{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/RoyElkabetz/Tensor-Networks-Simple-Update/blob/main/Quantum_Ising_Model_Phase_Transition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If running from google colab change to TRUE\n",
    "run_from_colab = False  # True if using google colab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cloning the latest tnsu repo from github\n",
    "You can clone the latest `tnsu` repo from github or run \n",
    "```\n",
    "!pip install tnsu\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q7oPsCWpkslc",
    "outputId": "4dbd72b0-b87a-4f61-d5a8-ee4429699a10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'Tensor-Networks-Simple-Update' already exists and is not an empty directory.\r\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "if run_from_colab:\n",
    "    # clone the git reposetory\n",
    "    !git clone https://github.com/RoyElkabetz/Tensor-Networks-Simple-Update\n",
    "\n",
    "    # add path to .py files for import\n",
    "    sys.path.insert(1, \"/content/Tensor-Networks-Simple-Update/src\")\n",
    "\n",
    "    # mount google drive\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/gdrive')\n",
    "    \n",
    "    # path for saving the networks\n",
    "    save_path = '/content/gdrive/MyDrive/tmp'\n",
    "else:\n",
    "    # clone the git reposetory\n",
    "    !git clone https://github.com/RoyElkabetz/Tensor-Networks-Simple-Update\n",
    "    \n",
    "    # add path to .py files for import\n",
    "    sys.path.insert(1, \"Tensor-Networks-Simple-Update/src\")\n",
    "    \n",
    "    # path for saving the networks\n",
    "    save_path = '../tmp/networks'\n",
    "    \n",
    "if not os.path.exists(save_path):\n",
    "    os.makedirs(save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Z0WLUhyGlAZj"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tnsu.tensor_network import TensorNetwork\n",
    "import tnsu.simple_update as su\n",
    "\n",
    "\n",
    "np.random.seed(216)\n",
    "\n",
    "plt.rcParams.update({'font.size': 16, \n",
    "                     \"figure.facecolor\": 'white', \n",
    "                     \"axes.facecolor\": 'white', \n",
    "                     \"savefig.facecolor\": 'white', \n",
    "                     'savefig.edgecolor' : 'white', \n",
    "                     'figure.edgecolor' : 'white'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dB_3Wdmql1Vu"
   },
   "source": [
    "# Simulating the Quantum Phase Transition of an Ising Transverse Field (ITF) Hamiltonian on an infinite $2D$ Lattice\n",
    "\n",
    "The ITF Hamiltonian is given by\n",
    "\n",
    "\\begin{align}\n",
    "    H = -J\\sum_{<i, j>}\\sigma_i^z \\cdot \\sigma_j^z - h\\sum_{i}\\sigma_i^x\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "c3459cfa365748819f769a0a56ce5e35",
      "42572937b4f44b6583d1858b86a60205",
      "9a18c1e737a340efaac39f706a112b27",
      "78eaeef960dd481a94f1e22fe98f88cf",
      "a0027bc389b7489d91cf94ccdbf9c291",
      "bf62e84e68234879b724349b333fadff",
      "d2f75953be0c4aa9a3dd328fb97bd25d",
      "2ae0bcb18f6b4f0caddd91998dd5bea3"
     ]
    },
    "id": "xktZ755blDNm",
    "outputId": "00a8947c-167e-4dd5-c7ad-bb6f8b495db3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function tqdm.__del__ at 0x7f7df822b5e0>\n",
      "Traceback (most recent call last):\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tqdm/std.py\", line 1145, in __del__\n",
      "    self.close()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tqdm/notebook.py\", line 283, in close\n",
      "    self.disp(bar_style='danger', check_delay=False)\n",
      "AttributeError: 'tqdm' object has no attribute 'disp'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Simple Update converged. final error is 0.0000000000 < 0.0000010000.\n",
      "| D max:   2 | h: -0.0000000000 | Energy: -1.9999999997 | Mz: 0.9999999999 | Mx: 0.0000120168 |\n",
      "==> Simple Update converged. final error is 0.0000000011 < 0.0000010000.\n",
      "| D max:   2 | h: -0.0677966102 | Energy: -2.0005745609 | Mz: -0.9998562105 | Mx: 0.0169572669 |\n",
      "==> Simple Update converged. final error is 0.0000000018 < 0.0000010000.\n",
      "| D max:   2 | h: -0.1355932203 | Energy: -2.0022984096 | Mz: -0.9994249695 | Mx: 0.0339055010 |\n",
      "==> Simple Update converged. final error is 0.0000000029 < 0.0000010000.\n",
      "| D max:   2 | h: -0.2033898305 | Energy: -2.0051720406 | Mz: -0.9987049077 | Mx: 0.0508700458 |\n",
      "==> Simple Update converged. final error is 0.0000000046 < 0.0000010000.\n",
      "| D max:   2 | h: -0.2711864407 | Energy: -2.0091962803 | Mz: -0.9976943962 | Mx: 0.0678493320 |\n",
      "==> Simple Update converged. final error is 0.0000000067 < 0.0000010000.\n",
      "| D max:   2 | h: -0.3389830508 | Energy: -2.0143722883 | Mz: 0.9963909671 | Mx: 0.0848485028 |\n",
      "==> Simple Update converged. final error is 0.0000000095 < 0.0000010000.\n",
      "| D max:   2 | h: -0.4067796610 | Energy: -2.0207015590 | Mz: 0.9947914672 | Mx: 0.1018721674 |\n",
      "==> Simple Update converged. final error is 0.0000000109 < 0.0000010000.\n",
      "| D max:   2 | h: -0.4745762712 | Energy: -2.0281859371 | Mz: 0.9928919761 | Mx: 0.1189253465 |\n",
      "==> Simple Update converged. final error is 0.0000000112 < 0.0000010000.\n",
      "| D max:   2 | h: -0.5423728814 | Energy: -2.0368276047 | Mz: -0.9906877745 | Mx: 0.1360132323 |\n",
      "==> Simple Update converged. final error is 0.0000000117 < 0.0000010000.\n",
      "| D max:   2 | h: -0.6101694915 | Energy: -2.0466290810 | Mz: -0.9881733257 | Mx: 0.1531410128 |\n",
      "==> Simple Update converged. final error is 0.0000000123 < 0.0000010000.\n",
      "| D max:   2 | h: -0.6779661017 | Energy: -2.0575932480 | Mz: -0.9853422367 | Mx: 0.1703139373 |\n",
      "==> Simple Update converged. final error is 0.0000000127 < 0.0000010000.\n",
      "| D max:   2 | h: -0.7457627119 | Energy: -2.0697233480 | Mz: 0.9821872055 | Mx: 0.1875373340 |\n",
      "==> Simple Update converged. final error is 0.0000000133 < 0.0000010000.\n",
      "| D max:   2 | h: -0.8135593220 | Energy: -2.0830229877 | Mz: 0.9786998844 | Mx: 0.2048169880 |\n",
      "==> Simple Update converged. final error is 0.0000000138 < 0.0000010000.\n",
      "| D max:   2 | h: -0.8813559322 | Energy: -2.0974961544 | Mz: -0.9748710528 | Mx: 0.2221579837 |\n",
      "==> Simple Update converged. final error is 0.0000000144 < 0.0000010000.\n",
      "| D max:   2 | h: -0.9491525424 | Energy: -2.1131472160 | Mz: 0.9706901963 | Mx: 0.2395663700 |\n",
      "==> Simple Update converged. final error is 0.0000000153 < 0.0000010000.\n",
      "| D max:   2 | h: -1.0169491525 | Energy: -2.1299809381 | Mz: 0.9661456323 | Mx: 0.2570480867 |\n",
      "==> Simple Update converged. final error is 0.0000000159 < 0.0000010000.\n",
      "| D max:   2 | h: -1.0847457627 | Energy: -2.1480024979 | Mz: -0.9612242914 | Mx: 0.2746094447 |\n",
      "==> Simple Update converged. final error is 0.0000000167 < 0.0000010000.\n",
      "| D max:   2 | h: -1.1525423729 | Energy: -2.1672174856 | Mz: 0.9559118547 | Mx: 0.2922562475 |\n",
      "==> Simple Update converged. final error is 0.0000000175 < 0.0000010000.\n",
      "| D max:   2 | h: -1.2203389831 | Energy: -2.1876319308 | Mz: -0.9501921915 | Mx: 0.3099952663 |\n",
      "==> Simple Update converged. final error is 0.0000000185 < 0.0000010000.\n",
      "| D max:   2 | h: -1.2881355932 | Energy: -2.2092523090 | Mz: -0.9440474448 | Mx: 0.3278333305 |\n",
      "==> Simple Update converged. final error is 0.0000000193 < 0.0000010000.\n",
      "| D max:   2 | h: -1.3559322034 | Energy: -2.2320855694 | Mz: -0.9374577057 | Mx: 0.3457777383 |\n",
      "==> Simple Update converged. final error is 0.0000000204 < 0.0000010000.\n",
      "| D max:   2 | h: -1.4237288136 | Energy: -2.2561391383 | Mz: -0.9304012220 | Mx: 0.3638351679 |\n",
      "==> Simple Update converged. final error is 0.0000000218 < 0.0000010000.\n",
      "| D max:   2 | h: -1.4915254237 | Energy: -2.2814209503 | Mz: 0.9228533553 | Mx: 0.3820137144 |\n",
      "==> Simple Update converged. final error is 0.0000000239 < 0.0000010000.\n",
      "| D max:   2 | h: -1.5593220339 | Energy: -2.3079394664 | Mz: 0.9147867891 | Mx: 0.4003215070 |\n",
      "==> Simple Update converged. final error is 0.0000000261 < 0.0000010000.\n",
      "| D max:   2 | h: -1.6271186441 | Energy: -2.3357037116 | Mz: 0.9061708967 | Mx: 0.4187673444 |\n",
      "==> Simple Update converged. final error is 0.0000000285 < 0.0000010000.\n",
      "| D max:   2 | h: -1.6949152542 | Energy: -2.3647232868 | Mz: -0.8969722399 | Mx: 0.4373588920 |\n",
      "==> Simple Update converged. final error is 0.0000000310 < 0.0000010000.\n",
      "| D max:   2 | h: -1.7627118644 | Energy: -2.3950084023 | Mz: -0.8871524235 | Mx: 0.4561059990 |\n",
      "==> Simple Update converged. final error is 0.0000000338 < 0.0000010000.\n",
      "| D max:   2 | h: -1.8305084746 | Energy: -2.4265699127 | Mz: -0.8766685544 | Mx: 0.4750184800 |\n",
      "==> Simple Update converged. final error is 0.0000000367 < 0.0000010000.\n",
      "| D max:   2 | h: -1.8983050847 | Energy: -2.4594193523 | Mz: -0.8654722035 | Mx: 0.4941067294 |\n",
      "==> Simple Update converged. final error is 0.0000000400 < 0.0000010000.\n",
      "| D max:   2 | h: -1.9661016949 | Energy: -2.4935689751 | Mz: -0.8535084653 | Mx: 0.5133817833 |\n",
      "==> Simple Update converged. final error is 0.0000000436 < 0.0000010000.\n",
      "| D max:   2 | h: -2.0338983051 | Energy: -2.5290317987 | Mz: 0.8407148004 | Mx: 0.5328553858 |\n",
      "==> Simple Update converged. final error is 0.0000000519 < 0.0000010000.\n",
      "| D max:   2 | h: -2.1016949153 | Energy: -2.5658216536 | Mz: -0.8270206581 | Mx: 0.5525386869 |\n",
      "==> Simple Update converged. final error is 0.0000000688 < 0.0000010000.\n",
      "| D max:   2 | h: -2.1694915254 | Energy: -2.6039532348 | Mz: -0.8123415800 | Mx: 0.5724477334 |\n",
      "==> Simple Update converged. final error is 0.0000000888 < 0.0000010000.\n",
      "| D max:   2 | h: -2.2372881356 | Energy: -2.6434421652 | Mz: -0.7965842192 | Mx: 0.5925940102 |\n",
      "==> Simple Update converged. final error is 0.0000001091 < 0.0000010000.\n",
      "| D max:   2 | h: -2.3050847458 | Energy: -2.6843050622 | Mz: 0.7796357163 | Mx: 0.6129943846 |\n",
      "==> Simple Update converged. final error is 0.0000001345 < 0.0000010000.\n",
      "| D max:   2 | h: -2.3728813559 | Energy: -2.7265595991 | Mz: -0.7613641023 | Mx: 0.6336656878 |\n",
      "==> Simple Update converged. final error is 0.0000001632 < 0.0000010000.\n",
      "| D max:   2 | h: -2.4406779661 | Energy: -2.7702246088 | Mz: 0.7416120989 | Mx: 0.6546261614 |\n",
      "==> Simple Update converged. final error is 0.0000001949 < 0.0000010000.\n",
      "| D max:   2 | h: -2.5084745763 | Energy: -2.8153201641 | Mz: -0.7201953193 | Mx: 0.6758909696 |\n",
      "==> Simple Update converged. final error is 0.0000002294 < 0.0000010000.\n",
      "| D max:   2 | h: -2.5762711864 | Energy: -2.8618676803 | Mz: 0.6968747640 | Mx: 0.6974884602 |\n",
      "==> Simple Update converged. final error is 0.0000002662 < 0.0000010000.\n",
      "| D max:   2 | h: -2.6440677966 | Energy: -2.9098900415 | Mz: -0.6713706611 | Mx: 0.7194353770 |\n",
      "==> Simple Update converged. final error is 0.0000003043 < 0.0000010000.\n",
      "| D max:   2 | h: -2.7118644068 | Energy: -2.9594117242 | Mz: 0.6433109922 | Mx: 0.7417631700 |\n",
      "==> Simple Update converged. final error is 0.0000003424 < 0.0000010000.\n",
      "| D max:   2 | h: -2.7796610169 | Energy: -3.0104589575 | Mz: -0.6122263293 | Mx: 0.7644965170 |\n",
      "==> Simple Update converged. final error is 0.0000003784 < 0.0000010000.\n",
      "| D max:   2 | h: -2.8474576271 | Energy: -3.0630598874 | Mz: -0.5774714847 | Mx: 0.7876723886 |\n",
      "==> Simple Update converged. final error is 0.0000004091 < 0.0000010000.\n",
      "| D max:   2 | h: -2.9152542373 | Energy: -3.1172447813 | Mz: -0.5381562763 | Mx: 0.8113254466 |\n",
      "==> Simple Update converged. final error is 0.0000004311 < 0.0000010000.\n",
      "| D max:   2 | h: -2.9830508475 | Energy: -3.1730462005 | Mz: -0.4928628913 | Mx: 0.8355424375 |\n",
      "==> Simple Update converged. final error is 0.0000004017 < 0.0000010000.\n",
      "| D max:   2 | h: -3.0508474576 | Energy: -3.2304991004 | Mz: -0.4391370362 | Mx: 0.8604882092 |\n",
      "==> Simple Update converged. final error is 0.0000003704 < 0.0000010000.\n",
      "| D max:   2 | h: -3.1186440678 | Energy: -3.2896391827 | Mz: -0.3714490838 | Mx: 0.8866746207 |\n",
      "==> Simple Update converged. final error is 0.0000002809 < 0.0000010000.\n",
      "| D max:   2 | h: -3.1864406780 | Energy: -3.3504475719 | Mz: 0.2617293979 | Mx: 0.9179402864 |\n",
      "==> Simple Update converged. final error is 0.0000001011 < 0.0000010000.\n",
      "| D max:   2 | h: -3.2542372881 | Energy: -3.4130312047 | Mz: -0.0157501399 | Mx: 0.9474613624 |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Simple Update converged. final error is 0.0000000995 < 0.0000010000.\n",
      "| D max:   2 | h: -3.3220338983 | Energy: -3.4773510383 | Mz: -0.0061220514 | Mx: 0.9499163307 |\n",
      "==> Simple Update converged. final error is 0.0000000996 < 0.0000010000.\n",
      "| D max:   2 | h: -3.3898305085 | Energy: -3.5418294421 | Mz: 0.0031098580 | Mx: 0.9521348522 |\n",
      "==> Simple Update converged. final error is 0.0000000998 < 0.0000010000.\n",
      "| D max:   2 | h: -3.4576271186 | Energy: -3.6064523146 | Mz: 0.0018442022 | Mx: 0.9541952569 |\n",
      "==> Simple Update converged. final error is 0.0000001000 < 0.0000010000.\n",
      "| D max:   2 | h: -3.5254237288 | Energy: -3.6712099285 | Mz: 0.0011625538 | Mx: 0.9561182326 |\n",
      "==> Simple Update converged. final error is 0.0000001002 < 0.0000010000.\n",
      "| D max:   2 | h: -3.5932203390 | Energy: -3.7360934829 | Mz: -0.0009802413 | Mx: 0.9579170474 |\n",
      "==> Simple Update converged. final error is 0.0000001004 < 0.0000010000.\n",
      "| D max:   2 | h: -3.6610169492 | Energy: -3.8010951146 | Mz: 0.0006927509 | Mx: 0.9596032237 |\n",
      "==> Simple Update converged. final error is 0.0000001007 < 0.0000010000.\n",
      "| D max:   2 | h: -3.7288135593 | Energy: -3.8662074842 | Mz: 0.0003816074 | Mx: 0.9611863173 |\n",
      "==> Simple Update converged. final error is 0.0000001010 < 0.0000010000.\n",
      "| D max:   2 | h: -3.7966101695 | Energy: -3.9314238638 | Mz: 0.0003206137 | Mx: 0.9626748199 |\n",
      "==> Simple Update converged. final error is 0.0000001013 < 0.0000010000.\n",
      "| D max:   2 | h: -3.8644067797 | Energy: -3.9967381540 | Mz: -0.0002894299 | Mx: 0.9640764964 |\n",
      "==> Simple Update converged. final error is 0.0000001016 < 0.0000010000.\n",
      "| D max:   2 | h: -3.9322033898 | Energy: -4.0621447229 | Mz: 0.0001679938 | Mx: 0.9653982247 |\n",
      "==> Simple Update converged. final error is 0.0000001019 < 0.0000010000.\n",
      "| D max:   2 | h: -4.0000000000 | Energy: -4.1276383352 | Mz: 0.0002015268 | Mx: 0.9666461585 |\n"
     ]
    }
   ],
   "source": [
    "# The Tensor Network structure matrix\n",
    "ising_structure_matrix = np.array([[1, 2, 3, 4, 0, 0, 0, 0],\n",
    "                                   [1, 2, 0, 0, 3, 4, 0, 0],\n",
    "                                   [0, 0, 1, 2, 0, 0, 3, 4,],\n",
    "                                   [0, 0, 0, 0, 1, 2, 3, 4]])\n",
    "# Simple-Update parameters\n",
    "d_max_ising = [2]\n",
    "error = 1e-6\n",
    "max_iterations = 200\n",
    "ising_energy = []\n",
    "ising_z_magnetizaton = []\n",
    "ising_x_magnetizaton = []\n",
    "\n",
    "j_ij_ising = [-1.] * 8\n",
    "dts = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
    "h_k = -np.linspace(0, 4, 60)\n",
    "\n",
    "# Pauli matrices\n",
    "pauli_x = np.array([[0, 1],\n",
    "                    [1, 0]])\n",
    "pauli_y = np.array([[0, -1j],\n",
    "                    [1j, 0]])\n",
    "pauli_z = np.array([[1, 0],\n",
    "                    [0, -1]])\n",
    "\n",
    "s_i = [pauli_z]\n",
    "s_j = [pauli_z]\n",
    "s_k = [pauli_x]\n",
    "\n",
    "\n",
    "# Run\n",
    "for d_max in d_max_ising:\n",
    "    for h in h_k:\n",
    "        ising = TensorNetwork(structure_matrix=ising_structure_matrix, \n",
    "                              virtual_dim=d_max)\n",
    "        ising_su = su.SimpleUpdate(tensor_network=ising, \n",
    "                                  dts=dts, \n",
    "                                  j_ij=j_ij_ising, \n",
    "                                  h_k=h, \n",
    "                                  s_i=s_i, \n",
    "                                  s_j=s_j, \n",
    "                                  s_k=s_k, \n",
    "                                  d_max=d_max, \n",
    "                                  max_iterations=max_iterations, \n",
    "                                  convergence_error=error, \n",
    "                                  log_energy=False,\n",
    "                                  print_process=False)\n",
    "        ising_su.run()\n",
    "        energy = ising_su.energy_per_site()\n",
    "        z_magnetization = ising_su.expectation_per_site(pauli_z)\n",
    "        x_magnetization = ising_su.expectation_per_site(pauli_x)\n",
    "        print('| D max: {:3d} | h: {:3.10f} | Energy: {:3.10f} | Mz: {:3.10f} | Mx: {:3.10f} |'\n",
    "        .format(d_max, h, energy, z_magnetization, x_magnetization))\n",
    "        ising_energy.append(energy)\n",
    "        ising_z_magnetizaton.append(z_magnetization)\n",
    "        ising_x_magnetizaton.append(x_magnetization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "_SxZljU3lLgP",
    "outputId": "d78fcd23-6a79-4567-8269-c6daf9c07c60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7df826bbb0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(17, 6))\n",
    "ax[0].plot(-h_k, ising_energy, '--o', label=r'$\\epsilon_0$', color='tab:red', \n",
    "           markerfacecolor='none', linewidth=1, markersize=10)\n",
    "ax[0].set_xlabel(r'h')\n",
    "ax[0].set_ylabel(r'$\\epsilon_0$')\n",
    "ax[0].set_title('$2D$ Ising model ground-state energy per-site')\n",
    "ax[0].tick_params(direction='in', axis='both')\n",
    "ax[0].grid()\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(-h_k, np.abs(np.array(ising_z_magnetizaton)), '--o', label=r'$M_z$', \n",
    "           color='tab:blue', markerfacecolor='none', linewidth=1, markersize=10)\n",
    "ax[1].plot(-h_k, ising_x_magnetizaton, '--o', label=r'$M_x$', color='tab:green', \n",
    "           markerfacecolor='none', linewidth=1, markersize=10)\n",
    "ax[1].set_xlabel(r'h')\n",
    "ax[1].set_ylabel(r'$M$')\n",
    "ax[1].set_title(r'$2D$ Ising model $\\hat{z}, \\hat{x}$ magnetizations per-site')\n",
    "ax[1].tick_params(direction='in', axis='both')\n",
    "ax[1].grid()\n",
    "ax[1].legend()"
   ]
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