{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "P9-FPzw4D3-B"
},
"source": [
"# Guided mode expansion of a PhC slab with vertical (kz) symmetry plane\n",
"\n",
"In this example we apply `legume` to calculate the bands of a PhC slab with vertical (kz) mirror planes, i.e., vertical planes that are defined by the wavevector ${\\bf k}$ and the $z$ axis. Such plane exist in symmetric lattices (like the square and hexagonal lattice) only along some symmetry-directions in k-space. This example is related to Sec. 3.1 of the CPC paper.\n",
"\n",
"This feature is new to Legume 1.0 (2024 version, CPC paper)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1708441233553,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "21sd2KrpVpKj",
"outputId": "545f8634-7154-444a-cc06-ef982e996732",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Version of the imported legume : 1.0.0\n"
]
}
],
"source": [
"import legume\n",
"print(f\"Version of the imported legume : {legume.__version__}\")\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import copy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a PhC slab with a square lattice"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u47pd9lU30m5"
},
"source": [
"We adopt the parameters of Fig. 6(b) of the CPC paper\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"executionInfo": {
"elapsed": 254,
"status": "ok",
"timestamp": 1708441055803,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "lMi5ujZg34dh",
"tags": []
},
"outputs": [],
"source": [
"D = 0.5 # slab thickness in units of a\n",
"r = 0.2 # hole radius in units of a\n",
"eps_c = 1 # dielectric constant of circular hole\n",
"eps_b = 3.54**2 # background dielectric constant of slab\n",
"eps_lower, eps_upper = 1.45**2, 1 # dielectric constants of lower and upper claddings\n",
"\n",
"lattice = legume.Lattice('square')\n",
"phc = legume.PhotCryst(lattice, eps_l=eps_lower, eps_u=eps_upper)\n",
"phc.add_layer(d=D, eps_b=eps_b)\n",
"phc.layers[-1].add_shape(legume.Circle(eps=eps_c, r=r, x_cent=0., y_cent=0))\n",
"gme = legume.GuidedModeExp(phc, gmax=4.5, truncate_g='abs')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calulate even and odd mode with respect to the vertical plane of symmetry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we exploit a new feature in legume 1.0, namely symmetry (or parity) separation with respect to reflection $\\sigma_{kz}$ in a vertical mirror plane that contains the 2D wavevector ${\\bf k}$. This mirror symmetry holds only along special symmetry directions of the lattice, e.g, the $\\Gamma-\\text{X}$ and $\\Gamma-\\text{M}$ directions of the square lattice, and the $\\Gamma-\\text{K}$ and $\\Gamma-\\text{M}$ directions of the triangular lattice.\n",
"\n",
"Symmetry separation is controlled by the keyword argument `symmetry` which can have four values:\n",
"* `symmetry = None`: no symmetry separation (in this case it is not a string)\n",
"* `symmetry = 'even'`: only modes with $\\sigma_{kz}=+1$ are calculated\n",
"* `symmetry = 'odd'`: only modes with $\\sigma_{kz}=-1$ are calculated\n",
"* `symmetry = 'both'`: both modes with $\\sigma_{kz}=+1$ and with $\\sigma_{kz}=-1$ are calculated separately"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 55750,
"status": "ok",
"timestamp": 1708441113153,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "siNlJJ7S29LK",
"outputId": "113729b7-e953-45ec-b40f-c9b6a0879b3d",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running gme k-points: │██████████████████████████████│ 131 of 131\r"
]
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Steps in GuidedModeExp: 97 plane waves and 4 guided modes ┃ Time (s) ┃ % vs total T ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ Guided modes computation with gmode_compute=' exact ' │ 7.229 │ │██████████----------│ 53% │\n",
"│ Inverse matrix of Fourier-space permittivity │ 0.002 │ │--------------------│ 0% │\n",
"│ Matrix diagionalization using the ' eigh ' solver │ 0.417 │ │--------------------│ 3% │\n",
"│ Creating GME matrix │ 4.272 │ │██████--------------│ 31% │\n",
"│ Creating change of basis matrix using dense matrices │ 1.632 │ │██------------------│ 12% │\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│ Total time for real part of frequencies for 131 k-points │ 13.612 │ │████████████████████│ 100% │\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n",
" \n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mSteps in GuidedModeExp: 97 plane waves and 4 guided modes\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime (s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m % vs total T\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36mGuided modes computation with gmode_compute='\u001b[0m\u001b[1;36mexact\u001b[0m\u001b[36m'\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m7.229 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████████----------│ 53%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mInverse matrix of Fourier-space permittivity\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.002 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 0%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mMatrix diagionalization using the '\u001b[0m\u001b[1;36meigh\u001b[0m\u001b[36m' solver\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.417 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 3%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating GME matrix\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m4.272 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████--------------│ 31%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating change of basis matrix using \u001b[0m\u001b[1;36mdense\u001b[0m\u001b[36m matrices\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m1.632 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██------------------│ 12%\u001b[0m\u001b[32m \u001b[0m│\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│\u001b[1;36m \u001b[0m\u001b[1;36mTotal time for real part of frequencies for 131 k-points\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m \u001b[0m│\u001b[1;35m \u001b[0m\u001b[1;4;35m13.612\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m│\u001b[1;32m \u001b[0m\u001b[1;32m│████████████████████│ 100%\u001b[0m\u001b[1;32m \u001b[0m│\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Skipping imaginary part computation, use run_im () to run it, or compute_rad () to compute the radiative rates of \n",
"selected eigenmodes\n",
" \n"
],
"text/plain": [
"Skipping imaginary part computation, use \u001b[1;35mrun_im\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to run it, or \u001b[1;35mcompute_rad\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to compute the radiative rates of \n",
"selected eigenmodes\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running gme k-points: │██████████████████████████████│ 131 of 131\r"
]
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Steps in GuidedModeExp: 97 plane waves and 4 guided modes ┃ Time (s) ┃ % vs total T ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ Guided modes computation with gmode_compute=' exact ' │ 7.453 │ │██████████----------│ 53% │\n",
"│ Inverse matrix of Fourier-space permittivity │ 0.001 │ │--------------------│ 0% │\n",
"│ Matrix diagionalization using the ' eigh ' solver │ 0.426 │ │--------------------│ 3% │\n",
"│ Creating GME matrix │ 4.412 │ │██████--------------│ 31% │\n",
"│ Creating change of basis matrix using dense matrices │ 1.668 │ │██------------------│ 12% │\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│ Total time for real part of frequencies for 131 k-points │ 14.021 │ │████████████████████│ 100% │\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n",
" \n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mSteps in GuidedModeExp: 97 plane waves and 4 guided modes\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime (s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m % vs total T\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36mGuided modes computation with gmode_compute='\u001b[0m\u001b[1;36mexact\u001b[0m\u001b[36m'\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m7.453 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████████----------│ 53%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mInverse matrix of Fourier-space permittivity\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.001 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 0%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mMatrix diagionalization using the '\u001b[0m\u001b[1;36meigh\u001b[0m\u001b[36m' solver\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.426 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 3%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating GME matrix\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m4.412 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████--------------│ 31%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating change of basis matrix using \u001b[0m\u001b[1;36mdense\u001b[0m\u001b[36m matrices\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m1.668 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██------------------│ 12%\u001b[0m\u001b[32m \u001b[0m│\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│\u001b[1;36m \u001b[0m\u001b[1;36mTotal time for real part of frequencies for 131 k-points\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m \u001b[0m│\u001b[1;35m \u001b[0m\u001b[1;4;35m14.021\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m│\u001b[1;32m \u001b[0m\u001b[1;32m│████████████████████│ 100%\u001b[0m\u001b[1;32m \u001b[0m│\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Skipping imaginary part computation, use run_im () to run it, or compute_rad () to compute the radiative rates of \n",
"selected eigenmodes\n",
" \n"
],
"text/plain": [
"Skipping imaginary part computation, use \u001b[1;35mrun_im\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to run it, or \u001b[1;35mcompute_rad\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to compute the radiative rates of \n",
"selected eigenmodes\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"path = lattice.bz_path(['X', 'G', 'M'], [50, 80])\n",
"gmode_inds, numeig, verbose, compute_im = [0, 1, 2, 3], 40, True, False\n",
"gmax = 5.5\n",
"\n",
"gme = legume.GuidedModeExp(phc, gmax=gmax, truncate_g='abs')\n",
"gme.run(kpoints=path['kpoints'], angles=path['angles'], gmode_inds=gmode_inds, kz_symmetry='even',\n",
" numeig=numeig, verbose=True, compute_im=compute_im)\n",
"freqs_even = gme.freqs\n",
"\n",
"gme = legume.GuidedModeExp(phc, gmax=gmax, truncate_g='abs')\n",
"gme.run(kpoints=path['kpoints'], angles=path['angles'], gmode_inds=gmode_inds, kz_symmetry='odd',\n",
" numeig=numeig, verbose=True, compute_im=compute_im)\n",
"freqs_odd = gme.freqs\n",
"(X0,X) = legume.viz.calculate_x(path[\"kpoints\"],numeig,k_units=True) # Create an array for bands plotting"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fEwxF10VBeK7"
},
"source": [
"Define light lines for the plot"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"executionInfo": {
"elapsed": 5,
"status": "ok",
"timestamp": 1708441113154,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "MS_nV4qkQUZk",
"tags": []
},
"outputs": [],
"source": [
"eps_clad = [gme.phc.claddings[0].eps_avg, gme.phc.claddings[-1].eps_avg]\n",
"light_lower_clad = np.sqrt(\n",
" np.square(gme.kpoints[0, :]) +\n",
" np.square(gme.kpoints[1, :])) / 2 / np.pi / np.sqrt(eps_clad[0])\n",
"light_upper_clad = np.sqrt(\n",
" np.square(gme.kpoints[0, :]) +\n",
" np.square(gme.kpoints[1, :])) / 2 / np.pi / np.sqrt(eps_clad[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "El_lGtuhBhIW"
},
"source": [
"Plot the photonic bands with even/odd separation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
},
"executionInfo": {
"elapsed": 969,
"status": "ok",
"timestamp": 1708441131735,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "zKhw3zK0FBk4",
"outputId": "4fe850df-d421-4566-dc6a-38ed10da24b4",
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 0.36)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATcAAAGbCAYAAACh/NenAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABrEElEQVR4nO2deVxU5f7HPzPDzLA5FKJoCKigoogguLAImrlkZlbe8lpipmb+5Gea2U0lMyuXfmkuNzBNjUxDMvOqNzUxNUXQ3EDDLXHBkEVEGRFlBOb3xzgjw5xz5pwz58zG8369eClnec4zMHzm+a6PRKvVakEgEAhOhtTWEyAQCAQxIOJGIBCcEiJuBALBKSHiRiAQnBIibgQCwSkh4kYgEJwSIm4EAsEpcbH1BOyV+vp63LhxA82aNYNEIrH1dAiEJodWq8Xdu3fx1FNPQSrlvg4j4kbDjRs34O/vb+tpEAhNnuvXr6NNmzac7yPiRkOzZs0AANdPbYeqmYe4D3twG9BUmL3s3PlKfJ9+DUeP3eb8iAFPt8Ss9zsbvq++chp3T+2hvb5Z90Fwb9eN83MIwMIvzmHv/jLW13f3v42XIkvQuWsLeHaOgcL7KQASQNWe9RjfrT2J9d/mmhzvHeOPBV8MZD0OJDKgeQTA01rR3DiD2xvHMV7j1msMVH2nmB1LrVbD39/f8LfIFQkpv6JGrVbDy8sLlZd+g6qZp7gPe1gFVN9gfXn+2UqsW38F2UfMC2JDwkJVGPbcU3hYfB4+9/9ER98qyuvcQ2Kg6vY0p7EJxuSfrcTylL9wJl/N+p6owAq82uMGwnoG4sm4fwKe7C2H/DOlmDBmK+W52XP7YtiLnSnPmaB8EvDqwPq5VKj3LUF1zlrGa7zHpkPhF848jv5vsLISKpWK8zyIuNFgVXGrfwjcvcL5Nr4ip0f/x9RQ5FQ9noN7+whe4xFMmTc/H7szSznd07djGWb8sw6quDFQtAxifd977+xE9qFCynOjx0YgaWq0+UE8AwF3X9bPpENTlIeqrK+hufQ75XlZyxB4PTeXUeCIuImEVcUNACovAajndasQIhfd/jbc/YLQKTYKoV28eI1DoIbP7yeklRrPdL4JVx9/BPfpg4iESPPPYVi9ASxXcN5hgIsb63maozp3C9S/zKE9r+w6DE8O/5zyHBE3kbC6uFVdB+ruWzRE/tlKFP5djdqHWuzYeYOTSdSQsFAVevXwRkzv5kToBMSSD6GnI4EFayeZvS5l+RFsSMulPc+4gpPKAZ/unOdmjoqMSdBcOkh7nk7giLiJhNXF7X4poKkUdEg+JlFjYqObo198C7jIJQho4y6q2OnFWezn2Jr8s5XIPnoLx45XcPoA6uyvwbB/RMDVuxUCAr0QGkZtPu74zzksmEdtDgLAmvUvUd/r2hxQsTeD2aIpykNF2ijGa1RDP4V7xAijY04hbqmpqfjiiy9QXFyM0NBQLFu2DPHx8ZTXZmVl4YMPPsD58+dRXV2NwMBAvP3223j33XcN16SlpeHNN980uff+/ftwdXVlNSeri5vmDnCffYSNLflnK7H6qxP446ww4zUUu9qHWt6i13CVWVL2wOQP/Z/PueGtf7ihrlp3TNk6CIrmfsK8CDuCT+BBz7NDO2DuZ89QnmNawX30aX8Meb6j6Ylm7QE3H87zYAObIINLmwgo28VCGRQPhV+444tbRkYGEhMTkZqairi4OKxatQpr1qzB2bNnERAQYHL9qVOncP78eXTr1g0eHh7IysrC22+/jaVLl2LixIkAdOI2depUXLhwwejeVq1asZ6X1cWt9j5w77rgw1ZfzoX6+E5cLPXEjTuuqK2X4EiBN04UPinoc/SmrG9LVyPho/r3wKEyVqbZ5yOMI7ou3n5QtmrnlELHd5UdFOyNfs+0Q0xcgMlqjG4FRyuKzSMAmYLzHNiiKcqDeu//ofbvU2avVQQnQOMXh9bxYxxX3Hr37o3IyEisXLnScKxz58548cUXsXDhQlZjvPzyy/Dw8MD3338PQCdu06ZNw507d3jPy+ripq0H1JcEHVKdtw/VF45Qnit0j0NGlhfvIIQ1mPrMJfTrVE55TtEqCJ6hfZxK5CwNDMXGB2DcW1FGIjfvw9+w+5e/TK41CS7IXIHm1slrvL3tA9T8ucPsdXcf1KHjZxd5i5tNk3g1Gg1OnDiBmTNnGh0fNGgQsrOzWY1x6tQpZGdn47PPPjM6XlVVhcDAQNTV1SEiIgKffvopunend5bW1NSgpqbG8L1azc8ZzxuJVOfQrX8oyHCaW0W0wgYAETHB6PW8n5F5yHZFZS2eeuIB7TlNSQEqSgqcSuRCu3hhyaII5F9WoLBIg1827sOJ8+zvzz5UiOxDhQgL90Wv6DaIiQtAr2h/SnFbMO93HNh35bEYKriLB1+eHP45qgN7MUZRhcCm4lZeXo66ujr4+hovp319fVFSUsJ4b5s2bXDz5k3U1tbi448/xoQJEwznQkJCkJaWhrCwMKjVaixfvhxxcXHIy8tDhw7UCYoLFy7EvHnzLH9RliBVCiZuVflZtOfcQ2IMYhDaxcvgLxs29Cnezm6hebl7EW2ScUP0IqcMCMWT0cOtMDPxCQ33Q2h3OYY83xG5B09iXWo2jl1g/6d6Jq8UZ/JKsXbVCYSF0+es6cVw9NgIJH2UKMTUWeMeMQK1FVfN+uEswaZm6Y0bN+Dn54fs7GzExMQYjs+fPx/ff/89zp+n/9i6cuUKqqqqcOTIEcycORNfffUVRo2ijsjU19cjMjISCQkJWLFiBeU1VCs3f39/65mlAPDgFlBzy+Jh9H42Krgk6dpiVderCzCiM331BBMuzf2gihjg2Ks4iRRQBZsczj14EhcP/IYDx2tx6rqw/lIAWLN9JkKjKIIMIsOU7OvQZqmPjw9kMpnJKq2srMxkNdeYdu3aAQDCwsJQWlqKjz/+mFbcpFIpevbsib/+Ml2e61EqlVAqlRxfgcDILH8+k59N0SqIU/UB1apOL3ZcgwNUxMU0R98+LVBS9gASwJBXp7nVCXV3K6Ctr0NdtRo1pVdQe6vI7Hi1t4pQ8dt3jm2qSqnfAxEJkYhIiMTA/atx5vBp/HjMT9Cg0KKZG/HquGfgIndBQHtfhEYKnxJChcIvHN4jV0JTlIeagkOouZLDKuDABpuKm0KhQFRUFDIzM/HSSy8ZjmdmZmL4cPYmhlarNVp1UZ3Pzc1FWFiYRfMVHQvFrfpyLqOfzTO0j0XjNxQ7PQ1NWQnAKlpqLn1E0dwPaCBMzbomQHOrCDXFBayEzqFNVTPvgSefnojo0AJ0CUvH2TN/4uQ1L+Rd98L5Ust8ZpfOXseCGWmG76Of7opBL0ZbTegUfuFQ+IWjWcL/QlOUh7qKa0BlJfDZGN5j2jxaqk8F+frrrxETE4PVq1fjm2++QX5+PgIDAzFr1iwUFRVh/fr1AICUlBQEBAQgJCQEgC7vbdq0aZgyZYohqDBv3jxER0ejQ4cOUKvVWLFiBb7//nscPnwYvXr1YjUvq0dL9fAsw2JasQHOVQyvuVUE9am9qK0wv5pzOFPVzRdQsMsZvL1/NWou6QJvF0s9BV/N6Rk9eQiSkl8RfFxzWJrnZvOWRyNHjsStW7fwySefoLi4GF27dsXOnTsRGBgIACguLkZh4eNi4Pr6esyaNQtXrlyBi4sLgoKCsGjRIrz99tuGa+7cuYOJEyeipKQEXl5e6N69Ow4ePMha2GyKTMm5DMtcZNSei+HZrPoar/IUzf3gM+AN3D6yDTWF+YzjO5ypymH1/uTTE6EJfQZVJ7ehI07jw+cv4GKpp2CrOT0bUneh35BIq5mqQmHzlZu9YrOVG48yrLt/HsS9s9TRUXtdsXHN6Yru5Y1BA3xNhE5zqwhV+VnQlBSwGsfuTVVVsC6owBFNWQHqKktw/9IRaP4+A0DY1dxHyydgyD9iLR6HCw5foWCvPBa3feI3q2wIxzIsJnPUHldsliaqAsCzA30xNznU6JhTmKpSBdCsrcXDaMoKUHVyGzTXTwOAIKu5NTuSrb5yI+ImEoYf7NU/oHKz4o+IQxkWU8qHPa7YUlZdwoZ06n5jXAkLVWFqUgeToAQbU1WP3Zmq8maAe2vBhtOUFUCdk47asseVLxdLPbHyQDtcvcXtA9sWfjdLxY3sfmUOBb8Wx7xh6XNR5+2jFTaPLn3sTth2/HJDMGEDgDP5akyYfAIpq4xL1p6MHg7vZ96AopX5VYampAAVv32H20e2CTYvi6BJA+GLomUQfIZ/CGXwY3Oyo28V/qcfc2PUnm1Ncy03pO7C1tQfBJ2f2JCVGw2GT42bf0NVb97UEZS7VxgrFZhWbAB0f9x2shoRwgw1x+z3QzBs6FMmxx3OVHV/CpCL49/V++S0dXWovnAQ67bVYOsp7q81JKAOPXr6IW5oX0QMHizCTB9DzFKRMPxg79yBSvOXrrDdWty7AdRSZ+g7UsoHWzO0W1cVekaZdhTZs7cUR/5gJ4pUfjg9XExVmwYcmrXT1Rdbgdv7V+OXHX8hZT9/P1rPzhL0HdgVZbc0kLo9gT7DBwjqlyPiJhJGP9j6YsEbSTJCU4aleZTWQIc9BRB2/HIDC75grvqOi2mONxPbMvaC01dFsBE6Oj8cwC2qapNVHE3ZlZhoHkiw8stMbNosXDea3r1aIj7aFyVFNwEAT4V0hWvLtqh9WIuSogpdyo+ft+H/Mf3DaAWRiJtIGP1gXaqBKuH8RWah2Q3LUVI+2KzY6ExJJtiauLHRzTFuDLVocjFVrRpwkLlx2u3KYqQKwCcCALAj/aBRdYK1aSyIrfxawEUhh1p9F2M+nk3ETWiMxM3dBaj403oPp9gNy1FSPtis2BJfC8DkifxXKWyeAQCjRwUg6W3q59idqap4AnBrKe4zGuLqY7Qv6ntjliH7t9PWez4Laus1OF6ykURLRcXF3Wq+EACPnvX4V8NUgeAeEmM3wpay6hKj6MTFNMea1CiLhA3Q1bM+O9D89nMb0guRf5banaCPqrp4m1+V1RTmo/y376BhUbzPGwGaJnCiUf+2cdOGMV7eua2IcxEJIm5skdsuJYSuN5s9pXyYS/WY/X4IFi8MF2zjl7nJoawEbtGS87QCpy/jUgZQByIaoi/jUuft4zxXVlhd3Ix/D6GRQRg9eQjt5a+8OwEr143E6BGt0DnAisE1CyBmKQ0mzsz7N3ltnMyb+2WA5g5j2oe9pHyY87FZaoYywdYPx2SiAtwCDqK4AXiWXfFC5gY0p+6QQ+d/e3ZEDOaueMvwfe6vv+La2b9QfS0XpVeuCVrLqsdSs5SIGw0m4lZXA9zKs94ENHegPrKJsTebd8JI682HhvyzlZgw+QTteT6BA77zWLTkPC4V3LNoLmwDDoL64QQqu2KNmy/QLJD29Lx3vsHuLTkmx2cvHothoxJMjutbFJ3NL8PfxQ9Qq3mIkqKbqLv9N7y11+Ai1aK2XoLyuwoAEvg0q0H5XYVZQbRU3GzeFcRhkCkfdeyg7xsnJNWXjovam00oCv+upj2X+FqAVYQN0PWam/leCKPQLvjiPE7m3qbNhwPYdxypKcxH+b07wqSM2Njf1pheCaGU4rZgRhoKL5ealGEp/MIBv3BEhQFRje7RC5+27iHq1MUAAJmqteH/F2+4Ggki0Dhayu8lAkTcuCFXAXU3RX+M+uiPqD5NX4HQcA8EW/PHMWpz0FortoaEdvHC6FEBjCayfvs8JoEDdAEHTYcejKaqYO2UBC67Ykaiex8zENCe3pe5IXUXAtr7Uq7gqNALHx1RMBVEPWq1Gvh4NqvnUEECClxg2UTQEjRlBYzCpurxnF0FEaj22hwyyNfqwqYn6e1grEmNQmy0N+01uzNLseMX0zzCxiia+8E7YSRUPZ5jvM7iGlUR9wo1Qe4BSGWMl5gLLiyYkYaU+ZuFnpngEHHjghWK6Guu0+caOUraR88e9MJiDfRb5M1+P4T2mgVfnDcpuqfDvX0Eq4iqPmWk+nIu7l89wz51xJpmqZlVm56k5Fcwe/FY2vMbUnch/yS7Hnq2gogbF6RyXc6bSKiP/oh7J6k//e1pxZZ/tpLR9AtoI97PiAvm8uE2pBeyWsEBOjOVbcqI+vhOVP6xg91qTr9frbXgsD/psFEJjCu4wsumq3Z7gogbV0TavLb6/EFac9SeVmwAkHOUfvvBxNcCBMtlEwJz+XALvjiPefPZVSpwaaekx2wCsDX9bRIp564jTCu4Pw6y+7mxRVOUh/tntqM6d4vu39OWtaIiqSA00Bbt1lQClReEfRZDAMGjSx8068rOeWsNmHLabBFEYIu5ki2mriJUcKlR1UOZPmLNsiuFF/BEJ163ck0P4crtbR+g5s8dRscs3beUrNy4ovAEIBFsOHMBBGXrDoI9y1KYqhCsmfbBh2FDn8LoUQG059kGGfRwqW7QQ7mKs6a/zQKro1cC9eu0NLigKcpD+XevmwibEBBx44pEJmhDQcYAQvhzULRsT3vemjAFEMa90Va0CgQhSXo72GyQga2JqoerqapPH6k4mKETOTsMJlBhLj1kR/pBTuNpivJQkfE/qEgbJdgmzI0h4sYHgfxujAGE+HFQ9XrV+gmeFJgLIMT2bm7F2ViGuSDD7sxSzgKnTxnxfuYNePUaBoWv+Q8kQ/rIQfr+fIIicbEoGCZkesjtbR+gIm0UNJd+5z0fNhBx44MA+W6MAYTw5+Ae8siPYc0cKBocKYDABnNBBq4mqh5Fcz+4tQ2Dd99/su84cikH5ds+g6ZM5LQKhQqQWOZOsTQ9REwTlAoSUKCBsQuoVguUnwS0dfzGZgogRA5Hs6iXHh/gsBuWGDhqAIEN5oIM5ort2cClb5xLy2Ao24RC6d8NipYCb6PXrK1ggYuU+ZuxIXUX5bnYZ7phyfppJsfV+5agOmctq/Hl7ePh3nUoKisr0Tp+DKkttSoSia4FkuYO51vNBhD8uxkfsKFZ6sgBBDYMG/oUTubepqyyAHR5cAFt3C16nWzKuPTUll1Cbdkl3Du5DQr/cHhGviCcyAmYwpSU/AoC2vtSdg/J/u008k8WGLUO1xTlsRI2RXBfePaZpCvZAvBQrbZonsQs5QvPN0vVye2053QBhEZvZmsneT7CGQIIbBAyD46Ohj45NqYqAGiu56Fi26eo2L3UcpNVqgRkrpaN0YhhoxIQ+0w3ynPZ+84Y/q8pykPlznmMY8n9usN7bDq8R640CJsQkJUbX3j43TRlBdBcp26bpIof99jP1hiZknGrP6FxpgACG+YmhyIy4klaMWdbbG8Oth1HGqK5noeK63lQBsfiyacn8nywOInn46YNo2xNvm7pdmhqHiIxptDsik3Z9QU8OXyRKPMjKze+uLhxXlHRpX0o/MPphQ2wctcI5wsgsEHoPDgmnoweDu8Bb0LR2AXBQM2lbJRv+wzV5w/i/l+Hua3mRBI3pgjqhtRd2L6O3kqRtQyB99h00YQNIAEFWlhtK1ZZQLkFH+V4DEEE7+FzmH0rNLthiYEzBxDYYC7IwLSzFice7XalKStA1clt0DDkO9LBejXn011U18aaxf/B2qXUQvZS9yKMiTENiHmPTTdrglq6tR9ZuVkCy09Ec2kfZp3GVgoqMJmjzhBAYIO5PLjsI7cwYfIJ1h1FaHn0O1W0DIL3s9PhPXwOPCKHw6Ule19mzaVs3PzpQ9w9sZV+JcfDwuBKTH/qluUAsPWUHy6WGie9u8dMENS3RgcRN0tg4XdTH/0R6kPrKM95RA7XJeqao9FuWGJB11XXmQIIbGCz+QzTzlqsaPSBpWgZhGZRL8Fn+IfwHj6HtcjV3f4b905uow8+yMV3IZhL8P3xmC6IogjuC++x6VD1ny76nAAibpYhUzBGoTinfTA+S/zVG11XXWcLILBhbnIoY6kWAKxbb8GGQQy/T0XLIPgM/xDK4FhOQ+ojrOXbPnu8mhPJ39aYpORXMOMd6hXcicIncdjlLcGjoeYg4mYpDG8e83WjHHKYRBY3pq66zhhAYIO5IEP2kQr+QQap+cqTJ5+eqPPHcvkQBAy5chXbPkXF1pnQFIm7sZG+TjSu9htEBVJ/QC5eccbq3XtJQIEG1s7MBxWA2tT/whRAYEz7oENTCdwXpzkgUxDho9mdMWRQa1Ge6yiY21mLc5CBx25XmrIC1FWW4P6lI9D8fcb8DY1waRMBZbtYKIPiBV09Na48uFjqiQ+2dKW9nkuLJEsDCkTcaGD9g62v1ZViNaD6/EFaP5t7+HPs/GyNEakMy9zWfGtSo5rsyq0h5n5OAIdyLXkzwJ3/B4YlEVYAUAQnwLXTQEhkcsi8A3mLnaYoDxVpo0yOr8/xx9ZT9MnKoycPMdlBiwpLxY0k8VqK1AVw8QBqdZ/qnOpGuSCSWdoUc9r4wGZnrQ3phegX38L8z8zCvEV9hFVTVoCa66dR83c+asvYR281lw5Cc+lxiyJ5+z5w7/o8Z6GrylpJeXxMzHU89cQDpOyndrtsSN2FfkMijUq0xICs3Gjg9KlRdR2oLoamrAAV2z6lvcxsPps57l4RtFKhqee08cFcHlxwkAdmvhfCLHDuTwnaExCwfDWnR2++ylStKVd2+n1IH5ZfRnX2atpx3GMm4Pscf9oC++Au/pj5+RhGgSNmqUhw+sFqKoE7F3D3xFba/my8zdGGVN/QJfQKAJOZlfhaQJNK/eAK04eCHkYTtVk70XLPDKu5oguoLaUXYS7oV3b3//wvNJezGK+VtQyB13NzDYK4I/0gZYG9HiYTlSTx2gPyZuwaT1qKgGVYdOZoU8tp44N+b9TgIA/aa2h31hK5EYIhX+61VF0VQHBfi8d8eDkLldtnmhU2AEbCBjAX2APibhFIxE0AqvO2sms8aSkC+d1SVl3C2u+uUp5rijltfAjt4oWZ7zHnwVF2FLFWnbBCBYVfOLxHroT32HR4xE+GS5vuoj6SrvJg3LRhjPct+mC9KAJnF+KWmpqKdu3awdXVFVFRUTh06BDttVlZWYiLi0Pz5s3h5uaGkJAQLF261OS6LVu2oEuXLlAqlejSpQu2bt0qytzV+5ZA/cscynOsKxDYIoC4mevRRgII7NEHGZgwaVtujVI6iYuudvURCr9wNEv4X/i8sRHeY9Ph9cIiKNr3EfSRiuB+cPEOxP0z203y6sxVMFw6ex0Ths3He2OWCSpyNve5ZWRkIDExEampqYiLi8OqVauwZs0anD17FgEBpm+cU6dO4fz58+jWrRs8PDyQlZWFt99+G0uXLsXEiboi4pycHMTHx+PTTz/FSy+9hK1bt+Kjjz5CVlYWevfuzWpebOx9ulC4HosDCFRUXgJQz+tWJl/RuDfa4q037WMzGkcj/2wl1q2/guwj1AmsQIMAjZuvIG3qGVF6A17mXQv64IC27iEeXNgr6J4GjQMT2rqH+PPkJSxfcw2Xrz1gvLdXTCCe/edAeLd0R3Tf7o4bUOjduzciIyOxcuXjsHLnzp3x4osvYuHChazGePnll+Hh4YHvv/8eADBy5Eio1Wrs2vU4UvPss8/iySefRHp6Oqsx2Yjb/TPbUbl9JuU5QQIIVFRdB+ruc76N5LOJD6u9URcOEbxxpAnN2gFuLTjfpinKQ02Bzmp6WHSalY+NK+aSfBtSW6/B8ZKNjpnnptFocOLECcycaSwQgwYNQnZ2NqsxTp06hezsbHz22WeGYzk5OXj33XeNrhs8eDCWLVtGO05NTQ1qamoM36tZtDh+cPkw5XFeFQhskSl5iRvJZxMfc23Ld2eWAvIszJ0/QNyJ8KwnVfiFU6Z9aOseQiKT497xdNTesKyUq6NvFV7qXsSY5CsUNvW5lZeXo66uDr6+xh0YfH19UVJSwnhvmzZtoFQq0aNHDyQlJWHChAmGcyUlJZzHXLhwIby8vAxf/v7+jM+vzt1CuYuPa3CseMIG8PLZMAUQZr8fQqKjAmJ2Z62dl7DjP+fEm4BMKZhfT+EXDrewF+AeMQIPLh+2WNj0jIm5js9H/ImogNuCjEeHXQQUJI22HNNqtSbHGnPo0CEcP34cX3/9NZYtW2ZibnIdc9asWaisrDR8Xb9OX+rEFERQtrGsFbVZOEbbSI8262Ouo8iCeb/jvXd2Iv+MCLXCFmy8TAfdB7kldPStwofPX8DnI/5EiK9lG8HQYVOz1MfHBzKZzGRFVVZWZrLyaky7du0AAGFhYSgtLcXHH3+MUaN0zv1WrVpxHlOpVEKpNC8c5nbykT3RxuwYFsFxH1OmfDYSQBAPcyZq9qFCZB8qxOixEUiaGi3cgwUOVrDZks+lTXco28VApmrNOTDR0bcKC0ecxbK9Qfj9Inc/IeO8BB2NIwqFAlFRUcjMzMRLLz2uuczMzMTw4cNZj6PVao38ZTExMcjMzDTyu+3Zswexsdz6Y1Ghd7hS4R4zAQr/KOBBucXPoUWfBMqiDIspOkry2cRHv6EMncABwIa0XPTr3w6hYcwf5qxRNBNmHOhWbEzCJvfrjmYD/2Xkp3OPGGEUmGgYLa1TFxuONfy/RCbHR0N10dSrp/+EprwQRwq8cfQqfZI0G2xeOD99+nQkJiaiR48eiImJwerVq1FYWIhJkyYB0JmLRUVFWL9+PQAgJSUFAQEBCAnRLfuzsrKwePFiTJkyxTDm1KlTkZCQgM8//xzDhw/Htm3bsHfvXmRlWRb9YfoUUw39FO4RI4D75eKKG8BqNyxz5igJIFgHcztrAcCizw5i5ocJlguci7tg1Q/mVmxMu1Y1DkywpVcE0As662hExTX8cewa+o3fyHkcPTYXt5EjR+LWrVv45JNPUFxcjK5du2Lnzp0IDAwEABQXF6Ow8PEfaX19PWbNmoUrV67AxcUFQUFBWLRoEd5++23DNbGxsdi0aRM+/PBDzJkzB0FBQcjIyGCd40YFkznqHjNBJ2yAdTqfSpUAmGtMmVqGE3PUugwb+hQK/76PDenXKM9fungLE8ZstdxEFei9Z871YvggFwmFXzjgF47ugWpg/Du8x7F5npu90jjP7e7Br3DvUKrJdR7xk9Es4X+ND946DdQxJypaBIvdsObNz6c0h0g+m42QuSH/igKLPjuISxfp03Jmz+2LYS925vcMr46A8gl+9zagImOSUUukhrjHTLDaHgikcN4KqPctoRQ2AFAGxZseFHv1ZibUT1qG2yEyJULDfDHzQ+Y0oQXzfse8D3/j8QCJIP42TVEerbCphn5qNWETAiJuZqg+vY3RHKX0LYhdXiOV6wILFKSsukTr3+nZw1vMWRGYePSBFBrmi9FjIxgv3f3LX5g4diu3VBG5JyCRWTBBHXQBM0VwX1FNUTEg4maGu79+RnncI34y/aeYXLiIFS0U+W5MQQQACGjjLuaMCEw0WG0nTY3GmvUvIbYPfdH9mbxSTBizFSnLj7AbXwBrgclC8ewzyeLxrQ0RN55QmqN69K3HxYTCNCUlVnZMo92uQsN8seTfz2H2XOZ+axvSctlVNFgobuYCZtbckk8oiLjxgNUv28p+N1JiZcdIFbRuhGEvdsazQzsw3m7WDyeRAS6WtS2n2w+B0UKxc4i4cYS1U1V0v9tjcSMlVnaOmQDQ3M+eMStwjH44eTPATLkiE9W5W2iDCIwWip1DxI0DRvls5pB7QtQfb4MyLNIy3M5hUQ8897Nn+PvhLLASmOqkFcF9HdIc1UPEjQWK4L7wHpvObXkukQq+w5HJ+FI5aRnuCLCsB+bth+MpbuaSdR0xiNAQIm5mePL1dfAeuZLfJ5jIfrf889WkxMoR4NiCiJMfTirXlV3xwGydtAOv2gA7KL9yahRewL2/RRs+5yh1DSspsbIjeO52NfezZwDofG107P7lLxQVVWPqpyrOGxyzqpN2cMjKzQy3N47TlaMU8WjU5+IuSGIlFSnLj2DtWuoUAWKO2hEW7HbFyg+XW4QJw+Zj3jvfsB6XdZ20g0PEjQWaSwdRkTYK6n1LuN0okYhimu74zzlsSMulPEfMUTvDwq64bP1wu7fkYOLwBax2j3LGtA8qiLhxoDpnLapzt3C7SeCUkJTlR7BgHnUzQBIdtUMEavnNxg935vgls1vkMdWOOnLaBxVE3Dii/mUOtxWcgG2f88+U0q7YAGKO2iUC7lPKJh8OALJ/O40Jw+YjZf5mk3NMtaOOHkBoDBE3HlTnrGXvg3NxNSm94UvOYfq6UWKO2ikC/e716P1wYeHmG1tuSN1ltIJzttpRcxBx4wlTGN0EAfxuKcuPYO0q6n1HSXmVncJQdmUJoWG+WJ32Ep592Xzz1UUfrEf+yQKnrB01B2lWSYO+UV7xofXA79SbQ7vHjIeq/3vmB3tQDqgv855L/hldZjoViW90xeQ3W/IemyAi8maAe2txxnbxALxDkX+yAOuW7UD2b6cZL+8V0QwjgnLQ0de4gzNls1U7gTSrFBn3bsPhHjOe8hxr89TCoELhtUrK4+MmRmHyVMs3vSGIhAVpIGZ5ZA2ERgZhyfppWLMjGcFd6Pfa/SP3Lj7Y0hXL9hrnPzpbEKEhRNxYoOr/HjziJ1OeY2WeSuWAzI338/84Qr2HamyfAN5JogQrIGAwwYRGro7QyCDM/HyM2dt+v9gSs37ugoulnk5rjuoh4sYSuk+4e4dS2UVPefrd8s+UUmapDxna4fFuSWL+ERH4w3GPWfZIKBuihkYGYfTkIWbvPl+iwgdbumJempRVXpyjQsSNJQq/cMvMU57iRhch7RndwAQR0/wh8EPMFbW8GW2gIin5FazZkYzYZ7qZHUafMvLu6C+x66dspxM6UlvKAVX/9yBxUVKG02sKDjEv8eUqABIA7OM3KcuP0Oa1BQQ28OORlZv9YQV/Gx16P9yJTWvw1f/txvlS5uuP7P8TR/b/CQCIfaYbxk0bxrlW1R4hKzeO8DZPpTJAzr71OFPCbuKbEcYb+BJxsz+s6G+jQr1vCfwKvsTCEWfRt+NN1kPrV3NcalXtFSJuHLHIPOVQrUBnjo6bGIXJ7zTauFcqB/lV2hliiZtEZnZ/jsY5bdMGFODzEX8iKuA268dwqVW1V8hfBA+Yoqd1FdS7igNg7XdjStil7RBBVm/2hVi/D7nKbEtxqgh+R98qfPj8BSyf2x5hPdglfOtrVR3VJ0d8bjxRBsVT+t5k3oH0N8k9dY5gbT3tJYwdPxqbo0YPVgJ195mmTLAmApddGTDzAWmuT1uviBHoNRGY98432L0lh9UjHdUnRyoUaGCTHU31RjJbtXDnAqChTsplCiCMmxiFt/6nJ/24mkrgPodNfAniIVUAzdqKM7Z3GOBCnTOpKcpDRdooynPuMRNM2hnlnyxA4eVS1D6sxY5NWThz/BLraUQ/3RWDXoxGQHtf0YTO0goFsnKzANdOA0zErTpnLVw7DaCPnCpUlOJmtuMHQ8NCAOKtFAjcEcsklcpphQ2gd4nQlViFRgYZhGnYqATeq7mwHsHoFd8FMf3D7GpFR8TNAujeTIxpITRBBcaOH0zmqB7ic7MfxEoDMWOS0rlE2JZYzV3xFv4xtj+Wz8vgtIo7c/wSzhy/hLVLtyP2mW7oNyQSLnIXUVd1bCDiZgF0b6Z7h1Khra2hNk9d3AGJC6CtNRxiMkdnz+2LYS92Nj8ZfdJo/UM2UyeIiViVCXLmGuUHF/aaHONaYhUaGYTV22ZzWsU1JPu300ZF/HrztfZhrdUFj4ibBejTQqgcuLTmqb71eE0FAPP5bKyETY9MScTNHhBrFc2wcqNraeTa6Rlej9Kv4vj65PQ0NF/1RD/dFV0i2kMCwNfPGy5yF9Q+rEVJUYXRMXUltW+aLUTcLISpaqGu4hpA9anZQNyY8tkYAwhUSJUAqsxeRhARscquZK6MK0K6Bg6070EWWOKTY4JK8KiorddY9BwibgJAlxairaNZRT36BGYyR80GEKggfjfbYwN/G1P6B2NqEkcar+YO7Dppto+cLSHiJgB05qn6lzmorbhq6nuTuSL/z9v88tmYIOJme0QzSan9bdbusNt4NZd/sgDZ+87g2KGzvMxWMSHiJhCq/u/Bxbst1L/MMTpO53vLySmmHIeXOapHKjebJEwQGdEqE0xbHAH05qi1Ouzqxe6tGS8a8ub2/OcIK7NTbIi4CYhERu1raez3SJm/GRtSqbdX42WONkRKKhVsihji5uIBSE3/VJnMUVt02NUL3ZB/xBolCLvIXWwieETcBITOv9HQ95Z/sgAbUndRXsfbHDWaBBE3myJGMjWFv83eN3xpaL4CMBG808cLkHvkAv6+WibaHEjhvIDQdQxpuNfpumU7KO+l7PbBB+J3sx0i7XZF5W9jqkaw113j9au6wsul+O+mQ6IKG0BWboLD5Hu7/LArbXTJYnNUD+nKaztE+WCR6houNOLB5cOUV9v7hi9MlovQkJWbCND53nJ+y6U8HhsfYLk5qke0vv0Es4ghbvpOMg3QFOWh5k9TC8C16ws2N0fNQWe5NCZuQDhmfPa6Rc+yC3FLTU1Fu3bt4OrqiqioKBw6RL+j1M8//4yBAweiRYsWUKlUiImJwa+//mp0TVpaGiQSicnXgwcPxH4pAKh9b+tz/LF+4wXK68e9FSXcw8luWLZDjFUzhb+NziRVtrfvbR53pB9kzIvr1jMY4999AWt2JGPxd1Mx5JU4i55nc7M0IyMD06ZNQ2pqKuLi4rBq1SoMGTIEZ8+eRUCAqal28OBBDBw4EAsWLMATTzyBb7/9FsOGDcPRo0fRvXt3w3UqlQoXLhiLiaurq+ivBzDNe7tY6omtp/wor00c30u4VZseUoZlG8RYuVH42+iSw4VM2BUapuqG4C7+mPn5GMFrTm3ez613796IjIzEypUrDcc6d+6MF198EQsXUu/03pjQ0FCMHDkSH330EQDdym3atGm4c+cO73lZ2ksKAKpzt0D9yxxs+qMNMo63MTk/7t0XdDlt94p4z5OSB7eAmlvCjklgRiIDVAIXhEtkgE+kUedduvQPqn5t9oK5sq01O5Iphc2hd5zXaDQ4ceIEBg0aZHR80KBByM7OZjVGfX097t69C29vb6PjVVVVCAwMRJs2bfD888/j1KlTjOPU1NRArVYbfVmKRCbH+hx/SmEDgNj+YZz2VWANiZhaH7FSQBoIG136h2rop3YrbDvSDzIKW2LSc6J1CbGpuJWXl6Ourg6+vsZmma+vL0pKSliNsWTJEty7dw+vvvqq4VhISAjS0tKwfft2pKenw9XVFXFxcfjrL9PNjfUsXLgQXl5ehi9/f3/aa9my+1AVvTmq/6XKPYRPHyDiZn2sYJLS+droAli2JmX+ZiyYkUZ7fvbisZg8+x+iPd8uAgqSRhteaLVak2NUpKen4+OPP0ZGRgZatmxpOB4dHY3Ro0cjPDwc8fHx+PHHH9GxY0f8+9//ph1r1qxZqKysNHxdv36d/wuC7hf7f/NN+2sBwMge1zE6+tEbVSKlLa3hjb4Mi2A9RImUGq/q6Xxq9uhrM5fyMWREDIaNShB1DjYNKPj4+EAmk5ms0srKykxWc43JyMjA+PHjsXnzZgwYMIDxWqlUip49ezKu3JRKJZRKYd6g5n6xkYGVxjWnCi/afRV4Q8qwrIvQ4iZVAC7GATAhmlFai5x9Z2jPPTsiFh+tmCD6HGz68a5QKBAVFYXMzEyj45mZmYiNpQ9rp6enY+zYsfjhhx8wdOhQs8/RarXIzc1F69atLZ4zG5h+sS93L0JHX13PNYOZwXLLP04Q09S6CO1za/SeELoZpZikzN+MtUu3U56bvXgs5lpB2AA7SAWZPn06EhMT0aNHD8TExGD16tUoLCzEpEmTAOjMxaKiIqxfvx6ATtjGjBmD5cuXIzo62rDqc3Nzg5eXzkcxb948REdHo0OHDlCr1VixYgVyc3ORkpIi+uvRFcVTr9qSni7AgM6Pd/82hPRlbiatxy2GiJv1EKPsqpG4idGMUgwYa6eTnhPdFG2IzcVt5MiRuHXrFj755BMUFxeja9eu2LlzJwIDdX6E4uJiFBY+7la7atUq1NbWIikpCUlJSYbjb7zxBtLS0gAAd+7cwcSJE1FSUgIvLy90794dBw8eRK9evUR9LTvSD9L+Yl99vjkGBBwxOmbU761Bd15BIGVY1kNkf5u1mlEKAW3t9Lsv4K0ZL1p1LjbPc7NXuObYMK3Y9L9Yfd5bY7zHpkPh/RRw94rF8zagrQfU9tU80Glx9QGU3uavY4vMDWgeBoD7XqS2ZEf6QdroKF0uGxNk31I7gGnFBjzKZ4OZfm+tQoSdFNkNy3oIvUpuYJJy3YvUVjB9uMc+080mW/wRcbMQpl8qYJykyBjKlykf9WKrEW5ypAzLOghtljYQN7pSK3vq/mHuw33ctGFWnM1jSDKUBZhL+WicpEjV703ZsJOD0NUKxO8mPhIZZZdcCwY0vA/U+5ZQujHsKf3DXKKumBUI5iArNwtgat9CFxlS9X8PdXfLDC1rav7cDnWzFo+CCl7Ag5sm9/CGREzFR+gUEBcPQCpjLLVyjxgh7DN5wubD3ZrR0caQlRtPmNq3MJWVUPXiqs5ZC01RHqAQuFKBiJv4iGSSOkKpVeHlUtpz1k77oIKs3HhgznnK9Eule9Ma8pVc3IHaakHmSXbDsgIiiZsjlFr9cTCf8ritV2x6yMqNI5Y6T81uIiN0tQLxu4mLkOImedxS3N5Lrei6fVijZpQtZOXGkvyTBVi3bAdjJ1E2zlOzGzjHjQfAriMKK8huWOIipM9N3gyQSO2+1IrJcumZEGrl2dBDxM0MuzYfxh+/X2AUNYDbUpxxA+eO/aFQSAAIlFtN/G7iIXTZlRl/mz2UWpkLIgS0F7irtAUQcTPD4g83wsXMpzMf5yltQu/t60CbDsDDu5zGo4WYpeIh9AeH3P79bUxNIWyZ9kEFETcL4es8ZXwDK1TCiRvZDUs8BPW3ueiCSY9QBPeF5tLvhu/twd/GZI7aSxChIUTcLMCScDeV700R3PfRf1TC7atAyrDEQ8hV8aOW4o2L5BXBCfDs8z82FzZ76vbBFhIt5UHcgHCs2ZFscYtkVf/3dEXzj0RNc+l3VKSNgvrgal3mu1AQv5s4CPlzVagoAwmaSweFe4YFMHX7ELNVuCWQlRsH4gaE482pzwvuV2hofgBA9ZG1cPXrBIV3S5o7OCJVAqgSZiyCDqHLrhQq1FUcoTxl60BC/skC2oCavimEPULEzQwzPnsdKi8vBLT3FcVZShsZq6oAhBI3snITHiFTQKRKQOZqt/uR0gURbNXtgy1E3Mww5JU43vuWsoE2sNBSwBZIRNyER2CTlGk/Ulv62xj7FNqo2wdbiM/NxlB1ClEE99X98UgFqiMku2EJj4Diprl13S73I2WqxrG3tA8qyDveDqANLBz7WbiHkHw3YRFQ3OrU5ZTHbVkkz9TKyJ6DCA0h4mZHmAQWTm6BpqxAmMGJaSosQvncXNwg86FeAdnK12auCsGegwgNIeJmJ9AGFioFqjMl4iYcQpZdyVV2VyRvrpWRvZujekhAwU6gDSw8GSDMA4hZKhyC+tv+trsieXtvZcQWsnKzE2gDC3IPYR5AyrCEQzBxk6BOTd15mW4lLzb5JwvsvpURW4i42RGUgYUfp0N99EfLB5dIhW+J3VQRahUs94CseTvKU7byt9HltNlTKyO2EHGzQ0wCC6d3ChNYIH43YRDq56jw0v2jryl+hK38bSnzN2Pt0u2U5+yplRFbiM/NzmAMLLS00JFLVm6WI2DZlTp7Par/+N7wvS2L5M0VxjtKEKEhZOVmZ9AGFrxaCTA4WblZjEAfEJqbV4yEDbBtkbwjFsabg4ibnUEVWHDvlQiFpas2gIibEAj0M6yrukN93AaBBEctjDcHMUvtEFX/9+DaaQDqKq5BW/cQEqkMmrLLULRsb9nAZDcsyxFI3GQ+HamP2yCQ4KiF8eYg4manKPzCob6w1ygHyr3bc1D1ftWygaVkwxiLEGr1K/ewi267jlwYbw4ibnYKVePC6tM74douyjITleyGZRkC+NzUf/yE6rz/Gr63VSDBGYMIDSE+NztFtHIs4nfjjwBlV5qyAiNhA2wXSKAzRx05iNAQIm52imhRU1KGxR8BPhjoPpysHUhgymlz5CBCQ4i42SmU5ViBPSwfmJRh8UcAcaP7cLJmIMHZzVE9RNzsGJNyrGvHUbHtU8vKsUgZFn8EWPUqWnWBsquxo97agQRnzGmjgoibAyB4ORbxu/FDgJ+b+tgW1Pz5WFyUXYdZtduus+a0UUHEzc4RJbBAVm7cEaDsSlNWgOqTPxkdq/lzBzRFeRaNywVnzWmjgoibnSNKYIGs3LgjwAeCrYMJTEEER89po4KIm51DWY4V/pzluW4EbggRTPBuS3Nc/GBCUwkiNIQk8ToA+nKsmoJDQH0dlD4W7mdKyrC4I8QHgg2rEphy2t6a8aLoz7cFRNwchAcNSrHuQYBSLFKGxQ0LxU199EdUn95p+N6aVQlMJVbOFkRoiF2YpampqWjXrh1cXV0RFRWFQ4cO0V77888/Y+DAgWjRogVUKhViYmLw66+/mly3ZcsWdOnSBUqlEl26dMHWrVvFfAmiQleKRSKmVsQCn5umrMBI2ADrVSU0RXNUDy9xy8rKQl6eMBGejIwMTJs2DcnJyTh16hTi4+MxZMgQFBYWUl5/8OBBDBw4EDt37sSJEyfw9NNPY9iwYTh16pThmpycHIwcORKJiYnIy8tDYmIiXn31VRw9elSQOVsbUSKmRNzYY2HZlS0DCXQ7WTlbThsVvH5jU6ZMwYkTJ0yOX7x4EZWVlZzG+vLLLzF+/HhMmDABnTt3xrJly+Dv74+VK1dSXr9s2TL861//Qs+ePdGhQwcsWLAAHTp0wI4dO4yuGThwIGbNmoWQkBDMmjULzzzzDJYtW8ZpbvaCKBFTUobFHgs/CGxZlUC3k5Uzm6N6eInbhQsX0LdvX5Pj+/fvx6hRo1iPo9FocOLECQwaNMjo+KBBg5Cdnc1qjPr6ety9exfe3t6GYzk5OSZjDh48mHHMmpoaqNVqoy8AOJd7me3LEQ3KiGn3Fy2MmJJcN9YIsMpVtI81+t4agYQd6Qdpd7JyZnNUD6+AgkqlQkVFBYKCjH9A8fHxSE5OZj1OeXk56urq4OtrvPmEr68vSkrYmVxLlizBvXv38Oqrj53rJSUlnMdcuHAh5s2bZ3L8f0cuRsLAHhg3bZhN3xAmDSwf3oWmrIC/wEmkuqhp/UNhJ+qMWLDKtVUggSmI4Ig7WfGB18rthRdewOLFi00Hk0qh0Wg4jyeRSIy+12q1JseoSE9Px8cff4yMjAy0bGmcHsF1zFmzZqGystLwdf36dcO57N9OY8Kw+XhvzDLknxRgFyqeKPzC8fDmX1D/MgeVe/7P8jpT4ndjB8+fk60CCUxBBMAxd7LiAy9xW7BgAQ4dOoQBAwbg9GldndqDBw/w+eefo1u3bqzH8fHxgUwmM1lRlZWVmay8GpORkYHx48fjxx9/xIABA4zOtWrVivOYSqUSKpXK6KsxthY5waOmxO9mHgvKrmwVSKDLaQOcP0LaEF7i5uPjg5ycHCgUCkRERMDNzQ3NmjXDjh078MUXX7AeR6FQICoqCpmZmUbHMzMzERsbS3OXbsU2duxY/PDDDxg6dKjJ+ZiYGJMx9+zZwzgmF/QilzJ/syDjsUXwqClZuZnHghQQWwQSmEqsZi8e6/QR0obw+kj66quv8Pbbb2Pnzp0oLCxEbm4u5HI5evfubeTYZ8P06dORmJiIHj16ICYmBqtXr0ZhYSEmTZoEQGcuFhUVYf369QB0wjZmzBgsX74c0dHRhhWam5sbvLx0m9xOnToVCQkJ+PzzzzF8+HBs27YNe/fuRVZWFp+XS8uG1F0IaO+LYaMSBB2XDsGjpkTczGPBz0jRMgju4c8bdd4VM5BgLqfNWu9Te4GXuE2dOhV+fn7o06cPAgICEBAQYDiXlZWFPn36sB5r5MiRuHXrFj755BMUFxeja9eu2LlzJwIDdX/IxcXFRjlvq1atQm1tLZKSkpCUlGQ4/sYbbyAtLQ0AEBsbi02bNuHDDz/EnDlzEBQUhIyMDPTu3ZvPy2VkwYw0FF4uRVLyK4KP3Rh91NRo0xhL6kxJGZZ5LPwAcO3QDxKV7u9DGRQvaiCBKafNWUusmJBotVot15ukUilcXFxQV1eHFi1aoFu3bujWrRsCAwPx8ccf49atW2LM1aqo1Wp4eXnhyO+n8OOaA7Q9sPTMXjzWap+MmqI8XdS09iEk1X9D5tWSv8BVXSdlWEx4BgAyV163No6UuseMh6r/e0LNzIR573xDmfqxZkeyQ/rZ9H+DlZWVlD5wc/CuLb18+TLKy8uRl5eH3NxcnDhxAps3b0ZcXBzfIe2SzhHtsWR9BPJPFmDdsh20ImftFZxg2/6R3bCY4elzo4qUVueshWunAaKs3vJPFjTpnDYqeIubPpgQERGBN954Q8g52SWhkUFYsn4adqQfxIIZaZTXbEjdhX5DIkV/Mwm67R/xu9FjQdkVY6RUBHGji5A2lZw2KngXzJ07dw4PHza9BNBhoxIwevIQ2vN0/emFRNCoKUkHoccC4bdmpJQpQtpUctqo4C1u/fv3h6enJ8LCwvDaa69h0aJF2LlzJ/7++28h52eXJCW/gtmLx1Key/7tNHaki5uoKWjUlJRh0WPhqlbRLsboezEipU2564c5eIlbp06dkJ+fj927d2PixInw8PDA1q1b8eqrrxqinM7OsFEJiH2GOmF5wYw0UXPgBO3OS3bDoofnqlZ99EdUbPsUmis6H5giOAHeY9NF2QjG2TdWtgRePrdz584BAEJCQvD0008bjmu1WhQU2K48ydqMmzaMNsAgtv+tYa2pzPMJKDz4RfQA6FZv9dzL5pweHis32pKrPv8j1KwMNNUmlGwRtFmlRCJBcHCwkEPaNaGRQYz+t2yGMhghUPiFwy3sBcDFA/f/OkzKsISEZ9mVtUquiDlqHs7idv/+fWRlZeHs2bMm5x48eGCoJGgqMPnf1i3dLnqJlnrfElSsT0TlgW/4F9KTiKkpPAXfWoGEptyEki2cxO3ixYvo3LkzEhISEBYWhn79+qG4uNhwvrKyEm+++abgk7R3mCKoG1J3iVZkL1ghPRE3U3gGWhQtg+De7TmjY2IEEppyE0q2cBK3Dz74AGFhYSgrK8OFCxegUqkQFxdH2xK8KZGU/ArGv/sC5TmxzFPBUkL0ZViEx1gg+K4d4uERPxke8ZNFCSQ09SaUbOHkVMjOzsbevXvh4+MDHx8fbN++HUlJSYiPj8f+/fvh4eEh1jwdgpj+YZT5RuuWboem5qHg1QuCpoSQ3bCM4SlujUuutLU1gq7aSBNK9nD6uL5//z5cXIz1MCUlBS+88AL69u2LixcvCjo5R4MpwLAhdZfg+W+CpoQQ09QYHukxdCVXmiJhNlMiTSi5wWnlFhISgmPHjqFz585Gx//9739Dq9XihReozbKmRFLyK1Aq5ZQrODHqT43aj1eXQlJbxa/9OBG3x/AsuxK75IouiACQCCkVnH6DL7/8MjZt2kR57quvvsKoUaPAo8mI0xHD4NQVI8BgaD++dyn/qClJB3kMT6EXO1JKF0Roak0o2cJJ3NRqNebOnUt7PjU1FfX1pDeYtfPfBImakjKsx1jSoLJttNH3QkVKmbp+NLUmlGzhZJYWFxdj2LBhkMlkGDZsGIYPH44BAwZAqSSf+o1JSn4FAe19KTuICB1gYIyasjVP9WVYpFKB1ypW7F2uSNcP7nBauX377bcoLS3Fjz/+iCeeeALvvfcefHx88PLLLyMtLQ3l5eVizdMhsVb+m2BRU+J308Hx5yD2Llek6wc/OHtNJRIJ4uPj8X//9384f/48/vjjD0RHR+Obb76Bn58fEhISsHjxYhQVFYkxX4fDGvlvgkVNSQE9r7IrMUuuSJkVf3g3q9TTuXNndO7cGf/6179w8+ZNbN++Hdu36z5lZsyYYfEEnQFr5L8Zoqa3rkImq4GiRVvug5CVGy+TVMxAAlPXj6a4LwIXBE1Lb9GiBcaPH49t27YRYWuAufw3ocxThV843LoN519IT8SN189ArJIrJnOUlFmZh9fKra6uDmvWrMH58+fRpk0bQ7vx5s2bCz0/p4Ep/63wcqlg5oV63xL+eyuQ3bB4RY01ZQWQ+7SFauinkMjkkHkHWixsxBy1HF7iNmXKFPz0008YOHAgUlJSIJVK8fDhQ/j5+SEiIsJglhKMoTNPhXIKC7K3QlMvw+K4cqPa4cotzPJkdrJNn+XwMkt//vlnfP/999i4cSOUSiWOHz+OFStW4MGDB02mEy8f6MzTA7tOCjK+IIX0Td005eBzE7PcinT9sBxe4lZVVYUuXboAAORyOWQyGZKSkjBr1iy4ulrQEbYJ0G9IpMkxofxugqSENGVxkyoAiYT15WJFSck2fcLAS9zat2+PGzduAAD8/PwMaR/Dhg3Dhg0bhJudE0JnbgiRFiJISkhTLsPiKOxiRUlJwq4w8BK3V155Bbt37wYA9OvXD+vWrQMAnD17FvfvN2F/DQvo/GtCde1V9X8P3mPT4fX8Z1DFj4P8ST9ShsUWjuImRpSUJOwKB6+Awpw5cwz/f//999GrVy+0aNECarUa48ePZ7iToPe7UUXChNpU5vGO9OsMx1hHTZtyGRaPVatruyhIPFsDMiWUQfEWCRuJkAqLxUm8AQEByM/Px86dO+Ht7Y2hQ4cKMS+nhiktJHvfGYvfxBZHTWXKpiluFkZKLW1MSRJ2hYWXuC1cuBC5ubkoLS2Fh4cHunTpguHDh6NPnz5Cz89pEbNqweJC+qZYhsWx7IouUuraaQAvgSPb9AkPL5/b6tWrcffuXbRp0wYuLi746aef0LdvXwwaNAh37twReIrOiZhVCxZHTZtixJSjSSpkpJSYo+LAa+V25coVk2N//PEHJk2ahKSkJGzcuNHiiTUFxDJP9VFTo0oFLlHTpihuNoyUkoRdcbDY56anV69eWLduHeLj44UaskkglnlqKKQvPgWZq5JjOkgTLMPiGCVWtAyCe/hQVOf9YjjGN1JKEnbFwWJx+/bbb+Hp6QmFQoH//Oc/aNGihRDzajKIGT1V+IVD87AadUVHoAG457s1pTIsHj3c5C06WFxPShJ2xcNicTt69Cg2b96MO3fu4LnnniN1pTwQyzy1qIhe1sTEjYPPTch6UpKwKx4Wtzz6+uuvUV5ejv/+97+4fPkyjh07JsS8mhx0m8rwTe61eF+FpuR341B2JWQ9KUnYFRde4paQkIAjR44YvpdIJBgyZAg2btyI5ORkwSbXlBA6empxEX1TKsPiIORCRUlJhFR8eJml3bp1Q58+fdCrVy+MGDECYWFh8PT0RHp6Oim/sgAhe75Zng7ShHLdOIibUFFSkrArPrxWbl999RVOnz6NTp064ZNPPsGzzz6LPn36IDU1FTNnzhR6jk0KOvO09mEtp3EsLqLXl2E1BTisUoWoJyUddq2DRGvhLsp1dXUoKCjAnTt3EBgYCF9f5/AVqNVqeHl5obKyEiqVyqrPpstWHz15COfUEE1RHuoqrkGmkEHxJMdIdnUx8PAut3sckWbtWVcnaMoKUFdZAq3bU5DI3ThHSfNPFmDCsPmU5xKTniObKzfA0r9Bi6OlMpkMHTt2tHQYQgPo9jzlkxqi8AsH/MKhufw77v91GDKvVty68sLJxY1D2ZUQUVKSsGs9BN0ghi+pqalo164dXF1dERUVhUOHDtFeW1xcjNdeew2dOnWCVCrFtGnTTK5JS0uDRCIx+Xrw4IGIr0JYXOTUf3B8+r6p9y1BRfr/oPLAN6jY9inUR39kd2NT8Lux9LcJFSUlCbvWw+bilpGRgWnTpiE5ORmnTp1CfHw8hgwZgsLCQsrra2pq0KJFCyQnJyM8nN4cUKlUKC4uNvpypC7BQvV9syglpCmkg7D0twkRJSUJu9bF5uL25ZdfYvz48ZgwYQI6d+6MZcuWwd/fHytXrqS8vm3btli+fDnGjBkDLy8v2nElEglatWpl9OVICJUaYlFKiL4My5lhKeBCREnpTFKSsCsONn3najQanDhxAoMGDTI6PmjQIGRnZ1s0dlVVFQIDA9GmTRs8//zzOHXqFOP1NTU1UKvVRl+2hmm3ero/lMZYnBLi7PluLE1vIaKkdBFvkrArDjYVt/LyctTV1ZlEWH19fVFSwmHHpkaEhIQgLS0N27dvR3p6OlxdXREXF4e//vqL9p6FCxfCy8vL8OXv78/7+UJClxrC9g/C4pQQZzdNOYi3qver8B65HF4vLIL32HSo+k9nfW/K/M0mASKAJOyKiWBdQSxB0qj0RavVmhzjQnR0NKKjow3fx8XFITIyEv/+97+xYsUKyntmzZqF6dMfv1nVarVdCBxdYf2BXSdZ/1EYOoRUXIPMpQ6K5n7sJ+DM4sZxtytNWQHqHkohaxnCOf2DKrVn9uKxGDYqgfU4BG7YdOXm4+MDmUxmskorKysTNF9OKpWiZ8+ejCs3pVIJlUpl9GUvCLEdoMIvXJe2IPfA/b8OkxpTgNNrUx/9ERXbPkXlznmoSBsF9b4lrO+lq0agi4gThMGm4qZQKBAVFYXMzEyj45mZmYiNjRXsOVqtFrm5uWjdurVgY1oTobYDVO9bgoqMadxSQpy5SsEKaSCkON522PyjY/r06UhMTESPHj0QExOD1atXo7CwEJMmTQKgMxeLioqwfv16wz25ubkAdEGDmzdvIjc3FwqFwrBR9Lx58xAdHY0OHTpArVZjxYoVyM3NRUpKitVfnxAwpYWwbWjJe9MYZ94NS4g0EAbzlBTH2xabi9vIkSNx69YtfPLJJyguLkbXrl2xc+dOBAbqonzFxcUmOW/du3c3/P/EiRP44YcfEBgYiKtXrwIA7ty5g4kTJ6KkpAReXl7o3r07Dh48iF69elntdQmJEA0tLdo0xll3wxI5DYRUI9gWm4sbAEyePBmTJ0+mPJeWlmZyzFw57NKlS7F06VIhpmY3WNoxxKKUEGcsw+JQdqVrKf48qvP+azjGJg2EVCPYFrsQNwI76PZbYNMxxKJNY5yxDItDCgifluKkGsH2EHFzIOjM0wUz0lB4udSs782QEnLrKmSyB1C0aMfuwc4YMWX5mvgWy5P24bbHyWtrnI+k5Fcwe/FYk+NsU0MUfuFw6zYcCr/uZq814IxlWCxWo3yjpCRCah842Tu2aSBIxxAFxzw+ZyvDYrFy41MsTyKk9gMRNwdEkI4hXMXN2UxTFmLNJ0rKFCEljSitCxE3B0SQjiEu7jpzky3OJG4sy674FMuTCKn9QAIKDoogm8nIVUDNLXYPdCZx4/BaVL1fhWvEKNTdKTIbJSURUvuCrNwcGIs3k+FimjpTGRYXoZa5QeEfBbewF8ymf5B+bfYFETcHhs48XTAjjZ3vjYu4OdNuWFyCIxx+RqRfm31BxM3BsSg1RKbkuIpxEtOUy+tQ0Hd7bgjp12Z/EHFzAixKDWH5xwvAOdJBOJRdARJA3szsVUz92kiE1HYQcXMCLEoNkXMwTZ2hDIvLqk3uAUhlZi8j/drsEyJuToBFqSFc/G7OYJZyWX2yEH5SjWC/EHFzEnhvJiN10eW8scEZyrAE9LeRagT7xsHfqYSG8E4NaUp+N7amtUSqM0sZINUI9g0RNyeCd2pIUzJN2YqzvJnZVSqpRrBviLg5GbxSQ+TNALDcBcqRxY3LbldmBJ9UI9g/RNycELooHa3vTSIF5J7sBndkcRPQ30aqEewfIm5OCF2UjjF6x9bv5shVCmzFTeICyNwYLyHVCPYPETcnhM73dmDXSfqb2PrdHLkMi62/TaFiNF9JNYJjQMTNSeG8kbOLhy57nw2OapqynTfDKpZUIzgORNycFDqfEL3fjV2pEQDHTAfhUnbFsIol1QiOAxE3J4XO98OY88bW7+aIZVhsBZmhmQCpRnAsiLg5Kbxy3tj63RzRLGU7Z5qSK1KN4HgQcXNiOOe8ubixaz3uiGVYrP1t1OJGqhEcDwd7hxK4wjnnje3qzdH8bmxNaZrXT2d2kmoE+4WIm5PD2fcmZ+t3czBxYyPGDJvmUKXREHPUviHi5uRw9r05o9+NbdkVzWun87f1fZbDxtYEq0PErQnAyfcmUwAyV/ODOpK4WRhMoEv/YGwlRbA5RNyaCJx8b2xWb45UpcBK3CSAwjTPj6R/OC5E3JoInHxvbPLdHKkMi42/Te5pUqFB0j8cGyJuTQROvje2lQqOYpqymSfFapWkfzg2RNyaEKx9b1IXXa2pORwhHYRt2RWFuNFFlEn6h2NAxK2Jwdr3xsbv5ghlWGxWbRIZ4GLcz450/nB8SLVvE4O1703hBVQXMw/mCGYpK39bM6NUEabOH8NGJQg5O4KIkJVbE4O1703uCbNvD0cow+Lhb6PztZHOH46Fnb8zCWLAyvfGtvW4vfvdeIgbr07GBLuDiFsThZXvjZXfzc7FzVy6ilRusm8rKbVyDoi4NVFY+d4cXdzYlF01qkogpVbOAxG3Jgor3xub1uP2LG48TFJSauU82IW4paamol27dnB1dUVUVBQOHTpEe21xcTFee+01dOrUCVKpFNOmTaO8bsuWLejSpQuUSiW6dOmCrVu3ijR7x8Ws700iMb96s+cqBY7iRkqtnAubi1tGRgamTZuG5ORknDp1CvHx8RgyZAgKCwspr6+pqUGLFi2QnJyM8PBwymtycnIwcuRIJCYmIi8vD4mJiXj11Vdx9OhRMV+KQ2LW90ZTTG7AnsuwzAU7GrQUJ6VWzofNxe3LL7/E+PHjMWHCBHTu3BnLli2Dv78/Vq5cSXl927ZtsXz5cowZMwZeXtQ1kMuWLcPAgQMxa9YshISEYNasWXjmmWewbNkyEV+JY2LW98amztReTVNz82rw2kiplfNhU3HTaDQ4ceIEBg0aZHR80KBByM7O5j1uTk6OyZiDBw9mHLOmpgZqtdroqylg1vfm4soi4miH4sam7KrBqpSUWjkfNhW38vJy1NXVwdfXePXg6+uLkpIS3uOWlJRwHnPhwoXw8vIyfPn7+/N+vqNh1vdmzu9mj2VYHPxtpNTKObG5WQoAkkbheq1Wa3JM7DFnzZqFyspKw9f169cter6jweh7MytudrhyM7eadHEHpC5kk2Unxqb1JD4+PpDJZCYrqrKyMpOVFxdatWrFeUylUgml0g7/SK0EY1a+uaCCvgxLWy/CzHjC0t9GSq2cF5uu3BQKBaKiopCZmWl0PDMzE7GxsbzHjYmJMRlzz549Fo3p7FD53p4dEaszy2QKQObGPIC9+d3MmcqPVqOk1Mp5sfnH0/Tp05GYmIgePXogJiYGq1evRmFhISZNmgRAZy4WFRVh/fr1hntyc3MBAFVVVbh58yZyc3OhUCjQpUsXAMDUqVORkJCAzz//HMOHD8e2bduwd+9eZGVlWf31ORJJya+gvPQOdm/JAQDs3pINH18vJCW/ohOD+/fpb5YpgTqG89aGUWwlhoacpNTKebG5uI0cORK3bt3CJ598guLiYnTt2hU7d+5EYGAgAF3SbuOct+7dH5fCnDhxAj/88AMCAwNx9epVAEBsbCw2bdqEDz/8EHPmzEFQUBAyMjLQu3dvq70uRyT/ZIFB2PRsSN2FfkMiERrqDdxnyNK3J7+bubIruScgkZJSKyfH5uIGAJMnT8bkyZMpz6WlpZkc02q1Zsf8xz/+gX/8gziEuUDnfyq8XIrQiLYAJABofvb2JG4W+tsKL5eSlZsTYBfRUoJ9wJjQK5Uxtx63pyoFs+Km87fR5bYRf5tzQMSNYMBsQi9TSog9lWEx+dskOpEmuW3ODxE3ghGMCb35Fcw324tpyjQPeTPkn7pMctuaAETcCCbQJvQW3mVuK24P6SDmyq4UXiS3rYlAxI1gAm3uV1Ar5j1N7aEMi4W/jeS2NQ2IuBFMoPK9xQ141F6Kye9mD2Yp0+pRKgdc3EhuWxOBiBuBkqTkV7BmRzJin+kGADi8Nw8Ths1HypL99DfZw25YTAKrUJHctiYEETcCI9m/nTb6fsPXe5H/Zzn9Dbb2uzEGE1SMuW0E54KIG4EWWiEoqqG/ydamKVM6isKL5LY1IUh4iEALreM92B8ATQcQW4obU9mVzBUpi7ZRmqTE3+ackJUbgRbawAJTpYItxY3h2fnn1CS3rYlBxI3ACGVg4cUvkLLiD+obbFmlwCBuhX9TdywhuW3OCxE3AitMAgvfnkT+GQqfnC3LsBiCGQEdAqmPE1+b00LEjWAW2sDCtUrqG2xlmtI999Hm0vrVpx7ia3NuyJqcYBbawEIgzbZ/UiWAu+JNiAqGsquUFUew4ZvDhu9jn+mGcdOGEWFzcsjKjWAW2sACnRloizIsmlVb/plSI2EDTE1sgnNCxI3ACsrAwuhNSFl+xPRiW5ilNEJbeI16/1mStOv8EHEjcMIksJCWaxpYsEUZFo2gBgS1pj5OAglODxE3Ams4BRasXYZFI24H9l8zOUYCCU0DIm4E1nAKLFjbNKVIP9H52w6ZHCdF8k0DIm4E1lAGFvp1pL7YmuJGU3ZVWEgdsSX+tqYBETcCJ0wCCwcuYsKYraaBBWuKG62/7Snq48Tf1iQg4kbghdnAgjWrFBiSd0nibtOFJPESOMMUWAgNe7Qq0pdh1WvEnxBF8CJl+RFsSMs1fE8Sd5seZOVG4AzrwIK1TNNGz8k/U2okbABJ3G2KEHEjcIYysBAfYHqhNdJBKMqu6GpeSSChaUHEjcALk8DCoULTwII1Vm4Uz6CreSWBhKYFETeCRTAGFqxRY0qzOozt39XoexJIaHqQgAKBN2YDC/oyLC1NS3IhaLRyI4EEgh6yciPwhlVgQWy/WwNxI4EEQkOIuBF4QxVYSHwz4nE6CCC+361BPh0JJBAaQsxSgkUkJb+CfkMikb3vDCQ1dxAT3dz4AjHFrVHZFQkkEBpCVm4Eizmw6yTWLd2OtakHrRsxbTR2aJgvnn2+i9ExEkhouhBxI1hE/skCky3zjCKmYpZhUQQTdv/3rOH7Z0fEkG37mjBE3AgWYbbHm5i7YUmZgwm7t+Qg/2SBOM8m2D1E3AgWwSpiKpZp2mBcEkwgNIaIG8EiWEVMxUgHaVR2RYIJhMaQaCnBYvQR08LLpajVaODy4G/knyl9LHBirNwoxozt1xHZBy4avifBhKYNETeCIIRGBuHArpNGwYXRYyOQNDVanDKsBqtBUpVAoIKYpQRBYIyairEb1qOVG6lKINBhF+KWmpqKdu3awdXVFVFRUTh0yHRTj4b8/vvviIqKgqurK9q3b4+vv/7a6HxaWhokEonJ14MHD8R8GU0as1FTof1uj8SNBBIIdNjcLM3IyMC0adOQmpqKuLg4rFq1CkOGDMHZs2cREGDaI+zKlSt47rnn8NZbb2HDhg04fPgwJk+ejBYtWmDEiBGG61QqFS5cuGB0r6urq+ivp6liNmoqUwJ194V74KP0EraBhLq6Ojx8+FC45xMsRi6XQyaTiTa+zcXtyy+/xPjx4zFhwgQAwLJly/Drr79i5cqVWLhwocn1X3/9NQICArBs2TIAQOfOnXH8+HEsXrzYSNwkEglatWpllddAeBw1bWiaGkVNhQwqNCi7Cg3zxeixEUamacNAglarRUlJCe7cuSPc8wmC8cQTT6BVq1aQUOxeZik2FTeNRoMTJ05g5syZRscHDRqE7OxsyntycnIwaNAgo2ODBw/G2rVr8fDhQ8jlcgBAVVUVAgMDUVdXh4iICHz66afo3p1+v8qamhrU1NQYvler1XxfVpPFKGp6rwIudXceR02FFLdGnUDaB7fA7MVj4SJ3QUB7X6NAgl7YWrZsCXd3d1H+iAjc0Wq1qK6uRllZGQCgdevWgj/DpuJWXl6Ouro6+PoamxC+vr4oKSmhvKekpITy+traWpSXl6N169YICQlBWloawsLCoFarsXz5csTFxSEvLw8dOnSgHHfhwoWYN2+eMC+sCUMbNX2nl3APeSRujaOkoycPwZB/xBq+r6urMwhb8+bNG49CsDFubm4AgLKyMrRs2VJwE9UuAgqNP021Wi3jJyzV9Q2PR0dHY/To0QgPD0d8fDx+/PFHdOzYEf/+979px5w1axYqKysNX9evX+f7cpo0tFHTP28KV4YlVVJGSTek7jIqt9L72Nzd3YV5LkFw9L8bMfyhNhU3Hx8fyGQyk1VaWVmZyepMT6tWrSivd3Fxof10lkql6NmzJ/766y/auSiVSqhUKqMvAncYo6ZCmaYyJacoKTFF7Rcxfzc2FTeFQoGoqChkZmYaHc/MzERsbCzlPTExMSbX79mzBz169DD42xqj1WqRm5sril1PMIYxaipEOsijsitSbkUwh83N0unTp2PNmjVYt24dzp07h3fffReFhYWYNGkSAJ25OGbMGMP1kyZNwrVr1zB9+nScO3cO69atw9q1azFjxgzDNfPmzcOvv/6Ky5cvIzc3F+PHj0dubq5hTIJ4MNaaCrFyezSGPkpq9BwnKrfq168fpk2bZutpODQ2TwUZOXIkbt26hU8++QTFxcXo2rUrdu7cicDAQABAcXExCgsLDde3a9cOO3fuxLvvvouUlBQ89dRTWLFihVEayJ07dzBx4kSUlJTAy8sL3bt3x8GDB9Grl4BObQItDaOmAc3vIjT0Sd0JIcqwGqz++vVvB4WqJSQyV8T0D3MaYSMIg0Sr98YTjFCr1fDy8kJlZSXxv1lAflYWCi9eRUCgl271pr5k2W5Ybq0AhYoyUpqU/IrRpQ8ePMCVK1cM1S+ORL9+/RAREWHI53RWmH5Hlv4N2twsJTgvKfM3Y8LIdfhkzr7H7cct9bvJ2EVKnY3du3fDy8sL69evpywtbNu2LeP92dnZSEhIgJubG/z9/fHOO+/g3r17AHSun+joaJN7unXrhrlz5xq+//bbb9G5c2e4uroiJCQEqamphnNXr16FRCLBzz//jKeffhru7u4IDw9HTk6OMD8AHhBxI4gCbUrI+XuWDSxV2KyeVFOUh/tntkNTlCfqcxqzadMmvPrqq1i/fj3GjBmD4uJiw9elS5cQHByMhIQE2vvPnDmDwYMH4+WXX8bp06eRkZGBrKws/O///i8A4PXXX8fRo0dRUPD4wyE/Px9nzpzB66+/DgD45ptvkJycjPnz5+PcuXNYsGAB5syZg++++87oWcnJyZgxYwZyc3PRsWNHjBo1CrW1tSL8VMxjc58bwTmhTQn5+wFCO/DMO3tUdmWLSKl63xJU56w1fO8eMx6q/u+J9jw9qampmD17NrZt24ann34aAAxlhVqtFiNGjICXlxdWrVpFO8YXX3yB1157zRCg6NChA1asWIG+ffti5cqV6Nq1K7p164YffvgBc+bMAQBs3LgRPXv2RMeOHQEAn376KZYsWYKXX34ZgM73ffbsWaxatQpvvPGG4VkzZszA0KFDAegCe6Ghobh06RJCQkKE/cGwgKzcCKJAmxLSzoJKAaNIqXEpnZiRUk1RnpGwAUB1zlrRV3BbtmzBtGnTsGfPHoOwNWT27NnIycnBf/7zH0O2f2hoKDw9PeHp6YkhQ3RR6xMnTiAtLc1w3NPTE4MHD0Z9fT2uXLkCQLd627hxIwCdaKanpxtWbTdv3sT169cxfvx4ozE+++wzo9UeoDNl9ehTr/QlVtaGrNwIokBbSN/NTxdU4EODHm7tOz2F2YvDKetJhaau4hr9cb9w0Z4bERGBkydP4ttvv0XPnj2NEl43bNiApUuX4sCBA2jTpo3h+M6dOw3Z/nrBq6+vx9tvv4133nnH5Bn6zjuvvfYaZs6ciZMnT+L+/fu4fv06/vnPfxruB3Smae/evY3ub1wy1TDXVD9f/f3WhogbQTQMKSEFJQjwvo3QsJa6E1IFUK/hPqBUabaeVAxk3oGcjgtFUFAQlixZgn79+kEmk+Grr74CoGseMWHCBKxatcokEKBPoWpIZGQk8vPzERwcTPusNm3aICEhARs3bsT9+/cxYMAAQ5WQr68v/Pz8cPnyZcNqzhEg4kYQldDIIN2q6vZ54OGjTisyJS9xyz9bSRkl7TckUtSVm8IvHO4x4xv53CZAIeKqTU/Hjh2xf/9+9OvXDy4uLpg5cyZeeukl/POf/8TgwYMNpYgymQwtWrSgHOODDz5AdHQ0kpKS8NZbb8HDwwPnzp1DZmamUb3166+/jo8//hgajQZLly41GuPjjz/GO++8A5VKhSFDhqCmpgbHjx/H7du3MX36dPF+ABZAxI1gHRSqx+ImVQK4y+1+iQyF16soTxVeLhU9gVfV/z24dhqAuoprkHkHWkXY9HTq1An79u1Dv379cOjQIZSWluK7774zilQGBgbi6tWrlPd369YNv//+O5KTkxEfHw+tVougoCCMHDnS6LpXXnkFU6ZMgUwmw4svvmh0bsKECXB3d8cXX3yBf/3rX/Dw8EBYWJhdV1GQJF4aSBKvwDysAm4/2g3+4T2guojb/S7uyL8sx4QxW01OrdmRTClujpzE21QgSbwEx8fFQ1f0DvArw5Iqnb6elCAsxCwlWAeJBJCrAM3tx7thcSnDehQpTZoajX4vDULh1QrRo6QEx4aIG8F6KB6JG6Dzu3HZMEbfUUSqRGiPEIT2EH56BOeCmKUE66Fo4Dfh2v5I38VXQfyfBHYQcSNYDxc3nUkKcBO3BrtdEXEjsIWIG8G66MWJi7g1vJaIG4ElRNwI1kX+qOidy2YxenFzcX+88iMQzEDEjWBd9CsviZS9wOl7wJFVG4EDRNwI1kWmAGSPkjXZmqb66+RE3AjsIeJGsD76FRibrryPdrsCJICimajTsifsYYOYsWPHmpRhNcYe5kkHETeC9eESVDCs2jwfVzgQCCwg4kawPnrzkk0ZFvG3EXhCxI1gfaQuulpTfRkWEzIiboDlG8ScOXMG/fv3h5ubG5o3b46JEyeiqupxl5W6ujpMnz4dTzzxBJo3b45//etfaNxT4969exgzZgw8PT3RunVrLFmyRIyXKhhE3Ai2ga3fTabUCaCLp/hzMkP+yQLs+inb6rtsWbpBTHV1NZ599lk8+eSTOHbsGDZv3oy9e/caNogBgCVLlhg2OM/KykJFRQW2bjXuwPL+++9j//792Lp1K/bs2YMDBw7gxIkTor1uSyG1pQTboPACqot14sVUYypV6MzYBi22bUHK/M1GLdOp9kkVAyE2iNF3112/fj08PDwAAF999RWGDRuGzz//HL6+vli2bBlmzZpl2Nz866+/xq+//moYo6qqCmvXrsX69esxcOBAAMB3331n1OLc3iDiRrANck8AUuaggr7sysYmKeU2hVboALxlyxaUlpYiKysLvXr1Mjmv3yDm2LFjRhvEXLum2/MhPj4eu3btwrlz5xAeHm4QNgCIi4tDfX09Lly4AFdXVxQXFyMmJsZw3sXFBT169DCYpgUFBdBoNEbXeHt7o1OnTqK8diEg4kawDRKpTuDqqumvsRN/G+02hSJ3ABZqgxitVmt0b0PojjfGEXvaEp8bwXYoVMxVCjKlLujgwnOfU4Gg3aZQxH1SAd0GMfv378e2bdswZcoUw3FzG8QEBwcjODgYfn5+AIAuXbogNzfXsMM8ABw+fBhSqRQdO3aEl5cXWrdujSNHjhjO19bWGvnTgoODIZfLja65ffs2Ll68KPjrFgoibgTboVAxl2FJlXZRlaDfprAh1uoArN8gRr+HaUlJickGMSUlJbh58ybtGK+//jpcXV3xxhtv4M8//8T+/fsxZcoUJCYmGna4mjp1KhYtWoStW7fi/PnzmDx5Mu7cuWMYw9PTE+PHj8f777+P3377DX/++SfGjh0LqdR+JYSYpQTboW89Trcblkxpc5NUj2GbwsulVu8AbOkGMe7u7vj1118xdepU9OzZE+7u7hgxYgS+/PJLwzXvvfceiouLDYI1btw4vPTSS6isrDRc88UXX6CqqgovvPACmjVrhvfee8/ovL1BNoihgWwQYyUq/wLUBcCDcuPjEhmgCgKah3NvbPkIskGM/UM2iCE4L3IVda6bTPn4i0DgARE3gm1RqKgFTKrU5cIRCDwh4kawLS5uumho46J4mX0EEwiOCxE3gu2hSgmxo2ACwTEh4kawPVSmqeKJR33cCAR+EHEj2B55I3GTKgDlE4INX1/PYfNnglUR83dDPhoJtkf2qDj+/qMyJ4FMUoVCAalUihs3bqBFixZQKBSsy40I4qLVaqHRaHDz5k1IpVIoFBw2DGIJETeCfeDWElD/pfu/zBWQW95SXCqVol27diguLsaNGzcsHo8gPO7u7ggICBCl0oGIG8E+UD6hM0frNTp/m7kmlixRKBQICAhAbW0t6urqBBmTIAwymQwuLi6iraaJuBHsA73frV4DuAlbkC6RSCCXyyGXkz1PmxJ2EVBITU01lF9ERUXh0KFDjNf//vvviIqKgqurK9q3b4+vv/7a5JotW7agS5cuUCqV6NKli0lXUYKdIZXpTFGJDHDzsfVsCE6AzcUtIyMD06ZNQ3JyMk6dOoX4+HgMGTIEhYWFlNdfuXIFzz33HOLj43Hq1CnMnj0b77zzDrZs2WK4JicnByNHjkRiYiLy8vKQmJiIV199FUePHrXWyyLwQdlc529z8TB/LYFgBpsXzvfu3RuRkZFYuXKl4Vjnzp3x4osvYuHChSbXf/DBB9i+fTvOnTtnODZp0iTk5eUhJycHADBy5Eio1Wrs2vW4e6q+h3x6ejqreZHCeRtQXQZUnAbaDLD1TAh2gKV/gzb1uWk0Gpw4cQIzZ840Oj5o0CBkZ2dT3pOTk4NBgwYZHRs8eDDWrl2Lhw8fQi6XIycnB++++67JNcuWLaOdS01NDWpqagzf61u5qNVqLi+JYAlaBVCnAsjPnIDHf3t81182Fbfy8nLU1dUZGubp8fX1RUlJCeU9JSUllNfX1taivLwcrVu3pr2GbkwAWLhwIebNm2dy3N/fn+3LIRAIInDr1i14eXFvomAX0dLGoWCmnu901zc+znXMWbNmYfr06Ybv79y5g8DAQBQWFvL6wRK4o1ar4e/vj+vXrxNXAAGVlZUICAiAt7c3r/ttKm4+Pj6QyWQmK6qysjKTlZeeVq1aUV7v4uKC5s2bM15DNyYAKJVKKJWmrXe8vLzIH5qVUalU5GdOMMA3wdem0VKFQoGoqChkZmYaHc/MzERsbCzlPTExMSbX79mzBz169DDkMdFdQzcmgUBwQrQ2ZtOmTVq5XK5du3at9uzZs9pp06ZpPTw8tFevXtVqtVrtzJkztYmJiYbrL1++rHV3d9e+++672rNnz2rXrl2rlcvl2p9++slwzeHDh7UymUy7aNEi7blz57SLFi3Suri4aI8cOcJ6XpWVlVoA2srKSuFeLIER8jMnNMTS94PNxU2r1WpTUlK0gYGBWoVCoY2MjNT+/vvvhnNvvPGGtm/fvkbXHzhwQNu9e3etQqHQtm3bVrty5UqTMTdv3qzt1KmTVi6Xa0NCQrRbtmzhNKcHDx5o586dq33w4AGv10TgDvmZExpi6fvB5nluBAKBIAY2r1AgEAgEMSDiRiAQnBIibgQCwSkh4kYgEJwSIm4EAsEpIeLWgLq6OsTGxmLEiBFGxysrK+Hv748PP/zQRjNzfkpKSjB16lQEBwfD1dUVvr6+6NOnD77++mtUV1fbenoEKzB27FhIJBJMmjTJ5NzkyZMhkUgwduxY1uMRcWuATCbDd999h927d2Pjxo2G41OmTIG3tzc++ugjG87Oebl8+TK6d++OPXv2YMGCBTh16hT27t2Ld999Fzt27MDevXttPUWClfD398emTZtw//59w7EHDx4gPT0dAQEBnMayi8J5e6JDhw5YuHAhpkyZgqeffhrHjh3Dpk2b8Mcff4iyQw9B96ns4uKC48ePw8PjcaPKsLAwjBgxgnfLG4LjERkZicuXL+Pnn3/G66+/DgD4+eef4e/vj/bt23Mai6zcKJgyZQrCw8MxZswYTJw4ER999BEiIiJsPS2n5NatW9izZw+SkpKMhK0hZDu+psWbb76Jb7/91vD9unXrMG7cOM7jEHGjQCKRYOXKlfjtt9/g6+tr0kyTIByXLl2CVqtFp06djI77+PjA09MTnp6e+OCDD2w0O4ItSExMRFZWFq5evYpr167h8OHDGD16NOdxiFlKw7p16+Du7o4rV67g77//Rtu2bW09Jaem8ersjz/+QH19PV5//XWjDskE58fHxwdDhw7Fd999B61Wi6FDh8LHh/umQWTlRkFOTg6WLl2Kbdu2ISYmBuPHjyd+H5EIDg6GRCLB+fPnjY63b98ewcHBcHNzs9HMCLZk3LhxSEtLw3fffcfLJAWIuJlw//59vPHGG3j77bcxYMAArFmzBseOHcOqVatsPTWnpHnz5hg4cCC++uor3Lt3z9bTIdgJzz77LDQaDTQaDQYPHsxrDCJujZg5cybq6+vx+eefAwACAgKwZMkSvP/++7h69aptJ+ekpKamora2Fj169EBGRgbOnTuHCxcuYMOGDTh//jxkMpmtp0iwMjKZDOfOncO5c+d4//6Jz60Bv//+O1JSUnDgwAGjyN1bb72Fn376CePHj8fevXtJ9E5ggoKCcOrUKSxYsACzZs3C33//bdhMe8aMGZg8ebKtp0iwAZa2mif93AgEglNCzFICgeCUEHEjEAhOCRE3AoHglBBxIxAITgkRNwKB4JQQcSMQCE4JETcCgeCUEHEjEAhOCRE3AoHglBBxIxAITgkRNwKB4JT8P7U10i90xb7UAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"markersize = 10\n",
"ymin, ymax = 0, 0.36\n",
"odd_color = \"#381a61\"\n",
"even_color = \"#E78429\"\n",
"both_color = \"#AB3329\"\n",
"\n",
"fig, ax = plt.subplots(nrows=1, ncols=1, constrained_layout=True, figsize=(3, 4))\n",
"\n",
"ax.scatter(X, freqs_even,c=even_color, s=markersize, label='kz-even')\n",
"ax.scatter(X, freqs_odd, c=odd_color, s=markersize, label='kz-odd')\n",
"ax.set_xlim([0, 1])\n",
"\n",
"ax.fill_between(X0, light_lower_clad, max(100, light_lower_clad.max()),\n",
" facecolor='#FFE9C9', zorder=0)\n",
"ax.fill_between(X0, light_upper_clad, max(100, light_upper_clad.max()),\n",
" facecolor='#FFDAA3', zorder=0)\n",
"ax.legend()\n",
"ax.set_ylabel(\"$\\omega a/2\\pi c$\")\n",
"ax.set_xticks(path['k_indexes'], path['labels'])\n",
"ax.set_ylim([ymin, ymax])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare separated and not-separated bands"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SRLzvdLVWUoe"
},
"source": [
"We can compare with the bands without symmetry separation (like in the original versione of legume, using the option `kz_symmetry=None`.\n",
"Also, we can calculate bands of both parities in one shot using the option `kz_symmetry='both'`. This is faster than calculating even and odd modes separately, but we have to extract the results in the proper way."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 44960,
"status": "ok",
"timestamp": 1708441197651,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "PsAPCleMXbg8",
"outputId": "f664a0a3-7108-4242-b80e-45bbb1e46335",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running gme k-points: │██████████████████████████████│ 131 of 131\r"
]
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Steps in GuidedModeExp: 97 plane waves and 4 guided modes ┃ Time (s) ┃ % vs total T ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ Guided modes computation with gmode_compute=' exact ' │ 8.251 │ │███████████---------│ 58% │\n",
"│ Inverse matrix of Fourier-space permittivity │ 0.001 │ │--------------------│ 0% │\n",
"│ Matrix diagionalization using the ' eigh ' solver │ 1.234 │ │█-------------------│ 9% │\n",
"│ Creating GME matrix │ 4.623 │ │██████--------------│ 33% │\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│ Total time for real part of frequencies for 131 k-points │ 14.172 │ │████████████████████│ 100% │\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n",
" \n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mSteps in GuidedModeExp: 97 plane waves and 4 guided modes\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime (s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m % vs total T\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36mGuided modes computation with gmode_compute='\u001b[0m\u001b[1;36mexact\u001b[0m\u001b[36m'\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m8.251 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│███████████---------│ 58%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mInverse matrix of Fourier-space permittivity\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.001 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 0%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mMatrix diagionalization using the '\u001b[0m\u001b[1;36meigh\u001b[0m\u001b[36m' solver\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m1.234 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│█-------------------│ 9%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating GME matrix\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m4.623 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████--------------│ 33%\u001b[0m\u001b[32m \u001b[0m│\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│\u001b[1;36m \u001b[0m\u001b[1;36mTotal time for real part of frequencies for 131 k-points\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m \u001b[0m│\u001b[1;35m \u001b[0m\u001b[1;4;35m14.172\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m│\u001b[1;32m \u001b[0m\u001b[1;32m│████████████████████│ 100%\u001b[0m\u001b[1;32m \u001b[0m│\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Skipping imaginary part computation, use run_im () to run it, or compute_rad () to compute the radiative rates of \n",
"selected eigenmodes\n",
" \n"
],
"text/plain": [
"Skipping imaginary part computation, use \u001b[1;35mrun_im\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to run it, or \u001b[1;35mcompute_rad\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to compute the radiative rates of \n",
"selected eigenmodes\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running gme k-points: │██████████████████████████████│ 131 of 131\r"
]
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Steps in GuidedModeExp: 97 plane waves and 4 guided modes ┃ Time (s) ┃ % vs total T ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ Guided modes computation with gmode_compute=' exact ' │ 8.021 │ │██████████----------│ 53% │\n",
"│ Inverse matrix of Fourier-space permittivity │ 0.001 │ │--------------------│ 0% │\n",
"│ Matrix diagionalization using the ' eigh ' solver │ 0.828 │ │█-------------------│ 5% │\n",
"│ Creating GME matrix │ 4.565 │ │██████--------------│ 30% │\n",
"│ Creating change of basis matrix using dense matrices │ 1.724 │ │██------------------│ 11% │\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│ Total time for real part of frequencies for 131 k-points │ 15.202 │ │████████████████████│ 100% │\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n",
" \n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mSteps in GuidedModeExp: 97 plane waves and 4 guided modes\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime (s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m % vs total T\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36mGuided modes computation with gmode_compute='\u001b[0m\u001b[1;36mexact\u001b[0m\u001b[36m'\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m8.021 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████████----------│ 53%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mInverse matrix of Fourier-space permittivity\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.001 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 0%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mMatrix diagionalization using the '\u001b[0m\u001b[1;36meigh\u001b[0m\u001b[36m' solver\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.828 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│█-------------------│ 5%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating GME matrix\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m4.565 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██████--------------│ 30%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating change of basis matrix using \u001b[0m\u001b[1;36mdense\u001b[0m\u001b[36m matrices\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m1.724 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██------------------│ 11%\u001b[0m\u001b[32m \u001b[0m│\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│\u001b[1;36m \u001b[0m\u001b[1;36mTotal time for real part of frequencies for 131 k-points\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m \u001b[0m│\u001b[1;35m \u001b[0m\u001b[1;4;35m15.202\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m│\u001b[1;32m \u001b[0m\u001b[1;32m│████████████████████│ 100%\u001b[0m\u001b[1;32m \u001b[0m│\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Skipping imaginary part computation, use run_im () to run it, or compute_rad () to compute the radiative rates of \n",
"selected eigenmodes\n",
" \n"
],
"text/plain": [
"Skipping imaginary part computation, use \u001b[1;35mrun_im\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to run it, or \u001b[1;35mcompute_rad\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to compute the radiative rates of \n",
"selected eigenmodes\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gme = legume.GuidedModeExp(phc, gmax=gmax, truncate_g='abs')\n",
"gme.run(kpoints=path['kpoints'], angles=path['angles'], gmode_inds=gmode_inds, kz_symmetry=None,\n",
" numeig=numeig, verbose=True, compute_im=compute_im)\n",
"freqs_none = gme.freqs\n",
"\n",
"gme = legume.GuidedModeExp(phc, gmax=gmax, truncate_g='abs')\n",
"gme.run(kpoints=path['kpoints'], angles=path['angles'], gmode_inds=gmode_inds, kz_symmetry='both',\n",
" numeig=numeig, verbose=True, compute_im=compute_im)\n",
"freqs_both = gme.freqs\n",
"kz_symms = gme.kz_symms # this is the array that contains the parities of the modes: +1 for even, -1 for odd\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JtuyCxVeBvm7"
},
"source": [
"Here we separate the array freqs into even and odd frequencies\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"executionInfo": {
"elapsed": 302,
"status": "ok",
"timestamp": 1708441237999,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "9ynl6Pc3cB4p",
"tags": []
},
"outputs": [],
"source": [
"freqs_both_even = copy.deepcopy(freqs_both)\n",
"freqs_both_even[kz_symms==-1] = None\n",
"freqs_both_odd = copy.deepcopy(freqs_both)\n",
"freqs_both_odd[kz_symms==1] = None"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"executionInfo": {
"elapsed": 228,
"status": "ok",
"timestamp": 1708441241559,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "LW8gU6gPxCRf",
"tags": []
},
"outputs": [],
"source": [
"eps_clad = [gme.phc.claddings[0].eps_avg, gme.phc.claddings[-1].eps_avg]\n",
"light_lower_clad = np.sqrt(\n",
" np.square(gme.kpoints[0, :]) +\n",
" np.square(gme.kpoints[1, :])) / 2 / np.pi / np.sqrt(eps_clad[0])\n",
"light_upper_clad = np.sqrt(\n",
" np.square(gme.kpoints[0, :]) +\n",
" np.square(gme.kpoints[1, :])) / 2 / np.pi / np.sqrt(eps_clad[1])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
},
"executionInfo": {
"elapsed": 1642,
"status": "ok",
"timestamp": 1708441246340,
"user": {
"displayName": "SIMONE ZANOTTI",
"userId": "14051006727806237496"
},
"user_tz": -60
},
"id": "fkr5iYrLwVcY",
"outputId": "32f8b186-3ea4-4b1f-f0a2-9ce53d510041",
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'violet=even, orange=odd')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"ymin, ymax = 0, 0.36\n",
"\n",
"fig, ax = plt.subplots(nrows=1, ncols=3, constrained_layout=True, figsize=(8, 4))\n",
"for a in ax: # Loop over all axis for common set-up (limits, lightcones,...)\n",
" a.set_xlim([0, 1])\n",
" a.fill_between(X0, light_lower_clad, max(100, light_lower_clad.max()),\n",
" facecolor='#FFE9C9', zorder=0)\n",
" a.fill_between(X0, light_upper_clad, max(100, light_upper_clad.max()),\n",
" facecolor='#FFDAA3', zorder=0)\n",
" a.set_xticks(path['k_indexes'])\n",
" a.set_xticklabels(path['labels'])\n",
" a.set_ylim([ymin, ymax])\n",
"\n",
"ax[0].scatter(X, freqs_none, c=both_color, s=markersize)\n",
"ax[0].set_title('None')\n",
"ax[0].set_ylabel(\"$\\omega a/2\\pi c$\")\n",
"ax[1].scatter(X, freqs_both,c=both_color, s=markersize)\n",
"ax[1].set_title('both')\n",
"\n",
"ax[2].scatter(X, freqs_both_even, c=even_color, s=markersize)\n",
"ax[2].scatter(X, freqs_both_odd, c=odd_color, s=markersize)\n",
"ax[2].set_title('violet=even, orange=odd')"
]
}
],
"metadata": {
"colab": {
"provenance": [
{
"file_id": "1TN27imDitpmfF4G-wrnLBeNT0H9qNkBb",
"timestamp": 1708350553707
}
]
},
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}