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In [1]:



    


from ipythonblocks import BlockGrid as bg
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
import random
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import Image



    


















    






:0: FutureWarning: IPython widgets are experimental and may change in the future.

















In [2]:



    


def array_city(x, p1, p2):
    p0 = 1-(p1+p2)
    p1 = 1-(p0+p2)
    p2 = 1-(p0+p1)
    grid = (np.random.choice([0, 1 , 2], size = (x, x), p = [p0, p1, p2]))
    return grid



    











In [3]:



    


def visualize(grid):
    g = bg(grid.shape[0], grid.shape[0])
    for row in range(g.height):
        for col in range(g.width):
            block = g[row,col]
            if grid[row,col] == 0:
                block.blue = 1000
                block.red = 1000
                block.green = 1000
            if grid[row,col] == 1:
                block.blue = 2000
                block.red = 0
                block.green = 0
            if grid[row,col] == 2:
                block.red = 2000
                block.green = 0
                block.blue = 0
    return g



    











In [4]:



    


def visualize_big(grid):
    g = bg(grid.shape[0], grid.shape[0],block_size = 5)
    for row in range(g.height):
        for col in range(g.width):
            block = g[row,col]
            if grid[row,col] == 0:
                block.blue = 1000
                block.red = 1000
                block.green = 1000
            if grid[row,col] == 1:
                block.blue = 2000
                block.red = 0
                block.green = 0
            if grid[row,col] == 2:
                block.red = 2000
                block.green = 0
                block.blue = 0
    return g

How does the amount of empty squares effect the number of loops required to reach satisfaction?

Example of high amount of empty squares





In [5]:



    


g = array_city(90,0.1,0.1)
visualize_big(g)



    


















    
Out[5]:

Example of low amount of empty squares





In [7]:



    


g = array_city(90,0.45,0.45)
visualize_big(g)



    


















    
Out[7]:
























In [8]:



    


def empties(Positive,Inverse):
    print("Do you think the amount of empties has a positive or Inverse relationship with the amount of loops required grid to reach satisfaction?")
    if Positive == True:
        print ('Incorrect')
    if Inverse == True:
        print('Correct!')



    











In [9]:



    


interact(empties, Positive = False, Inverse = False);



    


















    






Do you think the amount of empties has a positive or Inverse relationship with the amount of loops required grid to reach satisfaction?

















In [10]:



    


#data
p0 = [0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8]
loops = [138,125,107,99,93,81,63,59,56,38,23,18,15,10,8]



    











In [11]:



    


#percent of empties in 40x40 grid at 20% satisfaction
plt.figure(figsize=(15,5))
plt.xlim(0.1, 0.8)
plt.plot(p0, loops)
plt.grid(True)
plt.xlabel('Percent of Empty Boxes')
plt.ylabel('Number of Loops')
plt.title('Empties vs. Loops');

How does the percent difference between red and blue effect ability to become satisfied?

Example of low difference between reds and blues





In [12]:



    


#Example of small difference
g = array_city(100,0.45,0.45)
visualize_big(g)



    


















    
Out[12]:

Example of large difference between reds and blues





In [13]:



    


#Example of large difference
g = array_city(100,0.1,0.8)
visualize_big(g)



    


















    
Out[13]:
























In [14]:



    


def difference(Positive,Inverse):
    print("Do you think the difference between reds and blues has a positive or Inverse relationship with the amount of loops required grid to reach satisfaction?")
    if Positive == True:
        print ('Correct!')
    if Inverse == True:
        print('Incorrect')



    











In [15]:



    


interact(difference, Positive = False, Inverse = False);



    


















    






Do you think the difference between reds and blues has a positive or Inverse relationship with the amount of loops required grid to reach satisfaction?

















In [16]:



    


diff = [0.1,0.2,0.3,0.4,0.5,0.6,0.7]
loops = [51,55,58,90,92,93,95]



    











In [17]:



    


#difference between p1 and p2 in 40x40 grid at 20% satisfaction
plt.figure(figsize=(15,5))
plt.grid(True)
plt.plot(diff, loops)
plt.xlabel('Difference between reds and blues')
plt.ylabel('Number of Loops')
plt.title('Difference vs. Loops');

How does the size of the grid effect the speed of satisfaction?

Example of small grid





In [18]:



    


g = array_city(5,0.5,0.5)
visualize(g)



    


















    
Out[18]:

Example of large grid





In [19]:



    


g = array_city(150,0.4,0.4)
visualize_big(g)



    


















    
Out[19]: