"Unfold" a 3D point cloud onto a 2D map

I have a 3D point cloud from a FE-analysis with pressure values for each point. X-, y-, z-coordinates and pressure values are stored column-wise in a csv-file. Here is a 3D-scatter plot of the points:

As you can see, the coordinates create a curved surface/strip. Now imagine you were to place a strip of tape along this surface, project the pressure magnitude onto it and then flatten it. Then you would have a 2D-map with projected x-values on the x-axis, y-values the same as in the csv-file on the y-axis and then respective pressure magnitude-values for each point.

I would like to plot the pressure distribution as a colored 2D contour-map.

Here is my attempt scripting a solution but now im stuck:

clc

clear all

%importing csv file.

filename = "contpres_87905_0.11333.csv";

C=readtable(filename);

% coordinate system is rotated in ANSYS. geometry is in 2nd quadrant.

x=C.zcor;

y=C.ycor;

z=C.xcor;

A=C.Pressure;

%remove rows with only zeros

x = x(any(x,2),:);

y = y(any(y,2),:);

z = z(any(z,2),:);

A = A(any(A,2),:);

% Get new x-axis coordinates with custom function below

tc=unfoldCircum(x,z);

%length

l=length(A);

% interpolation of magnitudes

xv = linspace(min(tc),max(tc),l);

yv = linspace(min(y),max(y),l);

[X,Y] = ndgrid(xv,yv);

Z = griddata(tc, y, A, X, Y);

% plot

cMap=jet(16); %set the colomap using the "jet" scale

colormap(cMap);

h = surf(X,Y,Z);

set(h, 'edgecolor','none');

view(2);

colorbar;

xlim([min(tc) max(tc)])

%%%%%%%%%% function [tCoords] = unfoldCircum(x,y)

% This function takes a 2D coordinate data set and:

% - creates a smooth spline s(t) from the data

% - projects the 2D-points [xi,yi] onto the spline

% - returns the respective t-coordinate for each point

% x,y=column vectors of x- and y-coordinates with the same length

% Output

% tDist = Coordinate along tape from tape beginning

function [tCoords] = unfoldCircum(x,y)

np=length(x);

if(np ~= length(y))

error('input vectors are not the same size.')

end

tCoords=zeros(np,1);

%sort x and y coordinates.

[xsort, ind]=sort(x);

% new x-vector. the geometry have x-coordinates from ~-60 to 0mm

x_s = linspace(min(x), 0,np)';

% cubic smoothing spline using the SPLINEFIT package

% https://se.mathworks.com/matlabcentral/fileexchange/71225-splinefit

y_struct=splinefit(xsort,y(ind),20);

y_s=ppval(y_struct,x_s);

% TODO: calculate total spline length

l=100;

minDistV=zeros(np,2);

%for each coordinate [xi yi], find the nearest spline coordinate [xsj ysj].

for i=1:np

minDist=1e9;

for j = 1:np

dist2=sqrt((x(i)-x_s(j))^2 + (y(i)-y_s(j))^2);

if dist2<minDist

minDist=dist2;

minDistV(i,1)=j;

minDistV(i,2)=minDist;

end

end

%calculate t-coordinate by

tCoords(i)=minDistV(i,1)/np * l;

end

end