diff --git a/tracking_losses_ntuple_B_default.root b/tracking_losses_ntuple_B_default.root
new file mode 100644
index 0000000..ce10330
Binary files /dev/null and b/tracking_losses_ntuple_B_default.root differ
diff --git a/trackinglosses_energy.ipynb b/trackinglosses_energy.ipynb
index 64bdb5c..463df5d 100644
--- a/trackinglosses_energy.ipynb
+++ b/trackinglosses_energy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,41 +17,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "31618"
+       "31630"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
-    "file = uproot.open(\"tracking_losses_ntuple_B_match_default_weights.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.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
+    ")\n",
     "\n",
-    "\n",
-    "#selektiere nur elektronen von B->K*ee\n",
+    "# selektiere nur elektronen von B->K*ee\n",
     "allcolumns = file.arrays()\n",
-    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)] #B: 9056\n",
-    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] #B: 1466\n",
+    "found = allcolumns[\n",
+    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)\n",
+    "]  # B: 9056\n",
+    "lost = allcolumns[\n",
+    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)\n",
+    "]  # B: 1466\n",
     "\n",
     "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
-    "#ak.count(found, axis=None)\t"
+    "# ak.count(found, axis=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#found\n",
+    "# found\n",
     "\n",
     "brem_e_f = found[\"brem_photons_pe\"]\n",
     "brem_z_f = found[\"brem_vtx_z\"]\n",
@@ -61,22 +66,22 @@
     "\n",
     "brem_f = ak.ArrayBuilder()\n",
     "\n",
-    "for itr in range(ak.num(found,axis=0)):\n",
+    "for itr in range(ak.num(found, axis=0)):\n",
     "    brem_f.begin_record()\n",
-    "    #[:,\"energy\"] energy\n",
+    "    # [:,\"energy\"] energy\n",
     "    brem_f.field(\"energy\").append(e_f[itr])\n",
-    "    #[:,\"photon_length\"] number of vertices\n",
+    "    # [:,\"photon_length\"] number of vertices\n",
     "    brem_f.field(\"photon_length\").integer(length_f[itr])\n",
-    "    #[:,\"brem_photons_pe\",:] photon energy \n",
+    "    # [:,\"brem_photons_pe\",:] photon energy\n",
     "    brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
-    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
+    "    # [:,\"brem_vtx_z\",:] brem vtx z\n",
     "    brem_f.field(\"brem_vtx_x\").append(brem_x_f[itr])\n",
     "    brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
     "    brem_f.end_record()\n",
     "\n",
     "brem_f = ak.Array(brem_f)\n",
     "\n",
-    "#lost\n",
+    "# lost\n",
     "\n",
     "brem_e_l = lost[\"brem_photons_pe\"]\n",
     "brem_z_l = lost[\"brem_vtx_z\"]\n",
@@ -86,15 +91,15 @@
     "\n",
     "brem_l = ak.ArrayBuilder()\n",
     "\n",
-    "for itr in range(ak.num(lost,axis=0)):\n",
+    "for itr in range(ak.num(lost, axis=0)):\n",
     "    brem_l.begin_record()\n",
-    "    #[:,\"energy\"] energy\n",
+    "    # [:,\"energy\"] energy\n",
     "    brem_l.field(\"energy\").append(e_l[itr])\n",
-    "    #[:,\"photon_length\"] number of vertices\n",
+    "    # [:,\"photon_length\"] number of vertices\n",
     "    brem_l.field(\"photon_length\").integer(length_l[itr])\n",
-    "    #[:,\"brem_photons_pe\",:] photon energy \n",
+    "    # [:,\"brem_photons_pe\",:] photon energy\n",
     "    brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
-    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
+    "    # [:,\"brem_vtx_z\",:] brem vtx z\n",
     "    brem_l.field(\"brem_vtx_x\").append(brem_x_l[itr])\n",
     "    brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
     "    brem_l.end_record()\n",
@@ -104,109 +109,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "photon_cut = 0\n",
+    "photon_cut_ratio = 0.1\n",
     "\n",
     "cut_brem_found = ak.ArrayBuilder()\n",
     "\n",
     "for itr in range(ak.num(brem_f, axis=0)):\n",
     "    cut_brem_found.begin_record()\n",
-    "    cut_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
-    "    \n",
+    "    cut_brem_found.field(\"energy\").real(brem_f[itr, \"energy\"])\n",
+    "\n",
+    "    tmp_energy = brem_f[itr, \"energy\"]\n",
+    "\n",
     "    cut_brem_found.field(\"brem_photons_pe\")\n",
     "    cut_brem_found.begin_list()\n",
     "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
-    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000 or brem_f[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
+    "        if (\n",
+    "            brem_f[itr, \"brem_vtx_z\", jentry] > 5000\n",
+    "            or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
-    "            cut_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
-    "            \n",
-    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "            cut_brem_found.real(brem_f[itr, \"brem_photons_pe\", jentry])\n",
+    "            tmp_energy -= brem_f[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem_found.end_list()\n",
     "\n",
+    "    tmp_energy = brem_f[itr, \"energy\"]\n",
+    "\n",
     "    cut_brem_found.field(\"brem_vtx_x\")\n",
     "    cut_brem_found.begin_list()\n",
     "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
-    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000 or brem_f[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
+    "        if (\n",
+    "            brem_f[itr, \"brem_vtx_z\", jentry] > 5000\n",
+    "            or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
-    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_x\",jentry])\n",
+    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_x\", jentry])\n",
+    "            tmp_energy -= brem_f[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem_found.end_list()\n",
-    "    \n",
+    "\n",
+    "    tmp_energy = brem_f[itr, \"energy\"]\n",
+    "\n",
     "    cut_brem_found.field(\"brem_vtx_z\")\n",
     "    cut_brem_found.begin_list()\n",
     "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
-    "        if brem_f[itr, \"brem_vtx_z\", jentry]>5000 or brem_f[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
+    "        if (\n",
+    "            brem_f[itr, \"brem_vtx_z\", jentry] > 5000\n",
+    "            or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem_f[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
-    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "            cut_brem_found.real(brem_f[itr, \"brem_vtx_z\", jentry])\n",
+    "            tmp_energy -= brem_f[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem_found.end_list()\n",
-    "    \n",
     "\n",
-    "    \n",
     "    cut_brem_found.end_record()\n",
     "\n",
     "tuple_found = ak.Array(cut_brem_found)\n",
     "\n",
-    "\n",
-    "\n",
     "cut_brem_lost = ak.ArrayBuilder()\n",
     "\n",
     "for itr in range(ak.num(brem_l, axis=0)):\n",
     "    cut_brem_lost.begin_record()\n",
-    "    cut_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
-    "    \n",
-    "    \n",
+    "    cut_brem_lost.field(\"energy\").real(brem_l[itr, \"energy\"])\n",
+    "\n",
+    "    tmp_energy = brem_l[itr, \"energy\"]\n",
+    "\n",
     "    cut_brem_lost.field(\"brem_photons_pe\")\n",
     "    cut_brem_lost.begin_list()\n",
     "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
-    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000 or brem_l[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
+    "        if (\n",
+    "            brem_l[itr, \"brem_vtx_z\", jentry] > 5000\n",
+    "            or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
-    "            cut_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
-    "            \n",
-    "            #cut_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
+    "            cut_brem_lost.real(brem_l[itr, \"brem_photons_pe\", jentry])\n",
+    "            tmp_energy -= brem_l[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem_lost.end_list()\n",
     "\n",
+    "    tmp_energy = brem_l[itr, \"energy\"]\n",
+    "\n",
     "    cut_brem_lost.field(\"brem_vtx_x\")\n",
     "    cut_brem_lost.begin_list()\n",
     "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
-    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000 or brem_l[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
+    "        if (\n",
+    "            brem_l[itr, \"brem_vtx_z\", jentry] > 5000\n",
+    "            or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
-    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_x\",jentry])\n",
+    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_x\", jentry])\n",
+    "            tmp_energy -= brem_l[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem_lost.end_list()\n",
-    "    \n",
+    "\n",
+    "    tmp_energy = brem_l[itr, \"energy\"]\n",
+    "\n",
     "    cut_brem_lost.field(\"brem_vtx_z\")\n",
     "    cut_brem_lost.begin_list()\n",
     "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
-    "        if brem_l[itr, \"brem_vtx_z\", jentry]>5000 or brem_l[itr, \"brem_photons_pe\", jentry]<photon_cut:\n",
+    "        if (\n",
+    "            brem_l[itr, \"brem_vtx_z\", jentry] > 5000\n",
+    "            or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
+    "            or brem_l[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
+    "        ):\n",
     "            continue\n",
     "        else:\n",
-    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
+    "            cut_brem_lost.real(brem_l[itr, \"brem_vtx_z\", jentry])\n",
+    "            tmp_energy -= brem_l[itr, \"brem_photons_pe\", jentry]\n",
     "    cut_brem_lost.end_list()\n",
-    "        \n",
+    "\n",
     "    cut_brem_lost.end_record()\n",
     "\n",
-    "tuple_lost = ak.Array(cut_brem_lost)\n"
+    "tuple_lost = ak.Array(cut_brem_lost)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<pre>{energy: 3.26e+04,\n",
-       " brem_photons_pe: [824, 287, 1.26e+04, 4.49e+03],\n",
-       " brem_vtx_x: [-8.5, -8.52, -33.8, -133],\n",
-       " brem_vtx_z: [157, 158, 601, 2.33e+03]}\n",
-       "-------------------------------------------------\n",
+       " brem_photons_pe: [1.26e+04, 4.49e+03],\n",
+       " brem_vtx_x: [-33.8, -133],\n",
+       " brem_vtx_z: [601, 2.33e+03]}\n",
+       "---------------------------------------\n",
        "type: {\n",
        "    energy: float64,\n",
        "    brem_photons_pe: var * float64,\n",
@@ -218,33 +257,33 @@
        "<Record {energy: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#data in cut_brem_found and cut_brem_lost\n",
+    "# data in cut_brem_found and cut_brem_lost\n",
     "\n",
-    "length_found = ak.num(tuple_found[\"brem_photons_pe\"],axis=-1)\n",
+    "length_found = ak.num(tuple_found[\"brem_photons_pe\"], axis=-1)\n",
     "length_lost = ak.num(tuple_lost[\"brem_photons_pe\"], axis=-1)\n",
     "\n",
-    "tuple_found[1]\n"
+    "tuple_found[1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<pre>{energy: 2.12e+04,\n",
-       " brem_photons_pe: [460, 1.71e+03, 249, 401, 979],\n",
-       " brem_vtx_x: [5.01, 26.4, 26.4, 26.6, 64.9],\n",
-       " brem_vtx_z: [199, 956, 956, 962, 2.29e+03]}\n",
-       "-------------------------------------------------\n",
+       " brem_photons_pe: [],\n",
+       " brem_vtx_x: [],\n",
+       " brem_vtx_z: []}\n",
+       "-----------------------------------\n",
        "type: {\n",
        "    energy: float64,\n",
        "    brem_photons_pe: var * float64,\n",
@@ -256,7 +295,7 @@
        "<Record {energy: 2.12e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,22 +306,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "Z_found = ak.to_numpy(ak.sum(tuple_found[\"brem_photons_pe\"],axis=-1,keepdims=False)) / ak.to_numpy(tuple_found[\"energy\"])\n",
-    "Z_lost = ak.to_numpy(ak.sum(tuple_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)) / ak.to_numpy(tuple_lost[\"energy\"])"
+    "Z_found = ak.to_numpy(\n",
+    "    ak.sum(tuple_found[\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
+    ") / ak.to_numpy(tuple_found[\"energy\"])\n",
+    "Z_lost = ak.to_numpy(\n",
+    "    ak.sum(tuple_lost[\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
+    ") / ak.to_numpy(tuple_lost[\"energy\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -292,26 +335,42 @@
     }
    ],
    "source": [
-    "plt.hist(Z_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
-    "plt.hist(Z_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
+    "plt.hist(\n",
+    "    Z_lost,\n",
+    "    bins=100,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"darkorange\",\n",
+    "    label=\"lost\",\n",
+    ")\n",
+    "plt.hist(\n",
+    "    Z_found,\n",
+    "    bins=100,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    "    histtype=\"bar\",\n",
+    "    color=\"blue\",\n",
+    "    label=\"found\",\n",
+    ")\n",
     "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
     "plt.ylabel(\"a.u.\")\n",
-    "plt.title(r'$E_{ph}/E_0$')\n",
+    "plt.title(r\"$E_{ph}/E_0$\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "found:  79481 , lost:  7087\n",
-      "0.08916596419270015\n"
+      "found:  24074 , lost:  2400\n",
+      "0.09969261443881366\n"
      ]
     }
    ],
@@ -325,18 +384,18 @@
     "n_found = len(brem_x_found)\n",
     "n_lost = len(brem_x_lost)\n",
     "print(\"found: \", n_found, \", lost: \", n_lost)\n",
-    "stretch_factor = n_lost/n_found\n",
+    "stretch_factor = n_lost / n_found\n",
     "print(stretch_factor)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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97z3Re3H27NkT7cOwlKE9u7v9MLVnP+SgPbtbRr+0Zz+0RTfK0J7dLaMb7dkP+9EvZWjPg37/7//98Sf+xJ84URknuVXSIBrEcdMwjJmOe6ye5LNe5rNV1TZlthuEtqiqLm3xOm1xUNn20BbV51dlXVW2RdnttMXJ6yrbH+6nPvBXRMRXVVzncW52OjExETMzM3H16tWI6F7ftFMdT5CMjY1FrVbruOA3q9Vqcfv27dLbl7X35Po333ds79/NZrNr6wAAAKNtEMdNxkwAAMPr3qj+NlLHre8bvuEb9ic1qu53HmuCZGZmJt761rd2XPiez372swcetFKlsbGxaDQasb6+vn/ZTsTdS3ciIi5evNi1dQAAgNE2iOMmYyYAAE5Tu92OiYmJiOhe37RTHU+QXLx4MX7sx36s44Lf7CS/oupE6sEr165di2azGe12e38manFxMRYXF/efdN+tdQAAgNHVz+MmYyYAgNFzb0Tcdwp15tne3o73v//98fjjj8fU1FRE3J0cWV9fj/X19f31qux3HusKkn7VarVieXk5IiKee+65mJycjImJif2cG41GbG5uxvz8fNTr9Wi32zE/Px8zMzP7ZXRrHQAAYHT167jJmAkAgNM2Nja2P0myvLwck5OTUa/XD0yORFTb7+x4gmR2dvZYBXd7+5RGoxHLy8v7Hf6idVZXV48spxvrAAAAo6lfx03GTAAA9IM3T4YUqarfeU+nK77zne88duEf/OAHT7Q9h83NzZ12Cl3JYZjK6Icc+qGMfmjLiOFpC+3ZXzl0y7C0hfbsvzL6IYdhKqMfchimMkaNcdPpqPJYLVNXVducZLuq6qpyv7TFyesqQ1ucrC5tcbJtqq6rjH7fL23Rne36yZl4/UHtVb3OVLJn3VHLsiwru/ELL7wQGxsbsbW1dSh28+bN2NjYiM997nMnSnBYtFqtaDabsbm5GY1G41RyuHXrVjz00EPx+c9/Ph588MFTyWGYaM/u0p7dpT27S3t2l/bsLu3ZXdqze/7ZP/tn8dhjj8Xzzz8ff+JP/InTTudUGTd1psoxk8/667TF67TF67TFQdrjddriddriddridf3UB97rW/31iPiDFdf9uxHxtyJO9W/hner4Fltv9qM/+qPxN/7G34jU/EqvH8wOAADQz4ybAAA4Tf30kPZ+1PEttt5sZWUlLly4EJubm/HSSy8den3sYx+LCxcudDNXAACAgWLcBAAA/av0ZM74+HhcvXo1Hnnkkdz4Qw89FIuLi2WLBwAAGHjGTQAAnKa9Z5BUXeegKH0FyezsbLTb7eQ6n//858sWDwAAMPCMmwAAoH+VniD5kR/5kVhdXY2Pf/zjcevWrUOvF198MRYWFrqZKwAAwEAxbgIAgP5V+uqaW7duxdbWVtTr9W7mAwAAMDSMmwAAOE0e0p5WOtepqanY2NiIRqOR29l/6aWX4p/+0396ouSG0YULF+Ls2bO5sbm5uZibm6s4IwAAKGdpaSmWlpZyY7/3e79XcTb9ybjp+IyZAIB+pg88XEpPkNy4cSM2Njbi277t2wrXuXbtWtnih9b169ej0WicSt33339/XL58Oe6///5TqX/YaM/u0p7dpT27S3t2l/bsLu3ZXdrzeFJ/rP5X/+pfxR/9o380vuzLvqzirPqLcdPxVTFm8ll/nbZ4nbZ4nbY4SHu8Tlu8Tlu8btTaYtD6wB7SnlbLsiwrs+G3f/u3x9WrV+O9731vl1MaTq1WK5rNZmxubp7aBAkAAFRF//cu46bOOWYAgEHXT/2ZvVwuR8QjFdf9YkRcieiLdjhK6Ye0Ly8vx7PPPptc5+d//ufLFg8AADDwjJsAAKB/lb665kMf+lC0Wq34/u///hgbG8tdZ2VlJb7ru76rbBUAAAADzbgJAIDT5CHtaaVzfe6552JjYyO5Tq1WK1s8AADAwDNuAgBgGK1/6ZXni1UmckKlJ0imp6ejXq/H7Oxsbvxzn/tcXL16tXRiAAAAg864CQCA03Rv9OaKjj/1pVee346IJ3tQZy+UbpuLFy/GxMREvPOd7yxc561vfWvZ4gEAAAaecRMAAPSv0g9pf+ihh5Kd/Ii7T6kHAAAYVcZNAACcpr1nkFT5GolnkEREvPDCC7GxsRFbW1uHYjdv3oyNjY34vu/7vpNUAQAAMNCMmwAAoD+VniD50R/90fgbf+NvRJZlhet42CAAADDKjJsAAKB/lb7F1srKSly4cCE2NzfjpZdeOvT62Mc+FhcuXOhmrgAAAAPFuAkAgNPkFltppXMdHx+Pq1evxiOPPJIbf+ihh2JxcbFs8QAAAAPPuAkAAPpX6StIZmdno91uJ9f5/Oc/X7Z4AACAgWfcBADAaToTd6+SqPJ1ppI9647SEyQ/8iM/Equrq/Hxj388bt26dej14osvxsLCQjdzBQAAGCjGTQAA0L9K32Lr1q1bsbW1FfV6vZv5DL0LFy7E2bNnc2Nzc3MxNzdXcUYAAFDO0tJSLC0t5cZ2dnYqzqY/GTcdnzETANDP9IGHS+kJkqmpqdjY2IhGo5Hb2X/ppZfin/7Tf3qi5IbR9evXo9FonHYaAABwYqk/VrdarWg2mxVn1H+Mm47PmAkA6GeD1gfee0h71XUOitK53rhxIzY2NuLbvu3bCte5du1a2eIBAAAGnnETAAD0r9ITJOfPn4/x8fHkOu9///vLFg8AADDwjJsAADhNew9pr7rOQVH6Ie3Ly8vx7LPPJtf5+Z//+bLFAwAADDzjJgAA6F+lJ48+9KEPRavViu///u+PsbGx3HVWVlbiu77ru8pWAQAAMNCMmwAAOE2eQZJWOtfnnnsuNjY2kuvUarWyxQMAAAw84yYAAOhfpSdIpqeno16vx+zsbG78c5/7XFy9erV0YgAAAIPOuAkAAPpX6QmSixcvxsTERLzzne8sXOetb31r2eIBAAAGnnETAACnyUPa0zp+SPutW7cO/Puhhx5KdvIjIv7T//Q/LdweAABg2Bg3AQDA4Oh4gmRhYeFEFZ10ewAAgH5n3AQAQD/Ze0h7la+ReEg7AAAAAAAwep770ivPq1UmckIdT5Csra3Fu971rsiy7NiVbG9vx8rKil9DAQAAQ824CQCAUXDxS688H4mIv1BhLifR8QTJ1tZWvP/97y9dUa1WK70tAADAIDBuAgCgn9wb1d9GapBuW3WsCRIAAACKGTcBAMDg6HiC5J3vfGcv8wAAABh4xk0AAPSTvYe0V13noBikXIfChQsX4uzZs7mxubm5mJubqzgjAAAoZ2lpKZaWlnJjOzs7FWfDsDBmAgD6mT7wcDFBUrHr169Ho9E47TQAAODEUn+sbrVa0Ww2K86IYWDMBAD0s0HrA5+J6icBzlRc30ncc9oJAAAAAAAAVM0VJADASKrVrhTGsuxyhZlQlvcQAACAkzBBAgAAAAAAQ8hD2tN6eoutD37wg70sHgAAYOAZNwEAwOkoPUHy4z/+48n49evXY3Z2tmzxAAAAA8+4CQCA07R3BUmVr5G4gmRhYSF+/dd//dDyF198Mb7jO74jLl68eKLEAAAABp1xEwAA9K/SEySNRiN+5Ed+JJ588sn9ZT/+4z8e586di/X19Xjf+94XFy5c6EqSAAAAg8i4CQAA+lfpq13W1tbioYceimvXrsW73/3uiIhot9vxzne+M5aXl+N973tffP7zn+9aogDA6KrVrhTGsuxyqTLLbkf/qPo97MVxyPAzbgIA4DSdiepveXWm4vpOonTbfOhDH4pGoxHr6+uxtbUVERFjY2OxsbERjzzySEREPPTQQ11JEgAAYBAZNwEAQP8qfYutqampOHfuXKytrcXMzEy89NJLsbGxEVNTU/sPIrx161bXEgUAABg0xk0AAJyme89E3Hdvta97B+gSktITJBER73znO2NzczN+6qd+Kh566KFoNBpx48aNyLIs3vWud3ngIAAAMPKMmwAAoD+VvsXWxMRE/JN/8k9yYz/yIz8SFy5ciG//9m8vnRgAAMCgM24CAOA0nTkTce+JLpMoUeediNitts6ySjfN8vJyMl6v149cBwAAYJgZNwEAQP8qfQXJO9/5ziPXed/73le2+KF14cKFOHv2bG5sbm4u5ubmKs4IgGFXq10pjGXZ5QozKW9Q8mS4OQ4PW1paiqWlpdzYzs5Oxdn0J+Om4zNmAgD6mT7wXdduR1wruErkC1m1uZxE6QkSyrl+/Xo0Go3TTgMAAE4s9cfqVqsVzWaz4owYBsZMAEA/G7Q+8L33RNzXg4em/8CZiB8oiP3r3Yhv/kL36+yFiu8+BgAAAAAAcPpcQQIAAAAAAEPo3nsj7u3BFSTJOmvV1ncSriABAAAAAABGjgkSAAAAAABg5LjFFgAMuFrtSjKeZZcryqR3Uvs4DPsHx+UzAQAAdOLeMxH3VTwLMEiTDq4gAQAAAAAARo4Jkg612+3TTgEAAKBvGTMBAPSheyLiTMWvAZp1GKBUT25ycjJqtVrua2Nj48C6b45PT08fiLdarZieno75+fmYnZ2NtbW1KncFAACg64yZAAAYJYN0O7ATabfb0W63Y3FxMcbGxvaXb21txdWrV2NiYmJ/2crKSszMzMS5c+f2l70x3m63o9lsxubmZjQajYiIOHfuXNy8eTNmZmZ6vzMAAABdZswEADCEzkT1swB3Kq7vBEZmgmRjYyM2NzcPdPQj4lBHPyJidXU11tfXC8uanZ2NiYmJ/Y5+ROz/KkpnHwAAGETGTAAAjJqRmSAp6oQ/++yzMTs7u//vtbW1uHHjRkxPT8fk5OSh7ba3t2NjYyMWFxcPLD9//nxEvP5LKgDoF7XalcJYll2uMJPyyuY5KPs+KHnSPxwX9IIxEwAAo2aknkHyZtvb29FqteLixYv7y9bX12N7ezvW1tZidnY2Hn744QP32r1x40ZERNTr9QNl7f0yKvUrKgAAgEFizAQAMODuPaXXgBigVLvvueeei0ajceAS8uXl5VheXo5WqxXLy8uxsrISk5OTsbW1FfV6PdrtdkTEocvO9+zFi7zyyitx69at0jnff//9cf/995feHgAAOvHqq6/Gq6++Wnr7V155pYvZcFqMmQCAUaIPPHpGeoJkdXU1Hn/88dxYo9GI5eXlmJycjOnp6Zifn4/V1dXY2tqKiIjx8fHc7ba3t5N1PvbYYyfK+fLly/H000+fqAwAADjKwsJCXLlSfPs3RoMxEwAwSoayD3waD2nfrbi+ExjZCZK9++IuLy8n15uamoqpqalotVoREXHu3LmIiLh582bu+m++jPzNnn/++Xjve997/IS/xC+hAACowpNPPhlPPPFE6e1feOGFE/+hm9NlzAQAjBp94NEzshMkGxsbUa/Xj+ycR0RMTk7u31N3b/2iXz0dVd4DDzwQDz744PGSBQCAip30NkUPPPBAF7PhNBgzAQCjRh949IzsBMmzzz4bU1NTHa9//vz5A/99831z9/7dbDa7lCEAdCbLLvek3Fqt+LLiXtU5qka9PR1r0J+MmQDotqJ+nz4f9NA9cfc2W1XXOSAGKNXuWltbK7yX7putr6/H7OxsRNx90GCj0Yj19fUD6+z9WurixYvdTRQAAOAUGDMBADDsRnKCZG1tbb/T/katViuazWZcvXr1wLrj4+MHfjl17dq12NjYOPCLqMXFxVhcXIyxsbGe5w8AANBLxkwAAENi7yHtVb6qvmLlBEbyFlvPPvts7q+W6vV6jI+Px8LCQqyvr0ej0YjJyclDDyVsNBqxubkZ8/PzUa/Xo91ux/z8fMzMzFS1CwAAAD1jzAQAQMrSzYill/JjO3eqzeUkRnKCZHV1NXf52NjYocvAizQajcJyAAAABpkxEwAAKXPjd195WjsRzRcrTae0kZwgAQAAAACAobd326uq6xwQI/kMEgAAAAAAYLQN0FwOAFBGrXalMJZll0vF+klq/xgMg3KsAQBwMvp9cAruieofmj5Al2UMUKoAAAAAAADd4QoSAAAAAAAYRmei+lmAqq9YOQFXkAAAAAAAACPHBAkAAAAAADBy3GILAAAAAACG0b1R/SzAAM06DFCqw+HChQtx9uzZ3Njc3FzMzc1VnBEAwy7LLp92CidWqz1z2ikAOZaWlmJpaSk3trOzU3E2DAtjJgCgn+kDDxcTJBW7fv16NBqN004DAABOLPXH6larFc1ms+KMGAbGTABAPxu4PvA9Uf1D0wfowR4DlCoAAAAAAEB3mCABAAAAAAD6Wrvd7nqZJkgAAAAAAGAYnYnXH9Re1euIW3qtra1Fs9mMWq0WzWYzNjY2cter1WoHXtPT0wfirVYrpqenY35+PmZnZ2Ntbe0YDXOXZ5AAAAAAAAA9d/Xq1VhfX4/Z2dnY2tqKq1evxuTkZKyvr8fExMT+eisrKzEzMxPnzp3bX/bGeLvdjmazGZubm/vPrzt37lzcvHkzZmZmOs7HBAkADLla7Uqp7bLscpczOYnbhZH+yhNOX+oz7/MCAFShqD+iLwKnYO8KkqrrLPDhD3841tfX9//9+OOPR7PZjMXFxQMTIKurqwfWe7PZ2dmYmJjYnxyJiP0rSY4zQeIWWwAAAAAAQE9tbGzE4uLigWWNRiMajcaB54usra3FjRs3Ynp6OlZWVg6Vs729HRsbGzE5OXlg+fnz5yMicrcpYoIEAAAAAACGUdXPH9l75ZiYmIh6vZ4be+Py9fX12N7ejrW1tZidnY2HH374wHNKbty4cWibiNi/miR15cmbmSABAAAAAAAiIuLVOxG3bpd/vVJ8l+xc7Xb7wAPYl5eXI8uy2NzcjJmZmdje3o7Jycn9q0z2/js2NlZYXqc8gwQAAAAAAIiIiIV2xJWtaupaW1uLer2e+9yQRqMRy8vLMTk5GdPT0zE/Px+rq6uxtXU3ufHx8dwyt7e3O67fBAkAAAAAAAyjeyL50PQ8T74r4on8O2F15IVbEY/9amfrLiwsxOrqanKdqampmJqailarFRER586di4iImzdv5q5fdBuvPCZIAGDIZdnlUtvVale6XmbVUvuQktq/YWgXyhmU976fcgEARpP+CAy2+8/cfZX1QIezDvPz83Ht2rWOJjQmJyf3n0Oyt37RlSImSAAAAAAAYNSdiepnATqYXFlZWYnJycn9B6t34vz58wf+++Znjez9u9lsdlymh7QDAAAAAACVWFtbi4iIiYmJA8v3bqGVZ319PWZnZyPi7sPZG41GrK+vH1hn7wqTixcvdpyLK0gqduHChTh79mxubG5uLubm5irOCAAAyllaWoqlpaXc2M7OTsXZMCyMmQCAfqYPfDIbGxuxsLAQs7OzsbKysr98c3Nz/8qP97///fH444/HpUuXIuLuhMr4+HhMTU3tr3/t2rVoNpvRbrf3b6m1uLgYi4uLMTY21nE+Jkgqdv369WNdNgQAAP0q9cfqVqt1rEvbYY8xEwDQzwauD9xHt9hqtVoxOTkZEbF/NcgbvfTSSxERMT4+HgsLC7G+vh6NRiMmJydjeXn5wLqNRiM2Nzdjfn4+6vV6tNvtmJ+fj5mZmWOlaoIEAAAAAADoqUajEVmWHbnem2+dlSpvdXX1RDmZIAGACtVqVwpjWXa562WepNzhUNzVybKnSpU42u052rz3AACdKRqjFPWn8tbX94IuORMdPTS963UOCA9pBwAAAAAARo4rSAAAAAAAYBj10TNI+pErSAAAAAAAgJFjggQAAAAAABg5brEFAAAAAADDyC22kkyQAECFsuzyaacwoHRZoNdqtSuFMecuABgMRd/nVX+XH7c+fQ3gtPhrAwAAAAAADKMzUf0VHQN0BYlnkAAAAAAAACPHBAkAAAAAADBy3GILAAAAAACGkYe0J7mCBAAAAAAAGDmuIAEAAAAAgGHkCpIkEyQVu3DhQpw9ezY3Njc3F3NzcxVnBAD5suzyaafwBrdLbpf/ncvgqNWuFMb66RjtRZ6DsO9LS0uxtLSUG9vZ2ak4G4aFMRPAQUV9grz+QL/0EWCY6QPftfRvIpb+f/mxnbJD+FNggqRi169fj0ajcdppAADAiaX+WN1qtaLZbFacEcPAmAkA6GcD1wfu0RUkc++9+8rT+kxE87nu19kLnkECAAAAAACMHBMkAAAAAADAyHGLLQAAAAAAGEZnovqHpg/QQ9pdQQIAAAAAAIwcV5AAQJfValcKY1l2uQc1nk1GU/mkFXcTsuypkmWWVbyPtdrVxHY73U+FSvXmM9N9vciz6n1P1Vf9eQ0A2OO7FjiRHj2k/cg6B4QrSAAAAAAAgJFjggQAAAAAABg5brEFAAAAAADDyC22klxBAgAAAAAAjBxXkAAAAAAAwDA6E9Vf0TFAV5CYIAGALsuyyxXXdykZr9WeSWz7VLfT6ZHB6LLUalcKY1UfF4yuXhyHjl8A6D953/m+swGOZzD+2gAAAAAAAByPZ5AkeQYJAAAAAAAwclxBUrELFy7E2bNnc2Nzc3MxNzdXcUYAAFDO0tJSLC0t5cZ2dnYqzoZhYcwEAPQzfeDhYoKkYtevX49Go3HaaQAAwIml/ljdarWi2WxWnBHDwJgJAOhnA9cHdoutJLfYAgAAAAAARo4rSACghFrtSmEsyy53fbuyufRq27K5lvdyIpZ/G5bTUH27UKQXn7Wyqs7FcQgAo2EQv/OL+kWDuC8wMFxBkuQKEgAAAAAAYOSYIAEAAAAAAEaOW2wBAAAAAMAwOhM9ueXV0r+MWPq/8mM7r3W/vl4xQQIAAAAAAHRs7o/dfeVpfTKi+ZPV5lOWW2xFRLvdPu0UAAAA+pYxEwDAgNp7SHuVrwF6SPtIXkFSq9UO/LvRaMTm5ub+v1utViwsLES9Xo/t7e2YnJyMqampA9t0sg4A/aFWu1IYy7LLXd8upex2J3O2MJJllwpjqf2v3ttKbrfT1SwYHGU/a8PzuYeTMWYC6H95/ZZ+73cU5TeI+wIMh5GbIFlZWYmZmZk4d+7c/rKJiYn9/2+329FsNmNzczMajUZERJw7dy5u3rwZMzMzHa8DAAAwiIyZAACGyN4VJFXXOSBGboJkdXU11tfXC+Ozs7MxMTGx34mPiJifn4/Z2dn9jnwn6wAAAAwiYyYAAEbFSD2DZG1tLW7cuBHT09OxsrJyKL69vR0bGxsxOTl5YPn58+cj4u4vqTpZBwAAYBAZMwEAMEpGaoJkfX09tre3Y21tLWZnZ+Phhx+OjY2N/fiNGzciIqJerx/Ybu9XT+vr6x2tAwAAMIiMmQAAhsyZU3oNiJG6xdby8nIsLy9Hq9WK5eXlWFlZicnJydja2op6vR7tdjsiIsbGxnK3b7fbHa2T8sorr8StW7dK78P9998f999/f+ntAQCgE6+++mq8+uqrpbd/5ZVXupgNVTFmAgBGmT7w6BmpCZI9jUYjlpeXY3JyMqanp2N+fj5WV1dja2srIiLGx8dzt9ve3u5onZTHHnusfOIRcfny5Xj66adPVAbAaavVrhTGsuxy1+srW2Yvckkp3y5HfZ3vlMon4i0lt+uFl0tuN5JdHU6g6s99StXnyjdbWFiIK1eKc2C4GTMB9L/j9AeK+hX90vfplzxgKPvAHtKeNNJ/NZiamoqpqalotVoREXHu3LmIiLh582bu+vV6vaN1Up5//vl473vfWzLj8EsoAAAq8eSTT8YTTzxRevsXXnjhxH/o5vQZMwEAo0QfePSM9ARJRMTk5OT+PXX3OupFv2iq1+sdrZPywAMPxIMPPlguWQAAqMhJb1P0wAMPdDEbTpMxEwAwKvSBR8/IT5BERJw/f/7Af998T9y9fzebzY7WAQAAGCbGTAAAA8ottpLuOe0ETtv6+nrMzs5GxN2HCDYajVhfXz+wzt6vpS5evNjROgAAAMPCmAkAgGE1MhMkrVYrms1mXL16dX/Z2tpajI+Px9TU1P6ya9euxcbGxoFfOy0uLsbi4mKMjY11vA4AAMAgMWYCABhC98TdKzqqfA3QrMPI3GKrXq/H+Ph4LCwsxPr6ejQajZicnIzl5eUD6zUajdjc3Iz5+fmo1+vRbrdjfn4+ZmZmjrUOwCio1a4UxrLscqnYKDuNdkm9h1XXl97/tyS2+/5EfR/sJC2GUPljrX8MSp4MD2MmgOGlXwGQb2QmSMbGxg5d4l2k0WjE6urqidcBAAAYFMZMAACMmpGZIAEAAAAAgJFyb1Q/CzBAsw4DdDcwAAAAAACA7higuRwAAAAAAKBjZ6L6WYAzFdd3AiZIAAAAAACAji39k7uvPDtfrDaXkzBBAgAAAAAAw6hHV5DM/em7rzytdkRzvvt19oIJEgBKy7LLldZXq10pjKVySW1XXvFXaJY91fXa0vvwliO2frlkrcXlln0vyh8zOyW3e63kdlSp7PGUUvX5CQCgV4r6Svo7ACfnIe0AAAAAAMDIcQUJAAAAAAAMo3ui+oemD9BlGSZIKnbhwoU4e/Zsbmxubi7m5uYqzggAAMpZWlqKpaWl3NjOTtlb4zHqjJkAgH6mDzxcTJBU7Pr169FoNE47DQAAOLHUH6tbrVY0m82KM2IYGDMBAP1s4PrA90b1swADNOswQBe7AAAAAAAAdMcAzeUADJZa7UoynmWXK8rkdKT2P7XvZbdL6UVbp/Ks1Z5J5PJUqTLTbVZc39HelogVXxpc/fGbf6uVo729q1nQG8NwPuzFuausfsoFADi543x/F/UD9AEA8pkgAQAAAACAYXQmqp8FqPqh8CfgFlsAAAAAAMDIcQUJAAAAAAAMo3ui+is6BuiyjAFKFQAAAAAAoDtcQQIAAAAAAMPIM0iSTJAAHKFWu1Jquyy73OVMTia1H2Vz7UWZvZB+D88WRrLsUqn6suypktuVbbP7jog/mIjtlKyzam8vud1rhZGyx2/V2/WTo86Hg7IfZQ3Kezjs53QAoJjvbIDjcYstAAAAAABg5LiCBAAAAAAAhtG9Uf0swADNOriCBAAAAAAAGDkDNJcDAAAAAAB07J6o/qHpA3RZxgClCgAAAAAA0B2uIKnYhQsX4uzZs7mxubm5mJubqzgj4ChZdrnyOmu1K6W2S+Vadj9SuaTK7MU+lHUa72GRsu2Slv+98rpbidjtE5TbH2q1ZxLR70rEPpoos9xxnzIox2EvziP9phfv7zAos+9LS0vxgz/4dEE0dX6BYsZMAIOvqL9V1N+o1Z7NWffxruYE3bK0tBRLS0u5sZ2dnYqz4aRMkFTs+vXr0Wg0TjsNAAA4sbm5ufjBH/xsQfRTEbFSZToMCWMmAKCfpX6w0Wq1otlsVpzREc5ET2YBltbuvvLsvNr9+nrFBAkAAAAAANCxuam7rzytj0Y0v7fSdEozQQIAAAAAAMPo3qh+FmCAZh08pB0AAAAAABg5AzSXAwAAAAAAdKxHzyA5ss4BYYIEoA9l2eXTTmFfKpda7UrXt0spu13Z9ixbX0pv2vMnjqj1sURsIxH7I0eU2y/enog9VxjpxTHaT8oeT6Og7P4PSrtV/d4XlXn3AZUe0g4AHC3LHj/tFIAR5RZbAAAAAADAyHEFCQAAAAAADKN7ovpbXg3QZRkDlCoAAAAAAEB3uIIEAAAAAACGkYe0J7mCBAAAAAAAGDmuIAGIiFrtSuV1ZtnlUtulck2VWfV2KWW360WZZd/7sm1WVrrMP3LE1i8XRrLsqUSdHzyi3Cq9PRG7lYiNF0ZO43NfRtnPYC8+ZwwG7z0A3VbUH/Gdc7Sq265f3qui+volP4A9JkgAAAAAAGAY3RvVzwIM0KyDW2wBAAAAAAAjxwQJAAAAAAAMo3vi7kPTq3wdMeuwtrYWzWYzarVaNJvN2NjYOLROq9WK6enpmJ+fj9nZ2VhbWyu1zlEG6GKX4XDhwoU4e/Zsbmxubi7m5uYqzggAAMpZWlqKpaWl3NjOzk7F2TAsjJkAgH6mD3wyV69ejfX19ZidnY2tra24evVqTE5Oxvr6ekxMTERERLvdjmazGZubm9FoNCIi4ty5c3Hz5s2YmZnpeJ1OmCCp2PXr1/ffMAAAGGSpP1a3Wq1oNpsVZ8QwMGYCAPrZwPWBz0T1swBnikMf/vCHY319ff/fjz/+eDSbzVhcXNyfIJmdnY2JiYkDfcK9q0T2Jj86WacTbrEFAAAAAAD01MbGRiwuLh5Y1mg0otFoRLvdjoiI7e3t2NjYiMnJyQPrnT9/PiIiVlZWOlqnU64gAU6sVrtSarssu9zlTKqX2oey7XLUtr1ot5PkWqbM8vtQ7murbC6p7Xpx3Jd/H/JvQ/K68USdVxPbFf96t+pjNKKViBXvX8TbErGbiVj/dJGG4Vx5EtUfawDAm/nO7Uxev6UbbVfUH8oru6i+Wu0jBWU8Wj6xhOPkDIyOvStE8tTr9YiIuHHjxoF/79m7UmR9fX0/llqn06tI+mf0DwAAAAAAdE+JW2y9+mrEq18sX+Urrx5v/Xa7HbOzs/v/HxExNjZWuG4n63TKBAkAAAAAABAREQsfiLjyE9XUtba2FvV6ff+Kj62trYiIGB/Pv1vE9vZ2R+t0ygQJAAAAAAAMo3vj2LMAT/71iCfyn0PfkRf+TcRjf7azdRcWFmJ1dXX/3+fOnYuIiJs382+LXa/XO1qnUyZIAAAAAACAiIi4//67r7Ie+P2drTc/Px/Xrl07MKGx9/9FV4HU6/WO1unUPR2vCQAAAAAAcEIrKysxOTm5/2D1PefPn4+Iw88R2ft3s9nsaJ1OuYIE6Kksu1xqu1rtSt+UmdquF3meZNuq260XbZr+arqdKPNqYrti5XO5LxE7WyqXsvse8ekjyi3ONcsuFcZqtZ9JbFf++C7njyRiDyZiv1Wqtix7qtR2Kb06X1Qp/Xk5yee+nGFoUwBgePSq/1FUbl5fqGjdLHu0qzkdRV8M+kd2T0R2pvo6U9bW1iIiYmJi4sDyVqsVjUYjGo1GrK+vx6VLr//NYmNjIyIiLl68GGNjY0eu0ykTJAAAAAAAQM9tbGzEwsJCzM7OxsrKyv7yzc3NaDab0Wg04tq1a9FsNqPdbu/fLmtxcTEWFxdjbGwsIqKjdTphggQAAAAAAIbQ7pmI3YpnAXYLrlhptVoxOTkZERGzs7OH4i+99FJERDQajdjc3Iz5+fmo1+vRbrdjfn4+ZmZm9tftZJ1OmCABAAAAAAB6qtFoRJZlHa+7urp64nWOYoIEAAAAAACG0J1TuILkTsXPPDmJIx6XAgAAAAAAMHxcQVKxCxcuxNmzZ3Njc3NzMTc3V3FG8Lpa7ScS0ZcLI1l2ueu59FOZZber1a6U2u4kdabUas8kord7UF/Z/S+by06prcq/v6nPy63EdsXtksol/f59fSIWEZH/vXO33J9JbPfoEeUeX9n9T52DIm4mYm89KqXK9OJzXVbZ96FX+5A+9sseM+WUPXf14rui6nYpt++/9qVXnu5/tzAajJmA05D3Pdgv/bda7dnTTuHYivsVFwuWP3doSb+0P7zZ0tJSLC0t5cZ2dsr9bYLTY4KkYtevX49Go3HaaQAAQBd845deeT4VESsV5sKwMGYCAPpZ6gcbrVYrms1mxRml7Z6pxe0ztYrrzCKis2eNnDa32AIAAAAAAEaOK0gAAAAAAGAI7Z45E7v3VnudxO6ZOzEot9x1BQkAAAAAADByTJAAAAAAAAAjxy22gDc4WxjJsicKY7XaM4ntniqVSa12JVHm5VJllq2vrKPyTNXZi3zKX9pY/FWRen/Lvoep4ym1D2WPi/JtnfoKTbV12a/e4s9nxM0jtv1cIraTiN13RLnHV/7zO54o85sLY7Xkc+h+JbFdteeglLLHaCrPXu1DP7VbWf2UZ9Xt2e0y7z6g0kPaAaqU993RT99t/azq/tFx6suyxyutr5ey7NHc5bXaxYozgdFx58yZ2D1T7XUSd87Uwi22AAAAAAAA+pQJkg612+3TTgEAAKBvGTMBAPSf3bgnduNMxa/BmXYYnEy7ZG1tLZrNZtRqtWg2m7GxsZG7Xq1WO/Canp4+EG+1WjE9PR3z8/MxOzsba2trVaQPAADQU8ZMAACMipF6BsnVq1djfX09ZmdnY2trK65evRqTk5Oxvr4eExMT++utrKzEzMxMnDt3bn/ZG+PtdjuazWZsbm5Go9GIiIhz587FzZs3Y2ZmprodAgAA6CJjJgCA4bIbZ+J2nKm4zsExUhMkH/7wh2N9fX3/348//ng0m81YXFw80JlfXV09sN6bzc7OxsTExH5HPyL2fxWlsw8AAAwqYyYAADrx00uvxk8vfTE39oWdrOJsyhuZCZKNjY1YXFw8sKzRaESj0Thwr9y1tbW4ceNGTE9Px+Tk5KHO+/b2dm5Z58+fj4jXf0kFgyjLvr8wVqtdqTCT8lJ5ZtnlUmWmtkvVV6td7Um5Zfej7HuYZU91vcxa7ZlS26XLTOVytjCSfh9SeRaXGbGTiN2XiKW8nIj97hHb3k7EvikRe60wUv0xerHkdqlcUt2gVJt1X/nPZ3+dR3pRbtkyB0XV+zfKbc3RjJkYJnnnu1E4z43CPg6abrwnx+krFtVXq32kYP1HS+XUfc/lLHM8Qz/7y3P3x1+euz839hut3fiO5isVZ1TOyDyDZGJiIur1em7sjcvX19dje3s71tbWYnZ2Nh5++OED99y9cePGoW0iYv+XUalfUQEAAPQrYyYAgOFzJ87Ebtxb6etOxbf0OomRuYKkSLvdjtnZ2f1/Ly8vx/LycrRarVheXo6VlZWYnJyMra2tqNfr+7+cGhsbKywv5ZVXXolbt26Vzvf++++P++/Pn5kDAIBuefXVV+PVV18tvf0rrwzGL8Y4mjETADAq9IFHz0hPkKytrUW9Xs+9vLvRaMTy8nJMTk7G9PR0zM/Px+rqamxtbUVExPj4eG6Z29vbyTofe+yxE+V8+fLlePrpp09UBgAAHGVhYSGuXBmMW2zSO8ZMAMAoGcY+8G7cE7uVP6T9TqX1ncRIT5AsLCzE6upqcp2pqamYmpqKVqsVERHnzp2LiIibN2/mrl90Sfqe559/Pt773vceP9kv8UsoAACq8OSTT8YTTzxRevsXXnjhxH/o5vQZMwEAo0QfePSM7ATJ/Px8XLt27cjOeUTE5OTk/j1199Yv+tXTUeU98MAD8eCDDx4vWQAAqNhJb1P0wAMPdDEbToMxEwAwavSBR89ITpDs3SN37yGBnTh//vyB/775vrl7/242m13KEqpXq6UuIXxHIvbpbqcSWXa5MJbOs/t6VV/Zcmu1ZxLR24lY6pSf2q5Y6n1KqdWuJqKpPF9OxM4WRrLsUslc7kvEdrq+Xflj7ZEj4h8pWW6qTcu+92X3sbjdkueLdyeK/Fj+rV/uSh1r3Vf2nJeKlX2PUnpRZi/LLVK23apu75SyuVSdJ4PLmAnyFZ1/nV+7L6+ttfNBee1RqxWt+2iPs3lzfcd9r97TkzyAvYe0V3uLrTsDdIute047gaqtra1FRMTExMSB5XuXg+dZX1/ffyjh2NhYNBqNWF9fP7DO3q+lLl682M10AQAAKmXMBADAqBipK0g2NjZiYWEhZmdnY2VlZX/55ubm/q+Y3v/+98fjjz8ely7d/bXx2tpajI+Px9TU1P76165di2azGe12e//y8MXFxVhcXIyxsbHqdggAAKCLjJkAAIbLnVN4SPud2K20vpMYmQmSVqsVk5OTERH7v2x6o5deeikiIsbHx2NhYSHW19ej0WjE5ORkLC8vH1i30WjE5uZmzM/PR71ej3a7HfPz8zEzM9P7HQEAAOgBYyYAAEbNyEyQNBqNyLLsyPXefBl4qrzV1dWTpgUAANAXjJkAABg1IzNBAgAAAAAAo+R23BO3K77F1u0BevT54GQKAAAAAADQJa4ggT5Wq10pjGXZ5a7XlyqzVvvbiS1vJ7Yr3oeyuZTVi1zSZe6Uqu9oxe1dXvHXQXof31IYybInEtu9loh1v91qtWcS0fsSseL9K5/n1yZin0zEUu/7O46o88VELLWP7z6i3OMr+9mu1UpW+JWJ2MeK38Msu5TIpZ/OzcW5VJ3nUfopn6rr68W+n8Z7WKR4/z5VaR4AVein82+Vis71vWyPvLJrtWcL1n38xPXl7WM39q+XbZdf9nsK1s0voxttl1/fRwrqe7Qn9XXDaRznUIU7cW/sVjwNMEgPaXcFCQAAAAAAMHJcQQIAAAAAAEPoTtwTuxU/g+TOAF2XMTiZAgAAAAAAdIkJEgAAAAAAYOS4xRYAAAAAAAyh3VO4xdbuAF2XYYKkYhcuXIizZ8/mxubm5mJubq7ijKhCrXal1HZZdjlR5k8ktrydKPNSyTL/TCL2vyRixdL7V9xmZbeLyP/s3bVTaZlHK1tu6rT+YCJ2KxF7SyL2cmGk7HGfru++RCy1DympNku1deo9upmIfaZkmanYWxOxiLj3e4tjtz+d2DAVe3u6zm5LHBa12q8UBx/+5kShqeP3bxfGenHuSulFmWXrO0oqn0Fpt6rbO6UX/YeyinP5tS+98hT3RyDFmGk45Z1HunG+Kjo/VX3OHnb90p5Z9nju8m4cX73ax6Jyu3Psvidn+87bqLce7ZM8Otcvx3mRXp1HOb6lpaVYWlrKje3snORvQZwGEyQVu379ejQajdNOAwAAuuAbv/TK86mIWKkwF4aFMRMA0M9SP9hotVrRbDYrzihtN87E7R5cQbK69LlYW8r/geirO3e6Xl+vmCABAAAAAAA6Nj331piey7+jxUdbO/GXmlsVZ1TO4NwMDAAAAAAAoEtcQQIAAAAAAEPoTpyJ3YqnAe5U/FD4k3AFCQAAAAAAMHJcQQKnLMsuF8ZqtSslSy3+aJcv83bJ7YqVzaVWeyYRfUsitpOIjSditxKx10rmcpSXE7HUqTu1HylnS+aS8rZELHU85T/g666y+5faLlVfql3K1lf2s3Rfcah2RLtMJmL/8BcSwe9Jl1sg9dkufc7748XbxQvfXBx7KfX5TX2Wyh735ZQ/N3e/zNR7dBK9OC5S2/WizLIGpcyyinK5+4BKD2kH7uqn81an8r4vjrsf3Sij03K7VXY39MN+d6ONjlvGcfb7eO1RtO6zHeUQERETVR8bh3M7rn4/zimvV+cI8u3Gmdit+IqOqus7CVeQAAAAAAAAI8cVJAAAAAAAMITuxD2VX9FxZ4CuyxicTAEAAAAAALrEBAkAAAAAADBy3GILAAAAAACG0O4p3GJrd4CuyzBBAiMmyy4Xxmq1nyje8OyjxbGdsvX97eIN42YidjsRezkRO5uIvZaI3ZeIJXY+xhOxiCz73sJYrfaBxJapXFP7n9ou1TZvScTKtk2qvlS7Vf0+pbZLHYcpqfZMHfeJfX/PEVX+R6lgYh/fkmrTir2QiKXepngwEUu9959JFVoofc67UqrMVHctVWYql14pv4/FyrZparvTaJsivWizsvWVbbNa7WpB5JOdpgVQWi/P6d0ou1f59dN3WZ5u5Jf3nVVUbje+T/PLOKqjXbbc47ZR0f5dzCn38YI8ni0oI3/93ilq048eWtLvx3me7rzf+QaxPYocZ1+Ocy6AMkyQAAAAAADAENqNM3G78itIqq3vJAbnWhcAAAAAAIAucQVJxS5cuBBnz+bfWmZubi7m5uYqzggAAMpZWlqKiL9ZEE3dFhGKGTMBAP1saWnpS/3gw3Z2kveApg+ZIKnY9evXo9FonHYaAABwYnNzc/GDP/gfCqKfjIifrDIdhoQxEwDQz1I/2Gi1WtFsNivOKO1OnIndiqcB7rjFFgAAAAAAQP9yBQkcQ612pQdl/kQi+pZELP+2A3e9nKjvmXJlPpLY7CPFoXSbpU5Bb0/EUrfsSF3KWNwu6fZM1Zfah1R9EbXaB0vmczMRSx0zZU/5qVz+YCL26ZKx8ZK5pPYvdVykYqn6Hiy5Xep4Kvm+f0Vis4iIsVTwbcWh/7w4lPpsZ9nlIxIqYaLkdr+QiN0uWWZJqXZJt+dTpeor+51VNs+7Up/D4gbvxfdrWb3IJdWmZT8vvfgMli+z6Dz6hVJ5AIOh6JzRk35Al/Lol5xH1fHb/z0dl32c97uXZXRa7rF9dedl1GrPFkS+/uR5HMtx66s6v95wPum+brRp3mf5uOV2o4zTshv3VP7Q9N0Bui5jcDIFAAAAAADoEleQAAAAAADAELr7DJJqryAZpGeQmCABAAAAAAA69otLvxP/59IncmNf3NmtOJvyTJAAAAAAAAAd+865r4nvnPua3NhW61Y80fxXFWdUjgkSAAAAAAAYQrtxT9z2kPZCJkgYObXa1WQ8yy4louOJ7X4oUecziTLPlozdl4gV5xlxMxG7XRy6nNjsu9+WCFYt1WZvT8RS7fK1idhnSuZyVDx1en5XIpbaj7LH2qcTsc8lYqn2TnktEXtLIpY4fpNSn6XU+7BTsr6yX72Jz/XvHrHpjyZifyvRbi8Uh7IsdVLogbJ9udup9+nB1Ialqkuf74vLrLw9E2q1K6W3zbKnSpWb2v+y25VVNpeyypZZdZul8yw6rxlqwDDr5XdX0Tknv86LHZdxGt+3tdqzOXk8XmkZxefw9+QuPW5+nTpu+/cqj+PlcPJj5rjtH/HRE63bP5/NRwtKOXw833V4X2q1jtJK5EAnjve+DqZu7MswtQcHGbUAAAAAAMAQ2o0zsVvxNEDVD4U/icG51gUAAAAAAKBLTJAAAAAAAAAjxy22AAAAAABgCN2JM5Xf8uqOW2wBAAAAAAD0L1eQAAAAAADAENqNeyq/gmR3gK7LMEFSsQsXLsTZs2dzY3NzczE3N1dxRtWo1a4kom9LxBrFobPfVBjKfi9R5KOXEsEjfN8Pldvu3qeKY7dvFceeebA49rFEfVOJ2HcWh7IsVeT/XBi7/t33JSpMeUci1krE3p6IvSUR+0zJ7VL7lzqNpvYvIuIjidi7E7HfTcQeScQ+l4il9iOVy2uJWOqDeDMRS3zu49Mlc0l8lmInEUt8PpNtPZ6IbSRixee1iBeLQ//fxGYREWuJ2PP/ZXHso8Wh9Hm9B96fiH1H8fvUzP7vwthmLVVh6pyQcrvUVuXbs1xXLssul6wvLbUfvaizF/WVfS9S9aXLzO8X3pU6P5XLJaXcvv9aRPxmQezVUnnAsI+Z8j5rRZ/b2tfkl5H9TjczOtqxzg8/VHAO+kCP+g4/9Gju4uwnT/69c5z3qtjhDlVRexaVnWWPd1xGxHs6LreX8vM7nNtdX5+7NMsOv7fd2O9a7dmC+g63c5HjvIfd6TcX5XZ4X2r/Q8GqP5a/OO98ctxjNNdEwfKN/EFG9pmnDy/8A/l/pMh7D7uSc5/rzjkpT9Fns1rdeA+rOg6WlpZiaWkpPvKRvLHoF7taF71ngqRi169fj0Yj9cc/AAAYFN8YEf9NQezfRsRfrTAXhoUxEwDQz/Z+sJE/4frbEfFk1Skl7caZuF35FSSeQQIAAAAAANC3TJAAAAAAAAAjxy22AAAAAABgCN2JM7Fb8TTAHbfYAgAAAAAA6F+uIOFYarUriWjqcErFHknEXiwO7Ywntnt3cei3Epsd5RdLbnc70W5febk49tFEmRuJ2IuJ2FckYgk/FX+tMHY9fjax5a1EbCcRS72/ZxOxB0vGbpasL5Xn7UQsIqLsw0f/YCL2lkTs0yXrS0m9v6n9f0ciljouUu/h1yZiLydiqfe+7DHzSCL2TYlY6j16e3HoHyQ2i4j4+4nY9yVi/3lxKMuKz13p74qSZf7d1JZ/qzDyp+OLhbHN+PpEmcXvRdn9S0nte1ll8+zF/p2s3O53V8v2Z7LsqVL19dP7W1b6M/8rFWYCw+Bi56t+Iu/hrxERj58og1otf3mWFS0/fA7IfzBtRPaTBXV+IG9pUVs8V7A8x4++ml9f7R/kLC0aYOXn0YvzdxnHOedn2eFjo2j74+zf8ct4T8dlR/xGwfJHOy4hb7+L5fcBa7WP5CzNPxaL9juvnYrXLfp85xyPHypYdSFnvwsO82/5nX9UUMifPLSkK8f+z+Z/NrOvyi/7L+eMd366sPDUH0uGV6/OScf7/PS36s/beW3Xiv57SPs9lT80fXeArssYnEwBAAAAAAC6xAQJAAAAAAAwctxiCwAAAAAAhtDdh7RXe4stD2kHAAAAAADoY64gAQAAAACAIXSnRw9pf37pN+OfL30kN/bazu2u19crJkg4pFZ7JhH92kTsexOxFwsjb8uKL2T6zH/7NcVF/uFEdSm/WnK7iPhTn/r5wlit9n+XKvPPf+rvFsbGYrswdjZ+rzD2L+ObC2P/109+a0d5vdlS7ZXi4Ld+V3HslzYSpY4nYvclYm9LxM4mYqmT8x9JxFLvbXFbR3wyEYuI2EnEHkzEHknEbiZi7+pBme9IxFL7n3oPP5OIpd7fVOzjiVjqOEyVmdou1S6vJWKpYyJR33ckNotInYIjvrw49OjX/evCWK32vxfGsuxyYrsrpWLvy84Xxjb+SnF9/zq+rjD238V/VhiL73uiOPa54lD8r8X7kG6X4u/eLHuq5HbF9fVOqs6ybVO8XS+k2julN3kWd9XLtllv2vpXCpYf9T0Iw61WezY/8EOPd77userL/wNFlj16eOFXn7i63P2IiKj9cMH6X51z/vlzBet+oPPvsM135HdmmvH3Dy0rOgcWn/9O/l2aV2dRfd34LjnOsVS87kdzlr2noIzj5FzUnvl51BJ/Dji07mTOwsKhaM5nIiJy+yp5x21E1Gr5JeS/3wXVLeV/hmIu57O8UFDGRl7b5Zf7yz/8J/PL+MmCsk/qo/fnL/+q/MV/OX760LJa7dP5K+e9Lz/aYV4D7C/HTx1a9tPx104hk944nfHLyWTZ4WWtVkSzWX0up+Gxua+Lx+byx9yfaH02Fpv/W8UZlWOCBAAAAAAAhtDtOBO3K34mSNX1nYRnkAAAAAAAACPHFSQVu3DhQpw9m3/Llrm5uZibm6s4IwAAKGdpaSki/mZBNHVLQShmzAQA9LOlpaUv9YMP29lJ3TqbfmSCpGLXr1+PRqNx2mkAAMCJzc3NxQ/+4H8oiH4yendjc4aZMRMA0M9SP9hotVrR7LOHkNyJM7Fb8TTAHbfYAgAAAAAA6F+uIBlRtdpvJKLfVxi5mP2jwtizf6lWGPup//F7C2Ofrv1MYezPFEYifiERq8UXC2NPx5cltoyoRZbY9kJh7B/WireL/7w49Idqxe2Wkn/Tgbu+IxH7XPbriejXl8rl6V8qtw+b2XOFsV/4huniDbcThX4yEXtHIpYq87O3E8E/mIi9NRGLiPh0IpZ6h1O3LHlbIpa6zPP/TsRSXxUPJmIpqTfqZiL2lkTsViL2SMlc/kgitpGIpd6/X0nEUgdpyVQiksf3H258uDD2ufiKRKHF+1irPXNEQscv80P/5DuL6/uO4vr+SZb6HP5ucegfFIeyv1t8zqv9r+vFsT+fSCWeKN6udiW1YSmp9yjLLpcv9/2J4AdLF9t1qX1MtXdqu5O0W5lc+kvR98sXKs0CjpL3mSr67NZqz+YX8sk/d3jZR+/PX/dDj+cuzp4//D1S+2T+5+XpryrKL6fspUcL1v3I4XKzn81dN+LHCso43HbPZ/9n7rq/XdDv+p6HD/f9r/y3BWlk8wWBw/kVjQufju8+XF/t8LKIiG/J/mHu8kfio4eWffxT78yv8B0Fx8FX5yz7oYLvjA/kL444/B4WyzsOCo7nAnmfi7zjKO1w20Uc83vtE1V/D77nGDlczF1aq+W1f0EZc/llROT8veZn67lrfstXPZRT2zflrvtHx34td3ntA3l/yyh4v786/zwTn8hZ/0MF6xb4E38+L7/8PnL23x8+j175i/nl/rMf+Mbc5b9Uy2+PPJezxN97eqDoO+jp+P7DC7O/dsyyO/8uPGm53Sq7n/PIr+9TPanrJHbjntit+IqO3QG6LmNwMgUAAAAAAOgSEyQAAAAAAMDIcYstAAAAAAAYQncf0l7tLbY8pB0AAAAAAKCPuYIEAAAAAACG0G7cE7c9pL2QCZITaLVasbCwEPV6Pba3t2NycjKmpqZKlVWrPZOI3i4O/dDlwtDTH6gVx45OKV9xkXEltd3P/Uyp6n6h1FYRT8eXldwyIvuvi3fyyt9MbLeV2K5eOp1COyW3+7eb/0lhbKL5fxTG/njJ+lK+Mj5VHPzriQ1/MBH7lkTs3ydi24lYPJIKJrx2RHw8EftYyXIfTMRuJWLvKZnLzUQstX+/lYi9PRE7m4il2uU3ErGUT5esL9XWbytZ333FoXcmNotIfoD/WPzLwthWvKsw9u8S+59lTxXGarXUt0Xx+3vx23+2MPZcfFdhbC0eSNSXOEYXi0M//GeuJspMvPd/79nEdqnPUrEsK+4HpNu6WNnt7npH1+ssu49l+1a9aNOyZfYil7LS7QnFujlm6lTta/KXZ+efPrTsSu3wsojEeKncae5gnXn1/bdffqwyns77opo7RgEFQ5crBV+AT+cs+6XC4c+vFZR9DLX8PIryO6nvjZ/OX17L+e7+oYJClo5R4Y8ds4y5R4+xbl5LXyxYOb+PXKt9pON1s+zxgjLyluaVGxGRs39F6391/rrZel4O+X2vwpy/Jmf5JwpSK9qXvOPjA0VjraL9zvHR+/OXf9XhRT8dfzl31Z/ezl8eeX2KiYI+yEbRe3hY9sH8k0TtfU/nr/9zOQv/3kc7rq/IY7WfyV3+8exbDi37nsgfcxT3yHqj6Bi9UvvuLpTdm73pVbkR+f3eovp6mUen9bVarWg2VyrNYxS02+2o13vwB9YwQVJau92OZrMZm5ub0Wg0IiLi3LlzcfPmzZiZmTnl7AAAAE6XMRMAwOnbjTOxW/E0QOqZJ9vb27GwsBAREYuL+T9+qL1ppr3RaMTm5ub+v7v5IxwTJCXNzs7GxMTEfkc/ImJ+fj5mZ2d19gEAgJFnzAQAwBttbGzE8vJyrK2tFfYHV1ZWYmZmJs6dO7e/bGJiYv//u/0jHBMkJWxvb8fGxsahGa7z589HxOtvIgAAwCgyZgIA4M0mJiZiYmLi0BUib7S6uhrr6zn3TvySbv8IZ3CeltJHbty4ERFx6L5ne29K6g0EAAAYdsZMAAD94U6c+dJttqp73Sn5UPi1tbW4ceNGTE9Px8rK4We57P0IZ3Jy8sDyN/4I57hcQVJCu92OiIixsbFkPM8rr7wSt27lPcD1C4ka3/gg0XvD2wYAQO/djtf7oan+Z9FD77/Y3XQYKL0ZM3Uouz+iVvBAYwCAhFdffTVeffXV0tu/8sorXcxm9Kyvr8f29nasra3F2tpazM/Px+rq6v4ttjr5Ec5xryLxl/YStra2IiJifHw8N769vV247WOPPXbC2h+LiG/d/9ejP/mvC9f8L37yDxXG/tfa/3PCPIbXL/zNkhv+Z11NIyIi3pGIfbJkmf/hDxdfOPYt8UuFsd2S9aX8WDxZGFt55b8q3vCtiUL/TSL2QCL2uUQsXkvE7kvEiv5g1Em5b0nEPnNEuUXenoi1Sm63UzKWKjPVpimpP6SU3Yey7XIzEft9iVhK4iv7iEPtf/tL31EY+7O1f1wYO/v5lxKlvlAYqdWuFsay7HJiuw8Uxj4XX1EYe+jVhwtjK5OJc0kU1xd/rzj0z//KH0+UWdyeEd+ciBW/iVn2/YWxWu1KYrvitk45SZm12kcS0edK5VNeqk1Tx2Hx/pfVi/cppXv7988j4vkT58NoOtUx05++HPGdTx9a/K0/8A8PLfuW2p86WV1U6nJON/jK205e7vf8+fzvqO/90LOHF76v4Dw6UXDu3cgpo0D2A4/nLq/N5dT5YwX15eXxbwsq/ETB8qVHDy+by2+jou/+LDtcRm0yp9yIiI2i/sNvHF5UkHOt1nk/o1br/D2J+Pr8xV9dsC8fyHmvvrrgvfrEcfLIr++RePHQsp/54b+WX8SPFv3B9x8cXrRx3P7Qew4t+dn/5WLumtl/9XTu8tr/nOUszW+jb/0LOefzv1h0Ps85jiLip2uHTyhXCvtQf7JgeW8U3Xno6Z7Vl/9+96KfWkbVeSwsLMSVK90fE5ymO3FP8qHpeW6/ejt2Xy3/18AvvHLU38TyLS8vx/LycrRarVheXo6VlZWYnJyMra2tqNfrJ/oRThETJCXsPSDm5s38P4C9eQbrjZ5//vl473vfe2j5Qw8tdFi7twwAgCr88Yj4oxER8fnPF/+oocgLL7zQhR8HMah6MWbq1EM/7eoRAKCcJ598Mp544onS2w9LH/hfLvxK/Isr/+zU6m80GrG8vByTk5MxPT29fyXJSX6EU8Rf20vY68wXNXiqs//AAw/Egw8+mBP58i5kBgAA3fL6rV3z+69pDzyQunSTYdebMVOHyl6MCgCMvPvvvz/uv7/8jy2GpQ/8x5785vimJ8rfLufTL/z7+J8e+x9PnMfU1FRMTU1Fq3X3Dh8n+RFOERMkJew99OXNl+zs/bvZbFaeEwAAQL8wZgIA6A+7JW6xVbv/TNx7gotyzzzQvYsBJicnY2NjIyJO9iOcIsUPI6DQ2NhYNBqNWF9fP7B87426eDH/PosAAACjwJgJAIBu2fvxTS9+hGOCpKRr167FxsbGgTdjcXExFhcXCx8SAwAAMCqMmQAATt9unInbFb+Oe8VKyvr6eszOzkZEb36E4xZbJTUajdjc3Iz5+fmo1+vRbrdjfn4+ZmZmSpWXZZdLbVerXSmM/Scxm9jyW4pD39oojk0Uh7J/UyuMXfl7xdslaos/86Hi2JX3Fce+MVHm/5T9nUQ04unaXymORfE+1u5kpbZLef8fT5T5z8uVOXn/RmHsX8a3FsauxN8sVV/Kfxb/qjj4UmLDs4nYpxOx1BnvXCL2scSNrFNl7uY/MGpflkj27LuLYzuPJAp9ORFLJZvKNf++jne9vWR9v5aIPZqIfTIR+32JWFl/KhFLtUsqz7ckYq+Vy+Vdic0i4nayY1K8H7/vgZ3C2E7yg1j8manVPpDYrnj/P9T+04Wxt9U/UVzk5xLVpY77xGH4r37vjxbGvjz+9+INfyjxJZpqloRU/yHVR0gr/uweWebfKc4n+yvd7+v0Qvk+2TOJ6O2SZZbb99R2ZfcPyuj2mKlT2Q8URf7koSW1+PsF6340f/FEzmeo6DvjzxXlkeMfFCz/0Vfzl39Pzj0v/ouCMuY+cmjRx7NvyV31a2s/mbv8p+O7Dy373p/Lr672s/ljmOwP5Ixffiu/jEc+fDjniIjL8Z5Dy/5MwVjrF/KLzq/vf8mvL2oFy49j6fHDy3Lek4iIb41/lLv8a7PDZXx8sqC+J3OWFY2dP1Rw8Oatn3fsR0T82/zFtW60Xa7fOMa6RX8kK9jvr85Z9omC79RPHD4W78ppp088W1BfzrEREZHTtX362340v7ZvWDy07GcW/1p+uf+g6F45eee74/2BMcsOt+kj8Z/krvviN70zv5DpvHIL2uhXD3/ui3s/BWXknNceiRcLS6lSVvCnoCvl/gwEfafo9litVive//73x+OPPx6XLl2KiIi1tbUYHx+Pqamp/fWuXbsWzWYz2u32/i21TvIjHBMkJ9BoNGJ1dfW00wAAAOhLxkwAAOxptVqxvLwcERHPPfdcTE5OxsTERIyNjUW9Xo/x8fFYWFiI9fX1aDQaMTk5ub/+nm7/CMcECQAAAAAADKE7cSZ2K54GuFNwJ4tGoxHLy8uHJj0i7t4+6823zirSzR/heAYJAAAAAAAwclxBAgAAAAAAQ2g37unqQ9M7rXNQDE6mAAAAAAAAXeIKEgAAAAAAGEJ3n0FS7RUkRc8g6UcmSAZcll0utV2tdqU4+Eu/kogVHzK1+PfF2028vTj2B4pDMZ+IbSZi/0ciVvuNRDAiorht/mX2vyXKLd7uK7K/Whj7WLyrVJlnsv+6MPb2+Exh7P/6J99aXN+3F4dSnv4zWXHwF1rFsdrZRKm/nIgljqd4sTj0cmq7km7vnGDj14pDOzcT26WO4UcSsU8mYvclYu9JxFJuJ2ITidinE7HE5yW53XgilmrrVJt9cyJW9uv1wUQs8Vl6sZEs9V2xlYgWH0+f+0ffUhjLsicKY6nvmNLfW596tTD22U+/tXjD1GEYiXPQWHHoyx8rPudlidNhSu0DxW1Wq5UrM9XWyX5AutHS/mrivPZXis8zvThmqleu3dLvRepc0ov6yhmc9wgOy7LHT1xG4efqA0Wfjbz1L+av+pFH85dvfCRn2XO5q+Z9Rmtfk99vKvoeq33N4Xb6y9+W/92c/YWCMiZzCh/LXzdqz+Yvzw73S//IQ/mrNv/7nPrmCsqt/Xp+dTnHR61W0DfeKDgONvIW5h8bv1zLfw8jPp+zrGBfNg7nV3Sc176moLrIO76K+oxfX7A8b18KjvPcdSPyxyFF9eXt4zG/8z5xeFHRd1xx/+wYdX4ip50LPP3DP5a7/PKfXjy88H0Fx8Yn/1zu4uwHqv0e/+X4lvzAjx5eVHR+zX7u8LJvzb4xd92nC8q4XLbjfoqe/qHDOR/33ctv0/zPZmH7532vHGPd4tzyj91ufFfn19eNnPPK+FTJjDgtbrEFAAAAAACMHFeQAAAAAADAENqNe+K2h7QXMkFSsQsXLsTZs/m39Jibm4u5ubmKMwIAgLJ+Lb7u674uN7Kzc5LbUDLKjJkAgH62tLQUS0tLEfH/5kRPcMtiToUJkopdv349Go30veIBAGAwfGP85m/+Ym6k1WpFs9msOB+GgTETANDP9n6wUfwMkpWqU0rajXtjt+JpgKrrO4nBudYFAAAAAACgSwZnKoeuyrLLpbbLnxnd81PFoY23JbZ7MBG7WRxqfmNiu/FEbCMRS7dNrZba8l8XRn7w3R8s3uzTqTKfKYz8f/6bHy/e7N8nivxHidinikN//faXFcaevvdnEoW+NRFLuZWIfSwR+yOJ2K8mYn8wEXtHIpaSfHMjInXrkdS2b0/EPlmyvneVLDN16ehridgvJ2Kpc0Iqln8rjrs+koiV9WLJ7X4lEUu9t4nPxFj6V7YfjO8rV+d/VBxKfx8Un4NrteLzWspbd7+3MPa5F7+qeMOxVKmJz9l/l3qfir9H0t8T1Uq9R+X7Aen3L8vuK5VPqkvam/3ofpm9yeWpUtulpPs55dqlVvvlgsj/02FWMNiO+/nP/674jfyVN547Rp3HyOMTzxYEHi9YP+f8UNRFLmqP3K/Poj7aRwuWH/Zlv/mF/DTyugc/kL9/Ree/vOXHf7/z2vq45/DO2yPLDu9jfg7HK7dYfhnHa6f8dfPzLvis5CwvyqHo/c5bv/i78T0dL897T1Jy6/wHBfsSWc7Sgvf7HffnL88rood++Wv+ZH7gPz68qPg9PLw8+wv5xWbH2L/jflbK9v/Kyn6yR+VmjxZEOt+/7rRFN85JnetGzt+SfdOhZS+3PhabLqIeKCZIAAAAAABgCN2Je2K34oe03xmgG1cNTqYAAAAAAABd4goSAAAAAAAYQrs9uoLk3y79UvzbpV/Or3Mnddv1/mKCBAAAAAAA6Nh/PPet8R/PfWtu7HOtj8f/2Sz3DNKqmSABAAAAAIAhdCfOnMIzSKqt7yRMkHAsWXa5MFarXUls+WAi9q5Efd+UqO9mosy3JMp8IrHdEf56cSj7iUTb/ECizLFEbOF2cewLie3+cCL207+VCL67MPLdZ/5eYrvUe/G7pepLu5WItRKx8UQslefnErHUsX3U5YSpU/DvS8ReTsRS78XZROzjiVhKqm0Sx2+y3VLvb2offi8RS7VZytsTsV8uuV1qHz6diP3B4tCXJzaLiA/86o+kVyjyj4pDqe+DXqj9cCL47xKxB1Kllj0npD67xcd9+e/QlHK5pOpL5ZllTyWzKb8fqfNF/yi7f+k27f5nqeyxVj6XonNs6rwMHPTR3KVFn8u8z3Jvv5vf0/GaxeeZHuX3PffnL1/vvIjjtHORonXzyq7Vni1Y9/GO6+uGbux3cRl5S8v2Ezqp7zhld348F6+b/5nNW798/+gN/lzH1UXMnby6bvj4Dxe03SfyF3/L7xwefNRqv5q7bv7nqvPPYJGiz2BX3sM+UfU47jj6Obciv1z7fM7SVyrPg5PxkHYAAAAAAGDkuIIEAAAAAACG0G7cE7crvuXV7gBdlzE4mQIAAAAAAHSJK0gAAAAAAGAI7caZ2K14GqDqh8KfhCtIAAAAAACAkeMKkopduHAhzp49mxubm5uLubm5ijPqniy7XHGNH0jEig/tWu12stTkfvytnyiO/cQTxbG//bcTNeYfD0flUqt9MFHm2xOx302U+VzxZj/7VKLM1P7tJGKfTsReS8TK+kzJ7VLHTGr/it/bu1L7f7Nkne9IxNLHfrGy79N9iVhqH1LHb8qDiVhq328lYp9LxFL7l3rvy7ZL4n34h4nNIiL+6q8Wx575puLYjeJQrXbliEqPL3n+TfRYstXi2B+P9cLYv6h9MpHNy8X1lfy+S7dZ6pgp/pxlWfG5uRfv0Umkv9O6n2vlx2hC2Vx60bfq/vvwaxHx4YJYL77LGQXDPGaK6M5n++RlfPSY9T1+wvqKPFqwPH//8s9TvRuHHqedi9bNy/k46x5Xftlf34UyjqtX+9L5urXaswVr5x//x2v/9xTk0fln5Vj5feA4+1LQbj/UQVLdlN9EkWX5y2uf+tacpZ/PX/cYn6tuqP7vXfmq3u+iz0S/tMdpWlpaiqWlpXg052tsZ2cnXnyx8pQ4ARMkFbt+/Xo0Go3TTgMAALrgGyPijxXEPhUR/0OFuTAsjJkAgH6W+sFGq9WKZrNZcUZpd+JM5be8uuMWWwAAAAAAAP3LFSQAAAAAADCEduOeyq8g2R2g6zIGJ1MAAAAAAIAucQUJAAAAAAAMod04E7crv4JkcJ5BYoKEgZVllwtjtdqVUtsd7eVSdUaMJ2IfL1nm2xKx30rEziZiby8OPZ3YLNEuaTcTsVuJWKo9U2U+koh9LBFLnSpTufTqFJt6D4uPp3SuKak2fUsPtnstEUsda58sWd87ErHUPqTeh1Seqe1K7sNuYrOIiHiwOPS3Eps9clS5x1f23B1fkTh3/0Rx6F8sTiaySX3ui9+nWu2ZxHa3CyNlv39S7VL2+y5dZvH+ZdlThbGj6iy7Xfq7sPvK1pfe99T3QfEx04t9710f6bC7D6j0kHboR93+vHdb0bkqL+/i81p/7ONx2rob70teexSVe5zvmeOWcZx96d13/Udzlx5nX477ntRqz+aU8XhBHvnLT6r2NQWB9/SkumL5z6+O+IGC5d9z/6FFvWqjQdWrc3c3PscwyNxiCwAAAAAAGDmuIAEAAAAAgCF0J87EbsXTAHcG6BZbriABAAAAAABGjitIAAAAAABgCO3GPZU/NH13gK7LGJxMAQAAAAAAusQVJNAlWXa51Ha12pWSNX4mETtbGMmy7y+XyydSubwlEbudiO0kYg8mYuX2PeLFRCy1Dymp/fvoEdumcn1bIpZqt9R+pLa7WbLMlxOx8ZK5pMpM5ZlyXyL2sUQsdRymYp9MxFL7njomEsfaLyc2i4hku20nNrvxWmEodc4re15Llvnu4u2S9f1Y6tyc+vzeKoxk2VPlcikp3dbPlMqlF+/fSepMKbtd2TJ70W692IdUfb3Iper9A0bb8c4rF3uWR55ufNf3y3nzOHkU7Xcv9+U4bZ2XR1Fu3diX45TRnfo+UhB5ruMyYq5g+Q/06j0syvnR/MUbh9upVstftV8+Q3nvbb/kdhyDmDP94RNL/0f87tIv5sbu7Hyx4mzKM0ECAAAAAABD6O5D2rt/i62vmvuz8VVzfzY39nLrY7HZ/KGu19kLbrEFAAAAAACMHFeQAAAAAADAENqNe+K2h7QXMkFSsQsXLsTZs/n3mJ+bm4u5uaKbQgIAQL/5tfi6r/u63MjOTuqZS1DMmAkA6GdLS0uxtLSUG9MHHjwmSCp2/fr1aDQap50GAAB0wTfGb/5m/oMZW61WNJvNivNhGBgzAQD9LPWDjX7sA+/GmditeBqgF8886ZXBudYFAAAAAACgS1xBwlDKsst9VW6tdqVUmant0sp+tN9SHLr9WmK7z5SsLyVVX/4tF+4qeynjyyXrS203fkSdtxKxsm2a2v/UcZHKNZVLqm0+nYjdTsQSx2Eyltq/VLu8veR2H0vEUu2ZyvNmIpbwwBHxl363OJZ6C997X2GoF+e15Dn2PxSHkvW9v3i7tOJ9T+nNOb379ZXN5ajtqv6e7Ce92Pey+ikXgF7Lskcrrq8/vpeKztn9Nhauquy89igq9zj1daOdj7t/+XW+p+Oya7Wikqv+nv+NguVFn9nD+5JlXUumI8d9v/vlfACcjAkSAAAAAAAYQnfiTOW3vLrjFlsAAAAAAAD9yxUkAAAAAAAwhO7EPadwBcngXJcxOJkCAAAAAAB0iQkSAAAAAABg5LjFFlQgyy4Xxmq1KxVmcpL6fqUwUn7/ik9BWfZUqTLTuVxN5LKTiJ0tud2tRCwtyy4Vxsq/h7cTsZcTsbJfFfclYuOJ2M1ELJVn6n1KSb1Pqfc3tX+fTsRS70PJY+0TRx1rjxeHXn6xOPbPy733ZT+jSe9J1Zdo77/49kShqf1LvffllN33sp/50m2dUPV3Vq+U3Y+eHNsl9dPxVFzmp0rVBXAcReegqs/Lx9GNnPt5/7olr52K9rtX7XEa7ZxX53G+v7Msf3mtVjajcrIsfwzSz5/ZfsiB3jjO+WQY7Z7CQ9qrru8kXEECAAAAAACMHFeQAAAAAADAENqNe+J25VeQDM51GYOTKQAAAAAAQJe4ggQAAAAAAIbQ3WeQVDsN4BkkAAAAAAAAfcwVJBW7cOFCnD17Njc2NzcXc3NzFWfEacuyy4WxWu1KyVJfLlVfSq32wUTs+VL1pfavVnums8SOZacw0pv34XbJ7U5SZ1nFbZN2XyL2WiL2mUSs3FdTll0qjKWPp17se9n3/mYiNp6IffqIcvO/d+76hUTshwojZc8lpf1Sye1+biMRTB2HxW2W+nz24lxS/rxdLs9e5HKScmu1q4ntij/3lR+jCVW/92XLLJPn0tJSPPros7mxnZ2dePHFYxcJxkwc03tyl+ad0/rpu+Gkis7ZVe9jL/M4ThnD/n53Z1/yPyu9Uqt95Fh55K2fZY92MSNGWbfPB0tLS7G0tJQb29kp+zcGTosJkopdv349Go3GaacBAAAnlvpjdavVimazWXFGDANjJgCgnw1aH/hOnOnJLa+2l/5+bC/l/1jqzs6rXa+vV0yQAAAAAAAAHRub++4Ym/vu3NgXWh+J32nmx/qNCRIAAAAAABhCd+Keyh+afmeAHn0+OJkCAAAAAAB0iQkSAAAAAABg5LjFFvSxLLtcarta7UqpWPqU8PZSuZR3uzBStl1S+5dul5SzidhOyTKPUrwfWfZUYaz8PqaU3cfUsVb83pd/D8cTsVslt0vt+32J2M1E7C0ltzvKp0uW+1uJ2LtL5lJWKs/Ue/FaYSR1LqnVnim1XUq6vnLn7VSZZetLKX/+PYnun0vLtmk/vRdVK5fnp7qeB8CbZdnjp53CsXXj+7SojLzzdS+/v0+nb3BYv+TRD4q+s6tuoyx7NHd5rVa0xXM5y7yv0A234544U/Ettm4P0HUZg5MpAAAAAABAl5gg6UC73T7tFAAAAPqacRMAQP+5E/fGbsWvOwN046qRmiBZW1uLZrMZtVotms1mbGxs5K5Xq9UOvKanpw/EW61WTE9Px/z8fMzOzsba2loV6QMAAPSccRMAAKNicKZyTujq1auxvr4es7OzsbW1FVevXo3JyclYX1+PiYmJ/fVWVlZiZmYmzp07t7/sjfF2ux3NZjM2Nzej0WhERMS5c+fi5s2bMTMzU90OAQAAdJlxEwDAcLkT98Ruxc8guTNA12WMzATJhz/84VhfX9//9+OPPx7NZjMWFxcPdORXV1cPrPdms7OzMTExsd/Jj4j9X0Tp6AMAAIPMuAkAgFEyEhMkGxsbsbi4eGBZo9GIRqNx4D65a2trcePGjZieno7JyclDHfft7e3css6fPx8Rr/+KCk5bll0utV2tdiURvZmIjZeqr2yeKal9qLq+o6TySe/HUyVrPFtyu9cKI6lc0m1zX8nYTiKW+kpLbXc7EUsd96k8X07EUlK5pBxV37sTsbclYs+XKrPs5yL9GX0xEXtLIvb2wkjV54uUsueDWu2ZRJnlzhVV7/tRyrZN2TJ7sV3ZMsseo2W36/b+tVqtaDZXulomvWXcBIOv377H+1HR92Q/t91xcu7n/bjrudNOAOCAwbnW5QQmJiaiXq/nxt64fH19Pba3t2NtbS1mZ2fj4YcfPnC/3Rs3bhzaJiL2fxWV+gUVAABAPzNuAgAYPrtfusVWta/BmXYYiStIirTb7Zidnd3/9/LyciwvL0er1Yrl5eVYWVmJycnJ2Nrainq9vv+rqbGxscLyjvLKK6/ErVu3Sud8//33x/333196ewAA6MSrr74ar776auntX3nllS5mw2mqetxkzAQAnBZ94NEzshMka2trUa/Xcy/tbjQasby8HJOTkzE9PR3z8/OxuroaW1tbERExPp5/O6Ht7e0j633sscdOlPfly5fj6aefPlEZAABwlIWFhbhypfytJBkOpzFuMmYCAE7LMPaB79w5E7t3Kn5Ie8X1ncTITpAsLCzE6upqcp2pqamYmpqKVqsVERHnzp2LiIibN/PvSV90OfobPf/88/He9773eMm+gV9CAQBQhSeffDKeeOKJ0tu/8MILJ/5DN6fvNMZNxkwAwGnRBx49AzVB0mq1Yn5+vqN16/V6LC8v58bm5+fj2rVrHU1oTE5O7t9Pd2/9ol88dVLeAw88EA8++OCR6wEAwGk66W2KHnjggS5mw3EM+rjJmAkAOC36wKNnoCZIGo3GiR/ot3d/3L0HBHbi/PnzB/775nvm7v272WyeKDc4fW8rjGTZ9xfGarXiSw9TsZQsu1zpdim92odUualty24XsZPMp9u5pJXLJeJsYSTLLhXGyud5u+R25b5ee7MPERG/lYi9nIg9Wqq2XnwOI96RiKXuU5/a92K12jOFsSx7qlSZ6frKnmeKcyl/ruiNXuRT9vx0ss/T8XPpRZn99v7SX4ybAIoN4vdkUc55/YF+379+zw+G0e7uPRG3q73l1e7u4DykfXAy7YK1tbWIiJiYmDiwfO9S8Dzr6+v7DyQcGxvLHWzs/VLq4sWL3UwXAACgcsZNAACMioG6guQkNjY2YmFhIWZnZ2NlZWV/+ebm5v4vmN7//vfH448/Hpcu3f0F79raWoyPj8fU1NT++teuXYtmsxntdnv/0vDFxcVYXFyMsbGx6nYIAACgy4ybAACGy+7tMxG3q50G2K34ipWTGIkJklarFZOTkxER+79qeqOXXnopIiLGx8djYWEh1tfXo9FoxOTk5KH78TYajdjc3Iz5+fmo1+vRbrdjfn4+ZmZmer8jAAAAPWLcBADAqBmJCZJGoxFZlh25Xqf36W00GrG6unrStAAAAPqGcRMAAJ364vLfiddW/m5uLPvCFyrOpryRmCABAAAAAIBRc2f3TE8e0n7mr87Emb+af3XwnRd+PV597Fu7XmcvjNRD2gEAAAAAACJcQQIjp1a7koieLVVmll0ul0xJqX2oOpdUfem2Tiu7bdm2qdWe6UGZ5fe/2E6p+srmWbbNyqrVrpbc8pNHxF9LxBol66xaah9/NRH7rkTso4nY7XQ6XdaLc1fZMnt1jq36/JzSi1yq/m6q+v3tp+9eABg1Rd/Ded/Bx1m3n9RqH8lZ+lzuuv2+L9Bvdnfviazih6bf2R2c6zIGJ1MAAAAAAIAucQUJAAAAAAAMod3bZ+LOa9VeQVL1FSsn4QoSAAAAAABg5JggAQAAAAAARo5bbAEAAAAAwBDK7pyJbLfiaYA7g3OLLRMkFbtw4UKcPXs2NzY3Nxdzc3MVZ8SoybLLp53CifViH2q1K12v76jtelFnqsy0+xKx24n6nimMpfahfJ7VSueZ+gpNtedOYSTLniqZy+8mYhERLydijyZirx1R7vGVP+4/mdju+xP1/WqizNR7WHzc95PTOHcNA+12fEX7t7S0FLXaHyjYajA+R/QfYyaAg47Xz3hPz/I4jqL+VtG+ZFneuGS4+1cMrqWlpVhaWsqN7ewUj/npTyZIKnb9+vVoNBqnnQYAAJzY3Nxc/OAPfrYg+qmIWKkyHYaEMRMA0M9SP9hotVrRbDYrzugIt++JqPqh6bcH58keg5MpAAAAAABAl5ggAQAAAAAARo5bbAEAAAAAwDDaPVP9LbZ2PaQdAAAAAABg3/b2diwsLERExOLi4qF4q9WKhYWFqNfrsb29HZOTkzE1NXXsdTplggQgIrLscmGsVrtSart+rLPYTsntbnc1i15JtWdK2feobLuUzTNi/Ij4y4nY7yZi7y6RS1pvjt+UwThGy6q+PQdHP7VN9ef0YuXPawBAv8n77u6XPlC/5AFExG4t4nat+jpzbGxsxPLycqytrcXMzMyheLvdjmazGZubm9FoNCIi4ty5c3Hz5s399TtZ5zg8gwQAAAAAAOipiYmJWF1dLYzPzs7GxMTE/sRHRMT8/HzMzs4ea53jMEECAAAAAADDaDfu3lyhytfu8dPc3t6OjY2NmJycPLD8/PnzERGxsrLS0TrHZYIEAAAAAAA4NTdu3IiIiHq9fmD53pUi6+vrHa1zXJ5BAgAAAAAA3PXFV+++yvq9V469SbvdjoiIsbGxwngn6xyXCRIAAAAAABhGe7fYOo7lhYifutKLbAptbW1FRMT4+HhufHt7u6N1jssECcARsuzySNRZJJVLrfZMIlb8RdqL/etFfWXLTLXL8Xslnbh5RPwtidjbE7HPlcglrfz7dLZkjbcS9T1VskzKSL33Kf10Piyr6nNQWd0us9VqRbN5/HsAAwCdOc53d1G/ol/6Wnn59UtuMJL+6pMRf/GJ8tv/2xci/spjx9rk3LlzERFx82b+3zjq9XpH6xyXCRIAAAAAABhGew9OP4577o/48vvL1/llDxx7k73JjaKrQOr1ekfrHJeHtAMAAAAAAKfm/PnzEXH4OSJ7/242mx2tc1yuIKnYhQsX4uzZ/NuEzM3NxdzcXMUZAQBAOUtLS7G0tJQb29nZqTgbhoUxEwDQz/SBe2NsbCwajUasr6/HpUuX9pdvbGxERMTFixc7Wue4TJBU7Pr169FoNE47DQAAOLHUH6vvPoPk+L/gAmMmAKCfDVwf+HZEvHYKdRZIPUj92rVr0Ww2o91u798ua3FxMRYXF2NsbKzjdY7DBAkAAAAAANBTrVYrlpeXIyLiueeei8nJyZiYmNif2Gg0GrG5uRnz8/NRr9ej3W7H/Px8zMzM7JfRyTrHYYIEoEdqtSvJeJZd7nqdqTKPyqdcfU+V2q43ufRTe5b7eu3d+/eZROzlRCz/9iYnUf59KnuZ8oMltxtuqeOpF5+lXpV7GvtRpbL7MOztAsBoyPs+G9XvsaLv9rz2OM66vVSrPVuQx+MFW7ynd8nAqLsTEbunUGeORqMRy8vL+5MkReusrq4mi+9knU55SDsAAAAAADByXEECAAAAAADDaDeSzwTpWZ0DwhUkAAAAAADAyDFBAgAAAAAAjBy32AIAAAAAgGF0O6q/xVbV9Z2ACRKAHsmyy8l4rXal9LZlpMpM5ZKKla2vbC4pZbdLKf8+9KInkPrK3jli23cVRrLsvyyM1Wp/+4hyq1S8/7XaLyS2+73Edt3/DPbi81K2vrKfwUEyLPvRbdoFgEFS1J856ffZccvNW79fvlOPk0e/5HxcWfb4aacAjCgTJAAAAAAAMIw8pD3JM0gAAAAAAICRY4IEAAAAAAAYOW6xBQAAAAAAw8gttpJcQQIAAAAAAIwcV5AAAAAAAMAwcgVJkgmSil24cCHOnj2bG5ubm4u5ubmKMwJIy7LLpbar1a50OZPqc0ltl8qlF/ue7s3kf6+87lbJOndKbVW23dJaidh9idhrJesrp+rjonx7jrZeHKNl399evIfV7t+vxaOP/nZuZGen3DkEjJlgtPSqP3PccgexX5X3/dwv+5Flj592CtAzS0tLsbS0lBvTBx48Jkgqdv369Wg0GqedBgAAdME3xm/+5i/mRlqtVjSbzYrzYRgYMwEA/Sz1g42+7AP36gqSf7gU8Y/yJ4rii4MzUWSCBAAAAAAA6Nyfmrv7ytNuRfxIn00UFfCQdgAAAAAAYOS4ggQAAAAAAIbR7aj68ZzVPxT+BFxBAgAAAAAAjBxXkACckiy7fNopnFitdqUwVvX+lc2ln/YhJb0PP3HE1vclti3e/4izR5Sbrzft9o5E7DOJWP/8bKWfjqey0sfL4Hzuy+Y5DOeLfioTADiZ43w/F/VjfMfDCNj90qvqOgeEK0gAAAAAAICRY4IEAAAAAAAYOW6xBQAAAAAAw2g3qr/7tFtsAQAAAAAA9C9XkAAAAAAAwDByBUmSCRIASsuyy5XWV6tdKYz1IpdUfb1Qfv+O6uk8mIilugKvHVFulT6eiL0jEbuViL1cMpfRdZLPWS8+v1Wfg6qur6yqz5UAMAzyvj+H6XuzX/alqJ/SL/kBo8cECQAAAAAADCNXkCR5BgkAAAAAADByXEFSsQsXLsTZs2dzY3NzczE3N1dxRgAAUM7S0lIsLS3lxnZ2dirOhmFhzAQA9DN94OFigqRi169fj0ajcdppAADAiaX+WN1qtaLZbFacEcPAmAkA6GcD1we+HdXfYqvq+k7ALbYAAAAAAICR4woSACLLLpfarla70vX6UmWWzTOt+Kswy54qjFWf50m8lojdl4iNdzuRE3hHInYzESu+vLn/3qf+0KtjW3tXR1sDwPH18/dnUf/sODl3owxgQHlIe5IrSAAAAAAAgJFjggQAAAAAABg5brEFAAAAAADDyC22kkyQAAAAAAAAnfsXSxH/cik/9lrx80D7zchPkLTb7ajX66edBgAAQN8ybgIAGFC3I+K1HpT7TXN3X3k+2Yr4QLMHlXbfyE2Q1Gq1A/9uNBqxubm5/+9WqxULCwtRr9dje3s7JicnY2pq6sA2nawDMAqy7HKlZdZqV3qQS/F1pmXrq3q7iPQvM7LsUqLOq4ktP5kst9tS+x/xtYWR9P797VL19eLYHhSjvO8R1R8XjkP6lXETwHDpl35FUR55faJ+yRkYbiM1QbKyshIzMzNx7ty5/WUTExP7/99ut6PZbMbm5mY0Go2IiDh37lzcvHkzZmZmOl4HAABgUBk3AQAMkd2o/pkgnkHSn1ZXV2N9fb0wPjs7GxMTE/sd+IiI+fn5mJ2d3e/Ed7IOAADAoDJuAgBgVNxz2glUZW1tLW7cuBHT09OxsrJyKL69vR0bGxsxOTl5YPn58+cj4u6vqDpZBwAAYFAZNwEAMEpGZoJkfX09tre3Y21tLWZnZ+Phhx+OjY2N/fiNGzciIg49eHDvF0/r6+sdrQMAADCojJsAAIbMbtx9/GqVL7fY6j/Ly8uxvLwcrVYrlpeXY2VlJSYnJ2Nrayvq9Xq02+2IiBgbG8vdvt1ud7TOUV555ZW4detWqX2IiLj//vvj/vvvL709AAB04tVXX41XX3219PavvPJKF7OhKv0wbjJmAgBOiz7w6BmZCZI9jUYjlpeXY3JyMqanp2N+fj5WV1dja2srIiLGx8dzt9ve3u5onaM89thj5RL/ksuXL8fTTz99ojIABlWWXe6bMmu1K10vsxf7F1E+11rtmV6kUyidy9WSpd4uVR+jq+rjop+Pw4WFhbhypfj8wXA7zXGTMRPAQf3cX+iWUdhHBsNQ9oH3riCpus4BMXITJHumpqZiamoqWq1WREScO3cuIiJu3ryZu369Xu9onaM8//zz8d73vrdExnf5JRQAAFV48skn44knnii9/QsvvHDiP3Rz+k5j3GTMBACcFn3g0TNQEyStVivm5+c7Wrder8fy8nJyncnJyf376e510ot+zVSv1zta5ygPPPBAPPjgg0euBwAAp+mktyl64IEHupgNxzHo4yZjJgDgtOgDj56BmiBpNBpdf6Df+fPnD/z3zffD3ft3s9nsaB0AAIDTZNwEAMA+t9hKuue0EzhN6+vrMTs7GxF3HyCYN5DY+6XUxYsXO1oHAABgmBg3AQAwrEZigqTVakWz2YyrV19/wOva2lqMj4/H1NTU/rJr167FxsbGgV86LS4uxuLiYoyNjXW8DgAAwKAxbgIAGEK3I+K1il9VX7FyAgN1i62y6vV6jI+Px8LCQqyvr0ej0YjJyclD99ptNBqxubkZ8/PzUa/Xo91ux/z8fMzMzBxrHQD6S612pTCWZZdLlZnarhf19Uqt9kwiWq5H05v9f63kdiPR1YFTVfyZ/1SleXByxk0A/anouzavb32cdXupX/IAOMpI/NVgbGys43vwNhqNWF1dPfE6AAAAg8S4CQCAUTMSEyQAAAAAADBydqP6h6Z7SDsAAAAAAED/cgUJAAAAAAAMo92o/qHpriABAAAAAADoX64gAQAAAACAYdSrK0j+zdLdV57bOz2osDdMkAAw9LLsctfLrNWudL3M09H9XlIv2ru8z5x2ApyS1Ge0v47RwVfUnq1WK5rNlYqzAYDhM4h9l0HMGTimPzx395Xn/21FrDWrzackt9gCAAAAAABGjitIAAAAAABgGN2OiNdOoc4BYYKkYhcuXIizZ8/mxubm5mJuruCyJAAA6DNLS0uxtJR/3+GdncG57zD9xZgJAOhn+sDDxQRJxa5fvx6NRuO00wAAgBNL/bH67jNIBuO+w/QXYyYAoJ8NXB9490uvquscEJ5BAgAAAAAAjBxXkABACVl2uTBWq12pMJOIiPzbkOzJskulSq1+P1KKb2CazjPdNhzPUcdE6nNRtX7KBQCgKvpAAMdjggQAAAAAAIbRblT/0HS32AIAAAAAAOhfriABAAAAAIBh5AqSJFeQAAAAAAAAI8cVJAAAAAAAMIxuR8Rrp1DngDBBAgBdlmWXK67vUulta7UrXczktKS6MzuVZTEKqj62T0PqMzEK+w8AADBK3GILAAAAAAAYOa4gAQAAAACAYXQnqn9o+p2K6zsBV5AAAAAAAAAjxxUkAAAAAAAwjG5H9Q9NH6CHtLuCBAAAAAAAGDmuIKnYhQsX4uzZs7mxubm5mJubqzgjAIZdrfZMYSzLLleYyUmkuizFP02pev9qtSuFscFp69HmfTqepaWlWFpayo3t7OxUnA3DwpgJYPDVas/mLs+yxyvOBLpPH3i4mCCp2PXr16PRaJx2GgAAcGKpP1a3Wq1oNpsVZ8QwMGYCAPrZwPWBd6P6W15V/VD4EzBBAgAAAAAAdO7jS3dfeXYH50oaEyQAAAAAADCMbkfEaz0o96vm7r7y3GpF/FqfXUlTwEPaAQAAAACAkeMKEgAAAAAAGEZ3ovpngtypuL4TMEECAENvvNRWtdqVwliWXS6bTClZ9lRhLJVn1ftQdbtAN/XTZx4AGGxZ9vhppwDQEbfYAgAAAAAARo4rSAAAAAAAYBjtxt0HtVdd54BwBQkAAAAAADByXEECAAAAAADD6HZUfwVJ1fWdgCtIAAAAAACAkeMKEgAYcLXalWQ8yy5XlEnvpPYxtX+p7cqWSfd5L/qDtgYAuqWof1fU38hbX98EqIIJEgAAAAAAGEa3I+K1U6hzQLjFFgAAAAAAMHJMkAAAAAAAwDC6ExG7Fb/uHD/NdrtdavdOyi22KnbhwoU4e/Zsbmxubi7m5uYqzggAAMpZWlqKpaWl3NjOzk7F2TAsjJkAgH6mD9wdtVrtwL8bjUZsbm7u/7vVasXCwkLU6/XY3t6OycnJmJqa6noeJkgqdv369Wg0GqedBgAAnFjqj9WtViuazWbFGTEMjJkAgH42cH3g3aj+mSC76fDKykrMzMzEuXPn9pdNTEzs/3+73Y5msxmbm5v7/cJz587FzZs3Y2ZmpqupmiABAAAAAAAqsbq6Guvr64Xx2dnZmJiYOPCjmfn5+ZidnTVBAgAclGWXk/Fa7UrpbfvHWwojqf1LdXWy7KkT5EM3lT0O0+999+sDAKAzx+1v6Z/B6FhbW4sbN27E9PR0TE5OHprw2N7ejo2NjVhcXDyw/Pz58xHx+tUn3eIh7QAAAAAAMIxun9KrwPr6emxvb8fa2lrMzs7Gww8/HBsbG/vxGzduREREvV4/sN3e1SSpK0/KMEECAAAAAADcdefViN1b5V93Xiksenl5ObIsi83NzZiZmdl/AHu73Y6I2P/v2NhY7vZ78W5xiy0AAAAAABhGtyPitWNu87mFiJfK3dK4U41GI5aXl2NycjKmp6djfn4+VldXY2trKyIixsfHc7fb3t7uah4mSAAAAAAAgLvGn4x4+Iny27/6QsQnH+to1ampqZiamopWqxUREefOnYuIiJs3b+au/+Zbb52UCRIAAAAAAOCu2v13X6W3f+BYq09OTu4/h2RvAqToShETJADAAbVa+rLXLLtcqtyy2/XGTmEklWet9kwvkqFP9NcxCgDAnqIxSlH/LW99fT3okjsRsXsKdR7T+fPnD/z3zc8a2ft3s9k8WW5v4iHtAAAAAADAqVhfX4/Z2dmIuPtw9kajEevr6wfW2bvC5OLFi12t2wQJAAAAAAAMo924+6D2Kl8FV6y0Wq1oNptx9erV/WVra2sxPj4eU1NT+8uuXbsWGxsbB64iWVxcjMXFxRgbGztBYxzmFlsAAAAAAEBP1ev1GB8fj4WFhVhfX49GoxGTk5OxvLx8YL1GoxGbm5sxPz8f9Xo92u12zM/Px8zMTNdzMkECAAAAAADDaO8KkqrrzDE2Nnbo1llFGo1GrK6udjGpfG6xBQAAAAAAjBxXkFTswoULcfbs2dzY3NxczM3NVZwRAKOsVrtSGMuyyxVmkpZlT5XcsuqfyUD3DMLnc2lpKZaWlnJjOzs7FWfDsDBmAhh8x+2r9EvfBjqhDzxcTJBU7Pr169FoNE47DQAAOLHUH6v3HsAIx2XMBAD0s4HrA5/G7wYH6LeKbrEFAAAAAACMHFeQAAAAAADAMNqNiNop1DkgXEECAAAAAACMHBMkAAAAAADAyHGLLQAYcFl2+VS2BXrL5xMAADix07jdlVtsAQAAAAAA9C8TJB1ot9unnQIAAEBfM24CAOhDuxFxu+KXK0j6z+TkZNRqtdzXxsbGgXXfHJ+enj4Qb7VaMT09HfPz8zE7Oxtra2tV7goAAEBPGDcBADBKRuIZJO12O9rtdiwuLsbY2Nj+8q2trbh69WpMTEzsL1tZWYmZmZk4d+7c/rI3xtvtdjSbzdjc3IxGoxEREefOnYubN2/GzMxM73cGAACgB4ybAACG0O2IqFVcZ1ZxfScwEhMkGxsbsbm5eaCTHxGHOvkREaurq7G+vl5Y1uzsbExMTOx38iNi/xdROvoAAMCgMm4CAGDUjMQESVEH/Nlnn43Z2dn/f3v379vIeT4I/FEQ2FnDcLhrN2l8AFWlC0abNgYOIlKkOWCpdZniLtQBAr5FChELHGAbOECgehXUfg9wkcYW9y8gc8C6XhJOc8EVIuDm0iRa2nC83j375oqNyOWKpCiKGv6YzwcYZMn59fLxw8nz6uXM23/daDTiyZMnsbOzE6VS6cJ+vV4vWq1W1Gq1offv3r0bEYNfUQEAzMvGxidj16XpRxm25HrW5XPAOtNvAgBgav/v6OUy0rNMm3IduZmD5HW9Xi86nU7cv3+//16z2YxerxeNRiN2d3fj9u3bQ8/ZffLkSUREFIvFoWOd/ypq0i+oAAAAVo1+EwDAivvxhpZ0L2Ljf41e4lGGH/B6cnEHySiff/55JEkydPt4vV6Per0enU4n6vV6HB8fR6lUitPT0ygWi9HtdiMiLtxyfu58/STffvttfPPNNzO3+80334w333xz5v0BAGAaz58/j+fPn8+8/7fffjvH1rAoi+g36TMBAIuiBs6f3A6QnJycxIcffjhyXZIkUa/Xo1Qqxc7OTlSr1Tg5OYnT09OIiLhz587I/Xq93qXn/eCDD2Zuc0TERx99FB9//PG1jgEAAJc5ODiITz4Z/2g08mER/SZ9JgBgUda2Bl6hSdOzlssBkvNn4tbr9YnblcvlKJfL0el0IiJic3MzIiLOzs5Gbv/6LeSjPH78OH71q19drcGv8EsoAACy8ODBg/jjH/848/5ffvnltf/QzWItqt+kzwQALIoaOH9WaoCk0+lEtVqdattisTi2kG+1WlEsFqca0CiVSv3n6Z5vP+4XT9Mc7+2334533nnn0u0AAGCRrvuYorfffnuOreEqVr3fpM8EACyKGjh/VmqAJEmSuUzo99lnn0W5XJ56+7t37w797+vPzD1/vbW1de22AUCWNjbG3zqcph9l2BLGWZf/DjfxOeQvjKbfBAAA0/nJohuwCI1GY+xzdF/XbDZjd3c3Il5OMjiqs3H+S6n79+/Pt6EAAAALot8EAMC6y90ASaPR6Bfsr+p0OrG1tRWHh4dD2965c2foV1MPHz6MVqs19GuoWq0WtVotCoXCjbcfAADgpuk3AQCQByv1iK15+Oyzz0b+YqlYLMadO3fi4OAgms1mJEkSpVLpwvN4kySJdrsd1Wo1isVidLvdqFarUalUsvoIAAAAN0q/CQCAPMjdAMnJycnI9wuFwtTP6U2SZOxxAAAAVp1+EwAAeZC7R2wBAAAAAAAYIAEAAAAAAHInd4/YAgAG0vSjRTfhRq3758s7/30BAAAu80NE/N8FnHM1uIMEAAAAAADIHXeQAAAAAADAWvohsr+jwx0kAAAAAAAAS8sACQAAAAAAkDsesQUAAAAAAGvJJO2TGCDJ2L179+LWrVsj1+3t7cXe3l7GLQIAgNkcHR3F0dHRyHXPnj3LuDWsC30mAGCZqYHXiwGSjD169CiSJFl0MwAA4Nom/bG60+nE1tZWxi1iHegzAQDLbPVq4B8j+zs6fsz4fLMzQAIAAAAAAFzBv0fE/xiz7vssG3ItBkgAAAAAAIAr+C//Wkb5S0T8xwzbMjsDJAAAAAAAsJZM0j7JTxbdAAAAAAAAgKy5gwQAWGkbG5+MXZemH2XYEsin8d/B/5NpOwAAgFHcQTKJO0gAAAAAAIDccQcJAAAAAACspR8j+zs6fsz4fLNzBwkAAAAAAJA7BkgAAAAAAIDc8YgtAAAAAABYSyZpn8QACQCw0tL0o0U3AXJt3Hew0+nE1tZxxq0BAACYngESAAAAAABYSyZpn8QcJAAAAAAAQO64gyRj9+7di1u3bo1ct7e3F3t7exm3CAAAZnN0dBRHR0cj1z179izj1rAu9JkAgGWmBl4vBkgy9ujRo0iSZNHNAACAa5v0x+qXc5BsZdwi1oE+EwCwzFavBjZJ+yQesQUAAAAAAOSOO0gAAIDY2Phk7Lo0/SjDlgAAAPPzQ2R/R4c7SAAAAAAAAJaWO0gAAAAAAGAtmYNkEneQAAAAAAAAueMOEgAAAAAA4Aoa/1pGeZ5lQ67FAAkAAAAAAKylH+NmHnn1n/61jPK/I+I/38A5588ACQAAEGn60aKbAAAAkCkDJAAAAAAAsJZM0j6JSdoBAAAAAIDcMUACAAAAAADkjkdsAQAAAADAWrqpSdovO+dqcAcJAAAAAACQO+4gAQAAAACAtWSS9kkMkGTs3r17cevWrZHr9vb2Ym9vL+MWAQDAbI6OjuLo6GjkumfPnmXcGtaFPhMAsMzUwOvFAEnGHj16FEmSLLoZAABwbZP+WN3pdGJrayvjFrEO9JkAgGW2ejWwOUgmMQcJAAAAAACQOwZIAAAAAACA3PGILQAAAAAAWEsmaZ/EHSQAAAAAAEDuuIMEAAAAAADWkjtIJnEHCQAAAAAAkDsGSAAAAAAAgNzxiC0AAAAAAFhLP0T2j7zyiC0AAAAAAICl5Q4SAAAAAABYSyZpn8QACQAAAAAAcAV/joj/OWbdiywbci0esZUjz58/j48//jieP3++6KasBfGcL/GcL/GcL/GcL/GcL/GcL/GcnxcvXgz9LywT3/UBsRgQiwGxGCYeA2IxIBYDYjGwnDXwjzGYh2SeywcR8cmY5b9m8snmYSNN03TRjciDTqcTW1tb0W63I0mShbThm2++iZ///Ofx9ddfxzvvvLOQNqwT8Zwv8Zwv8Zwv8Zwv8Zwv8Zwv8ZyfL774Ij744IN4/Phx/OY3v1l0c1gBWfaZfNcHxGJALAbEYph4DIjFgFgMiMXAMtXA57VVxH+LiP+Q8dm/ioj/vtC/hU/LI7Yydu/evbh169bIdXt7e7G3t5dxiwAAYDZHR0dxdHQ0ct13332XcWtYF/pMAMAyUwOvFwMkGXv06NHSj5oBAMA0Jv2x+vzXc3BV+kwAwDJbvRrYJO2TmIMEAAAAAADIHXeQAAAAAADAWjqfpD3rc64Gd5AAAAAAAAC5Y4AEAAAAAADIHY/YAgAAAACAtWSS9kncQbJijo6OFt2EubRhnY6xDG1YhmMsQywj1icW4rlcbZiXdYmFeC7fMZahDet0jGVowzodA7KQZa7Ocq6s9rnOflmdK8vPJRbXP9csxOJ65xKL6+2T9blmseyfSyzmsx8rJCUT7XY7jYi03W5f6zi//OUvZ97366+/TiMi/frrrxfWhnU6hnjOd/91iucytEE853uMZYnnMsRiHscQz/keYx7xXIbPsSzHEM/5HePx48dpRKSPHz++djvIh+v0ma6aq9f5rs/yvchqn1n2W4VYZHUusRgQi2GzxkMsrncusbj+fmKR/bmWqQY+r60i/i2NqGW8/Ntc/haehbV6xFav14uDg4OIiKjVahfWdzqdODg4iGKxGL1eL0qlUpTL5RvZBgAAYBnpNwEAwEtrM0DSarWiXq9Ho9GISqVyYX23242tra1ot9uRJElERGxubsbZ2Vl/+3ltAwAAsIz0mwAA8sYcJJOszRwk29vbcXJyMnb97u5ubG9v94vziIhqtRq7u7tz3wYAAGAZ6TcBAMDA2gyQTNLr9aLVakWpVBp6/+7duxERcXx8PLdtAAAAVpF+EwAAeZOLAZInT55ERESxWBx6//zXTM1mc27bAAAArCL9JgCAdfTDgpbVsDZzkEzS7XYjIqJQKIxdP69txnn27FlEvJyo8Ntvv52i1aN999130el0Ztr3/LxffvllvP322zO34dmzZzO3YZ2OIZ7z3X+d4rkMbRDP+R5jWeK5DLGYxzHEc77HmEc8l+FzLMsxxHPgxYsX8c9//jO++OKLmfb/y1/+EhER33///cxtIFuL7jddp8/0aq6+8cYb8cYbb0zc/jrf9Vm+W1ntM8t+qxCLrM4lFgNiMWzWeIhF9u3L8lxZxmLW/cTi6vu8ePEiXrx40X991Xp4OWvgv+fknDNK10xEpJVKZei9/f39NCLSdrs9cvtisTi3bcb505/+lEaExWKxWCwWi8WSq+XTTz+doarnpkUsX79Jn8lisVgsFsu6LMtQA3/11VfpW2+9tbAYvPXWW+lXX3216DBcKhd3kGxubkZExNnZ2cj1xWJxbtuM89vf/jY+/fTT+MUvfhE/+9nPpm7766b5NRQAAFzX67+eu6rvv/8+/va3v8Xvfve7ObaKm7TofpM+EwCwaOtUA7///vvx17/+Nf7+98XczfHee+/F+++/v5BzX8XSDJB0Op2oVqtTbVssFqNer0997PMCvNfrjV0/r23Gee+99+L3v//9dA0GAAAYYZ37TfpMAADz9f7776/EIMUiLc0ASZIkNzZZ3927dyPi4rNuz19vbW3NbRsAAICbot8EAADz85NFNyALhUJhZEei1WpFRMT9+/fntg3rZ9wkkrBM5CmrQJ6yzOQn6DetO9c5LiNHmER+MIq8YB0szR0k8zDuFu6IiIcPH8bW1lZ0u93+Ld21Wi1qtVoUCoW5brOMOp1OHBwcRLFYjF6vF6VSKcrl8qKbtZQ2NjaGXidJEu12u/96mljmLd69Xi8ODg4i4uX34XXzille4npZPCPk6VU1Go04ODiITqcTSZJErVaL7e3toW3k6fSmiWeEPJ3Wq/E8fxyO/JzdNPGMkJ+zaLVasbOzE0+fPh16X36unjz3m/KUZ3m8zumXDOhTDNMfGFDLD6jDB9TQo6l/c2LRs8TPS7vdTiuVShoRaaFQSE9OTtKnT59e2KZcLqf7+/tpuVxO6/X6yOPMY5tlcnp6mkZE2m63++8Vi8Wlb/ci1Ov1tFKppLVarb+8GrdpYpm3eDebzbRcLqcRkVYqlQvr5xWzvMT1snimqTy9qlqtlm5vb6f1ej3d399PIyKNiLTZbPa3kafTmyaeaSpPp3Uep2azmTabzTRJkjQi0tPT0/428nN608Tz1e3k59UUi8W0UCgMvSc/V0+e+015yrM8Xuf0Swb0KYbpDwyo5QfU4QNq6PHUv/mwNgMkjLe9vZ1ub28PvVev19M1Gh+bm9fjNGr9ZbHMa7zHFd/zilne4jqpMyNPr6ZcLg+9brfbaUQMfX55Or1p4pmm8nRatVpt6PV5PE9OTvrvyc/pTRPPNJWfs9jf30+3t7cvdBDlJ6skT3mW5+ucfsmAPsVL+gMDavkBdfiAGno09W9+5GIOkjzr9XrRarWiVCoNvX8+ceLx8fEimrWUGo1GPHnyJHZ2dkbGZZpYivewecVMXAfk6dW0Wq0LjxRIkiSSJOk/K1WeTm+aeEbI06vY398fen3+2JkkSSJCfl7VZfGMkJ+zaLVa8e677w7FMUJ+slrylGeucxe5Xg3LU47oDwyo5YepwwfU0Bepf/PFAMmae/LkSURE/7m/586/4K9PnJhnzWYzer1eNBqN2N3djdu3b/cnkoyYLpbiPWxeMRPXAXl6Ndvb2xc+57nz9+Xp9KaJZ4Q8vY5GoxG1Wk1+zsnr8YyQn7Oo1+sXOs4R8pPVkqc8c527yPVqWJ5yRH9gQC0/mTp8QA2t/s0bAyRr7vxXAOMmQnz1VwJ5V6/XI03TaLfbUalU+pMjncdomliK97B5xUxcB+TpfHS73djZ2en/O0KeXser8YyQp7OqVqv9CfrOyc/ZjYpnhPy8qmq1OnZyX/nJKslTnrnOXeR6NUyO6A+8Si2vDn+VGlr9m0cGSNbc6elpRETcuXNn5Pper5dha1ZDkiRRr9fj5OQkIl5eGCOmi6V4D5tXzMT1Ink6u0ajEcViMSqVSkTI0+t6PZ6vkqfTOzw8jG63G71eb+i2dfk5m3HxfJX8vFyn04l333137K9N5SerJI955jo34Ho1Wl5zRH9gQC2vDn+VGlr9m1cGSNbc5uZmREScnZ2NXD/uC09EuVyOcrkcnU4nIqaLpXgPm1fMxHU8eXp1BwcH/WIuQp5e1+vxHEWeXm5/fz9OTk6i2WxGoVDo/2JJfs5mXDxHkZ/jHRwcjHy0wDn5ySrJc565zrleXSZvOaI/MKCWV4e/Sg2t/s0rAyRr7vwLNW7k0RduslKp1L9YTRNL8R42r5iJ62TydHrVajUePnw49Bnl6exGxXMceTqd7e3tqFQq/Vuq5ef1vB7PceTnRdVqtf/YhFeXiMFjAeQnqyTveZb365zr1eXykiP6AwNq+WHq8IG81tDq3/z66aIbwM26e/duRFx8dt35662trczbtGrOYzhNLMV72LxiJq6Xk6eXOz4+jlKp1J/07Jw8nc24eE4iT6fz61//ul8Qy8/rezWek8jPYa1WKw4PD0eu29zcjCRJ4s9//nNEyE9WgzzL93XO/59OZ91zRH9gQC0/mjp8II81tPo3v9xBsuYKhUIkSRLNZnPo/VarFRER9+/fX0SzVkaz2Yzd3d2ImC6W4j1sXjET18nk6eUajUZEvPwlzKs6nY48ncGkeI4jT6fX7Xb7sZWf1/dqPMeRnxe12+1I03Ro2d/fj0Kh0J+gU36ySvKeZ3m/zrleXW7dc0R/YEAtP546fCCPNbT6N8dS1l673U4jIj09Pe2/VywW01qttsBWLZd2u50mSTIUk5OTk7RSqVzY7rJY5jHeT58+TSPiQrzSdH4xy1Ncx8VTns6m2WymSZKk9Xp9aKlUKmm9Xk/TVJ5exWXxlKfTe/r0aVoul9OTk5P+e6enp+n29vbQdvJzOtPEU35ez/7+flooFIbek5+skjzkWd6vc/olA/oUA/oDA2r5l9ThA2roydS/+bCRpml6EwMvLJdOpxMHBwdRLBaj2+1GqVSKSqWy6GYtjV6vFzs7O/HkyZO4e/duJEkSpVJp5Gj5NLHMU7w7nU7U6/U4Pj6OQqEQDx8+jO3t7SgUCkPbzCNmeYjrpHjK06vrdDoTb019+vRpP1fl6eWmiWdEyNMrKJVK/ViVSqUoFotRLpcvbCc/p3NZPF1Hr6darcbx8XH/u35OfrJK1j3P8nyd0y8Z0KcY0B8YbpdafkAdPqCGHk/9mw8GSAAAAAAAgNwxBwkAAAAAAJA7BkgAAAAAAIDcMUACAAAAAADkjgESAAAAAAAgdwyQAAAAAAAAuWOABAAAAAAAyB0DJAAAAAAAQO4YIAEAAAAAAHLHAAkAAAAAAJA7P110AwAAgMt1u93Y3NyMYrEY5XI5IiIePHgQhUJh4e2q1+vR6/Xi888/j16vF0+fPl14uwAAWG3qX7JggAQAAFZItVqNSqWy6Gb0FYvFqNVqERFRKBTi8PBwwS0CAGCdqH+5SR6xBQAAzMW777676CYAAEBm1L+rzwAJAAAAAACQOwZIAAAgQ5ubm1EqlWJnZyd2d3djZ2cnNjY2YmNjIzqdzpWP1+v14vj4OEqlUhwfH0e3241SqRS3b9+OUqkUvV4vIiIODw9jc3Mzbt++HdVqdW77AwDAJOpflpk5SAAAIEPb29tRr9f7rw8PD6PRaESlUokkSa58vLOzs2i329FqtSIi4vT0NGq1WpydnfU7osViMXZ2dqLZbEa1Wo3Dw8P48MMPI0mSa+8PAACTqH9ZZgZIAAAgQzs7O/1/93q9qFarUSgU+hM9XlWxWIzd3d04Pj4emjAyIiJJkmi1WnF6ehrFYjEiIh48eBCNRiNarVYkSXLt/QEAYBL1L8vMI7YAACBD29vb/X//4Q9/iIiIWq0WhULh2sd+/Rjnnbo7d+5ceO/09HTu+wMAwOvUvywzAyQAALAArVYrGo1GJEkSlUpl0c0BAIAbpf5lGRkgAQCABdjd3Y2IiJOTkwW3BAAAbp76l2VkgAQAADJWrVaj2+3G/v5+/5Z9AABYV+pflpUBEgAAyFC3243Dw8ORE1MeHh4uqFUAAHAz1L8sMwMkAACQoXGPFuh0OvGPf/xjpmOenZ2NfL/X611Yf/7vUe/Nuj8AAIyj/mWZGSABAICMtFqtaLVaEfGyg7i7uxu7u7tRKpVia2srNjc3r3zMTqfT/yXe8fFxNBqN/r/Pz1WtVqPT6US3241qtdpvy/Hx8bX3BwCAcdS/LLuNNE3TRTcCAACYrNvtxubmZtTr9ahUKotuzkiHh4dRrVbj6dOnUSgUFt0cAABWmPqXLLiDBAAAVsj5bf/LaNZHJAAAwDjqX27STxfdAAAAYHoHBwf9jtiDBw8W/ku1brcb9Xo9IsIjBwAAmDv1LzfJI7YAAAAAAIDc8YgtAAAAAAAgdwyQAAAAAAAAuWOABAAAAAAAyB0DJAAAAAAAQO4YIAEAAAAAAHLHAAkAAAAAAJA7BkgAAAAAAIDcMUACAAAAAADkjgESAAAAAAAgdwyQAAAAAAAAufP/ATvoI3DMeKkGAAAAAElFTkSuQmCC",
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       "text/plain": [
        "<Figure size 2000x800 with 3 Axes>"
       ]
@@ -346,26 +405,45 @@
     }
    ],
    "source": [
-    "vmax=300\n",
-    "nbins=100\n",
-    "\n",
-    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,8))\n",
-    "\n",
-    "\n",
-    "a0 = ax0.hist2d(brem_z_found, brem_x_found, density=False, bins=nbins,  cmap=plt.cm.jet, cmin=1, vmax=vmax, range=[[-200,4000],[-1000,1000]])\n",
-    "ax0.set_ylim(-1000,1000)\n",
-    "ax0.set_xlim(-200,4000)\n",
+    "vmax = 300\n",
+    "nbins = 100\n",
+    "\n",
+    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
+    "\n",
+    "a0 = ax0.hist2d(\n",
+    "    brem_z_found,\n",
+    "    brem_x_found,\n",
+    "    density=False,\n",
+    "    bins=nbins,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax,\n",
+    "    range=[[-200, 4000], [-1000, 1000]],\n",
+    ")\n",
+    "ax0.vlines([770, 990, 2700], -1000, 1000)\n",
+    "ax0.set_ylim(-1000, 1000)\n",
+    "ax0.set_xlim(-200, 4000)\n",
     "ax0.set_xlabel(\"z [mm]\")\n",
     "ax0.set_ylabel(\"x [mm]\")\n",
     "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
     "\n",
-    "a1 = ax1.hist2d(brem_z_lost, brem_x_lost, density=False, bins=nbins, cmap=plt.cm.jet, cmin=1,vmax=vmax*stretch_factor, range=[[-200,4000],[-1000,1000]])\n",
-    "ax1.set_ylim(-1000,1000)\n",
-    "ax1.set_xlim(-200,4000)\n",
+    "a1 = ax1.hist2d(\n",
+    "    brem_z_lost,\n",
+    "    brem_x_lost,\n",
+    "    density=False,\n",
+    "    bins=nbins,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax * stretch_factor,\n",
+    "    range=[[-200, 4000], [-1000, 1000]],\n",
+    ")\n",
+    "ax1.vlines([770, 990, 2700], -1000, 1000)\n",
+    "ax1.set_ylim(-1000, 1000)\n",
+    "ax1.set_xlim(-200, 4000)\n",
     "ax1.set_xlabel(\"z [mm]\")\n",
     "ax1.set_ylabel(\"x [mm]\")\n",
     "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
-    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
+    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
     "\n",
     "plt.colorbar(a0[3], ax=ax1)\n",
     "\n",
@@ -374,7143 +452,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre>{energy: 3.26e+04,\n",
-       " brem_photons_pe: [824, 287, 1.26e+04, 4.49e+03],\n",
-       " brem_vtx_x: [-8.5, -8.52, -33.8, -133],\n",
-       " brem_vtx_z: [157, 158, 601, 2.33e+03]}\n",
-       "-------------------------------------------------\n",
-       "type: {\n",
-       "    energy: float64,\n",
-       "    brem_photons_pe: var * float64,\n",
-       "    brem_vtx_x: var * float64,\n",
-       "    brem_vtx_z: var * float64\n",
-       "}</pre>"
-      ],
-      "text/plain": [
-       "<Record {energy: 3.26e+04, ...} type='{energy: float64, brem_photons_pe: va...'>"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "tuple_found[1]"
+    "tuple_found[1]\n",
+    "\n",
+    "\n",
+    "def ratio(nominator, denom):\n",
+    "    denominator = ak.num(denom[\"energy\"], axis=0)\n",
+    "    return nominator / denominator\n",
+    "\n",
+    "\n",
+    "def eff(found, lost):\n",
+    "    return found / (found + lost)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
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+      "ratio of lost electrons emitting in Velo:  0.3855211513301352\n",
+      "ratio of lost electrons emitting in Rich1+UT:  0.3475795900566943\n"
      ]
     }
    ],
    "source": [
+    "n_elec_lost = ak.num(tuple_lost, axis=0)\n",
+    "\n",
+    "velo_lost = 0\n",
+    "\n",
+    "richut_lost = 0\n",
+    "\n",
+    "for jelec in range(ak.num(tuple_lost, axis=0)):\n",
+    "    veloemitted = False\n",
+    "    richemitted = False\n",
+    "    for jphoton in range(ak.num(tuple_lost[jelec][\"brem_photons_pe\"], axis=0)):\n",
+    "        if (tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 850) and (veloemitted == False):\n",
+    "            velo_lost += 1\n",
+    "            veloemitted = True\n",
+    "        elif (\n",
+    "            (tuple_lost[jelec, \"brem_vtx_z\", jphoton] > 850)\n",
+    "            and (tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 3000)\n",
+    "            and (richemitted == False)\n",
+    "        ):\n",
+    "            richut_lost += 1\n",
+    "            richemitted = True\n",
+    "\n",
+    "print(\"ratio of lost electrons emitting in Velo: \", ratio(velo_lost, brem_l))\n",
+    "print(\"ratio of lost electrons emitting in Rich1+UT: \", ratio(richut_lost, brem_l))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "electrons_lost = ak.ArrayBuilder()\n",
+    "\n",
+    "for jelec in range(ak.num(tuple_lost, axis=0)):\n",
+    "    electrons_lost.begin_record()\n",
+    "    electrons_lost.field(\"energy\").real(tuple_lost[jelec, \"energy\"])\n",
+    "\n",
+    "    tmp_velo = 0\n",
+    "    tmp_richut = 0\n",
+    "    for jphoton in range(ak.num(tuple_lost[jelec][\"brem_photons_pe\"], axis=0)):\n",
+    "        if tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 850:\n",
+    "            tmp_velo += tuple_lost[jelec, \"brem_photons_pe\", jphoton]\n",
+    "        elif (tuple_lost[jelec, \"brem_vtx_z\", jphoton] > 850) and (\n",
+    "            tuple_lost[jelec, \"brem_vtx_z\", jphoton] <= 3000\n",
+    "        ):\n",
+    "            tmp_richut += tuple_lost[jelec, \"brem_photons_pe\", jphoton]\n",
+    "\n",
+    "    electrons_lost.field(\"velo\").real(tmp_velo)\n",
+    "\n",
+    "    electrons_lost.field(\"rich\").real(tmp_richut)\n",
+    "\n",
+    "    electrons_lost.end_record()\n",
     "\n",
+    "electrons_lost = ak.Array(electrons_lost)\n",
     "\n",
+    "electrons_found = ak.ArrayBuilder()\n",
     "\n",
-    "for jelec in range(ak.num(tuple_lost,axis=0)):\n",
-    "    for jphoton in range(ak.num(tuple_lost[jelec][\"brem_photons_pe\"],axis=0)):\n",
-    "        print(jphoton)"
+    "for jelec in range(ak.num(tuple_found, axis=0)):\n",
+    "    electrons_found.begin_record()\n",
+    "    electrons_found.field(\"energy\").real(tuple_found[jelec, \"energy\"])\n",
+    "\n",
+    "    tmp_velo = 0\n",
+    "    tmp_richut = 0\n",
+    "    for jphoton in range(ak.num(tuple_found[jelec][\"brem_photons_pe\"], axis=0)):\n",
+    "        if tuple_found[jelec, \"brem_vtx_z\", jphoton] <= 850:\n",
+    "            tmp_velo += tuple_found[jelec, \"brem_photons_pe\", jphoton]\n",
+    "        elif (tuple_found[jelec, \"brem_vtx_z\", jphoton] > 850) and (\n",
+    "            tuple_found[jelec, \"brem_vtx_z\", jphoton] <= 3000\n",
+    "        ):\n",
+    "            tmp_richut += tuple_found[jelec, \"brem_photons_pe\", jphoton]\n",
+    "\n",
+    "    electrons_found.field(\"velo\").real(tmp_velo)\n",
+    "\n",
+    "    electrons_found.field(\"rich\").real(tmp_richut)\n",
+    "\n",
+    "    electrons_found.end_record()\n",
+    "\n",
+    "electrons_found = ak.Array(electrons_found)"
    ]
   },
   {
@@ -7518,13 +568,52 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "eff()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>{new: 15}\n",
+       "----------------\n",
+       "type: {\n",
+       "    new: float64\n",
+       "}</pre>"
+      ],
+      "text/plain": [
+       "<Record {new: 15} type='{new: float64}'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = ak.ArrayBuilder()\n",
+    "\n",
+    "for i in range(4):\n",
+    "    test.begin_record()\n",
+    "    test.field(\"new\")\n",
+    "    tmp = 0\n",
+    "    for j in range(5):\n",
+    "        tmp += j + i\n",
+    "    test.real(tmp)\n",
+    "    test.end_record()\n",
+    "\n",
+    "test = ak.Array(test)\n",
+    "test[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   },
   {
@@ -7570,8 +659,10 @@
    "source": [
     "cut = 4000\n",
     "\n",
-    "tf = tuple_found[ak.sum(tuple_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n",
-    "tl = tuple_lost[ak.sum(tuple_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n",
+    "tf = tuple_found[ak.sum(\n",
+    "    tuple_found[\"brem_photons_pe\"], axis=-1, keepdims=False) > cut]\n",
+    "tl = tuple_lost[ak.sum(tuple_lost[\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
+    "                > cut]\n",
     "\n",
     "cut_x_found = ak.to_numpy(ak.flatten(tf[\"brem_vtx_x\"]))\n",
     "cut_z_found = ak.to_numpy(ak.flatten(tf[\"brem_vtx_z\"]))\n",
@@ -7580,8 +671,10 @@
     "cut_z_lost = ak.to_numpy(ak.flatten(tf[\"brem_vtx_z\"]))\n",
     "\n",
     "# how many tracks of all are included?\n",
-    "ratio_f = tuple_found[ak.sum(tuple_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n",
-    "ratio_l = tuple_lost[ak.sum(tuple_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>cut]\n"
+    "ratio_f = tuple_found[ak.sum(\n",
+    "    tuple_found[\"brem_photons_pe\"], axis=-1, keepdims=False) > cut]\n",
+    "ratio_l = tuple_lost[ak.sum(\n",
+    "    tuple_lost[\"brem_photons_pe\"], axis=-1, keepdims=False) > cut]"
    ]
   },
   {
@@ -7601,26 +694,38 @@
     }
    ],
    "source": [
-    "vmax=500\n",
-    "\n",
-    "\n",
-    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,8))\n",
-    "\n",
-    "\n",
-    "a0 = ax0.hist2d(cut_z_found, cut_x_found, density=False, bins=200,  cmap=plt.cm.jet, cmin=1, vmax =vmax)\n",
-    "ax0.set_ylim(-1000,1000)\n",
-    "ax0.set_xlim(-200,4000)\n",
+    "vmax = 500\n",
+    "\n",
+    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
+    "\n",
+    "a0 = ax0.hist2d(\n",
+    "    cut_z_found,\n",
+    "    cut_x_found,\n",
+    "    density=False,\n",
+    "    bins=200,\n",
+    "    cmap=plt.cm.jet,\n",
+    "    cmin=1,\n",
+    "    vmax=vmax,\n",
+    ")\n",
+    "ax0.set_ylim(-1000, 1000)\n",
+    "ax0.set_xlim(-200, 4000)\n",
     "ax0.set_xlabel(\"z [mm]\")\n",
     "ax0.set_ylabel(\"x [mm]\")\n",
     "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
     "\n",
-    "a1 = ax1.hist2d(cut_z_lost, cut_x_lost, density=False, bins=200, cmap=plt.cm.jet, cmin=1, vmax=vmax)\n",
-    "ax1.set_ylim(-1000,1000)\n",
-    "ax1.set_xlim(-200,4000)\n",
+    "a1 = ax1.hist2d(cut_z_lost,\n",
+    "                cut_x_lost,\n",
+    "                density=False,\n",
+    "                bins=200,\n",
+    "                cmap=plt.cm.jet,\n",
+    "                cmin=1,\n",
+    "                vmax=vmax)\n",
+    "ax1.set_ylim(-1000, 1000)\n",
+    "ax1.set_xlim(-200, 4000)\n",
     "ax1.set_xlabel(\"z [mm]\")\n",
     "ax1.set_ylabel(\"x [mm]\")\n",
     "ax1.set_title(r\"$e^\\pm$ lost brem vertices\")\n",
-    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
+    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
     "\n",
     "plt.colorbar(a0[3], ax=ax1)\n",
     "\n",