Repository: PyEllips/pyElli (language: python)

Issue:
Reimplement the Result get methods
Make it possible again, to get specific matrix elements by their physical name: E.g. R_ss


Discussion / hints:

Fix patch:
diff --git a/examples/Bragg-mirror/Bragg-example.ipynb b/examples/Bragg-mirror/Bragg-example.ipynb
index e260b6ec..4ff398ea 100644
--- a/examples/Bragg-mirror/Bragg-example.ipynb
+++ b/examples/Bragg-mirror/Bragg-example.ipynb
@@ -143,8 +143,8 @@
     "\n",
     "data = s.evaluate(lbda_list, 0)\n",
     "\n",
-    "R = data.R[:, 1, 1]\n",
-    "T = data.T[:, 1, 1]"
+    "R = data.R_ss\n",
+    "T = data.T_ss"
    ]
   },
   {
@@ -157,13 +157,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "51990a35",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -177,8 +177,8 @@
    "source": [
     "fig = plt.figure()\n",
     "ax = fig.add_subplot(1, 1, 1)\n",
-    "ax.plot(lbda_list, R, label=\"$R$\")\n",
-    "ax.plot(lbda_list, T, label=\"$T$\")\n",
+    "ax.plot(lbda_list, R, label=\"$R_{ss}$\")\n",
+    "ax.plot(lbda_list, T, label=\"$T_{ss}$\")\n",
     "ax.legend(loc='center right')\n",
     "ax.set_xlabel(r\"Wavelength $\\lambda$ (nm)\")\n",
     "ax.set_ylabel(r\"Power reflection $R$ or transmission $T$\")\n",
@@ -212,7 +212,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x216 with 1 Axes>"
       ]
@@ -252,7 +252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.8"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/Bragg-mirror/Bragg-example.py b/examples/Bragg-mirror/Bragg-example.py
index c784a6ff..58311491 100755
--- a/examples/Bragg-mirror/Bragg-example.py
+++ b/examples/Bragg-mirror/Bragg-example.py
@@ -63,8 +63,8 @@
 
 data = s.evaluate(lbda_list, 0)
 
-R = data.R[:, 1, 1]
-T = data.T[:, 1, 1]
+R = data.R_ss
+T = data.T_ss
 
 # %% [markdown]
 # ## Plotting
@@ -72,8 +72,8 @@
 # %%
 fig = plt.figure()
 ax = fig.add_subplot(1, 1, 1)
-ax.plot(lbda_list, R, label="$R$")
-ax.plot(lbda_list, T, label="$T$")
+ax.plot(lbda_list, R, label="$R_{ss}$")
+ax.plot(lbda_list, T, label="$T_{ss}$")
 ax.legend(loc='center right')
 ax.set_xlabel(r"Wavelength $\lambda$ (nm)")
 ax.set_ylabel(r"Power reflection $R$ or transmission $T$")
diff --git a/examples/Bragg-mirror/validation-Bragg.ipynb b/examples/Bragg-mirror/validation-Bragg.ipynb
index 429edec4..b2433def 100644
--- a/examples/Bragg-mirror/validation-Bragg.ipynb
+++ b/examples/Bragg-mirror/validation-Bragg.ipynb
@@ -199,13 +199,13 @@
     "# Incidence angle Phi_i = 0, 's' polarization\n",
     "data = s.evaluate(lbda_list, 0)\n",
     "\n",
-    "R_ss_0 = data.R[:, 1, 1]\n",
+    "R_ss_0 = data.R_ss\n",
     "\n",
     "# Incidence angle Phi_i = pi/4, 's' and 'p' polarizations\n",
     "data2 = s.evaluate(lbda_list, np.rad2deg(pi/4))\n",
     "\n",
-    "R_ss = data2.R[:, 1, 1]\n",
-    "R_pp = data2.R[:, 0, 0]"
+    "R_ss = data2.R_ss\n",
+    "R_pp = data2.R_pp"
    ]
   },
   {
@@ -226,7 +226,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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hhBBCCIfx9fXNnTFjxqH333+/fnGdoA4ePOjp6+ub+9hjj50ZN25c0vbt26tebrupbt26NpvNptLT0xVAz549Lxw8eNAnLi7OKyMjQy1evLj23XffnVKSa0xKSnL38/PL8fb2LlMBh8xwiHIhalMUYQ3CsARa8h8DvPHTGzzX9Tnm7ZoHwEeDPsLDzYNBcwcxxTKFA2cO8PqPr2Ng8H89/49h84eRnZuNp5snQXWCmLd7HrZcG/N2z8t/3hH7TrZO5oF2D/DGT28wPHh4fgBkXm/Rx7HHYonsFok10Zr/WAghhBBlS7du3S4GBwdf/Pjjj2s//vjjZ+yf27p1a5WJEyc2dHNzw8PDw3j//ff/vNx2ez169Di3Zs2a6kOHDj3v6enJm2++eah///432Ww27rvvvlOhoaEZRV9TnFWrVtXo27fvOcd8tY6jDKNMBUAlFhoaamzZIkt2VERmQBF7LDY/yJj+83Resr7EFMsU4k/FM3/3/PzB/pQNU8jOzcbDzYMxHcfw8daP6dqwK98e+JawgDB2Ju3EwKBt/bZsT9qOgUG9avU4dv4Y9avVJzk9GYWibrW6nEw7CQpq+9TmzMUzoMDP248zGWeoW7UuZy6eQaFo4teEP8/9iULRtn5bfj/xO0opQuqGsC1pG63rtGbPqT082elJQhuE8sSqJwDyr9f+2jNyMni9z+u092/PXXPv4hXLK7T3by8BiRBCiDJBKbXVMIxQV593x44dB9u1a1dsgXZFs3HjxqrR0dH1ly5dmng9x+nXr1/z6OjoI23bts101LWV1I4dO+q0a9euaXHPScAhSs2lZi3m7ZrH4rjFjAgZwawds5himcLrP76OpamFRX8sosONHdh5QgcRdavWJSktCYPL/xwrFAYGNb1r4uXuRXJ6Mr5evpzPOk+Tmk1wd3Mn4WwCQTcE4abc+OPUHwTdEESukcu+M/vw8/EjJSOFxjUbYxgGh1MPU6dqHbJt2ZzLPIe7csdmXHm1zyoeVcjMyUQpRYvaLUg4m6CvTyk83Tx5stOTvL/lfaBwcDKy9cj8mZNXLK8w7tZx+cGHGZxJECKEEMIZJOBwjbfffvuGJ5544rSHx7UlIGVkZKiZM2fWfuKJJ047+NJK5HIBh9RwCJeyr68IaxBGxMIIpv88nQNnDjBs/jCGzhtKq7qt6NG4Bx9s+QAvdy/GrxnP6YunWfjHQgwMth7fikJR26c2x9OO07Z+W7o27ApA8A3BAAxoMYBaPrV4tsuz1PCqQQ3vGkzuMRkDgyxbFqPbjiYtK43RbUdzNuMsp9NPM7nHZI6nHefY+WP5j09eOMnotqM5l3GO0W1Hk5KRQmpmKpN7TCbLpuu3JveYTDWvatT0rsk9re9BoRgSNAQ/Hz8sTS0A9GvWj+6NunMx5yLBdYNpVLMR8afjUUqRnZtNli2LC9kXmLZpGqmZqaRmphK1KYoL2RfIzMlk54mdTFgzgbtb3c3UjVOZ/vN07pp7FwdTDhKxMIIDZw7k15SYNSNSPyKEEEI4XlJSkntwcHBI0X9JSUnXtQL5M888c83BBuiF/0or2LgSCTiE09gXTZtBhllfMf3n6fx69FceaPsA49eMZ23iWs5nnic1K5VnVz/L4rjFeCgPzmacpX61+gD0bNITPx8/Jtw6AR9PHzJtmUzuMZnElER2J+9mdNvRxJ+O5/Zmt/Pt/m+Z1GMSg24ahFJ6dsPPxw+FHuAvj19OdL9olsUvw5ZrK/T89ez77f5vie4XzabDm7i3zb2sP7ie0W1H88vRX/j95O9M7jGZI6lHOHvxLJN7TMbHw4ca3jUYGzoWb3dvACxNLYTeGMqJCyeo4V2DbFs2vxz9BQODL3d+yZmLZxi/ZjzVPKvxwZYPuLPFnXRr3E0HbPOHcuDMgfyAxMNN/+GSInYhhBDCMfz9/W1xcXF7iv7z9/e/cqpDJSVF48Kh7NOkzBmMESEjmLJhClMsU3jth9fo4N+B8WvGF0pDSjibwE033ESAbwDWg1aGBw/nh0M/MKDFAL7a+RW3N7udtQlrie4XTXv/9nzy2yeXDAwmWyfzaOijTN04leHBw1lyzxJAF2kvuWdJfoH5uFvHEX8qHoCRbUbmP++IfQFesr5EdL9o4k/F/yU4Kfq4Re0W+Hj44Kbc2Hx0Mx5uHkzuMZl3fn0HX29f7m93P7N2zCKoThCbj26mtk9tktOTAfhi5xd8sfMLANyVO+sS1/Hxbx8T3iqcqRunAlyyiF1qQ4QQQgjhbFLDIRzKmmglYmEEMeExWAItRP8UTeR3kTSq0YhDqYfy96vuWZ207DS6NerGnuQ9PB72ODM2z0ChGBw0mK92fsWjoY+yYM8CJnafmD9gXrBnAcODhzOyzUig+C5VZaG24Updtewfb0vaxkvWl1hx7wq2JW3jxe9fxMvdq1ANx9J7lrItaRsT1kygb7O+rE1Yy6i2o1i1bxWdAjrxzf5v6BzQmZMXTpKYUlBv5oYbueTSqEYjjqQeYXKPyfRq2iu/w5ZZGzJ149T890yCDyGEEPakhkOUhBSNC6eyH1yDDjoGzxuMn48fR1OP5hd0m4XXQ4KGsOnwpvzZC3PWougguLIUSJckOCmukH7qxqmMCBnBh1s+ZFTbUSyLX4ZC6cLz2PdpXbc1Px7+kRreNUjNLFgUtZZPLdKy0vBw86B/i/4sjVvKwJYDGX/reID8gBGocN9rIYQQV08CDlESlws4JKVKXJPiUqfG3TqO2KOx7Dq5i7SsNNKy0mhUoxEpGSnc2fJO5u+ez+3Nbs9PfcrJzSG6X/RfUp9ij8Uy7tZxtPdvT+yxWAAsgZb8gMb8WFHYD+jtH9t/nbHHYokJjyH2WCwr7l2R/1xxaVu9A3tTq0otJqyZkJ+K9reb/8bX+76mea3mbD2+lSoeVbiYc5ElcUuo4VWD7w58x9qEtXi7ezOyzUi2JW3Ln/UAZNZDCCGEENdMZjjEVbG/A2/eCT9z8Qz/WvsvDpw9AOg0Hi8PL4YEDSFmd8wlU6Mkhef6XE1b4eJmQ3KNXLo27Ir1oBVbro1ccgG4sfqNHE87zr97/psqnlXwcPOQlCshhKjEZIZDlISkVAmHsa/ROJtxlnsX3ZvfHrZRjUYcTj1MVY+qrLxvJbHHYnXxcyVJjSorrmbhRPvakN6BvdlwcAM5Rk7+sepWrUtyejKTbpuEr7evBB9CCFEJScAhSkJSqsR1sb+Tbgm08MXQLxgwewDZudnkGvquuFmX0SewT34alDkIrSypUWWF+X23//7m5Obkp2JFbYrKT18zZ0PMtKwtx7aQZcvC3c2dZn7N2HlyJwCv/vgqdarW4VT6Ke5odgdQOPiUwEMIIYQQlyIBh7gis0Zj/t3zSc1KZczKMWTaMgHw8fBhRMiI/OJvcxbDvlOVfZAhSsel6kTM2hCAqRun5gci03+eztf7vmZ029Gs3LuSJn5N2J60HYDVCatZm7gWHw8f7rv5Pqn3EEIIIcqImJiYGj/++KPv22+/fdTd/brWIXQoWfhPFMt+sT5LoIUP7/yQO2bfwbD5wzh14RQ+7j5U9ayKl7sXVTyq5Bd/WxOtWAIt+QXOomyL7BaJJdCSH3iYgeEvR38hul80beq1YVKPSexI2sGom0fh5+NHy9otsRk2LmRfYPbvsxm/ZjxPd3qa2GOxTP95OhELI/LrSWShQSGEEOWdu7t7x+Dg4JCWLVu27t27d4tTp045fCSflpamwsLCgnJyCtKac3JyaNWqVYjFYmlhbgsICLj5pptuCgkODg5p06ZNq6LH2bBhg2+nTp0urFu3rpq5LSMjQ4WGhgZlZ2c7+rJLTAIOUSxzVsOaaGV5/HL+ufyf5OTqXwJvD2/ub3c/K+9dydJ7lrI4bjHt/dsXCjIsgRa5w12OmIEHFMx6jLt1HGENwpi6cSrR/aK5uf7NTO4xmf1n9nNfm/vwcvMiPTsdgMnrJzPzt5lMWDOBid0n5td32AcfQgghRHnk7e2dGxcXt2ffvn27/fz8ct544426jj7Hu+++W2fw4MFnPTwKko9effXV+i1atLhYdN8NGzbsjYuL27Nr164/ij7n7u5uzJkzp3a7du0yzG0+Pj5Gz549U2fOnFnb0dddUpJSJfIVrdWYPXw2/Wf3J8uWhRtuVPeqTueAzsQei2Vkm5H5A1Qz0LAftIryq7iUK/sAwqz3qOJZBXebO+5u7tSvVp99Z/YB8Pza5/ly55cknE1g6T1LC63PIqlWQgghrsU/lv2j0a6Tu6o68pht6rVJ/9+Q/x2+mtd06dLlws6dO6sU91xqaqrb4MGDmx0/ftwrNzdXRUZGHnv44YfPXmq7/WtjYmJumDdvXoL5+YEDBzxXr15dc+LEicffeuut+iW9vhkzZhwrbnt4eHjK888/HzB27NgzJT2WI0nAIfKZsxox4TG0rtea5757Lr8DlbeHN8tHLi808JQaDdeJioKwMIiN1R8tFhgzRj83ciS88QY8p9cIzH88Ty++zkcfXfu+xEZiiQSrFd74KZaY+y5d7zEiZARf7/ua3Nxctidtx0258VvSb2w+uhlPd0+p8xBCCFGu5eTkYLVaff/5z38W2zlr8eLFNfz9/bPXr1+/H+D06dPul9tuysjIUIcPH/YOCgrKMrc9/vjjjaKioo6cO3fuL+lbffr0aamU4u9//3vyhAkTStTFKyws7OLOnTurXXlP55CAQ+Qzay/ujrkbm2EjNTOVKh5V6Nqoa6F6DPsaDQk0rp8ZTFgsBY+hcGBw4ID+fMQImDJF/5s3D2w2/fH//g+GDQPDgAkTYMgQyM4GT0+oVQvmzNH7zpkDTz0FQ4fq406erF9n7hsUVPi4I0fC9OkwdSoMHx4JB+H5lVF0ydLB5vQlVqz7fuHRZtEc3JvDK727MGHNBDoFdCL2aCwT1kygXrV6JF9IJrpf9F8CViGEEKIkrnYmwpEyMzPdgoODQ06cOOHZvHnzjKFDh6YWt1+HDh0uvvjii43Gjh0bMGTIkHP9+/dPu9x2U1JSkoevr29+8cbcuXNr1qlTJ+e2225LX7lypa/9vhs3bowLDAzMPnr0qEfv3r1vat26dcaAAQMKHa84Hh4eeHp6GmfPnnWrVatW7rV9J66drMMhCqVSbT66mZ6f9STDloGHmwdrRq0pdlZDXD/74CIiAmJiYNs2ePFF8PLSQcSUKTqImDoVfvoJZs+GRo3g8GHw94eTJ/Xz7u5gV2d21ZSCatUgLQ0CAvRx3dwgJAS2b4fHH4fu3fXshxmcvPQSPL8iigFtw/hpjoUuI638eGME9zWayMFDOTx9TzvunD0YG/qGjZtyo3+L/mw+ulnW8RBCiHKksq/DUbVq1fbp6enbzp8/79arV6+Ww4YNOztp0qSTxe174sQJ90WLFtX87LPP6vbs2TM1Ojr6+OW2AyQnJ7vfcsstIUePHv0d4PHHHw9YuHDhDR4eHkZmZqbbhQsX3Pr165eybNmyRPtzjRs3rkH16tVtU6ZMOVGSr6NWrVrtkpKSdnp7eztl8H+5dTikaFzkp1K9/fPb9Py8J1m2LDyUB97u3vn7SOepaxcVpVOS7B9brXrWIiJCBxnDhulZiYkT9ezCxYvwn//AuXOQmqoH/LNn62McPgze3pCUBM2bQ7t2Oti47Tbo3Vvvc9ddBbMYQUH649ChBdsGDIC+ffXjW2+Fjh11sOHnB0ePQpUq+pjbtumA5r//1bMd587pa/PxgfHjIfBYJCvetfDII+DfMZacOTHMfmIcjQ5FsmyxF7mZVbgxozc+yhd1sTbf7PuGWt61+Nz6I4Nek45WQgghyg9fX9/cGTNmHHr//ffrF9fx6eDBg56+vr65jz322Jlx48Ylbd++verltpvq1q1rs9lsKj09XQG89957R0+cOLHz6NGjv3/++ecJXbp0Ob9s2bLE1NRUt7Nnz7qBrhexWq012rZt+5ei8uIkJSW5+/n55Tgr2LgSCTgElkALL972Is+ueZbMnEyqeVVjzeg1rLh3RX6nKnM/uRNdMvZBRliYDiymT9dBxrBheuDfujWEhuqB+6xZcP48ZGXpGYRq1fQsQ6dOcIdeZ49Bg+CGG2D0aL3f6NFw4gQkJurUqB07YOtW/fiHH/T5R4+GvXv1x++/19smT4ZNm3Q9yOTJsHs37Nun9zl3Tn8EqF4dIiN1SpYZyHTvDm3a6PO6u+tjGwa8/jp8/nAkF/+wkJEBPxy28sGpCB72XcLsfuvA+m9s3qfxu9iWfWf2MeuH71mZM4G76/2L2AXS0UoIIUT50K1bt4vBwcEXP/744790fNq6dWuVW265pVVwcHDIa6+91uCll146frnt9nr06HFuzZo11S937iNHjnh06dIlOCgoKKRDhw6t+vXrlxIeHl5seldRq1atqtG3b99zJf06HU1Sqiop+zSqnSd20uOzHmTkZJBpy2Ryj8lMsUwBpLj3atjXYlitOsiYOFHPFLi763qMdu3g99/1LIbJzw9SUnSa0n33wZIlOsXpqafgnXf048GD4auv4NFHYcECfdyXX9bHcXcvnH5lPrZPfbrWfZcu1bMcEybAqFGwbFnh67n7bvjuO2jVCn75BRo31jMvWWFRcDQMDlqo3dHKmT4RuP80EeWeg1F/J7aQ2XC2EXin0/KG5pzIjeP/gpaSs89CZKT83AkhRFlS2VOqXGHjxo1Vo6Oj6y9dujTxyntfvX79+jWPjo4+0rZt20xnHB8un1IlReOVlJlGNaP/DMavGY9hGGTZshjddjQfbPkAS1OLdKAqITPQMGcyYvJqoTt31gP1Vq0gLk4P8Ldvh3r1oEkTPcMwYgSsXg1Vq4KHB7Rtqwf5hqEDEaX04ypVIDpaz0i88gqMGwfx8fo8ZuepJbppVP5js/PU9ew7bx4sXqzPHR+vryc7G5Yv19umTtVB0ocf6pmRZcv0tT7SPpIvdkLj1rDbJxa3hTHYEizQ1Aqd3qD6gVGkNVqCR2oL9lXdjFuuFy+/nMvIzjB9iZUpf0Rwj4qBbjp4i43Vsy1CCCFERdS9e/f0LVu2pObk5GC/FocjZGRkqMGDB6c4M9i4EpnhqMRW7l3J0HlDcVNu5OTmEN0vmnG3jpMC8atkzmaYgcbgwZCRoWcJzF+vBg10LcYjj8DMmYVnCQYO1GlVoGsvXnkF2rcv3KXKHHC7evBdXAct+xa606frWZEpU3RAMn++/pqLmxnJCrBiGx5BztwYjAQLdJkOd0zALaE/uQ02gWcmLaq3ZX9KPJ6LljLtUQvt28PQZ62MHBfLyEaREngIIUQpkBmOwpKSktx79eoVVHT7+vXr4/39/W3FvaYykBkOARROozIMg8+3f47NsGEzbIxuO5pxt44DpO1tSdgPxC0W+PJLuPNOPUuRltecrmFDXRMxcKAORqKjdSDx6aeFZy2mTtX7WyywcqUOKMzjmszHRbc7m/3g3nxsf/6cHFixoiAgMWdOipsZMW6M5aEaMcw5beFikJWsHlNp8Hs0x07kQOxDcPco9mfEgnsVwGDSJFDNrOQOj8A9KYaI8QVBncx6CCGEKC3+/v62uLi4PaV9HeWJBByViP3CftuTtrPoj0UAjG47mlX7V2FNtOYHGJJK9Vf2QYaZPvXEE/Djj3rwezGvT4Snpy4MX7BAD7hzcnTxt17LomBQHhurU5jaty8cZLgyoLhexQUkoL+emJjCgci2bZG89JIOUOYdjmXntzH8usTC6FGw7LyVC3hhS+gKjTaSPfIOsnfdCy1W0XZHDB+usPDoNCtv/BTLtm2RTJ0qwYcQQghRXkjAUYmYMxfD5w8nJTMFgOjboxnfdbykUZWAfY1GSIj+9+9/6+c8PHSrWDc3/djPr2D2whx42wcWUHqzFq5QXCASG1swG4I1ksXf5c2AZFqxtYjA9vkSRne3ELN4DZl3D4J2X8KOv7FzuQX3FlY+OB1Bpz9jmJA3W2RfnG8GH0IIIYQoeyTgqGTCAsIw0IUF97S+h/FddfGApFEVr2jq1Oef6zUssrJ0WlSzZpCQoBfqGzVKF1pDwSA4Jqb8zl442qVmQMbMisV9egxvPmEhPh483DzJtFXBM7su2W3ngDKwNV+DWhjD5gQLNWroupElS2DnTl0vYn5fZbZDCCGEKHtkHY5K5v4l93Mu8xwPtHuAdYnr8tfYAFlnw565joY5q7F2rS6ADg+HzEwdbAwZogvB+/TRsxojRxYEFfaBhgx+/yoysiBIaJ4UydK3LIwbBzS14nFfBGP9luD538M0cg+FtnPwSGsKib3o3BnS0+FCXSsbjSguXIB33y1YTDEiQr9nVqt+D4UQQghR+iTgqOCiNkXlBxXv/vouS+KW0K1RN0LqhhATHlNoYT9RwAw0AKZNg/794c039cxG9eq6Bezy5Xo9jLVr9V32iIiCxf4k0Ci5QsFHj1iW3BtDUyy88uV6Lvok0sSzPTn1tlDloTvpfsdJHn7NCiMiaOQWhs2mZzr69tVF+0OH6u5YZuABEnwIIYQQpU0CjgrOLBRfvX81U36YQi2fWsSdisvvVmWmUYnCq4NbLDB7tk6feuihgoX6fHx0oNGmTUGNhtVaeFZDXLvIbpFYAi2EjbAydb+uKXrM4zeG1nuO9IBVTM9pyQcpQxlbJ4Yn7rKwfDm4t7CSe2sUFy/CnDm6xfBzzxWu8QiTBcyFEEKIUiM1HBWcGVTcNfcu0rPTqeldk0URi6QbVTHsi8IbNNAdqDLzlsipUgW6di0IKMzZi/LcYaosiz0Wm9/AwBIJEMXw+ftZErcEct2o3/kHIntamL5Et81tvy+G3V463Qr0zNPMmXDihNR4CCGEEKVNAo5KoFXdVmTbsgF4qvNTEmDYKVoUHhOjU3MyMyE3F7y9wd1d12i8+KJ+jRmUSJDhPEVriayJVn489CPjbx3PW7+8xb/X/5tVsXvYfOp7orvE0L6/hWHf6/fKzU2nve3bp1+7YAFs2qRXc5d2ukIIISqDmJiYGj/++KPv22+/fdTd3b20L0dSqiqDh5c/THZuNo+HPc4HWz6Qmg075qyG1arrM956S6+nkZurZzUeeEAvxmfWaICkTrmafcvm6H7RrLx3JW7KjV8vxNCyZiva+1mIiNC1HK/OttLo3ihOnoR+/XQA8sEH8NprOtXq+ecl1UoIIcTVcXd37xgcHBzSsmXL1r17925x6tQpp43g09LSVFhYWFBOTk7+tpycHFq1ahVisVha2O8bEBBw80033RQSHBwc0qZNm1b2z23YsMG3U6dOF9atW1fN3JaRkaFCQ0ODsrOznXX5lyQBRwVlFot/tv0zVu5bybDgYdzd6m6GBw+XQnE75qxGeDi0aKHXifD2ht699QJ+0nmq9NmnVwH4ePjg6+VLTe+a7M36kUdXjtWzFk2tTPkjgmOxYURH6+5h332n38+MDH2syEgdiNjPUklRuRBCiMvx9vbOjYuL27Nv377dfn5+OW+88UZdZ53r3XffrTN48OCzHh4FSUivvvpq/RYtWlwsbv8NGzbsjYuL27Nr164/7Le7u7sbc+bMqd2uXbsMc5uPj4/Rs2fP1JkzZ9Z21vVfiqRUVVBmsXhVz6pU9azK/e3uz79LPLLNyEq93oZ9GhVAo0Y6herMGb2exqpVf11UTtKnSo99epU527HkniXc1uQ2On3SiW18yLPxP3N0y1HuUTGMfMtSqGbDxwe6ddPBYlaWDkIaNdLv87ZtkmYlhBDlwj/+0Yhdu6o69Jht2qTzv/8dvpqXdOnS5cLOnTurFPdcfHy8V//+/VvefPPN6bt27ap60003XVywYMFBX1/f3Ms9Z3+MmJiYG+bNm5dgfn7gwAHP1atX15w4ceLxt956q35Jr3PGjBnHitseHh6e8vzzzweMHTv2TEmP5Qgyw1FBWQItvNTzJQ6dO0SXgC48vOLhgiLcSr7ehn0a1W+/wS23wIULEBSkB6cm6TxV9tjPdni4eRD7cCy1fGqx48QO2vu356OJOtiwJloZMysqP9Vq3Tq9KnxWFjRvDocPwxtvSEcrIYQQJZeTk4PVavUdOnRoyqX2OXjwoM8TTzxxMiEhYbevr2+u/WzI5Z4DnfJ0+PBh76CgoCxz2+OPP94oKirqiJtb8UP2Pn36tGzdunWr6OjoOiX5GsLCwi7u3Lmz2pX3dCyZ4ajA1iaspYpHFb4/+D2Te0yutDMaRZmBxLBhOtDIyYF//UuvtyGzGmVb0UD5hz9/wGbYcFfufJfwHeO+HcegoEFELIxgeE5MobSpqVN1K+OcHB1ELlyoj/HCC/r93rdPOloJIUSZdZUzEY6UmZnpFhwcHHLixAnP5s2bZwwdOjT1Uvv6+/tn9evX7wLA6NGjT8+YMaMecOJKzwEkJSV5+Pr65hdvzJ07t2adOnVybrvttvSVK1f6Fj3Xxo0b4wIDA7OPHj3q0bt375tat26dMWDAgLTLfS0eHh54enoaZ8+edatVq1bu5fZ1JJnhqKD2JO9hefxylFJM7jFZisUpvM6Gt7duoZqTo9fUmDZNb5dZjfLDTK9aes9SvvnbN3i4efDWr29x19y7iAmPyZ/tAP1+xsTAuHF6BmP9er14o4+P/rd1a94K5hfyji2zHUIIIfKYNRyHDh363TAMpk2bVu9S+yqlLvn55Z4DqFatWm5WVlb+2Hzjxo3Vv/vuO7+AgICbH3zwwWa//PKL75AhQwLN5wMDA7MBAgICcu68886Un3/+uUQzF9nZ2apq1apGSfZ1FAk4Kqjxq8cD8NWwr5himSKrilOQSvX++3D77XoxPx8fnV5jtfu2SFF4+WCfXtWveT8m3DoBgPTsdNzdChqIWBOt0C3qL3U5s2bp7lXp6dCqlQ4+Bw/WKXZDhxbMcoEUlgshhABfX9/cGTNmHHr//ffrX6rT0/Hjx73Wrl1bDWD27Nm1u3btmlaS5wDq1q1rs9lsKj09XQG89957R0+cOLHz6NGjv3/++ecJXbp0Ob9s2bJEgNTUVLezZ8+6mY+tVmuNtm3bFltYbi8pKcndz88vx9vbWwIOcW3MzlRHU4+yJmENQ4KG4OfjR9SmKFlVHD14jIqCxx/XrW+rVYNvvtE5/mZNhyg/zFXJQQcVM7fNZETICADu+PIO9iTvyZ8FCWugpyrMmY6iaVYPPgjvvaePu2OHDkKy8jJoZbZDCCGEqVu3bheDg4Mvfvzxx8V2emratGnGu+++W69Zs2atU1JSPCZMmJBckudMPXr0OLdmzZrqV7qOI0eOeHTp0iU4KCgopEOHDq369euXEh4efslUL9OqVatq9O3b99yV9nM0qeGoQMzOVH0C+wAQERKR35kKKueq4vYdqY4f14XDHh76bvYzzxTcwbZveSvKF/t1OiyBFiZ9P4nXfnyNsE/CqOJRhQUjFuT/3NvPXNkHH6ADixo19L/Dh2HgQOjUCfbskdoOIYSozNLT07fZf/7999/vv9S+Hh4emLMQV/Oc6amnnkqOjo6uP3To0PP22++6667zd911V/62kJCQrPj4+D0l+woKzJ07t3Z0dPSRq33d9ZIZjgrEEmjhi6FfELM7huA6wTy9+ulC6xdURmYa1TffwKBBOuiw2XT+/gcfFMxqSBpV+VV0nY5Xe7/KwBYDSc9Ox0250a1xt/x9rYlWojbp3KjIyMJBhNnR6tAheP11vfjjL7/oNTwMo/B+MtshhBDCGbp3757eq1evVPuF/xwlIyNDDR48OKVt27aZDj/4FUjAUcGcvHASA4M9yXsYGzq2UgcboAeU8+bpjlRbt+qZjehonb8fEyOpVBWBfWoV6KBi87HNdG/UneT0ZHp81gPDMP6SXmWv6GxHly7g6wu1aunUqttv14tAyoKBQgghkpKS3IODg0OK/qtZs6Zt3759u4t7TVBQUNalnivqmWeeOW2/8J+j+Pj4GE888cRphx+4BCSlqoJ58+c3cVfuTOw+kQ+2fIClaeVLoyrKXPANYNQo3akI/rp6uCj/iqZX9f2iL+sOrqPLp11IOJtwyRk/+9ktcxZj2TLo2RMefRQ++QTmz9fF5EWLz4UQQlQu/v7+tri4uKtOZ6rMZIajApm1Yxa7Tu7iH7f8g1d6v1JpO1PZt79dv16vs+DpCb1769WlpSNVxVU0vWrN/WsI8A1g89HNDA4aXKLg2362w80N7r1XNxhwc9O1HLffXnimQwghhBCXJwFHBfLZts9ww42XLS8DVNrOVGbdxsKFMHw4KAVVqsCkSZJGVdEVTa/acHADGTkZ+Lj78Nm2z1gWtyz/Oft6jkLHKKa2Y8UK2LsXataEtWuhTp3CwYakVwkhhBCXJgFHOWe2ws3JzSHudBx33nQncafi8gdSlkDLX1ZnrugsFp3+ct99cO4cVK1a0GVIFvarPMz0qgUjFvDmHW9iYBC+IJx1CesuW89hz36249Ah3eGsTh2Ii4OuXSE7WwrJhRBCiCuRGo5yzmyF+0znZ0hKS8r/3GyFW1nFx+vBIMCzzxa+G20GHqJis0+vsgRa+CP5D/4b+18e/fpRUjJSStTBzUy3M4OKBQugRw/o21en69WuXZBqJW1zhRBCiOLJDEc5Z6ZNTflhCtU8qzFj84xK2QrXvm4jIUEXhnt46LoN+/a3ovIoml41Y8AMbrrhJvaf2c/wVsOv6nfEfqbD3V3/PHXtCmlpcP48nDmj95PZDiGEEOKvJOCoAMICwrDl2riQfaHStsI16zbWrdMtcDMypG5DFLb+4HqOnDuCj7sPn/72Kav3rwYuXcthz76uA/TP0t69uqDcMGDECHjiCSkmF0IIIYrjkoBDKdVfKRWvlNqvlHq+mOcbK6WsSqltSqmdSqmBrriuiiL6p2hsho3RbUfzwZYPKl1XKiiozRg6FHbuBB8f3dZU6jYEFNRzvNL7Fbw8vLAZNobNH8b0n6eXqJaj0LHsWuLOmQPvvaeDjvfeg1tvlWJyIYQQZVtMTEyNJ598MsBms7nsnE4POJRS7sB7wAAgBLhXKRVSZLdJQIxhGO2BkcD7zr6uisKaaGXaxmnU8K7B/4b8r9K2wgVo06agbmPChL/WbUQSVTACNKc7xozR/6xWGDhQf7R/bD5fdF9zFCkjynLBrOcYd+s4lt6zFB8PHy7mXOTF71+86hTEoosEtmoF1avrFL4VK2D8eL1d0quEEKJicHd37xgcHBzSsmXL1r17925x6tQp9+L2O3XqlPu0adPqmp/Hx8d7tWzZsrWzry8tLU2FhYUFmauTL1y4sEbTpk3bNG7cuM0LL7zgX3T/DRs2+Hbq1OnCunXrqpnbMjIyVGhoaFC2OZByMFfMcHQC9huGkWAYRhYwDxhSZB8DqJH3uCZwzAXXVSH8fORn3JQbESEReLh5VLpWuPaxw4MPQmYmDBoEb72Vt91+BzPv6uBBvdP06XoZ8tmz9dRI3746H8v+8Vdf6ZZXRfc9cEBvi4jQj81ARQKRMsm+nsMSaOGZzs8AUNWjKl0adrm6YxXTNnf5cvjjD/Dz0z8WAwdKepUQQrjSpEnUX7ECX/ttK1bgO2kS9a/32N7e3rlxcXF79u3bt9vPzy/njTfeqFvcfqdPn3b/9NNP613v+a7Wu+++W2fw4MFnPTw8yMnJ4dlnn238zTff7N27d+/uRYsW1d66dauP/f7u7u7GnDlzardr1y7D3Obj42P07NkzdebMmbWdcY2uCDgCgMN2nx/J22bv38AopdQR4BvgyeIOpJR6RCm1RSm1JTk52RnXWu7c4n8LF3MuMrzV8PxtlakVrhlDLO8eRfo3Vnr2hI7rovjsfivvDLVy9McDeofp02HzZr1s9IcfQkCAvhVdsyZcvKirf196SffRTU3Vo8pz5yA9XU+bjB+v87SysnSByG+/6WmURx7Rxx82DAYM0Le57W9tS+BR5lgTrczcNpP2/u05k3GGvy/7e6HnrlTPYc9+tqNFC9i+XS8yuWpVwarkID8GQgjhbJ07k37//TQzg44VK/C9/36ade5MuiPP06VLlwtHjx71Ku658ePHNzx8+LB3cHBwyJgxYxoC2Gw2Ro4c2aRFixatu3Xr1jItLU1d6tipqaluvXr1ahEUFBTSsmXL1p988kmt4rYVfV1MTMwNERERKQDr16+v1qRJk8yQkJAsHx8fY/jw4WcWLlzoZ7//jBkzjq1atSqhfv36hXKqwsPDU+bNm1duA46SuBf43DCMhsBA4Eul1F+uzTCMjw3DCDUMI7Ru3WKDy0rBXHsDYPEfi6nhXQM35XZVA6WKwhIbxernrbzzUxgxROC3zcp9D3gw4rOBLLAN5Udug44ddcAweTK8+qpOuN+7V/czPXwYGjWCoCC4cAE6ddKJ+DYbdOmiP09PhxtugJMnde6MzQZbtujjvP66DjTOn9fbJ06EIUP0ioPbthXOqZFRZ6kzazliwmN4s9+beLl5MX/3fD7c8mGJ1+awV7SYPCFBx6VKwcyZ+p+kVgkhhPMNGsT5WbNIuP9+mj3zDA3uv59ms2aRMGgQ5x11jpycHKxWq+/QoUNTinv+zTffPNKoUaPMuLi4PR999NERgEOHDvk89dRTJ/fv37+7Zs2atlmzZv0lYDAtXry4hr+/f3Z8fPyeffv27R4+fHhqcdvsX5ORkaEOHz7sHRQUlAVw+PBhr4CAgCzz+YYNG2ZdKkAqKiws7OLOnTurXXnPq+eKgOMo0Mju84Z52+z9E4gBMAzjZ8AHqOOCayuXzLU21iasZVn8MsIahDFqyairGihVGGFhhLwcQa4BEcQwL2MoLWc+D1lZeF5IZeTK0bB6NdSqpWcqOnXSeS/h4TpgGD0azp6FY8d0QBIXB3v26Md79ugFPUaP1n1PR4/WQUX16npxj5o1oXNnfdwGDfT1ZGXp4GP2bB3kDBoEGzcWpF9J8FGqiq7NsTBiIQrFhDUT8gORa+3yZgYWy5bBl1/qoOPhh3X8KalVQgjhfIMGcf6BB0h+5x1ufOABkh0VbGRmZroFBweH1K1bt11ycrLn0KFDU6/8Ki0gICCza9euFwHat2+ffvDgQe9L7duhQ4eLP/74Y42xY8cGfPvtt9VvuOEGW3Hb7F+TlJTk4evrm3PtX10BDw8PPD09jbNnzzo8PnBFwBELtFRKBSqlvNBF4cuL7HMI6AOglGqFDjgkZ+oSzDqN8JhwTqWfIvZobOVae8OuLiO7u4UHPOeyhGHMrPIUuVnZOgCoXh1CQ/X+99yjF08YPVrnwNx7r161LTpajw5tNh18+PnpUaL94+xsnaBfdN9Bg3QK1ubN+ripqVCtGvzznzqn5sIFfc7PPoP/+z8dfPTtCz/8IMFHKSq6NsegoEHcHXI3F7Iv0MG/w3X9DtmnV/3tb/DYY3p7RoZOtzLJ2y2EEM6xYgW+X3xB3aef5vgXX1C3aE3HtTJrOA4dOvS7YRhMmzatxHUaXl5ehvnY3d3dyMnJuWRKVdu2bTN/++23PTfffPPFyZMnB0yYMOHG4rbZv6ZatWq5WVlZ+eP5Ro0aFZrROHLkSKEZjyvJzs5WVatWNa6859VxesBhGEYO8ASwGvgD3Y1qt1JqilJqcN5u44GHlVI7gLnAg4ZhOPyLrUgsgRaC6wQDMDasEqy9UVzx95QpbGk5kowzafhynuYXd+HtnkO09wtk24B9+3QwEBOjU53atNGBwxdf6M/HjYORI/XocOlSWLsWliwp/HjUKB2wFN133jyYOlUfr0qVgkAlJASqVtXbqleH22/X26tW1a+ZMkUHH0OGwK+/SvBRyqyJVtYfXE/zWs35LuE7Fu5eeM3HKlpMPn8+3Hmnjlk7doRTpyS9SgghnMWs2Zg1i4S33+aYmV7lqKADwNfXN3fGjBmH3n///frFdXOqWbOm7cKFC9c8tj548KCnr69v7mOPPXZm3LhxSdu3b69a3Db719StW9dms9lUenq6AujZs+eFgwcP+sTFxXllZGSoxYsX17777rtTSnL+pKQkdz8/vxxvb2+Hj8E9HH3A4hiG8Q26GNx+20t2j/cA3VxxLRWFNdHKlmNbCPQL5NNtn3JH8zsqdtBhBhkxMUR9H8qwBj1o8X//hy8hxLAEw9OLbNzwrOLBkL/dgO0zhae7oQf+0dE6ODBvP7dvX7Aox0cfFZyjaB/dotvs97W/nR0VpYOTbdv0rMeKFXqf6dPh66910LNqFfTsqT96ecGnn+pru3hRz3xA4QUerFZ9jsjKUfxfGuzrOVbtX8WbP7/JqCWjuKHqDVgCLVgTrcQei73qBgz2b6PFAo8/Du+/D8HBOi6V9CohhHC8X3+lqn3NhlnT8euvVHVkHUe3bt0uBgcHX/z4449rP/7442fsn/P397d17NgxLa997rlx48advJpjb926tcrEiRMburm54eHhYbz//vt/Fret6Ot69Ohxbs2aNdWHDh163tPTkzfffPNQ//79b7LZbNx3332nQkNDM4o7X1GrVq2q0bdv33NXc80lpcrrREJoaKixZcuW0r6MUmFNtBK+IJwzF88wrc80OgV0uu788zIpKkoHGubo7Ouv4e67OZNVjVxDkeLbiBbnt5PtWZU5bqNo+/pI2rcH7roLXnmlILCIjHTNAN7+es1R58SJkJOju1dNmKBnTFatgtatYcOGgte6u+sA5L77dAG7fYAkwYdTRG2KIqxBWH5wcdecu0jPSWdMxzHc0/qea/6dKvpjC/rx+vW6P0Fion67Qd5aIUT5oJTaahhGqKvPu2PHjoPt2rU75erzljcbN26sGh0dXX/p0qWJ13Ocfv36NY+Ojj7Stm3bzGt5/Y4dO+q0a9euaXHPlZUuVeIqxB6LZWzoWAD6NOtT8dbeMNOnzFmNdev0wH3ECMjMpLZxhswuPal5/ggbffqQnu2hg41xecuKr1ypB/kWS8FIzv6xs9jn1JgzIOPG6a/DTL9q00Z/LT/8oGc+atXSAYbNplvzfv21DkwmTiwcuEgOjsMVXZtjYcRC3JQbS+KWXFcAX7RzldUKu3bpkqLDh/+6hoe8tUIIIa5H9+7d03v16pVqLvx3LTIyMtTgwYNTrjXYuBKZ4Sin/rnsnyyJW0Lyc8m4uxW74GX5ZZ+TcuwYPPCAHpC7uelaiGHDML78inFE8zbj+N9oK39fVYZXWSvJzEd4uE7LMv9YeHnBLbforllLlxYepcotcae5e/7dLI5bzKCbBrH83qK9La5e0fSqHj3gxx/1j0NiYtn9kRVCCHsyw1FYUlKSe69evYKKbl+/fn28v7+/rbjXOPL1ZdXlZjhcUsMhHMswDL5L+I7egb0rXrABegQ2bx4MHqyXDrfl/e75+MDy5dh+jWWKbzQvnJ/KzaPaE7nKwi0TY2gfG1s2R2/2wYF97Yc5Go2O1u13q1XTbXUNQ3/Nmzfr3JuTeSmg9qNX4XDWRCs/HPqBAN8Avt73Nd/s+4aBLQde1zHt327Qb2HTpnp7375/nQmRWFIIIco+f39/W1xc3J7Sen15JClV5dD+M/s5nHqYvs36lvalOI59F6ozZ+Cdd3SKUXa2rm3o00fPBgAvp0cy5fw4Vj0Ywz9ujiUmBvpNtWANKwcjteLSrtq3h8WL9QzH11/rkWhOjq7zsNl0d6zAQL10ddHRq3S0cgj7AvLBQYPJNXIZsWBE/gKbV7sCualoetUPP+g+ATVr6kZoL7yQd35JrxJCCFGByQxHObQ2YS1AxQo47Frd8p//6GR3AG9v8PRkXvMXCRoIt0REEEcMDRtaaPA3C1G/WYi06HF4WZ3guCTzVnZUVOFZj19+0bMeOTm6ynj0aDh4ULc4+s9/Cl4vHa0cxn5BQIBPt31Kti2bdYnrAPKDkethBhULFkDjxnDzzbq0JyFBlylJepUQQoiKSmY4yomoTVH5d1vXJq6lSc0mHEo5dE13XcsM+1kNiwUeeUSvlnbsmE4ruvNO3dFp6VKGz4/g5Zdh4YgYmp6KJSJCr99n3hF2RU2401yq2DwyEvz99XoeHTrogGP1ar22h7l8NcitcQcoWkD+6eBPyc7N5tv93zqsA5x9elXz5gXdk+fPh0cflWBDCCFExSUBRzkR1iCMiIURrE1Yy/eJ3xNSN4R7Ft1DWINyPNA0ZzW+/x5efRVef10Xhmdn6/axK1fqUZjFgteSGN4cGcuoTy28VzWSWbMq6B3hoqvHRUToVKutW+Hbb/Uq5jYbnD8PTzwhaVZOMqrtKEJvDGXr8a3c2+Zeh7SbLppe5eam306At94qiL3lLRRCCFHRSMBRTpitb0csGEFKRgobD20s/+tuWCwwd66eyZg8WY++fH3141WrCkZgefu6T4wkKwvS02Hs2AoYbBRVtOLYw0N36erSRc927NkDFy7Aubw1eqQQwGGsiVYSUhJwV+58vPXj/NlFhx0/762aOlU3JEtPh+HD/7rwvBBCCFERSMBRjlgCLXRq0AmAf7T/R/kMNuzTqHJy4LPPICNvAUxPT303f8oUPdCOiCgUdEyapD8+9RR88EHheKRCutRsx+uvQ40aBbMdw4frwnOZ7XAIs4B84YiFdG3UlUxbJnfH3H3dBeT2zFhy/HhYuFDHj5mZOtaukDN3QgghKjUJOMoRa6KVDX9uoIZXDWb/Ptvhd11dwkyjWrNGd1+aM0dv79AhvwsVoEdcZiU48N13etfOnXUDq2LikYrNHKFCQeCxerUONgC2b9e3yc0WwjLbcc3sC8if6fIMAE1rNiX2WGx+MHK9qYz2seSgQfDPf+ruVQ0aQK9e13f9QgghRFkjAUc5YQ50/Hz8uKPFHcSExxCxMKL8BR1mGtWgQbBokd725pu6RmHp0sJRhF0l+Jdf6jryF18seMouHqn4zBGqfZqVxaLrOHx94cYb9YxR//664Nx+tTlxVewLyIe3Gs59be5j24ltJJxNcFgBuT2rVcePN90E+/fDk08Wfk4mqYQQQrhCTExMjSeffDLAZnP82oMScJQTscdi+fDODzlx4QRdGnbJr+mIPVYORtz2aVSGoQONrCz9+ejReoAMf4ki7F924oTuEFulSsEArFx3prpWxaVZLV2qO3tFRuoZjrfeKljZ3CQj12v27sB38XTz5KOtHzE2dKzDgw0zNvzgAz3J99578MknMkklhBBlhbu7e8fg4OCQli1btu7du3eLU6dOFbvq8qlTp9ynTZtW1/w8Pj7eq2XLlq2dfX1paWkqLCwsKCcnJ39bTk4OrVq1CrFYLC3s9w0ICLj5pptuCgkODg5p06ZNK/vnNmzY4NupU6cL69atq2Zuy8jIUKGhoUHZ2dnXdY0ScJQTkd0i81cVv7XhrYCu6YjsVg5G3GYaldUKL78MH36ot48eXWxxuBlFmC+bPVtnYPXpU7gVbqVXtKi8f3/dQtfTU39f77tPb5eR63XZkbQDDzed7vffzf916Kyi/VvYu7deo0MpePppmaQSQoirMen7SfVXxK/wtd+2In6F76TvJ9W/3mN7e3vnxsXF7dm3b99uPz+/nDfeeKNucfudPn3a/dNPP613vee7Wu+++26dwYMHn/WwS01/9dVX67do0eJicftv2LBhb1xc3J5du3b9Yb/d3d3dmDNnTu127dplmNt8fHyMnj17ps6cObP29VyjBBzlyC9HfsHTzZP2N7Yv7Uu5OubMxeDBOuAAvbCd2dv2EsUY5sseeUR/vny5DMAKKW62Y/lyPdvRvLlOXQsMLDxylZmOq2KmMs4aOgtvd2+6Nurq0FTGoq1yhw7VceLFi9CsmfysCyFESXUO6Jx+/9L7m5lBx4r4Fb73L72/WeeAzumOPE+XLl0uHD161Ku458aPH9/w8OHD3sHBwSFjxoxpCGCz2Rg5cmSTFi1atO7WrVvLtLQ0daljp6amuvXq1atFUFBQSMuWLVt/8skntYrbVvR1MTExN0RERKSYnx84cMBz9erVNR9++OFTV/O1zZgx49iqVasS6tevXyinKjw8PGXevHkScFQWvxz5hfY3tsfHw6e0L6Vk7HOivL11UTPojkrjx+vHVyjG6NVLtw0FePxxGYBdkv2t8jp14I8/ICBAr1BerRrceqvMdFwDs4A8ISWBO5rfwZoDa3hv4Hv5BeSOXnjTatW9ABo2hM2bdXmT/XMSKwohRPEGBQ06P2vorIT7l97f7Jlvn2lw/9L7m80aOithUNCg8446R05ODlar1Xfo0KEpxT3/5ptvHmnUqFFmXFzcno8++ugIwKFDh3yeeuqpk/v3799ds2ZN26xZs/4SMJgWL15cw9/fPzs+Pn7Pvn37dg8fPjy1uG32r8nIyFCHDx/2DgoKyjK3Pf74442ioqKOuLkVP8zv06dPy9atW7eKjo6uU5KvOyws7OLOnTurXXnPS5OAo5zIyc0h9lgsXQK6lPallJyZExUTA3fdpbdVqQIJCZdMoyrqvfcgJUUvrF0pWuFeq6K3yjdu1H1W27eHP/+Exo1hxAiZIrpKZgF5WIMwNh7eiC3Xxs+Hf85fiNORC2/a13N8/LFeGPC55/SK5BIrCiHElQ0KGnT+gXYPJL/z6zs3PtDugWRHBRuZmZluwcHBIXXr1m2XnJzsOXTo0NQrv0oLCAjI7Nq160WA9u3bpx88eND7Uvt26NDh4o8//lhj7NixAd9++231G264wVbcNvvXJCUlefj6+uYXb8ydO7dmnTp1cm677bZiZ3Y2btwYt2fPnj/WrFmz75NPPqm3atWq6lf6Gjw8PPD09DTOnj17zXGDBBzlxO8nfic9O50uDctRwGGxwFdf6cKLs2f1onVff61b8pSgp63Vqgdcnp66S1Wla4V7rexHrr/9BrfdBsnJer2T4ODC+8kt8xKxBFpYOGIhnu6e/HfzfxmxYITDu1XZT1INGADvvqt7LIwZI/UcQghREiviV/h+seOLuk93fvr4Fzu+qFu0puNamTUchw4d+t0wDKZNm1biOg0vLy/DfOzu7m7k5ORcMqWqbdu2mb/99tuem2+++eLkyZMDJkyYcGNx2+xfU61atdysrKz88fzGjRurf/fdd34BAQE3P/jgg81++eUX3yFDhgSazwcGBmYDBAQE5Nx5550pP//8c4lmLrKzs1XVqlWNK+9ZPAk4yrioTVFYE638cuQXALo07OKUVA6n+fZbyM3Vj599tqCdawl62v7yi87EGjZMd36tdK1wr5X9yNVq1elVHTroVclbt4YDB+SW+TWwBFp48JYHyTFyaFu/rcMX3iw6SfXYY9C1Kxw/DnfcIcGGEEJcjlmzMWvorIS3+799zEyvclTQAeDr65s7Y8aMQ++//3794ro21axZ03bhwoVrHlsfPHjQ09fXN/exxx47M27cuKTt27dXLW6b/Wvq1q1rs9lsKj09XQG89957R0+cOLHz6NGjv3/++ecJXbp0Ob9s2bJE0DUi5ixFamqqm9VqrdG2bdtiC8vtJSUlufv5+eV4e3tLwFFRmakbS+OXUq9aPRLPJjo8lcOh7Os2vv4a3n5bF2H06VM4J+oyaVTmIW65Bc6dg7/9reBmfKVshXu1zJGr/UzH1q269dHZs3qWY/BgWZX8KlkTrSz6YxHNajVjw8ENrDmwxrnns8LevToLcc4c+Oabws/J2yWEEAV+PfprVfuaDbOm49ejv1a90muvRrdu3S4GBwdf/Pjjj/9SRO3v72/r2LFjWsuWLVubReNXY+vWrVVuueWWVsHBwSGvvfZag5deeul4cduKvq5Hjx7n1qxZc8XUqCNHjnh06dIlOCgoKKRDhw6t+vXrlxIeHn7F9LBVq1bV6Nu377mr/XrsKcO45mClVIWGhhpbtmwp7ctwCWuildu/vJ3mtZtz5uIZh6dyOJQ5yP3gA718clqabtW6dKl+vgS5IeYhbrlFj5PnzdNBh6SUXKWoqL+uxzF4sC4KAPjf/+Dvfy8cmMg3uFhmt6qY8BiybFn0n90fXy9flo1c5pTfRfu3ZMcOPTno5aUnDEHeLiGEaymlthqGEerq8+7YseNgu3btrqrTUmW0cePGqtHR0fWXLl2a6Izj9+vXr3l0dPSRtm3bZl5uvx07dtRp165d0+KekxmOcqD9je2xGTb2nt7r8IXHHM5igfnzdW/PtDRdt7F06VWlUlks8MUXsHat7uoqwcY1KpqjY7XCzz/D2LH683/+Ex59VEavJWB2q7IEWtietJ0mNZtQr1o9Nh/dDODwNEf7rLhnntFxYlaWfkvl7RJCCGGve/fu6b169Uq1X/jPUTIyMtTgwYNTrhRsXInHlXcRpe3z7Z8DcG+be/lgywdYmlrKdtBx8CCYuY1m3YbJDDyuwOyg+9tvMHmyDK6uW9FZjM6d4cEH4aOPdAuwooFJbKzkrtmxX2CzU0AnXv3xVdKy0ugU0KnQ7IfDzlfkW//ll7pV7pYt8OKL8vsghBClKSkpyb1Xr15BRbevX78+3t/f31bcaxz5+uI888wzp6/ldVfi4+NjPPHEE9d9bAk4yjhropVJ308C4I3b32Dv6b35g5syE3TYp+4cOwZPPQXu7tCzp06tKmGQYe/DD/WKyy+8cM2HEPaKrkreuLGuxE9Ph2XLYNIkePXVwoGJKJYl0MKC8AUMnDOQMSvHcDbjrNN/H7duLXj89tu6JEp+H4QQonT4+/vb4uLi9pTW68sjSakq42KPxdKzSU9uqHIDDXwbYAm0EBMeQ+yxMtSqyVxv4/vv9ccLF3Sl66RJ19TL9rvv9KH69dNjYGmH6wDFrUq+bJm+Ze7jA6+9BuHhkq9TQv1b9qdbo27sO7OPe9vc69Rgw3y7Ro6E0FDd9C08XG+X4nEhhBDlgQQcZVxkt0iS05NpW78tSunWzZZAS6EUj1Jn1mYMHQqbNukB7PLlV1W3YW/BAr3+wKOPFj68tMN1EPvZjltugfXr9XTSokW6iFw6V12RNdHKnlP65tTM32ZiTXReNGy+Xffeqzsa5+RA27a6mYJ0NhZCCFEeSMBRxtlybew6uYt29duV9qVcXqdOBY/Hj/9r3cZV1ANUqaJjlttvv+ZDiMspWkyenq6L+wGmT4fVq2WdjsswazYWjljI0OCheLp7ErEwwmlBh/l2WSw6JvT01DHi/PkyGSWEEKJ8kICjjNt/Zj8Xcy7Stn7b0r6Uyxs7Fs6fhwce0IXI15j/ZBi6qVW/flCtRGtfiutiBhYrVsDEiWCzwcCBerVFGc0Wy75j1WOhj5GamcojHR9xSZqjxaKXUwHdJve225x+SiGEEOK6ScBRxu08sROAdv5lbIbDfoG/uXN1G51bboGQkGsqujAPt2MHHDqkGydJRo8L2KdXvf469O2riwT8/CTYuITIbpH5NRu/Hf+NAN8ANhzckJ/m6OgWufasVvjkE7j5ZkhOhgkTCj8nvy9CCCHKIgk4yrgdJ3bgrtwJqRtS2pdSmFkobrXC1Kng4aEjBbNb1VUWXZiHe/ttXU7g5ycZPS5RtJh8+3YICIA//yycwyaj2WJ1CuhESkYKmw5vYkfSjvx0q7AGjv/BtW8g9vbbuhHcO+/o2n/JgBNCCFGWSVvcMm7niZ0E1QnCx8OntC+lMDOoGDYMzp3TNQALFxYMXq+yj615uNtv1+PdMWMko8el7EezOTk6reqNN+DGG/XMlbTKLZYl0MKc4XMYMn8I/1z+T/4896fTWuQW7Wz84Yfw8MPwxBOQkSG/L0IIIcoumeEo43ac2FF2C8Z79dJrOYBee+M6RztBQbqE4MgRXRIigycXsh/N3n47LF4Mbm56liM8XEazlzE4eDA317uZrce38lD7h5zWIrdorf9DD0HHjvr3ZcQIeXuEEEI4TkxMTI0nn3wywGa7pnUI/0ICjjIsJSOFQ+cOld2C8WnT9Ghn4ECYOfO6F8qYMUN/fOQRvdifrLvhQkVHs4MGwWOP6dmO3NzCXcgkvaoQa6KVQ+cOAfBe7HtObZFb6LxWOHhQF49/+qn8vgghhLO4u7t3DA4ODmnZsmXr3r17tzh16pR7cfudOnXKfdq0aXXNz+Pj471atmzZ2nVXCmlpaSosLCwoJycHgJycHFq1ahVisVha2O8XEBBw80033RQSHBwc0qZNm1ZFj7NhwwbfTp06XVi3bl1+C5+MjAwVGhoalJ2dfdXXJQFHGZZfMF6WZjjM6u516+Cll8DfX7fNGT78ulbns1p1XnqtWjrYkMX+SpnVqhd66NMHUlKgWzfdQkyKBQoxazYWRyymea3mNKvVzKktcvPPm/c2LFig48SsLJ3daP6+SEwohKh0Jk2qz4oVvoW2rVjhy6RJ9a/30N7e3rlxcXF79u3bt9vPzy/njTfeqFvcfqdPn3b/9NNP613v+a7Hu+++W2fw4MFnPTx01cSrr75av0WLFheL23fDhg174+Li9uzateuPos+5u7sbc+bMqd2uXbsMc5uPj4/Rs2fP1JkzZ9a+2uuSgKMMitoUhTXRmh9wtK3f1qmdb66KWd09bZq++/3gg/C3v+llkK9jdb7Nm/X6GwMH6kweWeyvFNnXc6xdC3feqduHhYbKSuRFmC1yezfrzT/a/4MdJ3bw1h1vOb1Frn0G3NNP69+ZmjX175HEhEKISqlz53Tuv79ZftCxYoUv99/fjM6d0x15mi5dulw4evSoV3HPjR8/vuHhw4e9g4ODQ8aMGdMQwGazMXLkyCYtWrRo3a1bt5ZpaWnqUseOj4/3CgwMbD148ODAZs2ate7fv3+z8+fPu11qe3HHiImJuSEiIiIF4MCBA56rV6+u+fDDD5+62q9zxowZx1atWpVQv379QjlV4eHhKfPmzZOAoyIIaxBGxMIIVu1bxQ1VbiD+VLzTOt9cNYtFt8G1WuGGG3QqlTnyuY7V+e64Q99I79ev8Klksb9SULQ6ecUKaNwYfvtNBx8SbOSzb5F7IesCCkXcqTint8i1z4AbMADGjdNN4mJjJSYUQlRSgwadZ9asBO6/vxnPPNOA++9vxqxZCQwadN5Rp8jJycFqtfoOHTo0pbjn33zzzSONGjXKjIuL2/PRRx8dATh06JDPU089dXL//v27a9asaZs1a1aty53j4MGDPk888cTJhISE3b6+vrnmbMqlttvLyMhQhw8f9g4KCsoCePzxxxtFRUUdcXMrfrjfp0+flq1bt24VHR1dp6Tfg7CwsIs7d+686pXSJOAogyyBFmLCY1iTsIbqXtW5Z9E9Tut8c03OntXV3adPO6y6e80a/dF+dXFRSorWc6xfDxcu6CmoWbN0QblJcnfy9W3WF093Tz7a8hG2XJtTW+QWNW0a1K2rVyIfM0aCDSFEJTVo0HkeeCCZd965kQceSHZUsJGZmekWHBwcUrdu3XbJycmeQ4cOTS3pawMCAjK7du16EaB9+/bpBw8e9L7c/v7+/ln9+vW7ADB69OjTP/30U/XLbbeXlJTk4evrmwMwd+7cmnXq1Mm57bbbip3h2bhxY9yePXv+WLNmzb5PPvmk3qpVq/5yvOJ4eHjg6elpnD179qpiCAk4yqheTXvhrtz589yfjA0dW3aCDcOASZP0IgAvvuiw6u7vvoM2bXQXVlGG2BcLzJih3/+ICJ1qJbk7hVgCLbxw2wucuniK0UtGE7EwwmU3Cn74ATIz9eN33pHaJyFEJbVihS9ffFGXp58+zhdf1P1LTcc1Mms4Dh069LthGEybNq3EdRpeXl6G+djd3d3Iycm5ZEoVgFKq2M8vtd1etWrVcrOystwANm7cWP27777zCwgIuPnBBx9s9ssvv/gOGTIk0Nw3MDAwGyAgICDnzjvvTPn5559LPGuRnZ2tqlatalx5zwIScJRRi/9YTKYtkzua38EHWz5wWeebK5o+Hfbu1Ynjr756XdXdZv35xYvw4486nUpumJcx9ulVDz0E48fr2a0xYyR3pxgTu0/Ex8OHubvmuuxGgRn3LVmiY7+qVaXhghCiEjJrNmbNSuDtt4/lp1c5KOgA8PX1zZ0xY8ah999/v35xnZpq1qxpu3DhwnWNrY8fP+61du3aagCzZ8+u3bVr17TLbbdXt25dm81mU+np6eq99947euLEiZ1Hjx79/fPPP0/o0qXL+WXLliUCpKamupkzFKmpqW5Wq7VG27Ztiy0sLyopKcndz88vx9vbWwKO8s6aaOWfy/8JwLNdniUmPMYlnW8uyYwMAD76SLeS6tdPb7+O6m6z/vzdd/Xd2Xr15IZ5mVM0veqNN6B1a0hI0K1zJdgoZNOhTQB4KA/ej33fJb+zZkzYuze0awcnT8K99xb8SkoQL4SoFH79tWqhmg2zpuPXX6s68jTdunW7GBwcfPHjjz/+S+G0v7+/rWPHjmktW7ZsbRaNX62mTZtmvPvuu/WaNWvWOiUlxWPChAnJl9teVI8ePc6tWbPmsulRR44c8ejSpUtwUFBQSIcOHVr169cvJTw8vERpYqtWrarRt2/fc1f7dSnDuKoApcwIDQ01tmzZUtqX4RRRm6I4kXaC6b9MJ+GpBAJrBWJNtBJ7LDa/GNWlzFuor74Kjz6qlzdessQhd7etVl2HnJkJtWvLDfMyz2qFu+4CpfTU1LJl+nOrVY9wK3GVv1mz8VLPl3hq1VM83+15Zm6b6dL6q++/h/79oXp1vUTOr7/KRJQQ4voppbYahhHq6vPu2LHjYLt27a66w1J5FR8f73XXXXe13Ldv3+6SbC/Oxo0bq0ZHR9dfunRpojOusV+/fs2jo6OPtG3bNrPoczt27KjTrl27psW9TmY4yqDIbpEopfB296ZxzcaAzg8vlWADCmYxnn5arzK2eLHDRjAWC9SoodeWk9XFyzgz8HzlFfD01G9aeLhOs5OpqfwWuU+EPUGgXyBbjm8hJjzG6S1y7fXuDW++qfs6DB4swYYQQlQ23bt3T+/Vq1equfCfI2VkZKjBgwenFBdsXInMcJRRg+cOJjElkd/H/l7al6IdOaJboxoGTJ4MU6Y45LArVuiBUY8esGePDI7KtKgoHVRYLAUzHenp4O0Nq1bJG5cnalMUu07u4qudX3Fk3BEa+DZw+Qxl8+Y66+3552HqVJecUghRgckMR2FJSUnuvXr1Ciq6ff369fH+/v624l7jyNeXVZeb4fBw8bWIEoo/Hc/N9W4u7csoMHGiDjaefFJ3pjLX3bgOViuMGqUf//vf+qPckS3D7NOlLBZ49ll47TWdXhUcXHrXVcaENQhj2sZpGBjM/X0uHW7skN+xyhWsVjhzRj9+911dbiW/T0II4Tj+/v62uLi4PaX1+vJIUqrKoGxbNglnEwi64S/Bb+lYvRpmz4YuXXRr1OvoTGUvNlavLO7pCZ07y+ri5YrVqhsI3HorZGTAsGE6IDWfq8RVypZAC4siFuHh5sEbP73h0va4ZtZbRAS0b68zIM1f1Ur+tgghhChFEnCUQQlnE8jJzSGoThkJOD7/XA8m/+//9OcOigwiI/XqyB076lae5qErcd1x+WCOamNi9AyHj4+uTo6MlLU58lgCLfQJ7MOJCycIDwl3WdG42bFq5EidUnX2LNxzD8ybJ2+LEEKI0iMBRxkUfzoegJtuuKn0LsK+Fe6hQ9CihZ6KMG+ROiAyuHhRD5B69LjOaxWuZb82h8UCK1fqhSCnT9dF5JITl1+zATBrxyyXtbQ2uxhbLLqRnKcnfPGFQ/s8CCGEK+Xm5uZedqE8UTbkvU+5l3peAo4yaO/pvQClm1JlLpLxySfw009w++36tqkDb5H++itkZ8NttznskMIViq7N0acPPPaY7lpVqxb06lVql1YWmO1xF45YSK+mvbihyg2lso6OxaJnN9LSoFMnCTaEEOXSruTk5JoSdJRtubm5Kjk5uSaw61L7SNF4GRR/Kp66VetSq0qt0rsIM21q4EDw8NCPFyxw6Kjlxx91vXG3bg47pCgNVivMnQstW8K+fTBpkk61Mp+rZOtzmO1xLYEW4k7F8dg3jzFz0Exij8W6LLUK9Lf+228hIEB/XLsW+vZ12emFEOK65eTkPJSUlDQzKSmpDXKTvCzLBXbl5OQ8dKkdJOAog+JPx5eN+o3OnfVd65wcfQfbwbdIf/wR2rTRN8VFOWVfz2Gz6VXnXn8dQkPBz6/guUrEvvXt3SF388SqJ0hMSeTV3q+67Brs35Zz53RN//Dhep1GmekQQpQXHTt2PAkMLu3rENdPosUyKP50fNnoUPXaa5CVBaNH61a419mVCgpKQ3JydKZWjx7SPadcs6/n6NtXp+CBXsVRehxTr1o9LE0txOyOwZVrHtm/LfHxegmdunVh82b9vPzOCSGEcCUJOMqYlIwUTl44WboF46BHJG+8Af7+ukuVg1rh2peGXLgAtWtL95xyrWg9x9//rqPIEyfgjjsqdbABehHAdvXbse/MPnac2AHoGo+oTc4d7du/LZ066W5VCQnQtq00EhNCCOF6EnCUEVGborAmWgsVjLtiYHJJ332nK7offRTc3BzWCtc8zHPP6c/fe6/S3wSvWKxWvWR8lSowZ45ew8X+uUp2Wz2sQRhf7PgCN9yYv2t+fkF5WAPXjfYtFli4UP8ajxkjE09CCCFcTwKOMiKsQRgRCyNY8scSQM90uHpgUqgVbrVq+mPLlg5thWsepnFj/fjxx2XgU2HYFw68/LJeu2XIkIJV5yrhbXVLoIUFIxbg7ubOh1s/dOkigPb69dMZb4cPw+DB8jsnhBDCtZQr84odKTQ01NiyZUtpX4ZDWROt3DXnLi7mXOSGqje4fmBiDgrnz9e3QqtXhyNHHH471GrVg5/gYDh5Uu62VhhRUTqgMN/MPn3g++/1auT79lXqN/quOXfx9b6veaTjI3x010cuP7/VCiNG6Ba5AKtWVdq3QghxDZRSWw3DCC3t6xDll8xwlCGWQAuBtQIxMBgbOtbld0Hz853uvhv274cDB5wSbISH6+ZX//iHw0pDRFlQtJ5j9mzw8oKff9apeZV0hGtNtPLzkZ8B+GrnVy5fj8O8j7BgATz1lO4DER4uv3NCCCFcRwKOMsSaaCX+dDxN/ZrywZYPXD4wAfSgsHlz/dgJrXBjY2H8eP24c2eHlYaIsuiPP/RS1wAzZlTKEa79IoDdG3fHv7q/yxcBtO9Y5emp177p3bvgd64SltYIIYRwMZcEHEqp/kqpeKXUfqXU85fYJ0IptUcptVspNccV11WWmAMTXy9f+gT2ISY8plRWJ2btWvjtNwgJgU8/dfggMTISLl4Ed3do315vc1BpiChLzNvqS5ZAvXp68Uj7qaxKMsq1XwRwePBwEs4m8NYdbxF7zHURtv3EU9++Ouj4+mudNVlJS2uEEEK4mNMDDqWUO/AeMAAIAe5VSoUU2aclMBHoZhhGa+AZZ19XWRN7LJYvh37J2YyzBPoFYgm0EBMe49KBSX6+k2HAK684Ld9p82a94J9Zly4qIPO2+u236xHvmTN6McDY2Eo1yo3sFpmfGjms1TAAktKSCi0O6EoWC7zzjg76w8OlY5UQQgjXcMUMRydgv2EYCYZhZAHzgCFF9nkYeM8wjLMAhmGcdMF1lSmR3SJp4tcEgKZ+TQFd0+HSgUlsLHTvrovFBwxwSr6TYeiAo1Mnhx1SlEX2t9XHj4fbboNvv4VjxyrtKDdmdwwtarVgSdyS/G2l0fp6zBho0kRPZo4ZU+neBiGEEKXAFQFHAHDY7vMjedvs3QTcpJTapJT6RSnVv7gDKaUeUUptUUptSU5OdtLllp7ElEQAAmsFls4FPPusLvAdPFivowAOz3favx9SUiTgqHS+/FLn0b3zjl6FvBKOcsMahHEs7Rg/Hf6J4+ePl8qaHKAnmM6e1Y/ffbdSltYIIYRwsbJSNO4BtAR6AfcCnyil/IruZBjGx4ZhhBqGEVq3bl3XXqELJJ7NCzj8XBxwmOtvrFunU1/uucdpOfabN+uPnTs7/NCiLEtI0B2roNKOci2BFv474L8APLT8oVJZk8PMZlu0SM9yBAZKlzghhBDO54qA4yjQyO7zhnnb7B0BlhuGkW0YRiKwFx2AVCoHUw7i4+GDf3V/1544LEyPOt5+G2rWBG9vp+XYb96sazdCQq68r6ggzFFuRAT4+kKzZgWj3EpSPG568JYHqe1Tm2/2f1Mqra/N0pq+fXUTuh07YOpU6RInhBDCuVwRcMQCLZVSgUopL2AksLzIPkvRsxsopeqgU6wSXHBtZUpiSiJNajZBKeXaE1sses2ENWv0bc9RoxyaY2+/gPnmzdCxI/zwQ6UaZ1Zu5ij3gQf0Aiy//QbPPQfz5lWa4nHT+oPruZhzETflxvux77u8C519aU16up502ry5IGuyksV/QgghXMTpAYdhGDnAE8Bq4A8gxjCM3UqpKUqpwXm7rQZOK6X2AFbgOcMwTjv72sqaxJTE0qvfyM3VFd07dzo8x96cQFmzBrZtA3//SjfOrNzMUa7FAosXg5sbvPmmflyJisfNmo1Xe79KrpHLU52fKp3W13l69tRvxRdf6EzKStQ8TAghhIu5pIbDMIxvDMO4yTCM5oZhvJa37SXDMJbnPTYMwxhnGEaIYRg3G4YxzxXXVdYknk10ff2G6YMP9MeJE/VjByZ1m82u7rkHMjNh1apKNc4U9vr1g2HD4ORJuPXWSvVDYK7J8WSnJ6npXZODKQdd3/rajsWiy2mysgoy3uT3UgghhDOUlaLxSu9cxrn8NThc7vvvYeVK3br09dedsv6GxQLduunHDz4og5pKy2qFDRugQQO9+tyqVaV9RS5jrsnh6e7JgJYD+Hrf1/Rs2rPU1uQAeOghaNRI94t49FH5vRRCCOEcEnCUEQdTDgIFa3C41NKlOqXqoYf0505Yf8NsguXtDXPnSlecSsnM2YmJ0a2Xc3P16nOVbPVxgEE3DeLkhZNsPrq5VK/DatVtqqHSNg8TQgjhAhJwlBGlugaHn59O5h44sGCbA9ffMMeZzZpBly5OW8BclHVm8bjFon8AvLwgJ6egW1UlKiDYe3ovbrixIn5F/jZXLwJofssXLIBataB9e/m9FEII4RwScJQRpbYGB8Dy5XqF8Tp1nHL42Fg9q/Hnn3DLLU6ZQBHlgX2LJIsFPv5YFxCsWlXpCgh6NumJu5s783brcrXSWATQjP/uuEM3ENu0CT76SH4vhRBCOJ4EHGXEwZSD+Hr5UrtKbdec0OxVe/CgbsY/eLDTUloiI6FxY7hwQQcc4PAFzEV59MAD0KEDbNkC991XaYIN0IsA/qP9P0g4m8DTq54ulUUA7eM/d3fIzob9+6VFrhBCCMeTgKOMSExJpKlfU9etwWH2qn3zTf15vXpOTWnZtk1/NAMOIfIDXjc3PdtRyXJ5nu3yLAAzNs8olUUA7d15J3h6wjvv6NKaSpbhJoQQwskk4CgjXL4Gh5nX9NFHOpVq3DinprRs364HNLLCuAAKRrQLF0LnzrpfciUrID+aehQ35UbL2i35YMsHpbYeB+hf++eeg2PH9MRTJctwE0II4WQScJSiqE1RWBOtGIbBwZSDBPoFurZwNDQUbDY4dcrhi/0VtX27Dja8vJx2ClGe2BeQP/OMXnSyRQu9vRLcXrcmWrln0T0MajmIo+eP8tWwr0p1EUCAyZPBxwe++srpfw6EEEJUMhJwlKKwBmFELIxgWfwy0rLSyLHluLZw1MyfGD3a4Yv9FbV9u6RTCTv2BQQREXoxwM2b4fDhSnF73VwE8KGOD5GenY6bcivVRQABfv4ZlNL/3nuv0mW4CSGEcCJlGEZpX8M1CQ0NNbZs2VLal3HdrIlWhscMJyUjhRreNVh6z1LX5HJbrQVtcM+d0y1qnDTQS0qCG2+Et9+Gp5926KFFRXH8uF6BzmbTt9qnTCntK3KJC1kXqB1Vm8fDHmf6HdNL7TrMSaXoaL0w58MPw5IlFT7uE0KUkFJqq2EYoaV9HaL8khmOUmYJtNAnsA8AI1uPdF3haGysbr5/++06z8mJvWp37NAfZYZDXFJcXEG+3X//W2lur1fzqkbPJj1Ztb90V1w3M9weeAB69tSLdM6bJy1yhRBCOIYEHKXMmmjl2/3fArDoj0Wuy+EePlzfVb7jjoJtTupVu327/tiuncMPLSoC8/b6l19ClSq6tqgSrUA3oMUA4k7FcTDlYKldg5nhFhUF3bpBQoIuq4mMrBT1+0IIIZxMAo5SZC72NaDFAKp6ViUmPMZ1haOrV+uP/fs77RTmUh/btkHTpnpBcxm8iL8wb6/ffbcuFF+7Vuf2mLfXK/APTdSmqPy1d8wbD65ecdxeWJjuUFyjhm5gVwnq94UQQriABBylyCwcVUrRsEZDejfr7brC0W+/hebN9T8nMZf6+OknnU4lgxdRLPsC8qef1rfWv/yy4PZ6Bf6hCWsQxoTvJlC/Wn1W7V9VKiuO2zMzK7OzYdEiGDFC6jiEEEJcPykaLwO6ftqVKp5VWHf/OueeKCpKD9y6dYPatXXCdni4vpPspGW/V63Stek9esCePTJ4ESUwciTMn697sy5YUOF/aKyJVgbOGYgt10ZNn5ouX3G8OGPHwocfQr9+BZOhQojKS4rGxfWSGY4y4EjqERrVaOT8E5lTDv/9L1y4oFtHOfnucd26+uMPP0hvf1FC//0veHjoVs2V4IfGEmhhcNBgsnOzGdBiQKkHG1arXo/xxhvh++8rTSmNEEIIJ5KAo5TZcm0cO3+MhjUaOv9kZr7EpEm62f7bbzv97vHChfrjY485fakPUVH8/rtelr6SLAhhTbSyLmEdCsWiPS5sHFHctVgLumNPnAg5Obq0poK/BUIIIZxMAo5SduLCCWyGzTUBB+jgws9P58k/9phTgw2rFWbM0Der33lHD2IqUfMhcS3MEe///gfu7rpHawX+oTFrNhaMWMCtjW6lsV/jUl1x3H4B+Hvv1XFf377SHlcIIcT1kYCjlB0+dxjAdQHHihW6HW6PHk6fcoiNhZtvhpAQHXQ4cakPUVGYI95Dh6B3b93c4JNP9PYK2K3KbBxhCbTQN7Ave0/v5X+D/1dqK47b1+//73/QqZNOhxw3Tm+rgG+BEEIIF5CAo5QdST0C4JoaDqsVRo3Sj6dMcfqUQ2SkXmW8TZuCbU5a6kNUFOaINyxMBxkXL+q+ymb9UQXrVhXZLTK/ZqNvs77kGrnk5OYQ2a30f0nCwnR224kTunC8gjcME0II4UQScJQyM+BwyQxHbKxeWdzHB7p0cfqUw/nz+kZ169ZOObyoyCwW3ZfVy0vfUq8E/Vm7NOxCda/qrE1YW9qXAuhv9YIFupTmuecKajsq8FsghBDCSSTgKGVHUo/g4+GTv/iXU0VGwv790LUreHvrbU6cctizR3+UgENcE4sF/vY3yMiADh0q/EjX092Tnk16sjaxbAQcoNvihobCH3/Agw9W+LdACCGEk0jAUcoOpx6mYY2GKKWcf7LTp2HHDpeNGnbt0h8l4BDXxGrVNUeNG8O6dbBmTWlfkVNFbYqiSc0m7D29l0PnDgGlu+o46Ldg3z79+MMPK2ztvhBCCCeTgKOUHUk94rqC8Q0b9EcXBRy7d0OVKhAY6JLTiYrEvj/rRx9Bbm6F788a1iCMubvmArAuYV2przpuvgWLFuk6rEaNKnTDMCGEEE4kAUcpc9mif6BHClWruqzqc/duaNVKdzcV4qrY92fdsQOaN4datWDzZv18BWyXZAm0sHDEQhSK6b9MJ2JhRKmuOm6+Bb17wwMP6LSq6GjpMieEEOLqScBRinKNXI6eP+rcGY6oqIJbklYrdO8Omza5ZLC2e7ekU4lrZN+ftVMnOHkSDh+Gm26q0O2SejfrTet6rdl1chePdny0VFcdt38LUlJ08fjevQUlXxUw5hNCCOEkEnCUohNpJ8jJzXFuwGG2E128WEcAjRu7ZLCWkgJHj0rAIRzAYtE/v25u8MQTFbpdkjXRyp8pfwLwXux7pbrquL0+ffRaOp98orPbKnDMJ4QQwgkk4ChFLmmJa7a+ffBB/fnChS4ZrO3erT/ar8EhxDXr21e3TDp2DO66q8IGGxELI5g5eCYAI9uMLNVVx+1ZLPD885CcrNOrKnDMJ4QQwgkk4ChFLlv0z2KB4GD9+PHHnTpKMDO4zICjdWtJvRAOYLXq4gFvb5gzp0JWLpurjke0jiC4TjAHUw4SEx5TaquOF/XCC/rb/9VXMHasBBtCCCFKTgKOUuSyRf+sVvjtN2jaVHf8ceJgzczg+vZbqFYNDhyQ1Atxncz8nQUL4OmnITsbwsMrXNBRaNXxwL5s+HMD3Rp3KxOrjgP8/LP+6OUFH3xQ4b79QgghnEgCjlJ0JPUIXu5e1Klax3knsVr1Ks25uToXIibGqb0tzQyulSuhZk0YOVJSL8R1su9Y5e6uq5f79i1ol1QBp9D6NutLenY6vxz5pbQvBSiI+V59FbKyCkppJOgQQghREhJwlCKXLPoXG6vbyhiG7lBlRgRO7G1psejUi2PHJPVCOIB9u6TbbwdPT/j6a3jssQpbvdyraS/clBtrE8rGquNmzPfss1C/Pvz+u9P/jAghhKhArhhwKKVGK6WSlVJHlFIP5G3ropR6VSm11fmXWHG5ZNG/yEhIS9Mdfjp31tssloLelk6wcqU+Ze/eknohHMxigTffhAsX9MxdBa1erulTk04BncpMwGHGfO7u+lu+ciV07OjUPyNCCCEqkJLMcLwEDARuAQKVUt8BCwAv4BmnXVkFFrUpCmuitdCif9ZEK1GbnJQWsmkT3HIL+Po65/h2rFYYPVo/fuIJp2dwicro8cchIEAXCj36aIULNkD/jWhZuyWbj27mXMY5wMl/I66CzQaZmbBkScG2CpjVJoQQwoFKEnCkGYYRaxjGKeBloB1ws2EYkYZh/Ojcy6uYwhqEEbEwIj+lymyHGdbACWkh2dnwyy86ncoFYmN1GhXoxlguyOASlY3VCqmp+vGMGRUymg1rEMby+OXYDBvrD6537t+Iq3T33XrC9L//1Z9X0Kw2IYQQDlSSgMNfKfWIUqonUB84YhhGinMvq2KzBFr46K6PyMnNYXvSdiIWRhATHuOcVYV37ID0dOjWzfHHLkZkpE67cHeH5s31NidncInKxBzdLloEDRvqH7IKOIVmCbQQMyIGgNd+fM25fyOuUu/euhnEli0wfnyFzWoTQgjhQCUJOP4PuBmYAuwBblZKrVVKvaGUus+pV1eBNa+lR+OrD6xmbOhY5w0kNm7UH10UcADEx0NgoG6fKYRDmdXLt98ON98M27bB1KkVsmNVv+b9aFarGbHHYp37N+IaPP+8/jh9ujSGEEIIcWVXDDgMw/jYMIwnDcPoaRhGbSAQeBM4BQxw9gVWVKv3rwbgwXYP8sGWDxy/mrC5At/GjXr0HxDgssFYXFzBOoNCOJR9x6pHH9UfFy3S2ytYbo810cqJtBMAvB/7fplYcdx06pSexWzYUBpDCCGEuLKrbotrGMYRwzBWGYbxH8MwRjvjoio6a6KVV398FYBJPSbp1YUXRjh2QGGuwPf993p2w0WDMZsN9u2DoCCnnkYIGDwYhgzRxePjxlWo3B6zZuM/ff8DwJOdnnT834hrZP4peeABOHIE3n23Qma1CSGEcCBZh6MUxB6L5Z7W9wDgX91f52uHxxB7zIGV1RYLvPUWnD0LZ864bDB26BBkZMgMh3CR/+gBOW+9VaFye2KPxRITHsMjHR+hmmc1TqWfcvzfiGu9trysthde0J8fOiSNIYQQQlyeMgyjtK/hmoSGhhpbtmwp7cu4Zs98+wz/2/Y/UiemOu8ks2fDqFH68eTJMGWK886V59tvYcAA+OEHuO02p59OVHZWK9xxB/j46NUmK8gMh707vrqDY+eP8fvY30v7Uv6iSxfdInfbttK+EiGEMymlthqGEVra1yHKL5nhKCXH045zo++Nzj3JwoX644svuizROj5ef5SUKuF0Zm7PSy/B+fN64ZcKmNvTq0kvdp3cxan0U6V9KYVERUFoKGzfDn/8obdVoJp9IYQQDiQBRylJSkvCv7q/805gterlgNu1g1dfddkKfHFxUKsW1K3r1NMIUZDbM3EiNG6sp9UqYG5Pr6a9APjhzx9K90KKCAuDuXNBKf2xgtXsCyGEcCAJOEqJ0wOOn34Cw4D+/fXnLlqBLz5ez24o5dTTCFHQserNN3Wb3O+/B3//go5VFeRWe2iDUKp6VmX9wfWlfSmFWCx6EtXDQy8CWIFq9oUQQjiYBByl5Pj549xY3YkpVX366JZRnTsXbHPBCnxxcZJOJVwsLAyWLtUjXzN1sALdan/rl7doVadVoYDDmmglalPpB1QWiy6hOXsWhg6VYEMIIUTxJOAoBReyLnA+67xzZzh+/VV/tA84nCw1FY4flw5VwsUsFliwQC8M8dFHMGJEhbrVHtYgjD9O/cHvJ3/nVPqp/Ja5YQ1KP6CyWuHnn/WM5uzZFa58RgghhINIwFEKTlzQi3k5NeD45Re9KleDBs47Rx5zjcG9e/XnQUEVKqNFlAcWC9x7L2Rl6SC7ggQbAJZAC9P6TgPgsa8fI2JhBDHhMaW+8rg5kbRggc5o8/OrkDX7QgghHEACjlJw/PxxAOemVP36q8tmN8w1Bhcv1p+fO1ehMlpEeWA2SahfH1av1vUcFciYjmPwcPNgwZ4FjA0dW+rBBhTU7FsselLp+HHdn6KC1ewLIYRwAAk4SkFSWhLgxBmO5GRITHRZwGHWo7/zjk6teO65CpXRIso681Z7TAy88oquXRo+vELdat90aBMKRb1q9fhgywdlYsVxs2YfdP2GuzscPOj0MjEhhBDlkAQcpcDpAYdZv9Gli3OOXwyLBZo21Y2xKtCCz6I8sL/Vft99ULMmdOhQYW61mzUbo9uO5uSFk3wy6BMiFkaUiaDDVKcO9O6t06vK6VqyQgghnEgCjlJwPO047sqdOlXrOPbAZjHFr7/q240dO7qsmMKs4Wje3GVrDAqh2d9qf+893aFt40a4/369rZwXFMUeiyUmPIZ/tP8HAIZhEBMeQ+yxshNQRUXBzTfDgQN6IUAo9992IYQQDuRR2hdQGSWlJVGvWj3c3dwde2CzmKJxY/1//19/LUg1cSIzo8XTEwYM0Nks0pNflIqwMJg2DbKzYeZM6NbNJb8DzhTZTecoZdmyqOJRhQ1/buDt/m+XiToOU1iYruNwc9Pf6pSUcv9tF0II4UAyw1EKjqcd50ZfJxSMWywwbx5s26b/z++iUX9srO5GevEitGzpsjUGhfgriwUWLdLRb1RUhYp8vdy96Nqoa5lbABD+2pm4An3bhRBCOIAEHKXAqauMN26sk6h/+81lxRSRkbo5EOiAA1yyxqAQxbNYYMgQOH9ep1dVoFFvr6a92HliJ2cunintS/kLiwX69dOLAA4bVqG+7UIIIa6TBBylICktCf9qTgo4vvhCf3zkEZcWU+zfrz+2aOGS0wlxaVYrrF8P1arBkiUVqqCoV9NeGBj88OcPpX0pf2G16uV/lIKvvqpQ33YhhBDXSQIOF7Pl2jiRdsI5KVVWK0yfrtNJ/vtfndPgopW49u3T6RRNmzr9VEJcmn2L3Kee0rUc4eEVYvQbtSmKC1kXqOJRJT+typpoJWpT6Vdm2y8C2KcP1KoliwAKIYQoIAGHi52+eBqbYXNOSlVsrM5p6tBBBx0uLKbYv18HG56eTj+VEJdm3yL34Yd1euGgQRWioCisQRijlowiuE4wG/7ckN8uN6xB6a+wWXQRwGPH4PXXK8S3XQghhAO4JOBQSvVXSsUrpfYrpZ6/zH53K6UMpVSoK66rNDh1DY4JEyAhAULtvn0uKqbYt6+gfkOIUmPfInfBAv27sG4djB+vt5XjXq2WQAsx4THEn45ne9J2RiwYQUx4TJnoVmX/bR82TBYBFEIIUZjTAw6llDvwHjAACAHuVUqFFLOfL/A08Kuzr6k0HT9/HIAbqzshpWrvXkhL0+tvuJBh6BkOqd8QZUpYmP6dOHIEvv22IO8nrPRnBK6VJdBCeEg4AH2a9SkTwUZRn30GbdsWXgSwHMd5QgghHMAVMxydgP2GYSQYhpEFzAOGFLPfK8B/gAwXXFOpceoMx9at+mOoayeIkpMhNVVmOEQZY7HAwoW6inncuArRq9WaaOWbfd/grtxZEb+iTK02bgoL0zOe+/bBzp0VIs4TQghxnVwRcAQAh+0+P5K3LZ9SqgPQyDCMry93IKXUI0qpLUqpLcnJyY6/UhdwasCxZQtUqQKtWjn+2Jexb5/+KDMcosy5/Xbo2lXPdPztb+U+2IhYGEFMeAzdGnejcc3GRCyMKHNBh8UCs2bpx088USHiPCGEENep1IvGlVJuwHRg/JX2NQzjY8MwQg3DCK1bt67zL86BojZFYU20cjztOL5evlTzqub4DjNbtsAtt4CHaxeQN1viygyHKHOsVvjjD/34k0/Kdduk2GOx+TUbPRr3YP+Z/Xw+5HNij5W9yuxhw3QTiY0b4dFHJdgQQojKzhUBx1Ggkd3nDfO2mXyBNsB6pdRBoAuwvKIVjoc1CCNiYQQ7TuzAv7q/4zvM2Gx6hXEXp1OBtMQVZZSZy7NwoZ7pqFatXPdqjewWmV+z0aNJD2yGDU93TyK7lb3KbKsVTp3Sj997r9x+y4UQQjiIKwKOWKClUipQKeUFjASWm08ahnHOMIw6hmE0NQyjKfALMNgwjC0uuDaXMTvMbDq0iUxbZn5qxHUXfUZF6f+bx8fDhQs64HBxhaa0xBVlkn2v1jFjdLHRuHEVolfrrY1uxV25s+HghtK+lL8w47zPP9flM4MHl+s4TwghhAM4PeAwDCMHeAJYDfwBxBiGsVspNUUpNdjZ5y9LLIEWfL19OXTuEGNDxzqmw0xYmP6/+Zdf6s9tNpdXaO7bJ/Ubogyy79W6d69eje6XXwp6tZbj1knVvarTsUFHfjhU9lYcN+O8u++G7t11LwsXLQckhBCijHJJDYdhGN8YhnGTYRjNDcN4LW/bS4ZhLC9m314VbXbDZE20cvbiWTo16MQHWz5wTLGnubjf22/r2o3ISJdUaJoTK2ZL3JYty/X4TVR0XbpARgasWAFHj1aI1kk9Gvdg89HNXMy+WNqXUoh9nBceDrt2QYMGsiZHWWfWGdo/tiZaGTh74F8ej1kxhjErxgDkPy7JvmbdosPrF4UQZV6pF41XFtZEKxELIjAwGNZqGDHhMY7rMGOxQJ06kJMDY8e6pELTnFhZskS3xDWMcj9+ExWZxaKLxg1Dd6uqAK2TejTpQZYti81HN5f2pVzSiRP646JFBdvkxkTpKy648HDzYNDcQfmPB84ZyND5Q+nbrC/D5g9j6Pyh9A7szdD5Q/lq51fM3TWXiWsnMuf3OXy580uGzBtC98bd8/c1X/fV718xf/d8pv88nbvm3sXBlINELIzgwJkD+cGJGXxIICJExaUMc2WmciY0NNTYsqX8TIREbYoiwDeAUUtG8dmQz3jwlgexJlqJPRZ7/UWf69bpotiOHfXyvi4aSFmtMHw4pKRAjRqwdGm5Hr+JyiAwUP+OTJoEr7xS2ldzzaI2RRF8QzBD5w/l5V4vM7nnZMf9PXEgqxX69dM1Xvv2FUwslfNYr1yK2hRFWIMwLIGW/KYlE7tPJP5UPPN3z8fAYMKtE/jPpv+QZcvKf13DGg35M+VPcsm9qvNV86zGxZyLuCk3mvk1Y++ZvbSu05o9p/bwWu/X6NKwC8PmD8PAYGTrkQTVCWLqxqn5tY1l8ee5MlNKbTUMo0I18xGuJQGHC/10+Ce6/a8bq/62iv4t+jvmoFarTpY+e1bfwW3e3KX/Rx86FJYtg8ce091ohCizrFYYMgTOn4eaNfX0XDkd9ZoDxpreNQmsFcgL3V9wXCMKBxs7Fj78UK/JMW+eBBuudKkgIyc3B8MwmLhuIu3qt2NX8i5suTYMCo8HfL18OZ91nuA6wdTwqsHmY5vpE9gHDzcPVh9YzU033MTe03sZfNNgsnOzWbV/FT2b9CTbls1PR36iea3mnM86z8kLJ/9ybTW9a3Ih+wLuyp2wBmFsOryJN25/A5thw8PNg8nWybxieYVxt47LDz7CGoRJEFJKJOAQ10tSqlzo+PnjANxY/UbHHTQ2Fp5+Wj9u166gpsMFFZpWK3z3nX4cEyNdaEQZZt5aX7BAF4937FiuWyeZXe+Onj/K+oPry2ywAQW1G//9r8syPis9M03KbMduTbRiGAYhdUIYv2Y803+ezvPrnsfAYPuJ7dT0rkmbem0A8Hb35qnOT1HDuwZuyo3JPSZz7Pwx4k/HM7nHZGKPxfLLkV8Y3XY0+07vY3Tb0az/cz0/Hf6JyT0msy1pG7uTdzO5x2SS05PJzMlkdNvRKBRDgoZQw7sGt9S/hXOZ56hdpTbZudlsPLwRA4PItZG8t/k9xq8Zz4DmA5i6cepfUrHMVvKSfiVE+SIBhws5ZZXxyEjdmcrNDdro/2FgsTi9QtMcv916KzRpogOOcjx+ExWd2Trpjjt0DcemTTBzZrlunWQJtHBnyzvJyc1h0E2DymSwATqDzcMDAgLggw/kb4Sz2NdlmIHGb8d/47bGtzFg9gD6fNknv6vZiQsn9KyFdw0m3DqB7NxsEs4mUNWzKt4e3jSp2QSFwsDAz8fvL4+zc7NZHr+c6H7RLItflj87cqV9Nx3exN9u/hs7TuxgdNvRZORk4Ovly8MdHqa6Z3Ua12jModRDACyKW0RqRirj14wnsGYgH275kIndJxJ7LJbpP0+X4EOIcsa1S1JXcklpSbgpN+pUrePYA+/YAUFBUKWKY497Geb47YUXdBaX/cSK3MEUZY4ZgEdFQdu2kJkJhw7p7Var/sEtZ22UrIlWrAf1ADNmdwyj244uc0GHeWPigQfg009h7lyp4XAk+5QpM8iY2H0iKRkpdAnowoTvJhTav3NAZ/ad2cedLe/kq51fEd0vmvb+7fnkt0/Iyc3h9T6v096/PXfNvYtXLK/Q3r89b/z0BkvuWQKQ/3jernkAjLt1HPGn4gEY2WbkFfcFeMn6EtH9ook/FZ8fnNzb5l6C6wQzYc0ERrcdzdf7viakTggbD2/Ew82D3ad2A/Cvtf8ipE4Iv5/8neh+0cQei2Vb0rb82g9Aaj+EKKOkhsOFHlr+EN/s+4Zj44859sBNmkDXrvr/5i5Wr56u4/j4Y5efWoirZ46Aa9fWAfr06XDPPeVuBGzm48eExzD267H4+fhx4OyBMpdWFRWlO9cFBOh7Im+9pTM/y2F8VybZ/xxYAi08veppZmyekT+Qr1u1LsnpyVTxqEJ4SHh+kJGTm4OHmwdTN05lePBwRrYZCZA/UHfWoN0+QDIfA8zbNY/FcYvz60s83DyYsGYCfZv1ZW3CWga0GMD6P9dTzbMayenJALgrd1rXa83vJ35nYMuBjL91PED+98P+6xHXT2o4xPWSgMOF7ppzF8fOH+O3Mb857qBnz+rB07Rp8K9/Oe64JXD+vO5ONXUqPP+8S08txLWzWvXy12lp4OcHixeXq2ADCg/cHlnxCDG7Y1gYsZDfjv9WZgdY7drpvxc//ljaV1K+2b/3AOsS1jF43mA83DxIzUzF082T7NxsBt80mPV/rs8fwJflTlDFFbePCBnBrB2zmGKZwtSNUxkRMoIPt3zI8FbDWb1/NVU9q3IyXReju+GGu5s7VTyqMLJN2f06yzMJOMT1khoOFzqedpwbfR1YMA6wc6f+2K6dY49bAgkJ+mPz5i4/tRDXzmLR1ctQkA9YzkR2i8wfcPZo0oNzmeeoU7VOmR5Q3X23Lp05fry0r6R8KloIvi5hHS+vf5kRC0aQnp1OamYqoQ1CqeFdg9FtR7Ni7wpua3wbK+9dydJ7lrI4bjHt/dsTEx5D7DFdu2QJtJSJnxn7n+fYY7HEhMfQ1K8pK+5dwbhbxzGx+0Rm7ZhFdL9oOgV04mXLyySnJ3N3q7up5lktv/g8NSuVVftXMWHNBP7V7V9S7yFEGSIBhwslpSXhX82BBeMA27frj7fc4tjjlsCBA/qjBByiXLFa4bPP4Oab4bffYNWq0r6i69KzSU8Afvjzh1K+kkuLioJGjfS6i0t0ir8sAHiVzEADYGL3idzx1R38e8O/SclIoYpHFf5289/YemwrL9z2Am3qtSG6XzS/Hv0VKOhqFnsstswEGZdiBh/2QUhObk5+8BHWIIypG6fmBx9TLFM4ffE0g24ahIfy4HDqYQwM/rX2X3y540smrJnAxO4TC82emMGHEMJ1pGjcRXKNXE6knXBshyrQBeP16oG/g49bAhJwiHLHfuU5pfTsxj336MVkyuFMB0Cjmo1o6teUH/78gac6P1Xal1OssDD9bW/UCBYuhFatCt4GcWn2qUaWQAvT+02n31f9yMnNyd+nikcVVt63kthjsXS4sUOhVKL2/u3zgwzzX3lkHyCZMyD2AYRZhF7NqxpZtiyUUtTyqcWu5F0AvLDuBeb+Ppe9Z/ay9J6l+d8HSbUSwnUk4HCRU+mnsBk2x6dUbd9eKrMboAOOG27Qa6gJUS6Y7dUsFn27vUULqFq13LdX69GkB6v2rcIwDJRSpX05f2F2sbvrLjh8WGeCLlhQrr/lLmHOanw59Et+OvITUzdOzQ82qnhUoWujrvnpUeaguaIEGZdSXPABMHXj1PwOWdN/ns7X+75maNBQVh9YTU5uDluOb8ENN1754RWyc7PxdPPMLzCXwEMI55OUKhdx6BocUVH6Tm12Nuzeres3SiE/4cABaNbMpacU4vpERhaMct94A3r21KPfYcP0tnKY5xO1KYp6VeuRnJ5M3Kk4oGzmqVssMGqUftyliwQbl2K/noYl0MLE7hO5c86dvPLDKygUVTyqUNWzKp7unrx424ssvWdp/uJ+5msqy8DZTLuyn/UA+OXoL0T3i+bWRrfyau9XycnNoVNAJwCsB630/6o/A2cPZNBNg9iWtE1qPIRwAQk4XMShAYeZn/Dll5CVBZ6e+vMw1+alJiRIOpUox8LCdEGBUvC//xWkW7n49+h6hTUI49NtnwK6jqOs5qlbrbohWK1asGaNLAB4Keasxtd7v2bsyrGMXzOeXHIB8HT3ZHTb0fmF4GZNh30heGVUXNF50XqPu1vdzZrRa/By98LAIMOWweydsxm/ZjxPd3paajyEcDJpi+siX2z/ggeXPcj+J/fTvLYDRulWKwwZonvT1qoFixa59JZhdrZexuD55+HVV112WiEcy2qF/v3Bywt8fMrdehym7xO+p++XfWldrzVJaUllbj0O+9KZ1av15FKtWpJWZSra6va9ze/x5KonMTDwcvPCx9OHsAZhxB6LlRqEq1D0+2pNtDJs/jBCG4Sy8dBGMm2Z+fs2rtmYlIspLB0p39/iSFtccb1khsNFzBmO+tXrO+aAFgu0bq0fP/aYy/+vfegQ2GwywyHKOYtFB+5padCnT7kd/fZu1ptWdVux6+QuHu34aJkKNqBw6czdd0NuLvzzn3q7KJjVWJuwltd+eI2nVj2Fgb4Z6OHuwdJ7lrL2/rWVOn3qWtjPfJizF0vuWcKLt72Ij4cPVTyqUN2rOk1rNuXQuUOkZqXy6bZPeXn9y9JOVwgHk4DDRZLSkqjuVZ3qXtUdc0CrFbZuhRtvhI8+cnl+gtmhSmo4RLlmtep/1arp9KpymudjTbRyKOUQAO/Hvp8/IC0r7EtnQkOhcWPYs6dyrzZetFbj47s+ZsDsAUyyTsLdzZ0a3jXoE9gHD7eC3i727W3F1bEvMDcDj6/v+5peTXrx57k/GXzTYDzdPJn9+2xe+/E1xq8ZT8cbOwIUSrWSwEOIayMBh4scTzvOjdUd1KHKzE+oWhUGDNC3DiMiXDpYkpa4otyzz/N5/HHIyYHw8HIXdJiDoXcGvAPAwx0eLnQXvKxRSn+b16yBc+dK+2pKjzmrYU20svnoZsasHJPfgcrTzVNmNRzsSgXm3Rp3Y/Wo1fi4+5Cdmw3A6gOrueOrOxg6f2ihYEVqPIS4ehJwuEhSWpLj1uCIjYUPPtD/t27XrqDnpAvzEw4cAG9vaNDAZacUwrHs83z+8Q+d53P33eUuz8ccQD14y4PUrlKb5PTkMn0XPCoKmjbV/S5WrtTbymFzsGtmzmyYsxVD5g2hy8wuJKcnU92rup7VcJdZDWe5VIG5GcR5e3jTJ7APfj5+3Fj9RrJzszmfeZ4nVz2ZH3jYp2nJbIcQJSMBh4scTzvuuDU4IiN1CggUrMFhsbg0PyEhQadTuclPkCiv7PN8li2DNm1gwwZ47jm9rZyMgs0BlJty47bGt/HDoR/K9F3wsDCYMkWv4bNoUbltDnbNzJmNdQnrWHNgDeezzuvicHcvlo9cLrMaLnSpGo+1969lco/JJKUl0TewLwC7k3eTlpnGtqRthfaX2Q4hSkaGiy6SlJaEfzUHrga+Y4f+2Lat445ZAuYSIAcOFKRTlZNxmRCXFhYGf/4Je/fCxo3ldhTco0kP9p/Zz7Hzx0r7Ui7JnJC9cAGWL4cRI8ptc7BrYgm08OXQLxk4ZyDTNk0D4Bb/W/Dx8Cm0j8xquFbRFczNdrrNajWjhncNvNy8yCWX8WvGUyeqDoPnDpbZDiGuggQcLpCenU5qZqrjUqpArzDetCn4+TnumCVgLgGyd6+e4Sin4zIhCrNYYN48XWDwxBMFtR3lbBTco0kPQK/HUZZZLPpbbLNBr17l7tt81ewLxM9ePMtrG18jy5YFwKibR7FtzDaZ1ShlxaVatfdvz+K4xSy5ZwnfjvqWf7T/B55unpy+eJq07DQ+2voRr2x4RTpaCVECEnC4gNkS12EpVaBnONq1c9zxSshigQ8/hIwMiI8vt+MyIf5q4EBo316vPP73v5fLH+pb/G+hulf1Mh9wWK3wzTe678XKleWuTv+qmWlUMbtjuO2z2/jp8E8A3N7sdr498G2hmg6Z1Sh9xRWYWwItjLp5FFU9q3Jb49vwdPNk/u75vP7j60xYM4Hnuz0viwcKcRkScLiAQ1cZB0hP11MMZv2Gi92YFzetXg1jx5bLcZkQf2W16uIk0E0ZyuEoePrP0wmuE1wo4Chrd1vtm4ONHq0nlVzcZM8lira9ffuOtxm5cCR7kvdgGAZv9nuTNaPXEBMekz+zIbMaZculajx++PsPrB61Gm93bzJsGRgYPL/ueTp90kkKy4W4BAk4nMj8H459wOGQPz67dumOOqUwwwEFnWXGji234zIhCjNHwYsW6eLxgIByOQoOaxDGnuQ97E7ezan0U2Xybqt9c7DwcD1b+tRT5a452BXZt73de3ovz3z7DEbef6PajmLcreMAqdcoL4q20wXw8fChd9PeVPeqjrtyJ/ZYLOlZ6aRkpABSWC6EPQk4nMj8H86G/2/vvMOjqNY//j2bXkgoAZQiRRGVoghRFIEsTRGkE0CIgiWCV69KUxSuCYqYSLjKvdafclFBISIdqdm1gSIoIjbABJCmFAu9JHl/f7yZ7OySstk2s8n7eZ7zZHZ2ypmTnXPOe9625xMAQO4fub7pfDSHcQMEDrsdeInD/WPmTENSgAiC79FmwV26APfdx/aCzz8fdLNgaxMrnu/KjsgPrnyQzXhcJklGow8O1rkzULMmN3dlSwKoCRIDswfi+tevx9EzRxEbHospnaZg1S+rnPKkiGbD/JSm7ci5OwfpSek4X3AeVydcjXzKx4DsAWj/f+2d3j/RdAhVHRE4/Ig24Lz5zZsAgNErR/tm8P/2WyAujp3GA8zmzUCHDkCDBkBkpCEpQATB9+hnwX/8AYSGAtu3O2bBQRSKLbVtKkJUCD748QOMaTfGVMKGK//+N3DjjcDy5cC5c7wviJr6IvRmVABQP64+zlw4g1MXTiEyNBLLhi7DVOtUJzMqIfgoLaLVyOtG4n99/wcFhU0HNwEAVuxcIU7lggAROPyOtYkVzROaA4B3g78WjxZgDUfr1pwzIMAj88SJHM5Sn2E8wClABMG/JCUBISHA7Nk8Cw6yUGwb922ERVlwaeyleHXLq6ae1CYmAhs2AMePA+vXB11TX4TejCrvzzwkvpGIswVncXXC1QgPCS8+Tsyogpuykgc2im+EuIg4NIprhKOnj2LmlzMxbu04jL9pvDiVC1UbIgrK0rZtWwoGbHk2Cn8mnC554RJKyEwgW57NwwvZiBISiNavJ4qNJerXjz/bPLyeF9StS3TPPQG/rSAEjowMIoBowADD3jNPsOXZKCEzgVIWpZAl3ULLfl7mXb8TAFavJlKK6Nprg6qpS8WWZ6OaGTUpZloMIQ00fs344v1m/18I3uH6Px6cPZiQBkIayJJuoeteu47ipsc5/QZseTbK+DzDqCq7DYAtZIK5n5TgLaLh8CPaSkbj+MZoc2kb79Tomu3S4MHAyZO8HGhAPNpTp4Dff3fWcAhCpWP8eCA+Hli0KKhCsWmrrXdfezcKqRChllDTr6Tfeiv76W/bBqSmBk1TO6E3pWpdtzUssODUhVNoXqs5XujxAgDRalQFXE2t7HvsSGmdgsjQSMSExeDb377FqfOnxKlcqJKIwOFHtM7n5IWTuDT2Uu8HHKsV6NqVt4cMMWRk1qKGisAhVGo++QS4cIG3X345aKIiaKYe7Ru0R6glFJ/u/dT0Dsl2Oyd5B4KqqZ3QTKk+2vUROv6vI46eOYqIkAj8dvI3cQ6vQmjvnyZIZA/Kxjv938G0LtNw8vxJtKrTCgVUgIHZA3HNy9dICF2hSiEChx+Z2GEiOjfujN9P/l6cg8OrAUfLlgUAS5caMjLn5vLfpk0DfmtBCAyaI8Hs2YDFAvTsGXSh2GLCY9CuXjt8+qv5EwAmJwMLFgCxsUDHjkHX1AC4X39/4PvoO78vfjr6E6LDorFq+CosHrJYnMOrIKU5lY9oPQKz+84GAPx09CecPH8SR04fASDaDqHyIwKHnzl2+hgKqMD7pH/ayHzddcDVVxsWj1YTOETDIVRatBC5Q4YAt93G79j77wddKLZOl3XC5gObcfrCaaOrUipaU992G9CrF7BpU/A0td6Miojw4Y8fIr8wHwAw7qZxxdmpxYyq6lGWU3nj+MaIi4jDlTWvRCEVYsjCIbjpzZskhK5Q6RGBw88cOnkIAHBptUu9u5A2Mu/fz0KHQfFoc3OB6tU5dr4gVEr0IXITEoCDB4EzZ4IuRG7nxp1xofACNu3fZHRVSkXf1IMGAUeOcICwYIh6p49I9eKXL+K1r18DAKS0TnGKDiZmVFWb0vJ37Hh4B97t/y4UFL488CVCVSia1mgqmg6h0iICh5/RZxn3iokTWdD49VdHwj8D4tHm5Yl2Q6hCjBgBKMVJAIGgitvaoWEHKCh8utfcZlUaPXsCUVGc7D0Y0LQX/Rb0w9i1nDV8RvcZeKf/O5JnQygR12zl9avVR1xEHBrGNcRvp35Dk5ea4Pb3bsf8gfPFr0OodIjA4Wd8JnAAwHff8d/rrvP+Wh6SmysCh1CF6N4dGDoU2LgReOwxFjYMiA5XUTI3ZOKbQ9/gukuuK/bjMPvE5eWXgXbtODBYYSHvM7syqVZ0LZy5cAYAMKzFMIy7eRwAiUgllExp2o5fH/sV/a/qDwLhbP5ZjFs7Do+ve1wSBgqVChE4/MyhE2xS5ROB49tv+a+m4Qgw+fnAnj3iMC5UMaZO5b8vvhg0IXI1c58m1Zvgi31fYG3uWtObaSQmchd36BDwxRfmVCbp/TaOnj6KHu/2QH5hPm5peAvW7V4nEakEt3F1LP/s188wotUIhFpCse33bZjxxQyMWzsOT3R4QhIGCpUCETj8zG8nf0NseCxiw2O9v9i2bUCdOsAlPhBePGDfPhY6RMMhVCn27QPCwth56dVXgyKEkrbCvn73epzJP4MhHwxxMuUwI1Yr8N57vD1unDmVSZogtz5vPXq82wO/n/od0WHRmGqdKmZUQoUoKYTuuwPexdoRaxEZGolCYjXfpJxJuGX2LRJCVwh6RODwM7+d+s032g2Al/8MNKeSHBxClUNbZp8wAfjrL+CJJ4Imbqu1iRX3X38/AOC6S64ztbCh0bs30KwZR6saPdpcwgbgEOT6vN8HW3/biqjQKCwftlwiUgke4+rXAQARIRHo0rgLYsNjUUiF2LBvA87mnwURAZAQukJwIgKHnzl04hAujfUyQhXASch++MEwcypAQuIKVRAtOtyUKUCNGsCWLYZEh/ME+2473t72NurE1MGGfRuCYuXdbgd+Y7c3/Pe/5pDr9GZUAPD3ub9xJp/9NsbfPN5poihmVEJFKc2vI+fuHKQnpaOQClE7ujbOF5xH97ndMTh7sITQFYISETj8zG8nfaTh+Pln4Px5QzQcmZk88OfmAuHhQP365nfmFASfoMVtnTULSEpij+ZWrXi/iV8CvZnGnS3vBADTm/toyqS5c4HQUODWW82hTNKHv939524MWzgMAHBnyzudwt8KgreUljBw7E1jkXp9KgqpEAt/WogWtVuIX4cQdIjA4WcOnfSRhmPbNv5rgIYjMZEH/i++ABo3Bj791HzOnILgVxITgY8/ZqF/7lxzejTr0E9crE2suFB4AU/d8pSpzX00ZVKfPkC3bvx5wQLjlUmaqVTywmQkvpGIswVn8eQtT2LewHnityH4lNISBj5xyxMY2nIoYsNjERkSiU/2foLLZ12OwR8MNr1vliBoiMDhR05fOI3j5457p+HQ1AvbtgEREUDz5gFfWdVyDG7cCBQUmNOZUxD8itXKCSJCQ4Fp00z/EugnLp0adYJFWfDXub9Mbe6jTwI4cCD7jNWoYY4kgNYmVjSt0RTHzh5Dp0adMK3rtOL94rch+IOSTK2WDV2G45OOo1WdVsj7Mw8dL+sowoYQNIjA4Ud+P/k7AC9D4mrqBZsNaNkS+OwzQ1ZWk5I4A3BubtBEBhUE32K1ArfdBhw9CtxxR9C8BNUjq6PNJW1g3xMcq/CZmZzgPSQEWLiQ9xlhvab33Uj/JB1fHfgKzWs1x9cHv5bwt0KJaOuD2l8AeOABLvrfcEV/z3qN5ee/fo5DJw8h+ZpkrMtbJ9o1IWgQgcNHlNTRPPpQFLDsNRz+oYXHHQ2sVrYr2LrVUPXC0qVsTdKjR9BEBhUE32K3s11hWBjHbw2il6BLky74Yt8XOH3htNFVKZfEROD++4HWrVngsNmMsV7TfDeeynkK6R+no2FcQxw7cwxTrVPFjKqKoB/PtW27Hbj99pK3c3P5t7pnD69JzJwJzJ8PzJsH9OvH38+cycfk5vI5mkAClC6cYMPFIXQXDF6A5cOWy29RCB6IKChL27ZtyUzYbEQJCURZWfz32WeJIqPPEcJOUEy1fLLZHMfYbBW8+IEDRACXKVP8Uv+ysNmIqlfn2y9b5sVzCEKwov/R33cfUUQEUa1aQfESZHyeQdM/m05IA63LXUdERLY8G2V8nmFwzUrHZiOKjeU+p0YN45p5zS9ryJJuobCpYVTz+Zpky+OKmL39hIqTkeH4nWnbWVlEMTGO7chIorg43o6P5+0ZM/hvbCzRm28SPfookVJEbdvyX6uVrxETQ3Trrbxv3DiiuXP5vOhovlZWFn+OieG/qamO+URqKtfhhrEZ1Puhot+gjeuZtchGPZ/1/28RwBYywdxPSvAWwyvgaTGDwKHvoIiIli4lCg/nTgkgQq2fCBF/UmhYASUnezFJnz6dL3jXXYbM9DMyiJ58kqvw/fe8T+vsBKFKoH/ZN23il+Gxx4LiJbDl2ahWRi2ypFnoyfVPki3PRgmZCcWTZ7Py2GPczJ06GVeHJ9c/SUgDIQ00xRb4xR7B95QkWNhsPKnXFg1TUx0CxZQpLASEhTlKo0ZEFgsLD0pR8XqgpyUsjP/WqkUUGsrziDZt+Nr/+AfRggVcH71wol/gDMSUQAQOKd4WwyvgaTGDwKFf9Ny4kahuXSruQC67rGi701SKiS0ggOiqq4jOn3c+v9z5is3GvR1A9NdfhqkXpk7lKpw+HdDbCoL5eP55oiZNiNq1c+wzuQRuy7NR6NRQqp9VPyiEDa2ba9iQKCQkcN1dxucZxW2z4dcNpNIUhU0No65vdw2KdhNKRy9caEOoXmuxciXRww/zONesGQsUJQkHcXH8t3lzFgoAou7difr04e2kJBYKundngaFnT4eG4847+fxOnRzndezI27Vr89+EBBY4Srq3xULUuDFvd+gQWCWrCBxSvC2GV8DTYgaBg8ghD2grHbGxRCkpvH1F0kZC5F8UH8/CBkB0+eVER45UQG7IyOAerHFj55sGeHIzciRRvXoBvaUgmBP9IsDWrUFjY9jhrQ6ENNDEtRONrkqZ6JvzpZcooGZVmvZn5c6VVG9GPVJpiuKeiyNbni1oNEOCA702Q2/2nJrKE//wcNYolCRcNG5MdOONvB0RwZqGuDgWJqZMKXlbG/vHjHHcS28mpQk5SvGxrufp902cyL/7rl25DjfcwPMHfR0ffjhwbSkChxRviziNe8nBg8CpU/z6R0YC6enAqlXAjBnAnk2tYCkMAxHwyivA0KHsKNa8eQV8vydO5NS7+vwbVmvAY0Xm5kqGcUEAwO/fvHm8ff/9pg+RC3BYze8Pfw8AeO3r10ztZKrl47BagQEDeN+AAYHJx6GFuR2YPRAHTx5EdFg0lgxdUpzPRELgmh+9o7cW5HHmTI73MGAAMG4cx2E5fpwDoQBAo0b8NykJqFkTmDwZ+OMP4PvvgehojkjftCmgFI/11auXvB0VxWP/228DkyYBY8fyuD98OLBkCTuQT5/Ox0RF8XkXLgDLlvG+pUs5NgwRB8SbPJmDJqSkcO7fo0d5Wyl2Sn///aCKXSFUdYyWeDwtRmk49Csmmzez7WVICFHTpg5HL+37hI4LqaF1pZNConNnKlbH6ilVaXHqFC+/PP20n57IPerVYy2HIAhFtGrFL/PjjxtdkzLRVuZX71pNUc9GUf/5/YNqpb59ezZdCRSrd60Wv40gozRNRno6aw0A1mRoZkma38TIkRdrGbKy+Brx8azZ0D5HRzu2e/Z0mGfpt7UxvLTxvCT/kdRULkSO7dJ8SsSHQ0owF8Mr4GkxSuDQOrOFC/mvUg5VqatlRf2s+jRqyaiLztXMq+66y3l/iZ2G5qC6aJH/H64UTp/mKkydalgVBMFc6EO3xcaa2pxK75dw67u30tX/vTpooixlZBCNHs3NnJvL+3xtUapvn2Onj1HN52uSJd1CSXOSgkowq4qU5JexZg1HiAIcDt0REVTsX6FFhnKNNlVSVCh3hAh/P5t+Wy+caPUJVL1E4JDibTG8Ap4WI304cnJ4dcRicYTM09Be/oLCAgqdGkpPrn+yeL/WIZ47x86QAFG/fuWsULzxBh+Yl+f/ByuF77/nKsybZ1gVBME8aC9zTg5R69ZsWB0EPhxERDM2zCCkgfb9vc/oqriFzUZUsyb3P5mZ/nGX0ftmWOdYCWmgmGkx4rdhUkrSZsyYQdS/v7OzdVQU/731Vj5G02D06uUQJvRaC6MEi2BBBA4p3hbx4fCAv/9mu8vCQrbR1Jtua+4Vx04fQ35hfnGWcb1dcng4JwwPDWW7zjIzd3/7LRAXBzRu7OenKp28PP4rPhyCAMfL3KUL8OCD7OCUlhYYJwMv6da0GwAgJy/H4Jq4h9XKyf9CQ4EXX/SPu4zmm9F3fl/Y99gRERKB5cOWi9+GiSjNL2PjRva7GD8eWLzY4ZPRtSsQE8P+DmvXsj9Fy5bsJ7FpEx9jtQIrVgD5+c5ukQa4SApClUAEDjfQd3anTwOjRwMWC883XLNuZ27IhH23Hb+d/A0AcEnsJeyg2SHTaZDMy2PBAwBmzSrD8WvbNnYYV8r3D+Ymubn8VwQOQQDPRrSX+fBh9v7ctMkxS9GnCTYZreq2Qp2YOliXt87oqriN1Qp06sQBOoYN849vfss6LZFfmA8AGH/zeFibOG5ibWLFxA4yAzUSTciw2/n//9BD7Pw9dSoLpDVq8HFRUSxk2GzOQsb06XyNsWNZYNXWBkS4EITAIQKHG+g7u9GjHXOMyZO589K+A4DEeolIXpiMVb+sAgAcOnkIyQuTkVgvsfh6djufM2QIUKsWUK+e4xpOc5XCQofAYSC5uaxkqVXL0GoIgvm45Ra24Jg/n0PIaC93YmL55xqARVnQrWk3rM9bDyIyujpuYbezohcA3nrLN1F5tIUhjWEfDsOZ/DO4sf6NeP3r100dxauqoF/os1p5rO3Th8fLtDTef/480KMHR3aKjgbCwhyRokTIEARzERCBQyl1m1Jqh1LqF6XUEyV8P1Yp9aNS6julVI5SqlEg6uUuWmc3aBAwdy53asuX837tu+LOrEgF/+ynzwIA0j5OQ/agbKcVM80iIyUFOHcO+OknXrGZP99lrrJ7N3DypCkEDi0koCAIOqxWjnl94QLHvzR5iNzMDZmoX60+fj/1e3GYXPtuOzI3mFMjo8lvCxcCHToAtWs7L/B4irYwZN9tx/OfP4+c3TmICInA9K7TkT0ou/g7IfBogoZ+oW/OHOC++3g4PHSIQ8vXqMFj6Lp1QMeObB61ZAmwaBHQpo0IGYJgOvztJAIgBEAugKYAwgFsA3CNyzFWANFF22MALCjvukY4jbduTQQQPfJI+cdqzodPrHuizOPWreOwujVr6pwhNa+4hQv5hps3G+rJ1rw50cCBhtxaEIKDyy7jd3XyZKNrUia2PBvVzKhJSANlbcwyvVO03kH45Ze5if/v/3zTFdrybFQroxZFPBNBljQLrd612um7YIjiVRnRBwZ45x0O0KI5gkdEECUnO8LXZmRcHBpWnL79A8RpXIqXxf83AG4CsEb3eRKASWUc3wbAhvKuG2iB4733qDjbZ3lRUmx5Nop8NpIin410azBPTuZr9++vXcDmCKthsRCtWmVYFJz8fI78MdHcyYkFwThsNqJq1fgl1mJkmxhbno1C0kOo6UtNTS1suHL4MC/O+DLtSeIbiYQ0OIUvFwKPXrAkIlqwwFnQ0KJOacKECBmBRwQOKd6WQJhU1QewT/d5f9G+0rgXwKqSvlBKpSqltiilthw5csSHVSwbux245x4gJAT48MOL/Tacjt1tR/LCZFx/6fW4staV5arn7XZ2cKtZk8207HY47LQWLOAvUlIMM9M4cIDtZMVhXBBKQG/zU7cu0KKFb2x+/Ii1iRVt67VF3p95uK/NfU7mnmbmf/8D2rbl7MqFhbzPG//8/371X2w+uBmJ9RKxfOdyMaEKMCVFnsrM5Azbd93FVooA+2R07cqmzACbRolfhiAEH6ZyGldKjQDQDsALJX1PRG8QUTsiale7dm2/10frEG02dkobNQrYtcvhg1FSFMzNBzcje1A2zuafRf1q9csMq6jNVbKzOdpGfj4wYIBO6IiMZEfUMuPm+gft2fURqkwcfEcQjEHrDHr0YCPzL7/ksHMmDpFr323HjqM7AACvbHklaCbaiYns7/brrxwO1RP/fM1ZfM0va/Do6kdRO7o2nu78NAZcNUD8NgKM3kcjKQno1Qt4/HFgzRoeb6OiHI7gTz3F/hl6WV6EDEEILgIhcBwA0FD3uUHRPieUUt0APAWgDxGdC0C9ykXrEHft4g6wc2fHAFdaZzexw0RYm1hx4PgBNIhrAKD0sIr63Bx33w3Ex7Oz2+bN4N71+HFe2nGNvRsAtGdfuZI///67qYPvCIIx6EPkasvu339v2hC5mgZ2waAFiAqNQpfGXYJmom21snYDAB591DP/fM1Z/MmcJ1FABXjspscwculIDG05VPJtBIDSIk/VqgW8/TaPgQCHjE9JcTiCJyfz/tIW+gRBCAL8bbMFIBRAHoAmcDiNt3A5pg3YsbyZu9cNlA/HkiVEAFGLFu67UZzLP0dIA6V/nO72fTIyiAYNIgoNJTq8wEYUH0/f4FrKuG+nf9LruoHNxnazSgVNImVBMA6bjR2eqlcnOnfOsPe2LDI+zyj22eg1rxc1fakp5eTmBJWDdIsW3CdPmuTZ+f/b+j9CGqjlKy2DyoelMqB/Jc6dI7rrLir20dD8I7t2vdgVSnw0jAfiwyHFy+J3DQcR5QN4CMAaAD8ByCaiH5RSU5VSfYoOewFALIAPlFLfKqWW+bte7tK0KVCnDvDDD+5bNh08cRAAUL9aWa4qziQmOky3vn5tM7Z2fBg9sA6JvepcHHs3QFitnOCcyBCrLkEILqxW4JlngL/+4iQ7JgyRq2lgAaDnFT2R92ceGsQ3CJrEdnY7sK/II/Dllyuu+CUivL3tbUSGROL7w99jTLsxQePDEqyUptWoWRN45x02mRo8mIe3SZOA9evFfEoQKiMB8eEgoo+I6EoiupyIphXt+xcRLSva7kZEdYnouqLSp+wrBo6jR9lSYsoU9y2b9h/fDwDFJlXuYLWy32lYGDDgy4nosW4Csms/BGu/eMcBAe5x7XZg507giisMseoShOBj/Hi2D1myhLOEmkjYcKVns54AgFW7SozRYTo0n40PPwQuuQRo1co9/3x9kr952+fh4z0fQymFrk264tUtrwaFOVkwo/fVOH2ax5KTJ4FTp4CICPbZaNfOkaxPyyYu5lOCULkwldO42XB16i4rOpWeA8fZRaUiAgfAneyAAcCZM8A94XNhvemsp1X3Gu3ZQ0KAO+5w/9kFoUrzySeczRMA/vMfU78wC39ciIZxDfHRLx8V7zNzEkDN561bN2D4cOCrr4D/+7/yJ6Wa38ayn5fh4VUPI0SFICwkDE91fEqS/PkRTbOhCQ8DBnAgtw8+YB+Nzp1Z4AAk8pQgVAVE4CgDvVM34P6qiycaDoA75/Xrgfjo85h9YjDsNQZ4Um2fsHkzz5fOn+esrrLiJAjloF+hqFnT/SV4g0isl4ijp4/CvtuOU+dPFTuUJ9YzZ2QIvX++FjZ1//7yJ6VapMAhHw7BX2f/QmRoJJYMWQJrE2uZUQQF79A0G+vXA599xpaGJ0+yFn/1auDjj8V0ShCqEiJwlIF+gNNwp0Pcf3w/YsJiEBcR5/a99HOV9Du+RjaSMXjRUMPmKhMnAgkJvH3llfxXBgNBKANthWL7dk4msGED8OKLvN9k0aoAnoinJ6XjQuEF3LvsXiQvTEb2oOyg8GlYvZpDdb/zjmNfWU0cGx6Ls/msMR5701inZywtiqBQcVz9NWbN4lfh6ad5X+vWHO5WQxayBKHqIAKHHzhwgkPiKqXcPkevTRnQdCus+Bj3D/7b0I54B4fqR/PmxtVBEIIGbYUiMZFnxErxi60t9ZowpvQ/b/wnwixhWPDDgqByoE5MBH77jZv3p5/KzslRUFiAEYtGQEFhws0TxG/Dj+j9NbKzgfvv50AoAIe53bZNtBqCUFURgcMP7D++v8LmVHptynvr62CZpS8+/qG2oeH8d+4EYmOBSy8N7H0FIajRR4B45RUOwWOyaFUaG/dthFIKsWGxQTURt1o5bwPA+RZdA4LpHcXHrhmLnX/sxPBWw5EQnSB+Gz7GVavx7rvArbdyoLYzZxw5NVatEodwQajKiMDhB/Yf34/6ce6HxHXlhr/XYZSagy83Kezc6VlGXV+wYwebU1VAUSMIAsCzqpQUdjRo08aUwobmszH+pvE4eeEk0jqnBdVEfOBAoFkzzjr+wAPOTaw5in/444d44+s3cEWNK7A6dzUS6yWK34aP0Ws19uwB/vlP/tkD7BS+ejWbvukDj4hWQxCqHiJw+JiCwgIcPHEQDapVTMNRzIULsO6Zg3d7LQAA3HuvceH8d+wQcypB8Ai7nW1HLruME+ysX290jS5i88HNyB6UjfE3j0eoJRT7ju8Lqom43Q78/jtvuwYE04SK4YuG43zheRw7c8zJP0X8NryjpNwad9zBAuCuXUB0NNC1Kyv5NESzIQhVGxE4fMzhU4dRQAUVNqkq7sF/+gk4fx63D43DsEvsuOnzTEPC+Z89C+zd63AYFwTBTfQRIF56iRP5DBhgumhVWhLAGlE1YG1sxeKfFyOpcVJQTMS1Jl64kHNytGx5cUAwAuFcwTkUUiEeuuGhoPFPCQb0Wo3CQmDuXM6rkZ8PREYCK1ZIAj9BEJwRgcPHaCFxK2xSpfXg770HANi6TWHWb8nYjESPMup6yy+/cIZx0XAIQgXRR4D4+Wd2gmrY0LG0a8KIVf2u6oedx3bi56M/G10Vt9CauHt34O67gU2bgNueycT8L7mjPF9wHiOXjISCQlKjpKDyTwkGNG3F4MHA1VcDs2ezNiMpiX02XI8TrYYgCCJw+BhPc3AU98wvvYQ9IZejYebD+DEtG59arOjdO/Dh/Hfu5L8icAhCBdFHgLjxRuD4ceDHH4FOnYxzyCoHLVnp4p8XF+8zcxJAfRMXFHCJ/CMRi0LZB6Xfv9Ox74/DiAqLwr86/0scxX2E3pSqSRPAYuGxIiSEfTU0S0LRagiC4IoIHD7mwAnPsowD4J65Vi00LsjFyRFj0OlpKzp1ArZsCfwqkRYSt1mzwN1TECodViuwYAFHXjDSIascujXthlBLKN7ZxoktzJ4EUM/tt/Pq+kcvW7FgYDb6vjcIq374DJYQYMWwFZLgz4doivj//pdjIRw5wsJGZKQjuIhoNQRBKAkROHzM/uP7ER4SjoTohIqfvG4dcOAAcOONaLzqVcBux6BB7NZRt25gV4l27ADq1QOqVQvcPQWhUtKrF9C+PWs5hgwxnbABsBP13dfejR3HduDR1Y8GVRJAqxUYNw44eBD439NJOPXZvUDjz/Bw+9GS4M8HuDqIP/AA8PDDnDk8NpaHreXLRashCELZiMDhY/Yf34961erBoirYtJqpBQA8+mhxDMEhdbgH//BD39azPHbuFIdxQfAJdjtL8CEhwFtvmc55XGPCzRMAAC9teimokgACwJQpvMo+9/OPUdj2ZXRr0g3zts8TEyofoGk1bDYgPR2YNs3x3WOPsXAhWg1BEMpDBA4f40nSPwDcU99zD28nJhb34N//bzNatOBoLBqB8DmVkLiC4AP04ZTuv5/D+AwaZEqh4+CJgwi1hKJebL2gcrLO3JCJ19bYUdDIBtx5ByyL3kOP6Mcx4KoB4rfhIa5ajblzgdtuA9LS2HwtPp6FvFdfFa2GIAjuIQKHj9Ay2x44caBY4KiQ0+XEicCJE0DNmkDTprzPagVNmIg9e4DvvuP45v70OdUGmWPHgD/+YA2HCQPqCELwoI9YFRfH3s1du5ouYpXms3Ffm/tw8ORBzOwxM2gm66GHEzF+UzIuHZIGhJ9Cu6SjeHzLMDTPH1q1/Db0UoK2/cADXADHtt3Oji92u/O27tjhnz6AX3s+gK0z7bjQ43Z8+JAdHS7YsQK3o6vFDvu/7Jgam8lxTvrZkfuA8b9hQRDMjQgcPkLLbLv3r72oX62+Z06XX30FtGvnlNrbagXefJO3R4/2r8+pS2RenDtnyoA6ghA86MMp3XYbxwxdvhy47z5TRazSkgA+1ekpKCjs/mt30EzW83dZMf6al/BrwRZEHeqKLfFPILNdNvJ3WYE9VmBDJVl214QIvWChFyJyc/n3NHMmb/fvz6qJBQt43/z5wLx5QL9+QLdu/L1+W3ds/c/mY7iah6bj+uHZL7oh85f+WIx+2HtFN8w/1x+tn+gJhIbCCjsWnu+NvQdDSxdk9EK1SQRsQRAMgIiCsrRt25bMxuKfFhPSQD3e6UEJmQlky7O5f/KpU0QhIURPPVXi15dcQgQQTZnio8qWgs1GFBvL96pRgz8LguAj3nqLX65OnYgSEkz5giXNSaLm/2lOhYWFRlfFLQoKC6jDWx0ofNyVhDQQrFNo0SJuWpM2cdlkZDgqrW3bbESpqfxAY8YQxcQQZWURxcXxdmwsfx4xgn9fLVsShYURhYYS1a3L++LjiZTiEhbG+wAii4UIoDOIoEPqUt4XEUGFStFJRFM6ptCfiKPzIRFErVvT+dBIOoFoyg8NJ4qOJnroIaJq1bguWVl8H/12RARv22x8vLadkcHPqN8WTAuALWSCuZ+U4C2GV8DTYkaBY8uBLTzgpYGm2CooGWzYwP+OJUsu+krrpwGimjX9P4DeeCPfqxTZRxAEb2jenF+wCROMrslFZHyeQWNXjyWkgb4++DUREdnybJTxubkmhBmfZxQv6Ly+5XVCGijq2Shq8++uhAkJdMlNNvMLG+UJFllZvK1N4OfMIbrrLv7tXFokGERFUbHg4FpiYojq1KHi1SOAqHVronbtePumm4huvpm3b7mFdre6gwigP2tdTgTQItWfpmIyEUDPq8dpd/Xr+NjwcNqNRiXfMyKCBZjQUKIbbiCKjOR9kZEXCySpqfw5IYG3tecXQcSUiMAhxdtieAU8LWYUOJ7KeYqQBhq9fHTFNRwvvsj/jgMHnHZrq3RvvMFfjx/v31U7m40Xvy69NEhXBwXBzNhsRNWr88scHW26F8yWZ6NaGbUoJD2Exq0ZR7Y8W8X7sgCg1WvhDwspZloMhaSHUNz0OLLl2ei6/jbChAS645/mqnMxeuFC62SzshyT8tWrif75T/6NNGlSrIG4qMTFUbG2rG9f3r7jDqJatYgmT+bv4+OJUlJYq5GS4tg3ZUqJ24e6p1ABFL2DFPoTcXQ6gr8/HxNHf6l42p2iO++xx1iYAIj69CHq0oW3mzUjql275DrXqMHCSEQEUfv2XK+pU7kNXAURTRtC5BA+RAgxDBE4pHhbDK+Ap8VsAoctz0bR06IJaaAzF85UfKAePpyoXr2Ldmt9bGEh0WWXcb/urz5XG/9q1CAaNSqITRIEwYzoX6gWLXiCWauW4wUzyWTKlmej8GfCKfa5WFMKGxq2PBtFPBNBKk1R7LRYsuXZyGbjJrVcbqPQzhnm6bv02gztd6BpMOLiiMLDeSIeEnKxgNG0KU/OAaLbb+dzSxIitH2ayVJ8PE/a4+Odza9cNA05vbPofAwfez46nsYii/5EHJ1Szseej+FjLzKZiori67oKMprQExtL1L8/Px/AWpeQEOdnjInhfRERRB078nOMHu1op+hoNiXTD0gmeV+qCiJwSPG2iNO4j9h8cDPa12+PxtUbIzI00r3Mtnrnv6++Am644SKnOs3nVCmgTx9OsnTjjf4JP7h5M/D668CffwKtWklsdUHwKfqIVY88Apw9C7Rty/tN5EBubWJFnyv74OT5k+jWpJtp83GczT+LcwXnQCA8dtNjwB4rkpOBDz4AHujBzuKGRiDW9+9aRI6ZM4ENG9hJe9w4/j0cPw6cP8/H1a8PFBYCnTtzxMIpU4CjRzn7a0oKsGoVMHgw/50xA1i6lCOfEQFRUbxv+nR2EF+8GBgxgpNNjh0LDB0KDB8OLFkCrF/P3y9ZgmsPr8fgkMX46soRmH16CF4KGYuFIUOxIHQ4tj7tODZs+RJ0KSg67+mngX/9i7effdZRh+rVebAiAmrU4G2LBbjlFiAmhut46hRnDHz4YY7c1rYt76tVi9vhs8/4/Ndf53PHjQOaNAFeew144gl+X2bO5PbMzRXHdEEIFoyWeDwtZtNwEBG1fb0t9Xi3h/snaCtdy5YRAUT33lumSmHtWj5s6VIfVbgE1q/ne6xf7797CIJARLfcwiu5EyaYSpWoaWdjpsVQ+DPhptRwnDx3kuq+UJdC0kNo0vpJlJCZQKnP2YqbcMcOhwIgoIvgpWkynnySV/kBrpjOWZvCwrjvL0tTUZqpUWqqw/+hAr4P+moSEY0d66haXNzFFl9lXkBvItaz58XbWVmswXA1HdM+u2pq7ruPtSJXXuncXtp2vXq8PXo00YoVFzuma5UWDYhPgWg4pHhZDK+Ap8VsAkdhYSFVe64aPbTyoYqdqA0oWhSRUiYdGRlEa9Zwn3zvvY5Tfd2fzpzJVTl82LfXFQTBhW+/peKJlL/Dz7mJ3hR0cs5kQhqoZkZNUwgdekfxIR8MIaSBHlzxYPF+vflXRgb7QyckEJ0+zef7df5Zkl/G7NmOSbOr30XXrrwdHW2IM7W+mv/+NznN6/U/RZ+0WUWd4/WCSPfu/PeOO1gIufxy5/bUBLfQUDZLq1ZNnNH9hAgcUrwthlfA02I2gePQiUOENNCsL2dV/OROnai8qDXaAJGUxCaw69f7Z1F05EiOoigIgp+x2XhlNjQ0MOHn3EA/qd/7116ypFto+IfDTRGlShMq3tjyBqk0RTe8cYOTkKGPpqVfx3ntNT/5o5WkzZgyhcP86f0watXiv337Ovtf9OrlmAwbEC42J8cR/TA01OGCETBlW0XC/2qfNW1IrVostGk+LprzuuYPAhANGUI0d27Jgpz4gVQYETikeFsMr4CnxWwCxyd7PiGkgVbvWl2xE7WwUHXrltvT22y8gANwoBt/DApt2/KikiAIfkSboL77Ls/2evc2lVkVEQsf7d9sT3VfqEvn888TkfEhctflrqPQqaEUPS2aamXUKlPzkpPjkOV81rQlCRnPPUeUnEx0xRXkZPoDEA0c6CxkZGXxNQyY+OqrXlhI9PjjjurqA6YZHixEH41Kq4RmlqVvP1dTrPvvd+QXCQ93/l+EhTmiYhks6AUrInBI8baI07iP2HlsJwDgylpXun+S5igaEsKOgNnZ/LkUL0erlZPKAsA11/g+23hBAfDDD+wwLgiCH9EcyA8eBHr1YifgrCyHA7kJnF4T6yXixyM/4vdTv2PpjqWw77YjeWEyEusZ59i++cBm5Bfm4/SF03gw8cEyHdq7dOFE2n/8AXTs6EV/WZrz95IlwBVXAE8+yf/LvDygdWs+LiqKnbwXLQImTQJatnQ4dCcmshO3PiKH1eqfSCA6tKrn5LC/dkYG72/TBggNdRxneLAQLVKK9hcA8vOB5cu53RITuR1nzOB21pzUr7oKiI7mfVFRwB138LlXXw2EhwNffsnHffQR0KMH0Ls3/2+mTuUfSm6uwxldC95gkndRECoFRks8nhazaTgmrJ1AEc9EUH5BvvsnZWQQvfoqEUA0fz7vK2N1RVt5atyYIwj6egXq55+5KnPm+Pa6giCUgs3GS/CRkUSDB5tgedmZdbnryJJuoUb/bmRIiFy9idd3v31HIekhFDY1jLq+3bXc+mhNWbs295cVDoRRkl/GRx9x8jy9D4HmVzBihLNfhglMeFydw9etc04yPmOGo1om+tmVTUmmWHrHdJuNNYauGpDRo/mY+vWd/38WC5tkdetmuBbKzEA0HFK8LIZXwNNiNoGj7/t9qcXLLSp+oualvX9/mYfpBwQtR2CNGr4dILKz+bpff+27awqCUA6aaYc/bSW9oNvb3QhpoFFLRgX83prfxtpf1lKzWc1IpSmKe44T/JWV60jfX955JzdttWpuzB1LizJ1553OfgKa8/eQIaX7ZZggWpK+GufOsQ+g9ggjRlx8bNDOp8vKc1KSM/rw4Y7/Z4MGzpHDwsKIrr/elP9PIxGBQ4q3RUyqfMTOYzsrZk6l8fnnHGO8fv0yD9OH8O/Th/cNHepbtff27Rz2/JprfHdNQRDKwWoFHnqIt6OjOQeDSbDvtmPrb1sRFRqFedvnwb47sEkttHxG/Rb0w64/diEqLApLhi6BtYm1zFxH+v5y1Ci2Wo2N5XRHZaY80eyO7HY++e67OQ/Ee+8B587xMT16sIlOSgrfRG8ytWlTUcV1dkkBMJcqDa0ayclcxY8/BiIjOb3H6tXO1rsGVtN79OZX2j9/7Fjg8suL841g/nyHKVZMDDdEVBQnnqpWjfOVhIfzvm++udj8qndvYOtWyf8hCJ5itMTjaTGThiO/IJ/CpobR4+ser9iJhYWs709JqfA9W7Ui6ty5wqeViLY41Lcv0VVX8b4quogjCIFHWz295hoigL159d8ZtTqu0yBM+3QaIQ1U/fnqATer+mzvZ4Q0ENJAU2yehQ+eNImbdvDgEkyHXO2OcnLY9KZmTT5Jy4odGWkK52930D/SiRNshqs9immcwwNNSSZypeUFGTCAw/A2aEAXheHt3ZvozTerXPQriIZDipdFNBw+YO/fe3Gh8ELFNRy7dgFHjrBHYwXp04eVI8eOVfjUi9AW9b76in0eTZT0WBAqN9rLlp0NzJrFS/EZGcDKlYa/iJsPbkb2oGxYm1iRX5iPmLAYtKzdslijYN9tR+YG36/oZm7ILNak/HnmTwzMHggLLOjcqDNe3fKqR1qWqVM5cfcHH3DgDevmEhzBs7KAe+4B/vEPznz9xx/sUR0Tw5onbfXbYOdvd9AeaflyoH17YM8e/mlFRjqOMdw5PNBoWhC9+is/n7UYrhqQG28E0tOBAweAO+8EIiL4GtWrAytWAPfdxxniz5wBfv4ZGD+eNV36LOjieC4Izhgt8XhazKThWLVrFSEN9Nnezyp24ptvEgFEP/5YodMyMoheeYVPfecd3uftgsqKFXy9pKQqtuolCEbiurr+8sv8IjZqZKoX0ZZno+hp0YQ00He/fVem/4Qv7pWQmUA5uTmUNCeJkAaKmRZTrt9Gmde0OdwuYmOJvsnSrXIXFhKNGkVOYVSjothvI4jt+BcvdqQD0cLeVjmtRkUozw8kKor/DhzIjXrVVc7hd8PCiNq0CerfTFlANBxSvCyGV8DTYiaB46UvXyKkgX4/+bt7J2gd28iRnMCosLBCnZHWh9WsSTRokG8GkU8+IbMlPRaEqokWBWnMGKNr4sSSn5aQSlN0xawr/B6xypZno9jnYglpoMhnIp3uVdFcIFr/mJNDNK16BvWKtlFCAtE3M3KI4uLoYOzldA5hLIkAPLHU+uMgMpXRz5cPHmQLPb9lD6/slGR+VVr0q7vv5t9MjRrkJLSGhrKkN2pUpch8LgKHFG+L4RXwtJhB4NBCNv5j5T8ofno8FRYWujcYap1YvXrsOOGBxGCzselpWJhvVqzuvZd/DePGyQqYIBiGzcaLEJGRPGGpcCxX/6JFrBqcPdjn19aHwP10z6ek0hQhDdRlThevrmvvmcEaDSL68CEbHUYCvdPwSbJX70cHUZcIoKON23DH17Wrw55fI0gmhNow8t57HKk3LMwxN5Y+3UMqGv1q4EAWXBs2pIuSQPbpwzHng9T3QwQOKd4WwyvgaTGDwKGp99u+3pYS30ismLp/wQJu/m7dPB4NtHCPyckeVF6HzcaDU6NGjs8yQAlCgNG/eJrZRmysaSYjWv8WMy2GVJqilTtXOn3nbQZy7frzt8+n6s9XJ0u6haKejaL46fHeaVP07frdd/R5ZFcqgKKdliupAIqOtunqcAR3Pd7kuFrkzZnDj2KxBO0jmZeS8n/YbCw4aIKIPgu6PpRyfDw5CSDh4URdugSV+ZUIHFK8LYZXwNNiBoGDiCgnN4cs6RZq+UrLipkZaP4bHtowaX1TZCT3ad4MJNOnc1CW1FTn65usvxOEyo1+QpOTw6sAkZFEkycbPmPUL6bM+nIWIQ0UPjXcK7+Kkli5cyVZ0i2ENFDstFjPr+86E3/vveKMd4VK0ZdoRwTQ1lYpQWc6pUf/s/j6a4efCnBx8MMgeaTgw13fj/79+R/TrBlrL7W8H6Gh/NusVs3UWg8ROKR4WwyvgKclEAKHXsWv4bqS9+PhHz0L2dixI3c2kyd7ZE6lnTJqFJuJejMX2baNnBzQBUEwAXPm8It59dWGL0+79oU95/YkpIFu+L8bvBI29Nc9c+EMdX27a4n9qVsalJImflOnEt1yi2OCB9ChpCF0AtH0NlLoiEooNrcyy8SuothsPJ8ND+cF82rVeA1LNBoGUBHfjzvvZGGkWrXi32axxDh6NNG0aaby/RCBQ4q3xfAKeFoCIXBoK2tzts6hc/nnSlxpG718NCEN9MiqR9wfeNeu5c7n9tuLblSx1Uv9uLpyJf8Xn3vO875HC4yTl+fZ+YIg+AmrlV/O/v2NrokTR04dofBnwglpoLGrxxbvr7BDd1Gfui53HfV9vy8hDaTSFKUsSqm4IKPvR8+dY5t5fQShatXoUPcUKoCi9xOzqG5doqF1bcVCR7DIG67KmxUrHNGoNH93IsOVYlWbivp+9O1brIEr/mdq+V8GDWIT7Ph4NmfIyjLE/EoEDineFsMr4GkJlEmVFoFqUPagiwZAW56NwqaGUeMXGxd/dmuQfOABbvoPP3Ts87DTOHeO+6GRIyt8ajHDhrH/emGh59cQBMHHaJMKzf57/nzn7wz254iZFkNIA1nSLbTmlzVu93+u2pLVu1ZT2NSwYs1G1sas4nu41Z+6mqLFxTlWjevVI31cWHvPDNo5JovOxSdQr2gbAUQzetto4Q0ZQTM51881581z+CS3ahW0/u6VG3d8P/TmV3FxjoWG6GgqFpotFhZKIiIc/+gASpUicEjxthheAU9LoASOCwUXisMzuppMTf14KoWkh9DEtROL97m1wvfEE6zi//tvr+uXkUHUowdR9eosfBBVfJC57DLvHc8FQfAh+onEu+9S8Yrn+vWGL13rBYEpOVMIaaCQ9BC3nbv15x85dYRavdKqWNhIWZRy0bEl9qclrSBPmMD28doE7brryow89UtqBoWHc1dco4b5hQ3XR46JcTzqtGmO/cEiOFV53DG/SkgguuMOKs7No0mXAMc9rlkzYP9sETikeFsMr4CnJVAChy3PRtHPcsKrmhk1nQbUxT8tJqSB7LvtFbvotdcSde7sm/rZHGafq1ZVfMDZu5fPnTXLJ9URBMEXuNrNjB3LL+oVVxg+o3TVUHSc3ZGQBoqfHk/Hzx4v3q8XFlzP0fJsRD4bSUgDRT8bTVNsU9w3o9J3dEePEnXoQE5mKF3dizz1yCN8+KWXOhZszIr2COvWET38MBXPO7t0ufg40WoEEaWZX+kjXmm+H3FxbGKl/c4nTAhYNUXgkOJtMbwCnpZA+nDM3TaXkAYauXik04CYuiyVqj1Xjc7nny//Ylqnsn8/N7v22Qcjw+rV3CdpC3ruzEW027/3Hlfnm29koBIE01JYSFS7Nr+sd9/t2G8C0yotNDjSQHVfqEvj14ynrI1ZlJCZQKnLUsmWZ6OsjVnF2cKX71jupNWIeCaiuE8t04zKVQhbu9aRrwQgatCAisMzuRF5SpvbtWrFpw0bRk7fmbEvXLnSObn18OGGy5+CL3HH9yMryzn6lWg4pARJMbwCnpZAR6nq8nYXavxiY7p/2f2UuiyVCgsLqcHMBjRgwYCKJfsbP56b/c03fTpSaMmJH3/cveO16vTty+bO69bJwCUIpkVTZWomFQsXGm4/4yocjF4xuliIQBoodWkqrdy5kuKnx1Pc9Dgas2IMRTwTUfx9+DPh1HF2R4qbHld6JvHSJmD33MO2oNrMu3dvtnd3zXJXiuTgasWiTeJd/XGNxjV7eNu2jkfWwt6aqb6CDynJ9yMri23pXP1AxIdDShAUwyvgaQlkHo6MzzPoqZynCGmgFza8QAmZCTR+zXhCGmj8mvEVMwMID+cZvg87CZuNfThc84S5c15ICFHTpjJgCYJp0c8otfw9FktAVzdLoqSw4QMXDGStxdQIJ+HDtUQ+G+meVsN1Nv344+SUQC06mmjEiApnuXNVlqxcyU0aHh5Qs/hy0R7lrbdYvtICGXXvbspUDYK/cf3hEkmUKilBUwyvgKclkAKHNiDGToulAQsG0CMfPVI8cLr6dZTJ8eMO9b8Hyf5KrFvRgJSTQ9SiBdGVV7ovPOzYQd7kHhQEIRC4TjL69eOXtlYt59ByJjGv0nwxOs3uREgDdZrdiTq81YGQBop6Noq6vt3Vfa0GkUO707AhP7fWh2oxYH2UuE8LHlijBtGJE863D2Szuj5+errjsSV7uGAUInBI8bYYXgFPS6AzjdvybMUOjvpSoWR/EyZwk48a5bORQj84zZrFl3/tNfcGyOHD+fhHH5WBSxCCAm2W2aIFv7w9ezrvN4l5VdbGrOJ8GnHT4yhmWgxFT4suFjTc1mqcP0/00ENUvDJyxRUsaJUSecpTyUC7ZZcufJsWLYjy841pVr1z+BNPULEyR29GpT9WNBtCIBCBQ4q3xfAKeFoCLXAQEY1dPZaQBkqak0Q1nq9BT+U8VTFzqtBQXqUrLPTLSJaWxir3Bx5wvm1JA9L69WxCkJjoOE6EDkEwMfqXtLCQqE0b7sI7dDD85dWbV2nCRNbGLEpdlkrx0+Mp4pkIytqY5SRoXOT7VlI+DS0EX2goUa9eflnid72MpkCqiLbYW1y1GgsWOAIRhYWx9ZxkDxeMRAQOKd4WwyvgaQmIwKEbBbSB8rn07jSxmwfJqTTtxvTpjn0+Xp6y2TgnUGQkW2+VNR7ffz9X5/33/VYdQRB8ieus9OxZorp1+UV2DbNt4MusFz60bb1wUa5T+LhxrMnQZwnPyfGZ6dRF9XVp1sJCTnGgKVTy8x3f+atZ9X314sWOvIW6nIUXHScIgUQEDineFsMr4GkJiMBR1Lt/M49DPH4zL4tOVY+h7P+McRIy3IpSNXkyqxT27/drlf/zHyq2tChrYLrrLl48PH3ar9URBMFf2GxsXpSQwC/9yJGO/cEyK9XX9eBBohtuICc/jeuuC3j6bK1KLVtyFa6/nujCBd83q6ugs2SJwyncYmGhw8eWY4LgMSJwSPG2GF4BT0vATKqWLaNTUaG0OzXZabRxS8jQRpSCAjaluu02v48WhYWOcPTjxpVcnRMnOLLefffJ4CUIQYl+9vv3344QsZ06mV/YcJ1pL1/OqlmLhZ9BexYD4r663qpnT65K9erO0au86TddE0zn5PB6VGQkFWs1NH/4kuokCEYgAocUb4sFQtk0aIDoswVo/EY2MGYMYLUCAKxNrJjYYWLZ5yYmAsnJwMyZwL59js+JiX6r7scfA6dO8fasWYDd7vguNBS44w7g2Wf5mFat/F4dQRD8webNQHY290dxccD27UDt2sCnnwKXXQYkJTmOtduBzEzDqgqA7691Rlo/+PzzQK9ewPDhwLlzQGEh0K8fcPQokJICrFrF51it/KybN/u9mvpmBYCPPgJatAD++ov7zHnzuDvX95vuNG9Jj791K3DzzUCPHtwnnz8PxMYCXbsCYWGOcwP4+IIgCP7DaInH0xIwDYfN5tBz63Xb7i5x2Wwc3D0iwu/LVPqVsCFDqNj8WZ/M6umn2e8yKkpWzQSh0qC94Jp24MYbOcKT0cvjrsv5NhvR6tWOKFsAe0fHxrJGw2RxX7Uq9OnjqC5ANGlS+S4lpeUsfP55ojFjnK8nvhqC2YFoOKR4WQyvgKclkD4clJnJNsUxMWwz7TrKlMX69RSoZBf6Ae7kSfYnVYqFjrg4oqVLiZo0CVh1BEEIBPrZaUEBG/5rCyQ1avjUwdotSptpjxzpbDdUpw45zbT95BTuKa6T/mHDqNi/AiBq1oz71xkzHFWPjnbIS1pS6Kws/n7yZCo2zwIc2c2josRXQzA/InBI8bYYXgFPS6CjVBVn+L3sMh5FShM29Ofk53Mab6WIkpICvmS1ciU5LSLWr8/bMTESYlEQKg0lZR9u1crx4g8cyLPiQE3kXWfqU6ZQsbpV65C6deNjTDzTLkluSklhoaJZM8ejWCyOoFq9e3MI2wcf5L8dOpCTkKGVm27ix46Odjy+aDUEMyMChxRvi+EV8LQYkYeDrFZusjZtnPfrB0j9qDF2LDl5AAZ4RLHZeKC7/noqHujCw0VtLwiVGu3FHjCAnGa5jz3mWy2CfkauN53KyGBP6NhYR9herURGmtJ0qixcq5eVxdUfMoQjSdWr5/yIrkWTs66/nhVO2uP36nXxsGASWUsQLkIEDineFnEadxe7nR0zGzdmb78773Ts13sQah5+gwcD//kPYLEAK1bw/gB6/2nVWrQImDGD/UpDQ8UZURAqNdqLn50NfPghMGIE7w8LA/79b+C114Dx44FJk/jF98YDWvN+ttu5c7n9dqBvX2DvXuC++4CTJ4HffweuugqoVg2IjgbCw4GoKO6Upk8PuFO4J+gdye12rvaMGcD11wNpacChQ9zMNWuyAzjAATkAYOBAID6efeC3bgUmTwZatuTzN23iY/SPb7UCE8uJRSIIghCMKCIyug4e0a5dO9qyZUtgbqYfxDt04LAlv/wCXH45cOQIsGQJjxbaoP3xx8Ds2cD+/bzvq68CU08dmZmO6mhVB4D581kI0UdiEQShkqC9+NrsODkZ6NmThY/GjYEff+TjwsKA9u2Bzz/n2W9+PgsNU6YAzzwDjB0LPPAAHzt0KPDCC8CECfxZ60QmTQJ+/pk/nz0LFBRwpCmAF1oiIoA+fbizuf12YNw4/k7fIW3eHFQz7JKad9IkR/ONHw906wasX+/4q2/e6dOdhZcge3yhCqOU+pqI2hldDyGICYQaBcBtAHYA+AXAEyV8HwFgQdH3mwA0Lu+aATWpcrWR/vtv1o1rCaqWLOHv4+NZx64l4goP530GmgqUZN4tantBqOSUZgeUnMxOXFFRVOyAEB7O9j5KEY0a5XDyjovjY+Pi+LPWv82Y4QjbpF1HcwLv1InM7AjuS0ry8RgzxuEoHh3NnyvhowtVEIhJlRQvi981HEqpEAA7AXQHsB/AZgDDiOhH3TEPAmhNRKOVUkMB9CeiIWVdN6AaDlfsdjaZuvxy1l4oBTRpAuzZ41jhi4kBli/nbW1FT1QKgiAEAneW4rt3Bz77DAgJYfMnjZAQ1lbUqAH8/Tfvi40Fjh93vkdkJGs2wsOB1FTgnXe4L2zXjpfulyxx9HmVfDlfa25N0a3XYGj7K+mjC1UE0XAI3hIIgeMmAGlEdGvR50kAQETTdcesKTrmC6VUKIDfANSmMipnmMChN6+yWoG33uLBtrCQBZDTp9mod8oUYOpUxzky4giCYATuCB+DBgFr13If9s03QJ06wOHDwJVXshCxYwdnqatWDVizxnG8do2nn+b+jogFDUAWWgShEiECh+AtoQG4R30A+3Sf9wO4sbRjiChfKfU3gFoAjuoPUkqlAkgFgMsuu8xf9S0b11S0TZvyIKyt6inFwsarrzocxbUiCIIQaPQLHa4e0MnJDieD9u0dmo/169nTeelSR5/20ku8nZICzJ3r7JfRuzf7frRp41hc0XtCC4IgCFWaQAgcPoOI3gDwBsAaDkMqoR+8tQF78WL+3L8/r/BpAoas8AmCYCbKEz5Gj2bTqBkzgPR0Nq0KCQGqV2dhg8g5yhTA569Y4RAutP5OFloEQRCEIgIhcBwA0FD3uUHRvpKO2V9kUhUP4FgA6uYd+gE7M9MheMgKnyAIZqck4WPzZvY9s1rZjApwRKnS929jxzq0GaLFFQRBEMohED4coWCn8a5gwWIzgDuJ6AfdMf8A0ErnND6AiJLLuq6hTuOCIAiCIAhVBPHhELzF7xqOIp+MhwCsARACYDYR/aCUmgoOs7YMwFsA3lVK/QLgDwBD/V0vQRAEQRAEQRD8T0B8OIjoIwAfuez7l277LIDBgaiLIAiCIAiCIAiBw2J0BQRBEARBEARBqLyIwCEIgiAIgiAIgt8QgUMQBEEQBEEQBL8hAocgCIIgCIIgCH5DBA5BEARBEARBEPyGCByCIAiCIAiCIPgNETgEQRAEQRAEQfAbInAIgiAIgiAIguA3ROAQBEEQBEEQBMFvKCIyug4eoZQ6AmCv0fUwCQkAjhpdiSBE2s1zpO08Q9rNc6TtPEfazjOk3Rw0IqLaRldCCF6CVuAQHCilthBRO6PrEWxIu3mOtJ1nSLt5jrSd50jbeYa0myD4DjGpEgRBEARBEATBb4jAIQiCIAiCIAiC3xCBo3LwhtEVCFKk3TxH2s4zpN08R9rOc6TtPEPaTRB8hPhwCIIgCIIgCILgN0TDIQiCIAiCIAiC3xCBQxAEQRAEQRAEvyECR5CglJqtlDqslPq+lO+HK6W+U0ptV0ptVEpdG+g6mpXy2k53XKJSKl8pNShQdTMz7rSbUipJKfWtUuoHpdQngayfmXHjfY1XSi1XSm0rartRga6jGVFKNVRK2ZVSPxa1yyMlHKOUUrOUUr8U9XnXG1FXM+Fmu8kYUQLutJ3uWBkjBMFDROAIHuYAuK2M73cD6ExErQA8A3F20zMHZbcdlFIhADIArA1EhYKEOSij3ZRS1QG8AqAPEbUAMDgw1QoK5qDs39w/APxIRNcCSAKQpZQKD0C9zE4+gHFEdA2A9gD+oZS6xuWYngCaFZVUAK8GtoqmxJ12kzGiZNxpOxkjBMFLROAIEojoUwB/lPH9RiL6s+jjlwAaBKRiQUB5bVfEwwA+BHDY/zUKDtxotzsBLCKiX4uOl7Yrwo22IwDVlFIKQGzRsfmBqJuZIaJDRPRN0fYJAD8BqO9yWF8A7xDzJYDqSqlLA1xVU+FOu8kYUTJu/uYAGSMEwStE4Kic3AtgldGVCBaUUvUB9IeslFaUKwHUUEp9rJT6Wil1l9EVCiL+C+BqAAcBbAfwCBEVGlslc6GUagygDYBNLl/VB7BP93k/Sp4gVknKaDc9MkaUQGltJ2OEIHhPqNEVEHyLUsoKHkxuMbouQcSLAB4nokJecBbcJBRAWwBdAUQB+EIp9SUR7TS2WkHBrQC+BdAFwOUA1imlPiOi44bWyiQopWLBq8mPSpu4jzvtJmNEyZTTdi9CxghB8AoROCoRSqnWAN4E0JOIjhldnyCiHYD5RQNJAoDblVL5RLTE0FqZn/0AjhHRKQCnlFKfArgWgAgc5TMKwPPEiZB+UUrtBnAVgK+MrZbxKKXCwBO/eUS0qIRDDgBoqPvcoGhflcaNdpMxohTcaDsZIwTBS8SkqpKglLoMwCIAKbLCXDGIqAkRNSaixgAWAnhQBhK3WArgFqVUqFIqGsCNYPtnoXx+BWuGoJSqC6A5gDxDa2QCinxa3gLwExHNLOWwZQDuKopW1R7A30R0KGCVNCHutJuMESXjTtvJGCEI3iMajiBBKfU+OJpNglJqP4CnAYQBABG9BuBfAGoBeKVoFSafiNoZU1tz4UbbCSVQXrsR0U9KqdUAvgNQCOBNIioz9HBVwY3f3DMA5iiltgNQYHONowZV10x0AJACYLtS6tuifU8CuAwobruPANwO4BcAp8HaoqqOO+0mY0TJuNN2giB4iWKNviAIgiAIgiAIgu8RkypBEARBEARBEPyGCByCIAiCIAiCIPgNETgEQRAEQRAEQfAbInAIgiAIgiAIguA3ROAQBEEQBEGohCilZiulDiulyo0gqJQarZTarpT6Vin1uVLqmkDUUagaSJQqQRAEQRCESohSqhOAkwDeIaKW5Rwbp2VZV0r1AecbuS0A1RSqAKLhEARBEARBqIQQ0acA/tDvU0pdrpRarZT6Win1mVLqqqJjj+sOiwEgK9KCzxCBQxCEoEUp9W+l1KO6z2uUUm/qPmcppcb6+J4nfXy96kqpB3WfG7tj/lB0bCul1F6l1JgyjolSSn2ilApx85rhSqlPlVKSGFYQKidvAHiYiNoCGA/gFe0LpdQ/lFK5ADIB/NOg+gmVEBE4BEEIZjYAuBkAlFIWAAkAWui+vxnARgPqVRGqA3iwvINKgoi2AxgK4K4yDrsHwCIiKnDzmucB5AAY4kmdBEEwL0qpWHC/+EFRZvXXAVyqfU9ELxPR5QAeBzDZkEoKlRIROARBCGY2AripaLsFgO8BnFBK1VBKRQC4GsA3SqklReYDPyilUrWTlVLPK6X+ofucppQar5QaoZT6qsh58vWStAOlHVOkofhJKfV/Rfdbq5SKKvpuilJqR5FD5vtKqfEAngdwedF1Xii6fEhJ55fCYTgLWa4MB7BUV7eflVJzlFI7lVLzlFLdlFIblFK7lFI3FJ2zpOg8QRAqFxYAfxHRdbpydQnHzQfQL7BVEyozInAIghC0ENFBAPlKqcvAq3ZfANgEFkLaAdhetGJ/T5H5QDsA/1RK1Sq6xAIAybpLJhedPwRAByK6DkABXCbfSqmryzmmGYCXiagFgL8ADFRKJQIYCOBaAD2L6gIATwDILRr4J5R2fhnN8DyACKVUI9cvlFLhAJoS0R7d7isAZAG4qqjcCeAWsGnFk0XHfA8gsYx7CoIQhBT5aexWSg0GAMVcW7TdTHdoLwC7DKiiUEkRG11BEIKdjWBh42YAMwHUL9r+G2xyBbCQ0b9ouyF4Qn+MiLYqpeoopeoBqA3gT7BA0BbAZqUUAESBtQh6upZzzG4i+rZo+2sAjcHmXkuJ6CyAs0qp5WU8U0nnX4RSqifYuXMlWMux1+WQBLDA4nrt7UXn/wAgh4hIKbVduw8RFSilziulqhHRiTLqKQiCiVFKvQ8gCUCCUmo/gKfBiyOvKqUmAwgDazO2AXhIKdUNwAVwX3i3IZUWKiUicAiCEOxofhytwCvz+wCMA3AcwP+UUkkAugG4iYhOK6U+BhCpO/8DAIMAXALWeCgAbxPRpDLuWd4x53TbBWCBpCKUe75SKhJABoA+AEYBaAngI5fDzsD5WV2vXaj7XAjnMSECwNkK1lsQBBNBRMNK+eqicLdE9IifqyNUYcSkShCEYGcjgN4A/iCiAiL6A+yIfVPRd/EA/iwSNq4C0N7l/AVgx+tBYOEjB8AgpVQdAFBK1SzBXMmdY1zZAOAOpVRkkeNm76L9JwBUq+hDgx063ykyl9oOFjicIKI/wf4grkJHmRSZnB0logse1EsQBEEQnBCBQxCEYGc72HToS5d9fxPRUQCrAYQqpX4C+zvojwMR/QCe8B8gokNE9CN4Mr9WKfUdgHXQRXEpOqfcY1whos0AlgH4DsAqXR2PAdiglPpe5zReJkqp5gC6A3hR97ylJfVaC/bRqAhWsJmWIAiCIHiNZBoXBEEIEEqpWCI6qZSKBvApgFQi+sbP97wewGNElFKBcxYBeIKIdvqvZoIgCEJVQXw4BEEQAscbSqlrwH4Vb/tb2AAAIvpGKWVXSoW4k4ujKLLVEhE2BEEQBF8hGg5BEARBEARBEPyG+HAIgiAIgiAIguA3ROAQBEEQBEEQBMFviMAhCIIgCIIgCILfEIFDEARBEARBEAS/IQKHIAiCIAiCIAh+QwQOQRAEQRAEQRD8hggcgiAIgiAIgiD4jf8HJV4UfWOtLmcAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -238,7 +238,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x216 with 1 Axes>"
       ]
@@ -309,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.8"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/Bragg-mirror/validation-Bragg.py b/examples/Bragg-mirror/validation-Bragg.py
index 699f8499..2a4cba41 100755
--- a/examples/Bragg-mirror/validation-Bragg.py
+++ b/examples/Bragg-mirror/validation-Bragg.py
@@ -122,13 +122,13 @@ def U(k):
 # Incidence angle Phi_i = 0, 's' polarization
 data = s.evaluate(lbda_list, 0)
 
-R_ss_0 = data.R[:, 1, 1]
+R_ss_0 = data.R_ss
 
 # Incidence angle Phi_i = pi/4, 's' and 'p' polarizations
 data2 = s.evaluate(lbda_list, np.rad2deg(pi/4))
 
-R_ss = data2.R[:, 1, 1]
-R_pp = data2.R[:, 0, 0]
+R_ss = data2.R_ss
+R_pp = data2.R_pp
 
 # %% [markdown]
 # ## Plotting
diff --git a/examples/Liquid crystals/cholesteric-example.ipynb b/examples/Liquid crystals/cholesteric-example.ipynb
index c6b52d38..56b26efa 100644
--- a/examples/Liquid crystals/cholesteric-example.ipynb
+++ b/examples/Liquid crystals/cholesteric-example.ipynb
@@ -109,10 +109,10 @@
    "source": [
     "data = s.evaluate(lbda_list, 0)\n",
     "\n",
-    "T_pp = data.T[:, 0, 0]\n",
-    "T_ps = data.T[:, 0, 1]\n",
-    "T_ss = data.T[:, 1, 1]\n",
-    "T_sp = data.T[:, 1, 0]\n",
+    "T_pp = data.T_pp\n",
+    "T_ps = data.T_ps\n",
+    "T_ss = data.T_ss\n",
+    "T_sp = data.T_sp\n",
     "\n",
     "# Transmission coefficients for incident unpolarized light:\n",
     "T_pn = 0.5 * (T_pp + T_ps)\n",
@@ -126,15 +126,15 @@
     "\n",
     "# Right-circular wave is reflected in the stop-band.\n",
     "# R_LR, T_LR close to zero.\n",
-    "R_RR = data.Rc[:, 1, 1]\n",
-    "R_LR = data.Rc[:, 0, 1]\n",
-    "T_RR = data.Tc[:, 1, 1]\n",
-    "T_LR = data.Tc[:, 0, 1]\n",
+    "R_RR = data.Rc_RR\n",
+    "R_LR = data.Rc_LR\n",
+    "T_RR = data.Tc_RR\n",
+    "T_LR = data.Tc_LR\n",
     "\n",
     "# Left-circular wave is transmitted in the full spectrum.\n",
     "# T_RL, R_RL, R_LL close to zero, T_LL close to 1.\n",
-    "T_LL = data.Tc[:, 0, 0]\n",
-    "R_LL = data.Rc[:, 0, 0]"
+    "T_LL = data.Tc_LL\n",
+    "R_LL = data.Rc_LL"
    ]
   },
   {
@@ -220,7 +220,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -274,7 +274,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x216 with 1 Axes>"
       ]
@@ -322,7 +322,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.8"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/Liquid crystals/cholesteric-example.py b/examples/Liquid crystals/cholesteric-example.py
index c14f4a78..e95692f2 100644
--- a/examples/Liquid crystals/cholesteric-example.py
+++ b/examples/Liquid crystals/cholesteric-example.py
@@ -56,10 +56,10 @@
 # %%
 data = s.evaluate(lbda_list, 0)
 
-T_pp = data.T[:, 0, 0]
-T_ps = data.T[:, 0, 1]
-T_ss = data.T[:, 1, 1]
-T_sp = data.T[:, 1, 0]
+T_pp = data.T_pp
+T_ps = data.T_ps
+T_ss = data.T_ss
+T_sp = data.T_sp
 
 # Transmission coefficients for incident unpolarized light:
 T_pn = 0.5 * (T_pp + T_ps)
@@ -73,15 +73,15 @@
 
 # Right-circular wave is reflected in the stop-band.
 # R_LR, T_LR close to zero.
-R_RR = data.Rc[:, 1, 1]
-R_LR = data.Rc[:, 0, 1]
-T_RR = data.Tc[:, 1, 1]
-T_LR = data.Tc[:, 0, 1]
+R_RR = data.Rc_RR
+R_LR = data.Rc_LR
+T_RR = data.Tc_RR
+T_LR = data.Tc_LR
 
 # Left-circular wave is transmitted in the full spectrum.
 # T_RL, R_RL, R_LL close to zero, T_LL close to 1.
-T_LL = data.Tc[:, 0, 0]
-R_LL = data.Rc[:, 0, 0]
+T_LL = data.Tc_LL
+R_LL = data.Rc_LL
 
 # %% [markdown]
 # ## Text output: 
diff --git a/examples/Liquid crystals/twisted-nematic.ipynb b/examples/Liquid crystals/twisted-nematic.ipynb
index c6e5a5be..e3583bea 100644
--- a/examples/Liquid crystals/twisted-nematic.ipynb
+++ b/examples/Liquid crystals/twisted-nematic.ipynb
@@ -125,11 +125,11 @@
    "source": [
     "TN.set_divisions(7)\n",
     "data = s.evaluate(lbda_list, 0)\n",
-    "T7 = np.real(data.T[:, 0, 0])\n",
+    "T7 = np.real(data.T_pp)\n",
     "\n",
     "TN.set_divisions(18)\n",
     "data2 = s.evaluate(lbda_list, 0)\n",
-    "T18 = np.real(data2.T[:, 0, 0])"
+    "T18 = np.real(data2.T_pp)"
    ]
   },
   {
@@ -267,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.8"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/Liquid crystals/twisted-nematic.py b/examples/Liquid crystals/twisted-nematic.py
index 6c625111..bc001073 100644
--- a/examples/Liquid crystals/twisted-nematic.py
+++ b/examples/Liquid crystals/twisted-nematic.py
@@ -67,11 +67,11 @@
 # %%
 TN.set_divisions(7)
 data = s.evaluate(lbda_list, 0)
-T7 = np.real(data.T[:, 0, 0])
+T7 = np.real(data.T_pp)
 
 TN.set_divisions(18)
 data2 = s.evaluate(lbda_list, 0)
-T18 = np.real(data2.T[:, 0, 0])
+T18 = np.real(data2.T_pp)
 
 # %% [markdown]
 # ## Plot 
diff --git a/examples/Liquid crystals/validation-cholesteric.ipynb b/examples/Liquid crystals/validation-cholesteric.ipynb
index 1a1e7087..b20a4cb5 100644
--- a/examples/Liquid crystals/validation-cholesteric.ipynb
+++ b/examples/Liquid crystals/validation-cholesteric.ipynb
@@ -122,7 +122,7 @@
     "# Jones matrices for the circular wave basis\n",
     "# Right-circular wave is reflected in the stop-band\n",
     "# R_LR, T_LR close to zero\n",
-    "R_RR = data.Rc[:, 1, 1]"
+    "R_RR = data.Rc_RR"
    ]
   },
   {
@@ -143,7 +143,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -239,7 +239,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.8"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/Liquid crystals/validation-cholesteric.py b/examples/Liquid crystals/validation-cholesteric.py
index a72192e3..eb2cb3b5 100644
--- a/examples/Liquid crystals/validation-cholesteric.py
+++ b/examples/Liquid crystals/validation-cholesteric.py
@@ -69,7 +69,7 @@
 # Jones matrices for the circular wave basis
 # Right-circular wave is reflected in the stop-band
 # R_LR, T_LR close to zero
-R_RR = data.Rc[:, 1, 1]
+R_RR = data.Rc_RR
 
 # %% [markdown]
 # ## Plotting
diff --git a/src/elli/result.py b/src/elli/result.py
index 820aac18..baf8ffba 100644
--- a/src/elli/result.py
+++ b/src/elli/result.py
@@ -1,123 +1,180 @@
 # Encoding: utf-8
+import numpy as np
+import numpy.typing as npt
+from numpy.lib.scimath import sqrt
 
 
 class Result:
     """Record of a simulation result."""
-    __result = None
 
     @property
-    def psi(self):
-        return self.__result.psi
+    def rho(self) -> npt.NDArray:
+        rho = np.dot(self.rho_matrix, self.experiment.jones_vector)
+        rho = rho[:, 0] / rho[:, 1]
+        return rho
 
     @property
-    def delta(self):
-        return self.__result.delta
+    def psi(self) -> npt.NDArray:
+        return np.rad2deg(np.arctan(np.abs(self.rho)))
 
     @property
-    def rho(self):
-        return self.__result.rho
+    def delta(self) -> npt.NDArray:
+        return -np.angle(self.rho, deg=True)
 
     @property
-    def rho_matrix(self):
-        return self.__result.rho_matrix
+    def rho_matrix(self) -> npt.NDArray:
+        r_ss = self.jones_matrix_r[..., 1, 1]
+        return self.jones_matrix_r / r_ss[:, None, None]
 
     @property
-    def psi_matrix(self):
-        return self.__result.psi_matrix
+    def psi_matrix(self) -> npt.NDArray:
+        return np.rad2deg(np.arctan(np.abs(self.rho_matrix)))
 
     @property
-    def delta_matrix(self):
-        return self.__result.delta_matrix
+    def delta_matrix(self) -> npt.NDArray:
+        return -np.angle(self.rho_matrix, deg=True)
 
     @property
-    def mueller_matrix(self):
-        return self.__result.mueller_matrix
+    def mueller_matrix(self) -> npt.NDArray:
+        a = np.array([[1, 0, 0, 1],
+                      [1, 0, 0, -1],
+                      [0, 1, 1, 0],
+                      [0, 1j, -1j, 0]])
 
-    @property
-    def jones_matrix_r(self):
-        return self.__result.jones_matrix_r
+        # Kronecker product of S and S*
+        s_kron_s_star = np.einsum('aij,akl->aikjl', np.conjugate(self.rho),
+                                  self.rho).reshape([self.rho.shape[0], 4, 4])
+
+        mueller_matrix = np.real(a @ s_kron_s_star @ np.linalg.inv(a))
+        mm11 = mueller_matrix[:, 0, 0]
+
+        return mueller_matrix / mm11[:, None, None]
 
     @property
-    def jones_matrix_t(self):
-        return self.__result.jones_matrix_t
+    def jones_matrix_t(self) -> npt.NDArray:
+        return self._jones_matrix_t
 
     @property
-    def jones_matrix_rc(self):
-        return self.__result.jones_matrix_rc
+    def jones_matrix_r(self) -> npt.NDArray:
+        return self._jones_matrix_r
 
     @property
-    def jones_matrix_tc(self):
-        return self.__result.jones_matrix_tc
+    def jones_matrix_tc(self) -> npt.NDArray:
+        c = 1 / sqrt(2) * np.array([[1, 1], [1j, -1j]])
+        return np.einsum('ij,...jk,kl->...il', np.linalg.inv(c), self._jones_matrix_t, c)
 
     @property
-    def R(self):
-        return self.__result.R
+    def jones_matrix_rc(self) -> npt.NDArray:
+        c = 1 / sqrt(2) * np.array([[1, 1], [1j, -1j]])
+        d = 1 / sqrt(2) * np.array([[-1, -1], [-1j, 1j]])
+        return np.einsum('ij,...jk,kl->...il', np.linalg.inv(d), self._jones_matrix_r, c)
 
     @property
-    def T(self):
-        return self.__result.T
+    def R(self) -> npt.NDArray:
+        return np.abs(self._jones_matrix_r) ** 2
 
     @property
-    def Rc(self):
-        return self.__result.Rc
+    def T(self) -> npt.NDArray:
+        return np.abs(self._jones_matrix_t) ** 2 * self._power_correction[:, None, None]
 
     @property
-    def Tc(self):
-        return self.__result.Tc
+    def Rc(self) -> npt.NDArray:
+        return np.abs(self.jones_matrix_rc) ** 2
 
-    def __init__(self, solver: "Solver") -> None:
+    @property
+    def Tc(self) -> npt.NDArray:
+        return np.abs(self.jones_matrix_tc) ** 2 * self._power_correction[:, None, None]
+
+    def __init__(self, experiment: "Experiment",
+                 jones_matrix_r: npt.NDArray,
+                 jones_matrix_t: npt.NDArray,
+                 power_correction: npt.NDArray = None) -> None:
+        """Creates result object, to store simulation data. Gets called by solvers.
+
+        Args:
+            experiment (Experiment): Evalatued experiment, with structure and experimental parameters.
+            jones_matrix_r (npt.NDArray): Jones matrix for the reflection direction.
+            jones_matrix_t (npt.NDArray): Jones matrix for the transmission direction.
+            power_correction (npt.NDArray): Correction factors, to get the power transmission values.
         """
+        self.experiment = experiment
+        self._jones_matrix_r = jones_matrix_r
+        self._jones_matrix_t = jones_matrix_t
+        if power_correction is None:
+            self._power_correction = np.ones_like(self.experiment.lbda)
+        else:
+            self._power_correction = power_correction
+
+    def get(self, name: str) -> npt.NDArray:
+        """Return the data for the requested variable 'name'.
+
+        Args:
+            name (str): Variable name to return.
+                Examples for 'name':
+                'r_sp' : Amplitude reflection coefficient from 's' to 'p' polarization.
+                'r_LR' : Reflection from circular right to circular left polarization.
+                'T_pp' : Power transmission coefficient from 'p' to 'p' polarization.
+                'Ψ_ps', 'Δ_pp' : Ellipsometry parameters.
+                'psi', 'delta', 'rho': Reduced ellipsometry parameters, the whole matricies are returned by 'psi_matrix'.
+
+        Returns:
+            npt.NDArray: Array of data.
+        """
+        return self.__getattr__(name)
+
+    def __getattr__(self, name: str) -> npt.NDArray:
+        """Return the data for the requested variable 'name'.
+
+        Args:
+            name (str): Variable name to return.
+                Examples for 'name'...
+                'r_sp' : Amplitude reflection coefficient from 's' to 'p' polarization.
+                'r_LR' : Reflection from circular right to circular left polarization.
+                'T_pp' : Power transmission coefficient from 'p' to 'p' polarization.
+                'Ψ_ps', 'Δ_pp' : Ellipsometry parameters. 
+                'psi', 'delta', 'rho': Reduced ellipsometry parameters, the whole matricies are returned by 'psi_matrix'.
+
+        Returns:
+            npt.NDArray: Array of data.
+        """
+        names = name.split('_', 1)
+
+        if names[0] == 'Ψ':
+            names[0] = 'psi'
+        elif names[0] == 'Δ':
+            names[0] = 'delta'
+        elif names[0] == 'ρ':
+            names[0] = 'rho'
 
+        if names[0] not in ['psi', 'delta', 'rho', 'mueller', 
+                            'r', 't', 'rc', 'tc', 'R', 'T', 'Rc', 'Tc']:
+            raise AttributeError(f"'Result' object has no attribute '{name}'")
+
+        if len(names) == 1:
+            return self.__getattribute__(names[0])
+
+        if names[0] in ['psi', 'delta', 'rho', 'mueller']:
+            if names[1] == 'matrix':
+                return self.__getattribute__(names[0] + '_matrix')
+
+            (i, j) = map(self._polar_index, names[1])
+            return self.__getattribute__(names[0] + '_matrix')[:, i, j]
+
+        (i, j) = map(self._polar_index, names[1])
+        return self.__getattribute__(names[0])[:, i, j]
+
+    def _polar_index(self, index: str) -> int:
+        """Return polarization index for character 'index'.
+
+        Args:
+            index (str): Polarisation index, valid are: 'p', 's', 'R', 'L'.
+        Returns:
+            int: 'p', 'L' -> 0 
+                 's', 'R' -> 1
         """
-        self.__result = solver
-
-    # def get(self, name):
-    #     """Return the data for the requested coefficient 'name'.
-    #
-    #     Examples for 'name'...
-    #     'r_sp' : Amplitude reflection coefficient from 's' to 'p' polarization.
-    #     'r_LR' : Reflection from circular right to circular left polarization.
-    #     'T_pp' : Power transmission coefficient from 'p' to 'p' polarization.
-    #     'Ψ_ps', 'Δ_pp' : Ellipsometry parameters.
-    #
-    #     Note : 'Ψ', 'Δ' are shortcuts for 'Ψ_pp' and 'Δ_pp', which are the only
-    #     non zero parameters for samples with isotropic layers.
-    #
-    #     For more information about the definition of the...
-    #     * ellipsomtery parameters see getEllipsometryParameters()
-    #     * circular polarization, see getCircularJones()
-    #
-    #     Returns : array of values
-    #     """
-    #     param = name[0]
-    #
-    #     # Read the requested indices...
-    #     (i, j) = map(self._polarIndex, name[2:4]) if len(name) > 1 else (0, 0)
-    #
-    #     # Select the requested array...
-    #     if param == 'r':
-    #         M = self.Tc_ri if self.circular else self.T_ri
-    #     elif param == 't':
-    #         M = self.Tc_ti if self.circular else self.T_ti
-    #     elif param == 'R':
-    #         M = self.Rc if self.circular else self.R
-    #     elif param == 'T':
-    #         M = self.Tc if self.circular else self.T
-    #     elif param == 'Ψ' or param == 'psi':
-    #         M = self.Psi
-    #     elif param == 'Δ' or param == 'delta':
-    #         M = self.Delta
-    #
-    #     # Return the requested data...
-    #     return M[..., i, j]
-    #
-    # def _polarIndex(self, char):
-    #     """Return polarization index for character 'char'.
-    #
-    #     Returned value : 'p', 'L' -> 0
-    #                      's', 'R' -> 1
-    #     """
-    #     if char in ['p', 'L']:
-    #         return 0
-    #     elif char in ['s', 'R']:
-    #         return 1
+        if index in ['p', 'L']:
+            return 0
+        if index in ['s', 'R']:
+            return 1
+
+        return ValueError('Wrong index given for variable.')
diff --git a/src/elli/solver.py b/src/elli/solver.py
index 0c65e708..ed5a40b4 100644
--- a/src/elli/solver.py
+++ b/src/elli/solver.py
@@ -1,5 +1,6 @@
 # Encoding: utf-8
 from abc import ABC, abstractmethod
+from copy import deepcopy
 
 from .result import Result
 
@@ -14,79 +15,21 @@ class Solver(ABC):
     Because of this, this class should never be called.
     """
 
+    experiment = None
     structure = None
     lbda = None
     theta_i = None
     jones_vector = None
     permittivity_profile = None
 
-    @property
-    def rho(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def psi(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def delta(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def rho_matrix(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def psi_matrix(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def delta_matrix(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def mueller_matrix(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def jones_matrix_t(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def jones_matrix_r(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def jones_matrix_tc(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def jones_matrix_rc(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def R(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def T(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def Rc(self):
-        raise ValueError('Value not calculated by solver')
-
-    @property
-    def Tc(self):
-        raise ValueError('Value not calculated by solver')
-
     @abstractmethod
     def calculate(self) -> Result:
         pass
 
     def __init__(self, experiment: "Experiment") -> None:
-        self.structure = experiment.structure
-        self.lbda = experiment.lbda
-        self.theta_i = experiment.theta_i
-        self.jones_vector = experiment.jones_vector
+        self.experiment = deepcopy(experiment)
+        self.structure = self.experiment.structure
+        self.lbda = self.experiment.lbda
+        self.theta_i = self.experiment.theta_i
+        self.jones_vector = self.experiment.jones_vector
         self.permittivity_profile = self.structure.get_permittivity_profile(self.lbda)
diff --git a/src/elli/solver2x2.py b/src/elli/solver2x2.py
index 72e3cb82..fe0fe78c 100644
--- a/src/elli/solver2x2.py
+++ b/src/elli/solver2x2.py
@@ -13,32 +13,6 @@ class Solver2x2(Solver):
     Cannot handle anisotropy or anything fancy,
     thus Jonas and Mueller matrices cannot be calculated (respective functions return None).
     '''
-    _rtots = None
-    _ttots = None
-    _rtotp = None
-    _ttotp = None
-
-    @property
-    def psi(self):
-        if self._rtotp is None or self._rtots is None:
-            return None
-        return np.rad2deg(np.arctan(np.abs(self._rtotp / self._rtots)))
-
-    @property
-    def rho(self):
-        if self._rtotp is None or self._rtots is None:
-            return None
-        return self._rtotp / self._rtots
-
-    @property
-    def delta(self):
-        if self._rtotp is None or self._rtots is None:
-            return None
-        return -np.angle(self._rtotp / self._rtots, deg=True)
-
-    @property
-    def R(self):
-        return np.abs(self._rtotp + self._rtots)
 
     def list_snell(self, n_list):
         angles = arcsin(n_list[0] * np.sin(np.deg2rad(self.theta_i)) / n_list)
@@ -90,12 +64,22 @@ def calculate(self) -> Result:
                            np.array([[em, rp * em],
                                      [rp * ep, ep]], dtype=complex) / tp)
 
-        self._rtots = Ms[1, 0] / Ms[0, 0]
-        self._ttots = 1 / Ms[0, 0]
-        self._rtotp = Mp[1, 0] / Mp[0, 0]
-        self._ttotp = 1 / Mp[0, 0]
+        rtots = Ms[1, 0] / Ms[0, 0]
+        ttots = 1 / Ms[0, 0]
+        rtotp = Mp[1, 0] / Mp[0, 0]
+        ttotp = 1 / Mp[0, 0]
 
-        return Result(self)
+        zeros = np.repeat(1, n_list.shape[1]) if n_list.ndim > 1 else 1
+
+        jones_matrix_r = np.moveaxis(np.array([[rtotp, zeros],
+                                               [zeros, rtots]]), 2, 0)
+        jones_matrix_t = np.moveaxis(np.array([[ttotp, zeros],
+                                               [zeros, ttots]]), 2, 0)
+
+        # TODO: Test if p and s correction formulas are needed.
+        power_correction = (((n_list[-1] * np.cos(th_list[-1])).real) / (n_list[0] * np.cos(th_list[0])).real)
+
+        return Result(self.experiment, jones_matrix_r, jones_matrix_t, power_correction)
 
     @staticmethod
     def fresnel(n_i, n_t, th_i, th_t):
@@ -118,7 +102,7 @@ def fresnel(n_i, n_t, th_i, th_t):
         r_s = (n_i * cos_i - n_t * cos_t) / (n_i * cos_i + n_t * cos_t)
         r_p = (n_t * cos_i - n_i * cos_t) / (n_t * cos_i + n_i * cos_t)
         t_s = 2 * n_i * cos_i / (n_i * cos_i + n_t * cos_t)
-        t_p = 2 * n_i * cos_i / (n_i * cos_i + n_i * cos_t)
+        t_p = 2 * n_i * cos_i / (n_t * cos_i + n_i * cos_t)
 
         return r_s, r_p, t_s, t_p
 
diff --git a/src/elli/solver4x4.py b/src/elli/solver4x4.py
index 9c041332..ab0612c8 100644
--- a/src/elli/solver4x4.py
+++ b/src/elli/solver4x4.py
@@ -168,9 +168,6 @@ def calculate_propagation(self, delta: npt.NDArray, h: float, lbda: npt.ArrayLik
 class Solver4x4(Solver):
     """Solver class to evaluate Experiment objects. Based on Berreman's 4x4 method.
     """
-    _s = None
-    _jones_matrix_t = None
-    _jones_matrix_r = None
 
     @staticmethod
     def build_delta_matrix(k_x: npt.ArrayLike, eps: npt.NDArray) -> npt.NDArray:
@@ -316,83 +313,6 @@ def get_k_z(material: "Material", lbda: npt.ArrayLike, k_x: npt.ArrayLike) -> np
         k_z2 = nx ** 2 - k_x ** 2
         return sqrt(k_z2)
 
-    @property
-    def rho(self) -> npt.NDArray:
-        rho = np.dot(self._s, self.jones_vector)
-        rho = rho[:, 0] / rho[:, 1]
-        return rho
-
-    @property
-    def psi(self) -> npt.NDArray:
-        return np.rad2deg(np.arctan(np.abs(self.rho)))
-
-    @property
-    def delta(self) -> npt.NDArray:
-        return -np.angle(self.rho, deg=True)
-
-    @property
-    def rho_matrix(self) -> npt.NDArray:
-        return self._s
-
-    @property
-    def psi_matrix(self) -> npt.NDArray:
-        return np.rad2deg(np.arctan(np.abs(self.rho_matrix)))
-
-    @property
-    def delta_matrix(self) -> npt.NDArray:
-        return -np.angle(self.rho_matrix, deg=True)
-
-    @property
-    def mueller_matrix(self) -> npt.NDArray:
-        a = np.array([[1, 0, 0, 1],
-                      [1, 0, 0, -1],
-                      [0, 1, 1, 0],
-                      [0, 1j, -1j, 0]])
-
-        # Kronecker product of S and S*
-        s_kron_s_star = np.einsum('aij,akl->aikjl', np.conjugate(self._s),
-                                  self._s).reshape([self._s.shape[0], 4, 4])
-
-        mueller_matrix = np.real(a @ s_kron_s_star @ np.linalg.inv(a))
-        mm11 = mueller_matrix[:, 0, 0]
-
-        return mueller_matrix / mm11[:, None, None]
-
-    @property
-    def jones_matrix_t(self) -> npt.NDArray:
-        return self._jones_matrix_t
-
-    @property
-    def jones_matrix_r(self) -> npt.NDArray:
-        return self._jones_matrix_r
-
-    @property
-    def jones_matrix_tc(self) -> npt.NDArray:
-        c = 1 / sqrt(2) * np.array([[1, 1], [1j, -1j]])
-        return np.einsum('ij,...jk,kl->...il', np.linalg.inv(c), self._jones_matrix_t, c)
-
-    @property
-    def jones_matrix_rc(self) -> npt.NDArray:
-        c = 1 / sqrt(2) * np.array([[1, 1], [1j, -1j]])
-        d = 1 / sqrt(2) * np.array([[-1, -1], [1j, -1j]])
-        return np.einsum('ij,...jk,kl->...il', np.linalg.inv(d), self._jones_matrix_r, c)
-
-    @property
-    def R(self) -> npt.NDArray:
-        return np.abs(self._jones_matrix_r) ** 2
-
-    @property
-    def T(self) -> npt.NDArray:
-        return np.abs(self._jones_matrix_t) ** 2 * self.power_correction[:, None, None]
-
-    @property
-    def Rc(self) -> npt.NDArray:
-        return np.abs(self.jones_matrix_rc) ** 2
-
-    @property
-    def Tc(self) -> npt.NDArray:
-        return np.abs(self.jones_matrix_tc) ** 2 * self.power_correction[:, None, None]
-
     def __init__(self, experiment: "Experiment", propagator: Propagator = PropagatorExpmScipy()) -> None:
         super().__init__(experiment)
         self.propagator = propagator
@@ -405,23 +325,23 @@ def calculate(self) -> Result:
         """
         # Kx = kx/k0 = n sin(Φ) : Reduced wavenumber.
         nx = self.structure.front_material.get_refractive_index(self.lbda)[:, 0, 0]
-        self.k_x = nx * np.sin(np.deg2rad(self.theta_i))
+        k_x = nx * np.sin(np.deg2rad(self.theta_i))
 
         layers = reversed(self.permittivity_profile[1:-1])
 
         if isinstance(self.structure.back_material, IsotropicMaterial):
-            t = self.transition_matrix_iso_halfspace(self.k_x, self.permittivity_profile[-1][1])
+            t = self.transition_matrix_iso_halfspace(k_x, self.permittivity_profile[-1][1])
         else:
             t = self.transition_matrix_halfspace(
-                self.build_delta_matrix(self.k_x, self.permittivity_profile[-1][1]))
+                self.build_delta_matrix(k_x, self.permittivity_profile[-1][1]))
 
         for d, epsilon in layers:
             p = self.propagator.calculate_propagation(
-                self.build_delta_matrix(self.k_x, epsilon), -d, self.lbda)
+                self.build_delta_matrix(k_x, epsilon), -d, self.lbda)
             t = p @ t
 
         lf = self.transition_matrix_iso_halfspace(
-            self.k_x, self.permittivity_profile[0][1], inv=True)
+            k_x, self.permittivity_profile[0][1], inv=True)
         t = lf @ t
 
         # Extraction of t_it out of t. "2::-2" means integers {2,0}.
@@ -435,23 +355,18 @@ def calculate(self) -> Result:
         # Then we have t_ri = t_rt * t_ti
         t_ri = t_rt @ t_ti
 
-        r_ss = t_ri[..., 1, 1]
-        s = t_ri / r_ss[:, None, None]
-
-        if isinstance(self.structure.back_material, IsotropicMaterial):
-            k_z_f = self.get_k_z(self.structure.front_material, self.lbda, self.k_x)
-            k_z_b = self.get_k_z(self.structure.back_material, self.lbda, self.k_x)
-            self.power_correction = k_z_b.real / k_z_f.real
-        else:
-            self.power_correction = np.ones_like(self.lbda)
+        jones_matrix_t = t_ti
+        jones_matrix_r = t_ri
+        
         # The power transmission coefficient is the ratio of the 'z' components
         # of the Poynting vector:       t = P_t_z / P_i_z
         # For isotropic media, we have: t = kb'/kf' |t_bf|^2
         # The correction coefficient is kb'/kf'
         # Note : For the moment it is only meaningful for isotropic half spaces.
+        if isinstance(self.structure.back_material, IsotropicMaterial):
+            k_z_f = self.get_k_z(self.structure.front_material, self.lbda, k_x)
+            k_z_b = self.get_k_z(self.structure.back_material, self.lbda, k_x)
+            power_correction = k_z_b.real / k_z_f.real
+            return Result(self.experiment, jones_matrix_r, jones_matrix_t, power_correction)
 
-        self._jones_matrix_t = t_ti
-        self._jones_matrix_r = t_ri
-        self._s = s
-
-        return Result(self)
+        return Result(self.experiment, jones_matrix_r, jones_matrix_t)


Test patch:
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 8b814437..a4013b2d 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -25,6 +25,7 @@ jobs:
         python -m pip install --upgrade pip
         pip install pytest pytest-cov
         pip install -r requirements.txt
+        pip install torch
     - name: Install module
       run: |
         pip install .
diff --git a/tests/test_bragg_mirror.py b/tests/test_bragg_mirror.py
index da45d273..3d7fa93e 100644
--- a/tests/test_bragg_mirror.py
+++ b/tests/test_bragg_mirror.py
@@ -89,7 +89,7 @@ def U(k):
 
     def test_normal_incidence(self):
         data = self.s.evaluate(self.lbda_list, 0)
-        R_ss_0 = data.R[:, 1, 1]
+        R_ss_0 = data.R_ss
         R_th_ss_0 = (np.abs(self.ReflectionCoeff(0, 's'))) ** 2  # Phi_i = 0
         np.testing.assert_allclose(R_ss_0, R_th_ss_0, 1e-10, 0)
 
@@ -102,8 +102,92 @@ def test_angle(self):
         # Incidence angle Phi_i = pi/4, 's' and 'p' polarizations
         data2 = self.s.evaluate(self.lbda_list, np.rad2deg(pi / 4))
 
-        R_ss = data2.R[:, 1, 1]
-        R_pp = data2.R[:, 0, 0]
+        R_ss = data2.R_ss
+        R_pp = data2.R_pp
 
         np.testing.assert_array_almost_equal(R_ss, R_th_ss, decimal=1)
         np.testing.assert_array_almost_equal(R_pp, R_th_pp, decimal=1)
+
+    def test_normal_incidence_4x4_scipy(self):
+        data = self.s.evaluate(self.lbda_list, 0)
+        R_ss_0 = data.R_ss
+        R_th_ss_0 = (np.abs(self.ReflectionCoeff(0, 's'))) ** 2  # Phi_i = 0
+        np.testing.assert_allclose(R_ss_0, R_th_ss_0, 1e-10, 0)
+
+    def test_angle_4x4_scipy(self):
+        R_th_ss = (np.abs(self.ReflectionCoeff(pi / 4, 's'))) ** 2  # Phi_i = pi/4
+        R_th_pp = (np.abs(self.ReflectionCoeff(pi / 4, 'p'))) ** 2
+
+        # Incidence angle Phi_i = 0, 's' polarization
+
+        # Incidence angle Phi_i = pi/4, 's' and 'p' polarizations
+        data2 = self.s.evaluate(self.lbda_list, np.rad2deg(pi / 4))
+
+        R_ss = data2.R_ss
+        R_pp = data2.R_pp
+
+        np.testing.assert_array_almost_equal(R_ss, R_th_ss, decimal=1)
+        np.testing.assert_array_almost_equal(R_pp, R_th_pp, decimal=1)
+
+    def test_normal_incidence_4x4_eig(self):
+        data = self.s.evaluate(self.lbda_list, 0, solver=elli.Solver4x4, propagator=elli.PropagatorEig())
+        R_ss_0 = data.R_ss
+        R_th_ss_0 = (np.abs(self.ReflectionCoeff(0, 's'))) ** 2  # Phi_i = 0
+        np.testing.assert_allclose(R_ss_0, R_th_ss_0, 1e-10, 0)
+
+    def test_angle_4x4_eig(self):
+        R_th_ss = (np.abs(self.ReflectionCoeff(pi / 4, 's'))) ** 2  # Phi_i = pi/4
+        R_th_pp = (np.abs(self.ReflectionCoeff(pi / 4, 'p'))) ** 2
+
+        # Incidence angle Phi_i = 0, 's' polarization
+
+        # Incidence angle Phi_i = pi/4, 's' and 'p' polarizations
+        data2 = self.s.evaluate(self.lbda_list, np.rad2deg(pi / 4), solver=elli.Solver4x4, propagator=elli.PropagatorEig())
+
+        R_ss = data2.R_ss
+        R_pp = data2.R_pp
+
+        np.testing.assert_array_almost_equal(R_ss, R_th_ss, decimal=1)
+        np.testing.assert_array_almost_equal(R_pp, R_th_pp, decimal=1)
+
+    def test_normal_incidence_4x4_torch(self):
+        data = self.s.evaluate(self.lbda_list, 0, solver=elli.Solver4x4, propagator=elli.PropagatorExpmTorch())
+        R_ss_0 = data.R_ss
+        R_th_ss_0 = (np.abs(self.ReflectionCoeff(0, 's'))) ** 2  # Phi_i = 0
+        np.testing.assert_allclose(R_ss_0, R_th_ss_0, 1e-10, 0)
+
+    def test_angle_4x4_torch(self):
+        R_th_ss = (np.abs(self.ReflectionCoeff(pi / 4, 's'))) ** 2  # Phi_i = pi/4
+        R_th_pp = (np.abs(self.ReflectionCoeff(pi / 4, 'p'))) ** 2
+
+        # Incidence angle Phi_i = 0, 's' polarization
+
+        # Incidence angle Phi_i = pi/4, 's' and 'p' polarizations
+        data2 = self.s.evaluate(self.lbda_list, np.rad2deg(pi / 4), solver=elli.Solver4x4, propagator=elli.PropagatorExpmTorch())
+
+        R_ss = data2.R_ss
+        R_pp = data2.R_pp
+
+        np.testing.assert_array_almost_equal(R_ss, R_th_ss, decimal=1)
+        np.testing.assert_array_almost_equal(R_pp, R_th_pp, decimal=1)
+
+    def test_normal_incidence_2x2(self):
+        data = self.s.evaluate(self.lbda_list, 0, solver=elli.Solver2x2)
+        R_ss_0 = data.R_ss
+        R_th_ss_0 = (np.abs(self.ReflectionCoeff(0, 's'))) ** 2  # Phi_i = 0
+        np.testing.assert_allclose(R_ss_0, R_th_ss_0, 1e-10, 0)
+
+    def test_angle_2x2(self):
+        R_th_ss = (np.abs(self.ReflectionCoeff(pi / 4, 's'))) ** 2  # Phi_i = pi/4
+        R_th_pp = (np.abs(self.ReflectionCoeff(pi / 4, 'p'))) ** 2
+
+        # Incidence angle Phi_i = 0, 's' polarization
+
+        # Incidence angle Phi_i = pi/4, 's' and 'p' polarizations
+        data2 = self.s.evaluate(self.lbda_list, np.rad2deg(pi / 4), solver=elli.Solver2x2)
+
+        R_ss = data2.R_ss
+        R_pp = data2.R_pp
+
+        np.testing.assert_array_almost_equal(R_ss, R_th_ss, decimal=1)
+        np.testing.assert_array_almost_equal(R_pp, R_th_pp, decimal=1)
\ No newline at end of file
diff --git a/tests/test_internal_reflection.py b/tests/test_internal_reflection.py
index 44ac20e8..323201a9 100644
--- a/tests/test_internal_reflection.py
+++ b/tests/test_internal_reflection.py
@@ -6,7 +6,7 @@
 from scipy.constants import pi
 
 
-def test_tir_thickness():
+class TestTirThickness:
     # Refractive indices
     n_f = 1.5
     n_s = 1.0
@@ -31,10 +31,8 @@ def test_tir_thickness():
     # Air thickness variation range
     d = np.linspace(0, 1000)
 
-    # %% [markdown]
-    # ## Analytical calculation
+    # Analytical calculation
 
-    # %%
     # Reduced wavenumber
     Kx = n_f * np.sin(Phi_i)
 
@@ -78,42 +76,132 @@ def test_tir_thickness():
     T_th_s = t2_th_s * correction
     T_th_p = t2_th_p * correction
 
-    # %% [markdown]
-    # ## Calculation with Berreman4x4
-
-    # %%
-    R = []
-    T = []
-    jt = []
-
-    for dd in d:
-        layer.set_thickness(dd)
-        data = s.evaluate(lbda, np.rad2deg(Phi_i))
-        R.append(data.R[0])
-        T.append(data.T[0])
-        jt.append(data.jones_matrix_t[0])
-
-    R = np.array(R)
-    T = np.array(T)
-    jt = np.array(jt)
-
-    # Extraction of the transmission and reflexion coefficients
-    R_p = R[:, 0, 0]
-    R_s = R[:, 1, 1]
-    T_p = T[:, 0, 0]
-    T_s = T[:, 1, 1]
-    t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
-    t2_s = np.abs(jt[:, 1, 1]) ** 2
-
-    np.testing.assert_allclose(R_p, R_th_p)
-    np.testing.assert_allclose(R_s, R_th_s)
-    np.testing.assert_allclose(T_p, T_th_p)
-    np.testing.assert_allclose(T_s, T_th_s)
-    np.testing.assert_allclose(t2_p, t2_th_p)
-    np.testing.assert_allclose(t2_s, t2_th_s)
-
-
-def test_tir_angle():
+    def test_tir_thickness_4x4_scipy(self):
+        R = []
+        T = []
+        jt = []
+
+        for dd in self.d:
+            self.layer.set_thickness(dd)
+            data = self.s.evaluate(self.lbda, np.rad2deg(self.Phi_i))
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+    def test_tir_thickness_4x4_eig(self):
+        R = []
+        T = []
+        jt = []
+
+        for dd in self.d:
+            self.layer.set_thickness(dd)
+            data = self.s.evaluate(self.lbda, np.rad2deg(self.Phi_i), solver=elli.Solver4x4, propagator=elli.PropagatorEig())
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+    def test_tir_thickness_4x4_torch(self):
+        R = []
+        T = []
+        jt = []
+
+        for dd in self.d:
+            self.layer.set_thickness(dd)
+            data = self.s.evaluate(self.lbda, np.rad2deg(self.Phi_i), solver=elli.Solver4x4, propagator=elli.PropagatorExpmTorch())
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+    def test_tir_thickness_2x2(self):
+        R = []
+        T = []
+        jt = []
+
+        for dd in self.d:
+            self.layer.set_thickness(dd)
+            data = self.s.evaluate(self.lbda, np.rad2deg(self.Phi_i), solver=elli.Solver2x2)
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+
+class TestTirAngle:
     n_f = 1.5
     n_s = 1.0
     n_b = 1.7
@@ -140,10 +228,8 @@ def test_tir_angle():
     # Variation of incidence angle
     Phi_list = np.deg2rad(np.linspace(0, 89, 90))
 
-    # %% [markdown]
     # Analytical calculation
-    # Incidence angle
-    # %%
+
     # Reduced wavenumber
     Kx = n_f * np.sin(Phi_list)
 
@@ -186,40 +272,127 @@ def test_tir_angle():
     T_th_s = t2_th_s * correction
     T_th_p = t2_th_p * correction
 
-    # %% [markdown]
-    # ## Calculation with Berreman4x4
-    # %%
-    R = []
-    T = []
-    jt = []
-
-    for Phi_i in Phi_list:
-        data = s.evaluate(lbda, np.rad2deg(Phi_i))
-        R.append(data.R[0])
-        T.append(data.T[0])
-        jt.append(data.jones_matrix_t[0])
-
-    R = np.array(R)
-    T = np.array(T)
-    jt = np.array(jt)
-
-    # Extraction of the transmission and reflexion coefficients
-    R_p = R[:, 0, 0]
-    R_s = R[:, 1, 1]
-    T_p = T[:, 0, 0]
-    T_s = T[:, 1, 1]
-    t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
-    t2_s = np.abs(jt[:, 1, 1]) ** 2
-
-    np.testing.assert_allclose(R_p, R_th_p)
-    np.testing.assert_allclose(R_s, R_th_s)
-    np.testing.assert_allclose(T_p, T_th_p)
-    np.testing.assert_allclose(T_s, T_th_s)
-    np.testing.assert_allclose(t2_p, t2_th_p)
-    np.testing.assert_allclose(t2_s, t2_th_s)
-
-
-def test_transition_matrix_halfspace():
+    def test_tir_angle_4x4_scipy(self):
+        R = []
+        T = []
+        jt = []
+
+        for Phi_i in self.Phi_list:
+            data = self.s.evaluate(self.lbda, np.rad2deg(Phi_i))
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+        
+    def test_tir_angle_4x4_eig(self):
+        R = []
+        T = []
+        jt = []
+
+        for Phi_i in self.Phi_list:
+            data = self.s.evaluate(self.lbda, np.rad2deg(Phi_i), solver=elli.Solver4x4, propagator=elli.PropagatorEig())
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+    def test_tir_angle_4x4_torch(self):
+        R = []
+        T = []
+        jt = []
+
+        for Phi_i in self.Phi_list:
+            data = self.s.evaluate(self.lbda, np.rad2deg(Phi_i), solver=elli.Solver4x4, propagator=elli.PropagatorExpmTorch())
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+    def test_tir_angle_2x2(self):
+        R = []
+        T = []
+        jt = []
+
+        for Phi_i in self.Phi_list:
+            data = self.s.evaluate(self.lbda, np.rad2deg(Phi_i), solver=elli.Solver2x2)
+            R.append(data.R[0])
+            T.append(data.T[0])
+            jt.append(data.jones_matrix_t[0])
+
+        R = np.array(R)
+        T = np.array(T)
+        jt = np.array(jt)
+
+        # Extraction of the transmission and reflexion coefficients
+        R_p = R[:, 0, 0]
+        R_s = R[:, 1, 1]
+        T_p = T[:, 0, 0]
+        T_s = T[:, 1, 1]
+        t2_p = np.abs(jt[:, 0, 0]) ** 2  # Before power correction
+        t2_s = np.abs(jt[:, 1, 1]) ** 2
+
+        np.testing.assert_allclose(R_p, self.R_th_p)
+        np.testing.assert_allclose(R_s, self.R_th_s)
+        np.testing.assert_allclose(T_p, self.T_th_p)
+        np.testing.assert_allclose(T_s, self.T_th_s)
+        np.testing.assert_allclose(t2_p, self.t2_th_p)
+        np.testing.assert_allclose(t2_s, self.t2_th_s)
+
+def test_4x4_transition_matrix_halfspace():
     # Refractive indices
     n_glass = 1.5
     n_air = 1.0
diff --git a/tests/test_liquid_crystals.py b/tests/test_liquid_crystals.py
index 8af2bbbf..2a2bf521 100644
--- a/tests/test_liquid_crystals.py
+++ b/tests/test_liquid_crystals.py
@@ -53,11 +53,12 @@ def test_cholesteric_lc():
 
     # Berreman simulation
     data = s.evaluate(lbda, 0)
-    R_RR = data.Rc[:, 1, 1]
+    R_RR = data.Rc_RR
 
     # Checks positions of local extrema
     np.testing.assert_allclose(argrelmax(R_RR)[0], argrelmax(R_th)[0], atol=3)
     np.testing.assert_allclose(argrelmin(R_RR)[0], argrelmin(R_th)[0])
+    np.testing.assert_array_almost_equal(R_RR, R_th, decimal=1)
 
 
 def test_twisted_nematic_lc():
@@ -89,7 +90,7 @@ def test_twisted_nematic_lc():
 
     # Berreman simulation
     data = s.evaluate(lbda_list, 0)
-    T_bm = np.real(data.T[:, 0, 0])
+    T_bm = np.real(data.T_pp)
 
     # Compare results
     np.testing.assert_array_almost_equal(T_bm, T_gt, decimal=2)