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aare/python/tests/test_Minuit2_scurve_1d.ipynb
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CI/CD: Integrate pre-commit hooks and GitHub Actions workflow (#303)
To improve codebase quality and reduce human error, this PR introduces
the pre-commit framework. This ensures that all code adheres to project
standards before it is even committed, maintaining a consistent style
and catching common mistakes early.

Key Changes:

- Code Formatting: Automated C++ formatting using clang-format (based on
the project's .clang-format file).
- Syntax Validation: Basic checks for file integrity and syntax.
- Spell Check: Automated scanning for typos in source code and comments.
- CMake Formatting: Standardization of CMakeLists.txt and .cmake
configuration files.
- GitHub Workflow: Added a CI action that validates every Pull Request
against the pre-commit configuration to ensure compliance.

The configuration includes a [ci] block to handle automated fixes within
the PR. Currently, this is disabled. If we want the CI to automatically
commit formatting fixes back to the PR branch, this can be toggled to
true in .pre-commit-config.yaml.

```yaml
ci:
  autofix_commit_msg: [pre-commit] auto fixes from pre-commit hooks
  autofix_prs: false
  autoupdate_schedule: monthly
```

The last large commit with the fit functions, for example, was not
formatted according to the clang-format rules. This PR would allow to
avoid similar mistakes in the future.

Python fomat with `ruff` for tests and sanitiser for `.ipynb` notebooks
can be added as well.
2026-04-14 11:52:23 +02:00

398 lines
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "efef8e20-6571-4561-8f0b-6048b57907de",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import random\n",
"import numpy as np\n",
"np.set_printoptions(suppress=True, precision=6)\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from scipy.special import erf\n",
"\n",
"import sys\n",
"sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
"\n",
"from aare import fit_scurve, fit_scurve2 # lmfit\n",
"from aare import RisingScurve, FallingScurve, fit # minuit2 (object based API)\n",
"\n",
"from pprint import pprint"
]
},
{
"cell_type": "markdown",
"id": "8449f9e9-59ee-4194-b477-d014b8efc93b",
"metadata": {},
"source": [
"## Model curves"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "35772e2c-37c6-4986-a9c0-7dca4a034827",
"metadata": {},
"outputs": [],
"source": [
"def scurve(x, p): # rising Scurve\n",
" p0, p1, p2, p3, p4, p5 = p\n",
" return (p0 + p1*x) + 0.5 *(1 + erf((x-p2) / (np.sqrt(2)*p3))) * (p4 + p5*(x-p2))\n",
"\n",
"def scurve2(x, p): #falling Scurve\n",
" p0, p1, p2, p3, p4, p5 = p\n",
" return (p0 + p1*x) + 0.5 *(1 - erf((x-p2) / (np.sqrt(2)*p3))) * (p4 + p5*(x-p2))"
]
},
{
"cell_type": "markdown",
"id": "a318ab93-fe18-4798-9ddb-6ffc2c2e3875",
"metadata": {},
"source": [
"## Generate data (1D)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "06c8fddb-56d9-4f84-8b21-4ffd8b7bd26f",
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(0,120, 121)\n",
"\n",
"rng = np.random.default_rng(42)\n",
"0\n",
"p_true_rising = np.array([100.0, 0.25, 60.0, 6.0, 120.0, 1.0])\n",
"p_true_falling = np.array([100.0, 0.25, 60.0, 6.0, 120.0, -1.0])\n",
"\n",
"y_true_rising = scurve(x, p_true_rising)\n",
"y_true_falling = scurve2(x, p_true_falling)\n",
"\n",
"noise_sigma = 4\n",
"\n",
"y_rising = y_true_rising + rng.normal(0, noise_sigma, size=x.shape)\n",
"# y_err_r = np.full_like(x, noise_sigma)\n",
"\n",
"y_falling = y_true_falling + rng.normal(0, noise_sigma, size=x.shape)\n",
"# y_err_f = np.full_like(x, noise_sigma)"
]
},
{
"cell_type": "markdown",
"id": "7e185144-3b72-41f4-80d0-3c3504e48bba",
"metadata": {},
"source": [
"## Plot synthetic data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9a0b3292-7c57-4483-aecc-7ef683ff0b62",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(12,4), sharex=True)\n",
"\n",
"ax[0].plot(x, y_true_rising, label=\"true\")\n",
"ax[0].scatter(x, y_rising, s=12, label=\"noisy\")\n",
"ax[0].set_title(\"Rising S-curve\")\n",
"ax[0].legend()\n",
"\n",
"ax[1].plot(x, y_true_falling, label=\"true\")\n",
"ax[1].scatter(x, y_falling, s=12, label=\"noisy\")\n",
"ax[1].set_title(\"Falling S-curve\")\n",
"ax[1].legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "95c826d1-9223-4fb2-9ada-d9fc1acad2fb",
"metadata": {},
"source": [
"## Fit the Rising S-curve"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "37d2fda4-40b8-4ea2-8f2c-6c3f2384d158",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Paramter list of the Scurve:\n",
"['p0', 'p1', 'mu', 'sigma', 'A', 'C']\n",
"6 \n",
"\n",
"== Tuned fit settings ==\n",
"max_calls : 500\n",
"tolerance : 0.01\n",
"compute_erros: True\n",
"\n",
"\n",
"== Results ==\n",
"True rising params: [100. 0.25 60. 6. 120. 1. ]\n",
"lmfit rising result: [ 99.467831 0.287017 60.078405 5.715461 117.257939 0.967234]\n",
"minuit_grad (obj api ) rising result:\n",
"{'par': array([100. , 0.269137, 60.050193, 5.729932, 117.777635,\n",
" 0.984073]),\n",
" 'par_err': array([1. , 0.054406, 0.747753, 0.909864, 7.887499, 0.174058]),\n",
" 'chi2': array([7.351743])}\n"
]
}
],
"source": [
"res_r_lmfit = fit_scurve(x, y_rising)\n",
"\n",
"model_r = RisingScurve()\n",
"print(\"Paramter list of the Scurve:\")\n",
"# print(model_r.GetParNames())\n",
"print(model_r.par_names)\n",
"print(model_r.n_par, '\\n')\n",
"\n",
"model_r.FixParameter('p0', 100) # Fixed p0 = 100 (optimizer does not touch the value)\n",
"model_r.SetParameter('A', 100) # Start value A = 100\n",
"\n",
"print(\"== Tuned fit settings ==\")\n",
"model_r.compute_errors = True\n",
"model_r.max_calls = 500\n",
"model_r.tolerance = 0.01\n",
"print(f\"max_calls : {model_r.max_calls}\")\n",
"print(f\"tolerance : {model_r.tolerance}\")\n",
"print(f\"compute_erros: {model_r.compute_errors}\")\n",
"print(\"\\n\")\n",
"print(\"== Results ==\")\n",
"res_r_munuit_obj = model_r.fit(x, y_rising, np.sqrt(y_rising))\n",
"\n",
"print(\"True rising params: \", p_true_rising)\n",
"print(\"lmfit rising result: \", res_r_lmfit)\n",
"print(\"minuit_grad (obj api ) rising result:\")\n",
"pprint(res_r_munuit_obj, sort_dicts=False)"
]
},
{
"cell_type": "markdown",
"id": "5ebf1328-954a-43ce-953b-c402fd7998f9",
"metadata": {},
"source": [
"## Fit the Falling S-curve"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5ce22d2d-1adb-47d9-bf36-6cb535fa8b77",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True falling params: [100. 0.25 60. 6. 120. -1. ]\n",
"lmfit falling result: [101.73448 0.225786 60.251971 6.092915 118.476735 -0.998677]\n",
"minuit_grad (obj api ) falling result\n",
"{'par': array([101.741754, 0.225723, 60.251604, 6.092887, 118.473268,\n",
" -0.998565]),\n",
" 'chi2': array([1865.728797])}\n"
]
}
],
"source": [
"res_f_lmfit = fit_scurve2(x, y_falling)\n",
"\n",
"model_f = FallingScurve()\n",
"model_f.SetParameter(2, 20) # set start p2 = 20\n",
"model_f.SetParameter(4, 90) # set start p4 = 90\n",
"model_f.SetParLimits(4, 80, 130)\n",
"model_f.max_calls = 150 # this is limits the number of calls done by the minimizer (increasing the `max_calls` may help if the fit fails) \n",
"res_f_munuit_obj = model_f.fit(x, y_falling)\n",
"\n",
"print(\"True falling params: \", p_true_falling)\n",
"print(\"lmfit falling result: \", res_f_lmfit)\n",
"print(\"minuit_grad (obj api ) falling result\")\n",
"pprint(res_f_munuit_obj, sort_dicts=False)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "fe72503a-fc77-4b17-9b17-b98055e2fc87",
"metadata": {},
"outputs": [
{
"data": {
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heUoKO+c/88wz9O3bl127dnHgwAHi4+PzVXrJbdfQoUN57LHHCtxGUc2hv5D77ruP++67D6fTyZ9//smUKVMYOHAgERERPPHEE9d8/+LmJj3a4pYzfvx4goKCGDFixCWXxPD19aVly5YMGzYMm83Gjh07rrodjRs3Jj093XMXOte8efMu6f3nJvzIdfz4cdLS0jw1RENCQgq803/77bdTsWJFZsyYUeBJNVe5cuVITEzMd4Fgs9lYtmzZJbUzV1BQEO3atWP27Nn8+OOPJCQk5Bs2DtC6dWv+/fffAttct27dArOfX4nc+toX69EoW7Ys/fv356GHHmLLli1Fsm8hhBDXh6Zpnr/3uX766SeOHTt2Xdvh6+tL3bp1+f7777HZbJ7lGRkZBWYnP5fdbi800M0dAp97zq9cuXKB582WLVsC7szfF1KuXLl8WcHBnZk7IyPjou3M68knn8TLy4vY2FhiY2MpXbo0zZs397xeuXJlKlWqxN9//13g+b5u3br5huNfjUs55+v1eu655x4++ugjADnniyIhPdrilhMUFMTQoUN59dVXmTt3Ll27di1wvWeffRZvb2/uvfdeoqKiSEhIYNy4cQQEBFCvXr2rbkf37t2ZNGkSXbt25e233+a2227j559/9gSwFyvX0adPH1JSUujQoQM1atRAr9eze/duJk2ahE6n47XXXrtoGz766CPatGlD/fr1efnllylbtiyHDx9m2bJlfPnllwB07tyZESNG8MQTT/DKK69gtVr573//i9PpvOxj7tmzJ/Pnz6d///6UKVOGZs2a5Xt94MCBfPvtt9x///28/PLL1KpVC5fLxeHDh1m+fDmDBw/mnnvuuez9nstisRATE8OiRYt48MEHCQ4OJjQ0lKCgIJo2bUqXLl2oUqUKFouFTZs2ebLFCiGEuHG0bt2a2NhYqlSpQq1atdi8eTPvv/9+kU+ZuhRjxozhkUceoUWLFrz00ks4nU7ef/99/Pz8SEpKuuB7U1NTKVeuHI8//jjNmjUjOjqajIwM1q5dy+TJk6latepFz1H33XcfTz/9NG+//TYnTpygdevWmM1mtm7dio+PDwMGDADc0+befPNNRowYQePGjdm5cycffvghAQEBl3W8gYGBtG/fntjYWFJSUhgyZMh51zWffPIJLVu2pEWLFvTo0YPSpUuTlJTErl272LJlC998881l7bMwNWvWBGDy5Ml0794do9FI5cqV+fLLL1m9ejWPPPIIZcuWxWq1ekbUnXt9IsQVKd5cbEJcO7kZMc/NYK2UUtnZ2aps2bKqUqVKyuFwKKXOz0w9a9Ys1bRpUxUREaFMJpMqVaqU6tSpk9q2bZtnncKyjvv6+p63z4KydB8+fFg99thjys/PT1ksFtWhQwe1ZMkSBahFixZd8PiWLVumevbsqapVq6YCAgKUwWBQUVFR6rHHHlO//fbbpXxESimlfvvtN9WyZUsVEBCgzGazqlixYr5srEoptWTJEnXnnXcqb29vVaFCBfXhhx8WmnW8X79+he7L6XSq6OjoC2Y+zcjIUMOHD1eVK1dWJpNJBQQEqJo1a6qXX35ZJSQkXPBYLjXruFJKrVy5UtWuXVuZzWYFqO7duyur1aqef/55VatWLeXv76+8vb1V5cqV1ciRI1VmZuYF9y2EEOL6utB5XimlkpOTVa9evVR4eLjy8fFRjRo1UuvXrz/vnHC1WccLOu+dm6VbKaUWLlyoatasqUwmkypbtqx699131YsvvqiCgoIueJw5OTlqwoQJqmXLlqps2bLKbDYrLy8vVbVqVfXqq6+q06dPX/D9uZxOp5o0aZKqUaOG5/zaoEEDtXjx4nz7evXVV1V0dLTy9vZWjRs3Vn/99VehWccL++yVUmr58uWeLOl79+4tcJ2///5bderUSYWHhyuj0agiIyPVAw88oD7++OOLHs+lZh1XSqmhQ4eqUqVKKZ1O57lu++2331T79u1VTEyMMpvNKiQkRDVu3Fj98MMPF923EJdCU+pMmkIhRInwzjvvMHz4cA4fPlwsd92FEEIIce3Z7XbuvPNOSpcuzfLly4u7OUKIIiZDx4UoRh9++CEAVapUwW63s3r1av773//StWtXCbKFEEKIm0ivXr146KGHPNPRPv74Y3bt2iVZroW4SUmgLUQx8vHxYdKkScTFxZGTk0PZsmV57bXXGD58eHE3TQghhBBFKD09nSFDhnDy5EmMRiN16tRhyZIlMh9YiJuUDB0XQgghhBBCCCGKkJT3EkIIIYQQQgghipAE2kIIIYQQQgghRBGSQFsIIYQQQgghhChCN2QyNJfLxfHjx7FYLGiaVtzNEUIIIVBKkZ6eTqlSpdDp5D52UZDzvRBCiJLkcs71N2Sgffz4caKjo4u7GUIIIcR5jhw5IuX5ioic74UQQpREl3KuvyEDbYvFArgP0N/fv5hbI4QQQkBaWhrR0dGec5S4enK+F0IIUZJczrn+hgy0c4eP+fv7y4lXCCFEiSJDnIuOnO+FEEKURJdyrpdJZEIIIYQQQgghRBGSQFsIIYQQQgghhChCEmgLIYQQQgghhBBF6Iaco32pnE4ndru9uJtxSzAajej1+uJuhhBCCCGEEDcdiWuuH5PJVCRlOm/KQFspRUJCAikpKcXdlFtKYGAgkZGRkghICCGEEEKIIiBxzfWn0+koX748JpPpqrZzUwbauV/G8PBwfHx8JPC7xpRSZGVlkZiYCEBUVFQxt0gIIYQQQogbn8Q115fL5eL48ePEx8dTtmzZq/q8b7pA2+l0er6MISEhxd2cW4a3tzcAiYmJhIeHyzByIYQQQgghroLENcUjLCyM48eP43A4MBqNV7ydmy4ZWu7cBR8fn2Juya0n9zOX+SNCCCGEEEJcHYlrikfukHGn03lV27nperRzybCK608+cyFESWO1O5mz8RBHkrKIDvaha/0YvIwy4ka4yfdDCHEjkGvs66uoPu+bNtAWQghxa7PanXSYtoGd8WnoNQ2nUizceoxvX2gowZSQ74cQQohr6qYbOi6EEEIAzNl4iJ3xaSgFDpdCKdgZn8acjYeKu2miBJDvhxBCiGtJAu0SpEmTJgwcOLC4myGEEDeFI0lZ6M8Z/qXXNI4kZRVTi0RJcjnfD6vdyfT1Bxi5aDvT1x/Aar+6eXtCCHGzk7hGho7fUJRSOJ1ODAb5sQkhxMVEB/vgVCrfMqdSRAdLUhlx6d8PGWIuhBBF71aIa6RHu4To0aMH69atY/LkyWiahqZpxMbGomkay5Yto27dupjNZtavX0+PHj1o165dvvcPHDiQJk2aeJ4rpRg/fjwVKlTA29ubO+64gwULFlzfgxJCiGLUtX4M1aL80TQw6DQ0DapF+dO1fgw4bJCekP8N6Qnu5eLmUsjPumu9qHzfD4Bwi5mDpzLz9VrLEHMhhLg8Ete43by3EPJQSpFdTMO8vI36S8pcN3nyZPbu3UuNGjUYM2YMADt27ADg1VdfZcKECVSoUIHAwMBL2u/w4cP57rvvmDZtGpUqVeKXX36ha9euhIWF0bhx4ys+HiGEuFF4GfV8+0LD87NKa074pjsk7oTuP2L1LYVX5nGY1RrCq8Hjs8BgKu7mi6LgsME33XGd2Mn86lPZl+FDNf1hHtr5BvHmcjx6xwQ6VzGyP83I8r3JnEjPYf6mI/l6rXOHmDvy9H7LFAQhRHEprrjmUmMakLgm1y0RaGfbnVQbsaxY9r1zTAt8TBf/mAMCAjCZTPj4+BAZGQnA7t27ARgzZgwPPfTQJe8zMzOTiRMnsnr1aho0aABAhQoV+PXXX/nkk09K9BdSCCGKkpdRT+/7KrifOGyQfRIAdWInv5324YP/fE942dv5MOtVSI5zr5edBJbI4mmwKFrZSbhO7ESXEkfDX7tzmwrmTm0fRs1FaraNH1es4FPvj7gvuDLz0nuhlMETUOf2WssUBCFESVJccc2lxjQgcU2uWyLQvtHVrVv3stbfuXMnVqv1vC+xzWajdu3aRdk0IYS4MZzp2XQm7GRs1GSWZozjuF1h0/7FfGA/o02nCQkuB91/lCD7ZmKJZH71qTRY34MXMNFQO0xlzQlKz7u2zkwzfkCU6zQJyRCiZeBQihQs2DF4eq2HtqrKwq3H8s3R9kxBEEIIcVlupbjmlgi0vY16do5pUWz7vlq+vr75nut0OtQ5d9ftdrvn/y6XC4CffvqJ0qVL51vPbDZfdXuEEOKG4LB5eqdV1mnWHbbz35QebDmRSY62n3TTl2TqN9FdWQhBg/YLIDC6uFstitjurABmO15ineFLlmq7GY2B+4hkuGkqpTUHx1Qo7wSOJSzrOFNMU9iroulrf4lAVzonkgMZt2QXrWtF0eaOUsSnZJ+dgiCJ0IQQxaC44pqiiGng1oprbolAW9O0Sx7qUJxMJhNO58XnXISFhbF9+/Z8y/766y+MRiMA1apVw2w2c/jw4RI9nEIIIYqK1e5kzsZDHD+VSoyPlRxTEPf/NZionDh+b/gJU7fBX8nPkKPtI900ikz9n1RQBkYqL7qc2Ybru+fQ9fhRgu2bTBWfVBrpF1Da0ZIVqjep+jWs1y+nmS6ZEFcojysDA2wTud28GwNONAXV+ZcPzR+y699yvOgciFXpuTfCwfTnm+Pl5V3ch1Sg3N+BfPkI5GaAEDcdiWtuHCX/p3QLKVeuHL///jtxcXH4+fl57uCc64EHHuD9999n9uzZNGjQgDlz5rB9+3bP8AmLxcKQIUN4+eWXcblcNGrUiLS0NDZs2ICfnx/du3e/noclhBDXVG75pX3xSXxkmMzt2hH62QbgbzDwmvM5/l6SSo6290wP9mYqKgOjlBdPYOSUCuZJ+4tMME4jJiUOV2xrdL2WyfDxm0V6Ap139EWnj+N23TGctu7UVbDA9hY7tRNkGJbxse53Pkk/SF0VzWuaFUe5vsw/PA6zygENAl1JhJLG2OQpnJhelZjnF7hHSngHl5ikeVKCTAhR0khcI+W9SpQhQ4ag1+upVq0aYWFhHD58uMD1WrRowZtvvsmrr75KvXr1SE9Pp1u3bvnWeeuttxgxYgTjxo2jatWqtGjRgsWLF1O+fPnrcShCCHHdzN2wn5PxhwhQ6VTiCHtUNDbNxOuOPvyBg1OmN0nwGkSU7m9muXzYgzddMWFAw6F0HHOF8GTOcA6pcI4YYtwBlLg5eAeji6iGK7AcW+6eyH8sX9LPuJgV5qHM8NvJ86opt1s/JsDRla1Y6ajF0yfuHfo6AziidGgoJhun8p15JDFaIpa0fXBiO8xo4c5cX0LKwUkJMiFESSNxDWjq3EHxN4C0tDQCAgJITU3F398/32tWq5WDBw9Svnx5vLy8iqmFtyb57IUQ103u/GvvYHZPaY9X8j662V4jnhDsGMnRdpNunEOm/i9uVwZGYaQTBvRoHFLhvGbrwwTjNMroTnPIFU5H20gMOo2W9aoxov2VJVe50LnpRjJu3Di+++47du/ejbe3Nw0bNuS9996jcuXKnnUKK/Eyfvx4XnnlFQCaNGnCunXr8r3euXNn5s2bd8ltKZLPNM93xVPWreNM8AqE2W3ITDnJz667me9ozHrNQbp+GVn6DWi4qKrK8bJy0FOXhFPpWVdjLM2OfgSpRyGoHPQ8k/m3mHu3Ry7azpe/H8bhOntJZ9BpPHVPWUa3rVFs7RJCXB25ti4eF/rcL+e8JEPHhRBC3DCsdidzN+yn4ZZBlLLFYegcy44MPz6zD+QwkVh1O8kwzCFTv43KysBo5U3HMwE2gNIZ+KvOeBxHIuh8KJy5prfZq6JJwYJDGSgVGlDMR1j81q1bR79+/ahXrx4Oh4Nhw4bRvHlzdu7c6UliEx8fn+89P//8M7169aJDhw75lj/77LOeGqoA3t7FML/ZYDo7FeDxWe6gG9y90qlH8Q2MpmOzZ+i4sA8HHaEscN7PV/ZOxOn/Yp9hGc/qD/OiK5hWKpK3to8GzQoBZaDrQkg7DgueOVt//ToPKc+dl73jeBpOl5QgE0KIkkQCbSGEEDcEqzWb3h8vZ++JdB4w7mO9KsekT/5iv+qGVbeDDONQMvX/UCVPgK1Dw4GenA4zMK8agZZyiLb/jqLF00voMMfJ4/EjSdf8cWh6Kdl0xtKlS/M9nzlzJuHh4WzevJn7778fwFMXNdeiRYto2rQpFSpUyLc8bw3VEiE36HbY3MExuHu3FzwDLgflDad4JeYwL6dsZH1KEF/bu7EYP1L0K/lOv55vsVPOVYHnMn0YtPA5TMe3gMsBSsHBdbBkyHULuvPOy9YBuWG2QSclyIQQoiS4rDna06ZNo1atWvj7++Pv70+DBg34+eefPa8rpRg1ahSlSpXC29ubJk2asGPHjnzbyMnJYcCAAYSGhuLr68ujjz7K0aNHi+ZohBBC3DwcNkhP8Pz/xPTOjE0ajFNpNLO9Tz/7S2zXkjhlep0T5teI0e3iG+XNDrx5xBjFEd87cGl6DDgxrx4Jj8e6h/uGV8PLP4xvX2hIn1YN6XRPBYa1qiqJowqRmpoKQHBwwXPXT5w4wU8//USvXr3Oe+3LL78kNDSU6tWrM2TIENLT069pWy+ZweQOhnsug4ga7uA4qBz0WgGPfYZB56Kp/m+m+U5nW/vb+C+hNM15h2D78xzXFEOdf2I5+ifNHdFsUMHgEwxfPQHJce6h6ce3wvQH88/jTk8Ahw2r3cn09QcYuWg709cfwGq/eFbeguSdl+3M05l9Z3SgfJ+FEKIEuKwe7TJlyvDuu+9y2223ATBr1izatm3L1q1bqV69OuPHj2fixInExsZy++238/bbb/PQQw+xZ88eLBYLAAMHDmTx4sXMmzePkJAQBg8eTOvWrdm8eTN6vZwQhBBC4A5OcufTdv8Rp2bg11M+zLC/ymkCseq2kWH4kkz9Dmqc6cFud6YHG8DXxwvfbrPAmnJ2aG9EDXdgdaaX0QvofV+FCzbjVqeUYtCgQTRq1IgaNQqe6ztr1iwsFguPPfZYvuVPPfUU5cuXJzIyku3btzN06FD+/vtvVqxYUej+cnJyyMnJ8TxPS0srmgMpSEFDynN7uyNrARp0+JzghX3oqY/jGcNytoe25OuTj/OVNYAThvWs1K9lBdlEHjPytCrLcP1x/B8YAV+0BXu2e9vZSeC0w6zWOEOr0vlUH7YlZF91dvAjSVnoNQ2Hyj8vu3opf/leCyFECXBZgXabNm3yPR87dizTpk1j48aNVKtWjQ8++IBhw4Z5TrazZs0iIiKCuXPn8txzz5Gamsrnn3/OF198QbNmzQCYM2cO0dHRrFy5khYtrn/xdSGEEMUvbx3sihYbHeqUwStxJ46kw/ww9Q0+1Lrwr7UrVt02Mo0fkKnfRa0zAXZbDGho2JWO1XmTVc1p7w6s8wTXUrbr8vTv359t27bx66+/FrrOjBkzeOqpp85LGPPss896/l+jRg0qVapE3bp12bJlC3Xq1ClwW+PGjWP06NFF0/jLkfe7kdvbnTv0+8wQc63jTGp6BVJzdhuGpZxgmasuX9leY6WWRLJ+Ge/r/2Ki8qfuN+MYShCPatnu2z6n9sH3z0PqUTKtDuJTjhOqIEVZUBg82cEvNziODvbBqWRethBClFRXXN7L6XQyb948MjMzadCgAQcPHiQhIYHmzZt71jGbzTRu3JgNGzYAsHnzZux2e751SpUqRY0aNTzrCCGEuDXkDqEd9t02mry/hvFL/qHh1sE0Wt+VPrEbmXvHLJo5PuDltM7sSN/CadMQEs3DqKTbxyLlzV94U1+FsclZBbvSYdRc1Nk3BTp94RkijnewO4AqIfWObyQDBgzghx9+YM2aNZQpU6bAddavX8+ePXvo3bv3RbdXp04djEYj+/btK3SdoUOHkpqa6nkcOXLkitt/VXID77xDzP1LuW/epB7FKzCStp2e5SuvyWzVL+ZdV33usL6Fj7Mpm3R7aaffTQiR9E6ycDT2CfeNn4AyzCg3gVJaMgtMo5lqnIwRB5FaCsdPpV52E7vWj6FalD+a5u7J1jRkXrYQQpQgl50M7Z9//qFBgwZYrVb8/PxYuHAh1apV8wTKERER+daPiIjg0CF3HceEhARMJhNBQUHnrZOQkFDoPq/rUDIhhBBFJren+khSFtHBPnStH4OXUZ8vkZMJB/4qnQCgvDrG766q7E/MYOjSo1h1h8k0vUOmfi91lIFR5lK0tqajnRki7lB6htifI4BMPjRPAf9KBJ8zRFxcHqUUAwYMYOHChaxdu/aCdUo///xz7rrrLu64446LbnfHjh3Y7XaioqIKXcdsNmM2m6+o3ddMIQnUXN88g87lIFJLoqnuL17QLeJPVYW5tuf5TnOQrF/F54bfmal8qeaszouZvnQ89h79jH9j1FzgUtRnO28bY+FYVXAsuKwEal5GPd++0LDA3y8hhBDF77ID7cqVK/PXX3+RkpLCt99+S/fu3fPVyTy3tqZSqtB6m5e6TrENJRNCCHHF8gbT585HnbthPyfjD2FQFj40TqY8x+lmf43m9vE40GPVbSHTMJZM/T7qnhki3hIDmjUDAqLJaf0RKXN7U0Z3kjnmd+loG8mwoAlM791chohfpX79+jF37lwWLVqExWLx3AgPCAjIV54rLS2Nb775hv/85z/nbePff//lyy+/pFWrVoSGhrJz504GDx5M7dq1uffee6/bsRSpc4aUHzHEgMpieE4PxppiMeuc3K12UTHYyFhnOotTbiPW8Sh/6naz07CSPs4UfFMr8rCrJsNIQKf5MsM8AaPmwmX3vuSs5YXdvBJCCFGyXHagbTKZPMnQ6taty6ZNm5g8eTKvvfYa4O61znu3OjEx0dPLHRkZic1mIzk5OV+vdmJiIg0bNix0n0OHDmXQoEGe52lpaURHR19u04UQQlxHebMi5yZs2hmf5qmD3cy0lxdyBrDbFc0oZzeOEYpVt5kM4xyydPu5WxkYrXy4SwvDv0ss2tdPeBJMmSOrENBvFWmftyLLVI7n69SjS8PbJOAoAtOmTQOgSZMm+ZbPnDmTHj16eJ7PmzcPpRRPPvnkedswmUysWrWKyZMnk5GRQXR0NI888ggjR468sROfGkxYvcKY89shvre/xElbPElY2KOiUS54yfEi95euyrMH+vG0YSUd1C90znmTKq5SfIuOeMN6vtX/ynd4Uc5Zi96qJgP0e7H4nsla7nK493Nie/763GeC7QvdvJLvvhBClCxXXUdbKUVOTo4ns+iKFSuoXbs2ADabjXXr1vHee+8BcNddd2E0GlmxYgWdOnUCID4+nu3btzN+/PhC91Eih5JdJ02aNOHOO+/kgw8+KO6mCCHEZTk3K7IRB6FaBqdPxhOac4SfnbU4SRATnI+TrfuTLONbZOoOUP9MgP0QejQ0jrp0fBdn5MnnN8DsNu6M0N7BeFlMePVdhb93MFVkiHiRUeck2CpMnz596NOnT4GvRUdH5xvtdrPIG+hqgEu5Ow362l8ikHROEoTXKSP3qXJEKQddct7AgYGBhsWM1VJY7arDDNvLrNAd57B+JcN0pxjjKkejI3qGaWVprD+M7qEx8E0PSHFPuyM7CatXGN+u+5PFe63sPJ6BIv/NqytJpiaEENfbrRbXXFag/cYbb9CyZUuio6NJT09n3rx5rF27lqVLl6JpGgMHDuSdd96hUqVKVKpUiXfeeQcfHx+6dOkCuIed9erVi8GDBxMSEkJwcDBDhgyhZs2anizk4sqtXbuWpk2bkpycTGBgYHE3Rwhxi4sJNBKskkgkCCMOphonU0E7xn9TP+QRx/skOOxk6/4g0ziHLN1BGigDY5QPD6LnhArmCVs//mOcRhndaRr/1pMvtdl06L4cL/+ws8NpZYi4uI7yjtLIezvCgYGTuIPuP49m0sXVl0DcNcMXmEZTWneaoyoUQ60OfL1rOCecfnzjfIhPHaHs129ilX4tqzES4axDl68+4lVdChF6A7T/DKsxgNf+G8ugtHcJd0WzGXdQn4IFOwb0msaRpKzr/2EIIcQ1dDPENZcVaJ84cYKnn36a+Ph4AgICqFWrFkuXLuWhhx4C4NVXXyU7O5u+ffuSnJzMPffcw/Llyz01tAEmTZqEwWCgU6dOZGdn8+CDDxIbG3tjDyUTQgiRn8NG96Mjaem9lY7Zb+DEwHZXDMOcPTmxL4ds3e9kmueQpYvj3jM92A+c6cEGcCgdR1Q4nWwj+cr0NnudpRm9KoG5O218+0IkXhfZvRDXQkG1q3UahPqZOZme4+5pdik4E3ibcLCPaFAw0f91Jh37L7gcROgz6OR/kD6pP/MXlZhp78O3Whqn9KuZqE9giqsMte31GPTlOBqaU5iQ9pc7gZp2JoGaKZa9Kpq+9pcIUunEBFYsts9ECCFEwS6rvNfnn39OXFwcOTk5JCYmsnLlSk+QDe5EaKNGjSI+Ph6r1cq6deuoUaNGvm14eXkxZcoUTp8+TVZWFosXL5b51mdkZmbSrVs3/Pz8iIqKOi/BzJw5c6hbty4Wi4XIyEi6dOlCYmIiAHFxcTRt2hSAoKAgNE3zzKVbunQpjRo1IjAwkJCQEFq3bs2///57XY9NCHGLcNggPQGyk9Cf2kWgM4ku5v+RoffnA2cH4nS7OWXux0nz29ylHWW18mE93jyIAU1nJKf955zQwiijO81X5rexo6ejbSR97S9hU2drDgtRHAqqXa2AssE+6HX5k7rqNagVE8aG2v/h1/vm8F6/LugiqrlLzz05D0NOCiadi9raPp7TbWSPbjVfOOvSyPYcRld5/tCv4omcH7kj2Uof531sd5YmWVmYYZ5AjC6R23VHqKIdYpH3W3Q/OtL9uyeEECWExDVXUUf7VpBb43Xkou1MX38Aq915Tff3yiuvsGbNGhYuXMjy5ctZu3Ytmzdv9rxus9l46623+Pvvv/n+++85ePCg50sXHR3Nt99+C8CePXuIj49n8uTJgPuLPmjQIDZt2sSqVavQ6XS0b98el8t1TY9HCHGLcdjgm+4wowVZ2Tl8VmUm99mnMN7ahkTXRk6Z+3LSPJZ62nHWKh9+wYemfmXQyjYAnQFcDsxr3yKgx1ekeZfhuLE8aZqFkwRhPzMAS4bJiuJUWO3qZtUizgvAXcDDNSIZ0b42TzW7By8v77M1ucs3Jt2/EodUOD1zhhCsZRCky6KD/le+Cd3FtgAjw+1dKOPoRLruKLHGxdTWJdNFleYTZ32SXL4sCOjJ3ICpRLkS0J/a5c5Snp7gCbiv9zWMEKJkk7jm+rvqZGg3q+ud2TMjI4PPP/+c2bNne0YJzJo1izJlynjW6dmzp+f/FSpU4L///S933303GRkZ+Pn5ERwcDEB4eHi+uQwdOnTIt6/PP/+c8PBwdu7ced6IAyGEyOuSSgk5bO6LfCAzYT9fnK7Bp5N+47TyJUv3N1nmOWTpjtJUGRjtHc19Waln36s3QNtpYE3xZFn2Kn0HXn1XsWtLKral+/PtyqkU0cE+1/iohShYYbWrARb/fTzfNUO1KH/Pa+f/HoUR0Xs+vT9ezh8nYC/RKAUTA17nvRe6EJn4D2/FtmSk08VaVy2mOJqxRneI3YaVDMDO6666PJi0hbu1QO4NMqDruhDSjnt+h6ztZ3i27dKMkp1ciFucxDXFQwLtQhRWluZaZfb8999/sdlsNGjQwLMsODiYypUre55v3bqVUaNG8ddff5GUlOS5c3P48GGqVat2wW2/+eabbNy4kVOnTuV7X0n7QgohikdBATVQ+IlZc56t8/tNdzIS9jOrwn/4LHUMyQ4HWfr/kWWYQ5buGA8qA6OUD40wQFYqBJSBdh/D989D6lGY097dy9dz2dm6wZZIujQM49u/EwsNXoQoDl5GfYHXAQUF4F5G/QUvcKf3a82cjYfYcOo/nLDYeK9xXbysJ2Hhs+ByYAgqR7Pmg3lwwTOcdvow19GKaZj4V/8HPxj/4CdXKKWS76Xn9A94NnsppTkBwNLlS3k3eTC7DOXoa38JhYGT8YeYuyGcno0rn9d2IcTNTeKa4iGBdiEKSnhyLYcsXqycSmZmJs2bN6d58+bMmTOHsLAwDh8+TIsWLbDZLjwvq02bNkRHR/PZZ59RqlQpXC4XNWrUuOj7hBC3hsICgTZ3lCq0DnbP46NwndjJ/PJvc2R3CHNz2pJ8IoUs/f/INM8hW3ech84E2BVUGH/WGQ/bX/TUwSa0EjyzFGa1dtcKzg2w8yis91B65ERJVFgAfrEL3PPeowW7fycAOs6EBc+guRyE6rN4sVQSAxL+4S9nWf5rf4JF2kmO6pczOtvKeFdt6rnu5qXybXloS38sWgZoEEoyoaTxkWkKWVtuh3sXnr1JJmXxhLglSFxTPCTQLkRBCU+u5ZDF2267DaPRyMaNGylbtiwAycnJ7N27l8aNG7N7925OnTrFu+++60ke9+eff+bbhsnkPmE6nWfnXJw+fZpdu3bxySefcN999wHw66+/XpNjEELcmAoLBLyN+kLrYKfEH2B2Um0+S8gmjeZk6deTZfiSLF08LZSBkcqHBmdOMcfQsdcWDM9vwDWrDUeMFZm1Mp5SoQF0fXpJ/nJd5ygseBHiRnHZF7gGEzw+C2vaSeb+k0FDVxlKeTswPfofvJa9hqYc1DYcZmbpcLKTEliY1ojJyp+/9Nv5xfglv275iRBHYzqpAB7TDjDZOJXaun0YNRdpNhOc2O4ZYs7jsyTYFuIWIHFN8ZBAuxBd68ewcOux6zZk0c/Pj169evHKK68QEhJCREQEw4YNQ6dz56srW7YsJpOJKVOm8Pzzz7N9+3beeuutfNuIiYlB0zR+/PFHWrVqhbe3N0FBQYSEhPDpp58SFRXF4cOHef3116/JMQghbkyFBQIGZT+vDnZpLZGJpyZSP2kE2Q4nmfpfyDbMIUt3gpZnAux7MJCgguicpw527wMvYX1wOb117/DHMXAdj8epjrNwq7+U6xI3tUu9wM07fSMywIvFfx9nV0I6XtpzWFQaUctNfBdWFb2Gu6fbKxDv2W3oov+dLsARVxj/tbdljpZJomElH2mZTHfWorKzDv1UNO31m/B/eDR8/bR7ygZgTTvJt1uO8m+6yX3jS0aMCHFTkrimeEjW8ULkDlkc1qoqT91TlmGtql7zJCLvv/8+999/P48++ijNmjWjUaNG3HXXXQCEhYURGxvLN998Q7Vq1Xj33XeZMGFCvveXLl2a0aNH8/rrrxMREUH//v3R6XTMmzePzZs3U6NGDV5++WXef//9a3YMQogbT0GBgE7ZGef8D4u836K0dopgLZNfXdXpbBvJijgrp9RqTpqf5bTpPzTRTvO78mUJ7iAb3HWwj2sRdLaPJF4XiW+Zmsz9J4P/nTBgUwYcLoVSSLkucdMrLFN53gvc3OkbY5fs4svfD/Pe0j3sjE9HKch26UlUQWxLyGZWmdHuXAb+pdy5DVKPQkA0dJhBtCGZ9w1rOKrbxFJnTZq5OqDhYJsplhcMK6jkupPuX69kTVIITv9orE98y2szl9JofVcabh3M+CX/0PujH7Fasy/52CSzuRA3BolrioemLjaIvgRKS0sjICCA1NRU/P39871mtVo5ePAg5cuXx8tL+kiuJ/nshbgx5Z2j7aU5sag0bo+wMJsRpCafYiqdmGV/gByXIlO/lmzDl2TpEmmtDIzETF3cJ2q70vGSrR9vGOdSRneaBH0kqxrMpkOdMnj5hzHyp718+fthHK6zpx2DTuOpe8oyum3JSmByJS50bhJX5mb5TC+WvX/6+gOMXbKLC12R5ftdyS2ll7jTM4+b5Dh3mbzSdd0BeNpRUpQvnzhr8qnmJE7/Jy4tFbOzKqVdjehkCqSX4wdu08VzyBXGcNszvG2OhbCqxDy/4KLzuAvK7VAtyl8ymwtRhOTaunhc6HO/nPOSDB0XQohbXO6d7rkb9tNwyyBK2eLIeOwHxm/5jC9OHCVD6cnUryLL/CXZulM8qgyMVL7UQU+CCmSTK4I7z8wBfc04n+dtA5lqnkKW32081aS250L9es8RE6KkuFiugYKmb5wr3+/KmXncnmA4b/I0n1CIbQlAoEnx2qN9eG1hH/5ylOYdV31+1h3ngPEz3nP58KFqQn3bw7TSUvnQNIUALZukNAMc3wrf9oTIWmfncacn5Au8r3cWYyGEuNFIoC2EELc6hw0vaxI96wRwanMyH6Xdx+ypf5OFngz9OqyGuWTpTtFOGXiRKJqS6Xmr0gzMCHuFY8fjmWKawl4VzR5i6GgbyfN16lElT2/Y9Z4jJsSNoqCbUAAaoNcV8rtypgwecDbotkS6e7sja7nf3eFzWNgHXA7uNCTwdeky5CQ5mJPWiMkY2KnfyErDEn5x3cb7jkd53GGmWswd9JndFs1xZgh5dhI47WcrBJwJvK93FmMhhLjRSKAthBC3IoctXx3sxPjDfFZ6LHOShpPltJGhX0224UuydUl0UAbeVL7cgR5UJsdcIQyyv3Am0dkp3s0aSb/IcTx+YiTpmj8OTU9YVAxdGt6Wb5dSrkuIghV0E6pKpIW2d5YmPiX74r8reYPuC/V2ewVint2GXvpt9AIOqyDedt7Bt1oS8cbPmYIRnwP38ZmzO711R3jM+S/hp/adrXkP7u0CMYHGazpC5WLD7YUQoqSTQFsIIW5iBV6sak7P/M7EltP5eH85vszshDUxhQz9KrK95mIliY64A+wwFcqfdcZT9a8BmJQVBRxQUXSyjWSe+W2yzOWZ/nxz5myKv+hFsZTrEuJ8RX4T6pze7tzs4i3/eIRg23Fc/tHoHhpN9MI+fKpt5hMFP7pq8a7y4w/d32w2rGSbqyzDk5vT8rMfeVofQdMgHcauCyHtOCx4hu6hVVkU2Yf4hOOka/5Ylb7IRqgUNP974dZjMv9bCHFDkUBbCCFuUuderOqUnbWb/2F6t7qkxB/l49P389WMY1i5hwz9CrKNX2ElmU4YeBNfqp9JcpZbBzuz/nzu3dCTXaocKViwYzg7RNzLWwJoIa7CtboJZVV6Osw5wL74JCKMUVTSHExULzNp1Rh0LgfoDGgRNWlz4h/auBykKi/ec9Rnls7KceNM5qDjO2dDSp9uT/fPvuBx6wJu5zCpGTk8W+UI92WPJ95cjg11JtKlpp/7Rh5XFwzL/G8hxM1AAm0hhLhJzd2wn5Pxh1AqCE3Z+cg4mZCkVJ75chybTw8lx2kjQ7+cbONcrKTSGQPDzwTYBdXBNj27nN67JvHHCVA6I5pSBQ4RF0KUHGeDVgMv2F4ikHRST1kYVDqGmCCg1QT4aQi4HLg0PSm+t/O64wTv5Jzgd1d5RqkQ1uj2sM+wjlHZUUxwtuBuZ2cetB+i999DCdBlYvEyUKV8Cq7Yxzhkqsis0qPddbmrm/HyDys0c3lhZP63EOJmIIG2EELcjBw2Gm4ZRDPTXp7IGY4VAz8572aJqz45xzJJ1y8j22seNlJ54kyAXTVPL5SDs3WwF3i/Q3iZmuj9w5jer7XMmxTiBpI3aLVj4CRBGDSNWaVHM6JZFHgH4wyrSmKalT7Z/UjNCeQrwyj8dVBTS8Hf3od9ho+Y77qTjzUnBwxfstzg4lfXPUxx9OIxbFTyv52+s9qic2ajV9n8fHwnOuXgkTXvEF6xNvrOsy8r2JYKBUKIm4EE2kIIcTPJTXIGlLLFkapc1NXtYbGrId+57iZD/zNW4zxySKMLRobjS+U8dbAXlh9Jx6TPKJN2lEV+4/j57liC6qxGf6ZXygtk6KYQN5DCgtZSoQGeedyzyozm4+2bSFRBGJWDnaocyqXRxfYGDgzoNB1DdAcYAuxRYYxQUfykHeKo+W0+coXjd+Ah5jgH01X/D/dq2ymrjvMf4zSiXKdJO/oP/mf+Jl2oLndeUqFACHEzkEBbCCFuFg6bJ8nZkbbf8d8yn/HdP6ew4yBD/wPZxnnYSOepMwH27WfqYP/hiqD2mTrYD5/4DF3XL2DBMwSHV8tXB1sIceO5lKD1UIqdJC0YzvR697W7h5gDLDCNprTuNEddobxjf5LJpo+Yr9uHSym+cFZmIgb+MSzgd8NXbHPdhcXRkuZaNn+r27C5DIwxvEnbXzbRdt9wdBHV8mdEL+Rvy7WsUCDZzIUQ14sE2iVIkyZNuPPOO/nggw+ueBvff/89Q4YM4eDBgwwYMIA777yTgQMHkpKSUmTtFEKUMHl6sQ8dT+DDpOZ89/EO7NjJ0C8l2zgfO+l0xcgLWmnuUeln36r0vOZ8Hosjg0+8PyK8TE2IqAE9l11y75MQouS6lKD13F7v3CHmJhzsVdHggv62AXxomoJRc2FXOna4ytFFF0d3zUWC8mOkiuJrLZ5E8zt8pYL5wdGMKEdrmp7eSYWNi9Dp43Aphe7gOlgy5GxN7kKC7sKSw11NoCzZzIW4fiSukUD7pvPcc8/xzDPP8OKLL2KxWDAYDLRq1crz+qhRo/j+++/566+/iq+RQoird04d7IPHT/BhyHC+PzUEu7KSYVhEtuFr7GTQDSNv4Mdt6ECln1MH+zRfeb/HqgazCarTwTNE3FMaSAhxw7tYRvML1fH+fMdIDhw+QjIWT9A93NaDCT6zMbpcKJ2ByNJ3MS31CJ+kpfGTM5qx+PGHYTF7jF9zyHkHC5wPU8cRSZvMA3Sd24tAlQpKwSUG3bmuNlCWbOZC3Fhu9LhGAu2bSEZGBomJibRo0YJSpUp5lnt7exdjq4QQRS7PEPF/H/yMD/dWZ1F2Fxwnk0k3/ITV8DV2MulxJsCuYCkNj32GbXbHgutg+90mQ8SFuEUU1iNcWK93j4bl6DBtAyfj03jRORCLSiMqqhShof/AqV1oHWeCTyi62JYANDfksNb7VWanj2c63szSTpNgGs9K5c9v1geY4HyV1roTdOII9859Er2yuxt2YjsseOZs0F3A36OrDZQlm7kQN46bIa7RFXcDSiyHDdIT8i9LT3Avvw7KlSvH22+/Tbdu3fDz8yMmJoZFixZx8uRJ2rZti5+fHzVr1uTPP/8EYO3atVgsFgAeeOABNE1j7dq1xMbGEhgYCEBsbCyjR4/m77//RtM0NE0jNjb2uhyPEKII5P5dyk5i/7GTvHTiER6ac4Jvs6uQbPiORK9uZBhm8jQ29uHHdLypgA6XpoegciysP5+jKsRTB/s4oXS0jWRDnYkSZAtxC8jtER67ZBdf/n6YsUt20WHaBqx2p6fXe3TbGvS+r4Knhzg3CB/Wqiqd7qlAn1YNmd+3sTuTeM9lULqOewRMZC0IiMbY7zfq3Fkbb83Au7oE4rU0fnOF0cwVidWwgkNeA5iu/47HTgbTMHscEx2dOFx/DHzTA5LjSDu8jfe/38D09QewJh3Ld92VGyjndTmBsmQzF7csiWuKhQTaBcntLZrRAlKOuJelHHE//6b7dftSTpo0iXvvvZetW7fyyCOP8PTTT9OtWze6du3Kli1buO222+jWrRtKKRo2bMiePXsA+Pbbb4mPj6dhw4b5tte5c2cGDx5M9erViY+PJz4+ns6dO1+XYxFCXAGHDWvSMaavP8CYhVs59HFHdk97iv7f7uOhUy+z0FWbZMO3JHp1J8Mwix7Y2Ycfn+KNtwrhSftwjqoQdGlHccW2pm29SrweNIl+jpfcdbA1pA62ELeQfD3CLoVSZ3uEL6TAIDzvFBODyd0L3XslmHzovKMvpbXTHFWhDLC/yF3YWaZLIB0d413RRGlpnDZNYpPXy4zS4qn//WGeTOzKAud9dEt+lhlb0vhhyY8kf/gAzvndPEFCTKDxqgLlrvVjqBblj6aBQaehaUg2c3Hzk7im2MjQ8YJkJ0HiTkiOg1mtof0nsPA59/Pc16/D/MVWrVrx3HPPATBixAimTZtGvXr1ePzxxwF47bXXaNCgASdOnCAyMpLw8HAAgoODiYw8v33e3t74+flhMBgKfF0IcX1dMKmPw4ZzfjeS/93KzOw3yMFEnFaPNa47cSYlkG5YTLZhAU6s9MbAUHwpm+feqUPpOOQMp5NzJF+Z3wZDDDFSB1uIW9o1HTqdG3g7bOgiquECNlV6m9F/v4rR5p7LbY6oySsn/uEVl4MdrkBGEsRP+nXEG37iO1clljtaEEYIjfiL5w2LiHQmkH5kG6Z9q7H9MJgGxnKU9uuPLeM06Zo/VqW/rED5WmYzF6LEkrim2EigXRBLJHT/0f1lTI5z3/EBCCrnXn6dkgTVqlXL8/+IiAgAatased6yxMTEEvsFE0IUrNCkPn3q4mVPBSDz6D8kO0xU4ihr1Z2sVJVJM3yN1bAAFzk8i4HX8SX6TIDt0vS84zWIHlkz3EnOTG/T0TaSJ+yjaFm6GiOkDrYQt7TrMnT6TO+2LjuJ9t7BkDYX1wkTS8sOpv7ucQS7HCidgSrhNZmXsA2FxixXOJPIYqfxI5KN0znmvJ+fHE9RHW9a6HbTbd7zRGjJpGTZCMzZx8deH5LuX4nf7ppIl4a3XVagfLHEcELcdCSuKTYydLwwgdHuOz55tf/Evfw6MRqNnv9rZ+YkFbTM5XJdtzYJIYpG3iGcmstOqEpmX3wSJ6Z3hhkt2P7vIdq7xtPK9i6r1W2kGuaS6NWdbMOXPIuTA/jyEd5E+5XCWaY+DvTolJOns2bxfM5ADrnC2auiScFCggqkVGhAcR+yEKKYXbeh07m92wYT1vYz6MYYBm4KYIs1ikMqnBE+w8lMScSAE5SeWq4YflbexCk/erpC0fS/keA1mF9M7/OuzUb9nLH0sQ1kUM5zfGj8L2U4QWl7HD3rBOBlPXndhr4KccOSuKZYSI92YVKOuIdV5LXwOfedn+v4pSxqJpMJp9NZ3M0Q4paVO1x8weajaIABB1ONk7ldO8KL9gH8eyqTt2wdWDnvFC4ySDN8T7ZhIWDjOYw8o0pRWztbBzvdDq9nP8sR6yNMMU1hr4pmDzF0tI0kTbPg0AwyB1EIARTP0Ok5m+L53wkDSsELtpcIJJ1Um4VH/aOJUHb65gxgDzHU4ABfm8cwXZfGJ8D3rkDGYWWL8VOSjTOZ67wXP1rwuH0kLVx/oi/fgtFpx/NnKr9IeTAhblkS1xQLCbQLkp5wdnhFULn8cxlmtXZn2bxBa8yWK1eOgwcP8tdff1GmTBksFgtms7m4myXELSHvcHETDkKUO2C+XTtCCn74kk1P6yCcZJBhmEOWYSEaNl7AyKv4UgodaOm4/Mtgb/MRKXN7E5GTwKvZQ+nISDraRpKCBTsGTmtB3B5hoeNdZWQOohDC43oPnc47L9yOgZMEYdA0/hPwBgfSjpBIEGEk84FpKkbNxUlDJCvLDKDzoRF0UJnE48ME5UOs/k9OGFaT5IrmX2dzfHelsHPPbjrpytLKtR/f41vh257u7Oe55cHSEyTwFkLimmIjQ8cL4h3svjuaO3ehbH33v0Hl3Mu9g4u7hVesQ4cOPPzwwzRt2pSwsDC++uqr4m6SEDc1q93J9PUHGLloOwPmbmFnfBoG5eBDw2QWmEaj0GhuG09b29usVzGkGmaT6NUNq2E+/VAcxJfXVRQfB47HrvMCQKfBgkPePGZ9M98Q8ZMEYT9z/1QBHe8qk69MjxCXYty4cdSrVw+LxUJ4eDjt2rXzZH/N1aNHD085ldxH/fr1862Tk5PDgAEDCA0NxdfXl0cffZSjR49ez0MRJUBh88IbVytNWFQMmgYZOn/2qmjidZFYnv6SJ1M/Q6ecoDMQHnkX72l2TqJY6vKjATmkGmI56tWdJfpFDHA1pN7pEbz+2XdsSfZCxW+DjAQ4tiV/VuVLKGWU9+/19PUHsNpLbk+ZEJdM4ppioyl1zl+/G0BaWhoBAQGkpqbi7++f7zWr1crBgwcpX748Xl5eV74Th+38LHxyZ/SCiuyzF+ImcW7CM81lJxB3L/YowyzmuR5gvasWTtLIMHxHluEHdNjph5FXMBFx5l7oMRXC/BqfMuiBCrhmteGIqSL97C+xM9FKiEr29GKDOwhXuOddfvtCQwmyr6MLnZtuJA8//DBPPPEE9erVw+FwMGzYMP755x927tyJr68v4A60T5w4wcyZMz3vM5lMBAefvWB74YUXWLx4MbGxsYSEhDB48GCSkpLYvHkzev2lfS9vls/0VlZQ4sfcv0+AZxh7TKCRLjX98PIPcwfHiTuh1QT4aQikxOHS9BzxqU6o/ThZtkQ+UkY+1pyc0NIwuKLwc7bAz9GMqnornYL38ljGPIJVijuY6Dgz/xDzAq7jLtRO+TsqiovENcXjQp/75ZyXZOh4YfLWh8x1gw6rEEIUj7kb9nMy/hBKBaEpO1ONk7EpPYMdL9DPMRAnqWQYZpJp+AEDDl7CyBB8CUeHXenoa+vHG8a5lNGdpveBl7A+uJzeunf44xg4sOJScJKgfPusUzaIh2tEynBxccWWLl2a7/nMmTMJDw9n8+bN3H///Z7lZrO50MywqampfP7553zxxRc0a9YMgDlz5hAdHc3KlStp0aLFtTsAUaKcOy88MsALTdMYt2RX4XPE8863jqgGGug6ziTGKxBmt8HXpmOU5mQkil9cPozDxkrDF6QY5pDkrM/fpx/mPdckmuv/4smqtWk4vxu6tDP1g7OT3P+eE2DkqzF+pg8qt8a4ZCkXNzyJa4qFBNpCCHEtOGw03DKIZqa9PJEznEzMTHO0YYu6HSeppBnmkm34ASMOBmFkML44VTCHVARB2j6MmovXTfN5wTaQT70/IrxMTWb9k+FJKpRX3l7sOb3vkQBbFKnUVHe5uby91QBr164lPDycwMBAGjduzNixYz11Tzdv3ozdbqd58+ae9UuVKkWNGjXYsGGDBNq3mNx54YWWNTy31zhvUJAbdIN7KHjqUQiIZmWZfjTe/gaNNY3G2EnCh4nKi5n6vzlu+JUkVxRznS34YXUVymuD6Oy7hcfbvUh4IQnULlRjPDeJpdTeFkJcDgm0hRDiChV48aU5saad5NstR3kgfT8HXaGEa8n8pSqxSTnJMEwn0/AjJpwMwcggfAk9M0Q83cuL/1UYzSFvGw/tfIMsczna12lBUM0O6P3DOPTT3vMuBHUakvRMXDNKKQYNGkSjRo2oUaOGZ3nLli15/PHHiYmJ4eDBg7z55ps88MADbN68GbPZTEJCAiaTiaCg/CMuIiIiSEhIKHR/OTk55OTkeJ6npaUV/UGJYnNFvca5QbfD5g6OATrOpM6spzBqLuxKx1ZXJUprp3hbd5q3gF+VD++RzVLDF6Qa5pDkvJe9Wa2YOG0XLfRbeErnSwPXTnQH18GSIRBejZhSowhWSSTnmYrjVIrIAK9LuzkghBDnkEBbCCGuQN6eGS/NiUWlsXhLHAtCPiHp37+Ynj2EMbxLDiacJJNh+JQMwxLMuHgFA4PwIgQdWKLgsc/g++expB5lYMLr7gygTVcR4B1MlTxDGwtKKpQ36ZkQRa1///5s27aNX3/9Nd/yzp07e/5fo0YN6tatS0xMDD/99BOPPfZYodtTSnlqpRZk3LhxjB49+uobLkqkC/UaX5TBlK8HOt2/EuknHfTLGUASAXxjcn9vHDpv7ms/lfsW9uGUy5vpysVk/SYSDOswusox39GKH+2DqJiUzdOzZ9BRdwJ/oEvDNFp6v8V2e2ledA7EqvRnao5rMqRcCHFFJOu4EEJcgdyeGYNy8F/9B3xjHI0rfjs/789mgPU5DlKKTDJIMX5CglcPnIbFvI6OQ77lGZsbZAPo9O5kPc8szZ8B1BJ5XoKSrvVjzlz4gUGnoWlIjWxxzQwYMIAffviBNWvWUKZMmQuuGxUVRUxMDPv27QMgMjISm81GcnJyvvUSExOJiIgodDtDhw4lNTXV8zhy5MjVH4goMQrLQB4d7HNpG8jt3TaYiOg9n2FBE9ihVSRZF8ROVY4TunCcXb+H1WPA5SBUZ+L1so045lua5cqH+7XTJBunccyrG5v5hjcdD1A/5yOGW97m2DevEeVK4B6/E/Ss48+wVlX5tmsFEpPcPdl5XfLNASHELU16tIUQ4gocP5VKpJaCQykqcYQDKgqXpuNF63M4OE2mcRoZ+qX44OINjAzEhyA0yDwJAWWg3cfw/fPu+Ya5dSx7LrtgBtBzkwrJXEFxLSilGDBgAAsXLmTt2rWUL1/+ou85ffo0R44cISoqCoC77roLo9HIihUr6NSpEwDx8fFs376d8ePHF7ods9lcomqgiqLVtX4MC7ceOy+z95XcLPTy8mZ6v9aev4dHAqdxd1Vv5u7KpqGrDFFedlZWf5cjWSZ6Jb7EQxh4CDiML1Nw8rF+Gcf1P5Lsqstn+9vyhWsQD+m30rtFU4bUrYeWuAO+eIbu+hjmqF6Eke6p8HBZNweEELcsCbSFEOJyOWx0PzaSboZdPGYbzUP297FjxMEpMo1TydAvwwcXw84E2FYVzP/qjKf19hfBnu3eRmgldy/2rNZne7EvocRGblIhIa6Vfv36MXfuXBYtWoTFYvHMqQ4ICMDb25uMjAxGjRpFhw4diIqKIi4ujjfeeIPQ0FDat2/vWbdXr14MHjyYkJAQgoODGTJkCDVr1vRkIRe3nqK+WZj372He6Txm+mBR6fC/NBaYR2PREjnqCuU9ZxcmGj7kfU3HGAx8qXS8q9vBv+ZNmFwVWOTowIpFSdRZ9gUD1FyaEkd0ELQMO8Wg1HfZp6Lpa3+JSlHBMpJICHFREmgLIcSlOlOHUinF3gxvptjdcwPdAfZ8MvTL8cXFm5h4EROBuIcbntAZaHbvPdBoA8xuA5G1zgbWF+nFFuJ6mzZtGgBNmjTJt3zmzJn06NEDvV7PP//8w+zZs0lJSSEqKoqmTZsyf/58LBaLZ/1JkyZhMBjo1KkT2dnZPPjgg8TGxl5yDW1xc7pWNwvzJlqzYsBKEEYc7HVFgwb9bQP40DTFk0At3b8KvTP30stlZ5XyYYyWwHrT+xhcs8iwd2SL80VqakcZWLk0E3cOR68lYjEbGHZvOJqmMf7HbZQKDZBRRUKIQkmgLYQQhTkTWOdmvFVfd2fFISf/9XqO7SndcGgnyTR+SLp+ORYUIzExABMBaNiVjv8EvEof2ywirPHw5aPuoLr3qvyBtdSxFCWMOrd+3Dm8vb1ZtmzZRbfj5eXFlClTmDJlSlE1TYhCFZRozY6BvvaXCDwz7HuvigYXjHI8w39yvgCXA6UZqORzBz86T3PAepjRWgaLjFNJM3xNhuNx/llXhnu0ZxgatJKqPT9hzRd/MjrldUqd6d2WDORCiMJIMrQb2Nq1a9E0jZSUlCLdbrly5fjggw+KdJtC3HAcNvimO8xogUo+zLK/DtB6Z1P6pHbnr8TDpJqmkuDVG02/jDEYOYQfvVUEe5xVsCsdRs3FM9bZWJ6ee9EkZ0IIIa5OQYnWwB1snyTIE3R3tI3kf6o6qZZKxOsiaWsdxcPJr9Aj9VlqYGIhBnbgSzstk2Tjx8Sbn2eN7iTtkvrTZeZfDEx+ixgtkUocIUClczL+EHM37C+GIxbi5nIzxjXSo30Da9iwIfHx8QQEBBTpdjdt2oSvr6/nuaZpLFy4kHbt2hXpfoQoboXVwSY7CQDXiV0sPx3KpPeXssdVGodmJMs4mTT9KgKAt5WRfpiwnBkinqb0DLE/RwCZfGieAv6VCI6oIcPDhRDiGsubaE0HOM/E3Hrt7P+VzsgpFUS1KH9W1BjP9OV/kqiCCFPJTDJNxYCTNK/SVH30Xb5Z8Aw7XToGammsMI3H5FrIhuQ+PO0aRh/9YuY6H8SIk3mmt8nacjvcu1D+xgtxFW7GuEYC7RuYyWQiMrLoh52GhYUV+TaFKGkKq4P9XeinqMTdjA16i8UpYzllB4eWSKbxA9L1q/FH4x2M9MWEHxpYosh59GNS5vamjO4kc8zv0tE2kmFBE5jeu/nZcjRCCCGumXMTrUUGeKFpGvEp2fn+n3tTddySXSRpwaCUZ1i5puDnsqN5bsWb4HJQRTPxmU91dub8yxB1hJ3mV8h0PMz79h5Y0DPdOIGyWiJpNtPZaUZCiCtyM8Y1MnS8BGnSpAkDBgxg4MCBBAUFERERwaeffkpmZibPPPMMFouFihUr8vPPPwPnD7GIjY0lMDCQZcuWUbVqVfz8/Hj44YeJj4/Pt4+BAwfm22+7du3o0aOH53neIRblypUDoH379mia5nkuxI2usDrYi/510SbxWWbu1pGQc4JU4yTizb0w6FczDiObVClexewOsgGXpsccVpGAfqtI8y5DVuDtPP9wPab3a42Xl3cxH6UQQtw6chOtjW5bgxea3MbzjSue9//e91XAy6jPN9Q877ByY+laOEOreoaVP5P8DLXsXmzDyGSXFw79SuK9nidRt4U+9sF84WqBqdcSd5CdnuCediSEkLgGCbRLnFmzZhEaGsoff/zBgAEDeOGFF3j88cdp2LAhW7ZsoUWLFjz99NNkZWUV+P6srCwmTJjAF198wS+//MLhw4cZMmTIFbdn06ZNgDvbbHx8vOe5EDcKq93J9PUHGLloO9PXH8BqdwJn62AHkk4ljrBHRWPVzAzK6s42vEg1TiTe3Bujfg3jMRGHL69gpqKWyjFXCE/ah3NUhaBLO4ortjVeZjP+fVdRZcBCejauLIlxhBCiBOtaP4ZqUf5oGhh0Gg7NQFhUDF0a3sasMqNpm/0mCSqYmYZxlNZOE6/CqFBnFnt8onkQG6fM44gzfs4wW2cenR3H7KW/4vq8hTu3hwTbQgAS19wyQ8ez7FnsPrX7uu+3SmgVfIw+l7z+HXfcwfDhwwEYOnQo7777LqGhoTz77LMAjBgxgmnTprFt27YC32+32/n444+pWLEiAP3792fMmDFX3P7c4RaBgYHXZDiHENdS3uHhek3DqZQ7Q2yfuufUwZ6AHQN2LYEs40TS9GsIQWM8Jp7HRLoKpqetHzNN4/HRbChgvzOKTs6RfGV+GwwxxMgcbCGEuGFcqKb3oRQ7SVowmrJ7MpV3dbxJe1swrY3e/Iyez1ze9NevJEG3E8fJ11iwzkFb00ks7ESXO4w8PUHyc4hrojjimsuNaUDimlsm0N59ajd3fXrXdd/v5j6bqRNV55LXr1Wrluf/er2ekJAQatas6VkWEREBQGJiIv7+/ue938fHx/NlBIiKiiIxMfFKmi7EDS9vXVWHUhhxcDL+EAvWG4nONDLR3t9dB1s7QYZhLun61fkCbN8zw8OTlY5jhPNwzrvMNY1llypHChbsGHjCPoqWpasxQi6khBDihlJYTe/cYeUqT3kwTYPeB16C7KNoAWWpUKYff2x/jce008SZB7HR9iodbCO52+iNcfUpqvj8S+cdfdFFVIPHZ0mwLYpUccQ1lxvTgMQ1t0ygXSW0Cpv7bC6W/V4Oo9GY77mmafmWadqZeaEu1yW/P29NVJ1Od16NVLvdflltFOJGkTs8PF4FYsTBVONkbOh5c/0Akmw9cWiJZBgnk65fRTDkC7DtSkd/ez9eN8yljO40iyzvMi5iEo/tH0OycgfZAAkqkFKhRZshUwghRPHJm8Fcae5M5bUivfENrQmnDND9R9avS+WkYxDbjRNpj4HlprFstT9HypFGRB5bR0/jB+i0RFxwtodbiCJSHHHN5cY0IHHNLRNo+xh9LvsuzM0oLCwsXxIBp9PJ9u3badq0aaHvMRqNOJ3O69E8IYqE1e5k7ob9tNz9Ot0NB3jSNpxsTHzueJiNqjoOTpFm/JpM/VKCgHfPZBFPV0HscEVQW7cPo+biDa9v+L3OZErtH05IRDXebt+YDp/+ycn4NAxnhqJXi/Kna/2Y4j5kIYQQRaSwYeV6rZEnu3gVn8P0NHyJDxo/o3geC5+ZPsZhTwRHW1BwiHA2VJ/KkxJkiyImcY1bSY9rbplAW7g98MADDBo0iJ9++omKFSsyadKkixaGL1euHKtWreLee+/FbDYTFBR0fRorxEUUVAcbp43eHy9n74l0vjEdQIeimi6Ola66/E+VJt34KRn6n7CgeBsjA3LLdAHJ59bBDq5M+4dbQHY98A7Gy2AqdE6fEEKIm0fBw8r1nrnXnXf0RaclckiF84qjL+/rp1JBg6GGhezVbHS1v06IlskdWWdGPMl8bSGKXEmPayTQvsX07NmTv//+m27dumEwGHj55ZcveNcH4D//+Q+DBg3is88+o3Tp0sTFxV2fxgpxAQUlOlu8JY4P9B8wNnkXndWbtMoZRw5GcrCSZphFluF7zDh5EyMvY8IfjQQVxFcVR/HowbcvqQ52YXP6hBBC3CK8g9FFVMMFbKg+lapZAcxNCGLI8ZcxqEBeMfzINuVLXecDxBiTsZ46hO3zVhw3lWNDnYl0aXib3KAVogiU9LhGU+cObL8BpKWlERAQQGpq6nkT561WKwcPHqR8+fJ4eXkVUwtvTfLZi+tp+voDjF2yC6XAiMOTrOY777GssNXkP47HSUNHuuFHsgzz0JPDSxjppsKpqqV5tnNUhbC+0Re0r11GLoTEVbnQuUlcGflMRYnlsHmGkZOegOvzFuhS4jiqQpiAjcm6YwTae9Bcu5OJhqmUVgkcVuF0tI0kLCqGb19oKOcYcVFybV08LvS5X855SXq0hRA3pCNJWeg1DU3ZmWqczO3aETrax9DC9j4ZDhcZ+pVkGmfjII3nMTAcXyLRgZbGMRXCEMcLvG+YRhnttHsIYP1lePVdhb93MFVkaJ8QQogLyTvaKU8P9/rqUymbkk3vXb2YboxllW0gc23300m/lidtw0kkiJPxaczZeEhGRwlxk5NAWwhxQ4oJNBKsklBACKmMd3TmpPIn27WZNPN0rLojPKGMvI0vAVooli4z4esnwJ6Nv5eROpXqsT6g4dnyKzJ3TgghxJUwmODxWeiykzyJz6z/fsmh2U+wwvgRE3PeZ5mzLscJBSBSS+H4qdTibLEQ4jqQQFsIcUPJzSjeYPMgmpn20yZnDB3so7Bpx0k2jSBbv4WGysgHypd6uIfluSw+6MIqwvMbYHYbLJG1eKVdQ/fFUf1lEmQLIYS4Onl6uK2nDpH8ZS9+0NKooIJINI0jPmcsESShx8VXxrfhWFVwLJBzjxA3Md3lrDxu3Djq1auHxWIhPDycdu3asWfPnnzr9OjRA03T8j3q16+fb52cnBwGDBhAaGgovr6+PProoxw9evTqj0YIcdOx2p1MX3+AkYu288nqXfT6cDEfL91EWsopXrX3Ihk9SYbZxJv7EaL7h++UN7/iRbQK5cfan0FAGXRpR2FWazD5QO9V8Pissxc3lki50BFCCFE00hOwfd6KKFcCp1yhLFBe6LR0DpimEaad5ivTW8RoiUQ7DrnneAshblqXFWivW7eOfv36sXHjRlasWIHD4aB58+ZkZmbmW+/hhx8mPj7e81iyZEm+1wcOHMjChQuZN28ev/76KxkZGbRu3bpIa5rdgDnebnjymYuilptZfOySXXz9+wEqrOnH2KRXyFEGnrANZzUZxHs9h9WwkJHKwD68aI8RDQ0nOvbaguGZpRBUDsLPDA+XwFoIIcS14h3McVM5DqtwOtlGMsQ2hgmu0lh1W/iffgu/uO7AFVgOXY8f81W0EOJC5Br7+iqqz/uyho4vXbo03/OZM2cSHh7O5s2buf/++z3LzWYzkZEF//FITU3l888/54svvqBZs2YAzJkzh+joaFauXEmLFi0u9xjyMRqNAGRlZeHt7X1V2xKXJysrCzj7MxDias3dsJ+T8YdQKgg/lUYZfSLvOzuThJMk4/tkGdbTVPkwA2/KaToSVBAv2frxH+M0yuhO0/vAS6BfBT1leLgQQojrwGBiQ52JfLx0E4m46/Ousz9Pa+Nn/Gj4ijE579GixWDCA6OLuaHiRiBxTfGw2WwA6PVXVxngquZop6a6EzkEBwfnW7527VrCw8MJDAykcePGjB07lvDwcAA2b96M3W6nefPmnvVLlSpFjRo12LBhQ4GBdk5ODjk5OZ7naWlp562TS6/XExgYSGJiIgA+Pj5omnblBykuSilFVlYWiYmJBAYGXvWXUggAHDYabhlEM9NensgZznFCaWd/i2TdZk57vYA3Nr5wefOUpkfD/TvuQMdxLYLO9pEs8H6H8DI1JcAWQghxXXVpeBvf/p3Iyfg0yminmWicRiiJRKhgDps+ZdT8bKYOjIbAaEhPkPOUKJTENdefy+Xi5MmT+Pj4YDBcXTqzK363UopBgwbRqFEjatSo4VnesmVLHn/8cWJiYjh48CBvvvkmDzzwAJs3b8ZsNpOQkIDJZCIoKCjf9iIiIkhISChwX+PGjWP06NGX3Lbc3vTcL6W4PgIDAwsdySDEZctOopQtDn8tkecMixnpeILjxhlkGH6miQrka4yEaTpOacFYuszEuLgfZdKOsshvHD/fHUtQndXo/cPk4kUIIcR15WXU8+0LDfl23Z+0/GMIwbZEXAExTNVF0j1lBfNsR+j86cs0fuoNWPCMe2pT3twhQuQhcc31p9PpKFu27FXf1LjiQLt///5s27aNX3/9Nd/yzp07e/5fo0YN6tatS0xMDD/99BOPPfZYodtTShV6MEOHDmXQoEGe52lpaURHFz7kRtM0oqKiCA8Px263X+ohiatgNBqlJ1sUDYfNnSDGEomp1xImTn6b/zjv4qR5MC7tOO+5gnhFc6CdSTERnJtRvOdSmNWa4PBqPNWktlywCCGEKDZeRr37XHTyTkg0oes4k24LnuFDZ3X+NM5iSNp7rP/6WbxS49xvOHPeE+JcEtdcfyaTCZ3uslKZFeiKAu0BAwbwww8/8Msvv1CmTJkLrhsVFUVMTAz79u0D3HdlbDYbycnJ+Xq1ExMTadiwYYHbMJvNmM3my26nXq+X4E+IG4nDBt90h8SduLot5v2NmXzojOakeQghmFilvKilOSGoPDz6IXz/PLrUMxnFey6TudhCCCFKjjP1tT1BdPcfWfh5Wyqke7NDN4c5pyvRM9SBrrskRhMXJ3HNjeeyQnWlFP379+e7775j9erVlC9f/qLvOX36NEeOHCEqKgqAu+66C6PRyIoVKzzrxMfHs3379kIDbSHELSI7CRJ34kg6zMDJs5n02wckmkZTXQWwXzmopelAZ4D2n0H5+ySjuBBCiJItb31t31JM4jn6OiuSrd/EeFdFBjn6YfUtVcyNFEJcC5cVaPfr1485c+Ywd+5cLBYLCQkJJCQkkJ2dDUBGRgZDhgzht99+Iy4ujrVr19KmTRtCQ0Np3749AAEBAfTq1YvBgwezatUqtm7dSteuXalZs6YnC7kQ4hbjsLkTwlgicXVbzCu6gcQ6/yDZ9BltVVn+0mUT0PkLCIwBlwMWPutePzDa3Ystc9uEEEKUcAvXbmRA2gTe0+3H4irHIeMCQpO3sHDtxuJumhDiGrisQHvatGmkpqbSpEkToqKiPI/58+cD7iEN//zzD23btuX222+ne/fu3H777fz2229YLBbPdiZNmkS7du3o1KkT9957Lz4+PixevFiGQwhxK8odLj6jBSr5MMPWJDPdsYF0/Y/0d1bmey0J/eOxUK0t9Pgpfw82SC+2EEKIki89gRabehOjJRKvQqlob4dNt5fpypeGf/R33zxOT3CfE4UQNwVN3YAV0NPS0ggICCA1NRV/f//ibo4Q4mqkJ7iD7KQ4RuufZ4L9TzL1a3nJVY0PdIcBcAWWQ9fjRymFIko0OTcVPflMxU3DYePQxx3h5C6eznmdYYa5PKlLIF1LY6jxGca80APmtJcM5EKUcJdzXrr6dGpCCHE1ziSIiTV1ZoJ9E5n6tbzovIP3taP0tb/MIRWOLiUOV2xrz/ByuQARQghxQzGYiOg9n2FBE7BqZqrqDzNK+eHQHWW6LYXk2V0gOQ4Sd7rzlQghbngSaAshikfuvGxgwylvBlnTyDSsoZezPpP1/9Lf9iJLnPV4Mmc4h1Q4RwwxZ4eLCyGEEDcYLy9vpvdrTZ9WDVlQYxrdfDOJctYhUTeX6acruadGSQZyIW4aEmgLIa6/PPOyD8ftp/OcsaQY5tPI0YTPdNsBeMM4l1Kc4jihPGEfxazSo6UnWwghxA3Ny6in930VGPR4MwK7TGe48sapneYj5U3Oox+7p0gJIW4KEmgLIa6/M2W8MpPiaT99KkeZQoyjIct120jSB3PUFUqMLpGvTG8TRjIJKpBSoQHF3WohhBCiaKQcgYXP8axuJ4HOOzluWMaPX01zLxdC3BQk0BZCXH9n5mUP0T3JVsOnBLiq86vuKKcJoG32KDrZRnDIFc4+oknVLFSL8qdr/ZjibrUQt4Rx48ZRr149LBYL4eHhtGvXjj179nhet9vtvPbaa9SsWRNfX19KlSpFt27dOH78eL7tNGnSBE3T8j2eeOKJ6304QpQ86QkwqzUkx2EMjqZHpe7YdYcZn+mLys1HIoS44UmgLYS4fvLMy152TGOGaxk65cd8ZaKMlsJLtv4cVaEcJ5SOtpFMjxzJq61q8u0LDfEySvk/Ia6HdevW0a9fPzZu3MiKFStwOBw0b96czMxMALKystiyZQtvvvkmW7Zs4bvvvmPv3r08+uij523r2WefJT4+3vP45JNPrvfhCFHyeAe7s4ufmZM9vP0LmF2V2Kb/jc1+jc/mI5FyX0Lc0AzF3QAhxC0id1524k5OP/49PRa8gU3bR29HO1oYVgIw0TiNJ23DOU4oybpgKpcOofd9FYq54ULcWpYuXZrv+cyZMwkPD2fz5s3cf//9BAQEsGLFinzrTJkyhbvvvpvDhw9TtmxZz3IfHx8iIyWxkxD5GEzuEl7ZSWCJJMRho7m5AYvts3nfOYD5BpN7CPms1lLuS4gbmPRoCyGuKavdyfT1B3j/+w2kHd6GSorj6Y/HkqDmU9HRhin6dWSawjiq8s/LdipFdLBPcTdfiFteamoqAMHBhWf9T01NRdM0AgMD8y3/8ssvCQ0NpXr16gwZMoT09PRr2VQhbhwGE1avMM/58Q3XYfSucJYcncPxHf/zDC2Xcl9C3LikR1sIcc1Y7U46TNvAzvg09JrGQterPGH4nuWGhXi5qvOt7gDJWgCx5aey8cBp/msbIfOyhShBlFIMGjSIRo0aUaNGjQLXsVqtvP7663Tp0gV/f3/P8qeeeory5csTGRnJ9u3bGTp0KH///fd5veF55eTkkJOT43melpZWdAcjRAly7vnxe9WDaobv+Ue/mI/mTmWsMU7KfQlxg5NAWwhxzczZeIid8WkoBQ6lOE4ob2pWXGTzuqsKdxiW0yFnJFu32XEpf16wvEPdKhV5NTyQrvVjZF62EMWsf//+bNu2jV9//bXA1+12O0888QQul4upU6fme+3ZZ5/1/L9GjRpUqlSJunXrsmXLFurUqVPg9saNG8fo0aOL7gCEKKHOPT8eI5SyjiZo+qXM0qyMURr69p9IuS8hbmAydFwIcc0cP5VKpJbiea7XtpGmX0YFR1te068B3POyI9UpAHZl+FA2PJDe91WQIFuIYjZgwAB++OEH1qxZQ5kyZc573W6306lTJw4ePMiKFSvy9WYXpE6dOhiNRvbt21foOkOHDiU1NdXzOHJESh2Jm9ORpCz0muZ5XopTzDBOI9BZnwT9/1jvqg4Ln5NyX0LcwCTQFkJcGw4b3Y+NZJ5xFKU4RSDJHDN9jlGVZyKJdLe9xiFXeL552XpN40hSVnG3XIhbmlKK/v37891337F69WrKly9/3jq5Qfa+fftYuXIlISEhF93ujh07sNvtREVFFbqO2WzG398/30OIm1F0sA9OpQAII5mvTG9zmy6eh3XROHUn+K++snuO9iwp9yXEjUoCbSHEtZGdRLTjEDFaIl+Z3+aUfjU5un9p5mhGbd1B4ojiSdtwDrnC2auiScEiCdCEKAH69evHnDlzmDt3LhaLhYSEBBISEsjOzgbA4XDQsWNH/vzzT7788kucTqdnHZvNXYro33//ZcyYMfz555/ExcWxZMkSHn/8cWrXrs29995bnIcnRInQtX4M1aL80TTI0PmzV0UTr4vkxQ6vY3CVYaU6QHpAZXfWce/CExEKIUouTakzt9NuIGlpaQQEBJCamip3u4UoyVKO4IptzXenfeik/weLsyF/Wmz8WXss29MtrNqdiCs9gXTNH6vSUy3KX2pmixvWzXJu0vIMZ81r5syZ9OjRg7i4uAJ7uQHWrFlDkyZNOHLkCF27dmX79u1kZGQQHR3NI488wsiRIy+YvfxcN8tnKkRBrHYnczYe4khSFjGBRrrU9MMcVIpK457nX9tMni/7HVFBpSkVGkDX6ma8/MOkzJcQxexyzkuSDE0Ice0ERkP7jxk0/Q00NF50laLSE89TqWx9AN7Mc5ERHewjCdCEKAEudv+9XLlyF10nOjqadevWFWWzhLjpeBn19L6vgue51e5k5i97aefw4T+4+PbfH/BX7Qh3bePhVWNJ96/Eb3dNpEvD2+RcKcQNQAJtIcS1k3KEmXPf44j+VyIcHRliWAYLt7rLlQRGn3eRIYQQQtyKcst9nYw/xEfG9UyjHqmG1dydU5OPTP+lDIkcSlV8vHQT3/6dKKO/hLgByBxtIUTRctjciVvSE3DGtmFE1il0+DKoZhcCgsMluYsQQghxjtxyX4kqiJdsA7jDWR2b7l+qGFYQo0vkkCucJ3OGk6iC2BmfxpyNh4q7yUKIi5BAWwhRdBw2+KY7zGgBtiym6+/huP43Ihxted7+DXRdCEHlJLmLEEIIkUfecl/HCcXorIdeBTFfy0YpGGR/geOEAkiFDiFuEBJoCyGKTnYSJO6E5DicXzzGqJR96AlkEE78T/8NJh/ouQwenyUJXYQQQogz8pb7KsUpPjZOwc/ZgHj9VnapMkw0TqMUpwCkQocQNwgJtIUQRccS6Z5/HVSOaaf9SHCuJcLenj4he93LLZHuhwTZQgghhEduua9wLZl55repqj9CPVdZnNppJriqEKNLZJ75bcK1ZKpF+dO1fkxxN1kIcRESaAshilZgNNmtP+JtzYZehTFEs2JuO8mdgVwIIYQQ5/Ey6vn2hYY8/3A9sgJvJ827DD2aPYtehbDS20WadxmyAm/n+YfrSSI0IW4QknVcCFGkrKcOMWf2GE7othBpf5pehp9JmrOJoL7L8QqVO/BCCCFEQbyMeno2rgz3LoTsJFrrQ/Bb3Yjj/MKJJ/+gStloqsiIMCFuGNKjLYQoOukJ2D5vxWSXFQ0zpZwNSFYWolzu5ZJpXAghhLgIgwkskQT4GKkf1RqnlsRHf2+UaVdC3GAk0BZCFB3vYNZqNdml34qfozmnKc2TtuEcVuEcN5WTTONCCCHEZXj6zqboXWF8v+fbswvTE9xVPoQQJZoE2kKIq5dbO9tgYoSuFC6yCHE+jBEHxwmlo20kG+pMlLvxQgghxKVy2Gix/y18nfdyxLqGuFPpkHLEXULzm+4SbAtRwkmgLYS4OnlqZycd/5edGV/j42zIJMP3TDNNxqQ5CIuKoUvD24q7pUIIIcSNIzuJ0OSt1HOVwaWlMGXZpzCrNSTHuUtpZicVdwuFEBcggbYQ4urkqZ395ucvYdeOUZN7aavbQB2veEY+GCkZUoUQQojLdaZk5lO+NvSuCBbu/tYdZAeVO1syUwhRYkmgLYS4OmcuBGwBFfjSHofJVZlBaicqqBzBfZfzVLN7JMgWQgghrkRgNC3bd8PHVZ/D+j2cUBZo/4mUzBTiBiCBthDiqll9S9Ex6ylS9TuIdDTlUf0GXnX2xepbqribJoQQQty4Uo4QuaIv1VxlcWpJxLrKwcLn3HO1hRAlmgTaQoirtnDtRg5al6Apb7rjwEuzMyBtAgvXbizupgkhhBA3pvQEz5zsx7x06JQvC/Rm9/DxWa2lZKYQJZwE2kKIK2a1O/ly5e9U2zCAXbpt+Dob86fzDg65wonREmmxqbdcCAghhBBXwjsYwqtBUDkefOx5vJx12c4hHIEV3MulZKYQJZqhuBsghLgxWe1OOkzbwL74JGrqKuI0bSXQ1pSdlOdJ23C+Mr8N/pUIlgsBIYQQ4vIZTPD4LMhO4k7fCEINDTjMuyxt9Aut77xHSmYKUcJJj7YQ4orM3bCfk/GHsCkDS/XxGF0x+KsYzJqDeC2UYUETiOg9Xy4EhBBCiCtlMIElEr1O4+HbHgalY8aOVXJuFeIGIIG2EOLyOWw03DKIBabRBHCYLN3v+Dke4hvTW8T6fsSbD9/G9H6t8fLyLu6WCiGEEDeFlpXLYXZV59ejy84uTE8Ah634GiWEKJQMHRdCXL7sJErZ4vDXErEYvwOgjQqkmu4IaUZFgzoBICW9hBBCiKLhsHH/rpH4OO/mpH02L837jfohLjrv6Isuopp7iLn0cgtRokiPthDi8lkiMfVawn4tmj91e/Bx1qefYR3xOvdyLJHF3UIhhBDi5pGdRODprdylIkCzs+qfL2n4aw90KXG4TuyE7KTibqEQ4hwSaAshrohXaAzf1RxIju4IFVx1qKftIbjrDLxCY4q7aUIIIcTNxRLJ/OpTuU9LweAqg0P/P2K0RA6pcOZXnyo3uIUogSTQFkJcmZQjfLHtK3TKQg8tCU0D8+K+kHKkuFsmhBBC3HR2ZwXwu6qOj/Nu9usOkqN0vOLoy+6sgOJumhCiABJoCyEuX3oCpz7vxG61HR/nvTzW6XkIKgfJcTCrtdTOFkIIIYpYFZ9UPjR8QISrMk4tlXmuaN43TKWKT2pxN00IUQAJtIUQl8VqdzJjSypjsivi0CVS3b8Z5WvdC91/dAfb4dVAamcLIYQQRSc9gc47+lJed4JG6NCUmdn4E6Ml0nlHX7nBLUQJJFnHhRCXzGp30mHaBnbGp5FkSEevD8Jlq4HV7sQrMBp6LnMH2ZL5VAghhCg63sHoIqrhAoKDH8Rr/zI2GzNx+ZdzZx2XG9xClDjSoy2EuGRzN+znZPwhXMpJpv5XfJyNcKafYu6G/e4VLJESZAshhBBFzWCCx2eh67WMwW3r4+WsTaprOyceXyClvYQooSTQFkJcGoeNhlsGscA0Gr1uM04tiQBnQxaaR9FwyyBw2Iq7hUIIIcTNy2ACSyRRAd7cHtgINAef79okQbYQJZQE2kKIS5OdRClbHGW1RGz6Nehd4QzRdlJWS6SULU5qeAohhBDXSfNKtdG7wli85+fibooQohASaAshLo0lElOvJfyP6sTr/8bibMTThtXE69zLpYanEEIIcX3cWykMb1dttp9eX9xNEUIUQgJtIcQl8wqNYVZMW1xaGo1UJMFaOsFdZ+AVGlPcTRNCFJFx48ZRr149LBYL4eHhtGvXjj179uRbRynFqFGjKFWqFN7e3jRp0oQdO3bkWycnJ4cBAwYQGhqKr68vjz76KEePHr2ehyLETat+WQs+rjpkqUNsOrLPvTA9QaZxCVGCSKAthLh0KUdYevBn9K4IumqHATAv7gspR4q5YUKIorJu3Tr69evHxo0bWbFiBQ6Hg+bNm5OZmelZZ/z48UycOJEPP/yQTZs2ERkZyUMPPUR6erpnnYEDB7Jw4ULmzZvHr7/+SkZGBq1bt8bpdBbHYQlx83DYCPixN3fpQkDpmPHnIvd5eEYL+Ka7BNtClBCaUkoVdyMuV1paGgEBAaSmpuLv71/czRHi1pCewMFPnqBSxj/4Ou8l7unXCfr5BUiOc9fP7rlMho+LW9rNem46efIk4eHhrFu3jvvvvx+lFKVKlWLgwIG89tprgLv3OiIigvfee4/nnnuO1NRUwsLC+OKLL+jcuTMAx48fJzo6miVLltCiRYtL2vfN+pkKcVXSE2BGCyYk1mOY4VdifCPZa06W87EQ18HlnJekR1sIcWm8g/lUux2nlsTdoc0Iur0hdP/RfVIPlxqeQtysUlNTAQgOdv+OHzx4kISEBJo3b+5Zx2w207hxYzZs2ADA5s2bsdvt+dYpVaoUNWrU8KxTkJycHNLS0vI9hBDnsERC9x9p6H8Kb2cdDmT9iSPpIK7Acu7zsgTZQpQIEmgLIS6NwcS3zix0yp+u9R5xLwuMdt85lxqeQtyUlFIMGjSIRo0aUaNGDQASEhIAiIiIyLduRESE57WEhARMJhNBQUGFrlOQcePGERAQ4HlER0cX5eEIcdOw+pbiG31L/F3VcWqZLFKhvOrsi9W3VHE3TQhxhgTaQogLstqdTF9/gCFfb+Vg5lq8nffwcI3SZ1ewREqQLcRNqn///mzbto2vvvrqvNc0Tcv3XCl13rJzXWydoUOHkpqa6nkcOSL5H4QoyMK1GxmYPoH7cKIpM/OwMCBtAgvXbizupgkhzjAUdwOEECWX1ZpN74+X878TBuzaERzmY5RSPQkw3XCpHYQQl2nAgAH88MMP/PLLL5QpU8azPDLSPSw1ISGBqKgoz/LExERPL3dkZCQ2m43k5OR8vdqJiYk0bNiw0H2azWbMZnNRH4oQN5f0BFps6k2wlkgN7ThmV3X+RwYxWiKWTb2h/hoZPi5ECXBZPdpS8kOIW4jDxonpnRmbPIQodYpM3W9oyotnnUc5Mb2zZDUV4iallKJ///589913rF69mvLly+d7vXz58kRGRrJixQrPMpvNxrp16zxB9F133YXRaMy3Tnx8PNu3b79goC2EuATewaT7V+KQCudb5314uWqRoDvMHlc46f6VJGeKECXEZQXaUvJDiFtIdhKWtH3EaIl8YpxIln4D3q67eFq/HkvaPshOKu4WCiGugX79+jFnzhzmzp2LxWIhISGBhIQEsrOzAfeQ8YEDB/LOO++wcOFCtm/fTo8ePfDx8aFLly4ABAQE0KtXLwYPHsyqVavYunUrXbt2pWbNmjRr1qw4D0+IG5/BRETv+QwLmsApAvFy3oHSrPT26UVE7/kynUuIEuKqynsVV8kPKfchxPXx1Yr/0fDXHvzsKssLhtXUtHVnseF/bGgUy5MP3VvczROiRLlZzk2FzaGeOXMmPXr0ANy93qNHj+aTTz4hOTmZe+65h48++siTMA3AarXyyiuvMHfuXLKzs3nwwQeZOnXqZSU4u1k+UyGuBavdyZyNh/h8/X5+tz1GmwrPsqjbpOJulhA3tetW3ut6lvwQQlx/7ZvUZ4r/EL5QXqD0PKnZmOI/hPZN6hd304QQ14hSqsBHbpAN7mB81KhRxMfHY7VaWbduXb4gG8DLy4spU6Zw+vRpsrKyWLx4sWQRF6IIeRn19L6vAk83rICXqyab4tcXd5OEEHlccTK0yy35cejQIc86l1vyIycnh5ycHM9zqaspxPXhlXmcMbpPCdcpzK7qPKrbTlX9DnSZrdylvYQQQghRrOpXCMFrRS0SrDPJzMnC1+xT3E0SQnAVPdrXs+SH1NUUohikJ8Cs1qxJ8iZTt51Iw91UC9bQpcTBrNbu14UQQghRrGqWDiBQXxuFnfn/rCzu5gghzriiQDu35MeaNWsKLfmRV2ElPwpb51xSV1OIYuAdDOHVmGkoA5qdVpXbovX4EYLKQXg1yWoqhBBClABGvY6G0XegUwEs3LHs7AvpCVIhRIhidFmBdnGV/DCbzfj7++d7CCGuMYMJ1TGWX7R0DK4oOt15t3u4eM9l8PgsyWoqhBBClAQOGw0yV+HlrMWvh9cwctF2vlrxP1yft4BvukuwLUQxuaw52v369WPu3LksWrTIU/ID3GU8vL2985X8qFSpEpUqVeKdd94ptORHSEgIwcHBDBkyREp+CFEC/ZOQTZLrdwK4jwYVQ9wLLZHF2yghhBBCnJWdxD22jXi57iDJOZW/f19MT+MMdFoiLkCXnSTnbiGKwWUF2tOmTQOgSZMm+ZbnLfnx6quvkp2dTd++fT0lP5YvX47FYvGsP2nSJAwGA506dfKU/IiNjUWv11/d0QghitTszetwaqdpEPUQZoP8fgohhBAljiWS3Xe8TsDav0jSXLQ0TCNGS+WQCmdD9ak8KUG2EMXisgLtSym5nVvyY9SoUYWuk1vyY8qUKZezeyHE9eCwwZm734v3/r+9O4+PqjofP/45d9YkZN8DSQAFWYIIiCBqFbUgglZFcYGKC+5a+KptXYtaLVpbW39VcC8oWK0i1gUXUMEFN8Ao+xpCAlnJMtkms9zz+2OSSQJBFhOGJM/79coLcufO5JyBybnPPc95znsoHcbk/icHjku6uBBCCHHU2eKOIUInYtFx/I8wbqCS3/tupn9tdKibJkSX9Yv20RZCdDI+D7wxFfPFsTzz7hfsqPmCMHMIZ31/i6zzEkIIIY5S/cIrudSyDId/ECtVNQCPW2fTL7wytA0ToguTQFsI0aSuDLNoPUbFDsq+fQqP2swxZi+SqtZhFq0PzHQLIYQQ4uhRVcil625mrGUVTjOLElXIWjOBTFXMpetulu04hQgRCbSFEE0iU3h94GxydRIf4AOluYI6cnUSrw+cLcVUhBBCiKNNWBxG8gCyYn1EMAiUyf875gYqnT3YrHvw0upK3F5/qFspRJdzSGu0hRCd38baaN703sKPljexm324yNgg67yEEEKIo5XVDpfMI6yujBELtrOrMIb3d/5Adu3dVKko3B9uZeGPxSy8aRROmxQ2FeJIkRltIUQL/cIrudD4hBrLjyT5+9NH7ZJ1XkIIIcTRzGqHyBRG9IrH6R/EHm82xTqWOtOC1rC+wMX8b3JD3UohuhQJtIUQTRrWeS0lDFNVE2kOZSdJss5LCCGE6ACG94rDaWZRb2zBxB08blGKvLLaELZMiK5HAm0hRJOwOFTSAJYqL4aOZESfMaw4dS5mTE+M5AEQFhfqFgohhBBiP4ZlxuI0B4HyU29sCB73a016XHgIWyZE1yNrtIUQTax2tpz+NCWbRhKhhzJ78kmE2S0w8qNAkC37aAshhBBHrW4OK4OTB1BYEY3bWEskQ/FrzSnJPqYMTw1184ToUmRGWwjRwtvrt+IxtjA44YxAkA2BauMSZAshhBBHN5+Hk33f4zQHYg9bx+QRGfzlzBhe5k84F10DPk+oWyhElyEz2kKIFhauew+AS7LGh7glQgghhDgkdWUM96/C4R/EHu8L/HFQKeHvToeKHaACj8tWnUIcGTKjLYQIqqzzsr7sC+zmMVxw/MBQN0cIIYQQhyIyheG//QtOMwut/CyZdzGU74DYnjD1PQmyhTiCJNAWQgRSyaoKWb65iFpjNamOk8mwuyTFTAghhOhg4tN60T+6N4aO5A0iAwcvfBZi0kPbMCG6GAm0hejqfB54Yyq8NJbXvv8AU7kYk34avDQ2cFyCbSGEEKLjqMhjhOcbHOZAVtAwhi+6ASryQtsuIboYCbSF6OrqyqB4PWZZLp/t+AhDR3DRjncCqWbF6wOPCyGEEOLoV1UI8yZwknclTn8WuUYp9TEZgTF93oTA40KII0ICbSG6usgU3JPfYRnDKLOspZs5iDN9KygwAsdlPZcQQgjRQYTFQdIAhsdW4zCzMPHw5egHAmu0kwYEHhdCHBESaAshmL/Bzx89F+FRmxliJmJXfm5z38D8Df5QN00IIYQQB8tqh0vm0f26/9IragBKh/HfnNVwzUdwyTzZqlOII0gCbSEErsLteI2VoDSX4gbgCdscXIXbQ9wyIYQQQhwSqz1QfbxXAg5zAMtzPw9kp0mQLcQRJYG2EF1dVSEXbb2HbUYeNrMni33jyTWTyFDFTNs+XdZzCSGEEB3Q8J5xOM0stlWuxmf6Qt0cIbocCbSF6OrC4ljZ7UzqLKuJ8J/ABqMPV3jvo8BIIaLHIFnPJYQQQnRAJ/WKw2Fm4dO1rNy1OtTNEaLLsYa6AUKIELPaeavbYPxl5QxJOYvT0jNIj+tH7MBPsUQlSqqZEEII0QEdm9iNJEd/irWdN9YuYWT6SaFukhBdigTaQnRxflOzbOdSlHby53ETOeWY1FA3SQghhBC/kGEoTuqZxOZt/fhk+zLg3lA3SYguRVLHhejisvMqKPN/TzcGM6JncqibI4QQQog2MjwjGqeZxcay7zC1GThYVQg+T2gbJkQXIIG2EF2VzwNVhXy4fjv1xnqGJJ2BtbZYBl8hhBCiM/B5GL7tXzjNLOpNF2uL1kJFHrw0Ft6YKuO9EO1MAm0huiKfJzDIvjSWRWsWg/Iz6djTZPAVQgDw+eefc95555GWloZSirfffrvF40qpVr8ef/zx4DlnnHHGPo9fdtllR7gnQnRhdWVkVX9FlNkTtJX7X/8nrmfPgfIdULwe6spC3UIhOjUJtIXoiurKoHg9xWXlbKr6GquZxqS1f5HBVwgBQE1NDYMHD+app55q9fGCgoIWXy+99BJKKSZOnNjivOuuu67Fec8+++yRaL4QAiAyBXPKIrKMYhxmH4rKPiKqLp8CIwX35HcCe2sLIdqNFEMToiuKTIGp7/HZ7Pupq/+QdPM4El3ZENsTpr4ng68QXdy4ceMYN27cfh9PSWn5O+J///sfo0ePpnfv3i2Oh4eH73OuEOLImb/BT4EZg8PMYo31fTQWbnPfwDkb/Ew7LdStE6JzkxltIbqqmHQWxZyG3yjiLN2whdeFz0JMemjbJYToUIqKinj//fe59tpr93lswYIFJCQkMHDgQO68806qqqp+9rXq6+txuVwtvoQQh89VuJ07bG/gNLOoVbVsxuQJ2xxchdtD3TQhOj0JtIXoojx7clm2+wvQNq41SgIHF90QKJQihBAHad68eURGRnLRRRe1OD558mT+85//sGzZMu6//34WLly4zzl7mzVrFtHR0cGv9HS58SfEYasqZNr26ZxjfE+42Re0wTwzgQxVzLTt0wPVx4UQ7UYCbSG6oqpCVr44g0rjJ6J0f0ZcOyeQNl6+A+ZNkMFXCHHQXnrpJSZPnozT6Wxx/LrrruPss88mKyuLyy67jDfffJOlS5eyevXq/b7W3XffTWVlZfArL09u/Alx2MLiiOgxiCpLDFac2PUxPKN7UGCkENFjEITFhbqFQnRqskZbiC7G7fXz6upKsuuOx228x0kJN2NknhxYmz1vAiQNkMFXCHFQvvjiCzZt2sTrr79+wHOHDh2KzWZjy5YtDB06tNVzHA4HDoejrZspRNdktWO59GViXSUMfWMnxXmDqHN8QczNq7FEJ4HVHuoWCtGpSaAtRBfi9vqZOGcF6wtc1CoNDh/VVUNxe/04Y9Lhmo8CQbYMvkKIg/Diiy8ybNgwBg8efMBz161bh9frJTU19Qi0TAgBgNWOM647155q4bMFWbjMt9it6jlGxnkh2p2kjgvRhcz/Jpf1BS60hlrLKqxmCqWV8Ux54Vte+GI7bmeiBNlCCKqrq8nOziY7OxuAnJwcsrOz2blzZ/Acl8vFG2+8wbRp0/Z5/rZt23jooYdYuXIlO3bsYPHixVxyySUMGTKEU0455Uh1QwjRYHjPOJzmANCK9zYtDXVzhOgSJNAWogvJK6vFohQaTZ2xijBzGArF6p3lPLJ4AxPnrMDt9Ye6mUKIEFu5ciVDhgxhyJAhANx+++0MGTKEP/3pT8FzXnvtNbTWXH755fs8326388knnzB27FiOO+44fve73zFmzBiWLl2KxWI5Yv0QQgTERtjpl5yCXffm3Y2fhLo5QnQJkjouRBeSGWMjTpexS1XjN4pwek8kkXIqdCRerKwvcDH/m1ymndb7wC8mhOi0zjjjDLTWP3vO9ddfz/XXX9/qY+np6Sxfvrw9miaEOEwn9Yrjm1VZrCr8KtRNEaJLkBltIboKn4ep+TN5zfkYdcZK0DZ6mOm8aX+Q2bYnseHDohR5ZbWhbqkQQggh2thJGdE4/QOp8OwmtyI3cLCqEHye0DZMiE5KAm0huoq6MiylG9jmS6DOspoIPZC3bI+SaRTTV+URQxV+rUmPCw91S4UQQgjRlnweTlr7AA5zIAAfbf0UKvLgpbHwxlQJtoVoBxJoC9FVRKbA1Pf40DoSt7GGU8xIelqKydVJTPHdT6mKZUBqFFNGZoa6pUIIIYRoS3VlpJSvoreqw2Zm8r/VCwNbepbvgOL1UFcW6hYK0enIGm0huhAzqgfvYAPl47dUA/DTiY9xptmX9LhwpozMxGmTQkVCCCFEp9Jws/2kJ1/iB18W3+7+InA8tidMfS/wuBCiTcmMthBdyJpNmyn0fYvdTOUSI7A+67ztD/Lg6dFMO623BNlCCCFEZxWTzvCRp+PwD2KPcpGPCRc+CzHpoW6ZEJ2SBNpCdBVVhXz05nPUWVbSXx2D49r3A3eyy3cE0seqCkPdQiGEEEK0l4o8Rqx9EKc5CICPNbDohsBabSFEm5NAW4iuIiyORf5o/KqMi4ZOhYyRgXSx2J6QNADC4kLdQiGEEEK0h6pCmDeBDNdqUhXYzJ68bomVm+1CtCNZoy1EF5Hn8rHBtxHDGsFNoy8PHIxJh2s+CgTZVntoGyiEEEKI9hEWhz+hP8WVbqq0A6d5PJ/xNQWWFJIS+mORm+1CtDmZ0Raii1i6oYg6y/d0DxtJYmRE0wORKRJkCyGEEJ2Z1c68Hg/ym7r7qcOJ0388XqOEc9w3MK/Hg3IdIEQ7kEBbiC7inbVr8RhbGdN7fKibIoQQQogjLLfCS5kKzFw7zSzQBrtULv/9oYgXvtiO2+sPcQuF6Fwk0BaiM/N5oKqQyjovX+UtAW1w8/FnBI4LIYQQostIjwvHrzUABt2w6964jTVsLqrikcUbmDhnhQTbQrQhCbSF6Kx8HnhjKrw0luU/bKTa+JYYy0CGfnhV4LgE20IIIUSXMWVkJgNSo1Aq8L3Tfzxuy0/4tUZrWF/gYv43uaFtpBCdiATaQnRCbq+fBct+oCwnG8p3sPjjt3AbPzJSN1QYLV4PdWWhbqYQQgghjhCnzcLCm0Zx77n9SYuy4jQH4Vd78KndAKSoCnaXVoa4lUJ0HhJoC9HJuL1+Js5ZwX2flPKb6rvZbKbxgceNVvVc6ysNbOc19b1AETQhhBBCdBlOm4VpJ/fgb+Gv4DAHgjZwGz+RRimv2R5g6q6ZkvEmRBuRQFuITmb+N7msL3ChNeSZ8dzkmUGZZTUOM5kLVT71580ObOslhBBCiK6nrowReg09qMWu+2C3fMt/HA+TqYpJ9+VKxpsQbUQCbSE6mbyyWiwNC7DSKOUYI59ayzecqLtjUVA2/xrcpbIGSwghhOiSIlOwXP0eI5w7cfqPp9pYR4YqwozpiXGVZLwJ0VYk0Baik2msKppIOS/bZrEUA1O58HrPJtdMItUsxPPiuVBVGOqmCiGEECIUYtI5+eTTcJqDqVZ1rMPEuOhZyXgTog1JoC1EJ9NYVbRSRfKueTKllh+wmAkU6lO53HMfO3USu+09ISwu1E0VQgghRChU5HHy2gdwmP1R2s77Glh0A1TkhbplQnQaEmgL0ck0VhX9w7mDeNUxiVrLCsLNk1EY7CaBiz0zWTH0CbDaQ91UIYQQQhxpVYUwbwI9XD+QbqnFYQ5kkbVbYFeSeRMk402INnLIgfbnn3/OeeedR1paGkop3n777RaPX3XVVSilWnyNHDmyxTn19fXcdtttJCQkEBERwfnnn09+fv4v6ogQoonTZuGqUT2p1Fvwqz2E+0dhNRRKQWJqJleMOjbUTRRCCCFEKITFQdIAVFxPRvbvjdM8gdW6gvqYDEgaIBlvQrSRQw60a2pqGDx4ME899dR+zznnnHMoKCgIfi1evLjF4zNmzGDRokW89tprfPnll1RXVzNhwgT8fv+h90AI0ZLPA1WFfLejjCLv51iI5tHTT+PK4ance25/Ft40CqfNEupWCiGEECIUrHa4ZB5c8xGnDcwkzD8Er65nxa8fCByXjDch2oT1UJ8wbtw4xo0b97PnOBwOUlJar1hYWVnJiy++yCuvvMLZZ58NwPz580lPT2fp0qWMHTv2UJskhGjk88AbU6F4PYtTnqHWsoKBUb/iqo03B+5SnzwPrBJkCyGEEF2a1Q6RKZxybD023RNDx/DOzm8ZPXBiqFsmRKfRLmu0ly1bRlJSEn379uW6666juLg4+NiqVavwer2MGTMmeCwtLY2srCxWrFjR6uvV19fjcrlafAkhWlFXBsXr8Zflsuinz/EZBVxRVx5Yd1W8XvbGFEIIIURQYqSDAakxOP2DeXfTR6FujhCdSpsH2uPGjWPBggV8+umn/P3vf+f777/nzDPPpL6+HoDCwkLsdjuxsbEtnpecnExhYevFF2bNmkV0dHTwKz1dth4QolWRKTD1Pb6OOJNd6kcMHcYt9WshtidMlb0xhRBCCNHSaX0SCDOHsq1yDaW1paFujhCdRpsH2pdeeinjx48nKyuL8847jw8++IDNmzfz/vvv/+zztNYopVp97O6776aysjL4lZcnWw8IsV8x6byVcBM1luUMNHvSTZlwoeyNKYQQQoh9nXpsAk7/YECzdPvSpgeqCgNL0oQQh6Xdt/dKTU0lMzOTLVu2AJCSkoLH46G8vLzFecXFxSQnJ7f6Gg6Hg6ioqBZfQojW1ZXk8r+tq/EZu7mOhgKDsjemEEIIIVpxUno3wlUsNjODJz+bz8z/reU/S77CfHFsoO6LBNtCHJZ2D7T37NlDXl4eqampAAwbNgybzcaSJUuC5xQUFLB27VpGjRrV3s0RonOrKmTpi/dSYlmBTUdzw9UvBNLGZW9MIYQQQrTC6atkuG07TnMIG/csY/13HzPqy6swKnZgFkl9FyEO1yEH2tXV1WRnZ5OdnQ1ATk4O2dnZ7Ny5k+rqau68806+/vprduzYwbJlyzjvvPNISEjgwgsvBCA6Opprr72WO+64g08++YQffviBKVOmMGjQoGAVciHEYQqLY5F/FDWWzxmROA57r9MCa7Nje8remEIIIYTYV2QKkRmDCPOfQIWq4WHbn8hUxeTqJF4fOFvquwhxmA55e6+VK1cyevTo4Pe33347AFOnTmXOnDmsWbOGl19+mYqKClJTUxk9ejSvv/46kZGRwef84x//wGq1MmnSJOrq6jjrrLOYO3cuFotsOyTEL1FWDx/WlmHaK5jxqxsCB2PS4ZqPAkG27I0phBBCiL1Yw2NxmINQ2sa72uQ4ZeH3vpvpXxsd6qYJ0WEprbUOdSMOlcvlIjo6msrKSlmvLQQE1k/VlfHKWjc3Lb4Wbd9M9fTvUOHxElwLcYTI2NT25D0V4shY8PGXPP5pPpvsfyPL2ML3QK6ZxKykxzlx8PFMGZmJ0yYTYkIcyrjU7mu0hRDtzOeBN6ZivjiWZz/LptbyNQO7nYV+6RwpYiKEOCyff/455513HmlpaSilePvtt1s8ftVVV6GUavE1cuTIFufU19dz2223kZCQQEREBOeffz75+flHsBdCiINSVcjl62/hV8ZPhPuHs4oa1poJZBrF3FPye55bvIKJc1bg9vpD3VIhOhQJtIXo6OrKMIvWs7Osjk01K9CqjnvKv5ciJkKIw1ZTU8PgwYN56qmn9nvOOeecQ0FBQfBr8eLFLR6fMWMGixYt4rXXXuPLL7+kurqaCRMm4PfLxboQR5WwOIzkAZwWuZswczha+ZnoG0eumcQmM51yHcn6Ahfzv8kNdUuF6FAOeY22EOIoE5nC6wNn8+OyhdRYlpFg9uACI59cncSKgbO5XIqYCCEO0bhx4xg3btzPnuNwOEhJaf33S2VlJS+++CKvvPJKsNDp/PnzSU9PZ+nSpYwdO7bN2yyEOExWO1wyj9GlRdie/Ambmclu60Yu9sykgki8WLEqRV5ZbahbKkSHIjPaQnQC66qjeM0/kDpjFZfpcAB+77uZjVLERAjRTpYtW0ZSUhJ9+/bluuuuo7i4OPjYqlWr8Hq9jBkzJngsLS2NrKwsVqxYsd/XrK+vx+VytfgSQhwBVjsJKemckB5DmH84tcZKionC2zAn59ea9LjwEDdSiI5FAm0hOgFfdSkV1k8wsPGgKgLgcets+oVXhrhlQojOaNy4cSxYsIBPP/2Uv//973z//feceeaZ1NfXA1BYWIjdbic2NrbF85KTkyksLNzv686aNYvo6OjgV3p6erv2QwjR0tn9kwkzT8JUlXiMLVgNhVIwIDWKKSMzQ908IToUCbSF6OiqCtm+5UeqrR9zgnksN/pmkquTyFTFXLruZqja/0WtEEIcjksvvZTx48eTlZXFeeedxwcffMDmzZt5//33f/Z5WmuUUvt9/O6776aysjL4lZeX19ZNF0L8jDP7JeEwj8PQkZzQZxuTR2Rw77n9WTilN04l9RWEOBSyRluIDm5btYNlvj34HXsYM/BR7M6RrAg/ifR1N2MkDwjsny2EEO0oNTWVzMxMtmzZAkBKSgoej4fy8vIWs9rFxcWMGjVqv6/jcDhwOBzt3l4hROv6JTjoYatjj/9EtlR8ygdXz4aKPJh3LiQNgEvmybahQhwkCbSF6Kga9s7+z6oyqqwfkGDrz6xzJwQCa6sdRn7U9HchhGhHe/bsIS8vj9TUVACGDRuGzWZjyZIlTJo0CYCCggLWrl3LX//611A2VQjxM5S7nDNta9lcP5xtrr+Sv+Edenx8P5TvCJxQVwZSZFWIgyKBthAdUcPe2e6izSyouA23sZpb+j0IL41tuuMsA6EQ4jBVV1ezdevW4Pc5OTlkZ2cTFxdHXFwcDzzwABMnTiQ1NZUdO3Zwzz33kJCQwIUXXghAdHQ01157LXfccQfx8fHExcVx5513MmjQoGAVciHEUSgyhbMmXM68/64BbfDua5dyk7JDbE+Y+p5cWwhxCCTQFqIjqiuD4vW8tyed3XoxFlsY9+e+AZW5TY/LYCiEOEwrV65k9OjRwe9vv/12AKZOncqcOXNYs2YNL7/8MhUVFaSmpjJ69Ghef/11IiMjg8/5xz/+gdVqZdKkSdTV1XHWWWcxd+5cLBbLEe+PEOLgjRx0HN3eysFpDuJlYxs3AVz4LMRIcUIhDoXSWutQN+JQuVwuoqOjqaysJCoqKtTNESIkzPKdjPn7B3xmvYvTzCSWGYWYMT0xrnpPBkMhQkDGprYn76kQIVCRx/X/+A8LfXmU256miAgSY3sHZrTl+kJ0cYcyLknVcSE6qI922fhBr8Wkkr+pMgD+4L8Zd0RaiFsmhBBCiA6pqhDmTeAs/1eE+08GpVgUHh1Yoz1vguxkIsQhkEBbiA7q0XdW47K+SZbuxYnKA8Btrr+xaNk3IW6ZEEIIITqksDhIGsBZcSXYVDQO/yBeickMrNFOkp1MhDgUEmgL0VH4PME7yas2bGV9zXJ8RiF3mXYm1s8k1wzsnT32+2lyx1kIIYQQh85qh0vmkXDdW4w6JoFw/2l8VfANJZctkK29hDhEEmgL0RE0VBnnpbFQkcfsb/ZQaX2DnmZPMojnJ30Ml3vuI1cnURXVR+44CyGEEOLwWO0QmcKE41MD6ePAovwVTUF2VWHgukQI8bMk0BaiI2ioMk75Dra8cA3vblmM19jBI9pPhlFEglFNgUrg3ti/kTztdbnjLIQQQohf5Jx+8TiIxOEfxMsr5wUOVuQFbvq/MVWCbSEOQLb3EqIjiEyBqe9hzp3AP4tPwmX9L8lmDybFxvBG1hzG1EaTHhfOlJGZOG2ydY4QQgghfpkYVcWpji286zuNrwufpmTzByR+cFegMBrIVqJCHIAE2kJ0EO6ING52T+d9dlNv2cj9/kzuMm/h4TNGSnAthBBCiLYVmcKEs8/ik/fXUa5ns+jVi7gee2Ar0anvSZAtxAFI6rgQHcRbn31NcWUl5bYX6G5252ajTKqMCyGEEKLdnH7CcdiIwmEez4sNYYNsJSrEwZFAW4ijWWOl8apCnF//k6+NrXjVTo71TGKnmSxVxoUQQgjRbpZ+s4qTjbVE+M/iO1XLNky5yS/EQZJAW4ijVbNK4966Gv7m/TUVtvkcb/bjXms2v/XcJVXGhRBCCNE+qgoZ+/00JlmWE+4fhaHD+Ye2yU1+IQ6SBNpCHK2aVRqf+8xfWMdHQD3/VSUcZ+ThVg6pMi6EEEKI9hEWR1VUH/qqPCzYCfefznMKtutEuckvxEGQQFuIo1VkCu7J7/ATfflb3fG4rO9ygXksESqW1wbM5vpzR/HCLRNwOsNC3VIhhBBCdDZWO8nTXueRuEfQKLr5xuBV5Vzb7Qq5yS/EQZBAW4ijTeO6bOCV9T6muP+PXNtzhOto5qp8ZnhuJC7tGKad1luqjQshhBCi3TidYbxwywSuGpWJXR+L3exJt7Q1TTf5qwplP+1Ozu3188IX25n5v7W88MV23F5/qJvUYcj2XkIcTRrXZRevh6nv8d36reRZP6Te2MgCM51Io5wnbHN4s/B4oHeoWyuEEEKITs5pszBzXB9WrP6RKu+v+WDbvympKSHR64Z5EyBpAFwyT2a4OyG318/EOStYX+DCohR+rVn0wy4W3jRKJnsOgsxoC3GUcHv9LFj2A2U52VC+g4LnJ/FZ7moqrK8y1hzAmWjyzQQyVDHTtk+XIiRCCCGEOCKUu5zL7V8Q4R+NacLLy/8cCLLLdwQmB+rKQt1E0Q7mf5PL+gIXWoPP1GgN6wtczP8mN9RN6xAk0BbiKNB4x/C+T0r5TfXd5JjJ3Fk+hlz700TrNP5FLZf4/syl3j9RYKQQ0WOQFCERQgghxJERmcKFV/+RcMII85/M0989jy7PgdieMPU9iEwJdQtFO8grq8WiVItjFqXIK6sNUYs6Fgm0hTgKvLpiKyUFuWgNeWY8l3nu5X+WL/CrUl7XNjYOf5wzRwzl6nNPI/bWT7Fc+rKkaAkhhBDiiIlJ7cW5fSOI9I8nR7n5EB9c+CzEpIe6aaKdpMeF49e6xTGfqVm32yXrtQ+CBNpChJrPw6jVt/Om/UHSKCWGKjZaP6DG+hnTzQGMNUo5b/uDPHh6dKAAWlx3CbKFEEIIcWRV5HH5nqdwmANxmscyCx8sugEq8gApmtUZTRmZyYDUKJQCS7OJ7ey8Ch5ZvIGJc1bIv/PPkEBbiFCrKyPNs4MMVcwD1rnstizDZXudcb4RPOx0QXR6YA3UvAmyLlsIIYQQR15VIcybwPDqZfSxltDNewlfKC/fl2+DeRNwl+1i4pwVPLJ4Awu+3SlBWCfhtFlYeNMo7j23P0MyYmmMtdtqvXZnvzkjgbYQoeLz4C7bxQvZtTyT+U9WmFncYp5Aie15BvhG8rylFMv1n8DVHwTWQCUNkHXZQgghhDjywuIgaQAqrifXjxlKuDkSB2k8ardB0gBeXVNNSUEuVu2TolmdjNNmYdppvRmYFoXFaLv12o31iTrzzRnZ3kuIUPB58L9+JeXbfuDfdfdQSAJPGMMosv+LZHMwXxnbCbvyLRwJmYHzr/koMMhJyrgQQgghjjSrPbCFV10ZF9pjeHLJRqo9F7CIZ9h+9kwqluXxpv1BNpnp3OydjhcrCnhzVT4QSEGW7aA6ttbWa/u1Jj0u/LBer0VF84bXbbw5M+20zrGFrcxoC9HOGtNiHlr0AwuWfhu4U1dXRk3+GlLNQv5kmUuFZT5F9n+R7h9BtioiRrmpf+N6nnhjaSCVxpkoQbYQQgghQsdqh8gUbJ4Kbgj7lAj/WVh0BH/75D6mbZ9Ohiqmr8ojhioATA2bi6o65UxlV9R8vbbVUCgFA1KjmDIy87BerytUNJcZbRFSbq+f+d/kkldWS3pceIe+49laXwAmzlnBloIyZtuepA95TPr2IQYNyMJ03s6Iqle4SplUWl9jgHc8y4zVfJx5N2fmP0VqXT4Xr72Ji1fPZNEPu1h406gO+94IIYQQopOITGHSdffw//7xNS7f+by48T/8iXBqjDSuqL+HPSoWGiY+zYY/O9tMZVfUuF67ra7b23qG/GgkgbYImca1GesLXFiUwq91hw0o99eX3wxKpKQgl2gNfcgj0yhmdv193PHdTWCs5G/WjfhVBSO8l7HU8il3em/EbxnJ7LooXrU9zGadTrmOpEQGKCGEEEKEWPNJhVPSwyjKPY8a69vcRz1PT3mJq3el8uaqfDYXVQWDbOh8M5VdVeN67bYwZWQmi37Y1eLa+ZfMkO9PKCf1JNAWIdOZ1mY074vSXhKoYkuBjyE1D/OmfTs319/Gbz138R/7I+Rj5VvrO+Rav8Dhz+I231k8av0Au/Iz0/karzhP5TOVyMWemVQQiRcrVhmghBBCCBFCzScVeqg9PGf9K8u4nxrvFF6yPcttb13JtGnLgB48sngDiZQHr2M620xlqHT0TNC9279g2gjeXJX/y/vj80BdGUSmNP0dqFNOrn5uOZuKa6lREXi0hWWr1vDCjWNwOsPauHf7kkBbhEzj2gxfs7SRDnXHs9mHendpJSmqArSXP9vm0kft4k/eq0ir3053Vcwix0ze9Q/gIp3C95ZsYCdpnqnMNXIY5FzJy72fYlL+I6TW5XPjjhm8qe+mmNjgj5IBSgghhBCh1DipkKDLmW/7M5lGMZdYlvO8fxx11neYUZ3Hpy+NZcpVS/hypZsHKx5kiw4UR+uTGtfmM5VdTUfPBP3F7fd5cLtKmL+unryyWjJjbFzRuw5/dC/y3ryL/JIy8gbeRN7G78mvMsnzx5Hni6WaMCAKNKRSwiPlj1D0Qn8yb3yz3esfSaAtQqYt1mb80jt7B3z+fu6QYe8Gb12HWbCGhf3/ybiNj3OtdQsKkxhVQ7jy8IL9b6z1Z7JGhfMIsayw/oDGJNI3gQvMnjxqW0gCFSwe8hLTJlwIFafBvAlEJPQn1ZlGSWFdu6bSCCGEEEIcrMYJkgodyWadDiZ8aI4k2mGjznsdyxwPsbimgPGV23lJPYShiol0WJn5qxQmnn5ihwgGj2YdPRP0Z9t/co/9Xm+7a13kl1Wz8+PZbNhdyb+9Z1FBFOmqiH/pSMrZBUwInP9lDTBgn5+dQAUJqpi7LG+RqYopc1mbfl47kkBbhMwvXZtxOHfGmgfWKdFO3v1xNxsKq3AqP91MF89/sZ2xx8VxXLSXiYOTcC65C0o2woXPwZdPQNHawAsl9MUs2YTh2sX4by6jigiSjQoAinQk68x65qlwXjd2U2qUYNEeuvkmMNw/hNutnzBXdceDBZsyOW/rA1B1MsSkwzUfYQmL43Vt6dCpQUIIIYToXBonSDRWbvZOJ4YqSlUslwzqzusrhxPmP57f6Y2MmTceGwpiexI39T0mx6SHuukdTmsTQR0yE7SV7E+f1lQQCcBxahe7CxPInT+L/OI95B0/nbz1K8h3+cnzxZJvxlJsRje82K9bvHSOTgv+PcpeT4LvJ7qpHOxGPkoVUa/KcCkXe3CzFT9+DM6gG7k6iRXDX+Dydg6yQQJtEUK/tHrhQd3Za/YBd7vrmPbMx2wuqqJKReEzNceQTxgpPGl9mr7WPKa7buL07HfoZ+yk6kuNw1aH8tZhvnQOfmXFpj0AuGrq8JmKOMChPGzBzRta8z7hfKUqqDJqUNpHmH8YSd6bOVl341rrEsbbH6PGmczGYwexImYe6etuxkgeENgjG4J31pzQIe5OCiGEEKJraD5BopWNUh3LgNQoHvpNFqXVHj7cNI0cx3Qe1Yr7lQMufDYwiSAOyf4mks4bnHbEqnQfVMboz2V9eqrB74HFv8dfvImiX8/mhO1zSTVc7CaeTTqDMiIpMyOYu6qYl7gg8Nxl1cDxLX6MxsRBITHGdroZ27Gp3ZiqGLcqp0JVU0o9azHRjqbnxGtFOop0DLIwyMBK34Zdrf8VdScPnzGyzd+z1iit9/oX6wBcLhfR0dFUVlYSFRUV6uZ0SkdrsYXm7Vq320V2XgU+U2PDRwxVlBtxXDk8lT+dnRr8gDfOSOe+8zDWknVoDRt1Bk48jDA2sMrsS5oqI8MoxqsVfqw4lReAGlssDm8lVkxMNLvQbMJkIybfY+FrbSVX1eBRPtAGdt0bp38QTvNE4s1enGNkM9X6MQPUDpZnPcLZ+U9DZT7E9oRrPgp0KixO9sgWohOQsantyXsqxNFlf9eHuTlb+fWzaym2vkaV9b98TTgnxR4DU98LBNtVhftc7xyt15pt4Zf07YUvtvPI4g3sHaENTY9hd2UdRVX1LTJBD7TGee9sTqUUBRV1+7Sr8byckmo+2VhMUVU9TuUnUrtITM1k4fUn4vRWBl60YQklhWvQV7xJyUePkVdQSL4/lnxfNPlmInn+WPJ8MezWcXh/Zm7XpAbDUkI0mwhTuViMAvyqhBpVSQU1FOHB22y77XAN6Rikoygxj+MCYysZGGQ0HEvHIBy1/58X0xPjqvcO+ybQoYxLEmiLfbR2J+1gPsjt4iBmpHeSwpO2p+mr8pjhvYl/dv+MTO92MP3grgRvLSgLXizBGeliM4o4VY1VmXi1wT2ea3jE/iLVymQnJjswycUkB81GLGwF8vFQr/wAKG3BqjOwmz2x6V44zD7YzT4k4eZXxk+Mt3zLCLWObfRggNqBTZmBD/Yl/4Y3r4akAXDJPAmwhehEOtPY9Pnnn/P444+zatUqCgoKWLRoERdccAEAXq+X++67j8WLF7N9+3aio6M5++yzefTRR0lLa0rlO+OMM1i+fHmL17300kt57bXXDrodnek9FaLTqiqEl8byRPGJPOk/n1LH70lUO1iDk8jYXnDxvtc+R/Ja80gH9L+0bzP/t5YF3+7EZ7YM0QwV2Jc8OcrB2f2T6ZUQccC+NG+LAfgbXjLMaBZA3zQK/B7unP1fPi6JQQFRugorPv5sm8ux7OI27y3clvQjvvo68vxx5Nt6kVdjkOeNJF8nUk/r17MaLz5VCqqQbioHp5GHUoV41B5qlIs91FHTcG0NYNHQvSFYTkc1BM8GGQ3HMlDEoVANgbQPC1aanu/VigqiSFSBGwI+bWBVJgBmtxQMiw0q85omvA4jffxQxiVJHRf7aM9iC81/2WXG2LhiUDeccd1/NuWkcUa66J2Heax8HdrWMCNtaTYjbZSRoYp5w/FnLOU28NcHXisiEfz1aNNHNT52YZKPJt8oJR+T7VqxQxnsdMzmKby4m33YDW3FopOw6FRsOoVwnUKU2QOb7o5VJ2Og6KUKOVbtYrCxjrHWeRyjdoPVAaYPpf2k2+pY0v9Jztn590CKeHJW4IMts9hCiKNYTU0NgwcP5uqrr2bixIktHqutrWX16tXcf//9DB48mPLycmbMmMH555/PypUrW5x73XXX8dBDDwW/Dwtr/+1UhBBHRuM13e7SSqZaMrkp4QeWcykri/9AvuNWblVe5nVLgTeuhoodgSc1TJ4cqcJeoajU/Uv71lqxYCC4L3lxVT29EiL2/1rNJqleXbGVkoJcEjTUEEYEdYEA2hLYIefWgtt47ctYRv14FzeU76SPMZTdOh5TKQp0PH/1XcZuHU814Vxf3KfFj9GY+CnHr3IwVRHhxk5sKh8dXB9dTaWqb/GcBK3IQNG3cQa6/4Wkb3ifjIagOhWF5WdmowFMZeF/Pe7k/Py/YdV+TAy2WY+lp3crNmUSoesoMGPxo7ArP17Tilbgdx5H5uX/hPkXBm76NC7bbEcSaHcBh3on73CLLTT/hZsZ7qbOmURxmYvMcDdKKXIq4LsteZRU1+NW4fzd8hSVy/Ng2v9wLv8zFP4EWoPpBcMOutmM9L/HkYYFm/KAArvpDc5ID7FsZkHajQwoeJpCXU++300+mu1Y2FlTyC5MSprNRgOgFTZiUCRiNROw6ATCdCLdzESsOhGrTsIgBoXCgp9U9tBdlTLAyKWP8SNxVHCisZUEw0WpkUh5RC+SvSba3gMjJQtOvR0WXU9c0gDOPf8yqBvTFFwfgeILQgjxS4wbN45x48a1+lh0dDRLlixpcexf//oXJ510Ejt37iQjIyN4PDw8nJQU+Z0nRGezdwA7X1/LScnwxORRTHzapN57My/b/8GYvC+YjC0wgzj1vcA1UFUhu0srj0hhr1BU6v6lRcuar4VHw94ht0UpdpdWBjIJGq8pfZ7AxFRc70BKd/F6uPA5Tln1IGPtm3CbFkqJYadOolDH8ZWZxVv6V2gUT3y8gQf0TS1+hkZjUoNfleAz1uFXJdhVHjZjF35VQp0qx0UNftXUuoiGlO6M4Iy0QYZ2BlO6e7SW0r3pU8AW/NarFWX7mZH2KTtW5ccw/VxQ9Rr+Hifhy/uOr/0DuLn+/+ipd/GU/V9s0d35k/dqvFiabi4YinHdB/Cn+N5HdMJLAu0O4JekvBzOnbyD3narlbTu7UXl/Nk6l2PVLqZ7buI22zv0UzvRaOzKzzRtRdtgm+7OsWoXyeYevM+fARGxUF0YeF1lCQTZgDs8nl2+WnbpevLR5GnNNmVlh1FFPibF+CjHjVnwaMNzA2ndFh2PRSc0/BlPOPFE6ngsOhGrjsei41AN//3teEhVZaSqPaRSRqI1h956Od1VKalGOd0pJkx5qdV2KnUEfgw20pNLvffxqvNxEo45kYSLXwjMwkPTh7f5B1mCayFEJ1ZZWYlSipiYmBbHFyxYwPz580lOTmbcuHHMnDmTyMjI/b5OfX099fVNMyAul6u9miyE+AX2DWCtfFUEn20s5p+XD+Wqf/tw+37iKsunxKM459d/DqyJrciDeROYaslkvr6W5qFIexT2CkWl7l+6fa1T+Vk4pTfz19WzdE0e23fmoWmakbZpH1N3zcF8cSf/O/Zh1tUnMzl/JpTvYFf8qeRVhZNXM4Kdz7xLvh5Lvp5MKTEtfobGg0+V4FOl+FUOPlWMRe3GogrwqVJqVCWehnpF0JTSndlsHXQGDjKc8aTXVZCOQSwEU7r31pjSHd4QQNfa4gj3V4LpA2UhP6wvyTWb9jsjjYKqqOPod/EDsOh6SBrAKyn38vrW5WzVPfBiZQ3HcLFnJhExiRRU+II3KGpxojSkJTRULz+C1+QSaB/lfmnKy+HcyZsyPJVlq9bwVZE1WAShb3IkU4bGB+6eQYsiCExeSNF/f89j5WtRtqZ9pN9wPNSisJhPG1iNwF0pi+mjHM0e/OzS1eysqmaLspOjLeRpKFKwh3pq6nICP081/hGOVce2CKRjGoJpi47HquMxiEZhoDCJx0WKKidZVZBkqyPZv45kykkOM0nx7iRFlxKralHdT4CCH8H0oa1h7PGH4zZhk87g957r+JdzDlVRx7BywD1oi40dNQ4uT4gmeuAELFGJgWDa0a3lGynBtRCiC3C73dx1111cccUVLdarTZ48mV69epGSksLatWu5++67+fHHH/eZDW9u1qxZPPjgg0ei2UKIX+DnAthpg2z8rttnPFl9G6XKxQXGKpa+MYVTL3kFlvwJyneQHgMnJcNXRRzWFq8H6+eC3vZau73f7WuHp+Iu28X8dfXsLq3kmEgPE4f2wBmVGHhisxlpZ/F6pl34HFfnPs2e8NXU+jQuItmpkyjWcSwsSiPfP4CdRZvJ0VW8yM1oDNgFGn8gpdsowWcU4FM/4VcloAqD66PdquWNhkRtEGYkMcwsDa6LTtdhwdnplP2ldNdVUe1Iod5rosw9QMtZaA82DO1vEUCbyqAq7Dj6xdsg9yvoeRpL0x/mrY8/5V+21meklYIbhw2nX8ZxwYmsnPc3s0X1avF/sNyIY0zfVCLyKw97++C2JIF2iB2oEuAvTXlp/oswWJlbxQXu5DWmmST2C5xcVwZ+D87Fv+dlNvLOyQ/Qf+vzpHm2EeG3YMxpSOlWQEJfKNkMrnx47nRiiSBK7QEFu81IivFQqLzkah+btYNtWNmJokD7KVH1VBoF+JSvWUsVFpxYiAsE0GY8Nh1PfLMA2qLjMQjHwCSeSpJUBUmqkiRVTrLaRZKxlngqCVMe+hl5JFCJX1mwYWLgx6OceJxRhDvsgfRubwbk5kHPX8Gl86F0M7x5NTuNdK7YfQkebaGCSLxYuaj+T9w4ejjXnH5c2/8nEEKIDsrr9XLZZZdhmiazZ89u8dh1110X/HtWVhZ9+vThxBNPZPXq1QwdOrTV17v77ru5/fbbg9+7XC7S02V7ICGONvsLYPtG1MC8yUz35rLTGcsi990U22cy1tjAl/+dzBAsENsTY+p7vBCRxsLlK9lWZSctIbpdipTtL+i9eFiPtl273SzLs3FGeuHqfHIqFb2iNRMHJ2F7cyrF23/k/bobuNX2Lv1ULpVfGdiOGYryuSnNXUtewunkuWzk1wwm79k3ydPDydPjKNBx+LA2pHRXB1K6VQl+Yys+9Q0+VYJWxWijiHoq0A2BLkC4DqyHzkQ3VecOpnQb9EDhRKHxoNjPrLuyQHg81BQHvu+WjHZXonxuKtx+tpvdOUYFUs4dyo9HW7EaBrVx/bh991n80zY7GED7lCUQNJ/SKxiHXKYtvLGmnEsKZuIiEndDiGo1FHXayYDUKK4YdWzgZzdMZO3v/2CvxG7cf97Ao6KivQTaIbS/SoBWo+kDf0J6zD53DBXw5qp8gKb/OD4PblfJPnfJekVBnC7D0lA5sI/axa3e2+gVlQGvToIdX0DmKWALg6K1wUrdhreWC364FgxroLCYG0wMavGzC5McTzUb/Ta26m7s9Brk46YEO+WqjlpjN7pxzYYCtCcYKFt0AlbdmMYdj5XG43FYUSQ0BNDJqpxEVUESFSQbOSSoCqKsPnqZO4nHhbVbfOAXmukHZaE4sj+xleuxKTOY4l1ILBvNDJ72/oZ/2mezle78yXU1vZJjeOGiMTitlqYbDVY7dB8K13zEvKUFFBUUtHjPy1QcuRVNKTRCCNHVeb1eJk2aRE5ODp9++ukBq68OHToUm83Gli1b9htoOxwOHA5Hq48JIY4e+wtgJ556PJQMwAL8/bdXYv2knP+uup8ixz2MUjm8ip1zRz+AIyYdZ0Uek9cF0oA5eR5YWw+EfsnMs9NmYeFNo/Z5/mFNZP3cvtHNsjz55AGchT8xuVndIZ3tp7jGS7E/iqstH7LBn86nnECeTiRvfTK7zDg82DHz3PhVaUNKdwk+I7vh+xL8DV9mswJjVq1IwkYmUGX2YgLVZGAnQzemdxtEs/+UbgwrjH8C3r8d1ZDG3SKgbijwi+kHTxVEpoFhQMrxvBlzLSd/cz0bzJ5M995CBHUA9E1P4dzjIpk4tAfR4XF4nlvJJQUzqVJRuJWlKWi2WiA1sGe2Exr+nbofcBuyRvvNHGg4t73W4B8KCbQPQnulljT/kDcr0xUs57++wEVatDN4t6ZxRrpEx7K5qIq/Ll5D9vdf8LcbLsD29g2Ub/uh5V2yLxVXRhicG2bi8/uJIZDSvcjxAAXfvYrp3oyh/Zg5X1Bliabev4ft2sJ6exJbtJMdWpFvaoqwU4Ybl6rG2/jhdtcAYKjIFmnbVh1PbHAGOpDabSeMZCpJbAigAzPRFSSr9SSpChIjrHTzlZHuzcGidIuUE7e2YsXEqkxqTTuVRLDHiCchdSgWvzuYchI1cR53PvMmt7seZYvZlHLSOCN9sWdm8O+7i2D+9wWBD2DDBzwoMoW0hFr8eneLw+2xbkgIITqqxiB7y5YtfPbZZ8THxx/wOevWrcPr9ZKamnoEWiiEaE/7C2CdNktgC6+6MiyRKTx0honvx2zeqp/FHtuTXGT9khlv/ZZHzBcJ/+whqAxMHAWD1lb22j6cmecDXbvvnfpuw0eCqm7K+DzATjh8+URgggqCWZ6uyj3kzZlCnjWT/LpB5OtE8nUSeTqBPJ1EDTb8qqwhiC5uNiNdGpyhNlXLuhQx2kEGVnqiORYfmSgydFhw66vkhsWSAF5VhA1n8LmN66JVw7poEwODhpluqwPt96FMH2UfzqI+6nhSXD+hmk++QWC3nIYCvyT2g3GPB/59wuJY+/5mnvD+mVKzG16s1OLEaiiO7ZHCxNP7N7z/25hwfCpqcNrPBs2N/6cOJTj+2f+DRwkJtPejtU3b23pbgNbSukuIDf7dqhQ9wuI5JdnXosjYLZ7b2K6786ztCUZWrqfkuX/Trb6YVLOQ/9pbros2awxi8LMNG0uNRHJNPzu0QV5NOYXEUqo8uFQ1tf58tPIHZqB9laAMLDoOC/FYdRoWHU9EMKAOBNBhOopE6klRZc0C6HKSVC5JRjYJykWk1STdn4ehdMsPeLfkQEVxnxvs3dli606h14U2zaZS/AS28WqckW6RcpI+nGuapZw4rXYe+91VLFyexX/WVFNY7Kb59oMlxAb/fqAiGD93h0wIIbqC6upqtm7dGvw+JyeH7Oxs4uLiSEtL4+KLL2b16tW89957+P1+CgsD9Tvi4uKw2+1s27aNBQsWcO6555KQkMD69eu54447GDJkCKecckqouiWEaEP7DYwai8BWFeJ7aTz/sOQTZU5lnvcPuHRf/mn9N1+8fR1zMcmKzgxUI/fUwsvnQcrxwb22qSrk1dWVhzzz3Dw4b6w1tOiHXSy8/kSoLWPh6ny25lcRZ5YFi4w9aXua/kYuX9ufhTcewSz4iRq3F0wvymIn3KZwu+vI90SQ98Ld5JNMnv908nUieXvS2Om/nArtC84+B2agd+Mzfmo2I70HmqV0O7WNRJx01xZ6Y5Lny2KadXWLlG5Hi5nofcM2rza4x3ctf7a+iE2ZeLVirdmbLCOnaV20jsVQFpIiDLAEloD6Ewfy+6KzmO76G1vq05hefQujE6v526WTAhmfe9/4aKVSd3pcOIU6pkVVdL/WpEQ7j9ge6UfLzPX+SKDdiuYfUEXTvnWHtS3A/tJMwuLIjLHRR+eQSwpP2p6mr8pjuucmbrW9Q/+GSt0RGzVWmx0iTWweF07qWeSYyVqzFwON7RRh46fyPBbFjMVR8yV5SlOASYn2UKFqqVFVeFVVQzGxMjBAaWfTumczPlhQrDG9O1xHk6Y0ybiaBc/lJKkSktXm4Ix0DNUoBQVmLEpBiioPdLmh/D6mHyxhEJEKSmGYzdZ4pxwPZz0ACy6ClOP5KuVeZn+cjW62zx/Q6oy0ValAGrfV3mJG2mmzMPnsEdQ5trNu8Yb9/pMcaHa6I9whE0KI9rRy5UpGjx4d/L5x3fTUqVN54IEHeOeddwA44YQTWjzvs88+44wzzsBut/PJJ5/w5JNPUl1dTXp6OuPHj2fmzJlYLPK7VIguISyO3faehNd6WGoOQ2GQ7BuPwzyWH23/4nhVyAXVlTy9ayWpb98E3sC1H9WFUFMKb17NKH8a6epy6rTRtFWTUhQWl0JVePDnAMHJl1dX5FBSkEuK9vFna2DZ5IyCmyh8/jHCyzdyut/Pr/HjtQcmdbbTnWPULuJxMeT7O1hm7Ul+7XGBtG6dxC6dSK7uRolqXqm7BJ+xBb/6OhBEW0vRzVK6DW0hhnC6awfpKI7BT38dxrGYwa2vooOVfgPBt9eyHhtNgaxXK6ptsXTzNcQOzXblaZyRtuHnD+HvsZkB9PWs5xtzIDd4/49j2MVTjn9RYM0kZ8SDXHhiT1REdHCHnHmrK1m0bitf6Kbr6/dLnJzQmPG5d0HfVgr87m9iSinVpluqtVdm8ZGgtG5lR/SjnMvlIjo6msrKygOuCTscL3yxnUcWb2B/74zVUEwekcGDv8lqOtjaGunBSTiX3IVZvJF3ejcrLGa3YqRk4ffUond8xUp/H9KMMjJUMV6t8GkrdcrKBt2NdTqc7crGDhR5yqBUuymnlmpVjVtVopW7RdsMHd0w4xzXLKU7MAPdTUeSioVU3CQ3FBGLp5JUo5xkmlK6o6hBKfBh4LHFEO4NfMCbp3QXmtFEqzrClId8M65hu67dgfL70c3K7zdPM2lMv4Gmu2JVhRAWh1tbWl2vbihazEwDKAX3ntv/oO5ktrb2vb3uqgkhurb2Hpu6InlPhejYXlq+iWc+/J5iHUsapbxge5y55jn81z8Kl+UjKm3/QeFmhE7md9RzcVQ8ltgMyF8Jpg+v4aTUH4ZfN2z1pK1opYlzKJxOJzX1fnY5ehNp8ZFWuRqVeQqb9vjoVrkJpf3BnXC8WuEikmIdzW6dwE6dSKGOJ18nsJ048oBS6huC6MYZ6KY/TVXd1CmtCCeCOB1OCjYyUfTFSw9tcKLhIgNFUrOU7r2ZGFSoKOJ0BQDFZhRxqhprKzPSXsOJLSIucPHbfMKqeUp30gDmptzL6x83bXUFkKTKufGc1gv5zvzfWhZ8uzO4XBX2E98cQGtB8KzFG9rktRtf/0jNjh+sQxmXDnlG+/PPP+fxxx9n1apVFBQUsGjRIi644ILg41prHnzwQZ577jnKy8sZMWIETz/9NAMHDgyeU19fz5133sl//vMf6urqOOuss5g9ezY9evQ41Ob8Yq39BzlQSrfSkBlzTIvN4f1vTttnjXTVlxqHtQ7DV8f4VVfj01bcysGa2ijW1hax1eMhR/dnF7DN0pt6XxhVqoY6VYlPlYBR1NRQbW0oGJaARadh1XHEBAPobqRgpQeaVKr3SuPeSpKxkmRVTreGoNyrDe7xXMvdtleDaSblRJHUsIbDra1YdWBdtPbXUqricZsaO00p3Rt0T2Z5LuVl+2Ns0E1FEForv99iQ/j9bIHVVAShZQX2vD01+6TuHyiNe+8Z6YMpqCCEEEIIIdrWFaOOZeGPxVCQy3/sD5OpirlF/Y91/kx66wTec8+hwvox31k+5TKjBEdVPX0qNcPI4ExMRjpspPjziVR1+GnaJlZ7Leh6E4UDf10eFVqRS19Kt3rII4VqswcFdGOr6aRIaYowqVG1+NUe/KoMv7EDn1qJX+3BVFUt2mzTEUTqSBJwkqat9NJJHKdiGEQtA5SHNBR2FCg30GzCS0GBmQAKjIYsz73XRWP6MEw/kcod2CtaG2zUGTi1hxHGBr4xB3KL/3Z6+vJ5NuxpknoPhvF/2/+EVcO1dmPVbl+BC2vD9XJiamZTpe69/NK9vhu1lrrdVq8Nh7dN8dHkkGe0P/jgA7766iuGDh3KxIkT9wm0H3vsMR555BHmzp1L3759efjhh/n888/ZtGkTkZGRANx00028++67zJ07l/j4eO644w7KyspYtWrVQaWUtdUdbre7jmnPfLzPftFnDOrNc0t/xKKbKnVP99zEbbZ36GfsxGoYJBzTVIzL7H4SO4tKqXXXsklHskGHk68UeWjygXJqqVS11CoXPlW2zwda6YhmVbmbUrgjdTeSsNIDgww8wVnoYDExykm0VBGhm9Yb+zGos8YE00yaf8C1xYHf78WKyU6dyG4znmHGZr41++PG3pCu3rQu+knHbGqjj6XnlXNYmF3E1jLNd1vyKKmup0pFUWdaSLdVUuSLwFS2drvL1JFTRoQQXYfMvrY9eU+F6PjcXj+vrthK/y9uI82bw+X19wHwhv1BlNIs9P+Kpf4T+B5NteUL6i3r8ahtoAJp0oaOwaoTsetIDO1AYQfsBDaJ9aPxoVUdJnWYqhaTKkzlapHKDYA2sBFNuI4kWoeRjI3uKHrjZwBuTohw0K82nwi1nwrdjfaqzO2yxOHw1+CgvkWWZ+PMe7gzbJ9ZaH/8cVxbMon1xW6qVBR+rTk7oZwTho5kl8sXuN4d6Ajssd18wuoA7/PBXi+350xxW752W828t6VDGZd+Ueq4UqpFoK21Ji0tjRkzZvDHP/4RCMxeJycn89hjj3HDDTdQWVlJYmIir7zyCpdeeikAu3fvJj09ncWLFzN27Ng27eB++TzkPnMxlGzgt/V3cb9tAQPUDrTSxNoVNX6Fx2diYuAignwdw2YiyEGxUyt2G4oSXUcFgXXQvoY7ZFp5mn6GVliIDQbQjUF0IIC2kYZBRtoQeuz+smU1bgJrohsLmu2joRS/bijF79WKdbo3A1UgzURbw9jjD8dtahz48WAN3hzwjvo/PG9MY7e9J9+c8BgX9aznzZ3d+HhdIdt35qFpWhfdWsrJ3h/ii4f14M1V+RIECyG6PAkK2568p0J0Ho1p5FrDm/YHyTSKydcJZA+dxYTtD1JS4eIrcxAbzXTW62Sy8VKiqqhTFfhUMaaqQlMf+FJelLYAFhQGDmwoHU4cJjEYJKBIwSRdeemj6+inauivXEQ1v05vTWQqWmtUdaDAY/Nlk15lw4KJof3NrrVho+7JXzyX8rLjMTaYPbnDfytOXUvf5EheuO4MnGbD2vNWlk2GciKpPSey2uq1W1vOe6AlpO0tZIH29u3bOeaYY1i9ejVDhgwJnveb3/yGmJgY5s2bx6effspZZ51FWVkZsbFNlaAHDx7MBRdcwIMPPnjAn9smA29VIWX/Gk2h28oH/gHsxKBYedmNSTFeylQd1aoanyrDr0rxUwGq6a1S2rHXOuh4InUECThIxUIm0BMvqcrVbE/oQDDtUL7g62jDGti3LvjCFnR4HKqmBGj5AdcWB0o37GUXk0kB8SSUZ+9T+KAivBc3V0zGoy3BwhFK0RQ0N3zAD7R9QqjXQAghREciQWHbk/dUiM6j8VpzS0EZs21P0kflMTPmUZ6ZOhLnvDGBrb5s4XD+U4G1xw3Xx25to5II6rU9WOHaQBOu3ETgxoEXpQJLI23Nqnq3sNcs9H53wonqDonHYZZsDlYdr/EZ+LRmk87gKe9vmB32DO7YPkzZfQke3bSVbCLlDOvXm+TYqBbBpWRmHr6jMT5p1zXaP6dxe4/k5OQWx5OTk8nNzQ2eY7fbWwTZjec0Pn9v9fX11Nc3pX+4XK5WzzskkSl8NPwFvlw2h9n2BS0eMnRUMIi2m72I1INIwEEyVtJR9NImmaomkMp96lSSVjxAIj9hV/79/LCmvewag+xiHUUc1VhNH1pZUGknQMGPYPpQnmrMyFRq6s2mbQUcgQJqzQsfvGj/P74pXskmszterKzhGC7zPkC8PYUiVR9cy1CLs6lSd0Pf9yaVtoUQQgghRHtpfq25ovTvFEV6eOb0E3Eqf2A3GhRMfLEpyFYWMKw4/fU4qcBUTcFx84moYh1FnK5ute5Qi51wPFUQmXbAnXC46HkMTzWRwNyVpcFdcRoD6vNr78dmxFOISfOwvtyIIzk2qkVK8+HuAy4COnp80i7be6m91jZorfc5trefO2fWrFkHNdN9qC48YyRvfbee0+vq6Y6ipzY5VnlIV1UkqXKSVQnxalvwg9yqb7Oh2Qy1iYHLiCLGrAAaCothYlMm3WgofEBD4QMChQ+KYk+kx5VvQelmePNqSOyHMe5xIg9Q+CDl63zW6cwW+9cV6hiy4qJYV1zcopkHU4TgaN+LTgghhBBCdFytX2taAntn15UFrnWTBmBqWu7YY7NQU1dHlVdhah2oQG5a0Qryrb2pj1SkVqziW7Mf9cpBP3KDyyb51R0HtxPOtE+a/t5QvDfHVUqZigtOXgGUEIuqNtk7Jbi1a+2OXszraNCR45M2DbRTUgIzpYWFhaSmpgaPFxcXB2e5U1JS8Hg8lJeXt5jVLi4uZtSoUa2+7t133x3cwxMCM9rp6em/uL3Omt28HvEUhnfHgU/eK+WEiMTAL4TGO24NM9KG6SPaqKdUx+M2aZFmUhHRmxv2XE6daVBBoDDcccYuTso4mT85ukH3oYdUqXt/+9c9fslgJr/w7T7Hf65StxBCCCGEECFhteN2JjL/61x22u5kVc121n9tx6lubCpWfHKgWLHWtFwaeeZwRp7SC3fBejZvDyO/rJbCSA8Th/bA0lhM7BCur5trrYI20CLINlTg+9autZvvZNTIohR5ZbWIzq9NA+1evXqRkpLCkiVLgmu0PR4Py5cv57HHHgNg2LBh2Gw2lixZwqRJkwAoKChg7dq1/PWvf231dR0OBw6Hoy2bGlinPG8CRsUOiO4RSCmpKgBaL8XfIuXEMAKVA7116Nyv2BU9jLlxDzMwrYjfbLkPI6kf3cY8xkfZRWyrsjM+IZrYgZP4YE01eR9ubfHhXKczuSAhuulAKx/y/fm5dIqOnGYhhBBCCCG6juYp1gowdWBmuE5bqCOWkiI45YQwElMzgxNJddrJgNSowBZWVgvO9BO4Zn/zcIdwfd1c80ktNPvMYhsK+iZHcvGwHq1ea7flVlei4znkQLu6upqtW7cGv8/JySE7O5u4uDgyMjKYMWMGf/nLX+jTpw99+vThL3/5C+Hh4VxxxRUAREdHc+2113LHHXcQHx9PXFwcd955J4MGDeLss89uu54dSENqCgBTFuH/8B721Pjw+P0tK3X3HIqltZSTsDjcPj93zv4vHxfGYBaV4tcGC5Mf4oWLxuB0hjH57J4tfuQVo/ws/LG4TWea95dO0ZHTLIQQQgghRNfRPMW6tSrNFqUorHQf8Ymk5pNXH64tZFVueYv2aeDiYT32e829v+xTyTLtGg450F65ciWjR48Oft+Y0j116lTmzp3LH/7wB+rq6rj55pspLy9nxIgRfPzxx8E9tAH+8Y9/YLVamTRpEnV1dZx11lnMnTv3oPbQbjNWe9N6kMgU5vV4kGfWfr9vOkr6cK7JOK7VlJP5X2/n/ZKEQMn5hrtVXxVZmf99wX6DX5lpFkIIIYQQoklrKdbNNc4Ch2IiqfFnThmZ2WoF7J8LmuXav2v7Rdt7hUp7bPdxsBuiNy/Rv263i+y8iqNqE3UhhBChIVtRtT15T4XoGlrbLxlarn9u70rdB7MNl2zVJUK2vVdHdjBrKPYu0d88wN7fc4QQQgghhBD7t3eKtc/UJEc5OLt/Mr0SIto9oD3YbbhkaaY4FBJoNziYNRStleiHwBZ8FkPWXQghhBBCCHGoQp1iLdtwifYggXaDg/mAt16iH4ZkxDIwLUpSSIQQQgghhDgMoZwtlm24RHuQQLuZA33AW0svN4FzslLkbpcQQgghhBAdkGzDJdqDEeoGdCRTRmYyIDUKpQJFz5RqfXN6IYQQQgghRMcg1/iiPciM9iEI9foRIYQQQgghRNuSa3zRHiTQPkRSbVAIIYQQQojORa7xRVuT1HEhhBBCCCGEEKINSaAthBBCCCGEEEK0IQm0hRBCCCGEEEKINiSBthBCCCGEEEII0YYk0BZCCCGEEEIIIdqQBNpCCCGEEEIIIUQbkkBbCCGEEEIIIYRoQxJoCyGEEEIIIYQQbcga6gYcDq01AC6XK8QtEUIIIQIax6TGMUr8cjLeCyGEOJocyljfIQPtqqoqANLT00PcEiGEEKKlqqoqoqOjQ92MTkHGeyGEEEejgxnrle6At95N02T37t1ERkailPrFr+dyuUhPTycvL4+oqKg2aGHHIv2X/kv/pf/S/1/ef601VVVVpKWlYRiyMqsttOV4L//Xpf/Sf+m/9F/6fyTH+g45o20YBj169Gjz142KiuqS//kaSf+l/9J/6X9X1Vb9l5nsttUe4738X5f+S/+l/12V9P/IjvVyy10IIYQQQgghhGhDEmgLIYQQQgghhBBtSAJtwOFwMHPmTBwOR6ibEhLSf+m/9F/6L/3vmv3vSrr6v7X0X/ov/Zf+S/+PbP87ZDE0IYQQQgghhBDiaCUz2kIIIYQQQgghRBuSQFsIIYQQQgghhGhDEmgLIYQQQgghhBBtSAJtIYQQQgghhBCiDXX5QHv27Nn06tULp9PJsGHD+OKLL0LdpHYxa9Yshg8fTmRkJElJSVxwwQVs2rSpxTlaax544AHS0tIICwvjjDPOYN26dSFqcfuZNWsWSilmzJgRPNYV+r5r1y6mTJlCfHw84eHhnHDCCaxatSr4eGd+D3w+H/fddx+9evUiLCyM3r1789BDD2GaZvCcztT/zz//nPPOO4+0tDSUUrz99tstHj+YvtbX13PbbbeRkJBAREQE559/Pvn5+UewF4fv5/rv9Xr54x//yKBBg4iIiCAtLY0rr7yS3bt3t3iNjtx/sS8Z65t0pt91B9IVx3sZ62WsbyRj/VEw1usu7LXXXtM2m00///zzev369Xr69Ok6IiJC5+bmhrppbW7s2LH63//+t167dq3Ozs7W48eP1xkZGbq6ujp4zqOPPqojIyP1woUL9Zo1a/Sll16qU1NTtcvlCmHL29Z3332ne/bsqY8//ng9ffr04PHO3veysjKdmZmpr7rqKv3tt9/qnJwcvXTpUr1169bgOZ35PXj44Yd1fHy8fu+993ROTo5+4403dLdu3fQ///nP4Dmdqf+LFy/W9957r164cKEG9KJFi1o8fjB9vfHGG3X37t31kiVL9OrVq/Xo0aP14MGDtc/nO8K9OXQ/1/+Kigp99tln69dff11v3LhRf/3113rEiBF62LBhLV6jI/dftCRjfdcb67XumuO9jPUy1jcnY33ox/ouHWifdNJJ+sYbb2xxrF+/fvquu+4KUYuOnOLiYg3o5cuXa621Nk1Tp6Sk6EcffTR4jtvt1tHR0fqZZ54JVTPbVFVVle7Tp49esmSJPv3004MDb1fo+x//+Ed96qmn7vfxzv4ejB8/Xl9zzTUtjl100UV6ypQpWuvO3f+9B5+D6WtFRYW22Wz6tddeC56za9cubRiG/vDDD49Y29tCaxcfe/vuu+80EAy8OlP/hYz1XW2s17rrjvcy1stY30jG+n2FYqzvsqnjHo+HVatWMWbMmBbHx4wZw4oVK0LUqiOnsrISgLi4OABycnIoLCxs8X44HA5OP/30TvN+3HLLLYwfP56zzz67xfGu0Pd33nmHE088kUsuuYSkpCSGDBnC888/H3y8s78Hp556Kp988gmbN28G4Mcff+TLL7/k3HPPBTp//5s7mL6uWrUKr9fb4py0tDSysrI63fsBgd+HSiliYmKArtf/zkzG+q431kPXHe9lrJexvpGM9fsKxVhvbZNX6YBKS0vx+/0kJye3OJ6cnExhYWGIWnVkaK25/fbbOfXUU8nKygII9rm19yM3N/eIt7Gtvfbaa6xevZrvv/9+n8c6e98Btm/fzpw5c7j99tu55557+O677/jd736Hw+Hgyiuv7PTvwR//+EcqKyvp168fFosFv9/PI488wuWXXw50jf8DjQ6mr4WFhdjtdmJjY/c5p7P9fnS73dx1111cccUVREVFAV2r/52djPVda6yHrj3ey1gvY30jGetbCtVY32UD7UZKqRbfa633OdbZ3Hrrrfz00098+eWX+zzWGd+PvLw8pk+fzscff4zT6dzveZ2x741M0+TEE0/kL3/5CwBDhgxh3bp1zJkzhyuvvDJ4Xmd9D15//XXmz5/Pq6++ysCBA8nOzmbGjBmkpaUxderU4Hmdtf+tOZy+drb3w+v1ctlll2GaJrNnzz7g+Z2t/11JV/psN+pqYz3IeC9jvYz1e5OxPrRjfZdNHU9ISMBisexzx6K4uHifuz+dyW233cY777zDZ599Ro8ePYLHU1JSADrl+7Fq1SqKi4sZNmwYVqsVq9XK8uXL+X//7/9htVqD/euMfW+UmprKgAEDWhzr378/O3fuBDr3vz/A73//e+666y4uu+wyBg0axG9/+1v+7//+j1mzZgGdv//NHUxfU1JS8Hg8lJeX7/ecjs7r9TJp0iRycnJYsmRJ8A43dI3+dxUy1nedsR5kvJexXsb6RjLWB4R6rO+ygbbdbmfYsGEsWbKkxfElS5YwatSoELWq/WitufXWW3nrrbf49NNP6dWrV4vHe/XqRUpKSov3w+PxsHz58g7/fpx11lmsWbOG7Ozs4NeJJ57I5MmTyc7Opnfv3p22741OOeWUfbZ42bx5M5mZmUDn/vcHqK2txTBa/rqzWCzBLT86e/+bO5i+Dhs2DJvN1uKcgoIC1q5d2ynej8aBd8uWLSxdupT4+PgWj3f2/nclMtZ3nbEeZLyXsV7G+kYy1h8lY32blFTroBq3/HjxxRf1+vXr9YwZM3RERITesWNHqJvW5m666SYdHR2tly1bpgsKCoJftbW1wXMeffRRHR0drd966y29Zs0affnll3fYLQ8OpHkVUq07f9+/++47bbVa9SOPPKK3bNmiFyxYoMPDw/X8+fOD53Tm92Dq1Km6e/fuwS0/3nrrLZ2QkKD/8Ic/BM/pTP2vqqrSP/zwg/7hhx80oJ944gn9ww8/BCttHkxfb7zxRt2jRw+9dOlSvXr1an3mmWd2mC0/fq7/Xq9Xn3/++bpHjx46Ozu7xe/D+vr64Gt05P6LlmSs77pjvdZda7yXsV7Gehnrj66xvksH2lpr/fTTT+vMzExtt9v10KFDg1tgdDZAq1///ve/g+eYpqlnzpypU1JStMPh0L/61a/0mjVrQtfodrT3wNsV+v7uu+/qrKws7XA4dL9+/fRzzz3X4vHO/B64XC49ffp0nZGRoZ1Op+7du7e+9957W/yy7Uz9/+yzz1r9vE+dOlVrfXB9raur07feequOi4vTYWFhesKECXrnzp0h6M2h+7n+5+Tk7Pf34WeffRZ8jY7cf7EvGev/HTynM/2uOxhdbbyXsV7Gehnrj56xXmmtddvMjQshhBBCCCGEEKLLrtEWQgghhBBCCCHagwTaQgghhBBCCCFEG5JAWwghhBBCCCGEaEMSaAshhBBCCCGEEG1IAm0hhBBCCCGEEKINSaAthBBCCCGEEEK0IQm0hRBCCCGEEEKINiSBthBCCCGEEEII0YYk0BZCCCGEEEIIIdqQBNpCCCGEEEIIIUQbkkBbCCGEEEIIIYRoQxJoCyGEEEIIIYQQbej/A0fhhYfy6BX1AAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(12,4))\n",
"\n",
"# rising\n",
"ax[0].plot(x, y_true_rising, label=\"true\")\n",
"ax[0].scatter(x, y_rising, s=12, label=\"data\")\n",
"ax[0].scatter(x, scurve(x, res_r_lmfit[:6]), s=20, marker=\"x\", label=\"lmfit\")\n",
"ax[0].plot(x, model_r(x, res_r_munuit_obj['par']), linewidth=1, color=\"green\", label=\"minuit\")\n",
"ax[0].set_title(\"Rising S-curve fits\")\n",
"ax[0].legend()\n",
"\n",
"# falling\n",
"ax[1].plot(x, y_true_falling, label=\"true\")\n",
"ax[1].scatter(x, y_falling, s=12, label=\"data\")\n",
"ax[1].scatter(x, scurve2(x, res_f_lmfit[:6]), s=20, marker=\"x\", label=\"lmfit\")\n",
"ax[1].plot(x, model_f(x, res_f_munuit_obj['par']), linewidth=1, color=\"green\", label=\"minuit\")\n",
"ax[1].set_title(\"Falling S-curve fits\")\n",
"ax[1].legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "2c42ad91-fd59-47ba-b90d-8300f97eb27a",
"metadata": {},
"source": [
"## Quick error check"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "3ba40e9a-0c64-4ef6-ba90-b1116f99b862",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rising abs error lmfit : [0.532169 0.037017 0.078405 0.284539 2.742061 0.032766]\n",
"Rising abs error minuit_grad: [0. 0.019137 0.050193 0.270068 2.222365 0.015927]\n",
"\n",
"\n",
"Falling abs error lmfit : [1.73448 0.024214 0.251971 0.092915 1.523265 0.001323]\n",
"Falling abs error minuit_grad: [1.741754 0.024277 0.251604 0.092887 1.526732 0.001435]\n"
]
}
],
"source": [
"print(\"Rising abs error lmfit : \", np.abs(res_r_lmfit[:6] - p_true_rising))\n",
"print(\"Rising abs error minuit_grad: \", np.abs(res_r_munuit_obj['par'] - p_true_rising))\n",
"print(\"\\n\")\n",
"print(\"Falling abs error lmfit : \", np.abs(res_f_lmfit[:6] - p_true_falling))\n",
"print(\"Falling abs error minuit_grad: \", np.abs(res_f_munuit_obj['par'] - p_true_falling))"
]
},
{
"cell_type": "markdown",
"id": "c9bbabfd-e24e-43ec-8622-983b3757d7a9",
"metadata": {},
"source": [
"## Benchmark"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "dc0b52b6-9fc3-453d-b6eb-e325a2b8342e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rising lmfit : 0.334 ms\n",
"Rising minuit2 : 0.239 ms\n",
"\n",
"Falling lmfit : 0.294 ms\n",
"Falling minuit2 : 0.214 ms\n"
]
}
],
"source": [
"def bench(fn, n_repeats=200):\n",
" # warmup\n",
" for _ in range(3):\n",
" fn()\n",
"\n",
" t0 = time.perf_counter()\n",
" for _ in range(n_repeats):\n",
" res = fn()\n",
" t1 = time.perf_counter()\n",
" return res, (t1 - t0) / n_repeats\n",
"\n",
"model_rising = RisingScurve()\n",
"model_falling = FallingScurve()\n",
"\n",
"res_r_lmfit, t_r_lmfit = bench(lambda: fit_scurve(x, y_rising))\n",
"res_r_minuit, t_r_minuit_grad = bench(lambda: model_rising.fit(x, y_rising))\n",
"\n",
"res_f_lmfit, t_f_lmfit = bench(lambda: fit_scurve2(x, y_falling))\n",
"res_f_minuit, t_f_minuit_grad = bench(lambda: model_falling.fit(x, y_falling))\n",
"\n",
"print(f\"Rising lmfit : {1e3*t_r_lmfit:.3f} ms\")\n",
"print(f\"Rising minuit2 : {1e3*t_r_minuit_grad:.3f} ms\")\n",
"print()\n",
"print(f\"Falling lmfit : {1e3*t_f_lmfit:.3f} ms\")\n",
"print(f\"Falling minuit2 : {1e3*t_f_minuit_grad:.3f} ms\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e694c18a-a203-4132-a472-c93c4d3ce853",
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}