diff --git a/customersim/.dockerignore b/customersim/.dockerignore new file mode 100644 index 0000000..965fae0 --- /dev/null +++ b/customersim/.dockerignore @@ -0,0 +1,13 @@ +**/.git/ +**/.idea/ +*.iws + + +**/__pycache__/ +*.py[cod] +*$py.class + +*.so + +**/.venv* +**/venv/ diff --git a/customersim/.environment.variables.sh b/customersim/.environment.variables.sh new file mode 100644 index 0000000..efe92d8 --- /dev/null +++ b/customersim/.environment.variables.sh @@ -0,0 +1,8 @@ +export STORE_HEIGHT=10 +export STORE_WIDTH=6 + +export CUSTOMERS_AVERAGE_IN_STORE=6 +export CUSTOMERS_LIST_FILE='customers.csv' + +export MQTT_HOST='127.0.0.1' +export MQTT_NAME=test1 diff --git a/customersim/.s2i/environment b/customersim/.s2i/environment deleted file mode 100644 index a585f44..0000000 --- a/customersim/.s2i/environment +++ /dev/null @@ -1 +0,0 @@ -APP_CONFIG=config.py diff --git a/customersim/Dockerfile b/customersim/Dockerfile new file mode 100644 index 0000000..a10fa56 --- /dev/null +++ b/customersim/Dockerfile @@ -0,0 +1,6 @@ +FROM tiangolo/uvicorn-gunicorn-fastapi:python3.8 + +COPY requirements.txt . +RUN pip install -r requirements.txt + +COPY ./app /app/app diff --git a/customersim/README.md b/customersim/README.md index 3aa0ba7..9320838 100644 --- a/customersim/README.md +++ b/customersim/README.md @@ -1,37 +1,182 @@ -# Flask Sample Application +# Functionality +This service generates messages that simulate customer behaviour in a reatail shop: +* customer entering the store +* customer movement +* customer exiting the store -This repository provides a sample Python web application implemented using the Flask web framework and hosted using ``gunicorn``. It is intended to be used to demonstrate deployment of Python web applications to OpenShift 3. -## Implementation Notes +## Table of contents +* [Functionality](#functionality) +* [Event payloads](#event-payloads) + * [customer/enter](#customerenter) + * [customer/move](#customermove) + * [customer/exit](#customerexit) -This sample Python application relies on the support provided by the default S2I builder for deploying a WSGI application using the ``gunicorn`` WSGI server. The requirements which need to be satisfied for this to work are: +* [Development](#development) + * [Dependencies](#dependencies) + * [Service configuration](#service-configuration) + * [Running the service](#running-the-service) + * [Testing with MQTT broker in docker](#testing-with-mqtt-broker-in-docker) + * [Testing without MQTT](#testing-without-mqtt) + * [Mock event endpoints](#mock-event-endpoints) -* The WSGI application code file needs to be named ``wsgi.py``. -* The WSGI application entry point within the code file needs to be named ``application``. -* The ``gunicorn`` package must be listed in the ``requirements.txt`` file for ``pip``. +* [Deployment](#deployment) + * [Docker image](#docker-image) + * [Connecting to a secured broker](#connecting-to-a-secured-broker) -In addition, the ``.s2i/environment`` file has been created to allow environment variables to be set to override the behaviour of the default S2I builder for Python. -* The environment variable ``APP_CONFIG`` has been set to declare the name of the config file for ``gunicorn``. -## Deployment Steps +## Event payloads +The service assumes the following data will be provided with given event types. -To deploy this sample Python web application from the OpenShift web console, you should select ``python:2.7``, ``python:3.3``, ``python:3.4`` or ``python:latest``, when using _Add to project_. Use of ``python:latest`` is the same as having selected the most up to date Python version available, which at this time is ``python:3.4``. +This script generates the following MQTT messages -The HTTPS URL of this code repository which should be supplied to the _Git Repository URL_ field when using _Add to project_ is: +### customer/enter -* https://github.com/OpenShiftDemos/os-sample-python.git +``` +{ + id: --ID representing customer--, + ts: --timestamp of the entrance, in seconds since epoch-- +} +``` + +### customer/move + +``` +{ + id: --ID representing customer--, + ts: --timestamp of the move, in seconds since epoch--, + x: --x coordinate of location sensor that fired--, + y: --y coordinate of location sensor that fired-- +} +``` + +### customer/exit + +``` +{ + id: --ID representing customer--, + ts: --timestamp of the exit, in seconds since epoch-- +} +``` + + + +# Development + +## Dependencies + +Dependencies of the project are contained in [requirements.txt](requirements.txt) file. All the packages are publicly +available. + +All the packages can be installed with: +`pip install -f requirements.txt` + +## Service configuration + +The service reads the following **environment variables**: + +| Variable | Description | Default | +|------------------------|--------------------------------------|--------------:| +| STORE_HEIGHT | | 10 | +| STORE_WIDTH | | 6 | +| CUSTOMERS_AVERAGE_IN_STORE | | 6 | +| CUSTOMERS_LIST_FILE | | customers.csv | +| MQTT_HOST | | - | +| MQTT_PORT | | 1883 | +| MQTT_NAME | | demoClient | +| ENTER_TOPIC | | customer/enter| +| MOVE_TOPIC | | customer/move | +| EXIT_TOPIC | | customer/exit | + +(Parameters with `-` in "Default" column are required.) + +Use [log_config.py](./app/utils/log_config.py) to **configure logging behaviour**. +By default, console and file handlers are used. The file appender writes to `messages.log`. + + +## Running the service -If using the ``oc`` command line tool instead of the OpenShift web console, to deploy this sample Python web application, you can run: +For my development I created a project with dedicated virtual environment (Python 3.8, all the dependencies installed +there). + +The code reads sensitive information (tokens, secrets) from environment variables. They need to be set accordingly in +advance. +`environment.variables.sh` can be used for that purpose. Then, in order to run the service the following commands can be +used: ``` -oc new-app https://github.com/OpenShiftDemos/os-sample-python.git +$ . .environment.variables.sh +$ . venv/bin/activate +(venv)$ uvicorn app.main:app --host 0.0.0.0 --reload --reload-dir app ``` +> Please, note `reload-dir` switch. Without it the reloader goes into an infinite loop because it detects log file changes (messages.log). + +## Testing with MQTT broker in docker -In this case, because no language type was specified, OpenShift will determine the language by inspecting the code repository. Because the code repository contains a ``requirements.txt``, it will subsequently be interpreted as including a Python application. When such automatic detection is used, ``python:latest`` will be used. +Quick way to **set up a simple MQTT broker** is to use Docker containers: +```shell +docker run -d --rm --name mosquitto -p 1883:1883 eclipse-mosquitto +``` +or +```shell +docker run -it -p 1883:1883 --name mosquitto eclipse-mosquitto mosquitto -c /mosquitto-no-auth.conf +``` -If needing to select a specific Python version when using ``oc new-app``, you should instead use the form: +To **publish to a topic**: +```shell +docker exec mosquitto mosquitto_pub -h 127.0.0.1 -t test -m "test message" ``` -oc new-app python:2.7~https://github.com/OpenShiftDemos/os-sample-python.git + +To **subscribe to a topic**: +```shell +docker exec mosquitto mosquitto_sub -h 127.0.0.1 -t test ``` + +### Testing without MQTT +There is an environment variable, `TESTING_MOCK_MQTT`, that will create an MQTT client mock instead of trying to connect +to a real MQTT broker. Instead of publishing the messages, they will be simply logged/printed out. + +This may be helpful for local development or testing. + +### Producing test messages + +```shell +curl http://127.0.0.1:8000/produce_entry -d '{"id": "997", "ts": 192326400}' + ``` + +```shell +curl http://127.0.0.1:8000/produce_exit -d '{"id": "997", "ts": 192326400}' + ``` + +```shell +curl http://127.0.0.1:8000/produce_move -d '{"id": "997", "ts": 192326400, "x": 2, "y": 3}' + ``` + + +# Deployment + +## Docker image +The docker image for the service is [Dockerfile](Dockerfile). +It is based on FastAPI "official" image. +See https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker +for the details on configuring the container (http port, log level, etc.) + +In order to build the image use: +``` +docker build -t customersim-service:0.0.1 . +``` + +> Set image name (`customersim-service`) and tag (`0.0.1`) according to +> your needs. + +To run the service as a Docker container run: +``` +docker run -d -e LOG_LEVEL="warning" --name customersim-service customersim-service:0.0.1 + +``` + +## Connecting to a secured broker +**TODO** Add info about setting user/password +**TODO** Add info about using client certificates (TLS) diff --git a/customersim/app/__init__.py b/customersim/app/__init__.py new file mode 100644 index 0000000..c10988d --- /dev/null +++ b/customersim/app/__init__.py @@ -0,0 +1,4 @@ +import logging + +logger = logging.getLogger(__name__) +logger.addHandler(logging.NullHandler()) diff --git a/customersim/app/config.py b/customersim/app/config.py new file mode 100644 index 0000000..07b59b7 --- /dev/null +++ b/customersim/app/config.py @@ -0,0 +1,34 @@ +import os +import sys + +from app import logger + + +def validate_and_crash(variable, message): + if not variable: + logger.error(message) + sys.exit(message) + + +logger.info('Reading environment variables...') + +STORE_HEIGHT = int(os.getenv('STORE_HEIGHT', 10)) +STORE_WIDTH = int(os.getenv('STORE_WIDTH', 6)) + +CUSTOMERS_AVERAGE_IN_STORE = int(os.getenv('CUSTOMERS_AVERAGE_IN_STORE', 6)) +CUSTOMERS_LIST_FILE = os.getenv('CUSTOMERS_LIST_FILE', 'customers.csv') + +MQTT_HOST = os.getenv('MQTT_HOST') +MQTT_PORT = int(os.getenv('MQTT_PORT', 1883)) +MQTT_NAME = os.getenv('MQTT_NAME', 'demoClient') + +CUSTOMER_ENTER_TOPIC = os.getenv('ENTER_TOPIC', 'customer/enter') +CUSTOMER_EXIT_TOPIC = os.getenv('EXIT_TOPIC', 'customer/exit') +CUSTOMER_MOVE_TOPIC = os.getenv('MOVE_TOPIC', 'customer/move') + +TESTING_MOCK_MQTT = os.getenv('TESTING_MOCK_MQTT', 'false') +TESTING_MOCK_MQTT = TESTING_MOCK_MQTT.lower() in ['1', 'yes', 'true'] + + +REQUIRED_PARAM_MESSAGE = 'Cannot read {} env variable. Please, make sure it is set before starting the service.' +validate_and_crash(MQTT_HOST, REQUIRED_PARAM_MESSAGE.format('MQTT_HOST')) diff --git a/customersim/app/domain_model.py b/customersim/app/domain_model.py new file mode 100644 index 0000000..3f69561 --- /dev/null +++ b/customersim/app/domain_model.py @@ -0,0 +1,70 @@ +import datetime +import random + + +# Represents a "square" in the store at a particular location, and contain all valid moves from that location +class Location: + def __init__(self, x: int, y: int, width: int, height: int): + self.x = x + self.y = y + # a list of all valid moves. Each move is a tuple of the form + # ("adjacent x location", "adjacent y location", "is this closer to the exit?") + self.validMoves = [(a, b, True if a <= self.x and b <= self.y else False) for a in + range(max(self.x - 1, 0), min(self.x + 2, width)) for b in + range(max(self.y - 1, 0), min(self.y + 2, height)) if not (a == self.x and b == self.y)] + + +class Store: + def __init__(self, width: int, height: int): + self.height = height + self.width = width + self.locations = [[Location(x, y, width, height) for y in range(0, height)] for x in range(0, width)] + + +class Customer: + def __init__(self, store, customer_id: str, name: str): + self.store = store + # customers enter and exit from the bottom left corner of the store + self.currentLocation = store.locations[0][0] + # the *average* amount of time this customer will spend on a square + self.meanDwellTime = random.uniform(1, 20) + # how consistently the customer spends that time. Higher means more inconsistent + self.consistency = random.uniform(1, 5) + self.nextMoveTime = self.get_next_move_time() + self.isExiting = False + # the time this customer will start to exit + self.exitTime = datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600)) + self.id = customer_id + self.name = name + + def get_next_move_time(self): + # amount of time spent at a location is a random value picked from a gaussian distribution, + # with a mean equal to the customer's average dwell time and a standard deviation + # equal to the customer's consistency + return datetime.datetime.now() + datetime.timedelta(0, random.gauss(self.meanDwellTime, self.consistency)) + + def move(self): + # if the customer is exiting, only move to an adjacent location that is towards the exit. + # If they are already at the door, don't move + if self.isExiting: + if self.currentLocation.x == 0 and self.currentLocation.y == 0: + (newX, newY) = (0, 0) + else: + (newX, newY, isTowardsExit) = random.choice( + [(x, y, e) for (x, y, e) in self.currentLocation.validMoves if e is True]) + else: + # if the customer is not exiting, pick any adjacent location + (newX, newY, isTowardsExit) = random.choice(self.currentLocation.validMoves) + + self.currentLocation = self.store.locations[newX][newY] + + def tick(self): + if not self.isExiting and self.exitTime < datetime.datetime.now(): + self.isExiting = True + + if self.nextMoveTime < datetime.datetime.now(): + self.nextMoveTime = self.get_next_move_time() + self.move() + return True + + return False diff --git a/customersim/app/events_model.py b/customersim/app/events_model.py new file mode 100644 index 0000000..a4217f9 --- /dev/null +++ b/customersim/app/events_model.py @@ -0,0 +1,32 @@ +from pydantic import BaseModel + + +class CustomerEnterEvent(BaseModel): + """ + id: --ID representing customer--, + ts: --timestamp of the entrance, in seconds since epoch-- + """ + id: str + ts: int + + +class CustomerExitEvent(BaseModel): + """ + id: --ID representing customer--, + ts: --timestamp of the exit, in seconds since epoch-- + """ + id: str + ts: int + + +class CustomerMoveEvent(BaseModel): + """ + id: --ID representing customer--, + ts: --timestamp of the move, in seconds since epoch--, + x: --x coordinate of location sensor that fired--, + y: --y coordinate of location sensor that fired-- + """ + id: str + ts: int + x: int + y: int diff --git a/customersim/app/log_config.py b/customersim/app/log_config.py new file mode 100644 index 0000000..2648efd --- /dev/null +++ b/customersim/app/log_config.py @@ -0,0 +1,25 @@ +import logging +import os + + +# TODO move to config +LOG_FILENAME = "messages.log" +LOG_FORMAT = "%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s]\t%(message)s" +LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO').upper() + +assert LOG_LEVEL in ['DEBUG', 'INFO', 'WARNING', 'ERROR'] + + +def configure_logger(): + logging.basicConfig(format=LOG_FORMAT, level=LOG_LEVEL) + # Basic console logger + logger = logging.getLogger("app") + # File logger + log_formatter = logging.Formatter(LOG_FORMAT) + file_handler = logging.FileHandler(LOG_FILENAME, encoding='UTF-8') + file_handler.setFormatter(log_formatter) + logger.addHandler(file_handler) + # Configuration done + logger.debug("Logger configured...") + return logger + diff --git a/customersim/app/main.py b/customersim/app/main.py new file mode 100644 index 0000000..901794b --- /dev/null +++ b/customersim/app/main.py @@ -0,0 +1,94 @@ +import asyncio + +from fastapi import FastAPI +from fastapi.responses import PlainTextResponse + +from app import logger +from app.config import CUSTOMER_ENTER_TOPIC, CUSTOMER_EXIT_TOPIC, CUSTOMER_MOVE_TOPIC +from app.events_model import CustomerMoveEvent, CustomerExitEvent, CustomerEnterEvent +from app.log_config import configure_logger +from app.mosquitto import get_mqtt_client +from app.simulation_engine import CustomerSimulator +from app.store_initializer import init_customer_list, init_store + +configure_logger() + +app = FastAPI() + + +@app.on_event("startup") +async def startup_event(): + app.state.customer_list = init_customer_list() + app.state.store = init_store() + + #################### + # init connections + app.state.mqttc = await get_mqtt_client() + + #################### + # background tasks + customer_sim = CustomerSimulator(app.state.store, app.state.customer_list, app.state.mqttc) + asyncio.create_task(customer_sim.run()) + + +#################### +# web handlers +logger.info('Defining web service handlers...') + + +@app.get('/') +async def root(): + logger.debug('/') + return {'message': 'Hello World'} + + +@app.get('/health') +async def health() -> PlainTextResponse: + """ + Service health check endpoint. + """ + logger.info('verify health') + return PlainTextResponse('OK') + + +class CustomerEnter(object): + pass + + +@app.post('/produce_entry') +async def produce_entry_event(event: CustomerEnterEvent) -> PlainTextResponse: + """ + Test endpoint that forces publication of "customer/enter". + """ + logger.info('produce_entry_event') + logger.debug(event) + + message = event.json() + result = app.state.mqttc.publish(CUSTOMER_ENTER_TOPIC, message) + return PlainTextResponse(str(result)) + + +@app.post('/produce_move') +async def produce_move_event(event: CustomerMoveEvent) -> PlainTextResponse: + """ + Test endpoint that forces publication of "customer/move". + """ + logger.info('produce_move_event') + logger.debug(event) + + message = event.json() + result = app.state.mqttc.publish(CUSTOMER_MOVE_TOPIC, message) + return PlainTextResponse(str(result)) + + +@app.post('/produce_exit') +async def produce_exit_event(event: CustomerExitEvent) -> PlainTextResponse: + """ + Test endpoint that forces publication of "customer/exit". + """ + logger.info('produce_exit_event') + logger.debug(event) + + message = event.json() + result = app.state.mqttc.publish(CUSTOMER_EXIT_TOPIC, message) + return PlainTextResponse(str(result)) diff --git a/customersim/app/mosquitto.py b/customersim/app/mosquitto.py new file mode 100644 index 0000000..6bb12a4 --- /dev/null +++ b/customersim/app/mosquitto.py @@ -0,0 +1,43 @@ +from paho.mqtt import client + +from app import logger +from app.config import TESTING_MOCK_MQTT, MQTT_HOST, MQTT_PORT, MQTT_NAME + +if TESTING_MOCK_MQTT: + class MQTTClient: + def __init__(self, mqtt_host: str, mqtt_port: int, mqtt_client_name: str): + logger.info(f'simulating a client to {mqtt_host}') + self.mqtt_client_name = mqtt_client_name + self.mqtt_host = mqtt_host + self.mqtt_port = mqtt_port + + def publish(self, topic, message): + logger.info(f'simulated publishing to {topic}. message: {message}') + + async def connect(self): + pass +else: + class MQTTClient: + def __init__(self, mqtt_host: str, mqtt_port: int, mqtt_client_name: str): + + logger.info(f'Creating MQTT client {mqtt_host}, {mqtt_port}, {mqtt_client_name}') + self.mqtt_client_name = mqtt_client_name + self.mqtt_host = mqtt_host + self.mqtt_port = mqtt_port + + def publish(self, topic, message): + logger.info(f' publishing to {topic}. message: {message}') + return self.mqttc.publish(topic, message) + + async def connect(self): + logger.info("before connect") + self.mqttc = client.Client(self.mqtt_client_name) + logger.info(f'connect({self.mqtt_host}, {self.mqtt_port})') + self.mqttc.connect(self.mqtt_host, self.mqtt_port) + logger.info("after connect") + + +async def get_mqtt_client(): + mqttc = MQTTClient(MQTT_HOST, MQTT_PORT, MQTT_NAME) + await mqttc.connect() + return mqttc diff --git a/customersim/app/simulation_engine.py b/customersim/app/simulation_engine.py new file mode 100644 index 0000000..1aec42d --- /dev/null +++ b/customersim/app/simulation_engine.py @@ -0,0 +1,81 @@ +import asyncio +import json +import datetime +import random + +from app import logger +from app.config import CUSTOMERS_AVERAGE_IN_STORE, CUSTOMER_ENTER_TOPIC, CUSTOMER_EXIT_TOPIC, CUSTOMER_MOVE_TOPIC +from app.domain_model import Store, Customer + +CUSTOMER_STATE_TEMPLATE = '{0} is Entering. MDT: {1:0.1f}, C: {2:0.1f}, E: {3}' + + +class CustomerSimulator: + def __init__(self, store: Store, customer_list: list, publisher, + next_entrance_time: datetime = datetime.datetime.now()): + self.customer_list = customer_list + self.store = store + self.customer_queue = [] # List of customers in the store + self.next_customer_entrance_time = next_entrance_time + self.is_running = True + self.mqttc = publisher + + def manage_customer_movements(self, c): + if c.tick(): + if c.isExiting and c.currentLocation.x == 0 and c.currentLocation.y == 0: + # remove the customer and signal the exit + timestamp_value = self.get_timestamp_value(datetime.datetime.now()) + # TODO convert to pydantic model usage + msg = {'id': str(c.id), 'ts': timestamp_value} + # TODO simulation engine don't need to know details like topic name or message marshaling + # it should be encapsulated in event publisher + self.mqttc.publish(CUSTOMER_EXIT_TOPIC, json.dumps(msg)) + logger.info(f'{c.name} is Exiting.') + self.customer_queue.remove(c) + else: + # signal move + timestamp_value = self.get_timestamp_value(datetime.datetime.now()) + msg = {'id': str(c.id), 'ts': timestamp_value, 'x': c.currentLocation.x, 'y': c.currentLocation.y} + self.mqttc.publish(CUSTOMER_MOVE_TOPIC, json.dumps(msg)) + + async def run(self): + # Check if any customers are due to enter, move, or exit. Sleep for one second, then repeat + while self.is_running: + if self.next_customer_entrance_time < datetime.datetime.now(): + # add the new customer, and signal the entrance + new_customer_prototype = random.choice(self.customer_list) + logger.info(f"new customer prototype {new_customer_prototype}") + new_customer = Customer(self.store, new_customer_prototype['customer_id'], + new_customer_prototype['name']) + self.customer_queue.append(new_customer) + + timestamp = datetime.datetime.utcnow() + timestamp_value = self.get_timestamp_value(timestamp) + + msg = {'id': str(new_customer.id), 'ts': timestamp_value} + self.mqttc.publish(CUSTOMER_ENTER_TOPIC, json.dumps(msg)) + + next_customer_entrance_time = self.get_new_entrance_time() + + logger.info('inspecting customer: ----------') + logger.info(self.customer_state_dump(new_customer)) + logger.info(f'Next Customer Entering at {next_customer_entrance_time}') + + [self.manage_customer_movements(c) for c in self.customer_queue] + await asyncio.sleep(1) + + @staticmethod + def get_timestamp_value(tmstmp: datetime): + # # For iso format use: + # timestamp_value = f'"{timestamp.isoformat()}"' + # For second since Epoch use: + timestamp_value = int(tmstmp.timestamp()) + return timestamp_value + + @staticmethod + def get_new_entrance_time(): + return datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600 / CUSTOMERS_AVERAGE_IN_STORE)) + + @staticmethod + def customer_state_dump(c: Customer): + return CUSTOMER_STATE_TEMPLATE.format(c.name, c.meanDwellTime, c.consistency, c.exitTime) diff --git a/customersim/app/store_initializer.py b/customersim/app/store_initializer.py new file mode 100644 index 0000000..93bb0d7 --- /dev/null +++ b/customersim/app/store_initializer.py @@ -0,0 +1,21 @@ +import csv + +from app import logger +from app.config import CUSTOMERS_LIST_FILE, STORE_WIDTH, STORE_HEIGHT +from app.domain_model import Store + + +def init_store(): + return Store(STORE_WIDTH, STORE_HEIGHT) + + +def init_customer_list(customer_file_path: str = CUSTOMERS_LIST_FILE): + try: + with open(customer_file_path, 'rt') as csvfile: + fake_reader = csv.DictReader(csvfile, delimiter=',') + customer_list = [row for row in fake_reader] + except IOError as e: + logger.error("Whoops....can't find the fake data file.\nTry generating the fake data file and try again\n") + exit(0) + + return customer_list diff --git a/customersim/config.cfg b/customersim/config.cfg deleted file mode 100755 index 48d3a8c..0000000 --- a/customersim/config.cfg +++ /dev/null @@ -1,22 +0,0 @@ -[Store] -# "Height" of the store (maximum Y value) -height=10 - -# "Width" of the store (maximum X value) -width=6 - -[Customers] -# Average number of customers in the store -averageCustomersInStore=6 -list=customers.dat - -[MQTT] -# MQTT host -host=172.30.106.185 - -# MQTT port -port=1883 - -# Name of MQTT device -name=demoQueue - diff --git a/customersim/config.py b/customersim/config.py deleted file mode 100644 index cf9afbf..0000000 --- a/customersim/config.py +++ /dev/null @@ -1,7 +0,0 @@ -import os - -workers = int(os.environ.get('GUNICORN_PROCESSES', '3')) -threads = int(os.environ.get('GUNICORN_THREADS', '1')) - -forwarded_allow_ips = '*' -secure_scheme_headers = { 'X-Forwarded-Proto': 'https' } diff --git a/customersim/customers.csv b/customersim/customers.csv new file mode 100644 index 0000000..28fd4fc --- /dev/null +++ b/customersim/customers.csv @@ -0,0 +1,1583 @@ +customer_id,name,age_range,marital_status,family_size,no_of_children,income_bracket,gender,mean_discount_used_by_cust,unique_items_bought_by_cust,mean_selling_price_paid_by_cust,mean_quantity_bought_by_cust,total_discount_used_by_cust,total_coupons_used_by_cust,total_price_paid_by_cust,total_quantity_bought_by_cust +1,Karen Montgomery,70+,Married,2,0,4,F,-1.75,463,99.22,1.0,-1832.94,78,103982.01,1048 +2,Tracey Newman,46-55,Married,2,0,1,F,-0.45,352,108.26,1.0,-189.97,4,45360.6,419 +3,Noah King,18-25,Married,2,0,2,M,-1.89,406,86.97,1.0,-1329.46,53,61312.59,707 +4,Jennifer West,46-55,Married,5,3,3,F,-0.08,125,138.34,1.0,-17.81,1,30434.3,220 +5,Joshua Willis,26-35,Single,3,2,3,M,-0.11,490,115.6,1.03,-90.83,2,91553.24,814 +6,Ricky Parker,46-55,Married,2,0,5,M,-0.63,429,102.43,1.0,-369.55,11,59716.13,583 +7,Lawrence Lawson,26-35,Married,3,1,3,M,-0.65,780,101.8,1.01,-687.47,17,107194.4,1066 +8,Mark Tate,26-35,Married,4,2,6,M,-3.72,719,126.24,1.25,-4899.4,192,166389.9,1643 +9,Karen Wyatt,26-35,Single,2,1,4,F,-0.77,405,90.97,1.05,-430.64,8,50672.81,585 +10,Christopher Gonzalez,46-55,Single,1,0,5,M,0.0,268,94.61,1.04,0.0,0,46455.82,509 +11,Marcus Smith,70+,Single,2,1,1,M,-1.6,282,116.04,1.11,-988.45,10,71594.7,683 +12,Thomas Morgan,46-55,Married,2,0,7,M,-0.96,436,119.82,1.03,-929.96,45,115504.13,993 +13,Sara Byrd,36-45,Single,1,0,2,F,-0.11,1192,83.27,1.03,-272.48,10,205750.82,2544 +14,Courtney Kemp,26-35,Married,2,0,6,F,-0.4,327,101.65,1.07,-492.72,27,126446.72,1328 +15,Pamela Knight,46-55,Married,2,0,6,F,-1.17,463,104.45,1.08,-967.07,39,86483.25,895 +16,Shannon Floyd,36-45,Married,2,0,1,F,0.0,328,136.27,1.08,0.0,0,72497.44,575 +17,Terry Smith,36-45,Single,1,0,5,M,-1.24,306,120.93,1.01,-708.13,5,69174.48,575 +18,Christina Schwartz,36-45,Married,2,0,5,F,0.0,315,82.29,1.0,0.0,0,41886.68,509 +19,Sharon Sanford,46-55,Single,1,0,3,F,-0.07,410,91.89,1.0,-90.82,4,127815.7,1391 +20,Susan Allen,36-45,Married,2,0,5,F,0.0,229,127.8,1.02,0.0,0,48181.07,385 +21,Christopher Ward,36-45,Married,2,0,4,M,-0.32,277,100.77,1.0,-120.75,3,38594.99,383 +22,Mrs. Wendy Santiago,36-45,Single,2,1,4,F,-7.84,329,130.09,1.1,-3334.06,154,55287.89,467 +23,Shawna Morales,46-55,Married,2,0,4,F,-0.15,467,104.15,1.11,-90.83,3,62072.75,661 +24,Nathan Park,70+,Single,1,0,4,M,-0.02,403,98.23,1.0,-12.47,1,63458.36,648 +25,Lindsay Roberts,46-55,Married,2,0,4,F,-0.09,96,107.29,1.07,-17.81,1,21028.23,210 +26,Collin Young,36-45,Married,3,1,3,M,-0.27,108,129.41,1.0,-29.68,1,14235.56,110 +27,Joshua Diaz,36-45,Married,2,0,8,M,-0.29,866,99.97,1.0,-334.71,14,116368.66,1167 +28,Angelica Wallace,46-55,Married,2,0,1,F,-0.11,1045,97.75,1.09,-231.52,7,200690.92,2242 +29,John Smith,70+,Single,3,2,1,M,0.0,232,91.09,1.0,0.0,0,47821.35,525 +30,Kelly Berry,70+,Single,1,0,5,F,-0.58,198,81.0,1.0,-325.92,18,45440.98,561 +31,William Garcia,36-45,Single,5,3,2,M,-0.72,190,66.47,1.03,-217.27,7,20139.08,313 +32,Michael Osborne,36-45,Single,1,0,9,M,-0.34,140,117.22,1.0,-71.24,1,24850.27,212 +33,Donna Smith,46-55,Married,5,3,9,F,-0.17,774,130.25,1.02,-169.2,3,132724.74,1040 +34,Brian Nicholson,46-55,Married,2,0,2,M,0.0,290,128.31,1.0,0.0,0,48887.4,381 +35,Robert Bates,18-25,Married,2,0,4,M,-0.82,170,107.59,1.05,-195.91,4,25712.86,250 +36,Michelle Stevenson,36-45,Married,2,0,4,F,-0.16,711,105.03,1.08,-158.86,8,105026.25,1081 +37,Anna Hamilton,36-45,Married,2,0,8,F,-0.04,276,97.55,1.0,-19.59,1,51801.68,531 +38,Thomas Morgan,46-55,Single,2,1,5,M,-0.02,697,110.31,1.02,-17.81,1,111413.62,1030 +39,Jennifer Allen,70+,Married,2,0,4,F,0.0,152,117.45,1.0,0.0,0,25956.71,221 +40,David Williams,56-70,Married,4,2,7,M,-0.59,696,106.64,1.0,-784.71,34,142365.91,1337 +41,Misty Ingram,46-55,Single,2,1,4,F,-0.29,295,94.35,1.05,-136.78,2,44626.62,495 +42,Allison Howell,26-35,Married,4,2,9,F,-0.72,983,107.26,1.15,-1199.48,45,179236.5,1916 +43,Tamara Rios,26-35,Single,1,0,5,F,-0.06,368,90.3,1.0,-37.4,2,55443.61,614 +44,Paul Anderson,36-45,Single,3,2,4,M,0.0,254,151.87,1.0,0.0,0,43890.5,289 +45,Shawn Hunt,46-55,Married,5,3,1,M,-2.88,728,105.31,1.03,-3849.98,163,140910.98,1373 +46,Karen Fuller,56-70,Married,2,0,5,F,-0.69,674,81.17,1.02,-768.49,24,90176.72,1137 +47,Melissa Anderson,26-35,Single,1,0,4,F,-0.02,828,98.48,1.0,-17.8,2,116401.41,1182 +48,Michael Scott,36-45,Married,2,0,3,M,-2.1,244,190.74,1.01,-810.35,12,73436.16,388 +49,Christine Newman,56-70,Married,2,0,7,F,-0.24,760,94.19,1.02,-391.46,2,152584.45,1648 +50,Terry Sutton,46-55,Married,3,1,4,M,0.0,431,109.45,1.09,0.0,0,65996.58,656 +51,Christina Lopez DDS,70+,Married,2,0,2,F,-0.53,259,75.6,1.0,-302.77,9,42942.81,568 +52,Jose Jones,36-45,Married,5,3,7,M,-0.42,858,100.29,1.0,-569.92,19,137394.07,1370 +53,Jessica Burns,56-70,Single,1,0,3,F,0.0,252,76.46,1.0,0.0,0,39682.81,519 +54,Stephen Hernandez,56-70,Single,1,0,3,M,-0.72,487,103.53,1.05,-532.52,23,76407.25,772 +55,Debra Sanchez,46-55,Married,2,0,5,F,-0.87,425,87.1,1.04,-456.83,11,45728.04,547 +56,Cindy Warner,18-25,Married,3,1,5,F,-0.06,799,91.86,1.0,-88.69,1,140914.43,1534 +57,Joe Williams,46-55,Married,2,0,6,M,0.0,208,96.0,1.04,0.0,0,23712.9,257 +58,Jeremy Russo,26-35,Single,1,0,4,M,-0.04,679,94.17,1.03,-41.85,2,102838.9,1125 +59,Edward Simmons,70+,Single,1,0,4,M,-0.07,523,113.24,1.01,-44.52,1,74965.42,666 +60,John Hart,36-45,Single,1,0,3,M,-0.57,1485,86.12,1.01,-1898.01,67,286593.68,3367 +61,Jason Bauer,46-55,Married,2,0,6,M,0.0,315,91.65,1.08,0.0,0,42708.98,501 +62,Steven Boyer,36-45,Married,3,1,1,M,0.0,252,88.86,1.01,0.0,0,24614.26,280 +63,Kathy Ramos,46-55,Single,1,0,2,F,-2.08,450,103.7,1.01,-2346.26,78,116973.33,1134 +64,Aaron Miller,46-55,Married,2,0,6,M,-1.1,775,103.82,1.06,-1206.09,17,113679.4,1161 +65,Daniel Allen,70+,Married,2,0,3,M,0.0,189,125.34,1.0,0.0,0,29454.17,235 +66,Nicholas Parker,46-55,Single,1,0,6,M,-0.01,681,96.39,1.06,-12.47,1,102173.46,1119 +67,Robert Kirk,36-45,Single,2,1,4,M,0.0,293,62.64,1.0,0.0,0,27747.96,443 +68,Robin Allen,26-35,Single,1,0,2,F,-0.32,941,82.8,1.01,-511.49,16,133560.54,1624 +69,Lindsey Johnson,46-55,Married,4,2,1,F,-0.38,335,97.62,1.08,-215.8,10,55839.46,620 +70,Megan Hernandez,26-35,Married,2,0,3,F,0.0,129,81.54,1.0,0.0,0,12068.23,148 +71,Garrett Cook,36-45,Married,5,3,4,M,-1.17,558,86.6,1.0,-1100.82,50,81229.51,942 +72,Danielle Johnson,26-35,Single,3,2,1,F,-0.16,884,71.6,1.0,-229.76,15,99884.29,1395 +73,Joseph Rodgers,36-45,Married,5,3,6,M,-0.1,439,104.17,1.02,-53.43,1,58127.59,571 +74,Brooke Wilson,26-35,Married,2,0,2,F,0.0,530,97.29,1.0,0.0,0,77055.52,792 +75,Amanda Powers,26-35,Single,1,0,4,F,-0.03,552,81.21,1.04,-26.71,1,63992.49,819 +76,Elizabeth Dawson,36-45,Married,2,0,4,F,-0.94,351,104.66,1.02,-400.35,15,44689.77,436 +77,Brandon Horton,56-70,Married,2,0,2,M,-0.23,136,104.38,1.19,-35.62,1,16387.1,187 +78,Mrs. Christina Valenzuela,36-45,Married,4,2,8,F,-0.48,1366,109.87,1.08,-1260.63,49,290936.29,2869 +79,Curtis Robles,26-35,Married,2,0,4,M,-0.05,488,96.9,1.02,-35.62,1,67734.62,715 +80,Nicholas Collier,36-45,Married,2,0,8,M,-1.02,838,129.26,1.08,-1705.95,50,217157.69,1814 +81,Douglas Carlson,36-45,Single,1,0,5,M,-1.57,99,101.86,1.0,-248.75,6,16094.07,158 +82,Miranda Williams,70+,Single,2,1,4,F,-0.44,484,80.74,1.01,-331.25,15,61039.57,763 +83,Dr. Gabriel Velez,46-55,Single,1,0,4,M,0.0,384,98.43,1.05,0.0,0,81205.81,865 +84,David West,46-55,Single,1,0,8,M,-0.54,326,122.88,1.0,-213.36,1,48169.8,392 +85,Mariah Murray,46-55,Married,3,1,3,F,-0.05,475,88.31,1.0,-32.65,2,53516.98,606 +86,Jeremy Knight,56-70,Married,3,1,3,M,-0.05,409,102.9,1.0,-26.71,1,55360.92,538 +87,Garrett Douglas,46-55,Single,2,1,4,M,-0.06,387,97.83,1.04,-35.62,1,62026.76,660 +88,John Perry,36-45,Single,1,0,4,M,-1.62,960,111.3,1.12,-2517.17,73,172846.55,1733 +89,Jacob Mcmillan,46-55,Single,1,0,3,M,-1.45,498,87.8,1.08,-970.34,49,58647.94,720 +90,Kelly Manning,46-55,Married,2,0,5,F,-0.46,888,107.83,1.03,-542.31,10,126272.5,1203 +91,Daniel Watson,36-45,Single,1,0,5,M,0.0,383,100.8,1.01,0.0,0,57052.5,569 +92,Brian Cross,46-55,Married,2,0,1,M,-0.27,417,109.59,1.0,-146.04,8,59508.78,543 +93,Alexandria Carpenter,56-70,Married,2,0,3,F,-0.21,750,110.3,1.01,-209.27,6,110076.58,1008 +94,Kelsey Stuart,46-55,Single,1,0,5,F,-0.05,555,88.76,1.0,-44.52,1,75181.31,847 +95,Jessica Gray,36-45,Single,2,1,4,F,-1.36,232,105.79,1.0,-356.19,12,27612.17,261 +96,Brian Tran,46-55,Single,1,0,6,M,-0.41,398,111.24,1.0,-216.4,8,58400.26,525 +97,Jessica Stewart,36-45,Single,1,0,6,F,-0.76,293,88.58,1.04,-375.94,10,43933.76,516 +98,Carolyn Adams,46-55,Married,2,0,3,F,-0.04,332,89.86,1.0,-20.07,1,49781.54,554 +99,Victoria Davis,26-35,Single,1,0,2,F,0.0,206,87.78,1.0,0.0,0,55828.0,636 +100,Taylor Mays,56-70,Married,2,0,7,M,-0.95,1011,90.91,1.04,-1821.35,58,174272.27,1994 +101,Leah Crawford,36-45,Single,2,1,4,F,0.0,370,117.04,1.0,0.0,0,55009.56,470 +102,Sarah Foster,46-55,Single,1,0,5,F,-0.25,237,118.3,1.0,-65.3,2,31349.23,265 +103,Brittany Reeves,46-55,Married,2,0,4,F,0.0,864,107.98,1.14,0.0,0,187337.79,1975 +104,Allen Horton,46-55,Single,1,0,4,M,-1.37,186,90.21,1.05,-348.17,14,23003.33,268 +105,Yvonne Hansen,36-45,Single,3,1,5,F,-0.25,296,113.68,1.01,-121.11,4,54794.34,486 +106,Sean Guerra,26-35,Married,5,3,3,M,-0.26,869,77.22,1.06,-347.1,14,101621.48,1391 +107,Kevin Newman,18-25,Married,2,0,2,M,-0.01,478,107.57,1.01,-8.9,1,64432.19,603 +108,Ann Allen,46-55,Single,1,0,5,F,-11.4,154,192.99,1.0,-3043.71,86,51529.28,267 +109,Danielle West,36-45,Married,2,0,8,F,0.0,178,90.92,1.02,0.0,0,19729.22,222 +110,Jordan Morris,18-25,Married,3,1,6,F,-0.04,424,90.75,1.03,-35.62,1,79137.98,896 +111,Emily Jones,26-35,Married,3,1,3,F,0.0,295,119.58,1.0,0.0,0,64213.63,537 +112,Joseph Sellers,46-55,Married,2,0,4,M,-0.26,701,86.62,1.01,-272.49,12,90950.58,1065 +113,Thomas Blankenship,56-70,Married,2,0,4,M,-0.93,309,92.4,1.13,-427.06,14,42505.39,520 +114,James Bishop,26-35,Married,3,1,5,M,0.0,227,110.83,1.0,0.0,0,45219.34,408 +115,Sean Thompson,36-45,Married,4,2,8,M,0.0,110,108.69,1.0,0.0,0,15651.35,144 +116,Curtis Wilson,26-35,Married,2,0,2,M,0.0,345,117.8,1.0,0.0,0,51123.96,434 +117,Benjamin Nelson,26-35,Single,1,0,9,M,-0.11,236,81.38,1.1,-35.62,2,26531.28,360 +118,Jennifer Cooper,26-35,Single,1,0,7,F,-0.17,270,92.92,1.0,-71.24,2,38190.54,411 +119,Kayla Wagner,46-55,Single,1,0,3,F,-0.31,467,116.58,1.12,-247.56,11,94200.17,901 +120,Theresa Thomas,46-55,Single,1,0,2,F,-0.31,304,86.02,1.05,-118.73,2,32772.56,399 +121,Belinda Freeman,26-35,Single,1,0,5,F,-0.73,165,71.17,1.0,-142.48,3,13949.05,196 +122,Tracey Rodriguez,46-55,Married,3,1,10,F,0.0,372,99.81,1.0,0.0,0,44314.42,444 +123,Blake Peterson,46-55,Married,2,0,4,M,-0.35,354,111.74,1.0,-276.06,13,87047.03,779 +124,Brenda Solis,46-55,Married,2,0,6,F,-0.16,377,110.51,1.24,-106.86,3,71721.55,803 +125,Kimberly Marsh,46-55,Married,2,0,6,F,0.0,282,111.55,1.02,0.0,0,35920.0,327 +126,Richard Rodriguez,46-55,Married,2,0,6,M,0.0,530,88.91,1.07,0.0,0,80192.83,968 +127,Kelly Cline,26-35,Married,2,0,5,F,-0.23,731,86.12,1.0,-292.27,14,107650.4,1256 +128,Leah Lane,36-45,Married,3,1,1,F,-0.27,1341,91.44,1.0,-605.18,12,204098.83,2232 +129,Jennifer Hammond,46-55,Married,2,0,6,F,0.0,132,100.65,1.0,0.0,0,15500.13,154 +130,Charles Green,26-35,Single,3,2,3,M,0.0,323,108.74,1.0,0.0,0,47085.34,435 +131,Brian Thompson,46-55,Married,2,0,5,M,-3.39,326,103.1,1.06,-2523.24,117,76813.06,786 +132,Patricia Chapman,46-55,Married,3,1,3,F,-2.47,584,113.68,1.06,-2174.63,41,100261.74,933 +133,Ashley Aguirre,36-45,Married,5,3,7,F,-0.16,467,94.33,1.0,-126.45,5,73389.73,778 +134,Dawn Brown,70+,Married,2,0,4,F,0.0,242,84.81,1.0,0.0,0,44692.52,527 +135,Ricardo Craig,36-45,Single,1,0,1,M,-0.16,614,97.87,1.01,-124.31,4,74673.18,770 +136,Annette Johnson,56-70,Single,2,1,1,F,-0.08,341,95.68,1.0,-55.21,3,65442.52,684 +137,George Williams,26-35,Single,1,0,5,M,-0.54,290,72.84,1.01,-231.53,8,31247.55,435 +138,Stephen Dean,36-45,Single,1,0,5,M,-0.08,222,113.26,1.0,-32.06,2,44283.2,391 +139,Denise Garcia,56-70,Married,2,0,9,F,-0.51,288,94.73,1.08,-206.6,9,38272.26,437 +140,Tara Green,46-55,Married,2,0,2,F,-0.09,725,115.38,1.13,-106.86,1,140882.46,1383 +141,John Case,56-70,Married,2,0,9,M,-0.03,897,120.18,1.01,-54.85,2,207434.32,1749 +142,Jasmine Johnson,46-55,Married,2,0,4,F,-0.22,348,83.89,1.08,-135.36,3,50671.48,655 +143,Eduardo Grant,70+,Single,1,0,2,M,-0.1,464,75.57,1.01,-64.12,4,50556.18,677 +144,Edward Williams,36-45,Married,2,0,4,M,-0.04,646,84.44,1.03,-53.43,1,121842.62,1491 +145,Michelle Thomas,46-55,Single,1,0,3,F,0.0,436,107.28,1.01,0.0,0,56751.89,534 +146,Paul Henry,46-55,Married,2,0,6,M,0.0,210,90.93,1.03,0.0,0,29096.32,330 +147,Michelle Salazar,36-45,Married,5,3,5,F,-0.04,227,52.98,1.0,-30.28,2,44129.64,835 +148,Mercedes Spencer,46-55,Married,2,0,4,F,-0.44,392,114.01,1.05,-204.82,4,53128.23,487 +149,Mark Martin,26-35,Single,1,0,3,M,-0.06,659,82.1,1.03,-65.3,3,90230.33,1132 +150,Erin Bartlett,36-45,Married,2,0,4,F,-0.4,1493,91.72,1.04,-1094.17,36,248736.32,2817 +151,Paul Schultz,36-45,Married,5,3,12,M,-0.06,991,118.91,1.05,-100.62,6,201314.36,1780 +152,Pamela Walsh,36-45,Single,1,0,6,F,-0.81,310,98.47,1.0,-332.44,12,40175.1,408 +153,Carol Hobbs,46-55,Single,1,0,5,F,-1.77,740,67.92,1.0,-2410.36,86,92716.3,1365 +154,Debra Johnson,46-55,Married,5,3,6,F,-0.22,640,101.56,1.0,-224.4,7,101455.6,999 +155,Kimberly Banks,70+,Married,2,0,5,F,-0.22,718,106.88,1.11,-242.2,11,117675.63,1218 +156,Joseph Garcia,46-55,Married,3,1,7,M,0.0,158,91.26,1.11,0.0,0,16335.33,198 +157,Jason Kirby,26-35,Single,1,0,4,M,0.0,147,88.17,1.0,0.0,0,18515.96,211 +158,Bryan Henry,36-45,Married,4,2,8,M,-2.38,975,105.35,1.0,-3022.65,106,133902.58,1271 +159,Tony Blake,36-45,Married,3,1,1,M,-3.83,268,103.94,1.01,-1674.14,14,45421.61,441 +160,Scott Garrett,36-45,Married,2,0,9,M,-0.04,378,100.31,1.0,-17.81,1,47345.28,474 +161,Nicole Lee,46-55,Single,1,0,1,F,-0.54,1235,76.96,1.0,-1537.74,60,219958.63,2872 +162,Caleb Martin,26-35,Married,2,0,5,M,-0.99,702,117.55,1.01,-1990.73,64,236634.44,2040 +163,Thomas Mayo,26-35,Single,3,2,3,M,-0.02,369,75.14,1.0,-11.58,1,46963.12,628 +164,Madison Gonzalez,46-55,Married,2,0,5,F,-1.11,673,65.34,1.01,-1117.04,40,65997.64,1020 +165,Melanie Baker,46-55,Single,1,0,8,F,-0.17,171,118.17,1.16,-35.62,1,25052.5,245 +166,Dr. Michael Mitchell,46-55,Single,1,0,4,M,-0.16,718,95.06,1.0,-175.73,5,106559.85,1121 +167,Michael Casey,46-55,Married,2,0,4,M,-0.17,543,93.11,1.0,-189.86,10,102239.32,1098 +168,Evan Jones,46-55,Married,2,0,6,M,0.0,358,97.94,1.01,0.0,0,51615.54,534 +169,Brandy Peters,36-45,Married,2,0,5,F,-0.04,351,89.93,1.0,-19.59,1,42445.21,473 +170,Dana Cortez,36-45,Married,5,3,6,F,-0.08,301,105.52,1.02,-42.67,2,56031.17,542 +171,Richard Moreno,70+,Married,2,0,4,M,0.0,85,119.71,1.0,0.0,0,14365.53,120 +172,Michelle Cooper,26-35,Married,2,0,6,F,0.0,286,114.92,1.0,0.0,0,48497.74,422 +173,Julie Hill,26-35,Married,2,0,4,F,-0.01,441,90.78,1.03,-5.34,1,93772.47,1068 +174,Jennifer Alexander,36-45,Single,1,0,4,F,-0.02,582,96.18,1.05,-17.81,1,95407.08,1042 +175,Courtney Flynn,70+,Married,2,0,1,F,0.0,316,118.55,1.0,0.0,0,48366.74,408 +176,Michelle Frank,26-35,Married,2,0,4,F,-1.08,218,97.64,1.08,-333.06,25,30074.28,334 +177,Maureen Armstrong,46-55,Married,2,0,5,F,0.0,208,112.8,1.06,0.0,0,31246.76,295 +178,Ashley Hall,36-45,Single,1,0,5,F,-0.41,184,100.78,1.0,-114.87,5,28118.85,280 +179,John Edwards,46-55,Single,1,0,4,M,-1.52,462,112.09,1.04,-1113.8,42,81939.43,757 +180,Nicole Sanchez,26-35,Married,2,0,5,F,-0.43,545,121.35,1.0,-308.83,6,87006.78,717 +181,Linda Rice,36-45,Single,1,0,2,F,-0.21,509,97.02,1.04,-167.4,7,76161.72,818 +182,Bonnie Santiago,46-55,Single,1,0,2,F,0.0,115,134.07,1.0,0.0,0,19708.82,147 +183,Dana Gutierrez,26-35,Married,4,2,2,F,-0.04,481,90.64,1.03,-26.71,1,57737.32,656 +184,Joseph Gamble,26-35,Married,2,0,5,M,0.0,418,102.12,1.08,0.0,0,53814.79,571 +185,Kirsten Stone,46-55,Single,1,0,7,F,0.0,518,90.91,1.13,0.0,0,80632.95,1003 +186,Nicole Maxwell,26-35,Married,2,0,4,F,-0.01,652,78.5,1.0,-17.81,1,117276.26,1494 +187,Karen Adams,36-45,Married,2,0,7,F,-0.04,329,80.29,1.0,-17.81,1,39663.35,494 +188,Gary Bowman,70+,Married,2,0,6,M,-0.04,546,103.48,1.0,-35.62,1,83711.32,809 +189,Antonio Blankenship,26-35,Married,4,2,5,M,-0.88,716,94.65,1.0,-1206.92,36,130053.59,1377 +190,Todd Butler,46-55,Single,1,0,3,M,-0.22,323,93.98,1.0,-89.05,1,38251.42,407 +191,Patricia Brown,56-70,Married,2,0,1,F,-0.04,750,104.54,1.0,-46.3,3,110083.82,1053 +192,Danielle Casey,36-45,Married,4,2,4,F,-0.07,826,111.01,1.0,-90.84,4,153971.82,1387 +193,Ronnie Campbell,46-55,Married,3,1,4,M,0.0,287,114.9,1.01,0.0,0,42284.87,371 +194,Christian Sanchez,46-55,Single,1,0,3,M,-0.26,600,101.83,1.02,-235.98,8,91141.24,909 +195,Dennis Frederick,26-35,Married,2,0,5,M,-0.06,1218,96.12,1.06,-118.73,2,176671.35,1945 +196,Andrew Herrera,70+,Married,2,0,6,M,-0.84,335,114.27,1.06,-509.35,14,69247.58,642 +197,Christina Lopez,18-25,Married,4,2,4,F,-2.03,547,79.81,1.05,-1971.96,76,77579.45,1020 +198,George Lee,36-45,Single,1,0,3,M,0.0,215,90.61,1.02,0.0,0,57806.61,648 +199,Christopher Frost,46-55,Married,3,1,5,M,0.0,240,126.82,1.04,0.0,0,41091.1,336 +200,Vicki Hunt,70+,Single,1,0,4,F,-2.55,387,115.68,1.0,-1439.19,36,65241.99,564 +201,Shelia Clark,46-55,Married,3,1,6,F,0.0,381,73.89,1.0,0.0,0,52685.48,713 +202,Anita Gray,46-55,Married,2,0,6,F,-0.57,626,107.12,1.0,-626.07,39,117405.33,1096 +203,Megan Black,36-45,Married,5,3,5,F,-0.42,169,98.43,1.01,-124.67,2,29529.22,304 +204,Hunter Griffin,46-55,Single,4,3,4,M,-0.05,600,84.72,1.06,-41.85,2,75319.67,938 +205,Kevin Hicks,46-55,Married,2,0,7,M,-1.72,533,113.79,1.0,-1667.11,85,110372.74,970 +206,Laura Singh,36-45,Married,4,2,5,F,-0.06,465,106.15,1.01,-35.62,1,59869.59,567 +207,Patrick Sanders,46-55,Single,1,0,5,M,-0.49,892,115.44,1.08,-740.88,28,175582.84,1636 +208,Mr. Mike Johnson Jr.,46-55,Married,3,1,10,M,-0.08,1439,117.81,1.03,-231.53,6,333162.8,2902 +209,Dylan Fernandez,26-35,Single,1,0,6,M,0.0,366,80.42,1.0,0.0,0,53964.24,671 +210,Kathryn Mitchell,46-55,Married,2,0,6,F,-0.09,217,71.2,1.03,-50.76,3,38235.93,553 +211,Zachary Cross,56-70,Married,2,0,10,M,-0.07,217,101.18,1.0,-19.59,1,28230.09,279 +212,Elizabeth Ramirez MD,36-45,Married,5,3,8,F,-0.08,873,89.55,1.0,-80.14,2,94559.55,1056 +213,Douglas Glass,36-45,Married,3,1,2,M,0.0,209,79.16,1.0,0.0,0,24776.17,313 +214,Luis Ramirez,46-55,Married,2,0,1,M,-1.2,763,88.78,1.01,-1556.83,44,114787.63,1302 +215,Michael Clark,36-45,Single,1,0,9,M,0.0,304,119.01,1.0,0.0,0,56648.83,476 +216,Kristi Vargas,46-55,Single,1,0,5,F,-0.16,278,91.91,1.05,-71.24,2,40439.23,464 +217,Michael Owens,46-55,Married,2,0,12,M,-0.68,115,82.65,1.03,-159.75,11,19340.91,241 +218,Jeremy Carpenter,46-55,Married,3,1,5,M,0.0,246,76.88,1.0,0.0,0,27063.52,352 +219,Allison Campbell,70+,Married,2,0,3,F,-0.34,377,74.13,1.0,-187.89,6,40623.74,548 +220,Cynthia Santos,56-70,Single,1,0,5,F,-0.21,354,92.33,1.0,-84.78,3,37949.56,411 +221,Shannon Hernandez,46-55,Single,1,0,6,F,-0.15,175,129.2,1.0,-32.94,2,28423.71,220 +222,Alicia Smith,70+,Married,2,0,3,F,-0.18,801,85.59,1.0,-260.02,7,122143.72,1427 +223,Joseph Adams,70+,Married,2,0,5,M,0.0,335,97.34,1.0,0.0,0,65316.78,671 +224,Angie Johnson,46-55,Single,1,0,1,F,0.0,353,78.3,1.0,0.0,0,35391.44,452 +225,Karen Bailey,46-55,Married,4,2,5,F,0.0,252,115.53,1.21,0.0,0,38704.17,404 +226,Johnny Wood,46-55,Single,1,0,5,M,-0.29,587,137.29,1.01,-377.21,10,177518.67,1304 +227,John Gibbs,46-55,Married,2,0,9,M,-0.09,454,109.94,1.0,-45.42,3,57937.84,527 +228,Jessica Hammond,26-35,Married,5,3,4,F,-1.02,1104,101.42,1.0,-2032.05,86,201221.05,1984 +229,Amber Allen,18-25,Single,2,1,5,F,-0.32,262,107.64,1.05,-160.29,3,54682.02,533 +230,Melissa Crawford,36-45,Married,2,0,5,F,-0.13,1192,115.83,1.0,-276.76,6,253657.11,2190 +231,Lynn Cannon DDS,46-55,Single,1,0,5,F,-0.27,1071,75.15,1.0,-578.46,13,161114.84,2144 +232,Andrea Johnson,70+,Single,1,0,5,F,-0.15,364,98.55,1.02,-92.26,4,60707.49,628 +233,Mrs. Heather Jenkins,36-45,Married,2,0,8,F,-0.19,426,110.73,1.01,-106.86,2,63560.95,577 +234,Barbara Gibson,46-55,Married,3,1,10,F,-0.13,508,117.23,1.02,-114.57,4,100581.98,879 +235,Jennifer Henry,56-70,Married,2,0,5,F,-3.16,764,113.55,1.0,-4619.7,210,166009.0,1462 +236,Christopher Lamb,18-25,Married,2,0,4,M,-0.15,155,108.04,1.04,-35.62,1,25173.39,243 +237,Traci Bradley,36-45,Single,2,1,4,F,0.0,188,89.13,1.01,0.0,0,18805.61,214 +238,Carly Robinson,70+,Married,2,0,5,F,-0.16,504,85.62,1.1,-125.92,7,67214.24,862 +239,Joshua Porter,36-45,Married,3,1,6,M,-0.13,1409,102.16,1.0,-401.6,13,319031.88,3123 +240,Ronald Booth,46-55,Single,1,0,2,M,-0.48,615,146.5,1.02,-500.8,9,153385.98,1072 +241,Eric Flores,26-35,Married,5,3,11,M,0.0,511,72.02,1.0,0.0,0,112776.33,1566 +242,Philip Williams,46-55,Married,2,0,5,M,0.0,374,90.65,1.0,0.0,0,56748.56,626 +243,Teresa Cruz,56-70,Married,2,0,5,F,-0.63,494,137.96,1.04,-550.91,23,121268.21,913 +244,John Martinez,70+,Married,2,0,2,M,-0.11,666,95.88,1.0,-189.97,4,171623.86,1790 +245,Stephen Christian,26-35,Single,2,1,4,M,-0.64,456,110.64,1.0,-365.09,7,63172.92,571 +246,Robert Graham,46-55,Single,1,0,5,M,-0.39,420,100.46,1.03,-388.22,17,100059.2,1029 +247,Bianca Cruz,36-45,Married,2,0,5,F,0.0,445,125.11,1.04,0.0,0,66308.11,550 +248,Michael Jones,36-45,Single,1,0,3,M,-0.56,819,68.15,1.02,-858.08,39,104202.24,1555 +249,Valerie Wolf,26-35,Single,1,0,5,F,-1.04,566,131.73,1.0,-843.89,28,107095.86,813 +250,Elizabeth Fritz,36-45,Single,1,0,9,F,-0.77,1134,108.35,1.03,-1977.97,79,277043.37,2637 +251,Alexis Sims,46-55,Single,1,0,3,F,-0.27,238,113.41,1.01,-89.05,1,38106.74,339 +252,Deanna Knox,70+,Single,1,0,3,F,-0.19,209,121.79,1.0,-46.3,3,28986.97,238 +253,Joseph Schmidt,36-45,Married,3,1,12,M,-0.01,848,131.17,1.0,-12.47,1,223909.04,1707 +254,Erica Oconnell,70+,Single,1,0,3,F,-0.2,723,95.29,1.0,-186.82,6,91094.91,956 +255,Michael Saunders,26-35,Married,2,0,4,M,0.0,246,121.52,1.0,0.0,0,33417.86,275 +256,Brenda Thompson,26-35,Married,3,1,4,F,-0.49,171,94.61,1.04,-144.56,6,28192.49,311 +257,Matthew Spears,26-35,Married,5,3,5,M,-0.62,652,105.44,1.07,-708.53,23,119782.95,1212 +258,Kenneth Williamson,36-45,Single,1,0,8,M,-0.03,402,75.15,1.04,-17.8,2,38627.46,532 +259,Kelly Washington,36-45,Married,3,1,9,M,-0.27,1653,134.68,1.05,-743.74,6,370091.54,2892 +260,Daisy Jenkins,36-45,Married,5,3,5,F,-0.38,216,99.03,1.09,-117.54,3,30601.77,337 +261,Matthew Jones,56-70,Married,2,0,5,M,-1.19,93,158.07,1.0,-130.85,5,17387.23,110 +262,Brandy Holland,26-35,Married,2,0,3,F,-0.02,670,129.22,1.02,-19.59,1,150925.52,1194 +263,Aaron Richards,26-35,Single,1,0,5,M,-0.07,345,76.66,1.23,-35.62,1,41242.22,660 +264,Calvin Adams,46-55,Single,1,0,5,M,-0.66,321,147.74,1.0,-262.7,3,58799.62,398 +265,Ashley Wagner,18-25,Single,2,1,3,F,-2.65,713,92.47,1.02,-2723.87,109,95057.11,1049 +266,Stephen Kane,56-70,Married,5,3,4,M,-0.56,194,82.66,1.0,-145.69,3,21409.2,259 +267,Matthew Cherry,36-45,Married,2,0,1,M,-0.16,540,127.99,1.05,-117.37,6,95990.19,788 +268,Sarah Brooks,70+,Married,2,0,2,F,-1.06,437,82.83,1.0,-709.89,16,55495.9,672 +269,Larry Weber,46-55,Single,1,0,7,M,-0.02,371,90.4,1.13,-19.59,1,79104.01,990 +270,Russell Manning,36-45,Married,2,0,1,M,-0.1,391,89.35,1.02,-56.1,4,50928.12,584 +271,Timothy Hamilton,36-45,Married,3,1,4,M,-0.03,706,104.33,1.05,-31.16,2,126236.06,1266 +272,Jennifer Green,56-70,Single,1,0,6,F,-1.04,234,121.91,1.04,-286.74,10,33646.07,286 +273,Robert Yang,46-55,Single,1,0,5,M,0.0,448,101.72,1.0,0.0,0,57673.55,567 +274,Alicia Ray,46-55,Married,2,0,10,F,-2.09,746,101.62,1.11,-2051.65,79,99996.18,1088 +275,Michael Moore,46-55,Married,5,3,8,M,-0.03,660,86.76,1.0,-37.4,2,109573.46,1263 +276,Debra Richardson,46-55,Married,2,0,5,F,-0.76,597,97.5,1.15,-922.55,53,118950.83,1409 +277,Christina Gonzalez,18-25,Married,2,0,5,F,-0.07,658,76.31,1.0,-80.14,3,84095.62,1102 +278,Tony Wilson,70+,Married,2,0,6,M,-0.99,137,103.89,1.01,-158.15,3,16519.23,161 +279,William Torres,18-25,Single,1,0,3,M,-0.25,438,106.76,1.02,-160.29,3,68327.76,650 +280,Madison Williamson,26-35,Married,4,2,8,F,0.0,139,79.3,1.0,0.0,0,16652.88,210 +281,Diane Ali,36-45,Married,3,1,2,F,0.0,526,90.88,1.0,0.0,0,93058.67,1025 +282,Anna Powell,46-55,Married,3,1,6,F,0.0,322,113.98,1.0,0.0,0,47643.2,418 +283,Alex Joyce,26-35,Married,5,3,5,M,-1.4,684,125.88,1.03,-1582.34,47,142744.07,1165 +284,Tammie Sanchez,36-45,Married,2,0,5,F,-1.43,357,104.21,1.0,-785.21,27,57109.82,548 +285,Theresa Brown,46-55,Single,1,0,1,F,0.0,68,111.88,1.0,0.0,0,11635.7,104 +286,Michelle Fischer,46-55,Single,1,0,5,F,-0.03,155,98.28,1.0,-12.47,1,45109.41,459 +287,Kevin Kramer,26-35,Single,2,1,2,M,-0.76,328,88.54,1.05,-304.2,10,35239.21,417 +288,Samantha Flores,46-55,Married,2,0,3,F,-0.79,269,90.64,1.0,-395.02,13,45137.46,498 +289,Brett Carson,46-55,Married,2,0,6,M,-0.09,385,126.0,1.0,-55.21,3,77614.86,616 +290,Sarah Hernandez,70+,Married,2,0,3,F,0.0,214,110.86,1.01,0.0,0,33147.11,302 +291,Kristina Phillips,46-55,Married,2,0,12,F,0.0,167,82.44,1.0,0.0,0,33304.3,404 +292,Michelle Bailey,26-35,Single,2,1,1,F,0.0,226,105.83,1.02,0.0,0,30160.28,291 +293,Zachary Davis,56-70,Single,1,0,4,M,-0.51,915,94.12,1.0,-866.58,27,160282.6,1703 +294,Alex Martin,18-25,Single,2,1,5,M,-0.39,724,143.37,1.0,-425.66,9,155264.33,1087 +295,Rebecca Jones,56-70,Married,2,0,4,F,-2.79,293,85.89,1.02,-1091.22,26,33584.71,399 +296,Dennis Hubbard,18-25,Single,1,0,1,M,-0.4,97,133.92,1.0,-81.92,4,27185.71,203 +297,Jerry Sanchez,70+,Single,1,0,3,M,-0.65,132,76.42,1.06,-110.42,6,13068.45,182 +298,Jeffrey Franklin,70+,Single,1,0,7,M,-0.28,290,107.17,1.0,-89.05,4,34081.09,318 +299,John Allen,26-35,Married,3,1,6,M,-0.63,330,109.08,1.13,-446.23,14,77443.58,801 +300,Kenneth Mccarthy,70+,Married,2,0,5,M,-4.45,540,90.62,1.02,-3516.34,152,71587.39,804 +301,Robert Young,18-25,Married,2,0,2,M,-0.25,360,106.89,1.0,-106.5,1,44895.4,420 +302,Holly Johnson,36-45,Married,3,1,12,F,-0.43,602,103.13,1.0,-436.34,8,104780.38,1020 +303,Christy Johnson,46-55,Single,1,0,6,F,-1.39,931,108.83,1.0,-2193.59,90,171184.55,1579 +304,Ashley Gutierrez,70+,Married,3,1,4,F,-0.06,231,113.13,1.04,-26.71,1,53172.27,488 +305,Brian Bates,36-45,Married,5,3,4,M,-0.63,1087,129.02,1.0,-1131.8,35,231593.86,1795 +306,Maurice Clark,46-55,Single,1,0,5,M,0.0,347,91.46,1.08,0.0,0,51859.48,613 +307,Christina Williams,70+,Married,2,0,9,F,-0.4,330,89.82,1.0,-153.16,5,34759.82,387 +308,Julie Cantu,70+,Married,3,1,5,F,-0.06,316,102.93,1.0,-26.71,1,42716.18,415 +309,Michael Cherry,26-35,Single,1,0,5,M,-0.5,400,87.79,1.0,-260.02,7,45298.74,516 +310,Adam Cardenas,46-55,Married,2,0,1,M,-0.43,549,103.6,1.07,-316.06,10,75835.96,781 +311,Anthony Wright,26-35,Married,5,3,11,M,-0.75,397,95.46,1.0,-400.72,4,50880.84,533 +312,Vicki Dean,46-55,Married,2,0,10,F,0.0,303,97.17,1.0,0.0,0,35757.45,369 +313,Marcus Mcdonald,46-55,Single,1,0,4,M,-0.43,398,109.56,1.0,-222.62,4,56093.97,512 +314,Keith Shaw,26-35,Married,2,0,2,M,-0.4,440,101.05,1.0,-240.43,3,60933.44,606 +315,Pamela Smith,36-45,Single,1,0,3,F,-1.03,448,85.14,1.15,-802.69,19,66153.72,896 +316,Barbara Brooks,56-70,Single,1,0,9,F,-0.01,584,94.72,1.08,-8.9,1,84583.93,963 +317,Randall Bond MD,36-45,Married,5,3,6,M,-4.08,502,96.32,1.0,-3436.12,160,81098.05,842 +318,Ashley Phillips,46-55,Married,2,0,6,F,0.0,527,120.9,1.07,0.0,0,82938.83,734 +319,Marc Green,46-55,Married,2,0,5,M,-1.0,716,107.08,1.07,-1439.9,45,154191.78,1539 +320,Tina Lyons,56-70,Single,1,0,5,F,-0.46,480,105.33,1.02,-449.52,29,102805.11,1000 +321,Emma Price,36-45,Married,3,1,5,F,-0.17,414,112.13,1.0,-89.05,2,60099.82,536 +322,Maria Norman,46-55,Single,1,0,4,F,0.0,187,94.15,1.0,0.0,0,20901.34,222 +323,Veronica Turner,26-35,Married,2,0,3,F,-0.03,436,85.4,1.0,-17.81,1,50643.09,593 +324,Nancy Ellis,36-45,Single,1,0,6,F,-0.09,753,99.68,1.0,-94.39,3,102366.34,1028 +325,Stephanie Peterson,46-55,Single,1,0,5,F,-0.12,274,82.76,1.02,-47.37,1,32357.21,399 +326,Veronica Richardson,36-45,Single,3,1,5,F,0.0,180,124.41,1.0,0.0,0,26623.34,214 +327,Frank Nelson,36-45,Married,3,1,12,M,-0.4,1131,88.17,1.04,-727.0,15,159242.52,1886 +328,Catherine Ramirez,46-55,Married,2,0,8,F,-0.47,426,88.36,1.06,-400.72,7,74925.29,895 +329,Mark Pineda,46-55,Married,2,0,6,M,-1.62,604,169.19,1.0,-1841.55,27,192882.02,1142 +330,John Norris,36-45,Single,1,0,8,M,0.0,217,78.88,1.0,0.0,0,34705.62,440 +331,Jason Rogers,26-35,Married,3,1,5,M,0.0,350,87.58,1.0,0.0,0,37923.42,433 +332,Isaac Bradford,26-35,Married,5,3,4,M,-0.07,635,107.57,1.03,-55.21,2,83367.33,797 +333,Edward Nguyen,46-55,Married,2,0,6,M,-2.59,604,97.42,1.0,-2383.83,70,89529.02,919 +334,Victoria Sanders,26-35,Married,2,0,8,F,-1.18,198,108.73,1.0,-265.35,14,24463.21,225 +335,Kimberly Wallace,36-45,Married,5,3,4,F,-0.13,330,81.72,1.0,-73.02,3,45765.55,561 +336,Daniel Johnson,70+,Single,1,0,4,M,-0.74,374,91.62,1.07,-543.02,15,66972.11,784 +337,Christine Taylor,26-35,Single,1,0,5,F,-0.48,332,127.51,1.0,-213.72,3,56357.35,444 +338,Ashley Mckee,46-55,Single,1,0,5,F,-0.1,500,99.98,1.06,-65.89,3,66285.75,706 +339,Rebecca Lowe,46-55,Single,1,0,5,F,-0.08,276,112.4,1.0,-53.43,3,78228.97,696 +340,Marcus Aguirre,36-45,Married,4,2,2,M,-0.06,400,85.11,1.0,-29.68,1,44342.16,521 +341,Natalie Carson,36-45,Married,2,0,5,F,-0.34,254,129.12,1.0,-97.94,4,36926.97,287 +342,Christopher Moore,46-55,Single,1,0,9,M,-0.1,372,104.42,1.0,-59.37,2,60562.12,580 +343,Jacqueline Allen,46-55,Single,1,0,4,F,0.0,294,50.22,1.0,0.0,0,28525.24,568 +344,Lisa Key,46-55,Married,3,1,8,F,-0.16,381,79.73,1.0,-80.14,3,39945.73,502 +345,David Smith,46-55,Single,1,0,5,M,0.0,244,123.54,1.0,0.0,0,35827.07,290 +346,Matthew Russell,36-45,Married,2,0,9,M,-0.02,473,118.73,1.01,-13.36,1,80263.71,682 +347,Thomas Ross,26-35,Single,1,0,5,M,-0.04,259,124.25,1.01,-14.25,1,46220.58,374 +348,Meagan Daniel,26-35,Single,1,0,4,F,-0.18,405,91.08,1.03,-101.52,6,50275.0,571 +349,Jessica Richmond,36-45,Married,3,1,4,F,-0.04,217,96.88,1.0,-24.58,1,66268.78,684 +350,George Bailey,70+,Single,1,0,2,M,-0.14,397,97.57,1.01,-62.33,2,44100.34,455 +351,Stephanie Meyer,36-45,Married,3,1,1,F,-0.2,444,79.16,1.01,-160.3,7,62377.47,792 +352,Linda Walker,46-55,Married,2,0,5,F,-0.27,282,95.37,1.09,-164.92,9,57987.28,661 +353,James Hoffman,36-45,Single,2,1,6,M,-0.14,674,109.69,1.01,-128.24,5,101902.89,934 +354,Rhonda Wilson,18-25,Single,1,0,12,F,-0.03,253,112.4,1.0,-12.47,1,46756.5,416 +355,Gabriel Sanford,46-55,Married,2,0,6,M,-0.12,967,92.63,1.01,-189.05,6,150609.96,1644 +356,Todd Calhoun,36-45,Single,5,3,2,M,0.0,342,97.75,1.0,0.0,0,56598.12,579 +357,Erin Jones,18-25,Married,2,0,4,F,-0.05,894,105.59,1.06,-64.11,3,134525.24,1349 +358,Andrew Contreras,70+,Married,2,0,6,M,0.0,285,131.28,1.02,0.0,0,51069.83,396 +359,Michelle Foster,26-35,Married,3,1,3,F,-0.36,422,96.41,1.05,-191.99,4,50903.96,555 +360,Kristen Walters,46-55,Single,1,0,5,F,-0.17,256,75.59,1.05,-53.42,2,23809.56,331 +361,Sarah Taylor,18-25,Single,1,0,3,F,0.0,323,88.2,1.01,0.0,0,50540.89,578 +362,Taylor Romero,18-25,Single,1,0,5,M,-0.07,851,91.08,1.0,-108.64,2,136619.16,1500 +363,Kelly King,36-45,Married,3,1,12,F,-0.13,714,137.26,1.01,-154.58,5,164845.93,1217 +364,Sarah Martin,56-70,Single,1,0,2,F,-0.28,149,109.27,1.0,-44.52,1,17264.57,158 +365,Charles Gomez,70+,Married,2,0,8,M,0.0,271,99.47,1.0,0.0,0,34615.92,349 +366,James Perez,46-55,Married,2,0,6,M,-0.8,418,126.25,1.04,-523.6,10,82567.69,679 +367,Michael Todd,36-45,Married,3,1,6,M,-6.27,747,109.79,1.02,-7896.29,293,138229.78,1283 +368,Alexandra Morrison,36-45,Single,1,0,5,F,-0.04,1199,95.35,1.02,-103.3,4,241619.47,2581 +369,Donna Wells,56-70,Married,2,0,4,F,-0.02,527,112.44,1.01,-17.81,1,87814.42,789 +370,Rodney Mcfarland,46-55,Married,2,0,5,M,-0.51,554,114.58,1.0,-437.95,19,98651.77,861 +371,Patrick Mcgrath,26-35,Single,1,0,5,M,-0.02,447,115.09,1.01,-12.47,1,63298.54,554 +372,Patricia Vega,46-55,Married,2,0,7,F,-0.36,109,163.75,1.01,-73.01,3,33405.95,206 +373,Linda Simon,26-35,Married,3,1,4,F,-0.17,682,93.17,1.0,-169.2,3,90936.44,980 +374,Lisa Reed,46-55,Married,3,1,8,F,-0.08,528,148.98,1.02,-51.95,2,102202.48,702 +375,Justin White,36-45,Single,1,0,2,M,0.0,339,130.66,1.0,0.0,0,59581.64,456 +376,Alexandra Davis,46-55,Single,1,0,8,F,-0.4,146,130.55,1.17,-71.24,1,23237.97,209 +377,Kimberly Mckenzie,18-25,Married,3,1,4,F,-0.13,653,92.81,1.03,-139.8,5,102744.84,1145 +378,Mark White,36-45,Married,5,3,5,M,-0.78,988,96.46,1.08,-1380.24,48,170254.62,1902 +379,Amanda Martin,18-25,Single,1,0,1,F,-0.35,1347,120.46,1.01,-946.4,20,321875.91,2690 +380,Lisa Roberts,46-55,Married,2,0,7,F,-0.32,419,106.55,1.02,-169.2,3,56152.94,537 +381,Sara Allen,26-35,Married,3,1,4,F,-0.24,465,99.08,1.0,-140.52,4,58654.37,592 +382,James Sims,46-55,Married,3,1,2,M,0.0,267,142.02,1.0,0.0,0,61495.95,433 +383,Vickie King,18-25,Single,2,1,1,F,-0.1,646,88.31,1.0,-88.87,2,81331.58,921 +384,Rose Weber,36-45,Single,1,0,3,F,-0.64,372,93.35,1.02,-382.91,16,55634.34,606 +385,Samantha Brown,26-35,Single,1,0,3,F,-3.27,456,114.79,1.08,-2173.44,84,76220.79,716 +386,Roy Mcmillan,36-45,Married,4,2,7,M,0.0,681,98.93,1.04,0.0,0,107540.54,1132 +387,James Russell,46-55,Single,1,0,5,M,0.0,600,78.12,1.06,0.0,0,74679.53,1014 +388,Allison Peterson,26-35,Single,1,0,4,F,0.0,215,115.99,1.0,0.0,0,45467.77,392 +389,Michael Shelton,26-35,Married,4,2,6,M,-1.48,659,91.38,1.06,-1815.99,46,112393.84,1298 +390,Lauren Diaz,46-55,Single,1,0,4,F,0.0,543,99.87,1.01,0.0,0,71707.89,725 +391,Chad Gonzalez,46-55,Single,1,0,8,M,-0.35,602,76.79,1.04,-352.98,14,77172.1,1047 +392,Ryan Hall,26-35,Single,5,3,3,M,-0.27,488,107.88,1.0,-252.9,5,102812.77,953 +393,Julie Lawson DVM,46-55,Single,1,0,5,F,-1.2,303,83.72,1.01,-568.14,6,39683.09,481 +394,Thomas Carter,46-55,Single,3,1,5,M,-0.07,383,95.08,1.02,-44.52,1,64940.98,699 +395,Ariel Hill,36-45,Married,4,2,2,F,-0.36,291,85.7,1.0,-242.21,8,56903.16,664 +396,Brian Ferguson,70+,Married,3,1,8,M,-0.16,655,92.73,1.12,-186.42,7,107936.42,1301 +397,Amanda Castro,46-55,Single,1,0,4,F,-0.09,248,110.4,1.0,-41.86,3,53435.34,484 +398,John Gonzalez,36-45,Single,1,0,2,M,-0.52,341,132.72,1.0,-220.84,6,56138.86,424 +399,Bonnie Hawkins,46-55,Married,3,1,4,F,-0.23,412,118.67,1.04,-142.48,5,73931.75,650 +400,Angela Best,46-55,Single,1,0,5,F,-0.09,578,82.55,1.06,-91.72,3,82135.77,1050 +401,Michelle Johnson,36-45,Married,3,1,4,F,-1.86,529,96.42,1.21,-1646.65,72,85233.6,1067 +402,Angela Dunlap,70+,Married,3,1,7,F,-0.11,623,84.97,1.09,-187.0,4,145724.18,1864 +403,Brianna Lopez,46-55,Single,1,0,3,F,-1.62,510,92.95,1.04,-1025.62,43,58933.19,657 +404,Kimberly Lee,36-45,Single,1,0,3,F,-2.47,595,106.77,1.04,-2693.23,64,116381.28,1133 +405,Brenda Love,36-45,Married,2,0,6,F,0.0,306,120.32,1.0,0.0,0,50054.28,416 +406,Daniel Scott,26-35,Single,1,0,3,M,0.0,47,141.52,1.0,0.0,0,11745.84,83 +407,Christina Brown,36-45,Married,2,0,6,F,-0.13,1007,72.93,1.01,-259.67,10,147099.74,2035 +408,Mark Smith,46-55,Married,2,0,5,M,-0.22,396,95.25,1.0,-125.56,4,54767.12,576 +409,Shannon Lutz,56-70,Married,2,0,5,F,-0.22,622,101.93,1.11,-237.58,10,110803.34,1212 +410,Sarah Hall,26-35,Married,2,0,5,F,0.0,124,89.16,1.0,0.0,0,16226.61,182 +411,Lindsey Parker,46-55,Single,1,0,4,F,-0.12,472,84.96,1.03,-89.05,3,60576.18,734 +412,Willie Orr,46-55,Married,2,0,3,M,-0.32,1284,109.88,1.0,-762.26,18,262507.32,2389 +413,Kathryn Griffin,36-45,Single,1,0,1,F,-0.03,372,128.1,1.01,-17.81,1,66869.9,525 +414,Rebecca Price,70+,Married,2,0,6,F,-0.42,566,81.35,1.03,-471.6,15,90782.22,1149 +415,Stephen Garcia,26-35,Married,2,0,5,M,-0.37,404,87.45,1.06,-178.1,2,42238.17,510 +416,Douglas Irwin,36-45,Married,4,2,5,M,-1.63,609,113.97,1.09,-1410.82,46,98353.98,942 +417,Jessica Murphy,36-45,Married,2,0,3,F,-0.11,351,95.11,1.0,-60.55,3,50410.43,530 +418,Robert Mendoza,36-45,Single,4,2,1,M,-0.05,1021,97.45,1.07,-89.05,2,183490.62,2020 +419,Katie Galloway,36-45,Married,2,0,3,F,-0.25,362,103.39,1.0,-126.44,6,53040.48,513 +420,Manuel Leonard,26-35,Married,3,1,4,M,-0.04,634,106.85,1.0,-40.07,2,110055.11,1030 +421,Jared Aguilar,56-70,Married,2,0,5,M,-0.29,917,113.48,1.03,-414.25,11,164438.22,1497 +422,Caitlyn Adkins,46-55,Single,1,0,5,F,0.0,440,177.18,1.0,0.0,0,200925.82,1134 +423,Anthony Weber,46-55,Married,2,0,4,M,-0.21,149,136.68,1.0,-53.43,2,35263.55,258 +424,Gavin Gibson,46-55,Single,1,0,4,M,-1.37,525,97.26,1.01,-1050.44,17,74308.75,769 +425,Anthony Key,70+,Single,1,0,1,M,-0.13,87,102.87,1.0,-17.81,1,13990.3,136 +426,Sara Medina,26-35,Single,1,0,7,F,-0.03,827,132.58,1.01,-44.52,2,170900.26,1301 +427,Laurie Valencia,56-70,Married,3,1,4,F,-1.23,680,100.52,1.04,-1246.68,43,101925.23,1055 +428,Bradley King,36-45,Single,1,0,4,M,-0.25,333,121.94,1.15,-160.29,10,79020.03,745 +429,Laurie Grant,36-45,Married,2,0,3,F,-0.59,336,86.88,1.03,-296.83,6,43962.95,520 +430,Nicholas Kelley,70+,Married,2,0,6,M,-0.01,615,98.7,1.0,-8.9,1,89620.29,908 +431,Julie Smith,36-45,Married,2,0,5,F,-0.55,329,89.22,1.06,-413.79,25,66736.78,793 +432,John White DVM,46-55,Married,2,0,6,M,-0.19,910,95.15,1.0,-391.64,6,194286.63,2042 +433,Lisa Keller,46-55,Single,1,0,5,F,-1.53,869,125.19,1.0,-2137.34,53,175139.14,1399 +434,Sydney Brown,46-55,Single,1,0,5,F,-0.15,491,89.17,1.0,-124.67,2,74901.02,840 +435,Cynthia Beck,36-45,Single,1,0,5,F,-0.3,209,126.18,1.01,-160.29,5,66748.72,532 +436,Ross Rogers,70+,Married,2,0,8,M,-1.01,534,96.42,1.14,-822.45,28,78289.9,927 +437,Jessica Castro,26-35,Single,1,0,2,F,0.0,491,82.49,1.01,0.0,0,52874.32,649 +438,Gregory Williamson,36-45,Married,2,0,1,M,-0.06,569,94.46,1.01,-71.24,2,111274.5,1188 +439,Matthew Martin,36-45,Married,3,1,6,M,-0.5,388,97.07,1.0,-297.41,17,57465.9,592 +440,Diana Harding,36-45,Married,3,1,8,F,-0.12,497,129.84,1.0,-71.24,1,78165.67,602 +441,Mary Lyons,70+,Single,1,0,2,F,-0.09,523,124.27,1.06,-64.11,4,91217.75,776 +442,Steven Weiss,46-55,Married,5,3,6,M,0.0,370,107.61,1.0,0.0,0,60475.01,562 +443,Mark Figueroa,70+,Single,1,0,4,M,-0.19,298,103.87,1.05,-137.13,5,75303.79,758 +444,Linda Taylor,46-55,Married,2,0,2,F,-0.12,960,115.31,1.0,-163.85,6,163742.07,1420 +445,Sarah Williams,26-35,Married,2,0,5,F,0.0,164,106.03,1.0,0.0,0,41138.1,388 +446,Zachary Baker,36-45,Single,3,2,4,M,-0.05,697,94.16,1.05,-71.24,2,123449.87,1371 +447,April Rocha,36-45,Married,2,0,5,F,-0.41,1214,117.35,1.08,-862.7,36,246437.23,2260 +448,John Andrews,18-25,Married,3,1,5,M,-0.07,470,100.21,1.04,-44.52,2,63433.7,656 +449,Karen Potts,70+,Single,1,0,4,F,-0.12,252,83.96,1.01,-35.62,2,24683.65,298 +450,Matthew Thomas,46-55,Married,2,0,5,M,-0.32,839,120.83,1.12,-473.86,14,177618.34,1642 +451,Emily Marks,56-70,Married,2,0,1,F,-0.23,280,113.64,1.01,-98.04,2,47956.21,426 +452,Jon Payne,26-35,Married,2,0,5,M,-0.11,517,102.73,1.05,-141.76,6,138176.97,1418 +453,Patrick Rodriguez,70+,Single,2,1,2,M,-0.17,703,117.84,1.12,-233.31,7,162269.19,1542 +454,Justin Barnett,36-45,Married,2,0,5,M,0.0,199,110.5,1.0,0.0,0,35137.47,318 +455,Mary Flores,46-55,Single,2,1,5,F,0.0,613,110.32,1.02,0.0,0,79653.01,734 +456,Pam Baker,26-35,Married,5,3,7,F,-0.04,1252,123.26,1.12,-104.48,4,295583.04,2675 +457,Crystal Morrison,36-45,Married,4,2,7,F,0.0,421,106.65,1.0,0.0,0,56099.52,526 +458,Sarah Morales,46-55,Single,2,1,4,F,-0.15,723,87.54,1.0,-173.94,4,99091.73,1132 +459,Jason Graham,26-35,Married,2,0,4,M,-0.69,638,89.29,1.01,-640.71,26,83310.54,942 +460,John Moore,70+,Married,2,0,5,M,-0.53,915,113.17,1.01,-1097.09,33,232103.75,2081 +461,Clifford Rodriguez,56-70,Married,3,1,2,M,-0.86,352,93.82,1.0,-591.3,19,64733.24,690 +462,Jennifer Ellis,56-70,Married,2,0,2,F,-1.36,467,85.78,1.01,-829.8,23,52325.33,614 +463,William Roberts,46-55,Married,5,3,6,M,-3.25,182,91.56,1.02,-728.42,29,20509.29,228 +464,Daniel Holden,46-55,Married,5,3,3,M,-1.47,2040,89.27,1.04,-6063.05,220,369053.18,4314 +465,Blake Young,26-35,Married,3,1,4,M,0.0,89,193.96,1.0,0.0,0,35300.99,182 +466,Brandon Phillips,36-45,Married,2,0,5,M,-0.58,698,109.83,1.01,-654.5,24,123780.54,1140 +467,Victor Sanchez,46-55,Single,1,0,3,M,-0.2,554,74.05,1.0,-276.06,8,102852.06,1389 +468,Ashley Woodward,56-70,Married,2,0,4,F,-0.04,320,104.69,1.0,-17.81,1,41874.09,400 +469,Courtney Bradshaw,18-25,Single,1,0,4,F,-0.04,550,99.59,1.0,-44.52,1,109452.94,1099 +470,Christopher Rosario,18-25,Married,3,1,4,M,-0.02,896,98.51,1.1,-39.19,3,202740.39,2266 +471,Brandi Morgan,18-25,Married,3,1,5,F,0.0,252,104.65,1.0,0.0,0,31812.86,304 +472,Brandi Wilson,46-55,Married,2,0,5,F,0.0,97,120.93,1.0,0.0,0,23702.96,196 +473,Michelle Hammond,46-55,Married,2,0,4,F,-0.35,501,105.67,1.08,-215.74,6,65094.98,664 +474,Tammy Fitzgerald,56-70,Married,4,2,6,F,-0.31,501,88.61,1.02,-266.55,12,77268.3,888 +475,Timothy Vaughan,46-55,Married,2,0,6,M,-1.26,210,82.15,1.0,-713.65,9,46414.89,565 +476,Daryl Moore,36-45,Married,5,3,7,M,0.0,146,108.21,1.04,0.0,0,18827.8,181 +477,Stephanie Barnes,70+,Married,2,0,2,F,0.0,268,101.83,1.0,0.0,0,29836.31,293 +478,Nicole Kane,36-45,Married,5,3,7,F,-0.03,477,86.18,1.0,-17.81,1,56192.32,652 +479,Gerald Welch,46-55,Married,2,0,10,M,-0.03,771,90.9,1.0,-44.52,2,117618.64,1294 +480,Eric Duncan,70+,Married,3,1,5,M,-0.06,398,91.81,1.01,-35.62,2,53250.29,586 +481,Wendy Barker,36-45,Married,4,2,2,F,-0.71,724,60.82,1.01,-1322.03,30,113545.34,1880 +482,Ronald Cruz,26-35,Married,4,2,8,M,-0.04,274,84.16,1.01,-17.81,1,37365.42,449 +483,Scott Williams,70+,Married,2,0,8,M,-1.54,570,99.18,1.07,-1130.33,53,72798.18,784 +484,Dawn Tucker,70+,Married,2,0,8,F,-0.04,877,89.67,1.0,-62.33,2,156384.85,1744 +485,Natalie Perkins,56-70,Married,2,0,7,F,-0.36,196,97.35,1.0,-92.26,2,25115.84,258 +486,Amy Thomas,46-55,Single,3,1,1,F,-0.02,454,94.28,1.0,-11.13,1,57509.36,612 +487,Clinton Sims,26-35,Single,1,0,4,M,0.0,214,88.73,1.01,0.0,0,26175.27,298 +488,Richard Mcintyre,36-45,Married,2,0,6,M,0.0,543,96.05,1.11,0.0,0,65893.59,763 +489,Rose Mccarthy,70+,Married,2,0,2,F,-0.07,130,113.74,1.0,-14.25,1,23998.79,211 +490,Gina Alvarez,36-45,Single,1,0,5,F,-0.83,466,82.99,1.85,-623.35,15,62245.13,1387 +491,Kathryn Turner,26-35,Married,2,0,4,F,-0.43,327,111.32,1.03,-183.73,7,47312.91,436 +492,Tyler Mendoza,46-55,Married,2,0,4,M,0.0,159,128.97,1.0,0.0,0,27341.28,212 +493,Patricia Washington,36-45,Married,2,0,4,F,-0.46,187,128.25,1.0,-142.48,1,40014.67,313 +494,Angela Dean,26-35,Married,5,3,8,F,-0.8,839,111.66,1.05,-991.47,22,138574.38,1297 +495,Sharon Simmons,36-45,Married,2,0,8,F,-0.01,1090,103.69,1.04,-19.59,1,163311.42,1640 +496,Joshua Gallagher,26-35,Single,1,0,5,M,-0.09,427,96.91,1.0,-64.11,3,69195.51,715 +497,Samantha Casey,36-45,Married,5,3,5,F,-1.11,391,102.17,1.04,-539.9,21,49656.77,506 +498,Janet Whitehead,46-55,Married,2,0,9,F,-0.4,912,114.45,1.0,-644.37,20,182435.33,1594 +499,Carol Briggs,36-45,Married,2,0,5,F,-0.14,735,76.89,1.06,-215.49,8,117866.2,1620 +500,Rebecca Benson DDS,46-55,Single,1,0,4,F,0.0,348,79.55,1.0,0.0,0,35478.17,448 +501,Kendra Griffin,46-55,Single,1,0,5,F,-0.09,1021,83.38,1.0,-178.8,6,175019.74,2100 +502,Henry James,26-35,Married,4,2,6,M,-0.03,465,95.95,1.0,-17.81,1,64480.74,675 +503,Barbara West,46-55,Married,3,1,4,F,-0.38,310,95.98,1.04,-239.82,12,60276.3,651 +504,Charles Reyes,70+,Married,3,1,8,M,0.0,408,135.5,1.0,0.0,0,70322.66,519 +505,Robin Powers,56-70,Married,2,0,9,F,0.0,109,86.65,1.0,0.0,0,15076.44,174 +506,Brenda Obrien,46-55,Married,2,0,7,F,-0.22,257,105.27,1.09,-89.05,4,43581.02,453 +507,Michelle Bass,70+,Married,2,0,6,F,0.0,179,100.45,1.14,0.0,0,32445.94,369 +508,Kathryn Patrick,18-25,Married,2,0,6,F,0.0,545,134.99,1.0,0.0,0,155368.79,1151 +509,Kristina Hawkins,26-35,Single,1,0,3,F,-0.44,193,114.68,1.0,-219.78,3,57225.55,499 +510,Nicholas Thomas,26-35,Single,2,1,1,M,-1.0,1019,86.01,1.03,-1634.66,56,141234.55,1684 +511,Grant Thomas,46-55,Single,1,0,5,M,-0.14,377,105.48,1.01,-71.24,2,54956.13,527 +512,Debra Buchanan,46-55,Single,1,0,3,F,-0.31,217,166.42,1.05,-82.99,3,44932.79,284 +513,Tiffany Chen,70+,Married,2,0,4,F,-0.11,231,89.1,1.0,-35.62,2,27797.71,312 +514,Christopher Cooke,26-35,Married,2,0,6,M,-0.04,379,144.69,1.0,-17.81,1,69163.7,478 +515,Nicholas Werner,26-35,Married,2,0,4,M,-0.02,903,97.06,1.0,-53.43,2,251472.19,2591 +516,Brandon Mckenzie,70+,Married,2,0,4,M,-0.79,235,138.27,1.0,-249.34,2,43415.58,314 +517,Alyssa Swanson,26-35,Married,2,0,6,F,0.0,256,110.94,1.0,0.0,0,40935.73,369 +518,Matthew Foster,56-70,Married,5,3,4,M,-0.9,633,79.35,1.0,-1111.68,7,97517.87,1229 +519,John Cain,46-55,Married,2,0,5,M,-0.18,354,82.42,1.02,-142.48,7,65774.04,811 +520,Casey Marsh,36-45,Married,2,0,6,F,-0.64,688,103.4,1.01,-693.69,26,112702.84,1105 +521,Brittney Silva,56-70,Single,1,0,6,F,-0.07,448,90.11,1.06,-53.43,2,70646.71,834 +522,Crystal Wheeler,46-55,Married,3,1,6,F,0.0,369,87.68,1.05,0.0,0,44718.54,533 +523,Adriana Li,36-45,Single,3,1,5,F,-1.08,127,91.67,1.0,-262.68,16,22276.25,243 +524,Dawn Brown,26-35,Single,1,0,5,F,-0.38,493,110.52,1.0,-274.8,9,80456.72,731 +525,Jacob Reed,36-45,Married,3,1,9,M,-0.15,614,105.82,1.0,-171.69,4,122327.85,1156 +526,Deborah Johnson,18-25,Married,2,0,2,F,-0.78,754,108.97,1.0,-737.32,18,103633.25,953 +527,Ronald Castillo,36-45,Married,3,1,5,M,0.0,299,121.27,1.06,0.0,0,46566.02,406 +528,Julie Murphy,46-55,Married,2,0,10,F,-0.72,1109,117.85,1.09,-1249.53,32,204122.66,1887 +529,Joshua Ramos,18-25,Single,1,0,5,M,-1.95,250,67.5,1.0,-751.2,27,26055.99,386 +530,Diane Chambers,46-55,Single,3,2,5,F,-0.25,831,126.42,1.0,-387.36,6,196458.65,1554 +531,Cynthia Baker,36-45,Married,3,1,5,F,0.0,168,112.42,1.06,0.0,0,22484.32,212 +532,Cindy Wood,56-70,Married,2,0,1,F,0.0,249,110.35,1.0,0.0,0,42595.66,386 +533,Nathan Walls,36-45,Married,5,3,6,M,-0.06,983,137.23,1.0,-92.61,4,219300.47,1598 +534,Tina Morales,26-35,Married,5,3,5,F,-0.17,177,113.49,1.08,-38.29,2,24968.18,238 +535,Rachel Wood,36-45,Married,2,0,5,F,-0.08,693,79.22,1.06,-108.64,2,113521.47,1521 +536,Ariana Pierce,46-55,Married,2,0,11,F,-0.8,441,136.71,1.0,-595.29,27,101712.02,747 +537,Alyssa Mills,18-25,Single,1,0,5,F,-0.15,745,115.16,1.01,-178.1,5,133813.01,1175 +538,Matthew Spears,46-55,Married,2,0,6,M,0.0,570,105.12,1.0,0.0,0,92291.63,878 +539,Haley Anderson,46-55,Married,2,0,9,F,-0.05,214,76.5,1.0,-14.25,1,20808.28,273 +540,Brittany Cummings,26-35,Married,4,2,4,F,-0.33,267,132.62,1.0,-106.86,2,43101.62,325 +541,Jennifer Gray,26-35,Single,1,0,10,F,-0.04,523,173.78,1.08,-69.46,3,271093.03,1690 +542,Kathleen Pratt,46-55,Single,1,0,6,F,0.0,89,94.38,1.0,0.0,0,9721.61,103 +543,David Davis,26-35,Married,2,0,5,M,-0.26,510,121.83,1.0,-213.72,4,99411.04,816 +544,Christopher Diaz,56-70,Single,1,0,4,M,-0.16,432,101.88,1.03,-145.45,6,91485.66,927 +545,Shannon Williams,70+,Married,3,1,4,F,-0.01,445,100.54,1.0,-8.9,1,88475.16,880 +546,Karen Roach,26-35,Single,1,0,5,F,-0.1,682,100.15,1.05,-135.35,4,136299.31,1425 +547,Dustin Walker,46-55,Married,2,0,1,M,0.0,159,121.96,1.0,0.0,0,26953.01,221 +548,Mark Hughes,46-55,Married,2,0,5,M,-0.05,267,90.29,1.0,-19.59,1,34038.93,377 +549,Amber Olson,46-55,Single,1,0,2,F,-0.13,242,83.34,1.04,-35.62,1,23668.72,295 +550,Sandra Peterson,36-45,Single,1,0,2,F,-0.71,687,103.13,1.0,-868.22,20,126741.56,1229 +551,Mark Roberts,46-55,Single,1,0,6,M,-1.6,576,94.97,1.08,-1513.03,59,89558.63,1019 +552,Timothy Holloway,70+,Married,2,0,4,M,-0.6,786,119.81,1.08,-651.47,23,129871.16,1171 +553,Nicole Evans,36-45,Married,3,1,11,F,0.0,747,136.33,1.0,0.0,0,200818.57,1475 +554,Spencer Snyder,26-35,Single,1,0,4,M,-0.36,453,64.0,1.04,-320.58,5,56380.02,916 +555,Michael Campbell,46-55,Single,1,0,5,M,0.0,522,96.62,1.06,0.0,0,102221.05,1125 +556,Tina Hubbard,46-55,Single,1,0,5,F,-3.45,340,122.19,1.03,-1919.14,65,68062.56,574 +557,Christopher Griffith,26-35,Single,3,2,4,M,-0.02,742,86.91,1.01,-35.62,1,129411.09,1499 +558,Kathy Lopez,36-45,Single,2,1,5,F,-0.74,259,98.27,1.11,-252.46,4,33510.0,377 +559,Tabitha Maynard,36-45,Married,2,0,6,F,-0.09,649,71.19,1.0,-133.57,8,101665.5,1428 +560,George Church Jr.,46-55,Married,2,0,5,M,-0.43,657,97.41,1.03,-408.72,13,92344.92,975 +561,Eric Jones,36-45,Married,2,0,4,M,-0.27,684,127.42,1.1,-284.96,7,134681.94,1162 +562,James Gomez,46-55,Single,1,0,9,M,0.0,122,83.64,1.0,0.0,0,12378.21,148 +563,Joshua Obrien,26-35,Single,1,0,2,M,0.0,268,115.48,1.03,0.0,0,42958.84,382 +564,Kimberly Brock,36-45,Married,2,0,7,F,-0.54,438,120.23,1.08,-411.41,18,91252.32,817 +565,Hayley Jones,26-35,Married,3,1,9,F,-1.81,924,106.25,1.0,-3453.35,45,202715.62,1908 +566,Alison Garcia,26-35,Married,4,2,5,F,-2.21,991,102.65,1.07,-5873.98,219,272420.99,2830 +567,Christine Medina,18-25,Single,1,0,3,F,-0.2,696,105.57,1.0,-262.7,11,135346.15,1282 +568,Peter Rios,56-70,Single,1,0,4,M,-0.05,419,99.35,1.0,-35.62,1,73918.95,747 +569,Cynthia Anderson MD,46-55,Single,1,0,9,F,-0.02,457,130.25,1.06,-17.81,1,138850.84,1129 +570,Amber Foster,46-55,Single,1,0,4,F,-0.49,955,99.13,1.02,-644.71,24,129663.22,1337 +571,Hannah Rodriguez,56-70,Married,2,0,8,F,-0.02,662,114.21,1.1,-19.59,1,126999.48,1225 +572,Carolyn Martin,36-45,Married,2,0,4,F,-0.04,1145,104.44,1.1,-106.5,2,249094.2,2627 +573,Elizabeth Jones,36-45,Single,1,0,4,F,-0.06,482,86.97,1.0,-35.62,1,54183.06,624 +574,Steven Scott,36-45,Single,1,0,4,M,-0.13,302,136.96,1.0,-53.43,2,57387.68,419 +575,Renee Cherry,56-70,Married,2,0,1,F,-0.08,275,93.05,1.14,-83.7,5,96118.37,1173 +576,Martin Lee,26-35,Single,1,0,5,M,0.0,242,89.75,1.0,0.0,0,32308.98,360 +577,Lisa Barker,46-55,Married,3,1,7,F,-0.3,602,96.25,1.04,-287.57,7,93264.06,1010 +578,Brad Leblanc,46-55,Married,2,0,6,M,-0.49,1489,111.88,1.1,-1129.69,45,258554.78,2541 +579,Charles Gray,26-35,Married,2,0,8,M,-0.09,384,101.21,1.03,-53.43,2,63154.69,641 +580,Jeffrey Henderson,26-35,Married,3,1,6,M,-0.1,183,116.73,1.01,-29.68,1,35370.69,307 +581,Joseph Mason DDS,46-55,Single,1,0,2,M,0.0,102,60.7,1.0,0.0,0,14204.24,234 +582,Joseph May,70+,Married,2,0,4,M,0.0,401,90.17,1.05,0.0,0,45627.33,532 +583,William Cooper,36-45,Single,1,0,2,M,-0.29,635,74.46,1.0,-322.0,10,81977.89,1101 +584,Karen Brown,46-55,Single,1,0,4,F,-0.35,295,68.93,1.0,-163.5,1,31912.34,465 +585,April Valencia,46-55,Married,5,3,4,F,-0.52,534,95.59,1.01,-441.34,9,81155.53,861 +586,Dr. Steven Johnson MD,46-55,Single,1,0,4,M,-1.94,354,118.08,1.05,-975.26,13,59273.83,528 +587,Courtney Watkins,26-35,Married,2,0,4,F,-0.49,368,110.72,1.1,-236.87,7,53700.43,532 +588,Mitchell Wyatt,46-55,Married,2,0,5,M,0.0,257,117.11,1.0,0.0,0,52467.42,448 +589,Erica Munoz,56-70,Single,1,0,4,F,0.0,77,89.8,1.0,0.0,0,12661.71,141 +590,Regina David,36-45,Married,2,0,1,F,-0.04,552,91.03,1.11,-55.21,3,116881.96,1427 +591,Gloria Sexton,26-35,Married,5,3,7,F,-0.49,288,100.99,1.0,-147.82,6,30699.57,304 +592,Steven Alvarez,70+,Married,2,0,4,M,-0.07,578,95.61,1.04,-55.21,2,71321.88,775 +593,Kimberly Hodge,46-55,Single,2,1,3,F,-0.18,650,68.68,1.04,-233.3,7,88533.59,1342 +594,Kelly Doyle,46-55,Single,1,0,10,F,-0.98,574,111.42,1.01,-961.73,31,109187.26,991 +595,Crystal Yang,18-25,Married,2,0,2,F,-0.14,734,100.11,1.0,-162.07,5,119533.46,1194 +596,Tiffany Jones,46-55,Single,1,0,1,F,-0.32,1454,75.91,1.01,-852.8,29,200179.32,2659 +597,Alexandra Richardson,26-35,Single,1,0,5,F,0.0,192,78.43,1.24,0.0,0,18667.11,296 +598,Vincent Powell,70+,Married,2,0,6,M,-2.07,872,103.32,1.01,-2744.39,96,137102.95,1338 +599,Jenny Howard,36-45,Single,1,0,3,F,0.0,303,117.95,1.0,0.0,0,50130.63,425 +600,Elizabeth Hobbs,46-55,Married,2,0,6,F,-0.07,529,66.26,1.0,-80.15,4,71958.6,1091 +601,Alan Beard,70+,Married,2,0,6,M,-0.1,230,95.1,1.0,-35.62,1,33474.47,352 +602,Ricky Smith DDS,18-25,Married,2,0,11,M,0.0,351,117.11,1.04,0.0,0,49771.22,441 +603,Amanda Cook,36-45,Single,1,0,3,F,-0.13,776,86.79,1.0,-199.46,11,128970.14,1486 +604,Omar Crosby,46-55,Married,2,0,1,M,-0.14,562,83.92,1.03,-136.24,4,79051.04,967 +605,Eddie Simmons,36-45,Married,3,1,2,M,-0.06,272,100.01,1.17,-19.59,1,34804.11,407 +606,Kimberly Conley,18-25,Single,1,0,1,F,-0.06,460,74.93,1.0,-35.62,1,46457.67,620 +607,Thomas Daniels,70+,Single,1,0,4,M,-0.95,250,84.21,1.01,-572.05,22,50945.69,609 +608,Samantha Collier,26-35,Married,2,0,5,F,-0.8,588,86.75,1.08,-888.32,34,96815.64,1209 +609,Jasmine Flores,26-35,Married,2,0,1,F,-0.08,385,89.1,1.02,-44.52,1,49184.49,561 +610,Deborah Ayala,46-55,Single,1,0,5,F,-0.03,382,79.48,1.0,-13.36,1,38787.34,488 +611,Michael Taylor,36-45,Married,3,1,4,M,-0.3,497,106.51,1.0,-215.5,8,76263.55,716 +612,Michael Cohen,18-25,Single,1,0,1,M,0.0,570,74.19,1.01,0.0,0,76412.51,1041 +613,Christopher Hobbs,46-55,Married,2,0,5,M,-0.08,679,104.94,1.15,-105.08,5,143555.16,1577 +614,Annette Murray,36-45,Single,1,0,6,F,-0.05,554,85.56,1.02,-49.27,2,82480.33,982 +615,Michele Malone,36-45,Married,5,3,8,F,0.0,242,134.29,1.0,0.0,0,57342.02,427 +616,Joann Buchanan,46-55,Single,2,1,7,F,-0.07,548,122.78,1.0,-53.43,1,94296.18,768 +617,Madeline Gonzalez,56-70,Married,2,0,3,F,-0.05,256,83.52,1.0,-19.59,1,34157.68,410 +618,Susan French,26-35,Married,2,0,4,F,-0.34,316,102.95,1.0,-135.36,5,41282.86,401 +619,James Peterson,36-45,Single,2,1,4,M,-0.13,996,96.89,1.0,-258.24,6,196878.97,2032 +620,Melissa Barajas,26-35,Married,3,1,4,F,0.0,545,105.45,1.0,0.0,0,78873.61,748 +621,Michael Sanders,36-45,Married,2,0,5,M,-0.39,548,111.91,1.0,-391.64,14,111686.15,1001 +622,Jennifer Cruz,36-45,Married,4,2,5,F,-0.01,1125,110.54,1.01,-17.81,1,245076.25,2236 +623,Kristin Ward,46-55,Married,4,2,8,F,0.0,239,106.05,1.13,0.0,0,75509.0,804 +624,Jose Gordon,26-35,Married,5,3,11,M,-0.13,562,115.62,1.0,-108.64,3,93656.14,814 +625,Susan Humphrey,18-25,Married,2,0,4,F,0.0,394,125.07,1.0,0.0,0,58906.56,471 +626,Zachary Howard,36-45,Single,2,1,4,M,-2.0,1451,93.27,1.01,-5655.2,171,264033.32,2866 +627,Martin Saunders,46-55,Married,4,2,4,M,-1.11,1124,94.29,1.07,-3807.38,128,323317.55,3683 +628,Christopher Smith,46-55,Married,3,1,4,M,-1.13,572,96.96,1.0,-865.54,36,74269.49,766 +629,Jessica Silva DDS,36-45,Married,2,0,11,F,-0.48,184,133.44,1.0,-96.18,3,26820.66,201 +630,Zachary Reynolds,26-35,Single,1,0,4,M,0.0,237,110.15,1.0,0.0,0,44061.96,400 +631,Daniel Dudley,26-35,Married,2,0,5,M,-0.05,352,105.48,1.0,-19.59,1,44195.03,419 +632,Michael Wilson,46-55,Married,2,0,6,M,-2.58,842,103.43,1.08,-3639.12,143,145942.91,1525 +633,Victoria Pollard,26-35,Married,5,3,4,F,-0.18,789,93.28,1.01,-168.3,4,89358.24,969 +634,Jonathan Riley DDS,56-70,Married,5,3,5,M,-0.54,418,96.14,1.0,-336.6,12,59797.25,622 +635,Michelle Bradford,46-55,Single,2,1,7,F,-0.12,274,125.65,1.0,-35.62,1,38824.74,309 +636,Christopher Robinson,56-70,Married,2,0,3,M,-2.56,232,106.4,1.01,-979.55,11,40749.64,385 +637,Brandon Jones,46-55,Single,1,0,6,M,-0.04,587,100.91,1.0,-44.52,2,114635.43,1136 +638,Courtney Kelly,36-45,Single,1,0,4,F,-0.4,1048,99.13,1.03,-680.86,22,168912.76,1757 +639,Kathryn Robles,46-55,Married,2,0,7,F,-0.25,354,97.38,1.0,-113.09,3,43723.53,449 +640,Mr. Adrian Mitchell,46-55,Single,1,0,4,M,-0.09,388,84.91,1.01,-89.04,4,83556.33,996 +641,Ashley Austin,46-55,Single,1,0,5,F,-0.63,324,104.01,1.0,-330.68,18,54811.98,527 +642,Peter English,36-45,Single,1,0,3,M,-0.08,395,104.17,1.0,-44.52,1,55733.45,535 +643,Richard Walters,26-35,Single,1,0,2,M,0.0,281,112.55,1.17,0.0,0,58186.59,605 +644,Shelly Vargas,26-35,Married,3,1,9,F,-0.07,272,72.3,1.0,-44.52,3,46631.68,645 +645,Rebecca James,18-25,Married,2,0,6,F,0.0,358,75.54,1.0,0.0,0,35351.19,469 +646,Doris Harris,18-25,Married,2,0,4,F,0.0,313,75.59,1.1,0.0,0,34468.31,501 +647,Cassandra Benson,56-70,Married,2,0,1,F,-0.42,382,105.66,1.1,-215.5,7,54839.83,570 +648,Theresa Morris,36-45,Married,5,3,4,F,-0.04,461,84.46,1.05,-19.59,1,47041.67,586 +649,Diana Williams,46-55,Married,4,2,2,F,-0.23,816,95.31,1.02,-236.52,10,96068.62,1024 +650,Frank Ford,46-55,Single,1,0,5,M,-0.29,456,80.26,1.0,-309.89,15,86362.67,1080 +651,Martha Jackson,46-55,Single,2,1,4,F,0.0,327,125.57,1.01,0.0,0,50980.95,410 +652,Diana Baker,70+,Married,2,0,4,F,-0.26,567,87.17,1.0,-211.75,9,72006.39,826 +653,Phillip Williams,46-55,Married,5,3,4,M,-0.74,253,113.9,1.03,-258.77,9,39863.27,360 +654,Erica Bowers,46-55,Married,2,0,5,F,0.0,206,119.23,1.0,0.0,0,28853.33,242 +655,Janet Ramos,46-55,Married,2,0,3,F,-0.08,968,107.24,1.0,-131.44,2,180593.28,1692 +656,Connor Bradford,36-45,Married,5,3,5,M,-0.27,328,99.77,1.0,-124.67,2,46594.0,467 +657,Robert Reyes,26-35,Married,4,2,1,M,-0.75,734,108.96,1.02,-1037.77,36,150359.22,1403 +658,Diane Kent,46-55,Married,4,2,2,F,-0.06,614,80.16,1.0,-51.95,2,69581.0,868 +659,Timothy Casey,26-35,Single,1,0,3,M,-0.26,681,100.52,1.13,-562.44,22,213800.89,2399 +660,Maureen Robertson,36-45,Married,2,0,6,F,0.0,483,99.22,1.11,0.0,0,84335.94,940 +661,Gary Moore,26-35,Single,2,1,8,M,-0.49,426,126.28,1.01,-285.4,8,73875.36,589 +662,Dawn Galloway,26-35,Married,2,0,5,F,0.0,91,116.63,1.07,0.0,0,12829.01,118 +663,Tina Mckee,56-70,Single,1,0,1,F,0.0,320,60.09,1.0,0.0,0,33169.78,552 +664,Cheryl Rodriguez,26-35,Married,5,3,3,F,-0.24,599,126.76,1.0,-222.63,8,119664.5,944 +665,James Johnson,26-35,Single,1,0,7,M,-0.29,565,113.32,1.0,-382.91,5,150832.0,1331 +666,Tamara Hernandez,26-35,Single,1,0,4,F,-0.02,956,88.53,1.0,-35.62,1,132090.15,1492 +667,Kelli Larson,26-35,Married,2,0,6,F,-0.12,834,74.91,1.01,-175.13,7,112066.97,1512 +668,Steven Baker,26-35,Married,2,0,1,M,-0.02,636,83.88,1.12,-19.59,1,81361.82,1082 +669,Brian Smith,46-55,Single,2,1,3,M,0.0,442,111.0,1.05,0.0,0,75256.96,713 +670,Jesus Miranda,26-35,Married,2,0,5,M,-0.78,175,107.02,1.0,-170.98,5,23438.31,219 +671,Lindsay Hernandez DDS,70+,Married,2,0,4,F,-0.66,225,110.87,1.12,-201.26,9,34038.04,343 +672,Sandra Bailey,26-35,Married,2,0,5,F,0.0,448,76.59,1.18,0.0,0,52844.81,817 +673,Katelyn Rose,46-55,Single,2,1,4,F,-0.07,392,101.18,1.01,-35.62,1,50590.02,506 +674,Benjamin Smith MD,56-70,Married,2,0,7,M,0.0,259,94.62,1.01,0.0,0,55162.87,591 +675,Nicholas Harper,26-35,Married,2,0,5,M,0.0,199,89.9,1.01,0.0,0,29037.59,325 +676,Tiffany Hall,46-55,Married,2,0,5,F,0.0,589,116.33,1.0,0.0,0,121684.75,1046 +677,Amanda Robertson,36-45,Single,1,0,5,F,-0.03,322,96.53,1.0,-44.52,1,124625.38,1291 +678,Dana Thomas,46-55,Married,5,3,3,F,-0.06,500,89.47,1.0,-55.21,2,79184.72,889 +679,Mason Jenkins,36-45,Single,2,1,4,M,0.0,469,112.11,1.0,0.0,0,153370.43,1368 +680,Steven Quinn,26-35,Single,1,0,5,M,0.0,553,103.05,1.01,0.0,0,85221.47,834 +681,Diana Cooper,70+,Married,2,0,2,F,-0.26,245,80.7,1.0,-119.33,2,36962.32,458 +682,Richard Hernandez,56-70,Single,1,0,4,M,-1.04,529,102.49,1.0,-768.15,30,75845.95,740 +683,Jason King,36-45,Single,1,0,4,M,-0.11,747,96.32,1.0,-115.76,3,102962.41,1069 +684,Michael Bryant,36-45,Married,2,0,3,M,-0.81,391,81.81,1.0,-687.99,16,69129.66,845 +685,Philip Moore,36-45,Married,5,3,8,M,-0.04,346,91.68,1.04,-17.81,1,37314.36,422 +686,Jessica Rubio,26-35,Married,2,0,4,F,-0.05,355,111.84,1.0,-26.71,1,56031.01,501 +687,Anna Young,36-45,Married,2,0,5,F,-0.15,638,90.65,1.12,-146.04,5,86384.86,1068 +688,Carol Lambert,18-25,Single,2,1,1,F,-0.09,452,68.15,1.0,-56.99,2,41710.75,612 +689,Chelsea Chan,70+,Single,1,0,4,F,0.0,534,65.59,1.0,0.0,0,60082.37,917 +690,Gary Cruz,26-35,Married,2,0,5,M,-0.1,373,102.37,1.04,-55.21,2,54666.75,557 +691,Charles Barnes,46-55,Married,4,2,4,M,-0.09,712,103.09,1.02,-120.51,4,136494.81,1348 +692,Rebekah Sanchez,18-25,Single,1,0,5,F,-0.44,213,100.52,1.02,-117.54,6,26738.95,271 +693,Bryan Schultz,26-35,Single,2,1,4,M,-0.11,255,131.04,1.0,-35.62,1,44421.42,339 +694,Kara Davis,26-35,Married,3,1,8,F,-0.21,679,98.81,1.16,-229.74,11,110662.8,1297 +695,Jesus Christensen,56-70,Married,2,0,6,M,-0.27,1028,118.96,1.0,-479.08,25,208425.62,1755 +696,Alan Martinez,46-55,Single,1,0,4,M,0.0,297,58.15,1.01,0.0,0,34250.6,593 +697,Tiffany Rosales,36-45,Single,1,0,3,F,-0.21,176,127.11,1.0,-47.19,3,28599.08,225 +698,Lauren Moody,26-35,Married,3,1,4,F,-0.03,277,87.35,1.0,-17.81,1,51798.23,593 +699,Desiree Johnson,36-45,Married,2,0,3,F,-0.03,830,88.57,1.04,-35.62,1,102567.0,1207 +700,Nancy Rose,36-45,Married,3,1,11,F,-0.47,475,88.45,1.08,-280.8,8,52981.27,647 +701,Derrick Cox DVM,56-70,Married,2,0,4,M,0.0,147,64.82,1.01,0.0,0,26901.54,420 +702,Justin Navarro,46-55,Single,1,0,5,M,-0.03,396,87.51,1.03,-17.81,1,46553.03,546 +703,Mary Davis,46-55,Married,2,0,6,F,-0.06,587,112.37,1.18,-54.85,2,106076.88,1110 +704,Veronica Johnson,46-55,Married,5,3,6,F,-0.38,689,101.25,1.01,-362.43,12,97601.59,969 +705,Kelly Ross,36-45,Single,1,0,4,M,-0.03,780,131.26,1.09,-35.62,1,152134.06,1258 +706,Rebecca Fowler,56-70,Single,1,0,4,F,-1.81,739,96.58,1.05,-3284.92,115,175388.58,1903 +707,Nancy Nichols,18-25,Single,2,1,5,F,-0.19,305,119.5,1.0,-68.86,3,43618.18,365 +708,Rachel Burke,36-45,Married,3,1,5,F,-0.1,692,100.77,1.08,-124.67,3,124646.34,1336 +709,Dylan Rodriguez,70+,Married,2,0,3,M,-0.05,504,111.81,1.0,-35.61,2,87096.93,779 +710,Amy Vega,36-45,Married,2,0,6,F,-10.93,472,184.86,1.0,-6370.77,118,107773.53,583 +711,Jonathan Williams,26-35,Single,1,0,9,M,-0.03,1250,85.77,1.0,-113.0,3,357911.09,4173 +712,Melissa Cruz,46-55,Married,3,1,5,F,-1.01,554,111.9,1.01,-774.44,47,85824.73,775 +713,Paul Atkinson,46-55,Married,3,1,5,M,-0.08,349,98.38,1.0,-41.22,3,52045.25,530 +714,Aaron Cline,46-55,Single,1,0,3,M,-0.12,507,108.52,1.06,-89.05,1,80844.19,788 +715,Anthony Murphy,36-45,Married,2,0,9,M,-0.27,462,89.92,1.01,-171.86,9,57820.55,647 +716,Samantha Owen,36-45,Single,4,2,1,F,-0.09,368,98.44,1.01,-44.52,1,46364.86,475 +717,Jennifer Herrera,36-45,Single,1,0,2,F,-0.35,434,96.66,1.0,-181.66,5,50652.44,524 +718,Ashley Acosta,36-45,Married,2,0,11,F,-0.06,266,79.15,1.02,-26.71,1,36804.45,472 +719,Kari Smith,56-70,Married,2,0,2,F,-0.15,377,139.67,1.0,-89.05,3,82544.52,591 +720,Ryan Peters,18-25,Married,2,0,6,M,-0.05,425,77.9,1.0,-26.71,2,44950.41,577 +721,Sharon Mercado,26-35,Married,2,0,5,F,0.0,150,101.94,1.01,0.0,0,16105.89,160 +722,Jason Allison,26-35,Single,1,0,4,M,-0.09,370,107.05,1.0,-44.52,1,54169.35,506 +723,Jacob Ramos,46-55,Single,2,1,3,M,-0.49,233,111.76,1.0,-133.57,2,30399.81,272 +724,Jacob Hines,26-35,Married,3,1,6,M,-0.14,488,92.06,1.01,-89.05,1,56709.17,621 +725,Patricia Lamb,56-70,Married,3,1,3,F,-0.86,1011,94.42,1.0,-1155.86,26,127371.68,1350 +726,Robert Mercer,46-55,Single,3,1,5,M,-0.13,393,91.73,1.0,-71.24,3,48890.55,533 +727,Amy Kennedy,46-55,Single,1,0,5,F,-0.07,371,92.02,1.0,-35.62,1,45823.85,498 +728,Sarah Richards,46-55,Married,5,3,9,F,0.0,160,91.04,1.02,0.0,0,17389.29,194 +729,Terry Smith,46-55,Single,1,0,3,M,-0.45,725,107.27,1.0,-434.21,6,103514.3,965 +730,Kristen Andrews,26-35,Single,1,0,4,F,-0.16,222,106.41,1.0,-41.86,2,28198.48,265 +731,Mary Mcclure,70+,Single,1,0,4,F,0.0,111,126.15,1.02,0.0,0,41378.18,334 +732,Valerie Russell,36-45,Married,2,0,2,F,0.0,266,94.43,1.0,0.0,0,75261.24,797 +733,Jacob Bailey,70+,Single,1,0,4,M,-0.45,575,134.25,1.0,-567.24,10,169155.74,1260 +734,Misty Mason,36-45,Married,2,0,5,F,0.0,158,81.19,1.0,0.0,0,15425.81,190 +735,Jeffery Perkins,46-55,Married,2,0,5,M,-0.37,948,115.96,1.06,-860.18,29,272030.9,2476 +736,Elizabeth Daugherty,56-70,Married,2,0,4,F,-0.13,493,106.54,1.0,-89.05,1,72873.69,684 +737,Elijah Vasquez,36-45,Single,4,3,4,M,-0.81,688,87.17,1.02,-1308.58,57,140174.48,1637 +738,Christopher Howard,46-55,Single,1,0,1,M,0.0,261,129.45,1.0,0.0,0,51261.16,396 +739,Erik Norris,36-45,Married,5,3,7,M,0.0,180,90.46,1.0,0.0,0,24786.19,274 +740,Douglas Martin,46-55,Married,2,0,4,M,-0.04,425,88.39,1.0,-35.62,2,70708.9,800 +741,Yvonne Peterson,18-25,Single,1,0,3,F,0.0,406,130.87,1.0,0.0,0,75512.23,577 +742,Melissa Bailey,46-55,Married,2,0,2,F,-0.11,418,81.24,1.02,-75.69,3,54429.55,684 +743,Cathy Davis,46-55,Married,2,0,2,F,-0.04,523,102.27,1.07,-39.18,2,90104.05,939 +744,Kimberly Turner,70+,Single,1,0,4,F,0.0,599,77.34,1.01,0.0,0,77029.5,1004 +745,Holly Robinson MD,36-45,Single,1,0,1,F,-0.07,724,107.42,1.08,-64.12,3,104413.19,1049 +746,Jamie Jensen,26-35,Single,1,0,2,F,0.0,387,111.44,1.0,0.0,0,90151.59,809 +747,Tyler Payne,26-35,Single,1,0,5,M,0.0,204,123.39,1.04,0.0,0,32575.83,274 +748,Heather Reed,46-55,Married,3,1,8,F,-4.25,1351,105.32,1.11,-9978.66,345,247401.42,2604 +749,Laura Hart,36-45,Married,4,2,5,F,-0.11,747,111.81,1.03,-126.45,4,131715.45,1216 +750,Christopher Pena,18-25,Married,2,0,2,M,-0.11,549,96.67,1.0,-142.47,5,121128.2,1255 +751,Laura Clark,26-35,Single,1,0,5,F,-0.02,540,137.1,1.0,-10.69,1,91585.37,668 +752,Eric Riddle,26-35,Married,5,3,1,M,0.0,447,77.0,1.0,0.0,0,44891.67,583 +753,Kyle Gonzalez,26-35,Married,2,0,5,M,-0.2,504,121.8,1.12,-222.61,10,132393.74,1218 +754,Jenny Hernandez,46-55,Single,1,0,2,F,-0.32,539,115.13,1.06,-281.04,5,99705.4,922 +755,Tina Warren,36-45,Married,2,0,5,F,-0.74,869,111.18,1.03,-1000.2,12,150320.47,1397 +756,Cheryl Mitchell,56-70,Married,2,0,7,F,0.0,552,90.39,1.1,0.0,0,77460.73,946 +757,Eric Washington,46-55,Married,4,2,5,M,-0.37,526,85.89,1.01,-240.43,10,55745.61,654 +758,Valerie Moreno,70+,Single,1,0,4,F,0.0,363,88.68,1.03,0.0,0,42210.37,489 +759,John Watson,46-55,Married,2,0,6,M,0.0,141,108.3,1.01,0.0,0,16677.6,155 +760,Taylor Potter,46-55,Married,5,3,5,F,-0.04,589,127.55,1.03,-29.38,2,91327.67,739 +761,Sarah Smith,36-45,Single,1,0,4,F,-0.41,404,88.25,1.0,-277.48,1,60187.29,682 +762,Vickie Lang,46-55,Single,1,0,4,F,-0.09,614,66.13,1.0,-89.05,4,66458.97,1008 +763,Patrick Kelley,26-35,Married,2,0,4,M,-0.31,543,88.74,1.06,-224.4,8,64604.76,775 +764,Lindsay Carrillo,18-25,Single,2,1,1,F,0.0,263,118.98,1.0,0.0,0,50092.04,421 +765,Tracy Heath,46-55,Married,2,0,5,F,-0.42,628,98.05,1.0,-501.88,11,116489.21,1190 +766,Stephen Scott,36-45,Married,2,0,9,M,-2.09,670,114.96,1.04,-2191.29,77,120588.07,1096 +767,Linda Fisher,26-35,Married,3,1,6,F,-0.37,630,110.97,1.02,-429.27,18,129505.2,1188 +768,Lori Smith,36-45,Married,5,3,5,F,-0.51,315,106.85,1.0,-195.91,4,41031.18,384 +769,Brandi Castro,46-55,Married,2,0,5,F,-0.12,924,96.91,1.0,-192.35,9,149632.39,1544 +770,Steven Fitzgerald,36-45,Single,1,0,4,M,-1.15,139,84.09,1.0,-246.37,11,17994.69,214 +771,Donald Howell,36-45,Single,1,0,2,M,-0.14,342,150.63,1.11,-122.44,5,131200.37,964 +772,Cindy Chavez,26-35,Single,1,0,4,F,-0.3,895,96.4,1.0,-415.69,9,135242.3,1403 +773,Christopher Drake,36-45,Single,1,0,5,M,-0.17,358,87.14,1.03,-80.14,2,41739.81,491 +774,Richard Moore,46-55,Married,4,2,4,M,-1.02,466,93.9,1.06,-675.81,35,62348.58,702 +775,Christine Moore,18-25,Married,3,1,2,F,0.0,138,89.25,1.0,0.0,0,14904.12,167 +776,Angelica Perez,46-55,Single,1,0,2,F,-0.65,751,101.45,1.0,-805.74,26,125901.73,1241 +777,Patricia Brown,56-70,Married,2,0,2,F,-0.13,226,104.75,1.0,-35.62,2,29645.39,283 +778,Julie Nelson,46-55,Single,1,0,4,F,-0.03,453,70.9,1.0,-17.81,1,48427.54,683 +779,Alicia Walton,26-35,Single,3,2,4,F,-0.15,792,89.24,1.0,-177.2,8,107708.81,1207 +780,Edward Carlson,26-35,Single,1,0,7,M,-0.16,595,112.41,1.02,-139.8,4,96110.04,869 +781,Leslie Mcclain,46-55,Single,1,0,7,F,-2.3,963,131.12,1.09,-4447.69,34,253071.12,2102 +782,Alex Ball,56-70,Married,2,0,9,M,-0.26,1228,114.54,1.1,-604.91,32,261714.49,2515 +783,Jason Lucas,46-55,Married,2,0,6,M,-0.27,311,83.01,1.07,-118.7,3,36359.97,470 +784,Mitchell Williams,46-55,Single,2,1,4,M,-0.09,705,120.89,1.0,-92.61,4,118476.56,980 +785,Hannah Weiss,36-45,Married,3,1,5,F,0.0,494,89.77,1.0,0.0,0,93813.25,1045 +786,Allison Buckley,26-35,Single,2,1,8,F,-0.18,147,111.46,1.0,-35.62,2,22402.46,201 +787,Nicole Burton,46-55,Married,4,2,1,F,-1.13,669,93.04,1.0,-1312.56,63,107738.87,1158 +788,Timothy Hodges Jr.,36-45,Single,1,0,3,M,0.0,313,98.38,1.0,0.0,0,47812.52,486 +789,James Pena,46-55,Married,2,0,1,M,-0.12,615,122.73,1.04,-147.28,2,156726.52,1324 +790,Cathy Krueger,46-55,Married,2,0,5,F,-0.13,460,98.12,1.03,-90.83,2,67702.06,711 +791,Danielle Ramos,46-55,Single,1,0,6,F,-0.12,535,122.15,1.03,-97.95,3,98330.51,833 +792,Jennifer Silva,36-45,Married,2,0,4,F,-0.31,560,92.03,1.0,-398.23,6,116423.28,1270 +793,George Holder,46-55,Single,2,1,5,M,-0.45,869,118.12,1.02,-771.88,25,203161.94,1758 +794,Justin Dean,46-55,Single,1,0,5,M,0.0,66,125.97,1.02,0.0,0,11085.63,90 +795,Samantha Carter,46-55,Single,2,1,5,F,-0.22,429,110.61,1.04,-178.1,5,90919.14,852 +796,Thomas Wilson,46-55,Married,3,1,5,M,0.0,199,105.37,1.02,0.0,0,24868.24,241 +797,Victor Watson,46-55,Single,1,0,6,M,-0.13,288,110.31,1.01,-82.37,4,69051.57,633 +798,Joseph Rodriguez,46-55,Single,1,0,8,M,0.0,108,89.43,1.0,0.0,0,20925.79,234 +799,Daniel Moreno,70+,Single,1,0,5,M,0.0,892,112.95,1.0,0.0,0,226583.11,2009 +800,Jeremiah Holland,46-55,Married,2,0,3,M,-0.07,504,83.58,1.02,-65.89,3,80736.72,982 +801,Taylor Hernandez,26-35,Single,1,0,2,M,-0.24,672,122.33,1.0,-255.04,6,129787.75,1061 +802,Michael Davis,46-55,Single,1,0,2,M,-1.78,776,132.78,1.02,-1908.34,26,142212.22,1097 +803,Joy Chambers,18-25,Married,3,1,4,F,-1.43,266,93.23,1.0,-530.06,17,34493.43,370 +804,Austin Anderson,26-35,Married,3,1,2,M,0.0,180,108.61,1.0,0.0,0,23568.7,217 +805,Stephanie Johnson,46-55,Married,2,0,1,F,-0.03,499,84.52,1.0,-17.81,1,53753.48,636 +806,Tony Torres,46-55,Single,1,0,2,M,0.0,312,123.75,1.0,0.0,0,47519.57,384 +807,Jonathan Morris,46-55,Married,2,0,7,M,0.0,472,89.92,1.0,0.0,0,76071.37,846 +808,Brent Morgan,70+,Married,2,0,4,M,-0.21,1006,106.18,1.0,-402.51,20,203329.53,1918 +809,Valerie Collins,46-55,Single,1,0,2,F,-0.27,782,107.71,1.01,-321.17,8,127525.97,1193 +810,Lawrence Higgins,46-55,Single,1,0,5,M,0.0,730,79.89,1.0,0.0,0,104341.63,1309 +811,Monica Wilson,56-70,Single,1,0,7,F,-0.04,720,85.68,1.0,-53.42,3,113015.13,1319 +812,George Moore,46-55,Single,1,0,6,M,0.0,232,75.14,1.0,0.0,0,27125.63,361 +813,Paul Taylor,26-35,Single,3,2,3,M,-0.54,540,77.04,1.01,-661.93,25,94608.88,1236 +814,Rebecca Reyes,26-35,Married,5,3,3,F,-0.49,413,80.34,1.0,-384.69,19,62507.54,778 +815,Morgan Blackburn,46-55,Married,2,0,3,F,0.0,318,107.31,1.01,0.0,0,39384.08,371 +816,John Morris,26-35,Single,1,0,3,M,-0.04,237,68.26,1.0,-17.81,1,33651.44,493 +817,Jerome Jones,56-70,Married,2,0,4,M,-0.31,428,90.29,1.0,-238.35,10,68620.59,760 +818,Evan West,36-45,Married,5,3,3,M,0.0,72,109.58,1.0,0.0,0,12820.65,117 +819,James Smith,46-55,Married,3,1,6,M,0.0,408,55.12,1.0,0.0,0,45748.81,830 +820,John Valenzuela,36-45,Married,2,0,8,M,-0.48,687,100.49,1.0,-460.9,20,96469.66,960 +821,Melanie Barajas,70+,Married,2,0,3,F,-0.34,219,105.61,1.0,-140.7,6,44355.01,420 +822,Mrs. Amy Owens,36-45,Single,1,0,6,F,0.0,190,103.16,1.03,0.0,0,24654.29,246 +823,Matthew West,36-45,Married,4,2,2,M,-1.98,876,94.13,1.05,-2711.34,109,128678.87,1431 +824,Rose Garcia,46-55,Married,4,2,5,F,-1.13,224,96.22,1.05,-316.66,7,27037.33,296 +825,Eric Johnston Jr.,36-45,Married,5,3,6,M,-0.44,350,104.03,1.0,-199.12,2,47435.49,456 +826,Ryan Miller Jr.,26-35,Single,1,0,4,M,-0.42,738,117.73,1.09,-402.59,6,111726.39,1032 +827,Sylvia Rollins,26-35,Married,2,0,5,F,-0.24,120,95.3,1.0,-39.18,2,15724.61,165 +828,Amanda Johnson,18-25,Single,2,1,1,F,-0.05,651,102.33,1.04,-85.49,4,161985.41,1652 +829,Jonathan Turner,46-55,Single,1,0,1,M,-0.07,522,81.39,1.0,-47.73,1,56239.26,692 +830,Morgan Buck,46-55,Single,1,0,2,F,0.0,195,97.9,1.05,0.0,0,32501.34,349 +831,Andrew Mason,70+,Married,2,0,3,M,-1.42,769,110.65,1.04,-1515.45,73,117734.98,1108 +832,Kristen Monroe,36-45,Single,1,0,5,F,0.0,1297,132.23,1.15,0.0,0,248591.42,2155 +833,Natalie Martinez,56-70,Married,2,0,4,F,-0.27,473,109.55,1.0,-191.45,8,77671.08,709 +834,Charles Obrien,36-45,Single,1,0,3,M,-0.25,617,93.14,1.0,-260.02,5,98547.08,1058 +835,Helen Le,70+,Married,2,0,5,F,-0.22,510,118.42,1.01,-195.9,7,105159.28,899 +836,Benjamin Jones,36-45,Married,4,2,5,M,-1.0,191,104.74,1.0,-213.72,7,22413.41,214 +837,Brittney Harris,18-25,Married,2,0,4,F,-0.19,242,70.6,1.0,-89.05,1,32476.96,460 +838,Christina Zimmerman,46-55,Single,1,0,5,F,0.0,291,118.1,1.0,0.0,0,43341.81,367 +839,Elizabeth Tyler,46-55,Single,1,0,1,F,-0.14,763,97.32,1.05,-170.08,7,116097.68,1254 +840,Donald Morrow,70+,Married,2,0,6,M,-0.21,291,112.01,1.04,-79.78,2,43570.55,404 +841,Kendra Johnson,36-45,Married,2,0,6,F,0.0,277,99.12,1.0,0.0,0,42523.41,429 +842,John Conner,46-55,Married,4,2,8,M,-0.35,848,114.42,1.0,-504.72,13,164764.47,1440 +843,James Allen,46-55,Single,1,0,4,M,-0.77,593,95.26,1.0,-866.39,16,107260.66,1126 +844,Brittany Velasquez,46-55,Married,4,2,5,F,-0.41,1390,97.2,1.05,-997.72,31,233863.71,2529 +845,Mitchell Horton,56-70,Married,2,0,7,M,-4.11,303,132.04,1.0,-1847.19,53,59286.15,449 +846,Tracy Armstrong,36-45,Single,2,1,6,F,-0.06,240,115.04,1.0,-17.81,1,34512.94,300 +847,Sarah Williams,26-35,Single,2,1,4,F,-0.44,273,136.8,1.04,-151.38,4,47058.62,359 +848,Benjamin Fowler,26-35,Single,3,2,1,M,-0.35,554,95.97,1.01,-432.37,10,117663.97,1243 +849,Kaitlyn Combs,46-55,Single,1,0,5,F,0.0,385,74.2,1.0,0.0,0,37990.91,514 +850,Kyle Willis,46-55,Married,2,0,5,M,-0.38,64,102.92,1.0,-39.18,2,10497.35,102 +851,Brian Mitchell,36-45,Married,2,0,6,M,-0.06,526,101.69,1.1,-55.21,2,98641.03,1064 +852,Doris Adams,56-70,Married,2,0,6,F,-0.15,676,84.58,1.0,-174.54,4,97355.72,1155 +853,Brittany Steele,70+,Single,2,1,2,F,-0.44,352,102.48,1.03,-195.91,6,45708.2,458 +854,Karen Hopkins,46-55,Married,5,3,8,F,-0.03,287,97.98,1.0,-19.59,1,55260.91,564 +855,Jessica Martinez,56-70,Married,2,0,3,F,-2.02,140,114.3,1.06,-688.2,24,38976.03,360 +856,Anne Smith,26-35,Single,2,1,2,F,-0.18,375,110.42,1.0,-106.86,3,66580.55,604 +857,Eric Glenn,70+,Single,1,0,7,M,-2.47,374,115.63,1.0,-1162.1,26,54462.72,471 +858,Jonathan Wilson,70+,Single,1,0,7,M,0.0,542,85.84,1.0,0.0,0,84900.08,992 +859,Daniel Bradley,36-45,Married,3,1,6,M,-0.48,1302,105.54,1.08,-970.35,37,212666.08,2185 +860,Thomas Richardson,26-35,Single,1,0,1,M,0.0,238,117.58,1.0,0.0,0,60200.42,512 +861,Mr. Craig Bennett,70+,Married,2,0,3,M,-0.06,502,95.16,1.0,-80.14,2,126367.93,1328 +862,Kelsey Marsh,46-55,Married,2,0,6,F,0.0,379,114.35,1.0,0.0,0,63809.23,558 +863,Sarah Herman,56-70,Married,2,0,4,F,-0.12,656,102.14,1.0,-106.5,1,94068.78,924 +864,Joseph Hester,56-70,Single,1,0,5,M,-1.96,1067,103.86,1.13,-3471.48,156,184358.5,1999 +865,Regina Thomas,26-35,Single,1,0,7,F,-0.04,597,94.3,1.01,-71.24,2,156728.99,1674 +866,Andrew Jackson,36-45,Married,4,2,2,M,-0.09,995,93.82,1.1,-190.56,7,209780.88,2457 +867,Michael Johnson,46-55,Married,2,0,5,M,-0.04,378,125.51,1.0,-25.83,3,83837.97,668 +868,Sean Mcclain,36-45,Single,1,0,5,M,0.0,150,125.05,1.0,0.0,0,29511.05,236 +869,Teresa Nelson,56-70,Married,2,0,4,F,-0.02,589,104.84,1.0,-17.81,1,112181.99,1070 +870,Paul Lowe,18-25,Married,2,0,4,M,0.0,189,110.21,1.0,0.0,0,40004.58,363 +871,Tammy Hall,36-45,Married,3,1,7,F,-0.58,759,105.69,1.06,-840.32,33,153353.36,1544 +872,Dawn Cooper,70+,Married,2,0,7,F,-0.18,262,124.73,1.0,-63.22,2,44029.58,353 +873,Joseph Lambert,56-70,Married,4,2,6,M,-0.15,629,95.35,1.0,-123.78,7,78757.29,826 +874,Jennifer Hernandez,46-55,Married,2,0,5,F,-0.34,954,106.8,1.11,-481.15,31,149627.4,1559 +875,Denise Heath,46-55,Married,2,0,2,F,-0.98,351,93.68,1.04,-532.58,20,50682.96,565 +876,Michael Pacheco,36-45,Single,1,0,9,M,-0.28,426,129.11,1.0,-208.37,10,95281.85,738 +877,Dr. Kevin Gordon MD,46-55,Married,2,0,5,M,-0.46,374,113.12,1.0,-322.17,11,79185.16,700 +878,Jason Mejia,70+,Married,2,0,8,M,-0.72,1058,95.77,1.02,-1202.65,29,160122.06,1699 +879,Stephanie Reynolds,46-55,Single,1,0,4,F,-0.65,436,99.57,1.0,-732.52,37,111519.6,1123 +880,Sara Campbell,56-70,Single,1,0,2,F,-0.02,326,93.65,1.02,-8.9,1,52071.05,569 +881,Thomas Miller,46-55,Single,1,0,5,M,-1.18,293,131.3,1.21,-642.35,15,71429.55,658 +882,Randall Green,26-35,Married,3,1,9,M,-0.36,267,133.39,1.01,-108.64,3,39750.28,300 +883,Tara Olson,46-55,Single,1,0,3,F,-0.11,443,124.36,1.06,-56.99,2,65167.05,558 +884,Kenneth Taylor,26-35,Single,1,0,3,M,0.0,186,92.88,1.0,0.0,0,28234.27,304 +885,Lori Lowery,46-55,Single,1,0,3,F,0.0,500,118.05,1.0,0.0,0,101880.84,863 +886,Andres Cruz,46-55,Married,3,1,5,M,-0.16,339,102.6,1.0,-124.66,6,81154.34,791 +887,Kimberly Garcia,46-55,Single,1,0,5,F,-0.1,264,80.6,1.08,-42.21,2,32400.13,436 +888,Laurie Johnson,26-35,Married,3,1,4,F,-0.09,359,128.09,1.01,-42.74,3,60586.7,476 +889,Margaret Shah,36-45,Single,1,0,4,F,-1.84,891,93.37,1.01,-3207.64,126,162925.68,1764 +890,Emily Powell,26-35,Married,4,2,8,F,-0.25,824,112.7,1.0,-253.61,9,114394.96,1020 +891,Brandon Martinez,36-45,Married,2,0,5,M,-0.53,485,96.06,1.11,-538.74,19,96731.56,1115 +892,Brenda Gray,46-55,Single,1,0,2,F,-0.09,143,84.5,1.01,-26.71,1,24675.29,295 +893,Justin Wu,46-55,Married,5,3,8,M,0.0,94,97.72,1.0,0.0,0,10651.21,109 +894,Justin Day,18-25,Single,3,1,5,M,-1.19,546,104.33,1.0,-1161.2,42,101516.53,973 +895,Chelsey Campbell,46-55,Married,2,0,12,F,-0.08,312,100.73,1.02,-35.62,1,44322.64,447 +896,Cynthia Johnson,56-70,Married,2,0,4,F,-0.59,617,93.43,1.05,-472.49,11,74557.7,841 +897,Cindy Suarez,46-55,Single,1,0,4,F,-4.44,97,94.12,1.0,-639.36,32,13553.21,144 +898,Robert Murphy,36-45,Married,3,1,4,M,-0.36,1318,107.13,1.11,-1162.08,27,347968.46,3593 +899,Laura Reynolds,46-55,Married,3,1,2,F,0.0,273,114.94,1.0,0.0,0,57700.91,502 +900,Kerri Williams,56-70,Single,1,0,9,F,-0.02,748,96.29,1.0,-17.81,1,102168.5,1061 +901,James Friedman,26-35,Married,3,1,2,M,-1.79,979,89.81,1.0,-2661.66,107,133364.64,1485 +902,Alexandra Davis,46-55,Single,1,0,1,F,0.0,282,121.21,1.0,0.0,0,41090.53,339 +903,Marcus Scott,46-55,Married,3,1,3,M,-1.1,295,89.66,1.07,-432.78,16,35145.08,421 +904,Johnny Murphy,36-45,Married,2,0,9,M,0.0,169,126.9,1.01,0.0,0,46573.42,370 +905,Angel Prince,36-45,Married,2,0,1,F,-0.37,535,125.81,1.0,-293.86,5,99892.69,794 +906,Wendy White,46-55,Married,3,1,6,F,-2.49,327,103.62,1.01,-1326.95,40,55124.42,539 +907,Melissa Williams,46-55,Married,5,3,9,F,-1.89,71,127.91,1.0,-151.38,3,10232.46,80 +908,John White,56-70,Married,2,0,3,M,0.0,105,154.14,1.0,0.0,0,31906.65,208 +909,Michael Campbell,46-55,Married,5,3,8,M,-3.35,685,110.45,1.09,-3107.09,99,102383.61,1013 +910,Denise Nunez,46-55,Married,3,1,3,F,0.0,260,44.91,1.01,0.0,0,32563.23,730 +911,Richard Miller,46-55,Married,3,1,8,M,-0.38,2073,117.1,1.0,-1352.4,49,421577.36,3617 +912,James Brown,70+,Married,2,0,6,M,-1.11,356,102.56,1.07,-625.89,19,57841.33,604 +913,Julie Turner,18-25,Married,2,0,11,F,0.0,347,95.09,1.0,0.0,0,46595.02,490 +914,Matthew Lee,26-35,Single,1,0,9,M,-0.83,408,93.8,1.04,-438.29,16,49242.72,546 +915,Jenna Hawkins,46-55,Single,1,0,3,F,-0.03,505,113.18,1.0,-17.81,1,80017.64,707 +916,Robert Campos,56-70,Single,1,0,4,M,-1.05,439,116.73,1.03,-585.04,23,65019.74,574 +917,Erin Sanchez,36-45,Married,2,0,3,F,-0.08,568,84.12,1.08,-71.24,2,79657.15,1026 +918,Jeremiah Snyder,26-35,Married,2,0,5,M,-0.21,610,95.39,1.01,-216.93,5,97774.06,1032 +919,Sara Romero,36-45,Married,4,2,5,F,-4.4,360,108.19,1.14,-2554.65,13,62752.55,661 +920,Kristin Keller,18-25,Single,1,0,12,F,-0.13,414,105.12,1.01,-71.24,2,58759.78,564 +921,Sherri Lee,36-45,Married,4,2,5,F,-0.07,211,61.15,1.01,-35.62,1,31918.77,527 +922,Rebecca Salazar MD,70+,Married,2,0,6,F,-0.02,524,113.54,1.04,-17.81,1,101727.53,936 +923,Sharon Bean,46-55,Single,1,0,4,F,-0.12,255,108.41,1.05,-35.62,1,31981.1,309 +924,Tina Williams,36-45,Married,2,0,10,F,-0.02,627,110.45,1.0,-17.81,1,91120.86,825 +925,Samantha Tucker,36-45,Single,1,0,5,F,0.0,276,106.91,1.0,0.0,0,46933.63,439 +926,Richard Austin,26-35,Single,1,0,5,M,-0.2,709,110.94,1.06,-403.02,16,220207.61,2099 +927,Luis Mathis,46-55,Single,2,1,5,M,0.0,560,122.39,1.2,0.0,0,144907.44,1415 +928,Heather Rodriguez,46-55,Single,1,0,2,F,-0.4,1041,92.04,1.01,-768.66,22,177089.11,1945 +929,Lauren Alexander,18-25,Single,1,0,4,F,-0.14,460,87.81,1.03,-84.58,4,51366.98,600 +930,Kaitlyn Castro,18-25,Single,1,0,1,F,-0.06,775,84.57,1.03,-70.52,2,92180.87,1118 +931,Mark Davis,18-25,Married,2,0,4,M,-1.26,559,89.63,1.0,-933.83,56,66324.68,740 +932,Sarah Blevins,36-45,Single,1,0,5,F,-0.64,232,154.04,1.0,-195.73,6,47291.58,307 +933,Amy Nicholson,26-35,Married,2,0,5,F,-1.08,477,94.47,1.01,-715.35,17,62445.4,665 +934,Gabrielle Giles,46-55,Single,1,0,6,F,-0.13,1368,95.98,1.04,-390.93,13,300022.11,3253 +935,Jason Garcia,26-35,Single,2,1,1,M,-0.11,547,116.61,1.07,-89.05,1,97018.23,888 +936,Joseph Moore,70+,Married,2,0,4,M,-0.06,476,111.19,1.0,-60.55,3,114863.9,1033 +937,Allison Nixon,70+,Married,2,0,2,F,-0.35,223,91.99,1.0,-89.05,1,23641.03,257 +938,Brett Williams,56-70,Married,4,2,7,M,-0.42,157,108.0,1.0,-77.47,3,20087.37,186 +939,Raymond Lin,70+,Single,1,0,3,M,0.0,396,80.29,1.0,0.0,0,53716.44,669 +940,Pamela Stephens,18-25,Married,3,1,4,F,0.0,363,58.63,1.0,0.0,0,35940.71,613 +941,Thomas Sanders,56-70,Married,2,0,2,M,-0.45,224,100.69,1.01,-147.67,6,33127.65,332 +942,Christopher Torres,70+,Married,3,1,8,M,-0.03,655,109.57,1.13,-35.62,1,132905.05,1367 +943,Robert Ford,46-55,Married,2,0,5,M,-0.14,419,107.26,1.0,-81.92,3,61569.54,574 +944,Laura Bates,46-55,Single,1,0,3,F,-0.32,424,98.08,1.0,-276.06,10,83862.28,855 +945,Brianna Wilson,70+,Single,1,0,4,F,0.0,263,84.43,1.0,0.0,0,52937.33,627 +946,Samantha Pierce,46-55,Single,1,0,7,F,-1.05,150,118.43,1.12,-225.15,9,25461.89,240 +947,Catherine Stewart,46-55,Married,2,0,5,F,-0.17,478,131.23,1.13,-163.25,3,124147.03,1066 +948,Gary Wilson,46-55,Married,2,0,5,M,-0.11,962,66.6,1.03,-270.71,7,164044.66,2525 +949,Jerry Phillips,26-35,Single,1,0,4,M,-0.08,455,103.58,1.0,-64.11,2,78517.34,758 +950,Shelly Johnson,36-45,Married,5,3,4,F,-0.28,284,90.85,1.11,-89.05,3,29345.69,359 +951,Ashley Romero,46-55,Single,1,0,5,F,-0.46,715,115.26,1.0,-455.04,22,113989.02,989 +952,Carl Wagner,46-55,Married,4,2,5,M,-0.52,519,99.34,1.01,-365.63,7,69540.68,709 +953,Alison Stephens,46-55,Single,1,0,2,F,0.0,205,114.12,1.0,0.0,0,26817.69,236 +954,Todd Romero,26-35,Married,4,2,5,M,-0.21,439,79.89,1.22,-130.62,8,49054.31,751 +955,Lindsey George,46-55,Married,2,0,4,F,-2.96,340,105.35,1.07,-1497.65,48,53305.19,540 +956,Jessica Fernandez,46-55,Single,1,0,1,F,0.0,484,79.5,1.0,0.0,0,66622.78,838 +957,Ronald Wilson,26-35,Married,2,0,5,M,-0.06,1104,112.72,1.13,-142.48,3,249459.6,2498 +958,Edward Pratt,36-45,Single,1,0,8,M,-0.1,1146,96.71,1.03,-159.75,3,153960.61,1645 +959,Dylan Delgado,46-55,Single,1,0,5,M,-1.31,851,101.66,1.07,-2296.33,87,177907.36,1864 +960,Alfred Mitchell,46-55,Married,2,0,7,M,0.0,107,113.54,1.05,0.0,0,17938.64,166 +961,Rebecca Hensley,46-55,Married,5,3,6,F,-0.52,230,119.51,1.08,-188.73,8,43143.99,391 +962,Wesley Chavez,26-35,Married,3,1,6,M,-0.68,585,102.02,1.09,-925.42,42,138740.74,1489 +963,John Gordon,18-25,Single,1,0,4,M,-0.03,808,91.69,1.01,-53.43,2,141024.46,1547 +964,Megan Pierce,46-55,Single,1,0,5,F,-0.28,785,114.83,1.07,-329.12,10,133207.0,1245 +965,Joseph Moreno,18-25,Married,3,1,4,M,0.0,423,91.54,1.05,0.0,0,50162.64,578 +966,Joel Turner,46-55,Married,4,2,2,M,-0.02,535,77.83,1.0,-17.81,1,68027.54,874 +967,Keith Roberts,36-45,Single,1,0,5,M,-1.45,658,117.49,1.0,-1979.65,81,160022.32,1366 +968,Lisa Sharp,36-45,Married,2,0,9,F,-0.7,265,116.98,1.0,-267.15,3,44685.72,382 +969,Edward Evans,18-25,Married,3,1,6,M,0.0,446,96.53,1.03,0.0,0,63321.73,678 +970,Ruth Casey,46-55,Married,2,0,4,F,-0.29,534,109.5,1.01,-226.18,8,86397.85,796 +971,Paul Middleton,46-55,Married,5,3,6,M,-0.15,615,71.23,1.0,-160.3,9,75072.18,1057 +972,Francisco Walters,46-55,Married,2,0,9,M,-0.66,92,65.98,1.28,-159.94,5,16099.53,313 +973,Vincent Cruz,56-70,Married,2,0,4,M,-0.15,579,86.44,1.0,-142.48,6,82288.34,954 +974,Sally Smith,46-55,Single,1,0,2,F,-1.11,269,83.45,1.04,-417.34,17,31377.25,391 +975,Andrea Henson,36-45,Married,3,1,2,F,0.0,162,88.54,1.0,0.0,0,18594.09,210 +976,John Olsen,36-45,Single,1,0,1,M,-0.26,328,94.06,1.0,-110.42,5,40163.5,427 +977,Julia Taylor,46-55,Married,2,0,4,F,-0.94,585,76.19,1.0,-924.1,47,75273.91,988 +978,Adam Baldwin,26-35,Married,4,2,7,M,-0.19,536,96.26,1.12,-217.88,11,108965.32,1263 +979,Lisa Payne,46-55,Single,4,3,4,F,-0.05,529,135.55,1.0,-35.62,1,101118.3,746 +980,Jason Frey,46-55,Married,3,1,6,M,0.0,436,123.73,1.01,0.0,0,72753.68,595 +981,James Peters,36-45,Married,2,0,6,M,-0.21,638,132.82,1.06,-212.82,7,134674.89,1071 +982,Jeffrey Calderon,46-55,Single,1,0,2,M,-0.04,294,66.9,1.0,-19.59,1,35054.54,524 +983,Tracy Smith,56-70,Single,1,0,4,F,-0.37,513,119.64,1.0,-267.14,7,86617.84,724 +984,Peter Nichols,56-70,Married,3,1,7,M,-0.02,295,76.3,1.0,-8.9,1,31204.91,409 +985,Andrew White,36-45,Married,2,0,6,M,-0.38,242,110.93,1.04,-106.5,2,31505.18,296 +986,April West,46-55,Married,2,0,4,F,-0.2,277,89.45,1.01,-71.24,2,31396.53,353 +987,Ricky Joseph,26-35,Single,3,2,4,M,-0.11,628,96.59,1.0,-106.86,1,93404.46,971 +988,Shawn Moore,46-55,Married,2,0,2,M,-0.06,431,75.25,1.01,-37.4,2,50944.65,684 +989,Jenna Richardson,26-35,Married,2,0,2,F,-0.09,1057,112.57,1.08,-187.0,6,227385.4,2190 +990,Stephanie Evans,18-25,Single,2,1,5,F,-0.02,296,49.34,1.0,-14.25,1,28173.59,573 +991,David Beck,46-55,Married,3,1,1,M,-0.52,131,131.52,1.0,-71.24,1,18018.2,137 +992,James Reynolds,36-45,Single,1,0,6,M,0.0,364,98.19,1.01,0.0,0,48703.68,500 +993,Craig Mullins,46-55,Single,1,0,5,M,-0.03,555,133.25,1.06,-32.94,2,138842.33,1109 +994,David Brown,46-55,Single,1,0,9,M,-0.09,899,137.28,1.06,-107.75,4,159521.57,1227 +995,Steven Dougherty,36-45,Single,1,0,1,M,0.0,182,105.81,1.0,0.0,0,38619.32,365 +996,Kevin Cantrell,26-35,Single,1,0,5,M,-0.74,121,80.94,1.05,-101.52,4,11169.86,145 +997,Frank Lewis,36-45,Single,1,0,5,M,-0.16,687,104.89,1.0,-178.1,5,115377.83,1102 +998,Jeffrey Thomas,36-45,Married,2,0,8,M,-0.25,702,115.09,1.0,-323.95,15,146623.77,1274 +999,Karen Chavez,26-35,Single,1,0,3,F,-1.25,423,95.77,1.05,-828.45,31,63594.41,695 +1000,Anne Fox,70+,Single,1,0,2,F,-0.15,179,127.97,1.08,-35.62,2,29946.05,252 +1001,Raymond Paul,46-55,Married,2,0,4,M,-0.42,320,103.99,1.0,-170.07,9,41805.57,402 +1002,Chase Duarte,46-55,Married,2,0,4,M,-0.22,351,94.02,1.02,-89.05,1,38924.4,424 +1003,Jeremy Johnson,46-55,Single,1,0,5,M,-0.09,545,118.64,1.08,-89.05,2,111399.74,1012 +1004,Amber Brown,46-55,Married,2,0,6,F,-0.07,1196,99.66,1.02,-129.42,6,180885.05,1855 +1005,Raven Vega,26-35,Married,2,0,4,F,-0.09,913,75.32,1.0,-142.48,2,125099.06,1661 +1006,Jennifer Jones,36-45,Married,2,0,7,F,-0.09,374,94.31,1.0,-35.62,1,39234.86,416 +1007,Carol Reyes MD,36-45,Married,3,1,9,F,-0.03,917,108.3,1.0,-39.18,2,140360.3,1296 +1008,Jo Price,36-45,Married,2,0,4,F,0.0,100,77.53,1.0,0.0,0,15428.28,199 +1009,Alicia Austin,26-35,Single,2,1,4,F,-5.39,255,78.73,1.0,-1520.64,70,22200.82,282 +1010,Angela Wong,46-55,Married,2,0,6,F,-1.15,112,119.18,1.0,-418.54,20,43263.49,363 +1011,Deborah Martinez,46-55,Married,5,3,8,F,-0.42,1134,109.11,1.1,-1618.52,70,421369.11,4253 +1012,Christina Shaffer,70+,Married,2,0,3,F,-0.15,617,108.23,1.0,-158.51,4,116780.7,1079 +1013,Sara Johnson,18-25,Single,1,0,4,F,-0.42,193,105.15,1.0,-92.62,6,22922.56,219 +1014,Daniel King,36-45,Single,1,0,3,M,0.0,512,96.65,1.0,0.0,0,90073.64,934 +1015,Andrew Scott,36-45,Married,2,0,6,M,-3.2,405,112.93,1.1,-2135.95,102,75437.48,735 +1016,Mr. Allen Mcknight,36-45,Married,2,0,8,M,-0.08,412,111.38,1.14,-44.52,2,62486.71,639 +1017,Scott Smith DVM,18-25,Single,3,1,5,M,-0.17,1113,87.85,1.05,-307.22,11,160686.26,1915 +1018,Jeffrey Lewis,46-55,Married,2,0,6,M,-0.2,399,98.9,1.0,-104.18,7,52416.29,530 +1019,Nancy Wright,36-45,Married,2,0,4,F,-0.02,435,103.3,1.03,-17.81,1,74166.4,739 +1020,Jacob Davis,46-55,Single,1,0,3,M,0.0,161,99.29,1.0,0.0,0,24127.03,243 +1021,Hannah Andersen,26-35,Single,1,0,4,F,-0.12,209,111.59,1.11,-35.62,1,32250.62,321 +1022,Anna Smith,70+,Married,2,0,4,F,-0.55,423,141.87,1.0,-577.75,24,147828.33,1042 +1023,Martha Briggs,18-25,Single,4,3,1,F,-0.15,1230,75.15,1.0,-398.94,11,205622.55,2738 +1024,Linda Jimenez,26-35,Married,2,0,3,F,0.0,222,111.05,1.07,0.0,0,39977.67,384 +1025,Jerry Schultz,46-55,Married,2,0,5,M,-1.54,1051,98.53,1.02,-2984.9,138,191434.34,1989 +1026,Tara Campbell,26-35,Married,5,3,4,F,-0.11,614,84.95,1.0,-135.34,7,107629.22,1268 +1027,Mr. Anthony Simon,46-55,Single,2,1,5,M,0.0,407,82.45,1.0,0.0,0,51285.96,622 +1028,Christina Noble,56-70,Married,2,0,4,F,-0.12,469,122.65,1.08,-94.4,4,95055.68,838 +1029,Jacob Beasley,26-35,Single,2,1,2,M,-0.29,251,78.12,1.0,-131.2,7,34996.98,449 +1030,Lisa Brown,36-45,Married,2,0,4,F,0.0,110,187.18,1.0,0.0,0,37997.8,203 +1031,Julie Davila,36-45,Single,1,0,2,F,-2.02,552,92.43,1.09,-1632.78,47,74681.98,880 +1032,Melanie Love,36-45,Married,3,1,6,F,-0.12,235,116.12,1.0,-35.62,1,35882.3,309 +1033,Elizabeth Wilson,36-45,Single,1,0,1,F,-0.21,1596,96.43,1.06,-589.15,15,271549.46,2985 +1034,Amber Dickson,46-55,Married,2,0,4,F,0.0,398,88.02,1.0,0.0,0,48232.84,548 +1035,Brian Hall,46-55,Married,2,0,5,M,-0.23,319,98.02,1.01,-89.05,1,37249.03,383 +1036,John Taylor,36-45,Married,2,0,9,M,-0.08,871,115.38,1.0,-124.67,2,179880.33,1559 +1037,Robert Salazar,46-55,Single,1,0,5,M,0.0,121,68.54,1.39,0.0,0,12405.47,252 +1038,Jordan Anderson,46-55,Single,1,0,4,F,0.0,112,142.98,1.01,0.0,0,27738.26,195 +1039,Trevor Anderson,36-45,Single,1,0,6,M,-0.37,157,104.18,1.0,-67.08,3,18855.91,181 +1040,Ricky French,46-55,Married,2,0,1,M,0.0,316,110.35,1.07,0.0,0,40386.41,390 +1041,Sarah Hughes,70+,Single,1,0,4,F,-0.03,306,92.92,1.0,-12.47,1,34658.19,373 +1042,Ian Duarte,70+,Married,2,0,6,M,-0.48,297,82.11,1.0,-172.16,3,29641.51,361 +1043,Nichole Dunlap,36-45,Married,5,3,8,F,-0.37,718,99.41,1.05,-382.54,10,103285.09,1095 +1044,Leslie Myers,36-45,Single,1,0,2,F,-0.98,729,103.84,1.01,-1333.7,42,141633.72,1374 +1045,Maria Parker,36-45,Married,2,0,6,F,-0.08,591,94.56,1.04,-76.58,3,90398.24,997 +1046,Colton Barnes,46-55,Married,2,0,6,M,-0.2,1093,123.4,1.0,-473.15,16,298616.63,2420 +1047,Jeremiah Davenport,46-55,Single,1,0,6,M,-0.06,483,110.15,1.0,-35.62,1,70495.85,642 +1048,Sonya Adkins,26-35,Married,2,0,5,F,0.0,322,104.49,1.04,0.0,0,53289.48,528 +1049,Kenneth Jordan,70+,Married,3,1,8,M,-0.25,225,142.24,1.01,-71.24,3,41106.26,292 +1050,Robert Johnson,26-35,Married,2,0,5,M,-0.19,216,101.08,1.0,-44.52,2,23957.11,237 +1051,Dr. Stephen Williams Jr.,46-55,Married,2,0,2,M,-0.32,177,82.89,1.01,-81.93,3,21303.96,260 +1052,Lauren Mcconnell,26-35,Single,1,0,4,F,-4.62,522,114.88,1.0,-3979.87,98,99027.94,862 +1053,John Young,46-55,Single,1,0,5,M,-0.29,208,164.25,1.08,-89.05,1,50918.77,335 +1054,Robert Lara,36-45,Single,1,0,5,M,0.0,415,75.74,1.01,0.0,0,39084.33,520 +1055,Kenneth Mcdaniel,26-35,Married,3,1,4,M,-0.12,996,99.22,1.02,-215.5,8,171458.68,1754 +1056,Christian Davis,36-45,Single,1,0,2,M,-0.86,646,84.66,1.02,-1405.86,46,139018.76,1678 +1057,Eddie Perez,36-45,Single,1,0,3,M,-0.11,240,86.42,1.03,-35.62,1,27482.68,326 +1058,Chelsea Smith,46-55,Married,3,1,5,F,0.0,428,111.93,1.0,0.0,0,68613.16,613 +1059,Jane Lopez,46-55,Married,2,0,5,F,-0.1,468,88.01,1.0,-73.02,3,63897.48,726 +1060,Lisa Schmidt,36-45,Married,2,0,2,F,0.0,398,72.96,1.0,0.0,0,39690.91,546 +1061,Ronnie Ruiz,36-45,Single,1,0,3,M,-0.85,818,103.47,1.0,-1231.29,48,150545.63,1455 +1062,Mark Marshall,46-55,Single,1,0,3,M,-0.11,392,109.91,1.0,-68.12,4,66276.55,603 +1063,Ryan Curtis,26-35,Married,3,1,4,M,-1.22,348,97.93,1.02,-603.38,25,48571.58,508 +1064,Ryan Simmons,36-45,Married,2,0,9,M,-0.26,950,115.0,1.03,-548.84,12,243574.25,2189 +1065,Heather Bryant,46-55,Single,1,0,5,F,-0.26,398,87.72,1.01,-144.26,5,48332.26,555 +1066,Samantha Cannon,46-55,Married,3,1,1,F,-0.39,142,122.36,1.0,-106.85,3,33650.22,275 +1067,Kelly Patton,46-55,Single,1,0,5,F,-0.22,533,114.02,1.01,-209.27,11,110597.4,981 +1068,Elizabeth Duarte,46-55,Married,2,0,2,F,-0.55,935,91.12,1.03,-1079.86,48,177419.11,2015 +1069,Kristine Cook,46-55,Married,2,0,5,F,0.0,135,202.68,1.01,0.0,0,54318.08,272 +1070,Stephanie Walsh,56-70,Single,1,0,6,F,-3.34,594,112.44,1.1,-3646.57,131,122668.62,1202 +1071,Richard Bennett,36-45,Single,1,0,2,M,-0.14,656,95.51,1.0,-103.3,4,72207.66,757 +1072,Joseph Price,70+,Married,2,0,2,M,-0.42,412,79.97,1.01,-218.12,7,41822.23,529 +1073,John Castro,36-45,Single,1,0,5,M,-0.55,582,92.82,1.0,-422.08,18,70913.74,764 +1074,Jon Watts,26-35,Single,2,1,2,M,0.0,171,117.15,1.0,0.0,0,23547.43,201 +1075,Vincent Anthony,36-45,Married,4,2,7,M,-0.05,303,125.75,1.03,-21.38,2,50804.6,418 +1076,Brenda Hernandez,18-25,Single,1,0,12,F,0.0,608,98.15,1.03,0.0,0,167927.65,1766 +1077,Elizabeth Smith,70+,Married,2,0,4,F,0.0,297,87.81,1.0,0.0,0,49610.63,566 +1078,Alicia Bush,26-35,Married,2,0,4,F,-0.4,116,91.35,1.0,-89.05,1,20188.28,221 +1079,Barbara Patterson,46-55,Married,2,0,1,F,0.0,317,97.39,1.0,0.0,0,64080.26,658 +1080,Christine Rogers,70+,Single,1,0,2,F,-0.1,288,126.83,1.01,-35.62,1,45786.64,365 +1081,Kevin Caldwell,56-70,Single,1,0,4,M,-0.03,351,88.27,1.09,-17.81,1,47668.48,590 +1082,Jacob Solis,56-70,Single,1,0,4,M,-0.58,331,115.65,1.15,-301.86,16,60370.67,599 +1083,Aaron Branch,26-35,Married,4,2,2,M,-0.71,653,85.81,1.0,-720.87,24,87610.29,1021 +1084,Kelly Torres,26-35,Married,2,0,4,M,-0.02,811,85.12,1.0,-27.61,3,133559.26,1573 +1085,Paul Rivera,46-55,Single,1,0,4,M,-0.35,450,79.16,1.06,-248.97,8,56912.61,763 +1086,Krystal Turner,26-35,Married,3,1,4,F,-3.78,506,93.65,1.04,-3061.8,92,75948.49,846 +1087,Tina Barajas,46-55,Single,1,0,4,F,-0.03,486,111.03,1.06,-17.81,1,72499.53,689 +1088,Loretta Beasley,36-45,Married,5,3,5,F,-0.26,279,102.37,1.0,-89.05,1,35216.02,344 +1089,Tyrone Alexander,36-45,Married,5,3,4,M,-0.04,723,81.7,1.0,-47.49,2,89627.15,1097 +1090,Stephanie Harris,70+,Married,2,0,6,F,0.0,204,94.27,1.0,0.0,0,22152.92,235 +1091,Johnny Gonzalez,46-55,Married,5,3,8,M,-0.42,1376,92.92,1.11,-1086.74,32,239362.16,2854 +1092,Jacob Cooper,46-55,Single,1,0,10,M,-0.08,307,145.77,1.0,-35.62,1,66764.46,458 +1093,Danielle Sherman,46-55,Married,4,2,6,F,-0.55,945,114.63,1.03,-746.53,33,155433.93,1403 +1094,Christina Watkins,46-55,Single,1,0,4,F,-0.05,529,94.77,1.01,-35.62,1,68612.92,731 +1095,Timothy Byrd,36-45,Single,1,0,2,M,-0.05,1518,87.68,1.0,-108.64,4,206410.02,2359 +1096,Morgan Rodriguez,46-55,Married,2,0,2,F,0.0,199,87.24,1.01,0.0,0,24514.86,283 +1097,Aaron Smith,26-35,Married,3,1,7,M,-0.05,564,136.57,1.01,-47.2,3,127285.82,938 +1098,Jordan Jensen,26-35,Married,2,0,4,M,-0.05,329,95.44,1.0,-35.62,1,65946.41,691 +1099,Kelly Evans,46-55,Single,1,0,5,F,-1.1,324,125.28,1.1,-525.38,23,60009.45,529 +1100,Nichole Lawson,46-55,Single,1,0,5,F,-0.59,645,98.27,1.0,-593.07,23,98069.27,998 +1101,Jessica Garza,46-55,Single,1,0,6,F,-0.34,492,85.82,1.0,-286.39,7,73206.51,853 +1102,Miranda Arroyo,56-70,Married,2,0,5,F,0.0,138,127.76,1.0,0.0,0,20186.87,158 +1103,Marilyn Morgan,26-35,Married,2,0,4,F,-0.16,819,81.02,1.14,-293.86,8,149963.89,2113 +1104,Anne Rogers,70+,Married,2,0,6,F,-0.14,588,87.3,1.01,-118.44,8,74990.16,871 +1105,Ashley White,46-55,Married,2,0,9,F,-0.06,455,119.72,1.07,-35.62,1,69077.26,615 +1106,Eduardo Aguilar,26-35,Married,2,0,3,M,-0.3,457,93.27,1.03,-213.72,5,66033.63,730 +1107,Mary Mcknight,46-55,Single,1,0,5,F,0.0,272,126.32,1.0,0.0,0,44591.82,353 +1108,Amy Mendez,26-35,Married,5,3,4,F,0.0,285,84.03,1.0,0.0,0,28569.5,340 +1109,Darius Cook,36-45,Married,2,0,6,M,-0.13,702,123.71,1.04,-178.1,5,175664.84,1482 +1110,Yesenia Baldwin,70+,Married,2,0,6,F,0.0,183,103.03,1.0,0.0,0,36471.21,354 +1111,Danielle Fletcher,46-55,Single,2,1,7,F,-1.56,305,101.45,1.02,-745.63,25,48592.78,490 +1112,Andres Smith,36-45,Single,3,2,5,M,-0.2,346,85.17,1.05,-133.57,2,56809.35,698 +1113,Melanie Hopkins,26-35,Married,2,0,4,F,-0.26,400,80.92,1.0,-142.48,4,44022.1,546 +1114,Kenneth Barry,46-55,Single,1,0,5,M,-0.26,591,95.99,1.0,-225.0,13,83412.64,870 +1115,Tammie Cochran,46-55,Married,2,0,3,F,-1.28,1208,89.86,1.01,-3315.5,98,233018.22,2623 +1116,Rebecca James,18-25,Single,1,0,4,F,0.0,314,92.42,1.02,0.0,0,46392.75,511 +1117,Holly Estrada MD,70+,Single,1,0,1,F,-0.26,610,111.1,1.0,-241.31,13,102212.17,920 +1118,Anthony Howell,56-70,Married,2,0,4,M,-0.61,438,92.12,1.0,-346.4,10,52045.72,565 +1119,Sierra King,36-45,Married,2,0,10,F,-0.35,403,112.97,1.26,-161.89,4,52982.39,590 +1120,Brandon Moran,46-55,Married,2,0,9,M,-0.03,508,111.59,1.0,-26.71,1,90837.21,814 +1121,Craig Barnes,18-25,Married,3,1,5,M,-0.1,1010,143.09,1.0,-222.26,8,323822.41,2263 +1122,Brian Lane,70+,Married,2,0,4,M,-0.36,897,112.94,1.09,-520.5,14,163870.05,1584 +1123,Danielle Stanley MD,36-45,Married,2,0,8,F,0.0,553,91.61,1.0,0.0,0,80799.12,885 +1124,Cassidy Jones,46-55,Married,2,0,1,F,-0.02,949,102.09,1.01,-35.62,1,152317.03,1505 +1125,Marcus Smith,26-35,Single,1,0,5,M,-0.24,207,118.81,1.0,-71.24,2,35880.54,302 +1126,Kathleen Howard,26-35,Single,1,0,5,F,-0.37,295,85.71,1.0,-144.26,5,33596.54,392 +1127,Jason Perkins,36-45,Single,1,0,3,M,-0.03,339,138.17,1.06,-17.81,1,78343.32,600 +1128,David Douglas,46-55,Single,1,0,5,M,-0.28,302,121.49,1.02,-106.86,2,45678.63,385 +1129,Brittany Wiggins,70+,Married,2,0,9,F,-0.66,607,127.33,1.0,-476.95,5,91552.56,721 +1130,Lisa Acosta,46-55,Married,2,0,3,F,-0.31,497,93.99,1.04,-198.4,5,59306.21,656 +1131,Kyle Hunt,18-25,Single,5,3,1,M,-1.36,707,90.57,1.05,-1226.73,38,81515.56,943 +1132,Kaitlyn Bowman,46-55,Married,2,0,5,F,-0.13,205,100.57,1.0,-35.62,2,27353.89,272 +1133,Bryan Aguirre MD,46-55,Married,2,0,9,M,0.0,540,96.86,1.01,0.0,0,84171.31,875 +1134,Brandon Bowman,26-35,Married,4,2,6,M,-0.04,525,106.08,1.02,-31.16,2,77334.73,740 +1135,Michael Madden,56-70,Married,2,0,4,M,-0.02,801,85.52,1.0,-17.81,1,83554.71,978 +1136,Madeline Wilson,46-55,Married,2,0,6,F,-2.0,957,98.73,1.07,-4036.85,137,199340.79,2151 +1137,Michelle Smith,36-45,Married,3,1,5,F,-0.17,374,104.19,1.0,-102.4,3,61368.24,589 +1138,Matthew Stewart,36-45,Single,1,0,9,M,0.0,246,124.02,1.06,0.0,0,45516.46,389 +1139,Teresa Tyler,56-70,Married,4,2,7,F,0.0,413,69.74,1.0,0.0,0,39614.35,568 +1140,Jennifer Taylor,26-35,Single,1,0,4,F,-0.15,722,116.31,1.0,-182.25,9,144694.52,1246 +1141,Phillip Turner,70+,Married,2,0,6,M,-0.41,448,120.38,1.07,-386.48,10,113153.54,1003 +1142,Jill Escobar,56-70,Single,1,0,6,F,-1.93,511,96.01,1.1,-1602.84,71,79689.22,909 +1143,Jonathon Barnes,46-55,Married,2,0,6,M,-0.18,250,84.36,1.0,-64.11,3,30114.98,357 +1144,Mark Bridges,26-35,Married,5,3,3,M,-1.15,432,105.44,1.0,-648.28,26,59466.92,565 +1145,Jennifer Murphy,46-55,Married,5,3,8,F,-0.41,560,108.66,1.02,-382.91,10,102362.12,960 +1146,Kelly Osborne,36-45,Married,2,0,5,F,-0.31,942,99.36,1.06,-609.99,15,198426.03,2121 +1147,Shelby Martin,46-55,Married,3,1,3,F,0.0,357,110.19,1.04,0.0,0,58950.7,555 +1148,Shane Hopkins,18-25,Single,1,0,12,M,-1.17,477,149.31,1.04,-873.74,19,111533.88,780 +1149,Sheryl Horton,46-55,Single,1,0,6,F,-0.3,193,59.73,1.0,-178.1,6,34941.06,585 +1150,James Weeks,36-45,Married,3,1,1,M,-0.23,70,74.49,1.0,-19.59,1,6480.43,87 +1151,William Harrington,46-55,Single,2,1,4,M,-0.2,220,97.43,1.0,-74.8,5,36048.76,370 +1152,Alexis Martin,46-55,Married,3,1,10,F,-0.17,1195,107.89,1.0,-474.28,13,292823.13,2717 +1153,Christopher Strickland,46-55,Single,1,0,10,M,-0.22,259,119.26,1.01,-89.05,1,48537.32,412 +1154,Angela Garza,46-55,Married,3,1,6,F,-0.07,840,121.32,1.02,-129.12,6,229904.66,1936 +1155,Julie Frazier,46-55,Married,5,3,1,F,-1.16,381,89.57,1.0,-670.18,13,51950.05,580 +1156,Isabella Gonzalez,70+,Married,2,0,6,F,-0.03,761,102.08,1.11,-30.28,2,106672.49,1160 +1157,Andrea Hall,18-25,Married,3,1,5,F,-0.21,1402,105.29,1.11,-425.16,15,210580.78,2220 +1158,Rhonda Long,36-45,Married,3,1,6,F,0.0,788,108.68,1.06,0.0,0,140201.64,1370 +1159,Stephen Thompson,36-45,Married,2,0,6,M,-1.74,824,93.01,1.0,-1928.11,67,103237.7,1112 +1160,Amanda Morrow,36-45,Married,2,0,4,F,-0.08,295,94.07,1.05,-34.35,2,38476.45,430 +1161,Natalie Hines,26-35,Single,1,0,9,F,-1.39,314,114.38,1.0,-760.47,28,62567.49,547 +1162,Kevin King,70+,Married,2,0,4,M,-0.01,243,121.44,1.0,-4.45,1,36431.43,300 +1163,Jessica Aguilar,46-55,Single,2,1,7,F,-0.52,150,99.05,1.04,-92.26,3,17532.22,184 +1164,Lindsey Campbell,26-35,Married,4,2,2,F,-0.43,354,89.61,1.0,-191.46,4,39698.58,443 +1165,Richard Webb,36-45,Married,5,3,5,M,-0.75,313,106.56,1.07,-335.7,10,47630.73,477 +1166,Jill Rivera,46-55,Married,5,3,8,F,-0.13,1083,123.6,1.04,-283.47,13,271916.85,2297 +1167,Omar Williams,46-55,Single,1,0,5,M,-0.07,585,103.99,1.0,-97.95,4,150676.87,1449 +1168,Joshua Boyer,18-25,Married,2,0,4,M,-1.28,639,91.73,1.01,-1084.98,40,77878.3,858 +1169,Kathryn Becker,46-55,Married,2,0,5,F,-0.45,429,129.8,1.05,-275.75,10,79696.82,646 +1170,Linda Green,36-45,Single,1,0,2,F,-0.12,850,84.01,1.06,-151.38,3,106861.26,1350 +1171,Kimberly Brooks,46-55,Married,2,0,3,F,0.0,302,115.15,1.0,0.0,0,42490.9,369 +1172,Kristina Parks,70+,Married,2,0,4,F,-0.2,203,71.66,1.0,-189.98,8,68002.28,949 +1173,Brittney Peterson,46-55,Single,1,0,7,F,-0.09,203,106.7,1.09,-35.62,1,42467.42,433 +1174,Brittany Dixon,26-35,Married,2,0,6,F,-0.15,509,92.19,1.01,-97.95,5,61767.6,674 +1175,Sara Little,70+,Married,2,0,6,F,-1.45,76,113.72,1.0,-178.1,2,13987.26,123 +1176,Amy Malone,70+,Single,1,0,2,F,-1.57,284,110.34,1.02,-572.92,18,40275.69,374 +1177,Alfred Lowery,26-35,Married,3,1,4,M,-0.33,226,118.6,1.0,-89.04,4,31548.39,266 +1178,Kurt Valdez,36-45,Married,3,1,4,M,-0.11,696,90.08,1.02,-128.23,3,107739.85,1222 +1179,Michael Leach,36-45,Married,2,0,7,M,-0.4,421,103.79,1.0,-207.46,11,53556.62,516 +1180,Shawn Jones,70+,Married,2,0,5,M,-2.36,74,204.43,1.0,-195.91,2,16967.64,83 +1181,Amber Ramirez,46-55,Married,2,0,5,F,0.0,528,100.04,1.01,0.0,0,67930.35,686 +1182,Kevin Liu,26-35,Married,3,1,3,M,-0.68,989,128.39,1.0,-1341.92,59,252546.08,1969 +1183,Christopher Taylor,46-55,Married,3,1,3,M,-0.05,672,127.01,1.0,-53.43,3,131327.46,1034 +1184,Tamara Mason,46-55,Married,3,1,4,F,-0.21,1874,100.36,1.0,-532.21,16,260236.54,2594 +1185,Barbara Barrett,36-45,Married,2,0,4,F,-0.15,271,121.66,1.01,-47.49,2,38445.76,320 +1186,Robert Garcia,46-55,Single,1,0,10,M,-0.02,721,119.54,1.04,-26.71,1,144877.87,1261 +1187,Robert Gray,26-35,Single,1,0,5,M,-0.63,909,79.15,1.05,-1014.4,35,128373.55,1706 +1188,Cole Hernandez,36-45,Married,2,0,5,M,0.0,198,83.37,1.12,0.0,0,25426.51,342 +1189,Danielle Harvey,46-55,Single,1,0,5,F,-0.36,342,83.96,1.0,-227.08,4,53316.04,635 +1190,Michele Stewart,56-70,Single,1,0,6,F,0.0,248,133.71,1.0,0.0,0,104295.08,780 +1191,Jordan Reynolds,46-55,Married,5,3,4,M,-0.16,491,93.24,1.0,-106.86,2,63215.93,680 +1192,Ashley Gutierrez,70+,Single,1,0,2,F,-0.2,938,93.91,1.0,-343.72,10,159740.01,1701 +1193,David Fowler,56-70,Married,2,0,4,M,-0.69,418,119.93,1.04,-378.76,19,65964.02,571 +1194,Julie Ramirez,36-45,Married,5,3,4,F,-1.05,526,90.59,1.05,-888.7,17,76910.8,888 +1195,Teresa Hunter,56-70,Married,2,0,5,F,-0.03,221,141.15,1.0,-8.9,1,39099.25,277 +1196,Michael Terry,36-45,Married,2,0,4,M,-0.84,150,89.03,1.04,-180.78,8,19051.39,223 +1197,Meredith Johnson,46-55,Single,1,0,4,F,-0.07,358,105.44,1.03,-35.62,1,54724.31,532 +1198,Michelle Allen,18-25,Single,1,0,4,F,-0.89,494,105.36,1.11,-669.63,27,79443.14,835 +1199,Robert Mcdonald,36-45,Single,1,0,9,M,-0.4,850,99.98,1.01,-549.62,8,136968.68,1380 +1200,Dr. Kari Walker PhD,36-45,Married,5,3,5,F,0.0,452,101.08,1.04,0.0,0,65092.54,671 +1201,Jasmine Ward,18-25,Single,1,0,1,F,0.0,667,101.6,1.0,0.0,0,89406.31,880 +1202,Ashley Rogers,36-45,Single,3,1,6,F,-0.99,338,85.21,1.03,-540.82,24,46526.21,565 +1203,Savannah Walter,36-45,Single,1,0,3,F,-0.02,522,87.55,1.0,-15.27,1,86232.99,985 +1204,Shannon Hernandez,46-55,Single,1,0,5,F,-0.08,457,107.0,1.0,-48.09,2,64305.65,601 +1205,Allison Bishop,46-55,Single,1,0,5,F,-0.05,284,103.21,1.0,-23.15,2,46959.6,455 +1206,Heather Clark,36-45,Married,3,1,12,F,-0.01,262,134.7,1.16,-3.92,1,47413.9,410 +1207,Megan Wagner,36-45,Single,4,3,4,F,-0.23,350,91.96,1.06,-106.5,1,41748.47,479 +1208,Kyle Adkins,56-70,Married,2,0,9,M,-3.84,493,121.62,1.13,-5535.66,203,175137.19,1626 +1209,Melissa Alexander,36-45,Married,4,2,7,F,-0.39,587,83.51,1.05,-415.56,8,88689.14,1111 +1210,Robert Murray,46-55,Married,2,0,5,M,-0.35,848,84.91,1.0,-614.44,23,148174.55,1751 +1211,Dr. Betty Taylor,36-45,Married,2,0,5,F,0.0,353,99.39,1.07,0.0,0,52078.36,563 +1212,Christopher Dunn,26-35,Married,2,0,6,M,-0.16,446,77.67,1.0,-124.67,5,61436.2,791 +1213,Tracy Stevens,36-45,Single,3,1,5,M,0.0,125,69.39,1.01,0.0,0,14641.56,213 +1214,Mark Robinson,46-55,Single,1,0,6,M,0.0,300,128.87,1.02,0.0,0,44847.28,354 +1215,Laurie Booth,36-45,Married,3,1,6,F,-0.08,738,102.72,1.02,-99.73,5,125519.31,1250 +1216,Katie Terry,26-35,Married,4,2,8,F,-0.69,752,121.92,1.08,-1034.73,38,183970.73,1624 +1217,Michelle Reynolds,26-35,Single,1,0,6,F,0.0,331,112.45,1.01,0.0,0,68370.9,613 +1218,Melissa Hill,36-45,Married,2,0,5,F,0.0,401,138.2,1.0,0.0,0,135159.68,980 +1219,Brittany Gillespie,36-45,Single,1,0,5,F,-0.41,269,75.21,1.0,-156.72,7,28431.14,378 +1220,Roberto Davis,46-55,Single,1,0,1,M,0.0,191,103.95,1.0,0.0,0,25363.51,244 +1221,Stephanie Bennett,46-55,Single,1,0,2,F,-0.75,111,87.41,1.0,-89.05,3,10401.32,119 +1222,Russell Mathews,46-55,Single,1,0,5,M,-0.03,397,91.23,1.0,-14.25,1,49809.5,546 +1223,Victor Jones,36-45,Married,2,0,5,M,0.0,169,106.81,1.01,0.0,0,31400.7,297 +1224,Tyler Flores,26-35,Single,1,0,4,M,-0.45,717,101.41,1.03,-630.15,27,141774.87,1443 +1225,Jamie Harris,46-55,Married,3,1,6,F,-0.26,301,110.52,1.0,-101.77,4,42883.22,388 +1226,Dwayne Valdez,46-55,Single,1,0,8,M,-0.58,652,104.89,1.1,-612.37,23,110553.58,1160 +1227,Jonathan Vasquez,46-55,Married,2,0,5,M,-0.13,292,89.9,1.0,-47.73,1,33531.16,373 +1228,Bryan Woods,70+,Married,2,0,3,M,-0.04,459,100.71,1.03,-26.71,3,61229.19,624 +1229,Amanda Stewart,26-35,Single,1,0,2,F,-0.92,411,108.34,1.0,-530.74,19,62297.3,576 +1230,Rebecca Farley,70+,Single,1,0,1,F,-0.46,416,78.75,1.0,-255.76,3,44102.31,560 +1231,Donald Adkins,26-35,Married,3,1,5,M,-0.89,445,99.86,1.22,-567.41,19,63514.08,774 +1232,Ronnie May MD,70+,Single,1,0,3,M,-0.13,766,87.31,1.0,-144.26,5,100314.44,1151 +1233,Dennis Perez,46-55,Single,1,0,3,M,0.0,248,78.74,1.0,0.0,0,39054.61,496 +1234,Susan Brown,46-55,Single,1,0,1,F,-0.23,275,86.38,1.0,-120.4,2,46038.76,533 +1235,Norman Vaughn,46-55,Married,5,3,1,M,-0.01,201,87.15,1.0,-3.56,1,23095.58,266 +1236,Joshua Roth,46-55,Single,1,0,5,M,-0.07,590,105.37,1.1,-73.03,5,110318.04,1152 +1237,Sean Lee,46-55,Single,1,0,4,M,-0.03,500,79.47,1.0,-17.81,1,53724.66,678 +1238,Jessica Harris,46-55,Single,1,0,5,F,-0.27,538,117.53,1.05,-311.31,6,137157.67,1225 +1239,Lindsey Rodriguez,36-45,Single,1,0,6,F,-1.61,1114,136.89,1.05,-3748.52,163,318531.62,2432 +1240,Jon Graham,26-35,Married,5,3,5,M,-0.2,603,95.5,1.0,-190.38,7,89862.13,941 +1241,Colin Campbell,46-55,Single,1,0,1,M,-0.12,247,88.38,1.04,-44.52,2,33494.31,395 +1242,Lisa White,46-55,Married,3,1,9,F,-2.7,134,119.71,1.03,-487.99,10,21667.37,187 +1243,Susan Gardner DVM,56-70,Married,2,0,5,F,-0.11,573,114.71,1.02,-89.05,1,94059.43,838 +1244,Allison Baker,46-55,Single,1,0,2,F,-0.03,143,89.13,1.0,-6.24,1,17291.43,194 +1245,Bridget Lamb,26-35,Single,1,0,7,F,0.0,328,70.68,1.07,0.0,0,59299.91,901 +1246,Edward Dunn,26-35,Married,5,3,5,M,-0.52,791,119.62,1.0,-703.66,26,161487.69,1350 +1247,Diana Murphy,56-70,Married,4,2,7,F,-0.18,491,77.3,1.0,-115.76,5,49860.08,645 +1248,Sarah Petersen,36-45,Married,4,2,8,F,-1.9,637,122.85,1.09,-1627.7,38,105407.55,937 +1249,Rebecca Williams,70+,Single,1,0,2,F,-0.81,292,85.56,1.03,-580.54,21,61002.33,737 +1250,Jaime Greene,36-45,Married,5,3,8,F,-1.82,409,99.26,1.11,-1252.31,60,68192.36,765 +1251,Brian Smith,70+,Single,1,0,1,M,-0.25,288,80.67,1.04,-89.04,5,28476.1,366 +1252,Francisco Harvey,26-35,Single,3,2,3,M,-0.16,1414,117.78,1.02,-376.96,11,286085.8,2482 +1253,Joanna White,46-55,Married,2,0,11,F,-0.03,318,148.73,1.0,-12.47,1,72431.35,487 +1254,Howard Frazier,26-35,Single,1,0,5,M,-0.08,499,123.91,1.03,-55.21,2,90327.51,749 +1255,William Bradley,46-55,Married,2,0,6,M,0.0,312,55.61,1.0,0.0,0,59886.93,1077 +1256,Beverly Solis,36-45,Single,1,0,4,F,-0.08,317,88.82,1.02,-35.62,1,39878.8,460 +1257,Judith Chen,46-55,Single,1,0,5,F,-1.76,738,106.12,1.0,-3774.69,172,227305.14,2142 +1258,Marcus Lawson,46-55,Single,1,0,1,M,-0.94,434,98.07,1.02,-697.25,24,72670.09,754 +1259,Kimberly Martinez,46-55,Married,3,1,5,F,-0.33,717,115.99,1.08,-407.85,17,145225.36,1358 +1260,Cory Thompson,46-55,Single,1,0,5,M,-0.79,360,107.7,1.0,-369.56,19,50080.68,465 +1261,Pamela Brown,46-55,Married,2,0,5,F,-0.53,330,137.48,1.02,-210.16,6,54168.86,402 +1262,Charles Gonzalez,46-55,Single,1,0,6,M,0.0,164,90.86,1.0,0.0,0,26168.07,288 +1263,Paula Coffey,46-55,Married,2,0,3,F,-0.35,896,90.04,1.04,-618.72,18,157745.3,1830 +1264,Paul Garcia,18-25,Single,1,0,3,M,-0.1,361,83.19,1.0,-39.18,2,34025.15,409 +1265,Marcus Lloyd,70+,Single,1,0,7,M,0.0,274,83.32,1.0,0.0,0,31660.61,380 +1266,James Levy,46-55,Single,1,0,4,M,-0.82,468,127.39,1.0,-846.28,38,131719.27,1034 +1267,Kimberly Miller,46-55,Single,1,0,5,F,-0.13,339,70.24,1.0,-80.14,2,41932.53,597 +1268,Daniel Valenzuela,46-55,Married,2,0,6,M,-0.03,767,78.55,1.07,-35.62,1,98265.36,1338 +1269,Wendy Boone,46-55,Single,1,0,5,F,-0.05,408,96.57,1.0,-26.71,1,54272.55,562 +1270,Patricia Maldonado,46-55,Single,1,0,2,F,-0.73,1080,97.34,1.0,-1593.57,40,212502.35,2188 +1271,Steven Armstrong,36-45,Married,5,3,7,M,-0.02,823,103.12,1.0,-35.62,1,158909.8,1541 +1272,Preston Bell,46-55,Single,1,0,4,M,-0.14,165,113.05,1.0,-35.62,1,28375.75,251 +1273,Natalie Davis,26-35,Single,2,1,8,F,-1.45,530,125.44,1.05,-1110.38,31,95837.62,801 +1274,Tammy Shelton,46-55,Married,2,0,9,F,-0.04,356,92.0,1.0,-17.81,1,43146.28,469 +1275,Tammy Reed,36-45,Married,2,0,8,F,-0.15,195,135.37,1.0,-35.62,1,33166.11,245 +1276,Christina Price,36-45,Single,1,0,6,F,-0.7,446,96.63,1.01,-381.38,21,52855.71,551 +1277,Timothy King,70+,Married,2,0,3,M,0.0,104,153.49,1.1,0.0,0,21796.06,156 +1278,Casey Moore,46-55,Single,1,0,1,F,-0.24,237,123.37,1.07,-75.16,4,38736.7,337 +1279,David Hanson,70+,Married,2,0,5,M,-0.23,368,71.94,1.08,-117.19,2,37190.74,556 +1280,Cheryl Caldwell,36-45,Married,5,3,8,F,-0.27,229,78.95,1.0,-83.71,3,24632.85,312 +1281,Casey Alexander,18-25,Single,1,0,5,M,-0.07,340,119.19,1.01,-28.14,1,50896.07,431 +1282,Cheryl Gonzalez,46-55,Single,2,1,4,F,0.0,259,95.08,1.0,0.0,0,35845.88,377 +1283,Paula Cain,36-45,Married,5,3,5,F,-0.1,221,105.83,1.01,-26.71,1,29631.11,284 +1284,Jared King,36-45,Single,2,1,4,M,-0.08,537,129.76,1.03,-71.24,2,112113.2,887 +1285,Cody Bradley MD,56-70,Married,2,0,7,M,-0.87,423,125.88,1.01,-605.61,26,87359.86,701 +1286,Elizabeth Rodriguez,36-45,Married,5,3,2,F,-0.04,276,78.93,1.0,-17.81,1,31808.69,405 +1287,Donna Perez,36-45,Married,2,0,3,F,0.0,202,99.05,1.0,0.0,0,24068.19,243 +1288,Steven Stephens,56-70,Married,2,0,4,M,0.0,123,136.85,1.03,0.0,0,18200.7,137 +1289,Danny Williams,18-25,Single,1,0,1,M,-0.22,687,92.34,1.07,-195.91,5,81901.65,949 +1290,Austin White,36-45,Married,5,3,3,M,0.0,587,114.18,1.12,0.0,0,89060.43,873 +1291,Felicia Thomas,46-55,Married,2,0,2,F,-0.1,538,107.27,1.06,-103.3,7,110376.29,1091 +1292,Jordan Herrera,46-55,Married,2,0,2,M,-0.05,415,102.46,1.02,-35.62,2,80632.68,800 +1293,Kimberly Martinez,26-35,Single,1,0,6,F,-0.03,242,121.69,1.02,-12.47,1,45024.12,378 +1294,Sydney Jones,70+,Married,2,0,4,F,-0.55,319,116.22,1.0,-270.36,4,57063.85,491 +1295,Katelyn Mullins,36-45,Married,2,0,4,F,-0.44,415,84.74,1.08,-239.18,8,46098.52,589 +1296,Robert Lyons,26-35,Single,2,1,8,M,-0.13,461,123.04,1.04,-120.66,7,111839.33,943 +1297,Brandon Rogers,46-55,Married,2,0,12,M,0.0,283,101.11,1.0,0.0,0,35691.2,353 +1298,Lisa Thompson MD,26-35,Married,2,0,3,F,-0.36,786,85.82,1.01,-480.86,22,115422.0,1358 +1299,Ricardo Kennedy,56-70,Married,2,0,6,M,-0.14,263,94.59,1.0,-55.21,2,37930.26,401 +1300,Ryan Fowler,18-25,Single,1,0,5,M,-0.18,686,117.4,1.01,-160.28,5,106250.8,910 +1301,James Garrett,26-35,Married,3,1,3,M,-0.52,419,105.61,1.0,-302.77,12,61150.7,579 +1302,April Merritt,56-70,Married,2,0,2,F,-0.45,335,123.22,1.01,-206.85,11,56187.45,461 +1303,Jacob Miller,56-70,Married,2,0,5,M,-0.56,472,85.7,1.01,-365.99,14,56133.77,662 +1304,Emily Martinez,36-45,Single,1,0,2,F,-0.01,326,100.05,1.0,-5.34,1,38218.66,382 +1305,Theresa Barnes,46-55,Single,1,0,5,F,-0.43,715,95.56,1.02,-469.29,10,103108.51,1099 +1306,Pamela Davis,36-45,Married,2,0,3,F,-0.03,237,109.74,1.0,-8.9,1,34350.05,313 +1307,Kenneth Peters,36-45,Married,2,0,8,M,-0.3,162,83.67,1.0,-65.89,4,18407.07,221 +1308,Jason Henderson,26-35,Married,3,1,3,M,-0.25,512,101.44,1.05,-192.35,6,77095.46,798 +1309,Vincent Bennett,26-35,Married,3,1,7,M,-0.41,745,93.47,1.08,-708.84,23,162732.27,1877 +1310,Joseph Blevins,56-70,Married,3,1,4,M,-0.18,244,167.52,1.1,-57.88,3,55113.24,362 +1311,Kevin Watkins,70+,Married,2,0,3,M,0.0,488,115.62,1.0,0.0,0,96770.69,839 +1312,Patrick Gonzalez,56-70,Single,1,0,3,M,-0.19,485,84.85,1.01,-295.65,11,133388.93,1590 +1313,Cynthia Kelly,26-35,Married,2,0,4,F,0.0,137,113.71,1.0,0.0,0,19557.52,172 +1314,Jamie Simpson,46-55,Married,3,1,1,F,-0.27,473,95.34,1.11,-196.8,6,70262.81,820 +1315,Tina Hill,26-35,Single,1,0,3,F,-1.13,653,112.75,1.06,-1377.55,58,137671.22,1294 +1316,Terri Armstrong,56-70,Married,2,0,8,F,-0.08,367,121.99,1.0,-35.62,1,53189.52,438 +1317,Jay Smith,36-45,Married,2,0,4,M,-0.28,1164,103.77,1.03,-581.72,25,214704.6,2141 +1318,Tina Ayala,46-55,Married,2,0,3,F,-0.07,419,103.67,1.0,-48.97,2,78060.16,755 +1319,Christopher Smith,46-55,Single,1,0,7,M,-0.24,872,97.49,1.07,-355.01,11,145949.43,1607 +1320,Whitney Young,26-35,Single,1,0,4,F,-0.17,565,81.39,1.0,-173.94,4,85375.69,1049 +1321,Diana Garner,36-45,Married,3,1,1,F,-2.55,366,90.34,1.07,-1548.37,58,54835.72,648 +1322,Brad Howard,46-55,Married,2,0,5,M,0.0,353,89.57,1.0,0.0,0,40398.29,451 +1323,Mr. David Harris Jr.,46-55,Married,2,0,5,M,-0.14,561,66.75,1.02,-128.23,5,63080.75,960 +1324,Johnathan Gibson,36-45,Married,5,3,2,M,-0.27,572,120.83,1.16,-272.43,6,123854.01,1193 +1325,Richard Cummings,26-35,Married,2,0,5,M,-0.13,101,109.25,1.0,-17.22,2,14858.11,136 +1326,Andrew Clark MD,70+,Single,1,0,2,M,0.0,52,79.21,1.16,0.0,0,6257.57,92 +1327,Theodore Baxter,70+,Married,2,0,4,M,-0.71,359,91.07,1.01,-401.79,13,51818.83,574 +1328,Michael Anthony,56-70,Married,2,0,10,M,-0.09,279,115.73,1.0,-39.18,2,50112.88,433 +1329,Tara Stone,36-45,Married,5,3,6,F,0.0,376,99.12,1.0,0.0,0,60464.96,610 +1330,Hannah Walls,46-55,Married,3,1,4,F,-0.16,273,91.13,1.0,-62.33,3,34446.29,379 +1331,Aaron Clark,36-45,Married,2,0,8,M,-0.05,225,93.16,1.0,-26.71,1,50400.75,541 +1332,Haley Thornton,36-45,Married,2,0,8,F,-0.44,1254,122.0,1.03,-848.82,9,233147.45,1972 +1333,Elaine Chase,46-55,Married,2,0,6,F,-8.39,336,125.32,1.05,-3928.63,132,58648.95,491 +1334,Ruth Miller,46-55,Married,5,3,4,F,-0.61,632,97.05,1.01,-527.88,17,83757.24,873 +1335,Keith Allen,46-55,Married,3,1,10,M,-1.35,375,85.13,1.01,-887.18,37,56018.38,663 +1336,Allison Alvarado,36-45,Married,5,3,9,F,-2.31,1089,107.46,1.15,-5023.1,172,233194.4,2503 +1337,Paul Walker,36-45,Married,5,3,6,M,-0.14,1587,100.76,1.04,-397.14,15,283041.99,2920 +1338,Destiny Hughes,46-55,Married,2,0,4,F,-1.5,221,75.95,1.0,-494.76,11,24988.93,329 +1339,Jeremy Martinez,56-70,Single,2,1,4,M,-1.19,599,111.17,1.09,-939.64,19,88048.23,865 +1340,Erin Ramirez,46-55,Married,2,0,6,F,-0.5,323,118.03,1.0,-341.35,10,80024.57,678 +1341,Melissa Elliott,26-35,Married,4,2,5,F,-1.42,681,75.74,1.14,-2073.35,79,110583.67,1662 +1342,Brittany Joyce,46-55,Single,2,1,5,F,-0.27,282,121.5,1.08,-89.05,2,40216.69,356 +1343,David Humphrey PhD,26-35,Single,2,1,4,M,-0.1,810,88.3,1.05,-125.47,5,112057.7,1336 +1344,Melissa Allen,26-35,Single,1,0,5,F,-2.41,576,122.18,1.01,-1705.08,66,86505.33,714 +1345,Nicholas Scott,46-55,Single,3,1,5,M,-0.13,565,102.09,1.0,-115.76,6,88208.84,864 +1346,Brian House,56-70,Married,2,0,7,M,0.0,624,128.61,1.04,0.0,0,132473.31,1070 +1347,Antonio Griffith,36-45,Single,1,0,4,M,-0.27,1015,108.19,1.0,-447.92,15,178946.27,1654 +1348,Amy Pope,36-45,Married,2,0,4,F,0.0,473,86.99,1.07,0.0,0,55673.44,682 +1349,Crystal Rogers,46-55,Single,1,0,5,F,-1.43,362,102.12,1.0,-607.3,19,43501.18,426 +1350,Wesley Mccoy,46-55,Married,2,0,1,M,-0.19,646,101.81,1.02,-203.04,7,107006.82,1076 +1351,Cindy Parker,46-55,Married,5,3,6,F,-0.17,484,122.96,1.0,-115.4,3,83735.33,681 +1352,Diana Flores,46-55,Single,1,0,2,F,-0.01,750,98.79,1.0,-14.25,1,146108.82,1479 +1353,Michelle Shannon,46-55,Married,2,0,6,F,-0.22,252,130.02,1.01,-63.05,1,37314.46,290 +1354,Kathryn Turner,36-45,Single,1,0,4,F,-0.17,182,130.45,1.02,-35.62,1,26741.68,209 +1355,Gregory Potts,46-55,Married,3,1,2,M,0.0,282,118.25,1.0,0.0,0,44816.27,379 +1356,John Thomas,26-35,Married,3,1,6,M,-0.21,622,85.82,1.01,-222.62,9,91398.14,1080 +1357,Sarah Brown,56-70,Married,4,2,4,F,-0.43,342,85.68,1.0,-328.58,16,65032.34,761 +1358,Jennifer Bailey,26-35,Married,2,0,2,F,-1.2,159,151.16,1.24,-320.58,5,40358.58,331 +1359,Marcus Neal,46-55,Married,2,0,4,M,-0.66,279,108.01,1.04,-243.28,4,39964.7,386 +1360,Eric Elliott,46-55,Married,2,0,5,M,-0.33,660,77.2,1.05,-481.77,24,113253.47,1536 +1361,Renee Deleon,26-35,Single,1,0,5,F,-1.86,348,74.53,1.14,-997.03,43,39949.64,610 +1362,David Baxter,46-55,Married,2,0,5,M,0.0,197,91.43,1.03,0.0,0,28708.83,324 +1363,Jordan Robinson,56-70,Married,2,0,9,M,0.0,86,104.62,1.33,0.0,0,10984.98,140 +1364,Sara Simon,46-55,Single,1,0,6,F,-6.64,567,185.74,1.1,-7898.5,98,221033.28,1314 +1365,Courtney Mayer,56-70,Single,1,0,4,F,-0.9,384,84.04,1.0,-452.02,6,42103.36,501 +1366,Joshua Davila,36-45,Married,5,3,6,M,0.0,362,84.3,1.0,0.0,0,48895.13,580 +1367,Kristy Lloyd,36-45,Single,1,0,5,F,-1.05,576,92.34,1.0,-1378.28,43,120686.45,1307 +1368,Scott Petersen,46-55,Married,2,0,5,M,-0.23,824,94.15,1.01,-376.98,8,151494.81,1622 +1369,Brandi Thompson,36-45,Married,2,0,8,F,-0.93,498,101.67,1.0,-815.32,41,89162.18,881 +1370,Mark Smith,26-35,Married,2,0,4,M,-0.14,315,120.16,1.01,-53.43,2,44337.56,372 +1371,Taylor Lamb,26-35,Married,3,1,6,F,-0.31,555,86.85,1.07,-250.76,4,70869.59,871 +1372,Daniel Thomas,46-55,Single,1,0,5,M,-0.09,718,89.7,1.02,-97.95,3,97328.79,1111 +1373,Corey Wilson,46-55,Married,3,1,4,M,0.0,198,123.46,1.02,0.0,0,27407.47,227 +1374,Ebony Herrera,56-70,Married,3,1,7,F,0.0,143,128.97,1.0,0.0,0,19604.05,152 +1375,Jeffery Powell,46-55,Married,2,0,6,M,-0.06,339,91.34,1.0,-29.68,1,43751.62,479 +1376,Jeffrey Cruz,70+,Married,2,0,3,M,-0.33,295,104.39,1.16,-134.76,4,42801.17,476 +1377,Elijah Conley,26-35,Single,1,0,5,M,0.0,699,116.14,1.06,0.0,0,110099.81,1007 +1378,Crystal Frank,26-35,Married,4,2,8,F,-0.49,915,119.89,1.02,-828.13,26,201536.88,1720 +1379,Keith Blanchard,36-45,Single,1,0,3,M,-0.07,456,125.22,1.0,-54.33,4,97044.98,775 +1380,Shelley Olson,46-55,Married,2,0,1,F,-0.19,231,153.12,1.0,-65.3,2,51908.7,339 +1381,Mary Taylor,70+,Single,2,1,4,F,0.0,383,94.63,1.02,0.0,0,54698.66,592 +1382,Roy Lane,36-45,Married,4,2,5,M,-0.34,961,92.44,1.12,-535.48,12,146149.06,1764 +1383,Elizabeth Rodriguez,26-35,Married,2,0,5,F,-0.05,1438,116.44,1.02,-90.83,2,232754.09,2039 +1384,Jeremy Sims,56-70,Single,1,0,3,M,-0.18,742,105.2,1.05,-234.14,4,135494.46,1356 +1385,Amanda Melton,36-45,Single,1,0,3,F,-0.73,713,91.61,1.03,-983.1,41,123765.43,1393 +1386,Mr. David Bryan,18-25,Married,3,1,5,M,-0.41,590,84.52,1.06,-562.78,18,116552.16,1461 +1387,Stephanie Haney,46-55,Single,1,0,1,F,-0.67,1104,86.49,1.05,-1415.86,41,183797.39,2228 +1388,Jenny Mcdaniel,46-55,Married,2,0,5,F,0.0,474,161.38,1.03,0.0,0,102796.18,657 +1389,Gary Walker,36-45,Married,2,0,5,M,-0.18,533,65.12,1.02,-146.92,4,52744.62,824 +1390,Jorge Lara,26-35,Married,2,0,5,M,-0.13,344,97.93,1.01,-71.24,2,53372.18,553 +1391,Charles Munoz,26-35,Single,1,0,5,M,0.0,979,80.85,1.0,0.0,0,109149.37,1353 +1392,Jared Perez,36-45,Single,3,2,5,M,-0.07,1170,101.12,1.01,-155.83,6,229548.14,2285 +1393,Patrick Morales,26-35,Married,5,3,4,M,0.0,751,101.24,1.12,0.0,0,106100.2,1172 +1394,Stanley Graham,46-55,Single,1,0,4,M,0.0,322,96.22,1.0,0.0,0,35600.16,370 +1395,Phillip Herrera,56-70,Single,1,0,3,M,-0.11,258,109.58,1.04,-35.62,1,35394.05,337 +1396,Julie Guerra,46-55,Married,2,0,2,F,-0.14,659,101.97,1.0,-138.92,5,98299.52,966 +1397,Daniel Scott,46-55,Single,1,0,5,M,0.0,322,107.1,1.0,0.0,0,51623.2,482 +1398,Jeffrey Rodriguez,36-45,Single,1,0,2,M,-0.71,749,102.31,1.04,-669.65,16,97089.33,984 +1399,Brooke Patel,70+,Single,1,0,4,F,0.0,442,89.73,1.0,0.0,0,61197.01,682 +1400,Brianna Cole,46-55,Single,1,0,2,F,-1.22,437,110.73,1.01,-757.76,34,68985.68,629 +1401,Matthew Perez,36-45,Single,1,0,5,M,-0.06,352,89.68,1.0,-35.62,1,56495.34,630 +1402,Debbie Dean,18-25,Single,1,0,12,F,0.0,586,114.82,1.0,0.0,0,112413.46,979 +1403,Erica Ortiz,70+,Single,1,0,5,F,0.0,471,110.57,1.02,0.0,0,72758.19,671 +1404,James Carpenter,36-45,Married,5,3,5,M,-0.44,1024,98.32,1.07,-807.66,29,181195.39,1964 +1405,Javier Davis,56-70,Single,1,0,4,M,-0.01,683,123.96,1.06,-12.47,1,122848.21,1047 +1406,Gabrielle Elliott,46-55,Single,1,0,5,F,0.0,209,100.09,1.0,0.0,0,23220.8,232 +1407,James Cline,36-45,Married,2,0,11,M,-1.96,613,110.51,1.01,-1544.95,52,87082.01,794 +1408,Joanne Randall,26-35,Single,1,0,9,F,-0.44,1033,99.63,1.07,-696.37,18,157110.37,1688 +1409,Charles Cross,70+,Married,2,0,1,M,-0.08,567,119.41,1.06,-83.71,4,122399.87,1085 +1410,Cathy Garcia,46-55,Married,3,1,10,F,-0.03,422,135.51,1.03,-14.25,1,69923.14,529 +1411,Scott Robertson,26-35,Single,1,0,9,M,-0.22,495,93.77,1.07,-150.31,4,65356.71,744 +1412,Holly Gordon,46-55,Single,1,0,5,F,-0.82,502,100.62,1.05,-751.28,35,92669.13,965 +1413,Brian Hamilton,46-55,Married,2,0,5,M,-0.48,363,79.93,1.0,-216.57,3,36368.68,455 +1414,Gabriella Rivas,46-55,Single,1,0,2,F,-0.42,270,100.42,1.01,-138.63,8,32938.16,332 +1415,Gwendolyn Murphy,36-45,Single,1,0,2,F,-0.69,579,104.91,1.0,-712.58,13,108899.89,1040 +1416,William Perry,36-45,Married,3,1,4,M,-0.16,297,86.53,1.01,-56.99,3,31495.19,366 +1417,Monica Henry,46-55,Married,2,0,5,F,-0.13,309,111.26,1.0,-71.24,2,61306.78,551 +1418,Christopher Charles,36-45,Married,3,1,8,M,-1.35,640,80.16,1.01,-1808.05,7,107419.88,1347 +1419,Jessica Frazier,46-55,Married,4,2,4,F,-0.02,487,81.41,1.01,-19.59,1,63903.72,795 +1420,Aaron Leon,70+,Married,2,0,5,M,-0.09,343,126.36,1.0,-35.62,1,52816.53,418 +1421,Timothy Hill,36-45,Single,1,0,6,M,0.0,161,103.23,1.0,0.0,0,17239.23,167 +1422,Timothy Cole,36-45,Single,1,0,6,M,-1.08,269,101.94,1.06,-387.9,6,36697.07,380 +1423,John Grant,46-55,Married,5,3,9,M,-0.19,288,100.16,1.0,-71.24,1,38461.79,384 +1424,Amber Mcgee,46-55,Married,4,2,5,F,-0.46,1009,89.97,1.05,-748.31,25,146650.32,1719 +1425,Antonio Rodriguez,18-25,Single,1,0,4,M,0.0,251,104.19,1.05,0.0,0,29588.84,298 +1426,Monique Higgins,46-55,Single,1,0,5,F,-0.33,219,115.58,1.0,-87.27,6,30167.22,261 +1427,Chris Bass,46-55,Single,1,0,3,M,-0.07,576,95.08,1.11,-85.49,4,124173.79,1450 +1428,Jennifer Burnett,36-45,Married,2,0,5,F,-0.08,922,107.68,1.08,-174.54,7,233350.82,2345 +1429,Jose Gibbs,26-35,Married,2,0,4,M,-0.22,204,98.14,1.02,-71.24,1,31601.37,328 +1430,Melissa Jackson,70+,Single,1,0,2,F,-0.05,921,85.41,1.0,-71.24,1,120602.43,1414 +1431,Andre Blackwell,46-55,Married,2,0,12,M,-0.1,1351,129.62,1.0,-233.31,9,316005.54,2443 +1432,Michele Edwards,26-35,Single,1,0,4,F,0.0,960,114.59,1.02,0.0,0,180360.78,1607 +1433,Angela Davis,56-70,Married,2,0,4,F,-0.4,264,100.82,1.0,-190.56,10,47686.02,473 +1434,Ashley Davis,26-35,Married,2,0,4,F,-0.09,898,75.01,1.0,-137.13,4,117017.63,1563 +1435,Michelle Garcia,56-70,Single,2,1,4,F,-0.09,209,102.49,1.0,-26.71,1,31670.25,309 +1436,Brandon Peterson,46-55,Married,2,0,9,M,-0.09,579,131.5,1.0,-65.3,2,98623.31,750 +1437,Matthew Sparks,26-35,Married,5,3,4,M,-1.13,356,89.95,1.0,-528.6,20,41914.54,466 +1438,Teresa Martin,36-45,Married,5,3,4,F,-0.05,639,80.99,1.0,-62.33,2,95321.49,1177 +1439,Carolyn Middleton,46-55,Single,1,0,6,F,0.0,149,149.18,1.02,0.0,0,24017.46,164 +1440,Heather Gregory,26-35,Married,2,0,5,F,-0.45,158,85.53,1.0,-89.05,2,16764.55,196 +1441,Cheryl Johnson,36-45,Married,3,1,2,F,-1.23,960,129.4,1.02,-2311.12,75,243015.89,1914 +1442,Kurt Harris,36-45,Married,3,1,12,M,-0.07,289,114.11,1.0,-35.62,1,54203.18,475 +1443,Edwin Sanders,56-70,Single,1,0,10,M,-0.6,157,88.03,1.0,-121.11,6,17781.84,202 +1444,James Hoffman,46-55,Married,2,0,12,M,-0.48,1680,121.41,1.02,-1367.8,13,342980.16,2870 +1445,Brian Oconnor,26-35,Married,4,2,8,M,0.0,141,123.6,1.04,0.0,0,20146.02,169 +1446,Denise Hernandez,18-25,Single,2,1,5,F,-0.18,351,106.56,1.01,-81.92,4,47526.32,449 +1447,Sarah Whitehead,26-35,Married,3,1,1,F,-0.19,343,112.19,1.0,-76.58,3,45660.12,407 +1448,Veronica Alvarez,36-45,Single,1,0,5,F,0.0,222,120.6,1.04,0.0,0,29788.93,256 +1449,Jeffery Ellison,46-55,Single,4,2,3,M,-0.08,1241,85.22,1.04,-167.41,5,174794.57,2124 +1450,Mariah Walker,36-45,Single,1,0,4,F,-0.12,1081,97.22,1.01,-226.19,4,181420.41,1880 +1451,Rhonda Lara,36-45,Married,2,0,8,F,-0.06,1385,92.61,1.0,-152.81,3,248194.38,2680 +1452,Karen Hanson,70+,Married,2,0,5,F,-0.61,361,104.8,1.0,-317.9,18,54704.18,522 +1453,Madeline Strickland,46-55,Married,2,0,5,F,-0.07,610,99.42,1.0,-64.12,4,94344.86,952 +1454,Debra Campbell MD,46-55,Married,3,1,3,F,-0.22,503,111.55,1.03,-194.13,6,98829.5,912 +1455,Mary Russo,36-45,Married,3,1,4,F,-0.23,358,82.35,1.01,-115.76,4,41502.75,508 +1456,Crystal Drake,46-55,Single,1,0,4,F,-0.17,244,151.57,1.01,-53.43,2,46834.38,312 +1457,Bobby Johnston,46-55,Single,1,0,5,M,-0.87,694,115.24,1.01,-1069.44,46,141517.23,1237 +1458,Michelle Day,46-55,Married,3,1,6,F,-0.36,628,94.98,1.04,-424.05,12,111980.79,1230 +1459,Kelly Brown,46-55,Single,2,1,5,F,-0.1,288,79.28,1.0,-40.25,2,32742.12,413 +1460,Pamela Williams,46-55,Married,2,0,12,F,-0.11,1034,108.94,1.0,-224.05,6,215258.23,1976 +1461,Thomas Fletcher,46-55,Married,2,0,8,M,-0.41,486,87.19,1.0,-424.75,17,90682.28,1043 +1462,Emily Gomez,56-70,Married,4,2,6,F,0.0,146,124.91,1.01,0.0,0,20235.99,164 +1463,Julie Franklin,36-45,Married,5,3,5,F,-0.53,1356,94.08,1.02,-1476.08,58,263427.89,2845 +1464,Anna Alexander,18-25,Single,1,0,1,F,-0.03,1630,99.32,1.02,-71.24,2,260113.05,2667 +1465,Joseph Green,56-70,Married,2,0,3,M,0.0,233,83.79,1.0,0.0,0,27649.63,330 +1466,Alejandra Vasquez,46-55,Single,1,0,10,F,-0.11,2088,130.45,1.07,-393.89,16,449140.38,3672 +1467,April Mueller,36-45,Married,2,0,5,F,-2.77,361,108.22,1.01,-1309.07,53,51190.17,476 +1468,Taylor Johns,46-55,Single,1,0,3,F,-1.15,403,70.92,1.02,-565.64,5,34822.13,499 +1469,Michael Morgan,46-55,Married,2,0,12,M,-0.62,1037,145.12,1.01,-1114.18,8,260483.14,1811 +1470,Ashley Galvan,36-45,Single,3,1,5,F,-0.28,572,100.61,1.02,-218.17,6,78171.13,790 +1471,Daniel Acosta,46-55,Single,1,0,5,M,-3.15,679,118.77,1.12,-2758.89,95,104158.82,980 +1472,Jorge Williams,36-45,Married,5,3,4,M,-1.14,797,83.67,1.07,-1531.84,64,112111.94,1429 +1473,Marissa Mercado,36-45,Single,1,0,5,F,-0.03,1198,111.55,1.0,-62.33,2,257009.56,2304 +1474,Kristen Reese,46-55,Married,2,0,1,F,-0.23,409,79.45,1.0,-133.57,2,46477.43,585 +1475,Leslie Shaw,70+,Single,3,2,1,F,-0.06,1229,68.56,1.0,-253.49,9,270468.17,3945 +1476,Jeffrey Mahoney,36-45,Single,1,0,5,M,-0.7,364,100.93,1.03,-302.4,7,43700.98,445 +1477,Annette Gates,46-55,Single,1,0,5,F,-0.06,926,79.14,1.0,-113.27,7,150911.69,1911 +1478,Thomas Gonzalez,56-70,Single,1,0,7,M,-1.36,291,115.73,1.38,-731.7,41,62376.54,744 +1479,Kathy Johnson,18-25,Single,1,0,5,F,-0.42,666,95.17,1.07,-404.29,15,90792.79,1022 +1480,Erica Andrade,18-25,Single,1,0,5,F,-0.78,419,102.65,1.01,-415.69,8,54607.83,539 +1481,William Garcia,56-70,Married,5,3,4,M,-0.09,644,102.68,1.01,-75.99,3,87794.71,862 +1482,Joel Sanchez,36-45,Married,2,0,6,M,-0.15,133,102.24,1.0,-35.62,1,24945.94,244 +1483,Annette Murphy,46-55,Married,2,0,4,F,-0.34,274,124.62,1.05,-115.76,5,42370.44,358 +1484,Cody Gomez,56-70,Single,1,0,10,M,-0.18,296,97.28,1.03,-67.68,4,36384.21,384 +1485,Jennifer Kelly,46-55,Married,2,0,6,F,-0.34,1200,105.36,1.07,-986.91,28,307332.44,3116 +1486,Andrew Parker,18-25,Married,2,0,4,M,0.0,434,105.77,1.08,0.0,0,84090.34,859 +1487,Cory Hobbs,36-45,Married,4,2,5,M,-0.2,503,84.77,1.0,-154.23,4,65527.38,773 +1488,Brandon Schwartz,46-55,Single,1,0,5,M,-0.13,156,118.4,1.0,-35.62,1,31257.55,264 +1489,Jonathan King,46-55,Married,2,0,3,M,-0.4,327,85.42,1.06,-225.23,10,48004.42,596 +1490,Mrs. Heather Simpson,26-35,Single,1,0,5,F,-0.2,903,91.93,1.13,-320.58,4,145707.14,1796 +1491,Pamela Newman,26-35,Married,2,0,4,F,0.0,355,115.91,1.0,0.0,0,104902.36,905 +1492,David Roberts,46-55,Married,2,0,3,M,-0.04,925,99.26,1.0,-71.24,2,200311.72,2018 +1493,Michele Foster,46-55,Single,1,0,6,F,-0.78,803,93.29,1.06,-991.31,26,118391.35,1350 +1494,Laura Williamson,46-55,Married,2,0,1,F,-0.06,789,87.13,1.0,-74.8,4,111266.07,1281 +1495,Brittany Mason,46-55,Single,1,0,3,F,-0.16,316,105.72,1.12,-71.24,2,45778.06,484 +1496,Brittany Wheeler,26-35,Married,2,0,1,F,-3.27,601,87.2,1.06,-3249.86,152,86674.49,1055 +1497,Jonathan Fisher,70+,Married,2,0,2,M,0.0,261,110.29,1.0,0.0,0,43013.92,390 +1498,Stacy Roberts,46-55,Married,5,3,6,F,-0.25,509,98.67,1.01,-162.07,4,64233.03,657 +1499,Dwayne Tucker,26-35,Single,3,2,1,M,-0.21,648,82.0,1.02,-193.77,7,77159.92,960 +1500,Ashlee Rodriguez,46-55,Married,2,0,9,F,-1.06,689,123.26,1.01,-954.01,41,110687.12,905 +1501,Amy Snyder,46-55,Single,1,0,5,F,-2.12,273,108.03,1.0,-771.16,31,39215.1,363 +1502,Craig Frost,46-55,Single,1,0,4,M,-0.57,408,106.27,1.04,-284.6,5,52604.13,516 +1503,Brianna Hernandez,36-45,Married,4,2,8,F,0.0,210,67.0,1.02,0.0,0,21505.56,326 +1504,Cheryl Padilla,26-35,Married,5,3,5,F,-0.29,756,113.25,1.01,-381.85,8,147454.23,1310 +1505,David Gaines,26-35,Married,2,0,5,M,0.0,173,116.44,1.06,0.0,0,23753.24,216 +1506,David Calderon,46-55,Single,1,0,5,M,-1.56,495,68.4,1.1,-1296.88,73,56769.14,912 +1507,Dr. David Simpson,46-55,Single,1,0,5,M,-0.44,313,67.08,1.03,-276.05,12,41993.96,642 +1508,Jonathon Larson,70+,Single,1,0,3,M,0.0,163,91.78,1.0,0.0,0,19457.7,212 +1509,Daniel Lee,70+,Married,2,0,4,M,-0.01,706,96.43,1.06,-8.9,1,121690.23,1336 +1510,Robert Carpenter,46-55,Single,1,0,5,M,-0.22,155,133.87,1.01,-35.62,1,21418.89,161 +1511,Krista Parrish,70+,Married,2,0,8,F,0.0,284,99.76,1.0,0.0,0,31822.74,319 +1512,Amanda Hernandez,26-35,Married,2,0,5,F,-0.06,200,103.86,1.0,-13.36,1,23471.27,226 +1513,Hector Martinez,36-45,Married,2,0,8,M,-2.67,75,95.48,1.1,-320.58,4,11457.73,132 +1514,Michelle Duncan,36-45,Single,1,0,6,F,-0.2,351,123.51,1.0,-103.3,4,63976.53,518 +1515,Cassandra Lee,46-55,Single,1,0,4,F,0.0,101,104.31,1.61,0.0,0,15750.84,243 +1516,Ricky Campos,46-55,Single,1,0,2,M,-0.08,704,82.06,1.0,-100.91,4,107176.67,1306 +1517,Mary Brown,26-35,Married,2,0,3,F,-0.49,290,103.27,1.0,-224.41,12,47506.03,460 +1518,Dustin Bowman,36-45,Married,3,1,3,M,-0.88,1194,91.06,1.07,-1949.98,86,202604.51,2383 +1519,Jillian Harvey,26-35,Single,1,0,2,F,-0.06,364,99.37,1.05,-35.62,1,58630.03,621 +1520,Patrick Martin DDS,26-35,Married,3,1,8,M,-0.22,463,127.9,1.06,-137.14,5,79044.45,657 +1521,Douglas Rodriguez,46-55,Married,2,0,7,M,-0.65,576,88.1,1.06,-676.42,20,92059.7,1110 +1522,Jennifer Allen,56-70,Married,2,0,6,F,-0.48,358,121.59,1.0,-237.76,6,60431.66,497 +1523,Kevin Sandoval,70+,Married,2,0,1,M,0.0,356,119.45,1.0,0.0,0,55185.01,462 +1524,Diana Herrera,36-45,Married,2,0,5,F,-0.19,1202,106.7,1.0,-491.2,16,273251.48,2561 +1525,Andrea Carroll,36-45,Single,1,0,2,F,0.0,219,88.42,1.0,0.0,0,41113.19,465 +1526,Joshua Dickerson,36-45,Single,1,0,3,M,0.0,450,130.61,1.01,0.0,0,86853.72,674 +1527,Laura Phillips,46-55,Married,2,0,10,F,0.0,188,98.48,1.02,0.0,0,25112.3,259 +1528,Terri Williams,46-55,Married,3,1,2,F,0.0,454,86.42,1.01,0.0,0,77345.55,906 +1529,Kyle Alvarado,46-55,Married,5,3,9,M,-1.27,1068,101.82,1.0,-2139.42,55,172067.37,1690 +1530,Glenda Simon,70+,Married,2,0,6,F,-0.63,456,86.59,1.01,-499.84,14,68230.22,792 +1531,Jordan Richmond,18-25,Single,5,3,1,M,0.0,625,103.67,1.0,0.0,0,98282.8,948 +1532,Mary Moreno,26-35,Single,1,0,9,F,-0.58,522,126.21,1.13,-404.28,12,88092.71,789 +1533,John Bullock,18-25,Single,1,0,1,M,-0.34,198,89.2,1.09,-148.54,2,38625.41,473 +1534,Jimmy Fox,36-45,Married,3,1,5,M,-0.61,848,96.3,1.06,-1053.84,41,165355.5,1825 +1535,Alison Wolf,46-55,Married,2,0,5,F,-0.31,238,95.25,1.0,-80.14,2,24955.02,262 +1536,Jonathan Fitzpatrick,46-55,Married,2,0,3,M,-0.29,376,89.87,1.02,-144.26,4,45202.1,513 +1537,Roger Miller,18-25,Single,1,0,4,M,-0.23,926,84.92,1.02,-354.06,10,130689.9,1567 +1538,Maria Edwards,36-45,Married,5,3,8,F,-0.14,590,119.76,1.0,-109.34,6,95809.9,800 +1539,Jordan Taylor,56-70,Married,5,3,4,M,0.0,309,108.98,1.13,0.0,0,43590.12,451 +1540,Hannah Douglas,70+,Single,1,0,5,F,0.0,112,86.35,1.0,0.0,0,12693.02,147 +1541,Jeremiah Holmes,70+,Married,3,1,8,M,-0.46,234,96.59,1.0,-142.48,1,30039.05,311 +1542,Melissa Lindsey,26-35,Married,3,1,5,F,-4.25,200,97.8,1.0,-1029.21,45,23667.42,242 +1543,Virginia Thompson,36-45,Married,5,3,5,F,-0.42,347,101.03,1.13,-273.92,10,65973.28,740 +1544,Julie Davis,46-55,Single,1,0,4,F,-0.55,1143,86.37,1.02,-893.43,27,140343.78,1657 +1545,Angela Ward,36-45,Married,2,0,8,F,-0.47,649,102.52,1.17,-505.51,28,110828.02,1262 +1546,Eric Johnson,36-45,Married,2,0,3,M,0.0,628,152.88,1.0,0.0,0,183920.5,1203 +1547,Vanessa Blevins,46-55,Married,2,0,5,F,-3.57,953,127.41,1.0,-6733.7,227,240038.7,1886 +1548,Brianna Barnett,56-70,Married,2,0,4,F,-2.92,460,90.45,1.08,-1711.71,61,53006.19,633 +1549,Teresa Lee,70+,Single,1,0,1,F,-2.87,856,110.79,1.01,-4354.98,197,168074.68,1533 +1550,Jessica Miller,46-55,Married,2,0,9,F,0.0,433,92.16,1.0,0.0,0,59353.22,645 +1551,Roger Baird,46-55,Single,1,0,5,M,-0.39,515,128.05,1.0,-258.23,10,85792.63,670 +1552,Jeffrey Nichols,46-55,Single,2,1,3,M,-0.27,460,136.95,1.0,-174.52,8,89700.97,655 +1553,Taylor Wells,46-55,Married,2,0,5,F,0.0,254,79.75,1.0,0.0,0,29665.85,372 +1554,Tonya Dominguez,46-55,Married,2,0,7,F,-1.93,320,92.36,1.01,-797.9,33,38144.06,416 +1555,Brian Luna,26-35,Single,1,0,7,M,-0.57,2432,94.82,1.02,-2594.37,75,428777.76,4628 +1556,Matthew Mason,36-45,Married,3,1,6,M,-1.07,855,110.03,1.0,-1624.9,77,167244.01,1523 +1557,Kenneth Gomez,46-55,Married,2,0,12,M,0.0,326,124.3,1.0,0.0,0,53447.04,430 +1558,Jordan Terry,36-45,Married,3,1,6,M,-1.67,1407,95.39,1.0,-4548.92,164,260142.02,2737 +1559,Dr. Lynn Ruiz DDS,46-55,Married,3,1,3,F,-0.47,494,97.9,1.0,-354.39,17,73229.48,748 +1560,Nancy Harris,36-45,Married,2,0,6,F,-4.39,137,96.55,1.02,-935.54,33,20565.24,218 +1561,Anthony Robertson,46-55,Single,1,0,5,M,0.0,166,111.86,1.0,0.0,0,19799.92,177 +1562,Sheila Smith,36-45,Single,2,1,4,F,-2.61,110,105.87,1.0,-468.99,17,19056.39,180 +1563,Monica Quinn,46-55,Single,1,0,5,F,-1.24,507,125.02,1.0,-1073.06,18,108267.7,870 +1564,Brandon Collier,36-45,Single,1,0,6,M,-0.23,163,112.32,1.1,-44.52,1,21565.86,212 +1565,James Gardner,26-35,Single,1,0,4,M,-0.06,448,86.66,1.0,-51.05,3,78429.31,905 +1566,Daniel Williams,26-35,Married,2,0,9,M,-0.45,1214,96.35,1.03,-1187.02,31,256394.87,2729 +1567,Dennis Hill,46-55,Single,1,0,5,M,-0.68,581,96.52,1.09,-551.78,30,78661.13,892 +1568,Richard Stein,36-45,Married,2,0,8,M,-0.16,487,87.11,1.0,-106.86,2,59062.29,678 +1569,Kevin Ortiz,46-55,Single,1,0,6,M,-0.07,57,58.66,1.0,-8.9,1,7391.78,126 +1570,Jordan Jones,36-45,Married,3,1,4,M,-0.06,795,106.79,1.06,-71.24,2,126762.78,1256 +1571,Zachary Brock,36-45,Single,1,0,5,M,0.0,341,95.79,1.02,0.0,0,38700.09,413 +1572,Traci Banks,36-45,Single,1,0,3,F,-0.16,648,76.1,1.01,-135.36,4,64075.33,851 +1573,Daisy Roberts,46-55,Single,1,0,4,F,-0.63,647,108.02,1.04,-745.27,26,127568.1,1230 +1574,Tracy Hayes,36-45,Married,2,0,5,F,-4.29,1689,99.46,1.02,-10415.74,430,241697.0,2469 +1575,Madison Coffey,46-55,Married,2,0,1,F,-0.11,315,84.11,1.01,-53.43,2,41636.19,502 +1576,Brianna Cox,26-35,Married,5,3,1,F,0.0,397,96.39,1.03,0.0,0,66217.37,710 +1577,Matthew Boyle,36-45,Married,2,0,5,M,-0.28,252,123.52,1.03,-80.14,2,35080.7,293 +1578,Jennifer Pruitt,46-55,Married,3,1,6,F,-0.78,481,92.87,1.03,-613.86,29,72719.29,810 +1579,John Murphy,46-55,Single,1,0,4,M,-0.13,639,114.29,1.0,-145.45,6,132804.28,1162 +1580,Miss Katrina Hamilton MD,26-35,Married,2,0,5,F,0.0,422,112.76,1.01,0.0,0,59651.72,534 +1581,Kristine Williams,26-35,Married,3,1,1,F,0.0,390,89.82,1.07,0.0,0,45356.97,541 +1582,Matthew Stevens,46-55,Single,3,2,5,M,-2.43,681,112.42,1.05,-2064.13,61,95556.72,895 diff --git a/customersim/requirements.txt b/customersim/requirements.txt index 31aacf2..778524e 100644 --- a/customersim/requirements.txt +++ b/customersim/requirements.txt @@ -1,3 +1,5 @@ -gunicorn -Flask -paho-mqtt +paho-mqtt~=1.5.1 +fastapi==0.63 +setuptools~=39.2.0 +uvicorn[standard]==0.13.4 +pydantic~=1.8.1 diff --git a/customersim/tools/customer_info.csv b/customersim/tools/customer_info.csv new file mode 100644 index 0000000..426a462 --- /dev/null +++ b/customersim/tools/customer_info.csv @@ -0,0 +1,1583 @@ +customer_id,age_range,marital_status,family_size,no_of_children,income_bracket,gender,mean_discount_used_by_cust,unique_items_bought_by_cust,mean_selling_price_paid_by_cust,mean_quantity_bought_by_cust,total_discount_used_by_cust,total_coupons_used_by_cust,total_price_paid_by_cust,total_quantity_bought_by_cust +1,70+,Married,2,0,4,F,-1.75,463,99.22,1.0,-1832.94,78,103982.01,1048 +2,46-55,Married,2,0,1,F,-0.45,352,108.26,1.0,-189.97,4,45360.6,419 +3,18-25,Married,2,0,2,M,-1.89,406,86.97,1.0,-1329.46,53,61312.59,707 +4,46-55,Married,5,3,3,F,-0.08,125,138.34,1.0,-17.81,1,30434.3,220 +5,26-35,Single,3,2,3,M,-0.11,490,115.6,1.03,-90.83,2,91553.24,814 +6,46-55,Married,2,0,5,M,-0.63,429,102.43,1.0,-369.55,11,59716.13,583 +7,26-35,Married,3,1,3,M,-0.65,780,101.8,1.01,-687.47,17,107194.4,1066 +8,26-35,Married,4,2,6,M,-3.72,719,126.24,1.25,-4899.4,192,166389.9,1643 +9,26-35,Single,2,1,4,F,-0.77,405,90.97,1.05,-430.64,8,50672.81,585 +10,46-55,Single,1,0,5,M,0.0,268,94.61,1.04,0.0,0,46455.82,509 +11,70+,Single,2,1,1,M,-1.6,282,116.04,1.11,-988.45,10,71594.7,683 +12,46-55,Married,2,0,7,M,-0.96,436,119.82,1.03,-929.96,45,115504.13,993 +13,36-45,Single,1,0,2,F,-0.11,1192,83.27,1.03,-272.48,10,205750.82,2544 +14,26-35,Married,2,0,6,F,-0.4,327,101.65,1.07,-492.72,27,126446.72,1328 +15,46-55,Married,2,0,6,F,-1.17,463,104.45,1.08,-967.07,39,86483.25,895 +16,36-45,Married,2,0,1,F,0.0,328,136.27,1.08,0.0,0,72497.44,575 +17,36-45,Single,1,0,5,M,-1.24,306,120.93,1.01,-708.13,5,69174.48,575 +18,36-45,Married,2,0,5,F,0.0,315,82.29,1.0,0.0,0,41886.68,509 +19,46-55,Single,1,0,3,F,-0.07,410,91.89,1.0,-90.82,4,127815.7,1391 +20,36-45,Married,2,0,5,F,0.0,229,127.8,1.02,0.0,0,48181.07,385 +21,36-45,Married,2,0,4,M,-0.32,277,100.77,1.0,-120.75,3,38594.99,383 +22,36-45,Single,2,1,4,F,-7.84,329,130.09,1.1,-3334.06,154,55287.89,467 +23,46-55,Married,2,0,4,F,-0.15,467,104.15,1.11,-90.83,3,62072.75,661 +24,70+,Single,1,0,4,M,-0.02,403,98.23,1.0,-12.47,1,63458.36,648 +25,46-55,Married,2,0,4,F,-0.09,96,107.29,1.07,-17.81,1,21028.23,210 +26,36-45,Married,3,1,3,M,-0.27,108,129.41,1.0,-29.68,1,14235.56,110 +27,36-45,Married,2,0,8,M,-0.29,866,99.97,1.0,-334.71,14,116368.66,1167 +28,46-55,Married,2,0,1,F,-0.11,1045,97.75,1.09,-231.52,7,200690.92,2242 +29,70+,Single,3,2,1,M,0.0,232,91.09,1.0,0.0,0,47821.35,525 +30,70+,Single,1,0,5,F,-0.58,198,81.0,1.0,-325.92,18,45440.98,561 +31,36-45,Single,5,3,2,M,-0.72,190,66.47,1.03,-217.27,7,20139.08,313 +32,36-45,Single,1,0,9,M,-0.34,140,117.22,1.0,-71.24,1,24850.27,212 +33,46-55,Married,5,3,9,F,-0.17,774,130.25,1.02,-169.2,3,132724.74,1040 +34,46-55,Married,2,0,2,M,0.0,290,128.31,1.0,0.0,0,48887.4,381 +35,18-25,Married,2,0,4,M,-0.82,170,107.59,1.05,-195.91,4,25712.86,250 +36,36-45,Married,2,0,4,F,-0.16,711,105.03,1.08,-158.86,8,105026.25,1081 +37,36-45,Married,2,0,8,F,-0.04,276,97.55,1.0,-19.59,1,51801.68,531 +38,46-55,Single,2,1,5,M,-0.02,697,110.31,1.02,-17.81,1,111413.62,1030 +39,70+,Married,2,0,4,F,0.0,152,117.45,1.0,0.0,0,25956.71,221 +40,56-70,Married,4,2,7,M,-0.59,696,106.64,1.0,-784.71,34,142365.91,1337 +41,46-55,Single,2,1,4,F,-0.29,295,94.35,1.05,-136.78,2,44626.62,495 +42,26-35,Married,4,2,9,F,-0.72,983,107.26,1.15,-1199.48,45,179236.5,1916 +43,26-35,Single,1,0,5,F,-0.06,368,90.3,1.0,-37.4,2,55443.61,614 +44,36-45,Single,3,2,4,M,0.0,254,151.87,1.0,0.0,0,43890.5,289 +45,46-55,Married,5,3,1,M,-2.88,728,105.31,1.03,-3849.98,163,140910.98,1373 +46,56-70,Married,2,0,5,F,-0.69,674,81.17,1.02,-768.49,24,90176.72,1137 +47,26-35,Single,1,0,4,F,-0.02,828,98.48,1.0,-17.8,2,116401.41,1182 +48,36-45,Married,2,0,3,M,-2.1,244,190.74,1.01,-810.35,12,73436.16,388 +49,56-70,Married,2,0,7,F,-0.24,760,94.19,1.02,-391.46,2,152584.45,1648 +50,46-55,Married,3,1,4,M,0.0,431,109.45,1.09,0.0,0,65996.58,656 +51,70+,Married,2,0,2,F,-0.53,259,75.6,1.0,-302.77,9,42942.81,568 +52,36-45,Married,5,3,7,M,-0.42,858,100.29,1.0,-569.92,19,137394.07,1370 +53,56-70,Single,1,0,3,F,0.0,252,76.46,1.0,0.0,0,39682.81,519 +54,56-70,Single,1,0,3,M,-0.72,487,103.53,1.05,-532.52,23,76407.25,772 +55,46-55,Married,2,0,5,F,-0.87,425,87.1,1.04,-456.83,11,45728.04,547 +56,18-25,Married,3,1,5,F,-0.06,799,91.86,1.0,-88.69,1,140914.43,1534 +57,46-55,Married,2,0,6,M,0.0,208,96.0,1.04,0.0,0,23712.9,257 +58,26-35,Single,1,0,4,M,-0.04,679,94.17,1.03,-41.85,2,102838.9,1125 +59,70+,Single,1,0,4,M,-0.07,523,113.24,1.01,-44.52,1,74965.42,666 +60,36-45,Single,1,0,3,M,-0.57,1485,86.12,1.01,-1898.01,67,286593.68,3367 +61,46-55,Married,2,0,6,M,0.0,315,91.65,1.08,0.0,0,42708.98,501 +62,36-45,Married,3,1,1,M,0.0,252,88.86,1.01,0.0,0,24614.26,280 +63,46-55,Single,1,0,2,F,-2.08,450,103.7,1.01,-2346.26,78,116973.33,1134 +64,46-55,Married,2,0,6,M,-1.1,775,103.82,1.06,-1206.09,17,113679.4,1161 +65,70+,Married,2,0,3,M,0.0,189,125.34,1.0,0.0,0,29454.17,235 +66,46-55,Single,1,0,6,M,-0.01,681,96.39,1.06,-12.47,1,102173.46,1119 +67,36-45,Single,2,1,4,M,0.0,293,62.64,1.0,0.0,0,27747.96,443 +68,26-35,Single,1,0,2,F,-0.32,941,82.8,1.01,-511.49,16,133560.54,1624 +69,46-55,Married,4,2,1,F,-0.38,335,97.62,1.08,-215.8,10,55839.46,620 +70,26-35,Married,2,0,3,F,0.0,129,81.54,1.0,0.0,0,12068.23,148 +71,36-45,Married,5,3,4,M,-1.17,558,86.6,1.0,-1100.82,50,81229.51,942 +72,26-35,Single,3,2,1,F,-0.16,884,71.6,1.0,-229.76,15,99884.29,1395 +73,36-45,Married,5,3,6,M,-0.1,439,104.17,1.02,-53.43,1,58127.59,571 +74,26-35,Married,2,0,2,F,0.0,530,97.29,1.0,0.0,0,77055.52,792 +75,26-35,Single,1,0,4,F,-0.03,552,81.21,1.04,-26.71,1,63992.49,819 +76,36-45,Married,2,0,4,F,-0.94,351,104.66,1.02,-400.35,15,44689.77,436 +77,56-70,Married,2,0,2,M,-0.23,136,104.38,1.19,-35.62,1,16387.1,187 +78,36-45,Married,4,2,8,F,-0.48,1366,109.87,1.08,-1260.63,49,290936.29,2869 +79,26-35,Married,2,0,4,M,-0.05,488,96.9,1.02,-35.62,1,67734.62,715 +80,36-45,Married,2,0,8,M,-1.02,838,129.26,1.08,-1705.95,50,217157.69,1814 +81,36-45,Single,1,0,5,M,-1.57,99,101.86,1.0,-248.75,6,16094.07,158 +82,70+,Single,2,1,4,F,-0.44,484,80.74,1.01,-331.25,15,61039.57,763 +83,46-55,Single,1,0,4,M,0.0,384,98.43,1.05,0.0,0,81205.81,865 +84,46-55,Single,1,0,8,M,-0.54,326,122.88,1.0,-213.36,1,48169.8,392 +85,46-55,Married,3,1,3,F,-0.05,475,88.31,1.0,-32.65,2,53516.98,606 +86,56-70,Married,3,1,3,M,-0.05,409,102.9,1.0,-26.71,1,55360.92,538 +87,46-55,Single,2,1,4,M,-0.06,387,97.83,1.04,-35.62,1,62026.76,660 +88,36-45,Single,1,0,4,M,-1.62,960,111.3,1.12,-2517.17,73,172846.55,1733 +89,46-55,Single,1,0,3,M,-1.45,498,87.8,1.08,-970.34,49,58647.94,720 +90,46-55,Married,2,0,5,F,-0.46,888,107.83,1.03,-542.31,10,126272.5,1203 +91,36-45,Single,1,0,5,M,0.0,383,100.8,1.01,0.0,0,57052.5,569 +92,46-55,Married,2,0,1,M,-0.27,417,109.59,1.0,-146.04,8,59508.78,543 +93,56-70,Married,2,0,3,F,-0.21,750,110.3,1.01,-209.27,6,110076.58,1008 +94,46-55,Single,1,0,5,F,-0.05,555,88.76,1.0,-44.52,1,75181.31,847 +95,36-45,Single,2,1,4,F,-1.36,232,105.79,1.0,-356.19,12,27612.17,261 +96,46-55,Single,1,0,6,M,-0.41,398,111.24,1.0,-216.4,8,58400.26,525 +97,36-45,Single,1,0,6,F,-0.76,293,88.58,1.04,-375.94,10,43933.76,516 +98,46-55,Married,2,0,3,F,-0.04,332,89.86,1.0,-20.07,1,49781.54,554 +99,26-35,Single,1,0,2,F,0.0,206,87.78,1.0,0.0,0,55828.0,636 +100,56-70,Married,2,0,7,M,-0.95,1011,90.91,1.04,-1821.35,58,174272.27,1994 +101,36-45,Single,2,1,4,F,0.0,370,117.04,1.0,0.0,0,55009.56,470 +102,46-55,Single,1,0,5,F,-0.25,237,118.3,1.0,-65.3,2,31349.23,265 +103,46-55,Married,2,0,4,F,0.0,864,107.98,1.14,0.0,0,187337.79,1975 +104,46-55,Single,1,0,4,M,-1.37,186,90.21,1.05,-348.17,14,23003.33,268 +105,36-45,Single,3,1,5,F,-0.25,296,113.68,1.01,-121.11,4,54794.34,486 +106,26-35,Married,5,3,3,M,-0.26,869,77.22,1.06,-347.1,14,101621.48,1391 +107,18-25,Married,2,0,2,M,-0.01,478,107.57,1.01,-8.9,1,64432.19,603 +108,46-55,Single,1,0,5,F,-11.4,154,192.99,1.0,-3043.71,86,51529.28,267 +109,36-45,Married,2,0,8,F,0.0,178,90.92,1.02,0.0,0,19729.22,222 +110,18-25,Married,3,1,6,F,-0.04,424,90.75,1.03,-35.62,1,79137.98,896 +111,26-35,Married,3,1,3,F,0.0,295,119.58,1.0,0.0,0,64213.63,537 +112,46-55,Married,2,0,4,M,-0.26,701,86.62,1.01,-272.49,12,90950.58,1065 +113,56-70,Married,2,0,4,M,-0.93,309,92.4,1.13,-427.06,14,42505.39,520 +114,26-35,Married,3,1,5,M,0.0,227,110.83,1.0,0.0,0,45219.34,408 +115,36-45,Married,4,2,8,M,0.0,110,108.69,1.0,0.0,0,15651.35,144 +116,26-35,Married,2,0,2,M,0.0,345,117.8,1.0,0.0,0,51123.96,434 +117,26-35,Single,1,0,9,M,-0.11,236,81.38,1.1,-35.62,2,26531.28,360 +118,26-35,Single,1,0,7,F,-0.17,270,92.92,1.0,-71.24,2,38190.54,411 +119,46-55,Single,1,0,3,F,-0.31,467,116.58,1.12,-247.56,11,94200.17,901 +120,46-55,Single,1,0,2,F,-0.31,304,86.02,1.05,-118.73,2,32772.56,399 +121,26-35,Single,1,0,5,F,-0.73,165,71.17,1.0,-142.48,3,13949.05,196 +122,46-55,Married,3,1,10,F,0.0,372,99.81,1.0,0.0,0,44314.42,444 +123,46-55,Married,2,0,4,M,-0.35,354,111.74,1.0,-276.06,13,87047.03,779 +124,46-55,Married,2,0,6,F,-0.16,377,110.51,1.24,-106.86,3,71721.55,803 +125,46-55,Married,2,0,6,F,0.0,282,111.55,1.02,0.0,0,35920.0,327 +126,46-55,Married,2,0,6,M,0.0,530,88.91,1.07,0.0,0,80192.83,968 +127,26-35,Married,2,0,5,F,-0.23,731,86.12,1.0,-292.27,14,107650.4,1256 +128,36-45,Married,3,1,1,F,-0.27,1341,91.44,1.0,-605.18,12,204098.83,2232 +129,46-55,Married,2,0,6,F,0.0,132,100.65,1.0,0.0,0,15500.13,154 +130,26-35,Single,3,2,3,M,0.0,323,108.74,1.0,0.0,0,47085.34,435 +131,46-55,Married,2,0,5,M,-3.39,326,103.1,1.06,-2523.24,117,76813.06,786 +132,46-55,Married,3,1,3,F,-2.47,584,113.68,1.06,-2174.63,41,100261.74,933 +133,36-45,Married,5,3,7,F,-0.16,467,94.33,1.0,-126.45,5,73389.73,778 +134,70+,Married,2,0,4,F,0.0,242,84.81,1.0,0.0,0,44692.52,527 +135,36-45,Single,1,0,1,M,-0.16,614,97.87,1.01,-124.31,4,74673.18,770 +136,56-70,Single,2,1,1,F,-0.08,341,95.68,1.0,-55.21,3,65442.52,684 +137,26-35,Single,1,0,5,M,-0.54,290,72.84,1.01,-231.53,8,31247.55,435 +138,36-45,Single,1,0,5,M,-0.08,222,113.26,1.0,-32.06,2,44283.2,391 +139,56-70,Married,2,0,9,F,-0.51,288,94.73,1.08,-206.6,9,38272.26,437 +140,46-55,Married,2,0,2,F,-0.09,725,115.38,1.13,-106.86,1,140882.46,1383 +141,56-70,Married,2,0,9,M,-0.03,897,120.18,1.01,-54.85,2,207434.32,1749 +142,46-55,Married,2,0,4,F,-0.22,348,83.89,1.08,-135.36,3,50671.48,655 +143,70+,Single,1,0,2,M,-0.1,464,75.57,1.01,-64.12,4,50556.18,677 +144,36-45,Married,2,0,4,M,-0.04,646,84.44,1.03,-53.43,1,121842.62,1491 +145,46-55,Single,1,0,3,F,0.0,436,107.28,1.01,0.0,0,56751.89,534 +146,46-55,Married,2,0,6,M,0.0,210,90.93,1.03,0.0,0,29096.32,330 +147,36-45,Married,5,3,5,F,-0.04,227,52.98,1.0,-30.28,2,44129.64,835 +148,46-55,Married,2,0,4,F,-0.44,392,114.01,1.05,-204.82,4,53128.23,487 +149,26-35,Single,1,0,3,M,-0.06,659,82.1,1.03,-65.3,3,90230.33,1132 +150,36-45,Married,2,0,4,F,-0.4,1493,91.72,1.04,-1094.17,36,248736.32,2817 +151,36-45,Married,5,3,12,M,-0.06,991,118.91,1.05,-100.62,6,201314.36,1780 +152,36-45,Single,1,0,6,F,-0.81,310,98.47,1.0,-332.44,12,40175.1,408 +153,46-55,Single,1,0,5,F,-1.77,740,67.92,1.0,-2410.36,86,92716.3,1365 +154,46-55,Married,5,3,6,F,-0.22,640,101.56,1.0,-224.4,7,101455.6,999 +155,70+,Married,2,0,5,F,-0.22,718,106.88,1.11,-242.2,11,117675.63,1218 +156,46-55,Married,3,1,7,M,0.0,158,91.26,1.11,0.0,0,16335.33,198 +157,26-35,Single,1,0,4,M,0.0,147,88.17,1.0,0.0,0,18515.96,211 +158,36-45,Married,4,2,8,M,-2.38,975,105.35,1.0,-3022.65,106,133902.58,1271 +159,36-45,Married,3,1,1,M,-3.83,268,103.94,1.01,-1674.14,14,45421.61,441 +160,36-45,Married,2,0,9,M,-0.04,378,100.31,1.0,-17.81,1,47345.28,474 +161,46-55,Single,1,0,1,F,-0.54,1235,76.96,1.0,-1537.74,60,219958.63,2872 +162,26-35,Married,2,0,5,M,-0.99,702,117.55,1.01,-1990.73,64,236634.44,2040 +163,26-35,Single,3,2,3,M,-0.02,369,75.14,1.0,-11.58,1,46963.12,628 +164,46-55,Married,2,0,5,F,-1.11,673,65.34,1.01,-1117.04,40,65997.64,1020 +165,46-55,Single,1,0,8,F,-0.17,171,118.17,1.16,-35.62,1,25052.5,245 +166,46-55,Single,1,0,4,M,-0.16,718,95.06,1.0,-175.73,5,106559.85,1121 +167,46-55,Married,2,0,4,M,-0.17,543,93.11,1.0,-189.86,10,102239.32,1098 +168,46-55,Married,2,0,6,M,0.0,358,97.94,1.01,0.0,0,51615.54,534 +169,36-45,Married,2,0,5,F,-0.04,351,89.93,1.0,-19.59,1,42445.21,473 +170,36-45,Married,5,3,6,F,-0.08,301,105.52,1.02,-42.67,2,56031.17,542 +171,70+,Married,2,0,4,M,0.0,85,119.71,1.0,0.0,0,14365.53,120 +172,26-35,Married,2,0,6,F,0.0,286,114.92,1.0,0.0,0,48497.74,422 +173,26-35,Married,2,0,4,F,-0.01,441,90.78,1.03,-5.34,1,93772.47,1068 +174,36-45,Single,1,0,4,F,-0.02,582,96.18,1.05,-17.81,1,95407.08,1042 +175,70+,Married,2,0,1,F,0.0,316,118.55,1.0,0.0,0,48366.74,408 +176,26-35,Married,2,0,4,F,-1.08,218,97.64,1.08,-333.06,25,30074.28,334 +177,46-55,Married,2,0,5,F,0.0,208,112.8,1.06,0.0,0,31246.76,295 +178,36-45,Single,1,0,5,F,-0.41,184,100.78,1.0,-114.87,5,28118.85,280 +179,46-55,Single,1,0,4,M,-1.52,462,112.09,1.04,-1113.8,42,81939.43,757 +180,26-35,Married,2,0,5,F,-0.43,545,121.35,1.0,-308.83,6,87006.78,717 +181,36-45,Single,1,0,2,F,-0.21,509,97.02,1.04,-167.4,7,76161.72,818 +182,46-55,Single,1,0,2,F,0.0,115,134.07,1.0,0.0,0,19708.82,147 +183,26-35,Married,4,2,2,F,-0.04,481,90.64,1.03,-26.71,1,57737.32,656 +184,26-35,Married,2,0,5,M,0.0,418,102.12,1.08,0.0,0,53814.79,571 +185,46-55,Single,1,0,7,F,0.0,518,90.91,1.13,0.0,0,80632.95,1003 +186,26-35,Married,2,0,4,F,-0.01,652,78.5,1.0,-17.81,1,117276.26,1494 +187,36-45,Married,2,0,7,F,-0.04,329,80.29,1.0,-17.81,1,39663.35,494 +188,70+,Married,2,0,6,M,-0.04,546,103.48,1.0,-35.62,1,83711.32,809 +189,26-35,Married,4,2,5,M,-0.88,716,94.65,1.0,-1206.92,36,130053.59,1377 +190,46-55,Single,1,0,3,M,-0.22,323,93.98,1.0,-89.05,1,38251.42,407 +191,56-70,Married,2,0,1,F,-0.04,750,104.54,1.0,-46.3,3,110083.82,1053 +192,36-45,Married,4,2,4,F,-0.07,826,111.01,1.0,-90.84,4,153971.82,1387 +193,46-55,Married,3,1,4,M,0.0,287,114.9,1.01,0.0,0,42284.87,371 +194,46-55,Single,1,0,3,M,-0.26,600,101.83,1.02,-235.98,8,91141.24,909 +195,26-35,Married,2,0,5,M,-0.06,1218,96.12,1.06,-118.73,2,176671.35,1945 +196,70+,Married,2,0,6,M,-0.84,335,114.27,1.06,-509.35,14,69247.58,642 +197,18-25,Married,4,2,4,F,-2.03,547,79.81,1.05,-1971.96,76,77579.45,1020 +198,36-45,Single,1,0,3,M,0.0,215,90.61,1.02,0.0,0,57806.61,648 +199,46-55,Married,3,1,5,M,0.0,240,126.82,1.04,0.0,0,41091.1,336 +200,70+,Single,1,0,4,F,-2.55,387,115.68,1.0,-1439.19,36,65241.99,564 +201,46-55,Married,3,1,6,F,0.0,381,73.89,1.0,0.0,0,52685.48,713 +202,46-55,Married,2,0,6,F,-0.57,626,107.12,1.0,-626.07,39,117405.33,1096 +203,36-45,Married,5,3,5,F,-0.42,169,98.43,1.01,-124.67,2,29529.22,304 +204,46-55,Single,4,3,4,M,-0.05,600,84.72,1.06,-41.85,2,75319.67,938 +205,46-55,Married,2,0,7,M,-1.72,533,113.79,1.0,-1667.11,85,110372.74,970 +206,36-45,Married,4,2,5,F,-0.06,465,106.15,1.01,-35.62,1,59869.59,567 +207,46-55,Single,1,0,5,M,-0.49,892,115.44,1.08,-740.88,28,175582.84,1636 +208,46-55,Married,3,1,10,M,-0.08,1439,117.81,1.03,-231.53,6,333162.8,2902 +209,26-35,Single,1,0,6,M,0.0,366,80.42,1.0,0.0,0,53964.24,671 +210,46-55,Married,2,0,6,F,-0.09,217,71.2,1.03,-50.76,3,38235.93,553 +211,56-70,Married,2,0,10,M,-0.07,217,101.18,1.0,-19.59,1,28230.09,279 +212,36-45,Married,5,3,8,F,-0.08,873,89.55,1.0,-80.14,2,94559.55,1056 +213,36-45,Married,3,1,2,M,0.0,209,79.16,1.0,0.0,0,24776.17,313 +214,46-55,Married,2,0,1,M,-1.2,763,88.78,1.01,-1556.83,44,114787.63,1302 +215,36-45,Single,1,0,9,M,0.0,304,119.01,1.0,0.0,0,56648.83,476 +216,46-55,Single,1,0,5,F,-0.16,278,91.91,1.05,-71.24,2,40439.23,464 +217,46-55,Married,2,0,12,M,-0.68,115,82.65,1.03,-159.75,11,19340.91,241 +218,46-55,Married,3,1,5,M,0.0,246,76.88,1.0,0.0,0,27063.52,352 +219,70+,Married,2,0,3,F,-0.34,377,74.13,1.0,-187.89,6,40623.74,548 +220,56-70,Single,1,0,5,F,-0.21,354,92.33,1.0,-84.78,3,37949.56,411 +221,46-55,Single,1,0,6,F,-0.15,175,129.2,1.0,-32.94,2,28423.71,220 +222,70+,Married,2,0,3,F,-0.18,801,85.59,1.0,-260.02,7,122143.72,1427 +223,70+,Married,2,0,5,M,0.0,335,97.34,1.0,0.0,0,65316.78,671 +224,46-55,Single,1,0,1,F,0.0,353,78.3,1.0,0.0,0,35391.44,452 +225,46-55,Married,4,2,5,F,0.0,252,115.53,1.21,0.0,0,38704.17,404 +226,46-55,Single,1,0,5,M,-0.29,587,137.29,1.01,-377.21,10,177518.67,1304 +227,46-55,Married,2,0,9,M,-0.09,454,109.94,1.0,-45.42,3,57937.84,527 +228,26-35,Married,5,3,4,F,-1.02,1104,101.42,1.0,-2032.05,86,201221.05,1984 +229,18-25,Single,2,1,5,F,-0.32,262,107.64,1.05,-160.29,3,54682.02,533 +230,36-45,Married,2,0,5,F,-0.13,1192,115.83,1.0,-276.76,6,253657.11,2190 +231,46-55,Single,1,0,5,F,-0.27,1071,75.15,1.0,-578.46,13,161114.84,2144 +232,70+,Single,1,0,5,F,-0.15,364,98.55,1.02,-92.26,4,60707.49,628 +233,36-45,Married,2,0,8,F,-0.19,426,110.73,1.01,-106.86,2,63560.95,577 +234,46-55,Married,3,1,10,F,-0.13,508,117.23,1.02,-114.57,4,100581.98,879 +235,56-70,Married,2,0,5,F,-3.16,764,113.55,1.0,-4619.7,210,166009.0,1462 +236,18-25,Married,2,0,4,M,-0.15,155,108.04,1.04,-35.62,1,25173.39,243 +237,36-45,Single,2,1,4,F,0.0,188,89.13,1.01,0.0,0,18805.61,214 +238,70+,Married,2,0,5,F,-0.16,504,85.62,1.1,-125.92,7,67214.24,862 +239,36-45,Married,3,1,6,M,-0.13,1409,102.16,1.0,-401.6,13,319031.88,3123 +240,46-55,Single,1,0,2,M,-0.48,615,146.5,1.02,-500.8,9,153385.98,1072 +241,26-35,Married,5,3,11,M,0.0,511,72.02,1.0,0.0,0,112776.33,1566 +242,46-55,Married,2,0,5,M,0.0,374,90.65,1.0,0.0,0,56748.56,626 +243,56-70,Married,2,0,5,F,-0.63,494,137.96,1.04,-550.91,23,121268.21,913 +244,70+,Married,2,0,2,M,-0.11,666,95.88,1.0,-189.97,4,171623.86,1790 +245,26-35,Single,2,1,4,M,-0.64,456,110.64,1.0,-365.09,7,63172.92,571 +246,46-55,Single,1,0,5,M,-0.39,420,100.46,1.03,-388.22,17,100059.2,1029 +247,36-45,Married,2,0,5,F,0.0,445,125.11,1.04,0.0,0,66308.11,550 +248,36-45,Single,1,0,3,M,-0.56,819,68.15,1.02,-858.08,39,104202.24,1555 +249,26-35,Single,1,0,5,F,-1.04,566,131.73,1.0,-843.89,28,107095.86,813 +250,36-45,Single,1,0,9,F,-0.77,1134,108.35,1.03,-1977.97,79,277043.37,2637 +251,46-55,Single,1,0,3,F,-0.27,238,113.41,1.01,-89.05,1,38106.74,339 +252,70+,Single,1,0,3,F,-0.19,209,121.79,1.0,-46.3,3,28986.97,238 +253,36-45,Married,3,1,12,M,-0.01,848,131.17,1.0,-12.47,1,223909.04,1707 +254,70+,Single,1,0,3,F,-0.2,723,95.29,1.0,-186.82,6,91094.91,956 +255,26-35,Married,2,0,4,M,0.0,246,121.52,1.0,0.0,0,33417.86,275 +256,26-35,Married,3,1,4,F,-0.49,171,94.61,1.04,-144.56,6,28192.49,311 +257,26-35,Married,5,3,5,M,-0.62,652,105.44,1.07,-708.53,23,119782.95,1212 +258,36-45,Single,1,0,8,M,-0.03,402,75.15,1.04,-17.8,2,38627.46,532 +259,36-45,Married,3,1,9,M,-0.27,1653,134.68,1.05,-743.74,6,370091.54,2892 +260,36-45,Married,5,3,5,F,-0.38,216,99.03,1.09,-117.54,3,30601.77,337 +261,56-70,Married,2,0,5,M,-1.19,93,158.07,1.0,-130.85,5,17387.23,110 +262,26-35,Married,2,0,3,F,-0.02,670,129.22,1.02,-19.59,1,150925.52,1194 +263,26-35,Single,1,0,5,M,-0.07,345,76.66,1.23,-35.62,1,41242.22,660 +264,46-55,Single,1,0,5,M,-0.66,321,147.74,1.0,-262.7,3,58799.62,398 +265,18-25,Single,2,1,3,F,-2.65,713,92.47,1.02,-2723.87,109,95057.11,1049 +266,56-70,Married,5,3,4,M,-0.56,194,82.66,1.0,-145.69,3,21409.2,259 +267,36-45,Married,2,0,1,M,-0.16,540,127.99,1.05,-117.37,6,95990.19,788 +268,70+,Married,2,0,2,F,-1.06,437,82.83,1.0,-709.89,16,55495.9,672 +269,46-55,Single,1,0,7,M,-0.02,371,90.4,1.13,-19.59,1,79104.01,990 +270,36-45,Married,2,0,1,M,-0.1,391,89.35,1.02,-56.1,4,50928.12,584 +271,36-45,Married,3,1,4,M,-0.03,706,104.33,1.05,-31.16,2,126236.06,1266 +272,56-70,Single,1,0,6,F,-1.04,234,121.91,1.04,-286.74,10,33646.07,286 +273,46-55,Single,1,0,5,M,0.0,448,101.72,1.0,0.0,0,57673.55,567 +274,46-55,Married,2,0,10,F,-2.09,746,101.62,1.11,-2051.65,79,99996.18,1088 +275,46-55,Married,5,3,8,M,-0.03,660,86.76,1.0,-37.4,2,109573.46,1263 +276,46-55,Married,2,0,5,F,-0.76,597,97.5,1.15,-922.55,53,118950.83,1409 +277,18-25,Married,2,0,5,F,-0.07,658,76.31,1.0,-80.14,3,84095.62,1102 +278,70+,Married,2,0,6,M,-0.99,137,103.89,1.01,-158.15,3,16519.23,161 +279,18-25,Single,1,0,3,M,-0.25,438,106.76,1.02,-160.29,3,68327.76,650 +280,26-35,Married,4,2,8,F,0.0,139,79.3,1.0,0.0,0,16652.88,210 +281,36-45,Married,3,1,2,F,0.0,526,90.88,1.0,0.0,0,93058.67,1025 +282,46-55,Married,3,1,6,F,0.0,322,113.98,1.0,0.0,0,47643.2,418 +283,26-35,Married,5,3,5,M,-1.4,684,125.88,1.03,-1582.34,47,142744.07,1165 +284,36-45,Married,2,0,5,F,-1.43,357,104.21,1.0,-785.21,27,57109.82,548 +285,46-55,Single,1,0,1,F,0.0,68,111.88,1.0,0.0,0,11635.7,104 +286,46-55,Single,1,0,5,F,-0.03,155,98.28,1.0,-12.47,1,45109.41,459 +287,26-35,Single,2,1,2,M,-0.76,328,88.54,1.05,-304.2,10,35239.21,417 +288,46-55,Married,2,0,3,F,-0.79,269,90.64,1.0,-395.02,13,45137.46,498 +289,46-55,Married,2,0,6,M,-0.09,385,126.0,1.0,-55.21,3,77614.86,616 +290,70+,Married,2,0,3,F,0.0,214,110.86,1.01,0.0,0,33147.11,302 +291,46-55,Married,2,0,12,F,0.0,167,82.44,1.0,0.0,0,33304.3,404 +292,26-35,Single,2,1,1,F,0.0,226,105.83,1.02,0.0,0,30160.28,291 +293,56-70,Single,1,0,4,M,-0.51,915,94.12,1.0,-866.58,27,160282.6,1703 +294,18-25,Single,2,1,5,M,-0.39,724,143.37,1.0,-425.66,9,155264.33,1087 +295,56-70,Married,2,0,4,F,-2.79,293,85.89,1.02,-1091.22,26,33584.71,399 +296,18-25,Single,1,0,1,M,-0.4,97,133.92,1.0,-81.92,4,27185.71,203 +297,70+,Single,1,0,3,M,-0.65,132,76.42,1.06,-110.42,6,13068.45,182 +298,70+,Single,1,0,7,M,-0.28,290,107.17,1.0,-89.05,4,34081.09,318 +299,26-35,Married,3,1,6,M,-0.63,330,109.08,1.13,-446.23,14,77443.58,801 +300,70+,Married,2,0,5,M,-4.45,540,90.62,1.02,-3516.34,152,71587.39,804 +301,18-25,Married,2,0,2,M,-0.25,360,106.89,1.0,-106.5,1,44895.4,420 +302,36-45,Married,3,1,12,F,-0.43,602,103.13,1.0,-436.34,8,104780.38,1020 +303,46-55,Single,1,0,6,F,-1.39,931,108.83,1.0,-2193.59,90,171184.55,1579 +304,70+,Married,3,1,4,F,-0.06,231,113.13,1.04,-26.71,1,53172.27,488 +305,36-45,Married,5,3,4,M,-0.63,1087,129.02,1.0,-1131.8,35,231593.86,1795 +306,46-55,Single,1,0,5,M,0.0,347,91.46,1.08,0.0,0,51859.48,613 +307,70+,Married,2,0,9,F,-0.4,330,89.82,1.0,-153.16,5,34759.82,387 +308,70+,Married,3,1,5,F,-0.06,316,102.93,1.0,-26.71,1,42716.18,415 +309,26-35,Single,1,0,5,M,-0.5,400,87.79,1.0,-260.02,7,45298.74,516 +310,46-55,Married,2,0,1,M,-0.43,549,103.6,1.07,-316.06,10,75835.96,781 +311,26-35,Married,5,3,11,M,-0.75,397,95.46,1.0,-400.72,4,50880.84,533 +312,46-55,Married,2,0,10,F,0.0,303,97.17,1.0,0.0,0,35757.45,369 +313,46-55,Single,1,0,4,M,-0.43,398,109.56,1.0,-222.62,4,56093.97,512 +314,26-35,Married,2,0,2,M,-0.4,440,101.05,1.0,-240.43,3,60933.44,606 +315,36-45,Single,1,0,3,F,-1.03,448,85.14,1.15,-802.69,19,66153.72,896 +316,56-70,Single,1,0,9,F,-0.01,584,94.72,1.08,-8.9,1,84583.93,963 +317,36-45,Married,5,3,6,M,-4.08,502,96.32,1.0,-3436.12,160,81098.05,842 +318,46-55,Married,2,0,6,F,0.0,527,120.9,1.07,0.0,0,82938.83,734 +319,46-55,Married,2,0,5,M,-1.0,716,107.08,1.07,-1439.9,45,154191.78,1539 +320,56-70,Single,1,0,5,F,-0.46,480,105.33,1.02,-449.52,29,102805.11,1000 +321,36-45,Married,3,1,5,F,-0.17,414,112.13,1.0,-89.05,2,60099.82,536 +322,46-55,Single,1,0,4,F,0.0,187,94.15,1.0,0.0,0,20901.34,222 +323,26-35,Married,2,0,3,F,-0.03,436,85.4,1.0,-17.81,1,50643.09,593 +324,36-45,Single,1,0,6,F,-0.09,753,99.68,1.0,-94.39,3,102366.34,1028 +325,46-55,Single,1,0,5,F,-0.12,274,82.76,1.02,-47.37,1,32357.21,399 +326,36-45,Single,3,1,5,F,0.0,180,124.41,1.0,0.0,0,26623.34,214 +327,36-45,Married,3,1,12,M,-0.4,1131,88.17,1.04,-727.0,15,159242.52,1886 +328,46-55,Married,2,0,8,F,-0.47,426,88.36,1.06,-400.72,7,74925.29,895 +329,46-55,Married,2,0,6,M,-1.62,604,169.19,1.0,-1841.55,27,192882.02,1142 +330,36-45,Single,1,0,8,M,0.0,217,78.88,1.0,0.0,0,34705.62,440 +331,26-35,Married,3,1,5,M,0.0,350,87.58,1.0,0.0,0,37923.42,433 +332,26-35,Married,5,3,4,M,-0.07,635,107.57,1.03,-55.21,2,83367.33,797 +333,46-55,Married,2,0,6,M,-2.59,604,97.42,1.0,-2383.83,70,89529.02,919 +334,26-35,Married,2,0,8,F,-1.18,198,108.73,1.0,-265.35,14,24463.21,225 +335,36-45,Married,5,3,4,F,-0.13,330,81.72,1.0,-73.02,3,45765.55,561 +336,70+,Single,1,0,4,M,-0.74,374,91.62,1.07,-543.02,15,66972.11,784 +337,26-35,Single,1,0,5,F,-0.48,332,127.51,1.0,-213.72,3,56357.35,444 +338,46-55,Single,1,0,5,F,-0.1,500,99.98,1.06,-65.89,3,66285.75,706 +339,46-55,Single,1,0,5,F,-0.08,276,112.4,1.0,-53.43,3,78228.97,696 +340,36-45,Married,4,2,2,M,-0.06,400,85.11,1.0,-29.68,1,44342.16,521 +341,36-45,Married,2,0,5,F,-0.34,254,129.12,1.0,-97.94,4,36926.97,287 +342,46-55,Single,1,0,9,M,-0.1,372,104.42,1.0,-59.37,2,60562.12,580 +343,46-55,Single,1,0,4,F,0.0,294,50.22,1.0,0.0,0,28525.24,568 +344,46-55,Married,3,1,8,F,-0.16,381,79.73,1.0,-80.14,3,39945.73,502 +345,46-55,Single,1,0,5,M,0.0,244,123.54,1.0,0.0,0,35827.07,290 +346,36-45,Married,2,0,9,M,-0.02,473,118.73,1.01,-13.36,1,80263.71,682 +347,26-35,Single,1,0,5,M,-0.04,259,124.25,1.01,-14.25,1,46220.58,374 +348,26-35,Single,1,0,4,F,-0.18,405,91.08,1.03,-101.52,6,50275.0,571 +349,36-45,Married,3,1,4,F,-0.04,217,96.88,1.0,-24.58,1,66268.78,684 +350,70+,Single,1,0,2,M,-0.14,397,97.57,1.01,-62.33,2,44100.34,455 +351,36-45,Married,3,1,1,F,-0.2,444,79.16,1.01,-160.3,7,62377.47,792 +352,46-55,Married,2,0,5,F,-0.27,282,95.37,1.09,-164.92,9,57987.28,661 +353,36-45,Single,2,1,6,M,-0.14,674,109.69,1.01,-128.24,5,101902.89,934 +354,18-25,Single,1,0,12,F,-0.03,253,112.4,1.0,-12.47,1,46756.5,416 +355,46-55,Married,2,0,6,M,-0.12,967,92.63,1.01,-189.05,6,150609.96,1644 +356,36-45,Single,5,3,2,M,0.0,342,97.75,1.0,0.0,0,56598.12,579 +357,18-25,Married,2,0,4,F,-0.05,894,105.59,1.06,-64.11,3,134525.24,1349 +358,70+,Married,2,0,6,M,0.0,285,131.28,1.02,0.0,0,51069.83,396 +359,26-35,Married,3,1,3,F,-0.36,422,96.41,1.05,-191.99,4,50903.96,555 +360,46-55,Single,1,0,5,F,-0.17,256,75.59,1.05,-53.42,2,23809.56,331 +361,18-25,Single,1,0,3,F,0.0,323,88.2,1.01,0.0,0,50540.89,578 +362,18-25,Single,1,0,5,M,-0.07,851,91.08,1.0,-108.64,2,136619.16,1500 +363,36-45,Married,3,1,12,F,-0.13,714,137.26,1.01,-154.58,5,164845.93,1217 +364,56-70,Single,1,0,2,F,-0.28,149,109.27,1.0,-44.52,1,17264.57,158 +365,70+,Married,2,0,8,M,0.0,271,99.47,1.0,0.0,0,34615.92,349 +366,46-55,Married,2,0,6,M,-0.8,418,126.25,1.04,-523.6,10,82567.69,679 +367,36-45,Married,3,1,6,M,-6.27,747,109.79,1.02,-7896.29,293,138229.78,1283 +368,36-45,Single,1,0,5,F,-0.04,1199,95.35,1.02,-103.3,4,241619.47,2581 +369,56-70,Married,2,0,4,F,-0.02,527,112.44,1.01,-17.81,1,87814.42,789 +370,46-55,Married,2,0,5,M,-0.51,554,114.58,1.0,-437.95,19,98651.77,861 +371,26-35,Single,1,0,5,M,-0.02,447,115.09,1.01,-12.47,1,63298.54,554 +372,46-55,Married,2,0,7,F,-0.36,109,163.75,1.01,-73.01,3,33405.95,206 +373,26-35,Married,3,1,4,F,-0.17,682,93.17,1.0,-169.2,3,90936.44,980 +374,46-55,Married,3,1,8,F,-0.08,528,148.98,1.02,-51.95,2,102202.48,702 +375,36-45,Single,1,0,2,M,0.0,339,130.66,1.0,0.0,0,59581.64,456 +376,46-55,Single,1,0,8,F,-0.4,146,130.55,1.17,-71.24,1,23237.97,209 +377,18-25,Married,3,1,4,F,-0.13,653,92.81,1.03,-139.8,5,102744.84,1145 +378,36-45,Married,5,3,5,M,-0.78,988,96.46,1.08,-1380.24,48,170254.62,1902 +379,18-25,Single,1,0,1,F,-0.35,1347,120.46,1.01,-946.4,20,321875.91,2690 +380,46-55,Married,2,0,7,F,-0.32,419,106.55,1.02,-169.2,3,56152.94,537 +381,26-35,Married,3,1,4,F,-0.24,465,99.08,1.0,-140.52,4,58654.37,592 +382,46-55,Married,3,1,2,M,0.0,267,142.02,1.0,0.0,0,61495.95,433 +383,18-25,Single,2,1,1,F,-0.1,646,88.31,1.0,-88.87,2,81331.58,921 +384,36-45,Single,1,0,3,F,-0.64,372,93.35,1.02,-382.91,16,55634.34,606 +385,26-35,Single,1,0,3,F,-3.27,456,114.79,1.08,-2173.44,84,76220.79,716 +386,36-45,Married,4,2,7,M,0.0,681,98.93,1.04,0.0,0,107540.54,1132 +387,46-55,Single,1,0,5,M,0.0,600,78.12,1.06,0.0,0,74679.53,1014 +388,26-35,Single,1,0,4,F,0.0,215,115.99,1.0,0.0,0,45467.77,392 +389,26-35,Married,4,2,6,M,-1.48,659,91.38,1.06,-1815.99,46,112393.84,1298 +390,46-55,Single,1,0,4,F,0.0,543,99.87,1.01,0.0,0,71707.89,725 +391,46-55,Single,1,0,8,M,-0.35,602,76.79,1.04,-352.98,14,77172.1,1047 +392,26-35,Single,5,3,3,M,-0.27,488,107.88,1.0,-252.9,5,102812.77,953 +393,46-55,Single,1,0,5,F,-1.2,303,83.72,1.01,-568.14,6,39683.09,481 +394,46-55,Single,3,1,5,M,-0.07,383,95.08,1.02,-44.52,1,64940.98,699 +395,36-45,Married,4,2,2,F,-0.36,291,85.7,1.0,-242.21,8,56903.16,664 +396,70+,Married,3,1,8,M,-0.16,655,92.73,1.12,-186.42,7,107936.42,1301 +397,46-55,Single,1,0,4,F,-0.09,248,110.4,1.0,-41.86,3,53435.34,484 +398,36-45,Single,1,0,2,M,-0.52,341,132.72,1.0,-220.84,6,56138.86,424 +399,46-55,Married,3,1,4,F,-0.23,412,118.67,1.04,-142.48,5,73931.75,650 +400,46-55,Single,1,0,5,F,-0.09,578,82.55,1.06,-91.72,3,82135.77,1050 +401,36-45,Married,3,1,4,F,-1.86,529,96.42,1.21,-1646.65,72,85233.6,1067 +402,70+,Married,3,1,7,F,-0.11,623,84.97,1.09,-187.0,4,145724.18,1864 +403,46-55,Single,1,0,3,F,-1.62,510,92.95,1.04,-1025.62,43,58933.19,657 +404,36-45,Single,1,0,3,F,-2.47,595,106.77,1.04,-2693.23,64,116381.28,1133 +405,36-45,Married,2,0,6,F,0.0,306,120.32,1.0,0.0,0,50054.28,416 +406,26-35,Single,1,0,3,M,0.0,47,141.52,1.0,0.0,0,11745.84,83 +407,36-45,Married,2,0,6,F,-0.13,1007,72.93,1.01,-259.67,10,147099.74,2035 +408,46-55,Married,2,0,5,M,-0.22,396,95.25,1.0,-125.56,4,54767.12,576 +409,56-70,Married,2,0,5,F,-0.22,622,101.93,1.11,-237.58,10,110803.34,1212 +410,26-35,Married,2,0,5,F,0.0,124,89.16,1.0,0.0,0,16226.61,182 +411,46-55,Single,1,0,4,F,-0.12,472,84.96,1.03,-89.05,3,60576.18,734 +412,46-55,Married,2,0,3,M,-0.32,1284,109.88,1.0,-762.26,18,262507.32,2389 +413,36-45,Single,1,0,1,F,-0.03,372,128.1,1.01,-17.81,1,66869.9,525 +414,70+,Married,2,0,6,F,-0.42,566,81.35,1.03,-471.6,15,90782.22,1149 +415,26-35,Married,2,0,5,M,-0.37,404,87.45,1.06,-178.1,2,42238.17,510 +416,36-45,Married,4,2,5,M,-1.63,609,113.97,1.09,-1410.82,46,98353.98,942 +417,36-45,Married,2,0,3,F,-0.11,351,95.11,1.0,-60.55,3,50410.43,530 +418,36-45,Single,4,2,1,M,-0.05,1021,97.45,1.07,-89.05,2,183490.62,2020 +419,36-45,Married,2,0,3,F,-0.25,362,103.39,1.0,-126.44,6,53040.48,513 +420,26-35,Married,3,1,4,M,-0.04,634,106.85,1.0,-40.07,2,110055.11,1030 +421,56-70,Married,2,0,5,M,-0.29,917,113.48,1.03,-414.25,11,164438.22,1497 +422,46-55,Single,1,0,5,F,0.0,440,177.18,1.0,0.0,0,200925.82,1134 +423,46-55,Married,2,0,4,M,-0.21,149,136.68,1.0,-53.43,2,35263.55,258 +424,46-55,Single,1,0,4,M,-1.37,525,97.26,1.01,-1050.44,17,74308.75,769 +425,70+,Single,1,0,1,M,-0.13,87,102.87,1.0,-17.81,1,13990.3,136 +426,26-35,Single,1,0,7,F,-0.03,827,132.58,1.01,-44.52,2,170900.26,1301 +427,56-70,Married,3,1,4,F,-1.23,680,100.52,1.04,-1246.68,43,101925.23,1055 +428,36-45,Single,1,0,4,M,-0.25,333,121.94,1.15,-160.29,10,79020.03,745 +429,36-45,Married,2,0,3,F,-0.59,336,86.88,1.03,-296.83,6,43962.95,520 +430,70+,Married,2,0,6,M,-0.01,615,98.7,1.0,-8.9,1,89620.29,908 +431,36-45,Married,2,0,5,F,-0.55,329,89.22,1.06,-413.79,25,66736.78,793 +432,46-55,Married,2,0,6,M,-0.19,910,95.15,1.0,-391.64,6,194286.63,2042 +433,46-55,Single,1,0,5,F,-1.53,869,125.19,1.0,-2137.34,53,175139.14,1399 +434,46-55,Single,1,0,5,F,-0.15,491,89.17,1.0,-124.67,2,74901.02,840 +435,36-45,Single,1,0,5,F,-0.3,209,126.18,1.01,-160.29,5,66748.72,532 +436,70+,Married,2,0,8,M,-1.01,534,96.42,1.14,-822.45,28,78289.9,927 +437,26-35,Single,1,0,2,F,0.0,491,82.49,1.01,0.0,0,52874.32,649 +438,36-45,Married,2,0,1,M,-0.06,569,94.46,1.01,-71.24,2,111274.5,1188 +439,36-45,Married,3,1,6,M,-0.5,388,97.07,1.0,-297.41,17,57465.9,592 +440,36-45,Married,3,1,8,F,-0.12,497,129.84,1.0,-71.24,1,78165.67,602 +441,70+,Single,1,0,2,F,-0.09,523,124.27,1.06,-64.11,4,91217.75,776 +442,46-55,Married,5,3,6,M,0.0,370,107.61,1.0,0.0,0,60475.01,562 +443,70+,Single,1,0,4,M,-0.19,298,103.87,1.05,-137.13,5,75303.79,758 +444,46-55,Married,2,0,2,F,-0.12,960,115.31,1.0,-163.85,6,163742.07,1420 +445,26-35,Married,2,0,5,F,0.0,164,106.03,1.0,0.0,0,41138.1,388 +446,36-45,Single,3,2,4,M,-0.05,697,94.16,1.05,-71.24,2,123449.87,1371 +447,36-45,Married,2,0,5,F,-0.41,1214,117.35,1.08,-862.7,36,246437.23,2260 +448,18-25,Married,3,1,5,M,-0.07,470,100.21,1.04,-44.52,2,63433.7,656 +449,70+,Single,1,0,4,F,-0.12,252,83.96,1.01,-35.62,2,24683.65,298 +450,46-55,Married,2,0,5,M,-0.32,839,120.83,1.12,-473.86,14,177618.34,1642 +451,56-70,Married,2,0,1,F,-0.23,280,113.64,1.01,-98.04,2,47956.21,426 +452,26-35,Married,2,0,5,M,-0.11,517,102.73,1.05,-141.76,6,138176.97,1418 +453,70+,Single,2,1,2,M,-0.17,703,117.84,1.12,-233.31,7,162269.19,1542 +454,36-45,Married,2,0,5,M,0.0,199,110.5,1.0,0.0,0,35137.47,318 +455,46-55,Single,2,1,5,F,0.0,613,110.32,1.02,0.0,0,79653.01,734 +456,26-35,Married,5,3,7,F,-0.04,1252,123.26,1.12,-104.48,4,295583.04,2675 +457,36-45,Married,4,2,7,F,0.0,421,106.65,1.0,0.0,0,56099.52,526 +458,46-55,Single,2,1,4,F,-0.15,723,87.54,1.0,-173.94,4,99091.73,1132 +459,26-35,Married,2,0,4,M,-0.69,638,89.29,1.01,-640.71,26,83310.54,942 +460,70+,Married,2,0,5,M,-0.53,915,113.17,1.01,-1097.09,33,232103.75,2081 +461,56-70,Married,3,1,2,M,-0.86,352,93.82,1.0,-591.3,19,64733.24,690 +462,56-70,Married,2,0,2,F,-1.36,467,85.78,1.01,-829.8,23,52325.33,614 +463,46-55,Married,5,3,6,M,-3.25,182,91.56,1.02,-728.42,29,20509.29,228 +464,46-55,Married,5,3,3,M,-1.47,2040,89.27,1.04,-6063.05,220,369053.18,4314 +465,26-35,Married,3,1,4,M,0.0,89,193.96,1.0,0.0,0,35300.99,182 +466,36-45,Married,2,0,5,M,-0.58,698,109.83,1.01,-654.5,24,123780.54,1140 +467,46-55,Single,1,0,3,M,-0.2,554,74.05,1.0,-276.06,8,102852.06,1389 +468,56-70,Married,2,0,4,F,-0.04,320,104.69,1.0,-17.81,1,41874.09,400 +469,18-25,Single,1,0,4,F,-0.04,550,99.59,1.0,-44.52,1,109452.94,1099 +470,18-25,Married,3,1,4,M,-0.02,896,98.51,1.1,-39.19,3,202740.39,2266 +471,18-25,Married,3,1,5,F,0.0,252,104.65,1.0,0.0,0,31812.86,304 +472,46-55,Married,2,0,5,F,0.0,97,120.93,1.0,0.0,0,23702.96,196 +473,46-55,Married,2,0,4,F,-0.35,501,105.67,1.08,-215.74,6,65094.98,664 +474,56-70,Married,4,2,6,F,-0.31,501,88.61,1.02,-266.55,12,77268.3,888 +475,46-55,Married,2,0,6,M,-1.26,210,82.15,1.0,-713.65,9,46414.89,565 +476,36-45,Married,5,3,7,M,0.0,146,108.21,1.04,0.0,0,18827.8,181 +477,70+,Married,2,0,2,F,0.0,268,101.83,1.0,0.0,0,29836.31,293 +478,36-45,Married,5,3,7,F,-0.03,477,86.18,1.0,-17.81,1,56192.32,652 +479,46-55,Married,2,0,10,M,-0.03,771,90.9,1.0,-44.52,2,117618.64,1294 +480,70+,Married,3,1,5,M,-0.06,398,91.81,1.01,-35.62,2,53250.29,586 +481,36-45,Married,4,2,2,F,-0.71,724,60.82,1.01,-1322.03,30,113545.34,1880 +482,26-35,Married,4,2,8,M,-0.04,274,84.16,1.01,-17.81,1,37365.42,449 +483,70+,Married,2,0,8,M,-1.54,570,99.18,1.07,-1130.33,53,72798.18,784 +484,70+,Married,2,0,8,F,-0.04,877,89.67,1.0,-62.33,2,156384.85,1744 +485,56-70,Married,2,0,7,F,-0.36,196,97.35,1.0,-92.26,2,25115.84,258 +486,46-55,Single,3,1,1,F,-0.02,454,94.28,1.0,-11.13,1,57509.36,612 +487,26-35,Single,1,0,4,M,0.0,214,88.73,1.01,0.0,0,26175.27,298 +488,36-45,Married,2,0,6,M,0.0,543,96.05,1.11,0.0,0,65893.59,763 +489,70+,Married,2,0,2,F,-0.07,130,113.74,1.0,-14.25,1,23998.79,211 +490,36-45,Single,1,0,5,F,-0.83,466,82.99,1.85,-623.35,15,62245.13,1387 +491,26-35,Married,2,0,4,F,-0.43,327,111.32,1.03,-183.73,7,47312.91,436 +492,46-55,Married,2,0,4,M,0.0,159,128.97,1.0,0.0,0,27341.28,212 +493,36-45,Married,2,0,4,F,-0.46,187,128.25,1.0,-142.48,1,40014.67,313 +494,26-35,Married,5,3,8,F,-0.8,839,111.66,1.05,-991.47,22,138574.38,1297 +495,36-45,Married,2,0,8,F,-0.01,1090,103.69,1.04,-19.59,1,163311.42,1640 +496,26-35,Single,1,0,5,M,-0.09,427,96.91,1.0,-64.11,3,69195.51,715 +497,36-45,Married,5,3,5,F,-1.11,391,102.17,1.04,-539.9,21,49656.77,506 +498,46-55,Married,2,0,9,F,-0.4,912,114.45,1.0,-644.37,20,182435.33,1594 +499,36-45,Married,2,0,5,F,-0.14,735,76.89,1.06,-215.49,8,117866.2,1620 +500,46-55,Single,1,0,4,F,0.0,348,79.55,1.0,0.0,0,35478.17,448 +501,46-55,Single,1,0,5,F,-0.09,1021,83.38,1.0,-178.8,6,175019.74,2100 +502,26-35,Married,4,2,6,M,-0.03,465,95.95,1.0,-17.81,1,64480.74,675 +503,46-55,Married,3,1,4,F,-0.38,310,95.98,1.04,-239.82,12,60276.3,651 +504,70+,Married,3,1,8,M,0.0,408,135.5,1.0,0.0,0,70322.66,519 +505,56-70,Married,2,0,9,F,0.0,109,86.65,1.0,0.0,0,15076.44,174 +506,46-55,Married,2,0,7,F,-0.22,257,105.27,1.09,-89.05,4,43581.02,453 +507,70+,Married,2,0,6,F,0.0,179,100.45,1.14,0.0,0,32445.94,369 +508,18-25,Married,2,0,6,F,0.0,545,134.99,1.0,0.0,0,155368.79,1151 +509,26-35,Single,1,0,3,F,-0.44,193,114.68,1.0,-219.78,3,57225.55,499 +510,26-35,Single,2,1,1,M,-1.0,1019,86.01,1.03,-1634.66,56,141234.55,1684 +511,46-55,Single,1,0,5,M,-0.14,377,105.48,1.01,-71.24,2,54956.13,527 +512,46-55,Single,1,0,3,F,-0.31,217,166.42,1.05,-82.99,3,44932.79,284 +513,70+,Married,2,0,4,F,-0.11,231,89.1,1.0,-35.62,2,27797.71,312 +514,26-35,Married,2,0,6,M,-0.04,379,144.69,1.0,-17.81,1,69163.7,478 +515,26-35,Married,2,0,4,M,-0.02,903,97.06,1.0,-53.43,2,251472.19,2591 +516,70+,Married,2,0,4,M,-0.79,235,138.27,1.0,-249.34,2,43415.58,314 +517,26-35,Married,2,0,6,F,0.0,256,110.94,1.0,0.0,0,40935.73,369 +518,56-70,Married,5,3,4,M,-0.9,633,79.35,1.0,-1111.68,7,97517.87,1229 +519,46-55,Married,2,0,5,M,-0.18,354,82.42,1.02,-142.48,7,65774.04,811 +520,36-45,Married,2,0,6,F,-0.64,688,103.4,1.01,-693.69,26,112702.84,1105 +521,56-70,Single,1,0,6,F,-0.07,448,90.11,1.06,-53.43,2,70646.71,834 +522,46-55,Married,3,1,6,F,0.0,369,87.68,1.05,0.0,0,44718.54,533 +523,36-45,Single,3,1,5,F,-1.08,127,91.67,1.0,-262.68,16,22276.25,243 +524,26-35,Single,1,0,5,F,-0.38,493,110.52,1.0,-274.8,9,80456.72,731 +525,36-45,Married,3,1,9,M,-0.15,614,105.82,1.0,-171.69,4,122327.85,1156 +526,18-25,Married,2,0,2,F,-0.78,754,108.97,1.0,-737.32,18,103633.25,953 +527,36-45,Married,3,1,5,M,0.0,299,121.27,1.06,0.0,0,46566.02,406 +528,46-55,Married,2,0,10,F,-0.72,1109,117.85,1.09,-1249.53,32,204122.66,1887 +529,18-25,Single,1,0,5,M,-1.95,250,67.5,1.0,-751.2,27,26055.99,386 +530,46-55,Single,3,2,5,F,-0.25,831,126.42,1.0,-387.36,6,196458.65,1554 +531,36-45,Married,3,1,5,F,0.0,168,112.42,1.06,0.0,0,22484.32,212 +532,56-70,Married,2,0,1,F,0.0,249,110.35,1.0,0.0,0,42595.66,386 +533,36-45,Married,5,3,6,M,-0.06,983,137.23,1.0,-92.61,4,219300.47,1598 +534,26-35,Married,5,3,5,F,-0.17,177,113.49,1.08,-38.29,2,24968.18,238 +535,36-45,Married,2,0,5,F,-0.08,693,79.22,1.06,-108.64,2,113521.47,1521 +536,46-55,Married,2,0,11,F,-0.8,441,136.71,1.0,-595.29,27,101712.02,747 +537,18-25,Single,1,0,5,F,-0.15,745,115.16,1.01,-178.1,5,133813.01,1175 +538,46-55,Married,2,0,6,M,0.0,570,105.12,1.0,0.0,0,92291.63,878 +539,46-55,Married,2,0,9,F,-0.05,214,76.5,1.0,-14.25,1,20808.28,273 +540,26-35,Married,4,2,4,F,-0.33,267,132.62,1.0,-106.86,2,43101.62,325 +541,26-35,Single,1,0,10,F,-0.04,523,173.78,1.08,-69.46,3,271093.03,1690 +542,46-55,Single,1,0,6,F,0.0,89,94.38,1.0,0.0,0,9721.61,103 +543,26-35,Married,2,0,5,M,-0.26,510,121.83,1.0,-213.72,4,99411.04,816 +544,56-70,Single,1,0,4,M,-0.16,432,101.88,1.03,-145.45,6,91485.66,927 +545,70+,Married,3,1,4,F,-0.01,445,100.54,1.0,-8.9,1,88475.16,880 +546,26-35,Single,1,0,5,F,-0.1,682,100.15,1.05,-135.35,4,136299.31,1425 +547,46-55,Married,2,0,1,M,0.0,159,121.96,1.0,0.0,0,26953.01,221 +548,46-55,Married,2,0,5,M,-0.05,267,90.29,1.0,-19.59,1,34038.93,377 +549,46-55,Single,1,0,2,F,-0.13,242,83.34,1.04,-35.62,1,23668.72,295 +550,36-45,Single,1,0,2,F,-0.71,687,103.13,1.0,-868.22,20,126741.56,1229 +551,46-55,Single,1,0,6,M,-1.6,576,94.97,1.08,-1513.03,59,89558.63,1019 +552,70+,Married,2,0,4,M,-0.6,786,119.81,1.08,-651.47,23,129871.16,1171 +553,36-45,Married,3,1,11,F,0.0,747,136.33,1.0,0.0,0,200818.57,1475 +554,26-35,Single,1,0,4,M,-0.36,453,64.0,1.04,-320.58,5,56380.02,916 +555,46-55,Single,1,0,5,M,0.0,522,96.62,1.06,0.0,0,102221.05,1125 +556,46-55,Single,1,0,5,F,-3.45,340,122.19,1.03,-1919.14,65,68062.56,574 +557,26-35,Single,3,2,4,M,-0.02,742,86.91,1.01,-35.62,1,129411.09,1499 +558,36-45,Single,2,1,5,F,-0.74,259,98.27,1.11,-252.46,4,33510.0,377 +559,36-45,Married,2,0,6,F,-0.09,649,71.19,1.0,-133.57,8,101665.5,1428 +560,46-55,Married,2,0,5,M,-0.43,657,97.41,1.03,-408.72,13,92344.92,975 +561,36-45,Married,2,0,4,M,-0.27,684,127.42,1.1,-284.96,7,134681.94,1162 +562,46-55,Single,1,0,9,M,0.0,122,83.64,1.0,0.0,0,12378.21,148 +563,26-35,Single,1,0,2,M,0.0,268,115.48,1.03,0.0,0,42958.84,382 +564,36-45,Married,2,0,7,F,-0.54,438,120.23,1.08,-411.41,18,91252.32,817 +565,26-35,Married,3,1,9,F,-1.81,924,106.25,1.0,-3453.35,45,202715.62,1908 +566,26-35,Married,4,2,5,F,-2.21,991,102.65,1.07,-5873.98,219,272420.99,2830 +567,18-25,Single,1,0,3,F,-0.2,696,105.57,1.0,-262.7,11,135346.15,1282 +568,56-70,Single,1,0,4,M,-0.05,419,99.35,1.0,-35.62,1,73918.95,747 +569,46-55,Single,1,0,9,F,-0.02,457,130.25,1.06,-17.81,1,138850.84,1129 +570,46-55,Single,1,0,4,F,-0.49,955,99.13,1.02,-644.71,24,129663.22,1337 +571,56-70,Married,2,0,8,F,-0.02,662,114.21,1.1,-19.59,1,126999.48,1225 +572,36-45,Married,2,0,4,F,-0.04,1145,104.44,1.1,-106.5,2,249094.2,2627 +573,36-45,Single,1,0,4,F,-0.06,482,86.97,1.0,-35.62,1,54183.06,624 +574,36-45,Single,1,0,4,M,-0.13,302,136.96,1.0,-53.43,2,57387.68,419 +575,56-70,Married,2,0,1,F,-0.08,275,93.05,1.14,-83.7,5,96118.37,1173 +576,26-35,Single,1,0,5,M,0.0,242,89.75,1.0,0.0,0,32308.98,360 +577,46-55,Married,3,1,7,F,-0.3,602,96.25,1.04,-287.57,7,93264.06,1010 +578,46-55,Married,2,0,6,M,-0.49,1489,111.88,1.1,-1129.69,45,258554.78,2541 +579,26-35,Married,2,0,8,M,-0.09,384,101.21,1.03,-53.43,2,63154.69,641 +580,26-35,Married,3,1,6,M,-0.1,183,116.73,1.01,-29.68,1,35370.69,307 +581,46-55,Single,1,0,2,M,0.0,102,60.7,1.0,0.0,0,14204.24,234 +582,70+,Married,2,0,4,M,0.0,401,90.17,1.05,0.0,0,45627.33,532 +583,36-45,Single,1,0,2,M,-0.29,635,74.46,1.0,-322.0,10,81977.89,1101 +584,46-55,Single,1,0,4,F,-0.35,295,68.93,1.0,-163.5,1,31912.34,465 +585,46-55,Married,5,3,4,F,-0.52,534,95.59,1.01,-441.34,9,81155.53,861 +586,46-55,Single,1,0,4,M,-1.94,354,118.08,1.05,-975.26,13,59273.83,528 +587,26-35,Married,2,0,4,F,-0.49,368,110.72,1.1,-236.87,7,53700.43,532 +588,46-55,Married,2,0,5,M,0.0,257,117.11,1.0,0.0,0,52467.42,448 +589,56-70,Single,1,0,4,F,0.0,77,89.8,1.0,0.0,0,12661.71,141 +590,36-45,Married,2,0,1,F,-0.04,552,91.03,1.11,-55.21,3,116881.96,1427 +591,26-35,Married,5,3,7,F,-0.49,288,100.99,1.0,-147.82,6,30699.57,304 +592,70+,Married,2,0,4,M,-0.07,578,95.61,1.04,-55.21,2,71321.88,775 +593,46-55,Single,2,1,3,F,-0.18,650,68.68,1.04,-233.3,7,88533.59,1342 +594,46-55,Single,1,0,10,F,-0.98,574,111.42,1.01,-961.73,31,109187.26,991 +595,18-25,Married,2,0,2,F,-0.14,734,100.11,1.0,-162.07,5,119533.46,1194 +596,46-55,Single,1,0,1,F,-0.32,1454,75.91,1.01,-852.8,29,200179.32,2659 +597,26-35,Single,1,0,5,F,0.0,192,78.43,1.24,0.0,0,18667.11,296 +598,70+,Married,2,0,6,M,-2.07,872,103.32,1.01,-2744.39,96,137102.95,1338 +599,36-45,Single,1,0,3,F,0.0,303,117.95,1.0,0.0,0,50130.63,425 +600,46-55,Married,2,0,6,F,-0.07,529,66.26,1.0,-80.15,4,71958.6,1091 +601,70+,Married,2,0,6,M,-0.1,230,95.1,1.0,-35.62,1,33474.47,352 +602,18-25,Married,2,0,11,M,0.0,351,117.11,1.04,0.0,0,49771.22,441 +603,36-45,Single,1,0,3,F,-0.13,776,86.79,1.0,-199.46,11,128970.14,1486 +604,46-55,Married,2,0,1,M,-0.14,562,83.92,1.03,-136.24,4,79051.04,967 +605,36-45,Married,3,1,2,M,-0.06,272,100.01,1.17,-19.59,1,34804.11,407 +606,18-25,Single,1,0,1,F,-0.06,460,74.93,1.0,-35.62,1,46457.67,620 +607,70+,Single,1,0,4,M,-0.95,250,84.21,1.01,-572.05,22,50945.69,609 +608,26-35,Married,2,0,5,F,-0.8,588,86.75,1.08,-888.32,34,96815.64,1209 +609,26-35,Married,2,0,1,F,-0.08,385,89.1,1.02,-44.52,1,49184.49,561 +610,46-55,Single,1,0,5,F,-0.03,382,79.48,1.0,-13.36,1,38787.34,488 +611,36-45,Married,3,1,4,M,-0.3,497,106.51,1.0,-215.5,8,76263.55,716 +612,18-25,Single,1,0,1,M,0.0,570,74.19,1.01,0.0,0,76412.51,1041 +613,46-55,Married,2,0,5,M,-0.08,679,104.94,1.15,-105.08,5,143555.16,1577 +614,36-45,Single,1,0,6,F,-0.05,554,85.56,1.02,-49.27,2,82480.33,982 +615,36-45,Married,5,3,8,F,0.0,242,134.29,1.0,0.0,0,57342.02,427 +616,46-55,Single,2,1,7,F,-0.07,548,122.78,1.0,-53.43,1,94296.18,768 +617,56-70,Married,2,0,3,F,-0.05,256,83.52,1.0,-19.59,1,34157.68,410 +618,26-35,Married,2,0,4,F,-0.34,316,102.95,1.0,-135.36,5,41282.86,401 +619,36-45,Single,2,1,4,M,-0.13,996,96.89,1.0,-258.24,6,196878.97,2032 +620,26-35,Married,3,1,4,F,0.0,545,105.45,1.0,0.0,0,78873.61,748 +621,36-45,Married,2,0,5,M,-0.39,548,111.91,1.0,-391.64,14,111686.15,1001 +622,36-45,Married,4,2,5,F,-0.01,1125,110.54,1.01,-17.81,1,245076.25,2236 +623,46-55,Married,4,2,8,F,0.0,239,106.05,1.13,0.0,0,75509.0,804 +624,26-35,Married,5,3,11,M,-0.13,562,115.62,1.0,-108.64,3,93656.14,814 +625,18-25,Married,2,0,4,F,0.0,394,125.07,1.0,0.0,0,58906.56,471 +626,36-45,Single,2,1,4,M,-2.0,1451,93.27,1.01,-5655.2,171,264033.32,2866 +627,46-55,Married,4,2,4,M,-1.11,1124,94.29,1.07,-3807.38,128,323317.55,3683 +628,46-55,Married,3,1,4,M,-1.13,572,96.96,1.0,-865.54,36,74269.49,766 +629,36-45,Married,2,0,11,F,-0.48,184,133.44,1.0,-96.18,3,26820.66,201 +630,26-35,Single,1,0,4,M,0.0,237,110.15,1.0,0.0,0,44061.96,400 +631,26-35,Married,2,0,5,M,-0.05,352,105.48,1.0,-19.59,1,44195.03,419 +632,46-55,Married,2,0,6,M,-2.58,842,103.43,1.08,-3639.12,143,145942.91,1525 +633,26-35,Married,5,3,4,F,-0.18,789,93.28,1.01,-168.3,4,89358.24,969 +634,56-70,Married,5,3,5,M,-0.54,418,96.14,1.0,-336.6,12,59797.25,622 +635,46-55,Single,2,1,7,F,-0.12,274,125.65,1.0,-35.62,1,38824.74,309 +636,56-70,Married,2,0,3,M,-2.56,232,106.4,1.01,-979.55,11,40749.64,385 +637,46-55,Single,1,0,6,M,-0.04,587,100.91,1.0,-44.52,2,114635.43,1136 +638,36-45,Single,1,0,4,F,-0.4,1048,99.13,1.03,-680.86,22,168912.76,1757 +639,46-55,Married,2,0,7,F,-0.25,354,97.38,1.0,-113.09,3,43723.53,449 +640,46-55,Single,1,0,4,M,-0.09,388,84.91,1.01,-89.04,4,83556.33,996 +641,46-55,Single,1,0,5,F,-0.63,324,104.01,1.0,-330.68,18,54811.98,527 +642,36-45,Single,1,0,3,M,-0.08,395,104.17,1.0,-44.52,1,55733.45,535 +643,26-35,Single,1,0,2,M,0.0,281,112.55,1.17,0.0,0,58186.59,605 +644,26-35,Married,3,1,9,F,-0.07,272,72.3,1.0,-44.52,3,46631.68,645 +645,18-25,Married,2,0,6,F,0.0,358,75.54,1.0,0.0,0,35351.19,469 +646,18-25,Married,2,0,4,F,0.0,313,75.59,1.1,0.0,0,34468.31,501 +647,56-70,Married,2,0,1,F,-0.42,382,105.66,1.1,-215.5,7,54839.83,570 +648,36-45,Married,5,3,4,F,-0.04,461,84.46,1.05,-19.59,1,47041.67,586 +649,46-55,Married,4,2,2,F,-0.23,816,95.31,1.02,-236.52,10,96068.62,1024 +650,46-55,Single,1,0,5,M,-0.29,456,80.26,1.0,-309.89,15,86362.67,1080 +651,46-55,Single,2,1,4,F,0.0,327,125.57,1.01,0.0,0,50980.95,410 +652,70+,Married,2,0,4,F,-0.26,567,87.17,1.0,-211.75,9,72006.39,826 +653,46-55,Married,5,3,4,M,-0.74,253,113.9,1.03,-258.77,9,39863.27,360 +654,46-55,Married,2,0,5,F,0.0,206,119.23,1.0,0.0,0,28853.33,242 +655,46-55,Married,2,0,3,F,-0.08,968,107.24,1.0,-131.44,2,180593.28,1692 +656,36-45,Married,5,3,5,M,-0.27,328,99.77,1.0,-124.67,2,46594.0,467 +657,26-35,Married,4,2,1,M,-0.75,734,108.96,1.02,-1037.77,36,150359.22,1403 +658,46-55,Married,4,2,2,F,-0.06,614,80.16,1.0,-51.95,2,69581.0,868 +659,26-35,Single,1,0,3,M,-0.26,681,100.52,1.13,-562.44,22,213800.89,2399 +660,36-45,Married,2,0,6,F,0.0,483,99.22,1.11,0.0,0,84335.94,940 +661,26-35,Single,2,1,8,M,-0.49,426,126.28,1.01,-285.4,8,73875.36,589 +662,26-35,Married,2,0,5,F,0.0,91,116.63,1.07,0.0,0,12829.01,118 +663,56-70,Single,1,0,1,F,0.0,320,60.09,1.0,0.0,0,33169.78,552 +664,26-35,Married,5,3,3,F,-0.24,599,126.76,1.0,-222.63,8,119664.5,944 +665,26-35,Single,1,0,7,M,-0.29,565,113.32,1.0,-382.91,5,150832.0,1331 +666,26-35,Single,1,0,4,F,-0.02,956,88.53,1.0,-35.62,1,132090.15,1492 +667,26-35,Married,2,0,6,F,-0.12,834,74.91,1.01,-175.13,7,112066.97,1512 +668,26-35,Married,2,0,1,M,-0.02,636,83.88,1.12,-19.59,1,81361.82,1082 +669,46-55,Single,2,1,3,M,0.0,442,111.0,1.05,0.0,0,75256.96,713 +670,26-35,Married,2,0,5,M,-0.78,175,107.02,1.0,-170.98,5,23438.31,219 +671,70+,Married,2,0,4,F,-0.66,225,110.87,1.12,-201.26,9,34038.04,343 +672,26-35,Married,2,0,5,F,0.0,448,76.59,1.18,0.0,0,52844.81,817 +673,46-55,Single,2,1,4,F,-0.07,392,101.18,1.01,-35.62,1,50590.02,506 +674,56-70,Married,2,0,7,M,0.0,259,94.62,1.01,0.0,0,55162.87,591 +675,26-35,Married,2,0,5,M,0.0,199,89.9,1.01,0.0,0,29037.59,325 +676,46-55,Married,2,0,5,F,0.0,589,116.33,1.0,0.0,0,121684.75,1046 +677,36-45,Single,1,0,5,F,-0.03,322,96.53,1.0,-44.52,1,124625.38,1291 +678,46-55,Married,5,3,3,F,-0.06,500,89.47,1.0,-55.21,2,79184.72,889 +679,36-45,Single,2,1,4,M,0.0,469,112.11,1.0,0.0,0,153370.43,1368 +680,26-35,Single,1,0,5,M,0.0,553,103.05,1.01,0.0,0,85221.47,834 +681,70+,Married,2,0,2,F,-0.26,245,80.7,1.0,-119.33,2,36962.32,458 +682,56-70,Single,1,0,4,M,-1.04,529,102.49,1.0,-768.15,30,75845.95,740 +683,36-45,Single,1,0,4,M,-0.11,747,96.32,1.0,-115.76,3,102962.41,1069 +684,36-45,Married,2,0,3,M,-0.81,391,81.81,1.0,-687.99,16,69129.66,845 +685,36-45,Married,5,3,8,M,-0.04,346,91.68,1.04,-17.81,1,37314.36,422 +686,26-35,Married,2,0,4,F,-0.05,355,111.84,1.0,-26.71,1,56031.01,501 +687,36-45,Married,2,0,5,F,-0.15,638,90.65,1.12,-146.04,5,86384.86,1068 +688,18-25,Single,2,1,1,F,-0.09,452,68.15,1.0,-56.99,2,41710.75,612 +689,70+,Single,1,0,4,F,0.0,534,65.59,1.0,0.0,0,60082.37,917 +690,26-35,Married,2,0,5,M,-0.1,373,102.37,1.04,-55.21,2,54666.75,557 +691,46-55,Married,4,2,4,M,-0.09,712,103.09,1.02,-120.51,4,136494.81,1348 +692,18-25,Single,1,0,5,F,-0.44,213,100.52,1.02,-117.54,6,26738.95,271 +693,26-35,Single,2,1,4,M,-0.11,255,131.04,1.0,-35.62,1,44421.42,339 +694,26-35,Married,3,1,8,F,-0.21,679,98.81,1.16,-229.74,11,110662.8,1297 +695,56-70,Married,2,0,6,M,-0.27,1028,118.96,1.0,-479.08,25,208425.62,1755 +696,46-55,Single,1,0,4,M,0.0,297,58.15,1.01,0.0,0,34250.6,593 +697,36-45,Single,1,0,3,F,-0.21,176,127.11,1.0,-47.19,3,28599.08,225 +698,26-35,Married,3,1,4,F,-0.03,277,87.35,1.0,-17.81,1,51798.23,593 +699,36-45,Married,2,0,3,F,-0.03,830,88.57,1.04,-35.62,1,102567.0,1207 +700,36-45,Married,3,1,11,F,-0.47,475,88.45,1.08,-280.8,8,52981.27,647 +701,56-70,Married,2,0,4,M,0.0,147,64.82,1.01,0.0,0,26901.54,420 +702,46-55,Single,1,0,5,M,-0.03,396,87.51,1.03,-17.81,1,46553.03,546 +703,46-55,Married,2,0,6,F,-0.06,587,112.37,1.18,-54.85,2,106076.88,1110 +704,46-55,Married,5,3,6,F,-0.38,689,101.25,1.01,-362.43,12,97601.59,969 +705,36-45,Single,1,0,4,M,-0.03,780,131.26,1.09,-35.62,1,152134.06,1258 +706,56-70,Single,1,0,4,F,-1.81,739,96.58,1.05,-3284.92,115,175388.58,1903 +707,18-25,Single,2,1,5,F,-0.19,305,119.5,1.0,-68.86,3,43618.18,365 +708,36-45,Married,3,1,5,F,-0.1,692,100.77,1.08,-124.67,3,124646.34,1336 +709,70+,Married,2,0,3,M,-0.05,504,111.81,1.0,-35.61,2,87096.93,779 +710,36-45,Married,2,0,6,F,-10.93,472,184.86,1.0,-6370.77,118,107773.53,583 +711,26-35,Single,1,0,9,M,-0.03,1250,85.77,1.0,-113.0,3,357911.09,4173 +712,46-55,Married,3,1,5,F,-1.01,554,111.9,1.01,-774.44,47,85824.73,775 +713,46-55,Married,3,1,5,M,-0.08,349,98.38,1.0,-41.22,3,52045.25,530 +714,46-55,Single,1,0,3,M,-0.12,507,108.52,1.06,-89.05,1,80844.19,788 +715,36-45,Married,2,0,9,M,-0.27,462,89.92,1.01,-171.86,9,57820.55,647 +716,36-45,Single,4,2,1,F,-0.09,368,98.44,1.01,-44.52,1,46364.86,475 +717,36-45,Single,1,0,2,F,-0.35,434,96.66,1.0,-181.66,5,50652.44,524 +718,36-45,Married,2,0,11,F,-0.06,266,79.15,1.02,-26.71,1,36804.45,472 +719,56-70,Married,2,0,2,F,-0.15,377,139.67,1.0,-89.05,3,82544.52,591 +720,18-25,Married,2,0,6,M,-0.05,425,77.9,1.0,-26.71,2,44950.41,577 +721,26-35,Married,2,0,5,F,0.0,150,101.94,1.01,0.0,0,16105.89,160 +722,26-35,Single,1,0,4,M,-0.09,370,107.05,1.0,-44.52,1,54169.35,506 +723,46-55,Single,2,1,3,M,-0.49,233,111.76,1.0,-133.57,2,30399.81,272 +724,26-35,Married,3,1,6,M,-0.14,488,92.06,1.01,-89.05,1,56709.17,621 +725,56-70,Married,3,1,3,F,-0.86,1011,94.42,1.0,-1155.86,26,127371.68,1350 +726,46-55,Single,3,1,5,M,-0.13,393,91.73,1.0,-71.24,3,48890.55,533 +727,46-55,Single,1,0,5,F,-0.07,371,92.02,1.0,-35.62,1,45823.85,498 +728,46-55,Married,5,3,9,F,0.0,160,91.04,1.02,0.0,0,17389.29,194 +729,46-55,Single,1,0,3,M,-0.45,725,107.27,1.0,-434.21,6,103514.3,965 +730,26-35,Single,1,0,4,F,-0.16,222,106.41,1.0,-41.86,2,28198.48,265 +731,70+,Single,1,0,4,F,0.0,111,126.15,1.02,0.0,0,41378.18,334 +732,36-45,Married,2,0,2,F,0.0,266,94.43,1.0,0.0,0,75261.24,797 +733,70+,Single,1,0,4,M,-0.45,575,134.25,1.0,-567.24,10,169155.74,1260 +734,36-45,Married,2,0,5,F,0.0,158,81.19,1.0,0.0,0,15425.81,190 +735,46-55,Married,2,0,5,M,-0.37,948,115.96,1.06,-860.18,29,272030.9,2476 +736,56-70,Married,2,0,4,F,-0.13,493,106.54,1.0,-89.05,1,72873.69,684 +737,36-45,Single,4,3,4,M,-0.81,688,87.17,1.02,-1308.58,57,140174.48,1637 +738,46-55,Single,1,0,1,M,0.0,261,129.45,1.0,0.0,0,51261.16,396 +739,36-45,Married,5,3,7,M,0.0,180,90.46,1.0,0.0,0,24786.19,274 +740,46-55,Married,2,0,4,M,-0.04,425,88.39,1.0,-35.62,2,70708.9,800 +741,18-25,Single,1,0,3,F,0.0,406,130.87,1.0,0.0,0,75512.23,577 +742,46-55,Married,2,0,2,F,-0.11,418,81.24,1.02,-75.69,3,54429.55,684 +743,46-55,Married,2,0,2,F,-0.04,523,102.27,1.07,-39.18,2,90104.05,939 +744,70+,Single,1,0,4,F,0.0,599,77.34,1.01,0.0,0,77029.5,1004 +745,36-45,Single,1,0,1,F,-0.07,724,107.42,1.08,-64.12,3,104413.19,1049 +746,26-35,Single,1,0,2,F,0.0,387,111.44,1.0,0.0,0,90151.59,809 +747,26-35,Single,1,0,5,M,0.0,204,123.39,1.04,0.0,0,32575.83,274 +748,46-55,Married,3,1,8,F,-4.25,1351,105.32,1.11,-9978.66,345,247401.42,2604 +749,36-45,Married,4,2,5,F,-0.11,747,111.81,1.03,-126.45,4,131715.45,1216 +750,18-25,Married,2,0,2,M,-0.11,549,96.67,1.0,-142.47,5,121128.2,1255 +751,26-35,Single,1,0,5,F,-0.02,540,137.1,1.0,-10.69,1,91585.37,668 +752,26-35,Married,5,3,1,M,0.0,447,77.0,1.0,0.0,0,44891.67,583 +753,26-35,Married,2,0,5,M,-0.2,504,121.8,1.12,-222.61,10,132393.74,1218 +754,46-55,Single,1,0,2,F,-0.32,539,115.13,1.06,-281.04,5,99705.4,922 +755,36-45,Married,2,0,5,F,-0.74,869,111.18,1.03,-1000.2,12,150320.47,1397 +756,56-70,Married,2,0,7,F,0.0,552,90.39,1.1,0.0,0,77460.73,946 +757,46-55,Married,4,2,5,M,-0.37,526,85.89,1.01,-240.43,10,55745.61,654 +758,70+,Single,1,0,4,F,0.0,363,88.68,1.03,0.0,0,42210.37,489 +759,46-55,Married,2,0,6,M,0.0,141,108.3,1.01,0.0,0,16677.6,155 +760,46-55,Married,5,3,5,F,-0.04,589,127.55,1.03,-29.38,2,91327.67,739 +761,36-45,Single,1,0,4,F,-0.41,404,88.25,1.0,-277.48,1,60187.29,682 +762,46-55,Single,1,0,4,F,-0.09,614,66.13,1.0,-89.05,4,66458.97,1008 +763,26-35,Married,2,0,4,M,-0.31,543,88.74,1.06,-224.4,8,64604.76,775 +764,18-25,Single,2,1,1,F,0.0,263,118.98,1.0,0.0,0,50092.04,421 +765,46-55,Married,2,0,5,F,-0.42,628,98.05,1.0,-501.88,11,116489.21,1190 +766,36-45,Married,2,0,9,M,-2.09,670,114.96,1.04,-2191.29,77,120588.07,1096 +767,26-35,Married,3,1,6,F,-0.37,630,110.97,1.02,-429.27,18,129505.2,1188 +768,36-45,Married,5,3,5,F,-0.51,315,106.85,1.0,-195.91,4,41031.18,384 +769,46-55,Married,2,0,5,F,-0.12,924,96.91,1.0,-192.35,9,149632.39,1544 +770,36-45,Single,1,0,4,M,-1.15,139,84.09,1.0,-246.37,11,17994.69,214 +771,36-45,Single,1,0,2,M,-0.14,342,150.63,1.11,-122.44,5,131200.37,964 +772,26-35,Single,1,0,4,F,-0.3,895,96.4,1.0,-415.69,9,135242.3,1403 +773,36-45,Single,1,0,5,M,-0.17,358,87.14,1.03,-80.14,2,41739.81,491 +774,46-55,Married,4,2,4,M,-1.02,466,93.9,1.06,-675.81,35,62348.58,702 +775,18-25,Married,3,1,2,F,0.0,138,89.25,1.0,0.0,0,14904.12,167 +776,46-55,Single,1,0,2,F,-0.65,751,101.45,1.0,-805.74,26,125901.73,1241 +777,56-70,Married,2,0,2,F,-0.13,226,104.75,1.0,-35.62,2,29645.39,283 +778,46-55,Single,1,0,4,F,-0.03,453,70.9,1.0,-17.81,1,48427.54,683 +779,26-35,Single,3,2,4,F,-0.15,792,89.24,1.0,-177.2,8,107708.81,1207 +780,26-35,Single,1,0,7,M,-0.16,595,112.41,1.02,-139.8,4,96110.04,869 +781,46-55,Single,1,0,7,F,-2.3,963,131.12,1.09,-4447.69,34,253071.12,2102 +782,56-70,Married,2,0,9,M,-0.26,1228,114.54,1.1,-604.91,32,261714.49,2515 +783,46-55,Married,2,0,6,M,-0.27,311,83.01,1.07,-118.7,3,36359.97,470 +784,46-55,Single,2,1,4,M,-0.09,705,120.89,1.0,-92.61,4,118476.56,980 +785,36-45,Married,3,1,5,F,0.0,494,89.77,1.0,0.0,0,93813.25,1045 +786,26-35,Single,2,1,8,F,-0.18,147,111.46,1.0,-35.62,2,22402.46,201 +787,46-55,Married,4,2,1,F,-1.13,669,93.04,1.0,-1312.56,63,107738.87,1158 +788,36-45,Single,1,0,3,M,0.0,313,98.38,1.0,0.0,0,47812.52,486 +789,46-55,Married,2,0,1,M,-0.12,615,122.73,1.04,-147.28,2,156726.52,1324 +790,46-55,Married,2,0,5,F,-0.13,460,98.12,1.03,-90.83,2,67702.06,711 +791,46-55,Single,1,0,6,F,-0.12,535,122.15,1.03,-97.95,3,98330.51,833 +792,36-45,Married,2,0,4,F,-0.31,560,92.03,1.0,-398.23,6,116423.28,1270 +793,46-55,Single,2,1,5,M,-0.45,869,118.12,1.02,-771.88,25,203161.94,1758 +794,46-55,Single,1,0,5,M,0.0,66,125.97,1.02,0.0,0,11085.63,90 +795,46-55,Single,2,1,5,F,-0.22,429,110.61,1.04,-178.1,5,90919.14,852 +796,46-55,Married,3,1,5,M,0.0,199,105.37,1.02,0.0,0,24868.24,241 +797,46-55,Single,1,0,6,M,-0.13,288,110.31,1.01,-82.37,4,69051.57,633 +798,46-55,Single,1,0,8,M,0.0,108,89.43,1.0,0.0,0,20925.79,234 +799,70+,Single,1,0,5,M,0.0,892,112.95,1.0,0.0,0,226583.11,2009 +800,46-55,Married,2,0,3,M,-0.07,504,83.58,1.02,-65.89,3,80736.72,982 +801,26-35,Single,1,0,2,M,-0.24,672,122.33,1.0,-255.04,6,129787.75,1061 +802,46-55,Single,1,0,2,M,-1.78,776,132.78,1.02,-1908.34,26,142212.22,1097 +803,18-25,Married,3,1,4,F,-1.43,266,93.23,1.0,-530.06,17,34493.43,370 +804,26-35,Married,3,1,2,M,0.0,180,108.61,1.0,0.0,0,23568.7,217 +805,46-55,Married,2,0,1,F,-0.03,499,84.52,1.0,-17.81,1,53753.48,636 +806,46-55,Single,1,0,2,M,0.0,312,123.75,1.0,0.0,0,47519.57,384 +807,46-55,Married,2,0,7,M,0.0,472,89.92,1.0,0.0,0,76071.37,846 +808,70+,Married,2,0,4,M,-0.21,1006,106.18,1.0,-402.51,20,203329.53,1918 +809,46-55,Single,1,0,2,F,-0.27,782,107.71,1.01,-321.17,8,127525.97,1193 +810,46-55,Single,1,0,5,M,0.0,730,79.89,1.0,0.0,0,104341.63,1309 +811,56-70,Single,1,0,7,F,-0.04,720,85.68,1.0,-53.42,3,113015.13,1319 +812,46-55,Single,1,0,6,M,0.0,232,75.14,1.0,0.0,0,27125.63,361 +813,26-35,Single,3,2,3,M,-0.54,540,77.04,1.01,-661.93,25,94608.88,1236 +814,26-35,Married,5,3,3,F,-0.49,413,80.34,1.0,-384.69,19,62507.54,778 +815,46-55,Married,2,0,3,F,0.0,318,107.31,1.01,0.0,0,39384.08,371 +816,26-35,Single,1,0,3,M,-0.04,237,68.26,1.0,-17.81,1,33651.44,493 +817,56-70,Married,2,0,4,M,-0.31,428,90.29,1.0,-238.35,10,68620.59,760 +818,36-45,Married,5,3,3,M,0.0,72,109.58,1.0,0.0,0,12820.65,117 +819,46-55,Married,3,1,6,M,0.0,408,55.12,1.0,0.0,0,45748.81,830 +820,36-45,Married,2,0,8,M,-0.48,687,100.49,1.0,-460.9,20,96469.66,960 +821,70+,Married,2,0,3,F,-0.34,219,105.61,1.0,-140.7,6,44355.01,420 +822,36-45,Single,1,0,6,F,0.0,190,103.16,1.03,0.0,0,24654.29,246 +823,36-45,Married,4,2,2,M,-1.98,876,94.13,1.05,-2711.34,109,128678.87,1431 +824,46-55,Married,4,2,5,F,-1.13,224,96.22,1.05,-316.66,7,27037.33,296 +825,36-45,Married,5,3,6,M,-0.44,350,104.03,1.0,-199.12,2,47435.49,456 +826,26-35,Single,1,0,4,M,-0.42,738,117.73,1.09,-402.59,6,111726.39,1032 +827,26-35,Married,2,0,5,F,-0.24,120,95.3,1.0,-39.18,2,15724.61,165 +828,18-25,Single,2,1,1,F,-0.05,651,102.33,1.04,-85.49,4,161985.41,1652 +829,46-55,Single,1,0,1,M,-0.07,522,81.39,1.0,-47.73,1,56239.26,692 +830,46-55,Single,1,0,2,F,0.0,195,97.9,1.05,0.0,0,32501.34,349 +831,70+,Married,2,0,3,M,-1.42,769,110.65,1.04,-1515.45,73,117734.98,1108 +832,36-45,Single,1,0,5,F,0.0,1297,132.23,1.15,0.0,0,248591.42,2155 +833,56-70,Married,2,0,4,F,-0.27,473,109.55,1.0,-191.45,8,77671.08,709 +834,36-45,Single,1,0,3,M,-0.25,617,93.14,1.0,-260.02,5,98547.08,1058 +835,70+,Married,2,0,5,F,-0.22,510,118.42,1.01,-195.9,7,105159.28,899 +836,36-45,Married,4,2,5,M,-1.0,191,104.74,1.0,-213.72,7,22413.41,214 +837,18-25,Married,2,0,4,F,-0.19,242,70.6,1.0,-89.05,1,32476.96,460 +838,46-55,Single,1,0,5,F,0.0,291,118.1,1.0,0.0,0,43341.81,367 +839,46-55,Single,1,0,1,F,-0.14,763,97.32,1.05,-170.08,7,116097.68,1254 +840,70+,Married,2,0,6,M,-0.21,291,112.01,1.04,-79.78,2,43570.55,404 +841,36-45,Married,2,0,6,F,0.0,277,99.12,1.0,0.0,0,42523.41,429 +842,46-55,Married,4,2,8,M,-0.35,848,114.42,1.0,-504.72,13,164764.47,1440 +843,46-55,Single,1,0,4,M,-0.77,593,95.26,1.0,-866.39,16,107260.66,1126 +844,46-55,Married,4,2,5,F,-0.41,1390,97.2,1.05,-997.72,31,233863.71,2529 +845,56-70,Married,2,0,7,M,-4.11,303,132.04,1.0,-1847.19,53,59286.15,449 +846,36-45,Single,2,1,6,F,-0.06,240,115.04,1.0,-17.81,1,34512.94,300 +847,26-35,Single,2,1,4,F,-0.44,273,136.8,1.04,-151.38,4,47058.62,359 +848,26-35,Single,3,2,1,M,-0.35,554,95.97,1.01,-432.37,10,117663.97,1243 +849,46-55,Single,1,0,5,F,0.0,385,74.2,1.0,0.0,0,37990.91,514 +850,46-55,Married,2,0,5,M,-0.38,64,102.92,1.0,-39.18,2,10497.35,102 +851,36-45,Married,2,0,6,M,-0.06,526,101.69,1.1,-55.21,2,98641.03,1064 +852,56-70,Married,2,0,6,F,-0.15,676,84.58,1.0,-174.54,4,97355.72,1155 +853,70+,Single,2,1,2,F,-0.44,352,102.48,1.03,-195.91,6,45708.2,458 +854,46-55,Married,5,3,8,F,-0.03,287,97.98,1.0,-19.59,1,55260.91,564 +855,56-70,Married,2,0,3,F,-2.02,140,114.3,1.06,-688.2,24,38976.03,360 +856,26-35,Single,2,1,2,F,-0.18,375,110.42,1.0,-106.86,3,66580.55,604 +857,70+,Single,1,0,7,M,-2.47,374,115.63,1.0,-1162.1,26,54462.72,471 +858,70+,Single,1,0,7,M,0.0,542,85.84,1.0,0.0,0,84900.08,992 +859,36-45,Married,3,1,6,M,-0.48,1302,105.54,1.08,-970.35,37,212666.08,2185 +860,26-35,Single,1,0,1,M,0.0,238,117.58,1.0,0.0,0,60200.42,512 +861,70+,Married,2,0,3,M,-0.06,502,95.16,1.0,-80.14,2,126367.93,1328 +862,46-55,Married,2,0,6,F,0.0,379,114.35,1.0,0.0,0,63809.23,558 +863,56-70,Married,2,0,4,F,-0.12,656,102.14,1.0,-106.5,1,94068.78,924 +864,56-70,Single,1,0,5,M,-1.96,1067,103.86,1.13,-3471.48,156,184358.5,1999 +865,26-35,Single,1,0,7,F,-0.04,597,94.3,1.01,-71.24,2,156728.99,1674 +866,36-45,Married,4,2,2,M,-0.09,995,93.82,1.1,-190.56,7,209780.88,2457 +867,46-55,Married,2,0,5,M,-0.04,378,125.51,1.0,-25.83,3,83837.97,668 +868,36-45,Single,1,0,5,M,0.0,150,125.05,1.0,0.0,0,29511.05,236 +869,56-70,Married,2,0,4,F,-0.02,589,104.84,1.0,-17.81,1,112181.99,1070 +870,18-25,Married,2,0,4,M,0.0,189,110.21,1.0,0.0,0,40004.58,363 +871,36-45,Married,3,1,7,F,-0.58,759,105.69,1.06,-840.32,33,153353.36,1544 +872,70+,Married,2,0,7,F,-0.18,262,124.73,1.0,-63.22,2,44029.58,353 +873,56-70,Married,4,2,6,M,-0.15,629,95.35,1.0,-123.78,7,78757.29,826 +874,46-55,Married,2,0,5,F,-0.34,954,106.8,1.11,-481.15,31,149627.4,1559 +875,46-55,Married,2,0,2,F,-0.98,351,93.68,1.04,-532.58,20,50682.96,565 +876,36-45,Single,1,0,9,M,-0.28,426,129.11,1.0,-208.37,10,95281.85,738 +877,46-55,Married,2,0,5,M,-0.46,374,113.12,1.0,-322.17,11,79185.16,700 +878,70+,Married,2,0,8,M,-0.72,1058,95.77,1.02,-1202.65,29,160122.06,1699 +879,46-55,Single,1,0,4,F,-0.65,436,99.57,1.0,-732.52,37,111519.6,1123 +880,56-70,Single,1,0,2,F,-0.02,326,93.65,1.02,-8.9,1,52071.05,569 +881,46-55,Single,1,0,5,M,-1.18,293,131.3,1.21,-642.35,15,71429.55,658 +882,26-35,Married,3,1,9,M,-0.36,267,133.39,1.01,-108.64,3,39750.28,300 +883,46-55,Single,1,0,3,F,-0.11,443,124.36,1.06,-56.99,2,65167.05,558 +884,26-35,Single,1,0,3,M,0.0,186,92.88,1.0,0.0,0,28234.27,304 +885,46-55,Single,1,0,3,F,0.0,500,118.05,1.0,0.0,0,101880.84,863 +886,46-55,Married,3,1,5,M,-0.16,339,102.6,1.0,-124.66,6,81154.34,791 +887,46-55,Single,1,0,5,F,-0.1,264,80.6,1.08,-42.21,2,32400.13,436 +888,26-35,Married,3,1,4,F,-0.09,359,128.09,1.01,-42.74,3,60586.7,476 +889,36-45,Single,1,0,4,F,-1.84,891,93.37,1.01,-3207.64,126,162925.68,1764 +890,26-35,Married,4,2,8,F,-0.25,824,112.7,1.0,-253.61,9,114394.96,1020 +891,36-45,Married,2,0,5,M,-0.53,485,96.06,1.11,-538.74,19,96731.56,1115 +892,46-55,Single,1,0,2,F,-0.09,143,84.5,1.01,-26.71,1,24675.29,295 +893,46-55,Married,5,3,8,M,0.0,94,97.72,1.0,0.0,0,10651.21,109 +894,18-25,Single,3,1,5,M,-1.19,546,104.33,1.0,-1161.2,42,101516.53,973 +895,46-55,Married,2,0,12,F,-0.08,312,100.73,1.02,-35.62,1,44322.64,447 +896,56-70,Married,2,0,4,F,-0.59,617,93.43,1.05,-472.49,11,74557.7,841 +897,46-55,Single,1,0,4,F,-4.44,97,94.12,1.0,-639.36,32,13553.21,144 +898,36-45,Married,3,1,4,M,-0.36,1318,107.13,1.11,-1162.08,27,347968.46,3593 +899,46-55,Married,3,1,2,F,0.0,273,114.94,1.0,0.0,0,57700.91,502 +900,56-70,Single,1,0,9,F,-0.02,748,96.29,1.0,-17.81,1,102168.5,1061 +901,26-35,Married,3,1,2,M,-1.79,979,89.81,1.0,-2661.66,107,133364.64,1485 +902,46-55,Single,1,0,1,F,0.0,282,121.21,1.0,0.0,0,41090.53,339 +903,46-55,Married,3,1,3,M,-1.1,295,89.66,1.07,-432.78,16,35145.08,421 +904,36-45,Married,2,0,9,M,0.0,169,126.9,1.01,0.0,0,46573.42,370 +905,36-45,Married,2,0,1,F,-0.37,535,125.81,1.0,-293.86,5,99892.69,794 +906,46-55,Married,3,1,6,F,-2.49,327,103.62,1.01,-1326.95,40,55124.42,539 +907,46-55,Married,5,3,9,F,-1.89,71,127.91,1.0,-151.38,3,10232.46,80 +908,56-70,Married,2,0,3,M,0.0,105,154.14,1.0,0.0,0,31906.65,208 +909,46-55,Married,5,3,8,M,-3.35,685,110.45,1.09,-3107.09,99,102383.61,1013 +910,46-55,Married,3,1,3,F,0.0,260,44.91,1.01,0.0,0,32563.23,730 +911,46-55,Married,3,1,8,M,-0.38,2073,117.1,1.0,-1352.4,49,421577.36,3617 +912,70+,Married,2,0,6,M,-1.11,356,102.56,1.07,-625.89,19,57841.33,604 +913,18-25,Married,2,0,11,F,0.0,347,95.09,1.0,0.0,0,46595.02,490 +914,26-35,Single,1,0,9,M,-0.83,408,93.8,1.04,-438.29,16,49242.72,546 +915,46-55,Single,1,0,3,F,-0.03,505,113.18,1.0,-17.81,1,80017.64,707 +916,56-70,Single,1,0,4,M,-1.05,439,116.73,1.03,-585.04,23,65019.74,574 +917,36-45,Married,2,0,3,F,-0.08,568,84.12,1.08,-71.24,2,79657.15,1026 +918,26-35,Married,2,0,5,M,-0.21,610,95.39,1.01,-216.93,5,97774.06,1032 +919,36-45,Married,4,2,5,F,-4.4,360,108.19,1.14,-2554.65,13,62752.55,661 +920,18-25,Single,1,0,12,F,-0.13,414,105.12,1.01,-71.24,2,58759.78,564 +921,36-45,Married,4,2,5,F,-0.07,211,61.15,1.01,-35.62,1,31918.77,527 +922,70+,Married,2,0,6,F,-0.02,524,113.54,1.04,-17.81,1,101727.53,936 +923,46-55,Single,1,0,4,F,-0.12,255,108.41,1.05,-35.62,1,31981.1,309 +924,36-45,Married,2,0,10,F,-0.02,627,110.45,1.0,-17.81,1,91120.86,825 +925,36-45,Single,1,0,5,F,0.0,276,106.91,1.0,0.0,0,46933.63,439 +926,26-35,Single,1,0,5,M,-0.2,709,110.94,1.06,-403.02,16,220207.61,2099 +927,46-55,Single,2,1,5,M,0.0,560,122.39,1.2,0.0,0,144907.44,1415 +928,46-55,Single,1,0,2,F,-0.4,1041,92.04,1.01,-768.66,22,177089.11,1945 +929,18-25,Single,1,0,4,F,-0.14,460,87.81,1.03,-84.58,4,51366.98,600 +930,18-25,Single,1,0,1,F,-0.06,775,84.57,1.03,-70.52,2,92180.87,1118 +931,18-25,Married,2,0,4,M,-1.26,559,89.63,1.0,-933.83,56,66324.68,740 +932,36-45,Single,1,0,5,F,-0.64,232,154.04,1.0,-195.73,6,47291.58,307 +933,26-35,Married,2,0,5,F,-1.08,477,94.47,1.01,-715.35,17,62445.4,665 +934,46-55,Single,1,0,6,F,-0.13,1368,95.98,1.04,-390.93,13,300022.11,3253 +935,26-35,Single,2,1,1,M,-0.11,547,116.61,1.07,-89.05,1,97018.23,888 +936,70+,Married,2,0,4,M,-0.06,476,111.19,1.0,-60.55,3,114863.9,1033 +937,70+,Married,2,0,2,F,-0.35,223,91.99,1.0,-89.05,1,23641.03,257 +938,56-70,Married,4,2,7,M,-0.42,157,108.0,1.0,-77.47,3,20087.37,186 +939,70+,Single,1,0,3,M,0.0,396,80.29,1.0,0.0,0,53716.44,669 +940,18-25,Married,3,1,4,F,0.0,363,58.63,1.0,0.0,0,35940.71,613 +941,56-70,Married,2,0,2,M,-0.45,224,100.69,1.01,-147.67,6,33127.65,332 +942,70+,Married,3,1,8,M,-0.03,655,109.57,1.13,-35.62,1,132905.05,1367 +943,46-55,Married,2,0,5,M,-0.14,419,107.26,1.0,-81.92,3,61569.54,574 +944,46-55,Single,1,0,3,F,-0.32,424,98.08,1.0,-276.06,10,83862.28,855 +945,70+,Single,1,0,4,F,0.0,263,84.43,1.0,0.0,0,52937.33,627 +946,46-55,Single,1,0,7,F,-1.05,150,118.43,1.12,-225.15,9,25461.89,240 +947,46-55,Married,2,0,5,F,-0.17,478,131.23,1.13,-163.25,3,124147.03,1066 +948,46-55,Married,2,0,5,M,-0.11,962,66.6,1.03,-270.71,7,164044.66,2525 +949,26-35,Single,1,0,4,M,-0.08,455,103.58,1.0,-64.11,2,78517.34,758 +950,36-45,Married,5,3,4,F,-0.28,284,90.85,1.11,-89.05,3,29345.69,359 +951,46-55,Single,1,0,5,F,-0.46,715,115.26,1.0,-455.04,22,113989.02,989 +952,46-55,Married,4,2,5,M,-0.52,519,99.34,1.01,-365.63,7,69540.68,709 +953,46-55,Single,1,0,2,F,0.0,205,114.12,1.0,0.0,0,26817.69,236 +954,26-35,Married,4,2,5,M,-0.21,439,79.89,1.22,-130.62,8,49054.31,751 +955,46-55,Married,2,0,4,F,-2.96,340,105.35,1.07,-1497.65,48,53305.19,540 +956,46-55,Single,1,0,1,F,0.0,484,79.5,1.0,0.0,0,66622.78,838 +957,26-35,Married,2,0,5,M,-0.06,1104,112.72,1.13,-142.48,3,249459.6,2498 +958,36-45,Single,1,0,8,M,-0.1,1146,96.71,1.03,-159.75,3,153960.61,1645 +959,46-55,Single,1,0,5,M,-1.31,851,101.66,1.07,-2296.33,87,177907.36,1864 +960,46-55,Married,2,0,7,M,0.0,107,113.54,1.05,0.0,0,17938.64,166 +961,46-55,Married,5,3,6,F,-0.52,230,119.51,1.08,-188.73,8,43143.99,391 +962,26-35,Married,3,1,6,M,-0.68,585,102.02,1.09,-925.42,42,138740.74,1489 +963,18-25,Single,1,0,4,M,-0.03,808,91.69,1.01,-53.43,2,141024.46,1547 +964,46-55,Single,1,0,5,F,-0.28,785,114.83,1.07,-329.12,10,133207.0,1245 +965,18-25,Married,3,1,4,M,0.0,423,91.54,1.05,0.0,0,50162.64,578 +966,46-55,Married,4,2,2,M,-0.02,535,77.83,1.0,-17.81,1,68027.54,874 +967,36-45,Single,1,0,5,M,-1.45,658,117.49,1.0,-1979.65,81,160022.32,1366 +968,36-45,Married,2,0,9,F,-0.7,265,116.98,1.0,-267.15,3,44685.72,382 +969,18-25,Married,3,1,6,M,0.0,446,96.53,1.03,0.0,0,63321.73,678 +970,46-55,Married,2,0,4,F,-0.29,534,109.5,1.01,-226.18,8,86397.85,796 +971,46-55,Married,5,3,6,M,-0.15,615,71.23,1.0,-160.3,9,75072.18,1057 +972,46-55,Married,2,0,9,M,-0.66,92,65.98,1.28,-159.94,5,16099.53,313 +973,56-70,Married,2,0,4,M,-0.15,579,86.44,1.0,-142.48,6,82288.34,954 +974,46-55,Single,1,0,2,F,-1.11,269,83.45,1.04,-417.34,17,31377.25,391 +975,36-45,Married,3,1,2,F,0.0,162,88.54,1.0,0.0,0,18594.09,210 +976,36-45,Single,1,0,1,M,-0.26,328,94.06,1.0,-110.42,5,40163.5,427 +977,46-55,Married,2,0,4,F,-0.94,585,76.19,1.0,-924.1,47,75273.91,988 +978,26-35,Married,4,2,7,M,-0.19,536,96.26,1.12,-217.88,11,108965.32,1263 +979,46-55,Single,4,3,4,F,-0.05,529,135.55,1.0,-35.62,1,101118.3,746 +980,46-55,Married,3,1,6,M,0.0,436,123.73,1.01,0.0,0,72753.68,595 +981,36-45,Married,2,0,6,M,-0.21,638,132.82,1.06,-212.82,7,134674.89,1071 +982,46-55,Single,1,0,2,M,-0.04,294,66.9,1.0,-19.59,1,35054.54,524 +983,56-70,Single,1,0,4,F,-0.37,513,119.64,1.0,-267.14,7,86617.84,724 +984,56-70,Married,3,1,7,M,-0.02,295,76.3,1.0,-8.9,1,31204.91,409 +985,36-45,Married,2,0,6,M,-0.38,242,110.93,1.04,-106.5,2,31505.18,296 +986,46-55,Married,2,0,4,F,-0.2,277,89.45,1.01,-71.24,2,31396.53,353 +987,26-35,Single,3,2,4,M,-0.11,628,96.59,1.0,-106.86,1,93404.46,971 +988,46-55,Married,2,0,2,M,-0.06,431,75.25,1.01,-37.4,2,50944.65,684 +989,26-35,Married,2,0,2,F,-0.09,1057,112.57,1.08,-187.0,6,227385.4,2190 +990,18-25,Single,2,1,5,F,-0.02,296,49.34,1.0,-14.25,1,28173.59,573 +991,46-55,Married,3,1,1,M,-0.52,131,131.52,1.0,-71.24,1,18018.2,137 +992,36-45,Single,1,0,6,M,0.0,364,98.19,1.01,0.0,0,48703.68,500 +993,46-55,Single,1,0,5,M,-0.03,555,133.25,1.06,-32.94,2,138842.33,1109 +994,46-55,Single,1,0,9,M,-0.09,899,137.28,1.06,-107.75,4,159521.57,1227 +995,36-45,Single,1,0,1,M,0.0,182,105.81,1.0,0.0,0,38619.32,365 +996,26-35,Single,1,0,5,M,-0.74,121,80.94,1.05,-101.52,4,11169.86,145 +997,36-45,Single,1,0,5,M,-0.16,687,104.89,1.0,-178.1,5,115377.83,1102 +998,36-45,Married,2,0,8,M,-0.25,702,115.09,1.0,-323.95,15,146623.77,1274 +999,26-35,Single,1,0,3,F,-1.25,423,95.77,1.05,-828.45,31,63594.41,695 +1000,70+,Single,1,0,2,F,-0.15,179,127.97,1.08,-35.62,2,29946.05,252 +1001,46-55,Married,2,0,4,M,-0.42,320,103.99,1.0,-170.07,9,41805.57,402 +1002,46-55,Married,2,0,4,M,-0.22,351,94.02,1.02,-89.05,1,38924.4,424 +1003,46-55,Single,1,0,5,M,-0.09,545,118.64,1.08,-89.05,2,111399.74,1012 +1004,46-55,Married,2,0,6,F,-0.07,1196,99.66,1.02,-129.42,6,180885.05,1855 +1005,26-35,Married,2,0,4,F,-0.09,913,75.32,1.0,-142.48,2,125099.06,1661 +1006,36-45,Married,2,0,7,F,-0.09,374,94.31,1.0,-35.62,1,39234.86,416 +1007,36-45,Married,3,1,9,F,-0.03,917,108.3,1.0,-39.18,2,140360.3,1296 +1008,36-45,Married,2,0,4,F,0.0,100,77.53,1.0,0.0,0,15428.28,199 +1009,26-35,Single,2,1,4,F,-5.39,255,78.73,1.0,-1520.64,70,22200.82,282 +1010,46-55,Married,2,0,6,F,-1.15,112,119.18,1.0,-418.54,20,43263.49,363 +1011,46-55,Married,5,3,8,F,-0.42,1134,109.11,1.1,-1618.52,70,421369.11,4253 +1012,70+,Married,2,0,3,F,-0.15,617,108.23,1.0,-158.51,4,116780.7,1079 +1013,18-25,Single,1,0,4,F,-0.42,193,105.15,1.0,-92.62,6,22922.56,219 +1014,36-45,Single,1,0,3,M,0.0,512,96.65,1.0,0.0,0,90073.64,934 +1015,36-45,Married,2,0,6,M,-3.2,405,112.93,1.1,-2135.95,102,75437.48,735 +1016,36-45,Married,2,0,8,M,-0.08,412,111.38,1.14,-44.52,2,62486.71,639 +1017,18-25,Single,3,1,5,M,-0.17,1113,87.85,1.05,-307.22,11,160686.26,1915 +1018,46-55,Married,2,0,6,M,-0.2,399,98.9,1.0,-104.18,7,52416.29,530 +1019,36-45,Married,2,0,4,F,-0.02,435,103.3,1.03,-17.81,1,74166.4,739 +1020,46-55,Single,1,0,3,M,0.0,161,99.29,1.0,0.0,0,24127.03,243 +1021,26-35,Single,1,0,4,F,-0.12,209,111.59,1.11,-35.62,1,32250.62,321 +1022,70+,Married,2,0,4,F,-0.55,423,141.87,1.0,-577.75,24,147828.33,1042 +1023,18-25,Single,4,3,1,F,-0.15,1230,75.15,1.0,-398.94,11,205622.55,2738 +1024,26-35,Married,2,0,3,F,0.0,222,111.05,1.07,0.0,0,39977.67,384 +1025,46-55,Married,2,0,5,M,-1.54,1051,98.53,1.02,-2984.9,138,191434.34,1989 +1026,26-35,Married,5,3,4,F,-0.11,614,84.95,1.0,-135.34,7,107629.22,1268 +1027,46-55,Single,2,1,5,M,0.0,407,82.45,1.0,0.0,0,51285.96,622 +1028,56-70,Married,2,0,4,F,-0.12,469,122.65,1.08,-94.4,4,95055.68,838 +1029,26-35,Single,2,1,2,M,-0.29,251,78.12,1.0,-131.2,7,34996.98,449 +1030,36-45,Married,2,0,4,F,0.0,110,187.18,1.0,0.0,0,37997.8,203 +1031,36-45,Single,1,0,2,F,-2.02,552,92.43,1.09,-1632.78,47,74681.98,880 +1032,36-45,Married,3,1,6,F,-0.12,235,116.12,1.0,-35.62,1,35882.3,309 +1033,36-45,Single,1,0,1,F,-0.21,1596,96.43,1.06,-589.15,15,271549.46,2985 +1034,46-55,Married,2,0,4,F,0.0,398,88.02,1.0,0.0,0,48232.84,548 +1035,46-55,Married,2,0,5,M,-0.23,319,98.02,1.01,-89.05,1,37249.03,383 +1036,36-45,Married,2,0,9,M,-0.08,871,115.38,1.0,-124.67,2,179880.33,1559 +1037,46-55,Single,1,0,5,M,0.0,121,68.54,1.39,0.0,0,12405.47,252 +1038,46-55,Single,1,0,4,F,0.0,112,142.98,1.01,0.0,0,27738.26,195 +1039,36-45,Single,1,0,6,M,-0.37,157,104.18,1.0,-67.08,3,18855.91,181 +1040,46-55,Married,2,0,1,M,0.0,316,110.35,1.07,0.0,0,40386.41,390 +1041,70+,Single,1,0,4,F,-0.03,306,92.92,1.0,-12.47,1,34658.19,373 +1042,70+,Married,2,0,6,M,-0.48,297,82.11,1.0,-172.16,3,29641.51,361 +1043,36-45,Married,5,3,8,F,-0.37,718,99.41,1.05,-382.54,10,103285.09,1095 +1044,36-45,Single,1,0,2,F,-0.98,729,103.84,1.01,-1333.7,42,141633.72,1374 +1045,36-45,Married,2,0,6,F,-0.08,591,94.56,1.04,-76.58,3,90398.24,997 +1046,46-55,Married,2,0,6,M,-0.2,1093,123.4,1.0,-473.15,16,298616.63,2420 +1047,46-55,Single,1,0,6,M,-0.06,483,110.15,1.0,-35.62,1,70495.85,642 +1048,26-35,Married,2,0,5,F,0.0,322,104.49,1.04,0.0,0,53289.48,528 +1049,70+,Married,3,1,8,M,-0.25,225,142.24,1.01,-71.24,3,41106.26,292 +1050,26-35,Married,2,0,5,M,-0.19,216,101.08,1.0,-44.52,2,23957.11,237 +1051,46-55,Married,2,0,2,M,-0.32,177,82.89,1.01,-81.93,3,21303.96,260 +1052,26-35,Single,1,0,4,F,-4.62,522,114.88,1.0,-3979.87,98,99027.94,862 +1053,46-55,Single,1,0,5,M,-0.29,208,164.25,1.08,-89.05,1,50918.77,335 +1054,36-45,Single,1,0,5,M,0.0,415,75.74,1.01,0.0,0,39084.33,520 +1055,26-35,Married,3,1,4,M,-0.12,996,99.22,1.02,-215.5,8,171458.68,1754 +1056,36-45,Single,1,0,2,M,-0.86,646,84.66,1.02,-1405.86,46,139018.76,1678 +1057,36-45,Single,1,0,3,M,-0.11,240,86.42,1.03,-35.62,1,27482.68,326 +1058,46-55,Married,3,1,5,F,0.0,428,111.93,1.0,0.0,0,68613.16,613 +1059,46-55,Married,2,0,5,F,-0.1,468,88.01,1.0,-73.02,3,63897.48,726 +1060,36-45,Married,2,0,2,F,0.0,398,72.96,1.0,0.0,0,39690.91,546 +1061,36-45,Single,1,0,3,M,-0.85,818,103.47,1.0,-1231.29,48,150545.63,1455 +1062,46-55,Single,1,0,3,M,-0.11,392,109.91,1.0,-68.12,4,66276.55,603 +1063,26-35,Married,3,1,4,M,-1.22,348,97.93,1.02,-603.38,25,48571.58,508 +1064,36-45,Married,2,0,9,M,-0.26,950,115.0,1.03,-548.84,12,243574.25,2189 +1065,46-55,Single,1,0,5,F,-0.26,398,87.72,1.01,-144.26,5,48332.26,555 +1066,46-55,Married,3,1,1,F,-0.39,142,122.36,1.0,-106.85,3,33650.22,275 +1067,46-55,Single,1,0,5,F,-0.22,533,114.02,1.01,-209.27,11,110597.4,981 +1068,46-55,Married,2,0,2,F,-0.55,935,91.12,1.03,-1079.86,48,177419.11,2015 +1069,46-55,Married,2,0,5,F,0.0,135,202.68,1.01,0.0,0,54318.08,272 +1070,56-70,Single,1,0,6,F,-3.34,594,112.44,1.1,-3646.57,131,122668.62,1202 +1071,36-45,Single,1,0,2,M,-0.14,656,95.51,1.0,-103.3,4,72207.66,757 +1072,70+,Married,2,0,2,M,-0.42,412,79.97,1.01,-218.12,7,41822.23,529 +1073,36-45,Single,1,0,5,M,-0.55,582,92.82,1.0,-422.08,18,70913.74,764 +1074,26-35,Single,2,1,2,M,0.0,171,117.15,1.0,0.0,0,23547.43,201 +1075,36-45,Married,4,2,7,M,-0.05,303,125.75,1.03,-21.38,2,50804.6,418 +1076,18-25,Single,1,0,12,F,0.0,608,98.15,1.03,0.0,0,167927.65,1766 +1077,70+,Married,2,0,4,F,0.0,297,87.81,1.0,0.0,0,49610.63,566 +1078,26-35,Married,2,0,4,F,-0.4,116,91.35,1.0,-89.05,1,20188.28,221 +1079,46-55,Married,2,0,1,F,0.0,317,97.39,1.0,0.0,0,64080.26,658 +1080,70+,Single,1,0,2,F,-0.1,288,126.83,1.01,-35.62,1,45786.64,365 +1081,56-70,Single,1,0,4,M,-0.03,351,88.27,1.09,-17.81,1,47668.48,590 +1082,56-70,Single,1,0,4,M,-0.58,331,115.65,1.15,-301.86,16,60370.67,599 +1083,26-35,Married,4,2,2,M,-0.71,653,85.81,1.0,-720.87,24,87610.29,1021 +1084,26-35,Married,2,0,4,M,-0.02,811,85.12,1.0,-27.61,3,133559.26,1573 +1085,46-55,Single,1,0,4,M,-0.35,450,79.16,1.06,-248.97,8,56912.61,763 +1086,26-35,Married,3,1,4,F,-3.78,506,93.65,1.04,-3061.8,92,75948.49,846 +1087,46-55,Single,1,0,4,F,-0.03,486,111.03,1.06,-17.81,1,72499.53,689 +1088,36-45,Married,5,3,5,F,-0.26,279,102.37,1.0,-89.05,1,35216.02,344 +1089,36-45,Married,5,3,4,M,-0.04,723,81.7,1.0,-47.49,2,89627.15,1097 +1090,70+,Married,2,0,6,F,0.0,204,94.27,1.0,0.0,0,22152.92,235 +1091,46-55,Married,5,3,8,M,-0.42,1376,92.92,1.11,-1086.74,32,239362.16,2854 +1092,46-55,Single,1,0,10,M,-0.08,307,145.77,1.0,-35.62,1,66764.46,458 +1093,46-55,Married,4,2,6,F,-0.55,945,114.63,1.03,-746.53,33,155433.93,1403 +1094,46-55,Single,1,0,4,F,-0.05,529,94.77,1.01,-35.62,1,68612.92,731 +1095,36-45,Single,1,0,2,M,-0.05,1518,87.68,1.0,-108.64,4,206410.02,2359 +1096,46-55,Married,2,0,2,F,0.0,199,87.24,1.01,0.0,0,24514.86,283 +1097,26-35,Married,3,1,7,M,-0.05,564,136.57,1.01,-47.2,3,127285.82,938 +1098,26-35,Married,2,0,4,M,-0.05,329,95.44,1.0,-35.62,1,65946.41,691 +1099,46-55,Single,1,0,5,F,-1.1,324,125.28,1.1,-525.38,23,60009.45,529 +1100,46-55,Single,1,0,5,F,-0.59,645,98.27,1.0,-593.07,23,98069.27,998 +1101,46-55,Single,1,0,6,F,-0.34,492,85.82,1.0,-286.39,7,73206.51,853 +1102,56-70,Married,2,0,5,F,0.0,138,127.76,1.0,0.0,0,20186.87,158 +1103,26-35,Married,2,0,4,F,-0.16,819,81.02,1.14,-293.86,8,149963.89,2113 +1104,70+,Married,2,0,6,F,-0.14,588,87.3,1.01,-118.44,8,74990.16,871 +1105,46-55,Married,2,0,9,F,-0.06,455,119.72,1.07,-35.62,1,69077.26,615 +1106,26-35,Married,2,0,3,M,-0.3,457,93.27,1.03,-213.72,5,66033.63,730 +1107,46-55,Single,1,0,5,F,0.0,272,126.32,1.0,0.0,0,44591.82,353 +1108,26-35,Married,5,3,4,F,0.0,285,84.03,1.0,0.0,0,28569.5,340 +1109,36-45,Married,2,0,6,M,-0.13,702,123.71,1.04,-178.1,5,175664.84,1482 +1110,70+,Married,2,0,6,F,0.0,183,103.03,1.0,0.0,0,36471.21,354 +1111,46-55,Single,2,1,7,F,-1.56,305,101.45,1.02,-745.63,25,48592.78,490 +1112,36-45,Single,3,2,5,M,-0.2,346,85.17,1.05,-133.57,2,56809.35,698 +1113,26-35,Married,2,0,4,F,-0.26,400,80.92,1.0,-142.48,4,44022.1,546 +1114,46-55,Single,1,0,5,M,-0.26,591,95.99,1.0,-225.0,13,83412.64,870 +1115,46-55,Married,2,0,3,F,-1.28,1208,89.86,1.01,-3315.5,98,233018.22,2623 +1116,18-25,Single,1,0,4,F,0.0,314,92.42,1.02,0.0,0,46392.75,511 +1117,70+,Single,1,0,1,F,-0.26,610,111.1,1.0,-241.31,13,102212.17,920 +1118,56-70,Married,2,0,4,M,-0.61,438,92.12,1.0,-346.4,10,52045.72,565 +1119,36-45,Married,2,0,10,F,-0.35,403,112.97,1.26,-161.89,4,52982.39,590 +1120,46-55,Married,2,0,9,M,-0.03,508,111.59,1.0,-26.71,1,90837.21,814 +1121,18-25,Married,3,1,5,M,-0.1,1010,143.09,1.0,-222.26,8,323822.41,2263 +1122,70+,Married,2,0,4,M,-0.36,897,112.94,1.09,-520.5,14,163870.05,1584 +1123,36-45,Married,2,0,8,F,0.0,553,91.61,1.0,0.0,0,80799.12,885 +1124,46-55,Married,2,0,1,F,-0.02,949,102.09,1.01,-35.62,1,152317.03,1505 +1125,26-35,Single,1,0,5,M,-0.24,207,118.81,1.0,-71.24,2,35880.54,302 +1126,26-35,Single,1,0,5,F,-0.37,295,85.71,1.0,-144.26,5,33596.54,392 +1127,36-45,Single,1,0,3,M,-0.03,339,138.17,1.06,-17.81,1,78343.32,600 +1128,46-55,Single,1,0,5,M,-0.28,302,121.49,1.02,-106.86,2,45678.63,385 +1129,70+,Married,2,0,9,F,-0.66,607,127.33,1.0,-476.95,5,91552.56,721 +1130,46-55,Married,2,0,3,F,-0.31,497,93.99,1.04,-198.4,5,59306.21,656 +1131,18-25,Single,5,3,1,M,-1.36,707,90.57,1.05,-1226.73,38,81515.56,943 +1132,46-55,Married,2,0,5,F,-0.13,205,100.57,1.0,-35.62,2,27353.89,272 +1133,46-55,Married,2,0,9,M,0.0,540,96.86,1.01,0.0,0,84171.31,875 +1134,26-35,Married,4,2,6,M,-0.04,525,106.08,1.02,-31.16,2,77334.73,740 +1135,56-70,Married,2,0,4,M,-0.02,801,85.52,1.0,-17.81,1,83554.71,978 +1136,46-55,Married,2,0,6,F,-2.0,957,98.73,1.07,-4036.85,137,199340.79,2151 +1137,36-45,Married,3,1,5,F,-0.17,374,104.19,1.0,-102.4,3,61368.24,589 +1138,36-45,Single,1,0,9,M,0.0,246,124.02,1.06,0.0,0,45516.46,389 +1139,56-70,Married,4,2,7,F,0.0,413,69.74,1.0,0.0,0,39614.35,568 +1140,26-35,Single,1,0,4,F,-0.15,722,116.31,1.0,-182.25,9,144694.52,1246 +1141,70+,Married,2,0,6,M,-0.41,448,120.38,1.07,-386.48,10,113153.54,1003 +1142,56-70,Single,1,0,6,F,-1.93,511,96.01,1.1,-1602.84,71,79689.22,909 +1143,46-55,Married,2,0,6,M,-0.18,250,84.36,1.0,-64.11,3,30114.98,357 +1144,26-35,Married,5,3,3,M,-1.15,432,105.44,1.0,-648.28,26,59466.92,565 +1145,46-55,Married,5,3,8,F,-0.41,560,108.66,1.02,-382.91,10,102362.12,960 +1146,36-45,Married,2,0,5,F,-0.31,942,99.36,1.06,-609.99,15,198426.03,2121 +1147,46-55,Married,3,1,3,F,0.0,357,110.19,1.04,0.0,0,58950.7,555 +1148,18-25,Single,1,0,12,M,-1.17,477,149.31,1.04,-873.74,19,111533.88,780 +1149,46-55,Single,1,0,6,F,-0.3,193,59.73,1.0,-178.1,6,34941.06,585 +1150,36-45,Married,3,1,1,M,-0.23,70,74.49,1.0,-19.59,1,6480.43,87 +1151,46-55,Single,2,1,4,M,-0.2,220,97.43,1.0,-74.8,5,36048.76,370 +1152,46-55,Married,3,1,10,F,-0.17,1195,107.89,1.0,-474.28,13,292823.13,2717 +1153,46-55,Single,1,0,10,M,-0.22,259,119.26,1.01,-89.05,1,48537.32,412 +1154,46-55,Married,3,1,6,F,-0.07,840,121.32,1.02,-129.12,6,229904.66,1936 +1155,46-55,Married,5,3,1,F,-1.16,381,89.57,1.0,-670.18,13,51950.05,580 +1156,70+,Married,2,0,6,F,-0.03,761,102.08,1.11,-30.28,2,106672.49,1160 +1157,18-25,Married,3,1,5,F,-0.21,1402,105.29,1.11,-425.16,15,210580.78,2220 +1158,36-45,Married,3,1,6,F,0.0,788,108.68,1.06,0.0,0,140201.64,1370 +1159,36-45,Married,2,0,6,M,-1.74,824,93.01,1.0,-1928.11,67,103237.7,1112 +1160,36-45,Married,2,0,4,F,-0.08,295,94.07,1.05,-34.35,2,38476.45,430 +1161,26-35,Single,1,0,9,F,-1.39,314,114.38,1.0,-760.47,28,62567.49,547 +1162,70+,Married,2,0,4,M,-0.01,243,121.44,1.0,-4.45,1,36431.43,300 +1163,46-55,Single,2,1,7,F,-0.52,150,99.05,1.04,-92.26,3,17532.22,184 +1164,26-35,Married,4,2,2,F,-0.43,354,89.61,1.0,-191.46,4,39698.58,443 +1165,36-45,Married,5,3,5,M,-0.75,313,106.56,1.07,-335.7,10,47630.73,477 +1166,46-55,Married,5,3,8,F,-0.13,1083,123.6,1.04,-283.47,13,271916.85,2297 +1167,46-55,Single,1,0,5,M,-0.07,585,103.99,1.0,-97.95,4,150676.87,1449 +1168,18-25,Married,2,0,4,M,-1.28,639,91.73,1.01,-1084.98,40,77878.3,858 +1169,46-55,Married,2,0,5,F,-0.45,429,129.8,1.05,-275.75,10,79696.82,646 +1170,36-45,Single,1,0,2,F,-0.12,850,84.01,1.06,-151.38,3,106861.26,1350 +1171,46-55,Married,2,0,3,F,0.0,302,115.15,1.0,0.0,0,42490.9,369 +1172,70+,Married,2,0,4,F,-0.2,203,71.66,1.0,-189.98,8,68002.28,949 +1173,46-55,Single,1,0,7,F,-0.09,203,106.7,1.09,-35.62,1,42467.42,433 +1174,26-35,Married,2,0,6,F,-0.15,509,92.19,1.01,-97.95,5,61767.6,674 +1175,70+,Married,2,0,6,F,-1.45,76,113.72,1.0,-178.1,2,13987.26,123 +1176,70+,Single,1,0,2,F,-1.57,284,110.34,1.02,-572.92,18,40275.69,374 +1177,26-35,Married,3,1,4,M,-0.33,226,118.6,1.0,-89.04,4,31548.39,266 +1178,36-45,Married,3,1,4,M,-0.11,696,90.08,1.02,-128.23,3,107739.85,1222 +1179,36-45,Married,2,0,7,M,-0.4,421,103.79,1.0,-207.46,11,53556.62,516 +1180,70+,Married,2,0,5,M,-2.36,74,204.43,1.0,-195.91,2,16967.64,83 +1181,46-55,Married,2,0,5,F,0.0,528,100.04,1.01,0.0,0,67930.35,686 +1182,26-35,Married,3,1,3,M,-0.68,989,128.39,1.0,-1341.92,59,252546.08,1969 +1183,46-55,Married,3,1,3,M,-0.05,672,127.01,1.0,-53.43,3,131327.46,1034 +1184,46-55,Married,3,1,4,F,-0.21,1874,100.36,1.0,-532.21,16,260236.54,2594 +1185,36-45,Married,2,0,4,F,-0.15,271,121.66,1.01,-47.49,2,38445.76,320 +1186,46-55,Single,1,0,10,M,-0.02,721,119.54,1.04,-26.71,1,144877.87,1261 +1187,26-35,Single,1,0,5,M,-0.63,909,79.15,1.05,-1014.4,35,128373.55,1706 +1188,36-45,Married,2,0,5,M,0.0,198,83.37,1.12,0.0,0,25426.51,342 +1189,46-55,Single,1,0,5,F,-0.36,342,83.96,1.0,-227.08,4,53316.04,635 +1190,56-70,Single,1,0,6,F,0.0,248,133.71,1.0,0.0,0,104295.08,780 +1191,46-55,Married,5,3,4,M,-0.16,491,93.24,1.0,-106.86,2,63215.93,680 +1192,70+,Single,1,0,2,F,-0.2,938,93.91,1.0,-343.72,10,159740.01,1701 +1193,56-70,Married,2,0,4,M,-0.69,418,119.93,1.04,-378.76,19,65964.02,571 +1194,36-45,Married,5,3,4,F,-1.05,526,90.59,1.05,-888.7,17,76910.8,888 +1195,56-70,Married,2,0,5,F,-0.03,221,141.15,1.0,-8.9,1,39099.25,277 +1196,36-45,Married,2,0,4,M,-0.84,150,89.03,1.04,-180.78,8,19051.39,223 +1197,46-55,Single,1,0,4,F,-0.07,358,105.44,1.03,-35.62,1,54724.31,532 +1198,18-25,Single,1,0,4,F,-0.89,494,105.36,1.11,-669.63,27,79443.14,835 +1199,36-45,Single,1,0,9,M,-0.4,850,99.98,1.01,-549.62,8,136968.68,1380 +1200,36-45,Married,5,3,5,F,0.0,452,101.08,1.04,0.0,0,65092.54,671 +1201,18-25,Single,1,0,1,F,0.0,667,101.6,1.0,0.0,0,89406.31,880 +1202,36-45,Single,3,1,6,F,-0.99,338,85.21,1.03,-540.82,24,46526.21,565 +1203,36-45,Single,1,0,3,F,-0.02,522,87.55,1.0,-15.27,1,86232.99,985 +1204,46-55,Single,1,0,5,F,-0.08,457,107.0,1.0,-48.09,2,64305.65,601 +1205,46-55,Single,1,0,5,F,-0.05,284,103.21,1.0,-23.15,2,46959.6,455 +1206,36-45,Married,3,1,12,F,-0.01,262,134.7,1.16,-3.92,1,47413.9,410 +1207,36-45,Single,4,3,4,F,-0.23,350,91.96,1.06,-106.5,1,41748.47,479 +1208,56-70,Married,2,0,9,M,-3.84,493,121.62,1.13,-5535.66,203,175137.19,1626 +1209,36-45,Married,4,2,7,F,-0.39,587,83.51,1.05,-415.56,8,88689.14,1111 +1210,46-55,Married,2,0,5,M,-0.35,848,84.91,1.0,-614.44,23,148174.55,1751 +1211,36-45,Married,2,0,5,F,0.0,353,99.39,1.07,0.0,0,52078.36,563 +1212,26-35,Married,2,0,6,M,-0.16,446,77.67,1.0,-124.67,5,61436.2,791 +1213,36-45,Single,3,1,5,M,0.0,125,69.39,1.01,0.0,0,14641.56,213 +1214,46-55,Single,1,0,6,M,0.0,300,128.87,1.02,0.0,0,44847.28,354 +1215,36-45,Married,3,1,6,F,-0.08,738,102.72,1.02,-99.73,5,125519.31,1250 +1216,26-35,Married,4,2,8,F,-0.69,752,121.92,1.08,-1034.73,38,183970.73,1624 +1217,26-35,Single,1,0,6,F,0.0,331,112.45,1.01,0.0,0,68370.9,613 +1218,36-45,Married,2,0,5,F,0.0,401,138.2,1.0,0.0,0,135159.68,980 +1219,36-45,Single,1,0,5,F,-0.41,269,75.21,1.0,-156.72,7,28431.14,378 +1220,46-55,Single,1,0,1,M,0.0,191,103.95,1.0,0.0,0,25363.51,244 +1221,46-55,Single,1,0,2,F,-0.75,111,87.41,1.0,-89.05,3,10401.32,119 +1222,46-55,Single,1,0,5,M,-0.03,397,91.23,1.0,-14.25,1,49809.5,546 +1223,36-45,Married,2,0,5,M,0.0,169,106.81,1.01,0.0,0,31400.7,297 +1224,26-35,Single,1,0,4,M,-0.45,717,101.41,1.03,-630.15,27,141774.87,1443 +1225,46-55,Married,3,1,6,F,-0.26,301,110.52,1.0,-101.77,4,42883.22,388 +1226,46-55,Single,1,0,8,M,-0.58,652,104.89,1.1,-612.37,23,110553.58,1160 +1227,46-55,Married,2,0,5,M,-0.13,292,89.9,1.0,-47.73,1,33531.16,373 +1228,70+,Married,2,0,3,M,-0.04,459,100.71,1.03,-26.71,3,61229.19,624 +1229,26-35,Single,1,0,2,F,-0.92,411,108.34,1.0,-530.74,19,62297.3,576 +1230,70+,Single,1,0,1,F,-0.46,416,78.75,1.0,-255.76,3,44102.31,560 +1231,26-35,Married,3,1,5,M,-0.89,445,99.86,1.22,-567.41,19,63514.08,774 +1232,70+,Single,1,0,3,M,-0.13,766,87.31,1.0,-144.26,5,100314.44,1151 +1233,46-55,Single,1,0,3,M,0.0,248,78.74,1.0,0.0,0,39054.61,496 +1234,46-55,Single,1,0,1,F,-0.23,275,86.38,1.0,-120.4,2,46038.76,533 +1235,46-55,Married,5,3,1,M,-0.01,201,87.15,1.0,-3.56,1,23095.58,266 +1236,46-55,Single,1,0,5,M,-0.07,590,105.37,1.1,-73.03,5,110318.04,1152 +1237,46-55,Single,1,0,4,M,-0.03,500,79.47,1.0,-17.81,1,53724.66,678 +1238,46-55,Single,1,0,5,F,-0.27,538,117.53,1.05,-311.31,6,137157.67,1225 +1239,36-45,Single,1,0,6,F,-1.61,1114,136.89,1.05,-3748.52,163,318531.62,2432 +1240,26-35,Married,5,3,5,M,-0.2,603,95.5,1.0,-190.38,7,89862.13,941 +1241,46-55,Single,1,0,1,M,-0.12,247,88.38,1.04,-44.52,2,33494.31,395 +1242,46-55,Married,3,1,9,F,-2.7,134,119.71,1.03,-487.99,10,21667.37,187 +1243,56-70,Married,2,0,5,F,-0.11,573,114.71,1.02,-89.05,1,94059.43,838 +1244,46-55,Single,1,0,2,F,-0.03,143,89.13,1.0,-6.24,1,17291.43,194 +1245,26-35,Single,1,0,7,F,0.0,328,70.68,1.07,0.0,0,59299.91,901 +1246,26-35,Married,5,3,5,M,-0.52,791,119.62,1.0,-703.66,26,161487.69,1350 +1247,56-70,Married,4,2,7,F,-0.18,491,77.3,1.0,-115.76,5,49860.08,645 +1248,36-45,Married,4,2,8,F,-1.9,637,122.85,1.09,-1627.7,38,105407.55,937 +1249,70+,Single,1,0,2,F,-0.81,292,85.56,1.03,-580.54,21,61002.33,737 +1250,36-45,Married,5,3,8,F,-1.82,409,99.26,1.11,-1252.31,60,68192.36,765 +1251,70+,Single,1,0,1,M,-0.25,288,80.67,1.04,-89.04,5,28476.1,366 +1252,26-35,Single,3,2,3,M,-0.16,1414,117.78,1.02,-376.96,11,286085.8,2482 +1253,46-55,Married,2,0,11,F,-0.03,318,148.73,1.0,-12.47,1,72431.35,487 +1254,26-35,Single,1,0,5,M,-0.08,499,123.91,1.03,-55.21,2,90327.51,749 +1255,46-55,Married,2,0,6,M,0.0,312,55.61,1.0,0.0,0,59886.93,1077 +1256,36-45,Single,1,0,4,F,-0.08,317,88.82,1.02,-35.62,1,39878.8,460 +1257,46-55,Single,1,0,5,F,-1.76,738,106.12,1.0,-3774.69,172,227305.14,2142 +1258,46-55,Single,1,0,1,M,-0.94,434,98.07,1.02,-697.25,24,72670.09,754 +1259,46-55,Married,3,1,5,F,-0.33,717,115.99,1.08,-407.85,17,145225.36,1358 +1260,46-55,Single,1,0,5,M,-0.79,360,107.7,1.0,-369.56,19,50080.68,465 +1261,46-55,Married,2,0,5,F,-0.53,330,137.48,1.02,-210.16,6,54168.86,402 +1262,46-55,Single,1,0,6,M,0.0,164,90.86,1.0,0.0,0,26168.07,288 +1263,46-55,Married,2,0,3,F,-0.35,896,90.04,1.04,-618.72,18,157745.3,1830 +1264,18-25,Single,1,0,3,M,-0.1,361,83.19,1.0,-39.18,2,34025.15,409 +1265,70+,Single,1,0,7,M,0.0,274,83.32,1.0,0.0,0,31660.61,380 +1266,46-55,Single,1,0,4,M,-0.82,468,127.39,1.0,-846.28,38,131719.27,1034 +1267,46-55,Single,1,0,5,F,-0.13,339,70.24,1.0,-80.14,2,41932.53,597 +1268,46-55,Married,2,0,6,M,-0.03,767,78.55,1.07,-35.62,1,98265.36,1338 +1269,46-55,Single,1,0,5,F,-0.05,408,96.57,1.0,-26.71,1,54272.55,562 +1270,46-55,Single,1,0,2,F,-0.73,1080,97.34,1.0,-1593.57,40,212502.35,2188 +1271,36-45,Married,5,3,7,M,-0.02,823,103.12,1.0,-35.62,1,158909.8,1541 +1272,46-55,Single,1,0,4,M,-0.14,165,113.05,1.0,-35.62,1,28375.75,251 +1273,26-35,Single,2,1,8,F,-1.45,530,125.44,1.05,-1110.38,31,95837.62,801 +1274,46-55,Married,2,0,9,F,-0.04,356,92.0,1.0,-17.81,1,43146.28,469 +1275,36-45,Married,2,0,8,F,-0.15,195,135.37,1.0,-35.62,1,33166.11,245 +1276,36-45,Single,1,0,6,F,-0.7,446,96.63,1.01,-381.38,21,52855.71,551 +1277,70+,Married,2,0,3,M,0.0,104,153.49,1.1,0.0,0,21796.06,156 +1278,46-55,Single,1,0,1,F,-0.24,237,123.37,1.07,-75.16,4,38736.7,337 +1279,70+,Married,2,0,5,M,-0.23,368,71.94,1.08,-117.19,2,37190.74,556 +1280,36-45,Married,5,3,8,F,-0.27,229,78.95,1.0,-83.71,3,24632.85,312 +1281,18-25,Single,1,0,5,M,-0.07,340,119.19,1.01,-28.14,1,50896.07,431 +1282,46-55,Single,2,1,4,F,0.0,259,95.08,1.0,0.0,0,35845.88,377 +1283,36-45,Married,5,3,5,F,-0.1,221,105.83,1.01,-26.71,1,29631.11,284 +1284,36-45,Single,2,1,4,M,-0.08,537,129.76,1.03,-71.24,2,112113.2,887 +1285,56-70,Married,2,0,7,M,-0.87,423,125.88,1.01,-605.61,26,87359.86,701 +1286,36-45,Married,5,3,2,F,-0.04,276,78.93,1.0,-17.81,1,31808.69,405 +1287,36-45,Married,2,0,3,F,0.0,202,99.05,1.0,0.0,0,24068.19,243 +1288,56-70,Married,2,0,4,M,0.0,123,136.85,1.03,0.0,0,18200.7,137 +1289,18-25,Single,1,0,1,M,-0.22,687,92.34,1.07,-195.91,5,81901.65,949 +1290,36-45,Married,5,3,3,M,0.0,587,114.18,1.12,0.0,0,89060.43,873 +1291,46-55,Married,2,0,2,F,-0.1,538,107.27,1.06,-103.3,7,110376.29,1091 +1292,46-55,Married,2,0,2,M,-0.05,415,102.46,1.02,-35.62,2,80632.68,800 +1293,26-35,Single,1,0,6,F,-0.03,242,121.69,1.02,-12.47,1,45024.12,378 +1294,70+,Married,2,0,4,F,-0.55,319,116.22,1.0,-270.36,4,57063.85,491 +1295,36-45,Married,2,0,4,F,-0.44,415,84.74,1.08,-239.18,8,46098.52,589 +1296,26-35,Single,2,1,8,M,-0.13,461,123.04,1.04,-120.66,7,111839.33,943 +1297,46-55,Married,2,0,12,M,0.0,283,101.11,1.0,0.0,0,35691.2,353 +1298,26-35,Married,2,0,3,F,-0.36,786,85.82,1.01,-480.86,22,115422.0,1358 +1299,56-70,Married,2,0,6,M,-0.14,263,94.59,1.0,-55.21,2,37930.26,401 +1300,18-25,Single,1,0,5,M,-0.18,686,117.4,1.01,-160.28,5,106250.8,910 +1301,26-35,Married,3,1,3,M,-0.52,419,105.61,1.0,-302.77,12,61150.7,579 +1302,56-70,Married,2,0,2,F,-0.45,335,123.22,1.01,-206.85,11,56187.45,461 +1303,56-70,Married,2,0,5,M,-0.56,472,85.7,1.01,-365.99,14,56133.77,662 +1304,36-45,Single,1,0,2,F,-0.01,326,100.05,1.0,-5.34,1,38218.66,382 +1305,46-55,Single,1,0,5,F,-0.43,715,95.56,1.02,-469.29,10,103108.51,1099 +1306,36-45,Married,2,0,3,F,-0.03,237,109.74,1.0,-8.9,1,34350.05,313 +1307,36-45,Married,2,0,8,M,-0.3,162,83.67,1.0,-65.89,4,18407.07,221 +1308,26-35,Married,3,1,3,M,-0.25,512,101.44,1.05,-192.35,6,77095.46,798 +1309,26-35,Married,3,1,7,M,-0.41,745,93.47,1.08,-708.84,23,162732.27,1877 +1310,56-70,Married,3,1,4,M,-0.18,244,167.52,1.1,-57.88,3,55113.24,362 +1311,70+,Married,2,0,3,M,0.0,488,115.62,1.0,0.0,0,96770.69,839 +1312,56-70,Single,1,0,3,M,-0.19,485,84.85,1.01,-295.65,11,133388.93,1590 +1313,26-35,Married,2,0,4,F,0.0,137,113.71,1.0,0.0,0,19557.52,172 +1314,46-55,Married,3,1,1,F,-0.27,473,95.34,1.11,-196.8,6,70262.81,820 +1315,26-35,Single,1,0,3,F,-1.13,653,112.75,1.06,-1377.55,58,137671.22,1294 +1316,56-70,Married,2,0,8,F,-0.08,367,121.99,1.0,-35.62,1,53189.52,438 +1317,36-45,Married,2,0,4,M,-0.28,1164,103.77,1.03,-581.72,25,214704.6,2141 +1318,46-55,Married,2,0,3,F,-0.07,419,103.67,1.0,-48.97,2,78060.16,755 +1319,46-55,Single,1,0,7,M,-0.24,872,97.49,1.07,-355.01,11,145949.43,1607 +1320,26-35,Single,1,0,4,F,-0.17,565,81.39,1.0,-173.94,4,85375.69,1049 +1321,36-45,Married,3,1,1,F,-2.55,366,90.34,1.07,-1548.37,58,54835.72,648 +1322,46-55,Married,2,0,5,M,0.0,353,89.57,1.0,0.0,0,40398.29,451 +1323,46-55,Married,2,0,5,M,-0.14,561,66.75,1.02,-128.23,5,63080.75,960 +1324,36-45,Married,5,3,2,M,-0.27,572,120.83,1.16,-272.43,6,123854.01,1193 +1325,26-35,Married,2,0,5,M,-0.13,101,109.25,1.0,-17.22,2,14858.11,136 +1326,70+,Single,1,0,2,M,0.0,52,79.21,1.16,0.0,0,6257.57,92 +1327,70+,Married,2,0,4,M,-0.71,359,91.07,1.01,-401.79,13,51818.83,574 +1328,56-70,Married,2,0,10,M,-0.09,279,115.73,1.0,-39.18,2,50112.88,433 +1329,36-45,Married,5,3,6,F,0.0,376,99.12,1.0,0.0,0,60464.96,610 +1330,46-55,Married,3,1,4,F,-0.16,273,91.13,1.0,-62.33,3,34446.29,379 +1331,36-45,Married,2,0,8,M,-0.05,225,93.16,1.0,-26.71,1,50400.75,541 +1332,36-45,Married,2,0,8,F,-0.44,1254,122.0,1.03,-848.82,9,233147.45,1972 +1333,46-55,Married,2,0,6,F,-8.39,336,125.32,1.05,-3928.63,132,58648.95,491 +1334,46-55,Married,5,3,4,F,-0.61,632,97.05,1.01,-527.88,17,83757.24,873 +1335,46-55,Married,3,1,10,M,-1.35,375,85.13,1.01,-887.18,37,56018.38,663 +1336,36-45,Married,5,3,9,F,-2.31,1089,107.46,1.15,-5023.1,172,233194.4,2503 +1337,36-45,Married,5,3,6,M,-0.14,1587,100.76,1.04,-397.14,15,283041.99,2920 +1338,46-55,Married,2,0,4,F,-1.5,221,75.95,1.0,-494.76,11,24988.93,329 +1339,56-70,Single,2,1,4,M,-1.19,599,111.17,1.09,-939.64,19,88048.23,865 +1340,46-55,Married,2,0,6,F,-0.5,323,118.03,1.0,-341.35,10,80024.57,678 +1341,26-35,Married,4,2,5,F,-1.42,681,75.74,1.14,-2073.35,79,110583.67,1662 +1342,46-55,Single,2,1,5,F,-0.27,282,121.5,1.08,-89.05,2,40216.69,356 +1343,26-35,Single,2,1,4,M,-0.1,810,88.3,1.05,-125.47,5,112057.7,1336 +1344,26-35,Single,1,0,5,F,-2.41,576,122.18,1.01,-1705.08,66,86505.33,714 +1345,46-55,Single,3,1,5,M,-0.13,565,102.09,1.0,-115.76,6,88208.84,864 +1346,56-70,Married,2,0,7,M,0.0,624,128.61,1.04,0.0,0,132473.31,1070 +1347,36-45,Single,1,0,4,M,-0.27,1015,108.19,1.0,-447.92,15,178946.27,1654 +1348,36-45,Married,2,0,4,F,0.0,473,86.99,1.07,0.0,0,55673.44,682 +1349,46-55,Single,1,0,5,F,-1.43,362,102.12,1.0,-607.3,19,43501.18,426 +1350,46-55,Married,2,0,1,M,-0.19,646,101.81,1.02,-203.04,7,107006.82,1076 +1351,46-55,Married,5,3,6,F,-0.17,484,122.96,1.0,-115.4,3,83735.33,681 +1352,46-55,Single,1,0,2,F,-0.01,750,98.79,1.0,-14.25,1,146108.82,1479 +1353,46-55,Married,2,0,6,F,-0.22,252,130.02,1.01,-63.05,1,37314.46,290 +1354,36-45,Single,1,0,4,F,-0.17,182,130.45,1.02,-35.62,1,26741.68,209 +1355,46-55,Married,3,1,2,M,0.0,282,118.25,1.0,0.0,0,44816.27,379 +1356,26-35,Married,3,1,6,M,-0.21,622,85.82,1.01,-222.62,9,91398.14,1080 +1357,56-70,Married,4,2,4,F,-0.43,342,85.68,1.0,-328.58,16,65032.34,761 +1358,26-35,Married,2,0,2,F,-1.2,159,151.16,1.24,-320.58,5,40358.58,331 +1359,46-55,Married,2,0,4,M,-0.66,279,108.01,1.04,-243.28,4,39964.7,386 +1360,46-55,Married,2,0,5,M,-0.33,660,77.2,1.05,-481.77,24,113253.47,1536 +1361,26-35,Single,1,0,5,F,-1.86,348,74.53,1.14,-997.03,43,39949.64,610 +1362,46-55,Married,2,0,5,M,0.0,197,91.43,1.03,0.0,0,28708.83,324 +1363,56-70,Married,2,0,9,M,0.0,86,104.62,1.33,0.0,0,10984.98,140 +1364,46-55,Single,1,0,6,F,-6.64,567,185.74,1.1,-7898.5,98,221033.28,1314 +1365,56-70,Single,1,0,4,F,-0.9,384,84.04,1.0,-452.02,6,42103.36,501 +1366,36-45,Married,5,3,6,M,0.0,362,84.3,1.0,0.0,0,48895.13,580 +1367,36-45,Single,1,0,5,F,-1.05,576,92.34,1.0,-1378.28,43,120686.45,1307 +1368,46-55,Married,2,0,5,M,-0.23,824,94.15,1.01,-376.98,8,151494.81,1622 +1369,36-45,Married,2,0,8,F,-0.93,498,101.67,1.0,-815.32,41,89162.18,881 +1370,26-35,Married,2,0,4,M,-0.14,315,120.16,1.01,-53.43,2,44337.56,372 +1371,26-35,Married,3,1,6,F,-0.31,555,86.85,1.07,-250.76,4,70869.59,871 +1372,46-55,Single,1,0,5,M,-0.09,718,89.7,1.02,-97.95,3,97328.79,1111 +1373,46-55,Married,3,1,4,M,0.0,198,123.46,1.02,0.0,0,27407.47,227 +1374,56-70,Married,3,1,7,F,0.0,143,128.97,1.0,0.0,0,19604.05,152 +1375,46-55,Married,2,0,6,M,-0.06,339,91.34,1.0,-29.68,1,43751.62,479 +1376,70+,Married,2,0,3,M,-0.33,295,104.39,1.16,-134.76,4,42801.17,476 +1377,26-35,Single,1,0,5,M,0.0,699,116.14,1.06,0.0,0,110099.81,1007 +1378,26-35,Married,4,2,8,F,-0.49,915,119.89,1.02,-828.13,26,201536.88,1720 +1379,36-45,Single,1,0,3,M,-0.07,456,125.22,1.0,-54.33,4,97044.98,775 +1380,46-55,Married,2,0,1,F,-0.19,231,153.12,1.0,-65.3,2,51908.7,339 +1381,70+,Single,2,1,4,F,0.0,383,94.63,1.02,0.0,0,54698.66,592 +1382,36-45,Married,4,2,5,M,-0.34,961,92.44,1.12,-535.48,12,146149.06,1764 +1383,26-35,Married,2,0,5,F,-0.05,1438,116.44,1.02,-90.83,2,232754.09,2039 +1384,56-70,Single,1,0,3,M,-0.18,742,105.2,1.05,-234.14,4,135494.46,1356 +1385,36-45,Single,1,0,3,F,-0.73,713,91.61,1.03,-983.1,41,123765.43,1393 +1386,18-25,Married,3,1,5,M,-0.41,590,84.52,1.06,-562.78,18,116552.16,1461 +1387,46-55,Single,1,0,1,F,-0.67,1104,86.49,1.05,-1415.86,41,183797.39,2228 +1388,46-55,Married,2,0,5,F,0.0,474,161.38,1.03,0.0,0,102796.18,657 +1389,36-45,Married,2,0,5,M,-0.18,533,65.12,1.02,-146.92,4,52744.62,824 +1390,26-35,Married,2,0,5,M,-0.13,344,97.93,1.01,-71.24,2,53372.18,553 +1391,26-35,Single,1,0,5,M,0.0,979,80.85,1.0,0.0,0,109149.37,1353 +1392,36-45,Single,3,2,5,M,-0.07,1170,101.12,1.01,-155.83,6,229548.14,2285 +1393,26-35,Married,5,3,4,M,0.0,751,101.24,1.12,0.0,0,106100.2,1172 +1394,46-55,Single,1,0,4,M,0.0,322,96.22,1.0,0.0,0,35600.16,370 +1395,56-70,Single,1,0,3,M,-0.11,258,109.58,1.04,-35.62,1,35394.05,337 +1396,46-55,Married,2,0,2,F,-0.14,659,101.97,1.0,-138.92,5,98299.52,966 +1397,46-55,Single,1,0,5,M,0.0,322,107.1,1.0,0.0,0,51623.2,482 +1398,36-45,Single,1,0,2,M,-0.71,749,102.31,1.04,-669.65,16,97089.33,984 +1399,70+,Single,1,0,4,F,0.0,442,89.73,1.0,0.0,0,61197.01,682 +1400,46-55,Single,1,0,2,F,-1.22,437,110.73,1.01,-757.76,34,68985.68,629 +1401,36-45,Single,1,0,5,M,-0.06,352,89.68,1.0,-35.62,1,56495.34,630 +1402,18-25,Single,1,0,12,F,0.0,586,114.82,1.0,0.0,0,112413.46,979 +1403,70+,Single,1,0,5,F,0.0,471,110.57,1.02,0.0,0,72758.19,671 +1404,36-45,Married,5,3,5,M,-0.44,1024,98.32,1.07,-807.66,29,181195.39,1964 +1405,56-70,Single,1,0,4,M,-0.01,683,123.96,1.06,-12.47,1,122848.21,1047 +1406,46-55,Single,1,0,5,F,0.0,209,100.09,1.0,0.0,0,23220.8,232 +1407,36-45,Married,2,0,11,M,-1.96,613,110.51,1.01,-1544.95,52,87082.01,794 +1408,26-35,Single,1,0,9,F,-0.44,1033,99.63,1.07,-696.37,18,157110.37,1688 +1409,70+,Married,2,0,1,M,-0.08,567,119.41,1.06,-83.71,4,122399.87,1085 +1410,46-55,Married,3,1,10,F,-0.03,422,135.51,1.03,-14.25,1,69923.14,529 +1411,26-35,Single,1,0,9,M,-0.22,495,93.77,1.07,-150.31,4,65356.71,744 +1412,46-55,Single,1,0,5,F,-0.82,502,100.62,1.05,-751.28,35,92669.13,965 +1413,46-55,Married,2,0,5,M,-0.48,363,79.93,1.0,-216.57,3,36368.68,455 +1414,46-55,Single,1,0,2,F,-0.42,270,100.42,1.01,-138.63,8,32938.16,332 +1415,36-45,Single,1,0,2,F,-0.69,579,104.91,1.0,-712.58,13,108899.89,1040 +1416,36-45,Married,3,1,4,M,-0.16,297,86.53,1.01,-56.99,3,31495.19,366 +1417,46-55,Married,2,0,5,F,-0.13,309,111.26,1.0,-71.24,2,61306.78,551 +1418,36-45,Married,3,1,8,M,-1.35,640,80.16,1.01,-1808.05,7,107419.88,1347 +1419,46-55,Married,4,2,4,F,-0.02,487,81.41,1.01,-19.59,1,63903.72,795 +1420,70+,Married,2,0,5,M,-0.09,343,126.36,1.0,-35.62,1,52816.53,418 +1421,36-45,Single,1,0,6,M,0.0,161,103.23,1.0,0.0,0,17239.23,167 +1422,36-45,Single,1,0,6,M,-1.08,269,101.94,1.06,-387.9,6,36697.07,380 +1423,46-55,Married,5,3,9,M,-0.19,288,100.16,1.0,-71.24,1,38461.79,384 +1424,46-55,Married,4,2,5,F,-0.46,1009,89.97,1.05,-748.31,25,146650.32,1719 +1425,18-25,Single,1,0,4,M,0.0,251,104.19,1.05,0.0,0,29588.84,298 +1426,46-55,Single,1,0,5,F,-0.33,219,115.58,1.0,-87.27,6,30167.22,261 +1427,46-55,Single,1,0,3,M,-0.07,576,95.08,1.11,-85.49,4,124173.79,1450 +1428,36-45,Married,2,0,5,F,-0.08,922,107.68,1.08,-174.54,7,233350.82,2345 +1429,26-35,Married,2,0,4,M,-0.22,204,98.14,1.02,-71.24,1,31601.37,328 +1430,70+,Single,1,0,2,F,-0.05,921,85.41,1.0,-71.24,1,120602.43,1414 +1431,46-55,Married,2,0,12,M,-0.1,1351,129.62,1.0,-233.31,9,316005.54,2443 +1432,26-35,Single,1,0,4,F,0.0,960,114.59,1.02,0.0,0,180360.78,1607 +1433,56-70,Married,2,0,4,F,-0.4,264,100.82,1.0,-190.56,10,47686.02,473 +1434,26-35,Married,2,0,4,F,-0.09,898,75.01,1.0,-137.13,4,117017.63,1563 +1435,56-70,Single,2,1,4,F,-0.09,209,102.49,1.0,-26.71,1,31670.25,309 +1436,46-55,Married,2,0,9,M,-0.09,579,131.5,1.0,-65.3,2,98623.31,750 +1437,26-35,Married,5,3,4,M,-1.13,356,89.95,1.0,-528.6,20,41914.54,466 +1438,36-45,Married,5,3,4,F,-0.05,639,80.99,1.0,-62.33,2,95321.49,1177 +1439,46-55,Single,1,0,6,F,0.0,149,149.18,1.02,0.0,0,24017.46,164 +1440,26-35,Married,2,0,5,F,-0.45,158,85.53,1.0,-89.05,2,16764.55,196 +1441,36-45,Married,3,1,2,F,-1.23,960,129.4,1.02,-2311.12,75,243015.89,1914 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+1482,36-45,Married,2,0,6,M,-0.15,133,102.24,1.0,-35.62,1,24945.94,244 +1483,46-55,Married,2,0,4,F,-0.34,274,124.62,1.05,-115.76,5,42370.44,358 +1484,56-70,Single,1,0,10,M,-0.18,296,97.28,1.03,-67.68,4,36384.21,384 +1485,46-55,Married,2,0,6,F,-0.34,1200,105.36,1.07,-986.91,28,307332.44,3116 +1486,18-25,Married,2,0,4,M,0.0,434,105.77,1.08,0.0,0,84090.34,859 +1487,36-45,Married,4,2,5,M,-0.2,503,84.77,1.0,-154.23,4,65527.38,773 +1488,46-55,Single,1,0,5,M,-0.13,156,118.4,1.0,-35.62,1,31257.55,264 +1489,46-55,Married,2,0,3,M,-0.4,327,85.42,1.06,-225.23,10,48004.42,596 +1490,26-35,Single,1,0,5,F,-0.2,903,91.93,1.13,-320.58,4,145707.14,1796 +1491,26-35,Married,2,0,4,F,0.0,355,115.91,1.0,0.0,0,104902.36,905 +1492,46-55,Married,2,0,3,M,-0.04,925,99.26,1.0,-71.24,2,200311.72,2018 +1493,46-55,Single,1,0,6,F,-0.78,803,93.29,1.06,-991.31,26,118391.35,1350 +1494,46-55,Married,2,0,1,F,-0.06,789,87.13,1.0,-74.8,4,111266.07,1281 +1495,46-55,Single,1,0,3,F,-0.16,316,105.72,1.12,-71.24,2,45778.06,484 +1496,26-35,Married,2,0,1,F,-3.27,601,87.2,1.06,-3249.86,152,86674.49,1055 +1497,70+,Married,2,0,2,M,0.0,261,110.29,1.0,0.0,0,43013.92,390 +1498,46-55,Married,5,3,6,F,-0.25,509,98.67,1.01,-162.07,4,64233.03,657 +1499,26-35,Single,3,2,1,M,-0.21,648,82.0,1.02,-193.77,7,77159.92,960 +1500,46-55,Married,2,0,9,F,-1.06,689,123.26,1.01,-954.01,41,110687.12,905 +1501,46-55,Single,1,0,5,F,-2.12,273,108.03,1.0,-771.16,31,39215.1,363 +1502,46-55,Single,1,0,4,M,-0.57,408,106.27,1.04,-284.6,5,52604.13,516 +1503,36-45,Married,4,2,8,F,0.0,210,67.0,1.02,0.0,0,21505.56,326 +1504,26-35,Married,5,3,5,F,-0.29,756,113.25,1.01,-381.85,8,147454.23,1310 +1505,26-35,Married,2,0,5,M,0.0,173,116.44,1.06,0.0,0,23753.24,216 +1506,46-55,Single,1,0,5,M,-1.56,495,68.4,1.1,-1296.88,73,56769.14,912 +1507,46-55,Single,1,0,5,M,-0.44,313,67.08,1.03,-276.05,12,41993.96,642 +1508,70+,Single,1,0,3,M,0.0,163,91.78,1.0,0.0,0,19457.7,212 +1509,70+,Married,2,0,4,M,-0.01,706,96.43,1.06,-8.9,1,121690.23,1336 +1510,46-55,Single,1,0,5,M,-0.22,155,133.87,1.01,-35.62,1,21418.89,161 +1511,70+,Married,2,0,8,F,0.0,284,99.76,1.0,0.0,0,31822.74,319 +1512,26-35,Married,2,0,5,F,-0.06,200,103.86,1.0,-13.36,1,23471.27,226 +1513,36-45,Married,2,0,8,M,-2.67,75,95.48,1.1,-320.58,4,11457.73,132 +1514,36-45,Single,1,0,6,F,-0.2,351,123.51,1.0,-103.3,4,63976.53,518 +1515,46-55,Single,1,0,4,F,0.0,101,104.31,1.61,0.0,0,15750.84,243 +1516,46-55,Single,1,0,2,M,-0.08,704,82.06,1.0,-100.91,4,107176.67,1306 +1517,26-35,Married,2,0,3,F,-0.49,290,103.27,1.0,-224.41,12,47506.03,460 +1518,36-45,Married,3,1,3,M,-0.88,1194,91.06,1.07,-1949.98,86,202604.51,2383 +1519,26-35,Single,1,0,2,F,-0.06,364,99.37,1.05,-35.62,1,58630.03,621 +1520,26-35,Married,3,1,8,M,-0.22,463,127.9,1.06,-137.14,5,79044.45,657 +1521,46-55,Married,2,0,7,M,-0.65,576,88.1,1.06,-676.42,20,92059.7,1110 +1522,56-70,Married,2,0,6,F,-0.48,358,121.59,1.0,-237.76,6,60431.66,497 +1523,70+,Married,2,0,1,M,0.0,356,119.45,1.0,0.0,0,55185.01,462 +1524,36-45,Married,2,0,5,F,-0.19,1202,106.7,1.0,-491.2,16,273251.48,2561 +1525,36-45,Single,1,0,2,F,0.0,219,88.42,1.0,0.0,0,41113.19,465 +1526,36-45,Single,1,0,3,M,0.0,450,130.61,1.01,0.0,0,86853.72,674 +1527,46-55,Married,2,0,10,F,0.0,188,98.48,1.02,0.0,0,25112.3,259 +1528,46-55,Married,3,1,2,F,0.0,454,86.42,1.01,0.0,0,77345.55,906 +1529,46-55,Married,5,3,9,M,-1.27,1068,101.82,1.0,-2139.42,55,172067.37,1690 +1530,70+,Married,2,0,6,F,-0.63,456,86.59,1.01,-499.84,14,68230.22,792 +1531,18-25,Single,5,3,1,M,0.0,625,103.67,1.0,0.0,0,98282.8,948 +1532,26-35,Single,1,0,9,F,-0.58,522,126.21,1.13,-404.28,12,88092.71,789 +1533,18-25,Single,1,0,1,M,-0.34,198,89.2,1.09,-148.54,2,38625.41,473 +1534,36-45,Married,3,1,5,M,-0.61,848,96.3,1.06,-1053.84,41,165355.5,1825 +1535,46-55,Married,2,0,5,F,-0.31,238,95.25,1.0,-80.14,2,24955.02,262 +1536,46-55,Married,2,0,3,M,-0.29,376,89.87,1.02,-144.26,4,45202.1,513 +1537,18-25,Single,1,0,4,M,-0.23,926,84.92,1.02,-354.06,10,130689.9,1567 +1538,36-45,Married,5,3,8,F,-0.14,590,119.76,1.0,-109.34,6,95809.9,800 +1539,56-70,Married,5,3,4,M,0.0,309,108.98,1.13,0.0,0,43590.12,451 +1540,70+,Single,1,0,5,F,0.0,112,86.35,1.0,0.0,0,12693.02,147 +1541,70+,Married,3,1,8,M,-0.46,234,96.59,1.0,-142.48,1,30039.05,311 +1542,26-35,Married,3,1,5,F,-4.25,200,97.8,1.0,-1029.21,45,23667.42,242 +1543,36-45,Married,5,3,5,F,-0.42,347,101.03,1.13,-273.92,10,65973.28,740 +1544,46-55,Single,1,0,4,F,-0.55,1143,86.37,1.02,-893.43,27,140343.78,1657 +1545,36-45,Married,2,0,8,F,-0.47,649,102.52,1.17,-505.51,28,110828.02,1262 +1546,36-45,Married,2,0,3,M,0.0,628,152.88,1.0,0.0,0,183920.5,1203 +1547,46-55,Married,2,0,5,F,-3.57,953,127.41,1.0,-6733.7,227,240038.7,1886 +1548,56-70,Married,2,0,4,F,-2.92,460,90.45,1.08,-1711.71,61,53006.19,633 +1549,70+,Single,1,0,1,F,-2.87,856,110.79,1.01,-4354.98,197,168074.68,1533 +1550,46-55,Married,2,0,9,F,0.0,433,92.16,1.0,0.0,0,59353.22,645 +1551,46-55,Single,1,0,5,M,-0.39,515,128.05,1.0,-258.23,10,85792.63,670 +1552,46-55,Single,2,1,3,M,-0.27,460,136.95,1.0,-174.52,8,89700.97,655 +1553,46-55,Married,2,0,5,F,0.0,254,79.75,1.0,0.0,0,29665.85,372 +1554,46-55,Married,2,0,7,F,-1.93,320,92.36,1.01,-797.9,33,38144.06,416 +1555,26-35,Single,1,0,7,M,-0.57,2432,94.82,1.02,-2594.37,75,428777.76,4628 +1556,36-45,Married,3,1,6,M,-1.07,855,110.03,1.0,-1624.9,77,167244.01,1523 +1557,46-55,Married,2,0,12,M,0.0,326,124.3,1.0,0.0,0,53447.04,430 +1558,36-45,Married,3,1,6,M,-1.67,1407,95.39,1.0,-4548.92,164,260142.02,2737 +1559,46-55,Married,3,1,3,F,-0.47,494,97.9,1.0,-354.39,17,73229.48,748 +1560,36-45,Married,2,0,6,F,-4.39,137,96.55,1.02,-935.54,33,20565.24,218 +1561,46-55,Single,1,0,5,M,0.0,166,111.86,1.0,0.0,0,19799.92,177 +1562,36-45,Single,2,1,4,F,-2.61,110,105.87,1.0,-468.99,17,19056.39,180 +1563,46-55,Single,1,0,5,F,-1.24,507,125.02,1.0,-1073.06,18,108267.7,870 +1564,36-45,Single,1,0,6,M,-0.23,163,112.32,1.1,-44.52,1,21565.86,212 +1565,26-35,Single,1,0,4,M,-0.06,448,86.66,1.0,-51.05,3,78429.31,905 +1566,26-35,Married,2,0,9,M,-0.45,1214,96.35,1.03,-1187.02,31,256394.87,2729 +1567,46-55,Single,1,0,5,M,-0.68,581,96.52,1.09,-551.78,30,78661.13,892 +1568,36-45,Married,2,0,8,M,-0.16,487,87.11,1.0,-106.86,2,59062.29,678 +1569,46-55,Single,1,0,6,M,-0.07,57,58.66,1.0,-8.9,1,7391.78,126 +1570,36-45,Married,3,1,4,M,-0.06,795,106.79,1.06,-71.24,2,126762.78,1256 +1571,36-45,Single,1,0,5,M,0.0,341,95.79,1.02,0.0,0,38700.09,413 +1572,36-45,Single,1,0,3,F,-0.16,648,76.1,1.01,-135.36,4,64075.33,851 +1573,46-55,Single,1,0,4,F,-0.63,647,108.02,1.04,-745.27,26,127568.1,1230 +1574,36-45,Married,2,0,5,F,-4.29,1689,99.46,1.02,-10415.74,430,241697.0,2469 +1575,46-55,Married,2,0,1,F,-0.11,315,84.11,1.01,-53.43,2,41636.19,502 +1576,26-35,Married,5,3,1,F,0.0,397,96.39,1.03,0.0,0,66217.37,710 +1577,36-45,Married,2,0,5,M,-0.28,252,123.52,1.03,-80.14,2,35080.7,293 +1578,46-55,Married,3,1,6,F,-0.78,481,92.87,1.03,-613.86,29,72719.29,810 +1579,46-55,Single,1,0,4,M,-0.13,639,114.29,1.0,-145.45,6,132804.28,1162 +1580,26-35,Married,2,0,5,F,0.0,422,112.76,1.01,0.0,0,59651.72,534 +1581,26-35,Married,3,1,1,F,0.0,390,89.82,1.07,0.0,0,45356.97,541 +1582,46-55,Single,3,2,5,M,-2.43,681,112.42,1.05,-2064.13,61,95556.72,895 diff --git a/customersim/tools/fake_customers.py b/customersim/tools/fake_customers.py new file mode 100644 index 0000000..26d859e --- /dev/null +++ b/customersim/tools/fake_customers.py @@ -0,0 +1,27 @@ +import csv +from faker import Faker + + +fake = Faker() +fake_data_file = 'customer_info.csv' +output_file = 'customers.csv' + +customers = [] +try: + with open(fake_data_file, 'rt') as csvfile: + fake_reader = csv.DictReader(csvfile, delimiter=',') + # customer_list = [row for row in fake_reader] + for row in fake_reader: + row['name'] = fake.name_male() if row['gender'] == 'M' else fake.name_female() + customers.append(row) +except IOError as e: + print("Whoops....can't find the fake data file.\nTry generating the fake data file and try again\n") + exit(0) + + +with open(output_file, 'w', newline='') as csvfile: + fieldnames = 'customer_id,name,age_range,marital_status,family_size,no_of_children,income_bracket,gender,mean_discount_used_by_cust,unique_items_bought_by_cust,mean_selling_price_paid_by_cust,mean_quantity_bought_by_cust,total_discount_used_by_cust,total_coupons_used_by_cust,total_price_paid_by_cust,total_quantity_bought_by_cust'.split(sep=',') + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + + writer.writeheader() + writer.writerows(customers) \ No newline at end of file diff --git a/customersim/wsgi.py b/customersim/wsgi.py deleted file mode 100644 index 692a85f..0000000 --- a/customersim/wsgi.py +++ /dev/null @@ -1,154 +0,0 @@ -from flask import Flask -import random, time, datetime, uuid, json, configparser, os -import paho.mqtt.client as mqtt -import csv -application = Flask(__name__) - -@application.route("/") - - -# This script generates the following MQTT messages -# -# 1. customer/enter -# -# { -# id: --ID representing customer--, -# ts: --timestamp of the entrance, in seconds since epoch-- -# } -# -# 2. customer/move -# -# { -# id: --ID representing customer--, -# ts: --timestamp of the move, in seconds since epoch--, -# x: --x coordinate of location sensor that fired--, -# y: --y coordinate of location sensor that fired-- -# } -# -# 2. customer/exit -# -# { -# id: --ID representing customer--, -# ts: --timestamp of the exit, in seconds since epoch-- -# } -# - -# Represents a "square" in the store at a particular location, and contain all valid moves from that location -class Location: - def __init__(self,x,y,width, height): - self.x = x - self.y = y - # a list of all valid moves. Each move is a tuple of the form ("adjacent x location", "adjacent y loaction", "is this closer to the exit?") - self.validMoves = [(a,b, True if a <= self.x and b <= self.y else False) for a in range(max(self.x - 1, 0), min(self.x + 2,width)) for b in range(max(self.y -1, 0), min(self.y + 2, height)) if not(a == self.x and b == self.y)] - -class Store: - def __init__(self, width, height): - self.height = height - self.width = width - self.locations = [[Location(x,y, width, height) for y in range(0,height)] for x in range(0, width)] - -class Customer: - def __init__(self, store, id, name): - self.store = store - self.currentLocation = store.locations[0][0] # customers enter and exit from the bottom left corner of the store - - self.meanDwellTime = random.uniform(1, 20) # the *average* amount of time this customer will spend on a square - self.consistancy = random.uniform(1,5) # how consistantly the customer spends that time. Higher means more inconsistant - self.nextMoveTime = self.getNextMoveTime() - self.isExiting = False - self.exitTime = datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600)) # the time this customer will start to exit - self.id = id - self.name = name - - def getNextMoveTime(self): - # amount of time spent at a location is a random value picked from a guassian distribution, with a mean equal to the customer's - # average dwell time and a standard devivation equal to the customer's consistancy - return (datetime.datetime.now() + datetime.timedelta(0, random.gauss(self.meanDwellTime, self.consistancy))) - - def move(self): - # if the customer is exiting, only move to an adjacent location that is towards the exit. If they are already at the door, don't move - if self.isExiting: - if self.currentLocation.x == 0 and self.currentLocation.y == 0: - (newX, newY) = (0,0) - else: - (newX, newY, isTowardsExit) = random.choice([(x,y,e) for (x,y,e) in self.currentLocation.validMoves if e is True]) - else: - # if the customer is not exiting, pick any adjacent location - (newX, newY, isTowardsExit) = random.choice(self.currentLocation.validMoves) - - self.currentLocation = self.store.locations[newX][newY] - - def tick(self): - if self.isExiting == False and self.exitTime < datetime.datetime.now(): - self.isExiting = True - - if self.nextMoveTime < datetime.datetime.now(): - self.nextMoveTime = self.getNextMoveTime() - self.move() - return True - - return False - -def main(): - - # configuration information - config = configparser.ConfigParser() - config.read('config.cfg') - mqttHost = config.get('MQTT', 'host') - mqttPort = config.get('MQTT', 'port') - mqttClientName = config.get('MQTT', 'name') - width = config.getint('Store', 'width') - height = config.getint('Store', 'height') - averageCustomersInStore = config.getint('Customers', 'averageCustomersInStore') - fakeDataFile = config.get('Customers', 'list') - - mqttc = mqtt.Client(mqttClientName) - mqttc.connect(mqttHost, mqttPort) - - # load customer list - try: - with open(fakeDataFile, 'rb') as csvfile: - fakereader = csv.reader(csvfile, delimiter='|') - customerList = [row for row in fakereader] - except IOError as e: - print("Whoops....can't find the fake data file.\nTry generating the fake data file and try again\n") - exit(0) - - myStore = Store(width, height) - customerQueue = [] # List of customers in the store - nextCustomerEntranceTime = datetime.datetime.now() - - def manageCustomerMovements(c): - if c.tick(): - if c.isExiting and c.currentLocation.x == 0 and c.currentLocation.y == 0: - # remove the customer and signal the exit - msg = {'id': str(c.id), 'ts': int(datetime.datetime.now().strftime("%s"))} - mqttc.publish("customer/exit", json.dumps(msg)) - print('%s is Exiting.') % (c.name) - customerQueue.remove(c) - else: - # signal move - msg = {'id': str(c.id), 'ts': int(datetime.datetime.now().strftime("%s")), 'x': c.currentLocation.x, 'y': c.currentLocation.y} - mqttc.publish("customer/move", json.dumps(msg)) - - # Check if any customers are due to entier, move, or exit. Sleep for one second, then repeat - while True: - if nextCustomerEntranceTime < datetime.datetime.now(): - # add the new customer, and signal the entrance - newCustomerPrototype = random.choice(customerList) - newCustomer = Customer(myStore, newCustomerPrototype[0], newCustomerPrototype[1]) - customerQueue.append(newCustomer) - - msg = {'id': str(newCustomer.id), 'ts': int(datetime.datetime.now().strftime("%s"))} - mqttc.publish("customer/enter", json.dumps(msg)) - - nextCustomerEntranceTime = datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600 / averageCustomersInStore)) - print('%s is Entering. MDT: %0.1f, C: %0.1f, E: %s') % (newCustomer.name, newCustomer.meanDwellTime, newCustomer.consistancy, newCustomer.exitTime) - print('Next Customer Entering at %s.') % (nextCustomerEntranceTime) - - [manageCustomerMovements(c) for c in customerQueue] - time.sleep(1) - -if __name__=='__main__': - main() - diff --git a/scenario-player/.environment.variables.sh b/scenario-player/.environment.variables.sh new file mode 100644 index 0000000..1f3a3eb --- /dev/null +++ b/scenario-player/.environment.variables.sh @@ -0,0 +1,8 @@ +export STORE_HEIGHT=10 +export STORE_WIDTH=6 +export CUSTOMERS_AVERAGE_IN_STORE=6 +export CUSTOMERS_LIST_FILE='customers.csv' +export MQTT_HOST='127.0.0.1' +export MQTT_NAME=test1 + +export LOG_LEVEL=INFO \ No newline at end of file diff --git a/scenario-player/README.md b/scenario-player/README.md new file mode 100644 index 0000000..baff830 --- /dev/null +++ b/scenario-player/README.md @@ -0,0 +1,154 @@ +# Project description + +This project was a part of broader demo. That broader demo analyzed customers movement in a retail store, determined +their behaviour (for example: "customer stopped in men's clothes department") and use Machine Learning to model for +purchase/product recommendation. The customer location was determined by movement sensors placed in the store. + +This service customer behaviour in a retail shop: + +* customer entering the store +* customer movement +* customer exiting the store + +by generating proper MQTT messages. + +# Usage + +This is a web service (implemented with FastAPI). By default, it works on port 8000. +(See [instructions](./development.md#running-the-service) for details on configuring and running the service.) + +When starting, it does the following: + +* connects to MQTT server +* waits for and registers new user movement scenarios (HTTP POST to `/scenario` endpoint) +* creates a background task (ran every second) that checks if there is something to be sent (if it is time for giving an + update on particular customer) + +### Main simulator loop +The main loop executes every second and tries to locate events (in the timeline) that should be +"replayed". If there are any, proper messages are constructed and published (via. `Publisher` object). + +> Please, note, that in the current implementation, the main simulator loop replays +> events from the timeline for **current timestamp** (current date/time). + +### Scenario definitions + +Scenario is a list of locations for given customer in certain moments: +``` +{ + "customer": { + "customer_id": "3" + }, + "path": [ + { + "type": "ENTER", + "location": {"x": 200, "y": 10}, + "timestamp": "2021-04-11T10:01:53.955760" + }, + { + "type": "MOVE", + "location": {"x": 320, "y": 150}, + "timestamp": "2021-04-11T10:13:49.614897" + }, + ... + { + "type": "EXIT", + "location": {"x": 200, "y": 10}, + "timestamp": "2021-04-11T12:31:17.437862" + } + ] +} +``` + +The service can register a scenario: +```shell +curl -X 'POST' \ + 'http://localhost:8000/scenario' \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -d '{"customer":{"customer_id":"1"},"path":[{"type":"ENTER","location":{"x":935,"y":50},"timestamp":1618323771180},{"type":"MOVE","location":{"x":588.9128630705394,"y":454.08039288409145},"timestamp":1618323772180},{"type":"EXIT","location":{"x":1075,"y":50},"timestamp":1618323773180}]}' +``` + +After receiving the request, the service all adds all scenario steps (from `path`) to the current timeline +with timestamps as defined in the payload. + +As the main loop uses current timestamp for "locating" the events on the timeline, +it is possible that registered events won't ever be published. +In case the timestamp of a given event _is in the past_, it will be never be retrieved and processed. + + +For the user convenience, it is possible to reuse a scenario definition that refers to the past. +`recalculate_time=true` parameter for `/scenario` request can be used here: +``` +curl -X 'POST' \ + 'http://localhost:8000/scenario?recalculate_time=true' \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -d '{"customer":{"customer_id":"1"},"path":[{"type":"ENTER","location":{"x":935,"y":50},"timestamp":1618323771180},{"type":"MOVE","location":{"x":588.9128630705394,"y":454.08039288409145},"timestamp":1618323772180},{"type":"EXIT","location":{"x":1075,"y":50},"timestamp":1618323773180}]}' +``` + +In this case, scenario will start with current time and step timestamps will be recalculated appropriately +(they will be "refreshed"). + + +## Messages payloads + +### **customer/exit** Channel + +| Name | Type | Description | Accepted values | +|---|---|---|---| +| id | string | ID of the customer | ...| +| ts | integer | Timestamp of the event, in seconds since epoch | ...| + +Example of payload: + +```json +{ + "id": "127", + "ts": 192322800 +} +``` + +### **customer/exit** Channel + +| Name | Type | Description | Accepted values | +|---|---|---|---| +| id | string | ID of the customer | ...| +| ts | integer | Timestamp (unix time) | ...| + +Example of payload: + +```json +{ + "id": "127", + "ts": 192322800 +} +``` + +### **customer/move** Channel + +| Name | Type | Description | Accepted values | +|---|---|---|---| +| id | string | ID of the customer | ...| +| ts | integer | Timestamp (unix time) | ...| +| x | integer | coordinate x | ...| +| y | integer | coordinate y | ...| + +Example of payload: + +```json +{ + "id": "127", + "ts": 192322800, + "x": 0, + "y": 0 +} +``` + +# Running the service +See [instructions](./development.md#running-the-service) for details on configuring and running the service locally. + +# Development information +See [development.md](./development.md) for information about configuring the service, how to run test and run the +service. + diff --git a/scenario-player/app/__init__.py b/scenario-player/app/__init__.py new file mode 100644 index 0000000..c10988d --- /dev/null +++ b/scenario-player/app/__init__.py @@ -0,0 +1,4 @@ +import logging + +logger = logging.getLogger(__name__) +logger.addHandler(logging.NullHandler()) diff --git a/scenario-player/app/backend/__init__.py b/scenario-player/app/backend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scenario-player/app/backend/base.py b/scenario-player/app/backend/base.py new file mode 100644 index 0000000..e946dc5 --- /dev/null +++ b/scenario-player/app/backend/base.py @@ -0,0 +1,43 @@ +from typing import List, Tuple + +from app.scenario.scenario_model import Scenario, Step + + +class BaseTimelineBackend: + def __init__(self): + """ + Initialize internal fields. + """ + pass + + async def initialize(self): + """ + Initialize the backend (for example, connect to the DB, etc.) + :return: + """ + pass + + async def store_scenario(self, scenario: Scenario): + """ + Persist timeline metadata. + :param scenario: + :return: + """ + raise NotImplementedError + + async def add_to_timeline(self, customer_id: str, step: Step): + """ + Store info about given step for a given customer. + :param customer_id: + :param step: + :return: + """ + raise NotImplementedError + + async def get_events(self, unix_time: int) -> List[Tuple[str, Step]]: + """ + + :param unix_time: + :return: list of tuples (customer_id, step) + """ + raise NotImplementedError diff --git a/scenario-player/app/backend/priority_queue.py b/scenario-player/app/backend/priority_queue.py new file mode 100644 index 0000000..d8ffd6b --- /dev/null +++ b/scenario-player/app/backend/priority_queue.py @@ -0,0 +1,56 @@ +from queue import PriorityQueue +from typing import List, Tuple + +from app import logger +from app.scenario.scenario_model import Scenario, Step + + +class PQueueTimelineBackend: + def __init__(self): + logger.info('Initializing PriorityQueue backend...') + self.timeline = PriorityQueue() + self.scenarios = {} + + async def initialize(self): + pass + + async def store_scenario(self, scenario: Scenario): + logger.info('store_scenario') + scenario_key = f'{scenario.customer.customer_id}' + self.scenarios.update({scenario_key: scenario}) + return scenario_key + + async def add_to_timeline(self, customer_id: str, step: Step): + logger.info(f'add_to_timeline: {customer_id} {step} ') + self.timeline.put((int(step.timestamp.timestamp()), (customer_id, step))) + return True + + # XXX TODO probably wrong return type definition (Tuple[int, Tuple[str, Step]) + def peek(self) -> Tuple[str, Step]: + return self.timeline.queue[0] if len(self.timeline.queue) > 0 else None + + async def get_events(self, unix_time: int, include_earlier: bool = False) -> List[Tuple[str, Step]]: + logger.debug(f'get events for timestamp {unix_time}') + earliest_element = self.peek() + logger.debug(f'earliest_element: {earliest_element}') + + result = [] + # proceed, if the next element in the queue is "old enough" for our query + if earliest_element and earliest_element[0] <= unix_time: + # consume elements until we reach desired timestamp + while True: + element = self.timeline.get_nowait() + print(element) + print(type(element)) + if element[0] < unix_time: + if include_earlier: + result.append(element[1]) + else: + result.append(element[1]) + + earliest_element = self.peek() + # if there is no elements left or if the next element is to new, break + if not earliest_element or earliest_element[0] > unix_time: + break + + return result diff --git a/scenario-player/app/backend/redis.py b/scenario-player/app/backend/redis.py new file mode 100644 index 0000000..b2af4a4 --- /dev/null +++ b/scenario-player/app/backend/redis.py @@ -0,0 +1,124 @@ +from datetime import datetime +from typing import List, Tuple + +import aioredis + +from app import logger +from app.backend.base import BaseTimelineBackend +from app.scenario.scenario_model import Scenario, Step, Location + +TIMELINE_KEY = f'TIMELINE:CURRENT' +SCENARIO_KEY = 'SCENARIO' + + +class RedisTimelineBackend(BaseTimelineBackend): + + def __init__(self, connection_url: str = 'redis://localhost', database: int = 0, redis_password: str = None): + """ + Initialize internal fields. + """ + super().__init__() + self.connection_url = connection_url + self.database = database + self.redis_password = redis_password + self.redis = None + + async def initialize(self): + """ + Initialize the backend (for example, connect to the DB, etc.) + :return: + """ + logger.info("initializing redis backend...") + logger.info(f'{self.connection_url}, {self.database},{self.redis_password}') + try: + self.redis = await aioredis.create_redis_pool(address=self.connection_url, db=self.database, + password=self.redis_password, + encoding='utf-8') + except Exception as e: + logger.error(f"Error while talking to redis: {e}") + # raise + + ### + # scenario related + # + + @staticmethod + def marshall_step(p: Step): + return f'{p.timestamp}|{p.type}|{p.location.x}|{p.location.y}' + + def serialize_steps(self, steps: List[Step]): + return [self.marshall_step(x) for x in steps] + + async def store_scenario(self, scenario: Scenario, namespace: str = SCENARIO_KEY): + logger.info('store_scenario') + result = None + try: + scenario_key = f'{namespace}:{scenario.customer.customer_id}' + values = self.serialize_steps(scenario.path) + length = len(values) + + if length >= 1: + logger.info(f'Sending multiple values ... {values}') + result = await self.redis.rpush(scenario_key, *values) + + result = scenario_key + except Exception as e: + logger.error(f"Error while talking to redis: {e}") + + return result + + ### + # timeline related + # + + @staticmethod + def marshall_event(client_id: str, p: Step): + return f'{client_id}|{p.location.x}|{p.location.y}|{p.type}|{p.timestamp}' + + @staticmethod + def unmarshall_event(s: str): + logger.debug(f'unmarshal event {s}') + parts = s.split(sep='|') + + client_id = parts[0] + loc = Location(x=int(parts[1]), y=int(parts[2])) + + return client_id, Step(location=loc, type=parts[3], + timestamp=datetime.strptime(parts[4], '%Y-%m-%d %H:%M:%S.%f')) + + async def add_to_timeline(self, customer_id: str, step: Step): + """ + Store info about given step for a given customer. + :param customer_id: + :param step: + :return: + """ + logger.info(f'add_to_timeline: {customer_id} {step} ') + + result = False + + try: + event_representation = self.marshall_event(customer_id, step) + logger.debug(f'marshalled event_representation: {event_representation}') + result = await self.redis.zadd(TIMELINE_KEY, int(step.timestamp.timestamp()), event_representation) + except Exception as e: + logger.error(f"Error while talking to redis: {e}") + + return result + + async def get_events(self, unix_time: int) -> List[Tuple[str, Step]]: + logger.debug(f'get events from redis for timestamp {unix_time}') + """ + Get events definitions for a given point in time + """ + result = [] + + try: + events = await self.redis.zrangebyscore(TIMELINE_KEY, min=unix_time, max=unix_time) + result = [self.unmarshall_event(e) for e in events] + mop = await self.redis.zremrangebyscore(TIMELINE_KEY, min=unix_time, max=unix_time) + except Exception as e: + logger.error(f"Error while talking to redis: {e}") + logger.error(type(e)) + + return result diff --git a/scenario-player/app/config.py b/scenario-player/app/config.py new file mode 100644 index 0000000..07b59b7 --- /dev/null +++ b/scenario-player/app/config.py @@ -0,0 +1,34 @@ +import os +import sys + +from app import logger + + +def validate_and_crash(variable, message): + if not variable: + logger.error(message) + sys.exit(message) + + +logger.info('Reading environment variables...') + +STORE_HEIGHT = int(os.getenv('STORE_HEIGHT', 10)) +STORE_WIDTH = int(os.getenv('STORE_WIDTH', 6)) + +CUSTOMERS_AVERAGE_IN_STORE = int(os.getenv('CUSTOMERS_AVERAGE_IN_STORE', 6)) +CUSTOMERS_LIST_FILE = os.getenv('CUSTOMERS_LIST_FILE', 'customers.csv') + +MQTT_HOST = os.getenv('MQTT_HOST') +MQTT_PORT = int(os.getenv('MQTT_PORT', 1883)) +MQTT_NAME = os.getenv('MQTT_NAME', 'demoClient') + +CUSTOMER_ENTER_TOPIC = os.getenv('ENTER_TOPIC', 'customer/enter') +CUSTOMER_EXIT_TOPIC = os.getenv('EXIT_TOPIC', 'customer/exit') +CUSTOMER_MOVE_TOPIC = os.getenv('MOVE_TOPIC', 'customer/move') + +TESTING_MOCK_MQTT = os.getenv('TESTING_MOCK_MQTT', 'false') +TESTING_MOCK_MQTT = TESTING_MOCK_MQTT.lower() in ['1', 'yes', 'true'] + + +REQUIRED_PARAM_MESSAGE = 'Cannot read {} env variable. Please, make sure it is set before starting the service.' +validate_and_crash(MQTT_HOST, REQUIRED_PARAM_MESSAGE.format('MQTT_HOST')) diff --git a/scenario-player/app/controller.py b/scenario-player/app/controller.py new file mode 100644 index 0000000..21852b6 --- /dev/null +++ b/scenario-player/app/controller.py @@ -0,0 +1,52 @@ +from datetime import datetime, timezone + +from app import logger +from app.backend.base import BaseTimelineBackend +from app.scenario.scenario_deployer import ScenarioDeployer +from app.scenario.scenario_model import Scenario +from app.scenario.scenario_producer import ScenarioProducer +from app.simulator.simulation_engine import CustomerSimulator + + +class TimelineController: + def __init__(self, backend: BaseTimelineBackend, scenario_producer: ScenarioProducer, + scenario_deployer: ScenarioDeployer, autostart=True): + self.backend = backend + self.scenario_producer = scenario_producer + self.scenario_deployer = scenario_deployer + self.autostart = autostart + + async def accept_scenario_draft(self, scenario: Scenario): + logger.info("Converting draft to scenario...") + + result = self.scenario_producer.expand(scenario) + + await self.backend.store_scenario(result) + if self.autostart: + await self.deploy_scenario(result) + + return result.json(exclude_none=True) + + async def deploy_scenario(self, scenario: Scenario, recalculate_time: bool = False): + logger.info('Deploying scenario...') + + if recalculate_time: + logger.debug('Recalculating scenario time...') + new_start = datetime.now(timezone.utc) + scenario = self.scenario_deployer.recalculate_time(scenario, new_start) + # persist the scenario + await self.backend.store_scenario(scenario) + + # add scenario steps to the timeline + result = await self.scenario_deployer.deploy_scenario(scenario) + return result + + async def get_current_state(self, timestamp: datetime): + logger.info(f'get_current_state({timestamp}):') + epoch = int(timestamp.timestamp()) + events_for_users = await self.backend.get_events(epoch) + logger.debug(f'events: {events_for_users}') + + customer_states = [CustomerSimulator.create_customer_state(efo[0], efo[1]) for efo in events_for_users] + + return customer_states diff --git a/scenario-player/app/log_config.py b/scenario-player/app/log_config.py new file mode 100644 index 0000000..f7836eb --- /dev/null +++ b/scenario-player/app/log_config.py @@ -0,0 +1,24 @@ +import logging +import os + + +# TODO move to config +LOG_FILENAME = "messages.log" +LOG_FORMAT = "%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s]\t%(message)s" +LOG_LEVEL = os.environ.get('LOG_LEVEL', 'DEBUG').upper() + +assert LOG_LEVEL in ['DEBUG', 'INFO', 'WARNING', 'ERROR'] + + +def configure_logger(): + logging.basicConfig(format=LOG_FORMAT, level=LOG_LEVEL) + # Basic console logger + logger = logging.getLogger("app") + # File logger + log_formatter = logging.Formatter(LOG_FORMAT) + file_handler = logging.FileHandler(LOG_FILENAME, encoding='UTF-8') + file_handler.setFormatter(log_formatter) + logger.addHandler(file_handler) + # Configuration done + logger.debug("Logger configured...") + return logger diff --git a/scenario-player/app/main.py b/scenario-player/app/main.py new file mode 100644 index 0000000..6dbce6f --- /dev/null +++ b/scenario-player/app/main.py @@ -0,0 +1,135 @@ +import asyncio +from datetime import datetime +from typing import List + +import uvicorn +from fastapi import FastAPI, HTTPException, Query +from fastapi.responses import PlainTextResponse + +from app import logger +from app.backend.priority_queue import PQueueTimelineBackend +from app.controller import TimelineController +from app.log_config import configure_logger +from app.publisher.mqtt_publisher import MQTTEventPublisher +from app.scenario.scenario_deployer import ScenarioDeployer +from app.scenario.scenario_model import Scenario, CustomerState +from app.scenario.scenario_producer import ScenarioProducer +from app.simulator.simulation_engine import CustomerSimulator + +configure_logger() + +app = FastAPI() + +# For bigger scale and volume, use Redis backend +USE_REDIS_BACKEND = False + + +async def init_backend(): + if USE_REDIS_BACKEND: + # XXX TODO add error handling + from app.backend.redis import RedisTimelineBackend + backend = RedisTimelineBackend('redis://127.0.0.1:6379', database=0, redis_password='redis123') + else: + backend = PQueueTimelineBackend() + + await backend.initialize() + return backend + + +@app.on_event("startup") +async def startup_event(): + app.state.backend = await init_backend() + app.state.scenario_producer = ScenarioProducer() + app.state.scenario_deployer = ScenarioDeployer(app.state.backend) + app.state.timeline_controller = TimelineController(app.state.backend, app.state.scenario_producer, + app.state.scenario_deployer) + + # app.state.event_publisher = LoggerEventPublisher() + app.state.event_publisher = MQTTEventPublisher(app) + await app.state.event_publisher.initialize() + + # #################### + # # background tasks + customer_sim = CustomerSimulator(app.state.backend, app.state.event_publisher) + + asyncio.create_task(customer_sim.run()) + + +#################### +# web handlers +logger.info('Defining web service handlers...') + + +@app.get('/') +async def root(): + logger.debug('/') + return {'message': 'Hello World'} + + +@app.get('/health') +async def health() -> PlainTextResponse: + """ + Service health check endpoint. + """ + logger.info('verify health') + return PlainTextResponse('OK') + + +# +# Timeline probably should be created on the app startup. +# Only modification to the timeline should be adding scenarios and rewinding current time of timeline. + +@app.post('/scenario_draft') +async def accept_scenario_draft(payload: Scenario) -> PlainTextResponse: + """ + """ + logger.info('accept_scenario') + logger.debug(payload) + + message = payload.json(exclude_none=True) + logger.debug(f'Received {message} payload') + + result = await app.state.timeline_controller.accept_scenario_draft(payload) + + if not result: + raise HTTPException(status_code=404, detail='Problem storing scenario.') + + # result = 'Scenario created' + return PlainTextResponse(result) + + +RECALCULATE_DESCRIPTION = 'if set to True, scenario will start with current time ' \ + '(and step timestamps will be recalculated appropriately)' + + +@app.post('/scenario') +async def deploy_scenario(payload: Scenario, + recalculate_time: bool = Query(default=False, + description=RECALCULATE_DESCRIPTION)) -> PlainTextResponse: + """ + Accepts a scenario definition and adds it to the current timeline. + """ + logger.info('deploy_scenario') + logger.debug(f'recalculate_time: {recalculate_time}') + logger.debug(payload) + + message = payload.json(exclude_none=True) + logger.debug(f'Received {message} payload') + + result = await app.state.timeline_controller.deploy_scenario(payload, recalculate_time) + + if not result: + raise HTTPException(status_code=404, detail='Problem storing scenario.') + + return PlainTextResponse(str(result)) + + +@app.get('/state') +async def get_current_state(timestamp: datetime) -> List[CustomerState]: + logger.info(f'get_current_state as for {timestamp}') + return await app.state.timeline_controller.get_current_state(timestamp) + + +# For debugging +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/scenario-player/app/publisher/__init__.py b/scenario-player/app/publisher/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scenario-player/app/publisher/base.py b/scenario-player/app/publisher/base.py new file mode 100644 index 0000000..04aeb4c --- /dev/null +++ b/scenario-player/app/publisher/base.py @@ -0,0 +1,28 @@ +from app import logger +from app.scenario.scenario_model import CustomerState + + +class BaseEventPublisher: + + async def initialize(self): + """ + Initialize the backend (for example, connect to the DB, etc.) + :return: + """ + raise NotImplemented + + def prepare_payload(self, customer_state: CustomerState): + return str(customer_state) + + async def publish_state(self, customer_state: CustomerState): + raise NotImplemented + + +class LoggerEventPublisher(BaseEventPublisher): + + async def initialize(self): + pass + + async def publish_state(self, customer_state: CustomerState): + message = self.prepare_payload(customer_state) + logger.info(f'Publishing {message}') diff --git a/scenario-player/app/publisher/mqtt_model.py b/scenario-player/app/publisher/mqtt_model.py new file mode 100644 index 0000000..e6733b8 --- /dev/null +++ b/scenario-player/app/publisher/mqtt_model.py @@ -0,0 +1,31 @@ +from pydantic import BaseModel, Field + + +class CustomerEnterEvent(BaseModel): + """ + Object representing the event of customer entering the store. + """ + id: str = Field(description='ID representing customer') + ts: int = Field(description='timestamp of the entrance, in seconds since epoch') + + +class CustomerExitEvent(BaseModel): + """ + id: --ID representing customer--, + ts: --timestamp of the exit, in seconds since epoch-- + """ + id: str + ts: int + + +class CustomerMoveEvent(BaseModel): + """ + id: --ID representing customer--, + ts: --timestamp of the move, in seconds since epoch--, + x: --x coordinate of location sensor that fired--, + y: --y coordinate of location sensor that fired-- + """ + id: str + ts: int + x: int + y: int diff --git a/scenario-player/app/publisher/mqtt_publisher.py b/scenario-player/app/publisher/mqtt_publisher.py new file mode 100644 index 0000000..bffe7e4 --- /dev/null +++ b/scenario-player/app/publisher/mqtt_publisher.py @@ -0,0 +1,116 @@ +from fastapi import FastAPI +from fastapi_mqtt import FastMQTT, MQTTConfig + +from app import logger +from app.config import TESTING_MOCK_MQTT, MQTT_HOST, MQTT_PORT, MQTT_NAME, CUSTOMER_EXIT_TOPIC, CUSTOMER_MOVE_TOPIC, \ + CUSTOMER_ENTER_TOPIC +from app.publisher.base import BaseEventPublisher +from app.publisher.mqtt_model import CustomerMoveEvent, CustomerEnterEvent, CustomerExitEvent +from app.scenario.scenario_model import CustomerState, STEP_TYPE_ENTER, STEP_TYPE_MOVE, STEP_TYPE_EXIT + +MAP = { + STEP_TYPE_ENTER: CUSTOMER_ENTER_TOPIC, + STEP_TYPE_MOVE: CUSTOMER_MOVE_TOPIC, + STEP_TYPE_EXIT: CUSTOMER_EXIT_TOPIC +} + +if TESTING_MOCK_MQTT: + class MQTTClient: + def __init__(self, mqtt_host: str, mqtt_port: int, mqtt_client_name: str, app: FastAPI): + logger.info(f'simulating a client to {mqtt_host}') + self.mqtt_client_name = mqtt_client_name + self.mqtt_host = mqtt_host + self.mqtt_port = mqtt_port + + def publish(self, topic, message): + logger.info(f'simulated publishing to {topic}. message: {message}') + + async def connect(self): + pass +else: + class MQTTClient: + def __init__(self, app: FastAPI, mqtt_host: str, mqtt_port: int, mqtt_client_name: str): + logger.info(f'Creating MQTT client {mqtt_host}, {mqtt_port}, {mqtt_client_name}') + self.mqtt_client_name = mqtt_client_name + self.mqtt_host = mqtt_host + self.mqtt_port = mqtt_port + self.app = app + + # TODO XXX there is now way to pass client name... + mqtt_config = MQTTConfig(host=self.mqtt_host, port=self.mqtt_port) + self.fast_mqtt = FastMQTT(config=mqtt_config) + + def publish(self, topic, message): + logger.info(f' publishing to {topic}. message: {message}') + return self.fast_mqtt.publish(topic, message) + + async def connect(self): + logger.info("before connect") + + self.fast_mqtt.init_app(self.app) + logger.info("after connect") + + +class MQTTEventMarshaller(object): + @staticmethod + def construct_entry_message(cs: CustomerState): + message = CustomerEnterEvent(id=cs.customer_description.customer_id, ts=int(cs.timestamp.timestamp())) + return message.json() + + @staticmethod + def construct_move_message(cs: CustomerState): + ts = int(cs.timestamp.timestamp()) + message = CustomerMoveEvent(id=cs.customer_description.customer_id, ts=ts, x=cs.location.x, y=cs.location.y) + return message.json() + + @staticmethod + def construct_exit_message(cs: CustomerState): + message = CustomerExitEvent(id=cs.customer_description.customer_id, ts=int(cs.timestamp.timestamp())) + return message.json() + + +class MQTTEventPublisher(BaseEventPublisher): + def __init__(self, app: FastAPI, mqtt_host=MQTT_HOST, mqtt_port=MQTT_PORT, mqtt_client_name=MQTT_NAME): + logger.info(f'Initializing MQTT client {mqtt_host}') + self.app = app + self.client = MQTTClient(app, mqtt_host, mqtt_port, mqtt_client_name) + + async def initialize(self): + logger.info('Initializing MQTT connection') + self.client.fast_mqtt.user_connect_handler = MQTTEventPublisher.on_connect + self.client.fast_mqtt.client.on_disconnect = MQTTEventPublisher.on_disconnect + + await self.client.connect() + + @staticmethod + def on_connect(client, flags, rc, properties): + logger.warning(f'Connected: , {client}, {flags}, {rc}, {properties}') + + @staticmethod + def on_disconnect(client, packet): + logger.warning(f'Disconnected: {client}, {packet}') + + @staticmethod + def get_topic_for_event_type(event_type): + return MAP[event_type] + + def prepare_payload(self, customer_state: CustomerState): + + if customer_state.status == STEP_TYPE_ENTER: + message = MQTTEventMarshaller.construct_entry_message(customer_state) + elif customer_state.status == STEP_TYPE_MOVE: + message = MQTTEventMarshaller.construct_move_message(customer_state) + elif customer_state.status == STEP_TYPE_EXIT: + message = MQTTEventMarshaller.construct_exit_message(customer_state) + else: + raise RuntimeError(f'Unknown message type ({customer_state.status})') + + return message + + async def publish_state(self, customer_state: CustomerState): + logger.debug('publish_state') + logger.debug(customer_state) + topic = self.get_topic_for_event_type(customer_state.status) + logger.warn(f'Publishing {customer_state} to {topic} topic') + message = self.prepare_payload(customer_state) + self.client.publish(topic, message) diff --git a/scenario-player/app/scenario/__init__.py b/scenario-player/app/scenario/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scenario-player/app/scenario/scenario_deployer.py b/scenario-player/app/scenario/scenario_deployer.py new file mode 100644 index 0000000..2292ba3 --- /dev/null +++ b/scenario-player/app/scenario/scenario_deployer.py @@ -0,0 +1,39 @@ +from datetime import datetime + +from app import logger +from app.backend.base import BaseTimelineBackend +from app.scenario.scenario_model import Scenario + + +class ScenarioDeployer(object): + def __init__(self, backend: BaseTimelineBackend): + logger.info(f'Initializing ScenarioDeployer') + self.backend = backend + + @staticmethod + def recalculate_time(scenario: Scenario, start_time: datetime): + logger.info(f'recalculate_time {scenario} (start from {start_time}') + + new_scenario = Scenario(customer=scenario.customer, path=[]) + + first_timestamp = scenario.path[0].timestamp + time_delta = start_time - first_timestamp + + # go trough draft steps + for s in scenario.path: + step = s.copy() + step.timestamp = s.timestamp + time_delta + + # add the step to the new scenario + new_scenario.path.append(step) + + return new_scenario + + async def deploy_scenario(self, scenario: Scenario): + logger.info(f'deploy_scenario {scenario}') + + # go trough draft steps + for step in scenario.path: + await self.backend.add_to_timeline(scenario.customer.customer_id, step) + + return True diff --git a/scenario-player/app/scenario/scenario_model.py b/scenario-player/app/scenario/scenario_model.py new file mode 100644 index 0000000..5be424c --- /dev/null +++ b/scenario-player/app/scenario/scenario_model.py @@ -0,0 +1,53 @@ +from datetime import datetime +from typing import Optional, List + +from pydantic import BaseModel, Field + +STEP_TYPE_MOVE = 'MOVE' +STEP_TYPE_FOCUS = 'FOCUS' +STEP_TYPE_ENTER = 'ENTER' +STEP_TYPE_EXIT = 'EXIT' + + +class Location(BaseModel): + """ + Object representing location of the customer in the store. + """ + x: int = Field(description='coordinate X') + y: int = Field(description='coordinate Y') + + +class Step(BaseModel): + type: str # TODO: introduce enum/Literal + location: Location + timestamp: Optional[datetime] + + +class CustomerDescription(BaseModel): + customer_id: str + # name: Optional[str] + # gender: Optional[str] + # age_bucket: Optional[str] + # preferred_vendors: Optional[List[str]] + + +class Scenario(BaseModel): + customer: CustomerDescription + path: Optional[List[Step]] + + +class CustomerState(BaseModel): + customer_description: CustomerDescription + location: Location + status: Optional[str] = STEP_TYPE_MOVE + timestamp: Optional[datetime] + + +class State(BaseModel): + customer_states: Optional[List[CustomerState]] + + +class Timeline(BaseModel): + name: str + from_timestamp: int + to_timestamp: int diff --git a/scenario-player/app/scenario/scenario_producer.py b/scenario-player/app/scenario/scenario_producer.py new file mode 100644 index 0000000..44db2a1 --- /dev/null +++ b/scenario-player/app/scenario/scenario_producer.py @@ -0,0 +1,84 @@ +from datetime import datetime, timedelta, timezone +import math + +from app import logger +from app.scenario.scenario_model import Scenario, Step, Location, STEP_TYPE_MOVE, STEP_TYPE_FOCUS + +CUSTOMER_AVERAGE_PACE = 0.03 # seconds per space unit +# XXX TODO: the speed per unit should be determined by the map size... + +SCENARIO_AUTOSTART_DELAY = 10 + + +class ScenarioProducer(object): + def __init__(self, store_map=None): + logger.info(f'Initializing ScenarioProducer with map {store_map}') + self.map = store_map + + def expand(self, scenario_draft: Scenario, start_timestamp=None): + logger.info(f'Expanding scenario {scenario_draft}') + if not start_timestamp: + start_timestamp = datetime.now(timezone.utc) + if SCENARIO_AUTOSTART_DELAY: + start_timestamp = start_timestamp + timedelta(seconds=SCENARIO_AUTOSTART_DELAY) + + # create new scenario + scenario = Scenario(customer=scenario_draft.customer, path=[]) + + last_step = None + + # go trough draft's steps + for i, s in enumerate(scenario_draft.path): + step = s.copy(update={'type': STEP_TYPE_MOVE}) + step.timestamp = self.compute_timestamp(last_step, s) if last_step else start_timestamp + + # add the step to the new scenario + scenario.path.append(step) + + # remember last step + last_step = step + + if s.type.upper() == STEP_TYPE_FOCUS: + additional_steps = self.generate_additional_steps(last_step, count=5, period=30) + scenario.path.extend(additional_steps) + last_step = additional_steps[-1] + + return scenario + + def compute_timestamp(self, previous_step: Step, current_step: Step, + customer_pace: float = CUSTOMER_AVERAGE_PACE) -> datetime: + logger.debug(f'compute_timestamp {previous_step}, {current_step}') + distance = self.get_distance(previous_step.location, current_step.location) + elapsed_time = distance * customer_pace + return previous_step.timestamp + timedelta(seconds=elapsed_time) + + def get_distance(self, l1: Location, l2: Location): + # In the future, use map and path finding to find distance + # for now use cartesian distance + + return math.dist([l1.x, l1.y], [l2.x, l2.y]) + + def generate_additional_steps(self, previous_step: Step, count: int, period: float): + """ + :param previous_step: + :param count: how many steps to produce + :param period: [seconds] the period to fill with the steps (time from the previous step to the last step) + :return: + """ + assert count >= 1 + + time_delta = period / (count - 1) + + result = [] + previous_timestamp = previous_step.timestamp + previous_location = previous_step.location + for i in range(0, count): + # TODO: add some movement around the same place (for example move left then right, then left + new_location = previous_location + new_timestamp = previous_timestamp + timedelta(seconds=time_delta) + + result.append(Step(type=STEP_TYPE_MOVE, location=new_location, timestamp=new_timestamp)) + previous_timestamp = new_timestamp + previous_location = new_location + + return result diff --git a/scenario-player/app/simulator/__init__.py b/scenario-player/app/simulator/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scenario-player/app/simulator/simulation_engine.py b/scenario-player/app/simulator/simulation_engine.py new file mode 100644 index 0000000..4b36b6d --- /dev/null +++ b/scenario-player/app/simulator/simulation_engine.py @@ -0,0 +1,46 @@ +import asyncio +from datetime import datetime, timezone + +from app import logger +from app.backend.base import BaseTimelineBackend +from app.publisher.base import BaseEventPublisher +from app.scenario.scenario_model import CustomerDescription, CustomerState, Step + +CUSTOMER_STATE_TEMPLATE = '{0} is Entering. MDT: {1:0.1f}, C: {2:0.1f}, E: {3}' + + +class CustomerSimulator: + def __init__(self, backend: BaseTimelineBackend, event_publisher: BaseEventPublisher, + start_time: datetime = datetime.now(timezone.utc)): + self.tick_time = start_time + self.last_tick_time: int = 0 + self.backend = backend + self.event_publisher = event_publisher + self.is_running = True + + @staticmethod + def create_customer_state(customer_id, step: Step): + customer_description = CustomerDescription(customer_id=customer_id) + return CustomerState(customer_description=customer_description, location=step.location, + timestamp=step.timestamp, status=step.type) + + async def run(self): + logger.info('Starting simulator loop') + while self.is_running: + logger.debug('another pass of simulator loop...') + tmpstmp = int(datetime.now(timezone.utc).timestamp()) + + # TODO get_events returns the events and REMOVES them from the queue, maybe change name to consume_events + # TODO maybe it should be: get events from the last tick 'till now + events_for_users = await self.backend.get_events(tmpstmp) + logger.debug(f'events: {events_for_users}') + + customer_states = [self.create_customer_state(efo[0], efo[1]) for efo in events_for_users] + for state in customer_states: + await self.event_publisher.publish_state(state) + + # remember last tick time + self.last_tick_time = tmpstmp + + # go to sleep + await asyncio.sleep(1) diff --git a/scenario-player/development.md b/scenario-player/development.md new file mode 100644 index 0000000..b41b4c2 --- /dev/null +++ b/scenario-player/development.md @@ -0,0 +1,141 @@ +# Functionality +This service generates messages that simulate customer behaviour in a retail shop: +* customer entering the store +* customer movement +* customer exiting the store + + +# Table of contents +* [Functionality](#functionality) + +* [Development](#development) + * [Dependencies](#dependencies) + * [Service configuration](#service-configuration) + * [Running the service](#running-the-service) + * [Testing with MQTT broker in docker](#testing-with-mqtt-broker-in-docker) + * [Testing without MQTT](#testing-without-mqtt) + * [Mock event endpoints](#mock-event-endpoints) + +* [Deployment](#deployment) + * [Docker image](#docker-image) + * [Connecting to a secured broker](#connecting-to-a-secured-broker) + + +# Development + +## Dependencies + +Dependencies of the project are contained in [requirements.txt](requirements.txt) file. All the packages are publicly +available. + +All the packages can be installed with: +`pip install -f requirements.txt` + +## Service configuration + +The service reads the following **environment variables**: + +| Variable | Description | Default | +|------------------------|--------------------------------------|--------------:| +| STORE_HEIGHT | | 10 | +| STORE_WIDTH | | 6 | +| CUSTOMERS_AVERAGE_IN_STORE | | 6 | +| CUSTOMERS_LIST_FILE | | customers.csv | +| MQTT_HOST | | - | +| MQTT_PORT | | 1883 | +| MQTT_NAME | | demoClient | +| ENTER_TOPIC | | customer/enter| +| MOVE_TOPIC | | customer/move | +| EXIT_TOPIC | | customer/exit | + +(Parameters with `-` in "Default" column are required.) + +Use [log_config.py](./app/utils/log_config.py) to **configure logging behaviour**. +By default, console and file handlers are used. The file appender writes to `messages.log`. + + +## Running the service + +For my development I created a project with dedicated virtual environment (Python 3.8, all the dependencies installed +there). + +The code reads sensitive information (tokens, secrets) from environment variables. They need to be set accordingly in +advance. +`environment.variables.sh` can be used for that purpose. Then, in order to run the service the following commands can be +used: + +``` +$ . .environment.variables.sh +$ . venv/bin/activate +(venv)$ uvicorn app.main:app --host 0.0.0.0 --reload --reload-dir app +``` +> Please, note `reload-dir` switch. Without it the reloader goes into an infinite loop because it detects log file changes (messages.log). + +## Testing with MQTT broker in docker + +Quick way to **set up a simple MQTT broker** is to use Docker containers: +```shell +docker run -d --rm --name mosquitto -p 1883:1883 eclipse-mosquitto +``` +or +```shell +docker run -it -p 1883:1883 --name mosquitto eclipse-mosquitto mosquitto -c /mosquitto-no-auth.conf +``` + +To **publish to a topic**: + +```shell +docker exec mosquitto mosquitto_pub -h 127.0.0.1 -t test -m "test message" +``` + +To **subscribe to a topic**: +```shell +docker exec mosquitto mosquitto_sub -h 127.0.0.1 -t test +``` + +### Testing without MQTT +There is an environment variable, `TESTING_MOCK_MQTT`, that will create an MQTT client mock instead of trying to connect +to a real MQTT broker. Instead of publishing the messages, they will be simply logged/printed out. + +This may be helpful for local development or testing. + +### Producing test messages + +```shell +curl http://127.0.0.1:8000/produce_entry -d '{"id": "997", "ts": 192326400}' + ``` + +```shell +curl http://127.0.0.1:8000/produce_exit -d '{"id": "997", "ts": 192326400}' + ``` + +```shell +curl http://127.0.0.1:8000/produce_move -d '{"id": "997", "ts": 192326400, "x": 2, "y": 3}' + ``` + + +# Deployment + +## Docker image +The docker image for the service is [Dockerfile](Dockerfile). +It is based on FastAPI "official" image. +See https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker +for the details on configuring the container (http port, log level, etc.) + +In order to build the image use: +``` +docker build -t customersim-service:0.0.1 . +``` + +> Set image name (`customersim-service`) and tag (`0.0.1`) according to +> your needs. + +To run the service as a Docker container run: +``` +docker run -d -e LOG_LEVEL="warning" --name customersim-service customersim-service:0.0.1 + +``` + +## Connecting to a secured broker +**TODO** Add info about setting user/password +**TODO** Add info about using client certificates (TLS) diff --git a/scenario-player/docs/asyncapi.md b/scenario-player/docs/asyncapi.md new file mode 100644 index 0000000..36508fb --- /dev/null +++ b/scenario-player/docs/asyncapi.md @@ -0,0 +1,97 @@ +# Customer movement simulator 0.1.0 documentation + +This service is responsible for simulating customer movement around retail store. +## Table of Contents + +* [Servers](#servers) +* [Channels](#channels) + +## Servers + +### **development** Server + +| URL | Protocol | Description | +|---|---|---| +| localhost | mqtt | Development mosquitto broker | + +## Channels + +### **customer/enter** Channel + +#### `publish` Operation + +##### Message + +###### Payload + +| Name | Type | Description | Accepted values | +|---|---|---|---| +| id | string | ID of the customer | _Any_ | +| ts | integer | Timestamp (unix time) | _Any_ | + +> Examples of payload _(generated)_ + +```json +{ + "id": "string", + "ts": 0 +} +``` + + + + +### **customer/exit** Channel + +#### `publish` Operation + +##### Message + +###### Payload + +| Name | Type | Description | Accepted values | +|---|---|---|---| +| id | string | ID of the customer | _Any_ | +| ts | integer | Timestamp (unix time) | _Any_ | + +> Examples of payload _(generated)_ + +```json +{ + "id": "string", + "ts": 0 +} +``` + + + + +### **customer/move** Channel + +#### `publish` Operation + +##### Message + +###### Payload + +| Name | Type | Description | Accepted values | +|---|---|---|---| +| id | string | ID of the customer | _Any_ | +| ts | integer | Timestamp (unix time) | _Any_ | +| x | integer | coordinate x | _Any_ | +| y | integer | coordinate y | _Any_ | + +> Examples of payload _(generated)_ + +```json +{ + "id": "string", + "ts": 0, + "x": 0, + "y": 0 +} +``` + + + + diff --git a/scenario-player/docs/asyncapi.yml b/scenario-player/docs/asyncapi.yml new file mode 100644 index 0000000..e98a0bb --- /dev/null +++ b/scenario-player/docs/asyncapi.yml @@ -0,0 +1,61 @@ +asyncapi: 2.0.0 +info: + title: Customer movement simulator + version: 0.1.0 + description: This service is responsible for simulating customer movement around retail store. +servers: + development: + url: localhost + protocol: mqtt + description: Development mosquitto broker +channels: + customer/enter: + publish: + message: + $ref: '#/components/messages/CustomerEnter' + customer/exit: + publish: + message: + $ref: '#/components/messages/CustomerEnter' + customer/move: + publish: + message: + $ref: '#/components/messages/CustomerMove' +components: + messages: + CustomerEnter: + payload: + type: object + properties: + id: + type: string + description: ID of the customer + ts: + type: integer + description: Timestamp (unix time) + CustomerExit: + payload: + type: object + properties: + id: + type: string + description: ID of the customer + ts: + type: integer + description: Timestamp (unix time) + CustomerMove: + payload: + type: object + properties: + id: + type: string + description: ID of the customer + ts: + type: integer + description: Timestamp (unix time) + x: + type: integer + description: coordinate x + y: + type: integer + description: coordinate y diff --git a/scenario-player/requirements.txt b/scenario-player/requirements.txt new file mode 100644 index 0000000..ecbfc2b --- /dev/null +++ b/scenario-player/requirements.txt @@ -0,0 +1,7 @@ +setuptools~=39.2.0 +fastapi==0.63 +uvicorn[standard]==0.13.4 +pydantic~=1.8.1 +aioredis~=1.3.1 +pip~=21.0.1 +fastapi-mqtt~=0.3.0 diff --git a/scenario-player/testing/feed.drafts.sh b/scenario-player/testing/feed.drafts.sh new file mode 100644 index 0000000..4fb7b79 --- /dev/null +++ b/scenario-player/testing/feed.drafts.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +for payload in $(jq '.[]' -c test_drafts.json); do + curl -X 'POST' -H 'accept: application/json' -H 'Content-Type: application/json' -d $payload 'http://localhost:8000/scenario_draft' + sleep $(( RANDOM % 5 )) +done diff --git a/scenario-player/testing/feed.scenarios.sh b/scenario-player/testing/feed.scenarios.sh new file mode 100644 index 0000000..557bd53 --- /dev/null +++ b/scenario-player/testing/feed.scenarios.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +for payload in $(jq '.[]' -c test.scenarios.json); do + curl -X 'POST' -H 'accept: application/json' -H 'Content-Type: application/json' -d $payload 'http://localhost:8000/scenario?recalculate_time=true' + sleep $(( RANDOM % 5 )) +done diff --git a/scenario-player/testing/sample.scenario.json b/scenario-player/testing/sample.scenario.json new file mode 100644 index 0000000..06089c3 --- /dev/null +++ b/scenario-player/testing/sample.scenario.json @@ -0,0 +1,103 @@ +{ + "customer":{ + "customer_id":"3" + }, + "path":[ + { + "type":"MOVE", + "location":{ + "x":100, + "y":10 + }, + "timestamp":"2021-01-01T00:00:00.000000" + }, + { + "type":"MOVE", + "location":{ + "x":354, + "y":222 + }, + "timestamp":"2021-01-01T00:00:02.000000" + }, + { + "type":"MOVE", + "location":{ + "x":650, + "y":375 + }, + "timestamp":"2021-01-01T00:00:04.000000" + }, + { + "type":"MOVE", + "location":{ + "x":138, + "y":450 + }, + "timestamp":"2021-01-01T00:00:06.000000" + }, + { + "type":"MOVE", + "location":{ + "x":568, + "y":794 + }, + "timestamp":"2021-01-01T00:00:08.000000" + }, + { + "type":"MOVE", + "location":{ + "x":568, + "y":794 + }, + "timestamp":"2021-01-01T00:00:10.000000" + }, + { + "type":"MOVE", + "location":{ + "x":568, + "y":794 + }, + "timestamp":"2021-01-01T00:00:12.000000" + }, + { + "type":"MOVE", + "location":{ + "x":568, + "y":794 + }, + "timestamp":"2021-01-01T00:00:14.000000" + }, + { + "type":"MOVE", + "location":{ + "x":568, + "y":794 + }, + "timestamp":"2021-01-01T00:00:16.000000" + }, + { + "type":"MOVE", + "location":{ + "x":568, + "y":794 + }, + "timestamp":"2021-01-01T00:00:18.000000" + }, + { + "type":"MOVE", + "location":{ + "x":879, + "y":743 + }, + "timestamp":"2021-01-01T00:00:20.000000" + }, + { + "type":"EXIT", + "location":{ + "x":1237, + "y":168 + }, + "timestamp":"2021-01-01T00:00:24.000000" + } + ] +} \ No newline at end of file diff --git a/scenario-player/testing/test.scenarios.json b/scenario-player/testing/test.scenarios.json new file mode 100644 index 0000000..b7f8937 --- /dev/null +++ b/scenario-player/testing/test.scenarios.json @@ -0,0 +1,5 @@ +[ +{"customer": {"customer_id": "2"}, "path": [{"type": "ENTER", "location": {"x": 935, "y": 50}, "timestamp": "2021-04-14T08:40:36.183000+00:00"}, {"type": "MOVE", "location": {"x": 1085, "y": 171}, "timestamp": "2021-04-14T08:40:41.183000+00:00"}, {"type": "MOVE", "location": {"x": 1235, "y": 293}, "timestamp": "2021-04-14T08:40:46.183000+00:00"}, {"type": "MOVE", "location": {"x": 1316, "y": 546}, "timestamp": "2021-04-14T08:40:51.183000+00:00"}, {"type": "MOVE", "location": {"x": 1397, "y": 799}, "timestamp": 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50}, "timestamp": "2021-04-14T08:41:50.513000+00:00"}]}, +{"customer": {"customer_id": "3"}, "path": [{"type": "ENTER", "location": {"x": 935, "y": 50}, "timestamp": "2021-04-14T08:41:08.954000+00:00"}, {"type": "MOVE", "location": {"x": 772, "y": 82}, "timestamp": "2021-04-14T08:41:13.954000+00:00"}, {"type": "MOVE", "location": {"x": 609, "y": 115}, "timestamp": "2021-04-14T08:41:18.954000+00:00"}, {"type": "MOVE", "location": {"x": 842, "y": 160}, "timestamp": "2021-04-14T08:41:23.954000+00:00"}, {"type": "MOVE", "location": {"x": 1075, "y": 205}, "timestamp": "2021-04-14T08:41:28.954000+00:00"}, {"type": "MOVE", "location": {"x": 1309, "y": 250}, "timestamp": "2021-04-14T08:41:33.954000+00:00"}, {"type": "MOVE", "location": {"x": 1104, "y": 405}, "timestamp": "2021-04-14T08:41:38.954000+00:00"}, {"type": "MOVE", "location": {"x": 900, "y": 560}, "timestamp": "2021-04-14T08:41:43.954000+00:00"}, {"type": "MOVE", "location": {"x": 832, "y": 566}, "timestamp": 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\ No newline at end of file