Depth_instertion_peptide.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "df27c681",
   "metadata": {},
   "outputs": [],
   "source": [
    "import MDAnalysis as mda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "29cb9742",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "416fa089",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "\n",
    "        \n",
    "root_directory = \"/data3/amiel/notebooks/P454/1stbatch/M2/2pep/\"\n",
    "\n",
    "\n",
    "universes = []\n",
    "\n",
    "# os.walk parcours les fichiers\n",
    "for directory_name, subdirectories, files in os.walk(root_directory):\n",
    "    for file_name in files:\n",
    "        if file_name.endswith('.tpr'):\n",
    "            try:\n",
    "                tpr_file = os.path.join(directory_name, file_name)\n",
    "                xtc_file = os.path.join(directory_name, file_name.replace('.tpr', '.xtc'))\n",
    "                \n",
    "            \n",
    "                u = mda.Universe(tpr_file, xtc_file)\n",
    "                \n",
    "         \n",
    "                universes.append(u)\n",
    "                \n",
    "             \n",
    "                \n",
    "            except Exception as e:\n",
    "                print(f\"Erreur lors du chargement du fichier {file_name}: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4ee19db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Universe with 14438 atoms>,\n",
       " <Universe with 14438 atoms>,\n",
       " <Universe with 14438 atoms>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "universes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "0253c815",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "m=mda.Universe('prod6667W.tpr','prod6667W.xtc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ae5eb744",
   "metadata": {},
   "outputs": [],
   "source": [
    "pep=m.residues[:31]\n",
    "membrane=m.select_atoms('resname POPC & POPS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af1a5974",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5453b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#w=mda.Universe('prodreplic2m3.tpr','prodreplic2m3.xtc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d121e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ALA', 'SER', 'ALA', 'HIS', 'TRP', 'GLY', 'GLN', 'ARG', 'ALA',\n",
       "       'LEU', 'GLN', 'GLY', 'ALA', 'GLN', 'ALA', 'VAL', 'ALA', 'ALA',\n",
       "       'ALA', 'GLN', 'ARG', 'LEU', 'VAL', 'HIS', 'ALA', 'ILE', 'ALA',\n",
       "       'LEU', 'MET', 'THR', 'GLN'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u.residues[:31].resnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9192f4dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/core/selection.py:521: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.zeros(len(group), dtype=np.bool)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 60,  60,  60, ..., 464, 464, 464])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u.select_atoms('resname POPC').resids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a28a90cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TRP', 'GLY', 'GLN', 'ARG', 'ALA', 'LEU', 'GLN', 'GLY', 'TRP',\n",
       "       'GLN', 'ALA', 'VAL', 'ALA', 'ALA', 'ALA', 'TRP', 'ARG', 'LEU',\n",
       "       'VAL', 'HIS', 'ALA', 'ILE', 'ALA', 'LEU', 'MET', 'THR', 'GLN',\n",
       "       'PHE', 'GLY', 'ARG', 'TRP'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "universes[1].residues[:30].resnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c7023ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def z_dis(univ):\n",
    "    \n",
    "    membrane=univ.select_atoms('resname POPC & POPS')\n",
    "    peptide=univ.residues[:30]\n",
    "    \n",
    "    z_displacements = []\n",
    "    time=[]\n",
    "    \n",
    "    for ts in univ.trajectory:\n",
    "\n",
    "        peptide_center_of_mass = peptide.center_of_mass()\n",
    "\n",
    "        membrane_COM=membrane.center_of_mass()\n",
    "        z_displacement = abs(peptide_center_of_mass[2]-membrane_COM[2])\n",
    "        z_displacements.append(z_displacement)\n",
    "        time.append(univ.trajectory.time)\n",
    "    z_displacements = np.array(z_displacements)\n",
    "    z_df=pd.DataFrame(z_displacements,index=time)    \n",
    "    \n",
    "    return z_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "66aef267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_df=pd.DataFrame()\n",
    "big_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6d1e157e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/core/selection.py:521: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.zeros(len(group), dtype=np.bool)\n"
     ]
    }
   ],
   "source": [
    "e=z_dis(universes[0])\n",
    "f=z_dis(universes[1])\n",
    "g=z_dis(universes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da3c46e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/core/selection.py:521: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.zeros(len(group), dtype=np.bool)\n"
     ]
    }
   ],
   "source": [
    "a=z_dis(universes[0])\n",
    "b=z_dis(universes[1])\n",
    "c=z_dis(universes[2])\n",
    "u=z_dis(universes[3])\n",
    "v=z_dis(universes[4])\n",
    "w=z_dis(universes[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dad11b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[e,f,g]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "553595a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    18.915995\n",
      "dtype: float64\n",
      "0    18.922642\n",
      "dtype: float64\n",
      "0    22.431734\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for i in data:\n",
    "    print(i.apply(lambda x: x.mean()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "599e2edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26.958792022702788, 1.031752324768103)\n",
      "(23.985384911948813, 0.6469306819030329)\n",
      "(23.67905952068113, 0.30653509989546635)\n"
     ]
    }
   ],
   "source": [
    "for i in data:\n",
    "    array=np.array(i)\n",
    "    print(error_estimate(array))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "fd7f22cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    26.958792\n",
       " dtype: float64,\n",
       " 0   NaN\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7fa7b3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qIu6xUUxrN+/giAc+5oHRC3Ityh7Hd6s38eqU73MthmHEJeFCOVX9Jtr27zqg/5iorIhUx1EOI1T1v259K1V1l6qWAM/hmJOiKQTaeN63BooS3c9Ijl+27ADg04WrciwJbNi2k207d+VajKxx+j8mctf/5uZaDMOIS+CV1CLSRESuFpHPcExPzRNcL8ALwAJVfcRzvIXnsrNwFt1F8xWwn4i0E5EawEDg3aCypoJFMeWWg4eO5dxnU7ZiVjq27NhzlKFReUkUxVQPpxP/DbA/MBJor6qtA9TdG7gQmCMiM91jdwCDRKQ7jsloGfA7914tgedVtb+qFovIdTgL9PKBF1V1XnIfzahszPmxSizKN4wqQ6IoplXAlzgrpyeqqorIWUEqVtWJ+PsSRse4vgjo73k/Ota1hpENtu3cRXGJUnevhBlpKh1vTS9k5vJfuO9XXXMtilGBSWRiugMnpcYzwO0i0iF8kXKDWZiMaI596FMOumdMrsUIhT+8OYtXp/yQazH2OKZ//wvrXN9fZSBRFNOjqnoEcAbObOB/QEsRuU1E9s+CfFkhyIZBz3++hC+Xrs2CNEZFYeWG7bkWwahi/PqZLxg4fEquxQhMICe1qi5R1ftVtSvOYrkGwAehSlbBuG/UAs77Z+6cqB8vWEnBkFEs/XlzzmQwKgZ3vzOXgiGjci2GkSILf9qYaxECkyiba7mhtarOUdU7VLVDrGsqIxU9iundWU6U76zl61KuY9P2Yt/jufjo/5q8jPvet4XxqfCvyf7rJ576dDEFQ0Yxu3Ad78z8MctSGZkm1u81mySaQXwqIteLSFvvQRGpISLHi8jLwMXhiZcdqoSGS8CkxT9z0D1jmLQ4Yf7FrHD3O/N4fuLSXItRpXjyk8UAnPHkJG58fWZuhTHS4j/TlnPQPWNYvCq3s41ECqIfsAt4TUSKRGS+iCwBFgGDcJLuvRSyjKHz04ZtLFixIddiBCLVlCBTXf/JV8vK+1H2BAVZUdEKOnX9Yc0WLnxhKlt25H4UmwxPfLyIw+//KNdipM0nC5zFq4tWbsqpHImc1NtU9WlV7Q3sC5wAHKqq+6rqlao6MxtChs3azTv4cd3WXIsRF+vEd1O0bis/b0rfgTyvaD1zs7z2YtnPmzPW6aoqS1bH7kBemrSUN6ctj3k+HsM+XMDni37mhc+X8k0WbOabtxfz0qSlaSvMR8Z9y+qN/m1DVXl6/GJWbdyW1j2ySa6HD4FXUqvqTlVdoarrQpSnynLT61/z8YKVKZcPs6HkuhH6sWl7MTt3lfie6zXsE3rcl/4occATEzntHxPTricZjvv7eDrfnZnQ2f/O+JHjH57A54v8k1QOfW8+f3xrdtw6rn/ta0pKlM0x7N0Pj/uWUx77LG1Z/ej14McM/+w7AO4fvYCh783n02/CS/syr2gDf/vwG2547euY12zaXsz079dy/6j5rFifu0FjxLOb6wlmYAVhpMf/ZhZx+cvTci1GpeGge8ZwxR7wvNLpACIrzyNmiFTMj+/NKuKvYxbS5Z4x/Pm9eSxfuyVhmU+/WRW3kw1K0fptPDB6IUDp2oBb35oTU+Ely+qN28vMECNZi+OlObl2xAx+/cxknvt8Kb9/Y2ZG5EiFihL6Ywoiy7zx1e7FSSs3bOPIBz6OayaIEGZ7EWDE1O9z7hCLZsK3TkexbeeuSuMj8uPp8Yv564cLcy1GTEa4C+b+b9IyrvyXo5TjKa5L/++r0qi67cW72LBtZ6D7zCtaT3GMWWGEnzdt58IXvgxUXyJOfnRC0jPE2YXrSl8X76qIc+vsEkhBiEgdEclzX+8vIme4mVqNJLnt7Tmlr0fNXsFPG7aVCVv899QfeHt6YVZlUuDOkXPp99jnWb1vUG59azanPv45azdXnhWoXv724Tc8M/670OpP1I3NL9oQd/2MN5wyllkvFgOHT+HgoWMTXrd41UYGPDGRh8Z8k1T96fDLlmCKKxbTvv8l50EEud6nJugM4jOgpoi0Aj4GLgVeCkuoikYYHfaYeT/5jrzuGDmHW96cFbNcJttr9KykOEsbBy1LcrHf9O9/AUjKufvkJ4sytpjsiwoSGhxNUDNE/yc+p+/fx4ciw9c/rAt03SrXcTzLM0LPNkFad/Q1W5NMQb9643ZGfu3fX0QHRHy8YCW/xBj0VJRtkIMqCFHVLcDZwD9U9Sygc3hi5YbPvl3tO4KK12FHM2XJGgqGjOK7OGajH9dt5XevTOexjxalJGeERSs3UjBkFJO/W5NWPV4W/pTYlLOrRNPahe6nDeFHkfx97LcZq+s3z0+N+32mQzoqObpzzkSnkkien9an+N1VIGtNmF3vFS9/xe/fmOUbSeU1d63fupPLX57G5S9/Ve66xas2lZpU/QaEJSXKU58uZn2aM6QgBFYQInIUcAEQGZZVuRSXF734JQ+7HcudI+cwdt5PSdfxzkzHNjtlSexOO52NcbwOtsnuPUbPWZFSXX6/2X6Pfc6DHyyIO/o+eOgY+vz1k9L3y9du4YUkFr3leYa+0YnLBg4vm87kxYlL0wpBzpSJYNO23K4H2LpjF9O/L7uGZWbUqvog5ghVTeuZJDJBbdi2s4xcD41Z6Ns2dhQnZ8rKFuk2l8jgp7gk/ueL+GKWrSkfFHDiIxNYEmeWPWHRah4a8w13vxv+hlNBFcRNwO3ASFWdJyLtgU9DkyqHLP3ZGSmOmPoDg1+ZnpV7JhOx4N2FLCzz6D8nLAGc1A0PflB+O9LNO3ZR5BlJXvTil/zl/fmsCbg2Id/T6sbOW8mzE3bb56csKdsJ3psgHcfKDdviZseM9YwyESFV+MsWTn50Qlbi6v/w5ix+/cxkRkxNb5vSEx6ZQNcAPoNUufylr/jVU5NKFclTn37HX96fX27Yfu/72d/e5Sc3bDWZn02ys7LI9cn8Nr9bvanU1DQmwKB0p6tcN28Pf9OpQLMAVZ0ATBCROu77JUBKe1RXdIJ8sSs3bOPov35Crer5fPqH42hcdy9vDQCUKNzzzlwuP7o9bRvXjnuPIPeMl/IqnoIpKdGY5xM1/YhD8fZTDwScPFAdmtUtd936rc5UN9bH2Ly9mDwRatXIB2Dou7s7/Vvfjh+n78XvOR3xwMfUyM/j2/tP9S8To66PklyT4q3nhYlLaVCrOt+u3Mi3KzfR8/6PmXn3STSsXSOpOiEyw0ncCc0tcuzXd44sP2pMZkawZHW4yR5nLXfkLCnXyMu+nbbsl1DliEZVuerVGUByyfKy4SQ+4eEJNKm7F9PuOpHfRQ1Kc22ZCxrFdJSIzAcWuO+7icjTCcq0EZFPRWSBiMwTkRvd4w+JyEIRmS0iI0WkYYzyy0RkjojMFJGsBcQH+ULeml7Izl3Khm3FjIph3pn343penvw91702I7MCAtO/X0vRuq3c8278Udj6rTtpf8do7n1/Pi9NSi/v0baduzjzqUn87pXyX0Ukuigvhibqcs8YDv3LuNL3md45bseuEiYu8nck3z9qQUYz4H79wy/85f35/CHKL3X6k6kvuPti8c/sf9cHccNFsx1Mk+r9EnWoU5aspXhXScy24mX0nBUUDBkVd23GqNkrOPnRCZQk8IlNWrzb5BvtP5u1fF1p+bCilrYXxx/tJ5MZIJtNIaiJ6THgFGANgKrOAo5JUKYYuEVVDwSOBK4Vkc7AOOAgVT0Y+BbHdBWLvqraXVV7BJQzK3yyMPFqT287iw7PTGURjNcmOWj4VB73OLhjVbfaNX3836RlbPCxoQdtaB8vWFn6eeKN/OJ9rGSjQXzrF9gYoxP97QtTd9/L46d5cdJSrvBxBKbKWU9/Ufra25ksX5u6n+TWt2ezo7iEoe+kZ3bZtjN9u34yM4xoP9WqjdvYGWDtwEcLVpLn6Xl+3rTdVyH972snI+28Iv/Aif98tZxr/z2Db1duYkcc38grk5eVznKhrIKY/v1aznxqEk+PX+xbNmkTU2QFNE4G5shneHOaf2STt2/w88sURfnfRs1ewdQl2duXJplUG9FJXeL+4t20HDPc1xtxZh+tVHWsqkZ6qylAkP2ts0Z0Q43ukLbsKC4Nu4TyneJrXzqPaYunQ/SOnv3u8dIXy+iXIJ2BN833jl0lpQ5qgJdjpH/O1GBo+dotpQ3f66SMfjbRo8KrXplOl7s/TPp+xz4U270VJI7+wKh7Znv0vaO4hGnL1gaK9lKg8BenE4gsPvO/LtfGBod4I92e938cqI5dJWXbylEPBiv347qtPP7RolLFHNQ8+ScfxRsJmf5xnTOIWuCanaKf8gOjF9D+dkcRLl61iZcmLeWL74KFPd/w2tfc5K7GDhL1d67PfjPRfr1r/z2DF9O0BiRDUAWxXER6Aeqm+v4DrrkpCCJSABwCTI06dRmxNx5SYKyITBeRwXHqHiwi00Rk2urVmViiX/aLjHbo/byx7Gxg5nJ/c8mGrbHNBSc+MqHcsWQ3EfkhQEqERE0y8hNNZIIRkdLIK287LxddJNDvsc/ocZ+jED+c9xOb46Q1iMX3PpEd4HT0qUSAZaprjc5X9FaM9THDPljIOc9OpsMdoxn03O7dw/47I/56moich9w7tnRFc+m5iqEfysygEhHL7CNS1qcWZNYBcPWr03n0o29ZvKp8yHE5n0cCVrm7BUaUTak0UdW8MuX70jZ/4iMTGPrefH7zXHQ3lhn89nrZUVzCgCc+Z2qcqMgwCaogrgKuBVoBhUB34JogBUWkLvA2cJOqbvAcvxPHDDUiRtHeqnoocCqOecrXpKWqw1W1h6r2aNq0acCPE5tE7eyYqNHt2zMKfVNURNJEpMvzny/J+O5hL0xcytc/BHcS7iguofu94xJfiKPoft6UuxXPfplHdxSXZMQP8ef3yo5EY63U9a4l8W5T++Hc+BEqkVHmL1t2Mm7+yrT9RumQiRnLS18si3kuL4HlZvXG7YydHwkkcGSJmA53qZaLXIv+3SYaSER/ukT7np319KS450vrKZUnMxp9yc+bmVe0gbt9ZkHZyNcUVEF0UtULVLW5qjZT1d8CByYq5KbjeBsYoar/9Ry/GDgNuEBjPElVLXL/rgJGAj0DypoWqXyta0LsEP/52ZJA1z3x8aKEjroIf3l/flIjwftHB5ssen/0/5q8LHD9mcQv8+iP67bS9+/jY4bhBl3vEiuNdFD8vp14/cjQ9+YH3lVsZYYXH46Zl3rm4QixlLIQO6Ahgp9JJnLkHx8vLjdgiZ5B3JRgw6SI4zuysVKivjbRivG+fx/PK5OXlYZ/J9IPQfuZeKYpVcfk5zV5Z5qgCuIfAY+V4m5F+gKwQFUf8RzvB9wGnOGuzvYrW0dE6kVeAycD4a8KITXNH2aGiqCd0iPjvuXzHKSE8DqEvZEifiOedNm0vTiu07D/4/FzSW3aXsy/p/5QbkY25L9zYpRIjVhNKJVBZaQ9xiv76TerOOKBxHb8dLYhTWX/Da8O8Ip/9YgZCWcQUTWVeee3VmDI23PKRDt96bMxlpeLXiybEDDd0fjSnzeX8XVkKlvAFwmyJPzqqUn8+pngg71kSbQn9VEicgvQVERu9vwbCuQnqLs3cCFwvBuqOlNE+gNPAvWAce6xZ917tRSR0W7Z5sBEEZkFfAmMUtXkvZ0pkEpfryglJcr1GUiBnA73vT+fh8d+w42vOzn+E3VIsWzoyeAN97zq1XAXFr40aVnc8/MTZHyduXwdj38cOwXHHSPn0DmOUz1o25ichr14flTEzrnPlndcRuNVzPFIZxvSm/8TP91Msqv5o0060SYhPyUQKeHXmY+as4LzfZy88dj/rvLuz0yN9aK/t+iBZyasQyK7AxzCItFCuRpAXfe6ep7jG4Bz4hVU1Yn4P4fRPsciJqX+7uslQLcEsoWCKslvWqKwcVsx78WJQkmWe96Zy7GdkvOpLFq1iUXuvsR3n9bZN6++t50+nYEMo8muafghhgM6CHl5kCCDQVxirTyNhBr+e+oPvufDJNrW3/+JsrOgSPBCNrOK+pkqNyVI6X3NiPLrfeJ1gtEziE+/KdtWy67xUc//kdDT8jKu9My2g2T+9YaVRsQJatJLlwoSc5CQuArCs4L6JVVNb41/JUFx8t0nWybWryHVfQxenvx9zPDVIDw74Tue+zxcJ+fm7buSdmZGO/mTwRl1pvfTimWiSrSnwZOfLMpA/Hl52dcFSLiW7ZTTfjOxZCVQje/4/TaJvZa/+WkT24t3m8diVRvPXh+9nsCP9XEiD/dUgibc2yIiDwFdgJqRg6p6fChSVTKcH4P/uaAhfJkmlnLIZOTDr5/5gtaNamWuwgTkCZR45O9+b/I5hWIptIUr4ocZf7Qg8azyP9OW8+i45LLIBvEdnPfPyezMUip2SD5kNBWS2dvj0Y+cZ9rRJ81LUBIFWvxvZhH3nN4lcH3rt+6kQa3Ut8QZkkSKmVwS1Ek9AlgItAP+DCwDMrc8tQKRymhN0bjrHioSye7FkIhsZuX8af32MovJgoy+vYS92OzWt2azIk467FRTpH+17Je0I6iSYWiCFC6pkAmdE1n/EFZ4564khBw4fEriizxE1xxkwOGluKQkbpRiWOnogyqIxqr6ArBTVSeo6mU46TMMYOYP63LuoA7K/2Zmzk8CuzeCCUIyG/748dGClXH3E07EyvXbWLkhex1tNPGUR0Vihk9IpyoJtwuNZl7Rbv9UdO6qdAiSxykVkjEhLlixoVy69TD5bvVmznwq9lqMizK0TWs0QRVEZKi2QkQGiMghVLAUGbnk4XHfhh5NUBU4O4m1F2EQnUrcSI5Ei8m8LFm9ma88ebuysUlUulz77+QSa/7qqUmBfBuZIl5ASKL9J1IlqA/iPhFpANyCs/6hPvD7UCSqpGTCBJDpFdMVjWTTiVQ1Kkq6jFRIVvToaKxMUjE243QIGvUU9ncf1sw46H4Q77sv1wN9Q5HEMHLIRE9I8PK1W0Jxvi8OyU6cDYTsR1PFIiwTUyqc/Ohn7Bu130u2iJXZOJMEUhAi0hS4EijwlnF9EVWKWInijMrPuq2xI2ee+GR3uuc+f/uUt6/ulfH7p7OPt7GbjVlaqxCUXPUZyYQKp0pQE9M7wOfARyRI813ZCZIl1aicJPODmpvhTY0qO6s2bEuYvsLw5+3phXy7snKaV4MqiNqqeluokhhGBWJnkhE7VZ2i9dtCS3Nd1Xlz+vJQAiSSWUuSKkGjmN538ygZxh7BMxlIQ2IYULmj54IqiBtxlMRWEdkgIhtFJLUcEoZRCViThdGZYVR0gkYx1Ut8lWEYhlGViKsgROQAVV0oIof6nY/sOW0YhmFUPRLNIG7BCW992OecApaszzAMo4qSKN33le5fWxxnGIaxh5HIxHR2vPPefaZ9yrYB/gXsA5QAw1X1cRHZG3gDZ9HdMuA8VS23qaq7NenjODvXPa+qw+J+EsMwDCOjJDIxne7+bQb0Aj5x3/cFxgMxFQRQDNyiqjPc/aWni8g44BLgY1UdJiJDgCE4e1SXIiL5wFPASUAh8JWIvKuq84N+MMMwDCM94oa5quqlqnopjr+hs6r+WlV/jbNxUFxUdUXEia2qG4EFQCvgTOBl97KXgV/5FO8JLFbVJaq6A3jdLWcYhmFkiaDrIApU1bsr+Upg/6A3EZEC4BBgKtA8Upf7t5lPkVbAcs/7QveYX92DRWSaiExbvbr8HsyGYRhGagRNtTFeRMYAr+HMJgYCgTYXFpG6wNvATaq6IWBOeb+LfDOdqepwYDhAjx49LBuaYRhGhgi6UO46ETkLOMY9NFxVRyYqJyLVcZTDCI9De6WItFDVFSLSAvDbe68QaON53xrI7FZohmEYRlwSKggRyQNmq+pBQEKl4CknwAvAAlV9xHPqXeBiYJj79x2f4l8B+4lIO+BHnBnLb4Le2zAMw0ifhD4IVS0BZolI2yTr7g1cCBwvIjPdf/1xFMNJIrIIJ0ppGICItBSR0e49i4HrgDE4zu3/qGrmd1I3DMMwYhLUB9ECmCciXwKbIwdV9YxYBVR1IrF3BzzB5/oioL/n/WhgdED5DMMwjAwTVEH8OVQpDMMwjApHUCf1BBFpDhzuHvpSVf2cy4ZhGEYVIdA6CBE5D/gSOBc4D5gqIueEKZhhGIaRW4KamO4EDo/MGkSkKc7+1G+FJZhhGIaRW4KupM6LMimtSaKsYRiGUQkJOoP40LOSGuB8LMLIMAyjShPUSf1HN/X30Tihq4FWUhuGYRiVl6AzCIBJwE6cnEhfhiOOYRiGUVFINorpHCyKyTAMY4/AopgMwzAMXyyKyTAMw/AlnSimD8IRyTAMw6gIWBSTYRiG4UtcBSEiHXG2CJ3kbvjzX/f4MSLSQVW/y4aQhmEYRvZJ5Ed4DNjoc3yLe84wDMOooiQyMRWo6uzog6o6TUQK4hUUkReB04BV7m50iMgbQCf3kobAOlXt7lN2GY5i2gUUq2qPBHIahmEYGSaRgqgZ51ytBGVfAp4E/hU5oKrnR16LyMPA+jjl+6rqzwnukRH2qV+TnzZsy8atDMMwKg2JTExficiV0QdF5HJgeryCqvoZsNbvnLtf9XnsjorKKXVrJrOg3DAMY88gUc94EzBSRC5gt0LoAdQAzkrjvn2Alaq6KMZ5BcaKiAL/VNXhsSoSkcHAYIC2bZPdNtswDMOIRVwFoaorgV4i0hc4yD08SlU/SfO+g4g/e+itqkUi0gwYJyIL3RmJn4zDgeEAPXr00FSEibVxtmEYxp5M0HUQnwKfZuKGIlINOBs4LM79ity/q0RkJNAT8FUQmZEprJoNwzAqL7lIl3EisFBVC/1OikgdEakXeQ2cDMwNUyCxOYRhGEY5QlMQIvIaMBnoJCKFrmMbYCBR5iURaSkikQ2ImgMTRWQWTgbZUar6YVhyGrnjzO4t+e2R5jcyjIpKaOE7qjooxvFLfI4VAf3d10uAbmHJZVQcHh94CCUlyqtTfsi1KIZh+GAZWTEfhGEYhh+mIIycUhWV87+vPCLU+sf9/phQ60+GWXefnGsRjBAxBWEYGabt3rXDrb9xbT6/tW+o9wA44YBmCa9pULt66HIYucMURBbZq1rVfdyNUuwoJAtTiD77NQn9Hl6a14+XoSZ1Dm3bEIBqeXm0CVkJAdTeyzIM7OlU3R6rAhDd9z1yXvecyJENDtu3Ua5FiEn7JnWyer/q+eH8rF4ffBRLHuhPfl58pZon8OUdJ6R9P9Vg607P7N4y7XuFyVHtG+dahEqLKQjCG8Vec1yHMu+r51dBg7vLsfs3zbUIVR4RyEugHCI0q18zbV9I0LQEFx21b1r3CZuurRvkWoSYvDH4SF69PFyfVTqYgiCx6adfl32yJEk4pDqCqlMjP/g9OpQ14zSpWyOle0ZzfAA7eEXk1IMqfptpWm+vlMu+dOnhXHVsZABUsQc+FVm6HgV7c3SWTaDJYAoCODJBB/rshTGzgiRFNuztfqSarfYPp3RKfJFLx2Z1U7pHEC4/ul1odYdFwxCct5luPc3rJ1AQcaYQx3VqxpBTD8isQCGRq99dEBJJ9s+AfU+9kPxFpiAIL9QyoAk3dJKR4+/n7l6jWKt68BlEOveMhwDN0hjpQnIdxP9dcnha96oIRB59rBQyTww6BIDGdeI/V42hIbLt9E+XCqwf4nLP6Z05pcs+XNKrIGcymIIAGtfJjDkkEZWhnZ5zWOvS19WSdLa2bBBO9M4JB2bHzHRln3Z0blk/5vlUI7W85KpzbVLXUQaPD+zOGd1aMuzsrjx2fve4ZWIp+VeibOaZ6oBPPLB5ZiqKIoh4x3UK5kN76dL0BhDXHNeBt68+KtC1+zevB0D3Ng3LHJ89NHtrT0xBAJf0KuDBs7vSq0O40Q5hjWTeubZ33PN/OGX/lOptWCu5DvGN3wVr+MlwRZ/2dGxWL9C1N5ywHwCdW8Tu5ONxatcWga89+5BW5Y51aFqHUTccDcTuXJ+7KPXdc4POhPyuOiqqbQ/s2ZZGGRoYZa5Z735oAX3xgQjy2O7ofyD3ntml9P3E2/r69gfHdUpvsHJS5+Yc1Co9p3n9mtlbe2IKAmekPKhnWw7YJ7WOJRbZsjAlCqs8YJ/6fHRz8NW3B7WqT9dWDZIeuTf2OKYz9dmjO7ZY/OaIttx80v4sGzaA0Tf2Seleh7ZtFLezK9NB+1x49qGt6dKyQelrP2p6zHbpOIkh9owmmWf/2pVHAlDbJyAh2ybSPM/zbZDk4CQeQbM1n394m9LXrRvVZsQVR/DAWV0zJgfAwa0bBr42GR0Z1ldlCqICc2T7vTm/R5uE1wUZISUz6nj/+j68d/3RiAgPnp3aDyReDH3rRvG3M78ioFO6zd5OPY8P7J6xH/Je1YL5XRJ1Oj3b7c19vzoo7jW3p+nknTTk+FJ/gpe/nOncN0i7iISA+l0ayweRLImiAH9/4v7ce2YXGtXePcB4ffBRXB0VJp4sdV3Hrd9sxM8cGv3diwi/OaIt9TK4JXGiNSxlBcjYbVPGFISHdk3CXZ3q/cG+cHFsU8PbV/dy5anLX885OK17fnLLsYATF59KvPWgnm1ZNmxAWjJ4ufmk/fno5mOZfteJpcf+eeFhPH3BoaXv/9gvWPRU9zaJF+clG24aL3VEooVjNQM69YdfeBif3HJszFlGUGrXqMYZ3couUnt8YHd+e2Rm1iVUy8tjbAbyPh2fYCbaqlEtLjqqoMyxTvvU47Z+jgKNFRHWNYGp5tiIX8FHU1bLz2P+vaeURnLFnTnGvUvyxBtcHLBPvdK/hxfs7VwfQICwdIkpCA+Z+mFFaB7HhHBo29id22H7NuL/Ljmce07vHOg+eXFaUPumu8NPE8Vb751BZ32f/co7/WrXyOfavh2pWT2fxnV3P5tTuuxDf4/9v0YGVyI/89vMhChH4/fILwzYfk7usk+Z7yXwPQNcc2b33b4R7/U13bU+1fLKPtvINdV91gLde2aXUkdpXLkS9GCJFojGOzv/3lOYcntqq8IjCj160N5nvyY8cl43ateoRr0AM+tYn88vV9Wv01T6ER4+r1tSK/LNxJQFRKSM7fOAfeql5VTMzxPu7H/g7vo9P4VEDsK+BzQrHZEmMp/USCLH00GtYvtZJt6WmQRw+Xniq4ym3XViclNsD3Wj4rwfPLtrRZiBlyH6e4j8aFP9zJmiXs1q3HVaZ67r25FTupSNFKqzVzVuPml/3rrqqHIpSbxKPB6J/AWnHdySQT2Dm0q9JsjaNaoFnplFM/BwZzOqQ6IGY69cfgQ93NF5EGLpv/t9fpfX9k3OLBZkdlCnRmITVyZ9Nl7C3FHuRRFZJSJzPceGisiPIjLT/dc/Rtl+IvKNiCwWkSFhyeiH14xwYIv6nNQ5vdC7K49pv3sRWVRjqFk92OOvs1fZH0j1fGHZsAG8f/3RZUw1QTgnzgindoCGGIRYM4CaAe37fkSbdwb1bOs5l3K1vsRavd2iwe6O6+Co9A3/vaZXzPoOL3A6qPZN08sJFetjPptglnTAPvVoUKs6fzilk2/o8g0n7EfHZvUYfWMferYL3nFGaNekTty0HtXz80rNRV5+d0x7AAqiTLvXHNcxaRn8OGb/piwbNiBQ+HW8JhSrD081KjGIb8c7mDzhwGYJI/PCWqga5gziJaCfz/FHVbW7+2909EkRyQeeAk4FOgODRCSYrSVEHkrDF/D21b0Y+/tj6B4VwTD6hj5J1zvqhqOZeNvxABzUqkG5UV7Yqab9aBcwGV7QPEK+ZX1+jZkIG+7hk2TwxRiL5c7rsVu5Nq9fs3R2eeKBzeKaDEtJU5HFenz9YvhZkl1BXLN6fspJF3t1aOIrX0TZ+tndh5x6AB/dfAyH7Rvc1u4lqBM9Xr2pNKFz3bVCmQxbv7ZvB26N4XsTEV6+rKfvuS+GOH3B2YeWD7vOBKEpCFX9DFibQtGewGJVXaKqO4DXgTMzKlwcIj+q9k3qcKMbVw9wQtQiniCreyPNt0Gt6uzfvB6N6tTgo5uPZcQVzmirfdO6nBsgSsnbAXRp2SBuOulcrBr1c962ahg/UikI3ugXv/TWkQivRJ1avGcSa+1D9OKkQ9o25GLPilZvlYlmMEHDLP90mjMOqubT0w6/8LCUU0YkM8NKZzbWySdM/OaTYq/BEZHAa1ySpVuICfoeOrcby4YNiOv7S5Y/nnIA1xzXkeuPd/qcto3Ltne/NgHQsmEtlg0bUMb3lEly4YO4TkRmuyYov192K2C5532he8wXERksItNEZNrq1aszJuTbV/eiIM7I+M2rUlsU1rFZXXp3LGufn3bXiXE71EShl94OOtUmG23jT5feHZuU7l8QlKd+cyhDT++MiPDlHSfw+KDupeeu8bHt9urYhGXDBmRkb4Szoha+/S9q8WFB4zqICPt4lHPCZ+1+L5F+JFHfe7JrzoweADx6fjdOTiFhZCr9Vzqhrc9ddBh/jMrfle6isCCks4I/yKeNpZj9Q4MD1Ben5Qw4uAXLhg0o93vMVdaebCuIZ4AOQHdgBfCwzzVJPXdVHa6qPVS1R9Om2Us5HW+RU9BolghN6u6VOHFaQIKmxziyvTOtf941kzQOmH3Vzz4f68vxOgKDKKABB7fgkt7OGohm9WuWUYynHdySv/461TUZZd97fSSRxhbUyffcRT1o2aAmR7QLvuq+oEkdLulVwPNxQpvjEXQGkgnSuVfrRrW5tm/6/oOgSqqXm0HY285eudzfFJOIVMJcU51BJBNUEot7z+yScJ1NJsiqglDVlaq6S1VLgOdwzEnRFAJeu0troCgb8uWSTGWcjLe+wkun5vVYNmwAHQI6tz64sQ8f3Xwsz/72sJj20L2q5dO4Tg3+4jZc78xmakY2sEm7CgDuPyv1H1bX1g344vYTaFC7emneJm/+Ki8RcfMEhp7RhQ4BQ1ujm0KmFqylyj2nd0645iAzJP4NvH/90aWvbz2lE5/9sS8tGu6eQUTCVr1PrKBxnYR5niLXn3NYa+4acGCZc5HfSPSeJxEFUb9mtdIAhKCbLCVLdL0XHVWQ8bB8P7KqIETEa/A9C5jrc9lXwH4i0k5EagADgXezIR8En5ZHj7TSTcIWGWGns49CuyZ12LdxfIfxWYe05sj2e3NV1CrVRB/7wBb16disLjWq5ZXL0RRpu/l5wvQ/neTbYdbJ4faV0d+pN2zyGPdHf3q34HmYIkTsv4lyOCU7Ks90H5NMdX7t/9Le7XjP0zGnQv1a1TjrkFZx9xiJzE67xUlH4TVZVcvPo23j2lTPi9+NVcvPizl7i/64fz+3G1f0aV/mWMTkV84RHDEd6m4fQXHJ7qcdz/8SCX0O+l2X5GiMEGaY62vAZKCTiBSKyOXA30RkjojMBvoCv3evbSkiowFUtRi4DhgDLAD+o6rzwpIzFtHfR6KfuDclb2R/iS5xMoNGE9mzIeKoTIZk2k6D2tV5ffBRZUI2k60jV2TKJ9jEE/nVsVldlg0bUBpJk0ki8ev1a8VWjh/dfCw3nLBfWuttYpHK49rX9eccESPcNVV7v4jw6Pnd6RtnA6iTOjfnm/v6JfRb3H1aZw70hH36peTIpFEuUld0Zx7xGyuQ7yqpXZ6e/AZvkMsBzSjwOJ7fubY31x/fMXBkX65mkaEN61R1kM/hF2JcWwT097wfDZQLgc0l0V9PZHXooJ5tePDssqGqAw5uwVEdTkppZXI6kRG5Wo51RR//3EkVZT+MaIImALzmuA48Pf67lO/zq0NasWHbzjJrNqLp2Kxu6Uhz+dotQHlFmK4PIhmzx37uymm/FcEz/nRSwt0XE3H/WV1RJWZIZ5BcWJcd3Y7LPPm6wp6dxvpJRszCJars37wuC1ZsoFb1fGrk57FjV0mZay87ul2Z4JSDWjVIzoGfo99S7ub9lZxq+XnMHnpyzFWOqaatyFWfmmoXtPTB/jH9J6mugE2Edz1CEFLtYG/td4DTgf9nVkrl8/OES3sHSzxYUThs30ZMGnK870whE6lYGtSqzlOevFuZ4tnfHlYmcMTvd1SjWh47ikt8zgQjehSf5zExPXh2V846pBXtm9blveuPZvw3q1K+jx+RzAtB0+9kClMQUSTTlWQyL3ui+75zbW+qxchpU9C4Dmcf2ooro2ynYdO1VYO4zvVr+3bkyU8XZ1GissSKHa9spGpeKPH4hpIh3TUsh7RtyNc/rEurjmjq1azGxm3FMc9H1grNWh77vlNuP4EtO8rWESws1b1Wo4/vnkHUrlGtdK+ITvvUo5ObdO+IdnszdWkqy8HKUj0/L6NJM4NiCiKKdEbw7113NGu37MiYLF66RS3c8pKfJzxyXvdyx4NYq1o1rMX+zeum5PtIRC3XIZlK+oZ4BLGYvHJ5TwoSOOyrOhF7eLb3ZB55TW8KhozKaJ1f3ZlcShk/9q5TI6VZUOT5lVMQAda3dGvTkKlL1wYOI69omIIISJCfWNcMrN7MZJhckKpqVMtj7O+PTan+IP3O9LtOzEkEkzeb7A0ndGTlhm2MmrMi6Xoio+9szEZaN6rFpb0LuOCItpz4yGdp1xfJz5ONcMho3r/+aKYsWZOx+sIyVwb5VmNds1e1PLq0rB937ccfT+lE/64tMr4ZWbYwBRFFrowS2R7lZYugGUGTIdlH1bB2DZ664FBGpTCq7d+1BXN/XJ+RBWCJEBHuOb1L4gsD0rTeXjkxS0AKTthKQLnIRhFG3RB/98Lq+Xnl0rZUJizddwzijeT9MlOmyy0n7U+PfRvFzCSaDJ0C5PA3glE9P487B3SmYe3cmQgqajRYRaSdu2AtUxlhgdIFkYl2QqyK2AwiIJF45fZN66S9FaIfBU3q8NbVsVNGJ8M71/XmgD99mJG6/KgK/dVLlx7OLyH5i3JBp+b1+GblxlyLkXPq16ye8VnT5Ue348j2javcjCgIpiAC0qBWdYad3bV05W1FJpM7ssUjV0axTIyoIxEnFZW969Rg7WZHgQUxqb17fW+Kd1UF1Z1d8mI4oL2IyB6pHMBMTOWI5wsY2LMtLTOQxtpIjWwmrcs1M/50Er/q3jLxhS57VcvPaTqTysqzFx7Gpb0L2C+kDXcqO6YgoqjthmZWVaexUfkwH0R4tGtSh3tO75LWZlZVGRtyRDHiiiP4YO5PGVk1mitMt1UNbJBi5BqbQUSxb+M6XHVs5p3QhmEYlQ1TEIZhGIYvpiCqIGaaqBpE9iEIutudYWQa80EYSRPWrllGWW4+aX8ObFEvI4snDSMVTEFUYQ4vaBTuDWymEio1quVxZvdWiS80jJAITUGIyIvAacAqVT3IPfYQcDqwA/gOuFRV1/mUXQZsBHYBxaqa+e22qjgf3tSH1o1qJ76wEpLq/KXPfk3oW8EXyBlGRSJMH8RLQL+oY+OAg1T1YOBb4PY45fuqandTDqlxwD71S/e5rjKkOWF55fIjyuxEZhhGfEJTEKr6GbA26thYd89pgClAcluDGRWCyD4PzetlPlOrYRgVh1wOMS8D3ohxToGxIqLAP1V1eKxKRGQwMBigbdvYe/8ameOAferz6PndOOHA5tm9sfnGDSOr5ERBiMidQDEwIsYlvVW1SESaAeNEZKE7IymHqzyGA/To0cO6kCxx1iG5m/yZa9wwskPW10GIyMU4zusLNEa8pKoWuX9XASOBntmT0Kjo2CjAMLJDVhWEiPQDbgPOUNUtMa6pIyL1Iq+Bk4G52ZPSqLDY1MEwskpoCkJEXgMmA51EpFBELgeeBOrhmI1misiz7rUtRWS0W7Q5MFFEZgFfAqNUNbzdbwzDMAxfQvNBqOogn8MvxLi2COjvvl4CdAtLLsMwDCMYlovJMAzD8MUUhFFpqJ4v7l9rtoaRDarYUlujKnPawS1Z+NNGrjmuY65FMYw9AlMQRqWhen4et596YK7FMIw9BpurG4ZhGL6YgjAMwzB8MQVhGIZh+GIKwjAMw/DFFIRhGIbhiykIwzAMwxdTEIZhGIYvpiAMwzAMXyTGlgyVEhFZDXyfYvEmwM8ZFCdTmFzJYXIlh8mVHFVRrn1VtanfiSqlINJBRKapao9cyxGNyZUcJldymFzJsafJZSYmwzAMwxdTEIZhGIYvpiB2MzzXAsTA5EoOkys5TK7k2KPkMh+EYRiG4YvNIAzDMAxfTEEYhmEYvlR5BSEi/UTkGxFZLCJDfM6LiDzhnp8tIocGLRuyXBe48swWkS9EpJvn3DIRmSMiM0VkWpblOk5E1rv3nikidwctG7Jcf/TINFdEdonI3u65MJ/XiyKySkTmxjifq/aVSK5cta9EcuWqfSWSK1ftq42IfCoiC0Rknojc6HNNeG1MVavsPyAf+A5oD9QAZgGdo67pD3wACHAkMDVo2ZDl6gU0cl+fGpHLfb8MaJKj53Uc8H4qZcOUK+r604FPwn5ebt3HAIcCc2Ocz3r7CihX1ttXQLmy3r6CyJXD9tUCONR9XQ/4Npt9WFWfQfQEFqvqElXdAbwOnBl1zZnAv9RhCtBQRFoELBuaXKr6har+4r6dArTO0L3TkiukspmuexDwWobuHRdV/QxYG+eSXLSvhHLlqH0FeV6xyOnziiKb7WuFqs5wX28EFgCtoi4LrY1VdQXRCljueV9I+Ycb65ogZcOUy8vlOCOECAqMFZHpIjI4QzIlI9dRIjJLRD4QkS5Jlg1TLkSkNtAPeNtzOKznFYRctK9kyVb7Ckq221dgctm+RKQAOASYGnUqtDZWLWkpKxficyw6rjfWNUHKpkrgukWkL84P+GjP4d6qWiQizYBxIrLQHQFlQ64ZOLlbNolIf+B/wH4By4YpV4TTgUmq6h0NhvW8gpCL9hWYLLevIOSifSVDTtqXiNTFUUo3qeqG6NM+RTLSxqr6DKIQaON53xooCnhNkLJhyoWIHAw8D5ypqmsix1W1yP27ChiJM5XMilyqukFVN7mvRwPVRaRJkLJhyuVhIFHT/xCfVxBy0b4CkYP2lZActa9kyHr7EpHqOMphhKr+1+eS8NpYGI6VivIPZ4a0BGjHbidNl6hrBlDWwfNl0LIhy9UWWAz0ijpeB6jnef0F0C+Lcu3D7gWWPYEf3GeX0+flXtcAx45cJxvPy3OPAmI7XbPevgLKlfX2FVCurLevIHLlqn25n/1fwGNxrgmtjVVpE5OqFovIdcAYHI/+i6o6T0Sucs8/C4zGiQJYDGwBLo1XNoty3Q00Bp4WEYBidbI1NgdGuseqAf9W1Q+zKNc5wNUiUgxsBQaq0xpz/bwAzgLGqupmT/HQnheAiLyGE3nTREQKgXuA6h65st6+AsqV9fYVUK6st6+AckEO2hfQG7gQmCMiM91jd+Ao+NDbmKXaMAzDMHyp6j4IwzAMI0VMQRiGYRi+mIIwDMMwfDEFYRiGYfhiCsIwDKOSkijJYNS1j3oSDn4rIusSlTEFYRguItLY8wP6SUR+dF9vEpGnQ7rnTSJyUQrlmopIJsMpjcrJSzipPxKiqr9X1e6q2h34B+C36K4MVXodhGEkgzqribsDiMhQYJOq/j2s+4lINeAynCyiSaGqq0VkhYj0VtVJmZfOqAyo6mdujqZSRKQD8BTQFGddxJWqujCq6CCctR5xsRmEYSRAnD0K3ndfDxWRl0VkrLsPwNki8jd3P4AP3bQIiMhhIjLBTeA2xs2uGc3xwAxVLXbLjBeRx8TZn2GuiPR0jx/rmdl8LSL13PL/Ay4I/QEYlY3hwPWqehjwB6DM7FdE9sVZXf1JoopMQRhG8nTASW9wJvAq8KmqdsVZ+TvAVRL/AM5xf6QvAvf71NMbmB51rI6q9gKuccuB8yO/1jUN9HHvAzDNfW8YQGlSv17Am+7K63/i7CnhZSDwlqruSlSfmZgMI3k+UNWdIjIHJ4VBxBcwByefTyfgIJzMnrjXrPCppwVOfn8vr0Gp6aC+iDQEJgGPiMgI4L+qWuheuwpomakPZVQJ8oB17mAiFgOBa4NWZhhGcmwHUNUSYKfuzldTgjPoEmBexCGoql1V9WSferYCNaOORee+UVUdBlwB1AKmiMgB7rma7J5NGAbqpAJfKiLnQul2pN7tZDsBjYDJQeozBWEYmecboKmIHAVOumbPxjdeFgAdo46d75Y5GlivqutFpIOqzlHVv+KYlSIKYn8gYXijUXVxkwxOBjqJSKGIXI7jl7pcRGYB8yi7i9wg4HXPoCYuZmIyjAyjqjtE5BzgCRFpgPM7ewznx+rlA+CVqGO/iMgXQH2cCCeAm9yNfXYB89m9+1tfYFTmP4FRWVDVQTFO+Ya+qurQZOq3bK6GkUNEZCRwq6ouEpHxwB9UdVrAsp/hbPbzS8KLDSMFzMRkGLllCOWjTBIiIk2BR0w5GGFiMwjDMAzDF5tBGIZhGL6YgjAMwzB8MQVhGIZh+GIKwjAMw/DFFIRhGIbhy/8D4+LjJvrH5xgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f.plot(title='Z-translation wild')\n",
    "plt.ylabel('Coordinates (A°)')\n",
    "plt.xlabel('Time (ps)')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "501dcc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#old version test\n",
    "z_displacements_mut = []\n",
    "\n",
    "for ts in m.trajectory:\n",
    "\n",
    "    peptide_center_of_mass = pep.center_of_mass()\n",
    "\n",
    "    membrane_COM=membrane.center_of_mass()\n",
    "    #z_displacement = peptide_center_of_mass[2]-membrane_COM[2]\n",
    "    z_displacement = abs(peptide_center_of_mass[2]-membrane_COM[2])\n",
    "    z_displacements_mut.append(z_displacement)\n",
    "\n",
    "\n",
    "z_displacements_mut = np.array(z_displacements_mut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7696529b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#old version test\n",
    "z_displacements = []\n",
    "\n",
    "for ts in w.trajectory:\n",
    "\n",
    "    p454_center_of_mass = p454.center_of_mass()\n",
    "\n",
    "    membrane_COM=membrane_wild.center_of_mass()\n",
    "    z_displacement = abs(p454_center_of_mass[2]-membrane_COM[2])\n",
    "    #z_displacement = p454_center_of_mass[2]-membrane_COM[2]\n",
    "    z_displacements.append(z_displacement)\n",
    "\n",
    "\n",
    "z_displacements = np.array(z_displacements)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d51b8e5",
   "metadata": {},
   "source": [
    " # Averages and Error Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "76771a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_estimate(array):\n",
    "    \n",
    "    bloc = len(array)/2\n",
    "    err_est = abs((array[0:int(bloc)].mean()) - (array[int(bloc):-1].mean()))\n",
    "    return(err_est)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0524871",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(error_estimate(e))\n",
    "#print(error_estimate(z_displacements_mb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f96be317",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[e,f,g]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a30c27e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTM=[a,b,c]\n",
    "data2pep=[u,v,w]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f7f2ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erreur lors du chargement du fichier errest.xvg\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Chemin du fichier\n",
    "\n",
    "import os\n",
    "\n",
    "        \n",
    "root_directory = \"/data3/amiel/P454/martini2/1stbatch/2_peptides/POPC_POPS/prodrome\"\n",
    "\n",
    "\n",
    "ee_values = []\n",
    "\n",
    "# os.walk parcours les fichiers\n",
    "for directory_name, subdirectories, files in os.walk(root_directory):\n",
    "    for file_name in files:\n",
    "        if file_name.startswith('err'):\n",
    "            file_path = os.path.join(directory_name, file_name)\n",
    "            try:\n",
    "                with open(file_path, 'r') as file:\n",
    "                    for line in file:\n",
    "        # Si la ligne commence par \"@ s1 legend \"ee \", extraire la valeur\n",
    "                        if line.startswith('@ s1 legend \"ee '):\n",
    "                            ee_value = float(line.split('\"')[2])\n",
    "                            ee_values.append(ee_value)\n",
    "\n",
    "# Vérifier si des valeurs \"ee\" ont été trouvées\n",
    "                if ee_values:\n",
    "    # Utiliser NumPy pour effectuer des opérations sur les valeurs si nécessaire\n",
    "                    ee_array = np.array(ee_values)\n",
    "                    average_ee = np.mean(ee_array)\n",
    "                    print(f'The average \"ee\" value is: {average_ee}')\n",
    "                else:\n",
    "                    print('No \"ee\" values found in the file.')\n",
    "\n",
    "                \n",
    "                \n",
    "            except Exception as e:\n",
    "                print(f\"Erreur lors du chargement du fichier {file_name}\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "270df3be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The \"ee\" value is: 0.00577899\n",
      "The \"ee\" value is: 0.0042322\n",
      "The \"ee\" value is: 0.12959\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "root_directory = \"/data3/amiel/P454/martini2/1stbatch/2_peptides/POPC_POPS/prodrome\"\n",
    "ee_values = []\n",
    "\n",
    "\n",
    "for directory_name, subdirectories, files in os.walk(root_directory):\n",
    "    for file_name in files:\n",
    "        if file_name.startswith('err'):\n",
    "            file_path = os.path.join(directory_name, file_name)\n",
    "            try:\n",
    "                with open(file_path, 'r') as file:\n",
    "                    file_content = file.read()\n",
    "                ee_match = re.search(r'@ s1 legend \"ee (\\d+\\.\\d+)\"', file_content)\n",
    "                if ee_match:\n",
    "                    ee_value = float(ee_match.group(1))\n",
    "                    ee_values.append(ee_value)\n",
    "                    print(f'The \"ee\" value is: {ee_value}')\n",
    "                else:\n",
    "                    print('No \"ee\" value found in the file.')\n",
    "\n",
    "            except Exception as e:\n",
    "                print(f\"Erreur lors du chargement du fichier {file_name}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d6cacb81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.00577899, 0.0042322, 0.12959]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ee_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1a6c7717",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cd /data3/amiel/P454/martini2/1stbatch/2_peptides/POPC_POPS/prodrome/\n",
    "file_path=\"/data3/amiel/P454/martini2/1stbatch/2_peptides/POPC_POPS/prodrome/\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3783686a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data3/amiel/notebooks'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6944452f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ee_values = []\n",
    "file_path=\"/data3/amiel/P454/martini2/1stbatch/2_peptides/POPC_POPS/prodrome/replic1/errest.xvg\"\n",
    "with open(file_path, 'r') as file:\n",
    "    for line in file:\n",
    "        if line.startswith('@ s1 legend \"ee '):\n",
    "            parts = line.split('\"')\n",
    "            if len(parts) > 2 and parts[2].strip():  # Vérifiez si la partie après le deuxième guillemet n'est pas une chaîne vide\n",
    "                ee_value = float(parts[2])\n",
    "                ee_values.append(ee_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "42976924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The \"ee\" value is: 0.0042322\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Read the content of the file\n",
    "  # Replace with the actual file path\n",
    "with open(file_path, 'r') as file:\n",
    "    file_content = file.read()\n",
    "\n",
    "# Use regular expression to find the value associated with \"ee\"\n",
    "ee_match = re.search(r'@ s1 legend \"ee (\\d+\\.\\d+)\"', file_content)\n",
    "\n",
    "# Check if the match is found\n",
    "if ee_match:\n",
    "    ee_value = float(ee_match.group(1))\n",
    "    print(f'The \"ee\" value is: {ee_value}')\n",
    "else:\n",
    "    print('No \"ee\" value found in the file.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "41244ebb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17823966369225985\n"
     ]
    }
   ],
   "source": [
    "z=np.array(e)\n",
    "print(error_estimate(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9786da1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Générer des données aléatoires (à remplacer par vos propres données)\n",
    "\n",
    "\n",
    "# Fonction pour calculer les moyennes par blocs\n",
    "def block_average(data, block_size):\n",
    "    num_blocks = len(data) // block_size\n",
    "    block_means = [np.mean(data[i * block_size:(i + 1) * block_size]) for i in range(num_blocks)]\n",
    "    return block_means\n",
    "\n",
    "# Calcul des moyennes par blocs\n",
    "block_size = 10  # Taille des blocs\n",
    "block_means = block_average(data, block_size)\n",
    "\n",
    "# Calcul de la moyenne globale et de l'écart-type pour l'estimation d'erreur\n",
    "global_mean = np.mean(block_means)\n",
    "global_std = np.std(block_means, ddof=1)  # Utilisation de ddof=1 pour une estimation non biaisée de l'écart-type\n",
    "\n",
    "# Création du graphique avec Matplotlib\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Tracé des données\n",
    "plt.scatter(np.arange(len(block_means)), block_means, label='Block Averages')\n",
    "\n",
    "# Barres d'erreur pour les estimations d'erreur\n",
    "plt.errorbar(0, global_mean, yerr=global_std, fmt='o', color='red', label='Global Mean ± Error')\n",
    "\n",
    "plt.xlabel('Block Index')\n",
    "plt.ylabel('Block Average')\n",
    "plt.title('Block Averaging with Error Estimates')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "050b3d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def block_average(data, block_size):\n",
    "    num_blocks = len(data) // block_size\n",
    "    data=np.array(data)\n",
    "    block_means = [np.mean(data[i * block_size:(i + 1) * block_size]) for i in range(num_blocks)]\n",
    "    return block_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a157347b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'e' 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/tmp/ipykernel_34996/795022116.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplot_acf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'e' is not defined"
     ]
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "\n",
    "\n",
    "plot_acf(e[0])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ef471efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(block_average(e,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b23d4c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.32921073729896, 1.3364074962002164, 1.3363654780440495]\n"
     ]
    }
   ],
   "source": [
    "err_values=[]\n",
    "for i in dataTM:\n",
    "    \n",
    "    err_values.append(np.std(i[0]))\n",
    "    \n",
    "print(err_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "38b3f13a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17823966369225985\n",
      "0.1508077649720363\n",
      "0.8500102304226687\n"
     ]
    }
   ],
   "source": [
    "for i in data:\n",
    "    i=np.array(i)\n",
    "    f= error_estimate(i)\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ab412d36",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_values=[]\n",
    "err_values=[]\n",
    "for df in data:\n",
    "    mean_z = df[0].mean()\n",
    "    mean_values.append(mean_z)\n",
    "\n",
    "# Tracé du graphique\n",
    "#plt.figure(figsize=(8, 6))\n",
    "plt.bar([f\"replicate {i+1}\" for i in range(len(data))], mean_values,yerr=ee_values,capsize=24)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f730cc4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_values=[]\n",
    "err_values=[]\n",
    "for df in data:\n",
    "    mean_z = df[0].mean()\n",
    "    mean_values.append(mean_z)\n",
    "    df=np.array(df)\n",
    "    err_values.append(np.std(df))\n",
    "# Tracé du graphique\n",
    "#plt.figure(figsize=(8, 6))\n",
    "plt.bar([f\"replicat {i+1}\" for i in range(len(data))], mean_values,yerr=err_values)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "3c3ab192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mean_values=[]\n",
    "err_values=[]\n",
    "for df in data2pep:\n",
    "    mean_z = df[0].mean()\n",
    "    mean_values.append(mean_z)\n",
    "    df=np.array(df)\n",
    "    err_values.append(np.std(df))\n",
    "    \n",
    "# Tracé du graphique\n",
    "#plt.figure(figsize=(8, 6))\n",
    "plt.bar([f\"peptide {i+1}\" for i in range(len(data2pep))], mean_values,yerr=err_values)\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "62567534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMq0lEQVR4nO3dX4yl9V3H8fdHwD8B2kKY4gbRbRokIqmLTlFBzVhsQ+kFJcEoKkUlbmtLCwlekN5YL0y2SQsXSmi3gkVFjAmQYluLG0JFLGJnyZYurkiDq6XdskNr+ZMaDfTrxTyLk2Fm5+ycc3b3u/N+JZM555nnnPM9+e2+59lnzplNVSFJ6ud7jvQAkqT1MeCS1JQBl6SmDLgkNWXAJamp4w/ng5122mm1efPmw/mQktTezp07n62qmeXbD2vAN2/ezPz8/OF8SElqL8l/rLTdUyiS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLU1JoBT3JmkgeS7EnyeJJrh+0fSvK1JLuGj0umP64kwdzcHHNzc0d6jCNulLfSvwRcX1WPJjkZ2Jlkx/C1m6rqI9MbT5K0mjUDXlX7gH3D5ReS7AHOmPZgkqSDO6Rz4Ek2A+cBjwybrknyWJLbkpyyym22JplPMr+wsDDetJKkV4wc8CQnAXcB11XV88AtwBuBLSweoX90pdtV1faqmq2q2ZmZV/02REnSOo0U8CQnsBjvO6rqboCqeqaqXq6q7wKfAM6f3piSpOVGeRVKgFuBPVV145Ltm5bsdhmwe/LjSZJWM8qrUC4ErgS+nGTXsO2DwBVJtgAF7AXePYX5JEmrGOVVKA8BWeFLn538OJKkUflOTElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqalR/kMHSRvE5hs+c6RHGMk3nvom0GdegL3b3jHx+/QIXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDPgVzc3PMzc0d6TEkHeMMuDY8v+GqKwMuSU2tGfAkZyZ5IMmeJI8nuXbYfmqSHUmeHD6fMv1xJUkHjHIE/hJwfVX9GPAzwPuSnAPcANxfVWcB9w/XJUmHyZoBr6p9VfXocPkFYA9wBnApcPuw2+3AO6c0oyRpBYd0DjzJZuA84BHg9KraB4uRB16/ym22JplPMr+wsDDmuJKkA0YOeJKTgLuA66rq+VFvV1Xbq2q2qmZnZmbWM6MkaQUjBTzJCSzG+46qunvY/EySTcPXNwH7pzOiJGklo7wKJcCtwJ6qunHJl+4FrhouXwV8avLjSZJWc/wI+1wIXAl8OcmuYdsHgW3AXye5GvhP4JenMqEkLfODv7btSI9wVFgz4FX1EJBVvnzRZMeRJI3Kd2JKUlMGXJKaMuCS1NQoP8Q8Kmy+4TNHeoSRfeOpbwJ9Zt677R1Tud8uz7/besH01ky9eAQuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUVJvfRtiJ/91TL66XuvIIXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqak1A57ktiT7k+xesu1DSb6WZNfwccl0x5QkLTfKEfgngYtX2H5TVW0ZPj472bEkSWtZM+BV9SDwrcMwiyTpEIxzDvyaJI8Np1hOmdhEkqSRrDfgtwBvBLYA+4CPrrZjkq1J5pPMLywsrPPhJEnLrSvgVfVMVb1cVd8FPgGcf5B9t1fVbFXNzszMrHdOSdIy6wp4kk1Lrl4G7F5tX0nSdKz5nxonuROYA05L8jTw+8Bcki1AAXuBd09vREnSStYMeFVdscLmW6cwiyTpEPhOTElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTa0Z8CS3JdmfZPeSbacm2ZHkyeHzKdMdU5K03ChH4J8ELl627Qbg/qo6C7h/uC5JOozWDHhVPQh8a9nmS4Hbh8u3A++c7FiSpLWs9xz46VW1D2D4/PrVdkyyNcl8kvmFhYV1Ppwkabmp/xCzqrZX1WxVzc7MzEz74SRpw1hvwJ9Jsglg+Lx/ciNJkkax3oDfC1w1XL4K+NRkxpEkjWqUlxHeCTwMnJ3k6SRXA9uAtyZ5EnjrcF2SdBgdv9YOVXXFKl+6aMKzSJIOge/ElKSmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLU1PHj3DjJXuAF4GXgpaqancRQkqS1jRXwwS9W1bMTuB9J0iHwFIokNTVuwAv4uyQ7k2xdaYckW5PMJ5lfWFgY8+EkSQeMG/ALq+ongbcD70vyC8t3qKrtVTVbVbMzMzNjPpwk6YCxAl5VXx8+7wfuAc6fxFCSpLWtO+BJTkxy8oHLwNuA3ZMaTJJ0cOO8CuV04J4kB+7nL6vqcxOZSpK0pnUHvKqeAn5igrNIkg6BLyOUpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktTUWAFPcnGSJ5J8JckNkxpKkrS2dQc8yXHAzcDbgXOAK5KcM6nBJEkHN84R+PnAV6rqqar6X+CvgEsnM5YkaS3Hj3HbM4CvLrn+NPDTy3dKshXYOlx9MckTYzxmJ6cBzx7pIUaRDx/pCY4KbdYLXLPBRlqzH1lp4zgBzwrb6lUbqrYD28d4nJaSzFfV7JGeQ6NxvfpxzcY7hfI0cOaS6z8EfH28cSRJoxon4F8EzkryhiTfC/wqcO9kxpIkrWXdp1Cq6qUk1wD3AccBt1XV4xObrL8Nd9qoOdernw2/Zql61WlrSVIDvhNTkpoy4JLUlAGfgCRzSS5Ycv09Sd61wn6bk+w+xPu+Lcn+Q72dDm5aa5bkzCQPJNmT5PEk105q5o1siuv1/Un+OcmXhvX6g0nNfDiM8zpw/b854EXgCwBV9bEJ3vcngT8G/myC96nprdlLwPVV9WiSk4GdSXZU1b9M6P43qjmms17/A7ylql5McgLwUJK/rap/mtD9T9WGDXiSzcDngEeA84B/A95VVd9J8lPAjcBJLL7T6zeral+SzwO7WPw1Aq8BfhvYD7wHeDnJbwDvBy4CXqyqjwz3dRvwHeChJY9/HLCNxT+Y3wfcXFUfXz5nVT04zLrhdVizqtoH7Bsuv5BkD4vvWt5wAW+yXsXiNwaAE4aPNq/s2OinUM4GtlfVm4DngfcO34X/CLi8qg78wfjDJbc5saouAN7L4ksn9wIfA26qqi1V9Q/LHuNPgQ9U1c8u23418FxVvRl4M/A7Sd4w4ed3LGqzZkPAzmMxYBvVUb9eSY5LsovFbxQ7qqrNem3YI/DBV6vqH4fLfwF8gMUjhnOBHUlg8TXu+5bc5k545cj4NUlet9qdJ3kt8Lqq+vth05+z+NsbAd4GvCnJ5cP11wJnAf8+7pM6xrVYsyQnAXcB11XV84f6JI8hR/16VdXLwJbhce5Jcm5VtfiZ00YP+PJ/KhWLv+Pl8RW+mx/sNqvJQb4e4P1Vdd+aU2qpo37NhiPMu4A7qurug+27ARz16/XKg1R9eziFczHQIuAb/RTKDyc58IfoChbPnz0BzBzYnuSEJD++5Da/Mmz/ORb/efYc8AJw8vI7r6pvA88N+wL8+pIv3wf87vCXnSQ/muTEiT2zY9dRvWZZPKS8FdhTVTeO9UyPDUf7es0cOMJP8gPALwH/uv6ne3ht9IDvAa5K8hhwKnDL8LvNLwc+nORLLP5A5YIlt/mvJF9g8Zzc1cO2vwEuS7Iryc8ve4zfAm5O8jDw30u2/wmLP9h6dHjZ08dZ4V9ESe4EHgbOTvJ0kquX77PBHO1rdiFwJfCW4b53JblkvKfc2tG+XpuAB4b5vsjiOfBPj/WMD6MN+1b64QdMn66qcw/hNp8Hfq+q5qc1l1bnmvXiek3fRj8Cl6S2NuwRuCR15xG4JDVlwCWpKQMuSU0ZcElqyoBLUlP/B5Ja9h3iYhQ1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mean_values=[]\n",
    "err_values=[]\n",
    "for df in data2pep:\n",
    "    mean_z = df[0].mean()\n",
    "    mean_values.append(mean_z)\n",
    "    err_values.append(np.std(df[0],ddof=1))\n",
    "# Tracé du graphique\n",
    "#plt.figure(figsize=(8, 6))\n",
    "plt.bar([f\"peptide {i+1}\" for i in range(len(data2pep))], mean_values,yerr=err_values)\n",
    "\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7740e37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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a3pF0yt2flXSqdA4EJZPJqK+vr6ytr69PmUwmpoqA6ZkywN39B5KuP9T8uqQjpddHJG2e27KAysvlcspms8rn8yoWi8rn88pms8rlcnGXBkQy05uYje5+WZLc/bKZrXzcG82sXVK7JK1Zs2aGlwPm3tiNys7OTg0MDCiTyWjv3r3cwEQwIt3ENLNmSSfcvaV0PuTuSyd8f9DdpxwH5yYmAEzfXK+FcsXMVpV+8CpJV2dTHABg+mYa4B9I2l56vV3S+3NTDgAgqijTCHskfSLpK2Z2ycyykvZJesXMzkt6pXQOAJhHU97EdPfH3dF5eY5rAQBMA2uhAECgCHAACBQBDgCBIsABIFAEOBKN9cARMtYDR2KxHjhCx3rgSKyWlhZt3rxZx48fH18LZeyc9cBRTR73KD09cCTWz372M925c0fd3d3jPfAdO3bo4sWLcZcGREKAI7EWLFigDRs2lK1GuGHDBl2+fDnu0oBIuImJxLp3756OHTumHTt26NatW9qxY4eOHTume/fuxV0aEAkBjsSqr6/X1q1b1d3drSVLlqi7u1tbt25VfX193KUBkRDgSKz79+/r448/VldXlwqFgrq6uvTxxx/r/v37cZcGRMIYOBLr+eef1+bNm8vGwL/5zW/q+PHjcZcGREIPHImVy+V09OjRsh740aNH2RMTwaAHjsRiT0yEjgd5AKDKzfWemACAmBHgABAoAhwAAkWAA0CgCHAkGuuBI2RMI0RisR44Qsc0QiRWS0uLurq61NbWNt6Wz+fV2dnJeuCoKo+bRkiAI7FqampUKBRUV1c33lYsFpVOpzUyMhJjZUA55oEDD8lkMtqzZ0/ZGPiePXuUyWTiLg2IhABHYrW1tendd98tWw/83XffLRtSAaoZAY7Eyufz2r17d9l64Lt371Y+n4+7NCASxsCRWIyBIxSMgQMPyWQy6uvrK2vr6+tjDBzBIMCRWLlcTtlsVvl8XsViUfl8XtlslvXAEQwe5EFisR44QscYOABUuYqMgZvZq2b2P2b2CzN7ZzY/CwAwPTMOcDOrkfQPkv5A0vOStpnZ83NVGADgyWbTA18v6Rfu/kt3vy/pPUmvz01ZAICpzCbAmyR9OuH8UqmtjJm1m1m/mfVfu3ZtFpcDAEw0mwC3SdoeuSPq7gfcvdXdW1esWDGLywEAJprNNMJLklZPOP+ypM+f9IEzZ858YWYXZ3FNoFKekfRF3EUAj7F2ssYZTyM0s1pJ/yvpZUmfSfqRpD9195/OtEIgLmbWP9k0LaCazbgH7u7DZvaXkv5DUo2kbsIbAObPvD7IA1QreuAIEWuhAA8ciLsAYLrogQNAoOiBA0CgCHAACBQBjkQzs24zu2pm5+KuBZguAhxJd1jSq3EXAcwEAY5Ec/cfSLoedx3ATBDgABAoAhwAAkWAA0CgCHAACBQBjkQzsx5Jn0j6ipldMrNs3DUBUfEoPQAEih44AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCB+n9xqMm/rAv0RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,df in enumerate(data):\n",
    "    plt.figure()\n",
    "    plt.boxplot(data[i])\n",
    "\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37f8bdb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,df in enumerate(data):\n",
    "    plt.figure()\n",
    "    plt.boxplot(data[i])\n",
    "\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "373a0a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.68720688, 16.88221852, 17.31511397, ..., 19.09015044,\n",
       "       19.03608042, 21.95179202])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_displacements_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "791d892b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "time1 = []\n",
    "protein = m.select_atoms(\"protein\")\n",
    "for ts in m.trajectory:\n",
    "    time1.append(m.trajectory.time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "42f6f032",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mut=pd.DataFrame(z_displacements_mut,index=time1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "3f2eb10a",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>18.687207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>16.882219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000.0</th>\n",
       "      <td>17.315114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000.0</th>\n",
       "      <td>17.362921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8000.0</th>\n",
       "      <td>18.176737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19992000.0</th>\n",
       "      <td>19.864732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19994000.0</th>\n",
       "      <td>20.122141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996000.0</th>\n",
       "      <td>19.090150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998000.0</th>\n",
       "      <td>19.036080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000000.0</th>\n",
       "      <td>21.951792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0\n",
       "0.0         18.687207\n",
       "2000.0      16.882219\n",
       "4000.0      17.315114\n",
       "6000.0      17.362921\n",
       "8000.0      18.176737\n",
       "...               ...\n",
       "19992000.0  19.864732\n",
       "19994000.0  20.122141\n",
       "19996000.0  19.090150\n",
       "19998000.0  19.036080\n",
       "20000000.0  21.951792\n",
       "\n",
       "[10001 rows x 1 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09aa9e31",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' 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/tmp/ipykernel_312930/4025612102.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0merr_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_mut\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmean_z\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_mut\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mut6667W'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmean_z\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "err_values=np.std(df_mut[0])\n",
    "\n",
    "plt.figure(figsize=(1, 7))\n",
    "mean_z=df_mut[0].mean()\n",
    "plt.bar(['mut6667W'],mean_z,yerr=err_values)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c785d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_mut=z_mut.iloc[:2500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6239bbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_df=z_df.iloc[:2500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a06ec79b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'z_mut' 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/tmp/ipykernel_3092882/4196967398.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz_trans_mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_mut\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mrolling_z\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_mut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mz_trans_mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Z-translation mutant'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Coordinates (A°)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Time (ps)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'z_mut' is not defined"
     ]
    }
   ],
   "source": [
    "z_trans_mb=pd.DataFrame(z_mut,index=time)\n",
    "rolling_z = pd.DataFrame(z_mut.rolling(50).mean(),index=time)\n",
    "z_trans_mb.plot(title='Z-translation mutant')\n",
    "plt.ylabel('Coordinates (A°)')\n",
    "plt.xlabel('Time (ps)')\n",
    "z_trans=pd.DataFrame(z_df,index=time1)\n",
    "z_trans.plot(title='Z-translation wild')\n",
    "plt.ylabel('Coordinates (A°)')\n",
    "plt.xlabel('Time (ps)')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "775cc04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_displacements = []\n",
    "\n",
    "for ts in u.trajectory:\n",
    "\n",
    "    peptide_center_of_mass = pep.center_of_mass()\n",
    "\n",
    "    membrane_COM=membrane_wild.center_of_mass()\n",
    "    z_displacement = peptide_center_of_mass[2]\n",
    "    z_displacements.append(z_displacement)\n",
    "\n",
    "\n",
    "z_displacements = np.array(z_displacements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a56dd4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_mut=z_mut.rename(columns={0:'dis'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c89b49c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function ReaderBase.__del__ at 0x7f6a5ed4e790>\n",
      "Traceback (most recent call last):\n",
      "  File \"/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/coordinates/base.py\", line 2152, in __del__\n",
      "    self.close()\n",
      "  File \"/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/coordinates/XDR.py\", line 179, in close\n",
      "    self._xdr.close()\n",
      "AttributeError: 'XTCReader' object has no attribute '_xdr'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#z_trans_mb=pd.DataFrame(z_mut,index=time)\n",
    "#rolling_z = pd.DataFrame(z_mut.rolling(50).mean(),index=time)\n",
    "rolling_mut=z_mut.rolling(50).mean()\n",
    "plt.plot(z_mut['dis'])\n",
    "plt.plot(rolling_mut, color = \"red\")\n",
    "\n",
    "plt.ylabel('Coordinates (A°)')\n",
    "plt.xlabel('Time (ps)')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "895254df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#z_trans=pd.DataFrame(z_df,index=time1)\n",
    "rolling_zdf=z_df.rolling(50).mean()\n",
    "\n",
    "plt.plot(z_df)\n",
    "plt.plot(rolling_zdf['dis'], color = \"red\")\n",
    "\n",
    "plt.ylabel('Coordinates (A°)')\n",
    "plt.xlabel('Time (ps)')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed660be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_trans_mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7ea677",
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "412c7d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_df=z_df.rename(columns={0:'dis'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d336e1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[120. 120. 120.  90.  90.  90.]\n"
     ]
    }
   ],
   "source": [
    "print(u.dimensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "da1792c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[121.8443 121.8443 109.4176  90.      90.      90.    ]\n"
     ]
    }
   ],
   "source": [
    "print(w.dimensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "503fa7b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "distance_array(): configuration.shape must be (3,) or (n, 3), got (6,).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_663810/577787742.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Calculer la distance entre le centre de masse et l'extrémité de la boîte\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdistance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistance_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmembrane_COM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimensions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/lib/util.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2052\u001b[0m                 \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mposargnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2053\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2054\u001b[0;31m                     \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_single\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_coords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2055\u001b[0m                     \u001b[0mall_single\u001b[0m \u001b[0;34m&=\u001b[0m \u001b[0mis_single\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2056\u001b[0m                     \u001b[0mncoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/softwares/Anaconda3/2021.11/lib/python3.9/site-packages/MDAnalysis/lib/util.py\u001b[0m in \u001b[0;36m_check_coords\u001b[0;34m(coords, argname)\u001b[0m\n\u001b[1;32m   2002\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mallow_single\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2003\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m                     raise ValueError(\"{}(): {}.shape must be (3,) or (n, 3), \"\n\u001b[0m\u001b[1;32m   2005\u001b[0m                                      \"got {}.\".format(fname, argname,\n\u001b[1;32m   2006\u001b[0m                                                       coords.shape))\n",
      "\u001b[0;31mValueError\u001b[0m: distance_array(): configuration.shape must be (3,) or (n, 3), got (6,)."
     ]
    }
   ],
   "source": [
    "from MDAnalysis.analysis import distances\n",
    "\n",
    "# Calculer la distance entre le centre de masse et l'extrémité de la boîte\n",
    "distance = distances.distance_array(membrane_COM, u.dimensions)\n",
    "\n"
   ]
  }
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