0_hAMP_properties.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tqdm\\std.py:648: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n"
     ]
    }
   ],
   "source": [
    "from common_dirs_fns import *\n",
    "from propy_functions import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "import subprocess\n",
    "from Bio.SeqUtils.ProtParam import ProteinAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify matplotlib formatting\n",
    "%matplotlib inline\n",
    "mpl.rcParams['svg.fonttype']='none'\n",
    "mpl.rcParams['font.sans-serif'] = 'Arial'\n",
    "mpl.rcParams['axes.linewidth'] = 1.\n",
    "mpl.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams.update({'font.size': 7})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify figures path and import human AMP information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify directory for figures corresponding to library properties\n",
    "library_props_figs_path = figures_path+'library_properties/'\n",
    "\n",
    "# Import human AMP peptide information scraped from APD3\n",
    "peptides_df = pd.read_excel(peptides_df_path,index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n",
      "0.11\n",
      "0.12\n",
      "0.13\n",
      "0.13999999999999999\n",
      "0.14999999999999997\n",
      "0.15999999999999998\n",
      "0.16999999999999998\n",
      "0.17999999999999997\n",
      "0.18999999999999995\n",
      "0.19999999999999996\n",
      "0.20999999999999996\n",
      "0.21999999999999995\n",
      "0.22999999999999995\n",
      "0.23999999999999994\n",
      "0.24999999999999992\n",
      "0.2599999999999999\n",
      "0.2699999999999999\n",
      "0.2799999999999999\n",
      "0.2899999999999999\n",
      "0.29999999999999993\n",
      "0.30999999999999994\n",
      "0.3199999999999999\n",
      "0.32999999999999985\n",
      "0.33999999999999986\n",
      "0.34999999999999987\n",
      "0.3599999999999999\n",
      "0.3699999999999999\n",
      "0.3799999999999999\n",
      "0.3899999999999999\n",
      "0.3999999999999998\n",
      "0.4099999999999998\n",
      "0.4199999999999998\n",
      "0.4299999999999998\n",
      "0.43999999999999984\n",
      "0.44999999999999984\n",
      "0.45999999999999985\n",
      "0.46999999999999986\n",
      "0.47999999999999976\n",
      "0.48999999999999977\n",
      "0.4999999999999998\n",
      "0.5099999999999998\n",
      "0.5199999999999998\n",
      "0.5299999999999998\n",
      "0.5399999999999998\n",
      "0.5499999999999998\n",
      "0.5599999999999997\n",
      "0.5699999999999997\n",
      "0.5799999999999997\n",
      "0.5899999999999997\n",
      "0.5999999999999998\n",
      "0.6099999999999998\n",
      "0.6199999999999998\n",
      "0.6299999999999997\n",
      "0.6399999999999997\n",
      "0.6499999999999997\n",
      "0.6599999999999997\n",
      "0.6699999999999997\n",
      "0.6799999999999997\n",
      "0.6899999999999997\n",
      "0.6999999999999996\n",
      "0.7099999999999996\n",
      "0.7199999999999996\n",
      "0.7299999999999996\n",
      "0.7399999999999997\n",
      "0.7499999999999997\n",
      "0.7599999999999997\n",
      "0.7699999999999997\n",
      "0.7799999999999997\n",
      "0.7899999999999996\n",
      "0.7999999999999996\n",
      "0.8099999999999996\n",
      "0.8199999999999996\n",
      "0.8299999999999996\n",
      "0.8399999999999996\n",
      "0.8499999999999996\n",
      "0.8599999999999995\n",
      "0.8699999999999996\n",
      "0.8799999999999996\n",
      "0.8899999999999996\n",
      "0.8999999999999996\n",
      "0.9099999999999996\n",
      "0.9199999999999996\n",
      "0.9299999999999996\n",
      "0.9399999999999996\n",
      "0.9499999999999995\n",
      "0.9599999999999995\n",
      "0.9699999999999995\n",
      "0.9799999999999995\n",
      "0.9899999999999995\n"
     ]
    }
   ],
   "source": [
    "# Generate a fasta file for all peptides in stats_table as an input for usearch\n",
    "to_fasta(peptides_df,\n",
    "         'Sequence',\n",
    "         'Sequence',\n",
    "         analysis_path + 'hAMP_clusters/hAMPs_table.fasta')\n",
    "\n",
    "# Cluster peptides using usearch\n",
    "linkages = np.arange(0.1, 1., 0.01)\n",
    "cluster_sizes = []\n",
    "\n",
    "clusters_dict = {}\n",
    "\n",
    "for linkage_num in linkages:\n",
    "    print(linkage_num)\n",
    "    prefix = '\"' + analysis_path + 'hAMP_clusters/hAMPs_table'\n",
    "    infile = prefix+'.fasta\"'\n",
    "    mxfile = prefix+'_mx.txt\"'\n",
    "    outfile = prefix+'_'+'{:.2f}'.format(linkage_num).split('.')[1]+'_clusters.txt\"'\n",
    "    subprocess.run(usearch_path+' -calc_distmx '+infile+' -tabbedout '+mxfile)\n",
    "    \n",
    "    # Average linkage uses distance between cluster centers\n",
    "    subprocess.run(usearch_path+' -cluster_aggd '+mxfile+' -clusterout '+outfile\n",
    "                   +' -id '+str(linkage_num)+' -linkage avg')\n",
    "\n",
    "    # Read back cluster information and add it to stats_tablel\n",
    "    clusters = pd.read_csv(outfile[1:-1], sep=\"\\t\", header=None)\n",
    "    clusters.columns = ['ClusterNum','Query']\n",
    "    clusters = clusters.set_index('Query')\n",
    "    \n",
    "    clusters_dict[np.round(linkage_num, decimals=2)] = clusters\n",
    "    cluster_sizes.append(len(clusters['ClusterNum'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Threshold of 0.7 yields reasonable clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x18cd805ef88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot number of thresholds per linkage threshold\n",
    "\n",
    "plt.figure(figsize=(2., 2.), dpi=200)\n",
    "\n",
    "plt.plot(linkages, cluster_sizes)\n",
    "plt.xlim([0., 1.05])\n",
    "plt.xlabel('Average linkage\\ndistance threshold')\n",
    "plt.ylabel('Number of clusters\\nfor given threshold')\n",
    "plt.axvline(0.7, zorder=-1, color='k', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0\n",
      "human neutrophil peptide-1\n",
      "ACYCRIPACIAGERRYGTCIYQGRLWAFCC\n",
      "human neutrophil peptide-2\n",
      "CYCRIPACIAGERRYGTCIYQGRLWAFCC\n",
      "human neutrophil peptide-3\n",
      "DCYCRIPACIAGERRYGTCIYQGRLWAFCC\n",
      "\n",
      "\n",
      "Cluster 1\n",
      "human LEAP-1\n",
      "DTHFPICIFCCGCCHRSKCGMCCKT\n",
      "Hepcidin\n",
      "ICIFCCGCCHRSKCGMCCKT\n",
      "\n",
      "\n",
      "Cluster 2\n",
      "Human TC-2\n",
      "NLAKGKEESLDSDLYAELRCMCIKTTSGIHPKNIQSLEVIGKGTHCNQVEVIATLKDGRKICLDPDAPRIKKIVQKKLAGDES\n",
      "Thrombocidin-1\n",
      "AELRCMCIKTTSGIHPKNIQSLEVIGKGTHCNQVEVIATLKDGRKICLDPDAPRIKKIVQKKLAGDES\n",
      "\n",
      "\n",
      "Cluster 3\n",
      "Beta-amyloid peptide (1-40)\n",
      "DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVV\n",
      "Beta-amyloid peptide (1-42)\n",
      "DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA\n",
      "\n",
      "\n",
      "Cluster 4\n",
      "Defensin DEFB126\n",
      "NWYVKKCLNDVGICKKKCKPEEMHVKNGWAMCGKQRDCCVPADRRANYPVFCVQTKTTRISTVTATTATTTLMMTTASMSSMAPTPVSPTG\n",
      "hBD-26\n",
      "WYVKKCLNDVGICKKKCKPEEMHVKNGWAMCGKGRDCCVPAD\n",
      "\n",
      "\n",
      "Cluster 5\n",
      "LL-23\n",
      "LLGDFFRKSKEKIGKEFKRIVQR\n",
      "TLN-58\n",
      "TLNQARGSFDISCDKDNKRFALLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES\n",
      "human LL-29\n",
      "LLGDFFRKSKEKIGKEFKRIVQRIKDFLR\n",
      "ALL-38\n",
      "ALLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES\n",
      "human KR-20\n",
      "KRIVQRIKDFLRNLVPRTES\n",
      "Human KS-27\n",
      "KSKEKIGKEFKRIVQRIKDFLRNLVPR\n",
      "LL-37\n",
      "LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES\n",
      "human RK-31\n",
      "RKSKEKIGKEFKRIVQRIKDFLRNLVPRTES\n",
      "human KS-30\n",
      "KSKEKIGKEFKRIVQRIKDFLRNLVPRTES\n",
      "\n",
      "\n",
      "Cluster 6\n",
      "SgII peptide A\n",
      "KQEGRDHDKSKGHFHMIVIHHKGGQAHHG\n",
      "SgI-29\n",
      "HNKQEGRDHDKSKGHFHRVVIHHKGGKAH\n",
      "\n",
      "\n",
      "Cluster 7\n",
      "CXCL1\n",
      "ASVATELRCQCLQTLQGIHPKNIQSVNVKSPGPHCAQTEVIATLKNGRKACLNPASPIVKKIIEKMLNSDKSN\n",
      "CXCL2\n",
      "APLATELRCQCLQTLQGIHLKNIQSVKVKSPGPHCAQTEVIATLKNGQKACLNPASPMVKKIIEKMLKNGKSN\n",
      "CXCL3\n",
      "ASVVTELRCQCLQTLQGIHLKNIQSVNVRSPGPHCAQTEVIATLKNGKKACLNPASPMVQKIIEKILNKGSTN\n",
      "\n",
      "\n",
      "Cluster 8\n",
      "Catestatin\n",
      "SSMKLSFRARAYGFRGPGPQL\n",
      "Cateslytin\n",
      "RSMRLSFRARGYGFR\n",
      "\n",
      "\n",
      "Cluster 9\n",
      "RNase 8\n",
      "KPKDMTSSQWFKTQHVQPSPQACNSAMSIINKYTERCKDLNTFLHEPFSSVAITCQTPNIACKNSCKNCHQSHGPMSLTMGELTSGKYPNCRYKEKHLNTPYIVACDPPQQGDPGYPLVPVHLDKVV\n",
      "RNase 7\n",
      "KPKGMTSSQWFKIQHMQPSPQACNSAMKNINKHTKRCKDLNTFLHEPFSSVAATCQTPKIACKNGDKNCHQSHGAVSLTMCKLTSGKYPNCRYKEKRQNKSYVVACKPPQKKDSQQFHLVPVHLDRVL\n",
      "\n",
      "\n",
      "Cluster 10\n",
      "human Histatin 7\n",
      "RKFHEKHHSHRGY\n",
      "human Histatin 4\n",
      "RKFHEKHHSHRGYRSNYLYDN\n",
      "Human histatin 9\n",
      "RKFHEKHHSHRGYR\n",
      "human Histatin 6\n",
      "DSHAKRHHGYKRKFHEKHHSHRGYR\n",
      "human Histatin 5\n",
      "DSHAKRHHGYKRKFHEKHHSHRGY\n",
      "human Histatin 8\n",
      "KFHEKHHSHRGY\n",
      "human Histatin 1 (XXP)\n",
      "DSHEKRHHGYRRKFHEKHHSHREFPFYGDYGSNYLYDN\n",
      "human Histatin 2\n",
      "RKFHEKHHSHREFPFYGDYGSNYLYDN\n",
      "human Histatin 3\n",
      "DSHAKRHHGYKRKFHEKHHSHRGYRSNYLYDN\n",
      "\n",
      "\n",
      "Cluster 11\n",
      "HD-5(7-32)\n",
      "TGRCATRESLSGVCEISGRLYRLCCR\n",
      "human defensin 5\n",
      "ATCYCRTGRCATRESLSGVCEISGRLYRLCCR\n",
      "HD-5(1-9)\n",
      "ATCYCRTGR\n",
      "HD-5(1-13)\n",
      "ATCYCRTGRCATR\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Threshold of 0.7 yields reasonable clusters\n",
    "cluster_i = clusters_dict[0.7]\n",
    "\n",
    "for cn in cluster_i['ClusterNum'].unique():\n",
    "    if len(cluster_i[cluster_i['ClusterNum']==cn])<2:\n",
    "        continue\n",
    "    print('Cluster ' + str(cn))\n",
    "    for c in cluster_i[cluster_i['ClusterNum']==cn].index:\n",
    "        print(peptides_df.set_index('Sequence').loc[c, 'SimpleName'])\n",
    "        print(c)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate human AMP properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataframe to store AMP properties\n",
    "hAMP_df = peptides_df.copy()\n",
    "hAMP_df = hAMP_df.set_index('Sequence')\n",
    "hAMP_df['Cluster'] = hAMP_df.index.map(lambda x: clusters_dict[0.7].loc[x, 'ClusterNum'])\n",
    "\n",
    "# Charge\n",
    "hAMP_df['Charge'] = hAMP_df.index.map(calculate_charge)\n",
    "\n",
    "# Grand average of hydropathicity\n",
    "hAMP_df['GRAVY'] = hAMP_df.index.map(lambda x: ProteinAnalysis(x).gravy())\n",
    "\n",
    "# Length\n",
    "hAMP_df['Length'] = hAMP_df.index.map(lambda x: len(x))\n",
    "\n",
    "# Only AMPs <= 104 aa in length are in the library\n",
    "hAMP_df['InLibrary'] = hAMP_df['Length'] <= 104\n",
    "\n",
    "# Save new dataframe to Analysis\n",
    "hAMP_df.to_excel(analysis_path+'human_AMP_properties.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Lp5PPvmEUaNGcfTRRzNt2rR9ThQbVI8//jiNGjVKdhiVghKSpIzCk28eyvsIkhUrVpCbmxt3fr/S+OSTTwC45pprGDlyZHmGljCDBg1KdgiVhk7ZiUjSRCaRzczMTHIkEgRKSJIySrq+s2TJEsaNG0fz5s3JyMigTZs23H333eWyj4j8/Hx+85vfUL9+fWrVqkWXLl147bXXYtrk5ORw2GGH8Y9//IMjjzySGjVqRP/63r17N1OnTqVjx47Url2b9PR0jjjiCEaMGBEzqevixYsxM+677z66d+9ORkYGzZo1Y8SIEZgZzzzzTLHY5s6di5klfAh3ixYtGDp0KABDhw7FzFi8eHH0c5k7dy6dOnWKfiY7d+4E4N1336Vnz57UrVs3WnfbbbdF6yPMjFGjRvHUU09x0kknUb16dZo3b86kSZNwd5599ln+67/+ixo1anDMMccc8MwiRa8hRaxatYoLLriAWrVqUbduXfr168eqVauK9cEFF1zA5MmTycrKok6dOtE5CDdu3MiYMWM49thjqVGjBjVq1KBdu3bccccdMRPGTpw4ETNj4cKFtG3bloyMDLp06cJZZ51F1apVWb9+fbHYfv3rX5OWlsY333xzQO+5ouiUnaS8oUOHUqtWLUaPHk21atWYMWMGo0ePpk6dOtEvzIN1yy230LBhQ8aNG0dBQQHTp0+nR48ePP/88/Tq1SvabufOnfTv35/rr7+erKwsjjzySCA0s/QLL7xATk4OI0aMYOfOnSxcuJAHH3yQr776ildeeSVmfzfeeCNnnnkm99xzD+vWrWP48OE89NBDPP744zErqwI89thj1KlTh4svvrhc3mtp/eUvf2HhwoU88MADXHnllfzyl7+kbdu20cEAI0aMoHfv3gwfPpz8/HyqV6/OvHnzuOyyy2jQoEF0BdxXX32ViRMn8tJLL7Fo0aKYWbz//ve/8+STT3LNNddw5ZVXMnPmTH7/+9/z/vvv8/bbb0dPFd53333RP0r69etXLu/voosuokOHDtx1112sXbuW6dOn8/rrr7Ns2TJatGgRbbdkyRI++ugjbr/9dvLy8ujWrRtbt27llFNOYfPmzVx11VXRVYEfe+wxxo8fT1paGr/97W9j9te3b1+GDx9O69atycjIoFq1aixZsoSnnnoqZlXgXbt2MW/ePLp160bz5s3L5b2Wm/KaFE8PTa4a9MlVi06WGplY88QTT/Rdu3ZFy7/++ms3M+/cufM+txeZqLXwZJcl7SM7O9s3b94cLc/NzfXatWt7ixYtfO/eve7+86Ss48aNi9lPZFLNa665plgMHTt2dMC///57d/95stgjjjjCt2/fHtO2W7duXq1aNf/uu++iZevXr/e0tDQfMWLEPt9rSQ52ctV4k5tGyjp16uQFBQXR8q1bt3pWVpY3aNDAN27cGLOdW2+91QGfOHFitIzwpKzvvfdetCzSl2YWM8nqZ5995oAPGjSozPFSaLJe958/xwsvvDD62bq7/+1vf3PABw8eHC078sgjHfAFCxbE7Ofuu+92oNjEq5s3b/b09HRv3759tCwyOe2ll14a0/bHH3/0OnXq+EknnRRTPm/ePAf8iSee2Od73Z+KmFxVp+wk5fXt25f09PToz82bN6dRo0bFFmQ7GKNHjyYrKyv6c9OmTRk8eDBr164ttnZOz549Y34+8cQT+eGHH7jzzjtjyr/99tvoNguv3QOhwRVF1/sZNmwYu3fvjlk/ac6cOezdu7fcjgTL04UXXhhz+vPVV19ly5YtXHvttTRs2DCm7e9+9ztq1arF008/HVN+1FFHxSwv365dOyC07PgvfvGLaHlkGfLwZKHlYvz48TErEV944YW0a9eOF154IWb9pIyMDLp37x7z2muvvZaNGzfSu3fvmPK8vDwyMzOLfd5Q/PemZs2a9OvXj3/96198+umn0fJHH32UzMxMLrroooN6fxVBCUlSXuPGjYuVZWRkRJcJLw+RL8LCWrduDVDsukJJ8Tz33HMMHjyY008/nSZNmtCoUaPodaiiy1vH28bFF19MVlYWc+bMiZY9+uijHHvssZx22mllf1MVrOh7iCxmeNxxxxVrW716dY455hjWrFmzz21Uq1YNgCZNmsSUR9ZhKs9lwtu2bVusrHXr1sVW/61fv37cdaDS0tKYOnUqffr0oUOHDmRmZtKqVSu+++67uHHG+8yHDRsGhIamQ+i61Msvv0z//v1LXKAwmZSQJOUV/is2kfuIfKkU/TIq+vMPP/zA6aefTk5ODl999RUdOnRgwoQJLFu2jIEDB8bdX7wvuOrVq3PZZZfx3nvvsXLlSj766COWL18ec2NvkBR9Dx5eKqekm5v37t1LRkZGTFkkARVVnjdIlyQtLa1YWbzPPN5ntXz5clq3bh0drHHuuedy77338uWXX5Z43Sfedk499VTatWvHE088gbvzxBNPsGfPnuB+5skOQCQVFF2qHIiuFBs5UirJtGnTWLZsGTNnzix2r05ZTysOGzaM++67j2eeeYb8/HzS0tIYPHhwmbaRLEcffTQQ+rIueiprx44dfPnll9E2QbB69epiR3Off/45devWpV69evt87ejRo9myZQvLly+POdLavXs3eXl5xU5Z7svQoUMZN24cS5cu5dlnn6VNmzaceuqpZXszCaIjJJEEuP/++9m1a1f05zVr1vD444/Tpk0b2rdvv8/X5uXlAXDCCSfElC9dupQlS5YAxAwD3pdf/OIXnHDCCcyfP58XXniB7t27k52dXZa3EmP8+PH8z//8D+PHjz/gbZTWueeeS506dZg2bVrMKS+AO++8k+3bt9OnT58Kj6O0it468PTTT7NixQouvfTS/R6h5eXlUatWLY466qiY8mnTprFjx45Sf94Al19+OdWqVWPmzJm89957gbxeGKEjJJEE2LBhA6effjrDhg0jLy+P6dOn4+7MmjVrv19OPXv2ZNq0aQwcOJCrr76azMxMli1bxqOPPkrVqlXZvXs3W7ZsKXUsQ4cOZcyYMQBMmDDhoN7XlVdeeVCvL4vMzExmzJjB5ZdfzgknnMDIkSNp2LAhr7/+OvPnz6dDhw6MGzcuYfHsz1//+lc2bdpEjx49+Pjjj5k5cyYtW7Zk0qRJ+31tr169uP322+nevTv9+/enoKCAhQsXsmDBAmrUqFGmmTEaNmzI+eefz+OPPx74I2IdIYkkwIwZM2jfvj233norU6ZM4eSTT+af//wnZ5xxxn5f27VrV+bOnUtWVhYTJ07k5ptv5oMPPmDSpEk899xzALz00kuljmXw4MGkp6dz+OGHx9wDdSgYOHAgb7zxBieffDJ33303Y8eOZcWKFUyaNIm33347UBfqX3vtteiowLlz53L55ZezdOnSUi1m+Pvf/57bb7+d3NxcxowZw+TJk/nxxx+ZP38+Y8aMIT8/n7fffrvUsQwfPhyAHj16FBvQESQWuVAoFcfM1gHZ2dnZrFu3LtnhlIuxY8fut82B3vkuFWvTpk00adKEkSNHMm3atGSHIwnw4osvcuGFF/Lcc8+V2w3QzZo1iwyTz3X3ZuWxTZ2yE0kxM2bM4KeffiqXyUzXr1/P3r17SUtLC/Rf3qnM3bnnnnto2rRpsXuVgqbCE5KZPQzc7+7vlVDfDbjR3XtUdCwiqWzw4MHk5eXx8ssv06dPn7j385RVx44dyc3NpTId/VcWq1ev5tZbb2XVqlV8+OGHTJ8+Pe7Q8CBJxDWkHOCofdR3CT9EpAJt2LCBJUuW0KtXL2bNmpXscKSCZWVl8eabb7J69WpuueUWrr766mSHtF/lni7N7GjgU6DwHWlzzGxOCS8BWF7ecYhIrFdffTXZIUgC1atXj//85z/JDqNMyj0huftqMxsPnBcuOhP4AtgYp/necPld5R2HiIgcWirkhKK7TwGmAJhZATDJ3Z+siH2JiEjlUOFXuNxd9zolgIZhB5c+G5HSSdiQi/C1pcZA8RkHAXd/M1GxiIhI8CRi2HcjYC6ha0n7EjdRHcR+TwHeAbq5++IidUcAk4BzgEzgE+AP7v638oxBRERKLxFHSH8kNKz7deADYNe+mx88M2sFzCfOsHYzawy8CdQFpgG5wHDgBTMbqGtdIiLJkYiE9CvgKXePv3BLOTOzi4CHgMNLaDIBOAI4w93fCb9mNvAucLeZveDuPyYiVhER+VkiEtJhwKIE7AczWwCcD/wbeAm4rEh9GjAIWBpJRgDuvtPM7gYeBi4AYtdBFpG4Xn/9dfbs2RP4GQDk0JCI36JPgWMTsB+ANsBvgf8FbolTfxyhBPlunLrI1EanoIQkUirHHpuo/9qSChKRkO4EZpvZPHdfVsH7aufuu6DEJYojM9J+HacuMhFXy7Ls0MxKM8ttuQ7YEBGpjBKRkM4CvgfeM7OVwAagoEgbd/duB7ujSDLah8zwc36cuu3h51pl3O03ZWwvIiJxJCIhjSr079bhR1GJWpTJijzHq9uboFgkTDeOHrqefPJJtm/fTs2aNRkwYECyw5FDXKrN1LAt/FwzTl2krPRrA4c0L0WbDwjdFCxSqdx4443R5SeUkORgpdrQmDXh53jXfSJlZToF5+77XQTGzHTUJSKyH4mYqeHy0rRz98cqOhbgc0JHQKfEqYuUvROnTkREKlgijpAeoXTXiCo8Ibn7HjN7GhhhZp0L3RhbHbiO0FIYCys6DhERKS4RCWloCfttDPQjNL3PsATEETEB6AksNLP/JZSEhgPtgf7uvjOBsYiISFgiBjU8WlKdmU0F3ic0vdD7FR1LOJ4NZtaZ0Bx71xFa2fYT4AJ3/0ciYhARkeKSOgIufN/QbGBIBWx7ortb0Zm+w3Vr3L2fu9d199ru3lnJSEQkuYIwym4v0CjZQaSCRN/vU5r9iYhEJPUIycyaA1cDXyYzDhERSb5EDPsuKdnUABoQSorXVHQcIlL+GjduHPMscjASccquCvGHfecDK4CH9zXwQUSCa9myip4vWVJJIkbZtajofYiIyKEvodeQLKSBmdVJ5H5FRCT4EpKQzKyemT0EbCa0/MRmM9tsZrPMrH4iYhARkWBLxKCGLELzw7UCVgFvEFqwri2hGRLONrOT3f2Hio5FRMrXyJEj2bRpE3Xr1uX+++9PdjhyiEvEoIbfAccAI9z9ocIVZjYMmEVoufF4S46LSIAtWLAguvyEyMFKxCm7i4DHiyYjAHd/mNCkqhcnIA4REQmwRCSkZsDSfdS/CxyRgDhERCTAEpGQNgNH7qO+JaDrRyIiKS4RCekN4Goza120wszaAFcBixMQh4iIBFgiBjXcRmj9oX+Z2ePAvwnN3HA8MCj87zsSEIeIiARYImZq+NzM/ht4GBhRpPpL4Ap3X17RcYiISLAlZPkJd38rfMruv4CjAQNWA//n7gWJiEFERIItYeshubsDHwEfmVkj4HslIxERiaiwhBS+6fUaoEOcxDMVONfMJri7bu8OEC2qVzap3l+XXXYZmzdv5vDDD092KFIJVEhCMrO7gHHAHkJTBn1RpMluoC4ww8zauft1FRGHiFSsP/3pT8kOQSqRch/2bWYXEEpGi4HW7l40GeHuwwhdS/oAGGVm55Z3HCIicmipiPuQrgbWAj3cfW1Jjdz9G6AHkAeMqoA4RETkEFIRCakj8Ki7795fQ3ffCjwKnFoBcYiIyCGkIhJSbWBdGdqvADIrIA4RqWBt2rShTp06tGnTJtmhSCVQEQlpPdCkDO0bAN9WQBwiUsHy8/PZtm0b+fn5yQ5FKoGKSEgfAr3K0P5iQkdJIiKSwioiIT0CdDCzMftrGG5zcvg1IiKSwsr9PiR3f9HMngemmtkpwD3Ae+6+B8DMqgGdgd8AlwBLgLnlHYdULqW5AXXq1KkJiEQOFYsWLdpvm65duyYgEimtipqpIQfYC/QFLgX2mNn34f0dTujIzID5wLBIshIRkdRVIeshuXu+u/cD/ht4CvgKqAOkE7pedD/Qxd0vCQ/9FhGRFFehk6u6+yvAKxW5DxERqRwSsWKsiIjIfikhiYhIICRsPSQRqXxmzpzJjh07qFGjRrJDkUpACUlEDtgFF1yQ7BCkElFCkpQTxHuaSrvQn+61kspM15BERCQQdIQkIgfsww8/5Cm4J9oAAA2JSURBVKeffiI9PZ0OHTokOxw5xCkhicgB69WrF7m5uWRnZ7NuXVlWnREpTqfsREQkEJSQREQkEJSQREQkEJSQREQkEJSQREQkEDTKTiqN0t5cKqW3vz7dulWrx0j50RGSiIgEghKSiIgEghKSiIgEghKSiBywoUOHsnXrVj777LNkhyKVgAY1iMgBS09Pp06dOskOQyoJHSGJiEggKCGJiEgg6JRdwOneGimrRC72t2zZMiZOnEidOnW4/vrrD3p7ktqUkETkgH344YcsWbKE7OxsJSQ5aDplJyIigaCEJCIigaCEJCIigaCEJCIigaCEJCIigaCEJCIigaCEJCIigaD7kETiCOoNyeUZV1DfY2ksWrQoUNsB6Nq1a7ltK1UpIYnIAWvYsCHHH388DRo0SHYoUgkoIYnIAbvooovKZQoiEdA1JBERCQglJBERCQQlJBERCQQlJBE5YPPnz+e0006jZ8+eyQ5FKgENahCRA/btt9/y5Zdfkp2dnexQpBJQQhKRg7Zr165yuacnFe7lKU0/pUI/xKNTdiIiEggpnZDMrJ6Z3WNmX5nZDjP7f2Y2LNlxiYikopQ9ZWdmtYBXgPbAvcDnwKXAQ2bW2N3/kMz4RERSTcomJGAUcDJwmbvPBTCzB4B/ABPM7HF3/yaZAYqIpJJUPmU3BMiNJCMAd3dgCpAODEhWYCIiqSglj5DMLBNoA8yPU/1e+PmUUm6rWSmapZUyNBGRlGWhg4LUYmbtgE+Bv7j7mDj1m4Cv3P2/SrGtUndglSpVaNKkSZli3bp1a5naiyTSjz/+iLtTpUoV6tate9Dby8jIKFW7Xbt2HfS+ylt5xl7abSXT+vXrKSgoANjt7unlsc2UPEICMsPP+SXUbwdqlfdOCwoKyM3NLe/NiiRdQUEBeXl5yQ5DkqPcLv2kakKyIs/x6veWclvNS9FmBaHrUgXAt+GyNKBx+N8byrA/KZn6tGKoX8tfZejThoSSUbkdrqZqQtoWfq5ZQn1NoFQj7Nx9XSmaFdtP+NpTZB8dS7kd2Qf1acVQv5Y/9Wl8qTrKbg3gQLEBCeH7k7IoZUISEZHykZIJyd3zgc+ATnGqI6Pr3klcRCIikpIJKWwOcKSZ9Y8UmJkB4widE51b0gtFRKT8peo1JIC/AIOAR82sA6GBB32Bc4Bx7r4+mcGJiKSalE1I7r7DzM4C/gBcDtQGvgAud/fHkxmbiEgqStmEBODu3wEjwg8REUmiVL6GJCIiAZKSUweJiEjw6AhJREQCQQlJREQCQQlJREQCQQlJREQCQQlJREQCQQlJREQCQQlJREQCQQlJREQCQQlJREQCQQkpCcysnpndY2ZfmdkOM/t/ZjYs2XEdKszsQTPzEh45hdodZ2Z/NbNvzWybmb1uZqcnMfRAMbNTzGxveJLhonVHmNljZvYfM/vRzN41s54lbKezmb1mZpvNbIuZ/c3M2lX4Gwio/fTra/v43T2rSNuU69eUnlw1GcIr0r4CtAfuBT4HLgUeMrPG7v6HZMZ3iDgBWAv8Pk7dOwBm1hZ4G9gBTCO0bP1vgDfM7Fx3X5KYUIPJzFoB84nzR6mZNQbeBOoS6rtcYDjwgpkNdPcnC7XtQuj3eS2hmfOrAKOBpWZ2irt/XsFvJVD21a9hJwDLgLvj1H1WaDup2a/urkcCH8BNhJZP71+ozICFhBYGbJ7sGIP8IPQfczvw9H7aLQy3O6pQWT1CX66fEp7HMRUfwEXApvDvoQNnFam/DygAOhcqqw78C/gOqBUuM2A58B+gbqG2R4f7/h/Jfq8B69cm4fK79rOdlO1XnbJLvCFArrtHV6T10G/bFCAdGJCswA4RrYAahP7DxmVmjYD/Bua7+5eRcnf/HngQaEf85esrPTNbADwPrAeeilOfRmjhyqXu/k6k3N13Evqrvj5wQbi4I3AcMNvdNxVquxp4BuhhZk0q6K0Eyv76NeyE8HOJv7thKduvSkgJZGaZQBvgvTjVkbJTEhfRIenE8PNyADOrGf4SLSzSh+/GeX2q93Mb4LfAyYRWSS7qOOAwStd3p4afS2pbhdCXayrYX79C8d/dWmYW7zs4ZftVCSmxsgkdjn9dtMLdtwObgZaJDuoQE/lP/d9mthb4EdgeHrxwVLiuWfi5WD8D68LPqdrP7dz9TnffVUJ9WfpO/fyz/fUr/Py7O8TMNgD5wLbw4JEGhdqlbL9qUENiZYaf80uo3w7USlAsh6rIaY9TgUlAHtAZuA7obGad2Hc/bw8/p2Q/7+cLE8rWd+rnsFL0K/z8u3syMBbYCZxLaMXqU8KDFbaQwv2qhJRYVuQ5Xv3eBMVyqJoLfAT80d13hMv+ambvAs8BkwkNWoD4/RwpUz/Ht6/f0aJ9V5a2AjOB2sAUdy8Ilz1rZl8A/wOMA24lhftVp+wSa1v4uWYJ9TWBrQmK5ZDk7k+4+4RCyShS/jzwDdCDffdzpEz9HF9Z+k79XAbufq+7/7FQMoqYTijB9Aj/nLL9qoSUWGsIDftsVrQifH9SFqEvVTkwGwn9Bbom/HOxfi5Upn6Oryx9p34uB+7+E6Hrx7XDRSnbr0pICeTu+YRufos35DgycumdOHUCmFl9M/vYzJ6PU1eN0JDwVcAHhO6jiTeSTv28b58T+uu7NH23rxGLpxD64yveSLGUY2btzexTM5sWp64hoeH0q8JFKduvSkiJNwc40sz6RwrMzAidP95F6BqJxOHueYSue15oZh2KVN9C6GLwI+6+EXgN6FNo5B1mVg+4Avh/7v5/CQr7kOLue4CngV+aWedIuZlVJzRwZCOhm45x9w+AL4ArzKxuobZHA32Av4fv/ZJQsmkCXG5mRxSpuzP8/Aikdr9qUEPi/YXQjYePhr9UVwB9gXOAce6+PpnBHQKuBl4CXjezewnNvNAVuARYTKh/AW4AlgJvm9mfCSX73xCaDqdvgmM+1EwAegILzex/CSWh4YSmu+ofvkk2YhShBLU0/HlkAGMITdl0Y0KjDjB332Fmowj9Qfqumc0gdCTaC+gGPOHuzxR6SWr2a7KnikjFB9AAmEXoP/p24P+AwcmO61B5EBo2+wKhaVp2ETrN9DugepF2JwH/AH4AthA6ajo12fEH5QFMJM4UN+G6loSOlDYRusj+DnB+Cds5m9Dcd9sJTS00H2ib7PcX0H7tFv493EYoufyL0B9KVdSvHprPS0REJNl0DUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUlERAJBCUmkgpnZI2bmZnZWsmMpDTM7ptC/W4RjfySJIUmKUEISEQDMrI6ZvUVo9V2RhFNCEpGIusAZyQ5CUpcSkoiIBIISkkiAmFknM/u7mW0ys51mttzMbjCztEJtItd1xprZr8NtdprZf8xsmpnVLrLNamY23sxWhdv928yGmtmDZubhNjnAmvBLhsS75mVmV4b3tcvMcs3sbjM7rGJ7RFJJ1WQHICIhZtYTeBb4EpgC5APnAlOB083sEnf3Qi+5GqgN3Ad8A/QBrgEygSGF2s0HfhV+/l/geGAWsKVQmzeBMcCfgbeAB4DPgBrh+j7hbcwA1gMXA9cCDYABB/3mRQCL/f0WkfIWHqE2BDjb3ReX0KYm8BXwNdDZ3XcVqruD0ECDfu4+z8xaEDqa2Qm0cfevwu2qEEoiLYEsd99uZn2AZ4B73P3aQtscADwB4O4WLots91F3zylSthM4wd1XhsurAl8AzYDa7v7TAXeQSJhO2YkEwzlAfUJHSLXNrH7kATwdbnNxkde8FUlGAO5eAHwEVAPqhYv7hZ8nFX6huz8JrCxDfG9HklH49XuAZUB6oX2JHBQlJJFgODb8/AfguyKPT8J1LYq8ZkOc7USOrCLXnI4FNrv7t3Ha/rsM8cXb147wc0YZtiNSIl1DEgmGSAIZDywtoc22Ij8XlGK76fycpIraWYrXR+wtQ1uRA6KEJBIMkRFuO9z9tcIV4VFzPQgNJiirFcAFZna4u28uUndsvBeIJItO2YkEw8uEjoDGmFnRazK3EhqYcP4BbPdpwIDRhQvN7BzgpCJtI0dB+l6QpNARkkjiXG9m/UuoGwOMAmYDn5jZA8B/gK6EBia8T2jIdVk9BQwDxptZW+ANoDWhIeM7geqF2uYRSkpnmdkI4JUD2J/IAVNCEkmcC/dRd7O7P2ZmXwM3AtcRShZfERohN9XdfyzrDt29IHx/00SgP9Cb0Oi6oYTuIzqpUNsdZnYTcDNwD3AVoQQmkhC6D0mkEjOzusCPhe9rKlS3Akh39xYJD0wkDp0rFqncRgDbzaxL4UIzOwVoRckj+kQSTkdIIpWYmbUkdB/TNmAmsA44GhhJ6A/Sju6+KnkRivxMCUmkkjOz44HfAr8EGhK62fZV4HZ3X7Ov14okkhKSiIgEgq4hiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIICghiYhIIPx/jGkNnyHLh9cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2., 1.), dpi=200)\n",
    "bins = np.arange(hAMP_df['Length'].min(), hAMP_df['Length'].max()+1, 5.5)\n",
    "plt.hist(hAMP_df[hAMP_df['InLibrary']]['Length'], bins=bins, color='#737373')\n",
    "plt.hist(hAMP_df[~hAMP_df['InLibrary']]['Length'], bins=bins, color='#bdbdbd')\n",
    "plt.axvline(104, color='k', linestyle='--', linewidth=1.)\n",
    "text_offset=8\n",
    "plt.text(104-text_offset, 16, 'Included\\nin library', horizontalalignment='right', verticalalignment='top')\n",
    "plt.text(104+text_offset, 16, 'Excluded\\nfrom library', horizontalalignment='left', verticalalignment='top')\n",
    "plt.xlabel('Length')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig(library_props_figs_path+'Length.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot charge and GRAVY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEBCAYAAAA3ndFoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAewgAAHsIBbtB1PgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAdU0lEQVR4nO3de5gU1bnv8e87KAiKoOEqg8IWDKIc45WAhsGIl5iYHSMiinLRJHjY+KgnEA9qtp6YI5GQGM2OIgZEkS0XgzFKPMqORkhQJF7iJaJGcCMTLio6kYvoMO/5o6pxpqd7pge6q9dM/z7P00/N1FpV9XYzzG9W1epqc3dERESKrazYBYiIiIACSUREAqFAEhGRICiQREQkCAokEREJggJJRESCoEASEZEgKJBERCQICiQREQmCAklERIKgQBIRkSAokEREJAgKJBERCYICSUREgqBAEhGRICiQREQkCAokEREJwj7FLqAUmNk2oA1QA2wucjkiIvnQhWhQs9Pd98/HDk0fYV54ZlYNtCp2HSIiBbDL3fMyuNEIKRk1QKuysjK6d+9e7FpEstqwYQM1NTXoZ1Uak/pZIfr9lhcKpGRsBnp0796d9evXF7sWkazKy8uprKxEP6vSmNTPCnm8DKFJDSIiEgQFkoiIBEGBJCIiQVAgiYhIEBRIIiIShBYXSGY2wMweNLP3zOxTM3vHzH5hZh3S+h1lZr81s81m9rGZ/cHMTi5W3SLNyY033oiZYWbccccdDfbt3bs3ZsbQoUOTKS5hu3bt4tZbb+XII4+kbdu29OrVi+uuu44dO3Y0uu3YsWN3v47ZHnPmzCn8kwhEi5r2bWZfBJ4BqoFfAeuAQcAVwFfNbJC7bzOzI4E/ATuA24GPgX8DnjKz09396aI8AZFmaNGiRUyYMCFj27PPPss777yTbEEJmzBhAjNnzuS8887jyiuv5IUXXmDq1Kk8//zzPPbYY5hZ1m3Hjx/PsGHD6q2vqqri6quvpn379i02yDNpUYFEFC6tgS+7+6vxurvM7AXgNmAC8FPg50S38jne3dcAmNn9wMvAHWZ2tOsWFiKN6tOnD8uWLWPTpk107dq1XvuCBQvo0qULmze3zDtmPffcc8ycOZPx48czY8aM3et79+7Ntddey6JFixgxYkTW7QcNGsSgQYPqrR85ciTV1dXMmzePXr16FaL0ILWYU3Zm1hr4CrC8Vhil3BcvK8ysK3AW8FAqjADc/QPg10B/4KQEShZp9kaMGEFNTQ2LFy+u1+buLFq0iPPPP78IlSUjdTrt+9//fp31V111Ffvttx/33HNPk/e5ePFiFixYwLhx4zjrrLPyUWaz0WICieg03VHA9zK0pf502wUMjL9+NkO/lfFyYIa2jMysvLEHuo+dtFBf/epX6dy5M4sWLarXtnz5ciorK7nwwgszbvvJJ59w00030a9fP9q0aUOnTp0YPnw4r76a/vckvPTSS1x44YWUl5fTunVrDjzwQAYPHszChQvr9Bs6dChHH300L7/8MmeffTYdOnTggAMO4IwzzmDVqlV1+s6ZMwczY+zYsXv8/J999lkOPvhg+vbtW2d927ZtGTBgACtXrsyyZWY7d+5k8uTJHHTQQfzkJz/Z47qaqxZzys7da4C1WZonxcungPL463UZ+qXuldK7CYd+twl9RVqUVq1a8e1vf5tf//rX9U7bzZ8/n549ezJ48OB623366aecccYZPPPMM4wePZqrr76ayspKZsyYwcCBA1m6dOnu7VauXElFRQU9e/Zk4sSJdO7cmbfffpuZM2dywQUXUF5eXucYGzduZMiQIXz9619n2rRprF27lltvvZVhw4axbt06OnSI5jcNGTKEuXPncvjhh+/x81+/fj2HHnpoxrby8nJWrVpFVVXV7mM2ZtasWaxZs4apU6fSuXPnPa6r2XL3Fv0ALgGcKIAOAKbE35+WoW+fuG1mE/bvuT569OjhIiHr0aNHTj+rN9xwgwP+1FNP+ZNPPumA33HHHbvbq6urvUuXLj558mR3dwe8oqJid/u0adMc8IULF9bZ74YNG7xTp07ev3//3evOPfdcb9Omjf/jH/+o03fJkiUO+BVXXLF7XUVFhQN+yy231Ol70003OeAzZ87M7YXIUevWrf2UU07J2DZq1CgHvLKyMqd91dTU+BFHHOEdOnTwqqqqfJZZEKmfFWC95+n3dUs6ZVePmY0B7gG2Aee5+1YgNeUl09SX1LpdTThMzxweG5tcvEgzUVFRQdeuXeuctnvyySfZvHkzI0eOzLjNAw88QMeOHTn11FN5//33dz/22Wcfvva1r/G3v/2N1atXA/Dggw+ybt26Oncfr66uTt1pmq1bt9bb/8UXX1zn+xNPPBGIRk/55J//YZqxDaJRZC6WLl3Km2++yeWXX86BBx6YtxqbkxZzyi6dmf0Q+BFQBXzD3VMnkD+Ol+0ybJZaV5Xrcdy90Vsim1lTAk6kWSkrK2P48OHMmDGDzZs306VLFxYsWEDfvn057rjjMm7zxhtvsH379gZPS73zzjv069ePsrIytmzZws9+9jNee+011q5dy9tvv82nn34KsDuYauvWrVud79u0aQNE7xlqqqqqqnrvKWrbti0dOnSgffv2bN++PeN2qfW5nq77zW9+A8All1zS5BpbihYXSGa2LzATGAtUAme7+8u1uqSuM5VTX2qdrguJNMGIESP41a9+xeLFi7nssst46KGHmDhxYtb+u3btok+fPtx5551Z+xxzzDEAzJs3j9GjR9OlSxcqKioYOXIkAwYMoLy8fPfIJ11ZWf5O/lx55ZXce++9ddaNGTOGOXPm0Lt3b9aty3Q5Orq+1KlTJ/bbb79Gj+HuPPLIIwwYMICjjjoqL3U3Ry0qkMysFfAAcB7Re4rOdvfKtG6riD5QaiCQ/hbz1Oy6FYWsU6SlOeWUUzjkkENYuHAhPXv2ZMuWLVlP10H0Pp2NGzcydOhQ9tmn7q+hFStWsG3bNtq1a8cnn3zC+PHj6dOnD6tWrapzKuvPf/5zwZ5PbT/4wQ/qnQI85JBDABg4cCDPP/88a9eupXfvz+dCbd++nVdeeYUzzzwzp2O89dZbbNiwgVGjRuWv8GaopV1DuokojJ4DhmQII9x9E/BfwHAz+5fUejP7AvAd4K/u/mJC9Yq0CKnTdsuWLePOO+/kmGOO4cgjj8zaf/jw4Xz00UdMnz69zvrKykrOOeccLrroIsrKytixYwfbtm2jV69edcKourp697bV1dWFeVKx/v37M2zYsDqP/v37A+wOkFtuuaXONrfddhs7d+7MeUr5X/7yFwBOOOGE/BXeDLWYEZKZHQpMJpr1sRg4J8MtOza5+1Lg+0S3GPqTmd0K7CS6ddDBQPa3VYtIVhdccAG33347S5YsYerUqQ32veaaa3jkkUeYMmUKq1at4rTTTuPDDz9kxowZfPTRR8ybN4+2bdvStm1bhgwZwhNPPMG4ceM4+eST2bJlC/PmzWP16tWUlZXx0Ucf7VG9a9asYcWKFRx++OEZ75aQi8GDBzN27FjuuusutmzZwplnnslzzz3H3XffzTnnnMO3vvWt3X03bdrE0qVL6dq1K6effnqd/bzxxhsAJXVXhkxaTCABQ/n8+WR7R9nTwFJ3f9XMvgLcDPyQ6BTeX4Ax7p7pDbMizcakSZMa75RFVVXO83nqGTRoED179uTdd99t8HQdwAEHHMCf/vQnpk6dyoMPPsijjz5Kx44dOfbYY7nvvvs49dRTd/dduHAhU6ZM4fHHH+eBBx6gW7dunHDCCdx3331MmDCB5cuXs337dtq1yzRPKbtly5Yxbtw4xowZs8eBBHD33XfTp08fZs+ezcMPP0x5eTnXX389U6ZMqXMfu9dff51LLrmEioqKeoH03nvvAdCxY8c9rqMlsGxTFiV/zGw90KNHjx6sX9/opDyRvbI3gXTXXXexdetW9LMqjSkvL6eyshKg0t0zTRJrspZ2DUlERJopBZKIiARBgSQiIkFQIImISBAUSCIiEoSWNO1bRIpsb2b4AfXeKCulRSMkEREJggJJRESCoEASEZEgKJBERCQICiQREQmCAklERIKgQBIRkSAokEREJAgKJBERCYICSUREgqBAEhGRICiQREQkCAokEREJggJJRESCoEASEZEgKJBERCQICiQREQmCAklERIKgQBIRkSAokEREJAgKJBERCYICSUREgqBAEhGRICiQREQkCAUPJDObbWYDG2g/zcweL3QdIiIStiRGSGOBf2mgvSJ+iIhICdsn3zs0s8OB14B9a62+38zub2CzV/Ndh4jsuaqqKiZNmlTsMqTE5D2Q3P1tM/t34GvxqiHAG8CmDN13xetvyXcdIiLSvOQ9kADcfRowDcDMaoAfu/t/FuJYIiLSMhQkkGpzd83kExGRRhU8kFLia0vdgFaZ2t19WVK1iIhIeAoeSGbWFZhPdC2pIRmDSkRESkMSI6SfEE3r/gOwCtiZwDFFRKSZSSKQvg484O6jEjiWiIg0U0lMODgAeDKB44iISDOWRCC9BnwxgeOIiEgzlkQgTQXGm9kJCRxLRESaqSSuIQ0FPgBWmtlbwEagJq2Pu/tpCdQiIiKBSiKQJtb6+oj4kc4TqENERAKmOzWIiEgQErtTg4jkTnfallKUxJ0aRufSz93vK3QtIiISriRGSHPI7RqRAklEpIQlEUjjshy3G3AB0dTzSxOoQ0REApbEpIZ7s7WZ2XTgOaLbCz1X6FpERCRcRZ0B5+47gXuAMcWsQ0REii+EKdm7gK7FLkJERIqrqNO+zawnMAFYU4B9DwRWAKe5+x/T2g4FfgwMAzoArwA3u/vv8l2HiORub6e7T58+PU+VSDEkMe07W9i0BToTjdKuyPMx+wIPkWEEaGbdgGXAwcDtQCVwGfCwmY1y9//MZy0iIpKbJEZIZWSe9r0VeBOY3dDEh6Yys3OBWcBBWbrcABwKnOLuK+Jt7gGeBW4zs4fdfVu+6hERkdwkMcuuV6GPkWJmS4Czgb8B/w+4MK29FXAx8EwqjOIaPzGz24DZwDeABUnVLCIikUQnNViks5kdWKBD9AOuBY4jGn2lO4roAwOfzdC2Ml4OLExpIiLSkEQmNZjZF4BpwHlA+3jdP4EHgSnu/n6eDtU/nkqOmWVqL4+X6zK0rY+XvZtyQDMrb7wXrZqyTxGRUpTEpIaORLPd+gJ/B54i+gV9JNFkglPN7Dh3/+feHisVRg3oEC+3ZmjbHi/3b+Jh321ifxERySCJU3bXA32A77r7Ee5+rrt/0937At8hGpFMSaAOAEtbZmrblVAtIiJSSxKn7M4F5rr7rPQGd59tZl8Bvk0yofRxvGyXoS21rqqJ++yZQ59VRPfuExGRLJIIpHLgmQbanwVGJlAHwNp4mem6T2pdk07Bufv6xvqYmUZdIiKNSOKU3YfAYQ209wb2+vpRjlYTjYAyzaRLrVuRoU1ERAosiUB6CphgZkekN5hZP+B/An9MoA7cvZroPUZfMbPBterYD7gS2AQ8lkQtIiJSVxKn7P4P8E3gJTObS/SmVQeOJnqTqgM3JVBHyg1xPY+Z2c+JQugyYAAw0t0/SbAWERGJJXGnhtVmdhbRXRC+m9a8BviOu79a6Dpq1bMxHh39hGhUtC/RzVW/4e6/T6oOERGpK5E3xrr78viU3bHA4URTrN8GXnT3mgId80bgxixta4k+rVZERAKR2MdPuLsDLwAvmFlX4INChZGIiDQ/BZvUYGaXmtmLZpbpGNOB9WY2vlDHFxGR5qUggWRmtwC/JrqZad8MXT4j+jyiO+K7bIuISInLeyCZ2TeAyURTuY9w9zfS+7j7pUTXklYBE83s9HzXISIizUshRkgTgHeAM939nWyd3P1d4EzgfWBiAeoQEZFmpBCBdCJwr7t/1lhHd68C7gW+XIA6RESkGSlEILXn888WysWbfP6xECIiUqIKEUgbgO5N6N8Z2FyAOkREpBkpRCA9D/xrE/p/m8wfNy4iIiWkEIE0BzjezK5urGPc57h4GxERKWF5DyR3fxRYDEw3s/lmdrKZ7b4jhJnta2YVZraQ6A2yTwPz812HiIg0L4W6ddBYoo8CHwGcD1Sb2Qfx8Q4iCkIDHgIujT8WQkRESlhBAsndtwIXmNksYDRwEnAIUE10vehp4AF3X16I44tIaZo0adIebzt9+vQ8ViJ7oqA3V3X3J4AnCnkMERFpGZL4xFgREZFGKZBERCQICiQREQmCAklERIKgQBIRkSAk9hHmIiIh25sp46Bp4/mgEZKIiARBgSQiIkFQIImISBAUSCIiEgQFkoiIBEGBJCIiQVAgiYhIEBRIIiISBAWSiIgEQYEkIiJBUCCJiEgQFEgiIhIEBZKIiARBd/tu4XQH4z2zt6+blB79X9t7GiGJiEgQFEgiIhIEBZKIiARBgSQiIkFQIImISBAUSCIiEgRN+w5cKU8/LuXnLlKKNEISEZEgKJBERCQICiQREQmCAklERIKgQBIRkSAokEREJAia9i0N2pup17p7sUgyWsqdxjVCEhGRICiQREQkCAokEREJggJJRESCoEASEZEgKJBERCQIJT3t28y+ANwIfBPoArwJ3Obus4tZl4iUHt3dvoQDycz2B54ABgC/AlYD5wOzzKybu99czPpEREpNyQYSMBE4DrjQ3ecDmNlM4PfADWY2193fLWaBIiKlpJSvIY0BKlNhBODuDkwDWgMXFaswEZFSVJIjJDPrAPQDHsrQvDJeDsxxX+U5dGuVY2kiIiXLokFBaTGz/sBrwC/c/eoM7VuA/3b3Y3PYV84vYFlZGd27d29SrVVVVU3qH5IOHTrs1fbN+bk3V9u2bcPdMTP233//YpcjCdmT/6sbNmygpqYG4DN3b52POkpyhASkXv2tWdq3A3n/31hTU0NlZWW+dxusrVuzvbwSOnfXv18J2ct/67xd+inVQLK0Zab2XTnuq2cOfd4kui5VA2zOcb8tTSugW/z1RnJ/fUWv3Z7S67Zncn3duhCF0c58HbhUA+njeNkuS3s7IKcZdu6+Podu2Y5TMuJrbanX9MQcXzdBr92e0uu2Z4r5upXqLLu1gAP1JiTE70/qSI6BJCIi+VGSgeTuW4HXgZMyNKdm161IriIRESnJQIrdDxxmZiNTK8zMgMlE50TnZ9tQRETyr1SvIQH8ArgYuNfMjieaeDACGAZMdvcNxSxORKTUlGwgufsOMxsK3AyMBtoDbwCj3X1uMWsTESlFJRtIAO7+HvDd+CEiIkVUyteQREQkICV56yAREQmPRkgiIhIEBZKIiARBgSQiIkFQIImISBAUSCIiEgQFkoiIBEGBJCIiQVAgiYhIEBRIIiISBAWSJM7M2pnZj83sTTP71Mw+NLMlZjaw8a0FwMz2NbMXzWxOsWsJkZl9wcx+aWb/bWY7zOyvZnZpsetqTsxsoJntim9CnQgFkiQq/syp3wLXAS8BVxJ9FMjxwHIzO62I5TULZtYKmAt8qdi1hCj+1OcngPHAYuAq4D1glpldW8zamgsz6ws8RMIZUdJ3+5aiGAmcDkx1992/HMxsNvAKcDtwVJFqC56ZHUoURkOKXUvAJgLHARe6+3wAM5sJ/B64wczmuvu7xSwwZGZ2LjALOCjpY2uEJEk7PV7eWXtl/Avij0B/M+uUdFHNgZldQvSZXScRfY6XZDYGqEyFEYBHd5GeBrQGLipWYaEzsyVEo8oNwANJH1+BJEmbDJwArM/Q1jVe7kqunGblS8CjwADg7iLXEiQz6wD0A1ZmaE6t07XK7PoB1xKNMN9M+uA6ZSeJcvcPgA/S15vZycCXgb+6+4eJF9Y8XOvuOwHMrFdxSwlWD8CAdekN7r7dzD4EeideVfPRv9bPWOIHVyBJXpjZz4F2jXT7ubvX+6vLzHoA8+Jvb8h3bSFryuuW+kUhDeoQL7dmad8O7J9QLc1OsX/GFEiSL5fy+S+DbOaTdhog/kt/KXAY8FN3f7gQxQVsj143ycrSlpnadUo4UAokyQt379jUbczsROB3QDfgZ+7+g7wXFrg9ed2kQR/Hy2yjznaAZtgFSpMapCjM7F+JZtV1BSa7+6TiViQtxFrAgfL0hvj9SR1RIAVLIyRJnJmdBywAqoER7v5gkUuSFsLdt5rZ60RT49OlZtetSLAkaQKNkCRRZvY/gPuBz4CzFEZSAPcDh5nZyNSK+A4hk4GdRNfkJEAaIUnSfgrsBywBys3s4gx9HnL3bcmWJS3IL4CLgXvN7HiiCSEjgGFEp4c3FLM4yU6BJIkxs32AofG3X48fmfQGFEiyR9x9R3xD0JuB0UB7ojtcjHb3ucWsTRpm0R01REREikvXkEREJAgKJBERCYICSUREgqBAEhGRICiQREQkCAokEREJggJJRESCoEASEZEgKJBERCQICiQREQmCAklERIKgQBIRkSAokEREJAgKJBERCYICSUREgqBAEikQMxtsZneb2etm9k8z2xp//Usz65Oh/1gz8yyPrWb2dzObYWbdGjnu0/E292Rpvztuv6SR/fSJ+z1uZj+Nv17cyDa3xf1+2VA/kUz0AX0ieWZmrYGfAROBdcAi4C3AgOOAi4B9gYvc/Te1thsL3AM8BKT/4u8KnEX0MdxvAse7+9YMxz48PtY2oBXQw90/TOvzZeAZ4HF3P6uB5/Ej4IfABcDvgJeALwIj3H1Rhv6DgeXA34Fj3X17tn2LZOTueuihRx4fwHTAgbuA1hnaDwXWAFuB8lrrx8bb3djAvmfHfa7M0v5/4/br4+VVWfq9BlQDXbO0G7AWeD/1HIBBwC5gI3BwWv/9gNXAZ8BJxf430KN5PnTKTiSPzOwY4H8RjSb+zd0/Te/j7uuAq4D9ge808RB3x8shGY5dBowG1gO3AzuB8Vn2M5toBHVBlvYhQC9gbuo5uPszwC+IRmu3pvX/EdHo6WZ3fy7H5yJShwJJJL/GEo0ubnD36gb6LQHOBG5p4v7rnaar5QygHHjC3f8JPA70M7NTM/SdSzSaGZVlX2Pi5ay09dcTnTIcbWbDAMzsOKIQfh64KZcnIZKJAkkkv4YRnSr7Q0Od3H2Xuz/h7juauP9z4+VfMrRdGi8XxMv58fLyDMffDDwKnJQ+wcLM2gHDgZXu/mradjvi49QA/2FmbYlGbZ8BlzQSwiINUiCJ5NehwPvuvi29wcw6ZXgclGEf7dL6dDGz/mY2BbiO6JTcf6Tt+2Dgm8B7fB6GvwO2A+eaWdcMx5kdL9NHSecC7ak/OgLA3f8M3EZ0iu6PRBM1/re7v56pv0iuFEgi+VVG9v9X72V4vJih3+S0PpuIJiHcCPwWONndP07bZhTQBljk7rsA4lD8HdGMvkup7zHgH9QPpDFEs/Tm19vic9cRzeY7CXiS6JqVyF5RIInk13rgYDNrk6Ht9LTHpiz7mFurz/lE14KcaFQyKp4UkS4VOH82s16pB7AsXv+9eNLDbnFw3Qv0NbMTAcysB3AasDBD6NXedgewMP72HnfX+0dkr+1T7AJEWpingX5EYfJo7QZ3/6/a35vZJ1n2sSat74Pxm1wnA4eZ2cjaAWBmXwK+FH87L8s+exG9j+n3aetnA1OIRkmrgIuJ/lDNeLpOpJA0QhLJr9R1mWvMrFUe93s50em9EcA1aW2p0dEsous/6Y97au2jDnf/O9EoaoSZGVEwrY6vE4kkSoEkkkfxe3B+DpwCzDGz/dP7mFlrM/sB0KMJ+91JdIeHT4AfxaOi1F0hLiJ6k+u/u/tv0x/AtcCnwNlm1jPD7mcD3YmuHQ1AoyMpEgWSSP5dA0wjCoq1ZnaHmU0ws8vN7HaiOyDcArwLXJHrTt19NdHptX2BufF1qm8BXwB+6+7/yLLdRqIJCq2A72Xosgj4J9Htjj4D7su1JpF8UiCJ5Jm7V7v7NUTToecBXyG6pc+tRFOzlxOdejvC3R9p4u5vA54CjgZ+DIyL19/RyHapOytcZmZ1rh17dM+5+cDBwCPxe5REEqebq4qISBA0QhIRkSAokEREJAgKJBERCYICSUREgqBAEhGRICiQREQkCAokEREJggJJRESCoEASEZEgKJBERCQICiQREQmCAklERIKgQBIRkSAokEREJAgKJBERCYICSUREgqBAEhGRICiQREQkCAokEREJggJJRESCoEASEZEgKJBERCQI/x8jDtAlfYqwWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stepsize={'Charge': 1,\n",
    "         'GRAVY': 0.2}\n",
    "\n",
    "for metric in ['Charge', 'GRAVY']:\n",
    "    plt.figure(figsize=(2., 1.), dpi=200)\n",
    "    bins = np.arange(hAMP_df[hAMP_df['InLibrary']][metric].min(),\n",
    "                            hAMP_df[hAMP_df['InLibrary']][metric].max()+stepsize[metric],\n",
    "                            stepsize[metric])\n",
    "    h = plt.hist(hAMP_df[hAMP_df['InLibrary']][metric],\n",
    "             bins=bins, color='#737373')\n",
    "    \n",
    "    mean = hAMP_df[hAMP_df['InLibrary']][metric].mean()\n",
    "    plt.axvline(mean, color='k', linestyle='-', linewidth=1.)\n",
    "    plt.text(mean+0.03*(h[1][-1] - h[1][0]), h[0].max(), 'Mean: '+'{:+.1f}'.format(mean))\n",
    "    plt.xlabel(metric)\n",
    "    plt.ylabel('Count')\n",
    "    plt.ylim([0, 22])\n",
    "    plt.savefig(library_props_figs_path+metric+'.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot secondary structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(1.8, 1.8), dpi=200)\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "data = hAMP_df[hAMP_df['InLibrary']]['3D Structure'].value_counts().reindex(['Combined','Helix','Beta','Bridge',\n",
    "                                                      'Rich','Not Helix or Beta','Unknown'])\n",
    "\n",
    "labels=data.index\n",
    "\n",
    "colors_dict = {'Combined':'#724279',\n",
    "               'Helix':'#E92929', #red\n",
    "               'Beta':'#3B54A3', #blue\n",
    "               'Bridge':'#57B8FF',\n",
    "               'Rich':'#FBB13C',\n",
    "               'Not Helix or Beta':'#41ab5d', #'#737373',\n",
    "               'Unknown':'#bdbdbd'}\n",
    "\n",
    "\n",
    "# Adapted from Matplotlib documentation:\n",
    "# https://matplotlib.org/3.2.1/gallery/pie_and_polar_charts/pie_and_donut_labels.html\n",
    "        \n",
    "def func(pct, allvals):\n",
    "    absolute = int(round(pct/100.*np.sum(allvals)))\n",
    "    return \"{:.0f}%\\n({:d})\".format(pct, absolute)\n",
    "\n",
    "wedges, texts, autotexts = ax.pie(data, startangle=90,\n",
    "                                  autopct=lambda pct: func(pct, data),\n",
    "                                  colors=pd.Series(labels).apply(lambda x: colors_dict[x]),\n",
    "                                  textprops=dict(color=\"w\"),\n",
    "                                  pctdistance=.75,\n",
    "                                  )\n",
    "\n",
    "kw = dict(arrowprops=dict(arrowstyle=\"-\"),\n",
    "          zorder=0, va=\"center\")\n",
    "\n",
    "for i, p in enumerate(wedges):\n",
    "    ang = (p.theta2 - p.theta1)/2. + p.theta1\n",
    "    y = np.sin(np.deg2rad(ang))\n",
    "    x = np.cos(np.deg2rad(ang))\n",
    "    horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
    "    connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\n",
    "    kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n",
    "    ax.annotate(labels[i], xy=(x, y), xytext=(1.1*np.sign(x), 1.1*y),\n",
    "                horizontalalignment=horizontalalignment,\n",
    "                verticalalignment='top',\n",
    "                **kw)\n",
    "\n",
    "fig.savefig(library_props_figs_path+'/SecondaryStructure.svg',dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot cluster distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(2., 1.), dpi=200)\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "bins = np.arange(0, 10, 1)\n",
    "cluster_sizes = pd.Series(Counter(hAMP_df[hAMP_df['InLibrary']]['Cluster'].value_counts()),\n",
    "                 index=bins)\n",
    "\n",
    "plt.bar(cluster_sizes.index, cluster_sizes.values,\n",
    "        color='#737373', width=0.9)\n",
    "\n",
    "plt.xlabel('Number of peptides per cluster')\n",
    "plt.ylabel('Number of clusters')\n",
    "\n",
    "for i in range(len(cluster_sizes)):\n",
    "    if np.isnan(cluster_sizes.values[i]):\n",
    "        continue\n",
    "    plt.text(cluster_sizes.index[i],\n",
    "             cluster_sizes.values[i]+3,\n",
    "             '{:.0f}'.format(cluster_sizes.values[i]),\n",
    "             horizontalalignment='center')\n",
    "    \n",
    "plt.xticks([1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
    "plt.ylim([0,90])\n",
    "\n",
    "fig.savefig(library_props_figs_path+'/cluster_sizes.svg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}