def case11():
y0 = [[1,7],[2,6]]
t_tuple = 2
stepsize = 0.01
order = 2
maxorder = 2
# start = time.time()
t_list, y_list = simulation_ode(mode2_1, y0, t_tuple, stepsize, eps=0)
for temp_y in y_list:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
plt.plot(y0_list,y1_list,'b')
plt.show()
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
print(len(tpar_list))
for i in range(0,len(tpar_list)):
print(tpar_list[i][0])
print(tpar_list[i][-1])
print(ypar_list[i][0])
print(ypar_list[i][-1])
G,labels = infer_dynamic_modes_pie(tpar_list, ypar_list, stepsize, maxorder, 0.02)
# modes, coefs, mdors = infer_dynamic_modes_ex(tpar_list, ypar_list, stepsize, maxorder, 0.01)
# print(modes)
# print(coefs)
# print(mdors)
print(len(labels))
print(G)
def case4():
y0 = [[5,5,5],[2,2,2]]
t_tuple = 5
stepsize = 0.01
maxorder = 2
t_list, y_list = simulation_ode(fvdp3_3, y0, t_tuple, stepsize, eps=0)
draw3D(y_list)
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
G,labels = infer_dynamic_modes_pie(tpar_list, ypar_list, stepsize, maxorder, 0.02)
print(labels)
print(G)
def case1():
y0 = [[1,7]]
t_tuple = 25
stepsize = 0.01
order = 2
maxorder = 4
# start = time.time()
t_list, y_list = simulation_ode(mode2_1, y0, t_tuple, stepsize, eps=0)
# end_simulation = time.time()
# result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(t_list, y_list, stepsize, order)
# end_inference = time.time()
# print(result_coef)
# print()
# print("Total time: ", end_inference-start)
# print("Simulation time: ", end_simulation-start)
# print("Calc-diff time: ", calcdiff_time)
# print("Pseudoinv time: ", pseudoinv_time)
tpar_list,ypar_list = parti(t_list,y_list,0.05)
print(len(tpar_list))
for i in range(0,len(tpar_list)):
print(tpar_list[i][0],tpar_list[i][-1])
print(ypar_list[i][0],ypar_list[i][-1])
for temp_y in y_list:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
plt.plot(y0_list,y1_list,'b')
plt.show()
# result_coef, calcdiff_time, pseudoinv_time = infer_dynamic([tpar_list[0],tpar_list[1]], [ypar_list[0],ypar_list[1]], stepsize, 4)
# print(result_coef)
# tstart = time.time()
# comt,comy = simulation_ode_stiff(ode_test(result_coef,4), [ypar_list[0][0],ypar_list[1][0]], [(tpar_list[0][0], tpar_list[0][-1]),(tpar_list[1][0], tpar_list[1][-1])], stepsize,eps=0)
# tend = time.time()
# print(dist(comy,[ypar_list[0],ypar_list[1]]))
# print(tend-tstart)
# print(dist([ypar_list[0],ypar_list[2],ypar_list[4]],comy))
# result_coef = np.matrix([[ 0, 0, 0, -0.26, 0.26, 0], [0, 0, 0, 0, 0, -1]])
# comt,comy = simulation_ode(ode_test(result_coef,2), [ypar_list[0][0],ypar_list[2][0],ypar_list[4][0]], [(tpar_list[0][0], tpar_list[0][-1]),(tpar_list[2][0], tpar_list[2][-1]),(tpar_list[4][0], tpar_list[4][-1])], stepsize,eps=0)
# print(dist([ypar_list[0],ypar_list[2],ypar_list[4]],comy))
# result_coef = np.matrix([[ 0, 0, 0, -0.26, 0.26, 0], [0, 0, 0, 0, 0, 1]])
# comt,comy = simulation_ode(ode_test(result_coef,2), [ypar_list[1][0],ypar_list[3][0],ypar_list[5][0]], [(tpar_list[1][0], tpar_list[1][-1]),(tpar_list[3][0], tpar_list[3][-1]),(tpar_list[5][0], tpar_list[5][-1])], stepsize,eps=0)
# print(dist([ypar_list[1],ypar_list[3],ypar_list[5]],comy))
tstart = time.time()
modes, coefs, mdors = infer_dynamic_modes_ex(tpar_list, ypar_list, stepsize, maxorder, 0.001)
# modes, coefs, mdors = infer_dynamic_modes_exx(tpar_list, ypar_list, stepsize, maxorder, 0.01)
tend = time.time()
print(modes)
print(coefs)
print(mdors)
print(tend-tstart)
def case5():
y0 = [[0,0],[1,0]]
t_tuple = 2
stepsize = 0.01
order = 2
maxorder = 3
t_list, y_list = simulation_ode(conti_test, y0, t_tuple, stepsize, eps=0)
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
print(len(tpar_list))
for i in range(0,len(tpar_list)):
print(tpar_list[i][0])
print(tpar_list[i][-1])
print(ypar_list[i][0])
print(ypar_list[i][-1])
for temp_y in y_list:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
plt.plot(y0_list,y1_list,'b')
plt.show()
# modes, coefs, mdors = infer_dynamic_modes_ex(tpar_list, ypar_list, stepsize, maxorder, 0.01)
# modes, coefs, mdors = infer_dynamic_modes_exx(tpar_list, ypar_list, stepsize, maxorder, 0.01)
# print(modes)
# print(coefs)
# print(mdors)
ttest_list=[]
ttest_list.append(tpar_list[0])
ttest_list.append(tpar_list[1])
ytest_list=[]
ytest_list.append(ypar_list[0])
ytest_list.append(ypar_list[1])
A, b = diff_method(ttest_list, ytest_list, 3, stepsize)
g = pinv2(A).dot(b)
print(g.T)
t_start = tpar_list[0][0]
t_end = tpar_list[0][-1]
t_start = 0
t_end = 0.01
t_points = np.arange(t_start, t_end + stepsize, stepsize)
tstart = time.time()
y_object = solve_ivp(ode_test(g.T,3), (t_start, t_end+stepsize), ypar_list[0][0], t_eval = t_points, rtol=1e-7, atol=1e-9)
y_points = y_object.y.T
tend = time.time()
print(y_points)
print(tend-tstart)
def case9():
y0 = [[4.7,0.2,-6,0,0,3]]
t_tuple = [(0,10)]
stepsize = 0.001
maxorder = 1
# start = time.time()
t_list, y_list = simulation_ode_2([mmode1, mmode2], event3, y0, t_tuple, stepsize)
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
print(tpar_list[0].shape)
tt = [tpar_list[0],tpar_list[2],tpar_list[4]]
yy = [ypar_list[0],ypar_list[2],ypar_list[4]]
A ,b = diff_method1(tt, yy, maxorder, stepsize)
# # clf = linear_model.LinearRegression(fit_intercept=False,normalize=True)
# # clf.fit(A,b)
# # g = clf.coef_
# bb = clf.predict(A)
g = pinv2(A).dot(b)
print("g=",g.T)
def case9():
y0 = [[0,0]]
t_tuple = 7
stepsize = 0.001
order = 2
maxorder = 4
t_list, y_list = simulation_ode(mode2_11, y0, t_tuple, stepsize, eps=0)
for temp_y in y_list:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
plt.plot(y0_list,y1_list,'b')
plt.show()
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
print(len(tpar_list))
for i in range(0,len(tpar_list)):
print(tpar_list[i][0])
print(tpar_list[i][-1])
print(ypar_list[i][0])
print(ypar_list[i][-1])
labels = infer_dynamic_modes_ex_dbs(tpar_list, ypar_list, stepsize, maxorder, 0.0001)
print(labels)
def case4():
y0 = [[0,0],[1,0]]
t_tuple = [(0,2.5),(0,2)]
stepsize = 0.01
order = 2
maxorder = 4
# start = time.time()
t_list, y_list = simulation_ode(conti_test, y0, t_tuple, stepsize, eps=0)
# end_simulation = time.time()
# result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(t_list, y_list, stepsize, order)
# end_inference = time.time()
# print(result_coef)
# print()
# print("Total time: ", end_inference-start)
# print("Simulation time: ", end_simulation-start)
# print("Calc-diff time: ", calcdiff_time)
# print("Pseudoinv time: ", pseudoinv_time)
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
print(len(tpar_list))
for i in range(0,len(tpar_list)):
print(tpar_list[i][0])
print(tpar_list[i][-1])
print(ypar_list[i][0])
print(ypar_list[i][-1])
for temp_y in y_list:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
plt.plot(y0_list,y1_list,'b')
plt.show()
modes, coefs, mdors = infer_dynamic_modes_ex(tpar_list, ypar_list, stepsize, maxorder, 0.0001)
# modes, coefs, mdors = infer_dynamic_modes_exx(tpar_list, ypar_list, stepsize, maxorder, 0.0001)
print(modes)
print(coefs)
print(mdors)
def case10():
y0 = [[0,0],[1,0],[0.3,0],[2.7,0]]
t_tuple = 2
stepsize = 0.01
order = 2
maxorder = 3
# start = time.time()
t_list, y_list = simulation_ode(conti_test, y0, t_tuple, stepsize, eps=0)
# end_simulation = time.time()
# result_coef, calcdiff_time, pseudoinv_time = infer_dynamic(t_list, y_list, stepsize, order)
# end_inference = time.time()
# print(result_coef)
# print()
# print("Total time: ", end_inference-start)
# print("Simulation time: ", end_simulation-start)
# print("Calc-diff time: ", calcdiff_time)
# print("Pseudoinv time: ", pseudoinv_time)
tpar_list,ypar_list = parti(t_list,y_list,0.2,1/3)
print(len(tpar_list))
for i in range(0,len(tpar_list)):
print(tpar_list[i][0])
print(tpar_list[i][-1])
print(ypar_list[i][0])
print(ypar_list[i][-1])
for temp_y in y_list:
y0_list = temp_y.T[0]
y1_list = temp_y.T[1]
plt.plot(y0_list,y1_list,'b')
plt.show()
G,labels = infer_dynamic_modes_pie(tpar_list, ypar_list, stepsize, maxorder, 0.15)
print(labels)
print(G)