fig2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Function Definitions \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "font = {'size'   : 8}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "from pro_plot import barPlotter, curvePlotter\n",
    "from standards import *\n",
    "\n",
    "DataPath = \"CA-prod-report\"\n",
    "OutPath = \"Graphs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 554.4x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  OPTIONS  #\n",
    "\n",
    "GENE = \"V\"\n",
    "n_choice = 7\n",
    "figsize = ( n_choice * 1.1, 2 )\n",
    "\n",
    "df = pd.read_csv(f'{DataPath}/CA-prod-{GENE}_usage.csv', index_col=0 )\n",
    "\n",
    "columns = [c for c in df.columns if 'err' not in c]\n",
    "errorbars = [ c + ' err' for c in columns ]\n",
    "colors = [ EXP_KOL[c] for c in columns ] \n",
    "\n",
    "df = df.iloc[np.arange(n_choice)]\n",
    "ax = barPlotter( df, colors=colors, hatch=None, grid=True,\n",
    "                columns=columns, figsize=figsize, errorbars=errorbars, rotation=0 )\n",
    "plt.margins(0)\n",
    "'''\n",
    "plt.legend(ncol=1, shadow=False, bbox_to_anchor=(1,.5), loc='center left',\n",
    "            edgecolor='inherit', framealpha=1)\n",
    "'''\n",
    "for i in np.arange( 1, ax.get_xlim()[1]-1, 2 ).astype(int) :\n",
    "    ax.axvspan( i, i+1, 0, 1, alpha=0.5, color=GREYS_DIC['silver'], ec=None, zorder=-10)\n",
    "\n",
    "plt.savefig( f\"{OutPath}/fig2A.pdf\", bbox_inches='tight' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Length Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 162x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  OPTIONS  #\n",
    "\n",
    "df = pd.read_csv(f'{DataPath}/CA-prod-lengths.csv', index_col=0 )\n",
    "\n",
    "figsize = ( 2.25, 1.75 )\n",
    "    \n",
    "columns =  ['gen'] + HEADERS\n",
    "errorbars = [c + ' err' for c in columns]\n",
    "colors = [ EXP_KOL[c] for c in columns ]\n",
    "linestyle = ['-'] + ['-'] * len(HEADERS)\n",
    "markerstyle = [''] + [''] * len(HEADERS)\n",
    "zorder = np.arange(len(columns))[::-1]\n",
    "zorder[0] = len(columns)\n",
    "\n",
    "ax = curvePlotter(\n",
    "    df, colors=colors, grid=False, legend=False, linestyle=linestyle, markerstyle=markerstyle,\n",
    "    columns=columns, figsize=figsize, zorder=zorder, errorbars=errorbars, fill_error=True, lw=1\n",
    "    )\n",
    "\n",
    "plt.margins(0)\n",
    "ax.set_xlim([8, 20])\n",
    "ax.set_ylim([0, np.max(df.values) * 1.1 ])\n",
    "\n",
    "ax.set_xticks([8,12,16,20])\n",
    "\n",
    "average_lengths = np.matmul( df.index.values, df[columns].values )\n",
    "ax.axvline(x=average_lengths[0], zorder =-2, ls='--', lw=1, color=GREYS_DIC['silver'])\n",
    "\n",
    "plt.savefig( f\"{OutPath}/fig2B.pdf\", bbox_inches='tight' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Amino Acid Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM4AAABvCAYAAACkX/+PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPG0lEQVR4nO2de3Bc5XmHn1daaS1ZXuti2dhGkmULQrDMeAxxuDSBTkjIxFxSZtpMTdoxJcCUDCVpp9Np00loSid0aKdNOp0QLpUg0IhLkgkuSYbShplgGhNkm3CzTYSNhWVbMojVrrX3ffvHrtZ702p3tRdp9T4zGp1vzznf+57Lb7893znn94mqYhhGYdRVOwHDWIyYcAyjCEw4hlEEJhzDKAITjmEUgaPaCaxatUo3bNhQ7TQyCIfDVYnrcFTvkCzFbc7F8PDwaVXtzDav6hlv2LCBV155pdppZDA+Pl6VuKtXr65KXFia25wLEXl3tnk181NtcHCw2ikYSwgTjmEUwaIXjnt6klPuMYLhIO7pyWqnYywR8hKOiPyLiPxSRL6d9vnXRGRMRO5J+7xJRE6KyNWlTDYb/pCPXQPXcvDka/hDvnKHMwwgD+GIyDagRVU/ATSKyMeSZj8E3JRltS8Br5UmxfxYv729kuGMJU4+vWqXAv8dn34euAz4NYCqnhKRjyYvLCKN8XX2zFahiNwG3Aawfv16hoeHmZlubm7m7bffBmDlypVs2rSJffv2AVBfX8/WrVs5ePAgQ0NDXHfddazbtAaAc7d3EAgGGB8fp7GxkZGREQDa2tro6enhwIEDADQ0NHDRRRfx5ptv4vPFWqjNmzczPj7OxMQEAD09PXi93kS5paWFtrY2RkdHYzvN4aCrq4vR0dFEF25XVxeTk5N4vV4AOjs7UVVOnz4NgMvlwuVy8d577wHQ2NjI+vXrOXbsGJFIJBH3nXfeYXIy9pNz06ZNBIPBRNw1a9bQ3t7OW2+9BcDy5cu54IILOHDgQKKObdu2MTIygtvtBuC8885jenqa48ePA7B27VpcLheHDh0CYMWKFZx//vns27ePUCgEQG9vLydOnMDv9yfW8fl8fPjhh4l96nQ6OXnyJADNzc2sXr2ao0ePAlBXV0dPTw9jY2MEAoHEsfV6vYm8Ojo6cDgcnDp1itHR0aKPU11dHUeOHEnUuW7dOl57Lfad7XQ66e/v5/XXX0/ksWXLFsbGxnj//fcT2xqNRnn33XcTx2316tW88cYb5ELmejpaRP4G2KeqP4//9LpcVb+ZNP8q4GpV/dt4+TZgFPg48KKqPp+r/ksuuUQL7Y72hyb59Kd28LPnnsYbiHLzwPUADN78X6xZua6gumZjKXbNLsVtzoWIDKvqJdnm5XON4wZc8WkX8GGOQA7gGlX9WaFJFkI44uPCT77HM/t3oBotZyjDyEo+wvk/4FPx6auBX+VYdg3QLSI/B74IfEtE2uaXYnau3NFR1HrWbW2UgjmFo6r7AL+I/BKIAMdE5GsAInIL8M/ATSLy76p6XFU/pqqfBR4D/lpVF1QfsQnHKAV5PXKjqnelffQP8c8fBh6eZZ2755WZYSxgFv0N0HwJun34xqeIhiIE3Xa/x5gfVX/IMx8mpuFM6Gy5vbHwOiKBEC/dPoD70AkigRDQVLL8jKXHomhxzoSg9/6zf9EC/EXSr2mu6d5e2uSMJcmiEE6xRKe8RKe8RMY/gGis2/qano9XOSujFqhp4ag/yI17juG5814wGyyjhNSkcKzL2Sg3NSWcsLOVo8Egp8NhjgaD2DMFRrmoKeH4EXoPj/CXl15B7+ERFPt5ZpSHmhKOYVSKJS2c5Gshuy4yCmFJCqexaRq8Rxl8+H7wHoWp0ww+9AB8cAq87mqnZywClqRwRKdhdy+7Nu+F3b0QCrDLdQb+8U8g6K92esYiYEkKZ4ZdVyZNbzm3eokYi44lLRzDKBYTjmEUQcntoUTkeyKyR0ReFJGLSp2wYSwEymEPda+qXgHcDHyjZJkaxgIinxYnmz0UELOHgtTb86p6JD4ZIvaqtWHUHPm8yNYKvBOfdgOb86z7W8B3ss0o1FctEAgCzqQasj9Kk251lVzM9XC0AhKfDgQDeE6fNl8181Wb/YQhP+HkbQ81g4h8BXhTVV/MNl9VHwAegJiv2sUXX5wyP73sdDpJRciGiKSVs09nrJccq9GJs30V0WiUlpaWlOV6e3tTyl1dXSnlzs5OOjtTh1NZsWJFzjq6u7tTyhs3bszIL913LH3/bN26NaXc19eXUna5XJxzzjk569i2bVuKr9ratWtT5i9btoy2tlTDovRtSS+vW5fqcdfe3k57e6rjam9vb8r2ped14YUXppS7u7sz9ll6nel19Pf3p5Q3bNhA+phMq1atyllHOqW2h0JEPgNcDtyTaznDWMyU1B4qvsq/Ab3AL0Tke+VJ2zCqS8ntoVT1I6VJzTAWLnYD1DCKwIRjGEVgwjGMIjDhGEYRmHAMowhMOIZRBCacGsT8E8qPCafGiE55GXjwISLjHxCd8lY7nZrFhFMATS2KsyXA0z9+DEezEm4I8PhTjxFuCCDOheHhpv4gXwi58Nx5L+oPVjudmsWEUwBRgjz76vW8OvqvBMIBbnnkBv5jz7e55ZEbCEULO0mHhobKlCXs7O2feyFjXphwiiB5/NFztxc2FmlrU5iOZR5++NTjtDaFS51aVip5zVPOL4SFhAlnDsKNrXgcHXgcHcxnd82cUPXqo/7ZPm7e/DL16kuZVy4qKZwnnngir+UWu8BMOHPgi9bT92Dsb66rmGwnw/I6J82RBp4aepLmSAMSf/sn2Zoq28k2MQ1H3bG/ienYZ4U6j4brHAt2+MZ8BbZQMeGUCG1qx7+sCc8KF+H4y2tDQ0NoKMreOx7lMn83e+94NO9xepJHoZsZxrFQ4URDEV66fYBLfV3x4RvLx8r6BtqigkNj0+ks9hYmHRNOifBRx19d/jv0jRyhNRCiLSr8cOiJxA7ONhJcnS6noy6MA6WjLkxrQ3kORyGj0CX/NPU4Ogg3tmYsk00EdaEwZ+66j8hvR6kLhTOWW+wtTDomnDJQHwhx5q77+EJ4Ze4Fw2Hq77sVGT1E/X23Uh/J3jPX2jiJ1z9GJBrE6x/DH5rMWKZU1zHJP037HqzHF60HYiIQZ6wLfujJH8zaBb9zw9kevRmxtDaFcdTFOkUq1SFSbsrhq9Yf91Tbs9R91fLtFt7VP4f9rvp4Zv8O3vf+hmf27yAcybxeKVcHQEeTG2dLAIczTFiD3PLIDRw8+XqiCz699cm2zfXqQ95/mfpn+2iI1iVa2XK1sJWgHL5qfw/8IfAH8WljDvL1rf7E59ozPpuIjz7nVy3PKHTq49lXr6f1ou8z4y60fnssD21q5/Gnn8azwoXK2VMp6mikOdJAnUqiQ2TXJ+Mzk1vZWVrYxUA+r05n81X7NcR81UTko2nLt6nqKICItGarcDHZQ7X2rgTqc0Sdg0IG7VUS9lAtXf3MbPPMyHIz94+CwQAej4eDBw+ybONGeg+PwLSP3sMj+JMcY5JDBwMB3hweTrGH2r17Nzt37kyxh0re3vRtnpmeuXflU+Hlz3yWvpEj+DvPxo0Eo+z98qNMHT7J3jse5XcHrz3bi5h8TKLKkSNHatYeqpXCfNWSW7GspkyLyR7K43BkXS5vcgXOksiMPdRRd/LHaXXUNeFsctK21sUZjbcxN3w+I8fk0I1OZ8p+vfgjffzFXc9x95/9KXjdCXuo5O3NqC8jX4HP/17W7ZiV5GNSJzVtD1Wor1ryl5SNX1sGAuEgB0++xq6Ba8+2stlO4CwkroWCfjh2yMYEKpKS+6oBH4jIuSKyDpiaT3JG6cgYhU4jc3dKzJNrureXtf5qUg5ftW8ATwBPAV8vS9ZGwWSMQoeWfTCtXPePyi3aclMOX7XfAFeUJDuj5CQ/6lNNdm05d1E78i/ejnTDqCImnEXKzL0UozqYcBYphb4HZJQWE45hFIEJxzCKwIRjGEVgwjEWDQvJL86EYyxoZsTinp7kgYce4JR7DPd05vtIlcaEYyxI/KHJxEt7Xv8YvtAZPN3H2TVwLf5Q9f0TTDjGgiQcib285+p/lGf270A1uqC64E04hlEEJhzDKAITjmEUgQnHWDAkmzBGC3xPvdJd1SYcY8GQbMKYr3CiU97EkCb5DG1SKoHN1x4qwwpKRH5fRF4Wkb0ickNJsjSMLAwODqL+IJ477+XGPcdmHdqkUAfUfJivPVQ2K6ivAlfF//68JFkaRhJhZytHg0HuHxjIamqREId/IvV1cf9EyXLIp8XJZg81Q5uqjqrqcWJuOAAjwHKgBfMcMMqAH6H38Ah7P31NwjormYRwwmdSXxcPRCEchA9OgdedsV4hzNceKpsV1I+B/fHyzdkqNF+12RPJ5auWnG+OKrKHztg3muLiVIiv2mx1Fppguq+atPcycyRybXOys09mCorH42F4eJgtG1fQyNnXxTUUSjj7BL76XV4/9Nuy+qrlsofKZgX1dWDGDOunwHMZm2a+arMmko+vWp62ZamhM/ZNarloX7W5ksgxL91XLd9tTs49PYVwXQPLlzVzYdd5NNSfTkv37MLOxlSfuXL4quWyh8pmBRUApoEzQGMe9RtGyYiGIrgPneCl2weyNnylcteZlz0U2a2gvgvsAV4i3qoYRiXJ5edWKkssSb8uqDQiMgG8W6bqVwGn51yqduJWM3Ytxu1R1c5sM6ounHIiIq+o6iVLJW41Yy+1uPbkgGEUgQnHMIqg1oVTrc6JanaKLLVtrkrcmr7GMYxyUestjmGUBROOYRRBTQpHRK4Ukf8VkRdE5H9EpGLDjojIVcmjcFcwpmdmzFURGRSRvgrFdonIs/F9/SsRqUjXsIisjMd8QUTc8f8DlYgNeY6Ps5gQkVXA3wHXq+qUiKwAKnISVZlR4EvAP1U47h8DP1LVh0XEATRVIqiquom9uoKIvKiqV1Ui7gy12OJ8DnhMVacAVNWjqvurnFMl+AlwnYjUz7lkaZkGLhORVaoaVlVPheNXhVoUzjrgBICI7Iy/oVrpb+FqEAF2AzdWOO73gWPAL0TkeRE5p8Lxq0ItCucEMfGgqv8JfJHY80xLgYeAWysZUFVDqvpNVd1CbFjLr1QyfrWoReH8FPgjEVkZL9fcddxsqOqHwCGgYsM9i0iPiDTEi+PU5jmVQc2dVKo6ISJ3Az8RkSgQBu6tcBo3icil8el7VPWFCsb+DvDlCsbbCjwpIj4gxCxv/dYa9uSAYRTBkmhWDaPUmHAMowhMOIZRBCYcwygCE45hFIEJxzCKwIRjGEXw/2m/IAjv31nbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 216x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from thymmatu.graphics.pro_plot import barPlotter\n",
    "\n",
    "#  OPTIONS  #\n",
    "Num = 1\n",
    "n_show_idx = 5\n",
    "figsize = ( 3, 1.5 )\n",
    "\n",
    "df = pd.read_csv(f'{DataPath}/CA-single_aa_usage.csv', index_col=0 )\n",
    "df = df.sort_values(by=\"gen\",ascending=False).head(n=n_show_idx)\n",
    "\n",
    "# BARPLOT\n",
    "columns = ['gen'] + HEADERS\n",
    "colors = [ EXP_KOL[c] for c in columns ] \n",
    "errorbars = [None] + [ c + \" err\" for c in HEADERS ]\n",
    "\n",
    "ax = barPlotter( df, colors=colors, grid=True, lw=0.75,\n",
    "                columns=columns, figsize=figsize, errorbars=errorbars, rotation=0 )\n",
    "\n",
    "#plt.legend(ncol=1, shadow=False, loc='center left', bbox_to_anchor=(1, 0.5), edgecolor='inherit', framealpha=1)\n",
    "\n",
    "for i in np.arange( 1, ax.get_xlim()[1]-1, 2 ).astype(int) :\n",
    "    ax.axvspan( i, i+1, 0, 1, alpha=0.5, \n",
    "                color=GREYS_DIC['silver'], ec=None, zorder=-10)\n",
    "\n",
    "plt.savefig( f\"{OutPath}/fig2C.pdf\", bbox_inches='tight' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 273.6x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "marker = { 'P' : 'H', '6' : 'o', '8' : 's', '9' : 'v' }\n",
    "Perc = pd.read_csv( f\"{DataPath}/pca-principal_percentuage.csv\",sep=',')\n",
    "Perc = Perc[ np.logical_and(Perc.Chain==\"CA\",Perc.Gene==GENE) ]\n",
    "\n",
    "# load erez PCA\n",
    "PCA_erez = pd.read_csv( f'{DataPath}/CA-V-pca_export.csv', index_col=0 )\n",
    "\n",
    "# define colors\n",
    "PCA_erez['color'] = [ EXP_KOL[ CC_to_HEAD[cc] ] if cc != 'Pgen' else EXP_KOL['gen'] for cc in PCA_erez['phenotype']  ]\n",
    "\n",
    "# define unique labels\n",
    "PCA_erez['aux'] = [ CC_to_HEAD[cc] if cc != 'Pgen' else CC_to_HEAD['gen'] for cc in PCA_erez['phenotype'] ]\n",
    "PCA_erez['label'] = '_'\n",
    "PCA_erez.at[PCA_erez['aux'].drop_duplicates().index, 'label'] = PCA_erez['aux'].drop_duplicates()\n",
    "PCA_erez.drop(columns='aux')\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (3.8, 3.8))\n",
    "\n",
    "# plt PCA scatter\n",
    "for _, row in PCA_erez.iterrows() :\n",
    "    ax.scatter( row['Dim.1'], row['Dim.2'], s=25, label=row.label, c=row.color, \n",
    "               ec='white', lw=.35, zorder=2, marker=marker[row['mouse']] )\n",
    "\n",
    "# plt options\n",
    "ax.set_xlabel(\"PCA1 $(\" + \"%.2f\" % Perc['Dim.1'].values + \"\\%)$\")\n",
    "ax.set_ylabel(\"PCA2 $(\" + \"%.2f\" % Perc['Dim.2'].values + \"\\%)$\")\n",
    "\n",
    "ax.minorticks_on()\n",
    "ax.axes.grid(which=\"major\", color=GREYS_DIC[\"silver\"], zorder = -5)\n",
    "\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "plt.savefig( f\"{OutPath}/fig2D.pdf\", bbox_inches='tight' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### insert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load erez PCA\n",
    "vec_erez = pd.read_csv( f'{DataPath}/CA-V-loadings_export.csv', index_col=0 )\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (1,1))\n",
    "\n",
    "# circle and axis\n",
    "plt.axis((-1.1, 1.1, -1.1 ,1.1))\n",
    "ax.add_patch( plt.Circle((0, 0), 1, fill=False, lw=0.5, ec=GREYS_DIC[\"pidgeon\"]) )\n",
    "ax.axvline(x=0, lw=0.5, color=GREYS_DIC[\"pidgeon\"], ls=\"--\", zorder=-5)\n",
    "ax.axhline(y=0, lw=0.5, color=GREYS_DIC[\"pidgeon\"], ls=\"--\", zorder=-5)\n",
    "\n",
    "\n",
    "# plt vector arrows\n",
    "prop = dict(arrowstyle=\"->,head_width=0.2,head_length=0.3\",\n",
    "            lw=0.5, shrinkA=0, shrinkB=0)\n",
    "for _, row in vec_erez.iterrows() :\n",
    "    norm = np.sqrt(np.sum(np.power(row[['x','y']],2)))\n",
    "    norm = 1\n",
    "    x = row['x'] / norm\n",
    "    y = row['y'] / norm\n",
    "    ax.annotate(\"\", xy=(x,y), xytext=(0,0), arrowprops=prop)\n",
    "\n",
    "# hide spines and ticks but not grid\n",
    "_ = [ ax.spines[s].set_visible(False) for s in ax.spines ]\n",
    "ax.minorticks_on()\n",
    "ax.tick_params(which='both', direction='out', length=0, width=0 )\n",
    "ax.get_xaxis().set_ticklabels([])\n",
    "ax.get_yaxis().set_ticklabels([])\n",
    "ax.axes.grid(which=\"both\", lw=.5, color=GREYS_DIC[\"silver\"], alpha=0.5, zorder = -10)\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "plt.savefig( f\"{OutPath}/fig2D-insert.pdf\", bbox_inches='tight' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "1ca6294cbb32705ba8e9d5f1c61ac24f2b2b51f4ed14a5137959c5b73ca963d8"
  },
  "kernelspec": {
   "display_name": "Python 3.7.4 ('base': conda)",
   "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": "3.7.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}