Ruff

Author: thewhaleking

bittensor_cli/src/__init__.py

@@ -17,7 +17,7 @@ class Constants:
         "archive": archive_entrypoint,
         "local": local_entrypoint,
         "dev": dev_entrypoint,
-        "rao": rao_entrypoint
+        "rao": rao_entrypoint,
     }
     delegates_detail_url = "https://raw.githubusercontent.com/opentensor/bittensor-delegates/main/public/delegates.json"
 

bittensor_cli/src/bittensor/async_substrate_interface.py

@@ -897,6 +897,7 @@ async def init_runtime(
 
         :returns: Runtime object
         """
+
         async def get_runtime(block_hash, block_id) -> Runtime:
             # Check if runtime state already set to current block
             if (block_hash and block_hash == self.last_block_hash) or (
@@ -2193,20 +2194,16 @@ async def runtime_call(
             params = {}
 
         try:
-            runtime_call_def = self.runtime_config.type_registry["runtime_api"][
-                api
-            ]["methods"][method]
+            runtime_call_def = self.runtime_config.type_registry["runtime_api"][api][
+                "methods"
+            ][method]
             runtime_api_types = self.runtime_config.type_registry["runtime_api"][
                 api
             ].get("types", {})
         except KeyError:
-            raise ValueError(
-                f"Runtime API Call '{api}.{method}' not found in registry"
-            )
+            raise ValueError(f"Runtime API Call '{api}.{method}' not found in registry")
 
-        if isinstance(params, list) and len(params) != len(
-            runtime_call_def["params"]
-        ):
+        if isinstance(params, list) and len(params) != len(runtime_call_def["params"]):
             raise ValueError(
                 f"Number of parameter provided ({len(params)}) does not "
                 f"match definition {len(runtime_call_def['params'])}"

bittensor_cli/src/bittensor/chain_data.py

@@ -571,9 +571,11 @@ def list_from_vec_u8(cls, vec_u8: bytes) -> list["SubnetInfo"]:
             )
         return result
 
+
 @dataclass
 class SubnetInfoV2:
     """Dataclass for subnet info."""
+
     netuid: int
     owner_ss58: str
     max_allowed_validators: int
@@ -776,7 +778,7 @@ def list_from_vec_u8(cls, vec_u8: bytes) -> List["SubnetInfoV2"]:
                 ["arbitration_block", "Compact<u64>"],
             ],
         },
-          "SubnetState": {
+        "SubnetState": {
             "type": "struct",
             "type_mapping": [
                 ["netuid", "Compact<u16>"],

bittensor_cli/src/commands/root.py

@@ -719,21 +719,22 @@ async def _get_list() -> tuple:
         )
         return sm, rn, di, ts
 
-
     with console.status(
         f":satellite: Syncing with chain: [white]{subtensor}[/white] ...",
         spinner="aesthetic",
     ):
-            
         senate_members, root_neurons, delegate_info, total_stakes = await _get_list()
-        total_tao = sum(float(Balance.from_rao(total_stakes[neuron.hotkey])) for neuron in root_neurons)
+        total_tao = sum(
+            float(Balance.from_rao(total_stakes[neuron.hotkey]))
+            for neuron in root_neurons
+        )
 
         table = Table(
             Column(
                 "[bold white]UID",
                 style="dark_orange",
                 no_wrap=True,
-                footer=f"[bold]{len(root_neurons)}[/bold]"
+                footer=f"[bold]{len(root_neurons)}[/bold]",
             ),
             Column(
                 "[bold white]NAME",
@@ -750,7 +751,7 @@ async def _get_list() -> tuple:
                 justify="right",
                 style="light_goldenrod2",
                 no_wrap=True,
-                footer=f"{total_tao:.2f} (\u03c4) "
+                footer=f"{total_tao:.2f} (\u03c4) ",
             ),
             Column(
                 "[bold white]SENATOR",
@@ -770,7 +771,7 @@ async def _get_list() -> tuple:
                 f"[red]Error: No neurons detected on the network:[/red] [white]{subtensor}"
             )
             raise typer.Exit()
-        
+
         sorted_root_neurons = sorted(
             root_neurons,
             key=lambda neuron: float(Balance.from_rao(total_stakes[neuron.hotkey])),