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  {
   "cell_type": "markdown",
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   "source": [
    "# Fit\n",
    "\n",
    "This example uses the `draw()` and `tween()` animations to animate a `scatter`, `plot`, `fill_between` and `text` plot objects.\n",
    "\n",
    "We first create our timeline and name our axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8ecc773",
   "metadata": {},
   "outputs": [],
   "source": [
    "import diplotocus as dpl\n",
    "import numpy as np\n",
    "\n",
    "tl = dpl.Timeline(xlim=(0,1),ylim=(0,1.5))\n",
    "tl.main_axis.set_xlabel('x')\n",
    "tl.main_axis.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb8c9bf",
   "metadata": {},
   "source": [
    "Then, we create the data we want to plot (data points, the fitted relation and its uncertainty):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "825677b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.uniform(0,1,100)\n",
    "y = np.random.normal(0.5*(1+x),0.2)\n",
    "\n",
    "popt, cov = np.polyfit(x,y,1,cov=True)\n",
    "err = np.sqrt(np.diag(cov))\n",
    "\n",
    "#Cheap & dirty way to get confidence bounds with numpy\n",
    "p_rand = np.random.normal(size=(10_000,2))*err + popt\n",
    "_x = np.linspace(0,1,1_000)\n",
    "_y = popt[0]*_x + popt[1]\n",
    "_ys = []\n",
    "for _p in p_rand:\n",
    "    _ys.append(_p[0]*_x + _p[1])\n",
    "y_max = np.max(_ys,axis=0)\n",
    "y_min = np.min(_ys,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4ade42",
   "metadata": {},
   "source": [
    "And finally, we animate it and render the video. To animate the confidence bounds growing, we start the `fill_between`'s `y1` and `y2` properties from the same y values as the line, and we `tween` them to `y_min` and `y_max`, the actual size of the $\\pm 1\\sigma$ bounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a0b36c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "e = dpl.easeInOutCubic()\n",
    "s = dpl.scatter(x=x,y=y)\n",
    "p = dpl.plot(x=_x,y=_y,c='k',lw=3)\n",
    "t = dpl.text(x=0.05,y=1.2,string=f'f(x) = {popt[0]:.2f}x + {popt[1]:.2f}',fontsize=14)\n",
    "f = dpl.fill_between(x=_x,y1=_y,y2=_y,alpha=0.2,fc='green')\n",
    "\n",
    "s.draw(duration=60,sort='x',easing=e)\n",
    "p.draw(duration=60,delay=60,easing=e)\n",
    "t.draw(duration=60,delay=60,easing=e)\n",
    "f.tween(property='y1',start=_y,end=y_min,duration=60,delay=120,easing=e)\n",
    "f.tween(property='y2',start=_y,end=y_max,duration=60,delay=120,easing=e)\n",
    "\n",
    "tl.animate((s,p,t,f))\n",
    "tl.wait(duration=60)\n",
    "\n",
    "tl.save_video('../../_static/examples/fit.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5077e4e",
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <video width=\"640\" height=\"460\" autoplay loop muted playsinline>\n",
       "    <source src=\"../../_static/examples/fit.mp4?randId={}\" type=\"video/mp4\">\n",
       "    </video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML, display\n",
    "display(HTML(\"\"\"\n",
    "    <video width=\"640\" height=\"460\" autoplay loop muted playsinline>\n",
    "    <source src=\"../../_static/examples/fit.mp4?randId={}\" type=\"video/mp4\">\n",
    "    </video>\n",
    "\"\"\"))"
   ]
  }
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