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 --
(first_list, second_list, offset, max_difference)
| 28 | return dict(list) |
| 29 | |
| 30 | def associate(first_list, second_list, offset, max_difference): |
| 31 | """ |
| 32 | Associate two dictionaries of (stamp, data). As the time stamps never match exactly, we aim |
| 33 | to find the closest match for every input tuple. |
| 34 | |
| 35 | Input: |
| 36 | first_list -- first dictionary of (stamp, data) tuples |
| 37 | second_list -- second dictionary of (stamp, data) tuples |
| 38 | offset -- time offset between both dictionaries (e.g., to model the delay between the sensors) |
| 39 | max_difference -- search radius for candidate generation |
| 40 | |
| 41 | Output: |
| 42 | matches -- list of matched tuples ((stamp1, data1), (stamp2, data2)) |
| 43 | """ |
| 44 | # Convert keys to sets for efficient removal |
| 45 | first_keys = set(first_list.keys()) |
| 46 | second_keys = set(second_list.keys()) |
| 47 | |
| 48 | potential_matches = [(abs(a - (b + offset)), a, b) |
| 49 | for a in first_keys |
| 50 | for b in second_keys |
| 51 | if abs(a - (b + offset)) < max_difference] |
| 52 | potential_matches.sort() |
| 53 | matches = [] |
| 54 | for diff, a, b in potential_matches: |
| 55 | if a in first_keys and b in second_keys: |
| 56 | first_keys.remove(a) |
| 57 | second_keys.remove(b) |
| 58 | matches.append((a, b)) |
| 59 | |
| 60 | matches.sort() |
| 61 | return matches |
| 62 | |
| 63 | dirs = glob.glob("data/tum/*/") |
| 64 | dirs = sorted(dirs) |