SIGNAL PROCESSING
Laboratory #2

Introduction to Python and PyLab environment

M. Kociński, P. Strumiłło, K. Kudryński, A. Borowska-Terka

Medical Electronics Division
Institute of Electronics

In [1]: 
import funkcje_do_lab2 as f2
f2.hide()
Out[1]:
The raw code for this IPython notebook is by default hidden for easier reading. To toggle on/off the raw code, click here.

PURPOSE:

To get acquainted with basic data visualization techniques and defining functions in Python.

TASKS:
1. In this exercise we will use different NumPy and PyLab methods and functions
2. In an interactive mode in the Editor of Canopy type the following sequence of commands:

dt=0.5

t=np.arange(0,10,dt) # time scale
a=9.81 # acceleration
v=at # velocity
s=(at**2)/2. # distance

plt.plot(t,v,'.') #plot distance in discrete time instances

plt.title('Velocity')
plt.xlabel('t[s]')
plt.ylabel('v[m/s]')

plt.figure(2)
plt.plot (t,s,'.r') #plot distance in discrete time instances
plt.title('Distance')
plt.xlabel('t[s]')
plt.ylabel('s[m]')

plt.figure(3),plt.plot (t,s,label='distance') # interpolated distance
plt.legend()
plt.grid()
plt.show()

EXPECTED RESULTS:

In [2]: 
%pylab inline
pylab.rcParams['figure.figsize'] = (14.0, 7.0)
f2.plotting()

Populating the interactive namespace from numpy and matplotlib

By selecting icon [floppy disc] displayed at the top of the figure windows save (in your defined directory D:/Biomed2014_1) the displayed plots in the jpg image file format.

3.Enter the subplot commands (consult subplot? or help(subplot) commands):

figure()
subplot(2,1,1), plot (t,v) # upper plot
subplot(2,1,2), plot (t,s) # lower plot

EXPECTED RESULTS:

In [3]: 
figure()
subplot(2,1,1), plot (f2.t,f2.v)	# upper plot
subplot(2,1,2), plot (f2.t,f2.s)	# lower plot
Out[3]:
(<matplotlib.axes.AxesSubplot at 0x75d6e30>,
 [<matplotlib.lines.Line2D at 0x7659410>])

4.Create a function motion(a,t) which returns the final velocity v and distance s of a body moving with acceleration a, after time t. Use the Canopy integrated editor to define and type in your function. Save the defined function under the name my_functions.py where all functions you write are stored. Now you can import your library of functions by using the command:

from my_functions import *

and then run the motion function for:
a = 5, t = 10,
a = 1, t = 5.

EXPECTED RESULTS:

In [4]: 
from my_functions import *
print 'The result of the function motion(a,t) for a = 5, t = 10:', motion(5,10)
print 'The result of the function motion(a,t) for a = 1, t = 5:', motion(1,5)

The result of the function motion(a,t) for a = 5, t = 10: (50, 250.0)
The result of the function motion(a,t) for a = 1, t = 5: (5, 12.5)

5.Write and test a new function my_sign(x) that checks, whether variable x is positive, negative or zero by printing appropriate texts, i.e. ‘positive’, ‘negative’, ‘zero’.

EXPECTED RESULTS:

In [5]: 
print 'The result of the function my_sign(x) for x = -10:', my_sign(-10)
print 'The result of the function my_sign(x) for x = 0:', my_sign(0)
print 'The result of the function my_sign(x) for x = 10:', my_sign(10)

The result of the function my_sign(x) for x = -10: negative
The result of the function my_sign(x) for x = 0: zero
The result of the function my_sign(x) for x = 10: positive

6.Write and test a new function my_stat_1d(x) which returns the minimum, average and maximum values of vector x = [1, -2, 4].

EXPECTED RESULTS:

In [6]: 
x=np.array([1, -2, 4])
print 'The result of the function my_stat_1d(x) for x = [1, -2, 4]:', my_stat_1d(x)

The result of the function my_stat_1d(x) for x = [1, -2, 4]: (-2, 1.0, 4)

7.You can also import a single function form my_functions in two possible methods:
a. from my_functions import my_stat_1d
b. from my_functions import my_stat_1d as myst

If you use method a. you invoke the my_stat_1d function e.g. by command: my_stat_1d(x)
If you use method b. you invoke the my_stat_1d function e.g. by command: myst(x)

In []: