diff --git a/notebooks/Manifest.toml b/notebooks/Manifest.toml
new file mode 100644
index 0000000..272a32a
--- /dev/null
+++ b/notebooks/Manifest.toml
@@ -0,0 +1,2106 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.12.1"
+manifest_format = "2.0"
+project_hash = "c21f61d67e2333fbac49834092feaba1f083431d"
+
+[[deps.AbstractFFTs]]
+deps = ["LinearAlgebra"]
+git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef"
+uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
+version = "1.5.0"
+weakdeps = ["ChainRulesCore", "Test"]
+
+    [deps.AbstractFFTs.extensions]
+    AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
+    AbstractFFTsTestExt = "Test"
+
+[[deps.AbstractTrees]]
+git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177"
+uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
+version = "0.4.5"
+
+[[deps.Accessors]]
+deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
+git-tree-sha1 = "3b86719127f50670efe356bc11073d84b4ed7a5d"
+uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
+version = "0.1.42"
+
+    [deps.Accessors.extensions]
+    AxisKeysExt = "AxisKeys"
+    IntervalSetsExt = "IntervalSets"
+    LinearAlgebraExt = "LinearAlgebra"
+    StaticArraysExt = "StaticArrays"
+    StructArraysExt = "StructArrays"
+    TestExt = "Test"
+    UnitfulExt = "Unitful"
+
+    [deps.Accessors.weakdeps]
+    AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
+    IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
+    LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+    StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
+    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+    Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
+
+[[deps.Adapt]]
+deps = ["LinearAlgebra", "Requires"]
+git-tree-sha1 = "7e35fca2bdfba44d797c53dfe63a51fabf39bfc0"
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+version = "4.4.0"
+weakdeps = ["SparseArrays", "StaticArrays"]
+
+    [deps.Adapt.extensions]
+    AdaptSparseArraysExt = "SparseArrays"
+    AdaptStaticArraysExt = "StaticArrays"
+
+[[deps.AdaptivePredicates]]
+git-tree-sha1 = "7e651ea8d262d2d74ce75fdf47c4d63c07dba7a6"
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+
+[[deps.AliasTables]]
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+uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"
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+
+[[deps.Animations]]
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+
+[[deps.ArgCheck]]
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+
+[[deps.ArgTools]]
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+uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
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+
+    [deps.ArrayInterface.extensions]
+    ArrayInterfaceBandedMatricesExt = "BandedMatrices"
+    ArrayInterfaceBlockBandedMatricesExt = "BlockBandedMatrices"
+    ArrayInterfaceCUDAExt = "CUDA"
+    ArrayInterfaceCUDSSExt = ["CUDSS", "CUDA"]
+    ArrayInterfaceChainRulesCoreExt = "ChainRulesCore"
+    ArrayInterfaceChainRulesExt = "ChainRules"
+    ArrayInterfaceGPUArraysCoreExt = "GPUArraysCore"
+    ArrayInterfaceMetalExt = "Metal"
+    ArrayInterfaceReverseDiffExt = "ReverseDiff"
+    ArrayInterfaceSparseArraysExt = "SparseArrays"
+    ArrayInterfaceStaticArraysCoreExt = "StaticArraysCore"
+    ArrayInterfaceTrackerExt = "Tracker"
+
+    [deps.ArrayInterface.weakdeps]
+    BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
+    BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
+    CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+    CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e"
+    ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
+    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+    GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
+    Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
+    ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
+    SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+    StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
+    Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
+
+[[deps.ArrayLayouts]]
+deps = ["FillArrays", "LinearAlgebra", "StaticArrays"]
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+    [deps.ArrayLayouts.extensions]
+    ArrayLayoutsSparseArraysExt = "SparseArrays"
+
+[[deps.Artifacts]]
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+
+[[deps.Automa]]
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+
+[[deps.AxisAlgorithms]]
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+[[deps.AxisArrays]]
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+
+[[deps.BangBang]]
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+
+    [deps.BangBang.extensions]
+    BangBangChainRulesCoreExt = "ChainRulesCore"
+    BangBangDataFramesExt = "DataFrames"
+    BangBangStaticArraysExt = "StaticArrays"
+    BangBangStructArraysExt = "StructArrays"
+    BangBangTablesExt = "Tables"
+    BangBangTypedTablesExt = "TypedTables"
+
+    [deps.BangBang.weakdeps]
+    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+    DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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+    Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+    TypedTables = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9"
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+    [deps.BlockArrays.extensions]
+    BlockArraysAdaptExt = "Adapt"
+    BlockArraysBandedMatricesExt = "BandedMatrices"
+
+    [deps.BlockArrays.weakdeps]
+    Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+    BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
+
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diff --git a/notebooks/Project.toml b/notebooks/Project.toml
new file mode 100644
index 0000000..0805551
--- /dev/null
+++ b/notebooks/Project.toml
@@ -0,0 +1,25 @@
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+T4AAdaptivePatchedTCI = "36ff05ac-192f-4444-92c7-2a2992a7023b"
+T4AITensorCompat = "864b39ca-388c-4f43-a593-a1076cf4b253"
+T4APartitionedTT = "7ada5264-53d5-494a-9572-4199565888e0"
+T4AQuantics = "99202d80-6772-4c79-995b-f2660cf2bd6d"
+T4AQuanticsTCI = "ef09ec98-57a2-49a0-bf0b-86b55a3d9051"
+T4ATensorCI = "14428447-6903-48c7-83df-f2cb08af9918"
+
+[compat]
+T4AITensorCompat = "0.8"
+T4APartitionedTT = "0.8"
+T4AQuantics = "0.6"
+T4ATensorCI = "0.10"
+
+[extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+HubbardAtoms = "4f3da25b-ea28-4d4f-a81f-380d3f5e56a6"
+ITensors = "9136182c-28ba-11e9-034c-db9fb085ebd5"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
+QuanticsGrids = "634c7f73-3e90-4749-a1bd-001b8efc642d"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
\ No newline at end of file
diff --git a/notebooks/adaptivematmul_tests.ipynb b/notebooks/bak/adaptivematmul_tests.ipynb
similarity index 100%
rename from notebooks/adaptivematmul_tests.ipynb
rename to notebooks/bak/adaptivematmul_tests.ipynb
diff --git a/notebooks/bse_3d.ipynb b/notebooks/bak/bse_3d.ipynb
similarity index 100%
rename from notebooks/bse_3d.ipynb
rename to notebooks/bak/bse_3d.ipynb
diff --git a/notebooks/gaussian.ipynb b/notebooks/gaussian.ipynb
new file mode 100644
index 0000000..ec68d13
--- /dev/null
+++ b/notebooks/gaussian.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Pkg\n",
+    "Pkg.Registry.add(RegistrySpec(url=\"https://github.com/tensor4all/T4ARegistry.git\"))\n",
+    "Pkg.activate(\".\")\n",
+    "Pkg.instantiate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Revise\n",
+    "using Random\n",
+    "import QuanticsGrids as QG\n",
+    "\n",
+    "# Make sure to load T4AITensorCompat first\n",
+    "# before importing other T4A packages\n",
+    "# to activate the ITensor-related extension\n",
+    "import ITensors: ITensors, Index\n",
+    "import T4AITensorCompat as T4AIT\n",
+    "import T4ATensorCI as TCI\n",
+    "import T4ATensorCI\n",
+    "import T4AQuanticsTCI as QTCI\n",
+    "import T4APartitionedTT as T4AP\n",
+    "import T4APartitionedTT: PartitionedTT, SubDomainTT, adaptive_patching, Projector\n",
+    "import T4AAdaptivePatchedTCI as PatchedTCI\n",
+    "using CairoMakie\n",
+    "using Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Random.seed!(1234)\n",
+    "\n",
+    "gaussian(x, y) = exp(- ((x-5)^2 + (y-5)^2)) + exp(- ((x+5)^2 + (y+5)^2))\n",
+    "\n",
+    "R = 20\n",
+    "xmax = 10.0\n",
+    "grid = QG.DiscretizedGrid{2}(R, (-xmax, -xmax), (xmax, xmax); unfoldingscheme=:interleaved)\n",
+    "localdims = fill(2, 2R)  # interleaved: 2R sites (R for x, R for y)\n",
+    "\n",
+    "# Convert gaussian function to quantics coordinates\n",
+    "qf = x -> gaussian(QG.quantics_to_origcoord(grid, x)...)\n",
+    "\n",
+    "tol = 1e-7\n",
+    "maxbonddim = 30\n",
+    "\n",
+    "# Create patch ordering (interleaved: 2R sites)\n",
+    "pordering = PatchedTCI.PatchOrdering(collect(1:2R))\n",
+    "\n",
+    "# Create TCI2PatchCreator\n",
+    "creator = PatchedTCI.TCI2PatchCreator(\n",
+    "    Float64,\n",
+    "    PatchedTCI.makeprojectable(Float64, qf, localdims),\n",
+    "    localdims,\n",
+    "    maxbonddim=maxbonddim,\n",
+    "    tolerance=tol,\n",
+    "    verbosity=1,\n",
+    "    ntry=10,\n",
+    ")\n",
+    "\n",
+    "# Run adaptive interpolation\n",
+    "results = PatchedTCI.adaptiveinterpolate(creator, pordering; verbosity=1)\n",
+    "patched_tt = PatchedTCI.ProjTTContainer(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@show patched_tt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#heatmap(-xmax:0.1:xmax, -xmax:0.1:xmax, gaussian)\n",
+    "fig = Figure()\n",
+    "ax = Axis(fig[1, 1])\n",
+    "hm = heatmap!(ax, -xmax:0.1:xmax, -xmax:0.1:xmax, gaussian)\n",
+    "Colorbar(fig[1, 2], hm)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_to_nested(x) = [[x_] for x_ in x]\n",
+    "_err(x...) = abs(patched_tt(_to_nested(QG.origcoord_to_quantics(grid, x))) - gaussian(x...))\n",
+    "fig = Figure()\n",
+    "ax = Axis(fig[1, 1])\n",
+    "hm = heatmap!(ax, -xmax:0.1:xmax, -xmax:0.1:xmax, _err)\n",
+    "Colorbar(fig[1, 2], hm)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function bound_idx(p::PatchedTCI.Projector, grid1d, sitedims)::Tuple{Float64,Float64}\n",
+    "    # p.data is Vector{Vector{Int}}, where p.data[isite][ilegg] is the projection value\n",
+    "    # 0 means no projection, >0 means projected to that value\n",
+    "    # sitedims is Vector{Vector{Int}} corresponding to p.sitedims\n",
+    "    \n",
+    "    # Create lower and upper bound MultiIndex\n",
+    "    # Lower bound: unprojected indices = 1, projected indices = projector value\n",
+    "    # Upper bound: unprojected indices = max value, projected indices = projector value\n",
+    "    lower_idx = TCI.MultiIndex(undef, sum(length.(sitedims)))\n",
+    "    upper_idx = TCI.MultiIndex(undef, sum(length.(sitedims)))\n",
+    "    \n",
+    "    idx_pos = 1\n",
+    "    for (isite, site_dim) in enumerate(sitedims)\n",
+    "        for (ilegg, dim) in enumerate(site_dim)\n",
+    "            proj_val = p.data[isite][ilegg]\n",
+    "            if proj_val == 0\n",
+    "                # Not projected: use full range\n",
+    "                lower_idx[idx_pos] = 1\n",
+    "                upper_idx[idx_pos] = dim\n",
+    "            else\n",
+    "                # Projected: use the projection value\n",
+    "                lower_idx[idx_pos] = proj_val\n",
+    "                upper_idx[idx_pos] = proj_val\n",
+    "            end\n",
+    "            idx_pos += 1\n",
+    "        end\n",
+    "    end\n",
+    "    \n",
+    "    # Convert quantics indices to original coordinates\n",
+    "    lower_coord = QG.quantics_to_origcoord(grid1d, lower_idx)[1]\n",
+    "    upper_coord = QG.quantics_to_origcoord(grid1d, upper_idx)[1]\n",
+    "    \n",
+    "    return (lower_coord, upper_coord)\n",
+    "end\n",
+    "\n",
+    "function patch_bound(p::PatchedTCI.Projector, xmin, xmax, ymin, ymax, R)::NTuple{4, Float64}\n",
+    "    xgrid = QG.DiscretizedGrid{1}(R, xmin, xmax)\n",
+    "    ygrid = QG.DiscretizedGrid{1}(R, ymin, ymax)\n",
+    "\n",
+    "    # For interleaved representation, x and y indices are interleaved\n",
+    "    # Structure: [x_1, y_1, x_2, y_2, ..., x_R, y_R]\n",
+    "    # p.data has 2R sites, where odd indices are x and even indices are y\n",
+    "    # p.sitedims[isite] has the dimensions for each leg at site isite\n",
+    "    \n",
+    "    # Extract x and y projectors\n",
+    "    x_data = Vector{Int}[]\n",
+    "    x_sitedims = Vector{Int}[]\n",
+    "    y_data = Vector{Int}[]\n",
+    "    y_sitedims = Vector{Int}[]\n",
+    "    \n",
+    "    for isite in 1:length(p.data)\n",
+    "        if isodd(isite)\n",
+    "            # x dimension (odd sites: 1, 3, 5, ...)\n",
+    "            push!(x_data, p.data[isite])\n",
+    "            push!(x_sitedims, p.sitedims[isite])\n",
+    "        else\n",
+    "            # y dimension (even sites: 2, 4, 6, ...)\n",
+    "            push!(y_data, p.data[isite])\n",
+    "            push!(y_sitedims, p.sitedims[isite])\n",
+    "        end\n",
+    "    end\n",
+    "    \n",
+    "    x_proj = PatchedTCI.Projector(x_data, x_sitedims)\n",
+    "    y_proj = PatchedTCI.Projector(y_data, y_sitedims)\n",
+    "    \n",
+    "    # Get bounds for x and y\n",
+    "    x_lower, x_upper = bound_idx(x_proj, xgrid, x_sitedims)\n",
+    "    y_lower, y_upper = bound_idx(y_proj, ygrid, y_sitedims)\n",
+    "    \n",
+    "    return (x_lower, x_upper, y_lower, y_upper)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to draw a box with red lines\n",
+    "function draw_box!(ax, x_lower, x_upper, y_lower, y_upper; color=:red, linewidth=2)\n",
+    "    # Create a closed rectangle: [x1, x2, x2, x1, x1] and [y1, y1, y2, y2, y1]\n",
+    "    x_box = [x_lower, x_upper, x_upper, x_lower, x_lower]\n",
+    "    y_box = [y_lower, y_lower, y_upper, y_upper, y_lower]\n",
+    "    lines!(ax, x_box, y_box, color=color, linewidth=linewidth)\n",
+    "end\n",
+    "\n",
+    "_to_nested(x) = [[x_] for x_ in x]\n",
+    "_err(x...) = abs(patched_tt(_to_nested(QG.origcoord_to_quantics(grid, x))) - gaussian(x...))\n",
+    "\n",
+    "fig = Figure()\n",
+    "ax = Axis(fig[1, 1])\n",
+    "hm = heatmap!(ax, -xmax:0.1:xmax, -xmax:0.1:xmax, _err)\n",
+    "Colorbar(fig[1, 2], hm)\n",
+    "\n",
+    "# Draw boxes for all patches\n",
+    "# patched_tt is ProjTTContainer, which contains ProjTensorTrain objects\n",
+    "# Each ProjTensorTrain has a projector field\n",
+    "for ptt in patched_tt\n",
+    "    proj = ptt.projector\n",
+    "    x_lower, x_upper, y_lower, y_upper = patch_bound(proj, -xmax, xmax, -xmax, xmax, R)\n",
+    "    draw_box!(ax, x_lower, x_upper, y_lower, y_upper)\n",
+    "end\n",
+    "\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the linkdims of patches in a single plot\n",
+    "fig = Figure()\n",
+    "ax = Axis(fig[1, 1], xlabel=\"Bond position\", ylabel=\"Link dimension\", title=\"Link dimensions of all patches\", yscale=log10)\n",
+    "\n",
+    "# Get linkdims for each patch\n",
+    "# patched_tt is ProjTTContainer, which contains ProjTensorTrain objects\n",
+    "for (patch_idx, ptt) in enumerate(patched_tt)\n",
+    "    # ptt is ProjTensorTrain, which has a data field (TensorTrain{T,3})\n",
+    "    tt = ptt.data\n",
+    "    # linkdims returns bond dimensions between tensors (length = length(tt) - 1)\n",
+    "    linkdims_vec = TCI.linkdims(tt)\n",
+    "    \n",
+    "    # Plot linkdims for this patch\n",
+    "    # Bond positions: 1 to length(linkdims_vec) (between sites 1-2, 2-3, ..., N-1-N)\n",
+    "    bond_positions = 1:length(linkdims_vec)\n",
+    "    lines!(ax, bond_positions, linkdims_vec, label=\"Patch $patch_idx\", linewidth=2)\n",
+    "end\n",
+    "\n",
+    "#axislegend(ax, position=:rt)\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.12.1",
+   "language": "julia",
+   "name": "julia-1.12"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/container.jl b/src/container.jl
index d7219d2..6b19baa 100644
--- a/src/container.jl
+++ b/src/container.jl
@@ -120,7 +120,18 @@ function Base.show(io::IO, obj::ProjContainer{T,V}) where {T,V}
 end
 
 function Base.show(io::IO, obj::ProjContainer{T,ProjTensorTrain{T}}) where {T}
-    return print(io, "ProjTTContainer{$T} with $(length(obj.data)) elements")
+    npatches = length(obj.data)
+    if npatches == 0
+        return print(io, "ProjTTContainer{$T} with 0 patches")
+    end
+    # Get max bond dimension from all patches
+    # TCI is imported as T4ATensorCI in the module
+    maxbonddims = [maximum(TCI.linkdims(ptt.data)) for ptt in obj.data]
+    maxbonddim = maximum(maxbonddims)
+    return print(
+        io,
+        "ProjTTContainer{$T} with $npatches patch$(npatches == 1 ? "" : "es"), max bond dimension: $maxbonddim",
+    )
 end
 
 function fulltensor(obj::ProjContainer{T})::Array{T} where {T}