Dirac_matrix_4lineari.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "confirmed-german",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import *\n",
    "init_printing(use_unicode = True)\n",
    "from sympy.physics.secondquant import *\n",
    "from IPython.display import display, Math\n",
    "from sympy.physics.quantum import *\n",
    "\n",
    "# FERMIONIC ALGEBRA\n",
    "\n",
    "fu1 = symbols('a_{u_1}', commutative=False)\n",
    "fdu1 = symbols('a_{u_1}^\\dagger', commutative=False)\n",
    "fd1 = symbols('a_{d_1}', commutative=False)\n",
    "fdd1 = symbols('a_{d_1}^\\dagger', commutative=False)\n",
    "fu2 = symbols('a_{u_2}', commutative=False)\n",
    "fdu2 = symbols('a_{u_2}^\\dagger', commutative=False)\n",
    "fd2 = symbols('a_{d_2}', commutative=False)\n",
    "fdd2 = symbols('a_{d_2}^\\dagger', commutative=False)\n",
    "\n",
    "fermi  = Matrix([fu1,fd1,fu2,fd2])\n",
    "fermid = Matrix([fdu1,fdd1,fdu2,fdd2])\n",
    "ops = [fdu1,fu1,fdd1,fd1,fdu2,fu2,fdd2,fd2]\n",
    "\n",
    "def dictionarize(strl):\n",
    "    d = []\n",
    "    counter = 0\n",
    "    for i in strl:\n",
    "        v = []\n",
    "        if (i == fdu1) or (i == fu1):\n",
    "            v.append(i)\n",
    "            v.append(1+counter)\n",
    "            d.append(v)\n",
    "            counter = counter + 1\n",
    "        elif (i == fdd1) or (i == fd1):\n",
    "            v.append(i)\n",
    "            v.append(100+counter)\n",
    "            d.append(v)\n",
    "            counter = counter + 1\n",
    "        elif (i == fdu2) or (i == fu2):\n",
    "            v.append(i)\n",
    "            v.append(200+counter)\n",
    "            d.append(v)\n",
    "            counter = counter + 1\n",
    "        elif (i == fdd2) or (i == fd2):\n",
    "            v.append(i)\n",
    "            v.append(300+counter)\n",
    "            d.append(v)\n",
    "            counter = counter + 1\n",
    "        elif i not in ops:\n",
    "            v.append(i)\n",
    "            v.append(0)\n",
    "            d.append(v)\n",
    "    return d\n",
    "\n",
    "def bubble_sort(strl):\n",
    "    bubbles = dictionarize(strl)\n",
    "    \n",
    "    swapped = True\n",
    "    asign = 1\n",
    "    while swapped:\n",
    "        swapped = False\n",
    "        for i in range(len(bubbles)-1):\n",
    "            if bubbles[i][1] > bubbles[i+1][1]:\n",
    "                bubbles[i], bubbles[i+1] = bubbles[i+1], bubbles[i]\n",
    "                asign = asign * (-1) # Update sign due to CAR\n",
    "                #print(asign)\n",
    "                #display(Math(latex(bubbles)))\n",
    "                swapped = True\n",
    "    \n",
    "    strl.clear()\n",
    "    for i in bubbles:\n",
    "        strl.append(i[0])\n",
    "            \n",
    "    strl.append(asign)\n",
    "    return strl\n",
    "\n",
    "def subd(expr):\n",
    "    return expr.subs({\n",
    "        fu1*fu1 : 0,\n",
    "        fdu1*fdu1 : 0,\n",
    "        fd1*fd1 : 0,\n",
    "        fdd1*fdd1 : 0,\n",
    "        fu2*fu2 : 0,\n",
    "        fdu2*fdu2 : 0,\n",
    "        fd2*fd2 : 0,\n",
    "        fdd2*fdd2 : 0\n",
    "    }).subs({\n",
    "        fdu1*fu1*fdu1*fu1 : fdu1*fu1,\n",
    "        fdd1*fd1*fdd1*fd1 : fdd1*fd1,\n",
    "        fdu2*fu2*fdu2*fu2 : fdu2*fu2,\n",
    "        fdd2*fd2*fdd2*fd2 : fdd2*fd2,\n",
    "    }).subs({\n",
    "        fdu1*fu1*fdu1*fu1 : fdu1*fu1,\n",
    "        fdd1*fd1*fdd1*fd1 : fdd1*fd1,\n",
    "        fdu2*fu2*fdu2*fu2 : fdu2*fu2,\n",
    "        fdd2*fd2*fdd2*fd2 : fdd2*fd2,\n",
    "    }).subs({\n",
    "        fdu1*fu1*fdu1*fu1 : fdu1*fu1,\n",
    "        fdd1*fd1*fdd1*fd1 : fdd1*fd1,\n",
    "        fdu2*fu2*fdu2*fu2 : fdu2*fu2,\n",
    "        fdd2*fd2*fdd2*fd2 : fdd2*fd2,\n",
    "    })\n",
    "\n",
    "def fermionize(expr): \n",
    "    listexpr = [x.as_ordered_factors() for x in expr.as_ordered_terms()]\n",
    "    listprov = []\n",
    "    for i in listexpr:\n",
    "        listprov.append(bubble_sort(i))\n",
    "    \n",
    "    exprout = 0\n",
    "    for i in range(len(listprov)):\n",
    "        prod = 1\n",
    "        for j in range(len(listprov[i])):\n",
    "            prod = prod*listprov[i][j]\n",
    "            \n",
    "        exprout = exprout + prod\n",
    "        \n",
    "    return subd(exprout)\n",
    "    \n",
    "mixed2= [fdu1*fu1,\\\n",
    "         fdd1*fd1,\\\n",
    "         fdu1*fd1,\\\n",
    "         fu1*fdd1,\\\n",
    "         fdu2*fu2,\\\n",
    "         fdd2*fd2,\\\n",
    "         fdu2*fd2,\\\n",
    "         fu2*fdd2,\\\n",
    "         fdu1*fd2,\\\n",
    "         fd1*fdu2,\\\n",
    "         fdd1*fu2,\\\n",
    "         fu1*fdd2,\\\n",
    "         fdu1*fu2,\\\n",
    "         fu1*fdu2,\\\n",
    "         fdd1*fd2,\\\n",
    "         fd1*fdd2]\n",
    "\n",
    "         \n",
    "mixed4 = [fd1*fdu2*fdd2*fd2,\\\n",
    "         fd1*fdu2*fu2*fdd2,\\\n",
    "         fdd1*fd1*fdd2*fd2,\\\n",
    "         fdd1*fd1*fdu2*fd2,\\\n",
    "         fdd1*fd1*fdu2*fu2,\\\n",
    "         fdd1*fd1*fu2*fdd2,\\\n",
    "         fdd1*fdu2*fu2*fd2,\\\n",
    "         fdd1*fu2*fdd2*fd2,\\\n",
    "         fdu1*fd1*fdd2*fd2,\\\n",
    "         fdu1*fd1*fdu2*fd2,\\\n",
    "         fdu1*fd1*fdu2*fu2,\\\n",
    "         fdu1*fd1*fu2*fdd2,\\\n",
    "         fdu1*fdd1*fd1*fd2,\\\n",
    "         fdu1*fdd1*fd1*fu2,\\\n",
    "         fdu1*fdd1*fu2*fd2,\\\n",
    "         fdu1*fdu2*fu2*fd2,\\\n",
    "         fdu1*fu1*fd1*fdd2,\\\n",
    "         fdu1*fu1*fd1*fdu2,\\\n",
    "         fdu1*fu1*fdd1*fd1,\\\n",
    "         fdu1*fu1*fdd1*fd2,\\\n",
    "         fdu1*fu1*fdd1*fu2,\\\n",
    "         fdu1*fu1*fdd2*fd2,\\\n",
    "         fdu1*fu1*fdu2*fd2,\\\n",
    "         fdu1*fu1*fdu2*fu2,\\\n",
    "         fdu1*fu1*fu2*fdd2,\\\n",
    "         fdu1*fu2*fdd2*fd2,\\\n",
    "         fdu2*fu2*fdd2*fd2,\\\n",
    "         fu1*fd1*fdu2*fdd2,\\\n",
    "         fu1*fdd1*fd1*fdd2,\\\n",
    "         fu1*fdd1*fd1*fdu2,\\\n",
    "         fu1*fdd1*fdd2*fd2,\\\n",
    "         fu1*fdd1*fdu2*fd2,\\\n",
    "         fu1*fdd1*fdu2*fu2,\\\n",
    "         fu1*fdd1*fu2*fdd2,\\\n",
    "         fu1*fdu2*fdd2*fd2,\\\n",
    "         fu1*fdu2*fu2*fdd2]\n",
    "\n",
    "  \n",
    "mixed6 = [fdd1*fd1*fdu2*fu2*fdd2*fd2,\\\n",
    "         fu1*fdd1*fdu2*fu2*fdd2*fd2,\\\n",
    "         fu1*fdd1*fd1*fdu2*fdd2*fd2,\\\n",
    "         fu1*fdd1*fd1*fdu2*fu2*fdd2,\\\n",
    "         fdu1*fd1*fdu2*fu2*fdd2*fd2,\\\n",
    "         fdu1*fu1*fdu2*fu2*fdd2*fd2,\\\n",
    "         fdu1*fu1*fd1*fdu2*fdd2*fd2,\\\n",
    "         fdu1*fu1*fd1*fdu2*fu2*fdd2,\\\n",
    "         fdu1*fdd1*fd1*fu2*fdd2*fd2,\\\n",
    "         fdu1*fu1*fdd1*fu2*fdd2*fd2,\\\n",
    "         fdu1*fu1*fdd1*fd1*fdd2*fd2,\\\n",
    "         fdu1*fu1*fdd1*fd1*fu2*fdd2,\\\n",
    "         fdu1*fdd1*fd1*fdu2*fu2*fd2,\\\n",
    "         fdu1*fu1*fdd1*fdu2*fu2*fd2,\\\n",
    "         fdu1*fu1*fdd1*fd1*fdu2*fd2,\\\n",
    "         fdu1*fu1*fdd1*fd1*fdu2*fu2]\n",
    "    \n",
    "mixed8 = [fdu1*fu1*fdd1*fd1*fdu2*fu2*fdd2*fd2]\n",
    "\n",
    "terms = mixed2 + mixed4 + mixed6 + mixed8 # List with all concatenated\n",
    "\n",
    "num_term = len(mixed2) + len(mixed4) + len(mixed6) + len(mixed8) # generate coefficients\n",
    "\n",
    "aliases = []\n",
    "for i in range(0,num_term):\n",
    "    strg = str(i+1)\n",
    "    if i<len(mixed2):\n",
    "        aliases.append(symbols('B_'+strg))\n",
    "    if i >=len(mixed2) and i<(len(mixed2)+len(mixed4)):\n",
    "        aliases.append(symbols('Q_'+strg))\n",
    "    if i>=(len(mixed2)+len(mixed4)) and i<(len(mixed2)+len(mixed4)+len(mixed6)):\n",
    "        aliases.append(symbols('T_'+strg))\n",
    "    if i>=(len(mixed2)+len(mixed4)+len(mixed6)):\n",
    "        aliases.append(symbols('Z_'+strg))\n",
    "        \n",
    "Id = Matrix([[1,0],[0,1]]) \n",
    "sx = Matrix([[0,1],[1,0]])\n",
    "sy = Matrix([[0,-I],[I,0]])\n",
    "isy = I*sy\n",
    "sz = Matrix([[1,0],[0,-1]])\n",
    "\n",
    "s = [sx,sy,sz]\n",
    "s_for_gamma = [sx,isy,sz,Id]\n",
    "names = ['sx','isy','sz','Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "separate-child",
   "metadata": {},
   "source": [
    "In this step we evaluate all the matrices of the kind\n",
    "$\\sigma_a\\otimes\\sigma_b\\otimes\\sigma_c\\otimes\\sigma_d$ which are invariant w.r.t. $(I\\otimes\\sigma_j)\\otimes(I\\otimes\\sigma_j)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "animal-usage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 64$"
      ],
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools #for combination\n",
    "\n",
    "combs4 = list(itertools.product(s_for_gamma, repeat=4))\n",
    "combnames = list(itertools.product(names, repeat=4))\n",
    "\n",
    "list4 = list(zip(combnames,combs4)) \n",
    "# ( (names),((matrices tuple)) )\n",
    "# ES.\n",
    "# (('sx', 'sx', 'sx', 'sx'),\n",
    "# (Matrix([\n",
    "#  [0, 1],\n",
    "#  [1, 0]]),\n",
    "#  Matrix([\n",
    "#  [0, 1],\n",
    "#  [1, 0]]),\n",
    "#  Matrix([\n",
    "#  [0, 1],\n",
    "#  [1, 0]]),\n",
    "#  Matrix([\n",
    "#  [0, 1],\n",
    "#  [1, 0]])))\n",
    "\n",
    "inv4 = []\n",
    "for i in list4:\n",
    "    prod = []\n",
    "    for j in s:\n",
    "        fact = 1\n",
    "        \n",
    "        var1 = j*i[1][1]*j\n",
    "        if var1 == -(i[1][1]):\n",
    "            fact = fact * (-1)\n",
    "            \n",
    "        var2 = j*i[1][3]*j\n",
    "        if var2 == -(i[1][3]):\n",
    "            fact = fact * (-1)\n",
    "        \n",
    "        prod.append(fact)\n",
    "        \n",
    "    if prod == [1,1,1]:\n",
    "        inv4.append(i)\n",
    "len(inv4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-smith",
   "metadata": {},
   "source": [
    "In the next steps we reorder the information contained in a tuple in the format\\\n",
    "($b_i$, (nomi), (matrici))\\\n",
    "e.g.:\n",
    "($b_0$, ($\\sigma_x$,$\\sigma_x$,$\\sigma_x$,$\\sigma_x$), (4 matrici $\\sigma_x$))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "supported-metadata",
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff_names = []\n",
    "for i in range(len(inv4)):\n",
    "    strg = str(i+1)\n",
    "    coeff_names.append(symbols('b_'+ strg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "after-factory",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat4info = []\n",
    "for i in range(len(inv4)):\n",
    "    mat4info.append( ( coeff_names[i], inv4[i][0], inv4[i][1] ) )\n",
    "# mat4info is a list with tuple containing a coefficient, a tuple with strings and one with matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "commercial-provider",
   "metadata": {},
   "source": [
    "Here we sum up all the 64 invariant matrices, each one weighted, namely we write explicitly\n",
    "the linear combination in terms of the matrix representation\\\n",
    "$\\sum_i b_i\\tilde{\\gamma}_{2,i}$\\\n",
    "where $b_i$ coefficient and $\\tilde{\\gamma}_{2,i}$ invariant matrix tensor product of 4 Pauli $\\sigma$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "current-oxford",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{array}{cccccccccccccccc}b_{43} + b_{44} + b_{47} + b_{48} + b_{59} + b_{60} + b_{63} + b_{64} & 0 & b_{41} + b_{42} + b_{45} + b_{46} + b_{57} + b_{58} + b_{61} + b_{62} & 0 & 0 & b_{35} + b_{36} + b_{39} + b_{40} + b_{51} + b_{52} + b_{55} + b_{56} & 0 & b_{33} + b_{34} + b_{37} + b_{38} + b_{49} + b_{50} + b_{53} + b_{54} & b_{11} + b_{12} + b_{15} + b_{16} + b_{27} + b_{28} + b_{31} + b_{32} & 0 & b_{10} + b_{13} + b_{14} + b_{25} + b_{26} + b_{29} + b_{30} + b_{9} & 0 & 0 & b_{19} + b_{20} + b_{23} + b_{24} + b_{3} + b_{4} + b_{7} + b_{8} & 0 & b_{1} + b_{17} + b_{18} + b_{2} + b_{21} + b_{22} + b_{5} + b_{6}\\\\0 & - b_{43} - b_{44} + b_{47} + b_{48} - b_{59} - b_{60} + b_{63} + b_{64} & 0 & - b_{41} - b_{42} + b_{45} + b_{46} - b_{57} - b_{58} + b_{61} + b_{62} & b_{35} + b_{36} - b_{39} - b_{40} + b_{51} + b_{52} - b_{55} - b_{56} & 0 & b_{33} + b_{34} - b_{37} - b_{38} + b_{49} + b_{50} - b_{53} - b_{54} & 0 & 0 & - b_{11} - b_{12} + b_{15} + b_{16} - b_{27} - b_{28} + b_{31} + b_{32} & 0 & - b_{10} + b_{13} + b_{14} - b_{25} - b_{26} + b_{29} + b_{30} - b_{9} & b_{19} + b_{20} - b_{23} - b_{24} + b_{3} + b_{4} - b_{7} - b_{8} & 0 & b_{1} + b_{17} + b_{18} + b_{2} - b_{21} - b_{22} - b_{5} - b_{6} & 0\\\\b_{41} - b_{42} + b_{45} - b_{46} + b_{57} - b_{58} + b_{61} - b_{62} & 0 & - b_{43} + b_{44} - b_{47} + b_{48} - b_{59} + b_{60} - b_{63} + b_{64} & 0 & 0 & b_{33} - b_{34} + b_{37} - b_{38} + b_{49} - b_{50} + b_{53} - b_{54} & 0 & - b_{35} + b_{36} - b_{39} + b_{40} - b_{51} + b_{52} - b_{55} + b_{56} & - b_{10} + b_{13} - b_{14} + b_{25} - b_{26} + b_{29} - b_{30} + b_{9} & 0 & - b_{11} + b_{12} - b_{15} + b_{16} - b_{27} + b_{28} - b_{31} + b_{32} & 0 & 0 & b_{1} + b_{17} - b_{18} - b_{2} + b_{21} - b_{22} + b_{5} - b_{6} & 0 & - b_{19} + b_{20} - b_{23} + b_{24} - b_{3} + b_{4} - b_{7} + b_{8}\\\\0 & - b_{41} + b_{42} + b_{45} - b_{46} - b_{57} + b_{58} + b_{61} - b_{62} & 0 & b_{43} - b_{44} - b_{47} + b_{48} + b_{59} - b_{60} - b_{63} + b_{64} & b_{33} - b_{34} - b_{37} + b_{38} + b_{49} - b_{50} - b_{53} + b_{54} & 0 & - b_{35} + b_{36} + b_{39} - b_{40} - b_{51} + b_{52} + b_{55} - b_{56} & 0 & 0 & b_{10} + b_{13} - b_{14} - b_{25} + b_{26} + b_{29} - b_{30} - b_{9} & 0 & b_{11} - b_{12} - b_{15} + b_{16} + b_{27} - b_{28} - b_{31} + b_{32} & b_{1} + b_{17} - b_{18} - b_{2} - b_{21} + b_{22} - b_{5} + b_{6} & 0 & - b_{19} + b_{20} + b_{23} - b_{24} - b_{3} + b_{4} + b_{7} - b_{8} & 0\\\\0 & b_{35} + b_{36} - b_{39} - b_{40} + b_{51} + b_{52} - b_{55} - b_{56} & 0 & b_{33} + b_{34} - b_{37} - b_{38} + b_{49} + b_{50} - b_{53} - b_{54} & - b_{43} - b_{44} + b_{47} + b_{48} - b_{59} - b_{60} + b_{63} + b_{64} & 0 & - b_{41} - b_{42} + b_{45} + b_{46} - b_{57} - b_{58} + b_{61} + b_{62} & 0 & 0 & b_{19} + b_{20} - b_{23} - b_{24} + b_{3} + b_{4} - b_{7} - b_{8} & 0 & b_{1} + b_{17} + b_{18} + b_{2} - b_{21} - b_{22} - b_{5} - b_{6} & - b_{11} - b_{12} + b_{15} + b_{16} - b_{27} - b_{28} + b_{31} + b_{32} & 0 & - b_{10} + b_{13} + b_{14} - b_{25} - b_{26} + b_{29} + b_{30} - b_{9} & 0\\\\b_{35} + b_{36} + b_{39} + b_{40} + b_{51} + b_{52} + b_{55} + b_{56} & 0 & b_{33} + b_{34} + b_{37} + b_{38} + b_{49} + b_{50} + b_{53} + b_{54} & 0 & 0 & b_{43} + b_{44} + b_{47} + b_{48} + b_{59} + b_{60} + b_{63} + b_{64} & 0 & b_{41} + b_{42} + b_{45} + b_{46} + b_{57} + b_{58} + b_{61} + b_{62} & b_{19} + b_{20} + b_{23} + b_{24} + b_{3} + b_{4} + b_{7} + b_{8} & 0 & b_{1} + b_{17} + b_{18} + b_{2} + b_{21} + b_{22} + b_{5} + b_{6} & 0 & 0 & b_{11} + b_{12} + b_{15} + b_{16} + b_{27} + b_{28} + b_{31} + b_{32} & 0 & b_{10} + b_{13} + b_{14} + b_{25} + b_{26} + b_{29} + b_{30} + b_{9}\\\\0 & b_{33} - b_{34} - b_{37} + b_{38} + b_{49} - b_{50} - b_{53} + b_{54} & 0 & - b_{35} + b_{36} + b_{39} - b_{40} - b_{51} + b_{52} + b_{55} - b_{56} & - b_{41} + b_{42} + b_{45} - b_{46} - b_{57} + b_{58} + b_{61} - b_{62} & 0 & b_{43} - b_{44} - b_{47} + b_{48} + b_{59} - b_{60} - b_{63} + b_{64} & 0 & 0 & b_{1} + b_{17} - b_{18} - b_{2} - b_{21} + b_{22} - b_{5} + b_{6} & 0 & - b_{19} + b_{20} + b_{23} - b_{24} - b_{3} + b_{4} + b_{7} - b_{8} & b_{10} + b_{13} - b_{14} - b_{25} + b_{26} + b_{29} - b_{30} - b_{9} & 0 & b_{11} - b_{12} - b_{15} + b_{16} + b_{27} - b_{28} - b_{31} + b_{32} & 0\\\\b_{33} - b_{34} + b_{37} - b_{38} + b_{49} - b_{50} + b_{53} - b_{54} & 0 & - b_{35} + b_{36} - b_{39} + b_{40} - b_{51} + b_{52} - b_{55} + b_{56} & 0 & 0 & b_{41} - b_{42} + b_{45} - b_{46} + b_{57} - b_{58} + b_{61} - b_{62} & 0 & - b_{43} + b_{44} - b_{47} + b_{48} - b_{59} + b_{60} - b_{63} + b_{64} & b_{1} + b_{17} - b_{18} - b_{2} + b_{21} - b_{22} + b_{5} - b_{6} & 0 & - b_{19} + b_{20} - b_{23} + b_{24} - b_{3} + b_{4} - b_{7} + b_{8} & 0 & 0 & - b_{10} + b_{13} - b_{14} + b_{25} - b_{26} + b_{29} - b_{30} + b_{9} & 0 & - b_{11} + b_{12} - b_{15} + b_{16} - b_{27} + b_{28} - b_{31} + b_{32}\\\\b_{11} + b_{12} + b_{15} + b_{16} - b_{27} - b_{28} - b_{31} - b_{32} & 0 & b_{10} + b_{13} + b_{14} - b_{25} - b_{26} - b_{29} - b_{30} + b_{9} & 0 & 0 & - b_{19} - b_{20} - b_{23} - b_{24} + b_{3} + b_{4} + b_{7} + b_{8} & 0 & b_{1} - b_{17} - b_{18} + b_{2} - b_{21} - b_{22} + b_{5} + b_{6} & - b_{43} - b_{44} - b_{47} - b_{48} + b_{59} + b_{60} + b_{63} + b_{64} & 0 & - b_{41} - b_{42} - b_{45} - b_{46} + b_{57} + b_{58} + b_{61} + b_{62} & 0 & 0 & - b_{35} - b_{36} - b_{39} - b_{40} + b_{51} + b_{52} + b_{55} + b_{56} & 0 & - b_{33} - b_{34} - b_{37} - b_{38} + b_{49} + b_{50} + b_{53} + b_{54}\\\\0 & - b_{11} - b_{12} + b_{15} + b_{16} + b_{27} + b_{28} - b_{31} - b_{32} & 0 & - b_{10} + b_{13} + b_{14} + b_{25} + b_{26} - b_{29} - b_{30} - b_{9} & - b_{19} - b_{20} + b_{23} + b_{24} + b_{3} + b_{4} - b_{7} - b_{8} & 0 & b_{1} - b_{17} - b_{18} + b_{2} + b_{21} + b_{22} - b_{5} - b_{6} & 0 & 0 & b_{43} + b_{44} - b_{47} - b_{48} - b_{59} - b_{60} + b_{63} + b_{64} & 0 & b_{41} + b_{42} - b_{45} - b_{46} - b_{57} - b_{58} + b_{61} + b_{62} & - b_{35} - b_{36} + b_{39} + b_{40} + b_{51} + b_{52} - b_{55} - b_{56} & 0 & - b_{33} - b_{34} + b_{37} + b_{38} + b_{49} + b_{50} - b_{53} - b_{54} & 0\\\\- b_{10} + b_{13} - b_{14} - b_{25} + b_{26} - b_{29} + b_{30} + b_{9} & 0 & - b_{11} + b_{12} - b_{15} + b_{16} + b_{27} - b_{28} + b_{31} - b_{32} & 0 & 0 & b_{1} - b_{17} + b_{18} - b_{2} - b_{21} + b_{22} + b_{5} - b_{6} & 0 & b_{19} - b_{20} + b_{23} - b_{24} - b_{3} + b_{4} - b_{7} + b_{8} & - b_{41} + b_{42} - b_{45} + b_{46} + b_{57} - b_{58} + b_{61} - b_{62} & 0 & b_{43} - b_{44} + b_{47} - b_{48} - b_{59} + b_{60} - b_{63} + b_{64} & 0 & 0 & - b_{33} + b_{34} - b_{37} + b_{38} + b_{49} - b_{50} + b_{53} - b_{54} & 0 & b_{35} - b_{36} + b_{39} - b_{40} - b_{51} + b_{52} - b_{55} + b_{56}\\\\0 & b_{10} + b_{13} - b_{14} + b_{25} - b_{26} - b_{29} + b_{30} - b_{9} & 0 & b_{11} - b_{12} - b_{15} + b_{16} - b_{27} + b_{28} + b_{31} - b_{32} & b_{1} - b_{17} + b_{18} - b_{2} + b_{21} - b_{22} - b_{5} + b_{6} & 0 & b_{19} - b_{20} - b_{23} + b_{24} - b_{3} + b_{4} + b_{7} - b_{8} & 0 & 0 & b_{41} - b_{42} - b_{45} + b_{46} - b_{57} + b_{58} + b_{61} - b_{62} & 0 & - b_{43} + b_{44} + b_{47} - b_{48} + b_{59} - b_{60} - b_{63} + b_{64} & - b_{33} + b_{34} + b_{37} - b_{38} + b_{49} - b_{50} - b_{53} + b_{54} & 0 & b_{35} - b_{36} - b_{39} + b_{40} - b_{51} + b_{52} + b_{55} - b_{56} & 0\\\\0 & - b_{19} - b_{20} + b_{23} + b_{24} + b_{3} + b_{4} - b_{7} - b_{8} & 0 & b_{1} - b_{17} - b_{18} + b_{2} + b_{21} + b_{22} - b_{5} - b_{6} & - b_{11} - b_{12} + b_{15} + b_{16} + b_{27} + b_{28} - b_{31} - b_{32} & 0 & - b_{10} + b_{13} + b_{14} + b_{25} + b_{26} - b_{29} - b_{30} - b_{9} & 0 & 0 & - b_{35} - b_{36} + b_{39} + b_{40} + b_{51} + b_{52} - b_{55} - b_{56} & 0 & - b_{33} - b_{34} + b_{37} + b_{38} + b_{49} + b_{50} - b_{53} - b_{54} & b_{43} + b_{44} - b_{47} - b_{48} - b_{59} - b_{60} + b_{63} + b_{64} & 0 & b_{41} + b_{42} - b_{45} - b_{46} - b_{57} - b_{58} + b_{61} + b_{62} & 0\\\\- b_{19} - b_{20} - b_{23} - b_{24} + b_{3} + b_{4} + b_{7} + b_{8} & 0 & b_{1} - b_{17} - b_{18} + b_{2} - b_{21} - b_{22} + b_{5} + b_{6} & 0 & 0 & b_{11} + b_{12} + b_{15} + b_{16} - b_{27} - b_{28} - b_{31} - b_{32} & 0 & b_{10} + b_{13} + b_{14} - b_{25} - b_{26} - b_{29} - b_{30} + b_{9} & - b_{35} - b_{36} - b_{39} - b_{40} + b_{51} + b_{52} + b_{55} + b_{56} & 0 & - b_{33} - b_{34} - b_{37} - b_{38} + b_{49} + b_{50} + b_{53} + b_{54} & 0 & 0 & - b_{43} - b_{44} - b_{47} - b_{48} + b_{59} + b_{60} + b_{63} + b_{64} & 0 & - b_{41} - b_{42} - b_{45} - b_{46} + b_{57} + b_{58} + b_{61} + b_{62}\\\\0 & b_{1} - b_{17} + b_{18} - b_{2} + b_{21} - b_{22} - b_{5} + b_{6} & 0 & b_{19} - b_{20} - b_{23} + b_{24} - b_{3} + b_{4} + b_{7} - b_{8} & b_{10} + b_{13} - b_{14} + b_{25} - b_{26} - b_{29} + b_{30} - b_{9} & 0 & b_{11} - b_{12} - b_{15} + b_{16} - b_{27} + b_{28} + b_{31} - b_{32} & 0 & 0 & - b_{33} + b_{34} + b_{37} - b_{38} + b_{49} - b_{50} - b_{53} + b_{54} & 0 & b_{35} - b_{36} - b_{39} + b_{40} - b_{51} + b_{52} + b_{55} - b_{56} & b_{41} - b_{42} - b_{45} + b_{46} - b_{57} + b_{58} + b_{61} - b_{62} & 0 & - b_{43} + b_{44} + b_{47} - b_{48} + b_{59} - b_{60} - b_{63} + b_{64} & 0\\\\b_{1} - b_{17} + b_{18} - b_{2} - b_{21} + b_{22} + b_{5} - b_{6} & 0 & b_{19} - b_{20} + b_{23} - b_{24} - b_{3} + b_{4} - b_{7} + b_{8} & 0 & 0 & - b_{10} + b_{13} - b_{14} - b_{25} + b_{26} - b_{29} + b_{30} + b_{9} & 0 & - b_{11} + b_{12} - b_{15} + b_{16} + b_{27} - b_{28} + b_{31} - b_{32} & - b_{33} + b_{34} - b_{37} + b_{38} + b_{49} - b_{50} + b_{53} - b_{54} & 0 & b_{35} - b_{36} + b_{39} - b_{40} - b_{51} + b_{52} - b_{55} + b_{56} & 0 & 0 & - b_{41} + b_{42} - b_{45} + b_{46} + b_{57} - b_{58} + b_{61} - b_{62} & 0 & b_{43} - b_{44} + b_{47} - b_{48} - b_{59} + b_{60} - b_{63} + b_{64}\\end{array}\\right]$"
      ],
      "text/plain": [
       "⎡b₄₃ + b₄₄ + b₄₇ + b₄₈ + b₅₉ + b₆₀ + b₆₃ + b₆₄                        0       \n",
       "⎢                                                                             \n",
       "⎢                      0                        -b₄₃ - b₄₄ + b₄₇ + b₄₈ - b₅₉ -\n",
       "⎢                                                                             \n",
       "⎢b₄₁ - b₄₂ + b₄₅ - b₄₆ + b₅₇ - b₅₈ + b₆₁ - b₆₂                        0       \n",
       "⎢                                                                             \n",
       "⎢                      0                        -b₄₁ + b₄₂ + b₄₅ - b₄₆ - b₅₇ +\n",
       "⎢                                                                             \n",
       "⎢                      0                        b₃₅ + b₃₆ - b₃₉ - b₄₀ + b₅₁ + \n",
       "⎢                                                                             \n",
       "⎢b₃₅ + b₃₆ + b₃₉ + b₄₀ + b₅₁ + b₅₂ + b₅₅ + b₅₆                        0       \n",
       "⎢                                                                             \n",
       "⎢                      0                        b₃₃ - b₃₄ - b₃₇ + b₃₈ + b₄₉ - \n",
       "⎢                                                                             \n",
       "⎢b₃₃ - b₃₄ + b₃₇ - b₃₈ + b₄₉ - b₅₀ + b₅₃ - b₅₄                        0       \n",
       "⎢                                                                             \n",
       "⎢b₁₁ + b₁₂ + b₁₅ + b₁₆ - b₂₇ - b₂₈ - b₃₁ - b₃₂                        0       \n",
       "⎢                                                                             \n",
       "⎢                      0                        -b₁₁ - b₁₂ + b₁₅ + b₁₆ + b₂₇ +\n",
       "⎢                                                                             \n",
       "⎢-b₁₀ + b₁₃ - b₁₄ - b₂₅ + b₂₆ - b₂₉ + b₃₀ + b₉                        0       \n",
       "⎢                                                                             \n",
       "⎢                      0                         b₁₀ + b₁₃ - b₁₄ + b₂₅ - b₂₆ -\n",
       "⎢                                                                             \n",
       "⎢                      0                          -b₁₉ - b₂₀ + b₂₃ + b₂₄ + b₃ \n",
       "⎢                                                                             \n",
       "⎢ -b₁₉ - b₂₀ - b₂₃ - b₂₄ + b₃ + b₄ + b₇ + b₈                          0       \n",
       "⎢                                                                             \n",
       "⎢                      0                          b₁ - b₁₇ + b₁₈ - b₂ + b₂₁ - \n",
       "⎢                                                                             \n",
       "⎣  b₁ - b₁₇ + b₁₈ - b₂ - b₂₁ + b₂₂ + b₅ - b₆                          0       \n",
       "\n",
       "                  b₄₁ + b₄₂ + b₄₅ + b₄₆ + b₅₇ + b₅₈ + b₆₁ + b₆₂               \n",
       "                                                                              \n",
       " b₆₀ + b₆₃ + b₆₄                        0                         -b₄₁ - b₄₂ +\n",
       "                                                                              \n",
       "                  -b₄₃ + b₄₄ - b₄₇ + b₄₈ - b₅₉ + b₆₀ - b₆₃ + b₆₄              \n",
       "                                                                              \n",
       " b₅₈ + b₆₁ - b₆₂                        0                         b₄₃ - b₄₄ - \n",
       "                                                                              \n",
       "b₅₂ - b₅₅ - b₅₆                         0                         b₃₃ + b₃₄ - \n",
       "                                                                              \n",
       "                  b₃₃ + b₃₄ + b₃₇ + b₃₈ + b₄₉ + b₅₀ + b₅₃ + b₅₄               \n",
       "                                                                              \n",
       "b₅₀ - b₅₃ + b₅₄                         0                         -b₃₅ + b₃₆ +\n",
       "                                                                              \n",
       "                  -b₃₅ + b₃₆ - b₃₉ + b₄₀ - b₅₁ + b₅₂ - b₅₅ + b₅₆              \n",
       "                                                                              \n",
       "                   b₁₀ + b₁₃ + b₁₄ - b₂₅ - b₂₆ - b₂₉ - b₃₀ + b₉               \n",
       "                                                                              \n",
       " b₂₈ - b₃₁ - b₃₂                        0                         -b₁₀ + b₁₃ +\n",
       "                                                                              \n",
       "                  -b₁₁ + b₁₂ - b₁₅ + b₁₆ + b₂₇ - b₂₈ + b₃₁ - b₃₂              \n",
       "                                                                              \n",
       " b₂₉ + b₃₀ - b₉                         0                         b₁₁ - b₁₂ - \n",
       "                                                                              \n",
       "+ b₄ - b₇ - b₈                          0                           b₁ - b₁₇ -\n",
       "                                                                              \n",
       "                    b₁ - b₁₇ - b₁₈ + b₂ - b₂₁ - b₂₂ + b₅ + b₆                 \n",
       "                                                                              \n",
       "b₂₂ - b₅ + b₆                           0                           b₁₉ - b₂₀ \n",
       "                                                                              \n",
       "                    b₁₉ - b₂₀ + b₂₃ - b₂₄ - b₃ + b₄ - b₇ + b₈                 \n",
       "\n",
       "          0                                               0                   \n",
       "                                                                              \n",
       " b₄₅ + b₄₆ - b₅₇ - b₅₈ + b₆₁ + b₆₂  b₃₅ + b₃₆ - b₃₉ - b₄₀ + b₅₁ + b₅₂ - b₅₅ - \n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "                                                                              \n",
       "b₄₇ + b₄₈ + b₅₉ - b₆₀ - b₆₃ + b₆₄   b₃₃ - b₃₄ - b₃₇ + b₃₈ + b₄₉ - b₅₀ - b₅₃ + \n",
       "                                                                              \n",
       "b₃₇ - b₃₈ + b₄₉ + b₅₀ - b₅₃ - b₅₄   -b₄₃ - b₄₄ + b₄₇ + b₄₈ - b₅₉ - b₆₀ + b₆₃ +\n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "                                                                              \n",
       " b₃₉ - b₄₀ - b₅₁ + b₅₂ + b₅₅ - b₅₆  -b₄₁ + b₄₂ + b₄₅ - b₄₆ - b₅₇ + b₅₈ + b₆₁ -\n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "                                                                              \n",
       " b₁₄ + b₂₅ + b₂₆ - b₂₉ - b₃₀ - b₉     -b₁₉ - b₂₀ + b₂₃ + b₂₄ + b₃ + b₄ - b₇ - \n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "                                                                              \n",
       "b₁₅ + b₁₆ - b₂₇ + b₂₈ + b₃₁ - b₃₂     b₁ - b₁₇ + b₁₈ - b₂ + b₂₁ - b₂₂ - b₅ + b\n",
       "                                                                              \n",
       " b₁₈ + b₂ + b₂₁ + b₂₂ - b₅ - b₆     -b₁₁ - b₁₂ + b₁₅ + b₁₆ + b₂₇ + b₂₈ - b₃₁ -\n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "                                                                              \n",
       "- b₂₃ + b₂₄ - b₃ + b₄ + b₇ - b₈      b₁₀ + b₁₃ - b₁₄ + b₂₅ - b₂₆ - b₂₉ + b₃₀ -\n",
       "                                                                              \n",
       "          0                                               0                   \n",
       "\n",
       "      b₃₅ + b₃₆ + b₃₉ + b₄₀ + b₅₁ + b₅₂ + b₅₅ + b₅₆                        0  \n",
       "                                                                              \n",
       "b₅₆                         0                        b₃₃ + b₃₄ - b₃₇ - b₃₈ + b\n",
       "                                                                              \n",
       "      b₃₃ - b₃₄ + b₃₇ - b₃₈ + b₄₉ - b₅₀ + b₅₃ - b₅₄                        0  \n",
       "                                                                              \n",
       "b₅₄                         0                        -b₃₅ + b₃₆ + b₃₉ - b₄₀ - \n",
       "                                                                              \n",
       " b₆₄                        0                        -b₄₁ - b₄₂ + b₄₅ + b₄₆ - \n",
       "                                                                              \n",
       "      b₄₃ + b₄₄ + b₄₇ + b₄₈ + b₅₉ + b₆₀ + b₆₃ + b₆₄                        0  \n",
       "                                                                              \n",
       " b₆₂                        0                        b₄₃ - b₄₄ - b₄₇ + b₄₈ + b\n",
       "                                                                              \n",
       "      b₄₁ - b₄₂ + b₄₅ - b₄₆ + b₅₇ - b₅₈ + b₆₁ - b₆₂                        0  \n",
       "                                                                              \n",
       "       -b₁₉ - b₂₀ - b₂₃ - b₂₄ + b₃ + b₄ + b₇ + b₈                          0  \n",
       "                                                                              \n",
       "b₈                          0                          b₁ - b₁₇ - b₁₈ + b₂ + b\n",
       "                                                                              \n",
       "        b₁ - b₁₇ + b₁₈ - b₂ - b₂₁ + b₂₂ + b₅ - b₆                          0  \n",
       "                                                                              \n",
       "₆                           0                          b₁₉ - b₂₀ - b₂₃ + b₂₄ -\n",
       "                                                                              \n",
       " b₃₂                        0                        -b₁₀ + b₁₃ + b₁₄ + b₂₅ + \n",
       "                                                                              \n",
       "      b₁₁ + b₁₂ + b₁₅ + b₁₆ - b₂₇ - b₂₈ - b₃₁ - b₃₂                        0  \n",
       "                                                                              \n",
       " b₉                         0                        b₁₁ - b₁₂ - b₁₅ + b₁₆ - b\n",
       "                                                                              \n",
       "      -b₁₀ + b₁₃ - b₁₄ - b₂₅ + b₂₆ - b₂₉ + b₃₀ + b₉                        0  \n",
       "\n",
       "                       b₃₃ + b₃₄ + b₃₇ + b₃₈ + b₄₉ + b₅₀ + b₅₃ + b₅₄   b₁₁ + b\n",
       "                                                                              \n",
       "₄₉ + b₅₀ - b₅₃ - b₅₄                         0                                \n",
       "                                                                              \n",
       "                       -b₃₅ + b₃₆ - b₃₉ + b₄₀ - b₅₁ + b₅₂ - b₅₅ + b₅₆  -b₁₀ + \n",
       "                                                                              \n",
       "b₅₁ + b₅₂ + b₅₅ - b₅₆                        0                                \n",
       "                                                                              \n",
       "b₅₇ - b₅₈ + b₆₁ + b₆₂                        0                                \n",
       "                                                                              \n",
       "                       b₄₁ + b₄₂ + b₄₅ + b₄₆ + b₅₇ + b₅₈ + b₆₁ + b₆₂     b₁₉ +\n",
       "                                                                              \n",
       "₅₉ - b₆₀ - b₆₃ + b₆₄                         0                                \n",
       "                                                                              \n",
       "                       -b₄₃ + b₄₄ - b₄₇ + b₄₈ - b₅₉ + b₆₀ - b₆₃ + b₆₄    b₁ + \n",
       "                                                                              \n",
       "                         b₁ - b₁₇ - b₁₈ + b₂ - b₂₁ - b₂₂ + b₅ + b₆     -b₄₃ - \n",
       "                                                                              \n",
       "₂₁ + b₂₂ - b₅ - b₆                           0                                \n",
       "                                                                              \n",
       "                         b₁₉ - b₂₀ + b₂₃ - b₂₄ - b₃ + b₄ - b₇ + b₈     -b₄₁ + \n",
       "                                                                              \n",
       " b₃ + b₄ + b₇ - b₈                           0                                \n",
       "                                                                              \n",
       "b₂₆ - b₂₉ - b₃₀ - b₉                         0                                \n",
       "                                                                              \n",
       "                        b₁₀ + b₁₃ + b₁₄ - b₂₅ - b₂₆ - b₂₉ - b₃₀ + b₉   -b₃₅ - \n",
       "                                                                              \n",
       "₂₇ + b₂₈ + b₃₁ - b₃₂                         0                                \n",
       "                                                                              \n",
       "                       -b₁₁ + b₁₂ - b₁₅ + b₁₆ + b₂₇ - b₂₈ + b₃₁ - b₃₂  -b₃₃ + \n",
       "\n",
       "₁₂ + b₁₅ + b₁₆ + b₂₇ + b₂₈ + b₃₁ + b₃₂                         0              \n",
       "                                                                              \n",
       "               0                         -b₁₁ - b₁₂ + b₁₅ + b₁₆ - b₂₇ - b₂₈ + \n",
       "                                                                              \n",
       "b₁₃ - b₁₄ + b₂₅ - b₂₆ + b₂₉ - b₃₀ + b₉                         0              \n",
       "                                                                              \n",
       "               0                          b₁₀ + b₁₃ - b₁₄ - b₂₅ + b₂₆ + b₂₉ - \n",
       "                                                                              \n",
       "               0                           b₁₉ + b₂₀ - b₂₃ - b₂₄ + b₃ + b₄ - b\n",
       "                                                                              \n",
       " b₂₀ + b₂₃ + b₂₄ + b₃ + b₄ + b₇ + b₈                           0              \n",
       "                                                                              \n",
       "               0                           b₁ + b₁₇ - b₁₈ - b₂ - b₂₁ + b₂₂ - b\n",
       "                                                                              \n",
       "b₁₇ - b₁₈ - b₂ + b₂₁ - b₂₂ + b₅ - b₆                           0              \n",
       "                                                                              \n",
       "b₄₄ - b₄₇ - b₄₈ + b₅₉ + b₆₀ + b₆₃ + b₆₄                        0              \n",
       "                                                                              \n",
       "               0                         b₄₃ + b₄₄ - b₄₇ - b₄₈ - b₅₉ - b₆₀ + b\n",
       "                                                                              \n",
       "b₄₂ - b₄₅ + b₄₆ + b₅₇ - b₅₈ + b₆₁ - b₆₂                        0              \n",
       "                                                                              \n",
       "               0                         b₄₁ - b₄₂ - b₄₅ + b₄₆ - b₅₇ + b₅₈ + b\n",
       "                                                                              \n",
       "               0                         -b₃₅ - b₃₆ + b₃₉ + b₄₀ + b₅₁ + b₅₂ - \n",
       "                                                                              \n",
       "b₃₆ - b₃₉ - b₄₀ + b₅₁ + b₅₂ + b₅₅ + b₅₆                        0              \n",
       "                                                                              \n",
       "               0                         -b₃₃ + b₃₄ + b₃₇ - b₃₈ + b₄₉ - b₅₀ - \n",
       "                                                                              \n",
       "b₃₄ - b₃₇ + b₃₈ + b₄₉ - b₅₀ + b₅₃ - b₅₄                        0              \n",
       "\n",
       "            b₁₀ + b₁₃ + b₁₄ + b₂₅ + b₂₆ + b₂₉ + b₃₀ + b₉                      \n",
       "                                                                              \n",
       "b₃₁ + b₃₂                        0                         -b₁₀ + b₁₃ + b₁₄ - \n",
       "                                                                              \n",
       "           -b₁₁ + b₁₂ - b₁₅ + b₁₆ - b₂₇ + b₂₈ - b₃₁ + b₃₂                     \n",
       "                                                                              \n",
       "b₃₀ - b₉                         0                         b₁₁ - b₁₂ - b₁₅ + b\n",
       "                                                                              \n",
       "₇ - b₈                           0                           b₁ + b₁₇ + b₁₈ + \n",
       "                                                                              \n",
       "             b₁ + b₁₇ + b₁₈ + b₂ + b₂₁ + b₂₂ + b₅ + b₆                        \n",
       "                                                                              \n",
       "₅ + b₆                           0                           -b₁₉ + b₂₀ + b₂₃ \n",
       "                                                                              \n",
       "             -b₁₉ + b₂₀ - b₂₃ + b₂₄ - b₃ + b₄ - b₇ + b₈                       \n",
       "                                                                              \n",
       "           -b₄₁ - b₄₂ - b₄₅ - b₄₆ + b₅₇ + b₅₈ + b₆₁ + b₆₂                     \n",
       "                                                                              \n",
       "₆₃ + b₆₄                         0                         b₄₁ + b₄₂ - b₄₅ - b\n",
       "                                                                              \n",
       "           b₄₃ - b₄₄ + b₄₇ - b₄₈ - b₅₉ + b₆₀ - b₆₃ + b₆₄                      \n",
       "                                                                              \n",
       "₆₁ - b₆₂                         0                         -b₄₃ + b₄₄ + b₄₇ - \n",
       "                                                                              \n",
       "b₅₅ - b₅₆                        0                         -b₃₃ - b₃₄ + b₃₇ + \n",
       "                                                                              \n",
       "           -b₃₃ - b₃₄ - b₃₇ - b₃₈ + b₄₉ + b₅₀ + b₅₃ + b₅₄                     \n",
       "                                                                              \n",
       "b₅₃ + b₅₄                        0                         b₃₅ - b₃₆ - b₃₉ + b\n",
       "                                                                              \n",
       "           b₃₅ - b₃₆ + b₃₉ - b₄₀ - b₅₁ + b₅₂ - b₅₅ + b₅₆                      \n",
       "\n",
       "   0                                               0                          \n",
       "                                                                              \n",
       "b₂₅ - b₂₆ + b₂₉ + b₃₀ - b₉     b₁₉ + b₂₀ - b₂₃ - b₂₄ + b₃ + b₄ - b₇ - b₈      \n",
       "                                                                              \n",
       "   0                                               0                          \n",
       "                                                                              \n",
       "₁₆ + b₂₇ - b₂₈ - b₃₁ + b₃₂     b₁ + b₁₇ - b₁₈ - b₂ - b₂₁ + b₂₂ - b₅ + b₆      \n",
       "                                                                              \n",
       "b₂ - b₂₁ - b₂₂ - b₅ - b₆     -b₁₁ - b₁₂ + b₁₅ + b₁₆ - b₂₇ - b₂₈ + b₃₁ + b₃₂   \n",
       "                                                                              \n",
       "   0                                               0                         b\n",
       "                                                                              \n",
       "- b₂₄ - b₃ + b₄ + b₇ - b₈     b₁₀ + b₁₃ - b₁₄ - b₂₅ + b₂₆ + b₂₉ - b₃₀ - b₉    \n",
       "                                                                              \n",
       "   0                                               0                         -\n",
       "                                                                              \n",
       "   0                                               0                         -\n",
       "                                                                              \n",
       "₄₆ - b₅₇ - b₅₈ + b₆₁ + b₆₂   -b₃₅ - b₃₆ + b₃₉ + b₄₀ + b₅₁ + b₅₂ - b₅₅ - b₅₆   \n",
       "                                                                              \n",
       "   0                                               0                         -\n",
       "                                                                              \n",
       "b₄₈ + b₅₉ - b₆₀ - b₆₃ + b₆₄  -b₃₃ + b₃₄ + b₃₇ - b₃₈ + b₄₉ - b₅₀ - b₅₃ + b₅₄   \n",
       "                                                                              \n",
       "b₃₈ + b₄₉ + b₅₀ - b₅₃ - b₅₄  b₄₃ + b₄₄ - b₄₇ - b₄₈ - b₅₉ - b₆₀ + b₆₃ + b₆₄    \n",
       "                                                                              \n",
       "   0                                               0                         -\n",
       "                                                                              \n",
       "₄₀ - b₅₁ + b₅₂ + b₅₅ - b₅₆   b₄₁ - b₄₂ - b₄₅ + b₄₆ - b₅₇ + b₅₈ + b₆₁ - b₆₂    \n",
       "                                                                              \n",
       "   0                                               0                         -\n",
       "\n",
       " b₁₉ + b₂₀ + b₂₃ + b₂₄ + b₃ + b₄ + b₇ + b₈                           0        \n",
       "                                                                              \n",
       "                     0                           b₁ + b₁₇ + b₁₈ + b₂ - b₂₁ - b\n",
       "                                                                              \n",
       " b₁ + b₁₇ - b₁₈ - b₂ + b₂₁ - b₂₂ + b₅ - b₆                           0        \n",
       "                                                                              \n",
       "                     0                           -b₁₉ + b₂₀ + b₂₃ - b₂₄ - b₃ +\n",
       "                                                                              \n",
       "                     0                         -b₁₀ + b₁₃ + b₁₄ - b₂₅ - b₂₆ + \n",
       "                                                                              \n",
       "₁₁ + b₁₂ + b₁₅ + b₁₆ + b₂₇ + b₂₈ + b₃₁ + b₃₂                         0        \n",
       "                                                                              \n",
       "                     0                         b₁₁ - b₁₂ - b₁₅ + b₁₆ + b₂₇ - b\n",
       "                                                                              \n",
       "b₁₀ + b₁₃ - b₁₄ + b₂₅ - b₂₆ + b₂₉ - b₃₀ + b₉                         0        \n",
       "                                                                              \n",
       "b₃₅ - b₃₆ - b₃₉ - b₄₀ + b₅₁ + b₅₂ + b₅₅ + b₅₆                        0        \n",
       "                                                                              \n",
       "                     0                         -b₃₃ - b₃₄ + b₃₇ + b₃₈ + b₄₉ + \n",
       "                                                                              \n",
       "b₃₃ + b₃₄ - b₃₇ + b₃₈ + b₄₉ - b₅₀ + b₅₃ - b₅₄                        0        \n",
       "                                                                              \n",
       "                     0                         b₃₅ - b₃₆ - b₃₉ + b₄₀ - b₅₁ + b\n",
       "                                                                              \n",
       "                     0                         b₄₁ + b₄₂ - b₄₅ - b₄₆ - b₅₇ - b\n",
       "                                                                              \n",
       "b₄₃ - b₄₄ - b₄₇ - b₄₈ + b₅₉ + b₆₀ + b₆₃ + b₆₄                        0        \n",
       "                                                                              \n",
       "                     0                         -b₄₃ + b₄₄ + b₄₇ - b₄₈ + b₅₉ - \n",
       "                                                                              \n",
       "b₄₁ + b₄₂ - b₄₅ + b₄₆ + b₅₇ - b₅₈ + b₆₁ - b₆₂                        0        \n",
       "\n",
       "                   b₁ + b₁₇ + b₁₈ + b₂ + b₂₁ + b₂₂ + b₅ + b₆   ⎤\n",
       "                                                               ⎥\n",
       "₂₂ - b₅ - b₆                           0                       ⎥\n",
       "                                                               ⎥\n",
       "                   -b₁₉ + b₂₀ - b₂₃ + b₂₄ - b₃ + b₄ - b₇ + b₈  ⎥\n",
       "                                                               ⎥\n",
       " b₄ + b₇ - b₈                          0                       ⎥\n",
       "                                                               ⎥\n",
       "b₂₉ + b₃₀ - b₉                         0                       ⎥\n",
       "                                                               ⎥\n",
       "                  b₁₀ + b₁₃ + b₁₄ + b₂₅ + b₂₆ + b₂₉ + b₃₀ + b₉ ⎥\n",
       "                                                               ⎥\n",
       "₂₈ - b₃₁ + b₃₂                         0                       ⎥\n",
       "                                                               ⎥\n",
       "                 -b₁₁ + b₁₂ - b₁₅ + b₁₆ - b₂₇ + b₂₈ - b₃₁ + b₃₂⎥\n",
       "                                                               ⎥\n",
       "                 -b₃₃ - b₃₄ - b₃₇ - b₃₈ + b₄₉ + b₅₀ + b₅₃ + b₅₄⎥\n",
       "                                                               ⎥\n",
       "b₅₀ - b₅₃ - b₅₄                        0                       ⎥\n",
       "                                                               ⎥\n",
       "                 b₃₅ - b₃₆ + b₃₉ - b₄₀ - b₅₁ + b₅₂ - b₅₅ + b₅₆ ⎥\n",
       "                                                               ⎥\n",
       "₅₂ + b₅₅ - b₅₆                         0                       ⎥\n",
       "                                                               ⎥\n",
       "₅₈ + b₆₁ + b₆₂                         0                       ⎥\n",
       "                                                               ⎥\n",
       "                 -b₄₁ - b₄₂ - b₄₅ - b₄₆ + b₅₇ + b₅₈ + b₆₁ + b₆₂⎥\n",
       "                                                               ⎥\n",
       "b₆₀ - b₆₃ + b₆₄                        0                       ⎥\n",
       "                                                               ⎥\n",
       "                 b₄₃ - b₄₄ + b₄₇ - b₄₈ - b₅₉ + b₆₀ - b₆₃ + b₆₄ ⎦"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat4 = zeros(16,16)\n",
    "for i in mat4info:\n",
    "    mat4 = mat4 + i[0]*TensorProduct(i[2][0],i[2][1],i[2][2],i[2][3])\n",
    "mat4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "timely-review",
   "metadata": {},
   "outputs": [],
   "source": [
    "transf = []\n",
    "for i in s:\n",
    "    transf.append(TensorProduct(Id,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "established-pricing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    }
   ],
   "source": [
    "# sanity check for the invariance\n",
    "\n",
    "def t4(mat):\n",
    "    return TensorProduct(mat,mat)\n",
    "\n",
    "for i in transf:\n",
    "    var = t4(i)*mat4*t4(i)\n",
    "    if (var - mat4 == zeros(16,16)):\n",
    "        print('ok, è invariante :)')\n",
    "    else:\n",
    "        print('Non è invariante, sorry :(')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "initial-expert",
   "metadata": {},
   "outputs": [],
   "source": [
    "difermi = TensorProduct(fermi, fermi)\n",
    "difermid = TensorProduct(fermid, fermid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "prime-celebration",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matrices to map all the combos\n",
    "m = [Matrix([[1,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,1,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,1,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,1],[0,0,0,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[1,0,0,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,1,0,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,1,0],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,1],[0,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[1,0,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,1,0,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,0,1,0],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,0,0,1],[0,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,0,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,1,0,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,1,0]]),\\\n",
    "     Matrix([[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,1]])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assigned-institute",
   "metadata": {},
   "source": [
    "Here we map each quadrilinear term to the matrix, called $A$, which gives the quadrilinear term as a result if we evaluate $(\\phi^\\dagger\\otimes\\phi^\\dagger)^T A (\\phi\\otimes\\phi)$\\\n",
    "where $\\phi = \\begin{bmatrix}a_{u1}\\\\ a_{d1}\\\\a_{u2}\\\\a_{d2} \\end{bmatrix}$ and $\\phi^\\dagger$ The same reasoning with the creation operators. We then build a list of tuples in order to classify the obtained information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "loved-functionality",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod4tuple = itertools.product(m, repeat=2)\n",
    "\n",
    "map4tuple = []\n",
    "for i in prod4tuple:\n",
    "    pten = TensorProduct(i[0],i[1])\n",
    "    var = fermionize((difermid.T*pten*difermi)[0])\n",
    "    if var.as_ordered_factors()[0] == -1:\n",
    "        map4tuple.append((var.as_ordered_factors()[0]*var, var.as_ordered_factors()[0]*pten, i[0], i[1]))\n",
    "    else:\n",
    "        map4tuple.append((var, pten, i[0], i[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "duplicate-abortion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left( a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2}, \\  \\left[\\begin{array}{cccccccccccccccc}0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right], \\  \\left[\\begin{matrix}0 & 0 & 0 & 0\\\\1 & 0 & 0 & 0\\\\0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0\\end{matrix}\\right], \\  \\left[\\begin{matrix}0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0\\end{matrix}\\right]\\right)$"
      ],
      "text/plain": [
       "⎛                                                   ⎡0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  1  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{u_2}, ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎜                                                   ⎢0  0  0  0  0  0  0  0  0\n",
       "⎜                                                   ⎢                         \n",
       "⎝                                                   ⎣0  0  0  0  0  0  0  0  0\n",
       "\n",
       "  0  0  0  0  0  0  0⎤                            ⎞\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥  ⎡0  0  0  0⎤  ⎡0  0  0  0⎤⎟\n",
       "                     ⎥  ⎢          ⎥  ⎢          ⎥⎟\n",
       "  0  0  0  0  0  0  0⎥  ⎢1  0  0  0⎥  ⎢0  0  0  0⎥⎟\n",
       "                     ⎥, ⎢          ⎥, ⎢          ⎥⎟\n",
       "  0  0  0  0  0  0  0⎥  ⎢0  0  0  0⎥  ⎢0  0  1  0⎥⎟\n",
       "                     ⎥  ⎢          ⎥  ⎢          ⎥⎟\n",
       "  0  0  0  0  0  0  0⎥  ⎣0  0  0  0⎦  ⎣0  0  0  0⎦⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎥                            ⎟\n",
       "                     ⎥                            ⎟\n",
       "  0  0  0  0  0  0  0⎦                            ⎠"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map4tuple[74] # example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "adverse-mortality",
   "metadata": {},
   "outputs": [],
   "source": [
    "map4tuple_compact = []\n",
    "fermion4set = set() \n",
    "for i in map4tuple:\n",
    "    fermion4set.add(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "victorian-county",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left\\{0, a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}, a_{d_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger, a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2}, a_{d_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger, a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}, a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2}, a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2}, a_{u_1} a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger, a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger, a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger, a_{u_1} a_{d_1}^\\dagger a_{d_2}^\\dagger a_{d_2}, a_{u_1} a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger, a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{d_2}, a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2}, a_{u_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}, a_{u_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger, a_{u_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2}, a_{u_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger, a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}, a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2}, a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{d_2}, a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{u_2}, a_{u_1}^\\dagger a_{d_1}^\\dagger a_{u_2} a_{d_2}, a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{d_2}^\\dagger, a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{u_2}^\\dagger, a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_1}, a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_2}, a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{u_2}, a_{u_1}^\\dagger a_{u_1} a_{d_2}^\\dagger a_{d_2}, a_{u_1}^\\dagger a_{u_1} a_{u_2} a_{d_2}^\\dagger, a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{d_2}, a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{u_2}, a_{u_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, a_{u_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2}, a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}\\right\\}$"
      ],
      "text/plain": [
       "{0, a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, a_{d_1}⋅a_{u_2}__\\dagge\n",
       "r⋅a_{u_2}⋅a_{d_2}__\\dagger, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger⋅a_{d_2},\n",
       " a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}⋅a_{d_2}__\\dagger, a_{d_1}__\\dagger⋅a_{d_1}⋅a\n",
       "_{u_2}__\\dagger⋅a_{d_2}, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, a_\n",
       "{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger⋅a_{d_2}, a_{d_1}__\\dagger⋅a_{u_2}__\\da\n",
       "gger⋅a_{u_2}⋅a_{d_2}, a_{u_1}⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger, a_{u_\n",
       "1}⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}\n",
       "⋅a_{u_2}__\\dagger, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, a_{u_1}⋅\n",
       "a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}__\\\n",
       "dagger⋅a_{d_2}, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{u_2}, a_{u_1}⋅a_{\n",
       "u_2}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, a_{u_1}⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_\n",
       "2}__\\dagger, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger⋅a_{d_2}, a_{u_1}__\\dagg\n",
       "er⋅a_{d_1}⋅a_{u_2}⋅a_{d_2}__\\dagger, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger\n",
       "⋅a_{d_2}, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, a_{u_1}__\\dagger⋅\n",
       "a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d_2}, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_\n",
       "{u_2}, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}, a_{u_1}__\\dagger⋅a_{\n",
       "u_1}⋅a_{d_1}⋅a_{d_2}__\\dagger, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}⋅a_{u_2}__\\dagg\n",
       "er, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}, a_{u_1}__\\dagger⋅a_{u_1\n",
       "}⋅a_{d_1}__\\dagger⋅a_{d_2}, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2},\n",
       " a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_2}__\\dagger⋅a_{d_2}, a_{u_1}__\\dagger⋅a_{u_1}⋅a\n",
       "_{u_2}⋅a_{d_2}__\\dagger, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}__\\dagger⋅a_{d_2}, a_\n",
       "{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, a_{u_1}__\\dagger⋅a_{u_2}⋅a_{d\n",
       "_2}__\\dagger⋅a_{d_2}, a_{u_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}, a_{u_\n",
       "2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger⋅a_{d_2}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fermion4set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "former-status",
   "metadata": {},
   "source": [
    "Let's put together all the terms from the list obtained, which are equal for each first entry of the tuple. In practice, we sum all the matrices that give us a given quadrilinear term. We do the same for all those matrices that, in bilinear form, would give us a null fermionic operator (e.g. $a_{u1}^{\\dagger 2} a_{u1}^2$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "loving-diploma",
   "metadata": {},
   "outputs": [],
   "source": [
    "map4 = []\n",
    "for i in fermion4set:\n",
    "    mat4map = zeros(16,16)\n",
    "    for j in map4tuple:\n",
    "        if i==j[0]:\n",
    "            mat4map = mat4map + j[1]\n",
    "    map4.append((i,mat4map))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adequate-soccer",
   "metadata": {},
   "source": [
    "The matrix that gives null operators tells us which entries of the matrix $\\sum_i b_i\\tilde{\\gamma}_{2,i}$ can be ignored in subsequent calculations, as no matter what values they have, they will always contribute 0 to the reconstruction of the invariant polynomial. That's why we use it as a \"mask\" for the $\\sum_i b_i\\tilde{\\gamma}_{2,i}$, meaning that in $\\sum_i b_i\\tilde{\\gamma}_{2,i}$ we set to zero the entries corresponding to the 1s in the matrix of null operators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "adjustable-playing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left( 0, \\  \\left[\\begin{array}{cccccccccccccccc}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\\end{array}\\right]\\right)$"
      ],
      "text/plain": [
       "⎛   ⎡1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1⎤⎞\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜0, ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎜   ⎢1  0  0  0  0  1  0  0  0  0  1  0  0  0  0  1⎥⎟\n",
       "⎜   ⎢                                              ⎥⎟\n",
       "⎝   ⎣1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1⎦⎠"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map4[0] # this is a mask for things to exclude in mat4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cosmetic-entrance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we rewrite only the conditions we are interested in linking, namely those that do not lead to terms that become 0 \n",
    "# once reordered in bilinear form\n",
    "\n",
    "list4zerofree = []\n",
    "for i in mat4 - map4[0][1]:\n",
    "    if (-1) in i.as_ordered_terms():\n",
    "        list4zerofree.append(0)\n",
    "    else:\n",
    "        list4zerofree.append(i)\n",
    "mat4zerofree = Matrix(16,16,list4zerofree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vertical-emission",
   "metadata": {},
   "source": [
    "This step is to familiarize myself with the notation used in the previous scripts and select the correct elements from the lists of tuples formed at the beginning of the program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "buried-spouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ \\left( 0, \\  a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}, \\  Q_{17}\\right), \\  \\left( 1, \\  a_{d_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger, \\  Q_{18}\\right), \\  \\left( 2, \\  a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2}, \\  Q_{19}\\right), \\  \\left( 3, \\  a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}, \\  Q_{20}\\right), \\  \\left( 4, \\  a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2}, \\  Q_{21}\\right), \\  \\left( 5, \\  a_{d_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger, \\  Q_{22}\\right), \\  \\left( 6, \\  a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2}, \\  Q_{23}\\right), \\  \\left( 7, \\  a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, \\  Q_{24}\\right), \\  \\left( 8, \\  a_{u_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2}, \\  Q_{25}\\right), \\  \\left( 9, \\  a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}, \\  Q_{26}\\right), \\  \\left( 10, \\  a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2}, \\  Q_{27}\\right), \\  \\left( 11, \\  a_{u_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger, \\  Q_{28}\\right), \\  \\left( 12, \\  a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{d_2}, \\  Q_{29}\\right), \\  \\left( 13, \\  a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{u_2}, \\  Q_{30}\\right), \\  \\left( 14, \\  a_{u_1}^\\dagger a_{d_1}^\\dagger a_{u_2} a_{d_2}, \\  Q_{31}\\right), \\  \\left( 15, \\  a_{u_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2}, \\  Q_{32}\\right), \\  \\left( 16, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{d_2}^\\dagger, \\  Q_{33}\\right), \\  \\left( 17, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{u_2}^\\dagger, \\  Q_{34}\\right), \\  \\left( 18, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_1}, \\  Q_{35}\\right), \\  \\left( 19, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_2}, \\  Q_{36}\\right), \\  \\left( 20, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{u_2}, \\  Q_{37}\\right), \\  \\left( 21, \\  a_{u_1}^\\dagger a_{u_1} a_{d_2}^\\dagger a_{d_2}, \\  Q_{38}\\right), \\  \\left( 22, \\  a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{d_2}, \\  Q_{39}\\right), \\  \\left( 23, \\  a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{u_2}, \\  Q_{40}\\right), \\  \\left( 24, \\  a_{u_1}^\\dagger a_{u_1} a_{u_2} a_{d_2}^\\dagger, \\  Q_{41}\\right), \\  \\left( 25, \\  a_{u_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, \\  Q_{42}\\right), \\  \\left( 26, \\  a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, \\  Q_{43}\\right), \\  \\left( 27, \\  a_{u_1} a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger, \\  Q_{44}\\right), \\  \\left( 28, \\  a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger, \\  Q_{45}\\right), \\  \\left( 29, \\  a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger, \\  Q_{46}\\right), \\  \\left( 30, \\  a_{u_1} a_{d_1}^\\dagger a_{d_2}^\\dagger a_{d_2}, \\  Q_{47}\\right), \\  \\left( 31, \\  a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{d_2}, \\  Q_{48}\\right), \\  \\left( 32, \\  a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2}, \\  Q_{49}\\right), \\  \\left( 33, \\  a_{u_1} a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger, \\  Q_{50}\\right), \\  \\left( 34, \\  a_{u_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}, \\  Q_{51}\\right), \\  \\left( 35, \\  a_{u_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger, \\  Q_{52}\\right)\\right]$"
      ],
      "text/plain": [
       "[(0, a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₁₇), (1, a_{d_1}⋅a_{u\n",
       "_2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₁₈), (2, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d\n",
       "_2}__\\dagger⋅a_{d_2}, Q₁₉), (3, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d\n",
       "_2}, Q₂₀), (4, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, Q₂₁), (5, a_\n",
       "{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₂₂), (6, a_{d_1}__\\dagger⋅a_\n",
       "{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}, Q₂₃), (7, a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\d\n",
       "agger⋅a_{d_2}, Q₂₄), (8, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₂\n",
       "₅), (9, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}, Q₂₆), (10, a_{u_1}_\n",
       "_\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, Q₂₇), (11, a_{u_1}__\\dagger⋅a_{d_1}\n",
       "⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₂₈), (12, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{d_1\n",
       "}⋅a_{d_2}, Q₂₉), (13, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}, Q₃₀),\n",
       " (14, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}, Q₃₁), (15, a_{u_1}__\\\n",
       "dagger⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}, Q₃₂), (16, a_{u_1}__\\dagger⋅a_{u_1}⋅a\n",
       "_{d_1}⋅a_{d_2}__\\dagger, Q₃₃), (17, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}⋅a_{u_2}__\n",
       "\\dagger, Q₃₄), (18, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}, Q₃₅), (\n",
       "19, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_2}, Q₃₆), (20, a_{u_1}__\\da\n",
       "gger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}, Q₃₇), (21, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{\n",
       "d_2}__\\dagger⋅a_{d_2}, Q₃₈), (22, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}__\\dagger⋅a_\n",
       "{d_2}, Q₃₉), (23, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, Q₄₀), (24\n",
       ", a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₄₁), (25, a_{u_1}__\\dagg\n",
       "er⋅a_{u_2}⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₄₂), (26, a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_\n",
       "2}__\\dagger⋅a_{d_2}, Q₄₃), (27, a_{u_1}⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dag\n",
       "ger, Q₄₄), (28, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger, Q₄₅), (29, \n",
       "a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger, Q₄₆), (30, a_{u_1}⋅a_{d_1}_\n",
       "_\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₄₇), (31, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}\n",
       "__\\dagger⋅a_{d_2}, Q₄₈), (32, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{u_2\n",
       "}, Q₄₉), (33, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₅₀), (34, a_\n",
       "{u_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₅₁), (35, a_{u_1}⋅a_{u_2}__\\\n",
       "dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₅₂)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(list(i for i in range(len(mixed4))), mixed4,aliases[16:52]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fabulous-competition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "34\n"
     ]
    }
   ],
   "source": [
    "# Questi sono i numeri da inserire nel blocco successivo per gli indici di map4\n",
    "\n",
    "for i in range(len(map4)):\n",
    "    if map4[i][0] == mixed4[1] or map4[i][0] == mixed4[34]:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "military-machine",
   "metadata": {},
   "source": [
    "We take the matrices that generate the pairs (or even just the single operator) of invariant quadrilinear operators and subtract them from the matrix $\\sum_i b_i\\tilde{\\gamma}_{2,i}$. It has been shown (in a previous script) that $\\sum_i b_i\\tilde{\\gamma}_{2,i}$ generates all and only the invariant quadrilinear operators. In this case, we simply specialize the matrix $\\sum_i b_i\\tilde{\\gamma}_{2,i}$ to a given pair or a given operator in order to rewrite the starting polynomial as a linear combination of quadratic forms of invariant matrices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "catholic-effects",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with an analogous technique we try to recover the conditions to map each single term\n",
    "\n",
    "# solvelist is the list with equations to solve\n",
    "solvelist = []\n",
    "for i in mat4zerofree - map4[10][1] - map4[34][1]:\n",
    "    if i != 0:\n",
    "        solvelist.append(i)\n",
    "        if (-2 in i.as_ordered_terms()) or (2 in i.as_ordered_terms()):\n",
    "            print('Ci sono dei 2, occhio')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-integrity",
   "metadata": {},
   "source": [
    "Let $B$ be the matrix that generates the pair of operators or the operator of interest. We set to zero all the values of the matrix mat4 $=\\sum_i b_i\\tilde{\\gamma}_{2,i}-B$. The result will be a tuple of $b_i$ values that identify the appropriate linear combination of matrices of the form $\\tilde{\\gamma}_{2}=\\sigma_a\\otimes\\sigma_b\\otimes\\sigma_c\\otimes\\sigma_d$, which yields the desired invariant operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "marine-trance",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy.solvers.solveset import linsolve\n",
    "sol = list(list(linsolve (solvelist, tuple(coeff_names)))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "drawn-special",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7} + \\frac{1}{4}, \\  b_{8} - \\frac{1}{4}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15} + \\frac{1}{4}, \\  b_{16} - \\frac{1}{4}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23} - \\frac{1}{4}, \\  b_{24} + \\frac{1}{4}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31} - \\frac{1}{4}, \\  b_{32} + \\frac{1}{4}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37} + \\frac{1}{4}, \\  b_{38} - \\frac{1}{4}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45} + \\frac{1}{4}, \\  b_{46} - \\frac{1}{4}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53} - \\frac{1}{4}, \\  b_{54} + \\frac{1}{4}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61} - \\frac{1}{4}, \\  b_{62} + \\frac{1}{4}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "[b₂₂, b₂₁, b₇ + 1/4, b₈ - 1/4, b₂₂, b₂₁, b₇, b₈, b₃₀, b₂₉, b₁₅ + 1/4, b₁₆ - 1/\n",
       "4, b₃₀, b₂₉, b₁₅, b₁₆, b₂₁, b₂₂, b₂₃ - 1/4, b₂₄ + 1/4, b₂₁, b₂₂, b₂₃, b₂₄, b₂₉\n",
       ", b₃₀, b₃₁ - 1/4, b₃₂ + 1/4, b₂₉, b₃₀, b₃₁, b₃₂, b₃₇ + 1/4, b₃₈ - 1/4, b₅₆, b₅\n",
       "₅, b₃₇, b₃₈, b₅₆, b₅₅, b₄₅ + 1/4, b₄₆ - 1/4, b₆₄, b₆₃, b₄₅, b₄₆, b₆₄, b₆₃, b₅₃\n",
       " - 1/4, b₅₄ + 1/4, b₅₅, b₅₆, b₅₃, b₅₄, b₅₅, b₅₆, b₆₁ - 1/4, b₆₂ + 1/4, b₆₃, b₆\n",
       "₄, b₆₁, b₆₂, b₆₃, b₆₄]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afraid-operation",
   "metadata": {},
   "source": [
    "In this case, for example, in order to recover $\\lambda_{18} a_{d_1} a^\\dagger_{u_2} a_{u_2} a^\\dagger_{d_2} + \\lambda_{18} a_{u_1} a^\\dagger_{u_2} a^\\dagger_{d_2} a_{d_2}$, we should write\\\n",
    "$\\lambda_{18}(\\frac{1}{8}\\tilde{\\gamma}_{2,3}-\\frac{1}{8}\\tilde{\\gamma}_{2,4}-\\frac{1}{8}\\tilde{\\gamma}_{2,7}+...)$\\\n",
    "Note that by using mat4zerofree (i.e., the matrix mat4 from which the \"mask\" of zeros has been removed), the uniqueness of the solution to the linear system is lost, and many terms become free. However, in the end, the invariant term is still recovered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "waiting-election",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{array}{cccccccccccccccc}4 b_{63} + 4 b_{64} & 0 & 2 b_{45} + 2 b_{46} + 2 b_{61} + 2 b_{62} & 0 & 0 & 4 b_{55} + 4 b_{56} & 0 & 2 b_{37} + 2 b_{38} + 2 b_{53} + 2 b_{54} & 2 b_{15} + 2 b_{16} + 2 b_{31} + 2 b_{32} & 0 & 4 b_{29} + 4 b_{30} & 0 & 0 & 2 b_{23} + 2 b_{24} + 2 b_{7} + 2 b_{8} & 0 & 4 b_{21} + 4 b_{22}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\2 b_{45} - 2 b_{46} + 2 b_{61} - 2 b_{62} & 0 & 0 & 0 & 0 & 2 b_{37} - 2 b_{38} + 2 b_{53} - 2 b_{54} & 0 & 0 & 0 & 0 & - 2 b_{15} + 2 b_{16} - 2 b_{31} + 2 b_{32} & 0 & 0 & 0 & 0 & - 2 b_{23} + 2 b_{24} - 2 b_{7} + 2 b_{8}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\4 b_{55} + 4 b_{56} & 0 & 2 b_{37} + 2 b_{38} + 2 b_{53} + 2 b_{54} & 0 & 0 & 4 b_{63} + 4 b_{64} & 0 & 2 b_{45} + 2 b_{46} + 2 b_{61} + 2 b_{62} & 2 b_{23} + 2 b_{24} + 2 b_{7} + 2 b_{8} & 0 & 4 b_{21} + 4 b_{22} & 0 & 0 & 2 b_{15} + 2 b_{16} + 2 b_{31} + 2 b_{32} & 0 & 4 b_{29} + 4 b_{30}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\2 b_{37} - 2 b_{38} + 2 b_{53} - 2 b_{54} & 0 & 0 & 0 & 0 & 2 b_{45} - 2 b_{46} + 2 b_{61} - 2 b_{62} & 0 & 0 & 0 & 0 & - 2 b_{23} + 2 b_{24} - 2 b_{7} + 2 b_{8} & 0 & 0 & 0 & 0 & - 2 b_{15} + 2 b_{16} - 2 b_{31} + 2 b_{32}\\\\2 b_{15} + 2 b_{16} - 2 b_{31} - 2 b_{32} & 0 & 0 & 0 & 0 & - 2 b_{23} - 2 b_{24} + 2 b_{7} + 2 b_{8} & 0 & 0 & 0 & 0 & - 2 b_{45} - 2 b_{46} + 2 b_{61} + 2 b_{62} & 0 & 0 & 0 & 0 & - 2 b_{37} - 2 b_{38} + 2 b_{53} + 2 b_{54}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- 4 b_{29} + 4 b_{30} & 0 & - 2 b_{15} + 2 b_{16} + 2 b_{31} - 2 b_{32} - 1 & 0 & 0 & - 4 b_{21} + 4 b_{22} & 0 & 2 b_{23} - 2 b_{24} - 2 b_{7} + 2 b_{8} - 1 & - 2 b_{45} + 2 b_{46} + 2 b_{61} - 2 b_{62} - 1 & 0 & - 4 b_{63} + 4 b_{64} & 0 & 0 & - 2 b_{37} + 2 b_{38} + 2 b_{53} - 2 b_{54} - 1 & 0 & - 4 b_{55} + 4 b_{56}\\\\0 & 0 & 0 & 1 & 0 & 0 & -1 & 0 & 0 & 1 & 0 & 0 & -1 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- 2 b_{23} - 2 b_{24} + 2 b_{7} + 2 b_{8} & 0 & 0 & 0 & 0 & 2 b_{15} + 2 b_{16} - 2 b_{31} - 2 b_{32} & 0 & 0 & 0 & 0 & - 2 b_{37} - 2 b_{38} + 2 b_{53} + 2 b_{54} & 0 & 0 & 0 & 0 & - 2 b_{45} - 2 b_{46} + 2 b_{61} + 2 b_{62}\\\\0 & 0 & 0 & -1 & 0 & 0 & 1 & 0 & 0 & -1 & 0 & 0 & 1 & 0 & 0 & 0\\\\- 4 b_{21} + 4 b_{22} & 0 & 2 b_{23} - 2 b_{24} - 2 b_{7} + 2 b_{8} - 1 & 0 & 0 & - 4 b_{29} + 4 b_{30} & 0 & - 2 b_{15} + 2 b_{16} + 2 b_{31} - 2 b_{32} - 1 & - 2 b_{37} + 2 b_{38} + 2 b_{53} - 2 b_{54} - 1 & 0 & - 4 b_{55} + 4 b_{56} & 0 & 0 & - 2 b_{45} + 2 b_{46} + 2 b_{61} - 2 b_{62} - 1 & 0 & - 4 b_{63} + 4 b_{64}\\end{array}\\right]$"
      ],
      "text/plain": [
       "⎡        4⋅b₆₃ + 4⋅b₆₄          0    2⋅b₄₅ + 2⋅b₄₆ + 2⋅b₆₁ + 2⋅b₆₂     0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢2⋅b₄₅ - 2⋅b₄₆ + 2⋅b₆₁ - 2⋅b₆₂  0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢        4⋅b₅₅ + 4⋅b₅₆          0    2⋅b₃₇ + 2⋅b₃₈ + 2⋅b₅₃ + 2⋅b₅₄     0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢2⋅b₃₇ - 2⋅b₃₈ + 2⋅b₅₃ - 2⋅b₅₄  0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢2⋅b₁₅ + 2⋅b₁₆ - 2⋅b₃₁ - 2⋅b₃₂  0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢       -4⋅b₂₉ + 4⋅b₃₀          0  -2⋅b₁₅ + 2⋅b₁₆ + 2⋅b₃₁ - 2⋅b₃₂ - 1  0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   1   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢-2⋅b₂₃ - 2⋅b₂₄ + 2⋅b₇ + 2⋅b₈   0                  0                   0   0  \n",
       "⎢                                                                             \n",
       "⎢              0                0                  0                   -1  0  \n",
       "⎢                                                                             \n",
       "⎣       -4⋅b₂₁ + 4⋅b₂₂          0   2⋅b₂₃ - 2⋅b₂₄ - 2⋅b₇ + 2⋅b₈ - 1    0   0  \n",
       "\n",
       "        4⋅b₅₅ + 4⋅b₅₆          0     2⋅b₃₇ + 2⋅b₃₈ + 2⋅b₅₃ + 2⋅b₅₄       2⋅b₁₅\n",
       "                                                                              \n",
       "              0                0                   0                          \n",
       "                                                                              \n",
       "2⋅b₃₇ - 2⋅b₃₈ + 2⋅b₅₃ - 2⋅b₅₄  0                   0                          \n",
       "                                                                              \n",
       "              0                0                   0                          \n",
       "                                                                              \n",
       "              0                0                   0                          \n",
       "                                                                              \n",
       "        4⋅b₆₃ + 4⋅b₆₄          0     2⋅b₄₅ + 2⋅b₄₆ + 2⋅b₆₁ + 2⋅b₆₂        2⋅b₂\n",
       "                                                                              \n",
       "              0                0                   0                          \n",
       "                                                                              \n",
       "2⋅b₄₅ - 2⋅b₄₆ + 2⋅b₆₁ - 2⋅b₆₂  0                   0                          \n",
       "                                                                              \n",
       "-2⋅b₂₃ - 2⋅b₂₄ + 2⋅b₇ + 2⋅b₈   0                   0                          \n",
       "                                                                              \n",
       "              0                0                   0                          \n",
       "                                                                              \n",
       "       -4⋅b₂₁ + 4⋅b₂₂          0    2⋅b₂₃ - 2⋅b₂₄ - 2⋅b₇ + 2⋅b₈ - 1    -2⋅b₄₅ \n",
       "                                                                              \n",
       "              0                -1                  0                          \n",
       "                                                                              \n",
       "              0                0                   0                          \n",
       "                                                                              \n",
       "2⋅b₁₅ + 2⋅b₁₆ - 2⋅b₃₁ - 2⋅b₃₂  0                   0                          \n",
       "                                                                              \n",
       "              0                1                   0                          \n",
       "                                                                              \n",
       "       -4⋅b₂₉ + 4⋅b₃₀          0   -2⋅b₁₅ + 2⋅b₁₆ + 2⋅b₃₁ - 2⋅b₃₂ - 1  -2⋅b₃₇ \n",
       "\n",
       " + 2⋅b₁₆ + 2⋅b₃₁ + 2⋅b₃₂     0           4⋅b₂₉ + 4⋅b₃₀           0  0      2⋅b\n",
       "                                                                              \n",
       "         0                   0                 0                 0  0         \n",
       "                                                                              \n",
       "         0                   0   -2⋅b₁₅ + 2⋅b₁₆ - 2⋅b₃₁ + 2⋅b₃₂  0  0         \n",
       "                                                                              \n",
       "         0                   0                 0                 0  0         \n",
       "                                                                              \n",
       "         0                   0                 0                 0  0         \n",
       "                                                                              \n",
       "₃ + 2⋅b₂₄ + 2⋅b₇ + 2⋅b₈      0           4⋅b₂₁ + 4⋅b₂₂           0  0     2⋅b₁\n",
       "                                                                              \n",
       "         0                   0                 0                 0  0         \n",
       "                                                                              \n",
       "         0                   0    -2⋅b₂₃ + 2⋅b₂₄ - 2⋅b₇ + 2⋅b₈   0  0         \n",
       "                                                                              \n",
       "         0                   0   -2⋅b₄₅ - 2⋅b₄₆ + 2⋅b₆₁ + 2⋅b₆₂  0  0         \n",
       "                                                                              \n",
       "         0                   0                 0                 0  0         \n",
       "                                                                              \n",
       "+ 2⋅b₄₆ + 2⋅b₆₁ - 2⋅b₆₂ - 1  0           -4⋅b₆₃ + 4⋅b₆₄          0  0   -2⋅b₃₇\n",
       "                                                                              \n",
       "         0                   1                 0                 0  -1        \n",
       "                                                                              \n",
       "         0                   0                 0                 0  0         \n",
       "                                                                              \n",
       "         0                   0   -2⋅b₃₇ - 2⋅b₃₈ + 2⋅b₅₃ + 2⋅b₅₄  0  0         \n",
       "                                                                              \n",
       "         0                   -1                0                 0  1         \n",
       "                                                                              \n",
       "+ 2⋅b₃₈ + 2⋅b₅₃ - 2⋅b₅₄ - 1  0           -4⋅b₅₅ + 4⋅b₅₆          0  0   -2⋅b₄₅\n",
       "\n",
       "₂₃ + 2⋅b₂₄ + 2⋅b₇ + 2⋅b₈      0          4⋅b₂₁ + 4⋅b₂₂         ⎤\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       "          0                   0   -2⋅b₂₃ + 2⋅b₂₄ - 2⋅b₇ + 2⋅b₈ ⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       "₅ + 2⋅b₁₆ + 2⋅b₃₁ + 2⋅b₃₂     0          4⋅b₂₉ + 4⋅b₃₀         ⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       "          0                   0  -2⋅b₁₅ + 2⋅b₁₆ - 2⋅b₃₁ + 2⋅b₃₂⎥\n",
       "                                                               ⎥\n",
       "          0                   0  -2⋅b₃₇ - 2⋅b₃₈ + 2⋅b₅₃ + 2⋅b₅₄⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       " + 2⋅b₃₈ + 2⋅b₅₃ - 2⋅b₅₄ - 1  0          -4⋅b₅₅ + 4⋅b₅₆        ⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       "          0                   0  -2⋅b₄₅ - 2⋅b₄₆ + 2⋅b₆₁ + 2⋅b₆₂⎥\n",
       "                                                               ⎥\n",
       "          0                   0                0               ⎥\n",
       "                                                               ⎥\n",
       " + 2⋅b₄₆ + 2⋅b₆₁ - 2⋅b₆₂ - 1  0          -4⋅b₆₃ + 4⋅b₆₄        ⎦"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix1851 = zeros(16,16)\n",
    "for i in range(len(inv4)):\n",
    "    matrix1851 = matrix1851 + sol[i]*TensorProduct(inv4[i][1][0],inv4[i][1][1],inv4[i][1][2],inv4[i][1][3])\n",
    "matrix1851"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "liable-acting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    }
   ],
   "source": [
    "# invariance check\n",
    "\n",
    "for i in transf:\n",
    "    var = t4(i)*matrix1851*t4(i)\n",
    "    if (var - matrix1851 == zeros(16,16)):\n",
    "        print('ok, è invariante :)')\n",
    "    else:\n",
    "        print('Non è invariante, sorry :(')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "noble-tower",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{d_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger + 4 a_{u_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger + 4⋅a_{u_1}⋅a_{u_2}__\\dagg\n",
       "er⋅a_{d_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check\n",
    "fermionize(expand((difermid.T*matrix1851*difermi)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "productive-viewer",
   "metadata": {},
   "source": [
    "# FURTHER VERIFICATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "breathing-combine",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle \\left[ \\left( 0, \\  a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}, \\  Q_{17}\\right), \\  \\left( 1, \\  a_{d_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger, \\  Q_{18}\\right), \\  \\left( 2, \\  a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2}, \\  Q_{19}\\right), \\  \\left( 3, \\  a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}, \\  Q_{20}\\right), \\  \\left( 4, \\  a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2}, \\  Q_{21}\\right), \\  \\left( 5, \\  a_{d_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger, \\  Q_{22}\\right), \\  \\left( 6, \\  a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2}, \\  Q_{23}\\right), \\  \\left( 7, \\  a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, \\  Q_{24}\\right), \\  \\left( 8, \\  a_{u_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2}, \\  Q_{25}\\right), \\  \\left( 9, \\  a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}, \\  Q_{26}\\right), \\  \\left( 10, \\  a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2}, \\  Q_{27}\\right), \\  \\left( 11, \\  a_{u_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger, \\  Q_{28}\\right), \\  \\left( 12, \\  a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{d_2}, \\  Q_{29}\\right), \\  \\left( 13, \\  a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{u_2}, \\  Q_{30}\\right), \\  \\left( 14, \\  a_{u_1}^\\dagger a_{d_1}^\\dagger a_{u_2} a_{d_2}, \\  Q_{31}\\right), \\  \\left( 15, \\  a_{u_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2}, \\  Q_{32}\\right), \\  \\left( 16, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{d_2}^\\dagger, \\  Q_{33}\\right), \\  \\left( 17, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{u_2}^\\dagger, \\  Q_{34}\\right), \\  \\left( 18, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_1}, \\  Q_{35}\\right), \\  \\left( 19, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_2}, \\  Q_{36}\\right), \\  \\left( 20, \\  a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{u_2}, \\  Q_{37}\\right), \\  \\left( 21, \\  a_{u_1}^\\dagger a_{u_1} a_{d_2}^\\dagger a_{d_2}, \\  Q_{38}\\right), \\  \\left( 22, \\  a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{d_2}, \\  Q_{39}\\right), \\  \\left( 23, \\  a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{u_2}, \\  Q_{40}\\right), \\  \\left( 24, \\  a_{u_1}^\\dagger a_{u_1} a_{u_2} a_{d_2}^\\dagger, \\  Q_{41}\\right), \\  \\left( 25, \\  a_{u_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, \\  Q_{42}\\right), \\  \\left( 26, \\  a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}, \\  Q_{43}\\right), \\  \\left( 27, \\  a_{u_1} a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger, \\  Q_{44}\\right), \\  \\left( 28, \\  a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger, \\  Q_{45}\\right), \\  \\left( 29, \\  a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger, \\  Q_{46}\\right), \\  \\left( 30, \\  a_{u_1} a_{d_1}^\\dagger a_{d_2}^\\dagger a_{d_2}, \\  Q_{47}\\right), \\  \\left( 31, \\  a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{d_2}, \\  Q_{48}\\right), \\  \\left( 32, \\  a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2}, \\  Q_{49}\\right), \\  \\left( 33, \\  a_{u_1} a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger, \\  Q_{50}\\right), \\  \\left( 34, \\  a_{u_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}, \\  Q_{51}\\right), \\  \\left( 35, \\  a_{u_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger, \\  Q_{52}\\right)\\right]$"
      ],
      "text/plain": [
       "[(0, a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₁₇), (1, a_{d_1}⋅a_{u\n",
       "_2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₁₈), (2, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d\n",
       "_2}__\\dagger⋅a_{d_2}, Q₁₉), (3, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d\n",
       "_2}, Q₂₀), (4, a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, Q₂₁), (5, a_\n",
       "{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₂₂), (6, a_{d_1}__\\dagger⋅a_\n",
       "{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}, Q₂₃), (7, a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\d\n",
       "agger⋅a_{d_2}, Q₂₄), (8, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₂\n",
       "₅), (9, a_{u_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}, Q₂₆), (10, a_{u_1}_\n",
       "_\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, Q₂₇), (11, a_{u_1}__\\dagger⋅a_{d_1}\n",
       "⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₂₈), (12, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{d_1\n",
       "}⋅a_{d_2}, Q₂₉), (13, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}, Q₃₀),\n",
       " (14, a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}, Q₃₁), (15, a_{u_1}__\\\n",
       "dagger⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}, Q₃₂), (16, a_{u_1}__\\dagger⋅a_{u_1}⋅a\n",
       "_{d_1}⋅a_{d_2}__\\dagger, Q₃₃), (17, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}⋅a_{u_2}__\n",
       "\\dagger, Q₃₄), (18, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}, Q₃₅), (\n",
       "19, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_2}, Q₃₆), (20, a_{u_1}__\\da\n",
       "gger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}, Q₃₇), (21, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{\n",
       "d_2}__\\dagger⋅a_{d_2}, Q₃₈), (22, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}__\\dagger⋅a_\n",
       "{d_2}, Q₃₉), (23, a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}__\\dagger⋅a_{u_2}, Q₄₀), (24\n",
       ", a_{u_1}__\\dagger⋅a_{u_1}⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₄₁), (25, a_{u_1}__\\dagg\n",
       "er⋅a_{u_2}⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₄₂), (26, a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_\n",
       "2}__\\dagger⋅a_{d_2}, Q₄₃), (27, a_{u_1}⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dag\n",
       "ger, Q₄₄), (28, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger, Q₄₅), (29, \n",
       "a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger, Q₄₆), (30, a_{u_1}⋅a_{d_1}_\n",
       "_\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₄₇), (31, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}\n",
       "__\\dagger⋅a_{d_2}, Q₄₈), (32, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{u_2\n",
       "}, Q₄₉), (33, a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₅₀), (34, a_\n",
       "{u_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger⋅a_{d_2}, Q₅₁), (35, a_{u_1}⋅a_{u_2}__\\\n",
       "dagger⋅a_{u_2}⋅a_{d_2}__\\dagger, Q₅₂)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(list(i for i in range(len(mixed4))), mixed4,aliases[16:52]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "attached-olympus",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy.solvers.solveset import linsolve\n",
    "\n",
    "def t4(mat):\n",
    "    return TensorProduct(mat,mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "trying-alcohol",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are the numbers to be inserted in the subsequent block for the indices of map4.\n",
    "\n",
    "def rebuilder(a,b): # a and b are the first number in the tuple in the box above associated with the other elements of the tuple.\n",
    "    part = []\n",
    "    for i in range(len(map4)):\n",
    "        if map4[i][0] == mixed4[a] or map4[i][0] == mixed4[b]:\n",
    "            part.append(i)\n",
    "        \n",
    "    # Using a similar technique, let's try to derive the conditions to map each individual term.\n",
    "    mats = mat4zerofree # By using mat4zerofree, the solution mats = mat4 becomes more general.\n",
    "    for i in part:\n",
    "        mats = mats - map4[i][1]\n",
    "    # solvelist is the list with the equations to solve\n",
    "    solvelist = []\n",
    "    for i in mats:\n",
    "        if i != 0:\n",
    "            solvelist.append(i)\n",
    "            if (-2 in i.as_ordered_terms()) or (2 in i.as_ordered_terms()):\n",
    "                print('Ci sono dei 2, occhio')\n",
    "\n",
    "    dicsol = list(linsolve (solvelist, tuple(coeff_names)))\n",
    "    if dicsol != []:\n",
    "        sol = list(dicsol[0])\n",
    "        display(Math(latex(sol)))\n",
    "    else:\n",
    "        return print('Non ci sono soluzioni :( Esco dalla funzione')\n",
    "\n",
    "    matrixinv = zeros(16,16)\n",
    "    for i in range(len(inv4)):\n",
    "        matrixinv = matrixinv + sol[i]*TensorProduct(inv4[i][1][0],inv4[i][1][1],inv4[i][1][2],inv4[i][1][3])\n",
    "    matrixinv\n",
    "    \n",
    "    for i in transf:\n",
    "        var = t4(i)*matrixinv*t4(i)\n",
    "        if (var - matrixinv == zeros(16,16)):\n",
    "            print('ok, è invariante :)')\n",
    "        else:\n",
    "            print('Non è invariante, sorry :(')\n",
    "        \n",
    "    return fermionize(expand((difermid.T*matrixinv*difermi)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ambient-fireplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7} + \\frac{1}{4}, \\  b_{8} - \\frac{1}{4}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15} + \\frac{1}{4}, \\  b_{16} - \\frac{1}{4}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23} - \\frac{1}{4}, \\  b_{24} + \\frac{1}{4}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31} - \\frac{1}{4}, \\  b_{32} + \\frac{1}{4}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37} + \\frac{1}{4}, \\  b_{38} - \\frac{1}{4}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45} + \\frac{1}{4}, \\  b_{46} - \\frac{1}{4}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53} - \\frac{1}{4}, \\  b_{54} + \\frac{1}{4}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61} - \\frac{1}{4}, \\  b_{62} + \\frac{1}{4}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARcAAAAcCAYAAABLY/ujAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAHaUlEQVR4Ae2d/3XVNhTHX3IyQEo3CBtA2SBsAGUCygbh5L/8x4ENgAlaugFsQMsGsEGBDdLvR7F8bD/ZlizJtt6zzhGWrav7W1dXsvM4ub293W2lbA3c3Nx8kARvdf1UtiQb9z4aKMXepz7ClAaD8lUvS+N747csDWx+NmyvgwwuwyIfZO9nSfXPQUq2CeXSQBH2PnNxfgDPilB+rJ61cn4Ujnuq31Xv6/53XZ/r+reuW8mvgVn9rDR7nxzSmUtH+d/kW0cx2ST3a8n6Stef+efTRmFpPyvF3geVuUjpj3H9hvJfHMlUuJCcv6luB7ozGHwFflaEvXvPXKTAB6pvZ7BVDhJW+TlwrxEn8p6vkbEhngr3MURbys+KsPfpgPE/qI/9fImlCOWXqNjEPJfsY6hi87MBh3AGF60oVwNjtq71aYAD3eznLfKLS9U/Uoi/+ViUFmexdxSHGrwXXGT0B3qOo2Z31ljmB8YXofwB/tfaxdaLGlUOxMfQweZnA56wF1wE+0zGfzcwZutanwZYCHD0UsrmY3GWKsLerbdFVao6eogruAvphtefvO79T5XvKp7o+Rtds5RAmsmUH0g3meyBdItZQSUXW+5V+hjGC9T7Un6WxN6Bsgb7dp25VIR+6krA6C3q57P6f1Vfqk0loOAsr9U+1zV5mUAzlfJnlxXlTZD3q4atPnORXCxKq/SxiXpfys+i7T3Bx1BRUGlmLi9E8OXQaPUTPDjhJ6g0gxDtb3pGJO8t6ifbYeV6qPaXXsBGh+Cm0Eyh/Cl0G5xPa06RV2OyZYzTpOgdld3HeimPdEzRu1Au4mex9p4oa61Bjfeaxya4CJg3AKOpqmBAei747pkMK7zPJ+fQuNJ4r8AiWEowTeFPMdmC6d6xG/3vUnRrxqU/7IRNu8V8mqB+18eJX/T8aXeAvVffXD5mSYZeg/UumUr1s2BZO8r0msdnUhCpKgGjmYl0cNW3fE7f+gpU41jhwcHfuYyVJwJojR8boP5Ymh4knCDHRrdWgmzqCh47Pcd+F7oGTSrBz+ljtRyBjWOyd6ysXvOYzAXDP5IDsN1pFl5J40g8J/C8UiWQdIMIjO4Etxc09Az4a1XSR9rPVP9U9SrV+CCaXohHgELpCp5VHn2hx3r1VpuffqjvR8juBIusUfIKB6vSlar31nOMrwT9WXxMsq5G71N0tIS9p9CsxgTPYzIXgoIrMPzgufrN5KgIoMNuhsPf85htjmCIaKTHnL8wSTj4fVzdM/lw/OeqocWLZihSD/hRusLBoR5/KvFG9Ra5VTm0RF7X1sKDrJ+OexB5paw9Y7M8li6S+5gYXZveY3Q36mfSYRfG0ptq7y6+5PP41HLouNpV1HQxYdTASViFTNEzggmTyP6WiAkkd70mkBCcWkLovnXeonuyo4+q0GsVPQuiKfhLVc50WllY975FxHEj+BC694Tincagi+ahNoHF6sVQEQz88coeHvnluFbw0X0IXYPT8Y9XyuoYt8QjbE41JVB+b72DXLhL97M7Je3/G2TvQB1DjYRgcB4Lp9Ovz7q8CpBIaAMIg5ion3Vln00W815t0u5fVdniEPF4Dc0zxtrCdqm5JWAitTIkjSEwQcvSU3OveNEULpw0ZQbhS9cET9Fny9eU/5Huu1tItkm/IGHFL9nhCfeN4kXXwld4rnU/aetp8cx5Fc/RPiYc3noXbPF+Zu2TyN4hPjY6j8Wb069dwcV5mIdwEoyVFca6pfWsUgCTvZm1EITqDAVcqmQxbKOIjs4CnDpa+CvA7jPvlUw4CXTwR1C7r4pyuoHPl66GmwLOZnDhnt9YIeDZbO3hHaj5F9rQaBXBetMVLDKk2nq2+Mh5I76jfazB36jeK/0X72ep7C08Xj5W0cPHBuex+p1+fdYwUrImzFcCGJxqs3LgBHxHQyT8SzVpEQ3vlUyEnZE2AUOWB2S1RuEVrAkulseKDgG1GyCrLu+LV8oqbPDSG0i9qa33b84G9R4g3yiotaGuqTLVUZoNgFntLRm95rHVScVn7ddZgktFhInDx3asrN9pq2KQrzCta67CxI7OICYwx0E18rI9wdn57qcOLGrXRTDwB6zNaOq+wMbklDWQjgEXv63sbgqODGO89Z6Y9hJ+Nqu9K315z+OuX2cLLpUjLuWMgyuZeDP9lfLqSBvjfMJJMKHa0mzbZzvBQY/AwqrAwW7QNyMWkcaRjdjsyD7e23qqw5my2gGlX6UHL71nknM2P1vK3qLLHB6dx4Lb8+vTTEr3QiuGOI+4EjDnJddqc/IdW+xKBl4mH843lkGMKi+WKcZLPrI4+PqhNv9hVO/Zg/oGi8aT/VFN0b3deiILK5wpep48kFrcpVzRjWrRfib+V2tv8eb064P6ge4QZ5dCiLTmR61xPNVJGUQIzdSw4pnUnLTVbj05V+EtHlvP1p9o6J6tGK+/Y7diQrMVXw1I38n8TLiKsvdRBhcZicnISm8L36fw1uggS0oHP0gFZRJqKT9bi72PMrhk8qVVol3KwVepjCNgak32/h/BZY+w61tCXAAAAABJRU5ErkJggg==",
      "text/latex": [
       "$\\displaystyle 4 a_{d_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger + 4 a_{u_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger + 4⋅a_{u_1}⋅a_{u_2}__\\dagg\n",
       "er⋅a_{d_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(1,34)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "chubby-planning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30} + \\frac{1}{2}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{30} + \\frac{1}{2}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64} + \\frac{1}{2}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64} + \\frac{1}{2}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{d_1}^\\dagger a_{d_1} a_{d_2}^\\dagger a_{d_2} + 4 a_{u_1}^\\dagger a_{u_1} a_{u_2}^\\dagger a_{u_2}$"
      ],
      "text/plain": [
       "4⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{d_2}__\\dagger⋅a_{d_2} + 4⋅a_{u_1}__\\dagger⋅a_{u_\n",
       "1}⋅a_{u_2}__\\dagger⋅a_{u_2}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(2,23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "nonprofit-detection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{22} - \\frac{1}{2}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22} - \\frac{1}{2}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64} + \\frac{1}{2}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64} + \\frac{1}{2}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{u_2} + 4 a_{u_1}^\\dagger a_{u_1} a_{d_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2} + 4⋅a_{u_1}__\\dagger⋅a_{u_\n",
       "1}⋅a_{d_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(4,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "varying-efficiency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7} - \\frac{1}{4}, \\  b_{8} + \\frac{1}{4}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15} - \\frac{1}{4}, \\  b_{16} + \\frac{1}{4}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23} - \\frac{1}{4}, \\  b_{24} + \\frac{1}{4}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31} - \\frac{1}{4}, \\  b_{32} + \\frac{1}{4}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37} - \\frac{1}{4}, \\  b_{38} - \\frac{1}{4}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45} - \\frac{1}{4}, \\  b_{46} - \\frac{1}{4}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53} + \\frac{1}{4}, \\  b_{54} + \\frac{1}{4}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61} + \\frac{1}{4}, \\  b_{62} + \\frac{1}{4}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{d_1}^\\dagger a_{u_2}^\\dagger a_{u_2} a_{d_2} + 4 a_{u_1}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{d_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2} + 4⋅a_{u_1}__\\dagger⋅a_{u_\n",
       "2}⋅a_{d_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(6,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "loaded-princeton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22} + \\frac{1}{2}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{22} + \\frac{1}{2}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56} + \\frac{1}{2}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56} + \\frac{1}{2}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1} a_{d_1}^\\dagger a_{u_2} a_{d_2}^\\dagger + 4 a_{u_1}^\\dagger a_{d_1} a_{u_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger + 4⋅a_{u_1}__\\dagger⋅a_{d_\n",
       "1}⋅a_{u_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(9,33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "loaded-acceptance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{30} - \\frac{1}{2}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30} - \\frac{1}{2}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56} + \\frac{1}{2}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56} + \\frac{1}{2}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1} a_{d_1}^\\dagger a_{u_2}^\\dagger a_{d_2} + 4 a_{u_1}^\\dagger a_{d_1} a_{u_2} a_{d_2}^\\dagger$"
      ],
      "text/plain": [
       "4⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{u_2}__\\dagger⋅a_{d_2} + 4⋅a_{u_1}__\\dagger⋅a_{d_\n",
       "1}⋅a_{u_2}⋅a_{d_2}__\\dagger"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(11,31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "guilty-meaning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7} + \\frac{1}{4}, \\  b_{8} + \\frac{1}{4}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15} + \\frac{1}{4}, \\  b_{16} + \\frac{1}{4}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23} + \\frac{1}{4}, \\  b_{24} + \\frac{1}{4}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31} + \\frac{1}{4}, \\  b_{32} + \\frac{1}{4}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37} + \\frac{1}{4}, \\  b_{38} + \\frac{1}{4}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45} + \\frac{1}{4}, \\  b_{46} + \\frac{1}{4}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53} + \\frac{1}{4}, \\  b_{54} + \\frac{1}{4}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61} + \\frac{1}{4}, \\  b_{62} + \\frac{1}{4}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1}^\\dagger a_{d_1}^\\dagger a_{d_1} a_{u_2} + 4 a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2} + 4⋅a_{u_1}__\\dagger⋅a_{u_\n",
       "1}⋅a_{d_1}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(13,19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "typical-taxation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7} - \\frac{1}{4}, \\  b_{8} - \\frac{1}{4}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15} - \\frac{1}{4}, \\  b_{16} - \\frac{1}{4}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23} + \\frac{1}{4}, \\  b_{24} + \\frac{1}{4}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31} + \\frac{1}{4}, \\  b_{32} + \\frac{1}{4}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37} - \\frac{1}{4}, \\  b_{38} + \\frac{1}{4}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45} - \\frac{1}{4}, \\  b_{46} + \\frac{1}{4}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53} - \\frac{1}{4}, \\  b_{54} + \\frac{1}{4}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61} - \\frac{1}{4}, \\  b_{62} + \\frac{1}{4}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARcAAAAcCAYAAABLY/ujAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAHd0lEQVR4Ae2d/3XVNhTHX3IyQJpuEDaAMkHDBlAmoGwQTv7LfxzYAJiA0g3oBrRsABuUZoP0+1Gsd2S/H9bVD9vP8T1HSLale3V/6kp2Hke3t7erOcL19fUn8fVO9V9z5G/haZHAkBJI8afjISe40CovAZSuclEe8+FgXGQwTV3NObh8kcj/nqbYl1ktEjg4CZj96eTgWOyZsFaxz+pypvJD5YGuf1P9QvWfqucIZqXPUAiLDCopNcefjmZ85vJG8n4t4dxUkvuoaDtK/67JzD2Ibsh7kcGGSKrdkKzN/jS7zCWQ7rnav6jM8kBXyn4Cr4HSX3J9n2CRwaDaNvvTzjMXKe6hyrtBp1+WGMI4LYtykti80ic5uX2TKmhjByuDffKZ2DOzPx3vYeCTnnF2scC0JWBW+oTYKWVjhyyDCamj7FS2BhetKJdlyYyCjQPdWZ63jCJNEZVdXKj8XoL+TGyshCgOBYfZnzaCi5T+UNzilItjHobazUrPYIttJiULKtjYkDLI4v0+Dd4ILmL+uZT/fgZCIDhidAtMTwJzsbHpSbbejMz+1Hpb1KSqvYe46ncuHng1xSvQf1X4huSp7r9VXQ2MdIusZkaaRXg30jQrvcgkE5GIN7bcpW2smAyMsk+UwuawMegaaZr9aZ25NIRuVBMwdoKe86n5Pyqv1KYQUDCWN2qfqq4CCXS/aSJZmUsCzWzeE2ialZ49yUQE4o1FqYaNFZFBguwTJdEeNgbdBJpmfwozl5ci+KrNdvtKzwkenPATVMIgRPu77rGCFIcUuhqTlUWl0AwZ13gyO1bpR2p/DZ/taifSNCt9F/0B7teysWwZJMo+W2S5dDV+EDsTHbM/ueCigbwB6E1V1QdGTtW/eyZDNlPz8/ox6ObSRJ6XklVUYFFfwEwzRel3pHb/K5zMHZ12wX2aoOfbPtj7qvvPugP8tZ5VszHhNhu+n1dQm2UfjM1p5tIdxM5SGDyRYkhVCRhhJrILF5+Yt7541TiyGXDwNz21YAy6uTSfShgtWUUIJ5dmBIn+LtLptuCx0n14Oldtcmb1X2xst9hzdT5ZOyNzQfGPZQBsd0LglTSGxH0Cz2sVAkk3iCCclfq1HEnXrHzgAPd6RVObnwhYXzN2H6gvNKPpbsMlHKwOlypRW5QUms2YK9EgRWe+z1U+qkRBCs0oxNPoVMXGSrFmlb36j2bbU7AzzSHKn8hcCAqtwIDSdP8/7qt2gUA1DgN0Mxz+xsWl/upDFKX9Q4U/H3ircstYFQ7yCDbb0m3d7oVeusLf7eORpqSOjO3i2+AVmirIhkPuJ801fKKAFypWiKJpRTpmf8mkuI0h5wo8dXFu6Fs0R7PtCdlZlD8d71EQDuMDykqM3egaI2EVcqB7BBMcyf9uinMuXZ+pvG+ehwe9BBbfV807UD8ypM8qa3rBMwtdP6xbm1JHzcNKk0BCIG4Zp65d0PWT0XUxPoXrQoUznVbG2b32tCdao++1zjV3k9zVv4gMjHTHtO3B7WyH3UT500l3sARNVPIBBOVhvF9Us88mi/mgNluMn1U+qhDdeQ3NPcau1HZOppqtgbvHfcFjlda2Sn0ITtDzNNXcgCi6fpRwYrBXKklblAaPhSZbQ/p7IIi2ssGSfDb8lc4M/dyr15p/CRtDxyVlEKVvzX1M2x7UzrwhNPZm9qeqv+eiSbG1eqbaOVpz/SuTVru7qhMIOBNh9UoGjcfoulsUrqPOW6yEG3rwyQ9TecMjgFLcW7WQJ7Wz+RQOAjHpOUGMwP5A9Uo1AZ7skYDvQG36IBPG0I8zr1bg070o0DhWLPOBbhRyYyfNJVoGoG76E9Swxywba/ANatuaMzoc1M4aPpP9aSNzAWFh8A7njZxrXku2gktBmtGpo2hmGxuGGhqr2mRi8Mo3Q6w0f6gUBdHwMu3NDEWYYPITE1DtDfQocUI4JWV0sMhAfWOy4xSevB6q27Z4GNzOGoH0+pPm5vlvLWDHKRI1jOFAkw/uWFExbL6FqRlYhN45dHgOAeOtlboxNm9wjMkFUmr4hDcETGDB8Ve6V9MZ4S0M0o7Xhj/IA4/uKvcvc0uej/ByrtT9xilAP0qzVwaaM9/glP4OawzbHsPOWCD3+hPPka8KRyfYvjv6OFGjGjQKDZUatovTFT0CGMWtKA0Btgjrw2L1YQXAITE4onI2CA/BqxXAspHGI9i7empuoSzgNzwbiqcy7Z57ZVBj6pIrthzac9iuQXI1tJ2JXpQ/idmtC1jtzKVXyGKAQzkymzOVK7XZ1yeBxrIqUxyAWw23mqsmAo8GzEWlCJ8BE9Grp2izBSS7GisIBtMu2oyWQVGqEcgq6Xwv5ZI0hSvKn9Rv6wJW9UB3rxQqPRSjBBNWZw5xOfRkK8CbrW961krpdZ19uCq8kwfxScbifqxcbV5fm76wnTyDkRO8L/qOFEdUN8nM4k8sYPxHhG6rPrvgEiWxptN9MDbxSJAlg/PAd0fu7ZK/Mfda/PoM9kq8ssDwaUX1bczc5RryJ3luLGD3MrgsxhaaxdJeJJAnAfnT1gXsf/MPU1Cf7k7kAAAAAElFTkSuQmCC",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1} a_{d_1}^\\dagger a_{d_1} a_{u_2}^\\dagger + 4 a_{u_1}^\\dagger a_{u_1} a_{d_1} a_{d_2}^\\dagger$"
      ],
      "text/plain": [
       "4⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}⋅a_{u_2}__\\dagger + 4⋅a_{u_1}__\\dagger⋅a_{u_\n",
       "1}⋅a_{d_1}⋅a_{d_2}__\\dagger"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(16,29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ambient-drawing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22} - \\frac{1}{4}, \\  b_{21} - \\frac{1}{4}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30} - \\frac{1}{4}, \\  b_{29} - \\frac{1}{4}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21} - \\frac{1}{4}, \\  b_{22} - \\frac{1}{4}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29} - \\frac{1}{4}, \\  b_{30} - \\frac{1}{4}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1}^\\dagger a_{d_1}^\\dagger a_{u_2} a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{u_1}__\\dagger⋅a_{d_1}__\\dagger⋅a_{u_2}⋅a_{d_2}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(14,14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "commercial-building",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56} + \\frac{1}{4}, \\  b_{55} + \\frac{1}{4}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64} + \\frac{1}{4}, \\  b_{63} + \\frac{1}{4}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55} + \\frac{1}{4}, \\  b_{56} + \\frac{1}{4}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63} + \\frac{1}{4}, \\  b_{64} + \\frac{1}{4}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1}^\\dagger a_{u_1} a_{d_1}^\\dagger a_{d_1}$"
      ],
      "text/plain": [
       "4⋅a_{u_1}__\\dagger⋅a_{u_1}⋅a_{d_1}__\\dagger⋅a_{d_1}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(18,18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "handmade-representation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56} + \\frac{1}{4}, \\  b_{55} - \\frac{1}{4}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64} + \\frac{1}{4}, \\  b_{63} - \\frac{1}{4}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55} - \\frac{1}{4}, \\  b_{56} + \\frac{1}{4}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63} - \\frac{1}{4}, \\  b_{64} + \\frac{1}{4}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(26,26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fossil-treat",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22} - \\frac{1}{4}, \\  b_{21} + \\frac{1}{4}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30} - \\frac{1}{4}, \\  b_{29} + \\frac{1}{4}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21} + \\frac{1}{4}, \\  b_{22} - \\frac{1}{4}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29} + \\frac{1}{4}, \\  b_{30} - \\frac{1}{4}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1} a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger$"
      ],
      "text/plain": [
       "4⋅a_{u_1}⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuilder(27,27)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0a6b08",
   "metadata": {},
   "source": [
    "## Other sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bd8bc847",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sollist(a,b):\n",
    "    part = []\n",
    "    for i in range(len(map4)):\n",
    "        if map4[i][0] == mixed4[a] or map4[i][0] == mixed4[b]:\n",
    "            part.append(i)\n",
    "        \n",
    "    mats = mat4zerofree\n",
    "    for i in part:\n",
    "        mats = mats - map4[i][1]\n",
    "    solvelist = []\n",
    "    for i in mats:\n",
    "        if i != 0:\n",
    "            solvelist.append(i)\n",
    "            if (-2 in i.as_ordered_terms()) or (2 in i.as_ordered_terms()):\n",
    "                print('Ci sono dei 2, occhio')\n",
    "\n",
    "    dicsol = list(linsolve (solvelist, tuple(coeff_names)))\n",
    "    if dicsol != []:\n",
    "        sol = list(dicsol[0])\n",
    "        display(Math(latex(sol)))\n",
    "    else:\n",
    "        return print('Non ci sono soluzioni :( Esco dalla funzione')\n",
    "        \n",
    "    return sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47b3ac9d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22} - \\frac{1}{4}, \\  b_{21} + \\frac{1}{4}, \\  b_{7}, \\  b_{8}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30} - \\frac{1}{4}, \\  b_{29} + \\frac{1}{4}, \\  b_{15}, \\  b_{16}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21} + \\frac{1}{4}, \\  b_{22} - \\frac{1}{4}, \\  b_{23}, \\  b_{24}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29} + \\frac{1}{4}, \\  b_{30} - \\frac{1}{4}, \\  b_{31}, \\  b_{32}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "soluz = sollist(27,27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3eb15b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle \\left[ - \\frac{1}{4}, \\  \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  - \\frac{1}{4}, \\  \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  \\frac{1}{4}, \\  - \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  \\frac{1}{4}, \\  - \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0\\right]$"
      ],
      "text/plain": [
       "[-1/4, 1/4, 0, 0, 0, 0, 0, 0, -1/4, 1/4, 0, 0, 0, 0, 0, 0, 1/4, -1/4, 0, 0, 0,\n",
       " 0, 0, 0, 1/4, -1/4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
       "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soluz_simple = []\n",
    "for i in soluz:\n",
    "    for j in coeff_names:\n",
    "        i = i.subs(j,0)\n",
    "    soluz_simple.append(i)\n",
    "soluz_simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51c5a2cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{u_1} a_{d_1} a_{u_2}^\\dagger a_{d_2}^\\dagger$"
      ],
      "text/plain": [
       "4⋅a_{u_1}⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{d_2}__\\dagger"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrixinv = zeros(16,16)\n",
    "for i in range(len(inv4)):\n",
    "    matrixinv = matrixinv + soluz_simple[i]*TensorProduct(inv4[i][1][0],inv4[i][1][1],inv4[i][1][2],inv4[i][1][3])\n",
    "    \n",
    "for i in transf:\n",
    "    var = t4(i)*matrixinv*t4(i)\n",
    "    if (var - matrixinv == zeros(16,16)):\n",
    "        print('ok, è invariante :)')\n",
    "    else:\n",
    "        print('Non è invariante, sorry :(')\n",
    "        \n",
    "fermionize(expand((difermid.T*matrixinv*difermi)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1e04b719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ b_{22}, \\  b_{21}, \\  b_{7} + \\frac{1}{4}, \\  b_{8} - \\frac{1}{4}, \\  b_{22}, \\  b_{21}, \\  b_{7}, \\  b_{8}, \\  b_{30}, \\  b_{29}, \\  b_{15} + \\frac{1}{4}, \\  b_{16} - \\frac{1}{4}, \\  b_{30}, \\  b_{29}, \\  b_{15}, \\  b_{16}, \\  b_{21}, \\  b_{22}, \\  b_{23} - \\frac{1}{4}, \\  b_{24} + \\frac{1}{4}, \\  b_{21}, \\  b_{22}, \\  b_{23}, \\  b_{24}, \\  b_{29}, \\  b_{30}, \\  b_{31} - \\frac{1}{4}, \\  b_{32} + \\frac{1}{4}, \\  b_{29}, \\  b_{30}, \\  b_{31}, \\  b_{32}, \\  b_{37} + \\frac{1}{4}, \\  b_{38} - \\frac{1}{4}, \\  b_{56}, \\  b_{55}, \\  b_{37}, \\  b_{38}, \\  b_{56}, \\  b_{55}, \\  b_{45} + \\frac{1}{4}, \\  b_{46} - \\frac{1}{4}, \\  b_{64}, \\  b_{63}, \\  b_{45}, \\  b_{46}, \\  b_{64}, \\  b_{63}, \\  b_{53} - \\frac{1}{4}, \\  b_{54} + \\frac{1}{4}, \\  b_{55}, \\  b_{56}, \\  b_{53}, \\  b_{54}, \\  b_{55}, \\  b_{56}, \\  b_{61} - \\frac{1}{4}, \\  b_{62} + \\frac{1}{4}, \\  b_{63}, \\  b_{64}, \\  b_{61}, \\  b_{62}, \\  b_{63}, \\  b_{64}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[ 0, \\  0, \\  \\frac{1}{4}, \\  - \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  \\frac{1}{4}, \\  - \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  - \\frac{1}{4}, \\  \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  - \\frac{1}{4}, \\  \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  \\frac{1}{4}, \\  - \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  \\frac{1}{4}, \\  - \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  - \\frac{1}{4}, \\  \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0, \\  - \\frac{1}{4}, \\  \\frac{1}{4}, \\  0, \\  0, \\  0, \\  0, \\  0, \\  0\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, è invariante :)\n",
      "ok, è invariante :)\n",
      "ok, è invariante :)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 4 a_{d_1} a_{u_2}^\\dagger a_{u_2} a_{d_2}^\\dagger + 4 a_{u_1} a_{u_2}^\\dagger a_{d_2}^\\dagger a_{d_2}$"
      ],
      "text/plain": [
       "4⋅a_{d_1}⋅a_{u_2}__\\dagger⋅a_{u_2}⋅a_{d_2}__\\dagger + 4⋅a_{u_1}⋅a_{u_2}__\\dagg\n",
       "er⋅a_{d_2}__\\dagger⋅a_{d_2}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soluz = sollist(1,34)\n",
    "soluz_simple = []\n",
    "for i in soluz:\n",
    "    for j in coeff_names:\n",
    "        i = i.subs(j,0)\n",
    "    soluz_simple.append(i)\n",
    "    \n",
    "display(Math(latex(soluz_simple)))\n",
    "\n",
    "matrixinv = zeros(16,16)\n",
    "for i in range(len(inv4)):\n",
    "    matrixinv = matrixinv + soluz_simple[i]*TensorProduct(inv4[i][1][0],inv4[i][1][1],inv4[i][1][2],inv4[i][1][3])\n",
    "    \n",
    "for i in transf:\n",
    "    var = t4(i)*matrixinv*t4(i)\n",
    "    if (var - matrixinv == zeros(16,16)):\n",
    "        print('ok, è invariante :)')\n",
    "    else:\n",
    "        print('Non è invariante, sorry :(')\n",
    "        \n",
    "fermionize(expand((difermid.T*matrixinv*difermi)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cubic-passing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/latex": [
       "$\\displaystyle Q_{18} \\left(b_{11} - b_{12} - b_{15} + b_{16} - b_{19} + b_{20} + b_{23} - b_{24} - b_{27} + b_{28} + b_{3} + b_{31} - b_{32} + b_{33} - b_{34} - b_{37} + b_{38} - b_{4} + b_{41} - b_{42} - b_{45} + b_{46} - b_{49} + b_{50} + b_{53} - b_{54} - b_{57} + b_{58} + b_{61} - b_{62} - b_{7} + b_{8}\\right) + Q_{19} \\left(2 b_{13} - 2 b_{26} - 2 b_{30} + 2 b_{43} + 2 b_{47} - 2 b_{60} - 2 b_{64} + 2 b_{9}\\right) + Q_{21} \\left(2 b_{1} - 2 b_{18} + 2 b_{22} - 2 b_{43} + 2 b_{47} - 2 b_{5} + 2 b_{60} - 2 b_{64}\\right) + Q_{23} \\left(- b_{11} + b_{12} + b_{15} - b_{16} - b_{19} + b_{20} + b_{23} - b_{24} - b_{27} + b_{28} - b_{3} + b_{31} - b_{32} - b_{33} - b_{34} + b_{37} + b_{38} + b_{4} - b_{41} - b_{42} + b_{45} + b_{46} + b_{49} + b_{50} - b_{53} - b_{54} + b_{57} + b_{58} - b_{61} - b_{62} + b_{7} - b_{8}\\right) + Q_{26} \\left(2 b_{1} - 2 b_{18} - 2 b_{22} + 2 b_{35} + 2 b_{39} + 2 b_{5} - 2 b_{52} - 2 b_{56}\\right) + Q_{28} \\left(- 2 b_{13} - 2 b_{26} + 2 b_{30} - 2 b_{35} + 2 b_{39} + 2 b_{52} - 2 b_{56} + 2 b_{9}\\right) + Q_{30} \\left(b_{11} + b_{12} - b_{15} - b_{16} + b_{19} + b_{20} - b_{23} - b_{24} + b_{27} + b_{28} + b_{3} - b_{31} - b_{32} + b_{33} + b_{34} - b_{37} - b_{38} + b_{4} + b_{41} + b_{42} - b_{45} - b_{46} + b_{49} + b_{50} - b_{53} - b_{54} + b_{57} + b_{58} - b_{61} - b_{62} - b_{7} - b_{8}\\right) + Q_{31} \\left(- 2 b_{1} - 2 b_{10} + 2 b_{13} + 2 b_{14} - 2 b_{17} - 2 b_{18} - 2 b_{2} + 2 b_{21} + 2 b_{22} - 2 b_{25} - 2 b_{26} + 2 b_{29} + 2 b_{30} + 2 b_{5} + 2 b_{6} - 2 b_{9}\\right) + Q_{33} \\left(- b_{11} - b_{12} + b_{15} + b_{16} + b_{19} + b_{20} - b_{23} - b_{24} + b_{27} + b_{28} - b_{3} - b_{31} - b_{32} - b_{33} + b_{34} + b_{37} - b_{38} - b_{4} - b_{41} + b_{42} + b_{45} - b_{46} - b_{49} + b_{50} + b_{53} - b_{54} - b_{57} + b_{58} + b_{61} - b_{62} + b_{7} + b_{8}\\right) + Q_{35} \\left(2 b_{35} + 2 b_{36} - 2 b_{39} - 2 b_{40} + 2 b_{43} + 2 b_{44} - 2 b_{47} - 2 b_{48} + 2 b_{51} + 2 b_{52} - 2 b_{55} - 2 b_{56} + 2 b_{59} + 2 b_{60} - 2 b_{63} - 2 b_{64}\\right) + Q_{36} \\left(b_{11} + b_{12} - b_{15} - b_{16} + b_{19} + b_{20} - b_{23} - b_{24} + b_{27} + b_{28} + b_{3} - b_{31} - b_{32} + b_{33} + b_{34} - b_{37} - b_{38} + b_{4} + b_{41} + b_{42} - b_{45} - b_{46} + b_{49} + b_{50} - b_{53} - b_{54} + b_{57} + b_{58} - b_{61} - b_{62} - b_{7} - b_{8}\\right) + Q_{38} \\left(2 b_{1} - 2 b_{18} + 2 b_{22} - 2 b_{43} + 2 b_{47} - 2 b_{5} + 2 b_{60} - 2 b_{64}\\right) + Q_{40} \\left(2 b_{13} - 2 b_{26} - 2 b_{30} + 2 b_{43} + 2 b_{47} - 2 b_{60} - 2 b_{64} + 2 b_{9}\\right) + Q_{42} \\left(- b_{11} + b_{12} + b_{15} - b_{16} - b_{19} + b_{20} + b_{23} - b_{24} - b_{27} + b_{28} - b_{3} + b_{31} - b_{32} - b_{33} - b_{34} + b_{37} + b_{38} + b_{4} - b_{41} - b_{42} + b_{45} + b_{46} + b_{49} + b_{50} - b_{53} - b_{54} + b_{57} + b_{58} - b_{61} - b_{62} + b_{7} - b_{8}\\right) + Q_{43} \\left(2 b_{35} - 2 b_{36} - 2 b_{39} + 2 b_{40} + 2 b_{43} - 2 b_{44} - 2 b_{47} + 2 b_{48} - 2 b_{51} + 2 b_{52} + 2 b_{55} - 2 b_{56} - 2 b_{59} + 2 b_{60} + 2 b_{63} - 2 b_{64}\\right) + Q_{44} \\left(- 2 b_{1} + 2 b_{10} + 2 b_{13} - 2 b_{14} + 2 b_{17} - 2 b_{18} + 2 b_{2} - 2 b_{21} + 2 b_{22} + 2 b_{25} - 2 b_{26} - 2 b_{29} + 2 b_{30} + 2 b_{5} - 2 b_{6} - 2 b_{9}\\right) + Q_{46} \\left(- b_{11} - b_{12} + b_{15} + b_{16} + b_{19} + b_{20} - b_{23} - b_{24} + b_{27} + b_{28} - b_{3} - b_{31} - b_{32} - b_{33} + b_{34} + b_{37} - b_{38} - b_{4} - b_{41} + b_{42} + b_{45} - b_{46} - b_{49} + b_{50} + b_{53} - b_{54} - b_{57} + b_{58} + b_{61} - b_{62} + b_{7} + b_{8}\\right) + Q_{48} \\left(- 2 b_{13} - 2 b_{26} + 2 b_{30} - 2 b_{35} + 2 b_{39} + 2 b_{52} - 2 b_{56} + 2 b_{9}\\right) + Q_{50} \\left(2 b_{1} - 2 b_{18} - 2 b_{22} + 2 b_{35} + 2 b_{39} + 2 b_{5} - 2 b_{52} - 2 b_{56}\\right) + Q_{51} \\left(b_{11} - b_{12} - b_{15} + b_{16} - b_{19} + b_{20} + b_{23} - b_{24} - b_{27} + b_{28} + b_{3} + b_{31} - b_{32} + b_{33} - b_{34} - b_{37} + b_{38} - b_{4} + b_{41} - b_{42} - b_{45} + b_{46} - b_{49} + b_{50} + b_{53} - b_{54} - b_{57} + b_{58} + b_{61} - b_{62} - b_{7} + b_{8}\\right)$"
      ],
      "text/plain": [
       "Q₁₈⋅(b₁₁ - b₁₂ - b₁₅ + b₁₆ - b₁₉ + b₂₀ + b₂₃ - b₂₄ - b₂₇ + b₂₈ + b₃ + b₃₁ - b₃\n",
       "₂ + b₃₃ - b₃₄ - b₃₇ + b₃₈ - b₄ + b₄₁ - b₄₂ - b₄₅ + b₄₆ - b₄₉ + b₅₀ + b₅₃ - b₅₄\n",
       " - b₅₇ + b₅₈ + b₆₁ - b₆₂ - b₇ + b₈) + Q₁₉⋅(2⋅b₁₃ - 2⋅b₂₆ - 2⋅b₃₀ + 2⋅b₄₃ + 2⋅b\n",
       "₄₇ - 2⋅b₆₀ - 2⋅b₆₄ + 2⋅b₉) + Q₂₁⋅(2⋅b₁ - 2⋅b₁₈ + 2⋅b₂₂ - 2⋅b₄₃ + 2⋅b₄₇ - 2⋅b₅ \n",
       "+ 2⋅b₆₀ - 2⋅b₆₄) + Q₂₃⋅(-b₁₁ + b₁₂ + b₁₅ - b₁₆ - b₁₉ + b₂₀ + b₂₃ - b₂₄ - b₂₇ +\n",
       " b₂₈ - b₃ + b₃₁ - b₃₂ - b₃₃ - b₃₄ + b₃₇ + b₃₈ + b₄ - b₄₁ - b₄₂ + b₄₅ + b₄₆ + b\n",
       "₄₉ + b₅₀ - b₅₃ - b₅₄ + b₅₇ + b₅₈ - b₆₁ - b₆₂ + b₇ - b₈) + Q₂₆⋅(2⋅b₁ - 2⋅b₁₈ - \n",
       "2⋅b₂₂ + 2⋅b₃₅ + 2⋅b₃₉ + 2⋅b₅ - 2⋅b₅₂ - 2⋅b₅₆) + Q₂₈⋅(-2⋅b₁₃ - 2⋅b₂₆ + 2⋅b₃₀ - \n",
       "2⋅b₃₅ + 2⋅b₃₉ + 2⋅b₅₂ - 2⋅b₅₆ + 2⋅b₉) + Q₃₀⋅(b₁₁ + b₁₂ - b₁₅ - b₁₆ + b₁₉ + b₂₀\n",
       " - b₂₃ - b₂₄ + b₂₇ + b₂₈ + b₃ - b₃₁ - b₃₂ + b₃₃ + b₃₄ - b₃₇ - b₃₈ + b₄ + b₄₁ +\n",
       " b₄₂ - b₄₅ - b₄₆ + b₄₉ + b₅₀ - b₅₃ - b₅₄ + b₅₇ + b₅₈ - b₆₁ - b₆₂ - b₇ - b₈) + \n",
       "Q₃₁⋅(-2⋅b₁ - 2⋅b₁₀ + 2⋅b₁₃ + 2⋅b₁₄ - 2⋅b₁₇ - 2⋅b₁₈ - 2⋅b₂ + 2⋅b₂₁ + 2⋅b₂₂ - 2⋅\n",
       "b₂₅ - 2⋅b₂₆ + 2⋅b₂₉ + 2⋅b₃₀ + 2⋅b₅ + 2⋅b₆ - 2⋅b₉) + Q₃₃⋅(-b₁₁ - b₁₂ + b₁₅ + b₁\n",
       "₆ + b₁₉ + b₂₀ - b₂₃ - b₂₄ + b₂₇ + b₂₈ - b₃ - b₃₁ - b₃₂ - b₃₃ + b₃₄ + b₃₇ - b₃₈\n",
       " - b₄ - b₄₁ + b₄₂ + b₄₅ - b₄₆ - b₄₉ + b₅₀ + b₅₃ - b₅₄ - b₅₇ + b₅₈ + b₆₁ - b₆₂ \n",
       "+ b₇ + b₈) + Q₃₅⋅(2⋅b₃₅ + 2⋅b₃₆ - 2⋅b₃₉ - 2⋅b₄₀ + 2⋅b₄₃ + 2⋅b₄₄ - 2⋅b₄₇ - 2⋅b₄\n",
       "₈ + 2⋅b₅₁ + 2⋅b₅₂ - 2⋅b₅₅ - 2⋅b₅₆ + 2⋅b₅₉ + 2⋅b₆₀ - 2⋅b₆₃ - 2⋅b₆₄) + Q₃₆⋅(b₁₁ \n",
       "+ b₁₂ - b₁₅ - b₁₆ + b₁₉ + b₂₀ - b₂₃ - b₂₄ + b₂₇ + b₂₈ + b₃ - b₃₁ - b₃₂ + b₃₃ +\n",
       " b₃₄ - b₃₇ - b₃₈ + b₄ + b₄₁ + b₄₂ - b₄₅ - b₄₆ + b₄₉ + b₅₀ - b₅₃ - b₅₄ + b₅₇ + \n",
       "b₅₈ - b₆₁ - b₆₂ - b₇ - b₈) + Q₃₈⋅(2⋅b₁ - 2⋅b₁₈ + 2⋅b₂₂ - 2⋅b₄₃ + 2⋅b₄₇ - 2⋅b₅ \n",
       "+ 2⋅b₆₀ - 2⋅b₆₄) + Q₄₀⋅(2⋅b₁₃ - 2⋅b₂₆ - 2⋅b₃₀ + 2⋅b₄₃ + 2⋅b₄₇ - 2⋅b₆₀ - 2⋅b₆₄ \n",
       "+ 2⋅b₉) + Q₄₂⋅(-b₁₁ + b₁₂ + b₁₅ - b₁₆ - b₁₉ + b₂₀ + b₂₃ - b₂₄ - b₂₇ + b₂₈ - b₃\n",
       " + b₃₁ - b₃₂ - b₃₃ - b₃₄ + b₃₇ + b₃₈ + b₄ - b₄₁ - b₄₂ + b₄₅ + b₄₆ + b₄₉ + b₅₀ \n",
       "- b₅₃ - b₅₄ + b₅₇ + b₅₈ - b₆₁ - b₆₂ + b₇ - b₈) + Q₄₃⋅(2⋅b₃₅ - 2⋅b₃₆ - 2⋅b₃₉ + \n",
       "2⋅b₄₀ + 2⋅b₄₃ - 2⋅b₄₄ - 2⋅b₄₇ + 2⋅b₄₈ - 2⋅b₅₁ + 2⋅b₅₂ + 2⋅b₅₅ - 2⋅b₅₆ - 2⋅b₅₉ \n",
       "+ 2⋅b₆₀ + 2⋅b₆₃ - 2⋅b₆₄) + Q₄₄⋅(-2⋅b₁ + 2⋅b₁₀ + 2⋅b₁₃ - 2⋅b₁₄ + 2⋅b₁₇ - 2⋅b₁₈ \n",
       "+ 2⋅b₂ - 2⋅b₂₁ + 2⋅b₂₂ + 2⋅b₂₅ - 2⋅b₂₆ - 2⋅b₂₉ + 2⋅b₃₀ + 2⋅b₅ - 2⋅b₆ - 2⋅b₉) +\n",
       " Q₄₆⋅(-b₁₁ - b₁₂ + b₁₅ + b₁₆ + b₁₉ + b₂₀ - b₂₃ - b₂₄ + b₂₇ + b₂₈ - b₃ - b₃₁ - \n",
       "b₃₂ - b₃₃ + b₃₄ + b₃₇ - b₃₈ - b₄ - b₄₁ + b₄₂ + b₄₅ - b₄₆ - b₄₉ + b₅₀ + b₅₃ - b\n",
       "₅₄ - b₅₇ + b₅₈ + b₆₁ - b₆₂ + b₇ + b₈) + Q₄₈⋅(-2⋅b₁₃ - 2⋅b₂₆ + 2⋅b₃₀ - 2⋅b₃₅ + \n",
       "2⋅b₃₉ + 2⋅b₅₂ - 2⋅b₅₆ + 2⋅b₉) + Q₅₀⋅(2⋅b₁ - 2⋅b₁₈ - 2⋅b₂₂ + 2⋅b₃₅ + 2⋅b₃₉ + 2⋅\n",
       "b₅ - 2⋅b₅₂ - 2⋅b₅₆) + Q₅₁⋅(b₁₁ - b₁₂ - b₁₅ + b₁₆ - b₁₉ + b₂₀ + b₂₃ - b₂₄ - b₂₇\n",
       " + b₂₈ + b₃ + b₃₁ - b₃₂ + b₃₃ - b₃₄ - b₃₇ + b₃₈ - b₄ + b₄₁ - b₄₂ - b₄₅ + b₄₆ -\n",
       " b₄₉ + b₅₀ + b₅₃ - b₅₄ - b₅₇ + b₅₈ + b₆₁ - b₆₂ - b₇ + b₈)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qil = fermionize(expand((difermid.T*mat4*difermi)[0]))\n",
    "\n",
    "lng = len(mixed2) + len(mixed4)\n",
    "for i in range(lng):\n",
    "    qil = qil.subs((mixed2+mixed4)[lng-1-i],aliases[lng-1-i])\n",
    "\n",
    "for i in aliases:\n",
    "    qil = collect(qil,i)\n",
    "qil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "incorporated-capital",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "nbconvert_exporter": "python",
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