notebooks/Scaling_Sororin_Nipbl_Wt.ipynb

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 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.6.3-final"
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  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
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  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python3",
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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pairtools\n",
    "import os\n",
    "import pairlib.scalings\n",
    "import pairlib\n",
    "import bioframe\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sbn\n",
    "from NGS import HiCTools as HT\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({\"pdf.fonttype\": 42})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recDict():\n",
    "    return defaultdict(recDict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set WD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"/groups/gerlich/experiments/Experiments_004800/004869/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairDir = {\"CAGATC_merged\":\"/groups/gerlich/experiments/Experiments_004800/004869/\"\n",
    "           \"Sequencing_data/Pooled/Pairs/\",\n",
    "           \"ACTTGA_merged\": \"/groups/gerlich/experiments/Experiments_004800/004869/\"\n",
    "           \"Sequencing_data/Pooled/Pairs/\",\n",
    "           \"GGCTAC_S1\": \"/groups/gerlich/experiments/Experiments_004600/004638/Sequencing_data/Novaseq/pairs/\",\n",
    "            \"GTGAAA_S11\": \"/groups/gerlich/experiments/Experiments_004600/004638/Sequencing_data/Novaseq/pairs\",\n",
    "            \"TTAGGC_merged\":\"/groups/gerlich/experiments/Experiments_004800/004869/\"\n",
    "           \"Sequencing_data/Pooled/Pairs/\",\n",
    "           \"GATCAG_merged\": \"/groups/gerlich/experiments/Experiments_004800/004869/\"\n",
    "           \"Sequencing_data/Pooled/Pairs/\",\n",
    "           \"GCCAAT\": \"/groups/gerlich/experiments/Experiments_004600/004655/Sequencing_data/Novaseq/Pairs/\",\n",
    "           \"G2\": \"/groups/gerlich/experiments/Experiments_004600/004661/Sequencing_data/pairs/\"}\n",
    "\n",
    "sampleMap = {\"CAGATC_merged\": \"Nipbl_w_auxin rep1\",\n",
    "             \"ACTTGA_merged\": \"Nipbl_w_auxin rep2\",\n",
    "             \"GGCTAC_S1\": \"Nipbl_w_auxin rep3\",\n",
    "             \"GTGAAA_S11\": \"Nipbl_w_auxin rep4\",\n",
    "             \"TTAGGC_merged\": \"Sororin_w_auxin rep1\",\n",
    "             \"GATCAG_merged\": \"Sororin_w_auxin rep2\",\n",
    "             \"GCCAAT\": \"Sororin_w_auxin rep3\",\n",
    "             \"G2\": \"WT pooled\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = {}\n",
    "for barcode, name in sampleMap.items():\n",
    "    pairs[name] = {}\n",
    "    pairs[name][\"cis\"] = HT.loadPairs(\n",
    "        os.path.join(pairDir[barcode], f\"{barcode}.cis.pairs.gz\"))\n",
    "    pairs[name][\"trans\"] = HT.loadPairs(\n",
    "        os.path.join(pairDir[barcode], f\"{barcode}.trans.pairs.gz\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairsMerged = recDict()\n",
    "pairsMerged[\"Nipbl_w_auxin\"][\"cis\"] = pd.concat((pairs[\"Nipbl_w_auxin rep1\"][\"cis\"], pairs[\"Nipbl_w_auxin rep2\"][\"cis\"], pairs[\"Nipbl_w_auxin rep3\"][\"cis\"], pairs[\"Nipbl_w_auxin rep4\"][\"cis\"]))\n",
    "pairsMerged[\"Nipbl_w_auxin\"][\"trans\"] = pd.concat((pairs[\"Nipbl_w_auxin rep1\"][\"trans\"], pairs[\"Nipbl_w_auxin rep2\"][\"trans\"], pairs[\"Nipbl_w_auxin rep3\"][\"trans\"], pairs[\"Nipbl_w_auxin rep4\"][\"trans\"]))\n",
    "pairsMerged[\"Sororin_w_auxin\"][\"cis\"] = pd.concat((pairs[\"Sororin_w_auxin rep1\"][\"cis\"], pairs[\"Sororin_w_auxin rep2\"][\"cis\"], pairs[\"Sororin_w_auxin rep3\"][\"cis\"]))\n",
    "pairsMerged[\"Sororin_w_auxin\"][\"trans\"] = pd.concat((pairs[\"Sororin_w_auxin rep1\"][\"trans\"], pairs[\"Sororin_w_auxin rep2\"][\"trans\"], pairs[\"Sororin_w_auxin rep3\"][\"trans\"]))\n",
    "pairsMerged[\"WT\"][\"cis\"] = pairs[\"WT pooled\"][\"cis\"]\n",
    "pairsMerged[\"WT\"][\"trans\"] = pairs[\"WT pooled\"][\"trans\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downsample pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairsNew = HT.downSamplePairs(pairsMerged, Distance=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get chromosome features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hg19_chromsizes = bioframe.fetch_chromsizes('hg19')\n",
    "arms = HT.getArmsHg19()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate scaling plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "scs = {sample: {rType: pairlib.scalings.compute_scaling(\n",
    "    pairsNew[sample][rType],\n",
    "    arms,\n",
    "    hg19_chromsizes\n",
    ") for rType in [\"cis\", \"trans\"]}\n",
    "    for sample in pairsNew.keys()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVaJChQps27aNXbt2UapUqTgzgwnPkgDsYi1amPXCTz0FUAiT1rKD3XkfXbtG/ZAQLs2f79kBilQj+i44OgBXqlQp5nXlypUf3oFwOwnAblCqlHk4V6cOQHrMpo2J2O57WXv/PmVat2bPg5IcQrhM9DxwWFgYgYGBVKxYkW3btrF161aqVKmS1MMTeHkAdkUuiMTKkQNWrowud6SAnphVEo9bzvsLqDJ6NDNr15bCn17I2ZphDzsSUqVKFZYvX06OHDnw9fUlR44cXL16lW3btlFJkkYlC14dgF2VCyKxfH1NuaN586Lnhatg5oWtdx/hQKeffqJ7QADhHqxHJbxbUFAQFy9epGLFipa2rFmzksuUfhFJTHJBeECrVlCiBDRrBn/++TgmmU8/YILlvEmnT/NrwYLMW7GCorVqJcVQhRfx9fXl+vXrlrYZcZTSiqtNeIZX3wEnJ0FBZl7Y5CVJg0nbMxOzdviBfXfuUKZ2bea+/77HxyiE8CwJwB6UIwesWBE9LwxmdcRW4CnLeTe0pu2wYfSsWdOuvpUQwntIAPYw+3nhEGAP0Mru3C/Xr6dSwYIcS2Q1D5G0UkO1mdTCXb+XEoCTSKtWsH179HrhrMBczFI16+L4fefPUzowkHmzZnl+kCLR0qVLx6VLlyQIewGtNZcuXSJdunQPP/kRyUO4JBQUBLt2QefOsGRJ9FK1ipi74T9jzrsREUGbjh3Z+NNPfDx5slv+IAjXyp8/P6dPn+aCB7ech4eHy58NN0mXLh358+d3eb9SlDMZ0Bo+/xz694d79wCuAd2ABXbnhhQrxrwVKyhSpIiHR/loUntRzqQgP/MkIUU5UzqloE8f+OUXCAgAMyUxD7NMzTolsff33ykdFMR82cIsRIonATgZKVcO9u6Fli3B/MXaizhXSYSH07p1azp36mS3zlMIkXJIAE5msmY1KyS++ALSpgUog1kl8aLduTNmzqRUUBAbNmzw7CCFEC4hATgZUgp69DCrJIoWBTMlMZ+4piT++vtvatasSb9+/WTNsBApjATgZCw4GHbvhnbt4MGUxHagpOU8rTWffPIJZcqUYe/evZ4fqBAiUSQAJ3OZM8Ps2fD112BWGIVgEvq8he0D2MOHD1O+fHlGjhxJhGRWEyLZkwCcAigF3brBjh0mqY/JH/ExJqnPk5ZzIyIiePfdd6lWrRp//PGH5wcrhHCYBOAUJCjITEn07BndUgM4AHSyO3f79u0EBwfz5Zdfym4sIZKpFBmAlVIllVLzlFJfKqVaJvV4PClDBpg4EZYvhzx5wDygmw78AFhzvN66dYuePXtSv359zpw54/nBCiES5PEArJSappQ6r5Q6aNNeTyl1VCl1TCn1sBo99YHxWusexFVwLRVo2BDCwqBx4+iW5sBBoLHduatXryYwMJAZM2bI3bAQyUhS3AHPAOrFblBK+WIy0dTHPOJvF3WXG6SUWm5z5AG+AdoqpcYAOT08/mQjTx5YsgS++grSp9dAXmAJMBWwVgG5evUqnTt3pmHDhpw6dSoJRiuEsJUkuSCUUoWA5VrrwKjXlYD3tdZ1o14PAtBaj3pIP77AD1rrpnG8FwqEAuTKlauMt2/d/fvv9Hz44dMcPZolquUEZm54k925GTJkoEePHjRs2BClnNrKHq+TJ09SqFAht/Qt4iY/c8+rUaOGc/8DubtoYDyFBAsBB2O9bglMifW6PTDhIddPBmYDVR/2ecWKFdOpwd27Wg8erLWPT6Q2KX4iNHyiIb0G7I5atWrpEydOuGUs69evd0u/In7yM08STsXC5PIQLq6/ReK9Nddan9Rah2qtX9Zab3HjuFIUf38YMQI2blQUyncX8AX6AvuB6nbnr1u3jsDAQCZOnEhkZKSHRyuESC4B+DRQINbr/MA/znaalGXpk1LVqrD/SBo6tInemlwUWI+pQ5fRcu7Nmzfp3bs3NWvW5NixYx4eqRCpW3IJwDuBokqpAKVUGqAtsNTZTnUSl6VPSlmywMzv07Fo4X3yZbqO+a3uDYQBNe3O37hxI6VKleLTTz/l/v37Hh6tEKlTUixDmwNsA4orpU4rpbpqrSMw0WE1cASYp7U+5OmxeaNmLXw5fDoLr9U9GdUSAPwETAIyW869ffs2b731FtWqVePAgQMeHacQqZHHA7DWup3W+nGttb/WOr/WempU+0qtdTGtdWGt9UhPj8ubZc0KX60qxMbZpyiW5iRmyj0UOITNikAAtm3bRkhICL169eLSpUueHawQqUhymYJwi9Q6Bxyf6i8VYP8/uRn8zGL8uIeZdl+J2UmX1XJuZGQkX3zxBUWLFmX8+PHcM7WShBAu5NUBODXPAccnXc6MjAhryq6BCynLLszdcCfgMHHtorty5QpvvPEGwcHB/PTTT54drBBezqsDsIiHUjw7qi3bN93lkyzvk4GbQD7MLrqlgH3Bz8OHD1O7dm2aNWvG8ePHPTxgIbyTBOBUzLdaZfr+3oOD5btSmzWYu+HGmJwSo7HdzgywZMkSSpYsycCBA7lx44ZnByyEl/HqACxzwA7Im5eAX75ldb+1zKQDubgApAUGAH8Ane0uuXv3LqNHj6ZYsWLMnDlTNnEIkUheHYBlDthBfn6osWPoML8Jv2cqQy8m4MN94DFgGrADqGh32blz5+jUqRMVK1Zk69atHh60ECmfVwdg8YhatiT7rrVMeOYrdlGWyvwS9UY5YCvwLWau2Grnzp1UqVKFl156ib///tuDAxYiZZMALKyKF4dffyXklUA2U40ZdCQP/2Lmh18GjgKDMdMUVnPmzKF48eIMGTKE27dve3bcQqRAXh2AZQ44kTJmhFmz8PnyCzqm+Z6jFOcNxkVNS2QCRmA2LL5od2l4eDgjRoygQ4cOzJo1S+aHhUiAVwdgmQN2glLQvTts2UK2J7MyjjfZSwhV2Rx1QgCwANgABNtdfvHiRTp27EiFChX45Zdf7N4XQnh5ABYuUK4c7NkD9epRijA2UZ1veIXHOBt1wnPALkwVjrx2l+/atYuqVavStm1b/vrrLw8OXIjkTwKweLicOWHFChg+HKUUrzCboxTnLT7Gn+i8w10wy9YGAmnsupg7dy4lSpTg3XffRaaEhDAkAAvH+PjAkCGwahXkzEkWbvAx/+MIT9OauVEnZQZGAb9hipxYhYeHM3LkSIoUKcLXX38taS9FqicBWDyaOnXMlESFCgAU5k/m0pZtVKQK0cVJAoD5wEagtF0X//77L6GhoQQHB7Nq1Sqp1CxSLa8OwLIKwk2efBI2bYI+fWKaKvIrm6nGQlpQxCc6V0R1TK796ZhNHVYHDx6kfv361K1bl/3793ti5EIkKw8NwEqp3kqp7J4YjKvJKgg3SpMGPvsMli+H3LkBs1K4BYs4FPk0n/M6OdP+h/kj1gn4HbN+OJ1dV2vXriUkJIQuXbrwzz9OV6ISIsVw5A74MWCnUmqeUqqeclcdc5EyNWwI+/dD7doxTWm4x+tM4PidJ3g7x9ekTROJmR8egXlQ1xHbOqxaa6ZPn07RokV577335EGdSBUeGoC11u9iqjpOxdzK/KGU+lApVdjNYxMpxeOPm4dzY8ea0sxRsnKdjy6HclQX55UyR6Ja8wMzgN3EVZvu1q1bDB8+nKJFizJlyhR5UCe8mkNzwNo8JTkXdUQA2YEFSqn/c+PYREri4wP9+sG2bVC0qOWtgveO8c3ukuyp3Ju6z9+Jag3B1KZbDjxt1925c+d49dVXCQ4OZuXKlfKgTnglR+aA31BK7Qb+D/gFCNJa9wDKENdeVJG6lSkDe/ZwtkEDu7dCtk5k1eGC/DRqJ2XKgJmGaAgcAL4Ecttdc/DgQRo2bEitWrXYtWuXe8cuhIc5cgecC2ihta6rtZ6vtb4HoLWOBBq5dXROklUQSSRTJo727w9z55qKoLH9+y+1BpVnR90hzP3uPkWKAPgB3YFjwDvE9aBu/fr1lCtXjnbt2vHnn3+6/SsI4QmOBOAArbVlD6lS6hsArfWRuC9JHmQVRBJr3do8oKtSxe4tnw9H0HpSLQ6vO8sXX0DevABZgJGYFRPtsX1QB/D9999TokQJ+vTpw8WLF907fiHczJEA/EzsF0opX8z0gxAPV7AgbNgA779v5olj27gR/3LB9Cj6E8eOwQcfQObMYKo1z8I8qKuNrXv37vH5559TuHBhRo0axa1bt9z9LYRwi3gDsFJqkFLqBlBKKXU96rgBnMdUbxTCMX5+8N57sG4dPGazIeP8eahTh0xj3uPdQfc5ftzs7zCLKUKANVGHfca169ev884771CsWDGmTZsmKyZEihNvANZaj9JaZwbGaK2zRB2ZtdY5tdaDPDhG4S1q1IB9+6BWLWu71jB8ONSpQ+775/jsMzh6FF56KfqE2pi74W+Agnbdnjlzhq5du/Lss8/y448/uvUrCOFKCd0Bl4j6z/lKqdK2h4fGJ7xN3rywerWZkrDd0/PzzxAcDOvXExAAs2fDzp0mbps/qq9gEv2MxayEtDp06BANGjSgWbNmnDx50r3fQwgXSGgOuF/Urx/HcYx187iEN/P1NVMSa9dCnjzW9/79F154wUwI379P2bImLi9fDiVLglkh0Q84DvQnrtJIS5Ys4emnn+aDDz4gPDzc7V9HiMRKaAri1ahfn4/jsN/CJMSjqlXLTEmYW9wHIiNh6FCoXx/On0epBzuev/7abLwzd8D/h1kx0QHbFRPh4eEMHTqUwMBAVq5c6YlvI8QjS2gKokVChycHKbzY44/DTz+ZXMO2UxJr15opiY0bAfMsr1s3+OMPGDbMlK6DJ4GZmKocley6P378OA0bNpRpCZEsJTQF0TiBI1lvwIgmGzFSCF9f8xBu1SrIlcv63tmzULMmfPihuTPGBN6hQ+H4cVO2ztcXTN7hLcA04tpRFz0tMWLECJmWEMmH1trrj2LFimnhWevXr0/chadPa12tmtZmbYT1qFtX6/Pn7S45ckTrxo1jn3pZQy8NPhqwOwoXLqxXrlzp3BdMhhL9MxfOcCo2JTQF8UrUr2/FdXjkbweR+jzxhHnqNnCg/XurV5spic2bLc0lSsDSpWbG4plnwMwPTyChaYno1RJ///23G76EEI5JaAoiY9SvmeM5hHAPPz8YNQpWrjQFQWP75x94/nn46KOYKYloL7xgnulNnBh9WQgPpiVspjYw0xJBQUHMmDFDsq2JpOHsLXRKOGQKwvNc9s/hU6e0rlIl7imJ+vW1vnAhzssuX9b6zTe19vNzbFqicePG+uzZs64ZcxKRKYgk4Z4piGhKqaeUUsuUUheUUueVUkuUUk+5+e8FIYz8+WH9enj7bfv3fvwRQkLgl1/s3sqeHT79FMLCwGTGjJ6W2AlUtDt/2bJlPPPMM8ybN8/FX0CI+DmSjOc7YB7wOJAPU+52jjsHJYSFv7+Zcli+HHLksL53+jQ89xz83//ZTUmAmR9escLE6hIlwKyW+AUYD6S3nHv58mXatGlD27ZtuXTpkpu+jBAPOBKAldb6G611RNTxLeafbUJ4VsOGZpK3ks2Dtfv3zR1ykyYQT+CsVw8OHIBx4yB7dh+gN7CfuB7SzZ07l8DAQJYtW+byryBEbAmtgsihlMoBrFdKDVRKFVJKFVRKDQBWeG6IQsRSoIDZmNG/v/17K1aYihxhYXFe6u8Pb7xhNnJ06wam1OFmYDSQxnLuuXPnaNKkCV26dOHatWsu/hJCGAndAe/GrONpA7wGrAc2AD2Azm4fmRDx8fc3Uw5Ll5rJ3tj++sskgF+1Kt7Lc+Y0W5rXr4eiRX2BAZg/7iF2506fPp2goCDWrVvn0q8gBCScCyJAa/1U1K+2hzyEE0mvcWPYuxcqVLC237hhpiu++CLBy2vUMNMSgweDn18g8CvwHuBrOe/UqVO88MIL9O7dm9u3b7vyG4hUzqGqyEqpQKVUa6VUh+jD3QMTwiEFC8KmTdCjh7U9MhJ69YI33zRzxPFIlw5GjIA9e6BCBX/gfUwgLml37sSJE6lcuTLHjx935TcQqZgjy9DewzwyHg88j0lB1cTN43IJyQWRSqRJY3ZffPaZfUKfceOgaVNzV5yAoCCzmm38eMiUqQxmSqI/tlnW9u3bR5kyZViyRIrCCIxdwBwAACAASURBVOc5cgfcEqgFnNNadwaeJa4krMmQlqKcqYdSppbRkiXRadIeWLECqlaFU6cS7MLXF3r3hsOHoXHjdJh7jc1AYct5165do1mzZgwYMICIiAiXfg2RujgSgG9rU4I+QimVBVMTTuaARfLUuDFs2WJySsR24ACULw+7dj20iwIFTByfNw/y5q0C7MHch1iNGTOGWrVqcfbsWdeMXaQ6jgTgXUqpbMDXmH+X7QF2uHVUQjgjOBh27IDSNpWzzp2D6tVh0aKHdqEUtGoFR45Aly5ZMHuRPgX8LOdt2rSJkJAQNmzY4KrRi1TkoQFYa91Ta31Va/0Vpjpix6ipCCGSr3z5zMO5pk2t7bdvw4svwpgxJkXEQ2TPDlOnwqJFipw53wQ2Ata763///ZdatWoxevRoIuPYjSdEfBxdBdFCKfUJ8Dq2E2JCJFcZM8LChfC//1nbtYYBAyA0FO7dc6irZs3MLEadOpUx/wi0VnaOjIxk4MCBNGvWjCtXrrhm/MLrObIK4gugOxAGHAReU0pNdPfAhHAJX19ztztpUnTpjAemTIFGjeDmTYe6ypfP5JT49NM8+PuvBobYnbNs2TLKlCnDnj17XDB44e0cuQN+DqirtZ6utZ4ONABquHVUQrhaaKjZHZc1q7V9zRqoUweuXnWoGx8fs7R41y5fAgOHAysBa4KgEydOUKlSZaZOneqasQuv5UgAPoqpfBitAHDAPcMRwo1eeAG2boWAAGv71q0myfu//zrcValSsHMn9OlTHzMlUc7y/t27d+jWrRsDBgyQeWERr4SS8SxTSi0FcgJHlFIblFIbgCPEVfVQiJSgZEnYvt2slIht3z6oVg0eoURRunRm78eqVQXJm3cz0MvunDFjxtCyZWvZwizi5JfAe2M9NgohPClPHpOJp1EjazL3P/4wGzbWroXixR3urm5dCAtLS7duE1i6tDLQDXgQcBctWki1aqdZuXIpefLkcd33ECleQsl4NkYfwG88qAV3JKpNiJQrWzYz/1u3rrX91ClzJ7x37yN1lzs3LF4Mkya9RNq0m4C8lvd37/6V0qUrcuTIEScHLryJI6sgWmM2XrQCWgO/KqXstwUJkdJkyGBSWrZqZW2/cMGkStuy5ZG6U8o869u2rSz58v0KPGN5/8yZE5QrV5n169c7N27hNRx5CDcYKKe17qi17gCUJ671N0KkRGnSwJw50LWrtf36dbM6IoG8wvEJCYG9ewtSufIvwAuW927evMoLL9RlxoxZTgxaeAtHArCP1vp8rNeXHLxOiJTB19dkaLfdsHH7tilzlIhCnXnywIYNWXnttZWANbhHRt6jc+eODBz4PtqB3XjCezkSSFcppVYrpToppTphyhGtdO+whPAwpUyVjZEjre337kHbtmbTxiPy94evvvJn0qSv8fH50O790aOH0axZB+7cuZPYUYsUzpFcEP2BSUApTCrKyVrrOGqEC5HCKQXvvAMTJljbtYZXX4WxiVsYFBqq2LRpEFmzfo9tJtelS7+lbNk6XL58OZGDFilZggFYKeWrlPpJa/2D1votrXVfrfXDU0m5kFLqKaXUVKXUglhtGZVSM5VSXyulXvbkeEQq0KsXfPON/dbl/v0THYSrVIGDB9tQvPg6zNL6Bw4e3ETx4pU4fvzPRA5YpFQJBmCt9X3gllIqa0LnxUcpNU0pdV4pddCmvZ5S6qhS6phSauBDxvCn1trmCQktgAVa61dJIdU5RArzyivwww+Q1qb2QP/+MCtxD9Dy54e9e6vQtOl2oJjlvYsXf6dUqeocPHg0kQMWKZEjc8DhQFjUXejn0YeD/c8A6sVuUEr5AhOB+pjCW+2UUiWVUkFKqeU2R3yr1vMD0eUN4i/4JYQzmjQx2XdsK6p06WKqbCRC+vSwaFERhg/fClSzvHfr1hnKlHmOXbsOJ3LAIqVRD3sKq5TqGFe71nqmQx+gVCFgudY6MOp1JeB9rXXdqNeDovob9ZB+FmitW0b9d3vgitZ6uVLqe6112zjODwVCAXLlylVm/vz5jgxXuMjJkycpVKhQUg/DJbLt2UOpgQPxiZW68n7atOwfO5brgYGJ7nf79owMGfIxERFzLe2+vrkYO3YswcEFH6k/b/qZpxQ1atRQDz8rAVrreA8gBFOL5emEzntIH4WAg7FetwSmxHrdHpiQwPU5ga+A48CgqLaMwHTgS+Dlh42hWLFiWnjW+vXrk3oIrjV/vtZKaW0eyZkje3atDx50qtvffruvM2XqpgHL4eOTQy9ZsvuR+vK6n3nKkKi4GH0klIxnKDAXeBFYoZR61alIH6vrONrivQ3XWl/SWnfXWhfWUXfJWuubWuvOWuseWuvZLhqXEPFr2RK++MLaduWK2cr811+J7rZ4cR+OHp1Erlw9Le2RkZdp1qwms2b9mui+RfKX0BxwGyBYa90Ok2sv1EWfeRqT0jJafuAfF/VtIWXphUt17w7DhlnbzpwxQfjixUR3my+fD8ePT6BAgTct7Vpfo2PH2nz22aNtiRYpR0IBOFxrfQvMXehDzn0UO4GiSqkApVQaoC2w1EV9W2gpSy9cbcgQs0wttqNHoUEDcOIv+ixZFH/88QlPP227xP4GffvWY+jQDYnuWyRfCQXVwkqppVHHMpvXDgVMpdQcYBtQXCl1WinVVWsdAfQGVmNyC8/TWh9y9osI4RFKwbhx9gl8du40xT7v3k1012nTKsLCRlGx4lCbd27ywQcN6NFjbaL7FslTQvmAbcrJPnp+4Kjpi7jaVyLbmUVK5etrNmpcvgzr1j1oX7MGOnWCb781tYsS1bVi27Zh1K+fhlWr3o31zm2++qoxFy78wLx5DRLbvUhmHMoHHNfhyUEmlswBC7dJmxYWLYIyZaztc+ZA374OlbxPyI8/DqZduzE2rXdYuLAZNWsucbSYs0jmvPrvUZkDFm6VOTOsXAlFi1rbP/8cRiW4rN0h3333P3r0GGfTeo+NG1tSrtx8bt1y+iNEEvPqACyE2+XJY6YeHn/c2j54sElx6aQvvniDQYO+smmNYP/+tlSsuFSCcArnSEWMVo60CZFqFSoUd8n77t0TldDd1ocfvsZHH03DuoQ+krCwttSosR2p95lyOXIHPMjBtmRH5oCFx5QqBcuWmVLJ0SIjoU0bOOT8Ip+33+7MxInfYP1f9jY7dzbihRd+lyCcQiW0E66+Umo88ETsJDxKqRlAhMdG6ASZAxYeVa0azJ1rXQFx/bqpvnz+fPzXOahnz5f5+OMvbVovsXVrPRo0OMfduzKjmNIk9Dv2D7ALkw1td6xjKVA3geuESL2aNIGPP7a2nTwJzZpBeLjT3b/1Vij9+tmWZDzBhg0NGTz4SaS4RsqS0DK0/dpkPAsCvtVaz4x6vQSQ32Yh4tOnD7z2mrVt2zZT+NMFNeDGjBlGu3adbVr3sGtXd5o3vydBOAVx5N8sa4D0sV6nB35yz3BcS+aARZJQCsaPhxesFZH57jsYMcIF3StmzpxEzZr1bN5ZzY8/hvLii1qCcArhSABOp7WOiWBR/53BfUNyHZkDFknG3x/mz4cSJaztQ4eaeWKnu/dnyZL5lCplsxGEGaxYMZTWrZ3aFS08xJEAfFMpVTr6hVKqDCDPXIV4mGzZYPlyyGmtAUfHjrB9u9PdZ8qUiTVrVlCw4FM274xg6dJJtGmD7JhL5hwJwG8C85VSm5VSmzE5gnu7d1hCeInChU1tOX//B2137kDTpk7lEY6WN29e1q5dRY4cuWze6cnixUtp21aCcHLmSFn6nUAJoAfQE1MdY7e7ByaE16he3X5X3Pnz0LixWabmpKJFi7Jy5XLSpIldQDQSaMsPP2ynXTsJwsmVowsHi2MKaIZgimh2cN+QhPBCHTvCIJv9S2Fh0K4d3He+rmyFChV4772h+PhYN2pAIxYu/J327V3yMcLFHNmK/B4wPup4Hvg/UkgpeFkFIZKVESOgRQtr28qV0K+fS7qvXLkyX35pv1ED6jF37jn69HHJKjjhQo7cAbcEagHntNadgWeBtAlfkjzIKgiRrPj4mDzCtiksx40Du8CZOKGhoQwZYr9RAxozcWI4I0e65GOEizgSgG9rrSOBCKVUFuA8YPvYVQjhiAwZYOlSeOIJa/vrr8Na11S8GDZsGJ07227U2AX0Y8gQlyRpEy7iSADepZTKBnyN2Yq8B9jh1lEJ4c3y5TOJezLEWk5//75J3PPnn053r5Ri0qRJ1Ktnu1HjC+B7unc3ueRF0nNkFURPrfVVrfVXQG2gY9RUhBAisUJCzM44FSvF5JUr0Lw53LzpdPf+/v7MmTOHp56y/cfqq0RGHqVdO9i0yemPEU5y5CFcTNErrfVJrfWB2G1CiERq2hQ+/NDaduCAy3JGZMuWjfnz55M2bexHNv8BLblz5xZNmpiPE0knoXSU6ZRSOYBcSqnsSqkcUUchIJ+nBugMWQUhkr2337avsDx3rn1GtUQqXbo048bZljU6CPTm2jWoVw9OnHDJR4lESOgO+DXMnG8JrOkolwAT3T8058kqCJHsKQXTpkFgoLX97bfhJ9fkvAoNDeWll16yaZ0OTOfsWahbFy5ccMlHiUeUUDrKcVrrAOB/WuuntNYBUcezWusJHhyjEN4tUybzVCxbtgdt0dU0XHB7Gv1QroRtYiB6AWH88Qc0aAA3bjj9UeIROfIQbrxSqrJS6iWlVIfowxODEyLVKFLE/qHc5ctm44YLKm9mypSJ+fPnkz597Myyt4FWwA127YIXX5QMap7myEO4b4CxQFWgXNRR1s3jEiL1qV/fPl/wvn0QGuqSh3KBgYFx7JQ7CoQCmrVroVMnc/MtPMORdcBlgSpRy9FejzrecPfAhEiVBg2y3648ezZ89plLuu/YsSNdunSxaf0e+AqAOXPgrbdky7KnOBKADwKPuXsgQgjMFMSMGVCypLW9f3/4+WeXfMT48eMJCgqyaX0T84zd7IweO9YlHyUewpEAnAs4rJRarZRaGn24e2BCpFqZM8PixZA164O26J1yLsghnCFDBubPn491ddBdzHzwVcAswpDdcu7nSAB+H2gGfAh8HOsQQrhL0aJm6iH2Q7mLF830xG3nC9IUL16cKVOm2LSeADoDGq3h5Zdht2T+ditHVkFsBH4DMkcdR6Lakj3ZiCFStIYNYdgwa9uePdC9u0smadu0aUOvXr1sWhcDnwImzjduDKdPO/1RIh6OrIJojUm+0wpoDfyqlGrp7oG5gmzEECne4MHQrJm1bdYsU3XZBT7++GPK2KbH5G1gGwBnz5ogLPcw7uHIFMRgoJzWuqPWugNQHrBNOCqEcAcfH5g507668ltvuSSbTtq0aZk/fz5ZY883EwG0B0xSoH374KWXpKKGOzgSgH201udjvb7k4HVCCFfIksU8lMuc+UHb/fvQujX884/T3QcEBDBz5kyb1uOYO2Fj2TIYMMDpjxI2HAmkq6JWQHRSSnUCVgA/undYQgiL4sXh22+tbf/+axL5uGD7WtOmTeOYD54IPEh8+MknMHmy0x8lYnHkIVx/YBJQClOOaLLWWv4uFMLTmjQB23JDW7eaNcIuMHr0aAoXLmxp8/XtAjyo3Nyzp8tyBAkSTkdZRClVBUBr/YPW+i2tdV/gklKqcHzXCSHc6L33TPqy2D7/3OSRcFLGjBmZMWMGKtbSt/v3/8bX961Yr6FlSzhyxOmPEyR8B/wZEFd+pFtR7wkhPM3X16wPLljQ2v7qq2R0QTmjqlWr0rdvX0vb/ftTgZUxr69dMyvkJIWl8xIKwIW01nb58rXWu4BCbhuRECJhOXPCggUQu9LFrVs8M3SoiY5OGjFihF3qyixZXgWuxLw+ccJUT7pzx+mPS9USCsDpEngvfQLvCSHcrWxZmGiti5DhzBno2NHpdGbp06dn5syZ+Pg8CA/Xr/9DkSLWHFy//OKy6kmpVkIBeKdS6lXbRqVUV6Kzdgghkk7XrtCtm7VtyRIYPdrprsuXL8/AgQMtbceOfUtQ0GJL2+zZ8MEHTn9cqqV0PH99KaXyAoswWTqiA25ZIA3QXGt9ziMjdIHixYvro0ePJvUwUpUNGzZQo0aNpB6G9wsPh6pVrUkbfHxg1SqoXdupru/cuUO5cuUICwuLacudOw9Zshzi+PFclnPnzbMvbZdKqIefEr+EShL9q7WuDAwDTkYdw7TWlVJK8JVcEMLrpUsHCxdCjhwP2iIjoV07+Ptvp7pOmzYts2bNws/PL6btwoXzFCvWk5w5red26mR2zIlH48g64PVa6/FRh2sSknqI5IIQqULBgjBnDjp25rRLl0yNofBwp7oODg5miM3a4x9/nE/v3nPx93/QdusWNG0K588jHoFsKRbCG9SpwwnbShe7dkGfPk53PWjQILuEPePH92TUKOs/hP/+26wRlrpyjpMALISX+Pull0zqstgmTzZl753g7+/PzJkzSZMmTUzb5cuX2bgxlF69rM+QNm+G11+XlRGOkgAshLfw8TGpKm22E9Ozp8kj7IRnnnmGD2yWOyxbtoyQkFk8/7z13MmTwa72p4iTBGAhvEm2bPDDDxC7/PydO2bXxL//OtV1v379qFSpkk1bH8aNO01AgPXcPn1gwwanPi5VkAAshLcpVco+bdnff5vE7k6UM/L19WXGjBmkjxXcr127xv/+15UlSzSxn3VHRJj54BMnEv1xqYIEYCG80SuvQO/e1rbt26FLF6cmaIsVK8aoUaMsbWvWrGH79il22TIvXTIrI2QVaPwkAAvhrT75xH4zxvff29eZe0Svv/46zz33nKWtX79+lC59iuHDreeGhUGHDk7vjvZaEoCF8Fb+/maLmm05o2HDnEpf6ePjw/Tp08mYMWNM240bNwgNDWXwYG23I27RIuwCszAkAAvhzbJlg+XLsdu61rmzSeaeSAEBAYy2yTmxatUqZs6cwfTpEBxsPX/YMLNhT1hJABbC2xUubGrKxVrHy9275qGcE0/JevToYTcV0bdvX65ePcPixZA7t/X8Dh3ggF2C29RNArAQqUHVqjBlirXtwgVo1CjROYR9fHyYOnWq3aqI1157jSef1CxcCLHSSHDrlqmqJIncH5AALERq0b49DB5sbTt8GNq0MevGEqFw4cJ2qyJWrFjBN998Q7VqdimL+esvkzXt3r1EfZzXkQAsRGoyfLh93sjVq+HNNxPd5euvv07VqlUtbX369OHs2bOEhpqNeLFt3Aj/+1+iP86rSAAWIjXx8YGZM6F8eWv7xIkwYUIiu/Rh2rRppEv3oIjO1atX6d69O1prPvsMbFNDf/652TWd2kkAFiK1SZ/eVM4oUMDa3qcP/PhjorosWrQoI0eOtLQtXbqUOXPmxKyGe/JJ6zWhoSZhW2qW7AOwUuoppdRUpdSChNqEEI/gscfM8rTY+4cjI8188MGDieqyT58+drkiXn/9dc6dO0fu3GY9cKyb5JgUFak5h7BbA7BSappS6rxS6qBNez2l1FGl1DGl1MD4rgfQWv+pte76sDYhxCMqVQrmzDHTEtFu3DArIxKRuMfX15dp06aRNla15suXL9OzZ0+01pQuDV9/bb3m9OnU/VDO3XfAM4B6sRuUUr7ARKA+UBJop5QqqZQKUkottznyuHl8QqRujRrBxx9b2/76Cxo0gOvXH7m7EiVKMNxm29uiRYuYN28eYFJU9O1rvWbTJujX75E/yiv4PfyUxNNab1JKFbJpLg8c01r/CaCU+h5oqrUeBTRy1WcrpUKBUIBcuXKxQXLjedTJkyflZ+5hif6ZP/ssRZs04YmlSx+07dnDleeeI2z0aCJjb+BwQJkyZShRogS//fZbTFtoaChp0qQhe/bsNGyo2LChFHv3Zo95f/x4yJDhN+rVSxHlJmM4XXhWa+3WAygEHIz1uiUwJdbr9sCEBK7PCXwFHAcGxdeW0FGsWDEtPGv9+vVJPYRUx6mf+d27Wtetq7XJlfbgaN5c63v3Hrm7Q4cO6TRp0mgg5mjVqlXM+xcuaF2woPWj0qbVeseOxH+FJOJUfEyKh3BxlXGONz+e1vqS1rq71rqwNnfJcbYJIZzg7w8LFkCFCtb2RYuge/dHTmFZsmRJ3n//fUvb/PnzWbDAPDfPlct0bZs3vkULp/PGpyhJEYBPA7HXv+QH/nHHB0lZeiEeQaZMsGIFPP20tX3qVBg06JG769+/v10xz549e3Lx4kUAQkLsd0efPg2tW6eeh3JJEYB3AkWVUgFKqTRAW2DpQ65JFC1l6YV4NDlzwpo19ot2R4+GsWMfqSs/Pz9mzJiBf6z69RcuXKB3rETxL70Eb71lvW7TJvs2b+XuZWhzgG1AcaXUaaVUV611BNAbWA0cAeZprQ+5cxxCiEeQP78JwrlyWdv794cZMx6pq8DAQIYOHWppmzt3Lt9//33M69GjoVYt63UTJjzyR6VMzk4ip4RDHsJ5njyE8zyX/8x37dI6UybrkzJfX60XL36kbu7evatDQkIsD+SyZcumT58+HXNOfA/lfv3VtV/JDVLcQziPkTlgIZxQpozZshx7Gdr9+2a33MaNDnfj7+/PrFmzSBOrn6tXr9KlS5folU7kymVSFsf1UO5cylqZ9ki8OgBrmQMWwjk1a9rvlrtzxyT23bvX4W4CAwP58MMPLW1r1qzhiy++iHkdHGye98V25ozZrhwenqjRJ3teHYCFEC7QogVMmmRtu34d6tWDP/5wuJu+ffvaVdDo378/R48ejXndrp19qsrt26FbN6eKOSdbEoCFEA/XrRvYJF7n/HmoUwf+cWwVqY+PDzNnziRz5swxbbdv36Z9+/bci7XubNQo021ss2fbf7w38OoALHPAQrjQ22/bJ204edJESwcnagsWLMj48eMtbTt37rRMT/j5wdy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      "text/plain": "<Figure size 360x360 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
    "\n",
    "cmapDict = {\"Nipbl_w_auxin\": \"red\",\n",
    "            \"Sororin_w_auxin\": \"blue\",\n",
    "            \"WT\": \"black\"}\n",
    "\n",
    "f, ax = plt.subplots(1, 1)\n",
    "plt.subplots_adjust(wspace=0.7)\n",
    "for index, sample in enumerate(pairsNew.keys()):\n",
    "    for rType in [\"cis\"]:\n",
    "        sc = scs[sample][rType]\n",
    "        sc_agg = (sc[0]\n",
    "                  .groupby(['min_dist', 'max_dist'])\n",
    "                  .agg({'n_pairs': 'sum', 'n_bp2': 'sum'})\n",
    "                  .reset_index())\n",
    "        ax.loglog(\n",
    "            np.sqrt(sc_agg.min_dist * sc_agg.max_dist),\n",
    "            sc_agg.n_pairs / sc_agg.n_bp2,\n",
    "            label=f'{sample}',\n",
    "            linewidth=4, color=cmapDict[sample])\n",
    "    ax.grid(lw=0.5, color='gray')\n",
    "    ax.set_xlim(10**4, 10**7)\n",
    "    ax.set_ylim(10**-12, 10**-8)\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"Genomic Distance, bp\")\n",
    "    ax.set_ylabel(\"Contact Probability\")\n",
    "f.set_size_inches(5, 5)\n",
    "sbn.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004869/Results\"\n",
    "f.savefig(os.path.join(resultsdir, \"Scaling_plot_cis_Nipbl_Sororin_WT.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"Scaling_plot_cis_Nipbl_Sororin_WT.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 360x360 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmapDict = {\"Nipbl_w_auxin\": \"red\",\n",
    "            \"Sororin_w_auxin\": \"blue\",\n",
    "            \"WT\": \"black\"}\n",
    "\n",
    "f, ax = plt.subplots(1, 1)\n",
    "plt.subplots_adjust(wspace=0.7)\n",
    "for index, sample in enumerate(pairsNew.keys()):\n",
    "    for rType in [\"trans\"]:\n",
    "        sc = scs[sample][rType]\n",
    "        sc_agg = (sc[0]\n",
    "                  .groupby(['min_dist', 'max_dist'])\n",
    "                  .agg({'n_pairs': 'sum', 'n_bp2': 'sum'})\n",
    "                  .reset_index())\n",
    "        ax.loglog(\n",
    "            np.sqrt(sc_agg.min_dist * sc_agg.max_dist),\n",
    "            sc_agg.n_pairs / sc_agg.n_bp2,\n",
    "            label=f'{sample}',\n",
    "            linewidth=4, color=cmapDict[sample])\n",
    "    ax.grid(lw=0.5, color='gray')\n",
    "    ax.set_xlim(10**4, 10**7)\n",
    "    ax.set_ylim(10**-12, 10**-8)\n",
    "    ax.legend()\n",
    "    ax.set_xlabel(\"Genomic Distance, bp\")\n",
    "    ax.set_ylabel(\"Contact Probability\")\n",
    "f.set_size_inches(5, 5)\n",
    "sbn.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsdir = \"/groups/gerlich/experiments/Experiments_004800/004869/Results\"\n",
    "f.savefig(os.path.join(resultsdir, \"Scaling_plot_trans_Nipbl_Sororin_WT.png\"), bbox_inches=\"tight\")\n",
    "f.savefig(os.path.join(resultsdir, \"Scaling_plot_trans_Nipbl_Sororin_WT.pdf\"), bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}