Expert Overview: Bromley vs Gillingham FC

The upcoming match between Bromley and Gillingham FC is set to be a thrilling encounter, with both teams eager to secure a win. The odds suggest a highly competitive match, with several betting options reflecting the potential for dynamic play and scoring opportunities. With an average of 2.97 total goals expected, this game promises to be action-packed. Both teams have shown resilience and skill, making predictions particularly intriguing.

Predictions for Bromley vs Gillingham FC

• Home Team To Score In 2nd Half: 98.80
• Away Team To Score In 2nd Half: 98.60
• Away Team Not To Score In 1st Half: 98.60
• Last Goal 73+ Minutes: 82.20
• Both Teams Not To Score In 1st Half: 81.20
• Goal In Last 15 Minutes: 81.70
• First Goal 30+ Minutes: 80.10
• Under 2.5 Goals: 74.50
• Over 1.5 Goals: 72.20
• Sum of Goals 2 or 3: 65.30
• Home Team To Score In 1st Half: 68.30
• Goal In Last 10 Minutes: 68.10
• Yellow Cards: 4.37

Additionajimmyschopf/simulations/simulations/Analysis/Graphs/NetworkStats/NetworkStats.py
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

class NetworkStats:
def __init__(self):
self.data = pd.DataFrame()
self.num_nodes = []

def add(self, data):
self.data = self.data.append(data)

def get_data(self):
return self.data

def show(self):
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)

self.data.boxplot(column=[‘degree’], ax=ax)

ax.set_title(“Degree Distribution”)

ax.set_ylabel(‘Number of Connections’)

ax.set_xlabel(‘Network Size’)

plt.show()

def show_averages(self):
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)

self.data.groupby(‘num_nodes’)[‘degree’].mean().plot(ax=ax)

ax.set_title(“Average Degree”)

ax.set_ylabel(‘Number of Connections’)

ax.set_xlabel(‘Network Size’)

plt.show()

def show_stdevs(self):
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)

self.data.groupby(‘num_nodes’)[‘degree’].std().plot(ax=ax)

ax.set_title(“Degree Standard Deviation”)

ax.set_ylabel(‘Number of Connections’)

ax.set_xlabel(‘Network Size’)

plt.show()

def show_density(self):
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)

self.data.groupby(‘num_nodes’)[‘density’].mean().plot(ax=ax)

ax.set_title(“Density”)

ax.set_ylabel(‘Density’)

ax.set_xlabel(‘Network Size’)

plt.show()

def show_avg_clustering_coefficient(self):
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)

self.data.groupby(‘num_nodes’)[‘avg_clustering_coefficient’].mean().plot(ax=ax)

ax.set_title(“Average Clustering Coefficient”)

ax.set_ylabel(‘Clustering Coefficient’)

ax.set_xlabel(‘Network Size’)

plt.show()

jimmyschopf/simulations/simulations/Analysis/Graphs/PopulationInfection/PopulationInfection.py
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

class PopulationInfection:
def __init__(self):
self.data = pd.DataFrame()

def add(self, data):
# print(data)
# print(type(data))
# print(data[‘infections’])
self.data = self.data.append(data)

def get_data(self):
return self.data

def show(self):
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)

#print(self.data)
#print(type(self.data[‘infections’]))
#print(type(np.array(self.data[‘infections’])))
#print(np.array(self.data[‘infections’]).shape)
for i in range(len(np.array(self.data[‘infections’]))[0]):
x = [x[i] for x in np.array(self.data[‘infections’])]
y = [y[i] for y in np.array(self.data[‘time’])]
#print(x)
#print(y)
if i == len(np.array(self.data[‘infections’]))[0]-1:
label = ‘Final Day’
else:
label = str(i+1) + ‘ Days’
if len(x) > len(y):
y.append(y[-1])
else:
x.append(x[-1])
#print(x,y,label)
ax.plot(y,x,label=label)

handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
legend_title=’Days’

leg = ax.legend(handles[::-1], labels[::-1], title=legend_title, loc=’upper left’, bbox_to_anchor=(0., -0.05), ncol=5)

for h in leg.legendHandles:
h._legmarker.set_markersize(5)

leg.get_title().set_fontsize(‘large’)

#plt.xticks(range(len(y)),y)

leg._legend_box.align=”left”

#ax.set_xticks(range(len(y)))

#ax.xaxis.grid(True,’major’)

#plt.xticks(np.arange(min(y), max(y)+1, step=1))

#ax.xaxis.set_major_locator(ticker.MultipleLocator(5))

plt.xticks(rotation=45)

#plt.xticks(np.arange(min(y), max(y)+1, step=5))

jimmyschopf/simulations/simulations/Simulation.py
import random
from .Agent import Agent
from .Location import Location
from .Person import Person
from .Worker import Worker
from .Household import Household
from .Graph import Graph
from .School import School
from .Business import Business
from .Settings import Settings
from datetime import datetime as dt
from copy import deepcopy

class Simulation:
def __init__(self):
self.settings = Settings()

def run_simulation(self):
#——————————————————————————————#
#——————————————————————————————#
#———————————–SET UP THE SIMULATION———————————–#
#——————————————————————————————#
#——————————————————————————————#

#Set up the simulation parameters and objects based on the user’s input in the settings file.
#The simulation can be run using either a preset or a custom configuration.

#Load the settings from the settings file.
settings_file_path=self.settings.get_settings_file_path()
settings_file=self.settings.read_settings(settings_file_path)

#Set up the simulation using the settings file.
if settings_file[‘preset’] == “true”:
preset=settings_file[‘preset_type’]
else:
preset=”custom”
if preset == “custom”:
num_agents=self.settings.get_custom_num_agents(settings_file)
num_households=self.settings.get_custom_num_households(settings_file,num_agents)
num_schools=self.settings.get_custom_num_schools(settings_file,num_agents,num_households)
num_businesses=self.settings.get_custom_num_businesses(settings_file,num_agents,num_households,num_schools)
else:
num_agents=self.settings.get_preset_num_agents(preset)
num_households=self.settings.get_preset_num_households(preset,num_agents)
num_schools=self.settings.get_preset_num_schools(preset,num_agents,num_households)
num_businesses=self.settings.get_preset_num_businesses(preset,num_agents,num_households,num_schools)

#create list of agents to store all agents.
agents=[]

#create dictionary to store household information.
household_dict={}

#create dictionary to store school information.
school_dict={}

#create dictionary to store business information.
business_dict={}

#create graph object to store the network structure of the population.
graph=Graph()

#create list of locations to store all locations.
locations=[]

#create list of days to store all days.
days=[]

#create list of people who are currently infected and will be removed from the simulation when recovered.
currently_infected=[]

#create list of people who have been infected throughout the simulation but are no longer infected.
removed=[]

#create list of people who have been infected throughout the simulation but are still infectious.
still_infectious=[]

#———————————RUN THE SIMULATION—————————————#

for day in range(settings_file[“num_days”]):

#——————————————————————————————#
#—————————–SET UP NEW DAY PARAMETERS————————————-#
#——————————————————————————————#

#print current day number (for debugging purposes).
print(day+1)

#create new day object and add it to days list.
day=Day(day+1,days,self.settings.get_time_step(),settings_file[“num_steps_per_day”],self.settings.get_agent_attribute(“household_size”),self.settings.get_agent_attribute(“school_size”),self.settings.get_agent_attribute(“business_size”),settings_file[“num_business_types”],settings_file[“num_school_types”])
days.append(day)

#if first day, initialize population.

if day.number==1:

#if first day, initialize population.

#——————————————————————————————#
#————————-INITIALIZE HOUSEHOLDS AND AGENTS———————————-#
#——————————————————————————————#

#create households based on number of households specified in settings file.

for household_number in range(num_households):

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#for each household create a household object and add it to household_dict.

household=Household(household_number,self.settings.get_agent_attribute(“household_size”))
household_dict[household_number]=household

#if first day, initialize population.

#for each household create agents and add them to agent list.

for agent_number in range(household.number_of_members):

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#if first day, initialize population.

#if first day, create agent object and add it to agent list.
agent=Agent(agent_number,self.settings.get_agent_attribute(“name”),self.settings.get_agent_attribute(“age_range”),self.settings.get_agent_attribute(“symptom_onset_rate”),self.settings.get_agent_attribute(“asymptomatic_rate”),self.settings.get_agent_attribute(“infectious_period”),self.settings.get_agent_attribute(“recovery_rate”),self.settings.get_agent_attribute(“household_size”),self.settings.get_agent_attribute(“school_size”),self.settings.get_agent_attribute(“business_size”),household_number,school_dict,business_dict,self.settings,get_infection_probability_function())
agents.append(agent)

#if first day, initialize population.

#if first day, initialize population.

#for each agent in household add it to household’s members list.

household.members.append(agent)

#if first day, initialize population.

#for each agent in household assign it a home location based on its household number and add it to locations list.

location=Location(agent.household_number,”home”,agent,self.locations_available_to_home(agent.household_number),graph,self.settings,get_infection_probability_function())
agent.home_location=location
locations.append(location)

#if first day, initialize population.

#for each agent in household assign it a work location based on its age and business location availability and add it to locations list.

if agent.age>=self.settings.get_agent_attribute(“work_age”):

work_location=random.choice(list(business_dict.keys()))

while len(business_dict[work_location].members)>=business_dict[work_location].max_capacity:

work_location=random.choice(list(business_dict.keys()))

if work_location not in agent.work_locations:

agent.work_locations.append(work_location)

location=Location(agent.household_number,”work”,agent,self.locations_available_to_work(agent.household_number),graph,self.settings,get_infection_probability_function())
business_dict[work_location].members.append(agent)

locations.append(location)

elif agent.age>=self.settings.get_agent_attribute(“school_age”):

school_location=random.choice(list(school_dict.keys()))

while len(school_dict[school_location].members)>=school_dict[school_location].max_capacity:

school_location=random.choice(list(school_dict.keys()))

if school_location not in agent.school_locations:

agent.school_locations.append(school_location)

location=Location(agent.household_number,”school”,agent,self.locations_available_to_school(agent.household_number),graph,self.settings,get_infection_probability_function())
school_dict[school_location].members.append(agent)

locations.append(location)

#else if agent is not old enough for work or school but is old enough for daycare:

elif agent.age>=self.settings.get_agent_attribute(“daycare_age”):

daycare_location=random.choice(list(business_dict.keys()))

while len(business_dict[daycare_location].members)>=business_dict[daycare_location].max_capacity:

daycare_location=random.choice(list(business_dict.keys()))

if daycare_location not in agent.daycare_locations:

agent.daycare_locations.append(daycare_location)

location=Location(agent.household_number,”daycare”,agent,self.locations_available_to_daycare(agent.household_number),graph,self.settings,get_infection_probability_function())
business_dict[daycare_location].members.append(agent)

locations.append(location)

#else if agent is too young for daycare but still needs care:

else:

caregiver=random.choice(household.members)

while caregiver.age=self.settings.get_agent_attribute(“daycare_age”):

if member not in member.caregivers:

member.c