Corbett/formatting

README.md

@@ -36,7 +36,7 @@ function MPO_new(os::OpSum, sites::Vector{<:Index}; kwargs...)::MPO
 ```
 
 The optional keyword arguments are
-* `basisOpCacheVec`: A list of operators to use as a basis for each site. The operators on each site are expressed as one of these basis operators.
+* `basis_op_cache_vec`: A list of operators to use as a basis for each site. The operators on each site are expressed as one of these basis operators.
 * `tol::Real`: The tolerance used in the sparse QR decomposition (which is done by SPQR). The default value is the SPQR default which is calculated separately for each QR decomposition. If you want a MPO that is accurate up to floating point errors the default tolerance should work well. If instead you  want to compress the MPO the value `tol` will differ from the `cutoff` passed to `ITensor.MPO` since the truncation method is completely different. If you want to replicate the same truncation behavior first construct the MPO with a suitably small (or default) `tol` and then use `ITensors.truncate!`.
 
 ## Examples: Fermi-Hubbard Hamiltonian in Real Space

examples/electronic-structure.jl

@@ -71,12 +71,12 @@ function electronic_structure(
       "Nup * Ndn",
     ]
 
-    opCacheVec = [
+    op_cache_vec = [
       [OpInfo(ITensors.Op(name, n), sites[n]) for name in operatorNames] for
       n in eachindex(sites)
     ]
 
-    return @time "\tConstrucing MPO" MPO_new(os, sites; basisOpCacheVec=opCacheVec)
+    return @time "\tConstrucing MPO" MPO_new(os, sites; basis_op_cache_vec=op_cache_vec)
   end
 end
 
@@ -154,7 +154,7 @@ function electronic_structure_OpIDSum(N::Int, complexBasisFunctions::Bool)::MPO
 
   sites = siteinds("Electron", N; conserve_qns=true)
   return @time "\tConstructing MPO" MPO_new(
-    os, sites, operatorNames; basisOpCacheVec=operatorNames
+    os, sites, operatorNames; basis_op_cache_vec=operatorNames
   )
 end
 

examples/fermi-hubbard.jl

@@ -73,7 +73,7 @@ function Fermi_Hubbard_momentum_space(
       ] for _ in 1:N
     ]
 
-    return @time "\tConstructing MPO" MPO_new(os, sites; basisOpCacheVec=operatorNames)
+    return @time "\tConstructing MPO" MPO_new(os, sites; basis_op_cache_vec=operatorNames)
   end
 end
 
@@ -126,7 +126,7 @@ function Fermi_Hubbard_momentum_space_OpIDSum(N::Int, t::Real=1.0, U::Real=4.0):
 
   sites = siteinds("Electron", N; conserve_qns=true)
   return @time "\tConstructing MPO" MPO_new(
-    os, sites, operatorNames; basisOpCacheVec=operatorNames
+    os, sites, operatorNames; basis_op_cache_vec=operatorNames
   )
 end
 

src/MPOConstruction.jl

@@ -12,29 +12,29 @@ BlockSparseMatrix{C} = Dict{Tuple{Int,Int},Matrix{C}}
   return sparse(ret.Q), ret.R, ret.prow, ret.pcol, rank(ret)
 end
 
-function for_non_zeros(f::Function, A::SparseMatrixCSC, maxRow::Int, maxCol::Int)::Nothing
-  @assert maxRow <= size(A, 1)
-  @assert maxCol <= size(A, 2)
+function for_non_zeros(f::Function, A::SparseMatrixCSC, max_row::Int, max_col::Int)::Nothing
+  @assert max_row <= size(A, 1)
+  @assert max_col <= size(A, 2)
 
   rows = rowvals(A)
   vals = nonzeros(A)
-  for col in 1:maxCol
+  for col in 1:max_col
     for idx in nzrange(A, col)
       row = rows[idx]
-      if row <= maxRow
+      if row <= max_row
         value = vals[idx]
         f(value, row, col)
       end
     end
   end
 end
 
-function for_non_zeros_batch(f::Function, A::SparseMatrixCSC, maxCol::Int)::Nothing
-  @assert maxCol <= size(A, 2) "$maxCol, $(size(A, 2))"
+function for_non_zeros_batch(f::Function, A::SparseMatrixCSC, max_col::Int)::Nothing
+  @assert max_col <= size(A, 2) "$max_col, $(size(A, 2))"
 
   rows = rowvals(A)
   vals = nonzeros(A)
-  for col in 1:maxCol
+  for col in 1:max_col
     range = nzrange(A, col)
     isempty(range) && continue
     f((@view vals[range]), (@view rows[range]), col)
@@ -58,19 +58,19 @@ end
   n::Int,
   sites::Vector{<:Index},
   tol::Real,
-  opCacheVec::OpCacheVec,
+  op_cache_vec::OpCacheVec,
 )::Tuple{Dict{QN,MPOGraph{C}},BlockSparseMatrix{ValType},Index} where {C}
-  hasQNs = hasqns(sites)
-  nextGraphs = Dict{QN,MPOGraph{C}}()
+  has_qns = hasqns(sites)
+  next_graphs = Dict{QN,MPOGraph{C}}()
 
   matrix = BlockSparseMatrix{ValType}()
 
   qi = Vector{Pair{QN,Int}}()
-  outgoingLinkOffset = 0
+  outgoing_link_offset = 0
 
-  mIdentityToID = Vector{Int}()
+  id_of_left_vertex_in_zero_flux_term = Vector{Int}()
 
-  for (inQN, g) in graphs
+  for (in_qn, g) in graphs
     W = sparse_edge_weights(g)
     Q, R, prow, pcol, rank = sparse_qr(W, tol)
 
@@ -84,28 +84,28 @@ end
       Q *= only(R)
     end
 
-    if hasQNs
-      append!(qi, [inQN => rank])
+    if has_qns
+      append!(qi, [in_qn => rank])
     end
 
     # Form the local transformation tensor.
     # At the 0th dummy site we don't need to do this.
     @timeit "constructing the sparse matrix" if n != 0
       ## TODO: This is wastefull
-      localMatrices = [
-        my_matrix([lv.opOnSite], opCacheVec[n]; needsJWString=lv.needsJWString) for
-        lv in g.leftVertexValues
+      local_matrices = [
+        my_matrix([lv.opOnSite], op_cache_vec[n]; needs_JW_string=lv.needs_JW_string) for
+        lv in g.left_vertex_values
       ]
 
       for_non_zeros(Q, left_size(g), rank) do weight, i, m
-        rightLink = m + outgoingLinkOffset
+        right_link = m + outgoing_link_offset
         lv = left_value(g, prow[i])
 
-        matrixElement = get!(matrix, (lv.link, rightLink)) do
+        matrix_element = get!(matrix, (lv.link, right_link)) do
           return zeros(C, dim(sites[n]), dim(sites[n]))
         end
 
-        matrixElement .+= weight * localMatrices[prow[i]]
+        matrix_element .+= weight * local_matrices[prow[i]]
       end
     end
 
@@ -115,72 +115,72 @@ end
     # Connect this output \tilde{A}_m to \tilde{B}_m = (R \vec{B})_m
     # If we are processing the last site then there's no need to do this.
     @timeit "constructing the next graphs" if n != length(sites)
-      nextGraphOfZeroFlux = get!(nextGraphs, inQN) do
+      next_graph_zero_flux = get!(next_graphs, in_qn) do
         return MPOGraph{C}()
       end
 
-      resize!(mIdentityToID, rank)
-      mIdentityToID .= 0
+      resize!(id_of_left_vertex_in_zero_flux_term, rank)
+      id_of_left_vertex_in_zero_flux_term .= 0
       for_non_zeros_batch(R, right_size(g)) do weights, ms, j
         j = pcol[j]
 
         rv = right_value(g, j)
         if !isempty(rv.ops) && rv.ops[end].n == n + 1
           onsite = pop!(rv.ops)
-          flux = opCacheVec[n + 1][onsite.id].qnFlux
-          newIsFermionic = xor(rv.isFermionic, opCacheVec[n + 1][onsite.id].isFermionic)
+          flux = op_cache_vec[n + 1][onsite.id].qnFlux
+          is_fermionic = xor(rv.is_fermionic, op_cache_vec[n + 1][onsite.id].is_fermionic)
 
-          rv = RightVertex(rv.ops, newIsFermionic)
+          rv = RightVertex(rv.ops, is_fermionic)
 
-          nextGraph = get!(nextGraphs, inQN + flux) do
+          next_graph = get!(next_graphs, in_qn + flux) do
             return MPOGraph{C}()
           end
 
-          add_edges!(nextGraph, rv, rank, ms, outgoingLinkOffset, onsite, weights)
+          add_edges!(next_graph, rv, rank, ms, outgoing_link_offset, onsite, weights)
         else
-          add_edges_id!(
-            nextGraphOfZeroFlux,
+          add_edges_vector_lookup!(
+            next_graph_zero_flux,
             rv,
             rank,
             ms,
-            outgoingLinkOffset,
+            outgoing_link_offset,
             OpID(1, n + 1),
             weights,
-            mIdentityToID,
+            id_of_left_vertex_in_zero_flux_term,
           )
         end
       end
 
-      if num_edges(nextGraphOfZeroFlux) == 0
-        delete!(nextGraphs, inQN)
+      if num_edges(next_graph_zero_flux) == 0
+        delete!(next_graphs, in_qn)
       end
 
-      for (_, nextGraph) in nextGraphs
-        empty!(nextGraph.leftVertexIDs)
+      for (_, next_graph) in next_graphs
+        empty!(next_graph.left_vertex_ids)
       end
     end
 
     empty!(g)
-    outgoingLinkOffset += rank
+    outgoing_link_offset += rank
   end
 
-  for (_, nextGraph) in nextGraphs
-    empty!(nextGraph.rightVertexIDs)
+  for (_, next_graph) in next_graphs
+    empty!(next_graph.right_vertex_ids)
   end
 
-  if hasQNs
-    outgoingLink = Index(qi...; tags="Link,l=$n", dir=ITensors.Out)
+  if has_qns
+    outgoing_link = Index(qi...; tags="Link,l=$n", dir=ITensors.Out)
   else
-    outgoingLink = Index(outgoingLinkOffset; tags="Link,l=$n")
+    outgoing_link = Index(outgoing_link_offset; tags="Link,l=$n")
   end
 
-  return nextGraphs, matrix, outgoingLink
+  return next_graphs, matrix, outgoing_link
 end
 
 @timeit function svdMPO_new(
   ValType::Type{<:Number},
   os::OpIDSum{C},
-  opCacheVec::OpCacheVec,
+  op_cache_vec::OpCacheVec,
   sites::Vector{<:Index};
   tol::Real=-1,
 )::MPO where {C}
@@ -195,8 +195,8 @@ end
 
   llinks = Vector{Index}(undef, N + 1)
   for n in 0:N
-    graphs, symbolicMatrix, llinks[n + 1] = at_site!(
-      ValType, graphs, n, sites, tol, opCacheVec
+    graphs, block_sparse_matrix, llinks[n + 1] = at_site!(
+      ValType, graphs, n, sites, tol, op_cache_vec
     )
 
     # For the 0th iteration we only care about constructing the graphs for the next site.
@@ -213,8 +213,8 @@ end
         dim(dag(sites[n])),
       )
 
-      for ((leftLink, rightLink), localOpMatrix) in symbolicMatrix
-        tensor[leftLink, rightLink, :, :] = localOpMatrix
+      for ((left_link, right_link), block) in block_sparse_matrix
+        tensor[left_link, right_link, :, :] = block
       end
 
       H[n] = itensor(
@@ -246,51 +246,59 @@ function MPO_new(
   ValType::Type{<:Number},
   os::OpIDSum,
   sites::Vector{<:Index},
-  opCacheVec::OpCacheVec;
-  basisOpCacheVec=nothing,
+  op_cache_vec::OpCacheVec;
+  basis_op_cache_vec=nothing,
   tol::Real=-1,
 )::MPO
-  opCacheVec = to_OpCacheVec(sites, opCacheVec)
-  basisOpCacheVec = to_OpCacheVec(sites, basisOpCacheVec)
-  os, opCacheVec = prepare_opID_sum!(os, sites, opCacheVec, basisOpCacheVec)
-  return svdMPO_new(ValType, os, opCacheVec, sites; tol=tol)
+  op_cache_vec = to_OpCacheVec(sites, op_cache_vec)
+  basis_op_cache_vec = to_OpCacheVec(sites, basis_op_cache_vec)
+  os, op_cache_vec = prepare_opID_sum!(os, sites, op_cache_vec, basis_op_cache_vec)
+  return svdMPO_new(ValType, os, op_cache_vec, sites; tol=tol)
 end
 
 function MPO_new(
-  os::OpIDSum, sites::Vector{<:Index}, opCacheVec; basisOpCacheVec=nothing, tol::Real=-1
+  os::OpIDSum,
+  sites::Vector{<:Index},
+  op_cache_vec;
+  basis_op_cache_vec=nothing,
+  tol::Real=-1,
 )::MPO
-  opCacheVec = to_OpCacheVec(sites, opCacheVec)
-  ValType = determine_val_type(os, opCacheVec)
-  return MPO_new(ValType, os, sites, opCacheVec; basisOpCacheVec=basisOpCacheVec, tol=tol)
+  op_cache_vec = to_OpCacheVec(sites, op_cache_vec)
+  ValType = determine_val_type(os, op_cache_vec)
+  return MPO_new(
+    ValType, os, sites, op_cache_vec; basis_op_cache_vec=basis_op_cache_vec, tol=tol
+  )
 end
 
 function MPO_new(
   ValType::Type{<:Number},
   os::OpSum,
   sites::Vector{<:Index};
-  basisOpCacheVec=nothing,
+  basis_op_cache_vec=nothing,
   tol::Real=-1,
 )::MPO
-  opIDSum, opCacheVec = op_sum_to_opID_sum(os, sites)
+  opID_sum, op_cache_vec = op_sum_to_opID_sum(os, sites)
   return MPO_new(
-    ValType, opIDSum, sites, opCacheVec; basisOpCacheVec=basisOpCacheVec, tol=tol
+    ValType, opID_sum, sites, op_cache_vec; basis_op_cache_vec=basis_op_cache_vec, tol=tol
   )
 end
 
 function MPO_new(
-  os::OpSum, sites::Vector{<:Index}; basisOpCacheVec=nothing, tol::Real=-1
+  os::OpSum, sites::Vector{<:Index}; basis_op_cache_vec=nothing, tol::Real=-1
 )::MPO
-  opIDSum, opCacheVec = op_sum_to_opID_sum(os, sites)
-  return MPO_new(opIDSum, sites, opCacheVec; basisOpCacheVec=basisOpCacheVec, tol=tol)
+  opID_sum, op_cache_vec = op_sum_to_opID_sum(os, sites)
+  return MPO_new(
+    opID_sum, sites, op_cache_vec; basis_op_cache_vec=basis_op_cache_vec, tol=tol
+  )
 end
 
 function sparsity(mpo::MPO)::Float64
-  numEntries = 0
-  numZeros = 0
+  num_entries = 0
+  num_zeros = 0
   for tensor in mpo
-    numEntries += prod(size(tensor))
-    numZeros += prod(size(tensor)) - ITensors.nnz(tensor)
+    num_entries += prod(size(tensor))
+    num_zeros += prod(size(tensor)) - ITensors.nnz(tensor)
   end
 
-  return numZeros / numEntries
+  return num_zeros / num_entries
 end