{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41876a00",
   "metadata": {},
   "source": [
    "# Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acd651a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import hepi \n",
    "import numpy as np\n",
    "from hepi.run import resummino as rs\n",
    "from hepi.run import spheno as sp\n",
    "sp.set_path(\"SPheno\")\n",
    "rs.set_path(\"resummino\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3adb99b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/apn/data/de.neuwirthinformatik.Alexander/Development/git/hepi/hepi/util.py:101: UserWarning: LHAPDF python binding not installed? Make sure you set PYTHONPATH correctly (i.e. correct python version) if you want to compute PDF uncertainties.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ec3fcae6bbf46a7b32b404f7ba9210a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QUEUEING TASKS | Checking input:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b340f949fc4a41d1bd1d76d67e9d6296",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PROCESSING TASKS | Checking input:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1bfdeebe573421ca05c258d580fe98d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "COLLECTING RESULTS | Checking input:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55ef2b564d6742bfbf1b50008a7741d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QUEUEING TASKS | Preparing:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5466e7a0e3ad4c268ffa6e9b2ef25a1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "225b52a5dfc44eb7969fef5ee2e78042",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "COLLECTING RESULTS | Preparing:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipped: 25 Not skipped: 0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0466f28103cc490b84e96c6d4042cb8c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QUEUEING TASKS | Running: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32b33dfbc4d24e978d255d3b8e9a55f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PROCESSING TASKS | Running: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60a8db67d0da4d10ab2bc5b9cff5e2cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "COLLECTING RESULTS | Running: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2630712fca8a4e2a957cf2a0a6d53e8e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6288809fc77c4a82bce410f4b6f73c39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3969c59974a6467f9b033fe3db45346e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for a,b in [(2000002,1000022)]:\n",
    "    for pdf,nlopdf in [(\"CT14lo\",\"CT14lo\")]:\n",
    "        li = [hepi.Input(hepi.Order.LO,13000,a,b,\"slha.in\",pdf,nlopdf,1., 1.)]\n",
    "        #li=hepi.slha_scan_rel(li,lambda r : [[\"EXTPAR\",1,r],[\"EXTPAR\",2,r]],np.linspace(500.,1500.,16*2))\n",
    "\n",
    "        li=hepi.mass_scan(li,a,np.linspace(500.,1500.,5))\n",
    "        li=hepi.mass_scan(li,b,np.linspace(500.,1500.,5))\n",
    "        dl = rs.run(li,True,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dffb427e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZpnF4GxknAAAswPDwqTrjX9eWlZXJbrebx78r29Saf/7zn2publZsbKzL8djYWB06dMite/z1r3/Va6+9pqFDh5rrp371q19pyJAhbRqTNxA4AQBgAU7Dw1Ldv6612+0ugZM/XXPNNXI6nf4ehgsCJwAALKCj7RweHR2t4OBglZeXuxwvLy9XXFycV7/Ll1jjBAAAvC40NFQjR47U9u3bzWNOp1Pbt29XWlqaH0fmGTJOAABYgLdKdReitrZWx44dMz8fP35cpaWlioqKUmJioubPn6+srCyNGjVKo0eP1tq1a1VXV2c+ZReICJwAALAAb71yJSUlRcHBwcrOzlZ2dvZ5rykuLtbYsWPNz/Pnz5ckZWVlaePGjZo+fbq++OILLV26VA6HQ8OHD9e2bdvOWjAeSAicAACAqaioyO3F4WPGjJFhGOftk5OTo5ycHG8MrUMgcAIAwAL8UarrjAicAACwAAIn3+CpOgAAADeRcQIAwALIOPkGgRMAABZA4OQblOoAAIApJSVFycnJys3N9fdQOiQyTgAAWIAhefiS3zMuZDuCzojACQAAC6BU5xsETgAAWACBk2+wxgkAAMBNZJwAALAAMk6+QeAEAIAFEDj5BqU6AAAANxE4AQBgAYZh87hJ7OPUGkp1AABYgFM2j/ZxarmWfZzOj4wTAACAm8g4AQBgASwO9w0CJwAALOCb65Taej1aR6kOAADATWScAACwAEp1vtFhMk75+fmaPHmy4uPjZbPZtHXrVvNcY2OjFi5cqCFDhqh79+6Kj4/XHXfcoZMnT7rco7KyUpmZmbLb7YqMjNTs2bNVW1vr0mfv3r269tprFR4eroSEBK1atcoX0wMAoF15azsCnF+HCZzq6uo0bNiwc+4b8dVXX2n37t1asmSJdu/erTfeeEOHDx/WTTfd5NIvMzNTBw4cUF5ent5++23l5+dr7ty55vmamhrdcMMN6tu3r0pKSvTkk0/q4Ycf1vPPP9/u8wMAoD0Z/8o4tbUROLmnw5TqJkyYoAkTJpzzXEREhPLy8lyOPfvssxo9erROnDihxMREHTx4UNu2bVNRUZFGjRolSXrmmWc0ceJErV69WvHx8dq8ebMaGhr04osvKjQ0VJdffrlKS0u1Zs0alwALAIDOKiUlRcHBwcrOzlZ2dra/h9PhdJjA6UJVV1fLZrMpMjJSklRQUKDIyEgzaJKk9PR0BQUFqbCwUFOmTFFBQYGuu+46hYaGmn0yMjL0xBNP6Msvv1SvXr3O+p76+nrV19ebn2tqatpvUgAAtJEhyTA8u15iA8zWdJhS3YU4ffq0Fi5cqNtuu838y3U4HIqJiXHpFxISoqioKDkcDrNPbGysS5+Wzy19vm3FihWKiIgwW0JCgrenAwCAx1p2DvekoXUBFzg1Njbqhz/8oQzD0Pr169v9+xYvXqzq6mqzlZWVtft3AgCAjimgSnUtQdNnn32mHTt2uKQS4+LiVFFR4dK/qalJlZWViouLM/uUl5e79Gn53NLn28LCwhQWFubNaQAA4HVsgOkbAZNxagmajh49qj/96U/q3bu3y/m0tDRVVVWppKTEPLZjxw45nU6lpqaaffLz89XY2Gj2ycvL04ABA865vgkAgEDhyRN1nu4B1Zl0mMCptrZWpaWlKi0tlSQdP35cpaWlOnHihBobG3XrrbequLhYmzdvVnNzsxwOhxwOhxoaGiRJgwYN0vjx4zVnzhzt2rVLf/3rX5WTk6MZM2YoPj5eknT77bcrNDRUs2fP1oEDB/Taa6/p6aef1vz58/01bQAAEEA6TKmuuLhYY8eONT+3BDNZWVl6+OGH9fvf/16SNHz4cJfr/vznP2vMmDGSpM2bNysnJ0fjxo1TUFCQpk2bpnXr1pl9IyIi9N577yk7O1sjR45UdHS0li5dylYEAICAZxgePlXnwbWdSYcJnMaMGSPjPH9r5zvXIioqSlu2bDlvn6FDh2rnzp0XPD4AADoy1jj5Rocp1QEAAHR0BE4AAFiAt95Vl5KSouTk5HO+Ag0dqFQHAADazmnYZPOg3NbyVB07h58fgRMAABbA4nDfoFQHAADgJjJOAABYwJmMkydP1XlxMBZG4AQAgAWwHYFvUKoDAABwExknAAAswPhX8+R6tI7ACQAAC6BU5xuU6gAAANxExgkAACugVucTBE4AAFiBh6U6UapzC6U6AAAANxE4AQBgAS2vXPGkSbzktzWU6gAAsABvPVXHS37Pj8CpjYZedFKhPUL9PYyAk1/d1d9DCGhfnwr39xACVmhtF38PIWB1ORXl7yEErOam01KZj77MsHm2Tok1Tm6hVAcAAOAmMk4AAFjAN9cptfV6tI7ACQAAK2AfJ5+gVAcAAOAmMk4AAFgA76rzDQInAACsgnJbu6NUBwAA4CYyTgAAWAClOt8gcAIAwAp4qs4nKNUBAAC4iYwTAACWYPtX8+R6tIbACQAAK6BU5xMETgAAWAGBk0+wxgkAAMBNBE4AAFiBYfO8SUpJSVFycrJyc3P9PKGOiVIdAAAWYBhnmifXS1JRUZHsdrt3BmVBZJwAAADcRMYJAAArYHG4TxA4AQBgBd9Yp9Tm69EqSnUAAABuIuMEAIAF2IwzzZPr0ToCJwAArIA1Tj5BqQ4AAMBNZJwAALACFof7BIETAABWQKnOJwicAACwAgInnyBwAgAAltTU1KQDBw6opqZGgwYNUnR0tMf3JHACAMAKyDi52LNnj6ZOnaqIiAiFhobqyJEjuuqqq/TMM88oKSmpzff16Km6r7/+Wp9//vlZxw8cOODJbQEAwIVqWRzuSbOQu+++Wy+++KJ2796tjz76SA6HQ9OnT9fEiRN16NChNt+3zYHTb3/7W/Xv31+TJk3S0KFDVVhYaJ6bOXNmmwcEAADgqVOnTun66683P4eGhmrmzJl6+eWXNW/evDbft82B0/Lly1VSUqLS0lK99NJLmj17trZs2SJJMgyL5fsAAOjgWnYO96RZSXBwsGpqas46npKSIofD0eb7tjlwamxsVGxsrCRp5MiRys/P13PPPadly5bJZrvwdF9+fr4mT56s+Ph42Ww2bd261eW8YRhaunSp+vTpo65duyo9PV1Hjx516VNZWanMzEzZ7XZFRkZq9uzZqq2tdemzd+9eXXvttQoPD1dCQoJWrVp1wWMFAKDDMbzQLOSee+7RrbfeqsrKSpfj1dXVcjqdbb5vmwOnmJgY7d271/wcFRWlvLw8HTx40OW4u+rq6jRs2DDl5uae8/yqVau0bt06bdiwQYWFherevbsyMjJ0+vRps09mZqYOHDigvLw8vf3228rPz9fcuXPN8zU1NbrhhhvUt29flZSU6Mknn9TDDz+s559//oLHCwAAOq477rhDt956q0aMGKE77rhDq1at0vLly3XVVVcpJyenzfe1GW7W1ZYvX64rrrhCI0eOVGxsrP7+97+rS5cuZtbpm/7617/q6quvbvugbDa9+eabuuWWWySdyTbFx8fr/vvv1wMPPCDpTMQYGxurjRs3asaMGTp48KCSk5NVVFSkUaNGSZK2bdumiRMn6u9//7vi4+O1fv16/exnP5PD4VBoaKgkadGiRdq6davbC8VqamoUERGhGdt/pNAeoW2eY2eVf+RSfw8hoIUdC/f3EAJWxKcW+7/TPhRxtM7fQwhYTU2n9efix1VdXS273d4u39HyeynxieUK6tr2nxHOr0/rxMKH2nWs/nDq1Cm98sor2r9/vyIiIjRp0iRdeeWVbb6f29sRLF261CzBxcXFmUFUy39efPHFZl9PgqZzOX78uBwOh9LT081jERERSk1NVUFBgWbMmKGCggJFRkaaQZMkpaenKygoSIWFhZoyZYoKCgp03XXXmUGTJGVkZOiJJ57Ql19+qV69enl13AAA+IpNnq1TstYzdf/Ws2dPl+qTp9wu1aWkpOjiiy/WQw89pEWLFik6OlpvvPGGbr31ViUmJiouLk4TJ0702sC+qWUR17ezW7GxseY5h8OhmJgYl/MhISGKiopy6XOue3zzO76tvr5eNTU1Lg0AAASu6dOnt/latzNOhYWF2rhxo/7f//t/SklJ0Zo1a/S9731P9fX1Ki0t1e7du/Xxxx+3eSAd1YoVK/TII4/4exgAAJwfL/l1W1FRUZuvvaDF4XfeeaeOHDmiAQMG6IorrtDixYvV3Nys1NRU3XXXXe22yDouLk6SVF5e7nK8vLzcPBcXF6eKigqX801NTaqsrHTpc657fPM7vm3x4sWqrq42W1lZmecTAgDA27z0VF1KSoqSk5O/82GtQPHQQw/p1Vdf1f79+9XU1OS1+17wU3U9evTQqlWrVFxcrP379+vSSy/Vyy+/7LUBnUtSUpLi4uK0fft281hNTY0KCwuVlpYmSUpLS1NVVZVKSkrMPjt27JDT6VRqaqrZJz8/X42NjWafvLw8DRgw4DvXN4WFhclut7s0AAA6HC8FTkVFRfrkk0+UnZ3t2/F7Scs2RL1791ZeXp5mz56tmJgYDR48WNOnT9ejjz561lZFF6JN76prampSfX29brvtNpWXl2vWrFm68cYbFRUV1eaB1NbW6tixY+bn48ePq7S0VFFRUUpMTNR9992n5cuXq3///kpKStKSJUsUHx9vPnk3aNAgjR8/XnPmzNGGDRvU2NionJwczZgxQ/Hx8ZKk22+/XY888ohmz56thQsXav/+/Xr66af11FNPtXncAACg44iIiNBvfvObs3YHP378uPbv36/9+/frP//zP9t8f7cDp5UrV2rfvn3at2+fDh06pPDwcA0dOlSjR4/Wf/3XfykiIqLNg5Ck4uJijR071vw8f/58SVJWVpY2btyoBx98UHV1dZo7d66qqqp0zTXXaNu2bQoP//ejl5s3b1ZOTo7GjRunoKAgTZs2TevWrTPPR0RE6L333lN2drZGjhyp6OhoLV261Kur7QEA8AdPd/+2ys7hhmHoueee05o1a2Sz2ZSSkqLbb79dKSkpSkpK0uTJkz26v9v7OAUFBalfv37KysrSbbfdpssuu8yjLw5U7OPkGfZx8gz7OLUd+zi1Hfs4tZ0v93Hqt/wxBYV7sI/T6dP620M/C/h9nIKCgtS7d2/NmDFD3bt3V0lJiXbu3KmcnBytXr3a4/u7nXG69tprVVpaqkceeUSrV6/W0KFDdcUVV5ht8ODBCg4O9nhAAAAAntiyZYtLOW7v3r26+eabdfHFF3v0gl/pAhaHf/DBB6qurtbhw4f1wgsv6Oqrr9bBgwd1//33a8SIEerRo4dGjx7t0WAAAEAb8a46SWdeAZeQkOBybOjQoXr22We1fv16j+9/wYvD+/fvr/79+2vGjBnmsePHj6u4uNiS+zgBABAIWON0xvDhw/XSSy/piSeecDl+6aWX6sSJEx7fv01P1X1bUlKSkpKS9IMf/MAbtwMAAGiT5cuXa+zYsTp58qR++tOfaujQoaqrq9Pjjz+upKQkj+/vlcAJAAD4GTuHS5KuvPJKffTRR7r33nt17bXXquUZuPDwcL3++use35/ACQAAK/B0nZJFSnWSNGzYML3//vuqqKhQSUmJuRl2dHS0x/cmcAIAAJYUExOjCRMmePWeBE4AAFgAi8N9g8AJAAAroFTnEwROAABYgYcZJwIn97i9ASYAAEBnR8YJAAAroFTnEwROAABYAYGTT1CqAwAAcBMZJwAALIDtCHyDjBMAAICbCJwAAADcRKkOAAArYHG4TxA4AQBgAaxx8g1KdQAAAG4i4wQAgFWQNWp3BE4AAFgBa5x8gsAJAAALYI2Tb7DGCQAAwE1knAAAsAJKdT5B4AQAgAVQqvMNSnUAAABuIuMEAIAVUKrzCQInAACsgMDJJyjVAQCADqmqqkqjRo3S8OHDNXjwYL3wwgv+HhIZp7YaH7VP3XoG+3sYAed/43r7ewgB7e81Mf4eQsDqUsO/17YKrQn39xACVlOj777LiovDe/bsqfz8fHXr1k11dXUaPHiwpk6dqt69/fe7hMAJAAArsGCpLjg4WN26dZMk1dfXyzAMGYZ/B0qpDgAAtEl+fr4mT56s+Ph42Ww2bd269aw+ubm56tevn8LDw5Wamqpdu3Zd0HdUVVVp2LBhuuSSS7RgwQJFR0d7afRtQ+AEAIAVGF5oF6iurk7Dhg1Tbm7uOc+/9tprmj9/vn7+859r9+7dGjZsmDIyMlRRUWH2aVm/9O128uRJSVJkZKT27Nmj48ePa8uWLSovL7/wgXoRpToAACzAW2ucampqXI6HhYUpLCzsnNdMmDBBEyZM+M57rlmzRnPmzNGsWbMkSRs2bNA777yjF198UYsWLZIklZaWujW+2NhYDRs2TDt37tStt97q1jXtgYwTAABW4KWMU0JCgiIiIsy2YsWKNg2noaFBJSUlSk9PN48FBQUpPT1dBQUFbt2jvLxcp06dkiRVV1crPz9fAwYMaNN4vIWMEwAAMJWVlclut5ufvyvb1Jp//vOfam5uVmxsrMvx2NhYHTp0yK17fPbZZ5o7d665KPzuu+/WkCFD2jQebyFwAgDAArxVqrPb7S6Bkz+NHj3a7VKerxA4AQBgBR1sO4Lo6GgFBweftZi7vLxccXFx3v0yH2KNEwAA8LrQ0FCNHDlS27dvN485nU5t375daWlpfhyZZ8g4AQBgBX7IONXW1urYsWPm5+PHj6u0tFRRUVFKTEzU/PnzlZWVpVGjRmn06NFau3at6urqzKfsAhGBEwAAFmD7V/PkeklKSUlRcHCwsrOzlZ2dfd5riouLNXbsWPPz/PnzJUlZWVnauHGjpk+fri+++EJLly6Vw+HQ8OHDtW3btrMWjAcSAicAAGAqKipye3H4mDFjWn0FSk5OjnJycrwxtA6BwAkAACvoYIvDrYrACQAAC/DWdgQ4PwInAACsgIyTT7AdAQAAgJvIOAEAYBVkjdpdwGScmpubtWTJEiUlJalr16763ve+p0cffdRlNb9hGFq6dKn69Omjrl27Kj09XUePHnW5T2VlpTIzM2W32xUZGanZs2ertrbW19MBAMCrWtY4edKkM9sRJCcnKzc3178T6qACJuP0xBNPaP369dq0aZMuv/xyFRcXa9asWYqIiNA999wjSVq1apXWrVunTZs2KSkpSUuWLFFGRoY++eQThYeHS5IyMzP1j3/8Q3l5eWpsbNSsWbM0d+5cbdmyxZ/TAwCgQ7iQ7Qg6o4AJnD788EPdfPPNmjRpkiSpX79+euWVV7Rr1y5JZ7JNa9eu1UMPPaSbb75ZkvTyyy8rNjZWW7du1YwZM3Tw4EFt27ZNRUVFGjVqlCTpmWee0cSJE7V69WrFx8f7Z3IAAHiKxeE+ETCluquuukrbt2/XkSNHJEl79uzRX/7yF02YMEHSmW3eHQ6H0tPTzWsiIiKUmpqqgoICSVJBQYEiIyPNoEmS0tPTFRQUpMLCQh/OBgAA7/JWqQ7nFzAZp0WLFqmmpkYDBw5UcHCwmpub9dhjjykzM1OS5HA4JOmsbdxjY2PNcw6HQzExMS7nQ0JCFBUVZfb5tvr6etXX15ufa2pqvDYnAAAQWAIm4/Sb3/xGmzdv1pYtW7R7925t2rRJq1ev1qZNm9r1e1esWKGIiAizJSQktOv3AQDQJoYXGloVMIHTggULtGjRIs2YMUNDhgzRzJkzNW/ePK1YsUKSFBcXJ0kqLy93ua68vNw8FxcXp4qKCpfzTU1NqqysNPt82+LFi1VdXW22srIyb08NAACPUarzjYAJnL766isFBbkONzg4WE6nU5KUlJSkuLg4bd++3TxfU1OjwsJCpaWlSZLS0tJUVVWlkpISs8+OHTvkdDqVmpp6zu8NCwuT3W53aQAAoHMKmDVOkydP1mOPPabExERdfvnl+vjjj7VmzRr9+Mc/liTZbDbdd999Wr58ufr3729uRxAfH69bbrlFkjRo0CCNHz9ec+bM0YYNG9TY2KicnBzNmDGDJ+oAAIHNS0/VpaSkKDg4WNnZ2crOzvbGyCwlYAKnZ555RkuWLNFPf/pTVVRUKD4+Xv/1X/+lpUuXmn0efPBB1dXVae7cuaqqqtI111yjbdu2mXs4SdLmzZuVk5OjcePGKSgoSNOmTdO6dev8MSUAALzHS4ET+zidX8AETj179tTatWu1du3a7+xjs9m0bNkyLVu27Dv7REVFsdklAMByPF2nxBon9wTMGicAAAB/C5iMEwAAOA92DvcJAicAACzAZhiyGW2Pfjy5tjOhVAcAAOAmMk4AAFgBpTqfIHACAMACeKrONyjVAQAAU0pKipKTk5Wbm+vvoXRIZJwAALACNsD0CQInAAAsgFKdb1CqAwAAcBMZJwAArICn6nyCwAkAAAugVOcbBE4AAFgBGSefYI0TAACAm8g4AQBgEZTb2h+BEwAAVmAYZ5on16NVlOoAAICJncPPj4wTAAAW4K2n6tg5/PwInAAAsAKeqvMJSnUAAABuIuMEAIAF2JxnmifXo3UETgAAWAGlOp+gVAcAAOAmMk4AAFgA76rzDQInAACsgA0wfYLACQAACyDj5BuscQIAAHATGScAAKyAp+p8gsAJAAALoFTnG5TqAACAiZf8nh8ZJwAArMBLT9Xxkt/zI3ACAMACKNX5BqU6AAAAN5FxAgDACniqzicInAAAsABKdb5BqQ4AAMBNZJwAALACp3GmeXI9WkXgBACAFbDGyScInAAAsACbPFzj5LWRWBtrnAAAANxExgkAACvw0s7hOD8CJwAALIDtCHyDUh0AAICbyDgBAGAFPFXnEwROAABYgM0wZPNgnZIn13YmBE5tdHP3U7J3D/b3MAJO4UWf+XsIAe33VT39PYSAVf9lD38PIWB9Xc3PurZqbuDPzmpY4wQAgBU4vdAkpaSkKDk5Wbm5ub4df4Ag4wQAgAV4q1RXVFQku93urWFZDhknAAAANwVU4PT555/rRz/6kXr37q2uXbtqyJAhKi4uNs8bhqGlS5eqT58+6tq1q9LT03X06FGXe1RWViozM1N2u12RkZGaPXu2amtrfT0VAAC8y/BCQ6sCJnD68ssvdfXVV6tLly764x//qE8++UT//d//rV69epl9Vq1apXXr1mnDhg0qLCxU9+7dlZGRodOnT5t9MjMzdeDAAeXl5entt99Wfn6+5s6d648pAQDgPS07h3vS0KqAWeP0xBNPKCEhQS+99JJ5LCkpyfzvhmFo7dq1euihh3TzzTdLkl5++WXFxsZq69atmjFjhg4ePKht27apqKhIo0aNkiQ988wzmjhxolavXq34+HjfTgoAAC9h53DfCJiM0+9//3uNGjVKP/jBDxQTE6MRI0bohRdeMM8fP35cDodD6enp5rGIiAilpqaqoKBAklRQUKDIyEgzaJKk9PR0BQUFqbCw8JzfW19fr5qaGpcGAAA6p4AJnD799FOtX79e/fv317vvvqu77rpL99xzjzZt2iRJcjgckqTY2FiX62JjY81zDodDMTExLudDQkIUFRVl9vm2FStWKCIiwmwJCQnenhoAAJ6jVOcTARM4OZ1OXXHFFXr88cc1YsQIzZ07V3PmzNGGDRva9XsXL16s6upqs5WVlbXr9wEA0BY2p+cNrQuYwKlPnz5KTk52OTZo0CCdOHFCkhQXFydJKi8vd+lTXl5unouLi1NFRYXL+aamJlVWVpp9vi0sLEx2u92lAQCAzilgAqerr75ahw8fdjl25MgR9e3bV9KZheJxcXHavn27eb6mpkaFhYVKS0uTJKWlpamqqkolJSVmnx07dsjpdCo1NdUHswAAoJ1QqvOJgHmqbt68ebrqqqv0+OOP64c//KF27dql559/Xs8//7wkyWaz6b777tPy5cvVv39/JSUlacmSJYqPj9ctt9wi6UyGavz48WaJr7GxUTk5OZoxYwZP1AEAApunezERN7klYAKnlJQUvfnmm1q8eLGWLVumpKQkrV27VpmZmWafBx98UHV1dZo7d66qqqp0zTXXaNu2bQoPDzf7bN68WTk5ORo3bpyCgoI0bdo0rVu3zh9TAgAAASZgAidJuvHGG3XjjTd+53mbzaZly5Zp2bJl39knKipKW7ZsaY/hAQDgN956Vx3OL6ACJwAA8B08XadE4OSWgFkcDgAA4G9knAAAsAJDkid7MZFwcguBEwAAFsAaJ98gcAIAwAoMebjGyWsjsTTWOAEAALiJjBMAAFbAU3U+QeAEAIAVOCXZPLweraJUBwAA4CYCJwAALKDlqTpPmnTmFWfJycnKzc3184w6Jkp1AABYgZfWOBUVFclut3tpUNZDxgkAAMBNZJwAALACnqrzCQInAACsgMDJJyjVAQAAuImMEwAAVsA+Tj5B4AQAgAXwkl/fIHACAMAKWOPkE6xxAgAAcBMZJwAArMBpSDYPskZOMk7uIHACAMAKKNX5BKU6AAAAN5FxAgDAEjzMOImMkzsInAAAsAJKdT5BqQ4AAMBNZJwAALACpyGPym08VecWAicAAKzAcJ5pnlyPVlGqAwAAcBMZJwAArIDF4T5B4AQAgBWwxsknCJwAALACMk4+wRonAAAAN5FxAgDACgx5mHHy2kgsjcAJAAAroFTnE5TqAAAA3ETGCQAAK3A6JXmwiaWTDTDdQeAEAIAVUKrzCUp1AAAAbiLjBACAFZBx8gkCJwAArICdw32CUh0AAICbyDgBAGABhuGUYbT9yThPru1MCJwAALACw/Cs3MYaJ7dQqgMAwApaFod70jqor776Sn379tUDDzzg76EQOAEAgI7tscce05VXXunvYUgicAIAwBqcTs9bB3T06FEdOnRIEyZM8PdQJBE4AQBgDX4o1eXn52vy5MmKj4+XzWbT1q1bz+qTm5urfv36KTw8XKmpqdq1a9cFfccDDzygFStWXPDY2guBEwAAaJO6ujoNGzZMubm55zz/2muvaf78+fr5z3+u3bt3a9iwYcrIyFBFRYXZZ/jw4Ro8ePBZ7eTJk/rd736nyy67TJdddpmvptSqgA2cVq5cKZvNpvvuu888dvr0aWVnZ6t3797q0aOHpk2bpvLycpfrTpw4oUmTJqlbt26KiYnRggUL1NTU5OPRAwDgXYbT6XGTpJqaGpdWX1//nd85YcIELV++XFOmTDnn+TVr1mjOnDmaNWuWkpOTtWHDBnXr1k0vvvii2ae0tFT79+8/q8XHx+ujjz7Sq6++qn79+umBBx7QCy+8oGXLlnn3D+4CBWTgVFRUpOeee05Dhw51OT5v3jy99dZbev311/XBBx/o5MmTmjp1qnm+ublZkyZNUkNDgz788ENt2rRJGzdu1NKlS309BQAAvMtLpbqEhARFRESYra1lsoaGBpWUlCg9Pd08FhQUpPT0dBUUFLh1jxUrVqisrEx/+9vftHr1as2ZM8fvv7MDLnCqra1VZmamXnjhBfXq1cs8Xl1drV/+8pdas2aNvv/972vkyJF66aWX9OGHH+qjjz6SJL333nv65JNP9Otf/1rDhw/XhAkT9Oijjyo3N1cNDQ3+mhIAAB1GWVmZqqurzbZ48eI23eef//ynmpubFRsb63I8NjZWDofDG0P1i4ALnLKzszVp0iSXCFaSSkpK1NjY6HJ84MCBSkxMNCPbgoICDRkyxOUvMSMjQzU1NTpw4MA5v6++vv6stCUAAB2O0/C8SbLb7S4tLCzMzxM7484779Tq1av9PYzA2jn81Vdf1e7du1VUVHTWOYfDodDQUEVGRroc/2Zk63A4zhn5tpw7lxUrVuiRRx7xwugBAGhHhiHJgy0FvLwBZnR0tIKDg89aa1xeXq64uDivfpcvBUzGqaysTPfee682b96s8PBwn33v4sWLXVKWZWVlPvtuAAACVWhoqEaOHKnt27ebx5xOp7Zv3660tDQ/jswzAZNxKikpUUVFha644grzWHNzs/Lz8/Xss8/q3XffVUNDg6qqqlyyTt+MbOPi4s7aP6IlEv6u6DcsLKzDpCkBAPguhtOQYWt71shoQ8aptrZWx44dMz8fP35cpaWlioqKUmJioubPn6+srCyNGjVKo0eP1tq1a1VXV6dZs2a1eZz+FjCB07hx47Rv3z6XY7NmzdLAgQO1cOFCJSQkqEuXLtq+fbumTZsmSTp8+LBOnDhhRrZpaWl67LHHVFFRoZiYGElSXl6e7Ha7kpOTfTshAAC8yXDKs1LdmWtTUlIUHBys7OxsZWdnn/eS4uJijR071vw8f/58SVJWVpY2btyo6dOn64svvtDSpUvlcDg0fPhwbdu27axlM4EkYAKnnj17avDgwS7Hunfvrt69e5vHZ8+erfnz5ysqKkp2u11333230tLSzPfb3HDDDUpOTtbMmTO1atUqORwOPfTQQ8rOziarBAAIaN7KOBUVFclut7t1zZgxY1rNVOXk5CgnJ6fN4+poAiZwcsdTTz2loKAgTZs2TfX19crIyNAvfvEL83xwcLDefvtt3XXXXUpLS1P37t2VlZXl9820AABAYAjowOn99993+RweHq7c3Nzv3Ppdkvr27as//OEPbf7Olsi6prZjvgyxo6uvbfT3EAJa81en/T2EwHU6oH/c+VVzg3eftupMmhvO/Jtty/qhC9Vk1JvltjZdL34+u4OfJBfo1KlTkqS+V/zNvwMJWJ/6ewAB7k1/DwBAG5w6dUoRERHtcu/Q0FDFxcXpL462JwVaxMXFKTQ01Aujsi6b4Ysw2EKcTqdOnjypnj17ymaznXW+pqZGCQkJKisrc7tGbBWdee5S555/Z5671Lnn35nnLrU+f8MwdOrUKcXHxysoqP12ADp9+rRX3oARGhrq0y1/AhEZpwsUFBSkSy65pNV+LTuudkadee5S555/Z5671Lnn35nnLp1//u2Vafqm8PBwAh4fCZgNMAEAAPyNwAkAAMBNBE5eFhYWpp///Oedcl+ozjx3qXPPvzPPXerc8+/Mc5eYf2fE4nAAAAA3kXECAABwE4ETAACAmwicAAAA3ETgBAAA4CYCpwu0cuVK2Ww23Xfffeax06dPKzs7W71791aPHj00bdo0lZeXu1x34sQJTZo0Sd26dVNMTIwWLFigpqYmH4++bT7//HP96Ec/Uu/evdW1a1cNGTJExcXF5nnDMLR06VL16dNHXbt2VXp6uo4ePepyj8rKSmVmZsputysyMlKzZ89WbW2tr6dywZqbm7VkyRIlJSWpa9eu+t73vqdHH33U5b1TVpl/fn6+Jk+erPj4eNlsNm3dutXlvLfmuXfvXl177bUKDw9XQkKCVq1a1d5Tc8v55t/Y2KiFCxdqyJAh6t69u+Lj43XHHXfo5MmTLvcI1Pm39nf/TT/5yU9ks9m0du1al+OBOnfJvfkfPHhQN910kyIiItS9e3elpKToxIkT5nmr/x7ANxhw265du4x+/foZQ4cONe69917z+E9+8hMjISHB2L59u1FcXGxceeWVxlVXXWWeb2pqMgYPHmykp6cbH3/8sfGHP/zBiI6ONhYvXuyHWVyYyspKo2/fvsadd95pFBYWGp9++qnx7rvvGseOHTP7rFy50oiIiDC2bt1q7Nmzx7jpppuMpKQk4+uvvzb7jB8/3hg2bJjx0UcfGTt37jQuvfRS47bbbvPHlC7IY489ZvTu3dt4++23jePHjxuvv/660aNHD+Ppp582+1hl/n/4wx+Mn/3sZ8Ybb7xhSDLefPNNl/PemGd1dbURGxtrZGZmGvv37zdeeeUVo2vXrsZzzz3nq2l+p/PNv6qqykhPTzdee+0149ChQ0ZBQYExevRoY+TIkS73CNT5t/Z33+KNN94whg0bZsTHxxtPPfWUy7lAnbthtD7/Y8eOGVFRUcaCBQuM3bt3G8eOHTN+97vfGeXl5WYfK/8egCsCJzedOnXK6N+/v5GXl2dcf/31ZuBUVVVldOnSxXj99dfNvgcPHjQkGQUFBYZhnPlHGRQUZDgcDrPP+vXrDbvdbtTX1/t0Hhdq4cKFxjXXXPOd551OpxEXF2c8+eST5rGqqiojLCzMeOWVVwzDMIxPPvnEkGQUFRWZff74xz8aNpvN+Pzzz9tv8F4wadIk48c//rHLsalTpxqZmZmGYVh3/t/+5eGtef7iF78wevXq5fK/+4ULFxoDBgxo5xldmPMFDy127dplSDI+++wzwzCsM//vmvvf//534+KLLzb2799v9O3b1yVwssrcDePc858+fbrxox/96DuvsfrvAbiiVOem7OxsTZo0Senp6S7HS0pK1NjY6HJ84MCBSkxMVEFBgSSpoKBAQ4YMUWxsrNknIyNDNTU1OnDggG8m0Ea///3vNWrUKP3gBz9QTEyMRowYoRdeeME8f/z4cTkcDpf5R0REKDU11WX+kZGRGjVqlNknPT1dQUFBKiws9N1k2uCqq67S9u3bdeTIEUnSnj179Je//EUTJkyQZP35t/DWPAsKCnTddde5vH09IyNDhw8f1pdffumj2XhHdXW1bDabIiMjJVl7/k6nUzNnztSCBQt0+eWXn3Xe6nN/5513dNlllykjI0MxMTFKTU11KedZ/fcAXBE4ueHVV1/V7t27tWLFirPOORwOhYaGmj88W8TGxsrhcJh9vvmPpeV8y7mO7NNPP9X69evVv39/vfvuu7rrrrt0zz33aNOmTZL+Pf5zze+b84+JiXE5HxISoqioqA4//0WLFmnGjBkaOHCgunTpohEjRui+++5TZmamJOvPv4W35hnI/xa+6fTp01q4cKFuu+0288WuVp7/E088oZCQEN1zzz3nPG/luVdUVKi2tlYrV67U+PHj9d5772nKlCmaOnWqPvjgA0nW/z0AVyH+HkBHV1ZWpnvvvVd5eXmd8s3TTqdTo0aN0uOPPy5JGjFihPbv368NGzYoKyvLz6Nrf7/5zW+0efNmbdmyRZdffrlKS0t13333KT4+vlPMH2drbGzUD3/4QxmGofXr1/t7OO2upKRETz/9tHbv3i2bzebv4fic0+mUJN18882aN2+eJGn48OH68MMPtWHDBl1//fX+HB78gIxTK0pKSlRRUaErrrhCISEhCgkJ0QcffKB169YpJCREsbGxamhoUFVVlct15eXliouLkyTFxcWd9XRFy+eWPh1Vnz59lJyc7HJs0KBB5tMkLeM/1/y+Of+KigqX801NTaqsrOzw81+wYIGZdRoyZIhmzpypefPmmdlHq8+/hbfmGcj/FqR/B02fffaZ8vLyzGyTZN3579y5UxUVFUpMTDR/Bn722We6//771a9fP0nWnbskRUdHKyQkpNWfg1b+PQBXBE6tGDdunPbt26fS0lKzjRo1SpmZmeZ/79Kli7Zv325ec/jwYZ04cUJpaWmSpLS0NO3bt8/lB0vLD91v/2PsaK6++modPnzY5diRI0fUt29fSVJSUpLi4uJc5l9TU6PCwkKX+VdVVamkpMTss2PHDjmdTqWmpvpgFm331VdfKSjI9Z9JcHCw+f9CrT7/Ft6aZ1pamvLz89XY2Gj2ycvL04ABA9SrVy8fzaZtWoKmo0eP6k9/+pN69+7tct6q8585c6b27t3r8jMwPj5eCxYs0LvvvivJunOXpNDQUKWkpJz35+DIkSMt/XsA3+Lv1emB6JtP1RnGmcdQExMTjR07dhjFxcVGWlqakZaWZp5veQz1hhtuMEpLS41t27YZF110UUA8hrpr1y4jJCTEeOyxx4yjR48amzdvNrp162b8+te/NvusXLnSiIyMNH73u98Ze/fuNW6++eZzPqY+YsQIo7Cw0PjLX/5i9O/fv8M9jn8uWVlZxsUXX2xuR/DGG28Y0dHRxoMPPmj2scr8T506ZXz88cfGxx9/bEgy1qxZY3z88cfmU2PemGdVVZURGxtrzJw509i/f7/x6quvGt26desQj6Sfb/4NDQ3GTTfdZFxyySVGaWmp8Y9//MNs33wiKlDn39rf/bd9+6k6wwjcuRtG6/N/4403jC5duhjPP/+8cfToUeOZZ54xgoODjZ07d5r3sPLvAbgicGqDbwdOX3/9tfHTn/7U6NWrl9GtWzdjypQpxj/+8Q+Xa/72t78ZEyZMMLp27WpER0cb999/v9HY2OjjkbfNW2+9ZQwePNgICwszBg4caDz//PMu551Op7FkyRIjNjbWCAsLM8aNG2ccPnzYpc///d//GbfddpvRo0cPw263G7NmzTJOnTrly2m0SU1NjXHvvfcaiYmJRnh4uPEf//Efxs9+9jOXX5ZWmf+f//xnQ9JZLSsryzAM781zz549xjXXXGOEhYUZF198sbFy5UpfTfG8zjf/48ePn/OcJOPPf/6zeY9AnX9rf/ffdq7AKVDnbhjuzf+Xv/ylcemllxrh4eHGsGHDjK1bt7rcw+q/B/BvNsP4xhbIAAAA+E6scQIAAHATgRMAAICbCJwAAADcROAEAADgJgInAAAANxE4AQAAuInACQAAwE0ETgAAAG4icAIAAHATgROAC3L99dfLZrPp8ccfdzluGIZSU1Nls9m0bNkyP40OANoXgRMAtxmGoY8//lh9+/bVvn37XM5t2rRJJ0+elCRdccUV/hgeALQ7AicAbjt69KhOnTqlrKwsl8Dp1KlTWrx4se68805J0siRI/00QgBoXwROANxWUlKibt266bbbbtPhw4fV0NAgSXr00Uc1atQoXXTRRYqLi1OfPn38PFIAaB8ETgDctnv3bg0dOlQDBgxQeHi4Dh06pKNHj2r9+vVas2aNdu/ebZbppkyZol69eunWW2/186gBwHsInAC4rSUwstlsGjp0qPbt26d58+bprrvuUv/+/VVSUmKW6e699169/PLLfh4xAHgXgRMAt30zozR8+HCtXbtWxcXFWrJkiU6fPq1Dhw6Z58eMGaOePXv6c7gA4HUETgDc8umnn6qqqsoMjEaMGKHi4mKtWLFCPXv21J49e9TU1MTCcACWFuLvAQAIDCUlJQoNDdXgwYMlSVlZWbrlllvUu3dvSWeyURdddJESEhL8OUwAaFcETgDcsnv3bg0ePFhdunSRJHXp0kXR0dEu50eMGOGv4QGAT9gMwzD8PQgA1vT+++/r2Wef1W9/+1t/DwUAvILACUC7SE9P1549e1RXV6eoqCi9/vrrSktL8/ewAMAjBE4AAABu4qk6AAAANxE4AQAAuInACQAAwE0ETgAAAG4icAIAAHATgRMAAICbCJwAAADcROAEAADgJgInAAAANxE4AQAAuInACQAAwE0ETgAAAG76/8sOF5/nSz46AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hepi.mapplot(dl,\"mass_2000002\",\"mass_1000022\",\"LO\",xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\",show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7999a3b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from smpl import data\n",
    "xx,yy = data.flatmesh(np.linspace(500,1500,5),np.linspace(500,1500,5))\n",
    "dll = hepi.interpolate_2d(dl,\"mass_1000022\",\"mass_2000002\",\"LO\",xx,yy,interpolator=\"linear\")\n",
    "hepi.mapplot(dll,\"mass_2000002\",\"mass_1000022\",\"LO\",xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\",show=True,fill_missing=False)\n",
    "#print(dll[\"LO\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e77461cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0+/-0.0016154263871670225,\n",
       "       1.5539029389273503e-16+/-0.0015909845253920855,\n",
       "       1.665699711616912e-16+/-0.0015822520490069067,\n",
       "       0.0+/-0.0015819508546576252, 0.0+/-0.001587055618200895,\n",
       "       0.0+/-0.0014827480507491498,\n",
       "       -1.3039619234566496e-16+/-0.001445349985972156,\n",
       "       1.257633877129178e-16+/-0.0014266141005402125,\n",
       "       2.384467240468002e-16+/-0.00142076671547182,\n",
       "       1.1118908908490573e-16+/-0.0014217864616282267,\n",
       "       0.0+/-0.0014085512991095304,\n",
       "       1.8508613222236997e-16+/-0.001367325296714199,\n",
       "       1.6602131115312832e-16+/-0.0013448653411302681,\n",
       "       1.4892845047421288e-16+/-0.0013341627341690635,\n",
       "       0.0+/-0.0013314060188204806, 0.0+/-0.001361907903727031,\n",
       "       -1.1779974384479693e-16+/-0.0013226213444905682,\n",
       "       0.0+/-0.0012988387152938962, 0.0+/-0.0012854820635719117,\n",
       "       0.0+/-0.001279159351555103, 0.0+/-0.0013320552441462043,\n",
       "       0.0+/-0.0012963384696511003,\n",
       "       2.2802366353531297e-16+/-0.001272302136817206,\n",
       "       1.9054767539780033e-16+/-0.0012573895051737369,\n",
       "       0.0+/-0.0012490700235172464], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dl[\"LO\"].to_numpy()-dll[\"LO\"].to_numpy())/dl[\"LO\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b14f324c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from smpl import data\n",
    "xx,yy = data.flatmesh(np.linspace(500,1500,16),np.linspace(500,1500,16))\n",
    "dll = hepi.interpolate_2d(dl,\"mass_1000022\",\"mass_2000002\",\"LO\",xx,yy,interpolator=\"cubic\")\n",
    "hepi.mapplot(dll,\"mass_2000002\",\"mass_1000022\",\"LO\",xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\",show=True,fill_missing=False)\n",
    "#print(dll[\"LO\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba1d705f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29030e42e2ea419b85acd7a24d484279",
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TBE5NTU0aNWrUeWfxnzx5Urt27dKCBQu0a9cuvfLKKzp48KC+/vWvR5QrKCjQvn37tGXLFr322msqLS3VrFmzwscbGhr01a9+VYMGDVJlZaWefPJJPfLII3rmmWccvz4AAJxC4OSeTjPHadKkSZo0adJ5j6WmpmrLli0R+55++mmNGzdOH374oQYOHKj9+/dr8+bNqqioUHZ2tiRpzZo1mjx5slasWKFAIKANGzaopaVFzz33nBITE3XFFVeoqqpKK1eujAiwAAAAzqfTZJwu1vHjx2UYhtLS0iRJZWVlSktLCwdNkpSfny+fz6fy8vJwmeuuu06JiYnhMhMnTtTBgwf18ccfn7ed06dPq6GhIWIDAKAzsSwjJpvEOk5t6TQZp4vR3NysuXPnatq0aeG1JmpqatSvX7+IcvHx8UpPT1dNTU24TGZmZkSZjIyM8LHevXuf09bSpUu1aNGic/b3TGhRfKJzac2W1jjH6pYkM+h8zBxyoQ0r5HwbZtD59HUose0y0TCCztYvSUbI+TZCLrRhhJz/fvsc/tyaIWd/f0iSL2i1XShKVsj5f6KMoMM/fCHn75N0Zg2naNdxOns+6zhdmOcyTq2trbr99ttlWZbWrl3reHvz58/X8ePHw9uRI0ccbxMAAHROnso4nQ2aDh8+rG3btkVExH6/X8eOHYsoHwwGVVdXJ7/fHy5TW1sbUebs12fLfFpSUpKSkpJieRkAAMQU6zi5xzMZp7NB06FDh/SHP/xBffr0iTiel5en+vp6VVZWhvdt27ZNpmkqNzc3XKa0tFStra3hMlu2bNHQoUPPO0wHAIAXxHKOEy6s0wROjY2NqqqqUlVVlSSpurpaVVVV+vDDD9Xa2qrbbrtNO3fu1IYNGxQKhVRTU6Oamhq1tLRIkoYNG6YbbrhBM2fO1I4dO/TnP/9ZxcXFmjp1qgKBgCTpzjvvVGJiombMmKF9+/bppZde0lNPPaXZs2d31GUDAAAP6TRDdTt37tSECRPCX58NZgoLC/XII4/oN7/5jSRp9OjREef98Y9/1Pjx4yVJGzZsUHFxsa6//nr5fD7deuutWr16dbhsamqq3njjDRUVFWns2LHq27evFi5cyFIEAABPY6jOPZ0mcBo/frws67OfPrjQsbPS09O1cePGC5YZOXKktm/fftH9AwCgs4rFUBtDdfZ0msAJAAC0jxWDjBOBkz2dZo4TAADoeCyAeWFknAAA8DhLko0ZLW3WIbEAZlsInAAA8DhThowYrRyOC2OoDgAAwCYyTgAAeBxP1bmHwAkAAI8zLUMG6zi5gqE6AAAAm8g4AQDgcZYVg6fqojy/uyBwAgDA45jj5B6G6gAAAGwicAIAwOPOZpyi3SRWDm8LQ3UAAHhcLJ+qY+XwCyNwAgDA45gc7h6G6gAAAGwi4wQAgMedyThF+1RdjDrTxRE4tVNKUrPik5z7lLUE4xyrW5KCIWfrlyQz6HxC0wy68PhsyPnfJkbI2eswQ45WL0kKudCG0/dJknxBx5tQKMHZ+o0k53/2DId/R0mSEXL+nygr2XS2/pCz9YfbYTkC1zBUBwAAYBMZJwAAPM765xZtHWgbgRMAAB7HUJ17GKoDAACwiYwTAABex1idawicAADwuhgM1YmhOlsYqgMAALCJwAkAAI87+8qVaDeJl/y2haE6AAA8LpZP1fGS3wsjcAIAwOssI/o5SsxxsoWhOgAAAJvIOAEA4HGfnKMUTR1oG4ETAABexzpOrmGoDgAAwCYyTgAAeBzvqnMPgRMAAF0BQ22uYKgOAADAJjJOAAB4HEN17iFwAgDA63iqzjUM1QEAANhExgkAAM8z/rlFWwfaQuAEAIDXMVTnGgKnduqV0KKEBOfqP5XoYOWSTged/9aHgs6PBIdCzrdhBZ3/K8xMdLb+UMj5azCCjjch03S+jZAL328j5Gz9Phe+36Ek53/2jFCc8204/LvQCrnwgyEROLmIOU4AAAA2ETgBAOB1lhGbTVJOTo6ysrJUUlLSwRfVOTFUBwCAx1nWmS3aOiSpoqJCKSkp0XeqiyLjBAAAYBMZJwAAvI7J4a4hcAIAwOs+MUcpqjrQJobqAAAAbCLjBACAxxnWmS3aOtA2AicAALyOOU6uYagOAADAJjJOAAB4HZPDXUPgBACA1zFU5xoCJwAAvI7AyTUETgAAoEsKBoPat2+fGhoaNGzYMPXt2zfqOgmcAADwOjJO59i9e7duueUWpaamKjExUe+9956uuuoqrVmzRpmZme2uN6qn6k6dOqWPPvronP379u2LploAAHAxzk4Oj3brQn7wgx/oueee065du/TWW2+ppqZGd9xxhyZPnqwDBw60u952B06/+tWvNGTIEN14440aOXKkysvLw8emT5/e7g4BAABE68SJE/rKV74S/joxMVHTp0/XCy+8oPvvv7/d9bY7cFqyZIkqKytVVVWln//855oxY4Y2btwoSbKsLpbvAwCgEzu7cni0W1cSFxenhoaGc/bn5OSopqam3fW2O3BqbW1VRkaGJGns2LEqLS3Vz372My1evFiGcfHpvtLSUk2ZMkWBQECGYWjTpk0Rxy3L0sKFC9W/f3/16NFD+fn5OnToUESZuro6FRQUKCUlRWlpaZoxY4YaGxsjyuzZs0fXXnutkpOTNWDAAC1fvvyi+woAQKdixWjrQu69917ddtttqquri9h//PhxmabZ7nrbHTj169dPe/bsCX+dnp6uLVu2aP/+/RH77WpqatKoUaNUUlJy3uPLly/X6tWrtW7dOpWXl+uSSy7RxIkT1dzcHC5TUFCgffv2acuWLXrttddUWlqqWbNmhY83NDToq1/9qgYNGqTKyko9+eSTeuSRR/TMM89cdH8BAEDnddddd+m2227TmDFjdNddd2n58uVasmSJrrrqKhUXF7e7XsOyOa62ZMkSXXnllRo7dqwyMjL017/+VQkJCeGs0yf9+c9/1tVXX93+ThmGXn31Vd18882SzmSbAoGAHnjgAf3oRz+SdCZizMjI0Pr16zV16lTt379fWVlZqqioUHZ2tiRp8+bNmjx5sv76178qEAho7dq1euihh1RTU6PExERJ0rx587Rp0ybbE8UaGhqUmpqqb2y5RwmXJLb7GttSe/JSx+qWpLqTPR2tX5KaTiY53kboZILjbRhNcY63EXfK2UmZTtcvSfEnHW9C8c1tl4m6DTeu45Szf9onnGz/X9N2xZ1yvo34k0Hn22hqdbT+YKhZ295+QsePH1dKSkrM6z/7b9LAZUvk65EcVV3mqWZ9OPdhx/raUU6cOKEXX3xRe/fuVWpqqm688UZ9+ctfbnd9tpcjWLhwYXgIzu/3h4Oos/+97LLLwmWjCZrOp7q6WjU1NcrPzw/vS01NVW5ursrKyjR16lSVlZUpLS0tHDRJUn5+vnw+n8rLy/WNb3xDZWVluu6668JBkyRNnDhRy5Yt08cff6zevXvHtN8AALjBUPRzlLrWM3X/0qtXr4jRp2jZHqrLycnRZZddpocffljz5s1T37599corr+i2227TwIED5ff7NXny5Jh17JPOTuL6dHYrIyMjfKympkb9+vWLOB4fH6/09PSIMuer45NtfNrp06fV0NAQsQEAAO+644472n2u7YxTeXm51q9frx//+MfKycnRypUr9fnPf16nT59WVVWVdu3apbfffrvdHemsli5dqkWLFp2zPy2hSYmJzqV4m0POrk162uH6Jak16PwQV3MwqqXIbLGCzv8dZprOXofhxjU4N3IdFgo534bhwnUYDl+HL+T899twoQ3Thd8hZtDZYVMz6NI607zk96JUVFS0+9yL+m19991367333tPQoUN15ZVXav78+QqFQsrNzdX3vvc9xyZZ+/1+SVJtbW3E/tra2vAxv9+vY8eORRwPBoOqq6uLKHO+Oj7ZxqfNnz9fx48fD29HjhyJ/oIAAIilGD5Vl5OTo6ysrM98WMsrHn74Yf3iF7/Q3r17FQzGbr7cRf+Ze+mll2r58uXauXOn9u7dqy984Qt64YUXYtah88nMzJTf79fWrVvD+xoaGlReXq68vDxJUl5enurr61VZWRkus23bNpmmqdzc3HCZ0tJStbb+K1O0ZcsWDR069DPnNyUlJSklJSViAwCgU4lh4FRRUaF3331XRUVFrl5CrJxdhqhPnz7asmWLZsyYoX79+mn48OG644479Oijj56zVNHFaFcOMRgM6vTp05o2bZpqa2t1zz336Gtf+5rS09Pb3ZHGxka9//774a+rq6tVVVWl9PR0DRw4UPfdd5+WLFmiIUOGKDMzUwsWLFAgEAg/eTds2DDdcMMNmjlzptatW6fW1lYVFxdr6tSpCgQCkqQ777xTixYt0owZMzR37lzt3btXTz31lH7605+2u98AAKDzSE1N1S9/+ctzVgevrq7W3r17tXfvXv3Hf/xHu+u3HTg98cQTeuedd/TOO+/owIEDSk5O1siRIzVu3Dh95zvfUWpqars7IUk7d+7UhAkTwl/Pnj1bklRYWKj169frwQcfVFNTk2bNmqX6+npdc8012rx5s5KT//X45YYNG1RcXKzrr79ePp9Pt956q1avXh0+npqaqjfeeENFRUUaO3as+vbtq4ULF8Z0tj0AAG6LxcrfXWXlcMuy9LOf/UwrV66UYRjKycnRnXfeqZycHGVmZmrKlClR1W97HSefz6fBgwersLBQ06ZN0xe/+MWoGvaqs2tm3POn25V4qXOzSP92KrpAtC3HHF4nSpLqm3o43kbzSedn8lonnZ/c6Tvl7OTwuCbnJ9HHn3K8CcW50IYb1+H4Ok4O1y9J8S6sFeVOG86uFRUMNutP5UscX8dp8JLH5EuOch2n5mb95eGHPL+Ok8/nU58+fTR16lRdcsklqqys1Pbt21VcXKwVK1ZEXb/tfxGuvfZaVVVVadGiRVqxYoVGjhypK6+8MrwNHz5ccXHOPwEBAABwIRs3bowYjtuzZ49uuukmXXbZZVG94Fe6iMnh//u//6vjx4/r4MGDevbZZ3X11Vdr//79euCBBzRmzBhdeumlGjduXFSdAQAA7cC76sLS09M1YMCAiH0jR47U008/rbVr10Zd/0WPQQwZMkRDhgzR1KlTw/uqq6u1c+fOLrmOEwAAnR1znP5l9OjR+vnPf65ly5ZF7P/CF76gDz/8MOr6YzJ5IzMzU5mZmfrmN78Zi+oAAADaZcmSJZowYYKOHj2q73//+xo5cqSampr0+OOPKzMzM+r6XVrSFAAAOIaVw8O+/OUv66233tIPf/hDXXvttTr7DFxycrJefvnlqOsncAIAwOtiMUepiwzVSdKoUaP0pz/9SceOHVNlZWV4Mey+fftGXTeBEwAA6JL69eunSZMmxbROAicAADyOyeHuIXACAMDrGKpzDYETAABeF4OME4GTPc6/hwEAAKCLIOMEAIDXMVTnGgInAAC8jsDJNQzVAQAA2ETGCQAAj2M5AveQcQIAALCJwAkAAMAmhuraKT3hpJISWh2rvymY5FjdknQyMcHR+iXpdND5j1co5Hzs3+JCG2bQ2ZdrGknO5+BDIedfEGqEHG9CpittOHuvQi5cgxF0/ufC58b3wuHrMH0u5SeYHO4aAicAADyOOU7uYagOAADAJjJOAAB0BWSMXEHgBACA1zHHyTUETgAAeBxznNzDHCcAAACbyDgBAOB1DNW5hsAJAACPY6jOPQzVAQAA2ETGCQAAr2OozjUETgAAeB2Bk2sYqgMAAJ1SfX29srOzNXr0aA0fPlzPPvtsR3eJjBMAAF7XVSeH9+rVS6WlperZs6eampo0fPhw3XLLLerTp0+H9YnACQAAr+uiQ3VxcXHq2bOnJOn06dOyLEuW1bEdZagOAAC0S2lpqaZMmaJAICDDMLRp06ZzypSUlGjw4MFKTk5Wbm6uduzYcVFt1NfXa9SoUbr88ss1Z84c9e3bN0a9bx8CJwAAvM6K0XaRmpqaNGrUKJWUlJz3+EsvvaTZs2frJz/5iXbt2qVRo0Zp4sSJOnbsWLjM2flLn96OHj0qSUpLS9Pu3btVXV2tjRs3qra29uI7GkMM1QEA4HGxnOPU0NAQsT8pKUlJSUnnPWfSpEmaNGnSZ9a5cuVKzZw5U/fcc48kad26dfrtb3+r5557TvPmzZMkVVVV2epfRkaGRo0ape3bt+u2226zdY4TyDgBAOB1Mcw4DRgwQKmpqeFt6dKl7epSS0uLKisrlZ+fH97n8/mUn5+vsrIyW3XU1tbqxIkTkqTjx4+rtLRUQ4cObVd/YoWMEwAACDty5IhSUlLCX39Wtqkt//jHPxQKhZSRkRGxPyMjQwcOHLBVx+HDhzVr1qzwpPAf/OAHGjFiRLv6EysETgAAeFwsh+pSUlIiAqeONG7cONtDeW4hcAIAwOs64XIEffv2VVxc3DmTuWtra+X3+2PbmIuY4wQAAGIuMTFRY8eO1datW8P7TNPU1q1blZeX14E9iw4Zp3bqk9Ck5ATnbl9jqH1jynY1hxIcrV+STgWdb6M1GOd4G8FW59sIJRmO1m8Gna1fkoxE59swQ443oVDQ+TYMh6/DcOH77Ut0vAmFXPncOps/CPlcyk90UMapsbFR77//fvjr6upqVVVVKT09XQMHDtTs2bNVWFio7OxsjRs3TqtWrVJTU1P4KTsvInACAMDjjH9u0dYhSTk5OYqLi1NRUZGKiooueM7OnTs1YcKE8NezZ8+WJBUWFmr9+vW644479Pe//10LFy5UTU2NRo8erc2bN58zYdxLCJwAAEBYRUWF7cnh48ePb/MVKMXFxSouLo5F1zoFAicAALyuE04O76oInAAA8LhYLkeACyNwAgDA68g4uYblCAAAAGwi4wQAQFdAxsgVZJwAAPC4s3Ocot2kM8sRZGVlqaSkpGMvqpMi4wQAAMIuZjmC7ojACQAAr2NyuGsInAAA8DiWI3APc5wAAABsIuMEAIDXMVTnGgInAAA8jqE69zBUBwAAYBOBEwAAXmfFaBPrOLWFoToAALwuhnOcWMfpwgicAADwOOY4ucczQ3WhUEgLFixQZmamevTooc9//vN69NFHZVn/+k5blqWFCxeqf//+6tGjh/Lz83Xo0KGIeurq6lRQUKCUlBSlpaVpxowZamxsdPtyAACAB3kmcFq2bJnWrl2rp59+Wvv379eyZcu0fPlyrVmzJlxm+fLlWr16tdatW6fy8nJdcsklmjhxopqbm8NlCgoKtG/fPm3ZskWvvfaaSktLNWvWrI64JAAAYiOGc5xwYZ4Zqvu///s/3XTTTbrxxhslSYMHD9aLL76oHTt2SDqTbVq1apUefvhh3XTTTZKkF154QRkZGdq0aZOmTp2q/fv3a/PmzaqoqFB2drYkac2aNZo8ebJWrFihQCDQMRcHAEAUDMuSYUUX+UR7fnfhmYzTVVddpa1bt+q9996TJO3evVtvvvmmJk2aJEmqrq5WTU2N8vPzw+ekpqYqNzdXZWVlkqSysjKlpaWFgyZJys/Pl8/nU3l5+XnbPX36tBoaGiI2AADQPXkm4zRv3jw1NDToS1/6kuLi4hQKhfTYY4+poKBAklRTUyNJysjIiDgvIyMjfKympkb9+vWLOB4fH6/09PRwmU9bunSpFi1adM7+z8U3qGdCXNTX9VlOhJIdq1uSmkJJjtYvSSeDCY630RJ0/iPcEnTu+3yWGXT2bxgr0fm/kcyQ83+tmkHD8TaMRMebkBl0uH4XriEUcr4NX6vz328zydmfDdPnUn6ClcNd45mM0y9/+Utt2LBBGzdu1K5du/T8889rxYoVev755x1td/78+Tp+/Hh4O3LkiKPtAQBwsc4+VRfthrZ5JuM0Z84czZs3T1OnTpUkjRgxQocPH9bSpUtVWFgov98vSaqtrVX//v3D59XW1mr06NGSJL/fr2PHjkXUGwwGVVdXFz7/05KSkpSU5Hx2BgCAziAnJ0dxcXEqKipSUVFRR3en0/FMxunkyZPyfSrlGRcXJ9M0JUmZmZny+/3aunVr+HhDQ4PKy8uVl5cnScrLy1N9fb0qKyvDZbZt2ybTNJWbm+vCVQAA4IAYPlVXUVGhd999l6DpM3gm4zRlyhQ99thjGjhwoK644gq9/fbbWrlypb797W9LkgzD0H333aclS5ZoyJAhyszM1IIFCxQIBHTzzTdLkoYNG6YbbrhBM2fO1Lp169Ta2qri4mJNnTqVJ+oAAJ7FApju8UzgtGbNGi1YsEDf//73dezYMQUCAX3nO9/RwoULw2UefPBBNTU1adasWaqvr9c111yjzZs3Kzn5XxOtN2zYoOLiYl1//fXy+Xy69dZbtXr16o64JAAA4DGeCZx69eqlVatWadWqVZ9ZxjAMLV68WIsXL/7MMunp6dq4caMDPQQAoIPwVJ1rPBM4AQCA82Oozj0ETgAAeB0ZJ9d45qk6AACAjkbGCQCALoChNncQOAEA4HWWdWaLtg60iaE6AAAQlpOTo6ysLJWUlHR0VzolMk4AAHhcLJ+qq6ioUEpKSvSd6qIInAAA8DqeqnMNQ3UAAAA2kXECAMDjDPPMFm0daBuBEwAAXsdQnWsYqgMAALCJjBMAAB7Hu+rcQ+AEAIDXsQCmawicAADwODJO7mGOEwAAgE1knNqpb1yDLomLc6z++oSejtUtSY2hJEfrl6SmxETH2zgZTHC8jdNB577PZ4WCzv4NEwwZjtYvSWbQ+TZCzn+kZLhyrxyuP+Rs/ZIUcuH77XP+15Tj3++QnL9PkniqzkUETgAAeBxDde5hqA4AAITxkt8LI+MEAIDXxfCpOl7ye2EETgAAeBxDde5hqA4AAMAmMk4AAHgdT9W5hsAJAACPY6jOPQzVAQAA2ETGCQAArzOtM1u0daBNBE4AAHgdc5xcQ+AEAIDHGYrBHKeY9KTrY44TAACATWScAADwuhiuHI4LI3ACAMDjWI7APQzVAQAA2ETGCQAAr+OpOtcQOAEA4HGGZcmIco5StOd3FwzVAQAA2ETgBACA15kx2iTl5OQoKytLJSUlrl6CVzBUBwCAx8VyqK6iokIpKSmx6FaXRMYJAADAJjJOAAB4HU/VuYbAqZ0y4ht1abxzCbt6s6djdUvS8YQejtYvSQ3BZMfbOJmY6HgbzcEEx9toaXX2RzGUEOdo/ZJkJZrOtxFyPklutjr/xi7T4Y9UKOhs/ZJkuNCGG9fhS3T2+20aLr0BjpXDXUPgBACAx7FyuHuY4wQAAGATGScAALyOoTrXEDgBAOBxhnlmi7YOtI2hOgAAAJvIOAEA4HUM1bmGwAkAAK9jHSfXMFQHAABgExknAAA8LpbvqsOFETgBAOB1zHFyDUN1AAAANpFxAgDA6yxJ0a7DRMLJFgInAAA8jjlO7iFwAgDA6yzFYI5TTHrS5THHCQAAwCYyTgAAeB1P1bmGwAkAAK8zJRkxqANtYqgOAADAJk8FTh999JG+9a1vqU+fPurRo4dGjBihnTt3ho9blqWFCxeqf//+6tGjh/Lz83Xo0KGIOurq6lRQUKCUlBSlpaVpxowZamxsdPtSAACImbNP1UW7SVJOTo6ysrJUUlLSwVfVOXlmqO7jjz/W1VdfrQkTJuj3v/+9Pve5z+nQoUPq3bt3uMzy5cu1evVqPf/888rMzNSCBQs0ceJEvfvuu0pOTpYkFRQU6G9/+5u2bNmi1tZW3XPPPZo1a5Y2btzYUZcGAEB0YjjHqaKiQikpKTHoVNfkmcBp2bJlGjBggH7+85+H92VmZob/37IsrVq1Sg8//LBuuukmSdILL7ygjIwMbdq0SVOnTtX+/fu1efNmVVRUKDs7W5K0Zs0aTZ48WStWrFAgEHD3ogAAgKd4ZqjuN7/5jbKzs/XNb35T/fr105gxY/Tss8+Gj1dXV6umpkb5+fnhfampqcrNzVVZWZkkqaysTGlpaeGgSZLy8/Pl8/lUXl5+3nZPnz6thoaGiA0AgE7lbMYp2g1t8kzG6YMPPtDatWs1e/Zs/fjHP1ZFRYXuvfdeJSYmqrCwUDU1NZKkjIyMiPMyMjLCx2pqatSvX7+I4/Hx8UpPTw+X+bSlS5dq0aJF5+zPiLPUK865D1l93HHH6pak4/E9Ha1fkhoTkh1voymY5HgbjQmJjrdxOsnZH8VgMM7R+iWpNej832Fma7SPDbXNSHT+Hw8z6Ox1GEFHq5ckma604fz3O+TwdZiW89cgieUIXOSZjJNpmrryyiv1+OOPa8yYMZo1a5ZmzpypdevWOdru/Pnzdfz48fB25MgRR9sDAACdl2cCp/79+ysrKyti37Bhw/Thhx9Kkvx+vySptrY2okxtbW34mN/v17FjxyKOB4NB1dXVhct8WlJSklJSUiI2AAA6FTNGG9rkmcDp6quv1sGDByP2vffeexo0aJCkMxPF/X6/tm7dGj7e0NCg8vJy5eXlSZLy8vJUX1+vysrKcJlt27bJNE3l5ua6cBUAAMReLJcjwIV5Zo7T/fffr6uuukqPP/64br/9du3YsUPPPPOMnnnmGUmSYRi67777tGTJEg0ZMiS8HEEgENDNN98s6UyG6oYbbggP8bW2tqq4uFhTp07liToAgHcxx8k1ngmccnJy9Oqrr2r+/PlavHixMjMztWrVKhUUFITLPPjgg2pqatKsWbNUX1+va665Rps3bw6v4SRJGzZsUHFxsa6//nr5fD7deuutWr16dUdcEgAA8BjPBE6S9LWvfU1f+9rXPvO4YRhavHixFi9e/Jll0tPTWewSANC1mJZkRJkxMsk42eGpwAkAAJwHQ3Wu8czkcAAAgI5GxgkAAM+LxcrfZJzsIHACAMDrGKpzDUN1AAAANpFxAgDA60xLUQ+18VSdLQROAAB4nWWe2aKtA21iqA4AAMAmMk4AAHgdk8NdQ+AEAIDXMcfJNQROAAB4HRkn1zDHCQAAwCYyTgAAeJ2lGGScYtKTLo/ACQAAr2OozjUM1QEAANhExgkAAK8zTUlRLmBpsgCmHQRO7ZTu66kUX5xj9feLa3Ssbkmqiz/haP2SVJ/Q0/E2GoI9HG+jMTHJ8TaagwnO1p/g/I96KMH5BHYo0fk2rKDzwxVmguFo/UbQ0eolSWarC204+2Nxpg2Hr8O1WIShOtcwVAcAAGATGScAALyOjJNrCJwAAPA6Vg53DUN1AAAANpFxAgDA4yzLlGVFNxM92vO7CwInAAC8zrKiH2pjjpMtDNUBAOB1ZyeHR7t1UidPntSgQYP0ox/9qKO7QuAEAAA6t8cee0xf/vKXO7obkgicAADwPtOMzdYJHTp0SAcOHNCkSZM6uiuSCJwAAPC+DhqqKy0t1ZQpUxQIBGQYhjZt2nROmZKSEg0ePFjJycnKzc3Vjh07LqqNH/3oR1q6dOlF980pBE4AAKBdmpqaNGrUKJWUlJz3+EsvvaTZs2frJz/5iXbt2qVRo0Zp4sSJOnbsWLjM6NGjNXz48HO2o0eP6te//rW++MUv6otf/KJbl9QmnqoDAMDjLNOUZcRmOYKGhoaI/UlJSUpKOv87OydNmnTBIbSVK1dq5syZuueeeyRJ69at029/+1s999xzmjdvniSpqqrqM89/66239Itf/EIvv/yyGhsb1draqpSUFC1cuPBiLi2myDgBAOB1MRyqGzBggFJTU8Nbe4fJWlpaVFlZqfz8/PA+n8+n/Px8lZWV2apj6dKlOnLkiP7yl79oxYoVmjlzZocGTRIZJwAA8AlHjhxRSkpK+OvPyja15R//+IdCoZAyMjIi9mdkZOjAgQNR9bEjETgBAOB1piUZsVkAMyUlJSJw6izuvvvuju6CJAInAAC8z7IkRbmcQIwXwOzbt6/i4uJUW1sbsb+2tlZ+vz+mbbmJOU4AACDmEhMTNXbsWG3dujW8zzRNbd26VXl5eR3Ys+iQcQIAwOMs05IV5VCd1Y6MU2Njo95///3w19XV1aqqqlJ6eroGDhyo2bNnq7CwUNnZ2Ro3bpxWrVqlpqam8FN2XkTgBACA11mmoh+qO3N+Tk6O4uLiVFRUpKKioguesnPnTk2YMCH89ezZsyVJhYWFWr9+ve644w79/e9/18KFC1VTU6PRo0dr8+bN50wY9xICJwAAPC6WGaeKigrbk8PHjx/fZqaquLhYxcXFUfWtM2GOEwAAgE1knC7S2ci6odHZlyE2tjpb/8nWkKP1S1JzS9DxNlpOtTreRmtzi+NtBE+edrT+0Enn/0YyTzr/glDzlPOfW6PZ+XvleBvOfpwkSYYbbTj/oyejJbZPkn1aqKVZUvvmD12MoHU6PNTW7jrk/O/TroDA6SKdOHFCkjToyr90bEeiVtt2EQBATJw4cUKpqakxrzcxMVF+v19v1vwuJvX5/X4lJibGpK6uyrCcDoO7GNM0dfToUfXq1UuGYXR0d9qloaFBAwYMOGd1WJyLe2UP98k+7pU9XeU+WZalEydOKBAIyOdzJtPY3NyslpbYpOcSExOVnJwck7q6KjJOF8nn8+nyyy/v6G7ERGddHbYz4l7Zw32yj3tlT1e4T05kmj4pOTmZYMdFTA4HAACwicAJAADAJgKnbigpKUk/+clP2v3G6+6Ee2UP98k+7pU93Cd0VkwOBwAAsImMEwAAgE0ETgAAADYROAEAANhE4AQAAGATgVMX8tFHH+lb3/qW+vTpox49emjEiBHauXNn+LhlWVq4cKH69++vHj16KD8/X4cOHYqoo66uTgUFBUpJSVFaWppmzJihxsZGty/FUaFQSAsWLFBmZqZ69Oihz3/+83r00Ucj3iXVHe9VaWmppkyZokAgIMMwtGnTpojjsbone/bs0bXXXqvk5GQNGDBAy5cvd/rSYu5C96q1tVVz587ViBEjdMkllygQCOiuu+7S0aNHI+roDveqrc/UJ333u9+VYRhatWpVxP7ucJ/gMRa6hLq6OmvQoEHW3XffbZWXl1sffPCB9frrr1vvv/9+uMwTTzxhpaamWps2bbJ2795tff3rX7cyMzOtU6dOhcvccMMN1qhRo6y33nrL2r59u/WFL3zBmjZtWkdckmMee+wxq0+fPtZrr71mVVdXWy+//LJ16aWXWk899VS4THe8V7/73e+shx56yHrllVcsSdarr74acTwW9+T48eNWRkaGVVBQYO3du9d68cUXrR49elg/+9nP3LrMmLjQvaqvr7fy8/Otl156yTpw4IBVVlZmjRs3zho7dmxEHd3hXrX1mTrrlVdesUaNGmUFAgHrpz/9acSx7nCf4C0ETl3E3LlzrWuuueYzj5umafn9fuvJJ58M76uvr7eSkpKsF1980bIsy3r33XctSVZFRUW4zO9//3vLMAzro48+cq7zLrvxxhutb3/72xH7brnlFqugoMCyLO6VZVnn/CMXq3vyn//5n1bv3r2t06dPh8vMnTvXGjp0qMNX5JwLBQRn7dixw5JkHT582LKs7nmvPus+/fWvf7Uuu+wya+/evdagQYMiAqfueJ/Q+TFU10X85je/UXZ2tr75zW+qX79+GjNmjJ599tnw8erqatXU1Cg/Pz+8LzU1Vbm5uSorK5MklZWVKS0tTdnZ2eEy+fn58vl8Ki8vd+9iHHbVVVdp69ateu+99yRJu3fv1ptvvqlJkyZJ4l6dT6zuSVlZma677rqIt69PnDhRBw8e1Mcff+zS1bjv+PHjMgxDaWlpkrhXZ5mmqenTp2vOnDm64oorzjnOfUJnRODURXzwwQdau3athgwZotdff13f+973dO+99+r555+XJNXU1EiSMjIyIs7LyMgIH6upqVG/fv0ijsfHxys9PT1cpiuYN2+epk6dqi996UtKSEjQmDFjdN9996mgoEAS9+p8YnVPampqzlvHJ9voapqbmzV37lxNmzYt/LJa7tUZy5YtU3x8vO69997zHuc+oTOK7+gOIDZM01R2drYef/xxSdKYMWO0d+9erVu3ToWFhR3cu87ll7/8pTZs2KCNGzfqiiuuUFVVle677z4FAgHuFWKqtbVVt99+uyzL0tq1azu6O51KZWWlnnrqKe3atUuGYXR0dwDbyDh1Ef3791dWVlbEvmHDhunDDz+UJPn9fklSbW1tRJna2trwMb/fr2PHjkUcDwaDqqurC5fpCubMmRPOOo0YMULTp0/X/fffr6VLl0riXp1PrO6J3+8/bx2fbKOrOBs0HT58WFu2bAlnmyTulSRt375dx44d08CBAxUfH6/4+HgdPnxYDzzwgAYPHiyJ+4TOicCpi7j66qt18ODBiH3vvfeeBg0aJEnKzMyU3+/X1q1bw8cbGhpUXl6uvLw8SVJeXp7q6+tVWVkZLrNt2zaZpqnc3FwXrsIdJ0+elM8X+dGPi4uTaZqSuFfnE6t7kpeXp9LSUrW2tobLbNmyRUOHDlXv3r1duhrnnQ2aDh06pD/84Q/q06dPxHHulTR9+nTt2bNHVVVV4S0QCGjOnDl6/fXXJXGf0El19Ox0xMaOHTus+Ph467HHHrMOHTpkbdiwwerZs6f13//93+EyTzzxhJWWlmb9+te/tvbs2WPddNNN532cfMyYMVZ5ebn15ptvWkOGDPH0I/bnU1hYaF122WXh5QheeeUVq2/fvtaDDz4YLtMd79WJEyest99+23r77bctSdbKlSutt99+O/wkWCzuSX19vZWRkWFNnz7d2rt3r/WLX/zC6tmzp+ceHb/QvWppabG+/vWvW5dffrlVVVVl/e1vfwtvn3zyqzvcq7Y+U5/26afqLKt73Cd4C4FTF/I///M/1vDhw62kpCTrS1/6kvXMM89EHDdN01qwYIGVkZFhJSUlWddff7118ODBiDL/7//9P2vatGnWpZdeaqWkpFj33HOPdeLECTcvw3ENDQ3WD3/4Q2vgwIFWcnKy9W//9m/WQw89FPGPWne8V3/84x8tSedshYWFlmXF7p7s3r3buuaaa6ykpCTrsssus5544gm3LjFmLnSvqqurz3tMkvXHP/4xXEd3uFdtfaY+7XyBU3e4T/AWw7I+sVwyAAAAPhNznAAAAGwicAIAALCJwAkAAMAmAicAAACbCJwAAABsInACAACwicAJAADAJgInAAAAmwicAAAAbCJwAnBRvvKVr8gwDD3++OMR+y3LUm5urgzD0OLFizuodwDgLAInALZZlqW3335bgwYN0jvvvBNx7Pnnn9fRo0clSVdeeWVHdA8AHEfgBMC2Q4cO6cSJEyosLIwInE6cOKH58+fr7rvvliSNHTu2g3oIAM4icAJgW2VlpXr27Klp06bp4MGDamlpkSQ9+uijys7O1uc+9zn5/X7179+/g3sKAM4gcAJg265duzRy5EgNHTpUycnJOnDggA4dOqS1a9dq5cqV2rVrV3iY7hvf+IZ69+6t2267rYN7DQCxQ+AEwLazgZFhGBo5cqTeeecd3X///fre976nIUOGqLKyMjxM98Mf/lAvvPBCB/cYAGKLwAmAbZ/MKI0ePVqrVq3Szp07tWDBAjU3N+vAgQPh4+PHj1evXr06srsAEHMETgBs+eCDD1RfXx8OjMaMGaOdO3dq6dKl6tWrl3bv3q1gMMjEcABdWnxHdwCAN1RWVioxMVHDhw+XJBUWFurmm29Wnz59JJ3JRn3uc5/TgAEDOrKbAOAoAicAtuzatUvDhw9XQkKCJCkhIUF9+/aNOD5mzJiO6h4AuMKwLMvq6E4A6Jr+9Kc/6emnn9avfvWrju4KAMQEgRMAR+Tn52v37t1qampSenq6Xn75ZeXl5XV0twAgKgROAAAANvFUHQAAgE0ETgAAADYROAEAANhE4AQAAGATgRMAAIBNBE4AAAA2ETgBAADYROAEAABgE4ETAACATQROAAAANhE4AQAA2ETgBAAAYNP/B28CdhVyMDbDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for a,b in [(2000002,1000022)]:\n",
    "    for pdf,nlopdf in [(\"CT14lo\",\"CT14lo\")]:\n",
    "        li = [hepi.Input(hepi.Order.LO,13000,a,b,\"slha.in\",pdf,nlopdf,1., 1.)]\n",
    "        #li=hepi.slha_scan_rel(li,lambda r : [[\"EXTPAR\",1,r],[\"EXTPAR\",2,r]],np.linspace(500.,1500.,16*2))\n",
    "\n",
    "        li=hepi.mass_scan(li,a,np.linspace(500.,1500.,16))\n",
    "        li=hepi.mass_scan(li,b,np.linspace(500.,1500.,16))\n",
    "        dl2 = rs.run(li,True,True)\n",
    "hepi.mapplot(dl2,\"mass_2000002\",\"mass_1000022\",\"LO\",xaxis=\"$M_1$\",yaxis=\"$M_2$\",zaxis=\"$\\sigma_{LO}$\",show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28d178a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.013+/-0.013\n",
      "0.22+/-0.25\n",
      "0.009+/-0.009\n",
      "0.0003+/-0.0004\n"
     ]
    }
   ],
   "source": [
    "from smpl import stat\n",
    "\n",
    "dll = hepi.interpolate_2d(dl,\"mass_1000022\",\"mass_2000002\",\"LO\",xx,yy,interpolator=\"cubic\")\n",
    "print(stat.mean(np.abs((dl2[\"LO\"].to_numpy()-dll[\"LO\"].to_numpy())/dl2[\"LO\"].to_numpy())))\n",
    "\n",
    "dll = hepi.interpolate_2d(dl,\"mass_1000022\",\"mass_2000002\",\"LO\",xx,yy,interpolator=\"linear\")\n",
    "print(stat.mean(np.abs((dl2[\"LO\"].to_numpy()-dll[\"LO\"].to_numpy())/dl2[\"LO\"].to_numpy())))\n",
    "\n",
    "# exponential dependency\n",
    "dll = hepi.interpolate_2d(dl,\"mass_1000022\",\"mass_2000002\",\"LO\",xx,yy,interpolator=\"linear\",pre=np.log,post=np.exp)\n",
    "print(stat.mean(np.abs((dl2[\"LO\"].to_numpy()-dll[\"LO\"].to_numpy())/dl2[\"LO\"].to_numpy())))\n",
    "\n",
    "dll = hepi.interpolate_2d(dl,\"mass_1000022\",\"mass_2000002\",\"LO\",xx,yy,interpolator=\"cubic\",pre=np.log,post=np.exp)\n",
    "print(stat.mean(np.abs((dl2[\"LO\"].to_numpy()-dll[\"LO\"].to_numpy())/dl2[\"LO\"].to_numpy())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efc9b48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       9.09030429285613e-06+/-0.0015844121424196667,\n",
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       "       1.2989387602054172e-05+/-0.0015811959210456758,\n",
       "       1.5105056790539695e-05+/-0.0015812344985740665,\n",
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       "       -3.785839264811367e-05+/-0.0013228209102155617,\n",
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       "       3.355802096449835e-05+/-0.0013446106194470145,\n",
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       "       0.00016584654860746296+/-0.0013309220044800249,\n",
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       "       0.00013052443412966847+/-0.001308445440093883,\n",
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       "       -2.7457196163413707e-05+/-0.0012994413625850836,\n",
       "       8.249971002769306e-05+/-0.0012971553583712091,\n",
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       "       -2.118177449748371e-05+/-0.001288604796748706,\n",
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       "       -0.0006220737559200378+/-0.0013245635953381774,\n",
       "       -0.00041132339535480435+/-0.001315273255445316,\n",
       "       -7.153873079302709e-05+/-0.0013068220309187621,\n",
       "       0.0001269753941258208+/-0.0012992177102487742,\n",
       "       8.427970058124029e-05+/-0.0012927860694650587,\n",
       "       -4.742879230333612e-05+/-0.0012873281270882407,\n",
       "       -0.00017058203309093455+/-0.0012825307574787727,\n",
       "       -0.00022968728014538542+/-0.001278296556620345,\n",
       "       -0.00020787127648808863+/-0.001274609332622356,\n",
       "       -0.00011694787245371972+/-0.001271423174009526,\n",
       "       6.069966948449475e-06+/-0.0012686442960820412,\n",
       "       0.00010337726457424577+/-0.001266287063084491,\n",
       "       0.00010507444419722617+/-0.001264423620388504,\n",
       "       -7.133885396628107e-05+/-0.001263134972474288,\n",
       "       -0.0002410739769788234+/-0.0013390603798948543,\n",
       "       -0.0005855094652249528+/-0.0013278504949617454,\n",
       "       -0.0006529959315203705+/-0.0013178320993784795,\n",
       "       -0.00043824702455191823+/-0.0013086746363412225,\n",
       "       -9.691678377124997e-05+/-0.0013002755050425254,\n",
       "       9.889536137057949e-05+/-0.0012927405604039898,\n",
       "       6.039548357847316e-05+/-0.001286315876717784,\n",
       "       -6.878424861027368e-05+/-0.0012807588265754063,\n",
       "       -0.0001877166114452211+/-0.0012758790028146482,\n",
       "       -0.00024859238182437784+/-0.0012715786830530362,\n",
       "       -0.00022579774517204394+/-0.0012678059221466287,\n",
       "       -0.00013427514106510572+/-0.0012644584344697163,\n",
       "       -1.0535537164217876e-05+/-0.0012615262541106722,\n",
       "       8.942457063640743e-05+/-0.0012590568816031771,\n",
       "       9.21749252418909e-05+/-0.0012570870689284294,\n",
       "       -8.642296967596324e-05+/-0.001255670886662506,\n",
       "       -3.5117582447420925e-16+/-0.001332055244145646,\n",
       "       -0.00035937095846981114+/-0.0013212454297169308,\n",
       "       -0.0004409792096084742+/-0.0013115559915172536,\n",
       "       -0.0002432852316549673+/-0.0013025906102734559,\n",
       "       7.767929102692437e-05+/-0.001294351732591518,\n",
       "       0.000256981222052556+/-0.0012869006720839112,\n",
       "       0.00021018666902285357+/-0.0012804582323263482,\n",
       "       7.028200240307518e-05+/-0.0012748346532102895,\n",
       "       -5.868459704079617e-05+/-0.0012699385580342223,\n",
       "       -0.00012617141125908254+/-0.0012655949706406222,\n",
       "       -0.00011252231730407305+/-0.0012616831987138997,\n",
       "       -2.777991257122016e-05+/-0.0012581935763035373,\n",
       "       8.919720156827438e-05+/-0.0012551529032158745,\n",
       "       0.00018462963780929018+/-0.0012526029315386102,\n",
       "       0.00018373391712617177+/-0.0012505698636003345,\n",
       "       -1.6102817093322409e-16+/-0.001249070023516848], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dl2[\"LO\"].to_numpy()-dll[\"LO\"].to_numpy())/dl2[\"LO\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91517bf5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AcIMKEwAAFsJTcuYgYQIAwEJOJUy+jmHyUzCNCAkTAI+l/fZPZofgoimDCwDUMxImAAAshKfkzEHCBACAhRg/L762cSH4n//5H4+PGTx4sJo3b+7xcSRMAABYiJUrTKtWrdLUqVPlcDg0ffp0/e53v/OpvRtvvNGj/W02m77++mt16dLF43ORMAEAgHpXXV2tKVOm6NNPP1VERIR69+6tUaNGqXXr1j61a7fbFR0dXad9W7Vq5fV5GCoJAICVGH5aGtjmzZt1+eWXq3379goLC1NGRoY++ugjn9rMzMz0qHvttttuU3h4uFfnImECAMBKfu6S82WRF11yGzZs0IgRIxQXFyebzaa33377F/vk5uaqc+fOatasmVJTU7V582bntoMHD6p9+/bOz+3bt9e3337r1S04bdGiRbVWjQzDkFHL3Al5eXlq06aNV+ciYQIAAG5VVFQoOTlZubm5tW5ftmyZpkyZokceeURffPGFkpOTNXToUB05cqTBYly4cKF69OihZs2aqVmzZurRo4f+/Oc/+6VtxjABAGAh/pzpu6yszGV9aGioQkNDaz0mIyNDGRkZ52zz6aef1oQJE3TnnXdKkhYsWKD33ntPr732mmbMmKG4uDiXitK3336rq666yrcLOcPs2bP19NNP67777lNaWpokKT8/X5MnT1ZJSYnmzp3rU/tUmAAAsBBfu+POfMouPj5eERERziUnJ8ermKqqqrR161alp6c71wUFBSk9PV35+fmSpKuuukq7du3St99+q2PHjumDDz7Q0KFDfb8hP8vLy9Orr76qnJwc3XDDDbrhhhuUk5OjV155RS+99JLP7VNhAgCgkdq/f7/LIOhzVZfc+f7771VTU6OYmBiX9TExMfrqq68kSU2aNNGf/vQnXXvttXI4HHrwwQd9fkLuTCdPnlSfPn1+sb53796qrq72uX0SJgAeq2kaWLME578zzewQgIbj5aDtX7QhKTw83OunxrxxuvJTH26//Xbl5eXp6aefdln/yiuvaOzYsT63T8IEAICF+HMMk7+0adNGwcHBOnz4sMv6w4cPKzY21r8nO8OUKVOcP9tsNv35z3/WRx99pL59+0qSNm3apJKSEo0bN87nc5EwAQAAn4SEhKh3795au3atc/Zth8OhtWvXKjs7u97Ou23bNpfPvXv3liTt27dP0qlErk2bNtq9e7fP56pzwtSQ72sBAADnYNLL5I4dO6aioiLn5+LiYhUWFioqKkodO3bUlClTlJmZqT59+uiqq67Ss88+q4qKCudTc/Xh008/rbe2z1bnhKkh39cCAABqZ9a75LZs2aJrr73W+fl0d1hmZqYWL16sMWPG6LvvvtPs2bNlt9t1xRVXaPXq1b8YCN4QTk9aabP5b7ylR9MK2O12ORyOOi0tWrTwW5AAAOAMfnotSkpKihITE885GeWZBg4c6JxB+8xl8eLFzn2ys7P173//W5WVldq0aZNSU1P9cbV1FhATVzbk+1oAAED9KygouGD+Vtf3xJV1TpgWLVqkXbt2qUePHnXaPy8vz+ugAABA7czqkgt0pyeuvPXWW53rbrjhBiUlJem+++5r2Jm+k5KSlJqaqldffVXl5eU+nRgAAHjB1+44fwwaD0D1PXGlRwnT+vXrdfnll2vq1Klq166dMjMz9dlnn/kcBAAAgC9OT1x5NlMmrrz66qt19dVX64UXXtCbb76pxYsX65prrlG3bt00fvx4ZWZm1usEVQAAwPbz4msbF56FCxeec+LKMye5PHs28LrwauLKli1b6s4779Sdd96poqIiLVq0SLm5uXr44Yc1bNgwr+ZsAgAAdWDSPEyBbteuXbryyisl/XLiyl27djn383aqAZ9n+u7WrZtmzZqlTp06aebMmXrvvfd8bRJAgDM86swHgPpX35NY+pQwbdiwQa+99preeustBQUF6Te/+Y3Gjx/vr9gAAMDZqDCZwuP/Tzx48KDmzZunSy65RAMHDlRRUZGef/55HTx4UK+++qqz3xAAANQDw+afRZ5NXBmIduzYIYfDUef9d+/e7fUTcx5VmDIyMvTxxx+rTZs2GjdunO666y51797dqxMDAABzWX3iyl69eslut6tt27Z12j8tLU2FhYVevbbNo4SpadOmWrFiha6//noFBwd7fDIAAOAbwzi1+NrGhcAwDD388MN1fh1bVVWV1+fyKGHi6TcAAEzGGCanAQMGaO/evXXePy0tzaPXvJ3J60Hfn332mV5++WXt27dPK1asUPv27fXf//3fSkhIUP/+/b1tFgAAnM8ZY5B8auMCsG7dugY7l1cJ01tvvaXbb79dY8eO1bZt21RZWSlJKi0t1bx58/T+++/7NUigoV0+8xmzQ3CxO2ey2SG4YFoBAI2NV//sPfbYY1qwYIFeffVVNW3a1Lm+X79++uKLL/wWHAAAcGUz/LPAM15VmPbu3asBAwb8Yn1ERISOHj3qa0wAAOBcGMNkCq8qTLGxsSoqKvrF+s8//9yrR/UAAAACmVcJ04QJEzRp0iRt2rRJNptNBw8e1JIlS/TAAw9o4sSJ/o4RAACcxsSVXlu2bJnXx3rVJTdjxgw5HA4NGjRIx48f14ABAxQaGqoHHnhA9913n9fBAAAAN/zYJWf1iSs9NW3aNI0ZM8arY71KmGw2m/7whz9o2rRpKioq0rFjx5SYmKiwsDCvggAAAKhvhg8zdnqUMP30009au3atrr/+eknSI4884pxSQJKaNGmiuXPnqlmzZl4HBAAAzoNB316z2byff8qjhOkvf/mL3nvvPWfC9OKLL+ryyy93zpr51VdfqV27dpo8ObDmjAE8FWjzHgUaI/jCmPQOsCQSpvNq27ZtrYmRYRg+PcnvUcK0ZMkSPfjggy7rli5d6nwy7q9//atyc3NJmAAAgCm+++67c2574403vG7Xo6fkioqK1LNnT+fnZs2aKSjoP01cddVV2rNnj9fBAAAAN/z4lFxjc3bRxxMeVZiOHj3qMmbp7CzO4XC4bAcAAP7lj5m6G+tM374M+vaowtShQwft2rXrnNt37NihDh06eB0MAABww/DT0gg12KDv6667TrNnz9bw4cN/8STcTz/9pDlz5mj48OFeBwPAGhppNR+ABZwe9H1mNen05wYb9D1r1iy9+eab6t69u7Kzs3XJJZdIOvVuuRdffFHV1dWaNWuW18EAAAD44nyDvn3hUcIUExOjjRs3auLEiZoxY4Yze7PZbBo8eLBeeuklxcTE+BxUTk6O/v73v+urr75S8+bN9atf/UpPPPGEunfv7tznxIkTmjp1qt544w1VVlZq6NChvzh/SUmJJk6cqE8//VRhYWHKzMxUTk6OmjTxar5OAABMZ5MfxjD9/N+UlBQFBwcrKytLWVlZvoYWEF5//fVzbrPZbLr99tu9atfjzCEhIUGrV6/Wjz/+6HwBb7du3RQVFeVVALVZv369srKylJKS4qxaDRkyRHv27FHLli0lSZMnT9Z7772n5cuXKyIiQtnZ2brpppv0j3/8Q5JUU1Oj4cOHKzY2Vhs3btShQ4c0btw4NW3aVPPmzfNbrAAAWNWF+GqUnTt3/mJdTU2NVqxYoUOHDnmdMNkMX4aMN5DvvvtO0dHRWr9+vQYMGKDS0lK1bdtWS5cu1c033yzp1KSZl112mfLz89W3b1998MEHuv7663Xw4EFn1WnBggWaPn26vvvuO4WEhLg9b1lZmSIiIlRaWnrBfaEAX1z5+2fMDsHFFy8z9xvMV99/M0633+nx/1KQj2/UcJw4oX/P+MMF//etqqpKCxcu1HPPPad+/fpp+vTpzuFEnqrzU3I7duyQw+Goc8O7d+9WdXW1V0GdrbS0VJKcVaytW7fq5MmTSk9Pd+5z6aWXqmPHjsrPz5ck5efnq2fPni5ddEOHDlVZWZl2797tl7gAAGhwPCXnVkVFhebPn6/ExET97//+rz7++GMtXLjQ62RJ8iBh6tWrl3744Yc6N5yWlqaSkhKvgjqTw+HQ/fffr379+qlHjx6SJLvdrpCQEEVGRrrsGxMTI7vd7tzn7PFUpz+f3udslZWVKisrc1kAAIB1zJ49W0lJSTp27Jg2bdqkZ555xi9THtV5DJNhGHr44YfVokWLOu1fVVXldVBnysrK0q5du/T555/7pb3zycnJ0Zw5c+r9PAAAeI13yZ3XY489prCwMOXl5WnBggXO9YZhyGaz6ciRI161W+eEacCAAdq7d2+dG05LS3O+lNdb2dnZWrVqlTZs2OCSHcbGxqqqqkpHjx51qTIdPnxYsbGxzn02b97s0t7hw4ed22ozc+ZMTZkyxfm5rKxM8fHxPl0DAAD+xEzf5+fJ8CFP1DlhWrduXb0EUBvDMHTfffdp5cqVWrdunRISEly29+7dW02bNtXatWs1evRoSafmgiopKVFaWpqkUwnbf/3Xf+nIkSOKjo6WJK1Zs0bh4eFKTEys9byhoaEKDQ2txysDvNNjWmANst7FIGvAPFSYTBGQExJlZWVp6dKleuedd9SqVSvnmKOIiAg1b95cERERGj9+vKZMmaKoqCiFh4frvvvuU1pamvr27StJGjJkiBITE3X77bdr/vz5stvteuihh5SVlUVSBAAAPBKQCVNeXp4kaeDAgS7rFy1apDvuuEOS9MwzzygoKEijR492mbjytODgYK1atUoTJ05UWlqaWrZsqczMTM2dO7ehLgMAAP+jwuSR6upqv0xYHZAJU12mhmrWrJlyc3OVm5t7zn06deqk999/35+hAQBgKsYweeaqq67SF1984XM7dZ5WAAAAwGr8NT93QFaYAADAORi2U4uvbVzATr9PzjAM/d///Z/L++XGjRvnVZteV5g+/PBDjRs3TpmZmbrjjjv00UcfObft2bNHTz31lA4ePChJWrlypbenAQAAZ/LjTN8pKSlKTEw87/AWKzIMw1lZOvO/vlSbvK4wvfnmmy4Z2+9//3sNGTJEkjRnzhzNmjVLc+bM0b333qs1a9Zo1KhRXgcJAAD870J8+a4kZWZmOn9+7rnnvK4qncnrhKmmpkZr165VfHy89u/fr5MnTzq3RUZGKjk5WQsWLNDkyZO1Y8cOnwMFAAAM+vaU6WOYXnzxRa1cuVIFBQXq0KGDnn/+eee2wYMHS5JsNpueeeYZPfXUU75HCjSgSx4LrIkimwabHQGAgMG0Ah45+60f3vI6YTp58qRuv/12SdJ1112nwYMHKywsTJJ08803O/ez2WyaNm2aj2ECAAB4rmnTpn5px+tB3y+++KKzj/D3v/+9rr32Wr3xxhu/2G/ChAneRwcAAFwZ/+mW83ZpTBUmf/G6wvR///d/uuaaayRJI0eOVP/+/TVhwgStXLlSN998s3bu3KmNGzeqvLzcb8ECANDo0SVXZ8eOHZMkZw+YL7yuMJWXl+vAgQOSpD/96U8aPHiwdu7cqfLycv3+97/X119/raefflobN270OUgAAPAzP04rcKF6/vnnFR8fr06dOqljx47q2LGjXnjhBZ/a9LrC9Oqrrzp/fuaZZ7R582bFxcVJkg4cOKDf/e53eu655/T888+rZcuWPgUJAABQF3PmzNHWrVv12WefqXPnzpKk4uJiTZ48Wd9//73mzJnjVbt+eTXKl19+6UyWJKlDhw5avXq1evfurb59+/rjFAAAQL6PX/LHtASBbMmSJVqxYoUzWZKkhIQELVu2TEuXLvW6Xa8qTGVlZVq0aJHsdrsSEhJ0xRVXqEePHmrRooXLfvfee6+GDRvmdXAAAACeCgkJ+cW60NBQ2WzevxLGq4Tppptu0vbt25WSkqJ3331Xe/fulSR17dpVycnJWrZsmXPfLl26eB0cGo+EF/5kdgguih+aanYILhL/EFjzQgFAoLrkkkv0xhtv6JZbbnFZv2zZMnXr1s3rdr1KmPLz87Vu3TqlpKRIkiorK7Vz504VFhZq+/btXgcDAADc4Cm588rLy9OoUaP08ssv68orr5QkffHFFyorK/Pp3bZeJUxJSUlq0uQ/h4aGhqpPnz7q06eP14EAAAD3eDXK+cXHx2vLli1au3at9uzZI+nUBNuDBg3SsmXL1LFjR6/a9Sphmj9/vmbPnq0VK1YoNDTUqxMDAADUl0GDBmnQoEEu66ZNm6YxY8Z41Z5XCVPnzp1VVlamxMREjRkzRn379lWvXr0UHx/vVRAAAMADF3CFqD758iJer6YVGD16tL755hv169dPGzduVGZmpjp37qy2bdtqyJAhXgcDAADc8OPElSkpKUpMTFRubm6DXoJZGvwpuV27dik/P1/JycnOdd988422bdumHTt2eB0MAABoOAUFBQoPDzc7DL9q27ZtrYmRYRg6evSo1+16lTClpKSooqLCZV3nzp3VuXNnjRo1yutg0HgV3xdYj/F3eyKwHuNv6pcpZgFcCBj0fX7fffddvbTr1T/DkyZN0qOPPupTpgYAALzAu+RM4VWF6eabb5YkXXzxxRo1apRSU1PVq1cv9ejRo9bZNQEAgH9QYTq/119//bzbx40b51W7XiVMxcXF2r59u3Oiynnz5umbb75RkyZN1L17d8YxAQAAU+zcufMX62pqarRixQodOnSoYROmTp06qVOnTrrhhhuc68rLy1VYWEiyBABAfWKm7/N68sknnT9XVVVp4cKFeu655zR48GBNnz7d63a9Sphq06pVK1199dW6+uqr/dUkAAA4GwmTWxUVFcrNzdUrr7yiESNG6OOPP1aHDh18atNvCRNwISmaPtnsEFxcNjuwntoDgEA1e/ZsLVmyRGPHjtWmTZvUunVrv7RLwgQAgIUw6Lt2x44dU1hYmB577DGFhYUpLy9PCxYscG43DEM2m01Hjhzxqn0SJgAArIQuuVpFRETozTfflMPhqJf2mQ4PAABYnmEYevnll9WvXz/1799fkydPVkFBgd/aJ2ECAMBKmLjynLZt26Yrr7xS/fv3165du3T11VfrgQce8EvbdMkBAGAhjGE6t6VLl2rw4MHOzzt27NDIkSPVvn17TZ7s28M8VJgAAIDlRUVFKT4+3mVdUlKSXnzxReXl5fncPhUmoBZdnnna7BBc/GvuFLNDABAoGPRdqyuuuEKLFi3SE0884bK+W7duKikp8bl9EiYAACyELrnaPfbYY7r22mt18OBB3XvvvUpKSlJFRYXmzZunhIQEn9snYQIAwEqoMNWqb9+++uc//6lJkybp6quvlmGcushmzZpp+fLlPrdPwgQAAC4IycnJWrdunY4cOaKtW7fK4XAoNTVVbdq08bltEiYAAKyECpNb0dHRysjI8GubJEwAAFiI7efF1zbgGaYVAACgkUpJSVFiYqJyc3PNDiXgUWECAMBK/NglV1BQoPDwcF8jahRImAAAsBCmFTAHXXIAAABuUGFCQOi8+An3OzWgoGB+NQAEKJ6SMwV/FQAAsBoSngZHlxwAAIAbVJgAALAQBn2bg4QJAAArYQyTKUiYAACwECpM5mAMEwAAgBtUmAAAsBK65ExBwgRJUuLbj5p6/m/uMPf8Z0t48U9mhwAAtaJLzhx0yQEAALhBhQkAACuhS84UJEwAAFgJCZMp6JIDAABwgwoTAAAWwqBvc5AwAQBgJXTJmYIuOQAAADeoMEGStOfGR009f5el80w9/9mM4BCzQwCAWtkMQzbDtxKRr8c3RiRMAABYCV1ypiBhAgDAQhj0bQ7GMAEAALhBhQkAACuhS84UJEwICP/67SyzQ3DR+eWnzA4BAGpFl5w56JIDAABwIyATpg0bNmjEiBGKi4uTzWbT22+/7bL9jjvukM1mc1mGDRvmss+PP/6osWPHKjw8XJGRkRo/fryOHTvWgFcBAEA9MPy0wCMBmTBVVFQoOTlZubm559xn2LBhOnTokHP529/+5rJ97Nix2r17t9asWaNVq1Zpw4YNuvvuu+s7dAAA6tXpLjlfF3gmIMcwZWRkKCMj47z7hIaGKjY2ttZtX375pVavXq2CggL16dNHkvTCCy/ouuuu01NPPaW4uDi/xwwAgNWkpKQoODhYWVlZysrKMjucgBaQCVNdrFu3TtHR0brooov061//Wo899phat24tScrPz1dkZKQzWZKk9PR0BQUFadOmTRo1alStbVZWVqqystL5uaysrH4vAgAAT/nxKbmCggKFh4f7GlGjEJBdcu4MGzZMr7/+utauXasnnnhC69evV0ZGhmpqaiRJdrtd0dHRLsc0adJEUVFRstvt52w3JydHERERziU+Pr5erwMAAG/QHdfwLFlhuuWWW5w/9+zZU0lJSeratavWrVunQYMGed3uzJkzNWXKFOfnsrIykiYAAGDNhOlsXbp0UZs2bVRUVKRBgwYpNjZWR44ccdmnurpaP/744znHPUmnxkWFhobWd7ioxSVv/dHsEFx88/uHzQ4BAGpnGKcWX9uARyzZJXe2AwcO6IcfflC7du0kSWlpaTp69Ki2bt3q3OeTTz6Rw+FQamqqWWECAOAznpIzR0BWmI4dO6aioiLn5+LiYhUWFioqKkpRUVGaM2eORo8erdjYWO3bt08PPvigunXrpqFDh0qSLrvsMg0bNkwTJkzQggULdPLkSWVnZ+uWW27hCTkAgLXxahRTBGSFacuWLerVq5d69eolSZoyZYp69eql2bNnKzg4WDt27NANN9ygSy65ROPHj1fv3r312WefuXSnLVmyRJdeeqkGDRqk6667Tv3799crr7xi1iUBAAALC8gK08CBA2Wcp3/1ww8/dNtGVFSUli5d6s+wAAAwnc1xavG1DXgmIBMmAABwDnTJmSIgu+QAAAACCRUmAAAsxB9PufGUnOdImAAAsBLmYTIFXXIAAABuUGEKQJmbxzf4OUuORTb4Oc8UHMTLHwGgLuiSMwcJEwAAVsJTcqagSw4AAMANKkwAAFgIXXLmIGECAMBKeErOFCRMAABYCBUmczCGCQAAwA0qTAHoL1ctNDuEBpf07myzQwAAa+ApOVOQMAEAYCF0yZmDLjkAAAA3qDABAGAlDuPU4msb8AgJEwAAVsIYJlPQJQcAAOAGFSYAACzEJj8M+vZLJI0LCRMAAFbCTN+mIGGCJGn0xntNPX+T4ChTzw8AwPmQMAEAYCHMw2QOEiYAAKyEp+RMQcIEAICF2AxDNh/HIPl6fGPEtAIAAABuUGGCJOmtX71k6vlTPphl6vkBwDIcPy++tgGPkDABAGAhdMmZgy45AAAAN6gwISDYmHYWAOqGp+RMQcIEAICVMNO3KeiSAwAAcIMKEwAAFsJM3+YgYQIAwErokjMFXXIAAABuUGGqg0++6aOWrYIb7HzbT3RssHOd9r8VsQ1+zjNtHvYXU88PAFZhc5xafG0DnqHCBACAlZzukvN1CUCjRo3SRRddpJtvvtnsUH6BhAkAACsx/LQEoEmTJun11183O4xakTABAICAMHDgQLVq1crsMGpFwgQAgIWcfpecr4unNmzYoBEjRiguLk42m01vv/32L/bJzc1V586d1axZM6Wmpmrz5s1+uOLAQMIEAICVmDSGqaKiQsnJycrNza11+7JlyzRlyhQ98sgj+uKLL5ScnKyhQ4fqyJEjzn2uuOIK9ejR4xfLwYMHvb4dDYWn5AAAaKTKyspcPoeGhio0NLTWfTMyMpSRkXHOtp5++mlNmDBBd955pyRpwYIFeu+99/Taa69pxowZkqTCwkL/BG4CKkwAAFiJIcnh4/JzgSk+Pl4RERHOJScnx6uQqqqqtHXrVqWnpzvXBQUFKT09Xfn5+V61GWioMAEAYCHejkE6uw1J2r9/v8LDw53rz1Vdcuf7779XTU2NYmJiXNbHxMToq6++qnM76enp2r59uyoqKtShQwctX75caWlpXsXkbyRMdfDrzltcvlD1bXCDnek/Jm271YSzAgDMFB4e3qB/39z5+OOPzQ7hnEiYAACwEkN+eJecXyJxatOmjYKDg3X48GGX9YcPH1ZsrLlvkvAXxjABAGAlATjTd0hIiHr37q21a9c61zkcDq1duzZgutR8RYUJAAC4dezYMRUVFTk/FxcXq7CwUFFRUerYsaOmTJmizMxM9enTR1dddZWeffZZVVRUOJ+aszoSJgAArMQhyeaHNjy0ZcsWXXvttc7PU6ZMkSRlZmZq8eLFGjNmjL777jvNnj1bdrtdV1xxhVavXv2LgeBWRcIEAICF+PMpuZSUFAUHBysrK0tZWVnnPWbgwIEy3Jw3Oztb2dnZPsUWqEiYAACwEn+MQfr5+IKCgoB6Si6QMegbAADADSpMAWhZUUqDnzMsOL7BzwkA8IIfK0yoOxImAACshITJFHTJAQAAuEGFCQAAKzFpWoHGjoQJAAAL8ee0Aqg7uuQAAADcIGECAMBK/PguuZSUFCUmJio3N9fkiwp8dMkFoDHdChr8nEW7r2/wcwIAvOAwJJuPXWoOJq70FBUmAAAAN6gwAQBgJczDZAoSJgAALMUPCZNImDxFwgQAgJVQYTIFY5gAAADcoMIESVJTW43ZIQAA6sJhyOcuNQcVJk8FZIVpw4YNGjFihOLi4mSz2fT222+7bDcMQ7Nnz1a7du3UvHlzpaen6+uvv3bZ58cff9TYsWMVHh6uyMhIjR8/XseOHWvAqwAAoB4YDv8s8EhAJkwVFRVKTk4+50Ra8+fP1/PPP68FCxZo06ZNatmypYYOHaoTJ0449xk7dqx2796tNWvWaNWqVdqwYYPuvvvuhroEywmyOUxdAAANj4kr6y4gu+QyMjKUkZFR6zbDMPTss8/qoYce0siRIyVJr7/+umJiYvT222/rlltu0ZdffqnVq1eroKBAffr0kSS98MILuu666/TUU08pLi6uwa7FKoJ5YgIArMGPg76ZuLLuArLCdD7FxcWy2+1KT093rouIiFBqaqry8/MlSfn5+YqMjHQmS5KUnp6uoKAgbdq0qcFjBgDAbxyGfxZ4JCArTOdjt9slSTExMS7rY2JinNvsdruio6Ndtjdp0kRRUVHOfWpTWVmpyspK5+eysjJJkuNwLzmOB/sl/rr4Z2V1g53rtMmX/avBzwkAgFVYrsJUn3JychQREeFc4uPjzQ4JAABXfnz5LurOcglTbGysJOnw4cMu6w8fPuzcFhsbqyNHjrhsr66u1o8//ujcpzYzZ85UaWmpc9m/f7+fowcAwEeG/JAwmX0R1mO5hCkhIUGxsbFau3atc11ZWZk2bdqktLQ0SVJaWpqOHj2qrVu3Ovf55JNP5HA4lJqaes62Q0NDFR4e7rIAAAAE5BimY8eOqaioyPm5uLhYhYWFioqKUseOHXX//ffrscce08UXX6yEhAQ9/PDDiouL04033ihJuuyyyzRs2DBNmDBBCxYs0MmTJ5Wdna1bbrmFJ+QAANbGq1FMEZAJ05YtW3Tttdc6P0+ZMkWSlJmZqcWLF+vBBx9URUWF7r77bh09elT9+/fX6tWr1axZM+cxS5YsUXZ2tgYNGqSgoCCNHj1azz//vFfxBMVsU1ADVpsi97dvsHMBACzG4ZDk4/x1Dua/85TNMEgzz6WsrEwREREqLS1t0O65PSYkTInx3zb4OQHgQlLffzNOt5/edryaBIX41Fa1o0off7ewwf++WZnlxjABAAA0NBImAACsxI/TCvBqlLoLyDFMAADgHByGfJ4XwMGrUTxFhQkAAMANKkwAAFiIYThkGL495ebr8Y0RCVMAigziwUUAwDkYfnh5Lg/Ie4wuOQAAADeoMAEAYCWGHwZ9U2HyGAkTAABW4nBINh/HIDGGyWN0yQEAALhBhQkAACuhS84UJEwAAFiI4XDI8LFLjmkFPEfCBACAlVBhMgUJUwCKCGpqdggAAOAMDPoGAMBKHIZ/FvHyXU9QYQIAwEoMQ5Kv0wrw8l1PUWECAABwgwoTAAAWYjgMGTbfBm0bDPr2GAlTAAq1MegbAHAOhkO+d8kxrYCn6JIDAABwgwoTAAAWQpecOUiYAlAQhT8AwLnQJWcKEqbzOJ2Bl5WVNeh5HeU1DXo+SQpq0bDXCAAXmtN/K+q7elOtkz5P9F2tk/4JphEhYTqPH374QZIUHx9vciQNIcLsAADgglBeXq6ICP//mxoSEqLY2Fh9bn/fL+3FxsYqJCTEL201BjaDjsxzOnr0qC666CKVlJTUy5ff6srKyhQfH6/9+/cz8VktuD/nx/05P+6Pe4F2jwzDUHl5ueLi4hQUVD9DK06cOKGqqiq/tBUSEqJmzZr5pa3GgArTeZz+wkdERATEL2OgCg8P5/6cB/fn/Lg/58f9cS+Q7lF9/891s2bNSHJMwuhiAAAAN0iYAAAA3CBhOo/Q0FA98sgjCg0NNTuUgMT9OT/uz/lxf86P++Me9wgNiUHfAAAAblBhAgAAcIOECQAAwA0SJgAAADcafcL06KOPymazuSyXXnqpc/uJEyeUlZWl1q1bKywsTKNHj9bhw4dNjLh+bdiwQSNGjFBcXJxsNpvefvttl+2GYWj27Nlq166dmjdvrvT0dH399dcu+/z4448aO3aswsPDFRkZqfHjx+vYsWMNeBX1y909uuOOO37xnRo2bJjLPhfqPcrJyVFKSopatWql6Oho3Xjjjdq7d6/LPnX5nSopKdHw4cPVokULRUdHa9q0aaqurm7IS6kXdbk/AwcO/MX355577nHZ50K9P5KUl5enpKQk59xKaWlp+uCDD5zbG/P3B+Zq9AmTJF1++eU6dOiQc/n888+d2yZPnqx3331Xy5cv1/r163Xw4EHddNNNJkZbvyoqKpScnKzc3Nxat8+fP1/PP/+8FixYoE2bNqlly5YaOnSoTpw44dxn7Nix2r17t9asWaNVq1Zpw4YNuvvuuxvqEuqdu3skScOGDXP5Tv3tb39z2X6h3qP169crKytL//znP7VmzRqdPHlSQ4YMUUVFhXMfd79TNTU1Gj58uKqqqrRx40b95S9/0eLFizV79mwzLsmv6nJ/JGnChAku35/58+c7t13I90eSOnTooMcff1xbt27Vli1b9Otf/1ojR47U7t27JTXu7w9MZjRyjzzyiJGcnFzrtqNHjxpNmzY1li9f7lz35ZdfGpKM/Pz8BorQPJKMlStXOj87HA4jNjbWePLJJ53rjh49aoSGhhp/+9vfDMMwjD179hiSjIKCAuc+H3zwgWGz2Yxvv/22wWJvKGffI8MwjMzMTGPkyJHnPKYx3aMjR44Ykoz169cbhlG336n333/fCAoKMux2u3OfvLw8Izw83KisrGzYC6hnZ98fwzCMa665xpg0adI5j2lM9+e0iy66yPjzn//M9wemosIk6euvv1ZcXJy6dOmisWPHqqSkRJK0detWnTx5Uunp6c59L730UnXs2FH5+flmhWua4uJi2e12l/sRERGh1NRU5/3Iz89XZGSk+vTp49wnPT1dQUFB2rRpU4PHbJZ169YpOjpa3bt318SJE50vcpYa1z0qLS2VJEVFRUmq2+9Ufn6+evbsqZiYGOc+Q4cOVVlZmbPKcKE4+/6ctmTJErVp00Y9evTQzJkzdfz4cee2xnR/ampq9MYbb6iiokJpaWl8f2CqRv8uudTUVC1evFjdu3fXoUOHNGfOHF199dXatWuX7Ha7QkJCFBkZ6XJMTEyM7Ha7OQGb6PQ1n/kP0enPp7fZ7XZFR0e7bG/SpImioqIazT0bNmyYbrrpJiUkJGjfvn2aNWuWMjIylJ+fr+Dg4EZzjxwOh+6//37169dPPXr0kKQ6/U7Z7fZav2Ont10oars/kvTb3/5WnTp1UlxcnHbs2KHp06dr7969+vvf/y6pcdyfnTt3Ki0tTSdOnFBYWJhWrlypxMREFRYW8v2BaRp9wpSRkeH8OSkpSampqerUqZPefPNNNW/e3MTIYFW33HKL8+eePXsqKSlJXbt21bp16zRo0CATI2tYWVlZ2rVrl8uYQPzHue7PmWPZevbsqXbt2mnQoEHat2+funbt2tBhmqJ79+4qLCxUaWmpVqxYoczMTK1fv97ssNDI0SV3lsjISF1yySUqKipSbGysqqqqdPToUZd9Dh8+rNjYWHMCNNHpaz77iZQz70dsbKyOHDnisr26ulo//vhjo7xnktSlSxe1adNGRUVFkhrHPcrOztaqVav06aefqkOHDs71dfmdio2NrfU7dnrbheBc96c2qampkuTy/bnQ709ISIi6deum3r17KycnR8nJyXruuef4/sBUJExnOXbsmPbt26d27dqpd+/eatq0qdauXevcvnfvXpWUlCgtLc3EKM2RkJCg2NhYl/tRVlamTZs2Oe9HWlqajh49qq1btzr3+eSTT+RwOJz/8Dc2Bw4c0A8//KB27dpJurDvkWEYys7O1sqVK/XJJ58oISHBZXtdfqfS0tK0c+dOl6RyzZo1Cg8PV2JiYsNcSD1xd39qU1hYKEku358L9f6ci8PhUGVlZaP//sBkZo86N9vUqVONdevWGcXFxcY//vEPIz093WjTpo1x5MgRwzAM45577jE6duxofPLJJ8aWLVuMtLQ0Iy0tzeSo6095ebmxbds2Y9u2bYYk4+mnnza2bdtm/Pvf/zYMwzAef/xxIzIy0njnnXeMHTt2GCNHjjQSEhKMn376ydnGsGHDjF69ehmbNm0yPv/8c+Piiy82br31VrMuye/Od4/Ky8uNBx54wMjPzzeKi4uNjz/+2LjyyiuNiy++2Dhx4oSzjQv1Hk2cONGIiIgw1q1bZxw6dMi5HD9+3LmPu9+p6upqo0ePHsaQIUOMwsJCY/Xq1Ubbtm2NmTNnmnFJfuXu/hQVFRlz5841tmzZYhQXFxvvvPOO0aVLF2PAgAHONi7k+2MYhjFjxgxj/fr1RnFxsbFjxw5jxowZhs1mMz766CPDMBr39wfmavQJ05gxY4x27doZISEhRvv27Y0xY8YYRUVFzu0//fSTce+99xoXXXSR0aJFC2PUqFHGoUOHTIy4fn366aeGpF8smZmZhmGcmlrg4YcfNmJiYozQ0FBj0KBBxt69e13a+OGHH4xbb73VCAsLM8LDw40777zTKC8vN+Fq6sf57tHx48eNIUOGGG3btjWaNm1qdOrUyZgwYYLLI86GceHeo9ruiyRj0aJFzn3q8jv1zTffGBkZGUbz5s2NNm3aGFOnTjVOnjzZwFfjf+7uT0lJiTFgwAAjKirKCA0NNbp162ZMmzbNKC0tdWnnQr0/hmEYd911l9GpUycjJCTEaNu2rTFo0CBnsmQYjfv7A3PZDMMwGq6eBQAAYD2MYQIAAHCDhAkAAMANEiYAAAA3SJgAAADcIGECAABwg4QJAADADRImAAAAN0iYAAAA3CBhAgAAcIOECYBbAwcOlM1mk81mc74M1gx33HGHM463337btDgAND4kTADqZMKECTp06JB69Ojhst5ut2vSpEnq1q2bmjVrppiYGPXr1095eXk6fvx4ndoeMWKEhg0bVuu2zz77TDabTTt27NBzzz2nQ4cO+XwtAOCpJmYHAMAaWrRoodjYWJd1//rXv9SvXz9FRkZq3rx56tmzp0JDQ7Vz50698sorat++vW644Qa3bY8fP16jR4/WgQMH1KFDB5dtixYtUp8+fZSUlCRJioiI8N9FAUAdUWECLOibb76RzWbTW2+9pQEDBqh58+ZKSUlRSUmJPvvsM/Xt21ctWrTQoEGDdPTo0XqL495771WTJk20ZcsW/eY3v9Fll12mLl26aOTIkXrvvfc0YsQISZLD4VBOTo4SEhLUvHlzJScna8WKFc52rr/+erVt21aLFy92af/YsWNavny5xo8fX2/XAAB1QYUJsKDt27dLkvLy8jRv3jy1bNlSI0eO1G233aZWrVrpxRdfVE1NjYYPH65FixZp8uTJkqTf/e53atWqlS666CJlZWUpJCRE2dnZCgoK0pgxY87ZLVabH374QR999JHz/LWx2WySpJycHP31r3/VggULdPHFF2vDhg267bbb1LZtW11zzTVq0qSJxo0bp8WLF+sPf/iD87jly5erpqZGt956qy+3CwB8RoUJsKDCwkJFRUVp2bJl6t+/v3r16qVrrrlG+/fv1/Lly9WnTx+lpqYqJSVFdrvdeVyHDh10+eWX6+uvv1br1q21cuVK3XbbbVq0aJGWLFniUQxFRUUyDEPdu3d3Wd+mTRuFhYUpLCxM06dPV2VlpebNm6fXXntNQ4cOVZcuXXTHHXfotttu08svv+w87q677tK+ffu0fv1657pFixZp9OjRdMMBMB0VJsCCtm/frlGjRql169bOdSUlJRozZoxatGjhsm7kyJHOz48++qikU5UmSfr222911VVXSfpPNchXmzdvlsPh0NixY1VZWamioiIdP35cgwcPdtmvqqpKvXr1cn6+9NJL9atf/UqvvfaaBg4cqKKiIn322WeaO3euX+ICAF9QYQIsqLCwUKmpqS7rtm/frr59+zo/nzhxQnv37lVycrLKysrUvn17Pfnkk5Kkd955RxkZGWrfvr0OHDjgVQzdunWTzWbT3r17XdZ36dJF3bp1U/PmzSWdGockSe+9954KCwudy549e1zGMUmnBn+/9dZbKi8v16JFi9S1a1ddc801XsUHAP5EwgRYTFlZmb755huX6kxxcbFKS0td1u3cuVOGYahnz54KDw/X5s2btXTpUn3//feaMWOGFi5cqFGjRmnJkiWaMGGCfvvb33oUR+vWrTV48GC9+OKLqqioOOd+iYmJCg0NVUlJibp16+ayxMfHu+z7m9/8RkFBQVq6dKlef/113XXXXX6rfAGAL+iSAyxm+/btCg4OdpkP6fSYpk6dOrms69q1q8LCwiRJ7du3V3l5uSZMmKCHH35YcXFxkk6NE/LWSy+9pH79+qlPnz569NFHlZSUpKCgIBUUFOirr75S79691apVKz3wwAOaPHmyHA6H+vfvr9LSUv3jH/9QeHi4MjMzne2FhYVpzJgxmjlzpsrKynTHHXd4HRsA+BMJE2Ax27dvV/fu3dWsWTOXdWdWl06vS05Odll38cUXq0mTJh5Xk86la9eu2rZtm+bNm6eZM2fqwIEDCg0NVWJioh544AHde++9kqQ//vGPatu2rXJycvSvf/1LkZGRuvLKKzVr1qxftDl+/HgtXLhQ1113nTOpAwCz2QzDMMwOAkD9O3DggHr27KkFCxZozJgxkqRt27bp8ccf18UXX6zHHnvsnMcOHDhQV1xxhZ599tkGivb8bDabVq5cqRtvvNHsUAA0EoxhAhoBwzA0fvx4DRkyRDt37nSu79Wrl5544ok6tfHSSy8pLCzM5fiGds899zi7GAGgIdElBzQCeXl5iouL06RJkzR16lSPj1+yZIl++uknSVLHjh39HV6dzZ07Vw888IAkqV27dqbFAaDxIWECLnD79u1Tbm6u8vPz1aJFC5WVlTnHCdVV+/bt6zHCuouOjlZ0dLTZYQBohBjDBDRixcXFeuihh/TVV19p8uTJuu2228wOCQACEgkTAACAGwz6BgAAcIOECQAAwA0SJgAAADdImAAAANwgYQIAAHCDhAkAAMANEiYAAAA3SJgAAADcIGECAABwg4QJAADADRImAAAAN0iYAAAA3Pj/XlFNj40hZy0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dl = hepi.load(\n",
    "    urllib.request.urlopen(\n",
    "        \"https://raw.githubusercontent.com/APN-Pucky/xsec/master/json/pp13_hinosplit_N2N1_NLO%2BNLL.json\"\n",
    "    ),\n",
    "    dimensions=2,\n",
    ")\n",
    "hepi.mapplot(dl,\"N1\",\"N2\",\"NLO_PLUS_NLL_NOERR\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{\\\\chi}_2^0}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\")\n",
    "plt.show()\n",
    "xx,yy = data.flatmesh(np.linspace(50,320,100),np.linspace(80,320,100))\n",
    "dll = hepi.interpolate_2d(dl,\"N1\",\"N2\",\"NLO_PLUS_NLL_NOERR\",xx,yy,interpolator=\"linearnd\",pre=np.log,post=np.exp)             \n",
    "hepi.mapplot(dll,\"N1\",\"N2\",\"NLO_PLUS_NLL_NOERR\",xaxis=\"$m_{\\\\tilde{\\\\chi}_1^0}$ [GeV]\",yaxis=\"$m_{\\\\tilde{\\\\chi}_2^0}$ [GeV]\" , zaxis=\"$\\\\sigma_{\\\\mathrm{NLO+NLL}}$ [pb]\",fill_missing=False)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d017b58",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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