rotate_cube.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "THETA = np.radians(-90)\n",
    "\n",
    "ROT_X_CW = lambda theta : np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0,np.sin(theta),np.cos(theta)]]) # rotate clockwise around x\n",
    "ROT_Y_CW = lambda theta : np.array([[np.cos(theta),0,np.sin(theta)],[0,1,0],[-np.sin(theta),0,np.cos(theta)]]) # rotate clockwise around y\n",
    "ROT_Z_CW = lambda theta : np.array([[np.cos(theta),-np.sin(theta),0],[np.sin(theta),np.cos(theta),0],[0,0,1]]) # rotate clockwise around z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "for i in [-1,0,1]:\n",
    "            for j in [-1,0,1]:\n",
    "                for k in [-1,0,1]:\n",
    "                    coords = np.array([[i],[j],[k]])\n",
    "                    lst.append(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = np.array(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1]\n",
      " [-1]\n",
      " [-1]]\n",
      "[[-1.]\n",
      " [-1.]\n",
      " [ 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(lst[0])\n",
    "rot_lst = ROT_X_CW(THETA) @ lst\n",
    "print(rot_lst[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.]\n",
      " [-1.]\n",
      " [ 1.]]\n",
      "[-1, -1, 0]\n"
     ]
    }
   ],
   "source": [
    "print(rot_lst[0])\n",
    "print(list(map(int,rot_lst[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(1.)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "cead19d0794b5db4c0b1bf734ae47d24812a90546e6b20fa1ccfd32912620d41"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}