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
"""
if __name__ == '__main__':
#parameter
luc_node = 80
hcc_node = 10
time = 15
luc_gro = 6
hcc_gro = 2
#immune_cell = 0.1
#Generate Graph
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 = 10**4
hccN0 = 10**4
LucN_init = 10**4
hccN_init = 10**4
