"""Agent and member data fixtures for comprehensive testing."""
from typing import Dict, Any, List, Optional
import random
from datetime import datetime, timedelta
class AgentDataFixtures:
"""Comprehensive agent and member data fixtures for testing."""
def __init__(self):
self.first_names = [
"John", "Jane", "Michael", "Sarah", "David", "Lisa", "Robert", "Emily",
"William", "Jessica", "James", "Ashley", "Christopher", "Amanda", "Daniel", "Jennifer",
"Matthew", "Stephanie", "Anthony", "Melissa", "Mark", "Nicole", "Steven", "Kimberly",
"Andrew", "Donna", "Kenneth", "Carol", "Paul", "Sandra", "Joshua", "Ruth"
]
self.last_names = [
"Smith", "Johnson", "Williams", "Brown", "Jones", "Garcia", "Miller", "Davis",
"Rodriguez", "Martinez", "Hernandez", "Lopez", "Gonzalez", "Wilson", "Anderson", "Thomas",
"Taylor", "Moore", "Jackson", "Martin", "Lee", "Perez", "Thompson", "White",
"Harris", "Sanchez", "Clark", "Ramirez", "Lewis", "Robinson", "Walker", "Young"
]
self.office_names = [
"Premier Realty", "Best Homes", "Elite Properties", "Top Agents", "Prime Real Estate",
"Century 21", "Keller Williams", "RE/MAX", "Coldwell Banker", "Berkshire Hathaway",
"Compass", "Realty One Group", "eXp Realty", "Better Homes and Gardens", "Sotheby's International",
"Douglas Elliman", "Corcoran Group", "BHHS PenFed Realty", "Windermere Real Estate",
"Howard Hanna", "Long & Foster", "John L. Scott", "Weichert Realtors"
]
self.cities = [
"Austin", "Dallas", "Houston", "San Antonio", "Fort Worth",
"El Paso", "Arlington", "Corpus Christi", "Plano", "Lubbock",
"Garland", "Irving", "Laredo", "Frisco", "McKinney",
"Grand Prairie", "Brownsville", "Pasadena", "Mesquite", "McAllen"
]
self.specializations = [
"Luxury Homes", "First-Time Buyers", "Investment Properties", "New Construction",
"Relocation Specialist", "Commercial Real Estate", "Farm and Ranch", "Waterfront Properties",
"55+ Communities", "Condominiums", "Foreclosures", "Short Sales", "Vacation Homes",
"Multi-Family Properties", "Land Development", "Property Management", "Leasing",
"International Clients", "Military Relocation", "Equestrian Properties"
]
self.designations = [
"ABR", "ABRM", "AHWD", "ALHS", "ASP", "AT", "BPOR", "CCIM", "CRB", "CRS",
"CIPS", "CRP", "CSDS", "e-PRO", "GRI", "MRP", "PSA", "RENE", "RSPS", "SFR",
"SRES", "SRS", "CCDS", "CDPE", "CPRES", "GREEN", "NAR", "PMN", "RESE", "RCC"
]
self.languages = [
"English", "Spanish", "French", "German", "Chinese", "Japanese", "Portuguese",
"Italian", "Russian", "Arabic", "Korean", "Hindi", "Vietnamese", "Tagalog"
]
def create_basic_agent(self, member_key: str = None, **overrides) -> Dict[str, Any]:
"""Create a basic real estate agent with realistic data."""
if not member_key:
member_key = f"AGENT{random.randint(100000, 999999)}"
first_name = random.choice(self.first_names)
last_name = random.choice(self.last_names)
city = random.choice(self.cities)
office = random.choice(self.office_names)
# Generate realistic contact information
area_codes = {"Austin": "512", "Dallas": "214", "Houston": "713", "San Antonio": "210"}
area_code = area_codes.get(city, "512")
agent_data = {
"MemberKey": member_key,
"MemberFirstName": first_name,
"MemberLastName": last_name,
"MemberFullName": f"{first_name} {last_name}",
"MemberNickname": first_name,
"MemberEmail": f"{first_name.lower()}.{last_name.lower()}@{office.lower().replace(' ', '').replace('&', '')}.com",
"MemberDirectPhone": f"({area_code}) {random.randint(200, 999)}-{random.randint(1000, 9999)}",
"MemberMobilePhone": f"({area_code}) {random.randint(200, 999)}-{random.randint(1000, 9999)}",
"MemberOfficePhone": f"({area_code}) {random.randint(200, 999)}-{random.randint(1000, 9999)}",
"MemberFax": f"({area_code}) {random.randint(200, 999)}-{random.randint(1000, 9999)}",
"MemberOfficeName": office,
"MemberOfficeKey": f"OFFICE{random.randint(1000, 9999)}",
"MemberCity": city,
"MemberStateOrProvince": "TX",
"MemberPostalCode": f"7{random.randint(8000, 8999)}",
"MemberAddress1": f"{random.randint(100, 9999)} {random.choice(['Main', 'Oak', 'Business', 'Commerce'])} St",
"MemberStateLicense": f"TX{random.randint(100000, 999999)}",
"MemberNationalAssociationId": f"NAR{random.randint(1000000, 9999999)}",
"MemberType": random.choice(["REALTOR", "Appraiser", "Broker", "Sales Agent"]),
"MemberStatus": random.choice(["Active", "Inactive", "Suspended"]),
"MemberLanguages": random.choice(self.languages),
"MemberSpecialty": random.choice(self.specializations),
"MemberDesignation": random.choice(self.designations),
"MemberPreferredPhone": random.choice(["Mobile", "Direct", "Office"]),
"MemberLoginId": f"{first_name.lower()}{last_name.lower()}{random.randint(100, 999)}",
"MemberPassword": None, # Never include actual passwords
"ModificationTimestamp": datetime.now().isoformat(),
"MemberMlsId": f"MLS{random.randint(100000, 999999)}"
}
# Apply any overrides
agent_data.update(overrides)
return agent_data
def create_top_producer(self, member_key: str = None, **overrides) -> Dict[str, Any]:
"""Create a top-producing agent with premium credentials."""
agent_data = self.create_basic_agent(member_key, **overrides)
# Upgrade to top producer status
top_producer_updates = {
"MemberType": "REALTOR",
"MemberStatus": "Active",
"MemberDesignation": random.choice(["CRS", "GRI", "ABR", "CRB", "CIPS"]),
"MemberSpecialty": random.choice(["Luxury Homes", "Investment Properties", "Commercial Real Estate"]),
"MemberYearsOfExperience": random.randint(10, 30),
"MemberVolumeSold": random.randint(50000000, 200000000), # $50M - $200M
"MemberTransactionsSold": random.randint(100, 500),
"MemberAwards": random.choice([
"Top Producer 2023", "Chairman's Circle", "President's Circle",
"Multi-Million Dollar Producer", "Platinum Producer"
]),
"MemberWebsite": f"www.{agent_data['MemberFirstName'].lower()}{agent_data['MemberLastName'].lower()}.com",
"MemberSocialMediaFacebook": f"facebook.com/{agent_data['MemberFirstName'].lower()}.{agent_data['MemberLastName'].lower()}",
"MemberSocialMediaLinkedIn": f"linkedin.com/in/{agent_data['MemberFirstName'].lower()}-{agent_data['MemberLastName'].lower()}",
"MemberBio": f"Top-producing {agent_data['MemberSpecialty']} specialist with {random.randint(10, 20)}+ years of experience in {agent_data['MemberCity']} real estate market."
}
agent_data.update(top_producer_updates)
return agent_data
def create_new_agent(self, member_key: str = None, **overrides) -> Dict[str, Any]:
"""Create a newly licensed agent."""
agent_data = self.create_basic_agent(member_key, **overrides)
# Adjust for new agent
new_agent_updates = {
"MemberYearsOfExperience": random.randint(0, 2),
"MemberVolumeSold": random.randint(0, 5000000), # $0 - $5M
"MemberTransactionsSold": random.randint(0, 20),
"MemberSpecialty": random.choice(["First-Time Buyers", "Residential Sales", "New Construction"]),
"MemberDesignation": random.choice(["", "NAR", "e-PRO"]), # Fewer designations
"MemberBio": f"Dedicated new agent specializing in {agent_data['MemberSpecialty']}. Committed to providing exceptional service to clients in {agent_data['MemberCity']}.",
"MemberMentorKey": f"MENTOR{random.randint(100000, 999999)}"
}
agent_data.update(new_agent_updates)
return agent_data
def create_broker(self, member_key: str = None, **overrides) -> Dict[str, Any]:
"""Create a broker with appropriate credentials."""
agent_data = self.create_basic_agent(member_key, **overrides)
# Upgrade to broker status
broker_updates = {
"MemberType": "Broker",
"MemberStatus": "Active",
"MemberYearsOfExperience": random.randint(15, 35),
"MemberDesignation": random.choice(["CRB", "CRS", "CCIM", "GRI"]),
"MemberSpecialty": random.choice(["Brokerage Management", "Commercial Real Estate", "Luxury Homes"]),
"MemberVolumeSold": random.randint(100000000, 500000000), # $100M - $500M
"MemberTransactionsSold": random.randint(200, 1000),
"MemberBrokerLicense": f"BROKER{random.randint(100000, 999999)}",
"MemberOfficeManager": True,
"MemberTeamLeader": True,
"MemberBio": f"Experienced broker with {random.randint(15, 25)}+ years in real estate. Leading {agent_data['MemberOfficeName']} {agent_data['MemberCity']} office."
}
agent_data.update(broker_updates)
return agent_data
def create_team_member(self, team_leader_key: str, member_key: str = None, **overrides) -> Dict[str, Any]:
"""Create a team member associated with a team leader."""
agent_data = self.create_basic_agent(member_key, **overrides)
# Team member specific updates
team_updates = {
"MemberTeamLeaderKey": team_leader_key,
"MemberTeamName": f"The {random.choice(['Elite', 'Premier', 'Top', 'Dynamic', 'Success'])} Team",
"MemberType": "Sales Agent",
"MemberSpecialty": random.choice([
"Buyer Specialist", "Listing Specialist", "Transaction Coordinator",
"Marketing Coordinator", "Client Care Specialist"
]),
"MemberYearsOfExperience": random.randint(2, 10),
"MemberBio": f"Dedicated team member specializing in {agent_data['MemberSpecialty']}. Part of {agent_data['MemberTeamName']} serving {agent_data['MemberCity']} area."
}
agent_data.update(team_updates)
return agent_data
def create_commercial_agent(self, member_key: str = None, **overrides) -> Dict[str, Any]:
"""Create a commercial real estate specialist."""
agent_data = self.create_basic_agent(member_key, **overrides)
# Commercial specialist updates
commercial_updates = {
"MemberSpecialty": "Commercial Real Estate",
"MemberDesignation": random.choice(["CCIM", "SIOR", "CRE", "MAI"]),
"MemberType": random.choice(["REALTOR", "Broker"]),
"MemberYearsOfExperience": random.randint(8, 25),
"MemberVolumeSold": random.randint(25000000, 150000000), # $25M - $150M
"MemberTransactionsSold": random.randint(30, 200),
"MemberCommercialPropertyTypes": random.choice([
"Office, Retail", "Industrial, Warehouse", "Multi-Family",
"Hotel, Hospitality", "Land Development", "Investment Properties"
]),
"MemberBio": f"Commercial real estate specialist with expertise in {agent_data['MemberCommercialPropertyTypes']}. Serving investors and businesses in {agent_data['MemberCity']} market."
}
agent_data.update(commercial_updates)
return agent_data
def create_agent_dataset(self, count: int, agent_types: List[str] = None) -> List[Dict[str, Any]]:
"""Create a dataset of diverse agents."""
if agent_types is None:
agent_types = ["basic", "top_producer", "new_agent", "broker", "commercial"]
agents = []
team_leaders = [] # Track team leaders for team member creation
for i in range(count):
agent_type = random.choice(agent_types)
member_key = f"DATASET{i:06d}"
if agent_type == "basic":
agent = self.create_basic_agent(member_key)
elif agent_type == "top_producer":
agent = self.create_top_producer(member_key)
elif agent_type == "new_agent":
agent = self.create_new_agent(member_key)
elif agent_type == "broker":
agent = self.create_broker(member_key)
team_leaders.append(member_key) # Brokers can be team leaders
elif agent_type == "commercial":
agent = self.create_commercial_agent(member_key)
elif agent_type == "team_member" and team_leaders:
team_leader = random.choice(team_leaders)
agent = self.create_team_member(team_leader, member_key)
else:
agent = self.create_basic_agent(member_key)
agents.append(agent)
# Randomly make some agents team leaders
if agent_type in ["top_producer", "broker"] and random.random() < 0.3:
team_leaders.append(member_key)
return agents
def create_office_data(self, office_key: str = None, **overrides) -> Dict[str, Any]:
"""Create office/brokerage data."""
if not office_key:
office_key = f"OFFICE{random.randint(1000, 9999)}"
office_name = random.choice(self.office_names)
city = random.choice(self.cities)
area_codes = {"Austin": "512", "Dallas": "214", "Houston": "713", "San Antonio": "210"}
area_code = area_codes.get(city, "512")
office_data = {
"OfficeKey": office_key,
"OfficeName": office_name,
"OfficePhone": f"({area_code}) {random.randint(200, 999)}-{random.randint(1000, 9999)}",
"OfficeFax": f"({area_code}) {random.randint(200, 999)}-{random.randint(1000, 9999)}",
"OfficeEmail": f"info@{office_name.lower().replace(' ', '').replace('&', '')}.com",
"OfficeAddress1": f"{random.randint(100, 9999)} {random.choice(['Main', 'Business', 'Commerce', 'Professional'])} {random.choice(['St', 'Ave', 'Dr', 'Blvd'])}",
"OfficeAddress2": random.choice(["", f"Suite {random.randint(100, 999)}", f"Floor {random.randint(1, 20)}"]),
"OfficeCity": city,
"OfficeStateOrProvince": "TX",
"OfficePostalCode": f"7{random.randint(8000, 8999)}",
"OfficeCountry": "US",
"OfficeBrokerKey": f"BROKER{random.randint(100000, 999999)}",
"OfficeManagerKey": f"MANAGER{random.randint(100000, 999999)}",
"OfficeStatus": random.choice(["Active", "Inactive"]),
"OfficeType": random.choice(["Main Office", "Branch Office", "Satellite Office"]),
"OfficeWebsite": f"www.{office_name.lower().replace(' ', '').replace('&', '')}.com",
"OfficeAffiliation": random.choice(["Independent", "Franchise", "Corporate"]),
"OfficeAgentCount": random.randint(5, 150),
"OfficeYearEstablished": random.randint(1970, 2020),
"ModificationTimestamp": datetime.now().isoformat()
}
# Apply any overrides
office_data.update(overrides)
return office_data
def get_agent_summary(self, agent_data: Dict[str, Any]) -> str:
"""Generate agent summary string."""
name = f"{agent_data.get('MemberFirstName', '')} {agent_data.get('MemberLastName', '')}".strip()
office = agent_data.get('MemberOfficeName', '')
city = agent_data.get('MemberCity', '')
state = agent_data.get('MemberStateOrProvince', '')
specialty = agent_data.get('MemberSpecialty', '')
phone = agent_data.get('MemberMobilePhone', agent_data.get('MemberDirectPhone', ''))
return f"{name} | {office} | {city}, {state} | {specialty} | {phone}"
