{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "211a6775",
   "metadata": {},
   "source": [
    "# How to analyse FACS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d5067e0",
   "metadata": {},
   "source": [
    "When we put cells on the sorter, we are measuring them on 1 dimension: fluorescence (whether that is GFP or GFP/RFP or something else). Our goal is to measure each tile's true average fluorescence value. We cannot determine the true measured fluoresence score of every cell (that would be a LOT of tubes), so we sort them into usually 4-8 different tubes depending on their fluorescence score. Each tube or 'bin' is defined by the median fluorescence value of the cells sorted into it. So for each tile, we end up with a discrete distribution of the number of cells at one of the 4-8 measured positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc6341c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.515, 1.1, 'Example potential distributions')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o67Qp0+s5a9GiRTZTXQUHBxvLli2zm/bWrVvG2LFjbd6/qlWrGjdv3nSqbj4+PoYko2LFisa6devSpE1KSjJGjx5tU58yZcrY/WykSG8aL2e4MvXfnj17jC5duhhz5841rl275rDcNWvW2Eyv5+3tbURERDjMk51T/23cuNHw8PAwy/fy8jK+/vpru6/rrFmzzO8966lYM/q7dmbqv+PHj9uU16BBgwyna9u1a5cxZMgQh38Drkw76Civp6enIcm45557jFmzZtlNn/qzntmp/1Je21KlStmd5jM+Pt5477330uSxN02sq3VJLbumeLyd+nXp0sWmTtWrV08z/aNhGEZcXJzx5ptvpnmd7P3mpkj9nZHyvdSnTx+7v3PR0dFG69atbfK8/fbb6ZbfsmVLM52/v78xc+ZMh9d67tw5Y8KECUb37t0dpgMAAO5DoAoAACCXSd3ZltK55+rDUSeSYSSvn2TdGV6mTJk06zENGTLEph7PPfdcll/vhg0bzM5LScbixYsdpk/92pQvX97hWj7nz5+3CYaldCZ369bNYef8kiVLnA5OpO6MT+kIP3v2rMNrGTBggE2eF198Md20rnR01qlTx6YeGXXgG0byWh9+fn5mvnfffTfDPM6yt/7Kgw8+aCQkJKSb59q1a0bt2rVt8vzxxx920165csUm8OTt7W2sX7/eYZ1WrFhh87krWLCgw+BIZgNVsbGxNuuD1a1b17h8+XKG+ebPn28TTJo0aZJTdUvpmLcXgLWWOlC6YsWKdNPmVKDKVRcuXLAJKL/55psO02dnoCokJMTmOidOnOgw/apVq2w+f1kVwJg0aZLNd93x48cze0kun9uZvClBi23btjmdP7OBqpS/6wMHDjgs/8MPP7TJ89BDD912XVLLbYGqFStWpPkdzej3auTIkTZ5WrVqlW7a1N8Zkoynn37aYfmXLl2yWbOsTJkydtPFx8fb/O188MEHDssFAAB3BtaoAgAAuAMkJia6/DDSWQMiRe3atW3Wazh58qT69u1r5vvrr79s1rOqW7dummmtskKTJk30wAMPmPvz5893Kf+YMWN0zz33pHu8aNGi6tatm7mflJQkf39/ff/99w6nmLr//vtVrlw5c3/jxo0u1eubb75RYGCgwzRfffWVihcvbu7/9NNP6a6z5awlS5Zox44d5v6kSZNUqlSpDPPVrVtXL774ork/fvz4DD9DmZU3b15NmDDB4dRV+fLl0w8//GDz3Lhx4+ymnTJliq5fv27uv/rqqwoJCXFYh5YtW9pMHXXlypVMr73iyI8//miuD+bp6alff/1VBQsWzDDfgw8+qO7du5v76V27PWPGjFGxYsUcphk8eLDNvquf79ygcOHCevnll819V787ssqePXu0YcMGc79Vq1Z66qmnHOYJDQ11aeo0Z509e9bcDggIUNmyZbP8HLdryJAhqlevXo6ca+TIkQoODnaY5s0331StWrXM/YULFyoiIiK7q+ZW3377rc3+d999l+Hv1dtvv606deqY+ytWrDCnGM1IUFBQmnOmVqhQIZspSE+ePGl3Wsro6Gib6RHr16/vVB0AAEDuRqAKAADgLvbMM8+oV69e5v78+fP1xRdf6MSJEzYdRgULFtSsWbOcWpcqM2rWrGlub9682el8ZcqUUfv27TNM16hRI5v9Rx55REWLFnUp3/79+52uV7ly5ZxaeyZ//vx68sknzf0rV67YrC2TGT/99JO5Xb16dbVu3drpvD179jS3z58/b64jk9UefvhhlShRIsN09evXV9OmTc395cuXm+uYWPvzzz/NbU9PT5vghSNDhgyxCVZal5NVrN+PlLW4nGX9fvzzzz+6evVqhnlKly7t1GevevXqKlKkiLlvvW7VncT6u2P//v02AcucMmfOHJt9Z9c4cvZz6op8+fKZ2+fOnUt3/Sl3evbZZ3PkPP7+/hkGDKXk74wXXnjB3E9MTEyz1t1/SXx8vBYtWmTuV6tWzeZmkfR4enrq1VdftXnO2e/Mp556Snny5MkwXYsWLWz27X0v5c2b12bf+sYMAABw5yJQBQAAkMuFhobKSJ6y2aVH7dq1nSp/woQJNp3nw4YN0wMPPGAuQC8lj8qpUKGCy3XftWuX3n33XT344IOqVKmSihYtKl9fX3l5edk8Ro0aZeY5deqU0+U3a9ZMFoslw3SpRxQ1a9bMqfKt812+fNnpej344INO1UtSmqCCK4E6e1avXm1up+70y0jlypVt9nfu3HlbdUnPQw895HRa69fHMAxt3brV5nhiYqLNcw0aNLAZpeZImTJlbEZ2bN68WUlJSU7XLSMxMTE2dbud9yMpKUm7d+/OMI+zfxOSbEYMuvL5zglnzpzR6NGj9eijj6pGjRoqVqyY/P3903x3dOjQwcyTlJTklsDMpk2bzG0vLy+1a9fOqXyVKlVStWrVsrQuDRs2NLcNw1DXrl11+PDhLD3H7ahQoYLKlCmTI+dq2bKlTeDOkaz+Hs7NduzYoZs3b5r7nTt3djpv586dbYL71iMJHXH2u8/6O0my/71UoEABVa1a1dx///33NXPmzCz97gYAADmPQBUAAMBdLl++fJo1a5b8/PwkSQkJCTpw4IB5/MUXX9TDDz/sUpn79+9XWFiYateurQ8//FALFy7UkSNHdOHCBcXHxzucptA6QJaRkiVLOpUu9R3YmcnnykgN62mkMlKzZk2bjj9XRm6ldu7cOZ0+fdrc//7779N07Dt6FCpUyKa8CxcuZLoujrjy+qQOuKZ+fU6fPm3z3lhPTeWMunXrmtvXrl2zef1u1969e3Xr1i1zf9iwYS69H6mv3Zn3w9nPtpT5z3d2unjxop5++mmVLl1ar776qmbOnKn9+/fr3Llzio2NTfPdkbpz2pXvj6xiPeojODjYqZEjKZy9ocBZjRo1UpMmTcz9LVu2KDg4WM2bN9dHH32k1atX3/b0orfDOsCQ3Vz5ngkMDLQJcN/O93Bud/DgQZt9V74z8+fPbxNAt24rOJLZ3+r0vpdeeeUVczsuLk6PPvqoypUrpxdeeEGzZs3K0u9xAACQMwhUAQAAQDVr1rS7fkT9+vX1xRdfuFTW+vXr1ahRI5uRPa6Ii4tzOq2/v79T6VKPMMlsPmdltD6QNT8/PxUoUMDcv52O9ujoaJt9wzBcXtvMmr1p9rKCK69P6rSpX5/U+0FBQS7VJXX6rAx0pH4/kpKSsv39SN3R64j15zu71iNzxdmzZ9WsWTNNnDgx06MjXPn+yCrWn5mM1vlJzZW/BWfNmDHDJiBkGIbWrVund955R2FhYSpYsKCaNGmid999V3v27Mny8zuSOhienVx9ba3TuyPgmVOy8jvT2dfJ2e+l1L+56X0vDRw40GaNQUmKiIjQ2LFj1aNHD5UqVUrly5dX//799eeffyo+Pt6p8wMAAPchUAUAAABJsrv+VNu2beXj4+N0GdeuXdMjjzxicxd0jRo19Mknn2jlypU6duyYrl27pvj4eJtpCocPH54l15BbuBIsSJ3+dka2ZPX0bdk1lZIrr09Gd9hfu3Yt02VLSjM1WOrybsed8n7kFk8++aTN6KTAwEC9/vrrmjdvnsLDw3X58mXFxcXZfHesXLnSpgx3BNysP5O387efVUqXLq3t27fr448/TjPtqZQ8anbTpk368MMPdd9996l169ZOTSuZFby9vXPkPJL7vodzu6z8zszK70tXjRkzRosXL1aLFi3s3lRy/PhxTZ06VV26dFH58uX1ww8/uKGWAADAWV7urgAAAADcLzw8XIMGDUrz/Geffab7779fYWFhTpUzbtw4RUVFmfuvvPKKvvzyywxHJv3XOgVv3LiR6fTOrqliT+qRYkOHDtWnn36a6fKyy40bN2xGkWWU1lrq1yd//vwO02ck9WcvdXm3I/X7MW7cOD377LNZVv5/yZYtW7Rw4UJzv0WLFpo3b16Gn5Pc8N2RL18+c7Tb7fztZyV/f3+99dZbGjp0qLZu3apVq1Zp48aNWr9+fZqRfitWrFCjRo00Z84cPfDAA9lSH3dw1/dwbpeV35lZ+X2ZGe3atVO7du0UERGh5cuXa926ddq4caPCw8NtgtZnzpzRwIEDtXHjRk2ePNmNNQYAAOlhRBUAAMBdLjY2Vj169DA7qzw9Pc01VhITE9W7d2+dO3fOqbLmzZtnbleqVEmff/65U9PnnT17NhM1z72cfb2k5KnKrl69au7fc889mT5v0aJFbfZTd0jnFq68Pqk/G6lfn9T71oFSZ6ROfzuvf2p3yvuRG1h/d1gsFv34449OBTNzw3eH9WfGlc+2lP319/DwUKNGjTR06FDNnTtX586d0+7du/XBBx+oTJkyZrq4uDj16dMn26b7dIfbeS/S+x7I7HSZuSGgmiIrvzOz8vvydpQuXVr9+vXTxIkTtW/fPp07d04//fST2rZta5NuypQp+uWXX9xUSwAA4AiBKgAAgLvc888/r71795r7I0aM0P/+9z9zPzIyUn369HFq2jHrRdrbtm0rT09Pp+qwbds2F2qc++3cudPptHv27LF5batXr57p8xYvXtxmDZjt27dnuqzs5Mrrs2vXLpv91K9PyZIlbUY/7Nixw6W6/PPPP+Z2/vz5VbJkSZfyO1K1alWbju3c+n7kBtbfHVWrVlW5cuWcypcbvjuqVatmbh88eNCldbJSf76zm8ViUc2aNfXOO+/o0KFDateunXnswoULmj17do7WJzu58j1z/vx5RUZGmvvpfQ9bT5MXExPjdPlnzpxxOm12Cw4Ottl35Tvz+vXrOnz4sLlvvRZablK0aFE9/vjj+vvvvzVlyhSbY0wBCABA7kSgCgAA4C72008/2XTitG3bVsOGDdOAAQP02GOPmc8vXbpUH330UYblWa/J4+yd1nv27NGBAwecr/QdYOHChU7fbf/XX3/Z7Ddq1CjT5/X09LSZpnH37t06duxYpsvLLvPnz3c6rfXrY7FY1KBBA5vjnp6eatiwobm/detWmw5nRyIiImyCR40aNZKHh/3/Inl5/f9Z0xMTE50qv2jRoqpVq5a5v2LFCpvRc3eK1OsKOXv9rsjMd8etW7c0d+7cLK+Lqxo3bmxuJyQk6O+//3Yq37///qv9+/dnV7Uy5Ovrqy+//NLmufQCZ9afgex4/7PDqlWrnJ7WztnvYevP5okTJ5wq2zAMrVu3zqm0Uva/1nXr1jVHTUvSn3/+6XTeP//80+bGipCQkCytW3bo16+f6tevb+7ndHAYAAA4h0AVAADAXSo8PFzPPfecuV+8eHH98ssvZkf9+PHjbe68HjlypFavXu2wTOv1Ko4fP+5UPT788EMXan1nOHbsmM1UZum5fv26zXoZBQsWVOvWrW/r3L179za3k5KSNGLEiNsqLzv8/vvvTgWTtm/frvXr15v7rVu3VsGCBdOk69y5s7mdmJiob775xql6jB492qbTtWvXrummtf5sX7p0yanyJdv34+rVq2kCA3eC1OvQuHL9mTmHswGAiRMnujxtWXbo0qWLzb71iFRHvv3222yojWsqVKhgsx8fH283XWY//+5048YNTZo0KcN0SUlJGjNmjLnv4eFh851izXqk1fnz5xUeHp5h+fPmzVNERIQTNU6W3a+1t7e32rdvb+6Hh4dr8eLFGeZLSkrS6NGjbZ5L/dnPraw/5+l9xgEAgHsRqAIAALgLxcTEqHv37jbrUk2fPl2BgYFmmnz58mnWrFny8/OT9P/Xqzp//ny65daoUcPcnjdvXobrr0ycOFEzZ868nUvJtQYPHuzwtZKk1157zWZKqCeeeEL+/v63dd5HHnnE5n346aefXO4Qj4uLswkQZbUbN27o2WefdTid5I0bNzRw4ECbkWmDBg2ym7Zfv342nbtfffWVNmzY4LAOq1evtgkoFCxYUH379k03vfVUdFu3bnVYtrXnnntOAQEB5v5HH32kOXPmOJ1fkq5cueLWKe5ST8PnyvU7y/oze/r0aS1cuNBh+p07d+qNN97I8npkxn333acmTZqY+8uWLdPUqVMd5lm3bp3GjRuX5XXZtWuXSx3xa9assdlPb8pF6+f/+ecfp6aCzQ2GDx9uM1WdPZ999pnNNIEPPvigzfpd1lKPIPr6668dln3+/Hm98MILTtU1RU681i+99JLN/nPPPZfh79Unn3xiMwK1TZs2Nn+3OSUqKsqlwF98fLw2bdpk7js7rSgAAMhZBKoAAADuQs8//7z27dtn7o8YMUKhoaFp0tWsWdMmyHHmzBmH61VZj0i5du2aOnbsaLdDKS4uTsOHD9fAgQMl2a778V/g6+urY8eOqXXr1tqzZ0+a4zdu3NCgQYP0/fffm88VLVpU77777m2f22KxaMqUKTZTO7388svq27dvhtMA7tq1S2+//bbKli2rzz///Lbrkh5fX1/99ddf6tGjhy5cuJDm+IkTJ9S+fXub9aNCQ0PTHfFUoEABffDBB+b+rVu39OCDD2rWrFl20//222966KGHlJCQYD736aefOvwcWgcitmzZopEjRzo1Kixv3rz64YcfzLWqEhMT9fDDD2vIkCE6d+5cuvkMw9CGDRv08ssvq0yZMvrll18yPFd2qV69us1Itg8//FCLFi1SbGxslp0j9ciMvn37au3atXbT/vbbb2rVqpWuX7+ea747vvrqK5tpI59++mmNGTPG7hSgs2fPVseOHZWQkCBfX98srcfo0aNVtmxZvfvuuxlOcbZmzRr179/f3LdYLOmOkLH+/EdGRur555/PldOKWvP19dXly5fVunXrNAE5KXmaxvfff1/Dhg2zyTNq1Kh0y2zRooVNoOOHH37Qt99+a/d93rBhg0JCQhQRESEfHx+n650Tr3VYWJjN9+mxY8fUsmVLu+t6xcfH65133tE777xjPufr66svvvgiS+vkrAMHDqhChQrq2bOn/vrrL4drwp0/f149e/bUyZMnzee6deuWE9UEAAAu8so4CQAAANxp9erVNuvjuOLIkSMqW7aszXNTp061uds/ZV2q9AwYMECrVq3StGnTJEl///23Pv74Y5tOqxQDBw7UV199pVOnTkmStm3bpsqVK+uBBx4wp0w6fvy4Fi9ebE5pVLVqVXXs2NFtnV7Z4eOPP9awYcO0Z88e1a5dWy1atFDdunXl5+enY8eOaeHChTZr8nh4eGjChAk2I29uR4MGDTR16lQ9/vjjunXrlqTkkVW//PKL6tatq/r166tIkSKSktcGOnr0qP75558MR8BllS+//FIvvPCC/vjjDy1atEgPPPCAqlSposTERO3du1fLli0z6y0lB/EmTZpkBnvseemll7Ry5UpzvZXLly+rR48eCg4OVqtWrVS0aFFFR0drxYoVOnjwoE3eRx99VM8++6zDOj/11FMaP368GaQdMWKERowYIW9vb5tO6D59+mj8+PE2eTt37qzPP/9cr7/+ugzDkGEY+uqrrzRmzBg1bNhQtWrVUpEiRXTr1i1dvnxZBw8e1I4dO3LNFGteXl7q37+/OXrkzJkz6tChgyTJz8/PJkCzaNEiNW/e3OVz1K1bV127djVHm0VHR6tFixZq1qyZGjVqJH9/f0VFRWnZsmVmp72/v78+/vhjvfzyy7d5hbevcePGGjZsmDmVaUJCgl588UV9/vnnateunYKCgnTx4kWtWrXKvEmgYsWK6tSpU5rp1G5XVFSUPvzwQ3344YcKCgpS3bp1VbFiRRUqVMg8vnnzZu3evdsm30svvaQqVarYLbNPnz4aPny4GZwcP368xo8fLy8vL5tgW/PmzbVo0aIsvZ7MGjZsmL755htFREQoNDRUDRs2VOPGjVWgQAFz1F7q77zPPvtM1apVS7dMi8WiDz/8UH369JGUHFB++eWX9f3336tNmzYqVKiQLly4oI0bN5qB9nvvvVf333+/vvrqK6fqnVOv9Q8//KBdu3bp6NGjkqR9+/apbt26atasmerVq6d8+fLp5MmTWrhwoaKjo23yjh492mb9vZyWkJCgGTNmaMaMGfLz89N9992ne++9V0WLFpWfn5+uXLmi/fv3a/Xq1TaBrDJlyuj11193W70BAED6CFQBAADcATK7oHrqu7z379+v559/3txPvS5VesaPH69t27aZHfwjRoxQ8+bN04zCyps3r+bOnau2bduanew3b97Un3/+aXfB9uDgYC1evFhTpkzJ1PXlVnXr1tXkyZP15JNP6ubNm1q1apVWrVplN62Xl5cmTZrkcH2kzHj00UdVunRpPfroo2bgMCkpSdu2bXNqGrmsHulh7fnnn9fBgwf1v//9TzExMZo9e3a6aQMCArR06VJVrFjRYZkWi0W///67BgwYoB9//NF8/uDBg2kCU9YGDhyo7777LsM6161b1ww2WY8ovHXrlk1QLb27+4cMGaJKlSqpX79+ZpAyPj5e69at07p16zI8f3a+H8748MMPtW3btjR1TT2qKrPfVZI0efJkHTlyxCaAkt7rkz9/fv3xxx/y9vbO9Pmy2gcffKDr16/bTAd38uRJ/fDDD2nSlixZUn/88YfL00C6KioqKsNpFKXkvwNH66cVL15cU6ZMUb9+/Ww+4wkJCTYjE7NylN3tKlOmjGbNmqWHH35Yly9f1pYtW7Rlyxa7aVMCUKmnxLPnscce08aNGzV27Fjzuf3792v//v1p0lavXl3z5s3LcCpIazn1WhcpUkTr16/Xgw8+aAbVDMPQ2rVr0x3N6O3trfHjx+vJJ5+8rXNnpdjYWG3evFmbN292mK5y5cpatGiR3XUOAQCA+zH1HwAAwF0iZV2qmJgYSfbXpUqPK+tV1atXT9u2bdNDDz2U7giYoKAgvfnmm9q6dWuaEV//Fb1799b69esVGhpq93WwWCwKCwvT9u3b9cQTT2RLHUJCQnT48GH973//M0e0OVKsWDH17t1bf/75pzmCLrt8++23mjZtmoKDg+0e9/X1Vb9+/bR//36n79z38vLS1KlT9ffffyskJCTdz5+Hh4eaNWumlStX6vvvv5enp6dT5b/66qvasWOHBg8erIYNG6pIkSIuTenVuXNnHT9+XB988IFT66SULVtWAwYM0PLly/Xpp586fZ7skDdvXq1atUq//fabevTooSpVqih//vwZBrldUahQIXO6w5TvmtT8/f3Vu3dv7dy5U23bts2yc2eV0aNHa968eapatard476+vurdu7d27NiRLSNS3n77bX344Ydq2rRphsFNT09P3X///Vq2bJlTfwePPvqo9u3bp2HDhqlZs2YKDAx0ewA1I61atdK2bdvUsWPHdK+vbt26WrVqlcORxamNGTNGEyZMUFBQkN3jBQoU0EsvvaStW7dmak2knHqtg4KCtHnzZo0bN87hzQB58uRRz549FR4e7vYgVYMGDfTjjz+qe/fuTrVfKlWqpFGjRmn37t0Z3vAAAADcx2LYm0wZAAAAyAKnT5/W2rVrderUKSUlJSkoKEjly5dXSEiI08GBO8GqVavUsmVLc3/lypUKCwsz948dO6atW7fq9OnTSkxMVIkSJdSsWTOVKVMmR+sZFRWlTZs26ezZs7p48aI8PDyUP39+lSlTRtWqVcu2TrwRI0Zo5MiR5n7q/4Js375d+/fv15kzZ5QnTx6VKVNGrVu3VoECBW7rvOfOndPatWsVFRWly5cvq1ChQipevLiaN2+eZdMs3o7jx49r27ZtOn/+vC5duiQvLy8VLFhQ5cqVU7Vq1XL885GbXL16VWvXrtW///6rGzduKDAwUCVLllTz5s2VL18+d1fPKTt37tSOHTt09uxZFShQQKVKlVKLFi3MKfiy282bN7V37179+++/ioyM1PXr1+Xt7a2CBQuqcuXKqlOnjgoXLpwjdckNoqKitGHDBp0+fVoxMTEqXry4GjVqlG6w3BmJiYnavHmz9uzZo4sXL6pQoUIqW7aswsLC5O/vn4W1zxkHDx7U9u3bde7cOcXGxqpo0aIqW7asmjdvnm7w2N2OHj2qAwcO6MSJE7p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\n",
      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# @hide2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1,3, dpi = 200, figsize = (10,3), sharey = True)\n",
    "\n",
    "axes[0].set_ylim(0,400)\n",
    "\n",
    "sns.barplot(x = [5,10,15,20,25,30,60,100], y = [350,200,100,50,25,10,0,0], ax = axes[0])\n",
    "sns.barplot(x = [5,10,15,20,25,30,60,100], y = [100,200,250,200,100,50,10,1], ax = axes[1])\n",
    "sns.barplot(x = [5,10,15,20,25,30,60,100], y = [0,0,10,25,50,100,200,350], ax = axes[2])\n",
    "\n",
    "for ax in axes:\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, fontsize = 9)\n",
    "\n",
    "axes[0].set_ylabel(\"Number of cells\", size = 12)\n",
    "\n",
    "axes[0].set_title(\"Low Activity\")\n",
    "axes[1].set_title(\"Average Activity\")\n",
    "axes[2].set_title(\"High Activity\")\n",
    "\n",
    "axes[1].set_xlabel(\"Median fluorescence of bin\", labelpad = 10, size = 12)\n",
    "\n",
    "fig.suptitle(\"Example potential distributions\", y = 1.1, x = 0.515, size = 14)"
   ]
  }
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