diff --git a/ContinuousCode/3_Data4DM/Capability trap - Brad_V4_Ross-test.2mdl b/ContinuousCode/3_Data4DM/Capability trap - Brad_V4_Ross-test.2mdl
new file mode 100644
index 0000000..9fd0cc0
--- /dev/null
+++ b/ContinuousCode/3_Data4DM/Capability trap - Brad_V4_Ross-test.2mdl	
@@ -0,0 +1,339 @@
+{UTF-8}
+Resources to Production IC=
+	1400
+	~	hours/Week [0,4000,80]
+	~		|
+
+Process Problems IC=
+	0.4
+	~	 [0,0.98,0.02]
+	~		|
+
+Problem Introduction=
+	(Unattended Process Problem Level-Process Problems)/Avg Process Erosion Time
+	~	
+	~	0.4/16*0.625*(Process Problems/0.4)^alpha
+	|
+
+alpha=
+	1
+	~	 [0,1,0.1]
+	~		|
+
+additional desired throughput=
+	300
+	~	 [200,500,10]
+	~		|
+
+Indicated Allocation to Production=
+	max(0,min(Available Time,Resource Gap+Resources to Production+pulse(time to impliment measure\
+		,shock duration)*Policy measure))
+	~	
+	~	How many hours do you need to update production to fufill throughput gap
+	|
+
+shock duration=
+	0
+	~	 [0,100,4]
+	~		|
+
+Policy measure=
+	0
+	~	 [-4000,0,100]
+	~		|
+
+Resources to Improve Process=
+	Available Time-Resources to Production
+	~	
+	~		|
+
+time to impliment measure=
+	0
+	~	 [0,200,5]
+	~		|
+
+Adjusting Allocation=
+	(Indicated Allocation to Production-Resources to Production)/Time to Adjust Allocation
+	~	
+	~		|
+
+Available Time=
+	4000
+	~	hours/Week
+	~		|
+
+Avg Process Erosion Time=
+	32
+	~	Week
+	~		|
+
+Defect Introduction=
+	Process Problems*Gross Process Throughput
+	~	units/Week
+	~		|
+
+Desired Throughput=
+	900+additional desired throughput
+	~	units/Week [300,1800,20]
+	~	900*1.2
+		900+STEP(180, 10 )
+		900+STEP(additional desired throughput,40)
+	|
+
+Gross Process Throughput=
+	Resources to Production*Productivity of Production Time
+	~	units/Week
+	~		|
+
+Net Process Throughput=
+	Gross Process Throughput-Defect Introduction
+	~	units/Week
+	~		|
+
+Net Productivity of Production Time=
+	Productivity of Production Time*(1-Process Problems)
+	~	units/hour
+	~	How much of the time are you being productive
+	|
+
+Problem Correction=
+	Problem Correction Effectiveness*(Process Problems/Time to Correct Problems)
+	~	
+	~		|
+
+Problem Correction Effectiveness=
+	Resources to Improve Process/Resources for Maximum Problem Correction
+	~	
+	~		|
+
+Process Problems= INTEG (
+	min(1,Problem Introduction-Problem Correction),
+		Process Problems IC)
+	~	units
+	~	Dimensionless index from 0 to 1
+	|
+
+Productivity of Production Time=
+	1
+	~	units/hour
+	~		|
+
+Resource Gap=
+	Throughput Gap/Net Productivity of Production Time
+	~	hour/Week
+	~	How many more hours would you really need to close throughput gap
+	|
+
+Resources for Maximum Problem Correction=
+	4000
+	~	hours/Week
+	~		|
+
+Resources to Production= INTEG (
+	Adjusting Allocation,
+		Resources to Production IC
+		)
+	~	hours/Week
+	~		|
+
+Throughput Gap=
+	Desired Throughput-Net Process Throughput
+	~	units/Week
+	~		|
+
+Time to Adjust Allocation=
+	12
+	~	Week [1,12,1]
+	~		|
+
+Time to Correct Problems=
+	16
+	~	Week
+	~		|
+
+Unattended Process Problem Level=
+	0.9
+	~	
+	~		|
+
+********************************************************
+	.Control
+********************************************************~
+		Simulation Control Parameters
+	|
+
+FINAL TIME=
+	500
+	~	Week
+	~	The final time for the simulation.
+	|
+
+INITIAL TIME  = 0
+	~	Week
+	~	The initial time for the simulation.
+	|
+
+SAVEPER  = 
+        TIME STEP
+	~	Week [0,?]
+	~	The frequency with which output is stored.
+	|
+
+TIME STEP  = 0.25
+	~	Week [0,?]
+	~	The time step for the simulation.
+	|
+
+\\\---/// Sketch information - do not modify anything except names
+V300  Do not put anything below this section - it will be ignored
+*View 1
+$-1--1--1,0,|12||-1--1--1|-1--1--1|-1--1--1|-1--1--1|-1--1--1|96,96,100,0
+10,1,Process Problems,403,186,43,23,3,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+12,2,48,180,187,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,3,5,2,100,0,0,22,0,192,0,-1--1--1,,1|(229,187)|
+1,4,5,1,4,0,0,22,0,192,0,-1--1--1,,1|(320,187)|
+11,5,0,275,187,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+10,6,Problem Introduction,275,218,58,23,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+12,7,48,624,186,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,8,10,1,100,0,0,22,0,192,0,-1--1--1,,1|(485,186)|
+1,9,10,7,4,0,0,22,0,192,0,-1--1--1,,1|(575,186)|
+11,10,0,530,186,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+10,11,Problem Correction,530,217,49,23,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+10,12,Time to Correct Problems,646,141,50,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+10,13,Avg Process Erosion Time,187,97,69,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+10,14,Unattended Process Problem Level,123,259,73,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||255-128-0,0,0,0,0,0,0
+10,15,Defect Introduction,799,263,60,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,16,Productivity of Production Time,538,312,73,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,17,Gross Process Throughput,685,337,58,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,18,Net Process Throughput,798,435,64,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,19,Throughput Gap,758,546,58,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,20,Desired Throughput,883,499,58,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,21,Net Productivity of Production Time,744,650,80,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,22,Resource Gap,610,586,50,23,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,23,Indicated Allocation to Production,439,597,63,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,24,Time to Adjust Allocation,279,554,51,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,25,Available Time,149,590,47,23,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+10,26,Resources to Improve Process,360,386,56,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-128-64,0,0,0,0,0,0
+10,27,Problem Correction Effectiveness,381,283,69,31,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-128-64,0,0,0,0,0,0
+10,28,Resources for Maximum Problem Correction,182,342,72,40,8,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||0-128-64,0,0,0,0,0,0
+1,29,12,10,1,0,0,0,0,192,0,-1--1--1,,1|(566,143)|
+1,30,28,27,1,0,0,0,0,192,0,-1--1--1,,1|(298,286)|
+1,31,26,27,1,0,0,0,0,192,0,-1--1--1,,1|(368,334)|
+1,32,25,26,1,0,0,0,0,192,0,-1--1--1,,1|(200,479)|
+10,33,Resources to Production,496,478,56,31,3,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+12,34,48,333,476,10,8,0,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,35,37,34,100,0,0,22,0,192,0,-1--1--1,,1|(364,476)|
+1,36,37,33,4,0,0,22,0,192,0,-1--1--1,,1|(418,476)|
+11,37,0,391,476,6,8,34,3,0,2,1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+10,38,Adjusting Allocation,391,507,51,23,40,3,0,2,-1,0,0,0,0-0-0,0-0-0,|||128-0-0,0,0,0,0,0,0
+1,39,24,38,1,0,0,0,0,192,0,-1--1--1,,1|(334,501)|
+1,40,23,38,1,0,0,0,0,192,0,-1--1--1,,1|(399,560)|
+1,41,33,23,1,0,0,0,0,192,0,-1--1--1,,1|(517,553)|
+1,42,22,23,1,0,0,0,0,192,0,-1--1--1,,1|(495,598)|
+1,43,19,22,1,0,0,0,0,192,0,-1--1--1,,1|(696,567)|
+1,44,21,22,1,0,0,0,0,192,0,-1--1--1,,1|(652,618)|
+1,45,20,19,1,0,0,0,0,192,0,-1--1--1,,1|(813,546)|
+1,46,18,19,1,0,0,0,0,192,0,-1--1--1,,1|(795,487)|
+1,47,17,18,1,0,0,0,0,192,0,-1--1--1,,1|(775,357)|
+1,48,15,18,1,0,0,0,0,192,0,-1--1--1,,1|(842,363)|
+1,49,16,17,1,0,0,0,0,192,0,-1--1--1,,1|(615,334)|
+1,50,33,17,1,0,0,0,0,192,0,-1--1--1,,1|(590,394)|
+1,51,33,26,1,0,0,0,0,192,0,-1--1--1,,1|(423,386)|
+1,52,27,11,1,0,0,0,0,192,0,-1--1--1,,1|(439,217)|
+1,53,1,15,1,0,0,0,0,192,0,-1--1--1,,1|(540,70)|
+1,54,33,37,1,0,0,0,0,192,0,-1--1--1,,1|(411,452)|
+10,55,Productivity of Production Time,914,662,56,31,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0
+1,56,55,21,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)|
+10,57,Process Problems,905,584,56,22,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0
+1,58,57,21,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)|
+1,59,25,23,1,0,0,0,0,192,0,-1--1--1,,1|(251,610)|
+1,60,17,15,1,0,0,0,0,64,0,-1--1--1,,1|(741,323)|
+1,61,1,10,1,0,0,0,0,192,0,-1--1--1,,1|(482,153)|
+12,62,0,1663,600,208,140,8,15,0,0,2,0,0,0,0,0,0,0,0,0
+Process Problems,Graph
+12,63,0,1663,323,208,140,8,15,0,0,2,0,0,0,0,0,0,0,0,0
+Resources to Production,Graph
+12,64,0,1248,601,208,140,8,15,0,0,2,0,0,0,0,0,0,0,0,0
+Net Process Throughput,Graph
+12,65,0,1248,324,208,140,8,15,0,0,2,0,0,0,0,0,0,0,0,0
+Problem Correction,Graph
+10,66,additional desired throughput,934,408,66,18,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,67,66,20,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)|
+10,68,FINAL TIME,982,88,41,22,8,2,0,3,-1,0,0,0,128-128-128,0-0-0,|||128-128-128,0,0,0,0,0,0
+10,69,Policy measure,507,708,45,23,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,70,69,23,0,0,0,0,0,192,0,-1--1--1,,1|(0,0)|
+10,71,time to impliment measure,387,713,62,18,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,72,71,23,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)|
+10,73,shock duration,598,688,53,10,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,74,73,23,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)|
+1,75,1,5,1,0,0,0,0,192,0,-1--1--1,,1|(342,123)|
+10,76,alpha,555,-97,21,10,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+10,77,Resources to Production IC,615,473,50,18,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,78,77,33,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)|
+10,79,Process Problems IC,405,72,46,18,8,3,0,0,-1,0,0,0,0,0,0,0,0,0
+1,80,79,1,0,0,0,0,0,64,1,-1--1--1,,1|(0,0)|
+1,81,13,6,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)|
+1,82,14,6,0,0,0,0,0,64,0,-1--1--1,,1|(0,0)|
+///---\\\
+:GRAPH Process_Problems
+:TITLE Process_Problems
+:SCALE
+:VAR Process Problems
+
+:GRAPH Resources_to_Production
+:TITLE Resources_to_Production
+:SCALE
+:VAR Resources to Production
+:L<%^E!@
+1:currentvectorv1.vdfx
+1:currentvectorv2.vdfx
+4:Time
+5:additional desired throughput
+9:currentvectorv1
+19:100,0
+24:0
+25:2000
+26:2000
+60:
+61:0
+62:0
+63:0
+64:0
+65:1
+66:0
+67:0
+68:0
+69:0
+70:1
+18:currentvectorv2.vsc
+20:currentvectorv1.lst
+15:0,0,0,0,0,0
+27:0,
+34:0,
+42:1
+72:0
+73:0
+35:Date
+36:YYYY-MM-DD
+37:2000
+38:1
+39:1
+40:2
+41:0
+95:0
+96:0
+97:0
+77:0
+78:0
+102:1
+93:0
+94:0
+92:0
+91:0
+90:0
+87:0
+75:
+43:
+103:8,8,8,3,8
+105:0,0,0,0,0,0,0,0,0,0
+104:Arial|14||0-0-0|0-0-0|-1--1--1|0-0-255|192-192-192|-1--1--1
diff --git a/ContinuousCode/3_Data4DM/Creating_spiral_graphs_DavidKeith_Method.m b/ContinuousCode/3_Data4DM/Creating_spiral_graphs_DavidKeith_Method.m
new file mode 100644
index 0000000..f06c03e
--- /dev/null
+++ b/ContinuousCode/3_Data4DM/Creating_spiral_graphs_DavidKeith_Method.m
@@ -0,0 +1,175 @@
+%Author: Ross Williams
+
+
+%%%%Untested Assumptions/Potential Errors%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%USER INPUTS%%%%%%%%%%%%%%%%%%%%%
+datafilename="currentvectorv1.xlsx"; %It must be an xlsx file
+
+%Smaller the numbers for both reduced data and reduce time will keep more of the data
+Reducedata=2; %Define, of the total initial conditions you have, by what whole number factor do you want to reduce by. See "reducedarray" for how it affects the code
+Reducetime=10; %Define how many time steps you want to skip.
+
+
+Normalization=1; %If you want normalization, make it 1, else put 0
+MaxTimeSeries1=1; %For timeseries 1, used to normalize.
+MaxTimeSeries2=4000; %For timeseries 2, used to normalize
+
+
+xlabelname="Process Problems";  %This will the name for your time series 1 (x-axis). Do not include normalization to title (will automatically do it)
+ylabelname="Resources for Production"; %This will the name for your time series 2 (y-axis). Do not include normalization to title (will automatically do it)
+titlename="Phase Plot of Process Problems and Resources for Production";%This will make your title for the graph
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+%%%COMMENT OUT TO SAVE TIME ONCE RUN ONCE%%%
+data=xlsread(datafilename); %Import data
+data(1,:)=[]; %Remove Time Step Row. ONLY RUN ONCE AND THEN COMMENT OUT THIS LINE
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+howmuchdata=length(data); %Determines how much data you imported
+array1=3:4:howmuchdata; %Array of rows for first time series
+array2=4:4:howmuchdata; %Array of rows for 2nd time series
+arraytot=cat(2,array1,array2); %concadenates arrays of rows for 1st and 2nd time series
+arraytotsort=sort(arraytot); %sorts row order of concadenated time series
+
+timeseries=data(arraytotsort,:); %Extracts only time series from data
+
+array3=1:2:(howmuchdata/2); %Array of rows for 1st time series in "timeseries" matrix 
+array4=2:2:(howmuchdata/2); %Array of rows from 2nd time series in "timeseries" matrix
+
+if Normalization==1
+timeseries(array3,:)=timeseries(array3,:)./MaxTimeSeries1; %x-axis previously known as the 1st time series
+timeseries(array4,:)=timeseries(array4,:)./MaxTimeSeries2; %y-axis previously known as the 2nd time series
+xlabelname=strcat("Normalized ",xlabelname);
+ylabelname=strcat("Normalized ",ylabelname);
+end
+
+x=timeseries(array3,:); %x-axis previously known as the 1st time series
+y=timeseries(array4,:); %y-axis previously known as the 2nd time series
+
+
+minx=min(x(:,1)); %Locate min x value
+miny=min(y(:,1)); %Locate min y value
+maxx=max(x(:,1)); %Locate max x value
+maxy=max(y(:,1)); %Locate max y value
+
+
+%Create the data
+MaxTSx=find(x(:,1)==maxx)*2-1; %Multiply by 2 as we'll need to extract data from real ts, not the x, and subtract 1 as it is x-series
+MaxTSx_Yequivalent=MaxTSx+1; %Finds y time series that goes along with MaxTSx
+MaxTSx_total=sort(cat(1,MaxTSx,MaxTSx_Yequivalent)); %Concatenates needed rows and sorts
+xmax=timeseries(MaxTSx_total,:); %All ts data relevant for xmax
+
+MinTSx=find(x(:,1)==minx)*2-1; %Multiply by 2 as we'll need to extract data from real ts, not the x, and subtract 1 as it is x-series
+MinTSx_Yequivalent=MinTSx+1; %Finds y time series that goes along with MinTSx
+MinTSx_total=sort(cat(1,MinTSx,MinTSx_Yequivalent)); %Concatenates needed rows and sorts
+xmin=timeseries(MinTSx_total,:); %All ts data relevant for xmin
+
+MaxTSy=find(y(:,1)==maxy)*2; %Multiply by 2 as we'll need to extract data from real ts and subtract 1 as it is x-series
+MaxTSy_Xequivalent=MaxTSy-1; %Finds x time series that goes along with MaxTSy
+MaxTSy_total=sort(cat(1,MaxTSy,MaxTSy_Xequivalent)); %Concatenates needed rows and sorts
+ymax=timeseries(MaxTSy_total,:); %All ts data relevant for ymax
+
+MinTSy=find(y(:,1)==miny)*2; %Multiply by 2 as we'll need to extract data from real ts and subtract 1 as it is x-series
+MinTSy_Xequivalent=MinTSy-1; %Finds x time series that goes along with MinTSy
+MinTSy_total=sort(cat(1,MinTSy,MinTSy_Xequivalent)); %Concatenates needed rows and sorts
+ymin=timeseries(MinTSy_total,:); %All ts data relevant for ymin
+
+
+%Reduction of data
+reducearrayX_x=1:2*Reducedata:size(xmax,1); %What x-rows from your time series from your xmin or xmax data 
+reducearrayY_x=1:2*Reducedata:size(ymax,1); %What x-rows from your time series from your ymin or ymax data 
+
+reducearrayX_y=reducearrayX_x+1; %The associated y-rows from your time series from your xmin or xmax data 
+reducearrayY_y=reducearrayY_x+1; %The associated y-rows from your time series from your ymin or ymax data 
+
+reducearrayX=sort(cat(2,reducearrayX_x,reducearrayX_y)); %Concatenating rows numbers and y-rows for xmin or xmax data
+reducearrayY=sort(cat(2,reducearrayY_x,reducearrayY_y)); %Concatenating row numbers and y-rows for ymin or ymax data
+
+reducetimestep=1:Reducetime:size(timeseries,2); %Reduces timestep
+
+xmax_final=xmax(reducearrayX,reducetimestep); %Reducing amount of lines and timesteps for xmax
+xmin_final=xmin(reducearrayX,reducetimestep); %Reducing amount of lines and timesteps for xmin
+ymax_final=ymax(reducearrayY,reducetimestep); %Reducing amount of lines and timesteps for ymax
+ymin_final=ymin(reducearrayY,reducetimestep); %Reducing amount of lines and timesteps for ymin
+
+
+%Begin final attackrun for plotting
+array5_xx=(1:2:size(xmax_final,1)); %Extract x time series data to plot for xmin and xmax
+array6_xy=(2:2:size(xmax_final,1)); %Extract y time series data to plot for xmin and xmax
+
+array7_yx=(1:2:size(ymax_final,1)); %Extract x time series data to plot for ymin and ymax
+array8_yy=(2:2:size(ymax_final,1)); %Extract y time series data to plot for ymin and ymax
+
+figure('Position',[10 10 1000 600],'Color','w'); %Create figure
+
+xmax_final_x=xmax_final(array5_xx,:); %Seperate out x time series data for xmax
+xmax_final_y=xmax_final(array6_xy,:); %Seperate out y time series data for xmax
+
+for i=1:size(xmax_final_x,1) %Run through all xmax data
+    for j=1:(size(xmax_final_x,2)-1)
+    u=xmax_final_x(i,j+1)-xmax_final_x(i,j); %Velocity x and points to next point
+    v=xmax_final_y(i,j+1)-xmax_final_y(i,j); %Velocity y and points to next point
+    quiver(xmax_final_x(i,j),xmax_final_y(i,j),u,v,'Alignment','tail','Color','black'); %Plot all xmax data
+    hold on %Use the same graph
+    end
+    plot(xmax_final_x(i,j+1), xmax_final_y(i,j+1),'black.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+
+xmin_final_x=xmin_final(array5_xx,:); %Seperate out x time series data for xmin
+xmin_final_y=xmin_final(array6_xy,:); %Seperate out y time series data for xmin
+
+for i=1:size(xmin_final_x,1) %Run through all xmin data
+    for j=1:(size(xmin_final_x,2)-1)
+    u=xmin_final_x(i,j+1)-xmin_final_x(i,j); %Velocity x and points to next point
+    v=xmin_final_y(i,j+1)-xmin_final_y(i,j); %Velocity y and points to next point
+    quiver(xmin_final_x(i,j),xmin_final_y(i,j),u,v,'Alignment','tail','Color','black'); %Plot all xmin data
+    hold on %Use the same graph
+    end
+    plot(xmin_final_x(i,j+1), xmin_final_y(i,j+1),'black.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+
+ymax_final_x=ymax_final(array7_yx,:); %Seperate out x time series data for ymax
+ymax_final_y=ymax_final(array8_yy,:); %Seperate out y time series data for ymax
+
+for i=1:size(ymax_final_x,1) %Run through all ymax data
+    for j=1:(size(ymax_final_x,2)-1)
+    u=ymax_final_x(i,j+1)-ymax_final_x(i,j); %Velocity x and points to next point
+    v=ymax_final_y(i,j+1)-ymax_final_y(i,j); %Velocity y and points to next point
+    quiver(ymax_final_x(i,j),ymax_final_y(i,j),u,v,'Alignment','tail','Color','black'); %Plot all ymax data
+    hold on %Use the same graph
+    end
+    plot(ymax_final_x(i,j+1), ymax_final_y(i,j+1),'black.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+
+ymin_final_x=ymin_final(array7_yx,:); %Seperate out x time series data for ymin
+ymin_final_y=ymin_final(array8_yy,:); %Seperate out y time series data for ymin
+
+for i=1:size(ymin_final_x,1) %Run through all ymin data
+    for j=1:(size(ymin_final_x,2)-1)
+    u=ymin_final_x(i,j+1)-ymin_final_x(i,j); %Velocity x and points to next point
+    v=ymin_final_y(i,j+1)-ymin_final_y(i,j); %Velocity y and points to next point
+    quiver(ymin_final_x(i,j),ymin_final_y(i,j),u,v,'Alignment','tail','Color','black'); %Plot all ymin data
+    hold on %Use the same graph
+    end
+    plot(ymin_final_x(i,j+1), ymin_final_y(i,j+1),'black.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+if Normalization==1
+xlim([0 1])
+ylim([0 1])
+end
+ylabel(ylabelname) %ylabel for graph
+xlabel(xlabelname) %xlabel for graph
+title(titlename) %title for graph
diff --git a/ContinuousCode/3_Data4DM/Creating_spiral_graphs_Hesam_Method.m b/ContinuousCode/3_Data4DM/Creating_spiral_graphs_Hesam_Method.m
new file mode 100644
index 0000000..14ccbf2
--- /dev/null
+++ b/ContinuousCode/3_Data4DM/Creating_spiral_graphs_Hesam_Method.m
@@ -0,0 +1,115 @@
+%Author: Ross Williams
+
+%Note: Coloring Function still needs work or it does work and I don't know why my data is wonked.
+
+%%%%Untested Assumptions/Potential Errors%%%
+%What if a time series has a minimum not equal to 0. Unsure if I will need to adjust the code.
+%I am not perfectly why I need to normalize velocities, but it definitely is needed.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%USER INPUTS%%%%%%%%%%%%%%%%%%%%%
+subtract=5; %Define the Position of how many time steps forward you want your arrow to be pointed towards
+Reducedata=1; %Define, of the total initial conditions you have, by what whole number factor do you want to reduce by. See "reducedarray" for how it affects the code
+MaxTimeSeries1=1; %For timeseries 1, used to normalize arrow position as well as arrow "velocities"
+MaxTimeSeries2=4000; %For timeseries 2, used to normalize arrow position as well as arrow "velocities"
+xlabelname="Process Problems";  %This will the name for your time series 1 (x-axis), and remember it will be normalized
+ylabelname="Normalized Resources for Production"; %This will the name for your time series 2 (y-axis), and remember it will be normalized
+titlename="Phase Plot of Process Problems and Normalized Resources for Production";%This will
+datafilename="current12.xlsx"; %It must be an xlsx file
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%OPTIONAL USER INPUTS FOR GRAPH%%%
+headWidth = 5;%Arrow head width
+headLength = 9; %Arrow head length
+LineLength = 0.01; %Put line lengths as close to 0 as possible. Else need to figure out how to normalize the line lengths,, 0.2
+ArrowAlignment = 'head'; %Where do you want
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%COMMENT OUT TO SAVE TIME ONCE RUN ONCE%%%
+%data=xlsread(datafilename); %Import data
+%data(1,:)=[]; %Remove Time Step Row. ONLY RUN ONCE AND THEN COMMENT OUT THIS LINE
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+howmuchdata=length(data); %Determines how much data you imported
+array1=3:4:howmuchdata; %Array of rows for first time series
+array2=4:4:howmuchdata; %Array of rows for 2nd time series
+arraytot=cat(2,array1,array2); %concadenates arrays of rows for 1st and 2nd time series
+arraytotsort=sort(arraytot); %sorts row order of concadenated time series
+
+
+
+timeseries=data(arraytotsort,:); %Extracts only time series from data
+timeseriescol2=timeseries(:,[1,subtract]); %Extracts two columns of interest from time series
+diff=timeseriescol2(:,2)-timeseriescol2(:,1); %Takes difference 
+
+array3=1:2:(howmuchdata/2); %Array of rows for 1st time series in "timeseries" matrix and "diff" matrix
+array4=2:2:(howmuchdata/2); %Array of rows from 2nd time series in "timeseries" matrix and "diff" matrix
+
+
+x=timeseries(array3,1); %x-axis previously known as the 1st time series
+y=timeseries(array4,1); %y-axis previously known as the 2nd time series
+
+diffx=diff(array3,1); %Find the difference in x-axis time series (x-axis previously known as the 1st time series)
+diffy=diff(array4,1); %Find the difference in y-axis time series (y-axis previously known as the 2nd time series)
+
+
+reducearray=1:Reducedata:(howmuchdata/4); %Takes a portion of all data that time series has. This line affects how many arrows come up. The max is the total amount of different initial conditions you have. 
+x_final=x(reducearray,1)./MaxTimeSeries1; %Reduces x-data and normalizes x-series
+y_final=y(reducearray,1)./MaxTimeSeries2; %Reduces y-data and normalizes y-series
+diffx_final=diffx(reducearray,1)./MaxTimeSeries1; %Reduces diffx-data and normalizes diffx-series
+diffy_final=diffy(reducearray,1)./MaxTimeSeries2; %Reduces diffy-data and normalizes diffy-series
+
+
+
+%Plotting
+
+
+
+%Plot with blue arrows that we don't really care about
+figure('Position',[10 10 1000 600],'Color','w'); %Just creates a plot setup; I stole this line from the web.
+scale=1; %Plays with size of arrows. TBH, just keep it at 1
+
+quiv=quiver(x_final,y_final,diffx_final*scale,diffy_final*scale,'AutoScale','off','Alignment',ArrowAlignment); %AutoScale I believe hurts this process, so I shut it off. 
+%Still recommended to further explore autoscale. Alignment=head means the head of the arrow will
+
+
+ylabel(ylabelname) %ylabel for graph
+xlabel(xlabelname) %xlabel for graph
+title(titlename) %title for graph
+
+
+%Plot with black arrows that we care about (I, Ross cares about)
+figure('Position',[10 10 1000 600],'Color','w'); %Just creates a plot setup; I stole this line from the web.
+
+
+%Making a graph with black/colored arrows (coloring is experimental atm)
+
+%Code to create black arrows found here: https://stackoverflow.com/questions/18776172/in-matlab-how-do-i-change-the-arrow-head-style-in-quiver-plot/18776822#18776822
+
+U = quiv.UData; %Extracts diffx_final (x-velocity) data 
+V = quiv.VData; %Extracts diffy_final (y-velocity) data 
+X = quiv.XData; %Extracts x_final (x-position) data 
+Y = quiv.YData; %Extracts y_final (y-position) data 
+
+
+
+
+for i = 1:length(X) %For all points
+    ah = annotation('arrow',...
+        'headStyle','cback1','color','black','HeadLength',headLength,'HeadWidth',headWidth,'LineWidth',0.01); %reformant arrow, color arrow, currently set as red spectrum
+    set(ah,'parent',gca); %Place arrow in current chart (tbh) I am unsure
+    set(ah,'position',[X(i,1) Y(i,1) LineLength*U(i,1) LineLength*V(i,1)]); %Place arrow here
+end
+
+ylabel(ylabelname) %ylabel for graph
+xlabel(xlabelname) %xlabel for graph
+title(titlename) %title for graph
+
+%Not used brokenish idea:
+
+%slope=V./U; %used for color of arrows
+%axSlope=max(abs(V./U)); %Used for colors arrows to normalize the values from [0,1]
+%colorarrow=abs(slope(i)/maxSlope); %Calculate each color arrow value
+
+%  ah = annotation('arrow','headStyle','cback1','color',[(1-colorarrow) 0 0],'HeadLength',headLength,'HeadWidth',headWidth,'LineWidth',0.01); %reformant arrow, color arrow, currently set as red spectrum
\ No newline at end of file
diff --git a/ContinuousCode/3_Data4DM/Creating_spiral_graphs_Ross_Method.m b/ContinuousCode/3_Data4DM/Creating_spiral_graphs_Ross_Method.m
new file mode 100644
index 0000000..c61e7d4
--- /dev/null
+++ b/ContinuousCode/3_Data4DM/Creating_spiral_graphs_Ross_Method.m
@@ -0,0 +1,154 @@
+%Author: Ross Williams
+
+%%%%Untested Assumptions/Potential Errors%%%
+%What if a time series has a minimum not equal to 0. Unsure if I will need to adjust the code.
+%I am unsure why I need to normalize velocities.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%USER INPUTS%%%%%%%%%%%%%%%%%%%%%
+datafilename="currentvectorv1.xlsx"; %It must be an xlsx file
+
+
+Reducedata=1; %Define, of the total initial conditions you have, by what whole number factor do you want to reduce by. See "reducedarray" for how it affects the code
+Reducetime=1; %Define how many time steps you want to skip.
+
+
+Normalization=1; %If you want normalization, make it 1, else put 0
+MaxTimeSeries1=1; %For timeseries 1, used to normalize.
+MaxTimeSeries2=4000; %For timeseries 2, used to normalize
+
+
+xlabelname="Process Problems";  %This will the name for your time series 1 (x-axis). Do not include normalization to title (will automatically do it)
+ylabelname="Resources for Production"; %This will the name for your time series 2 (y-axis). Do not include normalization to title (will automatically do it)
+titlename="Phase Plot of Process Problems and Resources for Production";%This will make your title for the graph
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%COMMENT OUT TO SAVE TIME ONCE RUN ONCE%%%
+%data=xlsread(datafilename); %Import data
+%data(1,:)=[]; %Remove Time Step Row.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+howmuchdata=length(data); %Determines how much data you imported
+array1=3:4:howmuchdata; %Array of rows for first time series
+array2=4:4:howmuchdata; %Array of rows for 2nd time series
+arraytot=cat(2,array1,array2); %concadenates arrays of rows for 1st and 2nd time series
+arraytotsort=sort(arraytot); %sorts row order of concadenated time series
+
+timeseries=data(arraytotsort,:); %Extracts only time series from data
+
+array3=1:2:(howmuchdata/2); %Array of rows for 1st time series in "timeseries" matrix 
+array4=2:2:(howmuchdata/2); %Array of rows from 2nd time series in "timeseries" matrix
+
+if Normalization==1
+timeseries(array3,:)=timeseries(array3,:)./MaxTimeSeries1; %x-axis previously known as the 1st time series
+timeseries(array4,:)=timeseries(array4,:)./MaxTimeSeries2; %y-axis previously known as the 2nd time series
+xlabelname=strcat("Normalized ",xlabelname);
+ylabelname=strcat("Normalized ",ylabelname);
+end
+
+x=timeseries(array3,:); %x-axis previously known as the 1st time series
+y=timeseries(array4,:); %y-axis previously known as the 2nd time series
+
+
+minx=min(x(:,1)); %Locate min x value
+miny=min(y(:,1)); %Locate min y value
+maxx=max(x(:,1)); %Locate max x value
+maxy=max(y(:,1)); %Locate max y value
+
+
+%Create the data
+MaxTSx=find(x(:,1)==maxx)*2-1; %Multiply by 2 as we'll need to extract data from real ts, not the x, and subtract 1 as it is x-series
+MaxTSx_Yequivalent=MaxTSx+1; %Finds y time series that goes along with MaxTSx
+MaxTSx_total=sort(cat(1,MaxTSx,MaxTSx_Yequivalent)); %Concatenates needed rows and sorts
+xmax=timeseries(MaxTSx_total,:); %All ts data relevant for xmax
+
+MinTSx=find(x(:,1)==minx)*2-1; %Multiply by 2 as we'll need to extract data from real ts, not the x, and subtract 1 as it is x-series
+MinTSx_Yequivalent=MinTSx+1; %Finds y time series that goes along with MinTSx
+MinTSx_total=sort(cat(1,MinTSx,MinTSx_Yequivalent)); %Concatenates needed rows and sorts
+xmin=timeseries(MinTSx_total,:); %All ts data relevant for xmin
+
+MaxTSy=find(y(:,1)==maxy)*2; %Multiply by 2 as we'll need to extract data from real ts and subtract 1 as it is x-series
+MaxTSy_Xequivalent=MaxTSy-1; %Finds x time series that goes along with MaxTSy
+MaxTSy_total=sort(cat(1,MaxTSy,MaxTSy_Xequivalent)); %Concatenates needed rows and sorts
+ymax=timeseries(MaxTSy_total,:); %All ts data relevant for ymax
+
+MinTSy=find(y(:,1)==miny)*2; %Multiply by 2 as we'll need to extract data from real ts and subtract 1 as it is x-series
+MinTSy_Xequivalent=MinTSy-1; %Finds x time series that goes along with MinTSy
+MinTSy_total=sort(cat(1,MinTSy,MinTSy_Xequivalent)); %Concatenates needed rows and sorts
+ymin=timeseries(MinTSy_total,:); %All ts data relevant for ymin
+
+
+%Reduction
+reducearrayX_x=1:2*Reducedata:size(xmax,1); %What x-rows from your time series from your xmin or xmax data 
+reducearrayY_x=1:2*Reducedata:size(ymax,1); %What x-rows from your time series from your ymin or ymax data 
+
+reducearrayX_y=reducearrayX_x+1; %The associated y-rows from your time series from your xmin or xmax data 
+reducearrayY_y=reducearrayY_x+1; %The associated y-rows from your time series from your ymin or ymax data 
+
+reducearrayX=sort(cat(2,reducearrayX_x,reducearrayX_y)); %Concatenating rows numbers and y-rows for xmin or xmax data
+reducearrayY=sort(cat(2,reducearrayY_x,reducearrayY_y)); %Concatenating row numbers and y-rows for ymin or ymax data
+
+reducetimestep=1:Reducetime:size(timeseries,2); %Reduces timestep
+
+xmax_final=xmax(reducearrayX,reducetimestep); %Reducing amount of lines and timesteps for xmax
+xmin_final=xmin(reducearrayX,reducetimestep); %Reducing amount of lines and timesteps for xmin
+ymax_final=ymax(reducearrayY,reducetimestep); %Reducing amount of lines and timesteps for ymax
+ymin_final=ymin(reducearrayY,reducetimestep); %Reducing amount of lines and timesteps for ymin
+
+
+%Begin final attackrun for plotting
+array5_xx=(1:2:size(xmax_final,1)); %Extract x time series data to plot for xmin and xmax
+array6_xy=(2:2:size(xmax_final,1)); %Extract y time series data to plot for xmin and xmax
+
+array7_yx=(1:2:size(ymax_final,1)); %Extract x time series data to plot for ymin and ymax
+array8_yy=(2:2:size(ymax_final,1)); %Extract y time series data to plot for ymin and ymax
+
+
+figure('Position',[10 10 1000 600],'Color','w'); %Create figure
+
+
+xmax_final_x=xmax_final(array5_xx,:); %Seperate out x time series data for xmax
+xmax_final_y=xmax_final(array6_xy,:); %Seperate out y time series data for xmax
+
+for i=1:size(xmax_final_x,1) %Run through all xmax data
+    plot(xmax_final_x(i,:),xmax_final_y(i,:),'-'); %Plot all xmax data
+    hold on %Use the same graph
+    plot(xmax_final_x(i,size(xmax_final,2)), xmax_final_y(i,size(xmax_final,2)),'.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+
+xmin_final_x=xmin_final(array5_xx,:); %Seperate out x time series data for xmin
+xmin_final_y=xmin_final(array6_xy,:); %Seperate out y time series data for xmin
+
+for i=1:size(xmin_final_x,1) %Run through all xmin data
+    plot(xmin_final_x(i,:),xmin_final_y(i,:),'-'); %Plot all xmin data
+    hold on %Use the same graph
+    plot(xmin_final_x(i,size(xmin_final,2)), xmin_final_y(i,size(xmin_final,2)),'.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+ymax_final_x=ymax_final(array7_yx,:); %Seperate out x time series data for ymax
+ymax_final_y=ymax_final(array8_yy,:); %Seperate out y time series data for ymax
+
+for i=1:size(ymax_final_x,1) %Run through all ymax data
+    plot(ymax_final_x(i,:),ymax_final_y(i,:),'-'); %Plot all ymax data
+    hold on %Use the same graph
+    plot(ymax_final_x(i,size(ymax_final,2)), ymax_final_y(i,size(ymax_final,2)),'.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+ymin_final_x=ymin_final(array7_yx,:); %Seperate out x time series data for ymin
+ymin_final_y=ymin_final(array8_yy,:); %Seperate out y time series data for ymin
+
+for i=1:size(ymin_final_x,1) %Run through all ymin data
+    plot(ymin_final_x(i,:),ymin_final_y(i,:),'-'); %Plot all ymin data
+    hold on %Use the same graph
+    plot(ymin_final_x(i,size(ymin_final,2)), ymin_final_y(i,size(ymin_final,2)),'.',MarkerSize=30); %Plot final point
+    hold on %Use the same graph
+end
+
+ylabel(ylabelname) %ylabel for graph
+xlabel(xlabelname) %xlabel for graph
+title(titlename) %title for graph
\ No newline at end of file