def update_all_parameter(diff):
#print 'each difference - %s' % diff
luc_node = int(30*diff)
hcc_node = int(5)
time = 10
#parameter
luc_gro = int(6*diff)
hcc_gro = int(2)
lucG = nx.barabasi_albert_graph(luc_node, luc_gro)
hccG = nx.barabasi_albert_graph(hcc_node, hcc_gro)
frequency = np.array([0.9, 0.1])
G_combine =nx.Graph()
G_combine = graph.merge_graph(G_combine, hccG, lucG, frequency)
frequency_1 = np.array([0.5, 0.5])
G_combine_1 =nx.Graph()
G_combine_1 = graph.merge_graph(G_combine_1, hccG, lucG, frequency_1)
#Time series cell volume
LucN = []
hccN = []
#Number of initial cell
LucN0 = 100
hccN0 = 100
LucN_init = 100
hccN_init = 100
for t in range(time):
LucN.append(calc.convert_volume(LucN0))
lucG = graph.update_graph(lucG, luc_gro)
LucN0 = LucN_init*calc.calc_entropy(lucG, t+1)
for t in range(time):
hccN.append(calc.convert_volume(hccN0))
hccG = graph.update_graph(hccG, hcc_gro)
hccN0 = hccN_init*calc.calc_entropy(hccG, t+1)
#Mix Number of cell
MixN0 = 100
MixN_init = 100
initial_populations = MixN0*frequency
G_comb_gro = ((frequency*np.array([luc_gro, hcc_gro])).sum())/2
MixN = []
x = []
for t in range(time):
x.append(t)
MixN.append(calc.convert_volume(MixN0))
G_combine = graph.update_graph(G_combine, G_comb_gro)
MixN0 = MixN_init*calc.calc_entropy(G_combine, t+1)
sim_ratio = np.array(LucN)/np.array(MixN)
return sim_ratio
"""
def update_all_parameter(diff):
#print 'each difference - %s' % diff
luc_node = int(30 * diff)
hcc_node = int(5)
time = 10
#parameter
luc_gro = int(6 * diff)
hcc_gro = int(2)
lucG = nx.barabasi_albert_graph(luc_node, luc_gro)
hccG = nx.barabasi_albert_graph(hcc_node, hcc_gro)
frequency = np.array([0.9, 0.1])
G_combine = nx.Graph()
G_combine = graph.merge_graph(G_combine, hccG, lucG, frequency)
frequency_1 = np.array([0.5, 0.5])
G_combine_1 = nx.Graph()
G_combine_1 = graph.merge_graph(G_combine_1, hccG, lucG, frequency_1)
#Time series cell volume
LucN = []
hccN = []
#Number of initial cell
LucN0 = 100
hccN0 = 100
LucN_init = 100
hccN_init = 100
for t in range(time):
LucN.append(calc.convert_volume(LucN0))
lucG = graph.update_graph(lucG, luc_gro)
LucN0 = LucN_init * calc.calc_entropy(lucG, t + 1)
for t in range(time):
hccN.append(calc.convert_volume(hccN0))
hccG = graph.update_graph(hccG, hcc_gro)
hccN0 = hccN_init * calc.calc_entropy(hccG, t + 1)
#Mix Number of cell
MixN0 = 100
MixN_init = 100
initial_populations = MixN0 * frequency
G_comb_gro = ((frequency * np.array([luc_gro, hcc_gro])).sum()) / 2
MixN = []
x = []
for t in range(time):
x.append(t)
MixN.append(calc.convert_volume(MixN0))
G_combine = graph.update_graph(G_combine, G_comb_gro)
MixN0 = MixN_init * calc.calc_entropy(G_combine, t + 1)
sim_ratio = np.array(LucN) / np.array(MixN)
return sim_ratio
"""
def num_read_cells(mm2):
mm3 = calc.convert_volume(mm2)*10**3
luc_data = np.loadtxt('data/100-0_r.csv', delimiter=",") + 1
hcc_data = np.loadtxt('data/0-100_r.csv', delimiter=",") + 1
mix_data = np.loadtxt('data/10-90_r.csv', delimiter=",", skiprows = 1) + 1
return luc_data*mm3, hcc_data*mm3, mix_data*mm3
def num_read_cells(mm2):
mm3 = calc.convert_volume(mm2) * 10**3
luc_data = np.loadtxt('data/100-0_r.csv', delimiter=",") + 1
hcc_data = np.loadtxt('data/0-100_r.csv', delimiter=",") + 1
mix_data = np.loadtxt('data/10-90_r.csv', delimiter=",", skiprows=1) + 1
return luc_data * mm3, hcc_data * mm3, mix_data * mm3
def exp_test():
#parameter
luc_node = 100
time = 10
luc_gro = 10
#Generate Graph
lucG = nx.barabasi_albert_graph(luc_node, luc_gro)
#Time series cell volume
LucN = []
#Number of initial cell
LucN0 = 100
LucN_init = 100
mm2 = 43
for t in range(time):
LucN.append(calc.convert_volume(LucN0))
lucG = graph.update_graph(lucG, luc_gro)
LucN0 = LucN_init * math.exp(1 / 10 * (t + 1))
r_LucN, r_hccN, r_MixN = graph.num_read_cells(mm2)
time_point = len(r_LucN[0]) #8
sim_tmp = len(LucN) / time_point #1.25
LucN_p = []
for t in range(time_point):
LucN_p.append(LucN[int(round(t * sim_tmp))])
corr_Luc = []
for i in range(len(r_LucN)): #times of experiments
tmp_Luc = np.corrcoef(r_LucN[i], LucN_p)
corr_Luc.append(tmp_Luc[0, 1])
print np.average(np.array(corr_Luc))
return 0
def exp_test():
#parameter
luc_node = 100
time = 10
luc_gro = 10
#Generate Graph
lucG = nx.barabasi_albert_graph(luc_node, luc_gro)
#Time series cell volume
LucN = []
#Number of initial cell
LucN0 = 100
LucN_init = 100
mm2 = 43
for t in range(time):
LucN.append(calc.convert_volume(LucN0))
lucG = graph.update_graph(lucG, luc_gro)
LucN0 = LucN_init*math.exp(1/10*(t+1))
r_LucN, r_hccN, r_MixN = graph.num_read_cells(mm2)
time_point = len(r_LucN[0])#8
sim_tmp = len(LucN)/time_point #1.25
LucN_p = []
for t in range(time_point):
LucN_p.append(LucN[int(round(t*sim_tmp))])
corr_Luc = []
for i in range(len(r_LucN)): #times of experiments
tmp_Luc = np.corrcoef(r_LucN[i], LucN_p)
corr_Luc.append(tmp_Luc[0,1])
print np.average(np.array(corr_Luc))
return 0
G_combine_1 =nx.Graph()
G_combine_1 = graph.merge_graph(G_combine_1, hccG, lucG, frequency_1)
#Time series cell volume
LucN = []
hccN = []
#Number of initial cell
LucN0 = 10**4
hccN0 = 10**4
LucN_init = 10**4
hccN_init = 10**4
for t in range(time):
LucN.append(calc.convert_volume(LucN0))
lucG = graph.update_graph(lucG, luc_gro)
LucN0 = LucN_init*calc.calc_entropy(lucG, t+1)
for t in range(time):
hccN.append(calc.convert_volume(hccN0))
hccG = graph.update_graph(hccG, hcc_gro)
hccN0 = hccN_init*calc.calc_entropy(hccG, t+1)
#Mix Number of cell
MixN0 = 10**4
MixN_init = 10**4
initial_populations = MixN0*frequency
G_comb_gro = ((frequency*np.array([luc_gro, hcc_gro])).sum())/2
MixN = []
x = []
def calculate_mm3(cells, mm2):
cells_per_mm3 = calc.convert_volume(mm2)*10*3
return cells/cells_per_mm3
def calculate_mm3(cells, mm2):
cells_per_mm3 = calc.convert_volume(mm2) * 10 * 3
return cells / cells_per_mm3
