replaced .set_label( by .relabel(

Author: manuschneider

benchmarks/linalg/Lanczos_bm.cpp

@@ -27,7 +27,7 @@ namespace BMTest_Lanczos {
      *         | |         +          | |             "pi"
      *         |_|--"vol" "po" "vor"--|_|
      *
-     * Then relabels ["vil", "pi", "vir"] -> ["vol", "po", "vor"]
+     * Then relabel ["vil", "pi", "vir"] -> ["vol", "po", "vor"]
      *
      * "vil":virtual in bond left
      * "po":physical out bond

docs/code/CytnxGuide_PythonCodeExamples_2023_06_30.ipynb

@@ -3664,12 +3664,12 @@
     "\n",
     "s0 = 1./s0\n",
     "\n",
-    "s0.set_labels(['0','1'])\n",
+    "s0.relabel_(['0','1'])\n",
     "\n",
     "s2 = cytnx.Contract(cytnx.Contract(sL,s0),sR)\n",
     "\n",
     "\n",
-    "s2.set_labels(['-10','-11'])\n",
+    "s2.relabel_(['-10','-11'])\n",
     "\n",
     "A.set_label(2,'-10')\n",
     "\n",

docs/code/iTEBD/iTEBD.py

@@ -79,10 +79,10 @@
 Elast = 0
 for i in range(10000):
 
-    A.set_labels([-1,0,-2])
-    B.set_labels([-3,1,-4])
-    la.set_labels([-2,-3])
-    lb.set_labels([-4,-5])
+    A.relabel_([-1,0,-2])
+    B.relabel_([-3,1,-4])
+    la.relabel_([-2,-3])
+    lb.relabel_([-4,-5])
 
     ## contract all
     X = cyx.Contract(cyx.Contract(A,la),cyx.Contract(B,lb))
@@ -103,7 +103,7 @@
     # Note that X,Xt contract will result a rank-0 tensor, which can use item() toget element
     XNorm = cyx.Contract(X,Xt).item()
     XH = cyx.Contract(X,H)
-    XH.set_labels([-4,-5,0,1])
+    XH.relabel_([-4,-5,0,1])
     XHX = cyx.Contract(Xt,XH).item() ## rank-0
     E = XHX/XNorm
 
@@ -141,8 +141,8 @@
     #       --lb-A'-la-B'-lb--
     #
     # again, but A' and B' are updated
-    A.set_labels([-1,0,-2]); A.set_rowrank(1);
-    B.set_labels([-3,1,-4]); B.set_rowrank(1);
+    A.relabel_([-1,0,-2]); A.set_rowrank(1);
+    B.relabel_([-3,1,-4]); B.set_rowrank(1);
 
     #A.print_diagram()
     #B.print_diagram()

example/DMRG/dmrg_two_sites_U1.py

@@ -19,7 +19,7 @@ def matvec(self, v):
             lbl = v.labels()
             self.anet.PutUniTensor("psi",v)
             out = self.anet.Launch()
-            out.relabels_(lbl)
+            out.relabel_(lbl)
             return out
 
     def optimize_psi(psi, functArgs, maxit=2, krydim=4):
@@ -98,7 +98,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         A[k] = cytnx.UniTensor([B1,B2,B3],rowrank=2)
 
         lbl = [str(2*k),str(2*k+1),str(2*k+2)]
-        A[k].set_labels(lbl)
+        A[k].relabel_(lbl)
 
         A[k].get_block_()[0] = 1
         lbls.append(lbl) # store the labels for later convinience.
@@ -131,12 +131,12 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
             psi.set_rowrank_(2) # maintain rowrank to perform the svd
             s,A[p],A[p+1] = cytnx.linalg.Svd_truncate(psi,new_dim)
-            A[p+1].relabels_(lbls[p+1]); # set the label back to be consistent
+            A[p+1].relabel_(lbls[p+1]); # set the label back to be consistent
 
             s = s/s.Norm().item() # normalize s
 
             A[p] = cytnx.Contract(A[p],s) # absorb s into next neighbor
-            A[p].relabels_(lbls[p]); # set the label back to be consistent
+            A[p].relabel_(lbls[p]); # set the label back to be consistent
 
             # update LR from right to left:
             anet = cytnx.Network()
@@ -152,7 +152,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
         A[0].set_rowrank_(1)
         _,A[0] = cytnx.linalg.Gesvd(A[0],is_U=False, is_vT=True)
-        A[0].relabels_(lbls[0]); #set the label back to be consistent
+        A[0].relabel_(lbls[0]); #set the label back to be consistent
 
         for p in range(Nsites-1):
             dim_l = A[p].shape()[0]
@@ -165,12 +165,12 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
             psi.set_rowrank_(2) # maintain rowrank to perform the svd
             s,A[p],A[p+1] = cytnx.linalg.Svd_truncate(psi,new_dim)
-            A[p].relabels_(lbls[p]); #set the label back to be consistent
+            A[p].relabel_(lbls[p]); #set the label back to be consistent
 
             s = s/s.Norm().item() # normalize s
 
             A[p+1] = cytnx.Contract(s,A[p+1]) ## absorb s into next neighbor.
-            A[p+1].relabels_(lbls[p+1]); #set the label back to be consistent
+            A[p+1].relabel_(lbls[p+1]); #set the label back to be consistent
 
             # update LR from left to right:
             anet = cytnx.Network()
@@ -186,7 +186,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
         A[-1].set_rowrank_(2)
         _,A[-1] = cytnx.linalg.Gesvd(A[-1],is_U=True,is_vT=False) ## last one.
-        A[-1].relabels_(lbls[-1]); #set the label back to be consistent
+        A[-1].relabel_(lbls[-1]); #set the label back to be consistent
 
     return Ekeep
 

example/DMRG/dmrg_two_sites_dense.py

@@ -18,7 +18,7 @@ def matvec(self, v):
             lbl = v.labels()
             self.anet.PutUniTensor("psi",v)
             out = self.anet.Launch()
-            out.relabels_(lbl)
+            out.relabel_(lbl)
             return out
 
     def optimize_psi(psi, functArgs, maxit=2, krydim=4):
@@ -61,7 +61,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
     lbls = [] # List for storing the MPS labels
     A = [None for i in range(Nsites)]
     A[0] = cytnx.UniTensor(cytnx.random.normal([1, d, min(chi, d)], 0., 1.), rowrank = 2)
-    A[0].relabels_(["0","1","2"])
+    A[0].relabel_(["0","1","2"])
     lbls.append(["0","1","2"]) # store the labels for later convinience.
 
     for k in range(1,Nsites):
@@ -70,7 +70,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         A[k] = cytnx.UniTensor(cytnx.random.normal([dim1, dim2, dim3],0.,1.), rowrank = 2)
 
         lbl = [str(2*k),str(2*k+1),str(2*k+2)]
-        A[k].relabels_(lbl)
+        A[k].relabel_(lbl)
         lbls.append(lbl) # store the labels for later convinience.
 
     LR = [None for i in range(Nsites+1)]
@@ -96,11 +96,11 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
         LR[p+1] = anet.Launch()
 
         # Recover the original MPS labels
-        A[p].relabels_(lbls[p])
-        A[p+1].relabels_(lbls[p+1])
+        A[p].relabel_(lbls[p])
+        A[p+1].relabel_(lbls[p+1])
 
     _,A[-1] = cytnx.linalg.Gesvd(A[-1],is_U=True,is_vT=False) ## last one.
-    A[-1].relabels_(lbls[-1]) # Recover the original MPS labels
+    A[-1].relabel_(lbls[-1]) # Recover the original MPS labels
 
     Ekeep = []
     for k in range(1, numsweeps+1):
@@ -116,12 +116,12 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
             psi.set_rowrank_(2) # maintain rowrank to perform the svd
             s,A[p],A[p+1] = cytnx.linalg.Svd_truncate(psi,new_dim)
-            A[p+1].relabels_(lbls[p+1]); # set the label back to be consistent
+            A[p+1].relabel_(lbls[p+1]); # set the label back to be consistent
 
             s = s/s.Norm().item() # normalize s
 
             A[p] = cytnx.Contract(A[p],s) # absorb s into next neighbor
-            A[p].relabels_(lbls[p]); # set the label back to be consistent
+            A[p].relabel_(lbls[p]); # set the label back to be consistent
 
             # update LR from right to left:
             anet = cytnx.Network()
@@ -138,7 +138,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
         A[0].set_rowrank_(1)
         _,A[0] = cytnx.linalg.Gesvd(A[0],is_U=False, is_vT=True)
-        A[0].relabels_(lbls[0]); #set the label back to be consistent
+        A[0].relabel_(lbls[0]); #set the label back to be consistent
 
         for p in range(Nsites-1):
             dim_l = A[p].shape()[0]
@@ -151,12 +151,12 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
             psi.set_rowrank_(2) # maintain rowrank to perform the svd
             s,A[p],A[p+1] = cytnx.linalg.Svd_truncate(psi,new_dim)
-            A[p].relabels_(lbls[p]); #set the label back to be consistent
+            A[p].relabel_(lbls[p]); #set the label back to be consistent
 
             s = s/s.Norm().item() # normalize s
 
             A[p+1] = cytnx.Contract(s,A[p+1]) ## absorb s into next neighbor.
-            A[p+1].relabels_(lbls[p+1]); #set the label back to be consistent
+            A[p+1].relabel_(lbls[p+1]); #set the label back to be consistent
 
             # update LR from left to right:
             anet = cytnx.Network()
@@ -174,7 +174,7 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
 
         A[-1].set_rowrank_(2)
         _,A[-1] = cytnx.linalg.Gesvd(A[-1],is_U=True,is_vT=False) ## last one.
-        A[-1].relabels_(lbls[-1]); #set the label back to be consistent
+        A[-1].relabel_(lbls[-1]); #set the label back to be consistent
     return Ekeep
 
 if __name__ == '__main__':

example/Network/Construct.cpp

@@ -9,10 +9,10 @@ int main(int argc, char* argv[]) {
   auto tc = UniTensor(arange(24).reshape(2, 3, 4), 1);
   auto td = UniTensor(arange(24).reshape(2, 3, 4), 1);
 
-  ta.set_labels({0, 1, 2});
-  tb.set_labels({0, 3, 4});
-  tc.set_labels({5, 1, 6});
-  td.set_labels({5, 7, 8});
+  ta.relabel_({0, 1, 2});
+  tb.relabel_({0, 3, 4});
+  tc.relabel_({5, 1, 6});
+  td.relabel_({5, 7, 8});
 
   UniTensor oot =
     Network::Contract(

example/iTEBD/iTEBD.py

@@ -64,10 +64,10 @@ def itebd_tfim(chi = 20, J = 1.0, Hx = 1.0, dt = 0.1, CvgCrit = 1.0e-10):
     Elast = 0
     for i in range(10000):
 
-        A.set_labels(['a','0','b'])
-        B.set_labels(['c','1','d'])
-        la.set_labels(['b','c'])
-        lb.set_labels(['d','e'])
+        A.relabel_(['a','0','b'])
+        B.relabel_(['c','1','d'])
+        la.relabel_(['b','c'])
+        lb.relabel_(['d','e'])
 
 
         ## contract all
@@ -89,7 +89,7 @@ def itebd_tfim(chi = 20, J = 1.0, Hx = 1.0, dt = 0.1, CvgCrit = 1.0e-10):
         # Note that X,Xt contract will result a rank-0 tensor, which can use item() toget element
         XNorm = cytnx.Contract(X,Xt).item()
         XH = cytnx.Contract(X,H)
-        XH.set_labels(['d','e','0','1'])
+        XH.relabel_(['d','e','0','1'])
 
 
         XHX = cytnx.Contract(Xt,XH).item() ## rank-0
@@ -133,7 +133,7 @@ def itebd_tfim(chi = 20, J = 1.0, Hx = 1.0, dt = 0.1, CvgCrit = 1.0e-10):
         # again, but A' and B' are updated
         lb_inv = 1./lb
         # lb_inv.print_diagram();
-        lb_inv.set_labels(['e','d'])
+        lb_inv.relabel_(['e','d'])
 
         A = cytnx.Contract(lb_inv,A)
         B = cytnx.Contract(B,lb_inv)

example/iTEBD/iTEBD_U1.py

@@ -73,10 +73,10 @@ def itebd_heisenberg(chi = 32, J  = 1.0, dt = 0.1, CvgCrit = 1.0e-12):
     Elast = 0
     for i in range(10000):
 
-        A.set_labels(["a","0","b"])
-        B.set_labels(["c","1","d"])
-        la.set_labels(["b","c"])
-        lb.set_labels(["d","e"])
+        A.relabel_(["a","0","b"])
+        B.relabel_(["c","1","d"])
+        la.relabel_(["b","c"])
+        lb.relabel_(["d","e"])
 
         ## contract all
         tmpA = cytnx.Contract(A,la)
@@ -101,7 +101,7 @@ def itebd_heisenberg(chi = 32, J  = 1.0, dt = 0.1, CvgCrit = 1.0e-12):
         ## <psi|H|psi>
         XH = cytnx.Contract(X,H)
         #XH.print_diagram()
-        XH.set_labels(['d','e','0','1'])
+        XH.relabel_(['d','e','0','1'])
         XHX = cytnx.Contract(Xt,XH).item()
         E = XHX/XNorm
 
@@ -141,7 +141,7 @@ def itebd_heisenberg(chi = 32, J  = 1.0, dt = 0.1, CvgCrit = 1.0e-12):
             T = lb_inv.get_block_(b);
             lb_inv.put_block_(1./T,b);
 
-        lb_inv.set_labels(['e','d'])
+        lb_inv.relabel_(['e','d'])
 
         A = cytnx.Contract(lb_inv,A)
         B = cytnx.Contract(B,lb_inv)

example/iTEBD/iTEBD_tag.py

@@ -66,10 +66,10 @@ def itebd_tfim_tag(chi = 20, J = 1.0, Hx = 1.0, dt = 0.1, CvgCrit = 1.0e-10):
     Elast = 0
     for i in range(10000):
 
-        A.set_labels(['a','0','b'])
-        B.set_labels(['c','1','d'])
-        la.set_labels(['b','c'])
-        lb.set_labels(['d','e'])
+        A.relabel(['a','0','b'])
+        B.relabel(['c','1','d'])
+        la.relabel(['b','c'])
+        lb.relabel(['d','e'])
 
 
 
@@ -93,7 +93,7 @@ def itebd_tfim_tag(chi = 20, J = 1.0, Hx = 1.0, dt = 0.1, CvgCrit = 1.0e-10):
         # Note that X,Xt contract will result a rank-0 tensor, which can use item() toget element
         XNorm = cytnx.Contract(X,Xt).item()
         XH = cytnx.Contract(X,H)
-        XH.set_labels(['d','e','0','1'])
+        XH.relabel_(['d','e','0','1'])
         XHX = cytnx.Contract(Xt,XH).item() ## rank-0
         E = XHX/XNorm
 
@@ -132,7 +132,7 @@ def itebd_tfim_tag(chi = 20, J = 1.0, Hx = 1.0, dt = 0.1, CvgCrit = 1.0e-10):
         #
         # again, but A' and B' are updated
         lb_inv = 1./lb
-        lb_inv.set_labels(['e','d'])
+        lb_inv.relabel_(['e','d'])
 
         A = cytnx.Contract(lb_inv,A)
         B = cytnx.Contract(B,lb_inv)

pybind/unitensor_py.cpp

@@ -164,7 +164,7 @@ void unitensor_binding(py::module &m) {
 
 
     .def("c_set_labels",[](UniTensor &self, const std::vector<std::string> &new_labels){
-                            return self.set_labels(new_labels);
+                            return self.relabel_(new_labels);
                         },py::arg("new_labels"))