def num_volume_corrcoef(LucN, hccN, MixN, mm2):
#Raw Data
r_LucN, r_hccN, r_MixN = graph.num_read_volume()
#Adjustment of Timeseries
time_point = len(r_LucN[0]) # Experiments
sim_time_point = len(LucN) # Simulation time
sim_tmp = (len(LucN)*0.66)/(time_point-1) #1.25
#Simulation timepoint
LucN_p = []
hccN_p = []
MixN_p = []
for t in range(time_point):
if t != 0:
print int(sim_time_point*0.3 + round(t*sim_tmp))
LucN_p.append(LucN[int(sim_time_point*0.3 + round(t*sim_tmp))])
hccN_p.append(hccN[int(sim_time_point*0.3 + round(t*sim_tmp))])
MixN_p.append(MixN[int(sim_time_point*0.3 + round(t*sim_tmp))])
else:
LucN_p.append(LucN[int(round(t*sim_tmp))])
hccN_p.append(hccN[int(round(t*sim_tmp))])
MixN_p.append(MixN[int(round(t*sim_tmp))])
print 'Simulation vs Raw'
print 'Luc'
LucN_v = check_number_of_volume(LucN_p, r_LucN, mm2,'Luc')
print 'Mix'
MixN_v = check_number_of_volume(MixN_p, r_MixN, mm2, 'Mix')
print 'hcc'
hccN_v = check_number_of_volume(hccN_p, r_hccN, mm2, 'HCC')
draw.all_cells_fig(LucN_v, r_LucN, MixN_v, r_MixN, hccN_v, r_hccN)
def num_volume_corrcoef(LucN, hccN, MixN, mm2):
#Raw Data
r_LucN, r_hccN, r_MixN = graph.num_read_volume()
#Adjustment of Timeseries
time_point = len(r_LucN[0]) # Experiments
sim_time_point = len(LucN) # Simulation time
sim_tmp = (len(LucN) * 0.66) / (time_point - 1) #1.25
#Simulation timepoint
LucN_p = []
hccN_p = []
MixN_p = []
for t in range(time_point):
if t != 0:
print int(sim_time_point * 0.3 + round(t * sim_tmp))
LucN_p.append(LucN[int(sim_time_point * 0.3 + round(t * sim_tmp))])
hccN_p.append(hccN[int(sim_time_point * 0.3 + round(t * sim_tmp))])
MixN_p.append(MixN[int(sim_time_point * 0.3 + round(t * sim_tmp))])
else:
LucN_p.append(LucN[int(round(t * sim_tmp))])
hccN_p.append(hccN[int(round(t * sim_tmp))])
MixN_p.append(MixN[int(round(t * sim_tmp))])
print 'Simulation vs Raw'
print 'Luc'
LucN_v = check_number_of_volume(LucN_p, r_LucN, mm2, 'Luc')
print 'Mix'
MixN_v = check_number_of_volume(MixN_p, r_MixN, mm2, 'Mix')
print 'hcc'
hccN_v = check_number_of_volume(hccN_p, r_hccN, mm2, 'HCC')
draw.all_cells_fig(LucN_v, r_LucN, MixN_v, r_MixN, hccN_v, r_hccN)
def num_cell_corrcoef(LucN, hccN, MixN, mm2):
#Raw Data
r_LucN, r_hccN, r_MixN = graph.num_read_cells(mm2)
#Adjustment of Timeseries
time_point = len(r_LucN[0]) # Experiments
sim_time_point = len(LucN) # Simulation time
sim_tmp = (len(LucN) * 0.66) / (time_point - 1) #1.25
#Simulation timepoint
LucN_p = []
hccN_p = []
MixN_p = []
for t in range(time_point):
if t != 0:
print int(sim_time_point * 0.3 + round(t * sim_tmp))
LucN_p.append(LucN[int(sim_time_point * 0.3 + round(t * sim_tmp))])
hccN_p.append(hccN[int(sim_time_point * 0.3 + round(t * sim_tmp))])
MixN_p.append(MixN[int(sim_time_point * 0.3 + round(t * sim_tmp))])
else:
LucN_p.append(LucN[int(round(t * sim_tmp))])
hccN_p.append(hccN[int(round(t * sim_tmp))])
MixN_p.append(MixN[int(round(t * sim_tmp))])
print 'Simulation vs Raw'
print 'Luc'
print check_number_of_cells(LucN_p, r_LucN, 'Luc')
print 'Mix'
print check_number_of_cells(MixN_p, r_MixN, 'Mix')
print 'hcc'
print check_number_of_cells(hccN_p, r_hccN, 'HCC')
draw.all_cells_fig(LucN_p, r_LucN, MixN_p, r_MixN, hccN_p, r_hccN)
corr_Luc = []
corr_hcc = []
corr_Mix = []
for i in range(len(r_LucN)): #times of experiments
tmp_Luc = np.corrcoef(r_LucN[i], LucN_p)
tmp_hcc = np.corrcoef(r_hccN[i], hccN_p)
tmp_Mix = np.corrcoef(r_MixN[i], MixN_p)
corr_Luc.append(tmp_Luc[0, 1])
corr_hcc.append(tmp_hcc[0, 1])
corr_Mix.append(tmp_Mix[0, 1])
print 'Average Correlation Luc = %s, HCC = %s, Mix ~ %s ' % (np.average(
np.array(corr_Luc)), np.average(
np.array(corr_hcc)), np.average(np.array(corr_Mix)))
return 0
def num_cell_corrcoef(LucN, hccN, MixN, mm2):
#Raw Data
r_LucN, r_hccN, r_MixN = graph.num_read_cells(mm2)
#Adjustment of Timeseries
time_point = len(r_LucN[0]) # Experiments
sim_time_point = len(LucN) # Simulation time
sim_tmp = (len(LucN)*0.66)/(time_point-1) #1.25
#Simulation timepoint
LucN_p = []
hccN_p = []
MixN_p = []
for t in range(time_point):
if t != 0:
print int(sim_time_point*0.3 + round(t*sim_tmp))
LucN_p.append(LucN[int(sim_time_point*0.3 + round(t*sim_tmp))])
hccN_p.append(hccN[int(sim_time_point*0.3 + round(t*sim_tmp))])
MixN_p.append(MixN[int(sim_time_point*0.3 + round(t*sim_tmp))])
else:
LucN_p.append(LucN[int(round(t*sim_tmp))])
hccN_p.append(hccN[int(round(t*sim_tmp))])
MixN_p.append(MixN[int(round(t*sim_tmp))])
print 'Simulation vs Raw'
print 'Luc'
print check_number_of_cells(LucN_p, r_LucN, 'Luc')
print 'Mix'
print check_number_of_cells(MixN_p, r_MixN, 'Mix')
print 'hcc'
print check_number_of_cells(hccN_p, r_hccN, 'HCC')
draw.all_cells_fig(LucN_p, r_LucN, MixN_p, r_MixN, hccN_p, r_hccN)
corr_Luc = []
corr_hcc = []
corr_Mix = []
for i in range(len(r_LucN)): #times of experiments
tmp_Luc = np.corrcoef(r_LucN[i], LucN_p)
tmp_hcc = np.corrcoef(r_hccN[i], hccN_p)
tmp_Mix = np.corrcoef(r_MixN[i], MixN_p)
corr_Luc.append(tmp_Luc[0,1])
corr_hcc.append(tmp_hcc[0,1])
corr_Mix.append(tmp_Mix[0,1])
print 'Average Correlation Luc = %s, HCC = %s, Mix ~ %s ' % (np.average(np.array(corr_Luc)), np.average(np.array(corr_hcc)), np.average(np.array(corr_Mix)))
return 0