Lsmlx 2017

In [1]:

    

print 1+1


    

    




2

In [2]:

    

countries = ['Portugal','Spain','United Kingdom']
print countries[:2]


    

    




['Portugal', 'Spain']

In [3]:

    

a=1
while a <= 3:
    print a
    a += 1


    

    




1
2
3

In [7]:

    

def greet(hour):
    if hour < 0 or hour > 23:
        raise ValueError("Invalid input value.")
    if hour < 12:
        print 'Good morning!'
    elif hour >= 12 and hour < 20:
        print 'Good afternoon!'
    else:
        print 'Good evening!'


    

In [11]:

    

greet(-1.4)


    

    




Invalid hour: it should be between 0 and 24.

In [2]:

    

import numpy as np
np.var
np.random.normal


    

    
Out[2]:





<function normal>

In [6]:

    

import my_tools
my_tools.toString("This works!")


    

    




This works!

In [9]:

    

# This will import the numpy library
# and give it the np abbreviation
import numpy as np
# This will import the plotting library
import matplotlib.pyplot as plt
# Linspace will return 1000 points,
# evenly spaced between -4 and +4
X = np.linspace(-4, 4, 1000)
# Y[i] = X[i]**2
Y = X**2
# Plot using a red line ('r')
plt.plot(X, Y, 'r')
# arange returns integers ranging from -4 to +4
# (the upper argument is excluded!)
Ints = np.arange(-4,5)
# We plot these on top of the previous plot
# using blue circles (o means a little circle)
plt.plot(Ints, Ints**2, 'bo')
# You may notice that the plot is tight around the line
# Set the display limits to see better
plt.xlim(-5,5)
plt.ylim(-1,17)
plt.show( )


    

In [12]:

    

import numpy as np
A = np.array([
    [1,2,3],
    [2,3,4],
    [4,5,6]])
print A[0,:] # This is [1,2,3]
print A[0] # This is [1,2,3] as well
print A[:,0] # this is [1,2,4]
print A[1:,0] # This is [ 2, 4 ]. Why?
# Because it is the same as A[1:n,0] where n is the size of the array.

B = np.array([[9,8,7],[6,5,4]])
print B[0,0]


    

    




[1 2 3]
[1 2 3]
[1 2 4]
[2 4]
9

In [13]:

    

import matplotlib.pyplot as plt
import numpy as np
X = np.linspace(0, 4 * np.pi, 1000)
C = np.cos(X)
S = np.sin(X)
plt.plot(X, C)
plt.plot(X, S)


    

    
Out[13]:





[<matplotlib.lines.Line2D at 0x107bfe7d0>]

In [14]:

    

import numpy as np
A = np.arange(100)
# These two lines do exactly the same thing
print np.mean(A)
print A.mean()
C = np.cos(A)
print C.ptp()


    

    




49.5
49.5
1.99996082639

In [15]:

    

import numpy as np
np.ptp?


    

In [18]:

    

import math
import numpy as np

np.sum(  )



def f( x ):
    return pow( x, 2 )

print f( 2 )


    

    




4

In [19]:

    

import numpy as np
m = 3
n = 2
a = np.zeros([m,n])
print a


    

    




[[ 0.  0.]
 [ 0.  0.]
 [ 0.  0.]]

In [21]:

    

print a.shape


    

    




(3, 2)

In [22]:

    

print a.dtype.name


    

    




float64

In [23]:

    

a = np.array([[2,3],[3,4]])
print a


    

    




[[2 3]
 [3 4]]

In [26]:

    

a = np.array([[2,3],[3,4]])
b = np.array([[1,1],[1,1]])
a_dim1, a_dim2 = a.shape
b_dim1, b_dim2 = b.shape
c = np.zeros([a_dim1,b_dim2])
for i in xrange(a_dim1):
    for j in xrange(b_dim2):
        for k in xrange(a_dim2):
            c[i,j] += a[i,k]*b[k,j]
print c
d = np.dot(a,b)
print d


    

    




[[ 5.  5.]
 [ 7.  7.]]
[[5 5]
 [7 7]]

In [27]:

    

I = np.eye(2)
x = np.array([2.3, 3.4])
print I
print np.dot(I,x)
[[ 1., 0.],
[ 0., 1.]]
[2.3, 3.4]


    

    




[[ 1.  0.]
 [ 0.  1.]]
[ 2.3  3.4]






    
Out[27]:





[2.3, 3.4]

In [ ]:

    

import galton as galton
galton_data = galton.load( )