Contact Tracing in Python - working with timeseries

Question

Let's say I have timeseries data (time on the x-axis, coordinates on the y-z plane.

Given a seed set of infected users, I want to fetch all users that are within distance d from the points in the seed set within t time. This is basically just contact tracing.

What is a smart way of accomplishing this?

The naive approach is something like this:

points_at_end_of_iteration = []
for p in seed_set:
    other_ps = find_points_t_time_away(t)
    points_at_end_of_iteration += find_points_d_distance_away_from_set(other_ps)

What is a smarter way of doing this - preferably keeping all data in RAM (though I'm not sure if this is feasible). Is Pandas a good option? I've been thinking about Bandicoot as well, but it doesn't seem to be able to do that for me.

Please let me know if I can improve the question - perhaps it's too broad.

Edit:

I think the algorithm I presented above is flawed.

Is this better:

for user,time,pos in infected_set:
    info = get_next_info(user, time) # info will be a tuple: (t, pos)
    intersecting_users = find_intersecting_users(user, time, delta_t, pos, delta_pos) # intersect if close enough to the user's pos/time
    infected_set.add(intersecting_users)
    update_infected_set(user, info) # change last_time and last_pos (described below)

infected_set I think should actually be a hashmap {user_id: {last_time: ..., last_pos: ...}, user_id2: ...}

One potential problem is that the users are treated independently, so the next timestamp for user2 may be hours or days after user1.

Contact tracing may be easier if I interpolate so that every user has information for every time point (say ever hour) though that would increase the amount of data by a huge amount.

Data Format/Sample

user_id = 123
timestamp = 2015-05-01 05:22:25
position = 12.111,-12.111 # lat,long

There is one csv file with all the records:

uid1,timestamp1,position1
uid1,timestamp2,position2
uid2,timestamp3,position3

There is also a directory of files (same format) where each file corresponds to a user.

records/uid1.csv
records/uid2.csv

Answer 1

First solution with interpolate:

# i would use a shelf (a persistent, dictionary-like object,
# included with python).
import shelve

# hashmap of clean users indexed by timestamp)
# { timestamp1: {uid1: (lat11, long11), uid12: (lat12, long12), ...},
#   timestamp2: {uid1: (lat21, long21), uid2: (lat22, long22), ...},
#   ...
# }
#
clean_users = shelve.open("clean_users.dat")

# load data in clean_users from csv (shelve use same syntax than
# hashmap). You will interpolate data (only data at a given timestamp
# will be in memory at the same time). Note: the timestamp must be a string

# hashmap of infected users indexed by timestamp (same format than clean_users)
infected_users = shelve.open("infected_users.dat")

# for each iteration
for iteration in range(1, N):

    # compute current timestamp because we interpolate each user has a location
    current_timestamp = timestamp_from_iteration(iteration)

    # get clean users for this iteration (in memory)
    current_clean_users = clean_user[current_timestamp]

    # get infected users for this iteration (in memory)
    current_infected_users = infected_user[current_timestamp]

    # new infected user for this iteration
    new_infected_users = dict()

    # compute new infected_users for this iteration from current_clean_users and
    # current_infected_users then store the result in new_infected_users

    # remove user in new_infected_users from clean_users

    # add user in new_infected_users to infected_users

# close the shelves
infected_users.close()
clean_users.close()

Second solution without interpolate:

# i would use a shelf (a persistent, dictionary-like object,
# included with python).
import shelve

# hashmap of clean users indexed by timestamp)
# { timestamp1: {uid1: (lat11, long11), uid12: (lat12, long12), ...},
#   timestamp2: {uid1: (lat21, long21), uid2: (lat22, long22), ...},
#   ...
# }
#
clean_users = shelve.open("clean_users.dat")

# load data in clean_users from csv (shelve use same syntax than
# hashmap). Note: the timestamp must be a string

# hashmap of infected users indexed by timestamp (same format than clean_users)
infected_users = shelve.open("infected_users.dat")


# for each iteration (not time related as previous version)
# could also stop when there is no new infected users in the iteration
for iteration in range(1, N):

    # new infected users for this iteration
    new_infected_users = dict()

    # get timestamp from infected_users
    for an_infected_timestamp in infected_users.keys():

        # get infected users for this time stamp 
        current_infected_users = infected_users[an_infected_timestamp]

        # get relevant timestamp from clean users
        for a_clean_timestamp in clean_users.keys():
            if time_stamp_in_delta(an_infected_timestamp, a_clean_timestamp):

                # get clean users for this clean time stamp
                current_clean_users = clean_users[a_clean_timestamp]

                # compute infected users from current_clean_users and
                # current_infected_users then append the result to
                # new_infected_users

        # remove user in new_infected_users from clean_users

        # add user in new_infected_users to infected_users

# close the shelves
infected_users.close()
clean_users.close()