Question: I need to process a simulation that was acquired using FE Bio ( of an LV ) . Below are the directions, deliverables, and helpful
I need to process a simulation that was acquired using FE Bio of an LV Below are the directions, deliverables, and helpful hints. I will also attach an image of what the final graph should look like.
Import all the csv files into MATLAB or Python
Identify the nodes on the periphery of the LV on the base plane to calculate the centroid.
Convert the triangle information the node numbers to coordinates using the position file and find the volume for all steps using the tetrahedron method described in class.
Extract the pressure values on the triangles associated with the LV and take the average again for all the steps
Plot the Pressure in mmHg vs Volume in micro Liter
Files required: feb geometry position.csv and pressure.csv
Copy all nodes and elements from the feb file and store them in arrays.
Nodes x coordinate, y coordinate, z coordinate
Elements ElemID NodeID NodeID NodeID NodeID
Extract nodes and faces belonging to the LV endocardium from the feb file
ie the faces on which the LV pressure is applied.
EndoFaces NodeID NodeID NodeID
EndoNodes stackuniqueEndoFaces
Find corresponding elements.
for face in EndoFaces:
if or Nodes of face in Elements:
EndoElements stackElemID
EndoElements uniqueEndoElements
Extracting pressure element data and volume node data for each
time point.
pressure ElemID t t tn
position NodeID xt yt zt xt yt zt
xtn ytn ztn
for timepoint in tn:
endopressure
endovolume
for i in sizepressure:
if pressurei in EndoElements:
endopressure stackpressuretimepoint
EndoPressureaverage meanendopressure
for i in sizeposition:
if positioni in EndoNodes:
endoposition stackxtimepoint, ytimepoint, ztimepoint
centroid meanendopositionbase nodes
positionvector endoposition centroid
for face in EndoFaces:
TetVolume positionvectorNodeID x positionvectorNodeID positionvectorNodeID x positionvectorNodeID
TetVolume stackTetVolume
EndoVolumeTotal sumTetVolume
Helpful commands
MATLAB: readmatrix, vertcat, horzcat, unique, mean, sum, cross, dot
Python: numpy.genfromtxt, numpy.vstack, numpy.hstack, numpy.sum
numpy.unique, numpy.mean, numpy.cross, numpy.dot
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