Party Time - Party Windows and Dead Zones

You run a mobile app called Party Time where people post about parties happening in their area. Users record when they hear about a party starting and ending. This information, along with location metadata, is submitted to the app.

Part 1: Compute Party Window

As part of an effort to understand which neighborhoods are the most active in partying, we want to determine the party window for each neighborhood.

A party window is a time range (inclusive) from the earliest party start to the latest party end in that neighborhood.

Example: If a neighborhood has two parties on a given day:

• One from 10AM to 1PM
• Another from 4PM to 8PM
• Then the party window would be 10AM to 8PM

Given JSON party data, implement compute_party_windows() to determine the party window for each neighborhood.

Part 2: Compute Dead Zone Time

Once we have party windows, we want to share data about party dead zones - time ranges for a given town when NO neighborhoods within that town have an active party window.

Example: Imagine a town with two neighborhoods:

• neighborhood_a has a party window from 8AM to 2PM
• neighborhood_b has a party window from 6PM to 11PM
• There is one dead zone from 2PM to 6PM (4 hours total)

Implement compute_dead_times() that maps towns to their total dead zone time in hours.

Data Format

Each party record contains:

• party_id: Unique identifier
• neighborhood: Name of the neighborhood
• town: Name of the town
• city: Name of the city
• state: State abbreviation
• start_time: ISO datetime string (e.g., "2024-01-15T10:00:00")
• end_time: ISO datetime string (e.g., "2024-01-15T14:00:00")

Assumptions

• All timestamps are on the hour (XX:00)
• All parties start and end on the same day
• A neighborhood belongs to exactly one town
• Only count dead zone time between party windows (not before first or after last)