https://github.com/borjaest/ndautomata

Multidimensional cellular automata in Python.
https://github.com/borjaest/ndautomata

cellular-automata cellular-automaton game-of-life numpy

Last synced: 2 months ago
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Multidimensional cellular automata in Python.

• Host: GitHub
• URL: https://github.com/borjaest/ndautomata
• Owner: BorjaEst
• License: gpl-3.0
• Created: 2022-10-04T07:37:57.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-07-12T08:46:50.000Z (about 2 years ago)
• Last Synced: 2025-06-25T21:40:45.777Z (3 months ago)
• Topics: cellular-automata, cellular-automaton, game-of-life, numpy
• Language: Python
• Homepage:
• Size: 800 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.ipynb
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

          
{

 "cells": [

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "
\n",

    "\n",

    "

\n",

    "\n",

    "![qc.sec](https://github.com/BorjaEst/ndautomata/actions/workflows/qc-sec.yml/badge.svg)\n",

    "![qc.sty](https://github.com/BorjaEst/ndautomata/actions/workflows/qc-sty.yml/badge.svg)\n",

    "![qc.uni](https://github.com/BorjaEst/ndautomata/actions/workflows/qc-uni.yml/badge.svg)\n",

    "[![DOI](https://zenodo.org/badge/545341562.svg)](https://zenodo.org/badge/latestdoi/545341562)\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Awesome cellular automata library created by Borja Esteban.\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Install it from PyPI\n",

    "\n",

    "```bash\n",

    "$ pip install ndautomata\n",

    "```\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Usage\n",

    "\n",

    "This package is designed in order to create your own automata definitions based on 2 attributes:\n",

    "\n",

    "- **neighbours**: List with relative indexes to cell neighbours.\n",

    "- **states**: Number possible cell states.\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "from ndautomata import BaseAutomaton, neighbours"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [],

   "source": [

    "class Automaton(BaseAutomaton):\n",

    "    neighbours = neighbours.regular(ndim=1, r=1)\n",

    "    states = 2"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "> Note the automata dimensions are defined with `neighbours.ndim`.\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "After you have defined your automaton, you can instantiate automata passing the initial configuration and the rule to the constructor function. Use the module `initializers`, based on [numpy](numpy.org/) to generate the required arrays passing the number of states and size.\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "from ndautomata import initializers"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "rule = initializers.random(states=2, size=[2, 2, 2])\n",

    "ic = initializers.center(states=2, size=[200])\n",

    "ca = Automaton(ic, rule)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "> Note the shape of the rule must always be `[states] * neighbours.size`.\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Plot the cellular automaton using matplotlib or your favorite tool.\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import matplotlib.pyplot as plt"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [

    {

     "data": {

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4xz8e8+bNi3Xr1sXExESsXbs2PvGJTyTpLAAAUF6F7nNRD+5zATTCTGvai9wLocj9DOpVt53M1dgWqWtsgTKbs/tcAAAAvE5wAQAAJCG4AAAAkqjpPhcArSDPuvU89yQoY912u0+CsQVoDDMXAABAEoILAAAgCcuigLZRJG1oMyqS6rTZGNupy6YqB6gnMxcAAEASggsAACAJwQUAAJCEPRdAy8qz9rwV1q2nOt52OtaZ6pZFO40t0NzMXAAAAEkILgAAgCQEFwAAQBL2XAAtpdb8/zOtaS/jOv08fU75WTSKsZ26vBXGFmgdZi4AAIAkBBcAAEASlkUBTa1Iis7Zlk1V3oj0nnmXtKT6LBq1lMbYnlizjy3QusxcAAAASQguAACAJAQXAABAEvZcAE2lSIrOerVbz3XrtaYcTdVmWds1tsXbnMt2gfZh5gIAAEhCcAEAACQhuAAAAJKw5wIotSL5/4usHy9yj4Vmb3eu7gthbFt3bIH2ZeYCAABIQnABAAAkIbgAAACSsOcCKJ0i+f9rrVsk/3+9+tSouq32WczV8RSp2wqfBUCEmQsAACARwQUAAJCEZVFAwxVJ0ZmnvBHv24x1y9inInXL2KdG1U35vpZJAVMxcwEAACQhuAAAAJIQXAAAAEnYcwHMuSJpNoHGcN0Cs2HmAgAASEJwAQAAJCG4AAAAkrDnApgTedZjv7FcLn1oHNctkJeZCwAAIAnBBQAAkIRlUUBdTLcsokhKS8stoH5ct0BRZi4AAIAkBBcAAEASggsAACAJey6AJPKsqc6T0nKmMmu5oXauWyA1MxcAAEASggsAACAJwQUAAJCEPRdATfLkvJ+qfC7atY4bJnPdAvVm5gIAAEhCcAEAACQhuAAAAJKw5wKYtTzrolOtqc67BvyN5XLpg+sWmFtmLgAAgCQEFwAAQBKWRQEnNN3yhCIpLYukpaxnXWgFrlugkcxcAAAASQguAACAJAQXAABAEvZcAFV51jbnTWlZxrq1ptmEMmnn63Y27w3MLTMXAABAEoWCiz/5kz+Jjo6O2LRpU/W5gwcPxsaNG2PZsmVx8sknx7p162JsbKxoPwEAgJKrObh4+umn4y/+4i/inHPOmfT8TTfdFI899lg88sgjsXPnzti3b19ceeWVhTsKAACUW03Bxauvvhrr16+PT33qU3HqqadWnx8fH49Pf/rT8bGPfSx++Zd/OdasWROf+cxn4p/+6Z/iySefTNZpII2Ojo5JjyzLJj2OLW920x1vqx0rrc11O/XxAo1XU3CxcePG+JVf+ZUYGhqa9PyePXvi8OHDk55fvXp1rFy5Mnbt2jXle01MTESlUpn0AAAAmk/ubFGf//zn45lnnomnn376uLLR0dFYuHBhLFmyZNLzPT09MTo6OuX7bd++PW6//fa83QAAAEom18zFyMhI/P7v/3589rOfjUWLFiXpwJYtW2J8fLz6GBkZSfK+AADA3Mo1c7Fnz5546aWX4l3velf1uSNHjsQTTzwR9913X3zlK1+JQ4cOxf79+yfNXoyNjUVvb++U79nZ2RmdnZ219R7ILc+9HZr9vhBF8v/LpU+ZpDqXm+E8znvtvbHcdQuNlyu4uOSSS+Lf//3fJz13zTXXxOrVq+MP//APY8WKFbFgwYIYHh6OdevWRUTE3r1744UXXojBwcF0vQYAAEonV3BxyimnxDve8Y5Jz5100kmxbNmy6vPXXnttbN68OZYuXRpdXV1x4403xuDgYFx00UXpeg0AAJRO7g3dM/n4xz8e8+bNi3Xr1sXExESsXbs2PvGJT6RuBpil6ZYJzLSEoEjdRpluCUi9PgtILc/52M7XbdG6QHodWcmutEqlEt3d3TE+Ph5dXV2N7g40PcHF1GXHlgsuKBPBxdRlM5W7bqE+8vx9XvMdugEAAN5IcAEAACSRfM8F0FhFUlbmKS/L8oNUx9vqaXkptyLpV4uUNft1m7eu6xbqz8wFAACQhOACAABIQnABAAAkYc8FtIBGpGJsVOrWImkp69WudJfUwnU7dbnrFpqbmQsAACAJwQUAAJCEZVHQhIqkcSyy/CBP3VTtFknRWWTZQ6rPOG+7tK5GnVOu25nbLNou8BNmLgAAgCQEFwAAQBKCCwAAIAl7LqAJFEnjWCQVYxnq5l0XXfbPwjru9uE8n937Nqpuo9LyQqszcwEAACQhuAAAAJIQXAAAAEnYcwElVCRHfKqydqs7V32yjru1FbmnxGzLitQtY58aVXeu7vkB7cbMBQAAkITgAgAASEJwAQAAJGHPBZREkTzvlJuxbV1F7qNAuRlbqI2ZCwAAIAnBBQAAkIRlUdAgeabVLaVpLsa2dRnb1mVsIQ0zFwAAQBKCCwAAIAnBBQAAkIQ9FzBHZlqTWyRd6XR1qT9j27ryjM+x5ca23Iwt1IeZCwAAIAnBBQAAkITgAgAASMKeC6ijPOtu8+RUn65cvvW5YWxbl7FtXcYW6s/MBQAAkITgAgAASMKyKEioSOrCVO3mTXXK7Bjb1mVsW5exhbln5gIAAEhCcAEAACQhuAAAAJKw5wIKyLN2NuW621TtWut7Ys0+tnnbbSfGtnUZW2g8MxcAAEASggsAACAJwQUAAJCEPReQw0xrY4vkVC+SF73Wutb6/kSrje1MddvJXI1tkbrGtnb1+v4ztlAbMxcAAEASggsAACAJwQUAAJCEPRcwgzzrX/PkNi9D3XbPt97KY3tsubE9sTKMT5G67Ty2Eem+/8pYt93GltZg5gIAAEhCcAEAACRhWRQco51StxZJxdiM2mlsI4qlw2w2xnbqsqnKm027pW5tp7GlNZm5AAAAkhBcAAAASQguAACAJOy5oO0VSWvYjOtfi6TobLa1ze08thG1H287HetMdcvC2J5Ys++vaaexpT2YuQAAAJIQXAAAAEkILgAAgCTsuaAt1ZpHvBnvC1Gv4ynjsUYY2xOVG9upy2ZT3gjGduqymcqN7YnfF+aKmQsAACAJwQUAAJCEZVG0hSKp/mZbNlV5I9IEpjrWvHUbNSVvbE8s1WdhbOvP2M7991QZj3Wm8mYYWzBzAQAAJJE7uPje974Xv/3bvx3Lli2LxYsXx9lnnx3//M//XC3Psiy2bdsWfX19sXjx4hgaGopnn302aacBAIDyyRVc/M///E9cfPHFsWDBgvjyl78c3/zmN+PP//zP49RTT62+5q677op77rknduzYEbt3746TTjop1q5dGwcPHkzeeQAAoDw6shyL8G699db4x3/8x/iHf/iHKcuzLIv+/v740Ic+FDfffHNERIyPj0dPT088+OCDcfXVV8/YRqVSie7u7hgfH4+urq7Zdg0mKZLqr17t1nP9ayNSMTYqvaexnV2fUrVZ1naNbfE2y9qusS3e5ly2S3vI8/d5rpmLv/u7v4vzzz8/fvM3fzOWL18e73znO+NTn/pUtfz555+P0dHRGBoaqj7X3d0dAwMDsWvXrinfc2JiIiqVyqQHAADQfHIFF//1X/8VDzzwQJx55pnxla98JT7wgQ/E7/3e78VDDz0UERGjo6MREdHT0zOpXk9PT7XsWNu3b4/u7u7qY8WKFbUcBwAA0GC5goujR4/Gu971rvjoRz8a73znO+P666+P6667Lnbs2FFzB7Zs2RLj4+PVx8jISM3vBQAANE6u+1z09fXFz//8z0967qyzzoq//uu/joiI3t7eiIgYGxuLvr6+6mvGxsbivPPOm/I9Ozs7o7OzM0834DhF8ogXWYdaJFd7GdrNu+Y2T91U7RrbuW/X2J64rNnbNbYnLmv2dpvhfi60h1wzFxdffHHs3bt30nPf+c534owzzoiIiFWrVkVvb28MDw9XyyuVSuzevTsGBwcTdBcAACirXDMXN910U7z73e+Oj370o/Fbv/Vb8dRTT8UnP/nJ+OQnPxkRr0XBmzZtijvuuCPOPPPMWLVqVWzdujX6+/vjiiuuqEf/AQCAksgVXFxwwQXx6KOPxpYtW+KP/uiPYtWqVXH33XfH+vXrq6+55ZZb4sCBA3H99dfH/v374z3veU88/vjjsWjRouSdBwAAyiPXfS7mgvtcMFtF8ojXWrdIHvF69alI3bk6niJ1W+2zmKvjKVK31T4L53nrHk+Ruq32WdTzeKBu97kAAAA4EcEFAACQRK49F9BIRVL95SlvxPs2qm4Z+9SoumXsU6PqlrFPReqWsU+NqlvGPjWqbhn7VKRuyve1TIoizFwAAABJCC4AAIAkBBcAAEAS9lxQWkXS9QEAs1MkjS0cy8wFAACQhOACAABIQnABAAAkYc8FpZF3Tecby60HBYDZy7Nv0Z5H8jBzAQAAJCG4AAAAkhBcAAAASdhzQUNNt26zyH0u7MEAgJ+Y7nexyH0u/N5yLDMXAABAEoILAAAgCcuimFN5pk/zpsabrsy0LQDtxO8tjWLmAgAASEJwAQAAJCG4AAAAkrDngroqkk62GdsFgEYokk52rtr1e9sezFwAAABJCC4AAIAkBBcAAEAS9lyQXJ71lW8sL7IeNG/dVO0CQKPU+nubt26qdv3etgczFwAAQBKCCwAAIAnLoihsumnOIinqyloXABoh1e9to+r6vW0PZi4AAIAkBBcAAEASggsAACAJey7ILc8aybyp8cpYt0i6PgCoVTv/3s7mvSknMxcAAEASggsAACAJwQUAAJCEPRfMqEj+62ZUJJ83ABTRTveFKHKPDMrLzAUAAJCE4AIAAEhCcAEAACRhzwVTyrPOsdnXSBbJI95q618BmFupfoOa4fcn72+mPY/NycwFAACQhOACAABIwrIoIqJY+tVmTN1aa6q/dkvLC0BaRdKvttPvbdG6NI6ZCwAAIAnBBQAAkITgAgAASMKeizZVJPVdnvKyrJFMdbytnpYXgLSKpF8tUtbsv7d56/q9LQ8zFwAAQBKCCwAAIAnBBQAAkIQ9F22kEfmii+SwbrV25eQGaA9l/71tVLt+b9uDmQsAACAJwQUAAJCEZVEtrEg6uCLTmHnqpprGLJLqb67aLUuaQADSatRvQa2/t3nrpmrX7217MHMBAAAkIbgAAACSEFwAAABJ2HPRQoqkgyuS0i1V3bxrJBtRt1GfBQDl0c6/T0XqNsNnQXFmLgAAgCQEFwAAQBKCCwAAIAl7LppYkXs7pCprt7pz1SfrQQHKpcg9JWZbVqRuGfvUqLpzec8PjmfmAgAASCJXcHHkyJHYunVrrFq1KhYvXhxve9vb4o//+I8nRXlZlsW2bduir68vFi9eHENDQ/Hss88m7zgAAFAuuYKLO++8Mx544IG477774lvf+lbceeedcdddd8W9995bfc1dd90V99xzT+zYsSN2794dJ510UqxduzYOHjyYvPMAAEB5dGQ5Fpf96q/+avT09MSnP/3p6nPr1q2LxYsXx1/91V9FlmXR398fH/rQh+Lmm2+OiIjx8fHo6emJBx98MK6++uoZ26hUKtHd3R3j4+PR1dVVwyG1tiL5oik3YwtQHkXuo0C5Gdv88vx9nmvm4t3vfncMDw/Hd77znYiI+Ld/+7f4xje+EZdddllERDz//PMxOjoaQ0ND1Trd3d0xMDAQu3btmvI9JyYmolKpTHoAAADNJ1e2qFtvvTUqlUqsXr065s+fH0eOHImPfOQjsX79+oiIGB0djYiInp6eSfV6enqqZcfavn173H777bX0HQAAKJFcMxdf/OIX47Of/Ww8/PDD8cwzz8RDDz0Uf/ZnfxYPPfRQzR3YsmVLjI+PVx8jIyM1v1cr6ujomPTIsqz6ONYby7IsO64u5WJsAcpjuu/kY7+XfSc3F2M7t3LNXPzBH/xB3HrrrdW9E2effXb893//d2zfvj02bNgQvb29ERExNjYWfX191XpjY2Nx3nnnTfmenZ2d0dnZWWP3AQCAssg1c/HjH/845s2bXGX+/Plx9OjRiIhYtWpV9Pb2xvDwcLW8UqnE7t27Y3BwMEF3AQCAsso1c3H55ZfHRz7ykVi5cmW8/e1vj3/5l3+Jj33sY/E7v/M7EfHatNOmTZvijjvuiDPPPDNWrVoVW7dujf7+/rjiiivq0X8AAKAkcgUX9957b2zdujU++MEPxksvvRT9/f3xu7/7u7Ft27bqa2655ZY4cOBAXH/99bF///54z3veE48//ngsWrQoeedb0Uwp0IqkK3V7+8YytgDlked79dhy38nlZmwbK9d9LuZCu9/nwh+grcvYApSHP0Bbl7FNr273uQAAADgRwQUAAJBErj0X1EeeKbbpyvPUdXv7uWFsAcrDd3LrMrblYeYCAABIQnABAAAkYVlUAxTJUpCq3bzZiJgdYwtQHr6TW5exLS8zFwAAQBKCCwAAIAnBBQAAkIQ9F3Mgzxq8lOv3UrXbrmsGZ8PYApRHs38n5223nRjb5mHmAgAASEJwAQAAJCG4AAAAkrDnog5mWmNXJDdzkfzKtdZt1zWDUzG2AOVSr++/Mn6ftxtj25zMXAAAAEkILgAAgCQEFwAAQBL2XCSSZx1dnhzJZajb7nmbjS1AeRS578Bsy8pSt92+k41tazBzAQAAJCG4AAAAkrAsqkbtlN6zSEq3ZmRsZ18OUG/tlt6zSKrTZmNspy6bqryZmLkAAACSEFwAAABJCC4AAIAk7LmYpSLp0ZpxHV2R9KvNtkbS2J5Ys48t0Hzyfq82+764VL9BrXasx5a389jOVLdszFwAAABJCC4AAIAkBBcAAEAS9lycQJH8/81474B6HU8ZjzWi9lzTxrb8Yws0nyL5/5vt3gH1Op4yHmuEsT1RecrPomzMXAAAAEkILgAAgCQEFwAAQBL2XLxBkfz/sy2bqrwR6+iK5F7OU16WNZKpjtfYnrisjOtfgXIqw/dUGb+TZypvhu9kY3tiqT6Lsv/emrkAAACSEFwAAABJtPWyqEaldCtDu3M1fdqo1GplbLedxzZlu0Dz8Z08uz6larOs7Rrb5mu3FmYuAACAJAQXAABAEoILAAAgibbac1EkPVqR9WzN3m7etXu1pvSdq2NtVLvtPLZF2wWai+/kuW93Lr+Ty/4732pjW6TdRvzemrkAAACSEFwAAABJCC4AAIAkWn7PRa15gYvki56runnX9pX9s5ir4ylSt9U+i7Ke50Dz8X1evE9F6pb1+7yVP4tWOM/rwcwFAACQhOACAABIouWWRRVJ3TXbsnarW8Y+NapuGftUpG5Z+mSZFDSfVL+3japbxj4VqVvGPjWqbhn71Ki6jfi9NXMBAAAkIbgAAACSEFwAAABJNP2eiyKpu4DGcN1C83HdQvMpkpa3VmYuAACAJAQXAABAEoILAAAgiabbc5F3bVijb4EOvCbPemzXLZSD6xaaT63Xbd66J2LmAgAASEJwAQAAJCG4AAAAkmiKPRfTrf8qknfbmlCon+muL9ctlJPrFppPqut2prqzZeYCAABIQnABAAAk0RTLoqabPs2bYmu6shTpt6Bd5Vn2UK/rdjbvDfyE6xaaTyOu20qlEt3d3bPqn5kLAAAgCcEFAACQROmWRb0+BVOpVBraj0a3D82uUdeQaxdq57qF5jMX18/rbcxmCWNHVrKFji+++GKsWLGi0d0AAADeYGRkJE4//fRpX1O64OLo0aOxb9++yLIsVq5cGSMjI9HV1dXobkFdVCqVWLFihfOcluY8px04z2llWZbFK6+8Ev39/TFv3vS7Kkq3LGrevHlx+umnV6dfurq6XKS0POc57cB5TjtwntOqZIsCAADmlOACAABIorTBRWdnZ9x2223R2dnZ6K5A3TjPaQfOc9qB8xxeU7oN3QAAQHMq7cwFAADQXAQXAABAEoILAAAgCcEFAACQhOACAABIQnABAAAkIbgAAACSEFwAAABJ/D+yxBMQkbgq5wAAAABJRU5ErkJggg==",

      "text/plain": [

       ""

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "plt.rcParams[\"image.cmap\"] = \"binary\"\n",

    "plt.matshow([next(ca) for _ in range(100)])\n",

    "plt.show()"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "See [examples](/examples) for more detailed use cases and examples on how to generate some plots.\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Development\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Fork the repository, pick one of the issues at the [issues](https://github.com/BorjaEst/ndautomata/issues) and create a [Pull request](https://github.com/BorjaEst/ndautomata/pulls).\n"

   ]

  }

 ],

 "metadata": {

  "kernelspec": {

   "display_name": "Python 3.9.2 64-bit",

   "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": "3.9.2"

  },

  "orig_nbformat": 4,

  "vscode": {

   "interpreter": {

    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"

   }

  }

 },

 "nbformat": 4,

 "nbformat_minor": 2

}