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You try to do |
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My labels are as follows:
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You can have labels applied during the extraction process...I think they should be applied by default actually, so I'm surprized they're not here. You can also add them to the network using spatial info, so: Ps = net['pore.coords'][:, 0] < lower_lim
net.set_label(pores=Ps, label='pore.left')
Ps = net['pore.coords'][:, 0] > upper_lim
net.set_label(pores=Ps, label='pore.right') Where upper_lim and lower_lim are physical distances that you consider to be 'on the right or left face' of the domain. For instance: lower_lim = pn['pore.coords'][:, 0].min() * 1.1
upper_lim = pn['pore.coords'][:, 0].min() * 0.9` |
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The "Snow_Advanced" extracted network does not give the labels of the pores; however, the older version of snow algorithm given the following example extracts the network specifying the labels of the pores as left, right,... |
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I generated the artificial image coded in the "snow_advanced" and extracted the network using PoreSpy. I then used pickle to save the extracted network python dictionary as follows:
After, I used OpenPNM to load the network:
However, when I am assigning the boundary conditions, I am getting the following error:
I think the problem is that openpnm can't find the pores(left, right,..). How can I solve this issue?
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