MaxEntPrep.11-05-18.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Script for Extraction/Processing and Preparation for MaxEnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First the extraction of the data from the PPM files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_ppms = ['out_{}.pdb'.format(i) for i in range(0,10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'out_9999.pdb'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files[9999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "################## LOADING THE FILES ############################\n",
    "\n",
    "# Path of the ppm files \n",
    "\n",
    "############## FOR 16Q ###############\n",
    "# pathppms = '/home/pedro/calculs/busra/16data/'\n",
    "# pathexp = '/home/pedro/calculs/busra/16data/'\n",
    "# files_ppms = ['/home/pedro/calculs/busra/16data/out_{}.pdb.ppm'.format(i) for i in range(0,10000)]\n",
    "# files_pdb = ['/home/pedro/calculs/busra/16data/out_{}.pdb'.format(i) for i in range(0,10000)]\n",
    "\n",
    "############# FOR 12Q #################\n",
    "# pathppms = '../reweighting/12q/pdbs/'\n",
    "# pathexp = '../reweighting/12q/'\n",
    "# files_ppms = ['../reweighting/12q/pdbs/out_{}.pdb.ppm'.format(i) for i in range(0,10000)]\n",
    "# files_pdb = ['../reweighting/12q/pdbs/out_{}.pdb'.format(i) for i in range(0,10000)]\n",
    "\n",
    "############## FOR 8Q ##################\n",
    "# pathppms = '../reweighting/8q/pdbs/'\n",
    "# pathexp = '../reweighting/8q/'\n",
    "# files_ppms = ['../reweighting/8q/pdbs/out_{}.pdb.ppm'.format(i) for i in range(0,10000)]\n",
    "# files_pdb = ['../reweighting/8q/pdbs/out_{}.pdb'.format(i) for i in range(0,10000)]\n",
    "\n",
    "############# FOR 4Q ##################\n",
    "# pathppms = '../reweighting/4q/pdbs/'\n",
    "pathexp = '../reweighting/4q/'\n",
    "files_ppms = ['../reweighting/4q/pdbs/out_{}.pdb.ppm'.format(i) for i in range(0,10000)]\n",
    "files_pdb = ['../reweighting/4q/pdbs/out_{}.pdb'.format(i) for i in range(0,10000)]\n",
    "\n",
    "####################### FILELIST ################################\n",
    "\n",
    "\n",
    "# filelist = pathppms+files_ppms\n",
    "# filelist.sort()\n",
    "\n",
    "############################ Checking ###########################\n",
    "# print(\"Number of files =\", len(filelist))\n",
    "# print(\"Path = \",filelist[0])\n",
    "# if len(filelist) == 10000:\n",
    "#     print(\"Selection Successfull, filelist has 10k files\")\n",
    "# else:\n",
    "#     print(\"Error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       " '../reweighting/4q/pdbs/out_881.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_882.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_883.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_884.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_885.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_886.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_887.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_888.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_889.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_890.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_891.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_892.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_893.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_894.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_895.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_896.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_897.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_898.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_899.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_900.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_901.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_902.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_903.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_904.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_905.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_906.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_907.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_908.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_909.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_910.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_911.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_912.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_913.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_914.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_915.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_916.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_917.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_918.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_919.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_920.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_921.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_922.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_923.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_924.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_925.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_926.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_927.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_928.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_929.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_930.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_931.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_932.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_933.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_934.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_935.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_936.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_937.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_938.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_939.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_940.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_941.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_942.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_943.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_944.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_945.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_946.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_947.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_948.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_949.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_950.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_951.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_952.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_953.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_954.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_955.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_956.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_957.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_958.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_959.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_960.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_961.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_962.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_963.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_964.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_965.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_966.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_967.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_968.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_969.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_970.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_971.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_972.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_973.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_974.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_975.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_976.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_977.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_978.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_979.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_980.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_981.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_982.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_983.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_984.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_985.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_986.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_987.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_988.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_989.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_990.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_991.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_992.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_993.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_994.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_995.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_996.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_997.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_998.pdb.ppm',\n",
       " '../reweighting/4q/pdbs/out_999.pdb.ppm',\n",
       " ...]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_ppms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extraction has been succesfull\n"
     ]
    }
   ],
   "source": [
    "############ EXTRACING THE VALUES (GENFROMTXT) ##############\n",
    "\n",
    "\n",
    "############## FOR 12Q and 16Q #####################\n",
    "# shifts_calc = []\n",
    "\n",
    "# for name in filelist:\n",
    "#     data = np.genfromtxt(name, \n",
    "#                     usecols=(5,8,11,14,17,21), \n",
    "#                     delimiter=(6,2,5,3,1,7,1,7,8,8,1,7,1,7,8,8,1,7,1,7,1,7,1,7),\n",
    "#                     skip_header=1,\n",
    "#                    missing_values='999.000,-999.000', usemask=True)\n",
    "#     data = data.filled(np.nan)\n",
    "#     shifts_calc.append(data)\n",
    "    \n",
    "# if np.array(shifts_calc).shape == (10000,20,6):\n",
    "#     print(\"Extraction has been succesfull\")\n",
    "# else:\n",
    "#     print(\"Error\")\n",
    "\n",
    "\n",
    "################# FOR 8 Q ####################\n",
    "\n",
    "shifts_calc = []\n",
    "\n",
    "for name in files_ppms:\n",
    "    data = np.genfromtxt(name, \n",
    "                    usecols=(5,8,11,14,17,21), \n",
    "                    delimiter=(6,2,5,3,1,7,1,7,8,8,1,7,1,7,8,8,1,7,1,7,1,7,1,7),\n",
    "                    skip_header=1,\n",
    "                   missing_values='999.000,-999.000', usemask=True)\n",
    "    data = data.filled(np.nan)\n",
    "    shifts_calc.append(data)\n",
    "    \n",
    "if np.array(shifts_calc).shape == (10000,16,6):\n",
    "    print(\"Extraction has been succesfull\")\n",
    "else:\n",
    "    print(\"Error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 16, 6)\n",
      "(6,)\n"
     ]
    }
   ],
   "source": [
    "###################### CONVERTING TO ARRAY ########################\n",
    "\n",
    "shifts_calc = np.array(shifts_calc)\n",
    "\n",
    "print(shifts_calc.shape)\n",
    "print(shifts_calc[0,0].shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraction from the Experimental Chemical Shifts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CA': array([     nan,      nan,      nan,      nan,      nan, 58.74703,\n",
       "        56.0724 , 55.88152, 55.83973, 55.87823, 56.52391, 56.29909,\n",
       "        56.07039, 55.85014,      nan,      nan]),\n",
       " 'CB': array([     nan,      nan,      nan,      nan,      nan, 63.36192,\n",
       "        41.87189, 41.86799, 41.91361, 42.05869, 28.97372, 29.19332,\n",
       "        29.38276, 29.39399,      nan,      nan]),\n",
       " 'C': array([   nan,    nan,    nan,    nan,    nan, 175.2 , 177.88, 177.87,\n",
       "        178.01, 178.17, 176.77, 176.46, 176.14, 175.82,    nan,    nan])}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################# LOADING THE EXP CHEMICAL SHIFTS ##################\n",
    "shifts = {}\n",
    "for key in ['CA', 'CB', 'C',]:\n",
    "    filename = pathexp+key+'shifts.dat' # CHANGE THE PATH EACH TIME\n",
    "    data = np.genfromtxt(filename, usecols=(1),missing_values='0.0',usemask=True)\n",
    "    data = data.filled(np.nan)\n",
    "    data = np.array(data)\n",
    "    shifts[key] = data\n",
    "shifts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing of the ppm Chemical Shifts according to the exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################### EXP VS PPM #########################\n",
    " \n",
    "# Putting NaN in the ppm where the EXPs are    \n",
    "\n",
    "for name,calc in zip(shifts,shifts_calc.T):\n",
    "    calc[np.isnan(shifts[name]),:] = np.nan\n",
    "    shifts[name] = shifts[name][~np.isnan(shifts[name])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 61)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = shifts_calc.swapaxes(1,2)\n",
    "q = q.reshape(q.shape[0], -1)\n",
    "q_valid = q[:, np.isfinite(q[0])]\n",
    "q_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q_exp = []\n",
    "\n",
    "for key in shifts:\n",
    "    Q_exp.append(shifts[key])\n",
    "\n",
    "Q_exp = np.concatenate(Q_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_exp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(\"../reweighting/16q/Q.npy\", Q_exp)\n",
    "# np.save(\"../reweighting/16q/q.npy\", q_valid)\n",
    "\n",
    "# np.save(\"../reweighting/12q/Q.npy\", Q_exp)\n",
    "# np.save(\"../reweighting/12q/q.npy\", q_valid)\n",
    "\n",
    "# np.save(\"../reweighting/8q/Q.npy\", Q_exp)\n",
    "# np.save(\"../reweighting/8q/q.npy\", q_valid)\n",
    "\n",
    "np.save(\"../reweighting/4q/Q.npy\", Q_exp)\n",
    "np.save(\"../reweighting/4q/q.npy\", q_valid)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f84dad934e0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(q_valid.mean(0)[:27], label=\"ppm\")\n",
    "plt.plot(Q_exp, label=\"exp\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}