Example adding trend line later in a different cell

SO Plot with trend line.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94dcaf35-a686-48ff-a624-2c7d3048217e",
   "metadata": {},
   "source": [
    "# SO Plot with trend line\n",
    "based on https://stackoverflow.com/q/78168545/8508004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad86baa1-e5b8-420b-80e2-564496ba42a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#based on https://stackoverflow.com/a/75562058/8508004\n",
    "import numpy as np\n",
    "x_data, y_data = np.repeat(np.linspace(0, 9, 100)[None,:], 2, axis=0) + np.random.rand(2, 100)*2\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(1,2, figsize=(12,3))\n",
    "axs[0].scatter(x_data, y_data)\n",
    "#z = np.polynomial.polynomial.polyfit(x_data, y_data, 1) comment https://stackoverflow.com/questions/26447191/how-to-add-trendline-to-a-scatter-plot#comment100202969_26447505 seems wrong\n",
    "z = np.polyfit(x_data, y_data, 1)\n",
    "p = np.poly1d(z)\n",
    "axs[0].plot(x_data,p(x_data), '-', color= \"red\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6110b46e-7334-4f7b-9c60-759405358b66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c4c7502-e434-4e51-a81b-814e07a3c9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /srv/conda/envs/notebook/lib/python3.10/site-packages (1.9.3)\n",
      "Requirement already satisfied: numpy<1.26.0,>=1.18.5 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from scipy) (1.23.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e696c2ee-c1f9-4398-b00e-d66deb6ac892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from https://stackoverflow.com/a/7187687/8508004\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def func(x, a, b, c):\n",
    "    return a*x**2 + b*x + c\n",
    "\n",
    "x = np.linspace(0,4,20)\n",
    "y = func(x, 5, 3, 4)\n",
    "# generate noisy ydata\n",
    "yn = y + 0.2 * y * np.random.normal(size=len(x))\n",
    "# generate error on ydata\n",
    "y_sigma = 0.2 * y * np.random.normal(size=len(x))\n",
    "\n",
    "popt, pcov = curve_fit(func, x, yn, sigma = y_sigma)\n",
    "\n",
    "# plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "#ax.errorbar(x, yn, yerr = y_sigma, fmt = 'o') # this gives `ValueError: 'yerr' must not contain negative values`\n",
    "ax.scatter(x, yn) # simplified because as written was getting `ValueError: 'yerr' must not contain negative values` and I didn't really care about that\n",
    "ax.plot(x, np.polyval(popt, x), '-', color= \"red\")\n",
    "ax.text(0.5, 100, r\"a = {0:.3f} +/- {1:.3f}\".format(popt[0], pcov[0,0]**0.5))\n",
    "ax.text(0.5, 90, r\"b = {0:.3f} +/- {1:.3f}\".format(popt[1], pcov[1,1]**0.5))\n",
    "ax.text(0.5, 80, r\"c = {0:.3f} +/- {1:.3f}\".format(popt[2], pcov[2,2]**0.5))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf07696-a587-409f-90ed-d686f4949eee",
   "metadata": {},
   "source": [
    "## Adjusting after-the-fact \n",
    "\n",
    "Imagine we made scatter plot and now want to adjust it.\n",
    "\n",
    "Starting point based on above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65f39548-8ec6-4fa3-b28d-432db0141bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#based on https://stackoverflow.com/a/75562058/8508004\n",
    "import numpy as np\n",
    "x_data, y_data = np.repeat(np.linspace(0, 9, 100)[None,:], 2, axis=0) + np.random.rand(2, 100)*2\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(1,2, figsize=(12,3))\n",
    "axs[0].scatter(x_data, y_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5b4a75-b060-40db-b2f1-811f8e00a62c",
   "metadata": {},
   "source": [
    "Because that gets closed after cell concludes running, there is no current plot. We can see that none are open by running `plt.gca()` and ` plt.gcf()` below (based on [here](https://stackoverflow.com/questions/36198050/update-subplots-in-matplotlib-figure-that-is-already-open#comment60035947_36198050)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a2212f6-c3a6-4024-b64e-c20318ea513f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gcf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8f39da7-58f5-4c53-83a3-7cf00b889b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e39fa46-32ec-4091-9eee-a67a413b2922",
   "metadata": {},
   "source": [
    "That's something but not what we want to modify."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8e2127-3fc8-448c-bf49-be28dcc3adaf",
   "metadata": {},
   "source": [
    "So both current figure and axis are something. What can we do with this knowledge?\n",
    "\n",
    "But if we put them in same cell, the current axes can be the last one modified. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd827a19-7402-4790-8386-79f0c3762dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.547727,0.11;0.352273x0.77)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9+/bp61//uqZMmaJFixYl6yMAAGBLrMMNIGlSJcNqdq3rZ64/PyDgbf/5Pmz8XA+/tjPiOdqvi/1S7T7NXVMb1TFmhMrW++98LOuBpzueTdbjngIA7CRezyUy3ACSIpUyrGY7j3fcr33GuWrXAVMBd/uma2YbsEXTqC1Stl6S5v35XfXO7qHzT2P9bAAAgK5gWTAACefPsHYsl/bPh66oa0jSyIKzIvCNtHxXsHWxYzkmEjPrhB/64rhmPLpVE5dutN1/CwAAgFRCwA0gocxkWMvWbW9bZ9oOrAh8/c3U/Pt3PF7qvC52LMdEEs064XZ9AQIAAJAqLA+4vV6v5s+fr4KCAvXs2VNDhw7VvffeyzIhACRF1wjMLqwKfP3N1Fw5gZlwV052yHnTsRwTTjTl53Z9AQIAAJAqLJ/DvXTpUq1YsUJPPvmkzj77bL311lu69tprlZOTo5tvvtnqywFIMbHOh062SGtdmw18Iy3fZdUxoUS7TnjHTugdpUrjOwAAgGSwPOB+4403NHXqVF166aWSpCFDhuiZZ55RdXW11ZcCkILi0QgsUawKfKNZvqsrx4Q6TyzrhAd7AZJKje8AAACSwfKS8vHjx6uyslIffPCBJOmdd97R5s2b9b3vfS/o/i0tLfJ4PAFfANJXPBqBdZXXZ6hq1wG9VLtPVbsOhC2f9ge+U0cNblv7OtWEKlMPp+MLkFRrfAcAAJAMlme4582bJ4/HozPPPFOZmZnyer1atGiRZsyYEXT/8vJylZWVWT0MADYVLsMaayOwrkjlLG1Xyrn92fotuw5o9tM1OvTF8aD7OdRaNt/+BUikxncOtc77njTMlZIvJAAAAKxieYb7ueee01NPPaWnn35aNTU1evLJJ/XAAw/oySefDLp/aWmpmpqa2r727t1r9ZAA2IzVjcBilcpZ2oq6Bk1culFXrNqiuWtqdcWqLVEv45WZ4dCE0wdoyf83Qg6ZbwiXio3vAAAAksHyDPevfvUrzZs3T5dffrkkacSIEfr4449VXl6umTNndtrf6XTK6XRaPQwASeDPuLo9R3Xw8xb1OzFLrpyeQTOvk4a51NvZQ1W790tqLdM+/7TElWibydLevfZ/1Tu7h/Z/3mKrhmD+FwUdx+5/URDtS4toG8KlauM7AACARLM84D5y5IgyMgIT55mZmfL5fFZfCoCNBCvN9utYoh1s3z/X/F9cyrjbl10P+JpTMqT9zS3af7glYpbW7WnRjEe3hvwcyRCvcu5oGsKlcuM7AACARLI84J4yZYoWLVqkU045RWeffbbefvttPfjgg/rpT39q9aUA2ESojKtfQ7vMqyRLs7ORxhXqJUAs4jHGaEVTzh2vTuiRlhYLNu8bAACgO7J8DvdDDz2kH/7wh7rxxht11lln6Ze//KV+/vOf695777X6UgBsIFzGtT1DrSXad68NnZ2VWrOz4bqEmxVqfnZXWD3GWNihnNvf+E4yP+8bAACgO7I8w927d28tW7ZMy5Yts/rUAGwoUsa1PbenJezPu5Kdbc/sS4BYWDVGszp2Ih/wNXM9L+Jdzh3tvG8AAIDuyPKAG0D3Eo9MalfPGc1LgFhFGmNXluzyC1YS7+rjVN9ePdR05HjSy7mjmfcNAADQHRFwA+iSeGRSu3rORHTHDjdGK9b2DjUvvtHT0rbNDuuYm533DQAA0B1ZPocbQPfib6BlJryLtI9DrYFpV7OzsQTsPxx9sn57+Sg9dV2hXH1Cf55IY7RibW8zncj79uqh3D7JXcccAAAA4ZHhBtAl/gZas1bXRNw33JxqK7OzkbpoB7N556da+sNzlJnh0N2XtX6eaDPIVi3ZZaYT+aEjx/XUdaOVkeGgnBsAAMCmyHAD6DJ/A628nOCZZTMxoJXZ2XBdtENxe1pUXX9Q0lefx5UTXQY5miW7wjFbEr+/uUVFQ/tr6qjBKhran2AbAADAZshwA+gSf3Owli99euCHIyWH9MnhFh38vEX9TszSweZjuveV9yKe54EfjtSE0wdYNq5QXbTDaR/oxtIQzKolu8yWxMe7EzkAAAC6hoAbQMzCNQe77oLTJEkv1e4zda79zeGXDIuFP2h+4u/1poL+jgFstA3BrAqU/SXx4V4UWDHXHQAAAPFFSTmAmJhtDpbsbG1mhkPXTCgI29itYyM0r89Q1a4Deql2n6p2HZDXZ24meKQGcmabwmVmOHTZyPCl9ZeNzKOEHAAAwOYIuAFELVJzMKm1OZjXZ1gWhHZFuDndHRuhVdQ1aOLSjbpi1RbNXVOrK1Zt0cSlG011F4/mOuF4fYbWvhP+emvfaTD9IgAAAADJQcANwDR/5vffN+ww3RzMqiC0q8w0QrNiSa9YG661F6n5mmSu+RoAAACSizncAEwJNl87En9zsFANzFz/nO+dqHWjwzVCs2pJr0jXMcOq5msAAABILgJuABH5M7/RFjC3n5fd1SDUKqEaoUWzpJeZRmrRNlxrL9nz3gEAAGANAm4AYYXL/IbiUGv2uuO87K4EofFmp6yyf967u+lo0Pse6v4CAADAXpjDDSAsM/OJ20vkvGwr2SmrbJd57wAAAOgaAm4AYUWb0Y2mOZid2KGbentWNF8DAABAclFSDiAssxndORd9QxO+MSAp87Kt4M8qz1pdI4cUUMqdrKyyXea9AwAAIDYE3ADCMjuf+NZJZ6R8IGiXburt2XneOwAAAMIj4AYQlh0zv/FEVhkAAABWIeAGbMrrM1Rdf1Dupi90sPmY+n3NKVef5AR/dsz8xhNZZQAAAFiBgBuwoYq6hk7BrV9ekoJcMr8AAABAdAi4AZupqGvQrNU1Ide9bmg6qlmra5LSqZrMLwAAAGAey4IBNuL1GSpbtz1ksN1e2brt8vrM7AkAkS1fvlxDhgxRdna2CgsLVV1dHXb/Q4cOafbs2crLy5PT6dQZZ5yh9evXJ2i0AACkBjLcgI1U1x8MWkbekaHWTPeWXQeUkeGgxBtAlzz77LMqKSnRypUrVVhYqGXLlmny5MnasWOHBg4c2Gn/Y8eOadKkSRo4cKD+9Kc/afDgwfr444/Vt2/fxA8eAAAbI+AGbOSTw5GD7fZmP12jQ18cb/s+WfO7AaS2Bx98UNdff72uvfZaSdLKlSv1yiuv6LHHHtO8efM67f/YY4/p4MGDeuONN9SjRw9J0pAhQxI5ZAAAUgIl5YCNDOydHdX+7YNtSXL/c353RV1DyGO8PkNVuw7opdp9qtp1ICFl6cm4ZiKk6+dC93Ls2DFt27ZNxcXFbdsyMjJUXFysqqqqoMesXbtWRUVFmj17tnJzczV8+HAtXrxYXq83UcMGACAlkOEGbGRcQT/l5WSbKisPxlDr2thl67Zr0jBXp/LyYN3P450VT8Y1EyFdPxe6n/3798vr9So3Nzdge25urt5///2gx+zevVsbN27UjBkztH79eu3cuVM33nijjh8/roULFwY9pqWlRS0tLW3fezwe6z4EAAA2RYYbsJHMDIcWThmmrszC9s/vrq4/GLDd3/28YzBvJiseq2RcMxHS9XMBZvl8Pg0cOFCPPPKIxowZo+nTp+vOO+/UypUrQx5TXl6unJyctq/8/PwEjhgAgOQg4AZs5uLheVpx5Wjl5QQvL+/bs4ep87SfDx6u+7l/m9Vdz5NxzURI18+F7mvAgAHKzMxUY2NjwPbGxka5XK6gx+Tl5emMM85QZmZm27azzjpLbrdbx44dC3pMaWmpmpqa2r727t1r3YcAAMCmKCkHbOji4XmaNMyl6vqDcjd9oYPNx9Tva065+mTLZxia8ejWiOdoPx88Uvfz9lnxUOtse32GqusPmu6IbsU17ShdPxe6r6ysLI0ZM0aVlZWaNm2apNYMdmVlpebMmRP0mAkTJujpp5+Wz+dTRkbru/sPPvhAeXl5ysrKCnqM0+mU0+mMy2cAAMCuCLgBm8rMcAQN2Lw+Q3k52XI3HQ2aZXVIcuW0BsR+Zrufd8yK+wPsj/Yf0TPVe+T2fPVzV59s3X1Z6PnKsVwzFaTr50L3VlJSopkzZ2rs2LEaN26cli1bpubm5rau5VdffbUGDx6s8vJySdKsWbP08MMPa+7cubrpppv04YcfavHixbr55puT+TEAALAdAm4gxfjnec9aXSOHFBB0+/PNC6cMC8g+m+1+7t8vWEOwjtyeo/rF6hqtvHJ00KA72muminT9XOjepk+frk8//VQLFiyQ2+3WqFGjVFFR0dZIbc+ePW2ZbEnKz8/Xq6++qltvvVXnnHOOBg8erLlz5+r2229P1kcAAMCWHIZh2GqiocfjUU5OjpqamtSnT59kDwewrWi6ZHt9hiYu3RgxK7759m9rw3a3Zq2uCbpfMCf16qG37prUqbw8mmuGK023m3T9XAiPZ5P1uKcAADuJ13OJDDeQotrP8440r9psVlxSyIZgoXx25Li27D6gCd8YENM1Uy0oTdfPBQAAAOvFpUv5vn37dOWVV6p///7q2bOnRowYobfeeiselwK6Nf8876mjBqtoaP+wQZ6/+7mrQ/dzV062VvyzLDxSQ7BQqnYdiPmaqShdPxcAAACsZXmG+7PPPtOECRN00UUX6b/+67/09a9/XR9++KFOOukkqy8FIEqRsuKxN/oKnROPJhOfStL1cwEAAMA6lgfcS5cuVX5+vh5//PG2bQUFBVZfBkCMQnU/l2Jv9FV02oCwPw93zVSWrp8LAAAA1rC8pHzt2rUaO3asfvSjH2ngwIE699xztWrVqpD7t7S0yOPxBHwBqcrrM1S164Beqt2nql0H5PXZqidhROMK+ikvJ1vR5Gj79uqh8wk6AQAAgE4sz3Dv3r1bK1asUElJie644w69+eabuvnmm5WVlaWZM2d22r+8vFxlZWVWDwNIuGi6hker/ZrY8SxdDtcQLJQlPxhBGTUAAAAQhOXLgmVlZWns2LF644032rbdfPPNevPNN1VVVdVp/5aWFrW0tLR97/F4lJ+fzzIhSCkVdQ1Bl9Lyh6FdaaQVz0A+mmt2DMBdfZy6+7KzaRCGboElrKzHPQUA2EnKLAuWl5enYcOGBWw766yz9Oc//zno/k6nU06n0+phAAnj9Rkhl9Iy1Bqolq3brknDXKYywe2z2R/tb9a///XDTvu4m45q1uqauHXEDtYQbMypJ2nbx5/RIAwAAAAwyfKAe8KECdqxY0fAtg8++ECnnnqq1ZcCbCHSUlqGpIamo6quPxixwVawzHKoc3YlkDcTMAdrCEaDMAAAAMA8ywPuW2+9VePHj9fixYv14x//WNXV1XrkkUf0yCOPWH0pwBbMLqUVab9QZemhdDWQt6IsPVFzywEAAIBUZHnAfd555+mFF15QaWmp7rnnHhUUFGjZsmWaMWOG1ZcCbMHsUlrh9gtXlh5JrIF8LGXpgeXuR/RM9R65PYmbWw4AAACkEssDbkn6/ve/r+9///vxODVgO/6ltNxNR4MGzA5JrpzW7G8okcrSw4k1kI+2LN1MuXu855YDAAAAqcTydbiB7sa/lJakTutX+79fOGVY2IDWbFl6x3PndTGQb1+WHo4/S25mbrnUGsSn2hrkAAAAgNUIuAELXDw8TyuuHC1XTmC22ZWTbSrba7Ys3c/qQD7cftGWu5sN4gEAAIB0F5eScqA7CraUltkmYpHK0jtydZgrHap5mRXzy2Mtd48law8AAACkEwJuwELBltIye9zCKcM0a3WNHFJA0O0P128pPkNDBvTqFMiH60A+aZiry/PLYw2co83aAwAAAOmGknLAQl6foapdB/RS7T5V7ToQ1TzmSGXpc4tP19RRg1U0tH9AsB1sbrW/edmG7e4uzy+Ppdw90txyAAAAoDsgw42UZbc1oK1Y6zqasnSzHcg33/5trbhydKexdSxLDyWacnezQTwAAADQHRBwIyVZEdxaPR6r1ro2W5YeTQfyrswvD1fu3pHZIB4AAADoDgi4kXKsDG6tYOVa19GItgN5rPPLpa/K3Ttlyfs4dcW4UzRkwIm2qDIAAAAA7ISAGyklWcFtONFkmmMJeOPZgTwaXcmSAwAAAN0RATdSSryD21hYsdZ1KPHuQB6trmTJAQAAgO6GLuVIKfEMbmMVr0xzIjqQm9WV7usAAABAd0WGGykl0WXUZkTq4h1LpjlRHcjNsFuDOgAAACBVEHAjpZhZoqpvrx7y+Qx5fUZC5heH6+Ida6Y5UR3II7FbgzoAAAAglVBSjpTiD26lzmXUfoeOHNeM32/VxKUbVVHXkJBx+bt4u3ICM+uunOyYgtJYO5BPHTVYRUP7W1ZGHi7LLrVm2SkvBwAAAIIjw42UE2qJqo4SnYW1MtNsh9J5OzaoAwAAAFIJATdSkj+43bL7gGY/VaNDXxzvtE8ylgmzqot3POaFR8tslt3d9IWqdh1gqTAAAACgAwJupKzMDIcyHI6gwbZfqmZh4zEvPFpms+f3vvKeDjYfa/uehmoAAABAK+ZwI6XZcZkwq1g9Lzxa/ix7pJC+fbAtfVXKn6j58wAAAIBdkeFGSrPDXOd4imcH8kjCZdnDSUYpPwAAAGBHZLiR0vxZ2HDy4jzXOd7i0YHcrFBZ9n4n9gh7XPtSfgAAAKC7IsONlJaZ4dBlI/P0n6/Xh9znspF5ZFm7IFiW3e05qlufrY14bCqW8gMAAABWIcONlOb1GVr7Tvi5wmvfaWCt6C7qmGV39UnvUn4AAADACgTcSGmR1oqWklva7PUZqtp1QC/V7lPVrgNpE/hHaqjmUOqX8gMAAABdRUk5Upqdu5RX1DWobN32gBcCZpfM8vqMpDRKM8sOy5YBAAAAdkeGGynNrl3KK+oaNGt1Tafsu5klsyrqGjRx6UZdsWqL5q6p1RWrtmji0o22W2Yr2cuWAbDW8uXLNWTIEGVnZ6uwsFDV1dWmjluzZo0cDoemTZsW3wECAJCCyHAjpflLm91NR4MuW+VQawCYyNJmr89Q2brtQccTacksf6De8Vh/oG63QDaZy5YBsM6zzz6rkpISrVy5UoWFhVq2bJkmT56sHTt2aODAgSGP++ijj/TLX/5SF1xwQQJHCwBA6iDDjZTmL22W1Gk+caTSZqvmV3c8z5bdB8LOKw+1ZNaxL32644V3QwbqUmugbrd54MlctgyANR588EFdf/31uvbaazVs2DCtXLlSvXr10mOPPRbyGK/XqxkzZqisrEynnXZaAkcLAEDqIMONlOcvbe44X9oVZr50V+ZXRzpP357h16j2az+vvKKuQXe8UKeDzcdD7t8+UC8a2t/0GK1g9znlAGJ37Ngxbdu2TaWlpW3bMjIyVFxcrKqqqpDH3XPPPRo4cKCuu+46/fd//3fE67S0tKilpaXte4/H07WBAwCQAgi4kRaiKW22qmw71HkOfRE6aG7PP6881HlCSXQDOKteTgCwp/3798vr9So3Nzdge25urt5///2gx2zevFm///3vVVtba/o65eXlKisr68pQAQBIOZSUI22YKW2ONL9aMle2He48kbRfMiuW8ySyAVxXmr8BSE+HDx/WVVddpVWrVmnAgAGmjystLVVTU1Pb1969e+M4SgAA7IEMN7qVSOt2my3bNrP+dzAd55VX7Qo/37vjsYlsANeV5m8AUseAAQOUmZmpxsbGgO2NjY1yuVyd9t+1a5c++ugjTZkypW2bz+eTJJ1wwgnasWOHhg4d2uk4p9Mpp9Np8egBALA3MtzoVqxat9vseTrO5+64ZFa05eGJXNs6mpcTAFJXVlaWxowZo8rKyrZtPp9PlZWVKioq6rT/mWeeqXfffVe1tbVtX5dddpkuuugi1dbWKj8/P5HDBwDA1shwo1uxat1us+dZ/pPRyshwhJxXbvY8/U7socX/OkKThrlUtetAQpqXWfVyAoD9lZSUaObMmRo7dqzGjRunZcuWqbm5Wddee60k6eqrr9bgwYNVXl6u7OxsDR8+POD4vn37SlKn7QAAdHdxD7iXLFmi0tJSzZ07V8uWLYv35YCwrFq32+x5zo+wTFak80hS/xOzVFX6HW18v1ETl25MWPMyq15OALC/6dOn69NPP9WCBQvkdrs1atQoVVRUtDVS27NnjzIyKIoDACBacX16vvnmm/rP//xPnXPOOfG8DGBaV9btTsZ5HJIW/etwbXy/MeHNy/wvA0J9gvbN3wCkvjlz5ujjjz9WS0uLtm7dqsLCwrafbdq0SU888UTIY5944gm9+OKL8R8kAAApJm4B9+eff64ZM2Zo1apVOumkk+J1GSBq/nW7XTmBmdmO86vtcp5Jw1yWdFaPllUvFQAAAIDuKm4l5bNnz9all16q4uJi3XfffSH3a2lpUUtLS9v3Ho8nXkMC2kSzbneyzxOpk7nZzuqx8L8M6LgOt4t1uAEAAICI4hJwr1mzRjU1NXrzzTcj7lteXq6ysrJ4DANpyOszuhzc+vnX7e6qeJ8n2c3LrHqpAAAAAHQ3lgfce/fu1dy5c7VhwwZlZ0duplRaWqqSkpK27z0eD0uKIKiKuoZOmdZ4Ng0zw8oXAKHYoXmZVS8VAAAAgO7E8oB727Zt+uSTTzR69Oi2bV6vV6+//roefvhhtbS0KDMzs+1nTqdTTqfT6mEgzVTUNWjW6ppO85j9TcOimTNt5ZgS8QLAqs7qAAAAABLL8qZp3/nOd/Tuu++qtra27Wvs2LGaMWOGamtrA4JtwAyvz0hK07Bw/C8AwnUN9/oMVe06oJdq96lq14GYx0fzMgAAACA1WZ7h7t27t4YPHx6w7cQTT1T//v07bQfMqK4/mLSmYcFEegHgkDTv+Xd199rtcnusyX7TvAwAAABIPXHrUg5YJdlNwzoy8wLg0JHjko4HbO9q+TvNywAAAIDUkpCAe9OmTYm4DOLEysZgsZzLDk3D2os1sPdnv8vWbdekYa6Y7iHNywAAAIDUQYYbYQVrDObq49QV407RkAEnRhWAx9pkzG5Nw7oS2Ce6/B0AAABA8ljeNA3pI2RjME+L/v2vH2rumlpdsWqLJi7dqIq6htjO1a7JWCh2axrmfwHQlaslqvwdAAAAQPIQcCOocI3BOooUNJvtMv73D/frpdp9+vuH+/X3nfsDunv7m4a5cgKzy66c7IQvCRbuBYBZiSp/BwAAAJA8lJQjqEiNwdqLNDfZbJfxGb/fGvTn7cvO7dI0LGTX8D5OHf3Sp6Yjx21R/g4AAAAgeQi4EVS0Jc/h5iZ3tXy6Y3dvu8x9DvUCYMN2t2atrpFDCgi6WTMbAAAA6F4oKUdQsZY8Bwuuu1o+3b7s3OszU+SeOP6u4VNHDVbR0P7KzHDYqvwdAAAAQPKQ4UZQkTqDhxIsuI71XO2lWndvO5W/AwAAAEgOMtwIKtrGYA61zrUONjfZiiZjfqnU3TtY9hsAAABA90HAjZAuHp6nG75VIEeEONHM3ORQZdbRors3AAAAgFRBSTlCqqhr0COv10csA3e16yIeTscy6wFfc+rfnqtVo6cl4jXo7g0AAAAg1RBwIygz63D37dlDy2eM1vmnmS+X9pdZ+9192dlBO3q3R3dvAAAAAKmIknIEZWYd7kNfHFeGw9GlINhMqTndvQEAAACkIjLcCMpsczIrmph1KjU/0Sk5pP2ft9DdGwAAAEDKIuBGUGabk1nVxKxjqbkZXp/BslsAAAAAbIuAG0FFWjs72U3MKuoaVLZue0DZe57J5m0AAAAAkAjM4UZQ/rWzQwXbUmATM6/PUNWuA3qpdp+qdh2Q1xep77g5wc5bUdegWatrOs0xdzcd1azVNaqoa7Dk2gAAAADQFWS400S8yqv79uqhQ0eOB2zL6dVDS34woi2THK9sc7Dzuvo4dfRLX9AXAYZaXwaUrduuScNclJcDAAAASCoC7jQQj4DXn0UOFtg2tQvAQ+3nzzbH2l085Hk9LWGPMyQ1NB1Vdf3BqOeEAwAAAICVKClPcfEorzazBnfZuu069qUv5H5Gu/2iLS83c/1IrOieDgAAAABdQcCdwsIFpl0JeCOtwe3PIv+/qo9M7Vddf9DS65thVfd0AAAAAIgVAXcKMxsYRxvwms0Of3zwiKXni3X/9hxqLadPVvd0AAAAAPAj4E5hZgPTaANYs9nhU/v1svR8se7vF6x7OgAAAAAkCwF3CjMbmEYbwPrX4A4VsvqzyFcVDTG1X7TZZjPX79urh1x9Aj+XKyc75iZtAAAAAGA1upSnMH9g6m46GnK9bFcMAa9/De5Zq2uCnlNqzSJnnZDRtp9DChhDV7LN7a8f6rxLfjBCk4a54rIUGgAAAABYgQx3CvMHppI6ZYOtKK/O6dWj07a+vXoEZJEvHp6nFVeOlivH2myzmfNmZjhUNLS/po4arKKh/Qm2AQAAANgKGe4U5w9MO67D7erCOtzh1uD+rN0a3O3HEI9sc7zOCwAAAACJQMCdBqwMTCOtge1Q61Jjk4a5As7vzzZbLV7nBQAEWr58uX7961/L7XZr5MiReuihhzRu3Lig+65atUp/+MMfVFdXJ0kaM2aMFi9eHHJ/AAC6K0rK04RV5dXxWmrM7rw+Q1W7Duil2n2q2nUg6rXLASCVPfvssyopKdHChQtVU1OjkSNHavLkyfrkk0+C7r9p0yZdccUVeu2111RVVaX8/Hx997vf1b59+xI8cgAA7I0Md5ry+oyYMt7xWmrMzirqGjqV5Od1oSQfAFLNgw8+qOuvv17XXnutJGnlypV65ZVX9Nhjj2nevHmd9n/qqacCvn/00Uf15z//WZWVlbr66qsTMmYAAFIBAXca6koAGa+lxuwq1Hx1d9NRzVpdwzJjANLesWPHtG3bNpWWlrZty8jIUHFxsaqqqkyd48iRIzp+/Lj69Qu9KkZLS4taWlravvd4PLEPGgCAFEFJeZrxB5Ady8L9AWRFXUPY482uwR3tUmNdFY+S73Dz1f3bytZtp7wcQFrbv3+/vF6vcnNzA7bn5ubK7XabOsftt9+uQYMGqbi4OOQ+5eXlysnJafvKz8/v0rgBAEgFBNxpxIoAMt5LjcWioq5BE5du1BWrtmjumlpdsWqLJi7dGPHlQSTddb46AFhpyZIlWrNmjV544QVlZ4eufiotLVVTU1Pb1969exM4SgAAkoOAO43EEkAGyxzHa23tWHQ1Yx9Od5yvDgAdDRgwQJmZmWpsbAzY3tjYKJfLFfbYBx54QEuWLNFf/vIXnXPOOWH3dTqd6tOnT8AXAADpzvI53OXl5Xr++ef1/vvvq2fPnho/fryWLl2qb37zm1ZfCh1EG0BGmuud7DWwI2XsQy1RZlZ3m68OAMFkZWVpzJgxqqys1LRp0yRJPp9PlZWVmjNnTsjj7r//fi1atEivvvqqxo4dm6DRAgCQWizPcP/tb3/T7NmztWXLFm3YsEHHjx/Xd7/7XTU3N1t9KXQQTQBpJnMcbKmxRC6fFe+Sb7vOVweARCspKdGqVav05JNP6r333tOsWbPU3Nzc1rX86quvDmiqtnTpUs2fP1+PPfaYhgwZIrfbLbfbrc8//zxZHwEAAFuyPMNdUVER8P0TTzyhgQMHatu2bfrWt75l9eXQjj+AdDcdDZoVdqi1LHzMqSfpwl+/FnXmONHLZ8W75Ns/X33W6ho5pID7kaz56gCQDNOnT9enn36qBQsWyO12a9SoUaqoqGhrpLZnzx5lZHz1jn7FihU6duyYfvjDHwacZ+HChbr77rsTOXQAAGwt7suCNTU1SVLIpUJYJsQ6ZgPIbR9/ZjpzXDS0v6TkLJ+ViJJv/3z1ji8SXKzDDaCbmTNnTsgS8k2bNgV8/9FHH8V/QAAApIG4Btw+n0+33HKLJkyYoOHDhwfdp7y8XGVlZfEcRrdiJoB8qXafqXP5M8dm51J/+8xcbfv4M8vmfJvN2He15NsO89UBAAAApJ+4BtyzZ89WXV2dNm/eHHKf0tJSlZSUtH3v8XhYm7OLIgWQ0WaOzc6lPr/8rzrYfLxte1fLzRNZ8u2frw4AAAAAVonbsmBz5szRyy+/rNdee00nn3xyyP1YJiQ+gjU884u2WZjZOdLtg23JmqW77LREGQAAAABEw/IMt2EYuummm/TCCy9o06ZNKigosPoSpnl9RkqUCSd6nNFmjmOdI23F0l0SJd8AAAAAUpPlAffs2bP19NNP66WXXlLv3r3ldrslSTk5OerZs6fVlwsp0R21Y5WscUbTLCzSXOpwgjVgiwUl3wAAAABSjcMwDEsXUnY4gmcdH3/8cV1zzTURj/d4PMrJyVFTU1PM5eWhOmr7R2aXUmQ7jNNsdt0/VklRB92S9NvLR2nqqMFdHC0AJIcVzyYE4p4CAOwkXs+luJSUJ5PZjtpdKXG2QrLGGSzANpM5DpUR739ilg40H4t4fFeW7gIAAACAVBT3dbgTzWxH7a6WOHdVMsbZ1fL1YHOpx5x6ki789WtxX7oLAAAAAFJN3LqUJ4vZjtpm94uXRI/TXxLeMciPtpN4x+7nWSdkaOGUYZLUqeu51Ut3AQAAAEAqSbuAO9o1ppMlkeOMVL4utZave32xTQdg6S4AAAAA6CztSsojddS2S4lzIsdpRfl6pOZqLN0FAAAAAIHSLuCOdo3pZEnkOLtavm527jdLdwEAAADAV9KupFxKnRLnRI2zK+XrVs39BgAAAIDuJu0y3H6pUuKciHHGWr6eKkusAQAAAIAdpW3ALaVOiXO8xxlr+XqqLLEGAAAAAHaUliXl6CyW8vVUWWINAAAAAOworTPcyRSpq3cyrhNt+XqqLLEGAAAAAHZEwB0HZrt6J+M60ZSvp8oSawAAAABgR5SUWyxRXb0TcR3/3G/pq7nefnZaYg0AAAAA7IiA20KRunpLrV29vb5ge9jvOpI0aZhLtxSfoZyePQK2222JNQAAAACwG0rKTTIzVzpRXb0TdZ1gJet9e/bQtROGaM63TyezDQAAAABhEHCbYHaudKK6eifiOv6S9Y458qYvjmvZXz/UN129yW4DAAAAQBiUlEcQzVzpRHX1jvd1ElmyDgAAAADpioA7jGgDz1H5fU2d1+x+ofi7h4cq6HaoNQMfa/fwaErWAQAAAADBEXCHEW3g+fTWj02d1+x+7Xl9hqp2HdBLtftUXX9Q8y89S1J8uocnqjQeAAAAANIZc7jDiDbw/PjgEVP7m93PL9Qc8hu+VaC17zQEbHdZsN53okrjAQAAACCdEXCHEW3geWq/Xqb2N7ufFLp5mbvpqB55vV7LfzJaJ52YFbZ7erT8JevupqNBy+kdag3sYy1ZBwAAAIDugJLyMKKdK31V0RBFinUzHK37mWFmDvm9r2zXuIJ+mjpqsIqG9rdkqa7MDIcWThkmKT4l6wAAAADQHRBwhxFt4Jl1Qoauv6Ag7Dmvv6BAWSeYu+3JbF528fA8rbhytFw5gVl+V062Vlw5miXBAAAAACACSsoj8AeeHedQh5orXXpJa4C+6r/r1X7VrAxHa7Dt/7kZyW5edvHwPE0a5lJ1/UFLS9YBAAAAoDsg4DYh2sCz9JJh+rfvnqn/V/WRPj54RKf266WrioaYzmz72aF5WWaGQ0VD+8ft/AAAAACQrgi4TYo28Mw6IUPXXXBal65J8zIAAAAASF3M4bYxmpcBAAAAQOoi4LY5mpcBAAAAQGqipDwF0LwMAAAAAFIPGe4U4Z9DbuV62wAA+C1fvlxDhgxRdna2CgsLVV1dHXb/P/7xjzrzzDOVnZ2tESNGaP369QkaKQAAqYOAGwCAbu7ZZ59VSUmJFi5cqJqaGo0cOVKTJ0/WJ598EnT/N954Q1dccYWuu+46vf3225o2bZqmTZumurq6BI8cAAB7cxiGEawBdtJ4PB7l5OSoqalJffr0SfZwAABI+2dTYWGhzjvvPD388MOSJJ/Pp/z8fN10002aN29ep/2nT5+u5uZmvfzyy23bzj//fI0aNUorV640dc10v6cAgNQSr+eS7eZw++N/j8eT5JEAANDK/0yy2TtqSxw7dkzbtm1TaWlp27aMjAwVFxerqqoq6DFVVVUqKSkJ2DZ58mS9+OKLIa/T0tKilpaWtu+bmpok8bwHANhDvJ71tgu4Dx8+LEnKz89P8kgAAAh0+PBh5eTkJHsYltq/f7+8Xq9yc3MDtufm5ur9998Peozb7Q66v9vtDnmd8vJylZWVddrO8x4AYCcHDhyw9Flvu4B70KBB2rt3r3r37i2HI7rGYB6PR/n5+dq7dy/laR1wb8Lj/oTGvQmP+xNaOt0bwzB0+PBhDRo0KNlDSVmlpaUBWfFDhw7p1FNP1Z49e9LuJUYypNP/b3bBPbUW99N63FNrNTU16ZRTTlG/fv0sPa/tAu6MjAydfPLJXTpHnz59+KULgXsTHvcnNO5NeNyf0NLl3qRrUDhgwABlZmaqsbExYHtjY6NcLlfQY1wuV1T7S5LT6ZTT6ey0PScnJy1+P+wiXf5/sxPuqbW4n9bjnlorI8PavuJ0KQcAoBvLysrSmDFjVFlZ2bbN5/OpsrJSRUVFQY8pKioK2F+SNmzYEHJ/AAC6K9tluAEAQGKVlJRo5syZGjt2rMaNG6dly5apublZ1157rSTp6quv1uDBg1VeXi5Jmjt3ri688EL95je/0aWXXqo1a9borbfe0iOPPJLMjwEAgO2kVcDtdDq1cOHCoCVr3R33JjzuT2jcm/C4P6Fxb1LH9OnT9emnn2rBggVyu90aNWqUKioq2hqj7dmzJ6DEbvz48Xr66ad111136Y477tDpp5+uF198UcOHDzd9TX4/rMX9tB731FrcT+txT60Vr/tpu3W4AQAAAABIB8zhBgAAAAAgDgi4AQAAAACIAwJuAAAAAADigIAbAAAAAIA4SJuAe/ny5RoyZIiys7NVWFio6urqZA/JFsrLy3Xeeeepd+/eGjhwoKZNm6YdO3Yke1i2tGTJEjkcDt1yyy3JHopt7Nu3T1deeaX69++vnj17asSIEXrrrbeSPayk83q9mj9/vgoKCtSzZ08NHTpU9957r7prD8rXX39dU6ZM0aBBg+RwOPTiiy8G/NwwDC1YsEB5eXnq2bOniouL9eGHHyZnsEioaJ/Nf/zjH3XmmWcqOztbI0aM0Pr16xM00tQQzf1ctWqVLrjgAp100kk66aSTVFxczN9GQcT69+OaNWvkcDg0bdq0+A4wxUR7Pw8dOqTZs2crLy9PTqdTZ5xxBv/fdxDtPV22bJm++c1vqmfPnsrPz9ett96qo0ePJmi09hbp75VgNm3apNGjR8vpdOob3/iGnnjiiegvbKSBNWvWGFlZWcZjjz1m/O///q9x/fXXG3379jUaGxuTPbSkmzx5svH4448bdXV1Rm1trXHJJZcYp5xyivH5558ne2i2Ul1dbQwZMsQ455xzjLlz5yZ7OLZw8OBB49RTTzWuueYaY+vWrcbu3buNV1991di5c2eyh5Z0ixYtMvr372+8/PLLRn19vfHHP/7R+NrXvmb89re/TfbQkmL9+vXGnXfeaTz//POGJOOFF14I+PmSJUuMnJwc48UXXzTeeecd47LLLjMKCgqML774IjkDRkJE+2z++9//bmRmZhr333+/sX37duOuu+4yevToYbz77rsJHrk9RXs/f/KTnxjLly833n77beO9994zrrnmGiMnJ8f4v//7vwSP3L5i/fuxvr7eGDx4sHHBBRcYU6dOTcxgU0C097OlpcUYO3ascckllxibN2826uvrjU2bNhm1tbUJHrl9RXtPn3rqKcPpdBpPPfWUUV9fb7z66qtGXl6eceuttyZ45PYU6e+Vjnbv3m306tXLKCkpMbZv32489NBDRmZmplFRURHVddMi4B43bpwxe/bstu+9Xq8xaNAgo7y8PImjsqdPPvnEkGT87W9/S/ZQbOPw4cPG6aefbmzYsMG48MILCbj/6fbbbzcmTpyY7GHY0qWXXmr89Kc/Ddj2gx/8wJgxY0aSRmQfHR9gPp/PcLlcxq9//eu2bYcOHTKcTqfxzDPPJGGESJRon80//vGPjUsvvTRgW2FhofHzn/88ruNMFV39W+fLL780evfubTz55JPxGmLKieWefvnll8b48eONRx991Jg5cyYBdzvR3s8VK1YYp512mnHs2LFEDTHlRHtPZ8+ebXz7298O2FZSUmJMmDAhruNMRWYC7ttuu804++yzA7ZNnz7dmDx5clTXSvmS8mPHjmnbtm0qLi5u25aRkaHi4mJVVVUlcWT21NTUJEnq169fkkdiH7Nnz9all14a8DsEae3atRo7dqx+9KMfaeDAgTr33HO1atWqZA/LFsaPH6/Kykp98MEHkqR33nlHmzdv1ve+970kj8x+6uvr5Xa7A/7/ysnJUWFhIf9Gp7FYns1VVVWd/h2ePHkyvyey5m+dI0eO6Pjx4zz//ynWe3rPPfdo4MCBuu666xIxzJQRy/1cu3atioqKNHv2bOXm5mr48OFavHixvF5vooZta7Hc0/Hjx2vbtm1tZee7d+/W+vXrdckllyRkzOnGqufSCVYOKhn2798vr9er3NzcgO25ubl6//33kzQqe/L5fLrllls0YcIEDR8+PNnDsYU1a9aopqZGb775ZrKHYju7d+/WihUrVFJSojvuuENvvvmmbr75ZmVlZWnmzJnJHl5SzZs3Tx6PR2eeeaYyMzPl9Xq1aNEizZgxI9lDsx232y1JQf+N9v8M6SeWZ7Pb7eb3JAQr/ta5/fbbNWjQIF4u/1Ms93Tz5s36/e9/r9ra2gSMMLXEcj93796tjRs3asaMGVq/fr127typG2+8UcePH9fChQsTMWxbi+We/uQnP9H+/fs1ceJEGYahL7/8Ur/4xS90xx13JGLIaSfUc8nj8eiLL75Qz549TZ0n5QNumDd79mzV1dVp8+bNyR6KLezdu1dz587Vhg0blJ2dnezh2I7P59PYsWO1ePFiSdK5556ruro6rVy5stsH3M8995yeeuopPf300zr77LNVW1urW265RYMGDer29waA/SxZskRr1qzRpk2beN7F6PDhw7rqqqu0atUqDRgwINnDSQs+n08DBw7UI488oszMTI0ZM0b79u3Tr3/9awLuGG3atEmLFy/W7373OxUWFmrnzp2aO3eu7r33Xs2fPz/Zw+u2Uj7gHjBggDIzM9XY2BiwvbGxUS6XK0mjsp85c+bo5Zdf1uuvv66TTz452cOxhW3btumTTz7R6NGj27Z5vV69/vrrevjhh9XS0qLMzMwkjjC58vLyNGzYsIBtZ511lv785z8naUT28atf/Urz5s3T5ZdfLkkaMWKEPv74Y5WXlxNwd+D/d7ixsVF5eXlt2xsbGzVq1KgkjQrxFsuz2eVy8SwPoSt/6zzwwANasmSJ/vrXv+qcc86J5zBTSrT3dNeuXfroo480ZcqUtm0+n0+SdMIJJ2jHjh0aOnRofAdtY7H8jubl5alHjx4Bf2udddZZcrvdOnbsmLKysuI6ZruL5Z7Onz9fV111lX72s59Jav37pLm5WTfccIPuvPNOZWSk/GzihAr1XOrTp4/p7LaUBsuCZWVlacyYMaqsrGzb5vP5VFlZqaKioiSOzB4Mw9CcOXP0wgsvaOPGjSooKEj2kGzjO9/5jt59913V1ta2fY0dO1YzZsxQbW1ttw62JWnChAmdlpD74IMPdOqppyZpRPZx5MiRTg+tzMzMtj++8JWCggK5XK6Af6M9Ho+2bt3Kv9FpLJZnc1FRUcD+krRhwwZ+TxT73zr333+/7r33XlVUVGjs2LGJGGrKiPaennnmmZ3+Zrjssst00UUXqba2Vvn5+Ykcvu3E8js6YcIE7dy5M+DZ+cEHHygvL6/bB9tSbPc01N8nkrrt0qVdYdlzKaoWaza1Zs0aw+l0Gk888YSxfft244YbbjD69u1ruN3uZA8t6WbNmmXk5OQYmzZtMhoaGtq+jhw5kuyh2RJdyr9SXV1tnHDCCcaiRYuMDz/80HjqqaeMXr16GatXr0720JJu5syZxuDBg9uWBXv++eeNAQMGGLfddluyh5YUhw8fNt5++23j7bffNiQZDz74oPH2228bH3/8sWEYrcuC9e3b13jppZeM//mf/zGmTp3KsmDdQKRn81VXXWXMmzevbf+///3vxgknnGA88MADxnvvvWcsXLiQZcHaifZ+LlmyxMjKyjL+9Kc/BTz/Dx8+nKyPYDvR3tOO6FIeKNr7uWfPHqN3797GnDlzjB07dhgvv/yyMXDgQOO+++5L1kewnWjv6cKFC43evXsbzzzzjLF7927jL3/5izF06FDjxz/+cbI+gq1E+ntl3rx5xlVXXdW2v39ZsF/96lfGe++9Zyxfvrz7LgtmGIbx0EMPGaeccoqRlZVljBs3ztiyZUuyh2QLkoJ+Pf7448kemi0RcAdat26dMXz4cMPpdBpnnnmm8cgjjyR7SLbg8XiMuXPnGqeccoqRnZ1tnHbaacadd95ptLS0JHtoSfHaa68F/Xdm5syZhmG0Lg02f/58Izc313A6ncZ3vvMdY8eOHckdNBIi3LP5wgsvbPsd8XvuueeMM844w8jKyjLOPvts45VXXknwiO0tmvt56qmnBv3/cuHChYkfuI1F+zvaHgF3Z9HezzfeeMMoLCw0nE6ncdpppxmLFi0yvvzyywSP2t6iuafHjx837r77bmPo0KFGdna2kZ+fb9x4443GZ599lviB21Ckv1dmzpxpXHjhhZ2OGTVqlJGVlWWcdtppMcVQDsOgvgAAAAAAAKul/BxuAAAAAADsiIAbAAAAAIA4IOAGAAAAACAOCLgBAAAAAIgDAm4AAAAAAOKAgBsAAAAAgDgg4AYAAAAAIA4IuAEAAAAAiAMCbgAAAAAA4oCAGwAAAACAOCDgBgAAAAAgDgi4AQAAAACIg/8fIz4+vr1mbMQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#based on https://stackoverflow.com/a/75562058/8508004\n",
    "import numpy as np\n",
    "x_data, y_data = np.repeat(np.linspace(0, 9, 100)[None,:], 2, axis=0) + np.random.rand(2, 100)*2\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(1,2, figsize=(12,3))\n",
    "axs[0].scatter(x_data, y_data)\n",
    "print(plt.gca());"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2f55d1-2f6a-4fc6-bf30-3d7015d8e3fd",
   "metadata": {},
   "source": [
    "If we do the same with the figure, we get something different thab above because of the `print()` we wrap it in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd07db1c-c66b-47ff-bbee-1a8d432877f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1200x300)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#based on https://stackoverflow.com/a/75562058/8508004\n",
    "import numpy as np\n",
    "x_data, y_data = np.repeat(np.linspace(0, 9, 100)[None,:], 2, axis=0) + np.random.rand(2, 100)*2\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(1,2, figsize=(12,3))\n",
    "axs[0].scatter(x_data, y_data)\n",
    "print(plt.gcf());"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a5d9800-53af-414e-9343-146783d21c4e",
   "metadata": {},
   "source": [
    "However, both of thse had a handle.   \n",
    "For example, we can recall `fig` already. (See [here](https://stackoverflow.com/a/36267710/8508004) and [here](https://stackoverflow.com/a/45766059/8508004).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b910771b-5b39-48ca-9652-5e04950c39a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337553c7-cff4-4039-aa86-4df59c70caba",
   "metadata": {},
   "source": [
    "So at least the figure object exists. We'll get back to that because recalling `fig` to display the current state of `fig` will be handy.\n",
    "\n",
    "Actually the Axes exist to because they had a handle applied. Because we are using subplot, there's more than one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15c9aa9d-7627-4012-b70a-384460a4bf7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.11;0.352273x0.77)\n",
      "AxesSubplot(0.547727,0.11;0.352273x0.77)\n"
     ]
    }
   ],
   "source": [
    "for ax_obj in axs:\n",
    "    print(ax_obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380ef2f1-986d-4624-9594-390ab9cd8981",
   "metadata": {},
   "source": [
    "### Modifying a specific subplot after-the-fact"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea6b76a-9886-4bb3-8684-429364b80296",
   "metadata": {},
   "source": [
    "Let's restore things to the starting point again where later  we want to add a line showing the fit; however, for now there is on a scatter plot produced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d38d4c07-2f0b-40ce-951d-6283b440e118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#based on https://stackoverflow.com/a/75562058/8508004\n",
    "import numpy as np\n",
    "x_data, y_data = np.repeat(np.linspace(0, 9, 100)[None,:], 2, axis=0) + np.random.rand(2, 100)*2\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(1,2, figsize=(12,3))\n",
    "axs[0].scatter(x_data, y_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5f1c6c-15a1-4442-93ca-42be791c270d",
   "metadata": {},
   "source": [
    "So we have a subplot without the fit line. Let's add the regression line after-the-fact and show the figure again like we did above using `fig`, which this time should be updated.\n",
    "\n",
    "We showed that because the axes have handles, we can access them after-the-fact. We can also modify them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a0cb940-b0c0-4996-bdf6-3ff91c1071d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.polyfit(x_data, y_data, 1)\n",
    "p = np.poly1d(z)\n",
    "axs[0].plot(x_data,p(x_data), '-', color= \"red\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83da75ff-1807-40fd-b161-f17f07b152cd",
   "metadata": {},
   "source": [
    "We could modify and reuse these. But I'm not sure OP wants to do that here? That's more complex.  \n",
    "What if we didn't get in situation in the first place:\n",
    "\n",
    "An option discussed [here](https://stackoverflow.com/a/77522237/8508004) is to use `plt.ioff()` to not clear the information after the cell is run. I don't think that is necessary given the handles but I add it in case OP is interested. Plus, I think `plt.ioff()` use can lead to complications, [see here](https://stackoverflow.com/q/45869825/8508004).  And so it is nice to have other options. \n",
    "\n",
    "\n",
    "---- \n",
    "\n",
    "Enjoy!"
   ]
  }
 ],
 "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}