#!/usr/bin/python
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"""
Juergen Sturm's ass function, used to find matches between two lists of things with timestamps.
Stripped out of ass.py, distributed with the TUM rgbd benchmark tools.
See: https://vision.in.tum.de/data/datasets/rgbd-dataset/tools
This is used when calculating Absolute Trajectory Error and Relative Pose Error benchmarks.

Original Comment below:
The Kinect provides the color and depth images in an un-synchronized way. This means that the set of time stamps from
the color images do not intersect with those of the depth images. Therefore, we need some way of associating
color images to depth images.

For this purpose, you can use the ''ass.py'' script. It reads the time stamps from the rgb.txt file and
the depth.txt file, and joins them by finding the best matches.
"""


def associate(first_list, second_list, offset, max_difference):
    """
    Associate two dictionaries of (stamp,data). As the time stamps never match exactly, we aim
    to find the closest match for every input tuple.

    Input:
    first_list -- first dictionary of (stamp,data) tuples
    second_list -- second dictionary of (stamp,data) tuples
    offset -- time offset between both dictionaries (e.g., to model the delay between the sensors)
    max_difference -- search radius for candidate generation

    Output:
    matches -- list of matched tuples ((stamp1,data1),(stamp2,data2))

    """
    first_keys = list(first_list.keys())  # copy both keys lists, so we can remove from them later
    second_keys = list(second_list.keys())
    potential_matches = [(abs(a - (b + offset)), a, b)
                         for a in first_keys
                         for b in second_keys
                         if abs(a - (b + offset)) < max_difference]
    potential_matches.sort()
    matches = []
    for diff, a, b in potential_matches:
        if a in first_keys and b in second_keys:
            first_keys.remove(a)
            second_keys.remove(b)
            matches.append((a, b))

    matches.sort()
    return matches
