def oppg_4_3(return_protein):
T_step = -30 # increase the temp by this amount each iteration
N = 15
T_max = 1500
d = 600
N_T = (T_max // abs(T_step))
count = 0
# Matrix with all diameters for all twists and temp
length = np.zeros((N_T, d))
# Vector with mean energy for each temp
L_mean = np.zeros(N_T)
# Calculate energy
protein = Protein(N, T_max)
for j in range(T_max, -T_step, T_step):
print(j)
protein.change_temp(j)
for i in range(0, d):
protein.twist()
length[count][i] = protein.diameter()
L_mean[count] = np.mean(length[count])
count += 1
protein.change_temp(10**(-6))
for i in range(0, d):
protein.twist()
length[N_T - 1] = protein.diameter()
L_mean[N_T - 1] = np.mean(length[N_T - 1])
L_mean = L_mean[::-1]
# Create temp vector for plotting the mean diameter
x = np.arange(0, T_max, -T_step)
# Plot the mean diameter
plt.figure('exercise-4-3')
plt.title('Mean diameter')
plt.grid()
plt.xlabel('T')
plt.ylabel('$<$L$>$')
plt.plot(x, L_mean, color='#000000', linestyle='-')
plt.show()
if return_protein:
return protein, N
def oppg_3():
T_step = 5 # Increase the temp by this amount each iteration
N = 15
T = 10**(-6) # Define temperature CLOSE to zero to avoid division by zero
s = 0.003
d_max = 10000
T_max = 1500
N_T = T_max // T_step
# Matrix with all diameters for all twists and temps
length = np.zeros((N_T, d_max))
# Vector with mean energy for each temp
L_mean = np.zeros(N_T)
# Calculate mean diameter for temp close to zero
protein = Protein(N, T)
for i in range(0, d_max):
protein.twist()
length[0][i] = protein.diameter()
L_mean[0] = np.mean(length[0][:d_max])
# Calculate mean diameter for all other temps up to T_max
for j in range(1, N_T):
print(j)
protein = Protein(N, j * T_step)
d = int(np.ceil(d_max * np.exp(-s * (j * T_step))))
for i in range(0, d):
protein.twist()
length[j][i] = protein.diameter()
L_mean[j] = np.mean(length[j][:d])
# Create temp vector for plotting the mean diameter
x = np.arange(0, T_max, T_step)
# Plot the mean diameter
plt.figure('exercise-3')
plt.title('Mean diameter')
plt.grid(color='#ffffff', linestyle='-')
plt.xlabel('T')
plt.ylabel('$<L>$')
plt.plot(x, L_mean, color='#000000', linestyle='-')
plt.show()