diff --git a/Project.toml b/Project.toml index 86e1560..262c349 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "ElectronGas" uuid = "8c98e03e-8755-48b1-a5ef-1e2e7812a7c9" authors = ["Xiansheng Cai, Pengcheng Hou, Kun Chen"] -version = "0.2.5" +version = "0.2.6" [deps] CompositeGrids = "b5136c89-beeb-4521-9139-60d2cac8be56" diff --git a/example/G0W0_2D.ipynb b/example/G0W0_2D.ipynb index cd7a26e..f93ccfb 100644 --- a/example/G0W0_2D.ipynb +++ b/example/G0W0_2D.ipynb @@ -26,7 +26,6 @@ "metadata": {}, "outputs": [], "source": [ - "using Revise\n", "using ElectronGas\n", "using GreenFunc\n", "using LaTeXStrings\n", @@ -37,15 +36,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}()\n" - ] - } - ], + "outputs": [], "source": [ "beta = 1000.0\n", "rs = 1.0\n", @@ -55,17 +46,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# sigma = SelfEnergy.G0W0(para, minK = 1e-6, Nk=12, order=4);\n", - "# Nk, order, minK, rtol = 11, 8, 1e-8, 1e-10\n", "Nk, order, minK, rtol = 8, 8, 1e-7, 1e-10\n", - "sigma = SelfEnergy.G0W0(para, 100*para.EF, rtol, Nk, 10 * para.kF, minK * para.kF, order, :rpa)\n", - "sigma_wn = GreenFunc.toMatFreq(sigma);\n", - "dlr = sigma_wn.dlrGrid\n", - "kgrid = sigma_wn.spaceGrid\n", + "sigma_dyn, sigma_ins = SelfEnergy.G0W0(para, 100*para.EF, rtol, Nk, 10 * para.kF, minK * para.kF, order, :rpa)\n", + "sigma_ωn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "wgrid, kgrid = sigma_ωn.mesh\n", + "dlr = wgrid.representation\n", + "ωn_grid = wgrid.representation.ωn;\n", "kFidx = searchsortedfirst(kgrid.grid, para.kF);" ] }, @@ -78,250 +68,324 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { + "image/png": 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xlabel=L\"$\\omega_n/E_F$\", ylabel=L\"$\\Sigma(i\\omega_n, k_F)$\", xlims=(-2, 2))\n", + "plot!(ωn_grid/para.EF, real(sigma_ωn[:, kFidx]),marker=2, label=\"real\")" ] }, { @@ -333,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": { "tags": [] }, @@ -343,521 +407,779 @@ "output_type": "stream", "text": [ "47\n", - "-1.8006326323142126, -1.8006232505919049 - 3.1140812186430085e-7im\n", - "-1.8006326323142126, -1.8006232505919049 - 3.1140812186430085e-7im\n" + "-1.8006326323142126, -1.800623250593426 - 3.1140629700555206e-7im\n", + "-1.8006326323142126, -1.800623250593426 - 3.1140629700555206e-7im\n" ] }, { "data": { + "image/png": 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"source": [ - "widx = searchsortedfirst(dlr.n, 0)\n", - "println(widx)\n", - "#println(imag(sigma_wn.dynamic[1, 1, :, widx]))\n", + "w0_idx = searchsortedfirst(dlr.n, 0)\n", + "println(w0_idx)\n", "\n", - "sig = sigma_wn.dynamic[1, 1, :, widx].+sigma_wn.instant[1, 1, :]\n", - "println(\"$(-para.kF*2*para.e0^2/π), $(sigma_wn.instant[1,1,kFidx])\")\n", - "plot(kgrid.grid/para.kF, imag(sigma_wn.dynamic[1, 1, :, widx]), xlim=(0.0,1.4), ylim=(-0.05,0.0),marker=2, label=\"imag\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\Sigma(i\\omega_0, k)$\")\n", + "sig = sigma_ωn[w0_idx, :] .+ sigma_ins[1, :]\n", + "println(\"$(-para.kF*2*para.e0^2/π), $(sigma_ins[1, kFidx])\")\n", + "plot(kgrid.grid/para.kF, imag(sigma_ωn[w0_idx, :]), xlim=(0.0,1.4), ylim=(-0.05,0.0),marker=2, label=\"imag\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\Sigma(i\\omega_0, k)$\")\n", "#plot!(kgrid.grid/para.kF, real(sig), marker=2, label=\"real\")\n", - "#plot!(kgrid.grid/para.kF, real(sigma_wn.dynamic[1, 1, :, widx]), marker=2, label=\"real dynamic\")\n", - "#plot!(kgrid.grid/para.kF, real(sigma_wn.instant[1, 1, :]), marker=2, label=\"real instant\")\n", "\n", - "sig = sigma_wn.dynamic[1, 1, :, widx+1].+sigma_wn.instant[1, 1, :]\n", - "println(\"$(-para.kF*2*para.e0^2/π), $(sigma_wn.instant[1,1,kFidx])\")\n", + "sig = sigma_ωn[w0_idx+1, :] .+ sigma_ins[1, :]\n", + "println(\"$(-para.kF*2*para.e0^2/π), $(sigma_ins[1, kFidx])\")\n", "plot!(kgrid.grid/para.kF, imag(sig), xlim=(0.0,1.4), ylim=(-0.05,0.0),marker=2, label=\"imag 1\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\Sigma(i\\omega_0, k)$\")\n", - "#plot!(kgrid.grid/para.kF, real(sig), marker=2, label=\"real 1\")\n", - "#plot!(kgrid.grid/para.kF, real(sigma_wn.dynamic[1, 1, :, widx+1]), marker=2, label=\"real dynamic 1\")\n", - "#plot!(kgrid.grid/para.kF, real(sigma_wn.instant[1, 1, :]), marker=2, label=\"real instant 1\")" + "#plot!(kgrid.grid/para.kF, real(sig), marker=2, label=\"real 1\")" ] }, { @@ -884,341 +1206,499 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rs = 1 has Z factor = 0.6626560160213353\n" + "rs = 1 has Z factor = 0.6626560199207896\n" ] }, { "data": { + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "w0idx = searchsortedfirst(dlr.n, 0)\n", - "#println(dlr.ωn[w0idx])\n", - "# zfactor_wn = @. 1 / (1 + imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * para.β)\n", - "zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx]) / π * para.β)\n", + "zfactor_wn = @. 1 / (1 - imag(sigma_ωn[w0idx, :]) / π * para.β)\n", "zfactor = zfactor_wn[kFidx]\n", "println(\"rs = 1 has Z factor = \", zfactor)\n", - "plot(kgrid.grid/para.kF, zfactor_wn,marker=2, label=\"z\", xlim=(0.0,5), ylim=(0,1), xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")" + "plot(kgrid.grid/para.kF, zfactor_wn, marker=2, label=\"z\", xlim=(0.0,5), ylim=(0,1), xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Warning: Some of the DLR coefficients are larger than 1e16. The quality of DLR fitting could be bad.\n", + "└ @ Lehmann /home/pchou/.julia/packages/Lehmann/v7X4o/src/operation.jl:370\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rs = 0.2 => 0.9028683441788833, with m*/m = 0.9609560793209351\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Warning: Some of the DLR coefficients are larger than 1e16. The quality of DLR fitting could be bad.\n", + "└ @ Lehmann /home/pchou/.julia/packages/Lehmann/v7X4o/src/operation.jl:370\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}()\n" + "rs = 0.5 => 0.7900843413960532, with m*/m = 0.9787571664643179\n" ] }, { @@ -1226,50 +1706,2056 @@ "output_type": "stream", "text": [ "┌ Warning: Some of the DLR coefficients are larger than 1e16. The quality of DLR fitting could be bad.\n", - "└ @ Lehmann /Users/kunchen/project/Lehmann.jl/src/operation.jl:330\n" + "└ @ Lehmann /home/pchou/.julia/packages/Lehmann/v7X4o/src/operation.jl:370\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "rs = 0.2 => 0.9028683441804687, with m*/m = 0.9609560792898801\n", - "Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}()\n" + "rs = 1.0 => 0.6643687873415336, with m*/m = 1.017292990475346\n" ] }, { - "ename": "LoadError", - "evalue": "InterruptException:", - "output_type": "error", - "traceback": [ - "InterruptException:", - "", - "Stacktrace:", - " [1] #RPA#2", - " @ ~/project/electron_gas/ElectronGas/src/interaction.jl:245 [inlined]", - " [2] interaction_dynamic(q::Float64, n::Int64, param::ElectronGas.Parameter.Para, int_type::Symbol, spin_state::Symbol; kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})", - " @ ElectronGas.LegendreInteraction ~/project/electron_gas/ElectronGas/src/legendreinteraction.jl:51", - " [3] interaction_dynamic", - " @ ~/project/electron_gas/ElectronGas/src/legendreinteraction.jl:40 [inlined]", - " [4] kernel_integrand2d(k::Float64, p::Float64, θ::Float64, n::Int64, channel::Int64, param::ElectronGas.Parameter.Para, int_type::Symbol, spin_state::Symbol)", - " @ ElectronGas.LegendreInteraction ~/project/electron_gas/ElectronGas/src/legendreinteraction.jl:114", - " [5] DCKernel_2d(param::ElectronGas.Parameter.Para, Euv::Float64, rtol::Float64, Nk::Int64, maxK::Float64, minK::Float64, order::Int64, int_type::Symbol, channel::Int64, spin_state::Symbol)", - " @ ElectronGas.LegendreInteraction ~/project/electron_gas/ElectronGas/src/legendreinteraction.jl:230", - " [6] #DCKernel_2d#7", - " @ ~/project/electron_gas/ElectronGas/src/legendreinteraction.jl:245 [inlined]", - " [7] G0W0(param::ElectronGas.Parameter.Para, Euv::Float64, rtol::Float64, Nk::Int64, maxK::Float64, minK::Float64, order::Int64, int_type::Symbol; kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})", - " @ ElectronGas.SelfEnergy ~/project/electron_gas/ElectronGas/src/selfenergy.jl:225", - " [8] G0W0", - " @ ~/project/electron_gas/ElectronGas/src/selfenergy.jl:218 [inlined]", - " [9] #G0W0#2", - " @ ~/project/electron_gas/ElectronGas/src/selfenergy.jl:242 [inlined]", - " [10] top-level scope", - " @ ./In[21]:10", - " [11] eval", - " @ ./boot.jl:360 [inlined]", - " [12] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)", - " @ Base ./loading.jl:1116" + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Warning: Some of the DLR coefficients are larger than 1e16. The quality of DLR fitting could be bad.\n", + "└ @ Lehmann /home/pchou/.julia/packages/Lehmann/v7X4o/src/operation.jl:370\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rs = 2.0 => 0.5208104758965416, with m*/m = 1.0749628398119586\n", + "rs = 4.0 => 0.3843177909438255, with m*/m = 1.1359383943785075\n", + "rs = 8.0 => 0.2714457809043789, with m*/m = 1.1822363450282063\n", + "rs = 16.0 => 0.18612520476305494, with m*/m = 1.201943904431772\n" + ] + }, + { + "data": { + "image/png": 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#sigma2=real(sigma_wn.dynamic[1, 1, k2, w0idx])+sigma_wn.instant[1, 1, k2]\n", - " #ds_dk = (sigma1-sigma2)/(kgrid.grid[k1]-kgrid.grid[k2])\n", - " #mratio = 1.0/z/(1+me/kF*ds_dk)\n", - " mratio = SelfEnergy.massratio(param, sigma)\n", + " sigma_dyn, sigma_ins = SelfEnergy.G0W0(param, minK = minK, Nk=Nk, order=order);\n", + " sigma_wn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "\n", + " z, _ = SelfEnergy.zfactor(param, sigma_wn)\n", + " mratio, _ = SelfEnergy.massratio(param, sigma_dyn, sigma_ins)\n", + "\n", " println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", " push!(zfactor, z)\n", " push!(mass, mratio)\n", " \n", " \n", - " zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * param.β)\n", - " #println(zfactor_wn)\n", + " zfactor_wn = @. 1 / (1 - imag(sigma_wn[w0idx+1, :]-sigma_wn[w0idx, :]) / (2π) * param.β)\n", " push!(zfactor_n, zfactor_wn)\n", " plot!(plt, kgrid.grid/param.kF, zfactor_wn, marker=2, label=\"rs=$_rs\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")\n", "end\n", @@ -1338,15 +3813,15 @@ "lastKernelId": null }, "kernelspec": { - "display_name": "Julia 1.6.2", + "display_name": "Julia 1.11.2", "language": "julia", - "name": "julia-1.6" + "name": "julia-1.11" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.6.2" + "version": "1.11.2" } }, "nbformat": 4, diff --git a/example/G0W0_3D.ipynb b/example/G0W0_3D.ipynb index 808c3cf..8508ae4 100644 --- a/example/G0W0_3D.ipynb +++ b/example/G0W0_3D.ipynb @@ -52,10 +52,11 @@ "outputs": [], "source": [ "#warning: take a minute or so.\n", - "sigma = SelfEnergy.G0W0(para, minK = 1e-6, Nk=12, order=6);\n", - "sigma_wn = GreenFunc.toMatFreq(sigma);\n", - "dlr = sigma_wn.dlrGrid\n", - "kgrid = sigma_wn.spaceGrid\n", + "sigma_dyn, sigma_ins = SelfEnergy.G0W0(para, minK = 1e-6, Nk=12, order=6);\n", + "sigma_ωn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "wgrid, kgrid = sigma_ωn.mesh\n", + "dlr = wgrid.representation\n", + "ωn_grid = wgrid.representation.ωn;\n", "kFidx = searchsortedfirst(kgrid.grid, para.kF);" ] }, @@ -73,565 +74,761 @@ "outputs": [ { "data": { + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 6, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "widx = searchsortedfirst(dlr.n, 0)\n", "println(widx)\n", - "sig = sigma_wn.dynamic[1, 1, :, widx].+sigma_wn.instant[1, 1, :]\n", + "\n", + "sig = sigma_ωn[widx, :] .+ sigma_ins[1, :]\n", "plot(kgrid.grid/para.kF, imag(sig), marker=2, label=\"imag\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\Sigma(i\\omega_0, k)$\", ylims=[-0.45, 0.0], xlims=[0.0, 3], legend =:bottomright)\n", "plot!(kgrid.grid/para.kF, real(sig), marker=2, label=\"real\")\n", - "#println(real(sig))\n", - "x=kgrid.grid\n", - "z=SelfEnergy.zfactor(para, sigma_wn)\n", + "x = kgrid.grid\n", + "z, _ = SelfEnergy.zfactor(para, sigma_ωn)\n", "plot!(x/para.kF, x.^2/(2*para.me)*(1/z-1).+real(sig[1]), marker=2, label=L\"$\\Sigma(i\\omega_0, 0)+\\left(\\frac{1}{z}-1\\right)\\frac{k^2}{2m}$\")" ] }, @@ -1457,370 +2162,532 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rs = 5.000000000000001 has Z factor = 0.5918707137562887\n" + "rs = 5.000000000000001 has Z factor = 0.5918707137703781\n" ] }, { "data": { + "image/png": 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"output_type": "display_data" } ], "source": [ "w0idx = searchsortedfirst(dlr.n, 0)\n", - "#println(dlr.ωn[w0idx])\n", - "zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * para.β)\n", + "zfactor_wn = @. 1 / (1 - imag(sigma_ωn[w0idx+1, :]-sigma_ωn[w0idx, :]) / (2π) * para.β)\n", "zfactor = zfactor_wn[kFidx]\n", "println(\"rs = $(para.rs) has Z factor = \", zfactor)\n", - "plot(kgrid.grid/para.kF, zfactor_wn,marker=2, label=\"z\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")" + "plot(kgrid.grid/para.kF, zfactor_wn, marker=2, label=\"z\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")" ] }, { @@ -1832,1220 +2699,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rs = 0.5 => 0.9226057675194066, with m*/m = 0.9679374331952681\n", - "rs = 1.0 => 0.8603682512692402, with m*/m = 0.9718851523199054\n", - "rs = 3.0 => 0.6930197532783634, with m*/m = 1.0172669146299085\n", - "rs = 4.0 => 0.6369961083916205, with m*/m = 1.0391144895449052\n", - "rs = 5.0 => 0.5915813359967018, with m*/m = 1.0583516495790941\n" + "rs = 0.5 => 0.9226090204113138, with m*/m = 0.9678509693636314\n", + "rs = 1.0 => 0.8603719175950142, with m*/m = 0.9718827268360345\n", + "rs = 3.0 => 0.6930238057874474, with m*/m = 1.0174682213993336\n", + "rs = 4.0 => 0.6370003335890603, with m*/m = 1.0394165559280977\n", + "rs = 5.0 => 0.5915854357607926, with m*/m = 1.0588987312553058\n" ] }, { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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=plot(xlims=(0.0, 2.01), ylims=(1.0, 1.8), legend = :topright, size=(600,600))\n", - "#plt =plot(xlims=(0.0, 3.01), legend = :topright)\n", "for _rs in rslist\n", " param = Parameter.rydbergUnit(1.0/beta/2, _rs, d)\n", " @unpack me, β, kF = param\n", - " sigma = SelfEnergy.G0W0(param, minK = 1e-6, Nk=12, order=6);\n", - " sigma_wn = GreenFunc.toMatFreq(sigma);\n", - " dlr = sigma_wn.dlrGrid\n", - " kgrid = sigma_wn.spaceGrid\n", - " kFidx = searchsortedfirst(kgrid.grid, kF)\n", - " #println(kgrid.grid[kFidx]-kF)\n", - " w0idx = searchsortedfirst(dlr.n, 0)\n", - " z = 1 / (1 - imag(sigma_wn.dynamic[1, 1, kFidx, w0idx+1]-sigma_wn.dynamic[1, 1, kFidx, w0idx]) / (2π) * β)\n", - " \n", - " #println(\"rs = $_rs => $z\")\n", - " #k1, k2 = kFidx, kFidx+3\n", - " #sigma1=real(sigma_wn.dynamic[1, 1, k1, w0idx])+sigma_wn.instant[1, 1, k1]\n", - " #sigma2=real(sigma_wn.dynamic[1, 1, k2, w0idx])+sigma_wn.instant[1, 1, k2]\n", - " #ds_dk = (sigma1-sigma2)/(kgrid.grid[k1]-kgrid.grid[k2])\n", - " #mratio = 1.0/z/(1+me/kF*ds_dk)\n", - " mratio = SelfEnergy.massratio(param, sigma)\n", + " sigma_dyn, sigma_ins = SelfEnergy.G0W0(param, minK = 1e-7, Nk=16, order=6);\n", + " sigma_wn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "\n", + " z, _ = SelfEnergy.zfactor(param, sigma_wn)\n", + " mratio, _ = SelfEnergy.massratio(param, sigma_dyn, sigma_ins)\n", + "\n", " println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", " push!(zfactor, z)\n", " push!(mass, mratio)\n", " \n", " \n", - " zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * param.β)\n", - " #println(zfactor_wn)\n", + " zfactor_wn = @. 1 / (1 - imag(sigma_wn[w0idx+1, :]-sigma_wn[w0idx, :]) / (2π) * param.β)\n", " plot!(plt, kgrid.grid/param.kF, 1 ./ zfactor_wn, marker=0, linewidth=2, label=\"rₛ = $_rs\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\frac{1}{z_k}=\\frac{\\partial G^{-1}(\\omega_0, k)}{\\partial \\omega}$\")\n", "end\n", "#savefig(plt, \"G0W0_z.pdf\")\n", @@ -3125,2270 +2784,26 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rs = 0.5 => 0.9251897145535224, with m*/m = 0.963887287888746\n", - "rs = 1.0 => 0.8658248509375787, with m*/m = 0.9631518360311019\n", - "rs = 3.0 => 0.7088872779699896, with m*/m = 0.9889445307074736\n", - "rs = 4.0 => 0.657265272858765, with m*/m = 1.0005706086914197\n", - "rs = 5.0 => 0.6154158970441141, with m*/m = 1.0101534418524212\n", - "rs = 10.0 => 0.4805190250156789, with m*/m = 0.9810042868150443\n", - "rs = 20.0 => 0.3483833836210729, with m*/m = 0.9947899588056527\n" + "rs = 0.5 => 0.925189714553559, with m*/m = 0.9638872885472264\n", + "rs = 1.0 => 0.8658248509377611, with m*/m = 0.9631518402804752\n", + "rs = 3.0 => 0.7088872779913278, with m*/m = 0.988944545440782\n", + "rs = 4.0 => 0.6572652727675898, with m*/m = 1.0005706018551648\n", + "rs = 5.0 => 0.6154158970789481, with m*/m = 1.0101534978493174\n", + "rs = 10.0 => 0.48051902501997634, with m*/m = 0.981004414200254\n", + "rs = 20.0 => 0.348383383554235, with m*/m = 0.9947900279052805\n" ] }, { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"stdout", + "output_type": "stream", + "text": [] } ], "source": [ @@ -5400,28 +2815,17 @@ "for _rs in rslist\n", " param = Parameter.rydbergUnit(1.0/beta/2, _rs, d)\n", " @unpack me, β, kF = param\n", - " sigma = SelfEnergy.G0W0(param, minK = 1e-6, Nk=16, order=6, int_type=:ko);\n", - " sigma_wn = GreenFunc.toMatFreq(sigma);\n", - " dlr = sigma_wn.dlrGrid\n", - " kgrid = sigma_wn.spaceGrid\n", - " kFidx = searchsortedfirst(kgrid.grid, kF)\n", - " #println(kgrid.grid[kFidx]-kF)\n", - " w0idx = searchsortedfirst(dlr.n, 0)\n", - " z = 1 / (1 - imag(sigma_wn.dynamic[1, 1, kFidx, w0idx+1]-sigma_wn.dynamic[1, 1, kFidx, w0idx]) / (2π) * β)\n", - " \n", - " #println(\"rs = $_rs => $z\")\n", - " #k1, k2 = kFidx, kFidx+3\n", - " #sigma1=real(sigma_wn.dynamic[1, 1, k1, w0idx])+sigma_wn.instant[1, 1, k1]\n", - " #sigma2=real(sigma_wn.dynamic[1, 1, k2, w0idx])+sigma_wn.instant[1, 1, k2]\n", - " #ds_dk = (sigma1-sigma2)/(kgrid.grid[k1]-kgrid.grid[k2])\n", - " #mratio = 1.0/z/(1+me/kF*ds_dk)\n", - " mratio = SelfEnergy.massratio(param, sigma)\n", + " sigma_dyn, sigma_ins = SelfEnergy.G0W0(param, minK = 1e-6, Nk=16, order=6, int_type=:ko);\n", + " sigma_wn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "\n", + " z, _ = SelfEnergy.zfactor(param, sigma_wn)\n", + " mratio, _ = SelfEnergy.massratio(param, sigma_dyn, sigma_ins)\n", + "\n", " println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", " push!(zfactor, z)\n", " push!(mass, mratio)\n", " \n", - " zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * param.β)\n", - " #println(zfactor_wn)\n", + " zfactor_wn = @. 1 / (1 - imag(sigma_wn[w0idx+1, :]-sigma_wn[w0idx, :]) / (2π) * param.β)\n", " plot!(plt, kgrid.grid/param.kF, zfactor_wn, marker=0, linewidth=2, label=\"rs=$_rs\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")\n", "end\n", "#savefig(plt, \"G0KO_z.pdf\")\n", @@ -5430,7 +2834,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -5438,336 +2842,369 @@ "output_type": "stream", "text": [ "0.5\n", + "rs = 0.5 => 0.9226090399410031, with m*/m = 0.967836335707776\n", "1.0\n", + "rs = 1.0 => 0.8603720207797995, with m*/m = 0.9718432954150146\n", "3.0\n", + "rs = 3.0 => 0.693024008862456, with m*/m = 1.0171638469643622\n", "4.0\n", - "5.0\n" + "rs = 4.0 => 0.6370006397861899, with m*/m = 1.0388706440721847\n", + "5.0\n", + "rs = 5.0 => 0.5915857919305341, with m*/m = 1.0581320068084474\n" ] }, { "data": { + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, @@ -5784,31 +3221,18 @@ " println(_rs)\n", " param = Parameter.rydbergUnit(1.0/beta/2, _rs, d)\n", " @unpack me, β, kF = param\n", - " sigma = SelfEnergy.G0W0(param, minK = 1e-6, Nk=16, order=6, int_type=:rpa);\n", - " sigma_wn = GreenFunc.toMatFreq(sigma);\n", - " dlr = sigma_wn.dlrGrid\n", - " kgrid = sigma_wn.spaceGrid\n", - " kFidx = searchsortedfirst(kgrid.grid, kF)\n", - " #println(kgrid.grid[kFidx]-kF)\n", - " w0idx = searchsortedfirst(dlr.n, 0)\n", - " z = 1 / (1 - imag(sigma_wn.dynamic[1, 1, kFidx, w0idx+1]-sigma_wn.dynamic[1, 1, kFidx, w0idx]) / (2π) * β)\n", - " \n", - " #println(\"rs = $_rs => $z\")\n", - " #k1, k2 = kFidx, kFidx+3\n", - " #sigma1=real(sigma_wn.dynamic[1, 1, k1, w0idx])+sigma_wn.instant[1, 1, k1]\n", - " #sigma2=real(sigma_wn.dynamic[1, 1, k2, w0idx])+sigma_wn.instant[1, 1, k2]\n", - " #ds_dk = (sigma1-sigma2)/(kgrid.grid[k1]-kgrid.grid[k2])\n", - " #mratio = 1.0/z/(1+me/kF*ds_dk)\n", - " #mratio = SelfEnergy.massratio(param, sigma)\n", - " #println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", - " #push!(zfactor, z)\n", - " #push!(mass, mratio)\n", - " \n", + " sigma_dyn, sigma_ins = SelfEnergy.G0W0(param, minK = 1e-6, Nk=16, order=6, int_type=:rpa);\n", + " sigma_wn = sigma_dyn |> to_dlr |> to_imfreq;\n", + " dlr = sigma_wn.mesh[1].representation\n", + "\n", + " z, _ = SelfEnergy.zfactor(param, sigma_wn)\n", + " mratio, _ = SelfEnergy.massratio(param, sigma_dyn, sigma_ins)\n", + "\n", + " println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", + " push!(zfactor, z)\n", + " push!(mass, mratio)\n", " \n", - " #zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * param.β)\n", - " #println(zfactor_wn)\n", - " #plot!(plt, kgrid.grid/param.kF, zfactor_wn, marker=2, label=\"rs=$_rs\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")\n", - " plot!(plt, dlr.ωn/para.EF, 1.0 .- imag(sigma_wn.dynamic[1, 1, kFidx, :])./dlr.ωn, marker=0, linewidth=2.0, label=\"rₛ=$(_rs)\", xlabel=L\"$\\omega_n/E_F$\", ylabel=L\"$\\operatorname{Im} G^{-1}(i\\omega_n, k_F)/\\omega_n$\")\n", + " plot!(plt, dlr.ωn/para.EF, 1.0 .- imag(sigma_wn[:, kFidx])./dlr.ωn, marker=0, linewidth=2.0, label=\"rₛ=$(_rs)\", xlabel=L\"$\\omega_n/E_F$\", ylabel=L\"$\\operatorname{Im} G^{-1}(i\\omega_n, k_F)/\\omega_n$\")\n", "end\n", "#savefig(plt, \"G0W0_w.pdf\")\n", "display(plt)" @@ -5816,7 +3240,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -5832,1253 +3256,2086 @@ }, { "data": { + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, @@ -7096,31 +5353,13 @@ " println(_rs)\n", " param = Parameter.rydbergUnit(1.0/beta/2, _rs, d)\n", " @unpack me, β, kF = param\n", - " sigma = SelfEnergy.G0W0(param, minK = 1e-6, Nk=16, order=6, int_type=:rpa);\n", - " sigma_wn = GreenFunc.toMatFreq(sigma);\n", - " dlr = sigma_wn.dlrGrid\n", - " kgrid = sigma_wn.spaceGrid\n", - " kFidx = searchsortedfirst(kgrid.grid, kF)\n", - " #println(kgrid.grid[kFidx]-kF)\n", + " sigma_dyn, sigma_ins = SelfEnergy.G0W0(param, minK = 1e-6, Nk=16, order=6, int_type=:rpa);\n", + " sigma_wn = sigma_dyn |> to_dlr |> to_imfreq;\n", + " dlr = sigma_wn.mesh[1].representation\n", + " kgrid = sigma_wn.mesh[2]\n", " w0idx = searchsortedfirst(dlr.n, 0)\n", - " sig = sigma_wn.dynamic[1, 1, :, w0idx].+sigma_wn.instant[1, 1, :]\n", - " #z = 1 / (1 - imag(sigma_wn.dynamic[1, 1, kFidx, w0idx+1]-sigma_wn.dynamic[1, 1, kFidx, w0idx]) / (2π) * β)\n", - " \n", - " #println(\"rs = $_rs => $z\")\n", - " #k1, k2 = kFidx, kFidx+3\n", - " #sigma1=real(sigma_wn.dynamic[1, 1, k1, w0idx])+sigma_wn.instant[1, 1, k1]\n", - " #sigma2=real(sigma_wn.dynamic[1, 1, k2, w0idx])+sigma_wn.instant[1, 1, k2]\n", - " #ds_dk = (sigma1-sigma2)/(kgrid.grid[k1]-kgrid.grid[k2])\n", - " #mratio = 1.0/z/(1+me/kF*ds_dk)\n", - " #mratio = SelfEnergy.massratio(param, sigma)\n", - " #println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", - " #push!(zfactor, z)\n", - " #push!(mass, mratio)\n", - " \n", + " sig = sigma_wn[w0idx, :] .+ sigma_ins[1, :]\n", " \n", - " #zfactor_wn = @. 1 / (1 - imag(sigma_wn.dynamic[1, 1, :, w0idx+1]-sigma_wn.dynamic[1, 1, :, w0idx]) / (2π) * param.β)\n", - " #println(zfactor_wn)\n", - " #plot!(plt, kgrid.grid/param.kF, zfactor_wn, marker=2, label=\"rs=$_rs\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")\n", " plot!(plt, kgrid.grid/param.kF, 1.0 .+ (real(sig) .- real(sig[1]))./(kgrid.grid .^2/(2*me)), marker=0, linewidth=2.0, label=\"rₛ=$(_rs)\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\operatorname{Re} (G^{-1}(\\omega_0, k)-G^{-1}(\\omega_0, 0))/(k^2/2m)$\")\n", "end\n", "#savefig(plt, \"G0W0_k.pdf\")\n", @@ -7141,15 +5380,15 @@ "lastKernelId": null }, "kernelspec": { - "display_name": "Julia 1.8.0", + "display_name": "Julia 1.11.2", "language": "julia", - "name": "julia-1.8" + "name": "julia-1.11" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.8.0" + "version": "1.11.2" } }, "nbformat": 4, diff --git a/example/G0_KO.ipynb b/example/G0_KO.ipynb index f4a04d3..9a32d65 100644 --- a/example/G0_KO.ipynb +++ b/example/G0_KO.ipynb @@ -41,7 +41,6 @@ "metadata": {}, "outputs": [], "source": [ - "using Revise\n", "using ElectronGas\n", "using GreenFunc, CompositeGrids\n", "using LaTeXStrings\n", @@ -86,9 +85,8 @@ " qTF: Float64 0.6990777111084904\n" ] }, - "execution_count": 2, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -110,10 +108,11 @@ "outputs": [], "source": [ "#warning: take a minute or so\n", - "sigma = SelfEnergy.G0W0(para, minK = 1e-6, Nk=12, order=4, int_type=:ko);\n", - "sigma_wn = GreenFunc.toMatFreq(sigma);\n", - "dlr = sigma_wn.dlrGrid\n", - "kgrid = sigma_wn.spaceGrid\n", + "sigma_dyn, sigma_ins = SelfEnergy.G0W0(para, minK = 1e-6, Nk=12, order=4, int_type=:ko);\n", + "sigma_ωn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "wgrid, kgrid = sigma_ωn.mesh\n", + "dlr = wgrid.representation\n", + "ωn_grid = wgrid.representation.ωn;\n", "kFidx = searchsortedfirst(kgrid.grid, para.kF);" ] }, @@ -131,281 +130,379 @@ "outputs": [ { "data": { + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 7, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "widx = searchsortedfirst(dlr.n, 0)\n", - "println(widx)\n", - "sig = sigma_wn.dynamic[1, 1, :, widx].+sigma_wn.instant[1, 1, :]\n", + "w0_idx = searchsortedfirst(dlr.n, 0)\n", + "println(w0_idx)\n", + "sig = sigma_ωn[w0_idx, :] .+ sigma_ins[1, :]\n", "plot(kgrid.grid/para.kF, imag(sig), marker=2, label=\"imag\", xlabel=L\"$k/k_F$\", ylabel=L\"$\\Sigma(i\\omega_0, k)$\", ylims=[-0.45, 0.0], xlims=[0.0, 3], legend =:bottomright)\n", "plot!(kgrid.grid/para.kF, real(sig), marker=2, label=\"real\")\n", - "#println(real(sig))\n", - "x=kgrid.grid\n", - "z=SelfEnergy.zfactor(para, sigma_wn)\n", + "x = kgrid.grid\n", + "z, _ = SelfEnergy.zfactor(para, sigma_ωn)\n", "plot!(x/para.kF, x.^2/(2*para.me)*(1/z-1).+real(sig[1]), marker=2, label=L\"$\\Sigma(i\\omega_0, 0)+\\left(\\frac{1}{z}-1\\right)\\frac{k^2}{2m}$\")" ] }, @@ -1303,298 +1829,400 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rs = 5.000000000000001 has Z factor = 0.6157944148914773\n" + "rs = 5.000000000000001 has Z factor = 0.6157944148918584\n" ] }, { "data": { + "image/png": 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"plot(kgrid.grid/para.kF, zfactor_ωnn,marker=2, label=\"z\", xlabel=L\"$k/k_F$\", ylabel=L\"$z(k)$\")" ] }, { @@ -1617,30 +2245,17 @@ ], "source": [ "#take a few minutes\n", - "zfactor = []\n", - "mass = []\n", "rslist = [0.5, 1.0, 2.0, 4.0, 5.0, 10.0]\n", "for _rs in rslist\n", " param = Parameter.rydbergUnit(1.0/beta, _rs, d)\n", " @unpack me, β, kF = param\n", " sigma = SelfEnergy.G0W0(param, minK = 1e-7, Nk=16, order=6, int_type=:ko);\n", - " sigma_wn = GreenFunc.toMatFreq(sigma);\n", - " dlr = sigma_wn.dlrGrid\n", - " kgrid = sigma_wn.spaceGrid\n", - " kFidx = searchsortedfirst(kgrid.grid, kF)\n", - " #println(kgrid.grid[kFidx]-kF)\n", - " w0idx = searchsortedfirst(dlr.n, 0)\n", - " z = 1 / (1 - imag(sigma_wn.dynamic[1, 1, kFidx, w0idx+1]-sigma_wn.dynamic[1, 1, kFidx, w0idx]) / (2π) * β)\n", - " \n", - " #println(\"rs = $_rs => $z\")\n", - " k1, k2 = kFidx, kFidx+3\n", - " sigma1=real(sigma_wn.dynamic[1, 1, k1, w0idx])+sigma_wn.instant[1, 1, k1]\n", - " sigma2=real(sigma_wn.dynamic[1, 1, k2, w0idx])+sigma_wn.instant[1, 1, k2]\n", - " ds_dk = (sigma1-sigma2)/(kgrid.grid[k1]-kgrid.grid[k2])\n", - " mratio = 1.0/z/(1+me/kF*ds_dk)\n", + " sigma_wn = sigma_dyn |> to_dlr |> to_imfreq;\n", + "\n", + " z, _ = SelfEnergy.zfactor(param, sigma_wn)\n", + " mratio, _ = SelfEnergy.massratio(param, sigma_dyn, sigma_ins)\n", + "\n", " println(\"rs = $_rs => $z, with m*/m = $mratio\")\n", - " push!(zfactor, z)\n", - " push!(mass, mratio)\n", "end" ] }, @@ -1651,6 +2266,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -1665,15 +2287,15 @@ "lastKernelId": null }, "kernelspec": { - "display_name": "Julia 1.8.0-rc3", + "display_name": "Julia 1.11.2", "language": "julia", - "name": "julia-1.8" + "name": "julia-1.11" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.8.0" + "version": "1.11.2" } }, "nbformat": 4, diff --git a/example/linearresponse.jl b/example/linearresponse.jl index 8750849..dc9451f 100644 --- a/example/linearresponse.jl +++ b/example/linearresponse.jl @@ -10,30 +10,30 @@ using DelimitedFiles export measure_chi function measure_chi(F_freq::GreenFunc.MeshArray) - F_dlr = F_freq |> to_dlr - F_ins = real(dlr_to_imtime(F_dlr, [F_freq.mesh[1].representation.β,])) * (-1) - kgrid = F_ins.mesh[2] - integrand = view(F_ins, 1, :) - return real(CompositeGrids.Interp.integrate1D(integrand, kgrid)) + F_dlr = F_freq |> to_dlr + F_ins = real(dlr_to_imtime(F_dlr, [F_freq.mesh[1].representation.β])) * (-1) + kgrid = F_ins.mesh[2] + integrand = view(F_ins, 1, :) + return real(CompositeGrids.Interp.integrate1D(integrand, kgrid)) end function measure_chi(dim, θ, rs; kwargs...) - param = Parameter.rydbergUnit(θ, rs, dim) - channel = 0 - lamu, R_freq, F_freq = BSeq.linearResponse(param, channel - ; kwargs...) - result = measure_chi(F_freq) - println("1/chi=", 1 / result) - - data = [1 / θ 1 / result lamu channel rs] - - dir = "./run/" - fname = "gap_chi_rs$(rs)_l$(channel).txt" - open(dir * fname, "a+") do io - writedlm(io, data, ' ') - end - - return 1 / result + param = Parameter.rydbergUnit(θ, rs, dim) + channel = 0 + lamu, R_freq, F_freq = BSeq.linearResponse(param, channel + ; kwargs...) + result = measure_chi(F_freq) + println("1/chi=", 1 / result) + + data = [1 / θ 1 / result lamu channel rs] + + dir = "./run/" + fname = "gap_chi_rs$(rs)_l$(channel).txt" + open(dir * fname, "a+") do io + writedlm(io, data, ' ') + end + + return 1 / result end end @@ -42,16 +42,16 @@ using Test using .MeasureChi @testset "measure chi" begin - # println(measure_chi(3, 1e-2, 2.0)) - dim = 3 - rs = 7.0 - num = 20 - # beta = [6.25 * 2^i for i in LinRange(0, num - 1, num)] - # beta = [2000, 2200.0, 2229.78,] - beta = [3200,] - # num = 18 - # beta = [6.25 * sqrt(2)^i for i in LinRange(0, num - 1, num)] - # chi = [measure_chi(dim, 1 / b, rs; sigmatype=:g0w0) for b in beta] - chi = [measure_chi(dim, 1 / b, rs; atol=1e-10, rtol=1e-10, verbose=true) for b in beta] - println(chi) -end \ No newline at end of file + # println(measure_chi(3, 1e-2, 2.0)) + dim = 3 + rs = 7.0 + num = 20 + # beta = [6.25 * 2^i for i in LinRange(0, num - 1, num)] + # beta = [2000, 2200.0, 2229.78,] + beta = [3200] + # num = 18 + # beta = [6.25 * sqrt(2)^i for i in LinRange(0, num - 1, num)] + # chi = [measure_chi(dim, 1 / b, rs; sigmatype=:g0w0) for b in beta] + chi = [measure_chi(dim, 1 / b, rs; atol = 1e-10, rtol = 1e-10, verbose = true) for b in beta] + println(chi) +end diff --git a/src/parameter.jl b/src/parameter.jl index b431d3f..741aa59 100644 --- a/src/parameter.jl +++ b/src/parameter.jl @@ -1,58 +1,57 @@ """ Template of parameter. A submodule of ElectronGas. - Use the convention where ħ=1, k_B=1. - Only stores parameters that might change for purposes. + Use the convention where ħ=1, k_B=1. + Only stores parameters that might change for purposes. """ module Parameter # using Parameters using ..Parameters -# using Roots, SpecialFunctions -# using Polylogarithms +using Roots, SpecialFunctions @with_kw struct Para - WID::Int = 1 - - dim::Int = 3 # dimension (D=2 or 3, doesn't work for other D!!!) - spin::Int = 2 # number of spins - - # prime parameters - ϵ0::Float64 = 1 / (4π) - e0::Float64 = sqrt(2) # electron charge - me::Float64 = 0.5 # electron mass - EF::Float64 = 1.0 #kF^2 / (2me) - β::Float64 = 200 # bare inverse temperature - μ::Float64 = 1.0 - - # artificial parameters - Λs::Float64 = 0.0 # Yukawa-type spin-symmetric interaction ~1/(q^2+Λs) - Λa::Float64 = 0.0 # Yukawa-type spin-antisymmetric interaction ~1/(q^2+Λa) - gs::Float64 = 1.0 # spin-symmetric coupling - ga::Float64 = 0.0 # spin-antisymmetric coupling - - # derived parameters - beta::Float64 = β * EF - Θ::Float64 = 1.0 / β / EF - T::Float64 = 1.0 / β - n::Float64 = (dim == 3) ? (EF * 2 * me)^(3 / 2) / (6π^2) * spin : me * EF / π - Rs::Float64 = (dim == 3) ? (3 / (4π * n))^(1 / 3) : sqrt(1 / (π * n)) - a0::Float64 = 4π * ϵ0 / (me * e0^2) - rs::Float64 = Rs / a0 - kF::Float64 = sqrt(2 * me * EF) - espin::Float64 = e0 - e0s::Float64 = e0 - e0a::Float64 = espin - NF::Float64 = (dim == 3) ? spin * me * kF / 2 / π^2 : spin * me / 2 / π - ωp::Float64 = (dim == 3) ? sqrt(4π * e0^2 * n / me) : 0.0 # plasma frequency - qTF::Float64 = (dim == 3) ? sqrt(4π * e0^2 * NF) : 0.0 # inverse thomas-fermi screening length - # for spin-2 and 3d case, ω_p=v_F*q_TF/sqrt(3) + WID::Int = 1 + + dim::Int = 3 # dimension (D=2 or 3, doesn't work for other D!!!) + spin::Int = 2 # number of spins + + # prime parameters + ϵ0::Float64 = 1 / (4π) + e0::Float64 = sqrt(2) # electron charge + me::Float64 = 0.5 # electron mass + EF::Float64 = 1.0 #kF^2 / (2me) + β::Float64 = 200 # bare inverse temperature + μ::Float64 = 1.0 + + # artificial parameters + Λs::Float64 = 0.0 # Yukawa-type spin-symmetric interaction ~1/(q^2+Λs) + Λa::Float64 = 0.0 # Yukawa-type spin-antisymmetric interaction ~1/(q^2+Λa) + gs::Float64 = 1.0 # spin-symmetric coupling + ga::Float64 = 0.0 # spin-antisymmetric coupling + + # derived parameters + beta::Float64 = β * EF + Θ::Float64 = 1.0 / β / EF + T::Float64 = 1.0 / β + n::Float64 = (dim == 3) ? (EF * 2 * me)^(3 / 2) / (6π^2) * spin : me * EF / π + Rs::Float64 = (dim == 3) ? (3 / (4π * n))^(1 / 3) : sqrt(1 / (π * n)) + a0::Float64 = 4π * ϵ0 / (me * e0^2) + rs::Float64 = Rs / a0 + kF::Float64 = sqrt(2 * me * EF) + espin::Float64 = e0 + e0s::Float64 = e0 + e0a::Float64 = espin + NF::Float64 = (dim == 3) ? spin * me * kF / 2 / π^2 : spin * me / 2 / π + ωp::Float64 = (dim == 3) ? sqrt(4π * e0^2 * n / me) : 0.0 # plasma frequency + qTF::Float64 = (dim == 3) ? sqrt(4π * e0^2 * NF) : 0.0 # inverse thomas-fermi screening length + # for spin-2 and 3d case, ω_p=v_F*q_TF/sqrt(3) end derived_para_names = (:beta, :Θ, :T, :n, :Rs, :a0, :rs, :kF, :espin, :e0s, :e0a, :NF, :ωp, :qTF) """ - function derive(param::Para; kws...) + function derive(param::Para; kws...) Reconstruct a new Para with given key word arguments. This is needed because the default reconstruct generated by Parameters.jl @@ -63,30 +62,30 @@ could not handle derived parameters correctly. - kws...: new values """ derive(param::Para; kws...) = _reconstruct(param, kws) -derive(param::Para, di::Union{AbstractDict,Tuple{Symbol,Any}}) = _reconstruct(param, di) +derive(param::Para, di::Union{AbstractDict, Tuple{Symbol, Any}}) = _reconstruct(param, di) # reconstruct(pp::Para, di) = reconstruct(Para, pp, di) # reconstruct(pp; kws...) = reconstruct(pp, kws) # reconstruct(Para::Type, pp; kws...) = reconstruct(Para, pp, kws) function _reconstruct(pp::Para, di) - # default reconstruct can't handle derived parameters correctly - di = !isa(di, AbstractDict) ? Dict(di) : copy(di) - ns = fieldnames(Para) - args = [] - for (i, n) in enumerate(ns) - if n ∉ derived_para_names - # if exist in di, use value from di - # the default value is from pp - push!(args, (n, pop!(di, n, getfield(pp, n)))) - else - pop!(di, n, getfield(pp, n)) - end - end - length(di) != 0 && error("Fields $(keys(di)) not in type $T") - - dargs = Dict(args) - return Para(; dargs...) + # default reconstruct can't handle derived parameters correctly + di = !isa(di, AbstractDict) ? Dict(di) : copy(di) + ns = fieldnames(Para) + args = [] + for (i, n) in enumerate(ns) + if n ∉ derived_para_names + # if exist in di, use value from di + # the default value is from pp + push!(args, (n, pop!(di, n, getfield(pp, n)))) + else + pop!(di, n, getfield(pp, n)) + end + end + length(di) != 0 && error("Fields $(keys(di)) not in type $T") + + dargs = Dict(args) + return Para(; dargs...) end # function Base.getproperty(obj::Para, sym::Symbol) @@ -115,22 +114,47 @@ end # end # end -# """ -# function chemical_potential(beta) +function polylog(s, z; max_iter = 1000, tol = 1e-17) + z = Complex(z) + + has_trans = false + if abs(z) > 1 + z = 1 / z + has_trans = true + end + sum = 0.0 + term = z + for n in 1:max_iter + sum += term / n^s + term *= z + if abs(term / n^s) < tol + break + end + end + + if has_trans + return (-1 + 0im)^(s + 1) * sum + (2π * im)^s / gamma(s) * zeta(1 - s, 0.5 + log(-1 / z) / (2π * im)) + else + return sum + end +end + +""" + function chemical_potential(beta) -# generate chemical potential μ with given beta and density conservation. +generate chemical potential μ with given beta and density conservation. -# #Arguments: -# - beta: dimensionless inverse temperature -# """ -# function chemical_potential(beta, dim) -# f(β, μ) = real(polylog(dim / 2, -exp(β * μ))) + 1 / gamma(1 + dim / 2) * (β)^(dim / 2) -# g(μ) = f(beta, μ) -# return find_zero(g, (-1e4, 1)) -# end +#Arguments: + - beta: dimensionless inverse temperature +""" +function chemical_potential(beta, dim) + f(β, μ) = real(polylog(dim / 2, -exp(β * μ))) + 1 / gamma(1 + dim / 2) * (β)^(dim / 2) + g(μ) = f(beta, μ) + return find_zero(g, (-1e4, 1)) +end """ - function fullUnit(ϵ0, e0, me, EF, β) + function fullUnit(ϵ0, e0, me, EF, β) generate Para with a complete set of parameters, no value presumed. @@ -141,31 +165,31 @@ generate Para with a complete set of parameters, no value presumed. - EF: Fermi energy - β: inverse temperature """ -@inline function fullUnit(ϵ0, e0, me, EF, β, dim=3, spin=2; kwargs...) - # μ = try - # chemical_potential(β * EF, dim) * EF - # catch e - # # if isa(e, StackOverflowError) - # EF - # end - μ = EF - # println(kwargs) - - para = Para(dim=dim, - spin=spin, - ϵ0=ϵ0, - e0=e0, - me=me, - EF=EF, - β=β, - μ=μ - ) - # return para - return derive(para, kwargs) +@inline function fullUnit(ϵ0, e0, me, EF, β, dim = 3, spin = 2; kwargs...) + μ = try + chemical_potential(β * EF, dim) * EF + catch e + # if isa(e, StackOverflowError) + EF + end + # μ = EF + # println(kwargs) + + para = Para(dim = dim, + spin = spin, + ϵ0 = ϵ0, + e0 = e0, + me = me, + EF = EF, + β = β, + μ = μ, + ) + # return para + return derive(para, kwargs) end """ - function defaultUnit(Θ, rs) + function defaultUnit(Θ, rs) assume 4πϵ0=1, me=0.5, EF=1 @@ -173,18 +197,18 @@ assume 4πϵ0=1, me=0.5, EF=1 - Θ: dimensionless temperature. Since EF=1 we have β=beta - rs: Wigner-Seitz radius over Bohr radius. """ -@inline function defaultUnit(Θ, rs, dim=3, spin=2; kwargs...) - ϵ0 = 1 / (4π) - e0 = (dim == 3) ? sqrt(2 * rs / (9π / (2spin))^(1 / 3)) : sqrt(sqrt(2) * rs) - me = 0.5 - EF = 1 - β = 1 / Θ / EF - return fullUnit(ϵ0, e0, me, EF, β, dim, spin; kwargs...) +@inline function defaultUnit(Θ, rs, dim = 3, spin = 2; kwargs...) + ϵ0 = 1 / (4π) + e0 = (dim == 3) ? sqrt(2 * rs / (9π / (2spin))^(1 / 3)) : sqrt(sqrt(2) * rs) + me = 0.5 + EF = 1 + β = 1 / Θ / EF + return fullUnit(ϵ0, e0, me, EF, β, dim, spin; kwargs...) end """ - function rydbergUnit(Θ, rs, dim = 3, spin = 2; kwargs...) + function rydbergUnit(Θ, rs, dim = 3, spin = 2; kwargs...) assume 4πϵ0=1, me=0.5, e0=sqrt(2) @@ -195,19 +219,19 @@ assume 4πϵ0=1, me=0.5, e0=sqrt(2) - spin: spin = 1 or 2 - kwargs: user may explicity set other paramters using the key/value pairs """ -@inline function rydbergUnit(Θ, rs, dim=3, spin=2; kwargs...) - ϵ0 = 1 / (4π) - e0 = sqrt(2) - me = 0.5 - kF = (dim == 3) ? (9π / (2spin))^(1 / 3) / rs : sqrt(4 / spin) / rs - EF = kF^2 / (2me) - β = 1 / Θ / EF - return fullUnit(ϵ0, e0, me, EF, β, dim, spin; kwargs...) +@inline function rydbergUnit(Θ, rs, dim = 3, spin = 2; kwargs...) + ϵ0 = 1 / (4π) + e0 = sqrt(2) + me = 0.5 + kF = (dim == 3) ? (9π / (2spin))^(1 / 3) / rs : sqrt(4 / spin) / rs + EF = kF^2 / (2me) + β = 1 / Θ / EF + return fullUnit(ϵ0, e0, me, EF, β, dim, spin; kwargs...) end """ - function atomicUnit(Θ, rs, dim = 3, spin = 2; kwargs...) + function atomicUnit(Θ, rs, dim = 3, spin = 2; kwargs...) assume 4πϵ0=1, me=1, e0=1 @@ -218,14 +242,14 @@ assume 4πϵ0=1, me=1, e0=1 - spin: spin = 1 or 2 - kwargs: user may explicity set other paramters using the key/value pairs """ -@inline function atomicUnit(Θ, rs, dim=3, spin=2; kwargs...) - ϵ0 = 1 / (4π) - e0 = 1 - me = 1 - kF = (dim == 3) ? (9π / (2spin))^(1 / 3) / rs : sqrt(4 / spin) / rs - EF = kF^2 / (2me) - β = 1 / Θ / EF - return fullUnit(ϵ0, e0, me, EF, β, dim, spin; kwargs...) +@inline function atomicUnit(Θ, rs, dim = 3, spin = 2; kwargs...) + ϵ0 = 1 / (4π) + e0 = 1 + me = 1 + kF = (dim == 3) ? (9π / (2spin))^(1 / 3) / rs : sqrt(4 / spin) / rs + EF = kF^2 / (2me) + β = 1 / Θ / EF + return fullUnit(ϵ0, e0, me, EF, β, dim, spin; kwargs...) end @@ -233,9 +257,9 @@ end """ - isZeroT(para) = (para.β == Inf) + isZeroT(para) = (para.β == Inf) - check if it is at zero temperature or not. + check if it is at zero temperature or not. """ isZeroT(para) = (para.β == Inf) diff --git a/test/parameter.jl b/test/parameter.jl index 4f18126..b6419e6 100644 --- a/test/parameter.jl +++ b/test/parameter.jl @@ -1,32 +1,39 @@ @testset "Parameter" begin - # test constructor and reconstruct of Para - - beta = 1e4 - rs = 2.0 - param = Parameter.defaultUnit(1 / beta, rs) - - @test param.me == 0.5 - @test param.EF == 1.0 - @test param.kF == 1.0 - @test param.β == beta - @test param.gs == 1.0 - @test param.ga == 0.0 - - @test param.ωp ≈ sqrt(4 * param.kF^3 * param.e0^2 / 3 / param.me / π) - @test param.qTF ≈ sqrt(4 * π * param.e0^2 * param.NF) - - newbeta = 1e3 - ga = 1.0 - newparam = Parameter.derive(param, β=newbeta, ga=ga) - - @test newparam.me == 0.5 - @test newparam.EF == 1.0 - @test newparam.kF == 1.0 - @test newparam.β == newbeta - @test newparam.ga == ga - - @test newparam.ωp ≈ sqrt(4 * newparam.kF^3 * newparam.e0^2 / 3 / newparam.me / π) - @test newparam.qTF ≈ sqrt(4 * π * newparam.e0^2 * newparam.NF) - - + # test constructor and reconstruct of Para + + beta = 1e4 + rs = 2.0 + param = Parameter.defaultUnit(1 / beta, rs) + + @test param.me == 0.5 + @test param.EF == 1.0 + @test param.kF == 1.0 + @test param.β == beta + @test param.gs == 1.0 + @test param.ga == 0.0 + @test param.μ ≈ 1.0 + + @test param.ωp ≈ sqrt(4 * param.kF^3 * param.e0^2 / 3 / param.me / π) + @test param.qTF ≈ sqrt(4 * π * param.e0^2 * param.NF) + + newbeta = 1e3 + ga = 1.0 + newparam = Parameter.derive(param, β = newbeta, ga = ga) + + @test newparam.me == 0.5 + @test newparam.EF == 1.0 + @test newparam.kF == 1.0 + @test newparam.β == newbeta + @test newparam.ga == ga + @test newparam.μ ≈ 1.0 + + @test newparam.ωp ≈ sqrt(4 * newparam.kF^3 * newparam.e0^2 / 3 / newparam.me / π) + @test newparam.qTF ≈ sqrt(4 * π * newparam.e0^2 * newparam.NF) + + beta = 2.0 + param = Parameter.rydbergUnit(1 / beta, rs) + @test param.beta == 2.0 + @test param.kF == (9π / 4)^(1 / 3) / rs + @test param.EF == param.kF^2 + @test param.μ ≈ param.EF * 0.743112084259 end diff --git a/test/polarization.jl b/test/polarization.jl index 9020321..6429cc8 100644 --- a/test/polarization.jl +++ b/test/polarization.jl @@ -1,86 +1,88 @@ # using Gaston @testset "Polarization" begin - beta, rs = 1e8, 1.0 - param3d = Parameter.defaultUnit(1 / beta, rs, 3) - param2d = Parameter.defaultUnit(1 / beta, rs, 2) + beta, rs = 1e8, 1.0 + param3d = Parameter.defaultUnit(1 / beta, rs, 3) + param2d = Parameter.defaultUnit(1 / beta, rs, 2) - @testset "Polarization: ZeroTemp vs. FiniteTemp" begin - # for low temp, Π from zero temp and finite temp should be close - qgrid = [-1.0, 0.0, 1e-16, 1e-8, 1e-7, 1e-6, 1e-5, 5e-5, 1e-4, 1e-3, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.0, 3.0] - ngrid = [n for n in -5:20] - for (qi, q) in enumerate(qgrid) - for (ni, n) in enumerate(ngrid) - # println("q=$q, n=$n") - @test isapprox( - Polarization.Polarization0_ZeroTemp(q, n, param3d), - Polarization.Polarization0_FiniteTemp(q, n, param3d), - rtol=1e-6 - ) - end - end + @testset "Polarization: ZeroTemp vs. FiniteTemp" begin + # for low temp, Π from zero temp and finite temp should be close + qgrid = [-1.0, 0.0, 1e-16, 1e-8, 1e-7, 1e-6, 1e-5, 5e-5, 1e-4, 1e-3, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.0, 3.0] + ngrid = [n for n in -5:20] + for (qi, q) in enumerate(qgrid) + for (ni, n) in enumerate(ngrid) + # println("q=$q, n=$n") + @test isapprox( + Polarization.Polarization0_ZeroTemp(q, n, param3d), + Polarization.Polarization0_FiniteTemp(q, n, param3d), + rtol = 1e-6, + ) + end + end - for (qi, q) in enumerate(qgrid) - for (ni, n) in enumerate(ngrid) - @test isapprox( - Polarization.Polarization0_ZeroTemp(q, n, param2d), - Polarization.Polarization0_FiniteTemp(q, n, param2d), - rtol=1e-4 - ) - end - end - end + for (qi, q) in enumerate(qgrid) + for (ni, n) in enumerate(ngrid) + @test isapprox( + Polarization.Polarization0_ZeroTemp(q, n, param2d), + Polarization.Polarization0_FiniteTemp(q, n, param2d), + rtol = 1e-4, + ) + end + end + end - qgrid = [-1.0, 0.0, 1e-16, 1e-8, 1e-7, 1e-6, 1e-5, 5e-5, 1e-4, 1e-3, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.0, 3.0] - ngrid = [n for n in -5:20] + qgrid = [-1.0, 0.0, 1e-16, 1e-8, 1e-7, 1e-6, 1e-5, 5e-5, 1e-4, 1e-3, 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.0, 3.0] + ngrid = [n for n in -5:20] - @testset "Polarization0!: 3D ZeroTemp vs. FiniteTemp" begin - # param = Parameter.defaultUnit(1 / beta, rs, 3) - nmesh = MeshGrids.ImFreq(param3d.β, BOSON; grid=ngrid) - PZ = MeshArray(nmesh, qgrid; dtype=Float64) - PF = similar(PZ) + @testset "Polarization0!: 3D ZeroTemp vs. FiniteTemp" begin + # param = Parameter.defaultUnit(1 / beta, rs, 3) + nmesh = MeshGrids.ImFreq(param3d.β, BOSON; grid = ngrid) + PZ = MeshArray(nmesh, qgrid; dtype = Float64) + PF = similar(PZ) - Polarization.Polarization0_ZeroTemp!(PZ, param3d) - Polarization.Polarization0_FiniteTemp!(PF, param3d) - for ind in eachindex(PZ) - @test isapprox(PZ[ind], PF[ind], rtol=1e-6) - end - end + Polarization.Polarization0_ZeroTemp!(PZ, param3d) + Polarization.Polarization0_FiniteTemp!(PF, param3d) + for ind in eachindex(PZ) + @test isapprox(PZ[ind], PF[ind], rtol = 1e-6) + end + end - @testset "Polarization: wrapped" begin - PZ = Polarization.Polarization0wrapped(100param3d.EF, 1e-10, qgrid, param3d, pifunc=Polarization.Polarization0_ZeroTemp!) - PF = Polarization.Polarization0wrapped(100param3d.EF, 1e-10, qgrid, param3d, pifunc=Polarization.Polarization0_FiniteTemp!) + @testset "Polarization: wrapped" begin + PZ = Polarization.Polarization0wrapped(100param3d.EF, 1e-10, qgrid, param3d, pifunc = Polarization.Polarization0_ZeroTemp!) + PF = Polarization.Polarization0wrapped(100param3d.EF, 1e-10, qgrid, param3d, pifunc = Polarization.Polarization0_FiniteTemp!) - PZ = Polarization.Polarization0wrapped(100param2d.EF, 1e-10, qgrid, param2d, pifunc=Polarization.Polarization0_ZeroTemp!) + PZ = Polarization.Polarization0wrapped(100param2d.EF, 1e-10, qgrid, param2d, pifunc = Polarization.Polarization0_ZeroTemp!) - # PZ = Polarization.Polarization0wrapped(100param.EF, 1e-10, qgrid, param, pifunc=Polarization.Polarization0_ZeroTemp) - # PF = Polarization.Polarization0wrapped(100param.EF, 1e-10, qgrid, param, pifunc=Polarization.Polarization0_FiniteTemp) - end + # PZ = Polarization.Polarization0wrapped(100param.EF, 1e-10, qgrid, param, pifunc=Polarization.Polarization0_ZeroTemp) + # PF = Polarization.Polarization0wrapped(100param.EF, 1e-10, qgrid, param, pifunc=Polarization.Polarization0_FiniteTemp) + end - @testset "Ladder" begin - para = Parameter.rydbergUnit(1.0 / 10, 4.0, 3) - ladder = Polarization.Ladder0_FiniteTemp(0.1 * para.kF, 1, para, gaussN=16, minterval=1e-8) - @test abs(real(ladder) - 0.0053666) < 3 * 1.7e-5 - @test abs(imag(ladder) - 0.006460) < 3 * 1.1e-5 + @testset "Ladder" begin + using Parameters + para = Parameter.rydbergUnit(1.0 / 10, 4.0, 3) + para = reconstruct(para, μ = para.EF) + ladder = Polarization.Ladder0_FiniteTemp(0.1 * para.kF, 1, para, gaussN = 16, minterval = 1e-8) + @test abs(real(ladder) - 0.0053666) < 3 * 1.7e-5 + @test abs(imag(ladder) - 0.006460) < 3 * 1.1e-5 - ladder = Polarization.Ladder0_FiniteTemp(0.1 * para.kF, 0, para, gaussN=16, minterval=1e-8) #zero frequency - @test abs(real(ladder) - 0.021199) < 3 * 2.0e-5 - @test abs(imag(ladder) - 0.0) < 3 * 3.3e-11 - end + ladder = Polarization.Ladder0_FiniteTemp(0.1 * para.kF, 0, para, gaussN = 16, minterval = 1e-8) #zero frequency + @test abs(real(ladder) - 0.021199) < 3 * 2.0e-5 + @test abs(imag(ladder) - 0.0) < 3 * 3.3e-11 + end - # while true - # print("Please enter a whole number between 1 and 5: ") - # input = readline(stdin) - # value = tryparse(Int, input) - # if value !== nothing && 1 <= value <= 5 - # println("You entered $(input)") - # break - # else - # @warn "Enter a whole number between 1 and 5" - # end - # end - # plt = plot(qgrid, polar, ytics = -0.08:0.01, ls = 1, Axes(grid = :on, key = "left")) - # display(plt) - # save(term = "png", output = "polar_2d.png", - # saveopts = "font 'Consolas,10' size 1280,900 lw 1") + # while true + # print("Please enter a whole number between 1 and 5: ") + # input = readline(stdin) + # value = tryparse(Int, input) + # if value !== nothing && 1 <= value <= 5 + # println("You entered $(input)") + # break + # else + # @warn "Enter a whole number between 1 and 5" + # end + # end + # plt = plot(qgrid, polar, ytics = -0.08:0.01, ls = 1, Axes(grid = :on, key = "left")) + # display(plt) + # save(term = "png", output = "polar_2d.png", + # saveopts = "font 'Consolas,10' size 1280,900 lw 1") end