Exercises-gameoflife_answer.ipynb

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    "### **Exercise: Game of Life (no xarray, just python practice)**\n",
    "\n",
    "Implement [Conway's game of life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life) using numpy array to hold data.\n",
    "Neighbours are defined as adjacent cells (all the 8).\n",
    "\n",
    " - Any LIVING cell with 2 or 3 neighbours survives.\n",
    " - Any DEAD cell with 3 neighbours comes alive.\n",
    " - Any OTHER LIVING cell dies.\n",
    " - All deaths and births occur simultaneously\n",
    "\n",
    "So, at each step you need:\n",
    "\n",
    " - calculate the number of neighnours\n",
    " - set \"dead\" or \"alive\" status according to the above\n",
    " - plot\n",
    "\n",
    "### Hints and directions:\n",
    "\n",
    "*Implement everything in a class*.\n",
    "\n",
    "For initial conditions: two files \"ships.txt\" and \"guns.txt\" are provided in data folder (but feel free to create your own if you wish), you can read them like\n",
    "\n",
    "```python\n",
    "field=np.genfromtxt(\"ships.txt\").transpose()\n",
    "```\n",
    "\n",
    "For plotting: Jupyter is very finicky with \"real-time\" plots and there is a high chance it will refuse to plot everything in one frame even if you try all the stackoverflow solutions, so I propose just using a script to run your program. Try the following code to produce \"animation\" to test that it works:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.axis([0, 10, 0, 1])\n",
    "\n",
    "for i in range(10):\n",
    "    y = np.random.random()\n",
    "    plt.scatter(i, y)\n",
    "    plt.pause(0.5)\n",
    "plt.show()\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2edc245d-0edd-4fe2-b1fb-2a1be863f366",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.animation import FuncAnimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "646ae670-4f06-4bef-a161-b2ad0d9a8089",
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class GameOfLife:\n",
    "    def __init__(self, initial_state):\n",
    "        self.grid = initial_state\n",
    "\n",
    "    def count_neighbors(self, x, y):\n",
    "        neighbors = [\n",
    "            self.grid[x-1, y-1], self.grid[x-1, y], self.grid[x-1, y+1],\n",
    "            self.grid[x, y-1],                     self.grid[x, y+1],\n",
    "            self.grid[x+1, y-1], self.grid[x+1, y], self.grid[x+1, y+1]\n",
    "        ]\n",
    "        return np.sum(neighbors)\n",
    "\n",
    "    def update(self):\n",
    "        new_grid = self.grid.copy()\n",
    "\n",
    "        for i in range(1, self.grid.shape[0] - 1):\n",
    "            for j in range(1, self.grid.shape[1] - 1):\n",
    "                count = self.count_neighbors(i, j)\n",
    "\n",
    "                # Apply Conway's Game of Life rules\n",
    "                if self.grid[i, j] == 1:  # Living cell\n",
    "                    if count not in [2, 3]:\n",
    "                        new_grid[i, j] = 0  # Dies\n",
    "                elif count == 3:\n",
    "                    new_grid[i, j] = 1  # Comes alive\n",
    "\n",
    "        self.grid = new_grid\n",
    "\n",
    "    def plot(self):\n",
    "        plt.imshow(self.grid, cmap='binary', interpolation='nearest')\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "# Create the GameOfLife instance with the 10 by 10 initial state\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dec4a85e-0119-4c36-b1a5-80a4cffb8e70",
   "metadata": {},
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.animation import FuncAnimation\n",
    "\n",
    "def generate_binary_matrix(N):\n",
    "    return np.random.choice([0, 1], size=(N, N))\n",
    "\n",
    "# Specify the size of the matrix (replace 5 with your desired size)\n",
    "N = 20\n",
    "\n",
    "# Generate the NxN binary matrix\n",
    "initial_state = np.genfromtxt(\"data/ships.txt\").transpose()\n",
    "\n",
    "\n",
    "def update(frame):\n",
    "    #old = laplace_solver.grid\n",
    "    gol.update()\n",
    "    im.set_array(gol.grid)\n",
    "    return [im]\n",
    "\n",
    "gol = GameOfLife(initial_state)\n",
    "\n",
    "fig, ax = plt.subplots()  \n",
    "im = ax.imshow(gol.grid, cmap='binary', interpolation='nearest')\n",
    "ax.set_title('Game of Life')\n",
    "ax.set_xlabel('X-axis')\n",
    "ax.set_ylabel('Y-axis')\n",
    "\n",
    "# Set up the animation\n",
    "animation = FuncAnimation(fig, update, frames=range(100), interval=2, blit=True)\n",
    "\n",
    "# Save the animation as a GIF\n",
    "animation.save('GOL.gif', writer='pillow')\n",
    "\n",
    "# Display the animation in the Jupyter Notebook\n",
    "#html_output = HTML(animation.to_jshtml())\n",
    "#display(html_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "797ad1e5-074d-4761-ac9d-1b13a52a91e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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