def tstep(self):
'''
takes a time step in the simulation
'''
start = time.time() # start clock
#======================================================================================#
#check destinations if active
#define motion vectors if destinations active and not everybody is at destination
active_dests = len(self.population[
self.population[:, 11] != 0]) # look op this only once
if active_dests > 0 and len(
self.population[self.population[:, 12] == 0]) > 0:
self.population = set_destination(self.population,
self.destinations)
self.population = check_at_destination(
self.population,
self.destinations,
wander_factor=self.Config.wander_factor_dest)
if active_dests > 0 and len(
self.population[self.population[:, 12] == 1]) > 0:
#keep them at destination
self.population = keep_at_destination(self.population,
self.Config.isolation_bounds)
#======================================================================================#
#gravity wells
if self.Config.gravity_strength > 0:
[self.population, self.last_step_change] = update_gravity_forces(
self.population, self.time, self.last_step_change,
self.Config.wander_step_size, self.Config.gravity_strength,
self.Config.wander_step_duration)
#======================================================================================#
#activate social distancing above a certain infection threshold
if not self.above_act_thresh and self.Config.social_distance_threshold_on > 0:
# If not previously above infection threshold activate when threshold reached
if self.Config.thresh_type == 'hospitalized':
self.above_act_thresh = sum(
self.population[:, 11] ==
1) >= self.Config.social_distance_threshold_on
elif self.Config.thresh_type == 'infected':
self.above_act_thresh = sum(
self.population[:, 6] ==
1) >= self.Config.social_distance_threshold_on
elif self.Config.social_distance_threshold_on == 0:
self.above_act_thresh = True
#deactivate social distancing after infection drops below threshold after using social distancing
if self.above_act_thresh and not self.above_deact_thresh and self.Config.social_distance_threshold_off > 0:
# If previously went above infection threshold deactivate when threshold reached
self.above_deact_thresh = sum(self.population[:,6][self.population[:,11] == 0] == 1) <= \
self.Config.social_distance_threshold_off
# activate social distancing at the onset of infection
if not self.Config.SD_act_onset:
act_social_distancing = self.above_act_thresh and not self.above_deact_thresh and sum(
self.population[:, 6] == 1) > 0
# activate social distancing from start of simulation
elif self.Config.SD_act_onset:
act_social_distancing = self.above_act_thresh and not self.above_deact_thresh
#activate social distancing only for compliant individuals
if self.Config.social_distance_factor > 0 and act_social_distancing:
self.population[(self.population[:,17] == 0) &\
(self.population[:,11] == 0)] = update_repulsive_forces(self.population[(self.population[:,17] == 0) &\
(self.population[:,11] == 0)], self.Config.social_distance_factor)
#======================================================================================#
#out of bounds
#define bounds arrays, excluding those who are marked as having a custom destination
if len(self.population[:, 11] == 0) > 0:
buffer = 0.0
_xbounds = np.array([[
self.Config.xbounds[0] + buffer,
self.Config.xbounds[1] - buffer
]] * len(self.population[self.population[:, 11] == 0]))
_ybounds = np.array([[
self.Config.ybounds[0] + buffer,
self.Config.ybounds[1] - buffer
]] * len(self.population[self.population[:, 11] == 0]))
self.population[self.population[:, 11] == 0] = update_wall_forces(
self.population[self.population[:, 11] == 0], _xbounds,
_ybounds)
#======================================================================================#
#update velocities
self.population[(self.population[:,11] == 0) |\
(self.population[:,12] == 1)] = update_velocities(self.population[(self.population[:,11] == 0) |\
(self.population[:,12] == 1)],
self.Config.max_speed,self.Config.dt)
#for dead ones: set velocity and social distancing to 0 for dead ones
self.population[:, 3:5][self.population[:, 6] == 3] = 0
self.population[:, 17][self.population[:, 6] == 3] = 1
#update positions
self.population = update_positions(self.population, self.Config.dt)
#======================================================================================#
#find new infections
if not self.above_test_thresh and self.Config.testing_threshold_on > 0:
# If not previously above infection threshold activate when threshold reached
self.above_test_thresh = sum(
self.population[:, 6] == 1) >= self.Config.testing_threshold_on
# self.above_test_thresh = sum(self.population[:,6] == 1) >= self.Config.social_distance_threshold_on
elif self.Config.testing_threshold_on == 0:
self.above_test_thresh = True
act_testing = self.above_test_thresh and sum(
self.population[:, 6] == 1) > 0
self.population, self.destinations = infect(
self.population,
self.Config,
self.frame,
send_to_location=self.Config.self_isolate,
location_bounds=self.Config.isolation_bounds,
destinations=self.destinations,
location_no=1,
location_odds=self.Config.self_isolate_proportion,
test_flag=act_testing)
#recover and die
self.population = recover_or_die(self.population, self.frame,
self.Config)
#======================================================================================#
#send cured back to population if self isolation active
#perhaps put in recover or die class
#send cured back to population
self.population[:, 11][self.population[:, 6] == 2] = 0
#======================================================================================#
#update population statistics
self.pop_tracker.update_counts(self.population, self.frame)
#======================================================================================#
#visualise
if self.Config.visualise and (
self.frame % self.Config.visualise_every_n_frame) == 0:
draw_tstep(self.Config, self.population, self.pop_tracker,
self.frame, self.fig, self.spec, self.ax1, self.ax2,
self.tight_bbox)
#report stuff to console
if (self.Config.verbose) and ((self.frame % self.Config.report_freq)
== 0):
end = time.time()
time_elapsed = end - start # elapsed time
sys.stdout.write('\r')
sys.stdout.write(
'%i: S: %i, I: %i, R: %i, in treatment: %i, F: %i, of total: %i, D: %.5f, GC: %.5f, time: %.5f'
% (self.frame, self.pop_tracker.susceptible[-1],
self.pop_tracker.infectious[-1],
self.pop_tracker.recovered[-1],
len(self.population[self.population[:, 10] == 1]),
self.pop_tracker.fatalities[-1], self.Config.pop_size,
self.pop_tracker.distance_travelled[-1],
self.pop_tracker.mean_perentage_covered[-1], time_elapsed))
#save popdata if required
if self.Config.save_pop and (self.frame %
self.Config.save_pop_freq) == 0:
save_population(self.population, self.frame,
self.Config.save_pop_folder)
#run callback
self.callback()
#======================================================================================#
#update frame
self.frame += 1
self.time += self.Config.dt
def tstep(self):
"""
takes a time step in the simulation
"""
# check destinations if active
# define motion vectors if destinations active and not everybody is at destination
active_dests = len(
self.population[self.population[:, 11] != 0]
) # look op this only once
if active_dests > 0 and len(self.population[self.population[:, 12] == 0]) > 0:
self.population = set_destination(self.population, self.destinations)
self.population = check_at_destination(
self.population,
self.destinations,
wander_factor=self.Config.wander_factor_dest,
speed=self.Config.speed,
)
if active_dests > 0 and len(self.population[self.population[:, 12] == 1]) > 0:
# keep them at destination
self.population = keep_at_destination(
self.population, self.destinations, self.Config.wander_factor
)
# out of bounds
# define bounds arrays, excluding those who are marked as having a custom destination
if len(self.population[:, 11] == 0) > 0:
_xbounds = np.array(
[[self.Config.xbounds[0] + 0.02, self.Config.xbounds[1] - 0.02]]
* len(self.population[self.population[:, 11] == 0])
)
_ybounds = np.array(
[[self.Config.ybounds[0] + 0.02, self.Config.ybounds[1] - 0.02]]
* len(self.population[self.population[:, 11] == 0])
)
self.population[self.population[:, 11] == 0] = out_of_bounds(
self.population[self.population[:, 11] == 0], _xbounds, _ybounds
)
# set randoms
if self.Config.lockdown:
if len(self.pop_tracker.infectious) == 0:
mx = 0
else:
mx = np.max(self.pop_tracker.infectious)
if len(self.population[self.population[:, 6] == 1]) >= len(
self.population
) * self.Config.lockdown_percentage or mx >= (
len(self.population) * self.Config.lockdown_percentage
):
# reduce speed of all members of society
self.population[:, 5] = np.clip(
self.population[:, 5], a_min=None, a_max=0.001
)
# set speeds of complying people to 0
self.population[:, 5][self.Config.lockdown_vector == 0] = 0
else:
# update randoms
self.population = update_randoms(
self.population, self.Config.pop_size, self.Config.speed
)
else:
# update randoms
self.population = update_randoms(
self.population, self.Config.pop_size, self.Config.speed
)
# for dead ones: set speed and heading to 0
self.population[:, 3:5][self.population[:, 6] == 3] = 0
# update positions
self.population = update_positions(self.population)
# find new infections
self.population, self.destinations = infect(
self.population,
self.Config,
self.frame,
send_to_location=self.Config.self_isolate,
location_bounds=self.Config.isolation_bounds,
destinations=self.destinations,
location_no=1,
location_odds=self.Config.self_isolate_proportion,
)
# recover and die
self.population = recover_or_die(self.population, self.frame, self.Config)
# send cured back to population if self isolation active
# perhaps put in recover or die class
# send cured back to population
self.population[:, 11][self.population[:, 6] == 2] = 0
# update population statistics
self.pop_tracker.update_counts(self.population)
# visualise
if self.Config.visualise:
draw_tstep(
self.Config,
self.population,
self.pop_tracker,
self.frame,
self.fig,
self.spec,
self.ax1,
self.ax2,
)
# report stuff to console
sys.stdout.write("\r")
sys.stdout.write(
"%i: healthy: %i, infected: %i, immune: %i, in treatment: %i, \
dead: %i, of total: %i"
% (
self.frame,
self.pop_tracker.susceptible[-1],
self.pop_tracker.infectious[-1],
self.pop_tracker.recovered[-1],
len(self.population[self.population[:, 10] == 1]),
self.pop_tracker.fatalities[-1],
self.Config.pop_size,
)
)
# save popdata if required
if self.Config.save_pop and (self.frame % self.Config.save_pop_freq) == 0:
save_population(self.population, self.frame, self.Config.save_pop_folder)
# run callback
self.callback()
# update frame
self.frame += 1
def run(input_file, output_dir, pop_size, max_gen, verbose):
if not path.exists('Worlds'):
mkdir('Worlds')
if output_dir == '':
dt = time.strftime("%Y%m%d_%H%M%S")
output_dir = 'World_' + dt
try:
mkdir('Worlds\\' + output_dir)
except FileExistsError:
print('Directory {} Exists! Potential Data Overwrite!'.format(
output_dir))
ans = '1'
while (ans != 'Y' and ans != 'N'):
ans = input('Continue[Y/N]? ')
if ans == 'N':
sys.exit(0)
# Generate Initial Population
if input_file == '':
pop.sixth_day(pop_size)
#pop.clones(10)
else:
pop.load_population(input_file)
generation = 0
# Wait for User to Press 'Space' (Start)
print('Waiting for User to Start...')
img = np.array(sct.grab(mon))
# cv2.imshow("img", img)
# cv2.waitKey(0)
while participant_lost(img):
img = np.array(sct.grab(mon))
print('Starting Evolution')
generaton = 0
while generation < max_gen:
generation += 1
print('\n\n***Generation {}***'.format(generation))
participant_num = 0
for participant in pop.participants:
best_fitness = -1000.0
participant_num += 1
print('G{}: {} {} ({}/{})'.format(generation,
participant.first_name,
participant.last_name,
participant_num,
len(pop.participants)))
for i in range(num_trials):
participant.current_move = 0
auto.press('space') # Restart Race
img = np.array(sct.grab(mon))
# Execute Moves in Order Until Participant has Lost
start_time = time.time()
time.sleep(
.5 / speedhack) #Wait for participant to reach the ground
while not participant_lost(img=np.array(sct.grab(mon))):
if (time.time() - start_time >= time_limit):
kill_participant()
else:
participant.nextMove()
img = np.array(sct.grab(mon))
# Read Score
img = np.array(sct.grab(mon))
score = read_score(img)
if score > best_fitness:
best_fitness = score
print('\t\tTrial Fitness: {}'.format(score))
participant.fitness = best_fitness
print('\tBest Fitness: {}'.format(best_fitness))
pop_save_name = 'Worlds\\' + output_dir + '\\' + 'Population.csv'.format(
generation)
stats_save_name = 'Worlds\\' + output_dir + '\\' + 'Stats.csv'.format(
generation)
pop.save_population(pop_save_name, generation, verbose)
pop.save_stats(stats_save_name, generation)
pop.display_stats()
pop.evolve()
