Party hours and quiet gaps by neighborhood and city

A community events app records every party in two separate lists that share a partyId:

• partyWindows: rows of [partyId, start, end]. start and end are hours as strings, and a party runs over the half-open interval [start, end).
• geoDataList: rows of [partyId, neighborhood, city].

Each neighborhood belongs to one city. Ignore a party that appears in only one of the two lists.

Implement a class PartyAggregator(partyWindows, geoDataList) with:

1. neighborhood_hours() -> dict[str, int]: for each neighborhood, the number of hours during which at least one of its parties is running. Overlapping parties must not be counted twice.
2. city_hours() -> dict[str, int]: the same measure for each city, over all parties in all of its neighborhoods.
3. city_gaps() -> dict[str, list[tuple[int, int]]]: for each city, the quiet stretches (from, to) between its first party start and its last party end when no party is running anywhere in the city, in time order. A party ending at 15 and another starting at 15 leave no gap.
4. report(): a list of (city, hours, gaps) sorted by hours descending, then city name ascending.

Raise ValueError for a window whose start is after its end. A zero-length window covers no time.

Constraints: up to 10^5 parties; hours fit in a 32-bit integer.

Example

partyWindows = [["a","10","14"], ["b","12","16"], ["c","20","21"], ["d","8","9"]]
geoDataList  = [["a","Harbor","Bayview"], ["b","Hilltop","Bayview"], ["c","Harbor","Bayview"], ["e","Old Town","Riverton"]]

• neighborhood_hours() → {"Harbor": 5, "Hilltop": 4}
• city_hours() → {"Bayview": 7} (10 to 16 is 6 hours, plus 20 to 21)
• city_gaps() → {"Bayview": [(16, 20)]}
• Party d has no location and e has no window, so both are ignored.

Reconstructed from the reported question; details may differ from what interviewers ask.