diff --git a/test.ipynb b/test.ipynb
index 9dc5c96..13cd8fe 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -11,7 +11,7 @@
        "'0.14.3'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,7 +47,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dat = sisl.io.siesta.eigSileSiesta(\n",
+    "    \"/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.EIG\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-12.806878959999999"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat.read_data().min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -74,13 +105,13 @@
       "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
       "================================================================================================================================================================\n",
       "Parameters for the contour integral:\n",
-      "Number of k points:  1000\n",
-      "k point directions:  x\n",
+      "Number of k points:  20\n",
+      "k point directions:  xy\n",
       "Ebot:  -13\n",
-      "Eset:  500\n",
+      "Eset:  600\n",
       "Esetp:  10000\n",
       "================================================================================================================================================================\n",
-      "Setup done. Elapsed time: 5447.39860525 s\n",
+      "Setup done. Elapsed time: 198.668371833 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -117,8 +148,8 @@
     "pairs = [\n",
     "    # isotropic should be -82 meV\n",
     "    dict(ai=0, aj=1, Ruc=np.array([0, 0, 0])),\n",
+    "    dict(ai=0, aj=2, Ruc=np.array([0, 0, 0])),\n",
     "    dict(ai=1, aj=2, Ruc=np.array([0, 0, 0])),\n",
-    "    #    dict(ai=0, aj=2, Ruc=np.array([0, 0, 0])),\n",
     "]\n",
     "\n",
     "\"\"\"\n",
@@ -145,10 +176,10 @@
     "\"\"\"\n",
     "\n",
     "# Brilloun zone sampling and Green function contour integral\n",
-    "kset = 1000\n",
-    "kdirs = \"x\"\n",
+    "kset = 20\n",
+    "kdirs = \"xy\"\n",
     "ebot = -13\n",
-    "eset = 500\n",
+    "eset = 600\n",
     "esetp = 10000\n",
     "\n",
     "\n",
@@ -190,15 +221,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-12.806739\n",
-      "-0.01254111\n",
       "xyz[-3:]: red, green, blue\n"
      ]
     },
@@ -214,7 +243,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 2 Axes>"
       ]
@@ -238,13 +267,11 @@
     "\n",
     "plt.figure(figsize=(15, 5))\n",
     "plt.subplot(121)\n",
-    "plt.plot(np.sort(dh.eig()))\n",
+    "plt.plot(np.sort(dh.eigh()))\n",
     "plt.grid()\n",
     "plt.subplot(122)\n",
     "DOS = sisl.physics.electron.DOS(np.linspace(-15, 85, 50), dh.eig())\n",
     "plt.plot(DOS, np.linspace(-15, 85, 50))\n",
-    "print(np.real(dh.eig()).min())\n",
-    "print(np.imag(dh.eig()).min())\n",
     "\n",
     "coords = dh.xyz[-3:]\n",
     "\n",
@@ -267,14 +294,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hamiltonian and exchange field rotated. Elapsed time: 5448.302713666 s\n",
+      "Hamiltonian and exchange field rotated. Elapsed time: 199.591100666 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -376,14 +403,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Site and pair dictionaries created. Elapsed time: 5448.330635 s\n",
+      "Site and pair dictionaries created. Elapsed time: 199.638437125 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -491,21 +518,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "k loop:   0%|          | 0/1000 [00:00<?, ?it/s]"
+      "k loop:   0%|          | 0/400 [00:00<?, ?it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "k set created. Elapsed time: 5448.339522166 s\n",
+      "k set created. Elapsed time: 199.64839675 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -526,14 +553,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rotations done perpendicular to quantization axis. Elapsed time: 5448.55170175 s\n",
+      "Rotations done perpendicular to quantization axis. Elapsed time: 199.979076 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -589,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -597,15 +624,15 @@
      "output_type": "stream",
      "text": [
       "Starting matrix inversions\n",
-      "Total number of k points: 1000\n",
-      "Number of energy samples per k point: 500\n",
+      "Total number of k points: 400\n",
+      "Number of energy samples per k point: 600\n",
       "Total number of directions: 3\n",
-      "Total number of matrix inversions: 1500000\n",
+      "Total number of matrix inversions: 720000\n",
       "The shape of the Hamiltonian and the Greens function is 84x84=7056\n",
       "Memory taken by a single Hamiltonian is: 0.015625 KB\n",
       "Expected memory usage per matrix inversion: 0.5 KB\n",
-      "Expected memory usage per k point for parallel inversion: 750.0 KB\n",
-      "Expected memory usage on root node: 732.421875 MB\n",
+      "Expected memory usage per k point for parallel inversion: 900.0 KB\n",
+      "Expected memory usage on root node: 351.5625 MB\n",
       "================================================================================================================================================================\n"
      ]
     },
@@ -613,14 +640,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "k loop: 100%|██████████| 1000/1000 [39:28<00:00,  2.37s/it]"
+      "k loop: 100%|██████████| 400/400 [2:12:33<00:00, 19.88s/it]    "
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculated Greens functions. Elapsed time: 7816.996438041 s\n",
+      "Calculated Greens functions. Elapsed time: 1375.51451525 s\n",
       "================================================================================================================================================================\n"
      ]
     },
@@ -724,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -755,10 +782,10 @@
       "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
       "================================================================================================================================================================\n",
       "Parameters for the contour integral:\n",
-      "Number of k points:  1000\n",
-      "k point directions:  x\n",
+      "Number of k points:  20\n",
+      "k point directions:  xy\n",
       "Ebot:  -13\n",
-      "Eset:  500\n",
+      "Eset:  600\n",
       "Esetp:  10000\n",
       "================================================================================================================================================================\n",
       "Atomic information: \n",
@@ -777,42 +804,68 @@
       "Magnetic entity1        Magnetic entity2       [i  j  k]       d [Ang]\n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "[3]Fe(2)        [4]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -29.367092812942317\n",
-      "DMI:  [-8.89792781e-01  4.16580540e+00 -3.59948517e-06]\n",
-      "Symmetric-anisotropy:  [-4.56670448  1.17667365  0.33235705  4.15578143 -0.03089926]\n",
+      "Isotropic:  -61.575578560021754\n",
+      "DMI:  [-9.32925576e-01 -6.69311832e-04 -1.27860983e-06]\n",
+      "Symmetric-anisotropy:  [ 3.27134566e-01 -2.18826996e-06  1.11817934e-06 -2.18826996e-06\n",
+      "  2.93842716e+00 -2.84299672e-05  1.11817934e-06 -2.84299672e-05\n",
+      " -3.26556173e+00]\n",
+      "Energies for debugging: \n",
+      "array([[-6.21988435e-02, -9.32897146e-04,  9.32954006e-04,\n",
+      "        -6.14460702e-02],\n",
+      "       [-6.74834371e-02,  6.68193653e-07, -6.70430011e-07,\n",
+      "        -6.66686592e-02],\n",
+      "       [-5.58282326e-02,  9.09660134e-10,  3.46687979e-09,\n",
+      "        -5.58282288e-02]])\n",
+      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
+      "array([-0.06666866, -0.05582823, -0.06219884])\n",
+      "Test J_xx = E(y,z) = E(z,y)\n",
+      "-0.06666865917296813 -0.055828228814894104\n",
+      "\n",
+      "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
+      "Isotropic:  -60.54611612367399\n",
+      "DMI:  [ 3.78484434e+00 -6.14206310e+00  1.63244853e-04]\n",
+      "Symmetric-anisotropy:  [ 0.20562843  0.07121269 -0.0931207   0.07121269  0.01213322 -0.04251871\n",
+      " -0.0931207  -0.04251871 -0.21776164]\n",
       "Energies for debugging: \n",
-      "array([[-0.02819042, -0.00085889,  0.00092069, -0.02597706],\n",
-      "       [-0.03690207, -0.00415578,  0.00417583, -0.0339338 ],\n",
-      "       [-0.03160427, -0.00033236, -0.00033235, -0.03122101]])\n",
+      "array([[-6.08734388e-02,  3.82736305e-03, -3.74232563e-03,\n",
+      "        -6.11763023e-02],\n",
+      "       [-6.06543168e-02,  6.23518381e-03, -6.04894240e-03,\n",
+      "        -6.08718006e-02],\n",
+      "       [-5.98916636e-02, -7.10494409e-05, -7.13759306e-05,\n",
+      "        -5.98091748e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.0339338 , -0.03160427, -0.02819042])\n",
+      "array([-0.0608718 , -0.05989166, -0.06087344])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.03393379729617979 -0.03122101431812066\n",
+      "-0.06087180064052234 -0.05980917475210674\n",
       "\n",
       "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -74.98944973858333\n",
-      "DMI:  [-6.82212886e+00  1.12891691e+01  5.77778279e-04]\n",
-      "Symmetric-anisotropy:  [-1.03411109  0.63716653 -0.16031601 10.67952816 -0.32649278]\n",
+      "Isotropic:  -60.54211382677383\n",
+      "DMI:  [-3.79969677e+00  6.15441158e+00  1.64058302e-04]\n",
+      "Symmetric-anisotropy:  [ 0.20695438  0.07120989  0.08986466  0.07120989  0.00574537  0.03644448\n",
+      "  0.08986466  0.03644448 -0.21269975]\n",
       "Energies for debugging: \n",
-      "array([[-0.07435228, -0.00649564,  0.00714862, -0.07459251],\n",
-      "       [-0.07593353, -0.01067953,  0.01189881, -0.07602356],\n",
-      "       [-0.0795701 ,  0.00016089,  0.00015974, -0.07975458]])\n",
+      "array([[-6.08700078e-02, -3.83614125e-03,  3.76325229e-03,\n",
+      "        -6.11810143e-02],\n",
+      "       [-6.06396194e-02, -6.24427624e-03,  6.06454692e-03,\n",
+      "        -6.08610806e-02],\n",
+      "       [-5.98917226e-02, -7.10458351e-05, -7.13739518e-05,\n",
+      "        -5.98092383e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.07602356, -0.0795701 , -0.07435228])\n",
+      "array([-0.06086108, -0.05989172, -0.06087001])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.07602356083253432 -0.07975458355080561\n",
+      "-0.06086108060807331 -0.059809238286325714\n",
       "\n",
       "================================================================================================================================================================\n",
       "Runtime information: \n",
-      "Total runtime: 2369.9501148750005 s\n",
+      "Total runtime: 1177.247012041 s\n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Initial setup: 0.17246508400057792 s\n",
-      "Hamiltonian conversion and XC field extraction: 0.904 s\n",
-      "Pair and site datastructure creatrions: 0.028 s\n",
-      "k set cration and distribution: 0.009 s\n",
-      "Rotating XC potential: 0.212 s\n",
-      "Greens function inversion: 2368.445 s\n",
-      "Calculate energies and magnetic components: 0.180 s\n"
+      "Initial setup: 0.12478720800001497 s\n",
+      "Hamiltonian conversion and XC field extraction: 0.923 s\n",
+      "Pair and site datastructure creatrions: 0.047 s\n",
+      "k set cration and distribution: 0.010 s\n",
+      "Rotating XC potential: 0.331 s\n",
+      "Greens function inversion: 1175.535 s\n",
+      "Calculate energies and magnetic components: 0.276 s\n"
      ]
     }
    ],
@@ -832,7 +885,10 @@
     "\n",
     "            # fill up the magnetic entities dictionary with the energies\n",
     "            magnetic_entities[tracker][\"energies\"].append(storage)\n",
-    "\n",
+    "        # convert to np array\n",
+    "        magnetic_entities[tracker][\"energies\"] = np.array(\n",
+    "            magnetic_entities[tracker][\"energies\"]\n",
+    "        )\n",
     "    print(\"Magnetic entities integrated.\")\n",
     "\n",
     "    # iterate over the pairs\n",
@@ -852,6 +908,8 @@
     "                    storage.append(np.trapz(-1 / np.pi * np.imag(traced * cont.we)))\n",
     "            # fill up the pairs dictionary with the energies\n",
     "            pairs[tracker][\"energies\"].append(storage)\n",
+    "        # convert to np array\n",
+    "        pairs[tracker][\"energies\"] = np.array(pairs[tracker][\"energies\"])\n",
     "\n",
     "    print(\"Pairs integrated.\")\n",
     "\n",
@@ -896,11 +954,136 @@
     "        pickle.dump(results, output_file)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (1445642317.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[35], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Magnetic entities integrated.\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "Magnetic entities integrated.\n",
+    "Pairs integrated.\n",
+    "Magnetic parameters calculated.\n",
+    "##################################################################### GROGU OUTPUT #############################################################################\n",
+    "================================================================================================================================================================\n",
+    "Input file: \n",
+    "Not yet specified.\n",
+    "Output file: \n",
+    "test.pickle\n",
+    "Number of nodes in the parallel cluster:  1\n",
+    "================================================================================================================================================================\n",
+    "Cell [Ang]: \n",
+    "[[ 3.79100000e+00  0.00000000e+00  0.00000000e+00]\n",
+    " [-1.89550000e+00  3.28310231e+00  0.00000000e+00]\n",
+    " [ 1.25954923e-15  2.18160327e-15  2.05700000e+01]]\n",
+    "================================================================================================================================================================\n",
+    "DFT axis: \n",
+    "[0 0 1]\n",
+    "Quantization axis and perpendicular rotation directions:\n",
+    "[1 0 0]  --»  [array([0, 1, 0]), array([0, 0, 1])]\n",
+    "[0 1 0]  --»  [array([1, 0, 0]), array([0, 0, 1])]\n",
+    "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
+    "================================================================================================================================================================\n",
+    "Parameters for the contour integral:\n",
+    "Number of k points:  20\n",
+    "k point directions:  x\n",
+    "Ebot:  -13\n",
+    "Eset:  600\n",
+    "Esetp:  10000\n",
+    "================================================================================================================================================================\n",
+    "Atomic information: \n",
+    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "[atom index]Element(orbitals)        x [Ang]        y [Ang]        z [Ang]        Sx        Sy        Sz        Q        Lx        Ly        Lz        Jx        Jy        Jz\n",
+    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "[3]Fe(2)        -7.339158738013707e-06        4.149278510690423e-06        11.657585837928032\n",
+    "\n",
+    "[4]Fe(2)        -7.326987662162937e-06        4.158274523275774e-06        8.912422537596708\n",
+    "\n",
+    "[5]Fe(2)        1.8954667088117545        1.0943913231921656        10.285002698393109\n",
+    "\n",
+    "================================================================================================================================================================\n",
+    "Exchange [meV]\n",
+    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "Magnetic entity1        Magnetic entity2       [i  j  k]       d [Ang]\n",
+    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "[3]Fe(2)        [4]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
+    "Isotropic:  -37.73293438752643\n",
+    "DMI:  [-1.95914479e+00  6.21772915e+00 -7.63210027e-06]\n",
+    "Symmetric-anisotropy:  [ 1.76549627e+00  1.32018786e+00  8.70141772e-04  1.32018786e+00\n",
+    "  3.02084625e+00 -1.61370176e-02  8.70141772e-04 -1.61370176e-02\n",
+    " -4.78634252e+00]\n",
+    "Energies for debugging: \n",
+    "array([[-0.03971497, -0.00194301,  0.00197528, -0.0357954 ],\n",
+    "       [-0.04532359, -0.0062186 ,  0.00621686, -0.03983102],\n",
+    "       [-0.03362878, -0.0013202 , -0.00132018, -0.03210385]])\n",
+    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
+    "array([-0.03983102, -0.03362878, -0.03971497])\n",
+    "Test J_xx = E(y,z) = E(z,y)\n",
+    "-0.0398310234439899 -0.03210385279422295\n",
+    "\n",
+    "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
+    "Isotropic:  -73.83015029521252\n",
+    "DMI:  [ 7.76660921e+00 -1.22515694e+01  6.25989582e-04]\n",
+    "Symmetric-anisotropy:  [-1.94961899 -0.25694518  1.17973546 -0.25694518 -0.9315989   0.68240007\n",
+    "  1.17973546  0.68240007  2.8812179 ]\n",
+    "Energies for debugging: \n",
+    "array([[-0.06985729,  0.00708421, -0.00844901, -0.06974285],\n",
+    "       [-0.07204057,  0.01107183, -0.0134313 , -0.07148291],\n",
+    "       [-0.07978065,  0.00025757,  0.00025632, -0.08007663]])\n",
+    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
+    "array([-0.07148291, -0.07978065, -0.06985729])\n",
+    "Test J_xx = E(y,z) = E(z,y)\n",
+    "-0.07148290532391263 -0.08007663325482241\n",
+    "\n",
+    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
+    "Isotropic:  -73.78915203163939\n",
+    "DMI:  [-7.75054625e+00  1.22185627e+01  6.29159323e-04]\n",
+    "Symmetric-anisotropy:  [-1.96064992 -0.25693115 -1.19384894 -0.25693115 -0.94861315 -0.70360485\n",
+    " -1.19384894 -0.70360485  2.90926307]\n",
+    "Energies for debugging: \n",
+    "array([[-0.06978756, -0.00704694,  0.00845415, -0.06969528],\n",
+    "       [-0.07197222, -0.01102471,  0.01341241, -0.07142338],\n",
+    "       [-0.07978025,  0.00025756,  0.0002563 , -0.08007622]])\n",
+    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
+    "array([-0.07142338, -0.07978025, -0.06978756])\n",
+    "Test J_xx = E(y,z) = E(z,y)\n",
+    "-0.07142338168884173 -0.08007622222171976\n",
+    "\n",
+    "================================================================================================================================================================\n",
+    "Runtime information: \n",
+    "Total runtime: 60.559684874999995 s\n",
+    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "Initial setup: 0.13768908300000016 s\n",
+    "Hamiltonian conversion and XC field extraction: 0.822 s\n",
+    "Pair and site datastructure creatrions: 0.036 s\n",
+    "k set cration and distribution: 0.025 s\n",
+    "Rotating XC potential: 0.245 s\n",
+    "Greens function inversion: 59.066 s\n",
+    "Calculate energies and magnetic components: 0.228 s"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (3012513526.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[13], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Magnetic entities integrated.\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "Magnetic entities integrated.\n",
     "Pairs integrated.\n",
@@ -988,18 +1171,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (1614865498.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[50], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Magnetic entities integrated.\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Magnetic entities integrated.\n",
     "Pairs integrated.\n",
@@ -1089,16 +1263,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (1097899960.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[40], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Magnetic entities integrated.\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Magnetic entities integrated.\n",
     "Pairs integrated.\n",
@@ -1201,16 +1366,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (1463926646.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[30], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Magnetic entities integrated.\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Magnetic entities integrated.\n",
     "Pairs integrated.\n",
@@ -1390,16 +1546,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (577185453.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[10], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Magnetic entities integrated.\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Magnetic entities integrated.\n",
     "Pairs integrated.\n",
@@ -1489,16 +1636,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (2508083104.py, line 2)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[10], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m    ================================================================================================================================================================\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "##################################################################### GROGU OUTPUT #############################################################################\n",
     "================================================================================================================================================================\n",
diff --git a/test.pickle b/test.pickle
index 292d728..1f35470 100644
Binary files a/test.pickle and b/test.pickle differ