fix requirement python>=3.8

.github/workflows/testing.yml

@@ -4,7 +4,7 @@ jobs:
   buildandtest:
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest, windows-latest]
 #        os: [ubuntu-latest, windows-latest, macos-latest]
     runs-on: ${{ matrix.os }}

README.md

@@ -16,7 +16,7 @@ Some routines are implemented in C++ for an increased execution speed.
 ### Requirements
 Following requirements are needed:
 
-- `python>=3.6`
+- `python>=3.8`
 - `torch>=1.7.0`
 - `numpy>=1.18`
 - [`opt_einsum`](https://pypi.org/project/opt-einsum/)

docs/install.rst

@@ -9,7 +9,7 @@ Requirements
 
 The requirements are the following:
 
-- ``python>=3.6``
+- ``python>=3.8``
 - ``torch>=1.7.0``
 - ``numpy>=1.18``
 - ``opt_einsum`` (https://pypi.org/project/opt-einsum/)

pyproject.toml

@@ -13,7 +13,7 @@ name = "torchTT"
 version = "0.3"
 description = "Tensor-Train decomposition in pytorch."
 readme = "README.md"
-requires-python = ">=3.7"
+requires-python = ">=3.8"
 dependencies = [
     "torch>=1.7", 
     "numpy>=1.18", 

torchtt/interpolate.py

@@ -34,7 +34,11 @@ def _LU(M):
 def _max_matrix(M):
 
     values, indices = M.flatten().topk(1)
-    indices = [tn.unravel_index(i, M.shape) for i in indices]
+    try:
+        indices = [tn.unravel_index(i, M.shape) for i in indices]
+    except:
+        indices = [np.unravel_index(i, M.shape) for i in indices]
+
     return values, indices
 
 
@@ -166,7 +170,10 @@ def function_interpolate(function, x, eps=1e-9, start_tens=None, nswp=20, kick=2
         rnew = min(N[k]*rank[k+1], rank[k])
         Jk = _maxvol(core)
         # print(Jk)
-        tmp = tn.unravel_index(Jk[:rnew], (rank[k+1], N[k]))
+        try:
+            tmp = tn.unravel_index(Jk[:rnew], (rank[k+1], N[k]))
+        except:
+            tmp = np.unravel_index(Jk[:rnew], (rank[k+1], N[k]))
         # if k==d-1:
         #    idx_new = tn.tensor(tmp[1].reshape([1,-1]))
         # else:
@@ -305,7 +312,10 @@ def function_interpolate(function, x, eps=1e-9, start_tens=None, nswp=20, kick=2
             _, Ps[k+1] = QR(tn.reshape(tmp, [rank[k]*N[k], rank[k+1]]))
 
             # calc Idx
-            tmp = tn.unravel_index(idx[:rank[k+1]], (rank[k], N[k]))
+            try:
+                tmp = tn.unravel_index(idx[:rank[k+1]], (rank[k], N[k]))
+            except:
+                tmp = np.unravel_index(idx[:rank[k+1]], (rank[k], N[k]))
             idx_new = tn.tensor(
                 np.hstack((Idx[k][tmp[0], :], tmp[1].reshape([-1, 1]))))
             Idx[k+1] = idx_new+0
@@ -423,7 +433,10 @@ def function_interpolate(function, x, eps=1e-9, start_tens=None, nswp=20, kick=2
             _, tmp = QR(tn.reshape(tmp, [rank[k+1], -1]).t())
             Ps[k+1] = tmp
             # calc Idx
-            tmp = tn.unravel_index(idx[:rank[k+1]], (N[k+1], rank[k+2]))
+            try:
+                tmp = tn.unravel_index(idx[:rank[k+1]], (N[k+1], rank[k+2]))
+            except:
+                tmp = np.unravel_index(idx[:rank[k+1]], (N[k+1], rank[k+2]))
             idx_new = tn.tensor(
                 np.vstack((tmp[0].reshape([1, -1]), Idx[k+2][:, tmp[1]])))
             Idx[k+1] = idx_new+0
@@ -511,7 +524,10 @@ def dmrg_cross(function, N, eps=1e-9, nswp=10, x_start=None, kick=2, dtype=tn.fl
         rnew = min(N[k]*rank[k+1], rank[k])
         Jk = _maxvol(core)
         # print(Jk)
-        tmp = tn.unravel_index(Jk[:rnew], (rank[k+1], N[k]))
+        try:
+            tmp = tn.unravel_index(Jk[:rnew], (rank[k+1], N[k]))
+        except:
+            tmp = np.unravel_index(Jk[:rnew], (rank[k+1], N[k]))
         # if k==d-1:
         #    idx_new = tn.tensor(tmp[1].reshape([1,-1]))
         # else:
@@ -639,7 +655,10 @@ def dmrg_cross(function, N, eps=1e-9, nswp=10, x_start=None, kick=2, dtype=tn.fl
             _, Ps[k+1] = QR(tn.reshape(tmp, [rank[k]*N[k], rank[k+1]]))
 
             # calc Idx
-            tmp = tn.unravel_index(idx[:rank[k+1]], (rank[k], N[k]))
+            try:
+                tmp = tn.unravel_index(idx[:rank[k+1]], (rank[k], N[k]))
+            except:
+                tmp = np.unravel_index(idx[:rank[k+1]], (rank[k], N[k]))
             idx_new = tn.tensor(
                 np.hstack((Idx[k][tmp[0], :], tmp[1].reshape([-1, 1]))))
             Idx[k+1] = idx_new+0
@@ -741,7 +760,10 @@ def dmrg_cross(function, N, eps=1e-9, nswp=10, x_start=None, kick=2, dtype=tn.fl
             _, tmp = QR(tn.reshape(tmp, [rank[k+1], -1]).t())
             Ps[k+1] = tmp
             # calc Idx
-            tmp = tn.unravel_index(idx[:rank[k+1]], (N[k+1], rank[k+2]))
+            try:
+                tmp = tn.unravel_index(idx[:rank[k+1]], (N[k+1], rank[k+2]))
+            except:
+                tmp = np.unravel_index(idx[:rank[k+1]], (N[k+1], rank[k+2]))
             idx_new = tn.tensor(
                 np.vstack((tmp[0].reshape([1, -1]), Idx[k+2][:, tmp[1]])))
             Idx[k+1] = idx_new+0