diff --git a/.gitignore b/.gitignore
index 77437de4..3235824e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,6 +12,9 @@ Cargo.lock
 # nano swp files
 .*.swp
 
+# SLURM scripts
+*.slurm
+
 # Local VSCode files
 .vscode/
 
@@ -37,3 +40,5 @@ kifmm/python/kifmm_py/_kifmm_rs/**/*.py
 *.o
 **/build/*
 *.clang-format
+
+*.log
\ No newline at end of file
diff --git a/hpc/archer2/data/weak/Weak Scaling.ipynb b/hpc/archer2/data/weak/Weak Scaling.ipynb
deleted file mode 100644
index 9c32e3bc..00000000
--- a/hpc/archer2/data/weak/Weak Scaling.ipynb	
+++ /dev/null
@@ -1,308 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f77a94f9-96b4-4f6b-b217-f6c03a3b5160",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib import rcParams"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e1278eec-53ac-4890-a9c0-87a9313af665",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fft = pd.read_csv('weak_fft_7913527.csv')\n",
-    "\n",
-    "# Strategy 1, where 1 MPI process per CCX, max 32 per archer node\n",
-    "blas1 = pd.read_csv('weak_blas_7913901.csv')\n",
-    "\n",
-    "# Strategy 2, 1 MPI process per CCD, max 16 per archer node\n",
-    "blas2 = pd.read_csv('weak_blas_7920032.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5c765e29-8f1d-4f38-98d9-a0ff75258a72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib import rcParams\n",
-    "\n",
-    "def plot_weak_scaling(df, title=\"Weak Scaling of Runtime vs. Particle Count\", save_svg=False, filename=\"weak_scaling_plot.svg\"):\n",
-    "    # Set global font to CMU and enable LaTeX\n",
-    "    rcParams['font.family'] = 'serif'\n",
-    "    rcParams['font.serif'] = ['CMU Serif']  # Use 'CMU Serif' for the main font\n",
-    "    rcParams['text.usetex'] = True\n",
-    "    rcParams['text.latex.preamble'] = r'\\renewcommand{\\rmdefault}{cmu}'\n",
-    "\n",
-    "    # Calculate total particles and runtimes for each experiment_id\n",
-    "    group_size = df.groupby('experiment_id').size()\n",
-    "    n_particles = group_size * df['n_points'].iloc[0]\n",
-    "    \n",
-    "    # Get maximum runtime per experiment_id\n",
-    "    runtimes = df.groupby('experiment_id')['runtime'].max()\n",
-    "    m2l_runtimes = df.groupby('experiment_id')['m2l'].max()\n",
-    "\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=300)  # Larger size and higher DPI for quality\n",
-    "\n",
-    "    # Set bar widths as a fraction of n_particles\n",
-    "    relative_width = 0.3\n",
-    "    widths = n_particles * relative_width\n",
-    "\n",
-    "    # Plot bars\n",
-    "    ax.bar(n_particles, runtimes, width=widths, align='center', color='skyblue', edgecolor='black')\n",
-    "\n",
-    "    # Set x-axis to logarithmic\n",
-    "    ax.set_xscale('log')\n",
-    "\n",
-    "    # Labeling the axes with LaTeX expressions for testing\n",
-    "    ax.set_xlabel(r'Total Number of Particles', fontsize=14)\n",
-    "    ax.set_ylabel(r'Runtime ($ms$)', fontsize=14)\n",
-    "    ax.set_title(title, fontsize=16)\n",
-    "\n",
-    "    # Customize ticks and grid\n",
-    "    ax.tick_params(axis='both', which='major', labelsize=12)\n",
-    "    ax.grid(True, which=\"both\", linestyle='--', linewidth=0.5, alpha=0.7)\n",
-    "\n",
-    "    # Save the plot as SVG if specified\n",
-    "    if save_svg:\n",
-    "        plt.savefig(filename, format=\"svg\", bbox_inches=\"tight\")\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "colors = [\"#87CEEB\", \"#FA8072\", \"#DAA520\", \"#3CB371\", \"#6A5ACD\", \"#FF7F50\", \"#9932CC\", \"#FF6347\", \"#40E0D0\"]\n",
-    "\n",
-    "\n",
-    "def plot_weak_scaling_stacked(df, ylims=None, title=\"Weak Scaling of Runtime vs. Particle Count\", save_svg=False, filename=\"weak_scaling_plot.svg\"):\n",
-    "    # Set global font to CMU and enable LaTeX\n",
-    "    rcParams['font.family'] = 'serif'\n",
-    "    rcParams['font.serif'] = ['CMU Serif']  # Use 'CMU Serif' for the main font\n",
-    "    rcParams['text.usetex'] = True\n",
-    "    rcParams['text.latex.preamble'] = r'\\renewcommand{\\rmdefault}{cmu}'\n",
-    "\n",
-    "    # Calculate total particles and runtimes for each experiment_id\n",
-    "    group_size = df.groupby('experiment_id').size()\n",
-    "    n_particles = group_size * df['n_points'].iloc[0]\n",
-    "    \n",
-    "    # Get maximum runtime per experiment_id\n",
-    "    runtimes = df.groupby('experiment_id')['runtime'].max()\n",
-    "    m2l_runtimes = df.groupby('experiment_id')['m2l'].max()\n",
-    "    p2p_runtimes = df.groupby('experiment_id')['p2p'].max()\n",
-    "    ghost_exchange_u = df.groupby('experiment_id')['ghost_exchange_u'].max()\n",
-    "    ghost_exchange_v = df.groupby('experiment_id')['ghost_exchange_v'].max()\n",
-    "    ghost_exchange_v_runtime = df.groupby('experiment_id')['ghost_exchange_v_runtime'].max()\n",
-    "\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(10, 6), dpi=300)  # Larger size and higher DPI for quality\n",
-    "\n",
-    "    # Set bar widths and offsets\n",
-    "    relative_width = 0.3\n",
-    "    widths = n_particles * relative_width\n",
-    "    offset = widths / 2  # Offset for second set of bars\n",
-    "    \n",
-    "    # Plot bars\n",
-    "    ax.bar(n_particles, m2l_runtimes, width=widths, align='center', color='skyblue', edgecolor='black', label='M2L Runtime')\n",
-    "    ax.bar(n_particles, p2p_runtimes, bottom=m2l_runtimes,  width=widths, align='center', color='salmon', edgecolor='black', label='P2P Runtime')\n",
-    "    ax.bar(n_particles, ghost_exchange_v_runtime, bottom=p2p_runtimes+m2l_runtimes, width=widths, align='center', color='slateblue', edgecolor='black', label='Ghost Exchange V Runtime')\n",
-    "\n",
-    "    # Plot the second set of stacked bars (ghost_exchange_u + ghost_exchange_v)\n",
-    "    ax.bar(n_particles + offset, ghost_exchange_u, width=widths, align='center', color='goldenrod', edgecolor='black', label='Ghost Exchange U')\n",
-    "    ax.bar(n_particles + offset, ghost_exchange_v, bottom=ghost_exchange_u, width=widths, align='center', color='MediumSeaGreen', edgecolor='black', label='Ghost Exchange V')\n",
-    "    \n",
-    "    # Set x-axis to logarithmic\n",
-    "    ax.set_xscale('log')\n",
-    "\n",
-    "    if ylims:\n",
-    "        ax.set_ylim(ylims)\n",
-    "    else:\n",
-    "        ax.set_ylim(0, runtimes.max()+150)\n",
-    "\n",
-    "    # Labeling the axes with LaTeX expressions for testing\n",
-    "    ax.set_xlabel(r'Total Number of Particles', fontsize=14)\n",
-    "    ax.set_ylabel(r'Runtime ($ms$)', fontsize=14)\n",
-    "    ax.set_title(title, fontsize=16)\n",
-    "\n",
-    "    # Customize ticks and grid\n",
-    "    ax.tick_params(axis='both', which='major', labelsize=12)\n",
-    "    ax.grid(True, which=\"both\", linestyle='--', linewidth=0.5, alpha=0.7)\n",
-    "\n",
-    "    ax.legend()\n",
-    "    # Save the plot as SVG if specified\n",
-    "    if save_svg:\n",
-    "        plt.savefig(filename, format=\"svg\", bbox_inches=\"tight\")\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "    return ax, fig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "4a8b8457-1b49-4fc7-9b52-a84d66479492",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 3000x1800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax1, fig1 = plot_weak_scaling_stacked(blas1, ylims=(0, 2500), title=\"Weak Scaling of Runtime vs. Particle Count FFT M2L\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "149a35b5-64f0-444d-a616-a314e7cdd3fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3000x1800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax2, fig2 = plot_weak_scaling_stacked(blas2, ylims=(0, 2500), title=\"Weak Scaling of Runtime vs. Particle Count BLAS M2L\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "2d78fc93-e2b7-49a6-9b22-2255091d7702",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 6000x1800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create a new combined figure\n",
-    "combined_fig, (combined_ax1, combined_ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=300)\n",
-    "\n",
-    "# Track unique labels to prevent duplicates\n",
-    "unique_labels = set()\n",
-    "\n",
-    "# Set lims\n",
-    "ylims = (0, 2500)\n",
-    "\n",
-    "# Transfer bar data from ax1 to combined_ax1 with relative widths\n",
-    "for container in ax1.containers:\n",
-    "    bars = container.get_children()\n",
-    "    x_values = [bar.get_x() + bar.get_width() / 2 for bar in bars]\n",
-    "    heights = [bar.get_height() for bar in bars]\n",
-    "    bottoms = [bar.get_y() for bar in bars]\n",
-    "    color = bars[0].get_facecolor() if bars else None\n",
-    "    widths = [x * 0.3 for x in x_values]\n",
-    "    \n",
-    "    # Only add label if it hasn't been added before\n",
-    "    label = container.get_label()\n",
-    "    for x, height, bottom, width in zip(x_values, heights, bottoms, widths):\n",
-    "        combined_ax1.bar(x, height, bottom=bottom, width=width, color=color, edgecolor='black', label=label if label not in unique_labels else \"\")\n",
-    "        unique_labels.add(label)  # Add label to set to prevent duplicates\n",
-    "\n",
-    "# Set labels and titles for combined_ax1\n",
-    "combined_ax1.set_xscale('log')\n",
-    "combined_ax1.set_xlabel(ax1.get_xlabel(), fontsize=14)\n",
-    "combined_ax1.set_ylabel(ax1.get_ylabel(), fontsize=14)\n",
-    "combined_ax1.set_title(ax1.get_title(), fontsize=16)\n",
-    "combined_ax1.set_ylim(ylims)\n",
-    "\n",
-    "# Repeat for combined_ax2 with unique labels\n",
-    "for container in ax2.containers:\n",
-    "    bars = container.get_children()\n",
-    "    x_values = [bar.get_x() + bar.get_width() / 2 for bar in bars]\n",
-    "    heights = [bar.get_height() for bar in bars]\n",
-    "    bottoms = [bar.get_y() for bar in bars]\n",
-    "    color = bars[0].get_facecolor() if bars else None\n",
-    "    widths = [x * 0.3 for x in x_values]\n",
-    "    \n",
-    "    label = container.get_label()\n",
-    "    for x, height, bottom, width in zip(x_values, heights, bottoms, widths):\n",
-    "        combined_ax2.bar(x, height, bottom=bottom, width=width, color=color, edgecolor='black', label=label if label not in unique_labels else \"\")\n",
-    "        unique_labels.add(label)\n",
-    "\n",
-    "# Set labels and titles for combined_ax1\n",
-    "combined_ax2.set_xscale('log')\n",
-    "combined_ax2.set_xlabel(ax2.get_xlabel(), fontsize=14)\n",
-    "combined_ax2.set_ylabel(ax2.get_ylabel(), fontsize=14)\n",
-    "combined_ax2.set_title(ax2.get_title(), fontsize=16)\n",
-    "combined_ax2.set_ylim(ylims)\n",
-    "\n",
-    "# Combine legend entries from both axes without duplicates\n",
-    "handles, labels = [], []\n",
-    "for handle, label in zip(*combined_ax1.get_legend_handles_labels()):\n",
-    "    if label not in labels:\n",
-    "        handles.append(handle)\n",
-    "        labels.append(label)\n",
-    "\n",
-    "# Create a single legend for the figure\n",
-    "combined_fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=3)\n",
-    "\n",
-    "# Show the combined figure\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "590b4ede-b2e4-42c1-8c94-7c68608f6945",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hpc/archer2/slurm/grid_search_1.slurm b/hpc/archer2/slurm/grid_search_1.slurm
deleted file mode 100644
index 7003a63d..00000000
--- a/hpc/archer2/slurm/grid_search_1.slurm
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/bin/bash
-# Here, we perform grid search where the number of MPI processes is set equal to the number of NUMA regions, i.e. 8 per node
-
-#SBATCH --job-name=grid_search
-#SBATCH --time=00:30:00
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=8
-#SBATCH --cpus-per-task=16
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/grid_search_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Set simulation parameters
-n_points=1000000 # points per MPI process
-n_tasks=8
-global_depth=1 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions
-local_depth=(5 6)
-n_samples=(100 1000)
-block_size=(128 256)
-n_threads=(8 16) # See if bandwidth saturates with different threading parameters for Rayon thread pool
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain,layout,\
-ghost_exchange_v,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u\
-expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT}
-
-# Perform grid search
-for i in ${!local_depth[@]}; do
-    for j in ${!n_samples[@]}; do
-        for k in ${!block_size[@]}; do
-            for l in ${!n_threads[@]}; do
-                experiment_id="${i}_${j}_${k}_${l}"
-                srun --ntasks=$n_tasks --cpus-per-task=16 --distribution=block:block --hint=nomultithread \
-                    ${WORK}/grid_search_mpi --id $experiment_id --n-points $n_points \
-                    --expansion-order 3 \
-                    --prune-empty \
-                    --global-depth $global_depth \
-                    --local-depth ${local_depth[$i]} \
-                    --n-samples ${n_samples[$j]} \
-                    --block-size ${block_size[$k]} \
-                    --n-threads ${n_threads[$l]} >> ${OUTPUT}
-            done
-        done
-    done
-done
diff --git a/hpc/archer2/slurm/grid_search_2.slurm b/hpc/archer2/slurm/grid_search_2.slurm
deleted file mode 100644
index 66944cfe..00000000
--- a/hpc/archer2/slurm/grid_search_2.slurm
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/bin/bash
-# Here, we perform grid search where the number of MPI processes is set equal to the number of CCX units, each of which
-# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node
-
-#SBATCH --job-name=grid_search
-#SBATCH --time=00:30:00
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=32
-#SBATCH --cpus-per-task=4
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/grid_search_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Script being run, expected in the work directory
-script_name="fmm_m2l_blas_mpi_f32"
-
-# Set simulation parameters
-n_points=250000 # points per MPI process
-n_tasks=32
-cpus_per_task=4
-global_depth=2 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions
-local_depth=(4 5)
-n_samples=(5000)
-block_size=(128)
-n_threads=(4) # See if bandwidth saturates with different threading parameters for Rayon thread pool
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/grid_search_blas_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain, layout,\
-ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\
-expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT}
-
-# Perform grid search
-for i in ${!local_depth[@]}; do
-    for j in ${!n_samples[@]}; do
-        for k in ${!block_size[@]}; do
-            for l in ${!n_threads[@]}; do
-                experiment_id="${i}_${j}_${k}_${l}"
-                srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \
-                    "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \
-                    --expansion-order 3 \
-                    --prune-empty \
-                    --global-depth $global_depth \
-                    --local-depth ${local_depth[$i]} \
-                    --n-samples ${n_samples[$j]} \
-                    --n-threads ${n_threads[$l]} >> ${OUTPUT}
-            done
-        done
-    done
-done
diff --git a/hpc/archer2/slurm/grid_search_3.slurm b/hpc/archer2/slurm/grid_search_3.slurm
deleted file mode 100644
index b3ad78d8..00000000
--- a/hpc/archer2/slurm/grid_search_3.slurm
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/bin/bash
-# Here we perform a naive run
-
-#SBATCH --job-name=grid_search
-#SBATCH --time=00:30:00
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=128
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/grid_search_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Set simulation parameters
-n_points=8000000 # points per MPI process
-n_tasks=1
-cpus_per_task=128
-global_depth=1 # Number of local roots matches the number of MPI processes, therefore the number of NUMA regions
-local_depth=(5)
-n_samples=(5000)
-block_size=(128)
-n_threads=(128) # See if bandwidth saturates with different threading parameters for Rayon thread pool
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/grid_search_fft_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain,layout,\
-ghost_exchange_v,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\
-expansion_order,n_points,local_depth, global_depth, block_size, n_threads, n_samples" >> ${OUTPUT}
-
-# Perform grid search
-for i in ${!local_depth[@]}; do
-    for j in ${!n_samples[@]}; do
-        for k in ${!block_size[@]}; do
-            for l in ${!n_threads[@]}; do
-                experiment_id="${i}_${j}_${k}_${l}"
-                srun --ntasks=$n_tasks --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \
-                    ${WORK}/fmm_m2l_fft_mpi_f32 --id $experiment_id --n-points $n_points \
-                    --expansion-order 3 \
-                    --prune-empty \
-                    --global-depth $global_depth \
-                    --local-depth ${local_depth[$i]} \
-                    --n-samples ${n_samples[$j]} \
-                    --block-size ${block_size[$k]} \
-                    --n-threads ${n_threads[$l]} >> ${OUTPUT}
-            done
-        done
-    done
-done
diff --git a/hpc/archer2/slurm/weak_1_blas.slurm b/hpc/archer2/slurm/weak_1_blas.slurm
deleted file mode 100644
index ab453a2e..00000000
--- a/hpc/archer2/slurm/weak_1_blas.slurm
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/bin/bash
-# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which
-# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node
-
-# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased
-# just to see what a single node is capable of (in the best parameter settings found)
-
-# This should give a good baseline of what to expect when communication costs are minimal
-
-#SBATCH --job-name=weak_scaling
-#SBATCH --time=00:30:00
-#SBATCH --nodes=16
-#SBATCH --ntasks-per-node=32
-#SBATCH --cpus-per-task=4
-#SBATCH --contiguous
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/weak_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Script being run, expected in the work directory
-script_name="fmm_m2l_blas_mpi_f32"
-
-# Set simulation parameters for FMM
-expansion_order=3
-n_points=250000 # points per MPI process (n_tasks)
-global_depth=(1 2 2 2 3 3 3) # Number of local roots
-local_depth=4
-n_samples=5000
-
-# Set parameters for weak scaling run
-n_tasks=(8 16 32 64 128 256 512)
-n_nodes=(1 1 1 2 4 8 16)
-n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool
-cpus_per_task=4
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain,layout,\
-ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\
-expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT}
-
-# Perform weak scaling
-for i in ${!n_tasks[@]}; do
-    experiment_id="${i}"
-    srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \
-        "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \
-        --expansion-order $expansion_order \
-        --prune-empty \
-        --global-depth ${global_depth[$i]} \
-        --local-depth $local_depth \
-        --n-samples $n_samples \
-        --n-threads $n_threads >> ${OUTPUT}
-done
-
diff --git a/hpc/archer2/slurm/weak_1_fft.slurm b/hpc/archer2/slurm/weak_1_fft.slurm
deleted file mode 100644
index c191b12a..00000000
--- a/hpc/archer2/slurm/weak_1_fft.slurm
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/bin/bash
-# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which
-# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node
-
-# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased
-# just to see what a single node is capable of (in the best parameter settings found)
-
-# This should give a good baseline of what to expect when communication costs are minimal
-
-#SBATCH --job-name=weak_scaling_fft
-#SBATCH --time=00:30:00
-#SBATCH --nodes=16
-#SBATCH --ntasks-per-node=32
-#SBATCH --cpus-per-task=4
-#SBATCH --contiguous
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/weak_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Script being run, expected in the work directory
-script_name="fmm_m2l_fft_mpi_f32"
-
-# Set simulation parameters for FMM
-expansion_order=3
-n_points=250000 # points per MPI process (n_tasks)
-global_depth=(1 2 2 2 3 3 3) # Number of local roots
-local_depth=4
-n_samples=5000
-block_size=128
-
-# Set parameters for weak scaling run
-n_tasks=(8 16 32 64 128 256 512)
-n_nodes=(1 1 1 2 4 8 16)
-n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool
-cpus_per_task=4
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain,layout,\
-ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\
-expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT}
-
-# Perform weak scaling
-for i in ${!n_tasks[@]}; do
-    experiment_id="${i}"
-    srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \
-        "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \
-        --expansion-order $expansion_order \
-        --prune-empty \
-        --global-depth ${global_depth[$i]} \
-        --local-depth $local_depth \
-        --n-samples $n_samples \
-        --block-size $block_size \
-        --n-threads $n_threads >> ${OUTPUT}
-done
-
diff --git a/hpc/archer2/slurm/weak_2_blas.slurm b/hpc/archer2/slurm/weak_2_blas.slurm
deleted file mode 100644
index b7d4b722..00000000
--- a/hpc/archer2/slurm/weak_2_blas.slurm
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/bin/bash
-# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which
-# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node
-
-# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased
-# just to see what a single node is capable of (in the best parameter settings found)
-
-# This should give a good baseline of what to expect when communication costs are minimal
-
-#SBATCH --job-name=weak_scaling_blas
-#SBATCH --time=00:30:00
-#SBATCH --nodes=128
-#SBATCH --ntasks-per-node=32
-#SBATCH --cpus-per-task=4
-#SBATCH --contiguous
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/weak_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Script being run, expected in the work directory
-script_name="fmm_m2l_blas_mpi_f32"
-
-# Set simulation parameters for FMM
-expansion_order=3
-n_points=250000 # points per MPI process (n_tasks)
-global_depth=(4 4 4) # Number of local roots
-local_depth=4
-n_samples=5000
-
-# Set parameters for weak scaling run
-n_tasks=(1024 2048 4096)
-n_nodes=(32 64 128)
-n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool
-cpus_per_task=4
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain,layout,\
-ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\
-expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT}
-
-# Perform weak scaling
-for i in ${!n_tasks[@]}; do
-    experiment_id="${i}"
-    srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \
-        "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \
-        --expansion-order $expansion_order \
-        --prune-empty \
-        --global-depth ${global_depth[$i]} \
-        --local-depth $local_depth \
-        --n-samples $n_samples \
-        --n-threads $n_threads >> ${OUTPUT}
-done
-
diff --git a/hpc/archer2/slurm/weak_2_fft.slurm b/hpc/archer2/slurm/weak_2_fft.slurm
deleted file mode 100644
index eab01c8e..00000000
--- a/hpc/archer2/slurm/weak_2_fft.slurm
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/bin/bash
-# Here, we perform a weak scaling run where the number of MPI processes is set equal to the number of CCX units, each of which
-# share an L3 cache, conist of 4 processors. There are 16 CCX regions per processor, and 32 per node
-
-# Running on just a single node, we try and examine the scaling performance as the number of tasks is increased
-# just to see what a single node is capable of (in the best parameter settings found)
-
-# This should give a good baseline of what to expect when communication costs are minimal
-
-#SBATCH --job-name=weak_scaling_fft
-#SBATCH --time=00:30:00
-#SBATCH --nodes=128
-#SBATCH --ntasks-per-node=32
-#SBATCH --cpus-per-task=4
-#SBATCH --contiguous
-
-#SBATCH --account=e738
-#SBATCH --partition=standard
-#SBATCH --qos=standard
-# Development environment for KiFMM
-
-# Restore AMD compiler env
-module load PrgEnv-aocc
-module load craype-network-ucx
-module load cray-mpich-ucx
-
-# Home and work directories
-export HOME="/home/e738/e738/skailasa"
-export WORK="/work/e738/e738/skailasa"
-
-# Create a scratch directory for this run
-export SCRATCH=${WORK}/weak_${SLURM_JOBID}
-
-# Load Spack
-source $HOME/spack/share/spack/setup-env.sh
-. "$HOME/.cargo/env"
-
-# Load BLAS
-spack load openblas
-
-# Ensure Rust can find the Cray libraries
-# export RUSTFLAGS="-L $(echo $CRAY_LD_LIBRARY_PATH)"
-# export LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH
-export RUSTFLAGS="-L $(spack location -i openblas)/lib"
-export LD_LIBRARY_PATH=$(spack location -i openblas)/lib:$LD_LIBRARY_PATH
-
-mkdir -p ${SCRATCH}
-cd ${SCRATCH}
-
-# Pass variable to SRUN from SBATCH
-export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
-
-# Script being run, expected in the work directory
-script_name="fmm_m2l_fft_mpi_f32"
-
-# Set simulation parameters for FMM
-expansion_order=3
-n_points=250000 # points per MPI process (n_tasks)
-global_depth=(4 4 4) # Number of local roots
-local_depth=4
-n_samples=5000
-block_size=128
-
-# Set parameters for weak scaling run
-n_tasks=(1024 2048 4096)
-n_nodes=(32 64 128)
-n_threads=4 # See if bandwidth saturates with different threading parameters for Rayon thread pool
-cpus_per_task=4
-export OMP_NUM_THREADS=1 # Need to set to 1 to avoid oversubsciption between Rayon and OpenMP
-
-# Create a CSV output file for analysis
-export OUTPUT=${SCRATCH}/weak_blas_${SLURM_JOBID}.csv
-touch ${OUTPUT}
-echo "
-experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,\
-source_tree,target_tree,source_domain,target_domain,layout,\
-ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,\
-source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,\
-expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples" >> ${OUTPUT}
-
-# Perform weak scaling
-for i in ${!n_tasks[@]}; do
-    experiment_id="${i}"
-    srun --nodes=${n_nodes[$i]} --ntasks=${n_tasks[$i]} --cpus-per-task=$cpus_per_task --distribution=block:block --hint=nomultithread \
-        "${WORK}/${script_name}" --id $experiment_id --n-points $n_points \
-        --expansion-order $expansion_order \
-        --prune-empty \
-        --global-depth ${global_depth[$i]} \
-        --local-depth $local_depth \
-        --n-samples $n_samples \
-        --block-size $block_size \
-        --n-threads $n_threads >> ${OUTPUT}
-done
-
diff --git a/hpc/ijhpca/README.md b/hpc/ijhpca/README.md
new file mode 100644
index 00000000..d4e27fab
--- /dev/null
+++ b/hpc/ijhpca/README.md
@@ -0,0 +1,2 @@
+# Experiments for Journal Submission to IJHPCA
+
diff --git a/hpc/ijhpca/Strong Scaling.ipynb b/hpc/ijhpca/Strong Scaling.ipynb
new file mode 100644
index 00000000..519199d6
--- /dev/null
+++ b/hpc/ijhpca/Strong Scaling.ipynb	
@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2137b1d9-7f65-410a-b79d-7ed81dcf9b46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b51de3af-2b62-4382-811b-1ea190101d64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "setup_cols = [\n",
+    "    'experiment_id', \n",
+    "    'dist_graph_create',\n",
+    "    'neighbour_all_to_all_v', \n",
+    "    'tree_all_to_all',\n",
+    "    'source_tree',\n",
+    "    'target_tree',\n",
+    "    'tree_all_gather',\n",
+    "    'n_points'\n",
+    "]\n",
+    "\n",
+    "runtime_cols = [\n",
+    "    'experiment_id', \n",
+    "    'runtime', \n",
+    "    'p2p', \n",
+    "    'm2l', \n",
+    "    'scatter_v_runtime',\n",
+    "    'gather_v_runtime',\n",
+    "    'neighbour_all_to_all_v_runtime', \n",
+    "    'n_points'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ae668ed4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df0 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=uniform_11277038.csv')\n",
+    "df1 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=uniform_11277036.csv')\n",
+    "df2 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=uniform_11278285.csv')\n",
+    "\n",
+    "df3 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11277037.csv')\n",
+    "df4 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=sphere_11277035.csv')\n",
+    "df5 = pd.read_csv('data/full/strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11277039.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "30777f75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def strong_scaling(dfs, names=None, title=\"Strong Scaling and Parallel Efficiency\", plot_err=True, y_scale='linear', stat='mean'):\n",
+    "    \"\"\"\n",
+    "    Plots strong scaling (runtime) and parallel efficiency for one or more DataFrames.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dfs : list of pandas.DataFrame or pandas.DataFrame\n",
+    "        One or more DataFrames with columns including:\n",
+    "        ['experiment_id', 'runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']\n",
+    "    names : list of str, optional\n",
+    "        Names/labels for each DataFrame. Must be same length as dfs.\n",
+    "    title : str\n",
+    "        Title for the figure.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Handle single DataFrame input\n",
+    "    if not isinstance(dfs, (list, tuple)):\n",
+    "        dfs = [dfs]\n",
+    "    if names is None:\n",
+    "        names = [f\"Run {i+1}\" for i in range(len(dfs))]\n",
+    "\n",
+    "    # Style setup\n",
+    "    sns.set_context(\"talk\", font_scale=1.1)\n",
+    "    sns.set_style(\"whitegrid\")\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "    ax_runtime, ax_eff = axes\n",
+    "\n",
+    "    # Marker and color configuration\n",
+    "    markers = ['o', 's', '^', 'D', 'v', 'P', '*']\n",
+    "    colors = sns.color_palette(\"deep\", len(dfs))\n",
+    "\n",
+    "    for df, label, color, marker in zip(dfs, names, colors, markers):\n",
+    "        stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']]\n",
+    "        n_ranks = stats.size()\n",
+    "\n",
+    "        if stat == \"mean\":\n",
+    "            runtime = stats.mean()['runtime']\n",
+    "        elif stat == \"median\":\n",
+    "            runtime = stats.median()['runtime']\n",
+    "        elif stat == \"max\":\n",
+    "            runtime = stats.max()['runtime']\n",
+    "        else:\n",
+    "            raise ValueError(\"Only supports 'mean' or 'median'\")\n",
+    "    \n",
+    "        runtime_err = stats.std()['runtime']\n",
+    "\n",
+    "        # Ideal scaling\n",
+    "        T1 = runtime.iloc[0]\n",
+    "        ideal_runtime = [T1 / (n_ranks.iloc[i] / n_ranks.iloc[0]) for i in range(len(n_ranks))]\n",
+    "\n",
+    "        # Parallel efficiency\n",
+    "        efficiency = (T1 / (runtime * (n_ranks / n_ranks.iloc[0]))) * 100  # in %\n",
+    "\n",
+    "        n_points = stats.mean()['n_points'] # points per rank\n",
+    "\n",
+    "        # Runtime plot\n",
+    "        if plot_err:\n",
+    "            ax_runtime.errorbar(\n",
+    "                n_points, runtime, yerr=runtime_err, fmt=marker+'-', color=color,\n",
+    "                label=label, capsize=4, linewidth=2, markersize=7\n",
+    "            )\n",
+    "        else:\n",
+    "            ax_runtime.plot(\n",
+    "                n_points, runtime,  marker=marker, color=color,\n",
+    "                label=label, linewidth=2, markersize=7\n",
+    "            )\n",
+    "        ax_runtime.plot(n_points, ideal_runtime, '--', color=color, alpha=0.7, linewidth=1.5)\n",
+    "\n",
+    "        # Efficiency plot\n",
+    "        ax_eff.plot(\n",
+    "            n_points, efficiency, marker=marker, linestyle='-', color=color,\n",
+    "            label=label, linewidth=2, markersize=7\n",
+    "        )\n",
+    "\n",
+    "        ax_eff.xaxis.set_inverted(True) \n",
+    "        ax_runtime.xaxis.set_inverted(True) \n",
+    "\n",
+    "\n",
+    "    # Configure runtime plot\n",
+    "    ax_runtime.set_xscale(\"log\", base=2)\n",
+    "    if y_scale == \"log\":\n",
+    "        ax_runtime.set_yscale(\"log\")\n",
+    "    ax_runtime.set_xlabel(\"Points Per Rank\", fontsize=13)\n",
+    "    ax_runtime.set_ylabel(\"Runtime (ms)\", fontsize=13)\n",
+    "    ax_runtime.set_title(\"Strong Scaling (Runtime)\", fontsize=14)\n",
+    "    ax_runtime.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n",
+    "    ax_runtime.legend(fontsize=11)\n",
+    "\n",
+    "    # Configure efficiency plot\n",
+    "    ax_eff.set_xscale(\"log\", base=2)\n",
+    "    ax_eff.set_ylim(0, 110)\n",
+    "    ax_eff.set_xlabel(\"Points Per Rank\", fontsize=13)\n",
+    "    ax_eff.set_ylabel(\"Parallel Efficiency (%)\", fontsize=13)\n",
+    "    ax_eff.set_title(\"Parallel Efficiency\", fontsize=14)\n",
+    "    ax_eff.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n",
+    "    ax_eff.legend(fontsize=11)\n",
+    "\n",
+    "    fig.suptitle(title, fontsize=16, y=1.03)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n",
+    "    \n",
+    "    stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "    n_ranks = stats.size()\n",
+    "    n_points = stats.mean()['n_points'] * n_ranks\n",
+    "\n",
+    "    if stat == \"max\":\n",
+    "        tmp = stats.max()\n",
+    "    elif stat == \"mean\":\n",
+    "        tmp = stats.mean()\n",
+    "    elif stat == \"median\":\n",
+    "        tmp = stats.median()\n",
+    "    else:\n",
+    "        return ValueError(\"invalid stat\")\n",
+    "    \n",
+    "    tmp = tmp.set_index(n_ranks)\n",
+    "    \n",
+    "    # Total communication time per experiment\n",
+    "    tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "    \n",
+    "    x = tmp.index.values.astype(float)\n",
+    "    y = tmp['comm_time'].values\n",
+    "    # c = y[-1]\n",
+    "\n",
+    "    # # log_curve\n",
+    "    # # log_curve = y[-1] * np.emath.logn(8, x / x[0]) + y[0]\n",
+    "    # m = (c-y)/np.emath.logn(8,x)\n",
+    "    # log_curve = -m*x + c\n",
+    "    \n",
+    "    # # linear curve\n",
+    "    # m = (c-y)/x\n",
+    "    # linear_curve = -m*x + c\n",
+    "    # log_curve = y[-1] * (np.emath.logn(8, x[-1]) / np.emath.logn(8, x))\n",
+    "    # linear_curve = y[-1] * (x[-1] / x)\n",
+    "    \n",
+    "    # Stacked bar for measured communication time\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].plot(\n",
+    "        kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\"\n",
+    "    )\n",
+    "        \n",
+    "    ax.set_xticks(range(len(x)))\n",
+    "    ax.set_xticklabels([int(v) for v in x], rotation=0)\n",
+    "    ax.set_xlabel(\"Number of MPI Ranks\")\n",
+    "    ax.set_ylabel(\"Runtime Communication (ms)\")\n",
+    "    \n",
+    "    # ax.set_yscale('log')\n",
+    "    if y_scale == \"log\":\n",
+    "        ax_runtime.set_yscale('log')\n",
+    "    \n",
+    "    # Overlay both theoretical trends, starting from first value\n",
+    "    # ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n",
+    "    # ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n",
+    "    \n",
+    "    # Style cleanupr\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ff40293-c3f5-421b-9d9e-312d64651764",
+   "metadata": {},
+   "source": [
+    "# Plot Strong Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "bff20d11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "strong_scaling([df0,df1,df2], names=[\"CCX\", \"CCD\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2 (Uniform)\", plot_err=True, stat='max', y_scale='log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "fcafbe0c-4a99-41a3-a5e2-79418587afc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "strong_scaling([df3,df4,df5], names=[\"CCX\", \"CCD\",\"Socket\"], title=\"Strong Scaling For 128e6 Points on 64 Nodes of ARCHER2 (Sphere)\", stat='max', y_scale='log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d2fd1a7-a20f-46a4-9297-f041381f2276",
+   "metadata": {},
+   "source": [
+    "# Plot Communication Breakdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "634fd841-be60-43c1-9531-dfd9143d131f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "breakdown_communication_times(df0, stat='max')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "679b41d5-a462-43f3-a12d-b73eb19bc1f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e158f706-d894-4fc5-9d33-098df1f79e56",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc6d9b48-6149-4245-8ff1-51f968bd027e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hpc/ijhpca/Weak Scaling.ipynb b/hpc/ijhpca/Weak Scaling.ipynb
new file mode 100644
index 00000000..856701bf
--- /dev/null
+++ b/hpc/ijhpca/Weak Scaling.ipynb	
@@ -0,0 +1,1693 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ae1633f-50c6-48ec-ad4b-d899118e24c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "86084f2e-5662-445c-8bac-e630a73d764f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=sphere_11277039.csv\n",
+      "strong_fft_n=128000000_p=64_points_per_rank=1000000_distribution=uniform_11278285.csv\n",
+      "strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=sphere_11277035.csv\n",
+      "strong_fft_n=128000000_p=64_points_per_rank=125000_distribution=uniform_11277036.csv\n",
+      "strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=sphere_11277037.csv\n",
+      "strong_fft_n=128000000_p=64_points_per_rank=62500_distribution=uniform_11277038.csv\n",
+      "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11279395.csv\n",
+      "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11282088.csv\n",
+      "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11290286.csv\n",
+      "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11291765.csv\n",
+      "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11293393.csv\n",
+      "weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11279368.csv\n",
+      "weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11277029.csv\n",
+      "weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=uniform_11279473.csv\n",
+      "weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11277033.csv\n",
+      "weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11277034.csv\n",
+      "weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11277027.csv\n",
+      "weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11279480.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ls data/full"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "178da76c-f2f3-4e55-859b-37cccde9f575",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df0 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=uniform_11279473.csv')\n",
+    "df1 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11279480.csv')\n",
+    "df2 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11277034.csv')\n",
+    "\n",
+    "df3 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11277029.csv')\n",
+    "df4 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11277027.csv')\n",
+    "df5 = pd.read_csv('data/full/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11277033.csv')\n",
+    "\n",
+    "df_big_uniform = pd.read_csv('data/full/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11279368.csv')\n",
+    "df_big_sphere = pd.read_csv('data/full/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11291765.csv')\n",
+    "df_big_sphere_1 = pd.read_csv('data/full/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=sphere_11293393.csv')\n",
+    "\n",
+    "# df0 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_11079232/weak_fft_n=4096000000_p=512_11079232.csv')\n",
+    "# df1 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=uniform_11262497.csv')\n",
+    "# df2 = pd.read_csv('data/uniform/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=uniform_11262498.csv')\n",
+    "\n",
+    "# df3 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11259998.csv')\n",
+    "# df4 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260013.csv')\n",
+    "# df5 = pd.read_csv('data/sphere/deeper_local_1/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260022.csv')\n",
+    "\n",
+    "# df3 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260141.csv')\n",
+    "# df4 = pd.read_csv('data/sphere/deeper_local_3/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260230.csv')\n",
+    "\n",
+    "# df3 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11260719.csv')\n",
+    "# df4 = pd.read_csv('data/sphere/deeper_local_4/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11260722.csv')\n",
+    "\n",
+    "# df3 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532/weak_fft_n=4096000000_p=512_points_per_rank=250000_distribution=sphere_11261532.csv')\n",
+    "# df4 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537/weak_fft_n=4096000000_p=512_points_per_rank=500000_distribution=sphere_11261537.csv')\n",
+    "# # df5 = pd.read_csv('data/sphere/deeper_local_5/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024/weak_fft_n=4096000000_p=512_points_per_rank=4000000_distribution=sphere_11260024.csv')\n",
+    "\n",
+    "# df_big = pd.read_csv('data/uniform/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163/weak_fft_n=32768000000_p=512_points_per_rank=32000000_distribution=uniform_11263163.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7d951bbf-72ad-4c6c-a696-1ee3d512d1d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['experiment_id', 'rank', 'runtime', 'p2m', 'm2m', 'l2l', 'm2l', 'p2p',\n",
+       "       'source_tree', 'target_tree', 'source_domain', 'target_domain',\n",
+       "       'layout', 'ghost_exchange_v', 'ghost_exchange_v_runtime',\n",
+       "       'ghost_exchange_u', 'gather_global_fmm', 'scatter_global_fmm',\n",
+       "       'source_to_target_data', 'source_data', 'target_data', 'global_fmm',\n",
+       "       'ghost_fmm_v', 'ghost_fmm_u', 'displacement_map', 'metadata_creation',\n",
+       "       'expansion_order', 'n_points', 'local_depth', 'global_depth',\n",
+       "       'block_size', 'n_threads', 'n_samples', 'source_local_trees_per_rank',\n",
+       "       'target_local_trees_per_rank', 'all_to_all', 'all_to_all_v',\n",
+       "       'neighbour_all_to_all', 'neighbour_all_to_all_v',\n",
+       "       'neighbour_all_to_all_v_runtime', 'gather', 'scatter', 'gather_v',\n",
+       "       'scatter_v', 'gather_v_runtime', 'scatter_v_runtime', 'all_gather',\n",
+       "       'all_gather_v', 'dist_graph_create', 'sort', 'tree_all_to_all',\n",
+       "       'tree_all_to_all_v', 'tree_neighbour_all_to_all',\n",
+       "       'tree_neighbour_all_to_all_v', 'tree_neighbour_all_to_all_v_runtime',\n",
+       "       'tree_gather', 'tree_scatter', 'tree_gather_v', 'tree_scatter_v',\n",
+       "       'tree_gather_v_runtime', 'tree_scatter_v_runtime', 'tree_all_gather',\n",
+       "       'tree_all_gather_v', 'tree_dist_graph_create', 'tree_sort'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df0.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02ff3f27-a583-4cc7-aca2-b3a3a69494c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "setup_cols = [\n",
+    "    'experiment_id', \n",
+    "    'dist_graph_create',\n",
+    "    'neighbour_all_to_all_v', \n",
+    "    'tree_all_to_all',\n",
+    "    'source_tree',\n",
+    "    'target_tree',\n",
+    "    'tree_all_gather',\n",
+    "    'n_points'\n",
+    "]\n",
+    "\n",
+    "runtime_cols = [\n",
+    "    'experiment_id', \n",
+    "    'runtime', \n",
+    "    'p2p', \n",
+    "    'm2l', \n",
+    "    'scatter_v_runtime',\n",
+    "    'gather_v_runtime',\n",
+    "    'neighbour_all_to_all_v_runtime', \n",
+    "    'n_points'\n",
+    "]\n",
+    "\n",
+    "fmm_op_cols = [\n",
+    "    # 'm2m',\n",
+    "    'm2l',\n",
+    "    'p2p',\n",
+    "    # 'l2l',\n",
+    "    # 'l2p'\n",
+    "]\n",
+    "tree_cols = [\n",
+    "    'source_tree',\n",
+    "    'source_domain',\n",
+    "    'layout',\n",
+    "    'ghost_exchange_v',\n",
+    "    'ghost_exchange_u',\n",
+    "    'tree_all_to_all',\n",
+    "    'tree_all_to_all_v',\n",
+    "    'n_points',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "abf6dd51-27cc-43ba-9923-eeeb79b9d32b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stat = \"median\"\n",
+    "df = df_big_sphere\n",
+    "stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "n_ranks = stats.size()\n",
+    "n_points = stats.mean()['n_points'] * n_ranks\n",
+    "\n",
+    "if stat == \"max\":\n",
+    "    tmp = stats.max()\n",
+    "elif stat == \"mean\":\n",
+    "    tmp = stats.mean()\n",
+    "elif stat == \"median\":\n",
+    "    tmp = stats.median()\n",
+    "else: pass\n",
+    "    # return ValueError(\"invalid stat\")\n",
+    "    \n",
+    "tmp = tmp.set_index(n_ranks)\n",
+    "    \n",
+    "# Total communication time per experiment\n",
+    "tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "    \n",
+    "x = tmp.index.values.astype(float)\n",
+    "y = tmp['comm_time'].values\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "53266195-0d63-4dde-804d-cf89ca5b4b80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>p2p</th>\n",
+       "      <th>m2l</th>\n",
+       "      <th>scatter_v_runtime</th>\n",
+       "      <th>gather_v_runtime</th>\n",
+       "      <th>neighbour_all_to_all_v_runtime</th>\n",
+       "      <th>n_points</th>\n",
+       "      <th>comm_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10733.5</td>\n",
+       "      <td>7245.5</td>\n",
+       "      <td>3180.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>32000000.0</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10407.5</td>\n",
+       "      <td>3785.0</td>\n",
+       "      <td>6183.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>32000000.0</td>\n",
+       "      <td>145.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10487.0</td>\n",
+       "      <td>6938.0</td>\n",
+       "      <td>3033.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>151.5</td>\n",
+       "      <td>150.5</td>\n",
+       "      <td>32000000.0</td>\n",
+       "      <td>310.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1024</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14782.0</td>\n",
+       "      <td>4402.5</td>\n",
+       "      <td>6126.5</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>1584.0</td>\n",
+       "      <td>657.5</td>\n",
+       "      <td>32000000.0</td>\n",
+       "      <td>2362.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      experiment_id  runtime     p2p     m2l  scatter_v_runtime  \\\n",
+       "2               0.0  10733.5  7245.5  3180.0                3.0   \n",
+       "16              1.0  10407.5  3785.0  6183.0               18.0   \n",
+       "128             2.0  10487.0  6938.0  3033.5                8.0   \n",
+       "1024            3.0  14782.0  4402.5  6126.5              121.0   \n",
+       "\n",
+       "      gather_v_runtime  neighbour_all_to_all_v_runtime    n_points  comm_time  \n",
+       "2                  0.0                            71.0  32000000.0       74.0  \n",
+       "16                 0.0                           127.0  32000000.0      145.0  \n",
+       "128              151.5                           150.5  32000000.0      310.0  \n",
+       "1024            1584.0                           657.5  32000000.0     2362.5  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "754504a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def summarize_by_stat(stats, stat):\n",
+    "    if stat == \"max\":\n",
+    "        return stats.max()\n",
+    "    elif stat == \"mean\":\n",
+    "        return stats.mean()\n",
+    "    elif stat == \"median\":\n",
+    "        return stats.median()\n",
+    "    elif stat in [\"p90\", \"p95\", \"p99\"]:\n",
+    "        q = float(stat[1:]) / 100.0\n",
+    "        return stats.quantile(q)\n",
+    "    else:\n",
+    "        raise ValueError(f\"Invalid stat: {stat}\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "def weak_scaling(dfs, names=None, title=\"Weak Scaling Performance\", plot_err=True, stat='mean', y_scale='linear'):\n",
+    "    \"\"\"\n",
+    "    Plots weak scaling results (runtime + efficiency) for one or more DataFrames.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dfs : list of pandas.DataFrame or pandas.DataFrame\n",
+    "        One or more DataFrames with columns including:\n",
+    "        ['experiment_id', 'runtime', 'p2m', 'm2l', 'm2m', 'l2l', 'source_tree', 'n_points', 'source_local_trees_per_rank']\n",
+    "    names : list of str, optional\n",
+    "        Names/labels for each DataFrame. Must be same length as dfs.\n",
+    "    title : str\n",
+    "        Figure title.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Handle single DataFrame\n",
+    "    if not isinstance(dfs, (list, tuple)):\n",
+    "        dfs = [dfs]\n",
+    "    if names is None:\n",
+    "        names = [f\"Run {i+1}\" for i in range(len(dfs))]\n",
+    "\n",
+    "    sns.set_context(\"talk\", font_scale=1.1)\n",
+    "    sns.set_style(\"whitegrid\")\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "    ax_runtime, ax_eff = axes\n",
+    "\n",
+    "    markers = ['o', 's', '^', 'D', 'v', 'P', '*']\n",
+    "    colors = sns.color_palette(\"deep\", len(dfs))\n",
+    "\n",
+    "    runtimes = []\n",
+    "    \n",
+    "    for df, label, color, marker in zip(dfs, names, colors, markers):\n",
+    "        # Aggregate per experiment\n",
+    "        stats = df.groupby('experiment_id')[['runtime', 'p2m', 'm2l', 'm2m', 'l2l',\n",
+    "                                             'source_tree', 'n_points', 'source_local_trees_per_rank']]\n",
+    "\n",
+    "        # if stat == \"mean\":\n",
+    "        #     runtime = stats.mean()['runtime']\n",
+    "        # elif stat == \"median\":\n",
+    "        #     runtime = stats.median()['runtime']\n",
+    "        # elif stat == \"max\":\n",
+    "        #     runtime = stats.max()['runtime']\n",
+    "        # else:\n",
+    "        #     raise ValueError(\"Only supports 'mean' or 'median'\")\n",
+    "        runtime = summarize_by_stat(stats, stat)['runtime']\n",
+    "\n",
+    "        \n",
+    "        runtime_err = stats.std()['runtime']\n",
+    "        n_ranks = stats.size()\n",
+    "        n_points = stats.mean()['n_points'] * n_ranks  # total DoFs = per-rank * ranks\n",
+    "\n",
+    "        # print(runtime)\n",
+    "        # Reference runtime\n",
+    "        T_ref = runtime.iloc[0]\n",
+    "        efficiency = (T_ref / runtime) * 100\n",
+    "\n",
+    "        if plot_err:\n",
+    "            # Runtime plot\n",
+    "            ax_runtime.errorbar(\n",
+    "                n_points, runtime, yerr=runtime_err,\n",
+    "                fmt=marker+'-', color=color, capsize=4, label=label,\n",
+    "                linewidth=2, markersize=7\n",
+    "            )\n",
+    "        else:\n",
+    "            ax_runtime.plot(\n",
+    "                n_points, runtime, marker=marker,\n",
+    "                color=color, label=label,\n",
+    "                linewidth=2, markersize=7\n",
+    "            )\n",
+    "\n",
+    "        \n",
+    "        runtimes.append(runtime)\n",
+    "\n",
+    "        # Efficiency plot\n",
+    "        ax_eff.plot(\n",
+    "            n_ranks, efficiency, marker=marker, linestyle='-', color=color,\n",
+    "            label=label, linewidth=2, markersize=7\n",
+    "        )\n",
+    "\n",
+    "        # print(\"efficiency\", efficiency)\n",
+    "        \n",
+    "    # --- Runtime plot formatting ---\n",
+    "    if y_scale == \"log\":\n",
+    "        ax_runtime.set_yscale('log')\n",
+    "\n",
+    "    min_runtime = runtime.min()\n",
+    "    for runtime in runtimes:\n",
+    "        if runtime.min() < min_runtime:\n",
+    "            min_runtime = runtime.min()\n",
+    "\n",
+    "    max_runtime = runtime.max()\n",
+    "    for runtime in runtimes:\n",
+    "        if runtime.max() > max_runtime:\n",
+    "            max_runtime = runtime.max()\n",
+    "\n",
+    "    ax_runtime.set_xscale('log')\n",
+    "    ax_runtime.set_xlabel('Total Number of Points (DoFs)', fontsize=13)\n",
+    "    ax_runtime.set_ylabel('Runtime (ms)', fontsize=13)\n",
+    "    ax_runtime.set_title('Weak Scaling (Runtime)', fontsize=14)\n",
+    "    ax_runtime.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n",
+    "    ax_runtime.legend(fontsize=11)\n",
+    "    ax_runtime.set_ylim(min_runtime-max_runtime*0.15, max_runtime+max_runtime*0.15)\n",
+    "\n",
+    "    # --- Efficiency plot formatting ---\n",
+    "    ax_eff.set_xscale('log', base=2)\n",
+    "    ax_eff.set_ylim(0, 120)\n",
+    "    ax_eff.set_xlabel('Number of Ranks', fontsize=13)\n",
+    "    ax_eff.set_ylabel('Parallel Efficiency (%)', fontsize=13)\n",
+    "    ax_eff.set_title('Parallel Efficiency', fontsize=14)\n",
+    "    ax_eff.grid(True, which=\"both\", ls=\"--\", lw=0.5)\n",
+    "    ax_eff.legend(fontsize=11)\n",
+    "\n",
+    "    # --- Global figure adjustments ---\n",
+    "    fig.suptitle(title, fontsize=16, y=1.03)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n",
+    "    \n",
+    "    stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "    n_ranks = stats.size()\n",
+    "    n_points = stats.mean()['n_points'] * n_ranks\n",
+    "\n",
+    "    tmp = summarize_by_stat(stats, stat)\n",
+    "    tmp = tmp.set_index(n_ranks)\n",
+    "    \n",
+    "    # Total communication time per experiment\n",
+    "    tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "    \n",
+    "    x = tmp.index.values.astype(float)\n",
+    "    y = tmp['comm_time'].values\n",
+    "    \n",
+    "    # log_curve\n",
+    "    log_curve = y[0] * np.emath.logn(8, x / x[0]) + y[0]\n",
+    "\n",
+    "    # linear curve\n",
+    "    linear_curve = y[0] * (x / x[0])\n",
+    "\n",
+    "    # log_curve = y[-1] * (np.emath.logn(8, x) / np.emath.logn(8, x[-1]))\n",
+    "    # linear_curve = y[-1] * (x / x[-1])\n",
+    "\n",
+    "    \n",
+    "    # Stacked bar for measured communication time\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].rename(columns={\n",
+    "            'scatter_v_runtime': 'ScatterV',\n",
+    "            'gather_v_runtime': 'GatherV',\n",
+    "            'neighbour_all_to_all_v_runtime': 'Neighbor AllToAllV'\n",
+    "\n",
+    "        }).plot(\n",
+    "        kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\"\n",
+    "    )\n",
+    "        \n",
+    "    ax.set_xticks(range(len(x)))\n",
+    "    ax.set_xticklabels([int(v) for v in x], rotation=0)\n",
+    "    ax.set_xlabel(\"Number of MPI Ranks\")\n",
+    "    ax.set_ylabel(\"Wall Time (ms)\")\n",
+    "    \n",
+    "    # ax.set_yscale('log')\n",
+    "    if y_scale == \"log\":\n",
+    "        ax.set_yscale('log')\n",
+    "\n",
+    "    # Overlay both theoretical trends, starting from first value\n",
+    "    # ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n",
+    "    # ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n",
+    "    \n",
+    "    # Style cleanupr\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def communication_trend(df, stat='max', y_scale=\"linear\"):\n",
+    "    \n",
+    "    stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "    n_ranks = stats.size()\n",
+    "    n_points = stats.mean()['n_points'] * n_ranks\n",
+    "    tmp = summarize_by_stat(stats, stat)\n",
+    "    \n",
+    "    tmp = tmp.set_index(n_ranks)\n",
+    "    \n",
+    "    # Total communication time per experiment\n",
+    "    tmp['comm_time'] = tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "    tmp['comp_time'] = tmp['runtime']-tmp['comm_time']\n",
+    "\n",
+    "    x = tmp.index.values.astype(float)\n",
+    "    y = tmp['comm_time'].values\n",
+    "\n",
+    "    print(tmp['comm_time'], tmp['comp_time'])\n",
+    "    # log_curve\n",
+    "    log_curve = y[0] * np.emath.logn(8, x / x[0]) + y[0]\n",
+    "\n",
+    "    # linear curve\n",
+    "    linear_curve = y[0] * (x / x[0])\n",
+    "\n",
+    "    # log_curve = y[-1] * (np.emath.logn(8, x) / np.emath.logn(8, x[-1]))\n",
+    "    # linear_curve = y[-1] * (x / x[-1])\n",
+    "\n",
+    "    \n",
+    "    # Stacked bar for measured communication time\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    tmp[['comm_time', 'comp_time']] \\\n",
+    "        .rename(columns={\n",
+    "            'comm_time': 'Communication Time',\n",
+    "            'comp_time': 'Computation Time'\n",
+    "        }) \\\n",
+    "        .plot(kind='bar', stacked=True, ax=ax, colormap='tab20')\n",
+    "\n",
+    "    ax.set_xticks(range(len(x)))\n",
+    "    ax.set_xticklabels([int(v) for v in x], rotation=0)\n",
+    "    ax.set_xlabel(\"Number of MPI Ranks\")\n",
+    "    ax.set_ylabel(\"Wall Time (ms)\")\n",
+    "    \n",
+    "    # ax.set_yscale('log')\n",
+    "    if y_scale == \"log\":\n",
+    "        ax.set_yscale('log')\n",
+    "\n",
+    "    # Overlay both theoretical trends, starting from first value\n",
+    "    ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n",
+    "    ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n",
+    "    \n",
+    "    # Style cleanupr\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "    plt.show()\n",
+    "\n",
+    "def summarize_by_stat(stats, stat):\n",
+    "    \"\"\"Return aggregation by requested statistic.\"\"\"\n",
+    "    if stat == \"max\":\n",
+    "        return stats.max()\n",
+    "    elif stat == \"mean\":\n",
+    "        return stats.mean()\n",
+    "    elif stat == \"median\":\n",
+    "        return stats.median()\n",
+    "    elif stat.startswith(\"p\") and stat[1:].isdigit():\n",
+    "        q = float(stat[1:]) / 100.0\n",
+    "        return stats.quantile(q)\n",
+    "    else:\n",
+    "        raise ValueError(f\"Invalid stat: {stat}\")\n",
+    "\n",
+    "\n",
+    "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n",
+    "    stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "    n_ranks = stats.size()\n",
+    "    n_points = stats.mean()['n_points'] * n_ranks\n",
+    "\n",
+    "    tmp = summarize_by_stat(stats, stat)\n",
+    "    tmp = tmp.set_index(n_ranks).sort_index()\n",
+    "\n",
+    "    # Total communication time per experiment\n",
+    "    tmp['comm_time'] = tmp[['scatter_v_runtime',\n",
+    "                            'gather_v_runtime',\n",
+    "                            'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "\n",
+    "    x = tmp.index.values.astype(float)\n",
+    "    y = tmp['comm_time'].values\n",
+    "\n",
+    "    # Theoretical scaling curves (normalized to first point)\n",
+    "    log_curve = y[0] * np.log2(x / x[0] + 1)\n",
+    "    linear_curve = y[0] * (x / x[0])\n",
+    "\n",
+    "    # --- Plot ---\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']] \\\n",
+    "        .rename(columns={\n",
+    "            'scatter_v_runtime': 'ScatterV',\n",
+    "            'gather_v_runtime': 'GatherV',\n",
+    "            'neighbour_all_to_all_v_runtime': 'Neighbor AllToAllV'\n",
+    "        }).plot(kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\")\n",
+    "\n",
+    "    ax.set_xticks(range(len(x)))\n",
+    "    ax.set_xticklabels([int(v) for v in x], rotation=0)\n",
+    "    ax.set_xlabel(\"Number of MPI Ranks\")\n",
+    "    ax.set_ylabel(\"Wall Time (ms)\")\n",
+    "    if y_scale == \"log\":\n",
+    "        ax.set_yscale('log')\n",
+    "\n",
+    "    # Overlay theoretical curves\n",
+    "    ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n",
+    "    ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n",
+    "\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "def summarize_by_stat(stats, stat):\n",
+    "    \"\"\"Return aggregation by requested statistic.\"\"\"\n",
+    "    if stat == \"max\":\n",
+    "        return stats.max()\n",
+    "    elif stat == \"mean\":\n",
+    "        return stats.mean()\n",
+    "    elif stat == \"median\":\n",
+    "        return stats.median()\n",
+    "    elif stat.startswith(\"p\") and stat[1:].isdigit():\n",
+    "        q = float(stat[1:]) / 100.0\n",
+    "        return stats.quantile(q)\n",
+    "    else:\n",
+    "        raise ValueError(f\"Invalid stat: {stat}\")\n",
+    "\n",
+    "\n",
+    "def breakdown_communication_times(df, stat='max', y_scale=\"linear\"):\n",
+    "    stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "    n_ranks = stats.size()\n",
+    "    n_points = stats.mean()['n_points'] * n_ranks\n",
+    "\n",
+    "    tmp = summarize_by_stat(stats, stat)\n",
+    "    tmp = tmp.set_index(n_ranks).sort_index()\n",
+    "\n",
+    "    # Total communication time per experiment\n",
+    "    tmp['comm_time'] = tmp[['scatter_v_runtime',\n",
+    "                            'gather_v_runtime',\n",
+    "                            'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "\n",
+    "    x = tmp.index.values.astype(float)\n",
+    "    y = tmp['comm_time'].values\n",
+    "\n",
+    "    # Theoretical scaling curves (normalized to first point)\n",
+    "    log_curve = y[0] * np.log2(x / x[0] + 1)\n",
+    "    linear_curve = y[0] * (x / x[0])\n",
+    "\n",
+    "    # --- Plot ---\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    tmp[['scatter_v_runtime', 'gather_v_runtime', 'neighbour_all_to_all_v_runtime']] \\\n",
+    "        .rename(columns={\n",
+    "            'scatter_v_runtime': 'ScatterV',\n",
+    "            'gather_v_runtime': 'GatherV',\n",
+    "            'neighbour_all_to_all_v_runtime': 'Neighbor AllToAllV'\n",
+    "        }).plot(kind=\"bar\", stacked=True, ax=ax, colormap=\"tab20\")\n",
+    "\n",
+    "    ax.set_xticks(range(len(x)))\n",
+    "    ax.set_xticklabels([int(v) for v in x], rotation=0)\n",
+    "    ax.set_xlabel(\"Number of MPI Ranks\")\n",
+    "    ax.set_ylabel(\"Wall Time (ms)\")\n",
+    "    if y_scale == \"log\":\n",
+    "        ax.set_yscale('log')\n",
+    "\n",
+    "    # Overlay theoretical curves\n",
+    "    # ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n",
+    "    # ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n",
+    "\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "    # plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def communication_trend(df, stat='max', y_scale=\"linear\"):\n",
+    "    stats = df.groupby('experiment_id')[runtime_cols]\n",
+    "    n_ranks = stats.size()\n",
+    "    n_points = stats.mean()['n_points'] * n_ranks\n",
+    "\n",
+    "    tmp = summarize_by_stat(stats, stat)\n",
+    "    tmp = tmp.set_index(n_ranks).sort_index()\n",
+    "\n",
+    "    # Total communication and computation times\n",
+    "    tmp['comm_time'] = tmp[['scatter_v_runtime',\n",
+    "                            'gather_v_runtime',\n",
+    "                            'neighbour_all_to_all_v_runtime']].sum(axis=1)\n",
+    "    tmp['comp_time'] = tmp['runtime'] - tmp['comm_time']\n",
+    "\n",
+    "    x = tmp.index.values.astype(float)\n",
+    "    y = tmp['comm_time'].values\n",
+    "\n",
+    "    # Theoretical scaling curves (normalized)\n",
+    "    log_curve = y[0] * np.log2(x / x[0] + 1)\n",
+    "    linear_curve = y[0] * (x / x[0])\n",
+    "\n",
+    "    print(tmp[['comm_time', 'comp_time']])\n",
+    "\n",
+    "    # --- Plot ---\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    tmp[['comm_time', 'comp_time']] \\\n",
+    "        .rename(columns={\n",
+    "            'comm_time': 'Communication Time',\n",
+    "            'comp_time': 'Computation Time'\n",
+    "        }).plot(kind='bar', stacked=True, ax=ax, colormap='tab20')\n",
+    "\n",
+    "    ax.set_xticks(range(len(x)))\n",
+    "    ax.set_xticklabels([int(v) for v in x], rotation=0)\n",
+    "    ax.set_xlabel(\"Number of MPI Ranks\")\n",
+    "    ax.set_ylabel(\"Wall Time (ms)\")\n",
+    "    if y_scale == \"log\":\n",
+    "        ax.set_yscale('log')\n",
+    "\n",
+    "    # Overlay theoretical trends\n",
+    "    ax.plot(range(len(x)), log_curve, color=\"red\", marker=\"o\", linestyle=\"--\", label=\"~ log(p) scaling\")\n",
+    "    ax.plot(range(len(x)), linear_curve, color=\"green\", marker=\"s\", linestyle=\"--\", label=\"~ O(p) scaling\")\n",
+    "\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "    ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "    # plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a787dad-8eff-4e58-a301-84ada4b956f2",
+   "metadata": {},
+   "source": [
+    "# Plot Weak Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "67d9abc3-f9bb-41ec-9778-9912fecbc924",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weak_scaling([df0, df1, df2], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Uniform)\", plot_err=False, stat='p99')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "b5461bbe-e6c7-4fec-a5e4-1d637566a545",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weak_scaling([df3, df4, df5], names=[\"CCX\", \"CCD\", \"Socket\"], title=\"Weak Scaling up to 4e9 Points on 512 nodes of ARCHER2 (Sphere)\", plot_err=False, stat='p99')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "10dc2192-ec20-4c00-bbe1-34697ecc73f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/XL2PdgHG5f/+7//UGH/55Zd12pk4cWLN4MGDVfmvvvpqnfF75plnEu/dcccd6r0RI0as8x5eDz/8cKK/6M9ZZ52l3j/zzDMT5RcuXFizzTbb1AwYMKDm7bffrmMDwTjj9fzzz9c7dqn6GPDZZ5+pz4YNG7aOzvDaY489ambNmpX47L333qvZeOONawYOHFgzc+bMer8b/UM7f/vb3+q8/+6776r3t91225rvvvsu8T50APvBZ7CTdHpNx5gxY2pOOeWUmh122EG1cfjhh2v1sz5gO0GfAioqKmr++Mc/qvdHjhxZp6+QL7hWFyxYkLHt4Dr79ddf1/kssPnvv/8+Zd3rr78+YVP1Edgg7CEdL774oioDu8McACBXcM1edtllSu6Ab775RtkpPoPMJq+BdGODaxPvHX300XX6gnHebbfd1Gew6/oIxu6www6ro6P58+fXHHLIIeozlEm+VoK5NTxf/vvf/1bvb7fddlo2e9xxxyXaGjJkSM1f/vKXmkMPPVTZDN7DNV5VVRX5us7ETz/9pPS74YYb1owfP77e8sH3oM7rr79eZ87HvIHP8DcoAG1utNFGNZtvvnnN6NGjE+9Dz3feeacqv9NOO9WsXLky8f7++++v3r///vsT5aHbCy+8MOVc9+mnn6r+o52wDJg/gjrhOeeuu+5S791yyy11ZIP9YszR16A/6YhyHYCg/5WVlTU6jBo1SpWHjYTHGn/zIPPUqVPXqRPYJq6XZDC+ixcvVusCzIfBPBa2r4svvli9D/1EIfw3A+Oe6YVrJFmf4frZkqvs2c77mTj//PPrrMV0+5lMMO+F57Fs58/ktVemeSOXOR42iTIBwRor6Os+++yj5tTkNSRep556auK6w/VxxBFHqPcffPDBlOMydOjQtH8rCSGkMcMt/IQQkgJsNUPEEyIcEAUVgOgAREkhegTgaT7K4ml+kDMNEWqohzJBRBUi5bB9EREniMIIR8YguiGIDH3ooYcS7yOiB5EA2L4ajvBCNFYQ7ZpuiyK2tyHiC9FXiLDCFjJd0DdElWH7FyIWsJUe0U/IA4noHEQ/QIbkg1UQGYbcr4g4wani4agu9AHjgW3giIyYP3++it5FRC62rIdBVAUibzLJpwOiIdF+MHboD6I+QHgrLLbKIrINUVPhiCPoFXnoWrVqJXFw/fXX14lSwzZc5OKELnXSB6QDkcTB4RDhbcywTeR5hP3BThAdmA2IbsGJ1UH6CmzDTpWmIRsQWY3Ib+gK/Q1AFBAi1zAmiJQLXw+I3kGkNq65XA6dCrajJx8aF77ugkgfXRDtiGsh/EKaBkR1I70A5ICcwTZvlIcuEGGLLdDhyG9EEQbzBCKHTV4D6Qi20mLeCvcFEfSYA2Cz9UUuI/oUUZL4bkQxhnO1tm/fXkWyIjIaJ44njy2iRhEJGp4vEWUF28X8k+lwpwDMSeD0009XUWuImkRkO17IbRpEUJoGfcPcjb8LmDeziVqGTeNgsLDtBZF2Yb2h34iixNZ6/K0IwFhjRwPmXES5BZGZ2FGB6FTYBf4+BEC3mAugj2Rga4iYRN7j5LyV0A0imBEFjr9xIJgPkvPGwn6xvRh/R+o78M70dZCOYF4N7x7BWCOCFTJnyvGNlBeIKAy/BgwYoMYccxT+HiLyHRHA4bQmwfikGutsgV4zveq7PpL7n/wKonJNyZ7tvJ8JpFICpnYD2Jg/Tds2IszD0eTJkfOYb8J2hTVcANoN/rZhXg1S1eDvaiqCaFlE2xJCCPkdOlAJIUQzDypuEOEchcMvyAEZOFPhgAu2igbb97GtMLxoxo00tq+n2lYIpyQWw9j2Fjhisc0Ri+hgayQItkLCkRs+pCAMHFm4eUZb2HaMRXq2YIs38nPBQYabZOTpChwfuCnD9jXktwvnYQ1uaLBlM9WWXmzfhyMF7eDmGnlOsb0sADescJjCWRs4TlPJpwtuNpLB1nwQzt0I5zdItV0TDt+wY8IWGI9UedyCPmXr3AzbAuwXtpVKPtxIwZkX5UbpwAMPVKdew0aQhxSOGeRyjXrDFU7pgG33gRM9LH/4vTCBjnK52Qtu9Ovbjp7NIVLYqpns1MC2d8gIJw0eVMDhkHwNYRt9+JCuAORpRD8x7jrXhu41kA48RMINPlJwwOEGpyMccsH8CMdTpgOWAA6FWbVqlXJGpHK24j3Mi3C0Jud1hpMhOW8t5tzgYD4dGeAgRR5AHHwXdubA4QOnIMB8ZhL8rUDOT+QaxUOLbOdgXb0Ff2swP6cCJ7yH7SoonypfKxyHQWqaAPwtCnKjpsoDC4cM/h6GywWnoSN/LVIt4OEHtmcHcwb6VN/WetPXQSrwNxu2gZQkwRwYgIdpgYM1XfuQAdu08cK1HH4QCBnhFETqg2RHaSBPLodUBSAfc6ZX8KA3HUH/073wt9yk7NnO++lAWVxjmJt0tvtHJdf507Rt15ceJzmlQDBPYr0aPKQLCFLxpLNvpAIC6fIoE0JIY4U5UAkhJA1wECDnFBxQiP4JTpNNvsmE8xOJ/+G8QXRAcJMaLod8VQB5QDMtgrEoh5M0cFbCAYPILDhDcTOOz8JOnlTOHDg44WDF4hzRs8jviUOfooCbE+TJwiuIusANMRw/yKsFJy9+h1MjiKxJPlE4Exg3HLSAmz3IGpyiXJ8TS4ew4zkgcKCEI6CCG4R0jqDgRsImcBSlIohI1TlUI52TAGOKGyk4gzPJl3wQWn0ENgp9n3baaYkIQ0T4ZTp0JxWIpMMBZ9ALHF2IqAsT6AgRj3ilI3DuRSFw6sCRl4rg/fqcP2EQeahzcFtAoOd0NocbYYw7dIVI7vryaupeA+mAbhGFjYNLcK0GB5wgyhwOeUT+1ufACGRKl18zkBcRzcl2npxvNyBwPOjIALsP52EMgyg5jAci6jCfBQ6HXMBDNlwPaBOOYewqSHXomgm91Te2ydd2UD5wAqUrH54/AkdRfc644BqFAw05VRH5jhyKeEFfcMbjoRsOxkoX5W3rOkgFHmbAeYS+pDrFHfMZ2oYzEI7CZGAreAgYBn+n8aABDxyQ/xV2kEyQ9zT54LiGILn/ukSVPdt5Px2wSzig080PwbWi87ArcGQHOwxMzp+mbTvTIYip+huso1KNU31rrGDnjU6UPyGENCboQCWEkDTAGYqbb2xHA0HUZ7IDNfgdDlTcAOBGAo6HcIL/YLGNiFBEPumAqCls80UUIaK04NDt06ePimjCjQG2AqcDkWHoFyI6rrzyShXVkc6BFgY3lHAA4GeqiEg4IvBCJBG2h8FhhYgKfFfySdqZwHhgS+3777+vokgQnYY2MT74XkSrZntic1QCp226G6JsIg4zkemGK902x+C7U0Wq6JDNDWTyAT7Zgu2ecKAGkdg64LuxpRcRgBgDbCPGYVrpxg6O2XTOHxDeHp4tcARiuzduXFNdo4ETqr6DX3JBR1/BWOSqL12wlRwRvojWGz16tJrn8CAFLzjJRo4cmfYwlVxt0MSDlExg7oHTAY4KRMnmCqKLsY0WEZeYE++4445IJ6jryl3f2CaPa33tJs9D4frpolxTPQTC3y1sdcbDNTx4xENIPFjEK9iJkMlZHcd1EBzOhBQ1QVRgKrCNP5UDNRVI6YOT0fG3Gakp8Hc7iAIOCNKoIMKwPjAOI0aMUI54XIPZOuLjREf2bOf9qH+zg2sOh+LVR5A2JK5UPbnYdn3Xb9R1QqY+ZLOuI4SQxgAdqIQQkgYs7BF1g6hRRDxgGzGcN8kRpHCUInoR208nT56s8nsiyiZM4HSBY0Yn6gOLemz7xGIbJzInR5DixjQd2DaH7ZOIoEF0J25gEUWGk5rrA3Ki77gBwc1uOsce5EWKAkTnBFGxgWMryIWXDE6YxdhgTLFtHs5TjCXypCVHsQVbPuMAzu4pU6aoKOFUkWrZbGELbnBSbc/EjXo60o1ZELkcJcIqiFiBkwg6wpimcqIj8hfUt90SjnVEQ6MvqaKLglxuultTcROMk4YRIY2oTjhfg7xsyQTXDxwZOBndBtA9bBJ5XFNtcQ5y3iG6yhbBNZQu9y90CGcfrsv6opFMAucCHnDgBeAkh2MHD5Vuu+22RJ7dTDIhIjMdgQ0in6ZJENmOviEC66KLLko5z2I8Ybu55qRERCPyDMLhgC3geHBl0qGRbmxhKxjbVHNXYEeBbME8G8wrySRHAAfzB2SCwysbZyWiYpGeAi9c65j/kTcXcy0cqHiAlkmucP9NXwfjx49XL4wHUpAk55IMHpggQhmpCbCTAw/3dMDfaugf+ZjhGERKg/BDH8xx+D78fUV0Xya7g2MXW+EhJ641lx2oOrJnO++nAw898LcWf1Nhm8nXWRDdmenvHoANLVu2TI2vzVQAPszxySAiP10ULiGENGaYA5UQQjIQ5DHFQUNYUCZHn4a38WOxju1rwe9hgrxwuGlKlTsLeQKRhw1b4uE0hbMGN/dw1qTafh9Ew6aKZsDNRHBDiPyl2KL4zDPPJHLU1be4x80Hvru+g4uQUgAEN+5BxCqi1FKBqBQcooObgyCqF87a5BsXfHfwedRtclFy3cJRnmrbNqLKdAkiX1Jte8MW5XTgBj1wIqVylOvkYU0VnQLnB7bOYhxTOd1x84kbWVDftns4RuH4wJbkVJE9gd4RTawDDgvBdyNqFCkhMt1EB9dPsIU8GdSHcxUPG6ISOE2D8QgzdepU5UCGs72+PHS5EMiJaM9UkT94YIFrHjkHUzl9TANd4+Cul156qc77m2yyifz973/XesCAiDs4fhDdm8rGMbb4DM4URNqZBNtyMY/95z//SbllOpArfOBfFDB3BLsFMIfjAZZt52nYXmAX6fJLhq/tYK6DfSU/6MDvwd+VAIwJoosxfwQpbMLAFo855hiVyiHIXwvnGL4v7DDHPITrKziAp75UG7avgyD6FNHV6erjoU3w9x7zXjbAFlAfDr7klCNwmOJBBP62IGIzHdgFgocUQV7MXKLr4yST7NnO++nAmgYP8mADqVLP4IEP5ml8jpzq6YB9BX+zdHbo5OMcn47gYQp2PRFCCPkdOlAJISQDwQ0nFvogkwM1uDHDojeoF4DtbDg0ADeOOLgkHGEJZxvew2mocNDAERbcLCFaB1GtAVhY42YODtFM+RrD34tIH9S79NJLtQ7cgAMAIGIVW+yC7XJhByeiqxDdBcdeEJGHNAGIxkVkD/Jghp27iFzElkWURRRuIB+cbuGbCDipcSBREP1Qn3wmgBMXN0+ImgluqADGClHAQV90ttUGzjU4ZsIRp3BeZzrRGWOFA1fCdoGbTPQJUSjpTkMOE+RwS47ePfbYY9VPRCGHt9dDr9AjHFg4yRhbMDMBvcGZArmQEzNsS3B4B7k+TzzxxDr1ENGJV/jBAewXJ3fDsYYt4PU5zuDowE05nMAoH7YtOG6wVRoOzlycm7hhhYzQVXC9B+MJ3eA7TzjhBKvbyuHIw8085gJE64WvPeSVhA5B4IgySWA/iMgKb8tGtOK9995bx1GBsQhSbIRPhU4FdDx06FDlhMNhMWFHJv6PNCP4TCc3Zrbg4BTMSxhHRIcG23UDu0GOa+gzUzRkfSCqHtGtcEAOHz5cHeAXF7ADRKohij/s/IR+MAfjgR0eUAUP4aArXOf4uwJbCh5Q4Seceami4oJDzmCPcHQHoA6ijxEliXrBaei4TrGTAe2H5wikSAge4tR3vdu8DtAnRAsDHICUiSDiGg9Qszk0CBHPcCQCzHPBQYUBePgARyraxTWRvAMBawLkBMVDN8z/wcMKH8gke7bzfibwYBAED1uTCXZJwK4R/ZwMUi0FDmocfhgXDTnHZ0MwrqlSORFCSGOGW/gJISQD2LYXbJOEYzSISE0G7+NGFtFYiM5KFS2CxTIWzYhSRVQjbh5w8w6HDSL6sFCF8xD07NlTRWYgsgk3cVh0w8GBG1g4NNAvRC3i5r0+4PSBswPlEaEXfEc68H2Q484771R9xk0ybrxxYwRnIhwP6C8iy8LpCDA+uCHBDTfqIocrHKZw0MGpiuhMRKECbPNDLjzc9CPyFmMGRxVy5eFGOxv5cgU3spATN7JwpODGDDqHnPh+bEVFNJVORBkcfXA2QWbIBacc2sCNMMYVKRVSgeg7nGKPg3kQoQJnFcYCDiXcAOocbhPkIMSWVNwQQg7cRMJ5Aht4+OGH1fZ3OFDQHhzacOhDPuhFJ9oFfcGNHXQL5wxsAPIhghoOFTiPkiOmMSYATklEpsFhDvsAGOcHH3ww7fdBDkQ444YbTlIcUgJHLRz7cJbCUYNxgsMIjuKoh6UBXIuImIJ8iCCEExzR2Lg+4dDAidTBYWq2QB9wDUEWONyhS5wEDTnRDzjpkGc5+dRwE8B+8LAGjpsgwhQPfWCTcHzhJ+YoXMdwViMCHVvuUb4+4CTF3AWbgY6CA4ngfINTE3aB6HQbYOv5UUcdpaKXIQPGE3MNHCiwWTg/w6eIZwscQdAP5gdE2KaTA2Nn2n5w/aH/sFc8uMADDkTmYS6BfuB8w7Udjq6Djf/5z39WqQ1gX3B8ojz+NmFsknNzJs8fsA1ct6gDeYNrM4jgDXJbv/nmm2qMg5yfmE9xHWGs63sgZPM6wIMptAPnetC3dMD+8XcPD40w52WTPgQR8XigCucdHlTBaRuMEdYHeKiIw6vQLqIO0Rc4u/FQAX8v4OiFLu+5556sDmbMBp1rDg9F8WAgG1LJjr8v2c77mUBkPJyxsIfgb0wYvAeHJKLosSbBmqJ3797KtjDPYX2B/yNnMeaFuGjIOV4X9AW7oPA3IZzLnxBCCB2ohBBSL4gmRaQIogbSObKQJwoOUdz4pItShaMOERg4eRZbK+FMwA0VFvW4oTz88MPrRGDhxheLfzhc4WiAgxY3fThsAYtvOCHhyMBNQqYbQWyfxE0MHENoDychB9FC6cANE24e0V98NyIX4ejAzSRu0nFzgoOqknOkol04Ce+77z4VXQoHMLbTIdIHzjXICuCYevbZZ5VzFmOGchhDOFIgG2780T5uxOF8sQ3kgTMI/caNPhy+cBrDQQxHAxyoOodMwLmECGHIBfnhtIHOEMkKedI5UNE2HBRwUGKrLJwS2LYJPdSnqwA4NuAEhmMaDnpETAVROIgIguMCjkfYCyJ7oQO0f/zxx2vnOYMsuBbgJIbOICNkRiQyblLTPWAIg8jlYHsgnDZ4pQNjFk4RAacmou0wRvhu2AlsBtuIc3GeBiACFVG/cAjh5h+Rs3iYAecUviOObdm4RmAnkBMR0RhnXHcYYzi+ktODmAJRtrjGYf+IGoOTARHsuNnHNQBnBZzVuMGHYwfjAUeHzqFamNdg33Aa4WEO8klj7oBuoWM4pmxtV8UDAmzjv//++9V4wnYCm4Ve6ztdvj6C1BV4MABnWCZsOOChB9gt5nboB45q6AfzKByfyfmT4RTB3AvHHOZXvOBcwu+Y51MdboT5Aw92kAoBD0swP8Kph79BeKgRPkAKfyOhZ8wRGGs8JMPfIMwd6A9sWCddgq3rIEhNU1/0KcBDS/y9fPrpp5WzK9v8y0ihg8MW4cyGHOGDHzEemM/wNxaywWGFaw/XCuZ8OPVgLza3lgeRuJnA9ZGtAzWV7HgAFWXeTwfGB/M/HOLYwZMqXzvSBcDRijkd64wgHQ8cuAcddJBaS8F5GTcNNcfrggdmeChpK984IYT4TEGNqeOFCSGEEA9BtC2iXuFoSXVjj5tAOP0QURU4gE2ByGY4qhF5lC53LCGEEELqggO2cAhVqoM2SXTgwMZOJzh3g7zuhBBCamEOVEIIIY0aREgh2hOpDZIPVkEkFZynSEVg2nlKCCGEkGhgVw129mDXBTGX+xSR6EgvQecpIYSsCyNQCSGENGpwaA7ykyIaFFv7kA4B2wGRIw2HrWCbIPIc6p4unw2MQCWEEEKigZ0hyMOMaNQhQ4Y0dHe8Z9iwYSrFD9JW6KTaIISQxgYjUAkhhDRqkH8UOdLOPPNMdbgHDnJA7j48X0Q+siD/LSGEEELcAbtHsOUc+cORh5hEB3mukUP5pptuovOUEELSwAhUQgghhBBCCCGEEEIISQMjUAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEEIIIYQQQgghhBBC0kAHKiGEEEIIIYQQQgghhKSBDlRCCCGEEEIIIYQQQghJAx2ohBBCCCGEEEIIIYQQkgY6UAkhhBBCCCGEEEIIISQNdKASQgghhBBCCCGEEEJIGuhAJYQQQgghhBBCCCGEkDTQgUoIIYQQQgghhBBCCCFpoAOVEOIk559/vmy44Yby2WefrfNZeXm5bLbZZurzO++8M2X9Qw45RDbeeGNZunSptT7i+4844ojI9SsqKmTkyJEydOhQ2WqrrZRMO++8s5x77rkyZswYsc3nn3+uZLj11lsT7+26666y0047SUNz1VVXyemnn574/ZhjjlF9TX4NGDBAttlmGzWG//nPf6S6ujq2Pv766691fm/IsRs/frxsueWWMn369Ab5fkIIIYRkD9axqdY3m266qfzhD3+Qv/zlLzJ27Fgn+vjJJ5+o37HWwO/nnXdepPZ010sol2pskl9YAwUsX75c/va3v6l19cCBA+Waa66RyspKufrqq2XbbbdVa22MabJMurzwwguq3rPPPhtJdkKI3xQ1dAcIISQV2223nbz88ssybtw4teBJdvzB+VhcXCwffvihnHnmmXU+x+IJiyksklq3bi0uAifwcccdJz/88IPstddess8++0jz5s3VohSLs9dff10uuugiVSZOLr74YqmpqZGG5IsvvlAL09dee22dzzAmbdu2Tfy+Zs0aNWbPPPOMcrrOmTNHOaBt849//EM+/vhjeffdd50YOziSYUeXXHKJPPLII1JQUNAg/SCEEEJI9hx++OHqQWhAVVWVzJo1Sz0c/uCDD+S+++6THXfcURojN954Y8bPu3btmvj/XXfdpdaPWBPBAd2/f3956qmn5PHHH1f3E/vtt58q3759e+nZs6f6PBu23npr1Z8tttgisjyEEH+hA5UQ4iTbb7+9+pnqqTucpiUlJWoR9NJLL8miRYvqONVQB461HXbYQVzliSeekG+++Ub++c9/yoEHHljns5NOOkkOPvhguemmm+SPf/yjdOvWLbZ+7b777tKQIIIUzkmMSa9evVL2r3v37uu8f/TRRysn9MMPP6yczu3atbPaT9zMFBUVOTV2eJCAPuDBQ7JNEUIIIcRdBg0aJAcccMA67++yyy5qVxWcdo3VgZpqXNIRRKMi8rRVq1bq/3jIHjyE32ijjRJlw//XpUePHupFCGmccAs/IcRJOnXqJH369JGvv/56nag+OFCx0Nxtt92Uww2RgGG++uor9dNlB2rQRyyMk0HU7JFHHqmcwA29bStu3nrrLZkyZYocddRRWdWDw3SPPfZQ27RgM42Rzp07q2vigQceaPAoYkIIIYTkziabbKKiJH/55RdZsmRJQ3fHebBDDQTO0/B7LVu2bLB+EULyAzpQCSFOb+NHDtMJEyYk3vvtt99k6tSp6ik8tuIgChAO1TBffvmlWjgh91EAtnZfdtllKucS8krBcYmn04heTeXEO+GEE+T//u//1MIVP0877TT5/vvvM/YX263OOusslRvptttuy1i2RYsW6ie2FKVydv35z39W2/v/9Kc/1Xkf7+E7MDbYPoSn8ohmDbeBheKDDz6oIhZQBvIit+qll14qCxYsyCovVZAjCjpAZCic0kiNsP/++6vo32QmTZqk+gfd4LtPPvlk9R7y0V544YVSH8gJu8EGG0SKCkAKBN08tcj7is+QDiKcz+tf//qXPP300yq6GXJi+xdSAyAtRLgc7GnGjBl18vBmGjvIDjuC4x/5XPEebA/b/vE+cnUh8hjO42ReffVVleMVdTGmcC6/9957KccA/Z44caKMGjUq6/EjhBBCiHsUFtbesuPBesCnn34qw4cPVzu2sFbF1vJjjz12nZyeWJuceOKJKgUA1hqDBw+Wxx57TH02d+5cufbaa2XPPfeUzTffXL2wm+fuu+9Wa9psyWatbSuvfxB4EM6RinUUwENm/I61XLocqG+++aba1YR0CsixjzVbuEy6HKjffvutuldAHawfsR7DrqiwznTXmgFY26NcsJ7H2v/UU0+V7777LpHyCu1dccUV64zH/PnzlV38/e9/NzK+hJBauIWfEOIsWBQi9xMWQ3CqgY8++kj9hAMVT5LhJMV7WGQg7+OqVavUwmLIkCGJLdbTpk1TTjQ4FpFjClvif/rpJ5UTafTo0epnsOUb+SOvv/565dQ644wzVJ5VOE7hLMRC5Z133km5PRwLTeTehPMVyetPOeWUjLKhP//973/ljjvuUIsxLDKx+MWCrUOHDutsDwdYwKFdyD1s2DAVpQtH2pVXXqmcecEi6a9//au8//77Kg0AHG+rV69WcmKxB8cdFmPZggXbeuutp35iHB999FG54IIL1HtBugU4StEvjAUWnJADC1FE0+oc7oSFPNIaHH/88Vn3D98Jp2GTJk1UPtCowBZWrFih+tylSxelT9ggHPk333yz0j220eGGAzc02A6GxWsm4ETu27evnHPOOcpB+u9//1stsuHwRR4u6AsHUuGGBtvwX3nllcTNEtI4wBkOxzXsC7pEflzcNKXKkYvFNer+73//U05zQgghhPgL1ndYX2HtGqw/sTbB2gEPp7EuxEN5rO+wzsOaA2vWcG5PrKPxOR5wL168WK0Vli1bptbEWN9gzYN8oPgMdbE2xXoa61ldsllrZ8vChQvTfoaUXlgXY52F9dk999yj1lT4P9aeWBM9+eST6kyFII9+un7AcQzZsa7DOq1p06aq33jAjc9S7RoDyIcPfSDFFMpifYfdcUjThbGHszacm76+tWYA8to///zzyvGN9rHLCoEXWGNjzYj7BugN9xN4II+xCIDTGGtjOF8JIeagA5UQ4ixwYsIhhsUHHHMA0aZwzAURinAs4cT6H3/8UT1pxfZtLDDC2/fxVHflypXy4osvqoVGALZ8w1mHxRKe3uIpMZ7QwwGHSEh8d3hb/UMPPaScqEhMHwYLFCwy3377bbXYQfRofWAxhMXY5Zdfrp5IYyEURATAWXzQQQepJ+DhxRDKYpGMHJdwngKMC5xocGgiwgBOSDhVURdRAAHo02GHHabGBwvRbBexvXv3Vo68YAGIKAUs4LCwCxyoWKxiQY5cU/gcIFoSi1Asnuvjs88+Uz8zRZ9icRleSEPXWLSjb5MnT1bRF1iMRmXevHnyxhtvJPJbHXrooSoyA45gnOCKRTGifm+55Rbl5NbJy4VFfXjsEEENHSFfKWwgYPbs2WoRDXuAnSKaAfWwwIbuA6Bv6BqLbNgitu4H4CYCfUdkCiGEEEL8AIeLhtc3cETCAYkdM1jr4OT4AKxVsRaGMy28+wa547HmxZor7EBF23AswnEagIe5M2fOVGtgrHMC4PzEug4P4rNxoOqutaMQ7ncyiCqFbBgPrMngnIQDNbw+w70DHKjp8ugDrCWxJoNTEpGjwfobO64wPrfffntKBypkxtofa3d8d1AP63DsRrv33nuVgxORvbprzWbNmqnddFhjI0oV671gDYlye++9t7IB9BcOUtgIgghwbkIA9AAnNu6lCCHmoAOVEOIs2IaPLUDBdhwsJrFFBwuEYCGBSFQsyhCdCQdqkFsU22EA8kUhQhXbieBcCi9O4ajD4gXReljUwWGKRScWQ2HnKRaeiEQFydtr8HT7vPPOU4seRIDqOE8DsBBDP+HsQh+xWMJiGXmu8NQaT4/hGIXzFg5iON6wIAucpwDjcMMNN6g+ohwco3AoBxGMAdi6H+SDwlPvbB2oSCUQfnoOvQRbhAAcp5ABjuvAeQowjoiW1HGgIj0DCC+8k4FjORWIhEWEJp785wK2SIUPB8A4wqGOsUdkBha12YJFc3js4FCFAxUL4DDBoVnYAocxwCmyQf3k6Au8h2sBNzjJKQpQF7qAYz9VJDMhhBBC3AKOM7yS6dixo3qIGo4kRKQpHiiHnadYIwdrv+S1KtawcAyGwXp13333rXMIK8B6A+vF5DYykc1aOwoIakiHqUNDEUWKQAoEB4SDF9q0aaOiQ1OliQKINEWKAjiJk8cMazU4UCF72IGqs9bEA3WAdsNrSKzxnnvuOWnfvn1iXYz7IARXBA5UHKT1888/q11N4bqEkNzhnRUhxGnw1BlPWfG0Frkd4SgMnKOBIw+LGziTsG0JTkgsSgInHJxycHLi1PRMT7CxNRpbdbBoggMST4ux3Rpbp2bNmpXIMZqcrxQRnYgUBPjubB14WNRiwRnkzsQiFJGseKoMpyl+YssRnowDHKyVTHLEJWTAk20s6iA/IhrhQA0WUTrb6ZPBk/3k7wi3hUUfHHaIVE2mX79+Wt8RLLjDif+TwZZ29AXfi+1oiBKATuBENnFoGG5UkglkDeexymXsAud88vvBjU8wpkE+VDjN0wH7TAaOdIwJxhOOZUIIIYS4DXaWhNe3WHtgTYL1bLITDA9HET2KyEts78daAGu9YP2QvM7DOjnVA1WsO7COQvokrOOw1sRDdhB+WF8f2a61syXY6WSTTOvsTOvYYK02YsQI9dJZq+msNaHPdP0Jp6qCnmA3GHs4X6FrRJ/CZg488MC0/SaERIMOVEKIFw5ULO6w/QYLgvACE4s/lMHWFSzMUC68bSdYRGLbTqaT3QOnFrYrIfIPiyUc2oPk+3h6jgUSco0mgwUptq7j+/H0F0+FsRUnE4gwxHZ95K4KP5EGZWVlaqs9DmHCtqdgK7ZuMn84YLHlGwtqpAlANCieTiNJPaJZkV8zCskRrclgexkIInXDlJaWZvUdmRyVOPwg2H4FO8AWduR5RQ4wbK+CnnVI9x02ntSnGhOd7wps96677kocOpZMqnQFgWzhKGpCCCGEuAvWnbqOQqQRwkFEWA9hrYd1MPJ2Yq14+umna63h8PAfB6aiDtac2NGFbf9YZyEKM5uH7dmutV0kWMdmuw4MAitwbgLOMUhF8hpO5zuy6Q+ik3EfguAJrIlxH4Ot++nSFRBCokMHKiHEabCQgwMOB0Mh/yicjsnbdRB5iIhRHK6D7fdhB2uweEAy/FQLUxwKFTyZx/Z/LDqwtRr5hMKLFkSapgIOSmyBQh8Q8RlEQtaXh/OBBx5Q0bPJDtQARNFiwRVsGQ/kSHVKO5ysyJOEbT5YQCFS9x//+Mc6i9hgu70NsP0c45Wqf8hNqkPwRD6b01oxznCcIkrz/PPPVwdyrb/++nUW69jWlozNsTBFoHOMC5z5ydEeGNdUW8oQeQp7NrWtjRBCCCFugMhTrCGxPsaD8fB282wekiNaElvOUSc4qDVw3GEdhgf6umSz1naV8Do7OeIU+WKxJR47wtLVQ2RtsuwYX6Q2SBVxmk1/cO8TBrlVkTrr0ksvVWtvBHsgFQPSiWEnGHad4aBXQoh5MocUEUJIA4OFIZ7oYos+trTjCXkywXtI3o7FGZ6kB2CbNOrDuYkt9mHgbERSfjzFB9j6AvAEPuw8hUMKkaWZIkGxMITTEgsaJJPPBLbb4IT077//Xh0SlApEs6Kt4MAq5HeFsxBO4mTnH7Zf4X0s0ALnY/LJ8IjeDeTXjWbNBizcEAGBhSK21oefzKN/2SwWcXOQDbiJQMQFtp3BiRqOLsUWdhwmEM5LhTGC7nMBjtkoqRCyIdA9Tm8N6ww3N1jE43AuRDMng/Hr2rUr814RQggheQZ2GmFtBUdZ2HmKAILgMFKdlENYCyFAIci/HoA24AjNJm1RNmttV0H0LNZNOJgrvObCeKPvWEcjv2syCNpAwAOcrMn56rGD7q9//WukNWeQzxTtJqcaQE5YpFwI1nnY6YTddwgEQUAF+oldbIQQ87j7GIgQQtYCxxxOoATh6NIAnEKOg3mwfR+J2ZNzaCL5PiIUEaGJ00XxpB3Re3C4wvEZPKWFIw6/48k+Fo/IO4UcRFiMwJkJgp+pwMmYeCHx+5NPPrnO4T5hrr32WnViPHJ6YssNThGFYxWOPpxGj1xGQ4YMSeS/hGMYKQSwCEVOo2HDhqkIQyS9h9Py7LPPVs5CPIXG4heORGzlRz5MOGqRDwlOPzjfMsmQC3DqoV94od9w6KJ/wSFg9Tn0ELmLPqJ8+LAEHeBMxIFKsIH7778/sYUNY4UE/hhrpFaA7NA7HL7JC91sQPJ+OPThHIbdJEeImrJ79BnOe2zJQqQzbpYQLYKtd9Bv+MAuAIcqbDZT3lRCCCGE+AmiI+H0fOmll9QuFDwwnzt3rlrn4bwAgAOm6gPrTuTZx9oYJ73DKYsDP7H+hGMV61G8p/swVnetHQUEFWQC9wDB4aZRQa5RrCWxZkT/cXgq5H/mmWdUgAXSJqQC62wEUGANvP/++6u6WI9jLY/1PdZpWK9lC+530B70Onv2bKUvnAPxxBNPqHuCCy+8sE55rJsfeeQRdQAt1oxRDj0lhNQPHaiEEOcJtsTgCW86RxUWGsj7mcrBisUltnYj2T6cm08//bRy7iHCD4624Ok7HJJwiGFbExZM2PoNpyacolgQovyHH36o8m2mA4soRMsiLyr6Ej5lM/lpPRZFWFjidE6c8IkFL2TEohMO1oMPPrjOwhUOVZSDHFgkIToAi0Ys6rD4DZxu6D+cwFgYw+GGaEQ4WLHoRt8hgw2HH/qNhR2+H05cLDyRgwnpEIYPH542F2gAnJpwRmL8siXIRYt8rxgfRPhiyxMczhhDLL4xphgL5PaCXs8555zIsp577rnqZgGyYoFrYzzBNddco9qGzSISFQ5mpCjA+6ly7QZjh4U2IYQQQvILrKWwewmBBXDQPfvss8phh1yoWPMgfRMiQetzfmL9izUFHLHXX3+92rKPqFasHZE2C9GTSJ2FdZwOumvtKCAoIBN//vOfc3agAqyV4UjFGhZrVzgh0e4///nPdR5Yh8HDeuwSg14QMYozGbDexNoXh4OlSrekA74XZxhAx1jjQkeI9EVUa/LhUliDo494wI77B0KIHQpqko+UJoQQQiKAyAc4hpMX7IgoRTQuEuyfeeaZGdvAzQAcm9hCtfXWW1vucf4BRz+iUJHLt76Dvwgh7oCHdpdddplcccUVaXcvIAoJWzfxECw41blbt27q4RoOg0mXZw8386iHeQH5kxFdBmcHvgeR7YQQQvwHkaeIHMZamhBiB95dEUIIMQIiABBpkJw3KzjUQCdKE/URKYvIBZIdyIeFA8UQ6UHnKSH+gIghHECYCeS2w5ZS7D5AXmekrsEL/8fOCUTCI11LMkhHgwcriKbCdlrsWkAUE/IUIpK+vpzdhBBC3AfBCkhjlSl9GCEkd3iHRQghxAjIv4SbeeQbRaoB5IHFdij83GWXXVKmV0gGjr+LL75Y/vvf/8rEiRNj6Xe+cMcdd6gcwPvss09Dd4UQogkeemCLJw7BSwfSuyB6Hz9xaCJyFGKLbPBC6hPkdMb2XeTIC4N0H2PGjFEPplAW23VxIjZyRWN7KnIsY3soIYQQ/0DKhL/97W9y8sknqzQCqdI7EULMQQcqIYQQI5x00kkqRxNOL7399ttV7ibkpf373/8ud911l/ZBBDhMCgtAtEX0+OGHH+S9995TY87oU0LcB45O5FDG1vv6DnxBXkE4SJHn8LbbblM/A5BnGzkLEVWKLf6vv/564jMcKIdc25h7kSs7nJMbeaKDQ0iQX7m6utqKnIQQQuyBXV84sBUH3yJvLQ+PIsQuPESKEEKIMQ444AD1ypUrr7zSSH8aC5tssonavkUIcZ+ffvpJPXBC3mgcgIdIfaQtCfKapjscDpH8LVu2XOdzHICI6HNEpuLwl8MOO0y9j8Pz8EALB4tstNFG69TDQSM4PAZ5k3FYzLbbbmtcVkIIIfbA7oT6zhcghJiDYSqEEEIIIYTEBCJD4TzFqdnYQn/qqadmLI+TnBFdjjQp6QjOhA3noB43bpz6ie9JRUlJiTrhGcCBSgghhBBC0sMIVEIIIYQQQmICWy1Hjhwp22+/vVZ5RJDilQ5s7w8coBtssEHifeSkBuGt+8l0795dHSgVlCWEEEIIIamhA5UQQgghhJCYgJMz7OjMlWuvvVZWrlwppaWlsvfeeyfeX7BgQWKLfzratGmjfi5atMhYfwghhBBC8hE6UPMc5MKqrKxUh4o0bdq0obtDCCGEEJKW1atXqwONiouLE9vLSeYTmF977TX1/9NOO63OAVOrVq1SPzOt/4LP4IA1BdeehBBCCMnH9ScdqHkOFrDIi4WcWDjxlRBCCCHEh/ULycxdd90ld955p/r/zjvvvE4u1SZNmqibAR0KCgqM9YtrT0IIIYTk4/qTDtQ8B0//sYDFwrhZs2aJ93EqK05+jUKUurp1TJbLRUYfcEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TjkXbME2jX1uRyQknG9Yv5D0Y3jVVVfJ008/rX5HLtXbb799nTFr3ry5LFmyREVVpCP4LLxGtLX2JH7OByQ3qEP/oQ79hvrzn6oYdKi7/qQl5TnYOoWn/1jADhgwIPH+xIkTpV+/fpHajFJXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc7tpW9cp54It2Kaxz+3jx49X6xZu/U7N8uXL5ayzzpKPP/5Y/b7nnnvKzTffLCUlJeuUbdu2rXKgLl68OG17Qe7TTHlSTa09iZ/zAckN6tB/qEO/of78Z2IMOtRdf/LxPiGEEEIIIY4ze/ZsOeKIIxLO0+OPP15uu+22lM5T0LdvX/VzxowZaducPn26+rn++utb6TMhhBBCSL5AB2ojpU+fPrHW1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQUZf5vbGxJw5c+SYY46RX375RW0vu+yyy+TCCy/MuNVs4MCB6ufYsWNTfl5RUSHff/+9+v/gwYMt9ZzkAq8X/6EO/Yc69Bvqz3/6OKRDOlAbKdOmTYu1rm4dk+VykdEHXJDPZh9Mth21LZu2rluWtu6GfLb7YKr9OOd207auUy4X+ZDXCAfquP6aOnVq3vYBOnDhevYNODqHDx+u9ILTYRF1evTRR9dbb++99044UH/++ed1Pn/++edl1apV0q1bN9lmm22s9J3kBq8X/6EO/Yc69JuG1p8v60+XX1NzWJdi/E3CHKiNlFxOt41SV7eOyXL5foKvC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1ykWRDw6imTNnKieU6UWUDXCoDxLZ52MfcKgQ9ACdlJaWGm8/X3nggQfkhx9+UP9H5CnynurQs2dPOeCAA+Tll19WeVPvueeexLb+UaNGyY033qj+D+csD9hwExf+/pHcoA79hzr0m4bQHw5SnDdvnixbtkwdgEQabl2KtSdSHXXt2tXI2pOrpUYKTmaNs65uHZPlcpHRB1yQz2YfTLYdtS2btq5blrbuhny2+2Cq/TjndtO2rlMu2z7CUYen1ljI+kK6fJb50IfgdFPoBM49OlHrBw7nRx99VP0fTs6XXnpJvdKx/fbby5lnnpn4/ZJLLpEJEybIjz/+KPvtt5/0799fXRe//fab+nzYsGFy2GGHxSAJ8fXvH8kN6tB/qEO/iVt/OIgI+cV9Wnu6TkkO61KsPeGANbX2pAO1kdKhQ4dY6+rWMVkuFxl9wAX5bPbBZNtR27Jp67plaetuyGe7D6baj3NuN23rOuWy7SMiT7GAxWIJ25R9iLLDdqNMeS197gOiMHBTgYUsdONSTitXQc7TJUuWJMYvXT7TgM6dO9f5vaysTJ588kkZOXKkvPHGG/Lrr78q3Q4aNEiGDh0qBx98sNX+E////pHcoA79hzr0m7j1N3/+fLX2xGnu6623nvrZpEmTWPuQb1TnsC7F2gmHaQY70nJde7p/J0GsAA98v379YqurW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUy+Y78cQZ0XsAzlMXIjt1QJ8bOjLTVh+gg44dOyonapBSAVuriMh7772X8v1NN900Zf7SbIAusU0fL+IXLvz9I7lBHfoPdeg3ceoP65oVK1Yk1p5wnpKGXZdi7QldTJo0ycjak4dIEUIIISTvwAIpyHnqQ+RpYyGIwgjrhxBCCCHEd8LrGhz6SNwguA8wsfakA7WRggiQOOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF27jg7LXZBxfkI8QXGsOcl+9Qh/5DHfoN9ec/RQ6tHelAbaTkktQ4Sl3dOibL5XviZhfks9kHk21HbcumreuWpa27IZ/tPphqP8653bSt65RzwRYIISQuOOf5D3XoP9Sh31B/xCR0oDZSFi5cGGtd3Tomy+Uiow+4IJ/NPphsO2pbNm1dtyxt3Q35bPfBVPtxzu2mbV2nnAu2AKqra2TV6ir10zRIdt/Q2OyDC/IR4guuzHkkOtSh/1CHfpMv+rO59nSdKofWju7EwhJCCCGEOMyUmUvk5VGTZPTXM6SyqlqKiwplp0Hd5IAhfaV317JY+vDKK6/I448/rk5nRxJ8nCZ62GGHybBhw4y0f8wxx6iT3G+66aZI9SsrK+U///mPHHfccUb6QwghhBDSWGksa89u3brJDTfc4Pzakw7URkrv3r1jratbx2S5XGT0ARfks9kHk21HbcumreuWpa27IZ/tPphqP8653bSt65RrSFsYNXa6jHhirODgzjVrn/5jIfvB2Ony/pjpcu6Rg2XI4O45f0+mE1ufe+45ufbaa+WSSy6RLbfcUiXC//jjj+Waa66R+fPnyxlnnCEmKCyMvkHptddek+uvvz7tIpYn0hLi198/khvUof9Qh37js/7iWntmIq61ZybqWzvWt/Y0CbfwN1JmzpwZa13dOibL5SKjD7ggn80+mGw7als2bV23LG3dDfls98FU+3HO7aZtXadcQ9kCnv5jAVtdU5NYwAbgd7yPz1EuV/AUPR1PPPGEHHLIIXLooYeqGwJEAOCpPRaM//73v8UU1dXVkevWd7ppJvkIIe79/SO5QR36D3XoN77qL861ZybiWnvmsnasb+1pEjpQGymrV6+Ota5uHZPlcpHRB1yQz2YfTLYdtS2btq5blrbuhny2+2Cq/TjndtO2rlOuoWwBW6fw9D8T+Pzl0ZOsOi8RGTpu3DhZsqTuYvmUU06Rp59+Wv1/1apVctttt8luu+0mm222mRxwwAHy1ltv1Sn/7bffqoXvFltsIdtvv71cfvnlsnLlypQ5p8466yzZeeedZerUqeq9OXPmyDnnnCNbbbWV/N///Z+cdtpp8uuvv6rPXnjhBbnooovU/zfccEP5/PPPs5KPEOLe3z+SG9Sh/1CHfuOr/uJce2bChbXnrFmzclp7moRb+BsppaWlsdbVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh9MtR/n3G7a1nXKmRqnj76ZIf958ydZubr+pPR4qr1waf2Lb0QDvPvlNBn381yVHyoTzZoWydF7DZAdBnbNavv8SSedpBaQO+20k1pAYiG57bbbqsVq69atVZlzzz1XfvzxR7niiiukV69ealvTX//6V7nrrrtk9913l2nTpsmxxx4rf/zjH9XCd9myZXLBBRfIlVdeWSf3FE6tPf/88+X777+Xxx57THr06CHl5eUq6mCTTTZRubDQ15EjR8rQoUPl1VdflX322Ue1d91118lHH30kZWVlRtMDENLYcOHvH8kN6tB/qEO/cUl/uuvPuNeemXBh7XnSSSfJpptuGnntaRI6UBspnTp1irWubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv0453bTtq5TztQ4vfD+RJk+d7nYQGfBq/rwwYSUi9iiovRLs7322ksd8IQtU8g/NWrUKPX++uuvrxaObdq0kXfffVfuu+8+9eQenHnmmfLTTz+p97CIfeaZZ1Q5lA++C3msEF0QBk/zv/nmG7WARWJ/8Prrr8vSpUvVAVNBXeTFwtN+tIvvatWqlXq/Y8eOKWXIJB8hxL2/fyQ3qEP/oQ79xiX92Vp/5rr2zERca8/q6uq0a084SHNZe5qEYQCNlN9++y3Wurp1TJbLRUYfcEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TjlT43TILv2l+3otpX1Zab2vdq2zO/QI5etrE9998M79U9avqKjI2P6gQYNkxIgR8umnn8qLL74oZ599tixfvlxOPvlk+fnnn1UZJPkPs/XWW6uTUwF+IoI07MhEJMHw4cMTv7/55pvqxFVEbYQXo4guwBYutIctWHjhu2bPni2TJultIatPPkKIW3//SG5Qh/5DHfqNS/rTXX/GvfasjzjWnv/973+trT1NwjAAQgghhDQq8PQ9myfwtz05Vp14mpzEP0yTwgLZecvucvawwWIDLBTvv/9+OfXUU1UkALYwbbzxxuqFp/v77bdfxq1gwaJVJwIUC9dbb71VTjjhBLX9CluzgugAHCBw7733rlOnefPmOclHCCGEEJLPZLP+bGxrz/XWW085aVOtPRHtimhWF9aejEBtpHTo0CHWurp1TJbLRUYfcEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TrmGsoUDhvSV+g74xOcH7NQ35+9Kt8gsKSmRZ599Vj2dTybIQYXFJxgzZkydz7/66ivp16+f+j9+4mk+8kwF/O9//5Ndd901cdAC8lsNHDhQzjvvPHnooYdULiqwwQYbqNNssVUKOa7w6tq1q9xyyy3y5ZdfqjL15eDiFn5C/Pr7R3KDOvQf6tBvfNVfnGvPdMS59tx6663Trj1xiFQua0+T0IFKCCGEEJKB3l3L5NwjB0thQYF62h8Gv+N9fI5ytmjXrp1Kon/77ber6NDx48erpPzvv/++nHHGGYnE/rvssotKyv/BBx/IlClT1FN85KbCE31w5JFHyqJFi9Tpp9j6hMXnjTfeqLZSNW1ad8vYsGHDZPPNN1c5qbD1fv/991fJ+XE6KnJUof6FF14oo0ePViefhqMBsPDFqayEEEIIISQ7uPa8yMm1Jx2ojZT58+fHWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynXkLYwZHB3ue3cIWqrVHFR7fIJP/E73sfnJqiqSn8yK3JOIXE+Fp7HHHOM7L333nL99dfL9ttvn9jahO1P2FZ1ySWXqEUnFrl33nmnOgQgOEzh4YcflsmTJ8uBBx6oTlbFwvcf//hHnW1XwRN9JPnHYviee+5RT/9xAmrbtm3lxBNPlEMPPVTmzJmj2uvbtzYCAothRBBgAYzvzkY+Qoh7f/9IblCH/kMd+o3P+otr7ZmJuNaeAanWnohIzWXtaRLuoyKEEEII0QBP+ZFn6qyhW0hF5RppWtIk1m1DAAtPvNKBp/CXXnqpeqUDCfifeOKJlJ/h5NPw03ssToNtVKBHjx5qUZwORAngVFRCCCGEEJIbjWXtGSZ57dm9e3dn1p50oDZSkDsizrq6dUyWy0VGH3BBPpt9MNl21LZs2rpuWdq6G/LZ7oOp9uOc203buk45F2wBFBYWSGnTImv5phoam31wQT5CfMGVOY9Ehzr0H+rQb/JFfzbXnq5T4tDakVv4GykIe46zrm4dk+VykdEHXJDPZh9Mth21LZu2rluWtu6GfLb7YKr9OOd207auU84FW7CNC1vcbfbBBfkI8YXGMOflO9Sh/1CHfkP9+U+VQ2tHOlAbKbkk141SV7eOyXL5fniFC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7aprq7O6z64IB8hvtAY5rx8hzr0H+rQb6g//6l2aO1IB2ojJfm0M9t1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm0KCwvzug8uyEeILzSGOS/foQ79hzr0G+rPfwodWju60xMSK127do21rm4dk+VykdEHXJDPZh9Mth21LZu2rluWtu6GfLb7YKr9OOd207auUy6b70SC/SDJvktbf+qjuLg4r/sQ6CSsH0KIu3//SG5Qh/5DHfpNnPoLr2sqKytj+958pzjHdWlwH2Bi7UkHaiNlypQpsdbVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh9MtR/n3G7a1nXKZfOdWCAFSednzJghFRUVaguQ66+VK1fmbR+gg2nTpimdQDd0oBLi/t8/khvUof9Qh34Tp/6wrmnRokVi7bl8+XLlSG3odZ3vr5U5rEux9oQuTK09G+cxXoQQQghpFFEHU6dOVfmvJk2aJD6AxV5Db1Wy2QdEAWA7HSN6CCGEEJJvdOjQQa07V69enXhoTBp+XdqkSRMja09GoDZS2rVrF2td3Tomy+Uiow+4IJ/NPphsO2pbNm1dtyxt3Q35bPfBVPtxzu2mbV2nXLZ9LC0tlZ49eyqHnS/Rjlgk5msfoANsw4JOoBtCiPt//0huUIf+Qx36Tdz6a968ufTt21fatm0rRUWMV2zodSnWnrgPMLX2pEYbKfDAx1lXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc7tpW9cpF0U+LJb69OkjNTU16uU6S5YskbKysrzsAxaxS5cupfOUEI/+/pHcoA79hzr0m4bQH76zc+fO6uXL+tNlluSwLjWdc58RqI2UefPmxVpXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc7tpW9cpl4t8WDxh+4/rrwULFuRtH6ADF65nQnyB14v/UIf+Qx36TUPrz5f1p8uvBTmsS03vQKMDlRBCCCGEEEIIIYQQQtJQUMN44rxm/PjxUl5ernJxDBgwIPE+TiMLTifOlih1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm0a+9yebt1C/IE6zK/5gOQGdeg/1KHfUH/+UxGDDnXXLoxAbaTMnz8/1rq6dUyWy0VGH3BBPpt9MNl21LZs2rpuWdq6G/LZ7oOp9uOc203buk45F2zBNi7I6MvcTki+w+vFf6hD/6EO/Yb685/5DumQDtRGCrzrcdbVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh9MtR/n3G7a1nXKuWALtnFBRl/mdkLyHV4v/kMd+g916DfUn/+UO6RDOlAbKcXFxbHW1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQUZf5nZC8h1eL/5DHfoPdeg31J//FDukQ+ZAzXPS5XKorq5Wp5JFIUpd3Tomy+Uiow+4IJ/NPphsO2pbNm1dtyxt3Q35bPfBVPtxzu2mbV2nnAu2YJvGPrczf6b/UIf5NR+Q3KAO/Yc69Bvqz3+qY9Ahc6CSjEyePDnWurp1TJbLRUYfcEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TjkXbME2Lsjoy9xOSL7D68V/qEP/oQ79hvrzn8kO6ZAOVEIIIYQQQgghhBBCCEkDHaiNlLZt28ZaV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNV+nHO7aVvXKeeCLdjGBRl9mdsJyXd4vfgPdeg/1KHfUH/+09YhHdKB2kgpKSmJta5uHZPlcpHRB1yQz2YfTLYdtS2btq5blrbuhny2+2Cq/TjndtO2rlPOBVuwjQsy+jK3E5Lv8HrxH+rQf6hDv6H+/KfEIR3SgdpImTNnTqx1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm1ckNGXuZ2QfIfXi/9Qh/5DHfoN9ec/cxzSIR2ohBBCCCGEEEIIIYQQkgY6UBsp3bt3j7Wubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv0453bTtq5TzgVbsI0LMvoytxOS7/B68R/q0H+oQ7+h/vynu0M6pAO1kbJo0aJY6+rWMVkuFxl9wAX5bPbBZNtR27Jp67plaetuyGe7D6baj3NuN23rOuVcsAXbuCCjL3M7IfkOrxf/oQ79hzr0G+rPfxY5pEM6UBspK1asiLWubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv0453bTtq5TzgVbsI0LMvoytxOS7/B68R/q0H+oQ7+h/vxnhUM6pAO1kVJUVBRrXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW4nJN/h9eI/1KH/UId+Q/35T5FDOiyoqampaehOEHuMHz9eysvLpXnz5jJgwICG7g4hhBBCSFq4bvEf6pAQQggh+bh2YQRqI2XixImx1tWtY7JcLjL6gAvy2eyDybajtmXT1nXL0tbdkM92H0y1H+fcbtrWdcq5YAu2cUFGX+Z2QvIdXi/+Qx36D3XoN9Sf/0x0SId0oBJCCCGEEEIIIYQQQkga6EBtpJSVlcVaV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNV+nHO7aVvXKeeCLdjGBRl9mdsJyXd4vfgPdeg/1KHfUH/+U+aQDulAbaQ0a9Ys1rq6dUyWy0VGH3BBPpt9MNl21LZs2rpuWdq6G/LZ7oOp9uOc203buk45F2zBNi7I6Mvcnk8888wzsuGGG8qTTz6Ztszq1avlvvvuk/32208222wz2XrrreXoo4+W119/PWPbUeuRhofXi/9Qh/5DHfoN9ec/zRzSIR2ojZTZs2fHWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnb84Vvv/1WbrjhhoxlVq1aJccff7zceuutMnnyZOnbt6+KyPjyyy/l3HPPlUsuucRoPeIGvF78hzr0H+rQb6g//5ntkA7pQCWEEEIIIaQB+PTTT+XEE0+UFStWZCx3zTXXyJgxY6Rfv37y1ltvyUsvvSTvvPOO3H///Soy47nnnpNnn33WWD1CSO5UV9fI6spq9ZMQQoj/0IHaSOnWrVusdXXrmCyXi4w+4IJ8Nvtgsu2obdm0dd2ytHU35LPdB1Ptxzm3m7Z1nXIu2IJtXJDRl7ndZ8rLy2XEiBFywgknyNKlSzOWnT59urz44otSUFAgt9xyi/To0SPx2c477ywXXnih+v+dd94p1dXVOdfzATikVq2uynvHFK8XP5kyc4nc9uRYOfSi1+TCB8ern/gd7xP/4HXoN9Sf/3RzSId0oDZS6lusm66rW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUc8EWbOOCjL7M7b7y008/yR577KGiQAsLC9VW+kw3By+//LJUVVWp/KUbbbTROp8ffPDBUlpaKnPmzJEvvvgi53q+OKYOu/j1vHdM8Xrxj1Fjp8vZI0bJB2OnS2VV7YMJ/Pxg7fv4nPgFr0O/of78Z6lDOqQDtZGybNmyWOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF27ggoy9zu68gMnTevHmy1VZbqS30p556asby48aNUz9RPhUlJSXKSQrCjtCo9VylMTqmeL34BRz5I54YK9U1NbImKToav+N9fJ6vDv98hdeh31B//rPMIR0WNXQHSMOAiIc46+rWMVkuFxl9wAX5bPbBZNtR27Jp67plaetuyGe7D6baj3NuN23rOuVcsAXbuCCjL3O7r/Ts2VNGjhwp22+/vVb5X3/9Vf0Mb8FPpnv37upgqKBsLvVcd0xJ0q79wFGFz3t2biW9u5ZJvpAv10sN9KZ+htQXvFf3V/VO8P/fP/v9jXXeS6pfk6HtcJ3fy9f9sppMbYfrpOjjU2//LFIQejMFBQUiL4+eJGcPG5y+EHGKfLkOGyvUn/8UOqRDOlAbKX369Im1rm4dk+VykdEHXJDPZh9Mth21LZu2rluWtu6GfLb7YKr9OOd207auU84FW7CNCzL6Mrf7ygYbbKBeuixYsED9bNeuXdoybdq0UT8XLVqUcz0XeXnUJOV4yuSYgnP10vs+kS7tWyScYjpOtd8dYb877tZx/CU5+Oq0ncHxF25by4GYso8T6nxnxrbT9TH0nakclslt/z42qfqTvu11x4okA4f/e19Ok6XLK6Rl82Jp2bxEWjYrrn01L5YWpaH38HuzYmla3ETlMiYNA/9u+Q315z99HNIhHaiNlEmTJknfvn1jq6tbx2S5XGT0ARfks9kHk21HbcumreuWpa27IZ/tPphqP8653bSt65RzwRZs44KMvsztjYVVq1apn02bNk1bJvhs5cqVOddzDRwUNfrrGetsiU7F0hUV6kWIy8CSvxw/R7t8UZNC5VCFM1U5XYP/N6vrgP39898dsM2aFtH5miP8u+U31J//THJIh3SgNlKSt6bYrqtbx2S5XGT0ARfks9kHk21HbcumreuWpa27IZ/tPphqP8653bSt65RzwRZs44KMvsztjYUmTZpIdXVtzs/6CDtKotYzBRy4EydOlN69e8uMGTOkoqJCmjVrJh07dpSpU6eqMh06dFA2EUTLrr/++jJ79mxVF87dLl26yM8TJidynkZBSbZWvIKQvIjFLFj7Tlj82rGoqbN1sGat87agEG0UJOy4dlshIk9rVBuFhU2kZu2Yo53CwoKEDhJl0VYBnGJFsqZ6TaJsk8JCWbNmzdp2arcr4vfCggIpKi6SNVWhsk2ayJo1Vep3/B9Ur22ruLhY1UOfULdJUROpqqwtW1RUW7b2ewpqy1ZVqehd9LW4qFgqK2sd0KiH+jiIDB0uKUHZNVJdU63qNi0pkdWrVydkQf3asrX5dfF/jIUqW9pUVq916BcVoWyhVFZWqt+bNi1R/Qn6hIPNVq1amSgL+SpWV6hxQbsYT7SN35s3ay4rVdkaadKkSIqLi2T1qtVr2226tmzt97Ro0UJWrixX44L+Qp7wQwbIVVUZLrtS9b9JUZHq48ry2j5BFoTYwp7RhxbNW6h2qtZUyTeTl1mJvq1aUy2Ll69Wr2xRfSwtltLiAmnWtFDatm4uJUUixU1qpHlpkXTv0lFWrVgipSUF0q5NS+nYrrWsWLpIle3Tq7vSxZIltTlb+/XrJ1OmTFG6atmypYpgR05n0KlTJ2UPixcvVr/D2YHrHHpu3ry5tG/fXqZNm6Y+W2+99ZQOFy5cqH7PdY4IUpDgO2BbyDEdpEvB/6FL2A5SlkyePFl91rZtW2X/c+fOTaQ6QX9WrFih7K5Xr17KaQPQr+XLl6vvBTj0D2OC92Cf6D/Kop+tW7dW9jNr1ixVtmvXrqocDsGBfWNc0AfYZqtWrVR5yA46d+6s+hoeb8iGsUKb6HN4vNGvYPcAIvQwvsF4Y9yCMcR4Qmfh8Z45c6bSF643tPXbb78lxhvMnz9f/cQ44KDBYLwhD2wg2N0A+cPjjXrl5eVqbDGm4fGGDtAWgC7Q92C8oVf8vQBlZWXKDsLjjfFDHkzoF7KGxxu2CHkA7AFthscbv6NtlEPb4fGGXGGbxTgE4w35wjaLsQ2PN3SRymbxf+g3bLOwh2C88b1hm0U/g/FOtlnIHh5vjFXYZvEdwXijbmCzuDahr/B4Q86wzYbHGzYTtlmMdXi8wzbbqlWrOuON77c9R0DXKGtzjkDbOtCB2kjBZBNnXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW5vLODGAjcnylmVhuAz3FjkWs8UuFHEzVRwkxImeD8AN9cBuNELM2DD/lJc9LOWE7W4qFCev2G/vIm4w40ybiSJH9z25Fh1qFmmaOkmhQUyZItuctyfNpHl5ZWyYlWl+rl8ZaWsKK9QP9X/1/6s/awi8V75qlontS5w6Na2Wfv7tHm1TuPfqXUkpOZn5WStjXAtkZbN5yQiX1s0WyUtmy9JRLsuriyvjX5t3Um9hzGA8y3TdR9OL5LLHJFcFg6hADigMpUN/02CoyVVWVyHcAaF6ybPmcnRceGymIvD13HyduRwWXwPnEMBcAZl6j8cQgHZjHdybuzkskF6l2zHG863TGXhfKtvvFP9jvGGA05nvOH8DI83HJbh35PHO3AaZzve2dhsNuOdjc1mM94Yh0xlk202PN6ZbLZFixZ1bBbO2XRl0Z/weOvabKq/habniPHjxyceAmaCDtRGCiaLOOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF27ggoy9ze2MBNwVwhAZRG6kIomLCN8hR67kGIht3GtRNyzG10xbd8sZ5Cni9+MUBQ/rK+2Nqo60yOTQP3LmftG1Vql7ZsmZNtaxYVVXrVC0POVqVs7Wizu8rkpyveGlkwqgDHLZ4zV2UfZqPpiVNQvldS+qmHsD/Q+kGwukJUBZ5X12B16HfUH/+09IhHdKB2khB2HWyJ95mXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW5vLCDSBtvPgm2HqQi2yIWjZ6LW89kxdcBObuRIMwWvF7/o3bVMzj1ysIx4YqzaOh92+MPBDxvF5ygXlSZNCqV1ixL1ipJPeOXqqt+draHo1+RI12QHLD7XyUMcZnXFGvVasCQ56lW0osnDztZ1HLCJXLB1D9zC/03nfeV16DfUn//MdEiHdKASQgghhBDiKAMHDpR3331Xxo4dm/Jz5O36/vvv1f8HDx6cc73G6pgixARDBneXnp1bycujJ8nocTNU6gk4AxEdDQd/Q9ooornhZMSrU7vmWdVFjkE4Q9NFuwYO2LqpB+CMhQO2Sioqa3P06oJxW7xstXpFkrM0FNG61gFb1yFbG/3aolnR2vQEte83Ly1W9RsLcKpDNyXFTRqV3ITktQP1mWeekcsuu0yuuOIKOeKII1KWwdP1Bx54QD788EOVLBdhvptssokccsghss8++2TM/TRy5Eh57bXXVNJg5GvacMMN1ffsu+++ztQzTXLOEdt1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm1ckNGXub2xsPfee8uIESOUI/Tnn39W68Ywzz//vDosAXm8ttlmm5zruYrLjilb8HrxE9ji2cMGy1lDt5BFS5ZJuzatvE8toQ74alqkXh3aZJ8zGU66sHO1Nvq1QssBu3L1mqydgsvKK9QrW9ThZE2LpMXaiFe8SksKpWzMskQU7O+O2bUO2FB0bFGT2gPgXGfKzCXy8qhJMvrr0Fw6qJuK9s+3uZTzqP90cUiHzjtQv/32W7nhhhsylvnqq6/k1FNPVaeK4bQyJK7F/z/66CP1evPNN+XWW29NnFIZgEXjCSecIGPGjFGfbbDBBqrel19+qV6ffPKJXHvttet8X9z1bIDT6JDwN666unVMlstFRh9wQT6bfTDZdtS2bNq6blnauhvy2e6DqfbjnNtN27pOORdswTYuyOjL3N5YwEEVBxxwgLz88sty1llnyT333JM4QGPUqFFy4403qv8PHz5cndCbaz1fHFNwyCDHou+OqUzwevEbRPStqVwlBQU8PA8Rjni1bR0t72vywVq1//89vcDvn9d9r3xVpYpQ1wVlkWMWr9qzzrOjWdMma6NfQxGvifQCdR2wv39e+z7GJw5GjZ2+TjQ/nKjIM41UKYjmxwOrfIHzqP+scEiHTq+WPv30U7Xgw4ClAw7Is88+W/3cZZdd5LrrrkskwodD8owzzpC33npLHnroITnllFPq1L3mmmuUMxP5FO67777E6WgffPCBavO5556TQYMGyWGHHdag9WywdOnSyKd6RqmrW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUc8EWbOOCjL7M7Y2JSy65RCZMmCA//vij7LffftK/f3/1YB67mcCwYcNSrh+j1vPBMYVIuHyH14v/UIe5g7yvZS2bqle2IBq1HHlf10a71uZ1DacXSOGADZVB/WxAtCxe8yPkfS0J8r4m0gus/X9p3QO3wvleg/QDpZoPkxB5CudpNTzFSaIFzlR8jmj/fIlE5TXoP0sd0qGTK4/y8nLlYMSW/Orq6oxlX3rpJZk3b55ymt5yyy11PNPbb7+9nHbaaer9p556qo4DFUnzX3zxRTXR4PPAmQl23nlnufDCC+Xyyy+XO++8U6UBKCwsbJB6tsjlaX2Uurp1TJbL54gEV+Sz2QeTbUdty6at65alrbshn+0+mGo/zrndtK3rlHPBFmzjgoy+zO2NibKyMnnyySdVGqg33nhDHQ6FtSIevA8dOlQOPvhgo/WIG/B68R/qsOEftgRb8bMFeV9XVayRH8ZPkPbrdU2Kfl2bcqCOQ7auAxZRndlQUVUtFctWy6IIeV+RC7ruAVuhQ7dCB259MHYaJMvYFkwWqVIQ7Z8P8Br0nwKHdFhQg5nBIX766Sc56aSTlFMU24kQgfr000+rHKepcqC+8847aos+timhbDLYogTHKbbM//DDD4nBv/vuu+WOO+6QzTffXJ599tmUifW33npr9ZT+0UcflW233bZB6uXK+PHjlUO6efPmMmDAACNtEkIIIYTYgOsW/6EOCSFEZHWQ9zUp12vYARv+PVwWjtuGBC6TPwzsWpuKoBSHaxUpZywO2WpRWlT7U/1epD5v1rSIh1CRRrF2cS4CFZGacJ5utdVWcumll6rOw4Gajt1331290vHdd9+pn4j4DHuux40bp37ie1KBXKqbbbaZyk36xRdfJByacdezxZQpU1Su2Ljq6tYxWS4XGX3ABfls9sFk21HbsmnrumVp627IZ7sPptqPc243bes65VywBdu4IKMvczsh+Q6vF/+hDhuvDpsWN1GvdhHyviJ6Fflba52uFUmHb9V9/3eHLFIUVKj8rbmCELsPv56Z9eFbzRHputbhWutsLZbmzYrWccK2DDlfa52xRVJaYscJy2vQf6Y4pEPnHKiIJMU2I2y/z4WVK1eqSM/7778/kSA/DLYwgfBW+mS6d++uHJpB2YaoZ4s1a9bEWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbCcl3eL34D3XoPw2hw+Ki6HlfkcsUztfaiNbfD9ZatqJC7nvxu6zzumZ7+NY8WRmpjXRO2ISzNUMEbCYnLK9B/1njkA6dc6DiZHq8ooKDo66//nqZNm2acqK2adNG/vGPf8iBBx5Yp9yCBQvUz+DAqVSgLli0aFGD1bNFy5YtY62rW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUc8EWbOOCjL7M7YTkO7xe/Ic69B/fdIh8qK2al6iXtK/72c+/LZIPxk5PHBiVrv4Om3eVo/beSMpXwilaudYhW5VwzMJRqv6Pz8Jl8P7KSpXPtSGcsPCdNktysjYpWCPtPl+yjhP29//XjZJFOgKXcm4SceoadM6Bmis4ZfSXX35J/I48Bp999pkMGTKkzsldyDUKmjZN/1Qn+AyO2IaqZwscKhBnXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW4nJN/h9eI/1KH/5JMODxjSV94fM71eR+ahu/WXrh2iO62CFAS1ztbUTtZUTliUL1dO1OwP4QLwC6vvXFkJr0rok8U5OWHXSUWgfg+VoRO20VyDeedA3X///WXYsGHqUCZsh//nP/8pr732mnz99dfywgsvJAYfh0pVV+tdlGHjj7ueKeDAnThxosodgQO5EOXaoUMH6dixo0ydOlWVwe84UyyIll1//fVl9uzZqi6cu126dFHpBVasWKFSLeAUV+SrBfgd/4fzF/lckY5g8uTJ6rO2bdsmDgUL0hgsXLhQtYP3evXqJZMmTVKfQW9oC98LunXrJkuWLJHly5erMUT/URa/oz8tWrSQWbNmqbJdu3ZV7y9dulSNIWRBHzHurVq1ktatWyvZQefOnVVf0Tbo16+fkq2qqkq1iT4jHy/o1KmT6lcQGdynTx8V4VxZWamSDGPcgjHEeCLEHPIB9HfmzJmyevVqKS0tVW399ttvifEG8+fPVz8xDnPmzEmMN+RBvo8gchnyh8cb44A2i4uL1ZiGxxs6QFsAukDfg/GGXmELANdDs2bN6ow3xm/ZsmVq7CArvgdjifHD0x/IAwJ7QF8x3n379lX9hfwoh7bD4w25Fi+u/eOFshiHYLwhH8YU4EEHxhbjj8/QB/wfOkBfwzaL/0O/YZuFPQTjje8N2yz6GYx3ss1C9vB4Y6zwO/qA8cV34IEMxjsY/yByHGMQjDfkh+xhmw2PN2wmsFmURf3weEOPgc3iNWHCBNUHjDe+P2yzwXhDXoxp2GbxXni8MWaBzbZv377OeEMPYZuF3lKNt+4cAfAd2cwRGNe5c+emnCPQ72BexHhBt/XNEYHNZpojMC7h8c40RwD0xdYcgd8xZrnOERh/9A/jjXqBzerMERh/2KjuHIG2cTBipjkCOkR5/Bw4cGC9cwR+R//TzRFoB2OGsQ2Pd65zRGCz2c4RYZvVnSMw3rg2080RaCf4ewkdY6wzzRHh8U43R2C80Q/dOQJ9w/fZmCPwkBs6TTdHBA+cCSGirjVcy8RfqEP/yScd9u5aJuceOVhGPDFWbZkPR6Ii8hTOU3yOcg2VgiCgsgqHcKWPdM3knLXjhNUHaQR+T0eQJh/sWidsyzR5YktLmtAJK6JSTkz+dZoM2LC/EweVFdRgpes4u+66q5q4rrjiCjniiCOyqosbiz/96U/qZuCMM86QM888U72/zTbbqJuM22+/Xfbaa6+UdW+44QaVj3WHHXaQhx9+uEHq2TpNDDdrUf8QRKmrW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUc8EWbNPY53ae4O4/1GF+zQckN6hD/8lHHU6ZuUReHj1JRo+boZyMcHjutEU3OWCnvjk7T10CTtjvf5wg63Xpvja6NXMEbNg5G6QtqFqTvRPWBKmcsHUO38rknF0bNdvUYyfsFNjoqEky+uuQjQ7qpqKobdio7tol7yJQk0H0BhyoTz75pDrdPgAROHBoBtEXqQiiW8J5S+OuZwtE3cRZV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNV+nHO7aVvXKeeCLdjGBRl9mdsJyXd4vfgPdeg/+ahDOKDOHjZYzhq6hVRUrvHa0ZaJ4qIm0r9P95xyaGJ8ap2rVSmdsL+/l6JMDk5YRF7i8C+8cnHC1hcB22LtAVzpyjSEbYwaO32dKGk4UZG/FykoECU9ZHB3aQi8d6DCKYnoVGwxC7Y7JoMtZCDYJhZsV8O2vWD7YCqCrW5B/YaoZwtskYs6kUSpq1vHZLlcZPQBF+Sz2QeTbUdty6at65alrbshn+0+mGo/zrndtK3rlHPBFmzjgoy+zO2E5Du8XvyHOvSffNYhHGylTb13CVnVX0lxE/Vq20pydsImcryG0gzUOaAr5IRNjoytWlMTyQm7rLxSvaKC1A7Nk/PBlgapBgJna2onbFCmabG+ExaRp3CeVmOjfJLIgTMVn/fs3KpBoqW9v1qOOuoolSNw+PDhcvbZZ6csE+S/Q86vAORie/fdd2Xs2LEp6yC31/fff6/+P3jw4AarZwtEwqZzONuoq1vHZLlcZPQBF+Sz2QeTbUdty6at65alrbshn+0+mGo/zrndtK3rlHPBFmzjgoy+zO2E5Du8XvyHOvQf6tBvXNDf707Y0kj1kXWzAgdzBY7VJCdswuEaioBNOGhzdMKuMeaELa7rZE1ywga5Xz8Yi3M6MvcTvlikoEAUddx470DdcccdlQP1+eefl1NPPVUdaBAGhx68/PLL6v+77LJL4v29995bRowYoRyaP//8s2y44YZ16qE9PK3AoRHIX9pQ9QghhBBCCCGEEEJI4wPRm4jixKtt69ycsMEBWclpBlKmIQjnhl1bZk3o4LHsnLAV6mUCtIf8vX89fIvY0wt4f4gUTrXdZ5991CFRf/jDH+S6665LRJriBNzzzjtPfvzxR3Vy7UsvvaROgQ04//zzlXMVW+bvuecetc0ejBo1SkWzIonsNddcI4cddlid74y7no1kuFB7VGOLUle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YprHP7TyAyH+ow/yaD0huUIf+Qx36DfVndixXV65Jk+s1nSPWjBM2Fc9ev6+UlpiJCW00h0jBWXr33XfLGWecIR999JHstttuykFZXV0tkydPVkrGqXn3339/HecpuOSSS1T0Khys++23n/Tv319Fgf7222/q82HDhqV0ZsZdzwb4zqi5VqPU1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQUZf5nZC8h1eL/5DHfoPdeg31J85CgoKlMMSr3Y5RMKurggdzBVKM7BiZYXc+8J3KndrfRQXFaqI3Ljx3oEKtt12W3n11VfloYceUtGcOKyppKRENt98c7V1Ho7J5K39oKysTJ588kkZOXKkvPHGG6peYWGhDBo0SIYOHSoHH3xwyu+Lu54NqqqqYq2rW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUc8EWbOOCjL7M7YTkO7xe/Ic69B/q0G+oPwedsE2L1Kt9ijOgfvp1kXwwdnrGKFXkVN1pi24NElnshQP1vffeq7dMly5d5NJLL1WvbCgtLVUHUOHlcj3TtGjRIta6unVMlstFRh9wQT6bfTDZdtS2bNq6blnauhvy2e6DqfbjnNtN27pOORdswTYuyOjL3E5IvsPrxX+oQ/+hDv2G+vOLA4b0lffHTM9YBklID9ipNh1m3BQ2yLeSBqddu3ax1tWtY7JcLjL6gAvy2eyDybajtmXT1nXL0tbdkM92H0y1H+fcbtrWdcq5YAu2cUFGX+Z2QvIdXi/+Qx36D3XoN9SfX/TuWibnHjlYCgsKVKRpGPyO9/E5yjUEdKA2UqZNmxZrXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW4nJN/h9eI/1KH/UId+Q/35x5DB3eW2c4fIzlt2V7lOAX7id7yPzxsKL7bwE0IIIYQQQgghhBBC8pveXcvk7GGD5ayhW8j4nyfIxhv1b5Ccp8kwArWRst5668VaV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNV+nHO7aVvXKeeCLdjGBRl9mdsJyXd4vfgPdeg/1KHfUH9+U1hYID26dXbCeQroQG2kVFZWxlpXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc7tpW9cp54It2MYFGX2Z2wnJd3i9+A916D/Uod9Qf/5T6ZAO6UBtpCxatCjWurp1TJbLRUYfcEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TjkXbME2Lsjoy9xOSL7D68V/qEP/oQ79hvrzn0UO6ZAOVEIIIYQQQgghhBBCCElDQU1NTU26D4n/jB8/XsrLy6V58+YyYMCAxPvV1dVSWBjNfx6lrm4dk+VykdEHXJDPZh9Mth21LZu2rluWtu6GfLb7YKr9OOd207auU84FW7BNY5/b061biD9Qh/k1H5DcoA79hzr0G+rPf6pj0KHu2oWW1EiZPn16rHV165gsl4uMPuCCfDb7YLLtqG3ZtHXdsrR1N+Sz3QdT7cc5t5u2dZ1yLtiCbVyQ0Ze5nZB8h9eL/1CH/kMd+g315z/THdIhHaiNlIqKiljr6tYxWS4XGX3ABfls9sFk21HbsmnrumVp627IZ7sPptqPc243bes65VywBdu4IKMvczsh+Q6vF/+hDv2HOvQb6s9/KhzSIR2ojZRmzZrFWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbCcl3eL34D3XoP9Sh31B//tPMIR3SgdpI6dixY6x1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm1ckNGXuZ2QfIfXi/9Qh/5DHfoN9ec/HR3SIR2ojZSpU6fGWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbCcl3eL34D3XoP9Sh31B//jPVIR3SgUoIIYQQQgghhBBCCCFpoAO1kcIt/P7jgny+bPPkFn6/cUE+buHPrTy38Ovjgoy+zO2E5Du8XvyHOvQf6tBvqD//6eiQDulAbaRUV1fHWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbCcl3eL34D3XoP9Sh31B//lPtkA7pQG2kLFiwINa6unVMlstFRh9wQT6bfTDZdtS2bNq6blnauhvy2e6DqfbjnNtN27pOORdswTYuyOjL3E5IvsPrxX+oQ/+hDv2G+vOfBQ7pkA5UQgghhBBCCCGEEEIISUNBTU1NTboPif+MHz9eysvLpXnz5jJgwIDE+1VVVVJUVBSpzSh1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm0a+9yebt1C/IE6zK/5gOQGdeg/1KHfUH/+UxWDDnXXLoxAbaTMmjUr1rq6dUyWy0VGH3BBPpt9MNl21LZs2rpuWdq6G/LZ7oOp9uOc203buk45F2zBNi7I6MvcTki+w+vFf6hD/6EO/Yb6859ZDumQrvhGyurVq2Otq1vHZLlcZPQBF+Sz2QeTbUdty6at65alrbshn+0+mGo/zrndtK3rlHPBFmzjgoy+zO1xUFlZKXPmzJFFixZJkyZNpH379tKhQwf1f0Js49v1QtaFOvQf6tBvqD//We2QDulAbaSUlpbGWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbbQFn6bPPPiujRo2S7777TjlRw5SUlMhWW20lO+20k/zpT3+Sdu3aNVhfSX7jw/VCMkMd+g916DfUn/+UOqRD5kDNc5gDNX+fEbggH3Og5laHOVD1cEE+5kDNrTxzoOrjgoyNNQfq/Pnz5bbbbpNXX31VqqurZbPNNpMNNthAevToIS1btlTvLV68WGbPni3ffPONTJgwQYqLi+WAAw6Q4cOHS+fOnWPtr6swB2p+zQckN6hD/6EO/Yb6858q5kAlDc2vv/4aa13dOibL5SKjD7ggn80+mGw7als2bV23LG3dDfls98FU+3HO7aZtXaecC7ZgGxdk9GVuN8ljjz0me+21l8yYMUOuv/56+fLLL+WJJ56QK664Qk488UQ5/PDD5YgjjlCO0iuvvFJeeukl+eyzz+Tiiy+WiRMnyr777qvaIKQxXC9EH+rQf6hDv6H+/OdXh3RIVzwhhBBCCGnUvPfee/Lwww/L5ptvrl2nVatWMnToUPX66quv5I477pBjjjnGaj8JIYQQQkjDQAdqIwWHIMRZV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNV+nHO7aVvXKeeCLdjGBRl9mdtNMnLkyJzqIyfqv//9b2P9IcTl64XoQx36D3XoN9Sf/7R3SIfcwt9IKSgoiLWubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv0453bTtq5TzgVbsI0LMvoytxOS7/B68R/q0H+oQ7+h/vynwCEd0oHaSMFBCXHW1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQUZf5vY4+fTTT+XSSy+VU045RS677DKV/5QQ2/h6vZDfoQ79hzr0G+rPf+Y7pEM6UAkhhBBCCEkDDpM6+eSTZdasWSrv6YQJE+T4449X7xNCCCGEkMZBQU1NTU1Dd4LYY/z48VJeXi7NmzeXAQMGJN6vqKiQkpKSSG1Gqatbx2S5XGT0ARfks9kHk21HbcumreuWpa27IZ/tPphqP8653bSt65RzwRZs09jn9nTrloZkzz33lHPOOUf22muvxHs33nijvPHGG/LBBx80aN9cxEUd+ooL8wHJDerQf6hDv6H+/KciBh3qrl0YgdpImTdvXqx1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm1ckNGXud0kxx57rIwdOzblZ1VVVevk38Lva9asial3pLHi6vVC9KEO/Yc69Bvqz3/mOaTDoobuAGkYVq5cGWtd3Tomy+Uiow+4IJ/NPphsO2pbNm1dtyxt3Q35bPfBVPtxzu2mbV2nnAu2YBsXZPRlbjfJpptuKieeeKJsscUWctZZZ8mgQYMSnx155JHyt7/9TZ555hlp166dzJgxQ8aNG6eiUgmxiavXC9GHOvQf6tBvqD//WemQDhmB2kjJJQQ6Sl3dOibL5Xuovgvy2eyDybajtmXT1nXL0tbdkM92H0y1H+fcbtrWdcq5YAu2cUFGX+Z2k/z973+Xd999VzbYYAM57rjjlDP122+/VZ/h/3fffbest956snjxYll//fXl3nvvVQdKxc2iRYvkpptukr333ls222wz5fA9+OCD5aGHHlJb3NKxevVque+++2S//fZT9bbeems5+uij5fXXX4+1/yQ/rheiD3XoP9Sh31B//lPikA6ZAzXPSZfLAdvOmjRpEqnNKHV165gsl4uMPuCCfDb7YLLtqG3ZtHXdsrR1N+Sz3QdT7cc5t5u2dZ1yLtiCbRr73O5C/kxsFfvXv/6lIk632WYbFZEKp2NDM23aNOX0nD17thrDXr16KacpImKxnEcfH3nkEWnZsmWdeqtWrZITTjhBxowZo+rBSbx8+XLVHjj00EPl2muvNdZPF3SYL7gwH5DcoA79hzr0G+rPf9bEoEPmQCUZmTJlSqx1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm1ckNGXud0WHTt2lEsuuUTefvtt6dmzpxx11FFy2mmnyQ8//NCg/Tr//POV87Rv377y6quvyn//+18VNfvYY49JmzZt5LvvvkvpCL3mmmuU87Rfv37y1ltvyUsvvSTvvPOO3H///dKsWTN57rnn5Nlnn20QmYj/1wvJDHXoP9Sh31B//jPFIR3SgUoIIYQQQho91dXV8vXXX8ubb74pn3/+ubRo0UIuu+wy5Ujt1KmTDBs2TIYPH66iFOLmt99+SxxydfXVVysnagC245933nnq/6+99pqKOA2YPn26vPjii+rQq1tuuUV69OiR+GznnXeWCy+8UP3/zjvvVPITQgghhJDU0IHaSMEhCHHW1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQUZf5naTTJo0SfbZZx/lJD377LPl2GOPld12201FbHbu3FmuvPJK5Vjt0KGDDB06VP7yl7/ITz/9FFv/Zs2alfj/RhtttM7nm2++ufqJLf3z589PvP/yyy9LVVWV2t6fqh7yp5aWlsqcOXPkiy++sNZ/kl/XC9GHOvQf6tBvqD//aeeQDulAbaQUFRXFWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbTYIt++3bt1fb4rEV/sMPP5QDDzxQLrjgAlm6dKkq061bNxX9+cYbb0jr1q3lsMMOi61/Xbt2Tfw/VQRs4MwtLi5Wh10FjBs3Tv3caqut0h7MEOR3pQPVPVy9Xog+1KH/UId+Q/35T5FDOqQDtZEyd+7cWOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF27ggoy9zu0nggEQEau/evZUTEnlQkfsU2+GxfT4MtsFff/31ypEaF8jF+oc//EH9//LLL6+TD+zbb7+Vm266Sf0ffQ6fVvvrr78m+pyO7t271ylL3MHV64XoQx36D3XoN9Sf/8x1SIfuuHIJIYQQQghpAAYOHCgPPvigcj4i0nTZsmXy+OOPqy37/fv3T1knk1PSBrfffruKlEVagX333Vd69eql8pbCwdu0aVM55ZRTVPqBMAsWLKh3+xsOoAKLFi2yLAEhhBBCiL/QgdpIyWXRH6Wubh2T5eK+sYkbF+Sz2QeTbUdty6at65alrbshn+0+mGo/zrndtK3rlHPBFmzjgoy+zO0mufnmm+WGG26Qq666SiorK9WhS4MGDZL77rtP5Qh1gcLCQtl4443VAVdwdk6ePDnxGQ68atWqlXKoNmnSJPF+cKAUHKzpCD5buXKl1f6T/LleiD7Uof9Qh35D/flPD4d0SAdqIwURCeF8Wrbr6tYxWS4XGX3ABfls9sFk21HbsmnrumVp627IZ7sPptqPc243bes65VywBdu4IKMvc7tJsGUfp9TDAblw4UIpKytTW/ldYfny5XL88cer7fqIiMWWfeQ1hbMX+Vrh/EX/x4wZI3fffXciXxicqZBJBziNTQLn7cSJE1VahBkzZqgDrpo1a6bGeurUqaoMInxramoSkbLrr7++zJ49W9WFY7dLly6J1ALIUQsn8rx58xJpDfB/OH4ROYxUBIFTuW3btkp/wbY/3HxBrytWrFBjg+hdHBwWRODCSY7vBYhAXrJkiRpzjB/6j7LoJ3LfwlkdHOoFW0Y55MnF+PXt21f1AWMOhzbKQ3aAw8jQV7QN+vXrp2TDIV9oE32ePn26+qxTp05qvPA96FufPn1k2rRpSt/NmzdX4xaMIcZzzZo1Sj6A/s6cOVNWr16t6qKtIA0F6oHgoDGMAw4QC8Yb8gTpIRC1DPnD44165eXlamwxpuHxhg7QFoAu4OQPxht6hS0AXFuwg/B4Y/wQ9Q39QtbweLds2VLJA2APaDM83ugv5Ec5tB0eb8i1ePFi9TvKYhyC8YZ8GFOAvMEY2yAKG32ALlLZLP4P/YZtFnoKxhvfG7ZZtBlcg8k2C9nD442xCtssviMYb9QN2yz0FR5vyBm22fB4w2bCNouxDo932GbxCo83vj9ss+HxRj/CNosxCI83xiywWYxFeLyhh7DNujxHBPbk4hwRtlnOEannCLSLvrs6R6CfwXhzjpiWco7AuOH7bc4RaFuHghp8G8lbcNAAjBpGOWDAgMT7uGBg4FGIUle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YprHP7enWLbbBDRdumHIBNya4YbDBbbfdJvfee6+6sXnppZfUDU8Y3HQccMABSo5rrrkmccDVNttso25ssP1/r732Stk2nK8jR46UHXbYQR5++OGc+9pQOsxHXJgPSG5Qh/5DHfoN9ec/E2PQoe7ahYdINVJyiaqIUle3jslyLkWO2MAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF27ggoy9zu0n23HNPeeSRR1R0RLZgwY2t/nvssYfY4s0331Q/jznmmHWcp0EkyiGHHKL+/+qrrybeR9QPCCI+UhFE1GTKk0oaBlevF6IPdeg/1KHfUH/+U+yQDulAbaQgXDnOurp1TJbLRUYfcEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TjkXbME2Lsjoy9xukn/961/yxhtvyE477aQOavrggw8ybudCtOmoUaPk/PPPV5Gbb7/9tmrDFsF2OWynTEcQnRFsTwy2yCW/l0ywvQ7b3ohbuHq9EH2oQ/+hDv2G+vOfng7pkA7URko4p0scdXXrmCyXi4w+4IJ8Nvtgsu2obdm0dd2ytHU35LPdB1Ptxzm3m7Z1nXIu2IJtXJDRl7ndJDic6emnn5aLLrpIbeM67bTTZMstt5Q//elP6v/nnXee/O1vf5MTTzxR9tlnH7U1Hu9PmDBBHTz13HPPyaabbmqtf8g5FnakpiLI/xVOIzBw4ED1c+zYsSnrwEn8/fffq/8PHjzYaJ9J/l4vRB/q0H+oQ7+h/vxnkkM65CFShBBCCCGk0YODHA488ED1gsMREab4+dNPP6kt8PgchxUgDykcq4hW3WSTTWLp27bbbiuvvfaaPPvsszJs2LB1trPBEfrKK6+o/2+33XaJ9/fee28ZMWKEkuPnn3+WDTfcsE69559/Xh20gIMq4BQmhBBCCCGpoQO1kYIT0eKsq1vHZLlcZPQBF+Sz2QeTbUdty6at65alrbshn+0+mGo/zrndtK3rlHPBFmzjgoy+zO02QTSmSxGZw4cPl//973/KCXrOOefI5Zdfrk6hBThBFmkHcPItTiQ+/vjj62x7w+FSL7/8spx11llyzz33JLb1w0F84403JtrH6b7ELXy5Xkh6qEP/oQ79hvrznzYO6ZArpUZK06ZNY62rW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtuyGf7T6Yaj/Oud20reuUc8EWbOOCjL7M7Y0J5De97bbbVBoBOFLff/996dOnjxQWFqrTaauqqtQNxt133y2dOnWqUxfOVaQa+PHHH2W//faT/v37q6jT3377TX2OiNbDDjusgSQjmeD14j/Uof9Qh35D/flPU4d0yByojZQ5c+bEWle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Qz7bfTDVfpxzu2lb1ynngi3YxgUZfZnbGxu77rqr2qZ/zDHHSPfu3ZUD9Ndff1WHP5100klqi/9WW221Tr2ysjJ58skn5eyzz1aOWNSZO3euDBo0SK677jq54oorGkQeUj+8XvyHOvQf6tBvqD//meOQDhmBSgghhBBCiAf06NFDLr300qzrlZaWqm36eBFCCCGEkOxhBGojBZELcdbVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh9MtR/n3G7a1nXKuWALtnFBRl/mdkLyHV4v/kMd+g916DfUn/+4pEM6UBspOE02zrq6dUyWy0VGH3BBPpt9MNl21LZs2rpuWdq6G/LZ7oOp9uOc203buk45F2zBNi7I6MvcTki+w+vFf6hD/6EO/Yb685/FDukwpy38M2bMkOnTp8uiRYukSZMm0r59e+natat07tzZXA+JFZYvXx5rXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW63yU033SR/+tOfZKONNmrorpBGjC/XC0kPdeg/1KHfUH/+s9whHWbtQP3ll1/k8ccflw8//FBmz56t3qupqVE/CwoK1M+ePXvKkCFD5NBDD5UNNtjAdJ+JAeDwjrOubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv0453bTtq5TzgVbsI0LMvoyt9vksccek4cffliddI+T6/FC/lFC4sSX64Wkhzr0H+rQb6g//2nikA4LagLvp4bjFCd1fvbZZyrCdOutt1bOUSwmW7ZsKdXV1Sq0Fk7Vb775Rr7++mtZuHChbL/99nLuuefKJptsYl8asg7jx4+X8vJyad68uQwYMKChu0MIIYQQ4vy6BdEOb731lrzxxhvy+eefy5o1a2TTTTdVUal77723dOzYscH65jqu6JAQQgghxOTaRSsH6g033CCHH364St769NNPywcffKC2Np188smy1157yR/+8AfZaaedZP/995dTTjlF7r77bvnoo4/kwQcflHbt2smRRx6p2iDuMHHixFjr6tYxWS4XGX3ABfls9sFk21HbsmnrumVp627IZ7sPptqPc243bes65VywBdu4IKMvc7tNEBxwyCGHyEMPPSSjR4+Wyy67TJ1k/89//lN23nlnOe644+S5556TZcuWNXRXSR7jy/VC0kMd+g916DfUn/9MdEiHRbpP4fEEvkuXLtoNYzv/DjvsoF5nnXWW3HPPPbn0kxBCCCGEkNgJggHwmjVrlgoiCCJTr776atl9993lhBNO4G4rQgghhJA8RsuBes011+T0Jdjmf/311+fUBjFLWVlZrHV165gsl4uMPuCCfDb7YLLtqG3ZtHXdsrR1N+Sz3QdT7cc5t5u2dZ1yLtiCbVyQ0Ze5PS4QZfrOO+/If//7X5XKqqKiQvr376+28yNg4IUXXpChQ4fKFVdcIYcddlhDd5fkET5eL6Qu1KH/UId+Q/35T5lDOsz6EKlMTJgwQQoLC6Vv374mmyUWQG6HOOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF27ggoy9zu02w+ypwmn7yySdSWVkpXbt2lT//+c/Kcbrhhhsmyh577LFqu/+tt95KBypplNcLSQ916D/Uod9Qf/7T3CEdauVATQbnTv3rX/+Siy66SP2OA6SQ+xQ5UHFK6YknnigrVqww3VdiEGxBi7Oubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv0453bTtq5TzgVbsI0LMvoyt9tku+22U+tcHIx68MEHy+OPPy7vvfeenHfeeXWcp6CkpEQFD7h0SizJD3y5Xkh6qEP/oQ79hvrzn1kO6TBSBCoS6o8YMUJ23HFH9TueziPB/p577qm2ND3wwAPqIKnzzz/fdH8JIYQQQgixyh577KGCArDWLSqqf7kMZ2ubNm1i6RshhBBCCPHEgfriiy/KH//4R7nzzjvV70ik36xZM3UyKU4oRfTpm2++SQeqw2AbWpx1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK0dTfks90HU+3HObebtnWdci7Ygm1ckNGXud0mt9xyi9rG/+yzz6odVi1atFDvP/fcc7Jy5Uq1VR9r3oBOnTo1YG9JvuLL9ULSQx36D3XoN9Sf/3R1SIeRtvBPmzZNdtppJ/V/5IT69NNPZZtttkksJLGNaf78+WZ7SowfiBBnXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+Wz3wVT7cc7tpm1dp5wLtmAbF2T0ZW63yYwZM+Sggw6Sq666SqZMmZJ4f+zYsXLttdcqB+rChQsbtI8k//HleiHpoQ79hzr0G+rPf5Y5pMNIDtTWrVurp/Lg888/l/Ly8oRDFUydOlU6dOhgrpfEOHSg+o8L8vlyk00Hqt+4IB8dqLmVpwNVHxdk9GVutx2Bir4+/PDDsummmybev+666+Q///mPChRAOitCbOLL9ULSQx36D3XoN9Sf/yxzSIeRtvBvscUWKpl+t27d5L777lO5oZArCtGo77//vjz55JOy++67m+8tMUZhYWGsdXXrmCyXi4w+4IJ8Nvtgsu2obdm0dd2ytHU35LPdB1Ptxzm3m7Z1nXIu2IJtXJAxXR+qq2ukonKNlBQ3kcLCAqNtu8YXX3whJ5xwgjpMKpktt9xSjjnmGHnqqacapG+k8eDL9ULSQx36D3XoN9Sf/xQ6pMOCmpqamiinYJ144okyefJkKSgoULlOjz/+eBWNeuyxx0qfPn3UE/vOnTvb6TXRZvz48SpCuHnz5jJgwICG7g4hhBBCPGPKzCXy8qhJMvrrGVJZVS3FRYWy06BucsCQvtK7a1lerlsGDx4sZ511lhx33HEpP//3v/+tIlC//vrr2PvmOq7okBBCCCHE5Nolkiu3S5cu8sorr8gzzzwjH3zwgXKego022kgtJp9//nk6Tx0Hzu846+rWMVkuFxl9wAX5bPbBZNtR27Jp67plaetuyGe7D6baj3NuN23rOuVcsAXbuCBjuA+jxk6Xs0eMkg/GTlfOU4CfH6x9H59HbdtlNt54Y3VoakVFxTqfYccV1sFY9xJiE1+uF5Ie6tB/qEO/of78Z7JDOiyKXLGoSDbffPM675WVlck+++xjol/EMtXV1bHW1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQcagD4g8HfHEWKnGZqWk/UprqmvfwOc9O7fSjkR1QT4dTj75ZDnttNPk8MMPVwdG9erVS+26Qp7/F154QX788Ue55557GrqbJM/x5Xoh6aEO/Yc69Bvqz3+qHdJhZAfqSy+9JB9//LHMmzcvpUBYZD766KO59o9YolWrVrHW1a1jslwuMvqAC/LZ7IPJtqO2ZdPWdcvS1t2Qz3YfTLUf59xu2tZ1yrlgC7ZxQcagD9i2X4BUpxmSPeHzl0dPkrOHDc6qbdcZMmSI3HzzzXLDDTfIVVddpda1AJmv2rVrp97feeedG7qbJM/x5Xoh6aEO/Yc69Bvqz39aOaTDSA7UW2+9Ve6//34pLi6W9u3bW0/qilQBl112mVxxxRVyxBFHpCwze/ZsGTlypHz44YcyY8YM9R4OucICGIcAdOzYMWW91atXq3qvvfaa/Pbbb1JaWiobbrih+p599903bZ/irmcaOlD9xwX56EDNrQ4dqHq4IB8dqLmVpwNVHxdkbNmypcycv1xt0w8iTdOBz0ePmyF/PXyLhJPRdfl0wboMO6u+//57tbZEwADSWG266aZqDUyIbXy6XkhqqEP/oQ79hvrzn1a+O1CRE+oPf/iD3HnnndKsWTOxybfffque8mfiq6++kuHDh8vSpUulSZMm0qNHD/X+r7/+KpMmTVLRsg888IBa8IZZtWqVcq6OGTNG1dtggw1k+fLl8uWXX6rXJ598Itdee+063xd3PRvMnDlT+vXrF1td3Tomy+Uiow+4IJ/NPphsO2pbNm1dtyxt3Q35bPfBVPtxzu2mbV2nnAu2YJu4ZURE5ZyF5TJx+mKZOG2x+jlh6iIpX71Guw3kRF1duUZKS+pfVvqmQziFN9tsM/UiJG58u17IulCH/kMd+g315z8zHdJhJAcqnH577rmndefpp59+qk5AXbFiRdoycJqeeeaZ6ueOO+4o1113nay33nrqs2nTpsn5558vY8eOlb/85S/y3//+V52qFXDNNdcoZyaUcd999yUcrzgY6+yzz5bnnntOBg0apHJfhYm7HiGEEEKIKWfppOlLZMK0ReonHKbLV1bm1G5xUaE0LW4i+cbEiRPVjqH58+fLmjVrUjpXse4khBBCCCH5T0ENVtNZ8te//lUdInXLLbdY6VR5eblyMCJqNJxfNdUW/kceeUSuv/565TSFgxTbzsIsXLhQ9tprL1myZIlyYAbOyenTpysnMBbEiFBNPkn1qaeekssvv1w6deqkHJxBmoK46+XK+PHj1XjCcTxgwIDE+3BKt2jRIlKbUerq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLAF25iSEcu7uYtWJqJK8Zo0fbEsK6/fWdqmZYn079lW5i9eKb/NWiqZdvE3KSyQnbfsrp0DtT750q1b4ubNN9+Uc889N+PBBXCgor/ETR3mA41hzst3qEP/oQ79hvrznxUx6FB37RIpAhX5SI8//nj529/+JrvvvrvKg5oq79XWW2+ddds//fSTnHTSSepwKjhpEZn59NNPJ/KaJvP555+rn7vssss6zlOARP9bbLGFckp+9913CQfqyy+/LFVVVbL55puv48wEBx98sHLMzpkzR7744gvZdtttG6SeLWAcUY0wSl3dOibL5SKjD7ggn80+mGw7als2bV23LG3dDfls98FU+3HO7aZtXaecC7ZgmygyJpyla52kE6Zl4Sxt1VT6dW+jXv17tJG+3cukumK5yh0/ZeYSOXvEqIynSOEx/AE79dXuqy86vPvuu6Vr164yYsQItW4rKSlp6C6RRogv1wtJD3XoP9Sh31B//lPukA6LouYgWLZsmbz++uvyxhtvpFzIR30qj0hNOE+32morufTSS5X3Fw7UdCD3KSI7e/funbZMEGQb3n41btw49RPfkwoslJHvCrlJww7NuOvZAhG56Q7WslFXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc7tpW9cp54It2KY+GbGmmbfWWfp73tIlsqy8Iitnab/uZdKvRxtp17p0nYfgEyfOUH3o3bVMzj1ysIx4YqygSPhAKUSeYnmFz1HOlHyugDz6F1xwgXrwTUhD4cv1QtJDHfoPdeg31J//LHFIh5EcqFdddZXKOXriiSfK+uuvryJFTdGzZ091Sv3222+vVR4L20yLW2zhh0MS4NCm8MIYBHlIU9G9e3fl0AzKNkQ9QgghhDROlLN08e/b8Gtzly7Wc5a2bKocpIgoDZym7cvWdZbWx5DB3aVn51by8uhJMnrcDHVgFHKe7rRFNxV5mo3z1Cc6d+6sDv8khBBCCCEERPJ8TpgwQc444ww5+eSTjY8inJxhR2eu4FT7lStXSmlpqey9996J9xcsWJDY4p+ONm3aqJ+LFi1qsHq2yOUUsyh1deuYLOfKSW22cEE+m30w2XbUtmzaum5Z2rob8tnug6n245zbTdu6TjkXbMG2s3Teyuby6X/HJ5ymS1fU7ywta1nye2Rpj+jO0nTjDCcpcpyeNXQLqahcI01Lmhhr21WOOuooefTRR1WKpUxrN0Js4sv1QtJDHfoPdeg31J//9HNIh0VRn8qbOuTIJvfcc486PRWcdtpp6qCpgCCqoGnTpmnrB5/BAdtQ9WwxZcqUjGkPTNfVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh9MtR/n3G7a1nXKuWALppyl8xev+n0b/trcpUuW6zlL+ya24de+OrSJ7izNZpwLCwuktGluu4580WFlZaUaU+T6R/olOFGTxxi/X3fddQ3WR5L/+HK9kPRQh/5DHfoN9ec/UxzSYaRVMA55uvPOO2XIkCFOeYPD3HXXXaqPYOedd5ZTTz21zudNmjTJeLJqmPCCOe56poADd+LEicrwcCAXUjDgJ3JJTJ06VZXp0KGDuqkLomWRnmH27NmqLpy7Xbp0UekFcAoaomThREe+2iD1Av4P5y/yuSIdweTJk9Vnbdu2ldWrV6vvD9IYILUC2kH6h169esmkSZPUZxUVFbJ8+XL1vaBbt24q5wXewxii/yiL3+fOnauSCc+aNUuVxWEPeB+yYQwhC/qAcW/VqpW0bt06cRgZHgKgr2gbwI4hGw76QpvoM/Lxgk6dOql+BZHBffr0kWnTpqmbK5zShnELxhDjiVy7kA+gv8gZDPkRBY22fvvtt8R4g/nz56ufGAccIhaMN+TBZAFw4wb5w+ON8cOYFhcXqzENjzd0gLYAdIG+B+MNvQa6KCsrk2bNmtUZb4wfchxDv5AV442xxPjhoDbIA2APSOiMtjDeffv2Vf2F/CiHtsPjDbkWL16sfkdZjEMw3pAPYwrwoANji36gbfQBuoAO0NewzeL/0G/YZmEPwXjje8M2i34G451ss5A9PN4Yq6APGF98B+TFeKNuYLOIHIe+gvGG/BjPsM2Gxxs2E9gsyqJeeLzDNotX0IdgvMM2G4w35MX4hm0W74XHG2MW2CwO/guPN/QQtlnoLdV4684RAN+RzRyBccU1nWqOQP+CMcR4Q7f1zRGBzWaaIzAuunMEsDlHoE9oL9c5IrAXjDfqBTarM0egLmTUnSPQNsg0R0CHKI+foL45Iuh/ujkiaA9jGx7vXOeIwGaznSPCNptujoCcK1aLjBs/TabNWynzl4lymC4rr5L6aNW8WHp1ai5d2hZLj07NZYctN5RlC2ep8Q7miJkzZ8iSBZJxjsB449rRnSPwM7AB03NEoON0c4Qr2+ZvueWWxP9Hjx6dsgwdqMQ24fMbiJ9Qh/5DHfoN9ec/axzSYUFNcMJSFlx99dXyzjvvqIUubhqwYMaNW52GCwrU1icT7LrrrmqxfsUVV8gRRxyRsSwW+cjRGhw8hVyq9957r7pRCrPNNtuom4zbb79d9tprr5Rt3XDDDSof6w477CAPP/xwg9TLFRzkhRsq3BDhQK4A3NDgxjEKUerq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt6zrlXLCFTCjn4ZJQZOm02ryli5evrrduq+Yl0r9HG+nctkgGbdRNRZl2bNPMykNVl+f2dOsW4g/UoTlcn/NI/VCH/kMd+g315z+zY9Ch7tolUgTq+++/rxymEAJRCkF0T0ODyKKzzjpLPv74Y/X7nnvuKTfffLOKHEkGEThwaAbRF6kIolvCua/irmeLIN9qXHV165gsl4uMPuCCfDb7YLLtqG3ZtHXdsrR1N+Sz3QdT7cc5t5u2dZ1yLthC2Fm6cOkq5SSdsPaAJzhNFy/Tc5b2w+FOa/OV4tWxba2zFBGYyQ9948aXuT0uEMWMCFxEWadaUxJiCx+vF1IX6tB/qEO/of78p41DOozkQH3vvffERa80DrX65Zdf1O/HH3+8nH/++WlztWK7GrbtBdsHUxFsdcMWtIaqZwt8V9T0C1Hq6tYxWS4XGX3ABfls9sFk21HbsmnrumVp627IZ7sPptqPc243bes65RrSFhYsWbn2YKdaR6m+s7RYRZMiujTIXbreWmdpY7R3F+TTBWkk8CD+o48+Uo7tYHfQiBEj5IILLlC5UQmxiU/XC0kNdeg/1KHfUH/+M90hHWo5UOH0Q+6vXEDuMmz3twHyuB1zzDEqBxccppdccokcffTRGesMHDhQ3n33XRk7dmzKz5Hb6/vvv1f/Hzx4cIPVI4QQQkj8ztIgonSC2oa/WBZpOEtbNiuuE1WK/2dylhJ3wUPvoUOHKt3tuOOO8r///U+9jx1YyCd8wgknyL///W8ZNGhQQ3eVEEIIIYS44kA95JBDZL/99lMRnjh4IFvH6b/+9S95++235fPPPxfTwPE4fPhw5TzFwQ1I+o+t+/Wx9957qwgCODR//vln2XDDDet8/vzzz6toAziOkb+0oerZIls95lpXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc7tpW9cpZ0MPahv+2nyl+Aln6cKlms7S7ogqLZP+Pdqqn53aNc/ZWZrv9u6CfDpgzYZUCi+++KLSKdaxAGu0N954Q+Xkx4GlDz74YEN3leQxvlwvJD3Uof9Qh35D/flPJ4d0mHp/exIvv/yyOml2t912k+OOO07+85//JE5wTpUTDA5ClDnqqKNkjz32UKexog0bPPDAA/LDDz+o/1922WVaztPgtNcDDjhA5bVC3tTgtFwwatQoufHGG9X/4ZzFSbsNVc8WOH03zrq6dUyWy0VGH3BBPpt9MNl21LZs2rpuWdq6G/LZ7oOp9uOc203buk65XMdp0dJV8sWPs+XJt36Sqx/6XI698k059sq31P+ffPtn+fLHOSmdpy2aFcvA/h3kkF36yQV/3koeuHh3eeLqveXq07aX4/bbRHYY2FU6t29hJNI03+3dBfl0+Oyzz5STtH379uvoFQv5I488MrFziJDGfr2Q9FCH/kMd+g315z+rHdKhlqcOC8U77rhDvvzyS3nkkUfkuuuuk2uuuUaaNWumIiZbtmypHKc4BAmOVkRSYrG56667KkeqrS3piD599NFHawUpKpKXXnpJvdKx/fbby5lnnpn4HVv9J0yYID/++KOKsO3fv7/qO3JegWHDhslhhx22Tjtx17MBDrPq0KFDbHV165gsl4uMPuCCfDb7YLLtqG3ZtHXdsrR1N+Sz3QdT7cc5t5u2dZ1y2XwnnKW1uUqXJKJLEW1aH3CWqgOeVHRpbe5SE5GluuS7vbsgn+4aE4dGpQO7nlxa0JP8xJfrhaSHOvQf6tBvqD//WeyQDrMKddx6663VCwc2jR49Wm1HRyQqBELu0S5dusiWW24p2267rfzhD3+wfpo8DozCyfagqqoqbX7RgM6dO9f5vaysTJ588kkZOXKk2o6FfFeQA/mskPfq4IMPTtlO3PUIIYQQkp5Fy1apnKVBvlI4Sxcs0XCWlhYlDnYKcpd2bh+fs5S4y0YbbaQOTcVuqmSw5nzllVfWScdECCGEEELyl4IahI6SvGX8+PFSXl4uzZs3lwEDBiTeh9qj3iBGqatbx2S5XGT0ARfks9kHk21HbcumreuWpa27IZ/tPphqP8653bSt65TD50uWV6yNLP09b2nWzlJEl/Yoky6Gtt2bJN/tvb62061b4ub999+X008/Xfbdd1+Vwuqcc85Ru6/atm0rDz30kIwbN05uu+027dRRjQlXdJgPuDAfkNygDv2HOvQb6s9/amLQoe7axX6yTeIkOHSrV69esdXVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh9MtR/n3G7a1lOVW7xsdeJgJ/z86dcFsnh5Zb3tNC8tSmzBD7bjI0dpYaH7C+h8t3cX5NNhl112kWuvvValrXr99dcTufaxiG/atKlccMEFdJ4S6/hyvZD0UIf+Qx36DfXnP1Md0iEdqI2UysrKWOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnuw+m2o9zbjdt60uWr5ZvJy2Uz375PXfp/MUr663XrGngLC1T+Up9cpY2Rnt3QT5dkFoJh6F+/PHHKmUVDgJF7n/k1EckKiG28el6IamhDv2HOvQb6s9/Kh3SIR2ojRSEJsdZV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNV+nHN7LrYOZ6nKWTp9USJ3qY6ztLSkUPr1aJvYho+8pV08dpY2Rnt3Qb5swEGpjDQlDYVv1wtZF+rQf6hDv6H+/Ke5QzqkA7WR0r59+1jr6tYxWS4XGX3ABfls9sFk21HbsmnrumVp627IZ7sPptqPc27XLQ9n6bQF1fLRj78kcpfOW6QTWdpE+nRro6JKg6347VsVS7NmpZLP5Lu9uyBfKi666CIZNmyYDBw4MPF7fSAfF7b4E9LYrheiD3XoP9Sh31B//tPeIR3SgdpIwVa0fv36xVZXt47JcrnI6AMuyGezDybbjtqWTVvXLUtbd0M+230w1X6cc3uq8ktXVNQ53Am5S+dm4Szt0FJkq017qcjSrh1arhNZOnHixAa3Bdvku727IF8qXnzxRbU1P3Cg4vf6oAOV2MbV64XoQx36D3XoN9Sf/0xzSIc5O1Dnzp0rs2bNkj59+qik+kVFRVJYWGimd4QQQghxkhWrqmTcz3MTUaVwmmbjLFU5S9ce9NStY62ztNZB2iOW/hMS5qeffsr4OyGEEEIIadxEdqCOGTNGnU46fvx49fvDDz8sa9askYsvvlguvPBC2WeffUz2kxhmvfXWi7Wubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqn1bc/uy8opQVClyly6WuQvL622ztATO0jLpuV5z2bjveipvadeOLaVJmpyl9fXfBVuwjQsy+jK32waHRn300Ufyf//3fypIALz77rsqUGCXXXZp6O6RRoBP1wtJDXXoP9Sh31B//rOeQzqM5ED99ttv5fjjj5cuXbrIscceK48++qh6v6ysTEWgnnfeedKiRQsZMmSI6f4SQ1RVVcVaV7eOyXK5yOgDLshnsw8m247alk1b1y1LW3dDPtt9MNW+ibl9OZylKqp0ScJpOkfDWdq0pIn07VamnKSIKkXu0sBZunDhQmnXrl3O/XfBFmzjgoy+zO02WbRokZx66qny3XffyQsvvCADBgxQ77/66qvy5ptvynbbbSd33323UwcbkPzDl+uFpIc69B/q0G+oP/+pckiHkRyot99+u3Tv3l0tKMvLy+WRRx5R72+22WbyyiuvyBFHHCH3338/HagOo3sza6qubh2T5XKR0QdckM9mH0y2HbUtm7auW5a27oZ8tvtgqv1s24GzNIgo/fqn6TJncZXMXqDnLO3arqls2r+zcpjigKdu67VKG1lqam53wRZs44KMvsztNrn11ltlwoQJctVVV0nfvn0T7994442y6667yuWXXy533nmnXHDBBbH37dNPP5X//Oc/8vXXX8vixYulTZs2su222yqHb//+/VPWwS6xp556Sp5//nmZNGmSNGnSRKXfOvjgg9XhWUy/5Sa+XC8kPdSh/1CHfkP9+c9Ch3QYyYE6btw4Of3006W0tFRWrqyb76xly5YydOhQueOOO0z1kRBCCCE5EjhLEzlLpy/Wdpb26VqmDnaCoxTRpd3XayVTJk9yJqE7IaYZNWqUHHfccXLYYYfVeb+kpET2339/5Vx94403Yneg3nzzzfLAAw+o/3fs2FE5QadMmaIiY9966y2566671glgQCqCc889V0XO4uArXLd4D9G1eL333nty7733SnFxcayyEEIIIYQ0ihyoWECmY/Xq1WphRtyld+/esdbVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y9LW3ZDPdh/QfnV1jVRUrpGS4ibrnD6fTTtg+cpKmaTylS6WCdNq85bOWrCi3vr47j5dW691lta+uq/XUpo0KbRu6zrlXLAF27ggoy9zu02WLl0q7du3T/t5586dZf78+bH26bnnnlPOU6TLQgQsnLtwiC5ZskSdPwBH6Pnnny/vvPOOtGrVKlEPu8LgPIXD9V//+pdsvPHG6n1EsP7lL3+RDz/8UDlezznnnFjlIflzvZD0UIf+Qx36DfXnP70d0mGk/ToDBw6U1157LeVn2NL/7LPPqu38xF1mzJgRa13dOibL5SKjD7ggn80+mGw7als2bV23LG3dDfls9mHKzCVy/chP5NCLXpPDLn5d/bztybHqfR1WrKyUbyfOkxfenyBXP/iRnHL9O3LEpW/Ipfd9IiNf+1E++mZmSucpnKUb9Wor++3QW84etoVcctRG8sy1+8hNZ+0kpx60uey2dU/p1aV1SuepDVvXKeeCLdjGBRl9mdttgm37iOisqalJ+TkOk4pzQY/ghJtuukn9H1Gv2O0F52lwBgE+g9MUW/rffvvtRL3ly5fLyJEj1f+vvPLKhPMUDBo0SG644Qb1f5xnAEcscQtfrheSHurQf6hDv6H+/GeGQzqMFIF61llnyTHHHCNHH3207LbbbmoBh4OlsJ3psccek5kzZ6pFGnGXioqKWOvq1jFZLhcZfcAF+Wz2wWTbUduyaeu6ZWnrbshnqw+jxk6XEU+MFZEaqV7rp6msqpYPxk6X98dMl3OPHCxDBnev4yydNGOxTJy2pDa6dPpimTVfI7K0qFB6dyuT/msPeEKEaY+kyNKJEyemdZbGYes65VywBdu4IKMvc7tNsM6Fo/K0006TI488Unr16qXWu1OnTpVnnnlG5SG99tprY+sPokvhHO3Ro4ccddRR63yOFFqXXnqpOvwqnAcV0ahwjHbo0EHlbk1mxx13VG1OmzZNlT3kkEOsy0Ly73oh6aEO/Yc69Bvqz38qHNJhJAfqFltsobYDYfvQP//5z0SyfYDtQSNGjFDJ7Im7NGvWLNa6unVMlstFRh9wQT6bfTDZdtS2bNq6blnauhvy2egDIkzhPK1OEeG2Zq039ZYnxqgt+IuWrlI5S2fqOkuTcpb27NSqXudotjKatnWdci7Ygm1ckNGXud0mBxxwgMydO1dtbR89enSdz7CF/q9//as6gCkuPvnkE/UTTlAcAJWKAw88MOW5BWDLLbdMRKwmg8/gQP3iiy/oQHUMX64Xkh7q0H+oQ7+h/vynmUM6jJwDdYcddpD//e9/8sMPP6hFF3KeduvWTTbddFO1sCRuA0d3nHV165gsl4uMPuCCfDb7YLLtqG3ZtHXdsrR1N+Sz0YeXR00S5c9IvUNYAd/qy6Mnpf28uKhQHfDUt3uZylfaq3ML6dO9nRRlEUkaVUbTtq5TzgVbsI0LMvoyt9vm5JNPlsMPP1w+/vhjtbsKJ9l37dpVtt9++9hPg/3555/VT0SXIq0AIlIRMTpr1ixp3bq1WpcfdNBB65xR8Ouvv6qfiDJNR/fu3euUJe7g0/VCUkMd+g916DfUn/90dEiHkXKgBuBJNhyme++9t+y7774qlxKdp36ALWhx1tWtY7JcLjL6gAvy2eyDybajtmXT1nXL0tbdkM9kH7BF/+epC9U2/SDSVAc4Szfo2Ub23n59OWvoILnjbzvLM9ftKzf/dScZfshA+eP/9ZLCysWRnKdRZDRt6zrlXLAF27ggoy9zexzAOYl17oknniinnHKK7LfffrE7TwEcuKC4uFiOO+44Of300+WFF15QqQSQq/Uf//iHikCdPn16nXoLFy5UPzP1uU2bNuontv8Tt/DteiHrQh36D3XoN9Sf/0x1SIdFUXMQ4BRQPJGfN2+eij5N5VzFk3FCCCGksVJdXSMz5i2XCdMWyYSpi+WXaYtk8oylUrVm3b+bmbjpzB3VlvyozlFCSGawVX+PPfaQDTbYIPF7fWCti1Ps42DFitr0HUidhYOhLrroItl///2lefPm8tlnn8k111wjkyZNUk5eOFZLS0tV+ZUrV6qfTZs2Tdt2cllCCCGEEGLIgYqk+U8//bR07txZbdsvLOQNnW/gMIE46+rWMVkuFxl9wAX5bPbBZNtR27Jp67plaetuyKfTB2yrnb94lXKW/jJ1kcpdiryl5auqcvpuRJ5u2Ktt2vyF2fbTVF3Ttq5TzgVbsI0LMvoyt5sEDlMcFOWqA3X16tWJiNLbb79d9tprr8RnO++8s/Tu3VvtBoMT9dlnn1WHYIF0+VJToTPHZMOqVavU4XToG07QRQAG8phhK14QTQJ7wNy5YMEC9fv6668vs2fPVnXh9O3SpUsitUD79u3VPQeCN0DPnj3V/+H4ReoCpCKYPHmy+qxt27YqWhd5bIMUBhg7OKKxWw66xlgFEbhwIuN7Ae5tcPAWHNUYP/QfZdFPRCS3aNFCpU4ASOmAckuXLlXj17dvX9UHBJe0atVKlQ9OD8Z9E/qKtkG/fv2UbFVVVapN9DmIIO7UqZMar8rKSjWGffr0USnT8Duc5hi3YAwxnkgvEUQbo7+IWIbNQC609dtvvyXGG8yfP1/9xDjMmTMnMd6QZ8qUKYmoZcgfHm/UKy8vV2OLMQ2PN3SAtgB0gYjmYLyhV8gBysrKlB2Exxvjt2zZMqVfyBoebxyQFkRgwx7QZni80V/Ij3JoOzzekAuHrwGUxTgE4w35MKZgvfXWU2MbRGGjD9BFKpvF/6HfsM3CHoLxxveGbRb1A9mTbRayh8cbYxW2WXxHMN6oG7ZZ6Cs83pAzbLPh8YbNhG0WYx0e77DN4hUeb3x/2GbD441+hG0WYxAeb4xZYLMYi/B4Qw9hm3V5jkBdjK2Lc0TYZjlHpJ4jIDvadnWOQD+D8eYcMS3lHAHZUdbmHKF7UFVBDb4tS5D7Ca+bb74526okZsaPH6+MGkY5YMCAxPu4+DGRRSFKXd06JsvlIqMPuCCfzT6YbDtqWzZtXbcsbd0N+VL1YVl5hYoqrXWY1v5ctKzWyZGJbh1bSP8ebVVk6qTpiyXTLv4mhQWy85bd5exhgyP3U5ds65q2dZ1yLtiCbRr73J5u3WKb77//Xi38g2jM4AarPnBzEweDBw9WN4XIgfraa6+lLHPhhRfKiy++KNttt5088sgj6j3kRf3xxx/l/PPPV2kIUvHYY4+pCFbcdCAdQK40lA7zERfmA5Ib1KH/UId+Q/35z6IYdKi7dokUgQpv8NZbb51L/0gDAw99VCOMUle3jslyucjoAy7IZ7MPJtuO2pZNW9ctS1t3Q75Zs+fJrMXVCUcpHKezFtRuqc1Eu9alKm8pHKb42a9HW2nZrFh9NmXmEjl7xKiMp0jhEecBO/V1cm43bes65VywBdu4IKMvc7vpA6POPfdcOeyww9TvcESGt/Q3NIiygQN1o402SlsGzlUQRHCAYKyDiI9UBBE1DZHblfh5vRB9qEP/oQ79hvrznwUO6TCSAxXbhv73v/+pk0kJIYSQfAL5SafOXpbYho+fU2cvzRgpClo0K5b+PeAs/d1h2r6sWdryvbuWyblHDpYRT4xVTtRw+4g8hfMUn6McIcQuiDoItn8FW/gRkemKAxVb3bC9LtMWs2C7Prb9hevhzIJMEbXB9jrISwghhBBCDDpQL7jgApWkftiwYbL77rsncjckg9NAiZvkskiOUle3jsly+X4j4IJ8Nvtgsu2obdm0dd2ytHW78iGLzaz5K+SXaYtlwlqHKbbVV1RlPuSppKhQ+nQrkw16tlUOU/zs3L6FFBZml0NwyODu0rNzK3npg4ny4TczpbKqWuU83WmLbiryNFvnaZxzu2lb1ymX77buioy+zO0m2XzzzeXOO+9UAQLIfQbuvfdeeeaZZ9LWwdr30UcfjaV/gwYNko8++ki+/fZbNW+lWncHueaQ0ytg4MCB6ue4cePStj127NhEmgDiFq5eL0Qf6tB/qEO/of78Z32HdBjJgTpmzBiVTwnJVr/++uuUZbCwowPVXZBYF8mE46qrW8dkuVxk9AEX5LPZB5NtR23Lpq3rlqWtm5Vv4dJVdSJLJ05bLMtXVmasA59ol/bNZOM+HRMO015dWktREzMHKMJJethOneSvwwZLReUaaVrSJPJhLnHO7aZtXadcvtu6KzL6MrebBKfb33DDDfLzzz+rLe24BnF4gSsn0++3335y9913qyjU119/Xf0eBtGzQW7UPffcM/H+LrvsovK64sCHDz74QB04FWb06NFqyz+cxkhZQNzC1euF6EMd+g916DfUn//MdkiHkRyoN954o0qu+ve//12d4JXNCZ/EDXAqWZx1deuYLJeLjD7ggnw2+2Cy7aht2bR13bK09ejyrVhZqRykv0z73WG6YEn9bXVu31w26NFW+q/NXdq3W5lMn/arOmDGpoyIXi1tWpRzO3HVNW3rOuXy3dZdkdGXud0ko0aNUjlQgygH5Bq9+OKL5U9/+pO4ANbbSJ311FNPyWWXXaa26QcOT5zee84556gcqZin9t1330Q9OEZPOOEEueeee+Siiy5STtgg0vSbb75R74E///nP6iRe4hauXi9EH+rQf6hDv6H+/GeVQzqMdKc2depU5Tw98sgjzfeIxELTpk1jratbx2S5XGT0ARfks9kHk21HbcumreuWpa3ryYfIzckzl6jDnZTDdOpidcp9fbRp1TSxBT/IXdq6RUmkPuSCqfbjnNtN27pOuXy3dVdk9GVuNwmCAy699NKEA7Vr165SVJTbAw3TwKE7d+5cee+99+TMM8+Uzp07qzRav/zyi1RWVkqXLl3ktttuW2eMhw8frrb+IwXAEUccofKiIsJ24sSJiSjVM844o4GkIj5eL0Qf6tB/qEO/of78p6lDOiyK+hR82bJl5ntDYgOL7Djr6tYxWS4XGX3ABfls9sFk21HbsmnrumVp6+vKt6a6RqbPCR3yNG2R/DpzqXo/E82aFv1+yNNah2nHNs20tsvbHmNT7cc5t5u2dZ1y+W7rrsjoy9xuEkR0Iv8pcoY2a9ZMbXlHZCd+ZgKO1jhvIBBJii38zz77rIwfP145QbGtDdGoxx13nLRr1y6lbPfff788/fTT8sILL6hcqWvWrFFRtki3dfTRRzvnLCZuXy9EH+rQf6hDv6H+/KeLQzosqEEm+ix599131VNwPK3fcccdpbDQTB44Yh4srnGyLFIuDBgwIPE+FtxRt6NGqatbx2S5XGT0ARfks9kHk21HbcumreuWbey2jj9RX4wbLxUFZYmt+DjkaVXFmoz1kJ+0T7fWKqJ0g7Vb8bt1bJn1IU9xjbGp9uOc203buk65fLZ1l2RsyLk93brFNjfddJM89NBDiQcq6Q5qStVf4oYO8xEX5gOSG9Sh/1CHfkP9+c/EGHSou3aJ9LgZT73xNPu0005TT8PbtGmzTh5ULDrfeeedKM0TQghppCxetlomTFskv0xdrH7CYbp0RUXGOvBxdF+vVcJRip/rd2ktxUXMz00I0QOpqbbeemt1iFRFRYXKFbr77rvLhhtu2NBdI4QQQgghDhDJgYok9cgRFeSJIv6BnFlx1tWtY7JcLjL6gAvy2eyDybajtmXT1nXL5rOtl6+qlEnTl9RxmM5dVP+J1+u1bVYnsrRv9zJpXlpsta+2x9hU+3HO7aZtXaecr7aeDS7I6MvcbhqcUB+cUv/iiy+q7e277bZbQ3eLNGJcvl6IHtSh/1CHfkP9+U97h3QYyYH62GOPme8JiZVc0i5Eqatbx2S5fE8t4YJ8Nvtgsu2obdm0dd2y+WLrlVXV8uusJQlHKX5On7tM6ksi06p5ifTp2lIG9O6YcJji4Ke4sT3GptqPc243bes65Xyw9VxxQUZf5nab4KAmQhoaX64Xkh7q0H+oQ7+h/vyn0CEdMmN8I2XevHlSVlYWW13dOibL5SKjD7ggn80+mGw7als2bV23rI+2Xl1dIzPmLa8TWTp5xlKpWlOdsV7TkibSr3vtIU8b9Ggr/Xu2kU7tmsukSZMaPHeR7TE21X6cc7tpW9cp55qt28AFGX2Z202CU+hxCNNWW22VeK+6ulqdcN+rVy91sFSYV155RS644ALmQCVWcfV6IfpQh/5DHfoN9ec/8xzSoZYDFduXcGhUsI1JZzsTc6ASQkj+g4NW5i9eVXvA09TanKUTpy+W8lVVGes1KSyQ9buuPeSpRxvp37Ot9FivpTRp4s4TRkJI4wFr1j333LPOe0uWLJGDDjpIHn74Ydluu+0arG+EEEIIIcQTB2rXrl3VaVTh34nf9OzZM9a6unVMlstFRh9wQT6bfTDZdtS2bNq6blnXbB0HOgWHO02Yulg5TnHwU31069hSRZQGkaV9upZJSbHeIU/5busm249zbjdt6zrlXLAF27ggoy9ze1wPiQhpKHy7Xsi6UIf+Qx36DfXnPz0d0mFRlJynzIGaH2HQ3bp1i62ubh2T5XKR0QdckM9mH0y2HbUtm7auW7YhbX3V6iqZNKP2kKfAWTp7QXm99dqXlcoGPdsmtuL37dFGWjaLfshTvtu6yfbjnNtN27pOORdswTYuyOjL3E5IvsPrxX+oQ/+hDv2G+vOfeQ7pMFIO1IsuukiGDRsmAwcOTPn5Z599Jg899JA88MADufaPWGLlypWx1tWtY7JcLjL6gAvy2eyDybajtmXT1nXLxmXryE/626ylKrL0l7Vb8afOXirV9QRftWhWXOsoXeswxat9Wd1cgbmS77Zusv0453bTtq5TzgVbsI0LMvoytxOS7/B68R/q0H+oQ7+h/vxnpUM6jORAffHFF2WHHXZI60D9/PPP1Yu4S0lJSax1deuYLJeLjD7ggnw2+2Cy7aht2bR13bI2bB1bUmfNX5FwlOLn5BlLpKIq8yFPJUWF0nftIU/IWYrcpV06tFA5r22S77Zusv0453bTtq5TzgVbsI0LMvoytxOS7/B68R/q0H+oQ7+h/vynxCEdajlQp02bJvvtt59UVFQk3vv73/+uXunYbLPNzPSQWKF79+6x1tWtY7JcLjL6gAvy2eyDybajtmXT1nXLmiizYMnKOpGleK1YWZmxTmFhgfTs1Or3rfg920rPzq2kqAEOecp3WzfZfpxzu2lb1ynngi3YxgUZfZnbCcl3eL34D3XoP9Sh31B//tPdIR1qOVB79Ogh//jHP+Srr75SkUsvvfSSbLnllur9ZAoLC6Vdu3ZyxBFH2OgvMcTkyZOlX79+sdXVrWOyXC4y+oAL8tnsg8m2o7Zl09Z1y2ZbZvnKSpm49pCnwGG6YMmqevvSpX2LRGQpfvbtVialTSNtUjBOvtu6yfbjnNtN27pOORdswTYuyOjL3G6axYsXy8yZMxO/L1myRP1cuHBhnffBokWLYu8faXy4fL0QPahD/6EO/Yb685/JDulQ++74kEMOUS8wY8YMOf3002W77baz2TdCCCEZqKhcI5NnLpFPvl0gL3+xRCZMXSQz5q2ot16bVk3V4U79e9Ye8tSvRxtp3cKdrRGEENIQXHfddeqVzHnnndcg/SGEEEIIIe4QKbzoscceM98TEitt27aNta5uHZPlcpHRB1yQz2YfTLYdtS2btq5bNiizprpGps1Zppykv6ht+Ivk15lL1fuZaNa0KHG4U+12/LbSoU2p9bylJsl3WzfZfpxzu2lb1ynngi3YxgUZfZnbTXLQQQc1dBcI8eZ6IfpQh/5DHfoN9ec/bR3SYeT9mRMnTpTXXntN5s+fL2vWrFnnc9ycp3qKT9yguLg41rq6dUyWy0VGH3BBPpt9MNl21LZs2nqmskiVMmdhuUyYuli+mzhHps4dLxOnL5bVFevOtWGQn7RPt9aJ6FI4S7t1bKnymfpMvtu6yfbjnNtN2Hq25VywBdu4IKMvc7tJrr/++obuAiHeXC9EH+rQf6hDv6H+/KfYIR1GcqC++eabcu6550p1dfoTm+lAdZu5c+dK69atY6urW8dkuVxk9AEX5LPZB5NtR23Lpq2Hyy5atqr2cKepi+UX5C+duliWlf9+aF8qEEDao1MrFVnatrRKtt+yv6zfpUyKi+I/5Mk2+W7rJtuPc26PYuu5lnPBFmzjgoy+zO2E5Du8XvyHOvQf6tBvqD//meuQDiM5UO+++27p2rWrjBgxQjbaaCMpKWHuPEII0aF8VaVMmr5EHfA0bvx0mfnUZJm3aGW99dZr20wd8LTB2oOecMhT89LixI6Afj3c2dpACCGEEEIIIYTkE5EcqL/++qtccMEFsvnmm5vvEYmFHj16xFpXt47JcrnI6AMuyGezDybbjtpWrrZeWbVGpsxcqqJL4TDFz+lzl0lN5rSl6kCnIGfp+p1byMZ91lMHP5nsp0+4IJ/tPphqP865PZvypuZ2F2zBNi7I6MvcTki+w+vFf6hD/6EO/Yb6858eDukwkgO1c+fOsmrVKvO9IbGxcOFC6dKli5G61dU16jTwkuImaXMt6n6fyXK5yOgDLshnsw8m247aVjb1cB3MmLdcvvh2isxbJsphCudp1Zr0qU5AaUkT6ds9fMhTG+nUrnnikKdZs2ZldJ5m208fcUE+230w1b7Jud1keVNzuwu2YBsXZPRlbick3+H14j/Uof9Qh35D/fnPQod0GMmBetRRR8mjjz4qBx98sLRr1858r4h1VqxYkXPdKTOXyMujJsnor2dIZVW1yr2406BucsCQvtK7a1mk7zNZLhcZfcAF+Wz2wWTbaEvH0a/bBxzyNG/xyrV5S2sjS/FauboqY3tNCgukd9fW6nCnDXq2kaY1y2T7rTdR72fbh2zL+IwL8tnug6n2TcztNsqbmttdsAXbuCCjL3M7IfkOrxf/oQ79hzr0G+rPf1Y4pMNIDtTKykoVHbX77rvLVlttpZyoQbRUAA+RcpuioqKc6o4aO11GPDFWHWSzprp2PzKcqB+MnS7vj5ku5x45WIYM7p7195ksl4uMPuCCfDb7YKptOPqfen+mjH1gfL2O/nR9WLqiQiZMWyS/TIWjtPaQp8XLV9f73d06tlSO0sBhiu+DAzecDiWT8zTch1zL+IwL8tnug6n2c53bbZU3Nbe7YAu2cUFGH+Z2QhoDvF78hzr0H+rQb6g//ylySIcFNQilyhIcHFVvwwUFMn78+Kj9IoaADsrLy6V58+YyYMCAxPtQe7LTW5fJMxbLObeOluoMplNYUCC3nTsk4aDS/T6T5XKR0QdckM9mH0y0HTj6paB2i30AnJYw32RHP1i1ukomzViy1mFaG106e0F5vd/VoaxUHe6ktuL3aCt9e7SRls1qD3mybccu2IJNXJDPdh9MtZ9LO9nWzaa8qbndBVuwTWOf29OtW2zz5z//Oes6kAM7sogbOsxHXJgPSG5Qh/5DHfoN9ec/NTHoUHftEsmV+9NPP+XSN+IAkyZNkn79+kWq+/jrX6vIU8nkei8QefGDiXLm0EHK2Cev/T7Uy2T8uv3SKZeLjD7ggnw2+5Br24g8hfNUOfqTbDWImg6iqFesqkpsxZ86e6mEfK0pgWMUjlI4TDdY+7Nd61L12cSJE6Vfv45afTRlxy7Ygk1ckM92H0y1n0s72dbNprypud0FW7CNCzK6PLfbYvr06Q3dBUK8uV6IPtSh/1CHfkP9+c8kh3ToTiws8QJE8Y2buDThgMpUDlv58fqdH9W/gRMVblT8xA7mgsLa3/F0oajJz6qQel/9xH8Q1bq2XkGBrKmqkuKSKb+/F7RV+PvvlZUVUtp0uhTgPUluC+0E7SX9jrKFtWVri68tU5j0e8a2MrQfLrN2PGrbrjsm4TEI6obHYMH8BfLjrKCfKcqkGBPtMVjn+36vmxiDQpEZc8qlumRR4v0gt2hOY7L2vVUVa6R8VWXt+0ofynrW6VM6kJ+3Pkc/nKs3PT4moy2XFBXWHvIU2orfpX0LPskkhJA84r333mvoLhBCCCGEEIeJ5EC96KKLtMpdf/31UZonMdCmTZtI9XAIT9WarLM+1EEFBCa2/6/b1urKzKeW/06lRpn681T6zeyG7gBiPS22XX+0e9ip/LsTvCYLOwq1VVggvTq3SjhKO7YS2XyjnlLUpNDKtaVT1lQZn3FBPtt9MNV+Lu1kW9e0reuUc8EWbOOCjDb74IJ82TJ37lyZNWuW9OnTR5o2bapycRXi6SQhlvHxeiF1oQ79hzr0G+rPf9o4pMNIDtQXX3wx4+ft27dXB0sRdyktrd1unC04AKeoSYGWExXOrI17ww4KpKqqSgoKCpW7FM5TBLDiZ03oJ6IB16ypVg6woIysff/332t/VlfDOQY3WY3gv7Vt1KRtP1yX5BeBbjPnlMjMcftuLAN6t5M+3cqktOT3aXH58uVZOU+zvbZ0ypoq4zMuyGe7D6baz6WdbOuatnWdci7Ygm1ckNFmH1yQT5cxY8bItddem8jp//DDD8uaNWvk4osvlgsv/P/2zgNMqiJ7+zVkBhTJCogoWUUEUb81oq4oiouiIigohlUxoGJi14QuYE6LicUVMSFiwF3EdQ0Luq6KIAZkRAHJQTLCAMMw/T1v7d7+9wzdMzW3q27X6X5/PPP0TPep0+fUObeoW7fCMHXaaadl2kSS5Ui6XkhyGEP5MIayYfzkU8ujGFrbAxUDWmvWrFFTp05VY8aMUQ899JAN+4gjVq1aFWofCczQ69JmT/XVT+Uv48chPd0Pa6Gu79c1YV/Iir/PplwqmbIDqv/9O5YwWPu/wdf/bUWQ+LeWDQZs47p2Hwgu+3fiQPB/PyvzN/6VJJTFv0CmJPnA8MqVq1STpk3LDEQHMqX/TvS1Yj+S+5VsYHrDxo2q3p714rLBwWJBvcX/1n4k1Gupukheb1u2blX5tfNT6P6/eorbVPJ/9barpEQtXb1FmVC9WhXV54Q2SZfkh7lOKlPGRNaWjGR88M+1Dbb0p6OnsmVt57qJnA+54BoffHRpgw/+mfDtt9+qiy++WO2zzz7qoosuih8WVa9ePT0D9aabblJ16tRRxx9/fKZNJVmMlOuFpIYxlA9jKBvGTz6rPIqhtT1QsZSpadOmurO5ZMkSdd9996lx48bZUk884vhDGqlZP24uVwaDWL2Pa618BANlVf+3n2ZVJZf5dbepNm1aZNYGwwHvTOh+bMJXatpXyyoc6D+uS3PuZ0oIIaQUjz/+uGrRooV688039amszz//vH6/U6dO6m9/+5vq37+/njDAAVRCCCGEkNzAyQZOBx54oPr6669dqCaWaN68eeiyR3RurYae31Uf7oMBqETwN97H5/s3q1fp77Mpl46PEvDBP5c2pKu79/Gt/7uyP42B/jA2VKaMrTz2IRdc4oN/rm2wpT8dPZUtazvXTeR8yAXX+OCjz217VMyePVv16dNHLxsr+5Ctbt26qm/fvuqnn37KmH0kN5ByvZDUMIbyYQxlw/jJp7lHMXQygDp9+nS9rIn4y6ZNm9Iqe3zXFuqxocfrZfpYAg3wir/xPj4P83025dLxUQI++OfShnR1YwA/GOgvM86fcqDfhg2VKWMrj33IBZf44J9rG2zpT7dtdyVvq233IRdc44OPPrftUVKjRo2Un+3YseN/+7ET4g5J1wtJDmMoH8ZQNoyffDZ5FMNQS/j/8Ic/JH2/qKhIzZs3Ty1YsEBdeOGF6dpGHILDcdIti4En7HE6pG8XVbRzl6pZo2rKpdCm32dTLh0fJeCDfy5tsKEbA/kt995DvTTlazV7wWa1s7hED/Rj2T5mnpY3eBrWhsqUsZXHPuSCS3zwz7UNtvTbaNtdyNtq233IBdf44KPvbXsUdO7cWU2ZMiVpfxZL+idNmqSX8xPiEinXC0kNYygfxlA2jJ98tngUw1ADqG+99VbKfVAbNWqkBg0apK6//vp0bSMOqVq1qrWyOFiqVs1qVr7Pplw6PkrAB/9c2mBLNwZJB5zcUt12WasKB/pt2FCZMrby2IdccIkP/rm2wZZ+m227TXlbbbsPueAaH3yU0La7ZsiQIWrgwIFqwIAB6qSTTtL/b+BgKSzbf/HFF9WKFSvU3XffnWkzSZYj5XohqWEM5cMYyobxk09Vj2KYF8Px1SRrKSgo0DMl8vPzVceOHTNtDiGEEEKIiH7Lp59+qu666y61bNmyUu83btxY3X777eqUU07JmG0+41MMCSGEEEJs9V2c7IEK3n77bVeqiQWwzUKUZU3L2JRLx0cJ+OCfSxts6g6ry2Wum8oy1/3wz7UNtvRH2bbbznUTOR9ywTU++CilbXfN0Ucfrd5//331+uuvq0cffVQ9/PDD6tVXX1X/+te/OHhKIkHS9UKSwxjKhzGUDeMnnwUexdB4CX9xcbH64IMP1DfffKMwaRWjsr169dptOu3y5cvVnXfeqf7zn/+o3r17u7CZWCCdicdhypqWsSmX7ZOrffDPpQ02dYfV5TLXTWWZ637459oGW/qjbNtt57qJnA+54BoffJTStkcBlu4ffPDB+oeQqJF2vZDdYQzlwxjKhvGTT8yjGBoNoK5bt05deuml+oCowHh0KJ999ln10ksvqXr1/nsQy/jx49Vjjz2mtm3bpg477DC3lpO02HPPPSMta1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltu03CHH6KvjD6voTk2vVCzGEM5cMYyobxk8+eHsXQaAD1kUceUT/88IPq37+/Ouuss1Tt2rXVxx9/rJ544gk1YsQINXLkSHXDDTeojz76SDv3xz/+UZ177rnurSehqVOnTqRlTcvYlEvHRwn44J9LG2zqDqvLZa6byjLX/fDPtQ229EfZttvOdRM5H3LBNT74KKVtt0nZfU4J8QFfrxdiDmMoH8ZQNoyffOp4FEOjAdTPPvtM9ejRQ2+kH9C2bVtVq1Yt9dBDD6nq1aurDz/8UJ9Ses8996iGDRu6tJlYYOXKlapNmzaRlTUtY1MuHR8l4IN/Lm2wqTusLpe5birLXPfDP9c22NIfZdtuO9dN5HzIBdf44KOUtt0mmARAiG/4er0QcxhD+TCGsmH85LPSoxgaHSK1du1a9Zvf/Ga394877ji9XB8HRuE00ieffJKDp4QQQgghhBBCCCGEkKzBaAZqUVGRqlu37m7vB++df/75asCAAfatI85o1qxZpGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1PvgopW23CfdAJT7i6/VCzGEM5cMYyobxk08zj2JoNIBaEZiJSmSxZcsWlZ+fH1lZ0zI25dLxUQI++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbbcI9UImP+Hq9EHMYQ/kwhrJh/OSzxaMYWhlArVbNihoSIZs3b1ZNmjSJrKxpGZty6fgoAR/8c2mDTd1hdbnMdVNZ5rof/rm2wZb+KNt227luIudDLrjGBx+ltO024R6oxEd8vV6IOYyhfBhD2TB+8tnsUQyNRz43btyoVqxYUeq9TZs26df169fv9plvU23J7svOoixrWsamXDo+SsAH/1zaYFN3WF0uc91Ulrnuh3+ubbClP8q23Xaum8j5kAuu8cFHKW17pkH/t0GDBpk2g2Qx2XS95CqMoXwYQ9kwfvLJ8yiGebFYLFaRUIcOHVIajeLJPsN7c+fOtWMlCU1BQYEqLCzUU547duyYaXMIIYQQQkT0WyZMmKA++eQTbU9JSUn8/V27dqmtW7eq+fPnqzlz5mTURh/xKYaEEEIIIbb6LlUq1KSUOuuss9SZZ56Z9CfVZ71791a2ee2111T79u11h7YiPv30Uy370EMPlSuHTvDLL7+s+vTpozp37qy6du2qzjnnHPXKK6+U6ixnupxtFi5cGGlZ0zI25dLxUQI++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbXTJ27Fh19913q3//+9/qxx9/VF9++aVaunSp+u6779SsWbPUokWL1MCBAzNtJslypFwvJDWMoXwYQ9kwfvJZ6FEMjZbw33vvvSrTfPvtt+q+++4zkl2yZIkaNmxYhXIYsBw6dKj6xz/+oWfMtmnTRr+HzjF+sB/W008/rapXr57Rci5IZ7A2TFnTMjblohyQzgQ++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbXfLmm2/qGQgvvvii2rBhgzr55JPVCy+8oLenmjhxovrTn/6kH4QT4hIp1wtJDWMoH8ZQNoyffEo8iqHRDNRM89lnn6lLL71UL5cymXp74YUXql9++aVC2TFjxujBzMaNG+uO8pQpU9TUqVN1x7hRo0Z62dYTTzyR8XIu2GOPPSIta1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltu0uWL1+uV1PVrVtX7bvvvqpevXpq5syZqmrVqur8889Xp512mho/fnymzSRZjpTrhaSGMZQPYygbxk8+e3gUQ68HULEHwSOPPKIuueQSffJWeezcuVM9//zzqm/fvmrlypUV6t6yZYsaN26c/h1LtA488MD4Z4ceemh8tis6x8FhWZko54o999wz0rKmZWzKpeOjBHzwz6UNNnWH1eUy101lmet++OfaBlv6o2zbbee6iZwPueAaH3yU0ra7pFq1aqpOnTrxv/fbbz81b968+N9HHnmkXsbvA0OGDCl32ypfto8i2Xu9kNQwhvJhDGXD+MlnT49i6O0A6g8//KB69OihZ21WqVJFL31v3rx5UtnVq1er008/XW81UFRUpPekQuewPD744AM9UImZnyeeeOJunx977LF6xsG2bdu0bKbKuZxZEWVZ0zI25dLxUQI++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbXdK6dWs1e/bs+N/7779/qQOj0KdDnzPTTJ48Wb333nspPw+2j7rnnnv04a7oa+6999566yg80L/88sv1JATiJ1KuF5IaxlA+jKFsGD/5LPcoht4OoC5btkytWbNGdevWTb3++uvqiiuuSCm7fv16tXjxYv30HTM4b7/9dj1zoDyCTvFhhx2m9yNNBj4DM2bMyFg5QgghhBASLZitie2WbrrpJr0iCg+/sYQfWy1h+yX0Nzt06JBRG7HiasSIEeXK+LR9FCGEEEKIZIwOkcoELVu21EvejzrqqApl69evr0aPHq1++9vf6tmqJgTLrvAkPhUtWrQoJZuJcq7A7IMoy5qWsSmXjo8S8ME/lzbY1B1Wl8tcN5Vlrvvhn2sbbOmPsm23nesmcj7kgmt88FFK2+6S/v37q1WrVuml73goj1VR3bt3jw82Ym9UDK5milgspg9M/fXXX1Xt2rX1CqaymGwfddlll+nBYGyXhX1eiV9IuV5IahhD+TCGsmH85LO3RzH0dgZqu3btjAZPgwpFx9Z08DSYtQoaNGiQUmavvfbSrzh9NVPlXJGso+2yrGkZm3Lp+CgBH/xzaYNN3WF1ucx1U1nmuh/+ubbBlv4o23bbuW4i50MuuMYHH6W07S7ZuHGjuuGGG9Tnn3+uatSooVcPPfPMM+qll17Sg6hYNt+lS5eM2YdBT9h2yimnqE6dOiWV8W37KJK91wtJDWMoH8ZQNoyffLZ5FENvZ6BGFYSaNWumlKlVq1Yp2UyUs8X27dvV/Pnz9R5e2EMCg7TYuwtLupYsWaJl0MHGjIZ169bpv1u1aqVnX6As7N5nn3307NitW7fq2RgYsMY2C8GMYfwO23Gjgdm0CxcujM8Qhs7gcCx01jGgHOjBwQwLFizQn8EmzKTA9wLse4tymEWBk29hP2TxN2zFAQ/BoWHNmjXT7+PAMdzo4HPMzMD+Xzi5DZsPB/tnYNAdtgY2tWnTRvtWXFysdcJmbCMBmjZtqu0KBrYPOOAAtXTpUr1nWH5+vq63oA5RnzisIRgwh70rVqxQO3bs0PGFLmw3EdQ3WLt2rX5FPWA/36C+4c/PP/8cH3iH/4n1DVnYX716dV2nifWNGOBzgFjA9qC+EVfkAsBsk7L1jfpDvSG+8BX1jbpE/WHGDfwByIdffvlF24D6xn5xsBf+Qw66E+sbfuGGFEAW9RDUN/xDnYImTZrousX3QDdsQCyC3EjMWfyO+CbmLPIhqG98b2LOws6gvsvmLHxPrG/IBzagfvEdWMaJ+kbZIGfx4APxCuob/uMnMWcT6xs5E+Qs5GB/Yn0jjkHO4iewAfWN70/M2aC+4S9kE3MW7yXWN+osyNmGDRuWqm/EITFnEbdk9W3aRgB8R2XaCNQr8ilZGwG7A79R34htRW1EkLPltRGol8T6Lq+NALDHVRuBvxGzdNuIIF9Q3ygX5KxJG4H6h4+mbQR0w5/y2gjogzxeIVtRGxHYn6qNgB74jrpNrO9024ggZyvbRiTmrGkbgfrGtZmqjUj8/xIxRl1X1EYE9Z2qjUB9ww7TNgK2BXpstxFBjFO1EZD1gTPPPFOde+656uqrry71PraWyjTILRyyijocPny4uu6665LKmW4fhWsL20edffbZTu0mlQfXCq4xIhfGUD6MoWwYP/ls8iiGOTuAipsWUxI7nVGXswVuFHEzFdyk4MYnOJQreD8AN9dltxUIgCxu9AKZxOVeZQ/5StSLG93Ev3ETlUwWunGjlyiLG7REcKMHOdzUlf0e3PgF70MGN5upbML3JF6IuNFLJQtwoxKAgYzyZBNnGpfdtqGsbDDzOFV9J5JY34kxTSaLG+uK6jvZ36hv3Fwn1ncqWdwQJ/6NG+vy6jsYEDKp78ScQc6WJ5uYs8nqOzFnE+u7vJwFGLgJ3sNARnmyQX3ju8ouM0hV35BFXSfWd9mcTbQBvyfmbFDf0FM2H2BPYn1XJmcrU9+VydmK6jvxhMXEnIV/FeVsIuXlbGIbAUzbCNjgso3AYFZQl+m0EWFyNnGwM/DRpI0IBv3Kq2/Yg/oOZCtqIzCgF7yXrL6hJ8iTxPpOt41IpDJtRGLOVqa+y2sj0Eksm7PltRFl6ztZG5FMtrw2Itk1Z6uNSMzRZG0EBtwxEJxp8H+QL531RDDIffPNN+s2A4Oo5a1s8m37KEIIIYQQyXi7hN81uCEB6ICmIpgFkXhzHnU5V5S92XFd1rSMTbl0fJSAD/65tMGm7rC6XOa6qSxz3Q//XNtgS3+UbbvtXDeR8yEXXOODj1Ladpf06tVLTZo0KT4j2ReefPJJ9f333+sZstj7vzx82z6KZO/1QlLDGMqHMZQN4yefNh7FMGcHUIPZEcHytWQEncnEjmfU5VyRzkyDMGVNy9iUy/bZFD7459IGm7rD6nKZ66ayzHU//HNtgy39UbbttnPdRM6HXHCNDz5Kadtdgu0FMBP3+OOPVz179lQDBgxQF154Yamfiy66KFKbvvnmG/WXv/xFz2C+/fbbK5TP9PZRJHeuF5IaxlA+jKFsGD/5LPIohjm7hB9LDj/99NP4/mvJCPYKS1xGGHU5V2A/syjLmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3zwUUrb7hL02YKH31g9FOw1mykwwHnLLbfo/W9HjRpVakuIVGR6+6iy++/7tLd24v77mdpb22T/fcgGW1L5sP9+ZffWdrH/frC3tov9913srY2YB75nam/txP33M7W3trT998ue0YG69bGN8O2MDh/bCPwebBPoYxvhw/77vrcRsVhMy7psI6DbiJggTjjhhFi7du1ir7zySoWyAwYM0LIPPvhg0s///ve/68+7d++eUsdJJ52kZV577bWMlUuXuXPnxmbOnKlfE1mxYkVonWHKmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3K9bU/Vb8l17rrrLt1HHD58uHGf98wzz9TvP/vssyn1vvDCC1qmR48e1mxlDLOrPSDpwRjKhzGUDeMnnxURxNC075KzS/hPOOEE/QQAo+fTpk3b7fOPP/5Yj4Dj6UOPHj0yVs4ViQc8RFHWtIxNuXR8lIAP/rm0wabusLpc5rqpLHPdD/9c22BLf5Rtu+1cN5HzIRdc44OPUtr2TBPMjHAN+ogTJkzQMyVwgJQpvm0fRXL7eslVGEP5MIayYfzkU9+jGObsACoGKi+55BL9+x/+8Af11VdfldpjCu8B7HGVeEJs1OVcEUyljqqsaRmbcun4KAEf/HNpg03dYXW5zHVTWea6H/65tsGW/ijbdtu5biLnQy64xgcfpbTtrsGg5VVXXaUGDRpUau/TCy64QB/idNxxx0Vix9SpU/Urlqt16dJFtW/fvtTPjBkz9Odjx47Vf5944onx5XHAl+2jSHZfLyQ5jKF8GEPZMH7yWeZRDHN2D1QwePBg9e2336p///vfqn///rqjiT0ogv0gMGv0mmuuyXg5QgghhBASHRiMfPjhh/UeZNjHCzM1sW8ZZnNiryysKho4cGAktmBws2vXrik///HHH/W+ZrAPe5dhbzDQuXNn/Tp79uyUZYMH+uXpJ4QQQgghOT6Aik7xmDFj1MSJE9Wbb76pN8fFhrcdOnTQMwtw4io26s10ORdgE98oy5qWsSmXjo8S8ME/lzbY1B1Wl8tcN5Vlrvvhn2sbbOmPsm23nesmcj7kgmt88FFK2+4S9NM6duyoXnzxRT14evLJJ6sXXnhBD1CiH/enP/0pPkDpmiuvvFL/pAIDuZiFesYZZ6ibbrop5fZR3bt3z+j2USR7rxeSGsZQPoyhbBg/+TT1KIaiBlA/+ugjY1l0eE3AgCWWYuGnMkRdzjbGp4xZKmtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbbtLsOx96NChevZpcHLvzJkz1VlnnaXOP/98NWvWLDV+/Hh16qmnKl8Jto966qmn9FZRTz75ZHymaSa2jyLZe72Q1DCG8mEMZcP4yafIoxjm7B6ouU5waEBUZU3L2JRLx0cJ+OCfSxts6g6ry2Wum8oy1/3wz7UNtvRH2bbbznUTOR9ywTU++CilbXcJHnhjADJgv/32U/PmzYv/feSRR6pFixYp38H2Ucccc4w+8ArbR5122mnq9NNPV3379lVr167l9lGeI+V6IalhDOXDGMqG8ZPPBo9iyAFUQgghhBBCEsA+9Yl7h+6///5qzpw58b83bdrk1YyIVATbR915553q4IMPVitXrtTL9rF91LBhw9To0aMj2z6KEEIIIUQyebFYLJZpI4g7CgoKVGFhocrPz9d7eQWUlJSoKlXCjZ+HKWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67J9bY9Vb8laiZMmKDuvvtu1atXL3XPPfeoTz75RF133XV6tuYBBxygRo0apWelvvzyyxmz0Vd8iWE24EN7QNKDMZQPYygbxk8+JRHE0LTvwkzKUTD7IMqypmVsyqXjowR88M+lDTZ1h9XlMtdNZZnrfvjn2gZb+qNs223nuomcD7ngGh98lNK2uwTL3a+44gp9+BJmaOKQJRzC9MQTT+i9Ubdv317qwCZCcvl6IalhDOXDGMqG8ZPPUo9iyDU7OcrOnTsjLWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbbtrbrjhBnXttdfGl7g/88wz+iCpjRs3qi5duqiGDRtm2kSS5Ui6XkhyGEP5MIayYfzks9OjGHIANUfB1OQoy5qWsSmXjo8S8ME/lzbY1B1Wl8tcN5Vlrvvhn2sbbOmPsm23nesmcj7kgmt88FFK2+6qkz5//nxVXFys2rRpo2rXrl3q827dumXMNpJ7+H69kIphDOXDGMqG8ZNPvkcx5ABqjtKoUaNIy5qWsSmXjo8S8ME/lzbY1B1Wl8tcN5Vlrvvhn2sbbOmPsm23nesmcj7kgmt88FFK226b559/Xj355JNqy5Yt8QOYzj//fHXjjTfyoCWSEXy+XogZjKF8GEPZMH7yaeRRDLkHao6yZMmSSMualrEpl46PEvDBP5c22NQdVpfLXDeVZa774Z9rG2zpj7Jtt53rJnI+5IJrfPBRSttuk8mTJ6v77rtP7bnnnuqCCy5QAwcOVK1atdKDqg888ECmzSM5iq/XCzGHMZQPYygbxk8+SzyKIR+nE0IIIYSQnOaVV15Rhx56qBo/fryqWbOmfi8Wi+l9UCdOnKgPjMKMVEIIIYQQkptwBmqO0rhx40jLmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3zwUUrbbpMFCxaoM844Iz54CvLy8tSgQYNUUVGRWrhwYUbtI7mJr9cLMYcxlA9jKBvGTz6NPYohB1BzlF27dkVa1rSMTbl0fJSAD/65tMGm7rC6XOa6qSxz3Q//XNtgS3+UbbvtXDeR8yEXXOODj1Ladpts27ZN7bHHHru936JFCz0TdfPmzRmxi+Q2vl4vxBzGUD6MoWwYP/ns8iiGHEDNUdavXx9pWdMyNuXS8VECPvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccI0PPkpp221SUlKiZ5yWpWrVqt513knu4Ov1QsxhDOXDGMqG8ZPPeo9iyAFUQgghhBBCCCGEEEIISUFeDOuSSNZSUFCgCgsLVX5+vurYsWP8fcykCGZVVJYwZU3L2JRLx0cJ+OCfSxts6g6ry2Wum8oy1/3wz7UNtvRH2bbbznUTOR9ywTW53ran6re4pkOHDuq2225TJ510Uqn3N23apM466yz18MMPqy5duuxWrlmzZpHZKIVMxTAb8aE9IOnBGMqHMZQN4yefXRHE0LTvwhmoOcqKFSsiLWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbbttRo0apQdQE3/69OmjP7vpppt2++y3v/1tpk0mWY7P1wsxgzGUD2MoG8ZPPis8imG1TBtAMsOOHTsiLWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbbtNMMuUEN/w9Xoh5jCG8mEMZcP4yWeHRzHkAGqOUqtWrUjLmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3zwUUrbbpN777030yYQIuZ6IeYwhvJhDGXD+Mmnlkcx5B6oWU6qvRx27typqlevHkpnmLKmZWzKpeOjBHzwz6UNNnWH1eUy101lmet++OfaBlv6o2zbbee6iZwPueCaXG/buX+mfBjD7GoPSHowhvJhDGXD+MlnZwQx5B6opFwWL14caVnTMjbl0vFRAj7459IGm7rD6nKZ66ayzHU//HNtgy39UbbttnPdRM6HXHCNDz5KadsJyXZ4vciHMZQPYygbxk8+iz2KIQdQCSGEEEIIIYQQQgghJAUcQM1RGjVqFGlZ0zI25dLxUQI++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbCcl2eL3IhzGUD2MoG8ZPPo08iiEHUAkhhBBCCCGEEEIIISQFHEDNUdauXRtpWdMyNuXS8VECPvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccI0PPkpp2wnJdni9yIcxlA9jKBvGTz5rPYohB1AJIYQQQgghhBBCCCEkBXmxWCyW6kMin4KCAlVYWKjy8/NVx44d4+/v3LlTVa9ePZTOMGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1ud62p+q3EDkwhtnVHpD0YAzlwxjKhvGTz84IYmjad+EM1Bxl9erVkZY1LWNTLh0fJeCDfy5tsKk7rC6XuW4qy1z3wz/XNtjSH2XbbjvXTeR8yAXX+OCjlLadkGyH14t8GEP5MIayYfzks9qjGHIANUfZvn17pGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1PvgopW0nJNvh9SIfxlA+jKFsGD/5bPcohhxAzVFq1qwZaVnTMjbl0vFRAj7459IGm7rD6nKZ66ayzHU//HNtgy39UbbttnPdRM6HXHCNDz5KadsJyXZ4vciHMZQPYygbxk8+NT2KIfdAzXJS7eWwa9cuVbVq1VA6w5Q1LWNTLh0fJeCDfy5tsKk7rC6XuW4qy1z3wz/XNtjSH2XbbjvXTeR8yAXX5Hrbzv0z5cMYZld7QNKDMZQPYygbxk8+uyKIIfdAJeXy888/R1rWtIxNuXR8lIAP/rm0wabusLpc5rqpLHPdD/9c22BLf5Rtu+1cN5HzIRdc44OPUtp2QrIdXi/yYQzlwxjKhvGTz88exZADqIQQQgghhBBCCCGEEJICDqDmKA0aNIi0rGkZm3Lp+CgBH/xzaYNN3WF1ucx1U1nmuh/+ubbBlv4o23bbuW4i50MuuMYHH6W07YRkO7xe5MMYyocxlA3jJ58GHsWQA6g5Sjp7SIQpa1rGply273Xig38ubbCpO6wul7luKstc98M/1zbY0h9l2247103kfMgF1/jgo5S2nZBsh9eLfBhD+TCGsmH85FPVoxhyADVHWbNmTaRlTcvYlEvHRwn44J9LG2zqDqvLZa6byjLX/fDPtQ229EfZttvOdRM5H3LBNT74KKVtJyTb4fUiH8ZQPoyhbBg/+azxKIYcQCWEEEIIIYQQQgghhJAU5MVisViqD4l8CgoKVGFhocrPz1cdO3aMv19UVKRq1KgRSmeYsqZlbMql46MEfPDPpQ02dYfV5TLXTWWZ637459oGW/qjbNtt57qJnA+54Jpcb9tT9VuIHBjD7GoPSHowhvJhDGXD+MmnKIIYmvZdOAM1R1m7dm2kZU3L2JRLx0cJ+OCfSxts6g6ry2Wum8oy1/3wz7UNtvRH2bbbznUTOR9ywTU++CilbSck2+H1Ih/GUD6MoWwYP/ms9SiGHEDNUTC6HmVZ0zI25dLxUQI++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbCcl2eL3IhzGUD2MoG8ZPPoUexZADqDlK9erVIy1rWsamXDo+SsAH/1zaYFN3WF0uc91Ulrnuh3+ubbClP8q23Xaum8j5kAuu8cFHKW07IdkOrxf5MIbyYQxlw/jJp7pHMeQeqFlOqr0cSkpKVJUq4cbPw5Q1LWNTLh0fJeCDfy5tsKk7rC6XuW4qy1z3wz/XNtjSH2XbbjvXTeR8yAXX5Hrbzv0z5cMYZld7QNKDMZQPYygbxk8+JRHEkHugknJZuHBhpGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1PvgopW0nJNvh9SIfxlA+jKFsGD/5LPQohhxAJYQQQgghhBBCCCGEkBRwADVHqV+/fqRlTcvYlEvHRwn44J9LG2zqDqvLZa6byjLX/fDPtQ229EfZttvOdRM5H3LBNT74KKVtJyTb4fUiH8ZQPoyhbBg/+dT3KIYcQM1RatSoEWlZ0zI25dLxUQI++OfSBpu6w+pymeumssx1P/xzbYMt/VG27bZz3UTOh1xwjQ8+SmnbCcl2eL3IhzGUD2MoG8ZPPjU8iiEHUHOU1atXR1rWtIxNuXR8lIAP/rm0wabusLpc5rqpLHPdD/9c22BLf5Rtu+1cN5HzIRdc44OPUtp2QrIdXi/yYQzlwxjKhvGTz2qPYsgBVEIIIYQQQgghhBBCCEkBB1BzlBYtWkRa1rSMTbl0fJSAD/65tMGm7rC6XOa6qSxz3Q//XNtgS3+UbbvtXDeR8yEXXOODj1LadkKyHV4v8mEM5cMYyobxk08Lj2LIAdQcZcOGDZGWNS1jUy4dHyXgg38ubbCpO6wul7luKstc98M/1zbY0h9l2247103kfMgF1/jgo5S2nZBsh9eLfBhD+TCGsmH85LPBoxhyADVH2bp1a6RlTcvYlEvHRwn44J9LG2zqDqvLZa6byjLX/fDPtQ229EfZttvOdRM5H3LBNT74KKVtJyTb4fUiH8ZQPoyhbBg/+Wz1KIbVMm0AyQzVqlWLtKxpGZty6fgoAR/8c2mDTd1hdbnMdVNZ5rof/rm2wZb+KNt227luIudDLrjGBx+ltO25yKpVq9S4cePUJ598opYvX67fa968uTr++OPVJZdcoho3brxbmR07dugyU6ZMUYsXL1a1atVS7du3V/3791enn356BrwgpvB6kQ9jKB/GUDaMn3yqeRTDvFgsFsu0EcQdBQUFqrCwUOXn56uOHTtm2hxCCCGEkJSw35KamTNnqsGDB6vNmzerqlWrqn333Ve/v3TpUrVr1y7VoEEDNXbsWHXwwQfHy2zfvl0PrM6aNUuXadeundqyZYsuA8455xw1cuRIq3YyhoQQQgiRhGnfhUv4c5T58+dHWta0jE25dHyUgA/+ubTBpu6wulzmuqksc90P/1zbYEt/lG277Vw3kfMhF1zjg49S2vZcAoOm1157rX499thj1bRp09R7770X/+natatav369uvrqq/UNQMCIESP04GmbNm203OTJk9UHH3ygxowZo2rXrq1ef/11NWnSpIz6RlLD60U+jKF8GEPZMH7yme9RDDmASgghhBBCiMe8+eabeoC0SZMm6rHHHtOvAZiJ+uSTT6p69erpJf7vvPOOfn/ZsmXqrbfeUnl5eerhhx+Oz1gF3bt3V8OGDdO/jx49WpWUlGTAK0IIIYQQOXAANUdBJzvKsqZlbMql46MEfPDPpQ02dYfV5TLXTWWZ637459oGW/qjbNtt57qJnA+54BoffJTStucSX3zxhX494YQTVN26dXf7HMv3u3Tpon//7rvv9Ovbb7+tiouLVadOnVSHDh12K9OnTx+9H+rq1avVjBkznPtAKg+vF/kwhvJhDGXD+Mmnnkcx5ABqjoJlW1GWNS1jUy4dHyXgg38ubbCpO6wul7luKstc98M/1zbY0h9l2247103kfMgF1/jgo5S2PZfA3qf333+/Ovvss1PKBMcaYD9UMHv2bP3arVu3pPI1atTQg6uAA6h+wutFPoyhfBhD2TB+8qntUQw5gJqjYIlXlGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1PvgopW3PJQ455BB15plnqs6dOyf9HMv7g0FQHBQFFi1apF8Tl+6XpUWLFqVkiV/wepEPYygfxlA2jJ98VnkUQw6gEkIIIYQQIpiRI0eqbdu26SX5PXv21O+tW7cuvrw/FXvttZd+3bBhQ0SWEkIIIYTIhAOoOUrz5s0jLWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbTv5L0899ZSaMmWK/v3KK6+MHzC1fft2/VqzZs2UZYPPMPhK/IPXi3wYQ/kwhrJh/OTT3KMYVsu0ASQzbN68OfReEmHKmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3zwUUrbTpR64okn1OjRo/Xv3bt3V1dccUX8s6pVq6qSkhIjPXl5edZtwwDu/Pnz1f7776+WL1+uioqKdOwbN26slixZomUaNWqk924NZsu2atVKL9VDWQzu7rPPPvHtBRo2bKiqVKmi1qxZo/9u2bKl/h2Dv9jPFdsRLFy4UH9Wv359Vb16dfXLL7/EtzHANgdbt25V1apVU/vtt59asGBBfBYuZu4GSwRxo7Zp0ya1ZcsWXYewH7Kwc88991R16tRRK1eu1LLNmjXTcshr1GHr1q21Daj3PfbYQ8vDd7D33ntrW6EbtGnTRvuGg76gEzYvW7ZMf9a0aVNdX7AJ9XDAAQeopUuXqp07d6r8/Hxdb0Edoj6x7y38A7B3xYoVaseOHdov6Fq8eHG8vsHatWv1K+oBh4gF9Q1/fv755/jMZfifWN8oV1hYqOsWdZpY34gBdAHEArOag/pGXJELwWEgyIPE+kb9/frrrzq+8DWxvnFoGvwByAfoTKxv2Av/IQfdifUNvzZu3Kj/hizqIahv+Ic6BXjogLoNZmLDBsQiWc7id8Q3MWeRD0F943sTcxbxxncmy1n4nljfqKvEnMV3BPWNsok5i3gl1jf8TMzZxPpGziTmLOo6sb4TcxY/ifWN70/M2cT6hh2JOYs6SKxv1FmQs6iLxPpGnSTmrM9tBHTCZh/biMScZRuRvI1AHcIeX9sI2BnUN9uIpUnbCMQD9eKyjYBuE/JiwY7zJCspKCjQSY2k7NixY/x9XDBI8DCEKWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67J9bY9Vb+FlAY3Fvfcc4+aOHGi/vuoo45STz/9tL45CzjiiCP0Tc3jjz+uTj311KR67rvvPjVu3Dh19NFHq+eee86KbYxhdrUHJD0YQ/kwhrJh/OQzP4IYmvZduIQ/R8HIe5RlTcvYlEvHRwn44J9LG2zqDqvLZa6byjLX/fDPtQ229EfZttvOdRM5H3LBNT74KKVtz1Uwg+Tyy/GzKnQAAD+eSURBVC+PD56ecsopasyYMaUGT4MZPyCY7ZGMYDZNefukkszB60U+jKF8GEPZMH7yqeJRDDkDNcvhLABCCCGESIH9lvLBsrTf//736scff9R/X3zxxeqWW25JenNx1VVXqQ8//FAPtt54441J9V1wwQVq5syZ6tprr1XXXHONFRsZQ0IIIYRIgjNQSbkE+2NEVda0jE25dHyUgA/+ubTBpu6wulzmuqksc90P/1zbYEt/lG277Vw3kfMhF1zjg49S2vZcA3uZDRw4UA+eYsD0jjvuUMOGDUs5M6Nz58769auvvkr6Ofb7mjNnjv69a9euDi0nYeH1Ih/GUD6MoWwYP/ks8CiGHEDNUdKZeBymrGkZm3LZPrnaB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltey6Bwc7BgwfrQxNwWMRjjz2mBgwYUG6Znj17xgdQ582bt9vnb7zxhj5kAYdUYL9U4h+8XuTDGMqHMZQN4yefmEcx5ABqjoIT66Isa1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltey4xduxY9f333+vfMfMU+55WBE6X7d27tz45d8iQIaVmcEyfPl098MAD+ncMzOJkX+IfvF7kwxjKhzGUDeMnnz09iiF7SzlK3bp1Iy1rWsamXDo+SsAH/1zaYFN3WF0uc91Ulrnuh3+ubbClP8q23Xaum8j5kAuu8cFHKW17Ls0+HT9+vP4dA52TJ0/WP6k46qij9J6m4LbbblM//fSTmjt3rurVq5dq27atnnW6ePFi/Xm/fv3UueeeG5EnpLLwepEPYygfxlA2jJ986noUQ85AzVFWrFgRaVnTMjbl0vFRAj7459IGm7rD6nKZ66ayzHU//HNtgy39UbbttnPdRM6HXHCNDz5KadtzBex5umnTJv17cXGxXpJf3s/ChQvjZevVq6cmTJigrr/+etWmTRu1aNEi9csvv6hDDz1UjRo1Sg0fPjyDnpGK4PUiH8ZQPoyhbBg/+azwKIacgUoIIYQQQoinHHzwwUn3MDWlVq1aepk+fgghhBBCSDg4AzVH2WeffSIta1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltOyHZDq8X+TCG8mEMZcP4yWcfj2IoYgD1tddeU+3bt9dLkFKxY8cO9cwzz+j9nTp16qQOP/xwfTrpO++8U65uKeVss3Xr1kjLmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3zwUUrbTki2w+tFPoyhfBhD2TB+8tnqUQy9H0D99ttv1X333VeuDDbDv/jii9Wjjz6q931q3bq13vPpyy+/VEOHDtUb6Esu54LNmzdHWta0jE25dHyUgA/+ubTBpu6wulzmuqksc90P/1zbYEt/lG277Vw3kfMhF1zjg49S2nZCsh1eL/JhDOXDGMqG8ZPPZo9i6PUA6meffaYuvfTSCkecR4wYoWbNmqU3x3/vvff0yaQffPCBGjNmjKpdu7Z6/fXX1aRJk8SWc0FeXl6kZU3L2JRLx0cJ+OCfSxts6g6ry2Wum8oy1/3wz7UNtvRH2bbbznUTOR9ywTU++CilbSck2+H1Ih/GUD6MoWwYP/nkeRTDvFgsFlOeUVhYqJe5jx07VpWUlMTfx0mh/fv3LyW7bNkydcopp6hdu3bpAckOHTqU+vzVV19Vd911l2ratKmaNm2aqlKliqhy6VJQUKDrMz8/X3Xs2NGKTkIIIYQQF7DfIh/GkBBCCCHZ2HfxbgbqDz/8oHr06KFnZWIQEUvbmzdvnlL+7bffVsXFxXo/0bKDkqBPnz769NHVq1erGTNmiCvnip9//jnSsqZlbMql46MEfPDPpQ02dYfV5TLXTWWZ637459oGW/qjbNtt57qJnA+54BoffJTSthOS7fB6kQ9jKB/GUDaMn3x+9iiG3g2gYqbmmjVrVLdu3fSS9iuuuKJc+dmzZ+tXyCejRo0aetASJA5MSinnCsyEjbKsaRmbcun4KAEf/HNpg03dYXW5zHVTWea6H/65tsGW/ijbdtu5biLnQy64xgcfpbTthGQ7vF7kwxjKhzGUDeMnn10exdC7AdSWLVuqcePGqZdfftlo2c+iRYv067777ptSpkWLFqVkJZVzRd26dSMta1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltOyHZDq8X+TCG8mEMZcP4yaeuRzGspjyjXbt2+seUdevW6dcGDRqklNlrr73064YNG8SVc0W9evUiLWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbTsh2Q6vF/kwhvJhDGXD+Mmnnkcx9G4GamXZvn27fq1Zs2ZKmeCzbdu2iSvniuXLl0da1rSMTbl0fJSAD/65tMGm7rC6XOa6qSxz3Q//XNtgS3+UbbvtXDeR8yEXXOODj1LadkKyHV4v8mEM5cMYyobxk89yj2Lo3QzUylK1alVVUlJiJJuXlyeunC0wgDt//ny1//776wTcunWrfm3cuLFasmSJlmnUqJGKxWLx2bKtWrVSq1at0mUxuLvPPvvo7QVQFrNkccgX9qsNtl7A7xj8xX6u2I5g4cKF+rP69evrA7Tw/cE2BuvXr9d6qlWrpvbbbz+1YMEC/VlRUZHasmWL/l6AA8Q2bdqk30Mdwn7Iouwvv/yi6tSpo1auXKllmzVrpuU2b94cr0PYgHrfY4891J577hm/+Pbee29tK3SDNm3aaN9gJ3TCZuzHC5o2bartCmYGH3DAAWrp0qVq586d+pQ21FtQh6hP7NEB/wDsXbFihdqxY4c+JAy6Fi9eHK9vsHbtWv2KesAhYkF9w59gw2TMXIb/ifUdxLR69eq6ThPrGzGALoBYwPagvhHXIBZ4mlO7du1S9Y36+/XXX3V84SvqG3mB+sP0efgDkA/wC7pQ361bt9b2wn/IQXdifcPejRs36r8hi3oI6hv+oU5BkyZNdN3CXuiGDYgFYgBbE3MWvyO+iTmLfAjqG9+bmLOwM6jvsjkL3xPrG3UV2ID6xXfgZD7UN8oGOYuZ44hXUN/wH/WZmLOJ9Y2cCXIWsiiXWN+JOYufwAbUN74/MWeD+oa/qN/EnMV7ifWNOgtytmHDhqXqG3FIzFnELVl9m7YRAN9RmTYC9YprOlkbge8M6hD1jdhW1EYEOVteG4F6MW0jgMs2Ar5CX7ptRJAvqG+UC3LWtI2Aj6ZtBHSD8toI6IQ8XkFFbURgf6o2ItCHuk2s73TbiCBnK9tGJOasaRuB+sa1maqNSPz/EjFGXZfXRiTWd6o2AvUNO0zbCOgPbLDdRgQxTtVGBA+cCSGEEEII8Ym8GHq6nnPiiSfqzvrw4cNV//79S312xBFH6JuFxx9/XJ166qlJy9933316X9Wjjz5aPffcc6LKpUtBQYG+ocINUeKesrhxC7uXRJiypmVsyqXjowR88M+lDTZ1h9XlMtdNZZnrfvjn2gZb+qNs223nuomcD7ngmlxv21P1W4gcGMPsag9IejCG8mEMZcP4yWdLBDE07buIX8KPmTQgmEWRjGCWSuL+o1LKuSKdGR5hypqWsSmX7bNYfPDPpQ02dYfV5TLXTWWZ637459oGW/qjbNtt57qJnA+54BoffJTSthOS7fB6kQ9jKB/GUDaMn3y2exRD8QOoWHZW0b4IwZI1LCWTVs4V5Q3kuihrWsamXDo+SsAH/1zaYFN3WF0uc91Ulrnuh3+ubbClP8q23Xaum8j5kAuu8cFHKW07IdkOrxf5MIbyYQxlw/jJZ6NHMRQ/gNq5c2f9+tVXXyX9HHt0zZkzR//etWtXceUIIYQQQgghhBBCCCGZQ/wAas+ePeMDk/Pmzdvt8zfeeENP+cXhD9iHVFo5VwQzYqMqa1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltOyHZDq8X+TCG8mEMZcP4yae1RzEUP4CKU1t79+6tT4wdMmRI/NRbMH36dPXAAw/o3wcPHqxPzJVWzhXBKc9RlTUtY1MuHR8l4IN/Lm2wqTusLpe5birLXPfDP9c22NIfZdtuO9dN5HzIBdf44KOUtp2QbIfXi3wYQ/kwhrJh/OSz2KMYuh+pi4DbbrtN/fTTT2ru3LmqV69eqm3btno2Z1DR/fr1U+eee67Yci4oLi6OtKxpGZty6fgoAR/8c2mDTd1hdbnMdVNZ5rof/rm2wZb+KNt227luIudDLrjGBx+ltO2EZDu8XuTDGMqHMZQN4yefYo9imBUDqPXq1VMTJkxQ48aNU1OnTlWLFi1SVapUUYceeqjq27ev6tOnj+hyLqhTp06kZU3L2JRLx0cJ+OCfSxts6g6ry2Wum8oy1/3wz7UNtvRH2bbbznUTOR9ywTU++CilbSck2+H1Ih/GUD6MoWwYP/nU8SiGebFYLJZpI4g7CgoKVGFhocrPz1cdO3aMv79jxw5Vs2bNUDrDlDUtY1MuHR8l4IN/Lm2wqTusLpe5birLXPfDP9c22NIfZdtuO9dN5HzIBdfketueqt9C5MAYZld7QNKDMZQPYygbxk8+OyKIoWnfRfweqCQcS5cujbSsaRmbcun4KAEf/HNpg03dYXW5zHVTWea6H/65tsGW/ijbdtu5biLnQy64xgcfpbTthGQ7vF7kwxjKhzGUDeMnn6UexZADqIQQQgghhBBCCCGEEJICDqDmKE2aNIm0rGkZm3Lp+CgBH/xzaYNN3WF1ucx1U1nmuh/+ubbBlv4o23bbuW4i50MuuMYHH6W07YRkO7xe5MMYyocxlA3jJ58mHsWQA6g5ys6dOyMta1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrvHBRyltOyHZDq8X+TCG8mEMZcP4yWenRzHkAGqOsmHDhkjLmpaxKZeOjxLwwT+XNtjUHVaXy1w3lWWu++Gfaxts6Y+ybbed6yZyPuSCa3zwUUrbTki2w+tFPoyhfBhD2TB+8tngUQw5gEoIIYQQQgghhBBCCCEpyIvFYrFUHxL5FBQUqMLCQpWfn686duwYf7+kpERVqRJu/DxMWdMyNuXS8VECPvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccE2ut+2p+i1EDoxhdrUHJD0YQ/kwhrJh/ORTEkEMTfsuzKQcZdmyZZGWNS1jUy4dHyXgg38ubbCpO6wul7luKstc98M/1zbY0h9l2247103kfMgF1/jgo5S2nZBsh9eLfBhD+TCGsmH85LPMoxhyADVHKSoqirSsaRmbcun4KAEf/HNpg03dYXW5zHVTWea6H/65tsGW/ijbdtu5biLnQy64xgcfpbTthGQ7vF7kwxjKhzGUDeMnnyKPYsgB1Byldu3akZY1LWNTLh0fJeCDfy5tsKk7rC6XuW4qy1z3wz/XNtjSH2XbbjvXTeR8yAXX+OCjlLadkGyH14t8GEP5MIayYfzkU9ujGHIANUdp3LhxpGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1PvgopW0nJNvh9SIfxlA+jKFsGD/5NPYohhxAzVGWLFkSaVnTMjbl0vFRAj7459IGm7rD6nKZ66ayzHU//HNtgy39UbbttnPdRM6HXHCNDz5KadsJyXZ4vciHMZQPYygbxk8+SzyKIQdQCSGEEEIIIYQQQgghJAUcQM1RuIRfPj74J2WZJ5fwy8YH/7iEPz15LuE3xwcfpbTthGQ7vF7kwxjKhzGUDeMnn8YexZADqDlKSUlJpGVNy9iUS8dHCfjgn0sbbOoOq8tlrpvKMtf98M+1Dbb0R9m22851EzkfcsE1PvgopW0nJNvh9SIfxlA+jKFsGD/5lHgUQw6g5ijr1q2LtKxpGZty6fgoAR/8c2mDTd1hdbnMdVNZ5rof/rm2wZb+KNt227luIudDLrjGBx+ltO2EZDu8XuTDGMqHMZQN4yefdR7FkAOohBBCCCGEEEIIIYQQkoK8WCwWS/UhkU9BQYEqLCxU+fn5qmPHjvH3i4uLVbVq1ULpDFPWtIxNuXR8lIAP/rm0wabusLpc5rqpLHPdD/9c22BLf5Rtu+1cN5HzIRdck+tte6p+C5EDY5hd7QFJD8ZQPoyhbBg/+RRHEEPTvgtnoOYoK1eujLSsaRmbcun4KAEf/HNpg03dYXW5zHVTWea6H/65tsGW/ijbdtu5biLnQy64xgcfpbTthGQ7vF7kwxjKhzGUDeMnn5UexZADqDnKjh07Ii1rWsamXDo+SsAH/1zaYFN3WF0uc91Ulrnuh3+ubbClP8q23Xaum8j5kAuu8cFHKW07IdkOrxf5MIbyYQxlw/jJZ4dHMeQAao5Sq1atSMualrEpl46PEvDBP5c22NQdVpfLXDeVZa774Z9rG2zpj7Jtt53rJnI+5IJrfPBRSttOSLbD60U+jKF8GEPZMH7yqeVRDLkHapbDPVCzd78TH/zjHqjpleEeqGb44B/3QE1PnnugmuODj9wDlaQDY5hd7QFJD8ZQPoyhbBg/+RRzD1SSaRYtWhRpWdMyNuXS8VECPvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccI0PPkpp2wnJdni9yIcxlA9jKBvGTz6LPIohB1AJIYQQQgghhBBCCCEkBRxAzVEaNmwYaVnTMjbl0vFRAj7459IGm7rD6nKZ66ayzHU//HNtgy39UbbttnPdRM6HXHCNDz5KadsJyXZ4vciHMZQPYygbxk8+DT2KIQdQc5S8vLxIy5qWsSmXjo8S8ME/lzbY1B1Wl8tcN5Vlrvvhn2sbbOmPsm23nesmcj7kgmt88FFK205ItsPrRT6MoXwYQ9kwfvLJ8yiGHEDNUdauXRtpWdMyNuXS8VECPvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccI0PPkpp24k5O3bsUM8884zq1auX6tSpkzr88MPVgAED1DvvvJNp00g58HqRD2MoH8ZQNoyffNZ6FEMeR0YIIYQQQkiWsn37dnXJJZeoWbNmqapVq6p27dqpLVu2qC+//FL//Oc//1EjR47MtJmEEEIIIV6TF4vFYpk2grijoKBAFRYWqvz8fNWxY8f4+0VFRapGjRqhdIYpa1rGplw6PkrAB/9c2mBTd1hdLnPdVJa57od/rm2wpT/Ktt12rpvI+ZALrsn1tj1Vv4WE5/bbb1eTJk1Sbdq00bNQ9913X/3+tGnT1PXXX6+2bdumRowYoc4991wr38cYZld7QNKDMZQPYygbxk8+RRHE0LTvwiX8OcqaNWsiLWtaxqZcOj5KwAf/XNpgU3dYXS5z3VSWue6Hf65tsKU/yrbddq6byPmQC67xwUcpbTupmGXLlqm33npL7x/28MMPxwdPQffu3dWwYcP076NHj1YlJSUZtJQkg9eLfBhD+TCGsmH85LPGoxhyADVHwWyDKMualrEpl46PEvDBP5c22NQdVpfLXDeVZa774Z9rG2zpj7Jtt53rJnI+5IJrfPBRSttOKubtt99WxcXFet/TDh067PZ5nz59VK1atdTq1avVjBkzMmIjSQ2vF/kwhvJhDGXD+Mlnm0cx5ABqjpLOFOgwZU3L2JTL9qn6Pvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccI0PPkpp20nFzJ49W79269YtZTwwuAo4gOofvF7kwxjKhzGUDeMnnxoexZADqDlK8+bNIy1rWsamXDo+SsAH/1zaYFN3WF0uc91Ulrnuh3+ubbClP8q23Xaum8j5kAuu8cFHKW07qZhFixbp18Sl+2Vp0aJFKVniD7xe5MMYyocxlA3jJ5/mHsWQA6g5ys8//xxpWdMyNuXS8VECPvjn0gabusPqcpnrprLMdT/8c22DLf1Rtu22c91EzodccI0PPkpp20nFrFu3Tr82aNAgpcxee+2lXzds2BCZXcQMXi/yYQzlwxjKhvGTz88exTAvFovFMm0EccfXX3+tdu3apQ8PqF27dvz97du36z2vwhCmrGkZm3Lp+CgBH/xzaYNN3WF1ucx1U1nmuh/+ubbBlv4o23bbuW4i50MuuCbX23bsc4WuadWqVdWhhx7qxIZcAifJ4nCoZ555Rp1wwglJZR599FH9eZcuXdSrr77qrO9JZLYHJD0YQ/kwhrJh/OSzPYIYmvY/qzm1gmSc4ERVJENhYWGpz8r+XRnClDUtY1MuHR8l4IN/Lm2wqTusLpe5birLXPfDP9c22NIfZdtuO9dN5HzIBdf44GOm23aeCG8H3AiY1iUGPF33PUnlYR3KhzGUD2MoG8ZPPoURxbCiPhMHULOc6tWrq507d6oqVaqomjVrZtocQgghhJCU7NixQ3de0X8h6ZOfn682bdqk6zUVwWe2Zouy70kIIYSQbOx/cgA1ywlOViWEEEIIIblF/fr19QDqxo0bU8oEe5+Wt09qZWDfkxBCCCHZCA+RIoQQQgghJAtp3bq1fl2+fHlKmWXLlunXVq1aRWYXIYQQQog0OIBKCCGEEEJIFtK5c2f9+tVXXyX9vKioSM2ZM0f/3rVr10htI4QQQgiRBAdQCSGEEEIIyUJ69uwZH0CdN2/ebp+/8cYb+nTb5s2bqyOOOCIDFhJCCCGEyIADqIQQQgghhGQhLVu2VL1799YHIwwZMkQtWLAg/tn06dPVAw88oH8fPHiwqlaNRyMQQgghhKQiLxaLxVJ+SgghhBBCCBELDpEaNGiQmjt3rqpSpYpq27atnnW6ePFi/Xm/fv3U3XffnWkzCSGEEEK8hgOohBBCCCGEZDEYMB03bpyaOnWqHjjFQGr79u1V3759VZ8+fVReXl6mTSSEEEII8RoOoBJCCCGEEEIIIYQQQkgKuAcqIYQQQgghhBBCCCGEpIADqIQQQgghhBBCCCGEEJICDqASQgghhBBCCCGEEEJICjiASgghhBBCCCGEEEIIISngACohhBBCCCGEEEIIIYSkgAOohBBCCCGEEEIIIYQQkgIOoBJCCCGEEEIIIYQQQkgKOIBKCCGEEEIIIYQQQgghKeAAKiFKqW+//VYNHDhQde7cWR1++OFq6NCh6pdffsm0WYRY57PPPlPnnnuuOuSQQ9SJJ56oRo8erYqKijJtFiFWGTRokLrzzjuTfrZo0SJ15ZVXqm7duqnf/OY36p577lFbt26N3EZCCKksH3zwQfz/cLRhgwcPVgsXLsy0WSQkkydPVu3bt1dffPFFpk0hlWDNmjXq1ltvVUceeaS+Dn//+9+rBQsWZNosUgl47y+XQRnu41ezqo0QgSxfvlxfiHXr1lXXX3+9vsj++te/qu+//169/fbbqlatWpk2kRArfPrpp+ryyy9XTZs2VUOGDFGbNm1SY8aM0bn+zDPPZNo8QqyAhwJ4UNCyZcvdPlu3bp268MILVbVq1fTAw+bNm9Vzzz2nlixZop599tmM2EsIISZMnz5dXX311Xrw9KabblJbtmxRL7zwgurfv7966623VLNmzTJtIqkE69evV/fee2+mzSCV5Ndff1UDBgxQGzdu1PePNWrUUOPGjdODcX//+99Vw4YNM20iqQDe+8tltAd9fA6gkpxn/Pjxatu2bWrSpEmqdevW+r22bdvqASb8R4gn/YRkAyNHjlS1a9dWr776qmrSpEk812+++Wb1/vvvq5NPPjnTJhISGsykfuCBB9SLL76YUgYdqQ0bNqh3331XtWjRQr+H19tvv113yPC0mhBCfASDbe3atVMTJkzQN4gA/2+feeaZ+uYw1Ywc4m+fjKsf5IGJB0uXLlUTJ05UnTp10u8dc8wx6ne/+51+76qrrsq0iaQCeO8vjyKP+vhcwk9ynp9//lnPyAsa0OA/QvDjjz9m0DJC7LFs2TK9vOjss8+OD56CM844QzVo0EA/cSVEKphNjVxGx+qyyy5LKYdO1dFHHx3vWIGzzjpL5efn688IIcTXJcPor55++unxwdPgph8/X3/9dUbtI5Xj448/Vv/4xz/UJZdckmlTSCWIxWK6v4wHF8HgKcA2DDfeeKO+Fon/8N5fFr718TmASnKefffdV61du1YvyUgcbAKNGzfOoGWE2GP16tX6tU2bNqXez8vL0//RFBQUZMgyQtIH7TdyGbOwMKM6GVhuh2VbBx10UKn3MRiBmx8s3SKEEB+pX7++HnBLNjMKbVvVqlUzYhepPJh1Onz4cHXxxRfr/3uIHHB/iH0yjzrqKP13SUmJKiws1L9jiyyu5JIB7/1l8atnfXwOoJKcB08ysF8N9pOaP3++mjNnjvrjH/+o38NTC0KyATx9A8mWi+HJHvaNIUQqe++9t5o6dao69thjU8oEhwNg1kFZ0GFetWqVUxsJISQsuAncf//9d9tf8V//+pdauXKl6tKlS8ZsI5XjscceU1WqVFHXXHNNpk0hlQQH1IC99tpLH05z2GGH6WsPy/c5C1wOvPeXxd6e9fG5ByrJebDpPhrSUaNGqWnTpun3sE/k888/z6dQJGvAMhUMouIEX2ycHoBl/djLiRDJJC5pTUXw8CDZ4QA1a9aMzyIhhBApy/oxkxFtGg6wITJO/n755ZfVX/7yFx5UI5BgxuJDDz2kDyDC9Ye9GZ9++mk9o/j1118vtSyc+Anv/WVRzbM+PmegkpznkUceUSNGjNDLMR599FG9qXvz5s3VpZdeqr777rtMm0eIFXBKKE4N/fLLL9Vdd92lB05nzpyprrvuOt0J5PI/kgt7l5UHZgQRQogEsFwRAwCYVYMDMrAklfjNzp071W233aZOO+20+H6LRBYYLAXbt29XL730kurdu7feVgOHEiG+GEgl/sN7/+wjFmEfnzNQSU6zefNmNW7cOL0EA/tqYH8NcOqpp+oODpZn4IQ+QrIBnC6Jpfqvvvqq/sETPQyqYtnDjBkzMm0eIZFsY7Fjx47dPsN7eJBACCESZp7i8CEcdnLttdfyxGgh/PWvf9X7LGIJ//r160vNmsLMRryHQz2Jv2CWIjjllFNUnTp14u/jAUbXrl31JAXiN7z3z07yI+zjcwCVqFzfywZPE3GqadCAAlxkJ510kpowYYJ+yshlNiQbqF69ul6ugoFUdOKD/dT69etX6sRCQrKRffbZp9Q+SYngvSZNmmTAKkIIMQczTi+66CLdf8X/5VdffXWmTSKGfPrpp3oZKQZpyhLEcd68eRmwjJgS7K9Ydi9igMHvuXPnZsAqUhl475+d7BNhH58DqETl+rLm4BTFsuzatUtPB69oSjghUpgyZYr+DwZPXbEhN9i2bZsqKChQ559/fqbNI8Qp9erV00u0fvjhh1LvFxcX65lcOASCEEJ8njmFmacYALjxxhv1qd9EDrfeequOYSJffPGFeuaZZ/RnHTp0yJhtxIy2bdvqyQgLFy7c7TOcAB4M4hB/4b1/dlIvwj4+N/wiKtf/I8Rm0W+++abeuyZgw4YN+rCdQw45JL5cgxDpPPfcc+r+++8v1TEYO3asfhLLJYAkF+jRo4eaPn26noEd8NZbb6WcFUQIIb4Q7F+Ovcs5eCqPgw8+WO+5mPjTrl07/dlBBx2k/yZ+g2X7xx9/vPrnP/9Z6gDWb775Rh8QhhmMxG9475+99Iioj88ZqCSnwcE5f/zjH9XQoUPVeeedp/r06aNn5GH6Pp4Sjx49OtMmEmINzFzBrBXcfOEAgzlz5qiJEyfqG7EDDjgg0+YR4hwcujJ58mR14YUXqkGDBuk957Av3QknnKCOPPLITJtHCCFJwUqRqVOn6ht/nCD99ttvl/p8jz32UCeeeGLG7CMkV7j55pv1IawXXHCB7ktgEA57agaHEBG/4b1/9nJZRH18DqASMbz22mvqjjvuUMOHD1f9+/dPKYeNgvEfGZYrL168WO9h0r59e10G+52UBU8ksO/JU089pR588EF9Shs2Ascm73gKRUi25HqvXr10Rw//mXz88cf6JuzOO+/k8n0iPrdNadSokXrxxRf1XsAPP/ywHnRAB/qGG24IrZMQQsLuZ4p27pNPPtHLfwEGYTDDDQ88MViauNQ7OEAKy73Lgj3NOYDqdwxJdsSwVatWerDtoYce0veOGJA7+uij1bBhw3SfgvgfQ977Z2e72SiiPn5ejJs8EAFgWQSeJOC0yvJuvLHpMy68WbNm6f/QsDRmy5Yt8WUW55xzjho5cmTE1hNiDnOdZCvMbUII+S+YwTZ48GA94wntHE7xBmjnsA8fDqTBFjtY9k38hDGUD2MoH8ZQFjOzIF7cA5V4z2effaaXROCmuyJGjBihb7rbtGmj3nvvPT2NG/uZjBkzRu9n8vrrr6tJkyZFYjchlYW5TrIV5jYhhPwX3Dhee+21+vXYY49V06ZN021d8IOZUFh6iJPZsXcb8Q/GUD6MoXwYQ1lszpJ4cQCVeAsunEceeUTPRCp7amUysGEwNgrOy8vT07aDJxqge/fuemkFwN4myU7eIyRTMNdJtsLcJoSQ0uDwEtwkNmnSRC8ZxWsA2rwnn3xSnyiMZY7vvPNORm0lyWEM5cMYyocxlMWbWRIvDqASL/nhhx/0SWqYcYR9SbDRM/bGKA9sqF9cXKw6deqkOnTosNvn2CQa++itXr1azZgxw6H1hJjDXCfZCnObEEJ2J9jPFAdbYB++smAJY5cuXfTv3333XeT2kYphDOXDGMqHMZTFF1kSLw6gEi/BLCRslN+tWze9XPOKK66osMzs2bP1K8oko0aNGvqmHPDGm/gCc51kK8xtQgjZHez/dv/996uzzz47pUxwRAX2hCP+wRjKhzGUD2Moi8FZEq9qmTaAkGS0bNlSn8521FFHGZdZtGiRfk1c8lmWFi1aqC+//DIuS0imYa6TbIW5TQghu4NTnss76RlLHIMHRDhEj/gHYygfxlA+jKEsDsmSeHEAlXgJLprKXjjr1q2LT/9OxV577aVfN2zYkKaFhNiBuU6yFeY2IYRUnpEjR6pt27bp7Up69uyZaXNICBhD+TCG8mEMZTFSSLy4hJ9kDdu3b9evNWvWTCkTfIaLkxCpMNdJtsLcJoTkMk899ZSaMmWK/v3KK68sdcgGkQFjKB/GUD6MoSyeEhQvzkAlWUPVqlWNT2XGCc+ESIW5TrIV5jYhJFd54okn1OjRo/Xv3bt3N9o3mvgFYygfxlA+jKEsnhAWLw6gkqwhPz9fbdq0Se3YsSOlTPBZ7dq1I7SMELsw10m2wtwmhOQaxcXF6p577lETJ07Uf2Pf6Mcff1xVqcKFglJgDOXDGMqHMZRFsdB4+W0dIZWgfv36+nXjxo0pZYI988rbX48Q32Guk2yFuU0IySW2bNmiLr/88vgN5CmnnKLGjBmj94AjMmAM5cMYyocxlMUWwfHiACrJGlq3bq1fly9fnlJm2bJl+rVVq1aR2UWIbZjrJFthbhNCcoVVq1ap/v37q08//VT/ffHFF6vHHntM1ahRI9OmEUMYQ/kwhvJhDGWxSni8OIBKsobOnTvr16+++irp50VFRWrOnDn6965du0ZqGyE2Ya6TbIW5TQjJBVavXq0GDhyofvzxR71c8Y477lDDhg3zfuki+T8YQ/kwhvJhDGWxOgviJcdSQiqgZ8+e8RvvefPm7fb5G2+8oU94bt68uTriiCMyYCEhdmCuk2yFuU0IyXbwIGjw4MFqyZIlqnr16nrmzYABAzJtFqkEjKF8GEP5MIayKMqSeHEAlWQNLVu2VL1799YnOA8ZMkQtWLAg/tn06dPVAw88oH/HhVutGs9PI3JhrpNshblNCMl2xo4dq77//nv9O2bfYO83IgvGUD6MoXwYQ1mMzZJ48e6DZBW33Xab+umnn9TcuXNVr169VNu2bfVspcWLF+vP+/Xrp84999xMm0lI2jDXSbbC3CaEZPMMnPHjx+vf8RBo8uTJ+icVOJX42muvjdBCUhGMoXwYQ/kwhrIoyqJ4cQCVZBX16tVTEyZMUOPGjVNTp05VixYt0ntqHHrooapv376qT58+mTaRECsw10m2wtwmhGQr2Pdt06ZN+vfi4uKU+z0H7L333hFZRkxhDOXDGMqHMZTFj1kUr7xYLBbLtBGEEEIIIYQQQgghhBDiI9wDlRBCCCGEEEIIIYQQQlLAAVRCCCGEEEIIIYQQQghJAQdQCSGEEEIIIYQQQgghJAUcQCWEEEIIIYQQQgghhJAUcACVEEIIIYQQQgghhBBCUsABVEIIIYQQQgghhBBCCEkBB1AJIYQQQgghhBBCCCEkBRxAJYQQQgghhBBCCCGEkBRwAJUQQgghhBBCCCGEEEJSwAFUQgghhBBCCCGEEEIISQEHUAkhThg2bJhq3759hT+QM2XdunWqsLCw0rYMHDhQnXjiieXKjB49WtvzwAMPpJSprL02gN2w32disZh68MEH1ZFHHqkOPfRQ9fLLLxvnRMeOHVXXrl3Vueeeq956661Q328S31Rs2bJFrV+/XtnkmmuuUU8//bT+/YsvvtjN5wMPPFAdccQR6oILLlBvv/126O9ZtmxZhdfXyJEjjfUtXbpUx3D16tWhbSKEEEKIHYJ+U6p+VdAPQB82SjLRH64sRUVF6g9/+IPuY+Lno48+StmHTNZ/Qn/25JNPVqNGjdJ9xVyvT0LIf6n2v1dCCLHKeeedp37zm9/E/541a5aaOHGifv+www6Lv9+yZUsjfdOnT1c33XSTHmTLz89Xrhg/frw688wzVbt27Zx9R7Yxbdo09eyzz6ru3bur3/72t6Ximwx0aOvXrx8ffEXH9G9/+5vuPG7YsEFdcskllfr+K6+8Um3btq3Sds+ZM0cNHjxYPfTQQ3rg0FZdfPXVV7sNxKMTjh9QXFysHwZ88MEH6pZbbtHyd999d+jv7Natm+rbt2/Sz1q3bm2sZ99991Wnnnqqvll4/PHHQ9tDCCGEEHs89thj6pRTTlGNGjXKtClieO2119Sbb76pevfurQ4//HB18MEHlytftt+2ceNGPeiK+4KFCxfqfi4hhHAAlRDihC5duuifgF27dukBVDzRRWemsnz77bdq8+bNyjUY3LrrrrvUK6+8ovLy8px/XzYwb948/Tp06FD9FL0iMMjaokWLUu+dc8456rTTTlNPPvmkGjBggKpRo4bx9x999NEhrFbqxx9/VL/88ouyRUlJiR58vOiii3Yb5Ee9lM37yy67TN16663q1Vdf1QO48D8MGPgMc00l4/LLL9cDvTNnztQDs4QQQgjJLOj/3nvvverhhx/OtCni+qZ33nmnqlu3boXyyfpRF154obriiiv0JA7chxxyyCFObCWEyIFL+AkhJAEsBceMwNdffz3Tpohh586d+rVOnTqhddSqVUvXPWaj/vTTT0oimKmwePFidcYZZxjJV6lSRQ/W16tXT40dO1b5QPPmzfXM8eeffz7TphBCCCHkf33TKVOmqM8++yzTpojrm5oMnqYCEymwKg18/fXX1mwjhMiFA6iEkIyD2W6DBg2Kz1rFE98vv/wy/jmWdj/xxBP695NOOqnUnqDvvvuunrGIZeNYnoNOJpbhYO+jMFx33XWqcePGell3RXtjptp7s+z7+BtPsLFk+3e/+53q1KmTOv300/UTbQwY4uk4lhdh4Aq/b9++fTedkyZN0r6jLPYL/eSTT3aTmT17trr44ovj9Yil8Hhingjsuv3229Uf//hH/ST9uOOOK9fPimIDfYmxCbsXKQhm/GK2sun3p6rvSy+9VH388ceqT58+us6OP/54vUcYZokC/I6tBAB0BuWxpQD8wVI5lDvqqKPUzTffrFauXFmh/Zi1jP1NmzVrZuwzOvYnnHCCmjt3rl7WH4CtDIYPH66OPfZYndew5y9/+UupuqksEyZM0IO7nTt31jNer7766qSD1VjGj8FgE58JIYQQ4hb022rXrq37BRX1b1PtnV/2ffx9zz336P4l+hjoE5599tm637hmzRrdH0a/C/2QRx55JN5/SuSZZ57Rn6Nfgb5U2T4n+Ne//qX69eunZdDXvfbaa9XPP/+82yodbFOALZnQ50EfGSvCUoH+NHTCZqyWQbkffvihlL5gX338ns5ZAqj3oH8YgL47ZgOjv4S+IuoJWyl9+OGHu+1PO3nyZPXoo4/q/nbQh//888/L/c4FCxbofhr0r127Vr+3YsUKXXfHHHOM1oNVS3j4niwuhBB3cACVEJJR0NlAxwaDNdiPEj/4HYNmQUcE+6YG+0di0AsdJYBO3/XXX6/22GMPvT8q9pPEDLq//vWvuiMWBgxoYXARex/hYCRbfP/991pvjx49tK0YIIPtWDK9fPlyvfwdg3XY5qDsPkvYq3PEiBG6swQ5LOXCgOx//vOfuMynn36q6/HXX3/VnV7UIzpbOKgIg5CJvPPOO3ppE+xBh69BgwahYwMdibHB32FAB3DGjBl66X6wb6fJ95e3PB/1iw4objyw1y4GRjGICGAz8gognwK7cTOAbQRwQ4DBbHR00VHHYHR5g5fYgxX2Y6C2srRt21a/Bp3/TZs26RsDzILGTQ3qFXWCzvqNN964W3ncTGEQvOzP1q1b4zLYYxY3Xhjgve222/RAO2ZaBzmTCA64gq///ve/K+0LIYQQQuyCvu1VV12lFi1apB+m2gL9G+x5jm2UcAAm9vrEIB36CFglgwkMOBNgzJgxux16+d5776lx48bp/goeyKIsBlETH8xiD1L03TAIiYfR6L/hYT/6nmUHUbHXKGaNos+Gvle1asl3GsSBWvg+yKJPDJ0YuO3fv398ABcTKYJtiPB7cN8QhmDCAvpPwUAq+uAvvfSS7kuir4g+IvryqMNg64AA1O/777+vZYYMGaIHVlEe9wHJQN8dsrgfQZ1g31v4im2fcC8Bf++44w61//7768keNvOBEGJAjBBCIuCNN96ItWvXTr8G7Ny5M3bcccfFjj/++Nivv/4af3/Tpk2xY489Vv8UFRXp9/785z/r8kuXLo3LnXrqqbHzzjsvVlJSspvOXr16xd8bMGBA7IQTTijXvrL6L7nkklj79u1jX375ZVwGn996660V6i37Pv5G2Y8++ij+3ksvvaTf69u3b/w9+AHb4VMA9EBu2rRp8fc2bNgQO+KII2JnnXWW/nvXrl2xk046KdavX79YcXFxXG7r1q2xk08+Oda7d+9S+jp06BBbtWpVufWRbmySgbqD3Pfffx9bt26d/vnll19is2fPjl133XX6s1GjRlX6+1PV94cffhh/b/v27bHDDz+8VN0GOfn555/H3+vZs2fs8ssvL2X3hAkTYr/73e9iixcvTunbZ599pnVNnTq11PvQjfdRR6l47bXXtMyUKVP03w8++KD++/333y8lN3z48FK5gPrG36l+EnP1sssui51++uml9EHPaaedFps5c2ap95GHnTt3jt1yyy0pbSaEEEKIW4J+E0CfB/+Pd+rUKbZo0aJS/YDEPgb6Q+gHlaXs+/gb/dwffvgh/t7999+v9V1//fWl+pIHHXRQbOjQofH3INOxY8dSZWHTgQceGLvmmmv03+i7de3aNXbDDTeUsgP9PvTHrrrqqlL6unXrFtu2bVu59bF+/XrdPznnnHNiO3bsiL+PesD7Z599dtK6K4+gzxj0S4Of+fPnx5566int56BBg+LyX3/9tZZH3zCRjz/+WL//3HPPxW3C3+jHog4D3nnnHf3+xIkTS/kPe/G9PXr00P3fxD71N998o2XefffdUn013Kuwr0ZItPAQKUJIxsCy5VWrVukZmYl7FO255556WT5m3GH2ZeJhVIlgVh1m/iUe9oRl0ChfWFiYlm14oozlzpi1h2VA1atXT0tfzZo19azGADw5Dpa9B8APzDJYvXp1qbJ4+p84s3GvvfbStr344ot6mRXkly5dqp++Y/ZiIlgejv0sIdO0aVP9HmZjBr+7ik15nHXWWbu9h5mnmA0ZzLBM9/sx26F79+6l6h91HiyFSsXee++tvvjiC/3UH0vI8OQfsyvwUx6of1D2cKzK7NMV5DGWz2PGKQ7bSgSzT7BNAGbfJuYDlnNhy4KyNGnSpJRfmKWMWbjYzwt2QkeyGbNBHmKWBCGEEEIyD/qh6JOiD4Sl91htlS7oDyYe/hn0TYOVRQCHYjZs2FD3NxNBnzax7H777aeXqWP1ClaxoM+Bpe7oyyRuFVW1alX1//7f/9PbWGGZfjDTFMvxsR9+eWAPWPT7MUM28bBR9GmwRRZWceFw0MT+jynYRqss6HNitiwO/AzAVgTYSirRVvgbLKVPXP0D0M9KPFi0Q4cO+rVsfaKuMMsUM1n//ve/l+pPwh/0zTATGOcNYHUV/LeRA4SQysEBVEJIxggGaIIOWyIHHHBAfClLqkEydCbRicHG+lg6tGTJkvg+khgASgd0BLG8HntlYokSfk8HDHomLkdCBxKgU5oI3k/cZylV/aDTC9DRQh0Fy5TwkwzIBIOmZb/TRWzKA1sjYGASYIkYOqgYMMQgp63vR31DdyLobFa0VxS2gcBys1GjRukTbw866CC9Txg60NgbNxXY8iHsYQVB2fr168d9TxxsD8D3o64Q87LvY/uH8sByNxyAgHzGT5s2bbRfWCYX5FIi8CPV8jJCCCGERA+WpeMhNJbGYzsmDOalQ7I+KCi7tVOyvmnQF0sE/Qk8BMaAKfrk4IYbbkj5/ZALBjtTbSeVSNA3TPbdwfZP6BuGGUBFXx/gHIKpU6fqewtMTMB2UGX7k+jPv/rqq3rrJhweCl+D8wvK1lNZv4KB37L9USzzx/fgfUwQSOz/4iE4tkDAXrQYZMWALAZ8sbVXz54943EjhLiHA6iEkIxRtpOR7LPyZn7+6U9/0nsQYV+iQw89VPXu3VsPqOF9GwfgYNAUT4GfeuopPRvRlGR7Zabayylx9mxlCOon6GwB7H2KekhGYmfTpKOVbmzKo2vXrhXO1Ez3+8t2dk3BzADs64U9r3DwAV7//Oc/6441ZjYEHfRU3xdmM/+CggKdB8FMjvJ8h/4w9Y7ON/Yvw+xazGCFX9g3C34999xzet/Tst/DDjkhhBDiFxhIwyAlHvKW3TPfl74p+g9Bfwh98lR9vnr16sV/T7fPkW7fNPFBNB4w40E/ZnxiRRv2ZU0c9MXDZ8x0Pfroo7Us+o6YuIH3w/ZHcZ4D9uHHnvz33Xefns2bWD9YadSrVy890IrZu5jhi/4cDqmqTB4QQtKDA6iEkIwRzBLF7NGyBJvLY+AnGZiFh8FTDJqWnXVZ0TJtU/CU+K677tJLhdABTNYpSnYaqq3vDyg74xDgIAGw7777xjvFeCJddiYiNtTHsv6KlkXZjI0NMvH9qEcc5ITZl9haIdheATMRMIMCh5bhQIXyZnEEs0lNwZItLHfDwH8wSwG+lz1cIVjuBfl99tmn0r4FhxpgxkKwTG3WrFnqoosu0ltBlB1AhR/pzuImhBBCiF3QV8D2RhjUS3ZgarK+KZbKY1VJshUntvumGAjEipqgDwF7y/ZN8TAXA6yJy/Ar2zcMlsIHBP1FW31D1DFWuaGPhCXzwbYG2EoJM2GxPVbisn8czJkO2OoAM4wR19///vd6qyps1RD0ydA/xQQEbOGAHwzsok+Kh/7o4yVup0AIcUe4KTqEEGIBLI/G8mOcjI6BoQD8jg4KPjv44INLPcENnjAHe31iKXIieCqLDhw6izZApw9PfDEbsSx4Oo0tAxL3LMWyGyznsQlO3cSeoIkDtNj/FR0tdFJRR6grdPIS915CPWLpEU5xr+yT/crExgVRfH/ZWaMYQMUJsli+n0iwRK68WQRBpx77tpqCXMZ3oROMJVmJ+9YuWLBAn46bSHDSauLerqZgdjK2J0icgYKZ25ipUdYvyGCwNsxALSGEEELccs455+jBtFR9UzyEDZaUA8xY3bFjh1UbsJIlsf/7448/6gfCmJGJGazoP2NrJsyODPZ6ByiDPd1xgnxlZ7oGOrF6JnGQGH0vrBjDPqom21SZgNm5mOWLftLdd9+tNm/eXOpBeeL9B/pzmNQB0r3/wMxTDNa+9tpr8UFZzDbFA2/EMQCTJnBGAuCKIUKigzNQCSEZA50SPGnF7L6zzz5bdwjB66+/rpfGYOl0MLgTzM5DRwydC+wR2axZM73cBZ1CPHHGbEsc+ITOVdlN3NMBT3gxMPvrr7+Weh8Dq9gjCU+KsU8SBlMxiNmqVatSncV0wRIeLN3BTFh0kl5++WXdQcPAaNl67NOnj65H1AFmTGIvKHRSUy3TshEbF0Tx/UFOYZAWg9I4mAsHWT399NN6z1DkGG5AsHQfh1LBjlRgkBWd2W+++Sbpdg+YHYAl9MEAJb4PA6SQx6Bt4mFiV1xxhfrnP/+pB7+RV8inzz//XL/Xo0ePpAc/VQTyB/U5aNAgdeqpp+rOPuzBtXP++eeXksVNEA5pSHagAiGEEEIyCwYecaAU+nxlB+zQN8WqKTyYxcFKeKiPwTjbq0owexT9B/Sb8CAYh29in3b0XYI+1tChQ/Ug5Hnnnadtga14CI6+R+LBTKZg0kCgE/0j9NvQ30c/Dg/DE5fa2wADlOg/4V4D+/ejXnEPgr4++mrom6K//+677+oJFOiX2rj/wDJ+DEZjFRz2u8WDdeyJetttt+lJFZhJjBm3uB9AX63sZBJCiDs4gEoIySgYzMEAIfYZffLJJ/VAHwajRo4cqWdYBmBQCgNI6Ehg03YMOGFGHvYJeuGFF/SAEDoU6HSgg4by6MzYmCWJ2Y4YyAuW0gSgQ3PnnXfq78f3oXODDi2W/EybNk3ZAgN5nTp10qdt4sk36gfLthJ9C+oRg3+oS3Ti2rZtq/+GnS5j4wrX349OJzbfxwwODFBicHLIkCH6AKo33nhD3X///XrAGrM80HFOtf9pcCOBJV4zZ85M+jn2rMJP4sFZmAH66KOP6kMAEsH3Y9AWMcb2AZj1gK0aMIMUA6BhwL5cGJRGruIQAtxoIH/Gjh2r7U4ES/th4zHHHBPquwghhBDiFizZxgNY7GOeCAY10VfEA2cM+GGp+xNPPKHlMNBpCwyKYiA3mMiAvgQmHGByQwD6LDjAFDNG0d/BdlJYYYQ+1WGHHRbqe6ETh0TBH/Rn8IAb2xBdc801TpaxY7YsBkgxKQEDthhAHTFihP5+3IOgnwqf0G+744479PYE6YI6xIGm8C84yBbfh8kDmGmLh/C4N0Gs4TchJDryYuWdVkEIIYQQIzCjFDNXMdC/3377Kan069dPLwHEDRchhBBCCCGEEO6BSgghhFgBs6Kx3B7bSEgFS/1mz56tLrnkkkybQgghhBBCCCHewAFUQgghxAJYynbjjTfudvCVJLAtBrZ8wLYFhBBCCCGEEEL+CwdQCSGEEEtgH1UMPj7//PNKGkuWLNH7tOLQAkIIIYQQQggh/wf3QCWEEEIIIYQQQgghhJAUcAYqIYQQQgghhBBCCCGEpIADqIQQQgghhBBCCCGEEJICDqASQgghhBBCCCGEEEJICjiASgghhBBCCCGEEEIIISngACohhBBCCCGEEEIIIYSkgAOohBBCCCGEEEIIIYQQkgIOoBJCCCGEEEIIIYQQQkgKOIBKCCGEEEIIIYQQQgghKeAAKiGEEEIIIYQQQgghhKjk/H/DzlZiaB1kPwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weak_scaling([df_big_uniform], names=[\"Socket\"], title=\"Weak Scaling up to 3.2e10 Points on 512 nodes of ARCHER2 (Uniform)\", plot_err=False, stat='max')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "acbc24f2-9682-4719-9e7e-43ceeb63d62e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weak_scaling([df_big_sphere, df_big_sphere_1], names=[\"Socket\"], title=\"Weak Scaling up to 3.2e10 Points on 512 nodes of ARCHER2 (Sphere)\", plot_err=False, stat='p90')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff5677a1-cd72-40ab-b6dc-843e928becc0",
+   "metadata": {},
+   "source": [
+    "# Breadown Communication Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7281b367-b065-43cb-b418-92405d3e1621",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>p2p</th>\n",
+       "      <th>m2l</th>\n",
+       "      <th>scatter_v_runtime</th>\n",
+       "      <th>gather_v_runtime</th>\n",
+       "      <th>neighbour_all_to_all_v_runtime</th>\n",
+       "      <th>n_points</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10676.000000</td>\n",
+       "      <td>7257.000000</td>\n",
+       "      <td>3128.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>32000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10596.562500</td>\n",
+       "      <td>3825.500000</td>\n",
+       "      <td>6263.000000</td>\n",
+       "      <td>21.187500</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>103.500000</td>\n",
+       "      <td>32000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10846.882812</td>\n",
+       "      <td>7249.820312</td>\n",
+       "      <td>3150.437500</td>\n",
+       "      <td>10.796875</td>\n",
+       "      <td>58.359375</td>\n",
+       "      <td>151.671875</td>\n",
+       "      <td>32000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14846.920898</td>\n",
+       "      <td>3823.790039</td>\n",
+       "      <td>5739.640625</td>\n",
+       "      <td>108.542969</td>\n",
+       "      <td>3879.185547</td>\n",
+       "      <td>914.461914</td>\n",
+       "      <td>32000000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               experiment_id       runtime          p2p          m2l  \\\n",
+       "experiment_id                                                          \n",
+       "0                        0.0  10676.000000  7257.000000  3128.000000   \n",
+       "1                        1.0  10596.562500  3825.500000  6263.000000   \n",
+       "2                        2.0  10846.882812  7249.820312  3150.437500   \n",
+       "3                        3.0  14846.920898  3823.790039  5739.640625   \n",
+       "\n",
+       "               scatter_v_runtime  gather_v_runtime  \\\n",
+       "experiment_id                                        \n",
+       "0                       0.000000          0.000000   \n",
+       "1                      21.187500          2.750000   \n",
+       "2                      10.796875         58.359375   \n",
+       "3                     108.542969       3879.185547   \n",
+       "\n",
+       "               neighbour_all_to_all_v_runtime    n_points  \n",
+       "experiment_id                                              \n",
+       "0                                   41.000000  32000000.0  \n",
+       "1                                  103.500000  32000000.0  \n",
+       "2                                  151.671875  32000000.0  \n",
+       "3                                  914.461914  32000000.0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_big_sphere_1.groupby('experiment_id')[runtime_cols].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e707dacb-1631-4268-b143-08b765c38186",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>p2p</th>\n",
+       "      <th>m2l</th>\n",
+       "      <th>scatter_v_runtime</th>\n",
+       "      <th>gather_v_runtime</th>\n",
+       "      <th>neighbour_all_to_all_v_runtime</th>\n",
+       "      <th>n_points</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98.994949</td>\n",
+       "      <td>1.414214</td>\n",
+       "      <td>31.112698</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>25.455844</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1703.371753</td>\n",
+       "      <td>645.033539</td>\n",
+       "      <td>1021.543473</td>\n",
+       "      <td>13.417495</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>54.166410</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3294.702213</td>\n",
+       "      <td>2296.363913</td>\n",
+       "      <td>998.870258</td>\n",
+       "      <td>1.096590</td>\n",
+       "      <td>25.046016</td>\n",
+       "      <td>55.296639</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9179.649659</td>\n",
+       "      <td>2623.943684</td>\n",
+       "      <td>4200.543822</td>\n",
+       "      <td>13.236332</td>\n",
+       "      <td>10294.242764</td>\n",
+       "      <td>1368.828403</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               experiment_id      runtime          p2p          m2l  \\\n",
+       "experiment_id                                                         \n",
+       "0                        0.0    98.994949     1.414214    31.112698   \n",
+       "1                        0.0  1703.371753   645.033539  1021.543473   \n",
+       "2                        0.0  3294.702213  2296.363913   998.870258   \n",
+       "3                        0.0  9179.649659  2623.943684  4200.543822   \n",
+       "\n",
+       "               scatter_v_runtime  gather_v_runtime  \\\n",
+       "experiment_id                                        \n",
+       "0                       0.000000          0.000000   \n",
+       "1                      13.417495         11.000000   \n",
+       "2                       1.096590         25.046016   \n",
+       "3                      13.236332      10294.242764   \n",
+       "\n",
+       "               neighbour_all_to_all_v_runtime  n_points  \n",
+       "experiment_id                                            \n",
+       "0                                   25.455844       0.0  \n",
+       "1                                   54.166410       0.0  \n",
+       "2                                   55.296639       0.0  \n",
+       "3                                 1368.828403       0.0  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_big_sphere_1.groupby('experiment_id')[runtime_cols].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ec3dfd92-0631-401d-871b-434224b957e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1024"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_big_sphere[df_big_sphere['experiment_id'] == 3].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "87c0da8c-5b2b-4119-b726-0bfe8e1d0ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "62"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col = df_big_sphere[df_big_sphere['experiment_id'] == 3]['gather_v_runtime']\n",
+    "df_big_sphere[df_big_sphere['experiment_id'] == 3][col > col.mean()].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "53f8cd8c-074f-4768-9028-644a9a95a3cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "breakdown_communication_times(df_big_uniform, stat='p93')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7bc99bae-b811-444c-aee4-efaf0e9779d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "breakdown_communication_times(df_big_sphere_1, stat='median')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "447bedda-cd0e-4ff3-9201-e46d6d66c62c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      comm_time  comp_time\n",
+      "2         112.8    10759.4\n",
+      "16        157.5    11036.0\n",
+      "128       238.8     9310.6\n",
+      "1024      567.7     9411.6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "communication_trend(df_big_uniform, stat='p90', y_scale='log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "f1583ffd-9eec-48e2-b58b-3baeeeec5cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      comm_time  comp_time\n",
+      "2          74.0    10659.5\n",
+      "16        145.0    10262.5\n",
+      "128       310.0    10177.0\n",
+      "1024     2362.5    12419.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "communication_trend(df_big_sphere, stat='median', y_scale='log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "279dacfc-380e-4a10-b16c-babee401697f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define quantile threshold\n",
+    "q = 0.95  # change to 0.95, 0.99, etc.\n",
+    "\n",
+    "# Loop through each experiment\n",
+    "for exp_id, group in df_big_sphere.groupby('experiment_id'):\n",
+    "    # print(exp_id, group)\n",
+    "    # Compute this experiment's threshold\n",
+    "    threshold = group['gather_v_runtime'].quantile(q)\n",
+    "\n",
+    "    # Filter rows within this experiment\n",
+    "    filtered = group[group['gather_v_runtime'] > threshold]\n",
+    "\n",
+    "    if exp_id == 3:\n",
+    "        # Plot histogram for this experiment\n",
+    "        plt.figure()\n",
+    "        plt.hist(sorted(filtered['rank']), bins=50)\n",
+    "        plt.title(f'Experiment {exp_id} — Ranks above {int(q*100)}th percentile runtime')\n",
+    "        plt.xlabel('Rank')\n",
+    "        plt.ylabel('Count')\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "1f75bd28-1504-4ec2-9346-9c2106e52861",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mzd33fve738Wcb9asWaHfD09vnaf8/1u0aFEwevTogvXr17v8vGzZslA66KV8EHnOLe78EX4u1XTt80i//vWvQ+kZbXos4ceBjpVt27YVmq784Ndd6RN+Ltdxqn2vacOHD4+6/LfeeivqNUj8/0877TR3TEWjfeH35cqVK4tMX7duXSj/P/bYYym/vkU77+zYsaOgTZs2bvovf/nLItP9d7XfIvll6xwRbvDgwe7/F154YaH/pyu/h/+mXk8++WRC1/FY53WdB8OFX8cWLFhQ5Ltvv/12aPozzzwTc7nR8kesdff7Xq9JkyYV+d5ll10Wmt6/f/8i0+fNmxeavmjRotD/dVzpWqD/33LLLVHP0YpffH589tlnCxKR8hJTtaNS+5N4Xr70K1qVvu6I1CZCJSAqKfJVQ8OHD3cN72NRUwC1nYmkYU18L+bwuzbdNfpqEd1pRqtaUq9oX4qju8LI9jSikh9/R50MDcXi75i8Zs2ahTr6qKQn2vLVViiyxEImTpzo3lUao3Z20ai6QL2+VfKpu+BY63XMMccU+b/azXm+rW86RLYVU0mDvyMuqa1Xtvjtb38b9fhQ+ymVvKikWHeeOiZUo6A2deF0x6r9oX2ldpzR2t4qX6stk8QqFRGVmoQ3n/HU1k1UIhNe1RtJx6lKMlQ6pvZZKp2JHEpIfJVirHaVKhnRflZVULxUCqHjVG2zYz28QNXIvmo0Vjoo/VTtGVkVqGPF17io5Nk3K1L6+zaQ6igUWZotSlO1AdR+UL4Nrw3SPlZpmv7vOx2F8+crlaSFV02n6hiPpP3lj2+V3EUbj1Qlqb4tqIRXEasERucybZPaJGodTj31VHfeVrtElTb6ks0PPvjAlbwkQ6U80arE/QgoSs9kz02qvoxsQqL8oFJK7Uttb3hPaOVT5QM1RYpVwqbmLL7kXOkajdpLRmuipmX7vKEq2WjNnVQNrxJ1nTv8MZeq65uuy9FqAGvWrOn2qSTzgJ1EpCu/RyquXXUq+Lb0kXS+9/ttc4rTUvtJ/V8ihcdKOp4iqZ2pFx5jqGmIb7YYq9mE4hf/wJVY+T0rqvLDq/R1YfLVJrq4iaqzdXAXRxelaJRw/mQbXoUX3sZNB7ZOuNFe/kSgnRHtRK0e5D6ITEasdpU+WFU1YjQ6CCW8U4ku0H4ddTGLtU062fnlxurl7YOZWOslpemkUhK159I+UluXF1980QU7qtLSxU03KflAVeu6qKhqWFUxkScBXSh1Qla1XLT8ryowBQC+ei/ahaek/R1+kY5VLaNqbAW2uqFUEKKbh8gxKj1ffa2qM7Vd0wUxvEOXLs5qQ3zyySfH3VZMVb933HGHq7ZSABpJVVGqNvM3NLEek6gq5VjtWtVMwvPt3HTT4Ku1i7s51TKVfyW8rZ6qZX3gH1mdr2NL1W4SXp2dymM8GuUz7TvdYCjIVxW1qnsVzKuZxs9+9jNX3ehvWsNvhjQQuNJZ6RN+YQunJkI+jePpGBmNvymPFH4jHf6QjnhpW2LtR7W51QU31nVEAbgffzjay6+zmotEu7H2y46kZi3++AgvFIiktq8K4Hwb6FRd31SoE6tdut+P6Rz8Pd353VNgqH2YTrHOsSrcUgCZjrRUE45oBQ7h5znf2TlafBF5nff5Sud5LaOk/K5rQ3EFImnv/KQTb2Tj72TceOON7o5UDbMVdOnAUMlRSWI13hY//JAuIjoAFUiG31FHu4uJRncOkYFkcaW48Yj1fX9RDs8g4aJlNt1t+XafahukV0li9TKMtV7hd97pfHZveKNp3d2p7Zfu7HRgqP2PblaiBSHhdECFB+7plmhD+fDH8upipcBOAZtqC3QxUh5VIBfPk67UZk/tx9avX+86Q6ihvT7He6LzJ8Zk9nd4KbZKzdTByLePi6QbT7V90zr6zk7K67owK/hTz9NYgUc81ElQgZFOiPoNvauNdngwGqvEXY++jUUXZ52IdZL1pRrhpfol5UVNVxvYyJoAlSzoxkNt67W//b5WiZfOV7pohadlKo/xaFQyq7abKv3T76s3t17h+UF/q6RQeTRani+uY5NKU3XjoYIHBRw6RhPtCBXr3BR+TkzmGe26ThT34BdNV81AeHr664iuVyqxLIku2jrOI0t8Y21TeGlVIuMXl8X1ze+3dNZgpTu/e2oXmUynqURkIi1rlRBfxIoxosUX4flK5/l48rvPV/F2ZA1Mr/xoVQfaYD8enjJMPCeu4h7VGF56ozswBab+6SSJiPad0j7BKlp1Z7JStU0StDEItT66WKrERicqBT8lBQNqChJe7ZhuKkFLlk4UulgpUFEVmTob6KSshuoKymOVQKlkSyXIGrUg8mKsfK7hVBRM+eMpGgU/sU5E8dDJVLUdelcJj9ZHwUe0YFcl7iolVScJVQupNM4/xlIv7TMFqVpGIo8zVPCpDpCRvf19SZouzgqIi2uOEKuUN3K6D/bDj52Sak38dyNL8rR/dPHQiV6lpr7Ey5egqpovfLiwVB7jsah2SvtGHS+UZgpAtd+UhqrWVSGA75AXbZSIeEq4Rftd253oE4DSNVxcSY/79fswPD2T3R+RgWms64hqiZK5VqQqn2T6OlAW+b2snkSZibSsksL4oiz2R7CijogewioR81SSoAuz70kWiy4WsQJYfzHQxd9fLP0O00VLvfByQfiFVe3a/Lh8uSI8UEln+9ZMU8mMxg9VnlfeVSmjSpii3XWq6tWPgafSDgUVChwUtKsEUEGnhikqLjAtLQWl6pmpUlrlObUfVfMClQhHozt0NVHQS6Nu6PhTFbCquXUc638qHVcby2jPY4928VbbRZVGKsBWGqhaXo/D1MsHQhpvr7jAtKTSZd9T3ZcwhAejJVUd++9GBrAKslQiqp7OakuowFTL8g+ciOyVXlbHuIagUy/iWDdDfviZaKV4CjiLK30Kr8Uo6WagLJW0//0+Di+F0nVEF161MQ4fdSFVwvOL0j1euXJ9y5ZrWiL7JptV+b98pVqtRPoBZHVgqhODGpmrRExVSgpENOyL2hXqolJcGxCdKKO1lRA/BIMOUh+8+ouV7th99X40JZ1kgyS8+rukRtRB2i5VOSsQU9WrAppY7W7DD/547gTVYSXaMGHZQB1p1HFPzWNUYqVhxSIHUVaVtQ9KFZhpnmiSGSA/EWpfqqBZLw3MrBOW2g8qqCqpGlGlo3ppGQre1K5cpamqRtaxX9wDFzwFdb6KXOeKaO0EfelccYo7ZvRdH9Sq/av4IVV8Hi7uufCaHn7eCadScm2Dqol1w6V21WoXq4ty5LaUxTGu40yl77Gapqj5haZr2eHHqva9mk6oWVdkXo02Lq9KYWON65kJJd3s+nFqw/e79qdqCdK1L8JLk3WNi1VLpJs6DZ+kpiDKT7lyfQvCNS28NilW07Dwku1cdsIJJ7iaQdV0pWNfBLLzkzovaLBqbZDG6lKQqotdeMAai6qcotF3/NiD4R0YfK80nWCjPXXBU5sWzavOOOFPSggipZU/cUWOtxhO6am0UGN6BYSpkuxJQSVQWl9dvDWwfyzhd/5+rNdcpnFj/UVQ4xZGpk34o1lV9R9rX/un86SzTXC0Xs0afSP8ZkLBowJoPU1HHWkiKRBSJyZfIhk+1mVx/FOZVJIVq/OK5vElXrHSQWMExio18x2RxA90r5JpX/1b3DOr1ZTCd3qK1iRDnSJ8yaPORWpzKmpvGxkcpvsYV1MZlYbEKi0VjR3ptyW8Kl/Bj9pPKkCKlY7qiOc7D0WOYpDpAEnrHvkkq/Cg0D/kRM0vIq8jqpGIHCElnGo9/NPNEmlHGD5CS7SHR4TvE405qfG0lY65cn3L9DUtstQ21s1tSU+GS1a5gN00+Hyl47i4bVZTLN2gqg9FIlX5gQtMdUJQdaC/yCoBVO3uOz75QcBjUXV/tBOD/u/vhDWsjqeTvu9hrkGKo2U4XUhVeqOEVSP9aM9wDxqVWvhSCZ+ekXTy0kVf21dcp7Fk29Ak2ltfpQK+t7Z64EcrudDF3T+xRyXfvjdzLtMFKXxoNR3s4QOfhz9txZfIhdMFUN/xd/Nl0RFMx6yGERGVgIe38VUQo9IlVfWrJDjaBVoBgN/GyCeaxeLTQaWs0R7ZquM3fFDq4ko9orVJ1vrqoi9q/+6HbtLv+s5rahMarcpUQbB+W7+pi4yG/InGV9lrUHZ/kx3r4RTpPMZ9z2EF4tHOpwrAfRCuICuchizy6R2tWlujQmgAbp0fVAoV+USZZM8fqaSBwyPzh/ahjiPlV/V5CH+8pt8X2jYF89E6XWl/qmmGzmEq0Uwk2FBTDwWNfuidaMe5/udvZtSkIAjXN98OOBXnnExe0yLPQ9EKTnTOSnaEiUTapR4sw468sSiG8vtWnSCjNWFQUyzlVZ1PVSMSqwN3mQSmOiBjDR0Q7RW+QeElompTplKT8ITw1YGqpot2YIoOPLU/0YldJwBVu6ha2N896aTph2zxjZ01ZqHPWBrrSw3+lbnVq09PE1FVqi50fhy7oN29xCrx8MNmqI2fAhtVEaoaUp1L9LfvuKASD38xSQXffldpphIxPwpCPHQDogyvk2Tv3r1dZx5V3Wh/aL9o/+hv7QON/5nKR8UFmUoA/Ph6ypfqMe2p5NHnSV0UlWcVTOjlh2MKHyuyuKf5pJKCL3+joRtDnaj8SdYHM7pYa+xVlSQqv2jf6uKqdrUKAHQyi/epYf4RtToHaSxVlcjpHKDAWCdIBY8aNqukdFCgqQ4/yl86z2gZCtB0XlGaKn8qsAqn8WU1lJDOXdoeXSDVdEjHm5paaJQRPw6l2s3GGjJG26p9qc5bOmYUVMR69G46j3E9GlXnRpUeal9oPykw949J9mN16sYwcogy7Xdfk6H9rnOm0l3pqO3S9vtSLzXRiGx6VZrzR6ooP6qDl0rPlZ4qFVKe8uuta1N48wNtr6+tUMCuYFujY+h6pHygwE/jDytPa/uSaVqkIeFUcqhrpq5JytPKj6pOVXMXpaum6cbPjzma6eub35cK5JR/EhkyKEjXNNFv+9EyVDimc4TSXumofaHzg2600hEf1AzrQKr9p2Mi/MlrZU15zI+nrRo7XatVm6d9rAIljdPs86POl76QImNtTP0A+4lUUfiBcFWF79vvqJ1cZO9INXrWidsHsBqvLfLZvMqM6iwROQi5aFDeaGNfqtOBfwyZElVj7EXSxVS9fYsbQy5IdELSQaqLpH8WcrSBrHVCVelQKp9xrMBf6aXSA982UNXR/lGDxVFbNQVd2geqJlCaR9s2lT75gbTzhS4wqsZTQKW2iLpZ00VdJQO6EOmRcrrAqBo9kkqXFdiqQ6HaLeqClmgv6GTomNV66i5f6692p8prCtQUrOhkpot9tOo5BaXKm/EOxaYSFR37Ogdp8PvIkjyfv5QW+r1YVZYKYP0jkcMHwvdtmlXyFC2Y0sVKeVyBsG6eoz22UifraPsnvFRG508/BqNKvmL14k3nMa710MVfN4oq3Y7W6VRV8L72IpzWV0OHKS0UOKgUKbIkSRdvjY+qjm+pPH+kgtJJx4pKvyM72Wi9FZRrrNZIumapxFGjTSgAjzYyhPKy2t0m+uxwUWGNgiEFyL69ebSAQfOoRDcI1zcVJimQ0nlLN9A6fhTsJ3O9yeQ1ze973fgrH2o/q1RdL0+FJLohVTv5ZIYpK87JJ5/smnPpxl35Ry8df5HPti9LOoerAEn7RDcG/nHykbV9KhSM1e8n8FX54VX4qiKJ9vQFtb/SQSm6cESr0teJRHfpGl5FGUUXDA1QrqBX1XCxOsuohEAXNb2rF7Pak6iDlE7QuuDpZKNp2USBhwIBP2yP7rZ1AtKdvkqyVCKkAzuZoV6Ko85pCi6VGZXeusEorhd0JFU/6U5epQ5alk5I2h+qOtX/VKIW3hwjX2h/qve96MSnEgLfTlJ3pAqYdFFXQKeTsi5OaieoC6nSU8eOP1kX1xYylbTP/BNFFIjqSUCiu2idxHViV5swBYvKm1p39e5XQKL9HO/Yi6LjVecQBTG64VXe0+8ofyv/68Ksm1lfAquSLAVdkZRfFZAq2NfFQMtQIKHSJqWj8mc0yqsaNUE3zQoudZwp7+qiomBXx6JGRihpuBj/tBQpqbQ4nce4Ahq1WVQVsoIiLVd5SgGG9puepR2rek7z63f1+2qLq/VROqpUWYUHurGKdtOZivNHKuhY0k2V8qL2odZbJcNKa38Nipb/NMC98qBuKPQdbbMuzsqPCqgU7CZScBPtxkol7yo9VQmelq3fVZopUFAQGC0IyNT1TTULyvvKl0oLXY+La4Mb1Guap7yv0mldf7Qu4ecGFbAlcr5KRIUKFVwAqG3WMafjoqxqvooL1HWuVa2m9rHyko4VvZQfdeOp82Wsp3QVu2w9l9SynBrZ+0cQqkTId0oAACAeKrjwJW3+EdgAyl5gSkwBAACQ3whMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAiEneuVnG42xqHEdNRivhlYAAADBp3HUNUyfhorS8F1IvZQPsI+SKSjV/YDGoizrJ5oAAIDSCcKjQXMVgWkGqKRUQakGqNVAx8lQYKsn+IiWkQ2PSc1GpHP6kcZlg3ROP9I499NZv6vf13Uc6UFgmgGqvldJqQ4o/+zfRCmwXbFihfusp5Ok+vFr+F+kc/qRxmWDdE4/0jj301mP39T1m2Z46UPIDwAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAiErxzHdvHmzjR071ubPn29bt2616tWr2xlnnGFXXnmlXXLJJUk9YmzcuHE2c+ZM27hxo1WpUsWNjdanTx/r1q1bWrYBAAAAWR6YLlmyxG6++WbbvXu3VapUyU455RT3+e2333avf/zjH/anP/0p7gF3v//+e+vXr58tXbrUfadJkyZueYsXL3avBQsW2PDhw9O+XQAAAPkuq6ryFTDeeuut7r1z5842b948e+WVV+zNN990JZ5HH320zZkzx5577rm4l/nggw+6oLRRo0buuzNmzLC5c+famDFj3JOZpk6dalOmTEnrdgEAACDLAlMFjV9//bXVrl3bRo4c6d69jh07Wv/+/d3nv/3tb3Etb9OmTTZ9+nT3nF0t78QTTwxN69Spkw0ZMsR9Hj16tB05ciTl2wMAAIAsDUzr1q1r3bt3d20/VToaSe1C5auvvrKCgoISl/fyyy/boUOHrEWLFtasWbMi03v06OHam6od66JFi1K0FQAAAMj6NqYXXnihe8WyatUq966ST5WClmT58uXuvX379lGnqw2rgla1NVVgevbZZye97gAAAMihwDSWffv2uXagahcqAwYMiOt7GzZscO/hVfiR6tev7wJTP28qqVT38OHDSX03/HvJLgMlU9qWL/+/FQukc3qQl8sG6Zx+pHHup3M8tbHI48BUPeZ///vf2xdffOGC05o1a9p9991nl19+eVzf37Ztm3sPb6saScuUHTt2WKppnVesWFHq5fiS4lRQENamTZuEvqOS51xpgxu5/Rqpobj0yKVtD4JU5mVkZzoneg4K6jEY5DTOJaRz7smqNqaR1qxZYx999JEL8GTv3r323nvv2b///e+4h4qSypUrx5zHT/O/AQAAgPTI6hLT//zP/7TevXvbgQMHXHX7I4884gbJVynkSy+9ZDVq1Cj2+yoNi/dOO542q4nScFS+w1aiVH3h7xTVDjbecVsT0e53r9veA9GrSapVqmBL7/2p+9yyZUvLRbG2Px+2vSyVRV5GdqZzth2D2ZjG2SiT6bxu3ToKqtIsqwPTOnXqhD536dLFZVD12tcwUC+88IINHDiw2O9Xq1bNdu3a5Z78FIufpiAy1RTspuKA0jLScWDqgrDvYMntd3L15BvP9ufqtmdKuvIysjOds/kYzJY0znZlnc7pKKRCDlXlRzrhhBNcYCrxDO9Uq1Yt975z586Y8/i2pcW1QwUAAECeBaYq3VS70m+++SbmPA0aNHDvGoi/JA0bNnTvmzdvjjmPSl/DlwsAAID0yKrA9JprrrErrrjC/vKXv8Sc58svv3Tvxx13XInLa9WqlXtftmxZ1Olqu/rBBx+4z23btk1yrQEAAJBzgel5553n3qdNmxa18fH27dvd05ykc+fOJS6va9euocBUDZoj6XfUc79evXrWoUOHFGwBAAAAciIw/cUvfmHVq1d3w0Hdcsst7lGh3vr16+2GG25wbUJPPfVU99hSb8uWLW663sOddNJJdtlll7me+YMGDXLzePPmzbMRI0aEBuyvWDGr+4kBAAAEXlZFW6qef/LJJ11Q+vbbb9sFF1zg2n4qsPz000/dExkaNWrkngAVPjbp4MGDXWcolXpOmDCh0DKHDh1qH3/8sWu7eumll1rjxo1dKenGjRvddA1H1bNnzzLfVgAAgHyTVYGp6Hn1r7zyij333HOuVFOPCtUz7TWWnarmFUgmMrSTxjqdOHGijRs3zmbPnu2WpyePtG7d2nr16mU9evRI6/YAAAAgSwNTOf744+2ee+5xr3hElpJGqlKliquu1wsAAACZkVVtTAEAAJC7CEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBQGAKAACAQCAwBQAAQCAQmAIAACAQCEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIFS0LPTVV1/ZuHHjbP78+bZ582b3v3r16tn5559v/fr1s2OPPTbuZRUUFFi7du1sz549xc737rvvWu3atUu97gAAAMiRwHTJkiU2YMAA+/bbb61ChQp24oknuv9v2LDB1q9fbzNmzLCxY8da8+bN41reF1984YLSo446ylq0aBFzPk0HAABA+mRVYKpgdODAge79vPPOs4ceesjq1KkTCjDvuusuW7Zsmf3qV7+yV1991apVq1biMj/88EP33qxZM5s4cWLatwEAAAA50Mb0pZdesu3bt7tgdNSoUaGgVFRy+uSTT1qNGjVcVf+sWbPiWubatWvde9OmTdO23gAAAMixwHThwoXuvXPnzla9evUi09UGtE2bNu7zqlWr4lqmLzElMAUAAMisrKrKV9vSLl262CmnnFJsZyY5fPhwXMtct26deycwBQAAyKysCkxbtmzpXrGomn/RokXuc5MmTUpc3q5du2zLli3u8zHHHGPPPvusLV++3Hbv3m1169a1Tp06uUC4fPn0FCwriI43gI4U/r1klxGLOpUluy65IJHtz7Vtz4R05mVkZzpn6zGYTWmczTKZzr7wC+mTVYFpSYYPH2779u2zKlWqWNeuXeNuX6rA86qrrrK9e/cWmq4e/q1atbInnniiUHvWVNG6rlixotTLibfZQjyUFr45RLxWrlxpR44csVyQ6Pbn0rYHQSrzMrIznXPlGAxyGucS0jn3ZFUb0+I89dRTNnPmTPe5f//+cQWSvn2pTmpnnXWWTZo0yd5//31777337JFHHnHjoervG2+80Q4cOJD2bQAAAMhnOVFiqhLN0aNHu8+qfr/55pvj+l7jxo3tmmuucR2pbr/99tD/VeJ6+eWXu9LSK664wpWsTp482fr27ZvS9a5atWrSbVtVfeHvFDX+aqLV76lUXPOKXJfP254qQcrLuSxX0zlIx2CupnHQZDKd1S9FtZ1In6wOTA8dOmTDhg1zJZ3SsWNHe+yxx+JuE3ruuee6VyzqZNW9e3cXlL722mspD0zLlSuXkgNKy8jkCTCfT775vO3pkOm8nC9yKZ2Duh25lMZBVtbprOs20itrA1N1UBo0aJC988477m91UvrjH/9olSpVSunvnHbaae5906ZNKV0uAAAAciAw1QD6avf50Ucfub+vv/5699SnZHrPq0pAbUxjPXLU98DjkaQAAADplXWdn7Zu3WrXXnutC0oViN577702ZMiQpIJSlbI2b97cxowZE3Oe1atXu/dGjRqVar0BAACQQ4GpesZrkP3PP//clWDqsaSlafepzk8qLVVv/mi97jdv3myvvvqq+9ytW7dSrTsAAAByKDAdO3ZsqARTJaUq8YyHBtFfv359aDB9T80BVNL62Wef2R133OEG6PfWrFlj/fr1c2ObnnnmmXbxxReneGsAAACQlW1MVaI5fvx497lixYpu8Hu9YlEP/YEDB7rPgwcPdk+E6tChg02YMCE0j4aDUq/++++/3+bMmWNvvvmm64m/f/9+27BhQ2goCg1Hla6nPwEAACDLAlO1KdUjRP0wUcuWLSt2fj1SNB49e/Z0wefzzz9vCxcutE8//dSNL6onj2ioqKuvvtoFwgAAAEivrIm41ElJA9smI7yUNJpmzZrZww8/nOSaAQAAIBWonwYAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBQGAKAACAQCAwBQAAQCAQmAIAACAQCEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBUNGy0FdffWXjxo2z+fPn2+bNm93/6tWrZ+eff77169fPjj322ISWt3//fre8mTNn2saNG61KlSrWtGlT69Onj3Xr1i1NWwEAAICsDkyXLFliAwYMsG+//dYqVKhgJ554ovv/hg0bbP369TZjxgwbO3asNW/ePK7lff/99y6YXbp0qVtekyZNbPfu3bZ48WL3WrBggQ0fPjzNWwUAAICsqspXMDpw4ED3ft5559lbb71lc+bMCb3atm1r27dvt1/96le2d+/euJb54IMPuqC0UaNGbhkKbOfOnWtjxoyxqlWr2tSpU23KlClp3zYAAIB8l1WB6UsvveQCzzp16tioUaPcu6eS0yeffNJq1KjhqvpnzZpV4vI2bdpk06dPt3LlytnIkSNDpa/SqVMnGzJkiPs8evRoO3LkSJq2CgAAAFkXmC5cuNC9d+7c2apXr15keu3ata1Nmzbu86pVq0pc3ssvv2yHDh2yFi1aWLNmzYpM79Gjh2tvunXrVlu0aFFKtgEAAADRZVVgqraljzzyiF155ZUx5ykoKHDvhw8fLnF5y5cvd+/t27ePOr1SpUouaBUCUwAAgPTKqs5PLVu2dK9YVM3vA0h1YiqJOkxJeBV+pPr167tOUH7eVFIQHU8AHU3495JdRizqBJbsuuSCRLY/17Y9E9KZl5Gd6Zytx6DWpXz58oFbr1yTybzsC7+QPlkVmJZEvef37dvnqt+7du1a4vzbtm0LNQGIpWbNmu59x44dlmpa1xUrVpR6OWq2oJOhb8ZQ1lauXJl0G9xE11ul3Ols75vo+sTa9nTsj9Jse9DSOZZ4muBkSrakYT6kc9WjKiQUxJbVeUPrEk8eibU+uZTHykqQ8zLyoCq/OE899ZQbh1T69+9fqGNUcUNFSeXKlWPO46cpiAQAAED65ESJ6RNPPOF6zvve9DfffHNc39Pdbbx3m+q5n2oajkoD+SdD1Rf+TlHtYMNLDdr97nXbe6Bo9Ubto4+yd4ZcEPc88SqueUUiYq1TtUoVbOm9P03pb6VKPOtTmv2Rjm0PWjoXl5eDKmhpmKvpnI37IlXrE7TtCpJM5uV169ZRUJVmWR2Yqkf9sGHDbNKkSe7vjh072mOPPRZq41OSatWq2a5du9yTn2Lx0xREppqC3VQcUFpG+HJ0Mtt3sOgJbd+BxOZJ5PdTIdY6peO3UiWe9SnN/kj0t7I9nSPzclAFOQ1zKZ2zcV+kan2Ctl1BVdZ5OR2FVMiRqnw9nemmm24KBaVdunRxg+KrfWm8atWq5d537twZcx7ftrS4dqgAAADI08BUA+jrOfbvvPOO+/v66693A+5reKdENGzY0L1v3ry52EH4pUGDBqVaZwAAAORYYKrB7q+99lr76KOPXJX9vffe657QFG/1fbhWrVq592XLlkWdfuDAAfvggw/cZz3uFAAAAOmTVYGpAkUNsv/555/bUUcd5UpJ+/btm/Ty/JBSCkzVoDnStGnTXM/9evXqWYcOHUq17gAAAMihwHTs2LG2evVq91klpWpXGo8tW7bY+vXr3Xu4k046yS677DLXM3/QoEFuHm/evHk2YsQI91nBcMWKWd1PDAAAIPAqZlNp6fjx491nBYkzZsxwr1jUQ3/gwIHu8+DBg90ToVTqOWHChELzDR061D7++GNbs2aNXXrppda4cWNXSrpx40Y3vXfv3tazZ8+0bhsAAACyKDBVm1IN7eSHiYrVLtSrW7duXMutUaOGTZw40caNG2ezZ892jx5Ve9XWrVtbr169rEePHilZfwAAAORIYNq8efOo7UDjEVlKGklDTKm6Xi8AAABkRla1MQUAAEDuIjAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAA5G9gqnFIAQAAgJQFpj//+c/tuuuuc09liocGyNfz6bt161aanwUAAEAOKtUA+3rMZ7ly5ezw4cNxzV9QUGCfffaZVa5cuTQ/CwAAgHwNTI8cOeIeAarAMpqlS5eWGGwePHjQPfLTP2kJAAAASDgw1bPjX3zxRXv11VcL/V+lpXLjjTfGs5jQd84666y45wcAAEB+iLuN6d13323Vq1d3pab+5YX/r7iXtG3b1oYOHZqerQEAAEDutzE99thjbc6cObZv3z73twLNCy+80JWAzpo1q9jqec1TsWJFq1mzplWqVCk1aw4AAID87fxUu3btQn+fcMIJLuisX78+HZoAAACQuV75b7zxRul+HQAAAPg/PPkJAAAA2V9i6i1cuNC1P928ebPt37/fDS9VHFX/jx8/PhU/DQAAgBxR6sD0N7/5jU2fPj30d6yxTqMNMwUAAACkJDD9+9//bi+99FIo2Dz55JPtRz/6ET3vAQAAULaB6ZQpU9y7AtIxY8ZYgwYNSrM4AAAA5LFSdX5au3atKym95557CEoBAACQucD00KFD7r1FixalWwsAAADkvVIFphpYX3bs2JGq9QEAAECeKlVgevHFF7te+OoEBQAAAGQsML3hhhvs1FNPtbFjxxYaMgoAAAAo0175r7/+ul199dU2atQoN57pyJEjrXnz5larVi2rWDH2otVhatiwYaX5aQAAAOSYUgWmd955Z2iwfFXpf/PNNzZv3ry4vktgCgAAgJQFpieccEJpvg4AAACkJjB94403SvN1AAAAIDWdnwAAAIBUITAFAABA9lflv/LKK0l/t3v37qX5aQAAAOSYlPXKT4S+Q2AKAACAlAWmfpioeFWrVs2OOeaY0v4kAAAAclCpAtN//vOfxU7//vvvbdeuXbZy5UobP368G+d00KBBdumll5bmZwEAAJCDShWY1qtXL6752rZta1dccYX16tXL7r77bmvcuLE1bdq0ND8NAACAHFNmvfJr1Khhv/71r+3gwYP2zDPPlNXPAgAAIEuU6XBRZ555pntftGhRWf4sAAAAskCZBqb79u1z72p3CgAAAGQsMJ04caJ7r1OnTln+LAAAAHK989OyZctKnOfQoUO2Y8cOe+2112z27NluDNPzzz/fUmny5Ml277332v333299+vRJeLirdu3a2Z49e4qd791337XatWuXck0BAACQlsD0Zz/7WUID7CsIVCeom266yVJFQ1E9/PDDSX//iy++cEHpUUcdZS1atIg5n6YDAAAgBwbYVwDboUMHGzp0qB133HGWCirF1LioJZV2FufDDz90782aNQs1NQAAAECWBaYvvPBCifOUL1/eqlat6sY8rVmzpqXC3r177emnn7axY8fakSNHSrWstWvXunfGVQUAAMjiwFQloGVNgeQvf/lL+/rrr61ixYp266232qRJk2zz5s2lKjElMAUAAMijXvmpsGnTJheUtm/f3qZOnWo333xzqZa3bt06905gCgAAkOVtTL1PP/3UlVwuXrzYvvzyS1fdrir8E044wdq0aeMeSdq8efNS/85JJ51k48aNs44dO5Z6WRpPdcuWLe7zMcccY88++6wtX77cdu/ebXXr1rVOnTpZly5dXHOEdFD73MOHDyf13fDv+c8VKlRI2boluy7JSGS9S/tbZbk+qd4fuZbO0X6rLH83l9Iwn9M5n88bQd+PuZiX4+1XgwwHpn/6059ce0/tsPCdtn//ftu5c6erLlfHon79+tntt99eqkCvSZMm7pUKvn2p1ueqq65ywXS4GTNmWKtWreyJJ55Iy9ireuDAihUrSr2cVatWuW3QDUAmaGSEZNv6JrrepfmtslyfdOyPXErn4vJyUGVLGuZbOufzeSPIeaysBDkvI0OB6e9//3vXCUoBaaVKlVy704YNG7rSUvWWX79+vStFPXjwoD333HPuILrrrrssCHz7Uq3TWWedZf3793e98xUwzps3z/74xz/a+++/bzfeeKNNmTLFbR8AAAACOsD++PHj3VBQF110kRvgPtog9Nu3b7dhw4bZP/7xD1cNf/HFF1vLli0t0xo3bmzXXHONVa9e3ZXkelWqVLHLL7/clZaqCYJKVjWIf9++fVP6+wrek23bquoLf6eo8VczVY0vZbkvg5Bv8mHby/K3gpSXczmv5mo6Z+O+SNX6BG278iEvq1+Kf7w6AhiYvvjii+79nHPOsccffzzmfApWVd3/3Xff2TvvvOPaogbhgDr33HPdK5ZTTjnFunfv7oJSPbkq1YGpAvpUHFBaRiYvMmX520G7mObqtmcqnTOdl1MpyNuRS+kcj6Bta6rWJ2jblQ95OZGHCiE5perVs3TpUreT/uu//qvEeTXfgAED3OclS5ZYtjjttNNCowEAAAAgoIHpN998E6oSj4efb+vWrRakKgG1f43Fd+bikaQAAAABDkzVRlLU8z4efr7KlStbEGgoKA1hNWbMmJjzrF692r03atSoDNcMAAAg/5QqMPXDNs2ePTuu+WfNmhWoIE8luOqRP3PmTDtw4ECR6Xqa1Kuvvuo+d+vWLQNrCAAAkD/Kl7bEUVXdKnF89913i51X0zWf78FfljSIvoat8oPpexoGSuPGffbZZ3bHHXe40QO8NWvWuHFXNbbpmWee6UYSAAAAQEB75ffq1cv++te/2oYNG9zz6y+55BL76U9/6sYxrVatmgvqFBCqR7tKHtWe8+STT7Y+ffpYWRo8eLAtWrTIjbE6YcKE0P81HJSGsdIwV3PmzLE333zT9cTXgwG0TX4oCg2wn66nPwEAACAFganaij711FMuKFVppKrE9YpGJavHH3+8KzUN0kD1PXv2dMHn888/bwsXLnSPVlXbWT19Q0NFXX311VaxYsqe3AoAAIAYSh1xnXrqqTZt2jR7+umnbfr06fbtt98WmecHP/iB9ejRww0rVaNGDUu1N954o9jp4aWk0ehpTw8//HCK1woAAACJSElRYK1atezuu++2IUOGuKcifPnll7Z7925XnV+vXj3XSYqqcAAAABQn6WhRvdjVfjScOjap9LFz586uGlxtSpcvX2579uxJ9mcAAACQJ5IKTNUe87zzzrMHHnig2Pn0KE91LrrgggvcdwAAAICUVOWrBPTXv/61/fOf/3SdmVauXGmHDh2K2jlI44OqtFTzqd3pI4884uYfOXIkz5oFAABA6UpM1UFo7ty5oR72t912W+iRnUUWXL68G35JpaonnHCCm09DRo0aNSqRnwQAAECeiDsw/eSTT9yYpSrt1GDzCjKvu+66Yp8h/8Mf/tANt6QhpM4991wXnD733HP2+eefp2r9AQAAkG+BqdqLqnpeA+SrOr5KlSpx/4h65z/++OOu5FTNAaZMmZLs+gIAACDfA9PFixe70lI9xrNChQoJ/9DRRx9tv/jFL1ypaUmPLwUAAED+iTsw3bRpk3tv2bJl0j+m6nyhKh8AAABJB6b79u1z78cdd5wl65hjjnHve/fuTXoZAAAAyPPAtHr16u79u+++S/rHtm3b5t71LHoAAAAgqcD01FNPde8ffPCBJWvt2rXuXZ2gAAAAgKQC03POOcd1XJo2bZola+LEia4DVatWrZJeBgAAAPI8ML3iiitcb/z58+cnNdyThptSz3655JJLEv4+AAAAclvcgWn9+vWtZ8+ertT0t7/9rY0bN849jrQkmkeD6g8bNsyVlp511ll29tlnl3a9AQAAkGOKPuS+GHfeeactX77c1q1bZyNGjLC//OUvriS1Q4cO1qBBA6tVq5YdPHjQduzY4YaE0nilekKUhppSQKu2pfoeAAAAUKrAVIPkP//88zZw4EBbsmSJbd682Z588kn3ikUBqbRu3dpGjRplderUSeQnAQAAkCcSCkxFpaITJkywqVOnuir6DRs2FDv/GWecYdddd511797dVeUDAAAAKQlMRQGm2pvqpSGgVL2vqvvdu3e7abVr13bDS7Vr187q1auXzE8AAAAgzyQVmIZr1qyZewEAAABl0isfAAAASCcCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBkBOB6eTJk61p06Y2ceLEpL6/f/9+e/rpp+3SSy+1Fi1a2Jlnnml9+/a1WbNmpXxdAQAAEF1Fy3IrV660hx9+OOnvf//999avXz9bunSpVahQwZo0aWK7d++2xYsXu9eCBQts+PDhKV1nAAAA5FiJ6bvvvms33HCD7dmzJ+llPPjggy4obdSokc2ZM8dmzJhhc+fOtTFjxljVqlVt6tSpNmXKlJSuNwAAAHIkMN27d689+uijrqTz22+/TXo5mzZtsunTp1u5cuVs5MiRduKJJ4amderUyYYMGeI+jx492o4cOZKSdQcAAECOBKZr1661iy66yJVoli9f3m6//XarV69eUst6+eWX7dChQ65dabNmzYpM79Gjh1WpUsW2bt1qixYtSsHaAwAAIGcCU5Vyfv3119a+fXtXzX7zzTcnvazly5e7dy0rmkqVKrmgVQhMAQAA0ivrOj+ddNJJNm7cOOvYsWOpl7Vhwwb3Hl6FH6l+/fquE5SfN5UKCgrs8OHDSX03/Hv+szpvZUKy2+Alst6l/a2yXJ9U749cS+dov1WWv5tLaZjP6ZzP542g78d00XarxtR/Lku6biO9si4wVa95vVJh27Zt7r127dox56lZs6Z737Fjh6Xavn37bMWKFaVezqpVq9xB2qZNG8vUyAjJtsFNdL1j/VZZbn/Vo/7/hSPdNwNl+VvpSGfVSiSSN5SXMyEd+ac0x0W6jp1o6ZzufZqpfZHosRNru8oyb6TqfBiPVO33TCxH+zOeedOdV5EeWVeVn0oaKkoqV64ccx4/TUEkAAAA0ifrSkxTSXdd8d5Nqed+qmk4Kj0YIBmqvvClHmoHm6lqfGnZsmWgfqvd7163vQeKVu/UPvooe2fIBXHPE49ULSdVv5Wq9SlNOlerVMGW3vvTuJcTpLxc1mmYKvHsizPOOKPEdE7VPg2aVG1X0PJGqpaT7vRJRzpnKq+uW7eOgqo0y+vAtFq1arZr1y735KdY/DQFkammYDcVF2EtI5MX87L87Xh+SyerfQeLnrD2HaiQ0DzxSNVyUvVbqVqf0qRzosuJnD/TgWlZpmGqJLovYqVzOvZpEKRqu4KWN1K1nHSnT6qWE8+5LtHfSlQ6CqlQWF5X5deqVcu979y5M+Y8vm1pce1QAQAAUHp5HZg2bNjQvW/evLnY4amkQYMGZbZeAAAA+SivA9NWrVq592XLlkWdfuDAAfvggw/c57Zt25bpugEAAOSbvA5Mu3btGgpM1aA50rRp01zPfT1ZqkOHDhlYQwAAgPyRF4Hpli1bbP369e49crD+yy67zPXMHzRokJvHmzdvno0YMcJ9HjBggFWsmNf9xAAAANIuL6KtwYMHu0eKqtRzwoQJhaYNHTrUPv74Y1uzZo1deuml1rhxY1dKunHjRje9d+/e1rNnzwytOQAAQP7Ii8C0ODVq1LCJEye6x5zOnj3bPXpUT6Bo3bq19erVy3r06JHpVQQAAMgLORGYvvHGG8VOjywljVSlShVXXa8XAAAAMiMv2pgCAAAg+AhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBQGAKAACAQCAwBQAAQCAQmAIAACAQCEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBQGAKAACAQKhoWWj//v02btw4mzlzpm3cuNGqVKliTZs2tT59+li3bt0SXl5BQYG1a9fO9uzZU+x87777rtWuXbsUaw4AAICcCUy///5769evny1dutQqVKhgTZo0sd27d9vixYvda8GCBTZ8+PCElvnFF1+4oPSoo46yFi1axJxP0wEAAJAeWReYPvjggy4obdSokT399NN24oknuv+/9dZbduutt9rUqVOtdevW1rNnz7iX+eGHH7r3Zs2a2cSJE9O27gAAAMiRNqabNm2y6dOnW7ly5WzkyJGhoFQ6depkQ4YMcZ9Hjx5tR44ciXu5a9eude9qDgAAAIDMyKrA9OWXX7ZDhw656naVbkbq0aOHa2+6detWW7RoUcIlpgSmAAAAmZNVgeny5cvde/v27aNOr1SpUqiNaCKB6bp169w7gSkAAEDmZFUb0w0bNrj38Cr8SPXr13edoPy8Jdm1a5dt2bLFfT7mmGPs2WefdQGwOlTVrVvXNRHo0qWLlS+f+hheowEcPnw4qe+Gf89/VmewTEh2G7xE1ru438rU9ueiVKVzPHkjWl7OhFTnn7LYlmT3ReS6pXqfllYmjuWyPLeU5fEVtHNvUNK5NNdtpFdWBabbtm1z78UN2VSzZk33vmPHjoTalyrwvOqqq2zv3r2Fps+YMcNatWplTzzxhNWpU8dSad++fbZixYpSL2fVqlVu/du0aWOZsHLlyoTa9IZLdL1j/VYmtz8XpSqdE80bysuZkI78U5rjIh3rvHr16qjpnO59mqhMHctleW7J1PGVjt8K8jk83XkV6VE+24aKksqVK8ecx09T0JdI+1Jl3rPOOssmTZpk77//vr333nv2yCOP2LHHHuv+vvHGG+3AgQMp2Q4AAABkeYmpivnjvftRz/14NG7c2K655hqrXr263X777aH/qxPV5Zdf7kpLr7jiCleyOnnyZOvbt6+lStWqVZNu16oqCl/qoXa1mazGbtmyZU7+Vj5LVTrHs5wg5eVczqtnnHFGStI5aNuVz+cx1rnsf0t9UuIt+EIeBKbVqlVzbUL15KdY/DQFffE499xz3SuWU045xbp37+6C0tdeey2lgamC51RchLWMTF7My/K3cyVoCbpUpXOiy8l0Xk6loG1H+PqUJp2Dtl35fB5jncv+t+It9EKeVOXXqlXLve/cuTPmPL5taSofHXraaaeFxlEFAABAemRVYNqwYUP3vnnz5pjz+OCxQYMGcS9XVYkHDx4ssRcejyQFAABIn6wKTNXeU5YtWxZ1ujonffDBB+5z27Zt41qmhoJq3ry5jRkzpsTerHoMKgAAANIjqwLTrl27hgJTPyh+uGnTprme+/Xq1bMOHTrE3flJHapmzpwZtde9SmdfffVV97lbt26l3gYAAADkQGB60kkn2WWXXeYCyUGDBtn69etD0+bNm2cjRoxwnwcMGGAVKxbu16VB9DW/H0zf0zBQGj/ts88+szvuuMO2b98emrZmzRrr16+fG9v0zDPPtIsvvjjt2wgAAJCvsqpXvgwdOtQ+/vhjFzReeumlrsRTpaQbN25003v37m09e/Ys8r3Bgwe7x5SqJHXChAmFmgcMGzbM7r//fpszZ469+eabrie+evf7p0dpaBUNsJ+Opz8BAAAgSwPTGjVq2MSJE23cuHE2e/ZsFzwqYGzdurX16tXLevTokfAyFcgq+Hz++edt4cKF9umnn7rhpvQUCg0VdfXVVxcpgQUAAEBqZWW0pcHvVV2vV7zCS0mjadasmT388MMpWDsAAAAkg7ppAAAABAKBKQAAAAKBwBQAAACBQGAKAACAQCAwBQAAQCAQmAIAACAQCEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAIBAIDAFAABAIBCYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBQGAKAACAQCAwBQAAQCAQmAIAACAQCEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAiEipaF9u/fb+PGjbOZM2faxo0brUqVKta0aVPr06ePdevWLTDLBAAAQA4Hpt9//73169fPli5dahUqVLAmTZrY7t27bfHixe61YMECGz58eMaXCQAAgByvyn/wwQddANmoUSObM2eOzZgxw+bOnWtjxoyxqlWr2tSpU23KlCkZXyYAAAByODDdtGmTTZ8+3cqVK2cjR460E088MTStU6dONmTIEPd59OjRduTIkYwtEwAAADkemL788st26NAha9GihTVr1qzI9B49eri2oVu3brVFixZlbJkAAADI8cB0+fLl7r19+/ZRp1eqVMkFmBJvEJmOZQIAACDHOz9t2LDBvYdXt0eqX7++67Dk583EMuNVUFBghw8fTuq74d/zn9VxS6pV+t/3SFUr/f/7kNLMEzkt2W3wSlrveH+rrLY/V+dJVTonmjc0T/ny5VOSl0ojFfknlcdFPBLdF7HSOdX7tLTK6lguy3NLpo6vIJx7g5jOpbluI73KFWRRKrdp08b27t1rjz32mF188cVR53n44YfdsE8dO3Z075lYZklWrFiRlgOmWrVqKV8mAADZSNf2dFEQ3bp167QtP59lVYmphnWSypUrx5zHT9u3b1/GllmSdHWiSudBCAAA/hedodMnqwJT3aHEmxnUyz5TyyzJUUcdZQcPHnTVasUFxAAAIDj0MB7FDLqOIz2yKjBVVfWuXbtcxojFT9P4o5laZkl8ZyoAAABkaa/8WrVqufedO3fGnGfHjh3uvXbt2hlbJgAAAHI8MG3YsKF737x5c7ED5kuDBg0ytkwAAADkeGDaqlUr975s2bKo0w8cOGAffPCB+9y2bduMLRMAAAA5Hph27do1FESuW7euyPRp06a5Xvb16tWzDh06ZGyZAAAAyPHA9KSTTrLLLrvM9YgbNGiQrV+/PjRt3rx5NmLECPd5wIABVrFi4X5dW7ZscfPrPVXLBAAAQJ4OsC/qQf+LX/zC1qxZ44Zbaty4sSvR3Lhxo5veu3dve+CBB4p879prr3WPFFWp54QJE1KyTAAAAKRO1hUB1qhRwyZOnOiewDR79mz3mFAFk3oCQ69evaxHjx6BWCYAAAByvMQUAAAAuSmr2pgCAAAgdxGYAgAAIBAITAEAABAIBKYAAAAIBAJTAAAABAKBKQAAAAKBwBQAAACBkHUD7Oe7/fv3uwcBzJw50z2ZqkqVKta0aVPr06ePdevWLdOrFzhfffWVS6/58+fb5s2b3f/q1atn559/vvXr18+OPfbYlKUx++b/0+N958yZYzfeeKPdcccdRaYfPnzY/va3v9m0adPcY4ArVKhgp556qnuYhZ60pgdcRJPs93LFu+++a3/9619txYoVtnPnTqtZs6adffbZdvPNN7sn1kVDWsdvx44d9uyzz9obb7xhmzZtco+hPuWUU9zxq6cHVqpUKer3OGeUbPLkyXbvvffa/fff77YvCOmYT+mfTRhgP4voMakKppYuXeouEk2aNLHdu3fbF1984aZfddVVNnz48EyvZmAsWbLEBgwYYN9++61LrxNPPNH9X+mli27t2rVt7Nix1rx581KnMfvm/5sxY4YNHjzYfY4WmB45csRuu+02+8c//mHlypWzRo0auf8p+JHzzjvP/vznP9tRRx2Vku/lij/+8Y8uv4puqJR/P/vsMztw4IALmJ544gl3wxWOtI6fjtW+ffu6m1kdwyeffLJLW93Q6jLZokULe/7556169eqFvsc5o2QrV650j/3es2dPzMC0rNMxn9I/6ygwRXYYOnRoQZMmTQouueSSgs8//zz0/zfffLOgVatWbtrkyZMzuo5BsWvXroKzzz7bpckNN9xQsHXr1tA0pV3v3r3dtP/4j/8o2LNnT6nTmH3zv7Zs2VLQrl07t716/eEPfygyz1NPPeWmnXvuuQWrV68O/X/58uUFHTt2dNMeffTRlH0vF0yZMsVt3+mnn14wadKkgiNHjrj/79y5s6B///5uWocOHQq+/fbbQt8jrePnzwldu3Yt+OSTT0L/X7RokUtbTRsyZEiR73HOKN6CBQsK2rdvHzonvPjii1HnK+t0zJf0z0YEplniiy++cBelpk2bFnz44YdFpk+cONEdSOedd17B4cOHC/LduHHjXHr8+Mc/Lvjuu++KTN+2bVvBmWeeWejkk2was2/+l4Kln//8525b/Yk9MjDVvvDpPnfu3CLL+Ne//hX6voKu0n4vF3z//fehwGj8+PFFpitt/M3A1KlTC/2ftI7Phg0bQoHTkiVLikzXOULTmjdvXrBv377Q/zlnxKYb/pEjRxY0a9YslLaxAtOyTsd8SP9slnuNhHLUyy+/bIcOHXLVSc2aNSsyXe2+1D5m69attmjRIst3CxcudO+dO3cuUvUmqgZt06aN+7xq1apSpTH75n+NHz/e3nvvPevSpYtLi2jmzp1ru3btsmOOOcZ+8pOfFJmuKmI1udi3b5+bt7TfywVq76j2pNq+a665psh05e977rnHhgwZUqidKWkdvy+//DL0Odox3LJlS/euqv1vvvkm9H/OGdGtXbvWLrroIhszZoxri3z77be7tv2xlHU65nr6ZzsC0yyxfPly996+ffuo09XGzAcDHEjm2pY+8sgjduWVV8acxzevVnvT0qQx+8Zc28NHH33UfvSjH7k2ZLH4tGrXrp1ruxiNpsVK40S/lwsWLFjg3hUkqi1cNJdffrldf/31oQBKSOv4nXDCCaHPH374YdRAS9Smtk6dOqH/c86ITh3Hvv76a7d9U6dOdZ3zilPW6Zjr6Z/tCEyzxIYNG9y778ATTf369QvNm890gdbFulWrVlGnb9++PXTCUaP30qRxvu+bgwcP2p133ul6uA4bNsyVRsdCGidu3bp17l2lobqZ+uc//2l3332360yi0Q8mTZrkSvIikdbxO+mkk+zHP/6x+/zb3/7WdSoL77jzhz/8wX1WiXV4z3zSOHZ6qre7RpA47bTTSpy/rNMx19M/2zFcVJbYtm2bey/uoq+hY/yQJyieeluqKlLVNV27di1VGuf7vnnyySdt9erV7kbgwgsvLHZe3RAkk1bJfi8XbNmyJVRap2BUzSXCaVguNaN45plnQhdTIa0T89hjj9nQoUNdemqoIPXK10gEGkaocuXKdtNNN9mtt95a6DucM6LTzb6/4Y9HWadjrqd/tqPENEtoaAvRCTIWP00BF2J76qmn3Lh10r9//1DVXLJpnM/75v3333cBUd26dV07x5L47S8urXSzED5vab6XCzTEjqhpyrJly1xpqcYzVdqrDZ9KfdSUQoGTz4tCWidGbSFPP/10F5Coec+nn37qSstUSn300UfbD37wAxeohuOckRplnY6kf7ARmGaJWG3LoonVLgzmxnpUyYh06tSpUNunZNM4X/eNTth33XWXu1g/9NBD7sJdEtI4cWoi4UsyVaWsUlOV9Cg4VB5+7rnnXGmqgtMpU6aEvkdax0/jV1533XWunbQ6fWmQfT3EYPHixe5/SpORI0faLbfc4jrNeKRxapR1OpL+wUZgmiWqVatW6CIVjZ9WtWrVMluvbKGLyX333WejR492f3fs2NEFqOFPr0k2jfN136gETyVKGiz73HPPjes78aSVL81INI2jfS8X+NJJtTG9+OKLi0xXlfOll17qPqv9qUdax0+BqNqSqhOU2kVq5AFt2w9/+ENXrf/iiy+6UtO33nrLpk+fHvoe54zUKOt0JP2DjcA0S9SqVcu9a9iYWHxbmOLazeQjlYaomlOdRETDGakK1F/wS5vG+bhv/vWvf9nEiRNdJwd1fIoXaZw4BUcSbVgbzw8T5Z9aI6R1/PSEK9FjR2vUqFFkuppL+BE+XnnlldD/SePUKOt0JP2DjcA0SzRs2NC9++e9xxqiQxo0aFBm6xV0erygSvTeeecd97eG1Bk1alTUZ14nm8b5uG9mz57t3j///HM3HqyeLx3+8iMe6BGa+tuPh0kaJ85ve7Se95FVk+H5mrROvIOZSp9j0WNZI9OFNE6Nsk5H0j/YCEyzhB/2SJ0fotFF64MPPnCf27ZtW6brFlQaHFklIB999JGrsr/33nvdIOTh1fepSON83Dc6WWtbYr38Qw3UKUp/N2/evFBa+XEEo/HpGC2NE/1eLmjdurV7V1WzH3s3kjrqiEqwPdI6fr59tA9Qo/E9ucMf2ME5IzXKOh1J/2AjMM0SfkgjHUh+XMNw06ZNc+2+9HSNDh06WL7TiUWD7KtETx1DVErat2/ftKRxPu4bjWagqvxYL/Vulu7du7u/H3/88dCTuNSEQgGA2utFayKg6mi159OTY7xkv5cL1H5UHTD0dKJZs2ZFDZj8KBNqpuKR1vE7++yz3bs6j2lc3mjnk7///e/u8znnnBP6P+eM1CjrdCT9g43ANEuoJOSyyy5zPaA1qLZ64Hrz5s2zESNGuM8KxipWZHhaVSFrbE1RSWn4BTvVacy+iZ+CmX79+rnPGvYovMRCwx/pf/Lzn/+8UFu/ZL+XC0455RS7+uqrQ3n5tddeC03T4zE1tqaGlFJVszrqeKR1/HRsanggBSm33Xabe2qRp8/qja9B99XeV82BPM4ZqVHW6Uj6B1u5glh1QwgcPb9aQ8WsWbPGVUerw4Pu6jQAtPTu3dseeOABy3cq3dBTXJReOqmEP6YxGvXQHzhwYKnSmH1TmJpQqJ3pjTfeaHfccUfU0uy333471N5LJYKffPJJqMROw3pFXhCS/V4uUA9hBaBvvPFGqImEHv+qZioq4Tv++OPdzZjvBOWR1vFT2v73f/+37d27123Xqaee6o5lbbNG9dD4pnqYRORjLDlnxEftzNWmU48sVrv/SGWdjvmW/tmEwDTL6MDRo97U+UQHkA4odS7p1auX9ejRgzHXzFzbIN+DNh6XXHKJ/elPfyp1GrNv4gtMRRd6jZLw0ksvufaRGtBcJYN6epSaXKj5RTTJfi8X6FStqnxVN+t57spvGt5I1el+bNNoSOv4qYmCnqI1f/5813RCx6yepqXxYpXGxx57bNTvcc4ofWCaiXTMp/TPJgSmAAAACATamAIAACAQCEwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgVM70CAJDu53MXR898P/roo61evXrWvn17u/766+3444+3oLj22mtt0aJFds4559jzzz+f6dUBgLSixBRAXjt48KDt3LnTVq9ebePHj7dLLrnE/vWvf2V6tQAgL1FiCiDntWvXzsaOHRt12oEDB1yp6uzZs11gunfvXrvtttvs1VdftTp16pT5ugJAPiMwBZDzKlSo4Krro9H/a9WqZc2bN7cmTZrY4MGDbffu3fbiiy/arbfeWubrCgD5jKp8APg/l19+udWtW9d9XrJkSaZXBwDyDiWmABDmuOOOs6+++sq+/vrrItO+++47mzRpkmuDun79etu1a5dVqlTJjj32WOvQoYNdd9111qhRo0Lf2bRpk11wwQXu87x58+zIkSP2zDPPuGXoN37wgx+4pgbqdNW2bduE1lVNEH72s5+59a1du7a98MIL1rhx41KmAABkDoEpAIS1N924caP7HNm+dMWKFda/f3/bsWNHkc5Te/bssQ0bNtj06dPtiSeesE6dOkVd/rJly+zee+91TQW8bdu22WuvvWavv/66PfTQQ9ajR4+41lVBrYJZBaU1a9a0cePGEZQCyHpU5QPA/1HnJ/XQl86dO4f+r0DylltucUGpAtaHH37YBZLvvvuuC0b79evnhp1SkDps2LCYy//Nb37jSlh/97vfudLT+fPn2wMPPGDVqlWzgoICe/DBB+3bb78tcT1VUqvfVBBdo0YNF5Q2a9YsRakAAJlDiSmAnHf48GFXqhlJwaCq5z/99FObOXOmCzLl1FNPtT59+oTme/nll0NV+yoRbdWqVWiaqtBPP/10K1++vD377LOuev2zzz6zU045Jep6TJgwoVB1f+/evd13VZKqdXz77bfdkFWxaNSAm266yT766CPXDEC/qd8HgFxAYAog5y1dujTu9puab9SoUVa1atXQ/zTg/jXXXOMC2fCgNJzamCpIlO3bt0cNTFUKG9kGVcKr/ot7IICaGvzqV79yzQo0moB+r2XLlnFtFwBkAwJTAHlPT306//zz7ac//al17Ngx6hOk9IpF7TxXrVpVqGQ0mlhB5I9+9KPQ5/3790edR8u8/fbbbcGCBe5vVfu3bt26mK0CgOxDYAog56k0U1Xo4SWPa9eutccee8xVnf/73/92waEe+1kctSFduHChrVmzxrXv/Pzzz13vfHVgCqeS1Wg0XmqscVb1UvAZ67uLFy8uNO2ll14qtsofALIRnZ8A5B11QFLppZ4G1aVLFxdwjh492kaMGBHzO7NmzXJV7jfccIONHDnSpk6d6p5hr2p7DczftWvXEn+3YsXkywIUlKqD1cUXX+z+Vsepv//970kvDwCCiBJTAHlLnY7Uw37dunVuuKf/+Z//saZNm7qB9sPp8aSqRvednS666CL3pKiGDRu6oLR69equJFXzpYuCUgXP5513nuuspc5Pv//9793fsUpiASDbUGIKIK9pqKY//OEPripdNNzTli1bCs3z6KOPuvf69eu74FNDPPXs2dN1lFJQKpHjm6Za+/btXecplbref//9Vq5cOVdaq+AUAHIFgSmAvKdqfT21STRkk8YZ9RT8qS2pqKRUg9lHozFNPT3dKZ30pCg/EL+GsnrnnXfS+nsAUFYITAHAzAYNGuR658sbb7xhc+fOLdIuVFXosTomqc2ppzar6XbnnXeGguT77rvP9u3bl/bfBIB0IzAFADM3bqkGufc0HJNKT3/4wx9aixYt3P/eeustV3WunvgqSV29erU98sgj7tGghw4dCn032mD+qaZ2pQpOZdOmTfb444+n/TcBIN0ITAHg/6gNp8YylS+//NJ1NvIlkmqLKs8//7wbpklDS6k6XR2mFJSqt76fR0NJlYUrr7zSVev7x6kqUAaAbEZgCgBh7rnnnlCA+cILL9iHH37o2qBOmzbN9davW7euq95XCWuDBg1ccDp58mS76667XAclee2118pkXdUBSh2htD4aA1XrHmtwfwDIBuUKYo3mDAAAAJQhSkwBAAAQCASmAAAACAQCUwAAAAQCgSkAAAACgcAUAAAAgUBgCgAAgEAgMAUAAEAgEJgCAAAgEAhMAQAAEAgEpgAAAAgEAlMAAAAEAoEpAAAAAoHAFAAAAIFAYAoAAAALgv8HojB9ckCilgAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define quantile threshold\n",
+    "q = 0.95  # change to 0.95, 0.99, etc.\n",
+    "\n",
+    "# Loop through each experiment\n",
+    "for exp_id, group in df_big_sphere_1.groupby('experiment_id'):\n",
+    "    # print(exp_id, group)\n",
+    "    # Compute this experiment's threshold\n",
+    "    threshold = group['gather_v_runtime'].quantile(q)\n",
+    "\n",
+    "    # Filter rows within this experiment\n",
+    "    filtered = group[group['gather_v_runtime'] > threshold]\n",
+    "\n",
+    "    if exp_id == 3:\n",
+    "\n",
+    "        # Plot histogram for this experiment\n",
+    "        plt.figure()\n",
+    "        plt.hist(sorted(filtered['rank']), bins=50)\n",
+    "        plt.title(f'Experiment {exp_id} — Ranks above {int(q*100)}th percentile runtime')\n",
+    "        plt.xlabel('Rank')\n",
+    "        plt.ylabel('Count')\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fb33d4b-b978-4469-93b8-486e766233bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "85e4532f-8c15-434e-8c5d-08900c84c40f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12        0\n",
+       "18       17\n",
+       "21        1\n",
+       "34       24\n",
+       "36      121\n",
+       "       ... \n",
+       "1044    620\n",
+       "1086    613\n",
+       "1094     17\n",
+       "1139     87\n",
+       "1153    622\n",
+       "Name: rank, Length: 114, dtype: int64"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered['rank']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "94cb41be-c8db-41bf-b4e2-862e452dae12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(filtered['rank'], bins=50)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8fb8789-dfb0-40e7-9749-104c713fa1f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "0768750f-b998-441e-8664-1ef6317fab05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "experiment_id\n",
+       "0       0.0\n",
+       "1       0.0\n",
+       "2      94.0\n",
+       "3    1001.6\n",
+       "Name: gather_v_runtime, dtype: float64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_big_sphere_1.groupby('experiment_id')['gather_v_runtime'].quantile(0.90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "cc40bb5d-3eb3-4351-8ba0-3be5119a8d1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(sorted(list(df_big_sphere_1[df_big_sphere_1['gather_v_runtime'] > 35000]['rank'])), bins=50)\n",
+    "plt.xlabel('Rank')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "ddfbe25b-bc60-4211-86bc-8f59186066ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>p2p</th>\n",
+       "      <th>m2l</th>\n",
+       "      <th>scatter_v_runtime</th>\n",
+       "      <th>gather_v_runtime</th>\n",
+       "      <th>neighbour_all_to_all_v_runtime</th>\n",
+       "      <th>n_points</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10664</td>\n",
+       "      <td>7244</td>\n",
+       "      <td>3169</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>69</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10803</td>\n",
+       "      <td>7247</td>\n",
+       "      <td>3191</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7735</td>\n",
+       "      <td>2684</td>\n",
+       "      <td>4385</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>171</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10047</td>\n",
+       "      <td>3673</td>\n",
+       "      <td>5863</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>212</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10174</td>\n",
+       "      <td>3675</td>\n",
+       "      <td>6083</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>165</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1165</th>\n",
+       "      <td>3</td>\n",
+       "      <td>26103</td>\n",
+       "      <td>8572</td>\n",
+       "      <td>15186</td>\n",
+       "      <td>103</td>\n",
+       "      <td>1482</td>\n",
+       "      <td>41</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1166</th>\n",
+       "      <td>3</td>\n",
+       "      <td>26220</td>\n",
+       "      <td>8566</td>\n",
+       "      <td>14955</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1247</td>\n",
+       "      <td>97</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1167</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30017</td>\n",
+       "      <td>8773</td>\n",
+       "      <td>15416</td>\n",
+       "      <td>123</td>\n",
+       "      <td>1506</td>\n",
+       "      <td>3634</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1168</th>\n",
+       "      <td>3</td>\n",
+       "      <td>27543</td>\n",
+       "      <td>9623</td>\n",
+       "      <td>15889</td>\n",
+       "      <td>86</td>\n",
+       "      <td>1090</td>\n",
+       "      <td>48</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1169</th>\n",
+       "      <td>3</td>\n",
+       "      <td>30765</td>\n",
+       "      <td>10459</td>\n",
+       "      <td>18371</td>\n",
+       "      <td>87</td>\n",
+       "      <td>1069</td>\n",
+       "      <td>52</td>\n",
+       "      <td>32000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1170 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      experiment_id  runtime    p2p    m2l  scatter_v_runtime  \\\n",
+       "0                 0    10664   7244   3169                  0   \n",
+       "1                 0    10803   7247   3191                  6   \n",
+       "2                 1     7735   2684   4385                 64   \n",
+       "3                 1    10047   3673   5863                 39   \n",
+       "4                 1    10174   3675   6083                 10   \n",
+       "...             ...      ...    ...    ...                ...   \n",
+       "1165              3    26103   8572  15186                103   \n",
+       "1166              3    26220   8566  14955                128   \n",
+       "1167              3    30017   8773  15416                123   \n",
+       "1168              3    27543   9623  15889                 86   \n",
+       "1169              3    30765  10459  18371                 87   \n",
+       "\n",
+       "      gather_v_runtime  neighbour_all_to_all_v_runtime  n_points  \n",
+       "0                    0                              69  32000000  \n",
+       "1                    0                              73  32000000  \n",
+       "2                    0                             171  32000000  \n",
+       "3                    0                             212  32000000  \n",
+       "4                    0                             165  32000000  \n",
+       "...                ...                             ...       ...  \n",
+       "1165              1482                              41  32000000  \n",
+       "1166              1247                              97  32000000  \n",
+       "1167              1506                            3634  32000000  \n",
+       "1168              1090                              48  32000000  \n",
+       "1169              1069                              52  32000000  \n",
+       "\n",
+       "[1170 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_big_sphere[runtime_cols]['"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f97097a2-a227-436a-afb8-cb59ecf9aa81",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hpc/ijhpca/strong_scaling.py b/hpc/ijhpca/strong_scaling.py
new file mode 100644
index 00000000..b7208bea
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling.py
@@ -0,0 +1,247 @@
+#!/usr/bin/env python3
+import numpy as np
+import argparse
+from pathlib import Path
+import json
+
+AMD_EPYC_ROME = {
+        "cores_per_node": 128, # total number of CPU cores per node
+        "sockets_per_node": 2, # two sockets per node
+        "cores_per_socket": 64, # A socket consists of 8 CCDs (64 cores total) common IO/memory controllers
+        "cores_per_ccd": 8, # Each CCD is a processor die, consists of two CCDs which are infinity linked together and connected to io
+        "cores_per_ccx": 4, # Each CCX shares an L3 cache (16MB)
+        "ccx_per_ccd": 2
+    }
+
+DISTRIBUTION = {
+    "uniform": 0,
+    "sphere": 1
+}
+
+def global_depth(rank):
+    """
+    will return the first power of 8 less than rank, the power corresponding to the level
+    """
+    level = 1
+    loop = True
+
+    while rank > 8**level:
+        level += 1
+
+    return level
+
+v_global_depth = np.vectorize(global_depth)
+
+def local_depth(points_per_rank, max_points_per_leaf, min_local_depth):
+
+    local_level = min_local_depth
+
+    while True:
+        n_leaves = 8**local_level # leaves in local tree
+        points_per_leaf = points_per_rank / n_leaves # estimate based on uniform tree
+        if points_per_leaf <= max_points_per_leaf:
+            return local_level
+        else:
+            local_level += 1
+
+v_local_depth = np.vectorize(local_depth)
+
+def pow2(x): return np.log2(x).is_integer()
+
+def pow8(x):
+    return np.emath.logn(8, x).is_integer()
+
+def parse_process_mapping(arch):
+    ccd_threads_per_rank = arch["cores_per_ccd"]
+    ccx_threads_per_rank = arch["cores_per_ccx"]
+    ccd_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccd"] # Mapping each rank to a CCD
+    ccx_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccx"] # Mapping each rank to a CCX
+    socket_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_socket"] # Mapping each rank to a socket
+    return {"socket": socket_max_ranks_per_node, "ccd": ccd_max_ranks_per_node, "ccx": ccx_max_ranks_per_node}
+
+
+def experiment_parameters(
+    min_nodes,
+    max_nodes,
+    max_ranks_per_node,
+    min_points_per_rank,
+    min_local_depth=4,
+    scaling_func=pow8,
+    arch=AMD_EPYC_ROME,
+    distribution="uniform"
+):
+
+    max_cpus=arch["cores_per_node"]
+
+    # Calculate the min/max number of ranks required to scale this problem by scaling_func given
+    # the resources specified by min/max nodes
+    min_ranks = int(min_nodes*max_ranks_per_node)
+    max_ranks = int(max_nodes*max_ranks_per_node)
+
+    # Want to scale the resources (ranks) by scaling function between min/max ranks
+    n_ranks = []
+    curr = min_ranks
+    while curr <= max_ranks:
+        if scaling_func == pow2:
+            n_ranks.append(curr)
+            curr *= 2
+
+        elif scaling_func == pow8:
+            n_ranks.append(curr)
+            curr *= 8
+        else:
+            raise ValueError("Unknown scaling func")
+
+    n_ranks = np.array(n_ranks)
+
+    # Can use the number of required ranks with this scaling function
+    # to calculate the number of required nodes
+    n_nodes = (n_ranks / max_ranks_per_node).astype(np.int32)
+
+    # we calculate the global depth as the least power of 8 smaller than or equal to the available
+    # ranks for each configuration
+    global_depth = v_global_depth(n_ranks)
+
+    local_trees_per_rank = 8**global_depth / n_ranks
+
+    # Problem size defined by largest valid rank set
+    n = min_points_per_rank * n_ranks[-1]
+
+    points_per_node = [n]
+    for i in range(len(n_nodes)-1):
+
+        if scaling_func == pow8:
+            p = points_per_node[i]/8
+        elif scaling_func == pow2:
+            p = points_per_node[i]/2
+        else:
+            raise ValueError("Unknown scaling function")
+
+        points_per_node.append(p)
+
+    points_per_node = np.array(points_per_node)
+
+    ranks_per_node = n_ranks/n_nodes
+    points_per_rank = np.int64(points_per_node/ranks_per_node)
+
+    min_points_per_leaf = points_per_rank[-1] / 8**min_local_depth
+
+    local_depth = v_local_depth(points_per_rank, min_points_per_leaf, min_local_depth)
+
+    max_threads_per_rank = int(max_cpus/ranks_per_node[0])
+
+    print(f"Total problem size n={n/1e6}M")
+    print(f"global depth {global_depth}")
+    print(f"local depth {local_depth}")
+    print(f"total depth {local_depth+global_depth}")
+    print(f"max local trees per rank {local_trees_per_rank}")
+    print(f"number of nodes {n_nodes} max ranks per node {max_ranks_per_node}")
+    print(f"number of ranks {n_ranks}")
+    print(f"points per rank {points_per_rank}")
+    print(f"points per node {points_per_node}")
+    print(f"min points per leaf {min_points_per_leaf}")
+    print(f"max threads per rank {max_threads_per_rank}")
+    print(f"distribution {distribution}")
+
+    # Test that same number of points being used in each experiment
+    assert(np.allclose(points_per_rank*n_ranks, points_per_node*n_nodes))
+    return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), local_depth.tolist(), points_per_rank, max_threads_per_rank, distribution
+
+
+
+def write_slurm(script_path, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, script_name="fmm_m2l_fft_mpi_f32", distribution="uniform"):
+    expansion_order = 3
+    n_samples = 500
+    block_size = 128
+    cpus_per_task = int(max_threads)
+
+    last_nodes = n_nodes[-1]
+    last_tasks = n_tasks[-1]
+    max_points = last_tasks * points_per_rank[-1]
+
+    slurm = f"""#!/bin/bash
+#SBATCH --job-name=strong_scaling_fft
+#SBATCH --time=00:30:00
+#SBATCH --nodes={last_nodes}
+#SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])}
+#SBATCH --cpus-per-task={cpus_per_task}
+#SBATCH --account=e738
+#SBATCH --partition=standard
+#SBATCH --qos=standard
+"""
+    slurm += f"""
+module load PrgEnv-aocc
+module load craype-network-ucx
+module load cray-mpich-ucx
+
+export HOME="/home/e738/e738/skailasa"
+export WORK="/work/e738/e738/skailasa"
+
+script_name="{script_name}"
+
+export SCRATCH=$WORK/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_distribution={distribution}_${{SLURM_JOBID}}
+mkdir -p $SCRATCH
+cd $SCRATCH
+
+export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
+export OMP_NUM_THREADS=1
+
+export OUTPUT=$SCRATCH/strong_fft_n={max_points}_p={last_nodes}_points_per_rank={points_per_rank[-1]}_distribution={distribution}_${{SLURM_JOBID}}.csv
+touch $OUTPUT
+echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" >> ${{OUTPUT}}
+"""
+
+    # loop of runs
+    slurm += "\n# Perform strong scaling\n"
+    for i, (ppr, nn, nt, gd, ld) in enumerate(zip(points_per_rank, n_nodes, n_tasks, global_depths, local_depths)):
+        slurm += f"""
+srun --nodes={nn} --ntasks={nt} --cpus-per-task={cpus_per_task} \\
+     --distribution=block:block --hint=nomultithread \\
+     $WORK/$script_name --id {i} --n-points {ppr} \\
+     --expansion-order {expansion_order} --prune-empty \\
+     --global-depth {gd} --local-depth {ld} \\
+     --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} --distribution {DISTRIBUTION[distribution]} \\
+     >> $OUTPUT 2> $SCRATCH/err_run_{i}.log
+"""
+
+    Path(script_path).write_text(slurm)
+    print(f"✅ Wrote SLURM script to {script_path}")
+
+if __name__ == "__main__":
+    ranks_per_node = parse_process_mapping(AMD_EPYC_ROME)
+
+    parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.")
+    parser.add_argument("--min-nodes", type=int, default=1)
+    parser.add_argument("--max-nodes", type=int, default=16)
+    parser.add_argument("--min-points-per-rank", type=int, default=250000)
+    parser.add_argument("--min-local-depth", type=int, default=4)
+    parser.add_argument("--scaling-func", type=int, default=2)
+    parser.add_argument("--method", type=str, default="ccx")
+    parser.add_argument("--output", type=str, default="job.slurm")
+    parser.add_argument("--distribution", type=str, default="uniform")
+    parser.add_argument("--config", action='append')
+
+    args = parser.parse_args()
+
+    if args.config is not None:
+        for fname in args.config:
+            with open(fname, 'r') as f:
+                parser.set_defaults(**json.load(f))
+
+    args = parser.parse_args()
+
+    if args.scaling_func == 2:
+        scaling_func = pow2
+    elif args.scaling_func == 8:
+        scaling_func = pow8
+    else:
+        raise ValueError("Unknown scaling function")
+
+    valid_methods = {"ccx", "ccd" "socket"}
+    if any(args.method in m for m in valid_methods):
+        n_nodes, n_tasks, global_depths, local_depths, points_per_rank, max_threads, distribution = experiment_parameters(
+            args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.min_points_per_rank, args.min_local_depth, scaling_func=scaling_func, distribution=args.distribution
+        )
+        write_slurm(args.output, n_nodes, n_tasks, local_depths, global_depths, max_threads, points_per_rank, distribution=distribution)
+    else:
+        raise ValueError("Unknown method")
\ No newline at end of file
diff --git a/hpc/ijhpca/strong_scaling_ccd.json b/hpc/ijhpca/strong_scaling_ccd.json
new file mode 100644
index 00000000..64d1c891
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling_ccd.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Strong scaling",
+    "min_nodes": 1,
+    "max_nodes": 128,
+    "min_points_per_rank": 125000,
+    "min_local_depth": 3,
+    "output": "strong_scaling_ccd_uniform.slurm",
+    "method": "ccd",
+    "scaling_func": 8,
+    "distribution": "uniform"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/strong_scaling_ccd_sphere.json b/hpc/ijhpca/strong_scaling_ccd_sphere.json
new file mode 100644
index 00000000..89b3d8a3
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling_ccd_sphere.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Strong scaling",
+    "min_nodes": 1,
+    "max_nodes": 128,
+    "min_points_per_rank": 125000,
+    "min_local_depth": 3,
+    "output": "strong_scaling_ccd_sphere.slurm",
+    "method": "ccd",
+    "scaling_func": 8,
+    "distribution": "sphere"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/strong_scaling_ccx.json b/hpc/ijhpca/strong_scaling_ccx.json
new file mode 100644
index 00000000..f8ae1828
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling_ccx.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Strong scaling",
+    "min_nodes": 1,
+    "max_nodes": 128,
+    "min_points_per_rank": 62500,
+    "min_local_depth": 3,
+    "output": "strong_scaling_ccx_uniform.slurm",
+    "method": "ccx",
+    "scaling_func": 8,
+    "distribution": "uniform"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/strong_scaling_ccx_sphere.json b/hpc/ijhpca/strong_scaling_ccx_sphere.json
new file mode 100644
index 00000000..534b9664
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling_ccx_sphere.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Strong scaling",
+    "min_nodes": 1,
+    "max_nodes": 128,
+    "min_points_per_rank": 62500,
+    "min_local_depth": 3,
+    "output": "strong_scaling_ccx_sphere.slurm",
+    "method": "ccx",
+    "scaling_func": 8,
+    "distribution": "sphere"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/strong_scaling_socket.json b/hpc/ijhpca/strong_scaling_socket.json
new file mode 100644
index 00000000..73714274
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling_socket.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Strong scaling",
+    "min_nodes": 1,
+    "max_nodes": 128,
+    "min_points_per_rank": 1000000,
+    "min_local_depth": 4,
+    "output": "strong_scaling_socket_uniform.slurm",
+    "method": "socket",
+    "scaling_func": 8,
+    "distribution": "uniform"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/strong_scaling_socket_sphere.json b/hpc/ijhpca/strong_scaling_socket_sphere.json
new file mode 100644
index 00000000..a533934f
--- /dev/null
+++ b/hpc/ijhpca/strong_scaling_socket_sphere.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Strong scaling",
+    "min_nodes": 1,
+    "max_nodes": 128,
+    "min_points_per_rank": 1000000,
+    "min_local_depth": 4,
+    "output": "strong_scaling_socket_sphere.slurm",
+    "method": "socket",
+    "scaling_func": 8,
+    "distribution": "sphere"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling.py b/hpc/ijhpca/weak_scaling.py
new file mode 100644
index 00000000..19d274b9
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling.py
@@ -0,0 +1,250 @@
+#!/usr/bin/env python3
+import numpy as np
+import argparse
+from pathlib import Path
+import json
+
+AMD_EPYC_ROME = {
+        "cores_per_node": 128, # total number of CPU cores per node
+        "sockets_per_node": 2, # two sockets per node
+        "cores_per_socket": 64, # A socket consists of 8 CCDs (64 cores total) common IO/memory controllers
+        "cores_per_ccd": 8, # Each CCD is a processor die, consists of two CCDs which are infinity linked together and connected to io
+        "cores_per_ccx": 4, # Each CCX shares an L3 cache (16MB)
+        "ccx_per_ccd": 2
+    }
+
+DISTRIBUTION = {
+    "uniform": 0,
+    "sphere": 1
+}
+
+MACHINE = {
+    "lumi": {
+        "project": "project_465001872",
+        "partition": "standard"
+    },
+
+    "archer2": {
+        "project": "e738",
+        "partition": "standard"
+    }
+}
+
+def parse_process_mapping(arch):
+    ccd_threads_per_rank = arch["cores_per_ccd"]
+    ccx_threads_per_rank = arch["cores_per_ccx"]
+    ccd_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccd"] # Mapping each rank to a CCD
+    ccx_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_ccx"] # Mapping each rank to a CCX
+    socket_max_ranks_per_node = arch["cores_per_node"] / arch["cores_per_socket"] # Mapping each rank to a socket
+    return {"socket": socket_max_ranks_per_node, "ccd": ccd_max_ranks_per_node, "ccx": ccx_max_ranks_per_node}
+
+
+def global_depth(rank):
+    """
+    Return the first power of 8 >= rank, the exponent (tree level).
+    """
+    level = 1
+    while rank > 8**level:
+        level += 1
+    return level
+
+v_global_depth = np.vectorize(global_depth)
+
+def pow2(x):
+    return np.log2(x).is_integer()
+
+def pow8(x):
+    return np.emath.logn(8, x).is_integer()
+
+
+def experiment_parameters(
+    min_nodes,
+    max_nodes,
+    max_ranks_per_node,
+    points_per_rank,
+    local_depth=4,
+    scaling_func=pow8,
+    arch=AMD_EPYC_ROME,
+    distribution="uniform"
+    ):
+
+    max_cpus=arch["cores_per_node"]
+
+    # Calculate the min/max number of ranks required to scale this problem by scaling_func given
+    # the resources specified by min/max nodes
+    min_ranks = int(min_nodes*max_ranks_per_node)
+    max_ranks = int(max_nodes*max_ranks_per_node)
+
+    # Want to scale the resources (ranks) by scaling function between min/max ranks
+    n_ranks = []
+    curr = min_ranks
+    while curr <= max_ranks:
+        if scaling_func == pow2:
+            n_ranks.append(curr)
+            curr *= 2
+
+        elif scaling_func == pow8:
+            n_ranks.append(curr)
+            curr *= 8
+        else:
+            raise ValueError("Unknown scaling func")
+
+    n_ranks = np.array(n_ranks)
+
+    # Can use the number of required ranks with this scaling function
+    # to calculate the number of required nodes
+    n_nodes = (n_ranks / max_ranks_per_node).astype(np.int32)
+
+    # we calculate the global depth as the least power of 8 smaller than or equal to the available
+    # ranks for each configuration
+    global_depth = v_global_depth(n_ranks)
+
+    local_trees_per_rank = 8**global_depth / n_ranks
+
+    # Problem size defined by largest valid rank set
+    n = points_per_rank * n_ranks[-1]
+
+    points_per_node = max_ranks_per_node*points_per_rank
+
+    points_per_leaf = points_per_rank / 8**local_depth
+
+    max_threads_per_rank = int(max_cpus/max_ranks_per_node)
+
+    print(f"Total problem size n={n/1e6}M")
+    print(f"global depth {global_depth}")
+    print(f"local depth {local_depth}")
+    print(f"total depth {local_depth+global_depth}")
+    print(f"max local trees per rank {local_trees_per_rank}")
+    print(f"number of nodes {n_nodes} max ranks per node {max_ranks_per_node}")
+    print(f"number of ranks {n_ranks}")
+    print(f"points per rank {points_per_rank}")
+    print(f"points per node {points_per_node}")
+    print(f"points per leaf {points_per_leaf}")
+    print(f"max threads per rank {max_threads_per_rank}")
+    print(f"distribution {distribution}")
+
+    return n_nodes.tolist(), n_ranks.tolist(), global_depth.tolist(), max_threads_per_rank, distribution
+
+
+def write_slurm(
+    machine,
+    script_path,
+    n_nodes,
+    n_tasks,
+    global_depths,
+    max_threads,
+    points_per_rank,
+    local_depth,
+    hrs,
+    mins,
+    distribution="uniform",
+    script_name="fmm_m2l_fft_mpi_f32",
+    ):
+    expansion_order = 3
+    n_points = points_per_rank
+    n_samples = 500
+    block_size = 128
+    cpus_per_task = int(max_threads)
+
+    last_nodes = n_nodes[-1]
+    last_tasks = n_tasks[-1]
+    max_points = last_tasks * n_points
+
+
+    slurm = f"""#!/bin/bash
+#SBATCH --job-name=weak_scaling_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}
+#SBATCH --time={hrs}:{mins}:00
+#SBATCH --nodes={last_nodes}
+#SBATCH --ntasks-per-node={int(n_tasks[-1] // n_nodes[-1])}
+#SBATCH --cpus-per-task={cpus_per_task}
+#SBATCH --account={MACHINE[machine]["project"]}
+#SBATCH --partition={MACHINE[machine]["partition"]}
+"""
+    if machine == "archer2":
+        slurm += """
+#SBATCH --qos=standard
+    """
+
+    slurm += f"""
+module load PrgEnv-aocc
+module load craype-network-ucx
+module load cray-mpich-ucx
+
+export UCX_UD_TIMEOUT=20m
+
+script_name="{script_name}"
+
+export SCRATCH=$WORK/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}_${{SLURM_JOBID}}
+mkdir -p $SCRATCH
+cd $SCRATCH
+
+export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK
+export OMP_NUM_THREADS=1
+
+export OUTPUT=$SCRATCH/weak_fft_n={max_points}_p={last_nodes}_points_per_rank={n_points}_distribution={distribution}_${{SLURM_JOBID}}.csv
+touch $OUTPUT
+echo "experiment_id,rank,runtime,p2m,m2m,l2l,m2l,p2p,source_tree,target_tree,source_domain,target_domain,layout,ghost_exchange_v,ghost_exchange_v_runtime,ghost_exchange_u,gather_global_fmm,scatter_global_fmm,source_to_target_data,source_data,target_data,global_fmm,ghost_fmm_v,ghost_fmm_u,displacement_map,metadata_creation,expansion_order,n_points,local_depth,global_depth,block_size,n_threads,n_samples,source_local_trees_per_rank,target_local_trees_per_rank,all_to_all,all_to_all_v,neighbour_all_to_all,neighbour_all_to_all_v,neighbour_all_to_all_v_runtime,gather,scatter,gather_v,scatter_v,gather_v_runtime,scatter_v_runtime,all_gather,all_gather_v,dist_graph_create,sort,tree_all_to_all,tree_all_to_all_v,tree_neighbour_all_to_all,tree_neighbour_all_to_all_v,tree_neighbour_all_to_all_v_runtime,tree_gather,tree_scatter,tree_gather_v,tree_scatter_v,tree_gather_v_runtime,tree_scatter_v_runtime,tree_all_gather,tree_all_gather_v,tree_dist_graph_create,tree_sort" >> ${{OUTPUT}}
+"""
+
+    # loop of runs
+    slurm += "\n# Perform weak scaling\n"
+    for i, (nn, nt, gd) in enumerate(zip(n_nodes, n_tasks, global_depths)):
+        slurm += f"""
+srun --nodes={nn} --ntasks={nt} --cpus-per-task={cpus_per_task} \\
+     --distribution=block:block --hint=nomultithread \\
+     $WORK/$script_name --id {i} --n-points {n_points} \\
+     --expansion-order {expansion_order} --prune-empty \\
+     --global-depth {gd} --local-depth {local_depth} \\
+     --n-samples {n_samples} --block-size {block_size} --n-threads {int(max_threads)} --distribution {DISTRIBUTION[distribution]} \\
+     >> $OUTPUT 2> $SCRATCH/err_run_{i}.log
+"""
+
+    Path(script_path).write_text(slurm)
+    print(f"✅ Wrote SLURM script to {script_path}")
+
+
+if __name__ == "__main__":
+
+    ranks_per_node = parse_process_mapping(AMD_EPYC_ROME)
+
+    parser = argparse.ArgumentParser(description="Generate a SLURM script for weak scaling runs.")
+    parser.add_argument("--min-nodes", type=int, default=1)
+    parser.add_argument("--max-nodes", type=int, default=16)
+    parser.add_argument("--points-per-rank", type=int, default=250000)
+    parser.add_argument("--local-depth", type=int, default=4)
+    parser.add_argument("--method", type=str, default="ccx")
+    parser.add_argument("--output", type=str, default="job.slurm")
+    parser.add_argument("--distribution", type=str, default="uniform")
+    parser.add_argument("--machine", type=str, default="archer2")
+    parser.add_argument("--hrs", type=str, default="00")
+    parser.add_argument("--mins", type=str, default="30")
+
+    parser.add_argument("--config", action='append')
+
+    args = parser.parse_args()
+
+    if args.config is not None:
+        for fname in args.config:
+            with open(fname, 'r') as f:
+                parser.set_defaults(**json.load(f))
+
+    args = parser.parse_args()
+
+    if args.scaling_func == 2:
+        scaling_func = pow2
+    elif args.scaling_func == 8:
+        scaling_func = pow8
+    else:
+        raise ValueError("Unknown scaling function")
+
+
+    valid_methods = {"ccx", "ccd" "socket"}
+    if any(args.method in m for m in valid_methods):
+        n_nodes, n_tasks, global_depths, max_threads, distribution = experiment_parameters(
+            args.min_nodes, args.max_nodes, ranks_per_node[args.method], args.points_per_rank, args.local_depth, distribution=args.distribution
+        )
+
+        write_slurm(args.machine, args.output, n_nodes, n_tasks, global_depths, max_threads, args.points_per_rank, args.local_depth, args.hrs, args.mins, distribution=distribution)
+
+    else:
+        raise ValueError("Unknown method")
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_big.json b/hpc/ijhpca/weak_scaling_big.json
new file mode 100644
index 00000000..02c7267d
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_big.json
@@ -0,0 +1,14 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 32000000,
+    "local_depth": 5,
+    "output": "weak_scaling_socket_32B.slurm",
+    "method": "socket",
+    "scaling_func": 8,
+    "distribution": "uniform",
+    "machine": "archer2",
+    "hrs": "1",
+    "mins": "00"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_big_sphere.json b/hpc/ijhpca/weak_scaling_big_sphere.json
new file mode 100644
index 00000000..cb4e4114
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_big_sphere.json
@@ -0,0 +1,14 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 32000000,
+    "local_depth": 6,
+    "output": "weak_scaling_socket_32B_sphere.slurm",
+    "method": "socket",
+    "scaling_func": 8,
+    "distribution": "sphere",
+    "machine": "archer2",
+    "hrs": "1",
+    "mins": "30"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_ccd.json b/hpc/ijhpca/weak_scaling_ccd.json
new file mode 100644
index 00000000..faaf8b9a
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_ccd.json
@@ -0,0 +1,14 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 500000,
+    "local_depth": 4,
+    "output": "weak_scaling_ccd_uniform.slurm",
+    "method": "ccd",
+    "scaling_func": 8,
+    "distribution": "uniform",
+    "machine": "archer2",
+    "hrs": "0",
+    "mins": "30"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_ccd_sphere.json b/hpc/ijhpca/weak_scaling_ccd_sphere.json
new file mode 100644
index 00000000..a09e086e
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_ccd_sphere.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 500000,
+    "local_depth": 4,
+    "output": "weak_scaling_ccd_sphere.slurm",
+    "method": "ccd",
+    "scaling_func": 8,
+    "distribution": "sphere"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_ccx.json b/hpc/ijhpca/weak_scaling_ccx.json
new file mode 100644
index 00000000..5e86038b
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_ccx.json
@@ -0,0 +1,14 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 250000,
+    "local_depth": 4,
+    "output": "weak_scaling_ccx_uniform.slurm",
+    "method": "ccx",
+    "scaling_func": 8,
+    "distribution": "uniform",
+    "machine": "lumi",
+    "hrs": "0",
+    "mins": "30"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_ccx_sphere.json b/hpc/ijhpca/weak_scaling_ccx_sphere.json
new file mode 100644
index 00000000..ca1e7810
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_ccx_sphere.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 250000,
+    "local_depth": 4,
+    "output": "weak_scaling_ccx_sphere.slurm",
+    "method": "ccx",
+    "scaling_func": 8,
+    "distribution": "sphere"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_socket.json b/hpc/ijhpca/weak_scaling_socket.json
new file mode 100644
index 00000000..92c9b360
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_socket.json
@@ -0,0 +1,14 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 4000000,
+    "local_depth": 5,
+    "output": "weak_scaling_socket_uniform.slurm",
+    "method": "socket",
+    "scaling_func": 8,
+    "distribution": "uniform",
+    "machine": "archer2",
+    "hrs": "0",
+    "mins": "30"
+}
\ No newline at end of file
diff --git a/hpc/ijhpca/weak_scaling_socket_sphere.json b/hpc/ijhpca/weak_scaling_socket_sphere.json
new file mode 100644
index 00000000..46c394b8
--- /dev/null
+++ b/hpc/ijhpca/weak_scaling_socket_sphere.json
@@ -0,0 +1,11 @@
+{
+    "_comment": "Weak scaling",
+    "min_nodes": 1,
+    "max_nodes": 512,
+    "points_per_rank": 4000000,
+    "local_depth": 5,
+    "output": "weak_scaling_socket_sphere.slurm",
+    "method": "socket",
+    "scaling_func": 8,
+    "distribution": "sphere"
+}
\ No newline at end of file
diff --git a/kifmm/include/kifmm_rs.h b/kifmm/include/kifmm_rs.h
index d446a301..7e8ef995 100644
--- a/kifmm/include/kifmm_rs.h
+++ b/kifmm/include/kifmm_rs.h
@@ -101,7 +101,7 @@ typedef enum MetadataType {
    */
   MetadataType_GhostFmmU,
   /**
-   * Pointer and Buffer Creation
+   * Pointer and Buffer Creationmp
    */
   MetadataType_MetadataCreation,
   /**
diff --git a/kifmm/pyproject.toml b/kifmm/pyproject.toml
index 4d726e02..e38c05d3 100644
--- a/kifmm/pyproject.toml
+++ b/kifmm/pyproject.toml
@@ -26,7 +26,7 @@ dependencies = [
     "cffi",
     "pandas==2.2.2",
     "seaborn==0.13.2",
-    "mayavi==4.8.1",
+#    "mayavi==4.8.1",
     "vtk==9.3.0",
     "pyqt5==5.15.10",
     "numpy-stl==3.1.1",
diff --git a/kifmm/src/fmm/builder/multi_node.rs b/kifmm/src/fmm/builder/multi_node.rs
index 52a29021..11cc5531 100644
--- a/kifmm/src/fmm/builder/multi_node.rs
+++ b/kifmm/src/fmm/builder/multi_node.rs
@@ -1,5 +1,5 @@
 //! Builder for constructing FMMs on multi-node.
-use std::collections::HashMap;
+use std::{collections::HashMap, time::Instant};
 
 use itertools::Itertools;
 use mpi::{
@@ -12,12 +12,11 @@ use rlst::{MatrixSvd, RlstScalar};
 use green_kernels::{traits::Kernel as KernelTrait, types::GreenKernelEvalType};
 
 use crate::{
-    fmm::types::PinvMode,
     fmm::{
         helpers::single_node::{level_index_pointer_single_node, ncoeffs_kifmm, optionally_time},
         types::{
             FmmEvalType, Isa, KiFmmMulti, Layout, MultiNodeBuilder, NeighbourhoodCommunicator,
-            Query,
+            PinvMode, Query,
         },
     },
     traits::{
@@ -27,7 +26,7 @@ use crate::{
         },
         fmm::{HomogenousKernel, Metadata, MetadataAccess},
         general::{multi_node::GlobalFmmMetadata, single_node::Epsilon},
-        types::{CommunicationType, MetadataType, OperatorTime},
+        types::{CommunicationType, MPICollectiveType, MetadataType, OperatorTime},
     },
     tree::{
         types::{Domain, SortKind},
@@ -186,6 +185,7 @@ where
                 source_layout: Layout::default(),
                 v_list_query: Query::default(),
                 u_list_query: Query::default(),
+                mpi_times: HashMap::default(),
             };
 
             // Global communication to set the source layout required
@@ -201,6 +201,14 @@ where
             fmm_tree.set_queries(true);
             fmm_tree.set_queries(false);
 
+            // TODO Fix timing (adding tree setup times to fmm tree reported times)
+            for (&op_type, op_time) in fmm_tree.source_tree.mpi_times.iter() {
+                fmm_tree
+                    .mpi_times
+                    .entry(op_type)
+                    .and_modify(|t| t.time += op_time.time);
+            }
+
             self.communicator = Some(global_communicator.duplicate());
             self.tree = Some(fmm_tree);
             self.communication_times = Some(communication_times);
@@ -289,10 +297,14 @@ where
         } else {
             let kernel = self.kernel.unwrap();
             let communicator = self.communicator.unwrap();
+
+            // TODO: clean operator times
+            let s = Instant::now();
             let neighbourhood_communicator_v = NeighbourhoodCommunicator::from_comm(&communicator);
             let neighbourhood_communicator_u = NeighbourhoodCommunicator::from_comm(&communicator);
             let neighbourhood_communicator_charge =
                 NeighbourhoodCommunicator::from_comm(&communicator);
+            let e = s.elapsed().as_millis() as u64;
             let rank = communicator.rank();
             let source_to_target = self.source_to_target.unwrap();
             let fmm_eval_type = self.fmm_eval_type.unwrap();
@@ -399,6 +411,7 @@ where
                 level_index_pointer_multipoles: Vec::default(),
                 potentials_send_pointers: Vec::default(),
                 metadata_times: HashMap::default(),
+                mpi_times: HashMap::default(),
                 ghost_requested_queries_v: Vec::default(),
                 ghost_requested_queries_key_to_index_v: HashMap::default(),
                 ghost_requested_queries_counts_v: Vec::default(),
@@ -419,6 +432,13 @@ where
                 ghost_received_queries_charge_displacements: Vec::default(),
             };
 
+            // TODO: cleanup timing
+            result
+                .mpi_times
+                .entry(MPICollectiveType::DistGraphCreate)
+                .and_modify(|t| t.time += s.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime { time: e });
+
             // Calculate required metadata
             let (_, duration) = optionally_time(timed, || result.source(PinvMode::svd(None, None)));
 
diff --git a/kifmm/src/fmm/charge_handler/multi_node.rs b/kifmm/src/fmm/charge_handler/multi_node.rs
index ac3b0b34..229a4b49 100644
--- a/kifmm/src/fmm/charge_handler/multi_node.rs
+++ b/kifmm/src/fmm/charge_handler/multi_node.rs
@@ -1,4 +1,6 @@
 //! Charge handling in multi-node setting
+use std::time::Instant;
+
 use green_kernels::traits::Kernel as KernelTrait;
 use itertools::izip;
 use mpi::{
@@ -14,7 +16,7 @@ use crate::{
         fmm::{ChargeHandler, HomogenousKernel},
         general::multi_node::GhostExchange,
         tree::{MultiFmmTree, MultiTree},
-        types::FmmError,
+        types::{FmmError, MPICollectiveType, OperatorTime},
     },
     KiFmmMulti,
 };
@@ -171,8 +173,15 @@ where
                     &self.charge_receive_queries_displacements[..],
                 );
 
+                let st = Instant::now();
                 self.neighbourhood_communicator_charge
                     .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+                self.mpi_times
+                    .entry(MPICollectiveType::NeighbourAlltoAllv)
+                    .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                    .or_insert(OperatorTime {
+                        time: st.elapsed().as_millis() as u64,
+                    });
             }
 
             self.charges = requested_queries;
diff --git a/kifmm/src/fmm/ghost_exchange.rs b/kifmm/src/fmm/ghost_exchange.rs
index aa57b474..ddf65d52 100644
--- a/kifmm/src/fmm/ghost_exchange.rs
+++ b/kifmm/src/fmm/ghost_exchange.rs
@@ -1,4 +1,7 @@
-use std::collections::{HashMap, HashSet};
+use std::{
+    collections::{HashMap, HashSet},
+    time::Instant,
+};
 
 use green_kernels::traits::Kernel as KernelTrait;
 use itertools::{izip, Itertools};
@@ -21,6 +24,7 @@ use crate::{
         fmm::{DataAccess, DataAccessMulti, HomogenousKernel},
         general::multi_node::{GhostExchange, GlobalFmmMetadata},
         tree::{MultiFmmTree, MultiTree, SingleFmmTree, SingleTree},
+        types::{MPICollectiveType, OperatorTime},
     },
     tree::{
         types::{Domain, MortonKey},
@@ -92,7 +96,14 @@ where
                 &displacements_buffers[..],
             );
 
+            let st = Instant::now();
             root_process.gather_varcount_into_root(&multipoles, &mut partition);
+            self.mpi_times
+                .entry(MPICollectiveType::GatherVRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             self.global_fmm.global_fmm_multipole_metadata(
                 self.tree.source_tree.all_roots.clone(),
@@ -110,7 +121,14 @@ where
                     .copy_from_slice(tmp);
             }
 
+            let st = Instant::now();
             root_process.gather_varcount_into(&multipoles);
+            self.mpi_times
+                .entry(MPICollectiveType::GatherVRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
     }
 
@@ -165,7 +183,14 @@ where
 
             // Send back coefficient data
             let partition = Partition::new(&send_buffer, counts, &displacements[..]);
+            let st = Instant::now();
             root_process.scatter_varcount_into_root(&partition, &mut receive_buffer);
+            self.mpi_times
+                .entry(MPICollectiveType::ScatterVRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             // Send back associated keys
             let partition = Partition::new(
@@ -173,10 +198,31 @@ where
                 &self.tree.target_tree.all_roots_counts[..],
                 &self.tree.target_tree.all_roots_displacements[..],
             );
+            let st = Instant::now();
             root_process.scatter_varcount_into_root(&partition, &mut expected_roots);
+            self.mpi_times
+                .entry(MPICollectiveType::ScatterVRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         } else {
+            let st = Instant::now();
             root_process.scatter_varcount_into(&mut receive_buffer);
+            self.mpi_times
+                .entry(MPICollectiveType::ScatterVRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
+            let st = Instant::now();
             root_process.scatter_varcount_into(&mut expected_roots);
+            self.mpi_times
+                .entry(MPICollectiveType::ScatterVRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // TODO might be broken if level locals is incorrectly set
@@ -253,8 +299,15 @@ where
                 neighbourhood_receive_displacements,
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_u
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             // Filter for locally available queries to send back
             let receive_counts_ = partition_receive.counts().iter().cloned().collect_vec();
@@ -364,8 +417,15 @@ where
             );
 
             // TODO: Investigate why all to all failing, and require all to all v
+            let st = Instant::now();
             self.neighbourhood_communicator_u
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Communicate expected query sizes
@@ -402,8 +462,15 @@ where
             );
 
             // TODO: Investigate why all to all failing, and require all to all v
+            let st = Instant::now();
             self.neighbourhood_communicator_u
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Create buffers to receive charge and coordinate data
@@ -457,8 +524,15 @@ where
                 &requested_queries_displacements[..],
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_u
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Communicate ghost coordinate data
@@ -475,8 +549,15 @@ where
                 &requested_coordinates_displacements[..],
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_u
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Communicate ghost charge data
@@ -493,8 +574,15 @@ where
                 requested_charges_displacements,
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_u
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Set metadata for Ghost FMM object
@@ -568,8 +656,15 @@ where
                 neighbourhood_receive_displacements,
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_v
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             // Then, filter for queries that actually exist at this rank and send back this information
             let receive_counts = partition_receive.counts().iter().cloned().collect_vec();
@@ -645,8 +740,15 @@ where
                 recv_displacements_,
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_v
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Create buffers to receive queries which have been requested from neighbours
@@ -677,8 +779,15 @@ where
                 &requested_queries_displacements[..],
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_v
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllv)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Store original ordering of received data temporarily
@@ -879,8 +988,15 @@ where
                 &requested_multipoles_displacements[..],
             );
 
+            let st = Instant::now();
             self.neighbourhood_communicator_v
                 .all_to_all_varcount_into(&partition_send, &mut partition_receive);
+            self.mpi_times
+                .entry(MPICollectiveType::NeighbourAlltoAllvRuntime)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
         }
 
         // Allocate ghost multipoles including sibling data, ordering dictated by tree construction
diff --git a/kifmm/src/fmm/tree/multi_node.rs b/kifmm/src/fmm/tree/multi_node.rs
index 8aeb1f0c..e4c8af6a 100644
--- a/kifmm/src/fmm/tree/multi_node.rs
+++ b/kifmm/src/fmm/tree/multi_node.rs
@@ -11,8 +11,14 @@ use num::Float;
 use rlst::RlstScalar;
 
 use crate::{
-    fmm::types::{Layout, Query},
-    traits::tree::{MultiFmmTree, MultiTree, SingleTree},
+    fmm::{
+        helpers::single_node::optionally_time,
+        types::{Layout, Query},
+    },
+    traits::{
+        tree::{MultiFmmTree, MultiTree, SingleTree},
+        types::{MPICollectiveType, OperatorTime},
+    },
     tree::{MortonKey, MultiNodeTree},
     MultiNodeFmmTree,
 };
@@ -144,9 +150,22 @@ where
         let mut receive_counts = vec![0i32; self.source_tree().communicator.size() as usize];
         let mut receive_marker = vec![0i32; self.source_tree().communicator.size() as usize];
 
-        self.source_tree
-            .communicator
-            .all_to_all_into(&send_counts, &mut receive_counts);
+        let (_, duration) = optionally_time(true, {
+            || {
+                self.source_tree
+                    .communicator
+                    .all_to_all_into(&send_counts, &mut receive_counts);
+            }
+        });
+
+        if let Some(d) = duration {
+            self.mpi_times
+                .entry(MPICollectiveType::AlltoAll)
+                .and_modify(|t| t.time += d.as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: d.as_millis() as u64,
+                });
+        };
 
         for (rank, &receive_count) in receive_counts.iter().enumerate() {
             if receive_count > 0 {
@@ -184,9 +203,22 @@ where
         let mut counts_ = vec![0i32; size as usize];
 
         // All gather to calculate the counts of roots on each processor
-        self.source_tree
-            .communicator
-            .all_gather_into(&n_roots, &mut counts_);
+
+        // TODO cleanup timing
+        let (_, duration) = optionally_time(true, || {
+            self.source_tree
+                .communicator
+                .all_gather_into(&n_roots, &mut counts_)
+        });
+
+        if let Some(d) = duration {
+            self.mpi_times
+                .entry(MPICollectiveType::AllGather)
+                .and_modify(|t| t.time += d.as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: d.as_millis() as u64,
+                });
+        };
 
         // Calculate displacements from the counts on each processor
         let mut displacements_ = Vec::new();
@@ -202,16 +234,31 @@ where
         let mut raw = vec![MortonKey::<T>::default(); n_roots_global as usize];
 
         // Store a copy of counts and displacements
+
+        // TODO: tidy timing
         let counts;
         let displacements;
-        {
-            let mut partition = PartitionMut::new(&mut raw, counts_, &displacements_[..]);
-            self.source_tree
-                .communicator
-                .all_gather_varcount_into(&roots, &mut partition);
-            counts = partition.counts().to_vec();
-            displacements = partition.displs().to_vec();
-        }
+        let mut partition = PartitionMut::new(&mut raw, counts_, &displacements_[..]);
+
+        let (_, duration) = optionally_time(true, {
+            || {
+                self.source_tree
+                    .communicator
+                    .all_gather_varcount_into(&roots, &mut partition);
+            }
+        });
+
+        counts = partition.counts().to_vec();
+        displacements = partition.displs().to_vec();
+
+        if let Some(d) = duration {
+            self.mpi_times
+                .entry(MPICollectiveType::AllGatherV)
+                .and_modify(|t| t.time += d.as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: d.as_millis() as u64,
+                });
+        };
 
         // Store as a set for easy lookup
         let raw_set = raw.iter().cloned().collect();
diff --git a/kifmm/src/fmm/types.rs b/kifmm/src/fmm/types.rs
index 222c3e0e..b5c399bd 100644
--- a/kifmm/src/fmm/types.rs
+++ b/kifmm/src/fmm/types.rs
@@ -21,7 +21,7 @@ use crate::{
 };
 
 #[cfg(feature = "mpi")]
-use crate::tree::MultiNodeTree;
+use crate::{traits::types::MPICollectiveType, tree::MultiNodeTree};
 
 #[cfg(feature = "mpi")]
 use std::collections::HashSet;
@@ -521,6 +521,9 @@ pub struct MultiNodeFmmTree<T: RlstScalar + Float + Equivalence, C: Communicator
 
     /// U list query
     pub u_list_query: Query,
+
+    /// MPI Collective Times
+    pub mpi_times: HashMap<MPICollectiveType, OperatorTime>,
 }
 
 /// Stores data and metadata for FFT based acceleration scheme for field translation.
@@ -1144,6 +1147,9 @@ where
     /// Metadata runtimes
     pub metadata_times: HashMap<MetadataType, OperatorTime>,
 
+    /// MPI Collective walltimes
+    pub mpi_times: HashMap<MPICollectiveType, OperatorTime>,
+
     /// Set to true if expansion order varies by level
     pub(crate) variable_expansion_order: bool,
 
diff --git a/kifmm/src/traits/types.rs b/kifmm/src/traits/types.rs
index 2c6e61f3..5c974e35 100644
--- a/kifmm/src/traits/types.rs
+++ b/kifmm/src/traits/types.rs
@@ -40,6 +40,56 @@ pub enum FmmOperatorType {
     P2P,
 }
 
+/// Enumeration of MPI collective types for timing
+#[repr(C)]
+#[derive(Debug, Clone, Copy, Hash, PartialEq, Eq)]
+pub enum MPICollectiveType {
+    /// All to All
+    AlltoAll,
+
+    /// All to All V
+    AlltoAllV,
+
+    /// Neighbour All to All
+    NeighbourAlltoAll,
+
+    /// Neighbour All to All V
+    NeighbourAlltoAllv,
+
+    /// Neighbour All to All V
+    NeighbourAlltoAllvRuntime,
+
+    /// Gather
+    Gather,
+
+    /// Scatter
+    Scatter,
+
+    /// Gather
+    GatherV,
+
+    /// Scatter
+    ScatterV,
+
+    /// Gather
+    GatherVRuntime,
+
+    /// Scatter
+    ScatterVRuntime,
+
+    /// All Gather
+    AllGather,
+
+    /// All Gather V
+    AllGatherV,
+
+    /// Cart dist-graph create
+    DistGraphCreate,
+
+    /// Parallel sort
+    Sort,
+}
+
 /// Enumeration of communication types for timing
 #[repr(C)]
 #[derive(Debug, Clone, Copy, Hash, PartialEq, Eq)]
@@ -97,7 +147,7 @@ pub enum MetadataType {
     /// Ghost FMM U
     GhostFmmU,
 
-    /// Pointer and Buffer Creation
+    /// Pointer and Buffer Creationmp
     MetadataCreation,
 
     /// Displacement Map Creation
diff --git a/kifmm/src/tree/helpers.rs b/kifmm/src/tree/helpers.rs
index 287ca510..47585544 100644
--- a/kifmm/src/tree/helpers.rs
+++ b/kifmm/src/tree/helpers.rs
@@ -52,9 +52,11 @@ pub fn points_fixture<T: Float + RlstScalar + rand::distributions::uniform::Samp
 /// * `max` - The maximum coordinate value along each axis.
 pub fn points_fixture_sphere<T: RlstScalar + rand::distributions::uniform::SampleUniform>(
     n_points: usize,
+    seed: Option<u64>,
 ) -> PointsMat<T> {
+    let seed = seed.unwrap_or(0);
     // Seeded random number generator for reproducibility
-    let mut rng = StdRng::seed_from_u64(0);
+    let mut rng = StdRng::seed_from_u64(seed);
     let pi = T::from(std::f64::consts::PI).unwrap();
     let two = T::from(2.0).unwrap();
 
diff --git a/kifmm/src/tree/multi_node.rs b/kifmm/src/tree/multi_node.rs
index ccf9c529..a320f170 100644
--- a/kifmm/src/tree/multi_node.rs
+++ b/kifmm/src/tree/multi_node.rs
@@ -1,5 +1,6 @@
 //! Implementation of constructors for MPI distributed multi node trees, from distributed point data.
 use std::collections::{HashMap, HashSet};
+use std::time::Instant;
 
 use itertools::Itertools;
 use mpi::{datatype::PartitionMut, topology::SimpleCommunicator, traits::Root};
@@ -10,6 +11,7 @@ use mpi::{
 use num::Float;
 use rlst::RlstScalar;
 
+use crate::traits::types::{MPICollectiveType, OperatorTime};
 use crate::{
     sorting::{hyksort, samplesort, simplesort},
     traits::tree::{MultiTree, SingleTree},
@@ -48,6 +50,7 @@ where
     ) -> Result<MultiNodeTree<T, SimpleCommunicator>, std::io::Error> {
         let dim = 3;
         let n_coords = coordinates_row_major.len() / dim;
+        let mut mpi_times: HashMap<MPICollectiveType, OperatorTime> = HashMap::default();
 
         let mut points = Points::default();
         for i in 0..n_coords {
@@ -68,7 +71,16 @@ where
         // Perform parallel Morton sort over encoded points
         match sort_kind {
             SortKind::Hyksort { subcomm_size } => hyksort(&mut points, subcomm_size, communicator)?,
-            SortKind::Samplesort { n_samples } => samplesort(&mut points, communicator, n_samples)?,
+            SortKind::Samplesort { n_samples } => {
+                let st = Instant::now();
+                samplesort(&mut points, communicator, n_samples)?;
+                mpi_times
+                    .entry(MPICollectiveType::Sort)
+                    .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                    .or_insert(OperatorTime {
+                        time: st.elapsed().as_millis() as u64,
+                    });
+            }
             SortKind::Simplesort => {
                 let splitters = MortonKey::root().descendants(global_depth).unwrap();
                 let mut splitters = splitters
@@ -227,7 +239,15 @@ where
             }
 
             let mut counts = vec![0 as Count; size as usize];
+            // TODO: sort times
+            let st = Instant::now();
             root_process.gather_into_root(&n_roots, &mut counts);
+            mpi_times
+                .entry(MPICollectiveType::Gather)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             // Calculate associated displacements from the counts
             let mut displacements = Vec::new();
@@ -244,7 +264,14 @@ where
             let mut partition =
                 PartitionMut::new(&mut global_roots, &counts[..], &displacements[..]);
 
+            let st = Instant::now();
             root_process.gather_varcount_into_root(&roots, &mut partition);
+            mpi_times
+                .entry(MPICollectiveType::GatherV)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             let mut global_roots_ranks = Vec::new();
             for (rank, &count) in counts.iter().enumerate() {
@@ -264,8 +291,22 @@ where
                 roots.push(tree.root())
             }
 
+            let st = Instant::now();
             root_process.gather_into(&n_roots);
+            mpi_times
+                .entry(MPICollectiveType::Gather)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
+            let st = Instant::now();
             root_process.gather_varcount_into(&roots);
+            mpi_times
+                .entry(MPICollectiveType::GatherV)
+                .and_modify(|t| t.time += st.elapsed().as_millis() as u64)
+                .or_insert(OperatorTime {
+                    time: st.elapsed().as_millis() as u64,
+                });
 
             all_roots = Vec::default();
             all_roots_counts = Vec::default();
@@ -276,6 +317,7 @@ where
         Ok(MultiNodeTree {
             domain,
             communicator: communicator.duplicate(),
+            mpi_times,
             rank,
             global_depth,
             local_depth,
diff --git a/kifmm/src/tree/types.rs b/kifmm/src/tree/types.rs
index caddf59a..13bddcb1 100644
--- a/kifmm/src/tree/types.rs
+++ b/kifmm/src/tree/types.rs
@@ -13,6 +13,9 @@ use mpi::{
 use num::Float;
 use rlst::RlstScalar;
 
+#[cfg(feature = "mpi")]
+use crate::traits::types::{MPICollectiveType, OperatorTime};
+
 /// Represents a three-dimensional box characterized by its origin and side-length along the Cartesian axes.
 #[repr(C)]
 #[derive(Debug, Clone, Copy, Default)]
@@ -148,6 +151,9 @@ where
     /// Roots associated with trees at this rank
     pub roots: Vec<MortonKey<T>>,
 
+    /// MPI collective times
+    pub mpi_times: HashMap<MPICollectiveType, OperatorTime>,
+
     /// All global roots, only populated at nominated rank
     pub all_roots: Vec<MortonKey<T>>,
 
diff --git a/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs b/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs
index 502c0241..b2b6c7af 100644
--- a/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs
+++ b/scripts/src/bin/fmm_m2l_blas_mpi_f32.rs
@@ -3,7 +3,10 @@ use clap::Parser;
 use green_kernels::laplace_3d::Laplace3dKernel;
 use kifmm::{
     traits::types::{CommunicationType, FmmOperatorType, MetadataType},
-    tree::{helpers::points_fixture, types::SortKind},
+    tree::{
+        helpers::{points_fixture, points_fixture_sphere},
+        types::SortKind,
+    },
     BlasFieldTranslationSaRcmp, DataAccessMulti, EvaluateMulti, FmmSvdMode, MultiNodeBuilder,
 };
 use mpi::traits::*;
@@ -48,6 +51,10 @@ struct Args {
     /// less than the number of samples and greater than 0
     #[arg(long, default_value_t = 10)]
     n_samples: usize,
+
+    /// Particle distribution (0 - uniform, 1 - sphere)
+    #[arg(long, default_value_t = 0)]
+    distribution: u64,
 }
 
 fn main() {
@@ -67,6 +74,7 @@ fn main() {
     let n_threads = args.n_threads;
     let n_samples = args.n_samples;
     let id = args.id;
+    let distribution = args.distribution;
 
     assert!(n_samples > 0 && n_samples < n_points);
 
@@ -84,7 +92,15 @@ fn main() {
         BlasFieldTranslationSaRcmp::<f32>::new(Some(threshold), None, FmmSvdMode::Deterministic);
 
     // Generate some random test data local to each process
-    let points = points_fixture::<f32>(n_points, None, None, Some(world.rank() as u64));
+    let points;
+    if distribution == 0 {
+        points = points_fixture::<f32>(n_points, None, None, Some(world.rank() as u64));
+    } else if distribution == 1 {
+        points = points_fixture_sphere::<f32>(n_points, Some(world.rank() as u64));
+    } else {
+        panic!("Unknown distribution")
+    }
+
     let charges = vec![1f32; n_points];
 
     let mut multi_fmm = MultiNodeBuilder::new(true)
diff --git a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs
index c463aeb4..517da33e 100644
--- a/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs
+++ b/scripts/src/bin/fmm_m2l_fft_mpi_f32.rs
@@ -2,8 +2,14 @@
 use clap::Parser;
 use green_kernels::laplace_3d::Laplace3dKernel;
 use kifmm::{
-    traits::types::{CommunicationType, FmmOperatorType, MetadataType},
-    tree::{helpers::points_fixture, types::SortKind},
+    traits::{
+        tree::MultiFmmTree,
+        types::{CommunicationType, FmmOperatorType, MPICollectiveType, MetadataType},
+    },
+    tree::{
+        helpers::{points_fixture, points_fixture_sphere},
+        types::SortKind,
+    },
     DataAccessMulti, EvaluateMulti, FftFieldTranslation, MultiNodeBuilder,
 };
 use mpi::traits::*;
@@ -48,6 +54,10 @@ struct Args {
     /// less than the number of samples and greater than 0
     #[arg(long, default_value_t = 10)]
     n_samples: usize,
+
+    /// Particle distribution (0 - uniform, 1 - sphere)
+    #[arg(long, default_value_t = 0)]
+    distribution: u64,
 }
 
 fn main() {
@@ -66,6 +76,7 @@ fn main() {
     let block_size = args.block_size;
     let n_threads = args.n_threads;
     let n_samples = args.n_samples;
+    let distribution = args.distribution;
     let id = args.id;
 
     assert!(n_samples > 0 && n_samples < n_points);
@@ -83,7 +94,16 @@ fn main() {
     let source_to_target = FftFieldTranslation::<f32>::new(Some(block_size));
 
     // Generate some random test data local to each process
-    let points = points_fixture::<f32>(n_points, None, None, Some(world.rank() as u64));
+
+    let points;
+    if distribution == 0 {
+        points = points_fixture::<f32>(n_points, None, None, Some(world.rank() as u64));
+    } else if distribution == 1 {
+        points = points_fixture_sphere::<f32>(n_points, Some(world.rank() as u64))
+    } else {
+        panic!("Unknown distribution")
+    }
+
     let charges = vec![1f32; n_points];
 
     let mut multi_fmm = MultiNodeBuilder::new(true)
@@ -112,9 +132,159 @@ fn main() {
     multi_fmm.evaluate().unwrap();
     let runtime = start.elapsed().as_millis();
 
+    let mut mean_roots_per_rank_source_tree = 0.;
+    if multi_fmm.rank() == 0 {
+        let mut m: HashMap<i32, f64> = HashMap::new();
+        for x in multi_fmm.tree().source_tree().all_roots_ranks.iter() {
+            *m.entry(*x).or_default() += 1.0;
+        }
+
+        let n_ranks = m.len();
+        let mut tmp = 0.0;
+        for (_rank, n_keys) in m.iter() {
+            tmp += n_keys;
+        }
+
+        mean_roots_per_rank_source_tree = tmp / (n_ranks as f64);
+    }
+
+    let mut mean_roots_per_rank_target_tree = 0.;
+    if multi_fmm.rank() == 0 {
+        let mut m: HashMap<i32, f64> = HashMap::new();
+        for x in multi_fmm.tree().target_tree().all_roots_ranks.iter() {
+            *m.entry(*x).or_default() += 1.0;
+        }
+
+        let n_ranks = m.len();
+        let mut tmp = 0.0;
+        for (_rank, n_keys) in m.iter() {
+            tmp += n_keys;
+        }
+
+        mean_roots_per_rank_target_tree = tmp / (n_ranks as f64);
+    }
+
     // Destructure operator times
     let mut operator_times = HashMap::new();
 
+    // Destructure FMM MPI times
+    let mut mpi_times = HashMap::new();
+
+    for (&op_type, op_time) in multi_fmm.mpi_times.iter() {
+        match op_type {
+            MPICollectiveType::AlltoAll => {
+                mpi_times.insert("all_to_all", op_time.time);
+            }
+            MPICollectiveType::AlltoAllV => {
+                mpi_times.insert("all_to_all_v", op_time.time);
+            }
+
+            MPICollectiveType::NeighbourAlltoAll => {
+                mpi_times.insert("neighbour_all_to_all", op_time.time);
+            }
+            MPICollectiveType::NeighbourAlltoAllv => {
+                mpi_times.insert("neighbour_all_to_all_v", op_time.time);
+            }
+
+            MPICollectiveType::NeighbourAlltoAllvRuntime => {
+                mpi_times.insert("neighbour_all_to_all_v_runtime", op_time.time);
+            }
+
+            MPICollectiveType::Gather => {
+                mpi_times.insert("gather", op_time.time);
+            }
+            MPICollectiveType::Scatter => {
+                mpi_times.insert("scatter", op_time.time);
+            }
+
+            MPICollectiveType::GatherV => {
+                mpi_times.insert("gather_v", op_time.time);
+            }
+            MPICollectiveType::ScatterV => {
+                mpi_times.insert("scatter_v", op_time.time);
+            }
+
+            MPICollectiveType::GatherVRuntime => {
+                mpi_times.insert("gather_v_runtime", op_time.time);
+            }
+            MPICollectiveType::ScatterVRuntime => {
+                mpi_times.insert("scatter_v_runtime", op_time.time);
+            }
+
+            MPICollectiveType::AllGather => {
+                mpi_times.insert("all_gather", op_time.time);
+            }
+            MPICollectiveType::AllGatherV => {
+                mpi_times.insert("all_gather_v", op_time.time);
+            }
+
+            MPICollectiveType::DistGraphCreate => {
+                mpi_times.insert("dist_graph_create", op_time.time);
+            }
+
+            MPICollectiveType::Sort => {
+                mpi_times.insert("sort", op_time.time);
+            }
+        }
+    }
+
+    for (&op_type, op_time) in multi_fmm.tree.mpi_times.iter() {
+        match op_type {
+            MPICollectiveType::AlltoAll => {
+                mpi_times.insert("tree_all_to_all", op_time.time);
+            }
+            MPICollectiveType::AlltoAllV => {
+                mpi_times.insert("tree_all_to_all_v", op_time.time);
+            }
+
+            MPICollectiveType::NeighbourAlltoAll => {
+                mpi_times.insert("tree_neighbour_all_to_all", op_time.time);
+            }
+            MPICollectiveType::NeighbourAlltoAllv => {
+                mpi_times.insert("tree_neighbour_all_to_all_v", op_time.time);
+            }
+            MPICollectiveType::NeighbourAlltoAllvRuntime => {
+                mpi_times.insert("tree_neighbour_all_to_all_v_runtime", op_time.time);
+            }
+
+            MPICollectiveType::GatherVRuntime => {
+                mpi_times.insert("tree_gather_v_runtime", op_time.time);
+            }
+            MPICollectiveType::ScatterVRuntime => {
+                mpi_times.insert("tree_scatter_v_runtime", op_time.time);
+            }
+
+            MPICollectiveType::Gather => {
+                mpi_times.insert("tree_gather", op_time.time);
+            }
+            MPICollectiveType::Scatter => {
+                mpi_times.insert("tree_scatter", op_time.time);
+            }
+
+            MPICollectiveType::GatherV => {
+                mpi_times.insert("tree_gather_v", op_time.time);
+            }
+            MPICollectiveType::ScatterV => {
+                mpi_times.insert("tree_scatter_v", op_time.time);
+            }
+
+            MPICollectiveType::AllGather => {
+                mpi_times.insert("tree_all_gather", op_time.time);
+            }
+            MPICollectiveType::AllGatherV => {
+                mpi_times.insert("tree_all_gather_v", op_time.time);
+            }
+
+            MPICollectiveType::DistGraphCreate => {
+                mpi_times.insert("tree_dist_graph_create", op_time.time);
+            }
+
+            MPICollectiveType::Sort => {
+                mpi_times.insert("tree_sort", op_time.time);
+            }
+        }
+    }
+
     for (&op_type, op_time) in multi_fmm.operator_times.iter() {
         match op_type {
             FmmOperatorType::P2M => {
@@ -224,8 +394,10 @@ fn main() {
     println!(
         "{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\
          {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \
-         {:?},{:?},{:?},{:?},{:?},{:?}, {:?}, {:?} \
-         {:?},{:?},{:?},{:?},{:?},{:?},{:?}",
+         {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \
+         {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}, \
+         {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},\
+         {:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?},{:?}",
         id,
         multi_fmm.rank(),
         runtime,
@@ -260,6 +432,42 @@ fn main() {
         args.global_depth,
         args.block_size,
         args.n_threads,
-        args.n_samples
+        args.n_samples,
+        mean_roots_per_rank_source_tree,
+        mean_roots_per_rank_target_tree,
+        mpi_times.get("all_to_all").unwrap_or(&0),
+        mpi_times.get("all_to_all_v").unwrap_or(&0),
+        mpi_times.get("neighbour_all_to_all").unwrap_or(&0),
+        mpi_times.get("neighbour_all_to_all_v").unwrap_or(&0),
+        mpi_times
+            .get("neighbour_all_to_all_v_runtime")
+            .unwrap_or(&0),
+        mpi_times.get("gather").unwrap_or(&0),
+        mpi_times.get("scatter").unwrap_or(&0),
+        mpi_times.get("gather_v").unwrap_or(&0),
+        mpi_times.get("scatter_v").unwrap_or(&0),
+        mpi_times.get("gather_v_runtime").unwrap_or(&0),
+        mpi_times.get("scatter_v_runtime").unwrap_or(&0),
+        mpi_times.get("all_gather").unwrap_or(&0),
+        mpi_times.get("all_gather_v").unwrap_or(&0),
+        mpi_times.get("dist_graph_create").unwrap_or(&0),
+        mpi_times.get("sort").unwrap_or(&0),
+        mpi_times.get("tree_all_to_all").unwrap_or(&0),
+        mpi_times.get("tree_all_to_all_v").unwrap_or(&0),
+        mpi_times.get("tree_neighbour_all_to_all").unwrap_or(&0),
+        mpi_times.get("tree_neighbour_all_to_all_v").unwrap_or(&0),
+        mpi_times
+            .get("tree_neighbour_all_to_all_v_runtime")
+            .unwrap_or(&0),
+        mpi_times.get("tree_gather").unwrap_or(&0),
+        mpi_times.get("tree_scatter").unwrap_or(&0),
+        mpi_times.get("tree_gather_v").unwrap_or(&0),
+        mpi_times.get("tree_scatter_v").unwrap_or(&0),
+        mpi_times.get("tree_gather_v_runtime").unwrap_or(&0),
+        mpi_times.get("tree_scatter_v_runtime").unwrap_or(&0),
+        mpi_times.get("tree_all_gather").unwrap_or(&0),
+        mpi_times.get("tree_all_gather_v").unwrap_or(&0),
+        mpi_times.get("tree_dist_graph_create").unwrap_or(&0),
+        mpi_times.get("tree_sort").unwrap_or(&0),
     );
 }