diff --git a/docs/examples/ts2img.ipynb b/docs/examples/ts2img.ipynb
index 099e92e..223625e 100644
--- a/docs/examples/ts2img.ipynb
+++ b/docs/examples/ts2img.ipynb
@@ -39,7 +39,7 @@
     },
     {
      "data": {
-      "text/plain": "<matplotlib.legend.Legend at 0x7f88234291b0>"
+      "text/plain": "<matplotlib.legend.Legend at 0x7f0ee33e7c10>"
      },
      "execution_count": 1,
      "metadata": {},
@@ -48,7 +48,7 @@
     {
      "data": {
       "text/plain": "<Figure size 1020x350 with 1 Axes>",
-      "image/png": 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"
+      "image/png": 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0u3FolSpVsG3bNowfP95yVSUuLk7Ur/y2t3oJguD02aWZM2di+vTpltcmkwlGo1H0BtOhoaEuv3BcuXJF1CIVYuzK2gQFBTnNzV6sGjVqWFbVA+48G7NmzRosX74cbdu2RfXq1S1XuEptqlSpgpCQEKt4VapUQdWqVa2OBQYGArhTJzVr1oROp8OkSZPw3HPPWWn466+/cPfdd0Ov18PPzw/Vq1d3mHfp8Tp16uD27dtWdkFBQfD397csAhIYGFgull6vR3BwsNVCIf7+/qhWrRpq166NgwcP4qmnnsLcuXPRt29flJSU4Ntvv8WSJUss9jqdDsHBwVZxAwIC4O/vj9q1a+P27dsA7tzOWPYqI3CnP5X1KywsRL169crlW/q6ZcuWyMrKsnq/9P9paWlWbRcYGIiaNWuWi1W2ToOCguDn51eu7QBY6sAVtWvXRkhICIA7z6/VrFkTMTExqFKlCsaOHYuffvrJUif/+Mc/sHDhwnIx6tWrZ1m8JzQ01Kpcf39/q76k0+nK9a3bt29b2rB0clWzZk3UqFED33//PWbNmoUlS5aga9euCA4OxuLFi5GWluayrmxp0KBBuTa7efMmAgICEB0dbak7R3FK+2Tp+1rfxFmLZZbacYNpz9hRm2fttKwtesAAZJnNaB4UhBib7zdiY6WZTMgym1FXr8fIMnckuKOtNEZZHVquN2qTZqdlbWLtvL3BtGoTs/T0dOTn56NTp06WY8XFxUhJScHKlSstzz1lZmbi2rVrKCoqQlhYGGJiYpw+ZxQREVHu6lh+fn65q2hlCQwMtEwopKDT6VzeTnjjxg1RtxyKsRMbSywHDx7EwIEDLZ2qpKQE2dnZlqtfjmjZsmW5BRqOHTtm9bpjx474+eefERUVZXX8ypUrltvJ9Ho9iouLXers0KEDTpw44dLOXfbv34/GjRvjlVdesWj79ddfrWxatGiBQ4cOYdSoUZZjZXOtW7cuGjRogF9++QUjRoyw8rW9IvXTTz8hNjbWoZ4nnngCw4cPx+eff46BAwdKTctC69atcf78eVy8eNFyy6BSS7WPGjUK8+bNw6pVqzB69Gh07NgR27ZtQ5MmTRAQUP70EhkZiSpVquDQoUMwGo0A7pyssrOz0aNHD4tdWFgYLl++bHmdnZ1tucJkj7179+K+++7D008/bTmWk5MjKaeuXbviiy++sDq2c+dO3HPPPZZJGSGE+BqbDQbsKPM87wyjEQsjI92KEZ+Tg0Wlz2eHh6MwJ0dejP/paOtWBEIqLqot/tG7d28cP34cmZmZlr977rkHI0aMQGZmJvz9/S22ISEhCAsLQ3Z2No4cOeL0y2rXrl3LrTi3c+dO3HfffR7Lxddp2rQpvvvuOxw4cAAnT57EpEmTnN76WcqkSZNw6tQpxMfHIysrC1u3brUszFJ6FS8+Ph6pqamYMmUKMjMzkZ2djaSkJMviH8CdfcxSUlLw22+/Wa3eZ0vfvn2xb98+mdmWJyoqCufPn8fmzZuRk5OD999/v9yqic8++yzWr1+PTZs2ITs7G/Pnz8fPP/9sdbVyzpw5WLBgAd555x1kZWXh+PHjSEhIsFqZ0Ww2Iz09vdziEmUZNmwYHn/8cQwbNgzz5s1DWloazp07hz179uCzzz6zGhtiiIuLQ4sWLTB69GgcO3YMe/futUxC5eLn54fnn38eb731FsxmM6ZMmYI///wTTzzxBA4dOoRffvkFO3fuxLhx4yy3hw4dOhQvvvgidu/ejZ9//hnjxo2Dn5+fVV326tUL69atQ0ZGBo4cOYLJkyc7nRRFRUXhyJEj+Pbbb5GVlYVZs2bZvUVaDJMnT8avv/6K6dOn4+TJk9iwYQPWr19v9Rzmjh070LJlSyu/M2fOIDMzE3l5eSgsLERmZiaOHz+uytYShBBSljSTCTtsrpAtys1FmhvP1KeZTFYTKiVjnHHy3C8hlQnVJmbBwcFo06aN1V/prVdt2rQBAHzyySdITk62LJnfp08fDBo0yOpL7ejRozFz5kzL69LbIxcuXIhTp05h4cKF2LVrl9VS3MSaF154AR07dkTfvn0RGxuLiIgIDBo0yKVf06ZN8d///hfbt29Hu3bt8N5771kWXim9AtmuXTvs2bMH2dnZeOCBB9ChQwfMmjXL6grmvHnzcO7cOURGRiIsLMxheSNHjsSJEydw+vRpeQnbMHDgQEybNg3PPPMM2rdvj0OHDmHWrFlWNiNGjMDMmTMtdXX27Fk88cQTVrdXTZgwAevWrcPGjRvRtm1b9OjRAxs3bkTjxo0tNp9//jkaNWqEBx54wKEenU6HLVu2YPny5fjqq6/Qu3dvtGjRAuPGjUP9+vXdnpz6+fnh008/xc2bN3HvvfdiwoQJ5VbPBO5sXSBlo/Fx48bh1q1bWLduHerXr4/9+/ejuLgYffv2RZs2bTB16lSEhIRYNgV//fXX0bVrV/Tv3x9xcXHo1q2bZYuBUpYsWYIGDRqge/fuGD58OF544QWn+7JNnjwZgwcPxtChQxETE4MrV65YXT1zh6ZNm+Krr75CcnIy2rdvj9dffx0rVqzAY489ZrExmUzl+uGECRPQoUMHrFmzBllZWejQoQN69uyJixcvStJBCCFKkeXgjgNHx70dI8/OHRaEVEY0PRIuXbqE6dOn4/Lly6hXrx5Gjx5d7gvz+fPnLV/4AOC+++6zbOA6a9YsREZGYsuWLeWe+6nMPPnkk1ZfwGvVqoXPPvvMqU9ycrLdRSIGDBhgtaz7K6+8goYNG1p9ye7cuTN27txp5Vc2VpcuXcrdAmmPWrVq4ZlnnsHSpUuxZs0aAHeuUs2ZM8cSz94+V8nJyeXKPHfunJXNokWLLCtOlj7DZzuZnzVrllX/i42NLXeL5vDhwzF8+HCHuS5btgyvvfaay1z9/PwwadIkTJo0qVysss/oONrX6+jRo1Z2zZs3L7dEf9kFYIA7deJsYhYbG2t3e4bq1avjzz//tMSKjo7G9u3bHcYJDg62Wgzm77//xty5c63uva5fvz4++eQTqxzK7o3XpEkTi5YrV64gMDAQCQkJ5bYwWLBggeX/jurKHj169HC6hP8TTzyBZ555xupYaT8ri9hnSwkhxJM0d/DDlqPj3o4R8b/ntAmp7GhqYmb7xea5554rt2iEKx8AePzxx/H4448rqIw4YtWqVejcuTNq166N/fv3Y+XKlXj22Wc9Vt4rr7yCf//73yguLnb7lj45mM1mrF69Gn379oW/vz8+/vhj7Nmzx62NmvPz8/H444/jiSee8KBSaZw6dQrBwcEYPXq0x8v68ccfkZeXh3vvvRfXrl3DvHnzAECR5+kIIYSUJ8ZgQD+b2xnjjUanC4DYizHDaLS6FVGpGFFltoAhpDKjqYkZ8T1Kn7f6888/0ahRIzz99NOYM2eOx8oLCQnByy+/7LH4jtDpdPjqq68wf/583Lx5Ey1atMDGjRsRFxcnOkZ4eLhlXzSt0bJlSxw/ftxr5b399ts4ffo09Ho9OnXqhL1796JOnTpeK58QQiobw0wmzIqNFbUqoyMWRkZicFgYssxm5KSkYI6ThazExCjVkajQglSE+DqcmBFZLFu2DMuWLbO8vnLlit3V+HydatWqYdeuXVbHXO3/RezTrl07pKenqy2DEEIqHTEGg6QJmb0YiTIWNlJCByEVkYr3DVpBxG4wLYaKtOmr1soUa0dt0uy0rE2sHbVJs1NbGzeYVt6O2jxrR23S7KhNmh21edauUm0w7QuI3WC6sm36qsUyxdpRmzQ7LWsTa0dt0uy4wbQ0O2qTZqdlbWLtqE2aHbVJs6M2z9pVmg2mCSGEEKIsaSaTrGeIiLY4o9fjg7w8We1Z2idyZOwVJrdfKdEv00wm7AsKQrTJJLsuOD6IVuHEjBBCCKkAbDYYsKPMNg8zjEYsjIxUURGRQ3xODhaFhwOnTgGQ1p7xOTn/fwXE8HAU5uTIiyFBhxL90qIhNBRrMjLk18X/dLR1KwIhnke1DaYJIYQQogxpNkuhA8Ci3FykmUwqKSJySDOZrCYRgPvtqYUYSvRLT+ZxRsZVREI8ASdmhBBCiI+TZTa7dZxoGyXaUwsxtKDBmW1eBVxFmvg2nJgRn+bKlSsIDw/H+fPnndrFxsbilVde8ZIqZZkzZw7at2+vSKybN2+iUaNGXlmuPjY2Fs8//7xTmyZNmmD58uUe1+JNzp07B51Oh8zMTLWleJTKkqev0DwoyK3jRNso0Z5aiKEFDc5sI27fFh2DEG/AiVklIj8/H5MmTUKjRo0QGBiIiIgI9O3bF4cPH1ZbmmQWLFiAf/zjH2jUqBEAIDk5GTqdDlevXrWy2759O1566SUVFMrnhRdewPfff69IrMDAQLzwwguIj48XZb9t2zYMGjQItWrVQlBQEFq0aIFx48bh6NGjLn23b9+O119/Xa5kKyr7ZODSpUsYPnw4WrRoAT8/P7sT3//85z944IEHUKtWLdSqVQtxcXE4dOiQ07il9Wr7p1S/I54nxmBAP5tbu+KNRi5w4KPEGAyYYTRaHXO3PbUQQ4l+6ck8omTsxUaIJ+A1XBW5VVKCKn5+Vq/1Hrzf+bHHHsOtW7ewadMmNGvWDJcvX8b333+Pv/76y2NlAnf2IvJEXoWFhVi/fj2++uorl7ahoaEQBEFxDZ5EEAQUFxejRo0aqFGjhmJxR4wYgRdffBEnT55EeHi4Q7v4+HgsWbIETz31FN588000bNgQ58+fx759+/Dyyy/j66+/tut369YtVKlSBaGhoYppFktpndlucm67B5bWcTRmbt68ibCwMLzyyitWG7uXZf/+/XjiiSdw3333oWrVqli0aBEefPBB/Pzzz2jQoIHTcnft2oW77rrL8trXxkxlZ5jJhFmxsVx1roKwMDIS1dLTEdm9u+T2XBgZicFhYXdWZUxJwZzYWFkxpOhQol+WakjYtQtj4+Jk10WpjsTUVLfjEOJJODFzgqc2mNbr9ahRowa++fNPvHH+PH76+2+0qV4drzRqhP61a+P69etOv0hKKfPatWvYt28fPv/8c7Rr1w4AUKNGDURGRqKgoABXrlwBAFy4cAEvvfQS9u7dC51Oh969e2PBggUIDw9HQUEBnnnmGVy7dg0ffPCBJfYrr7yC48ePIykpCQUFBRgwYABatWqFKlWqYOvWrWjRogW++OILnDp1CnPmzMHBgwchCALatm2Ld999F02bNgUAfPTRR3j33Xdx/vx5GI1GTJw4EUOGDHGY35dffgl/f380b94cBQUFOH/+PHr27AkAqFWrFgBg2LBhWLlyJQYMGIAWLVpg8eLFAIAOHTpg5MiRyMnJwY4dO1CrVi28+eabuPfee/HMM88gNTUVjRo1wooVK9ChQwdLmYcOHcK8efOQmZmJWrVqoX///nj11VdRvXp1AMCGDRvw3nvv4eLFizAYDOjSpQveffddAHe+5L777rvYtGkTLl++jMjISPzrX//CgAEDUFBQgH379mHQoEHYunUr3njjDZw4cQJbt27FgQMH8PXXXyM5OdnSpvbqaty4cQDufKl/6aWX8M033+DatWsIDw/HmDFjrK6udO7cGevXr8ezzz5rt26PHDmCRYsW4c0338Tw4cMtE8M2bdqgTZs2mDRpkqXPLFy4EF9//TVGjx6Nf//73zh//jzy8/MxcOBAtG3bFm+88QYA4Pfff8fUqVOxZ88e1K1bFzNnzkRJSQn+/vtvSyxH/beU0h8Rrl69iitXrjiss8WLF1v1waioKHz11Vc4ffo0Zs+ejdTUVAQFBSE2Nhbz58+37K2Vl5eHSZMm4euvv0aNGjXw7LPP4uuvv7bKo06dOlizZg0ee+wxi65mzZrhjTfewBNPPGGlsVatWiguLsb06dOxd+9e5Ofno0GDBhg3bhwmTZpkybN0XHXq1Alr166FXq8vd1WyoKAAwcHBeO211wAAa9aswY0bN8rV3ZIlS6wm8m+99RY++eQTfP755xg6dKjd+i3V7O/vjypVqljZ2Gsb27aoVasWSkpKMH36dBw4cADbtm2D0WhEnTp18Pbbb+Pbb7/Fvn37UL9+faxcuRK1a9fG888/j8zMTLRu3Rrvvfee5TxQVhs3mJZmpwOQ/b8/rWnTWplK2yld5uX9+xFVVOSwPcXG0/0vVqKIH6kcxbLtV2r0S93+/cg2m53What4ZXX4er+kNs/aqbHBNARSjmvXrgkAhGvXrpV7r7CwUDhx4oRQWFgoCIIg/PHHH6JilrUrKi4Wkn7/XdDt3i2gzJ9u924h6fffhaLiYtGxxNrdunVLqFGjhvD8888LN27csGtXUlIidOjQQbj//vuFI0eOCAcPHhQ6duwo9OjRw2I3ZswYYeDAgVb+U6dOtbLp0aOHUKNGDeHFF18UTp06JZw8eVK4cOGCEBoaKgwePFg4fPiwcPDgQWHDhg3CqVOnBEEQhPfff1+oV6+esG3bNuGXX34Rtm3bJoSGhgorV650mN/UqVOFhx56yFLu7du3hW3btgkAhNOnTwuXLl0Srl69KgiCIPTo0UOYNGmSxbdx48ZCaGiosHr1aiErK0v45z//KQQHBwsPPfSQsH79euH06dPCoEGDhFatWgklJSWCIAjCjz/+KNSoUUNYtmyZkJWVJXz11VdChw4dhCeffFIQBEE4fPiw4O/vL3z00UfCuXPnhIyMDOGdd96x1O/LL78stGzZUvjmm2+EnJwcISEhQQgMDBSSk5OFP/74Q9i9e7cAQGjXrp2wc+dO4cyZM8Iff/whzJ49W7j77rsteTqqq40bNwqCIAiLFy8WGjRoIKSkpAjnzp0T9u7dK3z00UdWdTdjxgwhNjbWYV967rnnhBo1agi3bt1y2d9mz54tVK9eXejVq5eQkZEhHDt2TCgpKRF69OghTJ061WL38MMPC23atBG+/vpr4ciRI8J9990nVKtWTVi2bJnduPbKPXv2rABAOHr0qCAIgsM6s+2DqampwsWLF4U6deoIM2fOFE6ePClkZGQIffr0EXr27GmJP3LkSKFx48bCrl27hOPHjwuPPvqoEBwcbJUHAOE///mPla6QkBAhISGhnMY//vhDKCoqEl577TXh0KFDwi+//CIkJiYKQUFBwpYtWyx5jhkzRqhRo4YwatQo4aeffhKOHz/usj5s69eRnclkEqpWrSp88cUXDu1KNRuNRiEsLEy47777hE8++cRp25fN8+LFi8Jjjz0mtG/fXrh8+bJVXTVo0EDYsmWLcPr0aeGRRx4RmjRpIvTq1Uv45ptvhBMnTghdunSxjGNbbbbn27J88MEHDrWpbUdt0uy0rE2sHbVJs6M2aXbU5lk7JWM5m1uUhVfMvETpL/IAUMXPD2+cPw/bm4QEAG+eP49/1KkjOpZYAgICsHHjRjz11FNYvXo1OnbsiB49emDYsGGW25t27dqFH3/8EWfPnoXxf/dif/DBB7jrrrtw+PBhNGvWTHR5UVFRWLRokeX1yy+/jJCQEGzevBlVqlTBlStXEBMTY3n/9ddfx5IlSzB48GAAQNOmTXHixAls2rQJU6ZMsVvGuXPnUL9+fctrf39/y+1z4eHhqFmzplONjzzyiOWqxWuvvYb33nsPnTt3xsCBA1G7dm3Ex8eja9euuHz5MiIiIrB48WIMHz7ccuUpNDQUK1asQI8ePfDee+/h/PnzqF69Ovr374/g4GA0btwYHTp0wJUrV/D3339j6dKl+OGHH9C1a1cAd66y7Nu3D2vWrLFcVQOAefPmoU+fPg51O6qrNWvWYMyYMTh//jyaNWuG+++/HzqdDo0bNy4Xo0GDBjh37pzDMrKystCsWTOrWwKXLl1quVoDAL/99htCQkIA3LmysWrVKrRo0cJhvK+//hoHDx5EVFQUateujfXr16NVq1YONbiDvTor2wevXLmCd955Bx07dsSbb75psdmwYQOMRiOysrJQr149bNmyBR999BF69+4NAEhISLDqY1KoUqUK5s6da3ndtGlTHDhwAFu3brW6Ily9enWsW7dO8dt+X3rpJTRo0ABxcXEObWrUqIGlS5eiW7du8PPzQ1JSEoYOHYqVK1fin//8p9P4BQUFeP7553H79m0kJydb+kQpY8eOteT53HPP4aGHHsKsWbPQt29fAMDUqVMxduxYmVkSQgghvg8nZirx099/u3VcCR577DH069cPe/fuRWpqKr755hssWrQIy5cvxzPPPIOTJ0/CaDRaJmUA0Lp1a9SsWRMnT550a2J2zz33WL3OzMzEAw88YHWbVCm///47cnNzMX78eDz11FOW47dv34bByX3khYWFqFq1qmhNtpTe0gkAdevWBQC0bdu23LH8/HxEREQgPT0dZ86cwYcffmixEQQBJSUlOHv2LPr06YPGjRujWbNmeOihh/DQQw/h0UcfBQCcOHECN27cKDd5KCoqsrpVEihfd2X5448/HNZV6RfiJ598EnFxcWjRogUeeugh9O/fHw8++KBVnGrVqsHsYqlhnU5n9XrcuHEYMGAA0tLSMHLkSKvnjxo3bow6Tn5QOHnyJAICAnDPPfdYFmZp2bKly8mzWOzVme2x9PR07N692+7zejk5OSgsLMStW7dw7733Wo6HhIQ4nGy6w+rVq7Fu3Tr8+uuvKCwsRFFRUbmVNtu2bav4pGzRokX4+OOPkZyc7HSs1KlTB9OmTbO8vueee/DXX3+Jmpg98cQTiIiIwJ49exBkZ+WzsuMsLCwMQPlxduPGDZhMJqfjnRBCCKnocGLmJa5cuWJ1patN9ep2N0ds879nldyJ5Q5Vq1ZFnz590KdPH7z22muYMGECFi5ciGeeeQaCIJT7Mg7A6rifn1+5BQFu3bpVzqe6TR62z+qVpaSkBACwdu1aq6toAGBysoFknTp1ZC1cUnaSWJqfvWOl+kpKSjBp0iQ899xzAO48Y1P6LFujRo2g1+uRkZGB5ORk7Ny5E6+99hrmzJmDb7/91hJjx44d5RZgCAwMtHptW3dlcVZX/v7+AICOHTsiIyMDaWlp2LVrF4YMGYK4uDj897//tdj++eefli/J9oiOjsa+ffus2rZmzZqoWbMmLly4UM7emWbg/y8iYa9/KYG98m2PlZSU4B//+AcWLlxYzrZevXrIzs62q9G2v+t0OlFjoJStW7di2rRpWLJkCbp27Yrg4GAsXrwYaWlpLnOQw9tvv40333wTu3btspociaVLly5Yt26dS7tHHnkEiYmJOHjwIHr16lXufXfHGXGfNJPpzuIOMib2pTG4aIh85NZlWX8taJDaH7TQL5XI44xejw/y8jRRl1JjyM3BVgPxHJyYqcCtkhK80qgRBv70k9XtjDoALzdqVG61Rk/SunVrfPrpp5b/nz9/Hrm5uZarZidOnMC1a9cst5yFhYXhp59+soqRmZlp90pYWdq1a4dNmzZZVuwrS926ddGgQQP88ssvGDFihNV7jhYeAO4s4JGYmGh1rPSKQ3FxsVM9UujYsSN+/vlnREVFWbTZTpADAgIQFxeHuLg4zJ49GzVr1sTevXsxePBgBAYG4vz58+jRo0e52M7yLEt4eLjDuipLcHAwhg4diqFDh+Lxxx/HQw89hD///NNyq+dPP/1U7kpdWZ544gm8++67WLVqFUaOHClKmzNatWqF27dv48iRI4iMjAQAnD59uty2Bp6kY8eO2LZtG5o0aVJu1UYAiIyMRJUqVXDo0CFL/zeZTMjOzrZqs7CwMFy+fNnyOjs72+nVx7179+K+++7D008/bTmWk5OjREoOWbx4MebPn49vv/3W6RVYZxw9etRy1dgZ//znP9G0aVMMGDAAO3bssNu/ieeIz8nBotzcOy/Cw1GYk4OF/xtjkmIAmGE0oq0Te+KYzQYDdmRkWF7PMBrdag/btuhnMMDdM7C99nRHg9wcymlQqV/KrQdLjPBw4NQpSTGUGFty85Cbgz0NUvolEQf3MVOBKn5+6F+7Nj5v0wZdDAbU8PdHF4MBn7dpg/61a3tkUnblyhX06tULiYmJlufIPvnkEyxatAgPP/wwACAuLg7t2rXDiBEjkJGRgUOHDmH06NHo0aOH5ctdr169cOTIEfznP/9BdnY2Zs+eXW6iZo9nnnkGJpMJw4YNw5EjR5CTk4MPPvgAp0+fBnBnE+UFCxbgnXfeQVZWFo4fP46EhASsWrXKYcy+ffvi559/trpq1rhxY+h0Onz55Zf4/fffRa9gKYb4+HikpqZiypQpyMzMRE5ODpKSkiwrG3755ZdYsWIFMjMz8euvv+I///kPSkpKEBUVheDgYLzwwguYNm0aNm3ahJycHBw9ehT//ve/sWnTJrd0OKqrpUuXAgCWLVuG7du349SpU8jKysInn3yCiIgIq9sG9+7dW+72xrJ07doV//rXv/Cvf/0Lr776Kvbt24dff/0VBw8exPr166HT6eDnRj8tva3yqaeeQnp6OtLT0zFhwgSnV1KVZsqUKfjzzz/xxBNP4NChQ/jll1+wc+dOjBs3DsXFxZbJ7Isvvojdu3fj559/xrhx4+Dn52d1Fa1Xr15Yt24dMjIycOTIEUyePNnpDxNRUVE4cuQIvv32W2RlZWHWrFmy9g7MzMxEZmYmCgoK8PvvvyMzMxMnTpywvL9ixQq8+uqr2LBhA5o0aYK8vDzk5eVZjYWVK1dabrMFgE2bNuGjjz7CyZMncfr0abz99ttYsWIFJkyYIErTU089hfnz56N///7Yt2+f5NyIe6SZTFZflgBgUW6u3bsx3I1xxoNbt1RU0kwm7LC5GuFOe9hrix0GgyLt6Y4GOTkoocFZDLH98oxe7zENctrT3bHlKQ1y68HdfknEw4mZSuh0OjwUGorUjh1x/YEHkNqxIx4KDVV0IlGWGjVqICYmBsuWLUP37t3Rpk0bzJo1C0899RTeeusti6bPPvsMtWrVQvfu3REXF4dmzZphy5Ytljh9+/bFrFmzMGPGDHTu3BnXr1/H6NGjXZZfu3Zt/PDDDygoKECPHj3Qu3dvrF271vKFdsKECVi3bh02btyItm3bokePHti4caPdhStKadu2Le655x5s3brVcqxBgwaYO3cuXnrpJdStWxfPPPOM1CorR7t27bBnzx5kZ2fjgQceQK9evTBr1izUq1cPwJ1b/bZv345evXqhVatWWL16NT7++GO0bNkSwJ1FO1577TUsWLAArVq1Qt++ffHFF19YLRMuBkd1VRqnRo0aePfdd3HPPfegc+fOOHfuHL766ivLRCo1NRXXrl3D448/7rSct99+Gx999BGOHz+O/v37Izo6Gv/3f/+HkpISpKamuv08UEJCAoxGIwYMGIDBgwdj4sSJ5fZRe/LJJxErYZ8dMdSvXx/79+9HcXEx+vbtizZt2mDq1KkICQmx1M3rr7+Orl27on///oiLi0O3bt3QqlUrq+ezlixZggYNGqB79+4YPnw4XnjhBbvPVpUyefJkDB48GEOHDkVMTAyuXLlidfXMXTp06IAOHTogPT0dH330ETp06IBHHnnE8n5CQgKKiorw+OOPo169epa/t99+22Lzxx9/lFv8Zf78+ZY+s3nzZmzYsMHl82Vlef755zF37lw88sgjOHDggOT8iHiyHFypdXTcHds8O1eViXPktocn29OXNDizFdsvHdlpoS7dGVsVpT2JeHjmVRHbK2NV/Pxg8tBGuIGBgViwYAEWLFhQ7r2yt9E1atQIn3/+udNYc+fOtVplzpbk5GS7x9u1a4dvv/3WUqbtbYDDhw/H8OHDHWqzx6xZs/DCCy9YVigsPTZr1qxymsrGsrciYelzQ6V2TZo0KfcsUefOnbFz5067Odx///12cy+Np9Pp8Nxzz1meUbO1iY2Ntbuh75w5czBnzhyrY/bqqpSnnnoKgwcPdvgc4tKlS/Hiiy+KWgBkyJAh6N27t9NnGkv12baVbV1ERETgyy+/tKq3UaNGWdmcO3fO6cTMtk1iY2Pxxx9/lFtExFEfjI6Oxvbt2x3GDw4Otlrc5e+//8bcuXMxceJEy7H69evjk08+saqTsrdkltV45coVBAYGIiEhAQkJCVZllR2LGzdudKjJFnt9pCxHjx51+QzqnDlzrPawGzNmDMaMGVPOztn4s80TAKZPn47p06c71NqoUaNyxxz1e+IaR896uPMMiCPbiNu3JWmqzMhtD0+2Z/OgIJd7f3lDg9wYYvulIztv5qHE2NJ6nyLKwytmxKcpXfL+0qVLakvxCW7evIm7777bagU+rXD9+nXk5OTghRdeUE3Djz/+iI8//hg5OTnIyMiwPMc3cOBA1TQR4ogYgwEzyqyiCwDxRqNbD/c7ihHloR8JKzIxBgP62dze5U572GuL/iaTIu3pjgY5OSihwVkMsf0yqqjIYxrktKe7Y8tTGuTWg7v9koiHV8ycsGXLlnLPwOj1ejRt2hR//fUX9Hq96FsPlbRTo0yxdmqUOXLkSBQUFLi8usZ6u8M///lPmM1mmM1mzWnLzMzEzZs3cfPmTVXq12w246233kJOTg6qVKmCu+++G1988QV0Op1V//LVtnfXTm1tRUVFKCgoQFJSEopsvszs379fVCw17LxZZlsAs/V65AUEID89HW1atUJiaqpbscrGiLh9G1EXLlT4evOUnXH/fszu2dOqLu21h6NYtm1xefduJIp4lqdsPHvtmZiaKjpPuTnYalCrX3az42+rwVW8tgAePXkS4Z06Oa0LT+XgKEapBjGx5OZgT4OYfqnlcSrWTslYhYWFomJxYuaEoUOHlnuO5saNGzh79ixq1aplee5E7NL1StqpUaZYO2qTZkdtnrUTY9OlSxccO3ZMsTLF2vl6vXnK7saNG7h69SoGDBhgdx82sSuGqmGnRpmJZrNmtYm107I2sXYjy2wcLzdWYlGROvWrZA6+3i8TEzGyzOMSXinTDTtRsRTOQWy/1GybumGnVCyTyWT1WIQjeCsjIYQQQgghhKgMJ2YS4cPqhBDiWXieJYQQUpngrYxuUrq8u9ls9uoeTIQQUtkofa7M399fZSXaI81kQpbZjOZBQZIfwpcbI81kwr6gIETLWAigrAZfRYkctNKeWWYzcmTsYaeUBrn1IKdfaqUt1B5bWqiHsjHk9EtfghMzN/H390fNmjWRn58P4M4Xhxs3brj0U9JOjTKpjdq0qE2sHbX5njaz2Yzff/8dQUFBCOCeWlbE5+RYbfg6w2jEwshIr8aw+IeGYk1GhiIa+hkMEPc0h3ZQIgcl2nOzwYAdGRmSY1hpCA9HYU6Oen1Kor9VDIn90p6Gtm4pqBhjSwt9spwOif3S1+CnnQQiIiIAAPn5+SgoKLDaw8gRStqpUSa1UZsWtYm1ozbf1Obn54dGjRpBp9O59KkspJlMVl+YAGBRbi4Gh4UpEkPMr9py/R3F2GEwIM2HluFWIgel6nKHja0W2tOdfnlGr/eoBjn1MNuNKzUVYWxpoU8qpcMX4cRMAjqdDvXq1UN4eDi2bduGAQMGuPRJSkpSzE7JWNSmDTtq86wdtfmmNr1eDz8/PgpdliwHm8Jnmc0QO311FkPMFx65/krFUBut1IPW21NMv8xzcFVcC/XgSJs3NWihHnytX/sqnJjJwN/fH0VFRXaXcbZFSTs1yqQ2atOiNrF21FbxtFVWHD0v0jwoCNkKxPCGv1Ix1EYr9aD19hTTLyNu3/aoBjn+jrR5U4MW6sHX+rWvwomZE+xtMG2Lr294p7QdtUmzozbP2lGbNDtqk2bn6TL7GQxWtwn1N5mQnZTkljZHMbJt7Bzhyt9VDvZitD9xAtkXLrj8Iq+ltlciB2d1KVZb++vXkdmqld0YzvSL0SAmB2cxxORwef9+9Hv4YZcaPN0v7flf3r0biTYb3DuL5c2x5SiW3H4pNwdAXJ90FUuJfinHjhtMawx7G0zbw5c3vPOEHbVJs6M2z9pRmzQ7apNm58kyR8LxamditTmLISZWqX/Crl0YGxcn6tYi23i2GrIvXNB0m9qzUyIHV20hdgPh6I4dXa6A5yhWWQ05KSmYI2GDadn9csAAUav4ebJf2ssh0WRyq029PbbsxZLbL+XmAEB0n3QWS4l+KdfO2xtMc2JGCCGE+BgxBoPs5yzkxogxGJAt83mPshrE3oqpNZTIQSvtGWMwiLo65GkNcpDbL7XSFmqPLS3UQ9kYcvqlL8GnqgkhhBBCCCFEZTgxI4QQQgghhBCV4a2MhBBCiBcQ8/yMJ/2BO/tFfZCXp6oGuTHk5mCrQa6/WvUgNwelNciph31BQYiWuIedVtpCTg5KadDS+UEqSuThy2hmYrZgwQK8/PLLmDp1KpYvXw7gziajL730Ej777DNcuXIFTZo0wXPPPYd//vOfDuNs3LgRY8eOLXe8sLCQyy8TQghRhficHKvNUmcYjWgr039hZKT7GsLDgVOnJMWQm4OjGG5rkJGDPQ39DAaIe7zfvr8UDZsNBuzIyJAcQ24O9mK4q0FuDlYaQkOxJiNDkT4ptz9I1iAxB0cafP38oEafrAho4lbGw4cP4/3330e7du2sjk+bNg3ffPMNEhMTcfLkSUybNg3PPvssPv/8c6fxDAYDLl26ZPXHSRkhhBA1SDOZrL5sAMCi3Fyc0etF+Z/R6+36p5lMsjWIjSE3B09qkFsPOwwGr2vYYXMlQG49uJODoxjuapCTg1Ia5LaF3LHlqT7pztjypAZf6pMVBdUnZgUFBRgxYgTWrl2LWrVqWb2XmpqKMWPGIDY2Fk2aNMHEiRNx991348iRI05j6nQ6REREWP054+bNmzCZTFZ/hBBCiBJkmc12j+cFiLtpxZGdo7ju2IqNITcHT2rQQj1Qg29qkDu2PFkPYsdWRWkLJWJUBFS/lXHKlCno168f4uLiMH/+fKv37r//fiQlJWHcuHGoX78+kpOTkZWVhXfeecdpzIKCAjRu3BjFxcVo3749Xn/9dXTo0MGh/YIFCzB37txyx7nBtPt21CbNjto8a0dt0uyoTZqdrU2OXg+Eh5ezy09PR6KILx35J08CffqUO56TkmK1hLQzbY402MZwFEtuDvv370ddFxpc1a3cHKTEsI0nNwd3NSiZQ9lYzmJc9kIO7sawF0tuDoC4saXkuLIXT87YEtMnXeXgTIPc84PYcSUmhhbO5XLsfGKD6c2bNyMjIwOHDx+2+/6KFSvw1FNPoWHDhggICICfnx/WrVuH+++/32HMli1bYuPGjWjbti1MJhPeeecddOvWDceOHUN0dLRdn5kzZ2L69OmW1yaTCUajkRtMS7SjNml21OZZO2qTZkdt0uxsbQptnp2INxrRplUr8ZsHG43l/OfExrqlzZ4GezE8kgOAkUOGuNTgKpbcHOzF6G8yid+4VoEcAOBIUpLVrYDO8lAyh7KxHOWRWFTklRycafBWDmLHlpLjyl48OWNLTJ90lYMjDXLOD26Nq//FUmJsKW1XaTaYzs3NxdSpU7Fz506Hz3+tWLECBw8eRFJSEho3boyUlBQ8/fTTqFevHuLi4uz6dOnSBV26dLG87tatGzp27Ih3330XK1assOsTGBiIwMBA+UkRQgghdlgYGYnBYWFWq40lpqbK8peioVp6OiK7d5cUQ24OjmK46y8nB3saspOSZPlL0TDMZMKs2FhZ9SAnB3sx3NUgN4eyGhJ27cLYuDhF+qRUDXLbQmoOjjT4+vlBjT5ZEVBtYpaeno78/Hx06tTJcqy4uBgpKSlYuXIlrl27hpdffhmffvop+vXrBwBo164dMjMz8fbbbzucmNni5+eHzp07Iztbyr7nhBBCiDLEGAyyvmjI9QeAqKIijHTx3LWnNciNITcHWw1Svh1ooR7k5qC0Bjkxss1myXG00hZyclBKg5bOD2r1SV9HtYlZ7969cfz4catjY8eORcuWLREfH4/i4mLcunULfn7W65P4+/ujpKREdDmCICAzMxNt27q7qC8hhBBCCCGEeAfVJmbBwcFo06aN1bHq1aujdu3aluM9evTAiy++iGrVqqFx48bYs2cP/vOf/2Dp0qUWn9GjR6NBgwZYsGABAGDu3Lno0qULoqOjYTKZsGLFCmRmZuLf//6395IjhBBCCCGEEDdQfVVGZ2zevBkzZ87EiBEj8Oeff6Jx48Z44403MHnyZIvN+fPnra6qXb16FRMnTkReXh5CQkLQoUMHpKSk4N5771UjBUIIIYQQQghxiaYmZsnJyVavIyIikJCQ4JbPsmXLsGzZMoWVEUIIIe6TZjLJepA9zWTCvqAgRJtMkp+7KKtBrr9UDWf0enyQlyerHuTkYBtDiga5OZTVkOPGxtz2/JXQILctpOagpIbKnAOg7LhSqz8pOa7knB+0gqYmZoQQQkhFId5m6ecZRiMWRka67x8aijUZGW7729PQz2CAuMWf7ftL1hAeDpw6JSmG3BzsxZCkQUYO5TSEh6MwJ0dWPSjRH2S1hYQcHGlwZxUA5lAmhoLjyt0cHMVQdVxB2vlBS/i5NiGEEEKIO6SZTFZfFgBgUW4u0kwmr/g7irHDYFBdg9x6cCcHT2nQQj24q+GMXq+6Bkcxzoi8csUcnGuQWw9ic/CkBm+e47QIr5g5YcuWLahWrZpTG1/fiVxpO2qTZkdtnrWjNml21CbNbv/+/dgXFASEhpZ7L2HXLmSbzS5jifF3pU1KDCVzcFeDvVhycxATw9M5uBvDEzkAwN4LF4DwcJcaPJWDsxh7f/kFiYmJTvUD6uQgdlx5KwdnGhzloXQOYs4PSo6r0jLlxnAVT4qNWLvCwkJRsTgxc8LQoUNhEHG/qy/vRO4JO2qTZkdtnrWjNml21CbNLjouDmsyMsodt92A1lGsaJNJlL8zbVJjWPxl5iBFg20suTmMHDlSVAxP5iAlhtI5AMCZrVvxg53j9jR4IgdnMR5o1gwjhwxxGgNQLwcx48pbOTjT4CwPJXMAxJ0flBxX9uIpcX5QwkaMnclkwsSJE13G4a2MhBBCiMLEGAyYYTRaHYs3GkU/3C7X31GM/m4sIuIpDXLrwZ0cPKVBC/XgroaooiLVNTiKEVVUJMqfOTjXILcexObgSQ3ePMdpEV4xI4QQQjzAwshIDA4Lk7xiWal/wq5don8BdqUhOylJlr9UDdXS0xHZvbusepCag70YUjTIycFWQ05KCubExkr2V0KD3LaQkoMjDYmpqbL8pZbvqzmUxlByXLmbg6MY7vorOa6knh+0BCdmhBBCiIeIMRhk/XobYzAg22yWHaPUP1umv1SiioowMiJCsr/cHGxjSEFuDmU1JLpxZcKevxIa5PpLzUFJDXL9fTkHQNlxpVYMJccVIP38oBV4KyMhhBBCCCGEqAwnZoQQQgghhBCiMryVkRBCSIUhzWSS9dyGEjHO6PX4IC9Psn9FyMFWg1x/teqhIuWQ48YeVZ7SILce9gUFIVri4g5aaYvKnoOtBrn+vrzQhz04MSOEEFIhiM/JsdpsdIbRiLYKxFgYGemef3g4cOqUdH8fz8Gehn4GA8QtOm3fX4qGzQYDdpRZSltuW/h8DuHhKMzJkd2W7mqQm4OVhtBQrMnIUGRcebsePJGDKucGGTnY06DGuNIynJg5gRtMu29HbdLsqM2zdtQmzc6XtJ3R6+9MJsqwKDcXj548CYjcMNVRjGrp6ZZlpJ1pE+Ovdg6u6lZuDo5i7DAYMGfrVqfLcZfGkptDaYwdIvOoTDk4a0t7sZzFuKxgDkqOK6VzUOPcIDYHXzq/OYohZlwplYMrfVJsxNpxg2kF4AbT0uyoTZodtXnWjtqk2fmKtg/y8ixXeMoS3qkTRg4eLKpMoXt3uzEiu3e3WjXMkTZHGmz9y6JGDs7qVm4OUmOUjSXG31X/cFcDc7Afy1mMqKIiRXNQclwpmQOgzrlBTA6+dH6TGqMscnNwpU+qjRg7bjBNCCGk0uDoWYWI27dlxxD7HISn/H0pB2pQxp8aqEGMHc8N7mvQOpyYEUII8XliDAbMMBqtjsUbjS5vjxETQ+zD5Z7y96UcHMXo78ZCAUpp6GcySY5RUXNQoi3d1SAnB6U0aKEeeG7QxrjSOryVkRBCSIVgYWQkBoeFWa3WlZiaKjuGu/7V0tMR2b27ZH9fz8GehuykJFn+UjQMM5kwKzZWVj1UpBxyUlIwJzbWbQ1y85CbQ1kNCbt2YWxcnCLjSqoGuW2hZA5qnBvk5GBPgxrjSstwYkYIIaTCEGMwyP6glhsjqqhI1PMSnipfiRhyc7DVkC3TXwkNcv19PYdEN66ueEqDHGIMBmSbzZLjaKUtKnsOthrUGldahbcyEkIIIYQQQojKcGJGCCGEEEIIISrDiRkhhBBCCCGEqIyoZ8xCQ0PdCqrT6ZCRkYHGjRtLEqUVuMG0+3bUJs2O2jxrR23S7HxF2xm9HnkBAYi4fVvUxrv22H7yJPZt3243hph4ZTWI2bTWXixHeSiZg6sNZNXMoXQDWUdtKTZWaYx8NzbfrQw5uDv+XOWhZA6ONiPWQg57L1zAGZEbIDuKUVaDkjmI3ahcjRy0do6WYqfZDaavXr2K5cuXIyQkxKWtIAh4+umnUVxcLEqAluEG09LsqE2aHbV51o7apNlpXVt8Tg4W5eZajs0wGrEwMtKtWPE5Ofi0YUOnMZzFs9XQ7+GHMXLAAJf6y8ZylYeSOdiLpYUcNhsMWF3mM9fdHMpp6NMH0S7asrLlIHb8He/a1eW4UjqHsrE0lUN4OH5w4u8slr08uimcg7NYaueglXO0HDvNbjA9bNgwjBkzxuXfk08+CX9/f7FhCSGEEEmkmUxWH/gAsCg3F2k2+yZ5MoY9/x0Gg+oafDGHHTY/hGqhLStbDsCdqxtq16Ov5+Asxhm9XpR/RchBC+doX0PUxKykpATh4eGig16/fh3NmjWTLIoQQghxRZbZ7NZxT8SoCBoqQg5a0FARcgCAvAD7N1P5Uj2qnYMzW0faxNr5Ug5aGBO+Bhf/IIQQ4pM0Dwpy67gnYlQEDRUhBy1oqAg5AEDE7duqaqgIOTizdaRNrJ0v5aCFMeFriJ6YpaSkiPojhBBCvEGMwYAZRqPVsXij0a2NR+XGsOff32RSXYMv5tDP5tYkLbRlZcsBuLO5uNr16Os5OIvhagGOUipCDlo4R/sa4q5FAoiNjXX4nk6ns/x7W+QsmhBCCJHLwshIDA4LQ5bZjOZBQZI+rBdGRqJaejoiu3eXFMNWQ3ZSkiQNcvKoCDkMM5kwKzZWdluWashJScEcJ99dXPlX1hyUiFGRckjYtQtj4+IU05CYmirLX0r5vpxDaQw55zdfQvTE7K+//rJ73Gw245133sGKFSv4XBkhhBCvE2MwyP6gjioqwsiICEU0ZCsQQwoVIQcl2rI0RqLIX/WV1lARclAiRkXJIdtsVrU/MIc7yD2/+QqiJ2a2S+WXlJRgw4YNmDt3Lvz8/PDvf/8bY8aMUVwgIYQQQgghhFR0RE/MyrJ9+3a8/PLL+P333zFz5kw8++yzCAwMVFqb6nCDafftqE2aHbV51o7apNlRmzQ7apNmp2VtYu2oTZodtUmzozbP2ml2g+lS9uzZg/j4eBw/fhxTp05FfHy8qE2nfRVuMC3Njtqk2VGbZ+2oTZpdRdaWZjJZnnuQEq+sv+1tOmJjRQ8YIOrZCyVzKGujpRzq6vUYOWSIqDJtYznKgzmI01+2fLVyGDlypNP+qGYO7pwb5OZQqmFfUBCiRTwXZi+WFnI4o9dDEPlMmFZzcLdMZ4jdYFr0xOyRRx7B999/j7Fjx+Kzzz5DRCW4z5MQQkjFJD4nx2rT0n4GA8R9/Nr3n2E0YmFkpFsaNhsM2JGRITlGhcshPByFOTlua5CbB3PQRg6K+iuYQ1uZ/pI1hIZiTUaGIvWoSg7h4cCpU5JiaCEHtRC9XP4333wDQRCwZcsWtG7dGqGhoXb/CCGEEC2TZjJZfWgDwA6DAWk2S527478oN1e0f2mMHTa/4roTo6LmIEWDnBjMQRs5nNHrZdeBp3I4o9eL8pebgzMNcuuBObiXg5qIvmKWkJDgSR2EEEKIV8gymx0eF7NymFx/LWioCDm4iqGT6c8cvJdDXoD9r6NayMGRNrF2WhhTzMG9HNRE9MSMKy4SQgipCJQ+jyX2uNL+WtBQEXJwFUPMkv/MQRs5RDjYA1cLOTjSJtZOC2OKObiXg5qIvpXR0yxYsAA6nQ7PP/+85VhBQQGeeeYZNGzYENWqVUOrVq3w3nvvuYy1bds2tG7dGoGBgWjdujU+/fRTDyonhBDiS8QYDJhhNFod628yif411Z5/vNHo1q+xMQYD+tncWuNOjIqagxQNcmIwB23kEFVUJLsOPJVDlMh92OTm4EyD3HpgDu7loCair5hlZ2djypQpMJlMeOuttxDr5i7uzjh8+DDef/99tGvXzur4tGnTsHv3biQmJqJJkybYuXMnnn76adSvXx8DBw60Gys1NRVDhw7F66+/jkcffRSffvophgwZgn379iEmJkYxzYQQQnyXhZGRGBwWZlm1KzspSZa/lA/9YSYTZsXGSo5R0XLISUnBHAnfLeTmwRy0kYOS/krmkJiaKstfqoaEXbswVsSqjGI0qJFDtfR0RIpclVGMBm/noBaiJ2bjx4/H8OHD0aFDBzzyyCO4fPkyAkTe7+mMgoICjBgxAmvXrsX8+fOt3ktNTcWYMWMsk8CJEydizZo1OHLkiMOJ2fLly9GnTx/MnDkTADBz5kzs2bMHy5cvx8cffyxbLyGEkIpBjMFg+cAWc7uVM38lNMj19/UcEkX+Iu5JDXL9mQNzUGpMZct4JkoLOUQVFWGkjBXctZCDGoi+lfHs2bPo0qULOnXqhOvXr+Pq1auKCJgyZQr69euHuLi4cu/df//9SEpKwm+//QZBELB7925kZWWhb9++DuOlpqbiwQcftDrWt29fHDhwwKHPzZs3YTKZrP4IIYQQQgghxFuIvuQ1YcIETJ48GfXq1UNcXBzq1Kkju/DNmzcjIyMDhw8ftvv+ihUr8NRTT6Fhw4YICAiAn58f1q1bh/vvv99hzLy8PNStW9fqWN26dZGXl+fQZ8GCBZg7d26541u2bEG1atWc5uDrO5ErbUdt0uyozbN21CbNjtqk2VGbNDstaxNrR23S7KhNmh21edZOyViFhYWiYomemM2ePRtxcXG4evWq0ytWYsnNzcXUqVOxc+dOVK1a1a7NihUrcPDgQSQlJaFx48ZISUnB008/bZkcOkKns16cVRCEcsfKMnPmTEyfPt3y2mQywWg0YujQoTCIuAyq1K7g7tipUaZYO2qTZkdtnrWjNml21CbNjtqk2WlZm1g7apNmR23S7KjNs3ZKxTKZTJg4caLLOG49JNatWzd3zJ2Snp6O/Px8dOrUyXKsuLgYKSkpWLlyJa5du4aXX34Zn376Kfr16wcAaNeuHTIzM/H22287nJhFRESUuzqWn59f7ipaWQIDAxEYGKhAVoQQQrRMmskk+4FwuTFK/XNEbpiqdPlKxGAOypWvRB34eg77goIQ7caqokpqYA7/P0Zlz0ELyF+9QyK9e/fG8ePHrY6NHTsWLVu2RHx8PIqLi3Hr1i34+Vk/Bufv74+SkhKHcbt27YrvvvsO06ZNsxzbuXMn7rvvPmUTIIQQ4lPE5+RgUW6u5fUMoxELIyPdirHZYMCOjAzJMaw0hIejMCdHur+E8pWIwRzkly+3H5XT4Os5hIZiTUaGvHaUoKEi5KDoeFIwh7ZSyi/j7+0ctIKoxT9CQ0Pxxx9/iA7aqFEj/Prrr05tgoOD0aZNG6u/6tWro3bt2mjTpg0MBgN69OiBF198EcnJyTh79iw2btyI//znP3j00UctcUaPHm1ZgRGA5fbIhQsX4tSpU1i4cCF27dpltT8aIYSQykWayWT1wQ8Ai3JzkebGYk9pJhN22PwK604MuRqUyOGMXq+qBuYgvx8ppYE5+H4OcseCEhoc+Z8ReRVXCzloCVFXzK5evYqvv/4aISEhooJeuXIFxcXFsoQBdxYHmTlzJkaMGIE///wTjRs3xhtvvIHJkydbbM6fP291Ve2+++7D5s2b8eqrr2LWrFmIjIzEli1buIcZIYRUYrLMZofHxd7yIjeG2v4AkOdgmxst5OD4SXBrfD0HrfdF5uA7OcgdC640yMnBkTaxdt7MQUuIvpVxzJgxntQBAEhOTrZ6HRERgYSEBLd8AODxxx/H448/rqAyQgghvkzzoCC3jnsihtr+ABBx+7aqGpz5i92Hzddz0HpfZA6+k4PcseBKgxx/R9rE2nkzBy0h6lbGkpISt/+aNWvmae2EEEKIKGIMBswwGq2OxRuNbv2aGmMwoJ/NrTHuxJCrQYkcooqKVNXAHOT3I6U0MAffz0HuWFBCgyP/KJGbfGshBy2h2uIfhBBCiDdZGBmJwWFhslb+GmYyYVZsrOQYZTXkpKRgTmysZH+pOciNwRzkly+3H9lq8PUcEnbtwti4OFntWFlzUHI8KZlDYmqqLH93kZuDVuDEzAncYNp9O2qTZkdtnrWjNml2FVWbDkD2//6kxrMXw51YOgCX9+9HoohflZXMoayN1nKQ0qa+noMz/WLjic3BUSxN5LB/P7LNZpe376mRg9hYauSg5FgAlM1BjfEMyM9BSpmqbDBdGeEG09LsqE2aHbV51o7apNlRmzQ7apNmp2VtYu2oTZodtUmzozbP2nl7g2lRz5gRQgghhBBCCPEcvGJGCCGkQpNmMsl6dqFsjByRe/MorUGpHPYFBSHaZFJFA3OQ34+U1MAc1BvPSsRQeywoEUMLOWgNSROznJwcJCQkICcnB++88w7Cw8PxzTffwGg04q677lJaIyGEECKJzQYDdmRkWF7PMBqxMDLSrRjxOTn/f/PS8HAU5uS4FcPKX4IGRXMIDcWajAy3YzAH+TnI7UflYsjVINdfwRzayvT3Zg5yy1cihtpjQYkYWshBi7h9K+OePXvQtm1bpKWlYfv27SgoKAAA/Pjjj5g9e7biAgkhhBAppJlM2GHzK+qi3Fyk2SyR7SpG2Q9/d2Mo4c8cfD8HueUrEeOMXq9qHTqLcUbklSu1c1CiDpiDNnLQKm5PzF566SXMnz8f3333HfRlBlLPnj2R6sbSmIQQQognyTKb3TruiRhq+2tBA3PQRh3kBdi/SUoLOTjSJtbOl9qROWgjB63i9sTs+PHjePTRR8sdDwsLw5UrVxQRRQghhMileVCQW8c9EUNtfy1oYA7aqIOI27dl+XsyB0faxNr5UjsyB23koFXcnpjVrFkTly5dKnf86NGjaNCggSKiCCGEELnEGAzoZ3NrS7zR6NZD4jEGA2YYjZJjKOHPHHw/B7nlKxEjqqhI1Tp0FiNKxD5ygPo5KFEHzEEbOWgVtxf/GD58OOLj4/HJJ59Ap9OhpKQE+/fvxwsvvIDRo0d7QqNqcINp9+2oTZodtXnWjtqk2VUEbcb9+zG7Z0/kBQQg4vZtRF24gEQ7t907i9cWwGy9HnkBAchPT0ebVq3sxnAUq6x/WQ1q5LD3l1/wQLNmDmOomYOrWL6eg7v9SOkc9u/fj24O/NXOwZ1zg7dzULofKZmDWmNBTA5aH88VZoPpN954A08++SQaNGgAQRDQunVrFBcXY/jw4Xj11VfdDadpuMG0NDtqk2ZHbZ61ozZpdhVC25AhipWZaDarU28K5ZCYmKhofbhlJ6JcUW1QEXIQ2Y/ExhNrp2QspXNQoz5UG88K2ak6FhSqt4qQgxg7sRtMuz0xq1KlCj788EPMmzcPR48eRUlJCTp06IDo6Gh3QxFCCCGEEEIIgYwNpiMjIxFZAfYLIIQQQgghhBC1ETUxmz59uuiAS5culSyGEEIIIYQQQiojoiZmR48etXqdnp6O4uJitGjRAgCQlZUFf39/dOrUSXmFhBBCiAqkmUzIMpvRPChI0mpfSvnniNx815MamIP0HOSWr0SMNJMJ+4KCEG0yqVqHcuvAl3PgWNDGWNA6oiZmu3fvtvx/6dKlCA4OxqZNm1CrVi0AwF9//YWxY8figQce8IxKQgghxIvE5+RgUW6u5fUMoxEL3bh9X1H/8HAU5uS45e9IQ1uZ/szBvRzklq9EDIt/aCjWZGR4vS9vNhiwIyNDsr+VBpVyULsfOdLAseB+DK3j9j5mS5YswYIFCyyTMgCoVasW5s+fjyVLligqjhBCCPE2aSaT1Yc/ACzKzUWazT5Wjjij18vyl1u+sxhnRP7SzRzk56BEHfh6DmkmE3bYXNVQqh94Kwe128BZDI4F92L4Am5PzEwmEy5fvlzueH5+Pq5fv66IKEIIIUQtssxmt47bkhdg/2YUsf5yy3dm60ibWDvm4N068PUcPNkPvKVB7TZwZsux4F4MX8Dtidmjjz6KsWPH4r///S8uXLiACxcu4L///S/Gjx+PwYMHe0IjIYQQ4jWaBwW5ddyWiNu3ZfnLLd+ZrSNtYu2Yg3frwNdz8GQ/8JYGtdvAmS3HgnsxfAG3l8tfvXo1XnjhBYwcORK3bt26EyQgAOPHj8fixYsVF6gmW7ZsQbVq1ZzaKLkruFg7NcoUa0dt0uyozbN21CbNrjJr62cwWN2C1d9kQnZSkqh4l/fvR7+HH7brny1Sm6Pysx16iMvh8u7dSCwq0mQO9mL5eg5y+lFFyaH99evIbNVKsn61c+B4lpaDFseCM31SbcTaFRYWiorl9sQsKCgIq1atwuLFi5GTkwNBEBAVFYXq1au7G0rzDB06FAYRK7748k7knrCjNml21OZZO2qTZldZtY2E49W/RGkbMEDU6mGOYpUtPyclBXOGDFEkh0STSXx9qJCDbSxfz0FuP1Iyh4RduzA2Lk7USnaK5pCYiOiOHSXr10IOHM8VYyxIsVMqlslkwsSJE13GkbzBdPXq1dGuXTup7oQQQoimiTEYZC3HrJS/mF/EPa1Brn9lzkFu+UrEiDEYkG02S46hdhuUxvDlHDgWtDEWtI7bE7OePXtCp9M5fP+HH36QJYgQQgghhBBCKhtuT8zat29v9frWrVvIzMzETz/9hDFjxiilixBCCCGEEEIqDW5PzJYtW2b3+Jw5c1BQUCBbECGEqEGayYR9QUGINpkk3yYh5t53T/vLyUFu+UrFUBO120ApDZW9H6mdg1L+OSL3qfKkBrVyYD9Svw2I95H8jJktI0eOxL333ou3335bqZCEEOIV4nNy7mxcGRqKNRkZmGE0YmFkpLQY/2OG0Yi2Mv3d0SA3B7n6HcVwtx7VRO02cKTB1/uREnXgSzko6h8ejsKcHN/tR4CkHNiPgM0GA3ZkZEj2V6IfEe/j9j5mjkhNTUXVqlWVCkcIIV4hzWSy+vAEgEW5uUgzmWTHOCPyV0q5GjzlL1a/EhrU5oxer2obOIshth3UzsGTdeArOajdBs5iVKZ+5Os5pJlMVsvCe7t8oh5uT8wGDx5s9ffoo4+iS5cuGDt2LCZNmuQJjYQQ4jGyzGa3jrtjmxcg7qYEuRo85S9WvxIa1MZRrt5qA2e2YttB7Rw8WQe+koPabeDMtjL1I1/PQW1/oh5u38poMBisVmX08/NDixYtMG/ePDz44IOKilMbbjDtvh21SbOjNs/aObPJ0euB8PDyx1NSHC4JbBvPUYz89HQkuvgg3L9/P+qK0ODJHOToVyoHR9rk2Lhjl3/yJNCnT7njtnXoKJ7a/QgQl4Ma/SgnJQWXRbapVseC2BzU6Ee28bzRj2zLtLJTuQ0A389BbPme7EfO4HcH9+08tsH0xo0b3XXxWbjBtDQ7apNmR22etXNmU2jzLEC80Yg5sbFuxbMXo02rVuK0DRkiSoMnc5CjX6kc3LVTtB8lJiLaaBRVh47iqd2PxObg7X40JzYWiUVFPj0WROegUj8Sk4PS/UjpHJTsRxUhhyNJSVa3M6rRj+Taafk7gVg7zW8w3axZMxw+fBi1a9e2On716lV07NgRv/zyi7shCSFEVRZGRmJwWBgSdu3C2Lg4SatflcYou4JWYmqqLH8p5UvNQa5+RzF8CbXbwJEGX+9H7uLrOSjpn5OSIurLtBgNavQjqTmwHwHDTCbMio1VtR8R7+P2xOzcuXMoLi4ud/zmzZv47bffFBFFCCHeJsZgQLbZLGsyEWMwqO4vJwe55SsVQ03UbgOlNFT2fqR2Dkr5O7vtzFsa5PpLzYH9SP02IN5H9MQsKSnJ8v9vv/0WISEhltfFxcX4/vvv0aRJE0XFEUIIIYQQQkhlQPSqjIMGDcKgQYOg0+kwZswYy+tBgwZh2LBh+O6777BkyRLJQhYsWACdTofnn3/eckyn09n9W7x4scM4GzdutOtz48YNydoIIYQQQgghxJOIvmJWUlICAGjatCkOHz6MOnXqKCbi8OHDeP/999GuXTur45cuXbJ6/fXXX2P8+PF47LHHnMYzGAw4ffq01bGKtsea3N3gy8aQuiO8Ehp8Hbl1oHYbnNHr8UFenmz9zYOC3Pa1F8MXc1Bbv60Guf5qt4Ea55I0kwn7goIQbTL5bBvIzUGpc5mcNlS7H/l6GygRQ+0c1P5MVVKDr34vUPt8XNlx+xmzs2fPKiqgoKAAI0aMwNq1azF//nyr9yIiIqxef/755+jZsyeaNWvmNKZOpyvnW5GQuxs8IH9HeLk72lcE5LaD2m0Qn5ODReHhwKlT0v3LlN/PYIC4tYscx/C1HNTWb0+Dr+Vgr/y2or3lYyk/NBRrMjJ8tw1k5CC3DZT4PFC7H2mxDeT2Q1/LQVF/CZ+pjjSo0o8AVb4XKPH9kshD1K2MK1assNwKuGLFCqd/7jJlyhT069cPcXFxTu0uX76MHTt2YPz48S5jFhQUoHHjxmjYsCH69++Po0ePOrW/efMmTCaT1Z9WkbsbfGkMNXe0rwjIbQe128AT/jsMBp/qh3JzUFu/oxi+lIMj/zMyfu12B7aB/DY4o9d7pA59KQe12xDw/RzU1u8sRmXpR0p8vyTyEXXFbNmyZRgxYgSqVq2KZcuWObTT6XR47rnnRBe+efNmZGRk4PDhwy5tN23ahODgYAwePNipXcuWLbFx40a0bdsWJpMJ77zzDrp164Zjx44hOjrars+CBQswd+7ccse1uMH0vqAgIDS03PGEXbuQbbNxpKNY7sSwF8+Zv66SbBQotx082QbZZrPH+5FU/RUpBzH+znJwt3w1clCyDUrLFOO/95dfkJiY6FK/GJRsA3vxfL0fyWkDANh74YLdjWxty6/IOcjtR0p8porJwZNjQW4OarSBbbzK3o+U+H7pro3W7TS7wXTZ2xeVupUxNzcXU6dOxc6dO0U9/7VhwwbL5NAZXbp0QZcuXSyvu3Xrho4dO+Ldd991eEVv5syZmD59uuW1yWSC0WjU5AbT0SYT1pS5zFyKoz027MVyN4ZtPGf+2WZzpdgoUG47eLINSv092Y/k6K8oOUTHxYnyd5SDlPLVyEHJNrCN58j/gWbNMHLIEJf65Y5nqfUnJgdf6Udy2+DM1q34wc5xe+VX1Bzk9iMlPlPF5uCpsSA3B7XaoGy8yt6PlPh+KcVG63be3mBa9KqMpcybNw9mOzP3wsJCzJs3T3Sc9PR05Ofno1OnTggICEBAQAD27NmDFStWICAgwGqvtL179+L06dOYMGGCu3Lh5+eHzp07Izs726FNYGAgDAaD1Z9WiTEY0M/msnK80ejWA5oxBgNmGI2SY8j1rwjIbQe128AT/v3dfFjc13NQW7+jGL6UgyP/KC/tucM2kN8GUUVFHqlDX8pB7TYEfD8HtfU7i1FZ+pES3y+JfNxe/GPu3LmYPHkygmxWnjKbzZg7dy5ee+01UXF69+6N48ePWx0bO3YsWrZsifj4ePj7+1uOr1+/Hp06dcLdd9/trlwIgoDMzEy0bevNx8k9i9zd4AH5O8LL3dG+IiC3HdRug4WRkaiWno7I7t1l628eFITsMnsdSo3hazmord+eBl/LwV75iampbsWQQ2n5Cbt2ib7K5SiGmm0gJwe5baDE54Ha/UiLbeAuvp6Dkv5SPlMdaVCjH6n1vUCJ75dEHm5PzARBgE6nK3f82LFjCLVzb6ojgoOD0aZNG6tj1atXR+3ata2Om0wmfPLJJw73SBs9ejQaNGiABQsWALgzcezSpQuio6NhMpmwYsUKZGZm4t///rdobb5AjEHebvBlY0jdEV4JDb6O3DpQuw2iioowUsYKpmXLd3xNWnwMKaidg9r6bTX4Yg5qn0tiDAZkm82KjGVAnTaQm4NS5zI5qN2PfL0NlIihdg5qf6YqqUGuv1rfC9Q+H1d2RE/MatWqZdmsuXnz5laTs+LiYhQUFGDy5MmKC9y8eTMEQcATTzxh9/3z58/Dz+//35F59epVTJw4EXl5eQgJCUGHDh2QkpKCe++9V3FthBBCCCGEEKIEoidmy5cvhyAIGDduHObOnYuQkBDLe3q9Hk2aNEHXrl1liUlOTi53bOLEiU4flrP1WbZsmdOVIwkhhBBCCCFEa4iemI0ZMwYA0LRpU9x3332oUqWKx0QRQgghhBBCSGXC7WfMevToYfl/YWEhbt26ZfW+llc0JHdIM5lkPdiphP++oCBEu7mKn1YozT9Hxia4WmgDOTloRX9zm0WIvFW+EjHUboMzej0+yMtjG6iYg5JtoIY/4Pv9SO7nkdptUFE+j9gGvp8DUQa3J2ZmsxkzZszA1q1bceXKlXLvl13m3tfR4gbTcmNtNhisdnbvZzLB6EY8e/7Dyiyv6kqbxT80FGsyMsr5i8lBqp0SsazyDw/HkaQkp/rtxfN0Gyidgxj9w0wm0fW75Pp1ZJbZK8VRH3AUz7b89tevA25sRuys/lTJQUI/kpvDZoMBO8LDgVOn5OuHuDYoG0/ueQRQpx+JzUHJNnAUT+p5RMk2UDoHb/cjuZ9Hvn4u11QOCraB0p9HanwnEJuDGv3IVTx3bLRup9kNpsvy4osvYvfu3Vi1ahVGjx6Nf//73/jtt9+wZs0avPXWW+6G0zRa3GBaTqw0kwk7bDYP3GEwYHbPnuI2T9TrsdqmPnYYDJgVG2v1C40jbY7Kt/V3loMzPL1RoFT9ZeN5qw2UzsGV/ln/W9LXVf2mmUxWH4KuyreNZ6/8zFatEN2xo6g2EJO/Gjm404/k5uBu+Uq2AQBEDxggqnxXOajVj8TmoGQb2MaTex5Rqg2UzsGb/chT50JfOZdXhBy8+Xnk7e8EYnNQqw3EaBNro3U7zW8w/cUXX2DVqlV4/PHHERAQgAceeACvvvoq3nzzTXz44YfuhiNeJMvOxuAAkBcgbn7uyM5RXLF2Yv3VRgn9vt4Gld1fCxoqu78WNGjVX+x5hG2gfhuofS53ZusrOfi6vxIx1G4DojxuT8z+/PNPNG3aFMCd58n+/PNPAMD999+PlJQUZdURRXF0D3/E7dui/B3ZiX02wJGd1GcLvI0S+n29DSq7vxY0VHZ/LWjQqr/Y8wjbQP02UPtc7szWV3LwdX8lYqjdBkR53J6YNWvWDOfOnQMAtG7dGlu3bgVw50pazZo1ldRGFCbGYMAMo9HqWLzRiCiRmxhGFRXZ9Rf7oKmj8n1lARAl9Pt6Gyjh38/m3nW55fd344FxpdpQ6Rx83b8itIE3c/CUv9jziCfPZWwD3ziXO4vhKzlo8fPI2+NI7TYgyuP2M2Zjx47FsWPH0KNHD8ycORP9+vXDu+++i9u3b2Pp0qWe0EgUZGFkJAaHhVmt3pOYmirLX0r5Cbt2YWxcnM8N/rL556SkYM7/7gOXGkPNNpCag9zyh5lMmBUbq4j+5kFByE5KkuUvpQ8qmYMabbAwMhLV0tMR2b0720ClHJRuA2+fR0pj+HI/kvt5pHYbVKTPIyXbwB3UPpfbxlDqXCDVX2oORDncnphNmzbN8v+ePXvi1KlTOHLkCCIjI3H33XcrKo54hhiDQdaESAn/bLPZ5yZlpZTmnyjyV0VnMdT2l5qDVvQDQLYK5SsRQ+02iCoqwsiICMn+FakNAHVyULIN1PAHfL8fyf08UrsNKsrnEdvA93MgyuD2rYy2NGrUCIMHD0ZoaCjGjRunhCZCCCGEEEIIqVTInpiV8ueff2LTpk1KhSOEEEIIIYSQSoPbtzIS30XuzvBn9Hp8kJenyM70UtCSfrX89wUFIdqNB+Q9oUHNHLSgP8tsRo5e77avkuVL9S8bw1dzUFu/r58HlYihdg6V/Vyutv7SGL6cg9rnEV/XTzwHJ2ZO2LJlC6pVq+bUxld2Ine2M7yYeJsNBuwIDwdOnSrnL0abbfntr18HEhNF5+BqZ3tXOYjV7yiWvfKNbrSDIvoNBiA0FGsyMpzqVzoHRdtARg6Oyhc7Zlzpd5WDlX94OI4kJTnVbxtPjn4AWHL9utVmqO6OQSk5KNkGSuTgqTZwVma58n34PAgo3AYK56BkGziK5+vncrXPg57KQck2cJUDz4PyxqBYOzW+Hyttp2SswsJCUbE4MXPC0KFDYRDxS4LWdyIXs7O7s3ju7gxvG8uef2arVoju2FHULzXRAwaIKt9RDlJ2ti8by5H/7J49MXLIEJf6z+j1WG1Tjqf1K52DGm1QNp4zf2flliK2DRzFkqq/NJ5c/Wkmk9UHuavylcxBqTaQm4Mn26AynAdLNSjdBkrnoGQb2Mbz9XO52udBT+agdBs4ykHtNvD186AzbVJttG6nVCyTyYSJEye6jCP6GbPBgwc7/Su7WiPRFmrvbl9R/fMCxP2u4cjOW/qd2YrNQattINZf7TZQ218LGujPPqBVf185l6t9HlRCQ2VvA1/3J55H9BWzkJAQl++PHj1atiCiPGrvbl9R/SNu3xbl78jOW/qd2YrNQatt0DwoSNQS22q3gVz9nuwDvpID/bXdB3zZ31fO5WqfB11pkONfWdpAq2NA6vOiRHlEXzFLSEgQ9Ue0R4xB3s7unvDv78YDw1rUH280Ikrkfh9RRUWq6ncWQ2wOWm0Dsf5qt4ES/v1sniFQqg/4Sg5a0O/L58HSGEq3Ac/l6uv31nlQCQ2VvQ18/TxIPA+fMaskKLEzfLX0dER2767IzvTZSUmy/NXWH2MwIDE11av6B4eFIWHXLoyNi5N0EvX1HJQqXwn/nJQUzPnfMwXeKn+YyYRZsbGyVtLy9RzU1u/r50FA2TbgubzynQfLxlAyBzXagOdBrsqoRTgxq0TEGOTtDB9VVISRERGKlC/mkr8zfykoqV8t/2yzWXUNauagBf0xBgMSRf6666ny5eDrOait39fPg0rEUDuHyn4uV1t/aQxfzkHt84iv6yeeQ7ENpgkhhBBCCCGESIMTM0IIIYQQQghRGd7K6ISKtMG0t+yoTZodtXnWjtqk2VGbNDtqk2anZW1i7ahNmh21SbOjNs/acYNpjaGVDabTTKZyD2m6u8G0q4c8XW3qmWU2o65eL2oDSDH63ckhesAAUQ+pOtsQ0x39trGc1Z+YHM7o9RBEPKjuSn/pcrbu9iNX7S9mU1Ix+h3Fktt/K0IOcvWnmUzYFxSEaJEP2is5BtU4h9jG08o5xJ0xyPOgNb5+HnRnDKpxDnEVT81ziNgceB50Hk/tc4gUOy1vHC3WztsbTHNipnHic3KwKDfX8nqG0Yi2Mv0XRkZK8w8PR2FOjnR/CeVvNhisdqn3Nf3xOTlYFB4OnDol3b9M+f0MBog7Rdj3V1u/u/3XUQxfykGxMRgaijUZGYroV83f188hcH8M2ovB86APngcVHoOV6RyiRAy1c6js50HiPfiMmYZJM5msBiIALMrNxRm9XpT/Gb3ern+azR4a7pbvTf8dNr/q+Jp+pf13GAw+rd+d/utJDd7KwdfHoK/r98Q5xJ0x6CiG2jn4un9lOg/6+hgEfD8HX9cv9xxCvAsnZhomy2y2ezwvQNyFTkd2juKKtaM//eX4i+2/ntTgrRx8fQz6un61/bWggf7a9K8s5xDA93Pwdf1KtCHxHpyYaZjSe+ltibh9W5S/IztHccXa0Z/+cvzF9l9PavBWDr4+Bn1dv9r+WtBAf236V5ZzCOD7Ofi6fiXakHgPTsw0TIzBgBlGo9WxeKMRUSI3FIwqKrLrL3ZTQUfle9O/n82ldl/Tr7R/f5PJp/W70389qcFbOfj6GPR1/Z44h7gzBh3FUDsHX/evTOdBXx+DgO/n4Ov65Z5DiHfh4h8aZ2FkJAaHhVmtpJOYmirLX2r5OSkpmBMbK1u/OwwzmTArNtZn9S+MjES19HREilwR0FX52UlJsvzV1u9u/3UUw11/NXNQagwm7NqFsSJXI/NE+b46BpU8h0gZg/Zi8Dzoe+dBpcdgZTqHKBFD7Rwq+3mQeA9OzHyAGINB1iBSyj/RjSsdnihfrr9a+qOKijAyIkKyf9nys2X6S0FJ/WrFUDsHJfyzzWbJMbSgvyKcQwBpY1BpDXL8eR6UXr6vj0E19SsRQ+0cfH0MKtGGxPNwYuYEbjDtvh21SbOjNs/aUZs0O2qTZkdt0uy0rE2sHbVJs6M2aXbU5lk7bjCtMbSywbRWyhRrR23S7KjNs3bUJs2O2qTZUZs0Oy1rE2tHbdLsqE2aHbV51o4bTBPFOKPX44O8PMn3E4vZ5d7T/vuCghDt5sP2SpavhH+OG/t2eaJ8X9WvZP+tjMitf4DnEC3orwjnEKljUEvnEPZf3+u/SsSo7G1AvA8nZhWU+JwcLAoPB06dAqDMLvdt3S1fxi71Fv/QUKzJyPBd/QAQHo7CnBzZ+t3x32wwYEdGhmR/tfUr3X/7GQwQ95tXxUBu/Vti8Bwiv3wl/H39HAL3x6DWziEVof9Wps9AJWKofQ5R+3OcqAOXy6+AeGqX+TMif3HRwi73cvSf0es1qd8d/x02v6z5mn6l/XcYDKL9fR25/RfgGJTr7+v6PXEOcWcMaiF/fgZqT7875zBfz0Htz3GiHpyYVUA8tUt8XoC4C6xq71IvV78jO7X10987/r6O3P7rzJZjUJy/r+unPz8DPVG+N8/hvp6D2v5EPTQzMVuwYAF0Oh2ef/55yzGdTmf3b/HixU5jbdu2Da1bt0ZgYCBat26NTz/91MPqtYWndomPuH1b1fK9pd+Rndr66e8df19Hbv91ZssxKM7f1/XTn5+Bnijfm+dwX89BbX+iHpqYmB0+fBjvv/8+2rVrZ3X80qVLVn8bNmyATqfDY4895jBWamoqhg4dilGjRuHYsWMYNWoUhgwZgrS0NE+noRliDJ7ZZT5K5N4bnirfW/qjioo0qd8d/342tyv4mn6l/ftLfHDbF5HbfwGOQbn+vq7fE+cQd8agFvLnZ6D29LtzDvP1HNT+HCfqofriHwUFBRgxYgTWrl2L+fPnW70XYbMZ5eeff46ePXuiWbNmDuMtX74cffr0wcyZMwEAM2fOxJ49e7B8+XJ8/PHHyiegURZGRqJaejoiu3dXbJf7xNRUWf5Syk/YtQtj4+J8Vn+W2YyclBTMiY2Vrd8dhplMmBUb67P6le6/2UlJbvn7OnLrvzQGzyHKnAN8cQwqeQ6RMga1dg6pCP23Mn0GKhFD7XOI2p/jRB1Un5hNmTIF/fr1Q1xcXLmJWVkuX76MHTt2YNOmTU7jpaamYtq0aVbH+vbti+XLlzv0uXnzJm7evGl5baogD0dGFRVhpM3k1h1iDOruMh9jMCDbbJYcQwv6YwwGJIr8ldJT5cv1V0u/kv03W3IU30Vu/QM8h2hBf0U4hwDSxqCWziFq+bP/yjuHsQ3ktQHxPqpOzDZv3oyMjAwcPnzYpe2mTZsQHByMwYMHO7XLy8tD3bp1rY7VrVsXeXl5Dn0WLFiAuXPnlju+ZcsWVKtWzWl5vr4TudJ21CbNjto8a0dt0uyoTZodtUmz07I2sXbUJs2O2qTZUZtn7ZSMVVhYKCqWahOz3NxcTJ06FTt37kTVqlVd2m/YsAEjRowQZavT6axeC4JQ7lhZZs6cienTp1tem0wmGI1GDB06FAYRv1T48k7knrCjNml21OZZO2qTZkdt0uyoTZqdlrWJtaM2aXbUJs2O2jxrp1Qsk8mEiRMnuoyj2sQsPT0d+fn56NSpk+VYcXExUlJSsHLlSty8eRP+/v4AgL179+L06dPYsmWLy7gRERHlro7l5+eXu4pWlsDAQAQGBkrMhBBCCCGEEELkodrErHfv3jh+/LjVsbFjx6Jly5aIj4+3TMoAYP369ejUqRPuvvtul3G7du2K7777zuo5s507d+K+++5TTjxxSprJJOuB3bL+apR/Rq/HB3l5iuhXy39fUBCiK9FKhOT/o2T/lYLa/dfXx58W9GeZzcgRuRmxp8r31f5b2T8/tKC/Mvdf4vuoNjELDg5GmzZtrI5Vr14dtWvXtjpuMpnwySefYMmSJXbjjB49Gg0aNMCCBQsAAFOnTkX37t2xcOFCDBw4EJ9//jl27dqFffv2eS4ZYmGzwYAdGRmW1zOMRiyMjBTtH5+TY7VbfT+DAeIuItv3l1R+eDhw6pR0f5vy24r2Vkh/bi4QGoo1GRlu+xPfRun+q8r4k9F/fX38yS1f0fNveDgKc3J8Vz/4+eFr/VdR/0rYf0nFQBP7mDlj8+bNEAQBTzzxhN33z58/j0uXLlle33fffdi8eTMSEhLQrl07bNy4EVu2bEFMTIy3JFda0kwm7LD5hWhRbi7SRK5ymWYyWZ2UAGCHwSDLX275SvifEfnLndr6iW/jif5TmcbfGb1ek/rd8Vf6/Ovr+tl/1dcv1t/X9avdf0nFQVMTs+Tk5HLL2k+cOBFmsxkhISEOfTZu3Gh17PHHH8epU6dQVFSEkydPulzJkShDltns1vHK4p8XIO7CtNr6iW+jdv/Rqr/Y8efITm399K/c/pWl//q6frX9ScVBUxMz4ts4uida7L3SFdU/4vZtVcvnveqVA7X7j1b9xY4/R3Zq66d/5favLP3X1/Wr7U8qDpyYEcWIMRjQz+aye7zRKPoB2BiDATOMRqtj/d14ANmev9zylfCPErmxo9r6iW/jif5TmcZfVFGRJvW746/0+dfX9bP/qq9frL+v61e7/5KKg6obTGsdbjDtvp1x/37M7tkTeQEBiLh9G1EXLiAxNVV0rLYAZuv1Fv/Lu3cjUcQ91qXxbP3Lli9Gf1sAj548ifBOnZzqdxTPXvnutKkz/WJyKPXf+8sveKBZM6f6xcQTa6N1u8qiTen+q+T4E5ODO/1XyfFXGq+bC/2ucpA7/hzVn9gcxJx/xejPCwhAfno62rRq5VS/bTy5518tf36IyUGtz4/SeK76r6c/P+T0XyXHn9L9V8nx5ywHKf23sny2qWVXqTaY9gW4wbQ0u5FDhigWK7GoyPv1lpiIkSKfS9RqmyYmJiraDlrub2LtKo02Bfuv0uNPrJ3Y/qvVNlV6/Lllp1C9JZrN/PyQYqfS54dYOzU+P8TaKXoOUbj/Kjn+xMYT23+1eh5Uq0yl7by9wTRvZSSEEEIIIYQQleHEjBBCCCGEEEJUhrcyEqIR0kwmZJnNaB4UJOmB37L+hLiD3L53Rq/HB3l5ivRdtfz3BQUhWuLD9lrQn2U2I0fknleeKt9X9SvZf6XA/uv7+tXsv6RiwYkZIRpgs8GAHRkZltczjEYsjIwU7R+fk2O1OWU/gwHi7oomlR3bviOp74WHA6dOSfe3Kb+taG+F9OfmAqGhWJOR4bv6ASA8HIU5ObL1u+Ov6LlLBf1K9193z73sv8rrr0z9l1Q8eCsjISqTZjJhh82vZItyc5EmYjWxUv+yJ3YA2GEwiPYnlRd7fUdu31PC/4zIX559Xf8ZvV6T+t3xV/rc5ev+7px7taif/dd39JOKCSdmhKhMltns1nGl/UnlRe2+58guL0DczRy+rt+Rndr66V+5/dl/fcOfVEw4MSNEZRw9lyD2eQW5/qTyonbfc2QXcfu2quV7S78jO7X1079y+7P/+oY/qZjwGTMncINp9+2oTZpd++vXkdmqleV1f5MJ2UlJyBYZq5/BYHVLRvsTJ5B94UI5fynatFxvYu2ozbGdbd8p2/fExHLmL0abPf/Lu3cjsahItn5HZUrVby+eVP0AcHn/fvR7+GGX5TvLwRP6s5OSRPcjpc9drvTbxtJa/3X33OvN/qvk+AMqT/9VUr9tPLn9V+3PD62VqbQdN5jWGNxgWpodtUmwS0xEdMeOolZmshdrJKxXdsq+cKFy1JsbdtRm386279j2PVexRgKYs3UrIrt3F7WqmG08e+Unmkyi682Vflc5lPon7NqFsXFxolZFKxtPjn4AGDlggKhV2RzFK1t+TkoK5ri5wbSz+hO7oblS5y6x+svG0lr/dffc6+3+q+T4A5Trv0qOP1dlWiGy/yo5/srGk9t/xdqoZadlbWLtvL3BNCdmhGiEGINB1lK5Zf1d/VpLSFnk9r2ooiKMjIhQrXwl/LPNZskxtKA/xmAQdZXDk+XL9VdLv5L9V8q5l/3X9/Wr2X9JxYLPmBFCCCGEEEKIynBiRgghhBBCCCEqw4kZIYQQQgghhKgMnzEjhBCiCmIWDfCWvxTkln9Gr8cHeXmayN9df8uCByI3I1aybKX9pVCZ277Uf19QEKJNJp/V78v9l1RcODEjhBDideJzcrAoN9fyeobRiIWRkaL9NxsM2JGRIdnftvx+BgPErb1l319S+eHhwKlT0v1tym8r2luefivf8HAU5uTI1s629422t/IPDcWajAzf1Q/4ZP8lFRveykgIIcSrpJlMVl9sAGBRbi7STCbR/jtsfmV219+2/B0Ggyx/ueUr4X9G5K//csrXQu5se3Xa3pP+YvWf0es1qd9b/ZdUfDgxI4QQ4lWyzGa3jtNfnF1egLibYOSUr9XcK7u/N9rek/5i9TuyU1u/t/xJxYe3Mjphy5YtqFatmlMbX9+JXGk7apNmR22etaM2aXaeKjNHrwfCw8u9n5OSgsSiIpfxXPm70uaOv70c6npRf2mZYvzz09OR6OILnhj99sqUqt02B2f+l0X0I7a9em0vRb9tPDn6ASD/5EmgTx+X5SvZf8Xo91b/daZNa3Za1ibWTslYhYWFomJxYuaEoUOHwuDmLvTesqsMu6R7wo7apNlpWZtYO2qTZuepMgttntOINxoxJzZWdLwjSUlWtwTZ+rvSZlt+f5MJc4YMEZMCRg4ZIlu/K/9yZdrEs+ffplUrcW0qQr+zHNzVbhvPkX9iUZEo/Wx79dpein7beHL0IzER0UajqPKV7L+u9Huz/0qxUctOy9rE2ikVy2QyYeLEiS7jcGJGCCHE6yyMjMTgsDDJK5MNM5kwKzZWsr9t+dlJSbL8pZRfLT0dkd27K6I/xmBAYmqqV/SX9c1JSRE1KVOqbIBtr2bbl/VP2LULY+PifFa/r/ZfUrHhxIwQQogqxBgMsr6UKOmfrUL5UUVFGBkRIdlfzfor9XV1+58nylban20vzT/bbJbdf+SUr4S/r/ZfUnHh4h+EEEIIIYQQojKcmBFCCCGEEEKIyvBWRkIIIcRLpJlMlmdL5PpLuRXqjF6PD/LyJPvLLV8J/31BQYg2mdz211LdSUGufkKI9uHEjBBCCPEC8TarufUzGCBuvS/7/jOMRiyMjHTPPzwcOHVKur9N+W1FeyukPzcXCA3FmowMt/y1VnfebntCiG/AWxkJIYQQD5NmMll9sQaAHQYD0kwmyf6LcnNV9z+j14vyP6PXq6Zfi3XnzbYnhPgOvGLmBG4w7b4dtUmzozbP2lGbNDtqk2Znz2ZfUBAQGlrueMKuXcgWsTGwGH9n2twt3zaWI/+9v/yCxMREp/oBYO+FC3Y31vWU/rLxXPm6alN3ytZi2zvTphU7apNmR22eteMG0xqDG0xLs6M2aXbU5lk7apNmR23S7Gxtok0mrMnIKGcndh+o6Lg4Uf6OtEkpv2wsR/4PNGuGkSI2Zz6zdSt+sHPck/pL44nxddam7pattbZ3pk1LdtQmzY7aPGvn7Q2meSsjIYQQ4mFiDAbMMBqtjvV3YwELe/7xRqPq/lEi94GKKipSTb8W686bbU8I8R14xYwQQgjxAgsjIzE4LMyysl52UpIsf3e/mC+MjES19HREdu8u2d+2/MTUVK/qHxwWhoRdu0RfbVKybCXrztttTwjxDTgxI4QQQrxEjMFg+VKdLdNfClFFRRgZESHZX275Svhnm82SYmip7tRoe0KI9tHMrYwLFiyATqfD888/b3X85MmTGDBgAEJCQhAcHIwuXbrg/PnzDuNs3LgROp2u3N+NGzc8nAEhhBBCCCGESEMTV8wOHz6M999/H+3atbM6npOTg/vvvx/jx4/H3LlzERISgpMnT6Jq1apO4xkMBpw+fdrqmCsfQgghhBBCCFEL1SdmBQUFGDFiBNauXYv58+dbvffKK6/gkUcewaJFiyzHmjVr5jKmTqdDhIzbDQghhBBCCCHEm6h+K+OUKVPQr18/xMXFWR0vKSnBjh070Lx5c/Tt2xfh4eGIiYnBZ5995jJmQUEBGjdujIYNG6J///44evSoU/ubN2/CZDJZ/RFCCCFEfdJMJnyQlyd5Q2U5/mqWTQipfKh6xWzz5s3IyMjA4cOHy72Xn5+PgoICvPXWW5g/fz4WLlyIb775BoMHD8bu3bvRo0cPuzFbtmyJjRs3om3btjCZTHjnnXfQrVs3HDt2DNHR0XZ9FixYgLlz55Y7zg2m3bejNml21OZZO2qTZkdt0uyoTZqdPZvNBgN2lFnwop/JBKMbZdrzH2YzSXKkzda3/fXrgIvNtMvGcla2VupXK3bUJs2O2jxrV6k2mM7NzcXUqVOxc+dOu89/lZSUAAAGDhyIadOmAQDat2+PAwcOYPXq1Q4nZl26dEGXLl0sr7t164aOHTvi3XffxYoVK+z6zJw5E9OnT7e8NplMMBqN3GBaoh21SbOjNs/aUZs0O2qTZkdt0uzK2qSZTNhhs7HyDoMBs3v2FLeptV6P1Taf4TsMBsyKjS23uqGtNntlZ7ZqheiOHV2ujDhy5EiH2suWrXb9as2O2qTZUZtn7SrNBtPp6enIz89Hp06dEBAQgICAAOzZswcrVqxAQEAAateujYCAALRu3drKr1WrVk5XZbTFz88PnTt3Rna248VpAwMDYTAYrP4IIYQQoh5ZZrPd43kB4n5TdmTnKK4YGzG+SvgTQionql0x6927N44fP251bOzYsWjZsiXi4+MRGBiIzp07l1tdMSsrC40bNxZdjiAIyMzMRNu2bRXRTQghhBDP0zwoyO7xiNu3Rfk7snMUV4yNGF8l/AkhlRPVrpgFBwejTZs2Vn/Vq1dH7dq10aZNGwDAiy++iC1btmDt2rU4c+YMVq5ciS+++AJPP/20Jc7o0aMxc+ZMy+u5c+fi22+/xS+//ILMzEyMHz8emZmZmDx5stdzJIQQQog0YgwGzDAarY7FG42IKioS5R9VVGTXX8wmzfbK7m8yid7g2ZF2bhBNCHGG6svlO+PRRx/F6tWrsWDBAjz33HNo0aIFtm3bhvvvv99ic/78efj5/f/55dWrVzFx4kTk5eUhJCQEHTp0QEpKCu699141UiCEEEKIRBZGRmJwWBiyzGY0DwpCjMGAxNRUWf5SfbOTkmRrJ4QQZ2hqYpacnFzu2Lhx4zBu3DjRPsuWLcOyZcsUVkYIIYQQNYgxGGRNauT4l/V1/KS6Z8omhFQ+VN/HjBBCCCGEEEIqO5yYEUIIIYQQQojKaOpWRq3BDabdt6M2aXbU5lk7apNmR23S7KhNmp2WtYm1ozZpdtQmzY7aPGtXqTaY9gW4wbQ0O2qTZkdtnrWjNml21CbNjtqk2WlZm1g7apNmR23S7KjNs3aVZoNpQgghhBBCCCF34MSMEEIIIYQQQlSGEzNCCCGEEEIIURlOzAghhBBCCCFEZTgxI4QQQgghhBCV4cSMEEIIIYQQQlSGEzNCCCGEEEIIURnuY2YHQRAA3NlzwBWFhYVet1OjTGqjNi1qE2tHbdRGbdTmDTtqozZq0742sXZKxip9v3SO4Qid4MqiEnLhwgUYjUa1ZRBCCCGEEEIqCLm5uWjYsKHD9zkxs0NJSQkuXryI4OBg6HQ6teX4DCaTCUajEbm5uTAYDGrLqbSwHbQB20F92AbagO2gPmwDbcB20AZqtIMgCLh+/Trq168PPz/HT5LxVkY7+Pn5OZ3NEucYDAaecDQA20EbsB3Uh22gDdgO6sM20AZsB23g7XYICQlxacPFPwghhBBCCCFEZTgxI4QQQgghhBCV4cSMKEZgYCBmz56NwMBAtaVUatgO2oDtoD5sA23AdlAftoE2YDtoAy23Axf/IIQQQgghhBCV4RUzQgghhBBCCFEZTswIIYQQQgghRGU4MSOEEEIIIYQQleHEjBBCCCGEEEJUhhMz4jYpKSn4xz/+gfr160On0+Gzzz6zel8QBMyZMwf169dHtWrVEBsbi59//lkdsRUYV+2wfft29O3bF3Xq1IFOp0NmZqYqOisyztrg1q1biI+PR9u2bVG9enXUr18fo0ePxsWLF9UTXEFxNRbmzJmDli1bonr16qhVqxbi4uKQlpamjtgKiqs2KMukSZOg0+mwfPlyr+mrLLhqhyeffBI6nc7qr0uXLuqIrcCIGQ8nT57EgAEDEBISguDgYHTp0gXnz5/3vtgKiqs2sB0HpX+LFy9WR/D/4MSMuM3ff/+Nu+++GytXrrT7/qJFi7B06VKsXLkShw8fRkREBPr06YPr1697WWnFxlU7/P333+jWrRveeustLyurPDhrA7PZjIyMDMyaNQsZGRnYvn07srKyMGDAABWUVmxcjYXmzZtj5cqVOH78OPbt24cmTZrgwQcfxO+//+5lpRUXV21QymeffYa0tDTUr1/fS8oqF2La4aGHHsKlS5csf1999ZUXFVYOXLVDTk4O7r//frRs2RLJyck4duwYZs2ahapVq3pZacXFVRuUHQOXLl3Chg0boNPp8Nhjj3lZqQ0CITIAIHz66aeW1yUlJUJERITw1ltvWY7duHFDCAkJEVavXq2CwsqBbTuU5ezZswIA4ejRo17VVNlw1galHDp0SAAg/Prrr94RVQkR0w7Xrl0TAAi7du3yjqhKhqM2uHDhgtCgQQPhp59+Eho3biwsW7bM69oqE/baYcyYMcLAgQNV0VNZsdcOQ4cOFUaOHKmOoEqImM+FgQMHCr169fKOICfwihlRlLNnzyIvLw8PPvig5VhgYCB69OiBAwcOqKiMEPW5du0adDodatasqbaUSktRURHef/99hISE4O6771ZbTqWhpKQEo0aNwosvvoi77rpLbTmVmuTkZISHh6N58+Z46qmnkJ+fr7akSkVJSQl27NiB5s2bo2/fvggPD0dMTIzT23+JZ7l8+TJ27NiB8ePHqy2FtzISZcnLywMA1K1b1+p43bp1Le8RUhm5ceMGXnrpJQwfPhwGg0FtOZWOL7/8EjVq1EDVqlWxbNkyfPfdd6hTp47asioNCxcuREBAAJ577jm1pVRqHn74YXz44Yf44YcfsGTJEhw+fBi9evXCzZs31ZZWacjPz0dBQQHeeustPPTQQ9i5cyceffRRDB48GHv27FFbXqVk06ZNCA4OxuDBg9WWggC1BZCKiU6ns3otCEK5Y4RUFm7duoVhw4ahpKQEq1atUltOpaRnz57IzMzEH3/8gbVr12LIkCFIS0tDeHi42tIqPOnp6XjnnXeQkZHBzwGVGTp0qOX/bdq0wT333IPGjRtjx44dmvhSWhkoKSkBAAwcOBDTpk0DALRv3x4HDhzA6tWr0aNHDzXlVUo2bNiAESNGaOIZP14xI4oSEREBAOWujuXn55e7ikZIZeDWrVsYMmQIzp49i++++45Xy1SievXqiIqKQpcuXbB+/XoEBARg/fr1asuqFOzduxf5+flo1KgRAgICEBAQgF9//RX/+te/0KRJE7XlVWrq1auHxo0bIzs7W20plYY6deogICAArVu3tjreqlUrrsqoAnv37sXp06cxYcIEtaUA4MSMKEzTpk0RERGB7777znKsqKgIe/bswX333aeiMkK8T+mkLDs7G7t27ULt2rXVlkT+hyAIvH3LS4waNQo//vgjMjMzLX/169fHiy++iG+//VZteZWaK1euIDc3F/Xq1VNbSqVBr9ejc+fOOH36tNXxrKwsNG7cWCVVlZf169ejU6dOmnnmmLcyErcpKCjAmTNnLK/Pnj2LzMxMhIaGolGjRnj++efx5ptvIjo6GtHR0XjzzTcRFBSE4cOHq6i64uGqHf7880+cP3/esm9W6YdARESE5comkYezNqhfvz4ef/xxZGRk4Msvv0RxcbHlSnJoaCj0er1asiscztqhdu3aeOONNzBgwADUq1cPV65cwapVq3DhwgX83//9n4qqKxauzke2P0pUqVIFERERaNGihbelVmictUNoaCjmzJmDxx57DPXq1cO5c+fw8ssvo06dOnj00UdVVF3xcDUeXnzxRQwdOhTdu3dHz5498c033+CLL75AcnKyeqIrGK7aAABMJhM++eQTLFmyRC2Z5VF7WUjie+zevVsAUO5vzJgxgiDcWTJ/9uzZQkREhBAYGCh0795dOH78uLqiKyCu2iEhIcHu+7Nnz1ZVd0XCWRuUblNg72/37t1qS69QOGuHwsJC4dFHHxXq168v6PV6oV69esKAAQOEQ4cOqS27QuHqfGQLl8v3DM7awWw2Cw8++KAQFhYmVKlSRWjUqJEwZswY4fz582rLrnCIGQ/r168XoqKihKpVqwp333238Nlnn6knuAIipg3WrFkjVKtWTbh69ap6Qm3QCYIgKDzXI4QQQgghhBDiBnzGjBBCCCGEEEJUhhMzQgghhBBCCFEZTswIIYQQQgghRGU4MSOEEEIIIYQQleHEjBBCCCGEEEJUhhMzQgghhBBCCFEZTswIIYQQQgghRGU4MSOEEEIIIYQQleHEjBBCSKXj3Llz0Ol0yMzM9Eh8nU6Hzz77TLJ/cnIydDoddDodBg0apJguezRp0sRS1tWrVz1aFiGEEMdwYkYIIcSrPPnkkx6fbLjCaDTi0qVLaNOmDYD/PxHS2sTk9OnT2Lhxo+V1QUEBhg0bhnr16mHYsGH4+++/AQCrV69GcHAwbt++bWVbpUoVPPDAA1Yx9+7dC51Oh6ysLADA4cOHsW3bNs8nQwghxCmcmBFCCKl0+Pv7IyIiAgEBAWpLcUp4eDhq1qxpeb18+XLUqFEDO3fuRFBQEJYvXw4A6NmzJwoKCnDkyBGL7d69exEREYHDhw/DbDZbjicnJ6N+/fpo3rw5ACAsLAyhoaFeyYcQQohjODEjhBCiKfbs2YN7770XgYGBqFevHl566SWrK0GxsbF47rnnMGPGDISGhiIiIgJz5syxinHq1Cncf//9qFq1Klq3bo1du3ZZ3V5Y9lbGc+fOoWfPngCAWrVqQafT4cknnwRw5za/0slPKe3bt7cqLzs7G927d7eU9d1335XL6bfffsPQoUNRq1Yt1K5dGwMHDsS5c+fcrpurV6+iefPmaNu2LVq2bIlr164BAFq0aIH69esjOTnZYpucnIyBAwciMjISBw4csDpemi8hhBDtwIkZIYQQzfDbb7/hkUceQefOnXHs2DG89957WL9+PebPn29lt2nTJlSvXh1paWlYtGgR5s2bZ5kQlZSUYNCgQQgKCkJaWhref/99vPLKKw7LNBqNllv5Tp8+jUuXLuGdd94RpbekpASDBw+Gv78/Dh48iNWrVyM+Pt7Kxmw2o2fPnqhRowZSUlKwb98+1KhRAw899BCKiorcqR4888wzWLNmDapUqYKEhARMnTrV8l5sbCx2795teb17927ExsaiR48eluNFRUVITU3lxIwQQjSItu/hIIQQUqlYtWoVjEYjVq5cCZ1Oh5YtW+LixYuIj4/Ha6+9Bj+/O78ntmvXDrNnzwYAREdHY+XKlfj+++/Rp08f7Ny5Ezk5OUhOTkZERAQA4I033kCfPn3slunv72+5lc/21kFX7Nq1CydPnsS5c+fQsGFDAMCbb76Jhx9+2GKzefNm+Pn5Yd26ddDpdACAhIQE1KxZE8nJyXjwwQdFl9ekSRNkZ2cjPz8fdevWtcQD7kzMpk2bhtu3b6OwsBBHjx5F9+7dUVxcjBUrVgAADh48iMLCQk7MCCFEg3BiRgghRDOcPHkSXbt2tZpwdOvWDQUFBbhw4QIaNWoE4M7ErCz16tVDfn4+gDtXvYxGo2VSBgD33nuvx/Q2atTIMikDgK5du1rZpKen48yZMwgODrY6fuPGDeTk5Lhdpp+fn1VupfTs2RN///03Dh8+jL/++gvNmzdHeHg4evTogVGjRuHvv/9GcnIyGjVqhGbNmrldLiGEEM/CiRkhhBDNIAiC1aSs9BgAq+NVqlSxstHpdCgpKXEYQyp+fn6W8ku5detWOW22WspSUlKCTp064cMPPyxnGxYWpohOAIiKikLDhg2xe/du/PXXX+jRowcAICIiAk2bNsX+/fuxe/du9OrVS7EyCSGEKAefMSOEEKIZWrdujQMHDlhNeA4cOIDg4GA0aNBAVIyWLVvi/PnzuHz5suXY4cOHnfro9XoAQHFxsdXxsLAwXLp0yfLaZDLh7NmzVnrPnz+PixcvWo6lpqZaxejYsSOys7MRHh6OqKgoq7+QkBBROYmlZ8+eSE5ORnJyMmJjYy3He/TogW+//RYHDx7kbYyEEKJRODEjhBDida5du4bMzEyrv/Pnz+Ppp59Gbm4unn32WZw6dQqff/45Zs+ejenTp1ueL3NFnz59EBkZiTFjxuDHH3/E/v37LYt/OLqS1rhxY+h0Onz55Zf4/fffUVBQAADo1asXPvjgA+zduxc//fQTxowZA39/f4tfXFwcWrRogdGjR+PYsWPYu3dvuYVGRowYgTp16mDgwIHYu3cvzp49iz179mDq1Km4cOGClOpzSM+ePbFv3z5kZmZarpgBdyZma9euxY0bNzgxI4QQjcKJGSGEEK+TnJyMDh06WP299tpraNCgAb766iscOnQId999NyZPnozx48fj1VdfFR3b398fn332GQoKCtC5c2dMmDDB4l+1alW7Pg0aNMDcuXPx0ksvoW7dunjmmWcAADNnzkT37t3Rv39/PPLIIxg0aBAiIyMtfn5+fvj0009x8+ZN3HvvvZgwYQLeeOMNq9hBQUFISUlBo0aNMHjwYLRq1Qrjxo1DYWEhDAaDu1XnlJ49e6KwsBBRUVGoW7eu5XiPHj1w/fp1REZGwmg0KlomIYQQZdAJ9m6QJ4QQQioQ+/fvx/33348zZ85YTay0SuleY3/99Zdbq0T6SnmEEELKw8U/CCGEVDg+/fRT1KhRA9HR0Thz5gymTp2Kbt26+cSkrCwNGzbEP/7xD3z88cceK+Ouu+7CL7/84rH4hBBCxMGJGSGEkArH9evXMWPGDOTm5qJOnTqIi4vDkiVL1JYlmpiYGGRnZwMAatSo4dGyvvrqK8tKk0rfWkkIIUQ8vJWREEIIIYQQQlSGi38QQgghhBBCiMpwYkYIIYQQQgghKsOJGSGEEEIIIYSoDCdmhBBCCCGEEKIynJgRQgghhBBCiMpwYkYIIYQQQgghKsOJGSGEEEIIIYSoDCdmhBBCCCGEEKIy/w/hTGJCYnXe3AAAAABJRU5ErkJggg=="
      },
      "metadata": {},
      "output_type": "display_data"
@@ -72,8 +72,8 @@
     "plt.pcolormesh(x, y, z, edgecolors='k', linewidth=0.5, alpha=0.2)\n",
     "plt.scatter(source_grid.activearrlon, source_grid.activearrlat, s=10, c='c')\n",
     "\n",
-    "plt.xlabel('Longitude [°N]')\n",
-    "plt.ylabel('Latitude [°W]')\n",
+    "plt.xlabel('Longitude [°W]')\n",
+    "plt.ylabel('Latitude [°N]')\n",
     "\n",
     "legend = [Line2D([0], [0], color='k', label='Target (image) Grid, Regular 0.1°'),\n",
     "          Line2D([0], [0], marker='o', color='w', markerfacecolor='c', label='Source (timeseries) Grid, Irregular 12.5 km')]\n",
@@ -119,7 +119,10 @@
     "        # The only relevant part is, that the reader has a function that takes a latitude and longitude\n",
     "        # and return a time series for the closest point.\n",
     "        gpi, _ = self.grid.find_nearest_gpi(lon, lat)\n",
-    "        return self.df.loc[gpi]"
+    "        if gpi not in self.df.index.get_level_values(0):\n",
+    "            return None\n",
+    "        else:\n",
+    "            return self.df.loc[gpi]"
    ]
   },
   {
@@ -140,7 +143,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<matplotlib.legend.Legend at 0x7f882332b940>"
+      "text/plain": "<matplotlib.legend.Legend at 0x7f0ee3176110>"
      },
      "execution_count": 3,
      "metadata": {},
@@ -219,16 +222,14 @@
     "The .calc function will then start the conversion. First we split the passed time stamps into chunks of the selected size. This is relevant for large datasets that don't fit into memory. In our small example case a single chunk is fine. Then we loop over all points in the image stack and look for the according time series (nearest neighbour with max distance). If a time series is found for a point, observations are written into the image stack, otherwise the fill value is kept. As this can be quite time-consuming for large data sets, there is a keyword to activate parallel processing (when `n_proc` is chosen greater than 1).\n",
     "\n",
     "Other arguments:\n",
-    "- `format_out` to define whether you want to store one time stamp / single image in each netcdf file (`slice`) or the whole chunk (`stack`).\n",
-    "- `fn_template` to set the file name of the output files (make sure that the {datetime} placeholder is included.\n",
-    "- `drop_empty` to drop any empty images after conversion. This can be useful if you expect that there are many time stamps without data, and you want to save storage space (otherwise these files would contain only fill values).\n",
-    "- `encoding` to efficiently store data without wasting too much space, you can convert variable to a certain data type (e.g integers)  and provide scale factors (precision when converting floats as integers for efficient storage). Common encoding keywords are `scale_factor`, `add_offset`, `_FillValue`, `dtype` (see also https://docs.xarray.dev/en/stable/user-guide/io.html#scaling-and-type-conversions)\n",
-    "- `glob_attrs` and `var_attrs` netcdf metadata included in each file and for certain variables.\n",
-    "- `zlib` to activate zlib compression\n",
-    "- `var_fillvalues` and `var_dtypes` can be used to force a certain value to use instead of NaN and convert to a different data type than the default float32. Note, it's better to use the encoding keyword!\n",
-    "\n",
-    "\n",
-    "For some points the time series reading will fail. That's fine, we will just continue with the next point in those cases (we could adapt the source reader class to handle this more elegantly)."
+    "* `format_out` to define whether you want to store one time stamp / single image in each netcdf file (`slice`) or the whole chunk (`stack`).\n",
+    "* `fn_template` to set the file name of the output files (make sure that the {datetime} placeholder is included.\n",
+    "* `drop_empty` to drop any empty images after conversion. This can be useful if you expect that there are many time stamps without data, and you want to save storage space (otherwise these files would contain only fill values).\n",
+    "* `encoding` to efficiently store data without wasting too much space, you can convert variable to a certain data type (e.g integers)  and provide scale factors (precision when converting floats as integers for efficient storage). Common encoding keywords are `scale_factor`, `add_offset`, `_FillValue`, `dtype` (see also https://docs.xarray.dev/en/stable/user-guide/io.html#scaling-and-type-conversions)\n",
+    "* `glob_attrs` and `var_attrs` netcdf metadata included in each file and for certain variables.\n",
+    "* `zlib` to activate zlib compression\n",
+    "* `var_fillvalues` and `var_dtypes` can be used to force a certain value to use instead of NaN and convert to a different data type than the default float32. Note, it's better to use the encoding keyword!\n",
+    "\n"
    ],
    "metadata": {
     "collapsed": false,
@@ -245,33 +246,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Images are stored in /tmp/tmpeh0_4n5o\n"
+      "Images are stored in /tmp/tmps22ozjzr\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Processed:   0%|          | 0/3 [00:00<?, ?it/s]ERROR:root:Error reading Time series data at lon: 9.499999999999998, lat: 47.500000000000014: 2399817\n",
-      "ERROR:root:Error reading Time series data at lon: 9.599999999999998, lat: 47.500000000000014: 2399817\n",
-      "ERROR:root:Error reading Time series data at lon: 9.699999999999998, lat: 47.500000000000014: 2399817\n",
-      "Processed:  33%|███▎      | 1/3 [00:02<00:04,  2.16s/it]ERROR:root:Error reading Time series data at lon: 13.499999999999984, lat: 47.80000000000002: 2412903\n",
-      "ERROR:root:Error reading Time series data at lon: 13.599999999999984, lat: 47.80000000000002: 2412903\n",
-      "Processed:  67%|██████▋   | 2/3 [00:06<00:03,  3.34s/it]ERROR:root:Error reading Time series data at lon: 16.599999999999973, lat: 47.70000000000002: 2408657\n",
-      "ERROR:root:Error reading Time series data at lon: 16.699999999999974, lat: 47.70000000000002: 2408657\n",
-      "ERROR:root:Error reading Time series data at lon: 16.799999999999972, lat: 47.70000000000002: 2408661\n",
-      "ERROR:root:Error reading Time series data at lon: 16.89999999999997, lat: 47.70000000000002: 2408661\n",
-      "ERROR:root:Error reading Time series data at lon: 16.699999999999974, lat: 47.80000000000002: 2412979\n",
-      "ERROR:root:Error reading Time series data at lon: 16.799999999999972, lat: 47.80000000000002: 2412983\n",
-      "ERROR:root:Error reading Time series data at lon: 16.89999999999997, lat: 47.80000000000002: 2412983\n",
-      "ERROR:root:Error reading Time series data at lon: 16.699999999999974, lat: 47.90000000000002: 2417293\n",
-      "ERROR:root:Error reading Time series data at lon: 16.799999999999972, lat: 47.90000000000002: 2417293\n",
-      "ERROR:root:Error reading Time series data at lon: 16.89999999999997, lat: 47.90000000000002: 2417297\n",
-      "ERROR:root:Error reading Time series data at lon: 16.99999999999997, lat: 47.90000000000002: 2417297\n",
-      "ERROR:root:Error reading Time series data at lon: 16.699999999999974, lat: 48.00000000000002: 2417293\n",
-      "ERROR:root:Error reading Time series data at lon: 16.89999999999997, lat: 48.00000000000002: 2417297\n",
-      "Processed: 100%|██████████| 3/3 [00:09<00:00,  3.01s/it]\n",
-      "Processed: 100%|██████████| 14/14 [00:00<00:00, 115.13it/s]\n"
+      "Processed: 100%|██████████| 3/3 [00:06<00:00,  2.04s/it]\n",
+      "Processed: 100%|██████████| 14/14 [00:00<00:00, 112.06it/s]\n"
      ]
     }
    ],
@@ -312,7 +295,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<matplotlib.collections.QuadMesh at 0x7f88134aa200>"
+      "text/plain": "<matplotlib.collections.QuadMesh at 0x7f0ed5683f70>"
      },
      "execution_count": 6,
      "metadata": {},
diff --git a/src/repurpose/ts2img.py b/src/repurpose/ts2img.py
index 2bed040..bb8b056 100644
--- a/src/repurpose/ts2img.py
+++ b/src/repurpose/ts2img.py
@@ -130,6 +130,9 @@ class Ts2Img:
         afterwards. Then convert time series time stamps to deltas (>0) from
         the image time stamps and store them in a new image variable
         'timedelta_seconds'.
+    loglevel: str, optional (default: 'WARNING')
+        Logging level.
+        Must be one of 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'.
     """
 
     # Some variables are generated internally and cannot be used.
@@ -140,7 +143,7 @@ class Ts2Img:
 
     def __init__(self, ts_reader, img_grid, timestamps,
                  variables=None, read_function='read',
-                 max_dist=18000, time_collocation=True):
+                 max_dist=18000, time_collocation=True, loglevel="WARNING"):
 
         self.ts_reader = ts_reader
         self.img_grid: CellGrid = Regular3dimImageStack._eval_grid(img_grid)
@@ -155,6 +158,7 @@ def __init__(self, ts_reader, img_grid, timestamps,
         self.max_dist = max_dist
         self.time_collocation = time_collocation
 
+        self.loglevel = loglevel
         self.stack = None
 
 
@@ -191,7 +195,7 @@ def _read_nn(self, lon: float, lat: float) -> Union[pd.DataFrame, None]:
             logging.error(f"Error reading Time series data at "
                           f"lon: {lon}, lat: {lat}: {e}")
             return None
-        if self.variables is not None:
+        if (self.variables is not None) and (ts is not None):
             ts = ts.rename(columns=self.variables)[self.variables.values()]
         return ts
 
@@ -201,8 +205,8 @@ def _calc_chunk(self, timestamps, log_path=None, n_proc=1):
         See: self.calc
         """
         self.timestamps = timestamps
-        logging.debug(f"Processing chunk from {timestamps[0]} to "
-                      f"{timestamps[-1]}")
+        logging.info(f"Processing chunk from {timestamps[0]} to "
+                     f"{timestamps[-1]}")
 
         # Transfer time series to images, parallel for cells
         STATIC_KWARGS = {'converter': self}
@@ -218,7 +222,7 @@ def _calc_chunk(self, timestamps, log_path=None, n_proc=1):
         stack = parallel_process_async(
             _convert, ITER_KWARGS, STATIC_KWARGS, n_proc=n_proc,
             show_progress_bars=True, log_path=log_path,
-            debug_mode=False)
+            verbose=False, ignore_errors=True)
 
         stack = xr.combine_by_coords(stack)
 
@@ -445,5 +449,7 @@ def store_netcdf_images(self, path_out, fn_template=f"{datetime}.nc",
                            'encoding': encoding},
             n_proc=n_proc,
             show_progress_bars=True,
+            verbose=False,
+            loglevel=self.loglevel,
             ignore_errors=True,
         )
diff --git a/src/repurpose/utils.py b/src/repurpose/utils.py
index 567bdb8..f93e24e 100644
--- a/src/repurpose/utils.py
+++ b/src/repurpose/utils.py
@@ -53,7 +53,8 @@ def parallel_process_async(
         show_progress_bars=True,
         ignore_errors=False,
         log_path=None,
-        debug_mode=False,
+        loglevel="WARNING",
+        verbose=False,
 ):
     """
     Applies the passed function to all elements of the passed iterables.
@@ -84,8 +85,11 @@ def parallel_process_async(
         Show how many iterables were processed already.
     log_path: str, optional (default: None)
         If provided, a log file is created in the passed directory.
-    debug_mode: float, optional (default: False)
-        Print logging messages to stdout, useful for debugging.
+    loglevel: str, optional (default: "WARNING")
+        Log level to use for logging. Must be one of
+        ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"].
+    verbose: float, optional (default: False)
+        Print all logging messages to stdout, useful for debugging.
 
     Returns
     -------
@@ -101,7 +105,7 @@ def parallel_process_async(
     if STATIC_KWARGS is None:
         STATIC_KWARGS = dict()
 
-    if debug_mode:
+    if verbose:
         logger.setLevel('DEBUG')
         logger.addHandler(streamHandler)
 
@@ -116,7 +120,7 @@ def parallel_process_async(
         os.makedirs(os.path.dirname(log_file), exist_ok=True)
         logging.basicConfig(
             filename=log_file,
-            level=logging.INFO,
+            level=loglevel.upper(),
             format="%(levelname)s %(asctime)s %(message)s",
             datefmt="%Y-%m-%d %H:%M:%S",
         )
@@ -187,7 +191,7 @@ def error(e) -> None:
     if pbar is not None:
         pbar.close()
 
-    if debug_mode:
+    if verbose:
         logger.handlers.clear()
 
     handlers = logger.handlers[:]
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 8509180..437233f 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,5 +1,3 @@
-import logging
-import os
 import numpy as np
 import pandas as pd
 import time
@@ -31,21 +29,7 @@ def test_apply_to_elements():
     static_kwargs = {'p': 2}
     with tempfile.TemporaryDirectory() as log_path:
         res = parallel_process_async(
-            func, iter_kwargs, static_kwargs, n_proc=2,
-            show_progress_bars=False, debug_mode=True,
+            func, iter_kwargs, static_kwargs, n_proc=1,
+            show_progress_bars=False, verbose=False, loglevel="DEBUG",
             ignore_errors=True, log_path=log_path)
         assert sorted(res) == [1, 4, 9, 16]
-
-        # no log files created with pytest, probably because pytest logs
-        # everything already. Keeping this for possible later inclusion
-
-        # logfile = os.listdir(log_path)
-        # assert len(logfile) == 1
-        # logfile = logfile[0]
-        # with open(os.path.join(log_path, logfile), 'r') as file:
-        #     cont = file.read().replace('\n', '')
-        #     assert "x=2, p=2" in cont
-        #     assert "INFO" in cont
-
-if __name__ == '__main__':
-    test_apply_to_elements()