t_noflow = 10 # in sec
t_flow = 50 # in sec
binsize = 0.1
current = 8 # in cm/s.
n_animals = 100
# generate data
track_data = {}
track_data['no_ll'] = []
for k in range(0, n_animals):
ipos = np.array([np.random.uniform(0,.68, 1), np.random.uniform(0,.15,1), np.random.uniform(0,.15,1)]).flatten()
data = wlk.randwalk(t_noflow, binsize, 0, ipos, latline = False, flow_version = FLOW_DYNAMICS)
data = wlk.randwalk(t_flow, binsize, current, data, latline = False, flow_version = FLOW_DYNAMICS)
data['track'] = data['track'] * 100
track_data['no_ll'].append(data)
track_data['ll'] = []
for k in range(0, n_animals):
ipos = np.array([np.random.uniform(0,.68, 1), np.random.uniform(0,.15,1), np.random.uniform(0,.15,1)]).flatten()
data = wlk.randwalk(t_noflow, binsize, 0, ipos, latline = True, flow_version = FLOW_DYNAMICS)
data = wlk.randwalk(t_flow, binsize, current, data, latline = True, flow_version = FLOW_DYNAMICS)
data['track'] = data['track'] * 100
track_data['ll'].append(data)
flow = np.ones(track_data['no_ll'][0]['track'].shape[0],'int') * current
flow[0: (float(t_noflow)/ binsize)] = 0
'lateral_line':np.zeros((TIME/binsize*numfrog)),
'state':np.zeros((TIME/binsize*numfrog))
}
frogData_F = {'time': np.zeros((TIME/binsize*numfrog)),
'track': np.zeros((TIME/binsize*numfrog,3)),
'velocity':np.zeros((TIME/binsize*numfrog,3)),
'orientation':np.zeros((TIME/binsize*numfrog)),
'lateral_line':np.zeros((TIME/binsize*numfrog)),
'state':np.zeros((TIME/binsize*numfrog))
}
for j in range(0,numfrog):
#(SIMLENGTH,BINSIZE,FLOW_SPEED,IPOS,LATLINE,VERSION) - Version refers to flow dynamics.
run_data_NF = wlk.randwalk(TIME,binsize,0, np.array([0.34, 0.075, 0.15]),latline = LATLINE,flow_version = FLOW_DYNAMICS)
# there is likely a more elegant way to pack these data up.
frogData_NF['time'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_NF['time']
frogData_NF['track'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_NF['track']
frogData_NF['velocity'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_NF['velocity']
frogData_NF['orientation'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_NF['orientation']
frogData_NF['lateral_line'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_NF['lateral_line']
frogData_NF['state'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_NF['state']
frogEndPoint = frogData_NF['track'][-1:,:]
run_data_F = wlk.randwalk(TIME,binsize, FLOWSPEED, frogEndPoint,latline = LATLINE,flow_version = FLOW_DYNAMICS)
frogData_F['time'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_F['time']
frogData_F['track'][(j+1)*froglen-froglen:(j+1)*froglen] = run_data_F['track']
frog1_F = frog1_F[index]
frog1_F = frog1_F[0::DOWNSAMPLE_RATE]
# reset the simulation length to reflect data
t_flow = len(frog1_F)*binsize*DOWNSAMPLE_RATE
# Append data into one structure:
frog1 = np.append(frog1_NF,frog1_F,axis=0)
frog1 = {'track': frog1,
'state': None}
FROG1NAME = 'Observed Data'
else:
# generate all data w/ lateral line switched off
LATLINE = False
frog1 = wlk.randwalk(t_noflow, binsize, 0, np.array([0.37, 0.075, 0.15]), latline = LATLINE, flow_version = FLOW_DYNAMICS)
frog1 = wlk.randwalk(t_flow, binsize, current, frog1,latline = LATLINE, flow_version = FLOW_DYNAMICS)
frog1['track'] = frog1['track']*100
FROG1NAME = 'NO Lateral Line Model'
# downsample
for data in frog1:
frog1[data] = frog1[data][0::DOWNSAMPLE_RATE]
# generate all data w/ lateral line switched on - First compute at high resolution before downsampling
LATLINE = True
frog2 = wlk.randwalk(t_noflow, binsize, 0, np.array([0.34, 0.075, 0.15]), latline = LATLINE, flow_version = FLOW_DYNAMICS)
frog2 = wlk.randwalk(t_flow, binsize, current, frog2,latline = LATLINE, flow_version = FLOW_DYNAMICS)
frog2['track'] = frog2['track']*100
state_colors = ['blue', 'black', 'red']
