Merge pull request #103 from tldr-group/feature-vertical-flux

deleted unused variables

Author: amirDahari1

.github/workflows/draft-pdf.yml

@@ -16,7 +16,7 @@ jobs:
                   # This should be the path to the paper within your repo.
                   paper-path: paper.md
             - name: Upload
-              uses: actions/upload-artifact@v1
+              uses: actions/upload-artifact@v3
               with:
                   name: paper
                   # This is the output path where Pandoc will write the compiled

taufactor/taufactor.py

@@ -47,8 +47,7 @@ def __init__(self, img, bc=(-0.5, 0.5), D_0=1, device=torch.device('cuda')):
                 f'Input image must only contain 0s and 1s. Your image must be segmented to use this tool. If your image has been segmented, ensure your labels are 0 for non-conductive and 1 for conductive phase. Your image has the following labels: {torch.unique(img).numpy()}. If you have more than one conductive phase, use the multi-phase solver.')
 
         # calculate
-        self.ph_bot = torch.sum(img[:, -1]).to(self.device) * self.bot_bc
-        self.ph_top = torch.sum(img[:, 0]).to(self.device) * self.top_bc
+        
         # init conc
         self.conc = self.init_conc(img)
         # create nn map
@@ -350,8 +349,7 @@ def __init__(self, img, cond={1: 1}, bc=(-0.5, 0.5), device=torch.device('cuda:0
         img = torch.tensor(img, dtype=self.precision, device=self.device)
 
         # calculate
-        self.ph_bot = torch.sum(img[:, -1]).to(self.device) * self.bot_bc
-        self.ph_top = torch.sum(img[:, 0]).to(self.device) * self.top_bc
+        
         # init conc
         self.conc = self.init_conc(img)
         # create nn map