Converting time-series values into a python list

Question

I am retrieving time-series values using odeint function. It solves a system of differential equation.

measurement_times = np.arange(0, 12,.1)
init = [.1,.1,.1,.1,.1]

def tar(y, measurement_times):
    T, U, V,W,I = y
    dT = 0.9*I*10.24 - T*0.0012
    dU = V*T*0.0154 - U*1*0.81
    dV = W*0.1*0.12 + U*1*0.81 - V*1.64 - V*T*0.015
    dW= V*1.64 + 0.7*1*0.47 - W*0.1*0.12 - W*U*1591.5*1
    dI= T*0.0012 + 0.8*U*1410.79*1- 0.9*I*10.24 - I*1*1934.77*1   
    return  dT, dU, dV, dW, dI
targetmodel= sp.integrate.odeint(tar, init, measurement_times)

If I print the values of dU it gives me some values like mentioned below.

for g in targetmodel:
    print(str(g[1]));
--------------------------------
    0.1
    0.09223727996210558
    0.0850835704105759
    0.07849011448256649

What I want is to convert the values into a list and assign that list to the variable data . Currently, I am doing it manually by copying the values and assigning to the variable data

 Manual way
    data = [0.1,0.09223727996210558,0.0850835704105759,0.078490114482]

I would like to find a way to do it automatically without assigning the values to variable data manually. Thanks

Answer 1

I hope that helps you

data = []
for g in targetmodel:
    data.append(g[1])

Answer 2

add the following code after targetmodel :

since the tagetmodel is of ndarray you can use basic numpy slicing to tell it to select all rows and column at index 1.(Index start at 0 )

print(targetmodel[:,1].tolist())