{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2770ffd07584667aa28bf7453fd13df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Rotx(l,rot):\n",
    "    xr = [0*np.cos(rot)-0*np.sin(rot),(l[0])*np.cos(rot)-0*np.sin(rot),(x)*np.cos(rot)-y*np.sin(rot)]\n",
    "    return xr\n",
    "\n",
    "def Roty(l,rot):\n",
    "    yr=[0*np.sin(rot)+0*np.cos(rot),(l[0])*np.sin(rot)+0*np.cos(rot),(x)*np.sin(rot)+y*np.cos(rot)]\n",
    "    return yr\n",
    "\n",
    "\n",
    "#Set the number of fibonacci triangles to display\n",
    "n=500\n",
    "\n",
    "fig, ax1= plt1.subplots(figsize=(10, 10))\n",
    "\n",
    "p= np.loadtxt(\"Primetable.txt\", delimiter=\",\", unpack=False)\n",
    "counter=np.arange(1,len(p)+1)\n",
    "\n",
    "tempcount=np.arange(1,n)\n",
    "\n",
    "ax1.clear()\n",
    "\n",
    "rot=0\n",
    "rot0=0\n",
    "x0=[0,0,0]\n",
    "y0=[0,0,0]\n",
    "\n",
    "for i in tempcount:\n",
    "\n",
    "    #Select 3 following primes\n",
    "    l=p[(i-1):(i)+2]\n",
    "    #print(l)\n",
    "\n",
    "    #Determine triangle charesteristics (cosine rule)\n",
    "    alpha=np.arccos((l[1]**2+l[2]**2-l[0]**2)/(2*l[1]*l[2]))\n",
    "    beta=np.arccos((l[0]**2+l[2]**2-l[1]**2)/(2*l[0]*l[2]))\n",
    "    gamma=np.arccos((l[0]**2+l[1]**2-l[2]**2)/(2*l[0]*l[1]))\n",
    "\n",
    "    #Height Fibonacci Prime Triangle\n",
    "    x=l[0]+l[1]*np.cos(np.pi-gamma)\n",
    "    y=l[2]*np.cos(np.pi/2-beta)\n",
    "\n",
    "    #Apply Rotation\n",
    "    rot=rot+np.pi-rot0\n",
    "    rot0=gamma\n",
    "    xr=Rotx(l,rot)\n",
    "    yr=Roty(l,rot)\n",
    "    \n",
    "    #Triangle not translation\n",
    "    xr =xr+x0\n",
    "    yr =yr+y0\n",
    "    x0=x*np.cos(rot)-y*np.sin(rot)+x0\n",
    "    y0=x*np.sin(rot)+y*np.cos(rot)+y0\n",
    "\n",
    "    color=str(i/n)\n",
    "    ax1.fill(xr,yr,zorder=(-i),c=color)\n",
    "\n",
    "ax1.axis([-1.5*l[2],1.5*l[2], -1.5*l[2],1.5*l[2]])\n",
    "ax1.text(0.05,0.95, 'Prime Triangle: [' +str(l[0]) +', '+ str(l[1]) +', '+  str(l[2]) + ']', transform=ax1.transAxes, fontsize=10,\n",
    "    verticalalignment='top')\n",
    "ax1.axes.get_xaxis().set_visible(False)\n",
    "ax1.axes.get_yaxis().set_visible(False)\n",
    "plt1.show()\n",
    "#plt1.savefig('Fibonacci Prime Traingle 5000', dpi=500, bbox_inches='tight')\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "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": "3.8.3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "44c092b33b7342b7abad0eec60fc9dd0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4a694c5b277849f3a0a79e125be05d91": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.7.3",
      "model_name": "ToolbarModel",
      "state": {
       "layout": "IPY_MODEL_44c092b33b7342b7abad0eec60fc9dd0",
       "toolitems": [
        [
         "Home",
         "Reset original view",
         "home",
         "home"
        ],
        [
         "Back",
         "Back to previous view",
         "arrow-left",
         "back"
        ],
        [
         "Forward",
         "Forward to next view",
         "arrow-right",
         "forward"
        ],
        [
         "Pan",
         "Pan axes with left mouse, zoom with right",
         "arrows",
         "pan"
        ],
        [
         "Zoom",
         "Zoom to rectangle",
         "square-o",
         "zoom"
        ],
        [
         "Download",
         "Download plot",
         "floppy-o",
         "save_figure"
        ]
       ]
      }
     },
     "9cc99c365ea94f7893b79747daae54b1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b2770ffd07584667aa28bf7453fd13df": {
      "model_module": "jupyter-matplotlib",
      "model_module_version": "^0.7.3",
      "model_name": "MPLCanvasModel",
      "state": {
       "_cursor": "default",
       "_figure_label": "Figure 1",
       "_height": 1000,
       "_image_mode": "diff",
       "_width": 1000,
       "layout": "IPY_MODEL_9cc99c365ea94f7893b79747daae54b1",
       "toolbar": "IPY_MODEL_4a694c5b277849f3a0a79e125be05d91",
       "toolbar_position": "left"
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}