from abapy.indentation import IndentationMesh
from matplotlib import pyplot as plt
Na = 2
Nb = 3
l, ltot = 1., 2
mesh = IndentationMesh(Na, Nb, l)
xe, ye, ze = mesh.get_edges()
plt.figure()
plt.gca().set_aspect('equal')
plt.plot(xe, ye, 'r-')
plt.plot(mesh.nodes.x, mesh.nodes.y, 'ob')
bbox = mesh.nodes.boundingBox()
plt.xlim(bbox[0])
plt.ylim(bbox[1])
plt.show()
ind_disp) # Applies the displacement to the indenter.
#plt.plot(xbi,ybi,'k-')
mesh.nodes.apply_displacement(
disp
) # This Nodes class method allows to deform a Nodes instance (and the Mesh instance it's nested in by the way) using a VectorFieldOutput. This allows very easy mesh tuning and deformed shape ploting.
xlim, ylim, zlim = mesh.nodes.boundingBox(
) # This little method allows nicer ploting producing a bounding box with a little gap around the mesh. This avoids the very tight boxes pyplot tends to use which cut thick lines on the border of the mesh.
xmin, xmax = 0., 2.
ymin, ymax = -2., 2.
plt.xlim([xmin, xmax])
plt.ylim([ymin, ymax])
x, y, z, tri = mesh.dump2triplot(
) # This method translates the whole mesh in matplotlib.pyplot.triplot syntax: x coords, y coords, (z coords useless here but can be nice for perspective effects) and label less connectivity.
xi, yi, zi, trii = indenter.mesh.dump2triplot()
xe, ye, ze = mesh.get_edges(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)
xb, yb, zb = mesh.get_border(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)
xei, yei, zei = indenter.mesh.get_edges(
xmin=xmin, xmax=xmax, ymin=ymin,
ymax=ymax) # Gives us a wireframe indenter representation.
xbi, ybi, zbi = indenter.mesh.get_border(
xmin=xmin, xmax=xmax, ymin=ymin,
ymax=ymax) # Gives us the border of the indenter.
plt.plot(xe, ye, '-k', linewidth=0.5) # Mesh ploting.
plt.plot(xb, yb, '-k', linewidth=1.) # Sample border ploting.
plt.plot(xei, yei, '-k', linewidth=0.5) # Mesh ploting.
plt.plot(xbi, ybi, '-k', linewidth=1.) # Sample border ploting.
grad = plt.tricontourf(
x, y, tri, stress.data,
levels) # Gradiant plot, Nlevels specifies the number of levels.
plt.tricontourf(xi, yi, trii, ind_stress.data, levels)
levels = np.linspace(smin, smax, Nlevels)
field_flag = r'$\sigma_{eq}$'
disp = fo['U'][step2plot]
ind_disp = fo['Uind'][step2plot]
indenter.apply_displacement(ind_disp) # Applies the displacement to the indenter.
#plt.plot(xbi,ybi,'k-')
mesh.nodes.apply_displacement(disp) # This Nodes class method allows to deform a Nodes instance (and the Mesh instance it's nested in by the way) using a VectorFieldOutput. This allows very easy mesh tuning and deformed shape ploting.
xlim, ylim, zlim = mesh.nodes.boundingBox() # This little method allows nicer ploting producing a bounding box with a little gap around the mesh. This avoids the very tight boxes pyplot tends to use which cut thick lines on the border of the mesh.
xmin, xmax = 0., 2.
ymin, ymax = -2., 2.
plt.xlim([xmin, xmax])
plt.ylim([ymin, ymax])
x, y, z, tri = mesh.dump2triplot() # This method translates the whole mesh in matplotlib.pyplot.triplot syntax: x coords, y coords, (z coords useless here but can be nice for perspective effects) and label less connectivity.
xi, yi, zi, trii = indenter.mesh.dump2triplot()
xe, ye, ze = mesh.get_edges(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax)
xb, yb, zb = mesh.get_border(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax)
xei, yei, zei = indenter.mesh.get_edges(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax) # Gives us a wireframe indenter representation.
xbi, ybi, zbi = indenter.mesh.get_border(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax) # Gives us the border of the indenter.
plt.plot(xe,ye,'-k', linewidth = 0.5) # Mesh ploting.
plt.plot(xb,yb,'-k', linewidth = 1.) # Sample border ploting.
plt.plot(xei,yei,'-k', linewidth = 0.5) # Mesh ploting.
plt.plot(xbi,ybi,'-k', linewidth = 1.) # Sample border ploting.
grad = plt.tricontourf(x, y, tri, stress.data, levels) # Gradiant plot, Nlevels specifies the number of levels.
plt.tricontourf(xi,yi,trii, ind_stress.data, levels)
#plt.tricontour(xi,yi,trii, ind_stress.data, levels, colors = 'black')
cbar = plt.colorbar(grad)
cbar.ax.set_ylabel(field_flag, fontsize=20)
#plt.tricontour(x, y, tri, stress.data, levels, colors = 'black') # Isovalue plot which make gradiant plot clearer in my (humble) opinion.
#plt.show()
plt.savefig(workdir + simname + '_field.png')