Temporarily don't reload snapshot

backend/api/asset_liability.py

@@ -1,3 +1,5 @@
+import functools
+
 from backend.state import BackendRequest
 from backend.state import BackendState
 from backend.utils.matrix import get_matrix
@@ -9,6 +11,7 @@
 router = APIRouter()
 
 
+@functools.lru_cache()
 async def _get_asset_liability_matrix(
     snapshot_path: str,
     vat: Vat,

backend/app.py

@@ -35,10 +35,10 @@ def clean_cache(state: BackendState) -> None:
 
     pickles = glob.glob("pickles/*")
 
-    # check for pickle folders with less than 8 files (error in write)
+    # check for pickle folders with less than 4 files (error in write)
     incomplete_pickles = []
     for pickle in pickles:
-        if len(glob.glob(f"{pickle}/*")) < 8:
+        if len(glob.glob(f"{pickle}/*")) < 4:
             incomplete_pickles.append(pickle)
 
     for incomplete_pickle in incomplete_pickles:
@@ -91,15 +91,27 @@ async def lifespan(app: FastAPI):
     if len(cached_vat_path) > 0:
         print("Loading cached vat")
         await state.load_pickle_snapshot(cached_vat_path[-1])
-        await repeatedly_clean_cache(state)
-        await repeatedly_retake_snapshot(state)
+        # await repeatedly_clean_cache(state)
+        # await repeatedly_retake_snapshot(state)
     else:
         print("No cached vat found, bootstrapping")
         await state.bootstrap()
         await state.take_pickle_snapshot()
-        await repeatedly_clean_cache(state)
-        await repeatedly_retake_snapshot(state)
+        # await repeatedly_clean_cache(state)
+        # await repeatedly_retake_snapshot(state)
     state.ready = True
+    # print("Checking price shock")
+    # await price_shock._get_price_shock(
+    #     state.current_pickle_path, state.vat, state.dc, 0.05, "ignore stables", 5
+    # )
+    # print("Checking asset liability matrix")
+    # await asset_liability._get_asset_liability_matrix(
+    #     state.current_pickle_path, state.vat, 0, 0
+    # )
+    import random
+    import time
+
+    time.sleep(random.randint(1, 10))
     print("Starting app")
     yield
 

backend/state.py

@@ -75,6 +75,8 @@ def initialize(
             self.spot_map,
             self.perp_map,
         )
+        self.ready = False
+        self.current_pickle_path = "bootstrap"
 
     async def bootstrap(self):
         with waiting_for("drift client"):

gunicorn_config.py

@@ -1,7 +1,7 @@
-workers = 4
+workers = 2
 worker_class = "uvicorn.workers.UvicornWorker"
 bind = "0.0.0.0:8000"
-timeout = 180
+timeout = 300
 keepalive = 65
 max_requests = 1000
 max_requests_jitter = 50
@@ -11,4 +11,4 @@
 
 # Restart workers that die unexpectedly
 worker_exit_on_restart = True
-worker_restart_delay = 2
+worker_restart_delay = 20

src/page/price_shock.py

@@ -60,18 +60,24 @@ def price_shock_plot(user_leverages, oracle_distort: float):
     num_scenarios = len(levs["leverages_down"])
     oracle_moves = generate_oracle_moves(num_scenarios, oracle_distort)
 
+    # Create and sort the DataFrame BEFORE plotting
     df_plot = pd.DataFrame(
         {
             "Oracle Move (%)": oracle_moves,
             "Total Bankruptcy ($)": total_bankruptcies,
             "Spot Bankruptcy ($)": spot_bankruptcies,
         }
-    ).sort_values("Oracle Move (%)")
+    )
+
+    # Sort by Oracle Move to ensure correct line connection order
+    df_plot = df_plot.sort_values("Oracle Move (%)")
 
+    # Calculate Perp Bankruptcy AFTER sorting
     df_plot["Perp Bankruptcy ($)"] = (
         df_plot["Total Bankruptcy ($)"] - df_plot["Spot Bankruptcy ($)"]
     )
 
+    # Create the figure with sorted data
     fig = go.Figure()
     for column in [
         "Total Bankruptcy ($)",
@@ -92,6 +98,7 @@ def price_shock_plot(user_leverages, oracle_distort: float):
         xaxis_title="Oracle Move (%)",
         yaxis_title="Bankruptcy ($)",
         legend_title="Bankruptcy Type",
+        template="plotly_dark",
     )
 
     return fig
@@ -151,19 +158,14 @@ def price_shock_page():
 
     fig = price_shock_plot(result, oracle_distort)
     st.plotly_chart(fig)
-
-    oracle_down_max = pd.DataFrame(
-        result["leverages_down"][-1][-1], index=result["user_keys"]
-    )
+    oracle_down_max = pd.DataFrame(result["leverages_down"][-1])
     with st.expander(
         str("oracle down max bankrupt count=")
         + str(len(oracle_down_max[oracle_down_max.net_usd_value < 0]))
     ):
         st.dataframe(oracle_down_max)
 
-    oracle_up_max = pd.DataFrame(
-        result["leverages_up"][-1][-1], index=result["user_keys"]
-    )
+    oracle_up_max = pd.DataFrame(result["leverages_up"][-1], index=result["user_keys"])
     with st.expander(
         str("oracle up max bankrupt count=")
         + str(len(oracle_up_max[oracle_up_max.net_usd_value < 0]))