C.C.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-D perclolation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from math import log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "L, T = 2000, 1000 ## latice num, simul time\n",
    "s = 3 ## cluster size\n",
    "p = 0.5 ## occupied probabiliy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "aver_list = np.zeros(T)\n",
    "## make lattice\n",
    "for j in tqdm(range(T)):\n",
    "    site = \"\"\n",
    "    aver =0\n",
    "    for i in range(L):\n",
    "        if np.random.random() < p:\n",
    "            site += \"1\"\n",
    "        else :\n",
    "            site += \" \"\n",
    "## make cluster size list\n",
    "    site_split = site.split()\n",
    "    cluster = [len(w) for w in site_split]\n",
    "## find cluster size s\n",
    "    for i in range(len(cluster)):\n",
    "        if cluster[i] == s:\n",
    "            aver += 1        \n",
    "    aver_list[j] = aver*s/L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.,   0.,   1.,\n",
       "          0.,   0.,   3.,   0.,   5.,   0.,   0.,  10.,   0.,  12.,  16.,\n",
       "          0.,  23.,   0.,  45.,  50.,   0.,  74.,  88.,   0., 129.,   0.,\n",
       "        134.,   0., 199., 230.,   0., 279.,   0., 306., 385.,   0., 436.,\n",
       "          0., 436., 479.,   0., 535.,   0., 588., 539.,   0., 558.,   0.,\n",
       "        538., 478.,   0., 508.,   0., 459., 375.,   0., 371., 322.,   0.,\n",
       "        274.,   0., 234.,   0., 184., 158.,   0., 125.,   0., 107.,  77.,\n",
       "          0.,  46.,  43.,   0.,  42.,   0.,  30.,  21.,   0.,  13.,   0.,\n",
       "         10.,   0.,   9.,   7.,   0.,   2.,   0.,   1.,   1.,   0.,   1.,\n",
       "          2.]),\n",
       " array([0.0465, 0.0474, 0.0483, 0.0492, 0.0501, 0.051 , 0.0519, 0.0528,\n",
       "        0.0537, 0.0546, 0.0555, 0.0564, 0.0573, 0.0582, 0.0591, 0.06  ,\n",
       "        0.0609, 0.0618, 0.0627, 0.0636, 0.0645, 0.0654, 0.0663, 0.0672,\n",
       "        0.0681, 0.069 , 0.0699, 0.0708, 0.0717, 0.0726, 0.0735, 0.0744,\n",
       "        0.0753, 0.0762, 0.0771, 0.078 , 0.0789, 0.0798, 0.0807, 0.0816,\n",
       "        0.0825, 0.0834, 0.0843, 0.0852, 0.0861, 0.087 , 0.0879, 0.0888,\n",
       "        0.0897, 0.0906, 0.0915, 0.0924, 0.0933, 0.0942, 0.0951, 0.096 ,\n",
       "        0.0969, 0.0978, 0.0987, 0.0996, 0.1005, 0.1014, 0.1023, 0.1032,\n",
       "        0.1041, 0.105 , 0.1059, 0.1068, 0.1077, 0.1086, 0.1095, 0.1104,\n",
       "        0.1113, 0.1122, 0.1131, 0.114 , 0.1149, 0.1158, 0.1167, 0.1176,\n",
       "        0.1185, 0.1194, 0.1203, 0.1212, 0.1221, 0.123 , 0.1239, 0.1248,\n",
       "        0.1257, 0.1266, 0.1275, 0.1284, 0.1293, 0.1302, 0.1311, 0.132 ,\n",
       "        0.1329, 0.1338, 0.1347, 0.1356, 0.1365]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(aver_list, bins = 100) ## show probability hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09394335000000001\n",
      "62.6289\n",
      "0.03131445\n"
     ]
    }
   ],
   "source": [
    "print(np.average(aver_list)) ## probability that we simulate\n",
    "print(np.average(aver_list*(L/s))) ## frequency that we simulate\n",
    "print(np.average(aver_list/s)) ## cluster number density that we simulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(s*((1-p)**2)*(p**s)) ## probability\n",
    "print(L*((1-p)**2)*(p**s)) ## frequency\n",
    "print(((1-p)**2)*(p**s)) ## cluster number density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e0861f3745f41a689ea95eff8a4f4bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "L, T = int(1e3), 1000 ## latice num, simul time\n",
    "p = 0.4 ## occupied probabiliy\n",
    "\n",
    "## simulate to find cluster number density changing s\n",
    "aver_list_04 = np.zeros(L)\n",
    "for s in tqdm(range(L)):\n",
    "    ## make lattice\n",
    "    for j in range(T):\n",
    "        site = \"\"\n",
    "        aver =0\n",
    "        for i in range(L):\n",
    "            if np.random.random() < p:\n",
    "                site += \"1\"\n",
    "            else :\n",
    "                site += \" \"\n",
    "    ## make cluster size list\n",
    "        site_split = site.split()\n",
    "        cluster = [len(w) for w in site_split]\n",
    "    ## find cluster size s\n",
    "        for i in range(len(cluster)):\n",
    "            if cluster[i] == s:\n",
    "                aver += 1        \n",
    "        aver_list_04[s] += aver/L\n",
    "    aver_list_04[s] /= T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "819e2f50766749b5b81db561b0040ef2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "L, T = int(1e3), 1000 ## latice num, simul time\n",
    "p = 0.905 ## occupied probabiliy\n",
    "\n",
    "## simulate to find cluster number density changing s\n",
    "aver_list_0905 = np.zeros(L)\n",
    "for s in tqdm(range(L)):\n",
    "    ## make lattice\n",
    "    for j in range(T):\n",
    "        site = \"\"\n",
    "        aver =0\n",
    "        for i in range(L):\n",
    "            if np.random.random() < p:\n",
    "                site += \"1\"\n",
    "            else :\n",
    "                site += \" \"\n",
    "    ## make cluster size list\n",
    "        site_split = site.split()\n",
    "        cluster = [len(w) for w in site_split]\n",
    "    ## find cluster size s\n",
    "        for i in range(len(cluster)):\n",
    "            if cluster[i] == s:\n",
    "                aver += 1        \n",
    "        aver_list_0905[s] += aver/L\n",
    "    aver_list_0905[s] /= T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29294fbcfb2b40afae765a951d61d5d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "L, T = int(1e3), 1000 ## latice num, simul time\n",
    "p = 0.99 ## occupied probabiliy\n",
    "\n",
    "## simulate to find cluster number density changing s\n",
    "aver_list_099 = np.zeros(L)\n",
    "for s in tqdm(range(L)):\n",
    "    ## make lattice\n",
    "    for j in range(T):\n",
    "        site = \"\"\n",
    "        aver =0\n",
    "        for i in range(L):\n",
    "            if np.random.random() < p:\n",
    "                site += \"1\"\n",
    "            else :\n",
    "                site += \" \"\n",
    "    ## make cluster size list\n",
    "        site_split = site.split()\n",
    "        cluster = [len(w) for w in site_split]\n",
    "    ## find cluster size s\n",
    "        for i in range(len(cluster)):\n",
    "            if cluster[i] == s:\n",
    "                aver += 1        \n",
    "        aver_list_099[s] += aver/L\n",
    "    aver_list_099[s] /= T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f83ad138590>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize = (20,10))\n",
    "axes.set_xlabel(\"cluster size\", size =10)\n",
    "axes.set_ylabel(\"cluster nuber density\", size = 10)\n",
    "axes.set_title(\"cluster number density\", size = 20)\n",
    "axes.loglog(range(L),aver_list_04, label = \"p = 0.4\")\n",
    "axes.loglog(range(L),aver_list_0905, label = \"p = 0.905\")\n",
    "axes.loglog(range(L),aver_list_099, label = \"p = 0.99\")\n",
    "axes.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5f5942115254e7ebdf47a70d0f8358d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "p_start = 1e-2\n",
    "T1, T2 = 100,100\n",
    "L = 100\n",
    "\n",
    "m = 0\n",
    "cluster_size_100 = np.zeros(T1)\n",
    "cluster_aver_size_100 = np.zeros(T1)\n",
    "for p in tqdm(np.linspace(p_start,1,T1)):\n",
    "    cluster_size_temp = 0\n",
    "    cluster_aver_size_temp = 0\n",
    "    aver_list = []\n",
    "    k = 0\n",
    "    for j in range(T2):\n",
    "        site = \"\"\n",
    "        for i in range(L):\n",
    "            if np.random.random() < p:\n",
    "                site += \"1\"\n",
    "            else :\n",
    "                site += \" \"\n",
    "        site_split = site.split()\n",
    "        cluster = np.array([len(w) for w in site_split])\n",
    "        if (len(cluster) != 0):\n",
    "            cluster_size_temp += np.max(cluster)\n",
    "            cluster_aver_size_temp += np.sum(cluster**2)/np.sum(cluster)\n",
    "    cluster_size_100[m] = cluster_size_temp/T2\n",
    "    cluster_aver_size_100[m] = cluster_aver_size_temp / T2\n",
    "    m += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "095ae85097444f999135215ed01803ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "p_start = 1e-2\n",
    "T1, T2 = 100,100\n",
    "L = 1000\n",
    "\n",
    "m = 0\n",
    "cluster_size_1000 = np.zeros(T1)\n",
    "cluster_aver_size_1000 = np.zeros(T1)\n",
    "for p in tqdm(np.linspace(p_start,1,T1)):\n",
    "    cluster_size_temp = 0\n",
    "    cluster_aver_size_temp = 0\n",
    "    aver_list = []\n",
    "    k = 0\n",
    "    for j in range(T2):\n",
    "        site = \"\"\n",
    "        for i in range(L):\n",
    "            if np.random.random() < p:\n",
    "                site += \"1\"\n",
    "            else :\n",
    "                site += \" \"\n",
    "        site_split = site.split()\n",
    "        cluster = np.array([len(w) for w in site_split])\n",
    "        if (len(cluster) != 0):\n",
    "            cluster_size_temp += np.max(cluster)\n",
    "            cluster_aver_size_temp += np.sum(cluster**2)/np.sum(cluster)\n",
    "    cluster_size_1000[m] = cluster_size_temp/T2\n",
    "    cluster_aver_size_1000[m] = cluster_aver_size_temp / T2\n",
    "    m += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0805b74063264358827fdfe1642b95d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "p_start = 1e-2\n",
    "T1, T2 = 100,100\n",
    "L = 10000\n",
    "\n",
    "m = 0\n",
    "cluster_size_10000 = np.zeros(T1)\n",
    "cluster_aver_size_10000 = np.zeros(T1)\n",
    "for p in tqdm(np.linspace(p_start,1,T1)):\n",
    "    cluster_size_temp = 0\n",
    "    cluster_aver_size_temp = 0\n",
    "    aver_list = []\n",
    "    k = 0\n",
    "    for j in range(T2):\n",
    "        site = \"\"\n",
    "        for i in range(L):\n",
    "            if np.random.random() < p:\n",
    "                site += \"1\"\n",
    "            else :\n",
    "                site += \" \"\n",
    "        site_split = site.split()\n",
    "        cluster = np.array([len(w) for w in site_split])\n",
    "        if (len(cluster) != 0):\n",
    "            cluster_size_temp += np.max(cluster)\n",
    "            cluster_aver_size_temp += np.sum(cluster**2)/np.sum(cluster)\n",
    "    cluster_size_10000[m] = cluster_size_temp/T2\n",
    "    cluster_aver_size_10000[m] = cluster_aver_size_temp / T2\n",
    "    m += 1\n",
    "\n",
    "x = np.linspace(0.01,1,T1,endpoint=False)\n",
    "y = [-1*(1/(log(a))) for a in x] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f83af46f410>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,figsize = (20,10))\n",
    "axes[0].set_xlabel(\"probabiliy\", size =10)\n",
    "axes[0].set_ylabel(\"giant cluster size density\", size = 10)\n",
    "axes[0].set_title(\"giant cluster size density\", size = 20)\n",
    "axes[0].plot(np.linspace(p_start,1,T1),cluster_size_100/100, label = \"L= 100\")\n",
    "axes[0].plot(np.linspace(p_start,1,T1),cluster_size_1000/1000, label = \"L=1000\")\n",
    "axes[0].plot(np.linspace(p_start,1,T1),cluster_size_10000/10000, label = \"L=10000\")\n",
    "axes[0].legend()\n",
    "axes[1].set_xlabel(\"probabiliy\", size =10)\n",
    "axes[1].set_ylabel(\"averege cluster size density\", size = 10)\n",
    "axes[1].set_title(\"averege cluster size density\", size = 20)\n",
    "axes[1].plot(np.linspace(p_start,1,T1),cluster_aver_size_100/100, label = \"L= 100\")\n",
    "axes[1].plot(np.linspace(p_start,1,T1),cluster_aver_size_1000/1000, label = \"L=1000\")\n",
    "axes[1].plot(np.linspace(p_start,1,T1),cluster_aver_size_10000/10000, label = \"L=10000\")\n",
    "axes[1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2-D percolation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_lattice(L,p):    \n",
    "    lattice_temp = np.zeros((L,L))\n",
    "    for i in range(L):\n",
    "        for j in range(L):\n",
    "            if p > np.random.random():\n",
    "                lattice_temp[j][i] = 1\n",
    "\n",
    "## make outside zero to avoid out or range in for sentence\n",
    "    for i in range(L):\n",
    "        lattice_temp[0][i] = 0\n",
    "        for j in range(L):\n",
    "            lattice_temp[j][0] = 0\n",
    "    return lattice_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "## labeling lattice with cluster\n",
    "def labelling(lattice, L):\n",
    "    labeled_lattice = np.zeros((L,L))\n",
    "    label = 0\n",
    "    for i in range(1,L):\n",
    "        for j in range(1,L):\n",
    "            if lattice[j][i] == 1:\n",
    "                if (lattice[j-1][i] != 1) and (lattice[j][i-1] != 1):\n",
    "                    label += 1\n",
    "                    labeled_lattice[j][i] = label\n",
    "                elif (lattice[j-1][i] == 1) and (lattice[j][i-1] != 1):\n",
    "                    labeled_lattice[j][i] = labeled_lattice[j-1][i]\n",
    "                elif (lattice[j-1][i] != 1) and (lattice[j][i-1] == 1):\n",
    "                    labeled_lattice[j][i] = labeled_lattice[j][i-1]\n",
    "                elif (lattice[j-1][i] == 1) and (lattice[j][i-1] == 1):\n",
    "                    if (labeled_lattice[j-1][i] > labeled_lattice[j][i-1]):\n",
    "                        value = labeled_lattice[j][i-1]\n",
    "                        change = labeled_lattice[j-1][i]\n",
    "                        for a in range(1,i+1):\n",
    "                            for b in range(1,L):\n",
    "                                if (labeled_lattice[b][a] == change):\n",
    "                                    labeled_lattice[b][a] = value\n",
    "                        labeled_lattice[j][i] = value\n",
    "                    elif (labeled_lattice[j-1][i] < labeled_lattice[j][i-1]):\n",
    "                        value = labeled_lattice[j-1][i]\n",
    "                        change = labeled_lattice[j][i-1]\n",
    "                        for a in range(1,i+1):\n",
    "                            for b in range(1,L):\n",
    "                                if (labeled_lattice[b][a] == change):\n",
    "                                    labeled_lattice[b][a] = value\n",
    "                        labeled_lattice[j][i] = value\n",
    "                    else :\n",
    "                        labeled_lattice[j][i] = labeled_lattice[j-1][i]\n",
    "    return labeled_lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "## show giant cluster size\n",
    "def show_clustersize(lattice,labeled_lattice):\n",
    "    occupied_lattice = int(np.sum(lattice))\n",
    "    if (occupied_lattice != 0):\n",
    "        cluster_size_array = np.zeros(int(np.max(labeled_lattice)))\n",
    "        for i in range(1,L):\n",
    "            for j in range(1,L):\n",
    "                if labeled_lattice[j][i] != 0:\n",
    "                    a = int(labeled_lattice[j][i] - 1)\n",
    "                    cluster_size_array[a] += 1\n",
    "        return np.max(cluster_size_array)\n",
    "    else :\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "## show avereage cluster size\n",
    "def show_aver_clustersize(lattice,labeled_lattice):\n",
    "    occupied_lattice = int(np.sum(lattice))\n",
    "    if (occupied_lattice != 0):\n",
    "        num = int(np.max(labeled_lattice))\n",
    "        cluster_size_array = np.zeros(num)\n",
    "        for i in range(1,L):\n",
    "            for j in range(1,L):\n",
    "                if labeled_lattice[j][i] != 0:\n",
    "                    a = int(labeled_lattice[j][i] - 1)\n",
    "                    cluster_size_array[a] += 1\n",
    "        aver_cluster_size = sum(cluster_size_array**2) / occupied_lattice\n",
    "\n",
    "        return aver_cluster_size\n",
    "    else :\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lattice size(out side lattice that i make +1 is trick to avoid index problem in for sentence) \\\n",
    "## simul time step for changing p, simul time step for same p\n",
    "L, T1, T2 = 20+1, 100, 10 \n",
    "p_start = 1e-2 ## start probability of occuping lattice\n",
    "lattice01 = make_lattice(L,0.1)\n",
    "lattice04 = make_lattice(L,0.4)\n",
    "lattice07 = make_lattice(L,0.7)\n",
    "lattice09 = make_lattice(L,0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f83acce38d0>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ploting labeling lattice and lattice when p = 0.5\n",
    "fig, axes = plt.subplots(2,2,figsize = (20,20))\n",
    "axes[0][0].set_title(\"p = 0.1\", size = 20)\n",
    "axes[0][1].set_title(\"p = 0.4\", size = 20)\n",
    "axes[1][0].set_title(\"p = 0.7\", size = 20)\n",
    "axes[1][1].set_title(\"p = 0.9\", size = 20)\n",
    "axes[0][0].set_xlim([0.5,20.5])\n",
    "axes[0][0].set_ylim([0.5,20.5])\n",
    "axes[0][1].set_xlim([0.5,20.5])\n",
    "axes[0][1].set_ylim([0.5,20.5])\n",
    "axes[1][0].set_xlim([0.5,20.5])\n",
    "axes[1][0].set_ylim([0.5,20.5])\n",
    "axes[1][1].set_xlim([0.5,20.5])\n",
    "axes[1][1].set_ylim([0.5,20.5])\n",
    "axes[0][0].imshow(lattice01, cmap=\"binary\")\n",
    "axes[0][1].imshow(lattice04, cmap=\"binary\")\n",
    "axes[1][0].imshow(lattice07, cmap=\"binary\")\n",
    "axes[1][1].imshow(lattice09, cmap=\"binary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lattice size(out side lattice that i make +1 is trick to avoid index problem in for sentence) \\\n",
    "## simul time step for changing p, simul time step for same p\n",
    "L, T1, T2 = 10+1, 100, 100 \n",
    "p_start = 1e-2 ## start probability of occuping lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e6e2e74a55b4264aa33035c4d746d58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "cluster_size_10_2d = np.zeros(T1)\n",
    "cluster_aver_size_10_2d = np.zeros(T1)\n",
    "for p in tqdm(np.linspace(p_start,1,T1)): ## simulating T1 step by increasing p\n",
    "    cluster_size_aver = 0\n",
    "    cluster_aver_size_aver = 0\n",
    "    for j in range(T2): ## simulating T2 times each probability\n",
    "        lattice = make_lattice(L,p)\n",
    "        labeled_lattice = labelling(lattice, L)\n",
    "        cluster_size_aver += show_clustersize(lattice,labeled_lattice)\n",
    "        cluster_aver_size_aver += show_aver_clustersize(lattice,labeled_lattice)\n",
    "    cluster_size_10_2d[i] = cluster_size_aver / T2 ## find giant cluster size\n",
    "    cluster_aver_size_10_2d[i] = cluster_aver_size_aver /T2 ## find average cluster size\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lattice size(out side lattice that i make +1 is trick to avoid index problem in for sentence) \\\n",
    "## simul time step for changing p, simul time step for same p\n",
    "L, T1, T2 = 50+1, 100, 10 \n",
    "p_start = 1e-2 ## start probability of occuping lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77dd17412d0f4bcc841895591bca28ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "cluster_size_50_2d = np.zeros(T1)\n",
    "cluster_aver_size_50_2d = np.zeros(T1)\n",
    "for p in tqdm(np.linspace(p_start,1,T1)): ## simulating T1 step by increasing p\n",
    "    cluster_size_aver = 0\n",
    "    cluster_aver_size_aver = 0\n",
    "    for j in range(T2): ## simulating T2 times each probability\n",
    "        lattice = make_lattice(L,p)\n",
    "        labeled_lattice = labelling(lattice, L)\n",
    "        cluster_size_aver += show_clustersize(lattice,labeled_lattice)\n",
    "        cluster_aver_size_aver += show_aver_clustersize(lattice,labeled_lattice)\n",
    "    cluster_size_50_2d[i] = cluster_size_aver / T2 ## find giant cluster size\n",
    "    cluster_aver_size_50_2d[i] = cluster_aver_size_aver /T2 ## find average cluster size\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lattice size(out side lattice that i make +1 is trick to avoid index problem in for sentence) \\\n",
    "## simul time step for changing p, simul time step for same p\n",
    "L, T1, T2 = 100+1, 100, 10 \n",
    "p_start = 1e-2 ## start probability of occuping lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20daf9b36bbc410ebb6deaae3920e6b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "cluster_size_100_2d = np.zeros(T1)\n",
    "cluster_aver_size_100_2d = np.zeros(T1)\n",
    "for p in tqdm(np.linspace(p_start,1,T1)): ## simulating T1 step by increasing p\n",
    "    cluster_size_aver = 0\n",
    "    cluster_aver_size_aver = 0\n",
    "    for j in range(T2): ## simulating T2 times each probability\n",
    "        lattice = make_lattice(L,p)\n",
    "        labeled_lattice = labelling(lattice, L)\n",
    "        cluster_size_aver += show_clustersize(lattice,labeled_lattice)\n",
    "        cluster_aver_size_aver += show_aver_clustersize(lattice,labeled_lattice)\n",
    "    cluster_size_100_2d[i] = cluster_size_aver / T2 ## find giant cluster size\n",
    "    cluster_aver_size_100_2d[i] = cluster_aver_size_aver /T2 ## find average cluster size\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f83af343790>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ploting giant cluster size density when size is L\n",
    "fig, axes = plt.subplots(1,2,figsize = (20,10))\n",
    "axes[0].set_title(\"Cluster size density\", size = 20)\n",
    "axes[0].set_xlabel(\"probability\", size = 10)\n",
    "axes[0].set_ylabel(\"Cluster size density\", size = 10)\n",
    "axes[0].plot(np.linspace(p_start,1,T1), cluster_size_10_2d/(100), label = \"lattice_10\")\n",
    "axes[0].plot(np.linspace(p_start,1,T1), cluster_size_50_2d/(2500), label = \"lattiec_50\")\n",
    "axes[0].plot(np.linspace(p_start,1,T1), cluster_size_100_2d/(10000), label = \"lattice_100\")\n",
    "axes[0].legend()\n",
    "## ploting averege cluster size density when size is L\n",
    "axes[1].set_title(\"Cluster aver_size density\", size = 20)\n",
    "axes[1].set_xlabel(\"probability\", size = 10)\n",
    "axes[1].set_ylabel(\"Cluster aver_size density\", size = 10)\n",
    "axes[1].plot(np.linspace(p_start,1,T1), cluster_aver_size_10_2d/(100), label = \"lattice_10\")\n",
    "axes[1].plot(np.linspace(p_start,1,T1), cluster_aver_size_50_2d/(2500), label = \"lattiec_50\")\n",
    "axes[1].plot(np.linspace(p_start,1,T1), cluster_aver_size_100_2d/(10000), label = \"lattice_100\")\n",
    "axes[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}