diff --git a/test.ipynb b/test.ipynb
index 63c260b..fb2d62d 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -2,16 +2,23 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Daniels-MacBook-Air.local:67834] shmem: mmap: an error occurred while determining whether or not /var/folders/yh/dx7xl94n3g52ts3td8qcxjcc0000gn/T//ompi.Daniels-MacBook-Air.501/jf.0/2346057728/sm_segment.Daniels-MacBook-Air.501.8bd60000.0 could be created.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'0.14.3'"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -57,21 +64,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 100,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-12.806878959999999"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "dat = sisl.io.siesta.eigSileSiesta(\n",
     "    \"/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.EIG\"\n",
@@ -81,21 +75,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 101,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-5.82448514"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "Ef = dat.read_fermi_level()\n",
     "Ef"
@@ -103,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -114,7 +95,7 @@
       "Input file: \n",
       "/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf\n",
       "Output file: \n",
-      "./Fe3GeTe2.pickle\n",
+      "./Fe3GeTe2_notebook.pickle\n",
       "Number of nodes in the parallel cluster:  1\n",
       "================================================================================================================================================================\n",
       "Cell [Ang]: \n",
@@ -130,13 +111,13 @@
       "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
       "================================================================================================================================================================\n",
       "Parameters for the contour integral:\n",
-      "Number of k points:  10\n",
+      "Number of k points:  15\n",
       "k point directions:  xy\n",
       "Ebot:  -13\n",
-      "Eset:  600\n",
-      "Esetp:  10000\n",
+      "Eset:  300\n",
+      "Esetp:  1000\n",
       "================================================================================================================================================================\n",
-      "Setup done. Elapsed time: 3484.176097333 s\n",
+      "Setup done. Elapsed time: 10.633095125 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -148,7 +129,7 @@
     "path = (\n",
     "    \"/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf\"\n",
     ")\n",
-    "outfile = \"./Fe3GeTe2\"\n",
+    "outfile = \"./Fe3GeTe2_notebook\"\n",
     "\n",
     "# this information needs to be given at the input!!\n",
     "scf_xcf_orientation = np.array([0, 0, 1])  # z\n",
@@ -163,7 +144,7 @@
     "]\n",
     "magnetic_entities = [\n",
     "    dict(atom=3, l=2),\n",
-    "    dict(atom=4),\n",
+    "    dict(atom=4, l=2),\n",
     "    dict(atom=5, l=2),\n",
     "]\n",
     "pairs = [\n",
@@ -174,13 +155,15 @@
     "    dict(ai=1, aj=2, Ruc=np.array([-1, -1, 0])),\n",
     "    dict(ai=0, aj=2, Ruc=np.array([-1, 0, 0])),\n",
     "    dict(ai=1, aj=2, Ruc=np.array([-1, 0, 0])),\n",
+    "    dict(ai=1, aj=2, Ruc=np.array([-2, 0, 0])),\n",
+    "    dict(ai=1, aj=2, Ruc=np.array([-3, 0, 0])),\n",
     "]\n",
     "# Brilloun zone sampling and Green function contour integral\n",
-    "kset = 10\n",
+    "kset = 15\n",
     "kdirs = \"xy\"\n",
     "ebot = -13\n",
-    "eset = 600\n",
-    "esetp = 10000\n",
+    "eset = 300\n",
+    "esetp = 1000\n",
     "################################################################################\n",
     "#################################### INPUT #####################################\n",
     "################################################################################\n",
@@ -228,42 +211,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 103,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "xyz[-3:]: red, green, blue\n",
-      "2.745163300331324\n",
-      "2.5835033632437767\n",
-      "2.583501767937866\n",
-      "2.583541444641373\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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WlPPaGPrygiCIRQqJYg3YsnEIwwN57BurZF4mYQDWDZhvsOcCxhguuOACbN26FTfccAM2bNiAu+++u2G/Cy64INHvuuuum5N9EQRBEEuQVq8+FlcBpUO1ev96I4htujKze6NTX8nIzgJ3PPFjDBZtjJbqr2DNR3AbYhmjNfDjvwN2fMD4o2oCS9cKg0BhCCgOAYUVplxYAdjB6SZuA8IOTjqF+XTlafpzCw5jcACEIZWk0vCkgq80fKngSgVfGpsnNXyl4PkanlLwfNMvaov195WCG9hq7UFfZfpIpaG1htQaUgFKaaigrpSG1IAKy0Gb0ojKkU0hmGO6+UzfxNhwvrA9NV8rf+RSa0hoQM7srw2RjZUWzQQ3AltUj7dzI/jFRLd0nhbe6oU5Xi/mcQaREvR4xtzhvFnrWiKci2fsu/HewpxOIxLE4oJEsQYIznDjFZtw7Z2PgiHpZSX8Z+7GKzbNyZWOnTt34pvf/CauuOIKrFu3Djt37sTBgwdxyimn4Oc//3mi3/33349LL70Ua9asSfQjCIIgiJbZcL4RuMZHkO1XLLgi+V9/Bjy905ws611rxjU4IZYUvgzDA3lcuXkYX3pgT90qWYIYMPfBbYhlzr/+FfCDT5jyypOAgWONsFUcqolcdeUVQH4Q4AvD4b75Bb29k5pLGR2IYzImlsWFOqm1EcbaFe7SIl1C/EsLdzWRr3799B6z1q/NN+2cOiYmNpkjEgMbzac0/KCvr1Q0zk/lzYRHP+jX/vm9pQVngMU5Boo2Tlvfj9OPGcBpxwzg9GMHsK4/T6IZQSwwSBRrwrbThvGF159V91K/bo6vcvT39+Phhx/GF7/4RYyPj2PDhg34xCc+gcsvvxxf+cpXEv0eeOAB3HrrrXX9CIIgCKJluAC23RJEn2zwVdC2jwKWA2zcWjc8fSLs6JSLd/zjo3XC18hYBV98YE/b25vL4DbEMmfqYKyijfibH0imXB/gFI2PK24BbGGIYUQ2LLz61+2NLFF0ICKGwpmvNKSMiWY6rKuGolrWWKXDPrVTiZFdxudQibmSawTzSh0JgMn9qQZzBmul95bad3wNTzYWB5UGXKlwcKKK7/36IL7369q/M6t6HWxaP4CVPQ4KjkDRFijmLBQdgR5HoOCYskmpcs70twT9G0QQnYR+XkzDttOGccmmdfPq9PeUU07BV7/6VfT394OnvoW8/fbbE/127NjRcJ54X4IgCIJoyqYrgdfeAb3jfWAxp/u6fz1YcEUy6zpklh8wzmYVx7IhcxHchljmvOKTwPBm4Hs3A4efNKklGJDrrxfQ6gS13uQVyMSVyhavT3LLOMIiiAUAYwyCgU4nAtHpukjQi4mBntLYN1bB48+O4RfPjuHxZ8fwxIFJHJp08cBvDk4/eQuE1zhtXotgG0bBDX3CheXLT1uHP7v4+bBJUCOIOkgUawHBGX0zTRAEQSwtlDRRJ2NXIXeo38NfVT6F49zHsAajOIBBPF3ZjA+p04GM65CN/IC14NZnRsxFcBtimSNs4PfeApz+WuBX9wJTB4DKWPPkVwBooDpm0th87HMmglpKWJuJH7Pp1rRyRvyz8iTcEYsCHVyfDa/Nqlhdag2tEF0vVTp5Ms6TGq6vTJISVb/eVivX7Gv7c1hRXIUzjhvEY8+M4Vcj4x15lvD0mmtqTfv+8rlxHDdUxB+ecUxH1iaIpQSJYgRBEASx3Nh1D7DjfUDsRFi5sA5fG/sTPKu24Flsiuxs3MPb73w0c5pGfsA6zVwHtyEI5PuBM/9Ta339KlAZjwllo40FNHcSkC4gvSC5sdwFlF8rx+1pQvv8/C/XPsJp4dRcv/HFltVmF7oqqumYQKKyRJO4b7J0u4rXk0JK6NOrWXvWeqGfMdMW7xuOrfk2i4+VYX9VL/g0mi/0R5aYu25srD32WWTOrTL2Gp+nbt9Zn0ltbPLza2FszH9aVrueoy9tFjrHDBZw3gl0yIMgsiBRjCAIgiCWE7vuCXyHJX8zyJX34fP2rbjW+3N8W22J7N3+/WGug9sQRNtYOaB3tUlzgdaBWJYhokkPUNl27VchfQ/Kd6F9F8qvRmUtjR0yrAfjfA86mI9JD/Ar4N4UuD8F4ZUgvCkIvwRLlprvWbrGP9vU7K+FfbX/Gvzfvj+N+XhK+nRKC0mh8/uG4lGWkJI6DUQQjeAM4MxEo3QsbpLgibJtceRSNsfisIM818DupMZF9tg4O2Pe0E4/EwmiM5AoRhAEQRDLBSXNCbEMqYsDUAButL+M+6rnQGFu/Y5kufPXqL+SOdfBbQiiHSqexGjJw9GSi6NTLo7GyhNVP3DeXe/MW8Z8DsUdgMcde6edhkcpmAfSR68eR78aw4AaxYAawwqMYVCPo6DLsOHDhg+HeXDgwwnr8GAziRy8oG7sNjNt8b4WU93+iLFp9Pt494HLur2NhrBIJDH+teKiSbzN1GtlwbPaM8by2lgxTTvP2ItZhzVYp/FYwZu3R+NjzyHSc/Npxgb9zVqN2zlLt8efq/FnkjUf59PPnZ4v/CwoSiRBLA9IFCMIgiCI5cLehxJXJtNwBqzHYWzhu/HvalPDfjMl/PXibRdtxD2PjWRGdp7v4DYE4UmFH+05goOTVYyWPByZcjFacnGk5GG05AailxG/Sm5zvz0zQ+MS/hNcyH+BflbCAKZi+RT6UUKBZVypnAd/2R4s+MyGH+ZBksyC5A5kUFfchuQ2FLMhuQPFbWhuyprbUMLkmueghamDG79kFiRsXYWtKuCMYe/G1+JTuQEIbhyFC84hOEyeEEAaCTotCB9Bf1EnADEwnhaQ6ucjCIIglg4kihEEQRDEcmFyf0vd1mC0I8txlnS6Hz/19d5tp+DhJw/gOz/YiUu3novzTlwTiV8U3IaYLyqexOv/5078eO/RlscIzrCiaGOw6GCo6GCwaGNF0UF/wYoiwIWCDo8LOwwQYXsg5FiC4dhnv4Wzf/TJOXzKDOwewOkBnCLg9AJ2Maj3BOUiYBdhCxt26GyfW0Ee1kWsnG6zUjYruy+36tpeRKITQRAEMY+QKEYQBEEQS5l4lMkWRbEDGJzVkuGvtJ993ZlY0ZPLPPUlOMO5G4dw+Fca59JpMKJLfOI7v44EsZPX9eGE1T1YUXSwIhC7hnpMeUWPEwlh/Xmrs6eFjrkI+FHnpmsJb8qkqXletxW41YLwZrUowIV1q0lbljjXzhpWrE/GGnwejvQRBEEQM4ZEMYIgCIJYqmREmQTjgM72G6Q0sA8r8Yg6udYdNd9f5AeMWGqUvdp1yKcOT+H4oSKqvsJ4xYOvNMquxJEpFz3jFoqOQNGx0OMIFHNhXaDHsVBwBHIWn5lYtvoFwE1j0/cLHfCHTvij3KuvKx+Qfnab9IxY3rAtNn/TNWTjtnDtun14yXpWKI9wXb/c/me5EGG8gcjXTHjLOHkXiWyicVtC5OuUkJixPp3mIwhiCUGiGEEQBEEsduKnwXrXAhvOB3Z/IzPKZCNBDDC+cz5t/Weoau1kQyhwAcD2e3eRHzBiSXHTFadiy8aV+OK//Ra/fG4c39nV2mnKLARnCaGsmBMo2iYPhbNIULNNXrAFBK859w4dkIfl8JolD8qcI2GzeA6c5ZNj7Vpfi/O6MVF5ITgUVzJbTKsT59Jt6bps0pYW9xqIhU2FxLjI16BNeoDO8DmnFSCrJi0VZi28Nb9C21Scs/ImAmyU5zJseUA4QT1Pp/UIgmgKiWKLjDe96U0YHR3F1772tW5vhSAIgphvGolf6dNgfcOAX0XmKYyQ9Imx/vVg2z6Kvzn5CvxhA4GrmfhFfsCIxYglOK7cvB5XvGgYO/ccwa/3TaDkSpRcH1NVibJn8pLro+RKTLkSpapf6+NKuL75/0gqjYmKj4mKD2BxCSAiJbrxmCgX+kVLCndICGtWQrhLi3moiXzxeVhyjJVoR2weG5w5iTlqY1En9glhhEGeEv9C8bEVoTC5TjxaYjISYtzBPw+jRWofrKmQ14rI10zIS5/SazDPjE8LpubLQgVti+U0H7cbiGnTCWq5mrAW7yOcQKwTqeuzQX3liUD/+m4/NUEQLUKiWCtk/RLCRVe28qlPfQpaN/klpw2eeuopbNy4ET/96U9xxhlndGROgiAIogO0Kn4VVgDlDAfhEyPTr6EV9pz9QRxmAyisOAYnn3sZhGVBoLHAJTgj8YtYkjDG8PsnrMTvn9D+329fKpQ8ibIrMRUJZhJTro9SQlDzgz41W9mTUEpDag2pNFSQR0nDtMfbYn1qYxG1K6XhB/awfbpXR6k0JDQwF8E1lyFhxEoRRK+siWo1sY+xmlDHGQPnFgSzg3LG2FjETB6LwhkJdKE4xzOEOxFG3GR1ETxNv5oYmhb+LEg4qgoHLmxdhaNd2NqFraom18ZuKWMTqhrVharCUlUI5ULIKixVgZCmD5dVCFkBj8pVcFkBk1VwvwLW7EuddlEe4HqAO9G5OafjLf8KHHv2/K1HEMSMIVFsOrL8sfSvB7bdAmy6ct63MzAwMO9rEgRBEPNI1s+dRuJXlq0N/vvDo7hHmauRww/8G/kBI4gZYAmOfsHRn7e7vZWG6JigphTgKwWlkCnGKW1EtYRYl+rry6RIlxTugvlDsa6p6BeORULEi/dRdWMBqVRCMJxubFpcrH++YA6pwLSEVj6YlubEl5bgQZlpBcEULEgIqCDJRN1isTYtYWkJrnTQJ2hLzVFr02Y+lp5fgcf6WcEc9W3JMSKaJ6gH5WhNVls/PketrsBZB8Wp5US+v9s7IAiiRUgUa8aue7L9sYyPGPtr75gzYezrX/86Pv7xj+PJJ59EsVjEmWeeia9//et4xzvekbg+qZTCLbfcgi996UvYt28fXvCCF+BDH/oQrr76agDA0aNH8c53vhPf+c53MDk5iWOPPRYf+MAH8OY3vxkbN24EAJx55pkAgIsvvhjf//738aMf/Qgf+MAH8NOf/hSe5+GMM87Af//v/x1nnXXWnDwrQRAEEdDo584sxa9GxKNM7hur4No7H8UXXn8WCWMEscRgjMESLPbi350bD9Py+P8Bvv5OoP8YYO0mID8Y+OmSgPaDJE0KrxhqWfNNpmJ2leqnVKzcYFz8316GBfsxLRR8WFCMG6mNiUCeM2Ujr3FI8KjNjyQ5FpUlOHzNISHgh/11UNZmjB/0CXNPC0gwU4aAp3gwP6/NGeSJ+WI2H/E91dbKbAvGhvuUwVr9xQK+8vbzccxQf3B9knyXEcRihESxRihpvqnPPLqrATBgx/uBk1/R8auUIyMjeMtb3oJbbrkFr371qzExMYEf/OAHmdcmb775Ztx555247bbbcNJJJ+GBBx7A61//eqxevRoXX3wxPvShD2HXrl341re+hVWrVuHJJ59EuWzu/z/yyCPYsmULvvvd7+LUU0+F4zgAgImJCVxzzTX4zGc+A601PvGJT+DlL385nnjiCfT19XX0WQmCIIiApj93OrxURpTJ4Ccbtt+7C5dsWkeO8gmCmF/8KvB/32bEqcNPmLRgYDXH8aH4EZaZiPmTErE+ItaWak+MSeWNxsx6rUbzNdhLYlzseaM2nvmLZMUzEVuPTLkYK3twfQVXKnhSwZc6Knu+ghev17VreLLBWBm0+UGb0nB9hYonUQ1yNQ8H3MZLwD88cgAffOWquV+MIIg5g0SxRux9KHl1pQ4NjD9r+m3c2tGlR0ZG4Ps+XvWqV+F5z3seAOD000+v61etVvGRj3wE3/3ud3HeeecBAE444QQ8+OCD+OIXv4iLL74Yv/vd73DmmWfinHPOAYBoPgBYvXo1AGDlypVYt25dZP+DP/iDxDpf+tKXMDg4iH/7t3/DK1/5yk4+KkEQxPJFSbC9D+KYIw+D7e03v3Q0/bkzw2U0ENe3wl8UtntvgELyW20NYGSsgkf2HCHfYQRBNMZ3AW8K8MqAWzLlRF4C3KkgD+pRuUkf5U+/9is+MTdiTyOhKhCAliNSaYyWXBydcHF40sXR0iSOTHk4MlXFkSkPR0tuJIAdmXJxtOSi5C5sx3SO4LAFg21x2IJHdUuEdQY7KNtWrW4F/RzBwTnDz58ZxcZVvXjzhRu7/UgEQcwSEsUaMdliSO5W+7XB5s2bcfHFF2Pz5s247LLLcOmll+Lqq6/GihUrEv2efPJJlEolXHLJJQm767rRlchrr70Wr3nNa/Doo4/i0ksvxVVXXYXzzz+/6fr79+/HBz/4QXz/+9/HgQMHIKVEqVTC7373u84+KEEQxHIl8BtmjT+HcwBg7xeAwmBHl1AaGEUvqnAwjCORfR9WYrv3BnxbbWk49sBEpaN7IQiiQ2htogL6FUC6JverQcqyhfZY2Y/1kal+CVujvpXWxKu54KovAGf8aXfWXsC4vkLZlSh5QaCHMJiDVyuXPRPkoRwEeTBttYAPU4ly0M+T0wZpyMLiDCt6HKwo2shZwohQgsMJhCiLs0Bw4lFbrd1ECA3LUZvgsC3TZvrGxKtUe1hOtIlaUAKCIIg4JIo1ondtZ/u1gRACd999Nx5//HF897vfxWc+8xn85V/+JXbu3JnoNzk5CQD4xje+gWOOOSbRlsvlAACXX3459u7di29+85u477778NKXvhTveMc78PGPf7zh+tdccw0OHz6MT33qU9iwYQNyuRzOO+88uK7b4SclCIJYhjT0GzY6o+nCX1hYxmmw67234D51Drbw3ViDURzAIB5RJ9edEEuzpi8/o70QBDEDjvwH8K9/DZSOxESttBgVE7rm4Yp1y3ALsHsAuwA4RVN2ioBdBJyeII/bCw36xPvG0hI6oaWUNkJULAppORCx4uUoQqlX369UrRe+yp6EJ+f270R/3sLK3hxWFG0M9TgY6nGwosfBUNFJ1FcGeV/OIvGJIIhFA4lijdhwvokyOT6C7JcPZto3ND91NVMYY7jggguwdetW3HDDDdiwYQPuvvvuRJ9NmzYhl8vhd7/7HS6++OKGc61evRrXXHMNrrnmGmzduhXvec978PGPfzzyISZl8pjzD3/4Q3z+85/Hy1/+cgDA008/jUOHDnX4CQmCIJYhLfgNC3171dmbiF+j6MUQJiN7+jTYvwcRJqeDAVg3kMeWjUMt9ScIogP85tvGwfx8k+sH8gNB3p/Mc30xW1afPiNuWc7877tDKGV8VFV9Ffm9coNy1Ze1cswe9qt40ghTblysMqerpmJl08f0q3hqzp/J4gwFR6DHsVB0RFQuOAJFR6AY2Ftt68lZGCzasMXSEScJgiDSkCjWCC6AbbcE3+YzJH+BCX4j2fbRjjvZB4CdO3fim9/8Jq644gqsW7cOO3fuxMGDB3HKKafg5z//edSvr68P7373u/Hf/tt/g1IKF154IcbGxvDDH/4Q/f39uOaaa3DDDTfg7LPPxqmnnopqtYp/+Zd/wSmnnAIAWLNmDQqFAnbs2IFjjz0W+XweAwMDOOmkk/DlL38Z55xzDsbHx/Ge97wHhUKh489JEASx7JjWX6X5CdPID1gj8Wsmp8Gy1gWAG6/YRE72CWI+OftNQHEVMHWw5m/Lnar3vRX534qV/Vlcda6OmzQTGAdEDrCCJHJGILPygHACW1C3nKDdlJVw4MGGCxsec+AzC15Q9mCjymy42oILB1VtoQoLFWWhqi34vod9uY2oKFYnVFW9lLAVlWVkr8bG+PPhiT3ro2NA0RYoxESoUJQyYlStLV5uJnaFZcci8YogCKJdSBRrxqYrgdfeYb7Vj/8S07/eCGKbrpyTZfv7+/Hwww/ji1/8IsbHx7FhwwZ84hOfwOWXX46vfOUrib5/9Vd/hdWrV+Pmm2/Gf/zHf2BwcBBnnXUWPvCBDwAAHMfB9ddfj6eeegqFQgFbt27FP/3TPwEALMvCpz/9aXz4wx/GDTfcgK1bt+L73/8+/u7v/g5ve9vbcNZZZ+G4447DRz7yEbz73e+ek2clCIJY8ihpxLDJ/cCB3S0NGUMvVrQpfjU6DZb1tY4GMFi0MVryIvu6gTxuvGITtp023OYDEgQxK+wC8KI/mtlYJbPFslbL0fVMN+aTLOVrTLrQfgVMx046aQX4ZZPahAPIBWkmPCw34XXeB2c4ujGOxZEL/FpFKajnIpuAIzjyNs84XWUFIlbSni7nbU5XCwmCIBYQJIpNx6YrgZNfUfuFpnetuTI5ByfEQk455RR89atfRX9/P3jKl8Ltt9+eqDPG8K53vQvvete7Muf64Ac/iA9+sPGLw1ve8ha85S1vSdjOPPNM/OhHP0rYrr766jaegCAIggAQOdRvN6rk/+v9V2jwtsSvOOGvW2+7aCPueWwEI2O10ySh+HXJpnV4ZM8RHJioYE2fuTJJJ8QIYpHBhbnOmO/v6LRKabz+73biod8ejmwCEg48OPDhwEOOecjF6/DgMD/qk4MLh/lRH5N7cFLjHObj1eLBlvc2XjwWW1YMoZgLxCi7XoCKXwnsCfrlLBETt+KCl3EET0IVQRDE8oREsVbgAti4tdu7IAiCIBYTDRzq6+A/Wb9/KW1Ohe1Um9q6/shZ7YolkDz19d5tpzQUv857/sr2n4sgiCXPlOsnBDEAkBAoQyA6G5Z1+3CGNxKPYYdwLm9+kvYG7xp8Rb4E1YoDjB1p2jcLxozPLcEZbM4hBIvqFuewRFhmENxESLQy+sTrItGH1+YXzetWEGExXo/W4CzaW/2+TETFeD1rX2GdhD6CIIjpIVGMIAiCIDpB/JpkcVVDh/rh9UWts53mb/fe0LIgFg7/7OvOxIqeXKbwJTgj8YsgiLboy9t48H0vwb2PjaBgc+RtgbxtfFYpreFLDV9pSKXgSwUlPSjfg/ar0NKD9l2TS5Mz3wOUC0gPkC5sfxK97mH0eofQ7x3EKpcD1eZ7emvuu3iw+OpgXQ1fqdg+kvUstAY8qeFJjQrm3un9QoAztCDe1QS+dL1uTFysa1u8Y7BStnSfnpxlolkWHQwUbHA6vUwQxDxAohhBEARBzJZd90DveB9Yi9cks768T0eMzKLZiTCCIBY5WhtxXbomKT8oeyYpL1mXbmDzWujnNxkb6x+N9XGsdHFtK/Mrb/pnaxfhAD2rjZ+zyhhQWIHjrv4i/rWFmxtaaygN+EpBKiOChaKZVClBL6jHRTVTrq+buVSibsarRF02EevifTyZPSZej+9Fxtr9jH1koTTgSgVIAHPwxzSXcAasKDoY6nGwosfByiAfCmxZKW/PnXsbgiCWLiSKEQRBEMRs2HUP9F1vhIZGu99pf9q7Ck/qY6eNGNnKiTCCIOYBFTiYTzisLzWIGhmPHjmZjCQZjov3n4HT+gULtwERJG4bkUtYQe4ATg/Qtw7oGzb+evuGg3pgK6zI/vagBRhjEAwQc+j/d6Ghtc4U61oR8NLiXFOxLjXeCHcqJho2r2ftoeorjJc9HC25ieAvSgOHp1wcnnJb/hx+73kr8JW3nUcnzAiCaAsSxQiCIAiiRaTSSf9cGwbg3vse5LTGTN7BH9KnJRznMwADRRt5S2DfeL1zfDoRRhBzyPhzwBPfAf7j34DSoQyRK0jzCQ+EpLjIlBCaQptT65uw2cl6lkjFU+2dmIt8Wc0rWgNSayitIQOBTMbKocCVrscFsLhYFT8RFxe+sk7ZNZorfeLOVxpTVR9Tro/Jio/JqsRk1UPF69xV1kOTLpTW4G1/RUUQxHJmWYhiSi0PvwHdhD5jgiCWEnXi18Yh3LdrH7bfuysRyfHlvU/g8/4+tPv+HTrUf0SdHNnCKT766tMpMiRBzBfjI8CP/ifwxLeBfb9ob6xdNMnpMckuAk4RsHtM7vTUymG/uv6xcVYBsJya0MQtgLcecIOYGzypMFb2MFryMFb2MF72MFp2MVnx4cZPQkkNr8m1zGzBKXZqStaueKavfHoNr1Ka8XqGAQ4WEo7F0Zuz0Juz0JOz0JczkUN78zZ6cwI9joXevJXoE6/35iys7c/DEvT/DEEQ7bGkRTHHccA5x3PPPYfVq1fDcZxFEYVFKQXXdVGpVMAX+MuQ1hqu6+LgwYPgnMNxnG5viSAIYlbseHykTvwaLNqJax0hVukg0OY/e40c6qdPg5FzfIKYB75xHfDrbwYVBhxzNnDSpcDK56dErp6k4GUVSLBahPz24CR+e2ASZU+i5JpUdn2MV3yMlYzYFQpg42Ujgk25stvbnhGMwUTZDB3ZpyJexp3qJ6JXCp7pCD9pb+yQ327ggN/mHAVH1AlZocDlWPT/E0EQ3WFJi2Kcc2zcuBEjIyN47rnWnB8vBLTWKJfLKBQKi0LEA4BisYjjjz9+wYt4BEEQzdjx+AiuvfPRupiRWYIYABzAYNtrxB3qf+DyF+CZJ3+FS7eei/NOXEOnwQhivnn2UZNf8mHgjP8E9Kzq7n6IOePbv9yHP/vyT+ZtPc6AomOh4AgUbIGiI1JlC0U7sDkiKpsxHAU76OMI2IEglY4GGRec0pEhya8WQRBEayxpUQwwp8WOP/54+L4PKRfHNz2e5+GBBx7ARRddBNu2u72daRFCwLKsRSPgEQRBZCGVxvZ7d9UJYs14RJ2M5/QQ1uFIpk8xc01yCH/hvR2rMR451NfgGB7I442/vwHfHt2Fc+l6JEF0h+q4yTf9IQliS5yVPfN7m0FpYLLqY7Lqz9kat/7xGbjqzGPmbH6CIIjlwJIXxQAThca27UUhMAFGZPJ9H/l8ftHsmSAIYjES9x12aKKauDLZCgoc27034gv2rVAaCWGsdk3yjXhYnRbZwy43XrGJhDCC6DbCBjwAU4eAFc/r9m6IWaDjDuYD/1sqFn1xeLCAH7z3JZEvr4qngmuUPiqeRNmTKLvGVvEkyuH1yli97NXq5uqlrI315Lz79vrhk4dIFCMIgpgly0IUIwiCIJYvWU7zBWeZvsNmwrfVFlzr/TlutO/AehyJ7OE1yZ35C4DY9cu47zDPy76WSRDEPDFwHFAZA/7+MuDUVwMv+mOgOATk+oFcH5DvB6z8ooumqJSGKxU8aaICelLB9VXN5tfaXV8lIxQqRA7ew+THypHgpGuRCaXWkbP5qC2o10VAzJgzc/6Yo/m0wCV1ba24ELaYsThDf8FGf9742CrYAnlbIG9z5GyBvGXKoe2YwSL+6Jxju71tgiCIRQ+JYgRBEMSSJUv4Gh7I48rNw/jSA3vauirZCAZgZ/4CXC0uwPFTj2ENRnEAg3i6dzM+9Een4/MUSZIgFi5XfAq470Zg74PAL+4yKQ23jECW6wNyA9C5XiinD9LuhQxyz+qFb/XAtfrgih64Vg8qoogK70WF96DMi6hqG64MxCjfiFVxYSoUsML2WpsO2mr9XJlh82sCmL/IBaJOI0In8pxBsJojeJGoc3CGhPN4zmL94nNwDsFrfeNtnMec1sfqvTkb/QULfXkjfPXlbQwULPTnbfTlbeRtTq5ICIIgugCJYgRBEMSSpJHT/JGxCr74wJ6OrBH++vLRV5+OSzatwyN7zs4UvyiSJEEsUI49B3jzN4Dnfgr8+23QIz+DKo8D1QlwbxIMGlA+UD5qEsz/9yJI7SA1gwcrSKJW1rFy2KZN2YUFP9bX1bW6G9h8naozAVdY0Rw+BCSzoLkDJWwwYQHcgbYcgDsAt8AsB5LbALehuQ1YNjSzYQneVABqLAjxRLvFA4FJZNW5EaZSc9bPz8Fj+6ibPyFG1fpyBhKbCIIgiIaQKEYQBEEsOWbiNL8ZDIAGMFi0E5Eo41chARK/CGIxsuu5cXzs2x6eOvw6PHv0KrhSAQAYFIqoog8l9LIy+lBGLyujF2X0sZKpo4wBXkE/L6GfVdAXtPeyMnp1CUWUUNBlcGgIpiHgIY/Uten51mtkkFq5vc1t43dN2IBwTOJWrZxos81V0+JKE7QgvwroWW3KPauAYpDbhTl+QIIgCIJoHRLFCIIgiCXHI3uOzNpXWJxQ/LqErkISRFvcdNNN2L59e8L2whe+ELt37+7SjurZ8fgIvvfrg3V2DY4pFDCFAnodCxs2DuHENb1Y0eOg6AgUbIGCI2BxDlswWIJDc4ay4HA5w0RwJc/mGraqwJYlWPBhaR82fHOGS/sQ2oNQHpjyAOkD0g2SByivVo5yr9ZHpfrLjP7tzJFGBePn0v2hcMzV1GO3AFd+GuhdM4eLEQRBEEQSEsUIgiCIJceBidkLYh96xSlY1Zejq5AEMUtOPfVUfPe7343qlrWwXj+vffGJOG6oiN8enMJTh6bw1OEp7D1cQtmTUZ/Jqo/7dx/A/bsPzNk+7EBEs0QOFs/DEhw2N2KbFfjAigtwVuD3KrJx088WHMJhka2AMlbKg3DgIQ8XDnw48JCDB1u7cODC0UFZleHIKThyCrY/BdufhO2XYPmTEN4khDcF4U2Cy8596QDpAqXDwG++BXz3JuCqz3duboIgCIKYhoX1VkIQBEEQHWBNX37GYxnMybA3XbCx/hSYksDeh4DJ/UDvWmDD+QBv17MQQSwvLMvCunXrur2NhhQcgT8657iETWuNgxNVPHW4FIhkU3j6SBlVX0IqDU+ayIie1PADx/Z+YPOlhhfkxl5vy8I4yZcdPZXVixIezL0Lg2yqc5O2SVVbqMKGCxsOfPSiDM6yP4PX/Ohk/OJH3wJjJuAnZ8b3WK2MoF4rhz7DOG+zf9b8vM3+WfPzNvtH8wc23rg/Q9jeZHxivYz98zb7Z3w+ba/JGBhPP1N9f4IgiG5AohhBEASx5NiycQjDA3nsG6s09SsW+gqL1wHgxis21Qtiu+4BdrwPGH+uZutfD2y7Bdh0ZWc2ThBLkCeeeALr169HPp/Heeedh5tvvhnHH398Zt9qtYpqtRrVx8fH52ubCRhjWNOfx5p+c1K0k2itIZURx7yYYCYDYS2MHhm2RcJa2hYKcJEoVxPnQgFOeVWUHn8eeqd+A0vP5R3IesZ1AePoQVXbkOBBEibXHD4EFDh8zU0Ogbfzu4M+DBICvhZQOtmnNpeZz0fQpoP50mu11Kc2d7Vurdj42J6zxtf6MMy/s7jFT3Mh0QiCadGNoV4YFawWeIEHQRw4ZxBBdFHOTUTSsC3dL4xSynlyrtoYtNgvY75gj1YQwCLsF6WoXyw6amy+ZD/U94uV4/MRBNEYEsUIgiCIJYfgDDdesQnX3vloQ+HrbRdtxD2PjSR8j6Ud50fsuge4642pmQCMjxj7a+8gYYwgMjj33HNx++2344UvfCFGRkawfft2bN26FY8//jj6+vrq+t988811PsiWGiyIumgJIG/Pw0nTbQ+ZXPqAVzLJnQK8cqxcMvWoXALcUpNyMD5eTvkk62dl9KO8bLUhBQHFODQzuYIFxQQ0M2KaZhyKGSFNMmH6g0Mxy9iC8RIcCgKSBXnQZmkPvXIUfXIMfXIURW1OA3554O34Vu+roLSG0kaEVRqpuoZSxqajtni5vq1uLjV9/7Y/s2B83c9aYtbUi2yAJXgk8iWExJQYZ4mYKBeIcUZcNEJj437J+U5d348/2ZL9hQhBdBMSxQiCIIglgVQ64QT/kk3r8IXXn4Xt9+5qKHy9d9sp0zvOV9KcEMt8SdcAGLDj/cDJr6CrlASR4vLLL4/KL3rRi3Duuediw4YNuOuuu/Cf//N/rut//fXX47rrrovq4+PjOO644+r6ETNAWIDoB/L9czO/9DLEtbKxa2kc+isV5H69LarLIGXYonqWrdU+PqBVZ/o0EW84JLiWwDyf0HvD2G14w1vfDfSuntd108RPRCZzc4qxZqudcEz3i+oy227GqKgt6wSm54dXnRVcX8OVCq4v4UkN11dB3SRPhv2MvRrZzNyLmfDz6TaXnz6MgYLd7W0QRIKui2KLISoRQRAEsXBIi19bNg7hvl376sSv4UD8evB9f9BQ+BKcZTvOj/sOm9yfvDJZhwbGnzX9N27t8NMSxNJicHAQL3jBC/Dkk09mtudyOeRyuXneFQEA0DoQguKClIyJQylRqM4uU+VwDgWtPGgloaWEUhI6GKelD618qGCsVj60lNDxekw400pCx+dXYV+/wd5NzuL70hIstOuwrsCCOZiWYEoFdh0kBImZBA7GBIT2u/2nVsftg+/Ed/6/30LpJzNPd2WeGss66aWyT5nJOTolRnSe9OmwMJnrm2lb7PQXBwTn0SmwWqq3hXOFp8HC02KJE2fC5KcM95MgRixIui6KAQs/KhFBEAQxv2QJX4Iz7Hh8pE78GizaGC3VfxO/b6yCa+98FF94/Vn11yFDshzn7/5Gve+wVpjc315/gliGTE5O4re//S3e8IY3dHsrCwKlYv7BghMxcf9ixibheS60W4Jyy9DuFLRXgnIr0ZVG7ZXBvDKYb3LuB2W/AiFNXchKlCxZhiUrEKoKW1WCVAWHmpPnjHvYWk7naZVmwfXHmh+zuG+yyDearm/zwTGqe3EQgzigB3FQD+KgHsABvQIHMYADehCTKKDufuo+ADjchaedOTPxJRY56W8hwAJjNZEn7lNsOpHHaiD8hCJPlm3Wc7UlZCXHJXyjUfACgmiZBaE+LfSoRARBEMTcIJXGzj1H8JNDDCv3HMF5J65peOrrys3D+NIDe+ouq2QJYkB0sRHb792FSzata81xfmEFUD46s4fpXTuzcQSxhHn3u9+NK664Ahs2bMBzzz2HG2+8EUIIvO51r+v21qZFa43Jqo+DE1UcnKjiQCo/PFVNXK0Knd3Hy+G1Li+85hW7BuYpBa2B1RjFB+07sY4dQR4uCqianLlR3WJzI1a1Q9q5vAJLiDs+REoEirfxmAgkUiJQqi2oh763jJ8tHvnoqvneEkE/YXx1hf2YgI7VNbMiv1468OkVjWECiNUR9bGgGQe4CHIr6gtuAeEaXABMgPFanTEBiNDOYxEWp48u2Siyo82AYxnDBp7dno4OOV10y05EnhTxdj6D+QIxioQbgiC6zYIQxToRlcjzPHje/N7ZnyvC51gqz9MOy/nZgeX9/PTsy+/Zv/3L/fjrb+7GvvEqAIE7nvgxBgsWRsv111FGxir44gN72l5DB2MffvIAzo1FkGO7/wXi/7wZgE58x64DQSzrFV03tDOgfz389b8HtPlnuFz/7EO6+fzL9TOfb5555hm87nWvw+HDh7F69WpceOGF+Pd//3esXt1df0dxfvncGL63+0Cd6HVwooqyJ+d8/bP5b/CH4qE5X6cZkgm4Vh9c0Qsp8iZZBUirCCXyUFYeWuQA4Zhk5QCRAywHsPLQ+QEgPwhdGATLrwAvDCJfHIRwCkGUvdopFsYzTgKlRBSCIAiCmC+Y1rqrt76/9a1vYXJyMhGV6Nlnn20YlSjLBxkA/OM//iOKxeJ8bJkgCIKYJY8dZvj73/CglpClMmyz540nSZy9KphbK1z6y+uQ947MepVwtz/a+F8wMvh7s5yNmE9KpRL+9E//FGNjY+jvnyPH48SsGR8fx8DAwJz9Of3imTG8+gs/hCcbvw735iys6cthVV8Oq/tyWN2bw5r+HFb15JCzeUL0sQSDxXmQm2tMtoi388ge9WUSzp5/hSgdgFAeuKwCftVEc0zkVcB3G+RZ/WPjuoVVAAqDQH4wlq8A8gOAUwTsIuD01sorTwTWn9G9/RIEQRBLgnbeH7ouiqUZHR3Fhg0b8MlPfjIzKlHWSbHjjjsOhw4dWjIvtZ7n4b777sMll1wC215ezgiX87MDy/v56dmXz7NLpfHiTzwQnBCbH+78f86JToqxvQ/CuvOqjsyr+4+BvORvoE9+5YzGL7c/+zTdfP7x8XGsWrWKRLEFzlyLYld+9kH8/JkxnHn8IC54/iqs6Tei1+q+HNb05bGqz0HRWRAXK1pDKSOGSddEfZSBaOaWAHcSqE4E+WSQj8fKkw36TALuhHFkPx9c8Wng7GvmZy2CIAhiSdLO+8OC+yk/06hEtm0vuV8oluIztcpyfnZgeT8/PfvSf/Yf//bwvAliDMC6gTzOO3FNzadYeZYOiC/7iPEf1rsWbMP5sPjs3UYvlz/7RnTj+Zfz503U2HNwCgBwy2tehBesrb+hMCOkD/hlwKsAfpC8sjm1NWN7UPfKtT6R6BUIYH7VRFhc7Awc0+0dEARBEMuIBSeKUVQigiCIpc2Bicr0nTpAeDXyxle+EGLvg7UIkz0z9WVkfIfh3LcDHRDCCIJYONiC1ypeGTi4G9j/S+DAr4DKWEysqqTKGWKVqveL2DWYqPkBE3Yqd4xPsGbtUTnXwJ5hsxr1TZdjNvo3lSAIgugSXRfFFnNUIoIgCKJ91vTlZzWeAYkIlGF9sGgnIlGuG8jj82c9gzPvuyQZYbJvGCgMBVEmW/UgEEhs2z5Kv7wRxBKCQ+Fi/hgGfvwYMPEbI4QdfrJzVwWFY/xq2XkjFkXlINmFmdszBa2wnCOxiSAIgiBaoOui2GKISkQQBEF0ji0bhzA8kMe+sUq7khTedtFG3PPYCEbGaqfN1g3kceMVm3DJpnV4ZM8RHJioYE1fHlsqD0L887tQJ3xN7EPSoX+GxFYYAspHaub+9UYQ23RlG09KEMRC50/xLbzP+V/Av6caCkPA2lNN6lkViFC5QJQKBap8YM9nCFeBCEaiFEEQBEEsaLouiv3TP/1Tt7dAEARBzCOCM9x4xSZce+ejbZ36uvGKTdh22jDeu+2UpPi1cSjyF3be81eaAUoCt74f2SfBtFmpsML8Ihs/RRaKXye/Atj7UO3K5Ybz6ZdbgliCXIXvAwDKx78YhRf+QSCEnWb+v2edjYJLEARBEMTCo+uiGEEQBLH82HbaML7w+rOw/d5drZ36iglfgrOa+NWIvQ8lxa46tDkJdvXXjdiVJX5t3DrLpyQIYqFzHPYBAA5f9Dc49sTTurwbgiAIgiDmGxLFCIIgiK6w7bRhXLJpHR5+8gC+84OduHTruYkokdMKX82Y3N9av9Ih4PSrZ74OQRCLGhaeJqWToARBEASxLCFRjCAIgugagjOcu3EIh3+lcW7sNNis6V3b2X4EQSxJdOixULfq4ZAgCIIgiKUEn74LQRAEQSwyNpxv/IOhkcjGgP5jTD+CIJYtMngVZpBd3glBEARBEN2ARDGCIAhi6cEFsO2WoJIWxoL6to/SlSmCWOaoUBTzyl3eCUEQBEEQ3YCuTxIEQRBLk01XAq+9A9jxvuwIk5uu7N7eCIJYEOzFMF6EJ7Hqvv8C7Hs10Lsa6FkN9KwBelaZcq6PIlESBEEQxBKFRDGCIAhi6bLpSuDkV5holFkRJgmCWNZcr9+B23EjVh/eDXz/I9mdrHwglK0KxLKg3BuUc/2AsABuA8IBhA1wK8jt5m1ckOBGEARBEF2ERDGCIAhiacMFsHFrt3dBEMQCZA/W41Xuh/GNC/diQB4Gpg4BUweByQOm7E0BfgUYe9qkuYDbRiSLRLS0mJZuayS4WQ3Gp9us5HwN526jjXES9wiCIIhFCYliBEEQBEEQxLLlGb0GY7//xxhYWaxvdKeMSJYQy8L6AVN3JwHpA8oDpBfkYd1NtiEjyqUKxnhz/qhzCxPmSwhumcR4rcxjbUzM3BbZ421ZawU5E01sjdbjqbWa7WG6fQkzH0EQBLFgIVGMIAiCIAiCILJwekxa8bzOzKdkTDjzkmXlNxDVGrUF7Y3alB8b36yt0dxeA6GvgbinJSClmZeIwVoT5hK2QJgrHwWOPlWb6vX/FzjxpV17EoIgiKUIiWIEQRAEQRDEsmbebv6FAgjy87TgHKFkUlRT0ohiyq/V62yqVo7sQb9GtsjeyOYnc93M1mi91L6kB/hVQFYB303myp/Bh6VrpwFny2P/H4liBEEQHYZEMYIgCIIgCIJYrigVnDiLnT6Ln0JrKHSF4pJK9Ynb4gLUNAJYwz4qVc+yZe2twVpN6z4yT8F1G6sAnPxy4OUf7/ZOCIIglhwkihEEQRAEQRDLEt1J/UPr2Amq8FqjW7uKmCk8pe1exvjAL9ms523QV8sOfghLnKbXHrPqDfydRbaMunCA1S8EjjkbGD4DyPd3+6kJgiCWNCSKEQRBEEsDJYG9DwGT+4HetcCG84NrSgRBEG2ilPm3ZOyZIPLkM7H0NFA6ki1eLQkYYOWSESwbijkph/NNHdA3cGjf1Ml+gz5NHei3uNd290YRNgmCIJYkJIoRBEEQi4ss8Wv3N4Ad7wPGn6v1618PbLsF2HRl9/ZKEMSiwNn/M+DfvhwIYE8DY892xgcUtwERJidWd2p2nqqH5chuBbljBJqwHNqz5kzYs+Zssif6MoEgCIJYRpAoRhAEQSwedt1TL34VVpgIXWnGR4C73gi89g4SxgiCaErfjz4F/MeO1jrnB4F1pwMDxwEDxwLFlYBdMFEq7WJQ7gXsfEzIsmviUyRsBWU6fUQQBEEQXYNEMYIgCGJxsOseI3KlnSBnCWJA0I8BO94PnPwKOv1AEERDRs//IIrHng5M7AMmD5iTqJMHgKkD9REHK6PAUz/o3OLcaiyYRWKalTz5lehvt9BmJU+JNWyL76XZuqk2JsyVQ4IgCIJYZJAoRhAEQSx8lDQnxNqOCqaB8WfNdcuNW+diZwRBLGL68hbKnsStP1N420V/jhPX9CY7KGWE98l9NaEsnk/sA7xSzZF96MRe+bG6H+SuicqYJoyo6Jfn56HnDDaHfsNm4DOs5fnbWXMaH2rTjqdTgQRBEAsNEsUIgiCIhc/eh5JXJttlcn/n9kIQxJLhwhNX4f/+9Fnc9eNncNePn8EZxw1iVW8ORUeg6AgUgrzo5FCwN6LonIhCj0BxhZVst62oXLAFOG8gfigVE87igllaVEu1xSNOJgS3Vtq8WECA6ebOassYnyXuQdcEPqIBrEWRbZo+wjKuA3pWB2lVrVxcZeqFFSTCEQRBtACJYgRBEMTCZ7aiVu/azuyDIIglxcf/aDP+8Mxj8OWH9+Jfd+/Hz54e7ci8eZuj6Fgo2CIlsAXimR3ajLiWtzk447B4HoIXwDmDxRk4Y7CEyUVo4wzCMnXBGQSrlePjEv1jfeLjwv4iXCvoPy1pcU8pQMuaKKakSZEtyLVK1WWtb1T3g/n91Phm87W7xkzXzNhD2qZlkw9OB59XB4I4zARuAye+DPjDzwE9K7uzB4IgiAUGiWIEQRDEwmfGohYzUSg3nN/R7RAEsTTgnOHiF6zGxS9YjWeOlvCTvUcxVZUouT7KrkTJkyZ3fZTcsBzak7ayVxNDKp5CxXO7+GSzI0twiwtnSRtgcW7ENw4IziEYGo7jzIIl7Ei4m26dtNCXGCMYhB0T99oWCMP9MnBunkNwBluwIOewOIMV5Qx28KwJlAL8Si25U4A7GeQZ5WqjtrAe2LxSZ/9glQf85lvAD28FLv2rzs5NEASxSCFRjCAIglj4bDjfiFvjI2jdr1jwS8u2j5KTfYIgpuXYFUUcu6I44/FKaVR8mRTPQnEtJaQlxTQfVU9Bag1faSilIcOkY2WloeJ9tIYvjS3eXynAVwpSwfSXCkobm1KI5mxGON/iQ8OBjxw85OEix9xaGR7yzK2V4SLfpD1X19dDDi7yUVu4hoccunTyK4bkDpTIQYk8wDgsfwrCm6zvl1+Bwyf+ETBRgc05RCD0WcIIh4yuXBIEscwgUYwgCIJY+HABbLsliD7JkBTGgnphCCgfqZn71xtBbNOV87tXgiCWJZwzFB0LRWfhv15rraE0kuKbVJDSh/RcaOlB+i6U70H5LqTvQUsPWgY2GdR9Y9NhXRqfZDrwkabrfKQZf2Us8k/mgyuTs8DOlA+uvSD3o5yHeSyJILeVC1u7sHUVvO2ALJ3F0wJV2KjAQQUOqtpGFQ4qsFHVQR7UK9rJ7BvZdINy0LcS9K3ChkaL0T8rAL70NICnM5vDU3W2qJ2aswXHmy94Ht520fM79jkRBEEsFBb+T22CIAhi+aGkca4/ud9cndxwvhG3XnuHiUIZd7ofil8nv6J+DJ0QIwhioaEk4JUBv2oiTnqV5NW7unqsr1+N1SvTOM9vHA2TKQ9CehAxcaprfq7mCA0GyXPwo+TAZTl4zIHLHLhw4DEH1aBchbGHQlMoQpW1jSllY0pamJQ2JqSFCV9g3LfqxKkKHEgs7p87oVBa9ZPBFB7ZcxRvu6hLmyIIgphDSBQjCIIguoeSYHsfxDFHHgbb2w+ccBGw+xsNhK9bjDDWTPzauLU7z0EQxNJCa2BiBNj/S6AyVi9ONRWyqk2ErPLii87IbUDYQW7F6lZkLyuG/ZMSPiz4EPAh4MXKrq7lnhbwYuWq4qauBKqao6oEXM3N+KBv1px+OCes2omp2GkqFxaia/QLhNBPWtxPmYidygr9lgle82GW2Se49hiWLc6Ca5DB2KgtWa9fK76H5LoinDu4XnnycF+3Pz6CIIg5gUQxgiAIojvsugfY8T5Y48/hHADY+wUTQr58tL7v+Ii5OvnaO4wwRuIXQRCdQmtgdC8w8lgyTR2c+7W5DdgFwMoBVpDbecCKpYb1XEuCVX3daj4u3pcLYBofUxMVD6ff9J25/6y6TM7icCwOR/BaOWZzLI6cJSJbTtTacxaHJXgU8dM4+zdXbjljMbu5vshYLZhAGMCAs1rQgFqORPAA0wdRoIHQzsLAB7H1eRBcoJmdp+YlCIJYipAoRhAEQcw/u+4J/IOlfL9kCWJA0I8BO95vTorRtUiCIDrF3X8G/Pwr2W2rXgj0rskQrtqpp4StqC2/JP4t681ZuOqM9fjaz56bvvMipuqruiuFy41IcGNJYS6M5slYTKDjiIlsjQW+MPJnveDXROCL1giFvJQ4GO4rtTZntfXr9zy9PfksSM3b+FkEiwmTLYqaFPCAIOYPEsUIgiCIjiCVxiN7juDARAVr+vLYsnEIIvhqWfo+du/8NspHn0VhcBibdr4PrG1nyBoYf9ZcnaSTYgRBdAolG7eNPW1ELGEDyjJ+t5QApAswHiSWOk0V/NumlTmFpqVJquZoHjJv5rDygMgBvEUn6QsQxhhu/ZMzceufnNnxubVORtWU2kTbVKlom2EEzngeDyRQs8f6huM1UvMFY1PrmL0k7UqHcyf3EM4d2lVs7tCefDYNqZF4tqw9p58tbtfxPeuYLbV29HyJ5zK26VAaUFKj9SjQxExhLC66xQS+SGQzgl3cnhASU0JexZX49f4JDBRs/P2bfg9nb1jR7UckiAUDiWIEQRDErNnx+Ai237sLI2OVyDY8kMeNV2zC2me/g/UPb8epONyZxSb3d2YegiAIAHj1/wD+4C+BA7uBA7uAg7tN+dCvAa8EjPxs7vcgnNqVSCtfX28lF079VUluTX9dMnHNUjRvm2cYC/xqzfvKyw+dEttq4mFKjIzZlU6LfRlCXFrYSwlzSSEzEBIT84XjEBMyw7kRmy++JpLPEImHgRhZJ6Im11RxEbbJno2o2Kr4WptTtqBAag34WqMltbINxsoe7tu1n0QxgohBP2MIgiCIWbHj8RFce+ejdd8b7xur4O5/vA1fsG81hk7dBOhd26GJCIIgYE5pDZ1g0skvr9mlDxzdAxz8NeBO1ZzoJ/IsWyt52Zwki9ZyTarO/+O3DqsX1iLhrJHPsnZEt4w5pp0/XQ/mt4uA01NLS+Ca6lxjrgsiOuG9EEkLd2kBq5GgF4lrWsOXGp5U8KSGLxU8peH5Cr4yNk8q00epwK5jdgU3GOcrDTew+TIsa/gq1ie0q1QfqeBKhapnruS6cv6u5b7i9GG866Unzdt6BLEYIFGMIAiCmDFSaWy/d1fmRQoGhRvtOwCgQw56mYlCueH8TkxGEATRHGEBq04yaS6QfgcEtlhZesHVTM9c04zqwZXNyJbRlrZnRsjUNfHOm5uPZM5IiGS9ScEsUW9UzmgTNoD6K57JE0utnSZqdC1yMV/xbPaMyRNnqWdpcvVTL7Nbm4wBNuewhYkUagsTPTSMCBqVgzaLc9gWhx1EEDV2jsGijVe+aD3OOn6QfJURRAYkihEEQRAz5pE9RxJXJuNs4buxnh3p0ErBS9y2j9I3/gRBLA2EBYheINfb7Z3Uo7XxtZYpsrUjupm68l1I34PyXSjfg/RdKOVDBzYtPShp+usgheXEvMoHkx6gTc60D6Z8cGXKXPng2oelqnBkCRzBCRyvZFIHI4pWtYUS8viF2og3e++FBP1sWgjUObZP+NwCLG6EJCcmKFnCCEl1IlPC3kyUajZPbD1u5nRaHLeQT+0RxFKCRDGCIAiiLeIO9Z/YP9mw3xqMzmB2BkADhSGgHBPU+tcbQWzTlTOYkyAIgmiVXc+N48rPPgh/Fr6MTJQ9B9AOPKVaPOGjYUPChh+koMxM3YEPK7A5zK/vF/QN+znw0MMqGMQkBtkUBjCFQTaJAUxiJZtAPyvN+PkAIMd85DCJM/mTKKCKSRQz+0UO01MRC5NRHOsjGWZGeuS1K47JSI/GHo+O2CwKY10EyAZO2uuiPWZETYxHY0xGYcyI9hg5iM+I6piyJ585I5plet3AThAE0S4kihEEQSxjmkaMzGi7b9e+Oof6jTiAwfY3FIpfJ7/CRJmc3G98iG04n06IEQRBzCVeBTj6FHbu+B4uxzOwuQ+bxUUqI0CFglQkUMUEqaSo5cPhPixRs4V9LRYfG4pfTaKAziOaccjcIKQzAJUfhMoNQOVXQOUHoPOD0PlBoLDCpPwg9KoX4MGelQkxKi4k0XU1giCIhQ2JYgRBEMuUZhEjAdS1DRZtjJZadyTziDoZz+khrMORTJ9iSgMH2BCGXvc/8POHvosztl4G64SLauLXxq0zezCCIAgim8o4cOQ/gNHfmTxKe4DxZwFovBnAm51ub9RQ1TY8CHiw4MGCCwu+jtdN2dcWPAi4gd2HBcUtKGZDcZN04IxfcwdaOKja/XDtfrj2AHxnwAhh+UEwpw85WyBnc+QsgZzFk2UrbAvKjCPnSeQsAW6ZH3YMJIYRBEEsFkgUIwiCWAakT30dnXLxjn/Mjhj59jsfzZyjHUEMADQ4tntvxBfsW6F00tl+eCtn5LybsPKEi/Hs7ils3nAhnQYjCIKYK556ELj9FZ2d0+kzPtFyfVHSTi+U0wff6oEXJJf3oCKKqPIelHkBZRRQ1jYqiqMiOcrKQklylCXHlM9RUgxlj6EiNSqeRMWTJlKfVKh6Eq5vovZV/Zp99hH8XAAHgtQ5eNZ1x9DfVd31yORVx2gcz7gCGT+VlmFvdnUyeY2x2RXO1PXH1JXPtD26Wpm6+lh3PTNlT1/JbPXaJJ3GIwiiE5AoRhAEsYRo9cojZ8iMGDmbwE4cClv4bqzBKA5gEE/3bsarrnw7Hnv2BKx/eDvW4nDU9wBbiZHzbsSZl10Dz1tsYcwIgiAWIXMRus+dMGliJDIxACJIudnMfe3DwNpNLXdXSsOVyohkvoxEs4oXE9D8dLsR2appkW2a/nWinN9YlFMaUFJjdj9hielIi2XN/LalnfFniXthO8/IOQNYOodZm7GaAMgQjgv7JuvN5k7UEV+rNle6njV3oo7UWjw81Vjz49Zo7ng9nCuam6fqQV8W21fDucP1eYO5M56j8dwkjhIzg0QxgiCIJULWdchGVx5n4T85k8v4I7jRviMRbVLn14PxW4DLroF86X/CL3d+G+Wjz6Kw4hicfO5lWGfRjyCCIIh5Y+NW4C/3myiMfhXwKyaX1WS9Lq8CfhkoHQFKh02aOgSUDgFThwFvam72+9t/bUsU45whzwXytgBgz82emhCKcr7SkEpDKQ2pY7k2fWTKLpWG1kjaozKgdNKutIZUgNQaOhiftsf7hnaV6tvIbnLU7T8aF19Dp8al9xuuEX0G8c8l+czxtdOfUSt6rlQaEhpYGK7piC6RFhPjdc6MUsmnEdwARAEcaiJdWpCLj8kQMmMiZDhXzRarM2SIfcn9QZv/53ylIWXt/zs/+HfGVwpKAb5SkV3Gk9bwpfl/LN72ht/fgD9/2UmwBO/in9jCgH4jIQiCWALseHwE195Zfx2y3SuPrZA+EbYCE/ic/am6fmx8BLjrjcBr74DYdCVOvaDD13YIgiCI1ji6Fxh/DnAngepEkE+2WA/yTp50EjnALgB2MZXnTXnDBcC5f9a59eaBUJQjOo/WNbEsEvEiMS1lTwhsMZEtZQ8FwXpxrzaf1ogERACRKBi2aQQCYrAOdLKuE3OE40JbrI50n9ozZ9Wz5s6ep76PypobyedSqbmy54n1jbcFc+l4PWPNOoE0+DOY/d+VYC5Tm/V8S5nPfu9JnH/iSpz//FXd3krXIVGMIAhikSOVxvZ7d83Lj/6sE2FS175FS6IBMGDH+000SfplgSAIYv757feAL181P2sVhoC+YaBvHdA/DPSuC8rrTd43DOT6ASsPiOX9a4iOBImkgGAEkFo5FCi0To6JCxrxMXERo+k8QXnaeVJ7VarJPIm+NfEnc57U88fFmax5Go6JPrsGa6PWb9q1U59ZXMBK2OqeuYV50OCzaPhn0+y5svbf2meKVD8VFNJ/p5Sq/T1VibUBZPydajiPRmBP/52ak/+tiDZ48QtX48zjVnR7GwuC5f3TiCAIYhGS9humtE5cmZwrLuOP4Av2rXV2wZq92WgT0WzvQxRNkiAIYp7RWkP2rgdffTLY5H7AqwB+BWyuvkYpHzHpwC+n7aqYBckd+DyHqtWHkrMSJXsIU/ZKTNlDmLKHMGENYdIewoQYwrhYAZfZdVcN09cAa1cDjV3r5tf/4tca42PjJ5DifTNFiJQY0Ujgio8hCGL2sITvttqVybAcv8rIUtccEzYAFmewLW5ywWELDkuEZZNbnMOxGCxu2pxEH9PP4rH+gsMRDFaD9vQa8flti8PmLNWHQ2SFdCdmBYliBEEQi4hMv2GFzvlOCVwX1JU5FG607zDlmfwsntw/+80RBEF0Ea01Jqo+xkoexsoeRkseRssuRoP6WNnDWMmDp1Rbwk2WHyYV9/WkYz6nGviDSl9FCueriS83xJ8ENiTycJGDhzxzkQvLcJFjHnLwIluOeVHfWv+YjbnIx/qH7elyDi5yzI92wbUPLn3YsoSCdxSD5d9N+2cwpos4pAdwEIM4pAcwpnswhTzKyKGkcyghh5LOo4QcqshhKiiXg3IZpo+/hH4FSvsvQp1IkLKnnKIDNb9GcR9MQNx5e8Y8dSJEsAdeb2skQiTnn/2YpACS3HcklGTOn5xv2nlSY+IO5OvmafRczeaZ1WeaHINUv7CctNfWBup9YoU3AdIO7rPWTj5z/TyNP1MGxtF4ntjfzfi6BNEJls5PBIIgiCVOQ79h5Zn7DeMs6XR/3UAeN15hHBvHxbctfHfiymTb9K6d+ViCIIg55OBEFT944mBC3BotuRgNhK/xsofRwN4Jnzfdh8GDBQ8WJhBE5At+2UxH5UtG74tF9mNBFL8g5ywZxS8xHzcOpQUDLKaRgwcHHvIsFNKq6FWT6JdHMSCPoE8eRb9/BH3+UfT6h9HrH0WvdxhC+xhgJQywEp6PkWmfshmS25CiCGUVIK0ipFWEsgtQVg+Ubeza7oG2i0HqiXJmOQDjYFwYtwBMgHEOcAHGBMCDtsAe9mOJfhYYZ2DcArgA57VxjFtgjIMJDs4ts5YQ4KGdc4q0RxAE0UFIFCMIgligxK9JrurJ4aZ7Ouc3LHyV/uzrzsSKnlx0FXPLxqHoWPYlJ6/G7iBi5NrKKPD4DFfqXw9sOL9DOycIgugcTx8p4crPPoijbQQlydscgwUHAwUbA0UbgwUbg0Ubg0UH/XkLtuAx0Qg1UahFEYmHdhYToWJ9s0Wr9Hy1sfXrIzb/IhFWtAYqo8DkgSDtB6YOApVxEwTAKwHulEmZ5ZLpp437baE8CDUGeGPdfa4ZwSIxDozHyjE7D9oCka5WTtvTc/CaPdE3mD/TzjPmSOXT7im1dlvzZfVtMl+rzyIcgNtmDoIgljQkihEEQcwDaT9gofgklcbOPUfwk0MMK/ccwXknroHgLPOa5ExgMFcgB4t2IhJleCJs22nD2QN33QOx4304dfy5Wa4OYNtHyck+QRALkvf9n5/jaMnDcUMFbD52EAOhwFVwYoKXE9kHCjbyNv17Nu8wBhRWmLT6hTObQ2tAutOIZ1nlSSOqhWXpAVoBShqRTSsYT+aysV0F9aiss+0tffWlAeUD8KftSXQAbhmBTNhBnlHmdsrepO+sy3a2nQQ8gpgxJIoRBEG0SSOBq1Hbfbv21QlcwwN5XLl5GPc8NhLYBe544seR/UsP7JnRqbDBgp24ThmKX5dsWtdwz3Xsuge4641o7eU8BuPm5T6kf70RxDZd2f6DEARBzAOPP2tOCn32dWdh83GD3d0MMbcwBlg5k4pD3d5NNlpnCGthWWfbm4lsCXuL88XtrYp5WjXYdyO7DATDLNEwbW9ReJztZ9LonUf5Js3cU8XC44//N3DKK7u9C4JYMJAoRhAE0QZZJ7iGG/jhAupPaIWMjFXwxQf2tGxvlc/96VngnGWKX+c9f+X0EygJ7Hgf2hPEAnHtNf8A9Kw011p615ork3RCjCCIBYwtzMkKOv1FLAji1/qaEQlXaUFIISlEtdOuGvSfrn0W6yk/EKh80x6JWmHdD/rG67JB3+nq08ylfEAtJeWrCb/4ZxLFCCIGiWIEQRAt0sjR/b6xCt5+56OZY7IEsbmAwZwK+/3nr2wvVLOSwN6HakKWVkC7VybpRBhBEE343Oc+h4997GPYt28fNm/ejM985jPYsmVLt7cFoCaGffBrv8CHXrkJwwMFDBbtSCwjWiQUaaRrhAUZJteIDdINrh2mTgJNJ74k2mYqBOkZCD0zXW8m8zXYX7O5iO7AuLlOyYTJI/9rJmACgsAIyXogcjatz3AuIOPvZcYJufjfzd7VwNa/6O7nSBALDBLFCIIgWkAqje33Zju673YsslACu/GKTe0JYrvuMafC4iJYYbC1sRe9B1h9Mp0IIwiiKV/5yldw3XXX4bbbbsO5556LW2+9FZdddhl+/etfY82aNd3eHj7w8lPw3q8+hh89dRRXfvaHkb03ZwXO822sCHyKrSg6kUP9wYKNFT02BgoOVhRrfsfa+jc4jQ58RWWJSdJLik3Ky2hzAekny2mRqk6watbWyvqxNYgFRuh4P+1sntU7sE84pk/350g6tG91vkbrx+ezYqJP6Ai/gSgUF48SYlILY9OC0kzHUsRPgliSkChGEATRAo/sOTJrp/dzxbRO87No5DesPNra+I0XAxu3tr4eQRDLkk9+8pN461vfije/+c0AgNtuuw3f+MY38Pd///d4//vf3+XdAa940TBedOwAbvj64/jp06MYK3vQGpis+pis+njmaLmt+frzFgqOgC04HIvDERy24LAFi2y24LigdD9ec/h/wNEVWNoHhw9LLzHH6dwKHJDbNWfkkeAwF+LLTIWZVoWhmc43UyGqnfWy2kjAIQiCaAUSxQiCIFrgwMTCEMQYgLX9OXzitWfg0GR1eqf5WczIb1hsB/3rzekwgiCIJriui5/85Ce4/vrrIxvnHC972cvw8MMPd3FnSY4bKuIf3myuc0qlMVHxcLTkYbTkYrTk4WiQj5ZcjJaTbaNlF6NTHiaqRtAar/gYr0wvbm2xHsegdWhOn2s2+LBQZXl4PAePO/BZDj7PQ4ocpMhDWzkoUQCsPGDnAbsAHianAOHkYTl52JYF23Zg2zZs24Jl2WDp00FhPX01bFpb+iQPXXklCIIg2odEMYIgiBZY05ef9zUZkrJVKHvddOWpuODEVe1NFvcdNrm/fb9h8R1s+yhdlyQIYloOHToEKSXWrl2bsK9duxa7d+/OHFOtVlGtVqP6+Pj4nO4xjeDMXI8sOgB6Wh7nSYWxsofRkoeKJ+FJBU9qeFLBlQqen66firvH3gTmTkH7FSBIzK8GuSlzVQWXFXBZhVAuLFWBkC6EcmHrKizlwtam7GgXNjzktAsHLvJwkWczu9ZowYelJwE5CSwaF1YsKZRlXYvLujqXuIrX5HpeR8S79JVBy5yiKwwBxZW1ZDnd/jAJgiCWDSSKEQRBtMCWjUMYHshj31hlTn2IhcLX2y7aiHseG0lc2YyuSW5aA+z5QXaUx7Tj/A3nA7u/Ue87rBUKK4Dy0VqdHOoTBDHH3Hzzzdi+fXu3t9E2tuBY1ZvDqt5cG6OeN1fbAQBoreFLBc+twq2W4LslyGoZfrUM361AuWVIrwTlVqC8CrRbhvIrgFeB9itQvgetfGjpQykJLT0o34eSPpQyuZYSWnlB7kMHUfxYFPlPgmkfXCtYTIJDwYKCgISAgoCChdAuwZmGBRm1W1BRm4CCzZopdNr4NlsK/s1y/UAxLpStStZ7Vpmf0bk+oGc10Leu2zsmCIJYtJAoRhAE0QKCM9x4xSZce+ejmSe4dEY5Xh8s2olIlMMDeVy5ebix8HXaMN677RQ8sucIDkxUatckd98L3JoSuPrXA9tuMeU6x/kpYasdrr7diG1Z4htBEMQ0rFq1CkII7N+/P2Hfv38/1q3L/iX++uuvx3XXXRfVx8fHcdxxx83pPpcqjDFYloBlFVEoFru6F08qlD2JiidRcRUqvkTZlbjr0Wfwvx7e29ZcDAr9DsOf/8EJ6LEZemygYGkULaBosaCskRfGnucAhxHroFSQ+4FoF7PJKuBVAL9scq8EeGVzis8rB+VyrBz2CdtLtb6z/fqsOm7S0ada63/JXwEX/NfZrUkQBLFMIVGMIAiiRbadNowvvP4sbL93V6aQBaBh2yWb1tULXJzhvdtOwcNPHsB3frATl249F+eduCbyDyagcB7fBYj9AF8L/Oow8M9vQt3L9vgIcNcbsjc9I0Es8Bu2cSuJYARBzBjHcXD22Wfj/vvvx1VXXQUAUErh/vvvxzvf+c7MMblcDrlcO6etiMWAHQQc6M/bCfvIWLltUUyDY8wFtu/Yk7A78HAsO4gNbD+ex/YF+X4Mskk4TMJhEjnmw4aEw0xgAwtB0j441Kyfs2vQz2qCIIgZQ6IYQRBEG2w7bbihwAWgadt5z19ZN5/gDOduHMLhX2mcG3eYv+ue+lNfjCP72+dOXugkv2EEQXSO6667Dtdccw3OOeccbNmyBbfeeiumpqaiaJTE8mbbacP49V9vQ6kqUfIkyq6PqapEyZUoez5KrkSpKlGulIHSIfCpgxClQ7ArB5GrHkZ/ZQQr3WewxnsOq9RBiOmELZ3Km+BrDg8WPFhwg9zXIip7EFG7p0Wirw8BTyfHRmN0rS6ZBc1tKG5BcweK29BBxE7NbeNbTDiAsMGEDSYck+wcmLBh2Q645YAdLsD+1m44gsESHIIzCM5gcQbOGCwR5JyBB/awj2CxcspeG8fBOWBxDsEBwbkZJ7LHR+sG6xEEQSxkSBQjCIJoE8FZpsA1XVvL7LoHuOuNqHtr1/PwLTb5DSMIooP88R//MQ4ePIgbbrgB+/btwxlnnIEdO3bUOd8nlglaA9IF3CmTvBJy7iRyU4ewYvIAMHUAmDwY5AeAqYPmCn+rp56dXmBoIzB0AvSKE+ANPA8VZwWq2kJZCVQkR0UJTEkjeLlaoKoEqjooa4GyFqhKAU+hFhhBKni+TtaDwAlVP6w36OObuisVdMe+w6oGaXGQEONYrcxTwl0zga6h8Dat4BcT8mKCXta4+B4b7iPYZ+Y4zmALDivMhREUbZFsE5yBMRILCWKhQKIYQRDEQkJJc0JsTt35p7jsI8ZnGPkNIwhiDnjnO9/Z8LoksUCRPuBNAW4pEK+CsheIWVE51Z7oWwLcyVg5qOsZhrNkwjiY71kD9K42+eBxwNAJtdSzGgjEBgbACdJCQaomEUkDUa0mqJnkBkJbVA+EtkQ9ms/UldLwlYbSQa40fKUgFSCVgtRBrnQyaQ1fpsfVz5ce56vm7yy+0oDScOfpc14M2IFgZol6IU1wBjtoswSHzVminyV4bXzQVusXzJma2xK1vvVzp8cYMTG+p9qY+rlJ5CMWOySKEQRBLCT2PtR+lMgZE/gOO/ftJIQRBEEsdx77CvCdDwKVMeN0fq7hNuD0mFRcCfSuqQlevWuT4lfvGqAwBHA+9/uaQ8yJI4G8vfR+5qpAVIsLZdniXL2oZsYZ0c5XCirMUyJd1ri6ObWGlMm9JPbUQPBrNi4SDOvGKSht9iqlsfuB8OlL0+7JbMHQCKISWALBUrP4+B9txtVnH9vtbRBESywIUexzn/scPvaxj2Hfvn3YvHkzPvOZz2DLli3d3hZBEMscqXRD/2AdQ0mwvQ/imCMPg+3tB0oHOzt/Q8h3GEEQBBHj4K/MtcW5RDhG5CquMNcjJ/cDlVHgFZ8EXrhtbtcm5hTOGTgYlqDeNyu0rglyvtLwg2u1vjLCmSdVppAmM2xRniirYF6N+3fvx8+fGev2IwMAfrN/ottbIIiW6boo9pWvfAXXXXcdbrvtNpx77rm49dZbcdlll+HXv/411qxZ0+3tEQSxTNnx+EhdJMnhIJLkttOGO7NI4EzfGn8O5wDA3i+Yb8vbhqF23TJejtULQ0D5SM1MvsMIgiCIOC+9Edj8OqAybq45hlcevYzrku6UEbPKR4M0an7G+JXma0gXGH/GpDjf/wiJYsSShLHweuPcr/X937QvagvOULQF8o5AwRbI2zzIBQqRLajbAgWHwxEicc3STl0BXdWbwwUnztK/LkHMI10XxT75yU/irW99axSF6LbbbsM3vvEN/P3f/z3e//73d3l3BEEsR3Y8PoJr73y0zqvXvrEKrr3zUXzh9We1L4wpaa5GTu4310JKh4F/fhPqfIeVDk8/F+NJp/uhwAXUR6wM205+RXJ98h1GEASxeCgdMSKVVoD0AL9qrjhmll2TEmU36NOo3OZY5Xfu2frWA1d9oXPzEcQy5W9f8yK87cs/wZ5DUy31781ZuGTTWvzByWtw6alrkZsP5Y4gFiBdFcVc18VPfvITXH/99ZGNc46XvexlePjhhzPHVKtVVKs1Pwfj4+MAAM/z4HlL41J2+BxL5XnaYTk/O7C8n7+bzy6Vxo/3HsWBiSpW9Ti46Z5fZrq51zDnrrbf+0u8+KSVLV+lZLv/BeI7HwCbqIlVmnEAGlkz1NZmYLFa2Fu+6n8CxaFI4NLHnVcTuJ5/KdjTD9e3SQUc+/vxhzZpAUB/75fnswPdff7l+pkTi5DH/gm4+8+6vYvmcNtcjbQcQORSZRuwAtvKE4FjzgKOORtY9UJAdP37eaKLaK2htHkPU8E1Q6U1lELk20vrmp8vpWD6Bb7EpI7ZYj7GMufUOnj1Sc0ZW081nSO2TmIOBHMk9yVV7epkYs6wT2rO2h4bPT+CORo/vwo+z4GCHbWX3MaBLSarPu7+6bO4+6fP4vmre3Dff7sYvNNuQghiEdDVn0SHDh2ClLIuLPfatWuxe/fuzDE333wztm/fXmf/zne+g2KxOCf77Bb33Xdft7fQNZbzswPL+/nn+9kfO8zwf5/iGHVbewnQAEbGqvjsV3bgpIHpI0QOj/4Iv7fnM3V2phsLUuFOqqIXOVnzyVC2V+DxY/8TRvZYAMYBFEz+y29nzNKsbWFCf++XL914/lKpNO9rEsSMEF2On2gVALsA2EXAzsfqeWOzcgC3THRILoKcm1PNcRvjxn74t8DRp2K2VD7dPFE/npo7PiZtEyYq5XTz1O0hqz2ca/bigVQ131KhvympNDxlnL57QYTImm+pVJ+U/ympdK2fqvmj8uN2Ga+rOlt8rvQaoYP5OhEnJiDViUKR0JMShbSGnsdA20Rzio4FpTV45te1BLG0WXRfz1x//fW47rrrovr4+DiOO+44XHrppejv7+/izjqH53m47777cMkll8C27W5vZ15Zzs8OLO/n78azf/uX+/EPDz+WeSpsOk449Qy8/EUZVyiVrJ3UKq6CuPerADCjVwzxyr+F3zccnfqyjzsPZ3KBM2cw10KG/t4vz2cHuvv84UlzgljwnPZqcwXenQr8fE0Zn19uvBy2BeVW23TjUyQRftmkuF9KAgq8lhiHAoMCh4zZJTikZpEtLBu7GSPjfcGhwSA1T9jr547PGRvXoI8ChwYHBwPXAhwmCXBoCAACAAfTAsZdvoAPAQ4OP+jnwYLQPGEHBBQ4AAENDqXNOBm0h6n2bO1HD+XM+L3ijEV5aBOcgTEGEbZxmH6MgXOTs1jf+NjEfJxBBPbkfEH/2HyRLTEHovbkHEEAgsSeYraGewzG1u0x9ozp9cP5mj4nknuMzekIDtYBoZcgFiNdFcVWrVoFIQT279+fsO/fvx/r1q3LHJPL5ZDL5erstm0vuV8oluIztcpyfnZgeT//XD17OpLk2RtW4G++9esZCWIAMDzYU7/PwHF+wqfXLLAGjwM2bu3IXIsB+nu/PJ8d6M7zL+fPm1iEWDmTMNS5ObU2vsPqBLPAyb7yjT9MrUxS0ohoQXmyUsWRyQqk70MpCSV9SCmhpIzqSkpoFbQrCS0ltAqTMrmWZu6gD7QEUypYSwIqWF9LMC3BtDZ5IPeISPIxZQEFBh3YYzamozKP5Rw6Ni7oz6Z/OwjlJvNZtviZswblZYJmHOAWNBPmdGGUYnVhgwV1luiT6pcxrml7Zj0rTTNGWOb0pnBiV4NT14RJXCKIRUVXRTHHcXD22Wfj/vvvx1VXXQUAUErh/vvvxzvf+c5ubo0giEVKWvzasnEI9+3aVxdJcqjHxpGp9n0KMQDrBsy8CXbdA9z1RrT+ZjzNKv3rjTN8giAIgpgLGAuuQOaNn8omaK3x1OESfvTUEfxozxH8eO/Rlp15zxecAY7FYQuOXJDbgkc2x+Jwgih5SZtJViyCnuCAwwGLaThCw2KAwxUspmFxwGGmbHMNiynY3JyVsriCzRQsaAiuYUHDYhoi6C+YhgUFwWrimxHkTB8OBd5AhEQoHmodK8ts4TKyhaJiOI8fCZCmHE9pW1j3pmlvUM+AaQVId+nrgTwUyGwjmFmhiBYvOzURLapn+eTL8s+XEuGiucL1GqxNPvwIIpOu/59x3XXX4ZprrsE555yDLVu24NZbb8XU1FQUjZIgiOVLlsAVOrdvVfwaLNoYLdWLXzMVxADgxis2JZ3sK2lOiHVKEANMxEiKDkkQBEF0mWeOlnDhLd+bk7kdwVFwBIqOiPKiY6HoCPQ4VrLNttCTM+WwrWALY7Mt5GwOmxtxyxIMVlCObMGVMWIe0LoFES1t89oX3uQMxrQsBsbXSdfDCK1edjRW5QHuAgzmwnhSIGvrRJ2I+Q5sZUxWvY11whScLpz1WozTCT6iIV0Xxf74j/8YBw8exA033IB9+/bhjDPOwI4dO+qc7xMEsbzY8fhIncA1PJDHjVdsAoCWxa8s20xZF6y/bdMaYM8PIl9f0GrmVyYZN+ND+tcbQWzTlZ3ZNEEQBEHMgk7+HE3jSgW3rDBWnh8BweJJwcziHHYgotmcQ3AGSwS2RJnXxgoOO9UmOAvmqbXF5wzbLB6cRgvn58kTauGcgtdstf71bWKhCn2MBdcMu/6r5vygzAk4yEAo86upshsT0tx6Uc13a+Ojclb/jLHSDcY0WC/+ha1WgF8xaTnSirCWuNorjOBZnTDXyquT5nPO4j/fBxy3ZX6fh+gYC+Jfqne+8510XZIgiIgdj4/g2jsfrTt3tW+sgrff+WjmmE6/tDMAa/tz+MRrz8ChyWrtpNrue4FbU37DCoMzXAHAa/4Bfn4AP/vBt3HG1stgnXARnRAjCIIgFgynHTOARz7wUuwbr4AzZiIhxqMXSh3ZwiiJJnJhss2XsUiJYWREqeCF0Q9jURM9WYuI6KXaknPGIiXGbUplRjb0gwiKQOMI0IsNOyHytSvM1cS+tDDXaM5QmIvbHMtcWTVJIGfHyhZHzubI26a8JB26cw7w4CryQkLr1Mk2tyaiRddifSPqzeQEnU6Pi/WRwfzRul5NuJNebS/xPSX6ucl+naDJ1d5Z8++fJ1FsEbMgRDGCIJYO7V55FJxBKo2de47gJ4cYVvz2MG66Z1fmRcT5itwdvqrddOWpuODEVbWGRn7DyqPtLxI7EaY9D8/+chybN1xIghhBEASx4FjTn8ea/gX2C/80xIUyKdsX6cK2uEhXJ+DFbWkBr84WWy+1h1Dsq5sztm8j5tXjSQ1PSmAB3tZrRCSgBUJZKKDl7QaimsWQtxgKFpAXQEFo5IVGzmLICSAnGHJCI2cBecHgCCDHAUeYVLAY+hxe87MW+mALr3lqVd8WBXhIjwl9t+mMMam5G7al58qw17XJ2LxN2ho9z4yftUFb4jPIaJu3t/YuY/cAw5uBS/+62zshZgGJYgRBdIx2rzwOD+Rx5eZh3PPYSGAXuOOJn8z3tjHU4+DIVO1bqOia5GnDtU6z8hvGgL5h4FW3AVMHzZXLDeeTAEYQBEEQc4TgDGIh/JwNHd0nnODL2umcLKf54YmbmE0rHzKI6On7HqRv6lJKKN+HUmH0Tw/Kl0E9FgE0nQfRQKOon8qLIoJC+dBKATo8RSRr5WBPLBBBmDb9WczRf7yNawmm66N+CqUg3CClIoVyKFiQNVsL0UCJRQzjsSRqZd7ILswV3WnbRHJuLmq+xbgN5AfMbY/8YPPc6TN7IZYsJIoRBNERZnLlcWSsgi8+sGfuN9eAMJLkv73nJfjJ3qOZp9si9j40Q79hwTyX3wKccPFst0wQBEEQi4/wGledg3Qvu55wbu5l1GXtelbdvE3qrayddqreUMCKiV3xfmmRq0MnZhhqv7g5HZlxnmDAfISblJoFchqHAoMCgwSHhrEXcg7ytlUvjmSKKmFbI1GFt2ava4sJNnVjGrXFy6xxWyMBqZlQ1NLn0MbzZu5tmmcisYlYAJAoRhDEjIhfhVzVk+v6lcd2iUeSdCyO856/svmAyf2tTVxYAZSP1urkOJ8gCIJYKPhV4BdfBY7uaSAgtSNMZQhIjYQpLbv95AsWCQHFRE3QYTwqS3DTHpU5JBiUNmVfc/iolaVmpq7NeD8xT62sgj5+2hYr++G6Ot7O6tvDMTo5T9O5dfa+wrJmFnKOhZzjIO/YyDk28raDfK6Wijk7yB305G30BFFLe3MWijkLPY5AMWeh17HQmxOwBYkvBEFkQ6IYQRBtk3VNcqHCYIS5dHTKxBVJJc1JsDCaZHi1MW5vVRS7+nYzNj0XQRAEQXQL3wXu3w48/Nlu7yQJt8w1Jh5EKoyivtnmZ6ewkxHiorqIjbNTdSs5b2Ke9Lw2pnxg/5RERQJlyVGWDBWfoeQDpTD3NEoeMOUDU55GydOYdDWmPI0pDzHBqj2BSGP+hJrIoT7nEC1G3kw62Y/ZYmPzfBoH/U2c9udtjqJjoScn0ONY6MkZYStnLUFn/ARBLFhIFCMIoi0aXZOcD0KBK12O1xuJX5dsWpcdAGDXPcZXWPxqZP964LSrgce/mrSzwElro931rwc2biURjCAIglhYPPC3XRTEGGDlAJEzuZUDhGNSeKWKi9qVqrDMY1etIlt4DUxkjMmaJ7i+FTn/BqAVJisunjgwhd37S3jqaBm+NgJV/YkqlhC4VOyklAMOAY4+xiKBy3Zs5G0Ljm3DcRzkHQs520Y+x5GzbXBhQQgLgnNwYQVJGJsQEFZQ5xYsy9RNf1O3LZESrGpRI20RCltG5ArbBDdiFIlMBEEQ2ZAoRhBEy0ilsf3e7GuS88G6Jk77pxW/lMR5fBcg9gN8LYDzgV3fyI4mOf4c8NCn6zfQTBADzDVJEsQIgiCIhcYxZ9fKxZXmlFTMMXrkCytua/gzr1004FdMqnZoylnSC+DMIHX8tyEvSKUOzwsAYCmBkKcExCy/UgyN/T41cFaebm/mX6qhbymW0dZo7aw1GjhLz/ShlbVOxvptPft0a7Tib6uVz5fESoLoNiSKEQTRMo/sOTJvVyYZgLX9OXzitWfg0GS1zgF+2+JX+jRY37DxrTITiS99Yoz8hhEEQRALmRdeDtw01t4YrYOUER0xLZ41a29qayDItTomYWtt7YeePIA9ByZikQ0VGHQi8qHJdSJaoilrcFaLksiiiIk61ieWM11vi81bm1vDYq2IkEHAAvgAuWlbIqSd5y8E0TG1JysP9K0z7859w0D/MNC3HuhdQ18GE0sCEsUIgmhK3KH+E/sn2x7f7MpjszEAcNMVJ+MC8aukwAXzw1dAtSZ+pR3fh0yMtP0sEVoBl33E+Awjv2EEQRDEUiQ6xcKND64lwnlaY+X+SUil4UkFVyp4vkI1yD2pUZISnq/hSgXXV/CkSa6v4EodlcPxUTkYnzUuy+7J5FsRm0Y4aya4MehYnzDmoo7mCoU/znTQN9ker7PY+izeJxgb32OiPTVvfA9GfIyvqRPPm72n5LpZa4TtIvZ8RrzUibUT41jys0o+R/LzYHXtEgwaTNfsDApcx8ptfeEaCp1LgOJK8158ypXARe9eUv9uEEsbEsUIgmhIJxzqN7vyODyQx5Wbh/EvP3sGx00+hjUYxQEM4unezfjs2SM4875L6n19bbsl2FyL4leWrRP0rgVOv3pu5iYIgiAIYk5gjOGF6/q6vQ0AgFIangoEMz8uvKmk8BYIdF6dEKfh+hK+0lBaQypAaQ2lNJQGZFTWsbL5wlPp2hitNaQyfXTQLoMxMhijAltUTswR2GNztryu1lBKgmsFHZzm48EpP6bTJ++SQmHjsp62b/ykYNP2uEDJstqzyxaTsCFhQcKCDwe+KUd2U5/e3qlrzPNA6bBJB3YBPauALW/t9o4IoiVIFCMIInEaLLyKeN+ufW071J/Rlcfd9+L9u98H5tYELi1WgD2cIWaNjwB3vSF78bkSvxrRu3Z+1yMIgiAIYmGjtbmmqfza9U3lp650+tG1Tq585JRELuorU+PiV0B9gElASID5AFfZfRNXR8P1VGrt4Apqi/sMyzroq5nZiw7HMQlAQof71KYOXVub6aCuFViwRpRDm5dIOnQ/j6T904W+6dKBLRrYAaB0xERb16m7vLl+4Pl/MP+PRBAzhEQxglhGNBK/6pzW9+dQ8VXbghgA3HTlqbjgxFWZfequPP7qMPDPbzIvQ/G5Ggpc3XLxHyeIMrnh/G5vhCAIgiCWPPETR/FTTFoFJ6EanYwKbNormfeKyih4eRSschS8OgpROQpeGYOojkJUR2FVRyG8CUD5YIE4ZIQck7Oorkw9Sgo8LC+I95S5g6Xy+cLX6aig9ZFCJTiUZg3b62wN52SpOadZs+GcbIbrp5+ptqfsvqxur/E+9/zXi3D8yr4MsYsc/BNECIliBLEEaVX8GizaGC15deP3jbcfHiq8JrnttOHsDrvuqb/yyDgWhtDVKhRlkiAIgph7tNaRD6qqJ4NcRXnVl3B9hWqUanU3ZvNl9hW26Npa4iocEtfhEgJU5hU5bQ4bpeaqCVIpm471b7Je+kredJzFfoN/cv4KDlu8nuddLeDBgg8BF5YpBzY3sBuxQ0SCh6+T4ki8TYLDT4gkIiGW+AlhRSQEGh+N5mXwtagTZbLW1+BQ3Hj50oEIowKn7jpKPBJpdCjWMAHNLdPGBBg3iXMGzgDBGDhj4BwQPCizoI0zMMZMHw5wxhr34WYuFvVBsEYwPqibubLWAGyeGh+sIzhL7LfhGok9psaHz5geH+wnMT72mSSfubZfgiCaQ6IYQSwxsvyANRK/smzt8M6XPB8nre2ruyZZx657gLveiDoBrGPh3mcKM77I7HzKd9kxwGmvAR7/aoZPM4oySRAEsRzRWuPBJw/hif2TsAWDJbj5pROoE61cKetFrLSwlSl4SSNsSQW9mL4z6iI32/9zUQtiAOAwCScdTjL1SqXBoFngCj6ICqjBAqHJRBKMxKbgapxGGHmQAzC5DvJ0xELGa+UwWiELyownbYzXxkTlwB71TUc1jKIZsrp1mkdZnM4em7euf3ydRlEaG0RrzGzP6pO2TTcPp1NaBLHAIFGMIJYQOx4fyfQDNlvxqxEXnLga5z1/ZfNOSpoTYgvuRFjwQnLFp4CTXwHsfcj4RYhHk3zZTdl2giAIYtnxfx59Fu/+58e6srYjOByLI2elcxHVs2y24O2fkuHpUzHpkzPpUy2tn5LJPtWSOuHSznjGwPYUgS9fVfuwcv1AfqCWcv2AUzT+vnTon0vHfHWpmH2aNqUajGnSpmSwdpZdodX3IxPxMPTVNRd/y4h5JS2UoV1xrVl7IwGvTSEwa58zEhNb2W8b69R9VjOcp2H/GXx2mXtqdR4SSbsNiWIEsUhJX5E8e8MKbL9317y8JzGY65JbNg5N33nvQ8nTVvMOA6CBwhBQPlIzp099bdxaP5SLbDtBEASx7Di5CxELC7ZAwRGweE00CsUqpQHXV/CVRtWXkaBlxfrFx3DGavNwcwUsFJoskewXH2tF/Y2gVivXrxG3J8ak+od7rdtX1K/JmNhzCs7AN1wMccMoOMPivCqmdUqwUylRTjWxxwS3jgt5qn5fLe8tLgQ2aWtpzy0+j0qt2aowidCuUfdMidSsXaNtpTIcSxDT8dov0y2VOYZEMYJYhGRdkRzqsXFkam5OhMUJXzdvvGJT9nVJJZOnqyZGOry6zijH6o3Er0anwQiCIAiiBU47ZgBPffQVUV0qjZLro+RKTFV9TFUlplzflF2JUtXHZDVoD+ylqqyzheNKVXOVMk7Zkyh7i/tq4HwSnnBLC2+NBcOwP49O0PGUAFcvGNbG2JzBFhyWMLktwjqHE1yxTdh52M+McbLGcg7HYrC4gG3ZsHkwj1Ub29BdBdE9InGzmbDWgviGYJ4wsmeUmtW9Bu3TzTHdvPFopU3mkG3Ml45USUzP6N5u72DJQ6IYQSwyGl2R7LQgxgAMFG3kLYF947HIlM0c6mc50y9Oc72y4epNBC6gfp1WxC869UUQBEF0CMEZ+vI2+vJ2x+Z0fYWSa0S1iieNE/yYU3sZc2IfOdFXNUf6UtXqMnJ6j2geX+mYY/1YXxmbIxzTbA1dWytqD8eoZD8Vy/3UmNDxv8zoL1VtTKuO95UGlDRBCpYyjMEIZJzBtgIhLRLhUkIdN4KaxVMCXUyUM0JcXLSrF/d6cxZeesoaFJ0Wf3187mfAAx8DetcAfeuB4hAiwWe2wlG6ve702hys0bQ96/TcLOcnFg7CqSUrF8tzgLBTtiDnNiCsILdNzkWtnGizTIraYva+YeCYs7r9CSx5SBQjiAVO/Jrkqp4cbrpn7q9Iht8/fvTVp+OSk1dj985vo3z0WRRWHIOTz70YwrLqT4SVDgP//CbUHR8vHW5hQZ58AWhF4CLxiyAIglhiOBaHYzkYLHZ7J90njHgZimK+MiKdL1UUjMD1FbwgD+thuWbXNXvQ3wvmSI9tbNeZ/WQr4THn5LMxAqoLAO78nbx550tOxLsve+H0Hb0y8D9fZk4wEfPADP2PcdGGT7DZ+hVrElCh03tIB1wQthGw0uJVncjVwLYYr2UTbUGiGEHMI2k/YPGIjVJp7NxzBD85xLByzxGcd+Ia3LdrX901yU4SXkAcKnC8oPo41mAUBzCIp3s340NXno5t/EfAp9+HU+MnsnauB067uj4yI+OY3p9C1pVHAK/5B/j5AfzsB9/GGVsvg3XCRdMLXOTviyAIglhixE9J+cqc4PKVqtlkSiiK6ipx4spP5fE+vkqe2jK5glSAVCphT/ZT2fPL1J6Vik6YRf3SdaXqx8kgj51CI2ZO+ippzT+buSpqcQ4e5iys13zIWalxRUdg22nrWlvcLgDn/hnw8Gfn9iHbhVuAla8JHuFpH8sJ7PFykIf1qG/WuHif0GbNUEwS07STo3aC6DQkihHEPJHlB2w4uIoIINYmcMcTP8Zg0e5Y1MhQikrPuW4gj8+f9QzO+OVHwXRN4NL59WDPXQ089BnUCV3jzwEPfbp+kVaOehdXAqVDtXrM2b32PDz7y3Fs3nAh+foiCIIgFhSeVJio+BgrexgvexiveEE5sFWM3ZR9jJe94CSRSghTCUFJKiiNhFilSQdqiXqhh2UKQHGBh8f8gyWEn0zhKGnPGpcUjgKfZPGcAUJwMz5jXDzAwEyeY7rn73rQgcv+xiQA8F3jY3b8WWDsWWD8mSB/Fhh7xuSt3CyYLcoH3Mm5XwcAwIITSk7talxUdky9MAS88pPA6hZO3xEEMWeQKEYQc0D6RNjRKRfv+Md6P2D7xip4+52PZs4xG0FsqMfBkSk3qod+wOquQg6MQ3z1XUgLX6yR8DVbtt1s7saTs3uCIAhigeBLhTse3ovfHSlFgldc7BoreyjN4xW1LOKnfizO64QSwdMCC68TXLL6NZ+rJvBMP1etT1xACutGJIrXWaIerccYhEjOn9wXX7xRJhc6mc7dmzlUn0l74BCe28DAsUDfWmB4s2nzq+bapVcGvCnALQFekLLK8xJvfTZoQLomNeOZH5MoRhBdhkQxgugwWSfCOMv+0d3pH+cMRgD7t/e8BD/ZezR5TXP3vfVXIVu68thB+obpyiNBEASxoPj7H+7BR765u6W+vTkL/XkL/QXbpLyNgYKN/oJl8nxot1BwREoI4smTPnVCVk2kSrQFkRCJBYL0AVk1Io50k3lYzooIKNMRArvdnpGWDax2WqvOuflMyk7NMXo75cIQcNyWbn8YBLHsIVGMIDpIo8iQ8+EWI3xdvvGKTXAsjvOeH4v6uOse4K43ok4Am7foNsxcldxw/jytRxAEQRDTo5TGZ/71ycy2vM1x0po+nLSmFyeu7cXzV/di/UABa/pzWNnjwBJ8nne7DNG6doooEqCq5jpeIq9mtGWIVmG7X2ncls79StK23CIDMlGLjsetIGpemEQQVS9WF/YM2kWDa4bTlYMoftOWU2IW3VIgCCIGiWIE0SGk0th+79xHhmxEeEVy22nDyQYlgR3vQ/eOmQdy3baP0ksIQRAEsaBgDNh26jrc89hzqPpJsaPiKfzi2TH84tmxunGcAT2OZa7+CXMCzFwDDMtxW2APy6LWx46uGHLYwekxW5jTYnZgNzZz0sxOrFHrn7l+bO303JZgsDmHCPNgjQVzLfH/vBX41b1GkFrI1+QYrzlcj0e3i4SfTolGXWpnAuAk/hIEsbQhUYwgOsQje47MWZTINAzAcJ+FL17soTr6nPEPdu7FEFbwv7SSwN6HjO+uyf3JKJFzvjme/BY15kyfIAiCIBYSjDF87I8242N/tBmeVDg86eLARAX7x6s4MFHBgURexf7xCg5NVqE0MFFdetfNwquboVhnR+Iar4lwmQJfUqwLIxkKBvDg+md4DTT0j8aZSYKj1h70ef1/PIY1frnbH0cCZfdA5wagc/1Avg86NwDmFAHhgAk7kaLTTNHJpHjditnT9VmMWyiCJkEQxCKDRDGC6BAHJuZGEONQ2MJ3Yw1GcQCD+JE6GZfwH+O/W/+Ewnf31TruXA9su8WUd7yvg0JYGLuylX4AXvMPQM9KcqZPEARBLCpswbFuII91A/mm/aTSODxZxZQr4UsFT5pokp5S8KWGH+RSaXjSRJ/0g2iTJq/18VXMluhr2szcYd+4zcwtg75eEMEy3t9TClJqeCq2l9hesyJdhhEyp3ENPud8AX+J49gBOPCRg4scPOSYZ3J4xpaoe8gxN+hfqyfbvdpcsfkceMjDhWDN33W4N2UcwE/O4xeNbSAhIJkVJAEFU1bMguQmTyRey3WU29A8KMfqOjhNFpUDIY4JG8J24Pcdi9KxFyHvCOQsgbzNkbMEcjZH3hbIWRyO4AvnJCJBEEQMEsUIokOs6Wv+Eg1kC1wS5li6gMLvpdpexn+M7c6XsQ61MNWj6MUAJsHSX6COjwB3vaFDTxO8tJz/X4DHv5oU2PqPAU57TYadToQRBEEQSx/BGdb0T/8zf6GjEkKeEeWMYNZEwAv6hoJcKNDFBbzQJpWG0iZJBVNWGjKea7MP0xdB33DcxmAeQGvTXyoNrYGS0pjQ2tiVhgzmSY5HVI7mj9aN78WsC+nD0lXY2oOlqijoMnr0FIq6hB5dQh5lFFFBQVfQyyoowuR5VJGHiwJcFFgVeXjGxowtDxcFNvcyo4CE0BLQ1TlfK4s/rH4Yj+kTG7YzBuRDoSwmnOVtjlwgnOVtEYlo+aBfrX+tnIuPDfJ8bI7Boo2iQ7/mEgTRGvSvBUFMg1Qaj+w5kozkGESBiret6slhXX8e+8crmeeqLuOP4Cb7DgyzI5FtP1biufNuBACsf3g71maIX2kGMIns79lm4XOj2ZXHl91Uu4oZP/nVyE4QBEEQxIKHc4YcF8h167cBrU1EROkmnd4n6l7MyX3g6F56yb7xftJN9vXdmc0J13w/uEwONinw4FSZgGIiOnWmotNnNZuEgA8BBQEPAp4WOMBWQfefgg3SRtVTqPgSFU+i6tdOJGoNlD2JsicBeHP+TOv68zhhdQ82rurBCat7ccKqHpywugfHDBYoSAZBEAlIFCOIJux4fATb792V8BU2HDi0B1DXNli0oVF/4XAbfwSft2+tc/ewBkew9uH/CqBe0mokfnX2/ayFK49cABu31g9tZCcIgiAIgtj9TeCfXtftXXQIlvLlZdUc1MfrTdvspGP7pm2xesIxvpWst9QWRne0s9fmFjjnmK1MdGmGTWsNVypUfWVEMk+h6ktUPBWJZhXP1LPstVzGxLbmc1Q9BVcq7BuvYN94BQ/99nBiT7ZgOH6oiOOGihgeKOCYwTzWDxaCcgHrBvJwLBLNCGI5QaIYQTRgx+MjuPbOR+vEqn1jFbz9zkczx4yVzDdfKwocL6g+jjUYxUH0Y7vz5cwvHFls9vq2eYCuPBIEQRAEMRc88e3OzidywDFn1yI9CjsW7TFIVmCPIkI26xu2ObG+WfPk6CT8DGGMGd9ilkB/3m7Yr3YNNnb1VgG+Uglb/Fqur+rtYTo85eLJA5N48sAknjgwgScOTEYn1jyp8duDU/jtwamWnmH9QB7/+Nbfx/NW9XTiIyEIYgFCohhBZCCVxvZ7d2VeSIzb0j7CHlEn4zL+Y2zXX8Za53DG6AXAZR8xp8HoyiNBEARBEFoD7hTgTgLVScCdMHWvbJJfmUFeAapjnd3n8b8PXHNPZ+fsIDrlxyzyZRb4LGvkSy201/tAq/ljiwtBSqUEIV2zRW1aQ0oV+Vrzw/EyFJ5U5OfNl+HcKlgrlkfjkzYZBHaoCVepccHz+ErV+sTaavsO+szCA8hc89xYBZ/51yfxiddu7vZWCIKYI0gUI4gMHtlzJLoWmSV8KXBcxh/BjfYdWB/zEXZE92IFgm+jFpwfCmZOhp37dhLCCIIgCGIxIz2gOhETsiZj9QmgOglVnYAOyiZP9mfuJJg7AeZOJU6uzwcaDD7PQYo8fJ6Dzx1wJSFUFUJVYakqLF3zO1XlRdzhvAFPf/3xhs7zQyf8caEoS4Bq5pw/miMStAKxKjZf1jqhY3+i83BmglsIziAYA+cMVlgPbEIk2zhjsAJb2C+0cZYazxkE5xAM4IxhvOJh/3gV+8crODBRxQmre3Dti0/o9sdAEMQcQqIYQWRwYMIIYlnC13N6CPf45+Nt1r/UjVsROMbnXRHE4p7M0l7Ngg1t+ygJYgRBEATRYbTWqPrGl1Ho06jqyahe9RVc3/g8Mnm6nOxTKyu4Qb3qKZw7+V38+fjHWtpTp7wilXQO4yhiXBcxEeTj6MGELmAcPRjXRUwhjwocVLWDCmxU4KCsc6jACZKNinZQDeouLLT97eFjALC3Q0/VHUKBhzOTTDlm4zUhh4ViUIbYw1lSFIqLPTwlFIW2RB6MT9ui8RnrxedO2+J5Yr8ZIlVT4Sq1F/M5LLhvmQmCWGKQKEYQIUpG0RRPnCrgcr4Tn7M/VddtHY7gzwJBLP1zen5+bgeCV2EIKNfEusg/GADseB8w/lx9G/kOIwiCIIhp+c3+CXzue09iouInxKtIxIoLXtLY54MzxNNAY9dMc0KRVVFEFevY0YZ9JDh8WPCZDY/ZkLDgMRs+cyCZsfvcCSIZOpDchmQ2JHcgmQ3FgzK3obkT1VVYF05k10FZWw60yEEHttAPmBY5QOTAhA0IB5zzSHARzIhQcfGJh8IUqwlCPDg1lBawBEdS0MqYM2sOwYzIRQIPQRDEwoNEMYIAgF33JISkUwF81uFgul7o6uwpsOlOdzURv05+RSTi1fkHa9ZGEARBEERTbvv+b/H1nz03fccGOBZHzuKBk3FTjtucaW21es7mcARHzhZwxNl4ZOr1KKgpWHYOjuBw4MGGD0t74NqFUB6YNDlXHpiqgksXTHlB7oJJkxAmvxoru4Cspspeqk8V8XcWAQUBFznt1ofT7jbNHOhnOeW30v0zxnILGHsGOPsaYPULu/2EBEEQxCwgUYwgdt0D3PVGpN/iBNTc+wVr5XRXM4Fr49bseblo3EYQBEEQRFMOTlbbHuNYHAMFG/15C0XHQsEWyDsCBZujYAsUHIG8bVIhSKY9SA6vtQX2vC1gcQbbMsKYuVK2tvsnjrQGlN+ioJYlrjUQ2loS5qYZq/zkXkP7XPDTLwPv/918XRUgCIIg5gASxYjljZJGjJqXrzUZ0DcMvOo2YOpg5uku/z8ewM9+8G2csfUyWCdcNL34RRAEQRBEx/mzi54PR3BMuT7KnkLFlSh7JlVciZInIVOe1V1f4eBEFQcn2hfU2oExwOYctmCwBIctOJyozGAHNisq12xh2eIcjsVg8cBusWBOM86JjXcy5grXcqJyAbboMW02g1NIzRX6hwLMu1d0Ss1rXlbe9H0SZc9EwAyjaUaBBaaCFETW7JRItvUvSBAjCIJY5JAoRixNYv7B6sSneNvk/uTprDkjeGG6/BbghIuzu3ABveFCPPvLcWzecCFddyQIgiCILnHhSatw4UmrmvbxpIpEskgw8xTKrkQlqJfd0F4r19eN6Fbxs/unoxpqDfjSB5M+GHwwSDD4ACTAJBCW4cfsJteQ0PCjXLGw7kNBBsnY7aAs4cOGhIQPn0n4Qd20mLLHfNhB2YYPDxJOYAv7ODB1zhba/co2cPqAF24DTv8j4ISXmKuWBEEQxKKGRDFi6ZHyDwYguIp4CwBA73gfWMeEsDb9gJGj+/+/vXuPj6su8P//PufMJUmTNL0kTQultBSphXLf1pRFuTd8WcDVhV0RpAuLWusqBb9L+a1SCl8sVb98d/XLt+IuUFh08fJYlQoCVQu6UqkCVaAFQUvLJWmB0iRtkrmc8/n9cWYmM5OZXNokM5nzej4ep+f2Oed8PpNJMnn3cz4HAICKEJarsNup+sQ+qbcjNWUvd6Ru68vv9ZTwl72EpITkJKRIah5K3f6XOSYpk3O8f8uiVXYDdx28uHGUVEgJOYorpIRCSpjUXKG8bX1ljBOW7URlhyKywxGFwlGFw1HZ4YgsJyIrFPH3pfbboaicsD+FwhE54ajCkSqFwhGFo1GFw1UKR6OKRKoUCqfHD4v0ze2wZI/UMz0BAOWCUAyVpcj4YOpsk753hUxqz6F1dE8dvfgfpRd+MPxxwAAAQHlJxqU3fyd1v9sXaPXsyw248gOv+P4xqdrQPrNYfQGOHRreshP2A59+y5FUuaEsR7KODUlOVJ4dVtIKKWGF5KbCraTCmWArbkJKGEcJz+91F0t62tcd177uhPZ1J/Red1z7uuN6rzuRmb/XHVdXb2rMsMRIvLqupJ7U1MexLUVD/hhv0eyHJoTzHqAQzn2YQnSI5SMhO3NraiSUuk01ZGduc42mlp2RfboTAKAAQjFUjgHHBzP+VlP46ZGmwFMmJckzkrFsf9D9tOxeX+fcPPxB8AEAQHkwRnr5EenxL0p7/3xw54jUSVUTc6fqBilaL4WrUkFRxA+LCi4PFEwNFlilw6jy+483W1IkNY2kpOupoyfRLyxLL/fEXcWSrmIJP2iLJV1/nshaTnqKJbKWk64Sbt/nR9cz6o676o67I1z74bEt5QRofeGZlROeZYdqkZCVKZt+QEP6mIjjKJy1vy+Iswqfq9j1Qun6WKV/6AMAHCJCMYxrbjKpl55+TD3vvakppkOzB7gt0sr8U2BfkUBMkpbHP6t/vOADOra+p3/wxVMeAQAo6Mgjj9TOnTtztq1Zs0YrV64sUY0K+MkK6Zl7/eXqSdLU9+UFXA2FA6/0vmi9H3BhzIQcW1Nqo5pSGx3R87qeUbxoiDZ4yNYTd3UgnlR3zJ8fiCV1IO6qO2tbep4/TlwxnpF6E556E97ghUuo9dhmffOKU0pdDQA4KPwWx7jgekZbduzVnq5eNdVVaeHsyfrDxvs1Y/NqHat3R+Wa7Zqi1Ykr9Ji3UOdPOFHHLjhsVK4DAECluuWWW3TNNddk1uvq6kpYmwJefsSfL1omnfXPUrTM6jcOGGOU9Izc1JTMzD1/7vbf7nnq259znJdT3jP5x3t55dP7PbnpergFzpm97hq5pvg184/z+m03Srr96x5kr717QMYYeo0BGJcIxVD2Hn2hTas3bFNbR29m20eqn9XXvK/5KyP4+/eWxOV6xzRojxq0xZsnT/6Aqk11VSN3EQAAAqKurk7Nzc2lrkZxidRYUqdeVZaBWCzp6t9/tUNP/vFtVYcdVYcdVYVtVUccRUOOqiOOHMvKC5AKBEd54U8mFHLzAqy84/qCLi8nVMoJuIKdBw0qZFtybEsh25Kdmju23bfd6dufvd2xs7fn7Xf6tjtW9jnswsc4fWUHumZ2nWwr63xZ5S1Z8oxJTX4oesLMBgIxAOMWoRjK2qMvtGnZA8/mjBJmy9MXvHv85WGMD2ZSJyl2q2S7pmi925oJwiQ/b2ue6PdMAwAAw3P77bfr1ltv1RFHHKHLLrtMK1asUChU/ONnLBZTLBbLrHd2do5uBSc0SrFO6YGPSC3LpeM+6t8WGRrpkbAOzk9+36avPvZyqatxUGxLOSGNnR/WOMVCnOxAyO4X1uSHOP3Omx/yOH3bHUtynAIhUPZ1nFTQNMA1M8c4WUFTXlgVsm3ZlgiLAKDMEYqhbLme0eoN2/oNm7/QfkkzrL1FjxtofDArtZwdpqX3rU5c0S8Qk6RVF87n6T8AAAzT5z73OZ188smaPHmynnrqKd14441qa2vTHXfcUfSYNWvWaPXq1WNXyTP/P+nRG6WO16VHV/qTJDlRqare7z0WrfPHDoum1gtt77etbkTGGztt7lRNqgnrve4RedTiIbMsqTrsqCbiqCo193uv+b3WqrPm0ZA/qHu030Dw2QO4Zw0CH8ofTL5vYPfsMmHbls3nMgDACCEUQ9nasmNv5pZJW54W2i+pSfs0135j2OdKjw8mSavC92uG9vbb93TVaVLWh87miVVadeF8tR43/RBbAgBAZVi5cqXWrl07YJnt27dr3rx5uu666zLbjj/+eEUiEX3qU5/SmjVrFI0WHiT9xhtvzDmus7NTM2fOHJnKF7Lgb6R5fyX9/jvS5v8nvfuKv92NSQfe9qdDEa7pC8hCVVlPncx7mmTOkyT7lpvtkJ5bnH7aZDjnqZWu5ShmHMU8Wz2eo56krR7XUrdrq9u1dSBh6UDS0v6kpa6Epa64tD9hqSO13BE36uiV3otLB5J2zn8MFmOMyuKpjOmnIeY/JbE67Gje9DqdcHiDjj98ot4/vV5V4fJ7MicAoHwQiqFs7enyA7El9hY/yBqgd1gh+eODGdmaWBPW3zin6YgDv1eT9mmPGvR67Qn60iUL9P/mN/cbzJ8eYgAA9Ln++uu1dOnSAcvMmTOn4PZFixYpmUzqtdde0zHHHFOwTDQaLRqYjZpwlT+m2KlXSZ4rxbpSU2ffcm9HgW2dqfX8bV1SMjVWWaLbn/bvHvFqO5JqUtOkQzlRyJ+MrExQZ+ywjB2SsUPy7Ig/t0Jy7bA8KyTPcuRaIblWWEn5y0k5SiqkpEJKyFFSjuLG3x4zYcUUUtyE/CDPhNVrHPWakHq8kHo9R72eox7PUbcbUrfnpMK9kPa7jmKe4x+vsBIKKeE6SriFw7ltbZ36r2fflOSHZ8c01+nEmQ36zBlzNaOh+lBeKQBABSIUQ9lqqqvSEnuL1oX/pd++4Y4Pli52+0cW6Nz5zdqy45SC4VfLUVNGoSUAAFSGxsZGNTY2HtSxW7dulW3bampqGuFajSDbkaob/OlQJONSfL8fmGWCsl7JS0puQnLjfcteIrWtyLKX9MsXXC50rrjkJgc/V94AFZZMal98JJ9hdGic1JTHWLaMHZHnRORaYbl2WEkrorhxtD/paF/cUq/xQ7j4npDie8J6+7V6zThyWqoXXsQfN86JSqFoqudeNLUtkrUc9dfrmqXm48a8+QCA0UcohvLhubJ2/rcO27tZ1s56LTxiseZE/kMy/QfUtyw/GMsfVD89PtjX7L/PuQ0g/1ZIwi8AAEbP5s2b9fTTT+vMM89UXV2dNm/erBUrVujyyy/XpEmH1K9pfAhFpNBkqaaMH9TjuQVDNTcRVzIRUzwRVzIeUzIRT22Ly036617SL+cl43KTcRk3IS8Zl0nG5bkJmdSUPr+VmuQlZLtx2Z4/OV5Ctokr5CXkGH8KmYTC6bmSCiuhqHLHVLOMJ8vtle329vtjZqrk/29ofrK3T9LWQ3i9Fn1aWrJGsge/zRQAMH4QiqE8bHtI5tEbFOp8S6dK0s51MjVTNE3v9v9Qk1Kol9gea4raWlbpq+d+QpdwKyQAACURjUb14IMP6uabb1YsFtPs2bO1YsWKnPHCgsoYo6RnFE96Srie4klP8dQ84Zq89ax5VtlEeu4axfLLFTy+wHmzzhPPOp/r5T/iKFvqXssxZMtTVHFNUEwTrB7Vqkd1qXm93aMGu1f1dq/qrV7VWz2qs3tUpx41RRNqiiZUqx7Z8dTtrYnug6/I09+Uqib6D2cAAFQMQjGU3raHZL73CRl/NIsM0/3ukLrvv37ccu2uOlLVkw7TvEVL1Jx61Du9wQAAKI2TTz5Zv/nNb0pdjRGRdD3t7Y7rna643j0Q07v743pnf0zvHojr3f0xvded8EOlQuFVkVCqNIxCchWSq7Dc1OhfriJyVWO5qV5ZrkJWai5XIctVte2p2nZV5XiK2p6qbFdVtr8csT1FLVdRy1XETs0tT2HLv0bYSk3KOqeSCpmkwiaukEnIMX6Psczcjcn24rK8uGw3LssMcVB/k5rSL29S0oERfgl7O0b4hACAUiMUQ2l5rno2/E9Fjel3i+RQO6fPPOV8zZx9+ohXDQAAVL7ueFKPPN+ut/b16N39Mb1TIPQaHqOo/B5KE6xeTVGPJqg31cupVzVOr6JKZIKpsOUqanl+6GS5itp+sBSxXEVSoVJmngm1kpmAyx/K3lXI+MPc2yYpxyTlGFe28ddtLyHbJA/thTKS3NRUKpadO95XKD0mWPa2gfYVKR+qSo0zNkD5cLVUP6OEjQcAjAZCMZSU+9qvVd3TXvQWyYFZ/oeTWYtHuloAACAA9h6Ia+m9W/SHN/weQFPVoUlWl2rVo2arV0epR7V2r2qtHjVGE5oaTmhSKKYGJ6Y6O6Zaq0fVpkcRt1vh5AGFkt0KJQ8cfACVDp7GimVLdjj11MlQapD59HI4tS/UV8aJDLAvnHeu/PVI7nKx0MpJhVSFtjn86QIAGFn8ZsGocT2jLQXG9creXv/K8zpzCOcyys/NUmutt/tPigIAABima7+7VX94o0OnVe/UTbU/0jFdTxcv7EmKpaahCk+QorVSpDY1r5MiE/qCniEFSIcSTg2yj0HjAQABRyiGUfHoC21avWGb2jp6M9umT6zSRSdM10O/b8ts/4DdrTMjg58vHpmkaPy9vg31M/xAbP5FI111AAAQEM+/sU83h9ZrqXlc6srbGa6RGudJ1ZP6Aq2cgKtWitblBl452yfwH3cAAJQ5QjGMuEdfaNOyB55V/rOL2jp6ddcvd+Rs2+LN01tmspq1t9+YYpLkGaldU7Tzo79WS+RVaf9uqXaaf8skHzQBAMAh+pD9+8I7Et3SW8/5/xHXMEuaVOP38LJsyUtKiR7JGCnZ6z/ZMPRu6ha/QmNSpW8XzB67KlL4UdoAAGDMEIphRLme0eoN2/oFYsV4srU68QmtC/+LPKOcYCz9RPCvh6/WbUc3S/b0Ea8vAAAILiPpI/HV+umFnpqtvdK+XdJ7O6V9O/15skfqfNOfdj018hVw8kOzoQwMP0jQFqrK3e9EsqbUrZnZt2Hm3FaZWiesAwAERElDsSOPPFI7d+7M2bZmzRqtXLmyRDXCodqyY2/OLZND8Zi3UMsS12pV+H7N0N7M9nZN0S2JK/ThS66SU6gbGQAAwCEwRtqneu0/+kNSU23/nQfeTgVlr/lBWfdeKRmT3JiUjKfmqcmNp5Z7+5bduL+eLuvGc6/hps5VbuxQbkjWLzgL9QVt2WOgDbtM9hhp4bz1rOP7hXmFyhDmAQCGr+Q9xW655RZdc801mfW6uroS1gaHak/X8AKxtMe8hdoYO1UL7ZfUpH3aowa9XnuCvnTJArUeRw8xAAAw8ozxu6UXzFIsS6pt8qfDTx2ZC3qeH4wVDNVS23JCtUKhW9b2/NAt/1yZcgm/rJdMXT/hT16if1An+eW8pN9Tbjyxw1LNZGlCk1Tb6M8nTPW/hpltWdudcKlrDAAosZKHYnV1dWpubi51NXCQ8p8wObU2etDn8mTrrCUf1huvbtelpy9Sy9wmeogBAIBRkx7uYcw+bdi2ZFdJ4aqxuuLgjJE8NxWWDRCcpdfdeGpb1npOueRBlCl0veTgdcrnJfzxZ/fvlnYPoe3Vk1OBWaN09LnSaZ8f8ZcXAFDeSh6K3X777br11lt1xBFH6LLLLtOKFSsUChWvViwWUyzW1828s7NTkpRIJJRIJEa9vmMh3Y5yb89jL+7W/3rkJbV39n09ptVF1FAdVkdPYsjjikn+h9HmiVF97JQZ+sW+bTr58Dp5blKeO+LVLmvj5Ws/Gmh7MNsuBbv9QW67VNr2B/U1R57hfFipVJaVuj0xJKmm1LXpLzuwcxN9PeTcuP+wg/h+KX7An2JdUleb1PGGPw5cR2o8uPj+wufu2etPb7/kP1Sh5bM8yAkAAsYy6X7jJXDHHXfo5JNP1uTJk/XUU0/pxhtv1N///d/rjjvuKHrMzTffrNWrV/fb/p3vfEc1NWX4i7xCeEb6U6elzoRUH5YOJKV7/2in9mb//2r+26n/PlueFtovZ26T3OIdI0+2rnqfpxOm8OkUADD6uru7ddlll6mjo0P19fWlrg6K6Ozs1MSJE0ft63Tcqse0P5bUpi+codlTJ4z4+cteOnDKvsWy35QosD/RN0ZaenvRc2RtS+ZvjxXZn94Wk4w3Om2vmy5NniNNnu3PjzpbmnHi6FwLADCmhvP5YcRDsZUrV2rt2rUDltm+fbvmzZvXb/s999yjT33qU9q/f7+i0cK34RXqKTZz5ky98847FfOhNpFIaOPGjTr33HMVDpd+rINCPcJsq+/pkPksSRNrQqoKOTnHTJ8Y1XWHvaQP/vkOTdO7me27NUWvL/ySTjj38rJr+1gLcvtpezDbLgW7/UFuu1Ta9nd2dmrq1KmEYmVutEOxY296VAfirp74whk6cqRDsezb/wqGSIWCo2IhUZEQKafnVJFAqt/+rLqMVuA0mrIH6c88XTN74P78bRFpwhRp8lGpECwVhEUCGIICQEAM5/PDiN8+ef3112vp0qUDlpkzZ07B7YsWLVIymdRrr72mY445pmCZaDRaMDALh8MV9wdFObTp0Rfa9I8P/r5f/69igZjk9wfb153Ut//+RDW88zv1vPemqicdpnkTu+T84IsyeWdr0l5N27JCOnKKdPT5ksqj7aUU5PbT9mC2XQp2+4Pcdqk07Q/y640+mTHFCg0qluiR9u/xn0C5f7e/vH+PdGBPav1t/9a7TA+pvMBqPAZO2U9ydCKpgCmcuy0UzQ2c+h0TkUL526OD7M+/VrEyEZ4wCQAYUSMeijU2NqqxsfGgjt26dats21ZTU9MI1woHw/WMVm/YdlDDbSyxt+jkH65QdW/WKKeWLcn0G8zWkpFkSY+ulI467+ArDAAAMAzGSHOstzTpV6uk+NtZodceKdY5shcbMPg5mBAqu0yhEGqYQZUTJnACAAROyQba37x5s55++mmdeeaZqqur0+bNm7VixQpdfvnlmjRpUqmqhSxbduxVW0fvsI9bYm/RuvC/yMo/dMD/MTVS55uyXt887OsBAAAcjKO1U+sjt6h+a5GB2J2oVDtNqm305xNS89omf6qZIoWqBu8NZYcInAAAKEMlC8Wi0agefPBB3XzzzYrFYpo9e7ZWrFih6667rlRVQp49XcMPxGx5WhW+X7IO8vHm+3dLqj6YIwEAAIZljb1Ok639ijcuUOSUy1NhV1boFa0nzAIAoIKVLBQ7+eST9Zvf/KZUl8cQNNVVDfuYhfZLmmHtPfiL1k6TNMK3KwAAABQwQ+9Ikvae83/UfMxflLg2AABgrNmlrgDK18LZkzV9YtWAPb7svJ3vqzlwkFezpPrDZGa2HOTxAAAAB8c4PHgBAIAgKllPMZQ/x7a06sL5WvbAs7KknAH301nY//3YSZo0Iao9Xb1qqqvSQqtOuv//DPNKqbO13i7ZzqFXHAAAYAisg3qcEAAAqBSEYhhQ63HTte7yk7V6w7acQfebJ1Zp1YXz1Xrc9NwDvNOk+hlSZ5tU7IOmZecOul8/ww/E5l8kJRIj3wgAAIABMW4YAABBRCiGQbUeN13nzm/Wlh17+3qEzZ4sJ//eScnv6dW6VvreJ6Ri/cs+eq80YYo/qH7tNGnWYnqIAQCAEqCnGAAAQUYohiFxbEstR00ZWuH5F0mX3i89eoPU+Vbf9uweYQAAAAAAACVEKIZD47nSzqf69/qaf5E074LC+wAAAMpAps+7xe2TAAAEEaEYDt62h4r0Blvrh2K2I80+vXT1AwAAAAAAKIJQDEOT3yOs+13p+0vVbyyOzjZ/PLFL7+c2SQAAME7QUwwAgCAiFMPgCvUIs2wVHpzWSLKkR1f6t09yuyQAAChTFgPtAwAQaHapK4Ayt+0hv+dXdiAmScYb4CAjdb7p9ywDAAAAAAAoQ4RiKM5z/R5iB/u/qPt3j2h1AAAARlKmpxgD7QMAEEiEYihu51P9e4gNR+20kasLAAAAAADACGJMMRR30D29LP8plLMWj2h1AAAARpJVYAkAAAQHPcVQ3EH19Ep9qGy9nUH2AQAAAABA2SIUQ3GzFvs9vgb631Mr7y1UP0O69H5p/kWjWjUAAAAAAIBDwe2TKM52pNa1/tMnZSl3wP1UUPbRe6UJU/xbLWun+UEaPcQAAMA4kB5o33D7JAAAgUQohoHNv8jv+fXoDbmD7tfP8G+RpEcYAAAAAAAYhwjFMLj5F0nzLvCfRkmPMAAAUDFSveAteooBABBEhGIYGtuRZp9e6loAAAAAAACMCAbaBwAAQCDRPwwAgGAjFAMAAEDAEY8BABBEhGIAAAAAAAAIHEIxAAAABJLFQPsAAAQaoRgAAAAAAAACh1AMAAAAwWZMqWsAAABKgFAMAAAAgeRlBtgnFAMAIIgIxQAAABBIJvVR2DJeiWsCAABKgVAMAAAAgeSmPwq78dJWBAAAlESo1BUAAAAASmG3JmuiDmjyz6+XDvyDVD0pd6pqkEKRUlcTAACMEkIxAAAABNI/e5/Sv9lr1ND+jPSjZwoXitSlQrKG/qFZ9SSpZnJeiBaV7FDe5PRft6zC1wMAAGOGUAwAAACB9Lzm6m/iq/Sjk55Tbfxtqec9f+reK/V2SDJSvMufOnaN7MWtAkHZqK8P95iDuc4QyxAKAgDKAKEYAAAAAutVc7jeO+cK1U6uyd3huX4w1vOe1LMvNd/bF5zlT917pd59kpuQvGTfVGwQf+NKriu5sdFuYnmy7CH0pssK8ixHsu2s5fR2O6+MU2bHHuo5CRABYDQRigEAAAD5bMe/NbJm8qGdx/P8ACw7KPMGWx9KmVIdM5T1rG3GLfy6GM9/wAEPORgCa+CgzZi+95gxfa+/Sb33DuXpqjMXSR978NC/DwCgTBGKAQAAAKPFtiXZkhMudU1KIzukGVY4l9pm3NyQJ7Ps9gVwOWXyt+edx41LyZjfoy8dyqWnZNzvuecmUmXiqXJZyznHjlUvP9P3moy115+WXnpYOvmKsb82AIwBQjEAAACgnHmuH8SY7LDHy1vP2p4dDg1WtlCglA6f+h1TZPuQy3qFg6oByw52nWLtKxCgyZT6Kzn+HPdR6dgPl7oWADBqCMUAAACAofJcKdEjJXv7z5O9UqJXSvbkzXsLl0/0+D2PCpXP3leKHkKBZPUfzyw9zpeVfcuinbfu5N7W6ET8noFORHKifcuhSIFtWcvZUyjSf1vR7eHUeSKMQQYAw0QoBgAAgGG77bbb9PDDD2vr1q2KRCLat29fvzK7du3SsmXLtGnTJtXW1urKK6/UmjVrFAqV2UdQY6T256WXfyrt2izF9hcPucotoCoYzqRDm9AgQU6hwMcpPOD7sMKhvEHkByvbL1gKDf96OeuhobWv33bCJAAImjL7RAIAAIDxIB6P65JLLlFLS4vuvvvufvtd19UFF1yg5uZmPfXUU2pra9MnPvEJhcNhffnLXy5Bjfs7Qu26LPSIpq//n1LXm8M/gRORQtVSuEoKVUnh6qx5NGtf/rxQ+aqsfQXO2a8HUyoMAgAAB41QDAAAAMO2evVqSdL69esL7n/88ce1bds2/exnP9O0adN04okn6tZbb9UNN9ygm2++WZFIZAxrW9ht9l36C2u71CU/iDrqTGnuOVJt0+ABVqiKUAoAgHGOUAwAAAAjbvPmzVqwYIGmTZuW2bZkyRItW7ZML774ok466aR+x8RiMcVifU/06+zsHNU6HqU3JEl7z/qqJn/gcilSM6rXAwAA5YX/3gIAAMCIa29vzwnEJGXW29vbCx6zZs0aTZw4MTPNnDlzVOtYbcUlSf9v1xH61c4DiiXdUb0eAAAoL/QUAwAAgCRp5cqVWrt27YBltm/frnnz5o3K9W+88UZdd911mfXOzs5RDcZsy5KM9OiL7fr3F7ZoQsTRXx49VTMaqlUddlQVdvx5xFFVyFZ1xMlsz+wL526PhmxZDNgOAMC4QCgGAAAASdL111+vpUuXDlhmzpw5QzpXc3OztmzZkrNt9+7dmX2FRKNRRaPRIZ1/JERCtpSQ/mpBs37w55De2R/TYy/uPuTzVoVtVYfzArSIk9meHapVpwI3P3hzsgI2WyHbluNYcixLIduSbefNLUuh1H7HLjBZlkK2LdtW7twSwR0AACIUAwAAQEpjY6MaGxtH5FwtLS267bbbtGfPHjU1NUmSNm7cqPr6es2fP39ErnGoLPnB0Mrz5+mfJs7SC2916Kk/vauOnoR6E656E6564q56E556Eq56Eq5iqXl6W2/cVW/SVcI1mfP2Jjz1Jjy9p0SpmjaodGhWLExLLxcK4exUSJd/XKEy6WNzz23LsZU7L3Jc5viB6jqE4DCzXCAkzD6HY1sKOxahIQAEBKEYAAAAhm3Xrl3au3evdu3aJdd1tXXrVknS3LlzVVtbq/POO0/z58/XFVdcoa985Stqb2/XF7/4RS1fvnxMe4MNifFk25aOP7xBxx/ecFCnSLheKkjz5z1ZoVpPoe2ZQM3LKpO1L+Ep6XpyjVHSNfKMUdIz8ry8eWq7mzclPTNgfV3PyJWRGEatoJBtKezYCjmWIo5dcDns2Apn5rnL6bLpcpGcY/rKhhxbkSLLxc5d6Dphx5ZjE+QBwHARigEAAGDYbrrpJt13332Z9fTTJDdt2qQzzjhDjuPoJz/5iZYtW6aWlhZNmDBBV155pW655ZZSVbm/iYdL77wsbVwlzf6g1DRfanq/VDN52KdKhxR1VaNQz4PkeUauyQ3K8sO09Hp+qOYf58n1pKTnyUvP80K6wsf1D+gGva4xct0Bjs85rq8+rlGmnv68+PkK1cMUyQ6TnlHSc1XGnf36sSz1D+BsS+FQKkCzLUVCfcHa35wyU39zyuGlrjYAlBShGAAAAIZt/fr1Wr9+/YBlZs2apUceeWRsKnQwFvyNtOk2aftD/pRWO80Px5rmS/WHSaGo5IQlJyqFIpITGWA5kiqft1yC2/Fs25ItS2FnzC8tSbnBVyqkSqaCq2Re6JUbgnn9QrH85YHKpNeTrqeE54d7Sbdvmz/31xOup1jSVSzpKZbw1JvM6tUXd9UdT6o34SnueqV5EYfBGCme9BRPDq2usaRHKAYg8AjFAAAAEEynXStNmSu1Py/t2S7t2Sbt2ynt3+1Pf35ixC5l7LAUiso4ERk7LM+OpKawPCdr2Q7LtSNyrYg8O6SkFUmth5S0wkpaYblWWInUclL+ckJhJeUoYUUUV0hxhZVQyF82IbnGUsJYSnhWZjnppedS0tiKp7d5lpJGinv+vsxxRUMqr/8+U7wXVhClx1wL2ZZCqV5b/lhnfq8ux7YUtu3MmGZOTjk769jUMallx7Yz5dO3UIZtf7vfWyyvTOoaIcdSy5wppX5ZAKDkCMUAAAAQTKGIdNxH/Ckttl/m7ZeUaHtRbvuLcve/LS8ek5fslZeMyyRjUjIu48ZluXFZbky2l5DtxeV4CYVMQo5JKJQ3WJflJaR4Qun+YiXqvHVIXGPJlS0jW65sebLkZS9btjzHkuvY8mTLS5X3MlNfeSNbnmVLqePS60aOjGXJWI6M/LksWyY1Sba/z7JT21P7bVtWat2ybMn2t1u2I1mOLNvfZtu2v+44sixHlu3vs1L7LCckO71s+8uW48ixQ7IdW7bj+PudkBzHlm2H5DghWbatUMgv74QcObYjJ+TvcxwnVQ//2sqqX+6yk7t9wH2MHwYAI4FQDAAAAIFljNGWHXt1/+ad+u1re9Udd3UgnpQxUyV96KDPa8tTWElFlFRECUWUVNjyl+tCrmpDRjV2UlV2UhHLVZWVVNRKKmq5/lwJRSxXUSt1bOY8CYXlKpxaDimpiEkopIRCJqmw8YO5kInLUVIhzw/pLBnZxpUlIyt7bvy4aigcy8iRqyGPzj/c3MbkzTEAawhhWnYAZ+Wtp5ftAmHdYPuKXXugfYcSABZqz8G0dYj7CgaYg+wDMG4RigEAACCQfvXK2/ryIy9pe1tn0TITIo5qoiF/HglpQjRvPuj+kGqijiZkbSvLpwR6nmQ8ybj+3HOzloe4z3Pzyg22L/t86XWTd/6sfQXrMdi+/PNn198U2VfguOztBfeZYdRrkNd1SIzkJUf1LYFhKBiYZYWKBfflh3zZoeJAAaAtuQkpvl+KH/CnWJc/9/KeDNHyWem8/0VwBwyAUCyIPFfa+ZQ/VkbtNGnWYv8H7GD7AAAAKsitP9mmP+7er6qwrb8+6XB99OTD1FgXzYRaVSFHdjkGWKPB9m9N5M+DMpAf1hUMDQfaN8ywsei+YoHiCIaNIxpEDiPE9ZJ9k5vww6RDCRmNK7lDDTTH0Ob/K/3F1dLkOaWuCVC2+K0XNNsekh69Qep8q29b/Qypda2/XGzf/IvGtp4AAACjrK2jV5L0X8tO0/wZ9SWuDcaNgUKkQtOw9mcFPWO+3+SGScPab7K2DXd//msynP2mwOubV99C+4NiyRpp0uxS1wIoa4RiQbLtIel7n5DyB2robJO+d0XhYzrb/GMuvZ9gDAAAVJSk638mqquq4I/E6dsU0yFOZj6a270C5UZq+2jVYxjnZdCzYMm+7THnNkYra1v+fjt3PX9/zthrtmSHpGidFK2Xqurz5g0FttVLkbpUD08Ah6KCPwEgh+f6vcAK/hIf6Be7kWRJj66U5l3ArZQAAADZPE9KdPdN8fzlA1KiJ2/5QKpM9nLvIYY9RUIdlIhVeCyodCBSMGSxDy1oGXC/1X98qvz9hcawsvLCn2Hvz5sG2z+s12qk9hcLvWzG4gICgFAsKHY+lXtb5LAYqfNN/xyzTx/RagEAAJSFvTukPz4q9byXCqp6UsHWIMvJnlLX/NBkBgHPntsFtttDLDec44e7fTjXG6ntg9XPzt2WHbYAAMoeoVhQ7N9dHucAAAAoExHFtcTerMb/ulN649eHfsJwTd8UGeZyuMq/hWqsQyAAAAKMUCwoaqeVxzkAAADKxNfsr+tc57fSG5JkSbM/KE09WgpXS+EJQwy2JvjlQ9WETAAAjDOjForddtttevjhh7V161ZFIhHt27evX5ldu3Zp2bJl2rRpk2pra3XllVdqzZo1CoXI6kbcrMX+kyQ72zT8wUEt/9hZi0ejZgAAACVxgl6VJHWd9CnVfegfpYaZJa4RAAAYS6P231nxeFyXXHKJli1bVnC/67q64IILFI/H9dRTT+m+++7T+vXrddNNN41WlYLNdqTWtamV/DEOrCLLWeuttzPIPgAAqCh1OiBJ6jr+KgIxAAACaNRCsdWrV2vFihVasGBBwf2PP/64tm3bpgceeEAnnniizj//fN1666268847FY/HR6tawTb/IunS+6X66bnb62dIl/6HPxXcd79/LAAAQCViUHQAAAKpZPcpbt68WQsWLNC0aX3jVC1ZskTLli3Tiy++qJNOOqlUVats8y+S5l3gP0ly/25/nLBZi/t6gQ20DwAAoAJ5ZrhDSwAAgEpQslCsvb09JxCTlFlvb28velwsFlMsFsusd3Z2SpISiYQSicQo1HTspdsxqu05/AN9y67nT0PZN8rGpO1lLMjtp+3BbLsU7PYHue1Sadsf1NccudIdxL762EtafEqNznn/NE2pjZa2UgAAYMwMKxRbuXKl1q5dO2CZ7du3a968eYdUqYGsWbNGq1ev7rf98ccfV01NzahdtxQ2btxY6iqUTJDbLgW7/bQ9uILc/iC3XSpN+7u7u8f8mig/jm1JnvTMzn368WvPy7ae14LDGzSpJqzqsKPqsKOqiNO3HLZVFXZUnb0tkt6Xvc3OLIccnkgJAEC5GlYodv3112vp0qUDlpkzZ86QztXc3KwtW7bkbNu9e3dmXzE33nijrrvuusx6Z2enZs6cqfPOO0/19fVDuna5SyQS2rhxo84991yFw+ExvbbrGf1u53va0xVTU11Up86a5H9gHCOlbHs5CHL7aXsw2y4Fu/1BbrtU2vane5oj2EK2LXnSP/zlbP3gz7ZeeLNTv39934heI+xYqgrlhWsRR9VhOydMyw/fQo6tkG3JtiyFHH/u2Kkpa1vItmTbuXMnq2xmX+qY7H05k2UpZNuybeVsc2xLFmOuAQAq1LBCscbGRjU2No7IhVtaWnTbbbdpz549ampqkuT/T3F9fb3mz59f9LhoNKpotH+39nA4XHF/UIxWm1zPaMuOvdrT1aumuiotnD1Zjm3p0RfatHrDNrV19GbKTp9YpVUXzlfrcdMHOOPIq8Sv53AEuf20PZhtl4Ld/iC3XSpN+4P8eqNPOupZuvhILb1glt54r1u/f71D3fGkepOeeuOuehKpKe6qN3t5oP0JV+lhyhKuUcJNqiuWLFk7D5VtKROYhWzbX3fsTCjnh29Z+2x7wKAuP4wruC0vwAvlBXzZ5fPDw5zrZp1zqHUKpwLJsGP7y056uW8fQSEAVIZRG1Ns165d2rt3r3bt2iXXdbV161ZJ0ty5c1VbW6vzzjtP8+fP1xVXXKGvfOUram9v1xe/+EUtX768YOiFkVEs+LrohOn61i93KH+Y2faOXi174Fmtu/zkMQ/GAAAAxtLhk2p0+KRDH47DGKO466k37uWEZj2JVHAWd9WbzA/avJz9Sc/IM8afe0ZJz0sN9erJNam5Z+R58vdltvXtcz0j1xi5bmru5U1Z27wBnjXgGSnuepIrSWM31mw5Czt+z7qQYymSE5zZmX2ZEC1/n2MrnA7fHFuR9LbsY0OWwnZf+fQ1MuVtW+FQ6jyh/iFeTp3s3GCPQA8A+oxaKHbTTTfpvvvuy6ynnya5adMmnXHGGXIcRz/5yU+0bNkytbS0aMKECbryyit1yy23jFaVAu/RF9q07IFn+wVfbR29uuuXOwoeY+T/L+rqDdt07vzmMb2VEgAAYDyyLEvRkKNoyNFEjY9eiSYVkPUP4/y5a4ySbv99hQK2IQVy6XPmXSd9fbfAtr7rZ4d/fUHhQOFhv+u6fdfJrmu6TXHXU9I1SriekgUSQ78HoCuNw2d2hGwrJ6ibOala6/9+oSZNiJS6agAw5kYtFFu/fr3Wr18/YJlZs2bpkUceGa0qIIvrGa3esK1fIDYURn5wtmXHXrUcNWWkqwYAAIASs1K3H4ac4mUKBWfpICl73Q/B/DA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-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -319,14 +268,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hamiltonian and exchange field rotated. Elapsed time: 3484.986699166 s\n",
+      "Hamiltonian and exchange field rotated. Elapsed time: 11.290487791 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -386,12 +335,16 @@
     "\n",
     "\n",
     "# symmetrizing Hamiltonian and overlap matrix to make them hermitian\n",
-    "# for i in range(dh.lattice.sc_off.shape[0]):\n",
-    "#    j = dh.lattice.sc_index(-dh.lattice.sc_off[i])\n",
-    "#    h1, h1d = hh[i], hh[j]\n",
-    "#    hh[i], hh[j] = (h1 + h1d.T.conj()) / 2, (h1d + h1.T.conj()) / 2\n",
-    "#    s1, s1d = ss[i], ss[j]\n",
-    "#    ss[i], ss[j] = (s1 + s1d.T.conj()) / 2, (s1d + s1.T.conj()) / 2\n",
+    "for i in range(dh.lattice.sc_off.shape[0]):\n",
+    "    j = dh.lattice.sc_index(-dh.lattice.sc_off[i])\n",
+    "    h1, h1d = hh[i], hh[j]\n",
+    "    hh[i], hh[j] = (h1 + h1d.T.conj()) / 2, (h1d + h1.T.conj()) / 2\n",
+    "    s1, s1d = ss[i], ss[j]\n",
+    "    ss[i], ss[j] = (s1 + s1d.T.conj()) / 2, (s1d + s1.T.conj()) / 2\n",
+    "\n",
+    "\n",
+    "###################################################################################\n",
+    "# either this is shit\n",
     "\n",
     "# identifying TRS and TRB parts of the Hamiltonian\n",
     "TAUY = np.kron(np.eye(NO), tau_y)\n",
@@ -403,12 +356,15 @@
     "traced = [spin_tracer(hTRB[i]) for i in range(dh.lattice.nsc.prod())]  # equation 77\n",
     "XCF = np.array(\n",
     "    [\n",
-    "        np.array([f[\"x\"] for f in traced]),\n",
-    "        np.array([f[\"y\"] for f in traced]),\n",
-    "        np.array([f[\"z\"] for f in traced]),\n",
+    "        np.array([f[\"x\"] / 2 for f in traced]),\n",
+    "        np.array([f[\"y\"] / 2 for f in traced]),\n",
+    "        np.array([f[\"z\"] / 2 for f in traced]),\n",
     "    ]\n",
     ")  # equation 77\n",
     "\n",
+    "###################################################################################\n",
+    "\n",
+    "\n",
     "# Check if exchange field has scalar part\n",
     "max_xcfs = abs(np.array(np.array([f[\"c\"] for f in traced]))).max()\n",
     "if max_xcfs > 1e-12:\n",
@@ -428,14 +384,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R [[1. 0. 0.]\n",
+      " [0. 1. 0.]\n",
+      " [0. 0. 1.]]\n",
+      "Z rot doesnt change anything in XCF:  True\n",
+      "MAX(myhk - sislhk) 2.974862582050264e-06 this is zero as should be\n",
+      "MAX(myhk_rot - sislhk) 2.974862582050264e-06 rotation fucks it up\n",
+      "MAX(rot_H - sislhk) 8.881784197001252e-16 rotation fucks it up\n"
+     ]
+    }
+   ],
+   "source": [
+    "myhk, sk = hsk(hh, ss, dh.sc_off, np.array([0.5, 0, 0]))\n",
+    "sislhk = dh.Hk(np.array([0.5, 0, 0])).toarray()\n",
+    "\n",
+    "R = RotMa2b(scf_xcf_orientation, ref_xcf_orientations[2][\"o\"])\n",
+    "print(\"R\", R)\n",
+    "rot_XCF = np.einsum(\"ij,jklm->iklm\", R, XCF)\n",
+    "print(\"Z rot doesnt change anything in XCF: \", np.allclose(XCF, rot_XCF))\n",
+    "\n",
+    "###################################################################################\n",
+    "# either this is shit\n",
+    "rot_H_XCF = sum(\n",
+    "    [np.kron(rot_XCF[i], tau) for i, tau in enumerate([tau_x, tau_y, tau_z])]\n",
+    ")\n",
+    "rot_H = hTRS + rot_H_XCF  # equation 76\n",
+    "###################################################################################\n",
+    "\n",
+    "\n",
+    "myhk_rot, sk = hsk(rot_H, ss, dh.sc_off, np.array([0.5, 0, 0]))\n",
+    "\n",
+    "print(\"MAX(myhk - sislhk)\", np.max(abs(myhk - sislhk)), \"this is zero as should be\")\n",
+    "print(\"MAX(myhk_rot - sislhk)\", np.max(abs(myhk_rot - sislhk)), \"rotation fucks it up\")\n",
+    "print(\"MAX(rot_H - sislhk)\", np.max(abs(rot_H - hh)), \"rotation fucks it up\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Site and pair dictionaries created. Elapsed time: 3485.081068083 s\n",
+      "Site and pair dictionaries created. Elapsed time: 11.50304025 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -552,21 +552,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "k loop:   0%|          | 0/100 [00:00<?, ?it/s]"
+      "k loop:   0%|          | 0/225 [00:00<?, ?it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "k set created. Elapsed time: 3485.092548083 s\n",
+      "k set created. Elapsed time: 11.526158208 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -587,14 +587,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rotations done perpendicular to quantization axis. Elapsed time: 3485.347085125 s\n",
+      "Rotations done perpendicular to quantization axis. Elapsed time: 11.790519875 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -614,12 +614,15 @@
     "    rot_H_XCF_uc = rot_H_XCF[uc_in_sc_idx]\n",
     "\n",
     "    # obtain total Hamiltonian with the rotated exchange field\n",
-    "    rot_H = (\n",
-    "        hTRS + rot_H_XCF\n",
-    "    )  # equation 76 #######################################################################################\n",
+    "    rot_H = hTRS + rot_H_XCF  # equation 76\n",
     "\n",
     "    hamiltonians.append(\n",
-    "        dict(orient=orient[\"o\"], H=rot_H)\n",
+    "        dict(\n",
+    "            orient=orient[\"o\"],\n",
+    "            H=rot_H,\n",
+    "            GS=np.zeros((eset, rot_H.shape[1], rot_H.shape[2]), dtype=\"complex128\"),\n",
+    "            GS_tmp=np.zeros((eset, rot_H.shape[1], rot_H.shape[2]), dtype=\"complex128\"),\n",
+    "        )\n",
     "    )  # store orientation and rotated Hamiltonian\n",
     "\n",
     "    # these are the rotations (for now) perpendicular to the quantization axis\n",
@@ -647,54 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x16b694a30>"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "HK, SK = hsk(hamiltonians[2][\"H\"], ss, dh.sc_off, np.array([0.3, 0, 0]))\n",
-    "\n",
-    "myhk = dh.Hk(k=np.array([0.3, 0, 0]))\n",
-    "\n",
-    "abs(HK - myhk.toarray()).max()\n",
-    "\n",
-    "plt.matshow(abs(HK))\n",
-    "plt.matshow(abs(myhk.toarray()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -702,15 +658,15 @@
      "output_type": "stream",
      "text": [
       "Starting matrix inversions\n",
-      "Total number of k points: 100\n",
-      "Number of energy samples per k point: 600\n",
+      "Total number of k points: 225\n",
+      "Number of energy samples per k point: 300\n",
       "Total number of directions: 3\n",
-      "Total number of matrix inversions: 180000\n",
+      "Total number of matrix inversions: 202500\n",
       "The shape of the Hamiltonian and the Greens function is 84x84=7056\n",
       "Memory taken by a single Hamiltonian is: 0.015625 KB\n",
       "Expected memory usage per matrix inversion: 0.5 KB\n",
-      "Expected memory usage per k point for parallel inversion: 900.0 KB\n",
-      "Expected memory usage on root node: 87.890625 MB\n",
+      "Expected memory usage per k point for parallel inversion: 450.0 KB\n",
+      "Expected memory usage on root node: 98.876953125 MB\n",
       "================================================================================================================================================================\n"
      ]
     },
@@ -718,23 +674,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "k loop: 100%|██████████| 100/100 [1:39:50<00:00, 59.90s/it]  "
+      "k loop: 100%|██████████| 225/225 [09:42<00:00,  2.59s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculated Greens functions. Elapsed time: 3878.870911625 s\n",
+      "Calculated Greens functions. Elapsed time: 593.774521541 s\n",
       "================================================================================================================================================================\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [
@@ -782,14 +731,15 @@
     "        # calculate Greens function\n",
     "        H = hamiltonian_orientation[\"H\"]\n",
     "        HK, SK = hsk(H, ss, dh.sc_off, k)\n",
-    "        # HK = HK - Ef * SK\n",
-    "        # Gk = inv(SK * eran.reshape(eset, 1, 1) - HK)\n",
     "\n",
     "        # solve Greens function sequentially for the energies, because of memory bound\n",
     "        Gk = np.zeros(shape=(eset, HK.shape[0], HK.shape[1]), dtype=\"complex128\")\n",
     "        for j in range(eset):\n",
     "            Gk[j] = inv(SK * eran[j] - HK)\n",
     "\n",
+    "        # saving this for total charge\n",
+    "        hamiltonian_orientation[\"GS_tmp\"] += Gk @ SK * wk\n",
+    "\n",
     "        # store the Greens function slice of the magnetic entities (for now) based on the on-site projections\n",
     "        for mag_ent in magnetic_entities:\n",
     "            mag_ent[\"Gii_tmp\"][i] += (\n",
@@ -811,6 +761,9 @@
     "\n",
     "# summ reduce partial results of mpi nodes\n",
     "for i in range(len(hamiltonians)):\n",
+    "    # for total charge\n",
+    "    comm.Reduce(hamiltonians[i][\"GS_tmp\"], hamiltonians[i][\"GS\"], root=root_node)\n",
+    "\n",
     "    for mag_ent in magnetic_entities:\n",
     "        comm.Reduce(mag_ent[\"Gii_tmp\"][i], mag_ent[\"Gii\"][i], root=root_node)\n",
     "\n",
@@ -830,13 +783,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Total charge:  39.907801636668175\n",
+      "Total charge:  39.90780156034552\n",
+      "Total charge:  39.992244200462636\n",
       "Magnetic entities integrated.\n",
       "Pairs integrated.\n",
       "Magnetic parameters calculated.\n",
@@ -845,7 +801,7 @@
       "Input file: \n",
       "/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf\n",
       "Output file: \n",
-      "./Fe3GeTe2.pickle\n",
+      "./Fe3GeTe2_notebook.pickle\n",
       "Number of nodes in the parallel cluster:  1\n",
       "================================================================================================================================================================\n",
       "Cell [Ang]: \n",
@@ -861,11 +817,11 @@
       "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
       "================================================================================================================================================================\n",
       "Parameters for the contour integral:\n",
-      "Number of k points:  10\n",
+      "Number of k points:  15\n",
       "k point directions:  xy\n",
       "Ebot:  -13\n",
-      "Eset:  600\n",
-      "Esetp:  10000\n",
+      "Eset:  300\n",
+      "Esetp:  1000\n",
       "================================================================================================================================================================\n",
       "Atomic information: \n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
@@ -873,7 +829,7 @@
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "[3]Fe(2)        -7.339158738013707e-06        4.149278510690423e-06        11.657585837928032\n",
       "\n",
-      "[4]Fe(all)        -7.326987662162937e-06        4.158274523275774e-06        8.912422537596708\n",
+      "[4]Fe(2)        -7.326987662162937e-06        4.158274523275774e-06        8.912422537596708\n",
       "\n",
       "[5]Fe(2)        1.8954667088117545        1.0943913231921656        10.285002698393109\n",
       "\n",
@@ -882,165 +838,198 @@
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Magnetic entity1        Magnetic entity2       [i  j  k]       d [Ang]\n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-      "[3]Fe(2)        [4]Fe(all)        [0 0 0]        d [Ang] 2.745163300331324\n",
-      "Isotropic:  -38.08908138560059\n",
-      "DMI:  [-3.50315141e-01  2.63885818e-04 -4.29362132e-06]\n",
-      "Symmetric-anisotropy:  [ 1.17647885e+00 -1.96468230e-05 -7.74651208e-07 -1.96468230e-05\n",
-      "  1.26142216e+00  3.57154955e-04 -7.74651208e-07  3.57154955e-04\n",
-      " -2.43790101e+00]\n",
-      "J:  [-3.69126025e+01 -1.96468230e-05 -7.74651208e-07 -1.96468230e-05\n",
-      " -3.68276592e+01  3.57154955e-04 -7.74651208e-07  3.57154955e-04\n",
-      " -4.05269824e+01]\n",
+      "[3]Fe(2)        [4]Fe(2)        [0 0 0]        d [Ang] 2.745163300331324\n",
+      "Isotropic:  -87.36625034925267\n",
+      "DMI:  [ 3.21770364e-02 -1.00594555e-03 -3.51968374e-07]\n",
+      "Symmetric-anisotropy:  [ 2.12625625e+00  7.29980046e-05 -4.82064787e-05  7.29980046e-05\n",
+      "  2.14479369e+00 -9.50239206e-06 -4.82064787e-05 -9.50239206e-06\n",
+      " -4.27104994e+00]\n",
+      "J:  [-8.52399941e+01  7.29980046e-05 -4.82064787e-05  7.29980046e-05\n",
+      " -8.52214567e+01 -9.50239206e-06 -4.82064787e-05 -9.50239206e-06\n",
+      " -9.16373003e+01]\n",
       "Energies for debugging: \n",
-      "array([[-4.04406378e-02, -3.50672296e-04,  3.49957987e-04,\n",
-      "        -4.01805196e-02],\n",
-      "       [-4.06133270e-02, -2.63111167e-07,  2.64660469e-07,\n",
-      "        -4.03504319e-02],\n",
-      "       [-3.34747989e-02,  1.53532017e-08,  2.39404444e-08,\n",
-      "        -3.34747732e-02]])\n",
+      "array([[-9.16002498e-02,  3.21865388e-05, -3.21675340e-05,\n",
+      "        -9.17228673e-02],\n",
+      "       [-9.16743508e-02,  1.05415203e-06, -9.57739074e-07,\n",
+      "        -9.17598493e-02],\n",
+      "       [-7.87200461e-02, -7.33499730e-08, -7.26460363e-08,\n",
+      "        -7.87201389e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.04035043, -0.0334748 , -0.04044064])\n",
+      "array([-0.09175985, -0.07872005, -0.09160025])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.04035043189424629 -0.03347477318614506\n",
+      "-0.09175984926880505 -0.07872013892300818\n",
       "\n",
       "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] 2.5835033632437767\n",
-      "Isotropic:  -65.6147625726105\n",
-      "DMI:  [ 3.55875702e+00 -6.14766359e+00  2.13990280e-05]\n",
-      "Symmetric-anisotropy:  [-0.08599962  0.03698877 -0.10152953  0.03698877 -0.29087488 -0.04979049\n",
-      " -0.10152953 -0.04979049  0.3768745 ]\n",
-      "J:  [-6.57007622e+01  3.69887710e-02 -1.01529530e-01  3.69887710e-02\n",
-      " -6.59056375e+01 -4.97904945e-02 -1.01529530e-01 -4.97904945e-02\n",
-      " -6.52378881e+01]\n",
+      "Isotropic:  -41.52730925965426\n",
+      "DMI:  [ 1.17785417e+00 -2.06225136e+00 -4.69189233e-05]\n",
+      "Symmetric-anisotropy:  [ 0.02746399  0.15418338 -0.07648137  0.15418338 -0.1972997  -0.04062499\n",
+      " -0.07648137 -0.04062499  0.16983571]\n",
+      "J:  [-4.14998453e+01  1.54183382e-01 -7.64813730e-02  1.54183382e-01\n",
+      " -4.17246090e+01 -4.06249865e-02 -7.64813730e-02 -4.06249865e-02\n",
+      " -4.13574736e+01]\n",
       "Energies for debugging: \n",
-      "array([[-6.53648847e-02,  3.60854752e-03, -3.50896653e-03,\n",
-      "        -6.58426415e-02],\n",
-      "       [-6.51108914e-02,  6.24919312e-03, -6.04613406e-03,\n",
-      "        -6.54759423e-02],\n",
-      "       [-6.59686334e-02, -3.69673720e-05, -3.70101700e-05,\n",
-      "        -6.59255821e-02]])\n",
+      "array([[-0.04145412,  0.00121848, -0.00113723, -0.04156529],\n",
+      "       [-0.04126082,  0.00213873, -0.00198577, -0.04129418],\n",
+      "       [-0.04188393, -0.00015423, -0.00015414, -0.04170551]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06547594, -0.06596863, -0.06536488])\n",
+      "array([-0.04129418, -0.04188393, -0.04145412])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06547594226467608 -0.06592558212190268\n",
+      "-0.04129418024412361 -0.041705510285739864\n",
       "\n",
-      "[4]Fe(all)        [5]Fe(2)        [0 0 0]        d [Ang] 2.583501767937866\n",
-      "Isotropic:  -63.461479457470716\n",
-      "DMI:  [-3.46188832e+00  5.96672549e+00  2.26187324e-05]\n",
-      "Symmetric-anisotropy:  [-0.0829036   0.03178786  0.09341113  0.03178786 -0.2854846   0.04272782\n",
-      "  0.09341113  0.04272782  0.3683882 ]\n",
-      "J:  [-6.35443831e+01  3.17878581e-02  9.34111270e-02  3.17878581e-02\n",
-      " -6.37469641e+01  4.27278222e-02  9.34111270e-02  4.27278222e-02\n",
-      " -6.30930913e+01]\n",
+      "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] 2.583501767937866\n",
+      "Isotropic:  -41.532465661632486\n",
+      "DMI:  [-1.16839004e+00  2.04641313e+00 -4.67852015e-05]\n",
+      "Symmetric-anisotropy:  [ 0.02908454  0.15418348  0.06718218  0.15418348 -0.19939635  0.03271419\n",
+      "  0.06718218  0.03271419  0.17031181]\n",
+      "J:  [-4.15033811e+01  1.54183477e-01  6.71821756e-02  1.54183477e-01\n",
+      " -4.17318620e+01  3.27141946e-02  6.71821756e-02  3.27141946e-02\n",
+      " -4.13621538e+01]\n",
       "Energies for debugging: \n",
-      "array([[-6.32236137e-02, -3.50461614e-03,  3.41916050e-03,\n",
-      "        -6.36877197e-02],\n",
-      "       [-6.29625688e-02, -6.06013662e-03,  5.87331436e-03,\n",
-      "        -6.33196114e-02],\n",
-      "       [-6.38062084e-02, -3.17652393e-05, -3.18104768e-05,\n",
-      "        -6.37691547e-02]])\n",
+      "array([[-0.0414585 , -0.0012011 ,  0.00113568, -0.04157913],\n",
+      "       [-0.0412658 , -0.0021136 ,  0.00197923, -0.04130058],\n",
+      "       [-0.0418846 , -0.00015423, -0.00015414, -0.04170618]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06331961, -0.06380621, -0.06322361])\n",
+      "array([-0.04130058, -0.0418846 , -0.0414585 ])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06331961140706378 -0.06376915470527415\n",
+      "-0.04130058163838069 -0.04170618060677052\n",
       "\n",
       "[3]Fe(2)        [5]Fe(2)        [-1 -1  0]        d [Ang] 2.5834973202859075\n",
-      "Isotropic:  -65.62000802135631\n",
-      "DMI:  [-7.07503443e+00  3.04878078e-04  3.59246447e-06]\n",
-      "Symmetric-anisotropy:  [-3.95641341e-01 -1.37462093e-04  1.36999229e-04 -1.37462093e-04\n",
-      "  1.87904813e-02  1.22453077e-01  1.36999229e-04  1.22453077e-01\n",
-      "  3.76850860e-01]\n",
-      "J:  [-6.60156494e+01 -1.37462093e-04  1.36999229e-04 -1.37462093e-04\n",
-      " -6.56012175e+01  1.22453077e-01  1.36999229e-04  1.22453077e-01\n",
-      " -6.52431572e+01]\n",
+      "Isotropic:  -41.516776604415945\n",
+      "DMI:  [-2.41730078e+00  8.52711009e-05  3.85154429e-05]\n",
+      "Symmetric-anisotropy:  [-3.24863466e-01 -1.57805287e-04  1.53264979e-04 -1.57805287e-04\n",
+      "  1.46547000e-01  7.51504636e-02  1.53264979e-04  7.51504636e-02\n",
+      "  1.78316466e-01]\n",
+      "J:  [-4.18416401e+01 -1.57805287e-04  1.53264979e-04 -1.57805287e-04\n",
+      " -4.13702296e+01  7.51504636e-02  1.53264979e-04  7.51504636e-02\n",
+      " -4.13384601e+01]\n",
       "Energies for debugging: \n",
-      "array([[-6.49844951e-02, -7.19748751e-03,  6.95258136e-03,\n",
-      "        -6.52968607e-02],\n",
-      "       [-6.55018192e-02, -4.41877308e-07,  1.67878849e-07,\n",
-      "        -6.60401128e-02],\n",
-      "       [-6.59055744e-02,  1.41054558e-07,  1.33869629e-07,\n",
-      "        -6.59911860e-02]])\n",
+      "array([[-4.11335642e-02, -2.49245124e-03,  2.34215032e-03,\n",
+      "        -4.11229167e-02],\n",
+      "       [-4.15433560e-02, -2.38536080e-07, -6.79938781e-08,\n",
+      "        -4.17094547e-02],\n",
+      "       [-4.16175425e-02,  1.96320730e-07,  1.19289844e-07,\n",
+      "        -4.19738255e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06604011, -0.06590557, -0.0649845 ])\n",
+      "array([-0.04170945, -0.04161754, -0.04113356])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06604011276766036 -0.06599118595746928\n",
+      "-0.04170945467884016 -0.041973825462496055\n",
       "\n",
-      "[4]Fe(all)        [5]Fe(2)        [-1 -1  0]        d [Ang] 2.583495745338251\n",
-      "Isotropic:  -63.4637006802454\n",
-      "DMI:  [ 6.84888899e+00 -7.88878725e-04  1.52778682e-05]\n",
-      "Symmetric-anisotropy:  [-3.84273968e-01 -1.41648052e-04  1.12499451e-04 -1.41648052e-04\n",
-      "  1.63124769e-02 -1.19287820e-01  1.12499451e-04 -1.19287820e-01\n",
-      "  3.67961491e-01]\n",
-      "J:  [-6.38479746e+01 -1.41648052e-04  1.12499451e-04 -1.41648052e-04\n",
-      " -6.34473882e+01 -1.19287820e-01  1.12499451e-04 -1.19287820e-01\n",
-      " -6.30957392e+01]\n",
+      "[4]Fe(2)        [5]Fe(2)        [-1 -1  0]        d [Ang] 2.583495745338251\n",
+      "Isotropic:  -41.514139744541524\n",
+      "DMI:  [ 2.41735105e+00 -1.91201970e-04  3.71831534e-05]\n",
+      "Symmetric-anisotropy:  [-3.21261199e-01 -1.57863970e-04  1.40654575e-04 -1.57863970e-04\n",
+      "  1.43245335e-01 -7.51485430e-02  1.40654575e-04 -7.51485430e-02\n",
+      "  1.78015864e-01]\n",
+      "J:  [-4.18354009e+01 -1.57863970e-04  1.40654575e-04 -1.57863970e-04\n",
+      " -4.13708944e+01 -7.51485430e-02  1.40654575e-04 -7.51485430e-02\n",
+      " -4.13361239e+01]\n",
       "Energies for debugging: \n",
-      "array([[-6.28360612e-02,  6.96817681e-03, -6.72960117e-03,\n",
-      "        -6.31426268e-02],\n",
-      "       [-6.33554172e-02,  6.76379274e-07, -9.01378176e-07,\n",
-      "        -6.38701956e-02],\n",
-      "       [-6.37521496e-02,  1.56925920e-07,  1.26370184e-07,\n",
-      "        -6.38257537e-02]])\n",
+      "array([[-4.11342275e-02,  2.49249959e-03, -2.34220250e-03,\n",
+      "        -4.11235752e-02],\n",
+      "       [-4.15380203e-02,  5.05473958e-08, -3.31856545e-07,\n",
+      "        -4.16963053e-02],\n",
+      "       [-4.16182136e-02,  1.95047123e-07,  1.20680816e-07,\n",
+      "        -4.19744966e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.0638702 , -0.06375215, -0.06283606])\n",
+      "array([-0.04169631, -0.04161821, -0.04113423])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06387019555954467 -0.06382575373745485\n",
+      "-0.04169630526113004 -0.04197449662603875\n",
       "\n",
       "[3]Fe(2)        [5]Fe(2)        [-1  0  0]        d [Ang] 2.583541444641373\n",
-      "Isotropic:  -65.60128795737425\n",
-      "DMI:  [ 3.57594200e+00  6.14751321e+00 -2.99860089e-05]\n",
-      "Symmetric-anisotropy:  [-0.08550091 -0.03685709  0.10144213 -0.03685709 -0.29158846 -0.04351401\n",
-      "  0.10144213 -0.04351401  0.37708937]\n",
-      "J:  [-6.56867889e+01 -3.68570877e-02  1.01442127e-01 -3.68570877e-02\n",
-      " -6.58928764e+01 -4.35140091e-02  1.01442127e-01 -4.35140091e-02\n",
-      " -6.52241986e+01]\n",
+      "Isotropic:  -41.518970343111015\n",
+      "DMI:  [1.16817703e+00 2.06220317e+00 7.21860779e-06]\n",
+      "Symmetric-anisotropy:  [ 0.03010538 -0.15396904  0.07631411 -0.15396904 -0.20084252 -0.03269774\n",
+      "  0.07631411 -0.03269774  0.17073714]\n",
+      "J:  [-4.14888650e+01 -1.53969044e-01  7.63141143e-02 -1.53969044e-01\n",
+      " -4.17198129e+01 -3.26977448e-02  7.63141143e-02 -3.26977448e-02\n",
+      " -4.13482332e+01]\n",
+      "Energies for debugging: \n",
+      "array([[-0.04144688,  0.00120087, -0.00113548, -0.04156696],\n",
+      "       [-0.04124959, -0.00213852,  0.00198589, -0.04128287],\n",
+      "       [-0.04187266,  0.00015398,  0.00015396, -0.04169486]])\n",
+      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
+      "array([-0.04128287, -0.04187266, -0.04144688])\n",
+      "Test J_xx = E(y,z) = E(z,y)\n",
+      "-0.04128287386394945 -0.041694856070965985\n",
+      "\n",
+      "[4]Fe(2)        [5]Fe(2)        [-1  0  0]        d [Ang] 2.5835398672184064\n",
+      "Isotropic:  -41.51853887711384\n",
+      "DMI:  [-1.17770380e+00 -2.04619919e+00  9.44971395e-06]\n",
+      "Symmetric-anisotropy:  [ 0.02607311 -0.15396929 -0.0672924  -0.15396929 -0.19537854  0.0406109\n",
+      " -0.0672924   0.0406109   0.16930543]\n",
+      "J:  [-4.14924658e+01 -1.53969287e-01 -6.72923993e-02 -1.53969287e-01\n",
+      " -4.17139174e+01  4.06108984e-02 -6.72923993e-02  4.06108984e-02\n",
+      " -4.13492334e+01]\n",
+      "Energies for debugging: \n",
+      "array([[-0.04144387, -0.00121831,  0.00113709, -0.04155449],\n",
+      "       [-0.04125459,  0.00211349, -0.00197891, -0.04128939],\n",
+      "       [-0.04187334,  0.00015398,  0.00015396, -0.04169554]])\n",
+      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
+      "array([-0.04128939, -0.04187334, -0.04144387])\n",
+      "Test J_xx = E(y,z) = E(z,y)\n",
+      "-0.041289392429914126 -0.04169553910557238\n",
+      "\n",
+      "[4]Fe(2)        [5]Fe(2)        [-2  0  0]        d [Ang] 5.951322298958084\n",
+      "Isotropic:  -1.7091250393227355\n",
+      "DMI:  [ 0.03576957  0.26364426 -0.18258214]\n",
+      "Symmetric-anisotropy:  [ 0.06980213  0.03737695  0.02629686  0.03737695 -0.14019341 -0.03727655\n",
+      "  0.02629686 -0.03727655  0.07039128]\n",
+      "J:  [-1.63932291  0.03737695  0.02629686  0.03737695 -1.84931845 -0.03727655\n",
+      "  0.02629686 -0.03727655 -1.63873376]\n",
       "Energies for debugging: \n",
-      "array([[-6.53521011e-02,  3.61945601e-03, -3.53242799e-03,\n",
-      "        -6.58308241e-02],\n",
-      "       [-6.50962960e-02, -6.24895534e-03,  6.04607109e-03,\n",
-      "        -6.54612308e-02],\n",
-      "       [-6.59549288e-02,  3.68271017e-05,  3.68870737e-05,\n",
-      "        -6.59123469e-02]])\n",
+      "array([[-1.60572733e-03,  7.30461189e-05,  1.50698510e-06,\n",
+      "        -1.77828935e-03],\n",
+      "       [-1.67174019e-03, -2.89941114e-04,  2.37347403e-04,\n",
+      "        -1.62742800e-03],\n",
+      "       [-1.92034755e-03, -2.19959089e-04,  1.45205198e-04,\n",
+      "        -1.65121781e-03]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06546123, -0.06595493, -0.0653521 ])\n",
+      "array([-0.00162743, -0.00192035, -0.00160573])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06546123082429328 -0.06591234690586369\n",
+      "-0.0016274280031076719 -0.0016512178077259976\n",
       "\n",
-      "[4]Fe(all)        [5]Fe(2)        [-1  0  0]        d [Ang] 2.5835398672184064\n",
-      "Isotropic:  -63.445681369296956\n",
-      "DMI:  [-3.44476779e+00 -5.96603346e+00 -2.37584314e-05]\n",
-      "Symmetric-anisotropy:  [-0.08466531 -0.031648   -0.0935334  -0.031648   -0.28385999  0.04835705\n",
-      " -0.0935334   0.04835705  0.3685253 ]\n",
-      "J:  [-6.35303467e+01 -3.16479979e-02 -9.35333990e-02 -3.16479979e-02\n",
-      " -6.37295414e+01  4.83570464e-02 -9.35333990e-02  4.83570464e-02\n",
-      " -6.30771561e+01]\n",
+      "[4]Fe(2)        [5]Fe(2)        [-3  0  0]        d [Ang] 9.638732176310562\n",
+      "Isotropic:  -0.09189981541370944\n",
+      "DMI:  [ 0.00859892  0.0105977  -0.36714056]\n",
+      "Symmetric-anisotropy:  [-0.05448044 -0.03796364 -0.01728604 -0.03796364  0.04913009 -0.00640036\n",
+      " -0.01728604 -0.00640036  0.00535035]\n",
+      "J:  [-0.14638025 -0.03796364 -0.01728604 -0.03796364 -0.04276972 -0.00640036\n",
+      " -0.01728604 -0.00640036 -0.08654947]\n",
       "Energies for debugging: \n",
-      "array([[-6.32065968e-02, -3.49312484e-03,  3.39641074e-03,\n",
-      "        -6.36666376e-02],\n",
-      "       [-6.29477154e-02,  6.05956686e-03, -5.87250006e-03,\n",
-      "        -6.33048155e-02],\n",
-      "       [-6.37924451e-02,  3.16242395e-05,  3.16717563e-05,\n",
-      "        -6.37558779e-02]])\n",
+      "array([[-4.10410032e-05,  1.49992875e-05, -2.19856035e-06,\n",
+      "        -1.34816950e-05],\n",
+      "       [-1.32057931e-04,  6.68833577e-06,  2.78837344e-05,\n",
+      "        -1.85874459e-04],\n",
+      "       [-7.20577538e-05, -3.29176918e-04,  4.05104201e-04,\n",
+      "        -1.06886051e-04]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06330482, -0.06379245, -0.0632066 ])\n",
+      "array([-1.85874459e-04, -7.20577538e-05, -4.10410032e-05])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06330481549381357 -0.06375587786178244\n",
+      "-0.00018587445917353333 -0.00010688605065147818\n",
       "\n",
       "================================================================================================================================================================\n",
       "Runtime information: \n",
-      "Total runtime: 395.5379132080002 s\n",
+      "Total runtime: 583.827944292 s\n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Initial setup: 0.14075858300020627 s\n",
-      "Hamiltonian conversion and XC field extraction: 0.811 s\n",
-      "Pair and site datastructure creatrions: 0.094 s\n",
-      "k set cration and distribution: 0.011 s\n",
-      "Rotating XC potential: 0.255 s\n",
-      "Greens function inversion: 393.524 s\n",
-      "Calculate energies and magnetic components: 0.702 s\n"
+      "Initial setup: 0.10415358400000052 s\n",
+      "Hamiltonian conversion and XC field extraction: 0.657 s\n",
+      "Pair and site datastructure creatrions: 0.213 s\n",
+      "k set cration and distribution: 0.023 s\n",
+      "Rotating XC potential: 0.264 s\n",
+      "Greens function inversion: 581.984 s\n",
+      "Calculate energies and magnetic components: 0.582 s\n"
      ]
     }
    ],
    "source": [
     "if rank == root_node:\n",
+    "    # Calculate total charge\n",
+    "    for hamiltonian in hamiltonians:\n",
+    "        GS = hamiltonian[\"GS\"]\n",
+    "        traced = np.trace((GS), axis1=1, axis2=2)\n",
+    "        integral = np.trapz(-1 / np.pi * np.imag(traced * cont.we))\n",
+    "        print(\"Total charge: \", integral)\n",
+    "\n",
     "    # iterate over the magnetic entities\n",
     "    for tracker, mag_ent in enumerate(magnetic_entities):\n",
     "        # iterate over the quantization axes\n",
@@ -1127,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1124,7 @@
      "evalue": "invalid syntax (3105939143.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[111], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    ========================================\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[13], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    ========================================\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1168,6 +1157,13 @@
     "Symmetric-anisotropy 0.26007 -0.00013243     0.12977   -0.069979   -0.042066\n",
     "--------------------------------------------------------------------------------\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {