diff --git a/Fe3GeTe2.pickle b/Fe3GeTe2.pickle
new file mode 100644
index 0000000..51c94ee
Binary files /dev/null and b/Fe3GeTe2.pickle differ
diff --git a/test.ipynb b/test.ipynb
index 8500f99..e6047a2 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -2,16 +2,23 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Daniels-Air:55387] shmem: mmap: an error occurred while determining whether or not /var/folders/yh/dx7xl94n3g52ts3td8qcxjcc0000gn/T//ompi.Daniels-Air.501/jf.0/1750007808/sm_segment.Daniels-Air.501.684f0000.0 could be created.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'0.14.3'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -23,12 +30,30 @@
     "\n",
     "import numpy as np\n",
     "import sisl\n",
+    "import sisl.viz\n",
     "from src.grogu_magn.useful import *\n",
     "from mpi4py import MPI\n",
     "import pickle\n",
     "from numpy.linalg import inv\n",
     "import warnings\n",
     "\n",
+    "\"\"\"    \n",
+    "# Some input parsing\n",
+    "parser = argparse.ArgumentParser()\n",
+    "parser.add_argument('--kset'    , dest = 'kset'   , default  = 2         , type=int  , help = 'k-space resolution of Jij calculation')\n",
+    "parser.add_argument('--kdirs'   , dest = 'kdirs'  , default  = 'xyz'                 , help = 'Definition of k-space dimensionality')\n",
+    "parser.add_argument('--eset'    , dest = 'eset'   , default  = 42        , type=int  , help = 'Number of energy points on the contour')\n",
+    "parser.add_argument('--eset-p'  , dest = 'esetp'  , default  = 10        , type=int  , help = 'Parameter tuning the distribution on the contour')\n",
+    "parser.add_argument('--input'   , dest = 'infile' , required = True                  , help = 'Input file name')\n",
+    "parser.add_argument('--output'  , dest = 'outfile', required = True                  , help = 'Output file name')\n",
+    "parser.add_argument('--Ebot'    , dest = 'Ebot'   , default  = -20.0     , type=float, help = 'Bottom energy of the contour')\n",
+    "parser.add_argument('--npairs'  , dest = 'npairs' , default  = 1         , type=int  , help = 'Number of unitcell pairs in each direction for Jij calculation')\n",
+    "parser.add_argument('--adirs'   , dest = 'adirs'  , default  = False                 , help = 'Definition of pair directions')\n",
+    "parser.add_argument('--use-tqdm', dest = 'usetqdm', default  = 'not'                 , help = 'Use tqdm for progressbars or not')\n",
+    "parser.add_argument('--cutoff'  , dest = 'cutoff' , default  = 100.0     , type=float, help = 'Real space cutoff for pair generation in Angs')\n",
+    "parser.add_argument('--pairfile', dest = 'pairfile', default  = False    ,             help = 'File to read pair information')\n",
+    "args = parser.parse_args()\n",
+    "\"\"\"\n",
     "# runtime information\n",
     "times = dict()\n",
     "times[\"start_time\"] = timer()\n",
@@ -40,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -49,7 +74,7 @@
        "-12.806878959999999"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -58,12 +83,13 @@
     "dat = sisl.io.siesta.eigSileSiesta(\n",
     "    \"/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.EIG\"\n",
     ")\n",
-    "dat.read_data().min()"
+    "siesta_eigs = dat.read_data()\n",
+    "siesta_eigs.min()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -74,7 +100,7 @@
       "Input file: \n",
       "/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf\n",
       "Output file: \n",
-      "Fe3GeTe2.pickle\n",
+      "./Fe3GeTe2.pickle\n",
       "Number of nodes in the parallel cluster:  1\n",
       "================================================================================================================================================================\n",
       "Cell [Ang]: \n",
@@ -90,13 +116,13 @@
       "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
       "================================================================================================================================================================\n",
       "Parameters for the contour integral:\n",
-      "Number of k points:  100\n",
+      "Number of k points:  20\n",
       "k point directions:  xy\n",
       "Ebot:  -13\n",
-      "Eset:  60\n",
+      "Eset:  100\n",
       "Esetp:  10000\n",
       "================================================================================================================================================================\n",
-      "Setup done. Elapsed time: 291.062782833 s\n",
+      "Setup done. Elapsed time: 3.879105916 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -108,7 +134,7 @@
     "path = (\n",
     "    \"/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf\"\n",
     ")\n",
-    "outfile = \"Fe3GeTe2\"\n",
+    "outfile = \"./Fe3GeTe2\"\n",
     "\n",
     "# this information needs to be given at the input!!\n",
     "scf_xcf_orientation = np.array([0, 0, 1])  # z\n",
@@ -130,25 +156,16 @@
     "    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=1, Ruc=np.array([1, 0, 0])),\n",
-    "    dict(ai=0, aj=2, Ruc=np.array([1, 0, 0])),\n",
-    "    dict(ai=1, aj=2, Ruc=np.array([1, 0, 0])),\n",
-    "    dict(ai=0, aj=1, Ruc=np.array([-1, 0, 0])),\n",
+    "    dict(ai=0, aj=2, Ruc=np.array([-1, -1, 0])),\n",
+    "    dict(ai=1, aj=2, Ruc=np.array([-1, -1, 0])),\n",
     "    dict(ai=0, aj=2, Ruc=np.array([-1, 0, 0])),\n",
     "    dict(ai=1, aj=2, Ruc=np.array([-1, 0, 0])),\n",
-    "    dict(ai=0, aj=1, Ruc=np.array([0, 1, 0])),\n",
-    "    dict(ai=0, aj=2, Ruc=np.array([0, 1, 0])),\n",
-    "    dict(ai=1, aj=2, Ruc=np.array([0, 1, 0])),\n",
-    "    dict(ai=0, aj=1, Ruc=np.array([0, -1, 0])),\n",
-    "    dict(ai=0, aj=2, Ruc=np.array([0, -1, 0])),\n",
-    "    dict(ai=1, aj=2, Ruc=np.array([0, -1, 0])),\n",
     "]\n",
-    "\n",
     "# Brilloun zone sampling and Green function contour integral\n",
-    "kset = 10\n",
+    "kset = 20\n",
     "kdirs = \"xy\"\n",
     "ebot = -13\n",
-    "eset = 60\n",
+    "eset = 100\n",
     "esetp = 10000\n",
     "################################################################################\n",
     "#################################### INPUT #####################################\n",
@@ -198,29 +215,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "xyz[-3:]: red, green, blue\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/danielpozsar/Documents/oktatás/elte/phd/grogu_project/.venv/lib/python3.9/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n",
-      "  return math.isfinite(val)\n",
-      "/Users/danielpozsar/Documents/oktatás/elte/phd/grogu_project/.venv/lib/python3.9/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n",
-      "  return np.asarray(x, float)\n"
+      "xyz[-3:]: red, green, blue\n",
+      "2.745163300331324\n",
+      "2.5835033632437767\n",
+      "2.583501767937866\n",
+      "2.583541444641373\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 2 Axes>"
       ]
@@ -230,7 +241,7 @@
     },
     {
      "data": {
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PP5zhw4dnzJgxeeGFFza6/U9/+tOcddZZmTZtWh5//PH86Ec/yg033JCvfe1rXVw5AJtL0AYAANCJLrvsspx66qmZPHly9t1338yaNSvbb799rr766o1uf//99+ejH/1oPv3pT2e33XbL0UcfnZNOOikPPvhgF1cOwOYStAEAAHSStWvXZuHChWloaGhbV1lZmYaGhixYsGCjYw499NAsXLiwLVh75plnctddd+XYY4/d5PusWbMmzc3N7RYAul5Z7zoKAADQm7300ktpaWlJXV1du/V1dXV54oknNjrm05/+dF566aUcdthhKZVKWbduXT73uc+941dHp0+fnm984xuF1g7A5nNGGwAAQDcyf/78XHjhhfnBD36Qhx9+OLfeemvuvPPOnH/++ZscM3Xq1DQ1NbUty5cv78KKAXibM9oAAAA6yc4775w+ffpk5cqV7davXLkygwYN2uiYc845JxMmTMgpp5ySJDnggAOyevXqfPazn83Xv/71VFZueL5EVVVVqqqqip8AAJvFGW0AAACdpF+/fjnooIMyb968tnWtra2ZN29eRo8evdExr7/++gZhWp8+fZIkpVKp84oFYKs5ow0AAKATTZkyJZMmTcrBBx+cQw45JDNmzMjq1aszefLkJMnEiRMzdOjQTJ8+PUkyduzYXHbZZRk5cmRGjRqVxYsX55xzzsnYsWPbAjcAuidBGwAAQCcaP358XnzxxZx77rlpbGzMiBEjMmfOnLYbJCxbtqzdGWxnn312KioqcvbZZ+e5557LLrvskrFjx+Zb3/pWuaYAQAdVlJx7vIHm5ubU1tamqakpNTU15S4HoMdzXG3P/gAoluPqhuwTgGJ19LjqGm0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFKGvQdu+992bs2LEZMmRIKioqcvvtt7d7vlQq5dxzz83gwYPTv3//NDQ05KmnnnrH1zzvvPNSUVHRbhk2bFgnzgKA7kyvAQAAukpZg7bVq1dn+PDhmTlz5kafv/jii/O9730vs2bNygMPPJD3vOc9GTNmTN588813fN399tsvK1asaFvuu+++zigfgB5ArwEAALpK33K++THHHJNjjjlmo8+VSqXMmDEjZ599do4//vgkyTXXXJO6urrcfvvtOfHEEzf5un379s2gQYM6pWYAeha9BgAA6Crd9hptS5YsSWNjYxoaGtrW1dbWZtSoUVmwYME7jn3qqacyZMiQ7LHHHvmHf/iHLFu27B23X7NmTZqbm9stAPR+XdVr9BkAANg2dNugrbGxMUlSV1fXbn1dXV3bcxszatSozJ49O3PmzMkVV1yRJUuW5GMf+1hWrVq1yTHTp09PbW1t21JfX1/MJADo1rqq1+gzAACwbei2QduWOuaYY3LCCSfkwAMPzJgxY3LXXXfl1VdfzY033rjJMVOnTk1TU1Pbsnz58i6sGICeZnN7jT4DAADbhm4btL193ZuVK1e2W79y5crNuibODjvskA9+8INZvHjxJrepqqpKTU1NuwWA3q+reo0+AwAA24ZuG7TtvvvuGTRoUObNm9e2rrm5OQ888EBGjx7d4dd57bXX8vTTT2fw4MGdUSYAPZheAwAAFKmsQdtrr72WRYsWZdGiRUnWX5R60aJFWbZsWSoqKnLGGWfkggsuyB133JFHH300EydOzJAhQzJu3Li21/jEJz6R73//+22Pv/SlL+VXv/pVli5dmvvvvz9/93d/lz59+uSkk07q4tkB0B3oNQAAQFfpW843f+ihh3LkkUe2PZ4yZUqSZNKkSZk9e3a+8pWvZPXq1fnsZz+bV199NYcddljmzJmT6urqtjFPP/10XnrppbbHf/rTn3LSSSfl//v//r/ssssuOeyww/Jf//Vf2WWXXbpuYgB0G3oNAADQVSpKpVKp3EV0N83NzamtrU1TU5Pr6AAUwHG1PfsDoFg94bg6c+bMXHLJJWlsbMzw4cNz+eWX55BDDtnk9q+++mq+/vWv59Zbb83LL7+cXXfdNTNmzMixxx7boffrCfsEoCfp6HG1rGe0AQAA9HY33HBDpkyZklmzZmXUqFGZMWNGxowZkyeffDIDBw7cYPu1a9fmqKOOysCBA3PzzTdn6NChefbZZ7PDDjt0ffEAbBZBGwAAQCe67LLLcuqpp2by5MlJklmzZuXOO+/M1VdfnbPOOmuD7a+++uq8/PLLuf/++7PddtslSXbbbbeuLBmALdRt7zoKAADQ061duzYLFy5MQ0ND27rKyso0NDRkwYIFGx1zxx13ZPTo0TnttNNSV1eX/fffPxdeeGFaWlo2+T5r1qxJc3NzuwWAridoAwAA6CQvvfRSWlpaUldX1259XV1dGhsbNzrmmWeeyc0335yWlpbcddddOeecc3LppZfmggsu2OT7TJ8+PbW1tW1LfX19ofMAoGMEbQAAAN1Ia2trBg4cmB/+8Ic56KCDMn78+Hz961/PrFmzNjlm6tSpaWpqaluWL1/ehRUD8DbXaAMAAOgkO++8c/r06ZOVK1e2W79y5coMGjRoo2MGDx6c7bbbLn369Glbt88++6SxsTFr165Nv379NhhTVVWVqqqqYosHYLM5ow0AAKCT9OvXLwcddFDmzZvXtq61tTXz5s3L6NGjNzrmox/9aBYvXpzW1ta2dX/84x8zePDgjYZsAHQfgjYAAIBONGXKlFx55ZX5yU9+kscffzyf//zns3r16ra7kE6cODFTp05t2/7zn/98Xn755Zx++un54x//mDvvvDMXXnhhTjvttHJNAYAO8tVRAACATjR+/Pi8+OKLOffcc9PY2JgRI0Zkzpw5bTdIWLZsWSor/3wORH19febOnZszzzwzBx54YIYOHZrTTz89X/3qV8s1BQA6qKJUKpXKXUR309zcnNra2jQ1NaWmpqbc5QD0eI6r7dkfAMVyXN2QfQJQrI4eV311FAAAAAAKIGgDAAAAgAII2gAAAACgAII2AAAAACiAoA0AAAAACtC33AUA0L09+WTyk58kzz+fDBqUTJiQ7LdfuasCoLd45ZXkmmuS3/0uqapKxo5NxoxJ+vQpd2UAsPkEbQBsVGtrcvrpyfe/n/Ttm5RKSUVFctFFycknJ7NmrV8PAFvqlluSf/zHZM2aPwdrs2Yl+++fzJmTDB1a3voAYHP56igAG/Wtb60P2ZJk3bqkpWX9/ybJ1VcnX/96+WoDoOd74IFk/Pj1IVuptL7HvN1nnngiOfroPz8GgJ5ii4K2j3/84/nGN76xwfpXXnklH//4x7e6KADK6/XXk0su2fTzpVLyve8lTU2d8/76DEDv9+1vrz9TulTa8Ll165I//CG5666ur+ttkyZNyr333lu+AgDokbYoaJs/f36+//3vZ9y4cVm9enXb+rVr1+ZXv/pVYcUBUB733pusWvXO27z5ZnL33Z3z/voMQO/W0pL83//7zmes9e2b3HZb19X0l5qamtLQ0JAPfOADufDCC/Pcc8+VrxgAeowt/uroPffck8bGxnzkIx/J0qVLCywJgHJ7/fWObffGG51Xgz4D0Hu9fUmCd9La2rl95t3cfvvtee655/L5z38+N9xwQ3bbbbccc8wxufnmm/PWW2+VrzAAurUtDtoGDx6cX/3qVznggAPy4Q9/OPPnzy+wLADKaf/9i91uS+gzAL1XVVWy++7rvzr6Tjqzz3TELrvskilTpuR3v/tdHnjggey1116ZMGFChgwZkjPPPDNPPfVUeQsEoNvZoqCt4v91xKqqqvz0pz/N6aefnr/5m7/JD37wg0KLA6A8PvjB5PDD/3wHuL/Up09y0EHJyJGd8/76DEDv9y//8s7PV1Ymn/lM19TyblasWJG77747d999d/r06ZNjjz02jz76aPbdd99897vfLXd5AHQjfbdkUOkvrlh69tlnZ5999smkSZMKKQqA8rvqqmT06OTVV9tfQ6dv3+S9701+8pPOe299BqD3O+205Oc/T+bPX/810bf16bP+a6VXXJEMGVK28vLWW2/ljjvuyI9//OP84he/yIEHHpgzzjgjn/70p1NTU5Mkue222/KZz3wmZ555ZvkKBaBb2aKgbcmSJdlll13arfvkJz+ZYcOG5aGHHiqkMADKa6+9kocfTqZPT2bPXn+dnKqq5B//Mfna15I99ui899ZnAHq/fv3W31X03/4tufzy5E9/Wr/+r/86mTo1Oeqo8tY3ePDgtLa25qSTTsqDDz6YESNGbLDNkUcemR122KHLawOg+6oo/eVpA6S5uTm1tbVpampq+2sVwLZs3bqkuTkZMCDZbrvNH++42p79AdBeqZQ0Na0P37bffvPHd8Zx9dprr80JJ5yQ6urqQl6vq+k1AMXq6HF1i85oA2Db0rdvstNO5a4CgN6qoiLpbieGTZgwodwlANADbfFdRwEAAACAPxO0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABShr0Hbvvfdm7NixGTJkSCoqKnL77be3e75UKuXcc8/N4MGD079//zQ0NOSpp55619edOXNmdtttt1RXV2fUqFF58MEHO2kGAHR3eg0AANBVyhq0rV69OsOHD8/MmTM3+vzFF1+c733ve5k1a1YeeOCBvOc978mYMWPy5ptvbvI1b7jhhkyZMiXTpk3Lww8/nOHDh2fMmDF54YUXOmsaAHRjeg0AANBVKkqlUqncRSRJRUVFbrvttowbNy7J+jMMhgwZkn/913/Nl770pSRJU1NT6urqMnv27Jx44okbfZ1Ro0blwx/+cL7//e8nSVpbW1NfX59/+Zd/yVlnndWhWpqbm1NbW5umpqbU1NRs/eQAtnHd5bjaXXpNd9kfAL2F4+qG7BOAYnX0uNptr9G2ZMmSNDY2pqGhoW1dbW1tRo0alQULFmx0zNq1a7Nw4cJ2YyorK9PQ0LDJMQBsu/QaAACgSH3LXcCmNDY2Jknq6urara+rq2t77i+99NJLaWlp2eiYJ554YpPvtWbNmqxZs6btcXNz85aWDUAP0lW9Rp8BAIBtQ7c9o60rTZ8+PbW1tW1LfX19uUsCoBfRZwAAYNvQbYO2QYMGJUlWrlzZbv3KlSvbnvtLO++8c/r06bNZY5Jk6tSpaWpqaluWL1++ldUD0BN0Va/RZwAAYNvQbYO23XffPYMGDcq8efPa1jU3N+eBBx7I6NGjNzqmX79+Oeigg9qNaW1tzbx58zY5JkmqqqpSU1PTbgGg9+uqXqPPAADAtqGs12h77bXXsnjx4rbHS5YsyaJFi7LTTjvl/e9/f84444xccMEF+cAHPpDdd98955xzToYMGdJ2t7gk+cQnPpG/+7u/yxe/+MUkyZQpUzJp0qQcfPDBOeSQQzJjxoysXr06kydP7urpAdAN6DUAAEBXKWvQ9tBDD+XII49sezxlypQkyaRJkzJ79ux85StfyerVq/PZz342r776ag477LDMmTMn1dXVbWOefvrpvPTSS22Px48fnxdffDHnnntuGhsbM2LEiMyZM2eDi1YDsG3QawAAgK5SUSqVSuUuortpbm5ObW1tmpqafL0HoACOq+3ZHwDFclzdkH0CUKyOHle77TXaAAAAeouZM2dmt912S3V1dUaNGpUHH3ywQ+Ouv/76VFRUtLukAQDdl6ANAACgE91www2ZMmVKpk2blocffjjDhw/PmDFj8sILL7zjuKVLl+ZLX/pSPvaxj3VRpQBsLUEbAABAJ7rsssty6qmnZvLkydl3330za9asbL/99rn66qs3OaalpSX/8A//kG984xvZY489urBaALaGoA0AAKCTrF27NgsXLkxDQ0PbusrKyjQ0NGTBggWbHPfNb34zAwcOzMknn9yh91mzZk2am5vbLQB0PUEbAABAJ3nppZfS0tKywZ2p6+rq0tjYuNEx9913X370ox/lyiuv7PD7TJ8+PbW1tW1LfX39VtUNwJYRtAEAAHQTq1atyoQJE3LllVdm55137vC4qVOnpqmpqW1Zvnx5J1YJwKb0LXcBAAAAvdXOO++cPn36ZOXKle3Wr1y5MoMGDdpg+6effjpLly7N2LFj29a1trYmSfr27Zsnn3wye+655wbjqqqqUlVVVXD1AGwuZ7QBAAB0kn79+uWggw7KvHnz2ta1trZm3rx5GT169AbbDxs2LI8++mgWLVrUtvzt3/5tjjzyyCxatMhXQgG6OWe0AQAAdKIpU6Zk0qRJOfjgg3PIIYdkxowZWb16dSZPnpwkmThxYoYOHZrp06enuro6+++/f7vxO+ywQ5JssB6A7kfQBgAA0InGjx+fF198Meeee24aGxszYsSIzJkzp+0GCcuWLUtlpS8bAfQGFaVSqVTuIrqb5ubm1NbWpqmpKTU1NeUuB6DHc1xtz/4AKJbj6obsE4BidfS46s8mAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABSg2wdtq1atyhlnnJFdd901/fv3z6GHHprf/va3m9x+/vz5qaio2GBpbGzswqoB6En0GgAAoAh9y13AuznllFPy+9//Ptdee22GDBmS6667Lg0NDfnDH/6QoUOHbnLck08+mZqamrbHAwcO7IpyAeiB9BoAAKAI3fqMtjfeeCO33HJLLr744vz1X/919tprr5x33nnZa6+9csUVV7zj2IEDB2bQoEFtS2Vlt54qAGWi1wAAAEXp1p8I1q1bl5aWllRXV7db379//9x3333vOHbEiBEZPHhwjjrqqPzmN795x23XrFmT5ubmdgsA24au6DX6DAAAbBu6ddA2YMCAjB49Oueff36ef/75tLS05LrrrsuCBQuyYsWKjY4ZPHhwZs2alVtuuSW33HJL6uvrc8QRR+Thhx/e5PtMnz49tbW1bUt9fX1nTQmAbqYreo0+AwAA24aKUqlUKncR7+Tpp5/OZz7zmdx7773p06dPPvShD+WDH/xgFi5cmMcff7xDr3H44Yfn/e9/f6699tqNPr9mzZqsWbOm7XFzc3Pq6+vT1NTU7to7AGyZ5ubm1NbWdtvjamf3Gn0GoHN19z5TDvYJQLE6elzt1me0Jcmee+6ZX/3qV3nttdeyfPnyPPjgg3nrrbeyxx57dPg1DjnkkCxevHiTz1dVVaWmpqbdAsC2o7N7jT4DAADbhm4ftL3tPe95TwYPHpxXXnklc+fOzfHHH9/hsYsWLcrgwYM7sToAegO9BgAA2Bp9y13Au5k7d25KpVL23nvvLF68OF/+8pczbNiwTJ48OUkyderUPPfcc7nmmmuSJDNmzMjuu++e/fbbL2+++Wauuuqq/Od//md+8YtflHMaAHRjeg0AAFCEbh+0NTU1ZerUqfnTn/6UnXbaKZ/85CfzrW99K9ttt12SZMWKFVm2bFnb9mvXrs2//uu/5rnnnsv222+fAw88MPfcc0+OPPLIck0BgG5OrwEAAIrQ7W+GUA4uHApQLMfV9uwPgGI5rm7IPgEoVq+5GQIAAEBPN3PmzOy2226prq7OqFGj8uCDD25y2yuvvDIf+9jHsuOOO2bHHXdMQ0PDO24PQPchaAMAAOhEN9xwQ6ZMmZJp06bl4YcfzvDhwzNmzJi88MILG91+/vz5Oemkk/LLX/4yCxYsSH19fY4++ug899xzXVw5AJtL0AYAANCJLrvsspx66qmZPHly9t1338yaNSvbb799rr766o1u/x//8R/5whe+kBEjRmTYsGG56qqr0tramnnz5nVx5QBsLkEbAABAJ1m7dm0WLlyYhoaGtnWVlZVpaGjIggULOvQar7/+et56663stNNOm9xmzZo1aW5ubrcA0PUEbQAAAJ3kpZdeSktLS+rq6tqtr6urS2NjY4de46tf/WqGDBnSLqz7S9OnT09tbW3bUl9fv1V1A7BlBG0AAADd1Le//e1cf/31ue2221JdXb3J7aZOnZqmpqa2Zfny5V1YJQBv61vuAgAAAHqrnXfeOX369MnKlSvbrV+5cmUGDRr0jmO/853v5Nvf/nbuueeeHHjgge+4bVVVVaqqqra6XgC2jjPaAAAAOkm/fv1y0EEHtbuRwds3Nhg9evQmx1188cU5//zzM2fOnBx88MFdUSoABXBGGwAAQCeaMmVKJk2alIMPPjiHHHJIZsyYkdWrV2fy5MlJkokTJ2bo0KGZPn16kuSiiy7Kueeem5/+9KfZbbfd2q7l9t73vjfvfe97yzYPAN6doA0AAKATjR8/Pi+++GLOPffcNDY2ZsSIEZkzZ07bDRKWLVuWyso/f9noiiuuyNq1a/OpT32q3etMmzYt5513XleWDsBmErQBAAB0si9+8Yv54he/uNHn5s+f3+7x0qVLO78gADqFa7QBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUABBGwAAAAAUQNAGAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEABBG0AAAAAUIBuH7StWrUqZ5xxRnbdddf0798/hx56aH7729++45j58+fnQx/6UKqqqrLXXntl9uzZXVMsAD2SXgMAABSh2wdtp5xySu6+++5ce+21efTRR3P00UenoaEhzz333Ea3X7JkSY477rgceeSRWbRoUc4444yccsopmTt3bhdXDkBPodcAAABFqCiVSqVyF7Epb7zxRgYMGJCf/exnOe6449rWH3TQQTnmmGNywQUXbDDmq1/9au688878/ve/b1t34okn5tVXX82cOXM69L7Nzc2pra1NU1NTampqtn4iANu47nxcLUev6c77A6AnclzdkH0CUKyOHle79Rlt69atS0tLS6qrq9ut79+/f+67776NjlmwYEEaGhrarRszZkwWLFiwyfdZs2ZNmpub2y0AbBu6otfoMwAAsG3o1kHbgAEDMnr06Jx//vl5/vnn09LSkuuuuy4LFizIihUrNjqmsbExdXV17dbV1dWlubk5b7zxxkbHTJ8+PbW1tW1LfX194XMBoHvqil6jzwAAwLahWwdtSXLttdemVCpl6NChqaqqyve+972cdNJJqawsrvSpU6emqampbVm+fHlhrw1A99fZvUafAQCAbUPfchfwbvbcc8/86le/yurVq9Pc3JzBgwdn/Pjx2WOPPTa6/aBBg7Jy5cp261auXJmampr0799/o2OqqqpSVVVVeO0A9Ayd3Wv0GQAA2DZ0+zPa3vae97wngwcPziuvvJK5c+fm+OOP3+h2o0ePzrx589qtu/vuuzN69OiuKBOAHkyvAQAAtka3D9rmzp2bOXPmZMmSJbn77rtz5JFHZtiwYZk8eXKS9V/HmThxYtv2n/vc5/LMM8/kK1/5Sp544on84Ac/yI033pgzzzyzXFMAoJvTawAAgCJ0+6Ctqakpp512WoYNG5aJEyfmsMMOy9y5c7PddtslSVasWJFly5a1bb/77rvnzjvvzN13353hw4fn0ksvzVVXXZUxY8aUawoAdHN6DQAAUISKUqlUKncR3U1zc3Nqa2vT1NSUmpqaDo9bsWpFZi+anT++/McM6DcgJ+x7Qg57/2GpqKjoxGoBur8tPa72Vlu6P95c92ZufOzG3PvsvalIRQ7f7fB8at9PpbpvdSdWC9D96TMb2pJ9Umptzb3/9/LcvOBHeW3dGxm24175p09fkrrd9+/kagG6v44eV7v9GW09xeUPXJ7679bn7F+enev++7pc8dAV+evZf52PX/PxNL3ZVO7yAOjhfvvcb/P+774/k26flJ/87ieZ/bvZmXDbhOw2Y7c8vOLhcpcHwLuYOXNmdtttt1RXV2fUqFF58MEH33H7m266KcOGDUt1dXUOOOCA3HXXXZ1a3ysrluSvp+yQIxadkVn9Hs112y/O19bOyV/9+IDMuuzTnfreAL2JoK0Atz5+a/73nP+dllJLWkutWde6Luta1yVJfv3srzP+5vFlrhCAnmzFqhU56tqj8vIbLydJuz7z0usvpeGahryw+oVylgjAO7jhhhsyZcqUTJs2LQ8//HCGDx+eMWPG5IUXNn7svv/++3PSSSfl5JNPziOPPJJx48Zl3Lhx+f3vf98p9ZVaW/PJiz6UBbWrkiTr+qxfWivX/+/nV/2f/Py6czvlvQF6G0HbViqVSvnGr76Rimz866EtpZbMfXpuFjUu6trCAOg1Zj00K6+tfS0tpZYNnmsptaRpTVOueviqMlQGQEdcdtllOfXUUzN58uTsu+++mTVrVrbffvtcffXVG93+3/7t3/I3f/M3+fKXv5x99tkn559/fj70oQ/l+9//fqfU99t7fpJf7vhqWjbx6bCyNblg4WWd8t4AvY2gbSs9t+q5/PfK/04pm77UXd+Kvrn9idu7rigAepWb/nDTRkO2t7WWWnPTYzd1YUUAdNTatWuzcOHCNDQ0tK2rrKxMQ0NDFixYsNExCxYsaLd9kowZM2aT2yfJmjVr0tzc3G7pqNt/fWX6brrNpLUyeWCH1Xlh6WMdfk2AbZWgbSu9/tbr77pNRUVF3njrjS6oBoDeaPVbqwvZBoCu99JLL6WlpSV1dXXt1tfV1aWxsXGjYxobGzdr+ySZPn16amtr25b6+voO1/j6ujc28f2c9t5Y/WqHXxNgWyVo20r1NfV5z3bvecdt3mp9K/sPdKceALbMyEEj07ey7yaf71vZNyMHjezCigDobqZOnZqmpqa2Zfny5R0ee8Dg4XnrXT4Z1q5JBu8xfCurBOj9BG1bqf92/XPyyJPTp6LPRp+vSEV2qN4hJ+x3QhdXBkBv8YUPf6Ht5gcbs651XT7/4c93YUUAdNTOO++cPn36ZOXKle3Wr1y5MoMGDdromEGDBm3W9klSVVWVmpqadktHnTjh4gxYm1Rs4mo4fVqTUysOTr/+7+3wawJsqwRtBfjmkd/MsJ2HbRC29anokz6VffIf/+s/Ut23ukzVAdDTHbXHUTntw6clSbub71T+vzY+5SNTcsRuR5SjNADeRb9+/XLQQQdl3rx5betaW1szb968jB49eqNjRo8e3W77JLn77rs3uf3Wes+OA3PdPl9LZSnp8xfXauvTmuzfXJ1zp/ysU94boLcRtBWgtro2v/nMb3LWYWdlp/47JUkqKyozdu+xuf8z9+fYDxxb5goB6MkqKipy+TGX58fH/zj77rJv2/r96/bPtX93bb5z9HfKWB0A72bKlCm58sor85Of/CSPP/54Pv/5z2f16tWZPHlykmTixImZOnVq2/ann3565syZk0svvTRPPPFEzjvvvDz00EP54he/2Gk1/u2Eb+W+0Vfm2FV1qWxdv27nNyrytXwsvz776Qx435BOe2+A3qSiVCpt+naZ26jm5ubU1tamqalps065Ttbf+a3pzaZsv932qepb1UkVAvQsW3Nc7Y22dn80r2lORSoyoGpAJ1QH0PP0hD7z/e9/P5dcckkaGxszYsSIfO9738uoUaOSJEcccUR22223zJ49u237m266KWeffXaWLl2aD3zgA7n44otz7LEd/wP+1uyTN197NW+seiW1A+tT2WfT1wgF2JZ09LgqaNuIntCoAXoSx9X27A+AYjmubsg+AShWR4+rvjoKAAAAAAUQtAEAAABAAQRtAAAAAFAAQRsAAAAAFEDQBgAAAAAFELQBAAAAQAEEbQAAAABQAEEbAAAAABRA0AYAAAAABRC0AQAAAEAB+pa7gO6oVColSZqbm8tcCUDv8Pbx9O3j67ZOnwEolj6zIb0GoFgd7TWCto1YtWpVkqS+vr7MlQD0LqtWrUptbW25yyg7fQagc+gzf6bXAHSOd+s1FSV/9tlAa2trnn/++QwYMCAVFRWbPb65uTn19fVZvnx5ampqOqHCbYP9WAz7sRj249YplUpZtWpVhgwZkspKVy3QZ96dOfZ8vX1+iTl2J/rMhram1/SUf/fuzn4shv1YDPtx63W01zijbSMqKyvzV3/1V1v9OjU1NX6AC2A/FsN+LIb9uOWcYfBn+kzHmWPP19vnl5hjd6HPtFdEr+kJ/+49gf1YDPuxGPbj1ulIr/HnHgAAAAAogKANAAAAAAogaOsEVVVVmTZtWqqqqspdSo9mPxbDfiyG/Uh3si38PJpjz9fb55eYI72Xf/di2I/FsB+LYT92HTdDAAAAAIACOKMNAAAAAAogaAMAAACAAgjaAAAAAKAAgjYAAAAAKICgbQvNnDkzu+22W6qrqzNq1Kg8+OCD77j9TTfdlGHDhqW6ujoHHHBA7rrrri6qtHvbnP04e/bsVFRUtFuqq6u7sNru5957783YsWMzZMiQVFRU5Pbbb3/XMfPnz8+HPvShVFVVZa+99srs2bM7vc7ubnP34/z58zf4WayoqEhjY2PXFMw2YVvoM5szxyuvvDIf+9jHsuOOO2bHHXdMQ0PDu+6T7mBz/x3fdv3116eioiLjxo3r3AK30ubO79VXX81pp52WwYMHp6qqKh/84Ae7/c/q5s5xxowZ2XvvvdO/f//U19fnzDPPzJtvvtlF1W4+v0tsu7aFPtMVfJ7Zeo5DxfCZpvsQtG2BG264IVOmTMm0adPy8MMPZ/jw4RkzZkxeeOGFjW5///3356STTsrJJ5+cRx55JOPGjcu4cePy+9//vosr7142dz8mSU1NTVasWNG2PPvss11YcfezevXqDB8+PDNnzuzQ9kuWLMlxxx2XI488MosWLcoZZ5yRU045JXPnzu3kSru3zd2Pb3vyySfb/TwOHDiwkypkW7Mt9JnNneP8+fNz0kkn5Ze//GUWLFiQ+vr6HH300Xnuuee6uPKO25I+lyRLly7Nl770pXzsYx/rokq3zObOb+3atTnqqKOydOnS3HzzzXnyySdz5ZVXZujQoV1cecdt7hx/+tOf5qyzzsq0adPy+OOP50c/+lFuuOGGfO1rX+viyjvO7xLbpm2hz3QFn2eK4ThUDJ9pupESm+2QQw4pnXbaaW2PW1paSkOGDClNnz59o9v//d//fem4445rt27UqFGlf/7nf+7UOru7zd2PP/7xj0u1tbVdVF3Pk6R02223veM2X/nKV0r77bdfu3Xjx48vjRkzphMr61k6sh9/+ctflpKUXnnllS6piW3PttBnNneOf2ndunWlAQMGlH7yk590VolbbUvmuG7dutKhhx5auuqqq0qTJk0qHX/88V1Q6ZbZ3PldccUVpT322KO0du3aripxq23uHE877bTSxz/+8XbrpkyZUvroRz/aqXUWxe8S245toc90BZ9niuc4VAyfacrLGW2bae3atVm4cGEaGhra1lVWVqahoSELFizY6JgFCxa02z5JxowZs8nttwVbsh+T5LXXXsuuu+6a+vr6HH/88Xnssce6otxew89isUaMGJHBgwfnqKOOym9+85tyl0MvsS30mS3tAf/T66+/nrfeeis77bRTZ5W5VbZ0jt/85jczcODAnHzyyV1R5hbbkvndcccdGT16dE477bTU1dVl//33z4UXXpiWlpauKnuzbMkcDz300CxcuLDtq2PPPPNM7rrrrhx77LFdUnNX6GnHGza0LfSZruDzTPn4eSyWzzTFE7RtppdeeiktLS2pq6trt76urm6T32VubGzcrO23BVuyH/fee+9cffXV+dnPfpbrrrsura2tOfTQQ/OnP/2pK0ruFTb1s9jc3Jw33nijTFX1PIMHD86sWbNyyy235JZbbkl9fX2OOOKIPPzww+UujV5gW+gzWzLHv/TVr341Q4YM2eAX7e5iS+Z433335Uc/+lGuvPLKrihxq2zJ/J555pncfPPNaWlpyV133ZVzzjknl156aS644IKuKHmzbckcP/3pT+eb3/xmDjvssGy33XbZc889c8QRR3Trr45uLr9L9HzbQp/pCj7PlI/jUDF8puk8fctdAHTU6NGjM3r06LbHhx56aPbZZ5/8+7//e84///wyVsa2Zu+9987ee+/d9vjQQw/N008/ne9+97u59tpry1gZbBu+/e1v5/rrr8/8+fN7zUWkV61alQkTJuTKK6/MzjvvXO5yOkVra2sGDhyYH/7wh+nTp08OOuigPPfcc7nkkksybdq0cpdXiPnz5+fCCy/MD37wg4waNSqLFy/O6aefnvPPPz/nnHNOucsDysznGboTn2k6j6BtM+28887p06dPVq5c2W79ypUrM2jQoI2OGTRo0GZtvy3Ykv34l7bbbruMHDkyixcv7owSe6VN/SzW1NSkf//+ZaqqdzjkkENy3333lbsMeoFtoc9sTQ/4zne+k29/+9u55557cuCBB3ZmmVtlc+f49NNPZ+nSpRk7dmzbutbW1iRJ37598+STT2bPPffs3KI3w5b8Gw4ePDjbbbdd+vTp07Zun332SWNjY9auXZt+/fp1as2ba0vmeM4552TChAk55ZRTkiQHHHBAVq9enc9+9rP5+te/nsrKnv9lEr9L9HzbQp/pCj7PlI/jUOfxmaYYPb/bd7F+/frloIMOyrx589rWtba2Zt68ee3+OvE/jR49ut32SXL33XdvcvttwZbsx7/U0tKSRx99NIMHD+6sMnsdP4udZ9GiRX4WKcS20Ge2tAdcfPHFOf/88zNnzpwcfPDBXVHqFtvcOQ4bNiyPPvpoFi1a1Lb87d/+bdsd1err67uy/He1Jf+GH/3oR7N48eK2ADFJ/vjHP2bw4MHdLmRLtmyOr7/++gZh2tvBYqlU6rxiu1BPO96woW2hz3QFn2fKx89j5/GZpiDlvhtDT3T99deXqqqqSrNnzy794Q9/KH32s58t7bDDDqXGxsZSqVQqTZgwoXTWWWe1bf+b3/ym1Ldv39J3vvOd0uOPP16aNm1aabvttis9+uij5ZpCt7C5+/Eb3/hGae7cuaWnn366tHDhwtKJJ55Yqq6uLj322GPlmkLZrVq1qvTII4+UHnnkkVKS0mWXXVZ65JFHSs8++2ypVCqVzjrrrNKECRPatn/mmWdK22+/fenLX/5y6fHHHy/NnDmz1KdPn9KcOXPKNYVuYXP343e/+93S7bffXnrqqadKjz76aOn0008vVVZWlu65555yTYFeZlvoM5s7x29/+9ulfv36lW6++ebSihUr2pZVq1aVawrvanPn+Je6+11HN3d+y5YtKw0YMKD0xS9+sfTkk0+Wfv7zn5cGDhxYuuCCC8o1hXe1uXOcNm1aacCAAaX/83/+T+mZZ54p/eIXvyjtueeepb//+78v1xTeld8ltk3bQp/pCj7PFMNxqBg+03QfgrYtdPnll5fe//73l/r161c65JBDSv/1X//V9tzhhx9emjRpUrvtb7zxxtIHP/jBUr9+/Ur77bdf6c477+ziirunzdmPZ5xxRtu2dXV1pWOPPbb08MMPl6Hq7uPtWzL/5fL2fps0aVLp8MMP32DMiBEjSv369SvtsccepR//+MddXnd3s7n78aKLLirtueeeperq6tJOO+1UOuKII0r/+Z//WZ7i6bW2hT6zOXPcddddN/r/02nTpnV94Zthc/8d/6fuHrSVSps/v/vvv780atSoUlVVVWmPPfYofetb3yqtW7eui6vePJszx7feeqt03nnntfWI+vr60he+8IXSK6+80vWFd5DfJbZd20Kf6Qo+z2w9x6Fi+EzTfVSUSr3kPHYAAAAAKCPXaAMAAACAAgjaAAAAAKAAgjYAAAAAKICgDQAAAAAKIGgDAAAAgAII2gAAAACgAII2AAAAACiAoA0AAAAACiBoAwAAAIACCNoAAAAAoACCNuhhXnzxxQwaNCgXXnhh27r7778//fr1y7x588pYGQC9xTXXXJP3ve99WbNmTbv148aNy4QJE8pUFQC9hc809GYVpVKpVO4igM1z1113Zdy4cbn//vuz9957Z8SIETn++ONz2WWXlbs0AHqBN954I4MHD86VV16ZE044IUnywgsvZOjQofnFL36RI488sswVAtDT+UxDbyVogx7qtNNOyz333JODDz44jz76aH7729+mqqqq3GUB0Et84QtfyNKlS3PXXXclSS677LLMnDkzixcvTkVFRZmrA6A38JmG3kjQBj3UG2+8kf333z/Lly/PwoULc8ABB5S7JAB6kUceeSQf/vCH8+yzz2bo0KE58MADc8IJJ+Scc84pd2kA9BI+09AbuUYb9FBPP/10nn/++bS2tmbp0qXlLgeAXmbkyJEZPnx4rrnmmixcuDCPPfZY/umf/qncZQHQi/hMQ2/kjDbogdauXZtDDjkkI0aMyN57750ZM2bk0UcfzcCBA8tdGgC9yBVXXJEZM2bkqKOOylNPPZW5c+eWuyQAegmfaeitBG3QA335y1/OzTffnN/97nd573vfm8MPPzy1tbX5+c9/Xu7SAOhFmpqaMmTIkKxbty7XXHNNxo8fX+6SAOglfKaht/LVUehh5s+fnxkzZuTaa69NTU1NKisrc+211+bXv/51rrjiinKXB0AvUltbm09+8pN573vfm3HjxpW7HAB6CZ9p6M2c0QYAwCZ94hOfyH777Zfvfe975S4FAKDbE7QBALCBV155JfPnz8+nPvWp/OEPf8jee+9d7pIAALq9vuUuAACA7mfkyJF55ZVXctFFFwnZAAA6yBltAAAAAFAAN0MAAAAAgAII2gAAAACgAII2AAAAACiAoA0AAAAACiBoAwAAAIACCNoAAAAAoACCNgAAAAAogKANAAAAAAogaAMAAACAAvz/e/gHozxT/KgAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -244,41 +255,64 @@
     "\n",
     "plt.figure(figsize=(15, 5))\n",
     "plt.subplot(121)\n",
-    "plt.plot(np.sort(dh.eigh()))\n",
+    "plt.plot(np.sort(dh.eigh()), marker=\"o\", linestyle=\" \", label=\"sisl\")\n",
+    "plt.plot(np.sort(siesta_eigs[0, 0]), marker=\"o\", linestyle=\" \", label=\"siesta\")\n",
+    "plt.ylim(None, 10)\n",
+    "plt.xlim(None, 75)\n",
+    "plt.legend()\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",
+    "DOS = sisl.physics.electron.DOS(np.linspace(-15, 85, 1000), dh.eigh())\n",
+    "plt.plot(DOS, np.linspace(-15, 85, 1000))\n",
+    "DOS = sisl.physics.electron.DOS(np.linspace(-15, 85, 1000), siesta_eigs[0, 0])\n",
+    "plt.plot(DOS, np.linspace(-15, 85, 1000))\n",
+    "plt.ylim(None, 10)\n",
     "\n",
     "coords = dh.xyz[-3:]\n",
     "\n",
+    "shift = np.array([-1, 0, 0]) @ simulation_parameters[\"cell\"]\n",
+    "\n",
     "\n",
     "plt.figure(figsize=(15, 5))\n",
     "plt.subplot(131)\n",
     "plt.scatter(coords[:, 0], coords[:, 2], color=[\"r\", \"g\", \"b\"])\n",
+    "plt.scatter(\n",
+    "    (coords + shift)[:, 0], (coords + shift)[:, 2], color=[\"r\", \"g\", \"b\"], marker=\"x\"\n",
+    ")\n",
     "plt.xlabel(\"x\")\n",
     "plt.ylabel(\"z\")\n",
     "plt.subplot(132)\n",
     "plt.scatter(coords[:, 1], coords[:, 2], color=[\"r\", \"g\", \"b\"])\n",
+    "plt.scatter(\n",
+    "    (coords + shift)[:, 1], (coords + shift)[:, 2], color=[\"r\", \"g\", \"b\"], marker=\"x\"\n",
+    ")\n",
     "plt.xlabel(\"y\")\n",
     "plt.ylabel(\"z\")\n",
     "plt.subplot(133)\n",
     "plt.scatter(coords[:, 0], coords[:, 1], color=[\"r\", \"g\", \"b\"])\n",
+    "plt.scatter(\n",
+    "    (coords + shift)[:, 0], (coords + shift)[:, 1], color=[\"r\", \"g\", \"b\"], marker=\"x\"\n",
+    ")\n",
     "plt.xlabel(\"x\")\n",
     "plt.ylabel(\"y\")\n",
-    "print(\"xyz[-3:]: red, green, blue\")"
+    "print(\"xyz[-3:]: red, green, blue\")\n",
+    "\n",
+    "print(np.linalg.norm(coords[0] - coords[1]))\n",
+    "print(np.linalg.norm(coords[0] - coords[2]))\n",
+    "print(np.linalg.norm(coords[2] - coords[1]))\n",
+    "print(np.linalg.norm(coords[0] - (coords + shift)[2]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hamiltonian and exchange field rotated. Elapsed time: 292.32726 s\n",
+      "Hamiltonian and exchange field rotated. Elapsed time: 4.926466833 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -380,25 +414,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Site and pair dictionaries created. Elapsed time: 292.347581791 s\n",
+      "Site and pair dictionaries created. Elapsed time: 4.962375291 s\n",
       "================================================================================================================================================================\n"
      ]
     }
    ],
    "source": [
     "# for every site we have to store 3 Greens function (and the associated _tmp-s) in the 3 reference directions\n",
-    "for i, mag_ent in enumerate(magnetic_entities):\n",
+    "for mag_ent in magnetic_entities:\n",
     "    parsed = parse_magnetic_entity(dh, **mag_ent)  # parse orbital indexes\n",
-    "    magnetic_entities[i][\"orbital_indeces\"] = parsed\n",
-    "    # calculate spin box indexes\n",
-    "    magnetic_entities[i][\"spin_box_indeces\"] = blow_up_orbindx(parsed)\n",
+    "    mag_ent[\"orbital_indeces\"] = parsed\n",
+    "    mag_ent[\"spin_box_indeces\"] = blow_up_orbindx(parsed)  # calculate spin box indexes\n",
     "    # if orbital is not set use all\n",
     "    if \"l\" not in mag_ent.keys():\n",
     "        mag_ent[\"l\"] = \"all\"\n",
@@ -422,12 +455,16 @@
     "\n",
     "    mag_ent[\"energies\"] = []  # we will store the second order energy derivations here\n",
     "\n",
+    "    # These will be the perturbed potentials from eq. 100\n",
+    "    mag_ent[\"Vu1\"] = []  # so they are independent in memory\n",
+    "    mag_ent[\"Vu2\"] = []\n",
+    "\n",
     "    mag_ent[\"Gii\"] = []  # Greens function\n",
     "    mag_ent[\"Gii_tmp\"] = []  # Greens function for parallelization\n",
-    "    # These will be the perturbed potentials from eq. 100\n",
-    "    mag_ent[\"Vu1\"] = [list([]) for _ in range(len(ref_xcf_orientations))]\n",
-    "    mag_ent[\"Vu2\"] = [list([]) for _ in range(len(ref_xcf_orientations))]\n",
     "    for i in ref_xcf_orientations:\n",
+    "        # Rotations for every quantization axis\n",
+    "        mag_ent[\"Vu1\"].append([])\n",
+    "        mag_ent[\"Vu2\"].append([])\n",
     "        # Greens functions for every quantization axis\n",
     "        mag_ent[\"Gii\"].append(\n",
     "            np.zeros((eset, spin_box_shape, spin_box_shape), dtype=\"complex128\")\n",
@@ -439,6 +476,12 @@
     "# for every site we have to store 2x3 Greens function (and the associated _tmp-s)\n",
     "# in the 3 reference directions, because G_ij and G_ji are both needed\n",
     "for pair in pairs:\n",
+    "    # calculate distance\n",
+    "    xyz_ai = magnetic_entities[pair[\"ai\"]][\"xyz\"]\n",
+    "    xyz_aj = magnetic_entities[pair[\"aj\"]][\"xyz\"]\n",
+    "    xyz_aj = xyz_aj + pair[\"Ruc\"] @ simulation_parameters[\"cell\"]\n",
+    "    pair[\"dist\"] = np.linalg.norm(xyz_ai - xyz_aj)\n",
+    "\n",
     "    # calculate size for Greens function generation\n",
     "    spin_box_shape_i = len(magnetic_entities[pair[\"ai\"]][\"spin_box_indeces\"])\n",
     "    spin_box_shape_j = len(magnetic_entities[pair[\"aj\"]][\"spin_box_indeces\"])\n",
@@ -495,22 +538,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "k loop:   0%|          | 0/100 [00:37<?, ?it/s]\n",
-      "k loop:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      "k loop:   0%|          | 0/400 [00:00<?, ?it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "k set created. Elapsed time: 292.813134416 s\n",
+      "k set created. Elapsed time: 4.990981666 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -538,7 +580,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rotations done perpendicular to quantization axis. Elapsed time: 135.307556041 s\n",
+      "Rotations done perpendicular to quantization axis. Elapsed time: 5.319766625 s\n",
       "================================================================================================================================================================\n"
      ]
     }
@@ -574,13 +616,10 @@
     "        Vu2 = 1 / 8 * commutator(commutator(Tu, rot_H_XCF_uc), Tu)  # equation 100\n",
     "\n",
     "        for mag_ent in magnetic_entities:\n",
+    "            idx = mag_ent[\"spin_box_indeces\"]\n",
     "            # fill up the perturbed potentials (for now) based on the on-site projections\n",
-    "            mag_ent[\"Vu1\"][i].append(\n",
-    "                Vu1[:, mag_ent[\"spin_box_indeces\"]][mag_ent[\"spin_box_indeces\"], :]\n",
-    "            )\n",
-    "            mag_ent[\"Vu2\"][i].append(\n",
-    "                Vu2[:, mag_ent[\"spin_box_indeces\"]][mag_ent[\"spin_box_indeces\"], :]\n",
-    "            )\n",
+    "            mag_ent[\"Vu1\"][i].append(Vu1[:, idx][idx, :])\n",
+    "            mag_ent[\"Vu2\"][i].append(Vu2[:, idx][idx, :])\n",
     "\n",
     "if rank == root_node:\n",
     "    times[\"reference_rotations_time\"] = timer()\n",
@@ -602,15 +641,15 @@
      "output_type": "stream",
      "text": [
       "Starting matrix inversions\n",
-      "Total number of k points: 100\n",
-      "Number of energy samples per k point: 60\n",
+      "Total number of k points: 400\n",
+      "Number of energy samples per k point: 100\n",
       "Total number of directions: 3\n",
-      "Total number of matrix inversions: 18000\n",
+      "Total number of matrix inversions: 120000\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: 90.0 KB\n",
-      "Expected memory usage on root node: 8.7890625 MB\n",
+      "Expected memory usage per k point for parallel inversion: 150.0 KB\n",
+      "Expected memory usage on root node: 58.59375 MB\n",
       "================================================================================================================================================================\n"
      ]
     },
@@ -618,14 +657,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "k loop: 100%|██████████| 100/100 [00:35<00:00,  2.82it/s]"
+      "k loop: 100%|██████████| 400/400 [04:23<00:00,  1.52it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Calculated Greens functions. Elapsed time: 170.554620458 s\n",
+      "Calculated Greens functions. Elapsed time: 268.00303675 s\n",
       "================================================================================================================================================================\n"
      ]
     },
@@ -669,7 +708,7 @@
     "\n",
     "# make energy contour\n",
     "# we are working in eV now  !\n",
-    "# and sisil shifts E_F to 0 !\n",
+    "# and sisl shifts E_F to 0 !\n",
     "cont = make_contour(emin=ebot, enum=eset, p=esetp)\n",
     "eran = cont.ze\n",
     "\n",
@@ -692,7 +731,7 @@
     "        # store the Greens function slice of the magnetic entities (for now) based on the on-site projections\n",
     "        for mag_ent in magnetic_entities:\n",
     "            mag_ent[\"Gii_tmp\"][i] += (\n",
-    "                Gk[:, mag_ent[\"spin_box_indeces\"]][..., mag_ent[\"spin_box_indeces\"]]\n",
+    "                Gk[:, mag_ent[\"spin_box_indeces\"], :][:, :, mag_ent[\"spin_box_indeces\"]]\n",
     "                * wk\n",
     "            )\n",
     "\n",
@@ -706,7 +745,7 @@
     "\n",
     "            # store the Greens function slice of the magnetic entities (for now) based on the on-site projections\n",
     "            pair[\"Gij_tmp\"][i] += Gk[:, ai][..., aj] * phase * wk\n",
-    "            pair[\"Gji_tmp\"][i] += Gk[:, aj][..., ai] * phase * wk\n",
+    "            pair[\"Gji_tmp\"][i] += Gk[:, aj][..., ai] / phase * wk\n",
     "\n",
     "# summ reduce partial results of mpi nodes\n",
     "for i in range(len(hamiltonians)):\n",
@@ -744,7 +783,7 @@
       "Input file: \n",
       "/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf\n",
       "Output file: \n",
-      "Fe3GeTe2.pickle\n",
+      "./Fe3GeTe2.pickle\n",
       "Number of nodes in the parallel cluster:  1\n",
       "================================================================================================================================================================\n",
       "Cell [Ang]: \n",
@@ -760,10 +799,10 @@
       "[0 0 1]  --»  [array([1, 0, 0]), array([0, 1, 0])]\n",
       "================================================================================================================================================================\n",
       "Parameters for the contour integral:\n",
-      "Number of k points:  10\n",
+      "Number of k points:  20\n",
       "k point directions:  xy\n",
       "Ebot:  -13\n",
-      "Eset:  60\n",
+      "Eset:  100\n",
       "Esetp:  10000\n",
       "================================================================================================================================================================\n",
       "Atomic information: \n",
@@ -781,295 +820,160 @@
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\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:  -37.10407661401345\n",
-      "DMI:  [-3.49488624e-01  2.55729732e-04 -2.76782696e-07]\n",
-      "Symmetric-anisotropy:  [ 1.17610396e+00 -2.12018279e-05 -1.68775278e-06 -2.12018279e-05\n",
-      "  1.26217741e+00 -6.17661677e-07 -1.68775278e-06 -6.17661677e-07\n",
-      " -2.43828137e+00]\n",
-      "J:  [-3.59279727e+01 -2.12018279e-05 -1.68775278e-06 -2.12018279e-05\n",
-      " -3.58418992e+01 -6.17661677e-07 -1.68775278e-06 -6.17661677e-07\n",
-      " -3.95423580e+01]\n",
-      "Energies for debugging: \n",
-      "array([[-3.94549786e-02, -3.49488006e-04,  3.49489241e-04,\n",
-      "        -3.91968963e-02],\n",
-      "       [-3.96297374e-02, -2.54041979e-07,  2.57417485e-07,\n",
-      "        -3.93690691e-02],\n",
-      "       [-3.24869021e-02,  2.09250452e-08,  2.14786106e-08,\n",
-      "        -3.24868762e-02]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.03936907, -0.0324869 , -0.03945498])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.0393690691402239 -0.03248687617162827\n",
-      "\n",
-      "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -65.61259672738467\n",
-      "DMI:  [ 3.55875363e+00 -6.14764701e+00  2.16400609e-05]\n",
-      "Symmetric-anisotropy:  [-0.0859418   0.03699169 -0.10152731  0.03699169 -0.29085831 -0.04979059\n",
-      " -0.10152731 -0.04979059  0.37680011]\n",
-      "J:  [-6.56985385e+01  3.69916940e-02 -1.01527307e-01  3.69916940e-02\n",
-      " -6.59034550e+01 -4.97905921e-02 -1.01527307e-01 -4.97905921e-02\n",
-      " -6.52357966e+01]\n",
-      "Energies for debugging: \n",
-      "array([[-6.53628254e-02,  3.60854422e-03, -3.50896304e-03,\n",
-      "        -6.58406049e-02],\n",
-      "       [-6.51087679e-02,  6.24917432e-03, -6.04611971e-03,\n",
-      "        -6.54738265e-02],\n",
-      "       [-6.59663052e-02, -3.69700539e-05, -3.70133340e-05,\n",
-      "        -6.59232506e-02]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06547383, -0.06596631, -0.06536283])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.06547382649599318 -0.06592325055847004\n",
-      "\n",
-      "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -65.61291582804701\n",
-      "DMI:  [-3.57656657e+00  6.16370250e+00  1.72769889e-05]\n",
-      "Symmetric-anisotropy:  [-0.08507475  0.03698984  0.09551784  0.03698984 -0.29208857  0.04348767\n",
-      "  0.09551784  0.04348767  0.37716332]\n",
-      "J:  [-6.56979906e+01  3.69898372e-02  9.55178378e-02  3.69898372e-02\n",
-      " -6.59050044e+01  4.34876702e-02  9.55178378e-02  4.34876702e-02\n",
-      " -6.52357525e+01]\n",
-      "Energies for debugging: \n",
-      "array([[-6.53645241e-02, -3.62005424e-03,  3.53307890e-03,\n",
-      "        -6.58436843e-02],\n",
-      "       [-6.51069809e-02, -6.25922034e-03,  6.06818466e-03,\n",
-      "        -6.54727085e-02],\n",
-      "       [-6.59663245e-02, -3.69725603e-05, -3.70071142e-05,\n",
-      "        -6.59232727e-02]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.06547271, -0.06596632, -0.06536452])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.0654727084967254 -0.0659232726509173\n",
-      "\n",
-      "[3]Fe(2)        [4]Fe(2)        [1 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -1.0251660485588765\n",
-      "DMI:  [5.56147340e-01 3.31683446e-04 6.04521494e-06]\n",
-      "Symmetric-anisotropy:  [-9.32615331e-01 -3.84767890e-05  1.75789862e-05 -3.84767890e-05\n",
-      "  3.05904797e-02  3.50989682e-05  1.75789862e-05  3.50989682e-05\n",
-      "  9.02024851e-01]\n",
-      "J:  [-1.95778138e+00 -3.84767890e-05  1.75789862e-05 -3.84767890e-05\n",
-      " -9.94575569e-01  3.50989682e-05  1.75789862e-05  3.50989682e-05\n",
-      " -1.23141198e-01]\n",
-      "Energies for debugging: \n",
-      "array([[ 4.97965048e-04,  5.56112241e-04, -5.56182439e-04,\n",
-      "         8.57005683e-04],\n",
-      "       [-7.44247443e-04, -3.49262432e-07,  3.14104460e-07,\n",
-      "        -8.04849198e-04],\n",
-      "       [-2.84615682e-03,  4.45220039e-08,  3.24315741e-08,\n",
-      "        -3.11071356e-03]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00080485, -0.00284616,  0.00049797])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.0008048491979002433 -0.003110713560414389\n",
-      "\n",
-      "[3]Fe(2)        [5]Fe(2)        [1 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -3.861520076312661\n",
-      "DMI:  [-3.85977504 -2.52369277 -4.22692922]\n",
-      "Symmetric-anisotropy:  [ 0.28001794 -0.01136935  0.01587327 -0.01136935 -0.12598869  0.05899784\n",
-      "  0.01587327  0.05899784 -0.15402925]\n",
-      "J:  [-3.58150213 -0.01136935  0.01587327 -0.01136935 -3.98750877  0.05899784\n",
-      "  0.01587327  0.05899784 -4.01554933]\n",
-      "Energies for debugging: \n",
-      "array([[-0.00437283, -0.00391877,  0.00380078, -0.0044349 ],\n",
-      "       [-0.00365826,  0.00250782, -0.00253957, -0.00358946],\n",
-      "       [-0.00354012, -0.00421556,  0.0042383 , -0.00357354]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00358946, -0.00354012, -0.00437283])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.003589463621997051 -0.003573540645545602\n",
-      "\n",
-      "[4]Fe(2)        [5]Fe(2)        [1 0 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -4.458620479895359\n",
-      "DMI:  [-4.33083787 -5.9385644  -4.22684544]\n",
-      "Symmetric-anisotropy:  [ 0.07272859 -0.01136933 -0.06310261 -0.01136933  0.39785991 -0.00993305\n",
-      " -0.06310261 -0.00993305 -0.4705885 ]\n",
-      "J:  [-4.38589189 -0.01136933 -0.06310261 -0.01136933 -4.06076057 -0.00993305\n",
-      " -0.06310261 -0.00993305 -4.92920898]\n",
-      "Energies for debugging: \n",
-      "array([[-0.00452019, -0.0043209 ,  0.00434077, -0.0045815 ],\n",
-      "       [-0.00533823,  0.00600167, -0.00587546, -0.00519834],\n",
-      "       [-0.00354003, -0.00421548,  0.00423821, -0.00357345]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00519834, -0.00354003, -0.00452019])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.005198338074976103 -0.0035734456953877562\n",
-      "\n",
-      "[3]Fe(2)        [4]Fe(2)        [-1  0  0]        d [Ang] Not yet.\n",
-      "Isotropic:  -0.6592236155327585\n",
-      "DMI:  [ 5.59127613e-01  3.58728563e-05 -2.86086982e-06]\n",
-      "Symmetric-anisotropy:  [-6.83716963e-01 -1.51898472e-05  5.53583253e-05 -1.51898472e-05\n",
-      "  1.41032214e-01 -2.42119712e-05  5.53583253e-05 -2.42119712e-05\n",
-      "  5.42684748e-01]\n",
-      "J:  [-1.34294058e+00 -1.51898472e-05  5.53583253e-05 -1.51898472e-05\n",
-      " -5.18191401e-01 -2.42119712e-05  5.53583253e-05 -2.42119712e-05\n",
-      " -1.16538867e-01]\n",
-      "Energies for debugging: \n",
-      "array([[ 5.05733178e-04,  5.59151825e-04, -5.59103401e-04,\n",
-      "         8.53125820e-04],\n",
-      "       [-7.38810912e-04, -9.12311817e-08, -1.94854690e-08,\n",
-      "        -7.84577563e-04],\n",
-      "       [-1.88950862e-03,  1.23289774e-08,  1.80507170e-08,\n",
-      "        -1.90130359e-03]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00078458, -0.00188951,  0.00050573])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.0007845775626293201 -0.0019013035936397693\n",
-      "\n",
-      "[3]Fe(2)        [5]Fe(2)        [-1  0  0]        d [Ang] Not yet.\n",
-      "Isotropic:  -5.329274876444571\n",
-      "DMI:  [4.32561582 5.93073478 4.17207926]\n",
-      "Symmetric-anisotropy:  [-0.2835386  -0.01607429  0.06200728 -0.01607429 -0.10548755  0.01356167\n",
-      "  0.06200728  0.01356167  0.38902615]\n",
-      "J:  [-5.61281347 -0.01607429  0.06200728 -0.01607429 -5.43476243  0.01356167\n",
-      "  0.06200728  0.01356167 -4.94024873]\n",
+      "[3]Fe(2)        [4]Fe(2)        [0 0 0]        d [Ang] 2.745163300331324\n",
+      "Isotropic:  -61.498296406506675\n",
+      "DMI:  [-9.32930006e-01 -6.96988637e-04 -1.36689594e-06]\n",
+      "Symmetric-anisotropy:  [ 3.64026416e-01 -9.75305726e-05  1.03837928e-05 -9.75305726e-05\n",
+      "  2.98408661e+00 -2.59876808e-05  1.03837928e-05 -2.59876808e-05\n",
+      " -3.34811302e+00]\n",
+      "J:  [-6.11342700e+01 -9.75305726e-05  1.03837928e-05 -9.75305726e-05\n",
+      " -5.85142098e+01 -2.59876808e-05  1.03837928e-05 -2.59876808e-05\n",
+      " -6.48464094e+01]\n",
       "Energies for debugging: \n",
-      "array([[-0.00454026,  0.00431205, -0.00433918, -0.00458051],\n",
-      "       [-0.00534024, -0.00599274,  0.00586873, -0.00521439],\n",
-      "       [-0.00628902,  0.00418815, -0.004156  , -0.00601124]])\n",
+      "array([[-6.21997924e-02, -9.32904018e-04,  9.32955993e-04,\n",
+      "        -6.14479990e-02],\n",
+      "       [-6.74930265e-02,  6.86604844e-07, -7.07372430e-07,\n",
+      "        -6.66882231e-02],\n",
+      "       [-5.55804206e-02,  9.61636767e-08,  9.88974686e-08,\n",
+      "        -5.55803169e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00521439, -0.00628902, -0.00454026])\n",
+      "array([-0.06668822, -0.05558042, -0.06219979])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.005214386566508839 -0.006011240381521584\n",
+      "-0.06668822305594396 -0.055580316925466514\n",
       "\n",
-      "[4]Fe(2)        [5]Fe(2)        [-1  0  0]        d [Ang] Not yet.\n",
-      "Isotropic:  -4.725061132133376\n",
-      "DMI:  [3.8792295  2.52874087 4.17199723]\n",
-      "Symmetric-anisotropy:  [-0.07053775 -0.01607386 -0.01391118 -0.01607386 -0.64500259 -0.04924493\n",
-      " -0.01391118 -0.04924493  0.71554034]\n",
-      "J:  [-4.79559888 -0.01607386 -0.01391118 -0.01607386 -5.37006372 -0.04924493\n",
-      " -0.01391118 -0.04924493 -4.00952079]\n",
+      "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] 2.5835033632437767\n",
+      "Isotropic:  -60.54618555554936\n",
+      "DMI:  [ 3.78486756e+00 -6.14165405e+00  5.59099134e-04]\n",
+      "Symmetric-anisotropy:  [ 0.20417082  0.07119707 -0.09312132  0.07119707  0.01235531 -0.04251649\n",
+      " -0.09312132 -0.04251649 -0.21652613]\n",
+      "J:  [-6.03420147e+01  7.11970660e-02 -9.31213222e-02  7.11970660e-02\n",
+      " -6.05338303e+01 -4.25164858e-02 -9.31213222e-02 -4.25164858e-02\n",
+      " -6.07627117e+01]\n",
       "Energies for debugging: \n",
-      "array([[-0.00436494,  0.00392847, -0.00382998, -0.00445121],\n",
-      "       [-0.0036541 , -0.00251483,  0.00254265, -0.00358006],\n",
-      "       [-0.00628891,  0.00418807, -0.00415592, -0.00601114]])\n",
+      "array([[-6.08726017e-02,  3.82738404e-03, -3.74235107e-03,\n",
+      "        -6.11758535e-02],\n",
+      "       [-6.06528217e-02,  6.23477538e-03, -6.04853273e-03,\n",
+      "        -6.08746935e-02],\n",
+      "       [-5.98918070e-02, -7.06379668e-05, -7.17561651e-05,\n",
+      "        -5.98093360e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00358006, -0.00628891, -0.00436494])\n",
+      "array([-0.06087469, -0.05989181, -0.0608726 ])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.0035800556270865367 -0.006011142134174699\n",
+      "-0.06087469345892933 -0.05980933600539501\n",
       "\n",
-      "[3]Fe(2)        [4]Fe(2)        [0 1 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -1.0496996447984108\n",
-      "DMI:  [-1.97020604e-01  3.93326251e-01 -7.99021874e-06]\n",
-      "Symmetric-anisotropy:  [-0.20217197  0.11453163 -0.03920952  0.11453163 -0.68803384 -0.03787685\n",
-      " -0.03920952 -0.03787685  0.8902058 ]\n",
-      "J:  [-1.25187161  0.11453163 -0.03920952  0.11453163 -1.73773348 -0.03787685\n",
-      " -0.03920952 -0.03787685 -0.15949384]\n",
+      "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] 2.583501767937866\n",
+      "Isotropic:  -60.54212757392337\n",
+      "DMI:  [-3.79967167e+00  6.15419703e+00  5.59764090e-04]\n",
+      "Symmetric-anisotropy:  [ 0.20573682  0.0711945   0.08981327  0.0711945   0.0059668   0.03643624\n",
+      "  0.08981327  0.03643624 -0.21170362]\n",
+      "J:  [-6.03363908e+01  7.11945020e-02  8.98132684e-02  7.11945020e-02\n",
+      " -6.05361608e+01  3.64362362e-02  8.98132684e-02  3.64362362e-02\n",
+      " -6.07538312e+01]\n",
       "Energies for debugging: \n",
-      "array([[-0.00047568, -0.00015914,  0.0002349 , -0.00043124],\n",
-      "       [ 0.00015669, -0.00035412,  0.00043254,  0.00040814],\n",
-      "       [-0.00304422, -0.00011454, -0.00011452, -0.00291188]])\n",
+      "array([[-6.08690792e-02, -3.83610790e-03,  3.76323543e-03,\n",
+      "        -6.11804573e-02],\n",
+      "       [-6.06385832e-02, -6.24401030e-03,  6.06438376e-03,\n",
+      "        -6.08633835e-02],\n",
+      "       [-5.98918643e-02, -7.06347379e-05, -7.17542661e-05,\n",
+      "        -5.98093980e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([ 0.00040814, -0.00304422, -0.00047568])\n",
+      "array([-0.06086338, -0.05989186, -0.06086908])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "0.0004081381450225469 -0.002911881367375347\n",
+      "-0.0608633834990491 -0.059809398001364436\n",
       "\n",
-      "[3]Fe(2)        [5]Fe(2)        [0 1 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -1.6451863500909631\n",
-      "DMI:  [ 0.24862131 -0.06829953 -0.0003067 ]\n",
-      "Symmetric-anisotropy:  [-0.17839237 -0.07756692 -0.00999969 -0.07756692 -0.01604251  0.00447534\n",
-      " -0.00999969  0.00447534  0.19443488]\n",
-      "J:  [-1.82357872 -0.07756692 -0.00999969 -0.07756692 -1.66122887  0.00447534\n",
-      " -0.00999969  0.00447534 -1.45075147]\n",
+      "[3]Fe(2)        [5]Fe(2)        [-1 -1  0]        d [Ang] 2.5834973202859075\n",
+      "Isotropic:  -60.53930274234898\n",
+      "DMI:  [-7.20262359e+00  3.30922312e-04 -5.12682125e-04]\n",
+      "Symmetric-anisotropy:  [-4.62470099e-02 -1.05109909e-04  1.11455444e-04 -1.05109909e-04\n",
+      "  2.54462404e-01  1.15662485e-01  1.11455444e-04  1.15662485e-01\n",
+      " -2.08215394e-01]\n",
+      "J:  [-6.05855498e+01 -1.05109909e-04  1.11455444e-04 -1.05109909e-04\n",
+      " -6.02848403e+01  1.15662485e-01  1.11455444e-04  1.15662485e-01\n",
+      " -6.07475181e+01]\n",
       "Energies for debugging: \n",
-      "array([[-1.33932568e-03,  2.44145966e-04, -2.53096646e-04,\n",
-      "        -1.15417279e-03],\n",
-      "       [-1.56217725e-03,  7.82992297e-05, -5.82998403e-05,\n",
-      "        -1.38940645e-03],\n",
-      "       [-2.16828494e-03,  7.72602195e-05,  7.78736263e-05,\n",
-      "        -2.25775098e-03]])\n",
+      "array([[-6.06216770e-02, -7.31828608e-03,  7.08696111e-03,\n",
+      "        -6.08002127e-02],\n",
+      "       [-6.08733593e-02, -4.42377757e-07,  2.19466868e-07,\n",
+      "        -6.12370668e-02],\n",
+      "       [-5.97694679e-02, -4.07572216e-07,  6.17792035e-07,\n",
+      "        -5.99340327e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00138941, -0.00216828, -0.00133933])\n",
+      "array([-0.06123707, -0.05976947, -0.06062168])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.001389406450933013 -0.0022577509836049143\n",
+      "-0.061237066792013795 -0.05993403271250145\n",
       "\n",
-      "[4]Fe(2)        [5]Fe(2)        [0 1 0]        d [Ang] Not yet.\n",
-      "Isotropic:  -1.925466529428356\n",
-      "DMI:  [ 5.35912969e-01 -3.81240204e-01 -3.13285724e-04]\n",
-      "Symmetric-anisotropy:  [-0.05397076 -0.07756762  0.06269827 -0.07756762  0.02186579  0.0475358\n",
-      "  0.06269827  0.0475358   0.03210497]\n",
-      "J:  [-1.97943729 -0.07756762  0.06269827 -0.07756762 -1.90360074  0.0475358\n",
-      "  0.06269827  0.0475358  -1.89336156]\n",
+      "[4]Fe(2)        [5]Fe(2)        [-1 -1  0]        d [Ang] 2.583495745338251\n",
+      "Isotropic:  -60.54260994469562\n",
+      "DMI:  [ 7.20261281e+00 -7.79978899e-04 -5.10345027e-04]\n",
+      "Symmetric-anisotropy:  [-4.49940133e-02 -1.04644259e-04  1.76885445e-05 -1.04644259e-04\n",
+      "  2.57575083e-01 -1.15643556e-01  1.76885445e-05 -1.15643556e-01\n",
+      " -2.12581070e-01]\n",
+      "J:  [-6.05876040e+01 -1.04644259e-04  1.76885445e-05 -1.04644259e-04\n",
+      " -6.02850349e+01 -1.15643556e-01  1.76885445e-05 -1.15643556e-01\n",
+      " -6.07551910e+01]\n",
       "Energies for debugging: \n",
-      "array([[-1.87844392e-03,  4.88377165e-04, -5.83448773e-04,\n",
-      "        -1.63886730e-03],\n",
-      "       [-1.90827920e-03,  3.18541935e-04, -4.43938473e-04,\n",
-      "        -1.70107327e-03],\n",
-      "       [-2.16833418e-03,  7.72543295e-05,  7.78809009e-05,\n",
-      "        -2.25780130e-03]])\n",
+      "array([[-6.06219750e-02,  7.31825637e-03, -7.08696926e-03,\n",
+      "        -6.08004936e-02],\n",
+      "       [-6.08884070e-02,  7.62290354e-07, -7.97667443e-07,\n",
+      "        -6.12410736e-02],\n",
+      "       [-5.97695761e-02, -4.05700767e-07,  6.14989286e-07,\n",
+      "        -5.99341343e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00170107, -0.00216833, -0.00187844])\n",
+      "array([-0.06124107, -0.05976958, -0.06062198])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.0017010732733817554 -0.0022578013038436714\n",
+      "-0.06124107359965607 -0.059934134316324196\n",
       "\n",
-      "[3]Fe(2)        [4]Fe(2)        [ 0 -1  0]        d [Ang] Not yet.\n",
-      "Isotropic:  -0.68223039919425\n",
-      "DMI:  [-1.89525393e-01  3.91001992e-01  1.58428064e-05]\n",
-      "Symmetric-anisotropy:  [-0.05823148  0.00509602  0.02845826  0.00509602 -0.47544519  0.02675952\n",
-      "  0.02845826  0.02675952  0.53367667]\n",
-      "J:  [-0.74046188  0.00509602  0.02845826  0.00509602 -1.15767559  0.02675952\n",
-      "  0.02845826  0.02675952 -0.14855373]\n",
+      "[3]Fe(2)        [5]Fe(2)        [-1  0  0]        d [Ang] 2.583541444641373\n",
+      "Isotropic:  -60.53142529259683\n",
+      "DMI:  [ 3.79939696e+00  6.14174265e+00 -4.60077462e-05]\n",
+      "Symmetric-anisotropy:  [ 0.2042796  -0.07107711  0.09303406 -0.07107711  0.01020684 -0.03654049\n",
+      "  0.09303406 -0.03654049 -0.21448644]\n",
+      "J:  [-6.03271457e+01 -7.10771078e-02  9.30340570e-02 -7.10771078e-02\n",
+      " -6.05212185e+01 -3.65404867e-02  9.30340570e-02 -3.65404867e-02\n",
+      " -6.07459117e+01]\n",
       "Energies for debugging: \n",
-      "array([[-4.64649982e-04, -2.16284909e-04,  1.62765877e-04,\n",
-      "        -4.17183456e-04],\n",
-      "       [ 1.67542528e-04, -4.19460248e-04,  3.62543736e-04,\n",
-      "         4.11325899e-04],\n",
-      "       [-1.89816773e-03, -5.08017987e-06, -5.11186549e-06,\n",
-      "        -1.89224965e-03]])\n",
+      "array([[-6.08543628e-02,  3.83593745e-03, -3.76285647e-03,\n",
+      "        -6.11652643e-02],\n",
+      "       [-6.06374606e-02, -6.23477670e-03,  6.04870859e-03,\n",
+      "        -6.08592084e-02],\n",
+      "       [-5.98771726e-02,  7.10311000e-05,  7.11231155e-05,\n",
+      "        -5.97950830e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([ 0.00041133, -0.00189817, -0.00046465])\n",
+      "array([-0.06085921, -0.05987717, -0.06085436])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "0.0004113258993876115 -0.0018922496544738186\n",
+      "-0.06085920839939711 -0.059795082988255574\n",
       "\n",
-      "[3]Fe(2)        [5]Fe(2)        [ 0 -1  0]        d [Ang] Not yet.\n",
-      "Isotropic:  -1.3572259825150497\n",
-      "DMI:  [-5.38839939e-01  3.89611153e-01  8.70420696e-05]\n",
-      "Symmetric-anisotropy:  [ 0.26558549  0.01373365 -0.05609544  0.01373365  0.28408137 -0.04203485\n",
-      " -0.05609544 -0.04203485 -0.54966686]\n",
-      "J:  [-1.09164049  0.01373365 -0.05609544  0.01373365 -1.07314461 -0.04203485\n",
-      " -0.05609544 -0.04203485 -1.90689284]\n",
+      "[4]Fe(2)        [5]Fe(2)        [-1  0  0]        d [Ang] 2.5835398672184064\n",
+      "Isotropic:  -60.52706082282313\n",
+      "DMI:  [-3.78470017e+00 -6.15374066e+00 -4.69844827e-05]\n",
+      "Symmetric-anisotropy:  [ 0.2054248  -0.07107452 -0.08989469 -0.07107452  0.00801517  0.04261804\n",
+      " -0.08989469  0.04261804 -0.21343998]\n",
+      "J:  [-6.03216360e+01 -7.10745234e-02 -8.98946890e-02 -7.10745234e-02\n",
+      " -6.05190457e+01  4.26180387e-02 -8.98946890e-02  4.26180387e-02\n",
+      " -6.07405008e+01]\n",
       "Energies for debugging: \n",
-      "array([[-1.89490240e-03, -4.96805090e-04,  5.80874787e-04,\n",
-      "        -1.64794979e-03],\n",
-      "       [-1.91888329e-03, -3.33515713e-04,  4.45706592e-04,\n",
-      "        -1.70084474e-03],\n",
-      "       [-4.98339441e-04, -1.36466118e-05, -1.38206959e-05,\n",
-      "        -4.82436238e-04]])\n",
+      "array([[-6.08579041e-02, -3.82731821e-03,  3.74208213e-03,\n",
+      "        -6.11607909e-02],\n",
+      "       [-6.06230975e-02,  6.24363535e-03, -6.06384598e-03,\n",
+      "        -6.08480575e-02],\n",
+      "       [-5.98773004e-02,  7.10275389e-05,  7.11215079e-05,\n",
+      "        -5.97952145e-02]])\n",
       "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00170084, -0.00049834, -0.0018949 ])\n",
+      "array([-0.06084806, -0.0598773 , -0.0608579 ])\n",
       "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.001700844742244848 -0.00048243623752327756\n",
-      "\n",
-      "[4]Fe(2)        [5]Fe(2)        [ 0 -1  0]        d [Ang] Not yet.\n",
-      "Isotropic:  -1.0714146881035242\n",
-      "DMI:  [-2.39958377e-01  5.49853449e-02  9.53349954e-05]\n",
-      "Symmetric-anisotropy:  [ 0.13849155  0.0137361   0.01047494  0.0137361   0.24743038 -0.00279692\n",
-      "  0.01047494 -0.00279692 -0.38592192]\n",
-      "J:  [-0.93292314  0.0137361   0.01047494  0.0137361  -0.82398431 -0.00279692\n",
-      "  0.01047494 -0.00279692 -1.45733661]\n",
-      "Energies for debugging: \n",
-      "array([[-1.34446326e-03, -2.37161457e-04,  2.42755297e-04,\n",
-      "        -1.14953629e-03],\n",
-      "       [-1.57020997e-03, -6.54602885e-05,  4.45104012e-05,\n",
-      "        -1.38331981e-03],\n",
-      "       [-4.98432325e-04, -1.36407658e-05, -1.38314358e-05,\n",
-      "        -4.82526480e-04]])\n",
-      "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-      "array([-0.00138332, -0.00049843, -0.00134446])\n",
-      "Test J_xx = E(y,z) = E(z,y)\n",
-      "-0.001383319805189735 -0.0004825264795417615\n",
+      "-0.060848057523824294 -0.05979521451219277\n",
       "\n",
       "================================================================================================================================================================\n",
       "Runtime information: \n",
-      "Total runtime: 36.825569167000026 s\n",
+      "Total runtime: 264.50463125 s\n",
       "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Initial setup: 0.20532049999999913 s\n",
-      "Hamiltonian conversion and XC field extraction: 0.972 s\n",
-      "Pair and site datastructure creatrions: 0.027 s\n",
-      "k set cration and distribution: 0.027 s\n",
-      "Rotating XC potential: 0.255 s\n",
-      "Greens function inversion: 35.247 s\n",
-      "Calculate energies and magnetic components: 0.092 s\n"
+      "Initial setup: 0.17141095800000006 s\n",
+      "Hamiltonian conversion and XC field extraction: 1.047 s\n",
+      "Pair and site datastructure creatrions: 0.036 s\n",
+      "k set cration and distribution: 0.029 s\n",
+      "Rotating XC potential: 0.329 s\n",
+      "Greens function inversion: 262.683 s\n",
+      "Calculate energies and magnetic components: 0.209 s\n"
      ]
     }
    ],
@@ -1166,783 +1070,13 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (1445642317.py, line 1)",
+     "evalue": "invalid syntax (3105939143.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[11], 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"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[11], line 1\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"
      ]
     }
    ],
-   "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": [],
-   "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:  1000\n",
-    "k point directions:  x\n",
-    "Ebot:  -13\n",
-    "Eset:  500\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:  -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",
-    "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",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.0339338 , -0.03160427, -0.02819042])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.03393379729617979 -0.03122101431812066\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",
-    "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",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07602356, -0.0795701 , -0.07435228])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07602356083253432 -0.07975458355080561\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 2369.9501148750005 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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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:  1000\n",
-    "k point directions:  x\n",
-    "Ebot:  -13\n",
-    "Eset:  100\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:  -29.36445059067917\n",
-    "DMI:  [-8.91784089e-01  4.16579134e+00 -3.59901207e-06]\n",
-    "Symmetric-anisotropy:  [-4.5685362   1.18301202  0.3323615   4.15576769 -0.02915828]\n",
-    "Energies for debugging: \n",
-    "array([[-0.02818144, -0.00086263,  0.00092094, -0.02597893],\n",
-    "       [-0.03690122, -0.00415577,  0.00417581, -0.03393299],\n",
-    "       [-0.03160383, -0.00033237, -0.00033236, -0.03122058]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.03393299, -0.03160383, -0.02818144])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.03393298678958339 -0.031220578858898954\n",
-    "\n",
-    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -74.99083333178646\n",
-    "DMI:  [-6.82222254e+00  1.12891474e+01  5.95795483e-04]\n",
-    "Symmetric-anisotropy:  [-1.03214008  0.64046775 -0.16031723 10.67951504 -0.32709547]\n",
-    "Energies for debugging: \n",
-    "array([[-0.07435037, -0.00649513,  0.00714932, -0.07459916],\n",
-    "       [-0.07593293, -0.01067952,  0.01189878, -0.07602297],\n",
-    "       [-0.0795695 ,  0.00016091,  0.00015972, -0.07975399]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07602297, -0.0795695 , -0.07435037])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07602297341278706 -0.07975398680085131\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 501.2763705420002 s\n",
-    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-    "Initial setup: 0.12531970899999578 s\n",
-    "Hamiltonian conversion and XC field extraction: 0.888 s\n",
-    "Pair and site datastructure creatrions: 0.062 s\n",
-    "k set cration and distribution: 0.024 s\n",
-    "Rotating XC potential: 0.245 s\n",
-    "Greens function inversion: 499.875 s\n",
-    "Calculate energies and magnetic components: 0.056 s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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:  1000\n",
-    "k point directions:  x\n",
-    "Ebot:  -13\n",
-    "Eset:  50\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:  -29.359042196157713\n",
-    "DMI:  [-8.93813379e-01  4.16574774e+00 -3.59758916e-06]\n",
-    "Symmetric-anisotropy:  [-4.57147523  1.18770348  0.33237646  4.15572505 -0.02850636]\n",
-    "Energies for debugging: \n",
-    "array([[-0.02817134, -0.00086531,  0.00092232, -0.02597527],\n",
-    "       [-0.03689861, -0.00415573,  0.00417577, -0.03393052],\n",
-    "       [-0.03160252, -0.00033238, -0.00033237, -0.03121927]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.03393052, -0.03160252, -0.02817134])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.03393051742760642 -0.031219274782673133\n",
-    "\n",
-    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -74.98947011735306\n",
-    "DMI:  [-6.82126017e+00  1.12890803e+01  6.51055432e-04]\n",
-    "Symmetric-anisotropy:  [-1.03170277  0.64211178 -0.16032097 10.67947464 -0.32714492]\n",
-    "Energies for debugging: \n",
-    "array([[-0.07434736, -0.00649412,  0.00714841, -0.07459988],\n",
-    "       [-0.07593108, -0.01067947,  0.01189869, -0.07602117],\n",
-    "       [-0.07956766,  0.00016097,  0.00015967, -0.07975216]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07602117, -0.07956766, -0.07434736])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07602117288317486 -0.07975215648287909\n",
-    "\n",
-    "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -75.04936117965987\n",
-    "DMI:  [ 6.85701206e+00 -1.13051160e+01  6.39732773e-04]\n",
-    "Symmetric-anisotropy:  [ -1.05318057   0.68103129  -0.16032    -10.70364791   0.31624841]\n",
-    "Energies for debugging: \n",
-    "array([[-0.07436833,  0.00654076, -0.00717326, -0.07467721],\n",
-    "       [-0.07601601,  0.01070365, -0.01190658, -0.07610254],\n",
-    "       [-0.07956796,  0.00016096,  0.00015968, -0.07975245]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07610254, -0.07956796, -0.07436833])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07610254174486064 -0.07975245001820208\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 261.890385875 s\n",
-    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-    "Initial setup: 0.1216103749998183 s\n",
-    "Hamiltonian conversion and XC field extraction: 1.045 s\n",
-    "Pair and site datastructure creatrions: 0.011 s\n",
-    "k set cration and distribution: 0.006 s\n",
-    "Rotating XC potential: 0.224 s\n",
-    "Greens function inversion: 260.450 s\n",
-    "Calculate energies and magnetic components: 0.033 s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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:  3000\n",
-    "k point directions:  x\n",
-    "Ebot:  -13\n",
-    "Eset:  50\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:  -29.437239263932703\n",
-    "DMI:  [-8.91096571e-01  4.16386016e+00 -3.68076797e-06]\n",
-    "Symmetric-anisotropy:  [-4.68204753  1.22980695  0.33079224  4.15200152 -0.03180686]\n",
-    "Energies for debugging: \n",
-    "array([[-0.02820743, -0.00085929,  0.0009229 , -0.025985  ],\n",
-    "       [-0.03707978, -0.004152  ,  0.00417572, -0.03411929],\n",
-    "       [-0.03167345, -0.0003308 , -0.00033079, -0.03129215]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.03411929, -0.03167345, -0.02820743])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.03411928679618277 -0.03129215156448653\n",
-    "\n",
-    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -74.98086703681366\n",
-    "DMI:  [-6.81158085e+00  1.13265508e+01  5.68487147e-04]\n",
-    "Symmetric-anisotropy:  [-1.03582622  0.63764125 -0.15959679 10.72110977 -0.32326625]\n",
-    "Energies for debugging: \n",
-    "array([[-0.07434323, -0.00648831,  0.00713485, -0.07458268],\n",
-    "       [-0.07592777, -0.01072111,  0.01193199, -0.07601669],\n",
-    "       [-0.07959289,  0.00016017,  0.00015903, -0.07977653]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07601669, -0.07959289, -0.07434323])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07601669325607426 -0.07977653333847946\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 808.34989875 s\n",
-    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-    "Initial setup: 0.1594020829999998 s\n",
-    "Hamiltonian conversion and XC field extraction: 0.995 s\n",
-    "Pair and site datastructure creatrions: 0.021 s\n",
-    "k set cration and distribution: 0.021 s\n",
-    "Rotating XC potential: 0.232 s\n",
-    "Greens function inversion: 806.884 s\n",
-    "Calculate energies and magnetic components: 0.037 s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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:  2000\n",
-    "k point directions:  x\n",
-    "Ebot:  -13\n",
-    "Eset:  50\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:  -29.313466612791018\n",
-    "DMI:  [-8.85106686e-01  4.16843252e+00 -3.50602459e-06]\n",
-    "Symmetric-anisotropy:  [-4.7530289   1.27630805  0.34469396  4.15760367 -0.03005581]\n",
-    "Energies for debugging: \n",
-    "array([[-0.02803716, -0.00085505,  0.00091516, -0.02583675],\n",
-    "       [-0.03702114, -0.0041576 ,  0.00417926, -0.0340665 ],\n",
-    "       [-0.03178903, -0.0003447 , -0.00034469, -0.03139132]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.0340665 , -0.03178903, -0.02803716])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.03406649551707385 -0.031391324924774755\n",
-    "\n",
-    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -74.97640334609967\n",
-    "DMI:  [-6.84069197e+00  1.12809486e+01  8.75098125e-04]\n",
-    "Symmetric-anisotropy:  [-1.03102826  0.63307456 -0.16123618 10.67285449 -0.32447457]\n",
-    "Energies for debugging: \n",
-    "array([[-0.07434333, -0.00651622,  0.00716517, -0.07457845],\n",
-    "       [-0.07591065, -0.01067285,  0.01188904, -0.07600743],\n",
-    "       [-0.07959609,  0.00016211,  0.00016036, -0.07978159]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07600743, -0.07959609, -0.07434333])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07600743160328334 -0.07978158717380361\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 534.5927785000001 s\n",
-    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-    "Initial setup: 0.14542879100008577 s\n",
-    "Hamiltonian conversion and XC field extraction: 0.903 s\n",
-    "Pair and site datastructure creatrions: 0.016 s\n",
-    "k set cration and distribution: 0.014 s\n",
-    "Rotating XC potential: 0.242 s\n",
-    "Greens function inversion: 533.223 s\n",
-    "Calculate energies and magnetic components: 0.050 s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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:  1000\n",
-    "k point directions:  x\n",
-    "Ebot:  -13\n",
-    "Eset:  50\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:  -29.359042196157713\n",
-    "DMI:  [-8.93813379e-01  4.16574774e+00 -3.59758916e-06]\n",
-    "Symmetric-anisotropy:  [-4.57147523  1.18770348  0.33237646  4.15572505 -0.02850636]\n",
-    "Energies for debugging: \n",
-    "array([[-0.02817134, -0.00086531,  0.00092232, -0.02597527],\n",
-    "       [-0.03689861, -0.00415573,  0.00417577, -0.03393052],\n",
-    "       [-0.03160252, -0.00033238, -0.00033237, -0.03121927]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.03393052, -0.03160252, -0.02817134])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.03393051742760642 -0.031219274782673133\n",
-    "\n",
-    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -74.98947011735306\n",
-    "DMI:  [-6.82126017e+00  1.12890803e+01  6.51055432e-04]\n",
-    "Symmetric-anisotropy:  [-1.03170277  0.64211178 -0.16032097 10.67947464 -0.32714492]\n",
-    "Energies for debugging: \n",
-    "array([[-0.07434736, -0.00649412,  0.00714841, -0.07459988],\n",
-    "       [-0.07593108, -0.01067947,  0.01189869, -0.07602117],\n",
-    "       [-0.07956766,  0.00016097,  0.00015967, -0.07975216]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.07602117, -0.07956766, -0.07434736])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.07602117288317486 -0.07975215648287909\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 272.760337666 s\n",
-    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-    "Initial setup: 0.13726933300000033 s\n",
-    "Hamiltonian conversion and XC field extraction: 0.970 s\n",
-    "Pair and site datastructure creatrions: 0.013 s\n",
-    "k set cration and distribution: 0.017 s\n",
-    "Rotating XC potential: 0.217 s\n",
-    "Greens function inversion: 271.373 s\n",
-    "Calculate energies and magnetic components: 0.034 s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##################################################################### 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:  xy\n",
-    "Ebot:  -15\n",
-    "Eset:  100\n",
-    "Esetp:  1000\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:  -63.51146633376187\n",
-    "DMI:  [-9.32966002e-01 -8.89800564e-04 -2.02862680e-06]\n",
-    "Symmetric-anisotropy:  [-3.33721683e+00  1.29672703e+00  6.03687008e-04 -8.09695186e-04\n",
-    " -6.00702131e-06]\n",
-    "Energies for debugging: \n",
-    "array([[-6.22147393e-02, -9.32959995e-04,  9.32972009e-04,\n",
-    "        -6.14709765e-02],\n",
-    "       [-6.75755955e-02,  8.09695186e-07, -9.69905943e-07,\n",
-    "        -6.68486832e-02],\n",
-    "       [-5.36624493e-02, -6.05715634e-07, -6.01658381e-07,\n",
-    "        -5.36632256e-02]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.06684868, -0.05366245, -0.06221474])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.06684868316242108 -0.053663225627735525\n",
-    "\n",
-    "[4]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -60.97776480541008\n",
-    "DMI:  [-3.79946502e+00  6.15248803e+00  3.52644581e-03]\n",
-    "Symmetric-anisotropy:  [0.09164177 0.11139034 0.07107136 6.24190815 0.03636875]\n",
-    "Energies for debugging: \n",
-    "array([[-6.08663745e-02, -3.83583377e-03,  3.76309627e-03,\n",
-    "        -6.11807969e-02],\n",
-    "       [-6.06347990e-02, -6.24190815e-03,  6.06306790e-03,\n",
-    "        -6.08861230e-02],\n",
-    "       [-5.98977554e-02, -6.75449092e-05, -7.45978008e-05,\n",
-    "        -5.98154343e-02]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.06088612, -0.05989776, -0.06086637])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.060886123032493376 -0.05981543426889457\n",
-    "\n",
-    "[3]Fe(2)        [5]Fe(2)        [0 0 0]        d [Ang] Not yet.\n",
-    "Isotropic:  -60.9832595805832\n",
-    "DMI:  [ 3.78505688e+00 -6.13845903e+00  3.52688440e-03]\n",
-    "Symmetric-anisotropy:  [ 0.08120798  0.11261323  0.07107254 -6.23157554 -0.04249828]\n",
-    "Energies for debugging: \n",
-    "array([[-6.08706463e-02,  3.82755516e-03, -3.74255860e-03,\n",
-    "        -6.11770808e-02],\n",
-    "       [-6.06458648e-02,  6.23157554e-03, -6.04534251e-03,\n",
-    "        -6.09020516e-02],\n",
-    "       [-5.98977110e-02, -6.75456589e-05, -7.45994277e-05,\n",
-    "        -5.98153841e-02]])\n",
-    "J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)\n",
-    "array([-0.06090205, -0.05989771, -0.06087065])\n",
-    "Test J_xx = E(y,z) = E(z,y)\n",
-    "-0.060902051599344476 -0.05981538413498572\n",
-    "\n",
-    "================================================================================================================================================================\n",
-    "Runtime information: \n",
-    "Total runtime: 203.083672208 s\n",
-    "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
-    "Initial setup: 0.1483790410000001 s\n",
-    "Hamiltonian conversion and XC field extraction: 0.861 s\n",
-    "Pair and site datastructure creatrions: 0.017 s\n",
-    "k set cration and distribution: 0.022 s\n",
-    "Rotating XC potential: 0.247 s\n",
-    "Greens function inversion: 201.715 s\n",
-    "Calculate energies and magnetic components: 0.073 s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "========================================\n",
     " \n",