Fixing ESLint warnings, ruff, prettier formatting + New Email List Question in Contact Page

.claude/hooks/format.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 """PostToolUse hook: format files Claude edits.
 
 .py -> ruff format (config in backend/pyproject.toml)

backend/api/models/__init__.py

@@ -1,6 +1,3 @@
-# ruff: noqa: F401
-
-
 from .request_models import (
     DPSeriesRequest,
     FilterRequest,

backend/api/models/request_models.py

@@ -3,7 +3,9 @@
 
 from pydantic import BaseModel, model_validator
 
-MAX_YEAR = datetime.now().year - 2
+from query.clock import EASTERN_STD_TIME
+
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 2
 
 
 class RangeFilter(BaseModel):

backend/api/routes/get_routes/get_wholedata.py

@@ -32,7 +32,7 @@ async def read_flood_data():
 
 # VT Municipalities Endpoint
 @router.get("/data/vermont/municipalities")
-async def read_municipalities_data():
+def read_municipalities_data():
     with open(
         DATA_DIR / "vermont" / "municipalities.json", "r", encoding="utf-8"
     ) as file:

backend/data_collection/acs5.py

@@ -20,6 +20,7 @@
 
 from data_collection.base import ALL_GEOS
 from data_collection.census import tidy_census
+from query.clock import EASTERN_STD_TIME
 
 # Define API key through the .env file
 API_KEY = os.environ.get("CENSUS_API_KEY")
@@ -36,7 +37,7 @@
 STORAGE_LOCATION = "Data/Census/ACS_5"
 ID_VARS = ["year", "geo_type", "table", "NAME", "state", "county"]
 
-MAX_YEAR = datetime.now().year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 YEARS = range(2009, MAX_YEAR)
 
@@ -73,7 +74,7 @@ def fetch_table(year, table, for_clause, in_clause):
         df["year"] = year
         df["table"] = table
         return df
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001 -- skip and keep the run going
         print(f"  SKIP {year} / {table} / {for_clause}: {e}")
         return None
 
@@ -133,7 +134,7 @@ def run_acs5_scrape(years: range = YEARS, geos: list = GEOS, append: bool = Fals
                 tidy_year["table"] = table
                 tidy_frames.append(tidy_year)
 
-            except Exception as e:
+            except Exception as e:  # noqa: BLE001 -- skip and keep the run going
                 print(f"  SKIP tidy {year} / {table}: {e}")
 
         if tidy_frames:
@@ -150,11 +151,11 @@ def run_acs5_scrape(years: range = YEARS, geos: list = GEOS, append: bool = Fals
 def merge_tidy_tables():
     """Merge the per-table tidy parquets into one combined file."""
     tidy_frames = []
-    for table, label in TABLES.items():
+    for label in TABLES.values():
         path = f"{STORAGE_LOCATION}/vt_acs5_{label}_data_tidy.parquet"
         try:
             tidy_frames.append(pd.read_parquet(path))
-        except Exception as e:
+        except Exception as e:  # noqa: BLE001 -- skip and keep the run going
             print(f"  SKIP {path}: {e}")
 
     if tidy_frames:

backend/data_collection/base.py

@@ -30,6 +30,7 @@
 import requests
 
 from data_collection.census import split_name_col
+from query.clock import EASTERN_STD_TIME
 
 # Define API key through the .env file
 API_KEY = os.environ.get("CENSUS_API_KEY")
@@ -52,7 +53,7 @@
 GEOS = [(k, *v) for k, v in ALL_GEOS.items()]
 
 
-MAX_YEAR = datetime.now().year - 2
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 2
 
 # ---------------------------------------------------------------------------
 # Data structures
@@ -101,7 +102,7 @@ def fetch(
             if c[0] == "B":
                 df[c] = pd.to_numeric(df[c], errors="coerce")
         return df
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001 -- skip and keep the run going
         print(f"  SKIP {year} / {for_clause}: {e}")
         return None
 
@@ -114,9 +115,13 @@ def fetch(
 def pct(val: float, total: float) -> float | None:
     try:
         if total and total > 0:
-            return round(val / total * 100, 1)
-    except Exception:
-        pass
+            return round((val / total) * 100, 1)
+    except OverflowError:
+        print(f"OverflowError computing pct({val}, {total})")
+    except FloatingPointError:
+        print(f"FloatingPointError computing pct({val}, {total})")
+    except ValueError:
+        print(f"ValueError computing pct({val}, {total})")
     return None
 
 

backend/data_collection/cdc.py

@@ -7,10 +7,11 @@
     Fetches Vermont CDC data from the CDC SODA API (In "Open Data Format")
 """
 
-import pandas as pd
+from io import BytesIO
+
 import geopandas as gpd
+import pandas as pd
 import requests
-from io import BytesIO
 
 # ---------------------------------------------------------------------------
 # API endpoints

backend/data_collection/demographics.py

@@ -13,6 +13,7 @@
 import pandas as pd
 
 from data_collection.base import ALL_GEOS, VarGroup, run_acs_b_scrape
+from query.clock import EASTERN_STD_TIME
 
 # ---------------------------------------------------------------------------
 # Age band definitions: (label, male_suffix_range, female_suffix_range)
@@ -29,7 +30,7 @@
     ("75 Plus", range(23, 26), range(47, 50)),
 ]
 
-MAX_YEAR = datetime.now().year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 YEARS = range(2009, MAX_YEAR)
 

backend/data_collection/economic.py

@@ -25,6 +25,7 @@
 import pandas as pd
 
 from data_collection.base import ALL_GEOS, VarGroup, run_acs_b_scrape
+from query.clock import EASTERN_STD_TIME
 
 SL = "Labor Force"
 SI = "Income"
@@ -86,7 +87,7 @@
     "B19301": ["B19301_001E"],
 }
 
-MAX_YEAR = datetime.now().year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 YEARS = range(2009, MAX_YEAR)
 

backend/data_collection/education.py

@@ -17,6 +17,7 @@
 import pandas as pd
 
 from data_collection.base import ALL_GEOS, VarGroup, run_acs_b_scrape
+from query.clock import EASTERN_STD_TIME
 
 TOTAL = "B15003_001E"
 S = "Educational Attainment"
@@ -41,7 +42,7 @@
     "B15003": [TOTAL] + [f"B15003_{str(i).zfill(3)}E" for i in range(2, 26)],
 }
 
-MAX_YEAR = datetime.now().year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 YEARS = range(2009, MAX_YEAR)
 

backend/data_collection/housing.py

@@ -20,6 +20,7 @@
 import pandas as pd
 
 from data_collection.base import ALL_GEOS, VarGroup, run_acs_b_scrape
+from query.clock import EASTERN_STD_TIME
 
 S = "Housing"
 
@@ -45,7 +46,7 @@
     "B25077": ["B25077_001E"],
 }
 
-MAX_YEAR = datetime.now().year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 YEARS = range(2009, MAX_YEAR)
 

backend/data_collection/qcew.py

@@ -25,6 +25,8 @@
 import pandas as pd
 import requests
 
+from query.clock import EASTERN_STD_TIME
+
 # ---------------------------------------------------------------------------
 # Constants
 # ---------------------------------------------------------------------------
@@ -89,7 +91,7 @@
 BASE_URL = "https://data.bls.gov/cew/data/api/{year}/{q}/area/{fips}.csv"
 QUARTERS = [1, 2, 3, 4]
 
-MAX_YEAR = datetime.now().year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 YEARS = range(2009, MAX_YEAR)
 
@@ -107,7 +109,7 @@ def fetch_quarter(year: int, quarter: int, area_fips: str) -> pd.DataFrame | Non
         df = pd.read_csv(StringIO(r.text), dtype=str)
         df.columns = df.columns.str.strip()
         return df
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001 -- skip and keep the run going
         print(f"  SKIP {year}Q{quarter} / {area_fips}: {e}")
         return None
 

backend/query/__init__.py

@@ -36,7 +36,7 @@
 }
 _NAME_TO_MODULE = {n: m for m, names in _EXPORTS.items() for n in names}
 
-__all__ = sorted(_NAME_TO_MODULE)
+__all__ = sorted(_NAME_TO_MODULE)  # noqa: PLE0605
 
 
 def __getattr__(name: str):

backend/query/clock.py

@@ -0,0 +1,5 @@
+from datetime import timedelta, timezone
+
+# Fixed UTC-5 (EST, no DST): MAX_YEAR only needs the year, and the year
+# boundary always falls in winter.
+EASTERN_STD_TIME = timezone(timedelta(hours=-5))

backend/query/comparison.py

@@ -21,6 +21,7 @@
 
 from api.models import FilterSource
 from query.cdc import build_cmap, to_rgba
+from query.clock import EASTERN_STD_TIME
 from query.production_db import get_db
 from query.sql_render import sql_filter_block
 
@@ -35,7 +36,7 @@
 ACS_SENTINEL = -666666666
 # Vintages this recent aren't final yet (ACS5/CDC Places both lag ~2 years),
 # so the newest year at or before this is the one used per table.
-MAX_YEAR = datetime.now().year - 2
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 2
 
 
 class NoDataError(ValueError):

backend/query/core_functions.py

@@ -117,15 +117,19 @@ def filter_tree(
     tree_labels: list[str],
     table: str,
     db=DB,
-    rangemap: dict = {},
-    badgemap: dict = {},
+    rangemap: dict | None = None,
+    badgemap: dict | None = None,
 ) -> FilterResponse:
     """
     Info for cascading filter UI.
     TODO: Right now, we pass in separate db.
     When we consolidate all SQL to one source/place, we'll update this to be just one.
     TODO: update to not pass in tree_labels but just use from schema
     """
+    if rangemap is None:
+        rangemap = {}
+    if badgemap is None:
+        badgemap = {}
     cols = [colmap.get(label) for label in tree_labels]
     select = ", ".join(f'"{col}"' for col in cols)
     order = ", ".join(str(i + 1) for i in range(len(cols)))

backend/run_data_cleaning.py

@@ -49,7 +49,7 @@ def run_master_clean(script_name: str | None = None):
                 cleaner.main(con)
                 print(f"Completed {name}")
 
-            except Exception as e:
+            except Exception as e:  # noqa: BLE001 -- isolate any cleaner failure so the rest still run
                 failed.append(name)
                 print(f"FAILED {name}: {e}")
 

backend/run_data_collection.py

@@ -1,5 +1,5 @@
 import argparse
-from datetime import datetime, timedelta, timezone
+from datetime import datetime
 
 from data_collection import (
     acs5,
@@ -20,6 +20,7 @@
     zoning,
 )
 from lake_build import get_connection, insert_year, replace_table
+from query.clock import EASTERN_STD_TIME
 
 YEARLY_SCRAPERS = [
     acs5,
@@ -43,8 +44,7 @@
     zoning,
 ]
 
-eastern_std_time = timezone(timedelta(hours=5))
-MAX_YEAR = datetime.now(eastern_std_time).year - 1
+MAX_YEAR = datetime.now(EASTERN_STD_TIME).year - 1
 
 
 def run_scraper(

backend/tests/test_base.py

@@ -5,7 +5,6 @@
 
 import pandas as pd
 import pytest
-
 from data_collection_OLD.base import VarGroup, compute_tidy_generic, pct
 
 # ---------------------------------------------------------------------------

backend/tests/test_lake.py

@@ -75,7 +75,7 @@ def inspect_schema(schema: str) -> None:
 
                 print(f"Years: {years[0]} → {years[1]} ({years[2]} distinct)")
 
-            except Exception as e:
+            except Exception as e:  # noqa: BLE001 -- skip and keep the run going
                 print(f"Could not inspect years: {e}")