flare9x

# Risk management simulations
using CSV, DataFrames, StatsBase, Random


##############################
## Trades to double
##############################

winPerc = .46
AvgWin = .14

flare9x

#####################################
# Export PSVs with EXERNAL / DEFAULT schedules (own export)
# Find PSVs with NO OVERHAUL schedule (own export)
# Compare - filter by no overhaul
#####################################

# load data

externals = read.csv("C:/Users/Andrew.Bannerman/Desktop/MARS/PSV KPI project/externals.csv")
internals = read.csv("C:/Users/Andrew.Bannerman/Desktop/MARS/PSV KPI project/overhauls.csv")

flare9x

#############################################################################################
# Calculating the Required Structural Thickness of Pipe Using Beam Stress Theory
#############################################################################################
# Input parameters
P = 850 # pressure
Do = 12.75 # outside diameter
S = 17900 # code allowable stress
E = .85 # weld joint efficieny
W = 1.0
Y = 0.4

flare9x

using Dates
using DataFrames
using Gadfly
using CSV
using Statistics

### methodology
# 1. amoritzation schedule per property
# 2. initial down payment + principal + any estimated equity due to renovations  (conservative estimates)
# 3. account for any refinances - amooritization schedule for each and join

flare9x

# Amortization 
# https://www.wikihow.com/Calculate-Amortization

# Step 1 - Gather the information you need to calculate the loan's amortization
property_value = 240000
loan_amount = .75 
down_payment = property_value-(loan_amount * property_value)
debt_borrowed = property_value - down_payment
interest_rate = 0.06 # annual interest rate
loan_term = 360 # months 

flare9x

library(ggmap)

google_map_api_key = ##

data = read.csv("C:/Users/Andrew.Bannerman/Desktop/Real Estate/County Tax Lists/harris_county_tax_list/Real_acct_owner/filtered_list.csv", stringsAsFactors = F,header=T, sep=",")
real_acct_names = c("account","tax_year","mailto","mail_addr_one","mail_addr_two","mail_city","mail_state","mail_zip","mail_country","undeliverable","str_pfx","str_num","str_num_sfx","str_name","str_sfx","str_sfx_dir","str_unit","site_addr_one","site_addr_two","site_addr_three","state_class","school_dist","map_facet","key_map",
                    "neighborhood_code","neighborhood_group","market_area_one","market_area_one_dscr","market_area_two","market_area_two_dscr","econ_area","econ_bld_class","center_code","yr_impr","yr_annexed","splt_dt","dsc_cd","nxt_building","total_building_area","total_land_area","acerage","cap_account","shared_cap_code","land_value","improvement_value","extra_feartures_value","ag_value","assessed_value","total_appraised_value",
                    "total_market_value

flare9x

data = read.csv("C:/Users/Andrew.Bannerman/Desktop/MARS/risk studies/for_r_plot.csv",header=T)

data = data[order(data$COF.Rank),]

#data = data[sample(nrow(data)),]

head(data)

names = c("Corrosion Loop","CL","Service","POF","Nominal Wall Thickness Category","COF Rank","Risk")
colnames(data) = names

flare9x

data = read.csv("C:/Users/Andrew.Bannerman/Desktop/MARS/15. PoC/3.24/PS_out_FCA.csv", header=T, stringsAsFactors = F)
FCAml = as.numeric(as.vector(data[6,2:length(data)]))
Rt = as.numeric(as.vector(data[9,2:length(data)]))
MAWPr = round(as.numeric(as.vector(data[18,2:length(data)])),digits=0)


MAWPr_P_intersection = ifelse(MAWPr > P,1,0)

df = data.frame(FCAml,Rt,MAWPr,MAWPr_P_intersection)

flare9x

using Distributions
using CSV
using DataFrames
using Statistics
using Cairo
using Gadfly

# read data
colnames = ["P&ID","Size","Service","Line_No","Sch","Spec","Exp Code","Design Temp"]

flare9x

# Plots for PArt 5 LTA sensitivity analysis
library(sm)
library(pheatmap)
library(ggplot2)

# original Grid 
M1 = c(0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300)
M2 = c(0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.100, 0.220, 0.280, 0.250, 0.240, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300)
M3 = c(0.300, 0.300, 0.300, 0.300, 0.300, 0.215, 0.255, 0.215, 0.145, 0.275, 0.170, 0.240, 0.250, 0.250, 0.280, 0.290, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300)
M4 = c(0.300, 0.300, 0.300, 0.300, 0.300, 0.170, 0.270, 0.190, 0.190, 0.285, 0.250, 0.225, 0.275, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300, 0.300)