my_list = ["blue", "red", "green"]

#1- Using sort or srted directly or with specifc keys
my_list.sort() #sorts alphabetically or in an ascending order for numeric data 
my_list = sorted(my_list, key=len) #sorts the list based on the length of the strings from shortest to longest. 
# You can use reverse=True to flip the order

#2- Using locale and functools 
import locale
from functools import cmp_to_key
my_list = sorted(my_list, key=cmp_to_key(locale.strcoll))

print(“Hello World”)

import copy
begining = [False,False,False,False,False,None,True,True,True,True,True]
#False = black True = white
its = [0]
def swap(layout, step):
    layoutCopy = copy.deepcopy(layout)
    layoutCopy[(step[0]+step[1])], layoutCopy[step[1]] = layoutCopy[step[1]], layoutCopy[(step[0]+step[1])]
    return layoutCopy
def isSolved(layout):
    for i in range(len(layout)):
        if(layout[i] == False):
            return (i >= (len(layout)/2))
def recurse(layout, its, steps = []):
    if isSolved(layout):
        its[0] += 1
        print(layout,list(x[0] for x in steps))
        return
    step = None
    for i in range(len(layout)):
        if(layout[i] == None):
            if(i >= 1): #If the empty space could have something to the left
                if(layout[i - 1] == False):
                    step = [-1,i]
                    recurse(swap(layout,step), its, (steps+[step]))
                if(i > 1): #If the empty space could have something 2 to the left
                    if(layout[i - 2] == False):
                        step = [-2,i]
                        recurse(swap(layout,step), its, (steps+[step]))
            if(i < (len(layout)-1)): #If the empty space could have something to the right
                if(layout[i + 1] == True):
                    step = [1,i]
                    recurse(swap(layout,step), its, (steps+[step]))
                if(i < (len(layout)-2)): #If the empty space could have something to the right
                    if(layout[i + 2] == True):
                        step = [2,i]
                        recurse(swap(layout,step), its, (steps+[step]))
    its[0] += 1
    #return None
recurse(begining,its,[])
print(its[0])

def format_timestamp(timestamp_epoch):
    """
    Convert epoch timestamp to formatted datetime string without using datetime package.
    
    Args:
        timestamp_epoch (int/float): Unix epoch timestamp (seconds since 1970-01-01 00:00:00 UTC)
        
    Returns:
        str: Formatted datetime string in 'YYYY-MM-DD HH:MM:SS' format
    """
    # Constants for time calculations
    SECONDS_PER_DAY = 86400
    SECONDS_PER_HOUR = 3600
    SECONDS_PER_MINUTE = 60
    
    # Handle negative timestamps and convert to integer
    timestamp = int(timestamp_epoch)
    
    # Calculate days since epoch and remaining seconds
    days_since_epoch = timestamp // SECONDS_PER_DAY
    remaining_seconds = timestamp % SECONDS_PER_DAY
    
    # Calculate hours, minutes, seconds
    hours = remaining_seconds // SECONDS_PER_HOUR
    remaining_seconds %= SECONDS_PER_HOUR
    minutes = remaining_seconds // SECONDS_PER_MINUTE
    seconds = remaining_seconds % SECONDS_PER_MINUTE
    
    # Calculate date (simplified, ignoring leap seconds)
    year = 1970
    days = days_since_epoch
    while days >= 365:
        is_leap = (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0)
        days_in_year = 366 if is_leap else 365
        if days >= days_in_year:
            days -= days_in_year
            year += 1
    
    # Month lengths (non-leap year for simplicity, adjusted later for leap years)
    month_lengths = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
    if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
        month_lengths[1] = 29
    
    month = 0
    while days >= month_lengths[month]:
        days -= month_lengths[month]
        month += 1
    
    # Convert to 1-based indexing for month and day
    month += 1
    day = days + 1
    
    # Format the output string
    return f"{year:04d}-{month:02d}-{day:02d} {hours:02d}:{minutes:02d}:{seconds:02d}"

# Example timestamp (Unix epoch seconds)
timestamp = 1697054700
formatted_date = format_timestamp(timestamp)
print(formatted_date + " UTC")  # Output: 2023-10-11 18:45:00

import pandas as pd



x = pd.read_excel(FILE_NAME)

print(x)

"""
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)