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,N_STEPS,offset+0,N_FRAMES,ignore=ignore,require_one=require_one,use_states=use_states) return data
def get_data_ids(d):
all_index = d['all_index']
choice = all_steer[all_index]
run_code = choice[3]
seg_num = choice[0]
offset = choice[1]
run_name = get_data_with_hdf5.Segment_Data['run_codes'][run_code]
#print run_name
"""
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
"""
#print 'here!'
seg_num_str = str(seg_num)
#aruco_matches = []
#print(run_code,seg_num,offset,N_STEPS,offset+0,N_FRAMES)
for i in [0]: #range(N_FRAMES):
timestamp = get_data_with_hdf5.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:
print(d2s('no aruco match',time.time()))
return None
"""
data = get_data_with_hdf5.get_data(run_code,
seg_num,
offset,
N_STEPS,
offset + 0,
N_FRAMES,
ignore=ignore,
require_one=require_one,
no_images=True)
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)
return data
def get_data_considering_high_low_steer_and_valid_trajectory_timestamp():
#print "get_data_considering_high_low_steer_and_valid_trajectory_timestamp"
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) # 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]
run_name = get_data_with_hdf5.Segment_Data['run_codes'][run_code]
#print run_name
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
#print 'here!'
seg_num_str = str(seg_num)
aruco_matches = []
#print(run_code,seg_num,offset,N_STEPS,offset+0,N_FRAMES)
for i in [0]: #range(N_FRAMES):
timestamp = get_data_with_hdf5.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 = get_data_with_hdf5.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)
return data
def get_data_considering_high_low_steer_and_valid_trajectory_timestamp__with_data_id(
d):
all_index = d['all_index']
#print "get_data_considering_high_low_steer_and_valid_trajectory_timestamp"
global counts
global high_loss_key_ctr
global high_loss_keys
choice = all_steer[all_index]
run_code = choice[3]
seg_num = choice[0]
offset = choice[1]
run_name = get_data_with_hdf5.Segment_Data['run_codes'][run_code]
#print run_name
#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
#print 'here!'
seg_num_str = str(seg_num)
#aruco_matches = []
#print(run_code,seg_num,offset,N_STEPS,offset+0,N_FRAMES)
for i in [0]: #range(N_FRAMES):
timestamp = get_data_with_hdf5.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 = get_data_with_hdf5.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)
else:
return None #assert(False)
return data