def get_data_considering_high_low_steer():
global ctr_low
global ctr_high
global low_steer
global high_steer
if ctr_low >= len_low_steer:
ctr_low = -1
if ctr_high >= len_high_steer:
ctr_high = -1
if ctr_low == -1:
random.shuffle(low_steer)
ctr_low = 0
if ctr_high == -1:
random.shuffle(high_steer)
ctr_high = 0
if random.random() < 0.5:
choice = low_steer[ctr_low]
ctr_low += 1
else:
choice = high_steer[ctr_high]
ctr_high += 1
run_code = choice[3]
seg_num = choice[0]
offset = choice[1]
data = get_data_with_hdf5.get_data(run_code,seg_num,offset,3*net.N_STEPS,offset+0,net.N_FRAMES,ignore=ignore,require_one=require_one,use_states=use_states)
return data
def get_data_considering_high_low_steer():
global ctr_low
global ctr_high
global low_steer
global high_steer
if ctr_low >= len_low_steer:
ctr_low = -1
if ctr_high >= len_high_steer:
ctr_high = -1
if ctr_low == -1:
random.shuffle(low_steer) # shuffle data before using (again)
ctr_low = 0
if ctr_high == -1:
random.shuffle(high_steer)
ctr_high = 0
if random.random(
) < 0.5: # len_high_steer/(len_low_steer+len_high_steer+0.0): # with some probability choose a low_steer element
choice = low_steer[ctr_low]
ctr_low += 1
else:
choice = high_steer[ctr_high]
ctr_high += 1
run_code = choice[3]
seg_num = choice[0]
offset = choice[1]
data = get_data_with_hdf5.get_data(run_code,
seg_num,
offset,
net.N_STEPS,
offset + 0,
net.N_FRAMES,
ignore=P['ignore'],
require_one=P['require_one'],
use_states=P['use_states'])
return data
def get_data_considering_high_low_steer(d):
N_STEPS = d['N_STEPS']
N_FRAMES = d['N_FRAMES']
ignore = d['ignore']
require_one = d['require_one']
use_states = d['use_states']
global ctr_low
global ctr_high
global low_steer
global high_steer
if ctr_low >= len_low_steer:
ctr_low = -1
if ctr_high >= len_high_steer:
ctr_high = -1
if ctr_low == -1:
random.shuffle(low_steer)
ctr_low = 0
if ctr_high == -1:
random.shuffle(high_steer)
ctr_high = 0
if random.random() < 0.5:
choice = low_steer[ctr_low]
ctr_low += 1
else:
choice = high_steer[ctr_high]
ctr_high += 1
run_code = choice[3]
seg_num = choice[0]
offset = choice[1]
data = Segment_Data.get_data(run_code,seg_num,offset,3*N_STEPS,offset+0,N_FRAMES,ignore=ignore,require_one=require_one,use_states=use_states)
return data
def get_data_considering_high_low_steer_and_valid_trajectory_timestamp(d):
N_STEPS = d['N_STEPS']
N_FRAMES = d['N_FRAMES']
ignore = d['ignore']
require_one = d['require_one']
use_states = d['use_states']
global ctr_low
global ctr_high
global low_steer
global high_steer
global counts
global high_loss_key_ctr
global high_loss_keys
if ctr_low >= len_low_steer:
ctr_low = -1
if ctr_high >= len_high_steer:
ctr_high = -1
if ctr_low == -1:
random.shuffle(low_steer)
ctr_low = 0
if ctr_high == -1:
random.shuffle(high_steer)
ctr_high = 0
if random.random() < 0.5:
choice = low_steer[ctr_low]
ctr_low += 1
else:
choice = high_steer[ctr_high]
ctr_high += 1
run_code = choice[3]
seg_num = choice[0]
offset = choice[1]
run_name = Segment_Data.Segment_Data['run_codes'][run_code]
if run_name not in Aruco_Steering_Trajectories.keys():
#print('Run name '+run_name+' not in Aruco_Steering_Trajectories')
return None
if len(Aruco_Steering_Trajectories[run_name].keys()) < 2:
#print('len(Aruco_Steering_Trajectories[run_name].keys()) <= 2')
return None
seg_num_str = str(seg_num)
aruco_matches = []
for i in [0]:#range(N_FRAMES):
timestamp = Segment_Data.Segment_Data['runs'][run_name]['segments'][seg_num_str]['left_timestamp'][offset+i]
behavioral_mode = np.random.choice(
['Direct_Arena_Potential_Field',
#'Furtive_Arena_Potential_Field',
'Follow_Arena_Potential_Field'])
#'Play_Arena_Potential_Field'])
desired_direction = np.random.choice([0,1])
if timestamp in Aruco_Steering_Trajectories[run_name][behavioral_mode][desired_direction].keys():
aruco_matches.append(timestamp)
if len(aruco_matches) < 1:
return None
data = Segment_Data.get_data(run_code,seg_num,offset,N_STEPS,offset+0,N_FRAMES,ignore=ignore,require_one=require_one)
if data != None:
data['behavioral_mode'] = behavioral_mode
data['desired_direction'] = desired_direction
for topic in Aruco_Steering_Trajectories[run_name][behavioral_mode][desired_direction][timestamp]:
data[topic] = Aruco_Steering_Trajectories[run_name][behavioral_mode][desired_direction][timestamp][topic]
data['id'] = (run_name,behavioral_mode,desired_direction,timestamp,run_code,seg_num,offset)
if P.other_cars_only and np.random.random() < 0.85:
if data == None:
return None
if data['other_car_inverse_distances'] == None:
return None
if len(data['other_car_inverse_distances']) == 0:
return None
return data