Running Median of a Stream

Track the median of a stream of numbers as they arrive, such as p50 latency on a live dashboard, without re-sorting everything each time.

The classic solution uses two heaps: a max-heap holding the smaller half and a min-heap holding the larger half, kept balanced so their sizes differ by at most one. The median is then at the top of one or both heaps.

Implement a class RunningMedian:

• add(x) in $O(\log n)$;
• median() in $O(1)$: the middle value for an odd count, the average of the two middle values for an even count. Raise ValueError if no numbers have been added.
• __len__ returns how many numbers have been added.

Python's heapq is a min-heap only; store negated values to simulate a max-heap.