Apply ruff formatting

app.py

@@ -22,6 +22,7 @@
 
 BASE_URL = "https://api.eia.gov/v2/electricity/rto/daily-fuel-type-data/data/"
 
+
 @st.cache_data(show_spinner=False)
 def load_fuel_data(api_key: str, start: str, end: str) -> pd.DataFrame:
     params = {
@@ -38,6 +39,7 @@ def load_fuel_data(api_key: str, start: str, end: str) -> pd.DataFrame:
     rows = fetch_all_pages(BASE_URL, params)
     return pd.json_normalize(rows)
 
+
 with st.spinner("Loading data..."):
     df = load_fuel_data(api_key, start, end)
 
@@ -51,9 +53,9 @@ def load_fuel_data(api_key: str, start: str, end: str) -> pd.DataFrame:
 # Aggregation by date and fuel type
 agg = (
     df.groupby(["period", "type-name"], as_index=False)[ycol]
-      .sum()
-      .rename(columns={ycol: "Demand"})
-) # type: ignore
+    .sum()
+    .rename(columns={ycol: "Demand"})
+)  # type: ignore
 
 # Keep top N fuel types by total
 agg = top_n_by_total(agg, "type-name", "Demand", top_n=top_n)

eia_api.py

@@ -3,7 +3,9 @@
 import requests
 
 
-def fetch_all_pages(base_url: str, params: dict[str, Any], timeout: int = 60) -> list[dict[str, Any]]:
+def fetch_all_pages(
+    base_url: str, params: dict[str, Any], timeout: int = 60
+) -> list[dict[str, Any]]:
     all_rows: list[dict[str, Any]] = []
     offset = 0
     length = params.get("length", 5000)

mainPage.py

@@ -8,4 +8,4 @@
 pg = st.navigation([main_page, page_2])
 
 # Run the selected page
-pg.run()
+pg.run()

region.py

@@ -2,63 +2,70 @@
 import pandas as pd
 import plotly.express as px
 
-from data_utils import convert_units, filter_to_timezone, parse_period_and_value, top_n_by_total
+from data_utils import (
+    convert_units,
+    filter_to_timezone,
+    parse_period_and_value,
+    top_n_by_total,
+)
 from eia_api import fetch_all_pages
-
-st.set_page_config(page_title="EIA Demand by Region (ET)", layout="wide")
-st.title("U.S. Electricity Demand by Region (Eastern Time)")
-
-# API Key Retrieval
-api_key = st.secrets.get("EIA_API_KEY", None)
-
-# Predefine time and unit values
-start = st.sidebar.text_input("Start date (YYYY-MM-DD)", value="2026-02-09")
-end = st.sidebar.text_input("End date (YYYY-MM-DD)", value="2026-02-16")
-units = st.sidebar.radio("Units", ["MWh", "GWh"], horizontal=True)
-
+
+st.set_page_config(page_title="EIA Demand by Region (ET)", layout="wide")
+st.title("U.S. Electricity Demand by Region (Eastern Time)")
+
+# API Key Retrieval
+api_key = st.secrets.get("EIA_API_KEY", None)
+
+# Predefine time and unit values
+start = st.sidebar.text_input("Start date (YYYY-MM-DD)", value="2026-02-09")
+end = st.sidebar.text_input("End date (YYYY-MM-DD)", value="2026-02-16")
+units = st.sidebar.radio("Units", ["MWh", "GWh"], horizontal=True)
+
 BASE_URL = "https://api.eia.gov/v2/electricity/rto/daily-region-data/data/"
-
-@st.cache_data(show_spinner=False)
-def load_region_data(api_key: str, start: str, end: str) -> pd.DataFrame:
-    params = {
-        "api_key": api_key,
-        "frequency": "daily",
-        "data[0]": "value",
-        "start": start,
-        "end": end,
-        "sort[0][column]": "period",
-        "sort[0][direction]": "asc",
-        "offset": 0,
-        "length": 5000,
-    }
-    rows = fetch_all_pages(BASE_URL, params)
-    df = pd.json_normalize(rows)
-    return df
-
-with st.spinner("Loading data from EIA..."):
-    df = load_region_data(api_key, start, end)
-
+
+
+@st.cache_data(show_spinner=False)
+def load_region_data(api_key: str, start: str, end: str) -> pd.DataFrame:
+    params = {
+        "api_key": api_key,
+        "frequency": "daily",
+        "data[0]": "value",
+        "start": start,
+        "end": end,
+        "sort[0][column]": "period",
+        "sort[0][direction]": "asc",
+        "offset": 0,
+        "length": 5000,
+    }
+    rows = fetch_all_pages(BASE_URL, params)
+    df = pd.json_normalize(rows)
+    return df
+
+
+with st.spinner("Loading data from EIA..."):
+    df = load_region_data(api_key, start, end)
+
 if df.empty:
     st.warning("No data returned. Double-check your dates and API key.")
     st.stop()
 
 df = parse_period_and_value(df)
 
-# Fix to Eastern Time 
+# Fix to Eastern Time
 df = filter_to_timezone(df, "eastern")
 
 df, ycol, ylabel = convert_units(df, units)
 
 # Plot Graph
 df = top_n_by_total(df, "respondent", "value", top_n=10)
 region_col = "respondent"
-
-fig = px.line(
-    df.sort_values("period"),
-    x="period",
-    y=ycol,
-    color=region_col,
-    title=f"U.S. electricity demand by region ({start} to {end}), Eastern Time",
-    labels={"period": "Date", ycol: ylabel, region_col: "Region"},
-)
-st.plotly_chart(fig, use_container_width=True)
+
+fig = px.line(
+    df.sort_values("period"),
+    x="period",
+    y=ycol,
+    color=region_col,
+    title=f"U.S. electricity demand by region ({start} to {end}), Eastern Time",
+    labels={"period": "Date", ycol: ylabel, region_col: "Region"},
+)
+st.plotly_chart(fig, use_container_width=True)

tests/test_data_utils.py

@@ -1,6 +1,11 @@
 import pandas as pd
 
-from data_utils import convert_units, filter_to_timezone, parse_period_and_value, top_n_by_total
+from data_utils import (
+    convert_units,
+    filter_to_timezone,
+    parse_period_and_value,
+    top_n_by_total,
+)
 
 
 def test_convert_units_to_gwh_creates_scaled_column():
@@ -49,7 +54,9 @@ def test_top_n_by_total_keeps_only_largest_categories():
 
 
 def test_parse_period_and_value_converts_types():
-    df = pd.DataFrame({"period": ["2026-02-01", "not-a-date"], "value": ["12.5", "oops"]})
+    df = pd.DataFrame(
+        {"period": ["2026-02-01", "not-a-date"], "value": ["12.5", "oops"]}
+    )
     parsed = parse_period_and_value(df)
     assert parsed["period"].notna().sum() == 1
     assert parsed["value"].tolist()[0] == 12.5