parallel_bic-after.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "import multiprocessing\n",
    "\n",
    "num_cores = multiprocessing.cpu_count()\n",
    "\n",
    "from functools import reduce\n",
    "\n",
    "import pickle\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data downloading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "D=np.load(\"X.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(D.dtype.names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt=0\n",
    "\n",
    "for r in range(D.shape[0]):\n",
    "    for c in range(D.shape[1]):\n",
    "        if D[r][c]==-1:\n",
    "            cnt+=1\n",
    "                \n",
    "                \n",
    "            \n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "553430"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequential code for object-attribute bicluster generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "start = timer()\n",
    "\n",
    "Objects={}\n",
    "Attributes={}\n",
    "idx=0\n",
    "\n",
    "for r in range(D.shape[0]):\n",
    "    Cols=[]\n",
    "    for c in range(D.shape[1]):\n",
    "        if D[r][c]==-1:\n",
    "            Cols.append(c)\n",
    "    #if r%1000==0: print(r)\n",
    "\n",
    "    Objects[r]=Cols\n",
    "    \n",
    "end = timer()\n",
    "print(\"Object primes\", end - start) # Time in seconds, e.g. 5.38091952400282        \n",
    "\n",
    "start = timer()\n",
    "\n",
    "for c in range(D.shape[1]):\n",
    "    Rows=[]\n",
    "    for r in range(D.shape[0]):\n",
    "        if D[r][c]==-1:\n",
    "            Rows.append(r)\n",
    "    #if c%1000==0: print(c)\n",
    "            \n",
    "    Attributes[c]=Rows\n",
    "    \n",
    "end = timer()\n",
    "print(\"Attribute primes\", end - start)\n",
    "\n",
    "start = timer()\n",
    "    \n",
    "for r in range(D.shape[0]):\n",
    "    for c in range(D.shape[1]):\n",
    "        if D[r][c]==-1:\n",
    "            if [Attributes[c],Objects[r]] not in B.values():\n",
    "                B[(r,c)]=(Attributes[c], Objects[r])\n",
    "                idx=idx+1\n",
    "                #if idx%1000==0: print(idx)\n",
    "            #B[(r,c)]=(Attributes[r], Objects[c])\n",
    "            #idx=idx+1\n",
    "            #if idx%1000==0: print(idx)\n",
    "\n",
    "end = timer()\n",
    "print(\"Pairing phase\", end - start)       \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paralle code for object-attribute bicluster generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object primes 155.13398409599995\n",
      "Attribute primes 109.33147086999998\n",
      "Pairing phase 3433.203342215\n"
     ]
    }
   ],
   "source": [
    "B={}\n",
    "Objects={}\n",
    "Attributes={}\n",
    "idx=0\n",
    "\n",
    "\n",
    "\n",
    "def objPrime(r,D):\n",
    "    Cols=[]\n",
    "    for c in range(D.shape[1]):\n",
    "        if D[r][c]==-1:\n",
    "            Cols.append(c)\n",
    "    if r%1000==0: print(r)\n",
    "\n",
    "    return Cols\n",
    "\n",
    "\n",
    "       \n",
    "\n",
    "\n",
    "start = timer()            \n",
    "\n",
    "    \n",
    "def attrPrime(c,D):    \n",
    "    Rows=[]\n",
    "    for r in range(D.shape[0]):\n",
    "        if D[r][c]==-1:\n",
    "            Rows.append(r)\n",
    "    if c%1000==0: print(c)\n",
    "            \n",
    "    return Rows\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "start = timer()\n",
    "\n",
    "Objects = Parallel(n_jobs=num_cores)(delayed(objPrime)(r,D) for r in range(D.shape[0]))\n",
    "\n",
    "end = timer()\n",
    "print(\"Object primes\", end - start) # Time in seconds, e.g. 5.38091952400282 \n",
    "\n",
    "start = timer()    \n",
    "\n",
    "#Objects = Parallel(n_jobs=num_cores/2)(delayed(objPrime)(r,D) for r in list(range(D.shape[0])))\n",
    "\n",
    "\n",
    "\n",
    "Attributes = Parallel(n_jobs=num_cores)(delayed(attrPrime)(c,D) for c in range(D.shape[1]))\n",
    "\n",
    "end = timer()\n",
    "print(\"Attribute primes\", end - start)\n",
    "\n",
    "start = timer()\n",
    "\n",
    " \n",
    "def biclGen(r,D,Objects,Attributes):\n",
    "    idx=0\n",
    "    B={}\n",
    "    for c in range(D.shape[1]):\n",
    "        if D[r][c]==-1:\n",
    "            \n",
    "             if [Attributes[c],Objects[r]] not in B.values():\n",
    "                B[(r,c)]=[Attributes[c], Objects[r]]\n",
    "                idx=idx+1\n",
    "                if idx%1000==0: print(idx)\n",
    "            #B[(r,c)]=(Attributes[r], Objects[c])\n",
    "            #idx=idx+1\n",
    "            #if idx%1000==0: print(idx)\n",
    "    return B\n",
    "            \n",
    "                \n",
    "\n",
    "    \n",
    "Bicl = Parallel(n_jobs=num_cores)(delayed(biclGen)(r,D, Objects, Attributes) for r in range(D.shape[0]))\n",
    "\n",
    "\n",
    "end = timer()\n",
    "print(\"Pairing phase\", end - start)    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduce 31.883283216000564\n"
     ]
    }
   ],
   "source": [
    "start = timer()\n",
    "\n",
    "B = reduce((lambda x, y: {**x, **y}), Bicl)\n",
    "\n",
    "end = timer()\n",
    "print(\"Reduce\", end - start)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Bicl' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-348-85cdb250caab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBicl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Bicl' is not defined"
     ]
    }
   ],
   "source": [
    "len(Bicl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383733"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(B)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def Density(D,b):\n",
    "    num=0\n",
    "    for i in b[0]:\n",
    "        for j in b[1]:\n",
    "            if D[i][j]==-1:\n",
    "                num=num+1\n",
    "    return num/(len(b[0])*len(b[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9835927375215471"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Density(D,B[(0,0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383733"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computation of basic statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done 100 tasks      | elapsed:  2.8min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 13000 tasks      | elapsed: 33.0min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 25900 tasks      | elapsed: 66.0min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 38800 tasks      | elapsed: 98.6min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 51500 tasks      | elapsed: 130.1min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 64200 tasks      | elapsed: 162.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 76900 tasks      | elapsed: 194.0min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 89600 tasks      | elapsed: 225.5min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 102200 tasks      | elapsed: 256.5min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 114700 tasks      | elapsed: 286.6min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 127200 tasks      | elapsed: 306.3min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 139700 tasks      | elapsed: 319.1min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 152200 tasks      | elapsed: 331.9min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 164700 tasks      | elapsed: 345.3min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 177200 tasks      | elapsed: 360.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 189700 tasks      | elapsed: 369.5min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 202300 tasks      | elapsed: 377.2min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 214800 tasks      | elapsed: 384.1min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 227400 tasks      | elapsed: 389.7min\n",
      "[Parallel(n_jobs=12)]: Done 227500 tasks      | elapsed: 389.7min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 240000 tasks      | elapsed: 391.5min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 252500 tasks      | elapsed: 393.6min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 265000 tasks      | elapsed: 396.6min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 277500 tasks      | elapsed: 403.3min\n",
      "[Parallel(n_jobs=12)]: Done 277600 tasks      | elapsed: 403.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 290000 tasks      | elapsed: 408.7min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 302500 tasks      | elapsed: 411.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 315100 tasks      | elapsed: 413.7min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 327600 tasks      | elapsed: 418.6min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 340100 tasks      | elapsed: 426.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 352600 tasks      | elapsed: 434.4min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 365100 tasks      | elapsed: 437.2min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 377600 tasks      | elapsed: 438.9min\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 382939 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 382946 tasks      | elapsed: 439.7min\n",
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      "[Parallel(n_jobs=12)]: Done 383205 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383212 tasks      | elapsed: 439.7min\n",
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      "[Parallel(n_jobs=12)]: Done 383233 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383240 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383247 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383254 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383261 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383268 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383275 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383282 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383284 tasks      | elapsed: 439.7min\n",
      "[Parallel(n_jobs=12)]: Done 383733 out of 383733 | elapsed: 439.8min finished\n",
      "Density calculation phase 26388.937483056\n"
     ]
    }
   ],
   "source": [
    "def biclDenLen(D,Bk,key):\n",
    "    den={}\n",
    "    den[key]=(Density(D,Bk), len(Bk[0]),len(Bk[1]))\n",
    "    if key[0]%10==0: print(key)\n",
    "    return den\n",
    "           \n",
    "keys=B.keys()\n",
    "\n",
    "import os\n",
    "from joblib import dump, load\n",
    "\n",
    "folder = './joblib_memmap2'\n",
    "try:\n",
    "    os.mkdir(folder)\n",
    "except FileExistsError:\n",
    "    pass\n",
    "\n",
    "data_filename_memmap = os.path.join(folder, 'data_memmap')\n",
    "dict_filename_memmap = os.path.join(folder, 'dict_memmap')\n",
    "\n",
    "dump(D, data_filename_memmap)\n",
    "dump(B, dict_filename_memmap)\n",
    "D = load(data_filename_memmap, mmap_mode='r')\n",
    "B = load(dict_filename_memmap, mmap_mode='r')\n",
    "                \n",
    "start = timer()\n",
    "    \n",
    "#Bdl = Parallel(n_jobs=num_cores, backend=\"threading\", verbose=100, pre_dispatch=\"100*n_jobs\", batch_size=100)(delayed(biclDenLen)(D,B[key],key) for key in keys)\n",
    "Bdl = Parallel(n_jobs=num_cores, verbose=100, pre_dispatch=\"100*n_jobs\", batch_size=100)(delayed(biclDenLen)(D,B[key],key) for key in keys)\n",
    "\n",
    "\n",
    "\n",
    "end = timer()\n",
    "print(\"Density calculation phase\", end - start) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383733"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Bdl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 0): (0.9835927375215471, 89, 2679)}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bdl[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f = open(\"Bi_stat.pkl\",\"wb\")\n",
    "pickle.dump(Bdl,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Bicl_stat=[]\n",
    "for d in Bdl:\n",
    "    Bicl_stat.append(list(list(d.values())[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Searching for relevant biclusters with constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_Bs=pd.DataFrame(Bicl_stat, columns =['Density', 'Extent', 'Intent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Bs['Square']=df_Bs['Extent']*df_Bs['Intent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_Bs['Density2Square']=(df_Bs['Density']**2)*df_Bs['Square']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "      <th>Square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>383733.000000</td>\n",
       "      <td>383733.000000</td>\n",
       "      <td>383733.000000</td>\n",
       "      <td>383733.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.635973</td>\n",
       "      <td>73.413710</td>\n",
       "      <td>1601.187646</td>\n",
       "      <td>119156.579174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.290122</td>\n",
       "      <td>47.826283</td>\n",
       "      <td>1032.256770</td>\n",
       "      <td>111572.910831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.009406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.399468</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>566.000000</td>\n",
       "      <td>18144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.703770</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1724.000000</td>\n",
       "      <td>70684.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.907211</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>2643.000000</td>\n",
       "      <td>242424.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>3423.000000</td>\n",
       "      <td>581910.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Density         Extent         Intent         Square\n",
       "count  383733.000000  383733.000000  383733.000000  383733.000000\n",
       "mean        0.635973      73.413710    1601.187646  119156.579174\n",
       "std         0.290122      47.826283    1032.256770  111572.910831\n",
       "min         0.009406       1.000000       4.000000       4.000000\n",
       "25%         0.399468      29.000000     566.000000   18144.000000\n",
       "50%         0.703770      91.000000    1724.000000   70684.000000\n",
       "75%         0.907211     104.000000    2643.000000  242424.000000\n",
       "max         1.000000     183.000000    3423.000000  581910.000000"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/Anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dd644b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc577472f10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5951a8bd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dd66ff50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5040x2160 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize = (70,30))\n",
    "ax = fig.gca()\n",
    "\n",
    "df_Bs.hist(ax = ax, bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection of dense and large biclusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Sel=[]\n",
    "for triple in range(len(Bicl_stat)):\n",
    "    if Bicl_stat[triple][0]>=0.90 and Bicl_stat[triple][1]>=3 and Bicl_stat[triple][1]<=1500 and Bicl_stat[triple][2]>=3 and Bicl_stat[triple][2]<=80000:\n",
    "        Sel.append(Bicl_stat[triple])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98529"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Sel=pd.DataFrame(Sel, columns =['Density', 'Extent', 'Intent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Sel['Square']=df_Sel['Extent']*df_Sel['Intent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       " ...]"
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     "execution_count": 414,
     "metadata": {},
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   "source": [
    "Sel"
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  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/Anaconda/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  \n"
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    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5db5202d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc59522cbd0>],\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5618cfc10>]],\n",
       "      dtype=object)"
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\n",
      "text/plain": [
       "<Figure size 1800x2160 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize = (25,30))\n",
    "ax = fig.gca()\n",
    "\n",
    "df_Sel.hist(ax = ax, bins=100, log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f = open(\"B.pkl\",\"rb\")\n",
    "Bp=pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383733"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Bp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "B=Bp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "91"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "165-170"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def Selection(Bicl_stat, density_min, density_max, ext_min, ext_max, int_min, int_max):\n",
    "    Sel=[]\n",
    "    for triple in range(len(Bicl_stat)):\n",
    "        if Bicl_stat[triple][0]>=density_min and Bicl_stat[triple][0]<=density_max and Bicl_stat[triple][1]>=ext_min and Bicl_stat[triple][1]<=ext_max and Bicl_stat[triple][2]>=int_min and Bicl_stat[triple][2]<=int_max:\n",
    "            Sel.append(Bicl_stat[triple])\n",
    "    return Sel            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.940876</td>\n",
       "      <td>122.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.940876</td>\n",
       "      <td>122.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.940876</td>\n",
       "      <td>122.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.940876</td>\n",
       "      <td>122.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.940876</td>\n",
       "      <td>122.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.940876</td>\n",
       "      <td>122.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.940876   122.0   455.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.940876   122.0   455.0\n",
       "25%    0.940876   122.0   455.0\n",
       "50%    0.940876   122.0   455.0\n",
       "75%    0.940876   122.0   455.0\n",
       "max    0.940876   122.0   455.0"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.9408,1, 122, 122, 3, 80000), columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc63765c390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc6376969d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc591bde6d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc591c11ed0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.9408,1, 122, 122, 3, 80000), columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.999637</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.999637</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.999637</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.999637</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.999637</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.999637</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2634.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.999637    89.0  2634.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.999637    89.0  2634.0\n",
       "25%    0.999637    89.0  2634.0\n",
       "50%    0.999637    89.0  2634.0\n",
       "75%    0.999637    89.0  2634.0\n",
       "max    0.999637    89.0  2634.0"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.999,1, 89, 89, 3, 80000), columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc591d9df10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc591d96b10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc591e1fc90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dce574d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9vT0iziM7q+j8/P2+CZwaEcP2S+g+2uIbko4k+w/7soi4ByAfjz41Ik6JiNWS/plsxxDg/OHaFoNpB7Ks+Pe832wEfCsi+gx436qgSSQtJ/sybVL+936yXwF6CTgtIu6U9E3gbOANsn/sqyJilqQ/Jxtn3hd4HviniLgpf92ryL51byMLgceBT0XEbyXdBxxI9h9BkJ1qdWNL3rCZDTm+krU1pgBPkN3E/9vAlZIUEecCPwdmRcQWebiPJTtN6jpgW+BY4N/Kzho5Fvg62TnBS4FvAkTER/Ple+Wv53A3G8Ec8K3xVERcERFvkp0NtB0woUrZI4HlEfHDiFgfEQ8BPyIbouh1S0Q8EBHrgXlkQzxmZm9TyBj8CPSH3icR8Wr+XeEWVcruCEyR9FLJvNHANZVej2w4ptprmdkI5oAvXvmXICuAeyPi0CIqY2bp8BBN8Z4luydPrzuAXSSdIGnj/LGfpF0H+HpmNkI54Iv3feAYSS9KujgiXiE7z3c62cUcfwAuAMbU+XpfA+ZKeknS3zSjwmY2PPg0STOzRHkP3swsUQ54M7NEOeDNzBLlgDczS1Rh58GPHz8+2traKi5bu3YtY8eObW2FhiC3Q6avdli0aNELEbFNi6tkNiwUFvBtbW0sXLiw4rKuri46OjpaW6EhyO2Q6asdJD3V2tqYDR81h2hU+9fuJeliSUvzn9fbp/HVNDOz/qpnDP4q+v6B28PJfoxgMjATuGzw1TIzs8GqGfB1/Nr9NODqyNwPbFX2a0xmZlaARozBV/vF8w1+Vk7STLK9fCZMmEBXV1fFF+zp6am6bCR5bvUaLpl3W7/W2WPiuCbVpjjuD2YD04iAr/sXzyNiNtnPU9He3h7Vvjjzl4uZS+bdxoWP9u+faPlxHc2pTIHcH8wGphHnwaf0i+dmZsloRMB3AifmZ9McAKyJiA2GZ8zMrLVqHv/X8Wv384EjyH4b9FXg082qrJmZ1a9mwEfEsTWWB/D5htXIzMwawveiMTNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MElVXwEs6TNITkpZKOrvC8hmSnpf0cP44pfFVNTOz/hhdq4CkUcClwKFAN/CgpM6IeLys6I0RMasJdTQzswGoZw9+f2BpRCyLiNeBG4Bpza2WmZkNVs09eGAisKJkuhuYUqHc0ZI+CjwJnBERK8oLSJoJzASYMGECXV1dFTfY09NTddlIMmEzOHOP9f1aJ8V2c38wG5h6Al4V5kXZ9O3A9RGxTtKpwFzgoA1WipgNzAZob2+Pjo6Oihvs6uqi2rKR5JJ5t3Hho/X8E71l+XEdzalMgdwfzAamniGabmCHkulJwMrSAhGxKiLW5ZNXAPs2pnpmZjZQ9QT8g8BkSTtJ2gSYDnSWFpC0XcnkVGBJ46poZmYDUfP4PyLWS5oF3AWMAuZExGJJ5wMLI6ITOF3SVGA9sBqY0cQ6m5lZHeoa4I2I+cD8snnnlTw/BzinsVUzM7PB8JWsZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZohzwZmaJcsCbmSXKAW9mligHvJlZouoKeEmHSXpC0lJJZ1dYPkbSjfnyBZLaGl1RMzPrn5oBL2kUcClwOLAbcKyk3cqKnQy8GBE7AxcBFzS6omZm1j/17MHvDyyNiGUR8TpwAzCtrMw0YG7+/GbgYElqXDXNzKy/RtdRZiKwomS6G5hSrUxErJe0BngX8EJpIUkzgZn5ZI+kJ6psc3z5uiNUv9tBaR479dUOO7ayImbDST0BX2lPPAZQhoiYDcyuuUFpYUS011G3pLkdMm4Hs4GpZ4imG9ihZHoSsLJaGUmjgXHA6kZU0MzMBqaegH8QmCxpJ0mbANOBzrIyncBJ+fNjgHsiYoM9eDMza52aQzT5mPos4C5gFDAnIhZLOh9YGBGdwJXANZKWku25Tx9kvWoO44wQboeM28FsAOQdbTOzNPlKVjOzRDngzcwS1fCAl7SDpP+RtETSYkl/V7LsC/ktDxZL+nY+b39JD+ePRyT9dT5/U0kP5PMWS/p6le3NkPR8yWuc0uj3NBCNaoeSdUZJ+qWkO6psb0jeLqKAdhiS/cGsEBHR0AewHbBP/vwdwJNktzj4S+BuYEy+bNv87+bA6JJ1nyP78lfAFvn8jYEFwAEVtjcD+EGj38dQaYeS1/sicB1wR5XtnQZcnj+fDtxYdBsU1A5Dsj/44UcRj4bvwUfEMxHxUP78FWAJ2ZWunwO+FRHr8mXP5X9fjYj1+eqbkl8gFZmefP7G+WPYfCPcqHYAkDQJ+CvgP/rY5JC8XUQB7WBmuaaOwefDBB8g2/veBTgwHz64V9J+JeWmSFoMPAqc2vsBzw/HHybbi/tZRCyosqmjJf1K0s2SdqhSpjCDbQfge8BZwB/72MzbbhcB9N4uYshoUTvAEO8PZq3StICXtAXwI+DvI+JlsmGXrYEDgH8Aburdw4yIBRHxfmA/4BxJm+bz34yIvcmunt1f0u4VNnU70BYRe5Id8s+tUKYwg20HSUcCz0XEolqbqjBvyBzxtLAdhnR/MGulpgS8pI3JPszzIuKWfHY3cEs+9PIA2V7Y+NL1ImIJsBbYvWz+S0AXcFj5tiJiVe9hPnAFsG8D38qgNKgdPgxMlbSc7E6eB0m6tsLmhuztIlrZDkO5P5i1WjPOohHZla1LIuK7JYtuBQ7Ky+wCbAK8oOwWCKPz+TsC7wOWS9pG0lb5/M2AQ4BfV9jediWTU8nGeAvXqHaIiHMiYlJEtJF9eXpPRBxfYZND8nYRrW6HodofzIpQz90k++vDwAnAo/n4OcBXgDnAHEmPAa8DJ0VESPoIcLakN8j24k6LiBck7QnMVfaDIxsBN0XEHQB6+20STpc0FVhPtsc6ownvaSAa0g59bUDNvV1Eo7S6HYZqfzBrOd+qwMwsUb6S1cwsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBL1/xRkDXui4ijdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.999,1, 89, 89, 3, 80000), columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.9906</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.9906</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.9906</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.9906</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.9906</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.9906</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1714.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Density  Extent  Intent\n",
       "count   1.0000     1.0     1.0\n",
       "mean    0.9906    36.0  1714.0\n",
       "std        NaN     NaN     NaN\n",
       "min     0.9906    36.0  1714.0\n",
       "25%     0.9906    36.0  1714.0\n",
       "50%     0.9906    36.0  1714.0\n",
       "75%     0.9906    36.0  1714.0\n",
       "max     0.9906    36.0  1714.0"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.99,1, 36, 40, 3, 80000), columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc575798750>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc637997a50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dcdcafd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dce0c810>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.99,1, 36, 40, 3, 80000), columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.960016</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.960016    18.0  1552.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.960016    18.0  1552.0\n",
       "25%    0.960016    18.0  1552.0\n",
       "50%    0.960016    18.0  1552.0\n",
       "75%    0.960016    18.0  1552.0\n",
       "max    0.960016    18.0  1552.0"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.96,1, 18, 18, 3, 80000),columns =['Density', 'Extent', 'Intent']).describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dcf58b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc637bbb1d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc637c8f7d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc550632f10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.96,1, 17, 22, 3, 80000),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.954429</td>\n",
       "      <td>166.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.954429</td>\n",
       "      <td>166.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.954429</td>\n",
       "      <td>166.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.954429</td>\n",
       "      <td>166.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.954429</td>\n",
       "      <td>166.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.954429</td>\n",
       "      <td>166.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.954429   166.0   108.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.954429   166.0   108.0\n",
       "25%    0.954429   166.0   108.0\n",
       "50%    0.954429   166.0   108.0\n",
       "75%    0.954429   166.0   108.0\n",
       "max    0.954429   166.0   108.0"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.954,1, 160, 175, 3, 80000),columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc591ec2510>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dd060810>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dd0b1d50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc550690590>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.954,1, 160, 175, 3, 80000),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.921795</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.921795</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.921795</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.921795</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.921795</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.921795</td>\n",
       "      <td>3.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.921795     3.0   520.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.921795     3.0   520.0\n",
       "25%    0.921795     3.0   520.0\n",
       "50%    0.921795     3.0   520.0\n",
       "75%    0.921795     3.0   520.0\n",
       "max    0.921795     3.0   520.0"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.92,1, 3, 1500, 520, 600),columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc574c628d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5918f3850>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc6371bea10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc59193dd10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.92,1, 3, 1500, 520, 600),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52228.02676"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.940876*122.0*455.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55510.0"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "122.0*455.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.900422</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.900422</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.900422</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.900422</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.900422</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.900422</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2052.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.900422     3.0  2052.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.900422     3.0  2052.0\n",
       "25%    0.900422     3.0  2052.0\n",
       "50%    0.900422     3.0  2052.0\n",
       "75%    0.900422     3.0  2052.0\n",
       "max    0.900422     3.0  2052.0"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.90,1, 3, 1500, 2000, 2500),columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dd026850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5600de050>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc575882ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5b643bb90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.9,1, 3, 1500, 2000, 2500),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.927514</td>\n",
       "      <td>14.0</td>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.927514</td>\n",
       "      <td>14.0</td>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.927514</td>\n",
       "      <td>14.0</td>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.927514</td>\n",
       "      <td>14.0</td>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.927514</td>\n",
       "      <td>14.0</td>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.927514</td>\n",
       "      <td>14.0</td>\n",
       "      <td>743.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.927514    14.0   743.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.927514    14.0   743.0\n",
       "25%    0.927514    14.0   743.0\n",
       "50%    0.927514    14.0   743.0\n",
       "75%    0.927514    14.0   743.0\n",
       "max    0.927514    14.0   743.0"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.9257,1, 14, 14, 3, 1000),columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5759d0450>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc575a04710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc575abcc50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5dd14cfd0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.9257,1, 14, 14, 3, 90000),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.975309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.975309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.975309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.975309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.975309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.975309</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  1.000000     1.0     1.0\n",
       "mean   0.975309     3.0   108.0\n",
       "std         NaN     NaN     NaN\n",
       "min    0.975309     3.0   108.0\n",
       "25%    0.975309     3.0   108.0\n",
       "50%    0.975309     3.0   108.0\n",
       "75%    0.975309     3.0   108.0\n",
       "max    0.975309     3.0   108.0"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.97,1, 3, 40, 3, 1000),columns =['Density', 'Extent', 'Intent']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5507bac50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc575bd81d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5507f7e50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc592037b10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.97,1, 3, 40, 3, 1000),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.910573</td>\n",
       "      <td>14.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.910573</td>\n",
       "      <td>14.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.910573</td>\n",
       "      <td>14.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.910573</td>\n",
       "      <td>14.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.910573</td>\n",
       "      <td>14.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.910573</td>\n",
       "      <td>14.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Density  Extent  Intent\n",
       "count  2.000000     2.0     2.0\n",
       "mean   0.910573    14.0   758.0\n",
       "std    0.000000     0.0     0.0\n",
       "min    0.910573    14.0   758.0\n",
       "25%    0.910573    14.0   758.0\n",
       "50%    0.910573    14.0   758.0\n",
       "75%    0.910573    14.0   758.0\n",
       "max    0.910573    14.0   758.0"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.91,1, 3, 40, 758, 758),columns =['Density', 'Extent', 'Intent']).describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc5920c7f90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc5a5588350>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc59215cb90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc59219ce90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Selection(Bicl_stat,0.9,1, 3, 40, 758, 758),columns =['Density', 'Extent', 'Intent']).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Incorporating RS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_dictionary = np.load('snp_dict.npy', allow_pickle='TRUE').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'rs12086311_0',\n",
       " 1: 'rs4475691_A',\n",
       " 2: 'rs9777703_G',\n",
       " 3: 'rs3934834_A',\n",
       " 4: 'rs3737728_A',\n",
       " 5: 'rs6687776_A',\n",
       " 6: 'rs4970405_G',\n",
       " 7: 'rs4970362_A',\n",
       " 8: 'rs9660710_A',\n",
       " 9: 'rs9729550_C',\n",
       " 10: 'rs3813199_A',\n",
       " 11: 'rs715643_A',\n",
       " 12: 'rs7524470_G',\n",
       " 13: 'rs2649588_G',\n",
       " 14: 'rs819980_G',\n",
       " 15: 'rs6690515_A',\n",
       " 16: 'rs6603793_A',\n",
       " 17: 'rs7531583_A',\n",
       " 18: 'rs742359_A',\n",
       " 19: 'rs6681938_G',\n",
       " 20: 'rs7525092_A',\n",
       " 21: 'rs2803291_A',\n",
       " 22: 'rs2254669_G',\n",
       " 23: 'rs884080_A',\n",
       " 24: 'rs908742_A',\n",
       " 25: 'rs3107151_A',\n",
       " 26: 'rs3128291_A',\n",
       " 27: 'rs6665593_A',\n",
       " 28: 'rs7512482_G',\n",
       " 29: 'rs2460000_A',\n",
       " 30: 'rs260513_A',\n",
       " 31: 'rs7547453_A',\n",
       " 32: 'rs11588312_A',\n",
       " 33: 'rs7553178_G',\n",
       " 34: 'rs10910047_A',\n",
       " 35: 'rs2279702_A',\n",
       " 36: 'rs2843157_A',\n",
       " 37: 'rs903904_A',\n",
       " 38: 'rs2843143_A',\n",
       " 39: 'rs2055204_G',\n",
       " 40: 'rs2840528_G',\n",
       " 41: 'rs903914_A',\n",
       " 42: 'rs7545940_G',\n",
       " 43: 'rs2843160_C',\n",
       " 44: 'rs2643901_A',\n",
       " 45: 'rs4648633_G',\n",
       " 46: 'rs3001336_G',\n",
       " 47: 'rs2494428_G',\n",
       " 48: 'rs6659405_A',\n",
       " 49: 'rs11581548_A',\n",
       " 50: 'rs10910078_A',\n",
       " 51: 'rs2494626_A',\n",
       " 52: 'rs2477703_G',\n",
       " 53: 'rs10797430_A',\n",
       " 54: 'rs2234167_A',\n",
       " 55: 'rs2495365_A',\n",
       " 56: 'rs4310388_A',\n",
       " 57: 'rs6698817_A',\n",
       " 58: 'rs28584125_A',\n",
       " 59: 'rs4648356_A',\n",
       " 60: 'rs10909880_A',\n",
       " 61: 'rs4609377_G',\n",
       " 62: 'rs12409348_A',\n",
       " 63: 'rs10797342_G',\n",
       " 64: 'rs2045333_A',\n",
       " 65: 'rs3100865_C',\n",
       " 66: 'rs1869970_G',\n",
       " 67: 'rs2925499_G',\n",
       " 68: 'rs10909845_A',\n",
       " 69: 'rs2485945_G',\n",
       " 70: 'rs12044030_G',\n",
       " 71: 'rs12046158_G',\n",
       " 72: 'rs2142569_A',\n",
       " 73: 'rs2297828_A',\n",
       " 74: 'rs2817168_G',\n",
       " 75: 'rs6690373_G',\n",
       " 76: 'rs2817185_G',\n",
       " 77: 'rs731031_G',\n",
       " 78: 'rs10752733_A',\n",
       " 79: 'rs10737190_G',\n",
       " 80: 'rs7539775_G',\n",
       " 81: 'rs10492941_A',\n",
       " 82: 'rs2817146_A',\n",
       " 83: 'rs2455118_G',\n",
       " 84: 'rs4648464_G',\n",
       " 85: 'rs3002686_A',\n",
       " 86: 'rs2455119_A',\n",
       " 87: 'rs10492940_A',\n",
       " 88: 'rs10909918_G',\n",
       " 89: 'rs4648377_A',\n",
       " 90: 'rs2455144_A',\n",
       " 91: 'rs12409345_G',\n",
       " 92: 'rs2483266_G',\n",
       " 93: 'rs2500271_G',\n",
       " 94: 'rs2483274_A',\n",
       " 95: 'rs2483289_A',\n",
       " 96: 'rs2483256_G',\n",
       " 97: 'rs2500286_G',\n",
       " 98: 'rs12135987_A',\n",
       " 99: 'rs1538693_G',\n",
       " 100: 'rs17399998_A',\n",
       " 101: 'rs871822_C',\n",
       " 102: 'rs6424074_G',\n",
       " 103: 'rs2493292_A',\n",
       " 104: 'rs1984069_G',\n",
       " 105: 'rs870171_A',\n",
       " 106: 'rs2493272_G',\n",
       " 107: 'rs2483250_A',\n",
       " 108: 'rs12239794_A',\n",
       " 109: 'rs2487670_A',\n",
       " 110: 'rs2185639_G',\n",
       " 111: 'rs12725009_A',\n",
       " 112: 'rs947344_A',\n",
       " 113: 'rs11585362_A',\n",
       " 114: 'rs6685801_G',\n",
       " 115: 'rs10909969_A',\n",
       " 116: 'rs947354_A',\n",
       " 117: 'rs2181484_A',\n",
       " 118: 'rs3765703_C',\n",
       " 119: 'rs3765705_A',\n",
       " 120: 'rs3765731_A',\n",
       " 121: 'rs3765736_A',\n",
       " 122: 'rs3753211_G',\n",
       " 123: 'rs12117836_A',\n",
       " 124: 'rs3737589_G',\n",
       " 125: 'rs1181888_G',\n",
       " 126: 'rs1181883_G',\n",
       " 127: 'rs2275819_A',\n",
       " 128: 'rs6667255_G',\n",
       " 129: 'rs6702935_A',\n",
       " 130: 'rs6663840_A',\n",
       " 131: 'rs6683156_A',\n",
       " 132: 'rs12738235_A',\n",
       " 133: 'rs7367066_A',\n",
       " 134: 'rs12724233_A',\n",
       " 135: 'rs10915433_G',\n",
       " 136: 'rs7522140_G',\n",
       " 137: 'rs12749761_A',\n",
       " 138: 'rs9792946_G',\n",
       " 139: 'rs10753358_C',\n",
       " 140: 'rs4436359_C',\n",
       " 141: 'rs10915423_A',\n",
       " 142: 'rs4481820_A',\n",
       " 143: 'rs7548756_C',\n",
       " 144: 'rs10915434_G',\n",
       " 145: 'rs7542208_A',\n",
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       " 959: 'rs6667212_G',\n",
       " 960: 'rs2185341_A',\n",
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       " 962: 'rs12145722_G',\n",
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       " 964: 'rs209709_G',\n",
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       " 968: 'rs2031749_A',\n",
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       " 970: 'rs2869513_G',\n",
       " 971: 'rs893964_A',\n",
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       " 973: 'rs12410046_A',\n",
       " 974: 'rs1561624_A',\n",
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       " 976: 'rs12723359_G',\n",
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       " 978: 'rs6667416_A',\n",
       " 979: 'rs4655134_C',\n",
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       " 981: 'rs722993_A',\n",
       " 982: 'rs309539_A',\n",
       " 983: 'rs4654828_A',\n",
       " 984: 'rs925826_A',\n",
       " 985: 'rs1208932_A',\n",
       " 986: 'rs765803_C',\n",
       " 987: 'rs11587694_G',\n",
       " 988: 'rs3845522_A',\n",
       " 989: 'rs628465_C',\n",
       " 990: 'rs655600_G',\n",
       " 991: 'rs1212034_G',\n",
       " 992: 'rs2275355_G',\n",
       " 993: 'rs3737646_G',\n",
       " 994: 'rs2077066_G',\n",
       " 995: 'rs7522616_A',\n",
       " 996: 'rs2075993_G',\n",
       " 997: 'rs2038027_A',\n",
       " 998: 'rs3218121_A',\n",
       " 999: 'rs909534_A',\n",
       " ...}"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 0): (0.9835927375215471, 89, 2679)}"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bdl[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bf={}\n",
    "for idx in range(len(B)):\n",
    "    #B[list(Bdl[idx].keys())[0]]\n",
    "    Bf[list(Bdl[idx].keys())[0]]=B[list(Bdl[idx].keys())[0]],Bicl_stat[idx]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FullSelection(Bdl,Bicl_stat, density_min, density_max, ext_min, ext_max, int_min, int_max):\n",
    "    Bf={}\n",
    "    for triple in range(len(Bicl_stat)):\n",
    "        if Bicl_stat[triple][0]>=density_min and Bicl_stat[triple][0]<=density_max and Bicl_stat[triple][1]>=ext_min and Bicl_stat[triple][1]<=ext_max and Bicl_stat[triple][2]>=int_min and Bicl_stat[triple][2]<=int_max:\n",
    "            Bf[list(Bdl[triple].keys())[0]]=B[list(Bdl[triple].keys())[0]],Bicl_stat[triple]\n",
    "    return Bf  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex=FullSelection(Bdl,Bicl_stat,0.91,1, 3, 40, 758, 758)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EncodeSelection(Bf, enc_dict):\n",
    "    B_enc={}\n",
    "    for key in Bf.keys():\n",
    "        enc=[]\n",
    "        for rs_idx in Bf[key][0][1]:\n",
    "            enc.append(enc_dict[rs_idx])\n",
    "            B_enc[(key[0],enc_dict[key[1]])]= Bf[key][0][0],enc,Bf[key][1][0],Bf[key][1][1],Bf[key][1][2],Bf[key][1][1]*Bf[key][1][2]\n",
    "    return B_enc       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex1=EncodeSelection(ex, read_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'rs3846306_C', 'rs156569_A', 'rs12503435_A', 'rs10517903_C', 'rs6855872_A', 'rs409190_A', 'rs6871960_C', 'rs151140_G', 'rs1502045_C', 'rs1446040_A', 'rs4146556_A', 'rs12187814_A', 'rs4702207_G', 'rs16886522_C', 'rs173585_A', 'rs4048810_G', 'rs10434626_A', 'rs576070_C', 'rs1864121_A', 'rs735200_A', 'rs292170_G', 'rs1020360_A', 'rs1424908_C', 'rs31242_C', 'rs10057304_A', 'rs2086675_G', 'rs6450431_G', 'rs2962014_G', 'rs158150_A', 'rs163192_C', 'rs920581_G', 'rs13153507_A', 'rs978180_A', 'rs2416258_G', 'rs306083_A', 'rs1838205_G', 'rs17336742_A', 'rs2662468_A', 'rs1368187_C', 'rs747013_A', 'rs12656417_G', 'rs337100_A', 'rs10519721_A', 'rs459380_G', 'rs389333_C', 'rs918440_C', 'rs10072362_C', 'rs2967806_G', 'rs248471_G', 'rs461355_C', 'rs6892131_G', 'rs7715908_G', 'rs806600_G', 'rs2659186_C', 'rs7703742_G', 'rs9502072_A', 'rs9394080_A', 'rs9462724_G', 'rs2237166_G', 'rs1359067_C', 'rs9367952_A', 'rs1555108_C', 'rs3131024_A', 'rs3095089_A', 'rs17589516_G', 'rs1433742_A', 'rs7741552_G', 'rs9395247_A', 'rs7738712_A', 'rs4712057_A', 'rs9453530_A', 'rs509417_A', 'rs1564609_A', 'rs9294116_A', 'rs1570933_A', 'rs9450248_A', 'rs589644_C', 'rs1556091_A', 'rs12191179_A', 'rs195851_C', 'rs2247210_C', 'rs643863_A', 'rs7765952_A', 'rs4946463_C', 'rs311239_G', 'rs9386625_G', 'rs2110904_G', 'rs1884351_A', 'rs12525721_G', 'rs2039683_A', 'rs12190331_C', 'rs11961682_A', 'rs9402950_A', 'rs9321861_C', 'rs7760387_C', 'rs12193181_A', 'rs4289669_A', 'rs9397085_A', 'rs11961360_G', 'rs240852_A', 'rs8191794_A', 'rs10046330_A', 'rs1885656_G', 'rs1106296_G', 'rs6964780_G', 'rs6463266_C', 'rs9655048_A', 'rs10279978_A', 'rs4724739_A', 'rs13236240_A', 'rs886727_G', 'rs17161716_G', 'rs1990103_C', 'rs2389577_A', 'rs1523635_A', 'rs11979495_G', 'rs6461521_A', 'rs4418236_A', 'rs17604090_A', 'rs10274825_C', 'rs12535229_A', 'rs12702272_C', 'rs1379183_A', 'rs12669559_C', 'rs4326334_G', 'rs4948060_C', 'rs6955170_A', 'rs11770537_G', 'rs2530427_A', 'rs10273681_C', 'rs7790360_A', 'rs4588797_A', 'rs3108450_G', 'rs798309_G', 'rs322005_A', 'rs2158376_A', 'rs4728584_A', 'rs10486905_G', 'rs2157814_A', 'rs767198_A', 'rs3816943_A', 'rs711427_C', 'rs42162_G', 'rs1227171_G', 'rs12670187_A', 'rs764891_C', 'rs2063993_A', 'rs629598_A', 'rs10276300_A', 'rs6946999_G', 'rs4424129_A', 'rs4540426_A', 'rs12542460_A', 'rs7000959_A', 'rs10503830_C', 'rs4732990_A', 'rs1413875_C', 'rs10504003_A', 'rs10095466_A', 'rs527238_G', 'rs1425892_C', 'rs2726556_G', 'rs623917_A', 'rs7003213_A', 'rs11997057_G', 'rs979843_C', 'rs2008566_C', 'rs10102377_A', 'rs12155981_C', 'rs9297941_C', 'rs12678345_A', 'rs11780016_G', 'rs1051624_A', 'rs3886489_A', 'rs1037698_C', 'rs2981248_A', 'rs4324901_A', 'rs1118409_G', 'rs1695715_A', 'rs10089630_C', 'rs4360333_G', 'rs10505399_A', 'rs16901313_A', 'rs398267_A', 'rs4074676_A', 'rs7829310_C', 'rs4313191_G', 'rs4909666_A', 'rs7816828_A', 'rs12681305_C', 'rs1536927_C', 'rs406322_A', 'rs17613887_G', 'rs7864527_A', 'rs4742605_C', 'rs668205_C', 'rs1978746_C', 'rs754819_A', 'rs6475848_C', 'rs1590444_A', 'rs10972658_C', 'rs10758418_C', 'rs894520_G', 'rs4298574_A', 'rs10780986_G', 'rs2125391_G', 'rs2273765_C', 'rs12344488_A', 'rs10867395_G', 'rs10908892_A', 'rs2613319_C', 'rs2498861_A', 'rs1323384_A', 'rs1211179_G', 'rs11795133_A', 'rs7022225_A', 'rs4978937_G', 'rs1329407_A', 'rs7023913_G', 'rs4837366_G', 'rs7863546_C', 'rs13301522_A', 'rs11250783_A', 'rs6601975_G', 'rs1157042_A', 'rs7917456_G', 'rs541045_C', 'rs4749728_A', 'rs12251362_G', 'rs7904993_G', 'rs1026580_G', 'rs7909832_A', 'rs3004928_C', 'rs660301_G', 'rs1938416_A', 'rs2506034_G', 'rs947696_A', 'rs7906426_G', 'rs4948627_A', 'rs11239374_A', 'rs7904463_A', 'rs12243919_A', 'rs11006576_G', 'rs1684895_C', 'rs1272018_G', 'rs880031_A', 'rs1250590_A', 'rs1856607_C', 'rs10509539_A', 'rs2026912_A', 'rs1125326_G', 'rs4917753_A', 'rs4919593_A', 'rs1936146_A', 'rs363294_A', 'rs82612_G', 'rs2186171_A', 'rs7093850_A', 'rs3125524_A', 'rs7115847_A', 'rs10765849_C', 'rs2736578_A', 'rs1507625_A', 'rs3884596_C', 'rs2035631_C', 'rs3935878_G', 'rs1124115_A', 'rs11024021_A', 'rs3740713_C', 'rs831465_A', 'rs1386253_G', 'rs1436094_C', 'rs2076622_A', 'rs7117886_C', 'rs11036050_A', 'rs2172998_A', 'rs1566729_A', 'rs17556442_A', 'rs11602122_A', 'rs1157343_A', 'rs4414243_C', 'rs7123143_C', 'rs17142769_G', 'rs948018_C', 'rs7943194_G', 'rs6592315_C', 'rs1986779_G', 'rs2129521_G', 'rs11225401_G', 'rs10895487_A', 'rs10890873_A', 'rs681200_C', 'rs2000571_A', 'rs478693_G', 'rs6589870_G', 'rs10893749_A', 'rs1873886_A', 'rs11600358_A', 'rs215231_A', 'rs4766100_G', 'rs980131_A', 'rs2364482_C', 'rs2364484_C', 'rs1558333_C', 'rs11614445_G', 'rs2604259_G', 'rs16912030_G', 'rs7960186_C', 'rs2728632_C', 'rs10506027_C', 'rs1260712_G', 'rs4768236_C', 'rs7306163_C', 'rs7980005_G', 'rs1894033_A', 'rs12425300_G', 'rs10877627_C', 'rs3741607_G', 'rs975917_G', 'rs2620777_G', 'rs10745432_A', 'rs10859005_A', 'rs10506999_C', 'rs10777647_G', 'rs1522307_G', 'rs732742_G', 'rs922970_A', 'rs11060870_A', 'rs9506634_A', 'rs1330918_C', 'rs2077899_C', 'rs2863937_A', 'rs3002216_A', 'rs6491070_G', 'rs694609_A', 'rs11618682_A', 'rs9578132_G', 'rs9805284_A', 'rs9634672_A', 'rs2324460_G', 'rs9549281_C', 'rs7993974_G', 'rs1172417_C', 'rs9316642_G', 'rs4142335_C', 'rs9543740_G', 'rs6563019_A', 'rs4584703_A', 'rs9547351_G', 'rs9516219_G', 'rs10492559_G', 'rs679331_A', 'rs4976845_A', 'rs7144752_G', 'rs433673_G', 'rs1955882_G', 'rs10131241_G', 'rs2010608_G', 'rs1597045_A', 'rs12432952_G', 'rs1957359_C', 'rs17127855_A', 'rs10498472_C', 'rs2016461_A', 'rs1189814_A', 'rs4435151_G', 'rs10150936_A', 'rs178377_G', 'rs10142253_A', 'rs242776_A', 'rs2273393_A', 'rs2656495_A', 'rs12594273_G', 'rs7183534_A', 'rs979328_A', 'rs6606877_A', 'rs641603_C', 'rs4779919_C', 'rs319889_G', 'rs4923705_G', 'rs1402111_A', 'rs488319_A', 'rs2554820_G', 'rs11634683_A', 'rs8039533_C', 'rs6495644_C', 'rs409211_G', 'rs2347452_A', 'rs11073896_A', 'rs4436756_A', 'rs4608314_C', 'rs11858431_A', 'rs4564540_A', 'rs2585243_G', 'rs1661812_A', 'rs9806826_A', 'rs13332500_C', 'rs4786640_C', 'rs1544616_A', 'rs4325583_G', 'rs1703492_A', 'rs8053423_A', 'rs1391349_A', 'rs9921767_G', 'rs2547416_G', 'rs6498039_A', 'rs12926138_G', 'rs8060086_A', 'rs12927233_G', 'rs7206599_A', 'rs17228232_A', 'rs7205341_G', 'rs7189335_A', 'rs8044752_G', 'rs235115_A', 'rs7193701_A', 'rs8044004_A', 'rs999675_G', 'rs6564396_C', 'rs282975_G', 'rs2178952_G', 'rs4435265_G', 'rs8054578_A', 'rs2679292_A', 'rs9928191_A', 'rs7199848_A', 'rs427585_G', 'rs456699_C', 'rs7500421_C', 'rs6503186_G', 'rs8081873_C', 'rs2072260_G', 'rs4357980_A', 'rs11869614_A', 'rs34046433_G', 'rs4377206_G', 'rs2306590_A', 'rs752313_A', 'rs8073596_A', 'rs16948490_G', 'rs2107441_G', 'rs9903614_C', 'rs8065114_G', 'rs4968360_A', 'rs2278445_A', 'rs4405611_A', 'rs11077382_C', 'rs936425_A', 'rs507757_A', 'rs1818871_A', 'rs8092935_A', 'rs17440876_C', 'rs1683390_A', 'rs2870108_G', 'rs9955902_C', 'rs11662508_A', 'rs1154244_A', 'rs546234_G', 'rs151245_C', 'rs4281798_A', 'rs9973037_A', 'rs12960377_A', 'rs4890712_C', 'rs732108_A', 'rs956778_A', 'rs917711_C', 'rs4940716_A', 'rs3809983_A', 'rs9948204_C', 'rs7228347_C', 'rs2332079_G', 'rs9636100_A', 'rs2016524_C', 'rs7237611_G', 'rs12604935_C', 'rs7236339_A', 'rs4799131_A', 'rs8110536_C', 'rs7252565_G', 'rs2240702_G', 'rs7254081_A', 'rs2241592_A', 'rs2287849_A', 'rs248943_A', 'rs12980897_A', 'rs7253365_A', 'rs3813587_C', 'rs10402888_C', 'rs2097765_C', 'rs7253927_A', 'rs4239554_G', 'rs8112651_C', 'rs1966961_A', 'rs2290682_A', 'rs3786772_G', 'rs1749320_C', 'rs188205_C', 'rs2180465_G', 'rs2422707_A', 'rs6116807_A', 'rs8119133_A', 'rs6077115_A', 'rs6055258_C', 'rs6039702_G', 'rs6134031_A', 'rs6079095_G', 'rs6132651_A', 'rs6016777_A', 'rs4810560_A', 'rs6063173_A', 'rs761685_A', 'rs1192511_G', 'rs2284803_A', 'rs1892317_C', 'rs4072474_G', 'rs2822429_A', 'rs2823008_G', 'rs2826558_A', 'rs2826596_A', 'rs2831315_C', 'rs4817242_G', 'rs2833470_A', 'rs2244151_C', 'rs2834609_A', 'rs2835955_G', 'rs3819595_A', 'rs9977968_A', 'rs2838358_G', 'rs1005873_G', 'rs1153418_G', 'rs5760813_C', 'rs738453_A', 'rs12627888_A', 'rs5749581_C', 'rs12628949_A', 'rs228129_A', 'rs738299_G', 'rs5750562_A', 'rs4365560_G', 'rs4634034_G', 'rs1555242_A', 'rs136026_G', 'rs5769526_G', 'rs12171249_A'], 0.91057293629853, 14, 758, 10612)}\n"
     ]
    }
   ],
   "source": [
    "print(ex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"B_full.pkl\",\"wb\")\n",
    "pickle.dump(Bf,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383733"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Bf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"B_full.pkl\",\"rb\")\n",
    "test=pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383733"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20,\n",
       "   21,\n",
       "   22,\n",
       "   23,\n",
       "   24,\n",
       "   25,\n",
       "   26,\n",
       "   27,\n",
       "   28,\n",
       "   29,\n",
       "   30,\n",
       "   31,\n",
       "   32,\n",
       "   33,\n",
       "   34,\n",
       "   35,\n",
       "   36,\n",
       "   37,\n",
       "   38,\n",
       "   39,\n",
       "   40,\n",
       "   41,\n",
       "   42,\n",
       "   43,\n",
       "   44,\n",
       "   45,\n",
       "   46,\n",
       "   47,\n",
       "   48,\n",
       "   49,\n",
       "   50,\n",
       "   51,\n",
       "   52,\n",
       "   53,\n",
       "   54,\n",
       "   55,\n",
       "   56,\n",
       "   57,\n",
       "   58,\n",
       "   59,\n",
       "   60,\n",
       "   61,\n",
       "   62,\n",
       "   63,\n",
       "   64,\n",
       "   65,\n",
       "   66,\n",
       "   67,\n",
       "   68,\n",
       "   69,\n",
       "   70,\n",
       "   71,\n",
       "   72,\n",
       "   73,\n",
       "   74,\n",
       "   75,\n",
       "   76,\n",
       "   77,\n",
       "   78,\n",
       "   79,\n",
       "   80,\n",
       "   81,\n",
       "   82,\n",
       "   83,\n",
       "   84,\n",
       "   85,\n",
       "   86,\n",
       "   87,\n",
       "   88],\n",
       "  [0,\n",
       "   1,\n",
       "   2,\n",
       "   15,\n",
       "   18,\n",
       "   22,\n",
       "   48,\n",
       "   49,\n",
       "   53,\n",
       "   55,\n",
       "   56,\n",
       "   57,\n",
       "   58,\n",
       "   75,\n",
       "   108,\n",
       "   111,\n",
       "   122,\n",
       "   140,\n",
       "   235,\n",
       "   257,\n",
       "   278,\n",
       "   337,\n",
       "   339,\n",
       "   344,\n",
       "   368,\n",
       "   382,\n",
       "   383,\n",
       "   384,\n",
       "   447,\n",
       "   450,\n",
       "   456,\n",
       "   527,\n",
       "   553,\n",
       "   558,\n",
       "   667,\n",
       "   685,\n",
       "   687,\n",
       "   700,\n",
       "   701,\n",
       "   711,\n",
       "   839,\n",
       "   909,\n",
       "   915,\n",
       "   944,\n",
       "   945,\n",
       "   976,\n",
       "   1022,\n",
       "   1057,\n",
       "   1079,\n",
       "   1085,\n",
       "   1088,\n",
       "   1128,\n",
       "   1150,\n",
       "   1192,\n",
       "   1193,\n",
       "   1203,\n",
       "   1204,\n",
       "   1227,\n",
       "   1228,\n",
       "   1229,\n",
       "   1230,\n",
       "   1231,\n",
       "   1237,\n",
       "   1239,\n",
       "   1245,\n",
       "   1253,\n",
       "   1265,\n",
       "   1357,\n",
       "   1385,\n",
       "   1386,\n",
       "   1390,\n",
       "   1391,\n",
       "   1401,\n",
       "   1453,\n",
       "   1490,\n",
       "   1492,\n",
       "   1631,\n",
       "   1637,\n",
       "   1641,\n",
       "   1727,\n",
       "   1730,\n",
       "   1752,\n",
       "   1809,\n",
       "   1878,\n",
       "   1889,\n",
       "   1891,\n",
       "   1917,\n",
       "   2001,\n",
       "   2355,\n",
       "   2410,\n",
       "   2413,\n",
       "   2420,\n",
       "   2615,\n",
       "   2619,\n",
       "   2621,\n",
       "   2648,\n",
       "   2654,\n",
       "   2665,\n",
       "   2704,\n",
       "   2758,\n",
       "   2777,\n",
       "   2836,\n",
       "   2849,\n",
       "   2983,\n",
       "   3034,\n",
       "   3135,\n",
       "   3136,\n",
       "   3213,\n",
       "   3272,\n",
       "   3321,\n",
       "   3480,\n",
       "   3491,\n",
       "   3506,\n",
       "   3544,\n",
       "   3600,\n",
       "   3642,\n",
       "   3647,\n",
       "   3650,\n",
       "   3652,\n",
       "   3661,\n",
       "   3691,\n",
       "   3695,\n",
       "   3696,\n",
       "   3697,\n",
       "   3710,\n",
       "   3713,\n",
       "   3714,\n",
       "   3715,\n",
       "   3717,\n",
       "   3719,\n",
       "   3722,\n",
       "   3725,\n",
       "   3733,\n",
       "   3737,\n",
       "   3785,\n",
       "   3807,\n",
       "   3818,\n",
       "   3845,\n",
       "   3850,\n",
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       "   30623,\n",
       "   30626,\n",
       "   30628,\n",
       "   30641,\n",
       "   30716,\n",
       "   30717,\n",
       "   30806,\n",
       "   30807,\n",
       "   30887,\n",
       "   31008,\n",
       "   31100,\n",
       "   31142,\n",
       "   31291,\n",
       "   31326,\n",
       "   31385,\n",
       "   31471,\n",
       "   31546,\n",
       "   31553,\n",
       "   31556,\n",
       "   31558,\n",
       "   31568,\n",
       "   31571,\n",
       "   31575,\n",
       "   31577,\n",
       "   31580,\n",
       "   31581,\n",
       "   31591,\n",
       "   31612,\n",
       "   31613,\n",
       "   31614,\n",
       "   31618,\n",
       "   31644,\n",
       "   31653,\n",
       "   31689,\n",
       "   31701,\n",
       "   31702,\n",
       "   31704,\n",
       "   31708,\n",
       "   31709,\n",
       "   31788,\n",
       "   31859,\n",
       "   31860,\n",
       "   31885,\n",
       "   31886,\n",
       "   31887,\n",
       "   31899,\n",
       "   ...]],\n",
       " [0.9835927375215471, 89, 2679])"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[(0,0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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