diff --git a/docs/_static/custom_fitting.png b/docs/_static/custom_fitting.png
new file mode 100644
index 00000000..c5505d56
Binary files /dev/null and b/docs/_static/custom_fitting.png differ
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 8f7f08b2..722fcd3d 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -17,8 +17,6 @@ sphinx-plotly-directive
sphinxcontrib-mermaid
matplotlib
h5py
-importlib-resources
-h5py
pyyaml
importlib-resources
rapidfuzz
diff --git a/examples/Basic Usage/Basic Usage.ipynb b/examples/Basic Usage/Basic Usage.ipynb
index 5488005d..7cc1cd05 100644
--- a/examples/Basic Usage/Basic Usage.ipynb
+++ b/examples/Basic Usage/Basic Usage.ipynb
@@ -185,7 +185,7 @@
"## Building the model\n",
"\n",
"For simple parameter estimation,\n",
- "the fit decorator (**@fit**) in conjuction with the model definition is used.\n",
+ "the fit decorator (**@fit**) in conjunction with the model definition is used.\n",
"The fitting decorator takes a pandas dataframe containing\n",
"the psi/delta measurement data (**psi_delta**) and the model parameters (**params**) as an input.\n",
"It then passes the wavelength from measurement dataframe (**lbda**)\n",
@@ -204,7 +204,7 @@
"This is done by calling the :code:`elli.IsotropicMaterial(...)` function\n",
"with a dispersion model as a parameter\n",
"or simply calling :code:`.get_mat()` on a dispersion model.\n",
- "These two approaches are equivalent.From these materials the layer is build,\n",
+ "These two approaches are equivalent. From these materials the layer is build,\n",
"which only consists of the SiO2 layer in this example.\n",
"The final structure consists of an incoming half-space,\n",
"the layers and an outgoing half space. Specifically,\n",
@@ -223,7 +223,7 @@
"Executing the cell below in a jupyter notebook displays a comparison of the simulated Ψ / Δ values\n",
"at the current parameter values with their measured counterparts.\n",
"Additionally, input fields for each model parameter are shown.\n",
- "You may change the parameters and the calcualted data will change accordingly.\n",
+ "You may change the parameters and the calculated data will change accordingly.\n",
"For clarification the modeled data is shown with `_calc` postfix in the legend.\n",
"\n"
]
diff --git a/examples/Custom fitting/Custom fitting example.ipynb b/examples/Custom fitting/Custom fitting example.ipynb
new file mode 100644
index 00000000..6aaf574b
--- /dev/null
+++ b/examples/Custom fitting/Custom fitting example.ipynb
@@ -0,0 +1,404 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "65555c05",
+ "metadata": {},
+ "source": [
+ "# Custom fitting example\n",
+ "\n",
+ "This is a short example for fitting data, which is not covered by the fitting widget.\n",
+ "It relies on calling lmfit manually. Therefore one needs to provide a fitting function, which calculates the numerical residual between measurement and model.\n",
+ "This notebook is about fitting multiple datasets from different angles of incidents simultaneously, but can be adapted to a wide range of different use cases."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "2094a05c-198a-475e-83f0-3d2b73fc3b1b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import elli\n",
+ "from elli.fitting import ParamsHist\n",
+ "from lmfit import minimize, fit_report\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cb2986be",
+ "metadata": {},
+ "source": [
+ "## Data import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "efb38395-95d5-4f4a-9330-10a76b3a47fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Ψ
\n",
+ "
Δ
\n",
+ "
\n",
+ "
\n",
+ "
Angle of Incidence
\n",
+ "
Wavelength
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
50
\n",
+ "
210.0
\n",
+ "
40.028156
\n",
+ "
146.370560
\n",
+ "
\n",
+ "
\n",
+ "
211.0
\n",
+ "
40.012478
\n",
+ "
146.607407
\n",
+ "
\n",
+ "
\n",
+ "
212.0
\n",
+ "
40.007420
\n",
+ "
146.799438
\n",
+ "
\n",
+ "
\n",
+ "
213.0
\n",
+ "
40.005928
\n",
+ "
147.055832
\n",
+ "
\n",
+ "
\n",
+ "
214.0
\n",
+ "
40.001190
\n",
+ "
147.294281
\n",
+ "
\n",
+ "
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
\n",
+ "
\n",
+ "
70
\n",
+ "
796.0
\n",
+ "
9.051100
\n",
+ "
174.423874
\n",
+ "
\n",
+ "
\n",
+ "
797.0
\n",
+ "
9.054414
\n",
+ "
174.494553
\n",
+ "
\n",
+ "
\n",
+ "
798.0
\n",
+ "
9.044289
\n",
+ "
174.405136
\n",
+ "
\n",
+ "
\n",
+ "
799.0
\n",
+ "
9.048944
\n",
+ "
174.376541
\n",
+ "
\n",
+ "
\n",
+ "
800.0
\n",
+ "
9.021362
\n",
+ "
174.418228
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
1773 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ψ Δ\n",
+ "Angle of Incidence Wavelength \n",
+ "50 210.0 40.028156 146.370560\n",
+ " 211.0 40.012478 146.607407\n",
+ " 212.0 40.007420 146.799438\n",
+ " 213.0 40.005928 147.055832\n",
+ " 214.0 40.001190 147.294281\n",
+ "... ... ...\n",
+ "70 796.0 9.051100 174.423874\n",
+ " 797.0 9.054414 174.494553\n",
+ " 798.0 9.044289 174.405136\n",
+ " 799.0 9.048944 174.376541\n",
+ " 800.0 9.021362 174.418228\n",
+ "\n",
+ "[1773 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = elli.read_nexus_psi_delta(\"SiO2onSi.ellips.nxs\").loc[(slice(None), slice(210, 800)), :]\n",
+ "lbda = data.loc[50].index.get_level_values(\"Wavelength\").to_numpy()\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6cc460c2",
+ "metadata": {},
+ "source": [
+ "## Setting up invariant materials and fitting parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7e74cd9c-a0c1-48a6-9773-9c8254521dda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rii_db = elli.db.RII()\n",
+ "Si = rii_db.get_mat(\"Si\", \"Aspnes\")\n",
+ "\n",
+ "params = ParamsHist()\n",
+ "params.add(\"SiO2_n0\", value=1.452, min=-100, max=100, vary=True)\n",
+ "params.add(\"SiO2_n1\", value=36.0, min=-40000, max=40000, vary=True)\n",
+ "params.add(\"SiO2_n2\", value=0, min=-40000, max=40000, vary=False)\n",
+ "params.add(\"SiO2_k0\", value=0, min=-100, max=100, vary=False)\n",
+ "params.add(\"SiO2_k1\", value=0, min=-40000, max=40000, vary=False)\n",
+ "params.add(\"SiO2_k2\", value=0, min=-40000, max=40000, vary=False)\n",
+ "params.add(\"SiO2_d\", value=20, min=0, max=40000, vary=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e12e3ea",
+ "metadata": {},
+ "source": [
+ "## Model helper function\n",
+ "\n",
+ "This model function is not strictly needed, but simplifies the fit function, as the model only needs to be defined once."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5deba07e-aab1-4bb5-b9a1-9a2087a0e80a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def model(lbda, angle, params):\n",
+ " SiO2 = elli.Cauchy(\n",
+ " params[\"SiO2_n0\"],\n",
+ " params[\"SiO2_n1\"],\n",
+ " params[\"SiO2_n2\"],\n",
+ " params[\"SiO2_k0\"],\n",
+ " params[\"SiO2_k1\"],\n",
+ " params[\"SiO2_k2\"],\n",
+ " ).get_mat()\n",
+ "\n",
+ " structure = elli.Structure(\n",
+ " elli.AIR,\n",
+ " [elli.Layer(SiO2, params[\"SiO2_d\"])],\n",
+ " Si,\n",
+ " )\n",
+ "\n",
+ " return structure.evaluate(lbda, angle, solver=elli.Solver2x2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb082e25",
+ "metadata": {},
+ "source": [
+ "## Defining the fit function\n",
+ "\n",
+ "The fit function follows the protocol defined by the lmfit package and needs the parameters dictionary as first argument.\n",
+ "It has to return a residual value, which will be minimized. Here psi and delta are used to calculate the residual, but could be changed to transmission or reflection data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3fec16e0-f069-413a-8ae4-6796319a264e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def fit_function(params, lbda, data):\n",
+ " residual = []\n",
+ "\n",
+ " for phi_i in [50, 60, 70]:\n",
+ " model_result = model(lbda, phi_i, params)\n",
+ "\n",
+ " resid_psi = data.loc[(phi_i, \"Ψ\")].to_numpy() - model_result.psi\n",
+ " resid_delta = data.loc[(phi_i, \"Δ\")].to_numpy() - model_result.delta\n",
+ "\n",
+ " residual.append(resid_psi)\n",
+ " residual.append(resid_delta)\n",
+ "\n",
+ " return np.concatenate(residual)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b07292b",
+ "metadata": {},
+ "source": [
+ "## Running the fit\n",
+ "\n",
+ "The fitting is performed by calling the minimize function with the fit_function and the needed arguments. \n",
+ "It is possible to change the underlying algorithm by providing the method kwarg."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "78dd21c3-4423-4bb3-b3d7-33862573bc2b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[Fit Statistics]]\n",
+ " # fitting method = leastsq\n",
+ " # function evals = 119\n",
+ " # data points = 3546\n",
+ " # variables = 3\n",
+ " chi-square = 157.748841\n",
+ " reduced chi-square = 0.04452409\n",
+ " Akaike info crit = -11031.1779\n",
+ " Bayesian info crit = -11012.6572\n",
+ "[[Variables]]\n",
+ " SiO2_n0: 1.63549687 +/- 0.03012997 (1.84%) (init = 1.452)\n",
+ " SiO2_n1: 27.6408772 +/- 8.75248284 (31.66%) (init = 36)\n",
+ " SiO2_n2: 0 (fixed)\n",
+ " SiO2_k0: 0 (fixed)\n",
+ " SiO2_k1: 0 (fixed)\n",
+ " SiO2_k2: 0 (fixed)\n",
+ " SiO2_d: 1.75831329 +/- 0.02404272 (1.37%) (init = 20)\n",
+ "[[Correlations]] (unreported correlations are < 0.100)\n",
+ " C(SiO2_n0, SiO2_d) = -0.9916\n",
+ " C(SiO2_n0, SiO2_n1) = -0.9596\n",
+ " C(SiO2_n1, SiO2_d) = +0.9259\n"
+ ]
+ }
+ ],
+ "source": [
+ "out = minimize(fit_function, params, args=(lbda, data), method=\"leastsq\")\n",
+ "print(fit_report(out))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67bf8ce3",
+ "metadata": {},
+ "source": [
+ "## Plotting the results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "826831fb-875b-4551-b21d-04bae98ae47d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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nU4JYsWOxrCBeW49ECJiptTg61+TIXJ3JSptaIyZMEtqJIo6NgHAdietKqs2QME7wfYcwUTRDjSs0kVb40qGZKLQ2XnpHCCotkxkUxpr5huoFjkaJph2bJ8vuutJGsCyHXpECXCmMG6Sbomx8IASusVgoBYEne+6VJcKlcy5AFGsygWPOSZQRJFqDivFURLMdknFNLEErMW4xT8TE2n3dctYzcRS1SJDzFC4K0apSUrPkvRpu1MbXDTwSnCjG1W3SUqEQtBUnFyHaiBAhHRydEGsQjkfgOmht3H5aJaSdmMBxaCkHEdXIygjH9VBKEynwhMITSWeykTiqjSsi2sJDIPCIUGi0ljjCiNVE+7gofKdJLAJQGk+0ARMX5AgFHWuCgxEeQmtcEaOUACXxCQHdsWhEHWuDxtMRWkikUOY9KpORJNEIbSwQItIQvfZL/ta/P69jGTX0CUEgXyMEOiKgY7UygTrKuHI6y0jP7OuEffcEARLhOOb6KjaCQAqIQ/AyaIERoU7afNGVAiHRnQhujUTI7lgUjuPhSgfiiNhLoaSD1BpXLBo7gHSJtSaJ2zhe2sQKRS3w09BxscVak6BxhMSlI3C0OnGc1kvOQatFH7gwy6c6J8hCkIegAIXVEOSMpctLm3POI1bsWCwrgEYr4thCg70TVY6Vm8zW27TbimYc02xrwjghQZHxXFphTCU0tTU8x0x8WoPrQrOtaIYKXY+M9aVjcWl23ExRYiwycfLWXT9dseI65nc3LiPlObhOd9ncdD1HkvUd2nFCxhP4rkM71kRK4XdcOGBiOkDiCEVAG1TyupidnKdwBDRNTCSeFxMnLjqBjKtwlUM6qjIkJimGFfriCQpxFY8Ekqgzd7mg4tcvS98Eh6IhBF+18KIaseMTygwpVcNXTROMKTwTX6I7ExtvLkI8oRFSotwAj4Sc08RJpZBa4ekWkHRiHlrGEqFBEiOlMineKkQkYTeFycS6dH7M8aqTttSNmzjHnMFLaDp/Z+F0JmNh3Bjd5cUWgq5QQBgLgNOxFiQxeKkTk7kQ5u/XHYgTmOW4DX7G7Itb4AbGMtG1SggFwjvx94feOQJOOrl33VZvOLmfShAIAY4PYaPzOp1z4jZkB6FdPXG+cMxYnRQ0Z8xvKaFdA9cH4UNUN5+P7+J2X8vLmNcNG2YZAXELF3C7+7QCNwVJ0jvH1Qo3bIAf9M4BvfS415zTex0/88bnxBFQhVTJ/B1VbN5vfmRZXFhnghU7FssFoGvBaUQxx+cb7B6vsOd4ldl6m8B1SHsOM9UWc82YjG+e1LSG+WaTVmjcRfUooRGaEu+tSJ2VxUUKI0Rcaaqsmh+J6wgkgpQncWQnAFU6OBLSnoMvoaV4w4yljJMQOBC3IxzRIuU4pDM+2UBRaTapRor+jEcxLWlG0IgSUlKRUXUK0TR9yQxBvICrmriRmaRcFeKGoXFdnFS4eKR0m4xs46dSZNOSdOCaLJeogYxb5klZSATq9cteyhj3oybaDdAqRmoFIkGrWYTSCAkqjnBUzfxdBIgkQkQt84EmMVLFoGNQsREdKkGojltGJ9BKzo8YWUJXUMglT/50nTnS7Txta3C8E8ctFiHdiZhOio/rm20qPvG0Lha/TmddektFxFuxFnTP6Y5ZdSZRFRuxoyOIQvB9Y0GJ2x1R5RiBELUhrEIqa8bYnai9jLGovJXJ/Y3OSWIjuoRjxuc4kOrvjNsHZGe7gsyQOU9F5v2l8pBbZd5v3ASGjAhyHHOMluZpR0hQAmRH9Cah2bf4uPN9Dg6k85Dug4FtsOoKI37OM1bsWCznkTBKGF9osGd8gcNzTfZPV5kot5FS4wmJdCSVVkwtiqi0E+YaEQdnY2phQjt+84ABzxGdgGBjcUm5xg3UFTOuNJlWGd9BKSimTTzJyYKNUx540kFpQcZJEEmIQ0IuEASeRyIkro4RcYMo6LqHTJBxkNTI6wWK8RyjzgxZ2cbTbTJhjJOAKyBIxzhSkoQejo7wPIkrFU5Uh5RGO4ERBnHLpPUmEUIrlJdCJ/HJhQsCqUNzMw4knopxkwRIQVwxE0cSmklTCjPRxO1OjMGiSVMnJ9a1Mr/PlTjpigLpnrBmOH7PBYKb6ggRjLBz/BPuFNlxocRR58lcGhHoZzpiQb9G4LyJiOhaG4K8+S2lmVRbZfO6QRGacycsBFHTjFO6JwTLKSwMy2YtWHxOV6yl+owIaNUgMwClErTmzU9mCDKDEFXN5+QVoG+9ER7nUxCoCJwM+Cnz3TKFg4yVxkkt3S6EEZLDV0J2wLiBMgXzWbtpyPR3rp90vjOcWJYd8X+yfadaPh/nuAH42fNu0elixY7Fch5QSjNdbfL8kXmePTLPVDUErSk3QpTW1NsJtXabdqxZaMaUGxHJSeZWKcDvuISygUnbzaVc0q6xyAhhHk6Na6nj68dUgXWlQxgpcoGDEiZNPBu4pt6FjnB1jE4ikkSDTvClQjoOg26Lje4sI06FftEg5YJ0XaSKUe0GxE1EuwokJnMlbCBFTILEFXWQPsJxTCBrswLSxxMSN26ZmCMhcbQy8QK6mxLbmaB152aKhrAJKoSWA0m7I0YiMymDufEmYcdcv0ioLDevFSddi0E3XqO7LB3zx/CCjvVJGUEhvY61w1nk0lkkQuI2pIrGEhDWzQSPMuuOB0Kb6wkHdNgRKDG9XgJe7oTw8LOLRMRpuimcwHy2jtcZn2PEhJfpiDDXTFzdXKELaWGQDrhZyBTNZyZcM5m6QScItmPxSxLj/soMQnEN5IY6IvYCCAI6QkYnxsr0RtvdVOezxmwXzgUTCxc79lOzWM4xYZTwysQCTxyY5cBsg7laC60F8/WQQ3MtKmFMvZ287jxHQjHlUcq4FNOuiW2RgihKcF1JynPJ+oJEmSDibOCQCxySxGQQeY5pO+B7DvnApRhI1mY1QzlJPudTDDy0inDbVZzmArp8BGoziMZcZ6JJEGENp91EOC6eELhB1lQYa0ybScfLmsk7qpjJmE4WhnA6T3MuEIEIIO5MuEloxEoS4UYN3LBmbuRxC6KGsUyo10a1vkVExwrS/UGccCnAiZiOrlgRjpnYHf/EEzTCuGp0Yt5rkDPvpXt93kSEdMXM6ywZrxEhTmA+AzcwxyUts78wZMYYLoBfMvviuhFETsp8YZZDRDiOEQlOBoK0GUe6vyN2JGSHIV00f/uVYmGAEyKsK3q7wixpm79Xd/9KEgupM9m+gsZ9EWI/PYvlHKGUZrzcYNfReZ46OMdcPUJ05r/dU1WOzLeWHB+4gnzKpRC45AKHjAdSugzlPVKeQ7WZ0IpjCjmfrG/cF/mUR9Zz6csHrC6lGcgFFLMuWRmjw4Yp8qYTnPosXnk/bu0ozvQcblTpmMs7FpPuJB+3oT1PtzYIoYlLMU/5GMEiHGNh0QkkTWOG99LmQb9dNa6PuAmtirFgJKGZyOPWieXTdgmJExaUxWKlZ0npuGq6gZ1amyd8OrEiTnDCZeVlOuJELxUepxQhmRMBr0loLDPSMS4eNwWtBWNVyA2ZCbZdMW4edxlEiI471+6HoAS5QciPdibqTryK7ozHz4KfN38jWD4R0f38Xd98lpqOEBQr28LguMaKs3jd8rbHfgsslnNAHCueOzLLEwfmOVZuUK6FuK5gvNLm+aPVXvzNUM5jMOfRn/EppFy01tTaEVnfYyDnEbgu2cCjkPIYygesH8gyVkrjexKtIlTUJhC6U9Zd4YgWbmUcZl6B6nGoTplJOayZNNegaCar5rSJX+hOBHHYEQwdC0gSmkmvG8fRrpmJrl0xcRpJ2BEubSNeuu4k/XoL1cnpWFacwIiWIGtiEYRnLCbdMbhpoFPjQ/rmtboipiuaXitcpNOJD8h1YiKaJsDTz525OwU6gqIPUjlz3SDbiaNxjMgIMidibWB5RQjCiC7pLnVjJLF1a1gsZ4D9L7FYlhmlNE8fmuGHr0zTCk2J9Vo74YWDVeYaxj2TciVbhtKUMh4p12TGpH2HoXyay4ayXLm6xGA+wHMlsTKl89OOJkUbwgo0KrBwDCpHoXzMCBqdQHNhUZAnRuS0FzqxIz40p8wTup8FpJnkk6hjiZk7YfKPW0Yk6PgMRQxLXUBuYESK43fERmwCLt30ya0sbvqEONCYsXlpYz2RrhFdjteZ8E8hXLpWkfwg5MdgYDOU1kAvnZkzd6cstijpxIztZLEU51SELLqe42Jv3xbL6WP/WyyWZUQpze7jZR7aN0uznVAPE3YdW2DvZN14AQRsG86yrj9FK4pxHMnawRwb+7JsGMywdiDD6lIW33PMxJm0jXipTMHMPlg4DOUjUJ2kEwjSiZHpZHa0FsxAdGgm626gb3seQkxMTLsbI9M0ouZ0g3h7IiYwAqSbGeJlT9Q/8TKdWJhO4KpwQEfg5Y0Fx3XeOAvG7QSYBv1QGDEpqqmCsZ44nnFRdS02p+OC8TNLXRrnBCtCLJaVjv2vtFiWCaU0r05XeWz/NMfn6zx/rMr+mUavSeRoPuCyoTSrimlSgWRtKct16/vYMJAlHbgEroPrSCNupo/B/GGYPwSze40VJ24bt4gKISybQOGuSweMi6k1ZywyUfMMxEzHpeSmTggUr5Pu7OWMC8jLdbKbQggGIDt0InA2bmMCa1NLg1tTGVNbI78aSmNmOTNwelkwXUuKzUSxWM4psYpR2liP3U6w9+JtAGEnEF8KiercT3zHB0BphdKKMAkJk7B3zuLzlVb4jk/Gy/Re43xj7xwWyzIxXW3y7KE5jlfafPfFaZqRuSmMFgKuHsuTCRyU1qzqS3PV6iJXrSkxlE8jO4XoiKpwfD8cfw7m9kNjpiMkhAkC1gm02kbkoMz+xqwRNWGNNwz6dTpixk0bEdF1K+VGTA2PqG5K1fspjHsrNuLFdZYGymYHOsXOEhOo66dPnZXTDW7tpjC/JaFib1UWy2JacYtW3EJpRcpN9YRIrGJc6fbWu8e8VoQorVhoL9BKWjjCQSUKz/GQUtJO2kRJRKPdoJ7USXRCK2rRTtoEXkDWy+Lg4Ds+URIxUZtgPpynHtapRTXTjkY6RCrCEQ4ZP0MpKLG1byuXD15Of7q/N57zhb2DWCzLQBgl7Dpa5tBcg++9OEUzUhRSLjvXFhjIB6bpoSvYua7ETZuGGCmkjasqDmFhHGZeheO7YGqPyXBy08blFDVM+la7bAqktReMBedk1hrHN/VQ3JRxM3kdd4+bgsKY0UJRFYRvrDVOCrxOQLDfZ2JcckOQ6esImUXBta8NlO0Vq7PWFosFTlhDumJisaWky2KBAq+3frx22ZXuSa0s47VxjlSPMNuaJUoiXOHiOz6udHGkQ5zEJCqhnbSphBWacZN20kYiEULQSlqEcYhCETgBAkFbtZlvzhOrmMALUEpRjaokOsGTnhFAKkIK004lTELTBFgnJDpBaYVG97ZpdO/z6Dan+fnLfh6Aq4evpnSeqyjbu5TF8hZRSrNnfIHnj5TZdXSBY+UWrhTcvm2AlO/SjhQjhRTv2tLP9esHSQWdAnPlo3D0GZh4HmrjJnNK+iAxtVQqx2DhiKlY+1qEYywyXifjJ8gacZPuN/u7N9k4hHSuI2KGoLAehjaZ9cWWmG520WkJGHvbsKx8YhX33C9dIXCq/V2LiNJqyXGvdfG8Vnh0RUs1rFINqzTCBq24he/6FIMiBb9A2ksTq7gnUKYb00w3pgnjECklkTJN3jSaVmKqhSM67iGliImJkoh20kZpRTNu0kyapgu99IhURC2sEesYKSQCQahCIhX1xtcVJFpr3npXvLNHa81ca46Z5gw5P3deXVr2rmWxvAW6cTpPHpxlotri8QPzANy4ocRYf5YwUhQzgg/sGOHqdf3GZRW14MhTcOhxmH/V9OnBxVQJrkB1HCqHT1QGRnQsLTnwC+Z3UDQuJ9kpMQ+QLpl+P+lBKKwyzfaKa4ylRusLXq7dYjkZXdHRdb90Y0HCJDylC6a77Ds+vuMviRdRWjHfmme2OUszbpLohLyXZyg9RC7IIYWkHtYZr4+z0F5grjFHJawQqYhYxWit8aRnrDCqRaQj4sRsj7Q5pjsujaYRN0hUYrzNnX5tsYpRShFpIzgiFdFWbdCgUD3rh+lMfwaZjsuMQPSsUBLz2xGOadiqwXVcHOGQqITACXCkYxIoEUaoCLPsOR4S4/7KeTkkkmbSpOAXKAZFAjdgLDfGQGqAWMdLrFvnC3vXs1jeAt04nYV2zMN754gSzUje55q1RYQyXb+vXlPk8tUlJNqkie/7IRz7sREzYR3QUN4P1WMmBqf75CU9yI5AaZ1JodZAVAMZQH4IsquguMoImsIqk7XkeEuDe62wsZwli90yi2M/fMdHCkmsjCXBd/yeZeR0LCHd5VjFVMMqk/VJphvTRDoi5aTw8Ih0xEJ7gZnmDPWo3nPBdONAwFhrBIJEJ0QqohE1qEU12nGbUIVoNK7s1K6KaoQqxJFm4o5U1HO5hEnYEx5d8dF1w1wIpJA4OCdEiFgkQjC97XzHx8ExlijHxZdGIAohEFrgOq4RIdJDYSxEaS9tREhsRIgrXFqqhYOD45jPRWuN53igoR7VSbtpFIooMX8bJRRaayN6lLFGCSEQiN53JJCB+fvjEjgBnvDIOBlzvla4wu3FGJ1P7J3QYjlLwijhxeMLHFto8uSBMhOVNq4U3LKpjzjRpFKCsWKKq9eU8KWGw0/Bqw/BzEvGjRXVYfJ5qE8tjcFJlaC0EUobOs0U05Ab7oiaRW6oxX2KrKh523IygdG1lJzqODh1vEisYuqRCTRdaBrBUW6VKYflnmtFSkkxKJpzNLjSpdKuUI2qqE5TVolECxO30Y6NC0ZhJsREJbRVm2pYPSGgkFTCCrW41hMjXZfMYkFyIYSIQOCI1wgQzOflSmP90Fr3hGBXmMhOqxCJ7FmsYhWbrCRcmknTBPsKh0bcIO/lcaRDqEKEFjiOQ6ziEwJGC+pRnZSTAgm1sEbBL/SEGtCzdHXjc6SQVMMqKZEyYknoEzFF2kWjSeIEJHjC6wlIT5rlWMW4whwXRiGBDPCkR03VyHk5Uk6KcrtMohNc6dJWbaSU+K5PGIdUqZLzc0RJhO/49Kf6GUwPnvesLHuHtFjOkulai8NzTRaaMU8cMHE179ncz0gxTRgpCoHPtWv7GMoFcOgJ2PePJ9ovNCbhyKMnRI6XMZlRxU0maNhJmUJ4o1fDqquNi8pLWzfUJcKpUnsXx450108VtNoVJc24SaIShDYTUzWqMteaY749TyNsGPeLimglLRKd9CbCrpXktcuxNmKpOxm14hZhElLwCzTiBhP1CbTQeKITLxLXjFVAaBJlrCLdYNbFLptzQdf10hUYjjQio+tmkcieJaQbzyIxx0ppPteu0IpURCADfMenGTfJ+bmemANeJzy01tSjOlkva9xZUYO0m8aVbi/DyRHOiSTJjsunHtZJO2kj2DT40kdogY8RQ1ppI0KERsfaCA2h0cpYUdJuGiTEsfmehMoEGqdl2nzOGnDA1W7PEoPACCgheiJEO5q8n6cem+9Q1s3S5/VRjau0VRtf+niuR1qnibQRn5lUBs/xiOKIvJenGBR73xOlFI508KRH4Aa9QGkpJbkgRylVYlNxE1v7t1IICufk+/BG2LumxXIWhFHCoZk6s/U23941gdJw2XCW69aXqLcVmZTL9etLbB7KISdegP3/ZCw5UpqeUUcfN0InPQhDl5sUbqU7sTajMHwlrLrSCB7Xv9Bv1/IaTpZ5A9CIGr3YkZMFuoIJZq1HdbTWNMJGz8VSbpaphBXCJKQe1c11OgGsEtlzTXQtJs24SStukfWypGSKhXCBclgmEIE5Voieq0ih8IRHmIQ046axvmBEVqzjE9k1+kR2TXfMsTbCarmsKV0R0P2MXOEusZp0RYonvV4MTtpL4+DQTtoU/AKe9AhVaM7tiBCtNSknhdaaSlgh42WQQlKLaqTdNJ70jBhQIKT5bLrxKVobsZIP8iY+R+reuGIdG4vIa4QHGlw6xySxESha045PuNySJCHlpfCER1MZUepIh7Zqo5TCd/1enM/i5YybIVYxbd0m7aYRStBMmqYfrGfef5t2T8yNpkcZSg8x05xhujnNcHqY4fQwC+ECc605+rJ9BG6AEGKJCMn65rsjECihCOOQYlLEk54RgVIaK5QwWV6BF6CVJkqM+GknJq6wL9XHcGqYgcwAfem+3t/N1tmxWC5ilNIcmKmxd6rGP700zWw9JOM73LZ1CIEg8CRXrS6wbTSPnNoDBx40DTIdF2rTcOhBU8cmOwxjN5xofzB8Oay+Hvo3QmHUipxl5GSuncW1SE433gRen3njOi5omGnNMN+ap9wq007Mk7EvfWPl0BGJTqiFNdCQD/KoRFGPTQ0TIYx7opua3NZtWompa5KoBIUyk+oiK0kvngbz9L443Vdr3RM0y40rXFx5IibEd/yeCydwOhMqDr5rYmpcXALXxHE0ogZ5P28sHFGdjJtBSEE7aRvh0ImpEQikNJaYSlgh62bN6+H1XjslU+a9d0QI0mQducLFE5753LRxqSmljFWjk+0YJzESSeAENOMmUWJSqhOVEOuYwA16n283JqWtXi88PMcz1xKSjJ8xFhCtyXt5FIoQY0VzhUvOydGmTUZmcKRD2kubTK0kXiJChDCWI6k7MS3CZDGFibHMeK5HzssxnBlmJDOCg0Mikt53W8QC4YleZtmAN8BwftjE4nB6Qd6vFSSL/1eAJf833f0ny3hbSazckVksK5TZeouDs3V2HS6ze6KKAD5w+Qj5lIsrBWOlFFevLuCXX4WDD5u6OGjTz+qV/9cUAUyVYM2Nxl01tAPW3gCrr7VuqtNgsVUlTMJeUTXgdTVMAOphnWO1Y0zWJ3s1RwInIJBBLz6hElWotCsnjTdZ7PapR3XTHN3xSJKEwAtoxS1q7RqRiCh4BVJuivnWPLWohsRMCr7r0w7bLMQLJkZCJzSiBm3VJtEJzbhJrONTvuflYrHbpzv5d4N4024aV7gkOiHtpgmcwLxX4eFIY41xhYuQojf5ZvwMcRLTjttkOv3YumIFbQJmtdC9dQ8Prc26L43lQCsNCmJhMph8x8cTHjEx7chkMC12wUSxcY8VgyJKKeq6Ts7LIRAshAsETkBf0EdTGctXykkRuAGucHtuOkc6PYtG3svjpbxeBtGgN0jRK9JKWjSSBiknZWJk4HXCoyvOUm6KjGusFr700UITxkY8tJM2WmgyXobhzDCrc6sZy45RSBlXzpnW2ekup9xUT2C04lYvmy3lpl63fjqc7nEXK/auarGcAWGUcHC6zlMH5/nbn0wAcMumfrYMZQBBJnC5YnWRIVE16eXNMiBMP6qXv2NcWUERNt5mCvutvgm2vM8U/ZPnNzthpdO1xiwWMM2oaUzzjTnGa+O0ddtklUStnjVjtjVLGIc4jmMmm7jdC9SMlSm2hqZXGbYb39GdDJuJKcCW9pY+xTuYwFFPmidkgUC1FCpR1OIaWmsmmTSptYmx0LSSlklpPk0h07WWpJyUERtgUn6Fg0Yb0eWYOBApjADrpvxm3Sxg0p+zbhbpSOIkNu4gZC9mpJtW3IpbBF6A0KK3LJGEifnsfOGbmA9lXGYIkNJcM9YxKceIhZiYtJcGYa7pCmMFOJklZLGrJu2Y7CCkiZtpJS0kknW5daTdNOP1cYQWDKeHX+eCiVQEEnKOqdUS65hau0bey5PyUmT9LGkn3YvBqUZVqm3jzhOYuJdCUCBwAvJBnr6gryeYpCNNxpFwyLgZMr4RMW+WXdZzwXW+Z4u/xycTKMvJa4XKpS5czgYrdiyWM2C+EfL04Tm+/uMjJEpz2XCWd6wrEcWafNpl26o8m/oC5KuPwfwBU9+mNgF7/tZUQw7ysO1fmNic9e+BDTefh0aVF46TuYYWx7XA659oARbaCxyvHudo5ShTzSkasXEZddNea+0aERE5J4dCMdWcIkkScl4ORzq9lNpWbOqkdCeybsZIKzLBuggTEJo4CUqauiituEWURKb2SqeuSjfAtxtwGyVRL9bldBEIUo6Z7DzpEcjABHLikPFMaq6DY4JF/azJWIrbpLyUsTRpjRbGCiKlNPE8iXGbhDIk5+VISIiSCE8ay0g3YLSdtHuWmVbSep3wWCJCXBPoGumIolvE8z3znnVk0oi9DEooGnGDOI4ZCobIeBnmWnNkZIZ8kH9DS8hiV40rXIpBkaHMEBknQ1t1ApuFYsvAFtZk1jCaH32dC6bb6qCdtE1FX2XcaMVUsbe/6545lZsSTrhnFscLnew4y8WP/UtaLKdJGCXsOjzPHz14kEaYMJjzuee61eRSLtVmhO8K1pfS+PP7YGKXaYg5ewBe+XvT1Ts7BJfdCYPbYOOtsOqqi96a81qX0uLCbt2+O2hIEhNTMNue5XjtOLONWSphZUkPncWuojA+ETibcTPExJSb5V5KLJin17l4jlpYI+tnkVoy156jEBTwhEcjbtDWbeIkZiqeIlJRryZLM26iMVlKXcvL2RY5k5hJ0ZNez42RclK9LJuulaGdtMn7efPUr43AaCpT9K7P66MW10wWlOOasSYJnuudPGhVpvGEh5Lmc/ekR6xjSn4JXGO1cnEppUvkvTy1pIYnPBPYexLh8VoR4uAQOAHFVJHB9CCjmVH6Un1oYT6zRCVUW1WaSRMhjaUo62V7hftOxxKyWFAsLibYiBo91+QbWSgGM4PnRJhYgXNpYv+qFstpoJRm15E5/sPfvcRcIyIXOHzomlU0o4Sk09Z8VTFFiQrM7DVtF44+BYceMhcoroOtP2MsOxvffVEJncXBvYuzippRk3JYZr4xz/Hq8SWNALsTtCc9PMdkzkzVppBS0p/uJ05i5sN5wthk1MQq7rmK2km7J4K6GUTdyTxKImpRrSdO2qpNI24g6yZrqZ2YGJizjX/purQ84fUEjCc9E6uC6IkqNASeSa9Nu2l86VMJKwQy6KUCS2kKq3U/L0c6vUJ8juOgUKRkiqJXJBEJOXJ4gXFhteM2ruMuCWB9bdBqd7y+9HvZSb7rk/WyZNwMA6kBBjOD+I5P3s8bVxNvHCOyeHmx9WOx6+VUafPL5Z45k7RkK0wsp4v9plgsb4JSmt3jZX7z//kJ4wtt0p7kI9eNdQKSJYEjGSmmWN/n4y28YjKvdn8bpl4wF1hzE6y7xRQBHHsHjFyxIoXOa0XN4gq347VxphpTLLQXUJgicYtdSk3VxMG4SWpJjVbU6rlDlDZpz92+PlqbeiG+8BFSmFowUlNtV1kIF1BCLalsG6v4rDOLuplDXcHSjXHxpEfGzRgrkaBX3TWQgSmMdpKYna7g6bo8ui6unJMjn8qT83KmlUCnxkoxKJKSqV6gayCNOFoczJp206S8FEqpXuxIwS+Q9/PkghzwenHyRq6WrphKuSlc6Z4Td8zJMnUslpWO/ZZaLG/Ckbkav/F/P8+huSa+K/nA5SM4AlxHkHIdXEfQn/MZknXc5hw0F04Ine0fhLGdplN4bhWsvu6CppSfykrTzVhaLGoacYNqu9qrFhsq8wSPxqRMk+AJk10jhSQmpqqqZPwMwu0EvbomuDbRCcIxVohW0qIW1WgmTSphhUpYOS1LTDd4t/vTXe92bQ7coFdYTSAopopmfGiyXpYkSWipVu9YX/o9YVALjQupEBTOOPMGaSxYA+kB+v1++tJ9uI7bSxt/baArvD6Y9WSxIxaLZfmw/1EWyylQSjO+UOe3/p/n2TNZwxHwM1cMs2koQ7kRMd+IKKRhYyHDlv4UhXDG1M/Z/4/mAoPbYNVOsy3dB2uuN20fziPdgOBW3HpDK003Y6krhpRWtKM2oT4RSBw4Qa/qrJQSRxtXDNKsd4VBWqeRQhLqkICAZtJkob1AJapQj+o0ksbrxtlNAU47adMNWbhLGhE6wiHv5U0WkJsi62Spx3VaSYvACegP+vE9n3pYN1YlL03Gz+Bg3EzFVJFiUCTn5kh7aRNzElVRQpFyUhS8whnFm5ws8+aN3D1WvFgsFxb7H2ixnASlNPunKvyX7+/liYNlAN63dYBcyqUZKoYLKVpRwtpShus39DOSkXDwOJSPwtEnzEVW32iEjuvCuhth6LJz4r46WcG8MAmZbc5yaOEQ4/VxjlePM92YNinYr7HS1KIakTbpyr0JXZi6Jyk31Wvw50u/VyMGbarQSm0yYUJtUrwrYYVyq0yoQqpRlUbcOKkLKpABGTdDzstR9IumeaCUPVdR4Jj93W7RRb/Ys7p4rnFBlVIlMjJjWhe4Xi/baSQzsqSGyWtjT2BRQb5F/YSWW5BYgWOxrBwu6H/jgw8+yP/+v//vPP3004yPj/PNb36TD33oQ739QoiTnvflL3+Zz33ucwDMzc3xmc98hu985ztIKbnnnnv4z//5P5PL5c7HW7BcgjRaES+Ol/nGE0f4u59MAnDlqjzD2RSBKwgTxVw9JOU7bBvJMVLIQG0cFg7B9Iumm3lmAAa3mKKB+REY2LLsQidMQqbqUxyrHWOiNsFsa5Z21KadtHtl4mMVmyweHVOLTA8jeI2VxpGkdIoE099IShOAq7RCaCOO4iSm1jKiqJmYirONxKSQt5LWG7qhpJAU/SJjmTHSbpqskyXjZ/DE0h46i11FaS/NQHqA4cwwa7Nre+nHiwXEYvGy3EGyFovl0uKC3hXq9TrXXHMNn/jEJ/jIRz7yuv3j4+NL1r/73e9y7733cs899/S2ffzjH2d8fJx//Md/JIoi/tW/+ld86lOf4i//8i/P+fgtlw6tMKbajjg8W+OFYws8vHeOf35lBg2s70uzaTBFWym8RJDxBY6UXDlWYMtIAakimD8MUQiv/rO54KbbID9mhE9+FKSzLOOMVUwrbjHbmGXv/F72z+9npjVDO2mjUFRaFZPN1GkKKYToCYGMl6GdtGlEjaVWGgWhNj2TwiQ0Vpqkbcroa5PhdDpp2b70ybgZsm6WQlCgP9XPUGqIPr+PwAvwXZ9SUGJ1djVDmaFT9tBZ7Cp6s/TjLlbgWCyWN+KC3iHuuusu7rrrrlPuHx0dXbL+t3/7t9x2221s2rQJgN27d/PAAw/w5JNPcv311wPw+7//+9x999185StfYWxs7KTXbbfbtNvt3nqlUnmrb8VyERInino74sB0lVdn6rx8vMrBuRq1VswzR6vESlNMu1y5Okcq8BAK2rGmP+uwti/N1WtK+J4DlSmoz5hqydVxkB4Mbjd1dlI5SBdBnL3Y6QYTzzXnOFI9wt65vRxYOMB8e94UxNMJEomQpmheohKTptypsKuUictpiAb1sM5ce46obiwz3e7Up0O3sm/X9dQX9JkGgJlhNhQ2sDq7+nWNALvdprvxK+eqgqzFYrG8ERfNHWdycpK///u/58///M972x577DFKpVJP6ADccccdSCl54okn+PCHP3zSa33xi1/kC1/4wjkfs2XlESeKZhgzUWlwfL7FnvEF9k83aMUJU5UmR8ptxhfaKA1pT7KzUx3ZB2JpPFGrCinesb6foXwaktj0vvIzcLhTU2fdzdC/BeIGuCnIDJ5Vv6swCZlpzDDbmuXYwjH2lvey0F5grj1HGIe9nkHdWBcUxNpk9jSjJrWoRiNpUI87tW/eQNRIIUk7adMTSQamv1Cqj76gj5HsCJuKm1iXX8dwZnhJEG+Xi6ERoMViefty0dyZ/vzP/5x8Pr/E3TUxMcHw8NLsFtd16e/vZ2Ji4pTX+vznP899993XW69UKqxdu3b5B21ZESwWOEfnmrwyUeHgbINaM6YSRtRbCa/ONplrnBADGV9y7ZoCxZRDmGhaiSYTSLaNFHn3ZYOsKmWRUkAYQWsBFo7D4cfNyeveCUnb/PSth+zgGY23ETWYrE2yd34vR6pHmGpMMV4dJ9IRLm4v+ylJEmPV0ZL5cJ7Z1iwL4QKNpHFKt1M38yjn5ch5OYbSQ/Sn+in6RUrpkrHUpIdfJ2qsmLFYLBczF82d60/+5E/4+Mc/Tir11vsIBUFAEATLMCrLSiROFO04IYwTZmqtJQJnvhrSVAmeEBwptzgw1ySMTQVkAQzmfDYNZEh5kPZdlNKkfYeRXIrtq/LcuHnABCR3aS3A/BHY+4CppVNcC6k+U0tnYPMZBSbHKuZA+QC7pnaxr7yP6cY0jnYQjiAkNKndhMRhTM7P0YgbHKkfYb49fyK7qoNEUvCNqEk5KUpeiXXFdYzlxhjJjjCaGT2plcaKGovFcilyUdzRHnroIV5++WX+6q/+asn20dFRpqamlmyL45i5ubnXxftYLm261puZWosjcw3GF5ocnK4zsRDSaEe0lAnEPVpuMVULqbaT3rmuFKwupdg4kDIp1UKQ8hwCT5IoxUghzY0b+rhydYm+7CKRnMRQOQ5xCAcfNNu2/gy4AajEWHVOs4BgmITsmtjFk5NPMt+eJ0oilFI0VANPefjSJ9IR5XaZ6dY05fky7eRE3JkjHAaCAdbl1lFMmXiaUrpEX9DHquwqNuQ30Jfp6wkZK2gsFsvbiYvibve1r32NnTt3cs011yzZfsstt1Aul3n66afZuXMnAD/4wQ9QSnHTTTddiKFazhOLrTflRsiRuQavTtfYP1Wj3k5oJQm1ZsxCM2KqHjLfiGlFS107xZTLuv4UxcBBCUHGc0BAI4oJXI+RQprtIznesb6fVaWMCUZeTNSE8iEYf8ZYePw8jF4ObgakC51eRG9EN338+enneXL8SVq6hS980KCFRmvds97Mh/NL3FOOcBhODbO5tJntfdvZ3r+dNYU1FPxCrw+S7/hkvIwVNhaL5W3NBb0D1mo19u3b11s/cOAAzz33HP39/axbtw4w8TR//dd/ze/93u+97vwdO3Zw55138slPfpL777+fKIr49Kc/zUc/+tFTZmJZLk66mVOtOKHajDhebjJZaXF8vsn4QotWlFCLYtptxUwjZKoaUm5EJPrENQRQSLmsKgQM5U3n7ELKRQOtKMGVkmzK47rVfWwbK7B2IMPqUvb1IqdLq2yysPZ/36xvuQPcNHgZ8FJv6L5SWjHTmOGl2Zf4ydRPOFQ7RCNskHJSREQIKaiGVZ6ffd50Du/gS5/B1CBD6SE2FzezY2AHm0ubWZNfY9oUvCZw2GKxWCwXWOw89dRT3Hbbbb31btDwL//yL/Nnf/ZnAHzjG99Aa83/+D/+jye9xte//nU+/elPc/vtt/eKCv6X//JfzvnYLeeeOFFUWyETlSYHpuocKzc5Ol9nuhKB0PiupN6KmayFVJoRM/WI2iL3FIDvCIppj9GCz0DWoxnGBJ5L4EqUUriOQ8pzuG51lu2ri6wqpRgtZEj5Dq7zBrE2cQiVCVMxeW4/CAkb3mPSzpuzkNsOzsnjwpRWHCgf4Lmp59g3t4+FeMF0y5Zhr1LxqwuvcrB6EI3Gkx7rc+tZk13DSHaEoawROpf1XcZAZqDX9NFisVgsJ0dorfWbH3ZpU6lUKBaLLCwsUCgULvRw3na0wphmlHS6aAvCJGGm1ubAVJ19UzX2z9SoNWMynkOkEo4ttCk3Y6qtmIVmzGu/wPnAoZD2GCsGZH1JM4pJeS6+YzKYfNdFOoJ1pQzbRgtsGsqxcTBHJuW+scBZTGUcjj8HD3/VtIdY9Q646iPg5Y37auO7obj6dacprXh1/lUePvow4/VxZpozaK0J3AA0HKoeYn9lP43Y9I8aTg+zc3Anm0qbWJ1fzWhmlFX5VQxnh3vVgy0Wi+XtyunO3/Zx0HJBWFyx+NBsg+lKyEKzTTOKabY15XpEO4mJlabSiplrxpQbEbUw4bXy3HcFpbTHQNb8OELQimI8VyCFoC/tI4RASMHmwRzbRgusH8iyrj9LLu2S8s/w36BbW6cxC0d/bLZd9fOQ6getoH8jZIdOeupcc44XZl6gGlZJOSlSXopm2KTcLLO/up/xhqka7kmPnYM7+Rdb/gXXDF5DKV3Cd3wbWGyxWCxngb1rWs4bJ6tYfHi+jiMEucBjoRVxaK5OrZ1Qaytq7Zgweb3h0XMEfWmPQtplIOcRSIEWkHZNA8so0RQCD9eRZAKPtf1p1vdn35rAWUzSqa3zwt8AGkavMR3OpQthFUprT5qFFSYh++b3MVGfINEJoQqJk5iXyy9zpHYEjUYg2FTcxHvG3sNPr/tptg9ttxYci8VieYtYsWM55yilma422T9dZdfhefbNNGiFCcfKDWrthHIzptZOXpct1SXjSUoZj2LaJXAF+cBFCoFG40tJpBRp1yHjuyAkhbTLpsEsW4bzjBRT9GcCMoHz1gTOYsIqzB5YlG5+F9QnIVWEwmrTBPS1n4FWHC4f5pX5V6iEFVxc9pb38uTkk72u4CW/xDtXvZPrhq/j+tHr2dS3qdcnymKxWCxnjxU7lnNKGCW8MrHAU4fmODLfZO9khSPzbY6WW0QnsdqkPUkucMgFDsW0CSpuRRFKS6QApTUCjSclmcBBCpe0L1lVTDNWSpvffWmGcqk3DzI+G5IYmguw5zugYujfDEM7AA1IKK45qVVnpjHDi3Mv0o7bJp28cYQfT/4YjWYgGGBLaQvXDlzLzlU72dy3mcHMoBU6FovFskxYsWM5JyilmW+0efbwHA/vneHF4xWOzreYqLR7AcW+IyhlPAqBS9qTZANpsqNcBwm04hitNRnPRQCR0qQ9h/5cwHAuzapSirFShrG+NH0ZH8+VBO45EDiL0QnMvAyvfNesX/4hI3pUaJp+pvKvOyVMQl6YeYFj1WM40sHB4SczP0GjWZ9fz0+v+Wk29W/iuuHrGM2N2pgci8ViWWbsXdVyTpiuNPnxgVkeeGGC7788Qys+4aLqy7isK6Uppk39GqVBdKw2jXZM2hX0Z32asaTWjsmnPTYO5tgylGfjYJbBfIDnSlznPIib16ISeOL/ZwTO8A4TrxPkANGpqyOWHq4Ve2f38sLMC0QqIidy1JIak81JJJJrh65lY/9Gblp1k7XmWCwWyznCih3LsqKUZrrW5J/3TPHYgRke2D1NojQpVzJWChjJB6QcxzSwlIKUI2kmMWnHJfAccn0mtibwXAYyPmN9aTYOZhktpsmlvfMrbE7G0SfhYKe7+RX/AwgHGjOmmGB+xKi2DkorDs4fZNfMLtpRG096NJMmP54wGVyXD1zO1r6tXDd0HcPZ4ZO9msVisViWASt2LMvKQjNk9/EFDs7VefTVeRKl6c94XLemgOcK4kThSkE7MWnhpVzAzv5+to0VGOtLM5hLkWiF0hA4DpngDGrfnGviEL7//8VkYF0N6RKEC+CmAGEClMWJastzzTn2VfbhSpeBzACNqMGhyiGO1I70rDobChsYyY1cqHdksVgsbwus2LEsG2GUsG+yyuHZJo+9Osd0NSRwJbdu6SeONaqTdxS4DpuH8ly+usC20fzpVSy+0CgFu74Bx5401ZI339ERNgKcFMiO2HHMv1SYhBypHKHSrhAmId1ApSenngTgioEr2Nq/le0DNrXcYrFYzjVW7FiWBaU0B2Zq/OR4mV1HFth1tALAjRtKDBZ8aq2EehizrpTmhvX9XL2mxEghfeq+UyuN+hQ89BWzvOmnYHA7tOYxKqYKA5uWpJyXW2VmW7METoDQgkQl7F3Yy2xrFl/63L7udq4evJrBzOAFeTsWi8XydsKKHcuyMF1t8pOjZQ7NNPh/X5gE4LKhDJsGM4QRZHyXtaU07946xI5VpYtH5IBxXz35NdPh3E3BuneCBHKjEDdNe4hFhQRjFVNpG7HXjtrUohr1qM4jxx8B4N2r3817Vr+HjaWNNiDZYrFYzgNW7FjeMmGU8OLxBfZOV/nmc+NEiWZVMeBdm/pxXYknHdYOpLhxwyCbhvJIKd78oiuJyjg89TWzvO1noLAKGvPgZ437Kj+yxKqjtKIe1Yl1jJCCQlDgHw7/A+2kzWBqkE9e+Um29G+xQsdisVjOE1bsWN4y07UWB2YafGfXJJVWTH/G484dwziuRCWaLaMZbt40yKpS9uITOkkMz/6F6YOVKsLIlRC3wM+ZPlhBH/RvWlJIMFYxU40p2lEbiWTXzC5+MvsTAD62/WO2MrLFYrGcZ6zYsbwlwijh0Eydx16d5fhCi8CVfOzGtYwWU9SaMY4ruH7DAKv7cxd6qGdHuwZP/6lZXvdOQEKrBioyAmft9ZBdGndTC2u0khZCCpRSfPegKUD4vjXv44bRG2zRQIvFYjnP2MdLy1nTDUp+abzKY/vnALhpYx+OFCw0I0Kl2DyUZVUxc4FH+hZ48v8wVp2gCNv+BaQKpm6gl4XCGujb0CkmaIhVTC2sUUqVSDtp/vzFP6cRN1idW82/2PgvyPk5a9WxWCyW84x9xLScNbP1FofnmzxzeJ56mFBIuVw5VsSVAgmMllJcMVa8uIKRuygFC0fh8T8w66uuNXE6+SHjworbkB8FufS9xSqmGlaJ4oj/tue/caBygLSb5lev/lV816fgF6xlx2KxWM4z9hHTclbEiWK2GjJbbfKDPdMAvHfrIDnfQQpBJnC5YnWRoXz6Ao/0LGnMwUO/B81OIPLwFaazefkw6Nj0wUovLSIIxoU1157j0fFHeWbqGQSCn7/s58n5OYbSQ5RSpQvzfiwWi+VtjH3EtJwVUaI4Mlfnr585TitWDOcDrhgrQKLxPIdtq/JsGrwIM6/ApJoffAJe/O9mffVNJ6okS8f0x3JTkBnsFREEU0hwoj5BFEf83at/B8CHt3yYW8ZuIVEJo9lRW0DQYrFYLgDWsmM5Y5TSHJ6t88TBOZ49XAbgtu1DBK4kFTikPYf1fdmL1301vgue+T+gXTFWnaErTLdzoaFZgXYd+ta/LjC53Coz3ZymEleox3UCJ+DqwatRSlEKSuT8izRI22KxWC5yrNixnDGz9RavztR4bP8cSsOGgQyjOZ9qO6YdaUaLAaXsRWjBiFrw6kPw4rfhyONm2+g7wAvADUyTTz8Lo5fDwJaTBiZnvAwvzbwEwOX9l7OusA5PehQCG6tjsVgsFwp797WcESbVvMHzRxd4cbwKwAcuH6GYCRBAX9ZjrD+Dt5L7XL0WpaByHPb90IicyechanRcVQNGBKXy5tjiahjavqSuDphCglpoU1dnehcA6wvracQNEp2Q83JW7FgsFssFwt59LWfEbK3N/qkKf/+TCQC2jeTIBw55X5IIQSZwGMj4K7up52KiFhx5Cg4+akTO8WegeszsG7rcCJ7GDBBDfg2sfsfr3FcAUkiaUZN6VOdY3Zy/Jr+G2eYsqzKrbGCyxWKxXECs2LGcNmGU8PJkhccPznN4rokjBe/a3E8jSjiy0GKkkGLjQJaBXOpCD/XNiUOojsOrj8LRx02W1ZFHTXVkBIxeDauuA9eDJILCatj8UzC0bYn7ajFaa6ab09SjOp70uHrwasIkJB/kbW0di8ViuYBYsWM5baZrLQ7ONnh43ywAN2woUUh5gEYBV48V2DxcWNkZWF2Rc+x5mHwRZl6B2Vdg6gVAm2KBa2+G7BBI37SEyAzCpvfCyI5TCp1YxcQq5kD5AACj2VFiFTOcHcaTHkqr8/gmLRaLxbIYK3Ysp0W3LcQj+2eZroakPMl7twwReA5RrBgqBmwYyq1coROHpkjgsV0w+ROoHIVWFY79uOOmAorrYWgHBPlO9lUCfh42vg9Grzql0AFTX6caVXsurO1923Gkg9IK3/GtZcdisVguIFbsWN6UxW0hHu1Ydd69ZYCUL0l7ksgRrO1Pk/ZX4NcpDqFyDA49CRO7oHwIoja0FmBql3FbCQkjV8PgNsCBpAHChdwYbHgXrHnHGwqdWMXUozoD6QFeXXgVgO0D23Gly1xrjpHMiA1OtlgslguIvQNb3pRuW4inDs5RDxOKaZetw3kaYUIca4YKPuv6MisrKFkpqE3BsWfh8BNG5OBAbRKmd0NoMsnwCzB6DaQHTMFAIcArweobYMv7oDD2hkIHTCZWohM8x+NI9QgAa3JrkEKS9/K2vo7FYrFcYKzYsbwh3bYQ05Um//yKcfe8r9MWQmvwPcn2VYWV1RYibMDEi3DocZg/APVpaEzDzB7T/gFAulDaCMMd95SOTTp5ca2J2Vl7PXinH2jdiBo8O/UsiU4o+AU25DeQ8lK40rVWHYvFYrnA2Luw5Q2JEsWxcpP//uxx2rFitBBw+aoCeqW1hUhiiOowvR+OPm1SyFtlaNdgdg/Up8xxQkJpPfRvA8cDr1MvJzMCa26BtdedljVnMY2oQTtp88r8KwBs7dvKbHuWbJJlU2mTFTsWi8VygbF3YcsbUm622T2+wLNHFgC468pRAtcBV+M7K6AtRDe7auZVOL4LpvYYF1VtEhZehcZs50ABxXVQWAdBAZzOtuyQseasvxmGd7yuWOCbvryKWWgvUEqVOFw9DMCW0hZ8x8d3fDJeZlnfrsVisVjOnAsaZPHggw/ywQ9+kLGxMYQQfOtb33rdMbt37+Znf/ZnKRaLZLNZbrjhBg4fPtzb32q1+LVf+zUGBgbI5XLcc889TE5Onsd3cekSRgmHZxr8w0tTJEqzfiDNmmKKKNEXvi1E2IDpvbD7u/DM1+HF/wcOPWSsOId+BONPdoSOgNwqWH8rjFwL6bxp3ukXYc31sPVOuOLnTLbVGQodMGKn3Coz25hlz9weAK4evJpV2VVW6FgsFssK4YJadur1Otdccw2f+MQn+MhHPvK6/fv37+fd73439957L1/4whcoFAq8+OKLpFInYik++9nP8vd///f89V//NcVikU9/+tN85CMf4ZFHHjmfb+WSo5uB9eDeGZ4/VgHgtq1DxEpTzDhkggvQFiJqmeacswdh4gWY3WuqHScxVCdg+iWIm+ZY4RiLTf/WTuyNBBVBug9WvQPWXAeDW0yvK+fs/w1qYY2FcIHZ1iyNuIHv+PSn+6mGVfK+LSZosVgsK4ELKnbuuusu7rrrrlPu/+3f/m3uvvtuvvzlL/e2bd68ube8sLDA1772Nf7yL/+Sn/qpnwLgT//0T9mxYwePP/44N99887kb/CXObL3Fwdk6//zyNAA7VuVZ25chGzi4UpLxz1NbiMUCZ+5VUwhw7gCgTMbV7MuwcMQIGQDpQf8myI6ZejlCm/ibzKAJSN5wE4xcAf5bt7osTjl/fNw0Dr2sdBme9GzKucVisawgVuydWCnF3//93/Nbv/VbfOADH+DZZ59l48aNfP7zn+dDH/oQAE8//TRRFHHHHXf0ztu+fTvr1q3jscceO6XYabfbtNvt3nqlUjmn7+Vio9vs87kjZV4aryIE/PSOITxHUm0mpAPNmr7CuW0LEbVg+mWY3G0EzvwhQEPU7MTjHIL2wonj3RQMbIXMEDgOyAD8lKmAXFwHa2+E1ddBbviMgo/fiG7KeSlV4mDlIACbiptsyrnFYrGsMFas2JmamqJWq/G//W//G7/zO7/Dl770JR544AE+8pGP8M///M+8973vZWJiAt/3KZVKS84dGRlhYmLilNf+4he/yBe+8IVz/A4uXmZrbQ7O1vnuCyb26erVRQLXwRGQS0s2DuWWNwMriSFpm99aQW0aDv8YJl+AqArVaZMyXjkC9UlzDNALMM6vgXTRxOEIB5K6qZmTGzE9rtbeCMWxs4rJeSOkkAgE5VaZ3XO7AWPZ8aVPIShYq47FYrGsEFbs3VgpM6H93M/9HJ/97GcBuPbaa3n00Ue5//77ee9733vW1/785z/Pfffd11uvVCqsXbv2rQ34EiFOFOMLLfZNVjk420AKeP8VwwSOJFSKsUKazYO55cnAWpxJVT1uRE510lhu6hOmnUNjxiwv7i3lZaC4wQidoGBcWFKagoDpPOS2wPCVsOpKKK1ZdpHTxZUuEsme2T3MNGeQQrKptInp5jRFv2jFjsVisawQVuzdeHBwENd1ufzyy5ds37FjBw8//DAAo6OjhGFIuVxeYt2ZnJxkdHT0lNcOgoAgCM7JuC92WmHC0fk63+/E6ly1ukDacYgSTSOMWVtKv3X31eI+VVMvmoJ/UWREzvyr0Jxd6qICcFLGBZUdMv2qHA+cjAk+jltm39A2GNwK/RuhMHrORE7vbagYhWKuPQfA2vxaCn4B3/HRQhOr2Aoei8ViWQGs2Dux7/vccMMNvPzyy0u2v/LKK6xfvx6AnTt34nke3//+97nnnnsAePnllzl8+DC33HLLeR/zxY5SmsNzdZ44MMfeqToCuGXTAFGiSPsuo5mAtQPZs3dfvbZP1czLRuCENahNQNRYenxQMDE4mSGTNaUT04ncS0NYh3QA+VUm4Hj1OyA/Yva9heyqM0FphUazt7wXgGuGrmE0OwoCmlHTdjq3WCyWFcIFFTu1Wo19+/b11g8cOMBzzz1Hf38/69at43Of+xy/8Au/wK233sptt93GAw88wHe+8x1++MMfAlAsFrn33nu577776O/vp1Ao8JnPfIZbbrnFZmKdBbP1FkfmGzx9qAzANWuLjBZSuI5ACsHa/szZNft8bZ+qyZ/A5PMnqhp3ERLS/Ubc5NeYTCoArY3rKmwYMZPpNzVyRq4wVpxz6Kp6MxpRg6cmngJgc2Ez9aiO53jGxWXTzi0Wi2VFcEHFzlNPPcVtt93WW+/G0fzyL/8yf/Znf8aHP/xh7r//fr74xS/yr//1v2bbtm38zd/8De9+97t753z1q19FSsk999xDu93mAx/4AP/1v/7X8/5eLna6GVi7jpbZM1kD4Mb1fWgN7UhTykhGi6kzTzVXymRV7f8RTO+BI4+bdPGu1SMoQKpkftIDRrRobbYL1wQoRy0jgtbeaAROaS0UVp1XK87JaEQNjtaOMt2cRgrJtoFtjNfHybq2TYTFYrGsJC7o3fh973sfWus3POYTn/gEn/jEJ065P5VK8Yd/+If84R/+4XIP723FbK3N/qkK395lstiuXl0gn3LxJLieZN1glsHsGcbqKGVSx1/+Lhx6GI4/bWrmgKl7M7gDghwoDUkLEBA2jfDxsyboONdvgo3X3rAiBE6XbpuIxSnnvuOjtLJtIiwWi2WFceFnDcsFJ4wSXp6s8PiBeY7ON3Gk4NatgwSuJNKaoYzPhv4z7IGlFMzshZe+BU//icmqAnB802k8N2JieLQER4P2ISh2UsaHID9k2jwMbYPhbWfUgfx80G0T0XVh7ejbQc7NkfWzxCq+wKOzWCwWy2Ks2LEwXWtxaK7Bw/tN08wbN/ThCkErTmjF6uwysGpTcOQpeO6/GaHj+DB8OeTWg4gBFzyMq8rPwtrtsOpa46JK5U29HD+z4kROl1pYo9w+UV/n6uGrqUQVFMq2ibBYLJYVhhU7b3NMrE6dR/bNMl0NSXmSWy8bJHAdolgxVDyLDKw4hPHnTXxO9bgJPN70/o4Lyoe4bXpYLXOfqvNFt01EI25QCSv4js/2/u2ESWjbRFgsFssKxN6R38Z0m32+ML7Aox2rzrs2D5DyJGlPEjmCtf3pM8/Aqk2aPlbHjYuHwe2mwrFSkDQg0wel65a1T9X5pNsmoptyvrW0lUhFtk2ExWKxrFCs2HkbM11t8pOjZR7eO0utnZD1Hdb1p6mHMXGsGSr4rOvLnFkGVhzC7KvmZ+ols23DreDnoD4NImXW19+yrH2qzjeNqMHjx03zz8v7Lyfn5gjcAFe61qpjsVgsKwx7V36bEkYJu46WeXG8wqP7TQXgd23pJ3AlYazIZz22ryowlE+f/kWVgtn9MPMKHHnUbBvYaqw60ofCMAxfDVvvuOisOYtpRMZ9tWd+D2DidWZaMzbl3GKxWFYo9q78NkQpzZ7xBXYdnuefdk8TK82avjSXj+ZxOpaWy4bPotlnYwZm90FYNW0gwNTGkb7JsipshnU3XtRCp5tyfrR2lEhFDKYGWZVdhUbblHOLxWJZoVix8zZDKc2r01WePDjLC+MVjsw3kQJu3zZINuWhFXieZP3AGaaad91XrQXY/W1TNHBou0kzT5rgZmD1NcZ1dRHTjdd5atLEI9286maGM8O40iVMwgs8OovFYrGcDCt23mZMV5vsOlpmvhHzxIEyANesLpBNuVSbMY6ELSNFhvJnmPLdmIX6DJSPwfFnAQGX3WlSx/3AuLMGtly0MTpdpJBorXns+GMAbO3bylxzjsANyHpZm3JusVgsKxB7Z34bESeKI3NNaq2Yh/bN0IoUfRmPHWN5oljhODBWSnHFWPHMrDpJDM2ysea88H+bbat3wsBlJu08MwSDl12w/lXLiStdXi2/SrldJnACrh66mljHTNQnEFrYeB2LxWJZgVix8zaiFSYcmauze6LKrqOmbcPdV4wwlE0RJ5pC4HPt2r4zC0oG0408rMKRH8PcPpAebLnTZF+1F2BgE2QHz8E7Ov/EKuaJiScAuHLgSmId40mP0ewoWmhbPdlisVhWIPYx9G1CGCW8MrHAy+NVvvvCif5Xg/kAgSafdrl+fYnNw4UzC0oGUIlxX/3kr8z6xvdCYQS0ML2v+jZc9O6rLrGKeXzcpJxfPXg1aMi4GXJBjnbcRnUbnFosFotlxWDFziWOUpr5RptnD8+x6/AC/7hnioVmTNZ3eOfmfoQAIQTXjBXZtqp45kIHTFDy8WehPgVeFq74H4DYVEQujplMrEuEA+UDHKoeQiC4eexmAjegHJaJdWzbRFgsFssKxd6ZL3Hm622efHWWXUcX2HVsgf0zDQBu3TJA1ndJuy6rCil2jBXOLE6nSxKbTuaTu8z6upsglQYvA9KFVNH0uboEiFXMj47+CIBNpU1kvAyudBEI5lpzZNyMjdmxWCyWFYi9M1/ChFHCs0fmeXGiwrG5Jo90igfesL7IuoEMviPJp102DucYzJ5lw80kgto0HDFxLIxdD04aghRENSN2LoJ+V6eD0oofT/wYgBtHbiRKItsmwmKxWC4CLo1ZyPI6uoUDnzk8T6UZ8U97pkmUZnUpxY0b+nCkJOVJ+rIeG/rPsKbOYsIq7PsniJqQGYChbdCYNiInP2a2XSI04ybPTT8HwPb+7aAh59k2ERaLxbLSsW6sSxClNK9MLPDEqzMs1EO+v3uaSsvE6dy2bYBmrKm1Y4SQbBnKMZA7S6tOHEL5KBx6xKyvvgFSJciOGNdVcc0lkW7e5ZFjjxCpiL6gj/WF9TiOw0xrhnKrTMEvWLFjsVgsKxQrdi4xjEWnzD/tmeRouckjr84xXmnjSsEd2wfxXYcojhnI+dy4se/ssq+6NGZh/gCMP2fW19wA9UlQMWT6IZVftvd1oYlVzA+P/BCAG0Zu6FVS9h3ftomwWCyWFY59FL2EUEqz+1iZf3p5kqlKkxfH6xycbSKA69cVSfsOSmlWFVO8b9swW0fPMvsKThQSPPJjSELIjZgigo5n6uyk+8zvS4QoiXrxOpcPXN5zYWX9rK2tY7FYLCscK3YuEVrtmKcPzvDwvlnGqy2OzDV57sgCAO/c3MfWkRxxolldzPCerYNvTejAiUKCXRfW2puN66q1YGrqjF5+yQQmAzw//TyzrVl86XPdyHVIIalEFRTKppxbLBbLCufSmY3epiilGS83eGTfFE8fmqcdayYX2jxz2AidK0ZzXDlWJFaKQtrj5k39b13ogCkkOL33RHfzoe0QLgAOuGkToHyJEKuYB48+CMC2/m1orXEd0/hzrjXHSGbExutYLBbLCsbeoS9C4kTRjhNaUczuYws8c2ievTN1VKKIlObJw2U0sLYvxZVjeaIkIZfyuGZN6ewLB76W1gIcfNj0wyqtg6ErIKqCF0B+5JIqJBirmMfGTePPHf07mGpMkXbTZL2sTTm3WCyWiwArdi4SWmFMPYyZr7c5Xm4yWWmxb7LKwZkGUoIC5uoxzx5ZIFaavozHjetKJBpSnsflowWuXdd39inmi+lmYR0zMSyMXA1xDbz0JVdIEOBY9Rj7yvsAuHXNreS8HM2kSeAElFIla9WxWCyWFY69S69wWu2YfVMVXpmqcmimweG5BlGcIB1BrWkCYx0heHmizuH5FgADWY9bL+tHo0l5DtetKfKODf30ZYLlGVRjFqZegtn9Zn3De424cVIgxSVVSDBWMY8efxSNZiw7RtbN4koXX/sshAuM5cas2LFYLJYVjr1Lr1CU0kxXmzz+6gy7ji5Qb0VUWxFhrAkThSsgUtCOFHuPNZhvRABsGcywY1WOQsoj7bu8e8sAN24cWh6LDpyw6hx4ENDQvxnSBSN22lXT4fwSKiSotOKZqWcA2Na3jYnGBGjI+3kKQcG6sCwWi+UiwIqdFYhSmkOzNX58YIYXjlWI4gQwwgYNsdKMV0MmKm2qbbPPlYKdawsM5k0Rv6F8wC2bBrlmbT+uu4yZQo1ZaMzAcSMATBaWBDcFAiitvaQKCQoEz049C8D1o9ezNr+WMAmpR3U86VmrjsVisVwE2Dv1CmS62uTpg3McLbdoRYpqM+aliRrzzZB6O0HpE8cKYCTvc+3aIoW0hxSwc12Rd142zKpidnmCkbt0a+ssHIX5V43IGdwKYd1kZw1svqSsOgCHKoeYbc3iCpfNxc0AeI6Hm9h/HYvFYrlYsHfsFUYYJew6WubAXINWFPPKVI0XjlVIFgkcKSDjO6wpBowVU+TTHoO5gKFcwLXrSly7tp9UcA7+tN3aOgc7tXWGL4fSRmgvAPqSs+oAPHr8UQA2FTcx355nvjVPMSgymBnEkx5Kqws8QovFYrG8GVbsrCC6zTufP1Jmrh7y/T0zTFXbAJTSLqMFn0LgkAokBc/D8zxKGY+Ng1m2DOdYP5hZfmvOkgEmsDAOR0waNiNXmSwsxwc/c0nV1unyyDEj7C4fuJxV2VUorYhUhNIK3/FtMUGLxWK5CLBiZ4WglObV6SpPHpxlrhny3RcmKTdjXCm4bm2egbRHNYwZzASs6c+wYTDLWCnDWF+aoVyKlO/gOud44m0twMwrUJswrSA2vA90fEnW1gGoR3WenTbxOqOZUabqU/Sl+5BC2mKCFovFchFxQR9LH3zwQT74wQ8yNjaGEIJvfetbS/b/y3/5LxFCLPm58847lxwzNzfHxz/+cQqFAqVSiXvvvZdarXYe38XyMF1t8uyhOSrtmEf3zlFumi7lH7p2lB2jRQYKAT+1bYRf/anL+OR7t3D3Nau5afMgG4fy5NLeuRc63Syso53aOkPbQSSXbG0dgMePP04zbpL1slw5eCVSSibqE7Tjti0maLFYLBcRF/SxtF6vc8011/CJT3yCj3zkIyc95s477+RP//RPe+tBsLRWzMc//nHGx8f5x3/8R6Io4l/9q3/Fpz71Kf7yL//ynI59OenG6RyeN/2sDs41kQLee1k/fRmfjO8wWshx67ZhVvddoAm2MQv1aThuLB2sf88lW1sHIEzCXpfzzcXNxCqm4BcopUokSUIhKFirjsVisVwkXNC79V133cVdd931hscEQcDo6OhJ9+3evZsHHniAJ598kuuvvx6A3//93+fuu+/mK1/5CmNjY8s+5uVmcZzO8YUWj+yfBeCWjX2sHcgSRopSOuD6DQOsKmYvzCCTGNoVKB+ExrSx5qy7EZLkkqytA1BulXlu+jkAtpS20EpahCok42VQWpHzclbsWCwWy0XCio+u/OEPf8jw8DDbtm3jV3/1V5mdne3te+yxxyiVSj2hA3DHHXcgpeSJJ5445TXb7TaVSmXJz4VAKc0rEws8eXCWhVbED/bMoDRsGEhzzboSaEEm5XL9+hKbhwvnLvD4zUgiE6+z/4dmfdV14OchOwhB9pLLwopVzHhtnEOVQwBsyG+g2q5Si2qUW2X6gj5KqdKFHaTFYrFYTpsVLXbuvPNO/uIv/oLvf//7fOlLX+JHP/oRd911F0liCulNTEwwPDy85BzXdenv72diYuKU1/3iF79IsVjs/axdu/acvo+T0Q1IfvzVGartmOcOL1BuRmQ8h9t3DCO1wHcF164pLl/zzrMlrEJtFo4+btbX3Aj1SYhqUFh9yVl1lFY8OfEkGs1gapCx/Bj96X4cTEzSWG4M37l0xJ3FYrFc6qxoO/xHP/rR3vJVV13F1VdfzebNm/nhD3/I7bffftbX/fznP899993XW69UKudd8HQDksutmMNzLX5yvArA+7YNkPMcpCMYK6a4ek1p+Vo9nA1JDM0FmHnZWHf8nEk5lw4gobjmkrLqgLHsPD5hhN3m0mbmm/OkvTSO45B1szYw2WKxWC4yzsqy89BDD/GLv/iL3HLLLRw7dgyA//P//D95+OGHl3Vwr2XTpk0MDg6yb5/pQD06OsrU1NSSY+I4Zm5u7pRxPmDigAqFwpKf88nigORaK+EfXpoEYPtIlrX9GbQUlNI+167tYyifPq9jex06gdY8HPihWV9zg/kdN8H1IJW/YEM7FyitOLZwjN1zuwFYn1+P7/kopQicgMH0oI3VsVgslouMMxY7f/M3f8MHPvAB0uk0zz77LO22KXq3sLDA7/7u7y77ABdz9OhRZmdnWbVqFQC33HIL5XKZp59+unfMD37wA5RS3HTTTed0LGfL4oDkdpTwo1emqbUTCimXd182SBQrSoHHTRv7L2ycTm/AiUk5P9KJgVp3C2T6IDdqau1wgce3zMw153hq6ikqYQVHOGwsbqQW1tBC4wmPnJ+zhQQtFovlIuOM79q/8zu/w/33388f//Ef43leb/u73vUunnnmmTO6Vq1W47nnnuO5554D4MCBAzz33HMcPnyYWq3G5z73OR5//HEOHjzI97//fX7u536OLVu28IEPfACAHTt2cOedd/LJT36SH//4xzzyyCN8+tOf5qMf/eiKzMRaXDiwHsXsmaiyd6qOAO64fBhHSNKByw0b+tg6eoHjdLq0FuDY0xA1IFWCwR2mEWhYM1lZYgWMcZkIk5AjlSO9LKyx7Bh5P89QeghPeiCg4NuUc4vFYrnYOGOx8/LLL3Prrbe+bnuxWKRcLp/RtZ566imuu+46rrvuOgDuu+8+rrvuOv79v//3OI7D888/z8/+7M+ydetW7r33Xnbu3MlDDz20pNbO17/+dbZv387tt9/O3Xffzbvf/W7+6I/+6Ezf1nmhG6dTDRPKtYhH9s8BcNOmPsbyqZUTkNylm3I+8bxZX3sjSH3ConOJFRKca86xZ34Pj42bdhjr8+uZqk/RiBskKqE/1W+zsCwWi+Ui5IwfUUdHR9m3bx8bNmxYsv3hhx9m06ZNZ3St973vfWitT7n/e9/73pteo7+//6IoILg4TqfaivmH3dMkGlYXA65clcfzVkhA8mKSCGpTJ6omr9oJThqClMnEuoQKCYZJyO6Z3fz1K3/NfHuerJvl6uGr8RyPZtRkNDvK2vxam4VlsVgsFyFnbNn55Cc/ya//+q/zxBNPIITg+PHjfP3rX+c3f/M3+dVf/dVzMcaLnsVxOs0w4YEXJ6mHCaW0y09fMUKioBCskIDkxYRV2Pd9iFuQGYTBy0xRwbBiYnYukZRzpRV7Z/fy3/f/d/aW9yIQ/PT6n0YnmmbcpBbVWJNbQ3+6/0IP1WKxWCxnwRk/lv/P//P/jFKK22+/nUajwa233koQBPzmb/4mn/nMZ87FGC9qFsfp1MKIx/fPM1Fp4zuCn7l6lMBxcF1x4QsHvpZuL6xDpus3q6/vdDUvgooumZRzpRUH5w/y/SPf5+FjJpvw5tGbGcuM4bkeEslQZog1hTU2MNlisVguUs5Y7Agh+O3f/m0+97nPsW/fPmq1Gpdffjm5nK09cjIWx+m8MlFnz6QJSH7/FcP0pXyEhKtXUpxOl8YszL4K48+Z9bU3mUKC6QHI9F8yKeczjRmenXqWv93/t8Q6Zig9xPa+7aS9NK50caTDmvwaUm7qQg/VYrFYLGfJWT+qHj58mCNHjnDVVVeRy+XeMPbm7criOJ0D0w0ee3UegOvWFlhVSOF5gnX96ZUVpwMnrDqHH4UkhNwIrLoWimvB8SHd1wlSvrhpRA2em3yOb+7/JlPNKXzp865V76IZN5lrz/U6nq/OrbYZWBaLxXIRc8ZiZ3Z2lttvv52tW7dy9913Mz4+DsC9997Lb/zGbyz7AC9WFsfpzNTafO/FSTRw2XCW6zf0ESd6ZcbpgLHqNGZg8gWzvvpGU1gwbEDSgiB/UQcmK62Yqk/x8JGH+c7+77BrZhcA71r1LvqDftJ+mjAJybpZrhi4gsHM4AUescVisVjeCmcsdj772c/ieR6HDx8mk8n0tv/CL/wCDzzwwLIO7mKlG6fz7OF5Flox33tpilasGMr5/NT2IQRyZTT4PBnddHPhwIQRAay9AZSGZtkEKl/EgclKK44sHOHR44/y7NSzPDFpiiVuLGxkLDuG4zigIetkuWboGjaWNtpYHYvFYrnIOePH83/4h3/ge9/7HmvWrFmy/bLLLuPQoUPLNrCLmdl6i/0zdXxf8uMD88zVTYPPu68exRMS4azQOB0w7SGUhokXTBZWuh8K64wlR/sXfYfzydokT00+xURtgoeOP0QzaZJ1s9w0chOOdEhUgi99rhi4gssGLrNCx2KxWC4Bzljs1Ov1JRadLnNzc0uK/b1diRPFfD3CdySP7Jvn5ckajhD87LWjlAJ35TT4PBXCMdWSD/7IrK9+R6c/Vs3E7FykVh2lFZO1SX505Ee8Mv8Ke8t7OVQ9hESyc2QnGo3neAQyYDQ/yhVDV9iaOhaLxXKJcMaPre95z3v4i7/4i966EAKlFF/+8pe57bbblnVwFyOJ1mjAdyQvHa8A8D/sXM3lq0oIZwU1+DwVWkFjDo49ZdZX7QQ/A9lBSBXgIrR0KK04UD7Ag0cf5OW5lxmvjfP8rKkKfdXAVazLrTNiR3j0pfu4evBqG6djsVgslxBnbNn58pe/zO23385TTz1FGIb81m/9Fi+++CJzc3M88sgj52KMFxWOEASuJJ9y+f/8zA4ePzDLO9b2sdBqM5QN2Lm+b+XF6SymMQvHn+30vspC/yYTw1NYC37aWHnO/GtzwQiTkJdnXuax8ceYbkyze343L829hEazOruaraWtxEkMAkazo1w3fB0bShus+8pisVguIc541rryyit55ZVX+IM/+APy+Ty1Wo2PfOQj/Nqv/VqvG/nbGdeRFNMezShhMOdzx44R6u2YkgzYPJhl01B+5Qqdbsr5kcfN+th1kBsCNwUqNi6ui6QXltKKcqvM85PP88T4E0y3pjlYOcjL5ZcBWJNdwzuG3wECFIqtpa28c/U7GcmNWKFjsVgslxhnJHaiKOLOO+/k/vvv57d/+7fP1ZgueoppE+tRacYEcUJf2qeUdRnIplau0AFj1alPn8jCWnUdtMrGwoO6qFLOy60yT088ze7Z3ZSjMi/MvsCx+jEAtpW2sb1vO45wGEgNMJIb4V1j72JV3op1i8ViuRQ5o5nL8zyef/75czWWSwYpBX3ZgHzKI9EaRwhcZ4VbC7op5+WDRvC4KVjzDkiUSTkf2HTRBCdX2hWeOP4Ee+b2UIkqPD7+ODOtGQSCy/su54qBK1BaIRCsyq1i58hORnIjF3rYFovFYjlHnPEM/Iu/+It87WtfOxdjueRwHUngOitf6IDpcN5agP0/NOtj7wC/YAKTg+xFkXIeq5i9c3v5u31/xz8d/idennuZBw4+wExrBle43DJ6C+sK62jrNo502N6/nVvGbmFtca11XVksFsslzBn7JOI45k/+5E/4p3/6J3bu3Ek2m12y/z/+x/+4bIOznEfCKtRm4WgnXmfNjaYXVqoIhdUr3qoTJiG7Jnbx5OSTzDRnmGvO8fzs84QqJHACbh65mVJQQgnFUDDE1v6t7BzdyXB2+EIP3WKxWCznmDMWOy+88ALveMc7AHjllVeW7BNiBcejWE5NEkNzAab3GOuOn4ORq0A6gFzRHc6VVsw0Znhm8hmeOP4EDdVg//z+XiByzstx/cD1lFIlQhUyEAxw7fC1XDV8FaVU6cIO3mKxWCznhTMWO//8z/98LsZhuZDoxPS+OvBDs77mRvM7bkIqt2I7nIdJyL65fTw1+RSvll9lvDbOi/MvshAuALA+v54dpR0gwJc+a/JreOeqd7JtcJstGGixWCxvIy6O1BrLuUUlMH/4RMr5undCpg8QIKX5vYLoWnNenH6RpyafYq49x0R9guemnyPWMa5wuar/Krb1byPRCY50WJtfy82rbmZL/xYbn2OxWCxvM85Y7Hz4wx8+qbtKCEEqlWLLli187GMfY9u2bcsyQMt5oLUAR358ohfW4DbT9dxNQ34EVoh7MlYxrbjFofIhXpx9kaPVo8y0Znhx9kUOVU1ftoJX4Iq+K8gHeVpJC1e6bChs4JZVt7ChzxYLtFgslrcjZyx2isUi3/rWtyiVSuzcuROAZ555hnK5zPvf/37+6q/+ii996Ut8//vf513veteyD9iyzHQLCR570qyPXg1R1aSeI0yA8gUuJNi15BxYOMDLsy/zSvkVmlGTicYEe+b3EKoQgK3FrWwrbaOZNImSCN/3uW7kOm4YvYGh7JAVOhaLxfI25YzFzujoKB/72Mf4gz/4A6Q0k4dSil//9V8nn8/zjW98g1/5lV/h3/7bf8vDDz+87AO2LDONWZg/CJM/Mesb3mvEjZMC2RE7F6iQYKxiamGN/fP7eWn2JSbqE4zXxjleP87B6kHqcR2AtJM2Pa4K6xAI0n6a/lQ/7xl7D9uHttv4HIvFYnmbc8az2Ne+9jUeeeSRntABkFLymc98hne+85387u/+Lp/+9Kd5z3ves6wDtZwDuladQ4+YOjvZYSisMmKnXb0ghQS7AmeyPsmhhUPsK+/jlblXaCdtWkmLvQt7ewHIrnDZWNjImswaEBDrmJRMsaG4gZtW3cSmvk3WmmOxWCyWs6uzs2fPHrZu3bpk+549e0iSBIBUKmXT0C8GGrMmNqfb4XztzSbd3E2ZmOTzVEiwFbdoRA1mm7Mcqhxi3/w+DpQPUItqxoXVnmG8Pt4TOVJI1mTXsD6/noHUAIlOiFXMqvQqdgzs4IqhKxjMDFqhY7FYLBbgLMTOL/3SL3Hvvffy7/7dv+OGG24A4Mknn+R3f/d3+Z/+p/8JgB/96EdcccUVyztSy/KSxKYNRPlwx4UlYPQqCOsmO2tg8zm16rTiFrWwxpHKEY5Uj3C0epTDlcM0ogaxjploTDDVnKISVtBoAASC4dQwW/u2knfzRDpCSkkgA7YUt3DL6ltYlV9l3VYWi8ViWcIZi52vfvWrjIyM8OUvf5nJyUkARkZG+OxnP8u//bf/FoD3v//93Hnnncs7UsvykkSwcAxe/nuzPrgV8mOmuzn6nFl1WnGLV+dfNe6p2VfYt7CPZtyklbSYac1Qbpd7AcddUk6KkcwII6mRnsUw0hEFv8Dq3GrWF9Zz7ci1thqyxWKxWE6K0Frrsz25UqkAUCgUlm1AF4JKpUKxWGRhYeGify+nTeU4vPQd+MEXjDVn573GmuMEkBuGy+4AP7NsL6e0YqI2wZPjT/LkxJM8OfUkE40JlFavO1YgKPgFBlIDjKZHkULiChdHOMQ6xpMe/el+tvVvY2vfVtYV11FKlazbymKxWN5mnO78fVZpNnEc88Mf/pD9+/fzsY99DIDjx49TKBTI5XJnN2LL+SMOTRHBY88YoZMqwZobTGBy3IbsUKdVxPLQilv8ZPInPDX5FA8ff5iX5l4i1jFghE3KSZFyU+TcHCW/RCko4UiHRtzAEQ6e9AjcANd1WZVaxY7BHVzWdxkDmQFSbgpX2tqYFovFYjk1ZzxLHDp0iDvvvJPDhw/Tbrf56Z/+afL5PF/60pdot9vcf//952KcluWkMQvVyRO1ddbeAs05SPeZ9hDp5amto7Si3Crz6NFH+bsDf8eLcy9SbpcBU/xvU2ETWTeL53i9c2phDY0m5+aQSCIiMl6GjcWNbOnbwmV9lzGSG7FxORaLxWI5bc5Y7Pz6r/86119/Pbt27WJg4EQA64c//GE++clPLuvgLOeAbrr57F6Y2w9CwsZbITdqemG5KcgMLkttnUq7wqPHHuXLT3+Z+fY8AI5wWJtdy8bCRpRSxCKmGTfxHZ+8l0drjS99fN9nY2kjG0sb2ZDfwFBuiJyfs1Yci8VisZwxZzxzPPTQQzz66KP4/tIn6w0bNnDs2LFlG5jlHNFNNz/c6YM1cpX53Zw3RQT71kN28C2/TJiEHFo4xB+/8MfMt+fxpc9lpctYn19PNawSq5hEJ6RFmqZoEoiArJ/lisEr2FLawlBmiKGsFTgWi8VieeuccUSnUqpXT2cxR48eJZ8/s+7YDz74IB/84AcZGxtDCMG3vvWtUx77K7/yKwgh+E//6T8t2T43N8fHP/5xCoUCpVKJe++9l1qtdkbjeNvQTTdvLcChh8y2dbeA44PjwdAOGNjSaf751phrzvHizIscWDgAwK1jt7K5sJmMl6HP7+tlVRUyBW4Zu4V7tt/DL+74RT6y7SPcuPpGLhu4jFKqZIWOxWKxWN4yZzyrvf/9718iOIQQ1Go1/sN/+A/cfffdZ3Ster3ONddcwx/+4R++4XHf/OY3efzxxxkbG3vdvo9//OO8+OKL/OM//iN/93d/x4MPPsinPvWpMxrH24ZuuvnefzBNPzODMHwFSNe4rfo3LEu6eaxiJuuTvDD3AhrNQGqAwcwgQgjacRvpSgbTg7x/4/v5l5f/S35++89z8+qb2dS/yVpyLBaLxbLsnPGs8nu/93t84AMf4PLLL6fVavGxj32MvXv3Mjg4yP/1f/1fZ3Stu+66i7vuuusNjzl27Bif+cxn+N73vsfP/MzPLNm3e/duHnjgAZ588kmuv/56AH7/93+fu+++m6985SsnFUdva1rzUD4IR54w6+veBWHVpJv7/aYP1nK8TNxioj7B3vm95mVy68i6WdCwEC2wLrOOnSM7uWrkKlJualle02KxWCyWU3HGYmfNmjXs2rWLb3zjGzz//PPUajXuvfdePv7xj5NOp5d1cEopfumXfonPfe5zJ63I/Nhjj1EqlXpCB+COO+5ASskTTzzBhz/84ZNet91u0263e+vdekGXNHEIs6/C1B6oHDXWnE3vAz+37Onm1XaVycYk+xf2A3Dl4JUIKSgEBUazo/zU2p9ifd96WxfHYrFYLOeFs/IXuK7LL/7iLy73WF7Hl770JVzX5V//63990v0TExMMDy+tmuu6Lv39/UxMTJzyul/84hf5whe+sKxjXdEoBbP7YWo3HPhns230Gojr4KWWNd08TEKmGlPsL+8nUhFFv8iOgR0kOqEZN9la2sra0lordCwWi8Vy3jgtsfPtb3/7tC/4sz/7s2c9mMU8/fTT/Of//J955plnlr2p6Oc//3nuu+++3nqlUmHt2rXL+horisYMzO6D2oT5LSRsug38Imi9rOnm5VaZ6dY0r5RfAWB7/3ZmW7PkvBw5L8fq/Gobk2OxWCyW88ppzTof+tCHlqwLIXhtl4muIDlZptbZ8NBDDzE1NcW6det625Ik4Td+4zf4T//pP3Hw4EFGR0eZmppacl4cx8zNzTE6OnrKawdBQBAEyzLOFU/XfdWYgz3/r9k2ttPU1WkvgJte1nTz8do40/Vpds/tBuCG0RsoBSU0mrX5tfSn+9/y61gsFovFciacli9BKdX7+Yd/+AeuvfZavvvd71IulymXy3z3u9/lHe94Bw888MCyDeyXfumXeP7553nuued6P2NjY3zuc5/je9/7HgC33HIL5XKZp59+unfeD37wA5RS3HTTTcs2loua2jTM7IPJF2H6JWPVWXuzidkJcjC8fOnm5VaZufYc+xf2k+iEsewYq3Oryfk58l6esdyYrXxssVgslvPOGfsT/s2/+Tfcf//9vPvd7+5t+8AHPkAmk+FTn/oUu3fvPu1r1Wo19u3b11s/cOAAzz33HP39/axbt25JhWYAz/MYHR1l27ZtAOzYsYM777yTT37yk9x///1EUcSnP/1pPvrRj9pMLDBWnandxn316g/MttFrIDsMSQi51TB42bKlm9fCGr7j8+OJHwNw4+iNSCFpRA3W5U2zTovFYrFYzjdn/Di/f/9+SqXS67YXi0UOHjx4Rtd66qmnuO6667juuusAuO+++7juuuv49//+35/2Nb7+9a+zfft2br/9du6++27e/e5380d/9EdnNI5LltokzB8wzT5n9wLCNPyMm8aFVVoe9xUYsVMNq7w8+zIHKgdwhMM7x95JX6qPjJuxVh2LxWKxXDDO2LJzww03cN//v707D2+ySh8+/k2apUnbJE1Lmxbowr6XzcG6sikgKoy8jkhVRMRxRmYUxZVFdEZxxGXE4efyexV0Bgd1BsFBARnZZBEFLJtIobYUSkvXtE2XbM/z/pG30coilZam5f5cV64hzzk5OTnN+Nw564MP8ve//534+HgATp48ycMPP8yvfvWrRpU1dOjQU+b+nM3pgim73c57773XqPe9KATn6pRD1urAtYT+EN0ZFDdEJEBMSpMMX0HgAM8KTwVbTmwBYEDcANx+NzXeGhwRDunVEUII0WIafad7++23KSgoICkpiS5dutClSxeSkpLIz8/nrbfeao46isaqX2p+8iCU50LxQUDz/1dgmcEQAfakwOTkJuBTfFR7qwHYVRSYP3VjpxtpZ25HmCYMR4RDenWEEEK0mEb37HTp0oW9e/eybt06vvvuOyAwd2bkyJFNvkRc/EKuIsjPBI8LcjYGrrXrAYaowLlYkfEQndIkS80BFFXBp/rYnL8ZRVVIsaSg1+rx+X1YjVYiDZFN8j5CCCHEL/GL7nYajYZrr72Wa6+9tqnrI86XzwMFe6HyWGByclU+aPWQfEVgro4hAhL6QGTcz5d1jrQaLTWeGjbkBTYsvDb5WuIj4qn2VqPX6mVfHSGEEC1K7kJtjesklH0fOPqhfgVW8uWBfXWgSZea/1i+K5+i2iL0Wj2XOC5BH6ZH55evlxBCiJYnd6O2pH5Scl0l5G2BOieE26D7dYHzr8LCISa1SZaaN3hbxcd3ZYEhzfaR7SmpKcFqtBJrjkWv1aOoSpO+nxBCCNEYckBRW/HjSck1pXB0a+B6ylXgqQF/HdhTAvN1mpjL4wrumNwnpg/xEfGEacNQVAVDmEHOwRJCCNGizvkutH79+iY7CkI0g/pJyd5qyNsKig+iEiCuXyA9qiMk9GuWXp1qbzV5VXkApFpTMelN6LQ6yurKMOvMMmdHCCFEizrnYOfuu++mXbt2TJo0iffff5/KysrmrJdoDE8NHNsB5dlQkQ8n9wWup1wNqgf0Zmif1qSTkuv5FB8nqk6Q78oHwGa0UVJTggYNUfooWYklhBCixZ1zsPP999+zceNGevXqxYsvvkh8fDzXXHMNr776Knl5ec1ZR3EmigLVJZD1X8jeCM58OLgikBbfB2K6Qpix2SYlQ2AIa2/JXlRU4kxxdLZ1RhemwxhmxG6yS6+OEEKIFteou1+/fv2YPXs2X331FdnZ2UyYMIHVq1fTvXt3+vfvz9y5c9m5c2dz1VX8lKsIsjfBid3g8wYmJdeWBpaXd7s+MGRltDTLpGT4YQjrRPUJADrZOmHUGTFoDVR4KmQISwghREj4xT/1ExMTuffee/n0008pKSlhzpw55ObmMnr0aJ599tmmrKMA8PsCZ1zVVgROMj+xD/atgNxtgQnJJzOhIi9wqnmn4YHhK29ts01KhkCw46xzklWeBYDD7JAhLCGEECGnSX52R0REMGHCBCZMmIDf76esrKwpihUQGKpyFUHJ4cDRD878wF46tWWBJeamaHA7f5ink3RF4FRzjR4sHZplUnI9l8dFubuc7IpsAPrH9Q8OYdnCbdKrI4QQIiQ0+d0oLCyMdu3aNXWxFydFgeJDkLMFnMcCPTi1zsAycm0Y4AdnDhTuDeSP6xM4FgINmO3QYXCzTEqGH4aw3H43tb5aDGEGuti64Pa7qfBUkBiZKMGOEEKIkCB3o1ClKIE9c7LWQlVBYCm5txpUJTBUBeDMBefRwL8jE6BjOqg+sCZBypXQrmuzTEqGwHlYftXPcddxAFItqdT56wjThMkQlhBCiJAiwU4oUpTAsFXOJqjMB9UfuOZzg+KB6kIoPQK+ukB+azJ0vDRwsGdke+g6EuJ7NlugA4HzsDRo2FO8B4BO1k6ggiHMgMVokV4dIYQQIUPuSKHIVQT534DbBfihuhRO7IKaEvC7f8injwj05ljbg8kO9k6QNARim69Hp55Oq0OLlm9LvwWgZ0xPNBoNxbXFWA1WCXaEEEKEjPO6I9XV1eHxeBpcs1gs51Whi57fF5iIXFcZWELuqYXsz37oxQEwREJUB0joH5h8HJUI7QcGJiNHxjV7oAOBOTuVnkpO1pwEoENkB/RaPY4IB6pGxaf4JOARQggREhp9N6qpqeGRRx7hgw8+oLS09JR0OVLiPHlroTwPfLVw8gB8++/AfB2THexdQR8FxkiwJIKtA9g6gaMnWBObbdXV6fgUH98UfQNAO1M7LAYLZp2ZSGMkbp9bDv8UQggRMhod7Dz88MNs2LCB1157jdtvv51FixaRn5/PG2+8wXPPPdccdbx4KEqgV6c8B0qy4MC/AtdtyZA6HDQaSL480IMTbgG9KdD7E3bhe1BcHhf7S/cD0N3eHaPOiNPjxKf6iDJEyeGfQgghQkaj75L/+c9/ePfddxk6dChTpkzhyiuvpEuXLiQnJ7N06VIyMjKao54Xh5qSQLATpoesTwPXHP2g63WgIXAERNcRYDC3ZC2Dy86PVR0DAiuxdFodHr+Hsroy4s3xMoQlhBAiZDT653dZWRmdOnUCAvNz6jcQvOKKK9i8eXPT1u5i4vNA6fdQUwa5m8FbE9gcsPfNYDSDtSN0GNjigQ4Egp2y2rLgZoL2cLvsnCyEECJkNTrY6dSpEzk5OQD06NGDDz74AAj0+Nhstiat3EXFVQxlOVB5AnK/CFzrNDwwTGWyQWwXiAiNzRpdHhffln1Lra8Wk85EWrs0OfxTCCFEyGp0sDNlyhT27AnsrfLYY4+xaNEiwsPDmTFjBg8//HCTV/Ci4PcFghxfXSDQUXwQnRLYEVnxgSkmsKz8Ak5APpP6Iax8Vz4AXWxdMOlNcvinEEKIkNXou9KMGTOC/x45ciTfffcdu3btokuXLvTr169JK3fR8NaCMw+qTsKx7YFrna8NhKLuCrBdBhGxLVrFevWHf9bvr9M+oj0lNSVE6CNkCEsIIURIanTPzrvvvovb/cPGdsnJydx000306NGDd999t0krd1GoX4FVegSOrA0cB2HvArb2EGaC6E4Qk3JB9s45Fy6PC6fbyWHnYQAGxg+UISwhhBAh7RcNY1VUVJxyvaqqiilTpjRJpS4q9SuwUKAgM3Ct2ygwWCAsDKKTQGdqwQr+oH4Iy6N4qPRUotPq6GrrKkNYQgghQlqjgx1VVdFoNKdcP378OFartUkqddHweaA4C6pLIHdLoFcnOjVw1pWvFnThgaMgWmAfndNRVAWf6uNQ+SEAOkZ2pLC6kFpvLZH6SBnCEkIIEZLO+S46YMAANBoNGo2GESNGoNP98FK/309OTg6jR49ulkq2SYoCpdlQdDAQ7Bz7MnA9dWhgn52wcLCnhswKLAgc/lnrrSWzKBOAfu36ER8RT7W3Gr1WL706QgghQtI5353Gjx8PQGZmJqNGjSIy8odf8QaDgZSUFCZMmNDkFWyzakqgLDewb07WzsCqK2tHSBwU2EDQFB0yK7B+TFXV4Hyd7tHd0Yfp0fklyBFCCBG6zvku9eSTTwKQkpLCLbfcQnh4eLNVqs3z+wL76vhqoLbih311kq+EunLQmyGmU8iswKrnU3wU1RRRVleGBg3mMDNV7ipizbHotXo5D0sIIURIavScncmTJzdZoLN582ZuuOEGEhMT0Wg0rFixokH6vHnz6NGjBxEREURHRzNy5Eh27NjRIE9ZWRkZGRlYLBZsNhtTp07F5XI1Sf2ajd8LzuPgPAZH1oHfDZHxENMFNGEQ1zPw7xBZgVXP5XGxpziwx1KKJYUkaxJh2jAUVcEQZpDzsIQQQoSkc7o7RUdHY7fbz+nRGNXV1aSlpbFo0aLTpnfr1o2//e1v7Nu3jy1btpCSksK1115LcXFxME9GRgYHDhxg3bp1rFq1is2bN3PPPfc0qh4XXF05VB0PTEg+ujVwrfMIMESCwRSYqxNiw1fB87BcgfOwutm7YdKb0Gl1lNWVyUosIYQQIeuc7k5//etfm+XNx4wZw5gxY86YPmnSpAbPX3rpJd566y327t3LiBEjOHjwIGvWrOHrr79m8ODBALz66qtcd911vPDCCyQmJjZLvc9L/RlY7ho48lmgVycqAeJ6gbcusHOyObqla3kKRVXwq36yyrMA6GLtQq2vFq1GK5sJCiGECGnnFOxMnjy5uevxszweD2+++SZWq5W0tDQAtm/fjs1mCwY6ENjVWavVsmPHDn7961+ftiy3291gY8TKysrmrXy9+hVYBfsDK7COfxW43nkEGC2Bc7CiO4JWf2Hq00gnq0+SV5UHQFdbVyJ1kRh1RnRanfTqCCGECFm/aJJFdnY2s2fP5tZbb6WoqAiA1atXc+DAgSatHMCqVauIjIwkPDycl19+mXXr1hEbG5i4W1hYSFxcXIP8Op0Ou91OYWHhGcucP38+Vqs1+OjYsWOT1/u0XEVQsA8UD+RuAsUP1iRIvAS0ukCwY44NmX11fqzGW8OB0sDfNyEigWhTNCV1JTjrnFgMFgl2hBBChKxGBzubNm2ib9++7Nixg+XLlwcnA+/Zsye4YqspDRs2jMzMTLZt28bo0aP5zW9+EwywfqnHH3+cioqK4OPYsWNNVNuz8PsCOyW7q8DvgcLARF+SL4fqk4HzsaKTQ24FFgTm61S4KzhadRQI9Or4VT+GMAOGMANmvbmFayiEEEKcWaODnccee4w///nPrFu3DoPhh0m0w4cP58svv2zSygFERETQpUsXLr30Ut566y10Oh1vvfUWAA6H45TAx+fzUVZWhsPhOGOZRqMRi8XS4NHsvLVQngceF3z7UWBycrseEN83sAIrtltIrsCCHw7/3Fe8D/hhCCshIkECHSGEECGv0XfWffv2nXYuTFxcHCUlJU1SqbNRFCU43yY9PR2n08muXbuC6evXr0dRFIYMGdLsdTlnigIlRwKPooNwcj9otNB1NOiMYLJBTOitwKrn8rgori0mtzIXgF6xvaj0VlLlqUKn1cmScyGEECGt0RMtbDYbBQUFpKamNrj+zTff0L59+0aV5XK5OHLkSPB5Tk4OmZmZ2O12YmJieOaZZ7jxxhtJSEigpKSERYsWkZ+fz8033wxAz549GT16NNOmTeP111/H6/Uyffp0Jk6cGDorsRQFSg5DwTeAH7LXBa4n9A8sNa+rgIR+gX12QlD9kvNydzl+1Y/NaCPOFIdH8VBWV0a8OV7m6wghhAhpjf5JPnHiRB599FEKCwvRaDQoisLWrVuZOXMmd9xxR6PK2rlzJwMGDGDAgAEAPPjggwwYMIC5c+cSFhbGd999x4QJE+jWrRs33HADpaWlfPHFF/Tu3TtYxtKlS+nRowcjRozguuuu44orruDNN99s7MdqPjUlgR4drR4qjgWe68IhdXigJycyHuJ7hWyvzimHf0Z15HjVcTn8UwghRKvR6J/kzz77LPfddx8dO3bE7/fTq1cv/H4/kyZNYvbs2Y0qa+jQoaiqesb05cuX/2wZdrud9957r1Hve8HUHwvhr4PqMvjuP4HrnUYEhq6MURDbJaQO+/yp+sM/6+frDIgbIId/CiGEaFUafacyGAz87//+L3PmzGH//v24XC4GDBhA165dm6N+rZvqB3cleGoCw1femsAGgkmXgVYDZntIHvb5U37Fz/cV3wPQ095TDv8UQgjRqvziO1ZSUhJJSUlNWZe2R/FDZUFgGCvv/x8L0X0sqD7w+iCmc0guNf8xRVU4WXuSOn8dJp2JGFMMqqoSa45Fp9HJ4Z9CCCFC3jkFOw8++OA5F/jSSy/94sq0KYoCJdlQmgsHV/z/pea9Aj07YUZo1z1wsnkILjX/qb3FewHobO2MFi1mnZlwXTgaNLISSwghRMg7p2Dnm2++afB89+7d+Hw+unfvDkBWVhZhYWEMGjSo6WvYGtWvwMr/Gkq+BWduYIfkbteCyR542FND9liIH6vx1vBt6bcA9LD3QB+mp6i2iAhvBJ1snWTOjhBCiJB3TneqDRs2BP/90ksvERUVxTvvvEN0dODAyvLycqZMmcKVV17ZPLVsbepXYKGF79cHrqVcCdGdA/vqhOkg3BqSx0L8WP1mgkecge0BOls7y87JQgghWp1Gj0G8+OKLzJ8/PxjoAERHR/PnP/+ZF198sUkr1yr5fVBTCmEGOPpF4N+maOgyKjBB2e8JzNMxx7R0TX+WT/GRVZ5FubscnUZHJ0sn2TlZCCFEq9PoYKeyspLi4uJTrhcXF1NVVdUklWrVVH9gfo7eCHnbA9fSMsDaEfThgR6dVrACCwI7J2cWZwLQydYJs9EsOycLIYRodRp9t/r1r3/NlClTWL58OcePH+f48eP8+9//ZurUqdx0003NUcfWRRMW2DTQEAXXvQSX3AMdfwXuisAREbHdQ34FFvywc/Jx13EAukV3Q6fVoUFDWV0ZZp1Z5usIIYRoFRp9t3r99deZOXMmkyZNwuv1BgrR6Zg6dSoLFixo8gq2OmG6wLCVty6wO3LvX4O7GjR6iO0MsV1bxQosRVXwq34Olx8GoJOlE7W+WrQaLVH6KNk5WQghRKvR6GDHbDbzP//zPyxYsIDs7GwAOnfuTERERJNXrtUKtwX+t9YJPkNg80CzHcyxrSLQqVdUUxTs2els7UykLhKjzohOq5NeHSGEEK3GL75jRURE0K9fv6asS9uh1QaCG6MlMIdHExbyK69+qsZbw6GywHlYceY4bCYbJXUlROhkybkQQojWRe5YzSlMR2tsYp/io8JdQX51PgCdrJ1kybkQQohWq/XdiUWzq99f50DJAYDgkvMIQwQ+xdfCtRNCCCEaR4IdcQqXx4XT7Qwe/tnd3p1KbyUKClGGKFlyLoQQolWRu5ZooH7JuU/14fK60Gl1JFuSZcm5EEKIVkuCHdGAoir4VF/wiIjEiEQKXAXUemuJ1EfKknMhhBCtjgQ7ogGtRkutt5Z9JfsA6BXTi/iIeFRU9Fq99OoIIYRodSTYEadQVZXcilwgsHOyPkyCHCGEEK2X3MFEA4qqoNVqg5sJJkYmoqoqseZYdBodiqq0cA2FEEKIxpFgR5ziUNkh/Kofi8FCjDEGs85MuC4cDRpZiSWEEKLVkTuXaKDGW8N3Zd8B0NnWGYPOQFFtEaU1pVgMFhnOEkII0epIsCOC6ndOzqvKAwKbCcrOyUIIIVo7+Zkugup3Tj5YdhAIHBMhOycLIYRo7STYEUEuj4vvK76nrK4MrUZLN3s32TlZCCFEqyd3LwH8sHNyUW0RACmWFCINkbJzshBCiFZPgh0BBJac+1V/cOfkLrYu1Ppq0Wq0ROmjZOdkIYQQrZb8VBdBNd4a9hTvAaCbrRuRukiMOiM6rU56dYQQQrRa0rMjgECgU1pbSkF1AQA9YnpQUleCs84pS86FEEK0anIHE8El54U1hQAkRCQQrgsHkCXnQgghWj0JdkTwpPO9JXuBwHwdVIgyRGHUGVu4dkIIIcT5kWEs8cNJ58WBk877xvbFqDNSWldKrbdWlpwLIYRo1Vr0LrZ582ZuuOEGEhMT0Wg0rFixIpjm9Xp59NFH6du3LxERESQmJnLHHXdw4sSJBmWUlZWRkZGBxWLBZrMxdepUXC7XBf4krZ/b5w7unNzF1gUADZqWrJIQQgjRJFo02KmuriYtLY1FixadklZTU8Pu3buZM2cOu3fvZvny5Rw6dIgbb7yxQb6MjAwOHDjAunXrWLVqFZs3b+aee+65UB+hTfApPrLKs4KHf9Z566hyVxFjiiFcFy4nnQshhGjVWnTOzpgxYxgzZsxp06xWK+vWrWtw7W9/+xu/+tWvyMvLIykpiYMHD7JmzRq+/vprBg8eDMCrr77KddddxwsvvEBiYuJpy3a73bjd7uDzysrKJvpErZPL42JfaWAIq4e9B45IB17Fi6IqGMIMMowlhBCiVWtVd7GKigo0Gg02mw2A7du3Y7PZgoEOwMiRI9FqtezYseOM5cyfPx+r1Rp8dOzYsbmrHrLqd06uH8LqauuKSW9Cp9XJzslCCCHahFYT7NTV1fHoo49y6623YrFYACgsLCQuLq5BPp1Oh91up7Cw8IxlPf7441RUVAQfx44da9a6hzKf4qOstoys8iwAYkwxlNSUoEEjOycLIYRoE1rFT3av18tvfvMbVFXltddeO+/yjEYjRqMsqYbAENZ35d9R66slPCyctNg0PKoHY5gRW7hNenWEEEK0eiHfs1Mf6Bw9epR169YFe3UAHA4HRUVFDfL7fD7KyspwOBwXuqqtTv0QVr4rH4DOts6YDWYMWgMVngoZwhJCCNEmhHSwUx/oHD58mP/+97/ExMQ0SE9PT8fpdLJr167gtfXr16MoCkOGDLnQ1W11fIoPZ52T78q+AyAxIlGGsIQQQrQ5Lfqz3eVyceTIkeDznJwcMjMzsdvtJCQk8H/+z/9h9+7drFq1Cr/fH5yHY7fbMRgM9OzZk9GjRzNt2jRef/11vF4v06dPZ+LEiWdciSV+4PK4qPBUcNh5GIABcQPQhelkCEsIIUSb0qJ3s507dzJs2LDg8wcffBCAyZMnM2/ePD7++GMA+vfv3+B1GzZsYOjQoQAsXbqU6dOnM2LECLRaLRMmTGDhwoUXpP6tWf0QlopKWV0ZWo2WbtHd0Gg0VHgqSIxMlGBHCCFEm9Cid7OhQ4eiquoZ08+WVs9ut/Pee+81ZbUuCvXnYdWvwkqISKC4ppgoQxQWo0WGsIQQQrQZIT1nRzSf+vOwviz4EgichxUfEY+Kil6rl14dIYQQbYYEOxcpRVWoqKtgf8l+AFItqbj9brRa+UoIIYRoW+TOdpFy1jn5zvkdNb4awsPC6RjVkTJ3GaYwk5yHJYQQok2RsYqLkE/x4fK4gvN1+rXrR5IlCZ/iw6f40Gq0ch6WEEKINkPuaBchn+KjylPF3uK9AKREpVDrq0VFpcZXQ6Q+UubsCCGEaDMk2LkIuTwuvq/8nuOu42jQ0DOmJ4XVhVS4K2hnaoct3NbSVRRCCCGajPx8v8h4/B4KqwvZXxyYmNwxqiMJEQkoKPgVP44IB4YwQwvXUgghhGg60rNzkXHWOSmuLeaQ8xAA3e3dKa4tRlEUbEab7K8jhBCizZFg5yLiU3xUuiup89Xxbem3APSJ6YPFaCFME4bFaJG5OkIIIdocCXYuIoqqUO2t5rDzMF7Fi9VgJSkqCZfbhdPtlInJQggh2iS5s11EFFWhxlfD4fLAwZ89Y3qioqLRaggPC5chLCGEEG2SBDsXGZ/PR2ZxJgDdo7uj0+qwhlmxGGQISwghRNskw1gXEZfHxdHqozjdTnQaHZ1tnanyVmHQGrCF22QjQSGEEG2S/JS/SNQvOc8sygSgk60TsaZYtGjxql6ZryOEEKLNkp/yFwlnnZOC6gL2FO8BYGDcQEprS/EpPqL0UTJfRwghRJslP+UvAh6/hwJXAd87v6ewppAwTRiXJlxKpCESv98vS86FEEK0aXKHuwg465yUucuCB3/2tPfE4/dQ56tDURUZwhJCCNGmyTBWG1d/wrleq+eb4m8AGBQ/CBWVCncF0cZoOQtLCCFEmyY/59u4+hPOD5cdpri2GL1Wz8C4gZj0Jqo91SRGJspZWEIIIdo06dlp4yrdlZxwnWBH4Q4AOls7U+gqxOVx4YhwSK+OEEKINk+CnTbMp/goqinCq3rZV7oPgEsTLkUXpsPtc8sJ50IIIS4KMozVhtX56sivyievMo9KTyUGrYFkSzImvQmdRke4LrylqyiEEEI0Owl22rAqdxVOj5N9JYFenX7t+mHSmTCFmTCGGWXHZCGEEBcFudu1UT7FR62vFovBwt6SvQBc4rgEnVaH0+0kQh8hQ1hCCCEuChLstFE+xUe5u5ys8ixcXhfhYeG0C2+HT/Wh1+pxRDhkbx0hhBAXBbnbtUGKqnCi8gQ5FTlsyd8CQFq7NAD0Gj2J1kTsJntLVlEIIYS4YKRnpw0qqy3jePVxgOCuyekJ6ViMFgxhBlmFJYQQ4qIiPTttjE/xUVZbRp2vjq8Lv0ZBoX1EeyxGC17FS6wpVg79FEJcFBRFwePxtHQ1xHnQ6/WEhYWddzkS7LQxiqrg8rio8lYFNxK8sv2VhIeFE0YYtnCbzNURQrR5Ho+HnJwcFEVp6aqI82Sz2XA4HGg0ml9chtz12hif4qO0rpTDpYcprSvFoDXQO7Y3qFCj1BChi5BgRwjRpqmqSkFBAWFhYXTs2BGtVmZstEaqqlJTU0NRUREACQkJv7isFr3rbd68mQULFrBr1y4KCgr46KOPGD9+fDB9+fLlvP766+zatYuysjK++eYb+vfv36CMuro6HnroIZYtW4bb7WbUqFH8z//8D/Hx8Rf2w4QIl8dFta+aPaV7AOgd05vy2nLCdeF0iOwgx0MIIdo8n89HTU0NiYmJmM3mlq6OOA8mkwmAoqIi4uLifvGQVouGu9XV1aSlpbFo0aIzpl9xxRX85S9/OWMZM2bM4D//+Q8ffvghmzZt4sSJE9x0003NVeWQ5vF7OFZ5jCp3FQfLDgKBvXVizbHEmGJIiEqQXh0hRJvn9/sBMBhkIUZbUB+wer3eX1xGi975xowZw5gxY86YfvvttwOQm5t72vSKigreeust3nvvPYYPHw7A4sWL6dmzJ19++SWXXnrpaV/ndrtxu93B55WVlb/wE4QORVXIc+aRXZHNN0Xf4Ff9OMwOkiKTiNBHoNFosBgsEuwIIS4a5zPHQ4SOpvg7tuqBzF27duH1ehk5cmTwWo8ePUhKSmL79u1nfN38+fOxWq3BR8eOHS9EdZtVWW0Zx1zH0Gv1wUM/+8b2xaN6cHldRBujZQhLCCHERalVBzuFhYUYDAZsNluD6/Hx8RQWFp7xdY8//jgVFRXBx7Fjx5q5ps0ruNzcX8fRiqMU1hSi0+gY5BiEQWMgPCycxMhE2VtHCCEuEvPmzTtljuvFrFUHO7+U0WjEYrE0eLRmPsXH8arjHKs6xrbCbQB0je6Kx++h2ltNO3M76dURQogQd+edd6LRaNBoNOj1euLj47nmmmt4++23z3sJ/Z133tlgAdD5SElJCdaz/vHcc881yLN3716uvPJKwsPD6dixI88//3yDdL/fz+9//3sSEhK47rrrgiuumkurDnYcDgcejwen09ng+smTJ3E4HC1TqQtMURWOOY9x2HmYAldBcGLyr+J/hUFjQKfTkRCRIL06QgjRCowePZqCggJyc3NZvXo1w4YN4/777+f666/H5/O1dPWCnn76aQoKCoKPP/zhD8G0yspKrr32WpKTk9m1axcLFixg3rx5vPnmm8E8y5YtIy8vj7Vr1zJw4EBmz57drPVt1cHOoEGD0Ov1fP7558Frhw4dIi8vj/T09Bas2YVTVltGjisHs87MrqJdAHS2dibKEIXb7ybOFCe9OkKIi5qqqtR4fC3yUFW1UXU1Go04HA7at2/PwIEDeeKJJ1i5ciWrV69myZIlwXxOp5O7776bdu3aYbFYGD58OHv27DltmfPmzeOdd95h5cqVwZ6YjRs3AvDoo4/SrVs3zGYznTp1Ys6cOee06ikqKgqHwxF8REREBNOWLl2Kx+Ph7bffpnfv3kycOJE//vGPvPTSS8E85eXlpKSk0KdPH/r27XtKp0VTa9GlOS6XiyNHjgSf5+TkkJmZid1uJykpibKyMvLy8jhx4gQQCGSAYONarVamTp3Kgw8+iN1ux2Kx8Ic//IH09PQzrsRqS+qXmjvrnHxf8T05lTloNVpGdBxBhC4Cm8lG+6j2sgJLCHFRq/X66TV3bYu897dPj8JsOL//Bg8fPpy0tDSWL1/O3XffDcDNN9+MyWRi9erVWK1W3njjDUaMGEFWVhZ2e8ODnmfOnMnBgweprKxk8eLFAME8UVFRLFmyhMTERPbt28e0adOIiorikUceOWudnnvuOf70pz+RlJTEpEmTmDFjBjpd4HNu376dq666qsHS/1GjRvGXv/yF8vJyoqOjue222xgxYgRGo5H4+Hg+/fTT82qjn9Oid8GdO3cybNiw4PMHH3wQgMmTJ7NkyRI+/vhjpkyZEkyfOHEiAE8++STz5s0D4OWXX0ar1TJhwoQGmwpeDIqqizjiPEKFu4JN+ZsA6BPThw6WDiiKgllvJtoYLcGOEEK0cj169GDv3r0AbNmyha+++oqioiKMRiMAL7zwAitWrOBf//oX99xzT4PXRkZGYjKZcLvdp0zx+PHwUUpKCjNnzmTZsmVnDXb++Mc/MnDgQOx2O9u2bePxxx+noKAg2HNTWFhIampqg9fUb/RbWFhIdHQ0NpuNXbt2UVhYSLt27Zrk/KuzadG74NChQ8/axXfnnXdy5513nrWM8PBwFi1adMaNCdsij99DQVUBW09s5WjlUXIrcimuLcagNdA3ti/5lfnERcSRHJWM3WT/+QKFEKINM+nD+PbpUS323k1BVdXgfjN79uzB5XIRExPTIE9tbS3Z2dmNKvf9999n4cKFZGdn43K58Pl8P7top75jAqBfv34YDAZ++9vfMn/+/GDwda4u1Pxa+cnfSvgUH3W+OkprSjlcfphDpYfIq87DHGZmT0lgnLZPbB+i9FEA9Lb3JtWWilbTqqdlCSHEedNoNOc9lNTSDh48GOwtcblcJCQkBOfd/NhPt2I5m+3bt5ORkcFTTz3FqFGjsFqtLFu2jBdffLFRdRsyZAg+n4/c3Fy6d++Ow+Hg5MmTDfLUP2+pxUOt+69/EVBUhZKaEnIqcjhcdpicihzK3eWEacLw+r0cLD1Ita+aCF0EA2IGYNQaiY+MJ9mWLIGOEEK0AevXr2ffvn3MmDEDgIEDB1JYWIhOpyMlJeWcyjAYDMFjNOpt27aN5ORkZs2aFbx29OjRRtcvMzMTrVZLXFwcAOnp6cyaNQuv14terwdg3bp1dO/enejo6EaX3xQk2AlhiqqQXZ7NrsJdHKs6xsnqk9T56tCiRdEoHKo4xPHq4wBc2f5KrCYrhjADHaI6EK4Lb+HaCyGEaCy3201hYSF+v5+TJ0+yZs0a5s+fz/XXX88dd9wBwMiRI0lPT2f8+PE8//zzdOvWjRMnTvDJJ5/w61//msGDB59SbkpKCmvXruXQoUPExMRgtVrp2rUreXl5LFu2jEsuuYRPPvmEjz766Kz12759Ozt27GDYsGFERUWxfft2ZsyYwW233RYMZCZNmsRTTz3F1KlTefTRR9m/fz+vvPIKL7/8ctM32DmSYCdEKarCkbIjfJH/BSerT+L1e1FQ0Gq1KH6FI1VHgoFOv9h+OCIcKIqCxWShfaSswBJCiNZozZo1JCQkoNPpiI6OJi0tjYULFzJ58mS02kBvvUaj4dNPP2XWrFlMmTKF4uJiHA4HV111VXAi8E9NmzaNjRs3MnjwYFwuFxs2bODGG29kxowZTJ8+HbfbzdixY5kzZ05wAdDpGI1Gli1bxrx583C73aSmpjJjxowG83isViufffYZ9913H4MGDSI2Npa5c+eeMnH6QtKojd0EoA2qrKzEarVSUVERErspK6pCbnkuWwu2crTiKIqioKBQWltKpaeS/Op8TtYGxj/TYtNIsaTQLrwdKdEp9I/tT0drRxnCEkJctOrq6sjJySE1NZXwcOnlbu3O9vc81/u3/PwPQSU1JRwoPUC1uxpVVSmuK2ZfyT7K3GWo/BCb9rP3IykqCavRSr+4fvSO7U2sOVYCHSGEEOJHJNgJMT7FR35VPlW+KswGM65KF1sLtuJVAjtaGrVGbEYbKVEp9IjpQWJEIgPiBtDZ3lmOhBBCCCFOQ4KdEFPnq6OgugCf38eB0gOsyVuDoirYjXY6Wzpj0VuIMcfQNbornW2dSbYmS2+OEEIIcRYS7ISYKncVFZ4KjlYc5dPcwPbZSZFJ/MrxK1DhEscl9I7tTYw5hnBduExEFkIIIX6G3ClDiMfvoaimCK/Py+qjqwHoG9uX4R2H4/a56WbrxuUdL8esN7dwTYUQQojWQ4KdEOKsc1JcV0xuVS5V3iqi9FFc3eFqIvWRxJpi6RvXVwIdIYQQopEk2AkRHr+HAlcBLreLTccDh3pe3v5y4s3xqKikWFLknCshhBDiF5BgJ0Q465yUucvIr86nqLYIQ5iBIY4hRBoiQYXEyERZbSWEEEL8ArKEJwT4FB+V7kr8ip/1eeuBwGaBKirVnmpiTbHYwm0tW0khhBCilZJgJwQoqkK1t5r86nyynFlo0HBN0jWEEQZIr44QQojGmTdvHv3792/paoQMCXZCgE/xUVRTxJbjWwDoZuuGWW8mLCyMCH1EYChLCCFEm3bnnXei0WjQaDTo9Xri4+O55pprePvtt1EU5bzLHj9+fNNUFPjkk08YMmQIJpOJ6OjoU8rOy8tj7NixmM1m4uLiePjhh/H5fA3yPPXUU3To0IErrriCrKysJqvb6UiwEwJcHhdOt5PM4kwAru54NaqqYgwzEmuKlb10hBDiIjF69GgKCgrIzc1l9erVDBs2jPvvv5/rr7/+lGChpfz73//m9ttvZ8qUKezZs4etW7cyadKkYLrf72fs2LF4PB62bdvGO++8w5IlS5g7d24wz9atW/nkk09YuXIlkyZNYvr06c1aZwl2Wlj9KqwDpQeo89dhNVjpENkBo86IXqMn0hApuyMLIcT5UFXwVLfMo5FnbRuNRhwOB+3bt2fgwIE88cQTrFy5ktWrV7NkyZJgPqfTyd133027du2wWCwMHz6cPXv2nLbMefPm8c4777By5cpgz9HGjRsBePTRR+nWrRtms5lOnToxZ84cvF7vGevn8/m4//77WbBgAffeey/dunWjV69e/OY3vwnm+eyzz/j222/5xz/+Qf/+/RkzZgx/+tOfWLRoER6PB4Dy8nISExPp168fgwYNwul0NqqdGku6DFpY/d46e0v2AvArx6/QaXUYw4xoNBosBov07AghxPnw1sCziS3z3k+cAEPEeRUxfPhw0tLSWL58OXfffTcAN998MyaTidWrV2O1WnnjjTcYMWIEWVlZ2O0NtymZOXMmBw8epLKyksWLFwME80RFRbFkyRISExPZt28f06ZNIyoqikceeeS0ddm9ezf5+flotVoGDBhAYWEh/fv3Z8GCBfTp0weA7du307dvX+Lj44OvGzVqFL/73e84cOAAAwYMYNSoUfztb3/DbDYTGRnJv/71r/Nqo58jXQYtyOP3cKzyGLnOXA47D6NBw4C4AWg0GlxeF9HGaFmFJYQQgh49epCbmwvAli1b+Oqrr/jwww8ZPHgwXbt25YUXXsBms502aIiMjMRkMgV7jRwOBwZDYNHL7Nmzueyyy0hJSeGGG25g5syZfPDBB2esx/fffw8Eeotmz57NqlWriI6OZujQoZSVlQFQWFjYINABgs8LCwsB0Ov1rFmzhvz8fE6ePMmIESPOr4F+hnQZtBBFVchz5pFdkc3XJ78GoLOtMzajjXBduOytI4QQTUVvDvSwtNR7NwFVVdFoNADs2bMHl8tFTExMgzy1tbVkZ2c3qtz333+fhQsXkp2djcvlwufzYbFYzpi/fqL0rFmzmDBhAgCLFy+mQ4cOfPjhh/z2t79t1PvHxcU1Kv8vJcFOCymrLeOY6xhh2jD2lewDoE9MH+r8daioJEUlSa+OEEI0BY3mvIeSWtrBgwdJTU0FwOVykZCQEJx382M2m+2cy9y+fTsZGRk89dRTjBo1CqvVyrJly3jxxRfP+JqEhAQAevXqFbxmNBrp1KkTeXl5ADgcDr766qsGrzt58mQwrSVIsNMCfIqPstoyvIqXrNIsqrxVmHVm+rbri0FjIDwsXHp1hBBCALB+/Xr27dvHjBkzABg4cCCFhYXodDpSUlLOqQyDwYDf729wbdu2bSQnJzNr1qzgtaNHj561nEGDBmE0Gjl06BBXXHEFAF6vl9zcXJKTkwFIT0/nmWeeoaioKNhzs27dOiwWS4Mg6UKSOTstQFEVXB4XLp+LnUU7AegX248abw3V3mramdtJr44QQlyE3G43hYWF5Ofns3v3bp599lnGjRvH9ddfzx133AHAyJEjSU9PZ/z48Xz22Wfk5uaybds2Zs2axc6dO09bbkpKCnv37uXQoUOUlJTg9Xrp2rUreXl5LFu2jOzsbBYuXMhHH3101vpZLBbuvfdennzyST777DMOHTrE7373OyAwaRrg2muvpVevXtx+++3s2bOHtWvXMnv2bO677z6MRmMTtta5k56dFuBTfJTWlVJYWUiWM7CR0uXtL8eqt+LFS0JEgvTqCCHERWjNmjUkJCSg0+mIjo4mLS2NhQsXMnnyZLTaQP+ERqPh008/ZdasWUyZMoXi4mIcDgdXXXXVKROD602bNo2NGzcyePBgXC4XGzZs4MYbb2TGjBlMnz4dt9vN2LFjmTNnDvPmzTtrHRcsWIBOp+P222+ntraWIUOGsH79eqKjowEICwtj1apV/O53vyM9PZ2IiAgmT57M008/3aRt1RgaVW3kJgBtUGVlJVarlYqKirNOzGoqRdVFbDuxjVXfr2JH4Q46WzszrtM4wnXhdIjswCWJlwQmKQshhGi0uro6cnJySE1NJTxc/lva2p3t73mu92/p2bnA6jcRrPPVBXdMTk9IJ9YcizHMSEJUguyrI4QQQjQhuateYPWbCGaVZ+H2u4kJj6GHvQcR+gjZRFAIIYRoBjJB+QKq79WprKtk4/GNAFyacClh2jDZRFAIIYRoJtKFcAGV1ZZxrOoYh8oOUVxbjEFroFd0LwxhBrRoZbm5EEII0QxatGdn8+bN3HDDDSQmJqLRaFixYkWDdFVVmTt3LgkJCZhMJkaOHMnhw4cb5CkrKyMjIwOLxYLNZmPq1Km4XK4L+CnOjU/xcbL6JDX+Gr46GdhsaUDcAHz4KKspI9YUK706QgghRDNo0WCnurqatLQ0Fi1adNr0559/noULF/L666+zY8cOIiIiGDVqFHV1dcE8GRkZHDhwgHXr1rFq1So2b97MPffcc6E+wjmr89WR78qnsKqQIxVH0KDh6g5XExMeA1qIM8dJr44QQgjRDFp0GGvMmDGMGTPmtGmqqvLXv/6V2bNnM27cOADeffdd4uPjWbFiBRMnTuTgwYOsWbOGr7/+msGDBwPw6quvct111/HCCy+QmNhCp9z+hKIq5Ffkc7TyKNsKtgHQNboriqrgVbxEG6KxGJt/ybsQQghxMQrZCco5OTkUFhYycuTI4DWr1cqQIUPYvn07EDjXw2azBQMdCOwsqdVq2bFjxxnLdrvdVFZWNng0p7LaMo5VHwPgu/LvALjUcSkmnQkVlfiIeNlXRwghhGgmIRvs1B8Df7pj4uvTCgsLTzkxVafTYbfbg3lOZ/78+Vit1uCjY8eOTVz7H3j8Ho5VHqPKU8Wuk7vwq37aR7QnISKBOl8dBq0BR4RDlpsLIYQQzSRkg53m9Pjjj1NRURF8HDt2rNney1nnpLSulFpfLbuKdwFweeLlGMIMROgjSLIkYTfZm+39hRBCXHzmzZtH//79W7oaISNkg536Y+Drj4Wvd/LkyWCaw+GgqKioQbrP56OsrOysx8gbjUYsFkuDR3PwKT4q3YEhso3HNuJTfLSPaE+v2F7oNDqsRisdozrKxGQhhBDceeedaDQaNBoNer2e+Ph4rrnmGt5++20URTnvssePH3/eddy4cWOwjj99fP3118F8e/fu5corryQ8PJyOHTvy/PPPNyjH7/fz+9//noSEBK677rpT7uVNLWSDndTUVBwOB59//nnwWmVlJTt27CA9PR0IHCPvdDrZtWtXMM/69etRFIUhQ4Zc8Dr/lKIqVHurcbqdwdPNr2h/Bc5aJ5WeSpItydKrI4QQImj06NEUFBSQm5vL6tWrGTZsGPfffz/XX389Pp+vpavHZZddRkFBQYPH3XffTWpqanD+bGVlJddeey3Jycns2rWLBQsWMG/ePN58881gOcuWLSMvL4+1a9cycOBAZs+e3az1btGJIi6XiyNHjgSf5+TkkJmZid1uJykpiQceeIA///nPdO3aldTUVObMmUNiYmIwOu3ZsyejR49m2rRpvP7663i9XqZPn87EiRNDYiWWT/FRUF3A+rz1eBUvDrODnvaeoIFIfSTto9qj1YRsvCmEEG2CqqrU+mpb5L1NOhMajeac8xuNxuDIRPv27Rk4cCCXXnopI0aMYMmSJdx9990AOJ1OZs6cycqVK3G73QwePJiXX36ZtLS0U8qcN28e77zzDkCwLhs2bGDo0KE8+uijfPTRRxw/fhyHw0FGRgZz585Fr9eftn4Gg6HByInX62XlypX84Q9/CJa9dOlSPB4Pb7/9NgaDgd69e5OZmclLL70U3BqmvLyclJQU+vTpw8GDB/n3v/99zm30S7RosLNz506GDRsWfP7ggw8CMHnyZJYsWcIjjzxCdXU199xzD06nkyuuuII1a9Y0OPV06dKlTJ8+nREjRqDVapkwYQILFy684J/lpxRV4UTlCXIqcviy8EsAhiUNQ4sWk95EvDleJiULIcQFUOurZch7LdPbv2PSDsx683mVMXz4cNLS0li+fHkw2Ln55psxmUysXr0aq9XKG2+8wYgRI8jKysJubzhiMHPmTA4ePEhlZSWLFy8GCOaJiopiyZIlJCYmsm/fPqZNm0ZUVBSPPPLIOdXt448/prS0lClTpgSvbd++nauuugqD4YcpGqNGjeIvf/kL5eXlREdHc9tttzFixAiMRiPx8fF8+umn59VGP6dF77ZDhw5FVdUzpms0Gp5++mmefvrpM+ax2+289957zVG981JWW0ZuVS4HSg/gVbzEm+NpH9EeVaOi1+iJNERKr44QQohz0qNHD/bu3QvAli1b+OqrrygqKsJoNALwwgsvsGLFCv71r3+dsrFuZGQkJpMJt9t9ynzWHw8fpaSkMHPmTJYtW3bOwc5bb73FqFGj6NChQ/BaYWEhqampDfLVr6wuLCwkOjoam83Grl27KCwspF27doSFhZ1jS/wy0rXQDOqXmxfXFP/Qq9NhGHHmOHyKDzTI6eZCCHGBmHQmdkw6895rzf3eTUFV1eAw0Z49e3C5XMTExDTIU1tbS3Z2dqPKff/991m4cCHZ2dm4XC58Pt85L9o5fvw4a9eu5YMPPmjUe/7Y2RYTNSW52zaD+uXmu4p24fa7iTPFkRyVTK2vFi1a7OF2OQdLCCEuEI1Gc95DSS3t4MGDwd4Sl8tFQkICGzduPCWfzWY75zK3b99ORkYGTz31FKNGjcJqtbJs2TJefPHFc3r94sWLiYmJ4cYbb2xw3eFwnHYldX1aS5Bgp4n5FB8uj4sIQwTfFH0DwOjU0cRFxFHjqSHSGCnLzYUQQpyz9evXs2/fPmbMmAHAwIEDKSwsRKfTkZKSck5lGAwG/H5/g2vbtm0jOTmZWbNmBa8dPXr0nMpTVZXFixdzxx13nDKZOT09nVmzZuH1eoNp69ato3v37kRHR59T+U1NJo00MUVVQAPR4dHMvnQ2t3S7hbR2aXgUDx7VI8vNhRBCnJHb7aawsJD8/Hx2797Ns88+y7hx47j++uu54447gMCxSOnp6YwfP57PPvuM3Nxctm3bxqxZs9i5c+dpy01JSWHv3r0cOnSIkpISvF4vXbt2JS8vj2XLlpGdnc3ChQv56KOPzqme69evJycnJzhh+scmTZqEwWBg6tSpHDhwgPfff59XXnkluAipJUiw08S0Gi1hmjBMOhMdojowttNYLAYLtnAb3aO7k2xNlonJQgghTmvNmjUkJCSQkpLC6NGj2bBhAwsXLmTlypXBSbwajYZPP/2Uq666iilTptCtWzcmTpzI0aNHTzliqd60adPo3r07gwcPpl27dmzdupUbb7yRGTNmMH36dPr378+2bduYM2fOOdXzrbfe4rLLLqNHjx6npFmtVj777DNycnIYNGgQDz30EHPnzj1l4vSFpFHPthzqIlFZWYnVaqWioqJJdlN21jkpri0mXBeOBg0evweP34MjwiFzdYQQopnV1dWRk5NDampqg61KROt0tr/nud6/Zc5OM7AYAw1e6anEq3jRaXXYw+3B60IIIYS4cCTYaQZajRZbuI1IQySKqqDVaGWZuRBCCNFC5A7cjCTAEUIIIVqezJQVQgghRJsmwY4QQog2SdbftA1N8XeUYEcIIUSbUr9E2+PxtHBNRFOoqakBOONJ7OdCJpUIIYRoU3Q6HWazmeLiYvR6PVqt/K5vjVRVpaamhqKiImw223kdFirBjhBCiDZFo9GQkJBATk7OOR9/IEKXzWY77zO1JNgRQgjR5hgMBrp27SpDWa2cXq8/rx6dehLsCCGEaJO0Wq3soCwAmaAshBBCiDZOgh0hhBBCtGkS7AghhBCiTZM5O/ywYVFlZWUL10QIIYQQ56r+vv1zGw9KsANUVVUB0LFjxxauiRBCCCEaq6qqCqvVesZ0jSr7aaMoCidOnCAqKgqNRtOo11ZWVtKxY0eOHTuGxWJpphq2HdJejSdt1jjSXo0j7dV40maN05ztpaoqVVVVJCYmnnXzSOnZIbA8sUOHDudVhsVikS99I0h7NZ60WeNIezWOtFfjSZs1TnO119l6dOrJBGUhhBBCtGkS7AghhBCiTZNg5zwZjUaefPJJjEZjS1elVZD2ajxps8aR9mocaa/GkzZrnFBoL5mgLIQQQog2TXp2hBBCCNGmSbAjhBBCiDZNgh0hhBBCtGkS7AghhBCiTZNg5zTmz5/PJZdcQlRUFHFxcYwfP55Dhw41yFNXV8d9991HTEwMkZGRTJgwgZMnTzbIk5eXx9ixYzGbzcTFxfHwww/j8/ku5Ee5IF577TX69esX3DAqPT2d1atXB9Olrc7uueeeQ6PR8MADDwSvSZs1NG/ePDQaTYNHjx49gunSXqfKz8/ntttuIyYmBpPJRN++fdm5c2cwXVVV5s6dS0JCAiaTiZEjR3L48OEGZZSVlZGRkYHFYsFmszF16lRcLteF/igXREpKyinfMY1Gw3333QfId+yn/H4/c+bMITU1FZPJROfOnfnTn/7U4IyqkPqOqeIUo0aNUhcvXqzu379fzczMVK+77jo1KSlJdblcwTz33nuv2rFjR/Xzzz9Xd+7cqV566aXqZZddFkz3+Xxqnz591JEjR6rffPON+umnn6qxsbHq448/3hIfqVl9/PHH6ieffKJmZWWphw4dUp944glVr9er+/fvV1VV2upsvvrqKzUlJUXt16+fev/99wevS5s19OSTT6q9e/dWCwoKgo/i4uJgurRXQ2VlZWpycrJ65513qjt27FC///57de3ateqRI0eCeZ577jnVarWqK1asUPfs2aPeeOONampqqlpbWxvMM3r0aDUtLU398ssv1S+++ELt0qWLeuutt7bER2p2RUVFDb5f69atUwF1w4YNqqrKd+ynnnnmGTUmJkZdtWqVmpOTo3744YdqZGSk+sorrwTzhNJ3TIKdc1BUVKQC6qZNm1RVVVWn06nq9Xr1ww8/DOY5ePCgCqjbt29XVVVVP/30U1Wr1aqFhYXBPK+99ppqsVhUt9t9YT9AC4iOjlb/7//9v9JWZ1FVVaV27dpVXbdunXr11VcHgx1ps1M9+eSTalpa2mnTpL1O9eijj6pXXHHFGdMVRVEdDoe6YMGC4DWn06kajUb1n//8p6qqqvrtt9+qgPr1118H86xevVrVaDRqfn5+81U+RNx///1q586dVUVR5Dt2GmPHjlXvuuuuBtduuukmNSMjQ1XV0PuOyTDWOaioqADAbrcDsGvXLrxeLyNHjgzm6dGjB0lJSWzfvh2A7du307dvX+Lj44N5Ro0aRWVlJQcOHLiAtb+w/H4/y5Yto7q6mvT0dGmrs7jvvvsYO3Zsg7YB+X6dyeHDh0lMTKRTp05kZGSQl5cHSHudzscff8zgwYO5+eabiYuLY8CAAfzv//5vMD0nJ4fCwsIGbWa1WhkyZEiDNrPZbAwePDiYZ+TIkWi1Wnbs2HHhPkwL8Hg8/OMf/+Cuu+5Co9HId+w0LrvsMj7//HOysrIA2LNnD1u2bGHMmDFA6H3H5CDQn6EoCg888ACXX345ffr0AaCwsBCDwYDNZmuQNz4+nsLCwmCeH3/p69Pr09qaffv2kZ6eTl1dHZGRkXz00Uf06tWLzMxMaavTWLZsGbt37+brr78+JU2+X6caMmQIS5YsoXv37hQUFPDUU09x5ZVXsn//fmmv0/j+++957bXXePDBB3niiSf4+uuv+eMf/4jBYGDy5MnBz3y6Nvlxm8XFxTVI1+l02O32NtlmP7ZixQqcTid33nknIP+fPJ3HHnuMyspKevToQVhYGH6/n2eeeYaMjAyAkPuOSbDzM+677z7279/Pli1bWroqIa179+5kZmZSUVHBv/71LyZPnsymTZtauloh6dixY9x///2sW7eO8PDwlq5Oq1D/axGgX79+DBkyhOTkZD744ANMJlML1iw0KYrC4MGDefbZZwEYMGAA+/fv5/XXX2fy5MktXLvQ99ZbbzFmzBgSExNbuioh64MPPmDp0qW899579O7dm8zMTB544AESExND8jsmw1hnMX36dFatWsWGDRvo0KFD8LrD4cDj8eB0OhvkP3nyJA6HI5jnpzP165/X52lLDAYDXbp0YdCgQcyfP5+0tDReeeUVaavT2LVrF0VFRQwcOBCdTodOp2PTpk0sXLgQnU5HfHy8tNnPsNlsdOvWjSNHjsh37DQSEhLo1atXg2s9e/YMDv3Vf+bTtcmP26yoqKhBus/no6ysrE22Wb2jR4/y3//+l7vvvjt4Tb5jp3r44Yd57LHHmDhxIn379uX2229nxowZzJ8/Hwi975gEO6ehqirTp0/no48+Yv369aSmpjZIHzRoEHq9ns8//zx47dChQ+Tl5ZGeng5Aeno6+/bta/CHXLduHRaL5ZT/CLVFiqLgdrulrU5jxIgR7Nu3j8zMzOBj8ODBZGRkBP8tbXZ2LpeL7OxsEhIS5Dt2Gpdffvkp22VkZWWRnJwMQGpqKg6Ho0GbVVZWsmPHjgZt5nQ62bVrVzDP+vXrURSFIUOGXIBP0TIWL15MXFwcY8eODV6T79ipampq0GobhhBhYWEoigKE4HesSac7txG/+93vVKvVqm7cuLHBUsSamppgnnvvvVdNSkpS169fr+7cuVNNT09X09PTg+n1yxCvvfZaNTMzU12zZo3arl27NrkM8bHHHlM3bdqk5uTkqHv37lUfe+wxVaPRqJ999pmqqtJW5+LHq7FUVdrspx566CF148aNak5Ojrp161Z15MiRamxsrFpUVKSqqrTXT3311VeqTqdTn3nmGfXw4cPq0qVLVbPZrP7jH/8I5nnuuedUm82mrly5Ut27d686bty40y4LHjBggLpjxw51y5YtateuXdvs0nNVVVW/368mJSWpjz766Clp8h1raPLkyWr79u2DS8+XL1+uxsbGqo888kgwTyh9xyTYOQ3gtI/FixcH89TW1qq///3v1ejoaNVsNqu//vWv1YKCggbl5ObmqmPGjFFNJpMaGxurPvTQQ6rX673An6b53XXXXWpycrJqMBjUdu3aqSNGjAgGOqoqbXUufhrsSJs1dMstt6gJCQmqwWBQ27dvr95yyy0N9oyR9jrVf/7zH7VPnz6q0WhUe/Toob755psN0hVFUefMmaPGx8erRqNRHTFihHro0KEGeUpLS9Vbb71VjYyMVC0WizplyhS1qqrqQn6MC2rt2rUqcEo7qKp8x36qsrJSvf/++9WkpCQ1PDxc7dSpkzpr1qwGy+xD6TumUdUfbXcohBBCCNHGyJwdIYQQQrRpEuwIIYQQok2TYEcIIYQQbZoEO0IIIYRo0yTYEUIIIUSbJsGOEEIIIdo0CXaEEEII0aZJsCOEEEKINk2CHSFEyNNoNKxYsaLZ32fJkiXYbLZmfx8hxIUlwY4Q4qKUkpLCX//61yYr7+jRo5hMJlwuV5OVKYRoGhLsCCFEE1i5ciXDhg0jMjKypasihPgJCXaEEEGrVq3CZrPh9/sByMzMRKPR8NhjjwXz3H333dx2220AlJaWcuutt9K+fXvMZjN9+/bln//8ZzDvm2++SWJiIoqiNHifcePGcddddwWfr1y5koEDBxIeHk6nTp146qmn8Pl8Z6znsWPH+M1vfoPNZsNutzNu3Dhyc3OD6XfeeSfjx4/nhRdeICEhgZiYGO677z68Xi8AQ4cO5ejRo8yYMQONRoNGo2lQ/tq1a+nZsyeRkZGMHj2agoKCn227lStXcuONN542bePGjWg0Gj7//HMGDx6M2Wzmsssu49ChQ8E88+bNo3///rz99tskJSURGRnJ73//e/x+P88//zwOh4O4uDieeeaZn62LEKIhCXaEEEFXXnklVVVVfPPNNwBs2rSJ2NhYNm7cGMyzadMmhg4dCkBdXR2DBg3ik08+Yf/+/dxzzz3cfvvtfPXVVwDcfPPNlJaWsmHDhuDry8rKWLNmDRkZGQB88cUX3HHHHdx///18++23vPHGGyxZsuSMN3Wv18uoUaOIioriiy++YOvWrcGgxOPxBPNt2LCB7OxsNmzYwDvvvMOSJUtYsmQJAMuXL6dDhw48/fTTFBQUNAhmampqeOGFF/j73//O5s2bycvLY+bMmWdtN6fTyZYtW84Y7NSbNWsWL774Ijt37kSn0zUI+ACys7NZvXo1a9as4Z///CdvvfUWY8eO5fjx42zatIm//OUvzJ49mx07dpz1fYQQP9Hk56gLIVq1gQMHqgsWLFBVVVXHjx+vPvPMM6rBYFCrqqrU48ePq4CalZV1xtePHTtWfeihh4LPx40bp951113B52+88YaamJio+v1+VVVVdcSIEeqzzz7boIy///3vakJCQvA5oH700UfBtO7du6uKogTT3W63ajKZ1LVr16qqqqqTJ09Wk5OTVZ/PF8xz8803q7fcckvweXJysvryyy83eN/FixergHrkyJHgtUWLFqnx8fFn/LyqqqpLly5VBw8efMb0DRs2qID63//+N3jtk08+UQG1trZWVVVVffLJJ1Wz2axWVlYG84waNUpNSUkJtpWqqmr37t3V+fPnn7U+QoiGpGdHCNHA1VdfzcaNG1FVlS+++IKbbrqJnj17smXLFjZt2kRiYiJdu3YFwO/386c//Ym+fftit9uJjIxk7dq15OXlBcvLyMjg3//+N263G4ClS5cyceJEtNrAf3727NnD008/TWRkZPAxbdo0CgoKqKmpOaV+e/bs4ciRI0RFRQXz2+126urqyM7ODubr3bs3YWFhwecJCQkUFRX97Oc3m8107ty5Ua872xDWj/Xr169BuUCDslNSUoiKigo+j4+Pp1evXsG2qr92Lp9DCPEDXUtXQAgRWoYOHcrbb7/Nnj170Ov19OjRg6FDh7Jx40bKy8u5+uqrg3kXLFjAK6+8wl//+lf69u1LREQEDzzwQIPhpBtuuAFVVfnkk0+45JJL+OKLL3j55ZeD6S6Xi6eeeoqbbrrplLqEh4efcs3lcjFo0CCWLl16Slq7du2C/9br9Q3SNBrNKXOHTud0r1NV9Yz5PR4Pa9as4YknnmhU2fXzhH5cp9O99y/9HEKIH0iwI4RooH7ezssvvxwMbIYOHcpzzz1HeXk5Dz30UDDv1q1bGTduXHDCsqIoZGVl0atXr2Ce8PBwbrrpJpYuXcqRI0fo3r07AwcODKYPHDiQQ4cO0aVLl3Oq38CBA3n//feJi4vDYrH84s9pMBiCE7HPx8aNG4mOjiYtLe28yxJCNA8ZxhJCNBAdHU2/fv1YunRpcCLyVVddxe7du8nKymrQs9O1a1fWrVvHtm3bOHjwIL/97W85efLkKWVmZGTwySef8PbbbwcnJtebO3cu7777Lk899RQHDhzg4MGDLFu2jNmzZ5+2fhkZGcTGxjJu3Di++OILcnJy2LhxI3/84x85fvz4OX/OlJQUNm/eTH5+PiUlJef8up/6+OOPz2kISwjRciTYEUKc4uqrr8bv9weDHbvdTq9evXA4HHTv3j2Yb/bs2QwcOJBRo0YxdOhQHA4H48ePP6W84cOHY7fbOXToEJMmTWqQNmrUKFatWsVnn33GJZdcwqWXXsrLL79McnLyaetmNpvZvHkzSUlJwflEU6dOpa6urlE9PU8//TS5ubl07ty5wfBXY0mwI0To06hnG4wWQghxRrt372b48OEUFxefMrdGCBE6pGdHCCF+IZ/Px6uvviqBjhAhTnp2hBBCCNGmSc+OEEIIIdo0CXaEEEII0aZJsCOEEEKINk2CHSGEEEK0aRLsCCGEEKJNk2BHCCGEEG2aBDtCCCGEaNMk2BFCCCFEmybBjhBCCCHatP8HmZYqz8dJmZcAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fit_50 = model(lbda, 50, out.params)\n",
+ "fit_60 = model(lbda, 60, out.params)\n",
+ "fit_70 = model(lbda, 70, out.params)\n",
+ "\n",
+ "fig = plt.figure(dpi=100)\n",
+ "ax = fig.add_subplot(1, 1, 1)\n",
+ "ax.scatter(lbda, data.loc[(50, \"Ψ\")], s=20, alpha=0.1, label=\"50° Measurement\")\n",
+ "ax.scatter(lbda, data.loc[(60, \"Ψ\")], s=20, alpha=0.1, label=\"Psi 60° Measurement\")\n",
+ "ax.scatter(lbda, data.loc[(70, \"Ψ\")], s=20, alpha=0.1, label=\"Psi 70° Measurement\")\n",
+ "psi50, = ax.plot(lbda, fit_50.psi, c=\"tab:blue\", label=\"Psi 50°\")\n",
+ "psi60, = ax.plot(lbda, fit_60.psi, c=\"tab:orange\", label=\"Psi 60°\")\n",
+ "psi70, = ax.plot(lbda, fit_70.psi, c=\"tab:green\", label=\"Psi 70°\")\n",
+ "ax.set_xlabel('wavelenth / nm')\n",
+ "ax.set_ylabel('psi / degree')\n",
+ "ax.legend(handles=[psi50, psi60, psi70], loc='lower left')\n",
+ "fig.canvas.draw()\n",
+ "\n",
+ "fig = plt.figure(dpi=100)\n",
+ "ax = fig.add_subplot(1, 1, 1)\n",
+ "ax.scatter(lbda, data.loc[(50, \"Δ\")], s=20, alpha=0.1, label=\"Delta 50° Measurement\")\n",
+ "ax.scatter(lbda, data.loc[(60, \"Δ\")], s=20, alpha=0.1, label=\"Delta 60° Measurement\")\n",
+ "ax.scatter(lbda, data.loc[(70, \"Δ\")], s=20, alpha=0.1, label=\"Delta 70° Measurement\")\n",
+ "delta50, = ax.plot(lbda, fit_50.delta, c=\"tab:blue\", label=\"Delta 50°\")\n",
+ "delta60, = ax.plot(lbda, fit_60.delta, c=\"tab:orange\", label=\"Delta 60°\")\n",
+ "delta70, = ax.plot(lbda, fit_70.delta, c=\"tab:green\", label=\"Delta 70°\")\n",
+ "ax.set_xlabel('wavelenth / nm')\n",
+ "ax.set_ylabel('delta / degree')\n",
+ "ax.legend(handles=[delta50, delta60, delta70], loc='lower right')\n",
+ "fig.canvas.draw()"
+ ]
+ }
+ ],
+ "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": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Custom fitting/SiO2onSi.ellips.nxs b/examples/Custom fitting/SiO2onSi.ellips.nxs
new file mode 100644
index 00000000..a44b6be4
Binary files /dev/null and b/examples/Custom fitting/SiO2onSi.ellips.nxs differ
diff --git a/examples/gallery/plot_01_basic_usage.py b/examples/gallery/plot_01_basic_usage.py
index 7530583a..4e9253d9 100644
--- a/examples/gallery/plot_01_basic_usage.py
+++ b/examples/gallery/plot_01_basic_usage.py
@@ -36,7 +36,7 @@
# Prior to defining our model, we have to set the parameters we want to use.
# We're going to use a :ref:`Cauchy model ` for SiO2 and load the Si values from
# `literature values `_.
-# The parameter names can be choosen freely,
+# The parameter names can be chosen freely,
# but you have to use the exact same name in the later model definition.
# The package uses lmfit as fitting tool and you may refer to their
# `documentation `_
@@ -71,7 +71,7 @@
# ------------------------
#
# For simple parameter estimation,
-# the fit decorator (**@fit**) in conjuction with the model definition is used.
+# the fit decorator (**@fit**) in conjunction with the model definition is used.
# The fitting decorator takes a pandas dataframe containing
# the psi/delta measurement data (**psi_delta**) and the model parameters (**params**) as an input.
# It then passes the wavelength from measurement dataframe (**lbda**)
@@ -90,7 +90,7 @@
# This is done by calling the :code:`elli.IsotropicMaterial(...)` function
# with a dispersion model as a parameter
# or simply calling :code:`.get_mat()` on a dispersion model.
-# These two approaches are equivalent.From these materials the layer is build,
+# These two approaches are equivalent. From these materials the layer is build,
# which only consists of the SiO2 layer in this example.
# The final structure consists of an incoming half-space,
# the layers and an outgoing half space. Specifically,
@@ -109,7 +109,7 @@
# Executing the cell below in a jupyter notebook displays a comparison of the simulated Ψ / Δ values
# at the current parameter values with their measured counterparts.
# Additionally, input fields for each model parameter are shown.
-# You may change the parameters and the calcualted data will change accordingly.
+# You may change the parameters and the calculated data will change accordingly.
# For clarification the modeled data is shown with `_calc` postfix in the legend.
@fit(psi_delta, params)
def model(lbda, params):
diff --git a/examples/gallery/plot_03_custom_fitting.py b/examples/gallery/plot_03_custom_fitting.py
new file mode 100644
index 00000000..c9150dce
--- /dev/null
+++ b/examples/gallery/plot_03_custom_fitting.py
@@ -0,0 +1,132 @@
+"""
+Custom fitting example
+======================
+
+This is a short example for fitting data, which is not covered by the fitting widget.
+It relies on calling lmfit manually. Therefore one needs to provide a fitting function, which calculates the numerical residual between measurement and model.
+This notebook is about fitting multiple datasets from different angles of incidents simultaneously, but can be adapted to a wide range of different use cases.
+
+"""
+
+# %%
+import numpy as np
+import elli
+from elli.fitting import ParamsHist
+from lmfit import minimize, fit_report
+import matplotlib.pyplot as plt
+
+# sphinx_gallery_thumbnail_path = '_static/custom_fitting.png'
+
+
+# %%
+# Data import
+# -----------
+data = elli.read_nexus_psi_delta("SiO2onSi.ellips.nxs").loc[
+ (slice(None), slice(210, 800)), :
+]
+lbda = data.loc[50].index.get_level_values("Wavelength").to_numpy()
+data
+
+# %%
+# Setting up invariant materials and fitting parameters
+# -----------------------------------------------------
+rii_db = elli.db.RII()
+Si = rii_db.get_mat("Si", "Aspnes")
+
+params = ParamsHist()
+params.add("SiO2_n0", value=1.452, min=-100, max=100, vary=True)
+params.add("SiO2_n1", value=36.0, min=-40000, max=40000, vary=True)
+params.add("SiO2_n2", value=0, min=-40000, max=40000, vary=False)
+params.add("SiO2_k0", value=0, min=-100, max=100, vary=False)
+params.add("SiO2_k1", value=0, min=-40000, max=40000, vary=False)
+params.add("SiO2_k2", value=0, min=-40000, max=40000, vary=False)
+params.add("SiO2_d", value=20, min=0, max=40000, vary=True)
+
+
+# %%
+# Model helper function
+# ---------------------
+# This model function is not strictly needed, but simplifies the fit function, as the model only needs to be defined once.
+def model(lbda, angle, params):
+ SiO2 = elli.Cauchy(
+ params["SiO2_n0"],
+ params["SiO2_n1"],
+ params["SiO2_n2"],
+ params["SiO2_k0"],
+ params["SiO2_k1"],
+ params["SiO2_k2"],
+ ).get_mat()
+
+ structure = elli.Structure(
+ elli.AIR,
+ [elli.Layer(SiO2, params["SiO2_d"])],
+ Si,
+ )
+
+ return structure.evaluate(lbda, angle, solver=elli.Solver2x2)
+
+
+# %%
+# Defining the fit function
+# -------------------------
+# The fit function follows the protocol defined by the lmfit package and needs the parameters dictionary as first argument.
+# It has to return a residual value, which will be minimized. Here psi and delta are used to calculate the residual, but could be changed to transmission or reflection data.
+
+
+def fit_function(params, lbda, data):
+ residual = []
+
+ for phi_i in [50, 60, 70]:
+ model_result = model(lbda, phi_i, params)
+
+ resid_psi = data.loc[(phi_i, "Ψ")].to_numpy() - model_result.psi
+ resid_delta = data.loc[(phi_i, "Δ")].to_numpy() - model_result.delta
+
+ residual.append(resid_psi)
+ residual.append(resid_delta)
+
+ return np.concatenate(residual)
+
+
+# %%
+# Running the fit
+# ---------------
+# The fitting is performed by calling the minimize function with the fit_function and the needed arguments.
+# It is possible to change the underlying algorithm by providing the method kwarg.
+
+out = minimize(fit_function, params, args=(lbda, data), method="leastsq")
+print(fit_report(out))
+
+# %%
+# Plotting the results
+# ----------------------------------------------
+
+fit_50 = model(lbda, 50, out.params)
+fit_60 = model(lbda, 60, out.params)
+fit_70 = model(lbda, 70, out.params)
+
+fig = plt.figure(dpi=100)
+ax = fig.add_subplot(1, 1, 1)
+ax.scatter(lbda, data.loc[(50, "Ψ")], s=20, alpha=0.1, label="50° Measurement")
+ax.scatter(lbda, data.loc[(60, "Ψ")], s=20, alpha=0.1, label="Psi 60° Measurement")
+ax.scatter(lbda, data.loc[(70, "Ψ")], s=20, alpha=0.1, label="Psi 70° Measurement")
+(psi50,) = ax.plot(lbda, fit_50.psi, c="tab:blue", label="Psi 50°")
+(psi60,) = ax.plot(lbda, fit_60.psi, c="tab:orange", label="Psi 60°")
+(psi70,) = ax.plot(lbda, fit_70.psi, c="tab:green", label="Psi 70°")
+ax.set_xlabel("wavelenth / nm")
+ax.set_ylabel("psi / degree")
+ax.legend(handles=[psi50, psi60, psi70], loc="lower left")
+fig.canvas.draw()
+
+fig = plt.figure(dpi=100)
+ax = fig.add_subplot(1, 1, 1)
+ax.scatter(lbda, data.loc[(50, "Δ")], s=20, alpha=0.1, label="Delta 50° Measurement")
+ax.scatter(lbda, data.loc[(60, "Δ")], s=20, alpha=0.1, label="Delta 60° Measurement")
+ax.scatter(lbda, data.loc[(70, "Δ")], s=20, alpha=0.1, label="Delta 70° Measurement")
+(delta50,) = ax.plot(lbda, fit_50.delta, c="tab:blue", label="Delta 50°")
+(delta60,) = ax.plot(lbda, fit_60.delta, c="tab:orange", label="Delta 60°")
+(delta70,) = ax.plot(lbda, fit_70.delta, c="tab:green", label="Delta 70°")
+ax.set_xlabel("wavelenth / nm")
+ax.set_ylabel("delta / degree")
+ax.legend(handles=[delta50, delta60, delta70], loc="lower right")
+fig.canvas.draw()