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Python: Numpay – P2

November 10, 2019 Leave a comment


Learning : Python Numpy – P2
Subject: Two Dimensional array and some basic commands

In real mathematics word we mostly using arrays with more than one dimensions, for example with two dimension array we can store a data as

So let’s start, if we want to create an array with 24 number in it starting from 0 to 23 we use the command np.range. as bellow :

 # We are using np.range to create an array of numbers between (0-23) 

m_array = np.arange(24)
print(m_array)
[Output]: 
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
 


And if we want the array to be in a range with certain incriminating amount we may use this command:

 # Create array between 2-3 with 0.1 interval 

m_array = np.arange(2, 3, 0.1)
print(m_array)
[Output]: 
[ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9]


Now if we want to create an array say 3×3 fill with random numbers from (0-10) we use random function in numpy as bellow:

 # create 3x3 Array with random numbers 0-10 
m_array = np.random.randint(10, size=(3,3))
print(m_array)
[Output]: 
[[6 0 7]
 [1 9 8]
 [5 8 9]] 



And if we want the random number ranges to be between two numbers we use this command:

# Array 3x3 random values between (10-60)
m_array = np.random.randint(10,60, size=(3,3))
[Output]: 
[[11 23 50]
 [36 44 18]
 [56 24 30]] 


If we want to reshape the array; say from 4×5 (20 element in the array) we can reshape it but with any 20-element size. Here is the code:

# To crate a randome numbers in an array of 4x5 and numbers range 10-60.
m_array = np.random.randint(10,60, size=(4,5))
print(m_array)

# We will reshape the 4x5 to 2x10
new_shape = m_array.reshape(2,10)
print ('\n   Tne new 2x10 array:\n',new_shape)
[Output]:
[[37 11 56 18 42]
 [17 12 22 16 42]
 [47 29 17 47 35]
 [49 55 43 13 11]]

Tne new 2x10 array:
[[37 11 56 18 42 17 12 22 16 42]
 [47 29 17 47 35 49 55 43 13 11]]


Also we can convert a list to an array,

# Convert a list l=([2,4,6,8]) to a 1D array
# l is a list with [2,4,6,8] values.
l=([2,4,6,8])
print('  l= ',l)
# Convert it to a 1D array.
ar = np.array(l)
print('\n  Type of l:',type(l),', Type of ar:',type(ar))
print('  ar = ',ar)

[Output]:
l=  [2, 4, 6, 8] 
Type of: class'list'  , Type of ar: class 'numpy.ndarray'
ar =  [2 4 6 8]


If we want to add a value to all elements in the array, we just write:

# Adding 9 to each element in the array

 
print('ar:',ar)
ar = ar + 9
print('ar after adding 9:',ar)

[Output]:
ar:  [2 4 6 8]
ar after adding 9: [11 13 15 17]


:: numpy Commands::

Command Comments and Outputs
my_array = np.array([1,2,3,4,5]) Create an array with 1 to 5 integer
len(my_array) Get the array length
np.sum(my_array) get the sum of the elements in the array

my_array = np.array([1,2,3,4,5])
print(np.sum(my_array))
[Output]: 15
np.max(my_array) # Get the maximum number in the array
my_array = np.array([1, 2, 3,4,5])
max_num = np.max(my_array)
[Output]: 5
np.min(my_array) # Get the minimum number in the array
my_array = np.array([1, 2, 3,4,5])
min_num = np.min(my_array)
[Output]: 1
my_array = np.ones(5)
Output: [ 1., 1., 1., 1., 1.]
create array of 1s (of length 5)
np.ones(5)
Output: [ 1., 1., 1., 1., 1.]
m_array = np.arange(24)
print(m_array)
# To create an array with 23 number.
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
m_array = np.arange(2, 3, 0.1)
print(m_array)
# Create an array from 2 to 3 with 0.1 interval value increments.
[ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9]
m_array = np.random.randint(10, size=(3,3))
print(m_array)
# Create a 3×3 array with random numbers between (0,10)
[[6 0 7]
[1 9 8]
[5 8 9]]
m_array = np.random.randint(10,60, size=(3,3)) # Create a 3×3 array with random numbers between (10,60)
[[11 23 50]
[36 44 18]
[56 24 30]]
# Create a 4×5 array with random numbers.
m_array = np.random.randint(10,60, size=(4,5))

# Reshape m_array from 4×5 to 2×10
new_shape = m_array.reshape(2,10)
print(m_array)
print(new_shape)

# m_array 4×5
[[37 11 56 18 42]
[17 12 22 16 42]
[47 29 17 47 35]
[49 55 43 13 11]]

# Tne new 2×10 array:
[[37 11 56 18 42 17 12 22 16 42]
[47 29 17 47 35 49 55 43 13 11]]

# convert a list to array:
l=[2,4,6,8]
ar = np.array(l)
# check data type for l and ar:
print(‘\n Type of l:’,type(l),’, Type of ar:’,type(ar))
[Output]:
l = [2, 4, 6, 8]
ar = [2, 4, 6, 8]
Type of l: class ‘list,’, Type of ar: class ‘numpy.ndarray’
# Adding 9 to each element in the array
ar = ar + 9
[11 13 15 17]


:: numpy Sessions ::

Sessions 1 Sessions 2 Sessions 3 Sessions 4




:: Some Code output ::

Create array with 24 numbers (0-23).
Reshape array to 4×6.
Create random array of numbers (0-10), size 3×3.
Reshape 4×5 array to 2×10.
Convert list to array.



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Python: Numpay – P1

November 7, 2019 1 comment


Learning : Python Numpy – P1
Subject: Numpay and some basic commands

In coming several posts I will talk about the numpay library and how to use some of its functions. So first what is numpy? Definition: NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Also known as powerful package for scientific computing and data manipulation in python. As any library or package in python we need to install it on our device (we will not go through this process)

Basic commands in numpy: First of all we need to import it in our code. so we will use

  import numpy as np


To create a 1 dimensional array we can use verey easy way as:

  # create an array using numpy array function.
my_array = np.array([1, 2, 3,4,5])


Later we will create a random array of numbers in a range.

Now, to get the length of the array we can use len command as


len(my_array)
Output: 5


To get the sum of all elements in the array we use..

np.sum(my_array)


And to get the maximum and minimum numbers in the array we use ..

 # Get the maximum and minimum numbers in the array
my_array = np.array([1, 2, 3,4,5])
np.max(my_array)
[Output]: 5 

np.min(my_array)
[Output]: 1 


Some time we may need to create an array with certain Number of elements only one’s, to do this we can use this commands:

#create array of 1s (of length 5) 
np.ones(5)
Output: [ 1.,  1.,  1.,  1.,  1.]


The default data type will be float, if we want to change it we need to pass the the ‘dtype’ to the command like this :

#create array of 1s (of length 5) as integer: 
np.ones(5, dtype = np.int)
Output: [ 1,  1,  1,  1,  1]


Code output:



So far we work on a one dimensional array, in the next post we will cover some commands that will help us in the arrays with multiple dimensions.



:: numpy Commands::

Command comment
my_array = np.array([1,2,3,4,5]) Create an array with 1 to 5 integer
len(my_array) Get the array length
np.sum(my_array) get the sum of the elements in the array

my_array = np.array([1,2,3,4,5])
print(np.sum(my_array))
[Output]: 15
np.max(my_array) # Get the maximum number in the array
my_array = np.array([1, 2, 3,4,5])
max_num = np.max(my_array)
[Output]: 5
np.min(my_array) # Get the minimum number in the array
my_array = np.array([1, 2, 3,4,5])
min_num = np.min(my_array)
[Output]: 1
my_array = np.ones(5)
Output: [ 1., 1., 1., 1., 1.]
create array of 1s (of length 5)
np.ones(5)
Output: [ 1., 1., 1., 1., 1.]


:: numpy Sessions ::

Sessions 1 Sessions 2 Sessions 3 Sessions 4



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Python and Lindenmayer System – P3

November 5, 2019 Leave a comment


Learning : Lindenmayer System P3
Subject: Drawing Fractal Tree using Python L-System

In the first two parts of the L-System posts (Read Here: P1, P2) we talk and draw some geometric shapes and patterns. Here in this part 3 we will cover only the Fractal Tree and looking for other functions that we may write to add leaves and flowers on the tree.

Assuming that we have the Pattern generating function and l-system drawing function from part one, I will write the rules and attributes to draw the tree and see what we may get.
So, first tree will have:


# L-System Rule to draw ‘Fractal Tree’
# Rule: F: F[+F]F[-F]F
# Angle: 25
# Start With: F
# Iteration : 4


and the output will be this:


If we need to add some flowers on the tree, then we need to do two things, first one is to write a function to draw a flower, then we need to add a variable to our rule that will generate a flower position in the pattern. First let’s write a flower function. We will assume that we may want just to draw a small circles on the tree, or we may want to draw a full open flower, a simple flower will consist of 4 Petals and a Stamen, so our flower drawing function will draw 4 circles to present the Petals and one middle circle as the Stamen. We will give the function a variable to determine if we want to draw a full flower or just a circle, also the size and color of the flowers.

Here is the code ..

font = 516E92
commint = #8C8C8C

Header here
# Functin to draw Flower
def d_flower () :

if random.randint (1,1) == 1 :

# if full_flower = ‘y’ the function will draw a full flower,

# if full_flower = ‘n’ the function will draw only a circle

full_flower = ‘y’

t.penup()

x1 = t.xcor()

y1 = t.ycor()

f_size = 2

offset = 3

deg = 90

if full_flower == ‘y’ :

t.color(‘#FAB0F4’)

t.fillcolor(‘#FAB0F4’)

t.goto(x1,y1)

t.setheading(15)

for x in range (0,4) : # To draw a 4-Petals

t.pendown()

t.begin_fill()

t.circle(f_size)

t.end_fill()

t.penup()

t.right(deg)

t.forward(offset)

t.setheading(15)

t.goto(x1,y1 – offset * 2 + 2)

t.pendown() # To draw a white Stamen

t.color(‘#FFFFF’)

t.fillcolor(‘#FFFFFF’)

t.begin_fill()

t.circle(f_size)

t.end_fill()

t.penup()

else: # To draw a circle as close flower

t.pendown()

t.color(‘#FB392C’)

t.end_fill()

t.circle(f_size)

t.end_fill()

t.penup()

t.color(‘black’)

Then we need to add some code to our rule and we will use variable ‘o’ to draw the flowers, also I will add a random number selecting to generate the flowers density. Here is the code for it ..

In the code the random function will pick a number between (1-5) if it is a 3 then the flower will be drawn. More density make it (1-2), less density (1-20)

And here is the output if we run the l-System using this rule: Rule: F: F[+F]F[-F]Fo

Using the concepts, here is some samples with another Fractal Tree and flowers.

Another Fractal Tree without any Flowers.


Fractal Tree with closed Pink Flowers.


Fractal Tree with closed Red Flowers.


Fractal Tree with open pink Flowers.




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Python and Lindenmayer System – P2

November 3, 2019 1 comment


Learning : Lindenmayer System P2
Subject: Drawing with python using L-System

In the first part of Lindenmayer System L-System post (Click to Read) we had wrote two functions: one to generate the pattern based on the variables and roles, and one to draw lines and rotate based on the pattern we have.

In this part I will post images of what Art we can generate from L-System
the codes will be the L-system that generate the patterns, so the code will include: the Rules, Angle (Right, Left) Iteration and Starting Variable.


L-System: Koch Curve

L-System: Minkowski Sausage

L-System: … but here the Iteration is: 3

L-System: Again … but here the Iteration is: 3

L-System: Square Sierpinski

L-System: Sierpinski Arrowhead.

L-system: Dragon Curve

L-System: Koch Snowflake

L-System:

L-System:


The possibilities to generate the putters and therefore drawing the output is endless, any slightly changes in the iterations or rotation (+ -) angles will take all output to a new levels. In the coming post, I will use the L-system to generate fractal tree and see what we can get from there.



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