M - N interaction diagram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from reinforced_concrete.sections import create_concrete_material, create_steel_material, Bars, ReinforcedConcreteSection\n",
    "\n",
    "plt.style.use([\"science\", \"retro\"])\n",
    "#plt.style.use([\"science\", \"notebook\",\"std-colors\"]) # faster but witout LaTeX render\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConcreteMaterial(name='C25/30', fck=25, rck=30, fcm=33, fcd=14.167, Ecm=31475.81, fctm=2.565, fctk=1.795, fctd=1.197, ec2=0.002, ecu2=0.0035, ec3=0.00175, ecu3=0.0035)\n",
      "SteelMaterial(name='B450C', fyk=450, Es=210000, esu=0.01)\n"
     ]
    }
   ],
   "source": [
    "cls  = create_concrete_material(\"EC2\",\"C25/30\") \n",
    "steel  = create_steel_material(\"NTC18\",\"B450C\")\n",
    "\n",
    "print(cls)\n",
    "print(steel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "As = Bars(n_bars=6, diameter=20, steel_material=steel)\n",
    "As1 = Bars(n_bars=3, diameter=12, steel_material=steel)\n",
    "section_1 = ReinforcedConcreteSection(b=300, d=410, d1=40, d2=40, concrete_material=cls, As=As, As1=As1, name=\"sec1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ReinforcedConcreteSection(b=300, d=410, d1=40, d2=40, concrete_material=ConcreteMaterial(name='C25/30', fck=25, rck=30, fcm=33, fcd=14.167, Ecm=31475.81, fctm=2.565, fctk=1.795, fctd=1.197, ec2=0.002, ecu2=0.0035, ec3=0.00175, ecu3=0.0035), As=Bars(n_bars=6, diameter=20, steel_material=SteelMaterial(name='B450C', fyk=450, Es=210000, esu=0.01)), As1=Bars(n_bars=3, diameter=12, steel_material=SteelMaterial(name='B450C', fyk=450, Es=210000, esu=0.01)), name='sec1')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "section_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = section_1.b\n",
    "As = section_1.As.area()\n",
    "As1 = section_1.As1.area()\n",
    "d = section_1.d\n",
    "d1 = section_1.d1\n",
    "d2 = section_1.d2\n",
    "h = section_1.h #TODO TOGLIERE LA H DALLE FORMULE E METTERLA COME D+D1\n",
    "\n",
    "fck = section_1.concrete_material.fck\n",
    "Ec = section_1.concrete_material.Ecm\n",
    "ec2 = section_1.concrete_material.ec2\n",
    "ecu = section_1.concrete_material.ecu2\n",
    "\n",
    "fyk = section_1.As.steel_material.fyk\n",
    "Es = section_1.As.steel_material.Es\n",
    "ese = section_1.As.steel_material.ese\n",
    "esu = section_1.As.steel_material.esu\n",
    "\n",
    "fyk1 = section_1.As1.steel_material.fyk\n",
    "ese1 = section_1.As1.steel_material.ese\n",
    "esu1 = section_1.As1.steel_material.esu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = 400\n",
    "# h = 450\n",
    "# As = 1407\n",
    "# As1 = 1206\n",
    "# d = 410\n",
    "# d1 = 40\n",
    "# d2 = 40\n",
    "# \n",
    "# fck = 25\n",
    "# fyk = 450\n",
    "# fyk1 = 450\n",
    "# Ec = 31475.806\n",
    "# Es = 200000\n",
    "# \n",
    "# ese = 1.9565/1000\n",
    "# ese1 = 1.9565/1000\n",
    "# esu = 10/1000 # campo 1 e 2\n",
    "# ecu = 3.5/1000\n",
    "# ec2 = 2/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25925925925925924"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi23 = ecu/(ecu + esu)\n",
    "xi23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6525767226404169"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi34 = ecu/(ecu + ese1)\n",
    "xi34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.10910444982312421"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi1a1b = (esu * d2 - ese1 * d)/(d*(esu - ese1) )\n",
    "xi1a1b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xi12 = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xi2a2b = 0.2393053249904227\n",
      "xi3a3b = 0.20863609015596798\n",
      "Snerva in 2, xi23 = 0.25925925925925924\n",
      "Snerva in 3, xi34 = 0.6525767226404169\n"
     ]
    }
   ],
   "source": [
    "xi2a2b = (d2/d * esu + abs(ese1)) / (esu + abs(ese1) )\n",
    "xi3a3b = ecu/(ecu-abs(ese1)) * d2/d\n",
    "\n",
    "print(f\"{xi2a2b = }\") \n",
    "print(f\"{xi3a3b = }\") \n",
    "\n",
    "if xi2a2b > xi23: \n",
    "    print(f\"Non snerva in 2, xi23 = {xi23}\")\n",
    "else:\n",
    "    print(f\"Snerva in 2, xi23 = {xi23}\")\n",
    "\n",
    "if xi3a3b > xi34: \n",
    "    print(f\"Non snerva in 3, xi34 = {xi34}\")\n",
    "else:\n",
    "    print(f\"Snerva in 3, xi34 = {xi34}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def storeToDict(dic,strainRange,N,M,nu,mu):\n",
    "    dic[strainRange][\"N\"] = N\n",
    "    dic[strainRange][\"M\"] = M\n",
    "    dic[strainRange][\"nu\"] = nu\n",
    "    dic[strainRange][\"mu\"] = mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strain Range 2\n",
    "def psi_2(xi):\n",
    "    if xi < 1/6:\n",
    "        return xi/(1-xi) * esu/(3 * ec2**2) * (3 * ec2 - xi/(1-xi) * esu)\n",
    "    else:\n",
    "        return 1 - ( ec2*(1-xi) ) / ( 3*esu*xi )\n",
    "\n",
    "def lamb_2(xi):\n",
    "# only for n = 2 C<C50/60\n",
    "    if xi == 0: #TODO perché si faceva ?\n",
    "        return 0\n",
    "    elif xi <= 1/6:\n",
    "        return (4*ec2 - esu*xi/(1-xi)) / (4*(3*ec2 - esu*xi/(1-xi))) \n",
    "    else:\n",
    "        return ( (6*esu**2 + 4*esu*ec2 +  ec2**2)*xi**2 - 2*ec2**2 * xi + ec2**2 - 4*esu*ec2*xi ) / ( 4*esu*xi*((3*esu+ec2)*xi - ec2 ))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def psi_6(xi):\n",
    "    return (xi**2 - 6/7*xi+125/1029)/(xi - 3/7)**2\n",
    "\n",
    "def lamb_6(xi, t):\n",
    "    return 3/14 * ((2401*xi**2 - 2058*xi + 185)/(1029*xi**2 - 882*xi + 125))\n",
    "    #return (1/2 + 1/3 * ec2/ecu * ((1-t/h)/(xi-t/h))**2 * (-1 + 1/4*ec2/ecu))/(1/(3*ecu) * (3*ecu - ec2*((1-t/h)/(xi-t/h))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strainDistribution(As,As1) -> dict[dict]:\n",
    "    \"\"\"\n",
    "    Return a dictionary with dictionaries for stain ranges (1-6), each with numpy arrays of M, N, mu and nu values\n",
    "    \n",
    "    Example: \n",
    "    As = 1407\n",
    "    As1 = 1206\n",
    "    MN_up = strainDistribution(As,As1)\n",
    "    MN_up[\"2\"][\"M\"] is an array with M values for strain range 2\n",
    "    \"\"\"\n",
    "    dic = {}\n",
    "    fcd = fck * 0.85 / 1.5\n",
    "    fyd = fyk/1.15 \n",
    "\n",
    "    # ---------------------------------------------------------------------\n",
    "    # ------------------------------ CAMPO 1 ------------------------------\n",
    "    # ---------------------------------------------------------------------\n",
    "    \n",
    "    strainRange = \"1\"\n",
    "    dic[strainRange]={}\n",
    "    xi = np.linspace(-50/d,0,20)\n",
    "    x = xi * d\n",
    "    es1 =  (esu * (d2 - x)) /(d - x) #TODO tanto vale mettere la formula con xi (ovunque)\n",
    "\n",
    "    fyd1 = []\n",
    "    for i in range(len(es1)):\n",
    "        if abs(es1[i]) > ese1:\n",
    "            fyd1.append(fyk1/1.15)\n",
    "        else:\n",
    "            fyd1.append(Es * es1[i])\n",
    "    fyd1 = np.array(fyd1)        \n",
    "\n",
    "    N =  - fyd1*As1 - fyd * As \n",
    "    M =  - fyd1*As1* (h/2 - d2) + fyd * As* (h/2 - d1)\n",
    "    nu = N/(b*d*fcd)\n",
    "    mu = M/(b*d**2*fcd)  \n",
    "    storeToDict(dic,strainRange,N,M,nu,mu)\n",
    "    # ---------------------------------------------------------------------\n",
    "    # ------------------------------ CAMPO 2 ------------------------------\n",
    "    # ---------------------------------------------------------------------\n",
    "    \n",
    "    strainRange = \"2\"\n",
    "    dic[strainRange]={}\n",
    "\n",
    "    xi = np.linspace(0,xi23,130)\n",
    "    x = xi * d\n",
    "    es1 = - (esu * (x - d2)) /(d - x) #TODO tanto vale mettere la formula con xi (ovunque)\n",
    "\n",
    "    fyd1 = []\n",
    "    for i in range(len(es1)):\n",
    "        if abs(es1[i]) > ese1 and es1[i] > 0:\n",
    "            fyd1.append(fyk1/1.15)\n",
    "        elif abs(es1[i]) > ese1 and es1[i] < 0:\n",
    "            fyd1.append(-fyk1/1.15)\n",
    "        else:\n",
    "            fyd1.append(Es * es1[i])\n",
    "    fyd1 = np.array(fyd1)        \n",
    "\n",
    "    psi = np.array([psi_2(xi_i) for xi_i in xi])\n",
    "    lamb = np.array([lamb_2(xi_i) for xi_i in xi])\n",
    "\n",
    "    N = b * psi * x * fcd  - fyd1*As1 - fyd * As \n",
    "    M = b * psi * x * fcd * (h/2 - lamb*x)  - fyd1*As1* (h/2 - d2) + fyd * As* (h/2 - d1)\n",
    "    nu = N/(b*d*fcd)\n",
    "    mu = M/(b*d**2*fcd)\n",
    "    \n",
    "    storeToDict(dic,strainRange,N,M,nu,mu)\n",
    "    \n",
    "    # ---------------------------------------------------------------------\n",
    "    # ------------------------------ CAMPO 3 ------------------------------\n",
    "    # ---------------------------------------------------------------------\n",
    "    \n",
    "    strainRange = \"3\"\n",
    "    dic[strainRange]={}\n",
    "    \n",
    "    fyd = fyk/1.15\n",
    "\n",
    "    xi = np.linspace(xi23,xi34,100)\n",
    "    x = xi * d\n",
    "\n",
    "    es1 = - ecu * (x - d2) / x\n",
    "\n",
    "   # fyd1 = -fyk1/1.15  \n",
    "    fyd1 = []\n",
    "    for i in range(len(es1)):\n",
    "        if abs(es1[i]) > ese1 and es1[i] > 0:\n",
    "            fyd1.append(fyk1/1.15)\n",
    "        elif abs(es1[i]) > ese1 and es1[i] < 0:\n",
    "            fyd1.append(-fyk1/1.15)\n",
    "        else:\n",
    "            fyd1.append(Es * es1[i])\n",
    "    fyd1 = np.array(fyd1)\n",
    "\n",
    "    psi = 17/21 # 0.8095238095238095\n",
    "    lamb = 99/238 # 0.4159663865546219\n",
    "\n",
    "    # solo campo 3B. Da capire il 3A perché non lo considera!\n",
    "    \n",
    "    N = b * psi * x * fcd  - fyd1*As1 - fyd * As  \n",
    "    M = b * psi * x * fcd * (h/2 - lamb*x)  - fyd1*As1* (h/2 - d2) + fyd * As* (h/2 - d1)\n",
    "    nu = N/(b*d*fcd)\n",
    "    mu = M/(b*d**2*fcd)\n",
    "    \n",
    "    storeToDict(dic,strainRange,N,M,nu,mu)\n",
    "    \n",
    "    # ---------------------------------------------------------------------\n",
    "    # ------------------------------ CAMPO 4 ------------------------------\n",
    "    # ---------------------------------------------------------------------\n",
    "    \n",
    "    strainRange = \"4\"\n",
    "    dic[strainRange]={}\n",
    "\n",
    "    xi = np.linspace(xi34,1,100)\n",
    "    x = xi * d\n",
    "\n",
    "    es1 = - ecu * (x - d2) / x\n",
    "    es = ecu * (d-x) / x\n",
    "\n",
    "    fyd1 = -fyk1/1.15  \n",
    "    fyd = Es*es\n",
    "\n",
    "    psi = 17/21 # 0.8095238095238095\n",
    "    lamb = 99/238 # 0.4159663865546219\n",
    "    \n",
    "    N = b * psi * x * fcd  - fyd1*As1 - fyd * As  \n",
    "    M = b * psi * x * fcd * (h/2 - lamb*x)  - fyd1*As1* (h/2 - d2) + fyd * As* (h/2 - d1)\n",
    "    nu = N/(b*d*fcd)\n",
    "    mu = M/(b*d**2*fcd)\n",
    "    \n",
    "    storeToDict(dic,strainRange,N,M,nu,mu)\n",
    "    \n",
    "    # ---------------------------------------------------------------------\n",
    "    # ------------------------------ CAMPO 5 ------------------------------\n",
    "    # ---------------------------------------------------------------------\n",
    "    \n",
    "    strainRange = \"5\"\n",
    "    dic[strainRange]={}\n",
    "\n",
    "    xi = np.linspace(1,1+d1/d,50)\n",
    "    x = xi * d\n",
    "\n",
    "    es1 = - ecu * (x - d2) / x\n",
    "    es = - ecu * (d-x) / x\n",
    "\n",
    "    fyd1 = -fyk1/1.15  \n",
    "    fyd = - Es*es\n",
    "    psi = 17/21 # 0.8095238095238095\n",
    "    lamb = 99/238 # 0.4159663865546219\n",
    "    \n",
    "    N = b * psi * x * fcd  - fyd1*As1 - fyd * As  \n",
    "    M = b * psi * x * fcd * (h/2 - lamb*x)  - fyd1*As1* (h/2 - d2) + fyd * As* (h/2 - d1)\n",
    "    nu = N/(b*d*fcd)\n",
    "    mu = M/(b*d**2*fcd)\n",
    "    \n",
    "    storeToDict(dic,strainRange,N,M,nu,mu)\n",
    "    \n",
    "\n",
    "    # ---------------------------------------------------------------------\n",
    "    # ------------------------------ CAMPO 6 ------------------------------\n",
    "    # ---------------------------------------------------------------------\n",
    "    \n",
    "    strainRange = \"6\"\n",
    "    dic[strainRange]={}\n",
    "\n",
    "    xi = np.linspace(1+d1/d,50,200)\n",
    "    x = xi * d\n",
    "    \n",
    "    t = (ecu-ec2)*(d+d1)/ecu # C<C50/60: 3/7 * h\n",
    "\n",
    "    es1 = - ec2 * (x - d2) / (x-t)\n",
    "    es = - ec2 * (x-d) / (x-t)\n",
    "\n",
    "    fyd1 = -fyk1/1.15  \n",
    "\n",
    "    #x6a6b = (ec2 * d + ese * t)/(ec2 + ese)\n",
    "\n",
    "    fyd = []\n",
    "    for i in range(len(x)):\n",
    "        if abs(es[i]) < ese:\n",
    "            fyd.append(Es * es[i])\n",
    "        else:\n",
    "            fyd.append(- fyk/1.15)\n",
    "    fyd = np.array(fyd)\n",
    "\n",
    "    psi = np.array([psi_6(xi_i) for xi_i in xi])\n",
    "    lamb = np.array([lamb_6(xi_i,t) for xi_i in xi])\n",
    "    psi[0] = 17/21 # 0.8095238095238095\n",
    "    lamb[0] = 99/238 # 0.4159663865546219\n",
    "\n",
    "    N = b * psi * h * fcd  - fyd1*As1 - fyd * As  \n",
    "    M = b * psi * h * fcd * (h/2 - lamb*h)  - fyd1*As1* (h/2 - d2) + fyd * As* (h/2 - d1)\n",
    "    nu = N/(b*d*fcd)\n",
    "    mu = M/(b*d**2*fcd)\n",
    "\n",
    "    storeToDict(dic,strainRange,N,M,nu,mu)\n",
    "    \n",
    "    return dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Above and below x axis: invert steel areas and change M sign in plotting function \n",
    "MN_up = strainDistribution(As,As1)\n",
    "MN_down = strainDistribution(As1,As) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_multiple_strainDistribution(As: list, As1: list) -> list[tuple[dict]]:\n",
    "    \"\"\"example [(MN_up_1, MN_down_1), (MN_up_2, MN_down_2), ...]\"\"\"\n",
    "    multiple_strain_distributions = []\n",
    "    for i in range(len(As1)):\n",
    "        # tup: (mn_up, mn_down)\n",
    "        tup = strainDistribution(As[i], As1[i]), strainDistribution(As1[i], As[i])\n",
    "        multiple_strain_distributions.append(tup)\n",
    "    return multiple_strain_distributions\n",
    "\n",
    "def a(n_bars: int , diameter: int, string = False):\n",
    "    if string == False:\n",
    "        return n_bars * 3.14 * diameter**2 / 4\n",
    "    else:\n",
    "        \"Return a string like:'2Ø4' \"\n",
    "        return f\"{n_bars}Ø{diameter}\"\n",
    "\n",
    "\n",
    "As_list = [a(4,12),a(4,14), a(4,16)]\n",
    "As1_list = [a(2,12),a(2,14), a(2,16)]\n",
    "\n",
    "mult = create_multiple_strainDistribution(As_list,As1_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As_list = [Bars(4,12,steel_material=steel), Bars(4,14,steel_material=steel), Bars(4,16,steel_material=steel)]\n",
    "# As_list_area = [bar.area for bar in As_list]\n",
    "# As_list_area #TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLORS = [list(plt.rcParams['axes.prop_cycle'])[col]['color'] for col in range(len(list(plt.rcParams['axes.prop_cycle'])))] #colors from the matplotlib style\n",
    "\n",
    "def interaction_diagram_single(dic_up,dic_down, NM=True,):\n",
    "    x = \"N\" if NM else \"nu\"\n",
    "    y = \"M\" if NM else \"mu\"\n",
    "\n",
    "    fig, ax = plt.subplots(1,1, figsize = (12, 7))\n",
    "    for i in range(1,7): \n",
    "        ax.plot(dic_up[f\"{i}\"][x],dic_up[f\"{i}\"][y],color=COLORS[i-1],label=f\" Campo {i}\")\n",
    "        ax.plot(dic_down[f\"{i}\"][x],-dic_down[f\"{i}\"][y],color=COLORS[i-1])\n",
    "        ax.set_xlabel(x) # \"r\"$\\bf\" + x + r\"}$\"\"\n",
    "        ax.set_ylabel(y)\n",
    "        ax.grid(\"True\",which=\"both\",linestyle='dashed')\n",
    "        ax.set_title(f\"{y} - {x} diagram\", fontsize=14)\n",
    "        ax.legend(loc = \"upper right\", frameon = \"True\")\n",
    "\n",
    "    ax.text(\n",
    "        x=0.5, \n",
    "        y=0.5, \n",
    "        s=f\"b = {b}\\nh = {h}\\nAs   = {As} \\nAs1 = {As1} \", \n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='center', \n",
    "        transform=ax.transAxes, #used to make x,y coords in global coordinates\n",
    "        bbox=dict(facecolor='grey', alpha=0.3)\n",
    "    )\n",
    "\n",
    "    #plt.savefig(f\"{x}_{y}_diagram.pdf\")\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "def interaction_diagram_multiple(\n",
    "        multiple_strainDistribution: create_multiple_strainDistribution, \n",
    "        NM=True,\n",
    "        NM_values: list = None\n",
    "    ):\n",
    "    x = \"N\" if NM else \"nu\"\n",
    "    y = \"M\" if NM else \"mu\"\n",
    "\n",
    "    fig, ax = plt.subplots(1,1, figsize = (12, 7))\n",
    "    ax.set_xlabel(x) # \"r\"$\\bf\" + x + r\"}$\"\"\n",
    "    ax.set_ylabel(y)\n",
    "    ax.grid(\"True\",which=\"both\",linestyle='dashed')\n",
    "    ax.set_title(f\"{y} - {x} diagram\", fontsize=14)\n",
    "    for area in range(len(multiple_strainDistribution)):\n",
    "        for i in range(1,7): \n",
    "            dic_up, dic_down = multiple_strainDistribution[area]\n",
    "            ax.plot(dic_up[f\"{i}\"][x],dic_up[f\"{i}\"][y],dic_down[f\"{i}\"][x],-dic_down[f\"{i}\"][y],color=COLORS[i-1])\n",
    "        #ax.plot([],[], label=f\"Area: {area}\")    \n",
    "        \n",
    "    ax.text(\n",
    "        x=0.5, \n",
    "        y=0.5, \n",
    "        s=f\"b = {b}\\nh = {h}\\nAs   = {As_list} \\nAs1 = {As1_list} \", \n",
    "        horizontalalignment='center',\n",
    "        verticalalignment='center', \n",
    "        transform=ax.transAxes, #used to make x,y coords in global coordinates\n",
    "        bbox=dict(facecolor='grey', alpha=0.3)\n",
    "    )\n",
    "    if NM_values:\n",
    "        x_points = NM_values[0]\n",
    "        y_points = NM_values[1]\n",
    "        ax.plot(x_points, y_points,\"rs\")\n",
    "\n",
    "    plt.savefig(f\"{y}_{x}_diagram_multiple.pdf\")\n",
    "    \n",
    "    return fig, ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = interaction_diagram_multiple(mult, NM = False) #, NM_values=[[10000,3454515],[2,6]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = interaction_diagram_single(MN_up,MN_down, NM=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>nu</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-869916.521739</td>\n",
       "      <td>1.118359e+08</td>\n",
       "      <td>-0.374426</td>\n",
       "      <td>0.117405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-869916.521739</td>\n",
       "      <td>1.118359e+08</td>\n",
       "      <td>-0.374426</td>\n",
       "      <td>0.117405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-869916.521739</td>\n",
       "      <td>1.118359e+08</td>\n",
       "      <td>-0.374426</td>\n",
       "      <td>0.117405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-866548.837172</td>\n",
       "      <td>1.124589e+08</td>\n",
       "      <td>-0.372977</td>\n",
       "      <td>0.118059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-863136.538845</td>\n",
       "      <td>1.130902e+08</td>\n",
       "      <td>-0.371508</td>\n",
       "      <td>0.118722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-859684.048548</td>\n",
       "      <td>1.137289e+08</td>\n",
       "      <td>-0.370022</td>\n",
       "      <td>0.119392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-856190.651968</td>\n",
       "      <td>1.143752e+08</td>\n",
       "      <td>-0.368518</td>\n",
       "      <td>0.120071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-852655.617764</td>\n",
       "      <td>1.150291e+08</td>\n",
       "      <td>-0.366997</td>\n",
       "      <td>0.120757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-849078.197060</td>\n",
       "      <td>1.156910e+08</td>\n",
       "      <td>-0.365457</td>\n",
       "      <td>0.121452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-845457.622909</td>\n",
       "      <td>1.163608e+08</td>\n",
       "      <td>-0.363899</td>\n",
       "      <td>0.122155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-841793.109751</td>\n",
       "      <td>1.170387e+08</td>\n",
       "      <td>-0.362321</td>\n",
       "      <td>0.122867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-838083.852843</td>\n",
       "      <td>1.177249e+08</td>\n",
       "      <td>-0.360725</td>\n",
       "      <td>0.123587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-834329.027668</td>\n",
       "      <td>1.184196e+08</td>\n",
       "      <td>-0.359109</td>\n",
       "      <td>0.124316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-830527.789327</td>\n",
       "      <td>1.191228e+08</td>\n",
       "      <td>-0.357473</td>\n",
       "      <td>0.125055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-826679.271901</td>\n",
       "      <td>1.198348e+08</td>\n",
       "      <td>-0.355816</td>\n",
       "      <td>0.125802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-822782.587800</td>\n",
       "      <td>1.205557e+08</td>\n",
       "      <td>-0.354139</td>\n",
       "      <td>0.126559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-818836.827073</td>\n",
       "      <td>1.212856e+08</td>\n",
       "      <td>-0.352441</td>\n",
       "      <td>0.127325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-814841.056704</td>\n",
       "      <td>1.220248e+08</td>\n",
       "      <td>-0.350721</td>\n",
       "      <td>0.128101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-810794.319876</td>\n",
       "      <td>1.227735e+08</td>\n",
       "      <td>-0.348979</td>\n",
       "      <td>0.128887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-806695.635207</td>\n",
       "      <td>1.235317e+08</td>\n",
       "      <td>-0.347215</td>\n",
       "      <td>0.129683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                N             M        nu        mu\n",
       "0  -869916.521739  1.118359e+08 -0.374426  0.117405\n",
       "1  -869916.521739  1.118359e+08 -0.374426  0.117405\n",
       "2  -869916.521739  1.118359e+08 -0.374426  0.117405\n",
       "3  -866548.837172  1.124589e+08 -0.372977  0.118059\n",
       "4  -863136.538845  1.130902e+08 -0.371508  0.118722\n",
       "5  -859684.048548  1.137289e+08 -0.370022  0.119392\n",
       "6  -856190.651968  1.143752e+08 -0.368518  0.120071\n",
       "7  -852655.617764  1.150291e+08 -0.366997  0.120757\n",
       "8  -849078.197060  1.156910e+08 -0.365457  0.121452\n",
       "9  -845457.622909  1.163608e+08 -0.363899  0.122155\n",
       "10 -841793.109751  1.170387e+08 -0.362321  0.122867\n",
       "11 -838083.852843  1.177249e+08 -0.360725  0.123587\n",
       "12 -834329.027668  1.184196e+08 -0.359109  0.124316\n",
       "13 -830527.789327  1.191228e+08 -0.357473  0.125055\n",
       "14 -826679.271901  1.198348e+08 -0.355816  0.125802\n",
       "15 -822782.587800  1.205557e+08 -0.354139  0.126559\n",
       "16 -818836.827073  1.212856e+08 -0.352441  0.127325\n",
       "17 -814841.056704  1.220248e+08 -0.350721  0.128101\n",
       "18 -810794.319876  1.227735e+08 -0.348979  0.128887\n",
       "19 -806695.635207  1.235317e+08 -0.347215  0.129683"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = pd.DataFrame(MN_up[\"1\"])\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>nu</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-806695.635207</td>\n",
       "      <td>1.235317e+08</td>\n",
       "      <td>-0.347215</td>\n",
       "      <td>0.129683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-805354.554972</td>\n",
       "      <td>1.237817e+08</td>\n",
       "      <td>-0.346638</td>\n",
       "      <td>0.129945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-803914.792292</td>\n",
       "      <td>1.240536e+08</td>\n",
       "      <td>-0.346018</td>\n",
       "      <td>0.130231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-802376.708837</td>\n",
       "      <td>1.243472e+08</td>\n",
       "      <td>-0.345356</td>\n",
       "      <td>0.130539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-800740.677008</td>\n",
       "      <td>1.246625e+08</td>\n",
       "      <td>-0.344652</td>\n",
       "      <td>0.130870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>-136827.636567</td>\n",
       "      <td>2.462714e+08</td>\n",
       "      <td>-0.058893</td>\n",
       "      <td>0.258535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>-131847.000462</td>\n",
       "      <td>2.469820e+08</td>\n",
       "      <td>-0.056749</td>\n",
       "      <td>0.259281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>-126866.364356</td>\n",
       "      <td>2.476882e+08</td>\n",
       "      <td>-0.054605</td>\n",
       "      <td>0.260022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>-121885.728251</td>\n",
       "      <td>2.483900e+08</td>\n",
       "      <td>-0.052462</td>\n",
       "      <td>0.260759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>-116905.092145</td>\n",
       "      <td>2.490874e+08</td>\n",
       "      <td>-0.050318</td>\n",
       "      <td>0.261491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 N             M        nu        mu\n",
       "0   -806695.635207  1.235317e+08 -0.347215  0.129683\n",
       "1   -805354.554972  1.237817e+08 -0.346638  0.129945\n",
       "2   -803914.792292  1.240536e+08 -0.346018  0.130231\n",
       "3   -802376.708837  1.243472e+08 -0.345356  0.130539\n",
       "4   -800740.677008  1.246625e+08 -0.344652  0.130870\n",
       "..             ...           ...       ...       ...\n",
       "125 -136827.636567  2.462714e+08 -0.058893  0.258535\n",
       "126 -131847.000462  2.469820e+08 -0.056749  0.259281\n",
       "127 -126866.364356  2.476882e+08 -0.054605  0.260022\n",
       "128 -121885.728251  2.483900e+08 -0.052462  0.260759\n",
       "129 -116905.092145  2.490874e+08 -0.050318  0.261491\n",
       "\n",
       "[130 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = pd.DataFrame(MN_up[\"2\"])\n",
    "c2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>nu</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-116905.092145</td>\n",
       "      <td>2.490874e+08</td>\n",
       "      <td>-0.050318</td>\n",
       "      <td>0.261491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-109432.880147</td>\n",
       "      <td>2.501028e+08</td>\n",
       "      <td>-0.047102</td>\n",
       "      <td>0.262557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-101960.668148</td>\n",
       "      <td>2.511081e+08</td>\n",
       "      <td>-0.043886</td>\n",
       "      <td>0.263612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-94488.456149</td>\n",
       "      <td>2.521032e+08</td>\n",
       "      <td>-0.040669</td>\n",
       "      <td>0.264657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-87016.244150</td>\n",
       "      <td>2.530882e+08</td>\n",
       "      <td>-0.037453</td>\n",
       "      <td>0.265691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>592955.047733</td>\n",
       "      <td>3.003393e+08</td>\n",
       "      <td>0.255217</td>\n",
       "      <td>0.315295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>600427.259732</td>\n",
       "      <td>3.003928e+08</td>\n",
       "      <td>0.258434</td>\n",
       "      <td>0.315351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>607899.471731</td>\n",
       "      <td>3.004361e+08</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.315396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>615371.683730</td>\n",
       "      <td>3.004693e+08</td>\n",
       "      <td>0.264866</td>\n",
       "      <td>0.315431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>622843.895728</td>\n",
       "      <td>3.004924e+08</td>\n",
       "      <td>0.268082</td>\n",
       "      <td>0.315455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                N             M        nu        mu\n",
       "0  -116905.092145  2.490874e+08 -0.050318  0.261491\n",
       "1  -109432.880147  2.501028e+08 -0.047102  0.262557\n",
       "2  -101960.668148  2.511081e+08 -0.043886  0.263612\n",
       "3   -94488.456149  2.521032e+08 -0.040669  0.264657\n",
       "4   -87016.244150  2.530882e+08 -0.037453  0.265691\n",
       "..            ...           ...       ...       ...\n",
       "95  592955.047733  3.003393e+08  0.255217  0.315295\n",
       "96  600427.259732  3.003928e+08  0.258434  0.315351\n",
       "97  607899.471731  3.004361e+08  0.261650  0.315396\n",
       "98  615371.683730  3.004693e+08  0.264866  0.315431\n",
       "99  622843.895728  3.004924e+08  0.268082  0.315455\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c3 = pd.DataFrame(MN_up[\"3\"])\n",
    "c3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>nu</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.228439e+05</td>\n",
       "      <td>3.004924e+08</td>\n",
       "      <td>0.268082</td>\n",
       "      <td>0.315455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.407943e+05</td>\n",
       "      <td>2.984046e+08</td>\n",
       "      <td>0.275808</td>\n",
       "      <td>0.313264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.586240e+05</td>\n",
       "      <td>2.963312e+08</td>\n",
       "      <td>0.283482</td>\n",
       "      <td>0.311087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.763347e+05</td>\n",
       "      <td>2.942720e+08</td>\n",
       "      <td>0.291105</td>\n",
       "      <td>0.308925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.939285e+05</td>\n",
       "      <td>2.922264e+08</td>\n",
       "      <td>0.298678</td>\n",
       "      <td>0.306778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.967377e+06</td>\n",
       "      <td>1.336147e+08</td>\n",
       "      <td>0.846791</td>\n",
       "      <td>0.140268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.978958e+06</td>\n",
       "      <td>1.319546e+08</td>\n",
       "      <td>0.851775</td>\n",
       "      <td>0.138525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.990504e+06</td>\n",
       "      <td>1.302931e+08</td>\n",
       "      <td>0.856745</td>\n",
       "      <td>0.136781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2.002016e+06</td>\n",
       "      <td>1.286301e+08</td>\n",
       "      <td>0.861700</td>\n",
       "      <td>0.135035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2.013493e+06</td>\n",
       "      <td>1.269657e+08</td>\n",
       "      <td>0.866640</td>\n",
       "      <td>0.133288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               N             M        nu        mu\n",
       "0   6.228439e+05  3.004924e+08  0.268082  0.315455\n",
       "1   6.407943e+05  2.984046e+08  0.275808  0.313264\n",
       "2   6.586240e+05  2.963312e+08  0.283482  0.311087\n",
       "3   6.763347e+05  2.942720e+08  0.291105  0.308925\n",
       "4   6.939285e+05  2.922264e+08  0.298678  0.306778\n",
       "..           ...           ...       ...       ...\n",
       "95  1.967377e+06  1.336147e+08  0.846791  0.140268\n",
       "96  1.978958e+06  1.319546e+08  0.851775  0.138525\n",
       "97  1.990504e+06  1.302931e+08  0.856745  0.136781\n",
       "98  2.002016e+06  1.286301e+08  0.861700  0.135035\n",
       "99  2.013493e+06  1.269657e+08  0.866640  0.133288\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c4 = pd.DataFrame(MN_up[\"4\"])\n",
    "c4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>nu</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.013493e+06</td>\n",
       "      <td>1.269657e+08</td>\n",
       "      <td>0.866640</td>\n",
       "      <td>0.133288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.019989e+06</td>\n",
       "      <td>1.260206e+08</td>\n",
       "      <td>0.869436</td>\n",
       "      <td>0.132296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.026475e+06</td>\n",
       "      <td>1.250750e+08</td>\n",
       "      <td>0.872227</td>\n",
       "      <td>0.131303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.032949e+06</td>\n",
       "      <td>1.241289e+08</td>\n",
       "      <td>0.875014</td>\n",
       "      <td>0.130310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.039413e+06</td>\n",
       "      <td>1.231823e+08</td>\n",
       "      <td>0.877796</td>\n",
       "      <td>0.129316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.045866e+06</td>\n",
       "      <td>1.222350e+08</td>\n",
       "      <td>0.880574</td>\n",
       "      <td>0.128322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.052308e+06</td>\n",
       "      <td>1.212873e+08</td>\n",
       "      <td>0.883346</td>\n",
       "      <td>0.127327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.058740e+06</td>\n",
       "      <td>1.203389e+08</td>\n",
       "      <td>0.886115</td>\n",
       "      <td>0.126331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.065161e+06</td>\n",
       "      <td>1.193899e+08</td>\n",
       "      <td>0.888879</td>\n",
       "      <td>0.125335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.071572e+06</td>\n",
       "      <td>1.184403e+08</td>\n",
       "      <td>0.891638</td>\n",
       "      <td>0.124338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.077973e+06</td>\n",
       "      <td>1.174901e+08</td>\n",
       "      <td>0.894393</td>\n",
       "      <td>0.123341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.084363e+06</td>\n",
       "      <td>1.165393e+08</td>\n",
       "      <td>0.897143</td>\n",
       "      <td>0.122342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.090742e+06</td>\n",
       "      <td>1.155879e+08</td>\n",
       "      <td>0.899889</td>\n",
       "      <td>0.121344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.097112e+06</td>\n",
       "      <td>1.146357e+08</td>\n",
       "      <td>0.902631</td>\n",
       "      <td>0.120344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.103471e+06</td>\n",
       "      <td>1.136830e+08</td>\n",
       "      <td>0.905368</td>\n",
       "      <td>0.119344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.109821e+06</td>\n",
       "      <td>1.127295e+08</td>\n",
       "      <td>0.908101</td>\n",
       "      <td>0.118343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.116160e+06</td>\n",
       "      <td>1.117754e+08</td>\n",
       "      <td>0.910829</td>\n",
       "      <td>0.117341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.122489e+06</td>\n",
       "      <td>1.108206e+08</td>\n",
       "      <td>0.913553</td>\n",
       "      <td>0.116339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.128808e+06</td>\n",
       "      <td>1.098650e+08</td>\n",
       "      <td>0.916273</td>\n",
       "      <td>0.115336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.135118e+06</td>\n",
       "      <td>1.089088e+08</td>\n",
       "      <td>0.918989</td>\n",
       "      <td>0.114332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.141417e+06</td>\n",
       "      <td>1.079518e+08</td>\n",
       "      <td>0.921700</td>\n",
       "      <td>0.113327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.147707e+06</td>\n",
       "      <td>1.069941e+08</td>\n",
       "      <td>0.924408</td>\n",
       "      <td>0.112322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.153987e+06</td>\n",
       "      <td>1.060357e+08</td>\n",
       "      <td>0.927111</td>\n",
       "      <td>0.111316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.160258e+06</td>\n",
       "      <td>1.050764e+08</td>\n",
       "      <td>0.929810</td>\n",
       "      <td>0.110309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.166518e+06</td>\n",
       "      <td>1.041165e+08</td>\n",
       "      <td>0.932504</td>\n",
       "      <td>0.109301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.172770e+06</td>\n",
       "      <td>1.031557e+08</td>\n",
       "      <td>0.935195</td>\n",
       "      <td>0.108292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.179012e+06</td>\n",
       "      <td>1.021942e+08</td>\n",
       "      <td>0.937882</td>\n",
       "      <td>0.107283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.185244e+06</td>\n",
       "      <td>1.012318e+08</td>\n",
       "      <td>0.940564</td>\n",
       "      <td>0.106273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.191467e+06</td>\n",
       "      <td>1.002687e+08</td>\n",
       "      <td>0.943243</td>\n",
       "      <td>0.105262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.197681e+06</td>\n",
       "      <td>9.930469e+07</td>\n",
       "      <td>0.945917</td>\n",
       "      <td>0.104250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.203885e+06</td>\n",
       "      <td>9.833990e+07</td>\n",
       "      <td>0.948588</td>\n",
       "      <td>0.103237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2.210080e+06</td>\n",
       "      <td>9.737427e+07</td>\n",
       "      <td>0.951254</td>\n",
       "      <td>0.102223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2.216266e+06</td>\n",
       "      <td>9.640779e+07</td>\n",
       "      <td>0.953917</td>\n",
       "      <td>0.101208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2.222443e+06</td>\n",
       "      <td>9.544046e+07</td>\n",
       "      <td>0.956575</td>\n",
       "      <td>0.100193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.228611e+06</td>\n",
       "      <td>9.447226e+07</td>\n",
       "      <td>0.959230</td>\n",
       "      <td>0.099177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2.234770e+06</td>\n",
       "      <td>9.350319e+07</td>\n",
       "      <td>0.961881</td>\n",
       "      <td>0.098159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2.240919e+06</td>\n",
       "      <td>9.253323e+07</td>\n",
       "      <td>0.964528</td>\n",
       "      <td>0.097141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2.247060e+06</td>\n",
       "      <td>9.156238e+07</td>\n",
       "      <td>0.967171</td>\n",
       "      <td>0.096122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.253192e+06</td>\n",
       "      <td>9.059063e+07</td>\n",
       "      <td>0.969810</td>\n",
       "      <td>0.095102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2.259316e+06</td>\n",
       "      <td>8.961797e+07</td>\n",
       "      <td>0.972446</td>\n",
       "      <td>0.094081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.265430e+06</td>\n",
       "      <td>8.864439e+07</td>\n",
       "      <td>0.975077</td>\n",
       "      <td>0.093058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2.271536e+06</td>\n",
       "      <td>8.766988e+07</td>\n",
       "      <td>0.977705</td>\n",
       "      <td>0.092035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2.277633e+06</td>\n",
       "      <td>8.669443e+07</td>\n",
       "      <td>0.980330</td>\n",
       "      <td>0.091011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2.283721e+06</td>\n",
       "      <td>8.571803e+07</td>\n",
       "      <td>0.982950</td>\n",
       "      <td>0.089986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.289801e+06</td>\n",
       "      <td>8.474068e+07</td>\n",
       "      <td>0.985567</td>\n",
       "      <td>0.088960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2.295872e+06</td>\n",
       "      <td>8.376236e+07</td>\n",
       "      <td>0.988180</td>\n",
       "      <td>0.087933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2.301935e+06</td>\n",
       "      <td>8.278308e+07</td>\n",
       "      <td>0.990790</td>\n",
       "      <td>0.086905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2.307989e+06</td>\n",
       "      <td>8.180280e+07</td>\n",
       "      <td>0.993396</td>\n",
       "      <td>0.085876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2.314035e+06</td>\n",
       "      <td>8.082154e+07</td>\n",
       "      <td>0.995998</td>\n",
       "      <td>0.084846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2.320073e+06</td>\n",
       "      <td>7.983928e+07</td>\n",
       "      <td>0.998597</td>\n",
       "      <td>0.083815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               N             M        nu        mu\n",
       "0   2.013493e+06  1.269657e+08  0.866640  0.133288\n",
       "1   2.019989e+06  1.260206e+08  0.869436  0.132296\n",
       "2   2.026475e+06  1.250750e+08  0.872227  0.131303\n",
       "3   2.032949e+06  1.241289e+08  0.875014  0.130310\n",
       "4   2.039413e+06  1.231823e+08  0.877796  0.129316\n",
       "5   2.045866e+06  1.222350e+08  0.880574  0.128322\n",
       "6   2.052308e+06  1.212873e+08  0.883346  0.127327\n",
       "7   2.058740e+06  1.203389e+08  0.886115  0.126331\n",
       "8   2.065161e+06  1.193899e+08  0.888879  0.125335\n",
       "9   2.071572e+06  1.184403e+08  0.891638  0.124338\n",
       "10  2.077973e+06  1.174901e+08  0.894393  0.123341\n",
       "11  2.084363e+06  1.165393e+08  0.897143  0.122342\n",
       "12  2.090742e+06  1.155879e+08  0.899889  0.121344\n",
       "13  2.097112e+06  1.146357e+08  0.902631  0.120344\n",
       "14  2.103471e+06  1.136830e+08  0.905368  0.119344\n",
       "15  2.109821e+06  1.127295e+08  0.908101  0.118343\n",
       "16  2.116160e+06  1.117754e+08  0.910829  0.117341\n",
       "17  2.122489e+06  1.108206e+08  0.913553  0.116339\n",
       "18  2.128808e+06  1.098650e+08  0.916273  0.115336\n",
       "19  2.135118e+06  1.089088e+08  0.918989  0.114332\n",
       "20  2.141417e+06  1.079518e+08  0.921700  0.113327\n",
       "21  2.147707e+06  1.069941e+08  0.924408  0.112322\n",
       "22  2.153987e+06  1.060357e+08  0.927111  0.111316\n",
       "23  2.160258e+06  1.050764e+08  0.929810  0.110309\n",
       "24  2.166518e+06  1.041165e+08  0.932504  0.109301\n",
       "25  2.172770e+06  1.031557e+08  0.935195  0.108292\n",
       "26  2.179012e+06  1.021942e+08  0.937882  0.107283\n",
       "27  2.185244e+06  1.012318e+08  0.940564  0.106273\n",
       "28  2.191467e+06  1.002687e+08  0.943243  0.105262\n",
       "29  2.197681e+06  9.930469e+07  0.945917  0.104250\n",
       "30  2.203885e+06  9.833990e+07  0.948588  0.103237\n",
       "31  2.210080e+06  9.737427e+07  0.951254  0.102223\n",
       "32  2.216266e+06  9.640779e+07  0.953917  0.101208\n",
       "33  2.222443e+06  9.544046e+07  0.956575  0.100193\n",
       "34  2.228611e+06  9.447226e+07  0.959230  0.099177\n",
       "35  2.234770e+06  9.350319e+07  0.961881  0.098159\n",
       "36  2.240919e+06  9.253323e+07  0.964528  0.097141\n",
       "37  2.247060e+06  9.156238e+07  0.967171  0.096122\n",
       "38  2.253192e+06  9.059063e+07  0.969810  0.095102\n",
       "39  2.259316e+06  8.961797e+07  0.972446  0.094081\n",
       "40  2.265430e+06  8.864439e+07  0.975077  0.093058\n",
       "41  2.271536e+06  8.766988e+07  0.977705  0.092035\n",
       "42  2.277633e+06  8.669443e+07  0.980330  0.091011\n",
       "43  2.283721e+06  8.571803e+07  0.982950  0.089986\n",
       "44  2.289801e+06  8.474068e+07  0.985567  0.088960\n",
       "45  2.295872e+06  8.376236e+07  0.988180  0.087933\n",
       "46  2.301935e+06  8.278308e+07  0.990790  0.086905\n",
       "47  2.307989e+06  8.180280e+07  0.993396  0.085876\n",
       "48  2.314035e+06  8.082154e+07  0.995998  0.084846\n",
       "49  2.320073e+06  7.983928e+07  0.998597  0.083815"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c5 = pd.DataFrame(MN_up[\"5\"])\n",
    "c5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>nu</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.320073e+06</td>\n",
       "      <td>7.983928e+07</td>\n",
       "      <td>0.998597</td>\n",
       "      <td>0.083815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.804346e+06</td>\n",
       "      <td>-2.558714e+06</td>\n",
       "      <td>1.207036</td>\n",
       "      <td>-0.002686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.981586e+06</td>\n",
       "      <td>-3.360490e+07</td>\n",
       "      <td>1.283323</td>\n",
       "      <td>-0.035278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.086624e+06</td>\n",
       "      <td>-5.212464e+07</td>\n",
       "      <td>1.328533</td>\n",
       "      <td>-0.054720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.155589e+06</td>\n",
       "      <td>-6.434673e+07</td>\n",
       "      <td>1.358216</td>\n",
       "      <td>-0.067551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>3.419849e+06</td>\n",
       "      <td>-1.118251e+08</td>\n",
       "      <td>1.471958</td>\n",
       "      <td>-0.117393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>3.419850e+06</td>\n",
       "      <td>-1.118252e+08</td>\n",
       "      <td>1.471958</td>\n",
       "      <td>-0.117394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>3.419851e+06</td>\n",
       "      <td>-1.118253e+08</td>\n",
       "      <td>1.471959</td>\n",
       "      <td>-0.117394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>3.419851e+06</td>\n",
       "      <td>-1.118254e+08</td>\n",
       "      <td>1.471959</td>\n",
       "      <td>-0.117394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>3.419852e+06</td>\n",
       "      <td>-1.118255e+08</td>\n",
       "      <td>1.471959</td>\n",
       "      <td>-0.117394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                N             M        nu        mu\n",
       "0    2.320073e+06  7.983928e+07  0.998597  0.083815\n",
       "1    2.804346e+06 -2.558714e+06  1.207036 -0.002686\n",
       "2    2.981586e+06 -3.360490e+07  1.283323 -0.035278\n",
       "3    3.086624e+06 -5.212464e+07  1.328533 -0.054720\n",
       "4    3.155589e+06 -6.434673e+07  1.358216 -0.067551\n",
       "..            ...           ...       ...       ...\n",
       "195  3.419849e+06 -1.118251e+08  1.471958 -0.117393\n",
       "196  3.419850e+06 -1.118252e+08  1.471958 -0.117394\n",
       "197  3.419851e+06 -1.118253e+08  1.471959 -0.117394\n",
       "198  3.419851e+06 -1.118254e+08  1.471959 -0.117394\n",
       "199  3.419852e+06 -1.118255e+08  1.471959 -0.117394\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c6 = pd.DataFrame(MN_up[\"6\"])\n",
    "c6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}