From 53a9fea63a38ecea4ad84a9f9e3dc55dd53f5618 Mon Sep 17 00:00:00 2001 From: Simon Guichandut <40605210+simonguichandut@users.noreply.github.com> Date: Tue, 14 May 2024 15:38:07 -0400 Subject: [PATCH] xrb layered updates (#396) This syncs up to the version that was used in the xrb_layered paper --- Exec/science/xrb_layered/.gitignore | 21 + Exec/science/xrb_layered/MaestroInitData.cpp | 168 +- Exec/science/xrb_layered/MaestroTagging.cpp | 71 + Exec/science/xrb_layered/_parameters | 60 +- .../xrb_layered/analysis/slice_multi_crop.py | 94 - .../xrb_layered/analysis/slice_small.py | 93 - .../create_initial_model/.gitignore | 6 + .../create_initial_model/Initial_Model.ipynb | 1219 ++++ .../create_initial_model/README.md | 27 +- .../ignition_analysis.ipynb | 258 - .../{ => mesa}/convert_mesa.py | 0 .../inputs_lagrangian_planar_xrb_layered | 0 .../{ => mesa}/periodic_table.list | 0 .../{ => mesa}/profile37.data | 0 .../{ => mesa}/profile37.raw | 0 .../{ => mesa}/profile38.data | 0 .../{ => mesa}/py_mesa_reader.py | 0 .../create_initial_model/mesa/readme.md | 23 + .../xrb_layered/create_initial_model/plot.py | 69 + .../create_initial_model/profile46.data | 1466 ---- .../create_initial_model/profile46.raw | 1487 ---- .../create_initial_model/profile47.data | 1491 ---- .../create_initial_model/toy_atm/.gitignore | 4 + .../create_initial_model/toy_atm/GNUmakefile | 33 + .../create_initial_model/toy_atm/Make.package | 3 + .../create_initial_model/toy_atm/_parameters | 40 + .../create_initial_model/toy_atm/init_1d.H | 624 ++ .../create_initial_model/toy_atm/inputs | 27 + .../create_initial_model/toy_atm/inputs_hot | 27 + .../toy_atm/inputs_hot_1.5 | 27 + .../create_initial_model/toy_atm/main.cpp | 62 + .../create_initial_model/toy_atm/model_util.H | 24 + .../toy_atm/toy_atm_3cm.hse | 1050 +++ .../toy_atm/toy_atm_3cm.hse.extras | 3076 +++++++++ .../toy_atm/toy_atm_hot_1.5cm.hse | 2074 ++++++ .../toy_atm/toy_atm_hot_1.5cm.hse.extras | 6148 +++++++++++++++++ .../toy_atm/toy_atm_hot_3cm.hse | 1050 +++ .../toy_atm/toy_atm_hot_3cm.hse.extras | 3076 +++++++++ .../create_initial_model/toy_atm_3cm.hse.png | Bin 0 -> 296921 bytes .../toy_atm_hot_3cm.hse.png | Bin 0 -> 294567 bytes .../create_initial_model/xrb_layered_3cm.hse | 1050 --- .../xrb_layered_3cm.uniform | 1050 --- .../xrb_layered/{inputs_2d_12cm => inputs} | 62 +- .../xrb_layered/plotfile_derive/.gitignore | 4 + .../xrb_layered/plotfile_derive/GNUmakefile | 40 + .../xrb_layered/plotfile_derive/Make.package | 1 + .../xrb_layered/plotfile_derive/README.md | 94 + .../xrb_layered/plotfile_derive/_parameters | 6 + .../plotfile_derive/amrex_astro_util.H | 230 + .../xrb_layered/plotfile_derive/main.cpp | 551 ++ Exec/science/xrb_layered/rms_tool/.gitignore | 3 + .../xrb_layered/rms_tool/AMReX_PPMUtil.H | 21 + .../xrb_layered/rms_tool/AMReX_PPMUtil.cpp | 98 + .../xrb_layered/rms_tool/CMakeLists.txt | 44 + Exec/science/xrb_layered/rms_tool/GNUmakefile | 32 + .../science/xrb_layered/rms_tool/Make.package | 3 + Exec/science/xrb_layered/rms_tool/frms.cpp | 391 ++ .../xrb_layered/scripts/average_vars.sh | 80 + Exec/science/xrb_layered/scripts/avg.submit | 83 + .../science/xrb_layered/scripts/chainslurm.sh | 56 + .../xrb_layered/scripts/conv_grad.submit | 72 + .../xrb_layered/scripts/derived.submit | 73 + .../xrb_layered/scripts/extrema.submit | 32 + .../xrb_layered/scripts/get_extrema.sh | 9 + Exec/science/xrb_layered/scripts/rms.submit | 78 + Exec/science/xrb_layered/scripts/rms_vars.sh | 81 + .../xrb_layered/scripts/run.submit.template | 105 + Exec/science/xrb_layered/toy_atm_hot_3cm.hse | 1 + Exec/science/xrb_layered/xrb_layered_3cm.hse | 1050 --- 69 files changed, 21040 insertions(+), 8258 deletions(-) create mode 100644 Exec/science/xrb_layered/.gitignore create mode 100644 Exec/science/xrb_layered/MaestroTagging.cpp delete mode 100755 Exec/science/xrb_layered/analysis/slice_multi_crop.py delete mode 100755 Exec/science/xrb_layered/analysis/slice_small.py create mode 100644 Exec/science/xrb_layered/create_initial_model/Initial_Model.ipynb delete mode 100644 Exec/science/xrb_layered/create_initial_model/ignition_analysis.ipynb rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/convert_mesa.py (100%) rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/inputs_lagrangian_planar_xrb_layered (100%) rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/periodic_table.list (100%) rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/profile37.data (100%) rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/profile37.raw (100%) rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/profile38.data (100%) rename Exec/science/xrb_layered/create_initial_model/{ => mesa}/py_mesa_reader.py (100%) create mode 100644 Exec/science/xrb_layered/create_initial_model/mesa/readme.md create mode 100644 Exec/science/xrb_layered/create_initial_model/plot.py delete mode 100644 Exec/science/xrb_layered/create_initial_model/profile46.data delete mode 100644 Exec/science/xrb_layered/create_initial_model/profile46.raw delete mode 100644 Exec/science/xrb_layered/create_initial_model/profile47.data create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/.gitignore create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/GNUmakefile create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/Make.package create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/_parameters create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/init_1d.H create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/inputs create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot_1.5 create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/main.cpp create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/model_util.H create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_3cm.hse create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_3cm.hse.extras create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_hot_1.5cm.hse create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_hot_1.5cm.hse.extras create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_hot_3cm.hse create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_hot_3cm.hse.extras create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm_3cm.hse.png create mode 100644 Exec/science/xrb_layered/create_initial_model/toy_atm_hot_3cm.hse.png delete mode 100644 Exec/science/xrb_layered/create_initial_model/xrb_layered_3cm.hse delete mode 100644 Exec/science/xrb_layered/create_initial_model/xrb_layered_3cm.uniform rename Exec/science/xrb_layered/{inputs_2d_12cm => inputs} (68%) create mode 100644 Exec/science/xrb_layered/plotfile_derive/.gitignore create mode 100644 Exec/science/xrb_layered/plotfile_derive/GNUmakefile create mode 100644 Exec/science/xrb_layered/plotfile_derive/Make.package create mode 100644 Exec/science/xrb_layered/plotfile_derive/README.md create mode 100644 Exec/science/xrb_layered/plotfile_derive/_parameters create mode 100644 Exec/science/xrb_layered/plotfile_derive/amrex_astro_util.H create mode 100644 Exec/science/xrb_layered/plotfile_derive/main.cpp create mode 100644 Exec/science/xrb_layered/rms_tool/.gitignore create mode 100644 Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.H create mode 100644 Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.cpp create mode 100644 Exec/science/xrb_layered/rms_tool/CMakeLists.txt create mode 100644 Exec/science/xrb_layered/rms_tool/GNUmakefile create mode 100644 Exec/science/xrb_layered/rms_tool/Make.package create mode 100644 Exec/science/xrb_layered/rms_tool/frms.cpp create mode 100755 Exec/science/xrb_layered/scripts/average_vars.sh create mode 100644 Exec/science/xrb_layered/scripts/avg.submit create mode 100755 Exec/science/xrb_layered/scripts/chainslurm.sh create mode 100644 Exec/science/xrb_layered/scripts/conv_grad.submit create mode 100644 Exec/science/xrb_layered/scripts/derived.submit create mode 100644 Exec/science/xrb_layered/scripts/extrema.submit create mode 100755 Exec/science/xrb_layered/scripts/get_extrema.sh create mode 100644 Exec/science/xrb_layered/scripts/rms.submit create mode 100755 Exec/science/xrb_layered/scripts/rms_vars.sh create mode 100644 Exec/science/xrb_layered/scripts/run.submit.template create mode 120000 Exec/science/xrb_layered/toy_atm_hot_3cm.hse delete mode 100644 Exec/science/xrb_layered/xrb_layered_3cm.hse diff --git a/Exec/science/xrb_layered/.gitignore b/Exec/science/xrb_layered/.gitignore new file mode 100644 index 000000000..153d2201c --- /dev/null +++ b/Exec/science/xrb_layered/.gitignore @@ -0,0 +1,21 @@ +*.ipynb_checkpoints/ +**/.vscode +*slurm* +*submit* +*out* +*debug* +log.md +analysis + +# some useful analysis scripts +scripts/* +!scripts/average_vars.sh +!scripts/avg.submit +!scripts/chainslurm.sh +!scripts/conv_grad.submit +!scripts/derived.submit +!scripts/get_extrema.sh +!scripts/extrema.submit +!scripts/rms.submit +!scripts/rms_vars.sh +!scripts/run.submit.template diff --git a/Exec/science/xrb_layered/MaestroInitData.cpp b/Exec/science/xrb_layered/MaestroInitData.cpp index 80cf8205d..d8662ed80 100644 --- a/Exec/science/xrb_layered/MaestroInitData.cpp +++ b/Exec/science/xrb_layered/MaestroInitData.cpp @@ -1,5 +1,6 @@ - #include +#include + using namespace amrex; // initializes data on a specific level @@ -38,121 +39,60 @@ void Maestro::InitLevelData(const int lev, [[maybe_unused]] const Real time, con const auto dx = geom[lev].CellSizeArray(); if (perturb_model) { - const auto xcen = center[0]; - const auto ycen = AMREX_SPACEDIM == 2 ? problem_rp::xrb_pert_height : center[1]; -#if AMREX_SPACEDIM == 3 - const auto zcen = problem_rp::xrb_pert_height; -#endif - - const auto rad_pert = - -problem_rp::xrb_pert_size * problem_rp::xrb_pert_size / (4.0 * std::log(0.5)); - - const bool perturb_temp_true = problem_rp::xrb_pert_type == 1; - - const auto xrb_pert_factor_loc = problem_rp::xrb_pert_factor; - const auto rad_pert_loc = rad_pert; - - ParallelFor(tileBox, [=] (int i, int j, int k) { - int r = AMREX_SPACEDIM == 2 ? j : k; - - const auto x = prob_lo[0] + (Real(i) + 0.5) * dx[0] - xcen; - const auto y = prob_lo[1] + (Real(j) + 0.5) * dx[1] - ycen; -#if AMREX_SPACEDIM == 3 - const auto z = prob_lo[2] + (Real(k) + 0.5) * dx[2] - zcen; -#else - const Real z = 0.0; -#endif - - const auto dist = std::sqrt(x * x + y * y + z * z); - - Real temp = 0.0; - Real dens = 0.0; - auto eos_input_flag = eos_input_tp; - - if (perturb_temp_true) { - Real t0 = s0_arr(lev, r, Temp); - temp = t0 * (1.0 + xrb_pert_factor_loc * - std::exp(-dist * dist / rad_pert_loc)); - dens = s0_arr(lev, r, Rho); - eos_input_flag = eos_input_tp; - } else { - Real d0 = s0_arr(lev, r, Rho); - dens = d0 * (1.0 + xrb_pert_factor_loc * - std::exp(-dist * dist / rad_pert_loc)); - temp = s0_arr(lev, r, Temp); - eos_input_flag = eos_input_rp; - } - - eos_t eos_state; - - eos_state.T = temp; - eos_state.p = p0_arr(lev, r); - eos_state.rho = dens; - for (auto comp = 0; comp < NumSpec; ++comp) { - eos_state.xn[comp] = - s0_arr(lev, r, FirstSpec + comp) / s0_arr(lev, r, Rho); - } - - eos(eos_input_flag, eos_state); - - scal(i, j, k, Rho) = eos_state.rho; - scal(i, j, k, RhoH) = eos_state.rho * eos_state.h; - scal(i, j, k, Temp) = eos_state.T; - for (auto comp = 0; comp < NumSpec; ++comp) { - scal(i, j, k, FirstSpec + comp) = - eos_state.rho * eos_state.xn[comp]; - } - }); - } - - if (problem_rp::apply_vel_field) { - const auto velpert_amplitude_loc = problem_rp::velpert_amplitude; - const auto velpert_scale_loc = problem_rp::velpert_scale; - const auto num_vortices_loc = problem_rp::num_vortices; - const auto velpert_height = problem_rp::velpert_height_loc; - - const Real offset = (prob_hi[0] - prob_lo[0]) / (problem_rp::num_vortices); - - // vortex x-coords - RealVector vortices_xloc(problem_rp::num_vortices); - for (auto i = 0; i < problem_rp::num_vortices; ++i) { - vortices_xloc[i] = (static_cast(i) + 0.5_rt) * offset; + + // Generate random seed (or get from input), and RNG + int seed = 0; + if (problem_rp::pert_seed != -1) { + seed = problem_rp::pert_seed; + } else { + std::random_device r; + seed = r(); } - - const Real* vortices_xloc_p = vortices_xloc.dataPtr(); - - ParallelFor(tileBox, [=] (int i, int j, int k) { - const auto x = prob_lo[0] + (Real(i) + 0.5) * dx[0]; - const auto y = prob_lo[1] + (Real(j) + 0.5) * dx[1]; - - Real ydist = y - velpert_height; - - Real upert = 0.0; - Real vpert = 0.0; - - for (auto vortex = 0; vortex < num_vortices_loc; ++vortex) { - Real xdist = x - vortices_xloc_p[vortex]; - - Real rad = std::sqrt(xdist * xdist + ydist * ydist); - - // e.g. Calder et al. ApJSS 143, 201-229 (2002) - // we set things up so that every other vortex has the same - // orientation - upert -= - ydist * velpert_amplitude_loc * - std::exp(-rad * rad / - (2.0 * velpert_scale_loc * velpert_scale_loc)) * - pow(-1.0, vortex + 1); - - vpert += - xdist * velpert_amplitude_loc * - std::exp(-rad * rad / - (2.0 * velpert_scale_loc * velpert_scale_loc)) * - pow(-1.0, vortex + 1); + std::default_random_engine generator(seed); + + // Gaussian distribution + std::normal_distribution noise(1.0, 0.001); // mean of 1, std dev 10^-3 + + // Loop through data and perturb + const auto lo = amrex::lbound(tileBox); + const auto hi = amrex::ubound(tileBox); + + // Why for and not parallelfor? + // Because RNG's are not thread safe. so if we wanted to use ParallelFor, we would have to create a new RNG inside + // every iteration, but that loses the ability to keep a fixed seed and be fully reproducible.. maybe that wouldn't be so bad + // but (probably?) because of this standard for, this initialization can't be run in parallel (with e.g. srun) + // Have to run as a single process until at least get an initial checkfile, from which we can run in parallel. + for (int k = lo.z; k<= hi.z; k++) { + for (int j = lo.y; j<= hi.y; j++) { + for (int i = lo.x; i<= hi.x; i++) { + int r = AMREX_SPACEDIM == 2 ? j : k; // j if 2D, k if 3D + + Real t0 = s0_arr(lev, r, Temp); + Real temp = t0 * noise(generator); + Real dens = s0_arr(lev, r, Rho); // no change + + // Create new eos object based on these modified values + eos_t eos_state; + eos_state.T = temp; + eos_state.p = p0_arr(lev, r); + eos_state.rho = dens; + for (auto comp = 0; comp < NumSpec; comp++) { + eos_state.xn[comp] = + s0_arr(lev, r, FirstSpec + comp) / s0_arr(lev, r, Rho); + } + + auto eos_input_flag = eos_input_tp; // temperature & pressure eos + eos(eos_input_flag, eos_state); + scal(i, j, k, Rho) = eos_state.rho; + scal(i, j, k, RhoH) = eos_state.rho * eos_state.h; // re-compute enthalpy + scal(i, j, k, Temp) = eos_state.T; + for (auto comp=0; comp < NumSpec; comp++) { + scal(i, j, k, FirstSpec + comp) = + eos_state.rho * eos_state.xn[comp]; + } + } } - vel(i, j, k, 0) += upert; - vel(i, j, k, 1) += vpert; - }); + } } } diff --git a/Exec/science/xrb_layered/MaestroTagging.cpp b/Exec/science/xrb_layered/MaestroTagging.cpp new file mode 100644 index 000000000..6757df811 --- /dev/null +++ b/Exec/science/xrb_layered/MaestroTagging.cpp @@ -0,0 +1,71 @@ + +#include + +using namespace amrex; + +void Maestro::RetagArray(const Box& bx, const int lev) { + // timer for profiling + BL_PROFILE_VAR("Maestro::RetagArray()", RetagArray); + + // re-compute tag_array since the actual grid structure changed due to buffering + // this is required in order to compute numdisjointchunks, r_start_coord, r_end_coord + + // Tag on regions including buffer cells + auto lo = bx.loVect3d()[AMREX_SPACEDIM - 1]; + auto hi = bx.hiVect3d()[AMREX_SPACEDIM - 1]; + const auto max_lev = base_geom.max_radial_level + 1; + + for (auto r = lo; r <= hi; ++r) { + tag_array[lev - 1 + max_lev * (r / 2)] = TagBox::SET; + } +} + +void Maestro::TagBoxes(TagBoxArray& tags, const MFIter& mfi, const int lev, + const Real time) { + // timer for profiling + BL_PROFILE_VAR("Maestro::TagBoxes()", TagBoxes); + + // tag all cells at a given height if any cells at that height were tagged + + const Array4 tag = tags.array(mfi); + const int* AMREX_RESTRICT tag_array_p = tag_array.dataPtr(); + const int max_lev = base_geom.max_radial_level + 1; + + const Box& tilebox = mfi.tilebox(); + + ParallelFor(tilebox, [=] AMREX_GPU_DEVICE(int i, int j, int k) { + int r = AMREX_SPACEDIM == 2 ? j : k; + + if (tag_array_p[lev + max_lev * r] > 0) { + tag(i, j, k) = TagBox::SET; + } + }); +} + +void Maestro::StateError(TagBoxArray& tags, const MultiFab& state_mf, + const MFIter& mfi, const int lev, const Real time) { + // timer for profiling + BL_PROFILE_VAR("Maestro::StateError()", StateError); + + const Box& tileBox = mfi.tilebox(); + + const auto tag = tags.array(mfi); + const Array4 state = state_mf.array(mfi); + int* AMREX_RESTRICT tag_array_p = tag_array.dataPtr(); + const int max_lev = base_geom.max_radial_level + 1; + + const Real dr_lev = base_geom.dr(lev); + + // Tag based on height + if (problem_rp::do_height_based_refinement) { + ParallelFor(tileBox, [=] AMREX_GPU_DEVICE(int i, int j, int k) { + int r = AMREX_SPACEDIM == 2 ? j : k; + Real height = (Real(r) + 0.5) * dr_lev; + + if (height > problem_rp::height_refine_bottom && height < problem_rp::height_refine_top) { + tag(i, j, k) = TagBox::SET; + tag_array_p[lev + max_lev * r] = TagBox::SET; + } + }); + } +} diff --git a/Exec/science/xrb_layered/_parameters b/Exec/science/xrb_layered/_parameters index bfb274ff1..e9d648c7a 100644 --- a/Exec/science/xrb_layered/_parameters +++ b/Exec/science/xrb_layered/_parameters @@ -1,18 +1,7 @@ @namespace: problem -# Magnitude of the perturbation. -xrb_pert_factor real 1.d-2 - -# Radius of the perturbation. -xrb_pert_size real 5.0d1 - -# Type of perturbation @@ -# 1 = Temperature perturbation @@ -# 2 = Density perturbation -xrb_pert_type integer 1 - -# the height location for the perturbation -xrb_pert_height real -1.0d0 +# seed for the random noise (-1 for random seed) +pert_seed integer -1 # Turn on (.true.) or off (.false.) sponging at the bottom of the domain. xrb_use_bottom_sponge integer 0 @@ -20,44 +9,7 @@ xrb_use_bottom_sponge integer 0 # minimum value to use for the sponge sponge_min real 0.01d0 -# Mass fraction used in {\tt diag.f90} to define where the burning layer @@ -# begins. -diag_define_layer real 0.1 - -# Velocity field initialization -apply_vel_field integer 0 - -# Initial velocity field vortex scale (2-d); thickness of velocity layer (3-d) -velpert_scale real 1.0d2 - -# Initial velocity field vortex amplitude -velpert_amplitude real 1.0d2 - -# Initial velocity field vortex height location (2-d); base of perturbation layer (3-d) -velpert_height_loc real 6.5d3 - -# thickness of velocity pert transition region (3-d) -velpert_steep real 12.d0 - -# number of vortices (2-d) -num_vortices integer 1 - -# look at the deltap diagnostic -do_deltap_diag integer 0 - -# minimum value to use for species in tag_boxes -tag_minval real 1.0d-4 - -# maximum value to use for species in tag_boxes -tag_maxval real 0.99d0 - -# factor to multiply the species by; for tagging off of spec -# instead of EGR -tag_xfac real 1.d-16 - -# density tagging -do_dens_tagging integer 0 - -lo_dens_tag real 5.4d5 -hi_dens_tag real 1.75d6 -dens_tag_lev_fac real 1.0d0 +# Do refinement above and below given heights (given in cm) +do_height_based_refinement integer 0 +height_refine_bottom real 1.0e3 +height_refine_top real 1.2e3 diff --git a/Exec/science/xrb_layered/analysis/slice_multi_crop.py b/Exec/science/xrb_layered/analysis/slice_multi_crop.py deleted file mode 100755 index 63cd53640..000000000 --- a/Exec/science/xrb_layered/analysis/slice_multi_crop.py +++ /dev/null @@ -1,94 +0,0 @@ -#!/usr/bin/env python3 - -import matplotlib -matplotlib.use('agg') - -import os -import sys -import yt -import matplotlib.pyplot as plt -import numpy as np -from functools import reduce - -from mpl_toolkits.axes_grid1 import ImageGrid - -# assume that our data is in CGS -from yt.units import cm, amu -from yt.frontends.boxlib.api import MaestroDataset - -plotfile = sys.argv[1] -if plotfile[-1] == '/': plotfile=plotfile[:-1] -#ds = MaestroDataset(plotfile) -ds = yt.load(plotfile, hint="maestro") - -xmin = ds.domain_left_edge[0] -xmax = ds.domain_right_edge[0] -xctr = 0.5*(xmin + xmax) -L_x = xmax - xmin - - -ymin = ds.domain_left_edge[1] -ymax = ds.domain_right_edge[1] -yctr = 0.5*(ymin + ymax) -L_y = ymax - ymin - - -fig = plt.figure() -#fig.set_size_inches(10.0, 12.0) - -fields = ["tfromp", "MachNumber", "Hnuc", "X(h1)", "X(he4)", "X(c12)"] - -#grid = ImageGrid(fig, 111, nrows_ncols=(1, len(fields)), -# axes_pad=1.0, cbar_pad=0.2, label_mode="L", cbar_mode="each") -grid = ImageGrid(fig, 111, nrows_ncols=(2, 3), - axes_pad=1.0, cbar_pad=0.2, label_mode="L", cbar_mode="each") - -for i, f in enumerate(fields): - - sp = yt.SlicePlot(ds, "z", f, center=[xctr, yctr, 0.0*cm], width=[L_x, L_y, 0.0*cm], fontsize="12") - sp.set_buff_size((2400,2400)) - - if f == "tfromp": - sp.set_zlim(f, 5.e7, 3.e9) - sp.set_cmap(f, "hot") - elif f == "Hnuc": - sp.set_zlim(f, 1.e16, 1.e20) - sp.set_cmap(f, "inferno") - elif f == "MachNumber": - sp.set_zlim(f, 1.e-4, 1.e0) - sp.set_cmap(f, "cividis") - elif "X" in f: # composition - #sp.set_log(f, False) - #sp.set_zlim(f, 0, 1) - - sp.set_zlim(f, 1e-3, 1) - - sp.set_cmap(f, "viridis") - - #if f != "density": - # # now do a contour of density - # sp.annotate_contour("density", ncont=2, clim=(1.e2, 2.e6), - # plot_args={"colors": "0.5", "linewidths": 1, "linestyle": ":"}) - - sp.set_axes_unit("cm") - - #sp.annotate_text((0.05, 0.05), "{:8.5f} s".format(float(ds.current_time.in_cgs())), - # coord_system="figure", text_args={"color": "black"}) - - plot = sp.plots[f] - plot.figure = fig - plot.axes = grid[i].axes - plot.cax = grid.cbar_axes[i] - if i < len(fields)-1: - grid[i].axes.xaxis.offsetText.set_visible(False) - - sp._setup_plots() - -#fig.text(0.5, 0.99, fr"time = {float(ds.current_time):8.5f} s", transform=fig.transFigure, fontsize=15) -fig.suptitle(fr"time = {float(ds.current_time):8.5f} s", fontsize=15) - -fig.set_size_inches(14,14.5) -plt.tight_layout() - -basename = os.path.basename(plotfile) -plt.savefig("plt/" + basename + "_slice.png") diff --git a/Exec/science/xrb_layered/analysis/slice_small.py b/Exec/science/xrb_layered/analysis/slice_small.py deleted file mode 100755 index 4887a0f13..000000000 --- a/Exec/science/xrb_layered/analysis/slice_small.py +++ /dev/null @@ -1,93 +0,0 @@ -#!/usr/bin/env python3 - -import matplotlib -matplotlib.use('agg') - -import os -import sys -import yt -import matplotlib.pyplot as plt -import numpy as np -from functools import reduce - -from mpl_toolkits.axes_grid1 import ImageGrid - -# assume that our data is in CGS -from yt.units import cm, amu -from yt.frontends.boxlib.api import MaestroDataset - -plotfile = sys.argv[1] -ds = MaestroDataset(plotfile) - -xmin = ds.domain_left_edge[0] -xmax = ds.domain_right_edge[0] -xctr = 0.5*(xmin + xmax) -L_x = xmax - xmin - - -ymin = ds.domain_left_edge[1] -ymax = ds.domain_right_edge[1] -yctr = 0.5*(ymin + ymax) -L_y = ymax - ymin - - -fig = plt.figure() -fig.set_size_inches(12.0, 9.0) - - -#fields = ["tfromp", "MachNumber", "Hnuc", "vort"] #, "density"] -fields = ["rho",] - -grid = ImageGrid(fig, 111, nrows_ncols=(1, len(fields)), - axes_pad=1.0, cbar_pad=0.2, label_mode="L", cbar_mode="each") - - -for i, f in enumerate(fields): - - sp = yt.SlicePlot(ds, "z", f, center=[xctr, yctr, 0.0*cm], width=[L_x, L_y, 0.0*cm], fontsize="12") - sp.set_buff_size((2400,2400)) - - if f == "tfromp": - sp.set_zlim(f, 5.e7, 1.e9) - sp.set_cmap(f, "viridis") - elif f == "Hnuc": - sp.set_zlim(f, 1.e16, 1.e19) - sp.set_cmap(f, "plasma_r") - elif f == "vort": - sp.set_zlim(f, -2.e6, 2.e6) - sp.set_log(f, False) - sp.set_cmap(f, "bwr") - elif f == "MachNumber": - sp.set_zlim(f, 1.e-4, 1.e-1) - sp.set_log(f, True) - sp.set_cmap(f, "cividis") - elif f == "rho": - sp.set_zlim([1e0,1e6]) - sp.set_log(f, True) - sp.set_cmap(f, "viridis") - - - #if f != "density": - # # now do a contour of density - # sp.annotate_contour("density", ncont=2, clim=(1.e2, 2.e6), - # plot_args={"colors": "0.5", "linewidths": 1, "linestyle": ":"}) - - sp.set_axes_unit("cm") - - #sp.annotate_text((0.05, 0.05), "{:8.5f} s".format(float(ds.current_time.in_cgs())), - # coord_system="figure", text_args={"color": "black"}) - - plot = sp.plots[f] - plot.figure = fig - plot.axes = grid[i].axes - plot.cax = grid.cbar_axes[i] - if i < len(fields)-1: - grid[i].axes.xaxis.offsetText.set_visible(False) - - sp._setup_plots() - -fig.text(0.05, 0.05, "time = {:8.5f} s".format(float(ds.current_time)), transform=fig.transFigure) - -fig.set_size_inches(19.2, 10.8) -plt.tight_layout() -plt.savefig("{}_slice.png".format(os.path.basename(plotfile))) diff --git a/Exec/science/xrb_layered/create_initial_model/.gitignore b/Exec/science/xrb_layered/create_initial_model/.gitignore index f22a19e65..1807e2322 100644 --- a/Exec/science/xrb_layered/create_initial_model/.gitignore +++ b/Exec/science/xrb_layered/create_initial_model/.gitignore @@ -1,2 +1,8 @@ .ipynb_checkpoints/ __pycache__/ +plots/ +*old* +**.ex + +construct_toy_model.ipynb +ignition_analysis.ipynb diff --git a/Exec/science/xrb_layered/create_initial_model/Initial_Model.ipynb b/Exec/science/xrb_layered/create_initial_model/Initial_Model.ipynb new file mode 100644 index 000000000..f63196e94 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/Initial_Model.ipynb @@ -0,0 +1,1219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b13874b8", + "metadata": {}, + "source": [ + "# Toy model for xrb_layered\n", + "\n", + "We describe the reasoning for how to build the model used in xrb_layered. The toy model is meant to capture the physics of the relevant zones in the MESA model, while being easy to build. The code to build it is using the complete Helmoltz EOS in ```toy_atm/init_1d.H```." + ] + }, + { + "cell_type": "markdown", + "id": "06094410-732d-43fc-ab5e-3593c9d3a898", + "metadata": {}, + "source": [ + "## MESA models\n", + "\n", + "The starting point is a MESA model as close as possible to ignition. We copy over profile37.data and profile38.data from the `make_ignition_model` run in github.com/simonguichandut/mesa_mixed_burst/." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "002fa146-8bd0-427d-b9ba-8e81b7a545eb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Models #: 2102 2103\n", + "dt=2.7e-01 s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import mesa.py_mesa_reader as mr\n", + "\n", + "# These were used in the first set of maestro runs for this problem\n", + "# They had a fe56 substrate with column y=4.6e10\n", + "# prof1 = mr.MesaData(\"profile46.data\")\n", + "# prof2 = mr.MesaData(\"profile47.data\")\n", + "\n", + "# These have a smaller substrate, y=5e9\n", + "prof1 = mr.MesaData(\"mesa/profile37.data\")\n", + "prof2 = mr.MesaData(\"mesa/profile38.data\")\n", + "\n", + "name1 = prof1.file_name.replace(\".data\",\"\")\n", + "name2 = prof2.file_name.replace(\".data\",\"\")\n", + "\n", + "yr = 3.1536e7\n", + "print(\"Models #:\",prof1.model_number,prof2.model_number)\n", + "print(f\"dt={(prof2.star_age-prof1.star_age)*yr:.1e} s\")\n", + "\n", + "fig,(ax1,ax2,ax3) = plt.subplots(3,1,sharex=True,figsize=(10,8))\n", + "fig.subplots_adjust(hspace=0)\n", + "\n", + "ax1.plot(prof1.R_cm-12e5, prof1.T/1e8, 'k-', label=prof1.file_name)\n", + "ax1.plot(prof2.R_cm-12e5, prof2.T/1e8, 'r-', label=prof2.file_name)\n", + "# ax1.set_ylim([])\n", + "ax1.set_ylabel(r'$T$ ($10^8$ K)')\n", + "ax1.legend(frameon=False)\n", + "\n", + "ax2.semilogy(prof1.R_cm-12e5, prof1.Rho, 'k-')\n", + "ax2.semilogy(prof2.R_cm-12e5, prof2.Rho, 'r-')\n", + "ax2.set_ylabel(r'$\\rho$ (g cm$^{-3}$)')\n", + "\n", + "ax3.plot(prof1.R_cm-12e5, prof1.h1, 'k-', label='h1')\n", + "ax3.plot(prof2.R_cm-12e5, prof2.h1, 'r-')\n", + "\n", + "ax3.plot(prof1.R_cm-12e5, prof1.he4, 'k--', label='he4')\n", + "ax3.plot(prof2.R_cm-12e5, prof2.he4, 'r--')\n", + "\n", + "ax3.plot(prof1.R_cm-12e5, prof1.c12, 'k:', label='c12')\n", + "ax3.plot(prof2.R_cm-12e5, prof2.c12, 'r:')\n", + "\n", + "ax3.set_ylabel('Mass fraction')\n", + "ax3.set_xlabel(r'r-R$_{\\rm NS}$ (cm)')\n", + "ax3.legend(frameon=False,loc=2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c8fdbcd7-b011-4184-84d6-d4ce3f7a8a89", + "metadata": {}, + "source": [ + "Only a small temperature bump seems necessary to ignite (notice the rapid burn he4->c12)\n", + "\n", + "Check the thin shell instability condition\n", + "\n", + "$$ d\\epsilon_{3\\alpha}/dT>d\\epsilon_{\\rm cool}/dT $$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b736e559-03b2-4005-bff4-0c3b9f1eca18", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Constants (cgs)\n", + "G = 6.6726e-8\n", + "c = 2.99792458e10\n", + "arad = 7.5657e-15\n", + "Msun = 1.989e33\n", + "\n", + "def dheat_dT(rho,T,Y):\n", + " T9 = T/1e9\n", + " eps_3alpha = 5.1e8 * rho**2 * Y**3 / T9**2 * np.exp(-4.4027/T9) # Hansen & Kawaler Eq. 6.80\n", + " return eps_3alpha / 1e9 * (-3/T9 + 4.4027/T9**2)\n", + "\n", + "g = G*1.4*Msun/12e5**2\n", + "# print(g/1e14)\n", + "\n", + "def dcool_dT(T,kappa,y):\n", + " eps_cool = arad*c*T**4 / (3*kappa*y**2)\n", + " return 4*eps_cool/T\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "plt.xlabel(r'r-R$_{\\rm NS}$ (cm)')\n", + "plt.ylabel(r'$d\\epsilon/dT$')\n", + "plt.semilogy(prof1.R_cm-12e5, dheat_dT(prof1.Rho,prof1.T,prof1.he4), 'r-', label=f'{name1} heat')\n", + "plt.semilogy(prof2.R_cm-12e5, dheat_dT(prof2.Rho,prof2.T,prof2.he4), 'r:', label=f'{name2} heat')\n", + "\n", + "plt.semilogy(prof1.R_cm-12e5, dcool_dT(prof1.T,prof1.opacity,prof1.column_depth), 'b-', label=f'{name1} cool')\n", + "plt.semilogy(prof2.R_cm-12e5, dcool_dT(prof2.T,prof2.opacity,prof2.column_depth), 'b:', label=f'{name2} cool')\n", + "\n", + "plt.legend(loc=2, frameon=False)\n", + "\n", + "plt.ylim([1e1,1e10])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1da384a1-8667-4cb3-866a-b60577f7c344", + "metadata": { + "tags": [] + }, + "source": [ + "Both models are past the thin shell instability, but note that this underestimates the cooling rate as it doesn't consider convection.\n", + "\n", + "**We'll use profile37 as the guide to build the toy model**" + ] + }, + { + "cell_type": "markdown", + "id": "f1bc5a53-adfa-4ae4-a39f-b76f51b22a7c", + "metadata": {}, + "source": [ + "## First, observe the temperature profile of the MESA model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "03ee98c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WLdWxY0fFxMRIkq5du6ZVq1YpOjpaZ8+e1Y4dO7R169aHfl5KSooSEhIyPADkrPr162v69Ok6c+aMvvjiC3l4eOjq1av65JNPVLlyZfXo0UPbtm1jnh4AYJVcU3A7duyoDz/8UN26dXvg8xMnTlRQUJAGDhyo2rVra9KkSXJ3d9f06dMlSRs2bFC1atVUsmRJFSxYUM8++6x+/fXXh37ehAkTVKxYMcvD3d09W34uAI9WvHhxjRkzRsePH9eKFSvUunVrpaWlacmSJWrZsqV8fHw0b948paSkGB0VAJAH5JqC+1du376t3bt3y9/fP8O6v7+/duzYIUlyd3fXjh07lJycrLS0NG3evFk1a9Z86HuOGzdO8fHxlgd/HQoYz97eXl26dNHPP/+sffv2KSgoSM7OztqzZ49eeeUVVaxYUePHj9f58+eNjgoAyMXyRMGNi4tTWlqa3NzcMqy7ubnpwoULkqQmTZqoU6dOatSokerXr6+qVavq+eeff+h7FihQQEWLFs3wAJB71K9fX+Hh4YqNjdXHH3+s8uXL69KlS3r//fdVqVIl9enTR5GRkUbHBADkQnmi4N7zv1sImc3mDGsfffSRDh8+rIMHD2ry5MlsOQTYAFdXV40bN06nTp3SokWL1KxZM925c0fffPONGjdurGbNmum7777TnTt3jI4KAMgl8kTBdXV1lb29veVs7T2XLl2676wuANvk6OioF198Udu3b1dkZKT69u0rR0dH/fLLL+rVq5cqV66sjz/+WHFxcUZHBQAYLE8UXCcnJ3l7e2v9+vUZ1tevX69mzZoZlAqAUXx8fDR//nzFxMRo/PjxKlOmjM6ePat33nlH7u7uGjJkiA4fPmx0TACAQXJNwU1MTFRUVJSioqIk/XnXo6ioKMs2YGPGjFF4eLjmzJmjw4cPa/To0YqJidHQoUOf6HNDQ0Pl6ekpX1/fJ/0RAOSwp556Sv/85z8VExOj+fPny8vLS8nJyQoLC5Onp6eeffZZbdiwgW3GACCfyTW36t28ebNat25933r//v311VdfSfrzRg+ffvqpzp8/r7p16yokJEStWrXKks/nVr1A3mc2mxUREaGJEyfqhx9+sBTbevXqacyYMerVq5cKFChgcEoAwOPKbF/LNQXXaBRcwLYcP35cX375pebMmWO5/a+bm5teffVVDR06VKVLlzY4IQDAWhRcK1FwAdt07do1zZo1S5MnT9bZs2clSc7OzurXr5+Cg4NVu3ZtgxMCADIrs30t18zgAkB2KFGihN58802dOnVK33zzjby9vTPM6Xbq1Ik5XQCwMRRcAPmCo6OjXn75ZUVGRmrr1q0KCAiQyWTS2rVr1a5dOzVo0EBz587ldsAAYAPyfcFlFwUgfzGZTGrZsqWWL1+uo0ePauTIkSpcuLD279+vwMBAVapUSR988IEuX75sdFQAwGNiBvcuZnCB/OvatWsKDw/X5MmTdebMGUl/zun27dtXwcHB8vT0NDghAEBiBhcAMq1EiRJ64403dPLkSS1cuFA+Pj5KTk7WrFmzVKdOHXXs2FHr169nThcA8ggKLgDc5ejoqF69emnnzp2KiIhQ165dZTKZ9OOPP8rf31/169fX3LlzlZycbHRUAMBfoOACwP8wmUxq0aKFli1bpmPHjmnUqFEqXLiwDhw4YJnTff/995nTBYBcihncu5jBBfBXrl+/btlP996cboECBdS3b1+NHj2aOV0AyAHM4AJAFipevLhlTvfbb7+Vj4+PUlJSFB4ezpwuAOQy+b7gsk0YAGs4OjrqpZdesszpduvW7b453Tlz5jCnCwAGYkThLkYUADyuEydOaPLkyZozZ44SExMlSWXKlNHw4cM1bNgwlSlTxuCEAGAbMtvXKLh3UXABPKnr169b9tONjY2V9P9zusHBwapTp47BCQEgb2MGFwByWPHixTV27FidOHFC3333nXx9fS1zunXr1lWHDh30008/MacLANmMggsAWczR0VE9e/bUb7/9pm3btumFF16QnZ2d1q1bp/bt21v2001JSTE6KgDYJAouAGQTk8mk5s2ba8mSJTp27Jhee+21+/bT/eCDDxQXF2d0VACwKczg3sUMLoCc8KD9dJ2dndW/f38FBwerVq1aBicEgNyLGVwAyIX+ez/dhQsXytvbW8nJyZo5c6Zq166t5557Tps2bWJOFwCeQL4vuOyDC8AIjo6O6tWrlyIjI7VlyxZ16dJFJpNJq1evVps2beTl5aUFCxbo9u3bRkcFgDyHEYW7GFEAYLRjx45p0qRJmjt3rpKSkiRJZcuW1ciRIzVkyBCVLFnS4IQAYCz2wbUSBRdAbnH16lXNnDlTU6ZM0fnz5yVJhQoV0oABA/Taa6+pevXqBicEAGMwgwsAeVTJkiU1btw4RUdHa/78+WrQoIFu3bql0NBQ1axZUwEBAYqIiGBOFwAegoILALmUk5OT+vbtq71792rjxo169tlnZTabtXLlSrVq1UqNGzfWt99+qzt37hgdFQByFQouAORyJpNJbdq00apVq3T48GENHjxYzs7O2rVrl15++WVVqVJFn332ma5fv250VADIFZjBvYsZXAB5yeXLlzVjxgxNnTpVly5dkiQVKVJEQUFBeu2111S5cmWDEwJA1mMGFwBsWOnSpfXuu+/q9OnTmj17turUqaPExER9+eWXqlatmrp3765ffvnF6JgAYAgKLgDkYc7OzgoMDNT+/fu1bt06tW/fXunp6Vq6dKmaNWumpk2b6vvvv1dqaqrRUQEgx1BwAcAGmEwm+fv768cff9T+/fsVGBgoJycn/frrr3rxxRdVrVo1hYSEKCEhweioAJDt8n3B5U5mAGxN3bp1NXv2bMXExOi9996Tq6urTp8+rTFjxsjd3V1jx45VTEyM0TEBINs80UVmd+7c0YULF3Tr1i2VLl06T99lh4vMANiqpKQkLViwQCEhIfrjjz8kSfb29urevbtef/11/oAPIM/ItovMEhMTNXPmTPn5+alYsWLy8PCQp6enSpcurUqVKmnQoEGKjIx8ovAAgKxTsGBBDR48WAcPHtTq1avVtm1bpaWladGiRWrcuLFatmyp5cuXKy0tzeioAJAlrCq4ISEh8vDw0KxZs9SmTRstW7ZMUVFROnLkiH755ReNHz9eqampateunTp06KBjx45lV24AgJXs7OzUqVMnbdiwQXv37lW/fv3k6Oiobdu2qVu3bqpRo4amTJmixMREo6MCwBOxakShR48eeu+991SvXr2/fF1ycrLmzJkjJycnDRw48IlD5gRGFADkR+fOnVNoaKimT5+ua9euSZKKFy+uIUOGaMSIEapQoYLBCQHg/2W2r2XLjR4SEhLyXEmk4ALIz27evKl58+YpJCREx48flyQ5ODjoxRdfVHBwMHO6AHKFbJvB/fzzzx/5wf7+/ta+LQDAQIULF9bw4cN15MgRrVy5Uq1atVJqaqoWLlyoxo0bq0WLFlq6dClzugDyBKsL7rvvvqu5c+c+8LnExES1b9+efRYBII+ys7PT888/ry1btmjXrl3q06ePHB0dtX37dnXv3l3VqlXTxIkTFR8fb3RUAHgoqwvuggULNHz4cK1YsSLDemJiovz9/XX16lVt2rQpq/IBAAzi7e2tBQsWKDo6Wu+8845KlSql6Ohovf7666pQoYJee+01nThxwuiYAHAfqwtu9+7dNWXKFL388suWIpuYmKgOHTooLi5OmzdvlpubW5YHBQAYo1y5cvrwww8VGxursLAweXp6KjExUZMnT1b16tXVtWtXbdmyRdlwSQcAPJbHupPZwIED9c9//lMBAQHavHmzOnbsqAsXLmjTpk0qW7ZsVmcEAOQCBQsW1KBBg3TgwAGtW7dOHTp0kNls1ooVK+Tn52c543v79m2jowLI555oF4Vx48bp008/lYeHh7Zs2ZKnt5NhFwUAsN6hQ4c0efJkzZ8/X0lJSZKkp556Sq+++qqGDh0qV1dXgxMCsCXZtk1Yt27dMny9Zs0aNWjQQOXLl8+wvmzZMmve1jChoaEKDQ1VWlqajh49SsEFgMdw5coVzZw5U1OnTtX58+clSc7OzurTp4+Cg4NVp04dgxMCsAXZVnAHDBiQqdc9bKeF3IozuADw5G7fvq3vv/9eISEh2r17t2W9Xbt2Gj16tNq3by87u8eajgMAY2/0kBdRcAEg65jNZm3fvl0hISFasWKF0tPTJUm1atVScHCw+vbtq0KFChmcEkBeQ8G1EgUXALLHqVOnNGXKFIWHh+vGjRuSpJIlS2rIkCF69dVX7xtxA4CHyZY7mcXExFgV4uzZs1a9HgBgeypXrqyJEyfqzJkzCgkJUeXKlXX16lVNmDBBHh4e6t27t3bt2mV0TAA2xKqC6+vrq0GDBmnnzp0PfU18fLxmzZqlunXr5pkLzQAA2a9o0aIKDg7WsWPHtGzZsgy3A/b19VXLli25HTCALGHViMLVq1f18ccfa86cOXJ0dJSPj4/KlSsnZ2dnXbt2TYcOHdLBgwfl4+Ojf/zjH+rYsWN2Zs9SjCgAQM7bvXu3Jk2apO+++06pqamSJA8PD40aNUqBgYEqVqyYwQkB5CbZOoObnJysNWvWKCIiQtHR0UpKSpKrq6saNWqk9u3bq27duk8U3ggUXAAwzrlz5zRt2jTNmDFDV65ckSS5uLgoMDBQo0aNUpUqVQxOCCA34CIzK1FwAcB4t27d0jfffKNJkybp0KFDkiSTyaQuXbpo9OjRatmypUwmk8EpARglWy4yAwAgOxUqVMhyO+Aff/wxw+2A//a3v8nHx4fbAQN4JAouACDXMZlMat++vdauXauDBw9q8ODBcnZ21p49e9SvXz9VqlRJH374oeLi4oyOCiAXsrrgrlu3zrJhNwAA2c3T01MzZ85UbGysPvroI5UtW1YXLlzQu+++K3d3dw0ePNgyzgAA0mPM4Nrb2+v8+fMqU6ZMdmUyBDO4AJA3POx2wP7+/pbbATOnC9imbJvB5Zo0AICRnJyc1Lt3b0VGRmrr1q3q1q2b7Ozs9NNPP6ljx46qU6eOZs6cqVu3bhkdFYBBmMEFAORJJpPJcnOI48ePKzg4WC4uLjp8+LCGDh0qd3d3vf3229xVE8iHHqvgTp06VevWrbOJ4f7Q0FB5enrK19fX6CgAgMdUuXJlhYSEPPR2wH369MkwzgDAtlk9g2tnZ6dSpUrpypUrMplMKl++vLy8vOTt7S0vLy95eXmpbNmy2ZU32zCDCwC2Iy0tTT/88INCQkIUERFhWW/RooVGjx6tLl26yN7e3sCEAB5Htt3owc7OThcuXFBqaqr27t2rPXv2WB6xsbEymUxyc3PTuXPnnviHyEkUXACwTQ+6HXDlypUttwPmdz6Qd2Rbwf2rXRSuXr2qXbt2KSoqSm+++ab1qQ1EwQUA23bu3DmFhoZqxowZunr1qiSpaNGiCgoK0qhRo+Th4WFsQACPlO1ncNkmDACQF926dUsLFizQpEmT9Mcff0j6879t3bp10+jRo9W0aVO2GQNyqWzbJmzt2rUqVqzYE4UDAMAohQoV0pAhQ3Tw4EGtWbNG7dq1U3p6upYsWaLmzZurSZMm+u6773Tnzh2jowJ4TFafwbVVnMEFgPxr//79mjRpkr755hulpKRIktzd3TVy5EgNGjRIxYsXNzYgAEnZOKJgqyi4AIBLly5p+vTpmjZtmi5duiRJKly4sAYMGKDXXntN1apVMzghkL9l24gCAAC2qkyZMho/frxOnz6t2bNnq27durp586amTp2qGjVqKCAgQFu2bOGunkAuR8EFAOB/ODs7KzAwUL///rvWr1+vTp06yWw2a+XKlfLz85O3t7cWLFig27dvGx0VwANQcAEAeAiTyaRnnnlGq1evttwCuGDBgtq7d6/69esnDw8Pffzxx7py5YrRUQH8F2Zw72IGFwCQGVeuXFFYWJimTJmi8+fPS5IKFiyofv36KTg4WLVq1TI4IWC7mMEFACAblCpVSuPGjVN0dLQWLFggLy8vJSUlaebMmapdu7Y6deqk9evXM6cLGIiCCwDAY3ByclKfPn20a9cubdmyRQEBATKZTFq7dq38/f3VqFEjff311+ynCxiAggsAwBMwmUxq1aqVli9frqNHj2rkyJEqXLiw9u3bp759+6pq1aoKCQnRjRs3jI4K5BvM4N7FDC4AIKtcvXpVM2bM0OTJk3Xx4kVJUrFixTRs2DCNGjVKZcuWNTghkDcxgwsAgEFKliypt99+W9HR0QoLC1PNmjUVHx+vf//73/Lw8FBQUJAOHz5sdEzAZlFwAQDIJs7Ozho0aJAOHTqkFStWqHnz5rp9+7bmzJkjT09Pde7cWREREVyQBmQxCi4AANnMzs5OXbp00bZt27Rjxw517dpVJpNJq1atUqtWrdS0aVMtXbpUaWlpRkcFbAIFFwCAHNS0aVMtW7ZMf/zxh4YMGaICBQrot99+U/fu3VWrVi1Nnz5dSUlJRscE8jQuMruLi8wAAEa4ePGipk6dqmnTpunq1auSJFdXV40YMUKvvvqqXF1dDU4I5B5cZJZJoaGh8vT0lK+vr9FRAAD5kJubmz744APFxMRo8uTJ8vDwUFxcnP75z3+qYsWKevXVV3XixAmjYwJ5Cmdw7+IMLgAgN0hNTdXSpUv12Wefaffu3ZL+nOF94YUX9MYbb3BCBvkaZ3ABAMiDHBwc1LNnT0VGRurnn39Whw4dlJ6eru+//16NGzeWn5+fVq9erfT0dKOjArkWBRcAgFzIZDKpdevWWrt2rX7//Xf169dPDg4O2rJli5577jnVq1dPc+fOVUpKitFRgVyHggsAQC5Xr149zZs3T6dOndLYsWPl4uKiQ4cOKTAwUFWqVNGnn36q+Ph4o2MCuQYzuHcxgwsAyCvi4+MVFhamSZMm6dy5c5IkFxcXDR48WMHBwapQoYLBCYHswQwuAAA2qlixYnrjjTd06tQpzZ07V3Xq1NGNGzf0xRdfqHLlyurXr59+//13o2MChqHgAgCQRzk5OemVV17R/v37tXr1avn5+Sk1NVULFixQgwYN1KFDB/3888/cChj5DgUXAIA8zmQyqVOnTtq0aZMiIyP14osvys7OTuvWrVPbtm3l4+Oj7777TqmpqUZHBXIEBRcAABvi4+OjRYsW6dixYxoxYoQKFiyoPXv2qFevXqpevbomT56smzdvGh0TyFZcZHYXF5kBAGxRXFycpk2bpqlTp+ry5cuSpBIlSmj48OEaOXKk3NzcDE4IZF5m+xoF9y4KLgDAliUlJWnevHn64osvdPz4cUlSgQIF1L9/f73++uuqUaOGwQmBR2MXBQAAYFGwYEENHTpUf/zxh5YuXaomTZooJSVFYWFhqlWrlrp27aodO3YYHRPIEhRcAADyEXt7e3Xr1k07duxQRESEOnfuLLPZrBUrVqh58+Zq3ry5VqxYwa2AkadRcAEAyIdMJpNatGihH374QYcOHVJQUJCcnJy0Y8cOde3aVbVr11ZYWJiSk5ONjgpYjYILAEA+V7t2bYWHhys6Olrjxo1T8eLFdfToUQ0ZMkSVKlXShx9+qCtXrhgdE8g0LjK7i4vMAAD4040bNzR79myFhIQoJiZG0p8zvIGBgRo9erSqVq1qcELkV1xkBgAAHouLi4uCg4N1/PhxLVy4UI0aNVJSUpJCQ0NVo0YN9ejRQ7/99pvRMYGHouACAIAHcnR0VK9evbR7925t3LhRHTt2VHp6upYsWaImTZqoZcuWWrlyJRekIdeh4AIAgL9kMpnUpk0brVmzRvv379eAAQPk6Oiobdu2KSAgwHJBWlJSktFRAUkUXAAAYIW6detqzpw5io6O1ltvvaVixYpluCDt/fffV1xcnNExkc9xkdldXGQGAID1HnZB2oABAzR69GhVq1bN4ISwJVxkBgAAst29C9JOnDihb7/9Vl5eXkpKStK0adNUo0YNvfDCC/rll1+Mjol8hoILAACemIODg1566SXt2rVLP//8szp16iSz2axly5apWbNmatq0qRYvXqzU1FSjoyIfoOACAIAsYzKZ1Lp1a61evVoHDhxQYGCgnJyc9Ouvv6pnz56qUqWKPvvsM127ds3oqLBhzODexQwuAADZ48KFC5o+fbqmT5+uy5cvS5IKFy6sV155RaNGjVKNGjUMToi8ghlcAACQKzz11FP617/+pZiYGM2ePVv16tXTzZs3FRoaqpo1a6pz587auHGjOOeGrELBBQAAOcLZ2VmBgYHat2+fNm7cqOeee06StGrVKj3zzDNq0KCB5syZo+TkZIOTIq+j4AIAgBx178YR//nPf3TkyBG9+uqrKlSokPbv36+goCBVrFhR48eP14ULF4yOijyKGdy7mMEFAMA4165dU3h4uKZMmaLY2FhJ/3+r4NGjR6thw4bGBkSuwAwuAADIM0qUKKE33nhDJ0+e1OLFi9W0aVPduXNH8+fPV6NGjeTn56elS5eyzRgyxWYK7pEjR9SwYUPLo2DBglqxYoXRsQAAgBUcHBzUo0cP7dixQ7/++qt69eolBwcHbdmyRd27d1flypX10Ucf6dKlS0ZHRS5mkyMKiYmJ8vDw0OnTp1W4cOFMfQ8jCgAA5E5nzpzRjBkzFBYWZtlmzMnJSS+++KJGjBihxo0by2QyGZwSOSFfjyj88MMPatu2babLLQAAyL0qVKigDz/8ULGxsVqwYIGefvpp3b59W19//bWaNGmixo0ba968eey+AItcU3C3bt2qzp07q1y5cjKZTA8cL5g2bZoqV64sZ2dneXt7KyIi4oHvtXjxYvXs2TObEwMAgJxUoEAB9enTR7/++qt27typ/v37q0CBAtq1a5deeeUVVahQQePGjdPp06eNjgqD5ZqCe/PmTTVo0EBTp0594POLFi1ScHCw3nnnHe3du1ctW7ZUx44dFRMTk+F1CQkJ2r59uzp16pQTsQEAgAF8fX311VdfKTY2VhMmTJC7u7uuXLmif//736pSpYq6du3KzSPysVw5g2symbR8+XIFBARY1p5++ml5eXlp+vTplrXatWsrICBAEyZMsKwtWLBA69at09dff/2Xn5GSkqKUlBTL1wkJCXJ3d2cGFwCAPCg1NVWrVq3S1KlTtXHjRst6rVq1NHz4cPXt21fFixc3LiCyhE3N4N6+fVu7d++Wv79/hnV/f3/t2LEjw1pmxxMmTJigYsWKWR7u7u5ZmhkAAOQcBwcHBQQEaMOGDTp48KBeffVVFSlSRH/88YdGjRqlcuXKKSgoSJGRkUZHRQ7IEwU3Li5OaWlpcnNzy7Du5uaW4S4n8fHx2rlzp9q3b//I9xw3bpzi4+Mtj3ubSgMAgLzN09NTU6dO1dmzZzVlyhTVqVNHSUlJmjNnjho3biwfHx+Fh4crMTHR6KjIJnmi4N7zv1uAmM3mDGvFihXTxYsX5eTk9Mj3KlCggIoWLZrhAQAAbEfRokU1YsQI7d+/X9u2bVOfPn3k5OSk3bt3a9CgQSpfvrzlediWPFFwXV1dZW9vf989qS9dunTfWV0AAID/ZjKZ1Lx5cy1YsEBnz57V559/rmrVqikhIUGhoaGqX7++WrRooa+//pqtxmxEnii4Tk5O8vb21vr16zOsr1+/Xs2aNTMoFQAAyGtcXV31+uuv68iRI9qwYYO6d+8uBwcHbd++XX379lX58uU1duxYHT161OioeAK5puAmJiYqKipKUVFRkqRTp04pKirKsg3YmDFjFB4erjlz5ujw4cMaPXq0YmJiNHToUANTAwCAvMjOzk5t27bV999/r5iYGH344YeqWLGirl69qi+++EI1a9bUM888oyVLlujOnTtGx4WVcs02YZs3b1br1q3vW+/fv7+++uorSX/e6OHTTz/V+fPnVbduXYWEhKhVq1ZP9LmhoaEKDQ1VWlqajh49yjZhAADkU2lpaVq7dq1mzJihNWvWWPbQfeqppxQUFKRBgwapUqVKBqfM3zK7TViuKbhGy+w/MAAAYPtOnz6tWbNmKTw8XBcvXpT05yxvp06dNHToUHXs2FH29vYGp8x/KLhWouACAID/defOHa1cuVIzZszIcAMJd3d3BQUFKTAwkL30cxAF10oUXAAA8FeOHj2qsLAwzZ07V1evXpX05yxvx44dNXjwYHXq1EkODg4Gp7RtFFwrUXABAEBmJCcna/ny5QoLC9PmzZst6+XKlVNgYKCCgoLk4eFhWD5bRsG1EgUXAABY6+jRowoPD9fcuXMVFxcn6c9ZXX9/fw0ePFidO3eWo6OjwSltBwU3k9hFAQAAPKmUlBStXLlSs2bN0oYNGyzrbm5uGjBggAYOHKiqVasamNA2UHCtxBlcAACQFU6cOGE5q3tvBwZJatu2rQYPHqyAgAA5OTkZmDDvouBaiYILAACy0p07d/Sf//xHs2bN0rp16yz76pYuXVr9+/fXoEGDVKNGDYNT5i0UXCtRcAEAQHaJjo7W7NmzNXv2bJ0/f96y7ufnp0GDBqlbt25ydnY2MGHeQMG1EgUXAABkt9TUVK1Zs0ZhYWFau3at0tPTJUklS5a0nNWtXbu2wSlzLwqulSi4AAAgJ8XGxmrOnDkKDw/XmTNnLOstWrTQoEGD1KNHDxUsWNDAhLkPBddKFFwAAGCEtLQ0rVu3TmFhYVq1apXS0tIkScWLF1ffvn01aNAg1atXz+CUuQMFN5PYJgwAAOQW586d09y5czVr1iydPn3ast6kSRMNGjRIPXv2VOHChQ1MaCwKrpU4gwsAAHKL9PR0rV+/XrNmzdLKlSuVmpoqSSpatKh69+6tIUOGqEGDBganzHkUXCtRcAEAQG504cIFffXVVwoPD9eJEycs640bN9aQIUPy1VldCq6VKLgAACA3S09P188//6ywsDAtX748w1ndPn36aPDgwTZ/VpeCayUKLgAAyCsuXryor776SrNmzcpXZ3UpuFai4AIAgLwmPT1dmzZt0syZM/PFWV0KrpUouAAAIC/LD2d1KbhWouACAABbYMtndSm4mcQ+uAAAwFbdO6sbFhamkydPWtaffvppDR48OM+d1aXgWokzuAAAwFY9bAeGYsWKacCAARo+fLiqV69ucMpHo+BaiYILAADyg4ed1W3fvr1GjBihjh07yt7e3sCED0fBtRIFFwAA5Cfp6en66aefNHXqVK1Zs0b3KqGHh4eGDx+uwMBAlSpVyuCUGVFwrUTBBQAA+dXJkyc1ffp0zZ49W9euXZMkOTs7q1evXhoxYoS8vLwMTvgnCq6VKLgAACC/u3Xrlr799ltNnTpVUVFRlvVWrVppzJgxeu655wwdX8hsX7PLwUwAAADIxQoVKqSgoCDt2bNH27dv10svvSQHBwdt3bpVAQEBqlWrlkJDQ3Xz5k2jo/4lzuDexRlcAACA+509e1ZTp07VjBkzdP36dUlSiRIlNHToUI0YMULlypXLsSycwQUAAMATK1++vCZMmKDY2FhNmTJFVatW1bVr1zRhwgR5eHjoo48+MjrifSi4AAAAeKQiRYpoxIgROnLkiJYvX66WLVvqzp07uXL/3Hw/osCdzAAAAB7P7t271aBBAzk4OOTI57GLgpWYwQUAAMjdmMEFAABAvkTBBQAAgE2h4AIAAMCmUHABAABgUyi4AAAAsCkUXAAAANgUCi4AAABsSs7sypsH3NsOOCEhweAkAAAAeJB7Pe1Rt3Gg4N5148YNSZK7u7vBSQAAAPBXbty4oWLFij30ee5kdld6errOnTsnFxcXmUwmo+PkKQkJCXJ3d1dsbCx3gbMxHFvbxvG1bRxf25Zfj6/ZbNaNGzdUrlw52dk9fNKWM7h32dnZqUKFCkbHyNOKFi2ar/4ly084traN42vbOL62LT8e3786c3sPF5kBAADAplBwAQAAYFMouHhiBQoU0Pjx41WgQAGjoyCLcWxtG8fXtnF8bRvH969xkRkAAABsCmdwAQAAYFMouAAAALApFFwAAADYFAouAAAAbAoFF9nq888/V506dVS3bl19/fXXRsdBFjpy5IgaNmxoeRQsWFArVqwwOhaykIODg+X4Dhw40Og4yEI3btyQr6+vGjZsqHr16mnWrFlGR0IW69q1q0qUKKHu3bsbHcUQ7KKAbLN//371799fO3bskCS1bdtWq1evVvHixY0NhiyXmJgoDw8PnT59WoULFzY6DrKIq6ur4uLijI6BbJCWlqaUlBQVKlRIt27dUt26dRUZGalSpUoZHQ1ZZNOmTUpMTNS8efO0ZMkSo+PkOM7gItscPnxYzZo1k7Ozs5ydndWwYUP9+OOPRsdCNvjhhx/Utm1byi2QR9jb26tQoUKSpOTkZKWlpYnzXbaldevWcnFxMTqGYSi4eKitW7eqc+fOKleunEwm0wP/+nnatGmqXLmynJ2d5e3trYiICMtzdevW1aZNm3T9+nVdv35dP//8s86ePZuDPwH+ypMe3/+2ePFi9ezZM5sTwxpZcXwTEhLk7e2tFi1aaMuWLTmUHJmRFcf3+vXratCggSpUqKA333xTrq6uOZQej5KVv5/zKwouHurmzZtq0KCBpk6d+sDnFy1apODgYL3zzjvau3evWrZsqY4dOyomJkaS5OnpqVGjRqlNmzbq2rWrfH195eDgkJM/Av7Ckx7fexISErR9+3Z16tQpJ2Ijk7Li+EZHR2v37t2aMWOG+vXrp4SEhJyKj0fIiuNbvHhx7du3T6dOndLChQt18eLFnIqPR8iq38/5mhnIBEnm5cuXZ1hr3LixeejQoRnWatWqZX7rrbce+B5BQUHmVatWZVdEPIEnOb7z58839+7dO7sj4glkxb+/HTp0MEdGRmZXRDyBrDi+Q4cONS9evDi7IuIJPMnx3bRpk/mFF17I7oi5Emdw8Vhu376t3bt3y9/fP8O6v7+/5aIySbp06ZKkP6+437lzp9q3b5+jOfF4Mnt8JcYT8qLMHN9r164pJSVFknTmzBkdOnRIVapUyfGssF5mju/FixctZ+QTEhK0detW1axZM8ezwnrW/H7Oz/j7YjyWuLg4paWlyc3NLcO6m5ubLly4YPk6ICBA169fV+HChTV37lxGFPKIzB7f+Ph47dy5U0uXLs3piHgCmTm+hw8f1pAhQ2RnZyeTyaQvv/xSJUuWNCIurJSZ43vmzBkFBQXJbDbLbDZrxIgRql+/vhFxYaXM/n5u37699uzZo5s3b6pChQpavny5fH19czquYWgbeCImkynD12azOcMaf5rM2x51fIsVK8bcXh72V8e3WbNm2r9/vxGxkEX+6vh6e3srKirKgFTIKo/6/bxu3bqcjpSrMKKAx+Lq6ip7e/sMf1qU/hxJ+N8/VSLv4fjaNo6vbeP42jaOb+ZQcPFYnJyc5O3trfXr12dYX79+vZo1a2ZQKmQVjq9t4/jaNo6vbeP4Zg4jCnioxMREHT9+3PL1qVOnFBUVpZIlS6pixYoaM2aM+vbtKx8fHzVt2lRhYWGKiYnR0KFDDUyNzOL42jaOr23j+No2jm8WMHAHB+RymzZtMku679G/f3/La0JDQ82VKlUyOzk5mb28vMxbtmwxLjCswvG1bRxf28bxtW0c3ydnMpu5Nx8AAABsBzO4AAAAsCkUXAAAANgUCi4AAABsCgUXAAAANoWCCwAAAJtCwQUAAIBNoeACAADAplBwAQAAYFMouAAAALApFFwAAADYFAouAOCJxMbGys/PT56enqpfv76+//57oyMByOdMZrPZbHQIAEDedf78eV28eFENGzbUpUuX5OXlpSNHjqhw4cJGRwOQT3EGFwBygStXrqhMmTKKjo42OorVypYtq4YNG0qSypQpo5IlS+rq1auSpO7du2vixIkGpgOQH1FwASAXmDBhgjp37iwPDw+jozyRXbt2KT09Xe7u7pKk9957Tx999JESEhIMTgYgP2FEAQAMlpSUpHLlymnNmjVq2rSp0XHu4+3trZSUlPvWf/rpJ5UrV87y9ZUrV9SyZUuFh4erWbNmGb5/4MCBGjZsWI7kBQDO4AJAFqtQoYKmTZuWYW3Hjh0qVKiQTp8+fd/r165dKwcHh/vK7Y0bN9S7d28VLlxYZcuWVUhIiPz8/BQcHPzQz05PT9cnn3yiatWqqUCBAqpYsaI++ugjy/N+fn4aOXKkgoODVaJECbm5uSksLEw3b97UgAED5OLioqpVq2rt2rWW79m9e7cOHDhw3+O/y21KSoq6du2qcePGZSi3kvT888/r22+/zdQ/OwDIChRcAMhiTZo0UWRkpOVrs9ms4OBgBQcHq1KlSve9fuvWrfLx8blvfcyYMdq+fbt++OEHrV+/XhEREdqzZ89ffva4ceP0ySef6N1339WhQ4e0cOFCubm5ZXjNvHnz5Orqqp07d2rkyJEaNmyYevTooWbNmmnPnj1q3769+vbtq1u3bmXq5zWbzXrllVfUpk0b9e3b977nGzdurJ07dz7wLDAAZAdGFAAgi33++ef66quvdODAAUnS/Pnz9eabb+rYsWNycXG57/UBAQEqVaqUZs+ebVm7ceOGSpUqpYULF6p79+6SpPj4eJUrV06DBg3SpEmT7nufGzduqHTp0po6daoGDhz4wGx+fn5KS0tTRESEJCktLU3FihVTt27dNH/+fEnShQsXVLZsWf3yyy9q0qTJI3/ebdu2qVWrVqpfv75lbcGCBapXr54k6ffff1eDBg0UHR39wIIPAFnNwegAAGBrmjRpor///e9KTEyUnZ2d3n77bX344YcPLLfSnzO4zs7OGdZOnjypO3fuqHHjxpa1YsWKqWbNmg/93MOHDyslJUVt27b9y3z/XUTt7e1VqlQpSxmVZDnje+nSpb98n3tatGih9PT0hz5fsGBBScr0GWEAeFIUXADIYj4+PrK3t9eePXu0YcMGlSpVSoGBgQ99vaurq65du5Zh7d5frplMpgeuP8i9Ivkojo6OGb42mUwZ1u595l+VVmvc2zKsdOnSWfJ+APAozOACQBZzdnZWgwYNtGzZMn3++eeaOHGi7Owe/uu2UaNGOnToUIa1qlWrytHRUTt37rSsJSQk6NixYw99n+rVq6tgwYLauHHjk/8QWejAgQOqUKGCXF1djY4CIJ+g4AJANmjSpIkmT56sZ5555pEjA+3bt9fBgwcznMV1cXFR//799cYbb2jTpk06ePCgAgMDZWdnd99Z3XucnZ3197//XW+++abmz5+vEydO6Ndff80w22uEiIgI+fv7G5oBQP5CwQWAbNCwYUM5ODjos88+e+Rr69WrJx8fHy1evDjD+sSJE9W0aVM999xzeuaZZ9S8eXPVrl37vnnd//buu+/q9ddf13vvvafatWurZ8+emZ6lzQ7Jyclavny5Bg0aZFgGAPkPuygAQDZo06aN6tev/8DdDh5kzZo1Gjt2rA4cOPDQcYabN2+qfPny+uKLLxQUFJSFabNPaGioVq5cqZ9++snoKADyES4yA4Askp6ersuXL2v27Nk6cuSIli9fnunv7dSpk44dO6azZ89abnO7d+9e/fHHH2rcuLHi4+P1/vvvS5K6dOmSLfmzg6Ojo6ZMmWJ0DAD5DGdwASCLbN68WW3atFGtWrU0d+5cPf3000/0fnv37tXAgQN15MgROTk5ydvbWxMnTsywpRcA4H4UXAAAANgULjIDAACATaHgAgAAwKZQcAEAAGBTKLgAAACwKRRcAAAA2BQKLgAAAGwKBRcAAAA2hYILAAAAm0LBBQAAgE2h4AIAAMCmUHABAABgUyi4AAAAsCkUXAAAANiU/wO673muBQ58LAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import mesa.py_mesa_reader as mr\n", + "mesa_prof = mr.MesaData(\"mesa/profile37.data\")\n", + "\n", + "GM = 6.6726e-8 * 1.4 * 1.989e33\n", + "RNS = 12e5\n", + "g0 = GM/RNS**2\n", + "\n", + "y_mesa = mesa_prof.pressure/g0\n", + "\n", + "plt.figure(figsize=(8,3))\n", + "plt.xlabel(r\"$y$ (g cm$^{-2}$)\")\n", + "plt.ylabel(r\"$T$ (K)\")\n", + "\n", + "plt.loglog(y_mesa, mesa_prof.T, 'k-')\n", + "plt.gca().invert_xaxis()\n", + "\n", + "# plt.xlim([1e9,1e7])\n", + "# plt.ylim([1e8,3e8])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0f92779b", + "metadata": {}, + "source": [ + "#### We can distinguish 4 broad zones with different temperature profiles:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8a88a488", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# These numbers are fit \"by eye\"\n", + "y1 = 4.5e8\n", + "y2 = 1.5e8\n", + "y3 = 2e7\n", + "\n", + "def zones_plot():\n", + "\n", + " plt.figure(figsize=(8,3))\n", + " plt.xlabel(r\"$y$ (g cm$^{-2}$)\")\n", + " plt.ylabel(r\"$T$ (K)\")\n", + "\n", + " plt.loglog(y_mesa, mesa_prof.T, 'k-', label=\"MESA\")\n", + " plt.gca().invert_xaxis()\n", + "\n", + " plt.xlim([1e10,1e6])\n", + " plt.ylim([7e7,3e8])\n", + "\n", + " plt.axvspan(1e10,y1, color='gray', alpha=0.3, lw=0)\n", + " plt.axvspan(y1,y2, color='green', alpha=0.3, lw=0)\n", + " plt.axvspan(y2,y3, color='red', alpha=0.3, lw=0)\n", + " plt.axvspan(y3, 1e6, color='blue', alpha=0.3, lw=0)\n", + "\n", + "\n", + " # plt.gca().twinx().semilogx(y_mesa,mesa_prof.grada)\n", + " # plt.gca().twinx().semilogx(y_mesa,mesa_prof.h1, 'b--')\n", + "\n", + "zones_plot()\n", + "\n", + "plt.text(3e9,1e8,'substrate')\n", + "\n", + "plt.text(4.3e8,1e8,'adiabatic')\n", + "plt.text(y1,7.5e7,r\"$y_1$\")\n", + "\n", + "plt.text(6e7,1e8,'stable,\\nno H',ha='center')\n", + "plt.text(y2,7.5e7,r\"$y_2$\")\n", + "\n", + "plt.text(2e6,1e8,'stable,\\nwith H',ha='center')\n", + "plt.text(y3,7.5e7,r\"$y_3$\")\n", + "\n", + "# We know the depletion depth from the physics\n", + "yd = 3.7e7\n", + "plt.axvline(yd,ls=':',color='m')\n", + "plt.text(0.95*yd,7.5e7,r\"$y_d$\",color='m')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c9253e06", + "metadata": {}, + "source": [ + "The zones are:\n", + "- **Gray**: The substrate/buffer layer. We set the composition to pure iron. We can assume an isothermal profile for simplicity.\n", + "- **Green**: The adiabatic zone. This is where convection will become unstable initially. It is composed of pure He plus a small percentage of CNO, but no hydrogen.\n", + "- **Red**: Stable, hydrogen deficient zone. Approximately nothing is burning here, so the flux is small, and therefore the T profile shallow.\n", + "- **Blue**: Stable, solar composition zone. Hydrogen is burning via hot CNO here, which steepens the T profile a bit.\n", + "\n", + "The exact composition is determined by the following analytic expression from Cumming & Bildsten 2000:\n", + "$$X(y)=X_0(1-y/y_d).$$\n", + "The depletion depth $y_d$ is inside of the red zone. This is because the flux from hot CNO ramps up from a small value, and it is only in the blue zone where it really sets the temperature profile." + ] + }, + { + "cell_type": "markdown", + "id": "738fda2e", + "metadata": {}, + "source": [ + "#### Get best fitting power-laws in each zone (except the iron zone where we assume isothermal)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "49d2a844", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8kIishIOYNWsWlixZwnUsQggHaB63irCytET0RE/kJB0BACxduhSnT5/mOBUhRNVQcVczDw8PLFu2TP583LhxePzqHYeJCCGqhoqbA1OmTEHbtm0BAM9kddBl+Vls//Mxx6kIIaqCipsDfD4fkZGR0LFsBMMBc1DAFyFk7w38mZLOdTRCiAqg4uZIo0aNsDFsPrKuHQAAyMCH/6Z4PH2bw3EyQoiyo+LmkK+vL6Z1sUXuwwQAQC4TwjvsILJy8zhORghRZlTcHJsdFIgxjXnIf5sKAHgPLbhN24i37+gDS0JIyai4Ocbj8bBofgi+bswgk3wAAGTo2qKVXwj+/PNPjtMRQpQRFbeSmDd1HKa01gGTyQoHmvZE11EzMH/+fFq7khBSBBW3Epk2rAcmdjCVP6/bfTIWLFkOc3PzUlfvIITUPlTcSmZmH2f0czKDCFKkHwgDk3zA69ev4efnh2HDhiE9naYMktpFkcWCX7x4gWHDhqFRo0bg8/kICAgots2mTZvQsWNH1K1bF3Xr1kXXrl1x+fLlKnwHlY+KW8nweDwsHtACJ6Z3xflff4G2tjYAoKudAGcP7kDDhg3xyy+/oKCggOOkhFQ9RRcLlkgkMDQ0RHBwMBwdHUvc5syZMxg6dChOnz6NS5cuwcrKCt26dUNqampVvpVKRTeZUnIfPnzAcN9B2NwkHnrqwP47Umy4moe3dVpg+YqV8PDw4DoiIVWmIosFe3h4wMnJCatWrfrkdgUFBahbty7Wrl1b5BaxVY1uMlWDaWlpYe9CPxiIeRDyeejfWITjI7Sws8M9HA7uDt8+Xrhx4wbXMQmpdJWxWHBZ5OTkID8/H/r6+pV2zKpG9+NWBTYdgU4zgYStQHbhMk72+nyEeWkgr+BP7P6+LQ4YeODLoHWw+f8lmwgpk4trgUvrPr+dqSMwbEfRse2+wIukz+/bbhLQ/huFo1VksWBFBAYGwtzcHF27dq20Y1Y1Km5VoGcOdAkG3GcCd46AXQ0HL+UMAEBNwMOw5iIAF3D6++ZYKh6BwMBA+Y3mCfkkSRaQ9fzz2+mZFx/LeVO2fSVZiuf6H+VZLLiswsLCEBsbizNnzkBD4/PrZyoLKm5VIhABTfqA16QPkP4A0svhyL+8BWKWCwC48ESKDac3YPPmzfD390dQUBDs7Mq3niCpJdR1AB2zz2+nWa/ksbLsq66jeC5UbLHgsli+fDkWLVqEkydPokWLFhU+XrViKiAjI4MBYBkZGVxHUT75H1n2pQiWMseBNTbVZgDkD6s6QvYg0IqlHVrM2McsrpMSojBXV1c2YcKEImONGzdmgYGBn93X3d2dTZkypcSfhYWFMV1dXXbp0qXKiFkuFek1+nBS1QnVodXWH7YLknH+r0eYM2eO/BPqUY4C2Km/h/HlxchZaIUXm4eBpf3FcWBCyk7RxYIBIDExEYmJicjOzsbr16+RmJiIW7duyX8eFhaGkJAQbNmyBTY2NkhLS0NaWhqys7Or9b1VBE0HrIHev3+P1atXo9XdFehdv/jP0zXtUbfrNPCb9wdE4uoPSIgCfv75Z4SFhckXC/7xxx/RqVMnAIC/vz8ePXqEM2fOyLcv6fq3tbU1Hj16BACwsbHB48fFFy6ZN28e5s+fXxVvoUQV6TUq7hosMzMTcevmQZC4Ff3s86GtVvQP+iNPDH6r4VDrMBHQp2vhhFQnKm7ySXl5edgdswUp+5aij9lrtDAWFPl5dHYHuIxdCQcHB44SElL7UHGTMmGM4cTx4zj0y3y0kiViSDMRcvMBs5VZ+CgFunTpgkmTJqGPW3MIRSKgrg3XkQmpsai4icKSkpKwZd0K3D67B8fvFl0ubc+X+uhXXwqJZUdodJgINOgGCGjmKCGVqdq+8i6VShESEgJbW1uIxWLY2dlhwYIFkP1zD+kSxMXFwcvLC4aGhtDV1UW7du1w7NgxhUKSyufo6IifNm7Fjj+eYeXKlahfv/BTTAMxD71s8sEDoPH0PLBjKHKW1If01CIgswxftiCEVDmFinvp0qXYsGED1q5di+TkZISFhWHZsmVYs2ZNqfucO3cOXl5eOHz4MK5du4bOnTvD29sb169fr3B4UnF169bF1KlTcefOHRw7dgzdvvgCC87l4dH7f/8x1sx/B+H5pShY0QSZm/oA908Bn/jHmhBStRS6VNK7d28YGxsjPDxcPjZgwABoamoqdKP/pk2bYsiQIZg7d26JP5dIJJBIJPLnmZmZsLS0pEsl1eTx48eI2LIZD09sxgDrDPRqIISAX3RGSoZAHwUjD0PfqjFHKQlRbdV2qcTNzQ2nTp3C3bt3ARReJ42Pj0fPnj3LfAyZTIasrKxP3olr8eLF0NPTkz8sLS0ViUkqyNraGvO//wFbzj+Dxqh9mPzAA6HxUjzP+vcs++mL1zC2bwFvb2/s2LEDHz584DAxIbWLQmfcjDHMnj0bS5cuhUAgQEFBAUJDQxEUFFTmF1y2bBmWLFmC5ORkGBkZlbgNnXErn/T0dMRGb0XK0XXobvAC++/m4+cr/66FqaWlhaPjrKHfrAsaDJgLka4hh2kJUX4VOeNWaKrAzp07ER0dje3bt6Np06ZITExEQEAAzMzMMHLkyM/uHxsbi/nz5+O3334rtbQBQF1dHerq6opEI1XMwMAA30yZCvZtABITE6G5PQbmz3fIVw1poJMLN51nwOOtyA3biit5thC0+QqtvL+CSE2N4/SE1CwKnXFbWloiMDAQkyZNko8tXLgQ0dHRuH379if33blzJ0aNGoVdu3ahV69eCoWk6YDKqaCgAOfPn0dMTAxMU3ZhQcfif0o3XgHX+I4w8pwEzx4+KnXrTEKqUrVd487JyQGfX3QXgUDwyemAQOGZtr+/P7Zv365waRPlJRAI4OHhgU2bNiH48EuccliEw+lWeP/x3wJvYQSMqpeETpe/QrSvEWaO7Ildu3bRNXFSZoosFlyW6ce1brFgb29vhIaG4tChQ3j06BH27t2LlStXol+/fvJt/nu3rtjYWPj5+WHFihVo27at/E5cGRkZlfcuCOfU1dXh6TsJPdf8BVHgA1wxH4X7uXXkP9dR52GsEw/tPp7B4MGDYWhoiAEDBiA2NhY5OTmlH5jUaoouFlyW6cc1YbFghe7HnZmZyaZMmcKsrKyYhoYGs7OzY8HBwUwikci3GTlyJHN3d5c/d3d3L3KP6H8eI0eOLPPr0v24VdfHh3+yR2v6stw5dRmbp8u62QuK/B0I+WAtrXTYmDFj2Pnz55lMJivTcRNeJpR5W6K6XF1d2fjx44uMOTg4lOl+3P9o0qQJ+/7770v9uVQqZTo6OiwqKqrcOcujIr1GCymQ6pGbwfIvR7ATx4+x8ePHMyMjIwaADWwiZGyeLjvlp8kGNREyhwZ2LDQ0lL18+bLUQ519epY1i2zGJp+azN7mvq3GN0Gqk0QiYQKBgMXFxRUZ//bbb1mnTp3KdIyCggJmaWnJ1qxZU+o2mZmZTENDgx04cKBCeRVVkV6jG1CQ6qGhC2Frf3QF0NWrG9auXYvz58/D+OhXAF6hi60QXWyFeJn9CuGnFqDjqgVo22MIJk+eDBcXF/lhcvJzMOfCHADA6aen8ff+vxHaMRRtTdty875UXNTfUdh6a+tnt2ui3wRrPIt+Q3ryqcm49fZWKXv8y6+JH0Y2/fyss/+qjMWCV6xYgQ8fPmDw4MGlbkOLBRNSRgKBAB7u7oD6VMgubwb/3QMAgLE2H7M7qiPQjeHo/V1Y8OV2vKvngonffIuBAwdCU6SJ+e3mY+7FuXgveY9Xua/w9fGv4d/UH5NbToZIIOL4namWD/kf8Crn1We3M9EyKTb2VvK2TPt+yK/YB9HlXSy4LNOPabFgQhTF4wHtJoLfdgLw6DxwdQvYrQPgMSn4PB56NhChZwMRnmXexOiQEZg1axYmT54MX19fxPWJQ3B8MC69uAQGhoi/I/DHiz+wtNNS2OrZcv3OVIaWSAtGmqV/p+If+urFv+msr65fpn21RFrlylaRxYJ37tyJMWPGYNeuXaWeSdNiwVWMrnHXIplpjJ1bzgpWNGFsnq780UCfX+RDzS+//JK9fvOaRd6MZE5bnVizyGasWWQz1jq6Ndt9Zzd9cFlDlGex4O3btzMNDQ22d+/eUrehxYIJqUw6xkDHaeAH3AC+3A3WqAfe1nWCQ4ei8/9jYmJgZ2uHe7H3sL7DevlZdq40F/Mvzcf6pPVcpCeVTNHFgssy/bgmLBZMZ9xE+UnzGWOMXbp0iTVs2JDx+UXPvrW1tdmM2TNY0O9BrFlkM9Y2pi17lvWM49Cksqxbt45ZW1szNTU11qpVK3b27Fn5z8oz/dja2rrEbebNm1d9b4pVrNdoBRyich4+fIglS5YgIiIC+fn/3uhKW1sbvnN80btPb/g4+HCYkJDPq7avvBOiDGxtbfHLL7/g/v37mDBhAtT+/yZW2dnZ2DxrM77q9BVWr14tv8Pkh/wPmH52Oh5nPuYyNiGVhs64icp7+vQpFi9ejE2bNkEqlcrHbWxssHDhQtyyvoX9D/ZDLBQjyDUIfev3LdN0MkKqEi0WTAiA+/fvY86cOdixY4d8jC/mo0loE+B/ZrN1s+6Gue3mQk9dj4OUhBSiSyWEAKhfvz5iY2Nx7do1dOvWDQAgy5Xh78C/8fbsW/l2xx8fx8ADA3E17SpXUQmpECpuUuO0atUKx44dw8mTJ+Hi4gKWx/A84jmerH2Cgg8FAIC0D2kYfWw0ViesRr4s/zNHJES5UHGTGsvT0xOXL19GbGwsrKyskHk1E/fn3Ed2cuF8XQaGTX9tgv8RfzzNfMpxWkLKjoqb1Gg8Hg++vr64ffs2fvjhB4g+ivAo7BHSdqWBSQs/3rnx5gbOPDvDbVBCFEDFTWoFsViMkJAQ3L17F34j/PDm0BukhKZAkiZB1l9Z2Dtnb6k35ydE2VBxk1rF3NwcUVFRuHz5MlqatsSD+Q/wbOMz7I3bi8aNG2PJkiXIy8tD2oey3TaUEC5QcZNaqXXr1oiPj0fkxkgYiA0AFK6pGhQUBMfejui+uzvWJa6DVCb9zJEIqX5U3KTW4vF4GDFiBO7cuYPJkyeDz+eDr8kH68EggwwbkjZg2P5heJb1jOuohBRBxU1qvTp16mD16tW4du0aXJ1c8fb0W7CCwg8ukzOS0Wd3H+y/t5/jlIT8i4qbkP/n5OSEC+cvYGm/pXi77i3yXuUBAPL5+Qi+GIyv932N7DwVuvUnqbGouAn5H3w+H6NGjcLNEzfh/sQd7y+8l//sUsYldInsgkuPL3EXkBBQcRNSIn19fWxcsxG/jvoVOAgU5BR+4zJXPRdfnfoK38d9z3FCUptRcRPyCe3atUPC9gT0y+qH3Pu5AAAmZVg0aRF8fX3LvNo4IZWJipuQzxCJRAidGYpDfoeglaSF59HPkZeWh507d6Jx48bYtGkTZDIZ1zFJLULFTUgZNWrQCJdWXMJPY3+CgUHh3O/3799j3KRxaDu9LW7euclxQlJbUHETogAejwc/Pz/cvn1bvkityRAT5DrmYuD+gQheE4yCggKOU5KajoqbkHKoV68eoqKisPvIbuh3LFylQWQkwm9av8H1W1f8nfw3xwlJTUbFTUgFDPhiAPb02QOdbB0AAE/AQ55rHvpE98GcsDlFllIjpLJQcRNSQY1MGuHshLPoUacHmKzwG5eaDTWxR3sPWg9vjRs3bnCckNQ0VNyEVAIRX4QwnzD80uUXaORpAAAEWgJIe0jRa00vhHwfgry8PI5TkpqCipuQStTBugNO+p2Ei46LfKyOWx1suLABrq6udPZNKgUVNyGVTE9dD1v6bcE813kQyATIuZuD9JPpSEpKgouLCxYtWkTXvkmFUHETUgV4PB4GNh6I/QP2Y0OfDWjWtBkAID8/H8HBwWjfqT1u377NcUqiqqi4CalCVrpW8GrjhatXryIwMBB8Ph9iOzGyfLPgNsoNP/74I33rkiiMxxhjXIf4nMzMTOjp6SEjIwO6urpcxyGk3E5fOI3JVyaDV5cHAHh75i3sHtshclMk7O3tOU5HqlNFeo3OuAmpRs6tneHZ3FP+XN9DHy+7vUTr3q2xYcMGqMB5FFECVNyEVCNdNV2s6rIK37f/Hmo8NQCAuqk6TKebIuS3EHTr3g1Pnz7lOCVRdlTchFQzHo+H/g36Y4/PHjjUcQAA8IV8mPqa4r7jfTi2d0R0dDSdfZNSUXETwhEbPRts770do5qNAg+F17y1m2nDeJYxxi8eD19fX7x9+5bjlEQZUXETwiGRQITvnL/Dpm6bUE+jHgCAr8GHNFOKX3/9Fc2bN8fJkyc5TkmUjULFLZVKERISAltbW4jFYtjZ2WHBggWfnc509uxZODs7Q0NDA3Z2dtiwYUOFQhNS07QxbYO9PnvRxbILvtD8ApofNAEAz58/h5eXFwICApCbm8txSqI0mAIWLlzIDAwM2MGDB9nDhw/Zrl27mLa2Nlu1alWp+6SkpDBNTU02ZcoUduvWLbZp0yYmEonY7t27y/y6GRkZDADLyMhQJC4hKkcmkzGZTMaePXvGvLy8GADGE/BYnfZ1WJMmTVhCQgLXEUklqUivKTSPu3fv3jA2NkZ4eLh8bMCAAdDU1MS2bdtK3GfWrFnYv38/kpOT5WPjx49HUlISLl0q22rZNI+b1EYymQxr165F2B9hqNu9LrL+ysLLyJeYN30eZsyYAYFAwHVEUgHVNo/bzc0Np06dwt27dwEASUlJiI+PR8+ePUvd59KlS+jWrVuRse7du+Pq1avIz88vcR+JRILMzMwiD0JqGz6fj37+/aDfvXChBp3mOrCZZ4PQHaHw8PDAw4cPOU5IuKJQcc+aNQtDhw6Fg4MDRCIRWrZsiYCAAAwdOrTUfdLS0mBsbFxkzNjYGFKpFG/evClxn8WLF0NPT0/+sLS0VCQmITWGpa4lNnhtQD1x4QeXQl0hbKba4IHNAzi5OCEyMpKmDdZCChX3zp07ER0dje3btyMhIQFRUVFYvnw5oqKiPrkfj8cr8vyfP7T/jv8jKCgIGRkZ8gd9IYHUZu3N2mNPnz3wsPCQjxl0NYDRd0YYP2c8Bg4cWOpJEKmZFCruGTNmIDAwEL6+vmjevDlGjBiBqVOnYvHixaXuY2JigrS0tCJjr169glAolK+U/V/q6urQ1dUt8iCkNtPX0MfqLqsR0iYEavzCb1xqWGjAfp49zmSeQYsWLWjaYC2iUHHn5OSAzy+6i0Ag+OR0wHbt2uHEiRNFxo4fPw4XFxeIRCJFXp6QWo3H42GIwxDs7L0TDes2BADwRXyYDTdDrn0uvLy8MH36dEgkEo6TkqqmUHF7e3sjNDQUhw4dwqNHj7B3716sXLkS/fr1k28TFBQEPz8/+fPx48fj8ePH+O6775CcnIwtW7YgPDwc06dPr7x3QUgtUr9ufWzvtR3DGw8HAIjSRci4lAEAWLFiBdq1a0f3+q7pFJk7mJmZyaZMmcKsrKyYhoYGs7OzY8HBwUwikci3GTlyJHN3dy+y35kzZ1jLli2Zmpoas7GxYevXr1doziLN4yakZOefnWcp71LYypUrmZqaGgPAADCxWMw2bNjAZDIZ1xFJKaptHjdXaB43IZ+XmJiIYcOG4eGHhzDzN0Pq5lT0cO2BzZs3l/p5EuEO3Y+bEAInJyfE/xGP5kHNoWmnCfv59jiXeQ4tWrTAqVOnuI5HKhEVNyE1yEf+R5ibmgMA+Gp8mPmZQThIiC/6f4GZM2ciLy+P44SkMlBxE1KDmGmbIbZXLIY5DJOP6bbUhf0Ce6w/vB7t27fHnTt3OExIKgMVNyE1jIZQA0FtgrDOcx3qqtcFAIjqiGAzwwapDVLRyrUVNm/eTN+4VGFU3ITUUJ0sOiHOJw4dzDvIx+p9UQ+m00wxae4kDBo0iBZqUFFU3ITUYPXE9fCz58+Y2XomRPzCL7xpmGtAoCHAnj170KJFC5w+fZrjlERRVNyE1HB8Hh8jmoxAbK9Y2OvZw0vLC+IMMQAgNTUVnp6eCAoKKvVunUT50DxuQmqRj9KPEPFFSHuRBj8/P/z+++8AHxDbitG0blNs374dDRo04DpmrUDzuAkhZaIh1ICAL4C5uTlOnDiBpUuXwtjHGHbBdnhq9xQtXVpiy5Yt9MGlkqPiJqSW4vP58BnrAyMfI/D4PBj2NITJdyaYEDwBQ4YMwbt377iOSEpBxU1ILdagbgNMc5kGIU8IABDbiFH/+/o4+fIkHB0dce7cOY4TkpJQcRNSi/F5fIxsOhIxvWJgo2tTOKbOh/loc8AH8OzlieDgYPrgUslQcRNC0MSgCXb23omBDQfKx/Rc9GC/wB4/xf0ENzc3PHjwgMOE5H9RcRNCAACaIk3MazcPqzxWQU9NDwAg0hfBZqYNkvOS0bJlS8TExHCckgBU3ISQ//C09sSePnvQxqQNAIC9Ycj+OxtZWVkYPnw4/Pz8kJWVxXHK2o2KmxBSjLGWMTZ224hpztMQMzQGfsP+XdVq27ZtaNWqFa5cucJhwtqNipsQUiI+jw//Zv5wsnBCZGQkYmJioKOjAzVTNeR65MKtqxuWLVv2yTVnSdWgb04SQsrszv07GLRvEJghQ156Hp798gwdrDsgKioKpqamXMdTKfTNSUJItWD6DDrmOgAANQM12AbaIkknCY4tHXH48GGO09UeVNyEkDJz0HfAnj574GLsAgDg8Xkw6mMEna910M+/H6ZOnQqJRMJxypqPipsQohATLRNs7rYZU1pNgYAnAABo2mvCfoE9Ii9Hok3bNrTKThWj4iaEKEzAF2Bs87HY1mMbLHUsC8fEAlh8bYH0tulw6eCC8PBwullVFaHiJoSUW3PD5tjlvQs+9j7yMT1XPcj0ZRg7dix8fX3x/v177gLWUFTchJAK0RJpYaHbQizrtAzaIm1Yploi514OAODXX3+Fk5MTLl68yHHKmoWKmxBSKb6w/QJ7ffZi/+z92LVrF+rUqQMAePzkMbr06YKFCxeioKCA25A1BBU3IaTSmGiZQMQXYeDAgUhKSoKbmxsMuhvA7gc7LD+0HJ6ennj27BnXMVUeFTchpEpYWVlh/Z71MBtiBoGmAJbjLfGg0QM4uTph3759XMdTaVTchJAqY1PHBr3te8uf12lfBwZTDTBs+jBMnDgRubm5HKZTXVTchJAqo62mjUUdF2FJxyXQEmoBANSM1GA32w67U3ejtWtr3Lx5k+OUqoeKmxBS5XrZ9cLuPrvhZOgEAOAJeDDub4wc7xy0694O69evpznfCqDiJoRUCwsdC0R8EYGJjhPBAw8AoNVIC5Yhlpi2Yhr69++P9PR0jlOqBipuQki1EfKFmOA0AVE9omCqWXg3QWmmFJJnEuzbtw+Ojo44e/YsxymVHxU3IaTatTRqid0+u+Ft542pDaeirnZdAEBqaio6d+6MOXPmQCqVcpxSedH9uAkhnEtNTcWIESNw+vRpiAxF0Guth/rv6yM2JhY2NjZcx6sSdD9uQohKMzc3x4kTJ7Bw0UJYjreEyWATvOz0Es4ezti5cyfX8ZQOFTchRCkIBAJ09e8KLfvCaYNaDlownmmMr5d/jTFjxuDDhw8cJ1QeVNyEEKXRxrQNIr6IgLHYGAAg0BLA6hsrHMMxOLd1RmJiIrcBlQQVNyFEqTgbOyOubxy6W3eXj9XtVBey4TJ0GtwJq1evrvVzvunDSUKIUmKM4bcHvyH0Uig+yj4WjkkZXsa9hCvPFZERkTA0NOQ4ZfnRh5OEkBqHx+Ohb/2+2OOzB030mxSOCXkwGWyC07dPw9HREb///jvHKbmhUHHb2NiAx+MVe0yaNKnUfWJiYuDo6AhNTU2Ymppi1KhR9O0oQkiZWelaIbpXNMY2HwseePhw7gNyH+TixYsX6Nq1K2bPno38/HyuY1YrhS6VvH79usiN0G/evAkvLy+cPn0aHh4exbaPj4+Hu7s7fvzxR3h7eyM1NRXjx49HgwYNsHfv3jKHpEslhBAASHyVCP18fYwdNRYnTpyQj7u2d8WO6B2wtbXlMJ1iqu1SiaGhIUxMTOSPgwcPwt7eHu7u7iVu/8cff8DGxgbffvstbG1t4ebmhnHjxuHq1asKhSSEEABwMnKClbkVjh49irCwMAiFQuh31sc773do3bs1duzYwXXEalHua9x5eXmIjo7G6NGjwePxStymffv2ePbsGQ4fPgzGGF6+fIndu3ejV69enzy2RCJBZmZmkQchhPyDz+djxowZ+PX3X2E6zBTqpuow/s4Y30R9g1GjR9X4Od/lLu59+/bh/fv38Pf3L3Wb9u3bIyYmBkOGDIGamhpMTExQp04drFmz5pPHXrx4MfT09OQPS0vL8sYkhNRgzo7OcDB0AADwhXyY+prijMEZOLs74/r169yGq0Llng7YvXt3qKmp4cCBA6Vuc+vWLXTt2hVTp05F9+7d8eLFC8yYMQOtW7dGeHh4qftJJBJIJBL588zMTFhaWtI1bkJIMfkF+ViTuAYRNyPkY9IsKV5ufYm5w+ZiypQppV4V4FJFrnGXq7gfP34MOzs7xMXFwcfHp9TtRowYgY8fP2LXrl3ysfj4eHTs2BHPnz+HqalpmV6PPpwkhHzOny/+xMwzM/E27618LP33dDi+c8TW8K1KN+e72udxR0REwMjI6LPXqnNycsDnF30JgUAAALX+m0+EkMrVxrQNfuv3GzpbdJaPGXQxwP229+HU2QmnTp3iMF3lUri4ZTIZIiIiMHLkSAiFwiI/CwoKgp+fn/y5t7c34uLisH79eqSkpODChQv49ttv4erqCjMzs4qnJ4SQ/1FHow5+6vIT5rabCxFEAAC+Oh9pD9Pg5eWFoKCgGjHnW+HiPnnyJJ48eYLRo0cX+9mLFy/w5MkT+XN/f3+sXLkSa9euRbNmzTBo0CA0atQIcXFxFUtNCCGl4PF4GNRwEHb33Y2Gug1het0UshwZGGNYsmQJ3NzckJKSwnXMCqF7lRBCaizGGBhjWLlyJYKCgiCVSiGsK4R+I32s+nYVhg4dylm2av9wsrpRcRNCKurKlSsYOmwoCvoXQLuJNtJPpsNLzQvrfloHbW3tas9DN5kihJDPaN26NVbsXwHtJoUlbdDVABftLsK5uzMSEhI4TqcYKm5CSK3Rx6EPgtsEQ4jCiRUaFhoQjhLii9lfYOWPK1VmthsVNyGk1uDxePB18MWuPrtgo2kDAOCL+DAeaozVT1fji/5f4NWrV9yGLAMqbkJIrVO/bn3s7r8bwxoNk4/pOOrgSecncBnkUuTOg8qIipsQUiupC9QR1DYI67uuhw5fBwAg1BNCb7QevEd7Y9asWUo755uKmxBSq7mZu+HAwANwNXQFALw9/RaSZxKEhYXBzc0NDx484DhhcVTchJBaz0BsgM09NmNB+wWY3HwyRKLCb11evnwZLVu2RHR0NMcJi6LiJoQQFH5w2a9BPwROC8TFixdRv359AICghQDBV4IxbOwwZGVlcZyyEBU3IYT8h4uLCxISEjB43GCYDjeFbitdJDRPgMtAF1y7do3reFTchBBSEh0dHcyYPwM64sIPLkV1RFAbpoY+K/tg2cplkMlknGWj4iaEkFK4mLjg0OBDaFmnpXxMv5s+fsn+Bd2GdsPLly85yUXFTQghn1BPXA+RfSIxreU08GWFlSm2FuOF5wu0+boNjh07Vu2ZqLgJIeQz+Dw+/Fv441efX2EsNC4cU+dDp78Ovj78NQKCApCXl1d9eartlQghRMU10m+EA0MOoI9VH/mY2E6MtevWokOHDrh//3615KDiJoQQBYiFYoR2DsVPHj9BQ6aBtC1pKMgqwNWrV6ttzjcVNyGElEMX6y44O/wsfo/4HQ0aNAAAZGdnw3+iPwaPH1ylc76puAkhpJw0RZpo1aoVEhIS4O/vDwAwH2OOv1v9jdajW+PKlStV8rpU3IQQUkHa2tqIiIjArG2zoOukC746H6LeIgzaNgiLVi6q9DnfVNyEEFJJ5vnOQ3eT7vLnOs462MrbCk9/z0qd803FTQghlUQsFGN59+VY3nE5RNLCG1WJ9EV43fk12s1sh8NHD1fK61BxE0JIJetu1x1HfI+ggXrhh5Y8Pg9aXbQw+dJkTAyeWOE531TchBBSBYy1jLF7yG583ehr4P8vcWvaaeKM1Rm0+6Jdhe7zTcVNCCFVhM/jY3LbydjhvQO6BboAgKyELCScTkDHjh3Lf9zKCkgIIaRkTes1xYkRJ9DLsBc0L2kCAD58+FDu41FxE0JINdAUaWJJzyW4dvEaRo0aVaFjUXETQkg10tbWxpYtW7B58+ZyH4OKmxBCODBo0KBy70vFTQghKoaKmxBCVAwVNyGEqBgqbkIIUTFU3IQQomKouAkhRMVQcRNCiIqh4iaEEBVDxU0IISqGipsQQlQMFTchhKgYKm5CCFExVNyEEKJiFCpuGxsb8Hi8Yo9JkyaVuo9EIkFwcDCsra2hrq4Oe3t7bNmypcLBCSGkthIqsvGVK1dQUFAgf37z5k14eXl98vaEgwcPxsuXLxEeHo769evj1atXkEql5U9MCCG1nELFbWhoWOT5kiVLYG9vD3d39xK3P3r0KM6ePYuUlBTo6+sDKDxr/xyJRAKJRCJ/npGRAQDIzMxUJC4hhCitf/qMMab4zqycJBIJMzAwYKGhoaVuM2HCBObp6clmzZrFzMzMWIMGDdi0adNYTk7OJ489b948BoAe9KAHPWr848GDBwr3r0Jn3P9r3759eP/+Pfz9/UvdJiUlBfHx8dDQ0MDevXvx5s0bTJw4EW/fvv3kde6goCB899138ufv37+HtbU1njx5Aj09vfJGrlUyMzNhaWmJp0+fQldXl+s4KoN+b4qj31n5ZGRkwMrKSn41QhHlLu7w8HD06NEDZmZmpW4jk8nA4/EQExMjL9yVK1di4MCBWLduHcRicYn7qaurQ11dvdi4np4e/WEoSFdXl35n5UC/N8XR76x8+HzFJ/eVazrg48ePcfLkSYwdO/aT25mamsLc3LzIWXLjxo3BGMOzZ8/K89KEEFLrlau4IyIiYGRkhF69en1yuw4dOuD58+fIzs6Wj929exd8Ph8WFhbleWlCCKn1FC5umUyGiIgIjBw5EkJh0SstQUFB8PPzkz8fNmwYDAwMMGrUKNy6dQvnzp3DjBkzMHr06FIvk5REXV0d8+bNK/HyCSkZ/c7Kh35viqPfWflU5PfGY0yxuSjHjx9H9+7dcefOHTRs2LDIz/z9/fHo0SOcOXNGPnb79m1MnjwZFy5cgIGBAQYPHoyFCxcqVNyEEEL+pXBxE0II4Rbdq4QQQlQMFTchhKgYKm5CCFExVNyEEKJilLq4s7KyEBAQAGtra4jFYrRv3x5XrlzhOpZSk0qlCAkJga2tLcRiMezs7LBgwQLIZDKuoym18tyymACpqakYPnw4DAwMoKmpCScnJ1y7do3rWEpr/vz5xf7GTExMFD5Oub/yXh3Gjh2LmzdvYtu2bTAzM0N0dDS6du2KW7duwdzcnOt4Smnp0qXYsGEDoqKi0LRpU1y9ehWjRo2Cnp4epkyZwnU8pVWeWxbXdu/evUOHDh3QuXNnHDlyBEZGRnjw4AHq1KnDdTSl1rRpU5w8eVL+XCAQKHwMpZ0OmJubCx0dHfz2229FvqHp5OSE3r17Y+HChRymU169e/eGsbExwsPD5WMDBgyApqYmtm3bxmEy1RIQEICDBw/i3r174PF4XMdRSoGBgbhw4QLOnz/PdRSVMX/+fOzbtw+JiYkVOo7SXiqRSqUoKCiAhoZGkXGxWIz4+HiOUik/Nzc3nDp1Cnfv3gUAJCUlIT4+Hj179uQ4merIy8tDdHQ0Ro8eTaX9Cfv374eLiwsGDRoEIyMjtGzZEps2beI6ltK7d+8ezMzMYGtrC19fX6SkpCh+EIVvBFuN2rVrx9zd3VlqaiqTSqVs27ZtjMfjsYYNG3IdTWnJZDIWGBjIeDweEwqFjMfjsUWLFnEdS6Xs3LmTCQQClpqaynUUpaaurs7U1dVZUFAQS0hIYBs2bGAaGhosKiqK62hK6/Dhw2z37t3sxo0b7MSJE8zd3Z0ZGxuzN2/eKHQcpS7u+/fvs06dOjEATCAQsNatW7Mvv/ySNW7cmOtoSis2NpZZWFiw2NhYduPGDbZ161amr6/PIiMjuY6mMrp168Z69+7NdQylJxKJWLt27YqMTZ48mbVt25ajRKonOzubGRsbsxUrVii0n1J/OGlvb4+zZ8/iw4cPyMzMhKmpKYYMGQJbW1uuoymtGTNmIDAwEL6+vgCA5s2b4/Hjx1i8eDFGjhzJcTrl988ti+Pi4riOovRMTU3RpEmTImONGzfGnj17OEqkerS0tNC8eXPcu3dPof2U9hr3/9LS0oKpqSnevXuHY8eOwcfHh+tISisnJ6fYjdkFAgFNByyjst6ymBTetvnOnTtFxu7evQtra2uOEqkeiUSC5ORkmJqaKrZjFf0PoFIcPXqUHTlyhKWkpLDjx48zR0dH5urqyvLy8riOprRGjhzJzM3N2cGDB9nDhw9ZXFwcq1evHps5cybX0ZReQUEBs7KyYrNmzeI6ikq4fPkyEwqFLDQ0lN27d4/FxMQwTU1NFh0dzXU0pTVt2jR25swZlpKSwv744w/Wu3dvpqOjwx49eqTQcZS6uHfu3Mns7OyYmpoaMzExYZMmTWLv37/nOpZSy8zMZFOmTGFWVlZMQ0OD2dnZseDgYCaRSLiOpvSOHTvGALA7d+5wHUVlHDhwgDVr1oypq6szBwcHtnHjRq4jKbUhQ4YwU1NTJhKJmJmZGevfvz/7+++/FT6O0s7jJoQQUjKVuMZNCCHkX1TchBCiYqi4CSFExVBxE0KIiqHiJoQQFUPFTQghKoaKmxBCVAwVNyGEqBgqbkIIUTFU3IQQomKouAkhRMX8H50xM0Wdd+2dAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import linregress\n", + "\n", + "plt.figure(figsize=(4,3))\n", + "\n", + "y,T = y_mesa[::-1], mesa_prof.T[::-1]\n", + "\n", + "dels = []\n", + "\n", + "i_left = np.argmin(abs(y-y1)) # index of left green boundary\n", + "\n", + "for y_right in (y2,y3,min(y)):\n", + " \n", + " i_right = np.argmin(abs(y-y_right))\n", + " \n", + " xx = np.log10(y[i_left:i_right])\n", + " ff = np.log10(T[i_left:i_right])\n", + " \n", + " res = linregress(xx,ff)\n", + " dels.append(res.slope)\n", + " \n", + " plt.plot(xx,ff,'k',lw=2)\n", + " plt.plot(xx, res.intercept+res.slope*xx, '--', lw=2, label=\"%.2f\"%res.slope)\n", + " \n", + " i_left = i_right\n", + " \n", + "plt.xlim([5,9])\n", + "plt.ylim([7.6,8.5])\n", + "plt.legend(frameon=False, title=r\"$\\nabla=d\\ln T/d\\ln y$\")\n", + "plt.gca().invert_xaxis()" + ] + }, + { + "cell_type": "markdown", + "id": "633cf0ca", + "metadata": {}, + "source": [ + "0.37 should be close to $\\nabla_{\\rm ad}=(8-6\\beta)/(32-24\\beta-3\\beta^2)$" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2646d3ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "del_ad (beta) is on average 0.3998 in that zone\n", + "del_ad (from MESA) is on average 0.3783 in that zone\n" + ] + } + ], + "source": [ + "beta = mesa_prof.pgas_div_ptotal\n", + "del_ad = (8-6*beta) / (32-24*beta-3*beta**2)\n", + "print(f\"del_ad (beta) is on average {np.mean(del_ad[np.logical_and(y_mesa < y1, y_mesa > y2)]):.4f} in that zone\")\n", + "print(f\"del_ad (from MESA) is on average {np.mean(mesa_prof.grada[np.logical_and(y_mesa < y1, y_mesa > y2)]):.4f} in that zone\")" + ] + }, + { + "cell_type": "markdown", + "id": "620349a1", + "metadata": {}, + "source": [ + "Which is the obviously close to the ideal gas value ($\\beta=1$, $\\nabla_{\\rm ad}=0.4$). We probably have corrections due to degenerate electrons which soften the gradient.\n", + "\n", + "We are now ready to:\n", + "\n", + "## Create the toy model\n", + "\n", + "We'll store it in a namedtuple object" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b6c71df9", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import namedtuple\n", + "toy = namedtuple(\"toy\", ['T','rho','y','X','Y','Zcno','Zfe'])" + ] + }, + { + "cell_type": "markdown", + "id": "06e9cccf", + "metadata": {}, + "source": [ + "#### Make the (continuous) temperature profile\n", + "\n", + "We need to set two temperatures: that of the isothermal iron layer, and at the base of the adiabatic zone" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c0b434e5", + "metadata": {}, + "outputs": [], + "source": [ + "# T_star = 2e8\n", + "T_star = 1e8\n", + "T_base = 2.75e8" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "19a5cc49", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kOl9fX06ePImVlRXNmjVLtO1/5+FOirdrvM2YX8bgmNWRSz6XGNxyMJVqViJfkXwEBQbx1adfceDvA691DSIiIkmlwC0iqW7ZsmUAfPDBB7i4uCTa9nngtrKO+0BlYop4FWHi8onk9MjJ3Zt3Gd97PB0GdaBs1bI8ffKUcb3G8ceiP17tAkRERF6CAreIpKqgoCDWr18PwCeffPLC9q/Sw/2cW143Ji2fROFShXn86DFjeo7hg6YfULtFbQwGA/PHzmfemHlER0e/9LFFRESSSoFbRFLV8uXLefLkCUWLFqV8+fIvbB8VFQUQ75SBSeGUzYkxv4yhYvWKRDyNYGL/iXgU8qDdwGezl2xft53AO4GvdGwREZGkUOBO5+7du8fgwYNZvHgxp06d0hKqkqZFRESwZMkSADp06JCk+bFfp4f7ORs7G4ZMH0LdT+piMBiYN2YeIY9CGPT9IIbOGIprHtdXPraIiKSc69evkjWrGadPn0jyPvXrv8+QIf1SrKZX8erfwSRNOHnyJH/88Qd//PFsLKqVlRXFihWjVKlSxlfevHm18IekCX/++ScBAQG4urpSt27dJO2THIEbwMLSgm7Du+Hs6sziqYv5fd7vVG9UnV7f9DK2OXPoDI5ZHfEo5PFa5xIRMYX69d+nZMnSjBs31dSlyH8ocKdzBQoUoFevXpw6dYpTp04RFBRk/P/nnJycKFmyZKwQni1bNhNWLW+imJgYFi5cCEDbtm2xtrZO0n5t27aldu3aPMr26LVrMDMzo0W3FjjncGb6sOnsWLeDoMAgvvjhCwL9A/m2x7eYmZnx5fQv8ark9drnExERAQ0pSffeeustevXqxdy5c9m/fz9btmxh8uTJfPrpp5QuXRpra2sePXrEP//8w6xZs+jWrRtVqlShZs2aDBgwgJ9//pljx44RHh5u6kuRDG7Tpk1cunQJBwcHWrRokeT9XF1dKVmyJDly50i2Wrw/8mbYj8Owtbfl+N7jfPXpV5hhRr4i+Qh9HMrITiP5e83fyXY+EZGU1qNHe/bu3cWPP/5A1qxmZM1qxvXrVwHYu3cX3t4VcXW1oWjRXIwc+aXx+Zjlyxfx1lvOPH36NNbxPv20Kd26fRrvuZ4P81izZiV161YlVy47atSowKVLFzh27DDVq5cnT57MNGtWh8DAAON+MTExTJw4muLF8+DqakPVqqXZtm1TrGMfPXqIatXKkDOnLdWrl+fUqeNxzn/unC/Nm9cjT57MFC7sSteubbl/P20/i6Me7gzEzMwMDw8PPDw8qF+/PvBszOz58+c5ffo0J0+e5PTp01y5coWbN29y8+ZNNm7cCIClpSWFCxc29oB7eXmRP39+zM31M5m8vujoaGbOnAlA+/btE13oJrWUq1qOMb+MYXTX0VzxvcLobqMZOnMoq+asYs/GPfww5Afu3rjLJ70/0ZAsEQEgNDQ0wc8sLCywtbVNUltzc3Ps7Oxe2DZTpkxJrm3cuB+4dOkCnp4lGDJkNADZs7tw+/YtWrSoxyeftGf27EVcvHiOvn07Y2try5dfjqRRo+Z88UUf/vrrDxo3bg7A/fuBbN68nt9+25TYKRk/fgRjx04lTx4Pevf+jE6dPsHBwZFx437A3t6eDh1aMHbscL7/fjYAP/74AzNmfMeUKXMoVaoMixf/RKtWDdm/34cCBQoRGhpKy5b1qVq1BnPmLObaNT+GDOkb65z+/neoX/89Pv20M2PGfE94+BNGjvyCDh1a8Mcf25P855XaFLgzOGtra0qWLEnJkiVp1aoVAMHBwZw5c8Y49OTUqVMEBgbi6+uLr68vy5cvByBz5syULFmSMmXKULp0aby8vHBycjLl5Ug69XwZd0dHRz79NP4ek4Rs376dK1eu4FbJjYIlCiZrXYVKFmLi8omM6DQC/+v+fN3+a4bOGkpO95ysmrOK5bOWc/fWXXp90+ul5wEXkYwnT57MCX72wQf1WLlyg/F94cI5CAsLi7ftO++8x/r1O43vvbzyxdtD+/ChIcm1OTk5YW1tjZ2dPa6uOY3bFyyYRe7c7kyaNAMzMzMKFy7KnTu3GTXqCwYPHo6dnR3NmrViyZKFxsC9atUS3Nzy8O677yd6zl69BuLtXRuArl370qnTJ6xb9zeVKr0DQJs2HVm27Gdj+xkzJtO37xc0bdoSgFGjJvDPPzuYPXsqkyfPZNWqJURHRzNjxk/Y29tTrFhxbt++yeefdzce46efZuPlVZbhw8cat02f/hMlSrhz6dIFChYsnOQ/s9SkwP0GcnR0pEqVKlSpUgUAg8HAnTt3YgVwHx8fQkJC2L9/P/v37zfuW6BAAUqXLm0M4W+99ZZ6wSVR0dHRzJo1C3g2M4mDg8NL7b9+/Xo2btxI5686J3vgBsjlkYuJyyYyuutoLp25xLAOw/hiyhf0+qYXs0bOYse6HXgU8qBpp6bJfm4RkZR24cJZKlSoHOs3dW+//Q4hISHcunUTd3cP2rXrTI0aFbh9+xZubrlZsmQhrVq1f+Fv94oXL2X8/xw5ns325OlZMta2gIB7wLPOvjt3bhvD+L9rOXPmpLHWEiW8sLe3N35eoULlWO1PnDjKnj074v3hx8/vsgK3pF1mZma4ubnh5uZGnTp1gGdzH1+6dIkTJ05w/PhxTpw4wbVr17h8+TKXL1/m999/B56Fdy8vL2MIL1WqFJkzJ9wDIG+eZcuW4efnh5OTE23btn3p/Y2zlFin3JerLM5ZGPPLGCb2n8jR3UcZ03MMPUb1YMScEfy1/C8aftowxc4tIunHzZshCX5mYWER6/2FC/cSbPvfjqqTJ6++Vl2JMRgM8QTnZz3nz7eXKlWGEiW8WL58Ed7etfH1Pc2yZX++8Nj/Xh/h+bH+uy0mJuY/e8Wu5d/1GQwv7tGPiYmhTp0GjBw5Ic5nrq65Xri/qShwS7wsLS0pWrQoRYsWpWXLZ7/6efDgQawAfvr0aYKDg9mzZw979uwBnv3jKlSokLEHvEyZMpqW8A129+5dpkyZAkC/fv1e6Yex53PLv+60gC9il8mOoTOHMnPETP5e/Tczhs3gk16fMGT6EOP9Gx0dze2rt3Ev4J6itYhI2vQyY6pTqm1irK2t46ycW6SIJ3/++XusYHvw4D4cHBxwc8ttbNe2bSdmz57CnTu3eP/9muTJk7xf5xwdHcmVy40DB/7hnXeqGbcfOrSPsmUrGmtdseJXnjx5YhzjfuTIgVjH8fIqy59//o6HRz4sLdNPjNVYAEmybNmyUaNGDT7//HN+/fVXDh8+zG+//cbXX3/Nhx9+SO7cuTEYDFy4cIEVK1YwZMgQ6tSpQ5UqVejWrRtz5szh4MGDCY5pk4xnwoQJhIaGUqpUqZeameTfkmse7qSwtLKkz5g+tOj+rNZlM5Yxc/hMoqOefQP7acJP9G/an/1b9yd2GBERk/DwyMfRowe5fv0q9+8HEhMTQ8eOPbh16waDB/fmwoVzbNy4jvHjR9Cjx4BYPe3Nm7fmzp1bLFo0j9atP0uR+nr3HsQPP0xg9eoVXLx4npEjv+T06RN06/bswchmzVphbm5O794dOXfOly1bNjJjxuRYx+jUqScPHz6gU6dPOHr0EFevXmH79i306vVZnB820pL086OBpDlWVlaUKFGCEiVK0KZNG+DZypcnTpwwvs6cOcPDhw/ZuXMnO3fuBJ792q1IkSLGHvAyZcqQO3du9YJnMHv27GHjxo2Ym5szcuTIOL9uTarngftVl3Z/WWZmZrTp2wZnV2fmjJ7DllVbCAoMov+E/ty5doeI8AjG9xnPZ198RsN2DXXfikia0avXQHr0aEelSp48efKEkyf98PDIx8qVGxk+fBBVq3qRNWs22rTpyMCBX8fa19HRkQYNmrJlywY+/LBxitTXtWsfHj8OZtiwzwkIuEeRIp4sXfoHBQoUAp5N1rBs2Z8MGNCN994rQ5EinowcOYFPP/3/Z2hy5XJj06a9jBz5BU2b1iYi4inu7nnx9q6Tpp8pMzMkZcCMvJbg4GCcnJw4cuTIGze+OSIigrNnzxqHoRw/fpy7d+/Gaefi4kLp0qWNIbx48eLY2NiYoGJJDiEhITRp0oQbN27Qrl07hgwZ8srHatmyJSdOnOCrGV9RqWalZKzyxQ78fYDJAyYT8TSCwl6FGTpjKMtmLmPT8mdTZX3Y+kM6fdXplX+YSAkNLpi6AknL/qSBqUtI8ywtw8mRww939/xYW9u+eIcMpEmTDyhcuBgTJkwzdSkpJiIinBs3/Lh3Lz9RUbH/fsPCgmnZ0olHjx4l+/S16uGWFGVtbY2XlxdeXv+/at+dO3eMPeDHjx/n7NmzBAQEsHXrVrZu3Qr8/xL1z8eCly5dmly50u7DEPL/DAYDI0aM4MaNG+TKlYvevXu/1vFSc0jJf1XyrsQ3C7/h2+7fcuHkBYa0GcKI+SPI5Z6LhZMWsmHJBu7dusfA7wZil8nuxQcUEUmDHj58wPbtW9i9ezsTJ84wdTkZknq4U8Gb3MOdFOHh4fj4+MQK4YGBcecjzZkzZ6wpCYsVK5bk5cEl9axYsYIRI0ZgYWHBr7/+StmyZV/reGfPniU4OBiLwhY4ZjXNgjk3r9xkRKcRBNwOIEv2LIyYMwL/G/5M+WIKEU8j8CznybjF49LE8BL1cEti1MP9Ym9iD3epUvkICnrIoEHD6N17oKnLSVGm6uFW4E4FCtwvx2AwcOvWLY4fP24cinL+/Pk4D0NYW1tTokQJYw946dKlyZEj+Zb/lpd37tw5WrRoQUREBIMGDaJjx47JduwLmDZJ3r97n9FdR+N3zg87ezu+nP4ldvZ2jO09lu4julP5g8ovPkgqUOCWxChwv9ibGLjfJArcGZgC9+sLCwvj9OnTxgB+4sQJgoKC4rTLnTt3rGEoRYoUSbWH7d50d+7coW3btty8eZP33nuP2bNnJ+sDLKYO3AChj0MZ13scpw6cwsLSgj5j+1DJu1Ks4SRPw59iY2u65w8UuCUxCtwvpsCdsSlwZ2AK3MnPYDBw9erVWMNQLl68GGfSfDs7O0qUKBFrXvCsWbOaqOqM686dO3z66afcuHEDd3d3Vq5cmWx/zqtWrSIyMpLCdQubbEjJv0VGRPLDkB/YvWE3AO0GtuOjjh9hZmZGwO0Avmj9BZ/0+oQPmn5gkvoUuCUxCtwvpsCdsemhSZGXYGZmRv78+cmfPz9NmjQBns2McfLkyVjTEj5+/JjDhw9z+PBh47558+aNNSVhwYIF09QsE+mNv78/7dq148aNG+TJk4dffvklWX+omTJlCg8ePGB6+elpInBbWVsxYNIAsuXIxtqFa/ll8i/c979PxyEd2bxqM4F3Apk+dDp3b96ldZ/WaWJct4gk3fN+G/VHZlTP/l5T+69XgVsyjMyZM/POO+/wzjvvAM+Wf71y5UqsYSiXL1/m2rVrXLt2jXXr1gHPVvgqVaoUpUuXNs6ool7wpLlz5w7t2rXj+vXr5MmTh0WLFuHm5pas5zDlLCUJMTc357MvPsPZ1ZkF4xewfvF6HgQ8oP+E/gCsnL2SlbNXcvfGXfqM7YOVtYY1iaQXMTFWxMRAREQYNjaafSijiYqKwGAAgyF1O9rSzncwkWRmbm5OwYIFKViwIM2bNwcgKCiIU6dOGYehnDx5ktDQUPbv38/+/f+/emDevHnx8vIyhvDChQtrLPh/nDx5kl69ehEQEJBiYRvSZuB+rlH7RmTLkY0pX0xh3+Z9PLr/iKEzh+Kax5VZI2axa/0uAvwDGDpjKA5ZHExdrogkgcFgQUhIFgID7wFgbW2v31RlEAZDDA8fBvDkiT0xMan7PUVjuFOBxnCnXdHR0Vy8eJETJ05w8uRJTp48yZUrV+K0s7W1pUSJEsYecC8vL1xdXU1QsekZDAZWrlzJmDFjiIiIoFChQsyZMydFwjZA8eLFiY6OZuGuhTi7OqfIOV7XqQOnGNtrLGEhYXgU8mDE3BHc8rvF+D7jCQsJI3f+3IxeMBoXN5cUr0VjuCUxGsOdVAYyZ/Ync+Yg0vDihfIKoqLMCQzMT0xM3GmF9dBkOqfAnb4EBQVx+vRpYwg/deoUwcHBcdrlypULLy8vSpYsSfHixfH09Ez2f6Bpzc2bNxk2bJjxtwE1atRg4sSJKXZfR0dHU7x4cQAW71+cJsZwJ8TvvB+jOo/iwb0HOLs6M3LeSABGdR2FY1ZHxv06LlUWx1HglsQocL8cM7NozM0jUQd3xmAwQHS0NRD/T1EK3OmcAnf6FhMTY5wR5Xkv+IULF4iJiYnT1sPDg+LFixtfnp6eODk5maDq5BUUFMTChQtZtGgRT548wcbGhr59+9K+fftknfrvv54+fWpcpXT5keXYZ7ZPsXMlh4DbAYzsPJIbl2+QySETQ2cOxS2fGwaDIdV65xW4JTEK3CIJU+BO5xS4M57Q0FDOnDnDyZMnOXPmDD4+Pty6dSvetu7u7hQvXpxixYpRqFAhChcujJubW4oG1eRy8+ZNVqxYwdKlSwkNDQWgfPnyfPvtt+TLly/Fzx8SEkL58uUB+P3U7+ni4cPHQY/5tse3nD12FksrSwZMGsC7dd41fv77vN8xNzen8WeNU2RcqAK3JEaBWyRhCtzpnAL3m+Hhw4f4+vri4+NjfN28eTPetvb29hQqVMgYwAsVKkSBAgVwcXEx+cM5Dx8+ZMeOHWzcuJF9+/YZe/KLFClC79698fb2TrUaIyMjOXjwIJGRkeR6L1e6+CEFni1+8/2g79m/dT9mZmZ0GtKJBp824NKZSwxoNgCAup/UpcvQLlhYJu+T8grckhgFbpGEKXCnMVOmTGH+/PkYDAZq1qzJDz/8kGgAUeB+cwUFBRlD+Pnz57l48SJXrlwxzrzxX3Z2dri7u5M3b148PDyMrzx58uDi4oKtbfIuwhATE8P169c5d+4cZ8+e5cCBA5w6dSrW/LNVqlShdevWVK9e3aSBNy2sNPkyoqOjmTdmHhuXbgTgo04f0bZ/W9b/up6fJvyEwWCg/HvlGfjdwGQdKqPALYlR4BZJmAJ3GhIQEEClSpXw8fHBysqKatWqMXnyZCpXrpzgPgrc8m+RkZFcu3aNixcvGl8XLlzgxo0b8Y4L/zdHR0dcXFzIkSMHLi4uZM2alUyZMsV62dnZYWZmZgzNUVFRhISE8OjRIx4/fkxwcDD379/nxo0bXL16lbCwsDjnKVKkCHXq1KFevXrkzZs3Rf4cXlZ6C9zwbEaX3+b+xq9TfgXgvQbv0WdMH47sOsJ3g74jIjyC/MXyM/zH4ck2xluBWxKjwC2SMK00mcZERUURHh4OPAtPOXLkMHFFkp5YWVkZ5wevW7eucXtERAS3b9/m+vXrXLt2jevXrxv//86dOzx9+pTg4GCCg4O5fPlystVjY2ND4cKFKVq0KKVKlaJq1arkzJkz2Y7/Oh49esTmzZuxt7encP3Cpi7npZmZmdG8a3Oy5cjGjGEz2PXnLoLuBzFk2hDGLhrLN92/we+sH4M+HsSwOcPIXyS/qUsWEZEUkGYC97hx41i9ejXnzp3Dzs6OKlWqMGHCBIoUKZJs59i9ezeTJk3i6NGj3LlzhzVr1tC4ceM47WbNmsWkSZO4c+cOxYsXZ+rUqVStWhUAFxcXBg4ciIeHB5aWlnTr1o0CBQokW43y5rK2tiZfvnzxPoxoMBh4/Pgx9+7dIyAgwPjfhw8fEhYWRmhoaKz/AsZhThYWFmTOnBknJyccHBxwcnIiS5Ys5MmTh7x585I3b14sLdPMl4JY7t69y/Dhw3F2dmZh/YWmLueVeTfxJkv2LEzoO4GT+04ypO0QRswZwaQVkxjdZTQ3r9zk4qmLCtwiIhlUmvkuu2vXLnr27EmFChWIiopi6NCh1KpVC19fXzJlyhSn/d69e6lYsWKc1f/OnTtHlixZ4u2hCw0NxcvLiw4dOtC0adN461ixYgX9+vVj1qxZvPPOO8yZM4e6devi6+uLh4cHDx8+ZP369Vy9ehU7Ozvq1q3L7t27qVatWvL8QYjEw8zMDEdHRxwdHSlYsKCpy0k1z8e6Z4RVPstVLcfYRWMZ1XUUfmf9+LLNl0xYMoGJyyayb+s+ajWrZeoSRUQkhaSZR/43bdpE+/btKV68OF5eXixcuJDr169z9OjROG1jYmLo2bMnrVq1Ijo62rj9woULVK9enUWLFsV7jrp16/Ltt9/y0UcfJVjH999/T8eOHenUqRPFihVj6tSpuLu7M3v2bAC2bdtGwYIFyZYtG3Z2dnz44YccOHDgNa9eROKTkQI3QMESBZm0fBKueVzxv+7PyC4jwYxYYTv4YTBrFqx54Xh+ERFJP9JM4P6vR48eAZAtW7Y4n5mbm7Nx40aOHz/Op59+SkxMDJcvX6ZGjRo0bNiQwYMHv9I5IyIiOHr0KLVqxe5pqlWrFvv27QOezam8b98+wsPDiY6OZufOnQkOe5k5cyaenp5UqFDhleoRedNltMANkNM9J6N/Gk2W7FnwO+vHyM4jCQt5NgwoOjqasb3GsnDSQr4b+B0RTyNMXK2IiCSHNBm4DQYDAwYM4N1336VEiRLxtnFzc2P79u3s3buXVq1aUaNGDby9vfnxxx9f+byBgYFER0fj6uoaa7urqyv+/v4AVKpUiXr16lGmTBlKlSpFgQIFaNiwYbzH69mzJ76+vhw+fPiVaxJ5kz0P3Gl1jPmryuWRi1ELRuHg5MCFkxcY3XU0YSFhWFhY8EGzD7CwtGDPxj0M6zCM4IfBpi5XREReU5oM3L169eLUqVMsW7Ys0XYeHh4sWrSIFStWYGlpyYIFC5JlQY7/HsNgMMTaNmbMGM6ePYuPjw/Tpk0z+UIlIhlVRMSzHt6M1MP9XP4i+Rm1YBSZHDLhe9SXL1p9QaB/IN5NvBk5fySZHDJx9thZBrcczO1rt01droiIvIY0F7h79+7NH3/8wY4dO8iTJ0+ibe/evUuXLl1o0KABYWFh9O/f/7XOnT17diwsLIy92c/du3cvTq+3iKS8jDik5N8KlijItz9/S1aXrFy7cI2BzQdy6sApvCp5MWHZBHK45eD2tdsM+ngQZ4+dNXW5IiLyitJM4DYYDPTq1YvVq1ezfft28udPfHqswMBAvL29KVasmHGflStXMnDgwFeuwdramnLlyrF169ZY27du3UqVKlVe+bgi8mpKlSrF9OnT6devn6lLSTEFihdg0vJJeBTy4EHAA4Z1GMbSaUvJnS83k1ZMomCJgjwOeszMETNjPSQuIiLpR5oJ3D179mTx4sUsXboUBwcH/P398ff358mTJ3HaxsTEUKdOHfLmzWscTlKsWDG2bdvGzz//zJQpU+I9R0hICCdOnODEiRMA+Pn5ceLECa5fv25sM2DAAObPn89PP/3E2bNn6d+/P9evX6dbt24pct0ikjBXV1c++OADKlWqZOpSUlSO3DmYvGIyHzT7AIPBwPJZy/my9ZcEPwxm7KKxVG9UnS9/+BILCwtTlyoiIq8gzSztntA46IULF9K+ffs427du3UrVqlWxtbWNtf3EiRM4Ozvj7u4eZ5+dO3dSvXr1ONvbtWvHzz//bHw/a9YsJk6cyJ07dyhRogRTpkx5rXm2tbS7yOtLj0u7v4pd63cxa8QsnoQ+wcLSgo86fkSL7i2wsbUxtvE96ksRryJYWMYN4FraXRKjpd1FEpaSS7unmcCdkSlwi7yaK1eu4OvrS+7cuclUJu4CWBlVwJ0A5n47l4N/HwTAxc2Ftv3bUu3Dapw6cIpRXUbhVdmLwVMGY5/ZPta+CtySGAVukYSlZOBOM0NKRET+659//mHgwIH8+uuvpi4lVbnkcmHozKEMmT6E7DmzE3A7gO8Hfc/nzT7nwskLWFhacGzPMYa0GUKgf6CpyxURkRdQ4BaRNCujz1LyIpU/qMzsv2bTpl8b7DLZcdn3Mot/WEz+ovnJ7JQZv3N+DGwxkCtnr5i6VBERSYQCt4ikWc8Dt7W1tYkrMR0bOxtadGvBnC1zqNeqHuYW5pw/cZ6QRyHY2Nnw4N4DhrQewtHdR01dqoiIJECBW0TSrIy88M3LyuKchW7DuzH7r9nG1SifPnkKwJOwJ3zT7Rsunblk4ipFRCQ+Ctwikma96UNK4pPLIxe9v+3NnM1zqNOyjnGmkpiYGKYNncbPf/9NaHg4T//3ZyciIqanwC0iaZYCd8Jy5M5Bj5E9mLdtHvXb1Mfa1pqr56/S4YcfcO/QAcePP6beqFHM27yZm4F6sFJExJQsTV2AiEhCFLhfLHvO7HT5uguf9PqELau2sO3n9dy6fx+Av44e5a+jz8Z2l8qXj3rlylGvfHkqFy2KpRbRERFJNZqHOxVoHm6RV3P69GmuXLlCoUKFsPRU/0BS1PGNYuU//zB21Sp8b9yIt82xKVMoU6AAANHR0VrB8g2iebhFEpaS83DrO5iIpFklS5akZMmSwJuz0uTrsrK0pPX779PqvffYf+4cU//4g9X79xMdEwOAhbk5i3bswN7GhiJ58vDZtGn43rhBvfLlqVeuHOULFlQAFxFJZurhTgXq4RZ5fQrcSRPfSpN3Hjzgp23bmLt5M9cDAozb3ytRguNXrhAcFmbclt3RkTply1KvXDlqlSmDczL38ohpqYdbJGFa2j2dU+AWeTUnTpzgwYMHFClShNDcoaYuJ11IbGn36OhoNh8/zo+bNrHhyBFi/tfrbWdtTa5s2bgbFERoeLixfen8+Tn+ww/G9waDATMzsxSrXVKeArdIwjSkRETeSHPnzmX79u2MHj2a0i1Km7qcdM/CwuLZ0JHy5bkREMD8rVv5+e+/uR4QwBV/fwDcs2cnv6srd4OCqFuunHHf0PBwSvbuTfWSJalXvjwflC6No729qS5FRCRdUeAWkTRLs5SkHHcXF0a1asWIli3Zefo0P2/fzu/79nEjMJAbgYGYmZlx5NIllu3aRaNKldhx+jR+d+/id/cuP23bhqWFBVU9PY1jv4u5u6v3W0QkAQrcIpJmKXCnPHNzc2p4eVHDy4uZXbvy2759/Pz33+z28WHriRNsPXGCTLa21K9QgdGtWxPw6BGbjx/nwq1b7Dh9mh2nTzNo4UJmd+9Ot7p1TX05IiJpkgK3iKRZCtypy8Heng41a9KhZk0u37nDoh07+HXHDvzu3mXFnj0AZHNwoGnlygxr0YL7jx+z6dgxdpw+TfX/zSYDsGTnTn7dscPY+13Qzc1UlyQikiYocItImqXAbToFcuViVKtWjPzkEw6eP8+yPXtYsWcPd4OCmLdlC/O2bMEtWzZavPsuXzVvTqF/heq1Bw6w+fhxNh8/Tt958yjk5mZcdKda8eLYWlub8MpERFKfZilJBZqlROTVNGnShLNnzzJv3jxcq7qaupx0IbFZSl5XdHQ0O8+cYdnu3fy+bx9Bof8/c0yBnDlpWqUKTatUwcHOjo1HjrDx6FF2+/gQFR1tbOdgZ8ftn38ms51dyhUqCdIsJSIJ0ywlIvJGUg932mJhYYG3lxfeXl7M7NaNzceOsWz3btYdPMhlf38mrl7NxNWr8XBx4aPKlRnVqhUlPDzYcfo0G48eZeORI3i4uMQK2x2nTcPZwYF65cvzTrFiWFnq25KIZDzq4U4F6uEWeTWbNm0iKCiI6tWr88j1kanLSRdSsoc7ISFPnrDx6FF+37ePDUeOxJrLO2fWrDSpVIlmVapQtXhxHoWFkf1/PUdBISFkb9PGuAqmo709H5QuTb1y5ahbrhy5smVL/YvJ4NTDLZIwLXyTzilwi7w+rTSZNKYI3P/25OlTNh8/zu/79vHn4cM8+tewk+yOjjR6+20avf023l5emAF/HDrExiNH+OvYMQIexf6hqm+DBkzt3DmVryBjU+AWSZiGlIiISLpgZ2ND40qVaFypEhGRkfx96hS/7d3LuoMHCQwOZsHWrSzYuhU7a2tqli5Nw4oVmdC+PQv79uXIpUvGsd+HL16kaJ48xuNevXuXr379lQ/Ll6d22bLGXnIRkfRAPdypQD3cIq9m//79mJmZ4eXlxQ27G6YuJ10wdQ93QqKio9l15gxrDxzgj0OHuB4QEOvztwsXpmHFijR8+22Ke3hwLygIW2trnDJlAmDmhg30mjMHADMzM94uXNg480mZt97C3Nw81a8pPVIPt0jCNKQknVPgFnk1ZcuWJSwsjK1bt/LE/Ympy0kX0mrg/jeDwcDpq1f549Ah/jh0iMMXL8b6PF+OHDR8+20aVqxIteLFsbK05PTVqyzbvZuNR49y0s8vVnvXLFnYMHw45QoWTM3LSJcUuEUSpsCdzilwi7yakiVLEhkZyc6dOwnOGWzqctKF9BC4/+v2/fusP3yYPw4d4u9TpwiPiDB+5mBnR00vL+qWK0edsmVxd3HhZmAgm44dY+ORI2w9eZLwiAjuL1mCo709AIu2b+fOw4fUK1eOEnnzasn5f1HgFkmYAnc6p8At8vIMBgPFihUDYO/evdx3vm/iitKH9Bi4/y00PJxtJ07wx6FD/Hn4cJwHKYt7eBjD97uenpgBp69di9W7XfHzz4295u7ZsxtXvKxRqtQbP/+3ArdIwvTQpIi8caKiooz/r3m43xyZbG1pVKkSjSpVIiYmhqOXL7Pp6FH+OnaMgxcu4HP9Oj7XrzN5zRoy2driXaoUdcqWxdnBgXyurhgMBj6rWZMcTk5sP3WKG4GBzNm0iTmbNmFtaUnjSpVYMXiwqS9TRN4wCtwikiY9X/QGFLjfVObm5lQoVIgKhQoxrGVL7gcHs/XECTYdO8amY8e4GxRkHAcOUDRPHuqULUutMmVYPmgQFubm7Dpzho1Hj7LhyBGu+PvHGl5iMBgYtmQJ7xYrxnslSmBnY2OqSxWRDE6BW0TSJAVu+S9nR0daVqtGy2rViImJ4YSfn7H3e/+5c5y7eZNzN28y9Y8/sLK0pFLhwtQsXZqWVasyuUMH/O7eNS6yA3D2xg3GrFwJgJ21NTVKlaJe+fLULVuW/DlzmuoyRSQDeq3AHRkZib+/P2FhYbi4uJBNq4KJSDJ5HrjNzMywsLAwcTWS1pibm1O2QAHKFijAVy1aEBQSwraTJ9l07BjbTp7k2r177PH1ZY+vLyOWLsXBzo73SpSgppcXBsDT3R0rS0u61qnDhiNHuBkYyIYjR9hw5AgAxdzdGdOmDU0qVzbthYpIhvDSgTskJIQlS5awbNkyDh06xNOnT42f5cmTh1q1atGlSxcqVKiQrIWKyJvFzs6OoUOHEh0drVkm5IWyZM5Ms3feodk772AwGLji78+2kyf5++RJ/j51igePH7P+8GHWHz4MPFtyvqaXF95eXnzVrBmPwsLYePQoG48cYe/Zs5y9cQNba2vj8U9fvcqB8+epV748uZ2dTXWZIpJOvdQsJVOmTGHMmDHky5ePhg0bUrFiRXLnzo2dnR0PHjzgzJkz7NmzhzVr1lCpUiWmT59OoUKFUrL+dEGzlIi8Pi3tnjTpfZaSlPB8+Mm2Eyf4+9Qpdvv4xJp6EJ7N/f2upydVPT0plS8f1wICaFixonFc9xc//8zE1asB8Mqf37joTqUiRbBMR7+B0SwlIglLM9MCNm/enOHDh1OyZMlE24WHh/PTTz9hbW1Np06dXrvI9E6BW+T1KXAnjQL3i4VHRLD/3Dn+PnWKbSdOcPjSJWL+NbYbwNnBgXeKFeNdT0/e9fTkxJUr/LJ9O4cuXuTf3zazZs5M7TJlmN29O1nSwdd3BW6RhKWZwJ1UwcHByV5oeqbALfLyQkJCOHfuHPb29nh6eipwJ5EC98t7HBbGgfPn+efsWfb4+HDg/Hme/KcH3NbamrcLF6bMW29hZWGB3927/H3qFA9DQnDNkoXbP/9sXF5+7YED5HZ2plyBAmluyXkFbpGEpal5uCdPnszAgQMT/Dw4OJhatWpx4MCB1ypMRN5sfn5+tGnTBjc3N7Zv327qciQDc7C354MyZfigTBkAIqOiOH7lCnt8fPjn7Fn+8fUlMDiYXWfOsOvMGeDZQ5ul8ubFu1QpPFxc8H/4EDdnZ2JiYug2axZ3g4JwcXKibtmy1CtfnlplypBVHS4ib6yXDtzDhg3D2dmZDh06xPksJCSE2rVrExysJZhF5PU8n6VEUwJKarOytKRi4cJULFyYz5s0wWAwcP7WLf7x9eWf/818csXfnxN+fpzw8wPg+3XreCtnTioWLEhuZ2dCwsMJePSIRTt2sGjHDszNzalStCif1axJh5o1TXyFIpLaXjpw//rrr7Rt25asWbPSuHFj4/aQkBBq1arFgwcP2L17d3LWKCJvoIj//Urf+l8zRYiYgpmZGUXz5KFonjx0qlULgNv377P37Fn2/C+En7x6lSv+/lzx9zfu52hvTw4nJx4/ecLdoCD+8fWlcpEixs+fPH3KpmPHqOnlhYO9fapfl4iknpcO3M2aNSMoKIhWrVqxYcMGqlevTkhICHXq1CEwMJBdu3bh6uqaErWKyBtEPdySlrk5O9P83Xdp/u67AASHhbH/3DljD/jBCxcIDgsjOCzMuI+VhQU7T59m+JIlvOvpSciTJzQdPx4rS0uqenoaZz4pmiePpsIUyWBeaeGbTp068eDBAxo3bsy6desYNmwY/v7+7Nq1i1y5ciV3jSLyBlLglvTE0d6e2mXLUrtsWQAiIiM5dvmysQf8n7NnefD4MYcvXeLwpUsAmJuZYWNlxdPISLafOsX2U6cYuHAh+XLkoF758vRv2JCCbm6mvCwRSSavvNLk4MGDefjwId7e3uTLl49du3aRO3fu5KxNRN5gz4eUKHBLemRtZUWlokWpVLQogz76iJiYGM7dvGkM33t8fLh67x5P//eDJWDs1b567x6zNm6ka506xs/O3byJtaUlb2nJeZF06aUD90cffRTrvZWVFdmzZ6dPnz6xtq/+3wIBIiKvQj3ckpGYm5vj6eGBp4cHXf4XpK/evcuO06eNr5uBgbH2qfbll9QqU4a65cqx7uBB1h08SJHcualXvjz1ypWjavHi2Ojfh0i68NLzcMc3O0l8Fi5c+EoFZUSah1vk5V28eJHt27fj5uZGgwYNNA93Emke7vTJYDBw+c4ddpw+zd+nTrHl+HEehoQkuk8mW1tqennxYfnydKpVK0njvjUPt0jC0t3CNxKbArfI61PgThoF7owhOjqaQxcv8tfRo/x19ChH/jfu+zkzMzPjipdl33qLo1OnGj876eeHp7s7VpZxf4mtwC2SsDS18I2IiIikLAsLCyoXLUrlokUZ3bo194KC2Hz8OH8dPcrm48d58Pixse0JPz8+GDaMJpUrU6NUKSp8/jn2NjbUKl2aeuXLU6dsWXJmzWrCqxGRl+rhvn79Oh4eHkk++K1bt/QgJerhFnkV9+7dIyAgAGdnZ3LmzKke7iRSD3fGFx0dzeGLF1l/5AhrDxzA5/r1WJ9bWlgQFR0da1u5ggWpV64c7t7jyJkzf2qWK5JupGQPt/nLNK5QoQKdO3fm0KFDCbZ59OgR8+bNo0SJEnpwUkRe2dq1a2natCnTpk0zdSkiaYqFhQWVihbl2zZtODNjBhd+/JEJ7dpRuWhRgFhh2/Z/C0cdvXSJb1asYPv2n01Rssgb76WGlJw9e5axY8dSp04drKysKF++PG5ubtja2vLw4UN8fX3x8fGhfPnyTJo0ibp166ZU3SKSwWmWEpGkKeTmxuCmTRnctCl3Hjxg3cGDrDlwgO2nThH+v+k1AZz+t5qlv/8VcuZ8i+vXfcicORvZsmn9DJGU9koPTYaHh7Nx40b27NnD1atXefLkCdmzZ6dMmTLUrl2bEiVKpESt6ZaGlIi8vKlTp/Ljjz/Spk0bvv76aw0pSSINKZHngkJCWHvwICv27GHbyZOxer4LFizPo0cBBAX5U69eD5o2/ZIsWXKYsFoR00tzD03a2try0UcfxZmTW0QkuaiHW+T1ZMmcmfbe3rT39uZ+cDCr9+9n5j/nOH16B5cuHTG2W7duChs2zKRu3e58/PHXODpmN2HVIhnTS43hFhFJLQrcIsnH2dGRzrVr880321i48Dbdus3C07Oq8fOoqAj+/PMH2rXLxcSJHxMUdM+E1YpkPArcIpImKXCLpIysWV2pV68748fvZv78q7RpM4bs2fMAEB0dxT//rKRr14LMmdOb8+cPouU6RF7fSwfuzZs3ExMTkxK1iIgYKXCLpLwcOfLSosVXLFhwne+/P0KFCvWxsrLhyZPHbNgwg0GDKtGxY14WLx5GYOBNU5crkm699EOTFhYW3Llzhxw59HBFUumhSZGXt23bNk6fPk2VKlV4++239dBkEumhSUlMUlaajI6O4sSJbezc+Sv79q0mMjIceLa6ZenSH1C7dhcqVGiAlZV1SpcrkqrS1NLu5ubm+Pv7K3C/BAVukdenwJ00CtySmJdd2v3ixSN88019goLuxtru6Jid6tU/5YMPOuLh4ZmcJYqYTJpZ+EZERETeHIUKlWfhwpv07bvQOM4bIDg4kHXrvqdXr+IMGlSZLVvmExb2OJEjibzZXilwz5gxg82bNxMYGJjc9YiIAHDr1i38/PwICQkxdSkibzQLC0u8vdszd+4VevacS/bs7sbPzMzMOX/+ADNmdKZ9+1z88MNn+Pru1YOWIv/xSkNKnJ2duX//PmZmZuTOnZuyZctSrlw5ypYtS9myZcmVS6tW/ZuGlIi8vM8++4x9+/YxceJEGjZsqCElSaQhJZKYlx1SEp/IyKds3bqAa9fO0LLlcLZvX8S2bQu4dev/b748eYpSq1ZnvL3b4+CQ7bXPKZIa0tzCNz4+PkRFRXH8+HGOHTvGsWPHmD9/Pjdu3MDMzAxXV1du376drIWKyJtFs5SIpE1WVjbUq9fD+L5p08FUrfoxXbsWwtrajsjIcG7ePMdPP33O4sVDeffdj6lbtzuFC1fEzMzMhJWLmM5LB+7n/1jc3Nxwc3Pjww8/NH724MEDjhw5wokTJ5KtQBF5Mylwi6Qft25dIFMmJ4KDnw01zZo1J5aWNgQEXGP79l/Yvv0X3nqrDHXqdOO991phZ6ff9sqb5aXHcCc2AiVbtmzUqlWLwYMHv1ZRIiLPA7e1taYeE0nrypT5gLlzr9C27VgyZ87Kw4f+BARcI2fOApQo8T6WltZcuXKcWbO60r69Gz/+2Itr186YumyRVPPSgfuvv/7CyckpJWoRETFSD7dI+mJv70Dz5kOYN8+PVq1GYW/viL//ZS5dOsyMGWf47LPvcHMrxJMnj9m4cSa9e5fkyy+rsmvXUiIjn5q6fJEU9dJDSmrXrp0SdYiIxKLALZI+ZcrkRMuWw6lfvzdr136PlZUNbm6FaNx4AA0b9mPDhhmcPr2TQ4f+wNf3H3x9/2H+/P7UqdOVOnW64ezsZupLEEl2r/TQpIhISlPgFknfMmfOSps238Ta5uu7h3nz+lK0aBU+/3wJN2+eY8uWedy/f4sVK77ht9/GUaVKM+rX703RopX1kKVkGArcIpImNWvWjAcPHuDi4mLqUkQkmdy8eQ5ra1vOndvHuXP7KFHiPfr3/5Xg4EDWr5+Gr+8/7NmznD17llOwYDnq1+9D1aofY2VlY+rSRV7LS8/DLS9P83CLvD7Nw500modbEpMc83C/rvv3b/Pbb+PYvHkuUVERAHh51aR162+wsrJh/frp7N79/+O6nZxcqF27C3XrdsfZObcpS5cMLiXn4VbgTgUK3CKvT4E7aRS4JTFpIXA/FxBwg1WrxrJt2wKioiLJmfMtZs++gIWFBcHBgWzePI+//ppFYOBN4NmKl5Urf0T9+n0oVqyKhptIslPgTucUuEVe3s2bN7GysiJ79uxYWFgocCeRArckJi0F7ufu3r3KypXfUqLE+1Sv3gaAyMgIbt48h4eHJwcOrGX9+un4+Ow27vPWW2Vo0KAPVau2xNra1lSlSwajwJ3OKXCLvLzixYsTHR3N7t27yZEjhwJ3EilwS2LSYuCOz6ZNc5k1qytVqjSlVatReHgUx8/vJOvXT2fXriVERIQD4OSUgw8/7Endut1xctLzHvJ6UjJwv/Q83CIiKS06Opro6GhAs5SIvIlu376AmZkZ+/b9Tu/eJZk06ROsrGzp3Xs+P/10k3btxpM9ex4ePbrH0qUj6NjRg5kzu3Lz5jlTly4SLwVuEUlzoqKijP+vwC3y5vnss8lMm3aKKlWaYTAY2LNnOb16eTJlSjtCQh7StOkXzJ17hc8/X0rBguWIiAhn8+a59OhRjNGj63Py5PZEV8YWSW0K3CKS5jyfgxu0tLvImypv3hJ8+eUqpk49TsWKDYmJiWHHjkXMndsbAEtLK9577xO+++4w48bt5u23G2FmZsaRIxsYNsybfv3KsmPHr0RGRpj4SkQUuEUkDfp34La01HIBIm+yt94qzddfr+O77w5TrlxdWrYcYfwsOPg+gYE3KV68KkOHrmX27PPUq9cTGxt7/PxOMGXKp3TunJ/ffhvP48cPTHgV8qZT4BaRNCci4lmPlKWlJebm+jIlIlCoUHlGjNhI0aKVjNtWrRpD164FmTOnNw8e3MHNrRDdus3gp59u0LbtWLJly8WDB7dZtGgIn33mzo8/9uL27UsmvAp5U+k7mYikOVrWXURexGAwcOPGWaKiItiwYQZdurzFggUDCAq6h4NDNpo3H8K8eVfp1+8X8uf34unTMDZunEn37oUZO7YJPj57NM5bUo2mBUwFmhZQ5OU8ePCAGTNmYG5uztdffw1o4Zuk0rSAkpj0Mi1gUhkMBk6d2sGSJcM4d24fADY29tSv35smTQbi6Jg9Vrt1677jyJGNxv2LFKnERx8N5u23G+m3aaJ5uNM7BW6R16fAnTQK3JKYjBa4nzMYDBw/voUlS4Zx8eJhABo27EenTlPitL1x4yzr1k1hx45FxuXjc+cuTJMmg6hevS1WVjapWrukHQrc6ZwCt8jrU+BOGgVuSUxGDdzPGQwGDh9ez2+/jefLL38jW7ZcAAQEXMfe3olMmZyMbR8+vMv69dPYuHEWoaFBAGTNmpOGDftRp063WG3lzaDAnc4pcIu8nIiICEJCQrC2tjb+m1HgThoFbklMRg/cCRk9+kPOndtP48YDadCgD3Z2//+9OCzsMVu2zOOPP6YQGHgTADs7B+rU6UbDhv1wdnYzVdmSyrTSpIi8UU6ePEmVKlVo0aKFqUsRkXQuLCyYu3f9CAl5yOLFQ+ncOT+rV0/i6dMwAOztHWjceABz5lymb9+fcXf35MmTx6xZM4nOnfMxbVpHrWApr02BW0TSHM1SIiLJxd7ekWnTTjNgwGLc3AoRHBzIzz8PpnPnt1i3bipPnz4BwMrKGm/vdkyffpphw/7E07MqUVGRbNv2Ez16FGPMmMacO7ffxFcj6ZUCt4ikOQrcIpKcLCwseP/91syc6Uvfvgtxdc1PUNBdFizoz44dv8Zqa25uToUK9Rk/fjcTJ+6jUqXGABw8uI7Bg6vw5ZdVOXToT2JiYkxwJZJeKXCLSJqjwC0iKcHCwhJv7/bMnn2enj3nUqzYO3h7tzN+fvv2JaKi/n+l26JFK/PVV2uYNessH3zQEUtLK3x9/+HbbxvSp08pdu5cQnR0lCkuRdIZBW4RSXOerzSpwC0iKcHS0oratTszYcI/xmkAo6OjGD26Ht27F+Hvv3+OFaTz5ClK797zmTfvKh99NBh7e0euX/fh++/b0L17ETZtmmOcYlAkPgrcIpLmqIdbRFLb7dsXjQ9Y/vBDB3r29PxfD3a0sY2zsxvt209g/vxrtGkzBkfH7Pj7X2HWrG507pyftWu/58mTEBNehaRVCtwikuYocItIanN3L8a8eVfo0GESjo7ZuX37It9/34Y+fUryzz8rY43Zzpw5Cy1afMWCBdfo3PkHsmfPw4MHd/jpp8/p1Ckvy5eP5vHjBya8GklrFLhFJM1xd3enUaNGVKpUydSliMgbxMbGniZNBjJ37hXath1L5sxZuXHjLBMnfmxcOv6/7Rs06MOcOZfp1Ws+uXIV5PHjByxdOoJOnfKycOFgHjy4Y4IrkbRGC9+kAi18I/L6tPBN0mjhG0nMm7rwzasKDX3En3/+wOXLx/jqqzWYmZkBz4af5MpV0Pj+uejoaPbt+41Vq8Zy9eopAKysbKhZ8zOaNBlEzpz5U/0aJOm00mQ6p8At8voUuJNGgVsSo8D9+oKD79O5cz7c3T1p1Wo0ZcrUihO8DQYDR45sZNWqMca5u83NLXjvvVY0azYEd/dipihdXkArTYrIGyUiIoInT57EelhJRCQtuHjxMDExMVy4cIiRI+vw5ZdVOXlye6w2ZmZmVKjwIRMm7GXs2J2UKVOLmJhoduz4lV69ijNuXFMuXjxioisQU1DgFpE058cff6RMmTKMGTPG1KWIiMRSrlwd5s69QqNG/bG2tuXs2b0MG+bN0KHV8fHZE6utmZkZJUq8x6hRm/nuu8NUrvwRBoOB/ftX8/nnFRgxojZnzuxCgw0yPgVuEUlzNEuJiKRlWbO60rHj98yZc5kPP+yFpaU1p0/v5OuvayT4kGShQuUZMuR3ZszwoXr1tpibW3D8+Ba++up9hgypxvHjWxW8MzAFbhFJcxS4RSQ9cHZ2o2vX6cyZc4k6dbpRp043smXLZfzc3/9KnH08PDzp338RP/54kbp1u2NpaY2v7z+MGFGLwYOrcPToXwreGZACt4ikOQrcIpKeuLi406PHbLp0mWbc5ud3iq5dCzJmTGOuXDkRZ5+cOfPTvfss5s27QoMGfbG2tuX8+QOMGlWPzz+vyKFDfyp4ZyAK3CKS5ihwi0h69O/ZSnx8dmNmZsbBg+vo168M48c34/p1nzj7ODvnpnPnqcyb50eTJgOxsbHn0qUjfPttQ/r1K8u+fatjLboj6ZMCt4ikOQrcIpLe1a/fixkzfKhatSVmZmbs2/c7vXuXZNKkT7h583yc9lmz5qRDh0nMn3+Vpk2/xM4uM35+Jxg/vil9+3rxzz8rNXNTOqbALSJpjgK3iGQEefIUZdCgZUybdooqVZphMBjYs2c5X39dnaioyHj3cXJyoV27ccybd5UWLb7G3t6Ra9fOMHHix/TpU5Jdu5YqeKdDCtwikuaUKlWKWrVqkS9fPlOXIiLy2vLmLcGXX65i6tTjvP12I5o0GYSl5bMOhZiYGAICbsTZx9HRmTZtvmH+/Kt88slIMmXKwo0bZ/nuu9b06uXJ9u2LiI6OSu1LkVeklSaTaMqUKcyfPx+DwUDNmjX54Ycf4qwslRCtNCny+rTSZNJopUlJjFaaTBsMBoMxQ+zd+xvffdeKmjU70rz5V7i4uMe7T2joIzZsmMG6dd/z+PEDAHLmfItmzb6ievW2WFlZp1r9GZVWmjSxgIAAZsyYwdGjRzl9+jRHjx7lwIEDpi5LRERE0qF/d9idOrWdqKhINm36ka5dCzJ3bp945/LOlMmJFi2GMm/eVdq1G4+Tkwv+/leYMaMT3bsXZtOmOURGPk3Ny5CXoMCdRFFRUYSHhxMZGUlkZCQ5cuQwdUkiGVZ0dLSmwxKRN0L37rMYO3YXJUq8R1RUBOvXT6dLl7dYsOBzgoLuxWlvb+9A06ZfMG+eH5999h1Zsrhy7941Zs3qRteuBdmwYSYREeEmuBJJzBsRuHfv3k2DBg1wc3PDzMyMtWvXxmkza9Ys8ufPj62tLeXKlWPPnv9fntXFxYWBAwfi4eGBm5sbNWvWpECBAql4BSJvlk8//RRPT0+2bt1q6lJERFJciRLVGDNmB9988zdFi1YhIiKcdeu+Z+LEFgnuY2ubicaNBzBvnh+dO/9AtmxuBAbeZM6cXnTp8hbr1k3l6dOwVLwKSYylqQtIDaGhoXh5edGhQweaNm0a5/MVK1bQr18/Zs2axTvvvMOcOXOoW7cuvr6+eHh48PDhQ9avX8/Vq1exs7Ojbt267N69m2rVqpngauIKDw/n3r24PwU/5+TkhJOTEwBPnz7l7t27CbZ1dHQkS5YsAERERODv759gWwcHB7JmzQo8m1Xizp34l7MFyJw5M9myZQOe9V7eunUrwbb29vZkz54dePYwyc2bNxNsa2dnh4uLi/H99evXE2xra2sb6zcTN27cSLAX1cbGBldXV+P7mzdvJjgPqrW1NTlz5jS+v3XrVoJPkFtaWuLm5mZ8f+fOHeOMHP9lYWFB7ty5je/9/f2JiIiIt625uTl58uQxvr979y5Pn8b/q0UzMzPc3f9/jOC9e/cID0+4N8Td3d3468+AgACePHmSYNs8efJgbv7s5/j79+8TGhqaYFs3NzcsLZ99CXrw4AEhISHGz0JCQjAYDMbPRUQyOjMzM7y8alCqVHWOH9/CkiXDaNr0C+PnT56EEB0dSebMWWPtZ2NjR4MGfahduwvbtv3Eb7+NJzDwBgsW9Of338fTtOmX1KnTFRsbu9S+JPmXN+6hSTMzM9asWUPjxo2N295++23Kli3L7NmzjduKFStG48aNGTduHKtWrWLnzp3MnDkTgEmTJmEwGBg8eHC853j69GmssBMcHIy7u3uKPTR56NAhPv300wQ/HzhwIJ06dQLg9OnTNG/ePMG2PXr0oE+fPgBcunSJ+vXrJ9i2Q4cOfPHFsy8Gt27dwtvbO8G2n3zyCSNGjADg4cOHVK5cOcG2jRo1YsKECQA8efKEMmXKJNi2Vq1aTJv2/yt7FS1aNMG21apVY+7cucb3ZcuWJSws/p/+y5cvz+LFi43vq1SpwoMHD+JtW7x4cX7//Xfje29v7wR/oChQoAAbNmwwvm/QoAEXL16Mt62bmxvbt283vm/evDmnT5+Ot22WLFliPVfw6aefcujQoXjb2tracuLECeP7rl27smvXrnjbApw9e9YYuPv168emTZsSbHv06FEyZcoEwJAhQ1izZk2Cbffu3YuzszMAo0ePZunSpXHazJs3j6pVqwJ6aDKp9NCkJEYPTaYfz+PZ86+/K1Z8y9q1k2nUaAANG/bD3j7+h/oiIyPYvv0XVq0aw7171wDIli0XTZt+Se3aXbC2tk2dC0iHUvKhyTe++ygiIoKjR4/y5Zdfxtpeq1Yt9u3bBzzr4du3bx/h4eFYWVmxc+dOunTpkuAxx40bx6hRo1K07n+zsLDA3t4+wc//3Utobm6eaFtra+tXamtmZpbktkCibW1sbEze1tY29hckOzu7BNvb2cXuNbC3t0+w7X+Pa2trm+TjJtb2v9sTa/syNfyXjY1NsrX990ND1tbWcdrmzp2bUqVKJelcIiIZzb+/RhoMBo4f30Jo6COWLh3Bn3/+QOPGA6lfvzd2drE78qysrKlduzM1arRj+/ZfWLnyWwICrjNvXl9+/30CzZoNoVatTgreqeyN7+G+ffs2uXPnZu/evVSpUsXYbuzYsfzyyy+cP/9sNaihQ4eyevVqzM3N8fb2TnRawNTu4RZ5E6iHO2nUwy2JUQ93+hUTE8PevatYtmwkN2+eA8DRMTtNm35BvXo9sLGJv4MjMjKCbdt+YtWqMQQGPhui6eycm+bNh/LBB59hZWUT735vIvVwp4L/hud/z5EJMGbMGMaMGZOkY9nY2MTpTRXT2bNnD507d060zdy5c9PMmHwREZH/Mjc3p2rVj6lSpRm7dy9j+fJR3LlziYULB+Hvf4Xu3WfFu5+VlTV163ajZs0ObN26gFWrxnL//i1+/LEHv/02jhYthuLt3UHzeKewNz5wZ8+eHQsLizgPB967dy/WQ3OSflWoUCHWrDMNGjTg448/pk2bNsZtzx/+FBERScssLCyoXr0N1aq1ZMeOX1m1aiyNGvU3fh4cfB87u8xxeq6trGyoV68HNWt+xpYt8/ntt3EEBt5g1qxu/PbbOJo3H4q3d3vjCpiSvN6IaQETY21tTbly5eJMP7Z169ZYQ0wk/bK1tcXFxQUXFxdiYmIICgqiXLlyxm0uLi6aDUNERNIVCwtLatbswOzZ53FzK2TcPm9eX7p2LcSmTXOJioo7C5a1tS316/di7tzLdO78A1mz5uTevWvMnNmFbt0Ks2XLgnj3k9fzRgTukJAQTpw4YZyZwc/PjxMnThinkBswYADz58/np59+4uzZs/Tv35/r16/TrVs3E1YtKcHX1xd4NrOIiIhIevd8KlZ4NnWgj8+u//Vcd6V79yL8/ffPREdHxdnP2tqWBg36MHfuFTp2nPK/BXSuMmNGJ3r0KJrgfvJq3ojAfeTIEcqUKWOcXm7AgAGUKVOG4cOHA/Dxxx8zdepURo8eTenSpdm9ezcbN24kb968pixbUoCvry+urq7G6egSEhwczIoVK1KpKhERkddnZ5eZ2bMv0LHjFJyccnD3rh8//NCBnj092blzSbxrRNjY2NGoUT/mzbtChw6TjUvG//BDB3r0KMb27YsUvJPBGzdLiSkEBwfj5OSkWUrSgJ49exITExNrzvX43Lx5k759+8aaX/tFoqOjsbCweN0SJQGapSRpNEuJJEazlLw5wsND2bhxFr//PoHHj+8D0KXLNOrX752k/VavnkhwcCAAbm6F+Pjj4VSr9kmG/j6XkrOUvBE93CLP+fr6xhlOEhoaSqdOnWjQoAENGjRgz549TJ06lcuXL9O4cWN+/PFHALp168ZHH31EgwYN2LJlC/AsmDdq1Ihhw4bRpEmTBFeCFBERSU22tpn46KNBzJvnR5s2Y8iVqyA1arQzfh4UdC/e1Zb/vV+7duNxcHDm9u2LTJnSll69irNr19IEV1OWhKmHOxWohztteL7C5axZs6hRo4Zx++bNm9m6dSuTJ0/GYDAQGhpKUFBQnB7uoKAgsmTJwuPHj2nRogUbN27k1q1b1K5dm9WrV1OkSBFTXNYbQz3cSaMebkmMerjfXDExMcbx3gaDgc8/r0hMTDStW4+mfPkPE1xbJCzsMRs2zGDt2sk8fvxsxWV392K0bDmCd95pHmsMeXqnHm6RZODj4wPEfWCycOHCHD16lEmTJnHixIkEfyj65ZdfaNSoEW3atOHOnTsEBAQAkC9fPoVtERFJ0/4djG/fvsitW+e4cuU433zTgEGDKnHs2OZ4e7zt7R1o3nwI8+b50br1N2TKlIUbN84yaVJL+vQpxT//rCImJiY1LyVdUuCWN8bZs2dxdnaOM796/vz5Wb16NYUKFWLs2LEsXrw4zr4HDhzg2LFjrFixgnXr1pErVy7j8JH/LpUuIiKSluXOXZh58/xo2vQLbGzsuXDhECNH1uHLL6ty8uT2ePext3fk44+/Zv78q7RqNYpMmZy4ft2HiRNb0Ldvafbt+13BOxEK3PLG6Ny5M3v37o2z/e7du9jb29O4cWM+/fRTzp07R6ZMmQgNDTW2CQ0NJUuWLNja2nLq1CmuXr2aipWLiIgkL0fH7LRrN565c6/QqFF/rK1tOXt2L8OGeXPy5N8J7pcpkxMtWw5n3ryrtGw5Ant7R65dO8348c3o378sBw6sjben/E2nwC1vvAsXLtCsWTMaN27M4sWLad++PVmzZsXT05MGDRrw448/8u677/L48WMaN27MkiVLKFy4sKnLFhEReW1Zs7rSseP3zJlzmQ8/7EWhQhUoWbK68fPnM5X8V+bMWWjVaiTz51+lRYuvsbNzwM/vJGPHNqF//3IcPPiHgve/6KHJVKCHJkVenx6aTBo9NCmJ0UOT8iL/nuI2PDyUzp3folCh8nzyySgKFSqf4H6PHz9g7drvWL9+Gk+ehABQsGA5WrUaTblydRN8KDMt0UOTIiIiIpLi/j3Pto/Pbh4/vs+RIxv5/PMKjBnTGD+/k/Hu5+CQjbZtx/xvbPiX2Npm4tKlo4we/SFffPEOJ0/+/Ub3eCtwi4iIiEgc5crVZdasc1Sv3hZzc3MOHlxH376lGT++Odev+8S7z7Ox4eOYN8+PJk0GYW1tx7lz+xk2rCZff10DX99/Uvkq0gYNKUlBM2fOZObMmURHR3PhwgUNKRF5DRpSkjQaUiKJ0ZASeVU3b55j2bJR/PPPCgwGA+bmFsyb54eLi3ui+z14cIfffhvHpk1ziIp6NrtXmTK1ad16NIULV0yN0pMsJYeUKHCnAo3hFnl9CtxJo8AtiVHgltd17doZli0bibm5BYMHrzBuf/z4AQ4O2RLcLyDgBqtWjWHr1gVER0cBULFiA1q1Gs1bb5VO6bKTRIE7nVPgFnl9CtxJo8AtiVHgluQSHR2FhYUlAP7+fvTqVZz33mtFixZf4+qaL8H9/P2vsGLFN+zYscg4b3eVKs1o1WoUHh6eqVF6gvTQpIiIiIikGc/DNsDBg2uJiHjC1q0L6N69MLNmdScg4Ea8++XM+RZ9+y5kxgxfqlX7BDMzM/bt+43evUvw3XdtuH37YmpdQqpS4BYRERGRV9aoUX8mTtyHl1dNoqIi2bTpR7p2LcjcuX148OBOvPvkyVOEgQOXMm3aKSpX/giDwcCuXUvo0aMY06d34t69a6l8FSlLQ0pSgYaUiLw+DSlJGg0pkcRoSImktDNndrFkyXB8fHYD4OSUg59+uoGVlXWi+12+fIwlS4Zz5MgGACwtrfjgg060aDEUZ+fcKV43aEiJiIiIiKQDJUq8x9ixO/nmm20ULVqZOnW6GsO2wWAgJCQo3v0KFCjL8OHrY/WU//XXbLp0KcD8+f15+PBuKl5F8lMPdypQD7fI61MPd9Koh1sSox5uSU0Gg4Ho6CgsLa0AOHnyb8aObUyDBv1o3HgAmTNnTXDfM2d2sXjxMHx99wBgY2NP/fq9adJkEI6OzilSr3q4RURERCRdMTMzM4ZtgN27l/HkSQgrV35L5875Wb58NKGhj+Ldt0SJ9xg3bhejRm2mcOGKPH0axu+/T6Bz5/wsXToiwf3SKvVwpwL1cIu8PvVwJ416uCUx6uEWU4qJieHAgbUsWzaCa9fOAJA5c1aaNBlE/fq9sbOLPyMZDAaOHNnA4sXD8PM7YdyvceOBNGjQJ8H9Xpbm4U7nFLhFXp8Cd9IocEtiFLglLYiJiWHv3lUsWzaSmzfPAVCs2DtMmJD4su/PAvsaliwZzo0bvsCzpeSbNv2SevV6YGNj91p1aUiJiIiIiGQI5ubmVK36MdOnn6F//1/Jlasg9er1MH4eGRlBRER4vPtVqdKUadNO8fnnS8iVqyDBwYEsXDiQrl0LsH79DCIjn6bmpSSZAreIiIiIpDoLCwuqV2/DrFlneffdj43bt26dT5cuBdi4cVa8AdrCwoL33mvFrFln6dPnJ3LkyMuDB3eYO7c3XbsWYvPmeURFRabmpbyQAreIiIiImIyFhSUWFhbG9zt2/MqDB7f58ceedOtWOMEAbWFhSc2aHZg9+wLdu8/G2Tk3gYE3mDmzCz16FGX79kVER0en5qUkSIFbRERERNKMsWN30rXrDLJly0VAwHVmzuxC9+5F+Pvvn4mOjorT3srKmrp1uzFnziU6dZqKk1MO/P2vMHVqO3r1Ks6ePSuIiYkxwZX8Pz00mQr00KTI69NDk0mjhyYlMXpoUtKTp0+fsGnTHH77bRyPHt0DoFatzvTqNTfR/cLDQ9mwYSarV0/g8eMHAOTNW5LWrUfz9tuNMDMzi3c/PTQpIiIiIm8UGxs7GjXqx7x5V2jffiKOjtmpXbuL8fOnT8Pi7bm2tc1E06aDmTfPj1atRmNv78i1a6cZO7YJn39egaNH/yK1+5vVw50K1MMt8vrUw5006uGWxKiHW9KziIhwrK1tje/nzu3DmTO7aNVqVKI91yEhD1m79jv++GMq4eGhABQtWpnWrb/Fy6uGsZ16uNOpmTNn4unpSYUKFUxdioiIiEi69u+w/fTpE/bsWcHVq6cYO7YJAwaU5/DhDfH2XGfOnJU2bb5l3jw/mjQZiLW1LefO7WfYMG+GDq2Or2/i838nB/VwpwL1cIu8PvVwJ416uCUx6uGWjOTx4wesW/c9f/75A0+ehABQuPDbtG49mtKlP0iwx/vBgzv89ts4Nm2aQ1RUBABlytSmWbMvGDq0hlaaTK8UuEVenwJ30ihwS2IUuCUjCg4OZPXqSWzYMIOnT8MA6NRpKg0b9k10v4CAG6xc+S3btv0Ua/YTDSkREREREfkXR8fstG8/gblzr9CoUX8cHJypVu0T4+fPQ/h/ubi407PnHGbPPk+NGu0S7BFPDurhTgXq4RZ5ferhThr1cEti1MMtb4KnT59gY2NnfD98eC3MzMxo1Wo0RYq8neB+Fy8e5fPPy6dID7dlsh5NRERERMSE/h2279y5zOnTO4iOjuL48S2UL/8hrVqNomDBcnH2y527UIrVpCElIiIiIpIh5cpVgNmzL1Cz5meYm1tw5MgGBgwoz9ixTfDzO5lqdShwi4iIiEiGlTNnfvr0WcCsWWepXr0t5ubmHDiwlr59S3P8+JZUqUGBW0REREQyPDe3QvTvv4jp089QterH5MxZgJIlqxs/j4gIT7Fzawy3iIiIiLwx3N2LMWjQcsLDQ7G0tAIgKiqSwYMrp9g51cMtIiIiIm8cW9tMxv8/eXIb/v5XUuxcCtwiIiIi8kYrV64uY8ZsT7HjK3CLiIiIyBuvQIG4UwUmFwVuEREREZEUpMAtIiIiIpKCFLhFRERERFKQAreIiIiISApS4BYRERERSUEK3CIiIiIiKUiBW0REREQkBSlwi4iIiIikIEtTF/AmMBgMAISEhJi4EpH0K4wwU5eQLgTrj0kSEUawqUsQSbPCwp79+3ie25KTAncKmjlzJjNnzuTp06cAvP/++6YtSEREREQSdf/+fZycnJL1mGaGlIjxEktQUBBZs2bl+vXr8f4FVqhQgcOHDye4/4s+T2qbV2n7Ku2Ta9+UPFZaPF9yCA4Oxt3dnRs3buDo6Bjnc91rqX+stHi+5KB7TfdaatG9pnsttTx69AgPDw8ePnxIlixZkvXY6uFOBebmz4bKOzk5xfvFwsLCIt7tSf08qW1epe2rtE+ufVPyWGnxfMnJ0dFR99pr0L2WdLrXXo/utaTTvfZ6dK8l3fPclqzHTPYjykvr2bPna32e1Dav0vZV2ifXvil5rLR4vtSgey31j5UWz5cadK+l/rHS4vlSg+611D9WWjxfWqchJakgODgYJycnHj16lG5/2pP0QfeapBbda5JadK9JaknJe0093KnAxsaGESNGYGNjY+pSJIPTvSapRfeapBbda5JaUvJeUw+3iIiIiEgKUg+3iIiIiEgKUuAWEREREUlBCtwiIiIiIilIgVtEREREJAUpcJtIkyZNyJo1K82aNYu1ff369RQpUoRChQoxf/58E1UnGdXkyZMpXrw4JUqUYPHixaYuRzKwKVOmULx4cTw9PenTpw96Pl9Swvnz5yldurTxZWdnx9q1a01dlmRQfn5+VK9eHU9PT0qWLEloaGiS99UsJSayY8cOQkJC+OWXX/jtt98AiIqKwtPTkx07duDo6EjZsmU5ePAg2bJlM3G1khGcPn2adu3asW/fPgC8vb3ZsGFDsi9fKxIQEEClSpXw8fHBysqKatWqMXnyZCpXrmzq0iQDCwkJIV++fFy7do1MmTKZuhzJgN577z2+/fZbqlatyoMHD3B0dMTSMmmLtquH20SqV6+Og4NDrG2HDh2iePHi5M6dGwcHB+rVq8fmzZtNVKFkNGfPnqVKlSrY2tpia2tL6dKl2bRpk6nLkgwqKiqK8PBwIiMjiYyMJEeOHKYuSTK4P/74A29vb4VtSRHPOxCqVq0KQLZs2ZIctkGB+5Xs3r2bBg0a4ObmhpmZWby/vpo1axb58+fH1taWcuXKsWfPnhce9/bt2+TOndv4Pk+ePNy6dSs5S5d07HXvuxIlSrBjxw6CgoIICgpi+/btur8kXq97r7m4uDBw4EA8PDxwc3OjZs2aFChQIBWvQNKL5Px+unLlSj7++OMUrljSq9e91y5evEjmzJlp2LAhZcuWZezYsS91fgXuVxAaGoqXlxczZsyI9/MVK1bQr18/hg4dyvHjx6latSp169bl+vXriR43vtE9ZmZmyVKzpH+ve989H0tbo0YNmjRpQoUKFV7qp3N5c7zuvfbw4UPWr1/P1atXuXXrFvv27WP37t2peQmSTiTX99Pg4GD27t1LvXr1UqNsSYde916LjIxkz549zJw5k/3797N161a2bt2a9AIM8loAw5o1a2Jtq1ixoqFbt26xthUtWtTw5Zdfxtq2Y8cOQ9OmTY3v9+7da2jcuLHxfZ8+fQxLlixJ/qIl3Xud++65jh07GtavX59SJUoG8Sr32sqVKw09evQwfjZx4kTDhAkTUrxWSd9e5+vaokWLDK1bt07pEiWDeJV7bd++fYbatWsbP5s4caJh4sSJST6neriTWUREBEePHqVWrVqxtteqVcv4sFpCKlasyJkzZ7h16xaPHz9m48aN1K5dOyXLlQwiqffdvXv3gGdP9h86dEj3l7y0pNxr7u7u7Nu3j/DwcKKjo9m5cydFihQxRbmSjr3M91MNJ5HXkZR7rUKFCty9e5eHDx8SExPD7t27KVasWJLPod8nJ7PAwECio6NxdXWNtd3V1RV/f3/j+9q1a3Ps2DFCQ0PJkycPa9asoUKFCnz33XdUr16dmJgYBg8ejLOzc2pfgqRDSb3vGjduTFBQEJkyZWLhwoUaUiIvLSn3WqVKlahXrx5lypTB3Nwcb29vGjZsaIpyJR1L6te1R48ecejQIX7//ffULlEyiKTca5aWlowdO5Zq1aphMBioVasW9evXT/I59N02hfx37LXBYIi1LaHZRxo2bKhvTPLKXnTfvei3LCJJ9aJ7bcyYMYwZMya1y5IM6EX3mpOTE3fv3k3tsiQDetG9VrduXerWrftKx9aQkmSWPXt2LCwsYv30Dc9+lf/fn5xEkovuO0ktutcktehek9SSGveaAncys7a2ply5cnGeXN26dStVqlQxUVWS0em+k9Sie01Si+41SS2pca9pSMkrCAkJ4dKlS8b3fn5+nDhxgmzZsuHh4cGAAQNo27Yt5cuXp3LlysydO5fr16/TrVs3E1Yt6Z3uO0ktutcktehek9Ri8nvtVaZTedPt2LHDAMR5tWvXzthm5syZhrx58xqsra0NZcuWNezatct0BUuGoPtOUovuNUktutcktZj6XjMzGOJZbUVERERERJKFxnCLiIiIiKQgBW4RERERkRSkwC0iIiIikoIUuEVEREREUpACt4iIiIhIClLgFhERERFJQQrcIiIiIiIpSIFbRERERCQFKXCLiIiIiKQgBW4RERERkRSkwC0iImnWjRs3eP/99/H09KRUqVKsWrXK1CWJiLw0M4PBYDB1ESIiIvG5c+cOd+/epXTp0ty7d4+yZcty/vx5MmXKZOrSRESSTD3cIiIZ3P3798mRIwdXr141dSkvLVeuXJQuXRqAHDlykC1bNh48eABAs2bN+P77701YnYhI0ihwi4hkcOPGjaNBgwbky5fP1KW8liNHjhATE4O7uzsAw4cPZ8yYMQQHB5u4MhGRxGlIiYhIBvbkyRPc3NzYuHEjlStXNnU5cZQrV46nT5/G2b5lyxbc3NyM7+/fv0/VqlWZP38+VapUibV/p06d6N69e6rUKyLyKtTDLSKSjuTJk4dZs2bF2rZv3z7s7e25du1anPZ//fUXlpaWccL248ePad26NZkyZSJXrlxMmTKF999/n379+iV47piYGCZMmEDBggWxsbHBw8ODMWPGGD9///336d27N/369SNr1qy4uroyd+5cQkND6dChAw4ODhQoUIC//vrLuM/Ro0c5c+ZMnNe/w/bTp09p0qQJQ4YMiRW2ARo2bMiyZcuS9GcnImIqCtwiIulIpUqVOHz4sPG9wWCgX79+9OvXj7x588Zpv3v3bsqXLx9n+4ABA9i7dy9//PEHW7duZc+ePRw7dizRcw8ZMoQJEyYwbNgwfH19Wbp0Ka6urrHa/PLLL2TPnp1Dhw7Ru3dvunfvTvPmzalSpQrHjh2jdu3atG3blrCwsCRdr8FgoH379tSoUYO2bdvG+bxixYocOnQo3l5yEZG0QkNKRETSkcmTJ/Pzzz9z5swZABYtWsTgwYO5ePEiDg4Ocdo3btwYZ2dnFixYYNz2+PFjnJ2dWbp0Kc2aNQPg0aNHuLm50blzZ6ZOnRrnOI8fP8bFxYUZM2bQqVOneGt7//33iY6OZs+ePQBER0fj5OTERx99xKJFiwDw9/cnV65c7N+/n0qVKr3wev/55x+qVatGqVKljNt+/fVXSpYsCcCpU6fw8vLi6tWr8f7AISKSFliaugAREUm6SpUq8cUXXxASEoK5uTlfffUV3377bbxhG56N4ba1tY217cqVK0RGRlKxYkXjNicnJ4oUKZLgec+ePcvTp0/x9vZOtL5/B2MLCwucnZ2N4Rgw9ojfu3cv0eM89+677xITE5Pg53Z2dgBJ7jEXETEFBW4RkXSkfPnyWFhYcOzYMbZt24azszOfffZZgu2zZ8/Ow4cPY217/otNMzOzeLfH53mwfRErK6tY783MzGJte37OxEL0y3g+RaCLi0uyHE9EJCVoDLeISDpia2uLl5cXq1evZvLkyXz//feYmyf8pbxMmTL4+vrG2lagQAGsrKw4dOiQcVtwcDAXL15M8DiFChXCzs6Ov//++/UvIhmdOXOGPHnykD17dlOXIiKSIAVuEZF0plKlSkybNo2aNWu+cIhH7dq18fHxidXL7eDgQLt27Rg0aBA7duzAx8eHzz77DHNz8zi93s/Z2tryxRdfMHjwYBYtWsTly5c5cOBArLHhprBnzx5q1apl0hpERF5EgVtEJJ0pXbo0lpaWTJo06YVtS5YsSfny5Vm5cmWs7d9//z2VK1emfv361KxZk3feeYdixYrFGe/9b8OGDePzzz9n+PDhFCtWjI8//jjJY7FTQnh4OGvWrKFz584mq0FEJCk0S4mISDpTo0YNSpUqFe9sIvHZuHEjAwcO5MyZMwkOPwkNDSV37tx89913dOzYMRmrTTkzZ85k3bp1bNmyxdSliIgkSg9NioikAzExMQQEBLBgwQLOnz/PmjVrkrxvvXr1uHjxIrdu3TIui378+HHOnTtHxYoVefToEaNHjwagUaNGKVJ/SrCysmL69OmmLkNE5IXUwy0ikg7s3LmTGjVqULRoURYuXMjbb7/9Wsc7fvw4nTp14vz581hbW1OuXDm+//77WFP4iYhI8lDgFhERERFJQXpoUkREREQkBSlwi4iIiIikIAVuEREREZEUpMAtIiIiIpKCFLhFRERERFKQAreIiIiISApS4BYRERERSUEK3CIiIiIiKUiBW0REREQkBSlwi4iIiIikIAVuEREREZEUpMAtIiIiIpKCFLhFRERERFLQ/wHk0tzGa+CAIQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yya = np.logspace(np.log10(max(y_mesa)), np.log10(y1))[:-1]\n", + "Ta = T_star*np.ones(len(yya))\n", + "\n", + "yyb = np.logspace(np.log10(y1), np.log10(y2))\n", + "Tb = T_base * (yyb/y1)**dels[0]\n", + "# Tb = T_base * (yyb/y1)**0.4\n", + "\n", + "yyc = np.logspace(np.log10(y2), np.log10(y3))[1:]\n", + "Tc = Tb[-1] * (yyc/y2)**dels[1]\n", + "\n", + "yyd = np.logspace(np.log10(y3), np.log10(min(y_mesa)))[1:]\n", + "Td = Tc[-1] * (yyd/y3)**dels[2]\n", + "\n", + "toy.y = np.concatenate((yya,yyb,yyc,yyd))\n", + "toy.T = np.concatenate((Ta,Tb,Tc,Td))\n", + "\n", + "zones_plot()\n", + "plt.loglog(toy.y, toy.T, 'k--', label=\"toy model\")\n", + "plt.legend()\n", + "\n", + "plt.text(2e9,T_star*0.95,r\"$T_{\\rm star}$\",ha='center',va='center')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6b6b943c", + "metadata": {}, + "source": [ + "We have some freedom in setting $T_{\\rm star}$. We might want to make that layer very cold to create a \"buoyancy wall\" in order to prevent convective undershoot into that region." + ] + }, + { + "cell_type": "markdown", + "id": "19669fc5", + "metadata": {}, + "source": [ + "#### Set the composition profile" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "aef7cac2", + "metadata": {}, + "outputs": [], + "source": [ + "X0 = 0.7\n", + "Zcno = 0.02" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5b25d5da", + "metadata": {}, + "outputs": [], + "source": [ + "# Metals\n", + "toy.Zfe = np.concatenate((np.ones(len(yya)), np.zeros(len(yyb)+len(yyc)+len(yyd))))\n", + "toy.Zcno = np.concatenate((np.zeros(len(yya)), Zcno*np.ones(len(yyb)+len(yyc)+len(yyd))))\n", + "\n", + "# Hydrogen\n", + "X0 = 0.7\n", + "toy.X = X0 * np.maximum(0, 1-toy.y/yd)\n", + "\n", + "toy.Y = 1 - toy.X - toy.Zfe - toy.Zcno\n", + "\n", + "# sanity check\n", + "assert max(toy.Y)<=1.0 and min(toy.Y)>=0" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "88827d3b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "plt.figure(figsize=(6,3))\n", + "plt.xlabel(r\"$y$ (g cm$^{-2}$)\")\n", + "plt.ylabel(r\"Mass fraction\")\n", + "\n", + "plt.loglog(y_mesa, mesa_prof.h1,'b-', label='h1')\n", + "plt.loglog(toy.y, toy.X, 'b--')\n", + "plt.loglog(y_mesa, mesa_prof.he4,'m-', label='he4')\n", + "plt.loglog(toy.y, toy.Y, 'm--')\n", + "\n", + "cno_mesa = mesa_prof.c12\n", + "for iso in (\"c13\",\"n13\",\"n14\",\"n15\",\"o14\",\"o15\",\"o16\",\"o17\",\"o18\",\"f17\",\"f18\",\"f19\"):\n", + " cno_mesa += mesa_prof.data(iso)\n", + "plt.loglog(y_mesa, cno_mesa, 'g-', label='cno')\n", + "plt.loglog(toy.y, toy.Zcno, 'g--')\n", + "\n", + "plt.loglog(y_mesa, mesa_prof.fe56,'-', color='gray', label='fe56')\n", + "plt.loglog(toy.y, toy.Zfe, '--', color='gray')\n", + "\n", + "plt.legend(loc=3, frameon=False)\n", + "plt.ylim([1e-2,1.1])\n", + "plt.xlim([1e5,1e10])\n", + "plt.gca().invert_xaxis()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "98e9e36b", + "metadata": {}, + "source": [ + "#### We can write down a simplified EOS (gas+radiation+degenerate electrons) to get the density profile" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "adcf3cce", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import fsolve\n", + "\n", + "# Constants\n", + "kB = 1.380658e-16\n", + "arad = 7.5657e-15\n", + "mp = 1.67e-24\n", + "\n", + "toy.rho = np.zeros(len(toy.T))\n", + " \n", + "for i in range(len(toy.T)):\n", + " \n", + " # Mean-molecular weights\n", + " if toy.Y[i]>0:\n", + " # If there is helium, then the Z is CNO with =15\n", + " mu_I = 1/(toy.X[i] + toy.Y[i]/4 + toy.Zcno[i]/15)\n", + " else:\n", + " # Otherwise, the Z is iron\n", + " mu_I = 56\n", + "\n", + " mu_e = 2/(1+toy.X[i])\n", + " mu = 1/(1/mu_I + 1/mu_e)\n", + " \n", + " T = toy.T[i]\n", + " \n", + " # ion pressure\n", + " def pgas(rho):\n", + " return rho*kB*T/mu_I/mp\n", + " \n", + " # Electron pressure\n", + " def pe(rho):\n", + " rY = rho/mu_e # rho*Ye = rho/mu_e\n", + " pednr = 9.91e12 * (rY)**(5/3) \n", + " pedr = 1.231e15 * (rY)**(4/3)\n", + " ped = 1/np.sqrt((1/pedr**2)+(1/pednr**2))\n", + " pend = kB/mp*rY*T\n", + " pe = np.sqrt(ped**2 + pend**2)\n", + " return pe\n", + " \n", + " # radiation pressure\n", + " prad = arad*T**4/3\n", + " \n", + " # Real total pressure\n", + " true_ptot = g0 * toy.y[i]\n", + " \n", + " # Solve for rho\n", + " error = lambda rho: true_ptot - (pgas(rho) + pe(rho) + prad)\n", + " \n", + " toy.rho[i] = fsolve(error, x0=1e7)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6065ab1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(4,3))\n", + "plt.xlabel(r\"$y$ (g cm$^{-2}$)\")\n", + "plt.ylabel(r\"$\\rho$ (g cm$^{-3}$)\")\n", + "\n", + "plt.loglog(y_mesa, mesa_prof.Rho, 'k-')\n", + "plt.loglog(toy.y, toy.rho, 'k--')\n", + "\n", + "# plt.xlim([1e7,1e10])\n", + "# plt.ylim([5e4,5e7])\n", + "\n", + "plt.gca().invert_xaxis()\n", + "\n", + "\n", + "# We can also get the radial extent by integrating the HSE equation\n", + "\n", + "axb = plt.gca().twinx()\n", + "axb.set_ylabel(\"z (cm)\", color='m')\n", + "axb.semilogx(y_mesa, mesa_prof.R_cm - 12e5, 'm-')\n", + "\n", + "from scipy.integrate import cumulative_trapezoid as ct\n", + "# z = -1/g0 * ct(1/toy.rho, x=toy.y*g0)\n", + "# z = -1 * ct(1/toy.rho, x=toy.y)\n", + "# axb.semilogx(toy.y[1:], z, 'm--')\n", + "\n", + "\n", + "# integrate in and out from the middle\n", + "i0 = np.argwhere(mesa_prof.fe56 > 1e-10)[0]\n", + "H0 = mesa_prof.R_cm[i0][0] - 12e5\n", + "axb.semilogx(y_mesa[i0], H0, 'mo')\n", + "\n", + "k0 = np.argmin(abs(toy.y - y_mesa[i0]))\n", + "\n", + "z1 = ct(-1/toy.rho[k0:], x=toy.y[k0:]) + H0\n", + "# axb.semilogx(toy.y[k0+1:], z1, 'm--')\n", + "\n", + "\n", + "# z2 = ct(-1/toy.rho[:k0], x=toy.y[:k0])\n", + "# axb.semilogx(toy.y[:k0-1], z2, 'm--')\n", + "\n", + "z2 = ct(-1/toy.rho[:k0+1][::-1], x=toy.y[:k0+1][::-1])[::-1] + H0\n", + "# axb.semilogx(toy.y[:k0], z2, 'm--')\n", + "\n", + "z = np.concatenate((z2,[H0],z1))\n", + "axb.semilogx(toy.y, z, 'm--')\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "380088d5", + "metadata": {}, + "source": [ + "There are a few additional details that were omitted here, but that are considered in the code to build the actual model:\n", + "- The transition from pure iron to no iron is done with a hyperbolic tangent function of width $\\delta$, another input parameter.\n", + "- The CNO species are split between $^{14}$O and $^{15}$O with ratio determined by their beta decay times (70.606/122.24)\n", + "- The integration begins at the base ($z=H_{\\rm star}$), using $T_{\\rm base}$ and also $\\rho_{\\rm base}$, instead of y1. We therefore need these values:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "77474bf8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "At y=y1, T=Tbase (y=y1)\n", + "Density = 1.45e+06\n", + "Height = 1120.0\n" + ] + } + ], + "source": [ + "print(f\"At y=y1, T=Tbase (y=y1)\")\n", + "print(f\"Density = {toy.rho[np.argmin(abs(toy.y-y1))]:.2e}\")\n", + "print(f\"Height = {z[np.argmin(abs(toy.y-y1))]:.1f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "39aebffd", + "metadata": {}, + "source": [ + "#### What is the burning like in this model? We can investigate with pynucastro" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "43fe6ec8", + "metadata": {}, + "outputs": [], + "source": [ + "import pynucastro as pyna\n", + "\n", + "# Code for CNO_extras network\n", + "# https://github.com/AMReX-Astro/Microphysics/blob/main/networks/CNO_extras/CNO_extras.py\n", + "rl = pyna.ReacLibLibrary()\n", + "\n", + "h_burn = rl.linking_nuclei([\"h1\", \"he4\",\n", + " \"c12\", \"c13\",\n", + " \"n13\", \"n14\", \"n15\",\n", + " \"o14\", \"o15\", \"o16\",\"o17\",\"o18\",\n", + " \"f17\", \"f18\",\"f19\",\n", + " \"ne18\", \"ne19\", \"ne20\",\n", + " \"mg22\", \"mg24\"],\n", + " with_reverse=True)\n", + "\n", + "rc = pyna.StarKillerCxxNetwork(libraries=[h_burn], inert_nuclei=[\"fe56\"])\n", + "# print(rc)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8b71df2a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "MeV2erg = 1.6021772e-06\n", + "\n", + "def burn_plot(ax, model, perturb_cno=False):\n", + "\n", + " comp = pyna.Composition(rc.get_nuclei())\n", + " eps_nuc_tot = []\n", + " eps_3a = []\n", + " eps_beta = []\n", + " eps_pg = []\n", + " eps_pa = []\n", + "\n", + " for i in range(len(toy.T)):\n", + "\n", + " comp.set_nuc(\"p\", model.X[i])\n", + " comp.set_nuc(\"he4\", model.Y[i])\n", + " comp.set_nuc(\"fe56\", model.Zfe[i])\n", + "\n", + " o15 = model.Zcno[i] / (1.0 + 70.606/122.24)\n", + " o14 = model.Zcno[i] - o15\n", + " \n", + " if perturb_cno:\n", + " # Convert 1/1000 of the o14 (o15) to n14 (n15)\n", + " \n", + " comp.set_nuc(\"n14\", 0.001*o14)\n", + " comp.set_nuc(\"n15\", 0.001*o15)\n", + " \n", + " o14 *= 0.999\n", + " o15 *= 0.999\n", + "\n", + " comp.set_nuc(\"o14\", o14)\n", + " comp.set_nuc(\"o15\", o15)\n", + "\n", + " rhoi,Ti = model.rho[i], model.T[i]\n", + "\n", + " eps_nuc_tot.append(rc.evaluate_energy_generation(rhoi,Ti,comp))\n", + "\n", + " eps_3a.append(0)\n", + " eps_beta.append(0)\n", + " eps_pg.append(0)\n", + " eps_pa.append(0)\n", + "\n", + " rates = rc.evaluate_rates(rhoi,Ti,comp)\n", + " for rate in rates:\n", + " eps = rates[rate] * rate.Q * MeV2erg / mp\n", + "\n", + " if rate.weak:\n", + " eps_beta[-1] += eps\n", + "\n", + " elif rate.fname == 'he4_he4_he4__c12':\n", + " eps_3a[-1] = eps\n", + "\n", + " elif pyna.Nucleus(\"p\") in rate.reactants:\n", + " if len(rate.products) == 1:\n", + " eps_pg[-1] += eps\n", + " elif pyna.Nucleus(\"he4\") in rate.products:\n", + " eps_pa[-1] += eps\n", + "\n", + " ax.loglog(model.y, eps_nuc_tot, 'k-', label='total', lw=5, alpha=0.3)\n", + " ax.loglog(model.y, eps_beta, 'b-', label=r'$\\beta$ decays')\n", + " ax.loglog(model.y, eps_3a, 'r-', label=r'$3\\alpha$')\n", + " ax.loglog(model.y, eps_pg, 'g-', label=r'p-$\\gamma$')\n", + " ax.loglog(model.y, eps_pa, 'm-', label=r'p-$\\alpha$')\n", + "\n", + " ax.set_ylim([1e10,1e20])\n", + " ax.set_xlim([1e6,1e9])\n", + " ax.set_ylabel(\"erg/g/s\")\n", + " ax.set_xlabel(r\"$y$ (g cm$^{-2}$)\")\n", + "\n", + "fig,axes = plt.subplots(1,2,figsize=(12,4))\n", + "\n", + "burn_plot(axes[0], model=toy)\n", + "axes[0].legend(frameon=False, loc=2)\n", + "\n", + "burn_plot(axes[1], model=toy, perturb_cno=True)\n", + "\n", + "axes[0].set_title(\"Original toy model\")\n", + "axes[1].set_title(\"Converting 1/1000 of the O14 (O15) to N14 (N15)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "76d3b2ff", + "metadata": {}, + "source": [ + "This model is close to runaway helium burning, but experience shows that we can accelerate the evolution by *kicking* the initial model, which we demonstrate in the next part.\n", + "\n", + "The plot on the right is more of a sanity check. We imagine perturbing the stable hot-CNO cycle by instantly decaying a small fraction of the oxygen. Then, new (p,g) and (p,alpha) reactions are available, which dominate the burning. But those reactions are only possible above the depletion column depth ($y" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# yya,Ta unchanged\n", + "\n", + "yyb = np.logspace(np.log10(y1), np.log10(y2_p))\n", + "Tb = T_base_p * (yyb/y1)**dels[0]\n", + "\n", + "yyc = np.logspace(np.log10(y2_p), np.log10(y3))[1:]\n", + "Tc = Tb[-1] * (yyc/y2_p)**dels[1]\n", + "\n", + "# yyd,Yd unchanged\n", + "\n", + "toy2.y = np.concatenate((yya,yyb,yyc,yyd))\n", + "toy2.T = np.concatenate((Ta,Tb,Tc,Td))\n", + "\n", + "zones_plot()\n", + "plt.loglog(toy.y, toy.T, 'k--', label=\"original toy model\")\n", + "plt.loglog(toy2.y, toy2.T, 'r-.', label=\"kicked toy model\")\n", + "plt.ylim([7e7,4e8])\n", + "\n", + "plt.axvline(y2_p, color='r', ls=':')\n", + "plt.text(y2_p, 8e7, r\"$y_2'$\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b10958e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New density where T=Tbase (y=y1) is 1.386e+06\n" + ] + } + ], + "source": [ + "# Set the rest of the model (same code as before but compact)\n", + "\n", + "toy2.Zfe = np.concatenate((np.ones(len(yya)), np.zeros(len(yyb)+len(yyc)+len(yyd))))\n", + "toy2.Zcno = np.concatenate((np.zeros(len(yya)), Zcno*np.ones(len(yyb)+len(yyc)+len(yyd))))\n", + "toy2.X = X0 * np.maximum(0, 1-toy2.y/yd)\n", + "toy2.Y = 1 - toy2.X - toy2.Zfe - toy2.Zcno\n", + "\n", + "toy2.rho = np.zeros(len(toy2.T))\n", + "for i in range(len(toy2.T)):\n", + " if toy2.Y[i]>0:\n", + " mu_I = 1/(toy.X[i] + toy.Y[i]/4 + toy.Zcno[i]/15)\n", + " else:\n", + " mu_I = 56\n", + " mu_e = 2/(1+toy2.X[i])\n", + " mu = 1/(1/mu_I + 1/mu_e)\n", + " T = toy2.T[i]\n", + " def pgas(rho):\n", + " return rho*kB*T/mu_I/mp\n", + " def pe(rho):\n", + " rY = rho/mu_e # rho*Ye = rho/mu_e\n", + " pednr = 9.91e12 * (rY)**(5/3) \n", + " pedr = 1.231e15 * (rY)**(4/3)\n", + " ped = 1/np.sqrt((1/pedr**2)+(1/pednr**2))\n", + " pend = kB/mp*rY*T\n", + " pe = np.sqrt(ped**2 + pend**2)\n", + " return pe\n", + " prad = arad*T**4/3\n", + " true_ptot = g0 * toy2.y[i]\n", + " error = lambda rho: true_ptot - (pgas(rho) + pe(rho) + prad)\n", + " toy2.rho[i] = fsolve(error, x0=1e7)[0]\n", + " \n", + "print(f\"New density where T=Tbase (y=y1) is {toy2.rho[np.argmin(abs(toy2.y-y1))]:.3e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a052a827", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+srIyPfzwwzLGaMaMGXVa/4033ijp8p1kr/TXv/7VfoffhmCz2RQQEOBQVF+8eFF//OMf67zO4OBgpzMGRo4cqZ07d2rLli0O7cuXL5fNZlNaWppD+xNPPKGvvvpKkyZNkr+/vx588ME6x+Su1NRUhYaG6k9/+pND++HDh/XJJ59o5MiRki4fUHXo0EFvv/22w1MRvvvuO61fv97hs7fddpvy8vJUVlamlJSUKq/GPvABADSuZ599VrNnz9Y777yje++912WeHjVqlPz9/bVkyRK31j1q1CitWbNGa9eu1W233Wa/HK5Hjx7q1q2btm3b5jS3pKSk2E8up6Wl6eOPP7aPWEuXj1WuPsaoTnBwsKSqswErcuLVOfNvf/ubCgsL7csrTJkyRUFBQbrvvvu0Z88ety/lq4/w8HANHjxYf//73x3iLy8v15/+9CfFxcWpe/fuktz/nUaPHq2AgAB9++231f72gK9i5B+og+uvv14LFy7U1KlTdcMNN+ixxx5Tp06dlJOTo0WLFunLL7/UwoULq0zTc1fv3r11zz336JVXXpG/v79GjBihHTt26JVXXlFUVJT8/BrmvN33vvc9LViwQPfee6/+7//9v8rLy9P8+fPtib4u+vbtq7/85S9asWKFrrnmGoWEhKhv37568skntXz5cn3ve9/TnDlzlJCQoPfff1+LFy/Www8/bE++FW655Rb16tVLn376qf3xgI2tZcuWevrpp/XLX/5S999/v+655x7l5eVp9uzZCgkJ0TPPPCPp8gjGc889pylTpmj8+PF68MEHdfbsWT377LNVpgfefffdeuuttzRmzBg98cQTGjRokAIDA3X48GF9+umnGjdunMaPH9/o3w0A0HhmzZolPz8/Pf300zLG6O2333Y6A6Bz58765S9/qeeee04XL17UPffco6ioKO3cuVOnTp3S7Nmzq3zmhhtu0Mcff6xbb71Vo0aN0urVqxUVFaXf/va3Sk9P1+jRozV58mR17NhRp0+f1q5du7Rlyxa9++67kqT//u//1j//+U+NGDFCs2bNUlhYmBYtWmQ/kVCTiscXvvjii/YZjf369dMtt9yi0aNH66mnnlJBQYGuv/56ffXVV3rmmWeUmJioiRMnOqynZcuWuv/++7VkyRIlJCRUuS9RY5k3b55uueUWpaWlafr06QoKCtLixYu1fft2vf322/YZme7+Tp07d9acOXM0c+ZM7d+/X7feeqtatWqlEydOaNOmTQoPD3f65wj4BO/daxBo+jZs2GAmTJhgYmJiTEBAgGnXrp254447zPr166v0rbjj68mTJ6tddqWioiIzbdo0065dOxMSEmKGDBliNmzYYKKiosyTTz5p71fd3f7Dw8Pd2s7vf/9706NHDxMcHGyuueYaM2/ePPPGG29UuQOwu3eiP3jwoBk1apSJiIiwP3avwnfffWfuvfde07p1axMYGGh69OhhXn75Zfsdia/27LPPGklm48aNNW63giTz6KOPOrQdOHDASDIvv/yyQ3vFb/fuu+86tL/++uumX79+JigoyERFRZlx48Y5fcTg66+/brp162aCgoJM9+7dze9//3szadIkh+9sjDElJSVm/vz5pn///iYkJMS0aNHCXHfddeYnP/mJ2bt3r70fd/sHAN9Xcbf/zZs3V1k2d+5c+2P8Ll26VO3d3pcvX24GDhxozwmJiYkOj+e7+lF/xhizfft20759e5OUlGQ/lti2bZv5wQ9+YNq1a2cCAwNN+/btzYgRI8xvfvMbh89+8cUX9sfgtW/f3vzXf/2XWbp0qVt3+y8uLjZTpkwxbdu2NTabzeEzFy9eNE899ZRJSEgwgYGBpkOHDubhhx82Z86ccbquzz77zEgyv/rVr1xu80rOfgtjjElISDDf+973qrQ7Ow5Yt26dGTFihAkPDzehoaFmyJAh5l//+leVz9bmd1q1apVJS0szkZGRJjg42CQkJJgJEyaYjz76yN6Hu/3D19iMuWLOKgCftn79el1//fV66623dO+993o7nEaXkpIim82mzZs3ezsUAABQTz/72c+0ZMkSHTp0yO0bGANoOEz7B3xURkaGNmzYoOTkZIWGhmrbtm361a9+pW7dulV7cx8rKCgo0Pbt2/Xee+8pKytLK1eu9HZIAACgHjZu3KhvvvlGixcv1k9+8hMKf8BLmvwN/w4dOqThw4erV69e6tevn/36Juny87Urbojy+uuvezFKoPYiIyP14YcfauLEiRo9erReeuklpaen6/PPP6/yCD8r2bJli66//notXbpUzzzzjL7//e97OyQAXkauB5q21NRUPfzww7rtttv0/PPPezscoNlq8tP+jx07phMnTmjAgAHKzc1VUlKS9uzZo+DgYPvNwiIjI5WUlKQvv/zS6XNCAQCA7yLXAwBQf01+5L9Dhw4aMGCAJKldu3aKjo7W6dOntWnTJvXu3VsdO3ZURESExowZozVr1ng3WAAAUGvkegAA6s/rxf/atWt1++23KzY2VjabTatWrarSZ/HixerSpYtCQkKUnJysdevWOV1XZmamysvLFR8fr6NHj6pjx472ZXFxcTpy5EhjfQ0AAFANcj0AAN7n9eK/sLBQ/fv312uvveZ0+YoVKzR16lTNnDlTW7du1bBhw5Senq6cnByHfnl5ebr//vu1dOlSSZKzqxkqnuMJAAA8h1wPAID3ef1u/+np6UpPT692+YIFC/TAAw9oypQpkqSFCxdqzZo1WrJkiebNmydJKi4u1vjx4zVjxgwNHTpUktSxY0eHs/+HDx/W4MGDq91OcXGxiouL7e/Ly8t1+vRptW7dmgMJAIBPMMbo3Llzio2NlZ+f18/fu41cDwCAexo11xsfIsmsXLnS/r64uNj4+/ubv//97w79Hn/8cXPjjTcaY4wpLy83d999t3nmmWcc+pSUlJiuXbuaw4cPm4KCAtO1a1dz6tSparf9zDPPGEm8ePHixYuXz78OHTrUYLnX0yRyPS9evHjx4lXTqzFyvddH/l05deqUysrKFBMT49AeExOj48ePS5K++OILrVixQv369bNfQ/jHP/5Rffv21SuvvKK0tDSVl5fr5z//uctnis6YMUPTpk2zv8/Pz1enTp106NAhRUZGNvyXAwCglgoKChQfH6+IiAhvh9JgyPUAAFRqzFzv08V/haun4hlj7G033HCDysvLnX5u7NixGjt2rFvbCA4OVnBwcJX2yMhIDggAAD7FilPUyfUAAFRqjFzv0xcMtmnTRv7+/vYz/xVyc3OrjBAAAICmh1wPAIBn+HTxHxQUpOTkZGVkZDi0Z2Rk2G/2AwAAmi5yPQAAnuH1af/nz5/Xvn377O8PHDig7OxsRUdHq1OnTpo2bZomTpyolJQUpaamaunSpcrJydFDDz3kxagBAIC7yPUAAHif14v/zMxMpaWl2d9X3Ihn0qRJWrZsme666y7l5eVpzpw5OnbsmPr06aPVq1crISHBWyEDAIBaINcDAOB9NmOM8XYQvqigoEBRUVHKz8/nJkAAAJ9AbmpY/J4AAF/TmLnJp6/5BwAAAAAA9UfxDwAAAACAxVH8AwAAAABgcRT/AAAAAABYHMU/AAAAAAAWR/EPAAAAAIDFUfwDAAAAAGBxFP8AAAAAAFgcxT8AAAAAABZH8Q8AAAAAgMVR/AMAAAAAYHEU/wAAAAAAWBzFPwAAAAAAFkfxDwAAAACAxVH8AwAAAABgcRT/AAAAAABYHMU/AAAAAAAWR/EPAAAAAIDFUfwDAAAAAGBxFP8AAAAAAFgcxT8AAAAAABZH8Q8AAAAAgMVR/AMAAAAAYHEU/wAAAAAAWBzFPwAAAAAAFkfxDwAAAACAxVH8AwAAAABgcRT/AAAAAABYHMU/AAAAAAAWR/EPAAAAAIDFUfwDAAAAAGBxFP8AAAAAAFgcxT8AAAAAABZH8Q8AAAAAgMVR/AMAAAAAYHEU/wAAAAAAWBzFPwAAAAAAFkfxDwAAAACAxVH8AwAAAABgcRT/AAAAAABYHMU/AAAAAAAWR/EPAAAAAIDFUfwDAAAAAGBxFP8AAAAAAFgcxT8AAAAAABZH8Q8AAAAAgMVR/AMAAAAAYHEU/wAAAAAAWJwliv/x48erVatWmjBhgkP7q6++qt69e6tXr156/PHHZYzxUoQAAKA+yPUAANSPJYr/xx9/XMuXL3doO3nypF577TVlZWXp66+/VlZWljZu3OilCAEAQH2Q6wEAqB9LFP9paWmKiIio0l5aWqqioiKVlJSopKRE7dq180J0AACgvsj1AADUj9eL/7Vr1+r2229XbGysbDabVq1aVaXP4sWL1aVLF4WEhCg5OVnr1q2rcb1t27bV9OnT1alTJ8XGxurmm2/Wtdde2wjfAAAAuEKuBwDA+7xe/BcWFqp///567bXXnC5fsWKFpk6dqpkzZ2rr1q0aNmyY0tPTlZOT43K9Z86c0XvvvaeDBw/qyJEjWr9+vdauXVtt/+LiYhUUFDi8AABA/ZHrAQDwPq8X/+np6Xr++ed1xx13OF2+YMECPfDAA5oyZYp69uyphQsXKj4+XkuWLHG53o8++khdu3ZVdHS0QkND9b3vfc/ldYDz5s1TVFSU/RUfH1+v7wUAAC4j1wMA4H1eL/5duXTpkrKysjRq1CiH9lGjRmn9+vUuPxsfH6/169erqKhIZWVl+uyzz9SjR49q+8+YMUP5+fn216FDhxrkOwAAgOqR6wEA8IwAbwfgyqlTp1RWVqaYmBiH9piYGB0/ftz+fvTo0dqyZYsKCwsVFxenlStXasiQIRozZowSExPl5+enkSNHauzYsdVuKzg4WMHBwY32XQAAQFXkegAAPMOni/8KNpvN4b0xxqFtzZo1Tj83d+5czZ07t1FjAwAA9UeuBwCgcfn0tP82bdrI39/f4cy/JOXm5lYZIQAAAE0PuR4AAM/w6eI/KChIycnJysjIcGjPyMjQ0KFDvRQVAABoKOR6AAA8w+vT/s+fP699+/bZ3x84cEDZ2dmKjo5Wp06dNG3aNE2cOFEpKSlKTU3V0qVLlZOTo4ceesiLUQMAAHeR6wEA8D6vF/+ZmZlKS0uzv582bZokadKkSVq2bJnuuusu5eXlac6cOTp27Jj69Omj1atXKyEhwVshAwCAWiDXAwDgfTZjjPF2EL6ooKBAUVFRys/PV2RkpLfDAQCA3NTA+D0BAL6mMXOTT1/zDwAAAAAA6o/iHwAAAAAAi6P4BwAAAADA4ij+AQAAAACwOIp/AAAAAAAsjuIfAAAAAACLo/gHAAAAAMDiKP4BAAAAALA4in8AAAAAACyO4h8AAAAAAIuj+AcAAAAAwOIo/gEAAAAAsDiKfwAAAAAALI7iHwAAAAAAi6P4BwAAAADA4ij+AQAAAACwOIp/AAAAAAAsjuIfAAAAAACLo/gHAAAAAMDiKP4BAAAAALA4in8AAAAAACyO4h8AAAAAAIsL8HYAAAAAAADUR15enk6ePKny8nLFxsaqZcuW3g7J51D8AwAAAACarB07dmj//v32999++6369OmjLl26eDEq38O0fwAAAABAk7Rz506Hwr/C9u3bdfHiRS9E5Lso/gEAAAAATc7evXv17bffVrs8Ly/Pg9H4Pop/AAAAAECTcvDgQe3evdtln+LiYg9F0zRQ/AMAAAAAmowjR47o66+/9nYYTQ7FPwAAAACgScjNzdXWrVvd6muMaeRomhaKfwAAAACAzzt9+rQ2b95MUV9HFP8AAAAAAJ+Wn5+vL7/8UuXl5W5/hpMEjij+AQAAAAA+q7CwUBs3blRpaam3Q2nSKP4BAAAAAD6pqKhIGzdu1KVLl2r9WUb+HVH8AwAAAAB8Tmlpqb788ktduHDB26FYAsU/AAAAAMCnlJeXKzMzUwUFBS77BQUFVbuMkX9HFP8AAAAAAJ+ybds2nTx50mWf9u3bq2vXrtUup/h3RPEPAAAAAPAZu3bt0uHDh132ad26tZKTk+XnR0nrLn4pAAAAAIBPOHjwoPbt2+eyT0REhAYOHCg/Pz/ZbLZq+zHy74jiHwAAAADgdcePH9fXX3/tsk9ISIiGDBmiwMBAD0VlHRT/AAAAAACvOn36tLKyslz2CQwM1JAhQxQSEmJvY+TffRT/AAAAAACvOX/+vDZt2qTy8vJq+/j5+WngwIGKiIjwYGTWQvEPAAAAAPCK4uJiffnllyopKXHZLzExUa1bt67Szsi/+yj+AQAAAAAeV15ers2bN+vChQsu+/Xp00exsbEeisq6KP4BAAAAAB6XnZ2tM2fOuOxz7bXXqkuXLtUuZ+TffRT/AAAAAACP2rNnj44cOeKyT8eOHdWzZ08PRWR9FP8AAAAAAI85fPiwvvnmG5d9WrdurQEDBrgc2ZcY+a8Nin8AAAAAgEfk5eVp27ZtLvuEh4dr4MCB8vOruVyl+HefJYr/8ePHq1WrVpowYYJD+4EDB5SWlqZevXqpb9++Kiws9FKEAACgPsj1AND0FRYWavPmzS4f6RcYGKjBgwcrMDDQg5E1D5Yo/h9//HEtX768SvvkyZM1Z84c7dy5U59//rmCg4O9EB0AAKgvcj0ANG0lJSU1PtLPz89PgwYNUnh4uNvrZeTffZYo/tPS0hQREeHQtmPHDgUGBmrYsGGSpOjoaAUEBHgjPAAAUE/kegBouioe6VfT7Kz+/fsrOjraQ1E1P14v/teuXavbb79dsbGxstlsWrVqVZU+ixcvVpcuXRQSEqLk5GStW7euxvXu3btXLVq00NixY5WUlKQXXnihEaIHAAA1IdcDQPP29ddfKy8vz2Wf7t27Ky4urtbrZuTffV4v/gsLC9W/f3+99tprTpevWLFCU6dO1cyZM7V161YNGzZM6enpysnJcbnekpISrVu3TosWLdKGDRuUkZGhjIyMxvgKAADABXI9ADRfBw4cqPHf844dO6pHjx4eiqj58nrxn56erueff1533HGH0+ULFizQAw88oClTpqhnz55auHCh4uPjtWTJEpfrjYuL08CBAxUfH6/g4GCNGTNG2dnZ1fYvLi5WQUGBwwsAANQfuR4AmqdTp05px44dLvtER0drwIABdd4GI//u83rx78qlS5eUlZWlUaNGObSPGjVK69evd/nZgQMH6sSJEzpz5ozKy8u1du1a9ezZs9r+8+bNU1RUlP0VHx/fIN8BAABUj1wPANZ04cIFZWZmuizAw8LC3H6kX11Q/Dvy6eL/1KlTKisrU0xMjEN7TEyMjh8/bn8/evRo3XnnnVq9erXi4uK0efNmBQQE6IUXXtCNN96ofv36qVu3brrtttuq3daMGTOUn59vfx06dKjRvhcAALiMXA8A1lNaWqpNmza5vLN/QECABg8erKCgoHpty9XIPxw1iVviXv0HaoxxaFuzZo3Tz6Wnpys9Pd2tbQQHB/N4IAAAvIRcDwDWYIzR1q1bde7cOZf9kpOT1aJFCw9FBcnHR/7btGkjf39/hzP/kpSbm1tlhAAAADQ95HoAsJZvvvmmyr/pV+vZs6fatWvXINvjmn/3+XTxHxQUpOTk5Cp37s3IyNDQoUO9FBUAoCkxxignJ0dff/219u7dW+MzhuFZ5HoAsI6jR4/qm2++cdmnY8eO6tq1a4Ntk+LffV6f9n/+/Hnt27fP/v7AgQPKzs5WdHS0OnXqpGnTpmnixIlKSUlRamqqli5dqpycHD300ENejBoA0BSUlpZq8+bNOnXqlL1t3759Sk1NVcuWLb0XWDNDrgcA6ysoKHD5xBVJioqKUv/+/T0TEKrwevGfmZmptLQ0+/tp06ZJkiZNmqRly5bprrvuUl5enubMmaNjx46pT58+Wr16tRISErwVMgCgidi2bZtD4S9dPiGwdetWh9yDxkWuBwBru3TpkjZt2qSysrJq+wQHB2vQoEHy9/f3WFyM/DuyGX4RpwoKChQVFaX8/HxFRkZ6OxwAQC199913+uqrr6pdfssttygkJMSDEdUfualh8XsCQP0ZY7Rx48YqJ9uv5Ofnp6FDh6pVq1YNvv1Tp05pw4YNTpe1b99eAwcObPBtNqbGzE0+fc0/AAB1UVBQoO3bt7vsc+nSJQ9FAwCAde3evdtl4S9J/fr1a5TCX+Ka/9qoU/F/8eJFXbhwwf7+u+++08KFC/Xhhx82WGAAANRFaWmpMjMzVV5e7u1QmjRyPQCgJseOHXO4p4sz11xzjeLj4z0UEVypU/E/btw4LV++XJJ09uxZDR48WK+88orGjRunJUuWNGiAAADUxtdff+3WHf0ZDXCNXA8AcOX8+fM13uCvbdu26tWrV6PGwci/++pU/G/ZskXDhg2TJP31r39VTEyMvvvuOy1fvly//vWvGzRAAADcdejQIR0+fNjbYVgCuR4AUJ2Kp+mUlpZW2yc0NFRJSUkui3N4Vp2K/wsXLigiIkKS9OGHH+qOO+6Qn5+fhgwZou+++65BAwQAwB3nzp3T119/7XZ/RgNcI9cDAKqTnZ2t8+fPV7vcz89PAwcOVFBQUKPHwsi/++pU/Hft2lWrVq3SoUOHtGbNGo0aNUqSlJuby91yAQAeV1ZWpqysLJePGLoaBwSukesBAM7s27dPx44dc9mnX79+ioqK8lBEcFediv9Zs2Zp+vTp6ty5swYPHqzU1FRJl0cGEhMTGzRAAABqsn37dp07d87bYVgKuR4AcLVTp05p9+7dLvskJCR49AZ/jPy7L6AuH5owYYJuuOEGHTt2TP3797e3jxw5UuPHj2+w4AAAqMmRI0eUk5NT689xQOAauR4AcKWLFy8qKyvLZf5s1aqV+vTp48GoKP5ro1bFf2xsrMaNG6exY8dq5MiRat++vcPyQYMGNWhwAAC4UlhYqK+++srbYVgKuR4AcLXy8nJlZmbq0qVL1fYJDg5WSkqK/PzqNLkcHlCrP5k///nPCgsL0+OPP642bdrozjvv1B//+EedPn26seIDAMCp8vJyZWVlubzTsCuMBjhHrgcAXG3nzp06e/ZstcttNpuSk5MVEhLiuaCu2HZ1yPWOalX8Dx8+XK+88or27t2rDRs2KCkpSYsWLVKHDh00fPhwvfrqq/r2228bK1YAAOx2796t/Pz8apcHBARwY7o6INcDAK509OhRHThwwGWfnj17qnXr1h6KCHVV5zkZvXv31owZM7Rx40bl5OTovvvu0yeffKK+ffuqT58+ev/99xsyTgAA7E6ePFljAdq/f3+FhYVVu5zRgJqR6wGgeSssLNS2bdtc9omNjdW1117roYiqYuTffXW64d/VYmJi9OCDD+rBBx9UYWGhPvzwQ4880xEA0PxcunRJ2dnZLvskJCQoNjZWR48erbYPBwS1Q64HgOalrKxMmZmZLi+vi4iIcLgpLHxbnYr/goICp+02m03BwcHcBRgA0Gi2bdumoqKiapdHRESod+/eHozImsj1ANC8bd++vdpcIEn+/v5KSUlRQECDjCfXGSP/7qvTn1TLli1d/shxcXGaPHmynnnmGe72CABoMAcPHtTx48erXe7n56ekpCT5+/tL4oCgPsj1ANB8HT58uMbH6Pbv318tWrTwUERoCHUq/pctW6aZM2dq8uTJGjRokIwx2rx5s/7whz/ov//7v3Xy5EnNnz9fwcHB+uUvf9nQMQMAmqFz585px44dLvv06tXL4SZ/ropXuEauB4Dm6dy5czU+RjchIUEdO3b0UESucaLffXUq/v/whz/olVde0Q9+8AN729ixY9W3b1/99re/1ccff6xOnTpp7ty5HBAAAOqtvLxcW7ZsUXl5ebV9YmJi1KVLF7fXyQGBa+R6AGh+SktLlZmZqbKysmr7REZGqk+fPh6Mqu7I9Y7qNE9vw4YNSkxMrNKemJioDRs2SJJuuOGGGqeKAADgjl27drm87jA4ONjpDYcYDag7cj0AND9fffWVzp8/X+3ygIAApaSk+NTlXszyc1+d/tTi4uL0xhtvVGl/4403FB8fL0nKy8tTq1at6hcdAKDZy83N1f79+132SUxMVHBwsIciah7I9QDQvOTk5OjIkSMu+wwYMEDh4eEeiqj+ONHvqE7T/ufPn68777xT//73vzVw4EDZbDZt3rxZu3fv1l//+ldJ0ubNm3XXXXc1aLAAgOaluLi4xsf6XXPNNWrbtq3TZYz81x25HgCaj4KCAn399dcu+1xzzTXq0KGDhyJyHyP/7qtT8T927Fjt2bNHv/3tb7Vnzx4ZY5Senq5Vq1apc+fOkqSHH364IeMEADQzxhhlZ2eruLi42j6RkZHq2bNntcs5IKg7cj0ANA8V1/m7uq9Oy5YtXeZbb+JEv/tqVfwvXbpUY8eOVfv27dW5c2fNmzevseICADRzBw4cUG5ubrXL/f39lZycXOfrDjkgcI5cDwDNy7Zt21RYWFjt8sDAQJ+7zh91U6s/wbfffludO3fW4MGD9cILL2jnzp2NFRcAoBkrKCjQrl27XPbp3bt3vZ4vTPHvHLkeAJqPAwcO6OjRoy77JCYmKjQ01EMR1R4j/+6rVfH/6aef6tixY/rpT3+q7Oxspaam6tprr9W0adP02WefuZwqAgCAO8rKymp8rF/79u2VkJBQ47qY9l975HoAaB7Onj1b4wnerl27KiYmxkMRobHVeu5Gq1at9MMf/lDvvPOOTp48qUWLFqmoqEgTJ05U27Ztdf/99+uvf/2ry6kjAABUZ+fOnTp37ly1y0NCQpw+1s8ZRgPqhlwPANZWUlKirKwslyd0o6Ojdd1113kwqroh17uvXhduBAUF6dZbb9XixYt16NAhrVmzRp07d9Zzzz2nBQsWNFSMAIBm4vjx4zp48KDLPomJiQoKCvJMQCDXA4AFZWdn68KFC9UuDwoKUnJyMjPoLKZOd/uvTkpKilJSUjRnzhyVlJQ05KoBABZXVFSkbdu2uezTtWtXtWnTxu11MhrQ8Mj1ANC07d+/X8ePH3fZJykpSSEhIR6KqH7I9e6rU/E/bdo0p+02m00hISHq1q2bxo4dq+jo6HoFBwBoHioe63fp0qVq+7Rs2VI9evSo1XoZsag7cj0AWM/Zs2drvKFu9+7d1bZtWw9F1Lgo/h3VqfjfunWrtmzZorKyMvXo0UPGGO3du1f+/v667rrrtHjxYk2bNk3/+c9/1KtXr4aOGQBgMfv379fJkyerXe7v76+kpKQGfcwQBwSukesBwFpKSkqUmZnp8jr/1q1bq3v37h6Mqv440e++Oh1FjRs3TjfffLOOHj2qrKwsbdmyRUeOHNEtt9yie+65R0eOHNGNN96oJ598sqHjBQBYTH5+vnbv3u2yT9++fRUeHl7rdXNAUHfkegCwluzsbF28eLHa5cHBwUpKSiJ3Wlidiv+XX35Zzz33nCIjI+1tkZGRevbZZ/XSSy8pLCxMs2bNUlZWVoMFCgCwHnce6xcbG6v4+PgG3zYj/66R6wHAOqx2nf+VuObffXUq/vPz85Wbm1ul/eTJkyooKJB0+dpMV9duAgCwfft2nT9/vtrloaGh6tevX53XzwFB3ZHrAcAazpw5o507d7rs071791rdUNeXkOvdV+dp/z/+8Y+1cuVKHT58WEeOHNHKlSv1wAMP6Pvf/74kadOmTU3uehEAgOccO3ZMOTk51S632WxKSkpSYGBgnbfB1MW6I9cDQNNXUlKirKwsl0VwmzZt+Le8majTDf9++9vf6sknn9Tdd9+t0tLSyysKCNCkSZP06quvSpKuu+46vf766w0XKQDAMtx9rF9j3kme0QDXyPUA0PRt3brV8tf5M/LvvloX/2VlZcrKytKLL76oV199Vfv375cxRtdee61atGhh7zdgwICGjBMAYBHGGG3dutXlM+JbtWrVIKMQHBDUDbkeAJq+b7/9VidOnKh2ecUMu+DgYA9GBW+qdfHv7++v0aNHa9euXerSpUu9rsUEADQ/3377rU6dOlXt8oCAgAZ/rB9qh1wPAE3bmTNntGvXLpd9mvJ1/lfiRL/76nRk1bdvX+3fv7+hYwEAWNzZs2fdeqxfWFhYg2yPA4K6I9cDQNNUXFyszMxMl3mubdu26tatmwejgi+oU/E/d+5cTZ8+Xe+9956OHTumgoIChxcAAFcrLS3Vli1bXB6MdOzYUXFxcQ22zaZ8DaO3kesBoOkxxigrK0tFRUXV9gkJCVFiYqKlcmR134UT/Y7qdMO/W2+9VZI0duxYhx/aGCObzaaysrKGiQ4AYBnbtm1TYWFhtcvDwsLUt29fj8XDAYFr5HoAaHp27dqlvLy8apc3t+v8yfWO6lT8f/rppw0dBwDAwg4ePKijR49Wu9xmsykxMbFej/WrLQ4IXCPXA0DTcvToUX377bcu+/To0UOtW7f2UESeY7PZyOtuqNO0/5tuukl+fn763e9+p1/84hfq2rWrbrrpJuXk5Mjf37+hYwQANGFnz57Vjh07XPbp3r17ozzWz0pTGj2NXA8ATce5c+eUnZ3tsk+7du3UtWtXzwQEn1Sn4v9vf/ubRo8erdDQUG3dulXFxcWSLu90L7zwQoMGCABoukpKSpSVlaXy8vJq+0RHRzfaTYe44V/dkesBoGkoKSnR5s2bXV6OFRYWpqSkJMueFOeaf/fUqfh//vnn9Zvf/Ea/+93vHKZoDh06VFu2bGmw4AAATVt2drYuXLhQ7fKgoCAlJydb9mCkKSPXA4DvM8Zo69atLu+p4+/vr4EDB3r00jpfQfHvqE7F/549e3TjjTdWaY+MjNTZs2frGxMAwAL27t2r48ePu+yTmJiokJCQRouBkf+6I9cDgO/btWuXTpw44bJPv379FBkZ6aGIvINBBPfUqfjv0KGD9u3bV6X9P//5j6655pp6BwUAaNqOHz+u3bt3u+zTrVs3tWvXzkMRobbI9QDg2w4dOlTjDf66dOnSoI/QRdNWp+L/Jz/5iZ544gl9+eWXstlsOnr0qN566y1Nnz5djzzySEPHCABoQgoKCmqcFt6mTRv16NGj0WNh5L/uyPUA4Lvy8vL01VdfuewTHR2tXr16eSgi7yLfu6dOj/r7+c9/rvz8fKWlpamoqEg33nijgoODNX36dD322GMNHSMAoIm4dOlSjTcdCg4O9thNh5gGWHfkegDwTefOndPmzZtd3kw3JCREKSkp8vOr01ivpRhjOB74X3Uq/iVp7ty5mjlzpnbu3Kny8nL16tVLLVq0aMjYAABNSHl5uTIzM13e4M9msyk5OVnBwcEejMw5RgJqRq4HAN9y8eJFbdy4USUlJdX28fPz08CBA30i13oKxb176nUqKCwsTCkpKRo0aJBXDwbGjx+vVq1aacKECVWWXbhwQQkJCZo+fboXIgOA5sEYo+zsbOXl5bns17dvX7Vu3dpDUTENsCGQ6wHAN1y6dElffvmlioqKXPZLTExUy5YtPROUjyDfu8cS80Aef/xxLV++3OmyuXPnavDgwR6OCACal507d+rIkSMu+3Tu3FkJCQkeiugyRgKsg1wPoDkrKSnRxo0bde7cOZf9evToodjYWA9FhabGEsV/WlqaIiIiqrTv3btXu3fv1pgxY7wQFQA0D/v27dP+/ftd9mnTpo169+7toYjcw0hA00KuB9BclZaWauPGjcrPz3fZLy4uTt27d/dQVL6FkX/3eL34X7t2rW6//XbFxsbKZrNp1apVVfosXrxYXbp0UUhIiJKTk7Vu3Tq31j19+nTNmzevgSMGAFTYt2+fdu3a5bJPWFiYkpOTvXLTIUb+fQO5HgDq5tKlS9qwYYPOnj3rsl/btm3Vv39/zwSFJsvrxX9hYaH69++v1157zenyFStWaOrUqZo5c6a2bt2qYcOGKT09XTk5OS7X+49//EPdu3d3++xXcXGxCgoKHF4AgOrt3bu3xsI/MDBQgwYNUlBQkIeich8jAZ5DrgeA2isqKtL69etrLPyjoqKa/Z39Gfl3T53v9t9Q0tPTlZ6eXu3yBQsW6IEHHtCUKVMkSQsXLtSaNWu0ZMkSl2f6N27cqL/85S969913df78eZWUlCgyMlKzZs1y2n/evHmaPXt2/b4MADQDxhjt2rVL3377rct+fn5+GjRokNOp2p7CwYBvINcDQO0UFBRo06ZNunjxost+LVq00ODBgxUQ4PWyDk2AT58eunTpkrKysjRq1CiH9lGjRmn9+vUuPztv3jwdOnRIBw8e1Pz58/Xggw9WezAgSTNmzFB+fr79dejQoQb5DgBgJWVlZcrMzKyx8K94pF90dLSHIqs+Dvg2cj0AODp27Jj+85//1Fj4h4eHKzU1tVk90q86nOx3j0+fIjp16pTKysoUExPj0B4TE6Pjx4/b348ePVpbtmxRYWGh4uLitHLlSg0cOLBW2woODuYvDgC4UFhYqKysrBpvOCRJ/fv3V/v27T0QVd1xMOAbyPUAcFl5ebl2795d4wl2SQoNDVVqaqpCQkI8EBmswqeL/wpXn8kxxji0rVmzxuXnJ0+e3BhhAUCzcejQIW3fvl2lpaU19h0wYIDi4+M9EFX9UPz7FnI9gOasoKBAW7dudeteJBUj/qGhoR6IrGlg5N89Pl38t2nTRv7+/g5n/iUpNze3yggBAKDhFRYWaseOHTpx4kSNfW02mwYMGKC4uDgPROYepv37PnI9gOaspKREe/bs0cGDB90qUqOiojR48GBmMdUCxX8ln77mPygoSMnJycrIyHBoz8jI0NChQ70UFQBYX1FRkXbs2KHPPvvMrcLfz89PycnJPlX4S4wENAXkegDNUUlJifbu3auPP/5YBw4ccCsntWnThmv8q8HJfvd4feT//Pnz2rdvn/39gQMHlJ2drejoaHXq1EnTpk3TxIkTlZKSotTUVC1dulQ5OTl66KGHvBg1AFjT2bNndfDgQR05ckTl5eVufSY4OFgDBw5Uq1atGjk6NFXkegC47MyZMzp8+LAOHTqksrIytz/XpUsX9erVq1k/zq+uONlfyevFf2ZmptLS0uzvp02bJkmaNGmSli1bprvuukt5eXmaM2eOjh07pj59+mj16tVKSEjwVsgAYBllZWU6c+aMTp48qaNHj+rChQu1+nxERIQGDRqksLCwRoqwfhj59w3kegDNVWlpqU6fPq3c3Fzl5uaqsLCwVp/38/NT37591alTp0aK0BoY+XePzXD041RBQYGioqKUn5+vyMhIb4cDAPVSWlqqixcv6uLFi7pw4YIKCgpUUFCg/Px8t0f4r5aQkKDevXvL39+/gaNtOMePH9fmzZudLouLi1NiYqKHI6ofclPD4vcE0FCKiop04cIFXbhwQYWFhbpw4YLy8/N17ty5Oq8zMjJSSUlJioiIaMBIrenzzz+v9maJI0eO9NlBCmcaMzd5feTf1+Xm5tb4jE0AvsOd85mu+lS3rCHWW7G8pv+v6X15ebnDy1VbWVmZiouLVVJSUmP87goKCmoSj/KTGPmHe8j1QOPwlX9nq8u51f33ynxaVlZW5f9LSkrsr0uXLqmkpKTOJ9Kdsdlsuvbaa9WjRw+m+aNBUfzXICsrq0mdKQKAxmKz2ZSQkKAePXooKCjI2+G4hWmAcAe5HoCvaNu2rXr37s1ofy1xst89FP8AgBq1bdtWvXr1stTUaA4GAAC+olWrVurevbvatWvn7VBgYRT/NZg5c4j8/a1zsAsAtREYGKSQkGD5+zfNdFFa2krnz98gSfLzM5owYZ8GDbr86EKKf1Qg1wPwlsDAQAUHBysgINDboTRp584NsD89ITk5V/fc8419Gfm+UtM8mvOgb79tJYkDAgBomgIlVT6C8F//6mIv/oEK5HoAaOoq/w3fu7elfvCDvfL3p+i/GsV/DX7+80wFB4d7OwwAaDQBAYGKjIxUVFSkIiMjFRwc4u2QGkxBQb52796tnTuj9be/dVNZWeU1gYwEoAK5HkBjCgkJVXh4uKKiohQVFaXAQEb5G9qOHTuUl1ekl19OljE2lZdLFQ8jIt9XovivQVLSSYWF1e55nADga/z9/RUWFqbQ0FCFhoYqLCxMkZGXi/2QEOsU+1fLyytVRESuSkouHwGUl3MDQFRFrgdQHzabTUFBQQoLC1NYWJjCw8Pt/x8ZGUmx7wGtWp3V4cP59veX873jkxxA8V+jDh06qEWLFt4OA0AdVHfnV1d3hK3p7vD1WeeV/3X3/5299/Pzs7+ufu+sPTAwUAEBzfufe5vtcuK/svjnYAAVyPWAb2isJ7S4m4evfO/v72/Po1f+v5+fnwICAhQUFKTAwEB7juXpMt535VMRjeHPw5nmfTTohgEDBljq7tYA0JxUHIz5+VWc/a9cRvGPCuR6AGj6KnK9JJWXezEQH+ZXcxcAAJq2igMCpv0DAGA9l2dsXFn8M9PPGYp/AIBlVU7rvPz+ymmAHAwAAGANly93rHxPvneO4h8AYHnOrvkHAADWceXIP9f8O0fxDwCwLK75BwDA+q4e+b/ymn/yfSWKfwCAZVUW/5ffcw0gAADWxT1+XKP4BwBYHtP+AQCwrsp7/FTM9ONkvzMU/wAAy2LaPwAAzUflyL+XA/FRFP8AAMtzNu0fAABYw9VP9+EyP+co/gEAlnX1NEAOBgAAsK7KmX7ke2co/gEAllX1GkBvRgMAABpD1ZP93ozGd1H8AwAsz9+fkQAAAKyOkX/XKP4BAJbFNYAAAFifq0f7ohLFPwDA8pgGCACAdbm6zI+T/ZUo/gEAllX1UX+M/AMAYFXOZvqhEsU/AMCymAYIAID1VeR77vHjGsU/AMDyeNQfAADW5yzfoxLFPwDAsq4e+ecaQAAArKfqDX4rl5HvK1H8AwAsz9k1/wAAwFrI965R/AMALOvqu/8y7R8AAOu5+ga/5HvnKP4BAJbHo/4AALA+8r1rFP8AAMuqvPvv5ffc/RcAAOupeo8f8r0zFP8AAMuqOu2/chkHAwAAWIuza/7J95Uo/gEAlsejfwAAsK6rT/aXlXkzGt9F8Q8AsCymAQIAYH1XP+qPfO8cxT8AwPKunAbIMQAAANbEo/5co/gHAFjW1dMAJdmLf0YCAACwhqqP+qtcRr6vRPEPALCsq6f9S1z3DwCAVVVM+yfXO0fxDwCwPMeRf9v//peRAAAArODqkX+u+XeO4h8AYFlXHwxIlVMBORgAAMBanD3aF5Uo/gEAlse0fwAArOvqy/yuzPWc7K9E8Q8AsCznN/xj2j8AAFZUke+5279zFP8AAMtzNu0fAABYQ9Vr/iuXcbK/EsU/AMDSbDYbI/8AAFhY1Uf9Me3fGYp/AICl2Ww2p9f8czAAAIC18Kg/1yj+AQCWZ7viGIBp/wAAWBOP+nON4h8AYGmVUwEvV/1M+wcAwFoqb/B7+T0n+p2j+AcANAvOHv8DAACaPq75dw/FPwDA0q5+3B8j/wAAWJOzaf+oRPEPAGgWKkcDvBwIAABoUK6m/XOyv5Iliv/x48erVatWmjBhgr3t0KFDGj58uHr16qV+/frp3Xff9WKEAABvqZwKePk9NwFqmsj1AICaXD3L7/L/k+srWKL4f/zxx7V8+XKHtoCAAC1cuFA7d+7URx99pCeffFKFhYVeihAA4G0VBwRc8980kesBANVxdc0/Klmi+E9LS1NERIRDW4cOHTRgwABJUrt27RQdHa3Tp097IToAgDddfc0/UwGbJnI9AKAmldf8ezkQH+X14n/t2rW6/fbbFRsbK5vNplWrVlXps3jxYnXp0kUhISFKTk7WunXrarWNzMxMlZeXKz4+voGiBgA0Fa6m/cMzyPUAgMZU9Zp/pv074/Xiv7CwUP3799drr73mdPmKFSs0depUzZw5U1u3btWwYcOUnp6unJwct9afl5en+++/X0uXLnXZr7i4WAUFBQ4vAIB1OBsN4IDAM8j1AIDGdPW0f675d87rxX96erqef/553XHHHU6XL1iwQA888ICmTJminj17auHChYqPj9eSJUtqXHdxcbHGjx+vGTNmaOjQoS77zps3T1FRUfYXIwcAYA1XT/svK+OAwNPI9QAAT+DJPq55vfh35dKlS8rKytKoUaMc2keNGqX169e7/KwxRpMnT9aIESM0ceLEGrc1Y8YM5efn21+HDh2qV+wAAN/CtH/fRK4HADQUpv27FuDtAFw5deqUysrKFBMT49AeExOj48eP29+PHj1aW7ZsUWFhoeLi4rRy5UoVFxdrxYoV6tevn/3awj/+8Y/q27ev020FBwcrODi40b4LAMA7mAro28j1AID6unqWHyf6nfPp4r9CxR9mBWOMQ9uaNWucfq6c+R4A0Oy5uts/fAe5HgBQVxX5wt+/6qP+ONFfyaen/bdp00b+/v4OZ/4lKTc3t8oIAQAArjAV0DeR6wEADaUy13s3Dl/l08V/UFCQkpOTlZGR4dCekZFR4019AACQmPbv68j1AID6cjXtn1xfyevT/s+fP699+/bZ3x84cEDZ2dmKjo5Wp06dNG3aNE2cOFEpKSlKTU3V0qVLlZOTo4ceesiLUQMAmhruAOw95HoAgCc4e6wvKnm9+M/MzFRaWpr9/bRp0yRJkyZN0rJly3TXXXcpLy9Pc+bM0bFjx9SnTx+tXr1aCQkJ3goZANCEVI4GXH7PtH/PI9cDABrT1bP8yPXOeb34Hz58eI1/II888ogeeeQRD0UEALASV9P+4RnkegCAJzg70Y9KPn3NPwAADcXZtH9GAwAAaPqqnuivXEaur0TxDwCwNG4CBABA88Alfq5R/AMAmgWmAgIAYE2urvlHJYp/AIClVRwQ+Psz8g8AgBUxy889FP8AgGah4oCAR/0BAGBNfv9b3VLvO0fxDwCwNB71BwCAtV097b+sjFzvDMU/AMDSeNQfAADNg7Np/6hE8Q8AaBZ41B8AANbEo/7cQ/EPALC0q6f9cxMgAACsiSf7uEbxDwBoFnj8DwAA1uTqUX+c6K9E8Q8AsLSqBwSVyzggAADAOpj27xrFPwDA0lxN+wcAAE2fqyf7oBLFPwCgWXB2B2BGAwAAsA7u9u8axT8AwNKY9g8AgLWR691D8Q8AaBa44R8AANZUWfxffs8sP+co/gEAlsaj/gAAaB4qpv1zot85in8AgKW5evwPAABo+njUn3so/gEAzULlHYAr2zggAADAOiqn/Xs3Dl9F8Q8AsLSrRwOY9g8AgLVUXuLHyL8rFP8AgGahsvj3ciAAAKBRVD7qz8uB+CiKfwCApV09GlBWxmgAAABWUpHr/f0Z+XeF4h8A0Cw4e/wPAACwjsr7+1D8O0PxDwCwtKtH/q88BuCAAACApq9qrudEvzMU/wAAS+NRfwAANA8Vs/yufLIPKlH8AwCaBaYCAgBgTTzZxz0U/wAAS+OAAAAAa+NRf+6h+AcANAuV0/69HAgAAGgUlTf39W4cvoriHwBgaZWjAZffMxoAAIC1MPLvHop/AICluZr2DwAArIOb+7pG8Q8AaBYqi//KNkYDAABo+qqe6K9cRq6vRPEPALA0pv0DANA8VOR6Zvk5R/EPAGgWnF0HCAAAmr6rR/7LyjjR7wzFPwDA0pgKCABA81Bxop9c7xzFPwDA0q4u/hn5BwDAWipz/eX3TPt3juIfANAscM0/AADWxqP+XKP4BwBYGtP+AQCwNmb5uYfiHwDQLHBAAACANVWd9l+5jBP9lSj+AQCWdvWj/rgOEAAAa+LJPq5R/AMAmgWuAwQAwJoqT/ST612h+AcAWBrX/AMA0Dz4+1fN9ahE8Q8AsLSrrwNkKiAAANbi6hI/TvRXovgHADQLFVMBGfkHAMCaKqf9ezkQH0XxDwCwNFeP/6H4BwCg6SPXu4fiHwDQLDDtHwAAa6t81B+53hmKfwCApV19B2Cm/QMAYC3c7d89FP8AAEtzNRUQAABYR+Usv8o2iv9KFP8AgGaB6wABALCmqo/15US/M5Yo/sePH69WrVppwoQJDu3vvfeeevTooW7duun111/3UnQAAG/i8T/WQK4HANSk8hI/cr0zlij+H3/8cS1fvtyhrbS0VNOmTdMnn3yiLVu26MUXX9Tp06e9FCEAwNucXfOPpoNcDwCoTtVr/r0Zje+yRPGflpamiIgIh7ZNmzapd+/e6tixoyIiIjRmzBitWbPGSxECALylcirg5fdM+2+ayPUAgOqQ693j9eJ/7dq1uv322xUbGyubzaZVq1ZV6bN48WJ16dJFISEhSk5O1rp162pc79GjR9WxY0f7+7i4OB05cqQhQwcANAHc8M/7yPUAAE/gmn/XvF78FxYWqn///nrttdecLl+xYoWmTp2qmTNnauvWrRo2bJjS09OVk5Pjcr3OzvBUHAACAJofHvXnPeR6AEBjcjXtn1xfKcDbAaSnpys9Pb3a5QsWLNADDzygKVOmSJIWLlyoNWvWaMmSJZo3b161n+vYsaPD2f/Dhw9r8ODB1fYvLi5WcXGx/X1+fr4kqaCgwO3vAgDwPefPn9eFCxdUUnJeUoFKSi6/l6Rz5841qX/nK2Jtagcy5HoAQGMyxujChQsqLi6VVKDyctlzfUBAQJP6d74xc73Xi39XLl26pKysLP3iF79waB81apTWr1/v8rODBg3S9u3bdeTIEUVGRmr16tWaNWtWtf3nzZun2bNnV2mPj4+vW/AAAJ/01VfS3Xd7O4r6ycvLU1RUlLfDaBDkegBAYyDXV+XTxf+pU6dUVlammJgYh/aYmBgdP37c/n706NHasmWLCgsLFRcXp5UrV2rgwIF65ZVXlJaWpvLycv385z9X69atq93WjBkzNG3aNPv7s2fPKiEhQTk5OT5/gDVw4EBt3ry5SWyjLuupzWfc7VtTP1fLq1tWUFCg+Ph4HTp0SJGRkW7F6y3sM7Xvyz7TNPaZuq6jqewz+fn56tSpk6Kjo92KtSkg17uvsf8eevPf7dp+zp2+9fk7WN1y/t1unG1YYZ8h13t2G419fOhu/6aW6326+K9w9fV7xhiHturu7Dt27FiNHTvWrW0EBwcrODi4SntUVJTP/0X19/dv9Bgbaht1WU9tPuNu35r6uVpe02cjIyPZZxpwG+wzvqGp7DN1XUdT22f8/Lx+y54GR66vWWP/PfTmv9u1/Zw7fevzd7Cm5fy73bDbsMI+Q6737DYa+/jQ3f5NLdf79NFDmzZt5O/v73DmX5Jyc3OrjBA0Z48++miT2UZd1lObz7jbt6Z+rpZ74vdubOwzte/LPtM09pm6roN9xnvI9e5r7P3Cm/9u1/Zz7vStz9/B2sbji5rKv9v1WY8v7TNNfX+RrL/P1PYzVtxnbMaH7hpks9m0cuVKff/737e3DR48WMnJyVq8eLG9rVevXho3bpzLmwDVV0FBgaKiopSfn+/zZ+ngG9hnUFvsM6gtK+wz5Ho0ZewzqC32GdRWY+4zXp/2f/78ee3bt8/+/sCBA8rOzlZ0dLQ6deqkadOmaeLEiUpJSVFqaqqWLl2qnJwcPfTQQ40aV3BwsJ555hmn0wMBZ9hnUFvsM6itprrPkOthFewzqC32GdRWY+4zXh/5/+yzz5SWllalfdKkSVq2bJkkafHixXrppZd07Ngx9enTR6+++qpuvPFGD0cKAADqglwPAID3eb34BwAAAAAAjcunb/gHAAAAAADqj+IfAAAAAACLo/gHAAAAAMDiKP4BAAAAALA4iv86OHDggNLS0tSrVy/17dtXhYWF3g4JPmzPnj0aMGCA/RUaGqpVq1Z5Oyz4uFdffVW9e/dWr1699Pjjj4t7s6Im8+fPV+/evdWnTx/96U9/8nY4TR65HrVBrkddkOtRW/XN9dztvw5uuukmPf/88xo2bJhOnz6tyMhIBQQEeDssNAHnz59X586d9d133yk8PNzb4cBHnTx5UkOGDNGOHTsUGBioG2+8UfPnz1dqaqq3Q4OP+vrrrzVp0iStX79ekjRy5Ei9//77atmypXcDa8LI9agrcj3cQa5HbTVErmfkv5Yq/oIOGzZMkhQdHc3BANz2z3/+UyNHjuRgADUqLS1VUVGRSkpKVFJSonbt2nk7JPiwXbt2aejQoQoJCVFISIgGDBigDz74wNthNVnketQHuR7uItejNhoi1ze74n/t2rW6/fbbFRsbK5vN5nRK1uLFi9WlSxeFhIQoOTlZ69atsy/bu3evWrRoobFjxyopKUkvvPCCB6OHN9R3n7nSO++8o7vuuquRI4a31Xefadu2raZPn65OnTopNjZWN998s6699loPfgN4Wn33mT59+ujTTz/V2bNndfbsWX3yySc6cuSIB7+BbyHXo7bI9agtcj1qyxdyfbMr/gsLC9W/f3+99tprTpevWLFCU6dO1cyZM7V161YNGzZM6enpysnJkSSVlJRo3bp1WrRokTZs2KCMjAxlZGR48ivAw+q7z1QoKCjQF198oTFjxngibHhRffeZM2fO6L333tPBgwd15MgRrV+/XmvXrvXkV4CH1XefqbhedMSIERo/frwGDhzYrEeqyfWoLXI9aotcj9ryiVxvmjFJZuXKlQ5tgwYNMg899JBD23XXXWd+8YtfGGOMWb9+vRk9erR92UsvvWReeumlRo8VvqEu+0yF5cuXm/vuu6+xQ4SPqcs+884775hHHnnEvuyll14yL774YqPHCt9Qn39nKjzwwAPmvffea6wQmxRyPWqLXI/aItejtryV65vdyL8rly5dUlZWlkaNGuXQPmrUKPuNFQYOHKgTJ07ozJkzKi8v19q1a9WzZ09vhAsf4M4+U4FpgJDc22fi4+O1fv16FRUVqaysTJ999pl69OjhjXDhA9z9dyY3N1fS5buOb9q0SaNHj/ZonE0FuR61Ra5HbZHrUVueyvXNd06gE6dOnVJZWZliYmIc2mNiYnT8+HFJUkBAgF544QXdeOONMsZo1KhRuu2227wRLnyAO/uMJOXn52vTpk3629/+5ukQ4WPc2WeGDBmiMWPGKDExUX5+fho5cqTGjh3rjXDhA9z9d+b73/++zp49q/DwcL355pvNetq/K+R61Ba5HrVFrkdteSrXc2TghM1mc3hvjHFoS09PV3p6uqfDgg+raZ+JiorSiRMnPB0WfFhN+8zcuXM1d+5cT4cFH1bTPnP1CCRcI9ejtsj1qC1yPWqrsXM90/6v0KZNG/n7+zucXZEuT6+4+iwMILHPoPbYZ1Bb7DMNi98TtcU+g9pin0FteWqfofi/QlBQkJKTk6vc0TcjI0NDhw71UlTwZewzqC32GdQW+0zD4vdEbbHPoLbYZ1Bbntpnmt20//Pnz2vfvn329wcOHFB2draio6PVqVMnTZs2TRMnTlRKSopSU1O1dOlS5eTk6KGHHvJi1PAm9hnUFvsMaot9pmHxe6K22GdQW+wzqC2f2Gdq9WwAC/j000+NpCqvSZMm2fssWrTIJCQkmKCgIJOUlGQ+//xz7wUMr2OfQW2xz6C22GcaFr8naot9BrXFPoPa8oV9xmaMMQ13KgEAAAAAAPgarvkHAAAAAMDiKP4BAAAAALA4in8AAAAAACyO4h8AAAAAAIuj+AcAAAAAwOIo/gEAAAAAsDiKfwAAAAAALI7iHwAAAAAAi6P4BwAAAADA4ij+AQAAAACwOIp/AJZw6NAhDR8+XL169VK/fv307rvvejskAADQwMj3QN3ZjDHG20EAQH0dO3ZMJ06c0IABA5Sbm6ukpCTt2bNH4eHh3g4NAAA0EPI9UHeM/AOwy8vLU7t27XTw4EFvh1JrHTp00IABAyRJ7dq1U3R0tE6fPi1JmjBhghYsWODF6AAA8B3ke6B5ovgHYDdv3jzdfvvt6ty5s7dDqZfMzEyVl5crPj5ekjRr1izNnTtXBQUFXo4MAADvI98DzRPT/gFIki5evKjY2FitXr1aqamp3g6niuTkZBUXF1dp//DDDxUbG2t/n5eXp2HDhun111/X0KFDHT4/ZcoUPfzwwx6JFwAAX0S+B5ovRv4Bi4qLi9PixYsd2tavX6+wsDB99913Vfr/+9//VkBAQJUDgXPnzum+++5TeHi4OnTooFdffVXDhw/X1KlTq912eXm5XnzxRXXt2lXBwcHq1KmT5s6da18+fPhw/fSnP9XUqVPVqlUrxcTEaOnSpSosLNSPfvQjRURE6Nprr9W///1v+2eysrK0ffv2Kq8rDwSKi4s1fvx4zZgxw+FAQJLGjh2rt99+263fDgCApoJ8T74H3EXxD1jUkCFDtHnzZvt7Y4ymTp2qqVOnKiEhoUr/tWvXKiUlpUr7tGnT9MUXX+if//ynMjIytG7dOm3ZssXltmfMmKEXX3xRTz/9tHbu3Kk///nPiomJcejzhz/8QW3atNGmTZv005/+VA8//LDuvPNODR06VFu2bNHo0aM1ceJEXbhwwa3va4zR5MmTNWLECE2cOLHK8kGDBmnTpk1ORxMAAGiqyPeOyPeACwaAJb388sumd+/e9vd/+MMfTExMjCkoKHDaf9y4cebHP/6xQ1tBQYEJDAw07777rr3t7NmzJiwszDzxxBNO11NQUGCCg4PN7373u2pju+mmm8wNN9xgf19aWmrCw8PNxIkT7W3Hjh0zksyGDRtcfs8K69atMzabzfTv39/++uqrr+zLt23bZiSZgwcPurU+AACaAvI9+R5wV4B3Tz0AaCxDhgzRU089pfPnz8vPz0+//OUv9fzzzysiIsJp/4sXLyokJMShbf/+/SopKdGgQYPsbVFRUerRo0e12921a5eKi4s1cuRIl/H169fP/v/+/v5q3bq1+vbta2+rGDnIzc11uZ4KN9xwg8rLy6tdHhoaKklujywAANAUkO8dke+B6lH8AxaVkpIif39/bdmyRR999JFat26tH//4x9X2b9Omjc6cOePQZv73fqA2m81puzMVSbcmgYGBDu9tNptDW8U2XSX42qh4DFDbtm0bZH0AAPgC8r0j8j1QPa75BywqJCRE/fv319///nfNnz9fCxYskJ9f9X/lExMTtXPnToe2a6+9VoGBgdq0aZO9raCgQHv37q12Pd26dVNoaKg+/vjj+n+JBrR9+3bFxcWpTZs23g4FAIAGQ753RL4HqkfxD1jYkCFD9Otf/1o333xzjdPyRo8erR07djiMBkRERGjSpEn6r//6L3366afasWOHfvzjH8vPz6/K6ECFkJAQPfXUU/r5z3+u5cuX69tvv9XGjRv1xhtvNOh3q61169Zp1KhRXo0BAIDGQL6vRL4HqkfxD1jYgAEDFBAQoJdffrnGvn379lVKSoreeecdh/YFCxYoNTVVt912m26++WZdf/316tmzZ5XrBa/09NNP62c/+5lmzZqlnj176q677nL7Wr7GUFRUpJUrV+rBBx/0WgwAADQW8v1l5HvANZtxdTEPgCZtxIgR6tevnxYuXOhW/9WrV2v69Onavn17tVMGCwsL1bFjR73yyit64IEHGjDaxrNo0SL94x//0IcffujtUAAAaHDk+8vI94Br3PAPsJjy8nKdPHlSb7zxhvbs2aOVK1e6/dkxY8Zo7969OnLkiOLj4yVJW7du1e7duzVo0CDl5+drzpw5kqRx48Y1SvyNITAwUP/zP//j7TAAAGgw5PuqyPeAa4z8Axbz2WefacSIEbruuuv05ptvavDgwfVa39atWzVlyhTt2bNHQUFBSk5O1oIFCxwe0wMAADyLfA+gtij+AQAAAACwOG74BwAAAACAxVH8AwAAAABgcRT/AAAAAABYHMU/AAAAAAAWR/EPAAAAAIDFUfwDAAAAAGBxFP8AAAAAAFgcxT8AAAAAABZH8Q8AAAAAgMVR/AMAAAAAYHEU/wAAAAAAWBzFPwAAAAAAFvf/AZly1f46V8piAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,axes = plt.subplots(1,2,figsize=(12,4))\n", + "burn_plot(axes[0], model=toy)\n", + "burn_plot(axes[1], model=toy2)\n", + "axes[0].set_title(\"Original toy model\")\n", + "axes[1].set_title(\"Kicked toy model\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3ae151e1", + "metadata": {}, + "source": [ + "We increase the peak burning by about an order of magnitude." + ] + }, + { + "cell_type": "markdown", + "id": "628597e1-f838-4616-bd8b-7257e0b19008", + "metadata": {}, + "source": [ + "Finally, let's load the model that we created in the actual script and compare." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e3d8ca54-f2dd-456b-9115-9531ba18471e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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MTp06RcOGDZkyZQqdOnXi9OnT1K9fn/nz5zNhwoQSTRrJysritdde46uvvuLMmTMUFBRw7do1Tpw4cdvv9fHx4Z577rEfBwUF0bhxY6pXr16s7fz58yW+3nbt2tn/bLFYqFu3rv379+/fT1hYGNWqVbOf06NHDwoLCzlw4ABBQUEl/hwRZ5aXl8fevXvZtm0bZ8+eBSAlpTWLF9v2k+/UaStjxmQwaNDwMvvMnz6eVVjogmFUjUdkFdQm+N/tzBtydS1+fKt8+fmWrceO3XVJt2QYxnWBa/zv3lBRe4cOHQgLC+Pjjz9m8ODBfPfdd6xYsaJE7//b3/6WNWvW8Le//Y1mzZrh7e3NmDFjyMvLu+33uru7Fzu2WCw3bLuTSSe3+v4b/bf46Xkild2FCxfYvn07u3fvJjs7l/x8Dzw9ba/5+mZitbrRpMkRHn54G2PGTLrh3tJ3q/iCJy4UFuaX2Xs7MgW1CX7SGTPt3Fvx8PAoNvmqTZs2xMTEFAupLVu24OvrS/369e3nTZ48mVmzZnH69GkGDhxIgwYNSvR5iYmJTJgwgVGjRgGQmZnJsZ/91vHzmszSpk0bFixYQFZWlr1XvXnzZlxcXGjRogXgOLWKlBWr1cr+/fvZvn07x44dIzOzOrt2dWLHjnCaNz/E0KErAQgJOckvfvEZw4Z5ER094aZ7S9+t4kuIVp2VyTTrW67TuHFjtm7dyrFjx7h48SJPP/00J0+e5Nlnn+X7779n2bJl/PGPf2T69OnFflt+9NFHOX36NO+//z4TJ04s8ec1a9aMxYsXk5yczO7du3nkkUeu+wvYuHFjEhISOH36NBcvXiyza71Tjz76KF5eXowfP569e/eyceNGnn32WcaNG2cf9m7cuDF79uzhwIEDXLx4kfz8qvFbv1Q+ubm5bNy4kVmzZvHll4uIj4dFi8Ywa9av2bBhAGlp/hw61JzCQgv+/v4MHDiAd94ZxoMPjip2+6ms2HrUPw59V5WgVo9arvOb3/yG8ePH06ZNG65du8bRo0eJjY3lt7/9LWFhYQQEBDBp0iR+//vfF/u+GjVqcP/997Ny5co7WvBj1qxZTJw4ke7duxMYGMiLL75Ienp6sXNmzJjBk08+yT333ENubq5pj2X4+PiwZs0afvWrX9GpUyd8fHy4//77eeutt+znTJkyhfj4eCIiIsjMzGTjxo307dvXlHpF7taZM2dYtGgRly9fJjm5PZs39+Dixdr210NCThIRsZ3hw3Pp0eNhmjVrVqbD3DdSfPesqrOEqMWoKldaDtLT0/Hz8yMtLY0aNWoUey0nJ4ejR4/SpEkTvLy8TKqw4g0aNIjWrVvzz3/+0+xSHF5V/RkRx2YYBlu2bGH9+vX2Huv69f35+uveuLvn0a7dHnr12sfQofUJDw+/4401SuP9998nKcngm2+6U6vWRWbP9qNDhw4V9vll6Vb58XPqUUuZuHz5MmvXrmXDhg28/fbbZpcjInchIyODpUuXsn//CXJzffD1tc18jYjYQY0a6QwdepVevcJo3frRUq0wdrdcXFyoX/8kY8YsAqCwcFiF12AGBbWUiY4dO3LlyhX++te/0rJly2Kv3XvvvRw/fvyG3/fee+/x6KOPVkSJJCYmEhUVddPXM281HV+kkjt06BBLly7lyJHqfPnlk3h7X2PChPm4uhZy772+vPRSJ9MfP7zZ0yeVnYJaysTPZ2n/VGxs7E0nVFXkX/yIiAiSk5Mr7PNEnEFBQQHr1q3jm2++Zfv2CNasGYLV6oavbzppaTUZOfJe+vTpg+vPnx01QdE9cNtyKppMJlJmGjVqZHYJAHh7e9OsWTOzyxBxGBcvXmTRokUcO3aV5csfZP/+NgA0b36QX/xiHePG3XdX+0aXFxcXF44ebczHH0+gdu3zREcfMbukCqGgLmdVZWhG7px+NsQshmGwa9cuVq1axdGjQSxaNJW0NH9cXKwMHBjHhAlXGTFiAj4+PmaXWozt8SzbGgV6PEtKrWh1q+zs7DJ/6F8qh+zsbOD6ldBEylNOTg4rVqxg3759GAasWhVFWpo/NWteZuzYJUyc2I6IiMEOudKebUe6Hxc8qSq/7Cqoy4mrqyv+/v72NaJ9fHwc8gdfKp5hGGRnZ3P+/Hn8/f0d4t6fVA0nTpwgJiaGtLQ0ACwWGD16MV9/3ZNf/GIH48YNp06dOiZXeXNa8KSKmjVrFh988AGGYTBw4ED7PsxloW7dugB3tCGEVB3+/v72nxGR8lRYWEhiYiLx8fH88ENjzp9vRdeuWwGoVesSf/7zGSIjxzv86M71a30rqCu9Cxcu8Pbbb7Nv3z7c3d3p3bs33377Ld26dSuT97dYLNSrV486depoGUkpxt3dXT1pqRBpaWksXryYo0dPEh/fj8TEXlgsBsHBZ2jZ8iIjRoygVatWZpdZIj8d+q5KK5NV6aAG26MJOTk5AOTn55fLsI+rq6v+URaRCpeSksLy5cs5d86TmJgJnDzZEIAOHXbSpYsHDz/81G1XxXIkLi4uuLkVAGC1ulaZHrVTb8qRkJDAsGHDCA4OxmKxsHTp0uvOmTt3rn2JxvDwcBITE+2v1a5dm9/85jc0bNiQ4OBgBg4cWGxvYxERZ5Sfn8+KFSv44osvSE5uxLvvTuXkyYZ4eubwwAOLmDUriylTHnWqkAZbUHt7X6NZs0Pcc88PVSaonbpHnZWVRVhYGI8//jj333//da8vXLiQadOmMXfuXHr06MF7771HVFQUKSkpNGzYkCtXrvDVV19x7NgxvL29iYqKIiEhgd69e5twNSIipXfu3DkWLVrEhQsXiIsbyJYtPQEIDj7N44+v4cknB5V4C1pH4+LiQvXqWTz66H8B2LOnBidPnjSllvDwcNq1a1chn+XUQR0VFXXLJSHfeustJk2axOTJkwGYPXs2a9as4Z133mHmzJmsW7eOZs2aERAQAMDQoUP59ttvbxrUubm55Obm2o9/vsOTiIhZDMMgKSmJuLg4Cgpsw8MBAZcB6NZtC88+e5ZRox5x6g1gfj7Rd86cEVit199WtFgM6tZNZfDgNfa2hQvHkpvrecP3DQy8SHR0rP14yZJRZGVVu+G5/v5Xue++r2jevPndXMJdceqgvpW8vDx27NjBSy+9VKw9MjKSLVu2ANCgQQO2bNlCTk4O7u7uxMfH88QTT9z0PWfOnMlrr71WrnWLiNyp7Oxsli1bxoEDB8jO9sbHxxbUHTvupGHDi0yZ0pGwsNFO/4joz9ekOHGiIQUFJZupfvx4I65du/ECLnl5HsWOjx1rTHq63w3PrV274p/iqbRBffHiRaxW63VrSQcFBZGamgpA165diY6OpkOHDri4uDBgwACGDx9+0/d8+eWXmT59uv04PT3daYeQRKRyOHLkCEuWLOHSpVxiY0dy7FhjnnzyXby9cwgOrsdzz42gVq1aZpdZJtq2bcv27dvJy8sDYNSoxRhG0S8fxX8J8fHJKnY8bNgKrFZXik8Ut32Pt/e1YucOGbKa/HzbLwA/n1ju5ZVLRau0QV3kRrut/LTtjTfe4I033ijRe3l6euLpeeOhExGRimS1Wtm4cSObN2/m7Nk6LFo0nkuXArFYCjlypClTpvjTv39/U7ajLC/16tXj6aef5ujRoxQUFDB06O2+4177n25/7o+7/t3+3GaEhITc7qQyU3n+H/yZwMBAXF1d7b3nIufPnzd9qzYRkdK4fPkyMTExnDp1mu3bO7FmzWD7jlePPLKS3/ymU6XdgMbf358OHTqYXUaFqrRB7eHhQXh4OHFxcYwaNcreHhcXx4gRI0r13nPmzGHOnDlYrdbSlikickf27NnDypUrSUuzXLfj1a9/vZtx44ZRvXp1k6uUsuTUQZ2Zmcnhw4ftx0ePHiU5OZmAgAAaNmzI9OnTGTduHBEREXTr1o158+Zx4sQJpk6dWqrPfeaZZ3jmmWdIT0/Hz+/GEw5ERMpSbm4usbGx7N69G4B164ayf38bXFysREau59VXfenWbYzTTxiT6zl1UG/fvp1+/frZj4smeo0fP56PPvqIsWPHcunSJWbMmMHZs2cJDQ0lNjbWYfZHFhEpidOnTxMTE8Ply5ftbQMGbODSpUDGjEli+vTe1KtXz8QKpTxZjKqyWGo5KOpRp6WlOd0KPyLi+AzDYMuWLaxfv570dG/27AmjW7ctFHWaO3ToQFRUFB4eHrd+I3E4d5IfTt2jFhGprDIyMli6dCk//PADR440YcmS0WRm+uLtfY2uXVMYNmwYoaGhZpcpFUBBfRc0mUxEytOhQ4dYsmQJmZk5xMf3JzGxF2Chdu3zdOyYz9SpU6lZs6bZZUoF0dB3KWjoW0TKUkFBAevWrePbb78lLc2PmJj77TtehYfvYMaMDAYP7qXd+CoBDX2LiDiZCxcuEBMTQ2pqKocONWPx4vvJyfHG0zOHBx9cx4wZoTRuHG52mWICBbWIiIkMw2DXrl2sWrWK/Px8AFxdreTkeBEcfJoXXtjJlCkD8PG58TrVUvkpqEVETHLt2jVWrFhBSkoKBQVuFK322bTpUR577DOefLIF3brdp2ejqzgF9V3QZDIRKa0TJ04QExNDWloae/a0Iy5uEI8/Pp+AgMvUqVOHp58eSJ06dcwuUxyAJpOVgiaTicidKiwsJCEhgU2bNpGb605sbDS7d7cHoFOnrfzxjxeJjIzE3b1k2zeKc9JkMhERB5SWlsbixYs5fvw4qalBLFr0gH3HqwEDNjN7dm3uvbeL2WWKg1FQi4hUgJSUFJYvX861aznX7Xj19NOb+d3vemhkTm5IQS0iUo7y8/NZvXo1O3bsAGD37jBiY20bHrdocZC//e0SQ4cOxsXFxcwyxYEpqEVEyklqaioxMTFcuHDB3ta27V527epIhw7H+Oc/m9KwYTcTKxRnoKC+C5r1LSK3YhgGSUlJxMXFkZ9vJTm5Pe3a7cHVtRBXVytvvrmN4cPvw8vLy+xSxQlo1ncpaNa3iPxcVlYWy5Yt4+DBg2RmVmPJktEcOXIPPXp8TXR0AtHR0YSFhenZ6CpOs75FRExw5MgRlixZQkZGBkeONGXx4tFkZVXHzS2fpk0LePLJJ6lVq5bZZYqTUVCLiJSS1Wpl48aNbN68GavVwsaN/fn66x93vPrznw8xcWJP3Nz0T67cOf3UiIiUwuXLl4mJieH06dOkpdUgJmaMfcerLl128/771WnbtofJVYozU1CLiNylPXv2sHLlSnJzcwEoKHAnNbUunp45TJmSxMyZHalevbrJVYqzU1CLiNyh3NxcYmNj2b17N4ZhoWheWK1alxg7NoYRI1py//29NGFMyoSC+i7o8SyRquv06dPExMRw+fJlLl2qRUzM/URGrqVx42PUqlWLJ5/sS7169cwuUyoRPZ5VCno8S6TqMAyDLVu2sH79egoLC9mzpx1ffXUf+fkeBAWl8u67W4mOjsLDw8PsUsUJ6PEsEZEylJGRwZIlSzhy5Ah5eR7Fdrxq2vQY77+fQ//+I8wtUiotBbWIyC0cPHiQpUuXkp2dfd2OV8OH7+KDD5oSGNjY7DKlElNQi4jcQEFBAXFxcWzduhWACxcC+eCDKfYdr2bMOMxzz3XQZhpS7hTUIiI/c+HCBWJiYkhNTbW3BQZepFWr7zEMbz75xJ2OHTuaWKFUJQpqEZH/MQyDnTt3snr1avLz8zl1qj61al3C2zsHiwVeeGE/998/lGrVfMwuVaoQBbWICHDt2jVWrFhBSkoKhmFh8+aebNjQn1atvufhhxcTFTWE8PBwPRstFU5BfRf0HLVI5XLixAliYmJIS0sjM7MaS5eO4ocfmgFQrZobEyY8QYMGdUyuUqoqPUddCnqOWsS5FRYWkpCQwKZNmzAM47odr55+ej9vvtkaDw93s0uVSkbPUYuI3EZaWhoxMTGcOHGCwkKX/+141ROwEBR0gfffz2DYsHZmlymioBaRqiclJYXly5eTk5MDQG6uB3v2tAMs9OlzgM8/r0fdurXNLVLkfxTUIlJl5Ofns3r1anbs2FGs3ds7hwceWEyDBl159dWWejZaHIqCWkSqhNTUVGJiYrhw4QIFBW6sXTuIevVS6dBhFzVr1mTy5EGEhISYXabIdRTUIlKpGYZBUlISa9euxWq1culSLRYtGkNqaj3c3fMYMcKFRx4ZhJeXl9mlityQglpEKq2srCyWLVvGwYMHAdi9ux0rV9p2vPLxyeb//b+zPP74fXo2WhyaglpEKqUjR46wZMkSMjIyrtvxqkWL08TEeBMaeo+5RYqUgIJaRCoVq9XKxo0b2bx5M4ZhUFDgxgcfTObChTpYLIWMG3eE999vgoeHq9mlipSIglpEKo3Lly8TExPD6dOn7W1ubgW0br2fvDxv5s5N45FHmplYocidU1CLSKWwZ88evvrqK/Ly8rh2zYvcXE/8/dMAmDjxFP36hdOwoWZ1i/NRUN8FrfUt4jhyc3NZuXIle/bsAeDkyRBiYsbg45PNlCkfMWRIP7p27aoJY+K0tNZ3KWitbxFznT59mpiYGC5fvvy/Ha96sGFDfwzDhcDAq6xYkUfXrtpMQxyPw6/1nZ+fT2pqKtnZ2dSuXZuAgAAzyhARJ2UYBps3b2bDhg0UFhaSmVmNJUtGc+SIbRZ3r16nWLy4DoGB/uYWKlIGKiyoMzMz+e9//8tnn31GUlISubm59tdCQkKIjIzkiSeeoFOnThVVkog4oYyMDJYsWcKRI0cA+OGHpixZYtvxyt09n1dfPcfvfx+CRrqlsqiQoJ41axZvvPEGjRs3Zvjw4bz00kvUr18fb29vLl++zN69e0lMTGTQoEF07dqVf/3rXzRv3rwiShMRJ3Lw4EGWLl1KdnY2AIYB8fH9yMqqTv36l1m0yI2uXTVhTCqXCrlH/cADD/CHP/yBtm3b3vK8nJwc/v3vf+Ph4cHkyZPLu6xS0z1qkYpRUFBAXFwcW7duve61q1drcvLkaD7+uD7VqmkzDXEOd5IfDjWZLD093akCT0EtUv4uXLjAokWLOHfuHADff9+K8+dr07t3IjVq1GD06NE0btzY3CJF7tCd5EeF/fr5t7/97Zavp6enExkZWUHViIijMwyDHTt2MG/ePM6dO0dBgRuxsVEsXPgQGzcOAHrx1FNPKaSl0quwyWSvvvoqtWrV4vHHH7/utczMTAYPHkx6enpFlSMiDuzatWusWLGClJQUgGI7XgH84hepvPxyfzw9NWNMKr8KC+pPPvmEcePGUbNmTUaOHGlvz8zMJDIyksuXL5OQkFBR5YiIgzpx4gQxMTGkpdlWFfvpjlfVq19j3rxcHn64rslVilScCgvqMWPGcPXqVR555BFWrlxJv379yMzMZMiQIVy8eJFNmzYRFBRUUeWIiIMpLCwkISGBTZs2UTR1ZuXKaLZv7wxAu3YXWb7cj0aNvM0sU6TCVeiCJ5MnT+by5cuMHDmSZcuW8eqrr5KamsqmTZuoV69eRZYiIg4kLS2NmJgYTpw4Uaw9JOQUO3ZE8Oyzl3nrrUBcteGVVEEVvjLZCy+8wJUrVxgwYACNGzdm06ZN1K9fv6LLEBEHkZKSwvLly8nJycEwICOjBjVq2OarjByZwSuvZBMREWhylSLmqbCgHj16dLFjd3d3AgMDee6554q1L168uKJKEhET5eXlsWbNGnbs2AHAtWteLF8+gtOn6/PUU/O4774u9OjRAxcXPRstVVuFBbWfn1+x44cffriiPlpEHExqaiqLFi3i4sWLAJw82YCYmPtJS/PH1dVKy5YT6NVLvWgRqMCgnj9/fkV9VLnTNpcid8cwDJKSkli7di1WqxXDsPD11z3YuNG241XdupnExLjTvbtCWqSIQ61M5my0MplIyWVlZbFs2TIOHjwIcN2OV1FRl/nss5r4+enZaKn8HG5lsp/P5Lyd06dPl1MlImKGI0eO8M4779hDGiA+vi9HjtyDu3sB//hHJitXBiikRW6gQoK6U6dOTJkyhaSkpJuek5aWxvvvv09oaKgmlIlUElarlbi4OD755BMyMzOLvTZo0Dq6dj3Pjh0WnnuuuralFLmJCrlHvX//fv7yl78wZMgQ3N3diYiIIDg4GC8vL65cuUJKSgr79u0jIiKCN998k6ioqIooS0TK0eXLl4mJibGPkF296seuXR3o2zceX9/qjBs3ipkz65hcpYjjq9B71Dk5OcTGxpKYmMixY8e4du0agYGBdOjQgcGDBxMaGlpRpZQJ3aMWubHdu3ezcuVK8vLyANi/vxXLl48gJ8ebCROSePvte6lWrZrJVYqYx2m3uXQ2CmqR4nJzc1m5ciV79uwBoKDAjbVrI9m2zbYMaJs2maxYUY2mTTXOLVXbneRHha9MJiKV0+nTp1m0aBFXrlwB4OLFWixa9ADnztk20HjqqUxmz66Oh4eZVYo4HwW1iJSKYRhs3ryZDRs2UFhYCEBKSmuWLh1Ffr4HNWrk8sknLgwfXt3kSkWcU4WtzbdmzRr7X2IRqRwyMjL45JNPWLduXbG/39WrZ2K1utG5cyb793syfLi7iVWKOLcK61FHR0dz9uxZ6tTRLE+RyuDgwYMsXbqU7OxsAHJzPfD0tE0e69EDHnggi8hIX+14JVJKFRbUmrMmUjkUFBQQFxfH1q1bATAM2LatExs39mfixH8zZkwb+vTpo800RMqI7lGLSIlduHCBRYsWce7cOeDHHa++/741AHl54+jXT09AiJSlCg3qt99+mx49ehAeHk5goBbdF3EWhmGwc+dOVq9eTX5+PnD9jlf/7/9Zef55hbRIWauw56hdXFyoVasWly5dwmKxUL9+fTp27Eh4eDgdO3akY8eO1KtXryJKKTN6jlqqgmvXrrFixQpSUlIArtvxqkGDHBYv9iQiQs9Gi5SUwz5HvW/fPgoKCti1axc7d+5k586dfPDBB5w8eRKLxUJQUBBnzpypyJJE5BaOHz/O4sWLSUtLs7ft3h3Ghg0DARg9OoePPvLC19esCkUqvwoLasv/VtwPDg4mODiYoUOH2l+7fPky27dvJzk5uaLKEZFbKCwsJCEhgU2bNl03EbRduz2cOtWdqVMDmDTJS5tpiJSzCh36Tk1NrVSPZ2noWyqjq1evsnjxYvv2tFarC9u2dSIiYjs1angyYsQIWrRoqYAWKQWHHPpetWoVfn5+FfVxInIX9u3bx4oVK8jJyQFsO17FxIzh1KkGFBY2YtGiEP1SKlLBKiyoBw8eXFEfJSJ3KC8vj9WrV7Nz50572/79rVm+fDg5Od5Uq1bApEmtqFFDz0aLVDQ9Ry1SxaWmprJo0SIuXrwIXL/jVfv2eSxe7EGTJmZWKVJ1VemgPnDgAGPHji12/NlnnzFy5EjzihKpIIZhsHXrVuLi4rBarQBcvhzAF188aN/x6te/LuCvf/XAXUt1i5imSgd1y5Yt7TPNMzMzady4MYMGDTK3KJEKkJWVxdKlSzl06FCxdoulkLQ0f2rWzOe//3UjKqpK/xMh4hD0t/B/li9fzoABA6hWrZrZpYiUqx9++IElS5aQmZkJQGGhCy4utp2v7r3Xh88+y6VzZz+Cg82sUkSKOPXMkISEBIYNG0ZwcDAWi4WlS5ded87cuXNp0qQJXl5ehIeHk5iYeMP3+uKLL4oNg4tUNlarlbi4OD755BN7SKem1mXu3Kc5fPgeevTowaRJkxg5UiEt4kicOqizsrIICwvj7bffvuHrCxcuZNq0abzyyivs2rWLXr16ERUVZX8+tEh6ejqbN28mOjq6IsoWqXCXL1/mww8/ZPPmzYBtx6ukpM588MFkLl0KJDn5QQYOHISr9qQUcTgVtuBJebNYLCxZsqTYRLAuXbrQsWNH3nnnHXtb69atGTlyJDNnzrS3ffLJJ6xZs4b//Oc/t/yM3NxccnNz7cfp6ek0aNBAC56IQ9u9ezcrV64kL8+2V/S1a94sXz7cvuPV0KEFLFjgRq1aZlYpUrXcyYInTt2jvpW8vDx27NhBZGRksfbIyEi2bNlSrK2kw94zZ87Ez8/P/tWgQYMyrVmkLOXm5rJ48WKWLFliD+mTJxvw3ntP8v33rXFzK2TWLIMVKxTSIo6s0gb1xYsXsVqtBAUFFWsPCgoiNTXVfpyWlkZSUlKJFmR5+eWXSUtLs3+dPHmyzOsWKQunTp3i3XffZc+ePfa2CxdqM3/+46Sl+dOkSQHffuvCtGkWLQUq4uAq/axvy8/+FTIMo1ibn58f586dK9F7eXp64unpWab1iZQlwzDYvHkzGzZsoLCwsNhrtWtfoF+/U9SpU5/33nNDd2tEnEOlDerAwEBcXV2L9Z4Bzp8/f10vW6QyyMjIYMmSJRw5csTeduRIE4KCzlGrViHDhg3jlVca4uaGetEiTqTSBrWHhwfh4eHExcUxatQoe3tcXBwjRowo1XvPmTOHOXPm2FdzEjHbgQMHWLZsGdnZ2YBtx6v4+H58/XVPQkNPsWmTLwEB/uYWKSJ3xamDOjMzk8OHD9uPjx49SnJyMgEBATRs2JDp06czbtw4IiIi6NatG/PmzePEiRNMnTq1VJ/7zDPP8Mwzz9hn7YmYpaCggLi4OLZu3Wpvu3rVj8WL7+fkyYYA9OgRQvXq6kKLOCunDurt27fTr18/+/H06dMBGD9+PB999BFjx47l0qVLzJgxg7NnzxIaGkpsbCyNGjUyq2SRMnPhwgUWLVpUbI7FT3e88vUt5N//dmHMGIW0iDOrNM9Rm+FOnoMTKSuGYbBz505Wr15Nfn4+cP2OV506FbJwoYt2vBJxUHeSH07dozaL7lGLWa5du8by5cvZv39/sXar1ZXDh5sD8MILBn/+s4t2vBKpJNSjLgX1qKUiHT9+nJiYGNLT0+1thmGbwR0UFESzZg9htdZkyBATixSRElGPWqQSKSwsZNOmTSQkJFD0e3VurgexsUOpX/8Uv/ylhcjISNzc9NdZpDLS32wRB3b16lUWL15cbCOZ1NS6fPnlA1y+XItDh9ry8ccuKKNFKi/99RZxUPv27WPFihXk5OQAtmHubds6s3ZtJFarG/XrF/LZZy4EBppcqIiUKwX1XdBkMilPeXl5rF69mp07d9rbfr7j1bBhBvPnu2gzDZEqQJPJSkGTyaSspaamsmjRIi5evGhvKyhwY+7cp7lyJQAPD4M337Tw7LNaBlTEmWkymYiTMQyDrVu3EhcXd91IjZtbAUOGnCEpyZ8vvnChY0eTihQRUyioRUyWlZXF0qVLOXTokL0tM7M6ubke1KuXydChQwkNvZdr1yxUr25ioSJiCgW1iIl++OEHlixZQmZm5k/a7mHJklH4+eWRlAT16wcAKKRFqigFtYgJrFYrGzZsYPPmzT9pc2Hjxv5s3twTgKZNDaxW3YgWqeoU1HdBs76lNC5fvsyiRYs4c+aMve3qVX9iYu7n1KkGADz1FPz97xa8vc2qUkQchWZ9l4JmfcudMAyDPXv2sHLlSvLy8uztKSmtWbHCtuOVn5/BBx9YGDPGxEJFpNxp1reIg8nNzeWrr77iu+++K9ZuGBaSkrqSk+NNly4Gn31m0Y5XIlKMglqknJ06dYqYmBiuXLly3Wu1a9di8WJvli2D3//eoh2vROQ6CmqRcmIYBps3b2bDhg0UFhba25OTw7h4MZAXXrjCkCFD8PDw0LPRInJTCmqRcpCRkcHixYs5evSova1ox6s9e8IA8PMDDw+zKhQRZ6GgFiljBw4cYNmyZWRnZ9vbzp6ty6JFth2vXFwMZsyw0LOniUWKiNNQUN8FPZ4lN1JQUMDatWtJSkqytxkGJCV1Ji7OtuNVSIhtwphCWkRKSo9nlYIez5IiFy5cYNGiRZw7d65Y+7JlI0hO7gDA8OHw73+jHa9E5I7yw6WCahKp1E6fPn1dSAP07ZuNh4fBP/8JS5cqpEXkzmnoW6QMhIWFcfjwYfbs2cfVqzUJCspgyJAhdOzYkenTLTRoYHaFIuKsFNQiZcBisRARcR+/+11nzp6tzTffZBMaaus+K6RFpDQ09C1SBtauhS5dvDh0qCGFhV6cPKkxbhEpGwpqkVLIz4eXXoLBg+H8eWjbFrZvtxAVZXZlIlJZaOhb5C4dPw4PPwzffGM7tu14hXa8EpEypaAWuUtvvmkLaT8/+PBDuP9+sysSkcpIQX0XtOCJAPz1r5CeDjNmQOPGZlcjIpWVFjwpBS14UrV8/z28955teNtFsztEpBS0H7VIGTIMWLAAnnkGsrOhSRN47jmzqxKRqkJBLXILGRnw9NPwn//YjgcMgAceMLcmEalaNIAnchO7dkF4uC2kXVzgz3+GNWugXj2zKxORqkQ9apEb+M9/YNIkyMuDkBD47DO045WImEI9apEbaNXKdm96+HDYvVshLSLmUY9a5H8uXIDatW1/joiApCQICwOLxdy6RKRqU49aqjyrFd54wzabOzn5x/b27RXSImI+BbVUaamptnW6f/97yMqCL74wuyIRkeI09C1V1tq1MG6cbTMNHx+YMwfGjze7KhGR4tSjvgtz5syhTZs2dOrUyexS5C78fMerdu1gxw6YMEFD3SLieLSEaCloCVHnNH8+TJxo+7N2vBIRM2gJUZFbGD8eVq+GBx/Ujlci4vg09C2VXk4OvP66bZ1usK0ytnChQlpEnIN61FKpff89jB0Le/bAqVO23a9ERJyJetRSKRkGfPSRba3uPXtsC5mMHm12VSIid049aql0fr7jVf/+tj9rMw0RcUbqUUulsm/fjzteubradrxau1YhLSLOSz1qqVT8/ODyZWjQAD79VJtpiIjzU1CL07t27cfnoENC4KuvoEULCAgwty4RkbKgoW9xal9/DS1bwrJlP7Z17aqQFpHKQ0EtTqlox6u+feHkSZg50zbTW0SkstHQtzids2dtm2msX287/sUvYO5crdMtIpWTglqcypo1tpC+cKH4jlcKaRGprBTU4jT27YOoKNsQd7t2tmVAW7UyuyoRkfKloL4Lc+bMYc6cOVitVrNLqVLuvRemTrX9WTteiUhVoW0uS0HbXJa/JUugSxcIDrYdFxbaNtUQEXFmd5If+idPHFJOjm0Z0NGjbZPFigYvFNIiUtVo6Fsczk93vALo3NnWk3Z1NbcuEREzKKjFYRgGLFgAzzxj2zu6dm345BMYPNjsykREzKOgFoeQmWmbKPbf/9qOBwywhbQ20xCRqk53/MQhWCywY4dtePuNN2zPSyukRUTUoxYTGYbty8UFqlWDL76A9HTo0cPsykREHId61GKKS5dg5Eh4660f29q2VUiLiPycgloq3NdfQ/v2sHw5/OlPttAWEZEbU1BLhfnpjlenTkHz5pCQALVqmV2ZiIjj0j1qqRA32/HK19fcukREHJ2CWsqdYfwY0j4+toB+7DHteCUiUhIKaikzV69CYiJs2mT737Vrwc/PFsidO9u2ptSOVyIid0abcpRCVd+U4/JlWygXfe3ebes9F/nqKxg61Pbns2ehZk3w8jKnVhERR3In+aEetZRYaqotaP39bcdffAFPPVX8nBYtoE8f21eXLj+2a/ESEZG7o6CWmzp16sfeckICHDgAc+bYdrUC2+ztNm1+DObevRXIIiJlTUEtxZw5A7//vS2cjxwp/prFAkeP/njcqhXs21ex9YmIVDUK6irKMOCHH2yB7OsLDz5oa69eHT7+2PbMs4sLdOjwY4+5Vy/bfWYREak4CuoqwjBs+zwnJPw4nH3mjO21Ll1+DOoaNWzLejZrZlvO08/PvJpFRERBXWWEhcF33xVv8/CwPTY1YEDx9ueeq7i6RETk1qp8UB89epSJEydy7tw5XF1d+fbbb6lWrZrZZd0xq9X2eFRRj/nwYdiz58dFRVq0gEOHoFu34rOyvb3NrVtERG6tyj9H3adPH/785z/Tq1cvLl++TI0aNXBzK9nvL2Y/R713L6xaZQvmr7+GtLTirx84YAtogHPnbI9VeXpWeJkiIvIzeo66hPbt24e7uzu9evUCICAgwOSKbi4vD7Zvt+065eNja5s/v/g2kb6+0LPnjz3mJk1+fC0oqELLFRGRMuLUu2clJCQwbNgwgoODsVgsLF269Lpz5s6dS5MmTfDy8iI8PJzExET7a4cOHaJ69eoMHz6cjh078pe//KUCq7+1nBxbT3nGDBg40NYb7tEDNm/+8ZzBg2H4cPj7320hfvkyxMbCiy9C167g7m5a+SIiUkacukedlZVFWFgYjz/+OPfff/91ry9cuJBp06Yxd+5cevTowXvvvUdUVBQpKSk0bNiQ/Px8EhMTSU5Opk6dOgwZMoROnToxaNAgE67G5ttvbUG7dSvk5hZ/LTDQtl52kchI25eIiFReTh3UUVFRREVF3fT1t956i0mTJjF58mQAZs+ezZo1a3jnnXeYOXMmISEhdOrUiQYNGgAQHR1NcnLyTYM6NzeX3J+kZ3p6ehlejY23t21CGEDduj+u+NWnD7RubXu2WUREqo5K+89+Xl4eO3bsIPJnXc7IyEi2bNkCQKdOnTh37hxXrlyhsLCQhIQEWrdufdP3nDlzJn5+fvavooAvS23bwgcf2CaCnTkDn39uW7Lz3nsV0iIiVVGl/af/4sWLWK1Wgn42iyooKIjU1FQA3Nzc+Mtf/kLv3r1p164dzZs357777rvpe7788sukpaXZv06ePFnmdbu4wKRJttna2q9ZRESceui7JCw/SzvDMIq13W74/Kc8PT3x1PNNIiJSgSptjzowMBBXV1d777nI+fPnr+tli4iIOKpKG9QeHh6Eh4cTFxdXrD0uLo7u3buX6r3nzJlDmzZt6NSpU6neR0RE5Haceug7MzOTw4cP24+PHj1KcnIyAQEBNGzYkOnTpzNu3DgiIiLo1q0b8+bN48SJE0ydOrVUn/vMM8/wzDPP2FeWERERKS9OHdTbt2+nX79+9uPp06cDMH78eD766CPGjh3LpUuXmDFjBmfPniU0NJTY2FgaNWpkVskiIiJ3pMqv9V0aZq/1LSIizulO8qPS3qMWERGpDBTUd0GTyUREpKJo6LsU0tLS8Pf35+TJkxr6FhGREktPT6dBgwZcvXr1tpOSnXoymdkyMjIAymUpURERqfwyMjJuG9TqUZdCYWEhZ86cwdfX97oV0BxJ0W9ulannr2tyDpXtmirb9YCuySyGYZCRkUFwcDAut9nIQT3qUnBxcSEkJMTsMkqsRo0aDvtDe7d0Tc6hsl1TZbse0DWZoaTrcGgymYiIiANTUIuIiDgwBXUV4OnpyR//+MdKtfOXrsk5VLZrqmzXA7omZ6DJZCIiIg5MPWoREREHpqAWERFxYApqERERB6agFhERcWAKahEREQemoBYREXFgCmoREREHpqAWERFxYApqERERB6agFhERcWAKahEREQemoBYREXFgbmYX4MwKCws5c+YMvr6+WCwWs8sREREnYRgGGRkZBAcH4+Jy6z6zgroUzpw5Q4MGDcwuQ0REnNTJkycJCQm55TkK6lLw9fUFbP+ha9SoYXI1IiLiLNLT02nQoIE9R25FQV0KRcPdNWrUUFCLiMgdK8ltU00mExERcWAKahEREQemoBYREXFgCmoREREHVqWD+sCBA7Rv397+5e3tzdKlS80uS0RExK5Kz/pu2bIlycnJAGRmZtK4cWMGDRpkblEiIiI/UaV71D+1fPlyBgwYQLVq1cwuRUREHJRhGBQUFFToZzp1UCckJDBs2DCCg4OxWCw3HLaeO3cuTZo0wcvLi/DwcBITE2/4Xl988QVjx44t54pFRMRZbd9+ij/+cQWrVq2q0M916qDOysoiLCyMt99++4avL1y4kGnTpvHKK6+wa9cuevXqRVRUFCdOnCh2Xnp6Ops3byY6OroiyhYRESeyf/85hg/fR7duQcyd253t25O5dOlShX2+Uwd1VFQUf/7znxk9evQNX3/rrbeYNGkSkydPpnXr1syePZsGDRrwzjvvFDtv2bJlDB48GC8vr1t+Xm5uLunp6cW+RESkcjp+/BIPP7yXDh38WLHiXgoK3PH2vkZmpg/x8fEVVodTB/Wt5OXlsWPHDiIjI4u1R0ZGsmXLlmJtJR32njlzJn5+fvYvbcghIlL5nDuXxpQp33HvvV58/nkoubleBAWl8vDDnzJx4of4+mZw8uRJ8vLyKqSeSjvr++LFi1itVoKCgoq1BwUFkZqaaj9OS0sjKSmJmJiY277nyy+/zPTp0+3HRYuqi4iI88vKyiIxMZHPP7/AggXjAAgIuETfvhsJDd2HxWJQrVo1evfuTXh4OG5uFROhlTaoi/x8wXPDMIq1+fn5ce7cuRK9l6enJ56enmVan4iImCs7O4fPP08mNXUDeXl5NGoEbdvuoVGjY7Rvn4yrayFeXl706NGDLl264OHhUaH1VdqgDgwMxNXVtVjvGeD8+fPX9bJFRKTqyc3NY/bsQ8yeXZsrV9rz3HOb8PEBiwVGj14MgLu7O1279qB79+54e3ubUmelDWoPDw/Cw8OJi4tj1KhR9va4uDhGjBhhYmUiImImq9XKe+8d5K9/9ePEiXsB8PTMITW1Hk2bHgHA1dWViIgIevXqRfXq1c0s17mDOjMzk8OHD9uPjx49SnJyMgEBATRs2JDp06czbtw4IiIi6NatG/PmzePEiRNMnTrVxKpFRMQMhYWF/Pe/h/nznz05eLA1AG5u+XTp8i09emzB2/saFouFsLAw+vbti7+/v7kF/49TB/X27dvp16+f/bhootf48eP56KOPGDt2LJcuXWLGjBmcPXuW0NBQYmNjadSokVkli4hIBSssLGTfvn2sWJHEq69OoLDQFRcXK+Hh2+nVKxFf30wA2rRpQ79+/ahdu7bJFRdnMQzDMLsIZ5Weno6fnx9paWnUqFHD7HJEROQnDMMgIeEge/eu48KFCwCsXBlNQYE7ffpswt//KgDNmjWjf//+BAcHV1htd5IfTt2jFhER+TnDMNiw4Qi//30+SUnNeeKJDRTNIY6OjqXowZ+GDRsyYMAAhx9lVVCLiEilYBgGX399hFdfzSExsRWFha4AHDzYnKAg22O4FgvUq1eP/v3706xZs+se4XVECmoREXFqhmGwbdtRfv/7TDZubE1BgTsATZv+QL9+GwgJOQ3YFrzq168fLVu2dIqALqKgFhERp2QYBkePHmX9+o28+OIorlxpCkCDBifo3389jRsfB6B27dr069eP1q1bO1VAF1FQi4iI00lJOc4332zg5ElbGHfqlMR337Wjf/8N3HPPYSwW28JXffv2pU2bNri4OO/WFgpqERFxGqdPn+bVV4/zxRftiI6uRps2tvYuXZLo2vVbLBYICAigb9++hIaGOnVAF1FQi4iIUzh+/Dh/+Usc8+dPBiA5uT1t2qQA4OJSSM2aNenTpw/t2rWrFAFdREEtIiJOIS4ujoMHf7wPPXbs54Btc6U+ffoQFhaGq6urmSWWCwW1iIg4tJMnTxIfH8/hwxc4cCAagNatU3B1LWTo0KF06NChwracNEPlvTIREXFahmFw/PhxNm3axJEjR9m0qQ9bt44hJ8cbMGja9Ci/+c1vTN8woyIoqEVExGEYhsEPP/xAQkICJ06cAGyLlKSm1iMnx5vatc8zcGAc0dEhVSKkQUEtIiIOwDAMDh48SEJCAt9/n8Y333SnS5c0/PzSAOjXbyNt2+4hIuIEPXt2o1u3biZXXHEU1CIiYhrDMEhJSSEhIYGDB6+xeXMPdu4Mx2p1Iz/fjaFDYwFo1iyb8eM7EB4+Cnd3d5OrrlgKahERqXCFhYXs3buXxMREDh7M5+uve5Kc3MG+PndIyElatTqAn58fPXv2rPQTxm6lal61iIiYwmq1snv3br7++msuX77MqlVRbNvWCcOwPffcuPFRevdOoGPHNHr37kW7du0q5SNXd0JBLSIi5a6goIBdu3bx9ddfk5aWZm93c8vHMFy4557D9O6dQHj4NXr16lVpVhUrCwpqEREpN/n5+ezYsYPNmzdz6FA1EhIG07lzEo0bHwOge/cttG69n4gIK71793bajTPKk4JaRETKXF5eHklJSXzzzTccPOhHQsJ9HDzYEoCcHC97UDdv7k+fPr1p0aKFAvomFNQiIlJmigJ6y5YtfP99LRISRvHDD80AsFgKuffeffTqlUDDhg3p3bs399xzjwL6NhTUIiJSaj8N6OzsbJYtG05yckfAFtDt2u2hV69EIiJq0KfPUBo1aqSALiEFtYiI3LWfBnRWVjZF2duw4Qn27Amjfftkevb8ms6dA+ndeyQNGjQwt2AnpKAWEZE7ZhgGO3fuJD4+nu+/9yU+fhStWn1PePgOAMLC9tCkyVG6dKlLnz4PEBwcbHLFzktBLSIid6SgoIBFixbx7bdnWbduMPv2hQJw6VItOnbcicVi0Lp1c/r06aOALgMKahERKbHc3Fw+++wzdu1K48MPp5CVVR0waNduD336xNOqVQsFdBlTUIuISIlkZ2fzySefcPhwGv/5zySysqpTp845Ro1aQs+e1enfX0Pc5UFBLSIit5WZmcmCBQtITb3Mp59O5PLlWvj5XWXixIVMnBhF8+bNzS6x0qryQe3m5kZoqO3+SkREBB988IHJFYmIOJbs7Gw+/vhjLly4gKsrtGmzjytXajJ5cgy/+tWD1K1b1+wSKzWLYRiG2UWYKTAwkIsXL97V96anp+Pn50daWho1atQo48pERMyXm5vLxx9/zOnTp4u1u7rW5ZlnHiQgIMCkypzbneSHVjwXEZEbKronvWtXPp9++gg5OZ4ABAQE8NxzDyukK4hTB3VCQgLDhg0jODgYi8XC0qVLrztn7ty5NGnSBC8vL8LDw0lMTCz2enp6OuHh4fTs2ZNNmzZVUOUiIo7t5MmTvPfeeyQkuPPvf0/k0KEWxMUNokaNGjz22GP4+fmZXWKV4dRBnZWVRVhYGG+//fYNX1+4cCHTpk3jlVdeYdeuXfTq1YuoqChOnDhhP+fYsWPs2LGDd999l8cee4z09PSKKl9ExOEYhsHmzZv58MP5fPVVO/7zn3Hk5nrRsOFxhg37hsceewx/f3+zy6xSKs09aovFwpIlSxg5cqS9rUuXLnTs2JF33nnH3ta6dWtGjhzJzJkzr3uPqKgoXn/9dSIiIkr0mbpHLSKVSWZmJsuWLSM5+QxLlvy4mUZYWDIPP7yJSZMeJTAw0OQqK4c7yY9KO+s7Ly+PHTt28NJLLxVrj4yMZMuWLQBcuXIFHx8fPD09OXXqFCkpKTRt2vSm75mbm0tubq79WL1vEaks9u3bx1dffcXRozX49NMnyciogZtbPtHRKxkx4gpjxjyuDolJKm1QX7x4EavVSlBQULH2oKAgUlNTAdi/fz9PPvkkLi4uWCwW/vGPf9xycsTMmTN57bXXyrVuEZGKlJ2dTWxsLHv37gXAz882yBoYeIEHHviSMWNa0bfvcFxcnPpOqVOrtEFd5OfbqBmGYW/r3r073333XYnf6+WXX2b69On24/T0dO0EIyJOyTAM9uzZw5o1azh2rDq1a4PFAt7eOTz66H8JCcnl4YeH33KUUSpGpQ3qwMBAXF1d7b3nIufPn7+ul11Snp6eeHp6lkV5IiKmuXTpEl999RUHDpxm48Z+fPttV0aMWEb79skA9OtXi6FDh1KtWjVzCxWgEge1h4cH4eHhxMXFMWrUKHt7XFwcI0aMMLEyERFzFBQU8PXXX5OYmMj+/fcQG/s0aWn+AJw9W5du3byJjo4mNDT0utFIMY9TB3VmZiaHDx+2Hx89epTk5GQCAgJo2LAh06dPZ9y4cURERNCtWzfmzZvHiRMnmDp1qolVi4hUvGPHjv1vslguq1ePJiXlXgD8/a8wdOhK7rvPjaFDn8bX19fkSuXnnDqot2/fTr9+/ezHRfePx48fz0cffcTYsWO5dOkSM2bM4OzZs4SGhhIbG0ujRo3MKllEpEJlZmYSFxfH7t27SUlpzfLlI8jN9cJiKaRbty0MH76LkSMjadmypdmlyk1UmueozaDnqEXEUVmtVrZu3cqmTZvsj5WeOhXChx9Opn79Uwwb9hUjRzahX79+eHh4mFxt1aPnqEVEqrDDhw+zevVqjh3L4dSpJrRq9T0AISGnGDduAT165DN8+Ajq1atncqVSEgpqEZFK4vLly6xZs4aUlEMkJXUmPr4fVqsrzzwzh5o1r+Dp6ckvf9maiIgIPRftRBTUIiJOLi8vj8TERLZs2cKhQw1ZvXoqFy7UASA4+DT5+W6EhYUxcOBATRZzQgpqEREnZRgG3333HXFxcZw86UJc3I+zuX18shgwYB3R0ee4774RhISEmFyt3C0FtYiIEzp79iyrVq3ixIkT5Oe7M2/er7l2zQeLpZBOnbYRHb2V4cN70b79cD0T7eQU1CIiTiQrK4v169ezc+cuwPbQjrt7Pp07J3H8eCOiotYyYkRj+vR5Ai8vL3OLlTKhoBYRcQI/fdzq8OHarFkziUGD4mjU6DgAvXsn0KxZE6KiRlO7dm2Tq5WypKAWEXFghmFw6NAh1qxZw5Ej+axbdx9797YFYOPGfkyY8BE1a9ZkyJAhtGjRQsPclZCCWkTEQV24cIE1a9awf/9xNm/uwebNPSgocAcMOnTYxeDBiQwYMIBu3brh5qZ/zisr/T8rIuJgrl27xqZNm0hKSiIlpTmrVv2S9HQ/ABo1OsbgwasZMiSIgQMn6nGrKkBBLSLiIAoLC9mxYwcbN24kOzsbgLw8T9LT/fD3v8KgQWsZNCiDqKj79LhVFaKgFhFxAEePHmX16tUcOnSNK1dq2yeJtW37HQUFrnTvfpSoqH60a9dO96GrGAW1iIiJrly5wtq1a9mz5zBbtnRn8+YeeHrm8uyz/8LDIw93d1emTfOlZ8+ntXlGFaWgFhExwY/Lfn7D7t2tiIv78T503bqpZGf70L59MwYNGkTNmjVNrlbMpKAWEalAhmGwZ88e1q1bx4ED1Vm9+jFOnmwIgJ/fVQYNiqNfv4tERY2gSZMmJlcrjkBBLSJSQU6dOsWqVas4ffo0V67U5IMPpmAYLri759Gz59cMGJDM4MG96djxfu1uJXYKahGRcpaens769etJTt5N0TywmjWvEBq6F4BBg9YzaFBr+vR5Cm9vbxMrFUekoBYRKSf5+fl88803JCQksndvUzZufIqHH/4Uf/80AEaOXEqLFk0ZPPgXWvZTbkpBLSJSxgzDYP/+/axdu5aDBz1Yvfphjh5tCkBiYm+GDVtBrVq1GDx4MM2bN9fjVnJLCmoRkTKUmprK6tWrSUk5R3x8P7Zvj8AwXHB1LaBbty0MGJDE4MGD6dy5M66urmaXK05AQS0iUgaysrLYuHEjO3bsYPv2jqxbN5acHNv95tatU4iMjGPAgKb07/8U1apVM7lacSYKahGRUrBarWzbto34+HhycnIASE+vQU6ON0FBqQwZspo+fQyGDHmQevXqmVytOCMFtYjIXfrhhx9YtWoVBw4YFBT4ERRkC+oePb7G3/8qvXsfZciQQbRp00b3oeWuKahFRO7Q1atXWbNmDbt2HSEhoQ9bt3YhKOgcU6a8j8ViUK0aTJ/uT/fuz+Du7m52ueLkFNQiIiWUn5/Pli1b2LTpa7Zta8uGDc+RnW2731y9eiY5OZ507tycgQMH4ufnZ3K1UlkoqEVEbsMwDA4cOMDq1avZvduP1asnkppqu99cq9ZFBg9eTZ8+2QwZ8ggNGzY0uVqpbBTUIiK3cPHiRVavXs3hw4c5dqwxCxZMAMDL6xp9+sTTp88+IiP70aFDBy37KeWiSgd1RkYG/fv3Jz8/H6vVynPPPceUKVPMLktEHEBubi4JCQl8++23WK1WABo1Ok5IyEnq1k2lX7+N9O0bSr9+z2jZTylXFsMwjNK+SX5+PqmpqWRnZ1O7dm0CAgLKorZyZ7Vayc3NxcfHh+zsbEJDQ9m2bRu1atUq0fenp6fj5+dHWloaNWrUKOdqRaQiGIbB3r17WbNmLUlJDfj226784hef4OGRD4DV6kKTJiFER0dTt25dk6sVZ3Un+XHXPerMzEz++9//8tlnn5GUlERubq79tZCQECIjI3niiSfo1KnT3X5EuXN1dcXHxweAnJwcrFYrZfB7i4g4qXPnzhEbG8u2bddYvXqUfdnPrVu70KvX1/j6+hIZGUloaKget5IKc1c3VGbNmkXjxo15//336d+/P4sXLyY5OZkDBw7wzTff8Mc//pGCggIGDRrEkCFDOHToUFnXDUBCQgLDhg0jODgYi8XC0qVLrztn7ty5NGnSBC8vL8LDw0lMTCz2+tWrVwkLCyMkJIQXXniBwMDAcqlVRBzXtWvXiI2NZdas+bz7bmvefXcqR482xc0tnz594unefRs9evTgl7/8JW3btlVIS4W6qx71li1b2LhxI23btr3h6507d2bixIm8++67fPjhh2zatInmzZuXqtAbycrKIiwsjMcff5z777//utcXLlzItGnTmDt3Lj169OC9994jKiqKlJQU+8xMf39/du/ezblz5xg9ejRjxowhKCiozGsVEcdTWFjIrl27WLduPZs3t2T9+mftj1vZlv1cS3h4LaKipuiXeDFNmdyjdgQWi4UlS5YwcuRIe1uXLl3o2LEj77zzjr2tdevWjBw5kpkzZ173Hk899RT9+/fngQceuOFn5ObmFhviT09Pp0GDBrpHLeKETp06RWxsLGfOnAFg0aIx7NsXSmDgBYYMWUV4+GWGDBlCy5Yt1YOWMleu96ivXLmCYRgEBARw4cIFEhISaNmyJaGhoXddcHnIy8tjx44dvPTSS8XaIyMj2bJlC2C7H+Xt7U2NGjVIT08nISGBp5566qbvOXPmTF577bVyrVtEyldmZibr168nIeEQFgtUr25rHzQojvr1T9G9+0769u1B9+4Pa1UxcQh3dI/6gw8+ICIigvDwcN555x1GjRrF+vXreeihh5g3b1551XhXLl68iNVqvW4YOygoiNTUVMD2G3Xv3r0JCwujZ8+e/PKXv6Rdu3Y3fc+XX36ZtLQ0+9fJkyfL9RpEpOxYrVa+/fZbZs2aw5w53rz99rOsWTPY/rqfXxoTJ6bxq189TZ8+fRTS4jDuqEf9r3/9i3379pGdnU3Dhg05evQotWvXJj09nd69e/PEE0+UV5137edDVoZh2NvCw8NJTk4u8Xt5enri6elZluWJSAU4fvw4K1euZMsWX1avnsSlS7b7zVeu1CQ/34169fyJiorinnvuMblSkevdUVC7urri5eWFl5cXzZo1o3bt2gDUqFHD4e7hBAYG4urqau89Fzl//rwmi4lUERkZGcTFxbFp00nWrBnMgQOtAKhWLZMBA9bRufN++vXrT5cuXXB1dTW5WpEbu6OgdnNzIycnBy8vLzZt2mRvz8jIKPPCSsvDw4Pw8HDi4uIYNWqUvT0uLo4RI0aYWJmIlDer1UpSUhLx8fGkpNTns8+ewWp1w8XFSufOSfTpE0+XLq0YOPCX+Pr6ml2uyC3dUVBv2LDBPvT7051hrl27xocffli2lZVAZmYmhw8fth8fPXqU5ORkAgICaNiwIdOnT2fcuHFERETQrVs35s2bx4kTJ5g6dWqF1yoiFePYsWPExsZy/vx5ABo0OIWPTza1a19gyJDVtG3rSnT0o9o8Q5xGqR7PSk1NNXUJvfj4ePr163dd+/jx4/noo48A24In//d//8fZs2cJDQ1l1qxZ9O7du0w+X0uIijiOjIwM1q5dy7p159m5syNDhqzGYrH985ae7kvt2vkMGNCfiIgIbZ4hpruT/ChVULdr1449e/bc7bc7PQW1iPmKhrljY79lzZrubN/eCcNwYcSIJbRvvxuADh06MHDgQKpVq2ZytSI2FbLWN6B1sUXEVMeOHeOrr2JZuzaE9eufsK8q1qbNPho3PkbdunUZOnQoDRo0MLlSkbtXqqB2tJneIlI1FA1zr159idjYEZw5Ux+A2rXPExW1itatzzJgwADCw8M1zC1Or0rvRy0izqVomHvjxo3k5uaxcuUTnD0bjKdnDn37xtOpUxIREWEMHDhGw9xSaSioRcQpHDt2jBUrVnH+/EVcXa1YLBAVFcuOHREMHLiOZs2qM3To4xrmlkqnVEHt4eFRVnWIiNxQ0TD3ypVXiY0dRatW39O3bzxge/SqefOLGuaWSq1UQb19+/ayqkNEpJiiYe7ly5OIje3Lnj1hAGRlVaNnz69xcyvQbG6pEjT0LSIOxzbMvZqvvmpMfPxU8vI8AYMOHXYyYMB6QkICNZtbqowyCeqcnBz27NnD+fPnKSwsLPba8OHDy+IjRKQKKBrmjou7wOLFo7lwoQ4AwcGniY6O5Z57LmmYW6qcUgf16tWreeyxx7h48eJ1r1ksFqxWa2k/QkQquZ/O5s7Ly8PT058rV2ri7Z3NwIHr6NBhFx07tmfgwEc0zC1VTqlWJgNo1qwZgwcP5g9/+EOV25VKK5OJlN6xY8dYvnw1337rS4sWh+zthw41JyTkFE2b+hMdHa1hbqlUKmxlMrBtGzl9+vQqF9IiUjpFw9xLl2axatUYLl0KZOLED2nQ4CQAbdue1DC3CGUQ1GPGjCE+Pl4brotIiVitVrZu3cqSJTtZubI/+/e3AWx7RF+75g1obW6Rnyr10Hd2djYPPPAAtWvXpm3btri7uxd7/bnnnitVgY5MQ98id+bYsWMsXbqa5ctbkJjYi4ICdyyWQjp33krfvvE0aRKgYW6pEip06PvTTz9lzZo1eHt7Ex8fX2z9b4vFUqmDWkRKpmiYe8+e75g/fyInT9r2gm7U6BhRUbE0apTOgAEDNcwtcgOlDurf//73zJgxg5deekl/wUSkmKJh7vj4ePLy8rBYoGPHHVy96k9k5FruvXcvHTt2YODA8RrmFrmJUgd1Xl4eY8eOVUiLSDHHjh1jyZI1LF3akjp1mnHvvSkAhIXtoU2b/TRqVIvo6Eka5ha5jVIH9fjx41m4cCG/+93vyqIeEXFyGRkZrFmzli+/zGfNmrGkpfnj65tO8+aH8PDIx9vbk/vu02xukZIqdVBbrVb+7//+jzVr1tCuXbvrJpO99dZbpf0IEXECRYuWfPnlHpYvH8APPzQDoEaNNAYPXo27e75mc4vchVIH9XfffUeHDh0A2Lt3b7HXfjqxTEQqr+PHjxMTs4bFi9vwzTeTKSx0xdW1gO7dt9CzZyKNGgVqmFvkLpU6qDdu3FgWdYiIE8rMzPzfbO49HD/eiM2bewLQvPlBhgxZTXBwNgMGRGqYW6QUtHuWiNyxwsJCtm3bRmzsZiyWdAAaNTpO9+6badjwOC1aHPzfbG4Nc4uUVqmDeubMmQQFBTFx4sRi7f/+97+5cOECL774Ymk/QkQcyIkTJ4iJieOLL9qwa9fTPPXUO/j5pQEwaFAcdevWZehQDXOLlJVSB/V7773Hp59+el37vffey0MPPaSgFqkksrKyWLs2jk8+sRIX9yCZmb4A7NvXhu7dv8HT05P+/fvTqVMnDXOLlKFSB3Vqair16tW7rr127dqcPXu2tG8vIiYrLCxk+/btfPrpdyxdOoDjxxsDEBBwiaioVTRrdpiwsDAGDRpE9erVzS1WpBIqdVA3aNCAzZs306RJk2LtmzdvJjg4uLRvLyImOnXqFCtXruSTT+5ly5bHMQwX3Nzy6d17E926fUP9+oFERz9Oo0aNzC5VpNIqdVBPnjyZadOmkZ+fT//+/QFYv349L7zwAs8//3ypCxSRipednc26devYuXMnABZLGwzDhdatUxg8eA116uTQr98gOnfurGFukXJW6qB+4YUXuHz5Mk8//TR5eXkAeHl58eKLL/Lyyy+XukARqTiFhYXs3LmT//53N7m5udSpY2vv1SuBxo2Pcs89R2jbti2RkZH4+vqaW6xIFVHqbS6LZGZmsn//fry9vWnevDmenp5l8bblbtSoUcTHxzNgwAAWLVp0R9+rbS6lMjl9+jSLFq3ls89akZTUheDgM0ya9CEWi+2fiNq1azN06FAaN25sbqEilUCFbnNZpHr16nTq1Kms3q7CPPfcc0ycOJEFCxaYXYqIKbKzs1m/fgPz5+exdu0DZGXZJoTVqJFOXp4Hvr4Gffv2pUuXLri6uppcrUjVU+UXPOnXrx/x8fFmlyFS4QzDYNeuXXzyyW4WL+7PiRO2CWG1al0kKmoV99zzA6GhoURGRmrESMRETj0LJCEhgWHDhhEcHIzFYmHp0qXXnTN37lyaNGmCl5cX4eHhJCYmVnyhIg7m7NmzfPjhh8yZs5N//GM8J040wt09j/791zF16jt06ZLGY489xpgxYxTSIiZz6h51VlYWYWFhPP7449x///3Xvb5w4UKmTZvG3Llz6dGjB++99x5RUVGkpKTQsGFDEyoWMde1a9fYsGED27dvxzAM6te3UK/eWfz80hg8eA2Bgdn06dOPbt26aZhbxEE4dVBHRUURFRV109ffeustJk2axOTJkwGYPXs2a9as4Z133mHmzJl3/Hm5ubnk5ubaj9PT0++8aBETGIbB7t27+fjjZOLiwhk+PBl393xcXAzGj1+Ah0cebdq0YfDgwfj5+Zldroj8RKmDevr06Tdst1gseHl50axZM0aMGEFAQEBpP+qO5OXlsWPHDl566aVi7ZGRkWzZsuWu3nPmzJm89tprZVGeSIVJTU3lyy/j+M9/mrNt22MYhgsBAZfp18+28129er5ER0dzzz33mFypiNxIqYN6165d7Ny5E6vVSsuWLTEMg0OHDuHq6kqrVq2YO3cuzz//PF9//TVt2rQpi5pL5OLFi1itVoKCgoq1BwUFkZqaaj8ePHgwO3fuJCsri5CQEJYsWXLT2esvv/xysV9M0tPTtfGAOKycnBw2bNjI++/nEBc3yj6bu02bfXTsuAN3d3d69+5Nt27dcHNz6sE1kUqt1H87i3rL8+fPt086SU9PZ9KkSfTs2ZMpU6bwyCOP8Otf/5o1a9aUuuA7ZbFYih0bhlGs7U5q8vT0dJrnw6XqMgyDPXv2sGBBMosW9ePkSdt8jFq1LhIdHUvTpkdo1aoVQ4YMwd/f39xiReS2Sh3Ub775JnFxccVmhtaoUYM//elPREZG8qtf/Yo//OEPREZGlvaj7khgYCCurq7Fes8A58+fv66XLVJZnDt3jtjYWI4fP86aNQ9w8mRD3N3z/rc297fUru1HVNSjNG/e3OxSRaSESh3UaWlpnD9//rph7QsXLtgnW/n7+9uXF60oHh4ehIeHExcXx6hRo+ztcXFxjBgxokJrESlvubm5bNgQz+bNO3Fzs014jIxci6urlQED1lGrVja9evWmR48eGuYWcTJlMvQ9ceJE/v73v9OpUycsFgtJSUn85je/YeTIkQAkJSXRokWL0n7UdTIzMzl8+LD9+OjRoyQnJxMQEEDDhg2ZPn0648aNIyIigm7dujFv3jxOnDjB1KlTy7wWETMYhsHevXuZP38nixb1w9+/HqNHLwbAzy+N0aMX07JlS4YMGULNmjVNrlZE7kapg/q9997j17/+NQ899BAFBQW2N3VzY/z48cyaNQuAVq1a8cEHH5T2o66zfft2+vXrZz8umug1fvx4PvroI8aOHculS5eYMWMGZ8+eJTQ0lNjYWG3JJ5XC+fPnWbRoHQsW3MO2beMwDBdSU+uSkVEdX99M/P39iYqKomXLlmaXKiKlUKabchw5cgTDMLjnnnuqxAby2pRDzJCbm8vGjZuYNy+btWsH2mdz33vvXiIj1xIQkE2PHj3o2bMn7u7uJlcrIjdS7ptynDhx4rqVvapXr067du1ueP7p06epX7/+3XyUiPyPYRikpKTw2Wff8PHHkfbZ3IGBF4iKWkXTpkdo3rw5UVFRFb5ugYiUn7ta67tTp05MmTKFpKSkm56TlpbG+++/T2hoKIsXL77rAkXEti7AJ598wpdffonVeoErV2ri7p7HwIFrmTr1XTp0uMRDDz3EI488opAWqWTuqke9f/9+/vKXvzBkyBDc3d2JiIggODgYLy8vrly5QkpKCvv27SMiIoI333zzlst8isjN5eXlER+fwIIFl2jW7AgWC3h65jJmzJfUrHmVmjWz6NGjB7169dIwt0glVap71Dk5OcTGxpKYmMixY8e4du0agYGBdOjQgcGDBxMaGlqWtToc3aOW8mIYBvv37+fDD3fyxRd9OHWqASNHLiYsbI/9nHvuuYfo6Ghq1aplYqUicjfK/R51ES8vL0aPHs3o0aNL8zYi8hOXLl3iyy/X8e9/N2H79kcwDBfc3fMoKLD1mGvUqMGQIUNo3br1dSvviUjlo5UPRBxEfn4+mzYlMmdOJmvX3kd2djXgx9nc/v6ZdO/ek969e+Ph4WFytSJSURTUIiYzDIMDBw6wevVqPv64F7t29Qdss7mjo2Np0uQoTZo0ITp6HLVr1za5WhGpaApqERNdvnyZVatWcejQIQDat9/Nvn2h9OkTT5cuW/H3r8bgwWO49957NcwtUkWV2YInVZEmk8ndys/PJyHha+bMyeTaNTe6dNlqfy0nxxMfn3y6du1Knz59tGObSCVUYZPJihw9epRNmzbh6elJx44dtWShyC0cPHiQefO2s3BhL06daoCbWz4tW36Pv38aAK1a1SM6Opo6deqYXKmIOIJSB/Xs2bN5/vnnqV69Om5ubly5coXw8HA++OADwsLCyqJGkUrhypUrLFq0nnnzGrJ9+8OABQ+PXPr0icfXN4Pq1avbH2vUMLeIFCl1UL/xxhu8/PLLzJgxAxcXF44ePcq//vUvunfvzpo1a+jZs2dZ1CnitAoKCkhM3Mw//5nO2rVR9tncoaHfERm5Fj+/LLp06ULfvn01zC0i1yl1UGdmZjJhwgRcXGyrkTZp0oS33nqLgIAAnn/+ebZu3XqbdxCpvA4dOsSqVas4csTKihXPYrW6Ubv2eaKjY2nc+BgNGzZk6NBfEBQUZHapIuKgSh3U7dq145tvvqFZs2bF2seOHcsbb7xR2rcXcUpXr15l+fI4jhzZB4C/P/TtuxFX10I6d95KjRreREaOol27dhrmFpFbKnVQ//3vf2f06NF4eHjw4IMP2v/R+eabb2jevHmpCxRxJgUFBWze/A2zZ6exdm00Dz+cRkjIKQB69tyMxWKhc+fO9OvXDy8vL5OrFRFnUOqg7tmzJx999BFTp07l2WefJSwsjLy8PPbt28d//vOfsqhRxCn88MMPvPvuNj77rCenT4cAkJTU2R7UDRo0YOjQodStW9fMMkXEyZTZc9S5ubmsX7+e+Ph4du3aRXJyMpcvXyYgIIC2bdvSrl07Zs+eXRYf5TD0HLUUWbduJy+/bGX79giKZnP37buRzp2T8PX1YtCgQbRv317D3CICmPAcNYCnpyfR0dFER0fb206ePElycjK7du1i165dZfVRIg7l88/hl7/swKVLthBu23YPgwatpUaNLCIiIujfvz/e3t4mVykizqpclxBt0KABDRo0YNiwYeX5MSKmys6GS5csNG2aTa9eC2nc+Dj169dn6NBHCA4ONrs8EXFyWutb5A5dugRHjkCnTrbjCRPA3R0efNCLL75w4957h9OhQwcNc4tImdBa36Wge9RVS2EhfPghvPwyeHnB999D9erFzzEMQwEtIrd1J/nhUkE1iTi17duhWzd44glbj7pmTThz5vrzFNIiUtYU1CK3cOkSTJ0KnTtDUhL4+sKsWbBzJ7RoYXZ1IlIV6B61yE2cOwf33msLa4Bf/AL+7/+gXj1z6xKRqkVBLXITQUEwYADs3w9vvw29e5tdkYhURRr6FvmfS5fg2Wfh9Okf2957zzbMrZAWEbNU+aAeNWoUNWvWZMyYMWaXIiaxWmHePNs957ffht/85sfX/P3BTeNOImKiKh/Uzz33HB9//LHZZYhJtm2Drl3hySfh8mVo2xaeftrsqkREflTlg7pfv374+vqaXYZUsIsXbY9adelie/SqRg2YPds2zN2rl9nViYj8yKGDOiEhgWHDhhEcHIzFYmHp0qXXnTN37lyaNGmCl5cX4eHhJCYmVnyh4nT++U94/30wDBg3Dg4cgF/9SsPcIuJ4HPqfpaysLMLCwnj88ce5//77r3t94cKFTJs2jblz59KjRw/ee+89oqKiSElJoWHDhgCEh4eTm5t73feuXbtW6zBXMXl54OFh+/MLL9iGvX/3O/WgRcSxOXRQR0VFERUVddPX33rrLSZNmsTkyZMBmD17NmvWrOGdd95h5syZAOzYsaPM6snNzS0W+unp6WX23lJ+Ll60BXJKCiQkgIuLbenPVavMrkxE5PYceuj7VvLy8tixYweRkZHF2iMjI9myZUu5fObMmTPx8/OzfzVo0KBcPkfKRtFs7pYtbcPcmzeD7oyIiLNx2qC+ePEiVquVoKCgYu1BQUGkpqaW+H0GDx7MAw88QGxsLCEhIWzbtu2m57788sukpaXZv06ePHnX9Uv5+vls7nbtbCHdp4/ZlYmI3BmHHvouiZ9vgnCnuxetWbOmxOd6enri6elZ4vOl4mVlwfTpP04Uq1EDXn/d9siVJoqJiDNy2n+6AgMDcXV1va73fP78+et62VJ1eHnZetOGAY89Bn/9K9Sta3ZVIiJ3z2mHvj08PAgPDycuLq5Ye1xcHN27dzepKjHD9u1w7Zrtz66u8MEHtkljCxYopEXE+Tl0UGdmZpKcnExycjIAR48eJTk5mRMnTgAwffp0PvjgA/7973+zf/9+fv3rX3PixAmmTp1qYtVSUYoWLenc2dZzLtKxox65EpHKw6GHvrdv306/fv3sx9OnTwdg/PjxfPTRR4wdO5ZLly4xY8YMzp49S2hoKLGxsTRq1MiskqUCWK3w4Yfw8su2iWIAZ87YhrvvYHqCiIhTsBiGYZhdhLNKT0/Hz8+PtLQ0atSoYXY5VcK2bbaJYdu3247btoU5c9SDFhHncif54dBD3yI/9d57WptbRKoehx76FvmpwYNts7rHjIH/+z9NFBORqkFBLQ5r2zZYt852LxqgcWM4dAjq1ze1LBGRCqWhb3E4ly7ZVhTr0sW2RvfmzT++ppAWkapGPWpxGIWFtmegfzqb+xe/gKZNza1LRMRMCmpxCNu2wTPP2P4XbLO5334bevc2ty4REbMpqMV0ubkwYgScPWubzT1jhi20tTa3iIiCWkxSWGhbnMRiAU9P28picXGazS0i8nOaTCYVrmgLys8//7Ft3Dj4+GOFtIjIzymopcL8dDb3tm22Ie7CQrOrEhFxbApqKXeFhTBvHrRoYftfw7D1oDduBBf9BIqI3JLuUUu5Sk21hfK6dbZjzeYWEbkz6s9Iudq50xbSPj4wa5btWCEtIlJy6lFLuYqOtgX04MHQurXZ1YiIOB/1qKVMnTgBw4bZ/rfItGkKaRGRu6UetZSZZcvg8cfhyhWwWiE21uyKREScn3rUUmq5ufDcczBypC2kO3WyTRgTEZHSU1BLqRw6BN26wb/+ZTt+/nn4+mttpCEiUlY09C13betWGDgQMjOhVi1YsACGDjW7KhGRykVBLXctLMzWc/b3h08/1V7RIiLlQUEtd+TQIVs4u7qClxesWQOBgdrpSkSkvOgetZSIYdiW/2zXDv7ylx/b69ZVSIuIlCf9Eyu3lZYGTzwBX3xhO05Ksq3frXW6RUTKn/6plVvatg06drSFtJsbvPmm7XlphbSISMVQj1puyDBg9mx48UXIz4fGjW37R3fpYnZlIiJVi/pFckNHjsDLL9tC+v77YdcuhbSIiBnUo5Ybuuce2+pieXnw1FNgsZhdkYhI1VSle9QZGRl06tSJ9u3b07ZtW95//32zSzKN1Qp//jN8++2PbZMnw9NPK6RFRMxkMQzDMLsIs1itVnJzc/Hx8SE7O5vQ0FC2bdtGrVq1SvT96enp+Pn5kZaWRo0aNcq52vJz9iz84hewYYPtXvTevVCtmtlViYhUXneSH1W6R+3q6oqPjw8AOTk5WK1WqtrvLWvW2FYY27DBFs6vvaaQFhFxJA4d1AkJCQwbNozg4GAsFgtLly697py5c+fSpEkTvLy8CA8PJzEx8Y4+4+rVq4SFhRESEsILL7xAYGBgGVXv2PLz4aWXYMgQuHDBtpDJ9u3w2GNmVyYiIj/l0EGdlZVFWFgYb99kz8SFCxcybdo0XnnlFXbt2kWvXr2IiorixIkT9nPCw8MJDQ297uvMmTMA+Pv7s3v3bo4ePcqnn37KuXPnKuTazJSWBn36wF//ajt+6inbvelWrcytS0REruc096gtFgtLlixh5MiR9rYuXbrQsWNH3nnnHXtb69atGTlyJDNnzrzjz3jqqafo378/DzzwwA1fz83NJTc3136cnp5OgwYNnO4etWHAiBGQkAAffmh7/EpERCpOlbhHnZeXx44dO4iMjCzWHhkZyZYtW0r0HufOnSM9PR2w/UdLSEigZcuWNz1/5syZ+Pn52b8aNGhw9xdQwXJyICvL9meLBebPtz0brZAWEXFsThvUFy9exGq1EhQUVKw9KCiI1NTUEr3HqVOn6N27N2FhYfTs2ZNf/vKXtGvX7qbnv/zyy6Slpdm/Tp48WaprqCgHD0K3bjB1qq03Dbb9o5s0MbcuERG5Padf8MTys4d8DcO4ru1mwsPDSU5OLvFneXp64unpeSflme4//7EFdFYWnDoFZ85o32gREWfitD3qwMBAXF1dr+s9nz9//rpedlWUmQkTJsC4cbaQ7tsXdu9WSIuIOBunDWoPDw/Cw8OJi4sr1h4XF0f37t1Nqsox7NkDnTrBggW2Xa5eew3WrYPgYLMrExGRO+XQQ9+ZmZkcPnzYfnz06FGSk5MJCAigYcOGTJ8+nXHjxhEREUG3bt2YN28eJ06cYOrUqSZWba78fNuM7mPHbMH86ae2R7FERMQ5OXRQb9++nX79+tmPp0+fDsD48eP56KOPGDt2LJcuXWLGjBmcPXuW0NBQYmNjadSokVklm87dHd5/H/7xD/j3v6F2bbMrEhGR0nCa56gdkaOs9Z2UBOfOwbBhppUgIiJ3oEo8Ry1QWAh//zv06AGPPgo/uUsgIiKVhEMPfcvNXbwI48dDbKzteMgQDXOLiFRG6lE7oU2bbDtexcaCpye8+y4sXAh+fmZXJiIiZU1B7UQMA15/Hfr3ty1c0qqV7f70k0/algUVEZHKR0HtRCwWuHrVdm96wgTbtpS3WPFUREQqAd2jdgL5+bbHrgBmzrQ9Fz18uLk1iYhIxVCP2oHl58MLL0C/frY/A3h4KKRFRKoS9agd1LFj8NBDsHWr7XjVKgW0iEhVpB61A4qJgfbtbSHt7287VkiLiFRNCmoHkpMDTz8NY8ZAWhp07Qq7dsHo0WZXJiIiZlFQO5DJk+Gdd2x/fvFFSEiAxo1NLUlEREymoHYgv/89NGkCq1fD//t/P870FhGRqkuTyRxIq1Zw8CC46f8VERH5H/WoHYxCWkREfkpBLSIi4sAU1CIiIg5MQS0iIuLAFNQiIiIOTEEtIiLiwBTUIiIiDkxBLSIi4sAU1CIiIg5MQS0iIuLAtA5WKRiGAUB6errJlYiIiDMpyo2iHLkVBXUpZGRkANCgQQOTKxEREWeUkZGBn5/fLc+xGCWJc7mhwsJCzpw5g6+vLxaLxexybio9PZ0GDRpw8uRJatSoYXY5ZULX5Bwq2zVVtusBXZNZDMMgIyOD4OBgXFxufRdaPepScHFxISQkxOwySqxGjRoO+0N7t3RNzqGyXVNlux7QNZnhdj3pIppMJiIi4sAU1CIiIg5MQV0FeHp68sc//hFPT0+zSykzuibnUNmuqbJdD+ianIEmk4mIiDgw9ahFREQcmIJaRETEgSmoRUREHJiCWkRExIEpqKWYjIwMOnXqRPv27Wnbti3vv/++2SWV2qhRo6hZsyZjxowxu5S7Vhmu4acq488ZgJubG+3bt6d9+/ZMnjzZ7HJK7cCBA/brad++Pd7e3ixdutTsskrlb3/7G/feey+hoaH85z//MbucEtGsbynGarWSm5uLj48P2dnZhIaGsm3bNmrVqmV2aXdt48aNZGZmsmDBAhYtWmR2OXelMlzDT1XGnzOAwMBALl68aHYZ5SIzM5PGjRtz/PhxqlWrZnY5d+W7775j/PjxbNmyBYABAwawcuVK/P39zS3sNtSjlmJcXV3x8fEBICcnB6vVWqLdXRxZv3798PX1NbuMUqkM1/BTlfHnrLJbvnw5AwYMcNqQBti/fz/du3fHy8sLLy8v2rdvz+rVq80u67YU1E4mISGBYcOGERwcjMViueEw1Ny5c2nSpAleXl6Eh4eTmJh4R59x9epVwsLCCAkJ4YUXXiAwMLCMqr9eRVyP2SrjNZbFNVXkz1lJlMU1paenEx4eTs+ePdm0aVMFVX5zZfmz98UXXzB27NhyrvjWSns9oaGhbNy4katXr3L16lU2bNjA6dOnK/AK7o6C2slkZWURFhbG22+/fcPXFy5cyLRp03jllVfYtWsXvXr1IioqihMnTtjPCQ8PJzQ09LqvM2fOAODv78/u3bs5evQon376KefOnXPq6zFbWVyjoymLa6rIn7OSKItrOnbsGDt27ODdd9/lscceM32v+rL62UtPT2fz5s1ER0dXRNk3VdrradOmDc899xz9+/dn1KhRdOrUCTc3J9ibyhCnBRhLliwp1ta5c2dj6tSpxdpatWplvPTSS3f1GVOnTjW++OKLuy3xjpTn9WzcuNG4//77S1tiqZXmGh3lGn6uLP5/q8ifs5Ioi2saMmSIsW3btvIq8Y6V5po+/vhj49FHHy3vEu9IWfx/NGnSJOOrr74qrxLLjHrUlUheXh47duwgMjKyWHtkZKR98sTtnDt3zt4LSE9PJyEhgZYtW5Z5rSVRFtfj6CrjNZbkmhzp56wkSnJNV65cITc3F4BTp06RkpJC06ZNK7zWkrqTnz1HGPa+nZJez/nz5wHbjPakpCQGDx5coXXeDSfo80tJXbx4EavVSlBQULH2oKAgUlNTS/Qep06dYtKkSRiGgWEY/PKXv6Rdu3blUe5tlcX1AAwePJidO3eSlZVFSEgIS5YsoVOnTmVd7l0p6TU68jX8XEmuyZF+zkqiJNe0f/9+nnzySVxcXLBYLPzjH/8gICDAjHJLpKQ/e2lpaSQlJRETE1PRJd6Rkl7PyJEjuXr1KtWqVWP+/PlOMfTt+BXKHbNYLMWODcO4ru1mwsPDSU5OLoeq7l5prgdgzZo1ZV1SmbvdNTrDNfzcra7JEX/OSuJW19S9e3e+++47M8oqldv97Pn5+Zk+f+BO3O56nHGkSkPflUhgYCCurq7X9TbPnz9/3W+ZzqCyXc+NVMZr1DU5h8p2TZXten5KQV2JeHh4EB4eTlxcXLH2uLg4unfvblJVd6+yXc+NVMZr1DU5h8p2TZXten5KQ99OJjMzk8OHD9uPjx49SnJyMgEBATRs2JDp06czbtw4IiIi6NatG/PmzePEiRNMnTrVxKpvrrJdz41UxmvUNemazFDZrqfETJptLndp48aNBnDd1/jx4+3nzJkzx2jUqJHh4eFhdOzY0di0aZN5Bd9GZbueG6mM16hr0jWZobJdT0lprW8REREHpnvUIiIiDkxBLSIi4sAU1CIiIg5MQS0iIuLAFNQiIiIOTEEtIiLiwBTUIiIiDkxBLSIi4sAU1CIiIg5MQS0iIuLAFNQi4tROnjxJ3759adOmDe3atePLL780uySRMqW1vkXEqZ09e5Zz587Rvn17zp8/T8eOHTlw4ADVqlUzuzSRMqEetYhw6dIl6tSpw7Fjx8wu5Y7Vq1eP9u3bA1CnTh0CAgK4fPkyAGPGjOGtt94ysTqR0lNQiwgzZ85k2LBhNG7c2OxSSmX79u0UFhbSoEEDAP7whz/wxhtvkJ6ebnJlIndPQ98iVdy1a9cIDg4mNjaWbt26mV3OdcLDw8nNzb2ufe3atQQHB9uPL126RK9evfjggw/o3r17se+fPHkyTz31VIXUK1LW1KMWqWRCQkKYO3dusbYtW7bg4+PD8ePHrzt/1apVuLm5XRfSGRkZPProo1SrVo169eoxa9Ys+vbty7Rp02762YWFhfz1r3+lWbNmeHp60rBhQ9544w3763379uXZZ59l2rRp1KxZk6CgIObNm0dWVhaPP/44vr6+3HPPPaxatcr+PTt27GDv3r3Xff00pHNzcxk1ahQvv/xysZAGGD58OJ999lmJ/tuJOCIFtUgl07VrV7Zt22Y/NgyDadOmMW3aNBo1anTd+QkJCURERFzXPn36dDZv3szy5cuJi4sjMTGRnTt33vKzX375Zf7617/y6quvkpKSwqeffkpQUFCxcxYsWEBgYCBJSUk8++yzPPXUUzzwwAN0796dnTt3MnjwYMaNG0d2dnaJrtcwDCZMmED//v0ZN27cda937tyZpKSkG/bKRZyCISKVyptvvmnce++99uMFCxYYQUFBRnp6+g3PHzFihDFx4sRibenp6Ya7u7vx5Zdf2tuuXr1q+Pj4GL/61a9u+D7p6emGp6en8f7779+0tj59+hg9e/a0HxcUFBjVqlUzxo0bZ287e/asARjffPPNLa+zSGJiomGxWIywsDD71549e+yv79692wCMY8eOlej9RByNm9m/KIhI2eratSsvvvgimZmZuLi48Lvf/Y4///nP+Pr63vD8a9eu4eXlVaztyJEj5Ofn07lzZ3ubn58fLVu2vOnn7t+/n9zcXAYMGHDL+tq1a2f/s6urK7Vq1aJt27b2tqIe+Pnz52/5PkV69uxJYWHhTV/39vYGKHEPXcTRKKhFKpmIiAhcXV3ZuXMn69ato1atWkycOPGm5wcGBnLlypVibcb/5phaLJYbtt9IUSDejru7e7Fji8VSrK3oM28Vvnei6FGt2rVrl8n7iVQ03aMWqWS8vLwICwtj8eLF/O1vf+Ott97CxeXmf9U7dOhASkpKsbZ77rkHd3d3kpKS7G3p6ekcOnTopu/TvHlzvL29Wb9+fekvogzt3buXkJAQAgMDzS5F5K4oqEUqoa5du/LPf/6TgQMH3nYoevDgwezbt69Yr9rX15fx48fz29/+lo0bN7Jv3z4mTpyIi4vLdb3sIl5eXrz44ou88MILfPzxx/zwww98++23fPjhh2V6bXcqMTGRyMhIU2sQKQ0FtUgl1L59e9zc3HjzzTdve27btm2JiIjgiy++KNb+1ltv0a1bN+677z4GDhxIjx49aN269XX3s3/q1Vdf5fnnn+cPf/gDrVu3ZuzYsSW+11wecnJyWLJkCVOmTDGtBpHS0oInIpVQ//79adeuHbNnzy7R+bGxsfzmN79h7969Nx0mz8rKon79+vz9739n0qRJZVht+ZkzZw7Lli1j7dq1Zpcictc0mUykkigsLOTChQt8+OGHHDhwgCVLlpT4e6Ojozl06BCnT5+2L7+5a9cuvv/+ezp37kxaWhozZswAYMSIEeVSf3lwd3fnX//6l9lliJSKetQilUR8fDz9+/enVatWzJ8/ny5dupTq/Xbt2sXkyZM5cOAAHh4ehIeH89ZbbxV7lEpEyp+CWkRExIFpMpmIiIgDU1CLiIg4MAW1iIiIA1NQi4iIODAFtYiIiANTUIuIiDgwBbWIiIgDU1CLiIg4MAW1iIiIA1NQi4iIODAFtYiIiANTUIuIiDgwBbWIiIgD+/+CnunrfagozgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# toy atm model\n", + "all_data = np.loadtxt(\"toy_atm/toy_atm_hot_3cm.hse\", skiprows=26).T\n", + "names = [\"density\",\"temperature\",\"pressure\",\"hydrogen-1\",\"helium-4\",\"carbon-12\",\"carbon-13\",\"nitrogen-13\",\"nitrogen-14\",\"nitrogen-15\",\"oxygen-14\",\"oxygen-15\",\"oxygen-16\",\"oxygen-17\",\"oxygen-18\",\"fluorine-17\",\"fluorine-18\",\"fluorine-19\",\"neon-18\",\"neon-19\",\"neon-20\",\"magnesium-22\",\"magnesium-24\",\"iron-56\"]\n", + "data = {name:all_data[i+1] for i,name in enumerate(names)}\n", + "z = all_data[0]\n", + "y=data[\"pressure\"]/g0\n", + "\n", + "fig,axes = plt.subplots(2,1,figsize=(5,6),sharex=True)\n", + "axes[1].set_xlabel(r\"$y$ (g cm$^{-2}$)\")\n", + "axes[0].set_ylabel(r\"$T$ (K)\")\n", + "axes[1].set_ylabel(r\"$\\rho$ (g cm$^{-3}$)\")\n", + "\n", + "axes[0].loglog(toy2.y, toy2.T, 'k-', lw=3, alpha=0.5, label='python')\n", + "axes[0].loglog(y, data[\"temperature\"], 'b--', label='toy_atm_hot')\n", + "\n", + "axes[1].loglog(toy2.y, toy2.rho, 'k-', lw=3, alpha=0.5)\n", + "axes[1].loglog(y, data[\"density\"], 'b--')\n", + "\n", + "# other models, just checking\n", + "# import os\n", + "# all_data = np.loadtxt(os.environ.get(\"PSCRATCH\")+\"/xrb_layered/RUN31/toy_atm_hot_delta5_3cm.hse\", skiprows=26).T\n", + "# data2 = {name:all_data[i+1] for i,name in enumerate(names)}\n", + "# y2=data2[\"pressure\"]/g0\n", + "# axes[0].loglog(y2, data2[\"temperature\"], 'r:', label='other')\n", + "# axes[1].loglog(y2, data2[\"density\"], 'r:')\n", + "# axes[0].set_ylim([1e8,4e8])\n", + "# # axes[1].set_ylim([1e6,5e6])\n", + "# # axes[1].set_xlim([1e6,2e9])\n", + "\n", + "axes[0].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b1c29534-c624-44c8-ac44-1f0ad4815311", + "metadata": {}, + "source": [ + "Entropy profile, using a python port of the Helmoltz EOS (https://github.com/msbc/helmeos)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3c05303a-57f0-44d3-8554-2daaccbbdbc7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing Helmholtz EOS table /global/homes/s/simongui/helmeos/helm_table.dat\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import sys\n", + "sys.path.append(\"/global/homes/s/simongui/\")\n", + "import helmeos\n", + "\n", + "abar = (toy2.X + toy2.Y/4 + toy2.Zcno/13 + toy2.Zfe/56)**-1\n", + "zbar = abar * (toy2.X + 2*toy2.Y/4 + 7*toy2.Zcno/13 + 26*toy2.Zfe/56)\n", + "\n", + "stot = helmeos.eos_DT(toy2.rho, toy2.T, abar, zbar)['stot']\n", + "sgas = helmeos.eos_DT(toy2.rho, toy2.T, abar, zbar)['sgas']\n", + "srad = helmeos.eos_DT(toy2.rho, toy2.T, abar, zbar)['srad']\n", + "\n", + "fig,ax = plt.subplots(1,1)\n", + "ax.set_ylabel('T')\n", + "ax.loglog(toy2.y, toy2.T, 'k-')\n", + "ax.invert_xaxis()\n", + "ax.set_xlim([1e7,1e9])\n", + "\n", + "axb = ax.twinx()\n", + "axb.set_ylabel('s')\n", + "axb.loglog(toy2.y, stot, 'r-', label='total')\n", + "axb.loglog(toy2.y, sgas, 'm--', label='gas')\n", + "axb.loglog(toy2.y, srad, 'c-', label='rad')\n", + "axb.set_ylim([2e8,4e8])\n", + "axb.legend()\n", + "\n", + "plt.axvline(yd,color='b',ls=':')\n", + "plt.axvline(y1,color='k',ls=':')\n", + "plt.axvline(y2_p,color='k',ls=':')\n", + "plt.axvline(y3,color='k',ls=':')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NERSC Python", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Exec/science/xrb_layered/create_initial_model/README.md b/Exec/science/xrb_layered/create_initial_model/README.md index c30d4527b..3f51df8b9 100644 --- a/Exec/science/xrb_layered/create_initial_model/README.md +++ b/Exec/science/xrb_layered/create_initial_model/README.md @@ -6,29 +6,8 @@ We then accrete a column $y\sim 2\times 10^4$ g/cm $^{2}$ of iron. This will be We then accrete a "solar" mixture (70% hydrogen, 28% helium, 2% carbon-12) for a few days, until ignition in a helium layer. -The last two profiles of the MESA run (`profile37.data` & `profile38.data`) are copied here. These steps are all done in [github.com/simonguichandut/mesa\_mixed\_burst](https://github.com/simonguichandut/mesa_mixed_burst). The relevant run is `run/make_ignition_model_for_hydro/`. +The last two profiles of the MESA run (`profile37.data` & `profile38.data`) are copied in `mesa/`. These steps are all done in [github.com/simonguichandut/mesa\_mixed\_burst](https://github.com/simonguichandut/mesa_mixed_burst). The relevant run is `run/make_ignition_model_for_hydro/`. -## HSE model +## Toy model -To convert `profile37` (the pre-ignited data which we want to start the simulation from) into a amrex-astro usable format: - -``` -python convert_mesa.py profile37.data -``` - -creates `profile37.raw` (note: this script uses [py\_mesa\_reader](https://github.com/wmwolf/py_mesa_reader)). - -To convert the MESA lagrangian grid into eulerian (and readjust HSE if needed), we use the routines in [AMReX-ASTRO/initial\_models/lagrangian\_planar](https://github.com/AMReX-Astro/initial_models). To get the correct list of species, the code there needs to be compiled with the CNO_extras network (`make NETWORK_DIR=CNO_extras`). The file `helm_table.dat` also needs to be copied over. - -Values for gravity, domain dimensions, cutoffs, are set in `inputs_lagrangian_planar_xrb_layered`. - -Run with: - -``` -{initial_models_directory}/lagrangian_planar/initialmodel1d.gnu.ex inputs_lagrangian_planar_xrb_layered -``` - -This creates the input file `xrb_layered_3cm.hse` used in the simulation. - -## Figuring out the initial perturbation -The MESA profile we use is pre-ignition. We need to give it a "kick" (i.e. manually increase the temperature at the base) to get the burst to start. We figure out how big this needs to be in `ignition_analysis.ipynb`. +See the notebook `Initial_Model.ipynb` for a detailed explanation of how we use the MESA model (profile37) to construct a simplified toy model, with a temperature kick at the bottom of the fuel layer. This is done in `toy_atm/`, which is a modified version of https://github.com/AMReX-Astro/initial_models/tree/main/toy_atm . \ No newline at end of file diff --git a/Exec/science/xrb_layered/create_initial_model/ignition_analysis.ipynb b/Exec/science/xrb_layered/create_initial_model/ignition_analysis.ipynb deleted file mode 100644 index 6e6fac4bd..000000000 --- a/Exec/science/xrb_layered/create_initial_model/ignition_analysis.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the two profiles (pre-ignition and ignited) with mesa_reader and compare T,rho,X" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dt=2.7e-01 s\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import py_mesa_reader as mr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# These were used in the first set of maestro runs for this problem\n", - "# They had a fe56 substrate with column y=4.6e10\n", - "# prof1 = mr.MesaData(\"profile46.data\")\n", - "# prof2 = mr.MesaData(\"profile47.data\")\n", - "\n", - "# These have a smaller substrate, y=5e9\n", - "prof1 = mr.MesaData(\"profile37.data\")\n", - "prof2 = mr.MesaData(\"profile38.data\")\n", - "\n", - "name1 = prof1.file_name.replace(\".data\",\"\")\n", - "name2 = prof2.file_name.replace(\".data\",\"\")\n", - "\n", - "yr = 3.1536e7\n", - "print(f\"dt={(prof2.star_age-prof1.star_age)*yr:.1e} s\")\n", - "\n", - "fig,(ax1,ax2,ax3) = plt.subplots(3,1,sharex=True)\n", - "fig.subplots_adjust(hspace=0)\n", - "\n", - "ax1.plot(prof1.R_cm-12e5, prof1.T/1e8, 'k-', label=name1)\n", - "ax1.plot(prof2.R_cm-12e5, prof2.T/1e8, 'r-', label=name2)\n", - "# ax1.set_ylim([])\n", - "ax1.set_ylabel('T (1e8 K)')\n", - "ax1.legend(frameon=False)\n", - "\n", - "ax2.semilogy(prof1.R_cm-12e5, prof1.Rho, 'k-')\n", - "ax2.semilogy(prof2.R_cm-12e5, prof2.Rho, 'r-')\n", - "# ax2.set_ylim([5e5,1e7])\n", - "ax2.set_ylabel('rho (g/cm3)')\n", - "\n", - "ax3.plot(prof1.R_cm-12e5, prof1.h1, 'k-', label='h1')\n", - "ax3.plot(prof2.R_cm-12e5, prof2.h1, 'r-')\n", - "\n", - "ax3.plot(prof1.R_cm-12e5, prof1.he4, 'k--', label='he4')\n", - "ax3.plot(prof2.R_cm-12e5, prof2.he4, 'r--')\n", - "\n", - "ax3.plot(prof1.R_cm-12e5, prof1.c12, 'k:', label='c12')\n", - "ax3.plot(prof2.R_cm-12e5, prof2.c12, 'r:')\n", - "\n", - "ax3.set_ylabel('Mass fraction')\n", - "ax3.set_xlabel(r'r-R$_{\\rm NS}$ (cm)')\n", - "ax3.legend(frameon=False,loc=2)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Only a small temperature bump seems necessary to ignite (notice the rapid burn he4->c12)\n", - "\n", - "Check the thin shell instability condition\n", - "\n", - "$$ d\\epsilon_{3\\alpha}/dT>d\\epsilon_{\\rm cool}/dT $$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10.0, 10000000000.0)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Constants (cgs)\n", - "G = 6.6726e-8\n", - "c = 2.99792458e10\n", - "arad = 7.5657e-15\n", - "Msun = 1.989e33\n", - "\n", - "def dheat_dT(rho,T,Y):\n", - " T9 = T/1e9\n", - " eps_3alpha = 5.1e8 * rho**2 * Y**3 / T9**2 * np.exp(-4.4027/T9) # Hansen & Kawaler Eq. 6.80\n", - " return eps_3alpha / 1e9 * (-3/T9 + 4.4027/T9**2)\n", - "\n", - "g = G*1.4*Msun/12e5**2\n", - "# print(g/1e14)\n", - "\n", - "def dcool_dT(T,kappa,y):\n", - " eps_cool = arad*c*T**4 / (3*kappa*y**2)\n", - " return 4*eps_cool/T\n", - "\n", - "plt.figure()\n", - "plt.xlabel(r'r-R$_{\\rm NS}$ (cm)')\n", - "plt.ylabel(r'$d\\epsilon/dT$')\n", - "plt.semilogy(prof1.R_cm-12e5, dheat_dT(prof1.Rho,prof1.T,prof1.he4), 'r-', label=f'{name1} heat')\n", - "plt.semilogy(prof2.R_cm-12e5, dheat_dT(prof2.Rho,prof2.T,prof2.he4), 'r:', label=f'{name2} heat')\n", - "\n", - "plt.semilogy(prof1.R_cm-12e5, dcool_dT(prof1.T,prof1.opacity,prof1.column_depth), 'b-', label=f'{name1} cool')\n", - "plt.semilogy(prof2.R_cm-12e5, dcool_dT(prof2.T,prof2.opacity,prof2.column_depth), 'b:', label=f'{name2} cool')\n", - "\n", - "plt.legend(frameon=False)\n", - "\n", - "plt.ylim([1e1,1e10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Both models are past the thin shell instability, but note that this underestimates the cooling rate as it doesn't consider convection." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Maestro applies a temperature perturbation at the beginning of the run, see `MaestroInitData.cpp`. We reproduce this here to see the effect." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'T (1e8 K)')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Values set in inputs_2d\n", - "\n", - "# for profile 46\n", - "# xrb_pert_size = 15\n", - "# # xrb_pert_factor = 1.0e-5\n", - "# xrb_pert_factor = 5.0e-2\n", - "# # xrb_pert_type = 1 # this just means temperature pert not density\n", - "# xrb_pert_height = 2000.0\n", - "\n", - "xrb_pert_size = 15\n", - "# xrb_pert_factor = 1.0e-5\n", - "xrb_pert_factor = 5.0e-2\n", - "# xrb_pert_type = 1 # this just means temperature pert not density\n", - "xrb_pert_height = 1100.0\n", - "\n", - "rad_pert = -xrb_pert_size**2/(4*np.log(0.5))\n", - "\n", - "# Note in the code the perturbation is 2D, i.e. depends on x. Here we just model at x=0\n", - "y = prof1.R_cm-12e5 - xrb_pert_height\n", - "Tnew = prof1.T * (1 + xrb_pert_factor * np.exp(-y**2/rad_pert**2))\n", - "\n", - "plt.figure()\n", - "plt.plot(prof1.R_cm-12e5, prof1.T/1e8, 'k-', label=name1)\n", - "plt.plot(prof1.R_cm-12e5, Tnew/1e8, 'g-', label=f'{name1} perturbed')\n", - "plt.plot(prof2.R_cm-12e5, prof2.T/1e8, 'r-', label=name2)\n", - "plt.ylim([1.5,2.9])\n", - "plt.legend(frameon=False,loc=3)\n", - "plt.xlabel(r'r-R$_{\\rm NS}$ (cm)')\n", - "plt.ylabel(r'T (1e8 K)')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NERSC Python", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "vscode": { - "interpreter": { - "hash": "ebe3ce01678291323dcff905f7209dd16179ba179af5ccfbed7b023ef5b6c3ff" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Exec/science/xrb_layered/create_initial_model/convert_mesa.py b/Exec/science/xrb_layered/create_initial_model/mesa/convert_mesa.py similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/convert_mesa.py rename to Exec/science/xrb_layered/create_initial_model/mesa/convert_mesa.py diff --git a/Exec/science/xrb_layered/create_initial_model/inputs_lagrangian_planar_xrb_layered b/Exec/science/xrb_layered/create_initial_model/mesa/inputs_lagrangian_planar_xrb_layered similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/inputs_lagrangian_planar_xrb_layered rename to Exec/science/xrb_layered/create_initial_model/mesa/inputs_lagrangian_planar_xrb_layered diff --git a/Exec/science/xrb_layered/create_initial_model/periodic_table.list b/Exec/science/xrb_layered/create_initial_model/mesa/periodic_table.list similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/periodic_table.list rename to Exec/science/xrb_layered/create_initial_model/mesa/periodic_table.list diff --git a/Exec/science/xrb_layered/create_initial_model/profile37.data b/Exec/science/xrb_layered/create_initial_model/mesa/profile37.data similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/profile37.data rename to Exec/science/xrb_layered/create_initial_model/mesa/profile37.data diff --git a/Exec/science/xrb_layered/create_initial_model/profile37.raw b/Exec/science/xrb_layered/create_initial_model/mesa/profile37.raw similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/profile37.raw rename to Exec/science/xrb_layered/create_initial_model/mesa/profile37.raw diff --git a/Exec/science/xrb_layered/create_initial_model/profile38.data b/Exec/science/xrb_layered/create_initial_model/mesa/profile38.data similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/profile38.data rename to Exec/science/xrb_layered/create_initial_model/mesa/profile38.data diff --git a/Exec/science/xrb_layered/create_initial_model/py_mesa_reader.py b/Exec/science/xrb_layered/create_initial_model/mesa/py_mesa_reader.py similarity index 100% rename from Exec/science/xrb_layered/create_initial_model/py_mesa_reader.py rename to Exec/science/xrb_layered/create_initial_model/mesa/py_mesa_reader.py diff --git a/Exec/science/xrb_layered/create_initial_model/mesa/readme.md b/Exec/science/xrb_layered/create_initial_model/mesa/readme.md new file mode 100644 index 000000000..feff8321e --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/mesa/readme.md @@ -0,0 +1,23 @@ +In an earlier version of xrb_layered, we directly converted the MESA profile to an eulerian grid using amrex initial models lagrangian routines. Instructions below, kept here for future reference. In the end, we decided to build the model from scratch, as explained in the parent directory readme. + +## HSE model + +To convert `profile37` (the pre-ignited data which we want to start the simulation from) into a amrex-astro usable format: + +``` +python convert_mesa.py profile37.data +``` + +creates `profile37.raw` (note: this script uses [py\_mesa\_reader](https://github.com/wmwolf/py_mesa_reader)). + +To convert the MESA lagrangian grid into eulerian (and readjust HSE if needed), we use the routines in [AMReX-ASTRO/initial\_models/lagrangian\_planar](https://github.com/AMReX-Astro/initial_models). To get the correct list of species, the code there needs to be compiled with the CNO_extras network (`make NETWORK_DIR=CNO_extras`). The file `helm_table.dat` also needs to be copied over. + +Values for gravity, domain dimensions, cutoffs, are set in `inputs_lagrangian_planar_xrb_layered`. + +Run with: + +``` +{initial_models_directory}/lagrangian_planar/initialmodel1d.gnu.ex inputs_lagrangian_planar_xrb_layered +``` + +This creates the input file `xrb_layered_3cm.hse` used in the simulation. \ No newline at end of file diff --git a/Exec/science/xrb_layered/create_initial_model/plot.py b/Exec/science/xrb_layered/create_initial_model/plot.py new file mode 100644 index 000000000..135a3234d --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/plot.py @@ -0,0 +1,69 @@ +import numpy as np +import matplotlib.pyplot as plt + +import mesa.py_mesa_reader as mr + +import sys +filename = sys.argv[1] + +by_column = False +#by_column = True +g = 1.29e14 + +fig,axes = plt.subplots(3,1,figsize=(6,8), sharex=True) +fig.subplots_adjust(hspace=0) + +axes[0].set_ylabel(r"$T$ (K)") +axes[1].set_ylabel(r"$\rho$ (g cm$^{-3}$)") +axes[2].set_ylabel(r"$X$") +axes[2].set_xlabel(r"r (cm)") +axes[2].set_ylim([1e-3,1.1]) + +# MESA model +prof = mr.MesaData("mesa/profile37.data") + +if by_column: + xx = prof.pressure/g +else: + xx = prof.R_cm-12e5 + +axes[0].plot(xx, prof.T, 'k-', label="MESA") +axes[1].semilogy(xx, prof.Rho, 'k-') + +axes[2].semilogy(xx, prof.h1, 'b-',label="h1") +axes[2].semilogy(xx, prof.he4, 'r-',label="he4") +cno = prof.c12 +for iso in ("c13","n13","n14","n15","o14","o15","o16","o17","o18","f17","f18","f19"): + cno += prof.data(iso) +axes[2].semilogy(xx, cno, 'g-',label="CNO") +axes[2].semilogy(xx, prof.fe56, 'k-',label="fe56") + +# toy atm model +all_data = np.loadtxt(filename, skiprows=26).T +names = ["density","temperature","pressure","hydrogen-1","helium-4","carbon-12","carbon-13","nitrogen-13","nitrogen-14","nitrogen-15","oxygen-14","oxygen-15","oxygen-16","oxygen-17","oxygen-18","fluorine-17","fluorine-18","fluorine-19","neon-18","neon-19","neon-20","magnesium-22","magnesium-24","iron-56"] +data = {name:all_data[i+1] for i,name in enumerate(names)} +z = all_data[0] + +if by_column: + xx = data["pressure"]/g +else: + xx = z + +axes[0].plot(xx, data["temperature"], 'k--', label="toy atm") +axes[1].semilogy(xx, data["density"], 'k--') + +axes[2].semilogy(xx, data["hydrogen-1"], 'b--') +axes[2].semilogy(xx, data["helium-4"], 'r--') +axes[2].semilogy(xx, data["carbon-12"]+data["oxygen-14"]+data["oxygen-15"], 'g--') +axes[2].semilogy(xx, data["iron-56"], 'k--') + +axes[0].legend(frameon=False, ncol=2, bbox_to_anchor = (0.8,1.15), bbox_transform=axes[0].transAxes) +axes[2].legend(loc=3) + +if by_column: + axes[2].set_xscale('log') + axes[0].set_yscale('log') + axes[2].set_xlim([1e4,1e10]) + axes[2].invert_xaxis() + +fig.savefig(filename.split("/")[-1] + ".png", bbox_inches="tight", dpi=500) diff --git a/Exec/science/xrb_layered/create_initial_model/profile46.data b/Exec/science/xrb_layered/create_initial_model/profile46.data deleted file mode 100644 index 24aa16a0c..000000000 --- a/Exec/science/xrb_layered/create_initial_model/profile46.data +++ /dev/null @@ -1,1466 +0,0 @@ - 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 - model_number num_zones initial_mass initial_z star_age time_step M_center R_center L_center Teff photosphere_L photosphere_r center_eta center_h1 center_he3 center_he4 center_c12 center_n14 center_o16 center_ne20 star_mass star_mdot star_mass_h1 star_mass_he3 star_mass_he4 star_mass_c12 star_mass_n14 star_mass_o16 star_mass_ne20 he_core_mass c_core_mass o_core_mass si_core_mass fe_core_mass neutron_rich_core_mass tau10_mass tau10_radius tau100_mass tau100_radius dynamic_time kh_timescale nuc_timescale power_nuc_burn power_h_burn power_he_burn power_neu burn_min1 burn_min2 time_seconds version_number compiler build MESA_SDK_version math_backend date - 2071 1460 1.0000000000000000E+000 2.0000000000000000E-002 1.6162944322133478E-002 1.1285251694263946E-008 2.7837738189494323E+033 1.2000000000000000E+006 1.8000000000000000E+034 3.3156311518358923E+006 3.2577602875009838E+001 1.7297378971668497E-005 3.4902971637580698E+001 1.0000000000000006E-099 1.0000000000000006E-099 1.0000000000000006E-099 1.0000000000000006E-099 1.0000000000000006E-099 1.0000000000000006E-099 1.0000000000000006E-099 1.4000000002048485E+000 3.0000000000000005E-010 1.1283560190115805E-013 7.6150983028677947E-025 4.0505714600979360E-012 5.8068514283771858E-013 3.9355852258097508E-014 8.5678525265105965E-017 5.1480957470263221E-024 1.4000000002045525E+000 0.0000000000000000E+000 0.0000000000000000E+000 0.0000000000000000E+000 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a/Exec/science/xrb_layered/create_initial_model/toy_atm/.gitignore b/Exec/science/xrb_layered/create_initial_model/toy_atm/.gitignore new file mode 100644 index 000000000..372c36f55 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/.gitignore @@ -0,0 +1,4 @@ +all_inputs +all_models +*.ex + diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/GNUmakefile b/Exec/science/xrb_layered/create_initial_model/toy_atm/GNUmakefile new file mode 100644 index 000000000..0b1faa572 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/GNUmakefile @@ -0,0 +1,33 @@ +PRECISION = DOUBLE +PROFILE = FALSE + +DEBUG = FALSE + +DIM = 1 + +COMP = g++ +FCOMP = gfortran + +USE_MPI = FALSE +USE_OMP = FALSE + +USE_REACT = TRUE +USE_CXX_EOS = TRUE + +MAX_ZONES := 16384 + +DEFINES += -DNPTS_MODEL=$(MAX_ZONES) + +# programs to be compiled +EBASE := initialmodel + +# EOS and network +EOS_DIR := helmholtz +NETWORK_DIR := CNO_extras + +Bpack := ./Make.package +Blocs := . .. + +EXTERN_SEARCH += . .. + +include $(MICROPHYSICS_HOME)/Make.Microphysics diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/Make.package b/Exec/science/xrb_layered/create_initial_model/toy_atm/Make.package new file mode 100644 index 000000000..d0bc096a6 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/Make.package @@ -0,0 +1,3 @@ +# the local sources needed to build the initial model generator +CEXE_headers += init_1d.H +CEXE_sources += main.cpp diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/_parameters b/Exec/science/xrb_layered/create_initial_model/toy_atm/_parameters new file mode 100644 index 000000000..349478518 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/_parameters @@ -0,0 +1,40 @@ +@namespace: problem + +model_prefix character "model" + +# Domain +nx integer 640 +xmin real 0.0 +xmax real 2.e3 + +# Gravity +g_const real -2.45e14 + +# Substrate +T_star real 1.e8 +H_star real 500.e0 +delta real 25.0 + +# Temperature profiles +T_base real 5.e8 +y1 real 5.e8 +del1 real 0.4 +y2 real 1.e8 +del2 real 0.1 +y3 real 1.e7 +del3 real 0.2 + +# Outer boundary +low_density_cutoff real 1.e-4 +T_lo real 5.e7 + +# Composition +X0 real 0.7 +Zcno real 0.02 +yd real 1.e7 +ash_name character "iron-56" + +# Other +index_base_from_temp integer 0 +small_temp real -1.e200 +small_dens real -1.e200 diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/init_1d.H b/Exec/science/xrb_layered/create_initial_model/toy_atm/init_1d.H new file mode 100644 index 000000000..0387ff225 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/init_1d.H @@ -0,0 +1,624 @@ +// Adapted from original AMReX-Astro code +// (github.com/AMReX-Astro/initial_models/tree/main/toy_atm/init_1d.H) +// by Simon Guichandut (2023). + +// Create a 1-d hydrostatic, atmosphere with an isothermal region +// (T_star) representing the NS, a hyperbolic tangent rise to a +// peak temperature (T_base) representing the base of an accreted +// layer at r=H_star, an isentropic profile representing the convective +// region at ignition, then a radiative profile from r=H_rad. +// Below, three different power-laws set the temperature profile +// T = T0*(y/y0)^del +// where y is the column depth (=pressure/gravity) and, del is the power-law +// index, and T0 and y0 are the values at the beginning of a given region. +// Those regions have different physics: 1) an adiabatic zone +// where helium is burning and generating convection, 2) a stable region +// where nothing is burning, 3) a stable region with hot CNO burning. + +// The composition transitions from pure ash, to helium and CNO, to a solar +// mixture including hydrogen, with initial hydrogen fraction X0 and CNO fraction +// Zcno (inputs file). The CNO is split between o14 and o15 with ratio +// determined by their beta decay times (70.621/122.266). Because hot CNO burns +// hydrogen at a constant rate: +// X = X0(1-y/yd), +// where yd is the depletion column depth, another input parameter. + +// This can serve as an initial model for a He-triggered XRB with an +// overlying hydrogen rich shell. + +// The temperature profile is: +// +// T +// ^ +// | +// T_base + /\ +// | / \ +// | / . \ +// T_star +-----+ \ T ~ y^del_1 (del_1~del_ad) +// | . . \ +// | . . \ +// | . . +__ +// | . . . `--- T ~ y^del_2 +// | . . . `---+ +// | . . . .`- +// | . . . . `- T ~ y^del_3 +// | . . . . `- +// | . . . . `- +// +-----+---+-----+---------------------> r +// | \ / +// | delta +// |< H_star>| +// |---------| +// |-------------------| +// +// The composition profile is: +// +// X +// ^ +// | star | reg 1 | reg 2 | region 3 +// 1.0 +--------*---------- +// | ash . \ +// | . \ +--------- X0 (hydrogen) +// | . \ / +// | . \/ +// | . /\ +// | . / \ +// | . / +--------- 1-X0-Zcno (helium) +// | .----------/--------------- Zcno (o14/o15) +// 0.0 +--------+---------+-----------------> r +// || +// |------------| +// | +// +// The transition from ash to fuel is a hyperbolic tangent, parametrized +// by delta (the half-width of the tanh), and starting at H_star. +// +// The model is placed into HSE by the following differencing: +// +// (1/dr) [

_i -

_{i-1} ] = (1/2) [ _i + _{i-1} ] g +// +// This will be iterated over in tandem with the EOS call, +// P(i-1) = P_eos(rho(i-1), T(i-1), X(i-1)) + + +#include + +#include + +#include +#include + +#include + +using namespace amrex; + +AMREX_INLINE void init_1d() +{ + + // define convenient indices for the scalars + + const int nvar = 3 + NumSpec; + const int idens = 0; + const int itemp = 1; + const int ipres = 2; + const int ispec = 3; + + const Real TOL = 1.e-10_rt; + + const int MAX_ITER = 250; + + const Real smallx = 1.e-10_rt; + + // get the species indices + int ih1 = network_spec_index("hydrogen-1"); + int ihe4 = network_spec_index("helium-4"); + int io14 = network_spec_index("oxygen-14"); + int io15 = network_spec_index("oxygen-15"); + int iash = network_spec_index(problem_rp::ash_name); + + Real xn[NumSpec] = {0.0_rt}; + + // set the composition of the underlying star + + Real xn_star[NumSpec] = {smallx}; + xn_star[iash] = 1.0; + + // the composition of the first layer above the star + // First we'll set the cno to the correct relative abundance, and the rest will be helium + // Later, we'll replace some of the helium with hydrogen according + // to column depth, or pressure + + Real xn_base[NumSpec] = {smallx}; + xn_base[io15] = problem_rp::Zcno / (1.0 + 70.621/122.266); + xn_base[io14] = problem_rp::Zcno - xn_base[io15]; + xn_base[ihe4] = 1.0 - problem_rp::Zcno; + + // check if they sum to 1 + double sum{0.0}; + for (auto e : xn_base) { + sum += e; + } + if (std::abs(sum) - 1.0_rt > NumSpec * smallx) { + amrex::Error("ERROR: fuel mass fractions don't sum to 1"); + } + + + + // Create a 1-d uniform grid that is identical to the mesh that we are + // mapping onto, and then we want to force it into HSE on that mesh. + + if (problem_rp::nx > NPTS_MODEL) { + amrex::Error("too many zones requested -- increase NPTS_MODEL"); + } + + Array1D xznl_hse; + Array1D xzn_hse; + Array1D xznr_hse; + + Array2D model_hse; + + // compute the coordinates of the new gridded function + + Real dCoord = (problem_rp::xmax - problem_rp::xmin) / static_cast(problem_rp::nx); + + for (int i = 0; i < problem_rp::nx; ++i) { + xznl_hse(i) = problem_rp::xmin + (static_cast(i)) * dCoord; + xzn_hse(i) = problem_rp::xmin + (static_cast(i) + 0.5_rt) * dCoord; + xznr_hse(i) = problem_rp::xmin + (static_cast(i) + 1.0_rt) * dCoord; + } + + // put the model onto our new uniform grid + + bool fluff = false; + + // determine the conditions at the base + + eos_t eos_state; + eos_state.T = problem_rp::T_base; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = xn_base[n]; + } + + // We know the pressure (from y1) and the temperature + Real pres_base = -problem_rp::g_const * problem_rp::y1; + + // However there is know (p,T) input option, so we need to iterate + // to find the base density. + // We know that it is roughly 1.3-1.5e6 (Initial_Model notebook) + + Real err_min = 1.0e99; + Real n_best = 0; + for (int n=0; n<200; ++n) { + eos_state.rho = (1.3 + n*0.001)*1.e6; + eos(eos_input_rt, eos_state); + + Real error = std::abs(eos_state.p - pres_base); + if (error < err_min) { + err_min = error; + n_best = n; + } + } + + Real dens_base = (1.3 + n_best*0.001)*1.e6; + + std::cout << "Best fitting base density = " << dens_base << std::endl; + + eos_state.rho = dens_base; + eos(eos_input_rt, eos_state); + + pres_base = eos_state.p; + + + // set an initial temperature profile and composition + + for (int i = 0; i < problem_rp::nx; ++i) { + + // Make hyperbolic tangent transition between star and base + + // Composition + for (int n = 0; n < NumSpec; ++n) { + model_hse(i, ispec+n) = xn_star[n] + + 0.5_rt * (xn_base[n] - xn_star[n]) * + (1.0_rt + std::tanh((xzn_hse(i) - (problem_rp::xmin + problem_rp::H_star - problem_rp::delta) + problem_rp::delta) / problem_rp::delta)); + } + + // Temperature + model_hse(i, itemp) = problem_rp::T_star + 0.5_rt * (problem_rp::T_base - problem_rp::T_star) * + (1.0_rt + std::tanh((xzn_hse(i) - (problem_rp::xmin + problem_rp::H_star - problem_rp::delta) + problem_rp::delta) / problem_rp::delta)); + + + // the density and pressure will be determined via HSE, + // for now, set them to the base conditions + model_hse(i, idens) = dens_base; + model_hse(i, ipres) = pres_base; + } + + + // find the index of the base height + int index_base = -1; + + if (problem_rp::index_base_from_temp == 1) { + for (int i = 0; i < problem_rp::nx; ++i) { + if (model_hse(i, itemp) > 0.9995 * problem_rp::T_base) { + index_base = i+1; + break; + } + } + } else { + for (int i = 0; i < problem_rp::nx; ++i) { + if (xzn_hse(i) >= problem_rp::xmin + problem_rp::H_star + problem_rp::delta) { + index_base = i+1; + break; + } + } + } + + if (index_base == -1) { + amrex::Error("ERROR: base_height not found on grid"); + } + + std::cout << "index_base = " << index_base << "; x=" << xzn_hse(index_base) << std::endl; + + + // make the base thermodynamics consistent for this base point -- that is + // what we will integrate from! + + eos_state.rho = model_hse(index_base, idens); + eos_state.T = model_hse(index_base, itemp); + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = model_hse(index_base, ispec+n); + } + + eos(eos_input_rt, eos_state); + + model_hse(index_base, ipres) = eos_state.p; + + Real y_base = -1.*eos_state.p/problem_rp::g_const; + std::cout << "Recovered base column depth y8 = " << y_base/1.e8 << std::endl; + + + // HSE solve + + // the HSE state will be done by fixing the temperature according to the power-law + // then iterating until the pressure from HSE and from EOS agree + + Real dens_zone; + Real temp_zone; + Real pres_zone; + Real y_zone; + + Real p_want; + Real y_want; + Real drho; + Real dtemp; + + // Keep track of which power-law we are currently in + // We know which one is first + Real current_T0 = eos_state.T; + Real current_y0 = y_base; + Real current_del = problem_rp::del1; + + bool cross_y2 = false; + bool cross_y3 = false; + + + // Integrate up to the top of the model + + for (int i = index_base+1; i < problem_rp::nx; ++i) { + + Real delx = xzn_hse(i) - xzn_hse(i-1); + + // we've already set initial guesses for density, temperature, and composition + dens_zone = model_hse(i, idens); + temp_zone = model_hse(i, itemp); + for (int n = 0; n < NumSpec; ++n) { + xn[n] = model_hse(i, ispec+n); + } + + // iteration loop + bool converged_hse = false; + + for (int iter = 0; iter < MAX_ITER; ++iter) { + + if (fluff) { + dens_zone = problem_rp::low_density_cutoff; + temp_zone = problem_rp::T_lo; + + // if here, assume we're at low column and X is constant + xn[ih1] = problem_rp::X0; + xn[ihe4] = 1.0 - problem_rp::X0 - problem_rp::Zcno; + + } else { + + p_want = model_hse(i-1, ipres) + + delx * 0.5_rt * (dens_zone + model_hse(i-1, idens)) * problem_rp::g_const; + + // The column depth determines both the temperature and hydrogen fraction + y_want = -p_want/problem_rp::g_const; + // std::cout << y_want << " " << current_del << std::endl; + + temp_zone = current_T0 * pow(y_want/current_y0, current_del); + + xn[ih1] = problem_rp::X0 * (1 - y_want/problem_rp::yd); + xn[ihe4] = 1.0 - problem_rp::Zcno - xn[ih1]; + if (xn[ih1] < 0) { + xn[ih1] = 0; + xn[ihe4] = 1.0 - problem_rp::Zcno; + } + + // set to isothermal if hit threshold + if (temp_zone < problem_rp::T_lo) { + temp_zone = problem_rp::T_lo; + } + + // std::cout << "T=" << temp_zone << std::endl; + } + + // (t, rho) -> (p) + eos_state.T = temp_zone; + eos_state.rho = dens_zone; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = xn[n]; + } + + eos(eos_input_rt, eos_state); + + pres_zone = eos_state.p; + + Real dpd = eos_state.dpdr; + + drho = (p_want - pres_zone) / (dpd - 0.5_rt * delx * problem_rp::g_const); + + dens_zone = amrex::max(0.9_rt * dens_zone, + amrex::min(dens_zone + drho, 1.1_rt * dens_zone)); + + + if (std::abs(drho) < TOL * dens_zone) { + converged_hse = true; + break; + } + + if (dens_zone <= problem_rp::low_density_cutoff) { + dens_zone = problem_rp::low_density_cutoff; + temp_zone = problem_rp::T_lo; + converged_hse = true; + if (!fluff) { + std::cout << "Hit fluff at x=" << xzn_hse(i) << ", y=" << y_want << std::endl; + } + fluff = true; + break; + } + } + + if (! converged_hse) { + std::cout << "\nError zone " << i << " did not converge in init_1d" << std::endl; + std::cout << "x = " << xzn_hse(i) << "(radiative layer)" << std::endl; + std::cout << "rho,T = " << dens_zone << " " << temp_zone << std::endl; + std::cout << "drho,dT = " << drho << " " << dtemp << std::endl; + amrex::Error("Error: HSE non-convergence"); + } + + // call the EOS one more time for this zone and then go on to the next + // (t, rho) -> (p) + + eos_state.T = temp_zone; + eos_state.rho = dens_zone; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = xn[n]; + model_hse(i, ispec+n) = xn[n]; + } + + eos(eos_input_rt, eos_state); + + pres_zone = eos_state.p; + + // update the thermodynamics in this zone + model_hse(i, idens) = dens_zone; + model_hse(i, itemp) = temp_zone; + model_hse(i, ipres) = pres_zone; + + // to make this process converge faster, set the density in the + // next zone to the density in this zone + model_hse(i+1,idens) = dens_zone; + + // Update the power-law + y_zone = -1*pres_zone/problem_rp::g_const; + + if ( !cross_y2 and y_zone < problem_rp::y2 ) { + current_T0 = temp_zone; + current_y0 = y_zone; + current_del = problem_rp::del2; + cross_y2 = true; + + std::cout << "crossed y2 at x=" << xzn_hse(i) << std::endl; + + } else if ( !cross_y3 and y_zone < problem_rp::y3 ) { + current_T0 = temp_zone; + current_y0 = y_zone; + current_del = problem_rp::del3; + cross_y3 = true; + + std::cout << "crossed y3 at x=" << xzn_hse(i) << std::endl; + } + + } + + std::cout << "top column depth y = " << -1.*pres_zone/problem_rp::g_const << std::endl; + + + // integrate down -- using the temperature profile defined above + + for (int i = index_base-1; i >= 0; --i) { + + Real delx = xzn_hse(i+1) - xzn_hse(i); + + // we already set the temperature and composition profiles + temp_zone = model_hse(i, itemp); + for (int n = 0; n < NumSpec; ++n) { + xn[n] = model_hse(i, ispec+n); + } + + // use our previous initial guess for density + + dens_zone = model_hse(i+1, idens); + + + // iteration loop + + // start off the Newton loop by saying that the zone has not converged + bool converged_hse = false; + + for (int iter = 0; iter < MAX_ITER; ++iter) { + + // get the pressure we want from the HSE equation, just the + // zone below the current. Note, we are using an average of + // the density of the two zones as an approximation of the + // interface value -- this means that we need to iterate for + // find the density and pressure that are consistent + + // HSE differencing + + p_want = model_hse(i+1, ipres) - + delx * 0.5_rt * (dens_zone + model_hse(i+1, idens)) * problem_rp::g_const; + + // we will take the temperature already defined in model_hse + // so we only need to zero: + // A = p_want - p(rho) + + // (t, rho) -> (p) + eos_state.T = temp_zone; + eos_state.rho = dens_zone; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = xn[n]; + } + + eos(eos_input_rt, eos_state); + + pres_zone = eos_state.p; + + Real dpd = eos_state.dpdr; + + Real A = p_want - pres_zone; + + drho = A / (dpd + 0.5_rt * delx * problem_rp::g_const); + + dens_zone = amrex::max(0.9_rt * dens_zone, + amrex::min(dens_zone + drho, 1.1_rt * dens_zone)); + + if (std::abs(drho) < TOL * dens_zone) { + converged_hse = true; + break; + } + + } + + if (! converged_hse) { + std::cout << "Error zone " << i << " did not converge in init_1d" << std::endl; + std::cout << "base isothermal layer" << std::endl; + std::cout << dens_zone << " " << temp_zone << std::endl; + std::cout << p_want << std::endl; + std::cout << drho << std::endl; + amrex::Error("Error: HSE non-convergence"); + } + + + // call the EOS one more time for this zone and then go on to the next + // (t, rho) -> (p) + eos_state.T = temp_zone; + eos_state.rho = dens_zone; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = xn[n]; + } + + eos(eos_input_rt, eos_state); + + pres_zone = eos_state.p; + + // update the thermodynamics in this zone + model_hse(i, idens) = dens_zone; + model_hse(i, itemp) = temp_zone; + model_hse(i, ipres) = pres_zone; + + } + + + // auto deltastr = num_to_unitstring(delta); + auto dxstr = num_to_unitstring(dCoord); + + // std::string outfile = problem_rp::model_prefix + ".hse.tanh.delta_" + deltastr + ".dx_" + dxstr; + std::string outfile = problem_rp::model_prefix + "_" + dxstr + ".hse"; + + std::string outfile2 = outfile + ".extras"; + + std::ofstream of(outfile); + std::ofstream of2(outfile2); + + of << "# npts = " << problem_rp::nx << std::endl; + of << "# num of variables = " << nvar << std::endl; + of << "# density" << std::endl; + of << "# temperature" << std::endl; + of << "# pressure" << std::endl; + + for (int n = 0; n < NumSpec; ++n) { + of << "# " << spec_names_cxx[n] << std::endl; + } + + for (int i = 0; i < problem_rp::nx; ++i) { + of << std::setprecision(12) << std::setw(20) << xzn_hse(i) << " "; + of << std::setprecision(12) << std::setw(20) << model_hse(i, idens) << " "; + of << std::setprecision(12) << std::setw(20) << model_hse(i, itemp) << " "; + of << std::setprecision(12) << std::setw(20) << model_hse(i, ipres) << " "; + for (int n = 0; n < NumSpec; ++n) { + of << std::setprecision(12) << std::setw(20) << model_hse(i, ispec+n) << " "; + } + of << std::endl; + } + + of.close(); + + std::cout << "Saved model to " << outfile << std::endl; + + // some metadata + of << "# generated by toy_atm_layered" << std::endl; + //of << "# inputs file: " << params_file << std::endl; + + // extras file + + of2 << "# npts = " << problem_rp::nx << std::endl; + of2 << "# num of variables = " << 2 << std::endl; + of2 << "# entropy" << std::endl; + of2 << "# c_s" << std::endl; + + for (int i = 0; i < problem_rp::nx; ++i) { + eos_state.rho = model_hse(i, idens); + eos_state.T = model_hse(i, itemp); + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = model_hse(i, ispec+n); + } + + eos(eos_input_rt, eos_state); + + of2 << std::setprecision(12) << std::setw(20) << xzn_hse(i) << std::endl; + of2 << std::setprecision(12) << std::setw(20) << eos_state.s << std::endl; + of2 << std::setprecision(12) << std::setw(20) << eos_state.cs << std::endl; + } + + // compute the maximum HSE error + + Real max_hse_error = -1.e30; + + for (int i = 1; i < problem_rp::nx-1; ++i) { + + Real delx = xzn_hse(i) - xzn_hse(i-1); + + Real dpdr = (model_hse(i, ipres) - model_hse(i-1, ipres)) / delx; + Real rhog = 0.5_rt * (model_hse(i, idens) + model_hse(i-1, idens)) * problem_rp::g_const; + + if (dpdr != 0.0_rt && model_hse(i+1, idens) > problem_rp::low_density_cutoff) { + max_hse_error = amrex::max(max_hse_error, std::abs(dpdr - rhog) / std::abs(dpdr)); + } + } + + std::cout << "maximum HSE error = " << max_hse_error << std::endl; + +} \ No newline at end of file diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs b/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs new file mode 100644 index 000000000..1d36d723c --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs @@ -0,0 +1,27 @@ +problem.model_prefix = "toy_atm" + +problem.nx = 1024 +problem.xmin = 0.0 +problem.xmax = 3072.0 + +problem.g_const = -1.29e14 + +problem.T_star = 1.0e8 +problem.H_star = 1120.0 +problem.delta = 5.0 + +problem.T_base = 2.75e8 + +problem.y1 = 4.5e8 +problem.del1 = 0.37 +problem.y2 = 1.5e8 +problem.del2 = 0.12 +problem.y3 = 2.0e7 +problem.del3 = 0.22 + +problem.low_density_cutoff = 1.e-4 +problem.T_lo = 1.e6 + +problem.X0 = 0.7 +problem.Zcno = 0.02 +problem.yd = 3.7e7 diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot b/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot new file mode 100644 index 000000000..8bf242a7a --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot @@ -0,0 +1,27 @@ +problem.model_prefix = "toy_atm_hot" + +problem.nx = 1024 +problem.xmin = 0.0 +problem.xmax = 3072.0 + +problem.g_const = -1.29e14 + +problem.T_star = 1.0e8 +problem.H_star = 1120.0 +problem.delta = 5.0 + +problem.T_base = 3.5e8 + +problem.y1 = 4.5e8 +problem.del1 = 0.37 +problem.y2 = 5.6e7 +problem.del2 = 0.12 +problem.y3 = 2.0e7 +problem.del3 = 0.22 + +problem.low_density_cutoff = 1.e-4 +problem.T_lo = 1.e6 + +problem.X0 = 0.7 +problem.Zcno = 0.02 +problem.yd = 3.7e7 diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot_1.5 b/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot_1.5 new file mode 100644 index 000000000..fa49a9ce8 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/inputs_hot_1.5 @@ -0,0 +1,27 @@ +problem.model_prefix = "toy_atm_hot" + +problem.nx = 2048 +problem.xmin = 0.0 +problem.xmax = 3072.0 + +problem.g_const = -1.29e14 + +problem.T_star = 1.0e8 +problem.H_star = 1120.0 +problem.delta = 5.0 + +problem.T_base = 3.5e8 + +problem.y1 = 4.5e8 +problem.del1 = 0.37 +problem.y2 = 5.6e7 +problem.del2 = 0.12 +problem.y3 = 2.0e7 +problem.del3 = 0.22 + +problem.low_density_cutoff = 1.e-4 +problem.T_lo = 1.e6 + +problem.X0 = 0.7 +problem.Zcno = 0.02 +problem.yd = 3.7e7 diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/main.cpp b/Exec/science/xrb_layered/create_initial_model/toy_atm/main.cpp new file mode 100644 index 000000000..37cc5388d --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/main.cpp @@ -0,0 +1,62 @@ +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include + +#include +#include +#include + +std::string inputs_name = ""; + +int +main (int argc, + char* argv[]) +{ + + // + // Make sure to catch new failures. + // + amrex::Initialize(argc,argv); + + // save the inputs file name for later + if (argc > 1) { + if (!strchr(argv[1], '=')) { + inputs_name = argv[1]; + // std::cout << "inputs file is " << inputs_name << std::endl; + } + } else { + std::cout << "No input file. Using defaults" << inputs_name << std::endl; + } + + + // initialize the runtime parameters + + init_extern_parameters(); + + // initialize C++ Microphysics + + eos_init(problem_rp::small_temp, problem_rp::small_dens); + + init_1d(); + + amrex::Finalize(); + + return 0; +} diff --git a/Exec/science/xrb_layered/create_initial_model/toy_atm/model_util.H b/Exec/science/xrb_layered/create_initial_model/toy_atm/model_util.H new file mode 100644 index 000000000..d7c4fa3b3 --- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/model_util.H @@ -0,0 +1,24 @@ +#ifndef MODEL_UTIL_H +#define MODEL_UTIL_H + +std::string num_to_unitstring(const double value) { + + std::string numstr{}; + std::stringstream ss; + + if (value > 1.e5) { + // work in km + ss << std::setprecision(2) << value / 1.e5; + numstr = ss.str() + "km"; + } else { + // work in cm + ss << std::setprecision(3) << value; + numstr = ss.str() + "cm"; + } + + return numstr; +} + + + +#endif diff --git 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--- /dev/null +++ b/Exec/science/xrb_layered/create_initial_model/toy_atm/toy_atm_hot_3cm.hse.extras @@ -0,0 +1,3076 @@ +# npts = 1024 +# num of variables = 2 +# entropy +# c_s + 1.5 + 23032475.387 + 337949793.287 + 4.5 + 23045768.0167 + 337712082.448 + 7.5 + 23059088.4243 + 337474124.625 + 10.5 + 23072436.7274 + 337235918.72 + 13.5 + 23085813.0449 + 336997463.656 + 16.5 + 23099217.4969 + 336758758.376 + 19.5 + 23112650.2045 + 336519801.842 + 22.5 + 23126111.2903 + 336280593.042 + 25.5 + 23139600.8778 + 336041130.983 + 28.5 + 23153119.0917 + 335801414.697 + 31.5 + 23166666.0578 + 335561443.239 + 34.5 + 23180241.9028 + 335321215.691 + 37.5 + 23193846.7545 + 335080731.158 + 40.5 + 23207480.7417 + 334839988.772 + 43.5 + 23221143.9939 + 334598987.69 + 46.5 + 23234836.6418 + 334357727.099 + 49.5 + 23248558.8166 + 334116206.211 + 52.5 + 23262310.6507 + 333874424.268 + 55.5 + 23276092.277 + 333632380.539 + 58.5 + 23289903.8293 + 333390074.325 + 61.5 + 23303745.442 + 333147504.953 + 64.5 + 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3.98092385968e-06 2.60441628774e-10 1.97930889343e-13 3.5457540675e-08 2.10622268206e-10 6.64437353286e-13 3.66708113144e-11 1.20161033038e-34 1e-98 - 1798.5 77978.6632801 130734580.336 1.58827971704e+21 0.480250190252 0.495372766555 6.07267624201e-06 1.56607562689e-09 6.02038276638e-05 9.58906500167e-05 1.47830282521e-09 0.00848194302521 0.0157283276639 5.19919516974e-07 9.00324480504e-09 5.74547484537e-19 4.02577760336e-06 2.6466415563e-10 2.04045334622e-13 3.4426510437e-08 2.06395668908e-10 6.86741491596e-13 3.39849645883e-11 9.80530091096e-35 1e-98 - 1801.5 76606.2998154 130306311.429 1.55836752669e+21 0.485301988571 0.490321522857 6.29226262857e-06 1.68733411428e-09 6.26318965714e-05 0.000100585836571 1.53966265714e-09 0.00847892285714 0.0157233965714 5.42877937143e-07 9.41898077142e-09 6.07435854285e-19 4.07387491429e-06 2.69241651429e-10 2.10729637143e-13 3.33243608572e-08 2.01850542857e-10 7.11278214285e-13 3.1218894e-11 7.73101217145e-35 1e-98 - 1804.5 75188.6634271 129694539.13 1.5289952013e+21 0.492319973913 0.483304371739 6.62450206521e-06 1.88112530435e-09 6.63249821739e-05 0.000107748510869 1.63312571739e-09 0.00847432626087 0.0157158804348 5.78010023913e-07 1.00470300652e-08 6.58568991304e-19 4.1416498913e-06 2.75874404348e-10 2.20645673913e-13 3.17831015218e-08 1.95404054348e-10 7.48055795652e-13 2.76641986957e-11 5.51435902176e-35 1e-98 - 1807.5 73876.6190309 129249143.137 1.50015106914e+21 0.49727155817 0.478353428758 6.88063154901e-06 2.03813552941e-09 6.91847593464e-05 0.000113305398039 1.70558768627e-09 0.00847076626144 0.015710049281 6.05363281699e-07 1.05303296732e-08 6.98958828104e-19 4.19049324837e-06 2.80793040523e-10 2.28141148366e-13 3.06804081046e-08 1.90732688235e-10 7.75989335294e-13 2.53209709151e-11 4.30282200655e-35 1e-98 - 1810.5 72627.8266642 128899155.822 1.4718024589e+21 0.501071144178 0.474554370891 7.09092351369e-06 2.17209600342e-09 7.15407702396e-05 0.000117888920548 1.76534036986e-09 0.00846783318836 0.0157052407877 6.27997172602e-07 1.09266571918e-08 7.32761831163e-19 4.22863795548e-06 2.84719548972e-10 2.34205342466e-13 2.98263077398e-08 1.87079775e-10 7.98595190068e-13 2.36121471576e-11 3.53524256851e-35 1e-98 - 1813.5 71409.1483474 128563231.164 1.44393130424e+21 0.504607068836 0.471018994178 7.30608310274e-06 2.31734840068e-09 7.39644380479e-05 0.00012261330411 1.82689927397e-09 0.00846481503767 0.0157002921575 6.5144799452e-07 1.13316914384e-08 7.68408646233e-19 4.2649567911e-06 2.88578829794e-10 2.40285068493e-13 2.9028725548e-08 1.83617275e-10 8.21183238014e-13 2.21516574315e-11 3.0123405137e-35 1e-98 - 1816.5 70208.3456666 128227306.507 1.41652831914e+21 0.508142993493 0.467483617466 7.52124269178e-06 2.46260079795e-09 7.63881058562e-05 0.000127337687671 1.88845817808e-09 0.00846179688699 0.0156953435274 6.74898816439e-07 1.17367256849e-08 8.04055461302e-19 4.30127562671e-06 2.92438110617e-10 2.46364794521e-13 2.82311433561e-08 1.80154775e-10 8.43771285959e-13 2.06911677055e-11 2.48943845889e-35 1e-98 - 1819.5 69002.2095008 127801366.176 1.38959107672e+21 0.512530159454 0.463097187815 7.80645107145e-06 2.66444411136e-09 7.96158157985e-05 0.000133639837815 1.97054205253e-09 0.0084577787479 0.0156887466176 7.06292164918e-07 1.22711844328e-08 8.52395775003e-19 4.34637033404e-06 2.97358664916e-10 2.54284278362e-13 2.7240877563e-08 1.75805977731e-10 8.72471631724e-13 1.89798310924e-11 1.94395794115e-35 1e-98 - 1822.5 67760.9951791 127155042.647 1.36312739661e+21 0.51900139895 0.456627145798 8.2631582143e-06 3.00483759875e-09 8.48120426893e-05 0.000143804795799 2.10287671639e-09 0.00845131236134 0.0156781142647 7.57131009456e-07 1.31225100631e-08 9.31813275003e-19 4.41295079622e-06 3.04877509454e-10 2.66707996849e-13 2.57788691596e-08 1.69287280252e-10 9.16136568699e-13 1.66543521008e-11 1.34319911763e-35 1e-98 - 1825.5 66585.693523 126650581.67 1.33713131235e+21 0.52396338167 0.451666119959 8.64097569858e-06 3.30278799593e-09 8.91337535031e-05 0.000152267594908 2.21309431161e-09 0.00844593334216 0.0156692660081 7.99719263137e-07 1.38249125458e-08 9.99481480653e-19 4.46442089817e-06 3.10874299593e-10 2.76796067414e-13 2.46707006314e-08 1.64280019552e-10 9.50148557842e-13 1.50127640529e-11 9.90452805903e-36 1e-98 - 1828.5 65447.5746389 126205016.667 1.31158287496e+21 0.5282885125 0.447341854167 8.98810049999e-06 3.58652670833e-09 9.31182470833e-05 0.000160075066667 2.31480983333e-09 0.008440973875 0.015661105 8.39168862499e-07 1.44690254167e-08 1.062851e-18 4.50954741667e-06 3.16249520833e-10 2.85950616667e-13 2.37125466667e-08 1.59908208333e-10 9.80066404166e-13 1.36689804167e-11 7.47577420836e-36 1e-98 - 1831.5 64330.8692381 125746266.667 1.28647074607e+21 0.5326102 0.443021166667 9.37138799999e-06 3.92193733332e-09 9.75459033332e-05 0.000168759566666 2.42810233333e-09 0.008435467 0.01565203 8.83398799999e-07 1.51765066666e-08 1.135421e-18 4.55509366667e-06 3.21910833333e-10 2.95828366666e-13 2.27650966667e-08 1.55507333333e-10 1.00966896667e-12 1.24559366667e-11 5.78328233338e-36 1e-98 - 1834.5 63237.4900471 125299535.97 1.26178626855e+21 0.536752237476 0.4388801371 9.75843338606e-06 4.2734588192e-09 0.000102034576497 0.000177565725612 2.54299976459e-09 0.00842988634652 0.0156428291714 9.28457455931e-07 1.58889045763e-08 1.21013373823e-18 4.59901031073e-06 3.2748653145e-10 3.05678085687e-13 2.18606845386e-08 1.51270279096e-10 1.03754498493e-12 1.13466082486e-11 4.39696455745e-36 1e-98 - 1837.5 62167.4498975 124866541.62 1.23752041267e+21 0.54068896064 0.434944430885 1.01497734991e-05 4.64339271752e-09 0.000106592983277 0.000186510923352 2.65973140302e-09 0.00842422137476 0.0156334845386 9.74463218645e-07 1.66069215254e-08 1.28729532015e-18 4.64106454802e-06 3.32964384558e-10 3.15495769303e-13 2.10054585499e-08 1.47220448587e-10 1.06344780979e-12 1.03558116384e-11 3.36056003765e-36 1e-98 - 1840.5 61094.509265 124287996.296 1.21366922357e+21 0.545787388889 0.429847437037 1.07030733334e-05 5.1971990741e-09 0.000113072555556 0.000199228714815 2.82596977778e-09 0.00841617459259 0.0156202 1.04037112593e-06 1.76169425926e-08 1.39988066667e-18 4.69632892593e-06 3.40427966667e-10 3.29127240741e-13 1.99026933333e-08 1.4192417037e-10 1.09534e-12 9.16290685181e-12 2.34067459256e-36 1e-98 - 1843.5 60054.5648113 123781285.668 1.19022687774e+21 0.550179013355 0.425457184365 1.12139003909e-05 5.73582711076e-09 0.0001190853557 0.000211029871336 2.98045154398e-09 0.00840871272313 0.015607872443 1.10197770033e-06 1.85451665472e-08 1.50684239088e-18 4.74402715635e-06 3.47092255049e-10 3.41504755375e-13 1.89686949837e-08 1.37379093811e-10 1.12064734853e-12 8.21641782734e-12 1.68310336807e-36 1e-98 - 1846.5 59054.2776361 123414488.329 1.16717931672e+21 0.553308765418 0.422328484438 1.1607323977e-05 6.17685936744e-09 0.000123744076225 0.000220169909222 3.10032004755e-09 0.00840293709942 0.0155983265562 1.15012790778e-06 1.92562418732e-08 1.59205400288e-18 4.77802367291e-06 3.52036716283e-10 3.50856317435e-13 1.83182617003e-08 1.34166830836e-10 1.13634568732e-12 7.60440624062e-12 1.34597977953e-36 1e-98 - 1849.5 58074.3185858 123086520.029 1.14451493335e+21 0.556087007493 0.419551193372 1.19680761383e-05 6.5909842954e-09 0.000128025992651 0.000228569044669 3.2105562147e-09 0.00839763090346 0.0155895556628 1.19453655332e-06 1.99068787608e-08 1.67123498271e-18 4.80823246254e-06 3.56504252306e-10 3.59366245389e-13 1.77460397983e-08 1.31323314986e-10 1.14935937608e-12 7.08306805618e-12 1.08904482276e-36 1e-98 - 1852.5 57098.2605366 122662598.026 1.12222903929e+21 0.559601810526 0.416037695395 1.24552141448e-05 7.17784277634e-09 0.000133836541448 0.000239962992764 3.3603249408e-09 0.00839043586184 0.0155776523684 1.25523709211e-06 2.07816655264e-08 1.78117973685e-18 4.84680827632e-06 3.62371032237e-10 3.70706967764e-13 1.70297436184e-08 1.27723525e-10 1.16371411842e-12 6.46089782234e-12 8.25533781571e-37 1e-98 - 1855.5 56121.0954257 122075295.395 1.10032109392e+21 0.564370757895 0.411270655921 1.31575496711e-05 8.05881514475e-09 0.000142249896711 0.000256456216448 3.57740586185e-09 0.00838002467763 0.0155604155263 1.34367788158e-06 2.20381128948e-08 1.94350605263e-18 4.89963064474e-06 3.70620308553e-10 3.86867691448e-13 1.60681317763e-08 1.22836025e-10 1.18035227632e-12 5.66704058551e-12 5.50825623679e-37 1e-98 - 1858.5 55176.082923 121609912.425 1.07878508991e+21 0.56806796473 0.407575063928 1.3755187976e-05 8.86500725252e-09 0.000149461014229 0.000270577261122 3.76378256914e-09 0.00837111450301 0.0155456574549 1.42034037275e-06 2.31005247896e-08 2.0876283467e-18 4.94086241683e-06 3.77369379359e-10 4.00346075551e-13 1.53429192585e-08 1.19086796794e-10 1.18852034469e-12 5.1156838537e-12 4.05810995188e-37 1e-98 - 1861.5 54247.5209626 121152183.902 1.05761162255e+21 0.571658378537 0.403986290244 1.43624552195e-05 9.71181506831e-09 0.000156812957561 0.000284966376098 3.95394691708e-09 0.00836203654146 0.0155306158537 1.49891571707e-06 2.41769676586e-08 2.23700348781e-18 4.98114752195e-06 3.8410733122e-10 4.13921302927e-13 1.46464472683e-08 1.15456942439e-10 1.1943139122e-12 4.60641660975e-12 2.90038043901e-37 1e-98 - 1864.5 53331.8265486 120634061.951 1.03679501881e+21 0.575607549268 0.400039095122 1.50921796098e-05 1.07930904341e-08 0.00016570290878 0.000302347498049 4.18422325853e-09 0.00835107337073 0.0155124329268 1.59488205854e-06 2.54637408293e-08 2.4232632439e-18 5.02621996098e-06 3.9195035561e-10 4.29980961463e-13 1.38934326342e-08 1.1147199122e-10 1.1962741561e-12 4.09407880488e-12 2.01952921952e-37 1e-98 - 1867.5 52433.9224079 120123935.429 1.01632934639e+21 0.579405629714 0.396242354286 1.58533251428e-05 1.19897482857e-08 0.000175031295428 0.000320563069143 4.42617776e-09 0.00833985110286 0.0154938728571 1.69659022857e-06 2.67995274857e-08 2.62477918285e-18 5.07037340571e-06 3.99909354857e-10 4.46500592e-13 1.31839298286e-08 1.07666205143e-10 1.19382400571e-12 3.64245692001e-12 1.40695277715e-37 1e-98 - 1870.5 51556.0258946 119670519.535 9.9620729139e+20 0.58269760186 0.392952940465 1.65657215814e-05 1.31732164186e-08 0.000183811433023 0.000337688207907 4.6541908279e-09 0.0083288458 0.0154755725116 1.79324063256e-06 2.80447690697e-08 2.82005114883e-18 5.10912225116e-06 4.07166793953e-10 4.61767579535e-13 1.25774128372e-08 1.04370097209e-10 1.18826455349e-12 3.28022751163e-12 1.01611362698e-37 1e-98 - 1873.5 50694.3123211 119237382.313 9.76421850946e+20 0.58576949932 0.389883959864 1.72751367347e-05 1.44038787755e-08 0.000192593514285 0.000354800049659 4.88246986394e-09 0.00831772182993 0.0154570414966 1.89066153061e-06 2.92810763945e-08 3.0199807551e-18 5.14592430612e-06 4.14258114966e-10 4.76829648979e-13 1.20163069388e-08 1.01289829932e-10 1.18056797279e-12 2.96181078912e-12 7.32213570753e-38 1e-98 - 1876.5 49847.4057634 118772756.463 9.56967028496e+20 0.588950859864 0.386704231973 1.80786673469e-05 1.58743277551e-08 0.000202594942857 0.000374260729932 5.14274197279e-09 0.00830566196599 0.0154370482993 2.00270030612e-06 3.06762872789e-08 3.25447055102e-18 5.18561206122e-06 4.22128182993e-10 4.93711689796e-13 1.14404293878e-08 9.80899659864e-11 1.16932239456e-12 2.65563255782e-12 5.21102074151e-38 1e-98 - 1879.5 49013.3739547 118238762.577 9.37837467621e+20 0.592517772086 0.383139867485 1.90529219325e-05 1.77650657669e-08 0.000214795974847 0.000397967372393 5.46065271472e-09 0.00829076134049 0.0154123038344 2.14091996932e-06 3.2362238773e-08 3.5501608681e-18 5.23106881595e-06 4.31457341104e-10 5.13973733742e-13 1.08076785583e-08 9.45274555215e-11 1.15296889877e-12 2.34017043865e-12 3.49423088037e-38 1e-98 - 1882.5 48195.6503248 117809257.895 9.19027521423e+20 0.595326769779 0.38033321545 1.9882021511e-05 1.94730117148e-08 0.000225242771817 0.000418233503226 5.73318375892e-09 0.00827797481834 0.0153910540238 2.26066231239e-06 3.37932223599e-08 3.81220681155e-18 5.26773255518e-06 4.39241258234e-10 5.31059494228e-13 1.03214596519e-08 9.1755252309e-11 1.13714913582e-12 2.11375274533e-12 2.5363073837e-38 1e-98 - 1885.5 47390.7837711 117444521.053 9.00531546425e+20 0.597685869949 0.377976203565 2.06085665026e-05 2.10170796265e-08 0.000234427101189 0.000436035116129 5.97293163668e-09 0.00826674188115 0.0153723816978 2.36659745671e-06 3.50456128523e-08 4.04682311036e-18 5.29895751273e-06 4.45994059593e-10 5.45961344822e-13 9.91923299659e-09 8.94455120712e-11 1.12246441596e-12 1.93409909507e-12 1.89682555007e-38 1e-98 - 1888.5 46597.562052 116970509.639 8.82344801508e+20 0.600688166265 0.374976768675 2.16152866265e-05 2.3318757229e-08 0.000247252701205 0.000460846859037 6.3082314217e-09 0.00825108151807 0.0153463366265 2.51684962651e-06 3.67764583133e-08 4.38922504821e-18 5.33982626506e-06 4.55147839759e-10 5.66438454218e-13 9.42631807227e-09 8.65706478312e-11 1.10060580723e-12 1.72942004819e-12 1.34962515661e-38 1e-98 - 1891.5 45815.9063257 116451960.113 8.64462795377e+20 0.603946053308 0.371722073724 2.27409930624e-05 2.59532218338e-08 0.00026163079811 0.000488644321173 6.68429891117e-09 0.00823353508128 0.0153171503592 2.68615168432e-06 3.87104621551e-08 4.77859684122e-18 5.38467308885e-06 4.65312574102e-10 5.89276585067e-13 8.89835709639e-09 8.3473997032e-11 1.0756582344e-12 1.51632285822e-12 8.52858368989e-39 1e-98 - 1894.5 45049.5717745 115988462.949 8.46880325365e+20 0.606722386011 0.368948916824 2.3840569622e-05 2.87820176749e-08 0.000275815750851 0.000515994850473 7.05597168999e-09 0.00821626211342 0.0152884093384 2.85681879207e-06 4.05990160303e-08 5.18659287147e-18 5.42580031002e-06 4.75044426654e-10 6.11433711721e-13 8.4620845066e-09 8.08671728355e-11 1.05036409452e-12 1.35859431379e-12 6.17317593944e-39 1e-98 - 1897.5 44294.4546984 115515568.154 8.29592256242e+20 0.609527921907 0.366146705071 2.49924175254e-05 3.183914357e-08 0.000290723543408 0.000544714688439 7.4467995781e-09 0.00819812147464 0.0152582206288 3.03755113185e-06 4.25787413591e-08 5.62438548479e-18 5.46823251116e-06 4.8519281501e-10 6.34630586816e-13 8.02734738133e-09 7.82565557403e-11 1.02378042475e-12 1.20571926166e-12 4.10162281337e-39 1e-98 - 1900.5 43552.0839809 115018103.651 8.12593951008e+20 0.612409808316 0.363268530629 2.62809266531e-05 3.54932287626e-08 0.000307521170182 0.000577014526166 7.88770769168e-09 0.00817771234686 0.015224246998 3.24459859635e-06 4.47968306085e-08 6.1400800081e-18 5.51407652738e-06 4.96430218661e-10 6.60545840365e-13 7.59662466329e-09 7.56360222719e-11 9.93825333469e-13 1.06553346045e-12 2.77219563288e-39 1e-98 - 1903.5 42821.3465167 114510786.957 7.95880692207e+20 0.615302369565 0.360380026087 2.76686321739e-05 3.96849817391e-08 0.000325734821739 0.000611976626086 8.36629921738e-09 0.00815561204348 0.0151874552174 3.47295034782e-06 4.71945430434e-08 6.72471365217e-18 5.56240382609e-06 5.08478034782e-10 6.88522013043e-13 7.17976300001e-09 7.30751773913e-11 9.61783365218e-13 9.37867000002e-13 1.82263508697e-39 1e-98 - 1906.5 42101.5450682 114004156.027 7.79448112685e+20 0.618153199554 0.357533457589 2.91152840402e-05 4.42671988617e-08 0.000344821301563 0.000648565834375 8.8682295134e-09 0.008132476 0.0151489347321 3.71546961161e-06 4.97029474777e-08 7.35870359152e-18 5.61208402455e-06 5.21010658259e-10 7.17767756697e-13 6.7811442366e-09 7.06073583035e-11 9.28669369196e-13 8.22079104017e-13 1.15607378705e-39 1e-98 - 1909.5 41392.1291134 113527303.348 7.63292086731e+20 0.620780744196 0.354910198661 3.05690050223e-05 4.9218437701e-08 0.000364154895313 0.000685554896876 9.37725317411e-09 0.008109076 0.0151099615179 3.9665334509e-06 5.22425747099e-08 8.03670693974e-18 5.6613690692e-06 5.33616082366e-10 7.47369587054e-13 6.43014557589e-09 6.84120279464e-11 8.96189949553e-13 7.27871202231e-13 7.83472581691e-40 1e-98 - 1912.5 40693.4597187 113049547.826 7.47408525292e+20 0.62336751087 0.352327997826 3.21058941304e-05 5.47954258696e-08 0.000384736473913 0.000724866804348 9.91965308696e-09 0.00808419509783 0.015068518913 4.23944558696e-06 5.4952666087e-08 8.79563041304e-18 5.71346867391e-06 5.47021801087e-10 7.78988406522e-13 6.09742486956e-09 6.63177863043e-11 8.62852193478e-13 6.43713298913e-13 5.25721711956e-40 1e-98 - 1915.5 40005.6018206 112558823.796 7.31793256884e+20 0.625986483286 0.349713845326 3.37443007932e-05 6.09979376769e-08 0.000406781361756 0.000766927428328 1.0500982068e-08 0.00805758655524 0.0150242008782 4.53608641926e-06 5.78624198866e-08 9.63689481585e-18 5.76915170255e-06 5.61389385835e-10 8.12967160623e-13 5.7691668442e-09 6.42430352692e-11 8.28145916431e-13 5.64332821248e-13 3.32024513883e-40 1e-98 - 1918.5 39332.5075202 111996982.436 7.16441332727e+20 0.628895038527 0.346810814164 3.57395727478e-05 6.91018555239e-08 0.000433841106798 0.000818464283851 1.12152625212e-08 0.00802507692351 0.0149700733994 4.90981588101e-06 6.14559524645e-08 1.07322602549e-17 5.83774971955e-06 5.79105958073e-10 8.55022231444e-13 5.41819913882e-09 6.20132703967e-11 7.87938692636e-13 4.85632962891e-13 1.99699273091e-40 1e-98 - 1921.5 38662.477312 111572048.963 7.01349303161e+20 0.631062459751 0.344648750207 3.7357166971e-05 7.62350280499e-08 0.000455981324067 0.000860550609129 1.18003053527e-08 0.00799823472199 0.0149253267635 5.22608697096e-06 6.44306031536e-08 1.16961729046e-17 5.89453558091e-06 5.93771356847e-10 8.89943245229e-13 5.17234423236e-09 6.0447689751e-11 7.5701349834e-13 4.3483869585e-13 1.41989939419e-40 1e-98 - 1924.5 38003.5091278 111128279.839 6.86514434785e+20 0.633273779839 0.342442993548 3.91069315323e-05 8.42919626211e-08 0.000480044997178 0.000906250826211 1.24364932258e-08 0.00796917533871 0.0148769020161 5.57638907259e-06 6.76836624194e-08 1.2786050242e-17 5.95649340726e-06 6.09802304436e-10 9.28188600001e-13 4.92491653225e-09 5.88703267742e-11 7.24903116128e-13 3.85656990322e-13 9.44700673382e-41 1e-98 - 1927.5 37357.5206971 110645497.581 6.71932075514e+20 0.635595997581 0.340126703226 4.11028629838e-05 9.39904243143e-08 0.000507663807661 0.000958640443144 1.31671383871e-08 0.00793601808065 0.0148216802419 5.9881910887e-06 7.14468962902e-08 1.4099723629e-17 6.02794360887e-06 6.28336316532e-10 9.72513599999e-13 4.66979798388e-09 5.72411816129e-11 6.90306341936e-13 3.3794005484e-13 6.10191600814e-41 1e-98 - 1930.5 36725.0295605 110102740.838 6.57597102039e+20 0.638152126702 0.337577819372 4.34851786911e-05 1.06476362304e-07 0.000540915942932 0.00102162348272 1.4047541623e-08 0.00789606785864 0.0147551398429 6.50242782722e-06 7.60383487434e-08 1.57997319372e-17 6.1146113822e-06 6.50927098953e-10 1.02676069319e-12 4.40185341361e-09 5.5529212199e-11 6.51488684293e-13 2.91260327225e-13 3.73155916231e-41 1e-98 - 1933.5 36099.2057298 109610807.143 6.43505612511e+20 0.64039955 0.335337407143 4.57563721429e-05 1.19139528572e-07 0.000572840064286 0.001082027 1.48932814286e-08 0.00785771407143 0.01469127 7.01251928572e-06 8.0503265e-08 1.75343328572e-17 6.19843685714e-06 6.72891135714e-10 1.07960764286e-12 4.17215292857e-09 5.40628185714e-11 6.16740235714e-13 2.53749978571e-13 2.32266799999e-41 1e-98 - 1936.5 35476.7074632 109214232.468 6.29655673308e+20 0.642134436364 0.33360855974 4.76885133766e-05 1.30602548052e-07 0.000600192710389 0.00113372927273 1.56183074026e-08 0.00782486650649 0.0146365881818 7.46494266233e-06 8.43835263636e-08 1.91153620779e-17 6.27086153247e-06 6.91982580519e-10 1.12559774026e-12 3.99768685715e-09 5.2951338052e-11 5.89329635065e-13 2.27379248052e-13 1.63891345455e-41 1e-98 - 1939.5 34864.5219272 108795161.749 6.16044645421e+20 0.643936415301 0.331813263934 4.98206191803e-05 1.44031851912e-07 0.000630582218032 0.00119112797814 1.64242143169e-08 0.00778839125683 0.014575869235 7.98079885791e-06 8.86926125136e-08 2.09696344808e-17 6.34759703279e-06 7.12989162295e-10 1.17667744262e-12 3.81964415301e-09 5.18015145355e-11 5.60675927323e-13 2.02013768853e-13 1.09207757432e-41 1e-98 - 1942.5 34263.9815271 108340407.65 6.02668280003e+20 0.64584481694 0.329912567213 5.22699101639e-05 1.60580089618e-07 0.000665788816394 0.00125756240437 1.73584151366e-08 0.00774616174863 0.014505574153 8.59727262842e-06 9.36819034973e-08 2.32573271038e-17 6.43117039344e-06 7.37033547541e-10 1.23583031148e-12 3.6359281694e-09 5.05908710929e-11 5.30050394535e-13 1.78242826229e-13 7.62423025135e-42 1e-98 - 1945.5 33670.9878935 107885653.552 5.8952286342e+20 0.647753218579 0.328011870492 5.47192011476e-05 1.77128327323e-07 0.000700995414755 0.0013239968306 1.82926159563e-08 0.00770393224044 0.014435279071 9.21374639892e-06 9.8671194481e-08 2.55450197268e-17 6.5147437541e-06 7.61077932788e-10 1.29498318033e-12 3.45221218579e-09 4.93802276502e-11 4.99424861748e-13 1.54471883606e-13 4.32768475947e-42 1e-98 - 1948.5 33092.4186554 107381247.458 5.76604144252e+20 0.649748774576 0.326026031638 5.77611549153e-05 2.01047351413e-07 0.000745515860453 0.00140789956497 1.9475130678e-08 0.00765057644633 0.0143465024859 1.00340851978e-05 1.04777906441e-07 2.88169871187e-17 6.59579515819e-06 7.89391805085e-10 1.36721979661e-12 3.26227576836e-09 4.80283216949e-11 4.66942641807e-13 1.34569608474e-13 2.98736711297e-42 1e-98 - 1951.5 32521.4513276 106874044.068 5.63907860411e+20 0.651749240678 0.324035396045 6.08364981356e-05 2.25381631074e-07 0.000790561029944 0.00149278642938 2.06716349153e-08 0.00759659381921 0.0142566846893 1.08659093503e-05 1.10947571695e-07 3.21444066102e-17 6.67670448023e-06 8.17946211865e-10 1.44019352542e-12 3.07198890395e-09 4.66684572881e-11 4.34355819774e-13 1.14885286441e-13 1.75725948587e-42 1e-98 - 1954.5 31970.9623215 106230330.435 5.5142857837e+20 0.654224713044 0.321573504348 6.50276952175e-05 2.62167934783e-07 0.000852718273914 0.00160986939131 2.23236195652e-08 0.00752214913043 0.0141328369565 1.20250595652e-05 1.18953078261e-07 3.70461965218e-17 6.75192452174e-06 8.53414530435e-10 1.53466295652e-12 2.83601021739e-09 4.48391991304e-11 3.9416233913e-13 9.3920737391e-14 8.82928121731e-43 1e-98 - 1957.5 31426.2437134 105613734.839 5.39161219002e+20 0.656536095484 0.319275988387 6.92653425162e-05 3.02212735484e-07 0.000916133072259 0.00172931769678 2.40095916774e-08 0.00744620607097 0.0140065494194 1.32094701936e-05 1.26680442581e-07 4.22616348388e-17 6.80044526452e-06 8.86392774839e-10 1.6256248129e-12 2.6134482e-09 4.29989452903e-11 3.56728379355e-13 7.66298379999e-14 4.34765856772e-43 1e-98 - 1960.5 30869.6507662 105216331.613 5.2710696342e+20 0.657960805161 0.317860956129 7.21879328387e-05 3.32453945161e-07 0.000960350217419 0.00181264463226 2.51854852258e-08 0.00739322849032 0.0139185478065 1.40288040645e-05 1.31613894193e-07 4.60627316128e-17 6.80862655484e-06 9.06284371613e-10 1.68342707097e-12 2.4719682e-09 4.17270710968e-11 3.33727766452e-13 6.74733880001e-14 2.9861879226e-43 1e-98 - 1963.5 30322.3263109 104801288.36 5.15266315856e+20 0.659420846561 0.316411396825 7.53383049205e-05 3.66807077247e-07 0.00100830861905 0.00190306850264 2.64610816402e-08 0.00733575528572 0.0138231190476 1.48972338624e-05 1.36583256085e-07 5.023758582e-17 6.80055603175e-06 9.25476656613e-10 1.74158555555e-12 2.32504786773e-09 4.03444452911e-11 3.10317493122e-13 5.86609940214e-14 1.8552574106e-43 1e-98 - 1966.5 29787.3474721 104343748.677 5.03635093979e+20 0.660966005291 0.31487865873 7.90374239682e-05 4.11066204232e-07 0.00106528004762 0.00201058953439 2.79768713227e-08 0.00726745171429 0.013709797619 1.58839402116e-05 1.416391291e-07 5.53128556613e-17 6.7533334127e-06 9.42984235979e-10 1.80060222222e-12 2.16502128042e-09 3.86950087831e-11 2.85920310582e-13 5.06774622752e-14 1.27971903757e-43 1e-98 - 1969.5 29259.00035 103886208.995 4.92209625675e+20 0.662511164021 0.313345920635 8.27365430159e-05 4.55325331218e-07 0.00112225147619 0.00211811056614 2.94926610053e-08 0.00719914814286 0.0135964761905 1.68706465609e-05 1.46695002116e-07 6.03881255027e-17 6.70611079365e-06 9.60491815344e-10 1.85961888889e-12 2.00499469312e-09 3.70455722751e-11 2.61523128042e-13 4.2693930529e-14 7.04180664541e-44 1e-98 - 1972.5 28751.7366009 103302229.301 4.80984548075e+20 0.664387644234 0.31148703535 8.78729393763e-05 5.2615025898e-07 0.00120275199433 0.00227042890359 3.16356170889e-08 0.00710249976937 0.0134363966541 1.81063813611e-05 1.51809734972e-07 6.75828041589e-17 6.56923540075e-06 9.73640207562e-10 1.91946134594e-12 1.80787100567e-09 3.47510820794e-11 2.33085183175e-13 3.49023481473e-14 3.94841985062e-44 1e-98 - 1975.5 28254.9002052 102680648.148 4.69953763853e+20 0.6663621 0.309531728704 9.34404408334e-05 6.05037561112e-07 0.00129032160185 0.00243621237037 3.39670047222e-08 0.00699733311111 0.0132622680556 1.94149258333e-05 1.56924256482e-07 7.54110351853e-17 6.4050219074e-06 9.85388252778e-10 1.97934646296e-12 1.59981592592e-09 3.22637312037e-11 2.03469661111e-13 2.71816929629e-14 1.64960047684e-44 1e-98 - 1978.5 27771.2356649 102005462.963 4.59112706562e+20 0.6684221 0.307494599074 9.99815241667e-05 7.11233672224e-07 0.00139498178704 0.00263514940741 3.67544769445e-08 0.00687134422222 0.0130540736111 2.07055091667e-05 1.6030990463e-07 8.44615537038e-17 6.13581264814e-06 9.84993530556e-10 2.01949275926e-12 1.38600851852e-09 2.9400851574e-11 1.74525772222e-13 2.09129892592e-14 7.69148162026e-45 1e-98 - 1981.5 27275.346368 101514425 4.48461192939e+20 0.669850075 0.306084425 0.0001049964 8.00614350002e-07 0.0014761185 0.002789907 3.89166025e-08 0.00677343775 0.01289259 2.155077e-05 1.61647375e-07 9.12429300001e-17 5.9022985e-06 9.78422525e-10 2.0360175e-12 1.2389625e-09 2.7293755e-11 1.5538845e-13 1.72095424999e-14 4.39332699994e-45 1e-98 - 1984.5 26783.2161526 101049248.069 4.38000861091e+20 0.671179516094 0.304772777253 0.000109948845923 8.96432874249e-07 0.00155703932618 0.00294481349356 4.10742548927e-08 0.00667553949785 0.0127314046781 2.22413432833e-05 1.61834651931e-07 9.76728763734e-17 5.65112330258e-06 9.66435818884e-10 2.03935766094e-12 1.10712011459e-09 2.52884718884e-11 1.38554629828e-13 1.42622212446e-14 2.59634572103e-45 1e-98 - 1987.5 26297.3353089 100586060.4 4.27729774383e+20 0.672483675048 0.303486905143 0.000115005728762 9.99376835806e-07 0.00164017393714 0.00310435861523 4.32919429142e-08 0.00657478026857 0.0125657010476 2.28570185714e-05 1.61367857905e-07 1.04066433524e-16 5.38505476572e-06 9.51130766477e-10 2.03528991619e-12 9.81036900003e-10 2.33000972953e-11 1.226335728e-13 1.16474287582e-14 1.39177923449e-45 1e-98 - 1990.5 25821.2251966 100101380.4 4.17644832925e+20 0.673772760762 0.302218162286 0.00012057350019 1.12546929295e-06 0.00173288079428 0.0032833460438 4.57679886285e-08 0.00646192369715 0.0123805867619 2.33434471428e-05 1.59596086476e-07 1.10742776381e-16 5.08265305144e-06 9.2891442362e-10 2.01679905905e-12 8.62446900003e-10 2.12910287239e-11 1.080358128e-13 9.53527332958e-15 8.45327291634e-46 1e-98 - 1993.5 25357.9698273 99557655.1111 4.07741658688e+20 0.675191185556 0.300823395556 0.000126981522222 1.2802186e-06 0.0018403518 0.00349172403333 4.86414423333e-08 0.00633066433334 0.0121656724444 2.37717003333e-05 1.56796853333e-07 1.18088435555e-16 4.73304193334e-06 9.00277162223e-10 1.98688543333e-12 7.37675002224e-10 1.90961385556e-11 9.2736678778e-14 7.45591352225e-15 4.07602964448e-46 1e-98 - 1996.5 24914.3538187 98895256.5574 3.98013964063e+20 0.67686673388 0.299178251366 0.000135098256831 1.49524767213e-06 0.00197794653006 0.00376028624044 5.23268409837e-08 0.00616173981967 0.0118898752459 2.4063130929e-05 1.51847050273e-07 1.26709954098e-16 4.28935268852e-06 8.58427697267e-10 1.93296645355e-12 6.0145977213e-10 1.65411425683e-11 7.61429505463e-14 5.45250376501e-15 1.64919148086e-46 1e-98 - 1999.5 24466.1345702 98332086.2258 3.8845883956e+20 0.678214450323 0.297857980645 0.000142310448387 1.70895974193e-06 0.00210167355806 0.00400407261935 5.56507114193e-08 0.00600866056129 0.0116409843871 2.40915993871e-05 1.46391880645e-07 1.33691837742e-16 3.9083880871e-06 8.1776192742e-10 1.87300150968e-12 5.01608995163e-10 1.45301547742e-11 6.41109433228e-14 4.17402536776e-15 7.9078828872e-47 1e-98 - 2002.5 24013.549171 97865195.9494 3.79078020756e+20 0.679262519937 0.296833671519 0.000148493117405 1.9102485443e-06 0.00220870861709 0.00421694282594 5.8536229905e-08 0.00587518073102 0.0114248293038 2.39887237975e-05 1.41293125e-07 1.39308700949e-16 3.59989251267e-06 7.82176304748e-10 1.81690723734e-12 4.29624230065e-10 1.29986902532e-11 5.55177948103e-14 3.34442776584e-15 4.37089133867e-47 1e-98 - 2005.5 23566.9079543 97398972.0962 3.69871202302e+20 0.680283604231 0.295837149038 0.000154828898269 2.13434799038e-06 0.002319174525 0.00443872603461 6.15266866538e-08 0.00573628735 0.0112008713269 2.37508673654e-05 1.3559452e-07 1.44513440962e-16 3.29655044423e-06 7.44414716731e-10 1.75357584615e-12 3.65778929616e-10 1.15694359231e-11 4.78480518846e-14 2.66669965193e-15 2.37909883462e-47 1e-98 - 2008.5 23120.2311706 96982935.5577 3.60837240881e+20 0.681182796538 0.294960514423 0.000160591955962 2.35073164423e-06 0.002420147025 0.00464296488077 6.42695981923e-08 0.00560850235 0.0109955270962 2.34431885192e-05 1.3009252e-07 1.48831075577e-16 3.02916563654e-06 7.09175159038e-10 1.69191430769e-12 3.13963275769e-10 1.03593051538e-11 4.15700557307e-14 2.15294792115e-15 1.33575768077e-47 1e-98 - 2011.5 22711.4740908 96294144.0824 3.51968805913e+20 0.68261337191 0.293568585393 0.000170381397379 2.76005131087e-06 0.00259283237454 0.00499801455806 6.90015472661e-08 0.00538683172284 0.0106417200749 2.27123155056e-05 1.20256078277e-07 1.55094113109e-16 2.60875517976e-06 6.48888324717e-10 1.58051576779e-12 2.42002971908e-10 8.56452983515e-12 3.26999207113e-14 1.50469319848e-15 5.12848683126e-48 1e-98 - 2014.5 22283.4950242 95826267.7367 3.43262279389e+20 0.683477140185 0.292732096305 0.000177216741109 3.07850822171e-06 0.00271386022171 0.00525184986836 7.23563456813e-08 0.00522882468822 0.0103912164896 2.21746313164e-05 1.13781291224e-07 1.59533288222e-16 2.35201927713e-06 6.10279910623e-10 1.50770145496e-12 2.02448338337e-10 7.52046063278e-12 2.78731223325e-14 1.18379407852e-15 2.7100883972e-48 1e-98 - 2017.5 21865.1763366 95324888.1651 3.34719511481e+20 0.684376476453 0.291862675229 0.000184663311315 3.45611176759e-06 0.00284571662997 0.00553401642508 7.60575706728e-08 0.00505376908257 0.0101154413639 2.14904714985e-05 1.06605337615e-07 1.63701787156e-16 2.09192809786e-06 5.68562785015e-10 1.42600768196e-12 1.66045304893e-10 6.50598915596e-12 2.33566598777e-14 9.09689960244e-16 1.34452873578e-48 1e-98 - 2020.5 21458.8161834 94766859.2308 3.26336318928e+20 0.685365663736 0.290907498901 0.00019306618022 3.92154861538e-06 0.00299428367033 0.00586030596703 8.02905376923e-08 0.0048523867033 0.0098002390989 2.05909026374e-05 9.82958879121e-08 1.67448364835e-16 1.81790993407e-06 5.21376759341e-10 1.3299203956e-12 1.3145599011e-10 5.48214771429e-12 1.89416191209e-14 6.68069263737e-16 5.84850349451e-49 1e-98 - 2023.5 21045.2098369 94316166.9231 3.18111789893e+20 0.686128454945 0.290172795604 0.000199935520879 4.33742246153e-06 0.00311545268132 0.00613490486813 8.37978607692e-08 0.00468413681319 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2.74959045763e-16 5.90983296612e-50 1e-98 - 2032.5 19872.9748988 92669025.5172 2.94362380199e+20 0.688635924828 0.28776994 0.000225327529655 6.21574857241e-06 0.00354661718621 0.0072130778069 9.72316217241e-08 0.00404417717931 0.00854097517241 1.65858806897e-05 6.8362584e-08 1.75603806207e-16 1.00933024966e-06 3.58476477931e-10 9.72289213104e-13 5.13599990345e-11 2.73461210345e-12 8.19403162759e-15 1.99856064138e-16 2.51873544138e-50 1e-98 - 2035.5 19485.0731084 92189542.7586 2.86746597909e+20 0.689244752414 0.28719194 0.000232669184828 6.87124098623e-06 0.00366678339311 0.00754826470346 1.01254345862e-07 0.00385299648965 0.0082390475862 1.56975703448e-05 6.25567839998e-08 1.77446013104e-16 8.75427904824e-07 3.27786408965e-10 9.0174831655e-13 4.09564335169e-11 2.31761555171e-12 6.74309921375e-15 1.50492202067e-16 1.18983682065e-50 1e-98 - 2038.5 19127.4952471 91488166.3013 2.79275065933e+20 0.690214315069 0.286267442466 0.000242900275343 7.92894287675e-06 0.00383078531507 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1.68308621478e-11 1.18377412081e-12 3.0085039866e-15 4.98288348327e-17 7.3619184398e-52 1e-98 - 2047.5 18022.8708967 89911429.4981 2.57713513856e+20 0.692027581081 0.284555864865 0.000262876057143 1.08535401545e-05 0.0041659346834 0.00926281608881 1.19321310039e-07 0.00299900266023 0.00670373015058 1.08171372201e-05 3.63407842471e-08 1.68409579537e-16 3.95029470656e-07 1.9148538417e-10 5.57589002316e-13 1.24442293436e-11 9.42212925094e-13 2.28966914285e-15 3.4406489884e-17 2.87243437063e-52 1e-98 - 2050.5 17667.9383861 89358640 2.5080734226e+20 0.6925756968 0.2840437648 0.0002684197152 1.209216112e-05 0.004279085896 0.00968303092001 1.231426616e-07 0.00281623436 0.00631146709599 9.75512831998e-06 3.13922377599e-08 1.650292552e-16 3.20171119198e-07 1.65978539999e-10 4.89210195199e-13 9.11702403193e-12 7.45062451196e-13 1.72846716799e-15 2.35217343998e-17 1.06654160317e-52 1e-98 - 2053.5 17306.7591188 88898779.61 2.44039738293e+20 0.692995644568 0.283654233983 0.000272067884958 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b/Exec/science/xrb_layered/create_initial_model/xrb_layered_3cm.uniform deleted file mode 100644 index 330229467..000000000 --- a/Exec/science/xrb_layered/create_initial_model/xrb_layered_3cm.uniform +++ /dev/null @@ -1,1050 +0,0 @@ -# npts = 1024 -# num of variables = 24 -# density -# temperature -# pressure -#hydrogen-1 -#helium-4 -#carbon-12 -#carbon-13 -#nitrogen-13 -#nitrogen-14 -#nitrogen-15 -#oxygen-14 -#oxygen-15 -#oxygen-16 -#oxygen-17 -#oxygen-18 -#fluorine-17 -#fluorine-18 -#fluorine-19 -#neon-18 -#neon-19 -#neon-20 -#magnesium-22 -#magnesium-24 -#iron-56 - 1.5 11624809.505 240650282.178 9.10195109986e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 4.5 11588776.1069 240553970.229 9.06119776412e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 7.5 11556971.9792 240468644.271 9.02527121949e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 10.5 11513280.7407 240351088.889 8.9759835895e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 13.5 11481577.4194 240265539.785 8.94026792878e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 16.5 11440160.1653 240153373.554 8.89367051561e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 19.5 11398009.8319 240038784.034 8.84632013634e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 22.5 11357060.2326 239926984.884 8.80038813348e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 25.5 11321093.3628 239828541.593 8.76010186194e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 28.5 11287936.2924 239737496.345 8.7230098877e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 31.5 11249749.3417 239632340.752 8.68034721031e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 34.5 11207711.7956 239516047.238 8.63345211446e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 37.5 11171057.0994 239414362.155 8.59262157038e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 40.5 11134086.1 239311389.5 8.55149506123e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 43.5 11091541.1034 239192510 8.50423836107e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 46.5 11053051.68 239084585.6 8.46155134447e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 49.5 11015691.6088 238979446.688 8.42017599104e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 52.5 10971915.9545 238855746.818 8.371769832e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 55.5 10936681.9646 238755854.224 8.3328672723e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 58.5 10909204.558 238677877.8 8.30256523595e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 61.5 10854907.7215 238522727.004 8.24277999789e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 64.5 10817601.2745 238415799.346 8.20177481701e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 67.5 10784174.3607 238319668.493 8.16508385589e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 70.5 10751825.0457 238226380.822 8.12962091902e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 73.5 10705899.4624 238093305.376 8.07935141264e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 76.5 10666110 237977600 8.03587121625e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 79.5 10628751.3953 237868619.302 7.99510902751e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 82.5 10598542.093 237780223.953 7.96219123902e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 85.5 10551718.2328 237642588.362 7.91124668283e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 88.5 10523158.4239 237558363.587 7.88021986312e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 91.5 10493991.4187 237472216.263 7.84856979918e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 94.5 10441565.5319 237316402.837 7.79177305182e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 97.5 10403716.6423 237203487.591 7.75084275707e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 100.5 10366651.5756 237092586.817 7.71082053413e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 103.5 10330199.9611 236983088.327 7.6715191e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 106.5 10292241.6766 236868618.563 7.63065491263e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 109.5 10258116.4486 236765370.405 7.59397113176e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 112.5 10220409.7742 236650884.839 7.5534968742e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 115.5 10182831.8456 236536338.591 7.51322308484e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 118.5 10146334.5667 236424720.609 7.47416717858e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 121.5 10115126.1358 236328959.485 7.44081765096e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 124.5 10072047.0588 236196158.824 7.39485398943e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 127.5 10041065.1751 236100339.689 7.36184860542e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 130.5 10009467.0165 236002398.347 7.32823083431e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 133.5 9960804.10663 235850697.406 7.27654473437e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 136.5 9928841.88685 235750719.572 7.24265463944e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 139.5 9891815.71059 235634497.647 7.20345253211e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 142.5 9859779.94588 235533556.471 7.16958391049e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 145.5 9820728.45338 235410091.64 7.1283608542e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 148.5 9782962.99401 235290202.994 7.0885609783e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 151.5 9747797.71847 235178153.959 7.05155953984e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 154.5 9711174.31104 235061049.164 7.01308363623e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 157.5 9676949.27416 234951166.081 6.97718266127e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 160.5 9649703.57996 234863485.94 6.9486410997e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 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0.00848690054707 0.0157364230373 4.82392444405e-07 8.31754742629e-09 5.21460186146e-19 3.94192066252e-06 2.56859373002e-10 1.92844317407e-13 3.63602902309e-08 2.14274328597e-10 6.46160212966e-13 3.92119551865e-11 1.44731406039e-34 1e-98 - 1795.5 78355.5659325 131132437.3 1.59295235154e+21 0.475479868739 0.500142598224 5.87338060035e-06 1.45816814742e-09 5.80041773357e-05 9.16419613676e-05 1.42274398046e-09 0.0084846785222 0.0157327942629 4.99166760568e-07 8.62583286145e-09 5.44995159502e-19 3.98092385968e-06 2.60441628774e-10 1.97930889343e-13 3.5457540675e-08 2.10622268206e-10 6.64437353286e-13 3.66708113144e-11 1.20161033038e-34 1e-98 - 1798.5 77089.0645546 130734580.336 1.56562311848e+21 0.480250190252 0.495372766555 6.07267624201e-06 1.56607562689e-09 6.02038276638e-05 9.58906500167e-05 1.47830282521e-09 0.00848194302521 0.0157283276639 5.19919516974e-07 9.00324480504e-09 5.74547484537e-19 4.02577760336e-06 2.6466415563e-10 2.04045334622e-13 3.4426510437e-08 2.06395668908e-10 6.86741491596e-13 3.39849645883e-11 9.80530091096e-35 1e-98 - 1801.5 75747.6208572 130306311.429 1.53655103286e+21 0.485301988571 0.490321522857 6.29226262857e-06 1.68733411428e-09 6.26318965714e-05 0.000100585836571 1.53966265714e-09 0.00847892285714 0.0157233965714 5.42877937143e-07 9.41898077142e-09 6.07435854285e-19 4.07387491429e-06 2.69241651429e-10 2.10729637143e-13 3.33243608572e-08 2.01850542857e-10 7.11278214285e-13 3.1218894e-11 7.73101217145e-35 1e-98 - 1804.5 73897.9758696 129694539.13 1.49628082554e+21 0.492319973913 0.483304371739 6.62450206521e-06 1.88112530435e-09 6.63249821739e-05 0.000107748510869 1.63312571739e-09 0.00847432626087 0.0157158804348 5.78010023913e-07 1.00470300652e-08 6.58568991304e-19 4.1416498913e-06 2.75874404348e-10 2.20645673913e-13 3.17831015218e-08 1.95404054348e-10 7.48055795652e-13 2.76641986957e-11 5.51435902176e-35 1e-98 - 1807.5 72587.564183 129249143.137 1.46756403876e+21 0.49727155817 0.478353428758 6.88063154901e-06 2.03813552941e-09 6.91847593464e-05 0.000113305398039 1.70558768627e-09 0.00847076626144 0.015710049281 6.05363281699e-07 1.05303296732e-08 6.98958828104e-19 4.19049324837e-06 2.80793040523e-10 2.28141148366e-13 3.06804081046e-08 1.90732688235e-10 7.75989335294e-13 2.53209709151e-11 4.30282200655e-35 1e-98 - 1810.5 71577.1598973 128899155.822 1.445314731e+21 0.501071144178 0.474554370891 7.09092351369e-06 2.17209600342e-09 7.15407702396e-05 0.000117888920548 1.76534036986e-09 0.00846783318836 0.0157052407877 6.27997172602e-07 1.09266571918e-08 7.32761831163e-19 4.22863795548e-06 2.84719548972e-10 2.34205342466e-13 2.98263077398e-08 1.87079775e-10 7.98595190068e-13 2.36121471576e-11 3.53524256851e-35 1e-98 - 1813.5 70636.5879795 128563231.164 1.42450953572e+21 0.504607068836 0.471018994178 7.30608310274e-06 2.31734840068e-09 7.39644380479e-05 0.00012261330411 1.82689927397e-09 0.00846481503767 0.0157002921575 6.5144799452e-07 1.13316914384e-08 7.68408646233e-19 4.2649567911e-06 2.88578829794e-10 2.40285068493e-13 2.9028725548e-08 1.83617275e-10 8.21183238014e-13 2.21516574315e-11 3.0123405137e-35 1e-98 - 1816.5 69696.0160616 128227306.507 1.40368535881e+21 0.508142993493 0.467483617466 7.52124269178e-06 2.46260079795e-09 7.63881058562e-05 0.000127337687671 1.88845817808e-09 0.00846179688699 0.0156953435274 6.74898816439e-07 1.17367256849e-08 8.04055461302e-19 4.30127562671e-06 2.92438110617e-10 2.46364794521e-13 2.82311433561e-08 1.80154775e-10 8.43771285959e-13 2.06911677055e-11 2.48943845889e-35 1e-98 - 1819.5 68536.3456302 127801366.176 1.37794718674e+21 0.512530159454 0.463097187815 7.80645107145e-06 2.66444411136e-09 7.96158157985e-05 0.000133639837815 1.97054205253e-09 0.0084577787479 0.0156887466176 7.06292164918e-07 1.22711844328e-08 8.52395775003e-19 4.34637033404e-06 2.97358664916e-10 2.54284278362e-13 2.7240877563e-08 1.75805977731e-10 8.72471631724e-13 1.89798310924e-11 1.94395794115e-35 1e-98 - 1822.5 66840.2615966 127155042.647 1.34018665458e+21 0.51900139895 0.456627145798 8.2631582143e-06 3.00483759875e-09 8.48120426893e-05 0.000143804795799 2.10287671639e-09 0.00845131236134 0.0156781142647 7.57131009456e-07 1.31225100631e-08 9.31813275003e-19 4.41295079622e-06 3.04877509454e-10 2.66707996849e-13 2.57788691596e-08 1.69287280252e-10 9.16136568699e-13 1.66543521008e-11 1.34319911763e-35 1e-98 - 1825.5 65544.9289613 126650581.67 1.31128161703e+21 0.52396338167 0.451666119959 8.64097569858e-06 3.30278799593e-09 8.91337535031e-05 0.000152267594908 2.21309431161e-09 0.00844593334216 0.0156692660081 7.99719263137e-07 1.38249125458e-08 9.99481480653e-19 4.46442089817e-06 3.10874299593e-10 2.76796067414e-13 2.46707006314e-08 1.64280019552e-10 9.50148557842e-13 1.50127640529e-11 9.90452805903e-36 1e-98 - 1828.5 64419.0125 126205016.667 1.28611740879e+21 0.5282885125 0.447341854167 8.98810049999e-06 3.58652670833e-09 9.31182470833e-05 0.000160075066667 2.31480983333e-09 0.008440973875 0.015661105 8.39168862499e-07 1.44690254167e-08 1.062851e-18 4.50954741667e-06 3.16249520833e-10 2.85950616667e-13 2.37125466667e-08 1.59908208333e-10 9.80066404166e-13 1.36689804167e-11 7.47577420836e-36 1e-98 - 1831.5 63298.4 125746266.667 1.26099199163e+21 0.5326102 0.443021166667 9.37138799999e-06 3.92193733332e-09 9.75459033332e-05 0.000168759566666 2.42810233333e-09 0.008435467 0.01565203 8.83398799999e-07 1.51765066666e-08 1.135421e-18 4.55509366667e-06 3.21910833333e-10 2.95828366666e-13 2.27650966667e-08 1.55507333333e-10 1.00966896667e-12 1.24559366667e-11 5.78328233338e-36 1e-98 - 1834.5 62225.1987571 125299535.97 1.23688839967e+21 0.536752237476 0.4388801371 9.75843338606e-06 4.2734588192e-09 0.000102034576497 0.000177565725612 2.54299976459e-09 0.00842988634652 0.0156428291714 9.28457455931e-07 1.58889045763e-08 1.21013373823e-18 4.59901031073e-06 3.2748653145e-10 3.05678085687e-13 2.18606845386e-08 1.51270279096e-10 1.03754498493e-12 1.13466082486e-11 4.39696455745e-36 1e-98 - 1837.5 61206.1818079 124866541.62 1.21395721101e+21 0.54068896064 0.434944430885 1.01497734991e-05 4.64339271752e-09 0.000106592983277 0.000186510923352 2.65973140302e-09 0.00842422137476 0.0156334845386 9.74463218645e-07 1.66069215254e-08 1.28729532015e-18 4.64106454802e-06 3.32964384558e-10 3.15495769303e-13 2.10054585499e-08 1.47220448587e-10 1.06344780979e-12 1.03558116384e-11 3.36056003765e-36 1e-98 - 1840.5 59883.5355555 124287996.296 1.18409496518e+21 0.545787388889 0.429847437037 1.07030733334e-05 5.1971990741e-09 0.000113072555556 0.000199228714815 2.82596977778e-09 0.00841617459259 0.0156202 1.04037112593e-06 1.76169425926e-08 1.39988066667e-18 4.69632892593e-06 3.40427966667e-10 3.29127240741e-13 1.99026933333e-08 1.4192417037e-10 1.09534e-12 9.16290685181e-12 2.34067459256e-36 1e-98 - 1843.5 58748.9900326 123781285.668 1.1584590812e+21 0.550179013355 0.425457184365 1.12139003909e-05 5.73582711076e-09 0.0001190853557 0.000211029871336 2.98045154398e-09 0.00840871272313 0.015607872443 1.10197770033e-06 1.85451665472e-08 1.50684239088e-18 4.74402715635e-06 3.47092255049e-10 3.41504755375e-13 1.89686949837e-08 1.37379093811e-10 1.12064734853e-12 8.21641782734e-12 1.68310336807e-36 1e-98 - 1846.5 57943.9613545 123414488.329 1.1402559911e+21 0.553308765418 0.422328484438 1.1607323977e-05 6.17685936744e-09 0.000123744076225 0.000220169909222 3.10032004755e-09 0.00840293709942 0.0155983265562 1.15012790778e-06 1.92562418732e-08 1.59205400288e-18 4.77802367291e-06 3.52036716283e-10 3.50856317435e-13 1.83182617003e-08 1.34166830836e-10 1.13634568732e-12 7.60440624062e-12 1.34597977953e-36 1e-98 - 1849.5 57230.0218732 123086520.029 1.12411199234e+21 0.556087007493 0.419551193372 1.19680761383e-05 6.5909842954e-09 0.000128025992651 0.000228569044669 3.2105562147e-09 0.00839763090346 0.0155895556628 1.19453655332e-06 1.99068787608e-08 1.67123498271e-18 4.80823246254e-06 3.56504252306e-10 3.59366245389e-13 1.77460397983e-08 1.31323314986e-10 1.14935937608e-12 7.08306805618e-12 1.08904482276e-36 1e-98 - 1852.5 56329.4217763 122662598.026 1.10371745924e+21 0.559601810526 0.416037695395 1.24552141448e-05 7.17784277634e-09 0.000133836541448 0.000239962992764 3.3603249408e-09 0.00839043586184 0.0155776523684 1.25523709211e-06 2.07816655264e-08 1.78117973685e-18 4.84680827632e-06 3.62371032237e-10 3.70706967764e-13 1.70297436184e-08 1.27723525e-10 1.16371411842e-12 6.46089782234e-12 8.25533781571e-37 1e-98 - 1855.5 55110.9941447 122075295.395 1.07609984248e+21 0.564370757895 0.411270655921 1.31575496711e-05 8.05881514475e-09 0.000142249896711 0.000256456216448 3.57740586185e-09 0.00838002467763 0.0155604155263 1.34367788158e-06 2.20381128948e-08 1.94350605263e-18 4.89963064474e-06 3.70620308553e-10 3.86867691448e-13 1.60681317763e-08 1.22836025e-10 1.18035227632e-12 5.66704058551e-12 5.50825623679e-37 1e-98 - 1858.5 54168.9687775 121609912.425 1.0547289534e+21 0.56806796473 0.407575063928 1.3755187976e-05 8.86500725252e-09 0.000149461014229 0.000270577261122 3.76378256914e-09 0.00837111450301 0.0155456574549 1.42034037275e-06 2.31005247896e-08 2.0876283467e-18 4.94086241683e-06 3.77369379359e-10 4.00346075551e-13 1.53429192585e-08 1.19086796794e-10 1.18852034469e-12 5.1156838537e-12 4.05810995188e-37 1e-98 - 1861.5 53254.3956097 121152183.902 1.03398251761e+21 0.571658378537 0.403986290244 1.43624552195e-05 9.71181506831e-09 0.000156812957561 0.000284966376098 3.95394691708e-09 0.00836203654146 0.0155306158537 1.49891571707e-06 2.41769676586e-08 2.23700348781e-18 4.98114752195e-06 3.8410733122e-10 4.13921302927e-13 1.46464472683e-08 1.15456942439e-10 1.1943139122e-12 4.60641660975e-12 2.90038043901e-37 1e-98 - 1864.5 52246.9078049 120634061.951 1.01108912998e+21 0.575607549268 0.400039095122 1.50921796098e-05 1.07930904341e-08 0.00016570290878 0.000302347498049 4.18422325853e-09 0.00835107337073 0.0155124329268 1.59488205854e-06 2.54637408293e-08 2.4232632439e-18 5.02621996098e-06 3.9195035561e-10 4.29980961463e-13 1.38934326342e-08 1.1147199122e-10 1.1962741561e-12 4.09407880488e-12 2.01952921952e-37 1e-98 - 1867.5 51277.0597143 120123935.429 9.89034283305e+20 0.579405629714 0.396242354286 1.58533251428e-05 1.19897482857e-08 0.000175031295428 0.000320563069143 4.42617776e-09 0.00833985110286 0.0154938728571 1.69659022857e-06 2.67995274857e-08 2.62477918285e-18 5.07037340571e-06 3.99909354857e-10 4.46500592e-13 1.31839298286e-08 1.07666205143e-10 1.19382400571e-12 3.64245692001e-12 1.40695277715e-37 1e-98 - 1870.5 50431.9813489 119670519.535 9.69795932205e+20 0.58269760186 0.392952940465 1.65657215814e-05 1.31732164186e-08 0.000183811433023 0.000337688207907 4.6541908279e-09 0.0083288458 0.0154755725116 1.79324063256e-06 2.80447690697e-08 2.82005114883e-18 5.10912225116e-06 4.07166793953e-10 4.61767579535e-13 1.25774128372e-08 1.04370097209e-10 1.18826455349e-12 3.28022751163e-12 1.01611362698e-37 1e-98 - 1873.5 49638.0239456 119237382.313 9.51705073981e+20 0.58576949932 0.389883959864 1.72751367347e-05 1.44038787755e-08 0.000192593514285 0.000354800049659 4.88246986394e-09 0.00831772182993 0.0154570414966 1.89066153061e-06 2.92810763945e-08 3.0199807551e-18 5.14592430612e-06 4.14258114966e-10 4.76829648979e-13 1.20163069388e-08 1.01289829932e-10 1.18056797279e-12 2.96181078912e-12 7.32213570753e-38 1e-98 - 1876.5 48806.1327891 118772756.463 9.32706847613e+20 0.588950859864 0.386704231973 1.80786673469e-05 1.58743277551e-08 0.000202594942857 0.000374260729932 5.14274197279e-09 0.00830566196599 0.0154370482993 2.00270030612e-06 3.06762872789e-08 3.25447055102e-18 5.18561206122e-06 4.22128182993e-10 4.93711689796e-13 1.14404293878e-08 9.80899659864e-11 1.16932239456e-12 2.65563255782e-12 5.21102074151e-38 1e-98 - 1879.5 47871.451411 118238762.577 9.11355299989e+20 0.592517772086 0.383139867485 1.90529219325e-05 1.77650657669e-08 0.000214795974847 0.000397967372393 5.46065271472e-09 0.00829076134049 0.0154123038344 2.14091996932e-06 3.2362238773e-08 3.5501608681e-18 5.23106881595e-06 4.31457341104e-10 5.13973733742e-13 1.08076785583e-08 9.45274555215e-11 1.15296889877e-12 2.34017043865e-12 3.49423088037e-38 1e-98 - 1882.5 47132.8471307 117809257.895 8.94486598199e+20 0.595326769779 0.38033321545 1.9882021511e-05 1.94730117148e-08 0.000225242771817 0.000418233503226 5.73318375892e-09 0.00827797481834 0.0153910540238 2.26066231239e-06 3.37932223599e-08 3.81220681155e-18 5.26773255518e-06 4.39241258234e-10 5.31059494228e-13 1.03214596519e-08 9.1755252309e-11 1.13714913582e-12 2.11375274533e-12 2.5363073837e-38 1e-98 - 1885.5 46511.1493378 117444521.053 8.80303883555e+20 0.597685869949 0.377976203565 2.06085665026e-05 2.10170796265e-08 0.000234427101189 0.000436035116129 5.97293163668e-09 0.00826674188115 0.0153723816978 2.36659745671e-06 3.50456128523e-08 4.04682311036e-18 5.29895751273e-06 4.45994059593e-10 5.45961344822e-13 9.91923299659e-09 8.94455120712e-11 1.12246441596e-12 1.93409909507e-12 1.89682555007e-38 1e-98 - 1888.5 45725.5454217 116970509.639 8.6238383683e+20 0.600688166265 0.374976768675 2.16152866265e-05 2.3318757229e-08 0.000247252701205 0.000460846859037 6.3082314217e-09 0.00825108151807 0.0153463366265 2.51684962651e-06 3.67764583133e-08 4.38922504821e-18 5.33982626506e-06 4.55147839759e-10 5.66438454218e-13 9.42631807227e-09 8.65706478312e-11 1.10060580723e-12 1.72942004819e-12 1.34962515661e-38 1e-98 - 1891.5 44874.449017 116451960.113 8.43015611997e+20 0.603946053308 0.371722073724 2.27409930624e-05 2.59532218338e-08 0.00026163079811 0.000488644321173 6.68429891117e-09 0.00823353508128 0.0153171503592 2.68615168432e-06 3.87104621551e-08 4.77859684122e-18 5.38467308885e-06 4.65312574102e-10 5.89276585067e-13 8.89835709639e-09 8.3473997032e-11 1.0756582344e-12 1.51632285822e-12 8.52858368989e-39 1e-98 - 1894.5 44137.4244423 115988462.949 8.26197740453e+20 0.606722386011 0.368948916824 2.3840569622e-05 2.87820176749e-08 0.000275815750851 0.000515994850473 7.05597168999e-09 0.00821626211342 0.0152884093384 2.85681879207e-06 4.05990160303e-08 5.18659287147e-18 5.42580031002e-06 4.75044426654e-10 6.11433711721e-13 8.4620845066e-09 8.08671728355e-11 1.05036409452e-12 1.35859431379e-12 6.17317593944e-39 1e-98 - 1897.5 43392.4663894 115515568.154 8.092372451e+20 0.609527921907 0.366146705071 2.49924175254e-05 3.183914357e-08 0.000290723543408 0.000544714688439 7.4467995781e-09 0.00819812147464 0.0152582206288 3.03755113185e-06 4.25787413591e-08 5.62438548479e-18 5.46823251116e-06 4.8519281501e-10 6.34630586816e-13 8.02734738133e-09 7.82565557403e-11 1.02378042475e-12 1.20571926166e-12 4.10162281337e-39 1e-98 - 1900.5 42626.7665923 115018103.651 7.91815020126e+20 0.612409808316 0.363268530629 2.62809266531e-05 3.54932287626e-08 0.000307521170182 0.000577014526166 7.88770769168e-09 0.00817771234686 0.015224246998 3.24459859635e-06 4.47968306085e-08 6.1400800081e-18 5.51407652738e-06 4.96430218661e-10 6.60545840365e-13 7.59662466329e-09 7.56360222719e-11 9.93825333469e-13 1.06553346045e-12 2.77219563288e-39 1e-98 - 1903.5 41860.3447826 114510786.957 7.74408308251e+20 0.615302369565 0.360380026087 2.76686321739e-05 3.96849817391e-08 0.000325734821739 0.000611976626086 8.36629921738e-09 0.00815561204348 0.0151874552174 3.47295034782e-06 4.71945430434e-08 6.72471365217e-18 5.56240382609e-06 5.08478034782e-10 6.88522013043e-13 7.17976300001e-09 7.30751773913e-11 9.61783365218e-13 9.37867000002e-13 1.82263508697e-39 1e-98 - 1906.5 41106.2566071 114004156.027 7.57321933841e+20 0.618153199554 0.357533457589 2.91152840402e-05 4.42671988617e-08 0.000344821301563 0.000648565834375 8.8682295134e-09 0.008132476 0.0151489347321 3.71546961161e-06 4.97029474777e-08 7.35870359152e-18 5.61208402455e-06 5.21010658259e-10 7.17767756697e-13 6.7811442366e-09 7.06073583035e-11 9.28669369196e-13 8.22079104017e-13 1.15607378705e-39 1e-98 - 1909.5 40411.5958928 113527303.348 7.41602171006e+20 0.620780744196 0.354910198661 3.05690050223e-05 4.9218437701e-08 0.000364154895313 0.000685554896876 9.37725317411e-09 0.008109076 0.0151099615179 3.9665334509e-06 5.22425747099e-08 8.03670693974e-18 5.6613690692e-06 5.33616082366e-10 7.47369587054e-13 6.43014557589e-09 6.84120279464e-11 8.96189949553e-13 7.27871202231e-13 7.83472581691e-40 1e-98 - 1912.5 39728.1595652 113049547.826 7.26162722962e+20 0.62336751087 0.352327997826 3.21058941304e-05 5.47954258696e-08 0.000384736473913 0.000724866804348 9.91965308696e-09 0.00808419509783 0.015068518913 4.23944558696e-06 5.4952666087e-08 8.79563041304e-18 5.71346867391e-06 5.47021801087e-10 7.78988406522e-13 6.09742486956e-09 6.63177863043e-11 8.62852193478e-13 6.43713298913e-13 5.25721711956e-40 1e-98 - 1915.5 39035.4860623 112558823.796 7.10551049875e+20 0.625986483286 0.349713845326 3.37443007932e-05 6.09979376769e-08 0.000406781361756 0.000766927428328 1.0500982068e-08 0.00805758655524 0.0150242008782 4.53608641926e-06 5.78624198866e-08 9.63689481585e-18 5.76915170255e-06 5.61389385835e-10 8.12967160623e-13 5.7691668442e-09 6.42430352692e-11 8.28145916431e-13 5.64332821248e-13 3.32024513883e-40 1e-98 - 1918.5 38260.9931445 111996982.436 6.93108935821e+20 0.628895038527 0.346810814164 3.57395727478e-05 6.91018555239e-08 0.000433841106798 0.000818464283851 1.12152625212e-08 0.00802507692351 0.0149700733994 4.90981588101e-06 6.14559524645e-08 1.07322602549e-17 5.83774971955e-06 5.79105958073e-10 8.55022231444e-13 5.41819913882e-09 6.20132703967e-11 7.87938692636e-13 4.85632962891e-13 1.99699273091e-40 1e-98 - 1921.5 37686.0428216 111572048.963 6.80191588852e+20 0.631062459751 0.344648750207 3.7357166971e-05 7.62350280499e-08 0.000455981324067 0.000860550609129 1.18003053527e-08 0.00799823472199 0.0149253267635 5.22608697096e-06 6.44306031536e-08 1.16961729046e-17 5.89453558091e-06 5.93771356847e-10 8.89943245229e-13 5.17234423236e-09 6.0447689751e-11 7.5701349834e-13 4.3483869585e-13 1.41989939419e-40 1e-98 - 1924.5 37093.4407661 111128279.839 6.66894460778e+20 0.633273779839 0.342442993548 3.91069315323e-05 8.42919626211e-08 0.000480044997178 0.000906250826211 1.24364932258e-08 0.00796917533871 0.0148769020161 5.57638907259e-06 6.76836624194e-08 1.2786050242e-17 5.95649340726e-06 6.09802304436e-10 9.28188600001e-13 4.92491653225e-09 5.88703267742e-11 7.24903116128e-13 3.85656990322e-13 9.44700673382e-41 1e-98 - 1927.5 36460.9064919 110645497.581 6.52705151749e+20 0.635595997581 0.340126703226 4.11028629838e-05 9.39904243143e-08 0.000507663807661 0.000958640443144 1.31671383871e-08 0.00793601808065 0.0148216802419 5.9881910887e-06 7.14468962902e-08 1.4099723629e-17 6.02794360887e-06 6.28336316532e-10 9.72513599999e-13 4.66979798388e-09 5.72411816129e-11 6.90306341936e-13 3.3794005484e-13 6.10191600814e-41 1e-98 - 1930.5 35765.0836649 110102740.838 6.37129357755e+20 0.638152126702 0.337577819372 4.34851786911e-05 1.06476362304e-07 0.000540915942932 0.00102162348272 1.4047541623e-08 0.00789606785864 0.0147551398429 6.50242782722e-06 7.60383487434e-08 1.57997319372e-17 6.1146113822e-06 6.50927098953e-10 1.02676069319e-12 4.40185341361e-09 5.5529212199e-11 6.51488684293e-13 2.91260327225e-13 3.73155916231e-41 1e-98 - 1933.5 35144.4707143 109610807.143 6.23260980507e+20 0.64039955 0.335337407143 4.57563721429e-05 1.19139528572e-07 0.000572840064286 0.001082027 1.48932814286e-08 0.00785771407143 0.01469127 7.01251928572e-06 8.0503265e-08 1.75343328572e-17 6.19843685714e-06 6.72891135714e-10 1.07960764286e-12 4.17215292857e-09 5.40628185714e-11 6.16740235714e-13 2.53749978571e-13 2.32266799999e-41 1e-98 - 1936.5 34651.9496104 109214232.468 6.12255430689e+20 0.642134436364 0.33360855974 4.76885133766e-05 1.30602548052e-07 0.000600192710389 0.00113372927273 1.56183074026e-08 0.00782486650649 0.0146365881818 7.46494266233e-06 8.43835263636e-08 1.91153620779e-17 6.27086153247e-06 6.91982580519e-10 1.12559774026e-12 3.99768685715e-09 5.2951338052e-11 5.89329635065e-13 2.27379248052e-13 1.63891345455e-41 1e-98 - 1939.5 34139.1842077 108795161.749 6.00821602581e+20 0.643936415301 0.331813263934 4.98206191803e-05 1.44031851912e-07 0.000630582218032 0.00119112797814 1.64242143169e-08 0.00778839125683 0.014575869235 7.98079885791e-06 8.86926125136e-08 2.09696344808e-17 6.34759703279e-06 7.12989162295e-10 1.17667744262e-12 3.81964415301e-09 5.18015145355e-11 5.60675927323e-13 2.02013768853e-13 1.09207757432e-41 1e-98 - 1942.5 33594.3071585 108340407.65 5.88690071595e+20 0.64584481694 0.329912567213 5.22699101639e-05 1.60580089618e-07 0.000665788816394 0.00125756240437 1.73584151366e-08 0.00774616174863 0.014505574153 8.59727262842e-06 9.36819034973e-08 2.32573271038e-17 6.43117039344e-06 7.37033547541e-10 1.23583031148e-12 3.6359281694e-09 5.05908710929e-11 5.30050394535e-13 1.78242826229e-13 7.62423025135e-42 1e-98 - 1945.5 33049.4301093 107885653.552 5.76620339527e+20 0.647753218579 0.328011870492 5.47192011476e-05 1.77128327323e-07 0.000700995414755 0.0013239968306 1.82926159563e-08 0.00770393224044 0.014435279071 9.21374639892e-06 9.8671194481e-08 2.55450197268e-17 6.5147437541e-06 7.61077932788e-10 1.29498318033e-12 3.45221218579e-09 4.93802276502e-11 4.99424861748e-13 1.54471883606e-13 4.32768475947e-42 1e-98 - 1948.5 32469.4541243 107381247.458 5.63747937362e+20 0.649748774576 0.326026031638 5.77611549153e-05 2.01047351413e-07 0.000745515860453 0.00140789956497 1.9475130678e-08 0.00765057644633 0.0143465024859 1.00340851978e-05 1.04777906441e-07 2.88169871187e-17 6.59579515819e-06 7.89391805085e-10 1.36721979661e-12 3.26227576836e-09 4.80283216949e-11 4.66942641807e-13 1.34569608474e-13 2.98736711297e-42 1e-98 - 1951.5 31887.5007345 106874044.068 5.50902374312e+20 0.651749240678 0.324035396045 6.08364981356e-05 2.25381631074e-07 0.000790561029944 0.00149278642938 2.06716349153e-08 0.00759659381921 0.0142566846893 1.08659093503e-05 1.10947571695e-07 3.21444066102e-17 6.67670448023e-06 8.17946211865e-10 1.44019352542e-12 3.07198890395e-09 4.66684572881e-11 4.34355819774e-13 1.14885286441e-13 1.75725948587e-42 1e-98 - 1954.5 31168.7804348 106230330.435 5.35083929375e+20 0.654224713044 0.321573504348 6.50276952175e-05 2.62167934783e-07 0.000852718273914 0.00160986939131 2.23236195652e-08 0.00752214913043 0.0141328369565 1.20250595652e-05 1.18953078261e-07 3.70461965218e-17 6.75192452174e-06 8.53414530435e-10 1.53466295652e-12 2.83601021739e-09 4.48391991304e-11 3.9416233913e-13 9.3920737391e-14 8.82928121731e-43 1e-98 - 1957.5 30492.6325161 105613734.839 5.20266610185e+20 0.656536095484 0.319275988387 6.92653425162e-05 3.02212735484e-07 0.000916133072259 0.00172931769678 2.40095916774e-08 0.00744620607097 0.0140065494194 1.32094701936e-05 1.26680442581e-07 4.22616348388e-17 6.80044526452e-06 8.86392774839e-10 1.6256248129e-12 2.6134482e-09 4.29989452903e-11 3.56728379355e-13 7.66298379999e-14 4.34765856772e-43 1e-98 - 1960.5 30063.4728387 105216331.613 5.10879526168e+20 0.657960805161 0.317860956129 7.21879328387e-05 3.32453945161e-07 0.000960350217419 0.00181264463226 2.51854852258e-08 0.00739322849032 0.0139185478065 1.40288040645e-05 1.31613894193e-07 4.60627316128e-17 6.80862655484e-06 9.06284371613e-10 1.68342707097e-12 2.4719682e-09 4.17270710968e-11 3.33727766452e-13 6.74733880001e-14 2.9861879226e-43 1e-98 - 1963.5 29620.4092064 104801288.36 5.01216025632e+20 0.659420846561 0.316411396825 7.53383049205e-05 3.66807077247e-07 0.00100830861905 0.00190306850264 2.64610816402e-08 0.00733575528572 0.0138231190476 1.48972338624e-05 1.36583256085e-07 5.023758582e-17 6.80055603175e-06 9.25476656613e-10 1.74158555555e-12 2.32504786773e-09 4.03444452911e-11 3.10317493122e-13 5.86609940214e-14 1.8552574106e-43 1e-98 - 1966.5 29143.8496825 104343748.677 4.90829894952e+20 0.660966005291 0.31487865873 7.90374239682e-05 4.11066204232e-07 0.00106528004762 0.00201058953439 2.79768713227e-08 0.00726745171429 0.013709797619 1.58839402116e-05 1.416391291e-07 5.53128556613e-17 6.7533334127e-06 9.42984235979e-10 1.80060222222e-12 2.16502128042e-09 3.86950087831e-11 2.85920310582e-13 5.06774622752e-14 1.27971903757e-43 1e-98 - 1969.5 28667.2901587 103886208.995 4.80504735301e+20 0.662511164021 0.313345920635 8.27365430159e-05 4.55325331218e-07 0.00112225147619 0.00211811056614 2.94926610053e-08 0.00719914814286 0.0135964761905 1.68706465609e-05 1.46695002116e-07 6.03881255027e-17 6.70611079365e-06 9.60491815344e-10 1.85961888889e-12 2.00499469312e-09 3.70455722751e-11 2.61523128042e-13 4.2693930529e-14 7.04180664541e-44 1e-98 - 1972.5 28080.775482 103302229.301 4.67802613784e+20 0.664387644234 0.31148703535 8.78729393763e-05 5.2615025898e-07 0.00120275199433 0.00227042890359 3.16356170889e-08 0.00710249976937 0.0134363966541 1.81063813611e-05 1.51809734972e-07 6.75828041589e-17 6.56923540075e-06 9.73640207562e-10 1.91946134594e-12 1.80787100567e-09 3.47510820794e-11 2.33085183175e-13 3.49023481473e-14 3.94841985062e-44 1e-98 - 1975.5 27461.7001852 102680648.148 4.54482050848e+20 0.6663621 0.309531728704 9.34404408334e-05 6.05037561112e-07 0.00129032160185 0.00243621237037 3.39670047222e-08 0.00699733311111 0.0132622680556 1.94149258333e-05 1.56924256482e-07 7.54110351853e-17 6.4050219074e-06 9.85388252778e-10 1.97934646296e-12 1.59981592592e-09 3.22637312037e-11 2.03469661111e-13 2.71816929629e-14 1.64960047684e-44 1e-98 - 1978.5 26809.7187037 102005462.963 4.40500662384e+20 0.6684221 0.307494599074 9.99815241667e-05 7.11233672224e-07 0.00139498178704 0.00263514940741 3.67544769445e-08 0.00687134422222 0.0130540736111 2.07055091667e-05 1.6030990463e-07 8.44615537038e-17 6.13581264814e-06 9.84993530556e-10 2.01949275926e-12 1.38600851852e-09 2.9400851574e-11 1.74525772222e-13 2.09129892592e-14 7.69148162026e-45 1e-98 - 1981.5 26342.22 101514425 4.30514319996e+20 0.669850075 0.306084425 0.0001049964 8.00614350002e-07 0.0014761185 0.002789907 3.89166025e-08 0.00677343775 0.01289259 2.155077e-05 1.61647375e-07 9.12429300001e-17 5.9022985e-06 9.78422525e-10 2.0360175e-12 1.2389625e-09 2.7293755e-11 1.5538845e-13 1.72095424999e-14 4.39332699994e-45 1e-98 - 1984.5 25905.8343133 101049248.069 4.21231616815e+20 0.671179516094 0.304772777253 0.000109948845923 8.96432874249e-07 0.00155703932618 0.00294481349356 4.10742548927e-08 0.00667553949785 0.0127314046781 2.22413432833e-05 1.61834651931e-07 9.76728763734e-17 5.65112330258e-06 9.66435818884e-10 2.03935766094e-12 1.10712011459e-09 2.52884718884e-11 1.38554629828e-13 1.42622212446e-14 2.59634572103e-45 1e-98 - 1987.5 25475.6804 100586060.4 4.12124243042e+20 0.672483675048 0.303486905143 0.000115005728762 9.99376835806e-07 0.00164017393714 0.00310435861523 4.32919429142e-08 0.00657478026857 0.0125657010476 2.28570185714e-05 1.61367857905e-07 1.04066433524e-16 5.38505476572e-06 9.51130766477e-10 2.03528991619e-12 9.81036900003e-10 2.33000972953e-11 1.226335728e-13 1.16474287582e-14 1.39177923449e-45 1e-98 - 1990.5 25036.0004 100101380.4 4.02828125224e+20 0.673772760762 0.302218162286 0.00012057350019 1.12546929295e-06 0.00173288079428 0.0032833460438 4.57679886285e-08 0.00646192369715 0.0123805867619 2.33434471428e-05 1.59596086476e-07 1.10742776381e-16 5.08265305144e-06 9.2891442362e-10 2.01679905905e-12 8.62446900003e-10 2.12910287239e-11 1.080358128e-13 9.53527332958e-15 8.45327291634e-46 1e-98 - 1993.5 24549.3745556 99557655.1111 3.92590760896e+20 0.675191185556 0.300823395556 0.000126981522222 1.2802186e-06 0.0018403518 0.00349172403333 4.86414423333e-08 0.00633066433334 0.0121656724444 2.37717003333e-05 1.56796853333e-07 1.18088435555e-16 4.73304193334e-06 9.00277162223e-10 1.98688543333e-12 7.37675002224e-10 1.90961385556e-11 9.2736678778e-14 7.45591352225e-15 4.07602964448e-46 1e-98 - 1996.5 23968.7306011 98895256.5574 3.80436828429e+20 0.67686673388 0.299178251366 0.000135098256831 1.49524767213e-06 0.00197794653006 0.00376028624044 5.23268409837e-08 0.00616173981967 0.0118898752459 2.4063130929e-05 1.51847050273e-07 1.26709954098e-16 4.28935268852e-06 8.58427697267e-10 1.93296645355e-12 6.0145977213e-10 1.65411425683e-11 7.61429505463e-14 5.45250376501e-15 1.64919148086e-46 1e-98 - 1999.5 23484.7346129 98332086.2258 3.70349849963e+20 0.678214450323 0.297857980645 0.000142310448387 1.70895974193e-06 0.00210167355806 0.00400407261935 5.56507114193e-08 0.00600866056129 0.0116409843871 2.40915993871e-05 1.46391880645e-07 1.33691837742e-16 3.9083880871e-06 8.1776192742e-10 1.87300150968e-12 5.01608995163e-10 1.45301547742e-11 6.41109433228e-14 4.17402536776e-15 7.9078828872e-47 1e-98 - 2002.5 23090.1356962 97865195.9494 3.62151798068e+20 0.679262519937 0.296833671519 0.000148493117405 1.9102485443e-06 0.00220870861709 0.00421694282594 5.8536229905e-08 0.00587518073102 0.0114248293038 2.39887237975e-05 1.41293125e-07 1.39308700949e-16 3.59989251267e-06 7.82176304748e-10 1.81690723734e-12 4.29624230065e-10 1.29986902532e-11 5.55177948103e-14 3.34442776584e-15 4.37089133867e-47 1e-98 - 2005.5 22701.5251731 97398972.0962 3.54114254667e+20 0.680283604231 0.295837149038 0.000154828898269 2.13434799038e-06 0.002319174525 0.00443872603461 6.15266866538e-08 0.00573628735 0.0112008713269 2.37508673654e-05 1.3559452e-07 1.44513440962e-16 3.29655044423e-06 7.44414716731e-10 1.75357584615e-12 3.65778929616e-10 1.15694359231e-11 4.78480518846e-14 2.66669965193e-15 2.37909883462e-47 1e-98 - 2008.5 22357.7194038 96982935.5577 3.47040628452e+20 0.681182796538 0.294960514423 0.000160591955962 2.35073164423e-06 0.002420147025 0.00464296488077 6.42695981923e-08 0.00560850235 0.0109955270962 2.34431885192e-05 1.3009252e-07 1.48831075577e-16 3.02916563654e-06 7.09175159038e-10 1.69191430769e-12 3.13963275769e-10 1.03593051538e-11 4.15700557307e-14 2.15294792115e-15 1.33575768077e-47 1e-98 - 2011.5 21803.5323595 96294144.0824 3.35681565983e+20 0.68261337191 0.293568585393 0.000170381397379 2.76005131087e-06 0.00259283237454 0.00499801455806 6.90015472661e-08 0.00538683172284 0.0106417200749 2.27123155056e-05 1.20256078277e-07 1.55094113109e-16 2.60875517976e-06 6.48888324717e-10 1.58051576779e-12 2.42002971908e-10 8.56452983515e-12 3.26999207113e-14 1.50469319848e-15 5.12848683126e-48 1e-98 - 2014.5 21433.2869746 95826267.7367 3.28115430089e+20 0.683477140185 0.292732096305 0.000177216741109 3.07850822171e-06 0.00271386022171 0.00525184986836 7.23563456813e-08 0.00522882468822 0.0103912164896 2.21746313164e-05 1.13781291224e-07 1.59533288222e-16 2.35201927713e-06 6.10279910623e-10 1.50770145496e-12 2.02448338337e-10 7.52046063278e-12 2.78731223325e-14 1.18379407852e-15 2.7100883972e-48 1e-98 - 2017.5 21042.5779511 95324888.1651 3.20169979174e+20 0.684376476453 0.291862675229 0.000184663311315 3.45611176759e-06 0.00284571662997 0.00553401642508 7.60575706728e-08 0.00505376908257 0.0101154413639 2.14904714985e-05 1.06605337615e-07 1.63701787156e-16 2.09192809786e-06 5.68562785015e-10 1.42600768196e-12 1.66045304893e-10 6.50598915596e-12 2.33566598777e-14 9.09689960244e-16 1.34452873578e-48 1e-98 - 2020.5 20614.5040659 94766859.2308 3.11518003651e+20 0.685365663736 0.290907498901 0.00019306618022 3.92154861538e-06 0.00299428367033 0.00586030596703 8.02905376923e-08 0.0048523867033 0.0098002390989 2.05909026374e-05 9.82958879121e-08 1.67448364835e-16 1.81790993407e-06 5.21376759341e-10 1.3299203956e-12 1.3145599011e-10 5.48214771429e-12 1.89416191209e-14 6.68069263737e-16 5.84850349451e-49 1e-98 - 2023.5 20272.9062637 94316166.9231 3.04650165544e+20 0.686128454945 0.290172795604 0.000199935520879 4.33742246153e-06 0.00311545268132 0.00613490486813 8.37978607692e-08 0.00468413681319 0.00953834239561 1.97975905495e-05 9.16229516484e-08 1.70061859341e-16 1.61440773627e-06 4.84184737363e-10 1.25211358242e-12 1.0820566044e-10 4.75238485715e-12 1.59280564836e-14 5.19264054947e-16 3.06943997806e-49 1e-98 - 2026.5 19866.9078481 93768554.8101 2.96518896728e+20 0.687010060759 0.289325677215 0.000208380098734 4.90573206329e-06 0.00326100868354 0.00648092392405 8.81933706329e-08 0.00447501163291 0.00921368702532 1.87543087342e-05 8.35821789874e-08 1.72295758228e-16 1.39172517722e-06 4.40227735443e-10 1.15634153165e-12 8.55572207596e-11 3.98716549368e-12 1.28987936709e-14 3.8442109114e-16 1.47395334811e-49 1e-98 - 2029.5 19454.1630508 93203611.5254 2.8830218234e+20 0.687878140678 0.288493483051 0.000217100810169 5.54555377966e-06 0.00340925281356 0.00685002966101 9.28016377966e-08 0.0042554771017 0.00887197474576 1.76391566102e-05 7.55151159322e-08 1.74066794915e-16 1.18244625424e-06 3.96645577966e-10 1.05916073729e-12 6.59906362713e-11 3.29018405085e-12 1.02281330509e-14 2.74959045763e-16 5.90983296612e-50 1e-98 - 2032.5 19070.109931 92669025.5172 2.80694424007e+20 0.688635924828 0.28776994 0.000225327529655 6.21574857241e-06 0.00354661718621 0.0072130778069 9.72316217241e-08 0.00404417717931 0.00854097517241 1.65858806897e-05 6.8362584e-08 1.75603806207e-16 1.00933024966e-06 3.58476477931e-10 9.72289213104e-13 5.13599990345e-11 2.73461210345e-12 8.19403162759e-15 1.99856064138e-16 2.51873544138e-50 1e-98 - 2035.5 18730.6409655 92189542.7586 2.73997846386e+20 0.689244752414 0.28719194 0.000232669184828 6.87124098623e-06 0.00366678339311 0.00754826470346 1.01254345862e-07 0.00385299648965 0.0082390475862 1.56975703448e-05 6.25567839998e-08 1.77446013104e-16 8.75427904824e-07 3.27786408965e-10 9.0174831655e-13 4.09564335169e-11 2.31761555171e-12 6.74309921375e-15 1.50492202067e-16 1.18983682065e-50 1e-98 - 2038.5 18243.9246575 91488166.3013 2.64484400949e+20 0.690214315069 0.286267442466 0.000242900275343 7.92894287675e-06 0.00383078531507 0.00806127134248 1.07052656165e-07 0.00357787602739 0.00778259116437 1.41006649315e-05 5.33130880819e-08 1.75263631507e-16 6.90172916433e-07 2.7939098493e-10 7.83043501366e-13 2.85561150682e-11 1.76624347944e-12 4.85072271228e-15 9.55311709576e-17 3.64783510948e-51 1e-98 - 2041.5 17870.7215885 90940390.4332 2.57230689212e+20 0.690885956679 0.285630985921 0.000250433020938 8.85534367868e-06 0.00395155193502 0.00847427688808 1.11491914079e-07 0.00336848063177 0.00741580942961 1.2929580722e-05 4.69479409388e-08 1.73491622744e-16 5.7237148123e-07 2.46317502889e-10 7.0001017437e-13 2.15733753792e-11 1.42901598557e-12 3.76608214804e-15 6.75654462461e-17 1.5494225235e-51 1e-98 - 2044.5 17551.8288255 90465461.0067 2.51067467569e+20 0.691431730201 0.285115862752 0.000256475735235 9.73472059393e-06 0.0040518624161 0.00883772785234 1.15201674832e-07 0.00319372673155 0.00709033213088 1.19368059732e-05 4.18374041277e-08 1.71376736913e-16 4.84166055372e-07 2.19865695303e-10 6.32023793626e-13 1.68308621478e-11 1.18377412081e-12 3.0085039866e-15 4.98288348327e-17 7.3619184398e-52 1e-98 - 2047.5 17185.8135135 89911429.4981 2.44032926937e+20 0.692027581081 0.284555864865 0.000262876057143 1.08535401545e-05 0.0041659346834 0.00926281608881 1.19321310039e-07 0.00299900266023 0.00670373015058 1.08171372201e-05 3.63407842471e-08 1.68409579537e-16 3.95029470656e-07 1.9148538417e-10 5.57589002316e-13 1.24442293436e-11 9.42212925094e-13 2.28966914285e-15 3.4406489884e-17 2.87243437063e-52 1e-98 - 2050.5 16826.54128 89358640 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2.36033764234e-08 1.5775285219e-16 2.14101582482e-07 1.25836703285e-10 3.78029261679e-13 5.07438715329e-12 4.77076989782e-13 1.01567974854e-15 1.16216286132e-17 1.88342988322e-53 1e-98 - 2059.5 15859.6739669 87822273.719 2.18918253571e+20 0.693858287603 0.282863864463 0.000277856491736 1.65149067769e-05 0.00462676318182 0.0107630209917 1.31506736364e-07 0.00238104365289 0.00520510431404 7.15982102478e-06 2.04570421487e-08 1.53713897521e-16 1.75413628925e-07 1.0957984876e-10 3.31574274379e-13 3.80592532229e-12 3.82966877684e-13 7.80810655367e-16 8.23031599993e-18 7.97343099157e-54 1e-98 - 2062.5 15440.5535468 87132909.6552 2.11113562834e+20 0.694350761084 0.282419738424 0.000278881641872 1.91057102955e-05 0.00481780473399 0.0111778843842 1.33951502956e-07 0.0022196774335 0.00470979827095 6.13315486209e-06 1.65566740887e-08 1.47312884729e-16 1.30778860099e-07 8.9355587094e-11 2.72483537932e-13 2.48612272908e-12 2.77063167982e-13 5.27624041876e-16 4.92968906901e-18 2.04810033009e-54 1e-98 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-7.45669878305e-56 -4.57215970574e-85 -2.6782874605e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3049.5 -51.0896128519 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.78580596833e-29 -4.51310637479e-26 -3.7541785645e-55 -8.91465838569e-88 -7.49596578302e-56 -4.59623230572e-85 -2.69239106049e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3052.5 -51.3598528517 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.79516656833e-29 -4.53676257477e-26 -3.77384506448e-55 -8.96134858565e-88 -7.53523278298e-56 -4.6203049057e-85 -2.70649466048e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3055.5 -51.6300928514 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.80452716832e-29 -4.56041877475e-26 -3.79351156447e-55 -9.00803878561e-88 -7.57449978294e-56 -4.64437750567e-85 -2.72059826046e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3058.5 -51.9003328512 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.81388776831e-29 -4.58407497473e-26 -3.81317806445e-55 -9.05472898556e-88 -7.61376678291e-56 -4.66845010565e-85 -2.73470186045e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3061.5 -52.1705728509 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.8232483683e-29 -4.6077311747e-26 -3.83284456443e-55 -9.10141918552e-88 -7.65303378287e-56 -4.69252270563e-85 -2.74880546044e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3064.5 -52.4408128507 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.83260896829e-29 -4.63138737468e-26 -3.85251106441e-55 -9.14810938548e-88 -7.69230078283e-56 -4.71659530561e-85 -2.76290906043e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3067.5 -52.7110528504 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.84196956828e-29 -4.65504357466e-26 -3.87217756439e-55 -9.19479958543e-88 -7.7315677828e-56 -4.74066790558e-85 -2.77701266041e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 - 3070.5 -52.9812928502 10000000 2.52197138341e+13 0.7 0.28 0.02 -1.85133016827e-29 -4.67869977464e-26 -3.89184406437e-55 -9.24148978539e-88 -7.77083478276e-56 -4.76474050556e-85 -2.7911162604e-69 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 diff --git a/Exec/science/xrb_layered/inputs_2d_12cm b/Exec/science/xrb_layered/inputs similarity index 68% rename from Exec/science/xrb_layered/inputs_2d_12cm rename to Exec/science/xrb_layered/inputs index c6a378f89..1dece7cf0 100644 --- a/Exec/science/xrb_layered/inputs_2d_12cm +++ b/Exec/science/xrb_layered/inputs @@ -1,10 +1,13 @@ # INITIAL MODEL -maestro.model_file = "xrb_layered_3cm.hse" +maestro.model_file = "toy_atm_hot_3cm.hse" maestro.perturb_model = true +maestro.use_soundspeed_firstdt = 1 +maestro.basestate_use_pres_model = 1 +#maestro.use_thermal_diffusion = true # PROBLEM SIZE geometry.prob_lo = 0.0 0.0 -geometry.prob_hi = 1536.0 3072.0 +geometry.prob_hi = 3072.0 3072.0 # BOUNDARY CONDITIONS # 0 = Interior 3 = Symmetry @@ -21,7 +24,7 @@ maestro.v = 1 # verbosity amrex.fpe_trap_invalid = 1 # floating point exception # GRIDDING AND REFINEMENT -amr.n_cell = 128 256 +amr.n_cell = 512 512 amr.max_grid_size = 64 amr.max_level = 0 # maximum level number allowed maestro.regrid_int = 2 # how often to regrid @@ -59,10 +62,12 @@ maestro.dpdt_factor = 0.0e0 maestro.species_pred_type = 3 -maestro.limit_conductivity = true +# maestro.limit_conductivity = true -maestro.anelastic_cutoff_density = 2.e3 -maestro.base_cutoff_density = 2.e3 +#maestro.anelastic_cutoff_density = 1.e2 +#maestro.base_cutoff_density = 1.e2 +maestro.anelastic_cutoff_density = 1.e3 +maestro.base_cutoff_density = 1.e3 maestro.do_sponge = true maestro.sponge_start_factor = 25.e0 @@ -71,21 +76,29 @@ maestro.ppm_type = 1 # PLOTFILES maestro.plot_base_name = PLOTS/xrb_ # root name of plot file -maestro.plot_int = 1000 # number of timesteps between plot files +maestro.plot_int = -1 # number of timesteps between plot files maestro.plot_deltat = 1.e-4 +maestro.plot_ad_excess = true maestro.small_plot_base_name = PLOTS/smallxrb_ -maestro.small_plot_deltat = 1.e-5 -maestro.small_plot_vars = Hnuc X(h1) X(he4) X(c12) magvel vort +maestro.small_plot_deltat = -1.0 +#maestro.small_plot_vars = Hnuc X(h1) X(he4) X(c12) magvel vort +maestro.small_plot_vars = rho p0 tfromp tpert vely X(H1) X(He4) X(C12) X(C13) X(N13) X(N14) X(N15) X(O14) X(O15) X(O16) X(O17) X(O18) X(F17) X(F18) X(F19) X(Ne18) X(Ne19) X(Ne20) X(Mg22) X(Mg24) X(Fe56) + +amr.write_plotfile_with_checkpoint = 0 +maestro.output_at_completion = 0 # CHECKPOINT maestro.check_base_name = CHECKS/chk maestro.chk_int = 1000 # tolerances for the initial projection -maestro.eps_init_proj_cart = 1.e-10 +#maestro.eps_init_proj_cart = 1.e-10 +maestro.eps_init_proj_cart = 1.e-8 +#maestro.eps_init_proj_cart = 1.e-4 maestro.eps_init_proj_sph = 1.e-10 # tolerances for the divu iterations -maestro.eps_divu_cart = 5.e-11 +#maestro.eps_divu_cart = 5.e-11 +maestro.eps_divu_cart = 1.e-8 maestro.eps_divu_sph = 1.e-10 maestro.divu_iter_factor = 100. maestro.divu_level_factor = 10. @@ -101,7 +114,7 @@ maestro.hg_level_factor = 10. maestro.eps_hg_bottom = 1.e-4 # BURNING -maestro.burner_threshold_species = "H1" +#maestro.burner_threshold_species = "H1" # LINEAR SOLVERS maestro.mg_bottom_solver = 4 @@ -110,18 +123,13 @@ maestro.max_mg_bottom_nlevels = 4 # override the default values of the probin namelist values here # temperature perturbation -problem.xrb_pert_size = 15 -problem.xrb_pert_factor = 5.0e-2 -problem.xrb_pert_type = 1 -problem.xrb_pert_height = 1100.0 - -# velocity perturbation -problem.apply_vel_field = 1 -problem.velpert_scale = 1.0e2 -problem.velpert_amplitude = 1.0e5 -problem.velpert_height_loc = 1100.0 -problem.velpert_steep = 12.0e0 -problem.num_vortices = 8 +problem.pert_seed = 42 + +# refinement +# Set this to 1, and amr.max_level = 1 +problem.do_height_based_refinement = 0 +problem.height_refine_bottom = 1.0e3 +problem.height_refine_top = 1.2e3 # Note that some of the parameters in this # namelist are specific to the default EOS, @@ -132,7 +140,7 @@ problem.num_vortices = 8 eos.use_eos_coulomb = 1 -integrator.jacobian = 2 -integrator.atol_spec = 1.e-8 -integrator.rtol_spec = 1.e-5 +integrator.jacobian = 1 +integrator.atol_spec = 1.e-6 +integrator.rtol_spec = 1.e-6 diff --git a/Exec/science/xrb_layered/plotfile_derive/.gitignore b/Exec/science/xrb_layered/plotfile_derive/.gitignore new file mode 100644 index 000000000..a8905d20a --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/.gitignore @@ -0,0 +1,4 @@ +*.ex +tmp_build_dir +Backtrace* +helm_table.dat diff --git a/Exec/science/xrb_layered/plotfile_derive/GNUmakefile b/Exec/science/xrb_layered/plotfile_derive/GNUmakefile new file mode 100644 index 000000000..7c41f42a0 --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/GNUmakefile @@ -0,0 +1,40 @@ +PRECISION = DOUBLE +PROFILE = FALSE + +DEBUG = FALSE + +DIM = 2 + +COMP = g++ +FCOMP = gfortran + +USE_MPI = FALSE +USE_OMP = FALSE + +USE_REACT = TRUE +USE_CXX_EOS = TRUE + +MAX_ZONES := 16384 + +DEFINES += -DNPTS_MODEL=$(MAX_ZONES) + +# programs to be compiled +EBASE := derive + +# EOS and network +EOS_DIR := helmholtz +NETWORK_DIR := CNO_extras + +# Conductivity +CONDUCTIVITY_HOME = $(MICROPHYSICS_HOME)/conductivity +USE_CONDUCTIVITY := TRUE +CONDUCTIVITY_DIR := stellar + +Bpack := ./Make.package +Blocs := . .. + +EXTERN_SEARCH += . .. + +USE_AMR_CORE = TRUE + +include $(MICROPHYSICS_HOME)/Make.Microphysics diff --git a/Exec/science/xrb_layered/plotfile_derive/Make.package b/Exec/science/xrb_layered/plotfile_derive/Make.package new file mode 100644 index 000000000..6b4b865e8 --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/Make.package @@ -0,0 +1 @@ +CEXE_sources += main.cpp diff --git a/Exec/science/xrb_layered/plotfile_derive/README.md b/Exec/science/xrb_layered/plotfile_derive/README.md new file mode 100644 index 000000000..06dfbcdc8 --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/README.md @@ -0,0 +1,94 @@ +# Derived quantities + +This tool evaluates the following quantities from the maestro plotfiles. +There is some hard code in here which assumes that the plotfiles are 2D cartesian, g=1.29e14. +This is based on [amrex-astro-diag](https://github.com/amrex-astro/amrex-astro-diag) + +### Thermodynamic gradients + +- The true temperature gradient in the model (`del`): +$$\nabla=\left(\frac{d\ln T}{d\ln P}\right)$$ + +- The adiabatic gradient (`del_ad`): +$$\nabla_{\rm ad}=\left(\frac{d\ln T}{d\ln P}\right)_S=\frac{\chi_T}{\Gamma_1c_V}\frac{P}{\rho T}$$ + +- The Ledoux gradient (`del_ledoux`):: +$$\nabla_{\rm L}=\nabla_{\rm ad}+B$$ +For the composition term $B$, we use MESA's formulation (Paxton et al. 2013, Equation 8), but with a centered difference instead. At some grid point $k$ with pressure $P_k=P(\rho_k,T_k,X_k)$: +$$B_k=-\frac{1}{\chi_T}\frac{\ln P(\rho_k,T_k,X_{k+1})-\ln P(\rho_k,T_k,X_{k-1})}{\ln P_{k+1}-\ln P_{k-1}}$$ +(The numerator terms are the local pressure evaluated with the composition of the above/below grid points.) + +### Vertical Velocity squared +$v_y^2$, variable name `vy2`. This is to compare to the MLT prediction + +### Fluxes + +- The true convective heat flux from the temperature excess `Fconv_dT` +$$F_{\rm conv}=\rho c_p v_y \delta T$$ +where $\delta T=T-\bar{T}$, which exists as a code variable in MAESTROeX. +We also output it with $T$ instead of $\Delta T$ as `Fconv_T`. These should be equivalent formulations if $=0$ because $ = = - T0 = $ + +The following expressions are from the mixing-length theory of Kippenhahn & Wiegert. We assume efficient convection ($\nabla_{\rm element}\approx\nabla_{\rm ad}$), and assume the mixing length $\ell$ is equal to the pressure scale height $H_p=p/\rho g$. We therefore indicate the normalization in terms of the mixing-length parameter $\alpha_{\rm MLT}=\ell/H_p$). + +- The *mixing-length theory* (MLT) convective heat flux `Fconv_mlt` +$$F_{\rm MLT}=\frac{1}{2}\rho c_pv_yT(\nabla-\nabla_{\rm ad})$$ +Missing a factor $\alpha_{\rm MLT}$ + +- The MLT flux can also be expressed as a function of the velocity `Fconv_mlt_v`, under the same assumptions as above: +$$F_{\rm MLT,v}=\frac{4\rho c_pT}{\delta gH_p}v^3$$ +where $\delta=\left(\frac{d\ln\rho}{d\ln T}\right)_P=\frac{\chi_T}{\chi_\rho}$ comes from the EOS. This ignores composition gradients. +Missing a factor $1/\alpha_{\rm MLT}$. +We also calculate it using $|v|^3$, as `Fconv_mlt_vabs`. + +- This is using the following form for the square of the velocity, which can be checked independently from the flux. We also output it here even though it's not a flux, `v2_mlt`. +$$v^2_{\rm MLT}=g\delta(\nabla-\nabla_{\rm ad})H_p/8$$ +Missing a factor $\alpha_{\rm MLT}^2$. + +- The kinetic energy flux `Fkin`: +$$F_{\rm kin}=\frac{1}{2}\rho v^3$$ +This should be similar as the MLT flux, up to some constant scaling (and using $v^3$ instead of $|v|^3$. + +- The radiative flux `Frad`: +$$F_{\rm rad}=-\frac{4ac T^3}{3\kappa\rho}\frac{dT}{dr}$$ +This requires that you include `CONDUCTIVITY_DIR` in the makefile + +- The chemical fluxes of species $i$ +$$F_{\rm X_i}=\rho v X_i$$ +Currently, only the hydrogen and carbon fluxes (`Fh1`,`Fc12`) are included in the code, but it is straightforward to include other species. + + +### Other numbers +- The pressure scale height `Hp` as $p/\rho g$ +- The heat capacity `cp` + +- ~~The Damkholer number, which is the ratio between mixing and reaction timescales. We write it as the ratio between the convective turnover time and the nuclear timescale: +$$Da=\frac{H_p/v_y}{c_pT/H_{\rm nuc}}$$~~ + +- ~~The Richardson number, which is the ratio between buoyancy and shear terms: +$$Ri=\frac{g}{\rho}\frac{\drho/dy}{(dv_x/dy)^2}$$~~ + +Unfortunately, I did not include Hnuc and vx in the small plotfiles so these cannot be evaluated. + + +To build, do: + +``` +make DIM=2 +``` + +changing the `DIM` line to match the dimension of your plotfile. + +It is also important that the network you build with matches +the one used for generating the plotfile. This is set via +the `NETWORK_DIR` parameter in the `GNUmakefile`. + +Runtime parameters are managed by AMReX's ParmParse. To run, +you specify the plotfile via `diag.plotfile`, either in an inputs +file or on the command line, e.g.: + +``` +./derived.gnu.ex diag.plotfile=plt00000 +``` + + + diff --git a/Exec/science/xrb_layered/plotfile_derive/_parameters b/Exec/science/xrb_layered/plotfile_derive/_parameters new file mode 100644 index 000000000..f8aaf3ac5 --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/_parameters @@ -0,0 +1,6 @@ +@namespace: diag + +small_temp real -1.e200 +small_dens real -1.e200 + +plotfile string "" diff --git a/Exec/science/xrb_layered/plotfile_derive/amrex_astro_util.H b/Exec/science/xrb_layered/plotfile_derive/amrex_astro_util.H new file mode 100644 index 000000000..6b87ee8db --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/amrex_astro_util.H @@ -0,0 +1,230 @@ +#ifndef AMREX_ASTRO_UTIL_H +#define AMREX_ASTRO_UTIL_H + +#include +#include +#include + +#include +#include +#include +#include + +#include + +using namespace amrex; + +/// +/// Gets the variable ``varname`` from the ``job_info`` file and returns as a +/// string +/// +inline +std::string GetVarFromJobInfo (const std::string pltfile, const std::string varname) { + std::string filename = pltfile + "/job_info"; + std::regex re("\\s*" + varname + "\\s*=\\s*([\\w\\d\\.\\-\\+]+)"); + + std::smatch m; + + std::ifstream jobfile(filename); + if (jobfile.is_open()) { + std::stringstream buf; + buf << jobfile.rdbuf(); + std::string file_contents = buf.str(); + + if (std::regex_search(file_contents, m, re)) { + return m[1]; + } else { + Print() << "Unable to find " << varname << " in job_info file!" << std::endl; + } + } else { + Print() << "Could not open job_info file!" << std::endl; + } + + return ""; +} + +/// +/// Get the center from the job info file and return as a Real Vector +/// +inline +Vector GetCenter (const std::string pltfile) { + auto center_str = GetVarFromJobInfo(pltfile, "center"); + + // split string + std::istringstream iss {center_str}; + Vector center; + + std::string s; + while (std::getline(iss, s, ',')) + center.push_back(stod(s)); + + return center; +} + +/// +/// return the radial coordinate of a zone from the center and the +/// volume of the zone +/// +inline +std::pair get_coord_info(const Array& p, + const Vector& center, + const Array& dx_level, + const int coord, const bool sphr) { + + + // compute radial coord and index + + Real r_zone{0.0_rt}; + Real vol = std::numeric_limits::quiet_NaN(); + +#if AMREX_SPACEDIM == 1 + // 1-d spherical geometry / spherical Sedov explosion + + AMREX_ASSERT(coord == 2); + + r_zone = p[0] - center[0]; + Real r_r = problo[0]+static_cast(i+1)*dx_level[0]; + Real r_l = problo[0]+static_cast(i)*dx_level[0]; + vol = (4.0_rt/3.0_rt) * M_PI * dx_level[0] * + (r_r*r_r + r_l*r_r + r_l*r_l); + +#elif AMREX_SPACEDIM == 2 + if (sphr) { + // 2-d axisymmetric geometry / spherical Sedov explosion + + AMREX_ASSERT(coord == 1); + + // axisymmetric V = pi (r_r**2 - r_l**2) * dz + // = pi dr * dz * (r_r + r_l) + // = 2 pi r dr dz + + r_zone = std::sqrt((p[0] - center[0]) * (p[0] - center[0]) + + (p[1] - center[1]) * (p[1] - center[1])); + vol = 2 * M_PI * p[0] * dx_level[0] * dx_level[1]; + + } else { + // 2-d Cartesian geometry / cylindrical Sedov explosion + + AMREX_ASSERT(coord == 0); + + r_zone = std::sqrt((p[0] - center[0]) * (p[0] - center[0]) + + (p[1] - center[1]) * (p[1] - center[1])); + vol = dx_level[0] * dx_level[1]; + + } + +#else + + AMREX_ASSERT(coord == 0); + + vol = dx_level[0] * dx_level[1] * dx_level[2]; + + if (sphr) { + // 3-d Cartesian geometry / spherical Sedov explosion + + r_zone = std::sqrt((p[0] - center[0]) * (p[0] - center[0]) + + (p[1] - center[1]) * (p[1] - center[1]) + + (p[2] - center[2]) * (p[2] - center[2])); + + } else { + // 3-d Cartesian geometry / cylindrical Sedov explosion + + r_zone = std::sqrt((p[0] - center[0]) * (p[0] - center[0]) + + (p[1] - center[1]) * (p[1] - center[1])); + + } +#endif + + return {r_zone, vol}; + +} + + +/// +/// return the index of the density variable by searching through +/// the list of variables in the plotfile. +/// +inline int +get_dens_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "density"); + if (idx == var_names_pf.cend()) { + // MAESTROeX uses "rho" + idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "rho"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find the density component"); + } + } + return std::distance(var_names_pf.cbegin(), idx); +} + +/// +/// return the index of the temperature variable by searching through +/// the list of variables in the plotfile. +/// +inline int +get_temp_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "Temp"); + if (idx == var_names_pf.cend()) { + // for MAESTROeX, we'll default to "tfromp" + idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "tfromp"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find the temperature component"); + } + } + return std::distance(var_names_pf.cbegin(), idx); +} + +/// +/// return the index of the pressure variable by searching through +/// the list of variables in the plotfile. +/// +inline int +get_pres_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "pressure"); + if (idx == var_names_pf.cend()) { + // for MAESTROeX, we'll default to "p0" + idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "p0"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find the pressure component"); + } + } + return std::distance(var_names_pf.cbegin(), idx); +} + + +/// +/// return the index of the first species component by searching through +/// the list of variables in the plotfile. +/// +inline int +get_spec_index(const std::vector& var_names_pf) { + + std::string first_spec_name = "X(" + short_spec_names_cxx[0] + ")"; + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), first_spec_name); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find the first species"); + } + int spec_comp = std::distance(var_names_pf.cbegin(), idx); + + // safety check -- make sure the species in the plotfile are identical to + // those defined in the network we built this tool with. + + for (int n = 0; n < NumSpec; ++n) { + std::string current_spec_name = "X(" + short_spec_names_cxx[n] + ")"; + if (current_spec_name != var_names_pf[spec_comp+n]) { + std::cout << current_spec_name << std::endl; + std::cout << var_names_pf[spec_comp+n] << std::endl; + std::cout << "Make sure to compile with the same network as the plotfile" << std::endl; + std::cout << " make NETWORK_DIR=aprox13 (for example)" << std::endl; + amrex::Error("Error: species don't match"); + } + } + return spec_comp; +} + +#endif + + diff --git a/Exec/science/xrb_layered/plotfile_derive/main.cpp b/Exec/science/xrb_layered/plotfile_derive/main.cpp new file mode 100644 index 000000000..9cf53bc3b --- /dev/null +++ b/Exec/science/xrb_layered/plotfile_derive/main.cpp @@ -0,0 +1,551 @@ +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include + +#include +#include +#include + +#include + +#include + +using namespace amrex; + +inline int +get_vx_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "velx"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find velx component"); + } + return std::distance(var_names_pf.cbegin(), idx); +} + +inline int +get_vy_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "vely"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find vely component"); + } + return std::distance(var_names_pf.cbegin(), idx); +} + +inline int +get_dT_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "tpert"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find tpert component"); + } + return std::distance(var_names_pf.cbegin(), idx); +} + +inline int +get_Hnuc_index(const std::vector& var_names_pf) { + + auto idx = std::find(var_names_pf.cbegin(), var_names_pf.cend(), "Hnuc"); + if (idx == var_names_pf.cend()) { + amrex::Error("Error: could not find Hnuc component"); + } + return std::distance(var_names_pf.cbegin(), idx); +} + +void main_main() +{ + const int narg = amrex::command_argument_count(); + + std::string pltfile(diag_rp::plotfile); + + if (pltfile.empty()) { + std::cout << "no plotfile specified" << std::endl; + std::cout << "use: diag.plotfile=plt00000 (for example)" << std::endl; + amrex::Error("no plotfile"); + } + + if (pltfile.back() == '/') { + pltfile.pop_back(); + } + + std::string outfile = pltfile + "/derived"; + std::cout << outfile << std::endl; + + PlotFileData pf(pltfile); + + const int ndims = pf.spaceDim(); + AMREX_ALWAYS_ASSERT(ndims <= AMREX_SPACEDIM); + + const int nlevs = pf.finestLevel() + 1; + + Vector varnames; + varnames = pf.varNames(); + + // find variable indices + // We want: + // density, temperature, pressure, species + // vertical velocity, temperature perturbation + // we will assume here that the species are contiguous, so we will find + // the index of the first species + + const Vector& var_names_pf = pf.varNames(); + + int dens_comp = get_dens_index(var_names_pf); + int temp_comp = get_temp_index(var_names_pf); + int pres_comp = get_pres_index(var_names_pf); + int spec_comp = get_spec_index(var_names_pf); + int dT_comp = get_dT_index(var_names_pf); + + int vy_comp = get_vy_index(var_names_pf); + //int vx_comp = get_vx_index(var_names_pf); + + //int Hnuc_comp = get_Hnuc_index(var_names_pf); + + // create the variable names we will derive and store in the output + // file + + Vector gvarnames; + + gvarnames.push_back("del"); + gvarnames.push_back("del_ad"); + gvarnames.push_back("del_ledoux"); + gvarnames.push_back("vy2"); + gvarnames.push_back("Fconv_dT"); + gvarnames.push_back("Fconv_T"); + gvarnames.push_back("Fconv_mlt"); + gvarnames.push_back("Fconv_mlt_v"); + gvarnames.push_back("Fconv_mlt_vabs"); + gvarnames.push_back("v2_mlt"); + gvarnames.push_back("Fkin"); + gvarnames.push_back("Frad"); + gvarnames.push_back("Fh1"); + gvarnames.push_back("Fc12"); + gvarnames.push_back("Hp"); + gvarnames.push_back("cp"); + //gvarnames.push_back("Da"); + //gvarnames.push_back("Ri"); + + // interpret the boundary conditions + + BCRec bcr_default; + Array is_periodic{AMREX_D_DECL(0,0,0)}; + IntVect ng(1); + for (int idim = 0; idim < AMREX_SPACEDIM; ++idim) { + if (idim < ndims) { + bcr_default.setLo(idim, BCType::hoextrapcc); + bcr_default.setHi(idim, BCType::hoextrapcc); + } else { + bcr_default.setLo(idim, BCType::int_dir); + bcr_default.setHi(idim, BCType::int_dir); + is_periodic[idim] = 1; + ng[idim] = 0; + } + } + + // we need the variables constructed with ghost cells + + Vector gmf(nlevs); + Vector geom; + for (int ilev = 0; ilev < nlevs; ++ilev) + { + + // output MultiFab + + gmf[ilev].define(pf.boxArray(ilev), pf.DistributionMap(ilev), gvarnames.size(), 0); + + Vector bcr{bcr_default}; + auto is_per = is_periodic; + + Geometry vargeom(pf.probDomain(ilev), RealBox(pf.probLo(),pf.probHi()), + pf.coordSys(), is_per); + geom.push_back(vargeom); + + PhysBCFunct> physbcf + (vargeom, bcr, GpuBndryFuncFab(FabFillNoOp{})); + + // fill the pressure and temperature mfs with ghost cells + // we also need all of the species + + MultiFab temp_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + MultiFab dens_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + MultiFab pres_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + MultiFab species_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), NumSpec, ng); + //MultiFab vx_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + MultiFab vy_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + MultiFab dT_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + //MultiFab Hnuc_mf(pf.boxArray(ilev), pf.DistributionMap(ilev), 1, ng); + + if (ilev == 0) { + + // temperature + { + MultiFab smf = pf.get(ilev, var_names_pf[temp_comp]); + FillPatchSingleLevel(temp_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + 0, 0, 1, vargeom, physbcf, 0); + } + + // density + { + MultiFab smf = pf.get(ilev, var_names_pf[dens_comp]); + FillPatchSingleLevel(dens_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + 0, 0, 1, vargeom, physbcf, 0); + } + + // pressure + { + MultiFab smf = pf.get(ilev, var_names_pf[pres_comp]); + FillPatchSingleLevel(pres_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + 0, 0, 1, vargeom, physbcf, 0); + } + + // species + { + for (int n = 0; n < NumSpec; ++n) { + MultiFab smf = pf.get(ilev, var_names_pf[spec_comp+n]); + FillPatchSingleLevel(species_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + 0, n, 1, vargeom, physbcf, 0); + } + } + + // v + //{ + // MultiFab smf = pf.get(ilev, var_names_pf[vx_comp]); + // FillPatchSingleLevel(vx_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + // 0, 0, 1, vargeom, physbcf, 0); + //} + + { + MultiFab smf = pf.get(ilev, var_names_pf[vy_comp]); + FillPatchSingleLevel(vy_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + 0, 0, 1, vargeom, physbcf, 0); + } + + // dT + { + MultiFab smf = pf.get(ilev, var_names_pf[dT_comp]); + FillPatchSingleLevel(dT_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + 0, 0, 1, vargeom, physbcf, 0); + } + + // Hnuc + //{ + // MultiFab smf = pf.get(ilev, var_names_pf[Hnuc_comp]); + // FillPatchSingleLevel(Hnuc_mf, ng, Real(0.0), {&smf}, {Real(0.0)}, + // 0, 0, 1, vargeom, physbcf, 0); + //} + + + + } else { + auto* mapper = (Interpolater*)(&cell_cons_interp); + + IntVect ratio(pf.refRatio(ilev-1)); + for (int idim = ndims; idim < AMREX_SPACEDIM; ++idim) { + ratio[idim] = 1; + } + + Geometry cgeom(pf.probDomain(ilev-1), RealBox(pf.probLo(),pf.probHi()), + pf.coordSys(), is_per); + PhysBCFunct> cphysbcf + (cgeom, bcr, GpuBndryFuncFab(FabFillNoOp{})); + + // temperature + { + MultiFab cmf = pf.get(ilev-1, var_names_pf[temp_comp]); + MultiFab fmf = pf.get(ilev , var_names_pf[temp_comp]); + FillPatchTwoLevels(temp_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + } + + // density + { + MultiFab cmf = pf.get(ilev-1, var_names_pf[dens_comp]); + MultiFab fmf = pf.get(ilev , var_names_pf[dens_comp]); + FillPatchTwoLevels(dens_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + } + + // pressure + { + MultiFab cmf = pf.get(ilev-1, var_names_pf[pres_comp]); + MultiFab fmf = pf.get(ilev , var_names_pf[pres_comp]); + FillPatchTwoLevels(pres_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + } + + // species + { + for (int n = 0; n < NumSpec; ++n) { + MultiFab cmf = pf.get(ilev-1, var_names_pf[spec_comp+n]); + MultiFab fmf = pf.get(ilev , var_names_pf[spec_comp+n]); + FillPatchTwoLevels(species_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + {&fmf}, {Real(0.0)}, 0, n, 1, cgeom, vargeom, + cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + } + } + + // v + //{ + // MultiFab cmf = pf.get(ilev-1, var_names_pf[vx_comp]); + // MultiFab fmf = pf.get(ilev , var_names_pf[vx_comp]); + // FillPatchTwoLevels(vx_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + // {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + // cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + //} + { + MultiFab cmf = pf.get(ilev-1, var_names_pf[vy_comp]); + MultiFab fmf = pf.get(ilev , var_names_pf[vy_comp]); + FillPatchTwoLevels(vy_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + } + + + // dT + { + MultiFab cmf = pf.get(ilev-1, var_names_pf[dT_comp]); + MultiFab fmf = pf.get(ilev , var_names_pf[dT_comp]); + FillPatchTwoLevels(dT_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + } + + // Hnuc + //{ + // MultiFab cmf = pf.get(ilev-1, var_names_pf[Hnuc_comp]); + // MultiFab fmf = pf.get(ilev , var_names_pf[Hnuc_comp]); + // FillPatchTwoLevels(Hnuc_mf, ng, Real(0.0), {&cmf}, {Real(0.0)}, + // {&fmf}, {Real(0.0)}, 0, 0, 1, cgeom, vargeom, + // cphysbcf, 0, physbcf, 0, ratio, mapper, bcr, 0); + //} + + } + + auto const& dx = pf.cellSize(ilev); + + const MultiFab& lev_data_mf = pf.get(ilev); + + for (MFIter mfi(temp_mf, TilingIfNotGPU()); mfi.isValid(); ++mfi) { + Box const& bx = mfi.tilebox(); + + // output storage + auto const& ga = gmf[ilev].array(mfi); + + // temperature, pressure and composition with ghost cells + auto const& T = temp_mf.const_array(mfi); + auto const& Rho = dens_mf.const_array(mfi); + auto const& P = pres_mf.const_array(mfi); + auto const& X = species_mf.const_array(mfi); + //auto const& Hnuc = Hnuc_mf.const_array(mfi); + //auto const& Vx = vx_mf.const_array(mfi); + + // all of the data without ghost cells + const auto& fab = lev_data_mf.array(mfi); + + amrex::ParallelFor(bx, [=] AMREX_GPU_DEVICE (int i, int j, int k) + { + + Real dT_dy = 0.0; + //Real drho_dy = 0.0; + //Real dvx_dy = 0.0; + Real del = 0.0; + + // y is the vertical + dT_dy = (T(i,j+1,k) - T(i,j-1,k)) / (2.0*dx[1]); + //drho_dy = (Rho(i,j+1,k) - Rho(i,j-1,k)) / (2.0*dx[1]); + //dvx_dy = (Vx(i,j+1,k) - Vx(i,j-1,k)) / (2.0*dx[1]); + Real dp = P(i,j+1,k) - P(i,j-1,k); + if (dp != 0.0) { + del = (T(i,j+1,k) - T(i,j-1,k)) / dp * (P(i,j,k) / T(i,j,k)); + } + + Real pres = fab(i,j,k,pres_comp); + Real rho = fab(i,j,k,dens_comp); + Real temp = fab(i,j,k,temp_comp); + //Real velx = fab(i,j,k,vx_comp); + Real vely = fab(i,j,k,vy_comp); + Real delT = fab(i,j,k,dT_comp); + //Real Hnuc = fab(i,j,k,Hnuc_comp); + + // Make EOS + eos_t eos_state; + eos_state.rho = rho; + eos_state.T = temp; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = fab(i,j,k,spec_comp+n); + } + eos(eos_input_rt, eos_state); + conductivity(eos_state); + + // Derive from EOS + Real cp = eos_state.cp; + Real chi_T = eos_state.dpdT * eos_state.T/eos_state.p; + Real chi_rho = eos_state.dpdr * eos_state.rho/eos_state.p; + Real delta = chi_T/chi_rho; // dlnd/dlnT = T/d dd/dT = T/d (dP/dT)/(dP/dd) = T/d chi_T/chi_d + Real del_ad = eos_state.p * chi_T / (eos_state.rho * eos_state.T * cp * chi_rho); + + + + // Stuff for del_ledoux + // del_ledoux = del_ad + B, where B is the composition term + // We calculate it like MESA, Paxton+ 2013 Equation 8 + // but we do a centered difference + + Real lnP_plus{0.0}; // pressure "above" + Real lnP_minus{0.0}; // pressure "below" + + Real lnPalt_plus{0.0}; // pressure with "above" species + Real lnPalt_minus{0.0}; // pressure with "below" species + + // Assuming non-spherical, ndims=2 + // y is the vertical + + lnP_plus = std::log(P(i,j+1,k)); + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = X(i,j+1,k,n); + } + eos(eos_input_rt, eos_state); + lnPalt_plus = std::log(eos_state.p); + + lnP_minus = std::log(P(i,j-1,k)); + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = X(i,j-1,k,n); + } + eos(eos_input_rt, eos_state); + lnPalt_minus = std::log(eos_state.p); + + Real denom = lnP_plus - lnP_minus; + Real B{0.0}; + if (denom != 0.0) { + B = -1 / chi_T * (lnPalt_plus - lnPalt_minus) / denom; + } + Real del_ledoux = del_ad + B; + + + // Restore eos + eos_state.rho = rho; + eos_state.T = temp; + for (int n = 0; n < NumSpec; ++n) { + eos_state.xn[n] = fab(i,j,k,spec_comp+n); + } + eos(eos_input_rt, eos_state); + conductivity(eos_state); + + // Convective grads + // varnames del,del_ad,del_ledoux,del-del_ad,del-del_ledoux + ga(i,j,k,0) = del; + ga(i,j,k,1) = del_ad; + ga(i,j,k,2) = del_ledoux; + //ga(i,j,k,3) = del-del_ad; + //ga(i,j,k,4) = del-del_ledoux; + + // Velocities : "vx2","vy2","v2","vy3","vyabs3" + //ga(i,j,k,5) = pow(velx,2); + // ga(i,j,k,6) = pow(vely,2); + // ga(i,j,k,7) = pow(velx,2) + pow(vely,2); + // ga(i,j,k,8) = pow(vely,3); + // ga(i,j,k,9) = pow(std::abs(vely),2); + + ga(i,j,k,3) = pow(vely,2); + //ga(i,j,k,6) = pow(vely,3); + //ga(i,j,k,7) = pow(std::abs(vely),2); + + // Gravity/Scale height + // auto grav = GetVarFromJobInfo(pltfile, "maestro.grav_const"); // really slow!! + // Real g = std::abs(std::stod(grav)); + // std::cout << g << std::endl; + Real g = 1.29e14; + Real Hp = pres/(rho*g); + + + // Fluxes + + // Convective heat flux : "Fconv_dT" + ga(i,j,k,4) = rho * cp * vely * delT; + + // Alternate version that should be equivalent : "Fconv_T" + // because = = - T0 = (the vertical velocity averages to zero) + ga(i,j,k,5) = rho * cp * vely * temp; + + // MLT heat flux in the efficient regime : "Fconv_mlt" + ga(i,j,k,6) = 0.5 * rho * cp * temp * vely * (del-del_ad); + + // MLT flux as a function of velocity : "Fconv_mlt_v3" and "Fconv_mlt_vabs3" + ga(i,j,k,7) = 4 * rho * cp * temp * pow(vely,3) / (delta * g * Hp); + ga(i,j,k,8) = 4 * rho * cp * temp * pow(std::abs(vely),3) / (delta * g * Hp); + + // MLT velocity squared : "v2_mlt" + ga(i,j,k,9) = g * delta * (del-del_ad) * Hp/8; + + // Kinetic flux : "Fkin" + ga(i,j,k,10) = 0.5 * rho * pow(vely,3); + + // Radiative flux : "Frad" + // conductivity is k = 4*a*c*T^3/(kap*rho) + // see Microphysics/conductivity/stellar/actual_conductivity.H + ga(i,j,k,11) = -eos_state.conductivity * dT_dy; + + // Hydrogen flux : "Fh1" + ga(i,j,k,12) = rho * vely * fab(i,j,k,spec_comp+0); // this is rho*v*X, not rho*v*dX + + // Hydrogen flux : "Fc12" + ga(i,j,k,13) = rho * vely * fab(i,j,k,spec_comp+2); + + // Other (scale height, cp, Damkholer number, Richardson number) + ga(i,j,k,14) = Hp; + ga(i,j,k,15) = cp; + //ga(i,j,k,20) = Hp * Hnuc / (vely*cp*temp); + //ga(i,j,k,21) = g * drho_dy / (rho * pow(dvx_dy, 2)); + + }); + } + } + + Vector level_steps; + Vector ref_ratio; + for (int ilev = 0; ilev < nlevs; ++ilev) { + level_steps.push_back(pf.levelStep(ilev)); + if (ilev < pf.finestLevel()) { + ref_ratio.push_back(IntVect(pf.refRatio(ilev))); + for (int idim = ndims; idim < AMREX_SPACEDIM; ++idim) { + ref_ratio[ilev][idim] = 1; + } + } + } + + WriteMultiLevelPlotfile(outfile, nlevs, GetVecOfConstPtrs(gmf), gvarnames, + geom, pf.time(), level_steps, ref_ratio); +} + +int main (int argc, char* argv[]) +{ + amrex::SetVerbose(0); + amrex::Initialize(argc, argv); + + // initialize the runtime parameters + + init_extern_parameters(); + + // initialize C++ Microphysics + + eos_init(diag_rp::small_temp, diag_rp::small_dens); + network_init(); + + main_main(); + amrex::Finalize(); +} diff --git a/Exec/science/xrb_layered/rms_tool/.gitignore b/Exec/science/xrb_layered/rms_tool/.gitignore new file mode 100644 index 000000000..8cf371c07 --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/.gitignore @@ -0,0 +1,3 @@ +*.ex +tmp_build_dir +Backtrace* diff --git a/Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.H b/Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.H new file mode 100644 index 000000000..67639c006 --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.H @@ -0,0 +1,21 @@ +#ifndef AMREX_PPM_UTIL_H_ +#define AMREX_PPM_UTIL_H_ + +#include +#include + +namespace amrex { + +int loadPalette (const std::string& filename, + Array& r, Array& g, + Array& b, Array& a); + +void storePPM (const std::string& filename, + unsigned char const* data, int width, int height, + Array const& r, + Array const& g, + Array const& b); + +} + +#endif diff --git a/Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.cpp b/Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.cpp new file mode 100644 index 000000000..b5d612deb --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/AMReX_PPMUtil.cpp @@ -0,0 +1,98 @@ + +#include +#include +#include +#include + +namespace amrex { + +namespace { + constexpr int NCOLOR = 256; + constexpr char PGM_MAGIC1 = 'P'; +// constexpr char RPGM_MAGIC2 = '5'; + constexpr char RPGM_MAGIC3 = '6'; +} + +int loadPalette (const std::string& filename, + Array& r, Array& g, + Array& b, Array& a) +{ + FILE* fp = std::fopen(filename.c_str(), "rb"); + if (!fp) { + amrex::Abort("loadPalette: cannot open "+filename); + } + + std::fseek(fp, 0, SEEK_END); + Long length = std::ftell(fp); + std::fseek(fp, 0, SEEK_SET); + + /* check for RGB or RGBA palette */ + int num_elements = static_cast(length/(NCOLOR*sizeof(unsigned char))); + + if ( num_elements != 3 && num_elements != 4 ) + { + amrex::Abort("loadPalette: cannot process palettel file " + filename + + ", num(r,g,b,a) = " + std::to_string(num_elements) + + ", must be 3 or 4"); + } + + if (std::fread(r.data(), 1, NCOLOR, fp) != NCOLOR) + { + amrex::Abort("loadPalette: fread() failed to read R"); + } + if (std::fread(g.data(), 1, NCOLOR, fp) != NCOLOR) + { + amrex::Abort("loadPalette: fread() failed to read G"); + } + if (std::fread(b.data(), 1, NCOLOR, fp) != NCOLOR) + { + amrex::Abort("loadPalette: fread() failed to read B"); + } + + if ( num_elements == 4 ) + { + if (std::fread(a.data(), 1, NCOLOR, fp) != NCOLOR) + { + amrex::Abort("loadPalette: fread() failed to read A"); + } + } + + std::fclose(fp); + + return num_elements; +} + +void storePPM (const std::string& filename, + unsigned char const* data, int width, int height, + Array const& r, + Array const& g, + Array const& b) +{ + FILE* fp = std::fopen(filename.c_str(), "w"); + if (!fp) { + amrex::Abort("storePPM: cannot open output file "+filename); + return; + } + + auto* image = (unsigned char*) std::malloc(3*width*height*sizeof(unsigned char)); + if (image == nullptr) { + amrex::Abort("storePPM: malloc failed"); + return; + } + + for (int i = 0; i < width*height; ++i) { + int j = static_cast(data[i]); + AMREX_ASSERT(j >= 0 && j < 256); + image[3*i+0] = r[j]; + image[3*i+1] = g[j]; + image[3*i+2] = b[j]; + } + + std::fprintf(fp, "%c%c\n%d %d\n%d\n", PGM_MAGIC1, RPGM_MAGIC3, width, height, 255); + std::fwrite(image, 1, 3*width*height, fp); + + std::free(image); + std::fclose(fp); +} + +} diff --git a/Exec/science/xrb_layered/rms_tool/CMakeLists.txt b/Exec/science/xrb_layered/rms_tool/CMakeLists.txt new file mode 100644 index 000000000..f462445c9 --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/CMakeLists.txt @@ -0,0 +1,44 @@ +project(AMReX-PlotfileTools + DESCRIPTION "AMReX tools to postprocess plot files" + ) + +# Plotfile tools requires only AMReX sources from Src/Base which get always +# compiled, so no need to check which part of AMReX is enabled by user + +# List of plotfile targets +set(_exe_names + fboxinfo + fcompare + fextract + fextrema + fgradient + fnan + fsnapshot + ftime + fvarnames + fvolumesum + ) + +# Build targets one by one +foreach( _exe IN LISTS _exe_names) + add_executable(${_exe} ${_exe}.cpp) + add_executable(AMReX::${_exe} ALIAS ${_exe}) + target_link_libraries(${_exe} PRIVATE amrex) + if (AMReX_CUDA) + set_source_files_properties(${_exe}.cpp PROPERTIES LANGUAGE CUDA) + endif() + + # Add prefix to each tool's name to make them unique when installed. + # This avoids potential collisions of names on user systems, e.g., in + # software packages (Spack/Conda/Debian/...). + set_target_properties(${_exe} PROPERTIES OUTPUT_NAME "amrex_${_exe}") +endforeach() + + +# target snapshot needs a special treatment +target_include_directories(fsnapshot PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) +target_sources(fsnapshot PRIVATE AMReX_PPMUtil.H AMReX_PPMUtil.cpp) +if (AMReX_CUDA) + set_source_files_properties(AMReX_PPMUtil.cpp PROPERTIES LANGUAGE CUDA) + target_compile_features(fsnapshot PUBLIC cxx_std_17) +endif() diff --git a/Exec/science/xrb_layered/rms_tool/GNUmakefile b/Exec/science/xrb_layered/rms_tool/GNUmakefile new file mode 100644 index 000000000..8fd4897c9 --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/GNUmakefile @@ -0,0 +1,32 @@ +AMREX_HOME ?= ../.. + +DEBUG = FALSE +DIM = 3 +COMP = gcc +PRECISION = DOUBLE +USE_MPI = FALSE +USE_OMP = FALSE +USE_CUDA = FALSE + +BL_NO_FORT = TRUE + +programs ?= + +ifeq ($(strip $(programs)),) + programs += frms +endif + +include $(AMREX_HOME)/Tools/GNUMake/Make.defs + +multiple_executables = $(addsuffix .$(machineSuffix).ex, $(programs)) +default: $(multiple_executables) + +include ./Make.package +include $(AMREX_HOME)/Src/Base/Make.package +include $(AMREX_HOME)/Src/Boundary/Make.package +include $(AMREX_HOME)/Src/AmrCore/Make.package + +include $(AMREX_HOME)/Tools/GNUMake/Make.rules + +clean:: + $(SILENT) $(RM) $(multiple_executables) diff --git a/Exec/science/xrb_layered/rms_tool/Make.package b/Exec/science/xrb_layered/rms_tool/Make.package new file mode 100644 index 000000000..4c0169751 --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/Make.package @@ -0,0 +1,3 @@ + +CEXE_headers += AMReX_PPMUtil.H +CEXE_sources += AMReX_PPMUtil.cpp diff --git a/Exec/science/xrb_layered/rms_tool/frms.cpp b/Exec/science/xrb_layered/rms_tool/frms.cpp new file mode 100644 index 000000000..d1f3dd0c5 --- /dev/null +++ b/Exec/science/xrb_layered/rms_tool/frms.cpp @@ -0,0 +1,391 @@ +// +// Get the root-mean-square (RMS) of a variable along the lateral direction +// x_rms = sqrt(<(x - )^2>), where denotes a horizontal averageG + +#include +// #include +#include +#include +#include +#include +#include + +using namespace amrex; + +std::string inputs_name = ""; + +void PrintUsage (); + + +int main(int argc, char* argv[]) +{ + + amrex::Initialize(argc, argv, false); + + { + + // timer for profiling + BL_PROFILE_VAR("main()", pmain); + + // Input arguments + std::string varname{"density"}; + bool do_favre{false}; + + const int narg = amrex::command_argument_count(); + + int farg = 1; + while (farg <= narg) { + const std::string& name = amrex::get_command_argument(farg); + if (name == "-v" || name == "--variable") { + varname = amrex::get_command_argument(++farg); + } else { + break; + } + ++farg; + } + + if (farg > narg) { + PrintUsage(); + return -1; + } + + const std::string& pltfile = amrex::get_command_argument(farg); + std::string slcfile = pltfile + ".slice"; + + PlotFileData pf(pltfile); + + int fine_level = pf.finestLevel(); + const int dim = pf.spaceDim(); + + // get the index bounds and dx. + Box domain = pf.probDomain(fine_level); + auto dx_fine = pf.cellSize(fine_level); + auto problo = pf.probLo(); + auto probhi = pf.probHi(); + + // compute the size of the radially-binned array -- we'll take the + // vertical direction to be the dimensionality + + int nbins = static_cast(std::abs(probhi[dim-1] - problo[dim-1]) / dx_fine[dim-1]); + + // height coordinate + Vector h(nbins); + + for (auto i = 0; i < nbins; i++) { + h[i] = (i + 0.5) * dx_fine[dim-1]; + } + + // find variable indices + const Vector& var_names_pf = pf.varNames(); + + // density can be call "density" in Castro or "rho" in MAESTROeX + int dens_comp = std::distance(var_names_pf.cbegin(), + std::find(var_names_pf.cbegin(), var_names_pf.cend(), "density")); + + if (dens_comp == var_names_pf.size()) { + dens_comp = std::distance(var_names_pf.cbegin(), + std::find(var_names_pf.cbegin(), var_names_pf.cend(), "rho")); + } + + if (dens_comp == var_names_pf.size() && do_favre) { + amrex::Error("density not found"); + } + + int var_comp = std::distance(var_names_pf.cbegin(), + std::find(var_names_pf.cbegin(), var_names_pf.cend(), varname)); + + if (var_comp == var_names_pf.size()) { + amrex::Error("variable " + varname + " not found"); + } + + + // allocate storage for data + Vector var_bin(nbins, 0.); + Vector volcount(nbins, 0.); + Vector var_bin_rms(nbins, 0.); + + // we will use a mask that tells us if a zone on the current level + // is covered by data on a finer level. + + // First pass to determine the horizontal average + + for (int ilev = 0; ilev <= fine_level; ++ilev) { + + Array dx_level = pf.cellSize(ilev); + Real vol = dx_level[0]; + if (dim >= 2) { + vol *= dx_level[1]; + } + if (dim == 3) { + vol *= dx_level[2]; + } + + if (ilev < fine_level) { + IntVect ratio{pf.refRatio(ilev)}; + for (int idim = dim; idim < AMREX_SPACEDIM; ++idim) { + ratio[idim] = 1; + } + const iMultiFab mask = makeFineMask(pf.boxArray(ilev), pf.DistributionMap(ilev), + pf.boxArray(ilev+1), ratio); + + const MultiFab& lev_data_mf = pf.get(ilev); + + for (MFIter mfi(lev_data_mf); mfi.isValid(); ++mfi) { + const Box& bx = mfi.validbox(); + if (bx.ok()) { + const auto& m = mask.array(mfi); + const auto& fab = lev_data_mf.array(mfi); + const auto lo = amrex::lbound(bx); + const auto hi = amrex::ubound(bx); + + for (int k = lo.z; k <= hi.z; ++k) { + for (int j = lo.y; j <= hi.y; ++j) { + for (int i = lo.x; i <= hi.x; ++i) { + if (m(i,j,k) == 0) { // not covered by fine + + Real height{0.0}; + if (dim == 1) { + height = problo[0] + static_cast(i+0.5)*dx_level[0]; + } else if (dim == 2) { + height = problo[1] + static_cast(j+0.5)*dx_level[1]; + } else { + height = problo[2] + static_cast(k+0.5)*dx_level[2]; + } + + int index = static_cast(height / dx_fine[dim-1]); + + // add to the bin, weighting by the size + + var_bin[index] += fab(i,j,k,var_comp) * vol; + volcount[index] += vol; + + } // mask + + } + } + } + + } // bx.ok() + + } // MFIter + + } else { + // this is the finest level + + const MultiFab& lev_data_mf = pf.get(ilev); + + for (MFIter mfi(lev_data_mf); mfi.isValid(); ++mfi) { + const Box& bx = mfi.validbox(); + if (bx.ok()) { + const auto& fab = lev_data_mf.array(mfi); + const auto lo = amrex::lbound(bx); + const auto hi = amrex::ubound(bx); + + for (int k = lo.z; k <= hi.z; ++k) { + for (int j = lo.y; j <= hi.y; ++j) { + for (int i = lo.x; i <= hi.x; ++i) { + + Real height{0.0}; + if (dim == 1) { + height = problo[0] + static_cast(i+0.5)*dx_level[0]; + } else if (dim == 2) { + height = problo[1] + static_cast(j+0.5)*dx_level[1]; + } else { + height = problo[2] + static_cast(k+0.5)*dx_level[2]; + } + + int index = static_cast(height / dx_fine[dim-1]); + + var_bin[index] += fab(i,j,k,var_comp) * vol; + volcount[index] += vol; + + } + } + } + + } // bx.ok() + + } // MFIter + + + } + + } // level loop + + + ParallelDescriptor::ReduceRealSum(var_bin.data(), static_cast(var_bin.size())); + ParallelDescriptor::ReduceRealSum(volcount.data(), static_cast(volcount.size())); + + //normalize + + for (int i = 0; i < nbins; i++) { + if (volcount[i] != 0.0) { + var_bin[i] /= volcount[i]; + } + } + + // var_bin is now the horizontal average + + + // Second pass to find the rms + + for (int ilev = 0; ilev <= fine_level; ++ilev) { + + Array dx_level = pf.cellSize(ilev); + Real vol = dx_level[0]; + if (dim >= 2) { + vol *= dx_level[1]; + } + if (dim == 3) { + vol *= dx_level[2]; + } + + if (ilev < fine_level) { + IntVect ratio{pf.refRatio(ilev)}; + for (int idim = dim; idim < AMREX_SPACEDIM; ++idim) { + ratio[idim] = 1; + } + const iMultiFab mask = makeFineMask(pf.boxArray(ilev), pf.DistributionMap(ilev), + pf.boxArray(ilev+1), ratio); + + const MultiFab& lev_data_mf = pf.get(ilev); + + for (MFIter mfi(lev_data_mf); mfi.isValid(); ++mfi) { + const Box& bx = mfi.validbox(); + if (bx.ok()) { + const auto& m = mask.array(mfi); + const auto& fab = lev_data_mf.array(mfi); + const auto lo = amrex::lbound(bx); + const auto hi = amrex::ubound(bx); + + for (int k = lo.z; k <= hi.z; ++k) { + for (int j = lo.y; j <= hi.y; ++j) { + for (int i = lo.x; i <= hi.x; ++i) { + if (m(i,j,k) == 0) { // not covered by fine + + Real height{0.0}; + if (dim == 1) { + height = problo[0] + static_cast(i+0.5)*dx_level[0]; + } else if (dim == 2) { + height = problo[1] + static_cast(j+0.5)*dx_level[1]; + } else { + height = problo[2] + static_cast(k+0.5)*dx_level[2]; + } + + int index = static_cast(height / dx_fine[dim-1]); + + // add to the bin, weighting by the size + + var_bin_rms[index] += std::pow(fab(i,j,k,var_comp) - var_bin[index], 2.0) * vol; + + } // mask + + } + } + } + + } // bx.ok() + + } // MFIter + + } else { + // this is the finest level + + const MultiFab& lev_data_mf = pf.get(ilev); + + for (MFIter mfi(lev_data_mf); mfi.isValid(); ++mfi) { + const Box& bx = mfi.validbox(); + if (bx.ok()) { + const auto& fab = lev_data_mf.array(mfi); + const auto lo = amrex::lbound(bx); + const auto hi = amrex::ubound(bx); + + for (int k = lo.z; k <= hi.z; ++k) { + for (int j = lo.y; j <= hi.y; ++j) { + for (int i = lo.x; i <= hi.x; ++i) { + + Real height{0.0}; + if (dim == 1) { + height = problo[0] + static_cast(i+0.5)*dx_level[0]; + } else if (dim == 2) { + height = problo[1] + static_cast(j+0.5)*dx_level[1]; + } else { + height = problo[2] + static_cast(k+0.5)*dx_level[2]; + } + + int index = static_cast(height / dx_fine[dim-1]); + + var_bin_rms[index] += std::pow(fab(i,j,k,var_comp) - var_bin[index], 2.0) * vol; + + } + } + } + + } // bx.ok() + + } // MFIter + + + } + + } // level loop + + + ParallelDescriptor::ReduceRealSum(var_bin_rms.data(), static_cast(var_bin_rms.size())); + + //normalize and take the square root + + for (int i = 0; i < nbins; i++) { + if (volcount[i] != 0.0) { + var_bin_rms[i] = std::sqrt(var_bin_rms[i]/volcount[i]); + } + } + + // var_bin_rms is now the rms + + + // now open the slicefile and write out the data + amrex::Print() << "outputting rms of " << varname << " to " << slcfile << std::endl; + + std::ofstream slicefile; + slicefile.open(slcfile); + slicefile.setf(std::ios::scientific); + slicefile.precision(12); + const auto w = 24; + + // write the header + slicefile << "# " << std::setw(w) << "height" << std::setw(w) << varname << std::endl; + + // write the data in columns + const auto SMALL = 1.e-20; + for (auto i = 0; i < nbins; i++) { + if (std::abs(var_bin[i]) < SMALL) var_bin[i] = 0.0; + + slicefile << std::setw(w) << h[i] << std::setw(w) << var_bin_rms[i] << std::endl; + } + + slicefile.close(); + + // destroy timer for profiling + BL_PROFILE_VAR_STOP(pmain); + + } + + amrex::Finalize(); +} + + + +// +// Print usage info +// +void PrintUsage () +{ + amrex::Print() << "\n" + << " Find the rms fluctuation of a variable\n" + << " Usage: \n" + << " faverage {[-v|--variable] name} plotfile\n" + << "\n" + << " -v name : variable to average (default: density)\n" + << std::endl; +} diff --git a/Exec/science/xrb_layered/scripts/average_vars.sh b/Exec/science/xrb_layered/scripts/average_vars.sh new file mode 100755 index 000000000..fed28a233 --- /dev/null +++ b/Exec/science/xrb_layered/scripts/average_vars.sh @@ -0,0 +1,80 @@ +#!/bin/bash +####################################################################### +# Description: AMREX's faverage takes only 1 variable, and writes to +# plotfile/.slice. This script loops through faverage for a given list +# of variables, and creates a column separated plotfile/averages +# +# Usage: averages_vars.sh plotfile var1 var2 var3 ... +####################################################################### + +faverage=$AMREX_HOME/Tools/Plotfile/faverage.gnu.x86-milan.ex +plotfile=$1 +curr_dir=$(pwd) + +# Append "/" to plotfile in case it's not there +if [ "${plotfile: -1}" != "/" ]; then + plotfile+="/" +fi + +# Check if plotfile exists +if [ ! -d "$plotfile" ]; then + echo "Error: Input file '$1' does not exist." + exit 1 +fi + +# restart log file +rm -f $plotfile/.faverage_log + +# Header line +header="height " + +i=0 +#for var in "$@"; do +IFS=' ' read -ra vars <<< "${@:2}" +for var in "${vars[@]}"; do + # Skip the first argument (the input file) + if [ "$var" = "$1" ]; then + continue + fi + + #echo $var + header+="$var " + + # Average current variable which creates (or overwrites) plotfile/.slice + $faverage -v "$var" "$plotfile" >> $plotfile/.faverage_log + + # go into there for file manipulation + cd $plotfile + + # backup for validating this script + #cp .slice .slice_"$var" + + # Delete header line + sed -i '1d' .slice # linux + #sed -i '' '1d' .slice # BSD (mac) + + # If first variable, copy to plotfile/averages + # otherwise, paste the second column into it + if [ $i -eq 0 ]; then + cp .slice averages + else + # extract second column and write to temp file + cut -b 30- .slice > _temp1 + + # paste and write to other temp file + paste -d ' ' averages _temp1 > _temp2 + + # overwrite averages file + cp _temp2 averages + fi + + rm -f _temp* + rm -f .slice + cd $curr_dir + ((i++)) +done + +# Recreate the header line +sed -i '1s/^/'"$header"' \n/' $plotfile/averages # linux +#sed -i '' '1s/^/'"$header"' \n/' $plotfile/averages # BSD (mac) + diff --git a/Exec/science/xrb_layered/scripts/avg.submit b/Exec/science/xrb_layered/scripts/avg.submit new file mode 100644 index 000000000..86a8c3a59 --- /dev/null +++ b/Exec/science/xrb_layered/scripts/avg.submit @@ -0,0 +1,83 @@ +#!/bin/bash +#SBATCH --account=m3018 +#SBATCH --constraint=cpu +#SBATCH --output=slurm.avgparallel.%j.out +#SBATCH --nodes=1 +#SBATCH --time=01:00:00 +#SBATCH --qos=regular +###SBATCH --time=00:05:00 +###SBATCH --qos=debug +###SBATCH --mail-type=all +###SBATCH --mail-user=simon.guichandut@mail.mcgill.ca + +# Run directory +RUN_DIR=$1 +echo $RUN_DIR + +# List of variables to horizontally average +#VARS="tfromp rho Hnuc" +#VARS="tfromp rho Hnuc velx vely X(h1) X(he4) X(c12) X(fe56)" +#VARS="p0 tfromp rho Hnuc entropy velx vely X(h1) X(he4) X(c12) X(fe56)" +#VARS="p0 tfromp rho Hnuc entropy velx vely X(h1) X(he4) X(c12) X(c13) X(n13) X(n14) X(n15) X(o14) X(o15) X(o16) X(o17) X(o18) X(f17) X(f18) X(f19) X(ne18) X(ne19) X(ne20) X(mg22) X(mg24) X(fe56)" +VARS="p0 tfromp rho Hnuc entropy velx vely conductivity abar X(H1) X(He4) X(C12) X(C13) X(N13) X(N14) X(N15) X(O14) X(O15) X(O16) X(O17) X(O18) X(F17) X(F18) X(F19) X(Ne18) X(Ne19) X(Ne20) X(Mg22) X(Mg24) X(Fe56)" + +PLOT_DIR=PLOTS +#PLOT_BASENAME="xrb_0" +PLOT_BASENAME="xrb_" + +script=$xrb/scripts/average_vars.sh + +cd $RUN_DIR + +# Find all plotfiles that do not already have an average +plots=() +for plotfile in $PLOT_DIR/$PLOT_BASENAME*; do + [[ ! -d "$plotfile" ]] && continue + [[ "$plotfile" == *.old.* ]] && continue + [[ -e "$plotfile/averages" ]] && continue + plots+=($plotfile) +done + +N0=${#plots[@]} +echo $N0" plotfiles to process" + +if [ $N0 -eq 0 ]; then + exit 0 +fi + +# If this is being run in a slurm job, do parallel +# Otherwise, loop through the plotfiles one by one + +if [ -n "$SLURM_JOB_ID" ]; then + + module load parallel + NTASK=32 + + echo "Running in parallel with "$NTASK" processes" + + # Temporary text file with all of the plotfiles + tmpfile=$(mktemp) + printf "%s\n" "${plots[@]}" > "$tmpfile" + + # Use srun parallel as in the "Running Many Tasks Inside a Single Node Allocation" example here + # https://docs.nersc.gov/jobs/workflow/gnuparallel/ + srun parallel --jobs $NTASK "$script {} \"$VARS\"" :::: "$tmpfile" + #parallel --jobs $NTASK "$script {} \"$VARS\"" :::: "$tmpfile" + + rm -f "$tmpfile" + +else + + for ((i=0; i<$N0; i++)); do + plotfile="${plots[$i]}" + #echo "Processing: $plotfile" + $script "$plotfile" $VARS + + # Print the number of remaining plotfiles + remaining=$((N0 - i - 1)) + echo $remaining + done + +fi + +echo "done" diff --git a/Exec/science/xrb_layered/scripts/chainslurm.sh b/Exec/science/xrb_layered/scripts/chainslurm.sh new file mode 100755 index 000000000..5dbcf87db --- /dev/null +++ b/Exec/science/xrb_layered/scripts/chainslurm.sh @@ -0,0 +1,56 @@ +#!/bin/sh -f + +if [ ! "$1" ]; then + echo "usage: chainslurm.sh jobid number script" + echo " set jobid -1 for no initial dependency" + exit -1 +fi + +if [ ! "$2" ]; then + echo "usage: chainslurm.sh jobid number script" + echo " set jobid -1 for no initial dependency" + exit -1 +fi + +if [ ! "$3" ]; then + echo "usage: chainslurm.sh jobid number script" + echo " set jobid -1 for no initial dependency" + exit -1 +fi + + +oldjob=$1 +numjobs=$2 +script=$3 + +if [ $numjobs -gt "20" ]; then + echo "too many jobs requested" + exit -1 +fi + +firstcount=1 + +if [ $oldjob -eq "-1" ] +then + echo chaining $numjobs jobs + + echo starting job 1 with no dependency + aout=`sbatch --parsable ${script}` + echo " " jobid: $aout + echo " " + oldjob=$aout + firstcount=2 + sleep 3 +else + echo chaining $numjobs jobs starting with $oldjob +fi + +for count in `seq $firstcount 1 $numjobs` +do + echo starting job $count to depend on $oldjob + aout=`sbatch --parsable -d afterany:${oldjob} ${script}` + echo " " jobid: $aout + echo " " + oldjob=$aout + sleep 3 +done diff --git a/Exec/science/xrb_layered/scripts/conv_grad.submit b/Exec/science/xrb_layered/scripts/conv_grad.submit new file mode 100644 index 000000000..5a4b4d851 --- /dev/null +++ b/Exec/science/xrb_layered/scripts/conv_grad.submit @@ -0,0 +1,72 @@ +#!/bin/bash +#SBATCH --account=m3018 +#SBATCH --constraint=cpu +#SBATCH --output=slurm.convgrad.%j.out +#SBATCH --nodes=1 +#SBATCH --qos=regular +#SBATCH --time=00:30:00 + +# Run directory +RUN_DIR=$1 + +echo $RUN_DIR + +PLOT_DIR=PLOTS +#PLOT_BASENAME="*xrb_0" +PLOT_BASENAME="xrb_0" + +script=~/amrex-astro-diag/source/convective_grad/fconvgrad2d.gnu.x86-milan.ex + +cd $RUN_DIR + +# Find all plotfiles that do not already have an average +plots=() +for plotfile in $PLOT_DIR/$PLOT_BASENAME*; do + [[ ! -d "$plotfile" ]] && continue + [[ "$plotfile" == *.old.* ]] && continue + [[ -e "$plotfile/convgrad" ]] && continue + plots+=($plotfile) +done + +N0=${#plots[@]} +echo $N0" plotfiles to process" + +if [ $N0 -eq 0 ]; then + exit 0 +fi + +# If this is being run in a slurm job, do parallel +# Otherwise, loop through the plotfiles one by one + +if [ -n "$SLURM_JOB_ID" ]; then + + module load parallel + NTASK=32 + + echo "Running in parallel with "$NTASK" processes" + + # Temporary text file with all of the plotfiles + tmpfile=$(mktemp) + printf "%s\n" "${plots[@]}" > "$tmpfile" + + # Use srun parallel as in the "Running Many Tasks Inside a Single Node Allocation" example here + # https://docs.nersc.gov/jobs/workflow/gnuparallel/ + srun parallel --jobs $NTASK "$script diag.plotfile={}" :::: "$tmpfile" + + rm -f "$tmpfile" + +else + + for ((i=0; i<$N0; i++)); do + plotfile="${plots[$i]}" + #echo "Processing: $plotfile" + $script diag.plotfile=$plotfile + + # Print the number of remaining plotfiles + remaining=$((N0 - i - 1)) + echo $remaining + done + +fi + +echo "done" diff --git a/Exec/science/xrb_layered/scripts/derived.submit b/Exec/science/xrb_layered/scripts/derived.submit new file mode 100644 index 000000000..1a756d07c --- /dev/null +++ b/Exec/science/xrb_layered/scripts/derived.submit @@ -0,0 +1,73 @@ +#!/bin/bash +#SBATCH --account=m3018 +#SBATCH --output=slurm.derived.%j.out +#SBATCH --constraint=cpu +#SBATCH --nodes=1 +#SBATCH --qos=regular +#SBATCH --time=02:00:00 + +# Run directory +RUN_DIR=$1 + +echo $RUN_DIR + +PLOT_DIR=PLOTS +#PLOT_BASENAME="*xrb_0" # includes the smallplot files +PLOT_BASENAME="*xrb_" # includes the smallplot files +#PLOT_BASENAME="xrb_0" + +script=~/MAESTROeX/Exec/science/xrb_layered/plotfile_derive/derive2d.gnu.x86-milan.ex + +cd $RUN_DIR + +# Find all plotfiles that do not already have an average +plots=() +for plotfile in $PLOT_DIR/$PLOT_BASENAME*; do + [[ ! -d "$plotfile" ]] && continue + [[ "$plotfile" == *.old.* ]] && continue + [[ -e "$plotfile/derived" ]] && continue + plots+=($plotfile) +done + +N0=${#plots[@]} +echo $N0" plotfiles to process" + +if [ $N0 -eq 0 ]; then + exit 0 +fi + +# If this is being run in a slurm job, do parallel +# Otherwise, loop through the plotfiles one by one + +if [ -n "$SLURM_JOB_ID" ]; then + + module load parallel + NTASK=32 + + echo "Running in parallel with "$NTASK" processes" + + # Temporary text file with all of the plotfiles + tmpfile=$(mktemp) + printf "%s\n" "${plots[@]}" > "$tmpfile" + + # Use srun parallel as in the "Running Many Tasks Inside a Single Node Allocation" example here + # https://docs.nersc.gov/jobs/workflow/gnuparallel/ + srun parallel --jobs $NTASK "$script diag.plotfile={}" :::: "$tmpfile" + + rm -f "$tmpfile" + +else + + for ((i=0; i<$N0; i++)); do + plotfile="${plots[$i]}" + #echo "Processing: $plotfile" + $script diag.plotfile=$plotfile + + # Print the number of remaining plotfiles + remaining=$((N0 - i - 1)) + echo $remaining + done + +fi + +echo "done" diff --git a/Exec/science/xrb_layered/scripts/extrema.submit b/Exec/science/xrb_layered/scripts/extrema.submit new file mode 100644 index 000000000..2f7f04195 --- /dev/null +++ b/Exec/science/xrb_layered/scripts/extrema.submit @@ -0,0 +1,32 @@ +#!/bin/bash +#SBATCH --account=m3018 +#SBATCH --output=slurm.extrema.%j.out +#SBATCH --constraint=cpu +###SBATCH --time=01:00:00 +#SBATCH --time=02:00:00 +#SBATCH --nodes=1 + +#SBATCH --qos=regular + +###SBATCH --qos=shared +###SBATCH --ntasks=1 +###SBATCH --mem=1GB + + +###SBATCH --mail-type=end +###SBATCH --mail-type=all +###SBATCH --mail-user=simon.guichandut@mail.mcgill.ca + +RUN_DIR=$1 + +PLOT_DIR=PLOTS +PLOT_BASENAME="xrb_0" + +VARS="tfromp MachNumber Hnuc" + +echo $RUN_DIR + +fextrema=$AMREX_HOME/Tools/Plotfile/fextrema.gnu.x86-milan.ex +$fextrema -v "$VARS" $RUN_DIR/$PLOT_DIR/$PLOT_BASENAME* > $RUN_DIR/"extrema.txt" + +echo "done" diff --git a/Exec/science/xrb_layered/scripts/get_extrema.sh b/Exec/science/xrb_layered/scripts/get_extrema.sh new file mode 100755 index 000000000..ca2c5c9f5 --- /dev/null +++ b/Exec/science/xrb_layered/scripts/get_extrema.sh @@ -0,0 +1,9 @@ +#!/bin/bash +RUN_DIR=$1 +PLOT_DIR=PLOTS +PLOT_BASENAME="xrb_0" + +VARS="tfromp MachNumber Hnuc" + +fextrema=$AMREX_HOME/Tools/Plotfile/fextrema.gnu.x86-milan.ex +$fextrema -v "$VARS" $RUN_DIR/$PLOT_DIR/$PLOT_BASENAME* > $RUN_DIR/"extrema.txt" diff --git a/Exec/science/xrb_layered/scripts/rms.submit b/Exec/science/xrb_layered/scripts/rms.submit new file mode 100644 index 000000000..e9d57b6fd --- /dev/null +++ b/Exec/science/xrb_layered/scripts/rms.submit @@ -0,0 +1,78 @@ +#!/bin/bash +#SBATCH --account=m3018 +#SBATCH --constraint=cpu +#SBATCH --output=slurm.rmsparallel.%j.out +#SBATCH --nodes=1 +#SBATCH --time=01:00:00 +#SBATCH --qos=regular +###SBATCH --time=00:05:00 +###SBATCH --qos=debug +###SBATCH --mail-type=all +###SBATCH --mail-user=simon.guichandut@mail.mcgill.ca + +# Run directory +RUN_DIR=$1 +echo $RUN_DIR + +# List of variables to rms +VARS="vely" + +PLOT_DIR=PLOTS +PLOT_BASENAME="*xrb_0" + +script=$xrb/scripts/rms_vars.sh + +cd $RUN_DIR + +# Find all plotfiles that do not already have a rms +plots=() +for plotfile in $PLOT_DIR/$PLOT_BASENAME*; do + [[ ! -d "$plotfile" ]] && continue + [[ "$plotfile" == *.old.* ]] && continue + [[ -e "$plotfile/rms" ]] && continue + plots+=($plotfile) +done + +N0=${#plots[@]} +echo $N0" plotfiles to process" + +if [ $N0 -eq 0 ]; then + exit 0 +fi + +# If this is being run in a slurm job, do parallel +# Otherwise, loop through the plotfiles one by one + +if [ -n "$SLURM_JOB_ID" ]; then + + module load parallel + NTASK=32 + + echo "Running in parallel with "$NTASK" processes" + + # Temporary text file with all of the plotfiles + tmpfile=$(mktemp) + printf "%s\n" "${plots[@]}" > "$tmpfile" + + # Use srun parallel as in the "Running Many Tasks Inside a Single Node Allocation" example here + # https://docs.nersc.gov/jobs/workflow/gnuparallel/ + srun parallel --jobs $NTASK "$script {} \"$VARS\"" :::: "$tmpfile" + #parallel --jobs $NTASK "$script {} \"$VARS\"" :::: "$tmpfile" + + rm -f "$tmpfile" + +else + + for ((i=0; i<$N0; i++)); do + plotfile="${plots[$i]}" + #echo "Processing: $plotfile" + $script "$plotfile" $VARS + + # Print the number of remaining plotfiles + remaining=$((N0 - i - 1)) + echo $remaining + done + +fi + +echo "done" diff --git a/Exec/science/xrb_layered/scripts/rms_vars.sh b/Exec/science/xrb_layered/scripts/rms_vars.sh new file mode 100755 index 000000000..a04bfbf8e --- /dev/null +++ b/Exec/science/xrb_layered/scripts/rms_vars.sh @@ -0,0 +1,81 @@ +#!/bin/bash +####################################################################### +# Description: AMREX's frms takes only 1 variable, and writes to +# plotfile/.slice. This script loops through frms for a given list +# of variables, and creates a column separated plotfile/averages +# +# Usage: averages_vars.sh plotfile var1 var2 var3 ... +####################################################################### + +#frms=$AMREX_HOME/Tools/Plotfile/frms.gnu.x86-milan.ex +frms=~/MAESTROeX/Exec/science/xrb_layered/rms_tool/frms.gnu.x86-milan.ex +plotfile=$1 +curr_dir=$(pwd) + +# Append "/" to plotfile in case it's not there +if [ "${plotfile: -1}" != "/" ]; then + plotfile+="/" +fi + +# Check if plotfile exists +if [ ! -d "$plotfile" ]; then + echo "Error: Input file '$1' does not exist." + exit 1 +fi + +# restart log file +rm -f $plotfile/.frms_log + +# Header line +header="height " + +i=0 +#for var in "$@"; do +IFS=' ' read -ra vars <<< "${@:2}" +for var in "${vars[@]}"; do + # Skip the first argument (the input file) + if [ "$var" = "$1" ]; then + continue + fi + + #echo $var + header+="$var " + + # Average current variable which creates (or overwrites) plotfile/.slice + $frms -v "$var" "$plotfile" >> $plotfile/.frms_log + + # go into there for file manipulation + cd $plotfile + + # backup for validating this script + #cp .slice .slice_"$var" + + # Delete header line + sed -i '1d' .slice # linux + #sed -i '' '1d' .slice # BSD (mac) + + # If first variable, copy to plotfile/averages + # otherwise, paste the second column into it + if [ $i -eq 0 ]; then + cp .slice rms + else + # extract second column and write to temp file + cut -b 30- .slice > _temp1 + + # paste and write to other temp file + paste -d ' ' rms _temp1 > _temp2 + + # overwrite averages file + cp _temp2 rms + fi + + rm -f _temp* + rm -f .slice + cd $curr_dir + ((i++)) +done + +# Recreate the header line +sed -i '1s/^/'"$header"' \n/' $plotfile/rms # linux +#sed -i '' '1s/^/'"$header"' \n/' $plotfile/rms # BSD (mac) + diff --git a/Exec/science/xrb_layered/scripts/run.submit.template b/Exec/science/xrb_layered/scripts/run.submit.template new file mode 100644 index 000000000..dac515c38 --- /dev/null +++ b/Exec/science/xrb_layered/scripts/run.submit.template @@ -0,0 +1,105 @@ +#!/bin/bash +#SBATCH --account=m3018 +#SBATCH --constraint=cpu +#SBATCH --output=slurm.run1.%j.out + +#SBATCH --nodes=1 +###SBATCH --nodes=2 +###SBATCH --nodes=4 + +#SBATCH --ntasks-per-node=64 +###SBATCH --ntasks-per-node=128 + +###SBATCH --ntasks-per-node=100 +###SBATCH --ntasks-per-node=50 +###SBATCH --ntasks-per-node=20 + +###SBATCH --time=00:02:00 +###SBATCH --qos=debug + +#SBATCH --time=10:00:00 +#SBATCH --qos=regular + +#SBATCH --mail-type=end +#SBATCH --mail-user=simon.guichandut@mail.mcgill.ca + +XRB=$MAESTROEX_HOME/Exec/science/xrb_layered +MAESTRO_EXEC=$XRB/Maestro2d.gnu.x86-milan.MPI.ex +XRB_SCRATCH=$PSCRATCH/xrb_layered + +INPUTS=inputs +RUN_DIR="RUN1" + +cd $XRB_SCRATCH/$RUN_DIR +mkdir -p PLOTS +mkdir -p CHECKS + +# Look for check file +# https://amrex-astro.github.io/workflow/nersc-workflow.html +function find_chk_file { + # find_chk_file takes a single argument -- the wildcard pattern + # for checkpoint files to look through + chk=$1 + + # find the latest 2 restart files. This way if the latest didn't + # complete we fall back to the previous one. + temp_files=$(find CHECKS -maxdepth 1 -name "${chk}" -print | sort | tail -2) + restartFile="" + for f in ${temp_files} + do + # the Header is the last thing written -- check if it's there, otherwise, + # fall back to the second-to-last check file written + if [ -f ${f}/Header ]; then + restartFile="${f}" + fi + done +} + +# look for 7-digit chk file +find_chk_file "*chk???????" + +# restartString will be empty if no chk files are found -- i.e. new run +if [ "${restartFile}" = "" ]; then + #restartString="" + + rm output.txt # we are starting over + + # In this case, we need to run the first step not in parallel + ${MAESTRO_EXEC} ${INPUTS} maestro.max_step=1 + restartString="maestro.restart_file=CHECKS/chk0000001" + +else + restartString="maestro.restart_file=${restartFile}" +fi +echo $restartString + +# Dump checkfile near end of job run time +(sleep 595m && echo -e "\n Nearing end of job, dumping checkfile now\n" && touch dump_and_continue)& + +# debug this bash script +#${MAESTRO_EXEC} ${INPUTS} ${restartString} + +# srun command +# -n is total number of tasks +# -c is cpus per task +# in perlmutter: 2*floor(128/#tasks per node) +# some examples below + +# 64 tasks +# 1 node, -c = 2*floor(128/64) = 4 +#srun -n 64 --cpu-bind=cores -c 4 ${MAESTRO_EXEC} ${INPUTS} ${restartString} >> output.txt + +# 128 tasks +# 1 node, -c = 2*floor(128/128) = 2 +##srun -n 64 --cpu-bind=cores -c 2 ${MAESTRO_EXEC} ${INPUTS} ${restartString} >> output.txt + +# 100 tasks +# 1 node, -c = 2*floor(128/100) = 2 +#srun -n 100 --cpu-bind=cores -c 2 ${MAESTRO_EXEC} ${INPUTS} ${restartString} >> output.txt +# 2 nodes, -c = 2*floor(128/50) = 4 +#srun -n 50 --cpu-bind=cores -c 4 ${MAESTRO_EXEC} ${INPUTS} ${restartString} >> output.txt +# 5 nodes, -c = 2*floor(128/20) = 12 +#srun -n 20 --cpu-bind=cores -c 12 ${MAESTRO_EXEC} ${INPUTS} ${restartString} >> output.txt + +# General command +srun -n $((SLURM_NNODES * SLURM_NTASKS_PER_NODE)) --cpu-bind=cores -c $((256 / SLURM_NTASKS_PER_NODE)) ${MAESTRO_EXEC} ${INPUTS} ${restartString} >> output.txt diff --git a/Exec/science/xrb_layered/toy_atm_hot_3cm.hse b/Exec/science/xrb_layered/toy_atm_hot_3cm.hse new file mode 120000 index 000000000..26f12edb9 --- /dev/null +++ b/Exec/science/xrb_layered/toy_atm_hot_3cm.hse @@ -0,0 +1 @@ +create_initial_model/toy_atm/toy_atm_hot_3cm.hse \ No newline at end of file diff --git a/Exec/science/xrb_layered/xrb_layered_3cm.hse b/Exec/science/xrb_layered/xrb_layered_3cm.hse deleted file mode 100644 index 476822f3a..000000000 --- a/Exec/science/xrb_layered/xrb_layered_3cm.hse +++ /dev/null @@ -1,1050 +0,0 @@ -# npts = 1024 -# num of variables = 24 -# density -# temperature -# pressure -#hydrogen-1 -#helium-4 -#carbon-12 -#carbon-13 -#nitrogen-13 -#nitrogen-14 -#nitrogen-15 -#oxygen-14 -#oxygen-15 -#oxygen-16 -#oxygen-17 -#oxygen-18 -#fluorine-17 -#fluorine-18 -#fluorine-19 -#neon-18 -#neon-19 -#neon-20 -#magnesium-22 -#magnesium-24 -#iron-56 - 1.5 11634311.0768 240650282.178 9.11268785169e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 4.5 11594573.5246 240553970.229 9.06773995999e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 7.5 11554908.5681 240468644.271 9.02294571214e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 10.5 11515343.4088 240351088.889 8.97830477457e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 13.5 11475837.4212 240265539.785 8.93381683966e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 16.5 11436427.519 240153373.554 8.889481607e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 19.5 11397098.3301 240038784.034 8.84529873448e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 22.5 11357846.5061 239926984.884 8.80126791622e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 25.5 11318665.3735 239828541.593 8.75738886574e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 28.5 11279558.649 239737496.345 8.71366130225e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 31.5 11240539.7379 239632340.752 8.67008491187e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 34.5 11201606.657 239516047.238 8.6266593586e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 37.5 11162743.2396 239414362.155 8.58338434155e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 40.5 11123959.3828 239311389.5 8.54025957198e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 43.5 11085264.1105 239192510 8.49728472452e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 46.5 11046640.6001 239084585.6 8.4544594889e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 49.5 11008093.9065 238979446.688 8.41178357763e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 52.5 10969637.2127 238855746.818 8.36925666792e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 55.5 10931244.145 238755854.224 8.32687846249e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 58.5 10892915.9158 238677877.8 8.28464871277e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 61.5 10854713.7767 238522727.004 8.24256704932e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 64.5 10816559.9617 238415799.346 8.20063313463e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 67.5 10778477.6844 238319668.493 8.15884673679e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 70.5 10740471.778 238226380.822 8.11720756958e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 73.5 10702568.4058 238093305.376 8.07571528682e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 76.5 10664732.2365 237977600 8.03436956008e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 79.5 10626969.8203 237868619.302 7.9931701166e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 82.5 10589272.6345 237780223.953 7.95211668745e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 85.5 10551683.4455 237642588.362 7.91120893743e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 88.5 10514139.2236 237558363.587 7.87044657057e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 91.5 10476673.9236 237472216.263 7.82982934713e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 94.5 10439328.7951 237316402.837 7.78935688187e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 97.5 10402034.8919 237203487.591 7.74902884313e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 100.5 10364817.2121 237092586.817 7.70884498431e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 103.5 10327676.0693 236983088.327 7.66880500981e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 106.5 10290615.2714 236868618.563 7.62890861607e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 109.5 10253624.9093 236765370.405 7.58915551132e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 112.5 10216718.5666 236650884.839 7.54954539669e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 115.5 10179889.3645 236536338.591 7.51007796035e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 118.5 10143135.4283 236424720.609 7.47075290737e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 121.5 10106448.8985 236328959.485 7.4315699617e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 124.5 10069861.6672 236196158.824 7.39252880076e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 127.5 10033328.932 236100339.689 7.35362912695e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 130.5 9996874.30554 236002398.347 7.31487068368e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 133.5 9960528.7381 235850697.406 7.27625310879e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 136.5 9924228.63146 235750719.572 7.23777610328e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 139.5 9888014.8837 235634497.647 7.19943941208e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 142.5 9851868.48731 235533556.471 7.16124273776e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 145.5 9815812.05357 235410091.64 7.12318577591e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 148.5 9779829.83433 235290202.994 7.08526820886e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 151.5 9743919.24142 235178153.959 7.0474897544e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 154.5 9708087.93455 235061049.164 7.00985012051e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 157.5 9672328.52672 234951166.081 6.97234901466e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 160.5 9636632.08519 234863485.94 6.93498617587e+23 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1e-98 1 - 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1744.5 109744.8492 138574175.901 2.23284740268e+21 0.364542864472 0.611073702236 3.38575630808e-06 4.54097276895e-10 3.12587467454e-05 4.10938431305e-05 7.52203621491e-10 0.00851779126335 0.0157864437888 2.54297323603e-07 3.95109895528e-09 2.26695815777e-19 3.12601365093e-06 1.9191431118e-10 1.11490094286e-13 5.55822558758e-08 2.85467154161e-10 3.79264205839e-13 1.47773936522e-10 5.83213049314e-33 1e-98 - 1747.5 107532.990724 138148604.672 2.19080414065e+21 0.372193488175 0.603423315912 3.48049263942e-06 4.82020784234e-10 3.22566422044e-05 4.29402988175e-05 7.77068678978e-10 0.00851655777226 0.0157844630803 2.6320288e-07 4.12569894891e-09 2.37099962482e-19 3.17277754015e-06 1.95240708613e-10 1.15245360876e-13 5.44841489343e-08 2.81707884817e-10 3.90766516205e-13 1.37339249781e-10 4.7932864248e-33 1e-98 - 1750.5 105342.965694 137694619.27 2.14961264309e+21 0.380252013723 0.595365053139 3.58369088759e-06 5.12735484964e-10 3.33438553431e-05 4.49521513723e-05 8.04161613285e-10 0.00851521456058 0.0157823039562 2.7291088e-07 4.31604566423e-09 2.48495407737e-19 3.2229979781e-06 1.98830548029e-10 1.19309988613e-13 5.3303151854e-08 2.77648337372e-10 4.03241129343e-13 1.26514592847e-10 3.78992087737e-33 1e-98 - 1753.5 103225.371943 137242328.214 2.10925466976e+21 0.388068948581 0.587548374958 3.69281494157e-06 5.46440991653e-10 3.44955652921e-05 4.70878250918e-05 8.32878555092e-10 0.00851379227045 0.015780016611 2.83221766277e-07 4.51800038731e-09 2.60773712354e-19 3.27369293656e-06 2.02509653756e-10 1.23529878798e-13 5.21080435058e-08 2.73489875292e-10 4.16276377295e-13 1.16513505676e-10 2.98082041236e-33 1e-98 - 1756.5 101177.366398 136791777.295 2.06970273989e+21 0.395637763272 0.579979810685 3.80802495826e-06 5.83218136895e-10 3.57135152087e-05 4.93506664941e-05 8.63263396494e-10 0.00851228876461 0.0157775975793 2.94151833055e-07 4.73187684808e-09 2.73958737396e-19 3.3248752404e-06 2.06280438397e-10 1.27909227713e-13 5.08984425042e-08 2.69229825209e-10 4.29887412354e-13 1.07358246912e-10 2.3712354374e-33 1e-98 - 1759.5 99170.29752 136318967.483 2.03093546692e+21 0.40334423118 0.572273634744 3.93545364143e-06 6.25321342762e-10 3.70632471492e-05 5.18636887528e-05 8.96956111136e-10 0.00851062273497 0.0157749132294 3.06296111581e-07 4.96908366592e-09 2.88796737416e-19 3.37907708463e-06 2.10332827171e-10 1.32673943875e-13 4.96156820045e-08 2.64663702895e-10 4.44808889978e-13 9.83822662138e-11 1.84676248552e-33 1e-98 - 1762.5 97203.6965699 135823600 1.99293709906e+21 0.4111902 0.564428 4.075265e-06 6.728221e-10 3.854653e-05 5.463025e-05 9.340011e-10 0.008508792 0.01577196 3.196709e-07 5.229934e-09 3.053099e-19 3.436339e-06 2.146706e-10 1.378292e-13 4.825878e-08 2.597874e-10 4.610584e-13 8.958797e-11 1.408544e-33 1e-98 - 1765.5 95357.2006674 135380901.205 1.95567656545e+21 0.417902175904 0.557716313253 4.21041680723e-06 7.21266100001e-10 3.99855468675e-05 5.73246909639e-05 9.69978979519e-10 0.00850701513253 0.0157690901205 3.32703912048e-07 5.48307906025e-09 3.2169850241e-19 3.48799321687e-06 2.18678672289e-10 1.42693248193e-13 4.70338836144e-08 2.55312556626e-10 4.76583942169e-13 8.25896206023e-11 1.13187893975e-33 1e-98 - 1768.5 93561.125462 134941306.599 1.91912086934e+21 0.424443068274 0.551175719543 4.35011220812e-06 7.72906317767e-10 4.14765333249e-05 6.01240001015e-05 1.00728263833e-09 0.00850517328934 0.0157661118274 3.4624483401e-07 5.74528027157e-09 3.38894860914e-19 3.53938251523e-06 2.22718187563e-10 1.47659179442e-13 4.58160009137e-08 2.50823232995e-10 4.92558080203e-13 7.60638032487e-11 9.01349989847e-34 1e-98 - 1771.5 91824.6939328 134515750.328 1.88324871329e+21 0.430625090591 0.544994028884 4.49141333041e-06 8.26873900437e-10 4.29884449672e-05 6.2970071663e-05 1.04513736324e-09 0.00850330464989 0.015763087965 3.60015840919e-07 6.0110196849e-09 3.56568789934e-19 3.5892311488e-06 2.2669453151e-10 1.52613961707e-13 4.46354112692e-08 2.46429259081e-10 5.08639005689e-13 7.01738376368e-11 7.19675908755e-34 1e-98 - 1774.5 90157.5299086 134118004.158 1.84803515297e+21 0.436227147484 0.539392365864 4.6299155186e-06 8.81623572211e-10 4.44742195843e-05 6.57738243983e-05 1.08236493436e-09 0.00850146723195 0.0157601142232 3.73590151641e-07 6.27195600876e-09 3.74186339169e-19 3.63591955142e-06 2.30480899125e-10 1.57397681619e-13 4.35304594092e-08 2.42274948359e-10 5.24324738731e-13 6.50696100655e-11 5.84339344199e-34 1e-98 - 1777.5 88541.6831531 133736876.49 1.81345685525e+21 0.441483315232 0.53413655596 4.77017345365e-06 9.3969153477e-10 4.59848859272e-05 6.86361201987e-05 1.12026021523e-09 0.00849959701324 0.0157570773841 3.87453470861e-07 6.53675805961e-09 3.92428908941e-19 3.6805506457e-06 2.34173930133e-10 1.62158612583e-13 4.24768931788e-08 2.38262398344e-10 5.40161870861e-13 6.06151204966e-11 4.83998493375e-34 1e-98 - 1780.5 86960.2288857 133360436.093 1.77949723527e+21 0.446641924503 0.528978294371 4.91092659934e-06 9.98695425498e-10 4.75025730133e-05 7.15149281457e-05 1.1583438245e-09 0.00849771754305 0.0157540227483 4.01398305298e-07 6.80265044371e-09 4.10847766557e-19 3.72460147351e-06 2.37840635431e-10 1.66913115894e-13 4.14378203311e-08 2.34289832119e-10 5.56041705298e-13 5.63438903642e-11 3.93528228475e-34 1e-98 - 1783.5 85394.0830483 132951948.765 1.74614667591e+21 0.452109650926 0.523510933025 5.07407287346e-06 1.07122587037e-09 4.92712709877e-05 7.4887034568e-05 1.20279375e-09 0.00849552418519 0.0157504598457 4.17746021297e-07 7.11142201544e-09 4.32815604939e-19 3.77206510803e-06 2.41893838889e-10 1.72309160494e-13 4.03234783333e-08 2.29960142284e-10 5.74459723766e-13 5.22212860493e-11 3.22260717591e-34 1e-98 - 1786.5 83852.5235691 132533708.025 1.71339745753e+21 0.457671456482 0.517949497839 5.24403444753e-06 1.14787309259e-09 5.11163635803e-05 7.84092753087e-05 1.24918125e-09 0.0084932352963 0.0157467422531 4.34825049074e-07 7.4332437747e-09 4.55863567902e-19 3.82056742284e-06 2.46064672222e-10 1.7790045679e-13 3.91862283333e-08 2.25521762654e-10 5.93650233025e-13 4.81439156789e-11 2.56837523147e-34 1e-98 - 1789.5 82263.8412602 131969804.908 1.68125394093e+21 0.464962692638 0.510658853988 5.48685431902e-06 1.26328855829e-09 5.37653040491e-05 8.3486844724e-05 1.31587026381e-09 0.00848994611043 0.015741388773 4.59478732516e-07 7.89335149081e-09 4.89657038038e-19 3.88542079141e-06 2.51775858896e-10 1.85738126994e-13 3.76730431901e-08 2.19531603681e-10 6.2099384724e-13 4.31866604293e-11 1.90016839262e-34 1e-98 - 1792.5 80757.0556161 131475439.076 1.64970939739e+21 0.471217257726 0.504404836234 5.71068415275e-06 1.37434239254e-09 5.62165343517e-05 8.81986380995e-05 1.37764859858e-09 0.00848690054707 0.0157364230373 4.82392444405e-07 8.31754742629e-09 5.21460186146e-19 3.94192066252e-06 2.56859373002e-10 1.92844317407e-13 3.63602902309e-08 2.14274328597e-10 6.46160212966e-13 3.92119551865e-11 1.44731406039e-34 1e-98 - 1795.5 79365.1646944 131132437.3 1.61872574776e+21 0.475479868739 0.500142598224 5.87338060035e-06 1.45816814742e-09 5.80041773357e-05 9.16419613676e-05 1.42274398046e-09 0.0084846785222 0.0157327942629 4.99166760568e-07 8.62583286145e-09 5.44995159502e-19 3.98092385968e-06 2.60441628774e-10 1.97930889343e-13 3.5457540675e-08 2.10622268206e-10 6.64437353286e-13 3.66708113144e-11 1.20161033038e-34 1e-98 - 1798.5 77978.6632801 130734580.336 1.58827971704e+21 0.480250190252 0.495372766555 6.07267624201e-06 1.56607562689e-09 6.02038276638e-05 9.58906500167e-05 1.47830282521e-09 0.00848194302521 0.0157283276639 5.19919516974e-07 9.00324480504e-09 5.74547484537e-19 4.02577760336e-06 2.6466415563e-10 2.04045334622e-13 3.4426510437e-08 2.06395668908e-10 6.86741491596e-13 3.39849645883e-11 9.80530091096e-35 1e-98 - 1801.5 76606.2998154 130306311.429 1.55836752669e+21 0.485301988571 0.490321522857 6.29226262857e-06 1.68733411428e-09 6.26318965714e-05 0.000100585836571 1.53966265714e-09 0.00847892285714 0.0157233965714 5.42877937143e-07 9.41898077142e-09 6.07435854285e-19 4.07387491429e-06 2.69241651429e-10 2.10729637143e-13 3.33243608572e-08 2.01850542857e-10 7.11278214285e-13 3.1218894e-11 7.73101217145e-35 1e-98 - 1804.5 75188.6634271 129694539.13 1.5289952013e+21 0.492319973913 0.483304371739 6.62450206521e-06 1.88112530435e-09 6.63249821739e-05 0.000107748510869 1.63312571739e-09 0.00847432626087 0.0157158804348 5.78010023913e-07 1.00470300652e-08 6.58568991304e-19 4.1416498913e-06 2.75874404348e-10 2.20645673913e-13 3.17831015218e-08 1.95404054348e-10 7.48055795652e-13 2.76641986957e-11 5.51435902176e-35 1e-98 - 1807.5 73876.6190309 129249143.137 1.50015106914e+21 0.49727155817 0.478353428758 6.88063154901e-06 2.03813552941e-09 6.91847593464e-05 0.000113305398039 1.70558768627e-09 0.00847076626144 0.015710049281 6.05363281699e-07 1.05303296732e-08 6.98958828104e-19 4.19049324837e-06 2.80793040523e-10 2.28141148366e-13 3.06804081046e-08 1.90732688235e-10 7.75989335294e-13 2.53209709151e-11 4.30282200655e-35 1e-98 - 1810.5 72627.8266642 128899155.822 1.4718024589e+21 0.501071144178 0.474554370891 7.09092351369e-06 2.17209600342e-09 7.15407702396e-05 0.000117888920548 1.76534036986e-09 0.00846783318836 0.0157052407877 6.27997172602e-07 1.09266571918e-08 7.32761831163e-19 4.22863795548e-06 2.84719548972e-10 2.34205342466e-13 2.98263077398e-08 1.87079775e-10 7.98595190068e-13 2.36121471576e-11 3.53524256851e-35 1e-98 - 1813.5 71409.1483474 128563231.164 1.44393130424e+21 0.504607068836 0.471018994178 7.30608310274e-06 2.31734840068e-09 7.39644380479e-05 0.00012261330411 1.82689927397e-09 0.00846481503767 0.0157002921575 6.5144799452e-07 1.13316914384e-08 7.68408646233e-19 4.2649567911e-06 2.88578829794e-10 2.40285068493e-13 2.9028725548e-08 1.83617275e-10 8.21183238014e-13 2.21516574315e-11 3.0123405137e-35 1e-98 - 1816.5 70208.3456666 128227306.507 1.41652831914e+21 0.508142993493 0.467483617466 7.52124269178e-06 2.46260079795e-09 7.63881058562e-05 0.000127337687671 1.88845817808e-09 0.00846179688699 0.0156953435274 6.74898816439e-07 1.17367256849e-08 8.04055461302e-19 4.30127562671e-06 2.92438110617e-10 2.46364794521e-13 2.82311433561e-08 1.80154775e-10 8.43771285959e-13 2.06911677055e-11 2.48943845889e-35 1e-98 - 1819.5 69002.2095008 127801366.176 1.38959107672e+21 0.512530159454 0.463097187815 7.80645107145e-06 2.66444411136e-09 7.96158157985e-05 0.000133639837815 1.97054205253e-09 0.0084577787479 0.0156887466176 7.06292164918e-07 1.22711844328e-08 8.52395775003e-19 4.34637033404e-06 2.97358664916e-10 2.54284278362e-13 2.7240877563e-08 1.75805977731e-10 8.72471631724e-13 1.89798310924e-11 1.94395794115e-35 1e-98 - 1822.5 67760.9951791 127155042.647 1.36312739661e+21 0.51900139895 0.456627145798 8.2631582143e-06 3.00483759875e-09 8.48120426893e-05 0.000143804795799 2.10287671639e-09 0.00845131236134 0.0156781142647 7.57131009456e-07 1.31225100631e-08 9.31813275003e-19 4.41295079622e-06 3.04877509454e-10 2.66707996849e-13 2.57788691596e-08 1.69287280252e-10 9.16136568699e-13 1.66543521008e-11 1.34319911763e-35 1e-98 - 1825.5 66585.693523 126650581.67 1.33713131235e+21 0.52396338167 0.451666119959 8.64097569858e-06 3.30278799593e-09 8.91337535031e-05 0.000152267594908 2.21309431161e-09 0.00844593334216 0.0156692660081 7.99719263137e-07 1.38249125458e-08 9.99481480653e-19 4.46442089817e-06 3.10874299593e-10 2.76796067414e-13 2.46707006314e-08 1.64280019552e-10 9.50148557842e-13 1.50127640529e-11 9.90452805903e-36 1e-98 - 1828.5 65447.5746389 126205016.667 1.31158287496e+21 0.5282885125 0.447341854167 8.98810049999e-06 3.58652670833e-09 9.31182470833e-05 0.000160075066667 2.31480983333e-09 0.008440973875 0.015661105 8.39168862499e-07 1.44690254167e-08 1.062851e-18 4.50954741667e-06 3.16249520833e-10 2.85950616667e-13 2.37125466667e-08 1.59908208333e-10 9.80066404166e-13 1.36689804167e-11 7.47577420836e-36 1e-98 - 1831.5 64330.8692381 125746266.667 1.28647074607e+21 0.5326102 0.443021166667 9.37138799999e-06 3.92193733332e-09 9.75459033332e-05 0.000168759566666 2.42810233333e-09 0.008435467 0.01565203 8.83398799999e-07 1.51765066666e-08 1.135421e-18 4.55509366667e-06 3.21910833333e-10 2.95828366666e-13 2.27650966667e-08 1.55507333333e-10 1.00966896667e-12 1.24559366667e-11 5.78328233338e-36 1e-98 - 1834.5 63237.4900471 125299535.97 1.26178626855e+21 0.536752237476 0.4388801371 9.75843338606e-06 4.2734588192e-09 0.000102034576497 0.000177565725612 2.54299976459e-09 0.00842988634652 0.0156428291714 9.28457455931e-07 1.58889045763e-08 1.21013373823e-18 4.59901031073e-06 3.2748653145e-10 3.05678085687e-13 2.18606845386e-08 1.51270279096e-10 1.03754498493e-12 1.13466082486e-11 4.39696455745e-36 1e-98 - 1837.5 62167.4498975 124866541.62 1.23752041267e+21 0.54068896064 0.434944430885 1.01497734991e-05 4.64339271752e-09 0.000106592983277 0.000186510923352 2.65973140302e-09 0.00842422137476 0.0156334845386 9.74463218645e-07 1.66069215254e-08 1.28729532015e-18 4.64106454802e-06 3.32964384558e-10 3.15495769303e-13 2.10054585499e-08 1.47220448587e-10 1.06344780979e-12 1.03558116384e-11 3.36056003765e-36 1e-98 - 1840.5 61094.509265 124287996.296 1.21366922357e+21 0.545787388889 0.429847437037 1.07030733334e-05 5.1971990741e-09 0.000113072555556 0.000199228714815 2.82596977778e-09 0.00841617459259 0.0156202 1.04037112593e-06 1.76169425926e-08 1.39988066667e-18 4.69632892593e-06 3.40427966667e-10 3.29127240741e-13 1.99026933333e-08 1.4192417037e-10 1.09534e-12 9.16290685181e-12 2.34067459256e-36 1e-98 - 1843.5 60054.5648113 123781285.668 1.19022687774e+21 0.550179013355 0.425457184365 1.12139003909e-05 5.73582711076e-09 0.0001190853557 0.000211029871336 2.98045154398e-09 0.00840871272313 0.015607872443 1.10197770033e-06 1.85451665472e-08 1.50684239088e-18 4.74402715635e-06 3.47092255049e-10 3.41504755375e-13 1.89686949837e-08 1.37379093811e-10 1.12064734853e-12 8.21641782734e-12 1.68310336807e-36 1e-98 - 1846.5 59054.2776361 123414488.329 1.16717931672e+21 0.553308765418 0.422328484438 1.1607323977e-05 6.17685936744e-09 0.000123744076225 0.000220169909222 3.10032004755e-09 0.00840293709942 0.0155983265562 1.15012790778e-06 1.92562418732e-08 1.59205400288e-18 4.77802367291e-06 3.52036716283e-10 3.50856317435e-13 1.83182617003e-08 1.34166830836e-10 1.13634568732e-12 7.60440624062e-12 1.34597977953e-36 1e-98 - 1849.5 58074.3185858 123086520.029 1.14451493335e+21 0.556087007493 0.419551193372 1.19680761383e-05 6.5909842954e-09 0.000128025992651 0.000228569044669 3.2105562147e-09 0.00839763090346 0.0155895556628 1.19453655332e-06 1.99068787608e-08 1.67123498271e-18 4.80823246254e-06 3.56504252306e-10 3.59366245389e-13 1.77460397983e-08 1.31323314986e-10 1.14935937608e-12 7.08306805618e-12 1.08904482276e-36 1e-98 - 1852.5 57098.2605366 122662598.026 1.12222903929e+21 0.559601810526 0.416037695395 1.24552141448e-05 7.17784277634e-09 0.000133836541448 0.000239962992764 3.3603249408e-09 0.00839043586184 0.0155776523684 1.25523709211e-06 2.07816655264e-08 1.78117973685e-18 4.84680827632e-06 3.62371032237e-10 3.70706967764e-13 1.70297436184e-08 1.27723525e-10 1.16371411842e-12 6.46089782234e-12 8.25533781571e-37 1e-98 - 1855.5 56121.0954257 122075295.395 1.10032109392e+21 0.564370757895 0.411270655921 1.31575496711e-05 8.05881514475e-09 0.000142249896711 0.000256456216448 3.57740586185e-09 0.00838002467763 0.0155604155263 1.34367788158e-06 2.20381128948e-08 1.94350605263e-18 4.89963064474e-06 3.70620308553e-10 3.86867691448e-13 1.60681317763e-08 1.22836025e-10 1.18035227632e-12 5.66704058551e-12 5.50825623679e-37 1e-98 - 1858.5 55176.082923 121609912.425 1.07878508991e+21 0.56806796473 0.407575063928 1.3755187976e-05 8.86500725252e-09 0.000149461014229 0.000270577261122 3.76378256914e-09 0.00837111450301 0.0155456574549 1.42034037275e-06 2.31005247896e-08 2.0876283467e-18 4.94086241683e-06 3.77369379359e-10 4.00346075551e-13 1.53429192585e-08 1.19086796794e-10 1.18852034469e-12 5.1156838537e-12 4.05810995188e-37 1e-98 - 1861.5 54247.5209626 121152183.902 1.05761162255e+21 0.571658378537 0.403986290244 1.43624552195e-05 9.71181506831e-09 0.000156812957561 0.000284966376098 3.95394691708e-09 0.00836203654146 0.0155306158537 1.49891571707e-06 2.41769676586e-08 2.23700348781e-18 4.98114752195e-06 3.8410733122e-10 4.13921302927e-13 1.46464472683e-08 1.15456942439e-10 1.1943139122e-12 4.60641660975e-12 2.90038043901e-37 1e-98 - 1864.5 53331.8265486 120634061.951 1.03679501881e+21 0.575607549268 0.400039095122 1.50921796098e-05 1.07930904341e-08 0.00016570290878 0.000302347498049 4.18422325853e-09 0.00835107337073 0.0155124329268 1.59488205854e-06 2.54637408293e-08 2.4232632439e-18 5.02621996098e-06 3.9195035561e-10 4.29980961463e-13 1.38934326342e-08 1.1147199122e-10 1.1962741561e-12 4.09407880488e-12 2.01952921952e-37 1e-98 - 1867.5 52433.9224079 120123935.429 1.01632934639e+21 0.579405629714 0.396242354286 1.58533251428e-05 1.19897482857e-08 0.000175031295428 0.000320563069143 4.42617776e-09 0.00833985110286 0.0154938728571 1.69659022857e-06 2.67995274857e-08 2.62477918285e-18 5.07037340571e-06 3.99909354857e-10 4.46500592e-13 1.31839298286e-08 1.07666205143e-10 1.19382400571e-12 3.64245692001e-12 1.40695277715e-37 1e-98 - 1870.5 51556.0258946 119670519.535 9.9620729139e+20 0.58269760186 0.392952940465 1.65657215814e-05 1.31732164186e-08 0.000183811433023 0.000337688207907 4.6541908279e-09 0.0083288458 0.0154755725116 1.79324063256e-06 2.80447690697e-08 2.82005114883e-18 5.10912225116e-06 4.07166793953e-10 4.61767579535e-13 1.25774128372e-08 1.04370097209e-10 1.18826455349e-12 3.28022751163e-12 1.01611362698e-37 1e-98 - 1873.5 50694.3123211 119237382.313 9.76421850946e+20 0.58576949932 0.389883959864 1.72751367347e-05 1.44038787755e-08 0.000192593514285 0.000354800049659 4.88246986394e-09 0.00831772182993 0.0154570414966 1.89066153061e-06 2.92810763945e-08 3.0199807551e-18 5.14592430612e-06 4.14258114966e-10 4.76829648979e-13 1.20163069388e-08 1.01289829932e-10 1.18056797279e-12 2.96181078912e-12 7.32213570753e-38 1e-98 - 1876.5 49847.4057634 118772756.463 9.56967028496e+20 0.588950859864 0.386704231973 1.80786673469e-05 1.58743277551e-08 0.000202594942857 0.000374260729932 5.14274197279e-09 0.00830566196599 0.0154370482993 2.00270030612e-06 3.06762872789e-08 3.25447055102e-18 5.18561206122e-06 4.22128182993e-10 4.93711689796e-13 1.14404293878e-08 9.80899659864e-11 1.16932239456e-12 2.65563255782e-12 5.21102074151e-38 1e-98 - 1879.5 49013.3739547 118238762.577 9.37837467621e+20 0.592517772086 0.383139867485 1.90529219325e-05 1.77650657669e-08 0.000214795974847 0.000397967372393 5.46065271472e-09 0.00829076134049 0.0154123038344 2.14091996932e-06 3.2362238773e-08 3.5501608681e-18 5.23106881595e-06 4.31457341104e-10 5.13973733742e-13 1.08076785583e-08 9.45274555215e-11 1.15296889877e-12 2.34017043865e-12 3.49423088037e-38 1e-98 - 1882.5 48195.6503248 117809257.895 9.19027521423e+20 0.595326769779 0.38033321545 1.9882021511e-05 1.94730117148e-08 0.000225242771817 0.000418233503226 5.73318375892e-09 0.00827797481834 0.0153910540238 2.26066231239e-06 3.37932223599e-08 3.81220681155e-18 5.26773255518e-06 4.39241258234e-10 5.31059494228e-13 1.03214596519e-08 9.1755252309e-11 1.13714913582e-12 2.11375274533e-12 2.5363073837e-38 1e-98 - 1885.5 47390.7837711 117444521.053 9.00531546425e+20 0.597685869949 0.377976203565 2.06085665026e-05 2.10170796265e-08 0.000234427101189 0.000436035116129 5.97293163668e-09 0.00826674188115 0.0153723816978 2.36659745671e-06 3.50456128523e-08 4.04682311036e-18 5.29895751273e-06 4.45994059593e-10 5.45961344822e-13 9.91923299659e-09 8.94455120712e-11 1.12246441596e-12 1.93409909507e-12 1.89682555007e-38 1e-98 - 1888.5 46597.562052 116970509.639 8.82344801508e+20 0.600688166265 0.374976768675 2.16152866265e-05 2.3318757229e-08 0.000247252701205 0.000460846859037 6.3082314217e-09 0.00825108151807 0.0153463366265 2.51684962651e-06 3.67764583133e-08 4.38922504821e-18 5.33982626506e-06 4.55147839759e-10 5.66438454218e-13 9.42631807227e-09 8.65706478312e-11 1.10060580723e-12 1.72942004819e-12 1.34962515661e-38 1e-98 - 1891.5 45815.9063257 116451960.113 8.64462795377e+20 0.603946053308 0.371722073724 2.27409930624e-05 2.59532218338e-08 0.00026163079811 0.000488644321173 6.68429891117e-09 0.00823353508128 0.0153171503592 2.68615168432e-06 3.87104621551e-08 4.77859684122e-18 5.38467308885e-06 4.65312574102e-10 5.89276585067e-13 8.89835709639e-09 8.3473997032e-11 1.0756582344e-12 1.51632285822e-12 8.52858368989e-39 1e-98 - 1894.5 45049.5717745 115988462.949 8.46880325365e+20 0.606722386011 0.368948916824 2.3840569622e-05 2.87820176749e-08 0.000275815750851 0.000515994850473 7.05597168999e-09 0.00821626211342 0.0152884093384 2.85681879207e-06 4.05990160303e-08 5.18659287147e-18 5.42580031002e-06 4.75044426654e-10 6.11433711721e-13 8.4620845066e-09 8.08671728355e-11 1.05036409452e-12 1.35859431379e-12 6.17317593944e-39 1e-98 - 1897.5 44294.4546984 115515568.154 8.29592256242e+20 0.609527921907 0.366146705071 2.49924175254e-05 3.183914357e-08 0.000290723543408 0.000544714688439 7.4467995781e-09 0.00819812147464 0.0152582206288 3.03755113185e-06 4.25787413591e-08 5.62438548479e-18 5.46823251116e-06 4.8519281501e-10 6.34630586816e-13 8.02734738133e-09 7.82565557403e-11 1.02378042475e-12 1.20571926166e-12 4.10162281337e-39 1e-98 - 1900.5 43552.0839809 115018103.651 8.12593951008e+20 0.612409808316 0.363268530629 2.62809266531e-05 3.54932287626e-08 0.000307521170182 0.000577014526166 7.88770769168e-09 0.00817771234686 0.015224246998 3.24459859635e-06 4.47968306085e-08 6.1400800081e-18 5.51407652738e-06 4.96430218661e-10 6.60545840365e-13 7.59662466329e-09 7.56360222719e-11 9.93825333469e-13 1.06553346045e-12 2.77219563288e-39 1e-98 - 1903.5 42821.3465167 114510786.957 7.95880692207e+20 0.615302369565 0.360380026087 2.76686321739e-05 3.96849817391e-08 0.000325734821739 0.000611976626086 8.36629921738e-09 0.00815561204348 0.0151874552174 3.47295034782e-06 4.71945430434e-08 6.72471365217e-18 5.56240382609e-06 5.08478034782e-10 6.88522013043e-13 7.17976300001e-09 7.30751773913e-11 9.61783365218e-13 9.37867000002e-13 1.82263508697e-39 1e-98 - 1906.5 42101.5450682 114004156.027 7.79448112685e+20 0.618153199554 0.357533457589 2.91152840402e-05 4.42671988617e-08 0.000344821301563 0.000648565834375 8.8682295134e-09 0.008132476 0.0151489347321 3.71546961161e-06 4.97029474777e-08 7.35870359152e-18 5.61208402455e-06 5.21010658259e-10 7.17767756697e-13 6.7811442366e-09 7.06073583035e-11 9.28669369196e-13 8.22079104017e-13 1.15607378705e-39 1e-98 - 1909.5 41392.1291134 113527303.348 7.63292086731e+20 0.620780744196 0.354910198661 3.05690050223e-05 4.9218437701e-08 0.000364154895313 0.000685554896876 9.37725317411e-09 0.008109076 0.0151099615179 3.9665334509e-06 5.22425747099e-08 8.03670693974e-18 5.6613690692e-06 5.33616082366e-10 7.47369587054e-13 6.43014557589e-09 6.84120279464e-11 8.96189949553e-13 7.27871202231e-13 7.83472581691e-40 1e-98 - 1912.5 40693.4597187 113049547.826 7.47408525292e+20 0.62336751087 0.352327997826 3.21058941304e-05 5.47954258696e-08 0.000384736473913 0.000724866804348 9.91965308696e-09 0.00808419509783 0.015068518913 4.23944558696e-06 5.4952666087e-08 8.79563041304e-18 5.71346867391e-06 5.47021801087e-10 7.78988406522e-13 6.09742486956e-09 6.63177863043e-11 8.62852193478e-13 6.43713298913e-13 5.25721711956e-40 1e-98 - 1915.5 40005.6018206 112558823.796 7.31793256884e+20 0.625986483286 0.349713845326 3.37443007932e-05 6.09979376769e-08 0.000406781361756 0.000766927428328 1.0500982068e-08 0.00805758655524 0.0150242008782 4.53608641926e-06 5.78624198866e-08 9.63689481585e-18 5.76915170255e-06 5.61389385835e-10 8.12967160623e-13 5.7691668442e-09 6.42430352692e-11 8.28145916431e-13 5.64332821248e-13 3.32024513883e-40 1e-98 - 1918.5 39332.5075202 111996982.436 7.16441332727e+20 0.628895038527 0.346810814164 3.57395727478e-05 6.91018555239e-08 0.000433841106798 0.000818464283851 1.12152625212e-08 0.00802507692351 0.0149700733994 4.90981588101e-06 6.14559524645e-08 1.07322602549e-17 5.83774971955e-06 5.79105958073e-10 8.55022231444e-13 5.41819913882e-09 6.20132703967e-11 7.87938692636e-13 4.85632962891e-13 1.99699273091e-40 1e-98 - 1921.5 38662.477312 111572048.963 7.01349303161e+20 0.631062459751 0.344648750207 3.7357166971e-05 7.62350280499e-08 0.000455981324067 0.000860550609129 1.18003053527e-08 0.00799823472199 0.0149253267635 5.22608697096e-06 6.44306031536e-08 1.16961729046e-17 5.89453558091e-06 5.93771356847e-10 8.89943245229e-13 5.17234423236e-09 6.0447689751e-11 7.5701349834e-13 4.3483869585e-13 1.41989939419e-40 1e-98 - 1924.5 38003.5091278 111128279.839 6.86514434785e+20 0.633273779839 0.342442993548 3.91069315323e-05 8.42919626211e-08 0.000480044997178 0.000906250826211 1.24364932258e-08 0.00796917533871 0.0148769020161 5.57638907259e-06 6.76836624194e-08 1.2786050242e-17 5.95649340726e-06 6.09802304436e-10 9.28188600001e-13 4.92491653225e-09 5.88703267742e-11 7.24903116128e-13 3.85656990322e-13 9.44700673382e-41 1e-98 - 1927.5 37357.5206971 110645497.581 6.71932075514e+20 0.635595997581 0.340126703226 4.11028629838e-05 9.39904243143e-08 0.000507663807661 0.000958640443144 1.31671383871e-08 0.00793601808065 0.0148216802419 5.9881910887e-06 7.14468962902e-08 1.4099723629e-17 6.02794360887e-06 6.28336316532e-10 9.72513599999e-13 4.66979798388e-09 5.72411816129e-11 6.90306341936e-13 3.3794005484e-13 6.10191600814e-41 1e-98 - 1930.5 36725.0295605 110102740.838 6.57597102039e+20 0.638152126702 0.337577819372 4.34851786911e-05 1.06476362304e-07 0.000540915942932 0.00102162348272 1.4047541623e-08 0.00789606785864 0.0147551398429 6.50242782722e-06 7.60383487434e-08 1.57997319372e-17 6.1146113822e-06 6.50927098953e-10 1.02676069319e-12 4.40185341361e-09 5.5529212199e-11 6.51488684293e-13 2.91260327225e-13 3.73155916231e-41 1e-98 - 1933.5 36099.2057298 109610807.143 6.43505612511e+20 0.64039955 0.335337407143 4.57563721429e-05 1.19139528572e-07 0.000572840064286 0.001082027 1.48932814286e-08 0.00785771407143 0.01469127 7.01251928572e-06 8.0503265e-08 1.75343328572e-17 6.19843685714e-06 6.72891135714e-10 1.07960764286e-12 4.17215292857e-09 5.40628185714e-11 6.16740235714e-13 2.53749978571e-13 2.32266799999e-41 1e-98 - 1936.5 35476.7074632 109214232.468 6.29655673308e+20 0.642134436364 0.33360855974 4.76885133766e-05 1.30602548052e-07 0.000600192710389 0.00113372927273 1.56183074026e-08 0.00782486650649 0.0146365881818 7.46494266233e-06 8.43835263636e-08 1.91153620779e-17 6.27086153247e-06 6.91982580519e-10 1.12559774026e-12 3.99768685715e-09 5.2951338052e-11 5.89329635065e-13 2.27379248052e-13 1.63891345455e-41 1e-98 - 1939.5 34864.5219272 108795161.749 6.16044645421e+20 0.643936415301 0.331813263934 4.98206191803e-05 1.44031851912e-07 0.000630582218032 0.00119112797814 1.64242143169e-08 0.00778839125683 0.014575869235 7.98079885791e-06 8.86926125136e-08 2.09696344808e-17 6.34759703279e-06 7.12989162295e-10 1.17667744262e-12 3.81964415301e-09 5.18015145355e-11 5.60675927323e-13 2.02013768853e-13 1.09207757432e-41 1e-98 - 1942.5 34263.9815271 108340407.65 6.02668280003e+20 0.64584481694 0.329912567213 5.22699101639e-05 1.60580089618e-07 0.000665788816394 0.00125756240437 1.73584151366e-08 0.00774616174863 0.014505574153 8.59727262842e-06 9.36819034973e-08 2.32573271038e-17 6.43117039344e-06 7.37033547541e-10 1.23583031148e-12 3.6359281694e-09 5.05908710929e-11 5.30050394535e-13 1.78242826229e-13 7.62423025135e-42 1e-98 - 1945.5 33670.9878935 107885653.552 5.8952286342e+20 0.647753218579 0.328011870492 5.47192011476e-05 1.77128327323e-07 0.000700995414755 0.0013239968306 1.82926159563e-08 0.00770393224044 0.014435279071 9.21374639892e-06 9.8671194481e-08 2.55450197268e-17 6.5147437541e-06 7.61077932788e-10 1.29498318033e-12 3.45221218579e-09 4.93802276502e-11 4.99424861748e-13 1.54471883606e-13 4.32768475947e-42 1e-98 - 1948.5 33092.4186554 107381247.458 5.76604144252e+20 0.649748774576 0.326026031638 5.77611549153e-05 2.01047351413e-07 0.000745515860453 0.00140789956497 1.9475130678e-08 0.00765057644633 0.0143465024859 1.00340851978e-05 1.04777906441e-07 2.88169871187e-17 6.59579515819e-06 7.89391805085e-10 1.36721979661e-12 3.26227576836e-09 4.80283216949e-11 4.66942641807e-13 1.34569608474e-13 2.98736711297e-42 1e-98 - 1951.5 32521.4513276 106874044.068 5.63907860411e+20 0.651749240678 0.324035396045 6.08364981356e-05 2.25381631074e-07 0.000790561029944 0.00149278642938 2.06716349153e-08 0.00759659381921 0.0142566846893 1.08659093503e-05 1.10947571695e-07 3.21444066102e-17 6.67670448023e-06 8.17946211865e-10 1.44019352542e-12 3.07198890395e-09 4.66684572881e-11 4.34355819774e-13 1.14885286441e-13 1.75725948587e-42 1e-98 - 1954.5 31970.9623215 106230330.435 5.5142857837e+20 0.654224713044 0.321573504348 6.50276952175e-05 2.62167934783e-07 0.000852718273914 0.00160986939131 2.23236195652e-08 0.00752214913043 0.0141328369565 1.20250595652e-05 1.18953078261e-07 3.70461965218e-17 6.75192452174e-06 8.53414530435e-10 1.53466295652e-12 2.83601021739e-09 4.48391991304e-11 3.9416233913e-13 9.3920737391e-14 8.82928121731e-43 1e-98 - 1957.5 31426.2437134 105613734.839 5.39161219002e+20 0.656536095484 0.319275988387 6.92653425162e-05 3.02212735484e-07 0.000916133072259 0.00172931769678 2.40095916774e-08 0.00744620607097 0.0140065494194 1.32094701936e-05 1.26680442581e-07 4.22616348388e-17 6.80044526452e-06 8.86392774839e-10 1.6256248129e-12 2.6134482e-09 4.29989452903e-11 3.56728379355e-13 7.66298379999e-14 4.34765856772e-43 1e-98 - 1960.5 30869.6507662 105216331.613 5.2710696342e+20 0.657960805161 0.317860956129 7.21879328387e-05 3.32453945161e-07 0.000960350217419 0.00181264463226 2.51854852258e-08 0.00739322849032 0.0139185478065 1.40288040645e-05 1.31613894193e-07 4.60627316128e-17 6.80862655484e-06 9.06284371613e-10 1.68342707097e-12 2.4719682e-09 4.17270710968e-11 3.33727766452e-13 6.74733880001e-14 2.9861879226e-43 1e-98 - 1963.5 30322.3263109 104801288.36 5.15266315856e+20 0.659420846561 0.316411396825 7.53383049205e-05 3.66807077247e-07 0.00100830861905 0.00190306850264 2.64610816402e-08 0.00733575528572 0.0138231190476 1.48972338624e-05 1.36583256085e-07 5.023758582e-17 6.80055603175e-06 9.25476656613e-10 1.74158555555e-12 2.32504786773e-09 4.03444452911e-11 3.10317493122e-13 5.86609940214e-14 1.8552574106e-43 1e-98 - 1966.5 29787.3474721 104343748.677 5.03635093979e+20 0.660966005291 0.31487865873 7.90374239682e-05 4.11066204232e-07 0.00106528004762 0.00201058953439 2.79768713227e-08 0.00726745171429 0.013709797619 1.58839402116e-05 1.416391291e-07 5.53128556613e-17 6.7533334127e-06 9.42984235979e-10 1.80060222222e-12 2.16502128042e-09 3.86950087831e-11 2.85920310582e-13 5.06774622752e-14 1.27971903757e-43 1e-98 - 1969.5 29259.00035 103886208.995 4.92209625675e+20 0.662511164021 0.313345920635 8.27365430159e-05 4.55325331218e-07 0.00112225147619 0.00211811056614 2.94926610053e-08 0.00719914814286 0.0135964761905 1.68706465609e-05 1.46695002116e-07 6.03881255027e-17 6.70611079365e-06 9.60491815344e-10 1.85961888889e-12 2.00499469312e-09 3.70455722751e-11 2.61523128042e-13 4.2693930529e-14 7.04180664541e-44 1e-98 - 1972.5 28751.7366009 103302229.301 4.80984548075e+20 0.664387644234 0.31148703535 8.78729393763e-05 5.2615025898e-07 0.00120275199433 0.00227042890359 3.16356170889e-08 0.00710249976937 0.0134363966541 1.81063813611e-05 1.51809734972e-07 6.75828041589e-17 6.56923540075e-06 9.73640207562e-10 1.91946134594e-12 1.80787100567e-09 3.47510820794e-11 2.33085183175e-13 3.49023481473e-14 3.94841985062e-44 1e-98 - 1975.5 28254.9002052 102680648.148 4.69953763853e+20 0.6663621 0.309531728704 9.34404408334e-05 6.05037561112e-07 0.00129032160185 0.00243621237037 3.39670047222e-08 0.00699733311111 0.0132622680556 1.94149258333e-05 1.56924256482e-07 7.54110351853e-17 6.4050219074e-06 9.85388252778e-10 1.97934646296e-12 1.59981592592e-09 3.22637312037e-11 2.03469661111e-13 2.71816929629e-14 1.64960047684e-44 1e-98 - 1978.5 27771.2356649 102005462.963 4.59112706562e+20 0.6684221 0.307494599074 9.99815241667e-05 7.11233672224e-07 0.00139498178704 0.00263514940741 3.67544769445e-08 0.00687134422222 0.0130540736111 2.07055091667e-05 1.6030990463e-07 8.44615537038e-17 6.13581264814e-06 9.84993530556e-10 2.01949275926e-12 1.38600851852e-09 2.9400851574e-11 1.74525772222e-13 2.09129892592e-14 7.69148162026e-45 1e-98 - 1981.5 27275.346368 101514425 4.48461192939e+20 0.669850075 0.306084425 0.0001049964 8.00614350002e-07 0.0014761185 0.002789907 3.89166025e-08 0.00677343775 0.01289259 2.155077e-05 1.61647375e-07 9.12429300001e-17 5.9022985e-06 9.78422525e-10 2.0360175e-12 1.2389625e-09 2.7293755e-11 1.5538845e-13 1.72095424999e-14 4.39332699994e-45 1e-98 - 1984.5 26783.2161526 101049248.069 4.38000861091e+20 0.671179516094 0.304772777253 0.000109948845923 8.96432874249e-07 0.00155703932618 0.00294481349356 4.10742548927e-08 0.00667553949785 0.0127314046781 2.22413432833e-05 1.61834651931e-07 9.76728763734e-17 5.65112330258e-06 9.66435818884e-10 2.03935766094e-12 1.10712011459e-09 2.52884718884e-11 1.38554629828e-13 1.42622212446e-14 2.59634572103e-45 1e-98 - 1987.5 26297.3353089 100586060.4 4.27729774383e+20 0.672483675048 0.303486905143 0.000115005728762 9.99376835806e-07 0.00164017393714 0.00310435861523 4.32919429142e-08 0.00657478026857 0.0125657010476 2.28570185714e-05 1.61367857905e-07 1.04066433524e-16 5.38505476572e-06 9.51130766477e-10 2.03528991619e-12 9.81036900003e-10 2.33000972953e-11 1.226335728e-13 1.16474287582e-14 1.39177923449e-45 1e-98 - 1990.5 25821.2251966 100101380.4 4.17644832925e+20 0.673772760762 0.302218162286 0.00012057350019 1.12546929295e-06 0.00173288079428 0.0032833460438 4.57679886285e-08 0.00646192369715 0.0123805867619 2.33434471428e-05 1.59596086476e-07 1.10742776381e-16 5.08265305144e-06 9.2891442362e-10 2.01679905905e-12 8.62446900003e-10 2.12910287239e-11 1.080358128e-13 9.53527332958e-15 8.45327291634e-46 1e-98 - 1993.5 25357.9698273 99557655.1111 4.07741658688e+20 0.675191185556 0.300823395556 0.000126981522222 1.2802186e-06 0.0018403518 0.00349172403333 4.86414423333e-08 0.00633066433334 0.0121656724444 2.37717003333e-05 1.56796853333e-07 1.18088435555e-16 4.73304193334e-06 9.00277162223e-10 1.98688543333e-12 7.37675002224e-10 1.90961385556e-11 9.2736678778e-14 7.45591352225e-15 4.07602964448e-46 1e-98 - 1996.5 24914.3538187 98895256.5574 3.98013964063e+20 0.67686673388 0.299178251366 0.000135098256831 1.49524767213e-06 0.00197794653006 0.00376028624044 5.23268409837e-08 0.00616173981967 0.0118898752459 2.4063130929e-05 1.51847050273e-07 1.26709954098e-16 4.28935268852e-06 8.58427697267e-10 1.93296645355e-12 6.0145977213e-10 1.65411425683e-11 7.61429505463e-14 5.45250376501e-15 1.64919148086e-46 1e-98 - 1999.5 24466.1345702 98332086.2258 3.8845883956e+20 0.678214450323 0.297857980645 0.000142310448387 1.70895974193e-06 0.00210167355806 0.00400407261935 5.56507114193e-08 0.00600866056129 0.0116409843871 2.40915993871e-05 1.46391880645e-07 1.33691837742e-16 3.9083880871e-06 8.1776192742e-10 1.87300150968e-12 5.01608995163e-10 1.45301547742e-11 6.41109433228e-14 4.17402536776e-15 7.9078828872e-47 1e-98 - 2002.5 24013.549171 97865195.9494 3.79078020756e+20 0.679262519937 0.296833671519 0.000148493117405 1.9102485443e-06 0.00220870861709 0.00421694282594 5.8536229905e-08 0.00587518073102 0.0114248293038 2.39887237975e-05 1.41293125e-07 1.39308700949e-16 3.59989251267e-06 7.82176304748e-10 1.81690723734e-12 4.29624230065e-10 1.29986902532e-11 5.55177948103e-14 3.34442776584e-15 4.37089133867e-47 1e-98 - 2005.5 23566.9079543 97398972.0962 3.69871202302e+20 0.680283604231 0.295837149038 0.000154828898269 2.13434799038e-06 0.002319174525 0.00443872603461 6.15266866538e-08 0.00573628735 0.0112008713269 2.37508673654e-05 1.3559452e-07 1.44513440962e-16 3.29655044423e-06 7.44414716731e-10 1.75357584615e-12 3.65778929616e-10 1.15694359231e-11 4.78480518846e-14 2.66669965193e-15 2.37909883462e-47 1e-98 - 2008.5 23120.2311706 96982935.5577 3.60837240881e+20 0.681182796538 0.294960514423 0.000160591955962 2.35073164423e-06 0.002420147025 0.00464296488077 6.42695981923e-08 0.00560850235 0.0109955270962 2.34431885192e-05 1.3009252e-07 1.48831075577e-16 3.02916563654e-06 7.09175159038e-10 1.69191430769e-12 3.13963275769e-10 1.03593051538e-11 4.15700557307e-14 2.15294792115e-15 1.33575768077e-47 1e-98 - 2011.5 22711.4740908 96294144.0824 3.51968805913e+20 0.68261337191 0.293568585393 0.000170381397379 2.76005131087e-06 0.00259283237454 0.00499801455806 6.90015472661e-08 0.00538683172284 0.0106417200749 2.27123155056e-05 1.20256078277e-07 1.55094113109e-16 2.60875517976e-06 6.48888324717e-10 1.58051576779e-12 2.42002971908e-10 8.56452983515e-12 3.26999207113e-14 1.50469319848e-15 5.12848683126e-48 1e-98 - 2014.5 22283.4950242 95826267.7367 3.43262279389e+20 0.683477140185 0.292732096305 0.000177216741109 3.07850822171e-06 0.00271386022171 0.00525184986836 7.23563456813e-08 0.00522882468822 0.0103912164896 2.21746313164e-05 1.13781291224e-07 1.59533288222e-16 2.35201927713e-06 6.10279910623e-10 1.50770145496e-12 2.02448338337e-10 7.52046063278e-12 2.78731223325e-14 1.18379407852e-15 2.7100883972e-48 1e-98 - 2017.5 21865.1763366 95324888.1651 3.34719511481e+20 0.684376476453 0.291862675229 0.000184663311315 3.45611176759e-06 0.00284571662997 0.00553401642508 7.60575706728e-08 0.00505376908257 0.0101154413639 2.14904714985e-05 1.06605337615e-07 1.63701787156e-16 2.09192809786e-06 5.68562785015e-10 1.42600768196e-12 1.66045304893e-10 6.50598915596e-12 2.33566598777e-14 9.09689960244e-16 1.34452873578e-48 1e-98 - 2020.5 21458.8161834 94766859.2308 3.26336318928e+20 0.685365663736 0.290907498901 0.00019306618022 3.92154861538e-06 0.00299428367033 0.00586030596703 8.02905376923e-08 0.0048523867033 0.0098002390989 2.05909026374e-05 9.82958879121e-08 1.67448364835e-16 1.81790993407e-06 5.21376759341e-10 1.3299203956e-12 1.3145599011e-10 5.48214771429e-12 1.89416191209e-14 6.68069263737e-16 5.84850349451e-49 1e-98 - 2023.5 21045.2098369 94316166.9231 3.18111789893e+20 0.686128454945 0.290172795604 0.000199935520879 4.33742246153e-06 0.00311545268132 0.00613490486813 8.37978607692e-08 0.00468413681319 0.00953834239561 1.97975905495e-05 9.16229516484e-08 1.70061859341e-16 1.61440773627e-06 4.84184737363e-10 1.25211358242e-12 1.0820566044e-10 4.75238485715e-12 1.59280564836e-14 5.19264054947e-16 3.06943997806e-49 1e-98 - 2026.5 20648.9087284 93768554.8101 3.10043977951e+20 0.687010060759 0.289325677215 0.000208380098734 4.90573206329e-06 0.00326100868354 0.00648092392405 8.81933706329e-08 0.00447501163291 0.00921368702532 1.87543087342e-05 8.35821789874e-08 1.72295758228e-16 1.39172517722e-06 4.40227735443e-10 1.15634153165e-12 8.55572207596e-11 3.98716549368e-12 1.28987936709e-14 3.8442109114e-16 1.47395334811e-49 1e-98 - 2029.5 20259.9826107 93203611.5254 3.02128107477e+20 0.687878140678 0.288493483051 0.000217100810169 5.54555377966e-06 0.00340925281356 0.00685002966101 9.28016377966e-08 0.0042554771017 0.00887197474576 1.76391566102e-05 7.55151159322e-08 1.74066794915e-16 1.18244625424e-06 3.96645577966e-10 1.05916073729e-12 6.59906362713e-11 3.29018405085e-12 1.02281330509e-14 2.74959045763e-16 5.90983296612e-50 1e-98 - 2032.5 19872.9748988 92669025.5172 2.94362380199e+20 0.688635924828 0.28776994 0.000225327529655 6.21574857241e-06 0.00354661718621 0.0072130778069 9.72316217241e-08 0.00404417717931 0.00854097517241 1.65858806897e-05 6.8362584e-08 1.75603806207e-16 1.00933024966e-06 3.58476477931e-10 9.72289213104e-13 5.13599990345e-11 2.73461210345e-12 8.19403162759e-15 1.99856064138e-16 2.51873544138e-50 1e-98 - 2035.5 19485.0731084 92189542.7586 2.86746597909e+20 0.689244752414 0.28719194 0.000232669184828 6.87124098623e-06 0.00366678339311 0.00754826470346 1.01254345862e-07 0.00385299648965 0.0082390475862 1.56975703448e-05 6.25567839998e-08 1.77446013104e-16 8.75427904824e-07 3.27786408965e-10 9.0174831655e-13 4.09564335169e-11 2.31761555171e-12 6.74309921375e-15 1.50492202067e-16 1.18983682065e-50 1e-98 - 2038.5 19127.4952471 91488166.3013 2.79275065933e+20 0.690214315069 0.286267442466 0.000242900275343 7.92894287675e-06 0.00383078531507 0.00806127134248 1.07052656165e-07 0.00357787602739 0.00778259116437 1.41006649315e-05 5.33130880819e-08 1.75263631507e-16 6.90172916433e-07 2.7939098493e-10 7.83043501366e-13 2.85561150682e-11 1.76624347944e-12 4.85072271228e-15 9.55311709576e-17 3.64783510948e-51 1e-98 - 2041.5 18756.8686305 90940390.4332 2.71944441522e+20 0.690885956679 0.285630985921 0.000250433020938 8.85534367868e-06 0.00395155193502 0.00847427688808 1.11491914079e-07 0.00336848063177 0.00741580942961 1.2929580722e-05 4.69479409388e-08 1.73491622744e-16 5.7237148123e-07 2.46317502889e-10 7.0001017437e-13 2.15733753792e-11 1.42901598557e-12 3.76608214804e-15 6.75654462461e-17 1.5494225235e-51 1e-98 - 2044.5 18382.5531456 90465461.0067 2.64757963409e+20 0.691431730201 0.285115862752 0.000256475735235 9.73472059393e-06 0.0040518624161 0.00883772785234 1.15201674832e-07 0.00319372673155 0.00709033213088 1.19368059732e-05 4.18374041277e-08 1.71376736913e-16 4.84166055372e-07 2.19865695303e-10 6.32023793626e-13 1.68308621478e-11 1.18377412081e-12 3.0085039866e-15 4.98288348327e-17 7.3619184398e-52 1e-98 - 2047.5 18022.8708967 89911429.4981 2.57713513856e+20 0.692027581081 0.284555864865 0.000262876057143 1.08535401545e-05 0.0041659346834 0.00926281608881 1.19321310039e-07 0.00299900266023 0.00670373015058 1.08171372201e-05 3.63407842471e-08 1.68409579537e-16 3.95029470656e-07 1.9148538417e-10 5.57589002316e-13 1.24442293436e-11 9.42212925094e-13 2.28966914285e-15 3.4406489884e-17 2.87243437063e-52 1e-98 - 2050.5 17667.9383861 89358640 2.5080734226e+20 0.6925756968 0.2840437648 0.0002684197152 1.209216112e-05 0.004279085896 0.00968303092001 1.231426616e-07 0.00281623436 0.00631146709599 9.75512831998e-06 3.13922377599e-08 1.650292552e-16 3.20171119198e-07 1.65978539999e-10 4.89210195199e-13 9.11702403193e-12 7.45062451196e-13 1.72846716799e-15 2.35217343998e-17 1.06654160317e-52 1e-98 - 2053.5 17306.7591188 88898779.61 2.44039738293e+20 0.692995644568 0.283654233983 0.000272067884958 1.32593976323e-05 0.00437680244847 0.0100231979136 1.25963391922e-07 0.00267628974095 0.00597911855989 8.9171995571e-06 2.77220277716e-08 1.61799751811e-16 2.68701706128e-07 1.47064581058e-10 4.37257059053e-13 7.06974505014e-12 6.13573770752e-13 1.37231967688e-15 1.73538067409e-17 5.18418072424e-53 1e-98 - 2056.5 16964.9542064 88319093.1752 2.37408161765e+20 0.69347654927 0.283211814964 0.000275853267518 1.48966717518e-05 0.00450492541606 0.0104343184307 1.29217278832e-07 0.00250995971168 0.00556334390876 7.94563025548e-06 2.36033764234e-08 1.5775285219e-16 2.14101582482e-07 1.25836703285e-10 3.78029261679e-13 5.07438715329e-12 4.77076989782e-13 1.01567974854e-15 1.16216286132e-17 1.88342988322e-53 1e-98 - 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