python 3 compatibility

README.md

@@ -72,9 +72,9 @@ for i in range(500):
 # Get Started
 
 Dependencies:
-* Python 2.7
-* numpy
-* scipy
+* Python (2 or 3)
+* NumPy
+* SciPy
 * PyTorch 1.0
 
 ## Installation using pip
@@ -89,7 +89,7 @@ pip install git+https://github.com/bruel-gabrielsson/TopologyLayer.git
 
 First, create a conda environment
 ```bash
-conda create -n toplayer python=2.7
+conda create -n toplayer # python=2 or python=3
 source activate toplayer
 ```
 

examples/cpp/simplicial_complex.py

+from __future__ import print_function
 from topologylayer.functional.cohom_cpp import SimplicialComplex, persistenceForward
 from topologylayer.util.process import remove_zero_bars
 import torch
@@ -45,5 +46,5 @@ s.extendFloat(f)
 ret = persistenceForward(s, 1)
 
 for k in range(2):
-    print "dimension %d bars" % k
-    print remove_zero_bars(ret[k])
+    print("dimension %d bars" % k)
+    print(remove_zero_bars(ret[k]))

examples/levelset/noisy_circle.py

+from __future__ import print_function
 import torch, torch.nn as nn, numpy as np, matplotlib.pyplot as plt
 from topologylayer.nn import LevelSetLayer2D, SumBarcodeLengths, PartialSumBarcodeLengths
 
@@ -49,7 +50,7 @@ for i in range(500):
     loss.backward()
     optimizer.step()
     if (i % 10 == 0):
-        print i, tlossi.item(), dlossi.item()
+        print(i, tlossi.item(), dlossi.item())
 
 
 # save figure

examples/paper/pointcloud/alpha.py

+from __future__ import print_function
 from topologylayer.nn import AlphaLayer
 from topologylayer.nn import *
 

topologylayer/__init__.py

-import nn
-import functional
+import topologylayer.nn
+import topologylayer.functional
 from topologylayer.functional.cohom_cpp import SimplicialComplex

topologylayer/functional/flag.py

@@ -24,7 +24,7 @@ class FlagDiagram(Function):
     def backward(ctx, *grad_dgms):
         # print(grad_dgms)
         X = ctx.X
-        y, = ctx.saved_variables
+        y, = ctx.saved_tensors
         grad_ret = list(grad_dgms)
         grad_y = persistenceBackwardFlag(X, y, grad_ret)
         return None, grad_y, None

topologylayer/functional/rips_dionysus.py

@@ -35,12 +35,12 @@ class Diagramlayer(Function):
         # F.sort() # this is done in computePersistence
 
         dgms, Tbl = computePersistence(F)
-        max_pts = np.max([len(dgms[i]) for i in xrange(maxdim+1)])
+        max_pts = np.max([len(dgms[i]) for i in range(maxdim+1)])
         num_dgm_pts = max_pts
         ''' -1 is used later '''
         dgms_inds = -1 * np.ones([maxdim+1, num_dgm_pts, 4])
         dgms_values = -np.inf * np.ones([maxdim+1, num_dgm_pts, 2]) # -1.0 * np.ones([3, num_dgm_pts, 2])
-        for dim in xrange(maxdim+1):
+        for dim in range(maxdim+1):
             if len(dgms[dim]) > 0:
                 dgm = np.array(dgms[dim])
                 dgm[dgm == np.inf] = SATURATION_VALUE

topologylayer/functional/utils_dionysus.py

+from __future__ import print_function
 import torch
 import numpy as np
 import time