Team:iBowu-China/Model equilibrium.py
equilibrium.py
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def model(N, A):
mr = 0.161 * 0.784 * N / 2.657
pr = 0.36 * mr / 2.658
mb = (1.13E-16 + 5.269 * 0.784 * N * (0.696 * A * pr)**2 / (1 + (0.696 * A * pr)**2)) / 0.231
pb = 2.297 * mb / 0.181
return pb
x_new = np.arange(0, 35, 1)
y_new = np.arange(0, 50, 0.01)
x_new, y_new = np.meshgrid(x_new, y_new)
z_new = np.zeros_like(x_new)
for i in range(x_new.shape[1]):
for j in range(y_new.shape[0]):
z_new[j, i] = model(x_new[0, i], y_new[j, 0])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x_new, y_new, z_new, cmap='rainbow', label = "simulated distribution")
ax.set_xlabel("DNA($ng/uL)$")
ax.set_ylabel("AHL($nM$)")
ax.set_zlabel("GFP($ng/ul$)")
ax.grid(False)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def model(N, A):
mr = 0.161 * 0.784 * N / 2.657
pr = 0.36 * mr / 2.658
mb = (1.13E-16 + 5.269 * 0.784 * N * (0.696 * A * pr)**2 / (1 + (0.696 * A * pr)**2)) / 0.231
pb = 2.297 * mb / 0.181
return pb
x_new = np.arange(0, 35, 1)
y_new = np.arange(0, 50, 0.01)
x_new, y_new = np.meshgrid(x_new, y_new)
z_new = np.zeros_like(x_new)
for i in range(x_new.shape[1]):
for j in range(y_new.shape[0]):
z_new[j, i] = model(x_new[0, i], y_new[j, 0])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x_new, y_new, z_new, cmap='rainbow', label = "simulated distribution")
ax.set_xlabel("DNA($ng/uL)$")
ax.set_ylabel("AHL($nM$)")
ax.set_zlabel("GFP($ng/ul$)")
ax.grid(False)
plt.show()