diff --git a/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc b/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc
index a682eac..7d3d19e 100644
Binary files a/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc and b/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc differ
diff --git a/methods/fit_linear_regression_model.py b/methods/fit_linear_regression_model.py
index b35a870..6f3889f 100644
--- a/methods/fit_linear_regression_model.py
+++ b/methods/fit_linear_regression_model.py
@@ -71,7 +71,8 @@ def fit_linear_regression_model(
     X_test_model = np.delete(X_test_model, remove, axis=1)
     poly_features = np.delete(poly_features, remove)
 
-    lin_reg = LinearRegression(fit_intercept=fit_intercept)  # Lasso(alpha=0.01)
+    # lin_reg = Lasso(alpha=0.01, fit_intercept=fit_intercept)
+    lin_reg = LinearRegression(fit_intercept=fit_intercept)
     lin_reg.fit(X_train_model, y_train)
     y_pred_test = lin_reg.predict(X_test_model)
     print(f"Parameterisation for {target_feat}:")
diff --git a/trackinglosses_endVelo_momEff.ipynb b/trackinglosses_endVelo_momEff.ipynb
index efaf4e7..741abbe 100644
--- a/trackinglosses_endVelo_momEff.ipynb
+++ b/trackinglosses_endVelo_momEff.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,21 +19,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "40402 10099\n",
-      "91861\n"
+      "41978 8523\n",
+      "92337\n"
      ]
     }
    ],
    "source": [
     "file = uproot.open(\n",
-    "    \"tracking_losses_ntuple_B_endVelo_idealstateP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    "    \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
     ")\n",
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
@@ -56,17 +56,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stretch factor:  0.24996287312509283\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
     "eta_found = ak.to_numpy(found[\"eta\"])\n",
@@ -76,12 +68,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABkMAAAL5CAYAAAADsVMKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC7E0lEQVR4nOz9cXRk15kYdn6vmzOc9pJNsDVpnsmsRLJA7dnIoUYqtOT1cD1ajwCPZyeZ2GqAyslmY2U9DYTGHGetHQND757TbCfHEGAlcSZpcwCOz6GPN5sVgdZkMzvWeADKkde0Y7FRI4u2NmfFLlJSRgo5UjfU5KSHUne//aMFqNF476LqoapQVf37nVOH7Pe9e+/3XlWh7q1b970sz/M8AAAAAAAAhtSRw04AAAAAAACgm0yGAAAAAAAAQ81kCAAAAAAAMNRMhgAAAAAAAEPNZAgAAAAAADDUTIYAAAAAAABDzWQIAAAAAAAw1EyGAAAAAAAAQ81kCAAAAAAAMNRMhgAAANBzjUYjlpaWYmxsLCYmJg47nQPrt+NpNpuxsrLSF7kAAPQDkyEAAENmbW0tpqamIsuyXY+tra226hkdHd1VfmxsLObn57uTNHeVjY2NmJmZ2fUaGx0djYmJiVhaWopGoxGNRiNGR0cPO9W2ra2txYMPPhhLS0uHnUpfazQa8ZnPfCbm5+ej0Wi0XG77y/07/7618uim1PGsra3F2NjYnny2X+9l9d35d3xqaiqazWZLuUxMTMTo6GjMzMzExsZGR44RAGDQZXme54edBAAAnbexsbHrF8Fzc3OxuLjYUtntCZVt4+Pjsb6+3vEcB9n25NLIyMih5tFLBz3m7S94m81m1Gq1mJmZifHx8RgZGYlmsxmNRiMWFhZ2tXPlypXOJN9BqfMwNjYWjUYjarVaXLp0qbeJDaCpqalYW1tr+2/M/Pz8zkTC+Ph4rK6u7no+tra24vLly9FsNncmKHox9E0dz8TExM7ERL1ej83NzX3rW1paivn5+VhcXIy5ubm2cllZWYmZmZmIiJ4cOwBAv7MyBABgSI2Pj+/6dzu/VF9YWNj1xaLLrOzV6q+0h8lBjnllZSXGxsai2WzG5ORkXLp0Kebm5qJer0etVovx8fGYm5uLK1euxPT0dERE26uZeiV1HhYXF2N8fLzlice73YkTJyqVu/1vUr1e3zMxNTIysvO6evHFFw+SYltSx7O6urrz/62uhvnOd74TIyMjbU+ERETUarW2ywAADDOTIQAAQ65er+/8fysTItuXKNr+Qjri7lr90Ir5+fm77tIzBznmtbW1nV+oT09P7/pSuMjy8nJMTk5Waqvb9jsP2ysC+jX/u9HIyEhfPB8jIyO7/q628vd4ZWVlVxkAAKozGQIAMOSefvrpnf9fWFjYd/+FhYWYnJyMD33oQ91Ma2Ctra3ddfeDOMgxb21t7brkWqsrJp577rmd8v3ibnzuh8V+E3C9cvvrf7+/xxsbG7G1tbXrbzgAANWZDAEAGHK1Wm3nV9FbW1uxsrJSuu/W1lasra21/eXb0tJSTExMxNjY2M6NsFPtbN+TZPuG7Ns3/M2ybM/Np5vNZszMzMSDDz64c9Ph1BfkW1tbu27OPTY2VvgF9va5uD3XlZWVXeWKboR85syZnX9/9KMfjQcffDAefPDBnfjtN0q+fQXB0tLSrtidN6Pv5jkps7a2FhMTEzE1NbXz/N35vO13zPu5/Qvfubm5llcZ3fkr+ohbXw5PTU3t5DgzM7Nz/Ldr9TWwrZPnYfseFWNjY8nja+c9U+W12qpWjr1TOczPz8fo6GiMjY2VttNpzWazcCXPYR3P7atUtv/elllcXIzJyck990Jp57Xdinb/fgMADKwcAIChFRH55uZmvrm5mUdEHhF5rVYr3X9ubi6v1+t5nuf56urqTpnl5eXC/Tc3N/NarZZPTk7ubLty5Uo+OTmZR0Q+Pj6eX7lyZSe2vr6ej4+P79Q7PT290+bc3Fw+PT29q83V1dW8Vqvt7Fer1fKI2MnxTuvr63m9Xs/X19fzPM/zS5cu7bR3e5nt/bbbWlxczCcnJ/N6vZ5PT0/vtBMRu/LfPr7t2Obm5p4cLl26tBPfzuN2IyMjeUTkc3NzPTknZbbbvP345ubmds5HO8eccvu5LDofrVhcXNxVz/Lycj4+Pr5zLiMiv3TpUp7nrb8GtnXqPGxubu56HkdGRgqPpcp7puprdT+tHvtBc7hy5Uper9f3HNv2MW8fdzvW19d3ym6/l4osLy/veR4P+3hu/xtR9vd4e5/b3zPtvrZvP0dF2n0tAgAMOpMhAABD7PYvbW//onZ1dbV0/+1YK5MhIyMjpV/6brdX9KVgKpftL/9HRkZ2fUmX57u/RNz+8vvO2J1fUt/+BfadX5puf5k+MjKy60vHK1eu7MSqTAykvvzf/qL1zly6cU7KbH9Jeudzt13XndurTobcXq6d/Parq1ar7ZzbxcXFfHp6elf+rb4GunEebn+uilR9z1R5raa0e+wHyaFeryfPRzcmQ65cuZKvr6/nIyMjpeflMI/n9vd70d+J7YnObVX+vu03GVL1tQgAMKhcJgsA4C5x+2WZiq5Vv7KysuuSWq3Ut7W1VXpz3+1r429sbOy5FMz2ZV/Gx8f3tLd9f4mtra2d+0Zsq9VqO2XvvITN/Px8jIyM7Lph/HZb29vuvPRLrVaLiFs39R4fH99V5tSpUxERcenSpcLjq6rsMlHdOCf72T7+bSdOnNhppxP36rh8+XKyvXaMjIzsOkfbz9fc3FwsLy9HRLXXQFFeBzkPqctjHeQ9063XajvHXiWHlZWVaDQapZfeu72eqpaWlnYuP7f9ePDBB/e9fNxhHs/tf4+L7qOzsrISMzMzu/av8tpOtV/1tQgAMKhMhgAA3CXGx8d3vjRrNBp7rqO/uLi45z4WKdtfkJXdaL1er+982fiZz3xmV2z7C9eiiYHtWJnteLPZ3JPP1tZWjI6O7nlsTxK08+X2du53ttMt3TgnZcbHx+PSpUuxubm5a/vt5e+cyKiiWzc/L5twaPc10I3zkHquDvKeSanyWu30sZflsP2l+p1f4m/b77Xdiunp6bh06dKux+bmZqyurrZ8j5o7dft4bv97vLGxsWsic/t1fPtERaf/vnXrtQgA0M/uOewEAADonaeffnpnlcHi4uLOr5jX1tbi8uXLpb8SLtLKF6+1Wi2azWblmzsXKfpyc7v+8fHxWF9f71hbg6LKF763rwhYW1uLz3zmMwdaubFfGxG3vqyt+uX0fqq+BnpxHrYd1nsm1da2bh379jF365xG3Hr9F9Vfr9c7PpnZyeO5/e/xwsJCrK6u7vz/7TdO78bft357LQIA9IKVIQAAd5HJycmdL/Fu/zXywsJC5YmQ1K/Ht9vqxCqDlO36e7WKY1isrKzE6OhoXL58OVZXV0sv/VPV7Ze2ioi4ePFiR+u/3UFeA90+D3fm1Q/vmW3dPPZ+eD/e/jfvoDp9PLfntra2tjPxcOdluDr9961fX4sAAN1mZQgAwF1mfn5+51r0CwsL8fTTT0ej0YgXX3yx5Tpu/3IxdZ+C7S/Cu/mr8Ij2LxNFxMTERGxsbMSlS5e6+vyMj4/vXJJnfX29I/eIKFL1NdCr89Bv75mI7h/77ZdsajabPTmmO9VqtY61243juf3v8fYluGq12q7LcHX671s/vhYBAHrByhAAgLvM9PT0zpdca2trcebMmV3bWnX7CpMy21/ebd+MuFvuvNxPma2trbZuMtwN3bqPRjvm5+djY2Mjpqenu/5F5+03fF9aWura8Vd5DfTyPNyeYz+8Z3px7Ld/od8Pl1q6894c7erG8dz+t3dlZSVWVlb23LupG3/f+um1CADQKyZDAADuQrdfgqXRaBTeOH2/S6Ns/5q50WiU/mJ5+wvDdm7MXsXt9ww4c+ZM6RfuU1NTHV2ZsN85KsqjHy45s/2F6ujoaNtl281/ZGRk5xfvEbeen1YtLS21/KVzlddAL89DRH+9Zw5y7O3Yfk4WFha62k4rtu/PcRDdOJ47L0125yULu/H3rZ9eiwAAvWIyBABgSG1/YVb0Rdfc3NzO/7dyTf2iL9/m5uZ2yt3+Zfe27S/Zbt9v20EmBLZz+c53vrNr+3YOW1tbMTY2tutL9EajEWNjY1Gv11v+FXxZjrevoLm9jdvP83Yby8vLu8re/qXinV/yd+OclNlu6zOf+cyu7bd/wXv7c97KMafMzc3F5ORkRNz6En5qaiq5QqTZbMbU1FRcunRp16/x99Pua6Ab52G7zk6/Z1KqvHbaPfaqOdz+nNw5GbG1tRUvvPBCRHT/Enfbbbf6eurl8dz+97js3k2d/vvWrdciAEBfywEAGErLy8t5ROSTk5OF8bm5uTwi8s3NzcL45ORkHhHJOi5dupTXarU8IvLFxcU926enpwvLjY+P5xGRj4+P74mtrq7utLu+vr4nPjIyUprT7Tnf+SjKJVVXvV7PIyKv1+ul+Y+MjOSLi4v5+Pj4rlwXFxd32q3X6/nk5GReq9XyxcXFPTleunSpq+ekyPT09E59tVptJ7/b29nefmd+ZcfcisXFxZ1ct5+T5eXlfH19PV9fX8+Xl5fzycnJfGRkJF9dXd1TPvVcbmvnNdCN87D9voqI/MqVK3vyq/qeqfpaLVPl2KvmcGdbc3Nz+dzcXF6r1XbKbZ/TVl9Tt5/n8fHx/NKlS7vO95UrV/JLly7ly8vLO23cmXc/Hk/Z3+M8b//v2/ZnQKdfiwAAg8pkCADAkFleXt75wvb2L+Rv/7Irz299WVj0xfvc3NyuL/RSdWzb/lJ4+8vAsi/KNzc3d32RuP0l3vYXgHd+Wb49gZDneb6+vr7nuKanp3e+qL3z+EdGRvKRkZHCXDY3N0vrunTp0p4vHW//QjjPb31ZePuXpUXHuri4uPNF4/j4+M4xTk9P55OTkztf9vfinBSZnp7eOUfT09M7X5ZuT+TUarVdx9XKMbdqe9Jj+/zc/sV70STI7efy9uMs2ne7/v1eA50+D0XPY6feMwd5raa0euydyGF1dXXnObl9kmVubi6fnJxs+fVU9Pet1cfy8nLHzmmnjmfblStXWprMauW1fenSpa68FgEABl2W53keAAAAAAAAQ8o9QwAAAAAAgKFmMgQAAAAAABhqJkMAAAAAAIChZjIEAAAAAAAYaiZDAAAAAACAoWYyBAAAAAAAGGomQwAAAAAAgKFmMgQAAAAAABhqJkMAAAAAAIChZjIEAAAAAAAYaiZDAAAAAACAoWYyBAAAAAAAGGomQwAAAAAAgKFmMgQAAAAAABhqJkMAAAAAAIChZjIEAAAAAAAYaiZDAAAAAACAoWYyBAAAAAAAGGomQwAAAAAAgKFmMgQAAAAAABhqJkMAAAAAAIChZjIEAAAAAAAYaiZDAAAAAACAoWYyBAAAAAAAGGomQwAAAAAAgKFmMgQAAAAAABhqJkMAAAAAAIChZjIEAAAAAAAYaiZDAAAAAACAoWYyBAAAAAAAGGomQwAAAAAAgKFmMgQAAAAAABhqJkMAAAAAAIChZjIEAAAAAAAYaiZDAAAAAACAoWYyBAAAAAAAGGomQwAAAAAAgKFmMgSAu1aj0YiVlZXDTgMAAACALjMZAkBXNBqNmJ+fj6mpqRgdHY2lpaXDTmlHs9mMqampGBsbi+Xl5eS+GxsbMTExEVmWRZZl8eCDD8bo6GiMjo7GxMREzM/PR7PZ7FHmAAAwXLbHDWNjYzE2Nnaouej7Awy3LM/z/LCTAGC4NBqN+OhHPxpXrlyJiIj5+fnY2trad+Kh17Isi3q9Hpubmy3tGxGxubkZ9Xo9IiLW1tbizJkzsbW1Fevr6zE+Pt7VfAEAYBg1Go0YGxtruW9eRbPZjBMnTsTIyMi+++r7Awynew47AQCGz8LCQpw4cWLn34uLi4eYTWfdPnianJyMiIipqamYmpramfwBAABatz3h0E1TU1Oxurra0mTINn1/gOHiMlkAdFyj0TjsFDqubNC0/Yuwra0tS+YBAKAPTU1NtTVG0fcHGE4mQwDomJWVlZiamopms7lzX46pqanY2NjY2WdraytmZmZifn4+JiYmYmJiYld8bW0tHnzwwciybGfAsrGxEVNTU5FlWUxNTe3Us7KyEmNjY7G2thYbGxsxNja2a5/bbbe7/ejUPUwuX77ckXoAAIC99hs/3LnPzMxMjI6OxsrKSkTcGl9sjytmZmbanhi5nb4/wGBzmSwAOmZ6ejqmp6djdHQ0IiJWV1d3xbfvJfLiiy/uLIVfWVmJiYmJWFxcjLm5uZicnIz19fWdwUvErV9g1Wq1WFtb29l2+fLlWF9fj0ajEcvLy1Gv1+O5556L5eXlWFlZiaWlpZibm4uIW9cHHhsbi9XV1Z1fc3VqMmQ7p3q9HrVarSN1AgAArY0fIiLOnDkTtVpt5/K8KysrsbW1FRG3Lm/18ssvx9LSUiwvLx+oz67vDzDYrAwBoGfOnDkTp06d2nVN4Onp6ajX6zE/P7+z1LxoWfrt9yCJiKjVavHxj388ImJnMFSv13du0r6+vr6z7/z8fJw6dWrXTQ63B07t2h5UNZvNWFpaivn5+ajX6/Hiiy9Wqg8AACjW6vjhzpUi09PTHWlf3x9guJgMAaAnms1mNBqNwpsjzszMRETsTGS0q2jyZHsJe7PZjLW1tZiYmKhU952mpqZidHQ0pqam4uWXX47l5eXY3Nxs60aMAABAWjvjh1qtFktLS7tWf1f98dPt9P0BhovLZAHQE6nr8p46dSoiois3Idyus1PL2NfX1y2JBwCALmtn/LC6uhpjY2MxPz8fy8vLsbq6WjiJ0i59f4DhYmUIAD21vdT8dtu/rLrzUlidsD1AcrNDAAAYPK2MH2q1Wrz22msxPj6+c7/A2+9BCAARJkMA6JHtX2bdeT3fiB8OcLZvvN5J27/k2tzc7HjdAABAd7Qzfmg2mzEyMhLr6+uxuroaET+8lBYAbDMZAkDHXb58ec9KjFqtFvV6PZrN5p7LYV28eDFGRkZ2bnT4rne9KyJ2XzZr+/+LfhmWsr2EfmVlpbBsq/Vt79du+wAAQPvaGT8sLi7uxCYnJ3fuJXJnOX1/gLubyRAAemZ1dTVGRkZ2/Upra2srFhcX47nnnttZ7r79K7D5+fnY2NiIlZWVnQHNxsbGzs3QW7n01cjIyM7NE8fGxmJjYyOazWbMz89HxK0B0u03WtyPy20BAEBvtDp+eOGFF3ZNfGxtbUWtVttZJb69gmR5eTmazWasra211L6+P8BwyfI8zw87CQCGQ6PRiOXl5Z3r805PT8fU1FSMj4/v7LO1tRVnzpzZGaBE3FrCfucNDpeWlmJhYWGnnsXFxRgdHY3Jycn4+Mc/HhERZ86ciUajEbVaLZaXl+PUqVMxPz+/0/7i4uLORMjKykosLi5Gs9mMer0eq6urMTExEZOTkzEzM1N6Y8SNjY1YXFzcWZ5fq9ViZmZmp14AAKC6O8cQi4uLMT09vTPR0cr4YWJiIprNZkxOTkbErR883T5ZEnHrh1HNZjOefPLJnR9a3UnfH2C4mQwBAAAAAACG2j2HncCd1tbWYmFhYdcvfW//RXHErV8NLCwsRK1Wi62trZ1f9h7WPgAAAL1k3AQAQL/b2trauerH7fd32tbrfmZfTYasrKzE5ubmzomZn5+PiYmJuHTp0s5SyGazGWNjY7G5ubmzJHJ0dDQuX768c+OsXu4DAADQS8ZNAAD0u42NjVheXo61tbXCPuFh9DP76jJZS0tLu67D2Gg0YmxsLFZXV3dme7Zvmru+vr6z38rKSszMzMT2ofRyHwAAgF4ybgIAYFBkWRbT09N77td0GP3MI22X6KI7b0i1faOr7Vmfra2t2NjY2DkJ206dOhURt05EL/cBAADoNeMmAAAG2WH1M/tqMuROa2trsbi4uLPU++LFixERO//ett3pX19f7+k+AAAAh824CQCAQXJY/cy+umfI7ebn52NlZSWee+65nW3NZjMifvjLpzs1m82e7lPm29/+dvz2b/92/MRP/ET82I/9WOl++/nRH/3R+NEf/dHK5QEAoBXf+9734nvf+17l8n/0R38U3/rWt+IXfuEX4sd//Mc7mBn7GdRxkzETAHDYhq0P/PWvfz2+/e1vt13uoOchIuInfuIn4r3vfW/L+3f7+/kyfTkZsrS0FM1mM7a2tmJqaiqWl5djeno6Ll26FBERJ06cKCy3tbXV033K/PZv/3Z84hOfKI0DAMAwev755+Mv/IW/cNhp3DUGedxkzAQADIt+6AN//etfj8cefji+f0jt/8iP/Ei8+uqr8Z73vKel/bv9/XyZtiZDrl69GhsbG9FsNqNer8fP/uzPtt1gK7avgbuxsRFTU1OxuLgY09PTMTo6GhERly9fLixXq9V6uk+Zn/iJn4iIiF/7tV+Ln/qpnyrdbz9lv3I6ffp0XLhwoXK9++lm/YOa+9tvvx0f+chH4gtf+ELcd999Ha8/wnk/jLq7/bw6772v23N6OHV3s35/fw+vfu/Vw6n/sHJv9ddgH3m9JPA//POIhb+80w++2xk37d6nSKfHTJ187/RjXZ2qp9N/f4f5XHWyLuf9cOpy3g+nrn497/14rjpZl/Nera52VkR8pFGw8dI/j/gv+6MP/O1vfzu+HxEfi4h216jc+MGjqu9ExG99//vx7W9/u+XJkG5/P1+m5cmQ7373u3Hq1Kldy09GR0djfX09Hn744bYbbsX4+HhMT0/H0tJSRPzwAMtmfWq1Wk/3KbO9zPunfuqn4md+5mdK96vq2LFjO9dG64Zu1j+ouV+9ejUiIj7wgQ/E8ePHO15/hPN+GHV3+3l13ntft+f0cOruZv3+/h5e/d6rh1N/3+e+z8jqIJc7GhbGTXv3KdLpMVMn3zv9WFen6un0399hPledrMt5P5y6nPfDqatfz3s/nqtO1uW896Cu6+WhfuoD/3hE/Ks9brPKBUu7/f18mZYnQ+bn5+PSpUsxPj4eIyMj0Wg04tVXX42xsbFK1yJr1Yc+9KGdA9u+U/yd1wPb/vfY2FhP9wEAALidcdPefQAA6I17ovf3xajS3mH1M4+0uuP2Mu/f/d3fjRdeeCFeffXVuHjxYty8eTP+zt/5O2033Kpmsxnj4+MRcetmKfV6fc+d4jc2NiIi4sknn+zpPgAAALczbtq7DwAA3O6w+pktT4bUarV45JFHdm2r1+vx3HPPxcWLF9tu+E7bN/1bW1vb2dZsNmN9fT2Wl5d3tj333HM7A4xti4uLsbi4uHNn+V7uAwAAsM24qXgfAAC6756I+JEeP1IrQ1I3OT+MfmbLq1gefPDBwu3j4+OxsrLSdsN3GhkZia2trThz5kwsLy/HxMRE1Gq1PTM/9Xo9Njc3Y35+Pmq1WjSbzZifn4/p6elD2QcAAIZJ1szLg3/8zZLAla7kMoiMm4ybAIDBln2+YONHzxVsfL3LmQy2RqOx82OdF154ISYmJnYuJRtxOP3MlidDVldX413veleMj4/H+Pj4zs14HnjggfjOd75TqfE73dmBL1Ov12N1dbVv9gEAAIgwbgIAgIhb/cPl5eVdq5eL9ullP7Ot+5vcnnytVtuZzRkdHe1IMrRmdnZ2YOsf5Ny7zXnvfd3d5rz3vu5u85weXv3d5Lz3vu5uc94Pr35uMW7qvU6+tvuxrn597w77uXLee19Pp+vqpH48xn7MqdOG/Vw574NfV785Gr2/gfrRHrd3EFme54l18D904sSJePLJJ+PSpUvx4osv/rCCLIuIW3dvf/LJJ2N8fDw+8IEPRETEb/zGb8Qv/dIvdT7rPveP/tE/io985CPxhS98IX7mZ37msNOhA65evRoPPPBAfPe73935dR+Dz/M6fDynw8dzOpw8r/0teZms0bLLZP33EfHn9H/DuKlVxkyHw9/fw+G8Hw7n/XA474fDee+s9i6T9Xxf9GcajUaMjY3FX4mI/2WP2/4fI+I/i4jNzc2o1+s9br09LU8ULS4uxpkzZ3b+/Xu/93vxmc98JjY2NqLRaMTFixdjc3MzIm5dx/bUqVNx8eLFu65TDwAA3L2MmwAAOCzbN1DvdZuDouVcb+/QR0R88IMfjA9+8IMREfHd7343NjY2Yn19fefu7uvr6zu/fgIAALgbGDcBAEB/6sjEzQMPPBCnT5+O06dPR0TEa6+9Fuvr6/Grv/qrnageAABg4Bk3AQDQTe4ZktaVc/Poo4/G9PS0XzgBAEAfyr6SCP7xRB/+noeKt998MOLmgVK6Kxk3AQAcnmyxJHB+76Y8P7tn2617oD3f0ZzoriPdrPzOJeIAAADsZtwEAADdN0j3NwEAAAAAAAq4gXpaV1eGAAAAAAAAHLZBmrgZGD/6oz+6678MvnvvvTfOnj0b995772GnQgd5XoeP53T4eE6Hk+d1GOn/0h5jpsPh7+/hcN4Ph/N+OJz3w+G8H45+7M+4gXpalud5fthJDJtGoxFjY2OxubkZ9Xr9sNMBAIBd0jdQT8TKRlZ5I+KG/i+tM2YCAA5bWzdQ//rebf3Un9nO5WxEPNLjtl+PiHMRfXEe9nPgiaLXX389lpeXo9lsxokTJ+Kxxx6LM2fOxPHjxzuRHwAAUMFj8S/Lg388NePx2fLQ9aslgddbyOjuZtwEAHA42pn0iCie+GA4HGgy5NOf/nTMz8/HnYtL/sbf+BvxG7/xG/Hn//yfP1ByAAAAg864CQCAXnAD9bTKub744osxNzcX9Xo9ZmZm4tSpUzEyMhJbW1vx8ssvx1/9q381Hn300fjABz7QwXQHy+nTp+PYsWOFsdnZ2Zidne1xRgAAUNXnIuLvl8S+18tEBopxU5oxEwDQz86fPx/nzxcvIbl27VqPs+GgKk+GLC4uxvLycpw5c2ZP7IMf/GA8+eST8fTTT8ezzz57oAQH2YULF/r+OmkAANCan4+IJ0pir8etKwVzJ+OmNGMmAKCfpX6csX2fjn5yT/R+pcYgrQw5cpDCRR36bSMjIwepGgAAYCgYNwEAwOGrPHHTyqxXs9msWj0AAMDAM24CAKBX3DMkrXKuV65ciX/+z/95/NRP/dSe2Ouvvx4zMzN+5QQAAF30p2K9NHYpeyRR8nOJWNmlsCIiGiXb30qUubsZNwEA9E72nr3b8q+X7Dzf1VToQ5UnQz71qU9FrVaLD33oQzvXeN3a2oqNjY1oNpsxMjISr732WscSBQAAGDTGTQAA0B8qT4aMjIzExsZGnDlzJhYXF3fF6vV6rK6uxvHjxw+cIAAAwKAybgIAoFdcJivtQLnW6/XY3NyM1157LRqNxs62Rx99tCPJAQAADDrjJgAAOHwdmbh59NFHCzvyV69e9SsnAACAMG4CAKC7jkbvV2oc7XF7B3Gkm5WvrKx0s3oAAICBZ9wEAADd1/JE0Yc+9KG2K280GvErv/IrbZcDAABueSz+ZWnsUjaRKPlSIvaTFcu9UbL9m4kydxfjJgCA7vtjV68Ubs+//uCebVl2rnjf/GxHcxpmvxUR/++S2Pd6mcgBtTwZkuf5zvVtW5VlWdsJAQAADCrjJgAADku3bqD+sR88inw1Iv5SF9rshpYvkzU+Ph6bm5tx8+bNlh9nzpzpZu4AAAB9xbgJAAD6U8srQz7+8Y/HBz/4wbYqn5mZaTshAACAQWXcBADAYXED9bSWV4aUdeivXr0an//853f+/eKLL+78u91BAAAAwCAzbgIAgP7U8mRIkaeeeioefPDB+DN/5s/sbPvoRz8aly5diqeffvrAyQEAAAw64yYAAHph+54hvXz0eiXKQVTO9Vd/9VdjeXk5RkZG9tzw78yZM/Fn/syfib/zd/5O/MW/+BcPnCQAAAyzH7/5+6Wx7xz944mSn03EXk3ETiZiDyVitMu4CQCguuzzJYFPPFi8/et7N+X52eK6s3PVktrxzQOWp9cqrwxZW1uLtbW1uHz5cnz0ox/dE5+YmIhPfepTB0oOAABgkBk3AQBAf6i8MqRWq8XHPvaxiIg9v3CKiHj55Zej2WxWzwwAAGDAGTcBANArbqCeVnllyMjIyM7/53m+K/Z7v/d7sba2FrVarXJiAAAAg864CQAA+kPliaKnn346fu7nfi4WFxd3fuH0+uuvx9raWszPz0eWZTEzM9OxRAfR6dOn49ixY4Wx2dnZmJ2d7XFGAABQ1W9FxG+WxL7fy0QGinFTmjETANDfvviDR5HrvUykJds3UO91m4Oi5Vy/9KUvxQc+8IGdf3/wgx+MhYWF+KVf+qVoNBqxtrYWET/8tdP8/Hz8yq/8SmezHTAXLlyIer1+2GkAAEAH/JsR8XhJ7OsR4b4XEcZN7TJmAgD624d/8CjyzYhY6WEuHFTLkyFnzpyJl19+ede2er0eFy9ejNdeey02Nzfjtddei1qtFuPj4/HAAw90PFkAABhUvxirpbHv/PhUouRXK7b4ZxOx30/EXq/YHhHGTQAAVWXZuZb3zfOzXan3licK2hvfs63RaMTYmMmQQdLyZMjm5ma8613viqeffjqmp6fj+PHjO7FHH300Hn300a4kCAAAMCiMmwAAOCz3RO8vWzVIl8lq+Qbq9Xo9PvWpT8Wrr74ajzzySHz84x+Pz3/+893MDQAAYKAYNwEAQH9qeTLk6aefjjNnzsSv//qvx+XLl+PJJ5+Mubm5eNe73hWf/vSn4+rVq93MEwAAoO8ZNwEAcFi2b6Dey8dQrgw5ffr0nn9fvHgxXn755Z1fPf3cz/1c/OZv/mbHkwQAABgExk0AANCfWp4MKVOr1XZ+9TQ9PR1/8S/+xXjXu94Vf+2v/bV4/fXXO5AiAADAYDNuAgCg247GD+8b0qvH0Z4cWWd0bBXLb/zGb8Ti4mJ897vfjTzP41Of+lRcuXIlnn322U41AQAAMNCMmwAA4HAcaDLk6tWrsbCwECsrK7G1tRV5nsfIyEg8/fTTMT09HQ888ECn8gQAgL73i7FaGvutbKpirRuJ2BOJWCMRezMReyydTqE/rFDm7mHcBADwQ9l7yiKpvu0ddWTFfeQ8H2+5jjw/2/K+DIeWJ0M++9nPxsc+9rGIiPjSl74UCwsLsba2FhEReZ5HrVaL+fn5OHPmTHcyBQAA6HPGTQAAHJbtG6j3us1B0XKuCwsLcfny5VhdXY2NjY3I8zwiIsbHx2N+fj4++tGPdi1JAACAQWDcBAAA/anlyZDNzc2YmZnZ6cxPTk7G4uJiPProo11LDgAAYJAYNwEAcFisDEk70s7OeZ7H3NxcXLlyJV544QUdegAAgDsYNwEAQP9peeKmVqtFo9GI48ePdzMfAACAgWXcBAAA/anlyZDl5WUdegAA7noz8Z+Xxn4r+w/LCz6YqPTKS4ngI4nYq4nYtUTs/kTsWGkkz3++cHuj0YixsUSVdxHjJgCAW7JsoyRS1ved27Mlz4v7pll2rqSO8YI6zpbsW6yo7nbrOCxHo/eXrTra4/YOouXLZLV7o79Pf/rT8frrr7ebDwAAwMAybgIAgP7U0kTRc889FysrKy1XurW1Fc1mMy5fvhx/42/8jcrJAQAADArjJgAADtM9RyN+JOtxm3lE3Ohtm1W1NBly6tSpmJmZabvy1dVVnXoAAOCuYNwEAAD9q6XLZH3wgx+MycnJuHnz5s5jcXExFhcXd227/TE3Nxfr6+vdzh8AAKAvGDcBAHCYjh6NuOee3j6ODtBNQ1q+Z8ji4uKufzebzfirf/Wvlu4/MzMTU1NT1TMDAAAYMMZNAADQn1q+ufyjjz7aVsXNZjMajUbbCQ2T06dPx7Fjxwpjs7OzMTs72+OMAABoxZ+K8l/q/+Mf+Q8TJd8oD135XKLcQ4nY64lYyhOJWKqfXnYML0aW/VJJ7HutpXQXMG5qjzETAAyH7D1FW18q3DfPz7Zeb3aurTqK9m9n372+GBFfjPe97zN7IteuXWuhPP2k5cmQO+V5Hv/wH/7D+NN/+k/viV29ejVmZmaiVqsdKLlBd+HChajX64edBgAAdMBHf/Ao8npEtDKYvPsYN6UZMwEA/e3DEfHh+MpX9k6oNBqNGBsb631KCfccifiRLly26te/H7Hy/eLYH+Wdb69bKk+GfOpTn4parRYf+tCHYmJiImq1Wly+fDk2NzdjZWUlIiKWl5c7ligAAMCgMW4CAGDQ/Qc/cutR5PduRDzxR73Np6rKkyEjIyNx8eLFmJ+fj7m5uciyLCJu/fIpImJubi5+6ZfKltEDAAAMP+MmAAB65Z57Iu7p8Q3N78l6295BVJ4MiYio1Wqxuroar732Wly6dClee+21qNVqcerUqXjggQc6lSMAAMDAMm4CAIDDd6DJkG2PPvpo4Y0CP/3pT8ev/MqvdKIJAACAgWbcBAAAh+dAkyGf/vSnY319PS5fvlwYbzQaOvUAAPSlX4zV0tg/zqYSJZ9PxD6WiB1PxK4lYtXk+ftLY1n2m4mSJxN1PlW4/dbNI91AvYxxEwAwLEq7yd/oTl8wz/feuLzbDqPNTrnnaMSPdGT5Qxtt9ra5A6mc68c//vFYXS0fQEbEzvVwAQAA7kbGTQAA0B+OVC24uroaMzMzcfPmzdLHmTNnOpkrAADAQDFuAgCgZ45ExNEePyrPMPRe5VTr9XrMzMwk91lcXKxaPQAAwMAzbgIAgP5QeTJkcXExPvOZzyT32dzcrFo9AADAwDNuAgCgZ47GrRtj9PJxtCdH1hGV7xmytbUVjUYjPv3pT8fIyEjhPouLi/HVr361ahMAAAADzbgJAAD6Q+XJkIWFhWg0GrG+vl66jxsBAgBwmP5ufLw09lv/aurX+m8kYscTsZf2S6nE1UTsrUTsZGkky5YS5R5PxF5J1HmuJPLNRH13N+MmAGBQZdnGnm15Pl6ybzv1Fvcp8/xs65WU6EQdDK/KkyHT09OxsbERH/948QDzO9/5TqysrFRODAAAYNAZNwEA0DPbl67qpZs9bu8AKp+aj3/84zExMRGPPvpo6T4f+tCHqlYPAAAw8IybAACgP1SeDHnggQfigQce2LP99ddfjxMnTsTx48fjgx/84IGSAwAAGGTGTQAA9Mz2DdR76UaP2zuAlk/N5z//+Z3//9mf/dnS/a5cuRKTk5Px3e9+NyYmJuJv/+2/fbAMAQAABoRxEwAA9Kcjre44Pj4e8/PzsbW1FRERV69e3fOIiPjgBz8YFy9ejEceeSSWl5e7kjQAAEA/Mm4CAID+1NaimZdffnnn/9fX12NhYSF+7/d+L2q1WszMzMSv/Mqv7MSXl5fjve99b+cyBQCAAv9ePFca+3s//Znygt+6mqj1s4nYsUTs1UTs/kQsJVXuzYrlUseQil1LxNhm3AQADJIsK+vjvVSwb/GeeX62A3mc60q9Zdqpuyi3iG92LplOORK3LpXV6zYHRMuTIbVabde/T58+HadPn44jR47E+vp6PPLII3v2T90kEAAAYNgYNwEAQH9qeTLkwQcfLNxeq9X2dOj3K3O3OH36dBw7VvzLutnZ2Zidne1xRgAAUNU/iaJfB95yvZeJ9DXjpvYYMwEA/ez8+fNx/vz5iPiDgmgf9oEP4wbqvV6JcgAHPjVZ2doo4sKFC1Gv1w87DQAA6ICfjogPlsS+GRErPcxl8Bg3FTNmAgD62faPM8ovk6UPPEgG6IpeAAAAAAAA7Wt5ZUiz2Yyvfe1rkef5nljR9itXrkSj0Th4hgAAAAPCuAkAgENzT/T+Mlm9bu8AWk71ypUre24GuK1sOwAAwN3EuAkAAPpTW/M2Rb9uSnFdXAAAOuHxeLk09i9OnCkveOVqotb/KhF7fyL25UQspfgm0d3zSCJ2PBE7mYi9WbL9x/bN5m5i3AQA9KPs852o5aWS7eN72yu8z0ZExBOFW/P8bMt1FO1btn/Zvu0oqqPRaMTYWJ/dM+RI9P6G5gN0I46WU63X63Hz5s22Hh/96Ee7mTsAAEBfMW4CAID+1PLKkKeffrrtymdmZtouAwAAMKiMmwAAODRHo/f38Oj1SpQDaHllyOnTp9uuvEoZAACAQWXcBAAA/WmArugFAAAAAADQvl4vmgEAAAAAADrtnuj9N/4DNMMwQKkCADDMzsWvlsb+RfZXKtb6SiL2UCL25Yrt3Z+IvZmInUzEriVibyViX0zEjiVi5fJ8rnB7o9GIsbFfq1QnAACdly0WbPzVc4X75vnZklr2bs+y4jqKtpfVW1ZHxHgbuXVPO8fCYDEZAgAAAAAAg+5IdOWG5ue/detR5NrNzrfXLSZDAAAAAACAQrM/cetRpPF2xNg/720+VQ3NDdSbzeZhpwAAANDXjJsAALhb9d1kyNraWoyNjUWWZTE2NhYbGxuF+2VZtusxNTW1K95oNGJqairm5+djZmYm1tbW9tTRqX0AAAB6ybgJAIA9jsYPb6Leq0cXLsvVLX11maylpaVYX1+PmZmZuHTpUiwtLcXExESsr6/H+PgPb6CzsrIS09PTMTo6urPt9niz2YyxsbHY3NyMer0eERGjo6Nx+fLlmJ6e7ug+AAAAvWTcBAAA7cvyPM8PO4ltU1NTsbq6uvPvRqMRY2NjMT4+Huvr6zvbtzv6ZSYmJiIidu2zsrISMzMzsX24ndqnyHbetw8GAADudp+Kv5KMP/3v/Gflwf/6pUTJNyrGHkrmU63OtxKx+yu292bFcidLI3n+VKUas+xcSeSbEbGi/9sjwzBuMmYCgM7IsuLVoRF7+895frakjuI+XtH+5f3B1sq3214ntJNzO3n0U39mJ5cPRdSP97jtqxFjL0dfnIf99M1lsjY2NmJxcXHXtnq9HvV6fdd1bdfW1uLixYsxNTUVKysre+rZ2tqKjY2NnQ75tlOnTkXErU55p/YBAADoJeMmAACopm8mQ8bHx6NWqxXGbt++vr4eW1tbsba2FjMzM/Hggw/uuj7uxYsX95SJiJ1ZqfX19Y7tAwAA0EvGTQAAlOr1/UK2HwOi71NtNpsxMzOz8+/l5eVYXl6ORqMRy8vLsbKyEhMTE3Hp0qWo1Wo7v4YaGRkpra9T++zn7bffjqtXr+67X5l777037r333srlAQCgFe+880688847Lez5RyXbv9fJdKhgUMdNxkwAwOG5/oPHbq32Td5+++0O50O39fVkyNraWtRqtcKb7tXr9VheXo6JiYmYmpqK+fn5WF1djUuXLkVExIkTJwrr3Nra6tg++/nIRz6y7z4pZ8+ejWeeeeZAdQAAwH4WFhbi3Ln2rqVM/xjkcZMxEwBweP4/EfGFPVsfeOBTvU+FnujryZCFhYVdNwYsMjk5GZOTk9FoNCIiYnR0NCIiLl++XLh/rVbr2D77+cIXvhAf+MAH9t2vjF84AQDQC08//XR88pOf3He/Bx5YKIn8TxHxfCdTog2DPG4yZgIADs+fiog/uWfrd7/7dEulv/SlLx34hx0ddyQijh5CmwOibydD5ufn47nnnmtp0mFiYmLn+rfb+5f9AqlWq3Vsn/3cd999cfz48X33AwAYFh+Pv1saeyH7z/Yp/UYi9uVK+UQcS8QeS8ReSsTeSsTur1juZCJW1ZulkSxbSpS7loj9WMn2H20lIbpg0MdNxkwA0Jrs88Xb83y8eP9sb382e8/B88jzswevpA1ZVrx6uZ08upXzfffd15V66Z6+nAzZvp7t9k33WnHq1Kld/73z2rTb/x4bG+vYPgAAAIfFuAkAgF2ORu+/8e/1SpQD6LtFLGtraxERMT6+e1Zzezl3kfX19Z2bBY6MjES9Xo/19fVd+2z/AurJJ5/s2D4AAACHwbgJAADa01crQzY2NmJhYSFmZmZiZWVlZ/vm5ubOL4rOnDkTH//4x2Nubi4ibg0CTpw4EZOTkzv7P/fcczE2NhbNZnNnWfbi4mIsLi7GyMhIR/cBAADoJeMmAABoX99MhjQajZiYmIiI2Pm10u2uXLkSEREnTpyIhYWFWF9fj3q9HhMTE7G8vLxr33q9HpubmzE/Px+1Wi2azWbMz8/H9PR0x/cBAADoFeMmAABKuUxWUt9MhtTr9cjzfN/97lx+napvdXW1J/sAAAD0gnETAABU0zeTIQAADIZ/L54rjb3w02cSJa/uU3P5vQ7SHkrEjiViv1+x3JuJ2MmK5a5VrPOtSnXm+VxpLMuWKtUJAEBn/Pvx63u25T/7H7RZyxN76/h68Z5ZVrb9XMHW4n5knqf6z3fue7bl9sr27YTi4+tum113NHq/UmOAVob03Q3UAQAAAAAAOsnKEAAAAAAAGHTuGZJkZQgAAAAAADDUTIYAAAAAAABDzWWyAAAAAABg0LlMVpLJEAAA9vgr8anS2N878avlBX8sVetL+7R6LRF7KxE7loi9WrG9k5Xay/NPlMay7FyizjcTsXJ5frZieymPJWJvlGz/o4ptAQDcvf7Y1SuF2689sLfP9XwU9+3K+oN5Pr5nW5ZttJFdsTxP9b0PWnd537ZVRcdYdC5ueeLA7TFYTIYAAAAAAMCgOxq9X6lhZQgAAAAAAHC3Wltbi/X19RgZGYlmsxm1Wi0WFxd37dNoNGJhYSFqtVpsbW3FxMRETE5OdiUfkyEAAAAAAEDHrK2txcLCQmxubu5sm5iYiPn5+Z0JkWazGWNjY7G5uRn1ej0iIkZHR+Py5csxPT3d8ZyOdLxGAAAAAACgt7ZvoN7LR8llspaXl+PUqVO7tk1MTMTa2trOv2dmZmJ8fHxnIiQiYn5+PmZmZqqegSSTIQAAAAAAQMdcvnw5NjZ239D+0qVLUavVIiJia2srNjY2YmJiYtc+2xMoKysrHc/JZAgAAAAAAAy6PloZMjMzE81mM6ampiLi1r1BXnjhhZ1LZF28eDEiYmdyZNv2KpH19fXq56GEe4Z00enTp+PYsWOFsdnZ2Zidne1xRgAAP/RX4lOlsb+V/fuJklcTsTcq55Ou9/5E7GuJ2IcTsVcSsfcnYhulkSw7lyhXTZ6fTbS3lChZ3A+9Va5Knl/8waPI9Qr1gTETAHeHbKp4e776YElgb/+v3f5b0f5l/cose6k4jUQ/9LBlWXGfPM/HW66jbN/d5+5WH/hf+9f+lT37Xbt2reW2+tk7N249qnr7+8Xbp6enY3NzM1ZWVmJ0dDRqtVq89tprMTIyEhG37hcSETv/vtN2vJNMhnTRhQsXdl3vDAAABteHo3xy6ZsR0fll7Aw/YyYAoL/d6gN/5St7J4YajUaMjY31PqWU7ZUhbVjYjDj3cleyieXl5bh48WI0Go1oNpuxsbERk5OTEXHrklkRESdOnCgsu7W11fF8TIYAAAAAAMBd6OlTEZ/8YPXyX/p2xEc+WxybmJiImZmZqNVqMTU1FVNTU7G6uhqTk5MxOjoaEbfuLVLkzstndYLJEAAAAAAAuAvde/TWo6r7SmYYZmZmIuLW5bIiIl577bV49NFH48yZMzE5ObnrRupFujEZ4gbqAAAAAAAw6I4e0qPACy+8sOtyqCMjI7G4uBhbW1vRaDTi1KlTEbH33iDb/+7GJchMhgAAAAAAAB1z4sSJPas+xsdv3bR+ZGQkRkZGol6vx/r6+q59NjY2IiLiySef7HhOLpMFADDEfjFWS2O/9SO/WrHWzyVi1xKxN/ap96GK9R4rjeT5z5fGsuz1RJ1fTcROJmKPJGKp9sqPIctKLsAbEXk+l6iz87JsqSTyYz3NAwCgX2XvaWPf7Fzh9jzfe7PuiOJ+X5ZttFxHWXuDKM/Hu1h30fkfEBVuoN6RNgvMzMzEwsJCLC4uxsjISERErK2tRb1e37kE1nPPPRdjY2PRbDZ3ti0uLu4q00kmQwAAAAAAgI6Zm5uLkZGRmJqa2rlc1tbWVrz44os7+9Tr9djc3Iz5+fmo1WrRbDZjfn5+5z4jnWYyBAAAAAAA6Kjp6el9Jzbq9XqsrpZf0aCTTIYAAAAAAMCg66PLZPUjN1AHAAAAAACGmpUhAAAAAAAw6I5G71dqDNDKEJMhAACD7p9lpaHfOpOXl7v+UsUGX0/EHkrE3tyn3mOJ2PsTsfLjyLLPVszn8UQs5Y2K5a4lYl8rjWTZ1Yp17vdcFMvzs4XbG41GjI39WqU6AQAGUZaV9bVa72OX9a2KLbWxb0SW7c2jrL0s22ij3nOF29s7FjgcJkMAAAAAAGDQuWdIknuGAAAAAAAAQ81kCAAAAAAAMNRcJgsAAAAAAAady2QlWRkCAAAAAAAMNStDAAAGwN+Nj5fGPvG/ySvW+lgi9tlEbDwR20jEHk+nk/TV0kiez5XGsuzZiu1dTcTqidhLFds7VhrJ87OVasyyc4noyUTszUrtAQAMq78Zf7lg6y+W7N1Of7C4X53ux7WmvT5kWc578yurt52cq/ZvO60s56L8ivf9Zocz6gArQ5KsDAEAAAAAAIaalSFddPr06Th2rPhXfrOzszE7O9vjjAAAoKovxvve977CyLVr13qcC8PCmAkA6Gfnz5+P8+fPR8QfFESv9zodDshkSBdduHAh6vXUZRQAAGBQfDi+8pXfLow0Go0YGxvrcT4MA2MmAKCfbf84o/wyWSu9TintaPT+slUukwUAAAAAANAfrAwBAAAAAIBB5wbqSSZDAAD6xT/LSkOfeCmvWOmXE7FHKtb5m4nY/YnYq/vU+2cTsd8pjWTZ84lyb+7TZpnjlXLJ87lKrRUvu9+OLSVKPpaInSyN5PlTifaer5DL7yfyAAAYDFlWdh+0X9yzJc/HS+p4qY32Ngq35/nZNuoo70e2qqy9orrLc3uiYN/ic1SmnWNp5xx1oo6ifW9dKrbPLpNFkskQAAAAAAAYdFaGJLlnCAAAAAAAMNRMhgAAAAAAAEPNZbIAAAAAAGDQHY3eX7bKZbIAAAAAAAD6g5UhAAA99IfvlP9s5r5/Ny8v+J1UrV9OxBqJ2KuJ2EMVY1dLI3n+VKJcRJY9n4ieTMS+logdS8SuJWJvJGLlsmwpEU0dQ3ksdd6y7NmK5VJ5lsvzucLtjUYjxsZ+rVKdAAC9ln2+vf3zfHxvHdm5kn3PFuy70XK9ZXUX1Zva3mq9naq77Fja0U577Wj3uAeaG6gnWRkCAAAAAAAMNStDAAAAAACAQuf/u1uPIte+38tMDsZkCAAAAAAADLouXSZrdvzWo0jjaxFj/1Hn2+wGl8kCAAAAAACGmpUhAAAAAAAw6I5E729oPkDLLUyGAAB02M/Ff1sa+90P3Sgv+J1EpVeeTwSPJ2IPJWIp9UTsc5VyybJUuYiIa4nYW4nYyUTszUrl8vyp0liWLSXqvD8RSx1fuXR71erM87mKuZwriXyzUn0AAN2Wvadg4zfK+jRPFNeRvdR6ewX9pTw/23L5sjzK+mHt1N1uHuV9v4PXfdAcunncDK8BmrcBAAAAAABon5UhAAAAAAAw6O6J3n/jP0AzDFaGAAAAAAAAQ22A5m0AAAAAAIBCR6P33/j3+obtB2AypItOnz4dx44dK4zNzs7G7OxsjzMCAIBqzp8/HxH/ZUn0ei9TYYgYMwEA/ez8+fM/6Afvde3atR5nw0GZDOmiCxcuRL1eP+w0AADgwGZnZ+OXf/nbJdFvRsRKL9NhSBgzAQD9LPXjjEajEWNjYz3OaB9WhiSZDAEAqOC/jZ8rjf3uiX9QXvDKVyu2+N5E7I2KsdQvmVLlUrlsJGIPJ2IREW8lYvdXKpfnZ0tjWXauUqy68vOdyjMly56tWG4pEW0/z1sDQZMhAMDhyRZLAt9op1/3Usn2J/ZsyfPx4jwK+pFZVtZHLm6vuM9V1l5x3WX5taNqH3VbWZ+6nXoPmgPczg3UAQAAAACAoWZlCAAAAAAADLoj0fvLVg3QcosBShUAAAAAAKB9VoYAAAAAAMCguyd6/43/AM0wWBkCAAAAAAAMtQGatwEA6K2X4/HS2L/1114pL3jlq4laE+XijURsPBF7KRErP4aI4xXr/FoidrJiuYiIh0sjef6J0liWPZuIndunzTLHErnMVWyvvM6ULHs+kctTFXMBABhMv13WL764ceC68/xs4faiflWWFfeXy+oolurj759D++11r+6iOjqRG3SSyRAAAAAAABh0R6P33/j3+obtB+AyWQAAAAAAwFCzMgQAAAAAAAbdkej9So0BWm4xQKkCAAAAAAC0z8oQAAAAAAAYdO4ZkmQyBAC4q/3hO+U9tw8/eqO84LdStb6SiF1NxJ5IxH4z1WDC8UTspUTs/kTsZCL2tUTsWCKWLptlz+5Ttkqb1yrVmGXnEtHUuXmzUnvpc9pb5cf+zZ7mAQDcHf79+PU9257P/lThvnleXEeWpfrYd+5b3NfJ87Nt1LG3j5nnxX3SdtprJ4fD0In8is7HYRx3ur9/O33gQeMyWQAAAAAAwFCzMgQAAAAAAAbdPdH7b/wHaIbByhAAAAAAAGCoDdC8DQAAAAAAUOhI9P6G5gO03GKAUgUAAAAAAGiflSFddPr06Th27FhhbHZ2NmZnZ3ucEQDcpT6XlYbu+1xeXu5bX67Y4EOJ2BOJ2GcTsWuJ2OOJWOePIc8/VhrLsqVEnaljiIgo7jfd8lYin7lEPs/u02b78vxsor1ziZLlx5cqV729k4nYm223d/78+fjlX/7rJaW+n2gLyhkzARAR8fWSvufz2esFW19qq+48H2953ywrrruoz1XeRyvqD5ftmxob9E6qv9mq9s7R4ORx/vz5OH/+fOF+165di9dfb6tqDpnJkC66cOFC1Ov1w04DAAAObHZ2Nn75l/+wJPr7EfFrvUyHIWHMBAD0s9SPMxqNRoyNjfU4o30cjd5/49/ry3IdgMkQAAAAAACg0Pm1W48i197pbS4HYTIEAAAAAAAG3T3RlW/8Z//tW48ijf8hYuzf7Xyb3eAG6gAAAAAAwFCzMgQAAAAAAAade4YkmQwBAIbDq1lpKPuP8oqVvr88lOpFXf9sIvh4IvZQIvZIInY8EXs1EUt5ozSSZZ+rWOfJiuUi8vyp0liWnUuUO5sot1Qplyx7tmJ75Xmm26uWZ8SbpZHO5/lHFcoAAHebbLEkcL6s73nt4G1mGwVbX2qrjqK+U1mfKdXP2rvveFt59IPyvuITB66jnXNXphN1MLxcJgsAAAAAABhqVoYAAAAAAMCgOxK9v2zVAC23GKBUAQAAAAAA2mdlCAAAAAAADDo3UE+yMgQAAAAAABhqVoYAAIPjn2Wloewf5OXlXk7Uef3LieDvJ8pdS5R7IxH7bCL23kTsq4lYKpdU7Fgi9lZpJM9/vjSWZa9UqvOWx/aJlzlZGsmypdJYns9VKhfxZitJFUid75T7E7HU81vVw4nY17rQHgAwjP5E/KO9G3/1e4X75vl44fYse6lga9G2sn0j8vzsgfYtU7Zvlp070L7t5tEJZXkU6URu3Ty+QTz/9I7JEAAAAAAAGHT3RO+/8R+gGQaXyQIAAAAAAIZa383brK2txcLCQjQajajX67G4uBjj47uXyjUajVhYWIharRZbW1sxMTERk5OTh7YPAABALxk3AQCwx5Ho/Q3NB2i5RV9NhiwtLcX6+nrMzMzEpUuXYmlpKSYmJmJ9fX2nY99sNmNsbCw2NzejXq9HRMTo6Ghcvnw5pqene74PAABALxk3AQBA+/pq3ubll1+O9fX1mJ6ejsXFxdjc3IyIiMXFxZ19ZmZmYnx8fKeTHRExPz8fMzMzh7IPAABALxk3AQBQ6Gj88L4hvXr0eiXKAfTNZMjGxsauzntERL1ej3q9Hs1mMyIitra2YmNjIyYmJnbtd+rUqYiIWFlZ6ek+AAAAvWTcBAAA1fTNZbLuvL7t7Wq1WkREXLx4cde/t23/Aml9fX0n1ot9LPkGgC54NSsNZf9VXl7uv3ijYoPvT8Qaidh7E7GridibFWMnE7FXE7FjiVjKtdJIli2VxvJ8LlHu2X3aLD9vqTbT7q9YZ/nxp5+LlFS5qq+L8uc39VykVWnvxyq2RbuMmwDoN38i/lHh9i9m/3Dvxk+dLdw3yzYKt+d50ede8Wdhlp0r3F5cb3EeZbpVd9m+3TyWdhy07rLjaKfeduvo1vnv5nmmd/pmMqRMs9ncWV69/UunkZGR0n17uc9+3n777bh6NfVlSNq9994b9957b+XyAADQinfeeSfeeeedH/zrj6rU0Ml0qGBQx03GTADAYdndB77d3v5wUX/l7bff7kJWB7R9maxetzkg+noyZG1tLWq12s4viS5duhQRESdOnCjcf2trq6f77OcjH/nIvvuknD17Np555pkD1QEAAPtZWFiIc+da/wUi/WWQx03GTADAYWmnD/zAA5/qcjb0Ql9PhiwsLMTq6urOv0dHRyMi4vLly4X712q1nu6zny984QvxgQ98YN/9yviFEwAAvfD000/HJz/5yYiIeOCBv1Whhm9GxHInU6INgzxuMmYCAA7L7X3g2z3wwMKebd/97tN7tn3pS1868A87Om77pua9bnNA9G2q8/Pz8dxzz+3qPG//f9mvi2q1Wk/32c99990Xx48f33c/AAA4TLsvNVTl/h++kD4sgz5uMmYCAA5L+eU29/aHi/or9913XxeyopuOHHYCRVZWVmJiYmLnpnvbTp06FRF7rzu7/e+xsbGe7gMAAHBYjJsAAKB1fbcyZG1tLSIixsfHd21vNBpRr9ejXq/H+vp6zM3N7cQ2NjYiIuLJJ5+MkZGRnu0DAFTzh++U32Htvg/n5QX/111IJqmeiDUSsYcSsdQvoF+tWC7V3iuJ2MlE7FppJM+fKo1l2WcTdabyjEgdf57PlcaybClRrjzXlCyrdv+MdLljlepMlUudl5Qse7ZSneXHV+Wm6xyEcRMAh+FPxD/as+2fxc8U7pvF9/Zsy+eL681+9aWSFsdLtu+V52eL6y7ov7Szb9n+VfuLrSjL76DaOb5O6ES9nXiuOtXmIMiPROQ9vqF53pfLLYr11WTIxsZGLCwsxMzMTKysrOxs39zcjLGxsajX6/Hcc8/F2NhYNJvNnSXXi4uLsbi4GCMjIxERPd0HAACgl4ybAACgfX0zGdJoNGJiYiIiImZmZvbEr1y5EhER9Xo9Njc3Y35+Pmq1WjSbzZifn4/p6emdfXu5DwAAQK8YNwEAUObG0YgbPf7G/0aPV6IcRN9MhtTr9cjzxGUx7th3dXW1b/YBAADoBeMmAACopm8mQwAAAAAAgGpuHsLKkJsDtDJkgG5vAgAAAAAA0D4rQwCArvjDd8p/HnLfl26UF7xytTz2T19KtHg8ETuZiDUSsdcTsWOJ2LVE7NVE7P5ELHFeknWmvFkayfO50liWLVVsbz+p408pP9/pXFPPU+r5LT9vKelzeq5SnVXl+VOlsVQueX62cHuj0YixsZXCGAAweP5m/OXC7f8sfm3Ptnb6MVlW1p9/omT/vXWX9UfK7a27LOf26+6dTuTcL8c3iMdSnPM3e5oDB2cyBAAAAAAABtyNo1lcP5r1uM08Ilq7p91hMxkCAAAAAAAUWv71PFZ+vXjC44/+qMfJHIDJEAAAAAAAGHA3jh6NG/d0/jbhv/TLtx5FvtS4GR/58PWOt9kNbqAOAAAAAAAMNZMhAAAAAADAUHOZLACgsj9852hp7L5//UZ5wVe/mqj1oeoJlXqpYnvHErFridjxROz+ROytirGTiVgqz/I6s+xcolzqvJTL87lkPMueT8SWEvWerVQu4vFEnR9L1Fnt3KRzKVf1+FLnO8ueTbRY7fkFAAbP10v6wnPveaN4+zf29oPK+ipZtlGwtbhfnufjJXXs3b+sL1aWR3Hdxe0Nu3bPXbfqaGffflGUc6PRiLGxlUPIptzNo0fjxtHern+4eTSLCJfJAgAAAAAAOHRWhgAAAAAAwIC7EUfiRpRfwaE7bQ4OK0MAAAAAAIChZmVIF50+fTqOHSu+5vLs7GzMzs72OCMAAKjqn8T73ve+wsi1a6l700A5YyYAoJ+dP38+zp8/Xxjrxz7wjTga160MKWUypIsuXLgQ9Xr9sNMAAIAO+On4ylf+m8LIrZtHjvU2HYaCMRMA0M9SP87QBx48JkMAgLTlrDR039N5ebk/l6jz1VSDqV/X/Hwi9uXSSJ5/ojSWZecSdX44EXslEUt5fyK2kYg9noi9kYgV/+L6lrcSsZTUc3SyNJJlSxXbi4i4P1Hvs4lyqVzLX4jp10XqnKakcimvM3Xe8nwuUS51DNWU5/L7HW8LAOiws3v79Q//9fXCXfOSbn6Wlfc99nqpjX3LPLFnS56Pd6Deg8vzs4Xbi/pgZfv2u2E6FogwGQIAAAAAAAPvZhyNGz3+yv9mT1s7GDdQBwAAAAAAhpqVIQAAAAAAMOBuxJG40fMbqA/O2hArQwAAAAAAgKFmMgQAAAAAABhqLpMFAHe7tSwZzv4wLw9e+Wp57Hfem6j1oURsIxG7moi9WRrJsrcS5T6ciD1WGsnzn0+097lEnS8lYscSsVcqlksd+/2JWPmxp3LJ86dKY1l2LlFnRPo4yp/fiJMVY6k6y3PJ87lEuWr2Pzfty/OzFdt7uEJr/3OFMgBAN2SfLwn89fWCjU8U11HWV3j33v5FO/2YVP+keP/xlttrt+52FLVZ1l7R9nZzbqe9drR//rt3TumOWzdQ7+1lsm66TBYAAAAAAEB/sDIEAAAAAAAG3M1DuIH6zbjR0/YOwsoQAAAAAABgqJkMAQAAAAAAhprLZAEAAAAAwIC7Hkfieo8vk3V9gNZbDE6mAAAAAAAAFVgZAgB3g/GsNJT9TJ4u+7dSwePloW89myj3SCL2aiJ2fyL2eCL2RiKW8lJpJMteqdjeyYq5vDcR+3Ii9lZpJM+fKo1l2VKizvJjyLJziXL7uVapzdQxpurM87OlsdRxpGKpOlN6nUvV9srKNRqNGBs7yHMPALTr6/FQceCjf6mkxBN7tuT5scI9s7Lhwzda/7wv6je021csqqNqf6sVZfm1cyy9zrmftXOO+qnuQXYz7okbPf7K3w3UAQAAAAAA+oSVIQAAAAAAMOBuxpG40eN7htxsY71Fs9mMtbW1iIiYnp6OkZGRiLi10nxhYSFqtVpsbW3FxMRETE5OdjxXkyEAAAAAAEBXNJvNmJ+fj62trVheXo5arbYrNjY2Fpubm1Gv1yMiYnR0NC5fvhzT09MdzcNlsgAAAAAAgI67dX/BsThx4kSsr6/vmgiJiJiZmYnx8fGdiZCIiPn5+ZiZmel4LiZDAAAAAABgwN34wWWyevson2LY2tqKj370o1Gr1WJ5ebkwvrGxERMTE7u2nzp1KiIiVlZWOnp+XCari06fPh3Hjh0rjM3Ozsbs7GyPMwJgqH0+Kw1l78nLy/3OPvVeSQWLP+duef8+FZd5LBG7moi9kYg9lIi9kk6nVD0RezURe6s0kudzpbEse37fjNqVZUsVcykvF3EyEXtz/6RK83mqUrksO1cpln5tX0vUWe2cpuT52UrtpaSOvay98+fPx/ve977C2LVr5ecEUoyZAFrzN+Mv79k2l71esndZ/+ClPVuy7Im28kj1Sw5SPt0v67ws2yjc3s7xlR/L3rrzfLzletvNo5918zh6dY7Onz8f58+fL4zpA+9v+9JYi4uLhfGLFy9GROxZLbK9SmR9fb2jl8oyGdJFFy5c2LW8BwAABlXqi+ntpe/QLmMmAKCfDVof+EYcjett3kD9e+/cjO+9k/gB5T7eers8tr2yY319Pebn56PZbMapU6d27hvSbDYjInZupH6n7XinmAwBAAAAAIC70PMLfxC/ce4POl5vo9GIiFurPGZmZmJxcTGazWZMTEzE6OhoXLlyJS5duhQRESdOnCisY2trq6M5mQwBAAAAAIC70Cee/lfi3/nkj1cu///70rX4Dz7y+p7t26s6ZmZmdi6DtX3vkImJiVhYWIjR0dGIiLh8+XJh3XdePuugTIYAAAAAAMCAuxlH40abX/kfvTfi2L3V2/yx+24Ubi+79NX4+K176GyvEokoXwHS6cmQ8lu9AwAAAAAAtOnUqVMRETuXwrrTiRMndva5894g2//u9D1ZrAwBgEHyC1lpKPsTiRuefTtR5z/dr9E3ErFXE7GridjxSnXm+VxpLMs+W7G9cun2lirVGXEyUee5RLmHE7G3KubyWCKX1PFdS8TuL43k+dlkNqnjr36+j1UslzrGVJ2pctWkXxfV7PdcAACH4+vxUOH2ucWiPvlGSS3Ffdg839uHKetntNNXaKevUlZvZ9p7oqTu8Za2tdtm+bHsrbvd/lyv+2qdeB3QH27E0bjR5g3UO9FmkZGRkRgfH4+Njd1/q7ZXgYyNjcXIyEjU6/VYX1+Pubkf/u3aLvPkk092NFcrQwAAAAAAgI5aXFyMRqOxa0JkZWUl6vV6TE9PR0TEc889FxsbG7tWhywuLsbi4mLppbaqsjIEAAAAAAAG3M040vOVITcT6y3q9Xpsbm7G/Px8rK6uxsjISGxtbcXm5mbhPrVaLZrNZszPz+9MlnSSyRAAAAAAAKDjti+Dtd8+q6urXc/FZbIAAAAAAIChZmUIAAAAAAAMuBuHcJmsGwO03sJkCAD0m6ey0lD2X9wsL/dziTpfTTX47D4J/R8SsccTsY1E7OcTsa+WRrLss4lyxxKxLydi9yfaez5RLuWxRCz1ZDyciL3ZhVxeScROVmzvrdJIli0lS+b52UTZc4mS5bnm+VPJNqvIsvL3TJ7PJcqljqH89Zs6Lymp9qrXWfYc/n6l+gDgrvWbxX3+h//DvHj/bxT1rV8qqbx4e1bQZNU+wW5PFG7N8/GWayjvtxTVffD22tWZ89R6vUXno1s5dLtu6CcmQwAAAAAAYMDdiKNxvecrQ3rb3kEMzhoWAAAAAACACkyGAAAAAAAAQ81lsgAAAAAAYMDdjKNxo8df+d90mSwAAAAAAID+YGUIAByGP5mVhrJ/LS8vdypR55WrFZN5Yp/4G4nYexOxVD5fTcQeSsReT8TeSsSuJWIpJxOx+0sjef6xSq1l2VKlcnk+l6jz2UTJhxOxryVixxKx8nOd52cT5SKy7FzFNlPPfaq9zp/vdLny408fe2+lcik7hkajEWNjv9atlABgsJ3d2/fPvlLS5/9GO/3W4j5Jnqf6TXfkkW2URF5qI4+yuovqKB577NdPPFgee/s23WyvHWX9rn7Jj8FzI470/IbmNwZovcXgZAoAAAAAAFCBlSFddPr06Th2rHg2fnZ2NmZnZ3ucEQAAVPXFeN/73lcYuXat6uor7nbGTABAPzt//nycP3++MNaPfeBb9wzp7cqQQbpniMmQLrpw4ULU6/XDTgMAADrgw/GVr/x2YeTWZbLGepwPw8CYCQDoZ6kfZ+gDDx6XyQIAAAAAAIaalSEAAAAAADDgbsSRuO4G6qVMhgBAtyxkpaEsy8vL/b2qDR5PxN5IxH5/n3ofS8Q+m4ilrp9aNdeUVJ5XE7FUnm9VyiTLziWiD1eqs3p7KSdLI3l+tlJ7qXIHy+epRD5LlVrL87lEndXOaffOTfvtpctVO2fl7X2zUn0AMEy+Hg8Vbn/4r68XbN1os/aXWtwWEVHc56jeZ0wr6+MUtZfn4y3vm6q7He3U0U4e7ezbjX7gIOjm8wpVmAwBAAAAAIABdyOOxo0ef+Xf6xu2H8TgrGEBAAAAAACowGQIAAAAAAAw1FwmCwAAAAAABtzNONrzy1bddJksAAAAAACA/mBlCAAAAAAADLgbcaTnK0NuDNB6C5MhAHAAX82y0tj/6k/m5QX/6dVErccTsVS5jUQs5aF94r+ZiD2eiD3Sfir7erNiLOVYInatUrk8P1say7JziTpPJup8qlKdVXOpmmeWLSXK7Sd1vqvJsmdLY6lzmq6z8+c73V75OU23l3ou7k/Eyp+HsvYajUaMja0k6gSA4fI34y/v2Tb3njdK9i7qp7/UVntFn8FZVtz/z97TVtUHzqPX+3ZTWR5F/bh+ybmfOUf0G5MhAAAAAAAw4G7E0bje85Uhg3PPEJMhAAAAAABAoY3zX43Pn79UGPvetRs9zqY6kyEAAAAAAECh8dn3xvjsewtjrzeuxDNj6z3OqBqTIQAAAAAAMOBuxtG40eOv/G8O0GWyBudW7wAAAAAAABVYGQIA+ziXZaWxZ87l5QXXUrW+UTH2aiJ2LBG7lojdn4jtF//JROx3ErFUPqnY44lY6vhT57TquSlvL8vOJcqlvFWx3MnSSPVcUt4sjeT52cq1ZtlSpViezyXKlR9/lj2bqLP8OKqe03SdzydKfq1SLlWfi/Q5K4t9s1JbANDvvh4PFW6fe09B//IbG4X75vl4wdaibRFZVlxH0Wdw+5/1e/fvRF+xrI6i/NrZt5/0c37dPKeded31tr1+qeOw3IgjPb+h+Y0BWm8xOJkCAAAAAABUYDIEAAAAAAAYai6TBQAAAAAAA+7WDdR7e5msQbqBusmQLjp9+nQcO1Z8PfHZ2dmYnZ3tcUYAAFDVF3/wKHK9l4kwRIyZAID+dqsP/L73fWZP5Nq11P0n6UcmQ7rowoULUa/XDzsNAADogA//4FHkmxGx0sNcGBbGTABAf7vVB/7KV/beQL3RaMTY2FjvU0q4eQg3UL85QHfiMBkCABFx9VhWGnvmfF5e8D9OVPqtVIvvTcS+mog9kYi9kYi9kkpmH+9PxH4zEfvzidhLpZE8f6o0lmXnEnU+nIh9LRGrKtVeKlYuzz9RGsuyZxMl30rEin9xfcv9idibpZE83zsQaEX6GCLyfC5Rtvy5z7KlSvmkz0251PFn2WcT5T6WiH0iUWfqdV9+DOnzXe35LT/XP5ZoCwD639+Mv1y4fW6qpI/9J/Zuyr8+Xrhrlm3s3Tcv3rdse0TZ9qL2ivsOxZ/xZeOL8v56a/UefN+I/fpBB6t7WPTzcZc/f8Wvu04cS7fqKD6Wbx64LXrLZAgAAAAAAAy463E0rvd4ZUiv2zuIwVnDAgAAAAAAUIHJEAAAAAAAYKi5TBYAAAAAAAy4m3E0bvT4K/+bLpMFAAAAAADQH6wMAeDu8VRWGnrg6by83H+cqPNbVxPBNxKxa4lYykuJ2GOJ2LFE7NVki3n+sdJYlt2fKPk7iTrnEnU+n6gzdRxVz2lVb1YsV55nlp2rWOfDldqLeCsRKz/XVfPM87PJeJY9W6lsKp+q5bJsqTSWOqf7HWO19sql30vV6kyVK2uv0WjE2NivVWoPAHrt6/HQnm1z2V8q3vndxZvzr7fTYlEffrydCgq12yfLso2W963ap+m0fsmj6Fy3k1vZc9XrOtpVVHc7efTL89cJRcdyqw+8cgjZlLsRR+JGj1dq3Big9RaDkykAAAAAAEAFJkMAAAAAAICh5jJZAAAAAAAw4G7dQL23l8lyA3UAAAAAAIA+MfCTIc1m87BTAAAA6GvGTQAAw+9GHInrcbSnj0G6gXpfXSZra2srFhYWIiJicXGxcJ8sy3b9u16vx+bm5s6/G41GLCwsRK1Wi62trZiYmIjJycldZTq1DwD959wdnxO3e+Y/ycsLPp+o9FtXy2MPHi+PXdlIVPpEIvbZROytRCyRS7yeiD2SiEVk2ecS0TcSsccq1plSXmc69mrF9lLHl3IsEftaaSTPz5bGsmypUp3VpY6hmix7vktlT1aut4r083QuUTJ1Tq9VKpdlz3a8zjyfS5SjHxg3AbTm6/FQ4faHp4r6eCV9928Uf7YXDzuK+/hFfYfsPcXN5V8v3l7Ux0j1SQ6qrE/TTpu9zrmb+uG4+/3c9cvz3S950J/6ZjJkY2MjlpeXY21tLaanpwv3WVlZienp6RgdHd3ZNj4+vvP/zWYzxsbGYnNzM+r1ekREjI6OxuXLl3fq7NQ+AAAAvWbcBABAmRtxNG70+Cv/Xt+j5CD6ZjJkfHw8xsfH9/yC6Xarq6uxvr5eGp+ZmYnx8fGdjnhExPz8fMzMzOx0xju1DwAAQK8ZNwEAQDUDc0GvtbW1uHjxYkxNTcXKysqe+NbWVmxsbMTExMSu7adOnYqIW7+O6tQ+AAAA/ci4CQAAig3MZMj6+npsbW3F2tpazMzMxIMPPhgbGz+8nuPFixcjIqJWq+0qt/0rpfX19Y7tAwAA0I+MmwAA7l434+gPLpXVu8dNl8nqvOXl5VheXo5GoxHLy8uxsrISExMTcenSpajVatFsNiMiYmRkpLB8s9ns2D6tevvtt+Pq1cRNd/dx7733xr333lu5PAAAtOb6Dx5Rqf/69ttvdzgfqhq0cZMxEwBwWN5555145513KpfXBx48AzMZsq1er8fy8nJMTEzE1NRUzM/Px+rqaly6dCkiIk6cOFFYbmtrq2P7tOojH/lIy/sWOXv2bDzzzDMHqgNgGJ1LXCf9mUs3ywv+uUSl307EHj9eHnslUS7pjUTskYp1Vv0yab+DOJmIvVkxVi7P50pjWbaUKJk6jmNdyOXZRMnUsZefzyw7V6lcSuoYUtLnOiV1rtOviW6c71S5PD+bzKea1PFf60J7nXmfPfPMM3Hu3K3X3wMPdOO80GuDMm4yZgI64bdjvHD7v5G9Xlzg3Xs35XlxHVFSd3G/7aXW6/hGWb+v9c/hsr5jWR+naP+yfdvpJ7WbRzvayfmg9aa002Z3+pid0Ynj7ufji2gvv4WFhZ0+8LC4GUd6fkPzm4Nz8anBmwzZNjk5GZOTk9FoNCIiYnR0NCIiLl++XLh/rVbr2D6t+sIXvhAf+MAHWt7/Tn7hBABALzz99NPxyU9+MiIiHnjgbyX2LJvQ+Z8i4vnOJkVH9Pu4yZgJADgst/eBq/jSl7504B920FsDOxkSETExMbFz/dvtznbZL5BqtVrH9mnVfffdF8ePJ35NDAAAfWD3pYZ+LLFnXrL9RzucEZ3Uz+MmYyYA4LAc9HKb9913XwezoRcGejIkIuLUqVO7/nvntWm3/z02NtaxfQAAAAaJcRMAwPC70aXLZDXOfzEa579YGLt+7XrH2+uWwbmgV4H19fWYmZmJiFs37qvX67G+vr5rn+1fQD355JMd2wcAAGBQGDcBAHAQ9dkPxy995ZcLH3/uwuD0+/pqMqRsiXWj0YixsbFYWvrhjTzX1tbixIkTMTk5ubPtueeei42NjV2/TlpcXIzFxcUYGRnp6D4AAACHwbgJAIAiN+JoXO/xo9c3bD+IvrlMVqPRiOXl5YiIeOGFF2JiYiLGx8djZGQkarVanDhxIhYWFmJ9fT3q9XpMTEzs7L+tXq/H5uZmzM/PR61Wi2azGfPz8zE9Pd3xfQDosv9rVhp65rfLrlkfEf9xos5XXkoET5aHvpUoFu9NxB5PxFK5vFmxzscSsVcSsfsTsYh0PonzFqlrwL9aGsmypdJY2rGK5cpuCL2f1HlJ5ZIql/JWxXLlsuxcaSzPz1YqV/18puX5U6Wx9HFULVft+LtRLqV6LuXvszyfazuPW1/Cr7RdjvYZNwF3vZ/cO074N366bHxQ0i/5xsaeTVn2RFtpFH3OZtneem9t3/t5XfWz/yB1FOdc3Jdop+5OHEs36+7W+R9E3Tqfnaobuq1vJkPq9XosLy/v6ahH3FrKfefy61Q9q6urPdkHAACgl4ybAACgmr6ZDAEAAAAAAKq5GUfjRo+/8r85QJfJ6qt7hgAAAAAAAHSalSEAAAAAADDgbsSRnt/Q/MYArbcYnEwBAAAAAAAqsDIEAAAAAAAG3K17hvR2Zcgg3TPEZAgAh2Ypy0pj8+N5ecEvJird+HIieLVi7PVUg4nYE4nYzydin0vEfjIRezURezwRS53QiIhjpZE8/0RpLMuerZjPtUQsdYypcuXHkMolfQwpqVxSx/5GIvZWxfaqybKljteZ52crt5nnc4mSJytmVP666PXxZ9m5RMnU67dqLuXnM51LmW9WTwYAivxk8VghO1owTlgr6wu9VLg1z8f31psV9+uL9i1X1l66D9SKTtTRrXrL+g7dyrnd9nqdR9E4sL3XUXHd3TqOdvVLHlCFy2QBAAAAAABDzcoQAAAAAAAYcDfiSFx3A/VSg5MpAAAAAABABVaGAAAAAADAgLsR98SNHn/l3+v2DsLKEAAAAAAAYKgNzrQNAAPpXJaVxp75Ql5e8KMVG3zs/eWxVxOxpK8mYr9Zsc7PJmJPJGK/k4hdS8SOpdOpKMuWEtFUPilvJmIPJ2Jfq9jeq4nY/YlY1XOaaq/8nOX52UqtpZ6j6nU+m6jzqUp13pI63ynlr5mqr9Hq5+ZcInqyUnupOqvmmVKlzkajEWNjKx3PBYC7wGvF44Xsm+slBTbaqPylku3je7bk+d5tERFZVtZf2NvHaOcztOzzvdd1lNddfJ7LzlMvlR1fN89HL+vtdt39oNfPFWwzGQIAAAAAAAPuZhyJGz2+gfrNAbr4lMmQLjp9+nQcO1b8q9HZ2dmYnZ3tcUYAAFDN+fPn4/z584Wxa9eqrgTjbmfMBAD0M33g4WIypIsuXLgQ9Xr9sNMAAIADS30xfesyWWM9zohhYMwEAPSzQesD3ziElSE3BmhlyOBkCgAAAAAAUIGVIQAAAAAAMOBuxtFDuGdIb9s7CJMhABzYuSwrjT3zb+blBX89Uen1lxLB+8tDrz6UKNdIxKr6s4nY7ydiqTzfSsQeS8SuJmKpa5meTMQiIr5WsWzieUpK1XmQ4yjzZiKWOoaquZS3l+dnS2NZdi5RZ/H19m8pzzPLlhLlUqpdG3e/9vJ8rlK9aalcy89b6nynnqf0c1h+/FXbq1oupRt1AkBERPzC3nFD9sfKxgsln0fvLvgs+sZG9Zy288iK+wx5XtZf2JtH2Wdo0ednJz5TO1FHuo9ZtH/ROO2JA+fRXg6tn+fDUJRfv+TWL5wPDovLZAEAAAAAAEPNyhAAAAAAABhwN+JIXHcD9VKDkykAAAAAAEAFVoYAAAAAAMCAuxFH40aPv/Lv9Q3bD8LKEAAAAAAAYKhZGQJAS85lWWns03/4B+UFH0tU+q2riWCqYKpcKnY8ETuZiL03EVtKxFLH8EoiVi7PP1Yay7LnEyWPJWJvVsplf9XazPO50liWlZ/vdLlziXJnK5VLHV+eP5UoV656np9NlEu9ZqodX8TDXagzIsueTURTr5nUuUm9R68l8+m88vZSx5BWfk6rvl8A4MDOFo8dsj+W7924tlFSScln1TfK9t+r7PM1y/bWkefjLe+b2r9b0n2s3drpV1Tvg+yvKOdOtNduHe3kUXae++WcAgdjMgQAAAAAAAbczTja88tW3XSZLAAAAAAAgP5gZQgAAAAAAAy4G3Gk5ytDbgzQeguTIQAAAAAAQKHXzv9OvH7+HxTGblz7Xo+zqc5kCAAAAAAADLgbcTSud2FlyLtnfyHePfsLhbHvNprxT8b+asfb7AaTIQDsOJdlpbG1/Iulsbf/Tz9eXukfJBq853h57Pq1RMFXErHHE7EnErHPJWIpfzYRe7VinW+URrLs2US5txKxxxKx1LmOiLi/NJLnT5XGsuxcotzZRLnPJnIpzzV1btLtpc7psUSsXOrY0x6uWO5qaSSdS+r4Uq+L8ljqXO+n+mum2jFWrTPLlhJ1zlVqL6Xqeamu/JyVH/vvdyEPAAZB0RjimRfz4p3/+kbBxpdKai7eXvTZl2X79WfvrGO8oI6yz9viMUTR/mWfy1m297iLckjpxGd+ezm33p8tq6OdnMvaK36+2+trt5NHt3I+DO0833C3GJwLegEAAAAAAFRgZQgAAAAAAAy4m3E0bvT4K/+bPb5h+0FYGQIAAAAAAAw1K0O66PTp03HsWPE1l2dnZ2N2drbHGQEAQFX/JCL+aUns+71MhCFizAQA9LPz58/H+fPnC2PXrrV3v6JeuBFH4kaPV2rcGKD1FiZDuujChQtRr9cPOw0AAOiAn/7Bo8jvR8Sv9TAXhoUxEwDQz1I/zmg0GjE2NtbjjDgIkyEAd5lzWVYae+Y/ycsLnkhUmvrsv/5sIvj+ROxkIvZQIvZmIraRiD2SiP0/ErFUnsW/dL3l8UTsaiKW8lYi9krFXCIiXi2NZNlSolz5ucmyz1VqL+LhRKz8uU/nmfo1T7X20s99qr3UMZxLlCuX52cTdZa/P/N8rlJ76XOdls61/PirHmM612rPYfU8U89v6r1U9XyXH0MqzzK3BoImQwCGWaNkHPH38y/s3fieslpearm9ss+jdvpEWfZESd3jLbdXXvfeYynLrcpna2s5lH2eV++P7aedYyk6H2Xl26m3W+ezXf2SR5l+z69VvX5fMdxMhgAAAAAAwIC7dQP13l4myw3UAQAAAAAA+oSVIQAAAAAAMOBuxJG47gbqpQYnUwAAAAAAgAqsDAEAAAAAgAF3I47GjR5/5d/re5QchJUhAAAAAADAULMyBGAIncuy0tgzX8jLC/7biUr/t4nYb305EXwqEftcInZ/IpZyNRH7WCL22UTs4UTszXQ6HVe1vdQx7Cf1XLyViFXN9WRpJM8/URrLsvLnMM/Ln/ssO1exvWcT5cpf9+n25iqVS6laLl3nUmksdQz711v1GMufi9TrMM/PdjyXlPRz3/lcUnWmVM0TgOHQKBlLjL27ZByRbRRsvHbgPDrzWfxSyfbxgvaKjiNVR+vaOZZ2Pmvz/FhJpPU6yo67LI+i/fN87/lM1dEPyp6T8uPeu38/H98waec5Se0PESZDAAAAAABg4N2Moz2/bNVNl8kCAAAAAADoD1aGAAAAAADAgLsZRw5hZcjgrLcYnEwBAAAAAAAqMBkCAAAAAAAMNZfJAhhQ57KsNPYb+VfLC/47FRv8rVSwkYi9lYjVE7GXErFjqWQS/m4idn/FOquWu5aIVT2+kxXLvbFP/JFE7CcTsfLnMM+fKo1l2VIi9tlEe68myp1L5HK2p+VSqrdXfs6q6vUxtBKv0mb1cqn3Yfn7t+ox9NpBnicAhkfRmOKZd+fFO38j1X+9U3HfpOgzJss2SvYdL9xetH/5vsWfd0Xbyz7/sqy4T1t8LK33Sdr9vG3nuMvraOe4i49lWPoJ7R7HsBx3vxj219dhunEIN1DvdXsHYWUIAAAAAAAw1KwMAQAAAACAAXcjjsT1nq8MGZz1FoOTKQAAAAAAQAVWhgAAAAAAwIC7dc+Q3n7l3849QzY2NmJqaiquXLmya3uj0YiFhYWo1WqxtbUVExMTMTk52elUTYYAAAAAAADdNTMzs2dbs9mMsbGx2NzcjHq9HhERo6Ojcfny5Zienu5o+yZDuuj06dNx7Nixwtjs7GzMzs72OCNgkJzLsmT8mf8+Lw/+9UTBlxOxP0i1+LlE7BOJ2FcTsVcTsccSsZcSsUcSsaquJWJvJWInE7GHErE3ErHHE7FXErH7E7H9pI7/NxOx8uPPsnMVcyn+XL0ldYzlsSxbKo3l+dn9U2pTqs7q56Wq1HObknoeyutMneuIiDyfS5Stdm6qn+/y46haZ7pc+tyUSz0X5aq8ts+fPx/nz58vjF27VvW1xN3OmAl65BeKxxbPTBaMKdbK/qaX9cH3bi/7nMmyjYJ9x0v2Lf5Mbe8z7ImS7anxRGvtHbTf1ol+X5YVH0cn+rDt1FF+LEXnv72ci+ruRh891V5Zm+08h93MuZv6+Rh71Z4+cOfMz89HrVaLy5cv79o+MzMT4+PjOxMh2/vOzMyYDBkkFy5c2PUkAgDAoEp9Md1oNGJsbKzHGTEMjJkAgH42aH3gm3G0rctWdarN/WxsbMS73vWuqNfrcfHixZ3tW1tbsbGxEYuLi7v2P3XqVERErKysdHRCxA3UAQAAAACArlheXo65ub1XAtieGKnVaru2b/9YZn19vaN5WBkCAAAAAAAD7mYcaXtlSP7O9yJ/53uV27z+9h8l4/Pz83tWfmxrNpsRETEyMpKMd4rJEAAAAAAAuAtdWXgurpx7tit1NxqNeNe73rVn5ce2S5cuRUTEiRMnCuNbW1sdzcdkCAAAAAAA3IUefPpMjHzyL1Qu/86X/of45kc+URhbWFiI1dXV0rKjo6MREXtuqr6tbBKlKpMhAIfoXJaVxn4j/2q68C8mYv/favnE9ZcSwSfKQ6lPk+vXEsE3KsbGE7GU4xXbe7Vie29WjKXOWdVYyv37xLtx/CcrlnslESs//jw/myhXLsuWEnXuvd5pK+2l6uyO1Ovi4dJInn+iNJZl5yplkjpn+zuWiFV77Vd/XaSOvzzP9HOfeh9We9+n8qx67AD0t/+iZHzxlz+VFxf4UMG2teJd87y4D17UZDt9hSxLjUFaraO4vbLPu0602V57GwX7Vh3T3F5ve32ybn3+lx/33vz6vQ/STn7tHPeg6vVrpki77++73fU4EkfbvYH6vcduPSq6ed99hdvn5+djYmJi16Wutv9/+7/bkx1lK0BMhgAAAAAAAH1rY2MjlpaKfxA2Ojoa9Xo9XnzxxYjYe2+Q7X+PjY11NKcjHa0NAAAAAADouZtxT9zo8eNmyXqLzc3NyPN812Nubi5GRkYiz/PY3NyMkZGRqNfrsb6+vqvsxsatFXVPPvlkR8+PyRAAAAAAAKDnnnvuudjY2Ni1OmRxcTEWFxdjZGSko225TBYAAAAAAAy4m3EkbrR7z5AOtHkQ9Xo9Njc3Y35+Pmq1WjSbzZifn4/p6ekOZfhDJkMAAAAAAICu2l7xcad6vR6rq6tdb99kCECXncuy0thG/rulsf/xrz2Wrvj1ROzVL5fHHnx/ouBXE7FEPtdfSZS7PxF7KBF7IxF7KRFLOV6x3LVE7FjFcg8nYm+m0ymR52dLY1l2LlEylWdExMmKZVPHWK29PH+qNJY+xnJZVnxDt4NI11n19VQuz+cqlUudsyx7NtFe1dfafvlUPW/V6kydt9RxpI+/Wnu9Nih5AlCuaJzxel7y+Z1tlNTSTr+6/PPvTmWflVlpHgf07vbaS32W762juE9QVEc7+5a3V5bz+IHq7aZOnOcy/fxclemX52VYOJ90kskQAAAAAAAYcDfiSBzp8WWybgzQbckHJ1MAAAAAAIAKrAwBAAAAAIABd/Pm0bhxs8c3UO9xewdhZQgAAAAAADDUTIYAAAAAAABDzWWyADrgXJaVxv5+/oXS2Bd/8WfKK724T6PfupoIvr88dCVV7olE7HOJ2EOJ2JdLI3n+VGksyz6bqPN4IvZGIlbVw4nYtUTssUTs1Yp13l8aybJnE+VSx/BmItZKvEq51DEeK41k2blEuZOJckuJcinleabqzPO5RLnU81TtXFc/vvJzHfFWxTrLpZ+//ZQ/v+m/JeVtHiyfMtVeM1XrzPOzlWpMv0arnJdvVsoDgP2VjTWeeTHfu3GxrJayz5KXCrYVjwnKPh+KPovKP0sK6n73eHG9Xy+uob3PqdT4pgveXfy5XJRz2Wd4nhefj25p53ktV/Q6iojYeyydaa9YO3V0oj12a+d13s772HPVnhs3jkRc7/EN1G8MznqLwckUAAAAAACgAitDuuj06dNx7FjxLy5nZ2djdna2xxkBAEBVX/zBo8j1XibCEDFmAgD62fnz5+P8+fOFsWvXUlc8OBw3rh+NuN7br/xv9HglykGYDOmiCxcuRL1eP+w0AACgAz78g0eRb0bESg9zYVgYMwEA/Sz144xGoxFjY2M9zoiDcJksAAAAAABgqFkZAgAAAAAAA+7mjaM9v4H6zRuDc5ksK0MAAAAAAIChZmUIQIvOZVlp7Jn/Pi8v+P5Epf9GIrbffbi+9Xoi+FYidn8iVnwD01veTMQeSsTKZdnziWjqBKTaq3oMJxOx44nY1xKx1POQUn4Mef5UaSzLnk3UmTqf3brpW6re1PNUXi7Pz5bGsuxcpXIpqTqrlqt6DCl5PlepXLq91HOUyqXzx3dL1fdTr6Xev9Wep6qqv55Sz+FSSeTHKrUFwG1+sni88cy7S8YaHy3qM5X9nX6ije1l+75UuDXLNkr2b03+9Tb3L/icyt5TsvM3Ws8ty4r7oO30I8uOpWgoWfY5XbXfWrXuTrRXVsfB+n6Dq+i1lOfFfcRevw46UW+Ztt4rXczjbnfjxpHIe74yZHDWWwxOpgAAAAAAABVYGQIAAAAAAAPuxvWjcfP7vV0Z0uuVKAdhZQgAAAAAADDUTIYAAAAAAABDzWWyAAAAAABgwOU3j0Z+o8df+d8cnMtkmQwBuM25LCuNPfP/zMsL/ueJSh9JxH49EbvyfCIYEfHzidjvl4cefCLR5ucSdZ4sD92TyOX684k6ryVixxKx8UTspUTsoYq5vJGIpfJMuT8Re6s0kmXPV2otz59K1PlspTpvKc81fYxvJmLl57Tq8adk2VJpLM/PdrzO9Pkuf59l2bnSWNU806/f8vdE+pzNVcxlP6n3aLnUual6Trtx/N14fqsee5VcGo1GjI39WuvJAdzlisYdz7y7ZLwxW1LJrxb1e4s/h/K8+DO/+G9+WX+67DNu7+di2edFcXvFffvyz6O945k8LxsftF53ezkXK6ujel+tmk4cSyfa68Rxt/Nc9Yuy91vxvt07lrv1/EOKyRAAAAAAABh0149E9PqG5tcH504cg5MpAAAAAABABSZDAAAAAACAodZXl8na2tqKhYWFiIhYXFzcE280GrGwsBC1Wi22trZiYmIiJicnD20fAACAXjNuAgCg0I2j3blM1t87H/F//9vFsT+qdn/Hw9A3kyEbGxuxvLwca2trMT09vSfebDZjbGwsNjc3o16vR0TE6OhoXL58eWf/Xu4DAADQa8ZNAAD03P9x9tajyL9oRPxbp3qbT0VZnuf5YSdxuyzLYnp6OpaXl3dtn5iYiIiI9fX1nW0rKysxMzMT24fQy31SGo3GnkEB0D/OZVlp7NN/+Aelsbd/6cfLK/2vq2bzUiL2+D5ljydizyZix/apt8xDFcv9ZCL2asVYyp9NxH6nYp0pjyViryRiqef3aiKW+sXFm4lYuTw/m4xn2blKZVPl0k5WLJc6/vLXfZ7PlcayrPy9lOdPtZJUW3VGvJWI3V+xXLVf6VR9btPllvZpM/VclJdNl+t8rqn2UrpRZ69l2fMlkdcj4pz+bw8N+rjJmIm7RdnY45n4n/dunCzur+SrxXVnWcFnfEkdsVbWH9j72bRf37AVhblFRJ7vza/8s/qJltvL8/GW921XWX5F5ynLNkpq2Tvua/c8F+XRieeK3mjndXQY+j2/flD8/v5qRPylvujPbPet4rObEX+8x7n8y0bExwajXzcQ9wzZ2tqKjY2Nnc72tlOnbs04rays9HQfAACAfmPcBAAA5QZiMuTixYsREVGr1XZt355pWl9f7+k+AAAA/ca4CQDgLncjIq73+HGjJ0fWEX1zz5CUZrMZEREjIyOl8V7u06q33347rl5NXeok7d5774177723cnkAAGjFO++8E++8804Le5ZdZqWVsnTbII6bjJkAgMPSeh84IuIPC7YNzo3DuWUgJkMuXboUEREnTpwojG9tbfV0n1Z95CMfaXnfImfPno1nnnnmQHUAAMB+FhYW4ty5qvf5oV8M4rjJmAkAOCz6wHefgZgMGR0djYiIy5cvF8ZrtVpP92nVF77whfjABz7Q8v538gsnAAB64emnn45PfvKT++73wAP/VUnk6xHxqY7mRPsGcdxkzAQAHJZW+8AREQ888A8Ltl6KiP9LR3M6sO3LZPW6zQExEJMh2x3psl8X1Wq1nu7Tqvvuuy+OHz/e8v5A55zLstLYV/LnS2P/8xvHyit9JNHgY4nY64nY9ScSwTcSsYiIjUTs/YlY1UtR1BOxX0/EHk/EHqqYS+J5it+pmMsrXSiXyrNquftLI3l+tjSWZUuJWPqXMFXrrXocEW8lcpmrmEv58uVel0ufz9RzkVqCnTrX5bGq5zN1DN2SzrX8vFU/3ydbSasnuXSj3J1uv9RQlTobjUaMjZkMOWyDOG4yZmKYFI1BnnkxL9752dbrzRbLIi/t3bTWxr5tKvt8KP5sKG4vy/ZuL/tsybLi8U6ejxcn2Ib9+r9Vlee2d3t75/Nw+l8HVXSM3TyOds9pL/VDDim9zq9fnqt2LreZ5//Wnm23+sCdzopuGojJkFOnTkXE3uvObv97bGysp/sAAAD0G+MmAIC73PZNzXvd5oA4ctgJtGJkZCTq9Xqsr6/v2r6xcetXAk8++WRP9wEAAOg3xk0AAFCuryZDUjfZe+6552JjY2PXL48WFxdjcXExRkZGer4PAADAYTBuAgCA9vXNZbIajUYsLy9HRMQLL7wQExMTMT4+vtOJrtfrsbm5GfPz81Gr1aLZbMb8/HxMT0/v1NHLfQAAAHrNuAkAgFLXI+L7h9DmgOibyZB6vR7Ly8s7HfuyfVZXV/etp1f7AAAA9JJxEwAAVNM3kyEA7TqXZaWxZy7dLC84UV4u/mWiwT+eiL36RiKY8tlE7GP7lH0sEXsrETueiJ1MxF5NxI5VLJdytWKd91dsL3U+ryVij5dG8vznS2NZtlSxvfLnqGqdeX42US4iy55NRKud7zx/KtFe+XFk2blEneXHkT6GhxJ17vc+bL+96seQKjdXqVwqln5fl6t6fAcpW/28VStXvc7y1/Z+56bT5arWWX7s3+x4HgD9qmwcsvjdy3s3lnQJ8oI5xNK/sWtPtJhZFXv7EFm20VYNRfvn+XjJ3mXb9yqrI91/ubOOdj4ni89z+bEcTDc+w7eVnaNutdnr9sr0+pwOe3vtttlOzr1+bdxVbkbEjUNoc0D01T1DAAAAAAAAOs3KEAAAAAAAGHQ3ovf38Oj1SpQDsDIEAAAAAAAYaiZDAAAAAACAoeYyWQAAAAAAMOiuR+8vk9Xr9g7AZAjQ185lWWnsmUs3ywv+rfJy8acSDW58tTz2rfcmCr6aiN2fiI0nYvv5zUTsWCI2l4g9m4g9koiljvGtROyVROxkIvZEIpZ4DuOLFdtLKT/XWZY6n9cqtvdQIvZmpRqz7Nw+ezyeiKWew6ptlp/TPD9bqc50uaVELuWqtle1zqqq5pLSjTz3bzP1PJW/n6oef9XXWirPPE/9/S2XPt+pv13lfxO68boAGDZlY5Fn4pniAg9s7t022UZ//90lf5u/0U6/8aU29o3I8739rixrr47iNouPu+gzrewzqezzrxOfYUV1lH/etv4cdiLnTtTRzc/5dp7DYXLQY2z3ee31Oe3W++ow3K2vUVpjMgQAAAAAAAadG6gnuWcIAAAAAAAw1EyGAAAAAAAAQ81lsgAAAAAAYNC5TFaSlSEAAAAAAMBQszIEAAAAAAAGnZUhSSZDuuj06dNx7Nixwtjs7GzMzs72OCPoT+eyrDT2zfxvlRf8++Xl4tlEg/+7VDbvLQ/9RKLYt+5PBH8nEXs8EXsjEduv7NVELHVynkjEXk3E3krEHkvE9jvGYnlenmeWvZQoeTJR51OJOlPn7Hgidi0RK/582N8riVj58UW8WbG9iPRz/3Ai9rXSSJ6fLY1l2bn9UypUfk6zbCmRy1zH20upfnzlz2/q+NLKX6Op5ygVS9nv2KvWW1U6n6rv0dT7vpqq75dOlzt//nxE/JclpXo94mJYGDPRL4rGJM+8Oy/c99i/uFK4/doDP7Z341rx50L2noLX/TeK983z4vdIlm0Ubi+uo/hve9HnQTv7dkJZveV5FB138RigE32LdvLrxHnul/5Qv+TXa+2ej1a1+74a9vNcpp33SplOn7vz58//oB+817Vrne/7010mQ7rowoULUa/XDzsNAAA4sNnZ2fjlX/52SfSbEbHSy3QYEsZMAEA/S/04o9FoxNjYWI8z2oeVIUnuGQIAAAAAAAw1kyEAAAAAAMBQc5ksAAAAAAAYdNcj4vtdqPcfnI/43eJ7p8T3BufeKSZDAAAAAACAYj83e+tR5LVGxNN9du+UEiZDgJ44l2WlsbX8i6Wxf/Gffqi80ucTDSaKxUYilvKtNxLBY4nY44nYY4nY68l0Io4nYg8lYk8kYqmT80oiljr+q4nYm4nYydJIlj2bKJfyVqLOpUS5+xOxV0sjeT5Xsb3UrypS57ra+Yz4WiIWkednk/EyWXauUizVXqpc+hjLz026znLpPFPPbzV5/lSivdR7ovzYe32uD6Lqa6a68vdh1fPWjWPozrG3396tm0e6gTrQ/8rGJc+8mO/d+IniOq796w8WByYLtv2zkkS+0Xr/o2wo1c5nQNX+TivayaNo37LcynNOjWdaq6Moj25+pvb687od/ZJblhWPRfN8vKd59EvfahAVvd/aPb5hOh+H5kb0/obmbqAOAAAAAADQH0yGAAAAAAAAQ81lsgAAAAAAYNDdiFs3Ue91mwPCyhAAAAAAAGCoWRkCAAAAAACDzsqQJJMhQMecy7LS2Ffy50tjvxS/URr7P3/uQ9WS+adfTgTfSsTeSMQeT8ReScSOJWJvJmKPJGL7tflEIvbridi1ROxkIpY6xq+VRvL8bGksyz6bKPexRLnnE7kcT8ReTcRSx1cuy5YS0fsrxqq+nsqfh/1k2bnSWOo5TL9mutFetTpT5zTP5xJ1Pp+oM/Veqib9eqp6rsvrTL8/q5Z7trXE2lT1NVP19VRVr9urel7Sz2/5ewKgn5SNTU7lHy0uMFWwbbak8osl29f2/t0t+3tbnF5ZH/6lkjpS/ZvW8jhovWX7lx/3RsHW1NilNd38jG33fBTpdR+gmw76fOf5eOG+ZdvvVu2c5261126bnfg7U1RHJ3KDbSZDAAAAAABg0FkZkuSeIQAAAAAAwFAzGQIAAAAAAAw1l8kCAAAAAIBBdz16f5msXrd3AFaGAAAAAAAAQ83KEKAt57KsNLaR/25p7B//tYnS2Av/3V8ob/CfJpL5k4lYvD8Re6M8dM8T5bHrn0vUeSwR+/lELFXntUQsIuL+RCxxjPHYPvUWy/OPlcayrNpxpMu9mih3LlHu8UTsldJInp+t2F5VJxOx1HNfHsvzudJY+hhSuUREvLlPvCyfpxL5PJsol3ounk+0mMqz/D2aOm8pef6JSuWybKlSLqnnsHou5c9D2n5/n4qlXhP7l029LqoeR7luvO/TdVZ7jaZfF+XnDGCYFI1RFr97uXDfa9mPFVfy7oJtZR9b58syqdan2Jbn4yWRsu17lX0ulG0v/qwoGxO91HId7XyODurnVT/k3d7z2hnt1F30mm4356L9++Hcd1uvj7Gd9jrxuuvWvoQbqO/DyhAAAAAAAGCoWRnSRadPn45jx4p/5Tc7Oxuzs7M9zggAAKr6J/G+972vMHLtWrXVQmDMBAD0s/Pnz8f588VLAvWBB4/JkC66cOFC1Ov1w04DAAA64KfjK1/5bwojjUYjxsbGepsOQ8GYCQDoZ6kfZ/RlH9hlspJcJgsAAAAAABhqVoYAAAAAAMCgux4R3z+ENgeEyRBgj3NZVhr7J/n/qzT2j5vj5ZX+i0SDL6eyeak89E8fr1YufrI8lPwDfjwRuz8R+3K1XKL4+tk/dLViLKX8epdZ9tmK7b2ViH0xEUs9v6n23kjETpZGsmwpUe7h0kiefyJR5/OJOr+WiKWUvy6y7FzFOqvLsmcT0Ye60GLV81auG+ctz88moqn3WXkuqTrTr9/+uY7twc51+fs3z586QL29k35dlKt63qq+ngD6Vdk45fW8oD/Sbnf4GwWflx8tG1Okxhq7JYZWBfsW/90u+5tdvP8TJXUkxmot71u8vSiPTnzOtPP512573aybtHbP5yCe/269J/pFN4+lnXPX7t9MiDAZAgAAAAAAg+9G9P4eHu4ZAgAAAAAA0B9MhgAAAAAAAEPNZbIAAAAAAGDQ3Yje39DcZbIAAAAAAAD6g5UhcJc6l2WlsT+df7g09hvxvvJK/2/ldcbnEslcv5oIPpGIpcodT8SOVazzpUTssYp1HsTXErHHK9b5Zmkkz5+qVGOWLSWiDydib1TKJctSL7ZXErFrlWJZdq40ludnE+VS5yWVy8mK5d5KxMqf91vK3zPp56L83KTqrFouz+cq1pl6HabOafl5q3oM6dfas4ly5VKvw26o+p7Yv97y90z6fKekXk+dP29V86yaS/X2yt9LAL1QNlZ5PS/+LHw++wt7N7677G/8RvHmd4/v2ZR/fe+2iIiyoVSe792/+mdUNUU5tKs857Kx2d7t7Rx32edcO59/Ze21U0fZvu3UnWXFr69OPC+t5nA368TroJ2623ntdjO3QdTN9+xdz8qQJCtDAAAAAACAoWYyBAAAAAAAGGoukwUAAAAAAIPOZbKSrAwBAAAAAACGmpUhAAAAAAAw6K5HxPe7UO/vnb/1KGzzWhca7A6TITDEzmVZaexa/kxpbDb+96Wx//GnHytv8H+RSOZfScS+9Xoi+FAidjUReyIRe6NirGqdVaXa20/q3KTOafnzm2XPJ8p9bZ98qnirNJJlS4lyf7Y0kuc/X7HOatJ1lncW8vxsos5zFbM5WbFcRCrXbtSZOv6U1LnpzjmtqrfPfdVyqVz+/+3dX4wkx33Y8V87gqRleOTwJJOQYR6lOeohTCjYM0spyAUmBM4YiRkjiG73+GSIBqJdHVZWEgXZkV683CTAajZAYARYnGYPiA9S8kDsnPIkycCM7dDBIRG1O5HERHqQdswjYxJHC3ejI5PlUXeqPKxmxL2p+s10Tf/f7wcYkNu/6qrqquqe6uvp7jj6aJJ4xsWq53p+ddHofX9JWe9ZzzyT3T4AcLGds8ybp6xpnzv1WXsmD4cpcfo5tvtYac/Dnt71XTP93DMIuo7IeN5h6xyGMbUQqcOktYtijubKI8x3nSutLe8o0kYhTNsl/b0fRZ9EUWbY8matX17nV3GN87y2x7H2myuHH5trPZGvVZOtjycekwUAAAAAAAAAAAqNO0MAAAAAAAAAAMi7O5L8C815gToAAAAAAAAAAEA2cGcIAAAAAAAAAAB5d0cOX6KedJk5wcWQGJ09e1bm5uassZWVFVlZcbx0BgAAAMiYra0t2drassYODg4Srg2KgnMmAACQZcyBi4WLITG6fPmyVCqVtKsBAAAAzEz7h+leryfVajXhGqEIOGcCAABZlrs5MHeGqLgYAuTcehA4Y8+9YNwrfl7J9CdK7MNK7GUl9roSUzO9T4n9WIlpV+e18v5KiWkn6jeVmFbPR5XYFSUmIvKQEnvZM6Z5U4k9qMTsv/YUETHmWWcsCDY9y3O3dxBMalM7Y857rRcEF5Soe4zq2/64EntJib2hxLT2FNHruj5h3fBlattvzKpXaXqb+npEiV1VYu59wr893YxZ8yov6boclunuJ21/0vbRSWVGvV4ctGMlAOSF67xl7Q/Hl31CLAtFRF51fDc9HOaY7ZgPvnrGstA173B9X9nSh5mD2OogYkzNutz2Xe36/gqCboi8py9PK3NW7m2ZfbunzVfLe9btjqI93f2a7DwmzLaE6Vct/axpMZ0sjCX6FVHiBeoAAAAAAAAAAKDQuDMEAAAAAAAAAIC8uy0iP0uhzJzgzhAAAAAAAAAAAFBo3BkCAAAAAAAAAEDe3ZHkX2ieoxeoc2cIAAAAAAAAAAAoNO4MAXJgPQicsU+ajztjz31TyXRBif0bJfawEntLiamuKLH7lNhNJXZCiX3ds7yeErumxHwdTIhrZc4psTc86iJizKozFgRam77kVZ7uo0pMG09amz7iWReNX1vr9fQda+4xYcx5dc0guKREr3qVqbeNtp5GW+9RJeYeo8asedbFLQg2Iy9Py9NXHNs+C22c6m2qHbv81vOltaneh+5jQvJjNPp2AVA8tvOXb5oXrGlflh+MLXvx1G/ZM37YsdzmVdex0z5XNKY2tiwI1qcvT0SMGZ+LuE7lbMfvIOha07rqEeY7wLZ9rrxd+YYpL4o6u9jbLlxfBcH4OAhbt7BlxsHVr0mLq1/jFOcYjUKYfTMreeRxHLhE0XbIJi6GAAAAAAAAAACQd3ck+Rea85gsAAAAAAAAAACAbODOEAAAAAAAAAAA8o47Q1TcGQIAAAAAAAAAAAqNO0MAAAAAAAAAAMi72yLysxTKzAkuhgAZsR4Ezthr5o+csSf7n3dn+kWlwB8qsRNKTKPlqR1tbh8oQS1WU2JXlNhDSkzzshKbU2LaNrzkVxUREXlcif1YiWkd/DFnJAjWlfUeVGLutgmCTWU9jda/vgP4qjMSBF9X1tPaWmuXNyZVyMqY886Y1kfGrCrrXZhQ6puTquWgjX3ffUajtbffvqaPUXc9jVlT1vMbo1pdtP7VaPXU93m//tPbZdI49auPbx/G0ff6Pqq3TbJ890EAx43rHGatP77sueZvWdO++MU/tyzthqqHMbbzAtd3g/14aztGhz02699V/uUFgTb3na4OrrzDbKN7+8a/v+P8XrPX44wjtb3toqjfrHm42jNsHxZdXPtm1tszC2M0qjzy6Lhu93HAY7IAAAAAAAAAAEChcWcIAAAAAAAAAAB593NJ/oXmP0+4vBlwZwgAAAAAAAAAACg07gwBAAAAAAAAACDvbkvyLzTP0QvUuTMEAAAAAAAAAABErt1uS7ValSAIpFqtSrfbHUvT6/VkcXFRGo2GLC8vS7vdjqUu3BkSo7Nnz8rc3Jw1trKyIisrKwnXCGlbDwJnbN9sO2Nf+/Zn3Jn+C6XAe5XY31Ji/0eJdb/vjr3nY+7Y7ZtKpp9SYl9XYi8psUeV2IES+7ESe0OJ2ff1Q48oMc19E+JaXR90Rox51hkLggtKnto2PqTE3lRi7r4wZs0ZC4L1GPLcVNZzj1HfuvjS+8iX1kci8fRT9LSxrdHqacyqsp57zOh5uttMo9XFl28f+dZlUnlJt41v3ydNG2u++6dPW29tbcljjz1mr8VB9Mc7HA+cM+WD6zzmHvMH1uWfkIXxhbuOzP/Ucjx6avwfSQ5dcSyvjS0JAlceLmemTunK23ZsdX/XjJfnSuv7/TgNW5mu8lzLgyD97wBjxsfAIfvyMNsdF3d7Tj8/i3PMpDEe4yov6TpHIQtjFIe2trZka2vLGmMOPNnm5qZ0Oh1ZXl6W/f192dzclHq9Lp1OR2q1w2N0v9+XarUqe3t7UqlURETk9OnTcv36dVlaWoq0PlwMidHly5dHHQgAAADkmfYP071eT6rVasI1QhFwzgQAALIsd3PgO5L8Y6uUF7Z/5zvfkU6nM/r7mWeekWq1Ks1mc3QxZHl5WWq12pE54fAOkagvhvCYLAAAAAAAAAAAEJlutyvNZvPIskqlIpVKRfr9voiIDAYD6Xa7Uq/Xj6Sbn58XEZHtbfeTdHxwMQQAAAAAAAAAgLy7LSI/S/jjuBOlVqtJuVy2xobLd3d3j/w9NLxL5N13lUSBx2QBAAAAAAAAAHAc/fzW4cfXnbdCJe/3+7K8vDz6fxGRUqnkTBslLoYAAAAAAAAAAJB3Pxf1HR5Wf7kh8vJ6HLUZ0263pVwuj94Fsr+/LyIiJ0+etKYfDAaRls/FECBi60HgjJWM+6U/X/v8Z9yZflgp8B8osa8psR/fVIL3KTGlMrd/pKz3khK7psTmPGO+HvUs70UldlWJPa7EtD4SEXlwQtwuCC55rSdyQolpfaitd+CMBMHmpAo5uPspCLQvd9/13IxZU/L02z5jzkeep9YPh9xjTWsbbfuT5lvPILigrLeqrHdpqnpFxX88+fVRXP3uu6/50vved8xE3zb6WHOXl5f9E0C22M5n1v7Qnvb35THr8heDJ8YXPhyiEl+u2Zdv2ZcHiyHydjBmPO8gmDRHmiZf+/HWdox2p+068ra0x8OOPE6Fq18YxozPo13fQdGUN55H+PLOzJxHuD6cPm1WZL1+RZd0+8e5zyIHHvmSyMNf8F//re+K/M8np0q6sbEhOzs7o79Pnz4tIiLXr1+3pnc9ZssXF0MAAAAAAAAAADiOfuV9hx9ff+PeqZI1Gg25ePHikQscw/933QHCxRAAAAAAAAAAAHDUHXG+0DzWMifY3t6Wer0+ejH60Pz8vIiMvxtk+He1Wo2mjr/wK5HmBgAAAAAAAAAAIIfvCRERqdWOPt6x1+tJqVSSSqUinU7nSKzbPXxE5Llz5yKtC3eGAAAAAAAAAACQd7cl+TtDlPK63a5sbGzI8vKybG9vj5bv7e1JtVqVSqUiFy9elGq1Kv1+f/RYrGazKc1mU0qlUqRV5WIIAAAAAAAAAACITK/Xk3q9LiIiy8vLY/EbN26IiEilUpG9vT1pNBpSLpel3+9Lo9GQpaWlyOvExRAgpPUgUOO/Zn7PGWvK590r/lcl03+kxDbU6ijuU2LXlFjPs7xHldjjSuwlzzz/ixL7JzGU96ASm1NiN5XYVSUmIvKIEjuYsK7Lm0pM236t3bR6au2m1UVr0xPOiDGrzlgQXPBcb9MrptHLW1fW1NpFo/WDiN4X7v7Vtl/bRo22/casecU0xpyPvC4afcz47ddaXfTx5Effl/R2iWP7ffm3m+9+6BbHWPPlrstridYDwOxc5zVrvza+LPiBsWfSdGT+Zcuy3amqdeiLXetiY2rW5dZj08OO4+Or9uNYEJyxLL0Sqh72fKP4rrXXw15nO2Oi/376ZT3Gt9H1/RSmPaL4jstKeWHyDtN2Yds56XlDFMJs93EV57griiLtE3lSqVTEGMccwpJ2Z2cn5hpxMQQAAAAAAAAAgPy7LSI/S6HMnCjMC9TvfuM8AAAAAOAozpsAAABwXOX2zpDgrlt6h88WG+r1erKxsSHlclkGg4HU63VZWFg4sk5UaQAAAAAgizhvAgAAOEZ+LiJ3UigzJ3J5MWR7e1uWlpbk9OnTo2W12i+f2dnv96Varcre3p5UKhURETl9+rRcv3599OKVqNIAAAAAQBZx3gQAAAD8Ui4vhuzs7Ein03HGl5eXpVarjSbiIiKNRkOWl5dHk/Go0gAAAABAFnHeBAAAcMzckeTf4ZH0nSgzyN07Q9rttuzu7sri4qJsb2+PxQeDgXS7XanX60eWz8/Pi8jhr6OiSgMAAAAAWcR5EwAAAHBU7u4M6XQ6MhgMpN1uS7vdlkajITs7O6PbvXd3d0VEpFwuH1lv+CulTqczis2ahl85Fdf6Xc9WfreS0fv9q+J+NvL+V/+2X4Uu+a0m8iMlduAOvedj7thtZT25qcTuU2JveubZVWKPKrEfK7FrnnVR2kzN8yUlNqfERETemBB3OeGMGLPqmeennJEg2PSqix7TuNslCNadMWPWvNYTeUSJXVVibnp5fvQ8H5whZ/c26m16QclTOya46WPNfezS6unLd6xp+6DvuNDbReM+Bun1dPftpG2Ioy+S5n8c1cyyj4bnM357vZ5Uq/yjd5Zw3oQh7dzGJviaGV/4rCNx2/H9+vD4d4h5JUQdgjOO5dq5yF1eDfu9eWVsiTE1Szo32/EzzHeb+/hrbw9jJp0v+AkC+3mWqz3CbGM07TFbvlFJuswo2nnWMRonV39HUb8sb3cU8tjfSQu73bbjYNjvBGRT7i6GtFotabVa0uv1pNVqyfb2ttTrddnf35dyuSz9fl9EREqlknX9fr8fWZpJ3nrrLbl5U/vHU9373vc+ed/73ue9PgAAADCNW7duya1bt37x19vOdK657VtvvRVDrTCLvJw3cc4EAADScnQO/G7/d2yJbb6SyTnwbUn+MVlJlzeD3F0MGapUKtJqtaRer8vi4uLol077+/siInLy5EnreoPBILI0kzz55JMT02jW1tbkueeemykPAAAAYJKNjQ1ZX5/8C9z77/9yArVBlLJ+3sQ5EwAASMu0c2ARkfvvj7kySERuL4YMLSwsyMLCgvR6PREROX36tIiIXL9+3Zq+XC5HlmaSF154QX7jN35jYjoXfuEEAACAJHzpS1+SL3zhCyIicv/9G850P/3pl6zLv/vd7878j9qIV1bPmzhnAgAAaXn3HPjd7r//z8eW/fSnnxxblsk58G0R+VkKZeZE7i+GiIjU63Xpdg+f5TacbLt+gVQulyNLM8m9994r992nvScBAAAASN/RRw2935nONbe99957Y6gVopbF8ybOmQAAQFrcj9v8m2NLbPMV5sD58ytpVyAq8/PzR/5797Nph39Xq9XI0gAAAABAnnDeBAAAgOOqEHeGdDodWV5eFpHDF/dVKhXpdDqyuro6SjP8BdS5c+ciS4N8Ww8CZ+zvmb/vjP12/yt6xv/Jna9oq7rfVSpy4/tK8MNK7ECJ/dgduv2msp72gst/qMR6Skyj/VJQ275rSkzbPi3POSV2VYk9rsQeVGKTvKHEtLo+5IwEwaZnXU4oMa1N/fI05rwzFgTa8z7d7R0El6aok43W99lhzJr3uv7jQqONXzdtO7S+991+fTxptH0w+vJm6d+oaftnnviOpzjGoW+b+tYlS+MJ0eK8qfhs5zhrv2NP+41vPGUPLI4v+vgrf2FN+mLwhD2PT4wvCgLXnNA2z1i1LHOlTV6Y7+swaV3HX3cetanzDsOY2fMNgu7UefvPt/zziOK7zraNSbddGmxtHWbspjHPiKa/Z9+Xw+QbRZ3zOKebdXxp6cOY/lj12sxlRe7nInInhTJzIld3hvR6PalWq7K5+csJULvdlpMnT8rCwsJo2cWLF6Xb7R75dVKz2ZRmsymlUinSNAAAAACQJZw3AQAAAONydWdIuVyWkydPysbGhnQ6HalUKlKv16XVah1JV6lUZG9vTxqNhpTLZen3+9JoNGRpaSnyNAAAAACQJZw3AQAAHFN3JPkXmid9J8oMcnUxpFQqSafTmSptpVKRnZ2dRNIAAAAAQFZw3gQAAACMy9XFEAAAAAAAAAAAYMGdIapcvTMEAAAAAAAAAAAgLO4MQaGtB4Ez9k/NB5yxX//qf3Nn+taEQr+ixF6fsK7LAx9zx95U1lOvBD+uxA70+jh9S4k9pMReUmIPKrE5JfaGEnvUM0+tnpprnuV9dEK+XSV2whkx5lPOWBBsOmP+40LbRo17cOv1dJdnzHmvPI1ZU9Zb96zLqlddtL7VBMEFJartLzqtbXzX07Zf347o+W6f3ofR08eve6zpeUY/tmcp078v/PL0LS+OuvhKdhtei7wsAG7aec7dfv8b9u/O528+Y1/h2+OLXgzeceR+xb74fM2Sr/27w7wyfqwKAm2eO6Mv24+NpjG+TP8utLF9B7q+o8+EKM/+3RqmflF8J4TrF/vYCDF0Y62zve3G++SwHpbxrCyftjzX9oXJNw1h+iWKtGHaLk5xlZnGtoQx63HGtX4U2x1n20077nq9nlSr27HVA9HjYggAAAAAAAAAAHmX9COy0irTE4/JAgAAAAAAAAAAhcadIQAAAAAAAAAA5N0dEQnxSMLIyswJ7gwBAAAAAAAAAACFxp0hAAAAAAAAAADA7u0tkVtb9pg5SLYuM+BiCHJvPXDf+/VJ83Fn7Nf/9bfdmX5FKfDtCRW6oQWvKbGHPPP8vhL7KyX2spap4mNKTNu++5TY4555vqHE3AdiYz7ljAXBBSXPE0rsQSWm1VOLTfKIErvqjATBurKeth3al5vvetr2zzkjxqw6Y/r2+QmCTc81/SYE/tvnOw4n1WfNsz7uPky6beIYF1q7aNunjSc9TzffMerfLn79N6k837Gmrefbppo4xpMv33bxzRNAsrRzHZuXzfhc9o//8rw17aUnP2vPZMWy7ItnQtVDvjO+yLxiTxoEXcvSK+HKC8E0XPWwfbeF225jbHMf+7HYfqy1l2fPV8Q2PMIe+231cOfh6pfxeoerRy1E2nDfU+56jJfpztdev3Btlw1Zr5+Nrc6uvsrj9tmPgSLGhNsv4jJrm7rWz3ofZqUeXuJ6ZNV7Vg4/1jJ7Iv+vGlPB0eIxWQAAAAAAAAAAoNC4MwQAAAAAAAAAgLy7IyIm4TJ/nnB5M+DOEAAAAAAAAAAAUGjcGRKjs2fPytyc/dmeKysrsrLieM4aAAAAkDkv/uJjczvJiqBAOGcCAABZtrW1JVtb9heHHxxk8MXht0Uk3CvHZpf0nSgz4GJIjC5fviyVSiXtagAAAAAR+PgvPjavich2gnVBUXDOBAAAskz7cUav15NqNR8vDschLoYgF9YD9yXND5tzztiTf/G8O9M/UQp8XYk9rsRERN5UYr/6kFLmj5QVryix+5TYTSX2YSX2shK75hnT6qJdSf+Y53qPOiNBsKmsd0KJaT7qGXP3rTHPqiXq22H/heVhvqtKnuvKemte68VBL8+97Xqb+dHaxZdvexpzPvI8J60bx/ZrtD70rUsc4z7pdtH2a3297IzfSZKuq1ZeHONCPz65v+/iqItPWx+eCHIxBJiFds5zt7XfsS8PTn12bNnz/+sZe+JLjswvWJY97J5fWT0xvigIuiEycH2vaedFRxlTsy5312M87yi+e8J8L7rq7E4/Xr/glCPtK6Gynro8F9d2R9Gmcc2zwuZrbf8YtzsuWa9z0ueaLrZjR9h91iaKPOJka/+sjA2XPNYZyeFiCAAAAAAAAAAAeXdHeEyWgheoAwAAAAAAAACAQuPOEAAAAAAAAAAAiiBHd2okjTtDAAAAAAAAAABAoXExBAAAAAAAAAAAFBqPyUJmrAfut/t80nzcGXvy88/7FfjXfqvJS5MSfMsdel1br6LEPqrEvq/EPqwVqHjTM3ZGiV1TYgee672hxGpK7KYSu6rE5pSY1g9am7m3PQi+rqwnInLCq8wgWFfW07bRjzFrXnUxZlVZ74JSojYufLdP6yetPd20dvFtM9+6TOZuN70P3dvhT+uLTaUu7vGUtDjGjLZPGHPeq7w4xlM8YyKeNvXlu//GIa72BjAb13nP71qWVfr2PD7xkRfsgcXxRQcXHrCn3bIvlhXLsm870r7q+F5+dnzuYIx9fh4EXctS13e6dr4xTb5aPa5YlrmO2/Z62PN21Xm8PJcw22JeceWR7HdQGK66ZeV7LFz9ph+jUdQjijYKm8es9Qjb3/HNH+37lWvfzMp4TFpc2x1nex7XvsJ0uDMEAAAAAAAAAAAUGhdDAAAAAAAAAABAoXExBAAAAAAAAAAAFBoXQwAAAAAAAAAAQKFxMQQAAAAAAAAAABQaF0MAAAAAAAAAAEChvSftCuB4WQ8CZ6xklpyxJz/fcme6qxT436+4Y+85o6x4U4ndp8Smift4XIld84xp9TyhxOaUmNLe8qhnnlpfPKjEukrsTSXmx5jzzlgQXFLWe1ZZb3OGGh0osUecEf/6uPsiCNYTXc+YNc88tXGoxfxoddG2QauLMatKeVr/aeNFz1ejlembp0bfft/21srzWy8O+jFIG/dann770iztknRdk+bbNr7b4NtPfuW95rEOUHza+c/dqgtmbNmzH/mKNe1LNx3nB9+2LGu7vucdc/cvWs6TFuzzD/OKfXlwyrIssNfDmJolrf1czZjpyzOvWJOGOsa5jpWuPOzd7TpHGt9Gd920c9fphN2WuMqLgq3OYcuLYhzY046PZ1d5UbRR2P6zlenKw1W/MHmEyTdsPWbNw9VXIq7ls4mir9IQBOP/tuJuuyjKi2dfOV5ui8jPUigzH7gzBAAAAAAAAAAAFBp3hgAAAAAAAAAAkHu3Jfk7NbgzBAAAAAAAAAAAIBO4GAIAAAAAAAAAAAqNx2QBAAAAAAAAAJB7vEBdw8WQGJ09e1bm5uassZWVFVlZWUm4RslYDwJn7DfNbztj//irLXemH1QKfEurzYPu0K8qq73+n5XgQ1qBInJTiV2dsK7LNc/Ym0rMvR3GnHfGguCCkqdmUrv5eMNzvRNeMb1d1pU87ceBybR66vWJgzGrzpi+/cp+qPahu930vthU1lvzWk/kwCtPjbYvxTGetP6bhVZX37bRubdf70NtHEZP78N80NszHv77k197Jz9+/fjWJer1tra25HOfe86xVn5OgJAtRTln0s6B7rbWty9/+SNfGVt26c8+a0/8VNe62Jja2LLgzxzfnU+dceQxnj4I7OU5N/vh8Xq42PK2bYdWD3vaK1Ondefh+p6wz6tsbScy/ba4jr/htnv2OYgrjzDfK3HOhWz1CFtemDyiaI8szStmZWuPKLbPPf6nLy+KPFxmzSOvY8B1PI6vvOy109bWlmxtbckPf/jXlihz4LzhYkiMLl++LJVKJe1qAAAAADNbWVmRz33uJ47oayKynWR1UBCcMwEAgCwb/jjDfmE0i3PgO5L8RZo7CZfnj3eGAAAAAAAAAACAQuNiCAAAAAAAAAAAKDQekwUAAAAAAAAAQO7xAnUNF0MAAAAAAAAAAIDDfxSRP3bE3k6yIjPhYgi8rAeBM/aa+SNn7Llv/jN3pv9cKfDGj5TgG0rsjDv0+hVlvY8psRNKTETkx0psTold88zzwLO8l5wRpXtFb+8HlZjWh5qrSuxxJebbD+7ts78saxruPvLPU6fnq22/Np60/nXnacx5ZT23INj0ihmz6lWetp7WnlrMmDUl5m4X33ERR7sc5ntByde9jXGYZTtcfPvQdz1fvvt10n2kmTS246irbx/CztWevV5PqtWsvTwSiJ52HjSrP37aMjf4hj3tJfm0dXlwyrLwVdf8zn4+FGYTjak58uiGSGurhz2t+/zONgdypXWdByrnjlOVJyIyfox0fdeE+86z183ezuG+S215hJX092mY8sK0hyttENj3IXvbTT++3PtEFGPGLkzbRTE2wsjKOHK1s215nH11XMW1f4ctL999GNedIb/3i4/NSyLyOzGUGT3eGQIAAAAAAAAAAAqNO0MAAAAAAAAAAMi9O5L8OzzuJFyeP+4MAQAAAAAAAAAAhcbFEAAAAAAAAAAAUGg8JgsAAAAAAAAAgNyL6wXqk8rMBy6GwGk9CJyx5/6Hca/475VM/60Su3FtYp3Cu6LEfqTE3lBij04o8yUl9qASm3NGjFl1xoLg6151MWZNyXNdyVOjtZsf33pq62mCYFOJuvtI5IQS09rFr98n0dvNvY1xjAvf9fS6XPDKU+M/7v3y9B2js4wLX8acd8bi2MY4xNMX2dk+kYO0KzCVWdqs+H0IIGu086G7zZunrMs/FPzp2LJ/95E/sKb9wTceG1t2Kfi0vcCHHXPIT9gWuuabtanzMDv2pEHQdeQ9fh7mbs4zlrSuY/54WpGwx3P7dgfB+HepMSHbziKa7xrX/H36tnPXQztnvjtvV2S8HsZM30Zh2bbFtd3h28OW1j4OgmC87eKcW9i2JYrywucRT9+GqUfYc7co8rblkZW5ZBTjPGzecZUXZv8OI5pjwXjaXq8n1eq2d72QPC6GAAAAAAAAAACQe7xAXcM7QwAAAAAAAAAAQKFxMQQAAAAAAAAAABQaj8kCAAAAAAAAACD3eIG6hjtDAAAAAAAAAABAoXFnyDG3HgTO2HMvGPeKf1fL9ZoS+9akKjkcKLE3ldgJJTbnjBiz6owFwbqSp4jIg0rsDaXM80qZF7zy1Ojb4d4GrZ7+5bn7Ig5BsKlEtTGj9Z97zMRl8li0M2Yt0fU02jb4bp9vedr2Jd3Wvny3bxZJb6OvvNRTU4RtmMVx3/4kpXEsAdLkOif6Xcuy8+YFa9rmzcetyw8WHhhfeMpRkU9Yli040rYd50evXhlf9nDNkXb6PILgjKMilvJERMSVfra0xji2JQoPT39O4jpOznqMDJtvFPPlohzXo2gjd1r7GLWVGcXYCJM2bHlZ6O+k2yisuPKO67gRNo+st39coqizve1emznf6N2W5O/U4M4QAAAAAAAAAACATODOkBidPXtW5ubsvy5ZWVmRlZWVhGsEAAAA+HpRHnvsMWvk4EC7ixdw45wJAABk2dbWlmxtbYnIX1uiWbwjgneGaLgYEqPLly9LpVJJuxoAAABABD4uP/jBN6yRXq8n1Wo14fqgCDhnAgAAWTb8cYb7MVnbSVcJM+AxWQAAAAAAAAAAoNC4MwQAAAAAAAAAgNy7I8k/tupOwuX542LIMfCo/G9nbF++6V7xyQtKrm8qMd9nRj/ijBhz3hkLAnc9fdez3/o2zHPNGZtFEGwqZa4q67nrKmJ//vIsefq2jRbTt8Etnjx929otrvEU11hMku82+PaFr6Tbugh9i+Mrje9QTdbqkwe0C4pqPQisy9f+0J7+nn95fXzhTXvag7/zgD3waneKmg3VxpaYV+wp3ecOZ0LU4cpUtdLSRnG8CAJb/ezlBcH0dXbVzfW9EGZb4jpOxllnF1v7GzM+FvU8xusXZlui2Y5s9Guc36H2fSWKfGdvO1fdbGPpuM4zsrLdYeuRhXpn+Rhx+KhYHpOVJ1wMAQAAAAAAAAAg93iBuoZ3hgAAAAAAAAAAgELjYggAAAAAAAAAACg0HpMFAAAAAAAAAEDu8QJ1DXeGAAAAAAAAAACAQuPOEAAAAAAAAAAAco8XqGu4GFIQQbCpRA88c53zytOYNc/y/BhzPtH14mLMqud60bd38n2Yj23wzTPp9jwO6Au7om8f8iFr4zBr9QGQjPUgGFt2j/kDa9qg+R/smdxvO+fRzrvGzXoMCoL1kOXVZs7Dnm+cx9IrsdTDtd15/F6Is862MROnuLYlj/0alr2vZu+/KNrONY5s++Fx6CtkD+MOQ1wMAQAAAAAAAAAg93hniIZ3hgAAAAAAAAAAgELjYggAAAAAAAAAACg0HpMFAAAAAAAAAEDu8QJ1DRdDAAAAAAAAAACAw7dE5E8csXeSrMhMuBhSEMaspl0FAAAAAEhUEHSty40xU+fxrxqOQGPOsnBt6nyjYMzs5UWRRxSCYN26PK76ZWW7jwPaGi6MDSANcd0ZUvvFx+YvReRLMZQZPd4ZAgAAAAAAAAAACo2LIQAAAAAAAAAAoNB4TBYAAAAAAAAAALl3W5J/oTkvUIeInD17VubmbM+ZFVlZWZGVlZWEawQAAAD42drakq2tLWvs4OAg4dqgKDhnAgAAWcYcuFi4GBKjy5cvS6VSSbsaAAAAwMy0f5ju9XpSrVYTrhGKgHMmAACQZfmbA8f1AvVJZeYDF0MAAAAAALlkTC3tKmBKxqylXQUAAHDM8QL1GLzzzjtH/ov8u3Xrljz33HNy69attKuCCNGvxUOfFg99Wkz0a/Ew/0VYjJl0cPxNB+2eDto9HbR7Omj3dGRzPnNHfvnekKQ+dxLZsihwMSQG2dwRMItbt27J+vo6XyoFQ78WD31aPPRpMdGvxcP8F2ExZtLB8TcdtHs6aPd00O7poN3TwXwmf7gYAgAAAAAAAAAACo13hgAAAAAAAAAAkHu8QF3DnSEAAAAAAAAAAKDQuDMEAAAAAAAAAIDcG75APeky84E7QwAAAAAAAAAAQKFxMQQAAAAAAAAAABQaj8kCAAAAAAAAACD3eIG6hjtDcmhrayu3+ee57nGj3ZPPO260e/J5x40+TS//ONHuyecdN9o9vfyBtEQ5trOYV1b33aK3Fe2efD5R5xWlLG5jFusUtaK3Fe2e/7yQL1wMyaE8nyTnue5xo92TzztutHvyeceNPk0v/zjR7snnHTfaPb38gbRk9R9IsviPSVEqelvR7snnE3VeUcriNmaxTlErelvR7vnPK3uGL1BP8pOfF6jzmKwp9Ho92djYkHK5LIPBQOr1uiwsLKRdLQAAAADIDM6bAAAAkGVcDJmg3+9LtVqVvb09qVQqIiJy+vRpuX79uiwtLaVcOwAAAABIH+dNAAAAWcA7QzQ8JmuC5eVlqdVqowm9iEij0ZDl5eUUawUAAAAA2cF5EwAAALKOiyGKwWAg3W5X6vX6keXz8/MiIrK9vZ1GtQAAAAAgMzhvAgAAQB5wMUSxu7srIiLlcvnI8uGvnTqdTuJ1AgAAAIAs4bwJAAAgK5J+efrwkw+8M0TR7/dFRKRUKqnxu7399tsiIvK9731vpvLf+973ynvf+96x5QcHB9Lr9WbKWxNn/nmt+1tvvSUiIt/97nfl3nvvjTx/Edo9jbzj7lfaPfm86dN08o4zf46/6eXPvppO/mnV/Z133pF33nnHO9/hvHc4D8bx4nPeFPU5U5T7ThbziiqfqI+/RW6rKPOi3dPJi3ZPJ6+stnsW2yrKvGh3v7yKOQf+yTEp05OB0+rqqhERs7e3NxYTEVMul63rXbp0yYgIHz58+PDhw4cPHz7H6nPp0qW4p+jIIJ/zJs6Z+PDhw4cPHz5F+WRhDnz16lVzzz33pNYG99xzj7l69WrazTARd4YoTp8+LSIi169ft8bvvg186Omnn5ZLly7Jhz70IXn/+9/vXb7rzhAAAAAgSrP+Ku7tt9+W119/XZ5++ukIa4W88Dlv4pwJAACkrUhz4FOnTskPf/hD+clP0rlL44Mf/KCcOnUqlbLD4GKIYjhpHwwGavxuH/zgB+XTn/50XNUCAAAAgMzwOW/inAkAACBap06dysUFiTTxAnXF/Py8iIw/43b4d7VaTbxOAAAAAJAlnDcBAAAgD7gYoiiVSlKpVKTT6RxZ3u12RUTk3LlzaVQLAAAAADKD8yYAAADkARdDJrh48aJ0u90jv3JqNpvSbDalVCqlVzHM7O5frgHIPvZbID/YX4HjhfOm9HC8xXHF2Mdxw5gHZhcYY0zalci6Xq8nGxsbUi6Xpd/vS71el6WlJTXdYDCQer0uCwsLKdQYNkEQHPm7UqnI3t7e6O9p+i+qNAhnMBjIxsaGiByeVN8tyb6jf6MzqV9F2G/zqN1uy8bGhvR6PalUKtJsNqVWqx1Jwz6bL9P0qQj7a968u1/L5bK0Wi32VcxsmvMm+np2HG/jxblHOjg3SA/z9+Qxv04H81+IiIhBJPb3942ImL29vdGycrlsWq1WirXCUKvVMktLS6bZbI4+7+6rafovqjQIp9PpmIWFBSMiZmlpaSyeZN/Rv9GZ1K/GsN/mUbPZNLVazbRaLbO6umpExIiI6XQ6ozTss/kyTZ8aw/6aN8P+6nQ6ptPpmEqlYkTE7O/vj9KwryIO9PXsON7Gi3OPdHBukB7m78ljfp0O5r8Y4mJIRGq1mqnVakeWtVotw/WmbLi7b2zxSf0XVRr4cU2Mk+w7+jd62gkP+23+LCwsHPl7b2/PiMiR9mWfzZdp+tQY9te8aTabR/4e9uvOzs5oGfsq4kBfz47jbTI490gH5wbJY/6ePObX6WD+iyHeGRKBwWAg3W5X6vX6keXz8/MiIrK9vZ1GtfAL7XZbdnd3ZXFx0doX0/RfVGkQrST7jv5NFvtt/nS73bFHGlQqFalUKqNn27LP5ss0fSrC/ppHq6urR/4evs+hUqmICPsq4kFfz47jbbo4NqaHsR8P5u/JY36dHua/GOJiSAR2d3dFRKRcLh9ZPtyhOp1O4nXCL3U6HRkMBtJut2V5eVkeeOAB6Xa7o/g0/RdVGkQryb6jf5PFfps/tVptrB2HhsvZZ/Nlmj4VYX8tgna7Lc1mk30VsaKvZ8fxNl0cG9PD2I8H8/fkMb/ODua/xxcXQyIwvHo7vKroiiMdrVZLjDGyt7cnS0tLo5cSDftlmv6LKg2ilWTf0b/JYr8tjn6/L4uLi6P/F2Gfzbt396kI+2veNRqN0Ysbh9hXEQf6enYcb9PFsTE9jP1kMX9PHvPrZDH/Pd64GBKB/f19ERE5efKkNT4YDBKsDVwqlYq0Wi3Z2dkRkcODn8h0/RdVGkQryb6jf9PBfptv7XZbyuWyLC0tiQj7bBHc3afvxv6aP5ubm9Lv92UwGBx5DAP7KuJAX0eH4206ODamj7EfP+bvyWN+nSzmv+BiSAROnz4tIiLXr1+3xl23wCEdCwsLsrCwIL1eT0Sm67+o0iBaSfYd/Zsu9tt82tjYGE3cRdhni+DuPrVhf82P1dVV2dnZkU6nI6VSafQMa/ZVxIG+jh7H22RxbMwOxn58mL8nj/l1spj/goshERgOUteVOwZx9tTr9dFBZ5r+iyoNopVk39G/6WO/zZdGoyEXL1480obss/lm61MX9td8qdVqsrS0NLoln30VcaCv48HxNjkcG7OFsR895u/JY36dHua/xxcXQyIwPz8vIuPPdBv+Xa1WE68TJhv22zT9F1UaRCvJvqN/s4H9Nh+2t7elXq+PXgA3xD6bX64+1bC/5ssTTzwxOvFiX0Uc6Ov4cLxNBsfG7GHsR4f5e/KYX6eP+e/xxMWQCJRKJalUKtLpdI4s73a7IiJy7ty5NKoFRafTkeXlZRGZrv+iSoNoJdl39G/62G/zod1ui8jhL23erdfrsc/mlNanLuyv+dPv90d9zL6KONDX8eB4mxyOjdnC2I8O8/fkMb/OBua/x5RBJPb29oyImP39/dGycrlsms1mirXC3t6eqVQqR/phZ2fHLC0tjaWb1H9RpUF4N27cMCIy1m/GJNt39G+0XP3KfptfnU7HVCoV02q1jnyWlpZMq9UyxrDP5s2kPmV/zZ8bN26YhYUFs7OzM1q2v79varXakXTsq4gDfe2P421yOPdIB+cG6WD+njzm18lj/ot3C4wxJvpLLMdTr9eTjY0NKZfL0u/3pV6vy9LSUtrVOtYGg4EsLi7K7u6uzM/PS6VSkXq9Pnb1XWS6/osqDabX6/Wk1WrJ9va2lEoluXjxotRqNSmVSkfSJNV39G80tH5lv82nXq+n3s5748aN0X7LPpsP0/SpiLC/5lC9Xh/1Wb1el3K5LAsLC2Pp2FcRB/raD/OjZHDukQ7ODdLB/D15zK/Tw/wXQ1wMAQAAAAAAAAAAhcY7QwAAAAAAAAAAQKFxMQQAAAAAAAAAABQaF0MAAAAAAAAAAEChcTEEAAAAAAAAAAAUGhdDAAAAAAAAAABAoXExBAAAAAAAAAAAFBoXQwAAAAAAAAAAQKFxMQQAAAAAAAAAABQaF0MAAACABDQaDQmCQIIgkAceeEAeeOAB599BEEi/30+7yiIisr29faRui4uLUq/X5fTp07K8vCyDwSDtKgIAACCDmP8ia7gYAgAAACRgMBhIrVaTGzdujD61Wk1ERC5evCg3btwQY4zs7e2N0mfB0tKSnDt3bvT/Ozs70ul0pNPpyPb2tlSr1czUFQAAANnB/BdZw8UQAAAAICGtVktKpZKaplKpyOrqajIVmtLwV3r1en20rFwuS61Wk36/L91uN62qAQAAIMOY/yJL3pN2BQAAAIDjoF6vS7lcnirt8vJyzLUJZ3iyN/wl39DwxDYrjzQAAABAdjD/RdZwZwgAAACQgIWFhanTlstlKZfL0m63pV6vS7fbHT27eHl5Wdrt9ug5xr1eT0QOT9gWFxdHzzV+t16vd+RZx41GY+q6DPMvl8tjv+obxiqVytT5AQAA4Hhg/ous4c4QAAAAIIPa7bY0Gg3p9/ujE7FyuSy7u7vSarVGzyweqtVqoxPId+v1etJoNKTT6YzyXVxclMFgIK1Wa2I9nn/++VH+d+c7rNvdMQAAACAs5r+IG3eGAAAAABm0sLAwelxAqVSSZrMpe3t7oxdM2p69fPLkybFln/nMZ6TZbB7Jt1Qqyfb29lQvfhw+ImD4vOTBYCDtdlueeuopKZVKo5NMAAAAYBbMfxE37gwBAAAAMmp4wvfEE094rd/v96XX68nGxoY1vru7q/6qbTAYjB4F8Pzzz8vGxoacPHlSyuWyNJtNWVpa8qoXAAAAYMP8F3HiYggAAACQcbZfwU1jeCK3s7Pjtf7wV3GVSsU7DwAAACAs5r+IA4/JAgAAAAqq3+8f+W9Yw0cAPPPMM5HVCQAAAIgL819ouBgCAAAAFFS5XBYRGXup5NDwl28uwzgviAQAAEAeMP+FhoshAAAAQMquX78eep0PfOADInL0V2/D/x++GHJ4EtdoNEaPDBja3t5W8+/3+9Lv96VUKkmlUgldPwAAAMCF+S/SwDtDAAAAgJTcffJ2t+FyW3x4gtZoNKRUKkm/35e9vT0ROfxFW71el06nI6urq7K5uSnValUWFhbkiSeekE6nI5VKRX0B5PBXcfPz855bBwAAABzF/Bdp4s4QAAAAIGHdbleWl5dHJ4MbGxvSaDSO/Mqt3W5Lq9USkcMTvrt/yVar1aTZbMr169dlcXFR9vf3pdVqSblcltXVVWk2myIi0mw2pdlsSrlcHuW5uLg4ittsb29Lo9EQEZHd3d2Jv6IDAAAANMx/kQWBMcakXQkAAAAAAAAAAIC4cGcIAAAAAAAAAAAoNC6GAAAAAAAAAACAQuNiCAAAAAAAAAAAKDQuhgAAAAAAAAAAgELjYggAAAAAAAAAACg0LoYAAAAAAAAAAIBC42IIAAAAAAAAAAAoNC6GAAAAAAAAAACAQuNiCAAAAAAAAAAAKDQuhgAAAAAAAAAAgELjYggAAAAAAAAAACg0LoYAAAAAAAAAAIBC42IIAAAAAAAAAAAotP8PRcXpDpLn4aAAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -94,13 +86,16 @@
     "endVeloP_found = ak.to_numpy(found[\"ideal_state_770_p\"])\n",
     "trueP_found = ak.to_numpy(found[\"p\"])\n",
     "\n",
+    "endVeloP_lost = ak.to_numpy(lost[\"ideal_state_770_p\"])\n",
+    "trueP_lost = ak.to_numpy(lost[\"p\"])\n",
+    "\n",
     "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"ideal_state_770_p\"])\n",
     "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n",
     "\n",
-    "stretch_factor = ak.num(trueP_notelectrons, axis=0) / ak.num(trueP_found, axis=0)\n",
+    "stretch_factor = ak.num(trueP_lost, axis=0) / ak.num(trueP_found, axis=0)\n",
     "\n",
     "nbins = 100\n",
-    "vmax = 50\n",
+    "vmax = 100\n",
     "\n",
     "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
     "\n",
@@ -115,12 +110,14 @@
     "    range=[[0, 30000], [0, 30000]],\n",
     ")\n",
     "ax0.set_xlabel(f\"True $P$\")\n",
-    "ax0.set_ylabel(f\"endVelo $P$\")\n",
+    "ax0.set_ylabel(f\"EndVelo $P$\")\n",
     "ax0.set_title(f\"found P\")\n",
     "\n",
     "a1 = ax1.hist2d(\n",
-    "    trueP_notelectrons,\n",
-    "    endVeloP_notelectrons,\n",
+    "    # trueP_notelectrons,\n",
+    "    # endVeloP_notelectrons,\n",
+    "    trueP_lost,\n",
+    "    endVeloP_lost,\n",
     "    density=False,\n",
     "    bins=nbins,\n",
     "    cmap=plt.cm.jet,\n",
@@ -129,20 +126,47 @@
     "    range=[[0, 30000], [0, 30000]],\n",
     ")\n",
     "ax1.set_xlabel(f\"True $P$\")\n",
-    "ax1.set_ylabel(f\"endVelo $P$\")\n",
-    "ax1.set_title(f\"notelectrons P\")\n",
+    "ax1.set_ylabel(f\"EndVelo $P$\")\n",
+    "ax1.set_title(f\"lost P\")\n",
     "\n",
-    "plt.suptitle(\"True and idealstate_endVelo Momentum\")\n",
+    "plt.suptitle(\"Momentum at Creation and EndVelo\")\n",
     "plt.colorbar(a0[3], ax=ax1)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a0 = plt.hist2d(\n",
+    "    trueP_found,\n",
+    "    endVeloP_found,\n",
+    "    density=False,\n",
+    "    bins=nbins,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax,\n",
+    "    range=[[0, 30000], [0, 30000]],\n",
+    ")\n",
+    "plt.xlabel(f\"True $P$\")\n",
+    "plt.ylabel(f\"EndVelo $P$\")\n",
+    "plt.title(f\"found P\")\n",
+    "plt.colorbar(a0[3])\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/trackinglosses_energy.ipynb b/trackinglosses_energy.ipynb
index dec0860..ea88ca8 100644
--- a/trackinglosses_energy.ipynb
+++ b/trackinglosses_energy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,43 +17,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "40402 10099\n",
+      "41978 8523\n",
       "50501\n"
      ]
     }
    ],
    "source": [
-    "#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
+    "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
     "file = uproot.open(\n",
-    "    \"tracking_losses_ntuple_B_default_radlength_endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    "    \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
     ")\n",
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = file.arrays()\n",
-    "found = allcolumns[\n",
-    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
-    "]  # B: 9056\n",
-    "lost = allcolumns[\n",
-    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
-    "]  # B: 1466\n",
+    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
+    "                   (allcolumns.fromB)]  # B: 9056\n",
+    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
+    "                  (allcolumns.fromB)]  # B: 1466\n",
     "\n",
     "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
     "\n",
     "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
-    "print(ak.num(electrons,axis=0))\n",
+    "print(ak.num(electrons, axis=0))\n",
     "# ak.count(found, axis=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -118,6 +116,7 @@
        "    fromPairProd: bool,\n",
        "    fromSignal: bool,\n",
        "    fromStrange: bool,\n",
+       "    ideal_state_770_p: float64,\n",
        "    ideal_state_770_qop: float64,\n",
        "    ideal_state_770_tx: float64,\n",
        "    ideal_state_770_ty: float64,\n",
@@ -195,7 +194,7 @@
        "<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +233,7 @@
        "<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,7 +247,6 @@
     "length = electrons[\"brem_vtx_z_length\"]\n",
     "rad_length = electrons[\"rad_length_frac\"]\n",
     "\n",
-    "\n",
     "brem = ak.ArrayBuilder()\n",
     "\n",
     "for itr in range(ak.num(electrons, axis=0)):\n",
@@ -272,12 +270,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
     "photon_cut = 0\n",
-    "photon_cut_ratio = 0.1\n",
+    "photon_cut_ratio = 0.25\n",
     "\n",
     "cut_brem = ak.ArrayBuilder()\n",
     "\n",
@@ -289,17 +287,16 @@
     "    cut_brem.field(\"energy\").real(brem[itr, \"energy\"])\n",
     "\n",
     "    ph_length = brem[itr, \"photon_length\"]\n",
-    "    \n",
+    "\n",
     "    tmp_energy = brem[itr, \"energy\"]\n",
     "\n",
     "    cut_brem.field(\"brem_photons_pe\")\n",
     "    cut_brem.begin_list()\n",
     "    for jentry in range(brem[itr, \"photon_length\"]):\n",
-    "        if (\n",
-    "            brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
-    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
-    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
-    "        ):\n",
+    "        if (brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
+    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "                or brem[itr, \"brem_photons_pe\",\n",
+    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
     "            ph_length -= 1\n",
     "            continue\n",
     "        else:\n",
@@ -312,11 +309,10 @@
     "    cut_brem.field(\"brem_vtx_x\")\n",
     "    cut_brem.begin_list()\n",
     "    for jentry in range(brem[itr, \"photon_length\"]):\n",
-    "        if (\n",
-    "            brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
-    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
-    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
-    "        ):\n",
+    "        if (brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
+    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "                or brem[itr, \"brem_photons_pe\",\n",
+    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
     "            continue\n",
     "        else:\n",
     "            cut_brem.real(brem[itr, \"brem_vtx_x\", jentry])\n",
@@ -328,35 +324,33 @@
     "    cut_brem.field(\"brem_vtx_z\")\n",
     "    cut_brem.begin_list()\n",
     "    for jentry in range(brem[itr, \"photon_length\"]):\n",
-    "        if (\n",
-    "            brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
-    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
-    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
-    "        ):\n",
+    "        if (brem[itr, \"brem_vtx_z\", jentry] > 3000\n",
+    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "                or brem[itr, \"brem_photons_pe\",\n",
+    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
     "            continue\n",
     "        else:\n",
     "            cut_brem.real(brem[itr, \"brem_vtx_z\", jentry])\n",
     "            tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem.end_list()\n",
-    "    \n",
+    "\n",
     "    cut_brem.field(\"photon_length\").integer(ph_length)\n",
     "\n",
     "    cut_brem.end_record()\n",
     "\n",
-    "ntuple = ak.Array(cut_brem)\n",
-    "\n"
+    "ntuple = ak.Array(cut_brem)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "44163\n",
+      "20636\n",
       "50501\n"
      ]
     },
@@ -367,11 +361,11 @@
        " lost: True,\n",
        " rad_length_frac: 0.129,\n",
        " energy: 1.17e+04,\n",
-       " brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03],\n",
-       " brem_vtx_x: [-6.97, -52.9, -55.2],\n",
-       " brem_vtx_z: [112, 859, 895],\n",
-       " photon_length: 3}\n",
-       "-------------------------------------------------\n",
+       " brem_photons_pe: [],\n",
+       " brem_vtx_x: [],\n",
+       " brem_vtx_z: [],\n",
+       " photon_length: 0}\n",
+       "-----------------------------------\n",
        "type: {\n",
        "    event_id: int64,\n",
        "    lost: bool,\n",
@@ -387,14 +381,14 @@
        "<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "print(ak.sum(ak.num(ntuple[\"brem_photons_pe\"], axis=1)))\n",
-    "print(ak.num(ntuple,axis=0))\n",
+    "print(ak.num(ntuple, axis=0))\n",
     "ntuple[0]"
    ]
   },
@@ -407,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +491,7 @@
        "<Record {event_id: 1, lost: False, ...} type='{event_id: int64, lost: bool,...'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -507,13 +501,13 @@
     "\n",
     "length_found = ak.num(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=0)\n",
     "length_lost = ak.num(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=0)\n",
-    "print(length_found+length_lost)\n",
+    "print(length_found + length_lost)\n",
     "ntuple[1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,12 +521,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHOCAYAAABttoiYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwWUlEQVR4nO3dz28baX7n8Y+S3VGMCO4yPZcssMB08TQ5GUX1JVjAA5hEH3cBkfIhA+QwMImBbkaiinKxfAlRRK4CUhQGmF30RSL9B0xYDtpYYIFFixXfAiTLcoA5zAIzTVcb2nXUSYZ70Fa1+Eui+LNKfL8AYSxW1cOnqz3kp5/nqe+z0ev1egIAAEip31l1BwAAAGZBmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAFw5wVBsOouAFggwgyAO69UKikMw1V3A8CCEGYALEStVtODBw+0sbGhjY0NFQqF+CebzcavL5rv+zJNU4ZhJLJ/AGb371bdAQB30/7+vjqdjur1uvb39+U4Tt/xIAhUKBQW3g/XdVWpVBLbPwCzY2QGwMKcnZ1J0shQYJqm8vn8wvvged7Y90lC/wDMboNdswEsSjRNM+5jJgzDoemfeWo2m2q1WnJdd+TxVfcPwHwwMgNgITzPk6Sh0Y1msxn/edFB4eTkZOQUk5SM/gGYD8IMgIVoNBqS+qdwwjDUycnJUt4/DEMFQSDLshLZPwDzQ5gBsBDRyMfJyYlyuZyy2awePHigzz77bCnvf3p6qqdPnya2fwDmh6eZAMxdNCpiGIba7Xb82pMnT6ZeVBuGoU5PT9Vut+U4zo1TQK7r6vXr13PrX6VSUTab1ddff63PPvtMxWJxqn8OAPNHmAEwd6enp5L616MYhqF8Pj922ucmhmEok8no9PR07ILeSBAEymQyYwPPbftXKpVkmqb29/clSblcLj4fwOoxzQRg7lqtlqThR54PDg5maverr77S7u7ujeeNqy0zTf+CIFCz2exr7+nTp0N1aQCsDmEGwNxF61EGg8fVkZLonNu2O0khu2azee000G365/u+pMu6MxHLsuR5HlskAAlBmAEwV0EQKAzDkVsIROr1+tDmj9H2As1mU7VaTdlsVrZtx8fDMJTv+8rn82o2m8pmsyNHX64rkjdN/7766quh8zKZjCSp2+2OfR8Ay0OYATBXUZ2WqyMZkTAMValUVKlUhkZFLMuKRzr29/fluq5qtVr8mud5Mk1TZ2dnMk1Ttm3HFXyvummK6bb9C8MwDi+D2I0bSAYWAAOYm1qtFo+meJ6nXC6nTCajbrcbP0EkScVicWi0o9VqybKssdNDrVZLpmkqk8nIsixVq1Vtb28Pnef7/thFxtP0L5vNxguGB40KRACWjzADYG729/fjJ35uy/O8vrowg7tdR9NHUVDxPC8ufBep1+vXjspM0z/TNIfWxkTTS4QZIBnYmwlAImxsbKjT6cQBIZvNynVd5fN5hWGoBw8exMeDIFA2mx3aUymXy+n169dz3YYgeq+rfavVajo5OYlr1ABYLdbMAFg5z/NkGEZfWCgWi/FCXs/zZFlWfDwapfF9Px41uam2zLRM01SxWOzbs+nk5IRHs4EEYZoJwMq1Wi1lMhl5nqcgCPoK1EmXTxRdnYIKw1Ddblfdbjeedrpp4e8sGo2GKpWKarWavv76a1UqFQrmAQnCNBOAlcvlcqpUKiqXy1O3USqVhtbQAFgPhBkAKze4XgYAboM1MwBWKpoaiirtAsBtrcXIzG9+8xv94he/0A9+8APdu3dv1d0BAAAT+Pjxo/7pn/5Jn3/+ub7//e+PPS9RC4ALhcLY/VparVa84M73fVWr1bj+Q6FQuHYfll/84hf68Y9/vJA+AwCAxfriiy/0x3/8x2OPJybMBEGgIAjkOE7fo5WdTke1Wi0OMkEQKJfLqd1ux08xZLNZdbvdsYsHf/CDH0i6vBk//OEP59bn8/NzPX78WG/evNHW1tbc2o3s7Ozo1atXqWl3UW1zn5fX9iLvdRrvB/d5OW1zn9Pd9iLv89///d/rxz/+cfw9PlYvIVzX7b1//37odcdxevl8Pv49n8/3/R5de90/Srvd7knqtdvtufW31+v1vvnmm56k3jfffDPXdiM//OEPU9XuotrmPi+v7UXe6zTeD+7zctrmPqe77UXe50m/vxe2APinP/2pfvazn018frlcHlns6uTkRKVSSdJlbQnP81QoFPrOifZnqdfr03cYAACk0kKmmd69eyfXdXV6eqqf/OQnU7cThqF83493r412yB18fDOabmq1WtfWqTg/P9eHDx+m7s/m5qY2Nzenvh4AgHVycXGhi4uLqa8/Pz+f6LyFhJlPP/1UruvOXDPi9PRUlmXFIzbRjrbjypVHx8d5/PjxTP158eKFDg8PZ2oDAIB1Ua1W9fLly4W/z8IWAD979mzmNhqNRl8J806nI0nKZDIjzx/c2XbQmzdv9OjRo6n7w6gMAACTOzg40PPnz6e+/u3btxMNRCwszHz++edyHGfq8BCtj3FdN34tm81Kkrrd7shrbhoJ2tra0v3796fqDwAAuJ1Zl2dM+nTU1GHm6ojJoCiInJ6eTh1mPM+TaZp9ASX687gRmLtWCn1vby9V7S667UXhPi9HGu8H93k5bXOf70bbqzR1BeDf+Z2bH4TKZrP6x3/8x2maV6lUkmmachwnfi0MQz148ECO4/TtqBsEgbLZrFzXHbkA2Pf9odo08/Dhwwd98skn+uabbxjxWSDu8/Jwr5eD+7wc3OflWOR9nvT7e+pHs4vFojqdjt6/fz/0c3Z2pnK5PHWQkaRmszk0+mMYhizLUqvV6ns9qhocPfUEAADWx9RhplKp6NNPP9Unn3wy9GNZlnK5nP7iL/5iqrabzWYcXAYdHx/L87y+J5ccxxmqHAwAANbD1Gtmnjx5cu1x0zT153/+5/rLv/zLW7d9cnIydpTFsiy1223Zti3TNBUEgWzbvra+DAAAuLumDjNv374deywKGNNqNBrXHrcs68ZzAADAepg6zFiWpY2NjbHHe72earXatM0DAABMZOowYxiGdnd3R65TefjwoSzLunEqCgAAYFZTh5nj42Pt7OzMsy8Lt7Ozo3v37o08tre3d2efvwcAIKmOjo50dHQ08tjHjx8namPqMJO2ICNJr169mmudmc3NTb148YJtDhaM+7w83Ovl4D4vB/d5OWa9z9cNJkR1Zm4yddG8m/z0pz/V9vb2TLtmz8uiiuZJ0qh9J9mLEgCA2S28aN513r17J9d1+6r0AgAALMJCNpr89NNP5brundsrCQAAJM/Cds3e3d3VJ598sqjmAQAAJC1omkmSXr9+rYODg0U1DwAAIGnGkZm//du/VavVUhiGfa93u135vq9ut6tqtTrLWwAAAFxrpjozlUrl2nPYLwkAACza1NNMruuq1Wrp/fv3+pu/+Rs5jqPf/va3+u1vf6tut6tyuay//uu/nmdfAQAAhkw9MpPP5+PtCvL5vI6Pj+NjhmEol8vp4OAgUdNMVAAGACBZVloB+Jtvvun7fXd3V3/1V3+lP/3TP41fazabiQoz864ADAAAZjOPCsBThxnTNPW7v/u7evDggc7OzrSzs6Pt7W21Wi0ZhqFmszlyE0oAAIB5mjrM/Nmf/Zl+85vf6O/+7u+UyWQkSaenpyoUCnr37p0kyXGc+fQSAHB3/Y/DVffgZn90uOoe4BozPZo9GFZM01Sn09G7d++UyWQomgcAABZuYdsZAACA7/i+r5OTE3meJ0lqt9sr7tHdsbAKwAAA4DuWZenp06fyfX+h7xMEwVAx27uOMAMAwJIs44naUqmkbre78PdJEsIMAAB3RKlUWvjITxItbNdsAAAwuTAMZdu2DMOIA4lt28rn8yPPCcNQnufJtm2Vy2U1m834ukqlIsMwdHBwsBb11QgzAACsmO/7evLkiV6/fh2Hj3q9rkKhIMdxtL+/L0l69uyZTNOMnyau1+vx+phisaivvvpKtVpNruvKNM2V/LOswlpNM+3s7OgP//APR/6MK6UMAMCiPXv2TNvb232jKOVyWZZlybZtBUEgSfGTUFfPSbujo6Ox3807OzsTtbFWIzNsZwAASJogCOT7fjz6clWlUlGlUpHrunIcR6Zpqlar6eHDh/H5o65Lk3lsZ7BWIzMAACTNdQt2t7e3JSkemWk0GjIMQ7ZtK5vNruVi31EIMwAAJMCo2jDRHofRtkGmaerdu3fK5/MKgkC5XE71en2JvUwmwgwAACsULX8YXA8jfRdwstmspMsRGsMw1Gq11Gg0JF1ORa07wgwAACtkmqYsy1IQBPF0UuTs7EyGYcQLfa/uiVgsFuW6riQNXUcFYAAAsFTRWpiroyxhGMpxHB0fH8fTTaenp33BJQxDmaYZP4YdjeC4rqsgCNRsNpf3D7FCa/U0EwAggf7ocNU9WArf9+ORFN/3VavVVC6XZRhGvBbm2bNnKhQKcThpNBp9T+Fub2+rUCioWCxKuhyRubphZblcluu6Oj09laT4/e46wgwAAEtgWZZc1x0bMAzDiNfBjNNqtW58n3XcjZtpJgAAkGprNTKzs7Oje/fujTx2XdEeAACwGEdHR2Or8H/8+HGiNtYqzFABGACAZKECMAAAWHuEGQAAkGqEGQAAkGqJXjNzteBP9Cy+dDmHVq1WZZqmwjDse+YeAACsl0SGmSAIZNu2wjCU67px8aDoWC6XU7vdjhfzZrNZdbvduNwzAABYH4mbZopWLmcyGbVarb4gI11uqJXP5/ueSrJtm422AABYU4kKM2EY6smTJzJNc2SFxDAM5XmeCoVC3+vb29uSxDboAACsoURNM0VTS1d3Bb3q7OxMkoZGa6JRmlarde1U0/n5uT58+DB1/zY3N7W5uTn19QAArJOLiwtdXFxMff35+flE5yUqzEQjK61WS7ZtKwgCbW9vx+tmop1Co4XAgwa3QB/0+PHjmfr34sULHR4eztQGAADrolqt6uXLlwt/n8SEGd/3JV2OslQqFTmOoyAIVCgUlM1m9f79e3U6HUlSJpMZ2UYYhte+x5s3b/To0aOp+8ioDAAAkzs4ONDz58+nvv7t27cTDUQkJsxEoyqVSiWeRorWzhQKBVWrVWWzWUlSt9sd2cbg9NOgra0t3b9/f469BgDMKg0D3mno47z5vq+zs7OZnhSedXnG1tbWROclZgHwuKmjfD4v6TLsRGFl3AjMTWEGAIAk8H1ftm2rVCopm82qVqutukuxIAhUKpWUy+VGPoyTRIkZmYmeSIqmkgZlMpn4nMG1MdHvk2xGBQDAKvm+rydPnuj9+/eSLh9+GffdtwqmaarRaGhjY2PVXZlYokZm8vm8PM/rez0ahcnlcjIMQ5ZlqdVq9Z0TXbO7u7uUvgIAMK1qtdq39tNxnNSMgCRVYsKMdPkv1Pf9vkBTr9dlWVY8Z3d8fCzP8/pGZxzHkeM4Y6eqAABIiuiBF8xPYqaZpMsnmdrttmzbVqPRkGEYCsNQ7XZ75DnR49q2bbOVAQAg0er1ulqtVvwf46VSSdJ3le2ly9kI27ZlGEYcemzbjo83m009e/Ys/m60LEue58l1XTWbTRWLRTUaDYVhqNPTU7muq4ODAxmGIdu25ft+fM5V0ftGogdu0iJRYUbSyGmkUecM/osAACDJyuWyyuVyHBQGv8eitTSvX7+Oi8HW63UVCgU5jqP9/X0Vi0W1Wq2+ivf5fF6macYbM0uXT/22Wi35vi/XdWVZlo6Pj+W6rur1umq1mvb39yV9t+dho9GIQ1OSFiRPIlHTTKn0yy+HfwAAuKVnz55pe3u7b+/Bcrksy7LiQrLS6Kd/B+uvmaapp0+fSlIchizLitfmXB00sG1b29vbcZCRFAedtCDMAACwYkEQyPf9viATiTZSnnaR8KjwE9VrC4JAzWZzaM/DtEncNNMi7ezs6N69eyOP7e3taW9vb8k9AgDg+kXB48qSzEPU5irrtB0dHeno6GjksY8fP07UxlqFmVevXo1MvQAAJMGoorDRyMq4rXxmEYWZcZX1l+G6wQTf9yeqIcc0EwAAKxb9h/ZgrTXpu4CziCeMohGZq08NpxFhBgCAJep2u0MjIaZpyrIsBUEwNJ10dnYmwzDiEiQPHz6U1D/tFP35pg2XB0VTWPV6feS1t21vVQgzAAAkQFRfLVrwK12GCcdxdHx8HE83RaM4tm3L8zzV6/V4cbDnefFi3kmmjgzDiJ9cyuVycVHaqOZMEASpeEx7rdbMAACSZ112pI5qvkSjHZVKRaVSKX4k2jRNvXv3Ts+ePVOhUIingBqNRt96z3w+L8dxVK1WVSqVVC6X5bquPM9TsVjU06dP4/eSLqvkm6ap7e3tOKT4vh/XmnEcR9lsVo7jqFAoxLXcoiJ8xWJxiXdpOhu9Xq+36k4sWrSAKKqWOE+HP/ly+LWf/Wiu7wEAwDqa9PubaSYAAJBqhBkAAJBqhBkAAJBqa7UAmArAAAAkCxWAb4kKwAAAJAsVgAEAwNojzAAAgFQjzAAAgFQjzAAAgFQjzAAAgFQjzAAAgFQjzAAAgFQjzAAAgFQjzAAAgFRbqwrAbGcAAECysJ3BLbGdAQAAycJ2BgAAYO0RZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKqtVdE8KgADAJAsVAC+JSoAAwCQLFQABgAAa48wAwAAUi0VYSYIglV3AQAAJFQi18xsbGz0/W5Zltrtdvy77/uqVqsyTVNhGKpQKKhYLC67mwAAIAESF2bq9brK5bKy2Wz8Wj6fj/8cBIFyuZza7Xa8mDebzarb7apcLi+9vwAAYLUSF2YajYZardbY45VKRfl8vu+pJNu2ValUCDMAAKyhRK2ZaTabOjs7U6lUUr1eHzoehqE8z1OhUOh7fXt7W5JGXgMAAO62RI3MtFothWGoZrOpZrMp27bVaDTiaaazszNJkmmafddFozStVuva0Znz83N9+PBh6v5tbm5qc3Nz6usBAFgnFxcXuri4mPr68/Pzic5LVJhxXVeu68r3fbmuq3q9rkKhoE6nI9M046eaDMMYef1NTz09fvx4pv69ePFCh4eHM7UBAMC6qFarevny5cLfJ1FhJmJZllzXVaFQUKlUikdoOp2OJCmTyYy8LgzDa9t98+aNHj16NHW/GJUBAGByBwcHev78+dTXv337dqKBiESGmUixWFSxWJTv+5IUP+HU7XZHnj84/TRoa2tL9+/fn28nAQDASLMuz9ja2provEQtAB6lUCjE4SUKK+NGYG4KMwAA4O5JfJiRvntaKfrfwbUx0e+TbEYFAADulsSHmVarpUqlIuly4a9lWUN1aDzPkyTt7u4uvX8AAGC1EhNmom2+a7Va/Fqz2VQmk+nbquD4+Fie5/WNzjiOI8dxxj7lBAAA7q7ELAA2TVOZTEbValWtVkuWZalQKMh13b7zon2abNuOH9e2bZvqvwAArKnEhBnDMK7dxuAqy7LUaDQW3CMAAJAGiZlmAgAAmAZhBgAApFpippmWYWdnR/fu3Rt5bG9vT3t7e0vuEQAA6+3o6EhHR0cjj338+HGiNtYqzLx69SrelBIAAKzedYMJ0ZPON2GaCQAApBphBgAApBphBgAApBphBgAApBphBgAApBphBgAApBphBgAApBphBgAApNpaFc2jAjAAAMlCBeBbogIwAADJQgVgAACw9ggzAAAg1QgzAAAg1QgzAAAg1QgzAAAg1QgzAAAg1QgzAAAg1QgzAAAg1QgzAAAg1daqAjDbGQAAkCxsZ3BLbGcAAECysJ0BAABYe4QZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQamtVNI8KwAAAJAsVgG+JCsAAACQLFYABAMDaI8wAAIBUI8wAAIBUS3SY8TxPDx48GHrd932VSiXZtq1KpaJms7mC3gEAgCRI9ALgSqUy9FoQBMrlcmq32/Fi3mw2q263q3K5vOwuAgCAFUvsyIxt2zJNc+j1SqWifD7f91RSNEIDAADWTyLDjOd5evjw4dBj1GEYyvM8FQqFvte3t7clSfV6fWl9BAAAyZDIaSbXddVoNGTbdt/rZ2dnkjQ0YhOFnlarde1U0/n5uT58+DB1vzY3N7W5uTn19QAArJOLiwtdXFxMff35+flE5yUuzNi2LcdxRh4LgkCSZBjGtcfHefz48Ux9e/HihQ4PD2dqAwCAdVGtVvXy5cuFv0+iwozv+3r48OHItTKS1Ol0JEmZTGbk8TAMr23/zZs3evTo0dT9Y1QGAIDJHRwc6Pnz51Nf//bt24kGIhIVZqrVqhqNxtjj2WxWktTtdkceHxeCIltbW7p///70HQQAABObdXnG1tbWROclJszYtq1CodA3VRT9OfrfKKyMG4G5KcwAAIC7JzFhxvM81Wq1kcey2awsy9Lr168lDa+NiX6fZDMqAABwtyTm0ex2u61er9f3s7+/L8Mw1Ov11G63ZRiGLMtSq9Xqu9bzPEnS7u7uKroOAABWKDFhZlLHx8fyPK9vdMZxHDmOM/YpJwAAcHclZpppUpZlqd1uxxWCgyCQbdtsZQAAwJpKdJiJRlwGWZZ17VNPAABgfaRumgkAAOAqwgwAAEi1RE8zzdvOzo7u3bs38tje3p729vaW3CMAANbb0dGRjo6ORh77+PHjRG2sVZh59erV0E7cAABgda4bTPB9f6IackwzAQCAVCPMAACAVCPMAACAVCPMAACAVCPMAACAVCPMAACAVCPMAACAVCPMAACAVFuronlUAAYAIFmoAHxLVAAGACBZqAAMAADWHmEGAACkGmEGAACkGmEGAACkGmEGAACkGmEGAACkGmEGAACkGmEGAACkGmEGAACk2lpVAGY7AwAAkoXtDG6J7QwAAEgWtjMAAABrjzADAABSjTADAABSjTADAABSjTADAABSjTADAABSjTADAABSjTADAABSba2K5lEBGACAZKEC8C1RARgAgGShAjAAAFh7hBkAAJBqhBkAAJBqiQszzWZTuVxOGxsbymaz8jxv6Bzf91UqlWTbtiqViprN5gp6CgAAkiBRC4Dr9bra7bYcx5Ek2batQqGgTqcj0zQlSUEQKJfLqd1ux4t5s9msut2uyuXyyvoOAABWI1EjM2EYynVd5fN55fN5HR8fS7ociYlUKhXl8/m+p5KiERoAALB+EhVm9vf3+343DEOS4uAShqE8z1OhUOg7b3t7W9LlyA4AAFgviZpmGtRsNuU4TjzFdHZ2Jknx75Eo7LRarWunms7Pz/Xhw4ep+7O5uanNzc2prwcAYJ1cXFzo4uJi6uvPz88nOi+xYca2bdXr9XiqSbpcLyN9N2IzKDo+zuPHj2fq04sXL3R4eDhTGwAArItqtaqXL18u/H0SGWZqtZqCIFAYhiqVSnJdV+VyWZ1OR5KUyWRGXheG4bXtvnnzRo8ePZq6X4zKAAAwuYODAz1//nzq69++fTvRQEQiw0y0dsbzPJVKJTmOo3K5rGw2K0nqdrsjrxucfhq0tbWl+/fvz7ezAABgpFmXZ2xtbU10XqIWAA/K5/Mql8vx9FEUVsaNwNwUZgAAwN2T6DAjSZ999lkcUqKnlgbXxkS/T7IZFQAAuFsSH2aCIFA+n5d0ufDXsiy1Wq2+c6Iqwbu7u0vvHwAAWK3EhJlose/VrQmCIFCr1ZLruvFrx8fH8jyvb3TGcRw5jjP2KScAAHB3JWYBsGEYCsNQz549k+u6KhQKMk1zaBTGsiy1223Zti3TNBUEgWzbZisDAADWVGLCjKSh4DKOZVlqNBoL7g0AAEiDxEwzAQAATIMwAwAAUi1R00yLtrOzo3v37o08tre3p729vSX3CACA9XZ0dKSjo6ORxz5+/DhRG2sVZl69ehVvSgkAAFbvusEE3/cnqiHHNBMAAEg1wgwAAEg1wgwAAEg1wgwAAEg1wgwAAEg1wgwAAEg1wgwAAEg1wgwAAEi1tSqaRwVgAACShQrAt0QFYAAAkoUKwAAAYO0RZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKoRZgAAQKqtVQVgtjMAACBZ2M7gltjOAACAZGE7AwAAsPYIMwAAINUIMwAAINUIMwAAINUIMwAAINUIMwAAINUIMwAAINUIMwAAINXWqmgeFYABAEgWKgDfEhWAAQBIFioAAwCAtUeYAQAAqUaYAQAAqZa4MNNsNpXL5bSxsaFcLifP84bO8X1fpVJJtm2rUqmo2WyuoKcAACAJErUAuFarqdVqqVKpqNPpqFarqVAoqNVqKZ/PS5KCIFAul1O73Y4X82azWXW7XZXL5VV2HwAArECiRma++uortVotlctlOY6jdrstSXIcJz6nUqkon8/3PZUUjdAAAID1k5gw43leX2iRJMuyZFmWgiCQJIVhKM/zVCgU+s7b3t6WJNXr9eV0FgAAJEZiwkw+n5dpmiOPRa+fnZ31/R6JRmlardYCewgAAJIoUWtmRgmCIJ5CikZoDMMYe+51zs/P9eHDh6n7srm5qc3NzamvBwBgnVxcXOji4mLq68/Pzyc6L9FhptlsyjTNeGFvp9ORJGUymZHnh2F4bXuPHz+eqT8vXrzQ4eHhTG0AALAuqtWqXr58ufD3SXSYqVarajQa8e/ZbFaS1O12R54/bpoq8ubNGz169Gjq/jAqAwDA5A4ODvT8+fOpr3/79u1EAxGJDTO2bev4+LgvoER/HjcCc1OY2dra0v379+fWRwAAMN6syzO2trYmOi8xC4CvqtfrKhQKQ5tCRk8tDa6NiX6fZDMqAABwtyQuzETVfKMieRHf92UYhizLGnpqKaoSvLu7u5xOAgCAxEjUNJPneapWq6pUKn01Y9rttnK5nCzL0vHxsXK5nIIgiKeVHMeR4zhjn3ICAAB3V2LCjO/7cTG8UdV8379/L+mypky73ZZt2zJNU0EQyLZttjIAAGBNJSbMWJalXq838blXn3ICAADrK3FrZgAAAG6DMAMAAFItMdNMy7Czs6N79+6NPLa3t6e9vb0l9wgAgPV2dHSko6Ojkcc+fvw4URtrFWZevXo1VLsGAACsznWDCb7vT1RDjmkmAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQamtVNI8KwAAAJAsVgG+JCsAAACQLFYABAMDaI8wAAIBUI8wAAIBUI8wAAIBUI8wAAIBUI8wAAIBUI8wAAIBUI8wAAIBUI8wAAIBUW6sKwGxnAABAsrCdwS2xnQEAAMnCdgYAAGDtEWYAAECqEWYAAECqEWYAAECqEWYAAECqEWYAAECqEWYAAECqEWYAAECqrVXRPCoAAwCQLFQAviUqAAMAkCxUAAYAAGuPMAMAAFKNMAMAAFItUWtmwjBUtVqVJDmOM3Tc931Vq1WZpqkwDFUoFFQsFpfdTQAAkCCJCTOe58l1XTWbTZXL5aHjQRAol8up3W7Hi3iz2ay63e7I8wEAwHpIzDRTPp9Xo9EYe7xSqSifz/c9jWTbtiqVyjK6BwAAEioxYeY6YRjK8zwVCoW+17e3tyVJ9Xp9Fd0CAAAJkIowc3Z2JkkyTbPv9WiUptVqLb1PAAAgGRKzZuY6QRBIkgzDuPb4Tc7Pz/Xhw4ep+7G5uanNzc2prwcAYJ1cXFzo4uJi6uvPz88nOi8VYabT6UiSMpnMyONhGE7UzuPHj2fqx4sXL3R4eDhTGwAArItqtaqXL18u/H1SEWay2awkqdvtjjw+OP00zps3b/To0aOp+8GoDAAAkzs4ONDz58+nvv7t27cTDUSkIsxEYWXcCMykYWZra0v379+fV7cAAMA1Zl2esbW1NdF5qVgAHD21NLg2Jvp9kk2oAADA3ZSKMGMYhizLGnpqyfM8SdLu7u4qugUAABIgUWHmuoW8x8fH8jyvb3TGcRw5jjP2KScAAHD3JWbNjO/7cl1XknR6eqpCoaB8Ph8HFcuy1G63Zdu2TNNUEASybZutDAAAWHOJCTOWZcl13TjQjDvnui0PAADA+knUNBMAAMBtEWYAAECqJWaaaRl2dnZ07969kcf29va0t7e35B4BALDejo6OdHR0NPLYx48fJ2pjrcLMq1ev4s0pAQDA6l03mOD7/kS15JhmAgAAqUaYAQAAqUaYAQAAqUaYAQAAqUaYAQAAqUaYAQAAqUaYAQAAqUaYAQAAqbZWRfOoAAwAQLJQAfiWqAAMAECyUAEYAACsPcIMAABINcIMAABINcIMAABINcIMAABINcIMAABINcIMAABINcIMAABINcIMAABItbWqAMx2BgAAJAvbGdwS2xkAAJAsbGcAAADWHmEGAACkGmFmBhcXF/rS/7n+9d++XXVX7rSLiwsdHh7q4uJi1V2587jXy8F9Xg7u83Ik4T4TZmZwcXGhN2//q/7t3/5l1V250y4uLvTy5Us+kJaAe70c3Ofl4D4vRxLuM2EGAACkGmEGAACkGmEGAACkGmEGAACk2loVzaMCMAAAyUIF4FuiAjAAAMlCBWAAALD2CDMJNm7YLantLrrtRZmoz//jsP9nXu1O6c7e54S1zX1eTtvc57vR9ioRZhIsjR8cafw/yuHhkQ4PFf/MC/e5XxrvB/d5OW1zn+9G26uUyjUzvu+rWq3KNE2FYahCoaBisbjqbiGt/vX/Sr/8Mv718CejTvpR32+HfzRBux9/PTyK80eHo84EAMwgdWEmCALlcjm12+14MW82m1W321W5XF5x77AuDn/y5Y3n/Dr8fR3+7Ef9L/7NInoDXEGIxhpKXZipVCrK5/N9TyXZtq1KpUKYQfL98su5NPPrX/3fmwPVf/zRrd//4tv/I0mq7v13bX7v9yVpOJBpeDpu5PTchGuL+tr5yZfDr414/yGDX+CTfHlP0b+J2xl8/8Fz/s+Ee9hM0sdp/lmnDTeT3GeCFFYgVWEmDEN5nifHcfpe397eliTV63UCDRD55ZdzaWaSUahJpuZ+/aufT9TWNO8/NAr2sxHXDIa7X/5o+JzBdifq84h2ht6//5xRoXHitm98r2FDgXDENdP8u5nIJIFs1GjSoEmC06AoNP7PqvT7mzf34zYm6fMoiwx3axwkUxVmzs7OJEmmafa9Ho3StFotwgyA0X755ap7sBQjpzcnMMk1vw5/fnNonCSA3dTuKFO8Vxwa/9t/uiE03t6oPk8UCOcV7iYxr9HHFEhVmAmCQJJkGMa1xwdFFQR939f5+fnU7/+9731P3/ve9+Lfo7b+99f/S9/7999VFvb9+1O/x1UfP36U7/tzaWsZ7S6q7eg+v337VltbW3NtW5L+9V8v9Kvf/ENq2l1k29/+y+X/Vwb/Ts9DGu8H93k5bd+V+1xx/sNc2v7Vb/7d3NoaavuX71X5z/VbXVP5L+1rj59//FbS6M/ob7/9Vt9+++3tOnnFP/zD5T2+sRJwL0X29/d7knrtdnvomKSeaZojr/viiy96kvjhhx9++OGHnxT+fPHFF9fmg1SNzGSzWUlSt9sdeXxw+iny+eef6+c//7n+4A/+QL/3e7839fsPjswAAIDxZh2Z+ed//mf96le/0ueff37teakKM1FYCcPw2uODvv/97+tP/uRPFtUtAACwQqmqABw9tTS4Nib6fZLNqAAAwN2SqjBjGIYsy1Kr1ep73fM8SdLu7u4qugUAAFZo4/8vnk2NaDvwTqcTTytls1lVKhXt7+/P9X2m2TKBrRZuZ9r71Ww2Va1W5fu+LMuS4zjK5/NL6HF6zePvpud5KpVKev/+/YJ6mX7zuM9BEKjZbEqSyuXy2Cc419ksnx2tVkuGYSgIApmmOVS7DJfCMFS1WpWkie/Ryr4D5/CQ0dK12+1esVjs7e/v94rFYs913bm23+l0elL/U1Omad74PtNet66mvV+O4/Ty+XzPdd34CTdJvVartegup9a8/m6aptkzDGPe3bszZr3PnU6nVywWe/l8vtfpdBbVzdSb9j43Go2eZVl9r+Xz+d7+/v5C+plmrVarVywWe5J65XJ5omtW+R2YyjCzaPl8vpfP5/tec123d1P2m/a6dTXt/SoWi32/t9vtnqShtvCdefzd3N/f7+XzecLMNWa5z+12u2cYxsRfHOtsls/owfvrOM7Ysh7o3SrMrPI7MFVrZpYh2jKhUCj0vX51y4R5Xreupr1fo7azsCxLlmWNLZq47ubxd9PzPD18+LBvTzT0m+U+h2GoJ0+eyDRNua670H6m3Sz3udvtxmssI1eXLGB6q/4OJMwMmGTLhHlet66mvV/5fH7sBw8fSKPN4++m67pzXZN2F81yn23bVhiGrN2YwCz3uVKpKAgClUolSZfrO05PT7nvc7Dq70DCzIBpt0yY9rp1Ne/7dfUDCv1mvde2bfNhP4FZ7nP0X62tVku5XE4PHjxQoVDgc2OEWe5zuVxWuVxWs9lUNpuVbdt69+4dI45zsOrvQMLMgE6nI0nKZDIjj48r2Dftdetqnver2WzKNE02GR1jlnvt+74ePnzIqNcEpr3P0V5mlmWpUqmo3W6r3W4rCAJls1k+OwbM+tnhum48Le153tC0E6az6u9AwsyAabdMmPa6dTXP+1WtVtVoNObSr7tolntdrVaZXprQtPc5+i/WSqUSn3N17Uz0aCwuzfrZUSgUVKlU4sezS6VS/Bg8prfq78BUbWewDNNumTDtdetqXvfLtm0dHx9zf68x7b22bXtoqiP6c/S/3PfvTHufxw3LR3WTmGrqN8tnR6VSkaR4FPfdu3f69NNP9ezZM+qBzWjV34GMzAyYdssEtlq4nXncr3q9rkKhwHz3Daa9157nqVKpKJvNxj/NZlNhGCqbzbJGacCsnx3RMP2gccP262qWz47T09O+zwvDMOQ4jsIwjKf7MJ1VfwcSZgZMu2UCWy3czqz3KxoWHqz6ywfSsGnvdbvdVu+yFlX8s7+/L8Mw1Ov11G63F973NJnlsyOfzw+t3Yj+C5f/EOo3y2dHJpMZGjmIPkOosjyblX8HLrySTQpFRdiuVuA0TbPnOE78e6fT6Zmm2Vd1dpLr8J1p73Or1epZltVzXbfvp1wuU215jGnv9aD9/X2K5l1j1s+Oq685jjNUrRaXpr3PjuP0DMPovX//vu817vNo79+/H1s0L2nfgayZGcGyLLXbbdm2LdM0FQSBbNvue1omDEN1u92+lD/JdfjONPfZ9/24KFM0/30VewaNNu3fadzOPD47Go2GDMNQGIaMfo0x7X2ORhZLpVI83RSGoV6/fr3sf4TE830/XoR+enqqQqGgfD4fj2Al7TswdRtNAgAAXMWaGQAAkGqEGQAAkGqEGQAAkGqEGQAAkGqEGQAAkGqEGQAAkGqEGQAAkGqEGQAAMLNVbopKmAEAADMrlUorqyBOmAEA4A6r1Wp68OCBNjY2tLGxoUKhEP9ks9n49Vn4vi/TNIc27FzGe0sSezMBAHCH7e/vq9PpqF6va39/X47j9B0PgiDe825aruuO3C9vGe8tMTIDAMCdd3Z2Jkkjg4Npmsrn8zO173ne2DYW/d4SG00CAHDnRVM5477ywzAcmiKaVLPZVKvVinfZXuZ7RxiZAQDgDvM8T5KGRkCazWb851nCxMnJycgppmW8d4QwAwDAHdZoNCT1T/OEYaiTk5OZ2w7DUEEQyLKspb/3VYQZAADusGh05OTkRLlcTtlsVg8ePNBnn302c9unp6d6+vTpSt77Kp5mAgDgjopGTgzDULvdjl978uTJXBbeuq6r169fz+29K5WKstmsvv76a3322WcqFosT9YMwAwDAHXV6eiqpf82KYRjK5/Njp4YmFQSBMpnM2DUvt33vUqkk0zS1v78vScrlcvH5N2GaCQCAO6rVakkafiz64OBg5rbH1ZaZ5r2DIFCz2exr7+nTp0N1acYhzAAAcEdFa1Z2d3f7Xr86mhKdI12Gimw2q1wuF78WhqFyuVzfE0jS5RNJ100D3ea9fd+XdFl3JmJZljzPm2iLBMIMAAB3UBAECsNw5DYDkXq93rdBpG3bchxHYRjGQaNarSoMw77gcl2RvGne+6uvvho6L5PJSJK63e5N/6ismQEA4C6KRlKujnZEwjCUbduq1+t6//59/PrTp09VLBbjMBKGoWq1WjxlFHFd99qpqtu+dxiGcXgZFATByHauIswAAHDH1Go12bYt6XIUJZfLKZPJqNvtxk8ZSVKxWOwbEYlGXyzLUhAEqlarKhaLQ6Mwvu+PXUA8zXtns9l4wfCgm4KMxHYGAABggO/7Ojk5UbPZVLvd7gs89XpdYRjGTx3NQ7PZVKlU6tvywPM8FQqFsdsgXMXIDAAA6GMYhmq1mhqNxtBalutqy0wrGuW5OqV03ejPIBYAAwCAPmEYKp/PDz2tdFNtmWmZpqlisdj3xNTJycnEj2YzzQQAAPrYtq2HDx8OTSXZtn2ryry3dbUCcDabVblcnug6wgwAAIiFYagHDx7Idd2hMFEqleLNI5OEaSYAABCr1+uSRj9FlMQgIxFmAADAFZ1OR6ZpzmUjymVhmgkAAPQJw3Dui3wXiTADAABSjWkmAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQaoQZAACQav8Px7LJkfhIqQQAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -542,7 +536,7 @@
     }
    ],
    "source": [
-    "xlim=0\n",
+    "xlim = 0\n",
     "\n",
     "plt.hist(\n",
     "    Z_lost,\n",
@@ -552,7 +546,7 @@
     "    histtype=\"bar\",\n",
     "    color=\"darkorange\",\n",
     "    label=\"lost\",\n",
-    "    range=[xlim,1]\n",
+    "    range=[xlim, 1],\n",
     ")\n",
     "plt.hist(\n",
     "    Z_found,\n",
@@ -562,7 +556,7 @@
     "    histtype=\"bar\",\n",
     "    color=\"blue\",\n",
     "    label=\"found\",\n",
-    "    range=[xlim,1]\n",
+    "    range=[xlim, 1],\n",
     ")\n",
     "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
     "plt.ylabel(\"a.u.\")\n",
@@ -573,15 +567,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found:  32898 , lost:  11265\n",
-      "0.34242203173445196\n"
+      "found:  16049 , lost:  4587\n",
+      "0.2858122001370802\n"
      ]
     },
     {
@@ -589,32 +583,32 @@
       "text/html": [
        "<pre>[-3.61,\n",
        " -33.8,\n",
-       " -133,\n",
        " 65.2,\n",
-       " -5.73,\n",
        " -26.6,\n",
-       " -4.26,\n",
-       " 6.83,\n",
-       " 10.7,\n",
-       " 26.2,\n",
-       " ...,\n",
-       " -11.6,\n",
-       " -13.1,\n",
-       " -25.6,\n",
-       " -4.27,\n",
-       " -4.27,\n",
+       " 31.6,\n",
+       " -52.1,\n",
+       " -44.7,\n",
        " -103,\n",
-       " 8.82,\n",
-       " 12.8,\n",
+       " -10.2,\n",
+       " -47.1,\n",
+       " ...,\n",
+       " -25.5,\n",
+       " -90.3,\n",
+       " 55.2,\n",
+       " 152,\n",
+       " -144,\n",
+       " 330,\n",
+       " -13.1,\n",
+       " -4.27,\n",
        " -17.8]\n",
        "---------------------\n",
-       "type: 32898 * float64</pre>"
+       "type: 16049 * float64</pre>"
       ],
       "text/plain": [
-       "<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='32898 * float64'>"
+       "<Array [-3.61, -33.8, 65.2, ..., -13.1, -4.27, -17.8] type='16049 * float64'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,12 +633,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -669,7 +663,7 @@
     "    vmax=vmax,\n",
     "    range=[[-200, 3000], [-1000, 1000]],\n",
     ")\n",
-    "ax0.vlines([770, 990, 2700], -1000, 1000,colors=\"red\")\n",
+    "ax0.vlines([770, 990, 2700], -1000, 1000, colors=\"red\")\n",
     "ax0.set_ylim(-1000, 1000)\n",
     "ax0.set_xlim(-200, 3000)\n",
     "ax0.set_xlabel(\"z [mm]\")\n",
@@ -702,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -727,14 +721,13 @@
     "    tmp_velo_length = 0\n",
     "    tmp_richut_length = 0\n",
     "    tmp_neither_length = 0\n",
-    "    \n",
+    "\n",
     "    for jphoton in range(ak.num(ntuple[jelec][\"brem_photons_pe\"], axis=0)):\n",
     "        if ntuple[jelec, \"brem_vtx_z\", jphoton] <= 770:\n",
     "            tmp_velo += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
     "            tmp_velo_length += 1\n",
-    "        elif (ntuple[jelec, \"brem_vtx_z\", jphoton] > 770) and (\n",
-    "            ntuple[jelec, \"brem_vtx_z\", jphoton] <= 2700\n",
-    "        ):\n",
+    "        elif (ntuple[jelec, \"brem_vtx_z\", jphoton]\n",
+    "              > 770) and (ntuple[jelec, \"brem_vtx_z\", jphoton] <= 2700):\n",
     "            tmp_richut += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
     "            tmp_richut_length += 1\n",
     "        else:\n",
@@ -746,13 +739,15 @@
     "\n",
     "    energy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n",
     "    energy_emissions.field(\"rich\").real(tmp_richut)\n",
-    "    \n",
+    "\n",
     "    energy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n",
     "    energy_emissions.field(\"downstream\").real(tmp_neither)\n",
-    "    \n",
-    "    energy_emissions.field(\"photon_length\").integer(tmp_neither_length+tmp_richut_length+tmp_velo_length)\n",
-    "    \n",
-    "    if (tmp_velo==0) and (tmp_richut==0):\n",
+    "\n",
+    "    energy_emissions.field(\"photon_length\").integer(tmp_neither_length +\n",
+    "                                                    tmp_richut_length +\n",
+    "                                                    tmp_velo_length)\n",
+    "\n",
+    "    if (tmp_velo == 0) and (tmp_richut == 0):\n",
     "        energy_emissions.field(\"quality\").integer(0)\n",
     "    else:\n",
     "        energy_emissions.field(\"quality\").integer(1)\n",
@@ -761,12 +756,12 @@
     "\n",
     "energy_emissions = ak.Array(energy_emissions)\n",
     "\n",
-    "print(ak.num(energy_emissions,axis=0))\n"
+    "print(ak.num(energy_emissions, axis=0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -800,7 +795,7 @@
        "<Record {lost: False, energy: 5.09e+04, ...} type='{lost: bool, energy: flo...'>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -811,26 +806,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found:  40402\n",
-      "lost:  10099\n",
+      "found:  41978\n",
+      "lost:  8523\n",
       "50501\n",
-      "VELO energy emission, eff:  0.2624700500980179\n",
-      "RICH1+UT energy emission, eff:  0.17696679273677748\n",
-      "Neither, eff:  0.3605869190709095\n",
-      "total efficiency:  0.8000237619057049\n",
-      "efficiency:  0.8000237619057048\n",
+      "VELO energy emission, eff:  0.15952159363180926\n",
+      "RICH1+UT energy emission, eff:  0.10419595651571256\n",
+      "Neither, eff:  0.5675135145838697\n",
+      "total efficiency:  0.8312310647313915\n",
+      "efficiency:  0.8312310647313914\n",
       "\n",
       "found in velo/(found + lost in velo)\n",
-      "VELO energy emission, eff:  0.807739183424741\n",
-      "RICH1+UT energy emission, eff:  0.7549417131272175\n",
-      "eff von e die nicht strahlen:  0.8183166314654204\n"
+      "VELO energy emission, eff:  0.8187823965850188\n",
+      "RICH1+UT energy emission, eff:  0.7830357142857143\n",
+      "eff von e die nicht strahlen:  0.8443815921277473\n"
      ]
     }
    ],
@@ -842,17 +837,17 @@
     "electrons_found = energy_emissions[~energy_emissions.lost]\n",
     "electrons_lost = energy_emissions[energy_emissions.lost]\n",
     "\n",
-    "anz_found = ak.num(electrons[~electrons.lost],axis=0)\n",
-    "anz_lost = ak.num(electrons[electrons.lost],axis=0)\n",
-    "print(\"found: \",anz_found)\n",
+    "anz_found = ak.num(electrons[~electrons.lost], axis=0)\n",
+    "anz_lost = ak.num(electrons[electrons.lost], axis=0)\n",
+    "print(\"found: \", anz_found)\n",
     "print(\"lost: \", anz_lost)\n",
     "\n",
     "num_velo_found = 0\n",
     "num_rich_found = 0\n",
     "num_no_up_rad_found = 0\n",
     "for itr in range(ak.num(electrons_found, axis=0)):\n",
-    "    if (electrons_found[itr, \"quality\"]==1):\n",
-    "        if (electrons_found[itr, \"velo\"] >= electrons_found[itr, \"rich\"]):\n",
+    "    if electrons_found[itr, \"quality\"] == 1:\n",
+    "        if electrons_found[itr, \"velo\"] >= electrons_found[itr, \"rich\"]:\n",
     "            num_velo_found += 1\n",
     "        else:\n",
     "            num_rich_found += 1\n",
@@ -863,25 +858,23 @@
     "num_rich_lost = 0\n",
     "num_no_up_rad_lost = 0\n",
     "for itr in range(ak.num(electrons_lost, axis=0)):\n",
-    "    if (electrons_lost[itr, \"quality\"]==1):\n",
-    "        if (electrons_lost[itr, \"velo\"] >= electrons_lost[itr, \"rich\"]):\n",
+    "    if electrons_lost[itr, \"quality\"] == 1:\n",
+    "        if electrons_lost[itr, \"velo\"] >= electrons_lost[itr, \"rich\"]:\n",
     "            num_velo_lost += 1\n",
     "        else:\n",
     "            num_rich_lost += 1\n",
     "    else:\n",
     "        num_no_up_rad_lost += 1\n",
     "\n",
-    "\n",
-    "\n",
-    "denom = ak.num(electrons,axis=0)\n",
+    "denom = ak.num(electrons, axis=0)\n",
     "print(denom)\n",
     "\n",
+    "eff_velo = num_velo_found / denom\n",
     "\n",
-    "eff_velo = num_velo_found/denom\n",
+    "eff_rich = num_rich_found / denom\n",
     "\n",
-    "eff_rich = num_rich_found/denom\n",
-    "\n",
-    "eff_other = ak.num(electrons_found[electrons_found.quality==0],axis=0)/denom\n",
+    "eff_other = ak.num(electrons_found[electrons_found.quality == 0],\n",
+    "                   axis=0) / denom\n",
     "\n",
     "print(\"VELO energy emission, eff: \", eff_velo)\n",
     "\n",
@@ -891,52 +884,51 @@
     "\n",
     "print(\"total efficiency: \", eff_velo + eff_rich + eff_other)\n",
     "\n",
-    "print(\"efficiency: \", anz_found/(anz_found+anz_lost))\n",
+    "print(\"efficiency: \", anz_found / (anz_found + anz_lost))\n",
     "\n",
     "print(\"\\nfound in velo/(found + lost in velo)\")\n",
     "\n",
-    "eff_velo = num_velo_found/(num_velo_found+num_velo_lost)\n",
-    "eff_rich = num_rich_found/(num_rich_found+num_rich_lost)\n",
+    "eff_velo = num_velo_found / (num_velo_found + num_velo_lost)\n",
+    "eff_rich = num_rich_found / (num_rich_found + num_rich_lost)\n",
     "\n",
-    "eff_no_rad = num_no_up_rad_found/(num_no_up_rad_found+num_no_up_rad_lost)\n",
+    "eff_no_rad = num_no_up_rad_found / (num_no_up_rad_found + num_no_up_rad_lost)\n",
     "\n",
     "print(\"VELO energy emission, eff: \", eff_velo)\n",
     "\n",
     "print(\"RICH1+UT energy emission, eff: \", eff_rich)\n",
     "\n",
-    "print(\"eff von e die nicht strahlen: \", eff_no_rad )"
+    "print(\"eff von e die nicht strahlen: \", eff_no_rad)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "40402\n",
-      "10099\n",
+      "41978\n",
+      "8523\n",
       "50501\n"
      ]
     }
    ],
    "source": [
-    "print(ak.num(electrons[~electrons.lost],axis=0))\n",
-    "print(ak.num(electrons[electrons.lost],axis=0))\n",
-    "print(ak.num(electrons,axis=0))\n",
-    "\n"
+    "print(ak.num(electrons[~electrons.lost], axis=0))\n",
+    "print(ak.num(electrons[electrons.lost], axis=0))\n",
+    "print(ak.num(electrons, axis=0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -947,16 +939,39 @@
    ],
    "source": [
     "# energyspektren angucken von velo und rich\n",
-    "diff_found = ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality==1)][\"velo\"]) - ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality==1)][\"rich\"])\n",
-    "diff_lost = ak.to_numpy(energy_emissions[(energy_emissions.lost) & (energy_emissions.quality==1)][\"velo\"]) - ak.to_numpy(energy_emissions[(energy_emissions.lost) & (energy_emissions.quality==1)][\"rich\"])\n",
+    "diff_found = ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (\n",
+    "    energy_emissions.quality == 1)][\"velo\"]) - ak.to_numpy(energy_emissions[\n",
+    "        (~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"])\n",
+    "diff_lost = ak.to_numpy(energy_emissions[(energy_emissions.lost) & (\n",
+    "    energy_emissions.quality == 1)][\"velo\"]) - ak.to_numpy(energy_emissions[\n",
+    "        (energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"])\n",
     "\n",
     "xlim = 20000\n",
+    "nbins = 80\n",
     "\n",
-    "plt.hist(diff_lost,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"lost\",range=[-xlim,xlim])\n",
-    "plt.hist(diff_found,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"found\", range=[-xlim,xlim])\n",
-    "plt.xlim(-20000,20000)\n",
+    "plt.hist(\n",
+    "    diff_lost,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"darkorange\",\n",
+    "    label=\"lost\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "plt.hist(\n",
+    "    diff_found,\n",
+    "    bins=nbins,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"blue\",\n",
+    "    label=\"found\",\n",
+    "    range=[-xlim, xlim],\n",
+    ")\n",
+    "plt.xlim(-20000, 20000)\n",
     "plt.title(\"emitted energy difference\")\n",
-    "plt.xlabel(r\"$E_{velo} - E_{rich}$ [MeV]\")\n",
+    "plt.xlabel(r\"$E_{VELO} - E_{RICH1+UT}$ [MeV]\")\n",
     "plt.ylabel(\"a.u.\")\n",
     "plt.legend()\n",
     "plt.show()"
@@ -964,12 +979,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -981,13 +996,32 @@
    "source": [
     "# number of brem vtx with E>x*E_0\n",
     "\n",
-    "number_velo = ak.to_numpy(energy_emissions[energy_emissions.quality==1][\"velo_length\"])\n",
-    "number_rich = ak.to_numpy(energy_emissions[energy_emissions.quality==1][\"rich_length\"])\n",
+    "number_velo = ak.to_numpy(\n",
+    "    energy_emissions[energy_emissions.quality == 1][\"velo_length\"])\n",
+    "number_rich = ak.to_numpy(\n",
+    "    energy_emissions[energy_emissions.quality == 1][\"rich_length\"])\n",
     "\n",
-    "\n",
-    "plt.hist(number_velo,bins=10,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"velo\",range=[0,10])\n",
-    "plt.hist(number_rich,bins=10,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"rich\",range=[0,10])\n",
-    "plt.xlim(0,10)\n",
+    "plt.hist(\n",
+    "    number_velo,\n",
+    "    bins=10,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"darkorange\",\n",
+    "    label=\"velo\",\n",
+    "    range=[0, 10],\n",
+    ")\n",
+    "plt.hist(\n",
+    "    number_rich,\n",
+    "    bins=10,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"blue\",\n",
+    "    label=\"rich\",\n",
+    "    range=[0, 10],\n",
+    ")\n",
+    "plt.xlim(0, 10)\n",
     "plt.title(\"number of photons emitted\")\n",
     "plt.xlabel(\"number of photons\")\n",
     "plt.ylabel(\"a.u.\")\n",
@@ -1004,7 +1038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1013,7 +1047,7 @@
        "' \\nphoton cut = x*E_0\\neffs, all photons included: x=0\\nfound in velo/(found + lost in velo)\\nVELO energy emission, eff:  0.8446167611094543\\nRICH1+UT energy emission, eff:  0.7961586121437423\\neff von e die nicht strahlen:  0.7954674220963173\\n'"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/trackinglosses_rad_length_endVelo.ipynb b/trackinglosses_rad_length_endVelo.ipynb
index 61ebc12..9c8c149 100644
--- a/trackinglosses_rad_length_endVelo.ipynb
+++ b/trackinglosses_rad_length_endVelo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,34 +14,38 @@
     "from scipy.optimize import curve_fit\n",
     "from methods.fit_linear_regression_model import fit_linear_regression_model\n",
     "import sklearn\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "40402 10099\n",
+      "41978 8523\n",
       "50501\n"
      ]
     }
    ],
    "source": [
     "file = uproot.open(\n",
-    "    \"tracking_losses_ntuple_B_default_radlength_endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    "    \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
     ")\n",
     "\n",
     "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = file.arrays()\n",
-    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
-    "                   (allcolumns.fromB)]  # B: 9056\n",
-    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
-    "                  (allcolumns.fromB)]  # B: 1466\n",
+    "found = allcolumns[\n",
+    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
+    "]  # B: 9056\n",
+    "lost = allcolumns[\n",
+    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
+    "]  # B: 1466\n",
     "\n",
     "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
     "\n",
@@ -52,22 +56,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "stretch factor:  0.24996287312509283\n"
+      "stretch factor:  0.20303492305493354\n"
      ]
     }
    ],
    "source": [
     "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
     "eta_found = ak.to_numpy(found[\"eta\"])\n",
+    "phi_found = ak.to_numpy(found[\"phi\"])\n",
     "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
     "eta_lost = ak.to_numpy(lost[\"eta\"])\n",
+    "phi_lost = ak.to_numpy(lost[\"phi\"])\n",
     "\n",
     "stretch_factor = ak.num(eta_lost, axis=0) / ak.num(eta_found, axis=0)\n",
     "print(\"stretch factor: \", stretch_factor)"
@@ -75,12 +81,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -122,12 +128,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -179,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -188,15 +194,15 @@
      "text": [
       "Parameterisation for rad_length_frac:\n",
       "intercept= 0.0\n",
-      "coef= {'1': 0.19436659767031483, 'x': -0.0002934072350452474, 'y': -0.00011050606423805956, 'tx': 0.22076931190684765, 'ty': 0.07813480395105185, 'qop': -11.837351954632934, 'x^2': -2.0111718729828063e-06, 'x y': 9.09756297060689e-06, 'x tx': -0.0071441555126283435, 'x ty': 0.01601317688273909, 'x qop': 0.6215416537184049, 'y^2': -6.877429438893236e-06, 'y tx': -0.03246512737106646, 'y ty': 0.009236443466565325, 'y qop': -0.7656982001003853, 'tx^2': 3.2721253340872036, 'tx ty': 6.990115604991107, 'tx qop': -728.51712427984, 'ty^2': -4.166569974702085, 'ty qop': 587.2224646668662, 'qop^2': 6.145749766573158, 'x^3': -1.6562683153707752e-07, 'x^2 y': -1.2001632657575304e-06, 'x^2 tx': 0.00040230376049067056, 'x^2 ty': -0.0003226579491818191, 'x^2 qop': 0.002985359658961018, 'x y^2': -5.185010866064232e-07, 'x y tx': 0.0029804615695217043, 'x y ty': 0.0012164498083769432, 'x y qop': 0.0029776397974184527, 'x tx^2': -0.3154073863149152, 'x tx ty': -0.4010820725541255, 'x tx qop': -2.1214929005307828, 'x ty^2': -0.6824284368708862, 'x ty qop': -14.944686049485084, 'x qop^2': -110.18157556408426, 'y^3': 2.4713398261155817e-07, 'y^2 tx': -8.884404196152218e-05, 'y^2 ty': -0.0005372353581591938, 'y^2 qop': -0.0036932151114263956, 'y tx^2': -1.5419692025080922, 'y tx ty': -0.11860718642094348, 'y tx qop': 11.982547200064268, 'y ty^2': 0.38784142586108383, 'y ty qop': 3.211190671760084, 'y qop^2': 46.063719746472096, 'tx^3': 79.89361864801454, 'tx^2 ty': 468.84573407547805, 'tx^2 qop': -100.98490043430814, 'tx ty^2': 186.00390862227553, 'tx ty qop': 7.2472113332685115, 'tx qop^2': 0.3801287848125358, 'ty^3': -92.81882058287101, 'ty^2 qop': 31.69467333269877, 'ty qop^2': 0.2896764307992765, 'qop^3': -0.004367926967413577, 'x^4': 4.0132150047611503e-08, 'x^3 y': -2.8422402209571374e-09, 'x^3 tx': -0.00012178298386800179, 'x^3 ty': -3.6970947007830546e-05, 'x^3 qop': -0.00037942687285408283, 'x^2 y^2': 7.386681488696922e-09, 'x^2 y tx': 4.123194972294186e-05, 'x^2 y ty': -5.2067299577629456e-05, 'x^2 y qop': 8.539712169408631e-06, 'x^2 tx^2': 0.1375456025515689, 'x^2 tx ty': 0.07276853278925399, 'x^2 tx qop': 0.5913446466084463, 'x^2 ty^2': 0.05481754041796982, 'x^2 ty qop': 0.12778897954363583, 'x^2 qop^2': 7.599038189537376, 'x y^3': 4.017000065914544e-09, 'x y^2 tx': 3.555973574975724e-05, 'x y^2 ty': 2.5015913422521407e-05, 'x y^2 qop': 0.0006923060020653793, 'x y tx^2': -0.0728178744632082, 'x y tx ty': -0.030849602066386872, 'x y tx qop': -0.053150965744121384, 'x y ty^2': -0.054271488256181, 'x y ty qop': -0.3845284787500334, 'x y qop^2': 3.5382762706451607, 'x tx^3': -68.2763499577661, 'x tx^2 ty': -35.09421296048657, 'x tx^2 qop': -209.5850008849592, 'x tx ty^2': -25.837301721685865, 'x tx ty qop': -72.99376890218326, 'x tx qop^2': -20.075729532656002, 'x ty^3': 24.168201409024896, 'x ty^2 qop': 77.70073016158322, 'x ty qop^2': -33.972956067372266, 'x qop^3': 4.163350823159558, 'y^4': 2.935763632194721e-09, 'y^3 tx': -3.516699649708244e-05, 'y^3 ty': -8.2068742131014e-06, 'y^3 qop': -1.523748721155016e-05, 'y^2 tx^2': -0.009411124024499173, 'y^2 tx ty': 0.06338416840865246, 'y^2 tx qop': -0.6891109275638628, 'y^2 ty^2': 0.008253872692153719, 'y^2 ty qop': 0.02346960416570875, 'y^2 qop^2': -0.4386626212085398, 'y tx^3': 32.94713780622537, 'y tx^2 ty': 19.368744361202825, 'y tx^2 qop': 17.426884247940652, 'y tx ty^2': -27.528264177777427, 'y tx ty qop': 341.8447125490805, 'y tx qop^2': -32.944204297917075, 'y ty^3': -3.4360959680136522, 'y ty^2 qop': -10.352300738708175, 'y ty qop^2': 79.19546293155958, 'y qop^3': 0.22012908047276727, 'tx^4': 12563.778412336536, 'tx^3 ty': 749.5755851917718, 'tx^3 qop': -9.94600150329609, 'tx^2 ty^2': 1299.398342139433, 'tx^2 ty qop': 0.1819109935589588, 'tx^2 qop^2': -0.05652058959156786, 'tx ty^3': 402.1468237499123, 'tx ty^2 qop': 0.5288025377553879, 'tx ty qop^2': -0.09503849406227534, 'tx qop^3': 0.005526136730138975, 'ty^4': 490.8007853758815, 'ty^3 qop': -0.032786661645227415, 'ty^2 qop^2': 0.21306953809390777, 'ty qop^3': 0.00041305791547802015, 'qop^4': 5.0463795291232325e-05}\n",
-      "r2 score= 0.052596793429776634\n",
-      "RMSE = 0.12333913500444761\n",
+      "coef= {'1': 0.19830920321074946, 'x': -4.49175976974402e-05, 'y': 0.00039490060416272056, 'tx': 0.00015102371088508598, 'ty': -0.3004315695136339, 'qop': -15.314945266490128, 'x^2': -1.8619394568578818e-05, 'x y': -4.953907513838906e-06, 'x tx': 0.021617503882699386, 'x ty': 0.03829244150062255, 'x qop': -0.41798007270055415, 'y^2': -2.4410328131494868e-05, 'y tx': -0.03443063985633742, 'y ty': 0.024201355785359608, 'y qop': 0.069823295273139, 'tx^2': -9.507076220830514, 'tx ty': -0.3980701633198789, 'tx qop': -0.04742639222342226, 'ty^2': -5.342167619183405, 'ty qop': 0.04842038611881145, 'qop^2': 0.2070268831284635, 'x^3': 1.5823479402461545e-07, 'x^2 y': -5.806838940825474e-07, 'x^2 tx': -0.00023418353598118923, 'x^2 ty': 0.0037081774556846224, 'x^2 qop': 0.01641641113222204, 'x y^2': 6.398758958085149e-08, 'x y tx': -0.002932641224303519, 'x y ty': -0.001396824762733282, 'x y qop': -0.020888196868450136, 'x tx^2': 0.09096908124129072, 'x tx ty': -2.939755357349759, 'x tx qop': -8.73057282483271, 'x ty^2': -0.15340975596199197, 'x ty qop': 9.249941815315987, 'x qop^2': 0.030205199863621846, 'y^3': 1.6478595155078324e-07, 'y^2 tx': 0.0013152209574444013, 'y^2 ty': -0.000257931039205234, 'y^2 qop': -0.0057816482028933735, 'y tx^2': 2.685350530706497, 'y tx ty': 0.17814134491255038, 'y tx qop': 9.050929476915277, 'y ty^2': 0.10064678584510746, 'y ty qop': 4.6142369495362185, 'y qop^2': -0.00046589334175238057, 'tx^3': -0.6242025517665986, 'tx^2 ty': -0.017658603327465147, 'tx^2 qop': -0.022216794668845363, 'tx ty^2': -0.01024816705930792, 'tx ty qop': 0.024042119917448937, 'tx qop^2': 6.093129132646114e-05, 'ty^3': 0.09834545208780196, 'ty^2 qop': 0.011664187426493774, 'ty qop^2': -2.1825340747940462e-05, 'qop^3': -1.559907925017188e-06, 'x^4': -2.9483981922595603e-09, 'x^3 y': -6.13444928188045e-09, 'x^3 tx': 7.101384723817716e-06, 'x^3 ty': 7.16725431293419e-06, 'x^3 qop': 4.00953960828232e-05, 'x^2 y^2': 1.0679747086683733e-08, 'x^2 y tx': 7.616826922074438e-06, 'x^2 y ty': -3.91052449297824e-05, 'x^2 y qop': 9.93899828579223e-05, 'x^2 tx^2': -0.005400741368580057, 'x^2 tx ty': -0.009338160688408294, 'x^2 tx qop': -0.0017215190824096578, 'x^2 ty^2': 0.0007665795500993852, 'x^2 ty qop': 0.08528819041114723, 'x^2 qop^2': 8.037042310903203, 'x y^3': 8.933181749881669e-09, 'x y^2 tx': 1.766907321343325e-05, 'x y^2 ty': -2.1412010806409754e-05, 'x y^2 qop': -7.010215747540322e-05, 'x y tx^2': -0.0021778144582400415, 'x y tx ty': 0.0326584774738, 'x y tx qop': -0.1598215452174385, 'x y ty^2': 0.012945427966444779, 'x y ty qop': -0.23950569088511311, 'x y qop^2': -0.8775916738593352, 'x tx^3': 1.366672968587086, 'x tx^2 ty': 1.7459886700480327, 'x tx^2 qop': 0.4423601484422016, 'x tx ty^2': -1.0803356692637864, 'x tx ty qop': -0.0706577637682464, 'x tx qop^2': 0.006422119173581787, 'x ty^3': -2.2905272843167253, 'x ty^2 qop': -0.0063092971067729734, 'x ty qop^2': -0.001963650254414034, 'x qop^3': -1.0318719588655238e-06, 'y^4': -2.213189409516758e-09, 'y^3 tx': 7.716181404937572e-08, 'y^3 ty': 3.7462658548648164e-06, 'y^3 qop': -2.6897178570957402e-05, 'y^2 tx^2': -0.019391135282039867, 'y^2 tx ty': 0.003922857934752042, 'y^2 tx qop': 0.30048105074735626, 'y^2 ty^2': -0.0014404468920953982, 'y^2 ty qop': 0.017062949506976018, 'y^2 qop^2': -0.5172314152946776, 'y tx^3': 1.1761566789450086, 'y tx^2 ty': -1.8639649790914088, 'y tx^2 qop': -0.07088661078488609, 'y tx ty^2': -2.1282820437243197, 'y tx ty qop': -0.001276549939024397, 'y tx qop^2': -0.0019180156335069092, 'y ty^3': -0.06849699842395515, 'y ty^2 qop': -0.0351395250211265, 'y ty qop^2': -0.0005408300561230844, 'y qop^3': 4.1258598459708434e-06, 'tx^4': -0.02399482130004447, 'tx^3 ty': 0.010297903626621132, 'tx^3 qop': 0.0018304232474417028, 'tx^2 ty^2': -0.01163526658236639, 'tx^2 ty qop': -0.00029701477688915344, 'tx^2 qop^2': 2.0001744822333693e-06, 'tx ty^3': -0.014645131120788562, 'tx ty^2 qop': -2.1232731978440055e-05, 'tx ty qop^2': -3.4544969537609295e-06, 'tx qop^3': 8.78704309226661e-09, 'ty^4': -0.001422061237110601, 'ty^3 qop': -0.0001708364957408537, 'ty^2 qop^2': -7.126783100939319e-07, 'ty qop^3': 6.1964331341077185e-09, 'qop^4': -5.174168949842998e-10, 'x^5': -1.5976409084572651e-10, 'x^4 y': -1.2852829911480512e-10, 'x^4 tx': 4.777915697529167e-07, 'x^4 ty': -9.081653267184464e-07, 'x^4 qop': -7.95855762181219e-07, 'x^3 y^2': -1.3157031020227805e-10, 'x^3 y tx': 1.2230534549573235e-06, 'x^3 y ty': -1.0267895049764775e-06, 'x^3 y qop': 6.863633592146812e-06, 'x^3 tx^2': -0.0005342802093432353, 'x^3 tx ty': 0.0011253536068463917, 'x^3 tx qop': 0.0006881448740720732, 'x^3 ty^2': 0.0033717855327176985, 'x^3 ty qop': -0.006259805047891221, 'x^3 qop^2': -0.0297856432575138, 'x^2 y^3': 1.382156611384744e-10, 'x^2 y^2 tx': 1.322090420252664e-06, 'x^2 y^2 ty': 5.288591697905076e-08, 'x^2 y^2 qop': 2.9553628222434014e-06, 'x^2 y tx^2': -0.0013788732936473938, 'x^2 y tx ty': -0.005451132472848938, 'x^2 y tx qop': -0.0016912696788365421, 'x^2 y ty^2': 0.00031313327173996576, 'x^2 y ty qop': -0.00464221485985505, 'x^2 y qop^2': -0.021052188879644034, 'x^2 tx^3': 0.2645245528224416, 'x^2 tx^2 ty': -0.33220588343665813, 'x^2 tx^2 qop': -0.1711975210821735, 'x^2 tx ty^2': -2.5912873965567513, 'x^2 tx ty qop': 0.6199222216667238, 'x^2 tx qop^2': 0.26554090739446995, 'x^2 ty^3': 0.21995419222883894, 'x^2 ty^2 qop': 0.5566329227084174, 'x^2 ty qop^2': 0.007138707803204316, 'x^2 qop^3': -0.0036233474117857143, 'x y^4': 3.4643399260403385e-11, 'x y^3 tx': -3.715471736942533e-07, 'x y^3 ty': 5.088992998114605e-07, 'x y^3 qop': -2.9562267287452926e-06, 'x y^2 tx^2': 0.001848217429633696, 'x y^2 tx ty': -0.0006914744675563748, 'x y^2 tx qop': -0.0005866344884493824, 'x y^2 ty^2': -0.0007084811094364334, 'x y^2 ty qop': 0.0021049811349424605, 'x y^2 qop^2': 0.010952363514219516, 'x y tx^3': 0.4012187913820339, 'x y tx^2 ty': 4.799588041207373, 'x y tx^2 qop': 1.3345269234332224, 'x y tx ty^2': -0.3492025592669184, 'x y tx ty qop': 0.7401379477245426, 'x y tx qop^2': 0.00853019020452141, 'x y ty^3': 0.2663977835465425, 'x y ty^2 qop': -0.609737555278061, 'x y ty qop^2': 0.04129006001688038, 'x y qop^3': 0.0008163990713127198, 'x tx^4': -48.942089737430436, 'x tx^3 ty': -0.7028902863652343, 'x tx^3 qop': 0.06643076319999941, 'x tx^2 ty^2': -3.4139167347725943, 'x tx^2 ty qop': 0.004841829970654066, 'x tx^2 qop^2': 0.0007186793516648628, 'x tx ty^3': -0.2579234851632811, 'x tx ty^2 qop': 0.005881970664042852, 'x tx ty qop^2': 3.936368237569365e-06, 'x tx qop^3': -6.671449197806545e-06, 'x ty^4': -0.7419058590355208, 'x ty^3 qop': 0.0013112782683548875, 'x ty^2 qop^2': 0.00010835312377364621, 'x ty qop^3': 2.0484219462302866e-06, 'x qop^4': 2.580463321616666e-08, 'y^5': -3.2720492981752614e-12, 'y^4 tx': -5.762284480681501e-07, 'y^4 ty': 2.5788644553159656e-09, 'y^4 qop': -2.837759991436428e-07, 'y^3 tx^2': 0.0006211299557630969, 'y^3 tx ty': 0.000747045380526546, 'y^3 tx qop': 0.0020744456340701305, 'y^3 ty^2': 4.4960392427497754e-06, 'y^3 ty qop': 0.001141710321238258, 'y^3 qop^2': 0.002696814911126153, 'y^2 tx^3': -2.1363036642104674, 'y^2 tx^2 ty': 0.0687773207312733, 'y^2 tx^2 qop': 0.9871972836921945, 'y^2 tx ty^2': -0.2701567032221813, 'y^2 tx ty qop': -0.848252914690415, 'y^2 tx qop^2': 0.03660886322324573, 'y^2 ty^3': -0.005452852127128314, 'y^2 ty^2 qop': -0.7021206166732931, 'y^2 ty qop^2': -0.08018559047526934, 'y^2 qop^3': -0.00011753193330693663, 'y tx^4': -0.6680420541418445, 'y tx^3 ty': -3.384167006412971, 'y tx^3 qop': 0.006637451265825805, 'y tx^2 ty^2': -0.2331985780185122, 'y tx^2 ty qop': 0.006353552479363033, 'y tx^2 qop^2': 5.615722587673721e-06, 'y tx ty^3': -0.7572903037298312, 'y tx ty^2 qop': 0.0005634713263895614, 'y tx ty qop^2': 0.00010068765868206922, 'y tx qop^3': 2.012241389483668e-06, 'y ty^4': 1.5039421870145853, 'y ty^3 qop': -0.004362654564735129, 'y ty^2 qop^2': -0.00021869646501481262, 'y ty qop^3': 2.17774577667171e-07, 'y qop^4': -5.1794510300353154e-09, 'tx^5': -0.3290348306030112, 'tx^4 ty': -0.004713501192249353, 'tx^4 qop': 0.00039080511691431735, 'tx^3 ty^2': -0.02293656439194589, 'tx^3 ty qop': 1.795982117437259e-05, 'tx^3 qop^2': 1.3703545654537173e-06, 'tx^2 ty^3': -0.0016697750693905097, 'tx^2 ty^2 qop': 2.67609154249412e-05, 'tx^2 ty qop^2': -3.12791956432394e-08, 'tx^2 qop^3': -1.1594178303086993e-08, 'tx ty^4': -0.005076176679563355, 'tx ty^3 qop': 1.1834454126493736e-05, 'tx ty^2 qop^2': 1.9879319916637808e-07, 'tx ty qop^3': 4.055187311820455e-09, 'tx qop^4': 4.214995181248244e-11, 'ty^5': 0.010165548951092954, 'ty^4 qop': -1.6822916965291464e-05, 'ty^3 qop^2': -4.4801213131046415e-07, 'ty^2 qop^3': 7.260260169082666e-10, 'ty qop^4': -1.2734042962503632e-11, 'qop^5': -3.472724885502462e-13}\n",
+      "r2 score= -0.008270330873300091\n",
+      "RMSE = 0.10823208615961777\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAHLCAYAAAAN9wgKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqTElEQVR4nO3de3xTdZ4//le5lUuhoaCIItBE7hchKY5zE50mjndG2sDud247O9rIdL+zu442sisCOhJSndmd2emMaZ0dd2b2+xtJcAQv45ig4owzjjQRtVwEegoKIgJpKIVeaHN+f+CJTZq0Sc5Jzkn6ej4eeejJub1PE3rePZ/P5/0pEEVRBBEREZHGDFM7ACIiIqJ4mKQQERGRJjFJISIiIk1ikkJERESaxCSFiIiINIlJChEREWkSkxQiIiLSJCYpREREpElMUog0LBAIoL6+fsiePxNCoRDq6+shCILaoRDRIJik0JAVCARgt9thtVphMBhQW1urdkgRgiDAarXCZDLB5XLlxPmln6fJZILJZMpwhOmpr69HaWkpbDYbQqGQYsfV8neJKJeNUDsAIjUEAgGUl5ejtbUVAGC329Hc3KxyVJ/R6/Vwu90oKCjImfMbjUYAQG1tbeT/1SYIAkpKSqDT6QAAVVVVaG5uVjSJ0Pp3iSiXMUmhIcnhcKCkpCSy7HQ6VYwmf2glOZFYrVa43e5IkgIAkyZNUvQc/C4RZQ6be2hICgQCaodAGWa1WrPyOfO7RJQ5TFJoSKmvr4fVaoUgCJF+F1arFT6fL7JNKBSCzWaD3W6HxWKBxWKJWu/xeDBx4kQUFBREblA+nw9WqxUFBQWwWq2R49TX18NkMsHj8cDn88FkMkVt05d0XumVSpOEx+OJxFlfX4+JEyfCZrNF1tfW1kauyWQyxe0MK+f8yQgEArBarbBYLDAYDLDb7VHnTuVnBSASp8Fg6JeQeDyeyLLNZoubsASDwcg5BzpPIoN9l5T+TKTvIpMiGlJEoiFIr9eLer2+3/t+v1/U6XSi3++PvOdyuUQAotPpjLxXVVUlAojarrm5WQQgVlZWRpYrKytFAKLZbBZrampEv98f2bfv8Zqbm0WdTid6vd7Ie06nUwQgGo3GAa/F7XaLer1eBCBWVVWJNTU1otFojOxXU1Mj9v2n7vV6RQBR55Jz/ljx9vH7/aLZbI6KWYpXOn+yPytRFEWj0SjW1NRE9gUgAhD1en3kmNJ1Nzc3R+0rXZfZbBadTueA50lGvO+SUp+JXq+Pil+n04k6nS7lGIlyFZMUGpISJSlGozHqZtr3/b43POkm0zdJaW1tjUpSRPGzm3HszU+6SUoqKyvjnjfZJEG68Uo37r7MZnPUjU2Ks++2cs8/2D5GozHqZyWKF2+4AMTW1lZRFJP/WUnb9T2elGT0vaEPlqS4XK4Bz5OsRN8luZ+J0Wjs97OQjin9zIjyHZt7iD4lCAICgUDczp/SY/p0hwP37bgpCQaDkfNKTQPpko6/bNmyfuvcbjf8fn9kubGxEQAiQ3CVOP9ApJ+rw+GINIn0bVqR4pEM9LMCgF27dvVbLx0vlaaQsrKyfudVsnaK3M8kEAjAbDZH7VdTUwNRFOP+jIjyEUf3EH1qoBucdEPLRAEw6Zh6vV72seLdvHQ6HXQ6HTweD55++ul+N00lzx+P9HN1u92KHE+K3+fz9UsotTa6CEjvM5F+ZkxGaKjjkxSiGPGKfEk3i75DTZUiJQl9nxYofXyTyQRBEOB2u1FTU5P18/f9r1yVlZUwm81wOBzw+XwIhUJwOp2oqanJWKKltGQ/E1bFpaGOSQrRp6S/wvuO5JFIiYvBYFD8vNKNte/jfyVZLBaUlJT0uxFm6/zS8T0eT9z18X7eg3G73TCbzZGy/U6nM6fqkwz2mUjfxURPn5i80FDBJIWGpGAw2O/JgV6vh9FojAwp7auxsRE6nQ5VVVUAPisI1nc76f9TLbcuNSXV19fH3VdO+XbpWvo2G0jHk64/k+cHEOlXYbfb+zWppTsvkFSkraamBjU1NQM288TGf/r0aQDKPTmK910aSKqfSWwSZ7fbM/JEj0iLmKQQ9SFVJ+1bz0JqTmhoaIjcWKSbot1uj9TBkDrV+ny+SCfUZG5eOp0u8he1yWSCz+eDIAiROiKCIAxas0S6ycXekKWbmcfjQX19Perr6yPHDQQCkacbcs+fyvVZrVbU1tbCYrGgubk5ksQke6OXbtzScerr66Pqokikp14ulyvSOXggoVBI0SYvuZ+J9GTIYrHAarVG6qkYDAb2VaGhQ+3hRUTZ1LcmBj6tYdG3NoUoXhwOKg3JraqqEquqqvoNnxXFi8NBpboV0tBRvV4fqfHh9/sjQ5f1er3o9XrF1tbWqPP3HWLqcrkitTWMRmOkTkZNTU2/YbR99a3Jodfr+w2tdblcok6nixxLFC8O2e0bdyrnl2KP94q9tr5DZZ1OZ8I4U/lZSXHFO79er486p9FoFHU6XaR2itvtjgx9loZFx56n789koGsd6Luk1GfidrsjPxej0djvu0qU7wpEURQzlwIRESkrEAjg6aefxtq1axEMBqOegLjdbhgMhoR9PYgot3AIMhHlDGlUTGtra2QYb196vT6tjrhEJI8gCBkZXZeXSYpUOEqv1yMUCsFisaCysjKlY/Rtw66qqmIbMJEGSJ2T7777bqxduzbSN0gQBPh8PjQ3N+fUKB+iXFVQUBC1bDQao0YIKnEfBoC8a+4RBAEGgwF+vz/yC0yazEwamTHY/na7HaFQCC6XK2fqLhANFbW1tXA4HFEdUo1GI5xOZ78KrUSkvPr6evj9/qiSDGazOeqPBjn34b7yLkmRRlV4vd7Ie/X19bDZbBjsUgOBAMrLy7Fq1aq0y58TUXZIfVH4hwRRdlkslqh7bLz1QHr34Vh5NQQ5FApFDf+U9K05MNC+5eXl0Ov1TFCIcoBOp2OCQpRlHo8HjY2NsFqtce+pcu7D8eRVkiJN0hX7i0t63DRQ5ic18bA9m4iIKD6v14tQKASPxwObzYaJEydGdVaXcx+OJ686zkqd6hJ1ch2olLSU3Xm9XtjtdgiCgLKysgH7pZw6dQovvPACpk6ditGjR6cd96hRozBq1Ki09ycioszq7u5Gd3d32vt3dnbi+PHjuPXWWzF58mQFI4v2wQcf4NSpU7LjBYCpU6di1qxZUe+5XC64XC4EAgG4XC7U19dHCjPq9XpZ9+G4MlWA5cyZM2JLS4u4Y8cOsaWlJVOniVJTUxMpshQLnxZVisfv90cVsBLF6IJRfYtD9fXUU08NWOyJL7744osvvvq+nnrqKcXuebGOHDkijlQw1pEjR4pHjhwZ8Jxut1sEIFZWVoqimP59OBHFn6S88sor2Lx5M3bs2AEAEEUxMlTJaDSitrYWN9xwg9KnBfBZGexEpa0TPRGRMjubzRbZRuqbYrFY4HA44jYDTZ06FQDw05/+FFdfffWg8X3zm9/Eb37zm37vx3uSUlFRga1btw56zHS2T3bb9vZ2LF++HDt37kRRUZFqcaS6bSrbp3qNWvhcUt02k9eohZ8Hv6fZiyOT22r9e5royUSi3+sA0HkhjF+/dQzHQh24piiE2ofuj9w3MuHUqVO4AKASQAmAXjnHArDtwgWcOnUK06dPT7hdZWUlKisrI9NSpHsfTkTRJGXr1q1wOBxYvXp1v0mwgsEg/H4/7r//ftxzzz246667lDw1gM8uPtGEaIl+OIkeS0nDGRM9npKaeK6++mpcd911g8Y3bty4pLYDgDFjxgw4aZqc7ZPdtq2tDQCwZMkSTJgwQbU4Ut02le1TvUYtfC6pbpvJa9TCz4Pf0+zFkcltc/V7muj3+pnzF7B+exN6JhXi5/+wACcP7kYtIKtrQLIuB3CFzGOMS2Fbi8US6ZeS7n04EUWTlMbGxkinmXjKy8tRU1ODBx54QMnTRki9h2OTCmnZZDINuF9zc3Pc9ZxxlIiIkvXJ2U489OwenOvugWPlYpROHoeTB9WOKrOk+2i69+FEFB3dI01fP5hMdRrS6XQwGo39eg9LGd6qVasS7mc2m/uV05YywVR/qERENDQdbT0Pu+dd9ITDcFZcTFCybTguPoGQ8xqewvm8Xm9k5vh078OJKJqkHDp0CGvXrsWRI0firj98+DAef/zxhE8slNDQ0BCZal7idDrhdDojzTpSNby+SYnT6UQgEIh6r76+HkajMeUKeURENPQc+qQdD2x9D2NHjcDmisW4XDdGlThGABgp8xWvmSUQCMBkMqG2tjbynsfjQUlJSVTJ+2Tuw6lcS0peeeUVfOUrX4m77oknnoDVakVpaSkKCgqg0+lQUlISmakUuNjJ5umnn071tEmT5g+w2+2R4VCxpXilSpWxZbWl/dxuN3Q6HUKhUNRcBERERPE0HTuDh5/fi2m6MdiwYgEmjB6pdkiK0+v1KCkpgcPhgNfrhdFohMVi6VcANZn7cLJSTlKsVitOnz6dcL3b7UZLSwv8fj8aGxsRCoVgMBig1+thNBpRWlqacpCpMhqNcLvdA65vbW2N+36qhWaIiGhoazwcxKYX92Hu1AlYd+t8jBmVSmOJ8qTmHrnHiKXT6ZK+Rw52H05WytfR2tqKa665Bm+99VbCbUpLS1FaWhp5/DPQ05ehpLq6OiPbZvrYWogjkz+PTB5XKz+PVGjlc8nFa+T3VF4cqdDK53LLP/4Aj7ywD8tmTETNTXMxaoT6hdyl5h65x9CClCcYvOqqq2A0GvH222/D5/NhxowZg+6zevXqjDbxqOX111+PjOtPdmhxLmlra0NxcTHOnDmT9NDOXMNrzH35fn0Ar1GrXnzvOJ7Y2Yzr51yKfy6fheHDChJum437hdRn5EEAg9+ZB3YEwA+BqJmM1ZBysuRyuVBeXg6PxwOz2QyPx5OwkNkzzzwDh8MRKfKSr771rW9h7NixcddVV1dn9K8JIiLKLlEU4W48it+8eQS3Xz0Vd31Jj2HDClBXV4e6urq4+5w/fz5r8WWquUcNKV9HeXk5gIsdYPV6PSoqKvDYY4/hzjvvBADs3r07Us8fiK44m69+/etf5+WTFCIiiiaKIn71xmH8/u1j+D+fm46/W3Zl5B430B+l0pOUbMin5h5ZcUg9eM1mM373u98hEAhAEARILUiVlZVxe/4SERHlmnBYxM9ePQTv3hO4+zo97rj6crVDynspJymPP/447rvvPgAXO8RKsyEGAgGIogiDwYCqqipUVVWhuLgYACu2EhFRbuvuCeNH3vfxZvNp/KtlFr4yd4raISUkFWSTewwtSDmOTZs2YdeuXfD5fAiFQlFPTfR6PXbs2AGbzRbV8amiokK5iImIiLKo80IvNr24D03HzmDtLfNwrT656upqGdLNPaFQCB6PB6Iowmg0wmazYdWqVZGnJh6PByaTacAOtURERLngbOcFPPzcXhw5fR4b7liAxdN0aoc0qGSTlBcAvJhgXf/5ntWRVrJUUVGBtWvXYunSpf3W9e1Q29DQgBtuuAHPPPMMVq5cKTtYIiKibAme68ZD25oQPNeNR+9ciFlTxqsdkqJu/fQVTzOAf81iLImknKTU1NRg8+bNA25jNBrR2NgIs9kMg8GAQCCQl0nKqFGjov6bbwoLC7F+/XoUFhaqHUrG8BpzX75fH8BrVMOJtk48+GwTLvSGsXnlYkyfFL/MRLKyeb/IpyHIKRdza2lpSam0vcViwSuvvILe3t6Ug9M6qXCO2sVuiIhIOR+cPo8HtzVh9IhheORrCzFlwmjZx8zG/UI6x88BzJJ5rIMAvgf1i7mlXL831bl3vF5vVubrISIikuvAibOwb30XxWNGwlmxWJEEhdKX0hOhtra2tMoVs04KERFp3TsfhvDoC/swY9JYPHT7fIzP0ZmM86m5J+nraGlpgdVqhcFggM1mS2nCQKlKbb6qqKjAmDFj4q5jWXwiIu37a/Np1P5xPxZfUYy1t8zD6JGp36YHKovf0dEhN8SkDckhyKWlpWhsbMTbb78Nl8sFm80Gs9kMm82GJUuWZDBE7du6dSv7pBAR5agd+07gpzsO4vOGyfjBjbMxcnh6MxkP9Eep1F+EUpNysrR06VI88cQTAC7enDdt2oSWlhasXr0aVVVVOTN7JRER0bbdx/Dkn1rw1QVT8L3rr8KwAWYyzhX51NyTXrr4qYqKCmzZsgU+nw/FxcX4yle+gq9+9at45plnlIqPiIhIcaIo4n//dgRP/qkFK41XoPqG/EhQgM+ae+S8tNLcIytJkRQXF+Puu+9GY2MjnnjiCbz11lu46qqrsGbNGuzevVuJUxARESkiHBZR/7qA3731Ib79hZn4zhdLIzMZk7YokqT0VVpais2bN+PQoUOorKzEpk2bMGvWLDz++ONoa2tT+nRERERJ6+kN4z99B/DCe8dRfYMBlaZpaoekuBEKvbRA8SSlr/LycmzZsgUHDx5kcxAREamquycMxx/2Y+fBU7jvxjm4aeFUtUPKCDb3pCG2OWj16tXZOjUREQ1x57t7sH77Huz+MISHbpuH62ZfonZIGSN1nJXz0krH2awnS1JzEBERUTac6biAjdv34FioAw+vWIAFlxerHRIlSZEk5fDhw5g5c6YShyIiIlLMqfYurHu2Ce1dPXCsXAT9JUVqh5RxyRZz+zWA3yRY16lcOLIokqSYTCY4nU7cddddUe+3tLRg0qRJeV87hRVniYi051ioAw892wQRwOaKxbhCF//3tFJyreLsdz99xfMegJsViyh9iiQp5eXl0Ov1/d4vLS3F448/DrPZnNdVaVlxlohIW4ST7Vi/fQ+KCkfgka8txOSiwoyfkxVnladIx9ktW7Zgy5YtOHz4cNT7hw8fxqlTp/J+7h4iItKOvR+1Ye0z72FyUSE2r1yclQRFS9hxNo4nnngCa9asgU6ngyAICAQCEAQBoiiisrJSqdMQEREl5D8SxKYX92P2lCKsu20+xo7SymDa7BkxHBgpszbdCBFAryLhyKLIp3fPPfegoaEBoihG3jOZTHjiiSdgNptRWlqqxGmIiIgSev3ASfzYewCmGRNRc9McFI7QyvMASpdizT3l5eVwuVxobm5GOBxGeXk5DAYDExQiIsq4l5qO4/GX38d1syZj7c1zh3SCMnw4MGKEvNdwjfz4FElSqqqq8PLLL+Puu++OJCWbN29Ga2srnnzySSVOQUREFJfHfxR1rzbjlkVT8S/m2RgxPGt1SjVpxDBg5HB5rxEa+REqEkai6rEVFRWYOHEi1q5dq8RpiIiIIkRRxFNvtOB//nIYq5ddCdt1+ryZyZguUiRJWbp0acJ1giCgvr5eidMQEREBuDiT8c9fa8bWwDHc9eVSfOPaGZzJ+FNym3qklxZkPIzKysq4NVSIiIjScaE3jB97D+Avh07h++WzYJk/Re2QNGXEcGCkzLu7RnKUzMdRWlrKzrNERKSIzgu92PyH/XjnaAj2m+fiC4bJaoekPcMgv9BJWIlA5NNKspTTWBafiCjz2rt68PBze9By6hzW374AS67UqR1SFK2Uxc8nTFIUwLL4RESZFTrfjYe27cHJs1344dcWYc5l49UOqR/NlMWXSs7KwScpREREg/ukrRMPPtuEzp4wNlcswoxJ49QOSduk2vZyDJUk5fHHH0dlZSVmzpyZ6VMREVGe+TB4Huu2NWHEsGGorViMy4pHqx1S3qg7e/EVT4cY//1sSylJaWhoSGk4cSgUgiAICAaD2LRpU8rBaYEgCBydRESkgoMnzmL99j2YOG4UHlmxECXjRqkdUm5IsrmneuLFVzyBLsB0TNGo0pJSklJWVgabzZbySdxud1aTlEAgAIfDAb1ej1AoBIvFkvQkh7Hj7I1GI/x+fybCJCKiBN47egaPPL8X0yeNxfrb52P86JFqh5Q7lBjdo5GKsyklKUuXLkVlZSW2bNkSee+xxx4DANx///1x93nggQdwzz33yAgxNYIgwGQywe/3RzqzGgwGBINBVFVVDbhvfX09qqqqYDAYIu+ZzeaMxkuUCQUFG6OWRXG9SpEQpe5vwmk4X9qPBZcX499umYcxozQykQxlXcp9UpxOZ9SyIAj4xS9+kXB7m80Gq9WKXbt2pR5dGmw2G8xmc9RoG7vdDpvNNmiS4na74fV6Mx0iEREl8Or+T/CfvgO4Vj8JP7hxDkZpZRKZXKLE6B6N5IUpf/qpFmYTBAGBQCDV06QlFArB5/PBYrFEvV9WVgYAA/an8Xg8aGxshNVqZRl/IiIVPP/uR/ix9wC+MncKam6aywQlXSMUemmA7DBEUcSrr76KG264od+6trY22Gy2rHU8bWxsBIB+55Oeqni93oRPU7xeL0KhEDweDzweD+x2O9xud1LNPe3t7Whra0s77sLCQhQWFqa9P1EsNu9QLhFFEU/v+hD/+7cP8LWlV+AfvzhTc/PwdHV1oaurK+3929vbFYxmEEO1T0o8mzdvhl6vx7Jly2CxWKDX6xEMBuH3+yNPJFwul+xAkyEIAgBAp9MNuD4el8sFl8uFQCAAl8uF+vp6WCwWNDc3D5pkLV++PO2YAWD9+vXYsGGDrGMQEeWicFjEf7/Rgm27P8I3r50Ba9k0zSUoAOBwOLBx48bBNyRFyU5SdDodGhsbYbfbUVNTE/lyieLFQdY1NTW466675J4mKc3NzQCAkpKSuOtDodCgxzAajXC5XLBYLLBarZEnKgPZuXMnlixZkmq4EXyKQkRDUW9YxH+9chCv7P8E9yw34NbFU9UOKaG1a9fi3nvvTXv/3bt3y/6DNml51CdFkVYnvV4Pt9uNlpYWCIIQqS1SVlaG4uJiJU6RFGlUTjAYTBhnsiorK1FZWZlUf5qioiJMmDAh6WMTEQ113T1hPPbH/XjrcCvutczG9XMuVTukAcltli8qKlIwmkEo0adEI31SFGt1amtrQ0tLC8rLy3H33XcDQNbri0hJSKInJqn2jbFYLAkTHiIiSk9Hdy82PrcH/iOtePDWeZpPUEg9iiQpa9aswcSJE3HjjTdG3isvL0dzczPWrl2rxCmSIo3iie17Ii2nM7mTdEwiIpKvrfMC/v3Z93Dwk3Y8vGIhls2M3zxPMkgdZ+W8NNJxVnYYDzzwAFwuF4qLi/s17dx9993w+/345S9/Kfc0SdHpdDAajf1qnfh8PgDAqlWrUjqe1+tNq8IuERH1d7q9C2u3vocTbZ3YdOciLLwie90BhhSpT4qcl0b6pMhOUqQhu8FgEOXl5f3WWywWbN68We5pktbQ0ACfzxf1NMXpdMLpdEZG/QiCAIPBEElepCm0a2trI/t4PB6UlJQkXU6fiIgSO36mA/at7+J8dw82r1yMqy7NYh8Nylmyu8bo9XqsXLkSQP95bwBg165dAw79VZo0147dboder4cgCLDb7VH1UUKhEILBYKTvil6vR0lJCRwOB7xeL4xGIywWS9aGThPR0DUUpjBoOXUOD21rwthRw7Fp5SJcOp4zGWcUR/d8pm9NEmnYseTtt9+Gx+OJmgsnG4xG44DDho1GI1pbWyPLOp2O5fCJiDJg3/E2bHxuD6ZMGI2NdyyAbixnMs64JEf31H1w8RVPR6+iEaVNdpKydu1afPWrX4XT6Yw8STl8+HCkamtBQQH7dRARDUGBD1qx6YV9uOrSIqy7bT7GFWpkXCsBAKqnX3zFE2gDTH/NbjzxyP7GLF26FA6HA3fddRcCgQA8Hg+A6GJu9913n9zTEBHlpXxs3gGANw6dwmN/fB9LrtThgZvnYvRIjbQfDAUsix/NaDSisbERLS0t8Pv9aGlpgV6vh9lszmoxN7VUVFRgzJgxcddVV1ejuro6yxEREanHu/cEfvbKQXxp1mT8q3k2RgzXyB0vw+rq6lBXVxd3XUdHR/YCYZ+Uz+zevTtSEr60tLTfLMnPPPNMpGNtvtq6dWtkEkMioqHs928fxX//+TBuWngZ1iw3YNgw7c3DkykD/VEqjSLNijxKUmSntw6HY8D1S5cuxZo1a+SehoiINEwURfzmr4fx338+jFVl0/C964dWgkKZkVaudebMmcjomFAohCNHjvQb2SOtc7lc2LJlC37xi1/Ii5SIiDQpHBbxxOvN+MN7H+M7X5yJlcZpaoc0tElVY+UeQwPSSlKCwSCsVivefvttAAPPiSOKYvYecRERUVb19IbxH74D+PPBU/i/X7kKNy64TO2QKI+ae9K6jNLSUjQ2NkYSlYGqshoMhsiEg0RElD86L/Ri8x/2Y/eHIdTcNBdfvGqy2iFRnpGVa7ndbmzduhUVFRVKxUNERDngXFcPHnl+Lw590o6Hbp8P4/SJaodEkiw+SfH5fLBarVEFUoGLHYUdDgf0ej1CoRAsFkta08zIHt0jJShtbW2YMGFC5P3Dhw+jpKQk6j0iIsp9ofPdWL99D060deKRry3EvKn8Pa8pWeyTEq9YqyAIMJlM8Pv9kZGvBoMBwWAwaoqaZMge3fP2229j0qRJmDgxOoueOXMmNm3ahCeffFLuKYiISCM+OdsJ+9Z3ETzXDcfKxUxQhjBpjrxYNpsNZrM5qjSH3W5Pq/q87CTFbrdDFMW4Mx1v3rwZTzzxBF599VW5pyEiIpUdbT0Pu+dd9IZFOCsWo3TyOLVDonik5h45r0GepPh8PkyaNKlfjbBQKASfzweLxRL1fllZGQCgvr4+pUtRpOJsMBhMuM5sNqOmpga7du1S4lSaxIqzRJSuXJkF+dAn7Vi/vQm6MaOwccUCTC4qVDskzRlKFWddLhfcbjfsdnvU+42NjQD6j/qVkhmv15tSk4/sJGWwGY4FQUAgEJB7Gk1jxVkiymdNx87g4ef3YtrEMdhwxwJMGD1S7ZA0STMVZwF09V58pav9QuJ1drsdTqcz7jpBEAAAOp1uwPXJkp2kiKKII0eOYMaMGf3W7dixAx6Ph3VSiIhy1K7DQThe3Id5UyfgwVvnY8wojRTQoMSGA47dwMYMNGAEAgFMmjQpYX205uZmAEBJSUnc9aFQKKXzyU5SnE4nTCYT1qxZg/Lycuh0OgiCALfbjfr6ehQUFGDt2rVyT0NERFm288BJ/Nh7ANfMnIj7vzoXo0YMjYkCc95wYO01wL0yng/sPgks39r/fYfDAbfbnXA/qXUlUTeQgYq/xiM7SSkuLsbLL7+MVatW4f7770dBwcW5GqQy+bW1tXk/wSARUbJypQ/Ki+8dxxM7m3HDnEvx/fJZGM55eHLHcKBwNCCn11BRe//37HY7LBZLVJON9P/Sf6UkJNETk6wnKdJJGxsb0dLSgkAgAEEQYDQaUVZWhuLiYiVOQUREWSCKItyNR/GbN4/gjqsvx3e/VMqJAgnAxRE9tbW1cdcZDAYYjUbs2LEDQP++J9Jyqt0/FElSJKWlpSgtLe33fmyhNyIi0h5RFPHfbxzGs28fw9c/Nx2rl10ZeTpOOSRDo3v8fn+/9+x2O+rr66MqzhqNRni9XtTU1ETe8/l8AIBVq1alFEZWGhhTHRdNRETZFQ6L+OmOQ3j27WOouk6Pv7tmOhOUXDVcoVeaGhoa4PP5op6mOJ1OOJ3OhKN+Ekk513ryySchCAI2bdoEAFi9evWAvXWDwSACgQDuu+++VE9FRJR3tNgHpbsnjB+9/D7eFE7jXsts3DD3UrVDohxmNBrh9/sjFWkFQYDdbk+5JD6QRpJSU1ODM2fORJIUURTh9XoH3IfZOBGRNnV092LTi/uw56Mz+Ldb5uFz+klqh0RyZXGCQekJSSyj0TjgKKBkpXwZfr8/6snJ6tWrsWzZMtx///1xtw+FQmllT0RElFlnOy9g43N78cHp89hwxwIsnqZTOyRSQhaTlExL+TJiO8ZWVFSgpaUl4fY6nS7v66SwLD4R5ZrguW6s29aE1nPdePTOhZg1ZbzaIeU8zZTFzyOKjO6JN6JH8vjjj6OyslKJ02gWy+ITUS75+EwnHny2CT3hMJwVi3FlyVi1Q8oLmimLP1SfpDQ0NKQ0UicUCkEQBASDwUgfFiIiUs+R0+ewbtsejB4xDLUVi3HphNFqh0RKkzk6J3IMDUgpSSkrK4PNZkv5JG63m0kKEZHK3v/4LDZs34NLxhfi4RULoBs7Su2QiAaUUpKydOlSVFZWYsuWLZH3HnvsMQBI2HH2gQcewD333CMjRCJt6lveXIvDSkmb1CqLv/vDEB59YS9KJ4/DQ7cvQFGhorU8SUvyqLkn5WJusUONBEFImKAAgM1mg9VqTT0yIiJSxF+aT2Hjc3uw4PJiPLxiIROUfCclKXJeGklSZI/uGYwgCAgEAqmehoiIFODbewL/9cpBfOGqybjXMhsjh3Mm47yXZJ+Uup1A3Z/ir+u4oGhEaZOdTouiiFdffRU33HBDv3VtbW2w2Wwpz3pIRETybdt9DE/+qQU3LbwMa5YbOFEgRalefvEVT+BDwNS/RlvWyU5SNm/eDL1ej2XLlsFisUCv1yMYDMLv90dGArlcLtmBEmkN+6FQOrLxvRFFEf/7tw/w9K4PUWG8At/+wsycqfytVp+dvJJHfVJkJyk6nQ6NjY2w2+2oqamJ/EMQRRHAxTL6d911l9zTEBFREsJhEfV/EvDCu8fx7S/MRKVpmtohUbYxSYmm1+vhdrvR0tICQRAgCAL0ej3KyspQXFysxCmIiGgQPb1h/HTHQbx24CSqbzDgpoVT1Q6JSBbFuni3tbWhpaUF5eXlKC8vx44dO+D3+/GVr3xFqVNoFsviE5Haunp6UfvS+/AfacV9N87BdbMvUTukIUczZfGHQf6TEI30r1YkSVmzZg3q6+tRUFCAnp4eAEB5eTkaGhrg9XrhcDiUOI1msSw+ESUrE30uznf34JHn9+LAiXasu20eTDNKZB9TLbncB0UzZfGlYcRyj6EBsnOlBx54AC6XC8XFxf2adu6++274/X788pe/lHsaIiKK48z5C/j33zdBOHkOj6xYmNMJClEs2UmKx+OBx+NBMBhEeXl5v/UWiwWbN2+WexoiIopx8mwXHnjmXZxq74Jj5SLMv3yC2iGRFgzlYm6x9Ho9Vq5cCQBxh7jt2rULgiDIPU1KAoEAHA4H9Ho9QqEQLBZLyjMx+3w+WK1WtLa2ZihKynUsi09qOhbqwLpnm1AAYHPFYlyhi98vjoYgju75jE6ni/y/NOxY8vbbb8Pj8cBgMMg9TdIEQYDJZILf74/0EzEYDAgGg6iqqkr6OOlMpEhElA3CyXas374H40ePwMMrFmJyUaHaIRFlhOzmnrVr1+KrX/0qdu/eHXmScvjwYTz++OMoKytDQUFBVm/4NpsNZrM5qiOr3W5PKQa73c4quUSkSXs+OoO1z7yHyUWFcKxczASF+pNG98h5aWR0j+wwli5dCofDgbvuugtutxvDhw+HwWBATU0NRFFETU0N7rvvPiViHVQoFILP54PFYol6v6ysDAAiFXAH4vP5MGnSJI7WISLN8R8J4qFte6C/ZBwevXMhiseMVDsk0iK5/VGUGB2kEEXCMBqNaGxsREtLC/x+P1paWqDX62E2m7NazK2xsREA+j0FkRIOr9c7aJOPy+WC2+2G3W7PTJCUN9gPhdKR7vfm9QMn8SPvAZTNmIiam+agcIRGOg2Q9rBPymfWrFkDv9+Pt956C6WlpSnPkqwkqYNu334y8dYnYrfb4XSmPqNSe3s72traUt5PUlhYiMJCPrIlovheajqOn7/WjOtnX4Lvl8/CCM5knHVdXV3o6upKe//29nYFoxk6ZCcpTz/9dFY7xg6kubkZAFBSEr9OQCgUSrhvIBDApEmT0uqLsnx5gmkkk7R+/Xps2LBB1jGIKD+5Gz/Er/96BLctnoq7v6znTMYqcTgc2Lhx4+AbakGSFWfrtgN1z8Vf19GtaERpk52kOJ3OQZ9QPPnkk1mZZFBKloLBYNz1AyUgDocDbrc7rfPu3LkTS5YsSWtfAHyKQkT9iKKIp/5yGM8EjuHvr5mOv7/mypyZyTgfrV27Fvfee2/a++/evVv2H7RJS7K5p3rlxVc8gQOAaY2iUaVFkTopgUAAa9euxbJly/o1tYRCITidzqwkKVISkuiJSaIkxW63w2KxRCVb0v9L/x0owSkqKsKECSyiRETKCIdF/Py1Q/jjnhO468ulWLHkioTbZqLMPvUnt1m+qKhIwWiGDkWepOzYsQOiKMbN8hO9nwnSKJ7YJzvScqJ5E3w+H2pra+OuMxgMMBqN8Pv9CkZKRBTfhd4wfvTyAfy1+RT+uXwWzPOnqB0S5Zo8mrtHdhhVVVUQBAE2my1uh9XW1takhv4qQafTwWg0wuv1oqamJvK+z+cDAKxatSrufvESELvdjvr6elacJaKs6bzQC8eL+/DusTN44OZ5+LxhktohUS7iLMifqaysREFBASoqKhJuk82OtQ0NDTCZTBAEIdJE43Q64XQ6I0mUIAiwWCxwuVwwm81Zi42IKJGznRfw8HN7cfj0Oay/fQGWXKlLaj8271A+k5WkHD58GAAGTFCSWa8kqWlGqhorCALsdntUfZRQKIRgMDjgaB8iomwJne/Gum17cOpsFx69cxFmTxmvdkiUy4Z6nZRXXnkFVqs1cpM3mUzw+Xya6TxqNBoHHKljNBoHbcaRnr4QEWXSibZOrHu2CV09YTgrFmP6pLFqh0S5Lo/6pKTc6tTS0gKz2YzW1laIoghRFNHY2MhmEyKiFH1w+jzsW99FWARqK5mgEMVKOVdyuVzQ6XRoaGiA2WxGMBiE2+3GAw88gF/+8pf47ne/m4k4ifLaYMNIOcw0/xw8cRbrt+9BybhReHjFQpSMG6V2SJQvhnJzTyAQQENDQ6SfSXFxcWQkzcsvv8wkhYhoEO8eDeGHz+/D9Eljsf72+Rg/mhMFkoKG8uielpaWuB1hrVZr2hVbc11FRQXGjBkTd111dTWqq6uzHBERadWbwmnUvrQfCy4vxr/fOg+jR2rkT1aSra6uDnV1dXHXdXR0ZC+QofwkJZHS0tKEo2Uef/xx3HfffUqdSnO2bt0amWmZiCiRV/afwE98B3GtYRJ+YJmDUSM08ucqKWKgP0oDgUDCgqKUWMpJyunTp/HOO++guLg46n0pQTly5AhEUYy8FwwG4XK58jpJIZJrsD4m7IOS+5575yPUvy7AMn8K/umGqzhRIGVOHo3uSTmMUCg04FODdGYRJiLKV6Io4ne7PsT/+9sHuHPpFfjOF2dyokDKrKHcJwVA5ElJvDL4fUlPV/gPkoiGonBYxC//3ILt73yEb147A9ayafx9SJpR92ug7rfx13V0ZjeWRFJOUsxmM15++eWU9rnxxhtTPQ0RpYBDlLWnNyzipzsO4tX3P8Ga6w24ZdHUuNvxsyPFJdlxtvofL77iCbwHmG5VNKq0pJyk2O32lE+Szj5ERLmquyeM2pf2Y9eRVvzgxjlYPvsStUOioWQoj+4pLy9P+STp7ENElIs6unvxyAt7sf94Gx68dR6WzSxROySinKWR/rtERLmvrfMCNmzbg6OhDjy8YiEWXlE8+E5EShvKo3uIaHDZ7mfAfgzqO9XehfXb9uBMxwVsunMRrrq0KKn9+NmR0sRhgCizuUbM5dE9FI0VZ4mGto9CHXhoWxN6wyI2VyzCtImcKHAo0kzF2TzCJEUBrDhLNHS1nDqHh7Y1Yeyo4di0chEuHT9a7ZBIJVqpONs7HOiVeXfvzdWOs0REdNG+423Y+NweTJkwGhvvWADdWM5kTOoLK5CkhJmkEOUv9jPIf/4jrXC8uA+zphThwVvnY1xhdn6dsq4KDaZ3eAF6hssrGtg7XAQgKhOQDExSiIhS9MahU3jsj+9j6XQdHrh5LgpHaOTPTqI8wySFiCgFL+/5GHWvHsKXZ12CfzHPwojhGhkGQfSp3uHD0Stzhu3e4WEAPcoEJAOTFCKiJD0TOIpfvXEYtyyaCtt1es5kTJoUHj4cvTKT5/DwAjBJIRoiMt2PgP0UMksURfzmzSNwNx7FqrJp+Ma1M1SbKJCfLQ0lTFKIiAYQDov4xc5mvNT0Mf7xSzNx59JpaodENKBeDEOvzMl3ehWKRS4mKURECVzoDeM/vAfwxqFT+H75LFjmT1E7JKJB9WI4epJIUn5V14Vf1XXHXdfZof7IHoBJClFWDPaIfrDmmtj1qR6fUtd5oReb/7Af7xwNwX7TXHzhqslqh0SkqO9UF+I71YVx170b6MVXTe1Zjqg/JikKYFl8ovxyrqsHjzy/F80n2/HQbfOxdPpEtUOiHKCVsvhhDEevzNt7WKFY5GKSogCWxSfKH6Hz3Xho2x58crYTj3xtIeZeNkHtkChHaKYsviJ9UrSRpjBJISL61CdnO7Hu2Sac7+7F5pWLMXPyOLVDIhrSmKQQacBgfUo4ZDnzPgyex7ptTRgxrAC1lYsxtTh+E26u4Wc99Fxs7pH3JCXMJylERNpw6JOzWL99D3RjRuHhFQswqSh+Z0KiXBBWoLknrJFByExSiGhIazp2Bg8/txfTSsZgwx0LMGH0SLVDIpKlB8OSGoI82DG0gEkKEQ1Zb7UEsfkP+zD/8gn491vmY8woThRIpCVMUohUkGo/AaX7FbBfAvDa+5/gP3wH8bnSEtx34xyMkjkhm1bxs5an77+9XPlZhjFCgSHI2mjuyc9/lUREA3jh3eP4sfcAbphzCew3zc3bBIWGJqlPipxXeID0wOPxwGQyoaCgAAaDAT6fr982gUAAVqsVdrsdNpsNHo8nrWvhkxQiGjJEUcSWxg/x2zc/wIoll+Mfv1jKmYyJUlBfXw+/3w+n0wkAsNvtsFgsaG5uhl6vBwAIggCTyQS/3x+pIWYwGBAMBlFVVZXS+ZikKIAVZylVqT42zpXHzFomiiJ++ecWbNv9Eb5x7XSsKrtStZmMKXek8m9PKxVnlSnmFv9JSigUgsvliiw3NDTAZDIhEAhEkhSbzQaz2RxV5FR6osIkRQWsOEukbb1hET975RB8+07AtlyP2xZfrnZIlIe0U3E2uQkGBztGPDU1NVHLOp0OACL3wFAoBJ/PF3nSIikrKwNw8UlMKokKkxQiymvdPWH86OX38aZwGvfeOBs3zLlU7ZCIMq67K4zurvRnMj7fnlzHWY/HA6fTGXmK0tjYCACRZYmUxHi9XiYpREQA0NHdi0df3Iu9H7Xh326Zh8/pJ6kdElHGhTEcv3Kcxi83nsjoeex2O+rr69HQ0BB5TxAEAJ89YYklrU8WkxQiFQw2pFjuegLOdl7Ahu178WHwPDbesRCLphWrHRJRVvRiOL6+9nKsundq2sc4uPs8/mn5gYTra2trIQgCQqEQrFYrXC4Xqqqq0NzcDAAoKSmJu18oFEopjrwcd5fu0KdkhlURkfYFz3XjgWfew/EzHXj0TiYoNPSMKhyGcROGp/0aUzRwn5aamhq43W54vV7odLpIHxSDwQAACAaDcfeLbQYaTN49SUl36FMyw6qISPs+PtOJB59tQk84DGfFYlxZMlbtkIiySpm5e5J7hmE2m1FVVYXa2loAnyUhiZ6YpHo/zbsnKQMNfRqINKzKbDbDbDZH2tgCgUBG4yUi5Rw5fQ41W9/F8GFALRMUGqJ6FSjmlmgIcjzLli2LJB/SKJ7YvifScqojnPLqSYqcoU+DDasikoN9SDLv/Y/PYsP2PbhkfCEeXrEAurGj1A6JSBWZHIIcjyAIMJvNAC7eO41GI7xeb9R9Veo+sWrVqpTiyKsnKckMfUpW7LAqItKu3R+G8OCz72F6yVhsWrmICQpRBkidZPv28xQEAV6vt1+BN5/PF/U0xel0wul0Jhz1k0hePUlRauhTvGFVA2lvb0dbW1tS28ZTWFiIwsLCtPcnGsr+cugUHnv5fVw9TYcHbp6L0SM5kzEpr6urC11dXWnv397ermA0AwtjuAITDPb/d6TT6RAKhXD33XfD5XLBYrFAr9f3ewBgNBrh9/tht9uh1+shCALsdnvK1WaBPEtSlBj6lGhY1UCWL1+ecqx9rV+/Hhs2bJB1DMo8OU02SpfBZ3PRRd69J/CzVw7ii1dNxr9aZmPk8Lx6OEwa4nA4sHHjxsE31IBMlsVPtkXCaDTC7XbLigHIsyRFiaFPUhuaz+eD1WqF0+kcNEnZuXMnlixZklqwffApClHqtu0+hif/1IKbFl6GNcsNnCiQMmrt2rW49957095/9+7dsv+gHYryKklRcuhT7LCqgRQVFWHChAlJH5uI0ieKIn77tw+wZdeHqDRNw7c+P4MTBVLGyW2WLyoqUjCagYU/HaEj9xhakFdJitJDn/oOqyIi9YXDIur/JOCFd4/jH74wExWmaWqHRKQ5vRimwOgebTSd5lWSovTQp77DqohS6QeS6SHHQ3FIc09vGD/ZcRA7D5xE9Q1X4aaFl6kdEhFlmDZSJQUlM/RJEISosvfJDqsiInV09fRi04v78aeDp1Dz1blMUIgG0Pvp6B55Lzb3ZEQyQ59CoRCCwWCk70qyw6qIKPvOd/fgkef34sCJdqy7bR5MM+KP3iOii9gnReMGG/pkNBrR2toa9R4TEiLtOXP+AtZvb8LxM514ZMVCzL+cHdSJlPJynYCX61riruvu6M1yNPHlZZJCpL5LB1wb26dkMLF9ToZCH5STZ7uw7tkmnOvugWPlIugvyd7oCKJclmydlPLqWSivnhV3XUugFetMrygdWsqYpBCR5hwLdWDds00oALC5YjGu0I1ROySinJHtuXsyiUmKAioqKjBmTPxfotXV1aiurs5yRES5SzjZjvXb92D86BF4eMVCTC5isUPKDXV1dairq4u7rqOjI2txZKosvhqYpChg69atnC2ZSAF7PjqDjc/txTTdGKy/YwGKx4xUOySipA30R2kgEEi5VhcxSSHKCFFcE7U8WB8UuX1M8qFuSuPhIBx/2I/ZU8Zj3W3zMHYUfz0RpSOTc/dkG38LEJHqXj9wEj/yHkDZjImw3zQXo0Zo4xckUS7iEGQiIoX84b3j+MXOZlw/+xL8s3k2hnOiQCL6FJMUoixItfkl1eahXGzeEUURbv9R/OavR3Db4qm4+8t6zmRMpICwAs09YTb3ENFQJYoinvrLYTwTOIa/v2Y6/v6aKzmTMZFCehQYgix3f6UwSSGirAqHRdS9eggv7z2Bu75cihVLrlA7JCLSKCYpRJQ13T1h/Nh7AH9tPoV/Mc9C+bwpaodElHdYJ4WIMmqwPiaDDTnW4pDkzgu92PTiPrx37AweuHkePm+YpHZIRHmJQ5CJiFJwtvMCHn5uL46cPo8Nty/A1Vfq1A6JiHIAkxQFsCw+UWKt57qxblsTTrd344d3LsTsKePVDokoI7RVFp91UuhTLItPFN+Jtk48+GwTunvCcFYsxvRJY9UOiShjtFIWvxfDFJhgkM09RPSpVPuYDEYLfVA+OH0e67Y1YdSIYaitXIwpE0arHRLRkNCbZMfZt+oCeKsuEHddT0eP0mGlhUkKESnu4ImzeGjbHkwqGoVHVizExHGj1A6JiGJcU23ENdXxWwE+CnyMetP/ZDmi/pikEJGi3j0awg+f34cZk8biodvnY/xozmRMlE3sk0JEUQoKfhG1HDsL8mBSHUKcavOP0kOSEx3vTeE0al/aj4VXFOPfbpmH0SO18YuOaChhWXwiohiv7D+Bn/gO4vOGybjXMpszGRORbExSiEi27e98hIbXBdw4fwqqb7iKEwUSqYjF3IiIPlV09WQ0vC5gpfEK/MMXZnKiQCKV9SowwaDcJEcpTFKIFDBYH5TB+pDE9hFRusy90kOSRXE9wmERT/5ZwHPvHMe3Pj8D1rIrFT0HERGTFCJKWW9YxE92HMRr73+C711vwM2LpqodEhF9ihMMUhSWxaehpLsnDOdL+9F4pBX33TgH182+RO2QiDRBK2Xx2SeForAsPg0VHd29eOSFvdh/vA3rbp2HspklaodEpBlaKYufT5ikEGXBYH1MBu9zcmkmwkrJmY4L2Lh9D46GOvDwioVYeEWx2iERURws5kZEQ8qp9i6s37YHZzouwLFyEQyXFKkdEhElwAkGiWjI+CjUgXXPNiEsinBWLsYVuvj9r4hIG3oxQnbHWbn7K0UbURDlOKWHGA9+voHL8CtVBl842Y712/dg3KgReORrC3HJ+MIE8aR2/UREyWCSQkRx7Tveho3P7cFlE0Zj4x0LUTyWEwUS5YJk5+7ZV/ca9tftjLuut+OC0mGlhUkKEfXjP9KKTS/uw+wpRVh323yMHcVfFUS5ItkhyLOryzG7ujzuutOBI3jR9KjSoaWMv3mIKMqfDp7Ej14+AOP0ibDfPAeFI7TRy5+Ihh4mKUQKkN/nInqI8eB9VBbJPF98LzV9jJ+/dgivb9mHbX/+COvF5PZjnxMi7eAQZIrCirOUD7b6j+KpvxzGrYun4rn/u0PtcIhyjpYqznIIMkWw4izlMlEU8eu/HoHHfxSrll2Jb3xuOgaeLpGI4mHFWeUxSSEawsJhEb/Y2YyXmj7Gd79Uiq8tvULtkIhIpl4FJhiU21ykFCYpRAoYrC7JYOv71zl5JuYMJwZcTqcuyoXeMP7DewBvHDqFHT9txPPf/6yJZ7D9U63rkmuUqjNDpIZ86pOijUYnIsqqzgu9ePSFffircBr2m+ai49AZtUMiIuonL5OUQCAAq9UKu90Om80Gj8eT1H4ejwcmkwkFBQUwmUzw+XwZjpQo+9q7evDQtibs+egM1t++AF+4arLaIRGRgqQ6KfJe2kgP8q65RxAEmEwm+P3+SGdWg8GAYDCIqqqqhPvV1tbC6/XCZrOhubkZtbW1sFgs8Hq9MJvN2Qqfckh0k8BgQ4IHG2IcO8vxlJjlxTHLsc0/A8X2WXNF6Hw3Htq2ByfPduGRry3E3MsmRK1P9XjJrs81uR4/DW29GK7A6B5tNPfkXZJis9lgNpujRttIT1QGSlJ27doFr9cbWV69ejVMJhOcTieTFMoLn7R1Yt22Jpzv7oVj5SLMnDxO7ZCIKAPCCnScZZ+UDAiFQvD5fLBYLFHvl5WVAQDq6+vj7ufz+eB0OqPeMxqNMBqNEAQhM8ESZdGHwfOo2fouesMiHqu8mgkKEeWEvEpSGhsbAQB6vT7qfempSt8nJX2ZzeZ++0gSvU+UK0ZMGo0HnnkX4wpHwFmxGJcVj1Y7JCLKIPZJ0SjpqYdOpxtwfSrHs9lsg27X3t6Otra2lI7dV2FhIQoLC9PeXyvk9ksYvN/DL2L2+CRmObZZ7otRS/+feOeA57+nO/b40c5suCz6jav6xDc1elXBdTE7T4wpj9Yauz5m+fqY5QdjlvvVhIodsnzxfKOmnMHE8r344+8/QdDXg593H/50/Xsx28f2iYnpA/Pl2M8itnrmopj10T/L2CHWsQb/bGPjG+yzj+703v+7NPAQ74HiTfV7Pthwbbn9gVLdfrDPRm68+aqrqwtdXV1p79/e3q5gNAPLpyHIeZWkNDc3AwBKSkrirg+FQkkfy+PxQK/XD9iPRbJ8+fKkjxvP+vXrsWHDBlnHIIpVOO00dNfvx4UTE9D66nGIPWpHRJS7HA4HNm7Mr/pAx+qew7G65+KuC3d0Zzma+PIqSTEYDACAYDAYd30qTTcOhwNutzupbXfu3IklS5YkfexY+fAUhbRltP4T6L54AJ0fTkLo9TlA+KDaIRHltLVr1+Lee+9Ne//du3fL/oM2WcnO3TOl+muYUv21uOvaAwex26T+vHN5laRISUiiJybJJil2ux0NDQ1Jb19UVIQJEyYktS1Rpo2dOwwTrjmAjkNTcOavVwFigdohEeU8uc3yRUVFCkYzMJbF1yhpFE9s3xNpOZnJnerr62GxWDhhYBoGq50x2PaDtXX3bzuP7ccQ3Qflwpnom/Ojg9yr/xXPDrzBQA6lvyuA/n1Ufj/Icoz1PwFEEdhy3oLfnr8Fo//4P1iwaxsKIALoX8UltkfKoP6U2ubry6OXdd3R/YHONEX375kh3hC1/Dv8fdSyE/ao5cNYGrX8dsvno5brSr8btVzwcnQ8M8T5UctmHI/e/t+it8fJvgv/J3rb2Nbl2M8yZntgWvT+/frH3ByzHFtjJ/ap2LSY5ZqY7WNWfzn635Gu++Oo5Z+JH0Yt/9MHT0QfL/Zn4xhsdt+nBlkf3b9Ibg0epfvGyTkWyaeN7rsK0el0MBqN/UbxSJVjV61aNeD+UmXa2LoogUBAwSiJlBcWC/DLc1/Db8/fgm+OexELdj0bSVCIaGgJyx7ZM1wzHWfzKkkBgIaGBvh8vqinKU6nE06nMzLqRxAEGAyGqLL3Pp8PDocDwMWnKdLLZrNFhjYTaVG4YBh+2v532NaxHLairVg11gs28BANXWEFhiCHNZIe5FVzD3CxJorf74fdboder4cgCLDb7VGjdEKhEILBYKTvSiAQiBSAizfkuLW13/NbIk3oHTYCgeu+ieGdi3Dv+N/ihtF+tUMiIlJMgSiKfCacpkAg0G+eIMpd/fu4/EPMcnSz3wZ8KZPhDKpnRCF23fCPOD1Fj5UPH0Jp6We1Pv5+ZUwnlrnRi0s2vRm13Apd1PKRH8fsEPuj6dcHJ7ZfRWyvl+heMaK4Mmo53+b+kUNrP4uhNm9TpmTjfiGdY7b/KYw1zpF1rPOB93HA9A+q39/y7kkK0VDQPWoM/ma24azuMlzrfQKlvxlsgkMiGiqSHYI82DG0QBtREFHSOsdMwBs3fx/nxk/GF/74M0w+0ax2SESkIdIQZHmvxEmOx+OByWRCQUEBTCZTVP9OSSAQgNVqjUzwKw1MSRWfpCigoqICY8aMibuuuroa1dXqF8ShZMQOhYz+TAsK+v9DzLZz4yfhr5bvYdpthbh/xa9weclZAMV4LXbD38cOC30qamm3Y+Bhn4itWRWzPPgj/pVIBZsIPqO1n8XgpQG0Fa+a6urqUFdXF3ddR8dgQ7VzQ21tLbxeL2w2G5qbm1FbWwuLxQKv1xsZGSsIQr+mLYPBgGAwmFQV976YpChg69at7JNCGdemm4q/3rgGI7o78e8VDZg8IaR2SETUx0B/lEr9RbIhk3P37Nq1K6rMx+rVq2EymeB0OiNJis1mg9lsjrovSk9UUk1S2NxDlAOCk2fgjZv+CaM72vCll37KBIWIEsrUEGSfzwen0xn1ntFohNFojJT9CIVC8Pl8kRGzEqnYan19fUrXwiSFSONOTp2NN2/8HsaHPsYXXvoZCjuzN5sqEZHEbDYnnC5Gel+qKxa7nfRUJbbY6mDY3EP0qVTb3guu+2x5w58GLp92aczyJ3G36u+j6YsRuO5bmHz8AMpe+xVG9F4AADyO+6K2+9G/PRizZ+yQ4Ghy+xGwHwKRdvVgGIZ19ULsSn8m4wvtXUlvKwhCpMaY9ERFKp4ab9tUMEkh0qgPrroG73z+73D5kd1Y+uf/xbBwr9ohEVEOCGMEWh1P4uzG/8r4uTweD/R6faSvSXPzxdGGJSWxk1pdlGgC4ESYpBBpUPP867GnbAVmHvgLFr3p4Tw8RJSSCWttGH/vd9Lev3v3Ppxa/vVBt3M4HHC73ZFlg8EAAAgGg3G3T9RclAiTFCINEQHsX3oLDi6yYNZ7Psx9+wXOw0NEKQljGMKFY4HCsekfpGj8oJvY7XY0NDREJR7S/yd6YsIkhShLxNc/+/+Ng2QSax6KXt74cJzjoQDvfW4lDs/5Eub7n8NVe16JrFv/T9HbFrwc0wfFEXu0KTHL0WXqWcqcKH/1YhiGZbjibH19PSwWS7/yG9Ionti+J9JyqsOwObqHSAPCBcMQ+PLXcWT2F3H1X5+OSlCIiLREqh4r1UWRBAIB6HQ6GI3GfqN4pKq0q1atSulcfJJCpLLe4SPQeP13cPLyOTC9/j+4/Mg7aodERDksHB6O3rDMYm4J9vf5fHA4HLDZbFE1T/x+P0wmE4xGIxoaGmAymSAIQqR5x+l0wul0Jhz1kwiTFAWwLD7FWv+b6OX935gRvX77EQDAueGj8cM5d+FUwZW4ZseTuPSj/cmd4LWY5atilg+diFpkcw5R5mmlLH5v7zCgR2ZzT2//hpZAIBAp0iYNOe6rtbUVwMWaKH6/H3a7HXq9HoIgwG63p1xtFmCSogiWxad0nBkxDuvn3YOPR0/C55/9BUpOHlY7JCKSQStl8Xt7hgM98m7vvXGSHKPRCFFMbqSh0WiMGvWTLvZJIVLBJ6N0sC/8Pk6PKoZjz8+YoBARxcEnKURZdqK1BM6F/4DhYhjOPT/F5Z2n1A6JiPJIuHe47OaecK+8/ZXCJIUoE2Lq4OsQAgAcOTkVP9u2CpcuDWHj0icwefQZAMD6H0Vvv/H/iznerJjluTHL/YYgL4paGmzIMYckE+WP3t5hEGUnKdpoaGGSQpQl7x+bgf98/pu4THcKjoX/hQkjz6sdEhGRpjFJIcqCdw7Pxs9e/HtcNfVD/POtv8WE95igEFFm9PYMR/iCvCcpcp/EKIVJClGG7TxtxE9e+DqunnEA37vpdxg5ghMFElHmiOHhEHtl3t5l1llRCpMUIgWsL49errvxuwCA99+7HG8dmI3QnMvxUfk1eGnYNwEAPyv7fNT2X1wSc7zpMSe4PWb5rzHLE2OWWz+JWmQfFCLKRUxSiDJAFIGmxhl4+81SzL36KN75kgEFwzhVIBFlQU+Sxdz+5+fAr5+Iv64ze8XnBsIkRQGsOEt9iQACbxiw5+0rcfXnDmPxssN4t+B6laMiokzTSsVZJDsE+ev/9+IrnqYAsKJM2bjSwCRFAaw4S5IwCvCzqavxytvLgOuK8fLVZXgZwJu4Nmq7y4SYHZ+KWd4evTizdF/0G7HNPf0M/H1k8w6R8rRScTafMEkhUkh3wQj86PJv4M0JiwHLRGDuOLVDIqKhqLcA6JHZvNyrjeZpbVRrIcpxnQWj8MMr78Ku8Quw9sP/ZoJCROrpBdAj86WRQYh8kkIk09nOC3h4xhocKZyKDR+4sPj8IbVDIqKhTEpS5B5DA5ikEMkQPNeNh7Y1wX31arR+eR7cJT+4uOK66H9aU5tC0TtOil4s3vNx1PKZC5dFb/ByzIm/vjHmjUUxyydilteAiCjXMEkhStOJtk48+GwTLvSGEbx+AXqKx6odEhHRZ002co+hAeyTQpSGD06fR43nXRQAcFYsZoJCRNrRA+CCzBeTFKLcdODEWdi3vosJY0bCWbEYUyaMVjskIqK8xOYeohS882EIj76wDzMmjcVDt8/H+NEjL66Y8Wj0hhNj6pC0vhGzHF3H5Ezh2Zgz/SV60RHb5yTWe1FLrINCNISFIb/ja1iJQORjkkKUpL82n0btH/dj8RXFWHvLPIweqY0JuIiIonB0D/XFsvj5b8e+E/jpjoP4vGEyfnDjbIwczpZSIoqmmbL4eYRJigJYFj+/bdt9DE/+qQVfXTAF37v+KgzjRIFEFIdmyuLn0egeJilECYiiiP/31gf43VsfYqXxCvzDF2aioCDJBKU1uo7J4H1EZg24XFAw8PFi1xPREMbmHqL8Fg6LaPiTgOffPY5vfX4GrGVXqh0SEZGyttcBz8VvnkK3NpqnmKQQxejpDeOnOw7itQMnUX2DATctnKp2SEREyUv2Scot1Rdf8RwKAP+s/qzNeZmkBAIBOBwO6PV6hEIhWCwWVFZWJrVvKBSCw+EAADidzkyGSRrU3ROG86X9aDzSivtunIPrZl+S1nEGa94ZrHkmdv/Bjschx0QUweYe7RIEASaTCX6/P9KZ1WAwIBgMoqqqasB9fT4fXC4XPB7PoNtS/jnf3YNHnt+HAyfO4qHb5sE0o0TtkIiIUpdHSUrejaO02Wwwm81Ro23sdjtsNtug+5rNZrjd7kyGRxp1puMCHvx9E4ST7Xh4xQImKEREGpBXSUooFILP54PFYol6v6ysDABQX1+vRlikcafau7D2mXdxsr0LjpWLsODyYrVDIiJKXx7N3ZNXzT2NjY0AAL1eH/W+9FTF6/WyGYeiHAt14KFnmyAC2FyxGFfo4hflG0yqfULYh4SIMqYX8ptrNNLck1dJiiAIAACdTjfgeqW1t7ejra0t7f0LCwtRWFioYESUDOFkO9Zv34OiwhF45GsLMbmInwERxdfV1YWurq60929vb1cwmqEjr5KU5uZmAEBJSfz+BKFQKCPnXb58uaz9169fjw0bNigTDCVl70dt2PjcHlyuG4MNty9A8diRaodERBrmcDiwcWOOFE3Mo46zeZWkGAwGAEAwGIy7PrYZSCk7d+7EkiVL0t6fT1Gyy38kiE0v7sfsKUVYd9t8jB2VV/8MiCgD1q5di3vvvTft/Xfv3i37D9qkMUnRJikJSfTEJFNJSlFRESZMmJCRY5OyXj9wEj/2HoBpxkTU3DQHhSM4kzERDU5us3xRUZGC0QwdeZWkSKN4YvueSMtZm9yJNOmlpo/x89cO4frZl+D75bMwgjMZE1E+yqMnKXn1W1qn08FoNMLr9Ua97/P5AACrVq1SIyzSgK3+o6h79RBuWTQV/2KezQSFiPJXj0IvDci739QNDQ3w+XxRT1OcTiecTmdk1I8gCDAYDJHkpa9Mda4ldYiiiKfeaMFTfzmM1cuuhO06PYYNS3ImYyIiUlVeNfcAF2ui+P1+2O126PV6CIIAu90eVR8lFAohGAz2S0gCgQBcLhcAYMuWLbBYLDCbzQmHNJO2hcMifrGzGS81fYy7vlyKFUuuUDskIqLMy6PmnrxLUoCLicpA5e2NRiNaW1vjvu9yuSKJCuWuC71h/Nh7AH85dArfL58Fy/wpaodERJQdySYpf64D/lIXf92FDiUjSlteJik0tHVe6MXmP+zHO0dDsN88F18wTFY7JCKi7JHK4g/mc9UXX/EcCwD/pf5gEyYplFfau3rw8HN70HLqHNbfvgBLrtSpHRIREaWJSYoCKioqMGZM/DlfqqurUV2dIFMlRYXOd+OhbXtw8mwXfvi1RZhz2Xi1QyKiIaSurg51dfGbTzo6sth8wrl7qK+tW7dGJjEkdXzS1okHn21CZ08YmysWYcakcWqHRERDzEB/lAYCgezV6mLHWSLt+DB4Huu2NWHEsGGorViMy4pHqx0SEREpgEkK5bSDJ85i/fY9mDhuFB5ZsRAl40apHRIRkbr4JIVIfe8dPYNHnt+L6ZPGYv3t8zF+NGcyJiJikkKksr8Jp+F8aT8WXF6Mf7tlHsaM4kSBRET5hkkK5ZxX93+C//QdwLX6SfjBjXMwakTeze5ARJS+ZOukDHYMDWCSQjnl+Xc/gmunAPO8Kfinr1yF4ZyHh4goGocgE2WXKIp4eteH+N+/fYAVSy7Hd79UioICJihERP2wTwpR9oTDIv77jRZs2/0RvnntDFjLpjFBISIaApikkKb1hkX81ysH8cr+T3DPcgNuXTxV7ZCIiLSNT1KoL5bFz4zunjAe++N+vHW4FfdaZuP6OZeqHRIRUUKaKYvPjrPUF8viK6+juxc/fGEv9h1vw4O3zsOymSVqh0RENCDNlMVP1t66i694erKYVA2ASQppTlvnBWzYvgdHgx14eMVCLLyiWO2QiIhyR7Kje+ZUX3zFcyoAPKd+UsUkhTTldHsXHtq2B6GObmxauQhXXVqkdkiqKCjYGLUsiutVioSIcg77pBAp7/iZDqx7tgm9YRGbVy7GlSVj1Q6JiIhUxCSFNKHl1Dk8tK0JY0cNx6aVi3DpeM5kTESUFj5JIVLOvuNt2PjcHkyZMBob71gA3VjOZExElDaO7iFSxtsftOLRF/bhqkuLsO62+RhXyK8kwD4oREQAkxRS0V8OnULtH9/Hkit1eODmuRg9kjMZExHJFob85pqwEoHIxySFVOHdewI/e+UgvjRrMv7VPBsjhnMmYyIiRfRAfnMNm3vyByvOpub3bx/Ff//5MG5aeBnWLDdgGGcyJqI8oJmKs+w4S32x4mxyRFHEb988gi2NR7GqbBq+ce0MThRIRHkj5yrOpiEUCsHhcAAAnE5nv/WBQAAOhwN6vR6hUAgWiwWVlZVpn49JCmVFOCziideb8Yf3PsZ3vjgTK43T1A6JiCg/ZWh0j8/ng8vlgsfjQVVVVb/1giDAZDLB7/dH/nA3GAwIBoNxt08GOwJQxvX0hvEj7/v4Y9PH+L9fuYoJChFRJkkdZ+W84nScNZvNcLvdCU9rs9lgNpujWhbsdjtsNlval8IkhTKq80IvHn1xH944dBo1N83FjQsuUzskIiJSWCgUgs/ng8ViiXq/rKwMAFBfX5/WcdncQxlzrqsHjzy/F4c+acdDt8+HcfpEtUMiIsp/vQC6u4BwV/rH6GpPafPGxkYAgF6vj3pfeqri9XrTavJhkkIZETrfjfXb9+BEWyce+dpCzJs6Qe2QiIiGhh4ARxzA0Y2DbqoUQRAAADqdbsD1qWKSQor75Gwn1j3bhPPdvXCsXIzSyePUDomIaGi5Yi0w9d709z+3G9i7POnNm5ubAQAlJSVx14dCobTCYJJCijraeh7rnm3C8GEFcFYsxuW6+PVjiIgoQ3oA9BYCKEz/GGJRSpsbDAYAQDAYjLs+thkoWUxSSDGHPmnH+u1N0I0ZhY0rFmBykYx/IERElB4VyuJLSUiiJyZMUkhVTcfO4OHn92LaxDHYcMcCTBg9Uu2QiIgoS6RRPLF9T6TldAvZMUlRwFAvi7/rcBCOF/dh3tQJePDW+RgzihMFEtHQM5TL4ut0OhiNRni9XtTU1ETe9/l8AIBVq1alFQaTFAUM5bL4Ow+cxI+9B3DNzIm4/6tzMWoES+8Q0dCkmbL4GZxgcKAOsA0NDTCZTBAEIdK843Q64XQ6E476GQyTFErbi+8dxxM7m3HDnEvx/fJZGM6JAomI1JehsviBQAAulwsAsGXLFlgsFpjN5kgCYjQa4ff7YbfbodfrIQgC7HZ72iXxASYplAZRFOFuPIrfvHkEd1x9Ob77pVLOZExElOeMRiNcLlckUUm0zUCl81PFJIVSIooi/vuNw3j27WP4+uemY/WyKzmTMRGRlqgwuidT8jJJSXeqaKWnmM434bCI/3rlEHz7TqDqOj1uv/pytUMiIqJYKnSczZS8S1LSnSo6E1NM55PunjB+9PL7eFM4jXsts3HD3EvVDomIiPJc3g3FSHeq6ExMMZ0vOrp78cjze7HrcBD/dss8JihERFomPUmR89LIk5S8SlLSnSo6U1NM54OznRewblsT3v/4LDbcsQCf009SOyQiIhqINLpHzktuc5FC8qq5J92pouVOMd3e3o62tra04y4sLERhofZKyAfPdWPdtia0nuvGo3cuxKwp49UOiYhIFV1dXejq6kp7//b2dgWjGTryKklJd6pouVNML1+e/EyR8axfvx4bNmyQdQylfXymEw8+24SecBjOisW4smSs2iEREanG4XBg48aNaoeRnF4AyQy67KwDuuJXyIWYxQq5A8irJCXdqaLlTjG9c+dOLFmyJKkY49HaU5QPTp/Hg9uaMHrEMNRWLMalE0arHRIRkarWrl2Le++9N+39d+/eLfsP2qQl259kRPXFV9xjBIDzWaqQO4C8SlLSnSpa7hTTRUVFmDBhQrJhatr7H5/Fhu17cMn4Qjy8YgF0Y0epHRIRkerkNssXFRUpGM3QkVdJSrpTRWdqiulcs/vDEB59YS9KJ4/DQ7cvQFFhXn09iIiGhl4AosxjsJib8tKdKjpTU0znkr80n8Jjf3wfV0/T4YGb52L0SM5kTESUk3qQXJ+UgchNchSSV0OQ+04V3ddgU0Wnu1++8O09Aecf9uNa/ST8+63zmKAQEeWyXoVeGpBXSQpwcapon88X9VQkdqpoQRBgMBgiSUiy++WjbbuP4Sc7DuLGBZfh/hvnYOTwvPtKEBFRjsqr5h4guamiQ6EQgsFgVB+UTEwxrWWiKOJ///YBnt71ISqMV+DbX5jJiQKJiPKFRppr5Mq7JAUYfKpoo9GI1tbWlPfLF+GwiIY/CXj+3eP49hdmotI0Te2QiIiI+snLJIUS6+kN46c7DuK1AydRfYMBNy2cqnZIREREcTFJGUK6enpR+9L78B9pxX03zsF1sy9ROyQiIqKEmKQooKKiAmPGjIm7rrq6GtXVCSr6ZdH57h488vxeHDjRjnW3zYNpRvzqukRElJ66ujrU1cUvM9/RoY0y87mGSYoCtm7dGpmMUIvOnL+ADc/twUehDjyyYiHmX54f1XGJiLRkoD9KA4HAkKi5pTQmKXnuVHsX1j3bhPauHjhWLoL+EpZmJiLKbz0ALihwDPUxScljx0IdWPdsEwoAbK5YjCt08ZukiIgon/RAfpLBJIUySDjZjvXb96CocAQe+dpCTC7S1kzLRESkticA1CdY15nNQBJikpKH9nx0Bg8/txeX68Zgwx0LUDxmpNohERFR1iTb3PPdT1/x7AawXKmA0sYkJc/4jwSx6cX9mD2lCOtum4+xo/gRExENLb2Q31yjjcl7eAfLI68fOIkfeQ+gbMZE1Nw0B4UjOFEgEdHQw46zpDEvNR3Hz19rxvWzL8H3y2dhBCcKJCKiHMckJQ94/EfxP385jNsWT8XdX9Zj2DBOFEhENHTxSQr1oVbFWVEU8dRfDuOZwDH83TVX4v9cM50zGRMRqUQ7FWfZJ4X6UKPibDgs4uevHcIf95zAXV8uxYolV2T1/EREFI0VZ5XHJCUHXegN40cvH8Bfm0/hn8tnwTx/itohERGRZrC5h1TSeaEXjhf34d1jZ/DAzfPwecMktUMiIiJNYXMPqeBs5wU8/NxeHD59DutvX4AlV+rUDomIiChjmKTkiND5bqzbtgenznbh0TsXYfaU8WqHREREmsTmHsqiE22dWPdsE7p6wnBWLMb0SWPVDomIiDSLEwxSlnxw+jwe2t6EEcOGobZyMaZMGK12SERERFnBJEXDDp44i/Xb96Bk3Cg8vGIhSsaNUjskIiLSPDb3UIa9ezSEHz6/D9MnjcX62+dj/GjOZExERMlIdnTPVgC/T7CuS7lwZGCSokFvCqdR+9J+LLi8GP9+6zyMHsmJAomIKFnJPkm549NXPAcA2BSLKF1MUhSgZFn8V/d/gv/0HcC1hkn4gWUORo3gRIFERLlAO2Xx8weTFAUoVRb/uXc+Qv3rAizzp+CfbriKEwUSEeUQ7ZTFZzE3UpAoivjdrg/x//72Ae5cegW+88WZnCiQiIjSxI6zpJBwWMQv/9yC7e98hG9eOwPWsmlMUIiIiACww4MM3d3dUf9NVW9YxE92HMRz736ENdcbsGrZlZpKULq6urBhwwZ0dWmjl3cm8BpzX75fH8BrzAdy7xepkZp75Ly00dzDJEUGOV+67p4wHC/uw2sHTuIHN87BLYumKh2ebF1dXdi4cWPe/tIAeI35IN+vD+A15oPsJilSc4+cF5t7hqyO7l788IW92He8DQ/eOg/LZpaoHRIREZHmMEnJsrbOC9iwbQ+Ohjrw8IqFWHhFsdohERFRXmHHWUrDqfYurN+2B2c6LmDTnYtw1aVFaodERER5hxMMUoo+CnXgoW1N6A2L2FyxCNMmciZjIiKigTBJyYKWU+fw0LYmjB01HJtWLsKl4zmTMRERZQqbe6iPb33rWxg7Nv6TEevd/4L3xy3ClAmjsfGOBdCN5UzGRET5aKCy+OfPn89iJKw4S338+te/xnXXXdfv/cAHrdj0wj7MmjwOD946H+MK+eMmIspXA5XFf/3117F8+fIsRZI/T1JYJyVD3jh0Cg8/txeLphVjwx0LMK5wRMIMO55Utk11+1SPrYU4MvnzyORxtfLzSIVWPpdcvEZ+T+XFkQqtfC6ZvEYCIFLadu7cKQIQd+7cGfX+H5uOi3f815/Ex17aL17o6Y28P2/evKSPncq2mTr2mTNnRADimTNnVI0j1W1T2T7Va9TC55Lqtpm8Ri38PPg9zV4cmdw237+nie4XSvL7/SIAEbhfBH6axKtSBKYkeJWIAES/35+xeJORd+0PgUAADocDer0eoVAIFosFlZWVSe0bCoXgcDgAAE6nM63zPxM4il+9cRg3L7oM91xn4EzGRESUZck293z+01c8RwH8h2IRpSuvkhRBEGAymeD3+2E0GgEABoMBwWAQVVVVA+7r8/ngcrng8XgG3TYeURTxmzePwN14FKvKpuEb187Q1Dw8REREuSav+qTYbDaYzeZIggIAdrsdNptt0H3NZjPcbnda5xVFET9/rRnuxqP4xy/NxDc/P5MJChERqYQTDGpOKBSCz+eDxWKJer+srAwAUF9fn7Fz/y7wMV7e8zG+Xz4Ldy6dlrHzEBERDS5/JhjMmySlsbERAKDX66Pel56qeL3ejJ17z/GzsN80F5b5UzJ2DiIioqEmb/qkCIIAANDpdAOuV1JnZycA4PMTQuj5aC9e/2jg7c+dO4fXX3+93/ujRo3CqFHRRd46OjoQCASSjiWV7ZPdtr29HQCwe/duFBUlN89QJuJIddtUtk/1GrXwuaS6bSavUQs/D35PsxdHJrfV+ve0u7sb3d3d/bZN9Hs91jvvvAPgs/tGZn0M+U9CTikRiHyqji1SUE1NTcLhUgBEvV6f1HEAiFVVVUlt+9RTT3063Isvvvjiiy++Bn899dRTKd3bUnHkyBFx7NixisU6duxY8ciRIxmLNxl58yTFYDAAAILBYNz1sc1ASrj11lvx1FNPYerUqRg9Ov35eOI9SSEiIu1I9CQlWZ2dnTh+/DhuvfVWBaOKNn36dOzbtw+nTinzFGTy5MmYPn26IsdKl+aSlEAgALvdntS2er0eLpcr8v/AxQ60ibZV2uTJk/Htb39b8eMSERGlY/r06aonFkrSXJJiNBrT6uQqjeKJ7XsiLZtMJvnBERERUdbkzegenU4XN8Hx+XwAgFWrVqkRFhEREaUpb5IUAGhoaIDP54t6muJ0OuF0OiOjfgRBgMFgiCQvfSVqKiIiIm3IxEhN0i7NNffIYTQa4ff7YbfbodfrIQgC7HZ7VJn7UCiEYDDYLyEJBAKR/i1btmyBxWKB2WzuN6RZ7bmBlJLudci5/mzLl89qIOleo8fjgcPhQCAQgNFohNPphNlszkLEqVHi+qS+a1q8PkCZf1M+nw9WqxWtra0ZilIeOdcYW71b+j2vJUp8hoIgwOPxAACqqqoSltMYclQdW5RjmpubRSB6mLNerxddLteg+3q9XrGyslIEkh/inCnpXoec68+2fPmsBpLuNTqdTtFsNosulysydB+A6PV6Mx1yStK9PpfLJVZVVYler1f0er2i0WgUAYjNzc2ZDjllSv2b0uv1ok6nUzo8Rci5RumzdDqdkZfas/LGkvsZNjc3i5WVlaLZbNbkd1RtTFJSYDabRbPZHPWey+USU8n1tHDjS/c6lLj+bMmXz2og6V5jZWVl1LI0vXvssdSW7vU5nc6oZen63G634jHKpcT3tKamRjSbzZpNUuRco9a+k/HIuT6/3y/qdDpN/55RW171SckkNecGUlK615FL159LsaYr3Wv0+Xz9mq+MRiOMRqOm2vrlfIY1NTVRy9Jj874Tj2qBEt9Tn8+HSZMmae7aJHKu0ePxoLGxEVarVbP/ZuVcXygUQnl5eVQpDeqPSUqS1JwbSEnpXkcuXX8uxZqudK/RbDYnrBmUiVpC6VLyM/R4PHA6nZq6PkCZa3S5XP2SMi2Rc41erxehUAgejwc2mw0TJ06MO+BBTXKuz263IxQKabrPmxYwSUmSGnMDZUK615FL159LsaZL6WsUBAFWq1VuWIpR6vrsdnukQ6PWyL1Gu92u+RucnGt0uVwQRRF+vx9VVVWRDqla+vcr5/qkpyxerxcmkwkTJ07U3PVpAZOUJDU3NwMASkpK4q7PleHL6V5HLl1/LsWaLiWv0ePxQK/XR42CU5sS11dbWwtBEBAKhTTZZCDnGgOBACZNmqTJ5KsvJT5Ho9EIl8sFt9sNAElXJM+GdK9PmrzQaDTCZrPB7/fD7/dHSmTkw+8opTBJSZIacwNlQrrXkUvXn0uxpkvJa3Q4HJEbgFYocX01NTVwu93wer3Q6XSae+og5xodDoemm3kkSn5PKysrUVlZmdJsxpmW7vVJT0tsNltkm759U6TyB5RndVKSkUtzA2VCuteRS9efS7GmS6lrtNvtaGho0NzPRMnP0Gw2o6qqCrW1tUqEpph0r9Fut/drFpD+X/qvVj5Ppf8tWiwWTfVLSff6EjUPSbV82OTzmSGXpAz1uYHSvY5cuv5cijVdSlxjfX09LBaLJkeGKP0ZLlu2TDM3bkm61+jz+RImXAaDQVPFzjLxb1E6phbI/X0qNRfFStR8NBSxuSdJ+TI3ULrXkUvXn0uxpkvuNUqVLWOrsGrlUbrSn6EgCJqrOJvuNfr9fogXa1xFXjU1NdDpdJGOplqh9Ofo9Xphs9kUi08uOb9PzWZzv6dC0hOZfPhDSjFqFmnJNVJRqL5VAfV6fVTxqObmZlGv18et3tna2qqJAmHpXkcy+2lFvnxWA0n3GqUqrC6XK+pVVVWlqerB6Vxfa2urWFlZGVW4rbm5WbNFweR+TyU1NTWaLeaWzjX6/X7RaDRGbeN2uzX571Hu79O+7zmdTtFoNGYn8Bwx5Jp75MjG3EDZkO51JLOfVuTLZzWQdK4xEAhECk/F+4tUS3O/pHN9Op0OoVAId999N1wuFywWC/R6vWZr48j5nuaKdK5Rr9ejpKQEDocDXq8XRqMRFotFk0XPlPh96na7I99dLT0J04ICURRFtYMgIiIiisU+KURERKRJTFKIiIhIk5ikEBERkSYxSSEiIiJNYpJCREREmsQkhYiIiDSJSQoRERFpEpMUIiIi0iQmKURERH1wFmLtYJJCRETUh9VqzdlpCPINkxQiIkrIbrejoKAABQUFmDhxIiZOnJhwuaCgIOefQgQCAej1+shcXdJcPH2vuba2NrK9z+eDwWCIrIud2ZjkYZJCREQJhUIhmM1mtLa2Rl5msxkA0NDQgNbWVoiiGJkYL9efQLhcrqjJN/V6PZxOJyorKwEAZrMZNTU1kfVmsxlGoxFGozHqZ0PKYJJCREQDcrlcg84CbjQao27eucrn88VNNNauXRtZ35fH40EgEODsxRnCJIWIiBKyWCzQ6/VJbWuz2QZNZrTM4/EkfBIiPS0JhUKRRCUQCMBut8Pr9WYzzCGFSQoRESUkNXMkQ6/XQ6/Xw+PxwGKxwOfzob6+HhMnToTNZoPH44n0YQkEAgAuPpmwWq0oKCiA1WqNOl4gEIDVaoXFYoHBYIDdblf02mI9/fTTUU09saR1LpcLoVAIVqsVbrc76SSOUsckhYiIFOPxeGC32+Hz+eB2u9Hc3Ay9Xo/GxkZUVlZi1apVUdubzWY4nc5+x5GeUrjdbni9XjidTtTW1g6YRPTdT0qMJFarFRMnTkzYsTcUCkEQBBiNxoTHlmL3eDwoLy+H0+kccHuSj0kKEREpprKyMpIc6HQ6OJ1O+P3+SJ+NeM1BJSUl/d67++67o5KXyspK6HQ61NfXD9g512g0wul0oqysDPX19QAuPgGx2WxwOp0Jn3ps2bIFq1evHvDadDpd5MmSXq9P6SkTpWeE2gEQEVF+kRKRZcuWpbW/IAgIBAJwOBxx1zc2Ng46isZqtcLn88Fms8Futw/aJONyubBjx45BY5OOIzVXxWOz2WAwGHD69GksW7aMyYwMTFKIiCgj0u1EKyUAbrc77XNLSYxOpxs0QREEASUlJYPG6/F44PP5oNfrI4lUbHOP1WqFXq+PjHQymUzQ6XQcmpwmNvcQEZGmSP1G5BSGkxKTZI4RWxslHqmvy44dO6I60PYlCAI8Hk/UsVavXh23zw0lh0kKERFpipRgeDyeuOuTqepqt9thNBoHbJaReDyeAZtk+o7k0el0qKqqAoBInxeJdK6+T26MRiN8Pl/OF7lTC5MUIiJKSzAYTHmfSZMmAYh+wiH9v3Qjl5pG7HZ7vyQjNjGIRxoCbbPZIAjCgE9mEhVv66u8vBwulyvStNO3A23fRGrXrl39moykTsHp/KyISQoREaUoNqmIJb0fb710o5eGKdfX10eaTXw+HywWC3Q6XVSfDqvVitraWlgsFjQ3N8dNKgRBQG1tLTweD4LBIMxmc2Q7l8uF2trauKOIBmvqkYrZxZ7TYrEAQFTn3lAoFPccUnyUBpGIiCgJXq9XrKqqEgGIAESdTifW1NSIzc3NkW3cbreo1+tFAKJerxddLle/4zidTlGn00X2F0VR1Ov1Yk1Njej3+6O2G+xYfc/b93iSyspKUafTRR23L71en/Ba+57b7XZHnctoNEZ+DpWVlaLX641cV19+v18EEPUzouQViKIoqpciERERqUOquaLUnEMejwdWqxV9b6vS0yHeatPDIchERDQkJVsbJVlSU5YgCFH1VFiVNn3sk0JERENOsrVRUiFVoe3bmfbpp5/mEGQZ2NxDRERDjt1uz1g12L4VZw0GQ2TIMqWOSQoREQ05Ut0T0jYmKURERKRJ7JNCREREmsQkhYiIiDSJSQoRERFpEpMUIiIi0iQmKURERKRJTFKIiIhIk5ikEBERkSYxSSEiIiJN+v8BswLLQ94MWoYAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -223,26 +229,24 @@
     "    y = found[\"ideal_state_9410_y\"]\n",
     "    qop = found[\"ideal_state_9410_qop\"]\n",
     "\n",
-    "data = ak.zip(\n",
-    "    {\n",
-    "        \"rad_length_frac\": rad_length_frac,\n",
-    "        \"x\": x,\n",
-    "        \"y\": y,\n",
-    "        \"tx\": slopex,\n",
-    "        \"ty\": slopey,\n",
-    "        \"qop\": qop,\n",
-    "    }\n",
-    ")\n",
+    "data = ak.zip({\n",
+    "    \"rad_length_frac\": rad_length_frac,\n",
+    "    \"x\": x,\n",
+    "    \"y\": y,\n",
+    "    \"tx\": slopex,\n",
+    "    \"ty\": slopey,\n",
+    "    \"qop\": qop,\n",
+    "})\n",
     "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n",
     "    data,\n",
     "    \"rad_length_frac\",\n",
     "    [\"x\", \"y\", \"tx\", \"ty\", \"qop\"],\n",
-    "    4,\n",
+    "    5,\n",
     "    include_bias=True,\n",
     ")\n",
     "\n",
     "nbins = 100\n",
-    "vmax = 50\n",
+    "vmax = 100\n",
     "\n",
     "a0 = plt.hist2d(\n",
     "    xx0_test,\n",
@@ -252,13 +256,12 @@
     "    cmap=plt.cm.jet,\n",
     "    cmin=1,\n",
     "    vmax=vmax,\n",
-    "    range=[[-0.1, 0.6], [-0.1, 0.6]],\n",
+    "    range=[[0, 0.5], [0, 0.5]],\n",
     ")\n",
-    "plt.plot([-0.1, 0.6], [-0.1, 0.6], marker=\"\", alpha=0.8)\n",
+    "plt.plot([0, 0.5], [0, 0.5], marker=\"\", alpha=0.8)\n",
     "plt.xlabel(f\"True $x/X_0$\")\n",
     "plt.ylabel(f\"Predicted $x/X_0$\")\n",
     "plt.title(f\"found rad_length_frac\")\n",
-    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
     "\n",
     "plt.colorbar(a0[3])\n",
     "plt.show()"
@@ -266,12 +269,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAHLCAYAAAAnR/mlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAx5klEQVR4nO3dTYgjeWLn/V+u/XROeZLqqKy5zMKCOwQ+F6Hsi/0wNVARDMxcFkqqunkMQ0kMeVjoxRlOXyrrYhGJrwkTSgyN2Uulonaf52CDLbVxDXjg2UqFy5eHZ5dVlMGHWXimldFF4prstUd7qImoVOollXpJhTK+H0i6S1JE/BWhVPzy/7rW6/V6AgAAyIl/s+wCAAAAXCfCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyBXCD3ItDEPV6/VlFyP34jhWvV5XFEXLLgquiN8hrCLCD3IpiiKVy2UVi0X5vt/3+J07d+b6Zb6IfU4qDEO5rqtisahisXjtx59EvV7XJ598omq1qjiOZ9pX8n7L5bIKhYL29/fnU8gpLfPaL9qw36FWq6VCoaC1tTWtra2pWCwqCIKBbYMgULFYTF8ThuHYY63C5xirhfCDXDJNU41GY+DxOI4Vx7E6nc5U+42iaOAGPus+Z2FZlh4/fnzpzeU6XTxHlUpFlUpl5v2GYagHDx7I8zw1Gg2VSqVrPedZu/aLNux3yLZttdvt9N/ValWlUmlg21KppGq1KsMw1G63ZVnW2GNl8XOM1Ub4Ac6xLEu9Xk+e5021fblcVrfbnes+Z3XZjeW6DTtHd+/enXm/tVpNm5ub6b89z+ur1Vu0LF77ZTAMQzs7O5I09A+MRLvd1u7u7sT7zdrnGKuN8APMSblc5i/TSyzyHC3z3HPt+yWhptVqjTwvR0dHc6nxA6ZB+MFKCIJAjuOo1WqpXq/rzp07qlar6fP7+/uqVqtpv4BhfSziOFa1Wk1/hvUHCYJA5XJZ5XJ54LlxxwiCIP2Sr1arfTfDcftMyuS6rhzHSd/j+efr9Xrad6LVaqV9JYbt76rCMFS5XJbjOCoUCnJdd6ZjJ+e2UCgMBIJx5yjR7XbTY076Huv1usrlsqIoSvuhlMvl9DzO+7OTXKfz1zfL136e13iS3yHpfe1P0txVq9UGng+CQLZtyzCMicp5mcvOJTCgB2Rco9HomabZk9SrVCq9nZ2dnmVZPcuyer1er7ezs9M7/1FuNps9Sb1ms5k+1ul0eoZh9D3meV5PUrqfTqeTPmbbdl8ZJjlG8ppOp9N33FH7bLfbPcMweu12O33M9/2epJ7neen2pVIp3X5nZ6fXbrd7lUql73WXOf8+zx//fJkajUZ6jqc5tmVZvZ2dnXRbST1JPdM0030OO0e9Xq/vHHmeN9V7NE2zZ5pm32Pz+uyYptlXZsMweoZhpP/O6rWf5zWe5HfovPOfgZOTk77nLMvq289l5UyM+hxfdi6Biwg/WAnJl2xycz3Ptu2+G9HJycnAa0ul0sANqNcb/mU67GY1yTFG3dhH7dOyrKFlsiyrbz/JjeDiF/mwfY4y7H1altV3w+j13t/Uz9+sJj128rrz+0tuoOfPx2Xhx/f9qd/jsPBzft/TfnYsyxp4/8k+k/OU1Ws/z2t8ld+hhG3bA+czCVFXLeeoY016LoHzaPbCSkiqxz/99NOB5xqNRt8Ik+PjY0lKR95EUZQ2fUzrsmNcVRRFCsNwaCfOpEnmYmfd800EiYsdbK96/FqtljbLnG/mSN7fpMd+9erVwPPJ/q7SF2Zra2vguLPO/TPrZycMQ9m23bfdzs6Oer3e0PNymeu69vO8xtP+DiVNV+ebEj3P6+vofNVyDnuPVzmXgCT95rILAFzFsC9owzBkGIaCINDz588HbnLJzdM0zZmOO+4YVzUuECQBYJET/iXHHzca5yqS89FqtQZuRFkZpTPNZyc5T9OEnFGu69rP8xpP+ztk27ZM01QURarX66pUKjo6OtKbN2/mUs5l/x5hdVHzg5UXRZGKxaKiKFKj0UiH2Z5/Xpq+lmSSY0xrWM1RcqM9P2x73pJzMq8bQ6lUkm3bqtVqarVaiuNYnudpZ2dnptC5aJN+dhZxA130tZ9n2Wf5HUpqfzzPUxAE2tra6guT8yjnsn6PsLoIP1h5juNoc3NzZCBJbr7nmzfmfYyrSmpDho1ISb7IC4XCXI41THJOhs2+O6pcl2k0GrJtO13uwPO8zM9vc9l1Ta7TqFqJaW7Y13Xt53mNZ/kdqlQqafPlkydP+kbazVrOZf8eYXURfrDSkuHN5/+STL70kr9Sk+rver0+9C/Ey/rtTHKMq+xPev+Fb1lWuu/zjo+PZRjGQudASfqwuK470HQw7VIM5XI5rT3Z2dkZ29x18Rx9+eWXkmarnet2u1fa/qqfnYs3WNd1B2oVsnTt53mNZ/0dOt/H5+KMz7OUc9m/R1hdhB+shOTL9eKXbHLzCYJA9Xpd9Xo9rWYPwzD9azL5y75YLKrVaimKovR1URSl85UMCzWTHCOO4/QvTN/30w6io/Ypva9NMAyj7y/hpLno8PAwvSnPEghGOT8Lb7FYVLlc1v7+vhzHUafTSW9Ikx47CQfJfur1et/8N4lR52iUOI5nfv+zfnaS2ivHcVQul9P5gAqFQnqNsnjt53mNL+5r3O/QMEkAGRZEJi3nKJOeS6DPsoebAZc5P1eLaZoDw6F93+8ZhtEzTTMdUlupVHqGYfQNsfV9P92PZVnp/C07Ozu9TqfTa7fb6Zwn+vXQ32SY7aTHsCyrZxhGOj/JuH32eu+HVidDiCuVSq9SqfQN+W232+mQXdM0e81ms3dycpIOI9eQIcrnnZ+zZdjxPc8beW6vcuzkXCaPn/8xTbPvmBfPUaPRSIc1J0OeLx5n2DD1Ue+xUqmkc8jM67PTaDTSc3FxjppR72vZ1z4xr2ucnK9xv0PjVCqVsa+5rJzjPseXnUvgorVer9dbXLQCkAdhGOr58+fa3d1Vt9vtq7FpNBoqFApz6y8FALNiqDuAmSQjpk5OTtKh4+eZpslSAwAyhT4/AGaSdDR98uRJXx+fZG4X3/fpdAogU2j2AjCz/f191Wq1vk7FlmXJ87xLO6wCwHUj/ACYm6SvT5YnNgQAwg8AAMgV+vwAAIBcycVor1/84hf6q7/6K/32b/+2bt26teziAACACbx7907/+I//qO9973v61re+Nbf95iL8/MVf/IX+4A/+YNnFAAAAU/j888/1wx/+cG77y0X4+fa3vy1JOjw8HLve0CwePnyoFy9eLGTfN+kYp6enun//vl6+fKmNjY2FHWfR74NrMbmbcK64Fvk5BtciW8cIw1BPnjxJ7+PzsrDw8+Mf/1hbW1v60Y9+tKhDTOwb3/iGJOl3fud3FhZ+bt26tbB936RjvH37VpJ079493b59e2HHWfT74FpM7iacK65Ffo7BtcjWMU5PTyV9uI/Py0I6PL9580a+7zOdPQAAyJyFhJ9PPvlEvu/r6OjoytsGQaBisai1tTUVCoWh0+KHYZiurlytVi9dGRoAACCxsGavJ0+eXHmber2udrstz/MkSa7rynEcdTqddNK0ZB2hdrudVrUVCgV1u12m0AcAAJda2Dw/3/ve9/T69esrbRPHsXzfl23bsm1bh4eHktS3XlC1WpVt231tjEkNEAAAwGWmrvl5/PjxyOfiOFar1dLR0ZHu3bs38T4v9hFKVodOgk6y36RmKLG1tSXpfc0RtT8AAGCcqcNPo9GY6DV/8id/Mu0hFASBPM9Lm7yOj48laWDdoCQcNZvNpYWf7e1tjpEhi34fXIvJ3YRzxbXI3zEW7aacp1W9FlOv7fXo0SN5nqfNzc2B5zqdjur1un7yk59MXTDXdVWv13V4eKhSqSTpfc1OtVpVs9kcWCl6bW1NlmWp3W4P7OunP/2p7t+/r7/8y7/U7/3e701dpvX1da2vr0+9Pd4PI/3444/11VdfLXQYKS7HtcgOrkV2cC3m6+zsTGdnZ1Nv/3d/93f6/ve/r5cvX+o73/nO3Mo1dc1PtVrVJ598MvQ5y7JULBb1x3/8x1PV/Ozv7yuKIsVxrHK5LN/3ValU1Ol0JGlo4JLeN4uN8/3vf//KZTnv6dOn2tvbm2kfAADkRa1W07Nnz5ZdjAFTh58HDx6Mfd40Tf3RH/3RVOEn6fvTarVULpfleZ4qlYoKhYIkqdvtjjzmOPOo+QEAAJPZ3d3VZ599NvX2Sc3PvE0dfsaN5IqiSK7rTrvrlG3bqlQq2t/fl/Qh3Iyq4bks/Hzzm9+kGhMAgGsya3eRb37zm3MszQdThx/LsrS2tjby+V6vl4aWWXz66adpqElGdUVR1Pea5N/FYnHm4wEAgJtt6vBjGIYePXqUDkc/7+7du7Is69KmsUlEUZR2bjYMQ5Zlqdls9g2LT2aBfvTo0czHAwAAN9vU4efw8FAPHz6cW0HiONaTJ0/0+PHjdHRXFEVqNptqNpt9xy0Wi4qiKK0R8jxPnucNDWIAAADnTR1+5hl8pPe1OkkA8n1fjuPINM2+4CMpHc7uuq5M00z7FzG5IQAAmMTC1vb68Y9/rK2tLf3oRz+aeJuLQWcUy7ImmmQx8dFHH/X9F4sxbBaAi4+tr6/r6dOnjJzLAK5FdnAtsoNrkS2Lun9PPcnhOG/evFGhUNCdO3f05Zdfznv3VxaG4cBiqJi/ScIPAACTWtT9eyE1P5988ol837906DkAAMB1W1iz16NHj/Txxx8vavcAAABT+TeL2vEXX3yh3d3dRe0eAABgKjPV/PzN3/yNms3mwIzL3W5XYRiq2+2qVqvNcoi5evjwoW7dujX0ue3t7ZVdnRYAgFV1cHCgg4ODoc+9e/duIcecaZ6farU69jVZG37+4sULOjwDAJAh4yofkg7P8zZ1s5fv+2o2mzo5OdFf//Vfy/M8/epXv9KvfvUrdbtdVSoV/eQnP5lnWQEAAGY2dfixbVsPHjzQxx9/LNu2dXx8nD5nGIaKxSJ9fgAAQOZMHX6++uqrvn8/evRIf/qnf9r3WBAE0+4eAABgIabu82Oapn7jN35Dd+7c0fHxsR4+fKitrS01m00ZhqEgCFhrCwAAZM7U4ecP//AP9Ytf/EJ///d/r83NTUnS0dGRHMfRmzdvJL1fcBQAACBLZhrqfjHcmKapTqejN2/eaHNzk0kOAWDV/Gxv2SWYzO/uLbsEWGELW94CAAAgixY2wzMAAFkXRdHARL2rsG/MhvADAMitcrmsbre7cvvGbAg/AIBcKpfLCsNw5faN2S1sVfcsYm0vAID0fh66JJxUq1UZhqHd3d10CaQwDFWr1RTHsaIoUqlU6hvkE8exXNeVYRiK41itVkuu66pSqVy6b/RbqbW9VhFrewEAJKlUKunVq1fa39+X7/syTTN9LgxDua6rZrMp6X1QKpfLiuNYvu9Lkp48eSLTNNNAVK/X0/494/aNQSu1thcAADfRkydP+mp5SqWSDMPoCzitVqtvm6wt5I3xclXzAwDAOFEUpU1ewxwfH8u2bZmmqf39fd29e1c7OzuSlP4X2Uf4AQDg15K+Oo1GY+zrGo2GisWiXNeV7/tqNBp0q1ghNHsBAPBrURT1/XcU0zT15s0b2batKIpULBZVr9evo4iYA8IPAAC/lnRODoJg6PNJX58oimQYhprNZlpLVK1Wr6eQmBnhBwCQa+dnYbZtW5Lkuu7APD3na3YudohORoFdrDFihudsIvxgofb2Bn8AIAsKhYIkyfd9RVGkIAhkGEbacblYLKpcLmt/f1+O46jT6aTh6OjoqC/oxHEs0zTTmqNh+0Z20OEZAPBBjlZLr1Qq8n1fR0dHkpTW3niep7t378r3/XTCwmQCw8TW1pYcx1GpVJL0vsan3W5fum9kA+EHAJBb5wPLeTs7O2OHricTIE6zbywfzV4AACBXclXzw9peAABkC2t7LRhrewEAkC2s7QUAALBghB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJAruVregrW9AGC8vb1ll2Ayq1LOccIw1PHxsSqVyrKLMtR1lY+1vRaMtb0AAMsWRZFc11UQBLIsKw0XURSpWCzK87ylBqJR5VsU1vYCAOCGM01TjUZj4PE4jhXHsTqdzlT7jaJIcRzPWLrR5btJclXzAwBAVlmWpV6vN/X25XJZjUZDhmHMr1A3FDU/AACsuHK5rDAMl12MlXFjan6iKJJpmssuBgBgBcRxrKOjI/m+L8/zFEWRfN9XFEWybVuHh4dpDUoQBPJ9X67rpv1hHj16JN/3Jb3vl1Kr1RTHsaIoUqlUkud5A8dzXTf9d6FQGChTEAR6/vy5JA00O53fPooiSZLnebIsS0EQpMGnWq3KMAzt7u6mfVznVb6bJHPhJwgC1Wo1hWEoy7LkeZ5s2x543draWt+/LctSu92+rmICAFaY67qq1+uSPoSI3d1dPX/+PA0TnU5HQRCkocc0TRmGIdM0dXx8LOl9sHBdV81mU9L7e1i5XFYcx2k4SjoyNxqN9H62v7/fV54oihRFkYIgGLjnRVEkx3HUbDbTP/Lv3LmjBw8e6OTkRKVSSa9evdL+/r583++rCJhX+W6aTDV7JReuWq1qZ2dHYRjKcRy1Wq2+19XrdVUqFXmel/4cHh4uqdQAgFWT1PhI75uMPM9TqVRKA0ASREqlkqrVqiTJMAx5nqd2u53+sf3kyZO+WpRSqSTDMFSv19POx67ramtrqy/U7Ozs9JXHNM2BxxLlclnVarUv1Ozu7qYdpMeZV/lumkzV/Lx69SpNp5L0+PHjdNjf+YvSaDT6XgcAwLQudpmoVqtqtVpqNptpWJCkTz/9tO91URSlTUrDHB8fyzRNBUEw0Mw0qeQYF//A39nZuTSgXEf5VlVmwk+r1Ro4+ZZlybKstH1Tel9ld3x8rHK5LMdxMjs5FABgNSV9Zc7feyQNjKJK+tmMGxaetFxM2yc1OcY0I7iuo3yrKjPNXrZtjzz55x9vNpuK41hBEKharerOnTsDzWIAAExrc3NT0uWBIAlHF0PSsNd0u92pyjLJMWbZdtbyrarMhJ9RoihSuVxO/+37vnq9ntrttiqViuI4luM4E30wTk9P9fbt26l/zs7OFvlWAQAZkASBy2YWTsJREARDn2+1Wulrph2Qk9RCjaq9GXfvu47yXebs7Gym++7p6elCypXp8BMEgUzTHNq0ZVmWfN9PPxDnh+iNcv/+fX388cdT/4xqNwUA3BxBEMgwjEu7VSR9UV3XHZhjJxlJtrW1lf57WOfkyzosn9/+YiuH67ppLdWw/V1H+S5Tq9Vmuu/ev39/puOPkunwU6vVLp1iu1QqqVQqTTS508uXL/XVV19N/bO7uzuvtwYAyIhkyLekdAj4+Q7GSQC4GAQMw0g7HReLRZXLZe3v78txHHU6Hdm2PfCaVquVzhUkva+5SYaVJ/s/3wSVjDCTJMdxVC6X5bquisWiCoVC2hcomZcnmasoCXDzLN80dnd3Z7rvvnz5cupjj5OZDs8Xua6rw8PDiTphDRsOP8zGxoZu3749j+IBAG4I0zRVLBbT+43v+2mtSTLBofT+vtTtdvtqhDzP0927d+X7fjo/kOu6A68pFAryPE+O48iyLDUajXQoffIHfNK6EIah9vf3ValU0nBimqZqtVq62OjFUdCVSkW+7+vo6Ch9D/Ms37TW19e1vr4+9fYbGxtTbzvOWm+WhUQWpF6vyzTNoZMbjnr9uOHvyaqw7XabVd0XaG9vvq8DgEXa399PJwCc9H6D67Wo+3fmmr2SjlkXP4jjmrWazWY6CRUAAMA4mWr2arVaqtVqqlaraWcs6X0v9KTX/ZMnT/T48eO0jTIIAm1ubs5ULQcAAPIjM+EnWcpC0tBanJOTE0nv51+o1WpqNpuyLEuO4/R1VgMA4DJxHKddJc6vaYV8yEz4sSxLk3Q/YlkLAMCs6vW6HMdJ/+je39+/8etZ4YPMhB8AAK4LQSffMtfhGQAAYJEIPwAAIFcIPwAAIFdy1efn4cOHunXr1tDntre3tb29fc0lAgAg3w4ODnRwcDD0uXfv3i3kmLkKPy9evGCGZwAAMmRc5UMyw/O80ewFAAByhfADAAByhfADAAByhfADAAByhfADAAByhfADAAByhfADAAByhfADAAByhfADAAByhfADAAByJVfLW7C2FwAA2cLaXgvG2l4AAGQLa3sBAAAsGOEHAADkCuEHAADkCuEHAADkCuEHAADkCuEHAADkCuEHAADkCuEHAADkCuEHAADkCuEHAADkSq6Wt2BtLwAAsoW1vRaMtb0AAMgW1vYCAABYMMIPAADIFcIPAADIFcIPAADIFcIPAADIFcIPAADIFcIPAADIFcIPAADIFcIPAADIFcIPAADIlVwtb8HaXgAAZAtrey0Ya3sBAJAtrO0FAACwYIQfAACQK4QfAACQK4QfAACQK5kLP0EQqFgsam1tTcViUa1Wa+A1YRiqXC7LdV1Vq1UFQbCEkgIAgFWUqdFe+/v7ajabqlar6nQ62t/fl+M4ajabsm1bkhRFkYrFotrtdjpyq1AoqNvtqlKpLLP4AABgBWSq5ufVq1dqNpuqVCryPE/tdluS5Hle+ppqtSrbtvuGrCc1QAAAAJfJTPhptVp9IUeSLMuSZVmKokiSFMexWq2WHMfpe93W1pYkqV6vX09hAQDAyspM+LFtW6ZpDn0uefz4+Ljv34mkFqjZbC6whAAA4CbIVJ+fYaIoSpu0khogwzBGvnac09NTvX37duqyrK+va319fertAQDIk7OzM52dnU29/enp6RxL80Gmw08QBDJNM+3I3Ol0JEmbm5tDXx/H8dj93b9/f6byPH36VHt7ezPtAwCAvKjVanr27NmyizEg0+GnVqup0Wik/y4UCpKkbrc79PWjms0SL1++1L1796YuD7U+AABMbnd3V5999tnU279+/XrmiothMht+XNfV4eFhX6BJ/n9UDc9l4WdjY0O3b9+eWxkBAMBos3YX2djYmGNpPshMh+fz6vW6HMcZWIE9GdV1sW9P8u9FrPwKAABulsyFn2S25mRSw0QYhjIMQ5ZlDYzqSmaBfvTo0fUUEgAArKxMNXu1Wi3VajVVq9W+OXva7baKxaIsy9Lh4aGKxaKiKEqbuTzPk+d5I0eBAQAAJDITfsIwTCcvHDZb88nJiaT3c/q02225rivTNBVFkVzXZWkLAAAwkcyEH8uy1Ov1Jn7t+VFgAAAAk8pcnx8AAIBFIvwAAIBcIfwAAIBcIfwAAIBcyUyH5+vw8OFD3bp1a+hz29vb2t7evuYSAQCQbwcHBzo4OBj63Lt37xZyzFyFnxcvXgzMGg0AAJZnXOVDGIYLWb2BZi8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJArhB8AAJAruVregrW9AADIFtb2WjDW9gIAIFtY2wsAAGDBCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXcrW8BWt7AQCQLazttWCs7QUAQLawthcAAMCCEX4AAECuEH4AAECuEH4AAECuEH4AAECuEH4AAECuEH4AAECuEH4AAECuEH4AAECu5GqGZ5a3AAAgW1jeYsFY3gIAgGxheQsAAIAFI/wAAIBcIfwAAIBcIfwAAIBcWfnwE0XRsosAAABWSKbCTxzHcl1XruuOfM3a2lrfT7lcvsYSAgCAVZeZoe6tVku+7ysIAlUqlaGvqdfrqlQqKhQK6WO2bV9XEQEAwA2QmfBj27Zs29ba2trI1zQaDTWbzWssFQAAuGkyE34uEwSBjo+PVS6X5TjOyNohXI+9vWWXAACA6axM+Gk2m4rjWEEQKAgCua6rRqNxpWav09NTvX37duoyrK+va319fertAQDIk7OzM52dnU29/enp6RxL88HKhB/f9+X7vsIwlO/7qtfrchxHnU5HpmlOtI/79+/PVIanT59qjyoPAAAmUqvV9OzZs2UXY8DKhJ+EZVnyfV+O46hcLqc1QJN4+fKl7t27N/WxqfUBAGByu7u7+uyzz6be/vXr1zNXXAyzcuEnUSqVVCqVFIbhxNtsbGzo9u3bCyzVDfWzvSEPDnsMAIAPZu0usrGxMcfSfJCpeX6uynEcdbvdZRcDAACskJUOP5K0tbW17CIAAIAVstLhp9lsqlqtLrsYAABghWQq/MRxPPTxMAxVLBa1v7+fPhYEgTY3N1Uqla6pdAAA4CbITPgJwzBd0+vo6EhBEKRhyDRNbW5uqlaryXEcua4rwzDk+/4SSwwAAFZRZkZ7JUPYhwUawzBY1gIAAMxFZmp+AAAArgPhBwAA5ArhBwAA5Epm+vxch4cPH+rWrVtDn9ve3tb29vY1lwgAgHw7ODjQwcHB0OfevXu3kGPmKvy8ePFClmUtuxgAAODXxlU+JFPdzBvNXgAAIFcIPwAAIFcIPwAAIFcIPwAAIFcIPwAAIFcIPwAAIFcIPwAAIFcIPwAAIFcIPwAAIFdyNcMzJvCzvWWXAACAhcpV+GFtLwAAsoW1vRaMtb0AAMgW1vYCAABYMMIPAADIFcIPAADIlVz1+cm1YaO4fnfIYwAA3HDU/AAAgFwh/AAAgFwh/AAAgFwh/AAAgFwh/AAAgFwh/AAAgFzJ1VB31vYCACBbWNtrwVjbCwCAbFnG2l65Cj+4YNjEhwAA3HD0+QEAALlC+AEAALlC+AEAALlC+AEAALlC+AEAALnCaC9M55/+dvCxf/fday4EAABXR80PAADIFcIPAADIFcIPAADIlVz1+WFtLwAAsoW1vRaMtb0AAMiWZaztRbMXAADIFcIPAADIFcIPAADIFcIPAADIFcIPAADIlUyN9orjWLVaTZLked7A82EYqlaryTRNxXEsx3FUKpWuu5gAAGCFZSb8tFot+b6vIAhUqVQGno+iSMViUe12Ox2uXigU1O12h74eAABgmMw0e9m2rUajMfL5arUq27b75ulxXVfVavU6igcAAG6IzISfceI4VqvVkuM4fY9vbW1Jkur1+jKKBQAAVlBmmr3GOT4+liSZptn3eFIL1Gw2J2r6Oj091du3b6cux/r6utbX16feHgCAPDk7O9PZ2dnU25+ens6xNB+sRPiJokiSZBjG2Ocvc//+/ZnK8fTpU+3t7c20DwAA8qJWq+nZs2fLLsaAlQg/nU5HkrS5uTn0+TiOJ9rPy5cvde/evanLQa0PAACT293d1WeffTb19q9fv5654mKYlQg/hUJBktTtdoc+f7E5bJSNjQ3dvn17buUCAACjzdpdZGNjY46l+WAlOjwn4WZUDc+k4QcAAGAlwk8yquti357k34tY7h4AANxMKxF+DMOQZVlqNpt9j7daLUnSo0ePllEsAACwgjIVfsZ1XD48PFSr1eqr/fE8T57njRwFBgAAcFFmOjyHYSjf9yVJR0dHchxHtm2nwcayLLXbbbmuK9M0FUWRXNdlaQsAAHAlmQk/lmXJ9/00AI16zbglMAAAAC6TqWYvAACARSP8AACAXCH8AACAXMlMn5/r8PDhQ926dWvoc9vb29re3r7mEgEAkG8HBwc6ODgY+ty7d+8WcsxchZ8XL16kK8EDAIDlG1f5EIbhQiYyptkLAADkCuEHAADkCuEHAADkSq76/GA6e3/23WUXAQCAuaHmBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5EquhrqzthcAANnC2l4LxtpeAABkC2t7AQAALBjhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5ArhBwAA5EqulrdgbS8AALKFtb0WjLW9AADIFtb2AgAAWDDCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyBXCDwAAyJVczfODy+392XeXXQQAABaKmh8AAJArhB8AAJAruWr2Ym0vAACyhbW9Foy1vQAAyBbW9gIAAFgwwg8AAMgVwg8AAMiVXPX5QT/m9AEA5NGNqfmJomjZRQAAACtgZcPP2tpa30+5XF52kQAAwApYyWaver2uSqWiQqGQPmbb9hJLBAAAVsVKhp9Go6Fms7nsYgAAgBW0cs1eQRDo+PhY5XJZ9Xp92cUBAAArZuVqfprNpuI4VhAECoJAruuq0WhM1Ox1enqqt2/fTn3s9fV1ra+vT709AAB5cnZ2prOzs6m3Pz09nWNpPli58OP7vnzfVxiG8n1f9XpdjuOo0+nINM2x296/f3+mYz99+lR7e3sz7QMAgLyo1Wp69uzZsosxYOXCT8KyLPm+L8dxVC6X0xqgcV6+fKl79+5NfUxqfS7xT387+Ni/++41FwIAkBW7u7v67LPPpt7+9evXM1dcDLOy4SdRKpVUKpUUhuGlr93Y2NDt27evoVQAAGDW7iIbGxtzLM0HK9fheRjHcdTtdpddDAAAsAJuRPiRpK2trWUXAQAArICVb/aS3o8Aq1aryy5Gdvxsb9klAAAgs1aq5icMQxWLRe3v76ePBUGgzc1NlUqlJZYMAACsipWq+TFNU5ubm6rVamo2m7IsS47jyPf9ZRct81jBHQCA91Yq/BiGwbIWAABgJivV7AUAADArwg8AAMgVwg8AAMiVlerzM6uHDx/q1q1bQ5/b3t7W9vb2NZcIAIB8Ozg40MHBwdDn3r17t5Bj5ir8vHjxQpZlLbsYAADg18ZVPiRT3MwbzV4AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXCD8AACBXcrW8BWt7AQCQLazttWCs7XUD/Gxv8LHfHfIYAGAlsLYXAADAghF+AABArhB+AABAruSqzw+W4J/+dsiD373eMgAAcA41PwAAIFcIPwAAIFdo9kJ2DRvWDgDAjAg/WcV8NgAALATNXgAAIFeo+UE2zNLERS0ZAOAKchV+WNsLAIBsYW2vBWNtLwAAsmUZa3vlKvysPJp3AACYGR2eAQBArhB+AABArhB+AABArhB+AABAruQi/Hz99dd9/8Xy/Mu/fq29vT2dnZ0tuyi5d3Z2xrXICK5FdnAtsmVR92/Cz6r72d7gT4b967/+Lz179owvlgw4OzvjWmQE1yI7uBbZsqj7N0Pdb6C9P/vusotwuf+nJn1zfdmlAADkUC5qfgAAABKEHwAAkCu5avb6/d//ff3Wb/3W0OdY2wsAgOs3bm2vf/7nf17IMXMVfv78z/9c3/nOd5ZdDAAA8GvjKh9++tOf6v79+3M/Js1eAAAgVwg/AAAgVwg/czKqvXIiE87Vc/Div05/jAn91//3vyz8GNfh4D9+f6HzH810vTN0jOtwE84V1yJ/x1i0m3KeVvVaEH7m5Fo+ZP/51cKP8er/+78XfozrsOhzdfCnewufXHJVv1QuuglfwFyL/B1j0W7KeVrVa7GSHZ7DMFStVpNpmorjWI7jqFQqLbtYuILan/+fWv/om+m/9370t0srCwAgX1Yu/ERRpGKxqHa7LcuyJEmFQkHdbleVSmXJpcPKmWeNzbB9/e4c978KMr68CgBIKxh+qtWqbNtOg48kua6rarVK+MF4y7gxT3rMmxyS3v3/g+fhJr9fAJm3UuEnjmO1Wi15ntf3+NbWliSpXq8TgIDrQi0PgBW1UuHn+PhYkmSaZt/jSS1Qs9nMXfhZiUVMJzDp+7ixfYOGBYlZakxmaYK7juY7asQALNFKhZ8oiiRJhmGMff6iX/7yl5Kkf/iHf5jp+B999JE++uijoc+9e/dOYRhevpN/8Kc+/ruzf1H4337e99jPf/Hfp97fMP/yL2dz3+d5X/+vd5Kk//nl/9BH/8etK29/8f1Lkv9/FQceG3auJjVsf9V/3+77989/8Zuqev927GtmNfQ9/Lfq9Du8sO3pu68lSa//03/Qxq3hn+t5HHeWa6FvTPA7pSv8/k1p0fs/PT2VJL1+/VobGxsLO86i38dNOAbXYr7H+Prrr/X1119Pvf/kvp3cx+emt0J2dnZ6knrtdnvgOUk90zSHbvf555/3JPHDDz/88MMPPyv48/nnn881T6xUzU+hUJAkdbvdoc9fbA5L/OAHP9Dnn3+ub3/72/rGN74x9fHH1fwAAIB+s9b8/PKXv9TPf/5z/eAHP5hjqVas2SsJN3Ecj33+om9961v64Q9/uKhiAQCAFbJSMzwno7ou9u1J/l0sDvbVAAAAOG+lwo9hGLIsS81ms+/xVqslSXr06NEyigUAAFbI2q87C6+MMAxVLBbV6XTSZq5CoaBqtaqdnZ0llw4AAGTdyoUfqX9tr3a7rZOTE9m2faV1vlgfbP6mPadBEKhWqykMQ1mWJc/zZNv2NZT45prH57vVaqlcLuvk5GRBpcyHeVyLKIoUBIEkqVKpjJzuA5eb5Xuq2WzKMAxFUSTTNAcm3MXk4jhWrVaTpInP41zv23MdO3bNOp1OT+of+m6aZs/3/YVsh9GmPaee5/Vs2+75vp9OZSCp12w2F13kG2ten2/TNHuGYcy7eLky67XodDq9UqnUs2271+l0FlXM3Jj2ejQajZ5lWX2P2bbd29nZWUg5b7pms9krlUo9Sb1KpTLRNvO+b690+LFtu2fbdt9jvu/3Lst0026H0aY9p6VSqe/f7Xa7J2lgX5jcPD7fOzs7Pdu2CT8zmuVatNvtnmEYE98ccLlZ7hkXr4PneSPnlsNkrhJ+5n3fXqkOz+cl63w5jtP3+Pl1vua5HUab9pwOW6fNsixZljVytm6MN4/Pd6vV0t27d/sWD8bVzXIt4jjWgwcPZJqmfH/6WeHxwSzXo9vtpgNrEuf7nWKxFnHfXtnwM8k6X/PcDqNNe05t2x755cGXynTm8fn2fZ/BA3Mwy7VwXVdxHNOnZI5muR7ValVRFKlcLkt63/fk6OiI63NNFnHfXtnwM+06X9Nuh9HmfU7Pf8ngama9Fq7r8oU+J7Nci+Qv2WazqWKxqDt37shxHL6fZjDL9ahUKqpUKgqCQIVCQa7r6s2bN9SOXpNF3LdXNvx0Oh1J0ubm5tDnR80CPe12GG2e5zQIApmmqUqlMo+i5c4s1yIMQ929e5datzmZ9loki0RalqVqtap2u612u60oilQoFPiOmtKs31O+76dN8q1Wa6AZDIuziPv2yoafadf5mnY7jDbPc1qr1dRoNOZSrjya5VrUajWau+Zo2muR/BVbrVbT15zv+5MMD8bVzPo95TiOqtVqOty9XC6n0w9gsRZx316ptb3Om3adr2m3w2jzOqeu6+rw8JBrMINpr4XrugPNKsn/J//lulzNtNdiVNV+MvcVTV/TmeV7qlqtSlJaI/3mzRt98sknevLkCfPDXYNF3LdXtuZn2nW+WB9s/uZxTuv1uhzHoQ19RtNei1arpWq1qkKhkP4EQaA4jlUoFOiDNYVZv6OSqv6LRlX9Y7xZvqeOjo76vpsMw5DneYrjOG2mxOIs4r69suFn2nW+WB9s/mY9p0nV8cVZnflSubppr0W73Vbv/bxf6c/Ozo4Mw1Cv11O73V542W+aWb6jbNse6FOS/NXLH2jTmeV7anNzc6DWIfm+YrbtxVvIfXuq2YEyIpkQ7/zMp6Zp9jzPS//d6XR6pmn2zRg8yXa4mmmvRbPZ7FmW1fN9v++nUqkw4/aUpr0WF+3s7DDJ4Yxm/Y46/5jneQOzDONqpr0enuf1DMPonZyc9D3G9ZjeycnJyEkOr+O+vbJ9fqT3oyHa7bZc15VpmoqiSK7r9o0UiuNY3W63L7VPsh2uZpprEYZhOmlV0qZ+HmtKTWfa3wvM3zy+oxqNhgzDUBzH1MDNaNrrkdSClsvltPkrjmN98cUX1/0WboQwDNMO/EdHR3IcR7Ztp7Vo13HfXsmFTQEAAKa1sn1+AAAApkH4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAM7u48GiWEX4AAMDMyuXyyiyZQ/gBAAAzCcNQpmmm63Mla2+tra1pbW1Nd+7c0f7+fvr6VqulQqGQPpes0H5dWNsLAADMpFqtqlwuy7btvsfL5bKCIFCpVFKj0Rh4LoqipSzYS/gBAAAzKRQK6nQ6A4+HYahisSjDMHRycpI+HgSBXNcdus11oNkLAABMLQiCgRqfhGVZsixLcRynTVthGMp1XTWbzessZh/CDwAAmNrz589VrVZHPp885/u+4jhWuVxWo9GQaZrXVcQBhB8AAJDWyNy5c6cvzJTLZd25c2foUPY4jhVFkSzLGrnfR48eSXpfQ/TgwQN5njf29deBPj8AACDlOI5arZZ6vV7akTmKIlUqlYHX1ut1xXGsnZ2dsfsc1/F5GX5z2QUAAADZUS6X1Wq1VK1W5bru2OYp3/f1xRdfXLrPZB9hGI58TbVaVaFQ0JdffqlPP/1UpVLp6oWfEOEHAACkks7LhmGMDT5RFGlzczOd22eUIAjUarVkmqaiKFIYhgPNXuVyWaZppjVIyQixUR2pZ0WfHwAAkEoCz2XLVfi+P7ajs/ShH9EXX3zR1/H5vCiKFARB374eP34sz/OmKf5E6PMDAABSruuq1WopjuOx8/CMmtsnEcexisWiGo1GOtz9zp07kqTz0SMIApXL5b7HWq2WHMfRycnJpTVL06DmBwAASHofRBzHUbVaVRRFae3PxVqgVqt1aZPUgwcP5Pt+2sRlGEbajycIgvR1r169Ggg4m5ubkqRutzvT+xmF8AMAQI5FUaT9/X0FQaButyvbttNg4/u+9vf30zCSuKzJy3EcmaY5EJAcx5Ek1Wq19LE4jgf2f75si0D4AQAgx8IwVK1W06tXr9Lh7KZpqlQqqV6vy7btgZqZYZ2WpQ8LlrZaLYVh2FfDEwRB2t8nDMN0VFmhUBhZw7OoiRDp8wMAACY26dw+kxrX52dREYWh7gAAYGKTzu0zqaQGKYqivvmAFjkLNM1eAABgIpPO7XMVSRPb+Say58+fM9QdAAAsn+u6C5t9+fwMz4VCYehyGvNC+AEAABNJVmRfdYQfAACQK/T5AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAuUL4AQAAufK/AQrsw39KaflPAAAAAElFTkSuQmCC",
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nbins = 100\n",
+    "vmax = 80\n",
+    "\n",
+    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))\n",
+    "\n",
+    "# ax0.set_aspect(\"equal\")\n",
+    "\n",
+    "a0 = ax0.hist2d(\n",
+    "    xx0_test,\n",
+    "    xx0_predicted,\n",
+    "    density=False,\n",
+    "    bins=nbins,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax,\n",
+    "    range=[[0, 0.5], [0, 0.5]],\n",
+    ")\n",
+    "ax0.plot([0, 0.5], [0, 0.5], marker=\"\", alpha=0.8)\n",
+    "ax0.set_box_aspect(1)\n",
+    "ax0.set_xlabel(f\"True $x/X_0$\")\n",
+    "ax0.set_ylabel(f\"Predicted $x/X_0$\")\n",
+    "ax0.set_title(f\"found rad_length_frac\")\n",
+    "plt.colorbar(a0[3], ax=ax0)\n",
+    "\n",
+    "ax1.hist(\n",
+    "    xx0_test,\n",
+    "    bins=100,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    color=\"darkorange\",\n",
+    "    histtype=\"bar\",\n",
+    "    label=\"test\",\n",
+    "    range=[0, 0.5],\n",
+    ")\n",
+    "ax1.hist(\n",
+    "    xx0_predicted,\n",
+    "    bins=100,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    color=\"blue\",\n",
+    "    histtype=\"bar\",\n",
+    "    label=\"predicted\",\n",
+    "    range=[0, 0.5],\n",
+    ")\n",
+    "ax1.set_xlim(0, 0.5)\n",
+    "ax1.set_title(\"radiation length fraction endVelo\")\n",
+    "ax1.set_xlabel(f\"$x/X_0$\")\n",
+    "ax1.set_ylabel(\"a.u.\")\n",
+    "ax1.set_box_aspect(1)\n",
+    "\n",
+    "ax1.legend()\n",
+    "\n",
+    "# plt.gca().set_aspect(\"equal\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -289,7 +365,7 @@
     "    color=\"darkorange\",\n",
     "    histtype=\"bar\",\n",
     "    label=\"test\",\n",
-    "    range=[0, 1],\n",
+    "    range=[0, 0.5],\n",
     ")\n",
     "plt.hist(\n",
     "    xx0_predicted,\n",
@@ -299,9 +375,9 @@
     "    color=\"blue\",\n",
     "    histtype=\"bar\",\n",
     "    label=\"predicted\",\n",
-    "    range=[0, 1],\n",
+    "    range=[0, 0.5],\n",
     ")\n",
-    "plt.xlim(0, 1)\n",
+    "plt.xlim(0, 0.5)\n",
     "# plt.yscale(\"log\")\n",
     "plt.title(\"radiation length fraction endVelo\")\n",
     "plt.xlabel(f\"$x/X_0$\")\n",
@@ -313,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -322,15 +398,15 @@
      "text": [
       "Parameterisation for rad_length_frac:\n",
       "intercept= 0.0\n",
-      "coef= {'1': 0.24562140630345797, 'x': -0.0006147519300623754, 'y': 0.00013149294035773212, 'tx': 0.40993139584024857, 'ty': -0.17613130993997955, 'qop': -2.431621847221756, 'x^2': -2.537929422256502e-05, 'x y': 1.6503609236837168e-05, 'x tx': 0.014114349319941343, 'x ty': -0.015914217992780068, 'x qop': -0.3138750804691517, 'y^2': 8.004082295422125e-06, 'y tx': -0.0007103518977325451, 'y ty': -0.012102735170731998, 'y qop': -0.01613366131755978, 'tx^2': -1.1805199392607937, 'tx ty': 2.630285174738731, 'tx qop': 0.2259863956524285, 'ty^2': -0.9000770143303645, 'ty qop': 0.035317808178541486, 'qop^2': 0.506257028947111, 'x^3': -1.054613771280784e-06, 'x^2 y': -7.542919321773425e-07, 'x^2 tx': 0.0016046817739745617, 'x^2 ty': 0.0041420285105903745, 'x^2 qop': 0.021289920901612722, 'x y^2': 1.6630958165414549e-06, 'x y tx': -0.0029322410866066016, 'x y ty': 0.00026646565396820643, 'x y qop': 0.008117926325135527, 'x tx^2': -0.6007987905052633, 'x tx ty': -2.98887506065023, 'x tx qop': -13.962516223136573, 'x ty^2': -0.8388571705095013, 'x ty qop': -3.036359977877559, 'x qop^2': 0.055324587199241985, 'y^3': -8.030979246897996e-08, 'y^2 tx': -0.002767856037518542, 'y^2 ty': 0.00010106006074174414, 'y^2 qop': 0.0087584836303898, 'y tx^2': 2.52086358169213, 'y tx ty': 1.771954217213756, 'y tx qop': -3.4960101511969945, 'y ty^2': -0.02778479572722497, 'y ty qop': -8.306825049361231, 'y qop^2': 0.011633837171348742, 'tx^3': -0.1376005395953692, 'tx^2 ty': 0.08788148993360365, 'tx^2 qop': -0.03627295634145932, 'tx ty^2': 0.03876102551307681, 'tx ty qop': -0.008986008881728739, 'tx qop^2': 9.888031370307247e-05, 'ty^3': -0.3359335900376418, 'ty^2 qop': -0.02333508540052829, 'ty qop^2': 9.573184641700597e-06, 'qop^3': -4.597612275351735e-06, 'x^4': -6.108581640518196e-09, 'x^3 y': -6.564809273967853e-08, 'x^3 tx': 1.5150502404637223e-05, 'x^3 ty': 3.104184179880676e-05, 'x^3 qop': 8.671096332828199e-05, 'x^2 y^2': -7.650740829062697e-09, 'x^2 y tx': 0.00011735105575993998, 'x^2 y ty': -3.2175113096499786e-05, 'x^2 y qop': 6.774919445144169e-05, 'x^2 tx^2': -0.011724689654149013, 'x^2 tx ty': -0.043137944250594654, 'x^2 tx qop': -0.0234816944437721, 'x^2 ty^2': -0.015910369617829878, 'x^2 ty qop': 0.47749147066068204, 'x^2 qop^2': 10.59413029512916, 'x y^3': 3.234245482541809e-08, 'x y^2 tx': 5.3043264426122505e-05, 'x y^2 ty': -5.8168920071072217e-05, 'x y^2 qop': -0.00018640913107006996, 'x y tx^2': -0.06797789037420698, 'x y tx ty': 0.044976962291121, 'x y tx qop': -0.5074481714386293, 'x y ty^2': 0.03709689469083238, 'x y ty qop': 0.42061549746065124, 'x y qop^2': 5.878761197856953, 'x tx^3': 2.928172660629093, 'x tx^2 ty': 15.518053547545511, 'x tx^2 qop': -0.4179045108625407, 'x tx ty^2': 2.863496001226073, 'x tx ty qop': 0.03316794782068423, 'x tx qop^2': 0.011365298564370092, 'x ty^3': -8.111988620415804, 'x ty^2 qop': -0.21644263264392571, 'x ty qop^2': 0.00843273298050687, 'x qop^3': -0.00016877332507871679, 'y^4': 6.023639254060242e-09, 'y^3 tx': -1.594750695611824e-05, 'y^3 ty': -1.437326358022517e-05, 'y^3 qop': -2.1442523502287515e-05, 'y^2 tx^2': -0.04753636763140216, 'y^2 tx ty': 0.018902432682699272, 'y^2 tx qop': -0.31014755455483073, 'y^2 ty^2': 0.011363729381750311, 'y^2 ty qop': 0.01681916414935703, 'y^2 qop^2': -5.242995516210874, 'y tx^3': 12.07113658017565, 'y tx^2 ty': 2.5063182953688687, 'y tx^2 qop': 0.05008258889078114, 'y tx ty^2': -5.840846904263012, 'y tx ty qop': -0.2222888950764648, 'y tx qop^2': 0.008403354774191822, 'y ty^3': -2.8576703684113554, 'y ty^2 qop': 0.156828601288708, 'y ty qop^2': -0.010489395507591974, 'y qop^3': 1.6753570524717795e-05, 'tx^4': -0.01910867380555672, 'tx^3 ty': 0.0837123623190646, 'tx^3 qop': -0.0014826028602247271, 'tx^2 ty^2': 0.0015368291104171875, 'tx^2 ty qop': 8.478961198550636e-05, 'tx^2 qop^2': 1.2316906385746789e-05, 'tx ty^3': -0.0271004544137963, 'tx ty^2 qop': -0.0008124467158542836, 'tx ty qop^2': 1.1953538675306295e-05, 'tx qop^3': -1.6561432689863052e-07, 'ty^4': -0.051346978654440316, 'ty^3 qop': 0.0005598437003601672, 'ty^2 qop^2': -1.8064704330828996e-05, 'ty qop^3': 3.8598127279103275e-08, 'qop^4': 3.326704284195817e-08, 'x^5': 6.821222753305989e-11, 'x^4 y': 2.526661102564276e-10, 'x^4 tx': -1.251389993850438e-07, 'x^4 ty': 3.4387986458384034e-08, 'x^4 qop': 8.496537125812775e-06, 'x^3 y^2': 5.454374729652045e-10, 'x^3 y tx': -5.823518507419578e-07, 'x^3 y ty': 1.573480655436832e-06, 'x^3 y qop': 1.1149119954367848e-05, 'x^3 tx^2': 7.080646439604904e-05, 'x^3 tx ty': -0.00019832304528768704, 'x^3 tx qop': -0.013548718492619474, 'x^3 ty^2': -0.0011831441371068785, 'x^3 ty qop': -0.007475115795384159, 'x^3 qop^2': -0.05297421345055594, 'x^2 y^3': -3.0988589472258354e-10, 'x^2 y^2 tx': -2.827730114507432e-06, 'x^2 y^2 ty': 3.25980575599516e-06, 'x^2 y^2 qop': -1.1472914959842484e-05, 'x^2 y tx^2': 0.0005880138985921626, 'x^2 y tx ty': -0.00028281256289024864, 'x^2 y tx qop': -0.011299091385256742, 'x^2 y ty^2': -0.0028883860783337767, 'x^2 y ty qop': 0.015857147862434813, 'x^2 y qop^2': -0.13581771520502617, 'x^2 tx^3': -0.011192107550488224, 'x^2 tx^2 ty': 0.12486137523256921, 'x^2 tx^2 qop': 5.225482032478654, 'x^2 tx ty^2': 0.7550762571921301, 'x^2 tx ty qop': 3.755310333424372, 'x^2 tx qop^2': 0.03529628798503831, 'x^2 ty^3': 0.0680249197813556, 'x^2 ty^2 qop': -2.149779516970109, 'x^2 ty qop^2': -0.10043851798514322, 'x^2 qop^3': -0.008357891977863841, 'x y^4': -3.010369731271112e-10, 'x y^3 tx': -2.5632317350865463e-06, 'x y^3 ty': -1.5046913748317792e-06, 'x y^3 qop': -7.480400594950254e-06, 'x y^2 tx^2': 0.0024216018281754056, 'x y^2 tx ty': 0.0007084178707292381, 'x y^2 tx qop': 0.00044348353393648763, 'x y^2 ty^2': 0.0026684932410411567, 'x y^2 ty qop': 0.004142697700575095, 'x y^2 qop^2': 0.04019255202302588, 'x y tx^3': -0.21609228057090962, 'x y tx^2 ty': -0.39898880238968853, 'x y tx^2 qop': 3.716497181247638, 'x y tx ty^2': 1.7005468399990162, 'x y tx ty qop': -1.6246665305727963, 'x y tx qop^2': -0.10861921254973068, 'x y ty^3': -1.0206195922719212, 'x y ty^2 qop': -2.2538410252773176, 'x y ty qop^2': 0.12471546683983396, 'x y qop^3': 0.0010355202027286007, 'x tx^4': -0.38054051948182677, 'x tx^3 ty': 0.02811376405424165, 'x tx^3 qop': 0.024914607795988558, 'x tx^2 ty^2': -0.2390526017685849, 'x tx^2 ty qop': 0.016195442265039908, 'x tx^2 qop^2': -7.88453042343562e-06, 'x tx ty^3': 0.15152568889899992, 'x tx ty^2 qop': -0.008761905989985623, 'x tx ty qop^2': -0.00028476668103488885, 'x tx qop^3': -1.3992733945114136e-05, 'x ty^4': -0.17984996125743222, 'x ty^3 qop': -0.010938588150706838, 'x ty^2 qop^2': 0.00028736668746421283, 'x ty qop^3': 1.0017755372740274e-06, 'x qop^4': 5.647205119564429e-08, 'y^5': 2.2355894913062002e-11, 'y^4 tx': 2.1532131875279248e-06, 'y^4 ty': -4.644247597607176e-08, 'y^4 qop': -6.709499208279457e-06, 'y^3 tx^2': 0.0016621305979105314, 'y^3 tx ty': -0.003130496631703698, 'y^3 tx qop': 0.0073473064465471, 'y^3 ty^2': 3.1438102391929956e-05, 'y^3 ty qop': 0.009790096490369568, 'y^3 qop^2': 0.007755101347045138, 'y^2 tx^3': -0.5967738984072392, 'y^2 tx^2 ty': -1.6414346613041646, 'y^2 tx^2 qop': -1.9221000353689144, 'y^2 tx ty^2': 1.1296103381428921, 'y^2 tx ty qop': -2.0138360070431838, 'y^2 tx qop^2': 0.10014966880153775, 'y^2 ty^3': -0.006765532531283773, 'y^2 ty^2 qop': -3.489466677603648, 'y^2 ty qop^2': -0.15818840693723135, 'y^2 qop^3': -0.006154290860857408, 'y tx^4': 0.06713941420974948, 'y tx^3 ty': -0.18765968488942172, 'y tx^3 qop': 0.01494671954085084, 'y tx^2 ty^2': 0.02688242692225791, 'y tx^2 ty qop': -0.008806488292543392, 'y tx^2 qop^2': -0.0002890003079734742, 'y tx ty^3': 0.0560088747156591, 'y tx ty^2 qop': -0.0091269069301046, 'y tx ty qop^2': 0.0002557631704771736, 'y tx qop^3': 9.13083085488923e-07, 'y ty^4': -0.11308027221966196, 'y ty^3 qop': -0.015522487055330275, 'y ty^2 qop^2': -0.0004358549211642052, 'y ty qop^3': -9.99916544547057e-06, 'y qop^4': -1.583944618976334e-08, 'tx^5': -0.0039057973828551464, 'tx^4 ty': 0.0011021677966443968, 'tx^4 qop': 8.364509298284483e-05, 'tx^3 ty^2': -0.0019793041595461795, 'tx^3 ty qop': 4.1168080154112314e-05, 'tx^3 qop^2': -2.463097953215033e-07, 'tx^2 ty^3': 0.0007208243773861416, 'tx^2 ty^2 qop': -2.5859488653297416e-05, 'tx^2 ty qop^2': -5.822330221226121e-07, 'tx^2 qop^3': -2.1963163726583706e-08, 'tx ty^4': 0.00020328501105408336, 'tx ty^3 qop': -2.7986469931730596e-05, 'tx ty^2 qop^2': 4.5313369723243955e-07, 'tx ty qop^3': 6.464504187809654e-10, 'tx qop^4': 9.133899373812518e-11, 'ty^5': -0.0025485559385795883, 'ty^4 qop': -5.08771174897897e-05, 'ty^3 qop^2': -9.412302186542877e-07, 'ty^2 qop^3': -1.55479697478695e-08, 'ty qop^4': -1.861631536431087e-11, 'qop^5': -1.2879922620235152e-12}\n",
-      "r2 score= 0.013501590753704495\n",
-      "RMSE = 0.2590497099709809\n",
+      "coef= {'1': 0.2484410418213911, 'x': -0.0007601095488043627, 'y': 0.0010569724392146917, 'tx': 0.6185505303064777, 'ty': -0.9394058560136732, 'qop': -9.741031889614183, 'x^2': -0.00016580416280366622, 'x y': 5.149038989659081e-05, 'x tx': 0.22996768886351043, 'x ty': -0.043161009059129354, 'x qop': -0.21658279194428842, 'y^2': 3.9826067539320166e-05, 'y tx': -0.033498957247677735, 'y ty': -0.08085122767618998, 'y qop': 0.06428923004582791, 'tx^2': -83.06687438225835, 'tx ty': 28.76266798578089, 'tx qop': -0.32072666519746007, 'ty^2': 32.80290436519906, 'ty qop': 0.29785759094660047, 'qop^2': 0.7177557091128425, 'x^3': -1.037888276319177e-06, 'x^2 y': 5.744977724613286e-07, 'x^2 tx': 0.0016261562680787358, 'x^2 ty': 0.00819223051446815, 'x^2 qop': 0.014940216048602184, 'x y^2': 1.55836456652794e-06, 'x y tx': -0.009042353485603404, 'x y ty': 0.002769481233443616, 'x y qop': 0.007035099510620806, 'x tx^2': -0.623629094925692, 'x tx ty': -0.5792857627094614, 'x tx qop': 3.819052794399519, 'x ty^2': -1.214138821848195, 'x ty qop': -4.179406422119741, 'x qop^2': -0.11723833703778899, 'y^3': -2.1060731703048674e-07, 'y^2 tx': -0.005136952674035623, 'y^2 ty': 0.0002523550890177065, 'y^2 qop': 0.020199755591199943, 'y tx^2': 0.8881296792413045, 'y tx ty': 2.1057062787476855, 'y tx qop': -4.322557205912296, 'y ty^2': -0.062185778412248593, 'y ty qop': -12.744670929978815, 'y qop^2': 0.10865361350283592, 'tx^3': -0.2772083890350773, 'tx^2 ty': -0.002155259913110253, 'tx^2 qop': 0.01973183125611695, 'tx ty^2': 0.2547275714314975, 'tx ty qop': -0.01267461996128659, 'tx qop^2': -0.0002315355231400779, 'ty^3': 0.12852489701010045, 'ty^2 qop': -0.03229168715651398, 'ty qop^2': 0.0001733290594162183, 'qop^3': -1.1131786479856305e-06, 'x^4': 1.4072631948636172e-09, 'x^3 y': -4.525309382774623e-08, 'x^3 tx': -5.048150129027817e-07, 'x^3 ty': 2.845994251238215e-07, 'x^3 qop': 6.161442924141475e-05, 'x^2 y^2': -1.8614020325102842e-08, 'x^2 y tx': 9.737839179148333e-05, 'x^2 y ty': -1.3038804363763035e-05, 'x^2 y qop': -8.415032085912991e-05, 'x^2 tx^2': -0.0010152847281566686, 'x^2 tx ty': -0.0032259545758118137, 'x^2 tx qop': -0.041785493301881166, 'x^2 ty^2': 0.013227443641328787, 'x^2 ty qop': 0.0035654519670473366, 'x^2 qop^2': 1.2621531315710728, 'x y^3': 3.906278722709544e-08, 'x y^2 tx': 6.858475435222999e-05, 'x y^2 ty': -5.862278080592809e-05, 'x y^2 qop': -0.00016236412225426912, 'x y tx^2': -0.06608751351613794, 'x y tx ty': -0.04864228625905696, 'x y tx qop': 0.06901548261804959, 'x y ty^2': 0.04159526642181612, 'x y ty qop': 0.28489071089527757, 'x y qop^2': 0.2535927965752249, 'x tx^3': 0.5018159233398294, 'x tx^2 ty': 0.9699678165589771, 'x tx^2 qop': 0.4741265130417677, 'x tx ty^2': 10.315681588678894, 'x tx ty qop': -0.22617149043686857, 'x tx qop^2': 0.00898570789717606, 'x ty^3': -12.737405175272935, 'x ty^2 qop': -0.014062351992903526, 'x ty qop^2': 0.005154120346378951, 'x qop^3': -4.609067543005094e-05, 'y^4': 9.455609628616912e-09, 'y^3 tx': -2.99210479094425e-05, 'y^3 ty': -2.583154312318925e-05, 'y^3 qop': 5.980622324156491e-05, 'y^2 tx^2': -0.016833583405836638, 'y^2 tx ty': 0.02458057322196644, 'y^2 tx qop': -0.169419352054439, 'y^2 ty^2': 0.02331391517451325, 'y^2 ty qop': -0.04589466917231106, 'y^2 qop^2': -1.206224538163147, 'y tx^3': 15.19059646701743, 'y tx^2 ty': 5.490478536183617, 'y tx^2 qop': -0.25278933198832354, 'y tx ty^2': -3.577260288354769, 'y tx ty qop': 0.03161480393545275, 'y tx qop^2': 0.005952281392129449, 'y ty^3': -6.821531531863169, 'y ty^2 qop': 0.09776598757839021, 'y ty qop^2': 0.0023614626852912395, 'y qop^3': -1.973415862163393e-05, 'tx^4': -0.05310859403428924, 'tx^3 ty': 0.023505514315239354, 'tx^3 qop': 0.0021762610547612377, 'tx^2 ty^2': 0.04763359558524625, 'tx^2 ty qop': -0.0009428124122745564, 'tx^2 qop^2': 1.2893218326397415e-05, 'tx ty^3': -0.018754843662608302, 'tx ty^2 qop': 4.8170564904883127e-05, 'tx ty qop^2': 1.6878183944633644e-05, 'tx qop^3': -4.12730477319448e-08, 'ty^4': 0.013919613173737298, 'ty^3 qop': 0.00022052138073417403, 'ty^2 qop^2': 9.713683885787424e-06, 'ty qop^3': -5.4325325107576684e-08, 'qop^4': 1.3416430509507416e-09, 'x^5': 2.37521113888306e-11, 'x^4 y': 6.566414079145488e-11, 'x^4 tx': -3.448894636548516e-08, 'x^4 ty': 4.817853991312404e-07, 'x^4 qop': -2.777657812425005e-06, 'x^3 y^2': 6.322311563167204e-10, 'x^3 y tx': -6.301214341419836e-07, 'x^3 y ty': 5.610926190335874e-06, 'x^3 y qop': 1.114123449319493e-05, 'x^3 tx^2': 1.3140964452713899e-05, 'x^3 tx ty': -0.0006698763738434144, 'x^3 tx qop': 0.004955643949611535, 'x^3 ty^2': -0.00020626810552818679, 'x^3 ty qop': -0.008992879337357176, 'x^3 qop^2': -0.01489312550273759, 'x^2 y^3': -1.0491474355944774e-10, 'x^2 y^2 tx': -7.058621529054676e-06, 'x^2 y^2 ty': 1.8916574067162628e-06, 'x^2 y^2 qop': 1.7843819655416482e-05, 'x^2 y tx^2': 0.0007719987709393639, 'x^2 y tx ty': -0.008372751086851777, 'x^2 y tx qop': -0.0094662761279594, 'x^2 y ty^2': -0.003982741017285164, 'x^2 y ty qop': -0.014247815997652827, 'x^2 y qop^2': -0.0933449616547058, 'x^2 tx^3': -0.00211924986590092, 'x^2 tx^2 ty': 0.1354990398386329, 'x^2 tx^2 qop': -2.4034242614405197, 'x^2 tx ty^2': 1.709794803412642, 'x^2 tx ty qop': 2.929391659772189, 'x^2 tx qop^2': -0.12899963468243164, 'x^2 ty^3': 0.41237245103458525, 'x^2 ty^2 qop': 0.9459129147227289, 'x^2 ty qop^2': 0.6716348674023994, 'x^2 qop^3': 0.0065213742878363744, 'x y^4': -3.1660363219998544e-10, 'x y^3 tx': -1.6554844943783564e-06, 'x y^3 ty': -1.8566065946856725e-06, 'x y^3 qop': -9.75035823502779e-06, 'x y^2 tx^2': 0.009680843788897421, 'x y^2 tx ty': 0.004952813049497684, 'x y^2 tx qop': -0.016925260798516226, 'x y^2 ty^2': 0.003475670476866588, 'x y^2 ty qop': 0.01417176786308785, 'x y^2 qop^2': -0.01761212366224941, 'x y tx^3': -0.15192958327351774, 'x y tx^2 ty': -0.060788822811239596, 'x y tx^2 qop': 4.3726462974483224, 'x y tx ty^2': 0.253839828387595, 'x y tx ty qop': 2.515358567018165, 'x y tx qop^2': 0.7384529496261274, 'x y ty^3': -1.4590050438376516, 'x y ty^2 qop': -4.598361895076147, 'x y ty qop^2': 0.31170679839759735, 'x y qop^3': -0.01024453299524337, 'x tx^4': 1.0757508065772434, 'x tx^3 ty': -1.6800363456087655, 'x tx^3 qop': 0.025130628317134973, 'x tx^2 ty^2': 1.1790180726236092, 'x tx^2 ty qop': 0.013852458993079927, 'x tx^2 qop^2': -0.0004548226751025624, 'x tx ty^3': -0.0007529047666109905, 'x tx ty^2 qop': 0.007719696430873407, 'x tx ty qop^2': 0.0018986412331720088, 'x tx qop^3': 7.660654451074145e-06, 'x ty^4': 1.3495131383656807, 'x ty^3 qop': -0.027518299643244655, 'x ty^2 qop^2': 0.0007835380395695195, 'x ty qop^3': -1.7215942880021808e-05, 'x qop^4': -4.814887567180262e-08, 'y^5': 1.1069811733932511e-11, 'y^4 tx': 2.5401641039479728e-06, 'y^4 ty': -3.659565095404105e-08, 'y^4 qop': -8.233519552314217e-06, 'y^3 tx^2': -0.0011440038498780267, 'y^3 tx ty': -0.003958853981003943, 'y^3 tx qop': 0.0019569523522222627, 'y^3 ty^2': 4.620362246071652e-05, 'y^3 ty qop': 0.013501593121770603, 'y^3 qop^2': 0.00013943149170339843, 'y^2 tx^3': -1.930662753166049, 'y^2 tx^2 ty': -0.6244835760091016, 'y^2 tx^2 qop': 9.706592754727314, 'y^2 tx ty^2': 1.567415053011399, 'y^2 tx ty qop': -1.8692848199146312, 'y^2 tx qop^2': 0.2576730572143735, 'y^2 ty^3': -0.02555256934016916, 'y^2 ty^2 qop': -5.570585900979827, 'y^2 ty qop^2': 0.4524728269219666, 'y^2 qop^3': -0.0012165533583740602, 'y tx^4': -1.705633938705921, 'y tx^3 ty': 0.9697939146444974, 'y tx^3 qop': 0.01417529417479696, 'y tx^2 ty^2': 0.24892206316288593, 'y tx^2 ty qop': 0.02112066742630421, 'y tx^2 qop^2': 0.001983588330826661, 'y tx ty^3': 1.0027301725129028, 'y tx ty^2 qop': -0.019989975028401157, 'y tx ty qop^2': 0.000703255023993435, 'y tx qop^3': -1.7780249221490402e-05, 'y ty^4': 5.039018301955912, 'y ty^3 qop': -0.03181545636834986, 'y ty^2 qop^2': 0.0013016040850667482, 'y ty qop^3': -4.788925524502292e-06, 'y qop^4': 8.952440180633341e-08, 'tx^5': 0.009735740982675399, 'tx^4 ty': -0.013441638488465971, 'tx^4 qop': 0.00015663907591491764, 'tx^3 ty^2': 0.008836930257445317, 'tx^3 ty qop': 4.602358365785842e-05, 'tx^3 qop^2': -1.2528198912759552e-06, 'tx^2 ty^3': 8.384431822878086e-05, 'tx^2 ty^2 qop': 3.2449454369351206e-05, 'tx^2 ty qop^2': 3.870208943851117e-06, 'tx^2 qop^3': 1.1062889336368561e-08, 'tx ty^4': 0.008284216728547011, 'tx ty^3 qop': -8.101830871580909e-05, 'tx ty^2 qop^2': 1.3999083905485367e-06, 'tx ty qop^3': -2.8838718241492492e-08, 'tx qop^4': -7.302022445954643e-11, 'ty^5': 0.033986172363938666, 'ty^4 qop': -0.00011487779463878022, 'ty^3 qop^2': 2.8267877400850516e-06, 'ty^2 qop^3': -1.0303917259481338e-08, 'ty qop^4': 1.343104287668482e-10, 'qop^5': -5.690015320531571e-16}\n",
+      "r2 score= 0.01281806793978646\n",
+      "RMSE = 0.2644569540509028\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -357,14 +433,16 @@
     "    y = lost[\"ideal_state_9410_y\"]\n",
     "    qop = lost[\"ideal_state_9410_qop\"]\n",
     "\n",
-    "data = ak.zip({\n",
-    "    \"rad_length_frac\": rad_length_frac,\n",
-    "    \"x\": x,\n",
-    "    \"y\": y,\n",
-    "    \"tx\": slopex,\n",
-    "    \"ty\": slopey,\n",
-    "    \"qop\": qop,\n",
-    "})\n",
+    "data = ak.zip(\n",
+    "    {\n",
+    "        \"rad_length_frac\": rad_length_frac,\n",
+    "        \"x\": x,\n",
+    "        \"y\": y,\n",
+    "        \"tx\": slopex,\n",
+    "        \"ty\": slopey,\n",
+    "        \"qop\": qop,\n",
+    "    }\n",
+    ")\n",
     "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n",
     "    data,\n",
     "    \"rad_length_frac\",\n",
@@ -398,44 +476,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre>[nan,\n",
-       " nan,\n",
-       " nan,\n",
-       " nan,\n",
-       " nan,\n",
-       " nan,\n",
-       " nan,\n",
-       " nan,\n",
-       " nan]\n",
-       "-----------------\n",
-       "type: 9 * float64</pre>"
-      ],
-      "text/plain": [
-       "<Array [nan, nan, nan, nan, nan, nan, nan, nan, nan] type='9 * float64'>"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "endT_x = lost[np.isnan(lost.ideal_state_9410_x)][\"ideal_state_9410_x\"]\n",
-    "endT_x"
+    "df = pd.DataFrame({\n",
+    "    \"phi\": phi_found,\n",
+    "    \"eta\": eta_found,\n",
+    "    \"rad_length_frac\": rad_length_found\n",
+    "})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "latex was not able to process the following string:\nb'sale price $'\n\nHere is the full command invocation and its output:\n\nlatex -interaction=nonstopmode --halt-on-error --output-directory=tmpg0jlxahb 82002a6a534310632a0ec6d082310260.tex\n\nThis is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(./82002a6a534310632a0ec6d082310260.tex\nLaTeX2e <2020-02-02> patch level 5\nL3 programming layer <2020-09-24>\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/article\n.cls\nDocument Class: article 2019/12/20 v1.4l Standard LaTeX document class\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/size10.\nclo))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/mathp\ntmx.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/type1cm/type\n1cm.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/cm-super/typ\ne1ec.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/t1cmr.f\nd))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/inputen\nc.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/geometry/geo\nmetry.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/graphics/key\nval.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifvt\nex.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifte\nx.sty)))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/underscore/u\nnderscore.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/textcom\np.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/l3backend/l3\nbackend-dvips.def)\nNo file 82002a6a534310632a0ec6d082310260.aux.\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1pt\nm.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1zt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omlzt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omszt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omxzt\nmcm.fd)\n! Extra }, or forgotten $.\n<recently read> }\n                 \nl.30 {\\rmfamily sale price $}\n                             %\nNo pages of output.\nTranscript written on tmpg0jlxahb/82002a6a534310632a0ec6d082310260.log.\n\n\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/backend_bases.py:2158\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2155\u001b[0m     \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m   2156\u001b[0m     \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m   2157\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2158\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2160\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3151\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3158\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axes/_base.py:3034\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3031\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m spine \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspines\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m   3032\u001b[0m         artists\u001b[38;5;241m.\u001b[39mremove(spine)\n\u001b[0;32m-> 3034\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3036\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxison:\n\u001b[1;32m   3037\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axes/_base.py:2978\u001b[0m, in \u001b[0;36m_AxesBase._update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2976\u001b[0m top \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(top, bb\u001b[38;5;241m.\u001b[39mymax)\n\u001b[1;32m   2977\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m title\u001b[38;5;241m.\u001b[39mget_text():\n\u001b[0;32m-> 2978\u001b[0m     \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# update offsetText\u001b[39;00m\n\u001b[1;32m   2979\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mget_text():\n\u001b[1;32m   2980\u001b[0m         bb \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mget_tightbbox(renderer)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axis.py:1352\u001b[0m, in \u001b[0;36mAxis.get_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1350\u001b[0m \u001b[38;5;66;03m# take care of label\u001b[39;00m\n\u001b[1;32m   1351\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m-> 1352\u001b[0m     bb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1353\u001b[0m     \u001b[38;5;66;03m# for constrained/tight_layout, we want to ignore the label's\u001b[39;00m\n\u001b[1;32m   1354\u001b[0m     \u001b[38;5;66;03m# width/height because the adjustments they make can't be improved.\u001b[39;00m\n\u001b[1;32m   1355\u001b[0m     \u001b[38;5;66;03m# this code collapses the relevant direction\u001b[39;00m\n\u001b[1;32m   1356\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m for_layout_only:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:956\u001b[0m, in \u001b[0;36mText.get_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    951\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    952\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    953\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwant to call \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure.draw_without_rendering()\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m first.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    955\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m--> 956\u001b[0m     bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    957\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_unitless_position()\n\u001b[1;32m    958\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform((x, y))\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:381\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    379\u001b[0m clean_line, ismath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_math(line)\n\u001b[1;32m    380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[0;32m--> 381\u001b[0m     w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    385\u001b[0m     w \u001b[38;5;241m=\u001b[39m h \u001b[38;5;241m=\u001b[39m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:69\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweakref\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:77\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(\u001b[38;5;241m4096\u001b[39m)\n\u001b[1;32m     74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[1;32m     75\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m     76\u001b[0m     \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:213\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    211\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_in_list([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTeX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m], ismath\u001b[38;5;241m=\u001b[39mismath)\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTeX\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 213\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[1;32m    216\u001b[0m     ox, oy, width, height, descent, font_image \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m    217\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmathtext_parser\u001b[38;5;241m.\u001b[39mparse(s, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdpi, prop)\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/backend_bases.py:652\u001b[0m, in \u001b[0;36mRendererBase.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    648\u001b[0m fontsize \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mget_size_in_points()\n\u001b[1;32m    650\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTeX\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    651\u001b[0m     \u001b[38;5;66;03m# todo: handle properties\u001b[39;00m\n\u001b[0;32m--> 652\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_texmanager\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    653\u001b[0m \u001b[43m        \u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    655\u001b[0m dpi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpoints_to_pixels(\u001b[38;5;241m72\u001b[39m)\n\u001b[1;32m    656\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/texmanager.py:363\u001b[0m, in \u001b[0;36mTexManager.get_text_width_height_descent\u001b[0;34m(cls, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    361\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tex\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 363\u001b[0m dvifile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_dvi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    364\u001b[0m dpi_fraction \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mpoints_to_pixels(\u001b[38;5;241m1.\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m renderer \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    365\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m dviread\u001b[38;5;241m.\u001b[39mDvi(dvifile, \u001b[38;5;241m72\u001b[39m \u001b[38;5;241m*\u001b[39m dpi_fraction) \u001b[38;5;28;01mas\u001b[39;00m dvi:\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/texmanager.py:295\u001b[0m, in \u001b[0;36mTexManager.make_dvi\u001b[0;34m(cls, tex, fontsize)\u001b[0m\n\u001b[1;32m    293\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m TemporaryDirectory(\u001b[38;5;28mdir\u001b[39m\u001b[38;5;241m=\u001b[39mcwd) \u001b[38;5;28;01mas\u001b[39;00m tmpdir:\n\u001b[1;32m    294\u001b[0m         tmppath \u001b[38;5;241m=\u001b[39m Path(tmpdir)\n\u001b[0;32m--> 295\u001b[0m         \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_checked_subprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlatex\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-interaction=nonstopmode\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m--halt-on-error\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[43m             \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m--output-directory=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtmppath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m             \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtexfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcwd\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m         (tmppath \u001b[38;5;241m/\u001b[39m Path(dvifile)\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39mreplace(dvifile)\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dvifile\n",
+      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/texmanager.py:258\u001b[0m, in \u001b[0;36mTexManager._run_checked_subprocess\u001b[0;34m(cls, command, tex, cwd)\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    255\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to process string with tex because \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcommand[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    256\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcould not be found\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m    257\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m subprocess\u001b[38;5;241m.\u001b[39mCalledProcessError \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m--> 258\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{prog}\u001b[39;00m\u001b[38;5;124m was not able to process the following string:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{tex!r}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    261\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHere is the full command invocation and its output:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    262\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{format_command}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    263\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{exc}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    264\u001b[0m             prog\u001b[38;5;241m=\u001b[39mcommand[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    265\u001b[0m             format_command\u001b[38;5;241m=\u001b[39mcbook\u001b[38;5;241m.\u001b[39m_pformat_subprocess(command),\n\u001b[1;32m    266\u001b[0m             tex\u001b[38;5;241m=\u001b[39mtex\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124municode_escape\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m    267\u001b[0m             exc\u001b[38;5;241m=\u001b[39mexc\u001b[38;5;241m.\u001b[39moutput\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackslashreplace\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m    268\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    269\u001b[0m _log\u001b[38;5;241m.\u001b[39mdebug(report)\n\u001b[1;32m    270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m report\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: latex was not able to process the following string:\nb'sale price $'\n\nHere is the full command invocation and its output:\n\nlatex -interaction=nonstopmode --halt-on-error --output-directory=tmpg0jlxahb 82002a6a534310632a0ec6d082310260.tex\n\nThis is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(./82002a6a534310632a0ec6d082310260.tex\nLaTeX2e <2020-02-02> patch level 5\nL3 programming layer <2020-09-24>\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/article\n.cls\nDocument Class: article 2019/12/20 v1.4l Standard LaTeX document class\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/size10.\nclo))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/mathp\ntmx.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/type1cm/type\n1cm.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/cm-super/typ\ne1ec.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/t1cmr.f\nd))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/inputen\nc.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/geometry/geo\nmetry.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/graphics/key\nval.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifvt\nex.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifte\nx.sty)))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/underscore/u\nnderscore.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/textcom\np.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/l3backend/l3\nbackend-dvips.def)\nNo file 82002a6a534310632a0ec6d082310260.aux.\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1pt\nm.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1zt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omlzt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omszt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omxzt\nmcm.fd)\n! Extra }, or forgotten $.\n<recently read> }\n                 \nl.30 {\\rmfamily sale price $}\n                             %\nNo pages of output.\nTranscript written on tmpg0jlxahb/82002a6a534310632a0ec6d082310260.log.\n\n\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(9, 6))\n",
+    "\n",
+    "contour = sns.kdeplot(\n",
+    "    data=df,\n",
+    "    x=\"phi\",\n",
+    "    y=\"eta\",\n",
+    "    cmap=\"Greens\",\n",
+    "    fill=True,\n",
+    "    cbar=True,\n",
+    "    # cbar_kws={\"label\": \"density of two variables (KDE weights)\", \"format\": \"{x:.1e}\"},\n",
+    ")\n",
+    "\n",
+    "ax.set_title(f\"Radiation Length Fraction $x/X_0$\", size=14)\n",
+    "ax.set_xlabel(f\"$\\phi$ [°]\", size=10)\n",
+    "ax.set_ylabel(f\"$\\eta$\", size=10)\n",
+    "\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",