fix linting issues

.gitignore

@@ -63,4 +63,4 @@ web/src/__generated__
 Pulumi*.yaml
 
 # performance profile files
-profiles
+profiles

api/graphql/filters.py

@@ -89,8 +89,10 @@ def to_internal_filter_mapped(self, f: Callable[[T], Y]) -> GenericFilter[Y]:
 # issue in strawberry graphql where the usage of generics in input types causes major
 # slowdowns.
 # @see https://github.com/strawberry-graphql/strawberry/issues/3544
-@strawberry.input(description='Filter for GraphQL queries')
+@strawberry.input(description='String filter for GraphQL queries')
 class GraphQLFilterStr(GraphQLFilter[str]):
+    """String filter for GraphQL queries"""
+
     eq: str | None = None
     in_: list[str] | None = None
     nin: list[str] | None = None
@@ -102,8 +104,10 @@ class GraphQLFilterStr(GraphQLFilter[str]):
     icontains: str | None = None
 
 
-@strawberry.input(description='Filter for GraphQL queries')
+@strawberry.input(description='Int filter for GraphQL queries')
 class GraphQLFilterInt(GraphQLFilter[int]):
+    """Int filter for GraphQL queries"""
+
     eq: int | None = None
     in_: list[int] | None = None
     nin: list[int] | None = None
@@ -115,8 +119,10 @@ class GraphQLFilterInt(GraphQLFilter[int]):
     icontains: int | None = None
 
 
-@strawberry.input(description='Filter for GraphQL queries')
+@strawberry.input(description='Bool filter for GraphQL queries')
 class GraphQLFilterBool(GraphQLFilter[bool]):
+    """Bool filter for GraphQL queries"""
+
     eq: bool | None = None
     in_: list[bool] | None = None
     nin: list[bool] | None = None
@@ -128,8 +134,10 @@ class GraphQLFilterBool(GraphQLFilter[bool]):
     icontains: bool | None = None
 
 
-@strawberry.input(description='Filter for GraphQL queries')
+@strawberry.input(description='Analysis status filter for GraphQL queries')
 class GraphQLFilterAnalysisStatus(GraphQLFilter[AnalysisStatus]):
+    """Analysis status filter for GraphQL queries"""
+
     eq: AnalysisStatus | None = None
     in_: list[AnalysisStatus] | None = None
     nin: list[AnalysisStatus] | None = None
@@ -141,8 +149,10 @@ class GraphQLFilterAnalysisStatus(GraphQLFilter[AnalysisStatus]):
     icontains: AnalysisStatus | None = None
 
 
-@strawberry.input(description='Filter for GraphQL queries')
+@strawberry.input(description='Datetime filter for GraphQL queries')
 class GraphQLFilterDatetime(GraphQLFilter[datetime.datetime]):
+    """Datetime filter for GraphQL queries"""
+
     eq: datetime.datetime | None = None
     in_: list[datetime.datetime] | None = None
     nin: list[datetime.datetime] | None = None
@@ -154,8 +164,10 @@ class GraphQLFilterDatetime(GraphQLFilter[datetime.datetime]):
     icontains: datetime.datetime | None = None
 
 
-@strawberry.input(description='Filter for GraphQL queries')
+@strawberry.input(description='Date filter for GraphQL queries')
 class GraphQLFilterDate(GraphQLFilter[datetime.date]):
+    """Date filter for GraphQL queries"""
+
     eq: datetime.date | None = None
     in_: list[datetime.date] | None = None
     nin: list[datetime.date] | None = None

api/server.py

@@ -59,15 +59,16 @@ async def app_lifespan(_: FastAPI):
     from pyinstrument import Profiler
     from pyinstrument.renderers.speedscope import SpeedscopeRenderer
 
-    @app.middleware("http")
+    @app.middleware('http')
     async def profile_request(request: Request, call_next):
-        profiler = Profiler(async_mode="enabled")
+        """Middleware to allow profiling of requests to the api"""
+        profiler = Profiler(async_mode='enabled')
         profiler.start()
         resp = await call_next(request)
         profiler.stop()
 
-        text_output = profiler.output_text()
         if 'text' in PROFILE_REQUESTS_OUTPUT:
+            text_output = profiler.output_text()
             print(text_output)
 
         timestamp = datetime.datetime.now().replace(microsecond=0).isoformat()

docs/installation.md

@@ -429,4 +429,4 @@ If you are working on performance issues it can be handy to generate a report th
 
 There are a few different options for outputting profiles which can be specified in the `SM_PROFILE_REQUESTS_OUTPUT` environment variable. The possible values are `text` which will print the profiling results to stdout, `html` which will generate an interactive pyinstrument report, or `json` which will generate a json profiling report which can be dropped into [speedscope](https://www.speedscope.app/) to explore the profile.
 
-You can output multiple report types by specifying the types in a list like: `export SM_PROFILE_REQUESTS_OUTPUT=json,text,html` 
+You can output multiple report types by specifying the types in a list like: `export SM_PROFILE_REQUESTS_OUTPUT=json,text,html`