{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softsusy example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2.10.1\n",
      "~/git/resummino\n",
      "softpoint.x\n"
     ]
    }
   ],
   "source": [
    "import hepi\n",
    "print(hepi.__version__)\n",
    "import smpl\n",
    "import numpy as np\n",
    "from hepi.run import resummino as rs\n",
    "from hepi.run import softsusy as sp\n",
    "import hepi.util as util\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rs.set_path(\"~/git/resummino\")\n",
    "sp.set_path(\"softpoint.x\")\n",
    "print (rs.get_path())\n",
    "print (sp.get_path())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipped: 2 Not skipped: 30\n"
     ]
    },
    {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n"
     ]
    },
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    {
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     "text": [
      "/home/apn/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"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipped: 2 Not skipped: 30\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
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     ]
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     "output_type": "stream",
     "text": [
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n",
      "warning: No Yukawa coupling Yd defined. Set to zero.\n",
      "warning: No Yukawa coupling Ys defined. Set to zero.\n",
      "warning: No Yukawa coupling Yu defined. Set to zero.\n",
      "warning: No Yukawa coupling Yc defined. Set to zero.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c48e955308a489c95a80c1c7903444e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "COLLECTING RESULTS | Running:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "299ee3d46bd244c08399164f667c0691",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QUEUEING TASKS | Parsing:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "971aa8a6a5f34fc9ae3b6d2569627554",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PROCESSING TASKS | Parsing:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49a14f740aa1437587fec2109c121bd5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "COLLECTING RESULTS | Parsing:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sq in [2000002,1000002]:\n",
    "    fig, axs = plt.subplots(3, 1, figsize=(12, 12), sharex=True)\n",
    "    fig.subplots_adjust(hspace=0)\n",
    "    for pdf,nlopdf in [(\"CT14lo\",\"CT14lo\")]:\n",
    "        li = [hepi.Input(hepi.Order.LO,13000,sq,1000022,\"pMSSM11_best_fit_LHC13-SUSYHIT.txt\",pdf,nlopdf,1., 1.,id=\"test\")]\n",
    "        li=hepi.slha_scan_rel(li,lambda r : [[\"EXTPAR\",1,510],[\"EXTPAR\",2,r]],np.linspace(470.,530.,32))\n",
    "        sp.run(li)\n",
    "        dl = rs.run(li,True,True)\n",
    "        for p in [1000022,1000023,1000025,1000035]:\n",
    "            hepi.slha_plot(li,[\"EXTPAR\",2],[\"MASS\",p],axes=axs[0],logy=True,xaxis=\"$M_2$ [GeV]\",yaxis=\"$M$\",label=\"$\"+hepi.get_name(p)+\"$\",tight=False)\n",
    "        for nm1 in [1]:\n",
    "            for nm2 in [1,2,3,4]:\n",
    "                hepi.slha_plot(li,[\"EXTPAR\",2],[\"NMIX\",(nm1,nm2)],fmt=\"-\",interpolate=False,xaxis=\"$M_2$ [GeV]\",yaxis=\"Mixing\",logy=False,axes=axs[1],label=\"$\"+\"N_{\"+ str(nm1)+ str(nm2)+\"}$\",tight=False)\n",
    "        hepi.vplot(hepi.slha_data(li,[\"EXTPAR\",2]),dl[\"LO\"],plot_data=True,axes=axs[2],xaxis=\"$M_2$ [GeV]\",yaxis= \"$\\sigma$ [pb]\",tight=False,label=\"$\\sigma_{LO}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}