Chapter 1 Solutions

Exercise 1.1

Run the following lines with matlab_1_1_free_falling_object.m

figure;
plot(tout,xout(:,1));
ylabel('position [m]');
xlabel('time [s]');

figure;
plot(tout,xout(:,2));
ylabel('velocity [m/s]');
xlabel('time [s]');

figure;
plot(tout,xout(:,3));
ylabel('m(t) [kg]');
xlabel('time [s]');

Exercise 1.2

Run the following lines with matlab_1_1_free_falling_object.m

figure;
subplot(221);
plot(tout,xout(:,1))
ylabel('position [m]');
xlabel('time [s]');
subplot(222);
plot(tout,xout(:,2))
ylabel('velocity [m/s]');
xlabel('time [s]');
subplot(212);
plot(tout,xout(:,3))
ylabel('m(t) [kg]');
xlabel('time [s]');

Exercise 1.3

Run the following lines with python_1_1_free_falling_object.py. The following codes are based on “https://towardsdatascience.com/matplotlib-multi-column-row-spanning-layouts-f026eb7c3c27”

from matplotlib.gridspec import GridSpec
fig = plt.figure(2)
gs = GridSpec(2,2,figure=fig)

ax1 = fig.add_subplot(gs[0,0])
ax1.plot(tout,xout[0,:])
ax1.set_ylabel('position [m]')
ax1.set_xlabel('time [s]')

ax2 = fig.add_subplot(gs[0,1])
ax2.plot(tout,xout[1,:])
ax2.set_ylabel('velocity [m/s]')
ax2.set_xlabel('time [s]')

ax3 = fig.add_subplot(gs[1,:])
ax3.plot(tout,xout[2,:])
ax3.set_ylabel('m(t) [kg]')
ax3.set_xlabel('time [s]')

The above codes are advanced levels in matplotlib. One of the good aspects of python is that the syntax and the commands are intuitive to understand. In the above program, GridSpec creates 2x2 tiles in the figure, where gs[0,0] indicates the first row and the first column of the tiles and gs[1,:] indicates the whole columns of the second row of the tiles.

Exercise 1.4

See the solution chapter of the book

Exercise 1.5

See the solution chapter of the book

Exercise 1.6

Check matlab_1_2_ligand_receptor_interactions.m and python_1_2_ligand_receptor_interactions.py