Merge pull request #450 from BalancerMaxis/issue/399

chore: make compatible with new bal tools release

.github/workflows/generate_permissions.yaml

@@ -25,7 +25,7 @@ jobs:
         id: update
         run: |
           pip3 install -r bal_addresses/requirements.txt
-          python3 generate_current_permissions.py
+          python3 gen_current_permissions.py
           git add -A
 
       - name: pull-request

gen_pools_and_gauges.py

@@ -1,38 +1,17 @@
 import json
 
 import pandas as pd
-import requests
 
 from bal_tools import BalPoolsGauges
-from bal_tools import Subgraph
-
-
-def query_swap_enabled_pools(chain, skip=0, step_size=100) -> list:
-    url = Subgraph(chain).get_subgraph_url("core")
-    query = f"""{{
-        pools(
-            skip: {skip}
-            first: {step_size}
-            where: {{swapEnabled: true}}
-        ) {{
-            address
-            symbol
-        }}
-    }}"""
-    r = requests.post(url, json={"query": query})
-    r.raise_for_status()
-    try:
-        result = r.json()["data"]["pools"]
-    except KeyError:
-        result = []
-    if len(result) > 0:
-        # didnt reach end of results yet, collect next page
-        result += query_swap_enabled_pools(chain, skip + step_size, step_size)
-    return result
-
-
-def process_query_swap_enabled_pools(result) -> dict:
-    df = pd.DataFrame(result)
+
+
+def process_query_pools(result) -> dict:
+    flattened_result = []
+    for pool_data in result:
+        flattened_result.append(
+            {"address": pool_data.address, "symbol": pool_data.symbol}
+        )
+    df = pd.DataFrame(flattened_result)
     if len(df) == 0:
         return
     # assert no duplicate addresses exist
@@ -46,25 +25,25 @@ def process_query_swap_enabled_pools(result) -> dict:
         print("Found duplicate symbols!")
         print(df[df["symbol"].duplicated(keep=False)].sort_values("symbol"))
         raise
-    return df.set_index("symbol")["address"].to_dict()
+    return df.sort_values("address").set_index("symbol")["address"].to_dict()
 
 
-def process_query_preferential_gauges(result) -> dict:
+def process_query_gauges(result) -> dict:
     df = pd.DataFrame(result)
     if len(df) == 0:
         return
     # assert no duplicate addresses exist
-    assert len(df["id"].unique()) == len(df)
+    assert len(df["address"].unique()) == len(df)
 
     # solve issue of duplicate gauge symbols
-    df["symbol"] = df["symbol"] + "-" + df["id"].str[2:6]
+    df["symbol"] = df["symbol"] + "-" + df["address"].str[2:6]
 
     # confirm no duplicate symbols exist, raise if so
     if len(df["symbol"].unique()) != len(df):
         print("Found duplicate symbols!")
         print(df[df["symbol"].duplicated(keep=False)].sort_values("symbol"))
         raise
-    return df.set_index("symbol")["id"].to_dict()
+    return df.sort_values("address").set_index("symbol")["address"].to_dict()
 
 
 def process_query_root_gauges(result, gauges) -> dict:
@@ -110,21 +89,18 @@ def main():
         chains = json.load(f)
     for chain in chains["BALANCER_PRODUCTION_CHAINS"]:
         print(f"Generating pools and gauges for {chain}...")
-        gauge_info = BalPoolsGauges(chain)
+        pool_gauge_info = BalPoolsGauges(chain)
         # pools
-        # TODO: consider moving to query object??
-        result = process_query_swap_enabled_pools(query_swap_enabled_pools(chain))
+        result = process_query_pools(pool_gauge_info.query_all_pools())
         if result:
             pools[chain] = result
         # gauges
-        result = process_query_preferential_gauges(
-            gauge_info.query_preferential_gauges()
-        )
+        result = process_query_gauges(pool_gauge_info.query_all_gauges())
         if result:
             gauges[chain] = result
         # cache mainnet BalPoolsGauges
         if chain == "mainnet":
-            gauge_info_mainnet = gauge_info
+            gauge_info_mainnet = pool_gauge_info
 
     # root gauges; only on mainnet
     result = process_query_root_gauges(gauge_info_mainnet.query_root_gauges(), gauges)