-%d")
2418
- df_agents['date'] = pd.to_datetime(df_agents['date'])
2419
-
2420
- daily_agents_df = df_agents.groupby('date').agg({'agent_count': 'sum'}).reset_index()
2421
- daily_agents_df.rename(columns={'agent_count': 'daily_agent_count'}, inplace=True)
2422
- # Sort by date to ensure proper running total calculation
2423
- daily_agents_df = daily_agents_df.sort_values('date')
2424
-
2425
- # Create week column
2426
- daily_agents_df['week'] = daily_agents_df['date'].dt.to_period('W').apply(lambda r: r.start_time)
2427
-
2428
- # Calculate running total within each week
2429
- daily_agents_df['running_weekly_total'] = daily_agents_df.groupby('week')['daily_agent_count'].cumsum()
2430
-
2431
- # Create final merged dataframe
2432
- weekly_merged_df = daily_agents_df.copy()
2433
- adjustment_date = pd.to_datetime('2024-11-15')
2434
- weekly_merged_df.loc[weekly_merged_df['date'] == adjustment_date, 'daily_agent_count'] -= 1
2435
- weekly_merged_df.loc[weekly_merged_df['date'] == adjustment_date, 'running_weekly_total'] -= 1
2436
- fig_agents_registered = go.Figure(data=[
2437
- go.Bar(
2438
- name='Daily nr of Registered Agents',
2439
- x=weekly_merged_df['date'].dt.strftime("%b %d"),
2440
- y=weekly_merged_df['daily_agent_count'],
2441
- opacity=0.7,
2442
- marker_color='blue'
2443
- ),
2444
- go.Bar(
2445
- name='Weekly Nr of Registered Agents',
2446
- x=weekly_merged_df['date'].dt.strftime("%b %d"),
2447
- y=weekly_merged_df['running_weekly_total'],
2448
- opacity=0.7,
2449
- marker_color='purple'
2450
- )
2451
- ])
2452
-
2453
- fig_agents_registered.update_layout(
2454
- xaxis_title='Date',
2455
- yaxis_title='Number of Agents',
2456
- title="Nr of Agents Registered",
2457
- barmode='group',
2458
- yaxis=dict(tickmode='linear', tick0=0, dtick=1),
2459
- xaxis=dict(
2460
- categoryorder='array',
2461
- categoryarray=weekly_merged_df['date'].dt.strftime("%b %d"),
2462
- tickangle=-45
2463
- ),
2464
- bargap=0.3,
2465
- height=600,
2466
- width=1200,
2467
- showlegend=True,
2468
- legend=dict(
2469
- yanchor="top",
2470
- xanchor="right",
2471
- ),
2472
- template='plotly_white',
2473
- )
2474
-
2475
- return fig_swaps_chain, fig_bridges_chain, fig_agents_registered,fig_tvl
2476
- """
2477
  # Placeholder figures for testing
2478
  fig_swaps_chain = go.Figure()
2479
  fig_swaps_chain.add_annotation(
@@ -2508,7 +2283,7 @@ def create_visualizations():
2508
  # Modify dashboard function to make the plot container responsive
2509
  def dashboard():
2510
  with gr.Blocks() as demo:
2511
- gr.Markdown("# Average Modius Agent Performance")
2512
 
2513
  # Create tabs for APR and ROI metrics
2514
  with gr.Tabs():
 
215
  logger.info("==== Starting APR data fetch ====")
216
 
217
  try:
218
+ # Step 1: Find the Optimus agent type
219
+ logger.info("Finding Optimus agent type")
220
+ optimus_type = get_agent_type_by_name("Optimus")
221
+ if not optimus_type:
222
+ logger.error("Optimus agent type not found, using placeholder data")
223
  global_df = pd.DataFrame([])
224
  return global_df
225
 
226
+ type_id = optimus_type["type_id"]
227
+ logger.info(f"Found Optimus agent type with ID: {type_id}")
228
 
229
  # Step 2: Find the APR attribute definition
230
  logger.info("Finding APR attribute definition")
 
237
  attr_def_id = apr_attr_def["attr_def_id"]
238
  logger.info(f"Found APR attribute definition with ID: {attr_def_id}")
239
 
240
+ # Step 3: Get all agents of type Optimus
241
+ logger.info(f"Getting all agents of type Optimus (type_id: {type_id})")
242
+ optimus_agents = get_agents_by_type(type_id)
243
+ if not optimus_agents:
244
+ logger.error("No agents of type 'Optimus' found")
245
  global_df = pd.DataFrame([])
246
  return global_df
247
 
248
+ logger.info(f"Found {len(optimus_agents)} Optimus agents")
249
+ logger.debug(f"Optimus agents: {[{'agent_id': a['agent_id'], 'agent_name': a['agent_name']} for a in optimus_agents]}")
250
 
251
+ # Step 4: Fetch all APR values for Optimus agents
252
+ logger.info(f"Fetching APR values for all Optimus agents (attr_def_id: {attr_def_id})")
253
+ apr_attributes = get_attribute_values_by_type_and_attr(optimus_agents, attr_def_id)
254
  if not apr_attributes:
255
+ logger.error("No APR values found for 'Optimus' agents")
256
  global_df = pd.DataFrame([])
257
  return global_df
258
 
 
267
  data = extract_apr_value(attr)
268
  if data["timestamp"] is not None:
269
  # Get agent name
270
+ agent_name = get_agent_name(attr["agent_id"], optimus_agents)
271
  # Add agent name to the data
272
  data["agent_name"] = agent_name
273
  # Add is_dummy flag (all real data)
 
670
  )
671
 
672
  # Save as static file for reference
673
+ fig.write_html("optimus_apr_combined_graph.html")
674
+ fig.write_image("optimus_apr_combined_graph.png")
675
 
676
  csv_file = None
677
  return fig, csv_file
 
715
  )
716
 
717
  # Save as static file for reference
718
+ fig.write_html("optimus_roi_graph.html")
719
+ fig.write_image("optimus_roi_graph.png")
720
 
721
  csv_file = None
722
  return fig, csv_file
 
744
  )
745
  return fig
746
 
747
+ # Define fixed start date (May 15, 2025)
748
+ fixed_start_date = datetime(2025, 5, 15)
749
  logger.info(f"Using fixed start date for ROI runtime calculation: {fixed_start_date}")
750
 
751
  # Calculate runtime for each agent from fixed start date
 
805
  type="rect",
806
  fillcolor="rgba(230, 243, 255, 0.3)",
807
  line=dict(width=0),
808
+ y0=0, y1=df['roi'].max() * 1.1 if not df.empty else 10, # Dynamic positive value based on data
809
  x0=min_time, x1=max_time,
810
  layer="below"
811
  )
 
815
  type="rect",
816
  fillcolor="rgba(255, 230, 230, 0.3)",
817
  line=dict(width=0),
818
+ y0=df['roi'].min() * 1.1 if not df.empty else -10, y1=0, # Dynamic negative value based on data
819
  x0=min_time, x1=max_time,
820
  layer="below"
821
  )
 
986
  # Update layout with average runtime information in the title
987
  fig.update_layout(
988
  title=dict(
989
+ text=f"Optimus Agents ROI (over avg. {avg_runtime:.1f} days runtime)",
990
  font=dict(
991
  family="Arial, sans-serif",
992
  size=22,
 
1042
  )
1043
  )
1044
 
1045
+ # Update y-axis with fixed range for ROI (-10 to 10)
1046
  fig.update_yaxes(
1047
  showgrid=True,
1048
  gridwidth=1,
1049
  gridcolor='rgba(0,0,0,0.1)',
1050
  # Use fixed range instead of autoscaling
1051
+ range=[-10, 10], # Set fixed range from -10 to 10
 
1052
  tickformat=".2f", # Format tick labels with 2 decimal places
1053
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
1054
  title=None # Remove the built-in axis title since we're using annotations
1055
  )
1056
 
1057
+ # Update x-axis with better formatting and autoscaling
1058
  fig.update_xaxes(
1059
  showgrid=True,
1060
  gridwidth=1,
1061
  gridcolor='rgba(0,0,0,0.1)',
1062
+ # Use autoscaling instead of fixed range
1063
+ autorange=True, # Enable autoscaling
 
1064
  tickformat="%b %d", # Simplified date format without time
1065
  tickangle=-30, # Angle the labels for better readability
1066
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
 
1069
 
1070
  try:
1071
  # Save the figure
1072
+ graph_file = "optimus_roi_graph.html"
1073
  fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1074
 
1075
  # Also save as image for compatibility
1076
+ img_file = "optimus_roi_graph.png"
1077
  try:
1078
  fig.write_image(img_file)
1079
  logger.info(f"ROI graph saved to {graph_file} and {img_file}")
 
1099
  x0=min_time, x1=max_time
1100
  )
1101
 
1102
+ # Add background shapes with dynamic values
1103
+ simple_fig.add_shape(
1104
+ type="rect",
1105
+ fillcolor="rgba(230, 243, 255, 0.3)",
1106
+ line=dict(width=0),
1107
+ y0=0, y1=df['roi'].max() * 1.1 if not df.empty else 10, # Dynamic positive value based on data
1108
+ x0=min_time, x1=max_time,
1109
+ layer="below"
1110
+ )
1111
+
1112
+ simple_fig.add_shape(
1113
+ type="rect",
1114
+ fillcolor="rgba(255, 230, 230, 0.3)",
1115
+ line=dict(width=0),
1116
+ y0=df['roi'].min() * 1.1 if not df.empty else -10, y1=0, # Dynamic negative value based on data
1117
+ x0=min_time, x1=max_time,
1118
+ layer="below"
1119
+ )
1120
+
1121
+ # Add background shapes with dynamic values
1122
+ simple_fig.add_shape(
1123
+ type="rect",
1124
+ fillcolor="rgba(230, 243, 255, 0.3)",
1125
+ line=dict(width=0),
1126
+ y0=0, y1=df['roi'].max() * 1.1 if not df.empty else 10, # Dynamic positive value based on data
1127
+ x0=min_time, x1=max_time,
1128
+ layer="below"
1129
+ )
1130
+
1131
+ simple_fig.add_shape(
1132
+ type="rect",
1133
+ fillcolor="rgba(255, 230, 230, 0.3)",
1134
+ line=dict(width=0),
1135
+ y0=df['roi'].min() * 1.1 if not df.empty else -10, y1=0, # Dynamic negative value based on data
1136
+ x0=min_time, x1=max_time,
1137
+ layer="below"
1138
+ )
1139
+
1140
  # Simply plot the average ROI data with moving average
1141
  if not avg_roi_data.empty:
1142
  # Add moving average as a line
 
1153
  # Simplified layout with adjusted y-axis range
1154
  simple_fig.update_layout(
1155
  title=dict(
1156
+ text="Optimus Agents ROI",
1157
  font=dict(
1158
  family="Arial, sans-serif",
1159
  size=22,
 
1169
  margin=dict(r=30, l=120, t=40, b=50)
1170
  )
1171
 
1172
+ # Update y-axis with fixed range for ROI (-10 to 10)
1173
  simple_fig.update_yaxes(
1174
  showgrid=True,
1175
  gridwidth=1,
1176
  gridcolor='rgba(0,0,0,0.1)',
1177
+ range=[-10, 10], # Set fixed range from -10 to 10
 
1178
  tickformat=".2f",
1179
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"),
1180
  title=None # Remove the built-in axis title since we're using annotations
1181
  )
1182
 
1183
+ # Update x-axis with better formatting and autoscaling
1184
  simple_fig.update_xaxes(
1185
  showgrid=True,
1186
  gridwidth=1,
1187
  gridcolor='rgba(0,0,0,0.1)',
1188
+ autorange=True, # Enable autoscaling
 
1189
  tickformat="%b %d",
1190
  tickangle=-30,
1191
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold")
1192
  )
1193
 
1194
  # Save the figure
1195
+ graph_file = "optimus_roi_graph.html"
1196
  simple_fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1197
 
1198
  # Return the simple figure
 
1205
  return None
1206
 
1207
  # Define the CSV file path
1208
+ csv_file = "optimus_roi_values.csv"
1209
 
1210
  # Save to CSV
1211
  df.to_csv(csv_file, index=False)
 
1247
  row=row, col=1
1248
  )
1249
 
1250
+ # Add background colors with dynamic values
1251
  fig.add_shape(
1252
  type="rect", fillcolor="rgba(230, 243, 255, 0.3)", line=dict(width=0),
1253
+ y0=0, y1=agent_data['apr'].max() * 1.1 if not agent_data.empty else 10,
1254
+ x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
1255
  row=row, col=1, layer="below"
1256
  )
1257
  fig.add_shape(
1258
  type="rect", fillcolor="rgba(255, 230, 230, 0.3)", line=dict(width=0),
1259
+ y0=agent_data['apr'].min() * 1.1 if not agent_data.empty else -10, y1=0,
1260
+ x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
1261
  row=row, col=1, layer="below"
1262
  )
1263
 
 
1337
  )
1338
 
1339
  # Save the figure (still useful for reference)
1340
+ graph_file = "optimus_apr_per_agent_graph.html"
1341
  fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1342
 
1343
  # Also save as image for compatibility
1344
+ img_file = "optimus_apr_per_agent_graph.png"
1345
  fig.write_image(img_file)
1346
 
1347
  logger.info(f"Per-agent graph saved to {graph_file} and {img_file}")
 
1379
  min_time = df['timestamp'].min()
1380
  max_time = df['timestamp'].max()
1381
 
1382
+ # Use the actual start date from the data instead of a fixed date
1383
+ x_start_date = min_time
1384
 
1385
  # CRITICAL: Log the exact dataframe we're using for plotting to help debug
1386
  logger.info(f"Graph data - shape: {df.shape}, columns: {df.columns}")
 
1438
  type="rect",
1439
  fillcolor="rgba(230, 243, 255, 0.3)",
1440
  line=dict(width=0),
1441
+ y0=0, y1=apr_data['apr'].max() * 1.1 if not apr_data.empty else 10, # Dynamic positive value based on data
1442
  x0=min_time, x1=max_time,
1443
  layer="below"
1444
  )
 
1448
  type="rect",
1449
  fillcolor="rgba(255, 230, 230, 0.3)",
1450
  line=dict(width=0),
1451
+ y0=apr_data['apr'].min() * 1.1 if not apr_data.empty else -10, y1=0, # Dynamic negative value based on data
1452
  x0=min_time, x1=max_time,
1453
  layer="below"
1454
  )
 
1755
  # Make chart responsive instead of fixed width
1756
  fig.update_layout(
1757
  title=dict(
1758
+ text="Optimus Agents",
1759
  font=dict(
1760
  family="Arial, sans-serif",
1761
  size=22,
 
1836
  )
1837
  )
1838
 
1839
+ # Update y-axis with fixed range (-10 to 10)
1840
  fig.update_yaxes(
1841
  showgrid=True,
1842
  gridwidth=1,
1843
  gridcolor='rgba(0,0,0,0.1)',
1844
  # Use fixed range instead of autoscaling
1845
+ range=[-10, 10], # Set fixed range from -10 to 10
 
1846
  tickformat=".2f", # Format tick labels with 2 decimal places
1847
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
1848
  title=None # Remove the built-in axis title since we're using annotations
1849
  )
1850
 
1851
+ # Update x-axis with better formatting and autoscaling
1852
  fig.update_xaxes(
1853
  showgrid=True,
1854
  gridwidth=1,
1855
  gridcolor='rgba(0,0,0,0.1)',
1856
+ # Use autoscaling instead of fixed range
1857
+ autorange=True, # Enable autoscaling
 
1858
  tickformat="%b %d", # Simplified date format without time
1859
  tickangle=-30, # Angle the labels for better readability
1860
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
 
1868
  write_debug_info(df, fig)
1869
 
1870
  # Save the figure (still useful for reference)
1871
+ graph_file = "optimus_apr_combined_graph.html"
1872
  fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
1873
 
1874
  # Also save as image for compatibility
1875
+ img_file = "optimus_apr_combined_graph.png"
1876
  try:
1877
  fig.write_image(img_file)
1878
  logger.info(f"Combined graph saved to {graph_file} and {img_file}")
 
1999
  )
2000
  )
2001
 
2002
+ # Simplified layout with fixed y-axis range (-10 to 10) and increased size
2003
  simple_fig.update_layout(
2004
  title=dict(
2005
+ text="Optimus Agents",
2006
  font=dict(
2007
  family="Arial, sans-serif",
2008
  size=22,
 
2013
  xaxis_title=None, # Remove x-axis title to use annotation instead
2014
  yaxis_title=None, # Remove the y-axis title as we'll use annotations instead
2015
  yaxis=dict(
2016
+ # Fixed range from -10 to 10
2017
+ range=[-10, 10], # Set fixed range from -10 to 10
2018
  tickformat=".2f", # Format tick labels with 2 decimal places
2019
  tickfont=dict(size=12) # Larger font for tick labels
2020
  ),
 
2074
  )
2075
  )
2076
 
2077
+ # Apply autoscaling to the x-axis for the fallback graph
2078
  simple_fig.update_xaxes(
2079
+ autorange=True, # Enable autoscaling
 
2080
  tickformat="%b %d", # Simplified date format without time
2081
  tickangle=-30,
2082
  tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"), # Adjusted font size
 
2109
  return None
2110
 
2111
  # Define the CSV file path
2112
+ csv_file = "optimus_apr_values.csv"
2113
 
2114
  # Save to CSV
2115
  df.to_csv(csv_file, index=False)
 
2117
 
2118
  # Also generate a statistics CSV file
2119
  stats_df = generate_statistics_from_data(df)
2120
+ stats_csv = "optimus_apr_statistics.csv"
2121
  stats_df.to_csv(stats_csv, index=False)
2122
  logger.info(f"Statistics saved to {stats_csv}")
2123
 
 
2230
  )
2231
  return fig
2232
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2233
  # Dummy blockchain functions to replace the commented ones
2234
  def get_transfers(integrator: str, wallet: str) -> str:
2235
  """Dummy function that returns an empty result"""
 
2249
 
2250
  # Function to create visualizations based on the metrics
2251
  def create_visualizations():
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2252
  # Placeholder figures for testing
2253
  fig_swaps_chain = go.Figure()
2254
  fig_swaps_chain.add_annotation(
 
2283
  # Modify dashboard function to make the plot container responsive
2284
  def dashboard():
2285
  with gr.Blocks() as demo:
2286
+ gr.Markdown("# Average Optimus Agent Performance")
2287
 
2288
  # Create tabs for APR and ROI metrics
2289
  with gr.Tabs():
app_trans_new.py DELETED
@@ -1,570 +0,0 @@
1
- import requests
2
- import pandas as pd
3
- import gradio as gr
4
- import plotly.express as px
5
- from datetime import datetime, timedelta
6
- import plotly.graph_objects as go
7
- import numpy as np
8
- import json
9
- from web3 import Web3
10
- import time
11
- import os
12
- from itertools import product
13
- # RPC URLs
14
- from dotenv import load_dotenv
15
- load_dotenv()
16
-
17
- OPTIMISM_RPC_URL = os.getenv('OPTIMISM_RPC_URL')
18
- BASE_RPC_URL = os.getenv('BASE_RPC_URL')
19
- ETH_RPC_URL = os.getenv('ETH_RPC_URL')
20
- MODE_RPC_URL = os.getenv('MODE_RPC_URL')
21
-
22
- print(f"Optimism RPC URL: {OPTIMISM_RPC_URL}")
23
-
24
- # Initialize Web3 instances
25
- print("Initializing Web3 instances...")
26
- web3_optimism = Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL))
27
- web3_base = Web3(Web3.HTTPProvider(BASE_RPC_URL))
28
- web3_eth = Web3(Web3.HTTPProvider(ETH_RPC_URL))
29
- web3_mode = Web3(Web3.HTTPProvider(MODE_RPC_URL)) # New chain
30
-
31
- # Contract addresses for service registries
32
- contract_address_optimism = '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44'
33
- contract_address_base = '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'
34
- contract_address_eth = '0x48b6af7B12C71f09e2fC8aF4855De4Ff54e775cA'
35
- contract_address_mode = '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE' # New chain
36
-
37
- # Load the ABI from a local JSON file
38
- with open('./contracts/service_registry_abi.json', 'r') as abi_file:
39
- contract_abi = json.load(abi_file)
40
-
41
- # Create the contract instances
42
- service_registry_optimism = web3_optimism.eth.contract(address=contract_address_optimism, abi=contract_abi)
43
- service_registry_base = web3_base.eth.contract(address=contract_address_base, abi=contract_abi)
44
- service_registry_eth = web3_eth.eth.contract(address=contract_address_eth, abi=contract_abi)
45
- service_registry_mode = web3_mode.eth.contract(address=contract_address_mode, abi=contract_abi) # New chain
46
- print("Service registry contracts loaded.")
47
-
48
- # Check if connection is successful
49
- if not web3_optimism.is_connected():
50
- raise Exception("Failed to connect to the Optimism network.")
51
- if not web3_base.is_connected():
52
- raise Exception("Failed to connect to the Base network.")
53
- if not web3_eth.is_connected():
54
- raise Exception("Failed to connect to the ETH network.")
55
- if not web3_mode.is_connected():
56
- raise Exception("Failed to connect to the Mode network.") # New chain
57
- print("Successfully connected to Ethereum, Optimism, Base, and Mode networks.")
58
-
59
-
60
-
61
- def fetch_service_safes(web3, registry_contract):
62
- print("\nFetching service safes...")
63
- total_services = registry_contract.functions.totalSupply().call()
64
- print(f"Total services: {total_services}")
65
- service_safes_dict = {}
66
- zero_address = '0x0000000000000000000000000000000000000000'
67
-
68
- for service_id in range(1, total_services + 1):
69
- print(f"Processing service ID: {service_id}")
70
- service = registry_contract.functions.getService(service_id).call()
71
- agent_ids = service[-1] # Assuming the last element is the list of agent IDs
72
- print(f"Agent IDs: {agent_ids}")
73
-
74
- if 40 in agent_ids or 25 in agent_ids:
75
- agent_instance_data = registry_contract.functions.getAgentInstances(service_id).call()
76
- service_safe = service[1]
77
- agent_addresses = agent_instance_data[1]
78
- if agent_addresses:
79
- agent_address = agent_addresses[0]
80
- if service_safe != zero_address:
81
- print(f"Found agent_address: {agent_address}")
82
- print(f"Found service safe: {service_safe}")
83
- service_safes_dict[agent_address] = service_safe
84
- else:
85
- print(f"Found zero address for service safe, skipping for agent: {agent_address}")
86
- else:
87
- print(f"No agent address found for service ID: {service_id}")
88
-
89
- print(f"Total unique agent addresses found (excluding zero safe addresses): {len(service_safes_dict)}")
90
- return service_safes_dict
91
-
92
- # Fetch service safes for each network
93
- service_safes_optimism = fetch_service_safes(web3_optimism, service_registry_optimism)
94
- service_safes_base = fetch_service_safes(web3_base, service_registry_base)
95
- service_safes_eth = fetch_service_safes(web3_eth, service_registry_eth)
96
- service_safes_mode = fetch_service_safes(web3_mode, service_registry_mode)
97
- # service_safes_eth = {safe for safe in service_safes_eth if safe.lower() != '0x0000000000000000000000000000000000000000'}
98
- print(f"Service safes for Mode: {service_safes_mode}")
99
- def get_block_range_for_date(chain_id, date_str, api_key, base_url):
100
- """Get the block range for a specific date."""
101
- target_date = datetime.strptime(date_str, "%Y-%m-%d").date()
102
- start_of_day = datetime.combine(target_date, datetime.min.time())
103
-
104
- if target_date == datetime.now().date():
105
- end_of_day = datetime.now() # Use the current time if the target date is today
106
- else:
107
- end_of_day = datetime.combine(target_date, datetime.max.time())
108
-
109
- start_timestamp = int(start_of_day.timestamp())
110
- end_timestamp = int(end_of_day.timestamp())
111
-
112
- # Get start block
113
- start_response = requests.get(
114
- f"{base_url}?module=block&action=getblocknobytime&timestamp={start_timestamp}&closest=before&apikey={api_key}"
115
- )
116
- if start_response.status_code == 200:
117
- start_data = start_response.json()
118
- start_block = start_data.get('result')
119
- else:
120
- print(f"Error fetching start block for {date_str} on chain {chain_id}")
121
- return None, None
122
-
123
- if start_block is None:
124
- print(f"No start block found for chain {chain_id} on {date_str}")
125
- return None, None
126
- print(f"Start block for chain {chain_id} on {date_str}: {start_block}")
127
-
128
- # Get end block
129
- time.sleep(1)
130
- end_response = requests.get(
131
- f"{base_url}?module=block&action=getblocknobytime&timestamp={end_timestamp}&closest=before&apikey={api_key}"
132
- )
133
- if end_response.status_code == 200:
134
- end_data = end_response.json()
135
- end_block = end_data.get('result')
136
- else:
137
- print(f"Error fetching end block for {date_str} on chain {chain_id}")
138
- return None, None
139
-
140
- if end_block is None:
141
- print(f"No end block found for chain {chain_id} on {date_str}")
142
- return None, None
143
- print(f"End block for chain {chain_id} on {date_str}: {end_block}")
144
-
145
- return start_block, end_block
146
-
147
- def get_transactions_mode_all(address):
148
- url = f'https://explorer.mode.network/api/v2/addresses/{address}/transactions'
149
- headers = {
150
- 'accept': 'application/json'
151
- }
152
-
153
- response = requests.get(url, headers=headers)
154
- return response.json()['items']
155
-
156
- def get_transactions(api_keys, wallet_address, chain_name, start_block, end_block):
157
- """Retrieve transactions for the given wallet address, chain, and block range using the Etherscan or similar API."""
158
- base_url = {
159
- 'optimism': "https://api-optimistic.etherscan.io/api",
160
- 'base': "https://api.basescan.org/api",
161
- 'ethereum': "https://api.etherscan.io/api",
162
- 'mode': "https://eth-sepolia.blockscout.com/api"
163
- }.get(chain_name)
164
-
165
- if not base_url:
166
- print(f"Invalid chain name: {chain_name}")
167
- return []
168
-
169
- params = {
170
- 'module': 'account',
171
- 'action': 'txlist',
172
- 'address': wallet_address,
173
- 'startblock': start_block,
174
- 'endblock': end_block,
175
- 'sort': 'asc',
176
- 'apikey': api_keys.get(chain_name)
177
- }
178
-
179
- response = requests.get(base_url, params=params)
180
- data = response.json()
181
-
182
- time.sleep(1)
183
-
184
- if data['status'] != '1':
185
- print(f"Error: {data['message']}")
186
- return []
187
-
188
- valid_transactions = [tx for tx in data['result'] if tx['isError'] == '0']
189
-
190
- return valid_transactions
191
-
192
- def get_mode_transactions(transactions_all_base,date_str):
193
- target_date = datetime.strptime(date_str, "%Y-%m-%d").date()
194
- start_of_day = datetime.combine(target_date, datetime.min.time())
195
-
196
- if target_date == datetime.now().date():
197
- end_of_day = datetime.now() # Use the current time if the target date is today
198
- else:
199
- end_of_day = datetime.combine(target_date, datetime.max.time())
200
-
201
- start_timestamp = start_of_day.isoformat()
202
- end_timestamp = end_of_day.isoformat()
203
- processed_list = []
204
- for transaction in transactions_all_base:
205
- # Filter transactions based on the timestamp
206
- if start_timestamp <= transaction['timestamp'] <= end_timestamp:
207
- transaction_timestamp = datetime.strptime(transaction['timestamp'], "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%Y-%m-%d %H:%M:%S")
208
- processed_transaction = {
209
- 'hash': transaction['hash'],
210
- 'timeStamp': transaction_timestamp,
211
- 'from': transaction['from']['hash'],
212
- 'to': transaction['to']['hash'],
213
- 'value': transaction['value']
214
- }
215
- processed_list.append(processed_transaction)
216
-
217
- return processed_list
218
-
219
-
220
- def date_range(start_date, end_date):
221
- """Generates a range of dates from start_date to end_date inclusive."""
222
- start_dt = datetime.strptime(start_date, "%Y-%m-%d")
223
- end_dt = datetime.strptime(end_date, "%Y-%m-%d")
224
- delta = timedelta(days=1)
225
- current_dt = start_dt
226
- while current_dt <= end_dt:
227
- yield current_dt.strftime("%Y-%m-%d")
228
- current_dt += delta
229
-
230
- def fetch_transactions():
231
- api_keys = {
232
- 'optimism': 'XQ72JA5XZ51QC7TG1W295AAIF4KTV92K1K',
233
- 'base': '4BFQMVW1QUKEPVDA4VW711CF4462682CY8',
234
- 'ethereum': '3GRYJGX55W3QWCEKGREF4H53AFHCAIVVR7',
235
- 'mode': 'f651444d-2916-4f37-bfd8-e70cc8232eb7'
236
- }
237
-
238
- base_urls = {
239
- 10: "https://api-optimistic.etherscan.io/api",
240
- 8453: "https://api.basescan.org/api",
241
- 1: "https://api.etherscan.io/api",
242
- 3443: "https://eth-sepolia.blockscout.com/api"
243
- }
244
-
245
- current_date = datetime.today().strftime("%Y-%m-%d")
246
- csv_filename = 'daily_transactions_new.csv'
247
-
248
- if os.path.exists(csv_filename):
249
- df_existing = pd.read_csv(csv_filename)
250
- if 'date' in df_existing:
251
- last_date_in_csv = df_existing['date'].max()
252
- else:
253
- df_existing['date'] = pd.to_datetime(df_existing['timestamp']).dt.date
254
- last_date_in_csv = df_existing['date'].max()
255
- else:
256
- df_existing = pd.DataFrame()
257
- last_date_in_csv = '2024-09-19'
258
-
259
- start_date = (datetime.strptime(last_date_in_csv, "%Y-%m-%d") + timedelta(days=1)).strftime("%Y-%m-%d")
260
-
261
- chains = {
262
- 10: ('optimism', service_safes_optimism),
263
- 8453: ('base', service_safes_base),
264
- 1: ('ethereum', service_safes_eth),
265
- 3443: ('mode', service_safes_mode)
266
- }
267
-
268
- all_transactions = df_existing.to_dict('records') if not df_existing.empty else []
269
-
270
- for chain_id, (chain_name, service_safes) in chains.items():
271
- base_url = base_urls[chain_id]
272
- api_key = api_keys[chain_name]
273
-
274
- for agent_address, safe_address in service_safes.items():
275
- print(f"\nProcessing {chain_name.capitalize()} for agent address {agent_address} (safe address {safe_address})...")
276
- if chain_name == 'mode':
277
- transactions_all_base = get_transactions_mode_all(safe_address)
278
-
279
- for single_date in date_range(start_date, current_date):
280
- if chain_name == 'mode':
281
- print(f"Processing date {single_date} for chain {chain_name}...")
282
- transactions = get_mode_transactions(transactions_all_base,single_date)
283
-
284
- else:
285
- print(f"Processing date {single_date} for chain {chain_name}...")
286
- start_block, end_block = get_block_range_for_date(chain_id, single_date, api_key, base_url)
287
- if start_block is None or end_block is None:
288
- print(f"Skipping date {single_date} for chain {chain_name} due to missing block data.")
289
- continue
290
- print(f"Start Block: {start_block}, End Block: {end_block} for date {single_date}")
291
- transactions = get_transactions(api_keys, safe_address, chain_name, start_block, end_block)
292
-
293
- if transactions:
294
- print(f"Found {len(transactions)} transactions on {single_date} for {chain_name.capitalize()} safe address {safe_address}:")
295
- for tx in transactions:
296
- if chain_name != 'mode':
297
- print(f"Transaction Hash: {tx['hash']} on {chain_name.capitalize()} at {tx['timeStamp']}")
298
- tx_time = datetime.fromtimestamp(int(tx['timeStamp']))
299
- all_transactions.append({
300
- 'chain': chain_name,
301
- 'agent_address': agent_address,
302
- 'safe_address': safe_address,
303
- 'date': single_date,
304
- 'transaction_hash': tx['hash'],
305
- 'timestamp': tx_time,
306
- 'from': tx['from'],
307
- 'to': tx['to'],
308
- 'value_eth': int(tx['value']) / 1e18 # Convert value to ETH
309
- })
310
- else:
311
- all_transactions.append({
312
- 'chain': chain_name,
313
- 'agent_address': agent_address,
314
- 'safe_address': safe_address,
315
- 'date': single_date,
316
- 'transaction_hash': tx['hash'],
317
- 'timestamp': tx['timeStamp'],
318
- 'from': tx['from'],
319
- 'to': tx['to'],
320
- 'value_eth': int(tx['value']) / 1e18 # Convert value to ETH
321
- })
322
-
323
- else:
324
- print(f"No transactions found for agent address {agent_address} (safe address {safe_address}) on {single_date} on {chain_name.capitalize()}.")
325
-
326
- df_transactions_new = pd.DataFrame(all_transactions)
327
- df_transactions_new.to_csv(csv_filename, index=False)
328
- return df_transactions_new
329
-
330
-
331
- def create_transcation_visualizations():
332
- df_transactions_new = fetch_transactions()
333
- df_transactions_new['timestamp'] = pd.to_datetime(df_transactions_new['timestamp'])
334
-
335
- # Group by date and chain, count transactions
336
- daily_counts = df_transactions_new.groupby([df_transactions_new['timestamp'].dt.date, 'chain']).size().unstack(fill_value=0)
337
-
338
- # Ensure required chains are present
339
- chains = ['optimism', 'base', 'ethereum', 'mode']
340
- for chain in chains:
341
- if chain not in daily_counts.columns:
342
- daily_counts[chain] = 0
343
-
344
- daily_counts = daily_counts[chains]
345
-
346
- # Read the original data
347
- daily_counts['timestamp'] = daily_counts.index
348
- daily_counts['timestamp'] = pd.to_datetime(daily_counts['timestamp'])
349
- daily_counts = daily_counts.reset_index(drop=True)
350
- # Get min and max dates
351
- min_date = daily_counts['timestamp'].min()
352
- max_date = daily_counts['timestamp'].max()
353
-
354
- # Create complete date range
355
- full_date_range = pd.date_range(start=min_date, end=max_date, freq='D')
356
-
357
- # Create a new dataframe with all dates
358
- complete_df = pd.DataFrame({'timestamp': full_date_range})
359
- complete_df = complete_df.merge(daily_counts, on='timestamp', how='left')
360
- complete_df = complete_df.fillna(0)
361
- daily_counts = complete_df
362
- # Convert timestamp to datetime
363
- daily_counts['timestamp'] = pd.to_datetime(daily_counts['timestamp'])
364
-
365
- # Create a new dataframe with 12-hour slots
366
- new_rows = []
367
-
368
- for _, row in daily_counts.iterrows():
369
- # Create first 12-hour slot (0-12)
370
- slot1 = row.copy()
371
- # slot1['timestamp'] = row['timestamp'].replace(hour=0)
372
-
373
- # # Create second 12-hour slot (12-24)
374
- new_rows.extend([slot1])
375
- slot2 = row.copy()
376
- if slot2['timestamp'].dayofweek == 6: # 6 represents Sunday
377
- slot2[chains] = 0
378
- slot2['timestamp'] = row['timestamp'].replace(hour=12)
379
- new_rows.extend([slot2])
380
-
381
-
382
-
383
- # Create new dataframe with 12-hour slots
384
- hourly_counts = pd.DataFrame(new_rows).sort_values('timestamp')
385
-
386
- # Prepare data for plotting
387
- dates = hourly_counts['timestamp'].tolist()
388
- values = hourly_counts[chains].to_numpy()
389
-
390
- # Create the figure
391
- fig = go.Figure()
392
- # Create arrays for positioning and width
393
- # Convert dates to datetime objects first
394
- date_objects = pd.to_datetime(dates)
395
-
396
- # Create numeric indices for x-axis
397
- x_numeric = np.arange(len(dates))
398
-
399
- # Create arrays for positioning and width
400
- width_array = []
401
-
402
- for i, date in enumerate(date_objects):
403
- width_array.append(1.0) # Full width for other days
404
-
405
- # Get Monday indices for tick positions
406
- monday_indices = [i for i, date in enumerate(date_objects) if date.dayofweek == 0]
407
- monday_labels = [date_objects[i].strftime('%m-%d') for i in monday_indices]
408
-
409
- # Add traces for each series
410
- fig.add_trace(go.Bar(
411
- name='Optimism',
412
- x=x_numeric,
413
- y=values[:,0],
414
- marker_color='blue',
415
- opacity=0.7,
416
- text=None,
417
- width=width_array,
418
- textposition='none',
419
- ))
420
-
421
- fig.add_trace(go.Bar(
422
- name='Base',
423
- x=x_numeric,
424
- y=values[:,1],
425
- marker_color='purple',
426
- opacity=0.7,
427
- text=None,
428
- textposition='none',
429
- width=width_array,
430
- ))
431
-
432
- fig.add_trace(go.Bar(
433
- name='Ethereum',
434
- x=x_numeric,
435
- y=values[:,2],
436
- marker_color='darkgreen',
437
- opacity=0.7,
438
- text=None,
439
- width=width_array,
440
- textposition='none',
441
- ))
442
-
443
- fig.add_trace(go.Bar(
444
- name='Mode',
445
- x=x_numeric,
446
- y=values[:,3],
447
- marker_color='orange',
448
- opacity=0.7,
449
- text=None,
450
- width=width_array,
451
- textposition='none',
452
- ))
453
-
454
- # Update layout with numeric x-axis
455
- fig.update_layout(
456
- title='Chain Daily Activity : Transactions',
457
- xaxis_title='Date',
458
- yaxis_title='Daily Transactions Count',
459
- barmode='stack',
460
- showlegend=True,
461
- legend_title_text='Transaction Chain',
462
- height=600,
463
- bargap=0,
464
- bargroupgap=0,
465
- xaxis=dict(
466
- tickangle=-45,
467
- tickmode='array',
468
- ticktext=monday_labels,
469
- tickvals=monday_indices,
470
- ),
471
- template='plotly_white',
472
- hoverlabel=dict(
473
- font_size=12,
474
- ),
475
- )
476
-
477
- # Update hover template
478
- for trace in fig.data:
479
- trace.update(
480
- hovertemplate="<b>Date:</b> %{text}<br>" +
481
- "<b>" + trace.name + ":</b> %{y}<br>" +
482
- "<extra></extra>",
483
- text=[d.strftime('%Y-%m-%d') for d in date_objects] # Add date text for hover
484
- )
485
-
486
- # Show the plot
487
- return fig
488
-
489
- def create_active_agents_visualizations():
490
- df_transactions_new = fetch_transactions()
491
- df_transactions_new['timestamp'] = pd.to_datetime(df_transactions_new['timestamp'])
492
- # Extract week and day information
493
- df_transactions_new['week_start'] = df_transactions_new['timestamp'].dt.to_period('W').apply(lambda r: r.start_time)
494
- df_transactions_new['weekday'] = df_transactions_new['timestamp'].dt.weekday
495
-
496
- # Count unique agents per day
497
- daily_agents = df_transactions_new.groupby(['week_start', 'weekday'])['agent_address'].nunique().reset_index()
498
-
499
- # Generate all possible combinations of week_start and weekday up to today
500
- today = datetime.today()
501
- yesterday = today - timedelta(days=1)
502
- valid_weekdays = [(ws, wd) for ws, wd in product(daily_agents['week_start'].unique(), range(7)) if ws + timedelta(days=wd) <= yesterday]
503
-
504
- all_combinations = pd.DataFrame(valid_weekdays, columns=['week_start', 'weekday'])
505
-
506
- # Merge with daily_agents to fill missing days with zero
507
- daily_agents = all_combinations.merge(daily_agents, on=['week_start', 'weekday'], how='left').fillna(0)
508
-
509
- # Compute average unique agents per week
510
- weekly_avg_agents = daily_agents.groupby('week_start')['agent_address'].mean().reset_index()
511
- weekly_avg_agents.rename(columns={'agent_address': 'avg_daily_active_agents'}, inplace=True)
512
-
513
- # Prepare data for plotting
514
- weeks = weekly_avg_agents['week_start'].unique()
515
- avg_agents_per_week = weekly_avg_agents['avg_daily_active_agents']
516
-
517
- # Create the bar chart
518
- fig = go.Figure()
519
-
520
- fig.add_trace(go.Bar(
521
- x=[f'{week.strftime("%b %d")}' for week in weeks],
522
- y=avg_agents_per_week,
523
-
524
- marker_color='blue',
525
- opacity=0.7,
526
- text=None,
527
- hoverlabel=dict(
528
- font_size=12,
529
- ),
530
- ))
531
-
532
- # Update layout
533
- fig.update_layout(
534
- title='Daily Active Agents: Weekly Average Number of Agents with at Least 1 Transaction Daily',
535
- xaxis_title='Week',
536
- yaxis_title='Average Number of Active Agents',
537
- xaxis=dict(
538
- tickangle=-45 # Rotate x-axis labels to 45 degrees
539
- ),
540
- height=600,
541
- width=1000,
542
- bargap=0,
543
- bargroupgap=0.2,
544
- template='plotly_white'
545
- )
546
-
547
- return fig
548
-
549
-
550
- # Gradio interface
551
- def dashboard():
552
- with gr.Blocks() as demo:
553
- gr.Markdown("# Valory Transactions Dashboard")
554
-
555
- # Fetch and display visualizations
556
- with gr.Tab("Transactions"):
557
- fig_tx_chain = create_transcation_visualizations()
558
- gr.Plot(fig_tx_chain)
559
- with gr.Tab("DAA"):
560
- fig_active_agents = create_active_agents_visualizations()
561
- gr.Plot(fig_active_agents)
562
-
563
- # Add more tabs as needed...
564
-
565
- return demo
566
-
567
-
568
- # Launch the dashboard
569
- if __name__ == "__main__":
570
- dashboard().launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app_value_locked.py DELETED
@@ -1,405 +0,0 @@
1
- import json
2
- import requests
3
- from web3 import Web3
4
- from datetime import datetime, timedelta
5
- from typing import List, Dict, Optional
6
- import pandas as pd
7
- import time
8
- import requests
9
- from requests.adapters import HTTPAdapter
10
- from requests.packages.urllib3.util.retry import Retry
11
- import time
12
- import os
13
-
14
- ADDRESSES = {
15
- "optimism": {
16
- "balancer_vault": "0xBA12222222228d8Ba445958a75a0704d566BF2C8",
17
- "uniswap_position_manager": "0xC36442b4a4522E871399CD717aBDD847Ab11FE88"
18
- },
19
- "base": {
20
- "balancer_vault": "0xBA12222222228d8Ba445958a75a0704d566BF2C8",
21
- "uniswap_position_manager": "0x03a520b32C04BF3bEEf7BEb72E919cf822Ed34f1"
22
- }
23
- }
24
-
25
- # Defining RPC URLs and initializing Web3 instances
26
- OPTIMISM_RPC_URL = os.getenv('OPTIMISM_RPC_URL')
27
- BASE_RPC_URL = os.getenv('BASE_RPC_URL')
28
- ETH_RPC_URL = os.getenv('ETH_RPC_URL')
29
-
30
- print("Initializing Web3 instances...")
31
- web3_optimism = Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL))
32
- web3_base = Web3(Web3.HTTPProvider(BASE_RPC_URL))
33
-
34
- # Contract addresses for service registries
35
- contract_address_optimism = '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44'
36
- contract_address_base = '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'
37
-
38
- # Load the ABI from a local JSON file
39
- with open('./contracts/service_registry_abi.json', 'r') as abi_file:
40
- contract_abi = json.load(abi_file)
41
-
42
- # Create the contract instances
43
- service_registry_optimism = web3_optimism.eth.contract(address=contract_address_optimism, abi=contract_abi)
44
- service_registry_base = web3_base.eth.contract(address=contract_address_base, abi=contract_abi)
45
- print("Service registry contracts loaded.")
46
-
47
- def load_abi(filename):
48
- with open(filename, 'r') as file:
49
- contract_json = json.load(file)
50
- return contract_json['abi']
51
-
52
- UNISWAP_ABI = load_abi('./contracts/NonfungiblePositionManager.json')
53
-
54
- def get_logs(api_key, chain_name, from_block, to_block, contract_address, service_safe):
55
- """Fetch logs for the given contract and wallet address with specified topics."""
56
- base_url = {
57
- 'optimism': "https://api-optimistic.etherscan.io/api",
58
- 'base': "https://api.basescan.org/api"
59
- }.get(chain_name)
60
-
61
- if not base_url:
62
- print(f"Invalid chain name: {chain_name}")
63
- return []
64
- print('formatted safe address for topic', f"0x000000000000000000000000{service_safe[2:].lower()}")
65
- params = {
66
- 'module': 'logs',
67
- 'action': 'getLogs',
68
- 'address': contract_address,
69
- 'fromBlock': from_block,
70
- 'toBlock': to_block,
71
- 'apikey': api_key,
72
- 'topic2': f"0x000000000000000000000000{service_safe[2:].lower()}" # Properly formatted topic2
73
- }
74
-
75
- response = requests.get(base_url, params=params)
76
- data = response.json()
77
-
78
- if data['status'] != '1':
79
- print(f"Error: {data['message']}")
80
- return []
81
-
82
- return data['result']
83
-
84
- def get_block_range_for_date(chain_id, date_str, api_key, base_url):
85
- """Get the block range for a specific date."""
86
- target_date = datetime.strptime(date_str, "%Y-%m-%d")
87
- start_of_day = datetime.combine(target_date, datetime.min.time())
88
- end_of_day = datetime.combine(target_date, datetime.max.time())
89
-
90
- start_timestamp = int(start_of_day.timestamp())
91
- end_timestamp = int(end_of_day.timestamp())
92
-
93
- # Get start block
94
- start_response = requests.get(
95
- f"{base_url}?module=block&action=getblocknobytime&timestamp={start_timestamp}&closest=before&apikey={api_key}"
96
- )
97
- if start_response.status_code == 200:
98
- start_data = start_response.json()
99
- start_block = start_data.get('result')
100
- else:
101
- print(f"Error fetching start block for {date_str} on chain {chain_id}")
102
- return None, None
103
-
104
- if start_block is None:
105
- print(f"No start block found for chain {chain_id} on {date_str}")
106
- return None, None
107
- print(f"Start block for chain {chain_id} on {date_str}: {start_block}")
108
-
109
- # Get end block
110
- end_response = requests.get(
111
- f"{base_url}?module=block&action=getblocknobytime&timestamp={end_timestamp}&closest=before&apikey={api_key}"
112
- )
113
- if end_response.status_code == 200:
114
- end_data = end_response.json()
115
- end_block = end_data.get('result')
116
- else:
117
- print(f"Error fetching end block for {date_str} on chain {chain_id}")
118
- return None, None
119
-
120
- if end_block is None:
121
- print(f"No end block found for chain {chain_id} on {date_str}")
122
- return None, None
123
- print(f"End block for chain {chain_id} on {date_str}: {end_block}")
124
-
125
- return start_block, end_block
126
-
127
- def date_range(start_date, end_date):
128
- """Generates a range of dates from start_date to end_date inclusive."""
129
- start_dt = datetime.strptime(start_date, "%Y-%m-%d")
130
- end_dt = datetime.strptime(end_date, "%Y-%m-%d")
131
- delta = timedelta(days=1)
132
- current_dt = start_dt
133
- while current_dt <= end_dt:
134
- yield current_dt.strftime("%Y-%m-%d")
135
- current_dt += delta
136
-
137
- def parse_transfer_log(chain_name, single_date, log):
138
- # ERC-721 Transfer event signature
139
- TRANSFER_EVENT_SIGNATURE = "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef"
140
-
141
- if log['topics'][0].lower() == TRANSFER_EVENT_SIGNATURE.lower():
142
- # This is a Transfer event
143
- return {
144
- "chain_name": chain_name,
145
- "date": single_date,
146
- "event": "Transfer",
147
- "topic_0": log['topics'][0],
148
- "from": "0x" + log['topics'][1][-40:], # Extract the address from the padded topic
149
- "to": "0x" + log['topics'][2][-40:], # Extract the address from the padded topic
150
- "token_id": int(log['topics'][3], 16) # Convert hex to decimal
151
- }
152
-
153
- # If no Transfer event is found
154
- return None
155
-
156
- def parse_pool_balance_log(chain_name, single_date, log):
157
- POOL_BALANCE_SIGNATURE = "0xe5ce249087ce04f05a957192435400fd97868dba0e6a4b4c049abf8af80dae78"
158
- if log['topics'][0].lower() == POOL_BALANCE_SIGNATURE.lower():
159
- # Event PoolBalanceChanged: (bytes32 poolId, address liquidityProvider, address[] tokens, int256[] deltas, uint256[] protocolFeeAmounts)
160
- return {
161
- "chain_name": chain_name,
162
- "date": single_date,
163
- "event": "PoolBalanceChanged",
164
- "poolId": log['topics'][1],
165
- "liquidityProvider": "0x" + log['topics'][2][-40:], # Extract the address from the padded topic
166
- "tokens": [log['data'][i:i + 64] for i in range(0, len(log['data']), 64)],
167
- "deltas": [int(log['data'][i:i + 64], 16) for i in range(0, len(log['data']), 64)],
168
- "protocolFeeAmounts": [int(log['data'][i:i + 64], 16) for i in range(0, len(log['data']), 64)]
169
- }
170
-
171
- def fetch_service_safes(web3, registry_contract):
172
- print("\nFetching service safes...")
173
- total_services = registry_contract.functions.totalSupply().call()
174
- print(f"Total services: {total_services}")
175
- service_safes = set()
176
-
177
- for service_id in range(1, total_services + 1):
178
- service = registry_contract.functions.getService(service_id).call()
179
- agent_ids = service[-1] # Assuming the last element is the list of agent IDs
180
-
181
- if 40 in agent_ids:
182
- service_safe = service[1]
183
- service_safes.add(service_safe)
184
-
185
- print(f"Total service safes found: {len(service_safes)}")
186
- return service_safes
187
-
188
- def get_uniswap_v3_position(web3, contract_address, token_id):
189
- """Fetch the Uniswap V3 position details including `token0`, `token1`."""
190
- position_manager_contract = web3.eth.contract(address=contract_address, abi=UNISWAP_ABI)
191
- position_data = position_manager_contract.functions.positions(token_id).call()
192
- return {
193
- 'token0': position_data[2],
194
- 'token1': position_data[3]
195
- }
196
-
197
- def get_uniswap_increase_liquidity(api_key, chain_name, token_id):
198
- """Fetch the IncreaseLiquidity event details including `amount0`, `amount1`."""
199
- base_url = {
200
- 'optimism': "https://api-optimistic.etherscan.io/api",
201
- 'base': "https://api.basescan.org/api"
202
- }.get(chain_name)
203
-
204
- if not base_url:
205
- print(f"Invalid chain name: {chain_name}")
206
- return {}
207
-
208
- params = {
209
- 'module': 'logs',
210
- 'action': 'getLogs',
211
- 'address': ADDRESSES[chain_name]['uniswap_position_manager'],
212
- 'topic0': "0x3067048beee31b25b2f1681f88dac838c8bba36af25bfb2b7cf7473a5847e35f",
213
- 'topic1': f"0x{token_id:064x}",
214
- 'apikey': api_key
215
- }
216
-
217
- response = requests.get(base_url, params=params)
218
- data = response.json()
219
-
220
- if data['status'] != '1':
221
- print(f"Error: {data['message']}")
222
- return {}
223
-
224
- log = data['result'][0] if data['result'] else None
225
- if not log:
226
- return {}
227
-
228
- # Extracting amounts from the data hex-string
229
- data_hex = log['data'][2:] # Remove '0x' prefix
230
- liquidity = int(data_hex[0:64], 16)
231
- amount0 = int(data_hex[64:128], 16)
232
- amount1 = int(data_hex[128:192], 16)
233
-
234
- return {
235
- 'liquidity': liquidity,
236
- 'amount0': amount0,
237
- 'amount1': amount1
238
- }
239
-
240
- def get_token_decimals(web3, token_address):
241
- """Fetch the number of decimals for a given ERC-20 token."""
242
- token_contract = web3.eth.contract(address=token_address, abi=[
243
- {"constant":True,"inputs":[],"name":"decimals","outputs":[{"name":"","type":"uint8"}],"payable":False,"stateMutability":"view","type":"function"}
244
- ])
245
- return token_contract.functions.decimals().call()
246
-
247
-
248
-
249
- def requests_retry_session(
250
- retries=3,
251
- backoff_factor=0.3,
252
- status_forcelist=(500, 502, 504),
253
- session=None,
254
- ):
255
- session = session or requests.Session()
256
- retry = Retry(
257
- total=retries,
258
- read=retries,
259
- connect=retries,
260
- backoff_factor=backoff_factor,
261
- status_forcelist=status_forcelist,
262
- )
263
- adapter = HTTPAdapter(max_retries=retry)
264
- session.mount('http://', adapter)
265
- session.mount('https://', adapter)
266
- return session
267
-
268
- def get_token_price_usd(chain, token_address):
269
- chain_dict = {"optimism": "optimistic-ethereum", "base": "base", "ethereum": "ethereum"}
270
- chain_name = chain_dict.get(chain, chain)
271
- time.sleep(4)
272
- url = f"https://api.coingecko.com/api/v3/simple/token_price/{chain_name}?contract_addresses={token_address}&vs_currencies=usd"
273
-
274
- headers = {
275
- "accept": "application/json",
276
- "x-cg-api-key": "CG-mf5xZnGELpSXeSqmHDLY2nNU"
277
- }
278
-
279
- try:
280
- response = requests_retry_session().get(url, headers=headers, timeout=10)
281
- response.raise_for_status()
282
- data = response.json()
283
-
284
- # Extract USD price
285
- key = token_address.lower()
286
- return data.get(key, {}).get('usd', None)
287
- except requests.exceptions.RequestException as e:
288
- print(f"An error occurred: {e}")
289
- return None
290
-
291
- def load_existing_transactions(file_path):
292
- """Load existing transactions from a CSV file."""
293
- try:
294
- return pd.read_csv(file_path)
295
- except FileNotFoundError:
296
- return pd.DataFrame()
297
-
298
- def get_last_processed_date(df):
299
- """Get the last processed date from the DataFrame."""
300
- if df.empty:
301
- return None
302
- return df['date'].max()
303
-
304
- # Main function to integrate the above based on provided context
305
- def fetch_daily_value_locked():
306
- api_keys = {
307
- 'optimism': 'XQ72JA5XZ51QC7TG1W295AAIF4KTV92K1K',
308
- 'base': '4BFQMVW1QUKEPVDA4VW711CF4462682CY8'
309
- }
310
-
311
- base_urls = {
312
- 10: "https://api-optimistic.etherscan.io/api",
313
- 8453: "https://api.basescan.org/api"
314
- }
315
-
316
- # Load existing transactions if any
317
- existing_transactions_path = 'daily_value_locked.csv'
318
- df_existing_transactions = load_existing_transactions(existing_transactions_path)
319
- last_processed_date = get_last_processed_date(df_existing_transactions)
320
-
321
- # Determine the start date based on the last processed date
322
- start_date = (pd.to_datetime(last_processed_date) + timedelta(days=1)).strftime('%Y-%m-%d') if last_processed_date else '2024-09-19'
323
- current_date = datetime.now().strftime('%Y-%m-%d') # Till present date
324
-
325
- chains = {
326
- 10: ('optimism', 'uniswap_position_manager', 'balancer_vault'),
327
- 8453: ('base', 'uniswap_position_manager', 'balancer_vault')
328
- }
329
-
330
- # Example service safe addresses - Replace these with actual addresses
331
- print("Fetching service safes for Optimism...")
332
- service_safes_optimism = fetch_service_safes(web3_optimism, service_registry_optimism)
333
- print(service_safes_optimism)
334
- print("Fetching service safes for Base...")
335
- service_safes_base = fetch_service_safes(web3_base, service_registry_base)
336
- print(service_safes_base)
337
-
338
- service_safes = {
339
- 'optimism': service_safes_optimism,
340
- 'base': service_safes_base
341
- }
342
-
343
- all_transactions = [] # List to hold all parsed logs
344
-
345
- for chain_id, (chain_name, uniswap_contract_key, balancer_contract_key) in chains.items():
346
- base_url = base_urls[chain_id]
347
- api_key = api_keys[chain_name]
348
-
349
- for service_safe in service_safes[chain_name]:
350
- print(f"Checking service safe {service_safe} for chain {chain_name}")
351
- for single_date in date_range(start_date, current_date):
352
- start_block, end_block = get_block_range_for_date(chain_id, single_date, api_key, base_url)
353
- if start_block is None or end_block is None:
354
- print(f"Skipping date {single_date} for chain {chain_name} due to missing block data.")
355
- continue
356
-
357
- print(f"Start Block: {start_block}, End Block: {end_block} for date {single_date} on chain {chain_name} for service safe {service_safe}")
358
-
359
- # Get logs for Uniswap and Balancer contracts
360
- for contract_key, topic_key in [(uniswap_contract_key, "transfer"), (balancer_contract_key, "pool_balance_changed")]:
361
- contract_address = ADDRESSES[chain_name][contract_key]
362
- print(api_key, chain_name, start_block, end_block, contract_address, service_safe)
363
- logs = get_logs(api_key, chain_name, start_block, end_block, contract_address, service_safe)
364
-
365
- for log in logs:
366
- parsed_log = parse_pool_balance_log(chain_name, single_date, log) if topic_key == "pool_balance_changed" else parse_transfer_log(chain_name, single_date, log)
367
- if parsed_log:
368
- if topic_key == "transfer":
369
- # If the event is a Transfer event, fetch uniswap position details and increase liquidity event details
370
- uniswap_v3_data = get_uniswap_v3_position(web3_base if chain_name == 'base' else web3_optimism, contract_address, parsed_log['token_id'])
371
- increase_liquidity_data = get_uniswap_increase_liquidity(api_key, chain_name, parsed_log['token_id'])
372
-
373
- token0_address = uniswap_v3_data['token0']
374
- token1_address = uniswap_v3_data['token1']
375
- decimals_token0 = get_token_decimals(web3_base if chain_name == 'base' else web3_optimism, token0_address)
376
- decimals_token1 = get_token_decimals(web3_base if chain_name == 'base' else web3_optimism, token1_address)
377
- print(decimals_token0,decimals_token1)
378
- increase_liquidity_data['amount0'] /= 10**decimals_token0
379
- increase_liquidity_data['amount1'] /= 10**decimals_token1
380
-
381
- usd_price_token0 = get_token_price_usd(chain_name, token0_address)
382
- usd_price_token1 = get_token_price_usd(chain_name, token1_address)
383
-
384
- if usd_price_token0 is not None and usd_price_token1 is not None:
385
- increase_liquidity_data['amount0_usd'] = increase_liquidity_data['amount0'] * usd_price_token0
386
- increase_liquidity_data['amount1_usd'] = increase_liquidity_data['amount1'] * usd_price_token1
387
-
388
- parsed_log.update(uniswap_v3_data)
389
- parsed_log.update(increase_liquidity_data)
390
- all_transactions.append(parsed_log)
391
-
392
- # Convert to DataFrame and append to existing transactions
393
- df_new_transactions = pd.DataFrame(all_transactions)
394
-
395
- if not df_existing_transactions.empty:
396
- all_data = pd.concat([df_existing_transactions, df_new_transactions])
397
- else:
398
- all_data = df_new_transactions
399
-
400
- all_data.to_csv(existing_transactions_path, index=False)
401
- print("Data saved to", existing_transactions_path)
402
-
403
- return all_data
404
-
405
- #print(df_transactions)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
apr_visualization.py DELETED
@@ -1,592 +0,0 @@
1
- import pandas as pd
2
- import numpy as np
3
- import matplotlib.pyplot as plt
4
- import matplotlib.dates as mdates
5
- import plotly.graph_objects as go
6
- import plotly.express as px
7
- from plotly.subplots import make_subplots
8
- import random
9
- from datetime import datetime, timedelta
10
- import requests
11
- import sys
12
- import json
13
- from typing import List, Dict, Any
14
- import logging
15
-
16
- logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
17
- logger = logging.getLogger(__name__)
18
-
19
- # Global variable to store the data for reuse
20
- global_df = None
21
-
22
- # Configuration
23
- API_BASE_URL = "https://afmdb.autonolas.tech"
24
-
25
- def get_agent_type_by_name(type_name: str) -> Dict[str, Any]:
26
- """Get agent type by name"""
27
- response = requests.get(f"{API_BASE_URL}/api/agent-types/name/{type_name}")
28
- if response.status_code == 404:
29
- logger.error(f"Agent type '{type_name}' not found")
30
- return None
31
- response.raise_for_status()
32
- return response.json()
33
-
34
- def get_attribute_definition_by_name(attr_name: str) -> Dict[str, Any]:
35
- """Get attribute definition by name"""
36
- response = requests.get(f"{API_BASE_URL}/api/attributes/name/{attr_name}")
37
- if response.status_code == 404:
38
- logger.error(f"Attribute definition '{attr_name}' not found")
39
- return None
40
- response.raise_for_status()
41
- return response.json()
42
-
43
- def get_agents_by_type(type_id: int) -> List[Dict[str, Any]]:
44
- """Get all agents of a specific type"""
45
- response = requests.get(f"{API_BASE_URL}/api/agent-types/{type_id}/agents/")
46
- if response.status_code == 404:
47
- logger.error(f"No agents found for type ID {type_id}")
48
- return []
49
- response.raise_for_status()
50
- return response.json()
51
-
52
- def get_attribute_values_by_type_and_attr(agents: List[Dict[str, Any]], attr_def_id: int) -> List[Dict[str, Any]]:
53
- """Get all attribute values for a specific attribute definition across all agents of a given list"""
54
- all_attributes = []
55
-
56
- # For each agent, get their attributes and filter for the one we want
57
- for agent in agents:
58
- agent_id = agent["agent_id"]
59
-
60
- # Call the /api/agents/{agent_id}/attributes/ endpoint
61
- response = requests.get(f"{API_BASE_URL}/api/agents/{agent_id}/attributes/", params={"limit": 1000})
62
- if response.status_code == 404:
63
- logger.error(f"No attributes found for agent ID {agent_id}")
64
- continue
65
-
66
- try:
67
- response.raise_for_status()
68
- agent_attrs = response.json()
69
-
70
- # Filter for the specific attribute definition ID
71
- filtered_attrs = [attr for attr in agent_attrs if attr.get("attr_def_id") == attr_def_id]
72
- all_attributes.extend(filtered_attrs)
73
- except requests.exceptions.RequestException as e:
74
- logger.error(f"Error fetching attributes for agent ID {agent_id}: {e}")
75
-
76
- return all_attributes
77
-
78
- def get_agent_name(agent_id: int, agents: List[Dict[str, Any]]) -> str:
79
- """Get agent name from agent ID"""
80
- for agent in agents:
81
- if agent["agent_id"] == agent_id:
82
- return agent["agent_name"]
83
- return "Unknown"
84
-
85
- def extract_apr_value(attr: Dict[str, Any]) -> Dict[str, Any]:
86
- """Extract APR value and timestamp from JSON value"""
87
- try:
88
- # The APR value is stored in the json_value field
89
- if attr["json_value"] is None:
90
- return {"apr": None, "timestamp": None, "agent_id": attr["agent_id"], "is_dummy": False}
91
-
92
- # If json_value is a string, parse it
93
- if isinstance(attr["json_value"], str):
94
- json_data = json.loads(attr["json_value"])
95
- else:
96
- json_data = attr["json_value"]
97
-
98
- apr = json_data.get("apr")
99
- timestamp = json_data.get("timestamp")
100
-
101
- # Convert timestamp to datetime if it exists
102
- timestamp_dt = None
103
- if timestamp:
104
- timestamp_dt = datetime.fromtimestamp(timestamp)
105
-
106
- return {"apr": apr, "timestamp": timestamp_dt, "agent_id": attr["agent_id"], "is_dummy": False}
107
- except (json.JSONDecodeError, KeyError, TypeError) as e:
108
- logger.error(f"Error parsing JSON value: {e} for agent_id: {attr.get('agent_id')}")
109
- return {"apr": None, "timestamp": None, "agent_id": attr["agent_id"], "is_dummy": False}
110
-
111
- def fetch_apr_data_from_db():
112
- """
113
- Fetch APR data from database using the API.
114
- """
115
- global global_df
116
-
117
- try:
118
- # Step 1: Find the Modius agent type
119
- modius_type = get_agent_type_by_name("Modius")
120
- if not modius_type:
121
- logger.error("Modius agent type not found, using placeholder data")
122
- global_df = pd.DataFrame([])
123
- return global_df
124
-
125
- type_id = modius_type["type_id"]
126
-
127
- # Step 2: Find the APR attribute definition
128
- apr_attr_def = get_attribute_definition_by_name("APR")
129
- if not apr_attr_def:
130
- logger.error("APR attribute definition not found, using placeholder data")
131
- global_df = pd.DataFrame([])
132
- return global_df
133
-
134
- attr_def_id = apr_attr_def["attr_def_id"]
135
-
136
- # Step 3: Get all agents of type Modius
137
- modius_agents = get_agents_by_type(type_id)
138
- if not modius_agents:
139
- logger.error("No agents of type 'Modius' found")
140
- global_df = pd.DataFrame([])
141
- return global_df
142
-
143
- # Step 4: Fetch all APR values for Modius agents
144
- apr_attributes = get_attribute_values_by_type_and_attr(modius_agents, attr_def_id)
145
- if not apr_attributes:
146
- logger.error("No APR values found for 'Modius' agents")
147
- global_df = pd.DataFrame([])
148
- return global_df
149
-
150
- # Step 5: Extract APR data
151
- apr_data_list = []
152
- for attr in apr_attributes:
153
- apr_data = extract_apr_value(attr)
154
- if apr_data["apr"] is not None and apr_data["timestamp"] is not None:
155
- # Get agent name
156
- agent_name = get_agent_name(attr["agent_id"], modius_agents)
157
- # Add agent name to the data
158
- apr_data["agent_name"] = agent_name
159
- # Add is_dummy flag (all real data)
160
- apr_data["is_dummy"] = False
161
-
162
- # Mark negative values as "Performance" metrics
163
- if apr_data["apr"] < 0:
164
- apr_data["metric_type"] = "Performance"
165
- else:
166
- apr_data["metric_type"] = "APR"
167
-
168
- apr_data_list.append(apr_data)
169
-
170
- # Convert list of dictionaries to DataFrame
171
- if not apr_data_list:
172
- logger.error("No valid APR data extracted")
173
- global_df = pd.DataFrame([])
174
- return global_df
175
-
176
- global_df = pd.DataFrame(apr_data_list)
177
- return global_df
178
-
179
- except requests.exceptions.RequestException as e:
180
- logger.error(f"API request error: {e}")
181
- global_df = pd.DataFrame([])
182
- return global_df
183
- except Exception as e:
184
- logger.error(f"Error fetching APR data: {e}")
185
- global_df = pd.DataFrame([])
186
- return global_df
187
-
188
- def generate_apr_visualizations():
189
- """Generate APR visualizations with real data only (no dummy data)"""
190
- global global_df
191
-
192
- # Fetch data from database
193
- df = fetch_apr_data_from_db()
194
-
195
- # If we got no data at all, return placeholder figures
196
- if df.empty:
197
- logger.info("No APR data available. Using fallback visualization.")
198
- # Create empty visualizations with a message using Plotly
199
- fig = go.Figure()
200
- fig.add_annotation(
201
- x=0.5, y=0.5,
202
- text="No APR data available",
203
- font=dict(size=20),
204
- showarrow=False
205
- )
206
- fig.update_layout(
207
- xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
208
- yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
209
- )
210
-
211
- # Save as static files for reference
212
- fig.write_html("modius_apr_per_agent_graph.html")
213
- fig.write_image("modius_apr_per_agent_graph.png")
214
- fig.write_html("modius_apr_combined_graph.html")
215
- fig.write_image("modius_apr_combined_graph.png")
216
-
217
- csv_file = None
218
- return fig, fig, csv_file
219
-
220
- # No longer generating dummy data
221
- # Set global_df for access by other functions
222
- global_df = df
223
-
224
- # Save to CSV before creating visualizations
225
- csv_file = save_to_csv(df)
226
-
227
- # Create per-agent time series graph (returns figure object)
228
- per_agent_fig = create_time_series_graph_per_agent(df)
229
-
230
- # Create combined time series graph (returns figure object)
231
- combined_fig = create_combined_time_series_graph(df)
232
-
233
- return per_agent_fig, combined_fig, csv_file
234
-
235
- def create_time_series_graph_per_agent(df):
236
- """Create a time series graph for each agent using Plotly"""
237
- # Get unique agents
238
- unique_agents = df['agent_id'].unique()
239
-
240
- if len(unique_agents) == 0:
241
- logger.error("No agent data to plot")
242
- fig = go.Figure()
243
- fig.add_annotation(
244
- text="No agent data available",
245
- x=0.5, y=0.5,
246
- showarrow=False, font=dict(size=20)
247
- )
248
- return fig
249
-
250
- # Create a subplot figure for each agent
251
- fig = make_subplots(rows=len(unique_agents), cols=1,
252
- subplot_titles=[f"Agent: {df[df['agent_id'] == agent_id]['agent_name'].iloc[0]}"
253
- for agent_id in unique_agents],
254
- vertical_spacing=0.1)
255
-
256
- # Plot data for each agent
257
- for i, agent_id in enumerate(unique_agents):
258
- agent_data = df[df['agent_id'] == agent_id].copy()
259
- agent_name = agent_data['agent_name'].iloc[0]
260
- row = i + 1
261
-
262
- # Add zero line to separate APR and Performance
263
- fig.add_shape(
264
- type="line", line=dict(dash="solid", width=1.5, color="black"),
265
- y0=0, y1=0, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
266
- row=row, col=1
267
- )
268
-
269
- # Add background colors
270
- fig.add_shape(
271
- type="rect", fillcolor="rgba(230, 243, 255, 0.3)", line=dict(width=0),
272
- y0=0, y1=1000, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
273
- row=row, col=1, layer="below"
274
- )
275
- fig.add_shape(
276
- type="rect", fillcolor="rgba(255, 230, 230, 0.3)", line=dict(width=0),
277
- y0=-1000, y1=0, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
278
- row=row, col=1, layer="below"
279
- )
280
-
281
- # Create separate dataframes for different data types
282
- apr_data = agent_data[agent_data['metric_type'] == 'APR']
283
- perf_data = agent_data[agent_data['metric_type'] == 'Performance']
284
-
285
- # Sort all data by timestamp for the line plots
286
- combined_agent_data = agent_data.sort_values('timestamp')
287
-
288
- # Add main line connecting all points
289
- fig.add_trace(
290
- go.Scatter(
291
- x=combined_agent_data['timestamp'],
292
- y=combined_agent_data['apr'],
293
- mode='lines',
294
- line=dict(color='purple', width=2),
295
- name=f'{agent_name}',
296
- legendgroup=agent_name,
297
- showlegend=(i == 0), # Only show in legend once
298
- hovertemplate='Time: %{x}<br>Value: %{y:.2f}<extra></extra>'
299
- ),
300
- row=row, col=1
301
- )
302
-
303
- # Add scatter points for APR values
304
- if not apr_data.empty:
305
- fig.add_trace(
306
- go.Scatter(
307
- x=apr_data['timestamp'],
308
- y=apr_data['apr'],
309
- mode='markers',
310
- marker=dict(color='blue', size=10, symbol='circle'),
311
- name='APR',
312
- legendgroup='APR',
313
- showlegend=(i == 0),
314
- hovertemplate='Time: %{x}<br>APR: %{y:.2f}<extra></extra>'
315
- ),
316
- row=row, col=1
317
- )
318
-
319
- # Add scatter points for Performance values
320
- if not perf_data.empty:
321
- fig.add_trace(
322
- go.Scatter(
323
- x=perf_data['timestamp'],
324
- y=perf_data['apr'],
325
- mode='markers',
326
- marker=dict(color='red', size=10, symbol='square'),
327
- name='Performance',
328
- legendgroup='Performance',
329
- showlegend=(i == 0),
330
- hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<extra></extra>'
331
- ),
332
- row=row, col=1
333
- )
334
-
335
- # Update axes
336
- fig.update_xaxes(title_text="Time", row=row, col=1)
337
- fig.update_yaxes(title_text="Value", row=row, col=1, gridcolor='rgba(0,0,0,0.1)')
338
-
339
- # Update layout
340
- fig.update_layout(
341
- height=400 * len(unique_agents),
342
- width=1000,
343
- title_text="APR and Performance Values per Agent",
344
- template="plotly_white",
345
- legend=dict(
346
- orientation="h",
347
- yanchor="bottom",
348
- y=1.02,
349
- xanchor="right",
350
- x=1
351
- ),
352
- margin=dict(r=20, l=20, t=30, b=20),
353
- hovermode="closest"
354
- )
355
-
356
- # Save the figure (still useful for reference)
357
- graph_file = "modius_apr_per_agent_graph.html"
358
- fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
359
-
360
- # Also save as image for compatibility
361
- img_file = "modius_apr_per_agent_graph.png"
362
- fig.write_image(img_file)
363
-
364
- logger.info(f"Per-agent graph saved to {graph_file} and {img_file}")
365
-
366
- # Return the figure object for direct use in Gradio
367
- return fig
368
-
369
- def create_combined_time_series_graph(df):
370
- """Create a combined time series graph for all agents using Plotly"""
371
- if len(df) == 0:
372
- logger.error("No data to plot combined graph")
373
- fig = go.Figure()
374
- fig.add_annotation(
375
- text="No data available",
376
- x=0.5, y=0.5,
377
- showarrow=False, font=dict(size=20)
378
- )
379
- return fig
380
-
381
- # Create Plotly figure
382
- fig = go.Figure()
383
-
384
- # Get unique agents
385
- unique_agents = df['agent_id'].unique()
386
-
387
- # Define a color scale for different agents
388
- colors = px.colors.qualitative.Plotly[:len(unique_agents)]
389
-
390
- # Add background shapes for APR and Performance regions
391
- min_time = df['timestamp'].min()
392
- max_time = df['timestamp'].max()
393
-
394
- # Add shape for APR region (above zero)
395
- fig.add_shape(
396
- type="rect",
397
- fillcolor="rgba(230, 243, 255, 0.3)",
398
- line=dict(width=0),
399
- y0=0, y1=1000,
400
- x0=min_time, x1=max_time,
401
- layer="below"
402
- )
403
-
404
- # Add shape for Performance region (below zero)
405
- fig.add_shape(
406
- type="rect",
407
- fillcolor="rgba(255, 230, 230, 0.3)",
408
- line=dict(width=0),
409
- y0=-1000, y1=0,
410
- x0=min_time, x1=max_time,
411
- layer="below"
412
- )
413
-
414
- # Add zero line
415
- fig.add_shape(
416
- type="line",
417
- line=dict(dash="solid", width=1.5, color="black"),
418
- y0=0, y1=0,
419
- x0=min_time, x1=max_time
420
- )
421
-
422
- # Add data for each agent
423
- for i, agent_id in enumerate(unique_agents):
424
- agent_data = df[df['agent_id'] == agent_id].copy()
425
- agent_name = agent_data['agent_name'].iloc[0]
426
- color = colors[i % len(colors)]
427
-
428
- # Sort the data by timestamp
429
- agent_data = agent_data.sort_values('timestamp')
430
-
431
- # Add the combined line for both APR and Performance
432
- fig.add_trace(
433
- go.Scatter(
434
- x=agent_data['timestamp'],
435
- y=agent_data['apr'],
436
- mode='lines',
437
- line=dict(color=color, width=2),
438
- name=f'{agent_name}',
439
- legendgroup=agent_name,
440
- hovertemplate='Time: %{x}<br>Value: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
441
- )
442
- )
443
-
444
- # Add scatter points for APR values
445
- apr_data = agent_data[agent_data['metric_type'] == 'APR']
446
- if not apr_data.empty:
447
- fig.add_trace(
448
- go.Scatter(
449
- x=apr_data['timestamp'],
450
- y=apr_data['apr'],
451
- mode='markers',
452
- marker=dict(color=color, symbol='circle', size=8),
453
- name=f'{agent_name} APR',
454
- legendgroup=agent_name,
455
- showlegend=False,
456
- hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
457
- )
458
- )
459
-
460
- # Add scatter points for Performance values
461
- perf_data = agent_data[agent_data['metric_type'] == 'Performance']
462
- if not perf_data.empty:
463
- fig.add_trace(
464
- go.Scatter(
465
- x=perf_data['timestamp'],
466
- y=perf_data['apr'],
467
- mode='markers',
468
- marker=dict(color=color, symbol='square', size=8),
469
- name=f'{agent_name} Perf',
470
- legendgroup=agent_name,
471
- showlegend=False,
472
- hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
473
- )
474
- )
475
-
476
- # Update layout
477
- fig.update_layout(
478
- title="APR and Performance Values for All Agents",
479
- xaxis_title="Time",
480
- yaxis_title="Value",
481
- template="plotly_white",
482
- height=600,
483
- width=1000,
484
- legend=dict(
485
- orientation="h",
486
- yanchor="bottom",
487
- y=1.02,
488
- xanchor="right",
489
- x=1,
490
- groupclick="toggleitem"
491
- ),
492
- margin=dict(r=20, l=20, t=30, b=20),
493
- hovermode="closest"
494
- )
495
-
496
- # Update axes
497
- fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
498
- fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
499
-
500
- # Save the figure (still useful for reference)
501
- graph_file = "modius_apr_combined_graph.html"
502
- fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
503
-
504
- # Also save as image for compatibility
505
- img_file = "modius_apr_combined_graph.png"
506
- fig.write_image(img_file)
507
-
508
- logger.info(f"Combined graph saved to {graph_file} and {img_file}")
509
-
510
- # Return the figure object for direct use in Gradio
511
- return fig
512
-
513
- def save_to_csv(df):
514
- """Save the APR data DataFrame to a CSV file and return the file path"""
515
- if df.empty:
516
- logger.error("No APR data to save to CSV")
517
- return None
518
-
519
- # Define the CSV file path
520
- csv_file = "modius_apr_values.csv"
521
-
522
- # Save to CSV
523
- df.to_csv(csv_file, index=False)
524
- logger.info(f"APR data saved to {csv_file}")
525
-
526
- # Also generate a statistics CSV file
527
- stats_df = generate_statistics_from_data(df)
528
- stats_csv = "modius_apr_statistics.csv"
529
- stats_df.to_csv(stats_csv, index=False)
530
- logger.info(f"Statistics saved to {stats_csv}")
531
-
532
- return csv_file
533
-
534
- def generate_statistics_from_data(df):
535
- """Generate statistics from the APR data"""
536
- if df.empty:
537
- return pd.DataFrame()
538
-
539
- # Get unique agents
540
- unique_agents = df['agent_id'].unique()
541
- stats_list = []
542
-
543
- # Generate per-agent statistics
544
- for agent_id in unique_agents:
545
- agent_data = df[df['agent_id'] == agent_id]
546
- agent_name = agent_data['agent_name'].iloc[0]
547
-
548
- # APR statistics
549
- apr_data = agent_data[agent_data['metric_type'] == 'APR']
550
- real_apr = apr_data[apr_data['is_dummy'] == False]
551
-
552
- # Performance statistics
553
- perf_data = agent_data[agent_data['metric_type'] == 'Performance']
554
- real_perf = perf_data[perf_data['is_dummy'] == False]
555
-
556
- stats = {
557
- 'agent_id': agent_id,
558
- 'agent_name': agent_name,
559
- 'total_points': len(agent_data),
560
- 'apr_points': len(apr_data),
561
- 'performance_points': len(perf_data),
562
- 'real_apr_points': len(real_apr),
563
- 'real_performance_points': len(real_perf),
564
- 'avg_apr': apr_data['apr'].mean() if not apr_data.empty else None,
565
- 'avg_performance': perf_data['apr'].mean() if not perf_data.empty else None,
566
- 'max_apr': apr_data['apr'].max() if not apr_data.empty else None,
567
- 'min_apr': apr_data['apr'].min() if not apr_data.empty else None,
568
- 'latest_timestamp': agent_data['timestamp'].max().strftime('%Y-%m-%d %H:%M:%S') if not agent_data.empty else None
569
- }
570
- stats_list.append(stats)
571
-
572
- # Generate overall statistics
573
- apr_only = df[df['metric_type'] == 'APR']
574
- perf_only = df[df['metric_type'] == 'Performance']
575
-
576
- overall_stats = {
577
- 'agent_id': 'ALL',
578
- 'agent_name': 'All Agents',
579
- 'total_points': len(df),
580
- 'apr_points': len(apr_only),
581
- 'performance_points': len(perf_only),
582
- 'real_apr_points': len(apr_only[apr_only['is_dummy'] == False]),
583
- 'real_performance_points': len(perf_only[perf_only['is_dummy'] == False]),
584
- 'avg_apr': apr_only['apr'].mean() if not apr_only.empty else None,
585
- 'avg_performance': perf_only['apr'].mean() if not perf_only.empty else None,
586
- 'max_apr': apr_only['apr'].max() if not apr_only.empty else None,
587
- 'min_apr': apr_only['apr'].min() if not apr_only.empty else None,
588
- 'latest_timestamp': df['timestamp'].max().strftime('%Y-%m-%d %H:%M:%S') if not df.empty else None
589
- }
590
- stats_list.append(overall_stats)
591
-
592
- return pd.DataFrame(stats_list)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
contracts/NonfungiblePositionManager.json DELETED
@@ -1,1228 +0,0 @@
1
- {
2
- "contractName": "",
3
- "abi": [
4
- {
5
- "inputs": [
6
- {
7
- "internalType": "address",
8
- "name": "_factory",
9
- "type": "address"
10
- },
11
- {
12
- "internalType": "address",
13
- "name": "_WETH9",
14
- "type": "address"
15
- },
16
- {
17
- "internalType": "address",
18
- "name": "_tokenDescriptor_",
19
- "type": "address"
20
- }
21
- ],
22
- "stateMutability": "nonpayable",
23
- "type": "constructor"
24
- },
25
- {
26
- "anonymous": false,
27
- "inputs": [
28
- {
29
- "indexed": true,
30
- "internalType": "address",
31
- "name": "owner",
32
- "type": "address"
33
- },
34
- {
35
- "indexed": true,
36
- "internalType": "address",
37
- "name": "approved",
38
- "type": "address"
39
- },
40
- {
41
- "indexed": true,
42
- "internalType": "uint256",
43
- "name": "tokenId",
44
- "type": "uint256"
45
- }
46
- ],
47
- "name": "Approval",
48
- "type": "event"
49
- },
50
- {
51
- "anonymous": false,
52
- "inputs": [
53
- {
54
- "indexed": true,
55
- "internalType": "address",
56
- "name": "owner",
57
- "type": "address"
58
- },
59
- {
60
- "indexed": true,
61
- "internalType": "address",
62
- "name": "operator",
63
- "type": "address"
64
- },
65
- {
66
- "indexed": false,
67
- "internalType": "bool",
68
- "name": "approved",
69
- "type": "bool"
70
- }
71
- ],
72
- "name": "ApprovalForAll",
73
- "type": "event"
74
- },
75
- {
76
- "anonymous": false,
77
- "inputs": [
78
- {
79
- "indexed": true,
80
- "internalType": "uint256",
81
- "name": "tokenId",
82
- "type": "uint256"
83
- },
84
- {
85
- "indexed": false,
86
- "internalType": "address",
87
- "name": "recipient",
88
- "type": "address"
89
- },
90
- {
91
- "indexed": false,
92
- "internalType": "uint256",
93
- "name": "amount0",
94
- "type": "uint256"
95
- },
96
- {
97
- "indexed": false,
98
- "internalType": "uint256",
99
- "name": "amount1",
100
- "type": "uint256"
101
- }
102
- ],
103
- "name": "Collect",
104
- "type": "event"
105
- },
106
- {
107
- "anonymous": false,
108
- "inputs": [
109
- {
110
- "indexed": true,
111
- "internalType": "uint256",
112
- "name": "tokenId",
113
- "type": "uint256"
114
- },
115
- {
116
- "indexed": false,
117
- "internalType": "uint128",
118
- "name": "liquidity",
119
- "type": "uint128"
120
- },
121
- {
122
- "indexed": false,
123
- "internalType": "uint256",
124
- "name": "amount0",
125
- "type": "uint256"
126
- },
127
- {
128
- "indexed": false,
129
- "internalType": "uint256",
130
- "name": "amount1",
131
- "type": "uint256"
132
- }
133
- ],
134
- "name": "DecreaseLiquidity",
135
- "type": "event"
136
- },
137
- {
138
- "anonymous": false,
139
- "inputs": [
140
- {
141
- "indexed": true,
142
- "internalType": "uint256",
143
- "name": "tokenId",
144
- "type": "uint256"
145
- },
146
- {
147
- "indexed": false,
148
- "internalType": "uint128",
149
- "name": "liquidity",
150
- "type": "uint128"
151
- },
152
- {
153
- "indexed": false,
154
- "internalType": "uint256",
155
- "name": "amount0",
156
- "type": "uint256"
157
- },
158
- {
159
- "indexed": false,
160
- "internalType": "uint256",
161
- "name": "amount1",
162
- "type": "uint256"
163
- }
164
- ],
165
- "name": "IncreaseLiquidity",
166
- "type": "event"
167
- },
168
- {
169
- "anonymous": false,
170
- "inputs": [
171
- {
172
- "indexed": true,
173
- "internalType": "address",
174
- "name": "from",
175
- "type": "address"
176
- },
177
- {
178
- "indexed": true,
179
- "internalType": "address",
180
- "name": "to",
181
- "type": "address"
182
- },
183
- {
184
- "indexed": true,
185
- "internalType": "uint256",
186
- "name": "tokenId",
187
- "type": "uint256"
188
- }
189
- ],
190
- "name": "Transfer",
191
- "type": "event"
192
- },
193
- {
194
- "inputs": [],
195
- "name": "DOMAIN_SEPARATOR",
196
- "outputs": [
197
- {
198
- "internalType": "bytes32",
199
- "name": "",
200
- "type": "bytes32"
201
- }
202
- ],
203
- "stateMutability": "view",
204
- "type": "function"
205
- },
206
- {
207
- "inputs": [],
208
- "name": "PERMIT_TYPEHASH",
209
- "outputs": [
210
- {
211
- "internalType": "bytes32",
212
- "name": "",
213
- "type": "bytes32"
214
- }
215
- ],
216
- "stateMutability": "view",
217
- "type": "function"
218
- },
219
- {
220
- "inputs": [],
221
- "name": "WETH9",
222
- "outputs": [
223
- {
224
- "internalType": "address",
225
- "name": "",
226
- "type": "address"
227
- }
228
- ],
229
- "stateMutability": "view",
230
- "type": "function"
231
- },
232
- {
233
- "inputs": [
234
- {
235
- "internalType": "address",
236
- "name": "to",
237
- "type": "address"
238
- },
239
- {
240
- "internalType": "uint256",
241
- "name": "tokenId",
242
- "type": "uint256"
243
- }
244
- ],
245
- "name": "approve",
246
- "outputs": [],
247
- "stateMutability": "nonpayable",
248
- "type": "function"
249
- },
250
- {
251
- "inputs": [
252
- {
253
- "internalType": "address",
254
- "name": "owner",
255
- "type": "address"
256
- }
257
- ],
258
- "name": "balanceOf",
259
- "outputs": [
260
- {
261
- "internalType": "uint256",
262
- "name": "",
263
- "type": "uint256"
264
- }
265
- ],
266
- "stateMutability": "view",
267
- "type": "function"
268
- },
269
- {
270
- "inputs": [],
271
- "name": "baseURI",
272
- "outputs": [
273
- {
274
- "internalType": "string",
275
- "name": "",
276
- "type": "string"
277
- }
278
- ],
279
- "stateMutability": "pure",
280
- "type": "function"
281
- },
282
- {
283
- "inputs": [
284
- {
285
- "internalType": "uint256",
286
- "name": "tokenId",
287
- "type": "uint256"
288
- }
289
- ],
290
- "name": "burn",
291
- "outputs": [],
292
- "stateMutability": "payable",
293
- "type": "function"
294
- },
295
- {
296
- "inputs": [
297
- {
298
- "components": [
299
- {
300
- "internalType": "uint256",
301
- "name": "tokenId",
302
- "type": "uint256"
303
- },
304
- {
305
- "internalType": "address",
306
- "name": "recipient",
307
- "type": "address"
308
- },
309
- {
310
- "internalType": "uint128",
311
- "name": "amount0Max",
312
- "type": "uint128"
313
- },
314
- {
315
- "internalType": "uint128",
316
- "name": "amount1Max",
317
- "type": "uint128"
318
- }
319
- ],
320
- "internalType": "struct INonfungiblePositionManager.CollectParams",
321
- "name": "params",
322
- "type": "tuple"
323
- }
324
- ],
325
- "name": "collect",
326
- "outputs": [
327
- {
328
- "internalType": "uint256",
329
- "name": "amount0",
330
- "type": "uint256"
331
- },
332
- {
333
- "internalType": "uint256",
334
- "name": "amount1",
335
- "type": "uint256"
336
- }
337
- ],
338
- "stateMutability": "payable",
339
- "type": "function"
340
- },
341
- {
342
- "inputs": [
343
- {
344
- "internalType": "address",
345
- "name": "token0",
346
- "type": "address"
347
- },
348
- {
349
- "internalType": "address",
350
- "name": "token1",
351
- "type": "address"
352
- },
353
- {
354
- "internalType": "uint24",
355
- "name": "fee",
356
- "type": "uint24"
357
- },
358
- {
359
- "internalType": "uint160",
360
- "name": "sqrtPriceX96",
361
- "type": "uint160"
362
- }
363
- ],
364
- "name": "createAndInitializePoolIfNecessary",
365
- "outputs": [
366
- {
367
- "internalType": "address",
368
- "name": "pool",
369
- "type": "address"
370
- }
371
- ],
372
- "stateMutability": "payable",
373
- "type": "function"
374
- },
375
- {
376
- "inputs": [
377
- {
378
- "components": [
379
- {
380
- "internalType": "uint256",
381
- "name": "tokenId",
382
- "type": "uint256"
383
- },
384
- {
385
- "internalType": "uint128",
386
- "name": "liquidity",
387
- "type": "uint128"
388
- },
389
- {
390
- "internalType": "uint256",
391
- "name": "amount0Min",
392
- "type": "uint256"
393
- },
394
- {
395
- "internalType": "uint256",
396
- "name": "amount1Min",
397
- "type": "uint256"
398
- },
399
- {
400
- "internalType": "uint256",
401
- "name": "deadline",
402
- "type": "uint256"
403
- }
404
- ],
405
- "internalType": "struct INonfungiblePositionManager.DecreaseLiquidityParams",
406
- "name": "params",
407
- "type": "tuple"
408
- }
409
- ],
410
- "name": "decreaseLiquidity",
411
- "outputs": [
412
- {
413
- "internalType": "uint256",
414
- "name": "amount0",
415
- "type": "uint256"
416
- },
417
- {
418
- "internalType": "uint256",
419
- "name": "amount1",
420
- "type": "uint256"
421
- }
422
- ],
423
- "stateMutability": "payable",
424
- "type": "function"
425
- },
426
- {
427
- "inputs": [],
428
- "name": "factory",
429
- "outputs": [
430
- {
431
- "internalType": "address",
432
- "name": "",
433
- "type": "address"
434
- }
435
- ],
436
- "stateMutability": "view",
437
- "type": "function"
438
- },
439
- {
440
- "inputs": [
441
- {
442
- "internalType": "uint256",
443
- "name": "tokenId",
444
- "type": "uint256"
445
- }
446
- ],
447
- "name": "getApproved",
448
- "outputs": [
449
- {
450
- "internalType": "address",
451
- "name": "",
452
- "type": "address"
453
- }
454
- ],
455
- "stateMutability": "view",
456
- "type": "function"
457
- },
458
- {
459
- "inputs": [
460
- {
461
- "components": [
462
- {
463
- "internalType": "uint256",
464
- "name": "tokenId",
465
- "type": "uint256"
466
- },
467
- {
468
- "internalType": "uint256",
469
- "name": "amount0Desired",
470
- "type": "uint256"
471
- },
472
- {
473
- "internalType": "uint256",
474
- "name": "amount1Desired",
475
- "type": "uint256"
476
- },
477
- {
478
- "internalType": "uint256",
479
- "name": "amount0Min",
480
- "type": "uint256"
481
- },
482
- {
483
- "internalType": "uint256",
484
- "name": "amount1Min",
485
- "type": "uint256"
486
- },
487
- {
488
- "internalType": "uint256",
489
- "name": "deadline",
490
- "type": "uint256"
491
- }
492
- ],
493
- "internalType": "struct INonfungiblePositionManager.IncreaseLiquidityParams",
494
- "name": "params",
495
- "type": "tuple"
496
- }
497
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498
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671
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672
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673
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676
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679
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680
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681
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682
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683
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684
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704
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705
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706
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707
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708
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711
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712
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713
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731
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732
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741
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742
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745
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774
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807
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809
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810
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811
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812
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813
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816
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817
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818
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827
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830
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832
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833
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835
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836
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837
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838
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841
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842
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843
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844
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845
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846
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853
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870
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871
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875
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882
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886
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906
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907
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910
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914
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915
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916
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917
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contracts/StakingToken.json DELETED
@@ -1,1274 +0,0 @@
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contracts/Vault.json DELETED
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- "name": "weth",
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- "name": "pauseWindowDuration",
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- "type": "constructor"
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- "inputs": [
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- {
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- "indexed": true,
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- "internalType": "contract IAuthorizer",
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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83
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84
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91
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96
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97
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98
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99
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101
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104
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105
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106
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109
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305
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315
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316
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320
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325
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327
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345
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348
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349
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350
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351
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352
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353
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386
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391
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397
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400
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401
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402
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403
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406
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408
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409
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413
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414
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415
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417
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418
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419
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420
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423
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424
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425
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434
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438
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439
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440
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441
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442
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443
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446
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448
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470
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473
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502
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507
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513
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517
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518
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520
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522
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523
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525
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526
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527
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529
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530
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531
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532
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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553
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554
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557
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558
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559
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561
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562
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563
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564
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565
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566
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569
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570
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571
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572
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574
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575
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576
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577
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578
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579
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580
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581
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583
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584
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585
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587
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588
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589
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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664
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665
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666
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667
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668
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670
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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752
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753
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754
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755
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756
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758
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759
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760
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761
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762
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763
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764
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766
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767
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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791
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793
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794
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795
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796
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797
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798
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799
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803
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804
- "name": "recipient",
805
- "type": "address"
806
- },
807
- {
808
- "components": [
809
- {
810
- "internalType": "contract IAsset[]",
811
- "name": "assets",
812
- "type": "address[]"
813
- },
814
- {
815
- "internalType": "uint256[]",
816
- "name": "maxAmountsIn",
817
- "type": "uint256[]"
818
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819
- {
820
- "internalType": "bytes",
821
- "name": "userData",
822
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823
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824
- {
825
- "internalType": "bool",
826
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827
- "type": "bool"
828
- }
829
- ],
830
- "internalType": "struct IVault.JoinPoolRequest",
831
- "name": "request",
832
- "type": "tuple"
833
- }
834
- ],
835
- "name": "joinPool",
836
- "outputs": [],
837
- "stateMutability": "payable",
838
- "type": "function"
839
- },
840
- {
841
- "inputs": [
842
- {
843
- "components": [
844
- {
845
- "internalType": "enum IVault.PoolBalanceOpKind",
846
- "name": "kind",
847
- "type": "uint8"
848
- },
849
- {
850
- "internalType": "bytes32",
851
- "name": "poolId",
852
- "type": "bytes32"
853
- },
854
- {
855
- "internalType": "contract IERC20",
856
- "name": "token",
857
- "type": "address"
858
- },
859
- {
860
- "internalType": "uint256",
861
- "name": "amount",
862
- "type": "uint256"
863
- }
864
- ],
865
- "internalType": "struct IVault.PoolBalanceOp[]",
866
- "name": "ops",
867
- "type": "tuple[]"
868
- }
869
- ],
870
- "name": "managePoolBalance",
871
- "outputs": [],
872
- "stateMutability": "nonpayable",
873
- "type": "function"
874
- },
875
- {
876
- "inputs": [
877
- {
878
- "components": [
879
- {
880
- "internalType": "enum IVault.UserBalanceOpKind",
881
- "name": "kind",
882
- "type": "uint8"
883
- },
884
- {
885
- "internalType": "contract IAsset",
886
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887
- "type": "address"
888
- },
889
- {
890
- "internalType": "uint256",
891
- "name": "amount",
892
- "type": "uint256"
893
- },
894
- {
895
- "internalType": "address",
896
- "name": "sender",
897
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898
- },
899
- {
900
- "internalType": "address payable",
901
- "name": "recipient",
902
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903
- }
904
- ],
905
- "internalType": "struct IVault.UserBalanceOp[]",
906
- "name": "ops",
907
- "type": "tuple[]"
908
- }
909
- ],
910
- "name": "manageUserBalance",
911
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912
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913
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914
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915
- {
916
- "inputs": [
917
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918
- "internalType": "enum IVault.SwapKind",
919
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920
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921
- },
922
- {
923
- "components": [
924
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925
- "internalType": "bytes32",
926
- "name": "poolId",
927
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928
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929
- {
930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
- "internalType": "bytes",
946
- "name": "userData",
947
- "type": "bytes"
948
- }
949
- ],
950
- "internalType": "struct IVault.BatchSwapStep[]",
951
- "name": "swaps",
952
- "type": "tuple[]"
953
- },
954
- {
955
- "internalType": "contract IAsset[]",
956
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957
- "type": "address[]"
958
- },
959
- {
960
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961
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962
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963
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964
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965
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966
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967
- "internalType": "bool",
968
- "name": "fromInternalBalance",
969
- "type": "bool"
970
- },
971
- {
972
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973
- "name": "recipient",
974
- "type": "address"
975
- },
976
- {
977
- "internalType": "bool",
978
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979
- "type": "bool"
980
- }
981
- ],
982
- "internalType": "struct IVault.FundManagement",
983
- "name": "funds",
984
- "type": "tuple"
985
- }
986
- ],
987
- "name": "queryBatchSwap",
988
- "outputs": [
989
- {
990
- "internalType": "int256[]",
991
- "name": "",
992
- "type": "int256[]"
993
- }
994
- ],
995
- "stateMutability": "nonpayable",
996
- "type": "function"
997
- },
998
- {
999
- "inputs": [
1000
- {
1001
- "internalType": "enum IVault.PoolSpecialization",
1002
- "name": "specialization",
1003
- "type": "uint8"
1004
- }
1005
- ],
1006
- "name": "registerPool",
1007
- "outputs": [
1008
- {
1009
- "internalType": "bytes32",
1010
- "name": "",
1011
- "type": "bytes32"
1012
- }
1013
- ],
1014
- "stateMutability": "nonpayable",
1015
- "type": "function"
1016
- },
1017
- {
1018
- "inputs": [
1019
- {
1020
- "internalType": "bytes32",
1021
- "name": "poolId",
1022
- "type": "bytes32"
1023
- },
1024
- {
1025
- "internalType": "contract IERC20[]",
1026
- "name": "tokens",
1027
- "type": "address[]"
1028
- },
1029
- {
1030
- "internalType": "address[]",
1031
- "name": "assetManagers",
1032
- "type": "address[]"
1033
- }
1034
- ],
1035
- "name": "registerTokens",
1036
- "outputs": [],
1037
- "stateMutability": "nonpayable",
1038
- "type": "function"
1039
- },
1040
- {
1041
- "inputs": [
1042
- {
1043
- "internalType": "contract IAuthorizer",
1044
- "name": "newAuthorizer",
1045
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1046
- }
1047
- ],
1048
- "name": "setAuthorizer",
1049
- "outputs": [],
1050
- "stateMutability": "nonpayable",
1051
- "type": "function"
1052
- },
1053
- {
1054
- "inputs": [
1055
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1056
- "internalType": "bool",
1057
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1058
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1059
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1060
- ],
1061
- "name": "setPaused",
1062
- "outputs": [],
1063
- "stateMutability": "nonpayable",
1064
- "type": "function"
1065
- },
1066
- {
1067
- "inputs": [
1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
- "name": "approved",
1081
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1082
- }
1083
- ],
1084
- "name": "setRelayerApproval",
1085
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1086
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1087
- "type": "function"
1088
- },
1089
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1090
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
- },
1103
- {
1104
- "internalType": "contract IAsset",
1105
- "name": "assetIn",
1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
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1117
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1119
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1120
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1121
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1122
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1123
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1124
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1125
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1126
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1127
- },
1128
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1129
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1130
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1131
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1132
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1134
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1135
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1136
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1137
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1138
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
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1159
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1161
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1162
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1163
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1164
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1165
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1166
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1168
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1169
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1170
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1171
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1172
- }
1173
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1174
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1175
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1176
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1177
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1178
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1179
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1180
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1182
- "bytecode": "",
1183
- "deployedBytecode": "",
1184
- "linkReferences": {},
1185
- "deployedLinkReferences": {}
1186
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
contracts/service_registry_abi.json DELETED
@@ -1 +0,0 @@
1
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15
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16
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17
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18
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19
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20
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21
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-28,0xed357b343ca86b7387c53a560568bed89f244f42e765879efb918f26139ed79e,2024-09-28 18:17:21,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
22
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-28,0x7ba1746736b944b47e175f3a87d9766b2e0406204ce228770f89bd5c0c6a1473,2024-09-28 18:17:45,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
23
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-28,0x03c98aef9e44614676d96481d58c3f6a65cc0518dbbe872998107421b73b0c19,2024-09-28 18:18:07,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
24
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-28,0x2a7837f17aafdab94dd4531f131995e53035ddae9a2e841a2175883296d3be89,2024-09-28 18:18:29,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
25
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-29,0x50195e96bf0fffbb03fc107a9dc7c8e6a8e3e56ce66b34ff07c6e9e03e1ff2f9,2024-09-29 17:56:05,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
26
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-29,0x6e8b7cde35dfa4e8685468efc4372a0ddc29dbe6402cdd7aa9558420e99b76d3,2024-09-29 18:21:53,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
27
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28
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-29,0xd4cde9b125d7f1fb6a3a643d0f1dcecfedafa35a56b082e45496a57df1e630da,2024-09-29 18:22:39,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
29
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-29,0x58afed267747fd871fcde3d259789b11527c813734c5385f024bfd65c0d80477,2024-09-29 18:23:03,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
30
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-30,0x3603d6513b26de8f6e8fa30c068afd9357543559a44b500770eebb39d70a37bb,2024-09-30 17:59:49,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
31
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-30,0xa5829b7d23f43925c76c444d8b3982e7e36a5c772fa1e52a195f170bec7bdeff,2024-09-30 18:25:43,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
32
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-30,0xaac5a58e3c17589b3fc738875f2ca63d4b8a931cd4d83b605c1c63904b3a8c7b,2024-09-30 18:26:07,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
33
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-30,0x23123c823bcc0bd6e7debb6cf898c09f9633b307fa0362a5b695860fcd26556d,2024-09-30 18:26:29,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
34
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-09-30,0x2ef4002702b6c5e822b7f1eb56ef8868b485cd6133ea75e324f52d9d961e7f39,2024-09-30 18:26:51,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
35
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-01,0x07edf2c1d52bd085b483a4d78d68f66f3b960c2f8b00a116c75f356d395014a2,2024-10-01 18:04:13,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
36
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-01,0x002d1e1a828b3a5bf0b9a1aafea84e3b2aa1baa3821f365f56740d7d2ad7a728,2024-10-01 18:30:05,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
37
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-01,0x1c13d5371972b9483d3fec56a30bc3a3037192077d4aa53dea7ca264fec9713e,2024-10-01 18:30:29,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
38
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-01,0x3b27d64ff2953ecf83ad012f6491c6811c53e4a741d6f001418c2341a72a005c,2024-10-01 18:30:53,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
39
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-01,0xab518b126e28e79cadd4787192bddf14982e53f1147596537020e29b263ea06e,2024-10-01 18:31:17,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
40
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0x03bed38602800fe269f10de492385ba526a84ec2cc7f9baa23619bd35da85fd7,2024-10-02 18:08:25,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
41
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0xd22d313ad6e3182c458f622bca14d43bb8b9ca9048d26091314120eb89327ae4,2024-10-02 18:34:21,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
42
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0x675ccf60999d108687723fbad766b4fe9c88fc4124922075f4eb66a419069f00,2024-10-02 18:34:45,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
43
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0xae6738f676a6df8468dd35631a84e236196f31781e10f42b59a5f2e06ec8d621,2024-10-02 18:35:11,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
44
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0x2f52592f281c86e3595310037a111a6aaec3a35e0c291197878f5ceffd060e47,2024-10-02 18:35:33,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
45
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0x4177e1d24e370c07740cb39146afb5bb8d94ea86a7cc7d99a91418063a2e51e1,2024-10-02 22:47:59,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
46
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0x67eef64284209d9ac03f57cdfa8f23ee9ad158ae12d92a3d5d70263c76d50311,2024-10-02 22:48:23,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
47
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0xe1b09e4690cad0315a50f6e651a1e4f7e7636f089fc897b92fdf9de3efb88848,2024-10-02 22:48:47,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
48
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-02,0xd716065247d7d17f2d5df0b0884efb1429b9fd8429390ecb8c368139d73269f8,2024-10-02 22:49:09,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
49
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-03,0xbdd16cc721c952ac2db3dacb6084babeb8a13c17ba1dbb44a642f766ef806c40,2024-10-03 18:09:11,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
50
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-03,0x2296dbb131d0a670822fbd3f682d550aa40fdc039759041ba5a178050b0e6e9c,2024-10-03 18:34:59,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
51
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-03,0x15926312822752070924174de37cda4d3d8f03f2f0970b07ab04bc2e153f8f28,2024-10-03 18:35:23,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
52
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-03,0xa8d6ced16e7154fbcbbafb4959cb3ca2a772c5184280e41818c3e0a77ea99cc9,2024-10-03 18:35:45,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
53
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-03,0xd34ce2ec71e543c0d043ab81500450c7ba15784af08cd162041ab9c861dffcd4,2024-10-03 18:36:09,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
54
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-04,0xa2f5c1d49938c2c522053a6e580950e47f13cbd8969b584f759e7455aeab190e,2024-10-04 18:14:23,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
55
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-04,0x255f06717a24abbf83a3e38458b7f319131f49ea78b407f7cc3b730044d0f903,2024-10-04 18:40:17,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
56
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-04,0x7edc08a131c9c98d38a6ab7f9d8e9f712919ec20cc9d4039b81af0ae3d7b73c7,2024-10-04 18:40:39,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
57
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-04,0xf791d1891ab8c19cd188f36733e7b2283e0d9c03b6eb1fae8ec02cf8ce743ab2,2024-10-04 18:41:01,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
58
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-04,0xb6a6a874ab5990e90df1a82071653b4ae76849945493bfa0c138943bcb4ec27a,2024-10-04 18:41:25,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
59
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-05,0xca9355317dbe951c691cbc00663c285a2f7b2ebe6ba6c97d56323b43d2ca5b4d,2024-10-05 18:16:33,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
60
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-05,0x8bc424a0b63e30bb90eae9f3d8b39331e9eeab3dca931398c55e812daeec67aa,2024-10-05 18:42:27,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
61
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-05,0x67e4281c16ef8943674c5a754bfb9424bedc76cee05cb18689ee6ae00f813c16,2024-10-05 18:42:49,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
62
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-05,0x5727658de3c9c2203db2f902c2ab793acd1743d48c34976f94d69ace7da13c85,2024-10-05 18:43:13,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
63
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-05,0x3d0716682ef361878ec806fcd13312c2bbb3992f6d1579972bcda7f3a071d0c9,2024-10-05 18:43:37,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
64
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-06,0x950db9e6fb2ffef17e18108f78cdee3ea67e50fe1f77bf217ddc2187391878c4,2024-10-06 18:18:17,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
65
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-06,0x082ee7e680fbe729a6d4762f44937472341f0dbbab0246bd2453e7d6789a7a04,2024-10-06 18:44:09,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
66
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-06,0xb48ca890464e5f5a936a2b37356b45d6d899ba35ac2dd786ed32106979bd182b,2024-10-06 18:44:33,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
67
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-06,0x01249bb16afd6e82b29107db57fb010aa1566e952808826609097850f28d6e95,2024-10-06 18:44:57,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
68
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-06,0xb01eeefc632f63e689de5b5b46cdd98f8ef5c363c1c3ae72c7fb34bdfdecc9b0,2024-10-06 18:45:19,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
69
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-07,0x566a02173d6d0d03acfc0c3d460db03793af4b55465e8ab2af17b4db03e1225b,2024-10-07 18:19:49,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
70
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-07,0xb12acc7e1b984e34b35763ae63b82e651b12b984e8e0a4ef6d4962984f1e59c1,2024-10-07 18:45:37,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
71
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-07,0x9be5b67d4cdf13c079aa44674c30554cbe2d6545463e3afc6327822cd4a6499f,2024-10-07 18:46:01,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
72
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-07,0xdc5c6fd66965fa021891c3914a9d3138bfee598902cf82a7327222193accf47c,2024-10-07 18:46:23,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
73
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-10-07,0xc6ec42440f16143e30decf95356285ff2c996a80b7377324e2db27c28922ac6f,2024-10-07 18:46:45,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050e90bea65c9ba6416996bb6fffa8f925885d40
74
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-10-04,0xdf58f14196a0693e1d767996fcec10b487efa8f6758ece46b47af91ecc820249,2024-10-04 19:09:29,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d
75
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-26,0x3cffb2e0ad7a1112415619dc7cdf87d4549eaa1eb15f6f349be8ab69aae5cd23,2024-09-26 18:16:59,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
76
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-26,0x0da11730c3f7bb0cc1ab62939d76cdbbcadb0cebf1af74319fbb5061816aac43,2024-09-26 18:17:17,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
77
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-26,0xa15b9e0cc87e4bb10cdfacc978353396432ff1fe90d6518930e5f0793a8b37a9,2024-09-26 18:17:37,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
78
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-26,0xca171f90a61c0894165d9927c4ecd3353a3e042db4b2c795042be91331d6de2f,2024-09-26 18:17:55,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
79
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-27,0x34ea9a3d8e63d645df4ea9358f8bf66a90a4c8a3725fdfdc235d1b4287ee9c84,2024-09-27 17:51:19,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
80
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-27,0xbebdf9ea646631ff785d04a11b4a1f9f9d34a28d941fa64162a75e11759ceaae,2024-09-27 17:51:37,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
81
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-27,0x74ce4551cc81a4d8a6e550aeb20f321d05bc119383e07587f4cb4b50f288ec01,2024-09-27 17:51:59,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
82
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-09-27,0x907910b3d3a536beaa703ed074348883356b9226a00d840787afdc9a77cfe3fc,2024-09-27 17:52:17,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
83
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-04,0x54a5d52d4bae5f2a448b6518f70bc804bb12b7094c906ea1069c0533df0ce8a1,2024-10-04 23:12:35,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
84
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-04,0x356719746d140a01e0958320beae6c7d2fd3c665d3735740aea4b37ed94d8e55,2024-10-04 23:12:53,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
85
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-04,0x858c90cfc4558e5cbd1bc5ba9224d38c227a52238e9a01df145021338cef23fb,2024-10-04 23:13:13,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
86
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-04,0xe410d7236f09f4c4f2e520417c5d392b0c90f8e3c146bf59a150fb7cd2b013b4,2024-10-04 23:13:31,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
87
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-10-07,0x64e43a3197333fc6efcd577837649b54e25c47ccbc9d00f91a11d368e25009e9,2024-10-07 11:57:33,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xa0ff35bfbd3c42e3afe29255742c7558498f5544
88
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-10-07,0xc0bdf9c0215249d766034fe8999569c6fedbc3e973386731f68e180c03d9a430,2024-10-07 12:12:29,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xa0ff35bfbd3c42e3afe29255742c7558498f5544
89
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-10-07,0x426ec7fe2dddc83ebc5ae45163841743465ed4f3a3a3fcd5e29d9224fb35fb1b,2024-10-07 12:13:21,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xa0ff35bfbd3c42e3afe29255742c7558498f5544
90
- base,0xaC55EEb3fdAcfdFae78c62cAA58934900Ad54ED2,2024-09-26,0x1927d44a64dcb7d49a853c7aecae452009100433458a575d207b6fe564bcb254,2024-09-26 17:51:17,0x66749af7e077bfa30aca7c671d56f07e95304722,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
91
- base,0xaC55EEb3fdAcfdFae78c62cAA58934900Ad54ED2,2024-09-26,0x60bad359d0e6a3f07979c8370ba1226f6a5effdfe47c901fbb7e4b1f44c805e5,2024-09-26 18:29:31,0x66749af7e077bfa30aca7c671d56f07e95304722,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
92
- base,0xaC55EEb3fdAcfdFae78c62cAA58934900Ad54ED2,2024-09-29,0x5d3f63be96d60a282a0d5b8216e22101dfc384999a5b5a5de59ade764c59367d,2024-09-29 17:33:13,0x66749af7e077bfa30aca7c671d56f07e95304722,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
93
- base,0xaC55EEb3fdAcfdFae78c62cAA58934900Ad54ED2,2024-09-29,0xcd4986fc3a3406e2ae5a3246a72f04b6b3500f6074c64433582947606a202486,2024-09-29 17:33:43,0x66749af7e077bfa30aca7c671d56f07e95304722,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
94
- ethereum,0xe1EC43B9e4aF80c0b50579AB820f0Ca4304D4DB0,2024-09-26,0x44c17afd9cf22261dc7881c0b72453f532d724d87a6b8a0fc41b5ee524223248,2024-09-26 17:49:59,0x66749af7e077bfa30aca7c671d56f07e95304722,0xe1ec43b9e4af80c0b50579ab820f0ca4304d4db0,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
95
- ethereum,0xe1EC43B9e4aF80c0b50579AB820f0Ca4304D4DB0,2024-09-26,0x4e1b99d3a5457ac8e6598dacd0b163217965cc69fe9dbe97fb7f1829e7879a52,2024-09-26 18:28:59,0x66749af7e077bfa30aca7c671d56f07e95304722,0xe1ec43b9e4af80c0b50579ab820f0ca4304d4db0,0.0,0x66749af7e077bfa30aca7c671d56f07e95304722
96
- ethereum,0xb0830c65499ff2A25D10D895970b152193b13FCf,2024-10-04,0x2d6cd40bc7862c05d8a1cf26b473a58aab79b322d98fecd304b261eda46ada20,2024-10-04 18:56:35,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0xb0830c65499ff2a25d10d895970b152193b13fcf,0.0,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d
97
- ethereum,0x654F17A32fE4ebAEBD6A0669AFD4f7392A7A47BE,2024-10-07,0x7928005ff789c7f3615ab40a8368695be812835ddba4b0645bc4043dd9ec5ae5,2024-10-07 11:45:59,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x654f17a32fe4ebaebd6a0669afd4f7392a7a47be,0.0,0xa0ff35bfbd3c42e3afe29255742c7558498f5544
98
- ethereum,0x654F17A32fE4ebAEBD6A0669AFD4f7392A7A47BE,2024-10-07,0xaf3e92969ca37a7999c81c509d33c5b50b2f212574752c22916cde6f7c58c49a,2024-10-07 12:11:11,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x654f17a32fe4ebaebd6a0669afd4f7392a7a47be,0.0,0xa0ff35bfbd3c42e3afe29255742c7558498f5544
99
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-08,0xaee13886847dc95d2e608ab7d56f9630dc59122ac807168f9ca952c6163c5a81,2024-10-08 15:32:07,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749AF7e077Bfa30aca7c671d56F07E95304722
100
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-08,0x1d913feaa55fc772cded5a1464e30b1a162d4d81ead9c353498ba272678b71ab,2024-10-08 15:32:27,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749AF7e077Bfa30aca7c671d56F07E95304722
101
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-08,0x9bac7a0e70877600504770e9baab57aca80e605ef154c4023fb58ece09c5cbc3,2024-10-08 15:32:41,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749AF7e077Bfa30aca7c671d56F07E95304722
102
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-08,0x84bde757adfa865b03b5a3aad8c535c80de02e89e679a79408c613ce68f7e53b,2024-10-08 15:32:59,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749AF7e077Bfa30aca7c671d56F07E95304722
103
- optimism,0x65F0fDeBCe6E62c195A3Bd5f2447c87991BEbFF1,2024-10-13,0xdc6264296464eec917a29cfa499671e1101b76493199a9d2d708fac9ed7dc2ad,2024-10-13 00:28:39,0x66749af7e077bfa30aca7c671d56f07e95304722,0x65f0fdebce6e62c195a3bd5f2447c87991bebff1,0.0,0x66749AF7e077Bfa30aca7c671d56F07E95304722
104
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106
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107
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108
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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126
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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139
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140
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141
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142
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143
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144
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145
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146
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147
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148
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
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178
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179
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180
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181
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182
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183
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184
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185
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186
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187
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188
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189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
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210
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211
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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252
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253
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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281
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282
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-18,0x83d1fdcd4e688fbc8621572c79da3ffda205d6846a93344dab272e8320e7ab6b,2024-11-18 20:19:27,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
283
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-19,0x400607f8419e10aa52c380451dc378b4803500a7b7757ad33fb72417b58af531,2024-11-19 19:53:41,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
284
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-19,0xab62b6f5fb23cabb39502daeb68f917e21e71cc881862dbf420b2028f0436d5b,2024-11-19 20:19:39,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
285
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-19,0xed1289ab3a8a3895117de1534edee9581ac32809ac14b4117810bdc75c063c07,2024-11-19 20:20:03,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
286
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-19,0x9d33a422c0617e720946fc6e3d2badcf8f1f36d9d3f310697ddff5fbf54910a5,2024-11-19 20:20:25,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
287
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-19,0x8cd54329d987fcf47a607d0d993e52950feebb3ceaa790cffe9bd3499601968f,2024-11-19 20:20:49,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
288
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-07,0xaaae37299329f78ac559ecc15645c370d0a005418c644ee74fc8e168b1315d5a,2024-11-07 04:09:03,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
289
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-07,0xb2fe13dc01083c55b85fb19839a10088c8b1f17f6c1785dc478103e70615f5b3,2024-11-07 04:09:33,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
290
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-07,0x05961dda08b3e88605517591d5a9f133f611d66d1c80f6ecb229f70c97b9aeb7,2024-11-07 04:10:03,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
291
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-07,0x0095018a7d02defd5c9a1f8fda0f4b3294a3a17d9ae97b1885c5ed0431ffbb16,2024-11-07 04:10:31,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
292
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-08,0xef508c7a5e05e4e18d783af57139a6522869843bb79c81f42b14c843ff134224,2024-11-08 12:15:43,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
293
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-08,0x0437ee97d0339ccdcec63fc82accd7dd8509ed44ab2207d0607272bb211a8d11,2024-11-08 12:16:13,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
294
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-08,0x92cf9d5e93b1a57212ad0719a626048eefdd6259fbcd08ca422f38e3baea2986,2024-11-08 12:16:41,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
295
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-08,0xfe09b2bd2bc210cef538b7d85b63f6a9910fdf68bd4c4d7c2c9c15b84e56cf4c,2024-11-08 12:17:09,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
296
- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-08,0x62392be6ff658aa8993f47963cc94c5b4bb0b70ea5c427969812057ea7927828,2024-11-08 19:31:21,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
297
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-12,0x23837e7d387b26e30b44178bdb0c6c643b8a5d802d916b4342a95305c762bc5a,2024-11-12 14:27:35,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
298
- ethereum,0xb0830c65499ff2A25D10D895970b152193b13FCf,2024-11-07,0x6d73ce5cfe1110def67b1dc776b349dec55b61a7ff1680d30c9fc6a2b6353a64,2024-11-07 13:21:59,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0xb0830c65499ff2a25d10d895970b152193b13fcf,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
299
- mode,0xA37e826fF12Af82e5eAed8c981218B78Cc606bac,2024-11-07,0xcd90dd2e5293269ee22f5fc4913f42e6c7f4e43fc42c90c8b716ad865b377ed7,2024-11-07 14:39:39,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D,0xA37e826fF12Af82e5eAed8c981218B78Cc606bac,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
300
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-15,0xe03a65fabb7468ac633dd5edabbb9609eabcf05be6cf1fc27f9841ed12796445,2024-11-15 18:58:13,0xA40c8a3088F892607AD01a24DBCD755EB720F725,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.00033,0x2302548951Ae698370e3C7943A843861641efB92
301
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-15,0x14588c9945c3d3f6d13fd58d91205a46dda2479a1403bba5b25c96735913310c,2024-11-15 17:56:05,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
302
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-15,0xc3624bc76dd73f83165c9dc483ad58358e8920b843fb7a46a3940f133f7a73f3,2024-11-15 17:55:41,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
303
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-15,0x450ccd2f3b50c6549890a68273176366357edd22b9bb92fb5732c213208fb37d,2024-11-15 17:55:15,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
304
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-15,0xd46245b2666e83243f782eb96876b30ece19c13af9b48497a5278726be12e338,2024-11-15 17:54:51,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
305
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-15,0x35433b80bc04e7de98a3afb506e2c8132a8cf579867be998c9010d02d4c1b78b,2024-11-15 17:28:33,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
306
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-18,0x5e7a0a4093f5463b8777bc1aee302046f3b7cbfe21cbb986ab2e02d6a714e055,2024-11-18 19:32:25,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
307
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-18,0x92defde2b1254a23adc285185324cf2b3ffeac41265dd70f3ef0476b35072c01,2024-11-18 19:32:01,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
308
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-18,0xd3c777e3522b1135bf3c00856ff892a7232745d68e3e3c78684c1e4e4f940354,2024-11-18 19:31:35,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
309
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-18,0xfbb08b7ec61f9c729a5a47dcba3281042ba435cb63b4ef71e729e4214cb8f7c8,2024-11-18 19:25:45,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
310
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-18,0x9842b8f77e8a3e61b9c7142bbea4af63f159f399b81bf77222f7562cce8b87b0,2024-11-18 09:10:05,0xA40c8a3088F892607AD01a24DBCD755EB720F725,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0051,0x2302548951Ae698370e3C7943A843861641efB92
311
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-18,0x2853a70afd78549f97b6d4bbc45952ef6b1d5000847cadb08f508b466aa1a1c9,2024-11-18 08:31:33,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
312
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-19,0x91e86fdb9a5523dd2386e9fa1deeffbb86ac609ac1e5d60795deeddf018409ba,2024-11-19 09:26:05,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
313
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-19,0x9588da03cbd1c6e76e7a90ca7b5ed827552907ca6d537e5cbe26b3b6cb47af45,2024-11-19 09:25:39,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
314
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-19,0xee1a6b8d8b96701221e749c0191d4e3d60cf19c24ac39750a89ba0a1343d25d6,2024-11-19 09:04:49,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
315
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-19,0x6cce2f632f82436fbda196a9fd9e6a14de5ce8c28557f6bb695a5e1c0bd0f40e,2024-11-19 09:04:09,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
316
- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-19,0x83523912a0693290f277b265f037ebd5aa953aa550cecfaadbec9013ef624d36,2024-11-19 08:57:57,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
317
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318
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-20,0x348a1064b12184ed4eefca86ae707a3c03f5bb14424d6986f98ec3c71aa71a58,2024-11-20 20:21:55,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
319
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-20,0xf04374541afe9676c6c1be148ad2d361ce343221100d5db91059f3f1f12bf763,2024-11-20 20:22:17,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
320
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-20,0x2878a6586bf53824255d79c4cd45a42b6559f4d11df379c7db99d00da2d0b557,2024-11-20 20:22:41,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
321
- optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-20,0xa5d0aa63e6dcc4a7c15df2a2ff27c7852aaa9aaacf41db864cc2bfa01ee7f7d9,2024-11-20 20:23:05,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
322
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0xd880358907e288a44179e4818d853c708df2eed8e6e22f1615ef2745b523c73d,2024-11-20 15:22:37,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
323
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0x73a4bd3298fe8cff0c8fbc7b2a4ed13c6b65487005f1e4941f62ec76c7d722a8,2024-11-20 15:23:05,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
324
- optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0x1c481b97d2f51165d16c3bb9ab9be3150ef3e9e498d62d505790b1696a1d711a,2024-11-20 15:23:39,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
325
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326
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- optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-20,0x0095018a7d02defd5c9a1f8fda0f4b3294a3a17d9ae97b1885c5ed0431ffbb16,2024-11-20 04:10:31,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
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- mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-20,0xe1243862f190fe30e8daaf89d1e9f499a93d2dbbd2bc717fdfa80437597a8e12,2024-11-20 09:27:13,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
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3
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modius_apr_values.csv DELETED
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1
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2
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