mydict = {'carl':40, 'alan':2, 'bob':1, 'danny':0}

# How to sort a dict by value Python 3>
sort = {key:value for key, value in sorted(mydict.items(), key=lambda kv: (kv[1], kv[0]))}
print(sort)

# How to sort a dict by key Python 3>
sort = {key:mydict[key] for key in sorted(mydict.keys())}
print(sort)

"""
Assignment 6

The goal is to make a graph of
who bit who and who was bitten.
There should be 10 nodes and 15 edges.
3 arrows of biting each other and
3 arrows of someone biting themselves.
Networkx can not do self biting
arrows, but it is in the code.
"""

from graphviz import Digraph as DDotGraph
from graphviz import Graph as UDotGraph
import networkx as nx
from networkx.algorithms.dag import transitive_closure
import graphviz as gv
import matplotlib.pyplot as plt
import numpy as np
from numpy.linalg import matrix_power

"""
class DGraph:
    def __init__(self):
        self.d = dict()

    def clear(self):
        self.d = dict()

    def add_node(self,n):
        if not self.d.get(n):
            self.d[n] = set()

    def add_edge(self,e):
        f,t=e
        self.add_node(f)
        self.add_node(t)
        vs=self.d.get(f)
        if not vs:
            self.d[f] = {t}
        else:
            vs.add(t)

    def add_edges_from(self,es):
        for e in es:
            self.add_edge(e)

    def edges(self):
        for f in self.d:
            for t in self.d[f]:
                yield (f,t)

    def number_of_nodes(self):
        return len(self.d)

    def __repr__(self):
        return self.d.__repr__()

    def show(self):
        dot = gv.Digraph()
        for e in self.edges():
            #print(e)
            f, t = e
            dot.edge(str(f), str(t), label='')
        #print(dot.source)
        show(dot)

# displays graph with graphviz
def show(dot, show=True, file_name='graph.gv'):
    dot.render(file_name, view=show)


def showGraph(g,label="",directed=True):
    if directed:
        dot = gv.Digraph()
    else:
        dot = gv.Graph()

    for e in g.edges():
        print(e)
        f, t = e
        dot.edge(str(f), str(t), label=label)
    print(dot.source)
    show(dot)


def bit():
    G = DGraph()
    G.add_edge(("Blade","Samara"))
    G.add_edge(("Shadow","Wolfe"))
    G.add_edge(("Raven", "Austin"))
    G.add_edge(("Blade", "Alice"))
    G.add_edge(("Alice","Brandon"))
    G.add_edge(("Blade", "Wolfe"))
    G.add_edge(("Samara", "Robin"))
    G.add_edge(("Samara", "Raven"))
    G.add_edge(("Samara", "Hamed"))
    G.add_edge(("Wolfe", "Blade"))
    G.add_edge(("Hamed", "Samara"))
    G.add_edge(("Wolfe", "Shadow"))
    G.add_edge(("Brandon", "Brandon"))
    G.add_edge(("Hamed", "Hamed"))
    G.add_edge(("Austin", "Austin"))
    showGraph(G, label="bit")

bit()

def bitten():
    G=DGraph()
    G.add_edge(("Samara","Blade"))
    G.add_edge(("Wolfe","Shadow"))
    G.add_edge(("Austin", "Raven"))
    G.add_edge(("Alice","Blade"))
    G.add_edge(("Brandon", "Alice"))
    G.add_edge(("Wolfe", "Blade" ))
    G.add_edge(("Robin", "Samara"))
    G.add_edge(("Raven", "Samara"))
    G.add_edge(("Hamed", "Samara"))
    G.add_edge(("Blade", "Wolfe"))
    G.add_edge(("Samara", "Hamed"))
    G.add_edge(("Shadow", "Wolfe"))
    G.add_edge(("Brandon", "Brandon"))
    G.add_edge(("Hamed", "Hamed"))
    G.add_edge(("Austin", "Austin"))
    showGraph(G, label="bitten by")

#bitten()

family = ["Blade", "Samara", "Shadow", "Wolfe", "Raven", "Alice"]
"""

#Do transitive closure call out and the
#matrix power operation should be the same
D = nx.DiGraph()
#D.add_nodes_from("SamaraBladeWolfeShadowAliceRavenBrandonRobinHamedAustin")
D.add_edge("Blade","Samara")
D.add_edge("Shadow","Wolfe")
D.add_edge("Raven", "Austin")
D.add_edge("Blade", "Alice")
D.add_edge("Alice","Brandon")
D.add_edge("Blade", "Wolfe")
D.add_edge("Samara", "Robin")
D.add_edge("Samara", "Raven")
D.add_edge("Samara", "Hamed")
D.add_edge("Wolfe", "Blade")
D.add_edge("Hamed", "Samara")
D.add_edge("Wolfe", "Shadow")
D.add_edge("Brandon", "Brandon")
D.add_edge("Hamed", "Hamed")
D.add_edge("Austin", "Austin")

T = transitive_closure(D)

for e in D.edges(): print(e)
for n in D.nodes(): print(n)

def show(H):
    nx.draw(H, with_labels=True, font_weight='bold')
    plt.show()
#Use nx.to_numpy_matrix instead of nx.adjacency_matrix

# M = nx.adjacency_matrix(D)
# MT = nx.adjacency_matrix(T)
M = nx.to_numpy_matrix(D)
MT = nx.to_numpy_matrix(T)
M2 = M@M

def mPower(M, k): #M is numpy matrix
    assert k >= 1
    P = M
    for _ in range(k):
       P = P @ M
    return P

def tc(M):
    #compute transitive closure
    pass

D1 = nx.DiGraph(M)
D2 = nx.DiGraph(M2)

print('Matrix for Original\n', M)
N = nx.to_numpy_array(D,dtype=int)
print('np_array for Original\n', N)
print('\nMatrix for Transitive Closure\n', MT)
N2 = nx.to_numpy_array(T,dtype=int)
print('np_array for Transitive Closure\n', N2)

show(D) #can use D, T, and numpy matrix power operation
show(T)
show(T)

import random
import time

def generate_maze(width, height):
    """Generate a random maze using depth-first search"""
    maze = [[1 for _ in range(width)] for _ in range(height)]
    
    def carve(x, y):
        maze[y][x] = 0
        directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]
        random.shuffle(directions)
        
        for dx, dy in directions:
            nx, ny = x + dx*2, y + dy*2
            if 0 <= nx < width and 0 <= ny < height and maze[ny][nx] == 1:
                maze[y + dy][x + dx] = 0
                carve(nx, ny)
    
    carve(1, 1)
    maze[0][1] = 0  # Entrance
    maze[height-1][width-2] = 0  # Exit
    return maze

def print_maze(maze, path=None):
    """Print the maze with ASCII characters"""
    if path is None:
        path = []
    
    for y in range(len(maze)):
        for x in range(len(maze[0])):
            if (x, y) in path:
                print('◍', end=' ')
            elif maze[y][x] == 0:
                print(' ', end=' ')
            else:
                print('▓', end=' ')
        print()

def solve_maze(maze, start, end):
    """Solve the maze using recursive backtracking"""
    visited = set()
    path = []
    
    def dfs(x, y):
        if (x, y) == end:
            path.append((x, y))
            return True
        
        if (x, y) in visited or maze[y][x] == 1:
            return False
            
        visited.add((x, y))
        path.append((x, y))
        
        for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
            if dfs(x + dx, y + dy):
                return True
                
        path.pop()
        return False
    
    dfs(*start)
    return path

# Generate and solve a maze
width, height = 21, 11  # Should be odd numbers
maze = generate_maze(width, height)
start = (1, 0)
end = (width-2, height-1)

print("Generated Maze:")
print_maze(maze)

print("\nSolving Maze...")
time.sleep(2)
path = solve_maze(maze, start, end)

print("\nSolved Maze:")
print_maze(maze, path)

print("hellur")

# Python program for Plotting Fibonacci 
# spiral fractal using Turtle 
import turtle 
import math 
  
def fiboPlot(n): 
    a = 0
    b = 1
    square_a = a 
    square_b = b 
  
    # Setting the colour of the plotting pen to blue 
    x.pencolor("blue") 
  
    # Drawing the first square 
    x.forward(b * factor) 
    x.left(90) 
    x.forward(b * factor) 
    x.left(90) 
    x.forward(b * factor) 
    x.left(90) 
    x.forward(b * factor) 
  
    # Proceeding in the Fibonacci Series 
    temp = square_b 
    square_b = square_b + square_a 
    square_a = temp 
      
    # Drawing the rest of the squares 
    for i in range(1, n): 
        x.backward(square_a * factor) 
        x.right(90) 
        x.forward(square_b * factor) 
        x.left(90) 
        x.forward(square_b * factor) 
        x.left(90) 
        x.forward(square_b * factor) 
  
        # Proceeding in the Fibonacci Series 
        temp = square_b 
        square_b = square_b + square_a 
        square_a = temp 
  
    # Bringing the pen to starting point of the spiral plot 
    x.penup() 
    x.setposition(factor, 0) 
    x.seth(0) 
    x.pendown() 
  
    # Setting the colour of the plotting pen to red 
    x.pencolor("red") 
  
    # Fibonacci Spiral Plot 
    x.left(90) 
    for i in range(n): 
        print(b) 
        fdwd = math.pi * b * factor / 2
        fdwd /= 90
        for j in range(90): 
            x.forward(fdwd) 
            x.left(1) 
        temp = a 
        a = b 
        b = temp + b 
  
  
# Here 'factor' signifies the multiplicative  
# factor which expands or shrinks the scale 
# of the plot by a certain factor. 
factor = 1
  
# Taking Input for the number of  
# Iterations our Algorithm will run 
n = int(input('Enter the number of iterations (must be > 1): ')) 
  
# Plotting the Fibonacci Spiral Fractal  
# and printing the corresponding Fibonacci Number 
if n > 0: 
    print("Fibonacci series for", n, "elements :") 
    x = turtle.Turtle() 
    x.speed(100) 
    fiboPlot(n) 
    turtle.done() 
else: 
    print("Number of iterations must be > 0")

# Function to multiply two matrices
def multiply_matrices(matrix1, matrix2):
    # Check if the matrices can be multiplied
    if len(matrix1[0]) != len(matrix2):
        print("Error: The number of columns in the first matrix must be equal to the number of rows in the second matrix.")
        return None

    # Create the result matrix filled with zeros
    result = [[0 for _ in range(len(matrix2[0]))] for _ in range(len(matrix1))]

    # Perform matrix multiplication
    for i in range(len(matrix1)):
        for j in range(len(matrix2[0])):
            for k in range(len(matrix2)):
                result[i][j] += matrix1[i][k] * matrix2[k][j]

    return result

# Example matrices
matrix1 = [[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]]

matrix2 = [[10, 11],
           [12, 13],
           [14, 15]]

# Multiply the matrices
result_matrix = multiply_matrices(matrix1, matrix2)

# Display the result
if result_matrix is not None:
    print("Result:")
    for row in result_matrix:
        print(row)