notebooks/fig4_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47fe4714",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73b3e0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif']='Myriad Pro'\n",
    "\n",
    "plt.rc('xtick', labelsize=14)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=14)    # fontsize of the tick labels\n",
    "\n",
    "# # set axes and tick width\n",
    "plt.rc('axes', linewidth=0.5)\n",
    "mpl.rcParams['xtick.major.size'] = 2\n",
    "mpl.rcParams['xtick.major.width'] = 0.5\n",
    "mpl.rcParams['xtick.minor.size'] = 2\n",
    "mpl.rcParams['xtick.minor.width'] = 0.5\n",
    "mpl.rcParams['ytick.major.size'] = 2\n",
    "mpl.rcParams['ytick.major.width'] = 0.5\n",
    "mpl.rcParams['ytick.minor.size'] = 2\n",
    "mpl.rcParams['ytick.minor.width'] = 0.5\n",
    "# # additional parameters\n",
    "mpl.rcParams['lines.linewidth'] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e080e002",
   "metadata": {},
   "outputs": [],
   "source": [
    "## modify this for the location of napari-mm3 on your computer\n",
    "helpers_folder = os.path.realpath('/Users/ryan/napari-mm3/src/napari_mm3/')\n",
    "\n",
    "if helpers_folder not in sys.path:\n",
    "    sys.path.insert(0, helpers_folder)\n",
    "    \n",
    "import utils_plotting as mm3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b366cd",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5ed709d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_otsu = pd.read_csv(\n",
    "    '/Users/ryan/Google Drive/My Drive/Jun_Lab/mm3_paper/data/20171225_fs104/otsu/20171225_otsu_complete_cells.csv')\n",
    "df_unet = pd.read_csv(\n",
    "    '/Users/ryan/Google Drive/My Drive/Jun_Lab/mm3_paper/data/20171225_fs104/unet/20171225_unet_complete_cells.csv')\n",
    "df_delta = pd.read_csv(\n",
    "    '/Users/ryan/Google Drive/My Drive/Jun_Lab/mm3_paper/data/20171225_fs104/delta/20171225_delta.csv')\n",
    "df_bac = pd.read_csv(\n",
    "    '/Users/ryan/Google Drive/My Drive/Jun_Lab/mm3_paper/data/20171225_fs104/bacmman/20171225_fs104_bacmman_0223.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac289b5",
   "metadata": {},
   "source": [
    "### Fig 4.3. Adder correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "638bc41f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_palette('Spectral',6,desat=.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51236621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "col_otsu = df_otsu['sb']/df_otsu['sb'].mean()\n",
    "row_otsu = df_otsu['delta']/df_otsu['delta'].mean()\n",
    "\n",
    "col_unet = df_unet['sb']/df_unet['sb'].mean()\n",
    "row_unet = df_unet['delta']/df_unet['delta'].mean()\n",
    "\n",
    "col_delta = df_delta['sb']/df_delta['sb'].mean()\n",
    "row_delta = df_delta['delta']/df_delta['delta'].mean()\n",
    "\n",
    "col_bac = df_bac['sb']/df_bac['sb'].mean()\n",
    "row_bac = df_bac['delta']/df_bac['delta'].mean()\n",
    "\n",
    "fig=plt.figure(figsize=(4,4))\n",
    "ax=plt.axes()\n",
    "\n",
    "x = np.linspace(0.4,1.6)\n",
    "y = np.ones(len(x))\n",
    "ax.plot(x,y,color='gray')\n",
    "ax.plot(x,2-x,color='gray')\n",
    "ax.plot(x,x,color='gray')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_otsu, row_otsu, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white',mfc='C0', color='C0',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2, label='Otsu')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_unet, row_unet, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1, ls='-',\n",
    "              lw=1, mew=1,mec='white', mfc='C1',color='C1',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='U-net')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_delta, row_delta, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white', mfc='C4', color='C4',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='DeLTA')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_bac, row_bac, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white', mfc='C5', color='C5',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='BACMMAN')\n",
    "\n",
    "ax.set_xticks([.4,1,1.6])\n",
    "ax.set_yticks([.4,1,1.6])\n",
    "ax.set_xlim(.4,1.6)\n",
    "ax.set_ylim(.4,1.6)\n",
    "ax.set_xlabel('$L_b$ /<$L_b$>',fontsize=20)\n",
    "ax.set_ylabel('$\\Delta$ /<$\\Delta$>',fontsize=20)\n",
    "plt.legend(frameon=False)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a77ee3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tau_fit(sb,a,b):\n",
    "    return (a/sb+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7eb3cd52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.06068765, 0.9413649 ]),\n",
       " array([[ 0.00019526, -0.00020083],\n",
       "        [-0.00020083,  0.00021252]]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curve_fit(tau_fit,bin_c,bin_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "532786e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1caa34640>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "col_otsu = df_otsu['sb']/df_otsu['sb'].mean()\n",
    "row_otsu = df_otsu['tau']/df_otsu['tau'].mean()\n",
    "\n",
    "col_unet = df_unet['sb']/df_unet['sb'].mean()\n",
    "row_unet = df_unet['tau']/df_unet['tau'].mean()\n",
    "\n",
    "col_delta = df_delta['sb']/df_delta['sb'].mean()\n",
    "row_delta = df_delta['tau']/df_delta['tau'].mean()\n",
    "\n",
    "col_bac = df_bac['sb']/df_bac['sb'].mean()\n",
    "row_bac = df_bac['tau']/df_bac['tau'].mean()\n",
    "\n",
    "fig=plt.figure(figsize=(4,4))\n",
    "ax=plt.axes()\n",
    "\n",
    "x = np.linspace(0.4,1.6)\n",
    "y = np.ones(len(x))\n",
    "\n",
    "fit1 = curve_fit(tau_fit,bin_c,bin_m)[0]\n",
    "\n",
    "ax.plot(x,tau_fit(x,*fit1),color='gray')\n",
    "\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_otsu, row_otsu, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white',mfc='C0', color='C0',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2, label='Otsu')\n",
    "\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_unet, row_unet, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1, ls='-',\n",
    "              lw=1, mew=1,mec='white', mfc='C1',color='C1',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='U-net')\n",
    "\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_delta, row_delta, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white', mfc='C4', color='C4',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='DeLTA')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_bac, row_bac, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white', mfc='C5', color='C5',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='BACMMAN')\n",
    "\n",
    "ax.set_xticks([.4,1,1.6])\n",
    "ax.set_yticks([.4,1,1.6])\n",
    "ax.set_xlim(.4,1.6)\n",
    "ax.set_ylim(.4,1.6)\n",
    "ax.set_xlabel('Rescaled birth length $L_b$ /<$L_b$>',fontsize=16)\n",
    "ax.set_ylabel('Rescaled doubling time $\\\\tau$ /<$\\\\tau$>',fontsize=16)\n",
    "sns.despine()\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0427260d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ca94a820>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_otsu = df_otsu['sb']/df_otsu['sb'].mean()\n",
    "row_otsu = df_otsu['elong_rate']/df_otsu['elong_rate'].mean()\n",
    "\n",
    "col_unet = df_unet['sb']/df_unet['sb'].mean()\n",
    "row_unet = df_unet['elong_rate']/df_unet['elong_rate'].mean()\n",
    "\n",
    "col_delta = df_delta['sb']/df_delta['sb'].mean()\n",
    "row_delta = df_delta['elong_rate']/df_delta['elong_rate'].mean()\n",
    "\n",
    "col_bac = df_bac['sb']/df_bac['sb'].mean()\n",
    "row_bac = df_bac['elong_rate']/df_bac['elong_rate'].mean()\n",
    "\n",
    "fig=plt.figure(figsize=(4,4))\n",
    "ax=plt.axes()\n",
    "\n",
    "x = np.linspace(0.4,1.6)\n",
    "y = np.ones(len(x))\n",
    "ax.plot(x,y,color='gray')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_otsu, row_otsu, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white',mfc='C0', color='C0',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2, label='Otsu')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_unet, row_unet, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1, ls='-',\n",
    "              lw=1, mew=1,mec='white', mfc='C1',color='C1',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='U-Net')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_delta, row_delta, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white', mfc='C4', color='C4',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='DeLTA')\n",
    "\n",
    "bin_c, bin_m, bin_e = mm3.binned_stat(col_bac, row_bac, bin_edges='sturges', binmin=100)\n",
    "\n",
    "ax.errorbar(bin_c, bin_m, yerr=bin_e, xerr=None,\n",
    "              marker='o', ms=8, alpha=1,ls='-',\n",
    "              lw=1, mew=1, mec='white', mfc='C5', color='C5',\n",
    "              elinewidth=0.5, capsize=1, capthick=0.5,\n",
    "              zorder=2,label='BACMMAN')\n",
    "\n",
    "ax.set_xticks([.4,1,1.6])\n",
    "ax.set_yticks([.4,1,1.6])\n",
    "ax.set_xlim(.4,1.6)\n",
    "ax.set_ylim(.4,1.6)\n",
    "ax.set_xlabel('Rescaled birth length $L_b$ /<$L_b$>',fontsize=16)\n",
    "ax.set_ylabel('Rescaled elongation rate $\\lambda$ /<$\\lambda$>',fontsize=16)\n",
    "sns.despine()\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d234df95",
   "metadata": {},
   "source": [
    "### Fig 4.4. Parameter distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1bd018c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_line_hist(data,bins=None,density=True):\n",
    "    if bins is None:\n",
    "        bin_vals, bin_edges = np.histogram(data,density=density)\n",
    "    else:\n",
    "        bin_vals, bin_edges = np.histogram(data,density=density,bins=bins)\n",
    "    bin_steps = np.diff(bin_edges)/2.0\n",
    "    bin_centers = bin_edges[:-1] + bin_steps\n",
    "    # add zeros to the next points outside this so plot line always goes down\n",
    "    bin_centers = np.insert(bin_centers, 0, bin_centers[0] - bin_steps[0])\n",
    "    bin_centers = np.append(bin_centers, bin_centers[-1] + bin_steps[-1])\n",
    "    bin_vals = np.insert(bin_vals, 0, 0)\n",
    "    bin_vals = np.append(bin_vals, 0)\n",
    "    return(bin_centers,bin_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9c8ff847",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvs_otsu = []\n",
    "cvs_unet = []\n",
    "cvs_delta = []\n",
    "cvs_bac = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2928d614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,6,figsize=(12,3))\n",
    "ax = np.ravel(axes)\n",
    "\n",
    "labels = ['Birth length ($\\mu$M)','Division length ($\\mu$M)','$\\Delta$ ($\\mu$M)','Elongation rate (1/hr)',\n",
    "         '$\\\\tau$ (minutes)','Septum position']\n",
    "\n",
    "titles = ['S$_{B}$','S$_{D}$','$\\Delta$','$\\lambda$','$\\\\tau$','L$_{1/2}$']\n",
    "\n",
    "float_columns = ['sb', 'sd', 'delta', 'elong_rate', 'tau', 'septum_position']\n",
    "\n",
    "for i,c in enumerate(float_columns):\n",
    "    mu1 = df_otsu[c].mean()\n",
    "    mu2 = df_unet[c].mean()\n",
    "    try:\n",
    "        mu3 = df_delta[c].mean()\n",
    "        cv3 = df_delta[c].std()/df_delta[c].mean()\n",
    "        mu4 = df_bac[c].mean()\n",
    "        cv4 = df_bac[c].std()/df_bac[c].mean()\n",
    "\n",
    "        cvs_delta.append(cv3)\n",
    "        cvs_bac.append(cv4)\n",
    "    except:\n",
    "        cvs_delta.append(None)\n",
    "        cvs_bac.append(None)\n",
    "    \n",
    "    cv1 = df_otsu[c].std()/df_otsu[c].mean()\n",
    "    cv2 = df_unet[c].std()/df_unet[c].mean()\n",
    "\n",
    "    cvs_otsu.append(cv1)\n",
    "    cvs_unet.append(cv2)\n",
    "\n",
    "    ax[i].set_title(titles[i],fontsize=14)\n",
    "    b1, v1 = make_line_hist(df_otsu[c],density=True)\n",
    "    ax[i].plot(b1,v1, ls='-',color='C0',\n",
    "               label='$\\mu$ = {:2.2f}\\nCV = {:2.2f}'.format(mu1,cv1)\n",
    "               ,lw=1)\n",
    "    b2, v2 = make_line_hist(df_unet[c],density=True)\n",
    "    ax[i].plot(b2,v2, ls='-',color='C1',\n",
    "               label='$\\mu$ = {:2.2f}\\nCV = {:2.2f}'.format(mu2,cv2),\n",
    "               lw=1)\n",
    "    \n",
    "    try:\n",
    "        b3, v3 = make_line_hist(df_delta[c],density=True)\n",
    "        ax[i].plot(b3,v3, ls='-',color='C4',\n",
    "               label='$\\mu$ = {:2.2f}\\nCV = {:2.2f}'.format(mu3,cv3),\n",
    "               lw=1)\n",
    "        \n",
    "        b4, v4 = make_line_hist(df_bac[c],density=True)\n",
    "        ax[i].plot(b4,v4, ls='-',color='C5',\n",
    "               label='$\\mu$ = {:2.2f}\\nCV = {:2.2f}'.format(mu4,cv4),\n",
    "               lw=1)\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    y_upper = np.max(np.maximum(np.maximum(v3,np.maximum(v1,v2)),v4))\n",
    "    \n",
    "    ax[i].set_xlabel(labels[i],fontsize=14)\n",
    "    ax[i].set_ylim(0,y_upper*1.3)\n",
    "    ax[i].set_yticks([])\n",
    "#     ax[i].legend(frameon=False,fontsize=10,loc=1)\n",
    "\n",
    "sns.despine(left=True)\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc8f4274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.set_palette('deep')\n",
    "fig=plt.figure(figsize=(3,3))\n",
    "ax=plt.axes()\n",
    "order = [5,1,0,3,2,4]\n",
    "order_delta = [1,0,3,2,4]\n",
    "plt.bar(range(6),np.array(cvs_otsu)[order],width=.2,color='C0')\n",
    "plt.bar(np.arange(6)+0.2,np.array(cvs_unet)[order],width=.2,color='C1')\n",
    "plt.bar(np.arange(1,6)+0.4,np.array(cvs_delta)[order_delta],width=.2,color='C4')\n",
    "plt.bar(np.arange(1,6)+0.6,np.array(cvs_bac)[order_delta],width=.2,color='C5')\n",
    "\n",
    "ax.set_yticks([0,.1,.2])\n",
    "ax.set_xticks([])\n",
    "labels = ['L$_{1/2}$','S$_{D}$','S$_{B}$','$\\lambda$','$\\Delta$','$\\\\tau$']\n",
    "ax.set_xticks(np.arange(6)+0.25,labels,fontsize=14)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41348d93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}